diff --git a/.coveragerc b/.coveragerc new file mode 100644 index 00000000..40c661b7 --- /dev/null +++ b/.coveragerc @@ -0,0 +1,5 @@ +[run] +omit = */tests/* +[report] +exclude_lines = + _log diff --git a/.cz.toml b/.cz.toml new file mode 100644 index 00000000..5edff1e7 --- /dev/null +++ b/.cz.toml @@ -0,0 +1,21 @@ +[tool.commitizen] +name = "cz_conventional_commits" +version = "2.0.0.post1" +version_files = [ + "setup.py", + "docs/source/conf.py", + "hypernetx/__init__.py" +] +update_changelog_on_bump = false +style = [ + ["qmark", "fg:#ff9d00 bold"], + ["question", "bold"], + ["answer", "fg:#ff9d00 bold"], + ["pointer", "fg:#ff9d00 bold"], + ["highlighted", "fg:#ff9d00 bold"], + ["selected", "fg:#cc5454"], + ["separator", "fg:#cc5454"], + ["instruction", ""], + ["text", ""], + ["disabled", "fg:#858585 italic"] +] diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 00000000..fd4246d0 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,61 @@ +name: Continuous Integration + + +on: + push: + branches: [master, develop, release/**] + pull_request: + types: [opened, synchronize, reopened] + workflow_dispatch: + inputs: + triggeredBy: + description: 'Name of team member who is manually triggering this workflow' + required: true + +defaults: + run: + shell: bash + +env: + LANG: en_US.utf-8 + LC_ALL: en_US.utf-8 + +jobs: + + run-tests: + + strategy: + matrix: + os: [ubuntu-22.04, macos-12, windows-2022] + python: ['3.8', '3.9', '3.10', '3.11'] + + runs-on: ${{ matrix.os }} + + steps: + + - run: | + echo "This workflow was triggered by: $TRIGGER_PERSON" + env: + TRIGGER_PERSON: ${{ inputs.triggeredBy }} + - run: echo "🎉 The job was automatically triggered by a ${{ github.event_name }} event." + - run: echo "🐧 This job is now running on a ${{ runner.os }} server hosted by GitHub!" + - run: echo "🔎 The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}." + + - name: Checkout + uses: actions/checkout@v3 + + - name: Set up Python ${{ matrix.python }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python }} + + - name: Install Pylint + run: pip install pylint + + # https://github.com/pre-commit/action + - name: Run pre-commit hooks + uses: pre-commit/action@v3.0.0 + + - name: Run tests + run: | + make test-ci-github diff --git a/.gitignore b/.gitignore index 5f496f23..c22f5005 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,6 @@ -.DS_Store -gitmerge.sh -dist +.DS_Store +gitmerge.sh +dist hypernetx.egg-info* hypernetx.egg-info/PKG-INFO hypernetx.egg-info/requires.txt @@ -26,4 +26,16 @@ vis_develop dist/ *.egg-info* .tox/ -venv* \ No newline at end of file +venv* +.coverage +.idea +*env* +.venv* +pylint-results.txt +*pytest.xml +*_alt* +docs/docs +docs/build +coverage.xml +cov.syspath.txt +*.whl diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..2bb5c4a3 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,45 @@ +# See https://pre-commit.com for more information +# See https://pre-commit.com/hooks.html for more hooks + +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.1.0 + hooks: + - id: check-yaml + - id: check-json + - id: check-toml + - id: end-of-file-fixer + exclude: ^(docs/|hypernetx.egg-info/) + - id: trailing-whitespace + exclude: ^(docs/|hypernetx.egg-info/|setup.cfg) + - id: check-merge-conflict + - id: no-commit-to-branch + args: ['--branch', 'master'] # blocks master commits. To bypass do git commit --allow-empty + +- repo: https://github.com/psf/black + rev: 22.6.0 + hooks: + - id: black + exclude: ^(docs/|hypernetx.egg-info/) + +# TODO: Uncomment once typing issues have been resolved and mypy has been +# correctly configured +#- repo: https://github.com/pre-commit/mirrors-mypy +# rev: v0.910-1 +# hooks: +# - id: mypy +# exclude: (?x)(docs/|tests/) +# args: [--no-strict-optional, --ignore-missing-imports] + +- repo: local + hooks: + - id: pylint + name: pylint + entry: pylint + language: system + types: [python] + args: + [ + "--rcfile=.pylintrc", + "--exit-zero" # Always return a 0 (non-error) status code, even if lint errors are found. This is primarily useful in continuous integration scripts. + ] diff --git a/.pylintrc b/.pylintrc new file mode 100644 index 00000000..7ebe9898 --- /dev/null +++ b/.pylintrc @@ -0,0 +1,13 @@ +[MAIN] + +# Specify a score threshold under which the program will exit with error. +fail-under=5.86 + +[REPORTS] +# Tells whether to display a full report or only the messages. +reports=yes + +# Set the output format. Available formats are text, parseable, colorized, json +# and msvs (visual studio). You can also give a reporter class, e.g. +# mypackage.mymodule.MyReporterClass. +output-format=colorized diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 00000000..62f12140 --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,31 @@ +# .readthedocs.yaml +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Set the version of Python and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: "3.11" + # You can also specify other tool versions: + # nodejs: "19" + # rust: "1.64" + # golang: "1.19" + +# Build documentation in the docs/ directory with Sphinx +sphinx: + configuration: docs/conf.py + +# If using Sphinx, optionally build your docs in additional formats such as PDF +formats: + - pdf + - htmlzip + - epub + +# Optionally declare the Python requirements required to build your docs +python: + install: + - requirements: docs/requirements.txt diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 00000000..1ecc2e51 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,134 @@ +# Contributor Covenant Code of Conduct + +Our shared values as software developers guide us in our day-to-day interactions and decision-making. Our open source projects are no exception. Trust, respect, collaboration and transparency are core values we believe should live and breathe within our projects. Our community welcomes participants from around the world with different experiences, unique perspectives, and great ideas to share. + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, religion, or sexual identity +and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the + overall community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or + advances of any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email + address, without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +hypernetx@pnnl.gov. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series +of actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or +permanent ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within +the community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.0, available at +https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. + +Community Impact Guidelines were inspired by [Mozilla's code of conduct +enforcement ladder](https://github.com/mozilla/diversity). + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see the FAQ at +https://www.contributor-covenant.org/faq. Translations are available at +https://www.contributor-covenant.org/translations. + +This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org/), +version 2.1, available at +https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 00000000..97893fc7 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,39 @@ +# Contributor orientation + +If you have ideas for improving this project, we would love to hear from you! + +## Orientation + +The world of open source is wide; it can be a lot to take in! If this is your first time working with an open source project, then welcome! If you're an experienced contributor, then welcome back! Either way, this online resource might help you [get oriented, or bursh up](https://opensource.guide/how-to-contribute/) on the process. + +## Report an issue, bug, or feature request + +Here are the [steps to creating an issue on github](https://docs.github.com/en/issues/tracking-your-work-with-issues/quickstart). When reporting a bug, + +- search for related issues on Github. You might be able to get answer without the hassle of creating an issue +- describe the current behavior and explain which behavior you expected to see instead and why. At this point you can also tell which alternatives do not work for you. + - (if applicable) provide error messages + - (if applicable) provide a step by step description of the problem; if possible include code that others can use to reproduce it + - You may want to **include screenshots and animated GIFs** which help you demonstrate the steps or point out the part which the suggestion is related to. You can use [this tool](https://www.cockos.com/licecap/) to record GIFs on macOS and Windows, and [this tool](https://github.com/colinkeenan/silentcast) or [this tool](https://github.com/GNOME/byzanz) on Linux. + - provide clear, specific title + - include details on your setup (operating system, python version, etc.) +- use the most recent version of this library and the source language (e.g. Rust); that fixes a lot of problems +- here are [more details on getting the most out of issue reporting!](https://marker.io/blog/how-to-write-bug-report) + +## Contribute new code + +Here is a [step-by-step guide to writing new code, and submiting it to the project](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) + +The more you know about a software library, the easier it is to get started writing code. The best way to learn about this project is its the documentation! See `README.md` to get started. + + +## Where can I go for help? + +If you're stuck or don't know where to begin, then you're in good company -- we've all been there! We're here to help, and we'd love to hear from you: + +- open a issue report on Github +- email us at + +# Code of conduct + +Our shared values as software developers guide us in our day-to-day interactions and decision-making. Our open source projects are no exception. Trust, respect, collaboration and transparency are core values we believe should live and breathe within our projects. Our community welcomes participants from around the world with different experiences, unique perspectives, and great ideas to share. Our [code of conduct](CODE_OF_CONDUCT) describes these values, and our standards for contributing. diff --git a/DISCLAIMER.txt b/DISCLAIMER.txt index 366dce85..cd85dd65 100644 --- a/DISCLAIMER.txt +++ b/DISCLAIMER.txt @@ -19,4 +19,4 @@ reflect those of the United States Government or any agency thereof. BATTELLE for the UNITED STATES DEPARTMENT OF ENERGY - under Contract DE-AC05-76RL01830 \ No newline at end of file + under Contract DE-AC05-76RL01830 diff --git a/LICENSE.rst b/LICENSE.rst index 27a3f68e..6401b05d 100644 --- a/LICENSE.rst +++ b/LICENSE.rst @@ -5,7 +5,7 @@ License HyperNetX -Copyright © 2018, Battelle Memorial Institute +Copyright © 2023, Battelle Memorial Institute Battelle Memorial Institute (hereinafter Battelle) hereby grants permission to any person or entity lawfully obtaining a copy of this software and associated diff --git a/LONG_DESCRIPTION.rst b/LONG_DESCRIPTION.rst new file mode 100644 index 00000000..4de24045 --- /dev/null +++ b/LONG_DESCRIPTION.rst @@ -0,0 +1,60 @@ +.. _long_description: + +HyperNetX +================= + +The HyperNetX library provides classes and methods for the analysis +and visualization of complex network data modeled as hypergraphs. +The library generalizes traditional graph metrics. + +HypernetX was developed by the Pacific Northwest National Laboratory for the +Hypernets project as part of its High Performance Data Analytics (HPDA) program. +PNNL is operated by Battelle Memorial Institute under Contract DE-ACO5-76RL01830. + +* Principal Developer and Designer: Brenda Praggastis +* Development Team: Madelyn Shapiro, Mark Bonicillo +* Visualization: Dustin Arendt, Ji Young Yun +* Principal Investigator: Cliff Joslyn +* Program Manager: Brian Kritzstein +* Principal Contributors (Design, Theory, Code): Sinan Aksoy, Dustin Arendt, Mark Bonicillo, Helen Jenne, Cliff Joslyn, Nicholas Landry, Audun Myers, Christopher Potvin, Brenda Praggastis, Emilie Purvine, Greg Roek, Madelyn Shapiro, Mirah Shi, Francois Theberge, Ji Young Yun + +The code in this repository is intended to support researchers modeling data +as hypergraphs. We have a growing community of users and contributors. +Documentation is available at: https://pnnl.github.io/HyperNetX + +For questions and comments contact the developers directly at: hypernetx@pnnl.gov + +New Features in Version 2.0 +--------------------------- + +HNX 2.0 now accepts metadata as core attributes of the edges and nodes of a +hypergraph. While the library continues to accept lists, dictionaries and +dataframes as basic inputs for hypergraph constructions, both cell +properties and edge and node properties can now be easily added for +retrieval as object attributes. + +The core library has been rebuilt to take advantage of the flexibility and speed of Pandas Dataframes. +Dataframes offer the ability to store and easily access hypergraph metadata. Metadata can be used for filtering objects, and characterize their +distributions by their attributes. + +**Version 2.0 is not backwards compatible. Objects constructed using version +1.x can be imported from their incidence dictionaries.** + +What's New +~~~~~~~~~~~~~~~~~~~~~~~~~ +#. The Hypergraph constructor now accepts nested dictionaries with incidence cell properties, pandas.DataFrames, and 2-column Numpy arrays. +#. Additional constructors accept incidence matrices and incidence dataframes. +#. Hypergraph constructors accept cell, edge, and node metadata. +#. Metadata available as attributes on the cells, edges, and nodes. +#. User-defined cell weights and default weights available to incidence matrix. +#. Meta data persists with restrictions and removals. +#. Meta data persists onto s-linegraphs as node attributes of Networkx graphs. +#. New hnxwidget available using `pip install hnxwidget`. + + +What's Changed +~~~~~~~~~~~~~~~~~~~~~~~~~ +#. The `static` and `dynamic` distinctions no longer exist. All hypergraphs use the same underlying data structure, supported by Pandas dataFrames. All hypergraphs maintain a `state_dict` to avoid repeating computations. +#. Methods for adding nodes and hyperedges are currently not supported. +#. The `nwhy` optimizations are no longer supported. +#. Entity and EntitySet classes are being moved to the background. The Hypergraph constructor does not accept either. diff --git a/Makefile b/Makefile index a02b78bf..c31b39e3 100644 --- a/Makefile +++ b/Makefile @@ -1,30 +1,80 @@ - SHELL = /bin/bash -# Variables -VENV = venv_test -PYTHON = $(VENV)/bin/python +VENV = venv-hnx +PYTHON_VENV = $(VENV)/bin/python3 +PYTHON3 = python3 -## Environment -venv: - @rm -rf VENV; - @python -m venv $(VENV); +## Test + +test: test-deps + @$(PYTHON3) -m tox -deps: - @$(PYTHON) -m pip install tox +test-ci: test-deps + @$(PYTHON3) -m pip install 'pytest-github-actions-annotate-failures>=0.1.7' + pre-commit install + pre-commit run --all-files + @$(PYTHON3) -m tox -e py38 -r + @$(PYTHON3) -m tox -e py38-notebooks -r -.PHONY: venv deps +test-ci-github: test-deps + @$(PYTHON3) -m pip install 'pytest-github-actions-annotate-failures>=0.1.7' + @$(PYTHON3) -m tox -## Test +.PHONY: test, test-ci, test-ci-github + +## Continuous Deployment +## Assumes that scripts are run on a container or test server VM + +### Publish to PyPi +publish-deps: + @$(PYTHON3) -m pip install -e .'[packaging]' + +build-dist: publish-deps clean + @$(PYTHON3) -m build --wheel --sdist + @$(PYTHON3) -m twine check dist/* + +## Assumes the following environment variables are set: TWINE_USERNAME, TWINE_PASSWORD, TWINE_REPOSITORY_URL, +## See https://twine.readthedocs.io/en/stable/#environment-variables +publish-to-pypi: publish-deps build-dist + @echo "Publishing to PyPi" + $(PYTHON3) -m twine upload dist/* + +.PHONY: build-dist publish-to-pypi publish-deps + +### Update version -test: venv deps - rm -rf .tox - @$(PYTHON) -m tox +version-deps: + @$(PYTHON3) -m pip install .'[releases]' -.PHONY: test +.PHONY: version-deps + +#### Documentation + +docs-deps: + @$(PYTHON3) -m pip install -e .'[documentation]' --use-pep517 + +commit-docs: + git add -A + git commit -m "Bump version in docs" + +.PHONY: commit-docs docs-deps + +## Environment + +clean-venv: + rm -rf $(VENV) clean: rm -rf .out .pytest_cache .tox *.egg-info dist build -.PHONY: clean \ No newline at end of file +venv: clean-venv + @$(PYTHON3) -m venv $(VENV); + +test-deps: + @$(PYTHON3) -m pip install -e .'[testing]' --use-pep517 + +all-deps: + @$(PYTHON3) -m pip install -e .'[all]' --use-pep517 + +.PHONY: clean clean-venv venv all-deps test-deps diff --git a/README.md b/README.md index d1ccf110..7475878b 100644 --- a/README.md +++ b/README.md @@ -1,73 +1,75 @@ HyperNetX -========= - -The HNX library provides classes and methods for modeling the entities and relationships -found in complex networks as hypergraphs, the natural models for multi-dimensional network data. -As strict generalizations of graphs, hyperedges can represent arbitrary multi-way relations -among entities, and in particular can distinguish cliques and simplices, and admit singleton edges. -As both vertex adjacency and edge -incidence are generalized to be quantities, -hypergraph paths and walks thereby have both length and *width* because of these multiway connections. -Most graph metrics have natural generalizations to hypergraphs, but since -hypergraphs are basically set systems, they also admit to the powerful tools of algebraic topology, -including simplicial complexes and simplicial homology, to study their structure. - -This library serves as a repository of the methods and algorithms we find most useful -as we explore what hypergraphs can tell us. We have a growing community of users and contributors. -To learn more about some of our research check out our publications below: - -Publications ------------- -Joslyn, Cliff A; Aksoy, Sinan; Callahan, Tiffany J; Hunter, LE; Jefferson, Brett ; Praggastis, Brenda ; Purvine, Emilie AH ; Tripodi, Ignacio J: (2020) "Hypernetwork Science: From Multidimensional Networks to Computational Topology", in: Int. Conf. Complex Systems (ICCS 2020), https://arxiv.org/abs/2003.11782, (in press) - -Feng, Song; Heath, Emily; Jefferson, Brett; Joslyn, CA; Kvinge, Henry; McDermott, Jason E ; Mitchell, Hugh D ; Praggastis, Brenda ; Eisfeld, Amie J; Sims, Amy C ; Thackray, Larissa B ; Fan, Shufang ; Walters, Kevin B; Halfmann, Peter J ; Westhoff-Smith, Danielle ; Tan, Qing ; Menachery, Vineet D ; Sheahan, Timothy P ; Cockrell, -Adam S ; Kocher, Jacob F ; Stratton, Kelly G ; Heller, Natalie C ; Bramer, Lisa M ; Diamond, Michael S ; Baric, Ralph S ; Waters, Katrina M ; Kawaoka, Yoshihiro ; Purvine, Emilie: (2020) "Hypergraph Models of Biological Networks to Identify Genes Critical to Pathogenic Viral Response", in: https://arxiv.org/abs/2010.03068, BMC Bioinformatics, 22:287, doi: 10.1186/s12859-021-04197-2 - -Aksoy, Sinan G; Joslyn, Cliff A; Marrero, Carlos O; Praggastis, B; Purvine, Emilie AH: (2020) "Hypernetwork Science via High-Order Hypergraph Walks", EPJ Data Science, v. 9:16, https://doi.org/10.1140/epjds/s13688-020-00231-0 +========== +[![Pytest](https://github.com/pnnl/HyperNetX/actions/workflows/ci.yml/badge.svg)](https://github.com/pnnl/HyperNetX/actions/workflows/ci.yml) +[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) +[![linting: pylint](https://img.shields.io/badge/linting-pylint-yellowgreen)](https://github.com/PyCQA/pylint) -Joslyn, Cliff A; Aksoy, Sinan; Arendt, Dustin; Firoz, J; Jenkins, Louis ; Praggastis, Brenda ; Purvine, Emilie AH ; Zalewski, Marcin: (2020) "Hypergraph Analytics of Domain Name System Relationships", in: 17th Wshop. on Algorithms and Models for the Web Graph (WAW 2020), Lecture Notes in Computer Science, v. 12901, ed. Kaminski, B et al., pp. 1-15, Springer, https://doi.org/10.1007/978-3-030-48478-1_1 -Joslyn, Cliff A; Aksoy, Sinan; Arendt, Dustin; Jenkins, L; Praggastis, Brenda; Purvine, Emilie; Zalewski, Marcin: (2019) "High Performance Hypergraph Analytics of Domain Name System Relationships", in: Proc. HICSS Symp. on Cybersecurity Big Data Analytics, http://www.azsecure-hicss.org/ +The HyperNetX library provides classes and methods for the analysis +and visualization of complex network data modeled as hypergraphs. +The library generalizes traditional graph metrics. -Notes ------ - -HNX was developed by the Pacific Northwest National Laboratory for the +HypernetX was developed by the Pacific Northwest National Laboratory for the Hypernets project as part of its High Performance Data Analytics (HPDA) program. PNNL is operated by Battelle Memorial Institute under Contract DE-ACO5-76RL01830. -* Principle Developer and Designer: Brenda Praggastis +* Principal Developer and Designer: Brenda Praggastis +* Development Team: Madelyn Shapiro, Mark Bonicillo * Visualization: Dustin Arendt, Ji Young Yun -* High Performance Computing: Tony Liu, Andrew Lumsdaine * Principal Investigator: Cliff Joslyn -* Program Manager: Mark Raugas, Brian Kritzstein -* Contributors: Sinan Aksoy, Dustin Arendt, Cliff Joslyn, Nicholas Landry, Andrew Lumsdaine, Tony Liu, Brenda Praggastis, Emilie Purvine, Mirah Shi, Francois Theberge +* Program Manager: Brian Kritzstein +* Principal Contributors (Design, Theory, Code): Sinan Aksoy, Dustin Arendt, Mark Bonicillo, Helen Jenne, Cliff Joslyn, Nicholas Landry, Audun Myers, Christopher Potvin, Brenda Praggastis, Emilie Purvine, Greg Roek, Madelyn Shapiro, Mirah Shi, Francois Theberge, Ji Young Yun The code in this repository is intended to support researchers modeling data as hypergraphs. We have a growing community of users and contributors. -Documentation is available at: -For questions and comments contact the developers directly at: +Documentation is available at: https://pnnl.github.io/HyperNetX -New Features of Version 1.0 ---------------------------- +For questions and comments contact the developers directly at: hypernetx@pnnl.gov -1. Hypergraph construction can be sped up by reading in all of the data at once. In particular the hypergraph constructor may read a Pandas dataframe object and create edges and nodes based on column headers. The new hypergraphs are given an attribute `static=True`. -2. A C++ addon called [NWHy](docs/build/nwhy.html) can be used in Linux environments to support optimized hypergraph methods such as s-centrality measures. -3. A JavaScript addon called [Hypernetx-Widget](docs/build/widget.html) can be used to interactively inspect hypergraphs in a Jupyter Notebook. -4. Four new tutorials highlighting the s-centrality metrics, static Hypergraphs, [NWHy](docs/build/nwhy.html), and [Hypernetx-Widget](docs/build/widget.html). +New Features in Version 2.0 +=========================== + +HNX 2.0 now accepts metadata as core attributes of the edges and nodes of a +hypergraph. While the library continues to accept lists, dictionaries and +dataframes as basic inputs for hypergraph constructions, both cell +properties and edge and node properties can now be easily added for +retrieval as object attributes. + +The core library has been rebuilt to take advantage of the flexibility and speed of Pandas Dataframes. +Dataframes offer the ability to store and easily access hypergraph metadata. Metadata can be used for filtering objects, and characterize their +distributions by their attributes. + +**Version 2.0 is not backwards compatible. Objects constructed using version +1.x can be imported from their incidence dictionaries.** + +What's New +---------- +1. The Hypergraph constructor now accepts nested dictionaries with incidence cell properties, pandas.DataFrames, and 2-column Numpy arrays. +1. Additional constructors accept incidence matrices and incidence dataframes. +1. Hypergraph constructors accept cell, edge, and node metadata. +1. Metadata available as attributes on the cells, edges, and nodes. +1. User-defined cell weights and default weights available to incidence matrix. +1. Meta data persists with restrictions and removals. +1. Meta data persists onto s-linegraphs as node attributes of Networkx graphs. +1. New hnxwidget available using `pip install hnxwidget`. + + +What's Changed +-------------- +1. The `static` and `dynamic` distinctions no longer exist. All hypergraphs use the same underlying data structure, supported by Pandas dataFrames. All hypergraphs maintain a `state_dict` to avoid repeating computations. +1. Methods for adding nodes and hyperedges are currently not supported. +1. The `nwhy` optimizations are no longer supported. +1. Entity and EntitySet classes are being moved to the background. The Hypergraph constructor does not accept either. -New Features of Version 1.1 ---------------------------- -1. Static Hypergraph refactored to improve performance across all methods. -2. Added modules and tutorials for Contagion Modeling, Community Detection, Clustering, and Hypergraph Generation. -3. Cell weights for incidence matrices may be added to static hypergraphs on construction. Tutorials may be run in your browser using Google Colab ------------------------------------------------------- +**Additional Tutorials may be found on in the Tutorials Folder.** + Open In Colab Tutorial 1 - HNX Basics @@ -92,107 +94,287 @@ Tutorials may be run in your browser using Google Colab
- + Open In Colab - Tutorial 5 - Homology mod2 for TriLoop Example + Tutorial 5 - s-Centrality
- + Open In Colab - Tutorial 6 - Static Hypergraphs and Entities + Tutorial 6 - Homology mod2 for TriLoop Example
- - Open In Colab - Tutorial 7 - s-Centrality - -
- + +Installation +==================== + +The recommended installation method for most users is to create a virtual environment and install HyperNetX from PyPi. + +HyperNetX may be cloned or forked from [Github](https://github.com/pnnl/HyperNetX). + +Prerequisites +------------- +HyperNetX officially supports Python 3.8, 3.9, 3.10 and 3.11. + +Create a virtual environment +---------------------------- + +### Using venv + + +```shell +python -m venv venv-hnx +source venv-hnx/bin/activate +``` + + +### Using Anaconda + + +```shell +conda create -n venv-hnx python=3.11 -y +conda activate venv-hnx +``` + + +### Using virtualenv + + +```shell +virtualenv env-hnx +source env-hnx/bin/activate +``` + + +### For Windows Users + +On both Windows PowerShell or Command Prompt, you can use the following command to activate your virtual environment: + +```shell +.\env-hnx\Scripts\activate +``` + +To deactivate your environment, use: + +```shell +.\env-hnx\Scripts\deactivate +``` + Installing HyperNetX ==================== -HyperNetX may be cloned or forked from: -To install in an Anaconda environment -------------------------------------- +Regardless of how you install HyperNetX, ensure that your environment is activated and that you are running Python >=3.8. + - >>> conda create -n python=3.7 - >>> source activate - >>> pip install hypernetx +Installing from PyPi +-------------------- -Mac Users: If you wish to build the documentation you will need -the conda version of matplotlib: +```shell +pip install hypernetx +``` - >>> conda create -n python=3.7 matplotlib - >>> source activate - >>> pip install hypernetx +Installing from Source +---------------------- -To use [NWHy](docs/build/nwhy.html) use python=3.9 and the conda version of tbb in your environment. -**Note** that [NWHy](docs/build/nwhy.html) only works on Linux and some OSX systems. See [NWHy documentation](docs/build/nwhy.html) for more.: +Ensure that you have [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) installed. - >>> conda create -n python=3.9 tbb - >>> source activate - >>> pip install hypernetx - >>> pip install nwhy +```shell +git clone https://github.com/pnnl/HyperNetX.git +cd HyperNetX +pip install . +``` -To install in a virtualenv environment --------------------------------------- +Post-Installation Actions +========================= - >>> virtualenv --python= +Running Tests +------------- -This will create a virtual environment in the specified location using -the specified python executable. For example: +```shell +python -m pytest +``` - >>> virtualenv --python=C:\Anaconda3\python.exe hnx +Development +=========== -This will create a virtual environment in .\hnx using the python -that comes with Anaconda3. +Install an editable version +--------------------------- + +``` +pip install -e . +``` + +Install an editable version with access to jupyter notebooks +------------------------------------------------------------ + +```shell +pip install -e .'[all]' +``` + +Install support for testing +----------------------------- + +> ℹ️ **NOTE:** This project has a pytest configuration file named 'pytest.ini'. By default, pytest will use those configuration settings to run tests. - >>> \Scripts\activate +```shell +pip install .'[testing]' -If you are running in Windows PowerShell use =.ps1 +# run tests +python -m pytest -If you are running in Windows Command Prompt use =.bat +# run tests and show coverage report +python -m pytest --cov=hypernetx -Otherwise use =NULL (no file extension). +# Generate an HTML code coverage report and view it on a browser +coverage html +open htmlcov/index.html +``` -Once activated continue to follow the installation instructions below. +Install support for tutorials +----------------------------- +``` shell +pip install .'[tutorials]' +``` -Install using Pip options -------------------------- -For a minimal installation: +Install support for documentation +--------------------------------- + +```shell +pip install .'[documentation]' +cd docs + +## This will generate the documentation in /docs/build/ +## Open them in your browser with docs/build/html/index.html +make html +``` + + +Code Quality +------------ +HyperNetX uses a number of tools to maintain code quality: - >>> pip install hypernetx +* Pylint +* Black -For an editable installation with access to jupyter notebooks: +Before using these tools, ensure that you install Pylint in your environment: - >>> pip install [-e] . +```shell +pip install .'[linting]' +``` -To install with the tutorials: - >>> pip install -e .['tutorials'] +### Pylint -To install with the documentation: +[Pylint](https://pylint.pycqa.org/en/latest/index.html) is a static code analyzer for Python-based projects. From the [Pylint docs](https://pylint.pycqa.org/en/latest/index.html#what-is-pylint): - >>> pip install -e .['documentation'] - >>> chmod 755 build_docs.sh - >>> sh build_docs.sh - ## This will generate the documentation in /docs/build/ - ## Open them in your browser with /docs/index.html +> Pylint analyses your code without actually running it. It checks for errors, enforces a coding standard, looks for code smells, and can make suggestions about how the code could be refactored. Pylint can infer actual values from your code using its internal code representation (astroid). If your code is import logging as argparse, Pylint will know that argparse.error(...) is in fact a logging call and not an argparse call. -To install and test using pytest: - >>> pip install -e .['testing'] - >>> pytest +We have a Pylint configuration file, `.pylintrc`, located at the root of this project. +To run Pylint and view the results of Pylint, run the following command: -To install the whole shabang: +```shell +pylint hypernetx --rcfile=.pylintrc +``` + +You can also run Pylint on the command line to generate a report on the quality of the codebase and save it to a file named "pylint-results.txt": + +```shell +pylint hypernetx --output=pylint-results.txt +``` + +For more information on configuration, see https://pylint.pycqa.org/en/latest/user_guide/configuration/index.html + +### Black + +[Black](https://black.readthedocs.io/en/stable/) is a PEP 8 compliant formatter for Python-based project. This tool is highly opinionated about how Python should be formatted and will automagically reformat your code. + + +```shell +black hypernetx +``` + +Documentation +=============== + +Build and view documentation locally +--------------------------- - >>> pip install -e .['all'] +``` +cd docs +make html +open docs/build/html/index.html +``` + +Editing documentation +---------------------- +NOTE: make sure you install the required dependencies using: `make docs-deps` + +When editing documentation, you can auto-rebuild the documentation locally so that you can view your document changes +live on the browser without having to rebuild every time you have a change. + +``` +cd docs +make livehtml +``` + +This make script will run in the foreground on your terminal. You should see the following: + +```shell +The HTML pages are in docs/html. +[I 230324 09:50:48 server:335] Serving on http://127.0.0.1:8000 +[I 230324 09:50:48 handlers:62] Start watching changes +[I 230324 09:50:48 handlers:64] Start detecting changes +[I 230324 09:50:54 handlers:135] Browser Connected: http://127.0.0.1:8000/install.html +[I 230324 09:51:02 handlers:135] Browser Connected: http://127.0.0.1:8000/ +``` + +Click on `http://127.0.0.1:8000/install.html` to open the docs on your browser. Since this will auto-rebuild, every time +you change a document file, it will automatically render on your browser, allowing you to verify your document changes. + + +Continuous Integration +====================== + +This project runs Continuous Integration (CI) using GitHub Actions. Normally, CI runs +on pull requests, pushes to certain branches, and other events. + +Maintainers of the GitHub repository can manually trigger CI using [GitHub CLI](https://cli.github.com/). See instructions below on how to manually trigger CI on GitHub Actions: + +```commandline +# login to Github +gh auth login --with-token < ~/.ssh/tokens/ + +# Trigger CI +gh workflow run ci.yml --repo pnnl/HyperNetX --ref --field triggeredBy="" + +# Get the status of the workflow +gh run list --workflow=ci.yml --repo pnnl/HyperNetX +``` + + +Versioning +---------- + +This project uses [`commitizen`](https://github.com/commitizen-tools/commitizen) to manage versioning. +The files where "version" will be updated are listed in the '.cz.toml' file. To create a new version and the associated tag, +run the following commands: + +```shell +# Install commitizen tool to environment +make releases + +# Updates version; values for '--increment' can be MAJOR, MINOR, or PATCH +# Autocreates a tag and commit for the updated version +cz bump --increment MAJOR --dry-run +cz bump --increment MAJOR +``` Notice ------- +====== This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor Battelle, nor any of their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. @@ -209,8 +391,6 @@ Reference herein to any specific commercial product, process, or service by trad License -------- +======= Released under the 3-Clause BSD license (see License.rst) - - diff --git a/build_docs.sh b/build_docs.sh deleted file mode 100755 index 63427d27..00000000 --- a/build_docs.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -rm -rf docs/build -rm -rf docs/source/classes -rm -rf docs/source/algorithms -rm -rf docs/source/drawing -rm -rf docs/source/reports - -sphinx-apidoc -o docs/source/classes hypernetx/classes -sphinx-apidoc -o docs/source/algorithms hypernetx/algorithms -sphinx-apidoc -o docs/source/drawing hypernetx/drawing -sphinx-apidoc -o docs/source/reports hypernetx/reports -sphinx-build -b html docs/source docs/build \ No newline at end of file diff --git a/docs/.nojekyll b/docs/.nojekyll deleted file mode 100755 index e69de29b..00000000 diff --git a/docs/Makefile b/docs/Makefile index 5d982c19..c0117614 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -2,10 +2,13 @@ # # You can set these variables from the command line. -SPHINXOPTS = -SPHINXBUILD = sphinx-build +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SPHINXAPIDOC ?= sphinx-apidoc +SPHINXAUTOBUILD ?= sphinx-autobuild PAPER = -BUILDDIR = docs +BUILDDIR = build +SOURCEDIR = source # User-friendly check for sphinx-build ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) @@ -13,180 +16,32 @@ $(error The '$(SPHINXBUILD)' command was not found. 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1.2.5 documentation - - - - - - - - - - - - - - - - -
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Source code for algorithms.contagion.animation

-from collections import defaultdict
-import hypernetx as hnx
-from celluloid import Camera
-
-
-
[docs]def contagion_animation(
-    fig,
-    H,
-    transition_events,
-    node_state_color_dict,
-    edge_state_color_dict,
-    node_radius=1,
-    fps=1,
-):
-    """
-    A function to animate discrete-time contagion models for hypergraphs. Currently only supports a circular layout.
-
-    Parameters
-    ----------
-    fig : matplotlib Figure object
-    H : HyperNetX Hypergraph object
-    transition_events : dictionary
-        The dictionary that is output from the discrete_SIS and discrete_SIR functions with return_full_data=True
-    node_state_color_dict : dictionary
-        Dictionary which specifies the colors of each node state. All node states must be specified.
-    edge_state_color_dict : dictionary
-        Dictionary with keys that are edge states and values which specify the colors of each edge state
-        (can specify an alpha parameter). All edge-dependent transition states must be specified
-        (most common is "I") and there must be a a default "OFF" setting.
-    node_radius : float, default: 1
-        The radius of the nodes to draw
-    fps : int > 0, default: 1
-        Frames per second of the animation
-
-    Returns
-    -------
-    matplotlib Animation object
-
-    Notes
-    -----
-
-    Example::
-
-        >>> import hypernetx.algorithms.contagion as contagion
-        >>> import random
-        >>> import hypernetx as hnx
-        >>> import matplotlib.pyplot as plt
-        >>> from IPython.display import HTML
-        >>> n = 1000
-        >>> m = 10000
-        >>> hyperedgeList = [random.sample(range(n), k=random.choice([2,3])) for i in range(m)]
-        >>> H = hnx.Hypergraph(hyperedgeList)
-        >>> tau = {2:0.1, 3:0.1}
-        >>> gamma = 0.1
-        >>> tmax = 100
-        >>> dt = 0.1
-        >>> transition_events = contagion.discrete_SIS(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax, dt=dt, return_full_data=True)
-        >>> node_state_color_dict = {"S":"green", "I":"red", "R":"blue"}
-        >>> edge_state_color_dict = {"S":(0, 1, 0, 0.3), "I":(1, 0, 0, 0.3), "R":(0, 0, 1, 0.3), "OFF": (1, 1, 1, 0)}
-        >>> fps = 1
-        >>> fig = plt.figure()
-        >>> animation = contagion.contagion_animation(fig, H, transition_events, node_state_color_dict, edge_state_color_dict, node_radius=1, fps=fps)
-        >>> HTML(animation.to_jshtml())
-    """
-
-    nodeState = defaultdict(lambda: "S")
-
-    camera = Camera(fig)
-
-    for t in sorted(list(transition_events.keys())):
-        edgeState = defaultdict(lambda: "OFF")
-
-        # update edge and node states
-        for event in transition_events[t]:
-            status = event[0]
-            node = event[1]
-
-            # update node states
-            nodeState[node] = status
-
-            try:
-                # update the edge transmitters list if they are neighbor-dependent transitions
-                edgeID = event[2]
-                if edgeID is not None:
-                    edgeState[edgeID] = status
-            except:
-                pass
-
-        kwargs = {"layout_kwargs": {"seed": 39}}
-
-        nodeStyle = {
-            "facecolors": [node_state_color_dict[nodeState[node]] for node in H.nodes]
-        }
-        edgeStyle = {
-            "facecolors": [edge_state_color_dict[edgeState[edge]] for edge in H.edges],
-            "edgecolors": "black",
-        }
-
-        # draw hypergraph
-        hnx.draw(
-            H,
-            node_radius=node_radius,
-            nodes_kwargs=nodeStyle,
-            edges_kwargs=edgeStyle,
-            with_edge_labels=False,
-            with_node_labels=False,
-            **kwargs
-        )
-        camera.snap()
-
-    return camera.animate(interval=1000 / fps)

-
- -
-
-
- -
- -
-

© Copyright 2021 Battelle Memorial Institute.

-
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- - - - \ No newline at end of file diff --git a/docs/build/_modules/algorithms/contagion/epidemics.html b/docs/build/_modules/algorithms/contagion/epidemics.html deleted file mode 100644 index 663a0d96..00000000 --- a/docs/build/_modules/algorithms/contagion/epidemics.html +++ /dev/null @@ -1,1162 +0,0 @@ - - - - - - algorithms.contagion.epidemics — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - -
- - -
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    • -
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    • -
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    • -
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Source code for algorithms.contagion.epidemics

-import random
-import heapq
-import numpy as np
-from collections import defaultdict
-from collections import Counter
-
-# Canned Contagion Functions
-
[docs]def collective_contagion(node, status, edge):
-    """
-    The collective contagion mechanism described in
-    "The effect of heterogeneity on hypergraph contagion models" by Landry and Restrepo
-    https://doi.org/10.1063/5.0020034
-
-    Parameters
-    ----------
-    node : hashable
-        the node uid to infect (If it doesn't have status "S", it will automatically return False)
-    status : dictionary
-        The nodes are keys and the values are statuses (The infected state denoted with "I")
-    edge : iterable
-        Iterable of node ids (node must be in the edge or it will automatically return False)
-
-    Returns
-    -------
-    bool
-        False if there is no potential to infect and True if there is.
-
-    Notes
-    -----
-
-    Example::
-
-        >>> status = {0:"S", 1:"I", 2:"I", 3:"S", 4:"R"}
-        >>> collective_contagion(0, status, (0, 1, 2))
-            True
-        >>> collective_contagion(1, status, (0, 1, 2))
-            False
-        >>> collective_contagion(3, status, (0, 1, 2))
-            False
-    """
-    if status[node] != "S" or node not in edge:
-        return False
-
-    neighbors = set(edge).difference({node})
-    for i in neighbors:
-        if status[i] != "I":
-            return False
-    return True

-
-
-
[docs]def individual_contagion(node, status, edge):
-    """
-    The individual contagion mechanism described in
-    "The effect of heterogeneity on hypergraph contagion models" by Landry and Restrepo
-    https://doi.org/10.1063/5.0020034
-
-    Parameters
-    ----------
-    node : hashable
-        The node uid to infect (If it doesn't have status "S", it will automatically return False)
-    status : dictionary
-        The nodes are keys and the values are statuses (The infected state denoted with "I")
-    edge : iterable
-        Iterable of node ids (node must be in the edge or it will automatically return False)
-
-    Returns
-    -------
-    bool
-        False if there is no potential to infect and True if there is.
-
-    Notes
-    -----
-
-    Example::
-
-        >>> status = {0:"S", 1:"I", 2:"I", 3:"S", 4:"R"}
-        >>> individual_contagion(0, status, (0, 1, 3))
-            True
-        >>> individual_contagion(1, status, (0, 1, 2))
-            False
-        >>> collective_contagion(3, status, (0, 3, 4))
-            False
-    """
-    if status[node] != "S" or node not in edge:
-        return False
-
-    neighbors = set(edge).difference({node})
-    for i in neighbors:
-        if status[i] == "I":
-            return True
-    return False

-
-
-
[docs]def threshold(node, status, edge, tau=0.1):
-    """
-    The threshold contagion mechanism
-
-    Parameters
-    ----------
-    node : hashable
-        The node uid to infect (If it doesn't have status "S", it will automatically return False)
-    status : dictionary
-        The nodes are keys and the values are statuses (The infected state denoted with "I")
-    edge : iterable
-        Iterable of node ids (node must be in the edge or it will automatically return False)
-    tau : float between 0 and 1, default: 0.1
-        The fraction of nodes in an edge that must be infected for the edge to be able to transmit to the node
-
-    Returns
-    -------
-    bool
-        False if there is no potential to infect and True if there is.
-
-    Notes
-    -----
-
-    Example::
-
-        >>> status = {0:"S", 1:"I", 2:"I", 3:"S", 4:"R"}
-        >>> threshold(0, status, (0, 2, 3, 4), tau=0.2)
-            True
-        >>> threshold(0, status, (0, 2, 3, 4), tau=0.5)
-            False
-        >>> threshold(3, status, (1, 2, 3), tau=1)
-            False
-    """
-    if status[node] != "S" or node not in edge:
-        return False
-
-    neighbors = set(edge).difference({node})
-    if len(neighbors) > 0:
-        fraction_infected = sum([status[i] == "I" for i in neighbors]) / len(neighbors)
-    # The isolated node case
-    else:
-        fraction_infected = 0
-    return fraction_infected >= tau

-
-
-
[docs]def majority_vote(node, status, edge):
-    """
-    The majority vote contagion mechanism. If a majority of neighbors are contagious,
-    it is possible for an individual to change their opinion. If opinions are divided equally,
-    choose randomly.
-
-
-    Parameters
-    ----------
-    node : hashable
-        The node uid to infect (If it doesn't have status "S", it will automatically return False)
-    status : dictionary
-        The nodes are keys and the values are statuses (The infected state denoted with "I")
-    edge : iterable
-        Iterable of node ids (node must be in the edge or it will automatically return False
-    Returns
-    -------
-    bool
-        False if there is no potential to infect and True if there is.
-
-    Notes
-    -----
-
-    Example::
-
-        >>> status = {0:"S", 1:"I", 2:"I", 3:"S", 4:"R"}
-        >>> majority_vote(0, status, (0, 1, 2))
-            True
-        >>> majority_vote(0, status, (0, 1, 2, 3))
-            True
-        >>> majority_vote(1, status, (0, 1, 2))
-            False
-        >>> majority_vote(3, status, (0, 1, 2))
-            False
-    """
-
-    if status[node] != "S" or node not in edge:
-        return False
-
-    neighbors = set(edge).difference({node})
-    if len(neighbors) > 0:
-        fraction_infected = sum([status[i] == "I" for i in neighbors]) / len(neighbors)
-    else:
-        fraction_infected = 0
-
-    if fraction_infected < 0.5:
-        return False
-    elif fraction_infected > 0.5:
-        return True
-    else:
-        return random.choice([False, True])

-
-
-# Auxiliary functions
-
-# The ListDict class is copied from Joel Miller's Github repository Mathematics-of-Epidemics-on-Networks
-class _ListDict_(object):
-    r"""
-    The Gillespie algorithm will involve a step that samples a random element
-    from a set based on its weight.  This is awkward in Python.
-
-    So I'm introducing a new class based on a stack overflow answer by
-    Amber (http://stackoverflow.com/users/148870/amber)
-    for a question by
-    tba (http://stackoverflow.com/users/46521/tba)
-    found at
-    http://stackoverflow.com/a/15993515/2966723
-
-    This will allow me to select a random element uniformly, and then use
-    rejection sampling to make sure it's been selected with the appropriate
-    weight.
-    """
-
-    def __init__(self, weighted=False):
-        self.item_to_position = {}
-        self.items = []
-
-        self.weighted = weighted
-        if self.weighted:
-            self.weight = defaultdict(int)  # presume all weights positive
-            self.max_weight = 0
-            self._total_weight = 0
-            self.max_weight_count = 0
-
-    def __len__(self):
-        return len(self.items)
-
-    def __contains__(self, item):
-        return item in self.item_to_position
-
-    def _update_max_weight(self):
-        C = Counter(
-            self.weight.values()
-        )  # may be a faster way to do this, we only need to count the max.
-        self.max_weight = max(C.keys())
-        self.max_weight_count = C[self.max_weight]
-
-    def insert(self, item, weight=None):
-        r"""
-        If not present, then inserts the thing (with weight if appropriate)
-        if already there, replaces the weight unless weight is 0
-
-        If weight is 0, then it removes the item and doesn't replace.
-
-        WARNING:
-            replaces weight if already present, does not increment weight.
-
-
-        """
-        if self.__contains__(item):
-            self.remove(item)
-        if weight != 0:
-            self.update(item, weight_increment=weight)
-
-    def update(self, item, weight_increment=None):
-        r"""
-        If not present, then inserts the thing (with weight if appropriate)
-        if already there, increments weight
-
-        WARNING:
-            increments weight if already present, cannot overwrite weight.
-        """
-        if (
-            weight_increment is not None
-        ):  # will break if passing a weight to unweighted case
-            if weight_increment > 0 or self.weight[item] != self.max_weight:
-                self.weight[item] = self.weight[item] + weight_increment
-                self._total_weight += weight_increment
-                if self.weight[item] > self.max_weight:
-                    self.max_weight_count = 1
-                    self.max_weight = self.weight[item]
-                elif self.weight[item] == self.max_weight:
-                    self.max_weight_count += 1
-            else:  # it's a negative increment and was at max
-                self.max_weight_count -= 1
-                self.weight[item] = self.weight[item] + weight_increment
-                self._total_weight += weight_increment
-                self.max_weight_count -= 1
-                if self.max_weight_count == 0:
-                    self._update_max_weight
-        elif self.weighted:
-            raise Exception("if weighted, must assign weight_increment")
-
-        if item in self:  # we've already got it, do nothing else
-            return
-        self.items.append(item)
-        self.item_to_position[item] = len(self.items) - 1
-
-    def remove(self, choice):
-        position = self.item_to_position.pop(choice)
-        last_item = self.items.pop()
-        if position != len(self.items):
-            self.items[position] = last_item
-            self.item_to_position[last_item] = position
-
-        if self.weighted:
-            weight = self.weight.pop(choice)
-            self._total_weight -= weight
-            if weight == self.max_weight:
-                # if we find ourselves in this case often
-                # it may be better just to let max_weight be the
-                # largest weight *ever* encountered, even if all remaining weights are less
-                #
-                self.max_weight_count -= 1
-                if self.max_weight_count == 0 and len(self) > 0:
-                    self._update_max_weight()
-
-    def choose_random(self):
-        # r'''chooses a random node.  If there is a weight, it will use rejection
-        # sampling to choose a random node until it succeeds'''
-        if self.weighted:
-            while True:
-                choice = random.choice(self.items)
-                if random.random() < self.weight[choice] / self.max_weight:
-                    break
-            # r = random.random()*self.total_weight
-            # for item in self.items:
-            #     r-= self.weight[item]
-            #     if r<0:
-            #         break
-            return choice
-
-        else:
-            return random.choice(self.items)
-
-    def random_removal(self):
-        r"""uses other class methods to choose and then remove a random node"""
-        choice = self.choose_random()
-        self.remove(choice)
-        return choice
-
-    def total_weight(self):
-        if self.weighted:
-            return self._total_weight
-        else:
-            return len(self)
-
-    def update_total_weight(self):
-        self._total_weight = sum(self.weight[item] for item in self.items)
-
-
-# Contagion Functions
-
[docs]def discrete_SIR(
-    H,
-    tau,
-    gamma,
-    transmission_function=threshold,
-    initial_infecteds=None,
-    initial_recovereds=None,
-    rho=None,
-    tmin=0,
-    tmax=float("Inf"),
-    dt=1.0,
-    return_full_data=False,
-    **args
-):
-    """
-    A discrete-time SIR model for hypergraphs similar to the construction described in
-    "The effect of heterogeneity on hypergraph contagion models" by Landry and Restrepo
-    https://doi.org/10.1063/5.0020034 and
-    "Simplicial models of social contagion" by Iacopini et al.
-    https://doi.org/10.1038/s41467-019-10431-6
-
-    Parameters
-    ----------
-    H : HyperNetX Hypergraph object
-    tau : dictionary
-        Edge sizes as keys (must account for all edge sizes present) and rates of infection for each size (float)
-    gamma : float
-        The healing rate
-    transmission_function : lambda function, default: threshold
-        A lambda function that has required arguments (node, status, edge) and optional arguments
-    initial_infecteds : list or numpy array, default: None
-        Iterable of initially infected node uids
-    initial_recovereds : list or numpy array, default: None
-        An iterable of initially recovered node uids
-    rho : float from 0 to 1, default: None
-        The fraction of initially infected individuals. Both rho and initially infected cannot be specified.
-    tmin : float, default: 0
-        Time at the start of the simulation
-    tmax : float, default: float('Inf')
-        Time at which the simulation should be terminated if it hasn't already.
-    dt : float > 0, default: 1.0
-        Step forward in time that the simulation takes at each step.
-    return_full_data : bool, default: False
-        This returns all the infection and recovery events at each time if True.
-    **args : Optional arguments to transmission function
-        This allows user-defined transmission functions with extra parameters.
-
-    Returns
-    -------
-    if return_full_data
-        dictionary
-            Time as the keys and events that happen as the values.
-    else
-        t, S, I, R : numpy arrays
-            time (t), number of susceptible (S), infected (I), and recovered (R) at each time.
-
-    Notes
-    -----
-
-    Example::
-
-        >>> import hypernetx.algorithms.contagion as contagion
-        >>> import random
-        >>> import hypernetx as hnx
-        >>> n = 1000
-        >>> m = 10000
-        >>> hyperedgeList = [random.sample(range(n), k=random.choice([2,3])) for i in range(m)]
-        >>> H = hnx.Hypergraph(hyperedgeList)
-        >>> tau = {2:0.1, 3:0.1}
-        >>> gamma = 0.1
-        >>> tmax = 100
-        >>> dt = 0.1
-        >>> t, S, I, R = contagion.discrete_SIR(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax, dt=dt)
-    """
-
-    if rho is not None and initial_infecteds is not None:
-        raise Exception("Cannot define both initial_infecteds and rho")
-
-    if initial_infecteds is None:
-        if rho is None:
-            initial_number = 1
-        else:
-            initial_number = int(round(H.number_of_nodes() * rho))
-        initial_infecteds = random.sample(list(H.nodes), initial_number)
-    else:
-        # check to make sure that the initially infected nodes are in the hypergraph
-        initial_infecteds = list(set(H.nodes).intersection(set(initial_infecteds)))
-
-    if initial_recovereds is None:
-        initial_recovereds = []
-    else:
-        # check to make sure that the initially recovered nodes are in the hypergraph
-        initial_recovereds = list(set(H.nodes).intersection(set(initial_recovereds)))
-
-    status = defaultdict(lambda: "S")
-
-    if return_full_data:
-        transition_events = dict()
-        transition_events[tmin] = list()
-
-    for node in initial_infecteds:
-        status[node] = "I"
-        if return_full_data:
-            transition_events[tmin].append(("I", node, None))
-
-    for node in initial_recovereds:
-        status[node] = "R"
-        if return_full_data:
-            transition_events[tmin].append(("R", node))
-
-    I = [len(initial_infecteds)]
-    R = [len(initial_recovereds)]
-    S = [H.number_of_nodes() - I[-1] - R[-1]]
-
-    t = tmin
-    times = [t]
-    newStatus = status.copy()
-
-    if H.isstatic:
-        edge_neighbors = lambda node: H.edges.memberships[node]
-    else:
-        edge_neighbors = lambda node: H.nodes[node].memberships
-
-    while t < tmax and I[-1] != 0:
-        # Initialize the next step with the same numbers of S, I, and R as the last step before computing the changes
-        S.append(S[-1])
-        I.append(I[-1])
-        R.append(R[-1])
-
-        if return_full_data:
-            transition_events[t + dt] = list()
-
-        for node in H.nodes:
-            if status[node] == "I":
-                # recover the node. If it is not healed, it stays infected.
-                if random.random() <= gamma * dt:
-                    newStatus[node] = "R"
-                    I[-1] += -1
-                    R[-1] += 1
-                    if return_full_data:
-                        transition_events[t + dt].append(("R", node))
-            elif status[node] == "S":
-                for edge_id in edge_neighbors(node):
-                    members = H.edges[edge_id]
-                    if (
-                        random.random()
-                        <= tau[len(members)]
-                        * transmission_function(node, status, members, **args)
-                        * dt
-                    ):
-                        newStatus[node] = "I"
-                        S[-1] += -1
-                        I[-1] += 1
-                        if return_full_data:
-                            transition_events[t + dt].append(("I", node, edge_id))
-                        break
-                # This executes after the loop has executed normally without hitting the break statement which indicates infection
-                else:
-                    newStatus[node] = "S"
-        status = newStatus.copy()
-        t += dt
-        times.append(t)
-    if return_full_data:
-        return transition_events
-    else:
-        return np.array(times), np.array(S), np.array(I), np.array(R)

-
-
-
[docs]def discrete_SIS(
-    H,
-    tau,
-    gamma,
-    transmission_function=threshold,
-    initial_infecteds=None,
-    rho=None,
-    tmin=0,
-    tmax=100,
-    dt=1.0,
-    return_full_data=False,
-    **args
-):
-    """
-    A discrete-time SIS model for hypergraphs as implemented in
-    "The effect of heterogeneity on hypergraph contagion models" by Landry and Restrepo
-    https://doi.org/10.1063/5.0020034 and
-    "Simplicial models of social contagion" by Iacopini et al.
-    https://doi.org/10.1038/s41467-019-10431-6
-
-    Parameters
-    ----------
-    H : HyperNetX Hypergraph object
-    tau : dictionary
-        Edge sizes as keys (must account for all edge sizes present) and rates of infection for each size (float)
-    gamma : float
-        The healing rate
-    transmission_function : lambda function, default: threshold
-        A lambda function that has required arguments (node, status, edge) and optional arguments
-    initial_infecteds : list or numpy array, default: None
-        Iterable of initially infected node uids
-    rho : float from 0 to 1, default: None
-        The fraction of initially infected individuals. Both rho and initially infected cannot be specified.
-    tmin : float, default: 0
-        Time at the start of the simulation
-    tmax : float, default: 100
-        Time at which the simulation should be terminated if it hasn't already.
-    dt : float > 0, default: 1.0
-        Step forward in time that the simulation takes at each step.
-    return_full_data : bool, default: False
-        This returns all the infection and recovery events at each time if True.
-    **args : Optional arguments to transmission function
-        This allows user-defined transmission functions with extra parameters.
-
-    Returns
-    -------
-    if return_full_data
-        dictionary
-            Time as the keys and events that happen as the values.
-    else
-        t, S, I : numpy arrays
-            time (t), number of susceptible (S), and infected (I) at each time.
-
-    Notes
-    -----
-
-    Example::
-
-        >>> import hypernetx.algorithms.contagion as contagion
-        >>> import random
-        >>> import hypernetx as hnx
-        >>> n = 1000
-        >>> m = 10000
-        >>> hyperedgeList = [random.sample(range(n), k=random.choice([2,3])) for i in range(m)]
-        >>> H = hnx.Hypergraph(hyperedgeList)
-        >>> tau = {2:0.1, 3:0.1}
-        >>> gamma = 0.1
-        >>> tmax = 100
-        >>> dt = 0.1
-        >>> t, S, I = contagion.discrete_SIS(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax, dt=dt)
-    """
-
-    if rho is not None and initial_infecteds is not None:
-        raise Exception("Cannot define both initial_infecteds and rho")
-
-    if initial_infecteds is None:
-        if rho is None:
-            initial_number = 1
-        else:
-            initial_number = int(round(H.number_of_nodes() * rho))
-        initial_infecteds = random.sample(list(H.nodes), initial_number)
-    else:
-        # check to make sure that the initially infected nodes are in the hypergraph
-        initial_infecteds = list(set(H.nodes).intersection(set(initial_infecteds)))
-
-    status = defaultdict(lambda: "S")
-
-    if return_full_data:
-        transition_events = dict()
-        transition_events[tmin] = list()
-
-    for node in initial_infecteds:
-        status[node] = "I"
-        if return_full_data:
-            transition_events[tmin].append(("I", node, None))
-
-    I = [len(initial_infecteds)]
-    S = [H.number_of_nodes() - I[-1]]
-
-    t = tmin
-    times = [t]
-    newStatus = status.copy()
-
-    if H.isstatic:
-        edge_neighbors = lambda node: H.edges.memberships[node]
-    else:
-        edge_neighbors = lambda node: H.nodes[node].memberships
-
-    while t < tmax and I[-1] != 0:
-        # Initialize the next step with the same numbers of S, I, and R as the last step before computing the changes
-        S.append(S[-1])
-        I.append(I[-1])
-        if return_full_data:
-            transition_events[t + dt] = list()
-
-        for node in H.nodes:
-            if status[node] == "I":
-                # recover the node. If it is not healed, it stays infected.
-                if random.random() <= gamma * dt:
-                    newStatus[node] = "S"
-                    I[-1] += -1
-                    S[-1] += 1
-                    if return_full_data:
-                        transition_events[t + dt].append(("S", node))
-            elif status[node] == "S":
-                for edge_id in edge_neighbors(node):
-                    members = H.edges[edge_id]
-
-                    if (
-                        random.random()
-                        <= tau[len(members)]
-                        * transmission_function(node, status, members, **args)
-                        * dt
-                    ):
-                        newStatus[node] = "I"
-                        S[-1] += -1
-                        I[-1] += 1
-                        if return_full_data:
-                            transition_events[t + dt].append(("I", node, edge_id))
-                        break
-                # This executes after the loop has executed normally without hitting the break statement which indicates infection, though I'm not sure we even need it
-                else:
-                    newStatus[node] = "S"
-        status = newStatus.copy()
-        t += dt
-        times.append(t)
-    if return_full_data:
-        return transition_events
-    else:
-        return np.array(times), np.array(S), np.array(I)

-
-
-
[docs]def Gillespie_SIR(
-    H,
-    tau,
-    gamma,
-    transmission_function=threshold,
-    initial_infecteds=None,
-    initial_recovereds=None,
-    rho=None,
-    tmin=0,
-    tmax=float("Inf"),
-    **args
-):
-    """
-    A continuous-time SIR model for hypergraphs similar to the model in
-    "The effect of heterogeneity on hypergraph contagion models" by Landry and Restrepo
-    https://doi.org/10.1063/5.0020034 and
-    implemented for networks in the EoN package by Joel C. Miller
-    https://epidemicsonnetworks.readthedocs.io/en/latest/
-
-    Parameters
-    ----------
-    H : HyperNetX Hypergraph object
-    tau : dictionary
-        Edge sizes as keys (must account for all edge sizes present) and rates of infection for each size (float)
-    gamma : float
-        The healing rate
-    transmission_function : lambda function, default: threshold
-        A lambda function that has required arguments (node, status, edge) and optional arguments
-    initial_infecteds : list or numpy array, default: None
-        Iterable of initially infected node uids
-    initial_recovereds : list or numpy array, default: None
-        An iterable of initially recovered node uids
-    rho : float from 0 to 1, default: None
-        The fraction of initially infected individuals. Both rho and initially infected cannot be specified.
-    tmin : float, default: 0
-        Time at the start of the simulation
-    tmax : float, default: float('Inf')
-        Time at which the simulation should be terminated if it hasn't already.
-    return_full_data : bool, default: False
-        This returns all the infection and recovery events at each time if True.
-    **args : Optional arguments to transmission function
-        This allows user-defined transmission functions with extra parameters.
-
-    Returns
-    -------
-    t, S, I, R : numpy arrays
-        time (t), number of susceptible (S), infected (I), and recovered (R) at each time.
-
-    Notes
-    -----
-
-    Example::
-
-        >>> import hypernetx.algorithms.contagion as contagion
-        >>> import random
-        >>> import hypernetx as hnx
-        >>> n = 1000
-        >>> m = 10000
-        >>> hyperedgeList = [random.sample(range(n), k=random.choice([2,3])) for i in range(m)]
-        >>> H = hnx.Hypergraph(hyperedgeList)
-        >>> tau = {2:0.1, 3:0.1}
-        >>> gamma = 0.1
-        >>> tmax = 100
-        >>> t, S, I, R = contagion.Gillespie_SIR(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax)
-    """
-    # Initial infecteds and recovereds should be lists or None. Add a check here.
-
-    if rho is not None and initial_infecteds is not None:
-        raise Exception("Cannot define both initial_infecteds and rho")
-
-    if initial_infecteds is None:
-        if rho is None:
-            initial_number = 1
-        else:
-            initial_number = int(round(H.number_of_nodes() * rho))
-        initial_infecteds = random.sample(list(H.nodes), initial_number)
-    else:
-        # check to make sure that the initially infected nodes are in the hypergraph
-        initial_infecteds = list(set(H.nodes).intersection(set(initial_infecteds)))
-
-    if initial_recovereds is None:
-        initial_recovereds = []
-    else:
-        # check to make sure that the initially recovered nodes are in the hypergraph
-        initial_recovereds = list(set(H.nodes).intersection(set(initial_recovereds)))
-
-    status = defaultdict(lambda: "S")
-
-    size_dist = np.unique(H.edge_size_dist())
-
-    for node in initial_infecteds:
-        status[node] = "I"
-
-    for node in initial_recovereds:
-        status[node] = "R"
-
-    I = [len(initial_infecteds)]
-    R = [len(initial_recovereds)]
-    S = [H.number_of_nodes() - I[-1] - R[-1]]
-
-    if H.isstatic:
-        edge_neighbors = lambda node: H.edges.memberships[node]
-    else:
-        edge_neighbors = lambda node: H.nodes[node].memberships
-
-    t = tmin
-    times = [t]
-
-    infecteds = _ListDict_()
-
-    infectious_edges = dict()
-    for size in size_dist:
-        infectious_edges[size] = _ListDict_()
-
-    for node in initial_infecteds:
-        infecteds.update(node)
-        for edge_id in edge_neighbors(node):
-            members = H.edges[edge_id]
-            for node in members:
-                is_infectious = transmission_function(node, status, members, **args)
-                if is_infectious:
-                    infectious_edges[len(members)].update((edge_id, node))
-
-    total_rates = dict()
-    total_rates[1] = gamma * infecteds.total_weight()
-
-    for size in size_dist:
-        total_rates[size] = tau[size] * infectious_edges[size].total_weight()
-
-    total_rate = sum(total_rates.values())
-
-    dt = random.expovariate(total_rate)
-    t += dt
-
-    while t < tmax and I[-1] != 0:
-        # choose type of event that happens
-        while True:
-            choice = random.choice(list(total_rates.keys()))
-            if random.random() <= total_rates[choice] / total_rate:
-                break
-
-        if choice == 1:  # recover
-            recovering_node = infecteds.random_removal()
-            status[recovering_node] = "R"
-
-            # remove edges that are no longer infectious because of this node recovering
-            for edge_id in edge_neighbors(recovering_node):
-                members = H.edges[edge_id]
-                for node in members:
-                    is_infectious = transmission_function(node, status, members, **args)
-                    if is_infectious:
-                        try:
-                            infectious_edges[len(members)].remove((edge_id, node))
-                        except:
-                            pass
-            times.append(t)
-            S.append(S[-1])
-            I.append(I[-1] - 1)
-            R.append(R[-1] + 1)
-
-        else:
-            _, recipient = infectious_edges[choice].choose_random()
-            status[recipient] = "I"
-
-            infecteds.update(recipient)
-
-            # remove the infectious links, because they can't infect an infected node.
-            for edge_id in edge_neighbors(recipient):
-                members = H.edges[edge_id]
-                try:
-                    infectious_edges[len(members)].remove((edge_id, recipient))
-                except:
-                    pass
-
-            # add edges that are infectious because of this node being infected
-            for edge_id in edge_neighbors(recipient):
-                members = H.edges[edge_id]
-                for node in members:
-                    is_infectious = transmission_function(node, status, members, **args)
-                    if is_infectious:
-                        try:
-                            infectious_edges[len(members)].update((edge_id, node))
-                        except:
-                            pass
-            times.append(t)
-            S.append(S[-1] - 1)
-            I.append(I[-1] + 1)
-            R.append(R[-1])
-
-        total_rates[1] = gamma * infecteds.total_weight()
-        for size in size_dist:
-            total_rates[size] = tau[size] * infectious_edges[size].total_weight()
-
-        total_rate = sum(total_rates.values())
-
-        if total_rate > 0:
-            dt = random.expovariate(total_rate)
-        else:
-            dt = float("Inf")
-        t += dt
-    return np.array(times), np.array(S), np.array(I), np.array(R)

-
-
-
[docs]def Gillespie_SIS(
-    H,
-    tau,
-    gamma,
-    transmission_function=threshold,
-    initial_infecteds=None,
-    rho=None,
-    tmin=0,
-    tmax=float("Inf"),
-    return_full_data=False,
-    sim_kwargs=None,
-    **args
-):
-    """
-    A continuous-time SIS model for hypergraphs similar to the model in
-    "The effect of heterogeneity on hypergraph contagion models" by Landry and Restrepo
-    https://doi.org/10.1063/5.0020034 and
-    implemented for networks in the EoN package by Joel C. Miller
-    https://epidemicsonnetworks.readthedocs.io/en/latest/
-
-    Parameters
-    ----------
-    H : HyperNetX Hypergraph object
-    tau : dictionary
-        Edge sizes as keys (must account for all edge sizes present) and rates of infection for each size (float)
-    gamma : float
-        The healing rate
-    transmission_function : lambda function, default: threshold
-        A lambda function that has required arguments (node, status, edge) and optional arguments
-    initial_infecteds : list or numpy array, default: None
-        Iterable of initially infected node uids
-    rho : float from 0 to 1, default: None
-        The fraction of initially infected individuals. Both rho and initially infected cannot be specified.
-    tmin : float, default: 0
-        Time at the start of the simulation
-    tmax : float, default: 100
-        Time at which the simulation should be terminated if it hasn't already.
-    return_full_data : bool, default: False
-        This returns all the infection and recovery events at each time if True.
-    **args : Optional arguments to transmission function
-        This allows user-defined transmission functions with extra parameters.
-
-    Returns
-    -------
-    t, S, I : numpy arrays
-        time (t), number of susceptible (S), and infected (I) at each time.
-
-    Notes
-    -----
-
-    Example::
-
-        >>> import hypernetx.algorithms.contagion as contagion
-        >>> import random
-        >>> import hypernetx as hnx
-        >>> n = 1000
-        >>> m = 10000
-        >>> hyperedgeList = [random.sample(range(n), k=random.choice([2,3])) for i in range(m)]
-        >>> H = hnx.Hypergraph(hyperedgeList)
-        >>> tau = {2:0.1, 3:0.1}
-        >>> gamma = 0.1
-        >>> tmax = 100
-        >>> t, S, I = contagion.Gillespie_SIS(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax)
-    """
-    # Initial infecteds and recovereds should be lists or None. Add a check here.
-
-    if rho is not None and initial_infecteds is not None:
-        raise Exception("Cannot define both initial_infecteds and rho")
-
-    if initial_infecteds is None:
-        if rho is None:
-            initial_number = 1
-        else:
-            initial_number = int(round(H.number_of_nodes() * rho))
-        initial_infecteds = random.sample(list(H.nodes), initial_number)
-    else:
-        # check to make sure that the initially infected nodes are in the hypergraph
-        initial_infecteds = list(set(H.nodes).intersection(set(initial_infecteds)))
-
-    status = defaultdict(lambda: "S")
-
-    size_dist = np.unique(H.edge_size_dist())
-
-    for node in initial_infecteds:
-        status[node] = "I"
-
-    I = [len(initial_infecteds)]
-    S = [H.number_of_nodes() - I[-1]]
-
-    if H.isstatic:
-        edge_neighbors = lambda node: H.edges.memberships[node]
-    else:
-        edge_neighbors = lambda node: H.nodes[node].memberships
-
-    t = tmin
-    times = [t]
-
-    infecteds = _ListDict_()
-
-    infectious_edges = dict()
-    for size in size_dist:
-        infectious_edges[size] = _ListDict_()
-
-    for node in initial_infecteds:
-        infecteds.update(node)
-        for edge_id in edge_neighbors(node):
-            members = H.edges[edge_id]
-            for node in members:
-                is_infectious = transmission_function(node, status, members, **args)
-                if is_infectious:
-                    infectious_edges[len(members)].update((edge_id, node))
-
-    total_rates = dict()
-    total_rates[1] = gamma * infecteds.total_weight()
-    for size in size_dist:
-        total_rates[size] = tau[size] * infectious_edges[size].total_weight()
-
-    total_rate = sum(total_rates.values())
-
-    dt = random.expovariate(total_rate)
-    t += dt
-
-    while t < tmax and I[-1] != 0:
-        # choose type of event that happens
-        # this can be improved
-        while True:
-            choice = random.choice(list(total_rates.keys()))
-            if random.random() <= total_rates[choice] / total_rate:
-                break
-
-        if choice == 1:  # recover
-            recovering_node = infecteds.random_removal()
-            status[recovering_node] = "S"
-
-            # remove edges that are no longer infectious because of this node recovering
-            for edge_id in edge_neighbors(recovering_node):
-                members = H.edges[edge_id]
-                for node in members:
-                    is_infectious = transmission_function(node, status, members, **args)
-                    if is_infectious:
-                        try:
-                            infectious_edges[len(members)].remove((edge_id, node))
-                        except:
-                            pass
-            times.append(t)
-            S.append(S[-1] + 1)
-            I.append(I[-1] - 1)
-
-        else:
-            _, recipient = infectious_edges[choice].choose_random()
-            status[recipient] = "I"
-
-            infecteds.update(recipient)
-
-            # remove the infectious links, because they can't infect an infected node.
-            for edge_id in edge_neighbors(recipient):
-                members = H.edges[edge_id]
-                try:
-                    infectious_edges[len(members)].remove((edge_id, recipient))
-                except:
-                    pass
-
-            # add edges that are infectious because of this node being infected
-            for edge_id in edge_neighbors(recipient):
-                members = H.edges[edge_id]
-                for node in members:
-                    is_infectious = transmission_function(node, status, members, **args)
-                    if is_infectious:
-                        try:
-                            infectious_edges[len(members)].update((edge_id, node))
-                        except:
-                            pass
-            times.append(t)
-            S.append(S[-1] - 1)
-            I.append(I[-1] + 1)
-
-        total_rates[1] = gamma * infecteds.total_weight()
-        for size in size_dist:
-            total_rates[size] = tau[size] * infectious_edges[size].total_weight()
-
-        total_rate = sum(total_rates.values())
-
-        if total_rate > 0:
-            dt = random.expovariate(total_rate)
-        else:
-            dt = float("Inf")
-        t += dt
-
-    return np.array(times), np.array(S), np.array(I)

-
- -
-
-
- -
- -
-

© Copyright 2021 Battelle Memorial Institute.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
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-
-
- - - - \ No newline at end of file diff --git a/docs/build/_modules/algorithms/generative_models.html b/docs/build/_modules/algorithms/generative_models.html deleted file mode 100644 index dd6cc67e..00000000 --- a/docs/build/_modules/algorithms/generative_models.html +++ /dev/null @@ -1,360 +0,0 @@ - - - - - - algorithms.generative_models — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - -
- - -
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    • -
  • Module code »
    • -
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    • -
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Source code for algorithms.generative_models

-import random
-import math
-import warnings
-from collections import defaultdict
-import numpy as np
-import pandas as pd
-from hypernetx import Hypergraph
-
-
-
[docs]def erdos_renyi_hypergraph(n, m, p, node_labels=None, edge_labels=None):
-    """
-    A function to generate an Erdos-Renyi hypergraph as implemented by Mirah Shi and described for
-    bipartite networks by Aksoy et al. in https://doi.org/10.1093/comnet/cnx001
-
-    Parameters
-    ----------
-    n: int
-        Number of nodes
-    m: int
-        Number of edges
-    p: float
-        The probability that a bipartite edge is created
-    node_labels: list, default=None
-        Vertex labels
-    edge_labels: list, default=None
-        Hyperedge labels
-
-    Returns
-    -------
-    HyperNetX Hypergraph object
-
-
-    Example::
-    
-    >>> import hypernetx.algorithms.generative_models as gm
-    >>> n = 1000
-    >>> m = n
-    >>> p = 0.01
-    >>> H = gm.erdos_renyi_hypergraph(n, m, p)
-
-    """
-
-    if node_labels is not None and edge_labels is not None:
-        get_node_label = lambda index: node_labels[index]
-        get_edge_label = lambda index: edge_labels[index]
-    else:
-        get_node_label = lambda index: index
-        get_edge_label = lambda index: index
-
-    bipartite_edges = []
-    for u in range(n):
-        v = 0
-        while v < m:
-            # identify next pair
-            r = random.random()
-            v = v + math.floor(math.log(r) / math.log(1 - p))
-            if v < m:
-                # add vertex hyperedge pair
-                bipartite_edges.append((get_edge_label(v), get_node_label(u)))
-                v = v + 1
-
-    df = pd.DataFrame(bipartite_edges)
-    return Hypergraph(df, static=True)

-
-
-
[docs]def chung_lu_hypergraph(k1, k2):
-    """
-    A function to generate an extension of Chung-Lu hypergraph as implemented by Mirah Shi and described for
-    bipartite networks by Aksoy et al. in https://doi.org/10.1093/comnet/cnx001
-
-    Parameters
-    ----------
-    k1 : dictionary
-        This a dictionary where the keys are node ids and the values are node degrees.
-    k2 : dictionary
-        This a dictionary where the keys are edge ids and the values are edge degrees also known as edge sizes.
-    Returns
-    -------
-    HyperNetX Hypergraph object
-
-    Notes
-    -----
-    The sums of k1 and k2 should be roughly the same. If they are not the same, this function returns a warning but still runs.
-    The output currently is a static Hypergraph object. Dynamic hypergraphs are not currently supported.
-
-    Example::
-
-    >>> import hypernetx.algorithms.generative_models as gm
-    >>> import random
-    >>> n = 100
-    >>> k1 = {i : random.randint(1, 100) for i in range(n)}
-    >>> k2 = {i : sorted(k1.values())[i] for i in range(n)}
-    >>> H = gm.chung_lu_hypergraph(k1, k2)
-    """
-
-    # sort dictionary by degree in decreasing order
-    Nlabels = [n for n, _ in sorted(k1.items(), key=lambda d: d[1], reverse=True)]
-    Mlabels = [m for m, _ in sorted(k2.items(), key=lambda d: d[1], reverse=True)]
-
-    m = len(k2)
-
-    if sum(k1.values()) != sum(k2.values()):
-        warnings.warn(
-            "The sum of the degree sequence does not match the sum of the size sequence"
-        )
-
-    S = sum(k1.values())
-
-    bipartite_edges = []
-    for u in Nlabels:
-        j = 0
-        v = Mlabels[j]  # start from beginning every time
-        p = min((k1[u] * k2[v]) / S, 1)
-
-        while j < m:
-            if p != 1:
-                r = random.random()
-                j = j + math.floor(math.log(r) / math.log(1 - p))
-            if j < m:
-                v = Mlabels[j]
-                q = min((k1[u] * k2[v]) / S, 1)
-                r = random.random()
-                if r < q / p:
-                    # no duplicates
-                    bipartite_edges.append((v, u))
-
-                p = q
-                j = j + 1
-
-    df = pd.DataFrame(bipartite_edges)
-    return Hypergraph(df, static=True)

-
-
-
[docs]def dcsbm_hypergraph(k1, k2, g1, g2, omega):
-    """
-    A function to generate an extension of DCSBM hypergraph as implemented by Mirah Shi and described for
-    bipartite networks by Larremore et al. in https://doi.org/10.1103/PhysRevE.90.012805
-
-    Parameters
-    ----------
-    k1 : dictionary
-        This a dictionary where the keys are node ids and the values are node degrees.
-    k2 : dictionary
-        This a dictionary where the keys are edge ids and the values are edge degrees also known as edge sizes.
-    g1 : dictionary
-        This a dictionary where the keys are node ids and the values are the group ids to which the node belongs.
-        The keys must match the keys of k1.
-    g2 : dictionary
-        This a dictionary where the keys are edge ids and the values are the group ids to which the edge belongs.
-        The keys must match the keys of k2.
-    omega : 2D numpy array
-        This is a matrix with entries which specify the number of edges between a given node community and edge community.
-        The number of rows must match the number of node communities and the number of columns
-        must match the number of edge communities.
-
-
-    Returns
-    -------
-    HyperNetX Hypergraph object
-
-    Notes
-    -----
-    The sums of k1 and k2 should be the same. If they are not the same, this function returns a warning but still runs.
-    The sum of k1 (and k2) and omega should be the same. If they are not the same, this function returns a warning
-    but still runs and the number of entries in the incidence matrix is determined by the omega matrix.
-
-    The output currently is a static Hypergraph object. Dynamic hypergraphs are not currently supported.
-
-    Example::
-
-    >>> n = 100
-    >>> k1 = {i : random.randint(1, 100) for i in range(n)}
-    >>> k2 = {i : sorted(k1.values())[i] for i in range(n)}
-    >>> g1 = {i : random.choice([0, 1]) for i in range(n)}
-    >>> g2 = {i : random.choice([0, 1]) for i in range(n)}
-    >>> omega = np.array([[100, 10], [10, 100]])
-    >>> H = gm.dcsbm_hypergraph(k1, k2, g1, g2, omega)
-    """
-
-    # sort dictionary by degree in decreasing order
-    Nlabels = [n for n, _ in sorted(k1.items(), key=lambda d: d[1], reverse=True)]
-    Mlabels = [m for m, _ in sorted(k2.items(), key=lambda d: d[1], reverse=True)]
-
-    # these checks verify that the sum of node and edge degrees and the sum of node degrees
-    # and the sum of community connection matrix differ by less than a single edge.
-    if abs(sum(k1.values()) - sum(k2.values())) > 1:
-        warnings.warn(
-            "The sum of the degree sequence does not match the sum of the size sequence"
-        )
-
-    if abs(sum(k1.values()) - np.sum(omega)) > 1:
-        warnings.warn(
-            "The sum of the degree sequence does not match the entries in the omega matrix"
-        )
-
-    # get indices for each community
-    community1Indices = defaultdict(list)
-    for label in Nlabels:
-        group = g1[label]
-        community1Indices[group].append(label)
-
-    community2Indices = defaultdict(list)
-    for label in Mlabels:
-        group = g2[label]
-        community2Indices[group].append(label)
-
-    bipartite_edges = list()
-
-    kappa1 = defaultdict(lambda: 0)
-    kappa2 = defaultdict(lambda: 0)
-    for id, g in g1.items():
-        kappa1[g] += k1[id]
-    for id, g in g2.items():
-        kappa2[g] += k2[id]
-
-    for group1 in community1Indices.keys():
-        for group2 in community2Indices.keys():
-            # for each constant probability patch
-            try:
-                groupConstant = omega[group1, group2] / (
-                    kappa1[group1] * kappa2[group2]
-                )
-            except:
-                groupConstant = 0
-
-            for u in community1Indices[group1]:
-                j = 0
-                v = community2Indices[group2][j]  # start from beginning every time
-                # max probability
-                p = min(k1[u] * k2[v] * groupConstant, 1)
-                while j < len(community2Indices[group2]):
-                    if p != 1:
-                        r = random.random()
-                        try:
-                            j = j + math.floor(math.log(r) / math.log(1 - p))
-                        except:
-                            j = np.inf
-                    if j < len(community2Indices[group2]):
-                        v = community2Indices[group2][j]
-                        q = min((k1[u] * k2[v]) * groupConstant, 1)
-                        r = random.random()
-                        if r < q / p:
-                            # no duplicates
-                            bipartite_edges.append((v, u))
-
-                            p = q
-                            j = j + 1
-
-    df = pd.DataFrame(bipartite_edges)
-    return Hypergraph(df, static=True)

-
- -
-
-
- -
- -
-

© Copyright 2021 Battelle Memorial Institute.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/_modules/algorithms/homology_mod2.html b/docs/build/_modules/algorithms/homology_mod2.html deleted file mode 100644 index 6f7d6ef6..00000000 --- a/docs/build/_modules/algorithms/homology_mod2.html +++ /dev/null @@ -1,1028 +0,0 @@ - - - - - - algorithms.homology_mod2 — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - -
- - -
- -
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    -
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    • -
  • Module code »
    • -
  • algorithms.homology_mod2
    • -
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-
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-
-
- -

Source code for algorithms.homology_mod2

-"""
-Homology and Smith Normal Form
-==============================
-The purpose of computing the Homology groups for data generated
-hypergraphs is to identify data sources that correspond to interesting
-features in the topology of the hypergraph.
-
-The elements of one of these Homology groups are generated by $k$
-dimensional cycles of relationships in the original data that are not
-bound together by higher order relationships. Ideally, we want the
-briefest description of these cycles; we want a minimal set of
-relationships exhibiting interesting cyclic behavior. This minimal set
-will be a bases for the Homology group.
-
-The cyclic relationships in the data are discovered using a **boundary
-map** represented as a matrix. To discover the bases we compute the
-**Smith Normal Form** of the boundary map.
-
-Homology Mod2
--------------
-This module computes the homology groups for data represented as an
-abstract simplicial complex with chain groups $\{C_k\}$ and $Z_2$ additions.
-The boundary matrices are represented as rectangular matrices over $Z_2$.
-These matrices are diagonalized and represented in Smith
-Normal Form. The kernel and image bases are computed and the Betti
-numbers and homology bases are returned.
-
-Methods for obtaining SNF for Z/2Z are based on Ferrario's work:
-http://www.dlfer.xyz/post/2016-10-27-smith-normal-form/
-
-"""
-
-import numpy as np
-import hypernetx as hnx
-import warnings
-import copy
-from hypernetx import HyperNetXError
-from collections import defaultdict
-import itertools as it
-import pickle
-from scipy.sparse import csr_matrix
-
-__all__ = [
-    "kchainbasis",
-    "bkMatrix",
-    "swap_rows",
-    "swap_columns",
-    "add_to_row",
-    "add_to_column",
-    "logical_dot",
-    "logical_matmul",
-    "matmulreduce",
-    "logical_matadd",
-    "smith_normal_form_mod2",
-    "reduced_row_echelon_form_mod2",
-    "boundary_group",
-    "chain_complex",
-    "betti",
-    "betti_numbers",
-    "homology_basis",
-    "hypergraph_homology_basis",
-    "interpret",
-]
-
-
-
[docs]def kchainbasis(h, k):
-    """
-    Compute the set of k dimensional cells in the abstract simplicial
-    complex associated with the hypergraph.
-
-    Parameters
-    ----------
-    h : hnx.Hypergraph
-    k : int
-        dimension of cell
-
-    Returns
-    -------
-     : list
-        an ordered list of kchains represented as tuples of length k+1
-
-    See also
-    --------
-    hnx.hypergraph.toplexes
-
-    Notes
-    -----
-    - Method works best if h is simple [Berge], i.e. no edge contains another and there are no duplicate edges (toplexes).
-    - Hypergraph node uids must be sortable.
-
-    """
-
-    import itertools as it
-
-    kchains = set()
-    for e in h.edges():
-        en = sorted(h.edges[e])
-        if len(en) == k + 1:
-            kchains.add(tuple(en))
-        elif len(en) > k + 1:
-            kchains.update(set(it.combinations(en, k + 1)))
-    return sorted(list(kchains))

-
-
-
[docs]def bkMatrix(km1basis, kbasis):
-    """
-    Compute the boundary map from $C_{k-1}$-basis to $C_k$ basis with
-    respect to $Z_2$
-
-    Parameters
-    ----------
-    km1basis : indexable iterable
-        Ordered list of $k-1$ dimensional cell
-    kbasis : indexable iterable
-        Ordered list of $k$ dimensional cells
-
-    Returns
-    -------
-    bk : np.array
-        boundary matrix in $Z_2$ stored as boolean
-
-    """
-    bk = np.zeros((len(km1basis), len(kbasis)), dtype=int)
-    for cell in kbasis:
-        for idx in range(len(cell)):
-            face = cell[:idx] + cell[idx + 1 :]
-            row = km1basis.index(face)
-            col = kbasis.index(cell)
-            bk[row, col] = 1
-    return bk

-
-
-def _rswap(i, j, S):
-    """
-    Swaps ith and jth row of copy of S
-
-    Parameters
-    ----------
-    i : int
-    j : int
-    S : np.array
-
-    Returns
-    -------
-    N : np.array
-    """
-    N = copy.deepcopy(S)
-    row = copy.deepcopy(N[i])
-    N[i] = copy.deepcopy(N[j])
-    N[j] = row
-    return N
-
-
-def _cswap(i, j, S):
-    """
-    Swaps ith and jth column of copy of S
-
-    Parameters
-    ----------
-    i : int
-    j : int
-    S : np.array
-        matrix
-
-    Returns
-    -------
-    N : np.array
-    """
-    N = _rswap(i, j, S.transpose()).transpose()
-    return N
-
-
-
[docs]def swap_rows(i, j, *args):
-    """
-    Swaps ith and jth row of each matrix in args
-    Returns a list of new matrices
-
-    Parameters
-    ----------
-    i : int
-    j : int
-    args : np.arrays
-
-    Returns
-    -------
-    list
-        list of copies of args with ith and jth row swapped
-    """
-    output = list()
-    for M in args:
-        output.append(_rswap(i, j, M))
-    return output

-
-
-
[docs]def swap_columns(i, j, *args):
-    """
-    Swaps ith and jth column of each matrix in args
-    Returns a list of new matrices
-
-    Parameters
-    ----------
-    i : int
-    j : int
-    args : np.arrays
-
-    Returns
-    -------
-    list
-        list of copies of args with ith and jth row swapped
-    """
-    output = list()
-    for M in args:
-        output.append(_cswap(i, j, M))
-    return output

-
-
-
[docs]def add_to_row(M, i, j):
-    """
-    Replaces row i with logical xor between row i and j
-
-    Parameters
-    ----------
-    M : np.array
-    i : int
-        index of row being altered
-    j : int
-        index of row being added to altered
-
-    Returns
-    -------
-    N : np.array
-    """
-    N = copy.deepcopy(M)
-    N[i] = 1 * np.logical_xor(N[i], N[j])
-    return N

-
-
-
[docs]def add_to_column(M, i, j):
-    """
-    Replaces column i (of M) with logical xor between column i and j
-
-    Parameters
-    ----------
-    M : np.array
-        matrix
-    i : int
-        index of column being altered
-    j : int
-        index of column being added to altered
-
-    Returns
-    -------
-    N : np.array
-    """
-    N = M.transpose()
-    return add_to_row(N, i, j).transpose()

-
-
-
[docs]def logical_dot(ar1, ar2):
-    """
-    Returns the boolean equivalent of the dot product mod 2 on two 1-d arrays of
-    the same length.
-
-    Parameters
-    ----------
-    ar1 : numpy.ndarray
-        1-d array
-    ar2 : numpy.ndarray
-        1-d array
-
-    Returns
-    -------
-    : bool
-        boolean value associated with dot product mod 2
-
-    Raises
-    ------
-    HyperNetXError
-        If arrays are not of the same length an error will be raised.
-    """
-    if len(ar1) != len(ar2):
-        raise HyperNetXError("logical_dot requires two 1-d arrays of the same length")
-    else:
-        return 1 * np.logical_xor.reduce(np.logical_and(ar1, ar2))

-
-
-
[docs]def logical_matmul(mat1, mat2):
-    """
-    Returns the boolean equivalent of matrix multiplication mod 2 on two
-    binary arrays stored as type boolean
-
-    Parameters
-    ----------
-    mat1 : np.ndarray
-        2-d array of boolean values
-    mat2 : np.ndarray
-        2-d array of boolean values
-
-    Returns
-    -------
-    mat : np.ndarray
-        boolean matrix equivalent to the mod 2 matrix multiplication of the
-        matrices as matrices over Z/2Z
-
-    Raises
-    ------
-    HyperNetXError
-        If inner dimensions are not equal an error will be raised.
-
-    """
-    L1, R1 = mat1.shape
-    L2, R2 = mat2.shape
-    if R1 != L2:
-        raise HyperNetXError(
-            "logical_matmul called for matrices with inner dimensions mismatched"
-        )
-
-    mat = np.zeros((L1, R2), dtype=int)
-    mat2T = mat2.transpose()
-    for i in range(L1):
-        if np.any(mat1[i]):
-            for j in range(R2):
-                mat[i, j] = logical_dot(mat1[i], mat2T[j])
-        else:
-            mat[i] = np.zeros((1, R2), dtype=int)
-    return mat

-
-
-
[docs]def matmulreduce(arr, reverse=False):
-    """
-    Recursively applies a 'logical multiplication' to a list of boolean arrays.
-
-    For arr = [arr[0],arr[1],arr[2]...arr[n]] returns product arr[0]arr[1]...arr[n]
-    If reverse = True, returns product arr[n]arr[n-1]...arr[0]
-
-    Parameters
-    ----------
-    arr : list of np.array
-        list of nxm matrices represented as np.array
-    reverse : bool, optional
-        order to multiply the matrices
-
-    Returns
-    -------
-    P : np.array
-        Product of matrices in the list
-    """
-    if reverse:
-        items = range(len(arr) - 1, -1, -1)
-    else:
-        items = range(len(arr))
-    P = arr[items[0]]
-    for i in items[1:]:
-        P = logical_matmul(P, arr[i]) * 1
-    return P

-
-
-
[docs]def logical_matadd(mat1, mat2):
-    """
-    Returns the boolean equivalent of matrix addition mod 2 on two
-    binary arrays stored as type boolean
-
-    Parameters
-    ----------
-    mat1 : np.ndarray
-        2-d array of boolean values
-    mat2 : np.ndarray
-        2-d array of boolean values
-
-    Returns
-    -------
-    mat : np.ndarray
-        boolean matrix equivalent to the mod 2 matrix addition of the
-        matrices as matrices over Z/2Z
-
-    Raises
-    ------
-    HyperNetXError
-        If dimensions are not equal an error will be raised.
-
-    """
-    S1 = mat1.shape
-    S2 = mat2.shape
-    mat = np.zeros(S1, dtype=int)
-    if S1 != S2:
-        raise HyperNetXError(
-            "logical_matadd called for matrices with different dimensions"
-        )
-    if len(S1) == 1:
-        for idx in range(S1[0]):
-            mat[idx] = 1 * np.logical_xor(mat1[idx], mat2[idx])
-    else:
-        for idx in range(S1[0]):
-            for jdx in range(S1[1]):
-                mat[idx, jdx] = 1 * np.logical_xor(mat1[idx, jdx], mat2[idx, jdx])
-    return mat

-
-
-# Convenience methods for computing Smith Normal Form
-# All of these operations have themselves as inverses
-
-
-def _sr(i, j, M, L):
-    return swap_rows(i, j, M, L)
-
-
-def _sc(i, j, M, R):
-    return swap_columns(i, j, M, R)
-
-
-def _ar(i, j, M, L):
-    return add_to_row(M, i, j), add_to_row(L, i, j)
-
-
-def _ac(i, j, M, R):
-    return add_to_column(M, i, j), add_to_column(R, i, j)
-
-
-def _get_next_pivot(M, s1, s2=None):
-    """
-    Determines the first r,c indices in the submatrix of M starting
-    with row s1 and column s2 index (row,col) that is nonzero,
-    if it exists.
-
-    Search starts with the s2th column and looks for the first nonzero
-    s1 row. If none is found, search continues to the next column and so
-    on.
-
-    Parameters
-    ----------
-    M : np.array
-        matrix represented as np.array
-    s1 : int
-        index of row position to start submatrix of M
-    s2 : int, optional, default = s1
-        index of column position to start submatrix of M
-
-    Returns
-    -------
-    (r,c) : tuple of int or None
-
-    """
-    # find the next nonzero pivot to put in s,s spot for Smith Normal Form
-    m, n = M.shape
-    if not s2:
-        s2 = s1
-    for c in range(s2, n):
-        for r in range(s1, m):
-            if M[r, c]:
-                return (r, c)
-    return None
-
-
-
[docs]def smith_normal_form_mod2(M):
-    """
-    Computes the invertible transformation matrices needed to compute the
-    Smith Normal Form of M modulo 2
-
-    Parameters
-    ----------
-    M : np.array
-        a rectangular matrix with data type bool
-    track : bool
-        if track=True will print out the transformation as Z/2Z matrix as it
-        discovers L[i] and R[j]
-
-    Returns
-    -------
-    L, R, S, Linv : np.arrays
-        LMR = S is the Smith Normal Form of the matrix M.
-
-    Note
-    ----
-    Given a mxn matrix $M$ with
-    entries in $Z_2$ we start with the equation: $L M R = S$, where
-    $L = I_m$, and $R=I_n$ are identity matrices and $S = M$. We
-    repeatedly apply actions to the left and right side of the equation
-    to transform S into a diagonal matrix.
-    For each action applied to the left side we apply its inverse
-    action to the right side of I_m to generate $L^{-1}$.
-    Finally we verify:
-    $L M R = S$ and  $LLinv = I_m$.
-    """
-
-    S = copy.copy(M)
-    dimL, dimR = M.shape
-
-    # initialize left and right transformations with identity matrices
-    L = np.eye(dimL, dtype=int)
-    R = np.eye(dimR, dtype=int)
-    Linv = np.eye(dimL, dtype=int)
-    for s in range(min(dimL, dimR)):
-        # Find index pair (rdx,cdx) with value 1 in submatrix M[s:,s:]
-        pivot = _get_next_pivot(S, s)
-        if not pivot:
-            break
-        else:
-            rdx, cdx = pivot
-        # Swap rows and columns as needed so that 1 is in the s,s position
-        if rdx > s:
-            S, L = _sr(s, rdx, S, L)
-            Linv = swap_columns(rdx, s, Linv)[0]
-        if cdx > s:
-            S, R = _sc(s, cdx, S, R)
-        # add sth row to every row with 1 in sth column & sth column to every column with 1 in sth row
-        row_indices = [idx for idx in range(s + 1, dimL) if S[idx, s] == 1]
-        for rdx in row_indices:
-            S, L = _ar(rdx, s, S, L)
-            Linv = add_to_column(Linv, s, rdx)
-        column_indices = [jdx for jdx in range(s + 1, dimR) if S[s, jdx] == 1]
-        for cdx in column_indices:
-            S, R = _ac(cdx, s, S, R)
-    return L, R, S, Linv

-
-
-
[docs]def reduced_row_echelon_form_mod2(M):
-    """
-    Computes the invertible transformation matrices needed to compute
-    the reduced row echelon form of M modulo 2
-
-    Parameters
-    ----------
-    M : np.array
-        a rectangular matrix with elements in $Z_2$
-
-    Returns
-    -------
-    L, S, Linv : np.arrays
-        LM = S where S is the reduced echelon form of M
-        and M = LinvS
-    """
-    S = copy.deepcopy(M)
-    dimL, dimR = M.shape
-
-    # method with numpy
-    Linv = np.eye(dimL, dtype=int)
-    L = np.eye(dimL, dtype=int)
-
-    s2 = 0
-    s1 = 0
-    while s2 <= dimR and s1 <= dimL:
-        # Find index pair (rdx,cdx) with value 1 in submatrix M[s1:,s2:]
-        # look for the first 1 in the s2 column
-        pivot = _get_next_pivot(S, s1, s2)
-
-        if not pivot:
-            return L, S, Linv
-        else:
-            rdx, cdx = pivot
-            if rdx > s1:
-                # Swap rows as needed so that 1 leads the row
-                S, L = _sr(s1, rdx, S, L)
-                Linv = swap_columns(rdx, s1, Linv)[0]
-            # add s1th row to every nonzero row
-            row_indices = [
-                idx for idx in range(0, dimL) if idx != s1 and S[idx, cdx] == 1
-            ]
-            for idx in row_indices:
-                S, L = _ar(idx, s1, S, L)
-                Linv = add_to_column(Linv, s1, idx)
-            s1, s2 = s1 + 1, cdx + 1
-
-    return L, S, Linv

-
-
-
[docs]def boundary_group(image_basis):
-    """
-    Returns a csr_matrix with rows corresponding to the elements of the
-    group  generated by image basis over $\mathbb{Z}_2$
-
-    Parameters
-    ----------
-    image_basis : numpy.ndarray or scipy.sparse.csr_matrix
-        2d-array of basis elements
-
-    Returns
-    -------
-     : scipy.sparse.csr_matrix
-    """
-    if len(image_basis) > 10:
-        msg = """
-        This method is inefficient for large image bases.
-        """
-        warnings.warn(msg, stacklevel=2)
-    if np.sum(image_basis) == 0:
-        return None
-    dim = image_basis.shape[0]
-    itm = csr_matrix(list(it.product([0, 1], repeat=dim)))
-    return csr_matrix(np.mod(itm * image_basis, 2))

-
-
-def _compute_matrices_for_snf(bd):
-    """
-    Helper method for smith normal form decomposition for boundary maps
-    associated to chain complex
-
-    Parameters
-    ----------
-    bd : dict
-        dict of k-boundary matrices keyed on dimension of domain
-
-    Returns
-    -------
-    L,R,S,Linv : dict
-        dict of matrices ranging over krange
-
-    """
-    L, R, S, Linv = [dict() for i in range(4)]
-
-    for kdx in bd:
-        L[kdx], R[kdx], S[kdx], Linv[kdx] = smith_normal_form_mod2(bd[kdx])
-    return L, R, S, Linv
-
-
-def _get_krange(max_dim, k=None):
-    """
-    Helper method to compute range of appropriate k dimensions for homology
-    computations given k and the max dimension of a simplicial complex
-    """
-    if k is None:
-        krange = [1, max_dim]
-    elif isinstance(k, int):
-        if k == 0:
-            msg = (
-                "Only kth simplicial homology groups for k>0 may be computed."
-                "If you are interested in k=0, compute the number connected components."
-            )
-            print(msg)
-            return
-        if k > max_dim:
-            msg = f"No simplices of dim {k} exist. k adjusted to max dim."
-            print(msg)
-        krange = [min([k, max_dim])] * 2
-    elif not len(k) == 2:
-        msg = f"Please enter krange as a positive integer or list of integers: [<min k>,<max k>] inclusive."
-        print(msg)
-        return None
-    elif not k[0] <= k[1]:
-        msg = f"k must be an integer or a list of two integers [min,max] with min <=max"
-        print(msg)
-        return None
-    else:
-        krange = k
-
-    if krange[1] > max_dim:
-        msg = f"No simplices of dim {krange[1]} exist. Range adjusted to max dim."
-        print(msg)
-        krange[1] = max_dim
-    if krange[0] < 1:
-        msg = (
-            "Only kth simplicial homology groups for k>0 may be computed."
-            "If you are interested in k=0, compute the number of connected components."
-        )
-        print(msg)
-        krange[0] = 1
-    return krange
-
-
-
[docs]def chain_complex(h, k=None):
-    """
-    Compute the k-chains and k-boundary maps required to compute homology
-    for all values in k
-
-    Parameters
-    ----------
-    h : hnx.Hypergraph
-    k : int or list of length 2, optional, default=None
-        k must be an integer greater than 0 or a list of
-        length 2 indicating min and max dimensions to be
-        computed. eg. if k = [1,2] then 0,1,2,3-chains
-        and boundary maps for k=1,2,3 will be returned,
-        if None than k = [1,max dimension of edge in h]
-
-    Returns
-    -------
-    C, bd : dict
-        C is a dictionary of lists
-        bd is a dictionary of numpy arrays
-    """
-    max_dim = np.max([len(h.edges[e]) for e in h.edges()]) - 1
-    krange = _get_krange(max_dim, k)
-    if not krange:
-        return
-    # Compute chain complex
-
-    C = defaultdict(list)
-    C[krange[0] - 1] = kchainbasis(h, krange[0] - 1)
-    bd = dict()
-    for kdx in range(krange[0], krange[1] + 2):
-        C[kdx] = kchainbasis(h, kdx)
-        bd[kdx] = bkMatrix(C[kdx - 1], C[kdx])
-    return C, bd

-
-
-
[docs]def betti(bd, k=None):
-    """
-    Generate the kth-betti numbers for a chain complex with boundary
-    matrices given by bd
-
-    Parameters
-    ----------
-    bd: dict of k-boundary matrices keyed on dimension of domain
-    k : int, list or tuple, optional, default=None
-        list must be min value and max value of k values inclusive
-        if None, then all betti numbers for dimensions of existing cells will be
-        computed.
-
-    Returns
-    -------
-    betti : dict
-        Description
-    """
-    rank = defaultdict(int)
-    if k:
-        max_dim = max(bd.keys())
-        krange = _get_krange(max_dim, k)
-        if not krange:
-            return
-        kvals = sorted(set(range(krange[0], krange[1] + 2)).intersection(bd.keys()))
-    else:
-        kvals = bd.keys()
-    for kdx in kvals:
-        _, S, _ = hnx.reduced_row_echelon_form_mod2(bd[kdx])
-        rank[kdx] = np.sum(np.sum(S, axis=1).astype(bool))
-
-    betti = dict()
-    for kdx in kvals:
-        if kdx + 1 in kvals:
-            betti[kdx] = bd[kdx].shape[1] - rank[kdx] - rank[kdx + 1]
-        else:
-            continue
-
-    return betti

-
-
-
[docs]def betti_numbers(h, k=None):
-    """
-    Return the kth betti numbers for the simplicial homology of the ASC
-    associated to h
-
-    Parameters
-    ----------
-    h : hnx.Hypergraph
-        Hypergraph to compute the betti numbers from
-    k : int or list, optional, default=None
-        list must be min value and max value of k values inclusive
-        if None, then all betti numbers for dimensions of existing cells will be
-        computed.
-
-    Returns
-    -------
-    betti : dict
-        A dictionary of betti numbers keyed by dimension
-    """
-    _, bd = chain_complex(h, k)
-    return betti(bd)

-
-
-
[docs]def homology_basis(bd, k=None, boundary=False, **kwargs):
-    """
-    Compute a basis for the kth-simplicial homology group, $H_k$, defined by a
-    chain complex $C$ with boundary maps given by bd $= \{k:\partial_k \}$
-
-    Parameters
-    ----------
-    bd : dict
-        dict of boundary matrices on k-chains to k-1 chains keyed on k
-        if krange is a tuple then all boundary matrices k \in [krange[0],..,krange[1]]
-        inclusive must be in the dictionary
-    k : int or list of ints, optional, default=None
-        k must be a positive integer or a list of
-        2 integers indicating min and max dimensions to be
-        computed, if none given all homology groups will be computed from
-        available boundary matrices in bd
-    boundary : bool
-        option to return a basis for the boundary group from each dimension.
-        Needed to compute the shortest generators in the homology group.
-
-    Returns
-    -------
-    basis : dict
-        dict of generators as 0-1 tuples keyed by dim
-        basis for dimension k will be returned only if bd[k] and bd[k+1] have
-        been provided.
-    im : dict
-        dict of boundary group generators keyed by dim
-    """
-    max_dim = max(bd.keys())
-    if k:
-        krange = _get_krange(max_dim, k)
-        kvals = sorted(
-            set(bd.keys()).intersection(range(krange[0], krange[1] + 2))
-        )  # to get kth dim need k+1 bdry matrix
-    else:
-        kvals = bd.keys()
-
-    L, R, S, Linv = _compute_matrices_for_snf(
-        {k: v for k, v in bd.items() if k in kvals}
-    )
-
-    rank = dict()
-    for kdx in kvals:
-        rank[kdx] = np.sum(S[kdx])
-
-    basis = dict()
-    im = dict()
-    for kdx in kvals:
-        if kdx + 1 not in kvals:
-            continue
-        rank1 = rank[kdx]
-        rank2 = rank[kdx + 1]
-        ker1 = R[kdx][:, rank1:]
-        im2 = Linv[kdx + 1][:, :rank2]
-        cokernel2 = Linv[kdx + 1][:, rank2:]
-        cokproj2 = L[kdx + 1][rank2:, :]
-
-        proj = matmulreduce([cokernel2, cokproj2, ker1]).transpose()
-        _, proj, _ = reduced_row_echelon_form_mod2(proj)
-        # proj = np.array(proj)
-        basis[kdx] = np.array([row for row in proj if np.any(row)])
-        if boundary:
-            im[kdx] = im2.transpose()
-    if boundary:
-        return basis, im
-    else:
-        return basis

-
-
-
[docs]def hypergraph_homology_basis(h, k=None, shortest=False, interpreted=True):
-    """
-    Computes the kth-homology groups mod 2 for the ASC
-    associated with the hypergraph h for k in krange inclusive
-
-    Parameters
-    ----------
-    h : hnx.Hypergraph
-    k : int or list of length 2, optional, default = None
-        k must be an integer greater than 0 or a list of
-        length 2 indicating min and max dimensions to be
-        computed
-    shortest : bool, optional, default=False
-        option to look for shortest representative for each coset in the
-        homology group, only good for relatively small examples
-    interpreted : bool, optional, default = True
-        if True will return an explicit basis in terms of the k-chains
-
-    Returns
-    -------
-    basis : list
-        list of generators as k-chains as boolean vectors
-    interpreted_basis :
-        lists of kchains in basis
-
-    """
-    C, bd = chain_complex(h, k)
-    if shortest:
-        basis = defaultdict(list)
-        tbasis, im = homology_basis(bd, boundary=True)
-        for kdx in tbasis:
-            imgrp = boundary_group(im[kdx])
-            if imgrp is None:
-                basis[kdx] = tbasis[kdx]
-            else:
-                for b in tbasis[kdx]:
-                    coset = np.array(
-                        np.mod(imgrp + b, 2)
-                    )  # dimensions appear to be wrong. See tests2 cell 5
-                    idx = np.argmin(np.sum(coset, axis=1))
-                    basis[kdx].append(coset[idx])
-        basis = dict(basis)
-
-    else:
-        basis = homology_basis(bd)
-
-    if interpreted:
-        interpreted_basis = dict()
-        for kdx in basis:
-            interpreted_basis[kdx] = interpret(C[kdx], basis[kdx], labels=None)
-        return basis, interpreted_basis
-    else:
-        return basis

-
-
-
[docs]def interpret(Ck, arr, labels=None):
-    """
-    Returns the data as represented in Ck associated with the arr
-
-    Parameters
-    ----------
-    Ck : list
-        a list of k-cells being referenced by arr
-    arr : np.array
-        array of 0-1 vectors
-    labels : dict, optional
-        dictionary of labels to associate to the nodes in the cells
-
-    Returns
-    ----
-    : list
-        list of k-cells referenced by data in Ck
-
-    """
-
-    def translate(cell, labels=labels):
-        if not labels:
-            return cell
-        else:
-            temp = list()
-            for node in cell:
-                temp.append(labels[node])
-            return tuple(temp)
-
-    output = list()
-    for vec in arr:
-        if len(Ck) != len(vec):
-            raise HyperNetXError("elements of arr must have the same length as Ck")
-        output.append([translate(Ck[idx]) for idx in range(len(vec)) if vec[idx] == 1])
-    return output

-
- -
-
-
- -
- -
-

© Copyright 2021 Battelle Memorial Institute.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
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- - - - \ No newline at end of file diff --git a/docs/build/_modules/algorithms/hypergraph_modularity.html b/docs/build/_modules/algorithms/hypergraph_modularity.html deleted file mode 100644 index 77b24423..00000000 --- a/docs/build/_modules/algorithms/hypergraph_modularity.html +++ /dev/null @@ -1,665 +0,0 @@ - - - - - - algorithms.hypergraph_modularity — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - -
- - -
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    • -
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    • -
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- -

Source code for algorithms.hypergraph_modularity

-"""
-Hypergraph_Modularity
----------------------
-Modularity and clustering for hypergraphs using HyperNetX.
-Adapted from F. Théberge's GitHub repository: `Hypergraph Clustering <https://github.com/ftheberge/Hypergraph_Clustering>`_ 
-See Tutorial 13 in the tutorials folder for library usage.
-
-References
----------- 
-.. [1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S. and Ravindran B. "A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering". In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24
-.. [2] Kamiński  B., Prałat  P. and Théberge  F. "Community Detection Algorithm Using Hypergraph Modularity". In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13
-.. [3] Kamiński  B., Poulin V., Prałat  P., Szufel P. and Théberge  F. "Clustering via hypergraph modularity", Plos ONE 2019, https://doi.org/10.1371/journal.pone.0224307
-"""
-
-from collections import Counter
-import numpy as np
-from functools import reduce
-import igraph as ig
-import itertools
-from scipy.special import comb
-
-################################################################################
-
-# we use 2 representations for partitions (0-based part ids):
-# (1) dictionary or (2) list of sets
-
-
-
[docs]def dict2part(D):
-    """
-    Given a dictionary mapping the part for each vertex, return a partition as a list of sets; inverse function to part2dict
-
-    Parameters
-    ----------
-    D : dict
-        Dictionary keyed by vertices with values equal to integer
-        index of the partition the vertex belongs to
-
-    Returns
-    -------
-    : list
-        List of sets; one set for each part in the partition
-    """
-    P = []
-    k = list(D.keys())
-    v = list(D.values())
-    for x in range(max(D.values()) + 1):
-        P.append(set([k[i] for i in range(len(k)) if v[i] == x]))
-    return P

-
-
-
[docs]def part2dict(A):
-    """
-    Given a partition (list of sets), returns a dictionary mapping the part for each vertex; inverse function
-    to dict2part
-
-    Parameters
-    ----------
-    A : list of sets
-        a partition of the vertices
-
-    Returns
-    -------
-    : dict
-      a dictionary with {vertex: partition index}
-    """
-    x = []
-    for i in range(len(A)):
-        x.extend([(a, i) for a in A[i]])
-    return {k: v for k, v in x}

-
-################################################################################
-
-
-
[docs]def precompute_attributes(HG):
-    """
-    Precompute some values on hypergraph HG for faster computing of hypergraph modularity. 
-    This needs to be run before calling either modularity() or last_step().
-
-    Note
-    ----
-
-    If HG is unweighted, v.weight is set to 1 for each vertex v in HG. 
-    The weighted degree for each vertex v is stored in v.strength.
-    The total edge weigths for each edge cardinality is stored in HG.d_weights.
-    Binomial coefficients to speed-up modularity computation are stored in HG.bin_coef.
-    Isolated vertices found only in edge(s) of size 1 are dropped.
-
-    Parameters
-    ----------
-    HG : Hypergraph
-
-    Returns
-    -------
-    H : Hypergraph
-      New hypergraph with added attributes 
-
-    """
-    H = HG.remove_singletons()
-    # 1. compute node strenghts (weighted degrees)
-    for v in H.nodes:
-        H.nodes[v].strength = 0
-    for e in H.edges:
-        try:
-            w = H.edges[e].weight
-        except:
-            w = 1
-            # add unit weight if none to simplify other functions
-            H.edges[e].weight = 1
-        for v in list(H.edges[e]):
-            H.nodes[v].strength += w
-    # 2. compute d-weights
-    ctr = Counter([len(H.edges[e]) for e in H.edges])
-    for k in ctr.keys():
-        ctr[k] = 0
-    for e in H.edges:
-        ctr[len(H.edges[e])] += H.edges[e].weight
-    H.d_weights = ctr
-    H.total_weight = sum(ctr.values())
-    # 3. compute binomial coeffcients (modularity speed-up)
-    bin_coef = {}
-    for n in H.d_weights.keys():
-        for k in np.arange(n // 2 + 1, n + 1):
-            bin_coef[(n, k)] = comb(n, k, exact=True)
-    H.bin_coef = bin_coef
-    return H

-
-################################################################################
-
-
-
[docs]def linear(d, c):
-    """
-    Hyperparameter for hypergraph modularity [2]_ for d-edge with c vertices in the majority class.
-    This is the default choice for modularity() and last_step() functions.
-
-    Parameters
-    ----------
-    d : int
-        Number of vertices in an edge
-    c : int
-        Number of vertices in the majority class
-
-    Returns
-    -------
-    : float
-      c/d if c>d/2 else 0
-    """
-    return c / d if c > d / 2 else 0

-
-# majority
-
-
-
[docs]def majority(d, c):
-    """    
-    Hyperparameter for hypergraph modularity [2]_ for d-edge with c vertices in the majority class.
-    This corresponds to the majority rule [3]_
-
-    Parameters
-    ----------
-    d : int
-        Number of vertices in an edge
-    c : int
-        Number of vertices in the majority class
-
-    Returns
-    -------
-    : bool
-      1 if c>d/2 else 0
-
-    """
-    return 1 if c > d / 2 else 0

-
-# strict
-
-
-
[docs]def strict(d, c):
-    """
-    Hyperparameter for hypergraph modularity [2]_ for d-edge with c vertices in the majority class.
-    This corresponds to the strict rule [3]_
-
-    Parameters
-    ----------
-    d : int
-        Number of vertices in an edge
-    c : int
-        Number of vertices in the majority class
-
-    Returns
-    -------
-    : bool
-      1 if c==d else 0
-    """
-    return 1 if c == d else 0

-
-#########################################
-
-
-def _compute_partition_probas(HG, A):
-    """
-    Compute vol(A_i)/vol(V) for each part A_i in A (list of sets)
-
-    Parameters
-    ----------
-    HG : Hypergraph
-     A : list of sets
-
-    Returns
-    -------
-    : list
-        normalized distribution of strengths in partition elements
-    """
-    p = []
-    for part in A:
-        vol = 0
-        for v in part:
-            vol += HG.nodes[v].strength
-        p.append(vol)
-    s = sum(p)
-    return [i / s for i in p]
-
-
-def _degree_tax(HG, Pr, wdc):
-    """
-    Computes the expected fraction of edges falling in 
-    the partition as per [2]_
-
-    Parameters
-    ----------
-    HG : Hypergraph
-
-    Pr : list
-        Probability distribution
-    wdc : func
-        weight function for edge contribution (ex: strict, majority, linear)
-
-    Returns
-    -------
-    float
-
-    """
-    DT = 0
-    for d in HG.d_weights.keys():
-        tax = 0
-        for c in np.arange(d // 2 + 1, d + 1):
-            for p in Pr:
-                tax += p**c * (1 - p)**(d - c) * HG.bin_coef[(d, c)] * wdc(d, c)
-        tax *= HG.d_weights[d]
-        DT += tax
-    DT /= HG.total_weight
-    return DT
-
-
-def _edge_contribution(HG, A, wdc):
-    """
-    Edge contribution from hypergraph with respect
-    to partion A.
-
-    Parameters
-    ----------
-    HG : Hypergraph
-
-    A : list of sets
-
-    wdc : func
-        weight function (ex: strict, majority, linear)
-
-    Returns
-    -------
-    : float
-
-    """
-    EC = 0
-    for e in HG.edges:
-        d = HG.size(e)
-        for part in A:
-            if HG.size(e, part) > d / 2:
-                EC += wdc(d, HG.size(e, part)) * HG.edges[e].weight
-    EC /= HG.total_weight
-    return EC
-
-# HG: HNX hypergraph
-# A: partition (list of sets)
-# wcd: weight function (ex: strict, majority, linear)
-
-
-
[docs]def modularity(HG, A, wdc=linear):
-    """
-    Computes modularity of hypergraph HG with respect to partition A.
-
-    Parameters
-    ----------
-    HG : Hypergraph
-        The hypergraph with some precomputed attributes via: precompute_attributes(HG)
-    A : list of sets
-        Partition of the vertices in HG
-    wdc : func, optional
-        Hyperparameter for hypergraph modularity [2]_ 
-
-    Note
-    ----
-    For 'wdc', any function of the format w(d,c) that returns 0 when c <= d/2 and value in [0,1] otherwise can be used.
-    Default is 'linear'; other supplied choices are 'majority' and 'strict'.
-
-    Returns
-    -------
-    : float
-      The modularity function for partition A on HG
-    """
-    Pr = _compute_partition_probas(HG, A)
-    return _edge_contribution(HG, A, wdc) - _degree_tax(HG, Pr, wdc)

-
-################################################################################
-
-
-
[docs]def two_section(HG):
-    """
-    Creates a random walk based [1]_ 2-section igraph Graph with transition weights defined by the
-    weights of the hyperedges.
-
-    Parameters
-    ----------
-    HG : Hypergraph
-
-    Returns
-    -------
-     : igraph.Graph
-       The 2-section graph built from HG
-    """
-    s = []
-    for e in HG.edges:
-        E = HG.edges[e]
-        # random-walk 2-section (preserve nodes' weighted degrees)
-        if len(E) > 1:
-            try:
-                w = HG.edges[e].weight / (len(E) - 1)
-            except:
-                w = 1 / (len(E) - 1)
-            s.extend([(k[0], k[1], w) for k in itertools.combinations(E, 2)])
-    G = ig.Graph.TupleList(s, weights=True).simplify(combine_edges='sum')
-    return G

-
-################################################################################
-
-
-
[docs]def kumar(HG, delta=.01):
-    """
-    Compute a partition of the vertices in hypergraph HG as per Kumar's algorithm [1]_
-
-    Parameters
-    ----------
-    HG : Hypergraph
-
-    delta : float, optional
-        convergence stopping criterion
-
-    Returns
-    -------
-    : list of sets
-       A partition of the vertices in HG
-
-    """
-    # weights will be modified -- store initial weights
-    W = {e: HG.edges[e].weight for e in HG.edges}  # uses edge id for reference instead of int
-    # build graph
-    G = two_section(HG)
-    # apply clustering
-    CG = G.community_multilevel(weights='weight')
-    CH = []
-    for comm in CG.as_cover():
-        CH.append(set([G.vs[x]['name'] for x in comm]))
-
-    # LOOP
-    diff = 1
-    ctr = 0
-    while diff > delta:
-        # re-weight
-        diff = 0
-        for e in HG.edges:
-            edge = HG.edges[e]
-            reweight = sum([1 / (1 + HG.size(e, c)) for c in CH]) * (HG.size(e) + len(CH)) / HG.number_of_edges()
-            diff = max(diff, 0.5 * abs(edge.weight - reweight))
-            edge.weight = 0.5 * edge.weight + 0.5 * reweight
-        # re-run louvain
-        # build graph
-        G = two_section(HG)
-        # apply clustering
-        CG = G.community_multilevel(weights='weight')
-        CH = []
-        for comm in CG.as_cover():
-            CH.append(set([G.vs[x]['name'] for x in comm]))
-        ctr += 1
-        if ctr > 50:  # this process sometimes gets stuck -- set limit
-            break
-    G.vs['part'] = CG.membership
-    for e in HG.edges:
-        HG.edges[e].weight = W[e]
-    return dict2part({v['name']: v['part'] for v in G.vs})

-
-################################################################################
-
-
-def _delta_ec(HG, P, v, a, b, wdc):
-    """
-    Computes change in edge contribution --
-    partition P, node v going from P[a] to P[b]
-
-    Parameters
-    ----------
-    HG : Hypergraph
-
-    P : list of sets
-
-    v : int or str
-        node identifier
-    a : int
-
-    b : int
-
-    wdc : func
-        weight function (ex: strict, majority, linear)
-
-    Returns
-    -------
-    : float
-    """
-    Pm = P[a] - {v}
-    Pn = P[b].union({v})
-    ec = 0
-    for e in list(HG.nodes[v].memberships):
-        d = HG.size(e)
-        w = HG.edges[e].weight
-        ec += w * (wdc(d, HG.size(e, Pm)) + wdc(d, HG.size(e, Pn))
-                   - wdc(d, HG.size(e, P[a])) - wdc(d, HG.size(e, P[b])))
-    return ec / HG.total_weight
-
-
-def _bin_ppmf(d, c, p):
-    """
-    exponential part of the binomial pmf
-
-    Parameters
-    ----------
-    d : int
-
-    c : int
-
-    p : float
-
-
-    Returns
-    -------
-    : float
-
-    """
-    return p**c * (1 - p)**(d - c)
-
-
-def _delta_dt(HG, P, v, a, b, wdc):
-    """
-    Compute change in degree tax --
-    partition P (list), node v going from P[a] to P[b]
-
-    Parameters
-    ----------
-    HG : Hypergraph
-
-    P : list of sets
-
-    v : int or str
-         node identifier
-    a : int
-
-    b : int
-
-    wdc : func
-        weight function (ex: strict, majority, linear)
-
-    Returns
-    -------
-    : float
-
-    """
-    s = HG.nodes[v].strength
-    vol = sum([HG.nodes[v].strength for v in HG.nodes])
-    vola = sum([HG.nodes[v].strength for v in P[a]])
-    volb = sum([HG.nodes[v].strength for v in P[b]])
-    volm = (vola - s) / vol
-    voln = (volb + s) / vol
-    vola /= vol
-    volb /= vol
-    DT = 0
-
-    for d in HG.d_weights.keys():
-        x = 0
-        for c in np.arange(int(np.floor(d / 2)) + 1, d + 1):
-            x += HG.bin_coef[(d, c)] * wdc(d, c) * (_bin_ppmf(d, c, voln) + _bin_ppmf(d, c, volm)
-                                                    - _bin_ppmf(d, c, vola) - _bin_ppmf(d, c, volb))
-        DT += x * HG.d_weights[d]
-    return DT / HG.total_weight
-
-
-
[docs]def last_step(HG, L, wdc=linear, delta=.01):
-    """
-    Given some initial partition L, compute a new partition of the vertices in HG as per Last-Step algorithm [2]_
-
-    Note
-    ----
-    This is a very simple algorithm that tries moving nodes between communities to improve hypergraph modularity.
-    It requires an initial non-trivial partition which can be obtained for example via graph clustering on the 2-section of HG,
-    or via Kumar's algorithm.
-
-    Parameters
-    ----------
-    HG : Hypergraph
-
-    L : list of sets
-      some initial partition of the vertices in HG
-
-    wdc : func, optional
-        Hyperparameter for hypergraph modularity [2]_ 
-
-    delta : float, optional
-            convergence stopping criterion    
-
-    Returns
-    -------
-    : list of sets
-      A new partition for the vertices in HG
-    """
-    A = L[:]  # we will modify this, copy
-    D = part2dict(A)
-    qH = 0
-    while True:
-        for v in list(np.random.permutation(list(HG.nodes))):
-            c = D[v]
-            s = list(set([c] + [D[i] for i in HG.neighbors(v)]))
-            M = []
-            if len(s) > 0:
-                for i in s:
-                    if c == i:
-                        M.append(0)
-                    else:
-                        M.append(_delta_ec(HG, A, v, c, i, wdc) - _delta_dt(HG, A, v, c, i, wdc))
-                i = s[np.argmax(M)]
-                if c != i:
-                    A[c] = A[c] - {v}
-                    A[i] = A[i].union({v})
-                    D[v] = i
-        Pr = _compute_partition_probas(HG, A)
-        q2 = _edge_contribution(HG, A, wdc) - _degree_tax(HG, Pr, wdc)
-        if (q2 - qH) < delta:
-            break
-        qH = q2
-    return [a for a in A if len(a) > 0]

-
-################################################################################
-
- -
-
-
- -
- -
-

© Copyright 2021 Battelle Memorial Institute.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/_modules/algorithms/laplacians_clustering.html b/docs/build/_modules/algorithms/laplacians_clustering.html deleted file mode 100644 index 22717a21..00000000 --- a/docs/build/_modules/algorithms/laplacians_clustering.html +++ /dev/null @@ -1,356 +0,0 @@ - - - - - - algorithms.laplacians_clustering — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - -
- - -
- -
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    • -
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    • -
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Source code for algorithms.laplacians_clustering

-# Copyright © 2021 Battelle Memorial Institute
-# All rights reserved.
-
-"""
-
-Hypergraph Probability Transition Matrices, Laplacians, and Clustering
-======================================================================
-We contruct hypergraph random walks utilizing optional "edge-dependent vertex weights", which are 
-weights associated with each vertex-hyperedge pair (i.e. cell weights on the incidence matrix).
-The probability transition matrix of this random walk is used to construct a normalized Laplacian 
-matrix for the hypergraph. That normalized Laplacian then serves as the input for a spectral clustering
-algorithm. This spectral clustering algorithm, as well as the normalized Laplacian and other details of
-this methodology are described in 
-
-K. Hayashi, S. Aksoy, C. Park, H. Park, "Hypergraph random walks, Laplacians, and clustering", 
-Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020.
-https://doi.org/10.1145/3340531.3412034
-
-Please direct any inquiries concerning the clustering module to Sinan Aksoy, sinan.aksoy@pnnl.gov
-
-"""
-
-import numpy as np
-from collections import defaultdict
-import networkx as nx
-import warnings
-import sys
-from scipy.sparse import csr_matrix, coo_matrix, diags, find, identity
-from scipy.sparse.linalg import eigs
-from sklearn.cluster import SpectralClustering, KMeans
-from sklearn import preprocessing
-from functools import partial
-from hypernetx import HyperNetXError
-
-try:
-    import nwhy
-
-    nwhy_available = True
-except:
-    nwhy_available = False
-
-sys.setrecursionlimit(10000)
-
-__all__ = [
-    "prob_trans",
-    "get_pi",
-    "norm_lap",
-    "spec_clus",
-]
-
-
-
[docs]def prob_trans(H, weights=False, index=True, check_connected=True):
-    """
-    The probability transition matrix of a random walk on the vertices of a hypergraph.
-    At each step in the walk, the next vertex is chosen by:
-
-    1. Selecting a hyperedge e containing the vertex with probability proportional to w(e)
-    2. Selecting a vertex v within e with probability proportional to a \gamma(v,e)
-
-    If weights are not specified, then all weights are uniform and the walk is equivalent
-    to a simple random walk.
-    If weights are specified, the hyperedge weights w(e) are determined from the weights
-    \gamma(v,e).
-
-
-    Parameters
-    ----------
-    H : hnx.Hypergraph
-        The hypergraph must be connected, meaning there is a path linking any two
-        vertices
-    weights : bool, optional, default : False
-         Use the cell_weights associated with the hypergraph
-         If False, uniform weights are utilized.
-    index : bool, optional
-        Whether to return matrix index to vertex label mapping
-
-    Returns
-    -------
-     P : scipy.sparse.csr.csr_matrix
-         Probability transition matrix of the random walk on the hypergraph
-     index: dict
-         mapping from row and column indices to corresponding vertex label
-    """
-    # hypergraph must be connected
-    if check_connected:
-        if not H.is_connected():
-            raise HyperNetXError("hypergraph must be connected")
-
-    # if no weighting function, each step in the random walk is chosen uniformly at random.
-    if weights == False:
-        R, index, _ = H.incidence_matrix(index=True)
-    else:
-        R, index, _ = H.incidence_matrix(index=True, weights=True)
-
-    # transpose incidence matrix for notational convenience
-    R = R.transpose()
-
-    # generates hyperedge weight matrix, has same nonzero pattern as incidence matrix,
-    # with values determined by the edge-dependent vertex weight standard deviation
-    edgeScore = {
-        i: np.std(R.getrow(i).data) + 1 for i in range(R.shape[0])
-    }  # hyperedge weights
-    vals = [edgeScore[i] for i in R.nonzero()[0]]
-    W = csr_matrix(
-        (vals, (R.nonzero()[1], R.nonzero()[0])), shape=(R.shape[1], R.shape[0])
-    )
-
-    # generate diagonal degree matrices used to normalize probability transition matrix
-    [rowSums] = R.sum(axis=1).flatten().tolist()
-    D_E = diags([1 / x for x in rowSums])
-
-    [rowSums] = W.sum(axis=1).flatten().tolist()
-    D_V = diags([1 / x for x in rowSums])
-
-    # probability transition matrix P
-    P = D_V * W * D_E * R
-
-    if index == False:
-        return P
-    else:
-        return P, index

-
-
-
[docs]def get_pi(P):
-    """
-    Returns the eigenvector corresponding to the largest eigenvalue (in magnitude),
-    normalized so its entries sum to 1. Intended for the probability transition matrix
-    of a random walk on a (connected) hypergraph, in which case the output can
-    be interpreted as the stationary distribution.
-
-    Parameters
-    ----------
-    P : csr matrix
-        Probability transition matrix
-
-    Returns
-    -------
-     pi : numpy.ndarray
-         Stationary distribution of random walk defined by P
-    """
-    rho, pi = eigs(
-        np.transpose(P), k=1, return_eigenvectors=True
-    )  # dominant eigenvector
-    pi = np.real(pi / np.sum(pi)).flatten()  # normalize as prob distribution
-    return pi

-
-
-
[docs]def norm_lap(H, weights=False, index=True):
-    """
-    Normalized Laplacian matrix of the hypergraph. Symmetrizes the probability transition
-    matrix of a hypergraph random walk using the stationary distribution, using the digraph
-    Laplacian defined in:
-
-    Chung, Fan. "Laplacians and the Cheeger inequality for directed graphs."
-    Annals of Combinatorics 9.1 (2005): 1-19.
-
-    and studied in the context of hypergraphs in:
-
-    Hayashi, K., Aksoy, S. G., Park, C. H., & Park, H.
-    Hypergraph random walks, laplacians, and clustering.
-    In Proceedings of CIKM 2020, (2020): 495-504.
-
-    Parameters
-    ----------
-    H : hnx.Hypergraph
-        The hypergraph must be connected, meaning there is a path linking any two
-        vertices
-    weight : bool, optional, default : False
-         Uses cell_weights, if False, uniform weights are utilized.
-    index : bool, optional
-        Whether to return matrix-index to vertex-label mapping
-
-    Returns
-    -------
-     P : scipy.sparse.csr.csr_matrix
-         Probability transition matrix of the random walk on the hypergraph
-     index: dict
-         mapping from row and column indices to corresponding vertex label
-    """
-    if weights == None:
-        P, index = prob_trans(H)
-    else:
-        P, index = prob_trans(H, weights=weights)
-    pi = get_pi(P)
-    gamma = diags(np.power(pi, 1 / 2)) * P * diags(np.power(pi, -1 / 2))
-    L = identity(gamma.shape[0]) - (1 / 2) * gamma + gamma.transpose()
-
-    if index:
-        return L, index
-    else:
-        return L

-
-
-
[docs]def spec_clus(H, k, existing_lap=None, weights=False):
-    """
-    Hypergraph spectral clustering of the vertex set into k disjoint clusters
-    using the normalized hypergraph Laplacian. Equivalent to the "RDC-Spec"
-    Algorithm 1 in:
-
-    Hayashi, K., Aksoy, S. G., Park, C. H., & Park, H.
-    Hypergraph random walks, laplacians, and clustering.
-    In Proceedings of CIKM 2020, (2020): 495-504.
-
-
-    Parameters
-    ----------
-    H : hnx.Hypergraph
-        The hypergraph must be connected, meaning there is a path linking any two
-        vertices
-    k : int
-        Number of clusters
-    existing_lap: csr matrix, optional
-        Whether to use an existing Laplacian; otherwise, normalized hypergraph Laplacian
-        will be utilized
-    weights : bool, optional
-         Use the cell_weights of the hypergraph. If False uniform weights are used.
-
-    Returns
-    -------
-     clusters : dict
-         Vertex cluster dictionary, keyed by integers 0,...,k-1, with lists of
-         vertices as values.
-    """
-    if existing_lap == None:
-        if weights == None:
-            L, index = norm_lap(H)
-        else:
-            L, index = norm_lap(H, weights=weights)
-    else:
-        L = existing_lap
-
-    # compute top eigenvectors
-    e, v = eigs(identity(L.shape[0]) - L, k=k, which="LM", return_eigenvectors=True)
-    v = np.real(v)  # ignore zero complex parts
-    v = preprocessing.normalize(v, norm="l2", axis=1)  # normalize
-    U = np.array(v)
-    km = KMeans(init="k-means++", n_clusters=k, random_state=0)  # k-means
-    km.fit(U)
-    d = km.labels_
-
-    # organize cluster assingments in dictionary of form cluster #: ips
-    clusters = {i: [] for i in range(k)}
-    for i in range(len(index)):
-        clusters[d[i]].append(index[i])
-
-    return clusters

-
- -
-
-
- -
- -
-

© Copyright 2021 Battelle Memorial Institute.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/_modules/algorithms/s_centrality_measures.html b/docs/build/_modules/algorithms/s_centrality_measures.html deleted file mode 100644 index 3ef7e931..00000000 --- a/docs/build/_modules/algorithms/s_centrality_measures.html +++ /dev/null @@ -1,490 +0,0 @@ - - - - - - algorithms.s_centrality_measures — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - -
- - -
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    • -
  • algorithms.s_centrality_measures
    • -
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    • -
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- -

Source code for algorithms.s_centrality_measures

-# Copyright © 2018 Battelle Memorial Institute
-# All rights reserved.
-
-"""
-
-S-Centrality Measures
-=====================
-We generalize graph metrics to s-metrics for a hypergraph by using its s-connected
-components. This is accomplished by computing the s edge-adjacency matrix and
-constructing the corresponding graph of the matrix. We then use existing graph metrics
-on this representation of the hypergraph. In essence we construct an *s*-line graph
-corresponding to the hypergraph on which to apply our methods.
-
-S-Metrics for hypergraphs are discussed in depth in:        
-*Aksoy, S.G., Joslyn, C., Ortiz Marrero, C. et al. Hypernetwork science via high-order hypergraph walks.
-EPJ Data Sci. 9, 16 (2020). https://doi.org/10.1140/epjds/s13688-020-00231-0*
-
-"""
-
-import numpy as np
-from collections import defaultdict
-import networkx as nx
-import warnings
-import sys
-from functools import partial
-
-try:
-    import nwhy
-
-    nwhy_available = True
-except:
-    nwhy_available = False
-
-sys.setrecursionlimit(10000)
-
-__all__ = [
-    "s_betweenness_centrality",
-    "s_harmonic_closeness_centrality",
-    "s_harmonic_centrality",
-    "s_closeness_centrality",
-    "s_eccentricity",
-]
-
-
-def _s_centrality(
-    func, H, s=1, edges=True, f=None, return_singletons=True, use_nwhy=True, **kwargs
-):
-    """
-    Wrapper for computing s-centrality either in NetworkX or in NWHy
-
-    Parameters
-    ----------
-    func : function
-        Function or partial function from NetworkX or NWHy
-    H : hnx.Hypergraph
-
-    s : int, optional
-        s-width for computation
-    edges : bool, optional
-        If True, an edge linegraph will be used, otherwise a node linegraph will be used
-    f : str, optional
-        Identifier of node or edge of interest for computing centrality
-    return_singletons : bool, optional
-        If True will return 0 value for each singleton in the s-linegraph
-    use_nwhy : bool, optional
-        If True will attempt to use nwhy centrality methods if availaable
-    **kwargs
-        Centrality metric specific keyword arguments to be passed to func
-
-    Returns
-    -------
-    dict
-        dictionary of centrality scores keyed by names
-    """
-    comps = H.s_component_subgraphs(
-        s=s, edges=edges, return_singletons=return_singletons
-    )
-    if f is not None:
-        for cps in comps:
-            if (edges and f in cps.edges) or (not edges and f in cps.nodes):
-                comps = [cps]
-                break
-        else:
-            return {f: 0}
-
-    stats = dict()
-    if H.isstatic:
-        for h in comps:
-            if edges:
-                vertices = h.edges
-            else:
-                vertices = h.nodes
-
-            if h.shape[edges * 1] == 1:
-                stats.update({v: 0 for v in vertices})
-            elif use_nwhy and nwhy_available and h.nwhy:
-                g = h.get_linegraph(s=s, edges=edges, use_nwhy=True)
-                stats.update(dict(zip(vertices, func(g, **kwargs))))
-            else:
-                g = h.get_linegraph(s=s, edges=edges, use_nwhy=False)
-                stats.update(
-                    {
-                        h.get_name(k, edges=edges): v
-                        for k, v in func(g, **kwargs).items()
-                    }
-                )
-            if f:
-                return {f: stats[f]}
-    else:
-        for h in comps:
-            if edges:
-                A, Adict = h.edge_adjacency_matrix(s=s, index=True)
-            else:
-                A, Adict = h.adjacency_matrix(s=s, index=True)
-            g = nx.from_scipy_sparse_matrix(A)
-            stats.update({Adict[k]: v for k, v in func(g, **kwargs).items()})
-            if f:
-                return {f: stats[f]}
-
-    return stats
-
-
-
[docs]def s_betweenness_centrality(
-    H, s=1, edges=True, normalized=True, return_singletons=True, use_nwhy=True
-):
-    r"""
-    A centrality measure for an s-edge(node) subgraph of H based on shortest paths.
-    Equals the betweenness centrality of vertices in the edge(node) s-linegraph.
-
-    In a graph (2-uniform hypergraph) the betweenness centrality of a vertex $v$
-    is the ratio of the number of non-trivial shortest paths between any pair of
-    vertices in the graph that pass through $v$ divided by the total number of
-    non-trivial shortest paths in the graph.
-
-    The centrality of edge to all shortest s-edge paths
-    $V$ = the set of vertices in the linegraph.
-    $\sigma(s,t)$ = the number of shortest paths between vertices $s$ and $t$.
-    $\sigma(s,t|v)$ = the number of those paths that pass through vertex $v$.
-
-    .. math::
-
-        c_B(v) = \sum_{s \neq t \in V} \frac{\sigma(s, t|v)}{\sigma(s,t)}
-
-    Parameters
-    ----------
-    H : hnx.Hypergraph
-    s : int
-        s connectedness requirement
-    edges : bool, optional
-        determines if edge or node linegraph
-    normalized
-        bool, default=False,
-        If true the betweenness values are normalized by `2/((n-1)(n-2))`,
-        where n is the number of edges in H
-    return_singletons : bool, optional
-        if False will ignore singleton components of linegraph
-
-    Returns
-    -------
-    dict
-        A dictionary of s-betweenness centrality value of the edges.
-
-    """
-    if use_nwhy and nwhy_available and H.nwhy:
-        func = partial(nwhy.Slinegraph.s_betweenness_centrality, normalized=False)
-    else:
-        use_nwhy = False
-        func = partial(nx.betweenness_centrality, normalized=False)
-    result = _s_centrality(
-        func,
-        H,
-        s=s,
-        edges=edges,
-        return_singletons=return_singletons,
-        use_nwhy=use_nwhy,
-    )
-
-    if normalized and H.shape[edges * 1] > 2:
-        n = H.shape[edges * 1]
-        return {k: v * 2 / ((n - 1) * (n - 2)) for k, v in result.items()}
-    else:
-        return result

-
-
-
[docs]def s_closeness_centrality(
-    H, s=1, edges=True, return_singletons=True, source=None, use_nwhy=True
-):
-    r"""
-    In a connected component the reciprocal of the sum of the distance between an
-    edge(node) and all other edges(nodes) in the component times the number of edges(nodes)
-    in the component minus 1.
-
-    $V$ = the set of vertices in the linegraph.
-    $n = |V|$
-    $d$ = shortest path distance
-
-    .. math::
-
-        C(u) = \frac{n - 1}{\sum_{v \neq u \in V} d(v, u)}
-
-
-    Parameters
-    ----------
-    H : hnx.Hypergraph
-
-    s : int, optional
-
-    edges : bool, optional
-        Indicates if method should compute edge linegraph (default) or node linegraph.
-    return_singletons : bool, optional
-        Indicates if method should return values for singleton components.
-    source : str, optional
-        Identifier of node or edge of interest for computing centrality
-    use_nwhy : bool, optional
-        If true will use the :ref:`NWHy library <nwhy>` if available.
-
-    Returns
-    -------
-    dict or float
-        returns the s-closeness centrality value of the edges(nodes).
-        If source=None a dictionary of values for each s-edge in H is returned.
-        If source then a single value is returned.
-    """
-    if use_nwhy and nwhy_available and H.nwhy:
-        func = partial(nwhy.Slinegraph.s_closeness_centrality)
-    else:
-        use_nwhy = False
-        func = partial(nx.closeness_centrality)
-    return _s_centrality(
-        func,
-        H,
-        s=s,
-        edges=edges,
-        return_singletons=return_singletons,
-        f=source,
-        use_nwhy=use_nwhy,
-    )

-
-
-
[docs]def s_harmonic_closeness_centrality(H, s=1, edge=None, use_nwhy=True):
-    msg = """
-    s_harmonic_closeness_centrality is being replaced with s_harmonic_centrality 
-    and will not be available in future releases. 
-    """
-    warnings.warn(msg)
-    return s_harmonic_centrality(H, s=s, edges=True, normalized=True, source=edge)

-
-
-
[docs]def s_harmonic_centrality(
-    H,
-    s=1,
-    edges=True,
-    source=None,
-    normalized=False,
-    return_singletons=True,
-    use_nwhy=True,
-):
-    r"""
-    A centrality measure for an s-edge subgraph of H. A value equal to 1 means the s-edge
-    intersects every other s-edge in H. All values range between 0 and 1.
-    Edges of size less than s return 0. If H contains only one s-edge a 0 is returned.
-
-    The denormalized reciprocal of the harmonic mean of all distances from $u$ to all other vertices.
-    $V$ = the set of vertices in the linegraph.
-    $d$ = shortest path distance
-
-    .. math::
-
-        C(u) = \sum_{v \neq u \in V} \frac{1}{d(v, u)}
-
-    Normalized this becomes:
-    $$C(u) = \sum_{v \neq u \in V} \frac{1}{d(v, u)}\cdot\frac{2}{(n-1)(n-2)}$$
-    where $n$ is the number vertices.
-
-    Parameters
-    ----------
-    H : hnx.Hypergraph
-
-    s : int, optional
-
-    edges : bool, optional
-        Indicates if method should compute edge linegraph (default) or node linegraph.
-    return_singletons : bool, optional
-        Indicates if method should return values for singleton components.
-    source : str, optional
-        Identifier of node or edge of interest for computing centrality
-    use_nwhy : bool, optional
-        If true will use the :ref:`NWHy library <nwhy>` if available.
-
-    Returns
-    -------
-    dict or float
-        returns the s-harmonic closeness centrality value of the edges, a number between 0 and 1 inclusive.
-        If source=None a dictionary of values for each s-edge in H is returned.
-        If source then a single value is returned.
-
-    """
-
-    if use_nwhy and nwhy_available and H.nwhy:
-        func = partial(nwhy.Slinegraph.s_harmonic_closeness_centrality)
-    else:
-        use_nwhy = False
-        func = partial(nx.harmonic_centrality)
-    result = _s_centrality(
-        func,
-        H,
-        s=s,
-        edges=edges,
-        return_singletons=return_singletons,
-        f=source,
-        use_nwhy=use_nwhy,
-    )
-
-    if normalized and H.shape[edges * 1] > 2:
-        n = H.shape[edges * 1]
-        return {k: v * 2 / ((n - 1) * (n - 2)) for k, v in result.items()}
-    else:
-        return result

-
-
-
[docs]def s_eccentricity(
-    H, s=1, edges=True, source=None, return_singletons=True, use_nwhy=True
-):
-    r"""
-    The length of the longest shortest path from a vertex $u$ to every other vertex in the linegraph.
-    $V$ = set of vertices in the linegraph
-    $d$ = shortest path distance
-
-    .. math::
-
-        \text{s-ecc}(u) = \text{max}\{d(u,v): v \in V\}
-
-    Parameters
-    ----------
-    H : hnx.Hypergraph
-
-    s : int, optional
-
-    edges : bool, optional
-        Indicates if method should compute edge linegraph (default) or node linegraph.
-    return_singletons : bool, optional
-        Indicates if method should return values for singleton components.
-    source : str, optional
-        Identifier of node or edge of interest for computing centrality
-    use_nwhy : bool, optional
-        If true will use the :ref:`NWHy library <nwhy>` if available.
-
-    Returns
-    -------
-    dict or float
-        returns the s-eccentricity value of the edges(nodes).
-        If source=None a dictionary of values for each s-edge in H is returned.
-        If source then a single value is returned.
-
-    """
-    if use_nwhy and nwhy_available and H.nwhy:
-        func = nwhy.Slinegraph.s_eccentricity
-    else:
-        use_nwhy = False
-        func = nx.eccentricity
-
-    if source is not None:
-        return _s_centrality(
-            func,
-            H,
-            s=s,
-            edges=edges,
-            f=source,
-            return_singletons=return_singletons,
-            use_nwhy=use_nwhy,
-        )
-    else:
-        return _s_centrality(
-            func,
-            H,
-            s=s,
-            edges=edges,
-            return_singletons=return_singletons,
-            use_nwhy=use_nwhy,
-        )

-
- -
-
-
- -
- -
-

© Copyright 2021 Battelle Memorial Institute.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/_modules/classes/entity.html b/docs/build/_modules/classes/entity.html deleted file mode 100644 index 5315be89..00000000 --- a/docs/build/_modules/classes/entity.html +++ /dev/null @@ -1,1163 +0,0 @@ - - - - - - classes.entity — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for classes.entity

-# Copyright © 2018 Battelle Memorial Institute
-# All rights reserved.
-
-from collections import defaultdict
-import warnings
-from copy import copy
-import numpy as np
-import networkx as nx
-from hypernetx import HyperNetXError
-
-__all__ = ["Entity", "EntitySet"]
-
-
-
[docs]class Entity(object):
-    """
-    Base class for objects used in building network-like objects including
-    Hypergraphs, Posets, Cell Complexes.
-
-    Parameters
-    ----------
-    uid : hashable
-        a unique identifier
-
-    elements : list or dict, optional, default: None
-        a list of entities with identifiers different than uid and/or
-        hashables different than uid, see `Honor System`_
-
-    entity : Entity
-        an Entity object to be cloned into a new Entity with uid. If the uid is the same as
-        Entity.uid then the entities will not be distinguishable and error will be raised.
-        The `elements` in the signature will be added to the cloned entity.
-
-    weight : float, optional, default : 1
-    props : keyword arguments, optional, default: {}
-        properties belonging to the entity added as key=value pairs.
-        Both key and value must be hashable.
-
-    Notes
-    -----
-
-    An Entity is a container-like object, which has a unique identifier and
-    may contain elements and have properties.
-    The Entity class was created as a generic object providing structure for
-    Hypergraph nodes and edges.
-
-    - An Entity is distinguished by its identifier (sortable,hashable) :func:`Entity.uid`
-    - An Entity is a container for other entities but may not contain itself, :func:`Entity.elements`
-    - An Entity has properties :func:`Entity.properties`
-    - An Entity has memberships to other entities, :func:`Entity.memberships`.
-    - An Entity has children, :func:`Entity.children`, which are the elements of its elements.
-    - :func:`Entity.children` are registered in the :func:`Entity.registry`.
-    - All descendents of Entity are registered in :func:`Entity.fullregistry()`.
-
-    .. _Honor System:
-
-    **Honor System**
-
-    HyperNetX has an Honor System that applies to Entity uid values.
-    Two entities are equal if their __dict__ objects match.
-    For performance reasons many methods distinguish entities by their uids.
-    It is, therefore, up to the user to ensure entities with the same uids are indeed the same.
-    Not doing so may cause undesirable side effects.
-    In particular, the methods in the Hypergraph class assume distinct nodes and edges
-    have distinct uids.
-
-    Examples
-    --------
-
-        >>> x = Entity('x')
-        >>> y = Entity('y',[x])
-        >>> z = Entity('z',[x,y],weight=1)
-        >>> z
-        Entity(z,['y', 'x'],{'weight': 1})
-        >>> z.uid
-        'z'
-        >>> z.elements
-        {'x': Entity(x,[],{}), 'y': Entity(y,['x'],{})}
-        >>> z.properties
-        {'weight': 1}
-        >>> z.children
-        {'x'}
-        >>> x.memberships
-        {'y': Entity(y,['x'],{}), 'z': Entity(z,['y', 'x'],{'weight': 1})}
-        >>> z.fullregistry()
-        {'x': Entity(x,[],{}), 'y': Entity(y,['x'],{})}
-
-
-    See Also
-    --------
-    EntitySet
-
-    """
-
-    def __init__(self, uid, elements=[], entity=None, weight=1.0, **props):
-        super().__init__()
-
-        self._uid = uid
-        self.weight = weight
-
-        if entity is not None:
-            if isinstance(entity, Entity):
-                if uid == entity.uid:
-                    raise HyperNetXError(
-                        "The new entity will be indistinguishable from the original with the same uid. Use a differen uid."
-                    )
-                self._elements = entity.elements
-                self._memberships = entity.memberships
-                self.__dict__.update(entity.properties)
-        else:
-            self._elements = dict()
-            self._memberships = dict()
-            self._registry = dict()
-
-        self.__dict__.update(props)
-        self._registry = self.registry
-
-        if isinstance(elements, dict):
-            for k, v in elements.items():
-                if isinstance(v, Entity):
-                    self.add_element(v)
-                else:
-                    self.add_element(Entity(k, v))
-        elif elements is not None:
-            self.add(*elements)
-
-    @property
-    def properties(self):
-        """Dictionary of properties of entity"""
-        temp = self.__dict__.copy()
-        del temp["_elements"]
-        del temp["_memberships"]
-        del temp["_registry"]
-        del temp["_uid"]
-        return temp
-
-    @property
-    def uid(self):
-        """String identifier for entity"""
-        return self._uid
-
-    @property
-    def elements(self):
-        """
-        Dictionary of elements belonging to entity.
-        """
-        return dict(self._elements)
-
-    @property
-    def memberships(self):
-        """
-        Dictionary of elements to which entity belongs.
-
-        This assignment is done on construction and controlled by
-        :func:`Entity.add_element()`
-        and :func:`Entity.remove_element()` methods.
-        """
-
-        return {
-            k: self._memberships[k]
-            for k in self._memberships
-            if not isinstance(self._memberships[k], EntitySet)
-        }
-
-    @property
-    def children(self):
-        """
-        Set of uids of the elements of elements of entity.
-
-        To return set of ids for deeper level use:
-        :code:`Entity.levelset(level).keys()`
-        see: :func:`Entity.levelset`
-        """
-        return set(self.levelset(2).keys())
-
-    @property
-    def registry(self):
-        """
-        Dictionary of uid:Entity pairs for children entity.
-
-        To return a dictionary of all entities at all depths
-        :func:`Entity.complete_registry()`
-        """
-        return self.levelset(2)
-
-    @property
-    def uidset(self):
-        """
-        Set of uids of elements of entity.
-        """
-        return frozenset(self._elements.keys())
-
-    @property
-    def incidence_dict(self):
-        """
-        Dictionary of element.uid:element.uidset for each element in entity
-
-        To return an incidence dictionary of all nested entities in entity
-        use nested_incidence_dict
-        """
-        temp = dict()
-        for ent in self.elements.values():
-            temp[ent.uid] = {item for item in ent.elements}
-        return temp
-
-    @property
-    def is_empty(self):
-        """Boolean indicating if entity.elements is empty"""
-        return len(self) == 0
-
-    @property
-    def is_bipartite(self):
-        """
-        Returns boolean indicating if the entity satisfies the `Bipartite Condition`_
-        """
-        if self.uidset.isdisjoint(self.children):
-            return True
-        else:
-            return False
-
-    def __eq__(self, other):
-        """
-        Defines equality for Entities based on equivalence of their __dict__ objects.
-
-        Checks all levels of self and other to verify they are
-        referencing the same uids and that they have the same set of properties.
-        If at any point we get duplicate addresses we stop checking that branch
-        because we are guaranteed equality from there on.
-
-        May cause a recursion error if depth is too great.
-        """
-        seen = set()
-        # Define a compare method to call recursively on each level of self and other
-
-        def _comp(a, b, seen):
-            # Compare top level properties: same class? same ids? same children? same parents? same attributes?
-            if (
-                (a.__class__ != b.__class__)
-                or (a.uid != b.uid)
-                or (a.uidset != b.uidset)
-                or (a.properties != b.properties)
-                or (a.memberships != b.memberships)
-            ):
-                return False
-            # If all agree then look at the next level down since a and b share uidsets.
-            for uid, elt in a.elements.items():
-                if isinstance(elt, Entity):
-                    if uid in seen:
-                        continue
-                    seen.add(uid)
-                    if not _comp(elt, b[uid], seen):
-                        return False
-                # if not an Entity then elt is hashable so we usual equality
-                elif elt != b[uid]:
-                    return False
-            return True
-
-        return _comp(self, other, seen)
-
-    def __len__(self):
-        """Returns the number of elements in entity"""
-        return len(self._elements)
-
-    def __str__(self):
-        """Return the entity uid."""
-        return f"{self.uid}"
-
-    def __repr__(self):
-        """Returns a string resembling the constructor for entity without any
-        children"""
-        return f"Entity({self._uid},{list(self.uidset)},{self.properties})"
-
-    def __contains__(self, item):
-        """
-        Defines containment for Entities.
-
-        Parameters
-        ----------
-        item : hashable or Entity
-
-        Returns
-        -------
-        Boolean
-
-        Depends on the `Honor System`_ . Allows for uids to be used as shorthand for their entity.
-        This is done for performance reasons, but will fail if uids are
-        not unique to their entities.
-        Is not transitive.
-        """
-        if isinstance(item, Entity):
-            return item.uid in self._elements
-        else:
-            return item in self._elements
-
-    def __getitem__(self, item):
-        """
-        Returns Entity element by uid. Use :func:`E[uid]`.
-
-        Parameters
-        ----------
-        item : hashable or Entity
-
-        Returns
-        -------
-        Entity or None
-
-        If item not in entity, returns None.
-        """
-        if isinstance(item, Entity):
-            return self._elements.get(item.uid, "")
-        else:
-            return self._elements.get(item)
-
-    def __iter__(self):
-        """Returns iterator on element ids."""
-        return iter(self.elements)
-
-    def __call__(self):
-        """Returns an iterator on elements"""
-        for e in self.elements.values():
-            yield e
-
-    def __setattr__(self, k, v):
-        """Sets entity property.
-
-        Parameters
-        ----------
-        k : hashable, property key
-        v : hashable, property value
-            Will not set uid or change elements or memberships.
-
-        Returns
-        -------
-        None
-
-        """
-        if k == "uid":
-            raise HyperNetXError(
-                "Cannot reassign uid to Entity once it"
-                " has been created. Create a clone instead."
-            )
-        elif k == "elements":
-            raise HyperNetXError("To add elements to Entity use self.add().")
-        elif k == "memberships":
-            raise HyperNetXError(
-                "Can't choose your own memberships, " "they are like parents!"
-            )
-        else:
-            self.__dict__[k] = v
-
-    def _depth_finder(self, entset=None):
-        """
-        Helper method when working with levels.
-
-        Parameters
-        ----------
-        entset : dict, optional
-            a dictionary of entities keyed by uid
-
-        Returns
-        -------
-        Dictionary extending entset
-        """
-        temp = dict()
-        for uid, item in entset.items():
-            temp.update(item.elements)
-        return temp
-
-
[docs]    def level(self, item, max_depth=10):
-        """
-        The first level where item appears in self.
-
-        Parameters
-        ----------
-        item : hashable
-            uid for an entity
-
-        max_depth : int, default: 10
-            last level to check for entity
-
-        Returns
-        -------
-        level : int
-
-        Note
-        ----
-        Item must be the uid of an entity listed
-        in :func:`fullregistry()`
-        """
-        d = 1
-        currentlevel = self.levelset(1)
-        while d <= max_depth + 1:
-            if item in currentlevel:
-                return d
-            else:
-                d += 1
-                currentlevel = self._depth_finder(currentlevel)
-        return None

-
-
[docs]    def levelset(self, k=1):
-        """
-        A dictionary of level k of self.
-
-        Parameters
-        ----------
-        k : int, optional, default: 1
-
-        Returns
-        -------
-        levelset : dict
-
-        Note
-        ----
-        An Entity contains other entities, hence the relationships between entities
-        and their elements may be represented in a directed graph with entity as root.
-        The levelsets are sets of entities which make up the elements appearing at
-        a certain level.
-        """
-        if k <= 0:
-            return None
-        currentlevel = self.elements
-        if k > 1:
-            for idx in range(k - 1):
-                currentlevel = self._depth_finder(currentlevel)
-        return currentlevel

-
-
[docs]    def depth(self, max_depth=10):
-        """
-        Returns the number of nonempty level sets of level <= max_depth
-
-        Parameters
-        ----------
-        max_depth : int, optional, default: 10
-            If full depth is desired set max_depth to number of entities in
-            system + 1.
-
-        Returns
-        -------
-        depth : int
-            If max_depth is exceeded output will be numpy infinity.
-            If there is a cycle output will be numpy infinity.
-
-        """
-        if max_depth < 0:
-            return 0
-        currentlevel = self.elements
-        if not currentlevel:
-            return 0
-        else:
-            depth = 1
-        while depth < max_depth + 1:
-            currentlevel = self._depth_finder(currentlevel)
-            if not currentlevel:
-                return depth
-            depth += 1
-        return np.inf

-
-
[docs]    def fullregistry(self, lastlevel=10, firstlevel=1):
-        """
-        A dictionary of all entities appearing in levels firstlevel
-        to lastlevel.
-
-        Parameters
-        ----------
-        lastlevel : int, optional, default: 10
-
-        firstlevel : int, optional, default: 1
-
-        Returns
-        -------
-        fullregistry : dict
-
-        """
-        currentlevel = self.levelset(firstlevel)
-        accumulater = dict(currentlevel)
-        for idx in range(firstlevel, lastlevel):
-            currentlevel = self._depth_finder(currentlevel)
-            accumulater.update(currentlevel)
-        return accumulater

-
-
[docs]    def complete_registry(self):
-        """
-        A dictionary of all entities appearing in any level of
-        entity
-
-        Returns
-        -------
-        complete_registry : dict
-        """
-        results = dict()
-        Entity._complete_registry(self, results)
-        return results

-
-    @staticmethod
-    def _complete_registry(entity, results):
-        """
-        Helper method for complete_registry
-        """
-        for uid, e in entity.elements.items():
-            if uid not in results:
-                results[uid] = e
-                Entity._complete_registry(e, results)
-
-
[docs]    def nested_incidence_dict(self, level=10):
-        """
-        Returns a nested dictionary with keys up to level
-
-        Parameters
-        ----------
-        level : int, optional, default: 10
-            If level<=1, returns the incidence_dict.
-
-        Returns
-        -------
-        nested_incidence_dict : dict
-
-        """
-        if level > 1:
-            return {ent.uid: ent.nested_incidence_dict(level - 1) for ent in self()}
-        else:
-            return self.incidence_dict

-
-
[docs]    def size(self):
-        """
-        Returns the number of elements in entity
-        """
-        return len(self)

-
-
[docs]    def clone(self, newuid):
-        """
-        Returns shallow copy of entity with newuid. Entity's elements will
-        belong to two distinct Entities.
-
-        Parameters
-        ----------
-        newuid : hashable
-            Name of the new entity
-
-        Returns
-        -------
-        clone : Entity
-
-        """
-        return Entity(newuid, entity=self)

-
-
[docs]    def intersection(self, other):
-        """
-        A dictionary of elements belonging to entity and other.
-
-        Parameters
-        ----------
-        other : Entity
-
-        Returns
-        -------
-        Dictionary of elements : dict
-
-        """
-        return {e: self[e] for e in self if e in other}

-
-
[docs]    def restrict_to(self, element_subset, name=None):
-        """
-        Shallow copy of entity removing elements not in element_subset.
-
-        Parameters
-        ----------
-        element_subset : iterable
-            A subset of entities elements
-
-        name: hashable, optional
-            If not given, a name is generated to reflect entity uid
-
-        Returns
-        -------
-        New Entity : Entity
-            Could be empty.
-
-        """
-        newelements = [self[e] for e in element_subset if e in self]
-        name = name or f"{self.uid}_r"
-        return Entity(name, newelements, **self.properties)

-
-
[docs]    def add(self, *args):
-        """
-        Adds unpacked args to entity elements. Depends on add_element()
-
-        Parameters
-        ----------
-        args : One or more entities or hashables
-
-        Returns
-        -------
-        self : Entity
-
-        Note
-        ----
-        Adding an element to an object in a hypergraph will not add the
-        element to the hypergraph and will cause an error. Use :func:`Hypergraph.add_edge <classes.hypergraph.Hypergraph.add_edge>`
-        or :func:`Hypergraph.add_node_to_edge <classes.hypergraph.Hypergraph.add_node_to_edge>` instead.
-
-        """
-        for item in args:
-            self.add_element(item)
-
-        return self

-
-
[docs]    def add_elements_from(self, arg_set):
-        """
-        Similar to :func:`add()` it allows for adding from an interable.
-
-        Parameters
-        ----------
-        arg_set : Iterable of hashables or entities
-
-        Returns
-        -------
-        self : Entity
-
-        """
-        for item in arg_set:
-            self.add_element(item)
-
-        return self

-
-
[docs]    def add_element(self, item):
-        """
-        Adds item to entity elements and adds entity to item.memberships.
-
-        Parameters
-        ----------
-        item : hashable or Entity
-            If hashable, will be replaced with empty Entity using hashable as uid
-
-        Returns
-        -------
-        self : Entity
-
-        Notes
-        -----
-        If item is in entity elements, no new element is added but properties
-        will be updated.
-        If item is in complete_registry(), only the item already known to self will be added.
-        This method employs the `Honor System`_ since membership in complete_registry is checked
-        using the item's uid. It is assumed that the user will only use the same uid
-        for identical instances within the entities registry.
-
-        """
-        checkelts = self.complete_registry()
-        if isinstance(item, Entity):
-            # if item is an Entity, descendents will be compared to avoid collisions
-            if item.uid == self.uid:
-                raise HyperNetXError(
-                    f"Error: Self reference in submitted elements."
-                    f" Entity {self.uid} may not contain itself. "
-                )
-            elif item in self:
-                # item is already an element so only the properties will be updated
-                checkelts[item.uid].__dict__.update(item.properties)
-            elif item.uid in checkelts:
-                # if item belongs to an element or a descendent of an element
-                # then the existing descendent becomes an element
-                # and properties are updated.
-                checkelts[item.uid]._memberships[self.uid] = self
-                checkelts[item.uid].__dict__.update(item.properties)
-                self._elements[item.uid] = checkelts[item.uid]
-            else:
-                # if item's uid doesn't appear in complete_registry
-                # then it is added as something new
-                item._memberships[self.uid] = self
-                self._elements[item.uid] = item
-        else:
-            # item must be a hashable.
-            # if it appears as a uid in checkelts then
-            # the corresponding Entity will become an element of entity.
-            # Otherwise, at most it will be added as an empty Entity.
-            if self.uid == item:
-                raise HyperNetXError(
-                    f"Error: Self reference in submitted elements."
-                    f" Entity {self.uid} may not contain itself."
-                )
-            elif item not in self._elements:
-                if item in checkelts:
-                    self._elements[item] = checkelts[item]
-                    checkelts[item]._memberships[self.uid] = self
-                else:
-                    self._elements[item] = Entity(item, _memberships={self.uid: self})
-
-        return self

-
-
[docs]    def remove(self, *args):
-        """
-        Removes args from entitie's elements if they belong.
-        Does nothing with args not in entity.
-
-        Parameters
-        ----------
-        args : One or more hashables or entities
-
-        Returns
-        -------
-        self : Entity
-
-
-        """
-        for item in args:
-            Entity.remove_element(self, item)
-        return self

-
-
[docs]    def remove_elements_from(self, arg_set):
-        """
-        Similar to :func:`remove()`. Removes elements in arg_set.
-
-        Parameters
-        ----------
-        arg_set : Iterable of hashables or entities
-
-        Returns
-        -------
-        self : Entity
-
-        """
-        for item in arg_set:
-            Entity.remove_element(self, item)
-        return self

-
-
[docs]    def remove_element(self, item):
-        """
-        Removes item from entity and reference to entity from
-        item.memberships
-
-        Parameters
-        ----------
-        item : Hashable or Entity
-
-        Returns
-        -------
-        self : Entity
-
-
-        """
-        if isinstance(item, Entity):
-            del item._memberships[self.uid]
-            del self._elements[item.uid]
-        else:
-            del self[item]._memberships[self.uid]
-            del self._elements[item]
-
-        return self

-
-
[docs]    @staticmethod
-    def merge_entities(name, ent1, ent2):
-        """
-        Merge two entities making sure they do not conflict.
-
-        Parameters
-        ----------
-        name : hashable
-
-        ent1 : Entity
-            First entity to have elements and properties added to new
-            entity
-
-        ent2 : Entity
-            elements of ent2 will be checked against ent1.complete_registry()
-            and only nonexisting elements will be added using add() method.
-            Properties of ent2 will update properties of ent1 in new entity.
-
-        Returns
-        -------
-        a new entity : Entity
-
-        """
-        newent = ent1.clone(name)
-        newent.add_elements_from(ent2.elements.values())
-        for k, v in ent2.properties.items():
-            newent.__setattr__(k, v)
-        return newent

-
-
-
[docs]class EntitySet(Entity):
-    """
-    .. _entityset:
-
-    Parameters
-    ----------
-    uid : hashable
-        a unique identifier
-
-    elements : list or dict, optional, default: None
-        a list of entities with identifiers different than uid and/or
-        hashables different than uid, see `Honor System`_
-
-    props : keyword arguments, optional, default: {}
-        properties belonging to the entity added as key=value pairs.
-        Both key and value must be hashable.
-
-    Notes
-    -----
-    The EntitySet class was created to distinguish Entities satifying the Bipartite Condition.
-
-    .. _Bipartite Condition:
-
-    **Bipartite Condition**
-
-    *Entities that are elements of the same EntitySet, may not contain each other as elements.*
-    The elements and children of an EntitySet generate a specific partition for a bipartite graph.
-    The partition is isomorphic to a Hypergraph where the elements correspond to hyperedges and
-    the children correspond to the nodes. EntitySets are the basic objects used to construct hypergraphs
-    in HNX.
-
-    Example: ::
-
-        >>> y = Entity('y')
-        >>> x = Entity('x')
-        >>> x.add(y)
-        >>> y.add(x)
-        >>> w = EntitySet('w',[x,y])
-        HyperNetXError: Error: Fails the Bipartite Condition for EntitySet.
-        y references a child of an existing Entity in the EntitySet.
-
-    """
-
-    def __init__(self, uid, elements=[], **props):
-        super().__init__(uid, elements, **props)
-        if not self.is_bipartite:
-            raise HyperNetXError(
-                "Entity does not satisfy the Bipartite Condition, elements and children are not disjoint."
-            )
-
-    def __str__(self):
-        """Return the entityset uid."""
-        return f"{self.uid}"
-
-    def __repr__(self):
-        """Returns a string resembling the constructor for entityset without any
-        children"""
-        return f"EntitySet({self._uid},{list(self.uidset)},{self.properties})"
-
-
[docs]    def add(self, *args):
-        """
-        Adds args to entityset's elements, checking to make sure no self references are
-        made to element ids.
-        Ensures Bipartite Condition of EntitySet.
-
-        Parameters
-        ----------
-        args : One or more entities or hashables
-
-        Returns
-        -------
-        self : EntitySet
-
-        """
-        for item in args:
-            if isinstance(item, Entity):
-                if item.uid in self.children:
-                    raise HyperNetXError(
-                        f"Error: Fails the Bipartite Condition for EntitySet. {item.uid} references a child of an existing Entity in the EntitySet."
-                    )
-                elif not self.uidset.isdisjoint(item.uidset):
-                    raise HyperNetXError(
-                        f"Error: Fails the bipartite condition for EntitySet."
-                    )
-                else:
-                    Entity.add_element(self, item)
-            else:
-                if not item in self.children:
-                    Entity.add_element(self, item)
-                else:
-                    raise HyperNetXError(
-                        f"Error: {item} references a child of an existing Entity in the EntitySet."
-                    )
-        return self

-
-
[docs]    def clone(self, newuid):
-        """
-        Returns shallow copy of entityset with newuid. Entityset's
-        elements will belong to two distinct entitysets.
-
-
-        Parameters
-        ----------
-        newuid : hashable
-            Name of the new entityset
-
-        Returns
-        -------
-        clone : EntitySet
-
-        """
-        return EntitySet(newuid, elements=self.elements.values(), **self.properties)

-
-
[docs]    def collapse_identical_elements(self, newuid, return_equivalence_classes=False):
-        """
-        Returns a deduped copy of the entityset, using representatives of equivalence classes as element keys.
-        Two elements of an EntitySet are collapsed if they share the same children.
-
-        Parameters
-        ----------
-        newuid : hashable
-
-        return_equivalence_classes : boolean, default=False
-            If True, return a dictionary of equivalence classes keyed by new edge names
-
-        Returns
-        -------
-         : EntitySet
-        eq_classes : dict
-            if return_equivalence_classes = True
-
-        Notes
-        -----
-        Treats elements of the entityset as equal if they have the same uidsets. Using this
-        as an equivalence relation, the entityset's uidset is partitioned into equivalence classes.
-        The equivalent elements are identified using a single entity by using the
-        frozenset of uids associated to these elements as the uid for the new element
-        and dropping the properties.
-        If use_reps is set to True a representative element of the equivalence class is
-        used as identifier instead of the frozenset.
-
-        Example: ::
-
-            >>> E = EntitySet('E',elements=[Entity('E1', ['a','b']),Entity('E2',['a','b'])])
-            >>> E.incidence_dict
-            {'E1': {'a', 'b'}, 'E2': {'a', 'b'}}
-            >>> E.collapse_identical_elements('_',).incidence_dict
-            {'E2': {'a', 'b'}}
-
-        """
-
-        shared_children = defaultdict(set)
-        for e in self.__call__():
-            shared_children[frozenset(e.uidset)].add(e.uid)
-        new_entity_dict = {
-            f"{next(iter(v))}:{len(v)}": set(k) for k, v in shared_children.items()
-        }
-        if return_equivalence_classes:
-            eq_classes = {
-                f"{next(iter(v))}:{len(v)}": v for k, v in shared_children.items()
-            }
-            return EntitySet(newuid, new_entity_dict), dict(eq_classes)
-        else:
-            return EntitySet(newuid, new_entity_dict)

-
-
[docs]    def incidence_matrix(self, sparse=True, index=False, weights=None):
-        """
-        An incidence matrix for the EntitySet indexed by children x uidset.
-
-        Parameters
-        ----------
-        sparse : boolean, optional, default: True
-
-        index : boolean, optional, default : False
-            If True return will include a dictionary of children uid : row number
-            and element uid : column number
-
-        weights : bdict, optional, default : None
-            cell weight dictionary keyed by (edge.uid, node.uid)
-
-        Returns
-        -------
-        incidence_matrix : scipy.sparse.csr.csr_matrix or np.ndarray
-
-        row dictionary : dict
-            Dictionary identifying row with item in entityset's children
-
-        column dictionary : dict
-            Dictionary identifying column with item in entityset's uidset
-
-        Notes
-        -----
-
-        Example: ::
-
-            >>> E = EntitySet('E',{'a':{1,2,3},'b':{2,3},'c':{1,4}})
-            >>> E.incidence_matrix(sparse=False, index=True)
-            (array([[0, 1, 1],
-                    [1, 1, 0],
-                    [1, 1, 0],
-                    [0, 0, 1]]), {0: 1, 1: 2, 2: 3, 3: 4}, {0: 'b', 1: 'a', 2: 'c'})
-        """
-        if sparse:
-            from scipy.sparse import csr_matrix
-
-        nchildren = len(self.children)
-        nuidset = len(self.uidset)
-
-        ndict = dict(zip(self.children, range(nchildren)))
-        edict = dict(zip(self.uidset, range(nuidset)))
-
-        if len(ndict) != 0:
-
-            if index:
-                rowdict = {v: k for k, v in ndict.items()}
-                coldict = {v: k for k, v in edict.items()}
-
-            if sparse:
-                # Create csr sparse matrix
-                rows = list()
-                cols = list()
-                data = list()
-                for e in self:
-                    for n in self[e].elements:
-                        if weights is not None:
-                            try:
-                                data.append(weights[(e, n)])
-                            except:
-                                data.append(1)
-                        else:
-                            data.append(1)
-                        rows.append(ndict[n])
-                        cols.append(edict[e])
-                MP = csr_matrix((data, (rows, cols)))
-            else:
-                # Create an np.matrix
-                MP = np.zeros((nchildren, nuidset), dtype=int)
-                for e in self:
-                    for n in self[e].elements:
-                        MP[ndict[n], edict[e]] = 1
-            if index:
-                return MP, rowdict, coldict
-            else:
-                return MP
-        else:
-            if index:
-                return np.zeros(1), {}, {}
-            else:
-                return np.zeros(1)

-
-
[docs]    def restrict_to(self, element_subset, name=None):
-        """
-        Shallow copy of entityset removing elements not in element_subset.
-
-        Parameters
-        ----------
-        element_subset : iterable
-            A subset of the entityset's elements
-
-        name: hashable, optional
-            If not given, a name is generated to reflect entity uid
-
-        Returns
-        -------
-        new entityset : EntitySet
-            Could be empty.
-
-        See also
-        --------
-        Entity.restrict_to
-
-        """
-        newelements = [self[e] for e in element_subset if e in self]
-        name = name or f"{self.uid}_r"
-        return EntitySet(name, newelements, **self.properties)

-
- -
-
-
- -
- -
-

© Copyright 2021 Battelle Memorial Institute.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/_modules/classes/hypergraph.html b/docs/build/_modules/classes/hypergraph.html deleted file mode 100644 index 491a0b52..00000000 --- a/docs/build/_modules/classes/hypergraph.html +++ /dev/null @@ -1,2716 +0,0 @@ - - - - - - classes.hypergraph — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for classes.hypergraph

-# Copyright © 2018 Battelle Memorial Institute
-# All rights reserved.
-
-import warnings
-import pickle
-import networkx as nx
-from networkx.algorithms import bipartite
-import numpy as np
-import pandas as pd
-from scipy.sparse import issparse, coo_matrix, dok_matrix, csr_matrix
-from collections import OrderedDict, defaultdict
-from hypernetx.classes.entity import Entity, EntitySet
-from hypernetx.classes.staticentity import StaticEntity, StaticEntitySet
-from hypernetx.exception import HyperNetXError
-from hypernetx.utils.decorators import not_implemented_for
-
-
-__all__ = ["Hypergraph"]
-
-
-
[docs]class Hypergraph:
-    """
-    Hypergraph H = (V,E) references a pair of disjoint sets:
-    V = nodes (vertices) and E = (hyper)edges.
-
-    An HNX Hypergraph is either dynamic or static.
-    Dynamic hypergraphs can change by adding or subtracting objects
-    from them. Static hypergraphs require that all of the nodes and edges
-    be known at creation. A hypergraph is dynamic by default.
-
-    *Dynamic hypergraphs* require the user to keep track of its objects,
-    by using a unique names for each node and edge. This allows for multi-edge graphs and
-    inseperable nodes.
-
-    For example: Let V = {1,2,3} and E = {e1,e2,e3},
-    where e1 = {1,2}, e2 = {1,2}, and e3 = {1,2,3}.
-    The edges e1 and e2 contain the same set of nodes and yet
-    are distinct and must be distinguishable within H.
-
-    In a dynamic hypergraph each node and edge is
-    instantiated as an Entity and given an identifier or uid. Entities
-    keep track of inclusion relationships and can be nested. Since
-    hypergraphs can be quite large, only the entity identifiers will be used
-    for computation intensive methods, this means the user must take care
-    to keep a one to one correspondence between their set of uids and
-    the objects in their hypergraph. See `Honor System`_
-    Dynamic hypergraphs are most practical for small to modestly sized
-    hypergraphs (<1000 objects).
-
-    *Static hypergraphs* store node and edge information in numpy arrays and
-    are immutable. Each node and edge receives a class generated internal
-    identifier used for computations so do not require the user to create
-    different ids for nodes and edges. To create a static hypergraph set
-    `static = True` in the signature.
-
-    We will create hypergraphs in multiple ways:
-
-    1. As an empty instance: ::
-
-        >>> H = hnx.Hypergraph()
-        >>> H.nodes, H.edges
-        ({}, {})
-
-    2. From a dictionary of iterables (elements of iterables must be of type hypernetx.Entity or hashable): ::
-
-        >>> H = Hypergraph({'a':[1,2,3],'b':[4,5,6]})
-        >>> H.nodes, H.edges
-        # output: (EntitySet(_:Nodes,[1, 2, 3, 4, 5, 6],{}), EntitySet(_:Edges,['b', 'a'],{}))
-
-    3. From an iterable of iterables: (elements of iterables must be of type hypernetx.Entity or hashable): ::
-
-        >>> H = Hypergraph([{'a','b'},{'b','c'},{'a','c','d'}])
-        >>> H.nodes, H.edges
-        # output: (EntitySet(_:Nodes,['d', 'b', 'c', 'a'],{}), EntitySet(_:Edges,['_1', '_2', '_0'],{}))
-
-    4. From a hypernetx.EntitySet or StaticEntitySet: ::
-
-        >>> a = Entity('a',{1,2}); b = Entity('b',{2,3})
-        >>> E = EntitySet('sample',elements=[a,b])
-        >>> H = Hypergraph(E)
-        >>> H.nodes, H.edges.
-        # output: (EntitySet(_:Nodes,[1, 2, 3],{}), EntitySet(_:Edges,['b', 'a'],{}))
-
-    All of these constructions apply for both dynamic and static hypergraphs. To
-    create a static hypergraph set the parameter `static=True`. In addition a static
-    hypergraph is automatically created if a StaticEntity, StaticEntitySet, or pandas.DataFrame object
-    is passed to the Hypergraph constructor.
-
-    5. | From a pandas.DataFrame. The dataframe must have at least two columns with headers and there can be no nans.
-       | By default the first column corresponds to the edge names and the second column to the node names.
-       | You can specify the columns by restricting the dataframe to the columns of interest in the order:
-       | :code:`hnx.Hypergraph(df[[edge_column_name,node_column_name]])`
-       | See :ref:`Colab Tutorials <colab>`  Tutorial 6 - Static Hypergraphs and Entities for additional information.
-
-
-    Parameters
-    ----------
-    setsystem : (optional) EntitySet, StaticEntitySet, dict, iterable, pandas.dataframe, default: None
-        See notes above for setsystem requirements.
-    name : hashable, optional, default: None
-        If None then a placeholder '_'  will be inserted as name
-    static : boolean, optional, default: False
-        If True the hypergraph will be immutable, edges and nodes may not be changed.
-    weights : array-like, optional, default : None
-        User specified weights corresponding to setsytem of type pandas.DataFrame,
-        length must equal number of rows in dataframe.
-        If None, weight for all rows is assumed to be 1.
-    keep_weights : bool, optional, default : True
-        Whether or not to use existing weights when input is StaticEntity, or StaticEntitySet.
-    aggregateby : str, optional, {'count', 'sum', 'mean', 'median', max', 'min', 'first','last', None}, default : 'sum'
-        Method to aggregate cell_weights of duplicate rows if setsystem  is of type pandas.DataFrame or
-        StaticEntity. If None all cell weights will be set to 1.
-    use_nwhy : boolean, optional, default : False
-        If True hypergraph will be static and computations will be done using
-        C++ backend offered by NWHypergraph. This requires installation of the
-        NWHypergraph C++ library. Please see the :ref:`NWHy documentation <nwhy>` for more information.
-    filepath : str, optional, default : None
-
-    """
-
-    # TODO: remove lambda functions from constructor in H and E.
-
-    def __init__(
-        self,
-        setsystem=None,
-        name=None,
-        static=False,
-        weights=None,
-        aggregateby="sum",
-        use_nwhy=False,
-        filepath=None,
-    ):
-        self.filepath = filepath
-        if use_nwhy:
-            static = True
-            try:
-                import nwhy
-
-                self.nwhy = True
-
-            except:
-                self.nwhy = False
-                print("NWHypergraph is not available. Will continue with static=True.")
-                use_nwhy = False
-        else:
-            self.nwhy = False
-        if not name:
-            self.name = ""
-        else:
-            self.name = name
-
-        if static == True or (
-            isinstance(setsystem, StaticEntitySet)
-            or isinstance(setsystem, StaticEntity)
-            or isinstance(setsystem, pd.DataFrame)
-        ):
-            self._static = True
-            if setsystem is None:
-                self._edges = StaticEntitySet()
-                self._nodes = StaticEntitySet()
-            else:
-                if weights is not None:
-                    E = StaticEntitySet(
-                        entity=setsystem, weights=weights, aggregateby=aggregateby
-                    )
-                else:
-                    E = StaticEntitySet(entity=setsystem)
-                self._edges = E
-                self._nodes = E.restrict_to_levels([1], weights=False, aggregateby=None)
-                self._nodes._memberships = E.memberships
-                for n in self._nodes:
-                    self._nodes[n].memberships = self._nodes._memberships[n]  ### a bit of a hack to get same functionality from static as dynamic
-                                                                            ### we will have to see if it slows things down too much
-        else:
-            self._static = False
-            if setsystem is None:
-                setsystem = EntitySet("_", elements=[])
-            elif isinstance(setsystem, Entity):
-                setsystem = EntitySet("_", setsystem.incidence_dict)
-            elif isinstance(setsystem, dict):
-                # Must be a dictionary with values equal to iterables of Entities and hashables.
-                # Keys will be uids for new edges and values of the dictionary will generate the nodes.
-                setsystem = EntitySet("_", setsystem)
-            elif not isinstance(setsystem, EntitySet):
-                # If no ids are given, return default ids indexed by position in iterator
-                # This should be an iterable of sets
-                edge_labels = [self.name + str(x) for x in range(len(setsystem))]
-                setsystem = EntitySet("_", dict(zip(edge_labels, setsystem)))
-
-            _reg = setsystem.registry
-            _nodes = {k: Entity(k, **_reg[k].properties) for k in _reg}
-            _elements = {j: {k: _nodes[k] for k in setsystem[j]} for j in setsystem}
-            _edges = {
-                j: Entity(j, elements=_elements[j].values(), **setsystem[j].properties)
-                for j in setsystem
-            }
-
-            self._edges = EntitySet(
-                f"{self.name}:Edges", elements=_edges.values(), **setsystem.properties
-            )
-            self._nodes = EntitySet(f"{self.name}:Nodes", elements=_nodes.values())
-        if self._static:
-            temprows, tempcols = self.edges.data.T
-            tempdata = np.ones(len(temprows), dtype=int)
-            self.state_dict = {
-                "data": (temprows, tempcols, tempdata)
-            }  # how can we incorporate the counts into the nwhy hypergraph?
-            if self.nwhy:
-                self.g = nwhy.NWHypergraph(*self.state_dict["data"])
-                self.nwhy_dict = {"snodelg": dict(), "sedgelg": dict()}
-            self.state_dict["snodelg"] = dict()
-            self.state_dict["sedgelg"] = dict()
-            if self.filepath is not None:
-                self.save_state(fpath=self.filepath)
-
-    @property
-    def edges(self):
-        """
-        Object associated with self._edges.
-
-        Returns
-        -------
-        StaticEntitySet or EntitySet
-            If self.isstatic the StaticEntitySet, otherwise EntitySet.
-        """
-        return self._edges
-
-    @property
-    def nodes(self):
-        """
-        Object associated with self._nodes.
-
-        Returns
-        -------
-        StaticEntitySet or EntitySet
-            If self.isstatic the StaticEntitySet, otherwise EntitySet.
-
-        """
-        return self._nodes
-
-    @property
-    def isstatic(self):
-        """
-        Checks whether nodes and edges are immutable
-
-        Returns
-        -------
-        Boolean
-
-        """
-        return self._static
-
-    @property
-    def incidence_dict(self):
-        """
-        Dictionary keyed by edge uids with values the uids of nodes in each edge
-
-        Returns
-        -------
-        dict
-
-        """
-        return self._edges.incidence_dict
-
-    @property
-    def shape(self):
-        """
-        (number of nodes, number of edges)
-
-        Returns
-        -------
-        tuple
-
-        """
-        if self.nwhy:
-            return (self.g.number_of_nodes(), self.g.number_of_edges())
-        else:
-            return (len(self._nodes.elements), len(self._edges.elements))
-
-    def __str__(self):
-        """
-        String representation of hypergraph
-
-        Returns
-        -------
-        str
-
-        """
-        return f"Hypergraph({self.edges.elements},name={self.name})"
-
-    def __repr__(self):
-        """
-        String representation of hypergraph
-
-        Returns
-        -------
-        str
-
-        """
-        return f"Hypergraph({self.edges.elements},name={self.name})"
-
-    def __len__(self):
-        """
-        Number of nodes
-
-        Returns
-        -------
-        int
-
-        """
-        if self.nwhy:
-            return self.g.number_of_nodes()
-        else:
-            return len(self._nodes)
-
-    def __iter__(self):
-        """
-        Iterate over the nodes of the hypergraph
-
-        Returns
-        -------
-        dict_keyiterator
-
-        """
-        return iter(self.nodes)
-
-    def __contains__(self, item):
-        """
-        Returns boolean indicating if item is in self.nodes
-
-        Parameters
-        ----------
-        item : hashable or Entity
-
-        """
-        if isinstance(item, Entity):
-            return item.uid in self.nodes
-        else:
-            return item in self.nodes
-
-    def __getitem__(self, node):
-        """
-        Returns the neighbors of node
-
-        Parameters
-        ----------
-        node : Entity or hashable
-            If hashable, then must be uid of node in hypergraph
-
-        Returns
-        -------
-        neighbors(node) : iterator
-
-        """
-        return self.neighbors(node)
-
-
[docs]    @not_implemented_for("dynamic")
-    def get_id(self, uid, edges=False):
-        """
-        Return the internally assigned id associated with a label.
-
-        Parameters
-        ----------
-        uid : string
-            User provided name/id/label for hypergraph object
-        edges : bool, optional
-            Determines if uid is an edge or node name
-
-        Returns
-        -------
-        : int
-            internal id assigned at construction
-        """
-        kdx = (edges + 1) % 2
-        # return list(self.edges.labs(kdx)).index(uid)
-        return int(np.argwhere(self.edges.labs(kdx) == uid)[0])

-
-
[docs]    @not_implemented_for("dynamic")
-    def get_name(self, id, edges=False):
-        """
-        Return the user defined name/id/label associated to an
-        internally assigned id.
-
-        Parameters
-        ----------
-        id : int
-            Internally assigned id
-        edges : bool, optional
-            Determines if id references an edge or node
-
-        Returns
-        -------
-        str
-            User provided name/id/label for hypergraph object
-        """
-        kdx = (edges + 1) % 2
-        return self.edges.labs(kdx)[id]

-
-
[docs]    @not_implemented_for("dynamic")
-    def get_linegraph(self, s, edges=True, use_nwhy=True):
-        """
-        Creates an ::term::s-linegraph for the Hypergraph.
-        If edges=True (default)then the edges will be the vertices of the line graph.
-        Two vertices are connected by an s-line-graph edge if the corresponding
-        hypergraphedges intersect in at least s hypergraph nodes.
-        If edges=False, the hypergraph nodes will be the vertices of the line graph.
-        Two vertices are connected if the nodes they correspond to share at least s
-        incident hyper edges.
-
-        Parameters
-        ----------
-        s : int
-            The width of the connections.
-        edges : bool, optional
-            Determine if edges or nodes will be the vertices in the linegraph.
-        use_nwhy : bool, optional
-            Requests that nwhy be used to construct the linegraph. If NWHy is not available this is ignored.
-
-        Returns
-        -------
-        nx.Graph
-            A NetworkX graph.
-        """
-        if use_nwhy and self.nwhy:
-            d = self.nwhy_dict
-        else:
-            d = self.state_dict
-        key = "sedgelg" if edges else "snodelg"
-        if s in d[key]:
-            return d[key][s]
-        else:
-            if use_nwhy and self.nwhy:
-                d[key][s] = self.g.s_linegraph(s=s, edges=edges)
-            else:
-                if edges:
-                    A = self.edge_adjacency_matrix(s=s)
-                else:
-                    A = self.adjacency_matrix(s=s)
-                d[key][s] = nx.from_scipy_sparse_matrix(A)
-                if self.filepath is not None:
-                    self.save_state(fpath=self.filepath)
-            return d[key][s]

-
-
[docs]    @not_implemented_for("dynamic")
-    def set_state(self, **kwargs):
-        """
-        Allow state_dict updates from outside of class. Use with caution.
-
-        Parameters
-        ----------
-        **kwargs
-            key=value pairs to save in state dictionary
-        """
-        self.state_dict.update(kwargs)
-        if self.filepath is not None:
-            self.save_state(fpath=self.filepath)

-
-
[docs]    @not_implemented_for("dynamic")
-    def save_state(self, fpath=None):
-        """
-        Save the hypergraph as an ordered pair: [state_dict,labels]
-        The hypergraph can be recovered using the command:
-
-            >>> H = hnx.Hypergraph.recover_from_state(fpath)
-
-        Parameters
-        ----------
-        fpath : str, optional
-        """
-        if fpath is None:
-            fpath = self.filepath or "current_state.p"
-        pickle.dump([self.state_dict, self.edges.labels], open(fpath, "wb"))

-
-
[docs]    @classmethod
-    def recover_from_state(cls, fpath="current_state.p", newfpath=None, use_nwhy=True):
-        """
-        Recover a static hypergraph pickled using save_state.
-
-        Parameters
-        ----------
-        fpath : str
-            Full path to pickle file containing state_dict and labels
-            of hypergraph
-
-        Returns
-        -------
-        H : Hypergraph
-            static hypergraph with state dictionary prefilled
-        """
-        temp, labels = pickle.load(open(fpath, "rb"))
-        recovered_data = np.array(temp["data"])[[0, 1]].T  # need to save counts as well
-        recovered_counts = np.array(temp["data"])[
-            [2]
-        ]  # ammend this to store cell weights
-        E = StaticEntitySet(data=recovered_data, labels=labels)
-        E.properties["counts"] = recovered_counts
-        H = Hypergraph(E, use_nwhy=use_nwhy)
-        H.state_dict.update(temp)
-        if newfpath == "same":
-            newfpath = fpath
-        if newfpath is not None:
-            H.filepath = newfpath
-            H.save_state()
-        return H

-
-
[docs]    @classmethod
-    def add_nwhy(cls, h, fpath=None):
-        """
-        Add nwhy functionality to a hypergraph.
-
-        Parameters
-        ----------
-        h : hnx.Hypergraph
-        fpath : file path for storage of hypergraph state dictionary
-
-        Returns
-        -------
-        hnx.Hypergraph
-            Returns a copy of h with static set to true and nwhy set to True
-            if it is available.
-
-        """
-
-        if h.isstatic:
-            sd = h.state_dict
-            H = Hypergraph(h.edges, use_nwhy=True, filepath=fpath)
-            H.state_dict.update(sd)
-            return H
-        else:
-            return Hypergraph(StaticEntitySet(h.edges), use_nwhy=True, filepath=fpath)

-
-
[docs]    def edge_size_dist(self):
-        """
-        Returns the size for each edge
-
-        Returns
-        -------
-        np.array
-
-        """
-        if self.isstatic:
-            dist = self.state_dict.get("edge_size_dist", None)
-            if dist:
-                return dist
-            else:
-                if self.nwhy:
-                    dist = self.g.edge_size_dist()
-                else:
-                    dist = list(np.array(np.sum(self.incidence_matrix(), axis=0))[0])
-
-                self.set_state(edge_size_dist=dist)
-                return dist
-        else:
-            return list(np.array(np.sum(self.incidence_matrix(), axis=0))[0])

-
-
[docs]    def convert_to_static(
-        self,
-        name=None,
-        use_nwhy=False,
-        filepath=None,
-    ):
-        """
-        Returns new static hypergraph with the same dictionary as original hypergraph
-
-        Parameters
-        ----------
-        name : None, optional
-            Name
-        use_nwhy : bool, optional, default : False
-            Description
-        filepath : None, optional, default : False
-            Description
-
-        Returned
-        ------------------
-        hnx.Hypergraph
-            Will have attribute static = True
-
-        Note
-        ----
-        Static hypergraphs store the user defined node and edge names in
-        a dictionary of labeled lists. The order of the lists provides an
-        index, which the hypergraph uses in place of the node and edge names
-        for faster processing.
-
-        """
-        if self.isstatic:
-            return self
-        else:
-            edict = self.incidence_dict
-            E = StaticEntitySet(edict)
-            return Hypergraph(E, use_nwhy=use_nwhy, filepath=filepath, name=name)

-
-
[docs]    def remove_static(self, name=None):
-        """
-        Returns dynamic hypergraph
-
-        Parameters
-        ----------
-        name : None, optional
-            User defined namae of hypergraph
-
-        Returns
-        -------
-        hnx.Hypergraph
-            A new hypergraph with the same dictionary as self but allowing dynamic
-            changes to nodes and edges.
-            If hypergraph is not static, returns self.
-        """
-        if not self.isstatic:
-            return self
-        else:
-            return Hypergraph(self.edges.incidence_dict, name=name)

-
-
[docs]    def translate(self, idx, edges=False):
-        """
-        Returns the translation of numeric values associated with hypergraph.
-        Only needed if exposing the static identifiers assigned by the class.
-        If not static then the idx is returned.
-
-        Parameters
-        ----------
-        idx : int
-            class assigned integer for internal manipulation of Hypergraph data
-        edges : bool, optional, default: True
-            If True then translates from edge index. Otherwise will translate from
-            node index, default=False
-
-        Returns
-        -------
-         : int or string
-            User assigned identifier corresponding to idx
-        """
-        if self.isstatic:
-            return self.get_name(idx, edges=edges)
-        else:
-            return idx

-
-
[docs]    def s_degree(self, node, s=1):  # deprecate this to degree
-        """
-        Same as `degree`
-
-        Parameters
-        ----------
-        node : Entity or hashable
-            If hashable, then must be uid of node in hypergraph
-
-        s : positive integer, optional, default: 1
-
-        Returns
-        -------
-        s_degree : int
-            The degree of a node in the subgraph induced by edges
-            of size s
-
-        Note
-        ----
-        The :term:`s-degree` of a node is the number of edges of size
-        at least s that contain the node.
-
-        """
-        msg = (
-            "s-degree is deprecated and will be removed in"
-            " release 1.0.0. Use degree(node,s=int) instead."
-        )
-
-        warnings.warn(msg, DeprecationWarning)
-        return self.degree(node, s)

-
-
[docs]    def degree(self, node, s=1, max_size=None):
-        """
-        The number of edges of size s that contain node.
-
-        Parameters
-        ----------
-        node : hashable
-            identifier for the node.
-        s : positive integer, optional, default: 1
-            smallest size of edge to consider in degree
-        max_size : positive integer or None, optional, default: None
-            largest size of edge to consider in degree
-
-        Returns
-        -------
-         : int
-
-        """
-        if self.isstatic:
-            ndx = self.get_id(node)
-            if self.nwhy:                
-                return self.g.degree(ndx, min_size=s, max_size=None)
-            else:
-                memberships = set(self.nodes.memberships[node])
-        else:
-            memberships = set(self.nodes[node].memberships)
-                                  
-        if max_size is not None:
-            return len(
-                set(
-                    e
-                    for e in memberships
-                    if len(self.edges[e]) in range(s, max_size + 1)
-                )
-            )
-        elif s > 1:
-            return len(set(e for e in memberships if len(self.edges[e]) >= s))
-        else:
-            return len(memberships)

-
-
[docs]    def size(self, edge, nodeset=None):
-        """
-        The number of nodes in nodeset that belong to edge.
-        If nodeset is None then returns the size of edge
-
-        Parameters
-        ----------
-        edge : hashable
-            The uid of an edge in the hypergraph
-
-        Returns
-        -------
-        size : int
-
-        """
-        if nodeset is not None:
-            return len(set(nodeset).intersection(set(self.edges[edge])))
-        else:
-            if self.nwhy:
-                edx = self.get_id(edge,edges=True)
-                return self.g.size(edx)
-            else:
-                return len(self.edges[edge])

-
-
[docs]    def number_of_nodes(self, nodeset=None):
-        """
-        The number of nodes in nodeset belonging to hypergraph.
-
-        Parameters
-        ----------
-        nodeset : an interable of Entities, optional, default: None
-            If None, then return the number of nodes in hypergraph.
-
-        Returns
-        -------
-        number_of_nodes : int
-
-        """
-        if nodeset:
-            return len([n for n in self.nodes if n in nodeset])
-        else:
-            if self.nwhy == True:
-                return self.g.number_of_nodes()
-            else:
-                return len(self.nodes)

-
-
[docs]    def number_of_edges(self, edgeset=None):
-        """
-        The number of edges in edgeset belonging to hypergraph.
-
-        Parameters
-        ----------
-        edgeset : an interable of Entities, optional, default: None
-            If None, then return the number of edges in hypergraph.
-
-        Returns
-        -------
-        number_of_edges : int
-        """
-        if edgeset:
-            return len([e for e in self.edges if e in edgeset])
-        else:
-            if self.nwhy == True:
-                return self.g.number_of_edges()
-            else:
-                return len(self.edges)

-
-
[docs]    def order(self):
-        """
-        The number of nodes in hypergraph.
-
-        Returns
-        -------
-        order : int
-        """
-        if self.nwhy:
-            return self.g.number_of_nodes()
-        else:
-            return len(self.nodes)

-
-
[docs]    def dim(self, edge):
-        """
-        Same as size(edge)-1.
-        """
-        return self.size(edge) - 1

-
-
[docs]    def neighbors(self, node, s=1):
-        """
-        The nodes in hypergraph which share s edge(s) with node.
-
-        Parameters
-        ----------
-        node : hashable or Entity
-            uid for a node in hypergraph or the node Entity
-
-        s : int, list, optional, default : 1
-            Minimum number of edges shared by neighbors with node.
-
-        Returns
-        -------
-         : list
-            List of neighbors
-
-        """
-        if not node in self.nodes:
-            print(f"Node is not in hypergraph {self.name}.")
-            return
-
-        if self.isstatic:
-            g = self.get_linegraph(s=s, edges=False)
-            ndx = self.get_id(node)
-            if self.nwhy == True:
-                nbrs = g.s_neighbors(ndx)
-            else:
-                nbrs = list(g.neighbors(ndx))
-            return [self.translate(nb, edges=False) for nb in nbrs]
-
-        else:
-            node = self.nodes[
-                node
-            ].uid  # this allows node to be an Entity instead of a string
-            memberships = set(self.nodes[node].memberships).intersection(
-                self.edges.uidset
-            )
-            edgeset = {e for e in memberships if len(self.edges[e]) >= s}
-
-            neighborlist = set()
-            for e in edgeset:
-                neighborlist.update(self.edges[e].uidset)
-            neighborlist.discard(node)
-            return list(neighborlist)

-
-
[docs]    def edge_neighbors(self, edge, s=1):
-        """
-        The edges in hypergraph which share s nodes(s) with edge.
-
-        Parameters
-        ----------
-        edge : hashable or Entity
-            uid for a edge in hypergraph or the edge Entity
-
-        s : int, list, optional, default : 1
-            Minimum number of nodes shared by neighbors edge node.
-
-        Returns
-        -------
-         : list
-            List of edge neighbors
-
-        """
-        if not edge in self.edges:
-            print(f"Edge is not in hypergraph {self.name}.")
-            return
-
-        if self.isstatic:
-            g = self.get_linegraph(s=s, edges=True)
-            edx = self.get_id(edge, edges=True)
-            if self.nwhy == True:
-                nbrs = g.s_neighbors(edx)
-            else:
-                nbrs = list(g.neighbors(edx))
-            return [self.translate(nb, edges=True) for nb in nbrs]
-
-        else:
-            node = self.edges[edge].uid
-            return self.dual().neighbors(node, s=s)

-
-
[docs]    @not_implemented_for("static")
-    def remove_node(self, node):
-        """
-        Removes node from edges and deletes reference in hypergraph nodes
-
-        Parameters
-        ----------
-        node : hashable or Entity
-            a node in hypergraph
-
-        Returns
-        -------
-        hypergraph : Hypergraph
-
-        """
-        if not node in self._nodes:
-            return self
-        else:
-            if not isinstance(node, Entity):
-                node = self._nodes[node]
-            for edge in node.memberships:
-                self._edges[edge].remove(node)
-            self._nodes.remove(node)
-        return self

-
-
[docs]    @not_implemented_for("static")
-    def remove_nodes(self, node_set):
-        """
-        Removes nodes from edges and deletes references in hypergraph nodes
-
-        Parameters
-        ----------
-        node_set : an iterable of hashables or Entities
-            Nodes in hypergraph
-
-        Returns
-        -------
-        hypergraph : Hypergraph
-
-        """
-        for node in node_set:
-            self.remove_node(node)
-        return self

-
-    @not_implemented_for("static")
-    def _add_nodes_from(self, nodes):
-        """
-        Private helper method instantiates new nodes when edges added to hypergraph.
-
-        Parameters
-        ----------
-        nodes : iterable of hashables or Entities
-
-        """
-        for node in nodes:
-            if node in self._edges:
-                raise HyperNetXError("Node already an edge.")
-            elif node in self._nodes and isinstance(node, Entity):
-                self._nodes[node].__dict__.update(node.properties)
-            elif node not in self._nodes:
-                if isinstance(node, Entity):
-                    self._nodes.add(Entity(node.uid, **node.properties))
-                else:
-                    self._nodes.add(Entity(node))
-
-
[docs]    @not_implemented_for("static")
-    def add_edge(self, edge):
-        """
-
-        Adds a single edge to hypergraph.
-
-        Parameters
-        ----------
-        edge : hashable or Entity
-            If hashable the edge returned will be empty.
-
-        Returns
-        -------
-        hypergraph : Hypergraph
-
-        Notes
-        -----
-        When adding an edge to a hypergraph children must be removed
-        so that nodes do not have elements.
-        Each node (element of edge) must be instantiated as a node,
-        making sure its uid isn't already present in the self.
-        If an added edge contains nodes that cannot be added to hypergraph
-        then an error will be raised.
-
-        """
-        if edge in self._edges:
-            warnings.warn("Cannot add edge. Edge already in hypergraph")
-        elif edge in self._nodes:
-            warnings.warn("Cannot add edge. Edge is already a Node")
-        elif isinstance(edge, Entity):
-            if len(edge) > 0:
-                self._add_nodes_from(edge.elements.values())
-                self._edges.add(
-                    Entity(
-                        edge.uid,
-                        elements=[self._nodes[k] for k in edge],
-                        **edge.properties,
-                    )
-                )
-                for n in edge.elements:
-                    self._nodes[n].memberships[edge.uid] = self._edges[edge.uid]
-            else:
-                self._edges.add(Entity(edge.uid, **edge.properties))
-        else:
-            self._edges.add(Entity(edge))  # this generates an empty edge
-        return self

-
-
[docs]    @not_implemented_for("static")
-    def add_edges_from(self, edge_set):
-        """
-        Add edges to hypergraph.
-
-        Parameters
-        ----------
-        edge_set : iterable of hashables or Entities
-            For hashables the edges returned will be empty.
-
-        Returns
-        -------
-        hypergraph : Hypergraph
-
-        """
-        for edge in edge_set:
-            self.add_edge(edge)
-        return self

-
-
[docs]    @not_implemented_for("static")
-    def add_node_to_edge(self, node, edge):
-        """
-
-        Adds node to an edge in hypergraph edges
-
-        Parameters
-        ----------
-        node: hashable or Entity
-            If Entity, only uid and properties will be used.
-            If uid is already in nodes then the known node will
-            be used
-
-        edge: uid of edge or edge, must belong to self.edges
-
-        Returns
-        -------
-        hypergraph : Hypergraph
-
-        """
-        if edge in self._edges:
-            if not isinstance(edge, Entity):
-                edge = self._edges[edge]
-            if node in self._nodes:
-                self._edges[edge].add(self._nodes[node])
-            else:
-                if not isinstance(node, Entity):
-                    node = Entity(node)
-                else:
-                    node = Entity(node.uid, **node.properties)
-                self._edges[edge].add(node)
-                self._nodes.add(node)
-
-        return self

-
-
[docs]    @not_implemented_for("static")
-    def remove_edge(self, edge):
-        """
-        Removes a single edge from hypergraph.
-
-        Parameters
-        ----------
-        edge : hashable or Entity
-
-        Returns
-        -------
-        hypergraph : Hypergraph
-
-        Notes
-        -----
-
-        Deletes reference to edge from all of its nodes.
-        If any of its nodes do not belong to any other edges
-        the node is dropped from self.
-
-        """
-        if edge in self._edges:
-            if not isinstance(edge, Entity):
-                edge = self._edges[edge]
-            for node in edge.uidset:
-                edge.remove(node)
-                if len(self._nodes[node]._memberships) == 1:
-                    self._nodes.remove(node)
-            self._edges.remove(edge)
-        return self

-
-
[docs]    @not_implemented_for("static")
-    def remove_edges(self, edge_set):
-        """
-        Removes edges from hypergraph.
-
-        Parameters
-        ----------
-        edge_set : iterable of hashables or Entities
-
-        Returns
-        -------
-        hypergraph : Hypergraph
-
-        """
-        for edge in edge_set:
-            self.remove_edge(edge)
-        return self

-
-
[docs]    def incidence_matrix(self, weights=False, index=False):
-        """
-        An incidence matrix for the hypergraph indexed by nodes x edges.
-
-        Parameters
-        ----------
-        weights : bool, default=False
-            If False all nonzero entries are 1.
-            If True and self.static all nonzero entries are filled by
-            self.edges.cell_weights dictionary values.
-
-        index : boolean, optional, default False
-            If True return will include a dictionary of node uid : row number
-            and edge uid : column number
-
-        Returns
-        -------
-        incidence_matrix : scipy.sparse.csr.csr_matrix or np.ndarray
-
-        row dictionary : dict
-            Dictionary identifying rows with nodes
-
-        column dictionary : dict
-            Dictionary identifying columns with edges
-
-        """
-        if self.isstatic:
-            if weights == False:
-                mat = self.state_dict.get("incidence_matrix", None)
-                if mat is None:
-                    mat = self.edges.incidence_matrix()
-                    self.state_dict["incidence_matrix"] = mat
-                if index:
-                    rdict = dict(enumerate(self.edges.labs(1)))
-                    cdict = dict(enumerate(self.edges.labs(0)))
-                    return mat, rdict, cdict
-                else:
-                    return mat
-            if weights == True:
-                mat = self.state_dict.get("weighted_incidence_matrix", None)
-                if mat is None:
-                    mat = self.edges.incidence_matrix(weights=True)
-                    self.state_dict["weighted_incidence_matrix"] = mat
-                if index:
-                    rdict = dict(enumerate(self.edges.labs(1)))
-                    cdict = dict(enumerate(self.edges.labs(0)))
-                    return mat, rdict, cdict
-                else:
-                    return mat
-        else:
-            return self.edges.incidence_matrix(index=index)

-
-    @staticmethod
-    def _incidence_to_adjacency(M, s=1, weights=False):
-        """
-        Helper method to obtain adjacency matrix from 
-        boolean incidence matrix for s-metrics.
-        Self loops are not supported.
-        The adjacency matrix will define an s-linegraph.
-
-        Parameters
-        ----------
-        M : scipy.sparse.csr.csr_matrix 
-            incidence matrix of 0's and 1's
-
-        s : int, optional, default: 1
-
-        # weights : bool, dict optional, default=True
-        #     If False all nonzero entries are 1.
-        #     Otherwise, weights will be as in product.
-
-        Returns
-        -------
-        a matrix : scipy.sparse.csr.csr_matrix
-
-        """
-        M = csr_matrix(M)
-        weights = False ## currently weighting is not supported
-
-        if weights == False:
-            A = M.dot(M.transpose())
-            A.setdiag(0)
-            A = (A >= s) * 1
-        return A
-
-
-
[docs]    def adjacency_matrix(self, index=False, s=1):## , weights=False):
-        """
-        The sparse weighted :term:`s-adjacency matrix`
-
-        Parameters
-        ----------
-        s : int, optional, default: 1
-
-        index: boolean, optional, default: False
-            if True, will return a rowdict of row to node uid
-
-        weights: bool, default=True
-            If False all nonzero entries are 1.
-            If True adjacency matrix will depend on weighted incidence matrix,
-
-        Returns
-        -------
-        adjacency_matrix : scipy.sparse.csr.csr_matrix
-
-        row dictionary : dict
-
-        """
-        weights = False ## Currently default weights are not supported.
-        M = self.incidence_matrix(index=index, weights=weights)
-        if index:
-            return Hypergraph._incidence_to_adjacency(M[0], s=s, weights=weights), M[1]
-        else:
-            return Hypergraph._incidence_to_adjacency(M, s=s, weights=weights)

-
-
[docs]    def edge_adjacency_matrix(self, index=False, s=1, weights=False):
-        """
-        The weighted :term:`s-adjacency matrix` for the dual hypergraph.
-
-        Parameters
-        ----------
-        s : int, optional, default: 1
-
-        index: boolean, optional, default: False
-            if True, will return a coldict of column to edge uid
-
-        sparse: boolean, optional, default: True
-
-        weighted: boolean, optional, default: True
-
-        Returns
-        -------
-        edge_adjacency_matrix : scipy.sparse.csr.csr_matrix or numpy.ndarray
-
-        column dictionary : dict
-
-        Notes
-        -----
-        This is also the adjacency matrix for the line graph.
-        Two edges are s-adjacent if they share at least s nodes.
-        If index=True, returns a dictionary column_index:edge_uid
-
-        """
-        weights=False  ## Currently default weights are not supported
-
-        M = self.incidence_matrix(index=index, weights=weights)
-        if index:
-            return (
-                Hypergraph._incidence_to_adjacency(
-                    M[0].transpose(), s=s, weights=weights
-                ),
-                M[2],
-            )
-        else:
-            return Hypergraph._incidence_to_adjacency(
-                M.transpose(), s=s, weights=weights
-            )

-
-
[docs]    def auxiliary_matrix(self, s=1, index=False):
-        """
-        The unweighted :term:`s-auxiliary matrix` for hypergraph
-
-        Parameters
-        ----------
-        s : int
-        index : bool, optional, default: False
-            return a dictionary of labels for the rows of the matrix
-
-
-        Returns
-        -------
-        auxiliary_matrix : scipy.sparse.csr.csr_matrix or numpy.ndarray
-            Will return the same type of matrix as self.arr
-
-        Notes
-        -----
-        Creates subgraph by restricting to edges of cardinality at least s.
-        Returns the unweighted s-edge adjacency matrix for the subgraph.
-
-        """
-
-        edges = [e for e in self.edges if len(self.edges[e]) >= s]
-        H = self.restrict_to_edges(edges)
-        return H.edge_adjacency_matrix(s=s, index=index, weights=False)

-
-
[docs]    def bipartite(self):
-        """
-        Constructs the networkX bipartite graph associated to hypergraph.
-
-        Returns
-        -------
-        bipartite : nx.Graph()
-
-        Notes
-        -----
-        Creates a bipartite networkx graph from hypergraph.
-        The nodes and (hyper)edges of hypergraph become the nodes of bipartite graph.
-        For every (hyper)edge e in the hypergraph and node n in e there is an edge (n,e)
-        in the graph.
-
-        """
-        B = nx.Graph()
-        E = self.edges
-        V = self.nodes
-        B.add_nodes_from(E, bipartite=1)
-        B.add_nodes_from(V, bipartite=0)
-        B.add_edges_from([(v, e) for e in E for v in self.edges[e]])
-        return B

-
-
[docs]    def dual(self, name=None):
-        """
-        Constructs a new hypergraph with roles of edges and nodes of hypergraph reversed.
-
-        Parameters
-        ----------
-        name : hashable
-
-        Returns
-        -------
-        dual : hypergraph
-        """
-        if self.isstatic:
-            E = self.edges.restrict_to_levels((1, 0))
-            return Hypergraph(E, name=name, use_nwhy=self.nwhy)
-        else:
-            E = defaultdict(list)
-            for k, v in self.edges.incidence_dict.items():
-                for n in v:
-                    E[n].append(k)
-            return Hypergraph(E, name=name)

-
-    def _collapse_nwhy(self, edges, rec):
-        """
-        Helper method for collapsing nodes and edges when hypergraph
-        is static and using nwhy
-
-        Parameters
-        ----------
-        edges : bool
-            Collapse the edges if True, otherwise the nodes
-        rec : bool
-            return the equivalence classes
-        """
-
-        if edges:
-            d = self.g.collapse_edges(return_equivalence_class=rec)
-        else:
-            d = self.g.collapse_nodes(return_equivalence_class=rec)
-
-        if rec:
-            en = {
-                self.get_name(
-                    k, edges=edges
-                ): f"{self.get_name(k,edges=edges)}:{len(v)}"
-                for k, v in d.items()
-            }
-            ec = {
-                f"{self.get_name(k,edges=edges)}:{len(v)}": {
-                    self.get_name(vd, edges=edges) for vd in v
-                }
-                for k, v in d.items()
-            }
-        else:
-            en = {
-                self.get_name(
-                    k, edges=edges
-                ): f"{self.get_name(k,edges=edges)}:{v.pop()}"
-                for k, v in d.items()
-            }
-            ec = {}
-        lev = self.edges.keys[1 - 1 * edges]
-        E = self.edges.restrict_to_indices(sorted(d.keys()), level=1 - 1 * edges)
-        E.labels[str(lev)] = np.array([en[k] for k in E.labels[lev]])
-        if rec:
-            return E, ec
-        else:
-            return E
-
-
[docs]    def collapse_edges(
-        self,
-        name=None,
-        use_reps=None,
-        return_counts=None,
-        return_equivalence_classes=False,
-    ):
-        """
-        Constructs a new hypergraph gotten by identifying edges containing the same nodes
-
-        Parameters
-        ----------
-        name : hashable, optional, default: None
-
-        return_equivalence_classes: boolean, optional, default: False
-            Returns a dictionary of edge equivalence classes keyed by frozen sets of nodes
-
-        Returns
-        -------
-        new hypergraph : Hypergraph
-            Equivalent edges are collapsed to a single edge named by a representative of the equivalent
-            edges followed by a colon and the number of edges it represents.
-
-        equivalence_classes : dict
-            A dictionary keyed by representative edge names with values equal to the edges in
-            its equivalence class
-
-        Notes
-        -----
-        Two edges are identified if their respective elements are the same.
-        Using this as an equivalence relation, the uids of the edges are partitioned into
-        equivalence classes.
-
-        A single edge from the collapsed edges followed by a colon and the number of elements
-        in its equivalence class as uid for the new edge
-
-
-        """
-        if use_reps is not None or return_counts is not None:
-            msg = """
-            use_reps ane return_counts are no longer supported keyword arguments and will throw
-            an error in the next release.
-            collapsed hypergraph automatically names collapsed objects by a string "rep:count"
-            """
-            warnings.warn(msg, DeprecationWarning)
-
-        if self.nwhy:
-            temp = self._collapse_nwhy(True, return_equivalence_classes)
-        else:
-            temp = self.edges.collapse_identical_elements(
-                "_", return_equivalence_classes=return_equivalence_classes
-            )
-        if return_equivalence_classes:
-            return Hypergraph(temp[0], name, use_nwhy=self.nwhy), temp[1]
-        else:
-            return Hypergraph(temp, name, use_nwhy=self.nwhy)

-
-
[docs]    def collapse_nodes(
-        self,
-        name=None,
-        use_reps=True,
-        return_counts=True,
-        return_equivalence_classes=False,
-    ):
-        """
-        Constructs a new hypergraph gotten by identifying nodes contained by the same edges
-
-        Parameters
-        ----------
-        name: str, optional, default: None
-
-        return_equivalence_classes: boolean, optional, default: False
-            Returns a dictionary of node equivalence classes keyed by frozen sets of edges
-
-        use_reps : boolean, optional, default: False - Deprecated, this no longer works and will be removed
-            Choose a single element from the collapsed nodes as uid for the new node, otherwise uses
-            a frozen set of the uids of nodes in the equivalence class
-
-        return_counts: boolean, - Deprecated, this no longer works and will be removed
-            if use_reps is True the new nodes have uids given by a tuple of the rep
-            and the count
-
-        Returns
-        -------
-        new hypergraph : Hypergraph
-
-        Notes
-        -----
-        Two nodes are identified if their respective memberships are the same.
-        Using this as an equivalence relation, the uids of the nodes are partitioned into
-        equivalence classes. A single member of the equivalence class is chosen to represent
-        the class followed by the number of members of the class.
-
-        Example
-        -------
-
-            >>> h = Hypergraph(EntitySet('example',elements=[Entity('E1', ['a','b']),Entity('E2',['a','b'])]))
-            >>> h.incidence_dict
-            {'E1': {'a', 'b'}, 'E2': {'a', 'b'}}
-            >>> h.collapse_nodes().incidence_dict
-            {'E1': {frozenset({'a', 'b'})}, 'E2': {frozenset({'a', 'b'})}} ### Fix this
-            >>> h.collapse_nodes(use_reps=True).incidence_dict
-            {'E1': {('a', 2)}, 'E2': {('a', 2)}}
-
-        """
-        if use_reps is not None or return_counts is not None:
-            msg = """
-            use_reps ane return_counts are no longer supported keyword arguments and will throw
-            an error in the next release.
-            collapsed hypergraph automatically names collapsed objects by a string "rep:count"
-            """
-            warnings.warn(msg, DeprecationWarning)
-
-        if self.nwhy:
-            temp = self._collapse_nwhy(False, return_equivalence_classes)
-            if return_equivalence_classes:
-                return Hypergraph(temp[0], name, use_nwhy=self.nwhy), temp[1]
-            else:
-                return Hypergraph(temp, name, use_nwhy=self.nwhy)
-        else:
-            temp = self.dual().edges.collapse_identical_elements(
-                "_", return_equivalence_classes=return_equivalence_classes
-            )
-
-            if return_equivalence_classes:
-                return Hypergraph(temp[0], name, use_nwhy=self.nwhy).dual(), temp[1]
-            else:
-                return Hypergraph(temp, name, use_nwhy=self.nwhy).dual()

-
-
[docs]    def collapse_nodes_and_edges(
-        self,
-        name=None,
-        use_reps=True,
-        return_counts=True,
-        return_equivalence_classes=False,
-    ):
-        """
-        Returns a new hypergraph by collapsing nodes and edges.
-
-        Parameters
-        ----------
-
-        name: str, optional, default: None
-
-        use_reps: boolean, optional, default: False
-            Choose a single element from the collapsed elements as a representative
-
-        return_counts: boolean, optional, default: True
-            if use_reps is True the new elements are keyed by a tuple of the rep
-            and the count
-
-        return_equivalence_classes: boolean, optional, default: False
-            Returns a dictionary of edge equivalence classes keyed by frozen sets of nodes
-
-        Returns
-        -------
-        new hypergraph : Hypergraph
-
-        Notes
-        -----
-        Collapses the Nodes and Edges EntitySets. Two nodes(edges) are duplicates
-        if their respective memberships(elements) are the same. Using this as an
-        equivalence relation, the uids of the nodes(edges) are partitioned into
-        equivalence classes. A single member of the equivalence class is chosen to represent
-        the class followed by the number of members of the class.
-
-        Example
-        -------
-
-            >>> h = Hypergraph(EntitySet('example',elements=[Entity('E1', ['a','b']),Entity('E2',['a','b'])]))
-            >>> h.incidence_dict
-            {'E1': {'a', 'b'}, 'E2': {'a', 'b'}}
-            >>> h.collapse_nodes_and_edges().incidence_dict   ### Fix this
-            {('E1', 2): {('a', 2)}}
-
-        """
-        if use_reps is not None or return_counts is not None:
-            msg = """
-            use_reps ane return_counts are no longer supported keyword arguments and will throw
-            an error in the next release.
-            collapsed hypergraph automatically names collapsed objects by a string "rep:count"
-            """
-            warnings.warn(msg, DeprecationWarning)
-
-        if return_equivalence_classes:
-            temp, neq = self.collapse_nodes(
-                name="temp", return_equivalence_classes=True
-            )
-            ntemp, eeq = temp.collapse_edges(name=name, return_equivalence_classes=True)
-            return ntemp, neq, eeq
-        else:
-            temp = self.collapse_nodes(name="temp")
-            return temp.collapse_edges(name=name)

-
-
[docs]    def restrict_to_edges(self, edgeset, name=None):
-        """
-        Constructs a hypergraph using a subset of the edges in hypergraph
-
-        Parameters
-        ----------
-        edgeset: iterable of hashables or Entities
-            A subset of elements of the hypergraph edges
-
-        name: str, optional
-
-        Returns
-        -------
-        new hypergraph : Hypergraph
-        """
-        if self._static:
-            E = self._edges
-            setsystem = E.restrict_to(sorted(E.indices(E.keys[0], list(edgeset))))
-            return Hypergraph(setsystem, name=name, use_nwhy=self.nwhy)
-        else:
-            inneredges = set()
-            for e in edgeset:
-                if isinstance(e, Entity):
-                    inneredges.add(e.uid)
-                else:
-                    inneredges.add(e)
-            return Hypergraph({e: self.edges[e] for e in inneredges}, name=name)

-
-
[docs]    def restrict_to_nodes(self, nodeset, name=None):
-        """
-        Constructs a new hypergraph by restricting the edges in the hypergraph to
-        the nodes referenced by nodeset.
-
-        Parameters
-        ----------
-        nodeset: iterable of hashables
-            References a subset of elements of self.nodes
-
-        name: string, optional, default: None
-
-        Returns
-        -------
-        new hypergraph : Hypergraph
-        """
-        if self.isstatic:
-            E = self.edges.restrict_to_levels((1, 0))
-            setsystem = E.restrict_to(sorted(E.indices(E.keys[0], list(nodeset))))
-            return Hypergraph(
-                setsystem.restrict_to_levels((1, 0)), name=name, use_nwhy=self.nwhy
-            )
-        else:
-            memberships = set()
-            innernodes = set()
-            for node in nodeset:
-                innernodes.add(node)
-                if node in self.nodes:
-                    memberships.update(set(self.nodes[node].memberships))
-            newedgeset = dict()
-            for e in memberships:
-                if e in self.edges:
-                    temp = self.edges[e].uidset.intersection(innernodes)
-                    if temp:
-                        newedgeset[e] = Entity(e, temp, **self.edges[e].properties)
-            return Hypergraph(newedgeset, name=name)

-
-
[docs]    def toplexes(self, name=None, collapse=False, use_reps=False, return_counts=True):
-        """
-        Returns a :term:`simple hypergraph` corresponding to self.
-
-        Warning
-        -------
-        Collapsing is no longer supported inside the toplexes method. Instead generate a new
-        collapsed hypergraph and compute the toplexes of the new hypergraph.
-
-        Parameters
-        ----------
-        name: str, optional, default: None
-
-        # collapse: boolean, optional, default: False
-        #     Should the hypergraph be collapsed? This would preserve a link between duplicate maximal sets.
-        #     If False then only one of these sets will be used and uniqueness will be up to sets of equal size.
-
-        # use_reps: boolean, optional, default: False
-        #     If collapse=True then each toplex will be named by a representative of the set of
-        #     equivalent edges, default is False (see collapse_edges).
-
-        return_counts: boolean, optional, default: True
-            # If collapse=True then each toplex will be named by a tuple of the representative
-            # of the set of equivalent edges and their count
-
-        """
-        # TODO: There is a better way to do this....need to refactor
-        if collapse:
-            if len(self.edges) > 20:  # TODO: Determine how big is too big.
-                warnings.warn(
-                    "Collapsing a hypergraph can take a long time. It may be preferable to collapse the graph first and pickle it then apply the toplex method separately."
-                )
-            temp = self.collapse_edges()
-        else:
-            temp = self
-
-        if collapse:
-            msg = """
-            collapse, return_counts, and use_reps are no longer supported keyword arguments 
-            and will throw an error in the next release.
-            """
-            warnings.warn(msg, DeprecationWarning)
-
-        thdict = dict()
-        if self.nwhy:
-            tops = self.g.toplexes()
-            E = self.edges.restrict_to(tops)
-            return Hypergraph(E, use_nwhy=True)
-        else:
-            if self.isstatic:
-                for e in temp.edges:
-                    thdict[e] = temp.edges[e]
-            else:
-                for e in temp.edges:
-                    thdict[e] = temp.edges[e].uidset
-            tops = list()
-            for e in temp.edges:
-                flag = True
-                old_tops = list(tops)
-                for top in old_tops:
-                    if set(thdict[e]).issubset(thdict[top]):
-                        flag = False
-                        break
-                    elif set(thdict[top]).issubset(thdict[e]):
-                        tops.remove(top)
-                if flag:
-                    tops += [e]
-            return self.restrict_to_edges(tops, name=name)

-
-
[docs]    def is_connected(self, s=1, edges=False):
-        """
-        Determines if hypergraph is :term:`s-connected <s-connected, s-node-connected>`.
-
-        Parameters
-        ----------
-        s: int, optional, default: 1
-
-        edges: boolean, optional, default: False
-            If True, will determine if s-edge-connected.
-            For s=1 s-edge-connected is the same as s-connected.
-
-        Returns
-        -------
-        is_connected : boolean
-
-        Notes
-        -----
-
-        A hypergraph is s node connected if for any two nodes v0,vn
-        there exists a sequence of nodes v0,v1,v2,...,v(n-1),vn
-        such that every consecutive pair of nodes v(i),v(i+1)
-        share at least s edges.
-
-        A hypergraph is s edge connected if for any two edges e0,en
-        there exists a sequence of edges e0,e1,e2,...,e(n-1),en
-        such that every consecutive pair of edges e(i),e(i+1)
-        share at least s nodes.
-
-        """
-
-        if self.isstatic:
-            g = self.get_linegraph(s=s, edges=edges)
-            if self.nwhy:
-                return g.is_s_connected()
-            else:
-                return nx.is_connected(g)
-        else:
-            if edges:
-                A = self.edge_adjacency_matrix(s=s)
-            else:
-                A = self.adjacency_matrix(s=s)
-            g = nx.from_scipy_sparse_matrix(A)
-            return nx.is_connected(g)

-
-
[docs]    def singletons(self):
-        """
-        Returns a list of singleton edges. A singleton edge is an edge of
-        size 1 with a node of degree 1.
-
-        Returns
-        -------
-        singles : list
-            A list of edge uids.
-        """
-        if self.nwhy:
-            return self.edges.translate(0, self.g.singletons())
-        else:
-            M, rdict, cdict = self.incidence_matrix(index=True)
-            idx = np.argmax(M.shape)  # which axis has fewest members? if 1 then columns
-            cols = M.sum(idx)  # we add down the row index if there are fewer columns
-            singles = list()
-            for c in range(cols.shape[(idx + 1) % 2]):  # index along opposite axis
-                if cols[idx * c, c * ((idx + 1) % 2)] == 1:
-                    # then see if the singleton entry in that column is also singleton in its row
-                    # find the entry
-                    if idx == 0:
-                        r = np.argmax(M.getcol(c))
-                        # and get its sum
-                        s = np.sum(M.getrow(r))
-                        # if this is also 1 then the entry in r,c represents a singleton
-                        # so we want to change that entry to 0 and remove the row.
-                        # this means we want to remove the edge corresponding to c
-                        if s == 1:
-                            singles.append(cdict[c])
-                    else:  # switch the role of r and c
-                        r = np.argmax(M.getrow(c))
-                        s = np.sum(M.getcol(r))
-                        if s == 1:
-                            singles.append(cdict[r])
-            return singles

-
-
[docs]    def remove_singletons(self, name=None):
-        """
-        Constructs clone of hypergraph with singleton edges removed.
-
-        Parameters
-        ----------
-        name: str, optional, default: None
-
-        Returns
-        -------
-        new hypergraph : Hypergraph
-
-        """
-        E = [e for e in self.edges if e not in self.singletons()]
-        return self.restrict_to_edges(E)

-
-
[docs]    def s_connected_components(self, s=1, edges=True, return_singletons=False):
-        """
-        Returns a generator for the :term:`s-edge-connected components <s-edge-connected component>`
-        or the :term:`s-node-connected components <s-connected component, s-node-connected component>`
-        of the hypergraph.
-
-        Parameters
-        ----------
-        s : int, optional, default: 1
-
-        edges : boolean, optional, default: True
-            If True will return edge components, if False will return node components
-        return_singletons : bool, optional, default : False
-
-        Notes
-        -----
-        If edges=True, this method returns the s-edge-connected components as
-        lists of lists of edge uids.
-        An s-edge-component has the property that for any two edges e1 and e2
-        there is a sequence of edges starting with e1 and ending with e2
-        such that pairwise adjacent edges in the sequence intersect in at least
-        s nodes. If s=1 these are the path components of the hypergraph.
-
-        If edges=False this method returns s-node-connected components.
-        A list of sets of uids of the nodes which are s-walk connected.
-        Two nodes v1 and v2 are s-walk-connected if there is a
-        sequence of nodes starting with v1 and ending with v2 such that pairwise
-        adjacent nodes in the sequence share s edges. If s=1 these are the
-        path components of the hypergraph.
-
-        Example
-        -------
-            >>> S = {'A':{1,2,3},'B':{2,3,4},'C':{5,6},'D':{6}}
-            >>> H = Hypergraph(S)
-
-            >>> list(H.s_components(edges=True))
-            [{'C', 'D'}, {'A', 'B'}]
-            >>> list(H.s_components(edges=False))
-            [{1, 2, 3, 4}, {5, 6}]
-
-        Yields
-        ------
-        s_connected_components : iterator
-            Iterator returns sets of uids of the edges (or nodes) in the s-edge(node)
-            components of hypergraph.
-
-        """
-        components = list()
-
-        if self.nwhy:
-            g = self.get_linegraph(s, edges=edges)
-            if return_singletons:
-                allobjects = set(self.edges) if edges == True else set(self.nodes)
-                for c in g.s_connected_components():
-                    comp = {self.get_name(nd, edges=edges) for nd in c}
-                    allobjects.difference_update(comp)
-                for c in g.s_connected_components():
-                    yield {self.get_name(nd, edges=edges) for nd in c}
-                for obj in allobjects:
-                    yield {obj}
-            else:
-                for c in g.s_connected_components():
-                    comp = {self.get_name(nd, edges=edges) for nd in c}
-                    yield comp
-
-        elif self.isstatic:
-            g = self.get_linegraph(s, edges=edges)
-            for c in nx.connected_components(g):
-                if not return_singletons and len(c) == 1:
-                    continue
-                yield {self.get_name(n, edges=edges) for n in c}
-        else:
-            if edges:
-                A, coldict = self.edge_adjacency_matrix(s=s, index=True)
-                G = nx.from_scipy_sparse_matrix(A)
-                # if not return_singletons:
-                #     temp = [c for c in nx.connected_components(G) if len(c) > 1]
-                # else:
-                #     temp = nx.connected_components(G)
-                for c in nx.connected_components(G):
-                    if not return_singletons and len(c) == 1:
-                        continue
-                    yield {coldict[n] for n in c}
-            else:
-                A, rowdict = self.adjacency_matrix(s=s, index=True)
-                G = nx.from_scipy_sparse_matrix(A)
-                for c in nx.connected_components(G):
-                    if not return_singletons:
-                        if len(c) == 1:
-                            continue
-                    yield {rowdict[n] for n in c}

-
-
[docs]    def s_component_subgraphs(self, s=1, edges=True, return_singletons=False):
-        """
-
-        Returns a generator for the induced subgraphs of s_connected components.
-        Removes singletons unless return_singletons is set to True. Computed using
-        s-linegraph generated either by the hypergraph (edges=True) or its dual
-        (edges = False)
-
-        Parameters
-        ----------
-        s : int, optional, default: 1
-
-        edges : boolean, optional, edges=False
-            Determines if edge or node components are desired. Returns
-            subgraphs equal to the hypergraph restricted to each set of nodes(edges) in the
-            s-connected components or s-edge-connected components
-        return_singletons : bool, optional
-
-        Yields
-        ------
-        s_component_subgraphs : iterator
-            Iterator returns subgraphs generated by the edges (or nodes) in the
-            s-edge(node) components of hypergraph.
-
-        """
-        for idx, c in enumerate(
-            self.s_components(s=s, edges=edges, return_singletons=return_singletons)
-        ):
-            if edges:
-                yield self.restrict_to_edges(c, name=f"{self.name}:{idx}")
-            else:
-                yield self.restrict_to_nodes(c, name=f"{self.name}:{idx}")

-
-
[docs]    def s_components(self, s=1, edges=True, return_singletons=True):
-        """
-        Same as s_connected_components
-
-        See Also
-        --------
-        s_connected_components
-        """
-        return self.s_connected_components(
-            s=s, edges=edges, return_singletons=return_singletons
-        )

-
-
[docs]    def connected_components(self, edges=False, return_singletons=True):
-        """
-        Same as :meth:`s_connected_components` with s=1, but nodes are returned
-        by default. Return iterator.
-
-        See Also
-        --------
-        s_connected_components
-        """
-        return self.s_connected_components(edges=edges, return_singletons=True)

-
-
[docs]    def connected_component_subgraphs(self, return_singletons=True):
-        """
-        Same as :meth:`s_component_subgraphs` with s=1. Returns iterator
-
-        See Also
-        --------
-        s_component_subgraphs
-        """
-        return self.s_component_subgraphs(return_singletons=return_singletons)

-
-
[docs]    def components(self, edges=False, return_singletons=True):
-        """
-        Same as :meth:`s_connected_components` with s=1, but nodes are returned
-        by default. Return iterator.
-
-        See Also
-        --------
-        s_connected_components
-        """
-        return self.s_connected_components(s=1, edges=edges)

-
-
[docs]    def component_subgraphs(self, return_singletons=False):
-        """
-        Same as :meth:`s_components_subgraphs` with s=1. Returns iterator.
-
-        See Also
-        --------
-        s_component_subgraphs
-        """
-        return self.s_component_subgraphs(return_singletons=return_singletons)

-
-
[docs]    def node_diameters(self, s=1):
-        """
-        Returns the node diameters of the connected components in hypergraph.
-
-        Parameters
-        ----------
-        list of the diameters of the s-components and
-        list of the s-component nodes
-        """
-        if self.nwhy:
-            g = self.get_linegraph(s, edges=False)
-            if g.is_s_connected():
-                return g.s_diameter()
-            else:
-                diameters = list()
-                nodelists = list()
-                for c in g.s_connected_components():
-                    tc = self.edges.labs(1)[c]
-                    nodelists.append(tc)
-                    diameters.append(self.restrict_to_nodes(tc).node_diameters(s=s))
-        else:
-            A, coldict = self.adjacency_matrix(s=s, index=True)
-            G = nx.from_scipy_sparse_matrix(A)
-            diams = []
-            comps = []
-            for c in nx.connected_components(G):
-                diamc = nx.diameter(G.subgraph(c))
-                temp = set()
-                for e in c:
-                    temp.add(coldict[e])
-                comps.append(temp)
-                diams.append(diamc)
-            loc = np.argmax(diams)
-            return diams[loc], diams, comps

-
-
[docs]    def edge_diameters(self, s=1):
-        """
-        Returns the edge diameters of the s_edge_connected component subgraphs
-        in hypergraph.
-
-        Parameters
-        ----------
-        s : int, optional, default: 1
-
-        Returns
-        -------
-        maximum diameter : int
-
-        list of diameters : list
-            List of edge_diameters for s-edge component subgraphs in hypergraph
-
-        list of component : list
-            List of the edge uids in the s-edge component subgraphs.
-
-        """
-        if self.nwhy:
-            g = self.get_linegraph(s, edges=True)
-            if g.is_s_connected():
-                return g.s_diameter()
-            else:
-                diameters = list()
-                edgelists = list()
-                for c in g.s_connected_components():
-                    tc = self.edges.labs(0)[c]
-                    edgelists.append(tc)
-                    diameters.append(self.restrict_to_edges(tc).edge_diameters(s=s))
-        else:
-            A, coldict = self.edge_adjacency_matrix(s=s, index=True)
-            G = nx.from_scipy_sparse_matrix(A)
-            diams = []
-            comps = []
-            for c in nx.connected_components(G):
-                diamc = nx.diameter(G.subgraph(c))
-                temp = set()
-                for e in c:
-                    temp.add(coldict[e])
-                comps.append(temp)
-                diams.append(diamc)
-            loc = np.argmax(diams)
-            return diams[loc], diams, comps

-
-
[docs]    def diameter(self, s=1):
-        """
-        Returns the length of the longest shortest s-walk between nodes in hypergraph
-
-        Parameters
-        ----------
-        s : int, optional, default: 1
-
-        Returns
-        -------
-        diameter : int
-
-        Raises
-        ------
-        HyperNetXError
-            If hypergraph is not s-edge-connected
-
-        Notes
-        -----
-        Two nodes are s-adjacent if they share s edges.
-        Two nodes v_start and v_end are s-walk connected if there is a sequence of
-        nodes v_start, v_1, v_2, ... v_n-1, v_end such that consecutive nodes
-        are s-adjacent. If the graph is not connected, an error will be raised.
-
-        """
-        if self.nwhy:
-            g = self.get_linegraph(s, edges=False)
-            if g.is_s_connected():
-                return g.s_diameter()
-            else:
-                raise HyperNetXError(f"Hypergraph is not s-connected. s={s}")
-        else:
-            A = self.adjacency_matrix(s=s)
-            G = nx.from_scipy_sparse_matrix(A)
-            if nx.is_connected(G):
-                return nx.diameter(G)
-            else:
-                raise HyperNetXError(f"Hypergraph is not s-connected. s={s}")

-
-
[docs]    def edge_diameter(self, s=1):
-        """
-        Returns the length of the longest shortest s-walk between edges in hypergraph
-
-        Parameters
-        ----------
-        s : int, optional, default: 1
-
-        Return
-        ------
-        edge_diameter : int
-
-        Raises
-        ------
-        HyperNetXError
-            If hypergraph is not s-edge-connected
-
-        Notes
-        -----
-        Two edges are s-adjacent if they share s nodes.
-        Two nodes e_start and e_end are s-walk connected if there is a sequence of
-        edges e_start, e_1, e_2, ... e_n-1, e_end such that consecutive edges
-        are s-adjacent. If the graph is not connected, an error will be raised.
-
-        """
-        if self.nwhy:
-            g = self.get_linegraph(s, edges=True)
-            if g.is_s_connected():
-                return g.s_diameter()
-            else:
-                raise HyperNetXError(f"Hypergraph is not s-connected. s={s}")
-        else:
-            A = self.edge_adjacency_matrix(s=s)
-            G = nx.from_scipy_sparse_matrix(A)
-            if nx.is_connected(G):
-                return nx.diameter(G)
-            else:
-                raise HyperNetXError(f"Hypergraph is not s-connected. s={s}")

-
-
[docs]    def distance(self, source, target, s=1):
-        """
-        Returns the shortest s-walk distance between two nodes in the hypergraph.
-
-        Parameters
-        ----------
-        source : node.uid or node
-            a node in the hypergraph
-
-        target : node.uid or node
-            a node in the hypergraph
-
-        s : positive integer
-            the number of edges
-
-        Returns
-        -------
-        s-walk distance : int
-
-        See Also
-        --------
-        edge_distance
-
-        Notes
-        -----
-        The s-distance is the shortest s-walk length between the nodes.
-        An s-walk between nodes is a sequence of nodes that pairwise share
-        at least s edges. The length of the shortest s-walk is 1 less than
-        the number of nodes in the path sequence.
-
-        Uses the networkx shortest_path_length method on the graph
-        generated by the s-adjacency matrix.
-
-        """
-        if self.isstatic:
-            g = self.get_linegraph(s=s, edges=False)
-            src = self.get_id(source, edges=False)
-            tgt = self.get_id(target, edges=False)
-            try:
-                if self.nwhy:
-                    d = g.s_distance(src, tgt)
-                    if d == -1:
-                        warnings.warn(f"No {s}-path between {source} and {target}")
-                        return np.inf
-                    else:
-                        return d
-                else:
-                    return nx.shortest_path(g, src, tgt)
-            except:
-                warnings.warn(f"No {s}-path between {source} and {target}")
-                return np.inf
-        else:
-            if isinstance(source, Entity):
-                source = source.uid
-            if isinstance(target, Entity):
-                target = target.uid
-            A, rowdict = self.adjacency_matrix(s=s, index=True)
-            g = nx.from_scipy_sparse_matrix(A)
-            rkey = {v: k for k, v in rowdict.items()}
-            try:
-                path = nx.shortest_path_length(g, rkey[source], rkey[target])
-                return path
-            except:
-                warnings.warn(f"No {s}-path between {source} and {target}")
-                return np.inf

-
-
[docs]    def edge_distance(self, source, target, s=1):
-        """XX TODO: still need to return path and translate into user defined nodes and edges
-        Returns the shortest s-walk distance between two edges in the hypergraph.
-
-        Parameters
-        ----------
-        source : edge.uid or edge
-            an edge in the hypergraph
-
-        target : edge.uid or edge
-            an edge in the hypergraph
-
-        s : positive integer
-            the number of intersections between pairwise consecutive edges
-
-        TODO: add edge weights
-        weight : None or string, optional, default: None
-            if None then all edges have weight 1. If string then edge attribute
-            string is used if available.
-
-
-        Returns
-        -------
-        s- walk distance : the shortest s-walk edge distance
-            A shortest s-walk is computed as a sequence of edges,
-            the s-walk distance is the number of edges in the sequence
-            minus 1. If no such path exists returns np.inf.
-
-        See Also
-        --------
-        distance
-
-        Notes
-        -----
-            The s-distance is the shortest s-walk length between the edges.
-            An s-walk between edges is a sequence of edges such that consecutive pairwise
-            edges intersect in at least s nodes. The length of the shortest s-walk is 1 less than
-            the number of edges in the path sequence.
-
-            Uses the networkx shortest_path_length method on the graph
-            generated by the s-edge_adjacency matrix.
-
-        """
-        if self.isstatic:
-            g = self.get_linegraph(s=s, edges=True)
-            src = self.get_id(source, edges=True)
-            tgt = self.get_id(target, edges=True)
-            try:
-                if self.nwhy:
-                    d = g.s_distance(src, tgt)
-                    if d == -1:
-                        warnings.warn(f"No {s}-path between {source} and {target}")
-                        return np.inf
-                    else:
-                        return d
-                else:
-                    return nx.shortest_path(g, src, tgt)
-            except:
-                warnings.warn(f"No {s}-path between {source} and {target}")
-                return np.inf
-        else:
-            if isinstance(source, Entity):
-                source = source.uid
-            if isinstance(target, Entity):
-                target = target.uid
-            A, coldict = self.edge_adjacency_matrix(s=s, index=True)
-            g = nx.from_scipy_sparse_matrix(A)
-            ckey = {v: k for k, v in coldict.items()}
-            try:
-                path = nx.shortest_path_length(g, ckey[source], ckey[target])
-                return path
-            except:
-                warnings.warn(f"No {s}-path between {source} and {target}")
-                return np.inf

-
-
[docs]    def dataframe(self, sort_rows=False, sort_columns=False, cell_weights=True):
-        """
-        Returns a pandas dataframe for hypergraph indexed by the nodes and
-        with column headers given by the edge names.
-
-        Parameters
-        ----------
-        sort_rows : bool, optional, default=True
-            sort rows based on hashable node names
-        sort_columns : bool, optional, default=True
-            sort columns based on hashable edge names
-        cell_weights : bool, optional, default=True
-            if self.isstatic then include cell weights
-
-        """
-        if self.isstatic:
-            mat, rdx, cdx = self.edges.incidence_matrix(index=True, weights=True)
-        else:
-            mat, rdx, cdx = self.edges.incidence_matrix(index=True)
-        index = [rdx[i] for i in rdx]
-        columns = [cdx[j] for j in cdx]
-        df = pd.DataFrame(mat.todense(), index=index, columns=columns)
-        if sort_rows:
-            df = df.sort_index()
-        if sort_columns:
-            df = df[sorted(columns)]
-        return df

-
-
[docs]    @classmethod
-    def from_bipartite(
-        cls, B, set_names=("edges", "nodes"), name=None, static=False, use_nwhy=False
-    ):
-        """
-        Static method creates a Hypergraph from a bipartite graph.
-
-        Parameters
-        ----------
-
-        B: nx.Graph()
-            A networkx bipartite graph. Each node in the graph has a property
-            'bipartite' taking the value of 0 or 1 indicating a 2-coloring of the graph.
-
-        set_names: iterable of length 2, optional, default = ['nodes','edges']
-            Category names assigned to the graph nodes associated to each bipartite set
-
-        name: hashable
-
-        static: bool
-
-        Returns
-        -------
-         : Hypergraph
-
-        Notes
-        -----
-        A partition for the nodes in a bipartite graph generates a hypergraph.
-
-            >>> import networkx as nx
-            >>> B = nx.Graph()
-            >>> B.add_nodes_from([1, 2, 3, 4], bipartite=0)
-            >>> B.add_nodes_from(['a', 'b', 'c'], bipartite=1)
-            >>> B.add_edges_from([(1, 'a'), (1, 'b'), (2, 'b'), (2, 'c'), (3, 'c'), (4, 'a')])
-            >>> H = Hypergraph.from_bipartite(B)
-            >>> H.nodes, H.edges
-            # output: (EntitySet(_:Nodes,[1, 2, 3, 4],{}), EntitySet(_:Edges,['b', 'c', 'a'],{}))
-
-        """
-        # TODO: Add filepath keyword to signatures here and with dataframe and numpy array
-        edges = []
-        nodes = []
-        for n, d in B.nodes(data=True):
-            if d["bipartite"] == 0:
-                nodes.append(n)
-            else:
-                edges.append(n)
-
-        if not bipartite.is_bipartite_node_set(B, nodes):
-            raise HyperNetXError(
-                "Error: Method requires a 2-coloring of a bipartite graph."
-            )
-
-        if static:
-            elist = []
-            for e in list(B.edges):
-                if e[0] in edges:
-                    elist.append([e[0], e[1]])
-                else:
-                    elist.append([e[1], e[0]])
-            df = pd.DataFrame(elist, columns=set_names)
-            E = StaticEntitySet(entity=df)
-            name = name or "_"
-            return Hypergraph(E, name=name, use_nwhy=use_nwhy)
-        else:
-            node_entities = {
-                n: Entity(n, [], properties=B.nodes(data=True)[n]) for n in nodes
-            }
-            edge_dict = {
-                e: [node_entities[n] for n in list(B.neighbors(e))] for e in edges
-            }
-            name = name or "_"
-            return Hypergraph(setsystem=edge_dict, name=name)

-
-
[docs]    @classmethod
-    def from_numpy_array(
-        cls,
-        M,
-        node_names=None,
-        edge_names=None,
-        node_label="nodes",
-        edge_label="edges",
-        name=None,
-        key=None,
-        static=False,
-        use_nwhy=False,
-    ):
-        """
-        Create a hypergraph from a real valued matrix represented as a 2 dimensionsl numpy array.
-        The matrix is converted to a matrix of 0's and 1's so that any truthy cells are converted to 1's and
-        all others to 0's.
-
-        Parameters
-        ----------
-        M : real valued array-like object, 2 dimensions
-            representing a real valued matrix with rows corresponding to nodes and columns to edges
-
-        node_names : object, array-like, default=None
-            List of node names must be the same length as M.shape[0].
-            If None then the node names correspond to row indices with 'v' prepended.
-
-        edge_names : object, array-like, default=None
-            List of edge names must have the same length as M.shape[1].
-            If None then the edge names correspond to column indices with 'e' prepended.
-
-        name : hashable
-
-        key : (optional) function
-            boolean function to be evaluated on each cell of the array,
-            must be applicable to numpy.array
-
-        Returns
-        -------
-         : Hypergraph
-
-        Note
-        ----
-        The constructor does not generate empty edges.
-        All zero columns in M are removed and the names corresponding to these
-        edges are discarded.
-
-
-        """
-        # Create names for nodes and edges
-        # Validate the size of the node and edge arrays
-
-        M = np.array(M)
-        if len(M.shape) != (2):
-            raise HyperNetXError("Input requires a 2 dimensional numpy array")
-        # apply boolean key if available
-        if key:
-            M = key(M)
-
-        if node_names is not None:
-            nodenames = np.array(node_names)
-            if len(nodenames) != M.shape[0]:
-                raise HyperNetXError(
-                    "Number of node names does not match number of rows."
-                )
-        else:
-            nodenames = np.array([f"v{idx}" for idx in range(M.shape[0])])
-
-        if edge_names is not None:
-            edgenames = np.array(edge_names)
-            if len(edgenames) != M.shape[1]:
-                raise HyperNetXError(
-                    "Number of edge_names does not match number of columns."
-                )
-        else:
-            edgenames = np.array([f"e{jdx}" for jdx in range(M.shape[1])])
-
-        if static or use_nwhy:
-            arr = np.array(M)
-            if key:
-                arr = key(arr) * 1
-            arr = arr.transpose()
-            labels = OrderedDict([(edge_label, edgenames), (node_label, nodenames)])
-            E = StaticEntitySet(arr=arr, labels=labels)
-            return Hypergraph(E, name=name, use_nwhy=use_nwhy)
-
-        else:
-            # Remove empty column indices from M columns and edgenames
-            colidx = np.array([jdx for jdx in range(M.shape[1]) if any(M[:, jdx])])
-            colidxsum = np.sum(colidx)
-            if not colidxsum:
-                return Hypergraph()
-            else:
-                M = M[:, colidx]
-                edgenames = edgenames[colidx]
-                edict = dict()
-                # Create an EntitySet of edges from M
-                for jdx, e in enumerate(edgenames):
-                    edict[e] = nodenames[
-                        [idx for idx in range(M.shape[0]) if M[idx, jdx]]
-                    ]
-                return Hypergraph(edict, name=name)

-
-
[docs]    @classmethod
-    def from_dataframe(
-        cls,
-        df,
-        columns=None,
-        rows=None,
-        name=None,
-        fillna=0,
-        transpose=False,
-        transforms=[],
-        key=None,
-        node_label="nodes",
-        edge_label="edges",
-        static=False,
-        use_nwhy=False,
-    ):
-        """
-        Create a hypergraph from a Pandas Dataframe object using index to label vertices
-        and Columns to label edges. The values of the dataframe are transformed into an
-        incidence matrix.
-        Note this is different than passing a dataframe directly
-        into the Hypergraph constructor. The latter automatically generates a static hypergraph
-        with edge and node labels given by the cell values.
-
-        Parameters
-        ----------
-        df : Pandas.Dataframe
-            a real valued dataframe with a single index
-
-        columns : (optional) list, default = None
-            restricts df to the columns with headers in this list.
-
-        rows : (optional) list, default = None
-            restricts df to the rows indexed by the elements in this list.
-
-        name : (optional) string, default = None
-
-        fillna : float, default = 0
-            a real value to place in empty cell, all-zero columns will not generate
-            an edge.
-
-        transpose : (optional) bool, default = False
-            option to transpose the dataframe, in this case df.Index will label the edges
-            and df.columns will label the nodes, transpose is applied before transforms and
-            key
-
-        transforms : (optional) list, default = []
-            optional list of transformations to apply to each column,
-            of the dataframe using pd.DataFrame.apply().
-            Transformations are applied in the order they are
-            given (ex. abs). To apply transforms to rows or for additional
-            functionality, consider transforming df using pandas.DataFrame methods
-            prior to generating the hypergraph.
-
-        key : (optional) function, default = None
-            boolean function to be applied to dataframe. Must be defined on numpy
-            arrays.
-
-        See also
-        --------
-        from_numpy_array())
-
-
-        Returns
-        -------
-        : Hypergraph
-
-        Notes
-        -----
-        The `from_dataframe` constructor does not generate empty edges.
-        All-zero columns in df are removed and the names corresponding to these
-        edges are discarded.
-        Restrictions and data processing will occur in this order:
-
-            1. column and row restrictions
-            2. fillna replace NaNs in dataframe
-            3. transpose the dataframe
-            4. transforms in the order listed
-            5. boolean key
-
-        This method offers the above options for wrangling a dataframe into an incidence
-        matrix for a hypergraph. For more flexibility we recommend you use the Pandas
-        library to format the values of your dataframe before submitting it to this
-        constructor.
-
-        """
-
-        if type(df) != pd.core.frame.DataFrame:
-            raise HyperNetXError("Error: Input object must be a pandas dataframe.")
-
-        if columns:
-            df = df[columns]
-        if rows:
-            df = df.loc[rows]
-
-        df = df.fillna(fillna)
-        if transpose:
-            df = df.transpose()
-
-        # node_names = np.array(df.index)
-        # edge_names = np.array(df.columns)
-
-        for t in transforms:
-            df = df.apply(t)
-        if key:
-            mat = key(df.values) * 1
-        else:
-            mat = df.values * 1
-
-        params = {
-            "node_names": np.array(df.index),
-            "edge_names": np.array(df.columns),
-            "name": name,
-            "node_label": node_label,
-            "edge_label": edge_label,
-            "static": static,
-            "use_nwhy": use_nwhy,
-        }
-        return cls.from_numpy_array(mat, **params)

-
-
-# end of Hypergraph class
-
-
-def _make_3_arrays(mat):
-    arr = coo_matrix(mat)
-    return arr.row, arr.col, arr.data
-
- -
-
-
- -
- -
-

© Copyright 2021 Battelle Memorial Institute.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/_modules/classes/staticentity.html b/docs/build/_modules/classes/staticentity.html deleted file mode 100644 index 8639ea2f..00000000 --- a/docs/build/_modules/classes/staticentity.html +++ /dev/null @@ -1,1400 +0,0 @@ - - - - - - classes.staticentity — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for classes.staticentity

-from collections import OrderedDict, defaultdict, UserList
-from collections.abc import Iterable
-import warnings
-from copy import copy
-import numpy as np
-import networkx as nx
-from hypernetx import *
-from hypernetx.exception import HyperNetXError
-from hypernetx.classes.entity import Entity, EntitySet
-from hypernetx.utils import (
-    HNXCount,
-    DefaultOrderedDict,
-    remove_row_duplicates,
-    reverse_dictionary,
-)
-from scipy.sparse import coo_matrix, csr_matrix, issparse
-import itertools as it
-import pandas as pd
-
-__all__ = ["StaticEntity", "StaticEntitySet"]
-
-
-
[docs]class StaticEntity(object):
-
-    """
-    .. _staticentity:
-
-    Parameters
-    ----------
-    entity : StaticEntity, StaticEntitySet, Entity, EntitySet, pandas.DataFrame, dict, or list of lists
-        If a pandas.DataFrame, an error will be raised if there are nans.
-    data : array or array-like
-        Two dimensional array of integers. Provides sparse tensor indices for incidence
-        tensor.
-    arr : numpy.ndarray or scip.sparse.matrix, optional, default=None
-        Incidence tensor of data.
-    labels : OrderedDict of lists, optional, default=None
-        User defined labels corresponding to integers in data.
-    uid : hashable, optional, default=None
-    weights : array-like, optional, default : None
-        User specified weights corresponding to data, length must equal number
-        of rows in data. If None, weight for all rows is assumed to be 1.
-    keep_weights : bool, optional, default : True
-        Whether or not to use existing weights when input is StaticEntity, or StaticEntitySet.
-    aggregateby : str, optional, {'count', 'sum', 'mean', 'median', max', 'min', 'first', 'last', None}, default : 'count'
-        Method to aggregate cell_weights of duplicate rows if setsystem  is of type pandas.DataFrame of
-        StaticEntity. If None all cell weights will be set to 1.
-
-    props : user defined keyword arguments to be added to a properties dictionary, optional
-
-    Attributes
-    ----------
-    properties : dict
-        Description
-
-    """
-
-    def __init__(
-        self,
-        entity=None,
-        data=None,
-        arr=None,
-        labels=None,
-        uid=None,
-        weights=None, ### in this context weights is just a column of values corresponding to the rows in data.
-        keep_weights=True,
-        aggregateby="sum",
-        **props,
-    ):
-        self._uid = uid
-        self.properties = {}
-        if entity is not None:
-            if isinstance(entity, StaticEntity) or isinstance(entity, StaticEntitySet):
-                self.properties.update(entity.properties)
-                self.properties.update(props)
-                self.__dict__.update(self.properties)
-                self.__dict__.update(props)
-                self._data = entity._data.copy()
-                if keep_weights:
-                    self._weights = entity._weights
-                    self._cell_weights = dict(entity._cell_weights)
-                else:
-                    self._data, self._cell_weights = remove_row_duplicates(
-                        entity.data, weights=weights, aggregateby=aggregateby
-                    )
-                self._dimensions = entity._dimensions
-                self._dimsize = entity._dimsize
-                self._labels = OrderedDict(
-                    (category, np.array(values))
-                    for category, values in entity._labels.items()
-                )
-                self._keys = np.array(list(self._labels.keys()))
-                # returns the index of the category (column)
-                self._keyindex = dict(
-                    zip(self._labels.keys(), np.arange(self._dimsize))
-                )
-                self._index = {
-                    cat: dict(zip(self._labels[cat], np.arange(len(self._labels[cat]))))
-                    for cat in self._keys
-                }
-                self._arr = None
-            elif isinstance(entity, pd.DataFrame):
-                self.properties.update(props)
-                (
-                    self._data,
-                    self._labels,
-                    self._cell_weights,
-                ) = _turn_dataframe_into_entity(
-                    entity, weights=weights, aggregateby=aggregateby
-                )
-                self.__dict__.update(self.properties)
-                self._arr = None
-                self._dimensions = tuple([max(x) + 1 for x in self._data.transpose()])
-                self._dimsize = len(self._dimensions)
-                self._keys = np.array(list(self._labels.keys()))
-                self._keyindex = dict(
-                    zip(self._labels.keys(), np.arange(self._dimsize))
-                )
-                self._index = {
-                    cat: dict(zip(self._labels[cat], np.arange(len(self._labels[cat]))))
-                    for cat in self._keys
-                }
-
-            else:  # For these cases we cannot yet add cell_weights directly, cell_weights default to duplicate counts
-                if isinstance(entity, Entity) or isinstance(entity, EntitySet):
-                    d = entity.incidence_dict
-                    (
-                        self._data,
-                        self._labels,
-                        self._cell_weights,
-                    ) = _turn_dict_to_staticentity(
-                        d
-                    )  # For now duplicate entries will be removed.
-                elif isinstance(entity, dict):  # returns only 2 levels
-                    (
-                        self._data,
-                        self._labels,
-                        self._cell_weights,
-                    ) = _turn_dict_to_staticentity(
-                        entity
-                    )  # For now duplicate entries will be removed.
-                else:  # returns only 2 levels
-                    (
-                        self._data,
-                        self._labels,
-                        self._cell_weights,
-                    ) = _turn_iterable_to_staticentity(entity)
-                self._dimensions = tuple([len(self._labels[k]) for k in self._labels])
-                self._dimsize = len(self._dimensions)  # number of columns
-                self._keys = np.array(
-                    list(self._labels.keys())
-                )  # These are the column headers from the dataframe
-                self._keyindex = dict(
-                    zip(self._labels.keys(), np.arange(self._dimsize))
-                )
-                self._index = {
-                    cat: dict(zip(self._labels[cat], np.arange(len(self._labels[cat]))))
-                    for cat in self._keys
-                }
-                self.properties.update(props)
-                self.__dict__.update(
-                    self.properties
-                )  # Add function to set attributes ###########!!!!!!!!!!!!!
-                self._arr = None
-        elif data is not None:
-            self._arr = None
-            self._data, self._cell_weights = remove_row_duplicates(
-                data, weights=weights, aggregateby=aggregateby
-            )
-            self._dimensions = tuple([max(x) + 1 for x in self._data.transpose()])
-            self._dimsize = len(self._dimensions)
-            self.properties.update(props)
-            self.__dict__.update(props)
-            if labels is not None:
-                self._labels = OrderedDict(
-                    (category, np.array(values)) for category, values in labels.items()
-                )  # OrderedDict(category,np.array([categorical values ....])) is aligned to arr
-                self._keyindex = dict(
-                    zip(self._labels.keys(), np.arange(self._dimsize))
-                )
-                self._keys = np.array(list(labels.keys()))
-                self._index = {
-                    cat: dict(zip(self._labels[cat], np.arange(len(self._labels[cat]))))
-                    for cat in self._keys
-                }
-            else:
-                self._labels = OrderedDict(
-                    [
-                        (int(dim), np.arange(ct))
-                        for dim, ct in enumerate(self.dimensions)
-                    ]
-                )
-                self._keyindex = defaultdict(_fd)
-                self._keys = np.arange(self._dimsize)
-                self._index = {
-                    cat: dict(zip(self._labels[cat], np.arange(len(self._labels[cat]))))
-                    for cat in self._keys
-                }
-
-        elif arr is not None:
-            self._arr = arr
-            self.properties.update(props)
-            self.__dict__.update(props)
-            self._state_dict = {"arr": arr * 1}
-            self._dimensions = arr.shape
-            self._dimsize = len(arr.shape)
-            self._data, self._cell_weights = _turn_tensor_to_data(arr * 1)
-            if labels is not None:
-                self._labels = OrderedDict(
-                    (category, np.array(values)) for category, values in labels.items()
-                )
-                self._keyindex = dict(
-                    zip(self._labels.keys(), np.arange(self._dimsize))
-                )
-                self._keys = np.array(list(labels.keys()))
-                self._index = {
-                    cat: dict(zip(self._labels[cat], np.arange(len(self._labels[cat]))))
-                    for cat in self._keys
-                }
-
-            else:
-                self._labels = OrderedDict(
-                    [
-                        (int(dim), np.arange(ct))
-                        for dim, ct in enumerate(self.dimensions)
-                    ]
-                )
-                self._keyindex = defaultdict(_fd)
-                self._keys = np.arange(self._dimsize)
-                self._index = {
-                    cat: dict(zip(self._labels[cat], np.arange(len(self._labels[cat]))))
-                    for cat in self._keys
-                }
-        else:  # no entity, data or arr is given
-
-            if labels is not None:
-                self._labels = OrderedDict(
-                    (category, np.array(values)) for category, values in labels.items()
-                )
-                self._dimensions = tuple([len(labels[k]) for k in labels])
-                self._data = np.zeros((0, len(labels)), dtype=int)
-                self._cell_weights = {}
-                self._arr = np.empty(self._dimensions, dtype=int)
-                self._state_dict = {"arr": np.empty(self.dimensions, dtype=int)}
-                self._dimsize = len(self._dimensions)
-                self._keyindex = dict(
-                    zip(self._labels.keys(), np.arange(self._dimsize))
-                )
-                self._keys = np.array(list(labels.keys()))
-                self._index = {
-                    cat: dict(zip(self._labels[cat], np.arange(len(self._labels[cat]))))
-                    for cat in self._keys
-                }
-            else:
-                self._data = np.zeros((0, 0), dtype=int)
-                self._cell_weights = {}
-                self._arr = np.array([], dtype=int)
-                self._labels = OrderedDict([])
-                self._dimensions = tuple([])
-                self._dimsize = 0
-                self._keyindex = defaultdict(_fd)
-                self._keys = np.array([])
-                # self._index = lambda category, value: None
-                self._index = {
-                    cat: dict(
-                        zip(self._labels[cat], [None for i in len(self._labels[cat])])
-                    )
-                    for cat in self._keys
-                }
-
-            # if labels is a list of categorical values, then change it into an
-            # ordered dictionary?
-            self.properties = props
-            self.__dict__.update(props)  # keyed by the method name and signature
-
-        if len(self._labels) > 0:
-            self._labs = {
-                kdx: self._labels.get(self._keys[kdx], {})
-                for kdx in range(self._dimsize)
-            }
-        else:
-            self._labs = {}
-
-        self._weights = [self._cell_weights[tuple(t)] for t in self._data]
-        self._memberships = None
-
-    @property
-    def arr(self):
-        """
-        Tensor like representation of data indexed by labels with values given by incidence or cell weight.
-
-        Returns
-        -------
-        numpy.ndarray
-            A Numpy ndarray with dimensions equal dimensions of static entity. Entries are cell_weights.
-            self.data gives a list of nonzero coordinates aligned with cell_weights.
-        """
-        if self._arr is not None:
-            if type(self._arr) == int and self._arr == 0:
-                print("arr cannot be computed")
-        else:
-            try:
-                imat = np.zeros(self.dimensions, dtype=int)
-                for d in self._data:
-                    imat[tuple(d)] = self._cell_weights[tuple(d)]
-                self._arr = imat
-            except Exception as ex:
-                print(ex)
-                print("arr cannot be computed")
-                self._arr = 0
-        return self._arr  # Do we need to return anything here
-
-    @property
-    def data(self):
-        """
-        Data array or tensor array of Static Entity
-
-        Returns
-        -------
-        numpy.ndarray
-            Two dimensional array. Each row has system ids of objects in the static entity.
-            Each column corresponds to one level of the static entity.
-
-        """
-
-        return np.array(self._data)
-
-    @property
-    def cell_weights(self):
-        """
-        User defined weights corresponding to unique rows in data.
-
-        Returns
-        -------
-        numpy.array
-            One dimensional array of values aligned to data.
-        """
-        return dict(self._cell_weights)
-
-    @property
-    def labels(self):
-        """
-        Ordered dictionary of labels
-
-        Returns
-        -------
-        collections.OrderedDict
-            User defined identifiers for objects in static entity. Ordered keys correspond
-            levels. Ordered values correspond to integer representation of values in data.
-        """
-        return dict(self._labels)
-
-    @property
-    def dimensions(self):
-        """
-        Dimension of Static Entity data
-
-        Returns
-        -------
-        tuple
-            Tuple of number of distinct labels in each level, ordered by level.
-        """
-        return self._dimensions
-
-    @property
-    def dimsize(self):
-        """
-        Number of categories in the data
-
-        Returns
-        -------
-        int
-            Number of levels in static entity, equals length of self.dimensions
-        """
-        return self._dimsize
-
-    @property
-    def keys(self):
-        """
-        Array of keys of labels
-
-        Returns
-        -------
-        np.ndarray
-            Array of label keys, ordered by level.
-        """
-        return self._keys
-
-
[docs]    def keyindex(self, category):
-        """
-        Returns the index of a category in keys array
-
-        Returns
-        -------
-        int
-            Index osition of particular label in keys equal to the level of the
-            category.
-        """
-        return self._keyindex[category]

-
-    @property
-    def uid(self):
-        """
-        User defined identifier for each object in static entity.
-
-        Returns
-        -------
-        str, int
-            Identifiers, which distinguish  objects within each level.
-        """
-        return self._uid
-
-    @property
-    def uidset(self):
-        """
-        Returns a set of the string identifiers for Static Entity
-
-        Returns
-        -------
-        frozenset
-            Hashable  set of keys.
-        """
-        return self.uidset_by_level(0)
-
-    @property
-    def elements(self):
-        """
-        Keys and values in the order of insertion
-
-        Returns
-        -------
-        collections.OrderedDict
-            Same as elements_by_level with level1 = 0, level2 = 1.
-            Compare with EntitySet with level1 = elements, level2 = children.
-
-        """
-        try:
-            return dict(self._elements)
-        except:
-            if len(self._keys) == 1:
-                self._elements = {k: UserList() for k in self._labels[self._keys[0]]}
-                return dict(self._elements)
-            else:
-                self._elements = self.elements_by_level(0, translate=True)
-                return dict(self._elements)
-
-    @property
-    def memberships(self):
-        """
-        Reverses the elements dictionary
-
-        Returns
-        -------
-        collections.OrderedDict
-            Same as elements_by_level with level1 = 1, level2 = 0.
-        """
-        try:
-            return dict(self._memberships)
-        except:
-            # self._memberships = reverse_dictionary(self.elements)
-            # return self._memberships
-            if len(self._keys) == 1:
-                return None
-            else:
-                self._memberships = self.elements_by_level(1, 0, translate=True)
-                return dict(self._memberships)
-
-    @property
-    def children(self):
-        """
-        Labels of keys of first index
-
-        Returns
-        -------
-        numpy.array
-            One dimensional array of labels in the second level.
-
-        """
-        try:
-            return set(self._labs[1])
-        except:
-            return
-
-    @property
-    def incidence_dict(self):
-        """
-        Same as elements.
-
-        Returns
-        -------
-        collections.OrderedDict
-            Same as elements_by_level with level1 = 0, level2 = 1.
-            Compare with EntitySet with level1 = elements, level2 = children.
-        """
-        return self.elements_by_level(0, translate=True)
-
-    @property
-    def dataframe(self):
-        """
-        Returns the entity data in DataFrame format
-
-        Returns
-        -------
-        pandas.core.frame.DataFrame
-            Dataframe of user defined labels and keys as columns.
-        """
-        return self.turn_entity_data_into_dataframe(self.data)
-
-    def __len__(self):
-        """
-        Returns the number of elements in Static Entity
-
-        Returns
-        -------
-        int
-            Number of distinct labels in level 0.
-        """
-        return self._dimensions[0]
-
-    def __str__(self):
-        """
-        Return the Static Entity uid
-
-        Returns
-        -------
-        string
-        """
-        return f"{self.uid}"
-
-    def __repr__(self):
-        """
-        Returns a string resembling the constructor for staticentity without any
-        children
-
-        Returns
-        -------
-        string
-        """
-        return f"StaticEntity({self._uid},{list(self.uidset)},{self.properties})"
-
-    def __contains__(self, item):
-        """
-        Defines containment for StaticEntity based on labels/categories.
-
-        Parameters
-        ----------
-        item : string
-
-        Returns
-        -------
-        bool
-        """
-        return item in np.concatenate(list(self._labels.values()))
-
-    def __getitem__(self, item):
-        """
-        Get value of key in E.elements
-
-        Parameters
-        ----------
-        item : string
-
-        Returns
-        -------
-        list
-        """
-        # return self.elements_by_level(0, 1, translate=True)[item]
-        return self.elements[item]
-
-    def __iter__(self):
-        """
-        Create iterator from E.elements
-
-        Returns
-        -------
-        odict_iterator
-        """
-        return iter(self.elements)
-
-    def __call__(self, label_index=0):
-        return iter(self._labs[label_index])
-
-
[docs]    def size(self):
-        """
-        The number of elements in E, the size of dimension 0 in the E.arr
-
-        Returns
-        -------
-        int
-        """
-        return len(self)

-
-
[docs]    def labs(self, kdx):
-        """
-        Retrieve labels by index in keys
-
-        Parameters
-        ----------
-        kdx : int
-            index of key in E.keys
-
-        Returns
-        -------
-        np.ndarray
-        """
-        return self._labs[kdx]

-
-
[docs]    def is_empty(self, level=0):
-        """
-        Boolean indicating if entity.elements is empty
-
-        Parameters
-        ----------
-        level : int, optional
-
-        Returns
-        -------
-        bool
-        """
-        return len(self._labs[level]) == 0

-
-
[docs]    def uidset_by_level(self, level=0):
-        """
-        The labels found in columns = level
-
-        Parameters
-        ----------
-        level : int, optional
-
-        Returns
-        -------
-        frozenset
-        """
-        return frozenset(self._labs[level])  # should be update this to tuples?

-
-
[docs]    def elements_by_level(self, level1=0, level2=None, translate=False):
-        """
-        Elements of staticentity by specified column
-
-        Parameters
-        ----------
-        level1 : int, optional
-            edges
-        level2 : int, optional
-            nodes
-        translate : bool, optional
-            whether to replace indices with labels
-
-        Returns
-        -------
-        collections.defaultdict
-
-        think: level1 = edges, level2 = nodes
-        """
-        # Is there a better way to view a slice of self._arr?
-        if level1 > self.dimsize - 1 or level1 < 0:
-            print(f"This StaticEntity has no level {level1}.")
-            return
-        if level2 is None:
-            level2 = level1 + 1
-
-        if level2 > self.dimsize - 1 or level2 < 0:
-            print(f"This StaticEntity has no level {level2}.")
-            return
-            # elts = OrderedDict([[k, UserList()] for k in self._labs[level1]])
-        elif level1 == level2:
-            print(f"level1 equals level2")
-            return
-            # elts = OrderedDict([[k, UserList()] for k in self._labs[level1]])
-
-        temp, _ = remove_row_duplicates(self.data[:, [level1, level2]])
-        elts = DefaultOrderedDict(UserList)
-        for row in temp:
-            elts[row[0]].append(row[1])
-
-        if translate:
-            telts = DefaultOrderedDict(UserList)
-            for kdx, vec in elts.items():
-                k = self._labs[level1][kdx]
-                for vdx in vec:
-                    telts[k].append(self._labs[level2][vdx])
-            return telts
-        else:
-            return elts

-
-
[docs]    def incidence_matrix(
-        self, level1=0, level2=1, weights=False, aggregateby=None, index=False
-    ):
-        """
-        Convenience method to navigate large tensor
-
-        Parameters
-        ----------
-        level1 : int, optional
-            indexes columns
-        level2 : int, optional
-            indexes rows
-        weights : bool, dict optional, default=False
-            If False all nonzero entries are 1.
-            If True all nonzero entries are filled by self.cell_weight
-            dictionary values, use :code:`aggregateby` to specify how duplicate
-            entries should have weights aggregated.
-            If dict, keys must be in (edge.uid, node.uid) form; only nonzero cells
-            in the incidence matrix will be updated by dictionary.
-        aggregateby : str, optional, {None, 'last', count', 'sum', 'mean', 'median', max', 'min', 'first', 'last'}, default : 'count'
-            Method to aggregate weights of duplicate rows in data. If None, then all cell weights
-            will be set to 1.
-        index : bool, optional
-
-        Returns
-        -------
-        scipy.sparse.csr.csr_matrix
-            Sparse matrix representation of incidence matrix for two levels of static entity.
-
-        Note
-        ----
-        In the context of hypergraphs think level1 = edges, level2 = nodes
-        """
-        if self.dimsize < 2:
-            warnings.warn("Incidence matrix requires two levels of data.")
-            return None
-        if not weights:  # transpose from the beginning
-            if self.dimsize > 2:
-                temp, _ = remove_row_duplicates(self.data[:, [level2, level1]])
-            else:
-                temp = self.data[:, [level2, level1]]
-            result = csr_matrix((np.ones(len(temp)), temp.transpose()), dtype=int)
-        else:  # transpose after cell weights are added
-            if self.dimsize > 2:
-                temp, temp_weights = remove_row_duplicates(
-                    self.data[:, [level1, level2]],
-                    weights=self._weights,
-                    aggregateby=aggregateby,
-                )
-            else:
-                temp, temp_weights = self.data[:, [level1, level2]], self.cell_weights
-
-            if isinstance(weights, dict):
-                cat1 = self.keys[level1]
-                cat2 = self.keys[level2]
-                for k, v in weights:
-                    try:
-                        tdx = (self.index(cat1, k[0]), self.index(cat2, k[1]))
-                    except:
-                        HyperNetXError(
-                            f"{k} is not recognized as belonging to this system."
-                        )
-                    if temp_weights[tdx] != 0:
-                        temp_weights[tdx] = v
-                # weights = {(self.index(cat1, k[0]), self.index(cat2, k[1])): v for k, v in weights.items()}
-                # for k in weights:
-                #     if temp_weights[k] != 0::
-                #         temp_weights[k]=weights[k]
-            temp_weights = [temp_weights[tuple(t)] for t in temp]
-            dtype = int if aggregateby == "count" else float
-            result = csr_matrix(
-                (temp_weights, temp.transpose()), dtype=dtype
-            ).transpose()
-
-        if index:  # give index of rows then columns
-            return (
-                result,
-                {k: v for k, v in enumerate(self._labs[level2])},
-                {k: v for k, v in enumerate(self._labs[level1])},
-            )
-        else:
-            return result

-
-
[docs]    def restrict_to_levels(self, levels, weights=False, aggregateby="count", uid=None):
-        """
-        Limit Static Entity data to specific levels
-
-        Parameters
-        ----------
-        levels : array
-            index of labels in data
-        weights : bool, optional, default : False
-            Whether or not to aggregate existing weights in self when restricting to levels.
-            If False then weights will be assigned 1.
-        aggregateby : str, optional, {None, 'last', count', 'sum', 'mean', 'median', max', 'min', 'first', 'last'}, default : 'count'
-            Method to aggregate cell_weights of duplicate rows in setsystem of type pandas.DataFrame.
-            If None then all cell_weights will be set to 1.
-        uid : None, optional
-
-        Returns
-        -------
-        Static Entity class
-            hnx.classes.staticentity.StaticEntity
-        """
-        if levels[0] >= self.dimsize:
-            return self.__class__()
-        # if len(levels) == 1:
-        #     if levels[0] >= self.dimsize:
-        #         return self.__class__()
-        #     else:
-        #         newlabels = OrderedDict(
-        #             [(self.keys[lev], self._labs[lev]) for lev in levels]
-        #         )
-        # return self.__class__(labels=newlabels)
-        else:
-            if weights:
-                weights = self._weights
-            else:
-                weights = None
-            if len(levels) == 1:
-                lev = levels[0]
-                newlabels = OrderedDict([(self._keys[lev], self._labs[lev])])
-                data = self.data[:, lev]
-                data = np.reshape(data, (len(data), 1))
-                return StaticEntity(
-                    data=data,
-                    weights=weights,
-                    aggregateby=aggregateby,
-                    labels=newlabels,
-                    uid=uid,
-                )
-            else:
-                data = self.data[:, levels]
-                newlabels = OrderedDict(
-                    [(self.keys[lev], self._labs[lev]) for lev in levels]
-                )
-                return self.__class__(
-                    data=data,
-                    weights=weights,
-                    aggregateby=aggregateby,
-                    labels=newlabels,
-                    uid=uid,
-                )

-
-
[docs]    def turn_entity_data_into_dataframe(
-        self, data_subset
-    ):  # add option to include multiplicities stored in properties
-        """
-        Convert rows of original data in StaticEntity to dataframe
-
-        Parameters
-        ----------
-        data : numpy.ndarray
-            Subset of the rows in the original data held in the StaticEntity
-
-        Returns
-        -------
-        pandas.core.frame.DataFrame
-            Columns and cell entries are derived from data and self.labels
-        """
-        df = pd.DataFrame(data=data_subset, columns=self.keys)
-        width = data_subset.shape[1]
-        for ddx, row in enumerate(data_subset):
-            nrow = [self.labs(idx)[row[idx]] for idx in range(width)]
-            df.iloc[ddx] = nrow
-        return df

-
-
[docs]    def restrict_to_indices(
-        self, indices, level=0, uid=None
-    ):  # restricting to indices requires renumbering the labels.
-        """
-        Limit Static Entity data to specific indices of keys
-
-        Parameters
-        ----------
-        indices : array
-            array of category indices
-        level : int, optional
-            index of label
-        uid : None, optional
-
-        Returns
-        -------
-        Static Entity class
-            hnx.classes.staticentity.StaticEntity
-        """
-        indices = list(indices)
-        idx = np.concatenate(
-            [np.argwhere(self.data[:, level] == k) for k in indices], axis=0
-        ).transpose()[0]
-        temp = self.data[idx]
-        df = self.turn_entity_data_into_dataframe(temp)
-        return self.__class__(entity=df, uid=uid)

-
-
[docs]    def translate(self, level, index):
-        """
-        Replaces a category index and value index with label
-
-        Parameters
-        ----------
-        level : int
-            category index of label
-        index : int
-            value index of label
-
-        Returns
-        -------
-         : numpy.array(str)
-        """
-        if isinstance(index, int):
-            return self._labs[level][index]
-        else:
-            return [self._labs[level][idx] for idx in index]

-
-
[docs]    def translate_arr(self, coords):
-        """
-        Translates a single cell in the entity array
-
-        Parameters
-        ----------
-        coords : tuple of ints
-
-        Returns
-        -------
-        list
-        """
-        assert len(coords) == self.dimsize
-        translation = list()
-        for idx in range(self.dimsize):
-            translation.append(self.translate(idx, coords[idx]))
-        return translation

-
-
[docs]    def index(self, category, value=None):
-        """
-        Returns dimension of category and index of value
-
-        Parameters
-        ----------
-        category : string
-        value : string, optional
-
-        Returns
-        -------
-        int or tuple of ints
-        """
-        if value is not None:
-            return self._keyindex[category], self._index[category][value]
-        else:
-            return self._keyindex[category]

-
-
[docs]    def indices(self, category, values):
-        """
-        Returns dimension of category and index of values (array)
-
-        Parameters
-        ----------
-        category : string
-        values : single string or array of strings
-
-        Returns
-        -------
-        list
-        """
-        return [self._index[category][value] for value in values]

-
-
[docs]    def level(self, item, min_level=0, max_level=None, return_index=True):
-        """
-        Returns first level item appears by order of keys from minlevel to maxlevel
-        inclusive
-
-        Parameters
-        ----------
-        item : string
-        min_level : int, optional
-        max_level : int, optional
-
-        return_index : bool, optional
-
-        Returns
-        -------
-        tuple
-        """
-        n = len(self.dimensions)
-        if max_level is not None:
-            n = min([max_level + 1, n])
-        for lev in range(min_level, n):
-            if item in self._labs[lev]:
-                if return_index:
-                    return lev, self._index[self._keys[lev]][item]
-                else:
-                    return lev
-        else:
-            print(f'"{item}" not found')
-            return None

-
-    # note the depth and registry methods may or may not be useful. We can add these later.
-
-
-
[docs]class StaticEntitySet(StaticEntity):
-
-    """
-    .. _staticentityset:
-    """
-
-    def __init__(
-        self,
-        entity=None,
-        data=None,
-        arr=None,
-        labels=None,
-        uid=None,
-        level1=0,
-        level2=1,
-        weights=None,
-        keep_weights=True,
-        aggregateby=None,
-        **props,
-    ):
-
-        if entity is None:
-            if data is not None:
-                data = data[:, [level1, level2]]
-                arr = None
-            elif arr is not None:
-                data, cell_weights = _turn_tensor_to_data(arr)
-                weights = [cell_weights[tuple(t)] for t in data]
-                data = data[:, [level1, level2]]
-            if labels is not None:
-                keys = np.array(list(labels.keys()))
-                temp = OrderedDict()
-                for lev in [level1, level2]:
-                    if lev < len(keys):
-                        temp[keys[lev]] = labels[keys[lev]]
-                labels = temp
-            super().__init__(
-                data=data, weights=weights, labels=labels, uid=uid, **props
-            )
-        else:
-            if isinstance(entity, StaticEntity):
-                data = entity.data[:, [level1, level2]]
-                if keep_weights:
-                    weights = entity._weights
-                labels = OrderedDict(
-                    [(entity._keys[k], entity._labs[k]) for k in [level1, level2]]
-                )
-                super().__init__(
-                    data=data,
-                    labels=labels,
-                    uid=uid,
-                    weights=weights,
-                    aggregateby=aggregateby,
-                    **props,
-                )
-            elif isinstance(entity, StaticEntitySet):
-                if keep_weights:
-                    aggregateby = "last"
-                super().__init__(
-                    entity,
-                    weights=weights,
-                    keep_weights=keep_weights,
-                    aggregateby=aggregateby,
-                    **props,
-                )
-
-            elif isinstance(entity, pd.DataFrame):
-                cols = entity.columns[[level1, level2]]
-                super().__init__(
-                    entity=entity[cols],
-                    uid=uid,
-                    weights=weights,
-                    aggregateby=aggregateby,
-                    **props,
-                )
-            else:
-                # this presumes entity is an iterable of iterables or a dictionary
-                super().__init__(entity=entity, uid=uid, **props)
-
-    def __repr__(self):
-        """
-        Returns a string resembling the constructor for entityset without any
-        children
-
-        Returns
-        -------
-        string
-        """
-        return f"StaticEntitySet({self._uid},{list(self.uidset)},{self.properties})"
-
-
[docs]    def incidence_matrix(self, index=False, weights=False):
-        """
-        Incidence matrix of StaticEntitySet
-
-        Parameters
-        ----------
-        index : bool, optional
-
-        weight: bool, dict optional, default=False
-            If False all nonzero entries are 1.
-            If True all nonzero entries are filled by self.cell_weight
-            dictionary values.
-            If dict, keys must be in self.cell_weight keys; nonzero cells
-            will be updated by dictionary.
-
-
-        Returns
-        -------
-        scipy.sparse.csr.csr_matrix
-            Sparse matrix representation of incidence matrix for static entity set.
-        """
-        return StaticEntity.incidence_matrix(self, weights=weights, index=index)

-
-
[docs]    def restrict_to(self, indices, uid=None):
-        """
-        Limit Static Entityset data to specific indices of keys
-
-        Parameters
-        ----------
-        indices : array
-            array of indices in keys
-        uid : None, optional
-
-        Returns
-        -------
-        StaticEntitySet
-            hnx.classes.staticentity.StaticEntitySet
-
-        """
-        return self.restrict_to_indices(indices, level=0, uid=uid)

-
-
[docs]    def convert_to_entityset(self, uid):
-        """
-        Convert Static EntitySet into EntitySet with given uid.
-
-        Parameters
-        ----------
-        uid : string
-
-        Returns
-        -------
-        EntitySet
-            hnx.classes.entity.EntitySet
-        """
-        return EntitySet(uid, self.incidence_dict)

-
-
[docs]    def collapse_identical_elements(
-        self,
-        uid=None,
-        return_equivalence_classes=False,
-    ):
-        """
-        Returns StaticEntitySet after collapsing elements if they have same children
-        If no elements share same children, a copy of the original StaticEntitySet is returned
-
-        Parameters
-        ----------
-        uid : None, optional
-        return_equivalence_classes : bool, optional
-            If True, return a dictionary of equivalence classes keyed by new edge names
-
-
-        Returns
-        -------
-        StaticEntitySet
-            hnx.classes.staticentity.StaticEntitySet
-        """
-        shared_children = DefaultOrderedDict(list)
-        for k, v in self.elements.items():
-            shared_children[frozenset(v)].append(k)
-        new_entity_dict = OrderedDict(
-            [
-                # (
-                #     f"{next(iter(v))}:{len(v)}",
-                #     sorted(set(k), key=lambda x: list(self.labs(1)).index(x)),
-                # )
-                (
-                    f"{next(iter(v))}:{len(v)}",
-                    sorted(set(k), key=lambda x: self.index(self._keys[1], x)),
-                )
-                for k, v in shared_children.items()
-            ]
-        )
-        if return_equivalence_classes:
-            eq_classes = OrderedDict(
-                [
-                    (
-                        f"{next(iter(v))}:{len(v)}",
-                        v
-                        # sorted(v, key=lambda x: self.index(self._keys[0], x)),  ## not sure why sorting is important here
-                    )
-                    for k, v in shared_children.items()
-                ]
-            )
-            return StaticEntitySet(uid=uid, entity=new_entity_dict), eq_classes
-        else:
-            return StaticEntitySet(uid=uid, entity=new_entity_dict)

-
-
-def _turn_tensor_to_data(arr):
-    """
-    Return list of nonzero coordinates in arr.
-
-    Parameters
-    ----------
-    arr : numpy.ndarray
-        Tensor corresponding to incidence of co-occurring labels.
-    """
-    temp = np.array(arr.nonzero()).T
-    return temp, {tuple(t): arr[tuple(t)] for t in temp}
-
-
-def _turn_dict_to_staticentity(dict_object):
-    """Create a static entity directly from a dictionary of hashables"""
-    d = OrderedDict(dict_object)
-    level2ctr = HNXCount()
-    level1ctr = HNXCount()
-    level2 = DefaultOrderedDict(level2ctr)
-    level1 = DefaultOrderedDict(level1ctr)
-    coords = list()
-    for k, val in d.items():
-        level1[k]
-        for v in val:
-            level2[v]
-            coords.append((level1[k], level2[v]))
-    coords, counts = remove_row_duplicates(coords, aggregateby="count")
-    level1 = np.array(list(level1))
-    level2 = np.array(list(level2))
-    data = np.array(coords, dtype=int)
-    labels = OrderedDict({"0": level1, "1": level2})
-    return data, labels, counts
-
-
-def _turn_iterable_to_staticentity(iter_object):
-    for s in iter_object:
-        if not isinstance(s, Iterable):
-            raise HyperNetXError(
-                "The entity data type not recognized. Iterables must be iterable of iterables."
-            )
-    else:
-        labels = [f"e{str(x)}" for x in range(len(iter_object))]
-        dict_object = dict(zip(labels, iter_object))
-    return _turn_dict_to_staticentity(dict_object)
-
-
-def _turn_dataframe_into_entity(
-    df, weights=None, aggregateby=None, include_unknowns=False
-):
-    """
-    Convenience method to reformat dataframe object into data,labels format
-    for construction of a static entity
-
-    Parameters
-    ----------
-    df : pandas.DataFrame
-        May not contain nans
-    weights : array-like, optional, default : None
-        User specified weights corresponding to data, length must equal number
-        of rows in data. If None, weight for all rows is assumed to be 1.
-    aggregateby : str, optional, {None, 'last', count', 'sum', 'mean', 'median', max', 'min', 'first', 'last'}, default : 'count'
-        Method to aggregate cell_weights of duplicate rows in data.
-    include_unknowns : bool, optional, default : False
-        If Unknown <column name> was used to fill in nans
-
-    Returns
-    -------
-    outputdata : numpy.ndarray
-    slabels : numpy.array of strings
-    cell_weights : dict
-
-    """
-    columns = df.columns
-    ctr = [HNXCount() for c in range(len(columns))]
-    ldict = OrderedDict()
-    rdict = OrderedDict()
-    for idx, c in enumerate(columns):
-        ldict[c] = defaultdict(ctr[idx])  # TODO make this an Ordered default dict
-        rdict[c] = OrderedDict()
-        if include_unknowns:
-            ldict[c][f"Unknown {c}"]
-            # TODO: update this to take a dict assign for each column
-            rdict[c][0] = f"Unknown {c}"
-        for k in df[c]:
-            ldict[c][k]
-            rdict[c][ldict[c][k]] = k
-        ldict[c] = dict(ldict[c])
-    dims = tuple([len(ldict[c]) for c in columns])
-
-    m = len(df)
-    n = len(columns)
-    data = np.zeros((m, n), dtype=int)
-    for rid in range(m):
-        for cid in range(n):
-            c = columns[cid]
-            data[rid, cid] = ldict[c][df.iloc[rid][c]]
-
-    output_data = remove_row_duplicates(data, weights=weights, aggregateby=aggregateby)
-
-    slabels = OrderedDict()
-    for cdx, c in enumerate(columns):
-        slabels.update({c: np.array(list(ldict[c].keys()))})
-    return output_data[0], slabels, output_data[1]
-
-
-# helpers
-def _fd():
-    return None
-
- -
-
-
- -
- -
-

© Copyright 2021 Battelle Memorial Institute.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/_modules/drawing/rubber_band.html b/docs/build/_modules/drawing/rubber_band.html deleted file mode 100644 index 193d4cfd..00000000 --- a/docs/build/_modules/drawing/rubber_band.html +++ /dev/null @@ -1,610 +0,0 @@ - - - - - - drawing.rubber_band — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for drawing.rubber_band

-# Copyright © 2018 Battelle Memorial Institute
-# All rights reserved.
-
-from hypernetx import Hypergraph
-from .util import (
-    get_frozenset_label,
-    get_collapsed_size,
-    get_set_layering,
-    inflate_kwargs,
-    transpose_inflated_kwargs,
-)
-
-import matplotlib.pyplot as plt
-from matplotlib.collections import PolyCollection, LineCollection, CircleCollection
-
-import networkx as nx
-
-from itertools import combinations
-from collections import defaultdict
-
-import numpy as np
-from scipy.spatial.distance import pdist
-from scipy.spatial import ConvexHull
-from scipy.spatial import Voronoi
-
-# increases the default figure size to 8in square.
-plt.rcParams["figure.figsize"] = (8, 8)
-
-N_CONTROL_POINTS = 24
-
-theta = np.linspace(0, 2 * np.pi, N_CONTROL_POINTS + 1)[:-1]
-
-cp = np.vstack((np.cos(theta), np.sin(theta))).T
-
-
-
-
-
-
[docs]def get_default_radius(H, pos):
-    """
-    Calculate a reasonable default node radius
-
-    This function iterates over the hyper edges and finds the most distant
-    pair of points given the positions provided. Then, the node radius is a fraction
-    of the median of this distance take across all hyper-edges.
-
-    Parameters
-    ----------
-    H: Hypergraph
-        the entity to be drawn
-    pos: dict
-        mapping of node and edge positions to R^2
-
-    Returns
-    -------
-    float
-        the recommended radius
-
-    """
-    if len(H) > 1:
-        return 0.0125 * np.median(
-            [pdist(np.vstack(list(map(pos.get, H.nodes)))).max() for nodes in H.edges()]
-        )
-    return 1

-
-
-
[docs]def draw_hyper_edge_labels(H, polys, labels={}, ax=None, **kwargs):
-    """
-    Draws a label on the hyper edge boundary.
-
-    Should be passed Matplotlib PolyCollection representing the hyper-edges, see
-    the return value of draw_hyper_edges.
-
-    The label will be draw on the least curvy part of the polygon, and will be
-    aligned parallel to the orientation of the polygon where it is drawn.
-
-    Parameters
-    ----------
-    H: Hypergraph
-        the entity to be drawn
-    polys: PolyCollection
-        collection of polygons returned by draw_hyper_edges
-    labels: dict
-        mapping of node id to string label
-    ax: Axis
-        matplotlib axis on which the plot is rendered
-    kwargs: dict
-        Keyword arguments are passed through to Matplotlib's annotate function.
-
-    """
-    ax = ax or plt.gca()
-
-    params = transpose_inflated_kwargs(inflate_kwargs(H.edges, kwargs))
-
-    for edge, path, params in zip(H.edges, polys.get_paths(), params):
-        s = labels.get(edge, edge)
-
-        # calculate the xy location of the annotation
-        # this is the midpoint of the pair of adjacent points the most distant
-        d = ((path.vertices[:-1] - path.vertices[1:]) ** 2).sum(axis=1)
-        i = d.argmax()
-
-        x1, x2 = path.vertices[i : i + 2]
-        x, y = x2 - x1
-        theta = 360 * np.arctan2(y, x) / (2 * np.pi)
-        theta = (theta + 360) % 360
-
-        while theta > 90:
-            theta -= 180
-
-        # the string is a comma separated list of the edge uid
-        ax.annotate(
-            s, (x1 + x2) / 2, rotation=theta, ha="center", va="center", **params
-        )

-
-
-
[docs]def layout_hyper_edges(H, pos, node_radius={}, dr=None):
-    """
-    Draws a convex hull for each edge in H.
-
-    Position of the nodes in the graph is specified by the position dictionary,
-    pos. Convex hulls are spaced out such that if one set contains another, the
-    convex hull will surround the contained set. The amount of spacing added
-    between hulls is specified by the parameter, dr.
-
-    Parameters
-    ----------
-    H: Hypergraph
-        the entity to be drawn
-    pos: dict
-        mapping of node and edge positions to R^2
-    node_radius: dict
-        mapping of node to R^1 (radius of each node)
-    dr: float
-        the spacing between concentric rings
-    ax: Axis
-        matplotlib axis on which the plot is rendered
-
-    Returns
-    -------
-    dict
-        A mapping from hyper edge ids to paths (Nx2 numpy matrices)
-    """
-
-    if len(node_radius):
-        r0 = min(node_radius.values())
-    else:
-        r0 = get_default_radius(H, pos)
-
-    dr = dr or r0
-
-    levels = get_set_layering(H)
-
-    radii = {
-        v: {v: i for i, v in enumerate(sorted(e, key=levels.get))}
-        for v, e in H.dual().edges.elements.items()
-    }
-
-    def get_padded_hull(uid, edge):
-        # make sure the edge contains at least one node
-        if len(edge):
-            points = np.vstack(
-                [
-                    cp * (node_radius.get(v, r0) + dr * (2 + radii[v][uid])) + pos[v]
-                    for v in edge
-                ]
-            )
-        # if not, draw an empty edge centered around the location of the edge node (in the bipartite graph)
-        else:
-            points = 4 * r0 * cp + pos[uid]
-
-        hull = ConvexHull(points)
-
-        return hull.points[hull.vertices]
-
-    return [get_padded_hull(uid, list(H.edges[uid])) for uid in H.edges]

-
-
-
[docs]def draw_hyper_edges(H, pos, ax=None, node_radius={}, dr=None, **kwargs):
-    """
-    Draws a convex hull around the nodes contained within each edge in H
-
-    Parameters
-    ----------
-    H: Hypergraph
-        the entity to be drawn
-    pos: dict
-        mapping of node and edge positions to R^2
-    node_radius: dict
-        mapping of node to R^1 (radius of each node)
-    dr: float
-        the spacing between concentric rings
-    ax: Axis
-        matplotlib axis on which the plot is rendered
-    kwargs: dict
-        keyword arguments, e.g., linewidth, facecolors, are passed through to the PolyCollection constructor
-
-    Returns
-    -------
-    PolyCollection
-        a Matplotlib PolyCollection that can be further styled
-    """
-    points = layout_hyper_edges(H, pos, node_radius=node_radius, dr=dr)
-
-    polys = PolyCollection(points, **inflate_kwargs(H.edges, kwargs))
-
-    (ax or plt.gca()).add_collection(polys)
-
-    return polys

-
-
-
[docs]def draw_hyper_nodes(H, pos, node_radius={}, r0=None, ax=None, **kwargs):
-    """
-    Draws a circle for each node in H.
-
-    The position of each node is specified by the a dictionary/list-like, pos,
-    where pos[v] is the xy-coordinate for the vertex. The radius of each node
-    can be specified as a dictionary where node_radius[v] is the radius. If a
-    node is missing from this dictionary, or the node_radius is not specified at
-    all, a sensible default radius is chosen based on distances between nodes
-    given by pos.
-
-    Parameters
-    ----------
-    H: Hypergraph
-        the entity to be drawn
-    pos: dict
-        mapping of node and edge positions to R^2
-    node_radius: dict
-        mapping of node to R^1 (radius of each node)
-    r0: float
-        minimum distance that concentric rings start from the node position
-    ax: Axis
-        matplotlib axis on which the plot is rendered
-    kwargs: dict
-        keyword arguments, e.g., linewidth, facecolors, are passed through to the PolyCollection constructor
-
-    Returns
-    -------
-    PolyCollection
-        a Matplotlib PolyCollection that can be further styled
-    """
-
-    ax = ax or plt.gca()
-
-    r0 = r0 or get_default_radius(H, pos)
-
-    points = [node_radius.get(v, r0) * cp + pos[v] for v in H.nodes]
-
-    kwargs.setdefault("facecolors", "black")
-
-    circles = PolyCollection(points, **inflate_kwargs(H, kwargs))
-
-    ax.add_collection(circles)
-
-    return circles

-
-
-
[docs]def draw_hyper_labels(H, pos, node_radius={}, ax=None, labels={}, **kwargs):
-    """
-    Draws text labels for the hypergraph nodes.
-
-    The label is drawn to the right of the node. The node radius is needed (see
-    draw_hyper_nodes) so the text can be offset appropriately as the node size
-    changes.
-
-    The text label can be customized by passing in a dictionary, labels, mapping
-    a node to its custom label. By default, the label is the string
-    representation of the node.
-
-    Keyword arguments are passed through to Matplotlib's annotate function.
-
-    Parameters
-    ----------
-    H: Hypergraph
-        the entity to be drawn
-    pos: dict
-        mapping of node and edge positions to R^2
-    node_radius: dict
-        mapping of node to R^1 (radius of each node)
-    ax: Axis
-        matplotlib axis on which the plot is rendered
-    labels: dict
-        mapping of node to text label
-    kwargs: dict
-        keyword arguments passed to matplotlib.annotate
-
-    """
-    ax = ax or plt.gca()
-
-    params = transpose_inflated_kwargs(inflate_kwargs(H.nodes, kwargs))
-
-    for v, v_kwargs in zip(H.nodes, params):
-        xy = np.array([node_radius.get(v, 0), 0]) + pos[v]
-        ax.annotate(
-            labels.get(v, v),
-            xy,
-            **{
-                k: (
-                    d[v]
-                    if hasattr(d, "__getitem__") and type(d) not in {str, tuple}
-                    else d
-                )
-                for k, d in kwargs.items()
-            }
-        )

-
-
[docs]def draw(
-    H,
-    pos=None,
-    with_color=True,
-    with_node_counts=False,
-    with_edge_counts=False,
-    layout=nx.spring_layout,
-    layout_kwargs={},
-    ax=None,
-    node_radius=None,
-    edges_kwargs={},
-    nodes_kwargs={},
-    edge_labels={},
-    edge_labels_kwargs={},
-    node_labels={},
-    node_labels_kwargs={},
-    with_edge_labels=True,
-    with_node_labels=True,
-    label_alpha=0.35,
-    return_pos=False,
-):
-    """
-    Draw a hypergraph as a Matplotlib figure
-
-    By default this will draw a colorful "rubber band" like hypergraph, where
-    convex hulls represent edges and are drawn around the nodes they contain.
-
-    This is a convenience function that wraps calls with sensible parameters to
-    the following lower-level drawing functions:
-
-    * draw_hyper_edges,
-    * draw_hyper_edge_labels,
-    * draw_hyper_labels, and
-    * draw_hyper_nodes
-
-    The default layout algorithm is nx.spring_layout, but other layouts can be
-    passed in. The Hypergraph is converted to a bipartite graph, and the layout
-    algorithm is passed the bipartite graph.
-
-    If you have a pre-determined layout, you can pass in a "pos" dictionary.
-    This is a dictionary mapping from node id's to x-y coordinates. For example:
-
-        >>> pos = {
-        >>> 'A': (0, 0),
-        >>> 'B': (1, 2),
-        >>> 'C': (5, -3)
-        >>> }
-
-    will position the nodes {A, B, C} manually at the locations specified. The
-    coordinate system is in Matplotlib "data coordinates", and the figure will
-    be centered within the figure.
-
-    By default, this will draw in a new figure, but the axis to render in can be
-    specified using :code:`ax`.
-
-    This approach works well for small hypergraphs, and does not guarantee
-    a rigorously "correct" drawing. Overlapping of sets in the drawing generally
-    implies that the sets intersect, but sometimes sets overlap if there is no
-    intersection. It is not possible, in general, to draw a "correct" hypergraph
-    this way for an arbitrary hypergraph, in the same way that not all graphs
-    have planar drawings.
-
-    Parameters
-    ----------
-    H: Hypergraph
-        the entity to be drawn
-    pos: dict
-        mapping of node and edge positions to R^2
-    with_color: bool
-        set to False to disable color cycling of edges
-    with_node_counts: bool
-        set to True to replace the label for collapsed nodes with the number of elements
-    with_edge_counts: bool
-        set to True to label collapsed edges with number of elements
-    layout: function
-        layout algorithm to compute
-    layout_kwargs: dict
-        keyword arguments passed to layout function
-    ax: Axis
-        matplotlib axis on which the plot is rendered
-    edges_kwargs: dict
-        keyword arguments passed to matplotlib.collections.PolyCollection for edges
-    node_radius: None, int, float, or dict
-        radius of all nodes, or dictionary of node:value; the default (None) calculates radius based on number of collapsed nodes; reasonable values range between 1 and 3
-    nodes_kwargs: dict
-        keyword arguments passed to matplotlib.collections.PolyCollection for nodes
-    edge_labels_kwargs: dict
-        keyword arguments passed to matplotlib.annotate for edge labels
-    node_labels_kwargs: dict
-        keyword argumetns passed to matplotlib.annotate for node labels
-    with_edge_labels: bool
-        set to False to make edge labels invisible
-    with_node_labels: bool
-        set to False to make node labels invisible
-    label_alpha: float
-        the transparency (alpha) of the box behind text drawn in the figure
-    """
-
-    ax = ax or plt.gca()
-
-    if pos is None:
-        pos = layout_node_link(H, layout=layout, **layout_kwargs)
-
-    r0 = get_default_radius(H, pos)
-    a0 = np.pi * r0 ** 2
-
-
-
-    def get_node_radius(v):
-        if node_radius is None:
-            return np.sqrt(a0 * get_collapsed_size(v) / np.pi)
-        elif hasattr(node_radius, "get"):
-            return node_radius.get(v, 1) * r0
-        return node_radius * r0
-
-    # guarantee that node radius is a dictionary mapping nodes to values
-    node_radius = {v: get_node_radius(v) for v in H.nodes}
-
-    # for convenience, we are using setdefault to mutate the argument
-    # however, we need to copy this to prevent side-effects
-    edges_kwargs = edges_kwargs.copy()
-    edges_kwargs.setdefault("edgecolors", plt.cm.tab10(np.arange(len(H.edges)) % 10))
-    edges_kwargs.setdefault("facecolors", "none")
-
-    polys = draw_hyper_edges(H, pos, node_radius=node_radius, ax=ax, **edges_kwargs)
-
-    if with_edge_labels:
-        labels = get_frozenset_label(
-            H.edges, count=with_edge_counts, override=edge_labels
-        )
-
-        draw_hyper_edge_labels(
-            H,
-            polys,
-            color=edges_kwargs["edgecolors"],
-            backgroundcolor=(1, 1, 1, label_alpha),
-            labels=labels,
-            ax=ax,
-            **edge_labels_kwargs
-        )
-
-    if with_node_labels:
-        labels = get_frozenset_label(
-            H.nodes, count=with_node_counts, override=node_labels
-        )
-
-        draw_hyper_labels(
-            H,
-            pos,
-            node_radius=node_radius,
-            labels=labels,
-            ax=ax,
-            va="center",
-            xytext=(5, 0),
-            textcoords="offset points",
-            backgroundcolor=(1, 1, 1, label_alpha),
-            **node_labels_kwargs
-        )
-
-    draw_hyper_nodes(H, pos, node_radius=node_radius, ax=ax, **nodes_kwargs)
-
-    if len(H.nodes) == 1:
-        x, y = pos[list(H.nodes)[0]]
-        s = 20
-
-        ax.axis([x - s, x + s, y - s, y + s])
-    else:
-        ax.axis("equal")
-
-    ax.axis("off")
-    if return_pos:
-        return pos

-
- -
-
-
- -
- -
-

© Copyright 2021 Battelle Memorial Institute.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/_modules/drawing/two_column.html b/docs/build/_modules/drawing/two_column.html deleted file mode 100644 index 572e9b20..00000000 --- a/docs/build/_modules/drawing/two_column.html +++ /dev/null @@ -1,319 +0,0 @@ - - - - - - drawing.two_column — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for drawing.two_column

-# Copyright © 2018 Battelle Memorial Institute
-# All rights reserved.
-
-import matplotlib.pyplot as plt
-from matplotlib.collections import LineCollection
-
-import networkx as nx
-
-from .util import get_frozenset_label
-
-
-
[docs]def layout_two_column(H, spacing=2):
-    """
-    Two column (bipartite) layout algorithm.
-
-    This algorithm first converts the hypergraph into a bipartite graph and
-    then computes connected components. Disonneccted components are handled
-    independently and then stacked together.
-
-    Within a connected component, the spectral ordering of the bipartite graph
-    provides a quick and dirty ordering that minimizes edge crossings in the
-    diagram.
-
-    Parameters
-    ----------
-    H: Hypergraph
-        the entity to be drawn
-    spacing: float
-        amount of whitespace between disconnected components
-    """
-    offset = 0
-    pos = {}
-
-    def stack(vertices, x, height):
-        for i, v in enumerate(vertices):
-            pos[v] = (x, i + offset + (height - len(vertices)) / 2)
-
-    G = H.bipartite()
-    for ci in nx.connected_components(G):
-        Gi = G.subgraph(ci)
-        key = {v: i for i, v in enumerate(nx.spectral_ordering(Gi))}.get
-        ci_vertices, ci_edges = [
-            sorted([v for v, d in Gi.nodes(data=True) if d["bipartite"] == j], key=key)
-            for j in [0, 1]
-        ]
-
-        height = max(len(ci_vertices), len(ci_edges))
-
-        stack(ci_vertices, 0, height)
-        stack(ci_edges, 1, height)
-
-        offset += height + spacing
-
-    return pos

-
-
-
[docs]def draw_hyper_edges(H, pos, ax=None, **kwargs):
-    """
-    Renders hyper edges for the two column layout.
-
-    Each node-hyper edge membership is rendered as a line connecting the node
-    in the left column to the edge in the right column.
-
-    Parameters
-    ----------
-    H: Hypergraph
-        the entity to be drawn
-    pos: dict
-        mapping of node and edge positions to R^2
-    ax: Axis
-        matplotlib axis on which the plot is rendered
-    kwargs: dict
-        keyword arguments passed to matplotlib.LineCollection
-
-    Returns
-    -------
-    LineCollection
-        the hyper edges
-    """
-    ax = ax or plt.gca()
-
-    pairs = [(v, e.uid) for e in H.edges() for v in e]
-
-    kwargs = {
-        k: v if type(v) != dict else [v.get(e) for _, e in pairs]
-        for k, v in kwargs.items()
-    }
-
-    lines = LineCollection([(pos[u], pos[v]) for u, v in pairs], **kwargs)
-
-    ax.add_collection(lines)
-
-    return lines

-
-
-
[docs]def draw_hyper_labels(
-    H, pos, labels={}, with_node_labels=True, with_edge_labels=True, ax=None
-):
-    """
-    Renders hyper labels (nodes and edges) for the two column layout.
-
-    Parameters
-    ----------
-    H: Hypergraph
-        the entity to be drawn
-    pos: dict
-        mapping of node and edge positions to R^2
-    labels: dict
-        custom labels for nodes and edges can be supplied
-    with_node_labels: bool
-        False to disable node labels
-    with_edge_labels: bool
-        False to disable edge labels
-    ax: Axis
-        matplotlib axis on which the plot is rendered
-    kwargs: dict
-        keyword arguments passed to matplotlib.LineCollection
-
-    """
-
-    ax = ax or plt.gca()
-
-    edges = [e.uid for e in H.edges()]
-
-    to_draw = []
-    if with_node_labels:
-        to_draw.append((H.nodes(), "right"))
-
-    if with_edge_labels:
-        to_draw.append((H.edges(), "left"))
-
-    for points, ha in to_draw:
-        for p in points:
-            ax.annotate(labels.get(p.uid, p.uid), pos[p.uid], ha=ha, va="center")

-
-
-
[docs]def draw(
-    H,
-    with_node_labels=True,
-    with_edge_labels=True,
-    with_node_counts=False,
-    with_edge_counts=False,
-    with_color=True,
-    edge_kwargs=None,
-    ax=None,
-):
-    """
-    Draw a hypergraph using a two-collumn layout.
-
-    This is intended reproduce an illustrative technique for bipartite graphs
-    and hypergraphs that is typically used in papers and textbooks.
-
-    The left column is reserved for nodes and the right column is reserved for
-    edges. A line is drawn between a node an an edge
-
-    The order of nodes and edges is optimized to reduce line crossings between
-    the two columns. Spacing between disconnected components is adjusted to make
-    the diagram easier to read, by reducing the angle of the lines.
-
-    Parameters
-    ----------
-    H: Hypergraph
-        the entity to be drawn
-    with_node_labels: bool
-        False to disable node labels
-    with_edge_labels: bool
-        False to disable edge labels
-    with_node_counts: bool
-        set to True to label collapsed nodes with number of elements
-    with_edge_counts: bool
-        set to True to label collapsed edges with number of elements
-    with_color: bool
-        set to False to disable color cycling of hyper edges
-    edge_kwargs: dict
-        keyword arguments to pass to matplotlib.LineCollection
-    ax: Axis
-        matplotlib axis on which the plot is rendered
-    """
-
-    edge_kwargs = edge_kwargs or {}
-
-    ax = ax or plt.gca()
-
-    pos = layout_two_column(H)
-
-    V = [v.uid for v in H.nodes()]
-    E = [e.uid for e in H.edges()]
-
-    labels = {}
-    labels.update(get_frozenset_label(V, count=with_node_counts))
-    labels.update(get_frozenset_label(E, count=with_edge_counts))
-
-    if with_color:
-        edge_kwargs["color"] = {
-            e.uid: plt.cm.tab10(i % 10) for i, e in enumerate(H.edges())
-        }
-
-    draw_hyper_edges(H, pos, ax=ax, **edge_kwargs)
-    draw_hyper_labels(
-        H,
-        pos,
-        labels,
-        ax=ax,
-        with_node_labels=with_node_labels,
-        with_edge_labels=with_edge_labels,
-    )
-    ax.autoscale_view()
-
-    ax.axis("off")

-
- -
-
-
- -
- -
-

© Copyright 2021 Battelle Memorial Institute.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/_modules/drawing/util.html b/docs/build/_modules/drawing/util.html deleted file mode 100644 index 83207295..00000000 --- a/docs/build/_modules/drawing/util.html +++ /dev/null @@ -1,254 +0,0 @@ - - - - - - drawing.util — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for drawing.util

-# Copyright © 2018 Battelle Memorial Institute
-# All rights reserved.
-
-from itertools import combinations
-
-import numpy as np
-
-import networkx as nx
-
-
-
[docs]def inflate(items, v):
-    if type(v) in {str, tuple, int, float}:
-        return [v] * len(items)
-    elif callable(v):
-        return [v(i) for i in items]
-    elif type(v) not in {list, np.ndarray} and hasattr(v, "__getitem__"):
-        return [v[i] for i in items]
-    return v

-
-
-
[docs]def inflate_kwargs(items, kwargs):
-    """
-    Helper function to expand keyword arguments.
-
-    Parameters
-    ----------
-    n: int
-        length of resulting list if argument is expanded
-    kwargs: dict
-        keyword arguments to be expanded
-
-    Returns
-    -------
-    dict
-        dictionary with same keys as kwargs and whose values are lists of length n
-    """
-
-    return {k: inflate(items, v) for k, v in kwargs.items()}

-
-
-
[docs]def transpose_inflated_kwargs(inflated):
-    return [dict(zip(inflated, v)) for v in zip(*inflated.values())]

-
-
-
[docs]def get_collapsed_size(v):
-    try:
-        if type(v) == str and ':' in v:
-            return int(v.split(':')[-1])
-    except:
-        pass
-    
-    return 1

-
-
[docs]def get_frozenset_label(S, count=False, override={}):
-    """
-    Helper function for rendering the labels of possibly collapsed nodes and edges
-
-    Parameters
-    ----------
-    S: iterable
-        list of entities to be labeled
-    count: bool
-        True if labels should be counts of entities instead of list
-
-    Returns
-    -------
-    dict
-        mapping of entity to its string representation
-    """
-
-    def helper(v):
-        if type(v) == str:
-            n = get_collapsed_size(v)
-            if count and n > 1:
-                return f"x {n}"
-            elif count:
-                return ""
-        return str(v)
-
-    return {v: override.get(v, helper(v)) for v in S}

-
-
-
[docs]def get_line_graph(H, collapse=True):
-    """
-    Computes the line graph, a directed graph, where a directed edge (u, v)
-    exists if the edge u is a subset of the edge v in the hypergraph.
-
-    Parameters
-    ----------
-    H: Hypergraph
-        the entity to be drawn
-    collapse: bool
-        True if edges should be added if hyper edges are identical
-
-    Returns
-    -------
-    networkx.DiGraph
-        A directed graph
-    """
-    D = nx.DiGraph()
-
-    V = {edge: set(nodes) for edge, nodes in H.edges.elements.items()}
-
-    D.add_nodes_from(V)
-
-    for u, v in combinations(V, 2):
-        if V[u] != V[v] or not collapse:
-            if V[u].issubset(V[v]):
-                D.add_edge(u, v)
-            elif V[v].issubset(V[u]):
-                D.add_edge(v, u)
-
-    return D

-
-
-
[docs]def get_set_layering(H, collapse=True):
-    """
-    Computes a layering of the edges in the hyper graph.
-
-    In this layering, each edge is assigned a level. An edge u will be above
-    (e.g., have a smaller level value) another edge v if v is a subset of u.
-
-    Parameters
-    ----------
-    H: Hypergraph
-        the entity to be drawn
-    collapse: bool
-        True if edges should be added if hyper edges are identical
-
-    Returns
-    -------
-    dict
-        a mapping of vertices in H to integer levels
-    """
-
-    D = get_line_graph(H, collapse=collapse)
-
-    levels = {}
-
-    for v in nx.topological_sort(D):
-        parent_levels = [levels[u] for u, _ in D.in_edges(v)]
-        levels[v] = max(parent_levels) + 1 if len(parent_levels) else 0
-
-    return levels

-
- -
-
-
- -
- -
-

© Copyright 2021 Battelle Memorial Institute.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/_modules/index.html b/docs/build/_modules/index.html deleted file mode 100644 index f6d6ee20..00000000 --- a/docs/build/_modules/index.html +++ /dev/null @@ -1,123 +0,0 @@ - - - - - - Overview: module code — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - -
- - -
- -
-
-
-
    -
  • »
    • -
  • Overview: module code
    • -
  • -
    • -
-
-
-
- -
-
- -
- -
-

© Copyright 2021 Battelle Memorial Institute.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/_modules/reports/descriptive_stats.html b/docs/build/_modules/reports/descriptive_stats.html deleted file mode 100644 index 6dd620f5..00000000 --- a/docs/build/_modules/reports/descriptive_stats.html +++ /dev/null @@ -1,523 +0,0 @@ - - - - - - reports.descriptive_stats — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - -
- - -
- -
-
-
-
    -
  • »
    • -
  • Module code »
    • -
  • reports.descriptive_stats
    • -
  • -
    • -
-
-
-
-
- -

Source code for reports.descriptive_stats

-"""
-This module contains methods which compute various distributions for hypergraphs:
-    * Edge size distribution
-    * Node degree distribution
-    * Component size distribution
-    * Toplex size distribution
-    * Diameter
-
-Also computes general hypergraph information: number of nodes, edges, cells, aspect ratio, incidence matrix density
-"""
-from collections import Counter
-import numpy as np
-import networkx as nx
-from hypernetx import *
-from hypernetx.utils.decorators import not_implemented_for
-
-__all__ = [
-    "centrality_stats",
-    "edge_size_dist",
-    "degree_dist",
-    "comp_dist",
-    "s_comp_dist",
-    "toplex_dist",
-    "s_node_diameter_dist",
-    "s_edge_diameter_dist",
-    "info",
-    "info_dict",
-    "dist_stats",
-]
-
-
-
[docs]def centrality_stats(X):
-    """
-    Computes basic centrality statistics for X
-
-    Parameters
-    ----------
-    X :
-        an iterable of numbers
-
-    Returns
-    -------
-    [min, max, mean, median, standard deviation] : list
-        List of centrality statistics for X
-    """
-    return [min(X), max(X), np.mean(X), np.median(X), np.std(X)]

-
-
-
[docs]def edge_size_dist(H, aggregated=False):
-    """
-    Computes edge sizes of a hypergraph.
-
-    Parameters
-    ----------
-    H : Hypergraph
-    aggregated :
-        If aggregated is True, returns a dictionary of
-        edge sizes and counts. If aggregated is False, returns a
-        list of edge sizes in H.
-
-    Returns
-    -------
-     edge_size_dist : list or dict
-        List of edge sizes or dictionary of edge size distribution.
-
-    """
-    if aggregated:
-        return Counter(H.edge_size_dist())
-    else:
-        return H.edge_size_dist()

-
-
-
[docs]def degree_dist(H, aggregated=False):
-    """
-    Computes degrees of nodes of a hypergraph.
-
-    Parameters
-    ----------
-    H : Hypergraph
-    aggregated :
-        If aggregated is True, returns a dictionary of
-        degrees and counts. If aggregated is False, returns a
-        list of degrees in H.
-
-    Returns
-    -------
-     degree_dist : list or dict
-        List of degrees or dictionary of degree distribution
-    """
-    if H.nwhy:
-        distr = H.g.node_size_dist()
-    else:
-        distr = [H.degree(n) for n in H.nodes]
-    if aggregated:
-        return Counter(distr)
-    else:
-        return distr

-
-
-
[docs]def comp_dist(H, aggregated=False):
-    """
-    Computes component sizes, number of nodes.
-
-    Parameters
-    ----------
-    H : Hypergraph
-    aggregated :
-        If aggregated is True, returns a dictionary of
-        component sizes (number of nodes) and counts. If aggregated
-        is False, returns a list of components sizes in H.
-
-    Returns
-    -------
-     comp_dist : list or dictionary
-        List of component sizes or dictionary of component size distribution
-
-    See Also
-    --------
-    s_comp_dist
-
-    """
-
-    distr = [len(c) for c in H.components()]
-    if aggregated:
-        return Counter(distr)
-    else:
-        return distr

-
-
-
[docs]def s_comp_dist(H, s=1, aggregated=False, edges=True, return_singletons=True):
-    """
-    Computes s-component sizes, counting nodes or edges.
-
-    Parameters
-    ----------
-    H : Hypergraph
-    s : positive integer, default is 1
-    aggregated :
-        If aggregated is True, returns a dictionary of
-        s-component sizes and counts in H. If aggregated is
-        False, returns a list of s-component sizes in H.
-    edges :
-        If edges is True, the component size is number of edges.
-        If edges is False, the component size is number of nodes.
-    return_singletons : bool, optional, default=True
-
-    Returns
-    -------
-     s_comp_dist : list or dictionary
-        List of component sizes or dictionary of component size distribution in H
-
-    See Also
-    --------
-    comp_dist
-
-    """
-    distr = list()
-    comps = H.s_connected_components(
-        s=s, edges=edges, return_singletons=return_singletons
-    )
-
-    distr = [len(c) for c in comps]
-
-    if aggregated:
-        return Counter(distr)
-    else:
-        return distr

-
-
-
[docs]@not_implemented_for("static")
-def toplex_dist(H, aggregated=False):
-    """
-
-    Computes toplex sizes for hypergraph H.
-
-    Parameters
-    ----------
-    H : Hypergraph
-    aggregated :
-        If aggregated is True, returns a dictionary of
-        toplex sizes and counts in H. If aggregated
-        is False, returns a list of toplex sizes in H.
-
-    Returns
-    -------
-     toplex_dist : list or dictionary
-        List of toplex sizes or dictionary of toplex size distribution in H
-    """
-    distr = [H.size(e) for e in H.toplexes().edges]
-    if aggregated:
-        return Counter(distr)
-    else:
-        return distr

-
-
-
[docs]def s_node_diameter_dist(H):
-    """
-    Parameters
-    ----------
-    H : Hypergraph
-
-    Returns
-    -------
-     s_node_diameter_dist : list
-        List of s-node-diameters for hypergraph H starting with s=1
-        and going up as long as the hypergraph is s-node-connected
-    """
-    i = 1
-    diams = []
-    while H.is_connected(s=i):
-        diams.append(H.diameter(s=i))
-        i += 1
-    return diams

-
-
-
[docs]def s_edge_diameter_dist(H):
-    """
-    Parameters
-    ----------
-    H : Hypergraph
-
-    Returns
-    -------
-     s_edge_diameter_dist : list
-        List of s-edge-diameters for hypergraph H starting with s=1
-        and going up as long as the hypergraph is s-edge-connected
-    """
-    i = 1
-    diams = []
-    while H.is_connected(s=i, edges=True):
-        diams.append(H.edge_diameter(s=i))
-        i += 1
-    return diams

-
-
-
[docs]def info(H, node=None, edge=None):
-    """
-    Print a summary of simple statistics for H
-
-    Parameters
-    ----------
-    H : Hypergraph
-    obj : optional
-        either a node or edge uid from the hypergraph
-    dictionary : optional
-        If True then returns the info as a dictionary rather
-        than a string
-        If False (default) returns the info as a string
-
-    Returns
-    -------
-     info : string
-        Returns a string of statistics of the size,
-        aspect ratio, and density of the hypergraph.
-        Print the string to see it formatted.
-
-    """
-    if not H.edges.elements:
-        return f"Hypergraph {H.name} is empty."
-    report = info_dict(H, node=node, edge=edge)
-    info = ""
-    if node:
-        info += f"Node '{node}' has the following properties:\n"
-        info += f"Degree: {report['degree']}\n"
-        info += f"Contained in: {report['membs']}\n"
-        info += f"Neighbors: {report['neighbors']}"
-    elif edge:
-        info += f"Edge '{edge}' has the following properties:\n"
-        info += f"Size: {report['size']}\n"
-        info += f"Elements: {report['elements']}"
-    else:
-        info += f"Number of Rows: {report['nrows']}\n"
-        info += f"Number of Columns: {report['ncols']}\n"
-        info += f"Aspect Ratio: {report['aspect ratio']}\n"
-        info += f"Number of non-empty Cells: {report['ncells']}\n"
-        info += f"Density: {report['density']}"
-    return info

-
-
-
[docs]def info_dict(H, node=None, edge=None):
-    """
-    Create a summary of simple statistics for H
-
-    Parameters
-    ----------
-    H : Hypergraph
-    obj : optional
-        either a node or edge uid from the hypergraph
-
-    Returns
-    -------
-     info_dict : dict
-        Returns a dictionary of statistics of the size,
-        aspect ratio, and density of the hypergraph.
-
-    """
-    report = dict()
-    if len(H.edges.elements) == 0:
-        return {}
-
-    if node:
-        report["membs"] = list(H.dual().edges[node])
-        report["degree"] = len(report["membs"])
-        report["neighbors"] = H.neighbors(node)
-        return report
-    if edge:
-        report["size"] = H.size(edge)
-        report["elements"] = list(H.edges[edge])
-        return report
-    else:
-        lnodes, ledges = H.shape
-        M = H.incidence_matrix(index=False)
-        ncells = M.nnz
-
-        report["nrows"] = lnodes
-        report["ncols"] = ledges
-        report["aspect ratio"] = lnodes / ledges
-        report["ncells"] = ncells
-        report["density"] = ncells / (lnodes * ledges)
-        return report

-
-
-
[docs]def dist_stats(H):
-    """
-    Computes many basic hypergraph stats and puts them all into a single dictionary object
-
-        * nrows = number of nodes (rows in the incidence matrix)
-        * ncols = number of edges (columns in the incidence matrix)
-        * aspect ratio = nrows/ncols
-        * ncells = number of filled cells in incidence matrix
-        * density = ncells/(nrows*ncols)
-        * node degree list = degree_dist(H)
-        * node degree dist = centrality_stats(degree_dist(H))
-        * node degree hist = Counter(degree_dist(H))
-        * max node degree = max(degree_dist(H))
-        * edge size list = edge_size_dist(H)
-        * edge size dist = centrality_stats(edge_size_dist(H))
-        * edge size hist = Counter(edge_size_dist(H))
-        * max edge size = max(edge_size_dist(H))
-        * comp nodes list = s_comp_dist(H, s=1, edges=False)
-        * comp nodes dist = centrality_stats(s_comp_dist(H, s=1, edges=False))
-        * comp nodes hist = Counter(s_comp_dist(H, s=1, edges=False))
-        * comp edges list = s_comp_dist(H, s=1, edges=True)
-        * comp edges dist = centrality_stats(s_comp_dist(H, s=1, edges=True))
-        * comp edges hist = Counter(s_comp_dist(H, s=1, edges=True))
-        * num comps = len(s_comp_dist(H))
-
-    Parameters
-    ----------
-    H : Hypergraph
-
-    Returns
-    -------
-     dist_stats : dict
-        Dictionary which keeps track of each of the above items (e.g., basic['nrows'] = the number of nodes in H)
-    """
-    if H.isstatic:
-        stats = H.state_dict.get("dist_stats", None)
-        if stats is not None:
-            return H.state_dict["dist_stats"]
-
-    cstats = ["min", "max", "mean", "median", "std"]
-    basic = dict()
-
-    # Number of rows (nodes), columns (edges), and aspect ratio
-    basic["nrows"] = len(H.nodes)
-    basic["ncols"] = len(H.edges)
-    basic["aspect ratio"] = basic["nrows"] / basic["ncols"]
-
-    # Number of cells and density
-    M = H.incidence_matrix(index=False)
-    basic["ncells"] = M.nnz
-    basic["density"] = basic["ncells"] / (basic["nrows"] * basic["ncols"])
-
-    # Node degree distribution
-    basic["node degree list"] = sorted(degree_dist(H), reverse=True)
-    basic["node degree centrality stats"] = dict(
-        zip(cstats, centrality_stats(basic["node degree list"]))
-    )
-    basic["node degree hist"] = Counter(basic["node degree list"])
-    basic["max node degree"] = max(basic["node degree list"])
-
-    # Edge size distribution
-    basic["edge size list"] = sorted(H.edge_size_dist(), reverse=True)
-    basic["edge size centrality stats"] = dict(
-        zip(cstats, centrality_stats(basic["edge size list"]))
-    )
-    basic["edge size hist"] = Counter(basic["edge size list"])
-    basic["max edge size"] = max(basic["edge size hist"])
-
-    # Component size distribution (nodes)
-    basic["comp nodes list"] = sorted(s_comp_dist(H, edges=False), reverse=True)
-    basic["comp nodes hist"] = Counter(basic["comp nodes list"])
-    basic["comp nodes centrality stats"] = dict(
-        zip(cstats, centrality_stats(basic["comp nodes list"]))
-    )
-
-    # Component size distribution (edges)
-    basic["comp edges list"] = sorted(s_comp_dist(H, edges=True), reverse=True)
-    basic["comp edges hist"] = Counter(basic["comp edges list"])
-    basic["comp edges centrality stats"] = dict(
-        zip(cstats, centrality_stats(basic["comp edges list"]))
-    )
-
-    # Number of components
-    basic["num comps"] = len(basic["comp nodes list"])
-
-    # # Diameters
-    # basic['s edge diam list'] = s_edge_diameter_dist(H)
-    # basic['s node diam list'] = s_node_diameter_dist(H)
-    if H.isstatic:
-        H.set_state(dist_stats=basic)
-    return basic

-
- -
-
-
- -
- -
-

© Copyright 2021 Battelle Memorial Institute.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/_sources/algorithms/algorithms.contagion.rst.txt b/docs/build/_sources/algorithms/algorithms.contagion.rst.txt deleted file mode 100644 index aaf64fec..00000000 --- a/docs/build/_sources/algorithms/algorithms.contagion.rst.txt +++ /dev/null @@ -1,29 +0,0 @@ -algorithms.contagion package -============================ - -Submodules ----------- - -algorithms.contagion.animation module -------------------------------------- - -.. automodule:: algorithms.contagion.animation - :members: - :undoc-members: - :show-inheritance: - -algorithms.contagion.epidemics module -------------------------------------- - -.. automodule:: algorithms.contagion.epidemics - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: algorithms.contagion - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/_sources/algorithms/algorithms.rst.txt b/docs/build/_sources/algorithms/algorithms.rst.txt deleted file mode 100644 index 5a819963..00000000 --- a/docs/build/_sources/algorithms/algorithms.rst.txt +++ /dev/null @@ -1,61 +0,0 @@ -algorithms package -================== - -Subpackages ------------ - -.. toctree:: - :maxdepth: 4 - - algorithms.contagion - -Submodules ----------- - -algorithms.generative\_models module ------------------------------------- - -.. automodule:: algorithms.generative_models - :members: - :undoc-members: - :show-inheritance: - -algorithms.homology\_mod2 module --------------------------------- - -.. automodule:: algorithms.homology_mod2 - :members: - :undoc-members: - :show-inheritance: - -algorithms.hypergraph\_modularity module ----------------------------------------- - -.. automodule:: algorithms.hypergraph_modularity - :members: - :undoc-members: - :show-inheritance: - -algorithms.laplacians\_clustering module ----------------------------------------- - -.. automodule:: algorithms.laplacians_clustering - :members: - :undoc-members: - :show-inheritance: - -algorithms.s\_centrality\_measures module ------------------------------------------ - -.. automodule:: algorithms.s_centrality_measures - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: algorithms - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/_sources/algorithms/modules.rst.txt b/docs/build/_sources/algorithms/modules.rst.txt deleted file mode 100644 index d755574d..00000000 --- a/docs/build/_sources/algorithms/modules.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -algorithms -========== - -.. toctree:: - :maxdepth: 4 - - algorithms diff --git a/docs/build/_sources/classes/classes.rst.txt b/docs/build/_sources/classes/classes.rst.txt deleted file mode 100644 index e6463304..00000000 --- a/docs/build/_sources/classes/classes.rst.txt +++ /dev/null @@ -1,37 +0,0 @@ -classes package -=============== - -Submodules ----------- - -classes.entity module ---------------------- - -.. automodule:: classes.entity - :members: - :undoc-members: - :show-inheritance: - -classes.hypergraph module -------------------------- - -.. automodule:: classes.hypergraph - :members: - :undoc-members: - :show-inheritance: - -classes.staticentity module ---------------------------- - -.. automodule:: classes.staticentity - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: classes - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/_sources/classes/modules.rst.txt b/docs/build/_sources/classes/modules.rst.txt deleted file mode 100644 index 6af3efe7..00000000 --- a/docs/build/_sources/classes/modules.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -classes -======= - -.. toctree:: - :maxdepth: 4 - - classes diff --git a/docs/build/_sources/core.rst.txt b/docs/build/_sources/core.rst.txt deleted file mode 100644 index f52bb844..00000000 --- a/docs/build/_sources/core.rst.txt +++ /dev/null @@ -1,12 +0,0 @@ -.. _core: - -================== -HyperNetX Packages -================== - -.. toctree:: - - Hypergraphs - Algorithms - Drawing - Reports diff --git a/docs/build/_sources/drawing/drawing.rst.txt b/docs/build/_sources/drawing/drawing.rst.txt deleted file mode 100644 index fd619876..00000000 --- a/docs/build/_sources/drawing/drawing.rst.txt +++ /dev/null @@ -1,37 +0,0 @@ -drawing package -=============== - -Submodules ----------- - -drawing.rubber\_band module ---------------------------- - -.. automodule:: drawing.rubber_band - :members: - :undoc-members: - :show-inheritance: - -drawing.two\_column module --------------------------- - -.. automodule:: drawing.two_column - :members: - :undoc-members: - :show-inheritance: - -drawing.util module -------------------- - -.. automodule:: drawing.util - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: drawing - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/_sources/drawing/modules.rst.txt b/docs/build/_sources/drawing/modules.rst.txt deleted file mode 100644 index d0a077a0..00000000 --- a/docs/build/_sources/drawing/modules.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -drawing -======= - -.. toctree:: - :maxdepth: 4 - - drawing diff --git a/docs/build/_sources/glossary.rst.txt b/docs/build/_sources/glossary.rst.txt deleted file mode 100644 index dcced646..00000000 --- a/docs/build/_sources/glossary.rst.txt +++ /dev/null @@ -1,135 +0,0 @@ -.. _glossary: - -===================== -Glossary of HNX terms -===================== - -.. glossary:: - :sorted: - - Entity - Class in entity.py. - The base class for nodes, edges, and other HNX structures. An entity has a unique id, a set of properties, and a set of other entities belonging to it called its :term:`elements ` (an entity may not contain itself). - If an entity A belongs to another entity B then A has membership in B and A is an element of B. For any entity A access a dictionary of its elements (keyed by uid) using ``A.elements`` and a dictionary of its memberships using ``A.memberships``. - - Entity.elements - Attribute in class Entity. Returns a dictionary of elements of the entity. - For any entity A, the elements equal the set of entities belonging to A. Use ``A.uidset`` to access the set of uids belonging to the elements of A and ``A.elements`` to access a dictionary of uid,entity key value pairs of elements of A. - - Entity.children - Attribute in class Entity. Returns a set of uids for the elements of the elements of entity. - For any entity A, the set of entities which belong to some entity belonging to A. Use ``A.children`` to access the set of uids belonging to the children of A and ``A.registry`` to access a dictionary of uid,entity key value pairs of the children of A. - See also :term:`Entity.levelset`. - - Entity.registry - Attribute in class Entity. - A dictionary of uid,entity key value pairs of the :term:`children ` of an entity. - - Entity.memberships - Attribute in class Entity. - A dictionary of uid,entity key value pairs of entities to which the entity belongs. - - Entity.levelset - Method in class Entity. - For any entity A, Level 1 of A is the set of :term:`elements ` of A. - The elements of entities in Level 1 of A belong to Level 2 of A. The elements of entities in Level k of A belong to Level k+1 of A. - The entities in Level 2 of A are called A's children. - A single entity may occupy multiple Level sets of an entity. An entity may belong to any of its own Level sets except Level 1 as no entity may contain itself as an element. - Note that if Level n of A is nonempty then Level k of A is nonempty for all k` belonging to an entity. - For any entity A, if A.elements is empty then it has depth 0 and no non-empty Levels. - If A.elements contains only Entities of depth 0 then A has depth 1. - If A.elements contains only Entities of depth 0 and depth 1 then A has depth 2. - If A.elements contains an entity of depth n and no Entities of depth more than n then it has depth n+1. - - entityset - An entity A satisfying the :term:`Bipartite Condition`, the property that the set of entities in Level 1 of A is disjoint from the set of entities in Level 2 of A, i.e. the elements of A are disjoint from the children of A. An entityset is instantiated in the class EntitySet. - - hypergraph - A pair of entitysets (Nodes,Edges) such that Edges has :term:`depth ` 2, Nodes have depth 1, and the children of Edges is exactly the set of elements of Nodes. Intuitively, every element of Edges is a (hyper)edge, which is either empty or contains elements of Nodes. Every node in Nodes has :term:`membership ` in some edge in Edges. Since a node has :term:`depth ` 0 it is distinguished by its uid, properties, and memberships. A hypergraph is instantiated in the class Hypergraph. - - subhypergraph - Given a hypergraph (Nodes,Edges), a subhypergraph is a pair of subsets of (Nodes,Edges). - - degree - Given a hypergraph (Nodes,Edges), the degree of a node in Nodes is the number of edges in Edges to which the node belongs. - See also: :term:`s-degree` - - incidence matrix - A rectangular matrix constructed from a hypergraph (Nodes,Edges) where the elements of Nodes index the matrix rows, and the elements of Edges index the matrix columns. Entry (i,j) in the incidence matrix is 1 if the node corresponding to i in Nodes belongs to the edge corresponding to j in Edges, and is 0 otherwise. - - s-adjacency matrix - For a hypergraph (Nodes,Edges) and positive integer s, a square matrix where the elements of Nodes index both rows and columns. The matrix can be weighted or unweighted. Entry (i,j) is nonzero if and only if node i and node j belong to at least s shared edges, and is equal to the number of shared edges (if weighted) or 1 (if unweighted). - - s-edge-adjacency matrix - For a hypergraph (Nodes,Edges) and positive integer s, a square matrix where the elements of Edges index both rows and columns. The matrix can be weighted or unweighted. Entry (i,j) is nonzero if and only if edge i and edge j share to at least s nodes, and is equal to the number of shared nodes (if weighted) or 1 (if unweighted). - - s-auxiliary matrix - For a hypergraph (Nodes,Edges) and positive integer s, the submatrix of the :term:`s-edge-adjacency matrix ` obtained by restricting to rows and columns corresponding to edges of size at least s. - - toplex - For a hypergraph (Nodes,Edges), a toplex is an edge in Edges whose elements (i.e. nodes) do not all belong to any other edge in Edge. - - dual - For a hypergraph (Nodes,Edges), its dual is the hypergraph constructed by switching the roles of Nodes and Edges. More precisely, if node i belongs to edge j in the hypergraph, then node j belongs to edge i in the dual hypergraph. - - s-node-walk - For a hypergraph (Nodes,Edges) and positive integer s, a sequence of nodes in Nodes such that each successive pair of nodes share at least s edges in Edges. - - s-edge-walk - For a hypergraph (Nodes,Edges) and positive integer s, a sequence of edges in Edges such that each successive pair of edges intersects in at least s nodes in Nodes. - - s-walk - Either an s-node-walk or an s-edge-walk. - - s-connected component, s-node-connected component - For a hypergraph (Nodes,Edges) and positive integer s, an s-connected component is a :term:`subhypergraph` induced by a subset of Nodes with the property that there exists an s-walk between every pair of nodes in this subset. An s-connected component is the maximal such subset in the sense that it is not properly contained in any other subset satisfying this property. - - s-edge-connected component - For a hypergraph (Nodes,Edges) and positive integer s, an s-edge-connected component is a :term:`subhypergraph` induced by a subset of Edges with the property that there exists an s-edge-walk between every pair of edges in this subset. An s-edge-connected component is the maximal such subset in the sense that it is not properly contained in any other subset satisfying this property. - - s-connected, s-node-connected - A hypergraph is s-connected if it has one s-connected component. - - s-edge-connected - A hypergraph is s-edge-connected if it has one s-edge-connected component. - - s-distance - For a hypergraph (Nodes,Edges) and positive integer s, the s-distances between two nodes in Nodes is the length of the shortest :term:`s-node-walk` between them. If no s-node-walks between the pair of nodes exists, the s-distance between them is infinite. The s-distance - between edges is the length of the shortest :term:`s-edge-walk` between them. If no s-edge-walks between the pair of edges exist, then s-distance between them is infinite. - - s-diameter - For a hypergraph (Nodes,Edges) and positive integer s, the s-diameter is the maximum s-Distance over all pairs of nodes in Nodes. - - s-degree - For a hypergraph (Nodes, Edges) and positive integer s, the s-degree of a node is the number of edges in Edges of size at least s to which node belongs. See also: :term:`degree` - - s-edge - For a hypergraph (Nodes, Edges) and positive integer s, an s-edge is any edge of size at least s. - - s-linegraph - For a hypergraph (Nodes, Edges) and positive integer s, an s-linegraph is a graph representing - the node to node or edge to edge connections according to the *width* s of the connections. - The node s-linegraph is a graph on the set Nodes. Two nodes in Nodes are incident in the node s-linegraph if they - share at lease s incident edges in Edges; that is, there are at least s elements of Edges to which they both belong. - The edge s-linegraph is a graph on the set Edges. Two edges in Edges are incident in the edge s-linegraph if they - share at least s incident nodes in Nodes; that is, the edges intersect in at least s nodes in Nodes. - - Bipartite Condition - Condition imposed on instances of the class EntitySet. - *Entities that are elements of the same EntitySet, may not contain each other as elements.* - The elements and children of an EntitySet generate a specific partition for a bipartite graph. - The partition is isomorphic to a Hypergraph where the elements correspond to hyperedges and - the children correspond to the nodes. EntitySets are the basic objects used to construct dynamic hypergraphs - in HNX. See methods :py:meth:`classes.hypergraph.Hypergraph.bipartite` and :py:meth:`classes.hypergraph.Hypergraph.from_bipartite`. - - simple hypergraph - A hypergraph for which no edge is completely contained in another. - - - - diff --git a/docs/build/_sources/index.rst.txt b/docs/build/_sources/index.rst.txt deleted file mode 100644 index 7e437e0e..00000000 --- a/docs/build/_sources/index.rst.txt +++ /dev/null @@ -1,55 +0,0 @@ -=============== -HyperNetX (HNX) -=============== - -.. image:: images/hnxbasics.png - :width: 300px - :align: right - -Description ------------ - -The `HNX`_ library provides classes and methods for modeling the entities and relationships -found in complex networks as hypergraphs, the natural models for multi-dimensional network data. -As strict generalizations of graphs, hyperedges can represent arbitrary multi-way relations -among entities, and in particular can distinguish cliques and simplices, and admit singleton edges. -As both vertex adjacency and edge -incidence are generalized to be quantities, -hypergraph paths and walks thereby have both length and *width* because of these multiway connections. -Most graph metrics have natural generalizations to hypergraphs, but since -hypergraphs are basically set systems, they also admit to the powerful tools of algebraic topology, -including simplicial complexes and simplicial homology, to study their structure. - -This library serves as a repository of the methods and algorithms we find most useful -as we explore what hypergraphs can tell us. We have a growing community of users and contributors. -To learn more about some of our research check out our :ref:`publications`. - - -For comments and questions you may contact the developers directly at: - hypernetx@pnnl.gov - -Contents --------- - -.. toctree:: - - Home - overview/index - install - Glossary - core - NWHypergraph C++ Optimization - HyperNetX Visualization Widget - Algorithms: Modularity and Clustering - Publications - license - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` - -.. _HNX: https://github.com/pnnl/HyperNetX diff --git a/docs/build/_sources/install.rst.txt b/docs/build/_sources/install.rst.txt deleted file mode 100644 index b8e059f4..00000000 --- a/docs/build/_sources/install.rst.txt +++ /dev/null @@ -1,87 +0,0 @@ -Installing HyperNetX -==================== - -HyperNetX may be cloned or forked from: https://github.com/pnnl/HyperNetX . - -To install in an Anaconda environment -------------------------------------- - - >>> conda create -n python=3.7 - >>> source activate - >>> pip install hypernetx - -Mac Users: If you wish to build the documentation you will need -the conda version of matplotlib: - - >>> conda create -n python=3.7 matplotlib - >>> source activate - >>> pip install hypernetx - -To use :ref:`NWHy ` use python=3.9 and the conda version of tbb in your environment. -**Note** that :ref:`NWHy ` only works on Linux and some OSX systems. See NWHy docs for more.: - - >>> conda create -n python=3.9 tbb - >>> source activate - >>> pip install hypernetx - >>> pip install nwhy - -To install in a virtualenv environment --------------------------------------- - - >>> virtualenv --python= - -This will create a virtual environment in the specified location using -the specified python executable. For example: - - >>> virtualenv --python=C:\Anaconda3\python.exe hnx - -This will create a virtual environment in .\hnx using the python -that comes with Anaconda3. - - >>> \Scripts\activate - -If you are running in Windows PowerShell use =.ps1 - -If you are running in Windows Command Prompt use =.bat - -Otherwise use =NULL (no file extension). - -Once activated continue to follow the installation instructions below. - - -Install using Pip options -------------------------- -For a minimal installation: - - >>> pip install hypernetx - -For an editable installation with access to jupyter notebooks: - - >>> pip install [-e] . - -To install with the tutorials: - - >>> pip install -e .['tutorials'] - -To install with the documentation: - - >>> pip install -e .['documentation'] - >>> chmod 755 build_docs.sh - >>> sh build_docs.sh - ## This will generate the documentation in /docs/build/ - ## Open them in your browser with /docs/index.html - -To install and test using pytest: - - >>> pip install -e .['testing'] - >>> pytest - -To install the whole shabang: - - >>> pip install -e .['all'] - - - - - - diff --git a/docs/build/_sources/license.rst.txt b/docs/build/_sources/license.rst.txt deleted file mode 100644 index 2a90cf46..00000000 --- a/docs/build/_sources/license.rst.txt +++ /dev/null @@ -1 +0,0 @@ -.. include:: ../../LICENSE.rst \ No newline at end of file diff --git a/docs/build/_sources/modularity.rst.txt b/docs/build/_sources/modularity.rst.txt deleted file mode 100644 index 8738ced1..00000000 --- a/docs/build/_sources/modularity.rst.txt +++ /dev/null @@ -1,114 +0,0 @@ -.. _modularity: - - -========================= -Modularity and Clustering -========================= - -.. image:: images/ModularityScreenShot.png - :width: 300px - :align: right - -Overview --------- -The hypergraph_modularity submodule in HNX provides functions to compute **hypergraph modularity** for a -given partition of the vertices in a hypergraph. In general, higher modularity indicates a better -partitioning of the vertices into dense communities. - -Two functions to generate such hypergraph -partitions are provided: **Kumar's** algorithm, and the simple **Last-Step** refinement algorithm. - -The submodule also provides a function to generate the **two-section graph** for a given hypergraph which can then be used to find -vertex partitions via graph-based algorithms. - - -Installation ------------- -Since it is part of HNX, no extra installation is required. -The submodule can be imported as follows:: - - import hypernetx.algorithms.hypergraph_modularity as hmod - -Using the Tool --------------- - - -Precomputation -^^^^^^^^^^^^^^ - -In order to make the computation of hypergraph modularity more efficient, some quantities need to be pre-computed. -Given hypergraph H, calling:: - - HG = hmod.precompute_attributes(H) - -will pre-compute quantities such as node strength (weighted degree), d-weights (total weight for each edge cardinality) and binomial coefficients. - -Modularity -^^^^^^^^^^ - -Given hypergraph HG and a partition A of its vertices, hypergraph modularity is a measure of the quality of this partition. -Random partitions typically yield modularity near zero (it can be negative) while positive modularity is indicative of the presence -of dense communities, or modules. There are several variations for the definition of hypergraph modularity, and the main difference lies in the -weight given to different edges. Modularity is computed via:: - - q = hmod.modularity(HG, A, wdc=linear) - -In a graph, an edge only links 2 nodes, so given partition A, an edge is either within a community (which increases the modularity) -or between communities. - -With hypergraphs, we consider edges of size *d=2* or more. Given some vertex partition A and some *d*-edge *e*, let *c* be the number of nodes -that belong to the most represented part in *e*; if *c > d/2*, we consider this edge to be within the part. -Hyper-parameters *0 <= w(d,c) <= 1* control the weight -given to such edges. Three functions are supplied in this submodule, namely: - -**linear** - $w(d,c) = c/d$ if $c > d/2$, else $0$. -**majority** - $w(d,c) = 1$ if $c > d/2$, else $0$. -**strict** - $w(d,c) = 1$ if $c == d$, else $0$. - -The 'linear' function is used by default. More details in [2]. - -Two-section graph -^^^^^^^^^^^^^^^^^ - -There are several good partitioning algorithms for graphs such as the Louvain algorithm and ECG, a consensus clustering algorithm. -One way to obtain a partition for hypergraph HG is to build its corresponding two-section graph G and run a graph clustering algorithm. -Code is provided to build such graph via:: - - G = hmod.two_section(HG) - -which returns an igraph.Graph object. - - -Clustering Algorithms -^^^^^^^^^^^^^^^^^^^^^ - -Two clustering (vertex partitioning) algorithms are supplied. The first one is a hybrid method proposed by Kumar et al. (see [1]) -that uses the Louvain algorithm on the two-section graph, but re-weights the edges according to the distibution of vertices -from each part inside each edge. Given hypergraph HG, this is called as:: - - K = hmod.kumar(HG) - -The other supplied algorithm is a simple method to improve hypergraph modularity directely. Given some -initial partition of the vertices (for example via Louvain on the two-section graph), move vertices between parts in order -to improve hypergraph modularity. Given hypergraph HG and initial partition A, this is called as:: - - L = hmod.last_step(HG, A, wdc=linear) - -where the 'wdc' parameter is the same as in the modularity function. - - -Other Features -^^^^^^^^^^^^^^ - -We represent a vertex partition A as a list of sets, but another conveninent representation is via a dictionary. -We provide two utility functions to switch representation, namely `A = dict2part(D)` and `D = part2dict(A)`. - -References -^^^^^^^^^^ -[1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S. and Ravindran B. “A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering”. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24 - -[2] Kamiński B., Prałat P. and Théberge F. “Community Detection Algorithm Using Hypergraph Modularity”. In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13 - diff --git a/docs/build/_sources/nwhy.rst.txt b/docs/build/_sources/nwhy.rst.txt deleted file mode 100644 index e918be96..00000000 --- a/docs/build/_sources/nwhy.rst.txt +++ /dev/null @@ -1,279 +0,0 @@ -.. _nwhy: - -==== -NWHy -==== - -Description ------------ -NWHy is an addon for HNX providing optimized C++ implementations of many of the hypergraph methods. -NWHy is a scalable, high-performance hypergraph library. It has three dependencies. - - 1. NWGraph library: provides graph data structures, a rich set of adaptors over the graph data structures, and various high-performance graph algorithms implementations. - 2. Intel OneAPI Threading Building Blocks (oneTBB): provides parallelism. - 3. Pybind11: encapsulate NWHy as a python module. - -The goal of the NWHy python API is to share an ID space between NWHy and its user for hypergraph processing, instead of copying the sparse matrix of the hypergraph back and forth between NWHy and its user. -NWHy was developed by Xu Tony Liu. The current version is preliminary and under active development. - -Installing NWHy ---------------- - -The NWHy library provides Pybind11_ APIs for analysis of complex data sets interpreted as hypergraphs. - -.. _Pybind11: https://github.com/pybind/pybind11 - -To install in an Anaconda environment -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - - >>> conda create -n python=3.9 - -Then activate the environment -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - - >>> conda activate - -Install Intel Threading Building Blocks(TBB) -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -To install TBB_: - -.. _TBB: https://github.com/oneapi-src/oneTBB - - >>> conda install tbb - -If a local TBB has been installed, we can specify TBBROOT - - >>> export TBBROOT=/opt/tbb/ - -Install using Pip -^^^^^^^^^^^^^^^^^ - -For installation: - - >>> pip install nwhy - -For upgrade: - - >>> pip install nwhy --upgrade - -or - - >>> pip install nwhy -U - - -Quick test with import -^^^^^^^^^^^^^^^^^^^^^^ - -For quick test: - - >>> python -c "import nwhy" - -If there is no import error, then installation is done. - -NWHy APIs ---------- - -.. _nwhy:: - :sorted: - - -nwhy module -^^^^^^^^^^^ - - _version - Attribute in nwhy module. - Return the version number of nwhy module. - - -NWHypergraph class -^^^^^^^^^^^^^^^^^^ - - NWHypergraph - Class in nwhy module. - The base class for hypergraph representation in nwhy. It accepts a directed edge list format of hypergraph, either weighted or unweighted, then construct the NWHypergraph object. - -NWHypergraph class attributes -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - - NWHypergraph.row - Attribute in class NWHypergraph. - Return a Numpy array of IDs, row of sparse matrix of the hypergraph. Note the number of entries in the Numpy lists, row, col and data must be equal. The row stores hyperedges. - NWHypergraph.col - Attribute in class NWHypergraph. - Return a Numpy array of IDs, columns of sparse matrix of the hypergraph. The col stores vertices. - NWHypergraph.data - Attribute in class NWHypergraph. - Return a Numpy array of IDs, weights of sparse matrix of the hypergraph. - -NWHypergraph class methods -^^^^^^^^^^^^^^^^^^^^^^^^^^ - - NWHypergraph.NWHypergraph(x, y) - Constructor of class NWHypergraph. - Return a NWHypergraph object. Here the hypergraph is unweighted. X is a Numpy array of hyperedges, and y is a Numpy array of vertices. - - NWHypergraph.NWHypergraph(x, y, data) - Constructor of class NWHypergraph. - Return a NWHypergraph object. Here the hypergraph is weighted. X is a Numpy array of hyperedges, y is a Numpy array of vertices, data is a Numpy array of weights associated with the pairs from hyperedges to vertices. - - NWHypergraph.collapse_edges(return_equal_class=False) - Method in class NWHypergraph. - Return a dictionary, where the key is a new ID of a hyperedge after collapsing the hyperedges if the hyperedges have the same vertices, and the value is the number of such hyperedges when `return_equal_class=False`, otherwise, the set of such hyperedges when `return_equal_class=True`. Note the weights associated with the pairs from hyperedges to vertices are not collapsed or combined. - - NWHypergraph.collapse_nodes(return_equal_class=False) - Method in class NWHypergraph. - Return a dictionary, where the key is a new ID of a vertex after collapsing the vertices if the vertices share the same hyperedges, and the value is the number of such vertices when `return_equal_class=False`, otherwise, the set of such vertices when `return_equal_class=True`. Note the weights associated with the pairs from hyperedges to vertices are not collapsed or combined. - - NWHypergraph.collapse_nodes_and_edges(return_equal_class=False) - Method in class NWHypergraph. - Return a dictionary, where the key is a new ID of a hyperedge after collapsing the hyperedges if the hyperedges share the same vertices, and the value is the number of such hyperedges when `return_equal_class=False`, otherwise, the set of such hyperedges when `return_equal_class=True`. This method is not equivalent to call `NWHypergraph.collapse_nodes()` then `NWHypergraph.collapse_edges()`. Note the weights associated with the pairs from hyperedges to vertices are not collapsed or combined. - - NWHypergraph.edge_size_dist() - Method in class NWHypergraph. - Return a list of edge size distribution of the hypergraph. - - NWHypergraph.node_size_dist() - Method in class NWHypergraph. - Return a list of vertex size distribution of the hypergraph. - - NWHypergraph.edge_incidence(edge) - Method in class NWHypergraph. - Return a list of vertices that are incident to hyperedge `edge`. - - NWHypergraph.node_incidence(node) - Method in class NWHypergraph. - Return a list of hyperedges that are incident to vertex `node`. - - NWHypergraph.degree(node, min_size=1, max_size=None) - Method in class NWHypergraph. - Return the degree of the vertex `node` in the hypergraph. For the hyperedges `node` incident to, if `min_size` or/and `max_size` are specified, then either/both criteria are used to filter the hyperedges. - - NWHypergraph.size(edge, min_degree=1, max_degree=None) - Method in class NWHypergraph. - Return the size of the hyperedge `edge` in the hypergraph. For the vertices `edge` incident to, if `min_degree` or/and `max_degree` are specified, then either/both criteria are used to filter the vertices. - - NWHypergraph.dim(edge) - Method in class NWHypergraph. - Return the dimension of the hyperedge `edge` in the hypergraph. - - NWHypergraph.number_of_nodes() - Method in class NWHypergraph. - Return the number of vertices in the hypergraph. - - NWHypergraph.number_of_edges() - Method in class NWHypergraph. - Return the number of edges in the hypergraph. - - NWHypergraph.singletons() - Method in class NWHypergraph. - Return a list of singleton hyperedges in the hypergraph. A singleton hyperedge is incident to only one vertex. - - NWHypergraph.toplexes() - Method in class NWHypergraph. - Return a list of toplexes in the hypergraph. For a hypergraph (Edges, Nodes), a toplex is a hyperedge in Edges whose elements (i.e. nodes) do not all belong to any other hyperedge in Edge. - - NWHypergraph.s_linegraph(s=1, edges=True) - Method in class NWHypergraph. - Return a Slinegraph object. Construct a s-line graph from the hypergraph for a positive integer `s`. In this s-line graph, the vertices are the hyperedges in the original hypergraph if `edges=True`; otherwise, the vertices are the vertices in the original hypergraph. Note this method create s-line graph on the fly, therefore it requires less memory compared with `NWHypergraph.s_linegraphs(l, edges=True)`. It is slower to construct multiple s-line graphs for different `s` compared with `NWHypergraph.s_linegraphs(l, edges=True)`. - - NWHypergraph.s_linegraphs(l, edges=True) - Method in class NWHypergraph. - Return a list of Slinegraph objects. For each positive integer in list `l`, construct a Slinegraph object from the hypergraph. In each s-line graph, the vertices are the hyperedges in the original hypergraph if `edges=True`; otherwise, the vertices are the vertices in the original hypergraph. Note this method creates multiple s-line graphs for one run, therefore it is significantly faster compared with `NWHypergraph.s_linegraph(s=1, edges=True)`, but it requires much more memory. - - -Slinegraph class -^^^^^^^^^^^^^^^^ - - Slinegraph - Class in nwhy module. - The base class for s-line graph representation in nwhy. It store an undirected graph, called an s-line graph of a hypergraph given a positive integer s. Slinegraph can be an 'edge' line graph, where the vertices in Slinegraph are the hyperedges in the original hypergraph; Slinegraph can also be a 'vertex' line graph, where the vertices in Slinegraph are the vertices in the original hypergraph. - -Slinegraph class attributes -^^^^^^^^^^^^^^^^^^^^^^^^^^^ - - Slinegraph.row - Attribute in class Slinegraph. - Return a Numpy array of IDs, row of sparse matrix of the s-line graph. Note the number of entries in the Numpy lists, row, col and data must be equal. - Slinegraph.col - Attribute in class Slinegraph. - Return a Numpy array of IDs, columns of sparse matrix of the s-line graph. - Slinegraph.data - Attribute in class Slinegraph. - Return a Numpy array of IDs, weights of sparse matrix of the s-line graph. The weights are not the hyperedge-vertex pair weights. Currently, if Slinegraph is an edge line graph, the weights are the number of overlapping vertices between two hyperedges in the original hypergraph. If the Slinegraph is a vertex line graph, the weights are the number of overlapping hyperedges between two vertices in the original hypergraph. - Slinegraph.s - Attribute in class Slinegraph. - Return s value of the s-line graph. - -Slinegraph class methods -^^^^^^^^^^^^^^^^^^^^^^^^ - - Slinegraph.Slinegraph(g, s=1, edges=True) - Constructor of class Slinegraph. - Return a new Slinegraph object. Given a positive integer `s`, construct a s-line graph from the hypergraph `g`. The vertices in the s-line graph are the hyperedges in `g` if `edges=True`, otherwise, the vertices in the s-line graph are the vertices in `g`. - - Slinegraph.Slinegraph(x, y, data, s=1, edges=True) - Constructor of class Slinegraph. - Return a new Slinegraph object. Given an edge list format of a s-line graph stored in three Numpy arrays, construct a s-line graph from the edge list. A positive integer `s` and a boolean `edges` are required to indicate the properties of the s-line graph. - - Slinegraph.get_singletons() - Method in class Slinegraph. - Return a list of singletons in the s-line graph. - - Slinegraph.s_connected_components() - Method in class Slinegraph. - Return a list of sets, where each set contains the vertices sharing the same component. - - Slinegraph.is_s_connected() - Method in class Slinegraph. - Return True or False. Check whether s-line graph is connected. - - Slinegraph.s_distance(src, dest) - Method in class Slinegraph. - Return the distance from `src` to `dest`. Return -1 if it is unreachable from `src` to `dest`. - - Slinegraph.s_diameter(src, dest) - Method in class Slinegraph. - Return the diameter of the s-line graph. Return 0 if every vertex is a singleton. - - Slinegraph.s_path(src, dest) - Method in class Slinegraph. - Return a list of vertices. The vertices are the vertices on the shortest path from `src` to `dest` in the s-line graph. The list will be empty if it is unreachable from `src` to `dest`. - - Slinegraph.s_betweenness_centrality(normalized=True) - Method in class Slinegraph. - Return a list of betweenness centrality score of every vertices in the s-line graph. The betweenness centrality score will be normalized by 2/((n-1)(n-2)) if `normalized=True` where n the number of vertices in s-line graph. Betweenness centrality of a vertex `v` is the sum of the fraction of all-pairs shortest paths that pass through `v`: - - .. math:: - - c_B(v) =\sum_{s,t \in V} \frac{\sigma(s, t|v)}{\sigma(s, t)} - - Slinegraph.s_closeness_centrality(v=None) - Method in class Slinegraph. - Return a list of closeness centrality scores of every vertices in the s-line graph. If `v` is specified, then the list returned contains only `v`'s score. Closeness centrality of a vertex `v` is the reciprocal of the average shortest path distance to `v` over all `n-1` reachable nodes: - - .. math:: - - C(v) = \frac{n - 1}{\sum_{v=1}^{n-1} d(u, v)}, - - - Slinegraph.s_harmonic_closeness_centrality(v=None) - Method in class Slinegraph. - Return a list of harmonic closeness centrality scores of every vertices in the s-line graph. If `v` is specified, then the list returned contains only `v`'s score. Harmonic centrality of a vertex `v` is the sum of the reciprocal of the shortest path distances from all other nodes to `v`: - - .. math:: - - C(v) = \sum_{v \neq u} \frac{1}{d(v, u)} - - Slinegraph.s_eccentricity(v=None) - Method in class Slinegraph. - Return a list of eccentricity of every vertices in the s-line graph. If `v` is specified, then the list returned contains only eccentricity of `v`. - - Slinegraph.s_neighbors(v) - Method in class Slinegraph. - Return a list of neighboring vertices of `v` in the s-line graph. - - Slinegraph.s_degree(v) - Method in class Slinegraph. - Return the degree of vertex `v` in the s-line graph. - diff --git a/docs/build/_sources/overview/index.rst.txt b/docs/build/_sources/overview/index.rst.txt deleted file mode 100644 index 30cb329a..00000000 --- a/docs/build/_sources/overview/index.rst.txt +++ /dev/null @@ -1,137 +0,0 @@ -.. overview: - -======== -Overview -======== - -.. image:: ../images/harrypotter_basic_hyp.png - :width: 300px - :align: right - -The `HyperNetX`_ (`HNX`_) library was developed to support researchers modeling data -as hypergraphs. We have a growing community of users and contributors. -For questions and comments you may contact the developers directly at: hypernetx@pnnl.gov - -`HyperNetX`_ was developed by the `Pacific Northwest National Laboratory `_ for the -Hypernets project as part of its High Performance Data Analytics (HPDA) program. -PNNL is operated by Battelle Memorial Institute under Contract DE-ACO5-76RL01830. - -* Principle Developer and Designer: Brenda Praggastis -* Visualization: Dustin Arendt, Ji Young Yun -* High Performance Computing: Tony Liu, Andrew Lumsdaine -* Principal Investigator: Cliff Joslyn -* Program Manager: Brian Kritzstein -* Mathematics, methods, and algorithms: Sinan Aksoy, Dustin Arendt, Cliff Joslyn, Nicholas Landry, Tony Liu, Andrew Lumsdaine, Brenda Praggastis, and Emilie Purvine, François Théberge - - - -New Features in Version 1.0 ---------------------------- - -#. Hypergraph construction can be sped up by reading in all of the data at once. In particular the hypergraph constructor may read a Pandas dataframe object and create edges and nodes based on column headers. -#. The C++ addon :ref:`nwhy` can be used in Linux environments to support optimized hypergraph methods such as s-centrality measures. -#. The JavaScript addon :ref:`widget` can be used to interactively inspect hypergraphs in a Jupyter Notebook. -#. We've added four new tutorials highlighting the s-centrality metrics, static Hypergraphs, :ref:`nwhy`, and :ref:`widget`. - -New Features in Version 1.1 ---------------------------- - -#. Cell weights for incidence matrices. -#. Support for edge and node properties in static hypergraphs. -#. Three new algorithms modules and their corresponding tutorials - - #. Contagion module for studying SIS and SIR contagion networks using hypergraphs. - #. Clustering module for clustering vertices based on hyperedge incidence and weighting. - #. Generator module for synthetic generation of ChungLu and DCSBM hypergraphs. - -New Features in Version 1.2 ---------------------------- -#. Added algorithm module and tutorial for Modularity and Clustering - - -.. _colab: - -COLAB Tutorials ---------------- -The following tutorials may be run in your browser using Google Colab. Additional tutorials are -available on `GitHub `_. - -.. raw:: html - - - - -Notice ------- -This material was prepared as an account of work sponsored by an agency of the United States Government. -Neither the United States Government nor the United States Department of Energy, nor Battelle, -nor any of their employees, nor any jurisdiction or organization that has cooperated in the development of -these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility -for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process -disclosed, or represents that its use would not infringe privately owned rights. -Reference herein to any specific commercial product, process, or service by trade name, -trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, -or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. -The views and opinions of authors expressed herein do not necessarily state or reflect -those of the United States Government or any agency thereof. - - -.. raw:: html - -
- 
-         PACIFIC NORTHWEST NATIONAL LABORATORY
-         operated by
-         BATTELLE
-         for the
-         UNITED STATES DEPARTMENT OF ENERGY
-         under Contract DE-AC05-76RL01830
-
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- -License -------- -HyperNetX is released under the 3-Clause BSD license (see :ref:`license`) - -.. toctree:: - :maxdepth: 2 - - -.. _HyperNetX: https://github.com/pnnl/HyperNetX -.. _HNX: https://github.com/pnnl/HyperNetX diff --git a/docs/build/_sources/publications.rst.txt b/docs/build/_sources/publications.rst.txt deleted file mode 100644 index 6632d5b4..00000000 --- a/docs/build/_sources/publications.rst.txt +++ /dev/null @@ -1,19 +0,0 @@ -.. _publications: - -============ -Publications -============ - -Joslyn, Cliff A; Aksoy, Sinan; Callahan, Tiffany J; Hunter, LE; Jefferson, Brett ; Praggastis, Brenda ; Purvine, Emilie AH ; Tripodi, Ignacio J: (2020) **"Hypernetwork Science: From Multidimensional Networks to Computational Topology"**, in: Int. Conf. Complex Systems (ICCS 2020), https://arxiv.org/abs/2003.11782, (in press) - - -Feng, Song; Heath, Emily; Jefferson, Brett; Joslyn, CA; Kvinge, Henry; McDermott, Jason E ; Mitchell, Hugh D ; Praggastis, Brenda ; Eisfeld, Amie J; Sims, Amy C ; Thackray, Larissa B ; Fan, Shufang ; Walters, Kevin B; Halfmann, Peter J ; Westhoff-Smith, Danielle ; Tan, Qing ; Menachery, Vineet D ; Sheahan, Timothy P ; Cockrell, Adam S ; Kocher, Jacob F ; Stratton, Kelly G ; Heller, Natalie C ; Bramer, Lisa M ; Diamond, Michael S ; Baric, Ralph S ; Waters, Katrina M ; Kawaoka, Yoshihiro ; Purvine, Emilie: (2020) **"Hypergraph Models of Biological Networks to Identify Genes Critical to Pathogenic Viral Response"**, in: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04197-2, BMC Bioinformatics, 22:287, doi: 10.1186/s12859-021-04197-2 - - -Aksoy, Sinan G; Joslyn, Cliff A; Marrero, Carlos O; Praggastis, B; Purvine, Emilie AH: (2020) **"Hypernetwork Science via High-Order Hypergraph Walks"**, EPJ Data Science, v. 9:16, https://doi.org/10.1140/epjds/s13688-020-00231-0 - - -Joslyn, Cliff A; Aksoy, Sinan; Arendt, Dustin; Firoz, J; Jenkins, Louis ; Praggastis, Brenda ; Purvine, Emilie AH ; Zalewski, Marcin: (2020) **"Hypergraph Analytics of Domain Name System Relationships"**, in: 17th Wshop. on Algorithms and Models for the Web Graph (WAW 2020), Lecture Notes in Computer Science, v. 12901, ed. Kaminski, B et al., pp. 1-15, Springer, https://doi.org/10.1007/978-3-030-48478-1_1 - - -Joslyn, Cliff A; Aksoy, Sinan; Arendt, Dustin; Jenkins, L; Praggastis, Brenda; Purvine, Emilie; Zalewski, Marcin: (2019) **"High Performance Hypergraph Analytics of Domain Name System Relationships"**, in: Proc. HICSS Symp. on Cybersecurity Big Data Analytics, http://www.azsecure-hicss.org/ diff --git a/docs/build/_sources/reports/modules.rst.txt b/docs/build/_sources/reports/modules.rst.txt deleted file mode 100644 index 96365041..00000000 --- a/docs/build/_sources/reports/modules.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -reports -======= - -.. toctree:: - :maxdepth: 4 - - reports diff --git a/docs/build/_sources/reports/reports.rst.txt b/docs/build/_sources/reports/reports.rst.txt deleted file mode 100644 index 4dde7d91..00000000 --- a/docs/build/_sources/reports/reports.rst.txt +++ /dev/null @@ -1,21 +0,0 @@ -reports package -=============== - -Submodules ----------- - -reports.descriptive\_stats module ---------------------------------- - -.. automodule:: reports.descriptive_stats - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: reports - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/build/_sources/widget.rst.txt b/docs/build/_sources/widget.rst.txt deleted file mode 100644 index 3c5ffcdc..00000000 --- a/docs/build/_sources/widget.rst.txt +++ /dev/null @@ -1,66 +0,0 @@ -.. _widget: - - -================ -Hypernetx-Widget -================ - -.. image:: images/WidgetScreenShot.png - :width: 300px - :align: right - -Overview --------- -The HyperNetXWidget_ is an addon for HNX, which extends the built in visualization -capabilities of HNX to a JavaScript based interactive visualization. The tool has two main interfaces, -the hypergraph visualization and the nodes & edges panel. -You may `demo the widget here `_ - -Installation ------------- -The HypernetxWidget_ is available on `GitHub `_ and may be -installed using pip: - - >>> pip install hnxwidget - -Using the Tool --------------- - -Layout -^^^^^^ -The hypergraph visualization is an Euler diagram that shows nodes as circles and hyper edges as outlines -containing the nodes/circles they contain. The visualization uses a force directed optimization to perform -the layout. This algorithm is not perfect and sometimes gives results that the user might want to improve upon. -The visualization allows the user to drag nodes and position them directly at any time. The algorithm will -re-position any nodes that are not specified by the user. Ctrl (Windows) or Command (Mac) clicking a node -will release a pinned node it to be re-positioned by the algorithm. - -Selection -^^^^^^^^^ -Nodes and edges can be selected by clicking them. Nodes and edges can be selected independently of each other, -i.e., it is possible to select an edge without selecting the nodes it contains. Multiple nodes and edges can -be selected, by holding down Shift while clicking. Shift clicking an already selected node will de-select it. -Clicking the background will de-select all nodes and edges. Dragging a selected node will drag all selected -nodes, keeping their relative placement. -Selected nodes can be hidden (having their appearance minimized) or removed completely from the visualization. -Hiding a node or edge will not cause a change in the layout, wheras removing a node or edge will. -The selection can also be expanded. Buttons in the toolbar allow for selecting all nodes contained within selected edges, -and selecting all edges containing any selected nodes. -The toolbar also contains buttons to select all nodes (or edges), un-select all nodes (or edges), -or reverse the selected nodes (or edges). An advanced user might: - -* **Select all nodes not in an edge** by: select an edge, select all nodes in that edge, then reverse the selected nodes to select every node not in that edge. -* **Traverse the graph** by: selecting a start node, then alternating select all edges containing selected nodes and selecting all nodes within selected edges -* **Pin Everything** by: hitting the button to select all nodes, then drag any node slightly to activate the pinning for all nodes. - -Side Panel -^^^^^^^^^^ -Details on nodes and edges are visible in the side panel. For both nodes and edges, a table shows the node name, degree (or size for edges), its selection state, removed state, and color. These properties can also be controlled directly from this panel. The color of nodes and edges can be set in bulk here as well, for example, coloring by degree. - -Other Features -^^^^^^^^^^^^^^ -Nodes with identical edge membership can be collapsed into a super node, which can be helpful for larger hypergraphs. Dragging any node in a super node will drag the entire super node. This feature is available as a toggle in the nodes panel. - -The hypergraph can also be visualized as a bipartite graph (similar to a traditional node-link diagram). Toggling this feature will preserve the locations of the nodes between the bipartite and the Euler diagrams. - -.. _HypernetxWidget: https://github.com/pnnl/hypernetx-widget diff --git a/docs/build/_static/_sphinx_javascript_frameworks_compat.js b/docs/build/_static/_sphinx_javascript_frameworks_compat.js deleted file mode 100644 index 8549469d..00000000 --- a/docs/build/_static/_sphinx_javascript_frameworks_compat.js +++ /dev/null @@ -1,134 +0,0 @@ -/* - * _sphinx_javascript_frameworks_compat.js - * ~~~~~~~~~~ - * - * Compatability shim for jQuery and underscores.js. - * - * WILL BE REMOVED IN Sphinx 6.0 - * xref RemovedInSphinx60Warning - * - */ - -/** - * select a different prefix for underscore - */ -$u = _.noConflict(); - - -/** - * small helper function to urldecode strings - * - * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL - */ -jQuery.urldecode = function(x) { - if (!x) { - return x - } - return decodeURIComponent(x.replace(/\+/g, ' ')); -}; - -/** - * small helper function to urlencode strings - */ -jQuery.urlencode = encodeURIComponent; - -/** - * This function returns the parsed url parameters of the - * current request. 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a/docs/build/_static/doctools.js +++ /dev/null @@ -1,264 +0,0 @@ -/* - * doctools.js - * ~~~~~~~~~~~ - * - * Base JavaScript utilities for all Sphinx HTML documentation. - * - * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ -"use strict"; - -const _ready = (callback) => { - if (document.readyState !== "loading") { - callback(); - } else { - document.addEventListener("DOMContentLoaded", callback); - } -}; - -/** - * highlight a given string on a node by wrapping it in - * span elements with the given class name. - */ -const _highlight = (node, addItems, text, className) => { - if (node.nodeType === Node.TEXT_NODE) { - const val = node.nodeValue; - const parent = node.parentNode; - const pos = val.toLowerCase().indexOf(text); - if ( - pos >= 0 && - !parent.classList.contains(className) && - !parent.classList.contains("nohighlight") - ) { - let span; - - const closestNode = parent.closest("body, svg, foreignObject"); - const isInSVG = closestNode && closestNode.matches("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.classList.add(className); - } - - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - parent.insertBefore( - span, - parent.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling - ) - ); - node.nodeValue = val.substr(0, pos); - - if (isInSVG) { - const rect = document.createElementNS( - "http://www.w3.org/2000/svg", - "rect" - ); - const bbox = parent.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute("class", className); - addItems.push({ parent: parent, target: rect }); - } - } - } else if (node.matches && !node.matches("button, select, textarea")) { - node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); - } -}; -const _highlightText = (thisNode, text, className) => { - let addItems = []; - _highlight(thisNode, addItems, text, className); - addItems.forEach((obj) => - obj.parent.insertAdjacentElement("beforebegin", obj.target) - ); -}; - -/** - * Small JavaScript module for the documentation. - */ -const Documentation = { - init: () => { - Documentation.highlightSearchWords(); - Documentation.initDomainIndexTable(); - Documentation.initOnKeyListeners(); - }, - - /** - * i18n support - */ - TRANSLATIONS: {}, - PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), - LOCALE: "unknown", - - // gettext and ngettext don't access this so that the functions - // can safely bound to a different name (_ = Documentation.gettext) - gettext: (string) => { - const translated = Documentation.TRANSLATIONS[string]; - switch (typeof translated) { - case "undefined": - return string; // no translation - case "string": - return translated; // translation exists - default: - return translated[0]; // (singular, plural) translation tuple exists - } - }, - - ngettext: (singular, plural, n) => { - const translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated !== "undefined") - return translated[Documentation.PLURAL_EXPR(n)]; - return n === 1 ? singular : plural; - }, - - addTranslations: (catalog) => { - Object.assign(Documentation.TRANSLATIONS, catalog.messages); - Documentation.PLURAL_EXPR = new Function( - "n", - `return (${catalog.plural_expr})` - ); - Documentation.LOCALE = catalog.locale; - }, - - /** - * highlight the search words provided in the url in the text - */ - highlightSearchWords: () => { - const highlight = - new URLSearchParams(window.location.search).get("highlight") || ""; - const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); - if (terms.length === 0) return; // nothing to do - - // There should never be more than one element matching "div.body" - const divBody = document.querySelectorAll("div.body"); - const body = divBody.length ? divBody[0] : document.querySelector("body"); - window.setTimeout(() => { - terms.forEach((term) => _highlightText(body, term, "highlighted")); - }, 10); - - const searchBox = document.getElementById("searchbox"); - if (searchBox === null) return; - searchBox.appendChild( - document - .createRange() - .createContextualFragment( - '

' + - '' + - Documentation.gettext("Hide Search Matches") + - "

" - ) - ); - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords: () => { - document - .querySelectorAll("#searchbox .highlight-link") - .forEach((el) => el.remove()); - document - .querySelectorAll("span.highlighted") - .forEach((el) => el.classList.remove("highlighted")); - const url = new URL(window.location); - url.searchParams.delete("highlight"); - window.history.replaceState({}, "", url); - }, - - /** - * helper function to focus on search bar - */ - focusSearchBar: () => { - document.querySelectorAll("input[name=q]")[0]?.focus(); - }, - - /** - * Initialise the domain index toggle buttons - */ - initDomainIndexTable: () => { - const toggler = (el) => { - const idNumber = el.id.substr(7); - const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); - if (el.src.substr(-9) === "minus.png") { - el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; - toggledRows.forEach((el) => (el.style.display = "none")); - } else { - el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; - toggledRows.forEach((el) => (el.style.display = "")); - } - }; - - const togglerElements = document.querySelectorAll("img.toggler"); - togglerElements.forEach((el) => - el.addEventListener("click", (event) => toggler(event.currentTarget)) - ); - togglerElements.forEach((el) => (el.style.display = "")); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); - }, - - initOnKeyListeners: () => { - // only install a listener if it is really needed - if ( - !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && - !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS - ) - return; - - const blacklistedElements = new Set([ - "TEXTAREA", - "INPUT", - "SELECT", - "BUTTON", - ]); - document.addEventListener("keydown", (event) => { - if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements - if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys - - if (!event.shiftKey) { - switch (event.key) { - case "ArrowLeft": - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; - - const prevLink = document.querySelector('link[rel="prev"]'); - if (prevLink && prevLink.href) { - window.location.href = prevLink.href; - event.preventDefault(); - } - break; - case "ArrowRight": - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; - - const nextLink = document.querySelector('link[rel="next"]'); - if (nextLink && nextLink.href) { - window.location.href = nextLink.href; - event.preventDefault(); - } - break; - case "Escape": - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; - Documentation.hideSearchWords(); - event.preventDefault(); - } - } - - // some keyboard layouts may need Shift to get / - switch (event.key) { - case "/": - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; - Documentation.focusSearchBar(); - event.preventDefault(); - } - }); - }, -}; - -// quick alias for translations -const _ = Documentation.gettext; - -_ready(Documentation.init); diff --git a/docs/build/_static/documentation_options.js b/docs/build/_static/documentation_options.js deleted file mode 100644 index fc9cc5db..00000000 --- a/docs/build/_static/documentation_options.js +++ /dev/null @@ -1,14 +0,0 @@ -var DOCUMENTATION_OPTIONS = { - URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '1.2.5', - LANGUAGE: 'en', - COLLAPSE_INDEX: false, - BUILDER: 'html', - FILE_SUFFIX: '.html', - LINK_SUFFIX: '.html', - HAS_SOURCE: true, - SOURCELINK_SUFFIX: '.txt', - NAVIGATION_WITH_KEYS: false, - SHOW_SEARCH_SUMMARY: true, - ENABLE_SEARCH_SHORTCUTS: true, -}; \ No newline at end of file diff --git a/docs/build/_static/file.png b/docs/build/_static/file.png deleted file mode 100644 index a858a410..00000000 Binary files a/docs/build/_static/file.png and /dev/null differ diff --git a/docs/build/_static/hnx_logo_smaller.png b/docs/build/_static/hnx_logo_smaller.png deleted file mode 100644 index 7f8d5447..00000000 Binary files a/docs/build/_static/hnx_logo_smaller.png and /dev/null differ diff --git a/docs/build/_static/jquery-3.6.0.js b/docs/build/_static/jquery-3.6.0.js deleted file mode 100644 index fc6c299b..00000000 --- a/docs/build/_static/jquery-3.6.0.js +++ /dev/null @@ -1,10881 +0,0 @@ -/*! - * jQuery JavaScript Library v3.6.0 - * https://jquery.com/ - * - * Includes Sizzle.js - * https://sizzlejs.com/ - * - * Copyright OpenJS Foundation and other contributors - * Released under the MIT license - * https://jquery.org/license - * - * Date: 2021-03-02T17:08Z - */ -( function( global, factory ) { - - "use strict"; - - if ( typeof module === "object" && typeof module.exports === "object" ) { - - // For CommonJS and CommonJS-like environments where a proper `window` - // is present, execute the factory and get jQuery. - // For environments that do not have a `window` with a `document` - // (such as Node.js), expose a factory as module.exports. - // This accentuates the need for the creation of a real `window`. - // e.g. var jQuery = require("jquery")(window); - // See ticket #14549 for more info. - module.exports = global.document ? - factory( global, true ) : - function( w ) { - if ( !w.document ) { - throw new Error( "jQuery requires a window with a document" ); - } - return factory( w ); - }; - } else { - factory( global ); - } - -// Pass this if window is not defined yet -} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { - -// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 -// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode -// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common -// enough that all such attempts are guarded in a try block. -"use strict"; - -var arr = []; - -var getProto = Object.getPrototypeOf; - -var slice = arr.slice; - -var flat = arr.flat ? function( array ) { - return arr.flat.call( array ); -} : function( array ) { - return arr.concat.apply( [], array ); -}; - - -var push = arr.push; - -var indexOf = arr.indexOf; - -var class2type = {}; - -var toString = class2type.toString; - -var hasOwn = class2type.hasOwnProperty; - -var fnToString = hasOwn.toString; - -var ObjectFunctionString = fnToString.call( Object ); - -var support = {}; - -var isFunction = function isFunction( obj ) { - - // Support: Chrome <=57, Firefox <=52 - // In some browsers, typeof returns "function" for HTML elements - // (i.e., `typeof document.createElement( "object" ) === "function"`). - // We don't want to classify *any* DOM node as a function. - // Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5 - // Plus for old WebKit, typeof returns "function" for HTML collections - // (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756) - return typeof obj === "function" && typeof obj.nodeType !== "number" && - typeof obj.item !== "function"; - }; - - -var isWindow = function isWindow( obj ) { - return obj != null && obj === obj.window; - }; - - -var document = window.document; - - - - var preservedScriptAttributes = { - type: true, - src: true, - nonce: true, - noModule: true - }; - - function DOMEval( code, node, doc ) { - doc = doc || document; - - var i, val, - script = doc.createElement( "script" ); - - script.text = code; - if ( node ) { - for ( i in preservedScriptAttributes ) { - - // Support: Firefox 64+, Edge 18+ - // Some browsers don't support the "nonce" property on scripts. - // On the other hand, just using `getAttribute` is not enough as - // the `nonce` attribute is reset to an empty string whenever it - // becomes browsing-context connected. - // See https://github.com/whatwg/html/issues/2369 - // See https://html.spec.whatwg.org/#nonce-attributes - // The `node.getAttribute` check was added for the sake of - // `jQuery.globalEval` so that it can fake a nonce-containing node - // via an object. - val = node[ i ] || node.getAttribute && node.getAttribute( i ); - if ( val ) { - script.setAttribute( i, val ); - } - } - } - doc.head.appendChild( script ).parentNode.removeChild( script ); - } - - -function toType( obj ) { - if ( obj == null ) { - return obj + ""; - } - - // Support: Android <=2.3 only (functionish RegExp) - return typeof obj === "object" || typeof obj === "function" ? - class2type[ toString.call( obj ) ] || "object" : - typeof obj; -} -/* global Symbol */ -// Defining this global in .eslintrc.json would create a danger of using the global -// unguarded in another place, it seems safer to define global only for this module - - - -var - version = "3.6.0", - - // Define a local copy of jQuery - jQuery = function( selector, context ) { - - // The jQuery object is actually just the init constructor 'enhanced' - // Need init if jQuery is called (just allow error to be thrown if not included) - return new jQuery.fn.init( selector, context ); - }; - -jQuery.fn = jQuery.prototype = { - - // The current version of jQuery being used - jquery: version, - - constructor: jQuery, - - // The default length of a jQuery object is 0 - length: 0, - - toArray: function() { - return slice.call( this ); - }, - - // Get the Nth element in the matched element set OR - // Get the whole matched element set as a clean array - get: function( num ) { - - // Return all the elements in a clean array - if ( num == null ) { - return slice.call( this ); - } - - // Return just the one element from the set - return num < 0 ? this[ num + this.length ] : this[ num ]; - }, - - // Take an array of elements and push it onto the stack - // (returning the new matched element set) - pushStack: function( elems ) { - - // Build a new jQuery matched element set - var ret = jQuery.merge( this.constructor(), elems ); - - // Add the old object onto the stack (as a reference) - ret.prevObject = this; - - // Return the newly-formed element set - return ret; - }, - - // Execute a callback for every element in the matched set. - each: function( callback ) { - return jQuery.each( this, callback ); - }, - - map: function( callback ) { - return this.pushStack( jQuery.map( this, function( elem, i ) { - return callback.call( elem, i, elem ); - } ) ); - }, - - slice: function() { - return this.pushStack( slice.apply( this, arguments ) ); - }, - - first: function() { - return this.eq( 0 ); - }, - - last: function() { - return this.eq( -1 ); - }, - - even: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return ( i + 1 ) % 2; - } ) ); - }, - - odd: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return i % 2; - } ) ); - }, - - eq: function( i ) { - var len = this.length, - j = +i + ( i < 0 ? len : 0 ); - return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); - }, - - end: function() { - return this.prevObject || this.constructor(); - }, - - // For internal use only. - // Behaves like an Array's method, not like a jQuery method. - push: push, - sort: arr.sort, - splice: arr.splice -}; - -jQuery.extend = jQuery.fn.extend = function() { - var options, name, src, copy, copyIsArray, clone, - target = arguments[ 0 ] || {}, - i = 1, - length = arguments.length, - deep = false; - - // Handle a deep copy situation - if ( typeof target === "boolean" ) { - deep = target; - - // Skip the boolean and the target - target = arguments[ i ] || {}; - i++; - } - - // Handle case when target is a string or something (possible in deep copy) - if ( typeof target !== "object" && !isFunction( target ) ) { - target = {}; - } - - // Extend jQuery itself if only one argument is passed - if ( i === length ) { - target = this; - i--; - } - - for ( ; i < length; i++ ) { - - // Only deal with non-null/undefined values - if ( ( options = arguments[ i ] ) != null ) { - - // Extend the base object - for ( name in options ) { - copy = options[ name ]; - - // Prevent Object.prototype pollution - // Prevent never-ending loop - if ( name === "__proto__" || target === copy ) { - continue; - } - - // Recurse if we're merging plain objects or arrays - if ( deep && copy && ( jQuery.isPlainObject( copy ) || - ( copyIsArray = Array.isArray( copy ) ) ) ) { - src = target[ name ]; - - // Ensure proper type for the source value - if ( copyIsArray && !Array.isArray( src ) ) { - clone = []; - } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { - clone = {}; - } else { - clone = src; - } - copyIsArray = false; - - // Never move original objects, clone them - target[ name ] = jQuery.extend( deep, clone, copy ); - - // Don't bring in undefined values - } else if ( copy !== undefined ) { - target[ name ] = copy; - } - } - } - } - - // Return the modified object - return target; -}; - -jQuery.extend( { - - // Unique for each copy of jQuery on the page - expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), - - // Assume jQuery is ready without the ready module - isReady: true, - - error: function( msg ) { - throw new Error( msg ); - }, - - noop: function() {}, - - isPlainObject: function( obj ) { - var proto, Ctor; - - // Detect obvious negatives - // Use toString instead of jQuery.type to catch host objects - if ( !obj || toString.call( obj ) !== "[object Object]" ) { - return false; - } - - proto = getProto( obj ); - - // Objects with no prototype (e.g., `Object.create( null )`) are plain - if ( !proto ) { - return true; - } - - // Objects with prototype are plain iff they were constructed by a global Object function - Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; - return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; - }, - - isEmptyObject: function( obj ) { - var name; - - for ( name in obj ) { - return false; - } - return true; - }, - - // Evaluates a script in a provided context; falls back to the global one - // if not specified. - globalEval: function( code, options, doc ) { - DOMEval( code, { nonce: options && options.nonce }, doc ); - }, - - each: function( obj, callback ) { - var length, i = 0; - - if ( isArrayLike( obj ) ) { - length = obj.length; - for ( ; i < length; i++ ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } else { - for ( i in obj ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } - - return obj; - }, - - // results is for internal usage only - makeArray: function( arr, results ) { - var ret = results || []; - - if ( arr != null ) { - if ( isArrayLike( Object( arr ) ) ) { - jQuery.merge( ret, - typeof arr === "string" ? - [ arr ] : arr - ); - } else { - push.call( ret, arr ); - } - } - - return ret; - }, - - inArray: function( elem, arr, i ) { - return arr == null ? -1 : indexOf.call( arr, elem, i ); - }, - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - merge: function( first, second ) { - var len = +second.length, - j = 0, - i = first.length; - - for ( ; j < len; j++ ) { - first[ i++ ] = second[ j ]; - } - - first.length = i; - - return first; - }, - - grep: function( elems, callback, invert ) { - var callbackInverse, - matches = [], - i = 0, - length = elems.length, - callbackExpect = !invert; - - // Go through the array, only saving the items - // that pass the validator function - for ( ; i < length; i++ ) { - callbackInverse = !callback( elems[ i ], i ); - if ( callbackInverse !== callbackExpect ) { - matches.push( elems[ i ] ); - } - } - - return matches; - }, - - // arg is for internal usage only - map: function( elems, callback, arg ) { - var length, value, - i = 0, - ret = []; - - // Go through the array, translating each of the items to their new values - if ( isArrayLike( elems ) ) { - length = elems.length; - for ( ; i < length; i++ ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - - // Go through every key on the object, - } else { - for ( i in elems ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - } - - // Flatten any nested arrays - return flat( ret ); - }, - - // A global GUID counter for objects - guid: 1, - - // jQuery.support is not used in Core but other projects attach their - // properties to it so it needs to exist. - support: support -} ); - -if ( typeof Symbol === "function" ) { - jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; -} - -// Populate the class2type map -jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), - function( _i, name ) { - class2type[ "[object " + name + "]" ] = name.toLowerCase(); - } ); - -function isArrayLike( obj ) { - - // Support: real iOS 8.2 only (not reproducible in simulator) - // `in` check used to prevent JIT error (gh-2145) - // hasOwn isn't used here due to false negatives - // regarding Nodelist length in IE - var length = !!obj && "length" in obj && obj.length, - type = toType( obj ); - - if ( isFunction( obj ) || isWindow( obj ) ) { - return false; - } - - return type === "array" || length === 0 || - typeof length === "number" && length > 0 && ( length - 1 ) in obj; -} -var Sizzle = -/*! - * Sizzle CSS Selector Engine v2.3.6 - * https://sizzlejs.com/ - * - * Copyright JS Foundation and other contributors - * Released under the MIT license - * https://js.foundation/ - * - * Date: 2021-02-16 - */ -( function( window ) { -var i, - support, - Expr, - getText, - isXML, - tokenize, - compile, - select, - outermostContext, - sortInput, - hasDuplicate, - - // Local document vars - setDocument, - document, - docElem, - documentIsHTML, - rbuggyQSA, - rbuggyMatches, - matches, - contains, - - // Instance-specific data - expando = "sizzle" + 1 * new Date(), - preferredDoc = window.document, - dirruns = 0, - done = 0, - classCache = createCache(), - tokenCache = createCache(), - compilerCache = createCache(), - nonnativeSelectorCache = createCache(), - sortOrder = function( a, b ) { - if ( a === b ) { - hasDuplicate = true; - } - return 0; - }, - - // Instance methods - hasOwn = ( {} ).hasOwnProperty, - arr = [], - pop = arr.pop, - pushNative = arr.push, - push = arr.push, - slice = arr.slice, - - // Use a stripped-down indexOf as it's faster than native - // https://jsperf.com/thor-indexof-vs-for/5 - indexOf = function( list, elem ) { - var i = 0, - len = list.length; - for ( ; i < len; i++ ) { - if ( list[ i ] === elem ) { - return i; - } - } - return -1; - }, - - booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + - "ismap|loop|multiple|open|readonly|required|scoped", - - // Regular expressions - - // http://www.w3.org/TR/css3-selectors/#whitespace - whitespace = "[\\x20\\t\\r\\n\\f]", - - // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram - identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + - "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", - - // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors - attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + - - // Operator (capture 2) - "*([*^$|!~]?=)" + whitespace + - - // "Attribute values must be CSS identifiers [capture 5] - // or strings [capture 3 or capture 4]" - "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + - whitespace + "*\\]", - - pseudos = ":(" + identifier + ")(?:\\((" + - - // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: - // 1. quoted (capture 3; capture 4 or capture 5) - "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + - - // 2. simple (capture 6) - "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + - - // 3. anything else (capture 2) - ".*" + - ")\\)|)", - - // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter - rwhitespace = new RegExp( whitespace + "+", "g" ), - rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + - whitespace + "+$", "g" ), - - rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), - rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + - "*" ), - rdescend = new RegExp( whitespace + "|>" ), - - rpseudo = new RegExp( pseudos ), - ridentifier = new RegExp( "^" + identifier + "$" ), - - matchExpr = { - "ID": new RegExp( "^#(" + identifier + ")" ), - "CLASS": new RegExp( "^\\.(" + identifier + ")" ), - "TAG": new RegExp( "^(" + identifier + "|[*])" ), - "ATTR": new RegExp( "^" + attributes ), - "PSEUDO": new RegExp( "^" + pseudos ), - "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + - whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + - whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), - "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), - - // For use in libraries implementing .is() - // We use this for POS matching in `select` - "needsContext": new RegExp( "^" + whitespace + - "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + - "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) - }, - - rhtml = /HTML$/i, - rinputs = /^(?:input|select|textarea|button)$/i, - rheader = /^h\d$/i, - - rnative = /^[^{]+\{\s*\[native \w/, - - // Easily-parseable/retrievable ID or TAG or CLASS selectors - rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, - - rsibling = /[+~]/, - - // CSS escapes - // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters - runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), - funescape = function( escape, nonHex ) { - var high = "0x" + escape.slice( 1 ) - 0x10000; - - return nonHex ? - - // Strip the backslash prefix from a non-hex escape sequence - nonHex : - - // Replace a hexadecimal escape sequence with the encoded Unicode code point - // Support: IE <=11+ - // For values outside the Basic Multilingual Plane (BMP), manually construct a - // surrogate pair - high < 0 ? - String.fromCharCode( high + 0x10000 ) : - String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); - }, - - // CSS string/identifier serialization - // https://drafts.csswg.org/cssom/#common-serializing-idioms - rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, - fcssescape = function( ch, asCodePoint ) { - if ( asCodePoint ) { - - // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER - if ( ch === "\0" ) { - return "\uFFFD"; - } - - // Control characters and (dependent upon position) numbers get escaped as code points - return ch.slice( 0, -1 ) + "\\" + - ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; - } - - // Other potentially-special ASCII characters get backslash-escaped - return "\\" + ch; - }, - - // Used for iframes - // See setDocument() - // Removing the function wrapper causes a "Permission Denied" - // error in IE - unloadHandler = function() { - setDocument(); - }, - - inDisabledFieldset = addCombinator( - function( elem ) { - return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; - }, - { dir: "parentNode", next: "legend" } - ); - -// Optimize for push.apply( _, NodeList ) -try { - push.apply( - ( arr = slice.call( preferredDoc.childNodes ) ), - preferredDoc.childNodes - ); - - // Support: Android<4.0 - // Detect silently failing push.apply - // eslint-disable-next-line no-unused-expressions - arr[ preferredDoc.childNodes.length ].nodeType; -} catch ( e ) { - push = { apply: arr.length ? - - // Leverage slice if possible - function( target, els ) { - pushNative.apply( target, slice.call( els ) ); - } : - - // Support: IE<9 - // Otherwise append directly - function( target, els ) { - var j = target.length, - i = 0; - - // Can't trust NodeList.length - while ( ( target[ j++ ] = els[ i++ ] ) ) {} - target.length = j - 1; - } - }; -} - -function Sizzle( selector, context, results, seed ) { - var m, i, elem, nid, match, groups, newSelector, - newContext = context && context.ownerDocument, - - // nodeType defaults to 9, since context defaults to document - nodeType = context ? context.nodeType : 9; - - results = results || []; - - // Return early from calls with invalid selector or context - if ( typeof selector !== "string" || !selector || - nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { - - return results; - } - - // Try to shortcut find operations (as opposed to filters) in HTML documents - if ( !seed ) { - setDocument( context ); - context = context || document; - - if ( documentIsHTML ) { - - // If the selector is sufficiently simple, try using a "get*By*" DOM method - // (excepting DocumentFragment context, where the methods don't exist) - if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { - - // ID selector - if ( ( m = match[ 1 ] ) ) { - - // Document context - if ( nodeType === 9 ) { - if ( ( elem = context.getElementById( m ) ) ) { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( elem.id === m ) { - results.push( elem ); - return results; - } - } else { - return results; - } - - // Element context - } else { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( newContext && ( elem = newContext.getElementById( m ) ) && - contains( context, elem ) && - elem.id === m ) { - - results.push( elem ); - return results; - } - } - - // Type selector - } else if ( match[ 2 ] ) { - push.apply( results, context.getElementsByTagName( selector ) ); - return results; - - // Class selector - } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && - context.getElementsByClassName ) { - - push.apply( results, context.getElementsByClassName( m ) ); - return results; - } - } - - // Take advantage of querySelectorAll - if ( support.qsa && - !nonnativeSelectorCache[ selector + " " ] && - ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && - - // Support: IE 8 only - // Exclude object elements - ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { - - newSelector = selector; - newContext = context; - - // qSA considers elements outside a scoping root when evaluating child or - // descendant combinators, which is not what we want. - // In such cases, we work around the behavior by prefixing every selector in the - // list with an ID selector referencing the scope context. - // The technique has to be used as well when a leading combinator is used - // as such selectors are not recognized by querySelectorAll. - // Thanks to Andrew Dupont for this technique. - if ( nodeType === 1 && - ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { - - // Expand context for sibling selectors - newContext = rsibling.test( selector ) && testContext( context.parentNode ) || - context; - - // We can use :scope instead of the ID hack if the browser - // supports it & if we're not changing the context. - if ( newContext !== context || !support.scope ) { - - // Capture the context ID, setting it first if necessary - if ( ( nid = context.getAttribute( "id" ) ) ) { - nid = nid.replace( rcssescape, fcssescape ); - } else { - context.setAttribute( "id", ( nid = expando ) ); - } - } - - // Prefix every selector in the list - groups = tokenize( selector ); - i = groups.length; - while ( i-- ) { - groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + - toSelector( groups[ i ] ); - } - newSelector = groups.join( "," ); - } - - try { - push.apply( results, - newContext.querySelectorAll( newSelector ) - ); - return results; - } catch ( qsaError ) { - nonnativeSelectorCache( selector, true ); - } finally { - if ( nid === expando ) { - context.removeAttribute( "id" ); - } - } - } - } - } - - // All others - return select( selector.replace( rtrim, "$1" ), context, results, seed ); -} - -/** - * Create key-value caches of limited size - * @returns {function(string, object)} Returns the Object data after storing it on itself with - * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) - * deleting the oldest entry - */ -function createCache() { - var keys = []; - - function cache( key, value ) { - - // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) - if ( keys.push( key + " " ) > Expr.cacheLength ) { - - // Only keep the most recent entries - delete cache[ keys.shift() ]; - } - return ( cache[ key + " " ] = value ); - } - return cache; -} - -/** - * Mark a function for special use by Sizzle - * @param {Function} fn The function to mark - */ -function markFunction( fn ) { - fn[ expando ] = true; - return fn; -} - -/** - * Support testing using an element - * @param {Function} fn Passed the created element and returns a boolean result - */ -function assert( fn ) { - var el = document.createElement( "fieldset" ); - - try { - return !!fn( el ); - } catch ( e ) { - return false; - } finally { - - // Remove from its parent by default - if ( el.parentNode ) { - el.parentNode.removeChild( el ); - } - - // release memory in IE - el = null; - } -} - -/** - * Adds the same handler for all of the specified attrs - * @param {String} attrs Pipe-separated list of attributes - * @param {Function} handler The method that will be applied - */ -function addHandle( attrs, handler ) { - var arr = attrs.split( "|" ), - i = arr.length; - - while ( i-- ) { - Expr.attrHandle[ arr[ i ] ] = handler; - } -} - -/** - * Checks document order of two siblings - * @param {Element} a - * @param {Element} b - * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b - */ -function siblingCheck( a, b ) { - var cur = b && a, - diff = cur && a.nodeType === 1 && b.nodeType === 1 && - a.sourceIndex - b.sourceIndex; - - // Use IE sourceIndex if available on both nodes - if ( diff ) { - return diff; - } - - // Check if b follows a - if ( cur ) { - while ( ( cur = cur.nextSibling ) ) { - if ( cur === b ) { - return -1; - } - } - } - - return a ? 1 : -1; -} - -/** - * Returns a function to use in pseudos for input types - * @param {String} type - */ -function createInputPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for buttons - * @param {String} type - */ -function createButtonPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return ( name === "input" || name === "button" ) && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for :enabled/:disabled - * @param {Boolean} disabled true for :disabled; false for :enabled - */ -function createDisabledPseudo( disabled ) { - - // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable - return function( elem ) { - - // Only certain elements can match :enabled or :disabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled - if ( "form" in elem ) { - - // Check for inherited disabledness on relevant non-disabled elements: - // * listed form-associated elements in a disabled fieldset - // https://html.spec.whatwg.org/multipage/forms.html#category-listed - // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled - // * option elements in a disabled optgroup - // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled - // All such elements have a "form" property. - if ( elem.parentNode && elem.disabled === false ) { - - // Option elements defer to a parent optgroup if present - if ( "label" in elem ) { - if ( "label" in elem.parentNode ) { - return elem.parentNode.disabled === disabled; - } else { - return elem.disabled === disabled; - } - } - - // Support: IE 6 - 11 - // Use the isDisabled shortcut property to check for disabled fieldset ancestors - return elem.isDisabled === disabled || - - // Where there is no isDisabled, check manually - /* jshint -W018 */ - elem.isDisabled !== !disabled && - inDisabledFieldset( elem ) === disabled; - } - - return elem.disabled === disabled; - - // Try to winnow out elements that can't be disabled before trusting the disabled property. - // Some victims get caught in our net (label, legend, menu, track), but it shouldn't - // even exist on them, let alone have a boolean value. - } else if ( "label" in elem ) { - return elem.disabled === disabled; - } - - // Remaining elements are neither :enabled nor :disabled - return false; - }; -} - -/** - * Returns a function to use in pseudos for positionals - * @param {Function} fn - */ -function createPositionalPseudo( fn ) { - return markFunction( function( argument ) { - argument = +argument; - return markFunction( function( seed, matches ) { - var j, - matchIndexes = fn( [], seed.length, argument ), - i = matchIndexes.length; - - // Match elements found at the specified indexes - while ( i-- ) { - if ( seed[ ( j = matchIndexes[ i ] ) ] ) { - seed[ j ] = !( matches[ j ] = seed[ j ] ); - } - } - } ); - } ); -} - -/** - * Checks a node for validity as a Sizzle context - * @param {Element|Object=} context - * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value - */ -function testContext( context ) { - return context && typeof context.getElementsByTagName !== "undefined" && context; -} - -// Expose support vars for convenience -support = Sizzle.support = {}; - -/** - * Detects XML nodes - * @param {Element|Object} elem An element or a document - * @returns {Boolean} True iff elem is a non-HTML XML node - */ -isXML = Sizzle.isXML = function( elem ) { - var namespace = elem && elem.namespaceURI, - docElem = elem && ( elem.ownerDocument || elem ).documentElement; - - // Support: IE <=8 - // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes - // https://bugs.jquery.com/ticket/4833 - return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); -}; - -/** - * Sets document-related variables once based on the current document - * @param {Element|Object} [doc] An element or document object to use to set the document - * @returns {Object} Returns the current document - */ -setDocument = Sizzle.setDocument = function( node ) { - var hasCompare, subWindow, - doc = node ? node.ownerDocument || node : preferredDoc; - - // Return early if doc is invalid or already selected - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { - return document; - } - - // Update global variables - document = doc; - docElem = document.documentElement; - documentIsHTML = !isXML( document ); - - // Support: IE 9 - 11+, Edge 12 - 18+ - // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( preferredDoc != document && - ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { - - // Support: IE 11, Edge - if ( subWindow.addEventListener ) { - subWindow.addEventListener( "unload", unloadHandler, false ); - - // Support: IE 9 - 10 only - } else if ( subWindow.attachEvent ) { - subWindow.attachEvent( "onunload", unloadHandler ); - } - } - - // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, - // Safari 4 - 5 only, Opera <=11.6 - 12.x only - // IE/Edge & older browsers don't support the :scope pseudo-class. - // Support: Safari 6.0 only - // Safari 6.0 supports :scope but it's an alias of :root there. - support.scope = assert( function( el ) { - docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); - return typeof el.querySelectorAll !== "undefined" && - !el.querySelectorAll( ":scope fieldset div" ).length; - } ); - - /* Attributes - ---------------------------------------------------------------------- */ - - // Support: IE<8 - // Verify that getAttribute really returns attributes and not properties - // (excepting IE8 booleans) - support.attributes = assert( function( el ) { - el.className = "i"; - return !el.getAttribute( "className" ); - } ); - - /* getElement(s)By* - ---------------------------------------------------------------------- */ - - // Check if getElementsByTagName("*") returns only elements - support.getElementsByTagName = assert( function( el ) { - el.appendChild( document.createComment( "" ) ); - return !el.getElementsByTagName( "*" ).length; - } ); - - // Support: IE<9 - support.getElementsByClassName = rnative.test( document.getElementsByClassName ); - - // Support: IE<10 - // Check if getElementById returns elements by name - // The broken getElementById methods don't pick up programmatically-set names, - // so use a roundabout getElementsByName test - support.getById = assert( function( el ) { - docElem.appendChild( el ).id = expando; - return !document.getElementsByName || !document.getElementsByName( expando ).length; - } ); - - // ID filter and find - if ( support.getById ) { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - return elem.getAttribute( "id" ) === attrId; - }; - }; - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var elem = context.getElementById( id ); - return elem ? [ elem ] : []; - } - }; - } else { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - var node = typeof elem.getAttributeNode !== "undefined" && - elem.getAttributeNode( "id" ); - return node && node.value === attrId; - }; - }; - - // Support: IE 6 - 7 only - // getElementById is not reliable as a find shortcut - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var node, i, elems, - elem = context.getElementById( id ); - - if ( elem ) { - - // Verify the id attribute - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - - // Fall back on getElementsByName - elems = context.getElementsByName( id ); - i = 0; - while ( ( elem = elems[ i++ ] ) ) { - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - } - } - - return []; - } - }; - } - - // Tag - Expr.find[ "TAG" ] = support.getElementsByTagName ? - function( tag, context ) { - if ( typeof context.getElementsByTagName !== "undefined" ) { - return context.getElementsByTagName( tag ); - - // DocumentFragment nodes don't have gEBTN - } else if ( support.qsa ) { - return context.querySelectorAll( tag ); - } - } : - - function( tag, context ) { - var elem, - tmp = [], - i = 0, - - // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too - results = context.getElementsByTagName( tag ); - - // Filter out possible comments - if ( tag === "*" ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem.nodeType === 1 ) { - tmp.push( elem ); - } - } - - return tmp; - } - return results; - }; - - // Class - Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { - if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { - return context.getElementsByClassName( className ); - } - }; - - /* QSA/matchesSelector - ---------------------------------------------------------------------- */ - - // QSA and matchesSelector support - - // matchesSelector(:active) reports false when true (IE9/Opera 11.5) - rbuggyMatches = []; - - // qSa(:focus) reports false when true (Chrome 21) - // We allow this because of a bug in IE8/9 that throws an error - // whenever `document.activeElement` is accessed on an iframe - // So, we allow :focus to pass through QSA all the time to avoid the IE error - // See https://bugs.jquery.com/ticket/13378 - rbuggyQSA = []; - - if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { - - // Build QSA regex - // Regex strategy adopted from Diego Perini - assert( function( el ) { - - var input; - - // Select is set to empty string on purpose - // This is to test IE's treatment of not explicitly - // setting a boolean content attribute, - // since its presence should be enough - // https://bugs.jquery.com/ticket/12359 - docElem.appendChild( el ).innerHTML = "" + - ""; - - // Support: IE8, Opera 11-12.16 - // Nothing should be selected when empty strings follow ^= or $= or *= - // The test attribute must be unknown in Opera but "safe" for WinRT - // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section - if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { - rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); - } - - // Support: IE8 - // Boolean attributes and "value" are not treated correctly - if ( !el.querySelectorAll( "[selected]" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); - } - - // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ - if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { - rbuggyQSA.push( "~=" ); - } - - // Support: IE 11+, Edge 15 - 18+ - // IE 11/Edge don't find elements on a `[name='']` query in some cases. - // Adding a temporary attribute to the document before the selection works - // around the issue. - // Interestingly, IE 10 & older don't seem to have the issue. - input = document.createElement( "input" ); - input.setAttribute( "name", "" ); - el.appendChild( input ); - if ( !el.querySelectorAll( "[name='']" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + - whitespace + "*(?:''|\"\")" ); - } - - // Webkit/Opera - :checked should return selected option elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - // IE8 throws error here and will not see later tests - if ( !el.querySelectorAll( ":checked" ).length ) { - rbuggyQSA.push( ":checked" ); - } - - // Support: Safari 8+, iOS 8+ - // https://bugs.webkit.org/show_bug.cgi?id=136851 - // In-page `selector#id sibling-combinator selector` fails - if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { - rbuggyQSA.push( ".#.+[+~]" ); - } - - // Support: Firefox <=3.6 - 5 only - // Old Firefox doesn't throw on a badly-escaped identifier. - el.querySelectorAll( "\\\f" ); - rbuggyQSA.push( "[\\r\\n\\f]" ); - } ); - - assert( function( el ) { - el.innerHTML = "" + - ""; - - // Support: Windows 8 Native Apps - // The type and name attributes are restricted during .innerHTML assignment - var input = document.createElement( "input" ); - input.setAttribute( "type", "hidden" ); - el.appendChild( input ).setAttribute( "name", "D" ); - - // Support: IE8 - // Enforce case-sensitivity of name attribute - if ( el.querySelectorAll( "[name=d]" ).length ) { - rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); - } - - // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) - // IE8 throws error here and will not see later tests - if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: IE9-11+ - // IE's :disabled selector does not pick up the children of disabled fieldsets - docElem.appendChild( el ).disabled = true; - if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: Opera 10 - 11 only - // Opera 10-11 does not throw on post-comma invalid pseudos - el.querySelectorAll( "*,:x" ); - rbuggyQSA.push( ",.*:" ); - } ); - } - - if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || - docElem.webkitMatchesSelector || - docElem.mozMatchesSelector || - docElem.oMatchesSelector || - docElem.msMatchesSelector ) ) ) ) { - - assert( function( el ) { - - // Check to see if it's possible to do matchesSelector - // on a disconnected node (IE 9) - support.disconnectedMatch = matches.call( el, "*" ); - - // This should fail with an exception - // Gecko does not error, returns false instead - matches.call( el, "[s!='']:x" ); - rbuggyMatches.push( "!=", pseudos ); - } ); - } - - rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); - rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); - - /* Contains - ---------------------------------------------------------------------- */ - hasCompare = rnative.test( docElem.compareDocumentPosition ); - - // Element contains another - // Purposefully self-exclusive - // As in, an element does not contain itself - contains = hasCompare || rnative.test( docElem.contains ) ? - function( a, b ) { - var adown = a.nodeType === 9 ? a.documentElement : a, - bup = b && b.parentNode; - return a === bup || !!( bup && bup.nodeType === 1 && ( - adown.contains ? - adown.contains( bup ) : - a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 - ) ); - } : - function( a, b ) { - if ( b ) { - while ( ( b = b.parentNode ) ) { - if ( b === a ) { - return true; - } - } - } - return false; - }; - - /* Sorting - ---------------------------------------------------------------------- */ - - // Document order sorting - sortOrder = hasCompare ? - function( a, b ) { - - // Flag for duplicate removal - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - // Sort on method existence if only one input has compareDocumentPosition - var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; - if ( compare ) { - return compare; - } - - // Calculate position if both inputs belong to the same document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? - a.compareDocumentPosition( b ) : - - // Otherwise we know they are disconnected - 1; - - // Disconnected nodes - if ( compare & 1 || - ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { - - // Choose the first element that is related to our preferred document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( a == document || a.ownerDocument == preferredDoc && - contains( preferredDoc, a ) ) { - return -1; - } - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( b == document || b.ownerDocument == preferredDoc && - contains( preferredDoc, b ) ) { - return 1; - } - - // Maintain original order - return sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - } - - return compare & 4 ? -1 : 1; - } : - function( a, b ) { - - // Exit early if the nodes are identical - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - var cur, - i = 0, - aup = a.parentNode, - bup = b.parentNode, - ap = [ a ], - bp = [ b ]; - - // Parentless nodes are either documents or disconnected - if ( !aup || !bup ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - return a == document ? -1 : - b == document ? 1 : - /* eslint-enable eqeqeq */ - aup ? -1 : - bup ? 1 : - sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - - // If the nodes are siblings, we can do a quick check - } else if ( aup === bup ) { - return siblingCheck( a, b ); - } - - // Otherwise we need full lists of their ancestors for comparison - cur = a; - while ( ( cur = cur.parentNode ) ) { - ap.unshift( cur ); - } - cur = b; - while ( ( cur = cur.parentNode ) ) { - bp.unshift( cur ); - } - - // Walk down the tree looking for a discrepancy - while ( ap[ i ] === bp[ i ] ) { - i++; - } - - return i ? - - // Do a sibling check if the nodes have a common ancestor - siblingCheck( ap[ i ], bp[ i ] ) : - - // Otherwise nodes in our document sort first - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - ap[ i ] == preferredDoc ? -1 : - bp[ i ] == preferredDoc ? 1 : - /* eslint-enable eqeqeq */ - 0; - }; - - return document; -}; - -Sizzle.matches = function( expr, elements ) { - return Sizzle( expr, null, null, elements ); -}; - -Sizzle.matchesSelector = function( elem, expr ) { - setDocument( elem ); - - if ( support.matchesSelector && documentIsHTML && - !nonnativeSelectorCache[ expr + " " ] && - ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && - ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { - - try { - var ret = matches.call( elem, expr ); - - // IE 9's matchesSelector returns false on disconnected nodes - if ( ret || support.disconnectedMatch || - - // As well, disconnected nodes are said to be in a document - // fragment in IE 9 - elem.document && elem.document.nodeType !== 11 ) { - return ret; - } - } catch ( e ) { - nonnativeSelectorCache( expr, true ); - } - } - - return Sizzle( expr, document, null, [ elem ] ).length > 0; -}; - -Sizzle.contains = function( context, elem ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( context.ownerDocument || context ) != document ) { - setDocument( context ); - } - return contains( context, elem ); -}; - -Sizzle.attr = function( elem, name ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( elem.ownerDocument || elem ) != document ) { - setDocument( elem ); - } - - var fn = Expr.attrHandle[ name.toLowerCase() ], - - // Don't get fooled by Object.prototype properties (jQuery #13807) - val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? - fn( elem, name, !documentIsHTML ) : - undefined; - - return val !== undefined ? - val : - support.attributes || !documentIsHTML ? - elem.getAttribute( name ) : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; -}; - -Sizzle.escape = function( sel ) { - return ( sel + "" ).replace( rcssescape, fcssescape ); -}; - -Sizzle.error = function( msg ) { - throw new Error( "Syntax error, unrecognized expression: " + msg ); -}; - -/** - * Document sorting and removing duplicates - * @param {ArrayLike} results - */ -Sizzle.uniqueSort = function( results ) { - var elem, - duplicates = [], - j = 0, - i = 0; - - // Unless we *know* we can detect duplicates, assume their presence - hasDuplicate = !support.detectDuplicates; - sortInput = !support.sortStable && results.slice( 0 ); - results.sort( sortOrder ); - - if ( hasDuplicate ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem === results[ i ] ) { - j = duplicates.push( i ); - } - } - while ( j-- ) { - results.splice( duplicates[ j ], 1 ); - } - } - - // Clear input after sorting to release objects - // See https://github.com/jquery/sizzle/pull/225 - sortInput = null; - - return results; -}; - -/** - * Utility function for retrieving the text value of an array of DOM nodes - * @param {Array|Element} elem - */ -getText = Sizzle.getText = function( elem ) { - var node, - ret = "", - i = 0, - nodeType = elem.nodeType; - - if ( !nodeType ) { - - // If no nodeType, this is expected to be an array - while ( ( node = elem[ i++ ] ) ) { - - // Do not traverse comment nodes - ret += getText( node ); - } - } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { - - // Use textContent for elements - // innerText usage removed for consistency of new lines (jQuery #11153) - if ( typeof elem.textContent === "string" ) { - return elem.textContent; - } else { - - // Traverse its children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - ret += getText( elem ); - } - } - } else if ( nodeType === 3 || nodeType === 4 ) { - return elem.nodeValue; - } - - // Do not include comment or processing instruction nodes - - return ret; -}; - -Expr = Sizzle.selectors = { - - // Can be adjusted by the user - cacheLength: 50, - - createPseudo: markFunction, - - match: matchExpr, - - attrHandle: {}, - - find: {}, - - relative: { - ">": { dir: "parentNode", first: true }, - " ": { dir: "parentNode" }, - "+": { dir: "previousSibling", first: true }, - "~": { dir: "previousSibling" } - }, - - preFilter: { - "ATTR": function( match ) { - match[ 1 ] = match[ 1 ].replace( runescape, funescape ); - - // Move the given value to match[3] whether quoted or unquoted - match[ 3 ] = ( match[ 3 ] || match[ 4 ] || - match[ 5 ] || "" ).replace( runescape, funescape ); - - if ( match[ 2 ] === "~=" ) { - match[ 3 ] = " " + match[ 3 ] + " "; - } - - return match.slice( 0, 4 ); - }, - - "CHILD": function( match ) { - - /* matches from matchExpr["CHILD"] - 1 type (only|nth|...) - 2 what (child|of-type) - 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) - 4 xn-component of xn+y argument ([+-]?\d*n|) - 5 sign of xn-component - 6 x of xn-component - 7 sign of y-component - 8 y of y-component - */ - match[ 1 ] = match[ 1 ].toLowerCase(); - - if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { - - // nth-* requires argument - if ( !match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - // numeric x and y parameters for Expr.filter.CHILD - // remember that false/true cast respectively to 0/1 - match[ 4 ] = +( match[ 4 ] ? - match[ 5 ] + ( match[ 6 ] || 1 ) : - 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); - match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); - - // other types prohibit arguments - } else if ( match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - return match; - }, - - "PSEUDO": function( match ) { - var excess, - unquoted = !match[ 6 ] && match[ 2 ]; - - if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { - return null; - } - - // Accept quoted arguments as-is - if ( match[ 3 ] ) { - match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; - - // Strip excess characters from unquoted arguments - } else if ( unquoted && rpseudo.test( unquoted ) && - - // Get excess from tokenize (recursively) - ( excess = tokenize( unquoted, true ) ) && - - // advance to the next closing parenthesis - ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { - - // excess is a negative index - match[ 0 ] = match[ 0 ].slice( 0, excess ); - match[ 2 ] = unquoted.slice( 0, excess ); - } - - // Return only captures needed by the pseudo filter method (type and argument) - return match.slice( 0, 3 ); - } - }, - - filter: { - - "TAG": function( nodeNameSelector ) { - var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); - return nodeNameSelector === "*" ? - function() { - return true; - } : - function( elem ) { - return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; - }; - }, - - "CLASS": function( className ) { - var pattern = classCache[ className + " " ]; - - return pattern || - ( pattern = new RegExp( "(^|" + whitespace + - ")" + className + "(" + whitespace + "|$)" ) ) && classCache( - className, function( elem ) { - return pattern.test( - typeof elem.className === "string" && elem.className || - typeof elem.getAttribute !== "undefined" && - elem.getAttribute( "class" ) || - "" - ); - } ); - }, - - "ATTR": function( name, operator, check ) { - return function( elem ) { - var result = Sizzle.attr( elem, name ); - - if ( result == null ) { - return operator === "!="; - } - if ( !operator ) { - return true; - } - - result += ""; - - /* eslint-disable max-len */ - - return operator === "=" ? result === check : - operator === "!=" ? result !== check : - operator === "^=" ? check && result.indexOf( check ) === 0 : - operator === "*=" ? check && result.indexOf( check ) > -1 : - operator === "$=" ? check && result.slice( -check.length ) === check : - operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : - operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : - false; - /* eslint-enable max-len */ - - }; - }, - - "CHILD": function( type, what, _argument, first, last ) { - var simple = type.slice( 0, 3 ) !== "nth", - forward = type.slice( -4 ) !== "last", - ofType = what === "of-type"; - - return first === 1 && last === 0 ? - - // Shortcut for :nth-*(n) - function( elem ) { - return !!elem.parentNode; - } : - - function( elem, _context, xml ) { - var cache, uniqueCache, outerCache, node, nodeIndex, start, - dir = simple !== forward ? "nextSibling" : "previousSibling", - parent = elem.parentNode, - name = ofType && elem.nodeName.toLowerCase(), - useCache = !xml && !ofType, - diff = false; - - if ( parent ) { - - // :(first|last|only)-(child|of-type) - if ( simple ) { - while ( dir ) { - node = elem; - while ( ( node = node[ dir ] ) ) { - if ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) { - - return false; - } - } - - // Reverse direction for :only-* (if we haven't yet done so) - start = dir = type === "only" && !start && "nextSibling"; - } - return true; - } - - start = [ forward ? parent.firstChild : parent.lastChild ]; - - // non-xml :nth-child(...) stores cache data on `parent` - if ( forward && useCache ) { - - // Seek `elem` from a previously-cached index - - // ...in a gzip-friendly way - node = parent; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex && cache[ 2 ]; - node = nodeIndex && parent.childNodes[ nodeIndex ]; - - while ( ( node = ++nodeIndex && node && node[ dir ] || - - // Fallback to seeking `elem` from the start - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - // When found, cache indexes on `parent` and break - if ( node.nodeType === 1 && ++diff && node === elem ) { - uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; - break; - } - } - - } else { - - // Use previously-cached element index if available - if ( useCache ) { - - // ...in a gzip-friendly way - node = elem; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex; - } - - // xml :nth-child(...) - // or :nth-last-child(...) or :nth(-last)?-of-type(...) - if ( diff === false ) { - - // Use the same loop as above to seek `elem` from the start - while ( ( node = ++nodeIndex && node && node[ dir ] || - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - if ( ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) && - ++diff ) { - - // Cache the index of each encountered element - if ( useCache ) { - outerCache = node[ expando ] || - ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - uniqueCache[ type ] = [ dirruns, diff ]; - } - - if ( node === elem ) { - break; - } - } - } - } - } - - // Incorporate the offset, then check against cycle size - diff -= last; - return diff === first || ( diff % first === 0 && diff / first >= 0 ); - } - }; - }, - - "PSEUDO": function( pseudo, argument ) { - - // pseudo-class names are case-insensitive - // http://www.w3.org/TR/selectors/#pseudo-classes - // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters - // Remember that setFilters inherits from pseudos - var args, - fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || - Sizzle.error( "unsupported pseudo: " + pseudo ); - - // The user may use createPseudo to indicate that - // arguments are needed to create the filter function - // just as Sizzle does - if ( fn[ expando ] ) { - return fn( argument ); - } - - // But maintain support for old signatures - if ( fn.length > 1 ) { - args = [ pseudo, pseudo, "", argument ]; - return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? - markFunction( function( seed, matches ) { - var idx, - matched = fn( seed, argument ), - i = matched.length; - while ( i-- ) { - idx = indexOf( seed, matched[ i ] ); - seed[ idx ] = !( matches[ idx ] = matched[ i ] ); - } - } ) : - function( elem ) { - return fn( elem, 0, args ); - }; - } - - return fn; - } - }, - - pseudos: { - - // Potentially complex pseudos - "not": markFunction( function( selector ) { - - // Trim the selector passed to compile - // to avoid treating leading and trailing - // spaces as combinators - var input = [], - results = [], - matcher = compile( selector.replace( rtrim, "$1" ) ); - - return matcher[ expando ] ? - markFunction( function( seed, matches, _context, xml ) { - var elem, - unmatched = matcher( seed, null, xml, [] ), - i = seed.length; - - // Match elements unmatched by `matcher` - while ( i-- ) { - if ( ( elem = unmatched[ i ] ) ) { - seed[ i ] = !( matches[ i ] = elem ); - } - } - } ) : - function( elem, _context, xml ) { - input[ 0 ] = elem; - matcher( input, null, xml, results ); - - // Don't keep the element (issue #299) - input[ 0 ] = null; - return !results.pop(); - }; - } ), - - "has": markFunction( function( selector ) { - return function( elem ) { - return Sizzle( selector, elem ).length > 0; - }; - } ), - - "contains": markFunction( function( text ) { - text = text.replace( runescape, funescape ); - return function( elem ) { - return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; - }; - } ), - - // "Whether an element is represented by a :lang() selector - // is based solely on the element's language value - // being equal to the identifier C, - // or beginning with the identifier C immediately followed by "-". - // The matching of C against the element's language value is performed case-insensitively. - // The identifier C does not have to be a valid language name." - // http://www.w3.org/TR/selectors/#lang-pseudo - "lang": markFunction( function( lang ) { - - // lang value must be a valid identifier - if ( !ridentifier.test( lang || "" ) ) { - Sizzle.error( "unsupported lang: " + lang ); - } - lang = lang.replace( runescape, funescape ).toLowerCase(); - return function( elem ) { - var elemLang; - do { - if ( ( elemLang = documentIsHTML ? - elem.lang : - elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { - - elemLang = elemLang.toLowerCase(); - return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; - } - } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); - return false; - }; - } ), - - // Miscellaneous - "target": function( elem ) { - var hash = window.location && window.location.hash; - return hash && hash.slice( 1 ) === elem.id; - }, - - "root": function( elem ) { - return elem === docElem; - }, - - "focus": function( elem ) { - return elem === document.activeElement && - ( !document.hasFocus || document.hasFocus() ) && - !!( elem.type || elem.href || ~elem.tabIndex ); - }, - - // Boolean properties - "enabled": createDisabledPseudo( false ), - "disabled": createDisabledPseudo( true ), - - "checked": function( elem ) { - - // In CSS3, :checked should return both checked and selected elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - var nodeName = elem.nodeName.toLowerCase(); - return ( nodeName === "input" && !!elem.checked ) || - ( nodeName === "option" && !!elem.selected ); - }, - - "selected": function( elem ) { - - // Accessing this property makes selected-by-default - // options in Safari work properly - if ( elem.parentNode ) { - // eslint-disable-next-line no-unused-expressions - elem.parentNode.selectedIndex; - } - - return elem.selected === true; - }, - - // Contents - "empty": function( elem ) { - - // http://www.w3.org/TR/selectors/#empty-pseudo - // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), - // but not by others (comment: 8; processing instruction: 7; etc.) - // nodeType < 6 works because attributes (2) do not appear as children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - if ( elem.nodeType < 6 ) { - return false; - } - } - return true; - }, - - "parent": function( elem ) { - return !Expr.pseudos[ "empty" ]( elem ); - }, - - // Element/input types - "header": function( elem ) { - return rheader.test( elem.nodeName ); - }, - - "input": function( elem ) { - return rinputs.test( elem.nodeName ); - }, - - "button": function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === "button" || name === "button"; - }, - - "text": function( elem ) { - var attr; - return elem.nodeName.toLowerCase() === "input" && - elem.type === "text" && - - // Support: IE<8 - // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" - ( ( attr = elem.getAttribute( "type" ) ) == null || - attr.toLowerCase() === "text" ); - }, - - // Position-in-collection - "first": createPositionalPseudo( function() { - return [ 0 ]; - } ), - - "last": createPositionalPseudo( function( _matchIndexes, length ) { - return [ length - 1 ]; - } ), - - "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { - return [ argument < 0 ? argument + length : argument ]; - } ), - - "even": createPositionalPseudo( function( matchIndexes, length ) { - var i = 0; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "odd": createPositionalPseudo( function( matchIndexes, length ) { - var i = 1; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? - argument + length : - argument > length ? - length : - argument; - for ( ; --i >= 0; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; ++i < length; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ) - } -}; - -Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; - -// Add button/input type pseudos -for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { - Expr.pseudos[ i ] = createInputPseudo( i ); -} -for ( i in { submit: true, reset: true } ) { - Expr.pseudos[ i ] = createButtonPseudo( i ); -} - -// Easy API for creating new setFilters -function setFilters() {} -setFilters.prototype = Expr.filters = Expr.pseudos; -Expr.setFilters = new setFilters(); - -tokenize = Sizzle.tokenize = function( selector, parseOnly ) { - var matched, match, tokens, type, - soFar, groups, preFilters, - cached = tokenCache[ selector + " " ]; - - if ( cached ) { - return parseOnly ? 0 : cached.slice( 0 ); - } - - soFar = selector; - groups = []; - preFilters = Expr.preFilter; - - while ( soFar ) { - - // Comma and first run - if ( !matched || ( match = rcomma.exec( soFar ) ) ) { - if ( match ) { - - // Don't consume trailing commas as valid - soFar = soFar.slice( match[ 0 ].length ) || soFar; - } - groups.push( ( tokens = [] ) ); - } - - matched = false; - - // Combinators - if ( ( match = rcombinators.exec( soFar ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - - // Cast descendant combinators to space - type: match[ 0 ].replace( rtrim, " " ) - } ); - soFar = soFar.slice( matched.length ); - } - - // Filters - for ( type in Expr.filter ) { - if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || - ( match = preFilters[ type ]( match ) ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - type: type, - matches: match - } ); - soFar = soFar.slice( matched.length ); - } - } - - if ( !matched ) { - break; - } - } - - // Return the length of the invalid excess - // if we're just parsing - // Otherwise, throw an error or return tokens - return parseOnly ? - soFar.length : - soFar ? - Sizzle.error( selector ) : - - // Cache the tokens - tokenCache( selector, groups ).slice( 0 ); -}; - -function toSelector( tokens ) { - var i = 0, - len = tokens.length, - selector = ""; - for ( ; i < len; i++ ) { - selector += tokens[ i ].value; - } - return selector; -} - -function addCombinator( matcher, combinator, base ) { - var dir = combinator.dir, - skip = combinator.next, - key = skip || dir, - checkNonElements = base && key === "parentNode", - doneName = done++; - - return combinator.first ? - - // Check against closest ancestor/preceding element - function( elem, context, xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - return matcher( elem, context, xml ); - } - } - return false; - } : - - // Check against all ancestor/preceding elements - function( elem, context, xml ) { - var oldCache, uniqueCache, outerCache, - newCache = [ dirruns, doneName ]; - - // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching - if ( xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - if ( matcher( elem, context, xml ) ) { - return true; - } - } - } - } else { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - outerCache = elem[ expando ] || ( elem[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ elem.uniqueID ] || - ( outerCache[ elem.uniqueID ] = {} ); - - if ( skip && skip === elem.nodeName.toLowerCase() ) { - elem = elem[ dir ] || elem; - } else if ( ( oldCache = uniqueCache[ key ] ) && - oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { - - // Assign to newCache so results back-propagate to previous elements - return ( newCache[ 2 ] = oldCache[ 2 ] ); - } else { - - // Reuse newcache so results back-propagate to previous elements - uniqueCache[ key ] = newCache; - - // A match means we're done; a fail means we have to keep checking - if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { - return true; - } - } - } - } - } - return false; - }; -} - -function elementMatcher( matchers ) { - return matchers.length > 1 ? - function( elem, context, xml ) { - var i = matchers.length; - while ( i-- ) { - if ( !matchers[ i ]( elem, context, xml ) ) { - return false; - } - } - return true; - } : - matchers[ 0 ]; -} - -function multipleContexts( selector, contexts, results ) { - var i = 0, - len = contexts.length; - for ( ; i < len; i++ ) { - Sizzle( selector, contexts[ i ], results ); - } - return results; -} - -function condense( unmatched, map, filter, context, xml ) { - var elem, - newUnmatched = [], - i = 0, - len = unmatched.length, - mapped = map != null; - - for ( ; i < len; i++ ) { - if ( ( elem = unmatched[ i ] ) ) { - if ( !filter || filter( elem, context, xml ) ) { - newUnmatched.push( elem ); - if ( mapped ) { - map.push( i ); - } - } - } - } - - return newUnmatched; -} - -function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { - if ( postFilter && !postFilter[ expando ] ) { - postFilter = setMatcher( postFilter ); - } - if ( postFinder && !postFinder[ expando ] ) { - postFinder = setMatcher( postFinder, postSelector ); - } - return markFunction( function( seed, results, context, xml ) { - var temp, i, elem, - preMap = [], - postMap = [], - preexisting = results.length, - - // Get initial elements from seed or context - elems = seed || multipleContexts( - selector || "*", - context.nodeType ? [ context ] : context, - [] - ), - - // Prefilter to get matcher input, preserving a map for seed-results synchronization - matcherIn = preFilter && ( seed || !selector ) ? - condense( elems, preMap, preFilter, context, xml ) : - elems, - - matcherOut = matcher ? - - // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, - postFinder || ( seed ? preFilter : preexisting || postFilter ) ? - - // ...intermediate processing is necessary - [] : - - // ...otherwise use results directly - results : - matcherIn; - - // Find primary matches - if ( matcher ) { - matcher( matcherIn, matcherOut, context, xml ); - } - - // Apply postFilter - if ( postFilter ) { - temp = condense( matcherOut, postMap ); - postFilter( temp, [], context, xml ); - - // Un-match failing elements by moving them back to matcherIn - i = temp.length; - while ( i-- ) { - if ( ( elem = temp[ i ] ) ) { - matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); - } - } - } - - if ( seed ) { - if ( postFinder || preFilter ) { - if ( postFinder ) { - - // Get the final matcherOut by condensing this intermediate into postFinder contexts - temp = []; - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) ) { - - // Restore matcherIn since elem is not yet a final match - temp.push( ( matcherIn[ i ] = elem ) ); - } - } - postFinder( null, ( matcherOut = [] ), temp, xml ); - } - - // Move matched elements from seed to results to keep them synchronized - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) && - ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { - - seed[ temp ] = !( results[ temp ] = elem ); - } - } - } - - // Add elements to results, through postFinder if defined - } else { - matcherOut = condense( - matcherOut === results ? - matcherOut.splice( preexisting, matcherOut.length ) : - matcherOut - ); - if ( postFinder ) { - postFinder( null, results, matcherOut, xml ); - } else { - push.apply( results, matcherOut ); - } - } - } ); -} - -function matcherFromTokens( tokens ) { - var checkContext, matcher, j, - len = tokens.length, - leadingRelative = Expr.relative[ tokens[ 0 ].type ], - implicitRelative = leadingRelative || Expr.relative[ " " ], - i = leadingRelative ? 1 : 0, - - // The foundational matcher ensures that elements are reachable from top-level context(s) - matchContext = addCombinator( function( elem ) { - return elem === checkContext; - }, implicitRelative, true ), - matchAnyContext = addCombinator( function( elem ) { - return indexOf( checkContext, elem ) > -1; - }, implicitRelative, true ), - matchers = [ function( elem, context, xml ) { - var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( - ( checkContext = context ).nodeType ? - matchContext( elem, context, xml ) : - matchAnyContext( elem, context, xml ) ); - - // Avoid hanging onto element (issue #299) - checkContext = null; - return ret; - } ]; - - for ( ; i < len; i++ ) { - if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { - matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; - } else { - matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); - - // Return special upon seeing a positional matcher - if ( matcher[ expando ] ) { - - // Find the next relative operator (if any) for proper handling - j = ++i; - for ( ; j < len; j++ ) { - if ( Expr.relative[ tokens[ j ].type ] ) { - break; - } - } - return setMatcher( - i > 1 && elementMatcher( matchers ), - i > 1 && toSelector( - - // If the preceding token was a descendant combinator, insert an implicit any-element `*` - tokens - .slice( 0, i - 1 ) - .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) - ).replace( rtrim, "$1" ), - matcher, - i < j && matcherFromTokens( tokens.slice( i, j ) ), - j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), - j < len && toSelector( tokens ) - ); - } - matchers.push( matcher ); - } - } - - return elementMatcher( matchers ); -} - -function matcherFromGroupMatchers( elementMatchers, setMatchers ) { - var bySet = setMatchers.length > 0, - byElement = elementMatchers.length > 0, - superMatcher = function( seed, context, xml, results, outermost ) { - var elem, j, matcher, - matchedCount = 0, - i = "0", - unmatched = seed && [], - setMatched = [], - contextBackup = outermostContext, - - // We must always have either seed elements or outermost context - elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), - - // Use integer dirruns iff this is the outermost matcher - dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), - len = elems.length; - - if ( outermost ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - outermostContext = context == document || context || outermost; - } - - // Add elements passing elementMatchers directly to results - // Support: IE<9, Safari - // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id - for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { - if ( byElement && elem ) { - j = 0; - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( !context && elem.ownerDocument != document ) { - setDocument( elem ); - xml = !documentIsHTML; - } - while ( ( matcher = elementMatchers[ j++ ] ) ) { - if ( matcher( elem, context || document, xml ) ) { - results.push( elem ); - break; - } - } - if ( outermost ) { - dirruns = dirrunsUnique; - } - } - - // Track unmatched elements for set filters - if ( bySet ) { - - // They will have gone through all possible matchers - if ( ( elem = !matcher && elem ) ) { - matchedCount--; - } - - // Lengthen the array for every element, matched or not - if ( seed ) { - unmatched.push( elem ); - } - } - } - - // `i` is now the count of elements visited above, and adding it to `matchedCount` - // makes the latter nonnegative. - matchedCount += i; - - // Apply set filters to unmatched elements - // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` - // equals `i`), unless we didn't visit _any_ elements in the above loop because we have - // no element matchers and no seed. - // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that - // case, which will result in a "00" `matchedCount` that differs from `i` but is also - // numerically zero. - if ( bySet && i !== matchedCount ) { - j = 0; - while ( ( matcher = setMatchers[ j++ ] ) ) { - matcher( unmatched, setMatched, context, xml ); - } - - if ( seed ) { - - // Reintegrate element matches to eliminate the need for sorting - if ( matchedCount > 0 ) { - while ( i-- ) { - if ( !( unmatched[ i ] || setMatched[ i ] ) ) { - setMatched[ i ] = pop.call( results ); - } - } - } - - // Discard index placeholder values to get only actual matches - setMatched = condense( setMatched ); - } - - // Add matches to results - push.apply( results, setMatched ); - - // Seedless set matches succeeding multiple successful matchers stipulate sorting - if ( outermost && !seed && setMatched.length > 0 && - ( matchedCount + setMatchers.length ) > 1 ) { - - Sizzle.uniqueSort( results ); - } - } - - // Override manipulation of globals by nested matchers - if ( outermost ) { - dirruns = dirrunsUnique; - outermostContext = contextBackup; - } - - return unmatched; - }; - - return bySet ? - markFunction( superMatcher ) : - superMatcher; -} - -compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { - var i, - setMatchers = [], - elementMatchers = [], - cached = compilerCache[ selector + " " ]; - - if ( !cached ) { - - // Generate a function of recursive functions that can be used to check each element - if ( !match ) { - match = tokenize( selector ); - } - i = match.length; - while ( i-- ) { - cached = matcherFromTokens( match[ i ] ); - if ( cached[ expando ] ) { - setMatchers.push( cached ); - } else { - elementMatchers.push( cached ); - } - } - - // Cache the compiled function - cached = compilerCache( - selector, - matcherFromGroupMatchers( elementMatchers, setMatchers ) - ); - - // Save selector and tokenization - cached.selector = selector; - } - return cached; -}; - -/** - * A low-level selection function that works with Sizzle's compiled - * selector functions - * @param {String|Function} selector A selector or a pre-compiled - * selector function built with Sizzle.compile - * @param {Element} context - * @param {Array} [results] - * @param {Array} [seed] A set of elements to match against - */ -select = Sizzle.select = function( selector, context, results, seed ) { - var i, tokens, token, type, find, - compiled = typeof selector === "function" && selector, - match = !seed && tokenize( ( selector = compiled.selector || selector ) ); - - results = results || []; - - // Try to minimize operations if there is only one selector in the list and no seed - // (the latter of which guarantees us context) - if ( match.length === 1 ) { - - // Reduce context if the leading compound selector is an ID - tokens = match[ 0 ] = match[ 0 ].slice( 0 ); - if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && - context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { - - context = ( Expr.find[ "ID" ]( token.matches[ 0 ] - .replace( runescape, funescape ), context ) || [] )[ 0 ]; - if ( !context ) { - return results; - - // Precompiled matchers will still verify ancestry, so step up a level - } else if ( compiled ) { - context = context.parentNode; - } - - selector = selector.slice( tokens.shift().value.length ); - } - - // Fetch a seed set for right-to-left matching - i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; - while ( i-- ) { - token = tokens[ i ]; - - // Abort if we hit a combinator - if ( Expr.relative[ ( type = token.type ) ] ) { - break; - } - if ( ( find = Expr.find[ type ] ) ) { - - // Search, expanding context for leading sibling combinators - if ( ( seed = find( - token.matches[ 0 ].replace( runescape, funescape ), - rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || - context - ) ) ) { - - // If seed is empty or no tokens remain, we can return early - tokens.splice( i, 1 ); - selector = seed.length && toSelector( tokens ); - if ( !selector ) { - push.apply( results, seed ); - return results; - } - - break; - } - } - } - } - - // Compile and execute a filtering function if one is not provided - // Provide `match` to avoid retokenization if we modified the selector above - ( compiled || compile( selector, match ) )( - seed, - context, - !documentIsHTML, - results, - !context || rsibling.test( selector ) && testContext( context.parentNode ) || context - ); - return results; -}; - -// One-time assignments - -// Sort stability -support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; - -// Support: Chrome 14-35+ -// Always assume duplicates if they aren't passed to the comparison function -support.detectDuplicates = !!hasDuplicate; - -// Initialize against the default document -setDocument(); - -// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) -// Detached nodes confoundingly follow *each other* -support.sortDetached = assert( function( el ) { - - // Should return 1, but returns 4 (following) - return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; -} ); - -// Support: IE<8 -// Prevent attribute/property "interpolation" -// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx -if ( !assert( function( el ) { - el.innerHTML = ""; - return el.firstChild.getAttribute( "href" ) === "#"; -} ) ) { - addHandle( "type|href|height|width", function( elem, name, isXML ) { - if ( !isXML ) { - return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); - } - } ); -} - -// Support: IE<9 -// Use defaultValue in place of getAttribute("value") -if ( !support.attributes || !assert( function( el ) { - el.innerHTML = ""; - el.firstChild.setAttribute( "value", "" ); - return el.firstChild.getAttribute( "value" ) === ""; -} ) ) { - addHandle( "value", function( elem, _name, isXML ) { - if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { - return elem.defaultValue; - } - } ); -} - -// Support: IE<9 -// Use getAttributeNode to fetch booleans when getAttribute lies -if ( !assert( function( el ) { - return el.getAttribute( "disabled" ) == null; -} ) ) { - addHandle( booleans, function( elem, name, isXML ) { - var val; - if ( !isXML ) { - return elem[ name ] === true ? name.toLowerCase() : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; - } - } ); -} - -return Sizzle; - -} )( window ); - - - -jQuery.find = Sizzle; -jQuery.expr = Sizzle.selectors; - -// Deprecated -jQuery.expr[ ":" ] = jQuery.expr.pseudos; -jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; -jQuery.text = Sizzle.getText; -jQuery.isXMLDoc = Sizzle.isXML; -jQuery.contains = Sizzle.contains; -jQuery.escapeSelector = Sizzle.escape; - - - - -var dir = function( elem, dir, until ) { - var matched = [], - truncate = until !== undefined; - - while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { - if ( elem.nodeType === 1 ) { - if ( truncate && jQuery( elem ).is( until ) ) { - break; - } - matched.push( elem ); - } - } - return matched; -}; - - -var siblings = function( n, elem ) { - var matched = []; - - for ( ; n; n = n.nextSibling ) { - if ( n.nodeType === 1 && n !== elem ) { - matched.push( n ); - } - } - - return matched; -}; - - -var rneedsContext = jQuery.expr.match.needsContext; - - - -function nodeName( elem, name ) { - - return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); - -} -var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); - - - -// Implement the identical functionality for filter and not -function winnow( elements, qualifier, not ) { - if ( isFunction( qualifier ) ) { - return jQuery.grep( elements, function( elem, i ) { - return !!qualifier.call( elem, i, elem ) !== not; - } ); - } - - // Single element - if ( qualifier.nodeType ) { - return jQuery.grep( elements, function( elem ) { - return ( elem === qualifier ) !== not; - } ); - } - - // Arraylike of elements (jQuery, arguments, Array) - if ( typeof qualifier !== "string" ) { - return jQuery.grep( elements, function( elem ) { - return ( indexOf.call( qualifier, elem ) > -1 ) !== not; - } ); - } - - // Filtered directly for both simple and complex selectors - return jQuery.filter( qualifier, elements, not ); -} - -jQuery.filter = function( expr, elems, not ) { - var elem = elems[ 0 ]; - - if ( not ) { - expr = ":not(" + expr + ")"; - } - - if ( elems.length === 1 && elem.nodeType === 1 ) { - return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; - } - - return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { - return elem.nodeType === 1; - } ) ); -}; - -jQuery.fn.extend( { - find: function( selector ) { - var i, ret, - len = this.length, - self = this; - - if ( typeof selector !== "string" ) { - return this.pushStack( jQuery( selector ).filter( function() { - for ( i = 0; i < len; i++ ) { - if ( jQuery.contains( self[ i ], this ) ) { - return true; - } - } - } ) ); - } - - ret = this.pushStack( [] ); - - for ( i = 0; i < len; i++ ) { - jQuery.find( selector, self[ i ], ret ); - } - - return len > 1 ? jQuery.uniqueSort( ret ) : ret; - }, - filter: function( selector ) { - return this.pushStack( winnow( this, selector || [], false ) ); - }, - not: function( selector ) { - return this.pushStack( winnow( this, selector || [], true ) ); - }, - is: function( selector ) { - return !!winnow( - this, - - // If this is a positional/relative selector, check membership in the returned set - // so $("p:first").is("p:last") won't return true for a doc with two "p". - typeof selector === "string" && rneedsContext.test( selector ) ? - jQuery( selector ) : - selector || [], - false - ).length; - } -} ); - - -// Initialize a jQuery object - - -// A central reference to the root jQuery(document) -var rootjQuery, - - // A simple way to check for HTML strings - // Prioritize #id over to avoid XSS via location.hash (#9521) - // Strict HTML recognition (#11290: must start with <) - // Shortcut simple #id case for speed - rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, - - init = jQuery.fn.init = function( selector, context, root ) { - var match, elem; - - // HANDLE: $(""), $(null), $(undefined), $(false) - if ( !selector ) { - return this; - } - - // Method init() accepts an alternate rootjQuery - // so migrate can support jQuery.sub (gh-2101) - root = root || rootjQuery; - - // Handle HTML strings - if ( typeof selector === "string" ) { - if ( selector[ 0 ] === "<" && - selector[ selector.length - 1 ] === ">" && - selector.length >= 3 ) { - - // Assume that strings that start and end with <> are HTML and skip the regex check - match = [ null, selector, null ]; - - } else { - match = rquickExpr.exec( selector ); - } - - // Match html or make sure no context is specified for #id - if ( match && ( match[ 1 ] || !context ) ) { - - // HANDLE: $(html) -> $(array) - if ( match[ 1 ] ) { - context = context instanceof jQuery ? context[ 0 ] : context; - - // Option to run scripts is true for back-compat - // Intentionally let the error be thrown if parseHTML is not present - jQuery.merge( this, jQuery.parseHTML( - match[ 1 ], - context && context.nodeType ? context.ownerDocument || context : document, - true - ) ); - - // HANDLE: $(html, props) - if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { - for ( match in context ) { - - // Properties of context are called as methods if possible - if ( isFunction( this[ match ] ) ) { - this[ match ]( context[ match ] ); - - // ...and otherwise set as attributes - } else { - this.attr( match, context[ match ] ); - } - } - } - - return this; - - // HANDLE: $(#id) - } else { - elem = document.getElementById( match[ 2 ] ); - - if ( elem ) { - - // Inject the element directly into the jQuery object - this[ 0 ] = elem; - this.length = 1; - } - return this; - } - - // HANDLE: $(expr, $(...)) - } else if ( !context || context.jquery ) { - return ( context || root ).find( selector ); - - // HANDLE: $(expr, context) - // (which is just equivalent to: $(context).find(expr) - } else { - return this.constructor( context ).find( selector ); - } - - // HANDLE: $(DOMElement) - } else if ( selector.nodeType ) { - this[ 0 ] = selector; - this.length = 1; - return this; - - // HANDLE: $(function) - // Shortcut for document ready - } else if ( isFunction( selector ) ) { - return root.ready !== undefined ? - root.ready( selector ) : - - // Execute immediately if ready is not present - selector( jQuery ); - } - - return jQuery.makeArray( selector, this ); - }; - -// Give the init function the jQuery prototype for later instantiation -init.prototype = jQuery.fn; - -// Initialize central reference -rootjQuery = jQuery( document ); - - -var rparentsprev = /^(?:parents|prev(?:Until|All))/, - - // Methods guaranteed to produce a unique set when starting from a unique set - guaranteedUnique = { - children: true, - contents: true, - next: true, - prev: true - }; - -jQuery.fn.extend( { - has: function( target ) { - var targets = jQuery( target, this ), - l = targets.length; - - return this.filter( function() { - var i = 0; - for ( ; i < l; i++ ) { - if ( jQuery.contains( this, targets[ i ] ) ) { - return true; - } - } - } ); - }, - - closest: function( selectors, context ) { - var cur, - i = 0, - l = this.length, - matched = [], - targets = typeof selectors !== "string" && jQuery( selectors ); - - // Positional selectors never match, since there's no _selection_ context - if ( !rneedsContext.test( selectors ) ) { - for ( ; i < l; i++ ) { - for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { - - // Always skip document fragments - if ( cur.nodeType < 11 && ( targets ? - targets.index( cur ) > -1 : - - // Don't pass non-elements to Sizzle - cur.nodeType === 1 && - jQuery.find.matchesSelector( cur, selectors ) ) ) { - - matched.push( cur ); - break; - } - } - } - } - - return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); - }, - - // Determine the position of an element within the set - index: function( elem ) { - - // No argument, return index in parent - if ( !elem ) { - return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; - } - - // Index in selector - if ( typeof elem === "string" ) { - return indexOf.call( jQuery( elem ), this[ 0 ] ); - } - - // Locate the position of the desired element - return indexOf.call( this, - - // If it receives a jQuery object, the first element is used - elem.jquery ? elem[ 0 ] : elem - ); - }, - - add: function( selector, context ) { - return this.pushStack( - jQuery.uniqueSort( - jQuery.merge( this.get(), jQuery( selector, context ) ) - ) - ); - }, - - addBack: function( selector ) { - return this.add( selector == null ? - this.prevObject : this.prevObject.filter( selector ) - ); - } -} ); - -function sibling( cur, dir ) { - while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} - return cur; -} - -jQuery.each( { - parent: function( elem ) { - var parent = elem.parentNode; - return parent && parent.nodeType !== 11 ? parent : null; - }, - parents: function( elem ) { - return dir( elem, "parentNode" ); - }, - parentsUntil: function( elem, _i, until ) { - return dir( elem, "parentNode", until ); - }, - next: function( elem ) { - return sibling( elem, "nextSibling" ); - }, - prev: function( elem ) { - return sibling( elem, "previousSibling" ); - }, - nextAll: function( elem ) { - return dir( elem, "nextSibling" ); - }, - prevAll: function( elem ) { - return dir( elem, "previousSibling" ); - }, - nextUntil: function( elem, _i, until ) { - return dir( elem, "nextSibling", until ); - }, - prevUntil: function( elem, _i, until ) { - return dir( elem, "previousSibling", until ); - }, - siblings: function( elem ) { - return siblings( ( elem.parentNode || {} ).firstChild, elem ); - }, - children: function( elem ) { - return siblings( elem.firstChild ); - }, - contents: function( elem ) { - if ( elem.contentDocument != null && - - // Support: IE 11+ - // elements with no `data` attribute has an object - // `contentDocument` with a `null` prototype. - getProto( elem.contentDocument ) ) { - - return elem.contentDocument; - } - - // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only - // Treat the template element as a regular one in browsers that - // don't support it. - if ( nodeName( elem, "template" ) ) { - elem = elem.content || elem; - } - - return jQuery.merge( [], elem.childNodes ); - } -}, function( name, fn ) { - jQuery.fn[ name ] = function( until, selector ) { - var matched = jQuery.map( this, fn, until ); - - if ( name.slice( -5 ) !== "Until" ) { - selector = until; - } - - if ( selector && typeof selector === "string" ) { - matched = jQuery.filter( selector, matched ); - } - - if ( this.length > 1 ) { - - // Remove duplicates - if ( !guaranteedUnique[ name ] ) { - jQuery.uniqueSort( matched ); - } - - // Reverse order for parents* and prev-derivatives - if ( rparentsprev.test( name ) ) { - matched.reverse(); - } - } - - return this.pushStack( matched ); - }; -} ); -var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); - - - -// Convert String-formatted options into Object-formatted ones -function createOptions( options ) { - var object = {}; - jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { - object[ flag ] = true; - } ); - return object; -} - -/* - * Create a callback list using the following parameters: - * - * options: an optional list of space-separated options that will change how - * the callback list behaves or a more traditional option object - * - * By default a callback list will act like an event callback list and can be - * "fired" multiple times. - * - * Possible options: - * - * once: will ensure the callback list can only be fired once (like a Deferred) - * - * memory: will keep track of previous values and will call any callback added - * after the list has been fired right away with the latest "memorized" - * values (like a Deferred) - * - * unique: will ensure a callback can only be added once (no duplicate in the list) - * - * stopOnFalse: interrupt callings when a callback returns false - * - */ -jQuery.Callbacks = function( options ) { - - // Convert options from String-formatted to Object-formatted if needed - // (we check in cache first) - options = typeof options === "string" ? - createOptions( options ) : - jQuery.extend( {}, options ); - - var // Flag to know if list is currently firing - firing, - - // Last fire value for non-forgettable lists - memory, - - // Flag to know if list was already fired - fired, - - // Flag to prevent firing - locked, - - // Actual callback list - list = [], - - // Queue of execution data for repeatable lists - queue = [], - - // Index of currently firing callback (modified by add/remove as needed) - firingIndex = -1, - - // Fire callbacks - fire = function() { - - // Enforce single-firing - locked = locked || options.once; - - // Execute callbacks for all pending executions, - // respecting firingIndex overrides and runtime changes - fired = firing = true; - for ( ; queue.length; firingIndex = -1 ) { - memory = queue.shift(); - while ( ++firingIndex < list.length ) { - - // Run callback and check for early termination - if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && - options.stopOnFalse ) { - - // Jump to end and forget the data so .add doesn't re-fire - firingIndex = list.length; - memory = false; - } - } - } - - // Forget the data if we're done with it - if ( !options.memory ) { - memory = false; - } - - firing = false; - - // Clean up if we're done firing for good - if ( locked ) { - - // Keep an empty list if we have data for future add calls - if ( memory ) { - list = []; - - // Otherwise, this object is spent - } else { - list = ""; - } - } - }, - - // Actual Callbacks object - self = { - - // Add a callback or a collection of callbacks to the list - add: function() { - if ( list ) { - - // If we have memory from a past run, we should fire after adding - if ( memory && !firing ) { - firingIndex = list.length - 1; - queue.push( memory ); - } - - ( function add( args ) { - jQuery.each( args, function( _, arg ) { - if ( isFunction( arg ) ) { - if ( !options.unique || !self.has( arg ) ) { - list.push( arg ); - } - } else if ( arg && arg.length && toType( arg ) !== "string" ) { - - // Inspect recursively - add( arg ); - } - } ); - } )( arguments ); - - if ( memory && !firing ) { - fire(); - } - } - return this; - }, - - // Remove a callback from the list - remove: function() { - jQuery.each( arguments, function( _, arg ) { - var index; - while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { - list.splice( index, 1 ); - - // Handle firing indexes - if ( index <= firingIndex ) { - firingIndex--; - } - } - } ); - return this; - }, - - // Check if a given callback is in the list. - // If no argument is given, return whether or not list has callbacks attached. - has: function( fn ) { - return fn ? - jQuery.inArray( fn, list ) > -1 : - list.length > 0; - }, - - // Remove all callbacks from the list - empty: function() { - if ( list ) { - list = []; - } - return this; - }, - - // Disable .fire and .add - // Abort any current/pending executions - // Clear all callbacks and values - disable: function() { - locked = queue = []; - list = memory = ""; - return this; - }, - disabled: function() { - return !list; - }, - - // Disable .fire - // Also disable .add unless we have memory (since it would have no effect) - // Abort any pending executions - lock: function() { - locked = queue = []; - if ( !memory && !firing ) { - list = memory = ""; - } - return this; - }, - locked: function() { - return !!locked; - }, - - // Call all callbacks with the given context and arguments - fireWith: function( context, args ) { - if ( !locked ) { - args = args || []; - args = [ context, args.slice ? args.slice() : args ]; - queue.push( args ); - if ( !firing ) { - fire(); - } - } - return this; - }, - - // Call all the callbacks with the given arguments - fire: function() { - self.fireWith( this, arguments ); - return this; - }, - - // To know if the callbacks have already been called at least once - fired: function() { - return !!fired; - } - }; - - return self; -}; - - -function Identity( v ) { - return v; -} -function Thrower( ex ) { - throw ex; -} - -function adoptValue( value, resolve, reject, noValue ) { - var method; - - try { - - // Check for promise aspect first to privilege synchronous behavior - if ( value && isFunction( ( method = value.promise ) ) ) { - method.call( value ).done( resolve ).fail( reject ); - - // Other thenables - } else if ( value && isFunction( ( method = value.then ) ) ) { - method.call( value, resolve, reject ); - - // Other non-thenables - } else { - - // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: - // * false: [ value ].slice( 0 ) => resolve( value ) - // * true: [ value ].slice( 1 ) => resolve() - resolve.apply( undefined, [ value ].slice( noValue ) ); - } - - // For Promises/A+, convert exceptions into rejections - // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in - // Deferred#then to conditionally suppress rejection. - } catch ( value ) { - - // Support: Android 4.0 only - // Strict mode functions invoked without .call/.apply get global-object context - reject.apply( undefined, [ value ] ); - } -} - -jQuery.extend( { - - Deferred: function( func ) { - var tuples = [ - - // action, add listener, callbacks, - // ... .then handlers, argument index, [final state] - [ "notify", "progress", jQuery.Callbacks( "memory" ), - jQuery.Callbacks( "memory" ), 2 ], - [ "resolve", "done", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 0, "resolved" ], - [ "reject", "fail", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 1, "rejected" ] - ], - state = "pending", - promise = { - state: function() { - return state; - }, - always: function() { - deferred.done( arguments ).fail( arguments ); - return this; - }, - "catch": function( fn ) { - return promise.then( null, fn ); - }, - - // Keep pipe for back-compat - pipe: function( /* fnDone, fnFail, fnProgress */ ) { - var fns = arguments; - - return jQuery.Deferred( function( newDefer ) { - jQuery.each( tuples, function( _i, tuple ) { - - // Map tuples (progress, done, fail) to arguments (done, fail, progress) - var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; - - // deferred.progress(function() { bind to newDefer or newDefer.notify }) - // deferred.done(function() { bind to newDefer or newDefer.resolve }) - // deferred.fail(function() { bind to newDefer or newDefer.reject }) - deferred[ tuple[ 1 ] ]( function() { - var returned = fn && fn.apply( this, arguments ); - if ( returned && isFunction( returned.promise ) ) { - returned.promise() - .progress( newDefer.notify ) - .done( newDefer.resolve ) - .fail( newDefer.reject ); - } else { - newDefer[ tuple[ 0 ] + "With" ]( - this, - fn ? [ returned ] : arguments - ); - } - } ); - } ); - fns = null; - } ).promise(); - }, - then: function( onFulfilled, onRejected, onProgress ) { - var maxDepth = 0; - function resolve( depth, deferred, handler, special ) { - return function() { - var that = this, - args = arguments, - mightThrow = function() { - var returned, then; - - // Support: Promises/A+ section 2.3.3.3.3 - // https://promisesaplus.com/#point-59 - // Ignore double-resolution attempts - if ( depth < maxDepth ) { - return; - } - - returned = handler.apply( that, args ); - - // Support: Promises/A+ section 2.3.1 - // https://promisesaplus.com/#point-48 - if ( returned === deferred.promise() ) { - throw new TypeError( "Thenable self-resolution" ); - } - - // Support: Promises/A+ sections 2.3.3.1, 3.5 - // https://promisesaplus.com/#point-54 - // https://promisesaplus.com/#point-75 - // Retrieve `then` only once - then = returned && - - // Support: Promises/A+ section 2.3.4 - // https://promisesaplus.com/#point-64 - // Only check objects and functions for thenability - ( typeof returned === "object" || - typeof returned === "function" ) && - returned.then; - - // Handle a returned thenable - if ( isFunction( then ) ) { - - // Special processors (notify) just wait for resolution - if ( special ) { - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ) - ); - - // Normal processors (resolve) also hook into progress - } else { - - // ...and disregard older resolution values - maxDepth++; - - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ), - resolve( maxDepth, deferred, Identity, - deferred.notifyWith ) - ); - } - - // Handle all other returned values - } else { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Identity ) { - that = undefined; - args = [ returned ]; - } - - // Process the value(s) - // Default process is resolve - ( special || deferred.resolveWith )( that, args ); - } - }, - - // Only normal processors (resolve) catch and reject exceptions - process = special ? - mightThrow : - function() { - try { - mightThrow(); - } catch ( e ) { - - if ( jQuery.Deferred.exceptionHook ) { - jQuery.Deferred.exceptionHook( e, - process.stackTrace ); - } - - // Support: Promises/A+ section 2.3.3.3.4.1 - // https://promisesaplus.com/#point-61 - // Ignore post-resolution exceptions - if ( depth + 1 >= maxDepth ) { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Thrower ) { - that = undefined; - args = [ e ]; - } - - deferred.rejectWith( that, args ); - } - } - }; - - // Support: Promises/A+ section 2.3.3.3.1 - // https://promisesaplus.com/#point-57 - // Re-resolve promises immediately to dodge false rejection from - // subsequent errors - if ( depth ) { - process(); - } else { - - // Call an optional hook to record the stack, in case of exception - // since it's otherwise lost when execution goes async - if ( jQuery.Deferred.getStackHook ) { - process.stackTrace = jQuery.Deferred.getStackHook(); - } - window.setTimeout( process ); - } - }; - } - - return jQuery.Deferred( function( newDefer ) { - - // progress_handlers.add( ... ) - tuples[ 0 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onProgress ) ? - onProgress : - Identity, - newDefer.notifyWith - ) - ); - - // fulfilled_handlers.add( ... ) - tuples[ 1 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onFulfilled ) ? - onFulfilled : - Identity - ) - ); - - // rejected_handlers.add( ... ) - tuples[ 2 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onRejected ) ? - onRejected : - Thrower - ) - ); - } ).promise(); - }, - - // Get a promise for this deferred - // If obj is provided, the promise aspect is added to the object - promise: function( obj ) { - return obj != null ? jQuery.extend( obj, promise ) : promise; - } - }, - deferred = {}; - - // Add list-specific methods - jQuery.each( tuples, function( i, tuple ) { - var list = tuple[ 2 ], - stateString = tuple[ 5 ]; - - // promise.progress = list.add - // promise.done = list.add - // promise.fail = list.add - promise[ tuple[ 1 ] ] = list.add; - - // Handle state - if ( stateString ) { - list.add( - function() { - - // state = "resolved" (i.e., fulfilled) - // state = "rejected" - state = stateString; - }, - - // rejected_callbacks.disable - // fulfilled_callbacks.disable - tuples[ 3 - i ][ 2 ].disable, - - // rejected_handlers.disable - // fulfilled_handlers.disable - tuples[ 3 - i ][ 3 ].disable, - - // progress_callbacks.lock - tuples[ 0 ][ 2 ].lock, - - // progress_handlers.lock - tuples[ 0 ][ 3 ].lock - ); - } - - // progress_handlers.fire - // fulfilled_handlers.fire - // rejected_handlers.fire - list.add( tuple[ 3 ].fire ); - - // deferred.notify = function() { deferred.notifyWith(...) } - // deferred.resolve = function() { deferred.resolveWith(...) } - // deferred.reject = function() { deferred.rejectWith(...) } - deferred[ tuple[ 0 ] ] = function() { - deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); - return this; - }; - - // deferred.notifyWith = list.fireWith - // deferred.resolveWith = list.fireWith - // deferred.rejectWith = list.fireWith - deferred[ tuple[ 0 ] + "With" ] = list.fireWith; - } ); - - // Make the deferred a promise - promise.promise( deferred ); - - // Call given func if any - if ( func ) { - func.call( deferred, deferred ); - } - - // All done! - return deferred; - }, - - // Deferred helper - when: function( singleValue ) { - var - - // count of uncompleted subordinates - remaining = arguments.length, - - // count of unprocessed arguments - i = remaining, - - // subordinate fulfillment data - resolveContexts = Array( i ), - resolveValues = slice.call( arguments ), - - // the primary Deferred - primary = jQuery.Deferred(), - - // subordinate callback factory - updateFunc = function( i ) { - return function( value ) { - resolveContexts[ i ] = this; - resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; - if ( !( --remaining ) ) { - primary.resolveWith( resolveContexts, resolveValues ); - } - }; - }; - - // Single- and empty arguments are adopted like Promise.resolve - if ( remaining <= 1 ) { - adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject, - !remaining ); - - // Use .then() to unwrap secondary thenables (cf. gh-3000) - if ( primary.state() === "pending" || - isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { - - return primary.then(); - } - } - - // Multiple arguments are aggregated like Promise.all array elements - while ( i-- ) { - adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject ); - } - - return primary.promise(); - } -} ); - - -// These usually indicate a programmer mistake during development, -// warn about them ASAP rather than swallowing them by default. -var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; - -jQuery.Deferred.exceptionHook = function( error, stack ) { - - // Support: IE 8 - 9 only - // Console exists when dev tools are open, which can happen at any time - if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { - window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); - } -}; - - - - -jQuery.readyException = function( error ) { - window.setTimeout( function() { - throw error; - } ); -}; - - - - -// The deferred used on DOM ready -var readyList = jQuery.Deferred(); - -jQuery.fn.ready = function( fn ) { - - readyList - .then( fn ) - - // Wrap jQuery.readyException in a function so that the lookup - // happens at the time of error handling instead of callback - // registration. - .catch( function( error ) { - jQuery.readyException( error ); - } ); - - return this; -}; - -jQuery.extend( { - - // Is the DOM ready to be used? Set to true once it occurs. - isReady: false, - - // A counter to track how many items to wait for before - // the ready event fires. See #6781 - readyWait: 1, - - // Handle when the DOM is ready - ready: function( wait ) { - - // Abort if there are pending holds or we're already ready - if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { - return; - } - - // Remember that the DOM is ready - jQuery.isReady = true; - - // If a normal DOM Ready event fired, decrement, and wait if need be - if ( wait !== true && --jQuery.readyWait > 0 ) { - return; - } - - // If there are functions bound, to execute - readyList.resolveWith( document, [ jQuery ] ); - } -} ); - -jQuery.ready.then = readyList.then; - -// The ready event handler and self cleanup method -function completed() { - document.removeEventListener( "DOMContentLoaded", completed ); - window.removeEventListener( "load", completed ); - jQuery.ready(); -} - -// Catch cases where $(document).ready() is called -// after the browser event has already occurred. -// Support: IE <=9 - 10 only -// Older IE sometimes signals "interactive" too soon -if ( document.readyState === "complete" || - ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { - - // Handle it asynchronously to allow scripts the opportunity to delay ready - window.setTimeout( jQuery.ready ); - -} else { - - // Use the handy event callback - document.addEventListener( "DOMContentLoaded", completed ); - - // A fallback to window.onload, that will always work - window.addEventListener( "load", completed ); -} - - - - -// Multifunctional method to get and set values of a collection -// The value/s can optionally be executed if it's a function -var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { - var i = 0, - len = elems.length, - bulk = key == null; - - // Sets many values - if ( toType( key ) === "object" ) { - chainable = true; - for ( i in key ) { - access( elems, fn, i, key[ i ], true, emptyGet, raw ); - } - - // Sets one value - } else if ( value !== undefined ) { - chainable = true; - - if ( !isFunction( value ) ) { - raw = true; - } - - if ( bulk ) { - - // Bulk operations run against the entire set - if ( raw ) { - fn.call( elems, value ); - fn = null; - - // ...except when executing function values - } else { - bulk = fn; - fn = function( elem, _key, value ) { - return bulk.call( jQuery( elem ), value ); - }; - } - } - - if ( fn ) { - for ( ; i < len; i++ ) { - fn( - elems[ i ], key, raw ? - value : - value.call( elems[ i ], i, fn( elems[ i ], key ) ) - ); - } - } - } - - if ( chainable ) { - return elems; - } - - // Gets - if ( bulk ) { - return fn.call( elems ); - } - - return len ? fn( elems[ 0 ], key ) : emptyGet; -}; - - -// Matches dashed string for camelizing -var rmsPrefix = /^-ms-/, - rdashAlpha = /-([a-z])/g; - -// Used by camelCase as callback to replace() -function fcamelCase( _all, letter ) { - return letter.toUpperCase(); -} - -// Convert dashed to camelCase; used by the css and data modules -// Support: IE <=9 - 11, Edge 12 - 15 -// Microsoft forgot to hump their vendor prefix (#9572) -function camelCase( string ) { - return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); -} -var acceptData = function( owner ) { - - // Accepts only: - // - Node - // - Node.ELEMENT_NODE - // - Node.DOCUMENT_NODE - // - Object - // - Any - return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); -}; - - - - -function Data() { - this.expando = jQuery.expando + Data.uid++; -} - -Data.uid = 1; - -Data.prototype = { - - cache: function( owner ) { - - // Check if the owner object already has a cache - var value = owner[ this.expando ]; - - // If not, create one - if ( !value ) { - value = {}; - - // We can accept data for non-element nodes in modern browsers, - // but we should not, see #8335. - // Always return an empty object. - if ( acceptData( owner ) ) { - - // If it is a node unlikely to be stringify-ed or looped over - // use plain assignment - if ( owner.nodeType ) { - owner[ this.expando ] = value; - - // Otherwise secure it in a non-enumerable property - // configurable must be true to allow the property to be - // deleted when data is removed - } else { - Object.defineProperty( owner, this.expando, { - value: value, - configurable: true - } ); - } - } - } - - return value; - }, - set: function( owner, data, value ) { - var prop, - cache = this.cache( owner ); - - // Handle: [ owner, key, value ] args - // Always use camelCase key (gh-2257) - if ( typeof data === "string" ) { - cache[ camelCase( data ) ] = value; - - // Handle: [ owner, { properties } ] args - } else { - - // Copy the properties one-by-one to the cache object - for ( prop in data ) { - cache[ camelCase( prop ) ] = data[ prop ]; - } - } - return cache; - }, - get: function( owner, key ) { - return key === undefined ? - this.cache( owner ) : - - // Always use camelCase key (gh-2257) - owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; - }, - access: function( owner, key, value ) { - - // In cases where either: - // - // 1. No key was specified - // 2. A string key was specified, but no value provided - // - // Take the "read" path and allow the get method to determine - // which value to return, respectively either: - // - // 1. The entire cache object - // 2. The data stored at the key - // - if ( key === undefined || - ( ( key && typeof key === "string" ) && value === undefined ) ) { - - return this.get( owner, key ); - } - - // When the key is not a string, or both a key and value - // are specified, set or extend (existing objects) with either: - // - // 1. An object of properties - // 2. A key and value - // - this.set( owner, key, value ); - - // Since the "set" path can have two possible entry points - // return the expected data based on which path was taken[*] - return value !== undefined ? value : key; - }, - remove: function( owner, key ) { - var i, - cache = owner[ this.expando ]; - - if ( cache === undefined ) { - return; - } - - if ( key !== undefined ) { - - // Support array or space separated string of keys - if ( Array.isArray( key ) ) { - - // If key is an array of keys... - // We always set camelCase keys, so remove that. - key = key.map( camelCase ); - } else { - key = camelCase( key ); - - // If a key with the spaces exists, use it. - // Otherwise, create an array by matching non-whitespace - key = key in cache ? - [ key ] : - ( key.match( rnothtmlwhite ) || [] ); - } - - i = key.length; - - while ( i-- ) { - delete cache[ key[ i ] ]; - } - } - - // Remove the expando if there's no more data - if ( key === undefined || jQuery.isEmptyObject( cache ) ) { - - // Support: Chrome <=35 - 45 - // Webkit & Blink performance suffers when deleting properties - // from DOM nodes, so set to undefined instead - // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) - if ( owner.nodeType ) { - owner[ this.expando ] = undefined; - } else { - delete owner[ this.expando ]; - } - } - }, - hasData: function( owner ) { - var cache = owner[ this.expando ]; - return cache !== undefined && !jQuery.isEmptyObject( cache ); - } -}; -var dataPriv = new Data(); - -var dataUser = new Data(); - - - -// Implementation Summary -// -// 1. Enforce API surface and semantic compatibility with 1.9.x branch -// 2. Improve the module's maintainability by reducing the storage -// paths to a single mechanism. -// 3. Use the same single mechanism to support "private" and "user" data. -// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) -// 5. Avoid exposing implementation details on user objects (eg. expando properties) -// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 - -var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, - rmultiDash = /[A-Z]/g; - -function getData( data ) { - if ( data === "true" ) { - return true; - } - - if ( data === "false" ) { - return false; - } - - if ( data === "null" ) { - return null; - } - - // Only convert to a number if it doesn't change the string - if ( data === +data + "" ) { - return +data; - } - - if ( rbrace.test( data ) ) { - return JSON.parse( data ); - } - - return data; -} - -function dataAttr( elem, key, data ) { - var name; - - // If nothing was found internally, try to fetch any - // data from the HTML5 data-* attribute - if ( data === undefined && elem.nodeType === 1 ) { - name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); - data = elem.getAttribute( name ); - - if ( typeof data === "string" ) { - try { - data = getData( data ); - } catch ( e ) {} - - // Make sure we set the data so it isn't changed later - dataUser.set( elem, key, data ); - } else { - data = undefined; - } - } - return data; -} - -jQuery.extend( { - hasData: function( elem ) { - return dataUser.hasData( elem ) || dataPriv.hasData( elem ); - }, - - data: function( elem, name, data ) { - return dataUser.access( elem, name, data ); - }, - - removeData: function( elem, name ) { - dataUser.remove( elem, name ); - }, - - // TODO: Now that all calls to _data and _removeData have been replaced - // with direct calls to dataPriv methods, these can be deprecated. - _data: function( elem, name, data ) { - return dataPriv.access( elem, name, data ); - }, - - _removeData: function( elem, name ) { - dataPriv.remove( elem, name ); - } -} ); - -jQuery.fn.extend( { - data: function( key, value ) { - var i, name, data, - elem = this[ 0 ], - attrs = elem && elem.attributes; - - // Gets all values - if ( key === undefined ) { - if ( this.length ) { - data = dataUser.get( elem ); - - if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { - i = attrs.length; - while ( i-- ) { - - // Support: IE 11 only - // The attrs elements can be null (#14894) - if ( attrs[ i ] ) { - name = attrs[ i ].name; - if ( name.indexOf( "data-" ) === 0 ) { - name = camelCase( name.slice( 5 ) ); - dataAttr( elem, name, data[ name ] ); - } - } - } - dataPriv.set( elem, "hasDataAttrs", true ); - } - } - - return data; - } - - // Sets multiple values - if ( typeof key === "object" ) { - return this.each( function() { - dataUser.set( this, key ); - } ); - } - - return access( this, function( value ) { - var data; - - // The calling jQuery object (element matches) is not empty - // (and therefore has an element appears at this[ 0 ]) and the - // `value` parameter was not undefined. An empty jQuery object - // will result in `undefined` for elem = this[ 0 ] which will - // throw an exception if an attempt to read a data cache is made. - if ( elem && value === undefined ) { - - // Attempt to get data from the cache - // The key will always be camelCased in Data - data = dataUser.get( elem, key ); - if ( data !== undefined ) { - return data; - } - - // Attempt to "discover" the data in - // HTML5 custom data-* attrs - data = dataAttr( elem, key ); - if ( data !== undefined ) { - return data; - } - - // We tried really hard, but the data doesn't exist. - return; - } - - // Set the data... - this.each( function() { - - // We always store the camelCased key - dataUser.set( this, key, value ); - } ); - }, null, value, arguments.length > 1, null, true ); - }, - - removeData: function( key ) { - return this.each( function() { - dataUser.remove( this, key ); - } ); - } -} ); - - -jQuery.extend( { - queue: function( elem, type, data ) { - var queue; - - if ( elem ) { - type = ( type || "fx" ) + "queue"; - queue = dataPriv.get( elem, type ); - - // Speed up dequeue by getting out quickly if this is just a lookup - if ( data ) { - if ( !queue || Array.isArray( data ) ) { - queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); - } else { - queue.push( data ); - } - } - return queue || []; - } - }, - - dequeue: function( elem, type ) { - type = type || "fx"; - - var queue = jQuery.queue( elem, type ), - startLength = queue.length, - fn = queue.shift(), - hooks = jQuery._queueHooks( elem, type ), - next = function() { - jQuery.dequeue( elem, type ); - }; - - // If the fx queue is dequeued, always remove the progress sentinel - if ( fn === "inprogress" ) { - fn = queue.shift(); - startLength--; - } - - if ( fn ) { - - // Add a progress sentinel to prevent the fx queue from being - // automatically dequeued - if ( type === "fx" ) { - queue.unshift( "inprogress" ); - } - - // Clear up the last queue stop function - delete hooks.stop; - fn.call( elem, next, hooks ); - } - - if ( !startLength && hooks ) { - hooks.empty.fire(); - } - }, - - // Not public - generate a queueHooks object, or return the current one - _queueHooks: function( elem, type ) { - var key = type + "queueHooks"; - return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { - empty: jQuery.Callbacks( "once memory" ).add( function() { - dataPriv.remove( elem, [ type + "queue", key ] ); - } ) - } ); - } -} ); - -jQuery.fn.extend( { - queue: function( type, data ) { - var setter = 2; - - if ( typeof type !== "string" ) { - data = type; - type = "fx"; - setter--; - } - - if ( arguments.length < setter ) { - return jQuery.queue( this[ 0 ], type ); - } - - return data === undefined ? - this : - this.each( function() { - var queue = jQuery.queue( this, type, data ); - - // Ensure a hooks for this queue - jQuery._queueHooks( this, type ); - - if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { - jQuery.dequeue( this, type ); - } - } ); - }, - dequeue: function( type ) { - return this.each( function() { - jQuery.dequeue( this, type ); - } ); - }, - clearQueue: function( type ) { - return this.queue( type || "fx", [] ); - }, - - // Get a promise resolved when queues of a certain type - // are emptied (fx is the type by default) - promise: function( type, obj ) { - var tmp, - count = 1, - defer = jQuery.Deferred(), - elements = this, - i = this.length, - resolve = function() { - if ( !( --count ) ) { - defer.resolveWith( elements, [ elements ] ); - } - }; - - if ( typeof type !== "string" ) { - obj = type; - type = undefined; - } - type = type || "fx"; - - while ( i-- ) { - tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); - if ( tmp && tmp.empty ) { - count++; - tmp.empty.add( resolve ); - } - } - resolve(); - return defer.promise( obj ); - } -} ); -var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; - -var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); - - -var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; - -var documentElement = document.documentElement; - - - - var isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ); - }, - composed = { composed: true }; - - // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only - // Check attachment across shadow DOM boundaries when possible (gh-3504) - // Support: iOS 10.0-10.2 only - // Early iOS 10 versions support `attachShadow` but not `getRootNode`, - // leading to errors. We need to check for `getRootNode`. - if ( documentElement.getRootNode ) { - isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ) || - elem.getRootNode( composed ) === elem.ownerDocument; - }; - } -var isHiddenWithinTree = function( elem, el ) { - - // isHiddenWithinTree might be called from jQuery#filter function; - // in that case, element will be second argument - elem = el || elem; - - // Inline style trumps all - return elem.style.display === "none" || - elem.style.display === "" && - - // Otherwise, check computed style - // Support: Firefox <=43 - 45 - // Disconnected elements can have computed display: none, so first confirm that elem is - // in the document. - isAttached( elem ) && - - jQuery.css( elem, "display" ) === "none"; - }; - - - -function adjustCSS( elem, prop, valueParts, tween ) { - var adjusted, scale, - maxIterations = 20, - currentValue = tween ? - function() { - return tween.cur(); - } : - function() { - return jQuery.css( elem, prop, "" ); - }, - initial = currentValue(), - unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), - - // Starting value computation is required for potential unit mismatches - initialInUnit = elem.nodeType && - ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && - rcssNum.exec( jQuery.css( elem, prop ) ); - - if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { - - // Support: Firefox <=54 - // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) - initial = initial / 2; - - // Trust units reported by jQuery.css - unit = unit || initialInUnit[ 3 ]; - - // Iteratively approximate from a nonzero starting point - initialInUnit = +initial || 1; - - while ( maxIterations-- ) { - - // Evaluate and update our best guess (doubling guesses that zero out). - // Finish if the scale equals or crosses 1 (making the old*new product non-positive). - jQuery.style( elem, prop, initialInUnit + unit ); - if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { - maxIterations = 0; - } - initialInUnit = initialInUnit / scale; - - } - - initialInUnit = initialInUnit * 2; - jQuery.style( elem, prop, initialInUnit + unit ); - - // Make sure we update the tween properties later on - valueParts = valueParts || []; - } - - if ( valueParts ) { - initialInUnit = +initialInUnit || +initial || 0; - - // Apply relative offset (+=/-=) if specified - adjusted = valueParts[ 1 ] ? - initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : - +valueParts[ 2 ]; - if ( tween ) { - tween.unit = unit; - tween.start = initialInUnit; - tween.end = adjusted; - } - } - return adjusted; -} - - -var defaultDisplayMap = {}; - -function getDefaultDisplay( elem ) { - var temp, - doc = elem.ownerDocument, - nodeName = elem.nodeName, - display = defaultDisplayMap[ nodeName ]; - - if ( display ) { - return display; - } - - temp = doc.body.appendChild( doc.createElement( nodeName ) ); - display = jQuery.css( temp, "display" ); - - temp.parentNode.removeChild( temp ); - - if ( display === "none" ) { - display = "block"; - } - defaultDisplayMap[ nodeName ] = display; - - return display; -} - -function showHide( elements, show ) { - var display, elem, - values = [], - index = 0, - length = elements.length; - - // Determine new display value for elements that need to change - for ( ; index < length; index++ ) { - elem = elements[ index ]; - if ( !elem.style ) { - continue; - } - - display = elem.style.display; - if ( show ) { - - // Since we force visibility upon cascade-hidden elements, an immediate (and slow) - // check is required in this first loop unless we have a nonempty display value (either - // inline or about-to-be-restored) - if ( display === "none" ) { - values[ index ] = dataPriv.get( elem, "display" ) || null; - if ( !values[ index ] ) { - elem.style.display = ""; - } - } - if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { - values[ index ] = getDefaultDisplay( elem ); - } - } else { - if ( display !== "none" ) { - values[ index ] = "none"; - - // Remember what we're overwriting - dataPriv.set( elem, "display", display ); - } - } - } - - // Set the display of the elements in a second loop to avoid constant reflow - for ( index = 0; index < length; index++ ) { - if ( values[ index ] != null ) { - elements[ index ].style.display = values[ index ]; - } - } - - return elements; -} - -jQuery.fn.extend( { - show: function() { - return showHide( this, true ); - }, - hide: function() { - return showHide( this ); - }, - toggle: function( state ) { - if ( typeof state === "boolean" ) { - return state ? this.show() : this.hide(); - } - - return this.each( function() { - if ( isHiddenWithinTree( this ) ) { - jQuery( this ).show(); - } else { - jQuery( this ).hide(); - } - } ); - } -} ); -var rcheckableType = ( /^(?:checkbox|radio)$/i ); - -var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); - -var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); - - - -( function() { - var fragment = document.createDocumentFragment(), - div = fragment.appendChild( document.createElement( "div" ) ), - input = document.createElement( "input" ); - - // Support: Android 4.0 - 4.3 only - // Check state lost if the name is set (#11217) - // Support: Windows Web Apps (WWA) - // `name` and `type` must use .setAttribute for WWA (#14901) - input.setAttribute( "type", "radio" ); - input.setAttribute( "checked", "checked" ); - input.setAttribute( "name", "t" ); - - div.appendChild( input ); - - // Support: Android <=4.1 only - // Older WebKit doesn't clone checked state correctly in fragments - support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; - - // Support: IE <=11 only - // Make sure textarea (and checkbox) defaultValue is properly cloned - div.innerHTML = ""; - support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; - - // Support: IE <=9 only - // IE <=9 replaces "; - support.option = !!div.lastChild; -} )(); - - -// We have to close these tags to support XHTML (#13200) -var wrapMap = { - - // XHTML parsers do not magically insert elements in the - // same way that tag soup parsers do. So we cannot shorten - // this by omitting or other required elements. - thead: [ 1, "", "
" ], - col: [ 2, "", "
" ], - tr: [ 2, "", "
" ], - td: [ 3, "", "
" ], - - _default: [ 0, "", "" ] -}; - -wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; -wrapMap.th = wrapMap.td; - -// Support: IE <=9 only -if ( !support.option ) { - wrapMap.optgroup = wrapMap.option = [ 1, "" ]; -} - - -function getAll( context, tag ) { - - // Support: IE <=9 - 11 only - // Use typeof to avoid zero-argument method invocation on host objects (#15151) - var ret; - - if ( typeof context.getElementsByTagName !== "undefined" ) { - ret = context.getElementsByTagName( tag || "*" ); - - } else if ( typeof context.querySelectorAll !== "undefined" ) { - ret = context.querySelectorAll( tag || "*" ); - - } else { - ret = []; - } - - if ( tag === undefined || tag && nodeName( context, tag ) ) { - return jQuery.merge( [ context ], ret ); - } - - return ret; -} - - -// Mark scripts as having already been evaluated -function setGlobalEval( elems, refElements ) { - var i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - dataPriv.set( - elems[ i ], - "globalEval", - !refElements || dataPriv.get( refElements[ i ], "globalEval" ) - ); - } -} - - -var rhtml = /<|&#?\w+;/; - -function buildFragment( elems, context, scripts, selection, ignored ) { - var elem, tmp, tag, wrap, attached, j, - fragment = context.createDocumentFragment(), - nodes = [], - i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - elem = elems[ i ]; - - if ( elem || elem === 0 ) { - - // Add nodes directly - if ( toType( elem ) === "object" ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); - - // Convert non-html into a text node - } else if ( !rhtml.test( elem ) ) { - nodes.push( context.createTextNode( elem ) ); - - // Convert html into DOM nodes - } else { - tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); - - // Deserialize a standard representation - tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); - wrap = wrapMap[ tag ] || wrapMap._default; - tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; - - // Descend through wrappers to the right content - j = wrap[ 0 ]; - while ( j-- ) { - tmp = tmp.lastChild; - } - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, tmp.childNodes ); - - // Remember the top-level container - tmp = fragment.firstChild; - - // Ensure the created nodes are orphaned (#12392) - tmp.textContent = ""; - } - } - } - - // Remove wrapper from fragment - fragment.textContent = ""; - - i = 0; - while ( ( elem = nodes[ i++ ] ) ) { - - // Skip elements already in the context collection (trac-4087) - if ( selection && jQuery.inArray( elem, selection ) > -1 ) { - if ( ignored ) { - ignored.push( elem ); - } - continue; - } - - attached = isAttached( elem ); - - // Append to fragment - tmp = getAll( fragment.appendChild( elem ), "script" ); - - // Preserve script evaluation history - if ( attached ) { - setGlobalEval( tmp ); - } - - // Capture executables - if ( scripts ) { - j = 0; - while ( ( elem = tmp[ j++ ] ) ) { - if ( rscriptType.test( elem.type || "" ) ) { - scripts.push( elem ); - } - } - } - } - - return fragment; -} - - -var rtypenamespace = /^([^.]*)(?:\.(.+)|)/; - -function returnTrue() { - return true; -} - -function returnFalse() { - return false; -} - -// Support: IE <=9 - 11+ -// focus() and blur() are asynchronous, except when they are no-op. -// So expect focus to be synchronous when the element is already active, -// and blur to be synchronous when the element is not already active. -// (focus and blur are always synchronous in other supported browsers, -// this just defines when we can count on it). -function expectSync( elem, type ) { - return ( elem === safeActiveElement() ) === ( type === "focus" ); -} - -// Support: IE <=9 only -// Accessing document.activeElement can throw unexpectedly -// https://bugs.jquery.com/ticket/13393 -function safeActiveElement() { - try { - return document.activeElement; - } catch ( err ) { } -} - -function on( elem, types, selector, data, fn, one ) { - var origFn, type; - - // Types can be a map of types/handlers - if ( typeof types === "object" ) { - - // ( types-Object, selector, data ) - if ( typeof selector !== "string" ) { - - // ( types-Object, data ) - data = data || selector; - selector = undefined; - } - for ( type in types ) { - on( elem, type, selector, data, types[ type ], one ); - } - return elem; - } - - if ( data == null && fn == null ) { - - // ( types, fn ) - fn = selector; - data = selector = undefined; - } else if ( fn == null ) { - if ( typeof selector === "string" ) { - - // ( types, selector, fn ) - fn = data; - data = undefined; - } else { - - // ( types, data, fn ) - fn = data; - data = selector; - selector = undefined; - } - } - if ( fn === false ) { - fn = returnFalse; - } else if ( !fn ) { - return elem; - } - - if ( one === 1 ) { - origFn = fn; - fn = function( event ) { - - // Can use an empty set, since event contains the info - jQuery().off( event ); - return origFn.apply( this, arguments ); - }; - - // Use same guid so caller can remove using origFn - fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); - } - return elem.each( function() { - jQuery.event.add( this, types, fn, data, selector ); - } ); -} - -/* - * Helper functions for managing events -- not part of the public interface. - * Props to Dean Edwards' addEvent library for many of the ideas. - */ -jQuery.event = { - - global: {}, - - add: function( elem, types, handler, data, selector ) { - - var handleObjIn, eventHandle, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.get( elem ); - - // Only attach events to objects that accept data - if ( !acceptData( elem ) ) { - return; - } - - // Caller can pass in an object of custom data in lieu of the handler - if ( handler.handler ) { - handleObjIn = handler; - handler = handleObjIn.handler; - selector = handleObjIn.selector; - } - - // Ensure that invalid selectors throw exceptions at attach time - // Evaluate against documentElement in case elem is a non-element node (e.g., document) - if ( selector ) { - jQuery.find.matchesSelector( documentElement, selector ); - } - - // Make sure that the handler has a unique ID, used to find/remove it later - if ( !handler.guid ) { - handler.guid = jQuery.guid++; - } - - // Init the element's event structure and main handler, if this is the first - if ( !( events = elemData.events ) ) { - events = elemData.events = Object.create( null ); - } - if ( !( eventHandle = elemData.handle ) ) { - eventHandle = elemData.handle = function( e ) { - - // Discard the second event of a jQuery.event.trigger() and - // when an event is called after a page has unloaded - return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? - jQuery.event.dispatch.apply( elem, arguments ) : undefined; - }; - } - - // Handle multiple events separated by a space - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // There *must* be a type, no attaching namespace-only handlers - if ( !type ) { - continue; - } - - // If event changes its type, use the special event handlers for the changed type - special = jQuery.event.special[ type ] || {}; - - // If selector defined, determine special event api type, otherwise given type - type = ( selector ? special.delegateType : special.bindType ) || type; - - // Update special based on newly reset type - special = jQuery.event.special[ type ] || {}; - - // handleObj is passed to all event handlers - handleObj = jQuery.extend( { - type: type, - origType: origType, - data: data, - handler: handler, - guid: handler.guid, - selector: selector, - needsContext: selector && jQuery.expr.match.needsContext.test( selector ), - namespace: namespaces.join( "." ) - }, handleObjIn ); - - // Init the event handler queue if we're the first - if ( !( handlers = events[ type ] ) ) { - handlers = events[ type ] = []; - handlers.delegateCount = 0; - - // Only use addEventListener if the special events handler returns false - if ( !special.setup || - special.setup.call( elem, data, namespaces, eventHandle ) === false ) { - - if ( elem.addEventListener ) { - elem.addEventListener( type, eventHandle ); - } - } - } - - if ( special.add ) { - special.add.call( elem, handleObj ); - - if ( !handleObj.handler.guid ) { - handleObj.handler.guid = handler.guid; - } - } - - // Add to the element's handler list, delegates in front - if ( selector ) { - handlers.splice( handlers.delegateCount++, 0, handleObj ); - } else { - handlers.push( handleObj ); - } - - // Keep track of which events have ever been used, for event optimization - jQuery.event.global[ type ] = true; - } - - }, - - // Detach an event or set of events from an element - remove: function( elem, types, handler, selector, mappedTypes ) { - - var j, origCount, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); - - if ( !elemData || !( events = elemData.events ) ) { - return; - } - - // Once for each type.namespace in types; type may be omitted - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // Unbind all events (on this namespace, if provided) for the element - if ( !type ) { - for ( type in events ) { - jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); - } - continue; - } - - special = jQuery.event.special[ type ] || {}; - type = ( selector ? special.delegateType : special.bindType ) || type; - handlers = events[ type ] || []; - tmp = tmp[ 2 ] && - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); - - // Remove matching events - origCount = j = handlers.length; - while ( j-- ) { - handleObj = handlers[ j ]; - - if ( ( mappedTypes || origType === handleObj.origType ) && - ( !handler || handler.guid === handleObj.guid ) && - ( !tmp || tmp.test( handleObj.namespace ) ) && - ( !selector || selector === handleObj.selector || - selector === "**" && handleObj.selector ) ) { - handlers.splice( j, 1 ); - - if ( handleObj.selector ) { - handlers.delegateCount--; - } - if ( special.remove ) { - special.remove.call( elem, handleObj ); - } - } - } - - // Remove generic event handler if we removed something and no more handlers exist - // (avoids potential for endless recursion during removal of special event handlers) - if ( origCount && !handlers.length ) { - if ( !special.teardown || - special.teardown.call( elem, namespaces, elemData.handle ) === false ) { - - jQuery.removeEvent( elem, type, elemData.handle ); - } - - delete events[ type ]; - } - } - - // Remove data and the expando if it's no longer used - if ( jQuery.isEmptyObject( events ) ) { - dataPriv.remove( elem, "handle events" ); - } - }, - - dispatch: function( nativeEvent ) { - - var i, j, ret, matched, handleObj, handlerQueue, - args = new Array( arguments.length ), - - // Make a writable jQuery.Event from the native event object - event = jQuery.event.fix( nativeEvent ), - - handlers = ( - dataPriv.get( this, "events" ) || Object.create( null ) - )[ event.type ] || [], - special = jQuery.event.special[ event.type ] || {}; - - // Use the fix-ed jQuery.Event rather than the (read-only) native event - args[ 0 ] = event; - - for ( i = 1; i < arguments.length; i++ ) { - args[ i ] = arguments[ i ]; - } - - event.delegateTarget = this; - - // Call the preDispatch hook for the mapped type, and let it bail if desired - if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { - return; - } - - // Determine handlers - handlerQueue = jQuery.event.handlers.call( this, event, handlers ); - - // Run delegates first; they may want to stop propagation beneath us - i = 0; - while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { - event.currentTarget = matched.elem; - - j = 0; - while ( ( handleObj = matched.handlers[ j++ ] ) && - !event.isImmediatePropagationStopped() ) { - - // If the event is namespaced, then each handler is only invoked if it is - // specially universal or its namespaces are a superset of the event's. - if ( !event.rnamespace || handleObj.namespace === false || - event.rnamespace.test( handleObj.namespace ) ) { - - event.handleObj = handleObj; - event.data = handleObj.data; - - ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || - handleObj.handler ).apply( matched.elem, args ); - - if ( ret !== undefined ) { - if ( ( event.result = ret ) === false ) { - event.preventDefault(); - event.stopPropagation(); - } - } - } - } - } - - // Call the postDispatch hook for the mapped type - if ( special.postDispatch ) { - special.postDispatch.call( this, event ); - } - - return event.result; - }, - - handlers: function( event, handlers ) { - var i, handleObj, sel, matchedHandlers, matchedSelectors, - handlerQueue = [], - delegateCount = handlers.delegateCount, - cur = event.target; - - // Find delegate handlers - if ( delegateCount && - - // Support: IE <=9 - // Black-hole SVG instance trees (trac-13180) - cur.nodeType && - - // Support: Firefox <=42 - // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) - // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click - // Support: IE 11 only - // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) - !( event.type === "click" && event.button >= 1 ) ) { - - for ( ; cur !== this; cur = cur.parentNode || this ) { - - // Don't check non-elements (#13208) - // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) - if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { - matchedHandlers = []; - matchedSelectors = {}; - for ( i = 0; i < delegateCount; i++ ) { - handleObj = handlers[ i ]; - - // Don't conflict with Object.prototype properties (#13203) - sel = handleObj.selector + " "; - - if ( matchedSelectors[ sel ] === undefined ) { - matchedSelectors[ sel ] = handleObj.needsContext ? - jQuery( sel, this ).index( cur ) > -1 : - jQuery.find( sel, this, null, [ cur ] ).length; - } - if ( matchedSelectors[ sel ] ) { - matchedHandlers.push( handleObj ); - } - } - if ( matchedHandlers.length ) { - handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); - } - } - } - } - - // Add the remaining (directly-bound) handlers - cur = this; - if ( delegateCount < handlers.length ) { - handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); - } - - return handlerQueue; - }, - - addProp: function( name, hook ) { - Object.defineProperty( jQuery.Event.prototype, name, { - enumerable: true, - configurable: true, - - get: isFunction( hook ) ? - function() { - if ( this.originalEvent ) { - return hook( this.originalEvent ); - } - } : - function() { - if ( this.originalEvent ) { - return this.originalEvent[ name ]; - } - }, - - set: function( value ) { - Object.defineProperty( this, name, { - enumerable: true, - configurable: true, - writable: true, - value: value - } ); - } - } ); - }, - - fix: function( originalEvent ) { - return originalEvent[ jQuery.expando ] ? - originalEvent : - new jQuery.Event( originalEvent ); - }, - - special: { - load: { - - // Prevent triggered image.load events from bubbling to window.load - noBubble: true - }, - click: { - - // Utilize native event to ensure correct state for checkable inputs - setup: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Claim the first handler - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - // dataPriv.set( el, "click", ... ) - leverageNative( el, "click", returnTrue ); - } - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Force setup before triggering a click - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - leverageNative( el, "click" ); - } - - // Return non-false to allow normal event-path propagation - return true; - }, - - // For cross-browser consistency, suppress native .click() on links - // Also prevent it if we're currently inside a leveraged native-event stack - _default: function( event ) { - var target = event.target; - return rcheckableType.test( target.type ) && - target.click && nodeName( target, "input" ) && - dataPriv.get( target, "click" ) || - nodeName( target, "a" ); - } - }, - - beforeunload: { - postDispatch: function( event ) { - - // Support: Firefox 20+ - // Firefox doesn't alert if the returnValue field is not set. - if ( event.result !== undefined && event.originalEvent ) { - event.originalEvent.returnValue = event.result; - } - } - } - } -}; - -// Ensure the presence of an event listener that handles manually-triggered -// synthetic events by interrupting progress until reinvoked in response to -// *native* events that it fires directly, ensuring that state changes have -// already occurred before other listeners are invoked. -function leverageNative( el, type, expectSync ) { - - // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add - if ( !expectSync ) { - if ( dataPriv.get( el, type ) === undefined ) { - jQuery.event.add( el, type, returnTrue ); - } - return; - } - - // Register the controller as a special universal handler for all event namespaces - dataPriv.set( el, type, false ); - jQuery.event.add( el, type, { - namespace: false, - handler: function( event ) { - var notAsync, result, - saved = dataPriv.get( this, type ); - - if ( ( event.isTrigger & 1 ) && this[ type ] ) { - - // Interrupt processing of the outer synthetic .trigger()ed event - // Saved data should be false in such cases, but might be a leftover capture object - // from an async native handler (gh-4350) - if ( !saved.length ) { - - // Store arguments for use when handling the inner native event - // There will always be at least one argument (an event object), so this array - // will not be confused with a leftover capture object. - saved = slice.call( arguments ); - dataPriv.set( this, type, saved ); - - // Trigger the native event and capture its result - // Support: IE <=9 - 11+ - // focus() and blur() are asynchronous - notAsync = expectSync( this, type ); - this[ type ](); - result = dataPriv.get( this, type ); - if ( saved !== result || notAsync ) { - dataPriv.set( this, type, false ); - } else { - result = {}; - } - if ( saved !== result ) { - - // Cancel the outer synthetic event - event.stopImmediatePropagation(); - event.preventDefault(); - - // Support: Chrome 86+ - // In Chrome, if an element having a focusout handler is blurred by - // clicking outside of it, it invokes the handler synchronously. If - // that handler calls `.remove()` on the element, the data is cleared, - // leaving `result` undefined. We need to guard against this. - return result && result.value; - } - - // If this is an inner synthetic event for an event with a bubbling surrogate - // (focus or blur), assume that the surrogate already propagated from triggering the - // native event and prevent that from happening again here. - // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the - // bubbling surrogate propagates *after* the non-bubbling base), but that seems - // less bad than duplication. - } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { - event.stopPropagation(); - } - - // If this is a native event triggered above, everything is now in order - // Fire an inner synthetic event with the original arguments - } else if ( saved.length ) { - - // ...and capture the result - dataPriv.set( this, type, { - value: jQuery.event.trigger( - - // Support: IE <=9 - 11+ - // Extend with the prototype to reset the above stopImmediatePropagation() - jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), - saved.slice( 1 ), - this - ) - } ); - - // Abort handling of the native event - event.stopImmediatePropagation(); - } - } - } ); -} - -jQuery.removeEvent = function( elem, type, handle ) { - - // This "if" is needed for plain objects - if ( elem.removeEventListener ) { - elem.removeEventListener( type, handle ); - } -}; - -jQuery.Event = function( src, props ) { - - // Allow instantiation without the 'new' keyword - if ( !( this instanceof jQuery.Event ) ) { - return new jQuery.Event( src, props ); - } - - // Event object - if ( src && src.type ) { - this.originalEvent = src; - this.type = src.type; - - // Events bubbling up the document may have been marked as prevented - // by a handler lower down the tree; reflect the correct value. - this.isDefaultPrevented = src.defaultPrevented || - src.defaultPrevented === undefined && - - // Support: Android <=2.3 only - src.returnValue === false ? - returnTrue : - returnFalse; - - // Create target properties - // Support: Safari <=6 - 7 only - // Target should not be a text node (#504, #13143) - this.target = ( src.target && src.target.nodeType === 3 ) ? - src.target.parentNode : - src.target; - - this.currentTarget = src.currentTarget; - this.relatedTarget = src.relatedTarget; - - // Event type - } else { - this.type = src; - } - - // Put explicitly provided properties onto the event object - if ( props ) { - jQuery.extend( this, props ); - } - - // Create a timestamp if incoming event doesn't have one - this.timeStamp = src && src.timeStamp || Date.now(); - - // Mark it as fixed - this[ jQuery.expando ] = true; -}; - -// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding -// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html -jQuery.Event.prototype = { - constructor: jQuery.Event, - isDefaultPrevented: returnFalse, - isPropagationStopped: returnFalse, - isImmediatePropagationStopped: returnFalse, - isSimulated: false, - - preventDefault: function() { - var e = this.originalEvent; - - this.isDefaultPrevented = returnTrue; - - if ( e && !this.isSimulated ) { - e.preventDefault(); - } - }, - stopPropagation: function() { - var e = this.originalEvent; - - this.isPropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopPropagation(); - } - }, - stopImmediatePropagation: function() { - var e = this.originalEvent; - - this.isImmediatePropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopImmediatePropagation(); - } - - this.stopPropagation(); - } -}; - -// Includes all common event props including KeyEvent and MouseEvent specific props -jQuery.each( { - altKey: true, - bubbles: true, - cancelable: true, - changedTouches: true, - ctrlKey: true, - detail: true, - eventPhase: true, - metaKey: true, - pageX: true, - pageY: true, - shiftKey: true, - view: true, - "char": true, - code: true, - charCode: true, - key: true, - keyCode: true, - button: true, - buttons: true, - clientX: true, - clientY: true, - offsetX: true, - offsetY: true, - pointerId: true, - pointerType: true, - screenX: true, - screenY: true, - targetTouches: true, - toElement: true, - touches: true, - which: true -}, jQuery.event.addProp ); - -jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { - jQuery.event.special[ type ] = { - - // Utilize native event if possible so blur/focus sequence is correct - setup: function() { - - // Claim the first handler - // dataPriv.set( this, "focus", ... ) - // dataPriv.set( this, "blur", ... ) - leverageNative( this, type, expectSync ); - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function() { - - // Force setup before trigger - leverageNative( this, type ); - - // Return non-false to allow normal event-path propagation - return true; - }, - - // Suppress native focus or blur as it's already being fired - // in leverageNative. - _default: function() { - return true; - }, - - delegateType: delegateType - }; -} ); - -// Create mouseenter/leave events using mouseover/out and event-time checks -// so that event delegation works in jQuery. -// Do the same for pointerenter/pointerleave and pointerover/pointerout -// -// Support: Safari 7 only -// Safari sends mouseenter too often; see: -// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 -// for the description of the bug (it existed in older Chrome versions as well). -jQuery.each( { - mouseenter: "mouseover", - mouseleave: "mouseout", - pointerenter: "pointerover", - pointerleave: "pointerout" -}, function( orig, fix ) { - jQuery.event.special[ orig ] = { - delegateType: fix, - bindType: fix, - - handle: function( event ) { - var ret, - target = this, - related = event.relatedTarget, - handleObj = event.handleObj; - - // For mouseenter/leave call the handler if related is outside the target. - // NB: No relatedTarget if the mouse left/entered the browser window - if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { - event.type = handleObj.origType; - ret = handleObj.handler.apply( this, arguments ); - event.type = fix; - } - return ret; - } - }; -} ); - -jQuery.fn.extend( { - - on: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn ); - }, - one: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn, 1 ); - }, - off: function( types, selector, fn ) { - var handleObj, type; - if ( types && types.preventDefault && types.handleObj ) { - - // ( event ) dispatched jQuery.Event - handleObj = types.handleObj; - jQuery( types.delegateTarget ).off( - handleObj.namespace ? - handleObj.origType + "." + handleObj.namespace : - handleObj.origType, - handleObj.selector, - handleObj.handler - ); - return this; - } - if ( typeof types === "object" ) { - - // ( types-object [, selector] ) - for ( type in types ) { - this.off( type, selector, types[ type ] ); - } - return this; - } - if ( selector === false || typeof selector === "function" ) { - - // ( types [, fn] ) - fn = selector; - selector = undefined; - } - if ( fn === false ) { - fn = returnFalse; - } - return this.each( function() { - jQuery.event.remove( this, types, fn, selector ); - } ); - } -} ); - - -var - - // Support: IE <=10 - 11, Edge 12 - 13 only - // In IE/Edge using regex groups here causes severe slowdowns. - // See https://connect.microsoft.com/IE/feedback/details/1736512/ - rnoInnerhtml = /\s*$/g; - -// Prefer a tbody over its parent table for containing new rows -function manipulationTarget( elem, content ) { - if ( nodeName( elem, "table" ) && - nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { - - return jQuery( elem ).children( "tbody" )[ 0 ] || elem; - } - - return elem; -} - -// Replace/restore the type attribute of script elements for safe DOM manipulation -function disableScript( elem ) { - elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; - return elem; -} -function restoreScript( elem ) { - if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { - elem.type = elem.type.slice( 5 ); - } else { - elem.removeAttribute( "type" ); - } - - return elem; -} - -function cloneCopyEvent( src, dest ) { - var i, l, type, pdataOld, udataOld, udataCur, events; - - if ( dest.nodeType !== 1 ) { - return; - } - - // 1. Copy private data: events, handlers, etc. - if ( dataPriv.hasData( src ) ) { - pdataOld = dataPriv.get( src ); - events = pdataOld.events; - - if ( events ) { - dataPriv.remove( dest, "handle events" ); - - for ( type in events ) { - for ( i = 0, l = events[ type ].length; i < l; i++ ) { - jQuery.event.add( dest, type, events[ type ][ i ] ); - } - } - } - } - - // 2. Copy user data - if ( dataUser.hasData( src ) ) { - udataOld = dataUser.access( src ); - udataCur = jQuery.extend( {}, udataOld ); - - dataUser.set( dest, udataCur ); - } -} - -// Fix IE bugs, see support tests -function fixInput( src, dest ) { - var nodeName = dest.nodeName.toLowerCase(); - - // Fails to persist the checked state of a cloned checkbox or radio button. - if ( nodeName === "input" && rcheckableType.test( src.type ) ) { - dest.checked = src.checked; - - // Fails to return the selected option to the default selected state when cloning options - } else if ( nodeName === "input" || nodeName === "textarea" ) { - dest.defaultValue = src.defaultValue; - } -} - -function domManip( collection, args, callback, ignored ) { - - // Flatten any nested arrays - args = flat( args ); - - var fragment, first, scripts, hasScripts, node, doc, - i = 0, - l = collection.length, - iNoClone = l - 1, - value = args[ 0 ], - valueIsFunction = isFunction( value ); - - // We can't cloneNode fragments that contain checked, in WebKit - if ( valueIsFunction || - ( l > 1 && typeof value === "string" && - !support.checkClone && rchecked.test( value ) ) ) { - return collection.each( function( index ) { - var self = collection.eq( index ); - if ( valueIsFunction ) { - args[ 0 ] = value.call( this, index, self.html() ); - } - domManip( self, args, callback, ignored ); - } ); - } - - if ( l ) { - fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); - first = fragment.firstChild; - - if ( fragment.childNodes.length === 1 ) { - fragment = first; - } - - // Require either new content or an interest in ignored elements to invoke the callback - if ( first || ignored ) { - scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); - hasScripts = scripts.length; - - // Use the original fragment for the last item - // instead of the first because it can end up - // being emptied incorrectly in certain situations (#8070). - for ( ; i < l; i++ ) { - node = fragment; - - if ( i !== iNoClone ) { - node = jQuery.clone( node, true, true ); - - // Keep references to cloned scripts for later restoration - if ( hasScripts ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( scripts, getAll( node, "script" ) ); - } - } - - callback.call( collection[ i ], node, i ); - } - - if ( hasScripts ) { - doc = scripts[ scripts.length - 1 ].ownerDocument; - - // Reenable scripts - jQuery.map( scripts, restoreScript ); - - // Evaluate executable scripts on first document insertion - for ( i = 0; i < hasScripts; i++ ) { - node = scripts[ i ]; - if ( rscriptType.test( node.type || "" ) && - !dataPriv.access( node, "globalEval" ) && - jQuery.contains( doc, node ) ) { - - if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { - - // Optional AJAX dependency, but won't run scripts if not present - if ( jQuery._evalUrl && !node.noModule ) { - jQuery._evalUrl( node.src, { - nonce: node.nonce || node.getAttribute( "nonce" ) - }, doc ); - } - } else { - DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); - } - } - } - } - } - } - - return collection; -} - -function remove( elem, selector, keepData ) { - var node, - nodes = selector ? jQuery.filter( selector, elem ) : elem, - i = 0; - - for ( ; ( node = nodes[ i ] ) != null; i++ ) { - if ( !keepData && node.nodeType === 1 ) { - jQuery.cleanData( getAll( node ) ); - } - - if ( node.parentNode ) { - if ( keepData && isAttached( node ) ) { - setGlobalEval( getAll( node, "script" ) ); - } - node.parentNode.removeChild( node ); - } - } - - return elem; -} - -jQuery.extend( { - htmlPrefilter: function( html ) { - return html; - }, - - clone: function( elem, dataAndEvents, deepDataAndEvents ) { - var i, l, srcElements, destElements, - clone = elem.cloneNode( true ), - inPage = isAttached( elem ); - - // Fix IE cloning issues - if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && - !jQuery.isXMLDoc( elem ) ) { - - // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 - destElements = getAll( clone ); - srcElements = getAll( elem ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - fixInput( srcElements[ i ], destElements[ i ] ); - } - } - - // Copy the events from the original to the clone - if ( dataAndEvents ) { - if ( deepDataAndEvents ) { - srcElements = srcElements || getAll( elem ); - destElements = destElements || getAll( clone ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - cloneCopyEvent( srcElements[ i ], destElements[ i ] ); - } - } else { - cloneCopyEvent( elem, clone ); - } - } - - // Preserve script evaluation history - destElements = getAll( clone, "script" ); - if ( destElements.length > 0 ) { - setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); - } - - // Return the cloned set - return clone; - }, - - cleanData: function( elems ) { - var data, elem, type, - special = jQuery.event.special, - i = 0; - - for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { - if ( acceptData( elem ) ) { - if ( ( data = elem[ dataPriv.expando ] ) ) { - if ( data.events ) { - for ( type in data.events ) { - if ( special[ type ] ) { - jQuery.event.remove( elem, type ); - - // This is a shortcut to avoid jQuery.event.remove's overhead - } else { - jQuery.removeEvent( elem, type, data.handle ); - } - } - } - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataPriv.expando ] = undefined; - } - if ( elem[ dataUser.expando ] ) { - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataUser.expando ] = undefined; - } - } - } - } -} ); - -jQuery.fn.extend( { - detach: function( selector ) { - return remove( this, selector, true ); - }, - - remove: function( selector ) { - return remove( this, selector ); - }, - - text: function( value ) { - return access( this, function( value ) { - return value === undefined ? - jQuery.text( this ) : - this.empty().each( function() { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - this.textContent = value; - } - } ); - }, null, value, arguments.length ); - }, - - append: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.appendChild( elem ); - } - } ); - }, - - prepend: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.insertBefore( elem, target.firstChild ); - } - } ); - }, - - before: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this ); - } - } ); - }, - - after: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this.nextSibling ); - } - } ); - }, - - empty: function() { - var elem, - i = 0; - - for ( ; ( elem = this[ i ] ) != null; i++ ) { - if ( elem.nodeType === 1 ) { - - // Prevent memory leaks - jQuery.cleanData( getAll( elem, false ) ); - - // Remove any remaining nodes - elem.textContent = ""; - } - } - - return this; - }, - - clone: function( dataAndEvents, deepDataAndEvents ) { - dataAndEvents = dataAndEvents == null ? false : dataAndEvents; - deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; - - return this.map( function() { - return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); - } ); - }, - - html: function( value ) { - return access( this, function( value ) { - var elem = this[ 0 ] || {}, - i = 0, - l = this.length; - - if ( value === undefined && elem.nodeType === 1 ) { - return elem.innerHTML; - } - - // See if we can take a shortcut and just use innerHTML - if ( typeof value === "string" && !rnoInnerhtml.test( value ) && - !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { - - value = jQuery.htmlPrefilter( value ); - - try { - for ( ; i < l; i++ ) { - elem = this[ i ] || {}; - - // Remove element nodes and prevent memory leaks - if ( elem.nodeType === 1 ) { - jQuery.cleanData( getAll( elem, false ) ); - elem.innerHTML = value; - } - } - - elem = 0; - - // If using innerHTML throws an exception, use the fallback method - } catch ( e ) {} - } - - if ( elem ) { - this.empty().append( value ); - } - }, null, value, arguments.length ); - }, - - replaceWith: function() { - var ignored = []; - - // Make the changes, replacing each non-ignored context element with the new content - return domManip( this, arguments, function( elem ) { - var parent = this.parentNode; - - if ( jQuery.inArray( this, ignored ) < 0 ) { - jQuery.cleanData( getAll( this ) ); - if ( parent ) { - parent.replaceChild( elem, this ); - } - } - - // Force callback invocation - }, ignored ); - } -} ); - -jQuery.each( { - appendTo: "append", - prependTo: "prepend", - insertBefore: "before", - insertAfter: "after", - replaceAll: "replaceWith" -}, function( name, original ) { - jQuery.fn[ name ] = function( selector ) { - var elems, - ret = [], - insert = jQuery( selector ), - last = insert.length - 1, - i = 0; - - for ( ; i <= last; i++ ) { - elems = i === last ? this : this.clone( true ); - jQuery( insert[ i ] )[ original ]( elems ); - - // Support: Android <=4.0 only, PhantomJS 1 only - // .get() because push.apply(_, arraylike) throws on ancient WebKit - push.apply( ret, elems.get() ); - } - - return this.pushStack( ret ); - }; -} ); -var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); - -var getStyles = function( elem ) { - - // Support: IE <=11 only, Firefox <=30 (#15098, #14150) - // IE throws on elements created in popups - // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" - var view = elem.ownerDocument.defaultView; - - if ( !view || !view.opener ) { - view = window; - } - - return view.getComputedStyle( elem ); - }; - -var swap = function( elem, options, callback ) { - var ret, name, - old = {}; - - // Remember the old values, and insert the new ones - for ( name in options ) { - old[ name ] = elem.style[ name ]; - elem.style[ name ] = options[ name ]; - } - - ret = callback.call( elem ); - - // Revert the old values - for ( name in options ) { - elem.style[ name ] = old[ name ]; - } - - return ret; -}; - - -var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); - - - -( function() { - - // Executing both pixelPosition & boxSizingReliable tests require only one layout - // so they're executed at the same time to save the second computation. - function computeStyleTests() { - - // This is a singleton, we need to execute it only once - if ( !div ) { - return; - } - - container.style.cssText = "position:absolute;left:-11111px;width:60px;" + - "margin-top:1px;padding:0;border:0"; - div.style.cssText = - "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + - "margin:auto;border:1px;padding:1px;" + - "width:60%;top:1%"; - documentElement.appendChild( container ).appendChild( div ); - - var divStyle = window.getComputedStyle( div ); - pixelPositionVal = divStyle.top !== "1%"; - - // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 - reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; - - // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 - // Some styles come back with percentage values, even though they shouldn't - div.style.right = "60%"; - pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; - - // Support: IE 9 - 11 only - // Detect misreporting of content dimensions for box-sizing:border-box elements - boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; - - // Support: IE 9 only - // Detect overflow:scroll screwiness (gh-3699) - // Support: Chrome <=64 - // Don't get tricked when zoom affects offsetWidth (gh-4029) - div.style.position = "absolute"; - scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; - - documentElement.removeChild( container ); - - // Nullify the div so it wouldn't be stored in the memory and - // it will also be a sign that checks already performed - div = null; - } - - function roundPixelMeasures( measure ) { - return Math.round( parseFloat( measure ) ); - } - - var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, - reliableTrDimensionsVal, reliableMarginLeftVal, - container = document.createElement( "div" ), - div = document.createElement( "div" ); - - // Finish early in limited (non-browser) environments - if ( !div.style ) { - return; - } - - // Support: IE <=9 - 11 only - // Style of cloned element affects source element cloned (#8908) - div.style.backgroundClip = "content-box"; - div.cloneNode( true ).style.backgroundClip = ""; - support.clearCloneStyle = div.style.backgroundClip === "content-box"; - - jQuery.extend( support, { - boxSizingReliable: function() { - computeStyleTests(); - return boxSizingReliableVal; - }, - pixelBoxStyles: function() { - computeStyleTests(); - return pixelBoxStylesVal; - }, - pixelPosition: function() { - computeStyleTests(); - return pixelPositionVal; - }, - reliableMarginLeft: function() { - computeStyleTests(); - return reliableMarginLeftVal; - }, - scrollboxSize: function() { - computeStyleTests(); - return scrollboxSizeVal; - }, - - // Support: IE 9 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Behavior in IE 9 is more subtle than in newer versions & it passes - // some versions of this test; make sure not to make it pass there! - // - // Support: Firefox 70+ - // Only Firefox includes border widths - // in computed dimensions. (gh-4529) - reliableTrDimensions: function() { - var table, tr, trChild, trStyle; - if ( reliableTrDimensionsVal == null ) { - table = document.createElement( "table" ); - tr = document.createElement( "tr" ); - trChild = document.createElement( "div" ); - - table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate"; - tr.style.cssText = "border:1px solid"; - - // Support: Chrome 86+ - // Height set through cssText does not get applied. - // Computed height then comes back as 0. - tr.style.height = "1px"; - trChild.style.height = "9px"; - - // Support: Android 8 Chrome 86+ - // In our bodyBackground.html iframe, - // display for all div elements is set to "inline", - // which causes a problem only in Android 8 Chrome 86. - // Ensuring the div is display: block - // gets around this issue. - trChild.style.display = "block"; - - documentElement - .appendChild( table ) - .appendChild( tr ) - .appendChild( trChild ); - - trStyle = window.getComputedStyle( tr ); - reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) + - parseInt( trStyle.borderTopWidth, 10 ) + - parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight; - - documentElement.removeChild( table ); - } - return reliableTrDimensionsVal; - } - } ); -} )(); - - -function curCSS( elem, name, computed ) { - var width, minWidth, maxWidth, ret, - - // Support: Firefox 51+ - // Retrieving style before computed somehow - // fixes an issue with getting wrong values - // on detached elements - style = elem.style; - - computed = computed || getStyles( elem ); - - // getPropertyValue is needed for: - // .css('filter') (IE 9 only, #12537) - // .css('--customProperty) (#3144) - if ( computed ) { - ret = computed.getPropertyValue( name ) || computed[ name ]; - - if ( ret === "" && !isAttached( elem ) ) { - ret = jQuery.style( elem, name ); - } - - // A tribute to the "awesome hack by Dean Edwards" - // Android Browser returns percentage for some values, - // but width seems to be reliably pixels. - // This is against the CSSOM draft spec: - // https://drafts.csswg.org/cssom/#resolved-values - if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { - - // Remember the original values - width = style.width; - minWidth = style.minWidth; - maxWidth = style.maxWidth; - - // Put in the new values to get a computed value out - style.minWidth = style.maxWidth = style.width = ret; - ret = computed.width; - - // Revert the changed values - style.width = width; - style.minWidth = minWidth; - style.maxWidth = maxWidth; - } - } - - return ret !== undefined ? - - // Support: IE <=9 - 11 only - // IE returns zIndex value as an integer. - ret + "" : - ret; -} - - -function addGetHookIf( conditionFn, hookFn ) { - - // Define the hook, we'll check on the first run if it's really needed. - return { - get: function() { - if ( conditionFn() ) { - - // Hook not needed (or it's not possible to use it due - // to missing dependency), remove it. - delete this.get; - return; - } - - // Hook needed; redefine it so that the support test is not executed again. - return ( this.get = hookFn ).apply( this, arguments ); - } - }; -} - - -var cssPrefixes = [ "Webkit", "Moz", "ms" ], - emptyStyle = document.createElement( "div" ).style, - vendorProps = {}; - -// Return a vendor-prefixed property or undefined -function vendorPropName( name ) { - - // Check for vendor prefixed names - var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), - i = cssPrefixes.length; - - while ( i-- ) { - name = cssPrefixes[ i ] + capName; - if ( name in emptyStyle ) { - return name; - } - } -} - -// Return a potentially-mapped jQuery.cssProps or vendor prefixed property -function finalPropName( name ) { - var final = jQuery.cssProps[ name ] || vendorProps[ name ]; - - if ( final ) { - return final; - } - if ( name in emptyStyle ) { - return name; - } - return vendorProps[ name ] = vendorPropName( name ) || name; -} - - -var - - // Swappable if display is none or starts with table - // except "table", "table-cell", or "table-caption" - // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display - rdisplayswap = /^(none|table(?!-c[ea]).+)/, - rcustomProp = /^--/, - cssShow = { position: "absolute", visibility: "hidden", display: "block" }, - cssNormalTransform = { - letterSpacing: "0", - fontWeight: "400" - }; - -function setPositiveNumber( _elem, value, subtract ) { - - // Any relative (+/-) values have already been - // normalized at this point - var matches = rcssNum.exec( value ); - return matches ? - - // Guard against undefined "subtract", e.g., when used as in cssHooks - Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : - value; -} - -function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { - var i = dimension === "width" ? 1 : 0, - extra = 0, - delta = 0; - - // Adjustment may not be necessary - if ( box === ( isBorderBox ? "border" : "content" ) ) { - return 0; - } - - for ( ; i < 4; i += 2 ) { - - // Both box models exclude margin - if ( box === "margin" ) { - delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); - } - - // If we get here with a content-box, we're seeking "padding" or "border" or "margin" - if ( !isBorderBox ) { - - // Add padding - delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - - // For "border" or "margin", add border - if ( box !== "padding" ) { - delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - - // But still keep track of it otherwise - } else { - extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - - // If we get here with a border-box (content + padding + border), we're seeking "content" or - // "padding" or "margin" - } else { - - // For "content", subtract padding - if ( box === "content" ) { - delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - } - - // For "content" or "padding", subtract border - if ( box !== "margin" ) { - delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - } - } - - // Account for positive content-box scroll gutter when requested by providing computedVal - if ( !isBorderBox && computedVal >= 0 ) { - - // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border - // Assuming integer scroll gutter, subtract the rest and round down - delta += Math.max( 0, Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - computedVal - - delta - - extra - - 0.5 - - // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter - // Use an explicit zero to avoid NaN (gh-3964) - ) ) || 0; - } - - return delta; -} - -function getWidthOrHeight( elem, dimension, extra ) { - - // Start with computed style - var styles = getStyles( elem ), - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). - // Fake content-box until we know it's needed to know the true value. - boxSizingNeeded = !support.boxSizingReliable() || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - valueIsBorderBox = isBorderBox, - - val = curCSS( elem, dimension, styles ), - offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); - - // Support: Firefox <=54 - // Return a confounding non-pixel value or feign ignorance, as appropriate. - if ( rnumnonpx.test( val ) ) { - if ( !extra ) { - return val; - } - val = "auto"; - } - - - // Support: IE 9 - 11 only - // Use offsetWidth/offsetHeight for when box sizing is unreliable. - // In those cases, the computed value can be trusted to be border-box. - if ( ( !support.boxSizingReliable() && isBorderBox || - - // Support: IE 10 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Interestingly, in some cases IE 9 doesn't suffer from this issue. - !support.reliableTrDimensions() && nodeName( elem, "tr" ) || - - // Fall back to offsetWidth/offsetHeight when value is "auto" - // This happens for inline elements with no explicit setting (gh-3571) - val === "auto" || - - // Support: Android <=4.1 - 4.3 only - // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) - !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && - - // Make sure the element is visible & connected - elem.getClientRects().length ) { - - isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; - - // Where available, offsetWidth/offsetHeight approximate border box dimensions. - // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the - // retrieved value as a content box dimension. - valueIsBorderBox = offsetProp in elem; - if ( valueIsBorderBox ) { - val = elem[ offsetProp ]; - } - } - - // Normalize "" and auto - val = parseFloat( val ) || 0; - - // Adjust for the element's box model - return ( val + - boxModelAdjustment( - elem, - dimension, - extra || ( isBorderBox ? "border" : "content" ), - valueIsBorderBox, - styles, - - // Provide the current computed size to request scroll gutter calculation (gh-3589) - val - ) - ) + "px"; -} - -jQuery.extend( { - - // Add in style property hooks for overriding the default - // behavior of getting and setting a style property - cssHooks: { - opacity: { - get: function( elem, computed ) { - if ( computed ) { - - // We should always get a number back from opacity - var ret = curCSS( elem, "opacity" ); - return ret === "" ? "1" : ret; - } - } - } - }, - - // Don't automatically add "px" to these possibly-unitless properties - cssNumber: { - "animationIterationCount": true, - "columnCount": true, - "fillOpacity": true, - "flexGrow": true, - "flexShrink": true, - "fontWeight": true, - "gridArea": true, - "gridColumn": true, - "gridColumnEnd": true, - "gridColumnStart": true, - "gridRow": true, - "gridRowEnd": true, - "gridRowStart": true, - "lineHeight": true, - "opacity": true, - "order": true, - "orphans": true, - "widows": true, - "zIndex": true, - "zoom": true - }, - - // Add in properties whose names you wish to fix before - // setting or getting the value - cssProps: {}, - - // Get and set the style property on a DOM Node - style: function( elem, name, value, extra ) { - - // Don't set styles on text and comment nodes - if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { - return; - } - - // Make sure that we're working with the right name - var ret, type, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ), - style = elem.style; - - // Make sure that we're working with the right name. We don't - // want to query the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Gets hook for the prefixed version, then unprefixed version - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // Check if we're setting a value - if ( value !== undefined ) { - type = typeof value; - - // Convert "+=" or "-=" to relative numbers (#7345) - if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { - value = adjustCSS( elem, name, ret ); - - // Fixes bug #9237 - type = "number"; - } - - // Make sure that null and NaN values aren't set (#7116) - if ( value == null || value !== value ) { - return; - } - - // If a number was passed in, add the unit (except for certain CSS properties) - // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append - // "px" to a few hardcoded values. - if ( type === "number" && !isCustomProp ) { - value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); - } - - // background-* props affect original clone's values - if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { - style[ name ] = "inherit"; - } - - // If a hook was provided, use that value, otherwise just set the specified value - if ( !hooks || !( "set" in hooks ) || - ( value = hooks.set( elem, value, extra ) ) !== undefined ) { - - if ( isCustomProp ) { - style.setProperty( name, value ); - } else { - style[ name ] = value; - } - } - - } else { - - // If a hook was provided get the non-computed value from there - if ( hooks && "get" in hooks && - ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { - - return ret; - } - - // Otherwise just get the value from the style object - return style[ name ]; - } - }, - - css: function( elem, name, extra, styles ) { - var val, num, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ); - - // Make sure that we're working with the right name. We don't - // want to modify the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Try prefixed name followed by the unprefixed name - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // If a hook was provided get the computed value from there - if ( hooks && "get" in hooks ) { - val = hooks.get( elem, true, extra ); - } - - // Otherwise, if a way to get the computed value exists, use that - if ( val === undefined ) { - val = curCSS( elem, name, styles ); - } - - // Convert "normal" to computed value - if ( val === "normal" && name in cssNormalTransform ) { - val = cssNormalTransform[ name ]; - } - - // Make numeric if forced or a qualifier was provided and val looks numeric - if ( extra === "" || extra ) { - num = parseFloat( val ); - return extra === true || isFinite( num ) ? num || 0 : val; - } - - return val; - } -} ); - -jQuery.each( [ "height", "width" ], function( _i, dimension ) { - jQuery.cssHooks[ dimension ] = { - get: function( elem, computed, extra ) { - if ( computed ) { - - // Certain elements can have dimension info if we invisibly show them - // but it must have a current display style that would benefit - return rdisplayswap.test( jQuery.css( elem, "display" ) ) && - - // Support: Safari 8+ - // Table columns in Safari have non-zero offsetWidth & zero - // getBoundingClientRect().width unless display is changed. - // Support: IE <=11 only - // Running getBoundingClientRect on a disconnected node - // in IE throws an error. - ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? - swap( elem, cssShow, function() { - return getWidthOrHeight( elem, dimension, extra ); - } ) : - getWidthOrHeight( elem, dimension, extra ); - } - }, - - set: function( elem, value, extra ) { - var matches, - styles = getStyles( elem ), - - // Only read styles.position if the test has a chance to fail - // to avoid forcing a reflow. - scrollboxSizeBuggy = !support.scrollboxSize() && - styles.position === "absolute", - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) - boxSizingNeeded = scrollboxSizeBuggy || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - subtract = extra ? - boxModelAdjustment( - elem, - dimension, - extra, - isBorderBox, - styles - ) : - 0; - - // Account for unreliable border-box dimensions by comparing offset* to computed and - // faking a content-box to get border and padding (gh-3699) - if ( isBorderBox && scrollboxSizeBuggy ) { - subtract -= Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - parseFloat( styles[ dimension ] ) - - boxModelAdjustment( elem, dimension, "border", false, styles ) - - 0.5 - ); - } - - // Convert to pixels if value adjustment is needed - if ( subtract && ( matches = rcssNum.exec( value ) ) && - ( matches[ 3 ] || "px" ) !== "px" ) { - - elem.style[ dimension ] = value; - value = jQuery.css( elem, dimension ); - } - - return setPositiveNumber( elem, value, subtract ); - } - }; -} ); - -jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, - function( elem, computed ) { - if ( computed ) { - return ( parseFloat( curCSS( elem, "marginLeft" ) ) || - elem.getBoundingClientRect().left - - swap( elem, { marginLeft: 0 }, function() { - return elem.getBoundingClientRect().left; - } ) - ) + "px"; - } - } -); - -// These hooks are used by animate to expand properties -jQuery.each( { - margin: "", - padding: "", - border: "Width" -}, function( prefix, suffix ) { - jQuery.cssHooks[ prefix + suffix ] = { - expand: function( value ) { - var i = 0, - expanded = {}, - - // Assumes a single number if not a string - parts = typeof value === "string" ? value.split( " " ) : [ value ]; - - for ( ; i < 4; i++ ) { - expanded[ prefix + cssExpand[ i ] + suffix ] = - parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; - } - - return expanded; - } - }; - - if ( prefix !== "margin" ) { - jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; - } -} ); - -jQuery.fn.extend( { - css: function( name, value ) { - return access( this, function( elem, name, value ) { - var styles, len, - map = {}, - i = 0; - - if ( Array.isArray( name ) ) { - styles = getStyles( elem ); - len = name.length; - - for ( ; i < len; i++ ) { - map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); - } - - return map; - } - - return value !== undefined ? - jQuery.style( elem, name, value ) : - jQuery.css( elem, name ); - }, name, value, arguments.length > 1 ); - } -} ); - - -function Tween( elem, options, prop, end, easing ) { - return new Tween.prototype.init( elem, options, prop, end, easing ); -} -jQuery.Tween = Tween; - -Tween.prototype = { - constructor: Tween, - init: function( elem, options, prop, end, easing, unit ) { - this.elem = elem; - this.prop = prop; - this.easing = easing || jQuery.easing._default; - this.options = options; - this.start = this.now = this.cur(); - this.end = end; - this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); - }, - cur: function() { - var hooks = Tween.propHooks[ this.prop ]; - - return hooks && hooks.get ? - hooks.get( this ) : - Tween.propHooks._default.get( this ); - }, - run: function( percent ) { - var eased, - hooks = Tween.propHooks[ this.prop ]; - - if ( this.options.duration ) { - this.pos = eased = jQuery.easing[ this.easing ]( - percent, this.options.duration * percent, 0, 1, this.options.duration - ); - } else { - this.pos = eased = percent; - } - this.now = ( this.end - this.start ) * eased + this.start; - - if ( this.options.step ) { - this.options.step.call( this.elem, this.now, this ); - } - - if ( hooks && hooks.set ) { - hooks.set( this ); - } else { - Tween.propHooks._default.set( this ); - } - return this; - } -}; - -Tween.prototype.init.prototype = Tween.prototype; - -Tween.propHooks = { - _default: { - get: function( tween ) { - var result; - - // Use a property on the element directly when it is not a DOM element, - // or when there is no matching style property that exists. - if ( tween.elem.nodeType !== 1 || - tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { - return tween.elem[ tween.prop ]; - } - - // Passing an empty string as a 3rd parameter to .css will automatically - // attempt a parseFloat and fallback to a string if the parse fails. - // Simple values such as "10px" are parsed to Float; - // complex values such as "rotate(1rad)" are returned as-is. - result = jQuery.css( tween.elem, tween.prop, "" ); - - // Empty strings, null, undefined and "auto" are converted to 0. - return !result || result === "auto" ? 0 : result; - }, - set: function( tween ) { - - // Use step hook for back compat. - // Use cssHook if its there. - // Use .style if available and use plain properties where available. - if ( jQuery.fx.step[ tween.prop ] ) { - jQuery.fx.step[ tween.prop ]( tween ); - } else if ( tween.elem.nodeType === 1 && ( - jQuery.cssHooks[ tween.prop ] || - tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { - jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); - } else { - tween.elem[ tween.prop ] = tween.now; - } - } - } -}; - -// Support: IE <=9 only -// Panic based approach to setting things on disconnected nodes -Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { - set: function( tween ) { - if ( tween.elem.nodeType && tween.elem.parentNode ) { - tween.elem[ tween.prop ] = tween.now; - } - } -}; - -jQuery.easing = { - linear: function( p ) { - return p; - }, - swing: function( p ) { - return 0.5 - Math.cos( p * Math.PI ) / 2; - }, - _default: "swing" -}; - -jQuery.fx = Tween.prototype.init; - -// Back compat <1.8 extension point -jQuery.fx.step = {}; - - - - -var - fxNow, inProgress, - rfxtypes = /^(?:toggle|show|hide)$/, - rrun = /queueHooks$/; - -function schedule() { - if ( inProgress ) { - if ( document.hidden === false && window.requestAnimationFrame ) { - window.requestAnimationFrame( schedule ); - } else { - window.setTimeout( schedule, jQuery.fx.interval ); - } - - jQuery.fx.tick(); - } -} - -// Animations created synchronously will run synchronously -function createFxNow() { - window.setTimeout( function() { - fxNow = undefined; - } ); - return ( fxNow = Date.now() ); -} - -// Generate parameters to create a standard animation -function genFx( type, includeWidth ) { - var which, - i = 0, - attrs = { height: type }; - - // If we include width, step value is 1 to do all cssExpand values, - // otherwise step value is 2 to skip over Left and Right - includeWidth = includeWidth ? 1 : 0; - for ( ; i < 4; i += 2 - includeWidth ) { - which = cssExpand[ i ]; - attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; - } - - if ( includeWidth ) { - attrs.opacity = attrs.width = type; - } - - return attrs; -} - -function createTween( value, prop, animation ) { - var tween, - collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), - index = 0, - length = collection.length; - for ( ; index < length; index++ ) { - if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { - - // We're done with this property - return tween; - } - } -} - -function defaultPrefilter( elem, props, opts ) { - var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, - isBox = "width" in props || "height" in props, - anim = this, - orig = {}, - style = elem.style, - hidden = elem.nodeType && isHiddenWithinTree( elem ), - dataShow = dataPriv.get( elem, "fxshow" ); - - // Queue-skipping animations hijack the fx hooks - if ( !opts.queue ) { - hooks = jQuery._queueHooks( elem, "fx" ); - if ( hooks.unqueued == null ) { - hooks.unqueued = 0; - oldfire = hooks.empty.fire; - hooks.empty.fire = function() { - if ( !hooks.unqueued ) { - oldfire(); - } - }; - } - hooks.unqueued++; - - anim.always( function() { - - // Ensure the complete handler is called before this completes - anim.always( function() { - hooks.unqueued--; - if ( !jQuery.queue( elem, "fx" ).length ) { - hooks.empty.fire(); - } - } ); - } ); - } - - // Detect show/hide animations - for ( prop in props ) { - value = props[ prop ]; - if ( rfxtypes.test( value ) ) { - delete props[ prop ]; - toggle = toggle || value === "toggle"; - if ( value === ( hidden ? "hide" : "show" ) ) { - - // Pretend to be hidden if this is a "show" and - // there is still data from a stopped show/hide - if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { - hidden = true; - - // Ignore all other no-op show/hide data - } else { - continue; - } - } - orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); - } - } - - // Bail out if this is a no-op like .hide().hide() - propTween = !jQuery.isEmptyObject( props ); - if ( !propTween && jQuery.isEmptyObject( orig ) ) { - return; - } - - // Restrict "overflow" and "display" styles during box animations - if ( isBox && elem.nodeType === 1 ) { - - // Support: IE <=9 - 11, Edge 12 - 15 - // Record all 3 overflow attributes because IE does not infer the shorthand - // from identically-valued overflowX and overflowY and Edge just mirrors - // the overflowX value there. - opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; - - // Identify a display type, preferring old show/hide data over the CSS cascade - restoreDisplay = dataShow && dataShow.display; - if ( restoreDisplay == null ) { - restoreDisplay = dataPriv.get( elem, "display" ); - } - display = jQuery.css( elem, "display" ); - if ( display === "none" ) { - if ( restoreDisplay ) { - display = restoreDisplay; - } else { - - // Get nonempty value(s) by temporarily forcing visibility - showHide( [ elem ], true ); - restoreDisplay = elem.style.display || restoreDisplay; - display = jQuery.css( elem, "display" ); - showHide( [ elem ] ); - } - } - - // Animate inline elements as inline-block - if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { - if ( jQuery.css( elem, "float" ) === "none" ) { - - // Restore the original display value at the end of pure show/hide animations - if ( !propTween ) { - anim.done( function() { - style.display = restoreDisplay; - } ); - if ( restoreDisplay == null ) { - display = style.display; - restoreDisplay = display === "none" ? "" : display; - } - } - style.display = "inline-block"; - } - } - } - - if ( opts.overflow ) { - style.overflow = "hidden"; - anim.always( function() { - style.overflow = opts.overflow[ 0 ]; - style.overflowX = opts.overflow[ 1 ]; - style.overflowY = opts.overflow[ 2 ]; - } ); - } - - // Implement show/hide animations - propTween = false; - for ( prop in orig ) { - - // General show/hide setup for this element animation - if ( !propTween ) { - if ( dataShow ) { - if ( "hidden" in dataShow ) { - hidden = dataShow.hidden; - } - } else { - dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); - } - - // Store hidden/visible for toggle so `.stop().toggle()` "reverses" - if ( toggle ) { - dataShow.hidden = !hidden; - } - - // Show elements before animating them - if ( hidden ) { - showHide( [ elem ], true ); - } - - /* eslint-disable no-loop-func */ - - anim.done( function() { - - /* eslint-enable no-loop-func */ - - // The final step of a "hide" animation is actually hiding the element - if ( !hidden ) { - showHide( [ elem ] ); - } - dataPriv.remove( elem, "fxshow" ); - for ( prop in orig ) { - jQuery.style( elem, prop, orig[ prop ] ); - } - } ); - } - - // Per-property setup - propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); - if ( !( prop in dataShow ) ) { - dataShow[ prop ] = propTween.start; - if ( hidden ) { - propTween.end = propTween.start; - propTween.start = 0; - } - } - } -} - -function propFilter( props, specialEasing ) { - var index, name, easing, value, hooks; - - // camelCase, specialEasing and expand cssHook pass - for ( index in props ) { - name = camelCase( index ); - easing = specialEasing[ name ]; - value = props[ index ]; - if ( Array.isArray( value ) ) { - easing = value[ 1 ]; - value = props[ index ] = value[ 0 ]; - } - - if ( index !== name ) { - props[ name ] = value; - delete props[ index ]; - } - - hooks = jQuery.cssHooks[ name ]; - if ( hooks && "expand" in hooks ) { - value = hooks.expand( value ); - delete props[ name ]; - - // Not quite $.extend, this won't overwrite existing keys. - // Reusing 'index' because we have the correct "name" - for ( index in value ) { - if ( !( index in props ) ) { - props[ index ] = value[ index ]; - specialEasing[ index ] = easing; - } - } - } else { - specialEasing[ name ] = easing; - } - } -} - -function Animation( elem, properties, options ) { - var result, - stopped, - index = 0, - length = Animation.prefilters.length, - deferred = jQuery.Deferred().always( function() { - - // Don't match elem in the :animated selector - delete tick.elem; - } ), - tick = function() { - if ( stopped ) { - return false; - } - var currentTime = fxNow || createFxNow(), - remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), - - // Support: Android 2.3 only - // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) - temp = remaining / animation.duration || 0, - percent = 1 - temp, - index = 0, - length = animation.tweens.length; - - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( percent ); - } - - deferred.notifyWith( elem, [ animation, percent, remaining ] ); - - // If there's more to do, yield - if ( percent < 1 && length ) { - return remaining; - } - - // If this was an empty animation, synthesize a final progress notification - if ( !length ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - } - - // Resolve the animation and report its conclusion - deferred.resolveWith( elem, [ animation ] ); - return false; - }, - animation = deferred.promise( { - elem: elem, - props: jQuery.extend( {}, properties ), - opts: jQuery.extend( true, { - specialEasing: {}, - easing: jQuery.easing._default - }, options ), - originalProperties: properties, - originalOptions: options, - startTime: fxNow || createFxNow(), - duration: options.duration, - tweens: [], - createTween: function( prop, end ) { - var tween = jQuery.Tween( elem, animation.opts, prop, end, - animation.opts.specialEasing[ prop ] || animation.opts.easing ); - animation.tweens.push( tween ); - return tween; - }, - stop: function( gotoEnd ) { - var index = 0, - - // If we are going to the end, we want to run all the tweens - // otherwise we skip this part - length = gotoEnd ? animation.tweens.length : 0; - if ( stopped ) { - return this; - } - stopped = true; - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( 1 ); - } - - // Resolve when we played the last frame; otherwise, reject - if ( gotoEnd ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - deferred.resolveWith( elem, [ animation, gotoEnd ] ); - } else { - deferred.rejectWith( elem, [ animation, gotoEnd ] ); - } - return this; - } - } ), - props = animation.props; - - propFilter( props, animation.opts.specialEasing ); - - for ( ; index < length; index++ ) { - result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); - if ( result ) { - if ( isFunction( result.stop ) ) { - jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = - result.stop.bind( result ); - } - return result; - } - } - - jQuery.map( props, createTween, animation ); - - if ( isFunction( animation.opts.start ) ) { - animation.opts.start.call( elem, animation ); - } - - // Attach callbacks from options - animation - .progress( animation.opts.progress ) - .done( animation.opts.done, animation.opts.complete ) - .fail( animation.opts.fail ) - .always( animation.opts.always ); - - jQuery.fx.timer( - jQuery.extend( tick, { - elem: elem, - anim: animation, - queue: animation.opts.queue - } ) - ); - - return animation; -} - -jQuery.Animation = jQuery.extend( Animation, { - - tweeners: { - "*": [ function( prop, value ) { - var tween = this.createTween( prop, value ); - adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); - return tween; - } ] - }, - - tweener: function( props, callback ) { - if ( isFunction( props ) ) { - callback = props; - props = [ "*" ]; - } else { - props = props.match( rnothtmlwhite ); - } - - var prop, - index = 0, - length = props.length; - - for ( ; index < length; index++ ) { - prop = props[ index ]; - Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; - Animation.tweeners[ prop ].unshift( callback ); - } - }, - - prefilters: [ defaultPrefilter ], - - prefilter: function( callback, prepend ) { - if ( prepend ) { - Animation.prefilters.unshift( callback ); - } else { - Animation.prefilters.push( callback ); - } - } -} ); - -jQuery.speed = function( speed, easing, fn ) { - var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { - complete: fn || !fn && easing || - isFunction( speed ) && speed, - duration: speed, - easing: fn && easing || easing && !isFunction( easing ) && easing - }; - - // Go to the end state if fx are off - if ( jQuery.fx.off ) { - opt.duration = 0; - - } else { - if ( typeof opt.duration !== "number" ) { - if ( opt.duration in jQuery.fx.speeds ) { - opt.duration = jQuery.fx.speeds[ opt.duration ]; - - } else { - opt.duration = jQuery.fx.speeds._default; - } - } - } - - // Normalize opt.queue - true/undefined/null -> "fx" - if ( opt.queue == null || opt.queue === true ) { - opt.queue = "fx"; - } - - // Queueing - opt.old = opt.complete; - - opt.complete = function() { - if ( isFunction( opt.old ) ) { - opt.old.call( this ); - } - - if ( opt.queue ) { - jQuery.dequeue( this, opt.queue ); - } - }; - - return opt; -}; - -jQuery.fn.extend( { - fadeTo: function( speed, to, easing, callback ) { - - // Show any hidden elements after setting opacity to 0 - return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() - - // Animate to the value specified - .end().animate( { opacity: to }, speed, easing, callback ); - }, - animate: function( prop, speed, easing, callback ) { - var empty = jQuery.isEmptyObject( prop ), - optall = jQuery.speed( speed, easing, callback ), - doAnimation = function() { - - // Operate on a copy of prop so per-property easing won't be lost - var anim = Animation( this, jQuery.extend( {}, prop ), optall ); - - // Empty animations, or finishing resolves immediately - if ( empty || dataPriv.get( this, "finish" ) ) { - anim.stop( true ); - } - }; - - doAnimation.finish = doAnimation; - - return empty || optall.queue === false ? - this.each( doAnimation ) : - this.queue( optall.queue, doAnimation ); - }, - stop: function( type, clearQueue, gotoEnd ) { - var stopQueue = function( hooks ) { - var stop = hooks.stop; - delete hooks.stop; - stop( gotoEnd ); - }; - - if ( typeof type !== "string" ) { - gotoEnd = clearQueue; - clearQueue = type; - type = undefined; - } - if ( clearQueue ) { - this.queue( type || "fx", [] ); - } - - return this.each( function() { - var dequeue = true, - index = type != null && type + "queueHooks", - timers = jQuery.timers, - data = dataPriv.get( this ); - - if ( index ) { - if ( data[ index ] && data[ index ].stop ) { - stopQueue( data[ index ] ); - } - } else { - for ( index in data ) { - if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { - stopQueue( data[ index ] ); - } - } - } - - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && - ( type == null || timers[ index ].queue === type ) ) { - - timers[ index ].anim.stop( gotoEnd ); - dequeue = false; - timers.splice( index, 1 ); - } - } - - // Start the next in the queue if the last step wasn't forced. - // Timers currently will call their complete callbacks, which - // will dequeue but only if they were gotoEnd. - if ( dequeue || !gotoEnd ) { - jQuery.dequeue( this, type ); - } - } ); - }, - finish: function( type ) { - if ( type !== false ) { - type = type || "fx"; - } - return this.each( function() { - var index, - data = dataPriv.get( this ), - queue = data[ type + "queue" ], - hooks = data[ type + "queueHooks" ], - timers = jQuery.timers, - length = queue ? queue.length : 0; - - // Enable finishing flag on private data - data.finish = true; - - // Empty the queue first - jQuery.queue( this, type, [] ); - - if ( hooks && hooks.stop ) { - hooks.stop.call( this, true ); - } - - // Look for any active animations, and finish them - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && timers[ index ].queue === type ) { - timers[ index ].anim.stop( true ); - timers.splice( index, 1 ); - } - } - - // Look for any animations in the old queue and finish them - for ( index = 0; index < length; index++ ) { - if ( queue[ index ] && queue[ index ].finish ) { - queue[ index ].finish.call( this ); - } - } - - // Turn off finishing flag - delete data.finish; - } ); - } -} ); - -jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { - var cssFn = jQuery.fn[ name ]; - jQuery.fn[ name ] = function( speed, easing, callback ) { - return speed == null || typeof speed === "boolean" ? - cssFn.apply( this, arguments ) : - this.animate( genFx( name, true ), speed, easing, callback ); - }; -} ); - -// Generate shortcuts for custom animations -jQuery.each( { - slideDown: genFx( "show" ), - slideUp: genFx( "hide" ), - slideToggle: genFx( "toggle" ), - fadeIn: { opacity: "show" }, - fadeOut: { opacity: "hide" }, - fadeToggle: { opacity: "toggle" } -}, function( name, props ) { - jQuery.fn[ name ] = function( speed, easing, callback ) { - return this.animate( props, speed, easing, callback ); - }; -} ); - -jQuery.timers = []; -jQuery.fx.tick = function() { - var timer, - i = 0, - timers = jQuery.timers; - - fxNow = Date.now(); - - for ( ; i < timers.length; i++ ) { - timer = timers[ i ]; - - // Run the timer and safely remove it when done (allowing for external removal) - if ( !timer() && timers[ i ] === timer ) { - timers.splice( i--, 1 ); - } - } - - if ( !timers.length ) { - jQuery.fx.stop(); - } - fxNow = undefined; -}; - -jQuery.fx.timer = function( timer ) { - jQuery.timers.push( timer ); - jQuery.fx.start(); -}; - -jQuery.fx.interval = 13; -jQuery.fx.start = function() { - if ( inProgress ) { - return; - } - - inProgress = true; - schedule(); -}; - -jQuery.fx.stop = function() { - inProgress = null; -}; - -jQuery.fx.speeds = { - slow: 600, - fast: 200, - - // Default speed - _default: 400 -}; - - -// Based off of the plugin by Clint Helfers, with permission. -// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ -jQuery.fn.delay = function( time, type ) { - time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; - type = type || "fx"; - - return this.queue( type, function( next, hooks ) { - var timeout = window.setTimeout( next, time ); - hooks.stop = function() { - window.clearTimeout( timeout ); - }; - } ); -}; - - -( function() { - var input = document.createElement( "input" ), - select = document.createElement( "select" ), - opt = select.appendChild( document.createElement( "option" ) ); - - input.type = "checkbox"; - - // Support: Android <=4.3 only - // Default value for a checkbox should be "on" - support.checkOn = input.value !== ""; - - // Support: IE <=11 only - // Must access selectedIndex to make default options select - support.optSelected = opt.selected; - - // Support: IE <=11 only - // An input loses its value after becoming a radio - input = document.createElement( "input" ); - input.value = "t"; - input.type = "radio"; - support.radioValue = input.value === "t"; -} )(); - - -var boolHook, - attrHandle = jQuery.expr.attrHandle; - -jQuery.fn.extend( { - attr: function( name, value ) { - return access( this, jQuery.attr, name, value, arguments.length > 1 ); - }, - - removeAttr: function( name ) { - return this.each( function() { - jQuery.removeAttr( this, name ); - } ); - } -} ); - -jQuery.extend( { - attr: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set attributes on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - // Fallback to prop when attributes are not supported - if ( typeof elem.getAttribute === "undefined" ) { - return jQuery.prop( elem, name, value ); - } - - // Attribute hooks are determined by the lowercase version - // Grab necessary hook if one is defined - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - hooks = jQuery.attrHooks[ name.toLowerCase() ] || - ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); - } - - if ( value !== undefined ) { - if ( value === null ) { - jQuery.removeAttr( elem, name ); - return; - } - - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - elem.setAttribute( name, value + "" ); - return value; - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - ret = jQuery.find.attr( elem, name ); - - // Non-existent attributes return null, we normalize to undefined - return ret == null ? undefined : ret; - }, - - attrHooks: { - type: { - set: function( elem, value ) { - if ( !support.radioValue && value === "radio" && - nodeName( elem, "input" ) ) { - var val = elem.value; - elem.setAttribute( "type", value ); - if ( val ) { - elem.value = val; - } - return value; - } - } - } - }, - - removeAttr: function( elem, value ) { - var name, - i = 0, - - // Attribute names can contain non-HTML whitespace characters - // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 - attrNames = value && value.match( rnothtmlwhite ); - - if ( attrNames && elem.nodeType === 1 ) { - while ( ( name = attrNames[ i++ ] ) ) { - elem.removeAttribute( name ); - } - } - } -} ); - -// Hooks for boolean attributes -boolHook = { - set: function( elem, value, name ) { - if ( value === false ) { - - // Remove boolean attributes when set to false - jQuery.removeAttr( elem, name ); - } else { - elem.setAttribute( name, name ); - } - return name; - } -}; - -jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { - var getter = attrHandle[ name ] || jQuery.find.attr; - - attrHandle[ name ] = function( elem, name, isXML ) { - var ret, handle, - lowercaseName = name.toLowerCase(); - - if ( !isXML ) { - - // Avoid an infinite loop by temporarily removing this function from the getter - handle = attrHandle[ lowercaseName ]; - attrHandle[ lowercaseName ] = ret; - ret = getter( elem, name, isXML ) != null ? - lowercaseName : - null; - attrHandle[ lowercaseName ] = handle; - } - return ret; - }; -} ); - - - - -var rfocusable = /^(?:input|select|textarea|button)$/i, - rclickable = /^(?:a|area)$/i; - -jQuery.fn.extend( { - prop: function( name, value ) { - return access( this, jQuery.prop, name, value, arguments.length > 1 ); - }, - - removeProp: function( name ) { - return this.each( function() { - delete this[ jQuery.propFix[ name ] || name ]; - } ); - } -} ); - -jQuery.extend( { - prop: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set properties on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - - // Fix name and attach hooks - name = jQuery.propFix[ name ] || name; - hooks = jQuery.propHooks[ name ]; - } - - if ( value !== undefined ) { - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - return ( elem[ name ] = value ); - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - return elem[ name ]; - }, - - propHooks: { - tabIndex: { - get: function( elem ) { - - // Support: IE <=9 - 11 only - // elem.tabIndex doesn't always return the - // correct value when it hasn't been explicitly set - // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ - // Use proper attribute retrieval(#12072) - var tabindex = jQuery.find.attr( elem, "tabindex" ); - - if ( tabindex ) { - return parseInt( tabindex, 10 ); - } - - if ( - rfocusable.test( elem.nodeName ) || - rclickable.test( elem.nodeName ) && - elem.href - ) { - return 0; - } - - return -1; - } - } - }, - - propFix: { - "for": "htmlFor", - "class": "className" - } -} ); - -// Support: IE <=11 only -// Accessing the selectedIndex property -// forces the browser to respect setting selected -// on the option -// The getter ensures a default option is selected -// when in an optgroup -// eslint rule "no-unused-expressions" is disabled for this code -// since it considers such accessions noop -if ( !support.optSelected ) { - jQuery.propHooks.selected = { - get: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent && parent.parentNode ) { - parent.parentNode.selectedIndex; - } - return null; - }, - set: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent ) { - parent.selectedIndex; - - if ( parent.parentNode ) { - parent.parentNode.selectedIndex; - } - } - } - }; -} - -jQuery.each( [ - "tabIndex", - "readOnly", - "maxLength", - "cellSpacing", - "cellPadding", - "rowSpan", - "colSpan", - "useMap", - "frameBorder", - "contentEditable" -], function() { - jQuery.propFix[ this.toLowerCase() ] = this; -} ); - - - - - // Strip and collapse whitespace according to HTML spec - // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace - function stripAndCollapse( value ) { - var tokens = value.match( rnothtmlwhite ) || []; - return tokens.join( " " ); - } - - -function getClass( elem ) { - return elem.getAttribute && elem.getAttribute( "class" ) || ""; -} - -function classesToArray( value ) { - if ( Array.isArray( value ) ) { - return value; - } - if ( typeof value === "string" ) { - return value.match( rnothtmlwhite ) || []; - } - return []; -} - -jQuery.fn.extend( { - addClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - if ( cur.indexOf( " " + clazz + " " ) < 0 ) { - cur += clazz + " "; - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - removeClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - if ( !arguments.length ) { - return this.attr( "class", "" ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - - // This expression is here for better compressibility (see addClass) - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - - // Remove *all* instances - while ( cur.indexOf( " " + clazz + " " ) > -1 ) { - cur = cur.replace( " " + clazz + " ", " " ); - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - toggleClass: function( value, stateVal ) { - var type = typeof value, - isValidValue = type === "string" || Array.isArray( value ); - - if ( typeof stateVal === "boolean" && isValidValue ) { - return stateVal ? this.addClass( value ) : this.removeClass( value ); - } - - if ( isFunction( value ) ) { - return this.each( function( i ) { - jQuery( this ).toggleClass( - value.call( this, i, getClass( this ), stateVal ), - stateVal - ); - } ); - } - - return this.each( function() { - var className, i, self, classNames; - - if ( isValidValue ) { - - // Toggle individual class names - i = 0; - self = jQuery( this ); - classNames = classesToArray( value ); - - while ( ( className = classNames[ i++ ] ) ) { - - // Check each className given, space separated list - if ( self.hasClass( className ) ) { - self.removeClass( className ); - } else { - self.addClass( className ); - } - } - - // Toggle whole class name - } else if ( value === undefined || type === "boolean" ) { - className = getClass( this ); - if ( className ) { - - // Store className if set - dataPriv.set( this, "__className__", className ); - } - - // If the element has a class name or if we're passed `false`, - // then remove the whole classname (if there was one, the above saved it). - // Otherwise bring back whatever was previously saved (if anything), - // falling back to the empty string if nothing was stored. - if ( this.setAttribute ) { - this.setAttribute( "class", - className || value === false ? - "" : - dataPriv.get( this, "__className__" ) || "" - ); - } - } - } ); - }, - - hasClass: function( selector ) { - var className, elem, - i = 0; - - className = " " + selector + " "; - while ( ( elem = this[ i++ ] ) ) { - if ( elem.nodeType === 1 && - ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { - return true; - } - } - - return false; - } -} ); - - - - -var rreturn = /\r/g; - -jQuery.fn.extend( { - val: function( value ) { - var hooks, ret, valueIsFunction, - elem = this[ 0 ]; - - if ( !arguments.length ) { - if ( elem ) { - hooks = jQuery.valHooks[ elem.type ] || - jQuery.valHooks[ elem.nodeName.toLowerCase() ]; - - if ( hooks && - "get" in hooks && - ( ret = hooks.get( elem, "value" ) ) !== undefined - ) { - return ret; - } - - ret = elem.value; - - // Handle most common string cases - if ( typeof ret === "string" ) { - return ret.replace( rreturn, "" ); - } - - // Handle cases where value is null/undef or number - return ret == null ? "" : ret; - } - - return; - } - - valueIsFunction = isFunction( value ); - - return this.each( function( i ) { - var val; - - if ( this.nodeType !== 1 ) { - return; - } - - if ( valueIsFunction ) { - val = value.call( this, i, jQuery( this ).val() ); - } else { - val = value; - } - - // Treat null/undefined as ""; convert numbers to string - if ( val == null ) { - val = ""; - - } else if ( typeof val === "number" ) { - val += ""; - - } else if ( Array.isArray( val ) ) { - val = jQuery.map( val, function( value ) { - return value == null ? "" : value + ""; - } ); - } - - hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; - - // If set returns undefined, fall back to normal setting - if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { - this.value = val; - } - } ); - } -} ); - -jQuery.extend( { - valHooks: { - option: { - get: function( elem ) { - - var val = jQuery.find.attr( elem, "value" ); - return val != null ? - val : - - // Support: IE <=10 - 11 only - // option.text throws exceptions (#14686, #14858) - // Strip and collapse whitespace - // https://html.spec.whatwg.org/#strip-and-collapse-whitespace - stripAndCollapse( jQuery.text( elem ) ); - } - }, - select: { - get: function( elem ) { - var value, option, i, - options = elem.options, - index = elem.selectedIndex, - one = elem.type === "select-one", - values = one ? null : [], - max = one ? index + 1 : options.length; - - if ( index < 0 ) { - i = max; - - } else { - i = one ? index : 0; - } - - // Loop through all the selected options - for ( ; i < max; i++ ) { - option = options[ i ]; - - // Support: IE <=9 only - // IE8-9 doesn't update selected after form reset (#2551) - if ( ( option.selected || i === index ) && - - // Don't return options that are disabled or in a disabled optgroup - !option.disabled && - ( !option.parentNode.disabled || - !nodeName( option.parentNode, "optgroup" ) ) ) { - - // Get the specific value for the option - value = jQuery( option ).val(); - - // We don't need an array for one selects - if ( one ) { - return value; - } - - // Multi-Selects return an array - values.push( value ); - } - } - - return values; - }, - - set: function( elem, value ) { - var optionSet, option, - options = elem.options, - values = jQuery.makeArray( value ), - i = options.length; - - while ( i-- ) { - option = options[ i ]; - - /* eslint-disable no-cond-assign */ - - if ( option.selected = - jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 - ) { - optionSet = true; - } - - /* eslint-enable no-cond-assign */ - } - - // Force browsers to behave consistently when non-matching value is set - if ( !optionSet ) { - elem.selectedIndex = -1; - } - return values; - } - } - } -} ); - -// Radios and checkboxes getter/setter -jQuery.each( [ "radio", "checkbox" ], function() { - jQuery.valHooks[ this ] = { - set: function( elem, value ) { - if ( Array.isArray( value ) ) { - return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); - } - } - }; - if ( !support.checkOn ) { - jQuery.valHooks[ this ].get = function( elem ) { - return elem.getAttribute( "value" ) === null ? "on" : elem.value; - }; - } -} ); - - - - -// Return jQuery for attributes-only inclusion - - -support.focusin = "onfocusin" in window; - - -var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, - stopPropagationCallback = function( e ) { - e.stopPropagation(); - }; - -jQuery.extend( jQuery.event, { - - trigger: function( event, data, elem, onlyHandlers ) { - - var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, - eventPath = [ elem || document ], - type = hasOwn.call( event, "type" ) ? event.type : event, - namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; - - cur = lastElement = tmp = elem = elem || document; - - // Don't do events on text and comment nodes - if ( elem.nodeType === 3 || elem.nodeType === 8 ) { - return; - } - - // focus/blur morphs to focusin/out; ensure we're not firing them right now - if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { - return; - } - - if ( type.indexOf( "." ) > -1 ) { - - // Namespaced trigger; create a regexp to match event type in handle() - namespaces = type.split( "." ); - type = namespaces.shift(); - namespaces.sort(); - } - ontype = type.indexOf( ":" ) < 0 && "on" + type; - - // Caller can pass in a jQuery.Event object, Object, or just an event type string - event = event[ jQuery.expando ] ? - event : - new jQuery.Event( type, typeof event === "object" && event ); - - // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) - event.isTrigger = onlyHandlers ? 2 : 3; - event.namespace = namespaces.join( "." ); - event.rnamespace = event.namespace ? - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : - null; - - // Clean up the event in case it is being reused - event.result = undefined; - if ( !event.target ) { - event.target = elem; - } - - // Clone any incoming data and prepend the event, creating the handler arg list - data = data == null ? - [ event ] : - jQuery.makeArray( data, [ event ] ); - - // Allow special events to draw outside the lines - special = jQuery.event.special[ type ] || {}; - if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { - return; - } - - // Determine event propagation path in advance, per W3C events spec (#9951) - // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) - if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { - - bubbleType = special.delegateType || type; - if ( !rfocusMorph.test( bubbleType + type ) ) { - cur = cur.parentNode; - } - for ( ; cur; cur = cur.parentNode ) { - eventPath.push( cur ); - tmp = cur; - } - - // Only add window if we got to document (e.g., not plain obj or detached DOM) - if ( tmp === ( elem.ownerDocument || document ) ) { - eventPath.push( tmp.defaultView || tmp.parentWindow || window ); - } - } - - // Fire handlers on the event path - i = 0; - while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { - lastElement = cur; - event.type = i > 1 ? - bubbleType : - special.bindType || type; - - // jQuery handler - handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] && - dataPriv.get( cur, "handle" ); - if ( handle ) { - handle.apply( cur, data ); - } - - // Native handler - handle = ontype && cur[ ontype ]; - if ( handle && handle.apply && acceptData( cur ) ) { - event.result = handle.apply( cur, data ); - if ( event.result === false ) { - event.preventDefault(); - } - } - } - event.type = type; - - // If nobody prevented the default action, do it now - if ( !onlyHandlers && !event.isDefaultPrevented() ) { - - if ( ( !special._default || - special._default.apply( eventPath.pop(), data ) === false ) && - acceptData( elem ) ) { - - // Call a native DOM method on the target with the same name as the event. - // Don't do default actions on window, that's where global variables be (#6170) - if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { - - // Don't re-trigger an onFOO event when we call its FOO() method - tmp = elem[ ontype ]; - - if ( tmp ) { - elem[ ontype ] = null; - } - - // Prevent re-triggering of the same event, since we already bubbled it above - jQuery.event.triggered = type; - - if ( event.isPropagationStopped() ) { - lastElement.addEventListener( type, stopPropagationCallback ); - } - - elem[ type ](); - - if ( event.isPropagationStopped() ) { - lastElement.removeEventListener( type, stopPropagationCallback ); - } - - jQuery.event.triggered = undefined; - - if ( tmp ) { - elem[ ontype ] = tmp; - } - } - } - } - - return event.result; - }, - - // Piggyback on a donor event to simulate a different one - // Used only for `focus(in | out)` events - simulate: function( type, elem, event ) { - var e = jQuery.extend( - new jQuery.Event(), - event, - { - type: type, - isSimulated: true - } - ); - - jQuery.event.trigger( e, null, elem ); - } - -} ); - -jQuery.fn.extend( { - - trigger: function( type, data ) { - return this.each( function() { - jQuery.event.trigger( type, data, this ); - } ); - }, - triggerHandler: function( type, data ) { - var elem = this[ 0 ]; - if ( elem ) { - return jQuery.event.trigger( type, data, elem, true ); - } - } -} ); - - -// Support: Firefox <=44 -// Firefox doesn't have focus(in | out) events -// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 -// -// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 -// focus(in | out) events fire after focus & blur events, -// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order -// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 -if ( !support.focusin ) { - jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { - - // Attach a single capturing handler on the document while someone wants focusin/focusout - var handler = function( event ) { - jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); - }; - - jQuery.event.special[ fix ] = { - setup: function() { - - // Handle: regular nodes (via `this.ownerDocument`), window - // (via `this.document`) & document (via `this`). - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ); - - if ( !attaches ) { - doc.addEventListener( orig, handler, true ); - } - dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); - }, - teardown: function() { - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ) - 1; - - if ( !attaches ) { - doc.removeEventListener( orig, handler, true ); - dataPriv.remove( doc, fix ); - - } else { - dataPriv.access( doc, fix, attaches ); - } - } - }; - } ); -} -var location = window.location; - -var nonce = { guid: Date.now() }; - -var rquery = ( /\?/ ); - - - -// Cross-browser xml parsing -jQuery.parseXML = function( data ) { - var xml, parserErrorElem; - if ( !data || typeof data !== "string" ) { - return null; - } - - // Support: IE 9 - 11 only - // IE throws on parseFromString with invalid input. - try { - xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); - } catch ( e ) {} - - parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ]; - if ( !xml || parserErrorElem ) { - jQuery.error( "Invalid XML: " + ( - parserErrorElem ? - jQuery.map( parserErrorElem.childNodes, function( el ) { - return el.textContent; - } ).join( "\n" ) : - data - ) ); - } - return xml; -}; - - -var - rbracket = /\[\]$/, - rCRLF = /\r?\n/g, - rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, - rsubmittable = /^(?:input|select|textarea|keygen)/i; - -function buildParams( prefix, obj, traditional, add ) { - var name; - - if ( Array.isArray( obj ) ) { - - // Serialize array item. - jQuery.each( obj, function( i, v ) { - if ( traditional || rbracket.test( prefix ) ) { - - // Treat each array item as a scalar. - add( prefix, v ); - - } else { - - // Item is non-scalar (array or object), encode its numeric index. - buildParams( - prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", - v, - traditional, - add - ); - } - } ); - - } else if ( !traditional && toType( obj ) === "object" ) { - - // Serialize object item. - for ( name in obj ) { - buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); - } - - } else { - - // Serialize scalar item. - add( prefix, obj ); - } -} - -// Serialize an array of form elements or a set of -// key/values into a query string -jQuery.param = function( a, traditional ) { - var prefix, - s = [], - add = function( key, valueOrFunction ) { - - // If value is a function, invoke it and use its return value - var value = isFunction( valueOrFunction ) ? - valueOrFunction() : - valueOrFunction; - - s[ s.length ] = encodeURIComponent( key ) + "=" + - encodeURIComponent( value == null ? "" : value ); - }; - - if ( a == null ) { - return ""; - } - - // If an array was passed in, assume that it is an array of form elements. - if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { - - // Serialize the form elements - jQuery.each( a, function() { - add( this.name, this.value ); - } ); - - } else { - - // If traditional, encode the "old" way (the way 1.3.2 or older - // did it), otherwise encode params recursively. - for ( prefix in a ) { - buildParams( prefix, a[ prefix ], traditional, add ); - } - } - - // Return the resulting serialization - return s.join( "&" ); -}; - -jQuery.fn.extend( { - serialize: function() { - return jQuery.param( this.serializeArray() ); - }, - serializeArray: function() { - return this.map( function() { - - // Can add propHook for "elements" to filter or add form elements - var elements = jQuery.prop( this, "elements" ); - return elements ? jQuery.makeArray( elements ) : this; - } ).filter( function() { - var type = this.type; - - // Use .is( ":disabled" ) so that fieldset[disabled] works - return this.name && !jQuery( this ).is( ":disabled" ) && - rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && - ( this.checked || !rcheckableType.test( type ) ); - } ).map( function( _i, elem ) { - var val = jQuery( this ).val(); - - if ( val == null ) { - return null; - } - - if ( Array.isArray( val ) ) { - return jQuery.map( val, function( val ) { - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ); - } - - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ).get(); - } -} ); - - -var - r20 = /%20/g, - rhash = /#.*$/, - rantiCache = /([?&])_=[^&]*/, - rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, - - // #7653, #8125, #8152: local protocol detection - rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, - rnoContent = /^(?:GET|HEAD)$/, - rprotocol = /^\/\//, - - /* Prefilters - * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) - * 2) These are called: - * - BEFORE asking for a transport - * - AFTER param serialization (s.data is a string if s.processData is true) - * 3) key is the dataType - * 4) the catchall symbol "*" can be used - * 5) execution will start with transport dataType and THEN continue down to "*" if needed - */ - prefilters = {}, - - /* Transports bindings - * 1) key is the dataType - * 2) the catchall symbol "*" can be used - * 3) selection will start with transport dataType and THEN go to "*" if needed - */ - transports = {}, - - // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression - allTypes = "*/".concat( "*" ), - - // Anchor tag for parsing the document origin - originAnchor = document.createElement( "a" ); - -originAnchor.href = location.href; - -// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport -function addToPrefiltersOrTransports( structure ) { - - // dataTypeExpression is optional and defaults to "*" - return function( dataTypeExpression, func ) { - - if ( typeof dataTypeExpression !== "string" ) { - func = dataTypeExpression; - dataTypeExpression = "*"; - } - - var dataType, - i = 0, - dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; - - if ( isFunction( func ) ) { - - // For each dataType in the dataTypeExpression - while ( ( dataType = dataTypes[ i++ ] ) ) { - - // Prepend if requested - if ( dataType[ 0 ] === "+" ) { - dataType = dataType.slice( 1 ) || "*"; - ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); - - // Otherwise append - } else { - ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); - } - } - } - }; -} - -// Base inspection function for prefilters and transports -function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { - - var inspected = {}, - seekingTransport = ( structure === transports ); - - function inspect( dataType ) { - var selected; - inspected[ dataType ] = true; - jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { - var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); - if ( typeof dataTypeOrTransport === "string" && - !seekingTransport && !inspected[ dataTypeOrTransport ] ) { - - options.dataTypes.unshift( dataTypeOrTransport ); - inspect( dataTypeOrTransport ); - return false; - } else if ( seekingTransport ) { - return !( selected = dataTypeOrTransport ); - } - } ); - return selected; - } - - return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); -} - -// A special extend for ajax options -// that takes "flat" options (not to be deep extended) -// Fixes #9887 -function ajaxExtend( target, src ) { - var key, deep, - flatOptions = jQuery.ajaxSettings.flatOptions || {}; - - for ( key in src ) { - if ( src[ key ] !== undefined ) { - ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; - } - } - if ( deep ) { - jQuery.extend( true, target, deep ); - } - - return target; -} - -/* Handles responses to an ajax request: - * - finds the right dataType (mediates between content-type and expected dataType) - * - returns the corresponding response - */ -function ajaxHandleResponses( s, jqXHR, responses ) { - - var ct, type, finalDataType, firstDataType, - contents = s.contents, - dataTypes = s.dataTypes; - - // Remove auto dataType and get content-type in the process - while ( dataTypes[ 0 ] === "*" ) { - dataTypes.shift(); - if ( ct === undefined ) { - ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); - } - } - - // Check if we're dealing with a known content-type - if ( ct ) { - for ( type in contents ) { - if ( contents[ type ] && contents[ type ].test( ct ) ) { - dataTypes.unshift( type ); - break; - } - } - } - - // Check to see if we have a response for the expected dataType - if ( dataTypes[ 0 ] in responses ) { - finalDataType = dataTypes[ 0 ]; - } else { - - // Try convertible dataTypes - for ( type in responses ) { - if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { - finalDataType = type; - break; - } - if ( !firstDataType ) { - firstDataType = type; - } - } - - // Or just use first one - finalDataType = finalDataType || firstDataType; - } - - // If we found a dataType - // We add the dataType to the list if needed - // and return the corresponding response - if ( finalDataType ) { - if ( finalDataType !== dataTypes[ 0 ] ) { - dataTypes.unshift( finalDataType ); - } - return responses[ finalDataType ]; - } -} - -/* Chain conversions given the request and the original response - * Also sets the responseXXX fields on the jqXHR instance - */ -function ajaxConvert( s, response, jqXHR, isSuccess ) { - var conv2, current, conv, tmp, prev, - converters = {}, - - // Work with a copy of dataTypes in case we need to modify it for conversion - dataTypes = s.dataTypes.slice(); - - // Create converters map with lowercased keys - if ( dataTypes[ 1 ] ) { - for ( conv in s.converters ) { - converters[ conv.toLowerCase() ] = s.converters[ conv ]; - } - } - - current = dataTypes.shift(); - - // Convert to each sequential dataType - while ( current ) { - - if ( s.responseFields[ current ] ) { - jqXHR[ s.responseFields[ current ] ] = response; - } - - // Apply the dataFilter if provided - if ( !prev && isSuccess && s.dataFilter ) { - response = s.dataFilter( response, s.dataType ); - } - - prev = current; - current = dataTypes.shift(); - - if ( current ) { - - // There's only work to do if current dataType is non-auto - if ( current === "*" ) { - - current = prev; - - // Convert response if prev dataType is non-auto and differs from current - } else if ( prev !== "*" && prev !== current ) { - - // Seek a direct converter - conv = converters[ prev + " " + current ] || converters[ "* " + current ]; - - // If none found, seek a pair - if ( !conv ) { - for ( conv2 in converters ) { - - // If conv2 outputs current - tmp = conv2.split( " " ); - if ( tmp[ 1 ] === current ) { - - // If prev can be converted to accepted input - conv = converters[ prev + " " + tmp[ 0 ] ] || - converters[ "* " + tmp[ 0 ] ]; - if ( conv ) { - - // Condense equivalence converters - if ( conv === true ) { - conv = converters[ conv2 ]; - - // Otherwise, insert the intermediate dataType - } else if ( converters[ conv2 ] !== true ) { - current = tmp[ 0 ]; - dataTypes.unshift( tmp[ 1 ] ); - } - break; - } - } - } - } - - // Apply converter (if not an equivalence) - if ( conv !== true ) { - - // Unless errors are allowed to bubble, catch and return them - if ( conv && s.throws ) { - response = conv( response ); - } else { - try { - response = conv( response ); - } catch ( e ) { - return { - state: "parsererror", - error: conv ? e : "No conversion from " + prev + " to " + current - }; - } - } - } - } - } - } - - return { state: "success", data: response }; -} - -jQuery.extend( { - - // Counter for holding the number of active queries - active: 0, - - // Last-Modified header cache for next request - lastModified: {}, - etag: {}, - - ajaxSettings: { - url: location.href, - type: "GET", - isLocal: rlocalProtocol.test( location.protocol ), - global: true, - processData: true, - async: true, - contentType: "application/x-www-form-urlencoded; charset=UTF-8", - - /* - timeout: 0, - data: null, - dataType: null, - username: null, - password: null, - cache: null, - throws: false, - traditional: false, - headers: {}, - */ - - accepts: { - "*": allTypes, - text: "text/plain", - html: "text/html", - xml: "application/xml, text/xml", - json: "application/json, text/javascript" - }, - - contents: { - xml: /\bxml\b/, - html: /\bhtml/, - json: /\bjson\b/ - }, - - responseFields: { - xml: "responseXML", - text: "responseText", - json: "responseJSON" - }, - - // Data converters - // Keys separate source (or catchall "*") and destination types with a single space - converters: { - - // Convert anything to text - "* text": String, - - // Text to html (true = no transformation) - "text html": true, - - // Evaluate text as a json expression - "text json": JSON.parse, - - // Parse text as xml - "text xml": jQuery.parseXML - }, - - // For options that shouldn't be deep extended: - // you can add your own custom options here if - // and when you create one that shouldn't be - // deep extended (see ajaxExtend) - flatOptions: { - url: true, - context: true - } - }, - - // Creates a full fledged settings object into target - // with both ajaxSettings and settings fields. - // If target is omitted, writes into ajaxSettings. - ajaxSetup: function( target, settings ) { - return settings ? - - // Building a settings object - ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : - - // Extending ajaxSettings - ajaxExtend( jQuery.ajaxSettings, target ); - }, - - ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), - ajaxTransport: addToPrefiltersOrTransports( transports ), - - // Main method - ajax: function( url, options ) { - - // If url is an object, simulate pre-1.5 signature - if ( typeof url === "object" ) { - options = url; - url = undefined; - } - - // Force options to be an object - options = options || {}; - - var transport, - - // URL without anti-cache param - cacheURL, - - // Response headers - responseHeadersString, - responseHeaders, - - // timeout handle - timeoutTimer, - - // Url cleanup var - urlAnchor, - - // Request state (becomes false upon send and true upon completion) - completed, - - // To know if global events are to be dispatched - fireGlobals, - - // Loop variable - i, - - // uncached part of the url - uncached, - - // Create the final options object - s = jQuery.ajaxSetup( {}, options ), - - // Callbacks context - callbackContext = s.context || s, - - // Context for global events is callbackContext if it is a DOM node or jQuery collection - globalEventContext = s.context && - ( callbackContext.nodeType || callbackContext.jquery ) ? - jQuery( callbackContext ) : - jQuery.event, - - // Deferreds - deferred = jQuery.Deferred(), - completeDeferred = jQuery.Callbacks( "once memory" ), - - // Status-dependent callbacks - statusCode = s.statusCode || {}, - - // Headers (they are sent all at once) - requestHeaders = {}, - requestHeadersNames = {}, - - // Default abort message - strAbort = "canceled", - - // Fake xhr - jqXHR = { - readyState: 0, - - // Builds headers hashtable if needed - getResponseHeader: function( key ) { - var match; - if ( completed ) { - if ( !responseHeaders ) { - responseHeaders = {}; - while ( ( match = rheaders.exec( responseHeadersString ) ) ) { - responseHeaders[ match[ 1 ].toLowerCase() + " " ] = - ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) - .concat( match[ 2 ] ); - } - } - match = responseHeaders[ key.toLowerCase() + " " ]; - } - return match == null ? null : match.join( ", " ); - }, - - // Raw string - getAllResponseHeaders: function() { - return completed ? responseHeadersString : null; - }, - - // Caches the header - setRequestHeader: function( name, value ) { - if ( completed == null ) { - name = requestHeadersNames[ name.toLowerCase() ] = - requestHeadersNames[ name.toLowerCase() ] || name; - requestHeaders[ name ] = value; - } - return this; - }, - - // Overrides response content-type header - overrideMimeType: function( type ) { - if ( completed == null ) { - s.mimeType = type; - } - return this; - }, - - // Status-dependent callbacks - statusCode: function( map ) { - var code; - if ( map ) { - if ( completed ) { - - // Execute the appropriate callbacks - jqXHR.always( map[ jqXHR.status ] ); - } else { - - // Lazy-add the new callbacks in a way that preserves old ones - for ( code in map ) { - statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; - } - } - } - return this; - }, - - // Cancel the request - abort: function( statusText ) { - var finalText = statusText || strAbort; - if ( transport ) { - transport.abort( finalText ); - } - done( 0, finalText ); - return this; - } - }; - - // Attach deferreds - deferred.promise( jqXHR ); - - // Add protocol if not provided (prefilters might expect it) - // Handle falsy url in the settings object (#10093: consistency with old signature) - // We also use the url parameter if available - s.url = ( ( url || s.url || location.href ) + "" ) - .replace( rprotocol, location.protocol + "//" ); - - // Alias method option to type as per ticket #12004 - s.type = options.method || options.type || s.method || s.type; - - // Extract dataTypes list - s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; - - // A cross-domain request is in order when the origin doesn't match the current origin. - if ( s.crossDomain == null ) { - urlAnchor = document.createElement( "a" ); - - // Support: IE <=8 - 11, Edge 12 - 15 - // IE throws exception on accessing the href property if url is malformed, - // e.g. http://example.com:80x/ - try { - urlAnchor.href = s.url; - - // Support: IE <=8 - 11 only - // Anchor's host property isn't correctly set when s.url is relative - urlAnchor.href = urlAnchor.href; - s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== - urlAnchor.protocol + "//" + urlAnchor.host; - } catch ( e ) { - - // If there is an error parsing the URL, assume it is crossDomain, - // it can be rejected by the transport if it is invalid - s.crossDomain = true; - } - } - - // Convert data if not already a string - if ( s.data && s.processData && typeof s.data !== "string" ) { - s.data = jQuery.param( s.data, s.traditional ); - } - - // Apply prefilters - inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); - - // If request was aborted inside a prefilter, stop there - if ( completed ) { - return jqXHR; - } - - // We can fire global events as of now if asked to - // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) - fireGlobals = jQuery.event && s.global; - - // Watch for a new set of requests - if ( fireGlobals && jQuery.active++ === 0 ) { - jQuery.event.trigger( "ajaxStart" ); - } - - // Uppercase the type - s.type = s.type.toUpperCase(); - - // Determine if request has content - s.hasContent = !rnoContent.test( s.type ); - - // Save the URL in case we're toying with the If-Modified-Since - // and/or If-None-Match header later on - // Remove hash to simplify url manipulation - cacheURL = s.url.replace( rhash, "" ); - - // More options handling for requests with no content - if ( !s.hasContent ) { - - // Remember the hash so we can put it back - uncached = s.url.slice( cacheURL.length ); - - // If data is available and should be processed, append data to url - if ( s.data && ( s.processData || typeof s.data === "string" ) ) { - cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; - - // #9682: remove data so that it's not used in an eventual retry - delete s.data; - } - - // Add or update anti-cache param if needed - if ( s.cache === false ) { - cacheURL = cacheURL.replace( rantiCache, "$1" ); - uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + - uncached; - } - - // Put hash and anti-cache on the URL that will be requested (gh-1732) - s.url = cacheURL + uncached; - - // Change '%20' to '+' if this is encoded form body content (gh-2658) - } else if ( s.data && s.processData && - ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { - s.data = s.data.replace( r20, "+" ); - } - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - if ( jQuery.lastModified[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); - } - if ( jQuery.etag[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); - } - } - - // Set the correct header, if data is being sent - if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { - jqXHR.setRequestHeader( "Content-Type", s.contentType ); - } - - // Set the Accepts header for the server, depending on the dataType - jqXHR.setRequestHeader( - "Accept", - s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? - s.accepts[ s.dataTypes[ 0 ] ] + - ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : - s.accepts[ "*" ] - ); - - // Check for headers option - for ( i in s.headers ) { - jqXHR.setRequestHeader( i, s.headers[ i ] ); - } - - // Allow custom headers/mimetypes and early abort - if ( s.beforeSend && - ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { - - // Abort if not done already and return - return jqXHR.abort(); - } - - // Aborting is no longer a cancellation - strAbort = "abort"; - - // Install callbacks on deferreds - completeDeferred.add( s.complete ); - jqXHR.done( s.success ); - jqXHR.fail( s.error ); - - // Get transport - transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); - - // If no transport, we auto-abort - if ( !transport ) { - done( -1, "No Transport" ); - } else { - jqXHR.readyState = 1; - - // Send global event - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); - } - - // If request was aborted inside ajaxSend, stop there - if ( completed ) { - return jqXHR; - } - - // Timeout - if ( s.async && s.timeout > 0 ) { - timeoutTimer = window.setTimeout( function() { - jqXHR.abort( "timeout" ); - }, s.timeout ); - } - - try { - completed = false; - transport.send( requestHeaders, done ); - } catch ( e ) { - - // Rethrow post-completion exceptions - if ( completed ) { - throw e; - } - - // Propagate others as results - done( -1, e ); - } - } - - // Callback for when everything is done - function done( status, nativeStatusText, responses, headers ) { - var isSuccess, success, error, response, modified, - statusText = nativeStatusText; - - // Ignore repeat invocations - if ( completed ) { - return; - } - - completed = true; - - // Clear timeout if it exists - if ( timeoutTimer ) { - window.clearTimeout( timeoutTimer ); - } - - // Dereference transport for early garbage collection - // (no matter how long the jqXHR object will be used) - transport = undefined; - - // Cache response headers - responseHeadersString = headers || ""; - - // Set readyState - jqXHR.readyState = status > 0 ? 4 : 0; - - // Determine if successful - isSuccess = status >= 200 && status < 300 || status === 304; - - // Get response data - if ( responses ) { - response = ajaxHandleResponses( s, jqXHR, responses ); - } - - // Use a noop converter for missing script but not if jsonp - if ( !isSuccess && - jQuery.inArray( "script", s.dataTypes ) > -1 && - jQuery.inArray( "json", s.dataTypes ) < 0 ) { - s.converters[ "text script" ] = function() {}; - } - - // Convert no matter what (that way responseXXX fields are always set) - response = ajaxConvert( s, response, jqXHR, isSuccess ); - - // If successful, handle type chaining - if ( isSuccess ) { - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - modified = jqXHR.getResponseHeader( "Last-Modified" ); - if ( modified ) { - jQuery.lastModified[ cacheURL ] = modified; - } - modified = jqXHR.getResponseHeader( "etag" ); - if ( modified ) { - jQuery.etag[ cacheURL ] = modified; - } - } - - // if no content - if ( status === 204 || s.type === "HEAD" ) { - statusText = "nocontent"; - - // if not modified - } else if ( status === 304 ) { - statusText = "notmodified"; - - // If we have data, let's convert it - } else { - statusText = response.state; - success = response.data; - error = response.error; - isSuccess = !error; - } - } else { - - // Extract error from statusText and normalize for non-aborts - error = statusText; - if ( status || !statusText ) { - statusText = "error"; - if ( status < 0 ) { - status = 0; - } - } - } - - // Set data for the fake xhr object - jqXHR.status = status; - jqXHR.statusText = ( nativeStatusText || statusText ) + ""; - - // Success/Error - if ( isSuccess ) { - deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); - } else { - deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); - } - - // Status-dependent callbacks - jqXHR.statusCode( statusCode ); - statusCode = undefined; - - if ( fireGlobals ) { - globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", - [ jqXHR, s, isSuccess ? success : error ] ); - } - - // Complete - completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); - - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); - - // Handle the global AJAX counter - if ( !( --jQuery.active ) ) { - jQuery.event.trigger( "ajaxStop" ); - } - } - } - - return jqXHR; - }, - - getJSON: function( url, data, callback ) { - return jQuery.get( url, data, callback, "json" ); - }, - - getScript: function( url, callback ) { - return jQuery.get( url, undefined, callback, "script" ); - } -} ); - -jQuery.each( [ "get", "post" ], function( _i, method ) { - jQuery[ method ] = function( url, data, callback, type ) { - - // Shift arguments if data argument was omitted - if ( isFunction( data ) ) { - type = type || callback; - callback = data; - data = undefined; - } - - // The url can be an options object (which then must have .url) - return jQuery.ajax( jQuery.extend( { - url: url, - type: method, - dataType: type, - data: data, - success: callback - }, jQuery.isPlainObject( url ) && url ) ); - }; -} ); - -jQuery.ajaxPrefilter( function( s ) { - var i; - for ( i in s.headers ) { - if ( i.toLowerCase() === "content-type" ) { - s.contentType = s.headers[ i ] || ""; - } - } -} ); - - -jQuery._evalUrl = function( url, options, doc ) { - return jQuery.ajax( { - url: url, - - // Make this explicit, since user can override this through ajaxSetup (#11264) - type: "GET", - dataType: "script", - cache: true, - async: false, - global: false, - - // Only evaluate the response if it is successful (gh-4126) - // dataFilter is not invoked for failure responses, so using it instead - // of the default converter is kludgy but it works. - converters: { - "text script": function() {} - }, - dataFilter: function( response ) { - jQuery.globalEval( response, options, doc ); - } - } ); -}; - - -jQuery.fn.extend( { - wrapAll: function( html ) { - var wrap; - - if ( this[ 0 ] ) { - if ( isFunction( html ) ) { - html = html.call( this[ 0 ] ); - } - - // The elements to wrap the target around - wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); - - if ( this[ 0 ].parentNode ) { - wrap.insertBefore( this[ 0 ] ); - } - - wrap.map( function() { - var elem = this; - - while ( elem.firstElementChild ) { - elem = elem.firstElementChild; - } - - return elem; - } ).append( this ); - } - - return this; - }, - - wrapInner: function( html ) { - if ( isFunction( html ) ) { - return this.each( function( i ) { - jQuery( this ).wrapInner( html.call( this, i ) ); - } ); - } - - return this.each( function() { - var self = jQuery( this ), - contents = self.contents(); - - if ( contents.length ) { - contents.wrapAll( html ); - - } else { - self.append( html ); - } - } ); - }, - - wrap: function( html ) { - var htmlIsFunction = isFunction( html ); - - return this.each( function( i ) { - jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); - } ); - }, - - unwrap: function( selector ) { - this.parent( selector ).not( "body" ).each( function() { - jQuery( this ).replaceWith( this.childNodes ); - } ); - return this; - } -} ); - - -jQuery.expr.pseudos.hidden = function( elem ) { - return !jQuery.expr.pseudos.visible( elem ); -}; -jQuery.expr.pseudos.visible = function( elem ) { - return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); -}; - - - - -jQuery.ajaxSettings.xhr = function() { - try { - return new window.XMLHttpRequest(); - } catch ( e ) {} -}; - -var xhrSuccessStatus = { - - // File protocol always yields status code 0, assume 200 - 0: 200, - - // Support: IE <=9 only - // #1450: sometimes IE returns 1223 when it should be 204 - 1223: 204 - }, - xhrSupported = jQuery.ajaxSettings.xhr(); - -support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); -support.ajax = xhrSupported = !!xhrSupported; - -jQuery.ajaxTransport( function( options ) { - var callback, errorCallback; - - // Cross domain only allowed if supported through XMLHttpRequest - if ( support.cors || xhrSupported && !options.crossDomain ) { - return { - send: function( headers, complete ) { - var i, - xhr = options.xhr(); - - xhr.open( - options.type, - options.url, - options.async, - options.username, - options.password - ); - - // Apply custom fields if provided - if ( options.xhrFields ) { - for ( i in options.xhrFields ) { - xhr[ i ] = options.xhrFields[ i ]; - } - } - - // Override mime type if needed - if ( options.mimeType && xhr.overrideMimeType ) { - xhr.overrideMimeType( options.mimeType ); - } - - // X-Requested-With header - // For cross-domain requests, seeing as conditions for a preflight are - // akin to a jigsaw puzzle, we simply never set it to be sure. - // (it can always be set on a per-request basis or even using ajaxSetup) - // For same-domain requests, won't change header if already provided. - if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { - headers[ "X-Requested-With" ] = "XMLHttpRequest"; - } - - // Set headers - for ( i in headers ) { - xhr.setRequestHeader( i, headers[ i ] ); - } - - // Callback - callback = function( type ) { - return function() { - if ( callback ) { - callback = errorCallback = xhr.onload = - xhr.onerror = xhr.onabort = xhr.ontimeout = - xhr.onreadystatechange = null; - - if ( type === "abort" ) { - xhr.abort(); - } else if ( type === "error" ) { - - // Support: IE <=9 only - // On a manual native abort, IE9 throws - // errors on any property access that is not readyState - if ( typeof xhr.status !== "number" ) { - complete( 0, "error" ); - } else { - complete( - - // File: protocol always yields status 0; see #8605, #14207 - xhr.status, - xhr.statusText - ); - } - } else { - complete( - xhrSuccessStatus[ xhr.status ] || xhr.status, - xhr.statusText, - - // Support: IE <=9 only - // IE9 has no XHR2 but throws on binary (trac-11426) - // For XHR2 non-text, let the caller handle it (gh-2498) - ( xhr.responseType || "text" ) !== "text" || - typeof xhr.responseText !== "string" ? - { binary: xhr.response } : - { text: xhr.responseText }, - xhr.getAllResponseHeaders() - ); - } - } - }; - }; - - // Listen to events - xhr.onload = callback(); - errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" ); - - // Support: IE 9 only - // Use onreadystatechange to replace onabort - // to handle uncaught aborts - if ( xhr.onabort !== undefined ) { - xhr.onabort = errorCallback; - } else { - xhr.onreadystatechange = function() { - - // Check readyState before timeout as it changes - if ( xhr.readyState === 4 ) { - - // Allow onerror to be called first, - // but that will not handle a native abort - // Also, save errorCallback to a variable - // as xhr.onerror cannot be accessed - window.setTimeout( function() { - if ( callback ) { - errorCallback(); - } - } ); - } - }; - } - - // Create the abort callback - callback = callback( "abort" ); - - try { - - // Do send the request (this may raise an exception) - xhr.send( options.hasContent && options.data || null ); - } catch ( e ) { - - // #14683: Only rethrow if this hasn't been notified as an error yet - if ( callback ) { - throw e; - } - } - }, - - abort: function() { - if ( callback ) { - callback(); - } - } - }; - } -} ); - - - - -// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) -jQuery.ajaxPrefilter( function( s ) { - if ( s.crossDomain ) { - s.contents.script = false; - } -} ); - -// Install script dataType -jQuery.ajaxSetup( { - accepts: { - script: "text/javascript, application/javascript, " + - "application/ecmascript, application/x-ecmascript" - }, - contents: { - script: /\b(?:java|ecma)script\b/ - }, - converters: { - "text script": function( text ) { - jQuery.globalEval( text ); - return text; - } - } -} ); - -// Handle cache's special case and crossDomain -jQuery.ajaxPrefilter( "script", function( s ) { - if ( s.cache === undefined ) { - s.cache = false; - } - if ( s.crossDomain ) { - s.type = "GET"; - } -} ); - -// Bind script tag hack transport -jQuery.ajaxTransport( "script", function( s ) { - - // This transport only deals with cross domain or forced-by-attrs requests - if ( s.crossDomain || s.scriptAttrs ) { - var script, callback; - return { - send: function( _, complete ) { - script = jQuery( " - - - - - - - - - - - - - -
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algorithms.contagion package

-
-

Submodules

-
-
-

algorithms.contagion.animation module

-
-
-algorithms.contagion.animation.contagion_animation(fig, H, transition_events, node_state_color_dict, edge_state_color_dict, node_radius=1, fps=1)[source]
-

A function to animate discrete-time contagion models for hypergraphs. Currently only supports a circular layout.

-
-
Parameters
-
    -

  • fig (matplotlib Figure object) –

    • -

  • H (HyperNetX Hypergraph object) –

    • -

  • transition_events (dictionary) – The dictionary that is output from the discrete_SIS and discrete_SIR functions with return_full_data=True

    • -

  • node_state_color_dict (dictionary) – Dictionary which specifies the colors of each node state. All node states must be specified.

    • -

  • edge_state_color_dict (dictionary) – Dictionary with keys that are edge states and values which specify the colors of each edge state -(can specify an alpha parameter). All edge-dependent transition states must be specified -(most common is “I”) and there must be a a default “OFF” setting.

    • -

  • node_radius (float, default: 1) – The radius of the nodes to draw

    • -

  • fps (int > 0, default: 1) – Frames per second of the animation

    • -
-
-
Return type
-

matplotlib Animation object

-
-
-

Notes

-

Example:

-
>>> import hypernetx.algorithms.contagion as contagion
->>> import random
->>> import hypernetx as hnx
->>> import matplotlib.pyplot as plt
->>> from IPython.display import HTML
->>> n = 1000
->>> m = 10000
->>> hyperedgeList = [random.sample(range(n), k=random.choice([2,3])) for i in range(m)]
->>> H = hnx.Hypergraph(hyperedgeList)
->>> tau = {2:0.1, 3:0.1}
->>> gamma = 0.1
->>> tmax = 100
->>> dt = 0.1
->>> transition_events = contagion.discrete_SIS(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax, dt=dt, return_full_data=True)
->>> node_state_color_dict = {"S":"green", "I":"red", "R":"blue"}
->>> edge_state_color_dict = {"S":(0, 1, 0, 0.3), "I":(1, 0, 0, 0.3), "R":(0, 0, 1, 0.3), "OFF": (1, 1, 1, 0)}
->>> fps = 1
->>> fig = plt.figure()
->>> animation = contagion.contagion_animation(fig, H, transition_events, node_state_color_dict, edge_state_color_dict, node_radius=1, fps=fps)
->>> HTML(animation.to_jshtml())
-
-
-
- -
-
-

algorithms.contagion.epidemics module

-
-
-algorithms.contagion.epidemics.Gillespie_SIR(H, tau, gamma, transmission_function=<function threshold>, initial_infecteds=None, initial_recovereds=None, rho=None, tmin=0, tmax=inf, **args)[source]
-

A continuous-time SIR model for hypergraphs similar to the model in -“The effect of heterogeneity on hypergraph contagion models” by Landry and Restrepo -https://doi.org/10.1063/5.0020034 and -implemented for networks in the EoN package by Joel C. Miller -https://epidemicsonnetworks.readthedocs.io/en/latest/

-
-
Parameters
-
    -

  • H (HyperNetX Hypergraph object) –

    • -

  • tau (dictionary) – Edge sizes as keys (must account for all edge sizes present) and rates of infection for each size (float)

    • -

  • gamma (float) – The healing rate

    • -

  • transmission_function (lambda function, default: threshold) – A lambda function that has required arguments (node, status, edge) and optional arguments

    • -

  • initial_infecteds (list or numpy array, default: None) – Iterable of initially infected node uids

    • -

  • initial_recovereds (list or numpy array, default: None) – An iterable of initially recovered node uids

    • -

  • rho (float from 0 to 1, default: None) – The fraction of initially infected individuals. Both rho and initially infected cannot be specified.

    • -

  • tmin (float, default: 0) – Time at the start of the simulation

    • -

  • tmax (float, default: float('Inf')) – Time at which the simulation should be terminated if it hasn’t already.

    • -

  • return_full_data (bool, default: False) – This returns all the infection and recovery events at each time if True.

    • -

  • **args (Optional arguments to transmission function) – This allows user-defined transmission functions with extra parameters.

    • -
-
-
Returns
-

t, S, I, R – time (t), number of susceptible (S), infected (I), and recovered (R) at each time.

-
-
Return type
-

numpy arrays

-
-
-

Notes

-

Example:

-
>>> import hypernetx.algorithms.contagion as contagion
->>> import random
->>> import hypernetx as hnx
->>> n = 1000
->>> m = 10000
->>> hyperedgeList = [random.sample(range(n), k=random.choice([2,3])) for i in range(m)]
->>> H = hnx.Hypergraph(hyperedgeList)
->>> tau = {2:0.1, 3:0.1}
->>> gamma = 0.1
->>> tmax = 100
->>> t, S, I, R = contagion.Gillespie_SIR(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax)
-
-
-
- -
-
-algorithms.contagion.epidemics.Gillespie_SIS(H, tau, gamma, transmission_function=<function threshold>, initial_infecteds=None, rho=None, tmin=0, tmax=inf, return_full_data=False, sim_kwargs=None, **args)[source]
-

A continuous-time SIS model for hypergraphs similar to the model in -“The effect of heterogeneity on hypergraph contagion models” by Landry and Restrepo -https://doi.org/10.1063/5.0020034 and -implemented for networks in the EoN package by Joel C. Miller -https://epidemicsonnetworks.readthedocs.io/en/latest/

-
-
Parameters
-
    -

  • H (HyperNetX Hypergraph object) –

    • -

  • tau (dictionary) – Edge sizes as keys (must account for all edge sizes present) and rates of infection for each size (float)

    • -

  • gamma (float) – The healing rate

    • -

  • transmission_function (lambda function, default: threshold) – A lambda function that has required arguments (node, status, edge) and optional arguments

    • -

  • initial_infecteds (list or numpy array, default: None) – Iterable of initially infected node uids

    • -

  • rho (float from 0 to 1, default: None) – The fraction of initially infected individuals. Both rho and initially infected cannot be specified.

    • -

  • tmin (float, default: 0) – Time at the start of the simulation

    • -

  • tmax (float, default: 100) – Time at which the simulation should be terminated if it hasn’t already.

    • -

  • return_full_data (bool, default: False) – This returns all the infection and recovery events at each time if True.

    • -

  • **args (Optional arguments to transmission function) – This allows user-defined transmission functions with extra parameters.

    • -
-
-
Returns
-

t, S, I – time (t), number of susceptible (S), and infected (I) at each time.

-
-
Return type
-

numpy arrays

-
-
-

Notes

-

Example:

-
>>> import hypernetx.algorithms.contagion as contagion
->>> import random
->>> import hypernetx as hnx
->>> n = 1000
->>> m = 10000
->>> hyperedgeList = [random.sample(range(n), k=random.choice([2,3])) for i in range(m)]
->>> H = hnx.Hypergraph(hyperedgeList)
->>> tau = {2:0.1, 3:0.1}
->>> gamma = 0.1
->>> tmax = 100
->>> t, S, I = contagion.Gillespie_SIS(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax)
-
-
-
- -
-
-algorithms.contagion.epidemics.collective_contagion(node, status, edge)[source]
-

The collective contagion mechanism described in -“The effect of heterogeneity on hypergraph contagion models” by Landry and Restrepo -https://doi.org/10.1063/5.0020034

-
-
Parameters
-
    -

  • node (hashable) – the node uid to infect (If it doesn’t have status “S”, it will automatically return False)

    • -

  • status (dictionary) – The nodes are keys and the values are statuses (The infected state denoted with “I”)

    • -

  • edge (iterable) – Iterable of node ids (node must be in the edge or it will automatically return False)

    • -
-
-
Returns
-

False if there is no potential to infect and True if there is.

-
-
Return type
-

bool

-
-
-

Notes

-

Example:

-
>>> status = {0:"S", 1:"I", 2:"I", 3:"S", 4:"R"}
->>> collective_contagion(0, status, (0, 1, 2))
-    True
->>> collective_contagion(1, status, (0, 1, 2))
-    False
->>> collective_contagion(3, status, (0, 1, 2))
-    False
-
-
-
- -
-
-algorithms.contagion.epidemics.discrete_SIR(H, tau, gamma, transmission_function=<function threshold>, initial_infecteds=None, initial_recovereds=None, rho=None, tmin=0, tmax=inf, dt=1.0, return_full_data=False, **args)[source]
-

A discrete-time SIR model for hypergraphs similar to the construction described in -“The effect of heterogeneity on hypergraph contagion models” by Landry and Restrepo -https://doi.org/10.1063/5.0020034 and -“Simplicial models of social contagion” by Iacopini et al. -https://doi.org/10.1038/s41467-019-10431-6

-
-
Parameters
-
    -

  • H (HyperNetX Hypergraph object) –

    • -

  • tau (dictionary) – Edge sizes as keys (must account for all edge sizes present) and rates of infection for each size (float)

    • -

  • gamma (float) – The healing rate

    • -

  • transmission_function (lambda function, default: threshold) – A lambda function that has required arguments (node, status, edge) and optional arguments

    • -

  • initial_infecteds (list or numpy array, default: None) – Iterable of initially infected node uids

    • -

  • initial_recovereds (list or numpy array, default: None) – An iterable of initially recovered node uids

    • -

  • rho (float from 0 to 1, default: None) – The fraction of initially infected individuals. Both rho and initially infected cannot be specified.

    • -

  • tmin (float, default: 0) – Time at the start of the simulation

    • -

  • tmax (float, default: float('Inf')) – Time at which the simulation should be terminated if it hasn’t already.

    • -

  • dt (float > 0, default: 1.0) – Step forward in time that the simulation takes at each step.

    • -

  • return_full_data (bool, default: False) – This returns all the infection and recovery events at each time if True.

    • -

  • **args (Optional arguments to transmission function) – This allows user-defined transmission functions with extra parameters.

    • -
-
-
Returns
-

    -

  • if return_full_data –

    -
    -
    dictionary

    Time as the keys and events that happen as the values.

    -
    -
    -
    • -

  • else –

    -
    -
    t, S, I, Rnumpy arrays

    time (t), number of susceptible (S), infected (I), and recovered (R) at each time.

    -
    -
    -
    • -
-

-
-
-

Notes

-

Example:

-
>>> import hypernetx.algorithms.contagion as contagion
->>> import random
->>> import hypernetx as hnx
->>> n = 1000
->>> m = 10000
->>> hyperedgeList = [random.sample(range(n), k=random.choice([2,3])) for i in range(m)]
->>> H = hnx.Hypergraph(hyperedgeList)
->>> tau = {2:0.1, 3:0.1}
->>> gamma = 0.1
->>> tmax = 100
->>> dt = 0.1
->>> t, S, I, R = contagion.discrete_SIR(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax, dt=dt)
-
-
-
- -
-
-algorithms.contagion.epidemics.discrete_SIS(H, tau, gamma, transmission_function=<function threshold>, initial_infecteds=None, rho=None, tmin=0, tmax=100, dt=1.0, return_full_data=False, **args)[source]
-

A discrete-time SIS model for hypergraphs as implemented in -“The effect of heterogeneity on hypergraph contagion models” by Landry and Restrepo -https://doi.org/10.1063/5.0020034 and -“Simplicial models of social contagion” by Iacopini et al. -https://doi.org/10.1038/s41467-019-10431-6

-
-
Parameters
-
    -

  • H (HyperNetX Hypergraph object) –

    • -

  • tau (dictionary) – Edge sizes as keys (must account for all edge sizes present) and rates of infection for each size (float)

    • -

  • gamma (float) – The healing rate

    • -

  • transmission_function (lambda function, default: threshold) – A lambda function that has required arguments (node, status, edge) and optional arguments

    • -

  • initial_infecteds (list or numpy array, default: None) – Iterable of initially infected node uids

    • -

  • rho (float from 0 to 1, default: None) – The fraction of initially infected individuals. Both rho and initially infected cannot be specified.

    • -

  • tmin (float, default: 0) – Time at the start of the simulation

    • -

  • tmax (float, default: 100) – Time at which the simulation should be terminated if it hasn’t already.

    • -

  • dt (float > 0, default: 1.0) – Step forward in time that the simulation takes at each step.

    • -

  • return_full_data (bool, default: False) – This returns all the infection and recovery events at each time if True.

    • -

  • **args (Optional arguments to transmission function) – This allows user-defined transmission functions with extra parameters.

    • -
-
-
Returns
-

    -

  • if return_full_data –

    -
    -
    dictionary

    Time as the keys and events that happen as the values.

    -
    -
    -
    • -

  • else –

    -
    -
    t, S, Inumpy arrays

    time (t), number of susceptible (S), and infected (I) at each time.

    -
    -
    -
    • -
-

-
-
-

Notes

-

Example:

-
>>> import hypernetx.algorithms.contagion as contagion
->>> import random
->>> import hypernetx as hnx
->>> n = 1000
->>> m = 10000
->>> hyperedgeList = [random.sample(range(n), k=random.choice([2,3])) for i in range(m)]
->>> H = hnx.Hypergraph(hyperedgeList)
->>> tau = {2:0.1, 3:0.1}
->>> gamma = 0.1
->>> tmax = 100
->>> dt = 0.1
->>> t, S, I = contagion.discrete_SIS(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax, dt=dt)
-
-
-
- -
-
-algorithms.contagion.epidemics.individual_contagion(node, status, edge)[source]
-

The individual contagion mechanism described in -“The effect of heterogeneity on hypergraph contagion models” by Landry and Restrepo -https://doi.org/10.1063/5.0020034

-
-
Parameters
-
    -

  • node (hashable) – The node uid to infect (If it doesn’t have status “S”, it will automatically return False)

    • -

  • status (dictionary) – The nodes are keys and the values are statuses (The infected state denoted with “I”)

    • -

  • edge (iterable) – Iterable of node ids (node must be in the edge or it will automatically return False)

    • -
-
-
Returns
-

False if there is no potential to infect and True if there is.

-
-
Return type
-

bool

-
-
-

Notes

-

Example:

-
>>> status = {0:"S", 1:"I", 2:"I", 3:"S", 4:"R"}
->>> individual_contagion(0, status, (0, 1, 3))
-    True
->>> individual_contagion(1, status, (0, 1, 2))
-    False
->>> collective_contagion(3, status, (0, 3, 4))
-    False
-
-
-
- -
-
-algorithms.contagion.epidemics.majority_vote(node, status, edge)[source]
-

The majority vote contagion mechanism. If a majority of neighbors are contagious, -it is possible for an individual to change their opinion. If opinions are divided equally, -choose randomly.

-
-
Parameters
-
    -

  • node (hashable) – The node uid to infect (If it doesn’t have status “S”, it will automatically return False)

    • -

  • status (dictionary) – The nodes are keys and the values are statuses (The infected state denoted with “I”)

    • -

  • edge (iterable) – Iterable of node ids (node must be in the edge or it will automatically return False

    • -
-
-
Returns
-

False if there is no potential to infect and True if there is.

-
-
Return type
-

bool

-
-
-

Notes

-

Example:

-
>>> status = {0:"S", 1:"I", 2:"I", 3:"S", 4:"R"}
->>> majority_vote(0, status, (0, 1, 2))
-    True
->>> majority_vote(0, status, (0, 1, 2, 3))
-    True
->>> majority_vote(1, status, (0, 1, 2))
-    False
->>> majority_vote(3, status, (0, 1, 2))
-    False
-
-
-
- -
-
-algorithms.contagion.epidemics.threshold(node, status, edge, tau=0.1)[source]
-

The threshold contagion mechanism

-
-
Parameters
-
    -

  • node (hashable) – The node uid to infect (If it doesn’t have status “S”, it will automatically return False)

    • -

  • status (dictionary) – The nodes are keys and the values are statuses (The infected state denoted with “I”)

    • -

  • edge (iterable) – Iterable of node ids (node must be in the edge or it will automatically return False)

    • -

  • tau (float between 0 and 1, default: 0.1) – The fraction of nodes in an edge that must be infected for the edge to be able to transmit to the node

    • -
-
-
Returns
-

False if there is no potential to infect and True if there is.

-
-
Return type
-

bool

-
-
-

Notes

-

Example:

-
>>> status = {0:"S", 1:"I", 2:"I", 3:"S", 4:"R"}
->>> threshold(0, status, (0, 2, 3, 4), tau=0.2)
-    True
->>> threshold(0, status, (0, 2, 3, 4), tau=0.5)
-    False
->>> threshold(3, status, (1, 2, 3), tau=1)
-    False
-
-
-
- -
-
-

Module contents

-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/algorithms/algorithms.html b/docs/build/algorithms/algorithms.html deleted file mode 100644 index 00006ccc..00000000 --- a/docs/build/algorithms/algorithms.html +++ /dev/null @@ -1,1267 +0,0 @@ - - - - - - - algorithms package — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

algorithms package

-
-

Subpackages

-
- -
-
-
-

Submodules

-
-
-

algorithms.generative_models module

-
-
-algorithms.generative_models.chung_lu_hypergraph(k1, k2)[source]
-

A function to generate an extension of Chung-Lu hypergraph as implemented by Mirah Shi and described for -bipartite networks by Aksoy et al. in https://doi.org/10.1093/comnet/cnx001

-
-
Parameters
-
    -

  • k1 (dictionary) – This a dictionary where the keys are node ids and the values are node degrees.

    • -

  • k2 (dictionary) – This a dictionary where the keys are edge ids and the values are edge degrees also known as edge sizes.

    • -
-
-
Return type
-

HyperNetX Hypergraph object

-
-
-

Notes

-

The sums of k1 and k2 should be roughly the same. If they are not the same, this function returns a warning but still runs. -The output currently is a static Hypergraph object. Dynamic hypergraphs are not currently supported.

-

Example:

-
>>> import hypernetx.algorithms.generative_models as gm
->>> import random
->>> n = 100
->>> k1 = {i : random.randint(1, 100) for i in range(n)}
->>> k2 = {i : sorted(k1.values())[i] for i in range(n)}
->>> H = gm.chung_lu_hypergraph(k1, k2)
-
-
-
- -
-
-algorithms.generative_models.dcsbm_hypergraph(k1, k2, g1, g2, omega)[source]
-

A function to generate an extension of DCSBM hypergraph as implemented by Mirah Shi and described for -bipartite networks by Larremore et al. in https://doi.org/10.1103/PhysRevE.90.012805

-
-
Parameters
-
    -

  • k1 (dictionary) – This a dictionary where the keys are node ids and the values are node degrees.

    • -

  • k2 (dictionary) – This a dictionary where the keys are edge ids and the values are edge degrees also known as edge sizes.

    • -

  • g1 (dictionary) – This a dictionary where the keys are node ids and the values are the group ids to which the node belongs. -The keys must match the keys of k1.

    • -

  • g2 (dictionary) – This a dictionary where the keys are edge ids and the values are the group ids to which the edge belongs. -The keys must match the keys of k2.

    • -

  • omega (2D numpy array) – This is a matrix with entries which specify the number of edges between a given node community and edge community. -The number of rows must match the number of node communities and the number of columns -must match the number of edge communities.

    • -
-
-
Return type
-

HyperNetX Hypergraph object

-
-
-

Notes

-

The sums of k1 and k2 should be the same. If they are not the same, this function returns a warning but still runs. -The sum of k1 (and k2) and omega should be the same. If they are not the same, this function returns a warning -but still runs and the number of entries in the incidence matrix is determined by the omega matrix.

-

The output currently is a static Hypergraph object. Dynamic hypergraphs are not currently supported.

-

Example:

-
>>> n = 100
->>> k1 = {i : random.randint(1, 100) for i in range(n)}
->>> k2 = {i : sorted(k1.values())[i] for i in range(n)}
->>> g1 = {i : random.choice([0, 1]) for i in range(n)}
->>> g2 = {i : random.choice([0, 1]) for i in range(n)}
->>> omega = np.array([[100, 10], [10, 100]])
->>> H = gm.dcsbm_hypergraph(k1, k2, g1, g2, omega)
-
-
-
- -
-
-algorithms.generative_models.erdos_renyi_hypergraph(n, m, p, node_labels=None, edge_labels=None)[source]
-

A function to generate an Erdos-Renyi hypergraph as implemented by Mirah Shi and described for -bipartite networks by Aksoy et al. in https://doi.org/10.1093/comnet/cnx001

-
-
Parameters
-
    -

  • n (int) – Number of nodes

    • -

  • m (int) – Number of edges

    • -

  • p (float) – The probability that a bipartite edge is created

    • -

  • node_labels (list, default=None) – Vertex labels

    • -

  • edge_labels (list, default=None) – Hyperedge labels

    • -
-
-
Return type
-

HyperNetX Hypergraph object

-
-
-

Example:

-
>>> import hypernetx.algorithms.generative_models as gm
->>> n = 1000
->>> m = n
->>> p = 0.01
->>> H = gm.erdos_renyi_hypergraph(n, m, p)
-
-
-
- -
-
-

algorithms.homology_mod2 module

-
-

Homology and Smith Normal Form

-

The purpose of computing the Homology groups for data generated -hypergraphs is to identify data sources that correspond to interesting -features in the topology of the hypergraph.

-

The elements of one of these Homology groups are generated by \(k\) -dimensional cycles of relationships in the original data that are not -bound together by higher order relationships. Ideally, we want the -briefest description of these cycles; we want a minimal set of -relationships exhibiting interesting cyclic behavior. This minimal set -will be a bases for the Homology group.

-

The cyclic relationships in the data are discovered using a boundary -map represented as a matrix. To discover the bases we compute the -Smith Normal Form of the boundary map.

-
-

Homology Mod2

-

This module computes the homology groups for data represented as an -abstract simplicial complex with chain groups \(\{C_k\}\) and \(Z_2\) additions. -The boundary matrices are represented as rectangular matrices over \(Z_2\). -These matrices are diagonalized and represented in Smith -Normal Form. The kernel and image bases are computed and the Betti -numbers and homology bases are returned.

-

Methods for obtaining SNF for Z/2Z are based on Ferrario’s work: -http://www.dlfer.xyz/post/2016-10-27-smith-normal-form/

-
-
-algorithms.homology_mod2.add_to_column(M, i, j)[source]
-

Replaces column i (of M) with logical xor between column i and j

-
-
Parameters
-
    -

  • M (np.array) – matrix

    • -

  • i (int) – index of column being altered

    • -

  • j (int) – index of column being added to altered

    • -
-
-
Returns
-

N

-
-
Return type
-

np.array

-
-
-
- -
-
-algorithms.homology_mod2.add_to_row(M, i, j)[source]
-

Replaces row i with logical xor between row i and j

-
-
Parameters
-
    -

  • M (np.array) –

    • -

  • i (int) – index of row being altered

    • -

  • j (int) – index of row being added to altered

    • -
-
-
Returns
-

N

-
-
Return type
-

np.array

-
-
-
- -
-
-algorithms.homology_mod2.betti(bd, k=None)[source]
-

Generate the kth-betti numbers for a chain complex with boundary -matrices given by bd

-
-
Parameters
-
    -

  • bd (dict of k-boundary matrices keyed on dimension of domain) –

    • -

  • k (int, list or tuple, optional, default=None) – list must be min value and max value of k values inclusive -if None, then all betti numbers for dimensions of existing cells will be -computed.

    • -
-
-
Returns
-

betti – Description

-
-
Return type
-

dict

-
-
-
- -
-
-algorithms.homology_mod2.betti_numbers(h, k=None)[source]
-

Return the kth betti numbers for the simplicial homology of the ASC -associated to h

-
-
Parameters
-
    -

  • h (hnx.Hypergraph) – Hypergraph to compute the betti numbers from

    • -

  • k (int or list, optional, default=None) – list must be min value and max value of k values inclusive -if None, then all betti numbers for dimensions of existing cells will be -computed.

    • -
-
-
Returns
-

betti – A dictionary of betti numbers keyed by dimension

-
-
Return type
-

dict

-
-
-
- -
-
-algorithms.homology_mod2.bkMatrix(km1basis, kbasis)[source]
-

Compute the boundary map from \(C_{k-1}\)-basis to \(C_k\) basis with -respect to \(Z_2\)

-
-
Parameters
-
    -

  • km1basis (indexable iterable) – Ordered list of \(k-1\) dimensional cell

    • -

  • kbasis (indexable iterable) – Ordered list of \(k\) dimensional cells

    • -
-
-
Returns
-

bk – boundary matrix in \(Z_2\) stored as boolean

-
-
Return type
-

np.array

-
-
-
- -
-
-algorithms.homology_mod2.boundary_group(image_basis)[source]
-

Returns a csr_matrix with rows corresponding to the elements of the -group generated by image basis over \(\mathbb{Z}_2\)

-
-
Parameters
-

image_basis (numpy.ndarray or scipy.sparse.csr_matrix) – 2d-array of basis elements

-
-
Return type
-

scipy.sparse.csr_matrix

-
-
-
- -
-
-algorithms.homology_mod2.chain_complex(h, k=None)[source]
-

Compute the k-chains and k-boundary maps required to compute homology -for all values in k

-
-
Parameters
-
    -

  • h (hnx.Hypergraph) –

    • -

  • k (int or list of length 2, optional, default=None) – k must be an integer greater than 0 or a list of -length 2 indicating min and max dimensions to be -computed. eg. if k = [1,2] then 0,1,2,3-chains -and boundary maps for k=1,2,3 will be returned, -if None than k = [1,max dimension of edge in h]

    • -
-
-
Returns
-

C, bd – C is a dictionary of lists -bd is a dictionary of numpy arrays

-
-
Return type
-

dict

-
-
-
- -
-
-algorithms.homology_mod2.homology_basis(bd, k=None, boundary=False, **kwargs)[source]
-

Compute a basis for the kth-simplicial homology group, \(H_k\), defined by a -chain complex \(C\) with boundary maps given by bd \(= \{k:\partial_k \}\)

-
-
Parameters
-
    -

  • bd (dict) – dict of boundary matrices on k-chains to k-1 chains keyed on k -if krange is a tuple then all boundary matrices k in [krange[0],..,krange[1]] -inclusive must be in the dictionary

    • -

  • k (int or list of ints, optional, default=None) – k must be a positive integer or a list of -2 integers indicating min and max dimensions to be -computed, if none given all homology groups will be computed from -available boundary matrices in bd

    • -

  • boundary (bool) – option to return a basis for the boundary group from each dimension. -Needed to compute the shortest generators in the homology group.

    • -
-
-
Returns
-

    -

  • basis (dict) – dict of generators as 0-1 tuples keyed by dim -basis for dimension k will be returned only if bd[k] and bd[k+1] have -been provided.

    • -

  • im (dict) – dict of boundary group generators keyed by dim

    • -
-

-
-
-
- -
-
-algorithms.homology_mod2.hypergraph_homology_basis(h, k=None, shortest=False, interpreted=True)[source]
-

Computes the kth-homology groups mod 2 for the ASC -associated with the hypergraph h for k in krange inclusive

-
-
Parameters
-
    -

  • h (hnx.Hypergraph) –

    • -

  • k (int or list of length 2, optional, default = None) – k must be an integer greater than 0 or a list of -length 2 indicating min and max dimensions to be -computed

    • -

  • shortest (bool, optional, default=False) – option to look for shortest representative for each coset in the -homology group, only good for relatively small examples

    • -

  • interpreted (bool, optional, default = True) – if True will return an explicit basis in terms of the k-chains

    • -
-
-
Returns
-

    -

  • basis (list) – list of generators as k-chains as boolean vectors

    • -

  • interpreted_basis – lists of kchains in basis

    • -
-

-
-
-
- -
-
-algorithms.homology_mod2.interpret(Ck, arr, labels=None)[source]
-

Returns the data as represented in Ck associated with the arr

-
-
Parameters
-
    -

  • Ck (list) – a list of k-cells being referenced by arr

    • -

  • arr (np.array) – array of 0-1 vectors

    • -

  • labels (dict, optional) – dictionary of labels to associate to the nodes in the cells

    • -
-
-
Returns
-

list of k-cells referenced by data in Ck

-
-
Return type
-

list

-
-
-
- -
-
-algorithms.homology_mod2.kchainbasis(h, k)[source]
-

Compute the set of k dimensional cells in the abstract simplicial -complex associated with the hypergraph.

-
-
Parameters
-
    -

  • h (hnx.Hypergraph) –

    • -

  • k (int) – dimension of cell

    • -
-
-
Returns
-

an ordered list of kchains represented as tuples of length k+1

-
-
Return type
-

list

-
-
-
-

See also

-

hnx.hypergraph.toplexes

-
-

Notes

-
    -

  • Method works best if h is simple [Berge], i.e. no edge contains another and there are no duplicate edges (toplexes).

    • -

  • Hypergraph node uids must be sortable.

    • -
-
- -
-
-algorithms.homology_mod2.logical_dot(ar1, ar2)[source]
-

Returns the boolean equivalent of the dot product mod 2 on two 1-d arrays of -the same length.

-
-
Parameters
-
    -

  • ar1 (numpy.ndarray) – 1-d array

    • -

  • ar2 (numpy.ndarray) – 1-d array

    • -
-
-
Returns
-

boolean value associated with dot product mod 2

-
-
Return type
-

bool

-
-
Raises
-

HyperNetXError – If arrays are not of the same length an error will be raised.

-
-
-
- -
-
-algorithms.homology_mod2.logical_matadd(mat1, mat2)[source]
-

Returns the boolean equivalent of matrix addition mod 2 on two -binary arrays stored as type boolean

-
-
Parameters
-
    -

  • mat1 (np.ndarray) – 2-d array of boolean values

    • -

  • mat2 (np.ndarray) – 2-d array of boolean values

    • -
-
-
Returns
-

mat – boolean matrix equivalent to the mod 2 matrix addition of the -matrices as matrices over Z/2Z

-
-
Return type
-

np.ndarray

-
-
Raises
-

HyperNetXError – If dimensions are not equal an error will be raised.

-
-
-
- -
-
-algorithms.homology_mod2.logical_matmul(mat1, mat2)[source]
-

Returns the boolean equivalent of matrix multiplication mod 2 on two -binary arrays stored as type boolean

-
-
Parameters
-
    -

  • mat1 (np.ndarray) – 2-d array of boolean values

    • -

  • mat2 (np.ndarray) – 2-d array of boolean values

    • -
-
-
Returns
-

mat – boolean matrix equivalent to the mod 2 matrix multiplication of the -matrices as matrices over Z/2Z

-
-
Return type
-

np.ndarray

-
-
Raises
-

HyperNetXError – If inner dimensions are not equal an error will be raised.

-
-
-
- -
-
-algorithms.homology_mod2.matmulreduce(arr, reverse=False)[source]
-

Recursively applies a ‘logical multiplication’ to a list of boolean arrays.

-

For arr = [arr[0],arr[1],arr[2]…arr[n]] returns product arr[0]arr[1]…arr[n] -If reverse = True, returns product arr[n]arr[n-1]…arr[0]

-
-
Parameters
-
    -

  • arr (list of np.array) – list of nxm matrices represented as np.array

    • -

  • reverse (bool, optional) – order to multiply the matrices

    • -
-
-
Returns
-

P – Product of matrices in the list

-
-
Return type
-

np.array

-
-
-
- -
-
-algorithms.homology_mod2.reduced_row_echelon_form_mod2(M)[source]
-

Computes the invertible transformation matrices needed to compute -the reduced row echelon form of M modulo 2

-
-
Parameters
-

M (np.array) – a rectangular matrix with elements in \(Z_2\)

-
-
Returns
-

L, S, Linv – LM = S where S is the reduced echelon form of M -and M = LinvS

-
-
Return type
-

np.arrays

-
-
-
- -
-
-algorithms.homology_mod2.smith_normal_form_mod2(M)[source]
-

Computes the invertible transformation matrices needed to compute the -Smith Normal Form of M modulo 2

-
-
Parameters
-
    -

  • M (np.array) – a rectangular matrix with data type bool

    • -

  • track (bool) – if track=True will print out the transformation as Z/2Z matrix as it -discovers L[i] and R[j]

    • -
-
-
Returns
-

L, R, S, Linv – LMR = S is the Smith Normal Form of the matrix M.

-
-
Return type
-

np.arrays

-
-
-
-

Note

-

Given a mxn matrix \(M\) with -entries in \(Z_2\) we start with the equation: \(L M R = S\), where -\(L = I_m\), and \(R=I_n\) are identity matrices and \(S = M\). We -repeatedly apply actions to the left and right side of the equation -to transform S into a diagonal matrix. -For each action applied to the left side we apply its inverse -action to the right side of I_m to generate \(L^{-1}\). -Finally we verify: -\(L M R = S\) and \(LLinv = I_m\).

-
-
- -
-
-algorithms.homology_mod2.swap_columns(i, j, *args)[source]
-

Swaps ith and jth column of each matrix in args -Returns a list of new matrices

-
-
Parameters
-
    -

  • i (int) –

    • -

  • j (int) –

    • -

  • args (np.arrays) –

    • -
-
-
Returns
-

list of copies of args with ith and jth row swapped

-
-
Return type
-

list

-
-
-
- -
-
-algorithms.homology_mod2.swap_rows(i, j, *args)[source]
-

Swaps ith and jth row of each matrix in args -Returns a list of new matrices

-
-
Parameters
-
    -

  • i (int) –

    • -

  • j (int) –

    • -

  • args (np.arrays) –

    • -
-
-
Returns
-

list of copies of args with ith and jth row swapped

-
-
Return type
-

list

-
-
-
- -
-
-
-
-

algorithms.hypergraph_modularity module

-
-

Hypergraph_Modularity

-

Modularity and clustering for hypergraphs using HyperNetX. -Adapted from F. Théberge’s GitHub repository: Hypergraph Clustering -See Tutorial 13 in the tutorials folder for library usage.

-

References

-
-
1(1,2)
-

Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S. and Ravindran B. “A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering”. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24

-
-
2(1,2,3,4,5,6)
-

Kamiński B., Prałat P. and Théberge F. “Community Detection Algorithm Using Hypergraph Modularity”. In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13

-
-
3(1,2)
-

Kamiński B., Poulin V., Prałat P., Szufel P. and Théberge F. “Clustering via hypergraph modularity”, Plos ONE 2019, https://doi.org/10.1371/journal.pone.0224307

-
-
-
-
-algorithms.hypergraph_modularity.dict2part(D)[source]
-

Given a dictionary mapping the part for each vertex, return a partition as a list of sets; inverse function to part2dict

-
-
Parameters
-

D (dict) – Dictionary keyed by vertices with values equal to integer -index of the partition the vertex belongs to

-
-
Returns
-

List of sets; one set for each part in the partition

-
-
Return type
-

list

-
-
-
- -
-
-algorithms.hypergraph_modularity.kumar(HG, delta=0.01)[source]
-

Compute a partition of the vertices in hypergraph HG as per Kumar’s algorithm 1

-
-
Parameters
-
    -

  • HG (Hypergraph) –

    • -

  • delta (float, optional) – convergence stopping criterion

    • -
-
-
Returns
-

A partition of the vertices in HG

-
-
Return type
-

list of sets

-
-
-
- -
-
-algorithms.hypergraph_modularity.last_step(HG, L, wdc=<function linear>, delta=0.01)[source]
-

Given some initial partition L, compute a new partition of the vertices in HG as per Last-Step algorithm 2

-
-

Note

-

This is a very simple algorithm that tries moving nodes between communities to improve hypergraph modularity. -It requires an initial non-trivial partition which can be obtained for example via graph clustering on the 2-section of HG, -or via Kumar’s algorithm.

-
-
-
Parameters
-
    -

  • HG (Hypergraph) –

    • -

  • L (list of sets) – some initial partition of the vertices in HG

    • -

  • wdc (func, optional) – Hyperparameter for hypergraph modularity 2

    • -

  • delta (float, optional) – convergence stopping criterion

    • -
-
-
Returns
-

A new partition for the vertices in HG

-
-
Return type
-

list of sets

-
-
-
- -
-
-algorithms.hypergraph_modularity.linear(d, c)[source]
-

Hyperparameter for hypergraph modularity 2 for d-edge with c vertices in the majority class. -This is the default choice for modularity() and last_step() functions.

-
-
Parameters
-
    -

  • d (int) – Number of vertices in an edge

    • -

  • c (int) – Number of vertices in the majority class

    • -
-
-
Returns
-

c/d if c>d/2 else 0

-
-
Return type
-

float

-
-
-
- -
-
-algorithms.hypergraph_modularity.majority(d, c)[source]
-

Hyperparameter for hypergraph modularity 2 for d-edge with c vertices in the majority class. -This corresponds to the majority rule 3

-
-
Parameters
-
    -

  • d (int) – Number of vertices in an edge

    • -

  • c (int) – Number of vertices in the majority class

    • -
-
-
Returns
-

1 if c>d/2 else 0

-
-
Return type
-

bool

-
-
-
- -
-
-algorithms.hypergraph_modularity.modularity(HG, A, wdc=<function linear>)[source]
-

Computes modularity of hypergraph HG with respect to partition A.

-
-
Parameters
-
    -

  • HG (Hypergraph) – The hypergraph with some precomputed attributes via: precompute_attributes(HG)

    • -

  • A (list of sets) – Partition of the vertices in HG

    • -

  • wdc (func, optional) – Hyperparameter for hypergraph modularity 2

    • -
-
-
-
-

Note

-

For ‘wdc’, any function of the format w(d,c) that returns 0 when c <= d/2 and value in [0,1] otherwise can be used. -Default is ‘linear’; other supplied choices are ‘majority’ and ‘strict’.

-
-
-
Returns
-

The modularity function for partition A on HG

-
-
Return type
-

float

-
-
-
- -
-
-algorithms.hypergraph_modularity.part2dict(A)[source]
-

Given a partition (list of sets), returns a dictionary mapping the part for each vertex; inverse function -to dict2part

-
-
Parameters
-

A (list of sets) – a partition of the vertices

-
-
Returns
-

a dictionary with {vertex: partition index}

-
-
Return type
-

dict

-
-
-
- -
-
-algorithms.hypergraph_modularity.precompute_attributes(HG)[source]
-

Precompute some values on hypergraph HG for faster computing of hypergraph modularity. -This needs to be run before calling either modularity() or last_step().

-
-

Note

-

If HG is unweighted, v.weight is set to 1 for each vertex v in HG. -The weighted degree for each vertex v is stored in v.strength. -The total edge weigths for each edge cardinality is stored in HG.d_weights. -Binomial coefficients to speed-up modularity computation are stored in HG.bin_coef. -Isolated vertices found only in edge(s) of size 1 are dropped.

-
-
-
Parameters
-

HG (Hypergraph) –

-
-
Returns
-

H – New hypergraph with added attributes

-
-
Return type
-

Hypergraph

-
-
-
- -
-
-algorithms.hypergraph_modularity.strict(d, c)[source]
-

Hyperparameter for hypergraph modularity 2 for d-edge with c vertices in the majority class. -This corresponds to the strict rule 3

-
-
Parameters
-
    -

  • d (int) – Number of vertices in an edge

    • -

  • c (int) – Number of vertices in the majority class

    • -
-
-
Returns
-

1 if c==d else 0

-
-
Return type
-

bool

-
-
-
- -
-
-algorithms.hypergraph_modularity.two_section(HG)[source]
-

Creates a random walk based 1 2-section igraph Graph with transition weights defined by the -weights of the hyperedges.

-
-
Parameters
-

HG (Hypergraph) –

-
-
Returns
-

The 2-section graph built from HG

-
-
Return type
-

igraph.Graph

-
-
-
- -
-
-
-

algorithms.laplacians_clustering module

-
-

Hypergraph Probability Transition Matrices, Laplacians, and Clustering

-

We contruct hypergraph random walks utilizing optional “edge-dependent vertex weights”, which are -weights associated with each vertex-hyperedge pair (i.e. cell weights on the incidence matrix). -The probability transition matrix of this random walk is used to construct a normalized Laplacian -matrix for the hypergraph. That normalized Laplacian then serves as the input for a spectral clustering -algorithm. This spectral clustering algorithm, as well as the normalized Laplacian and other details of -this methodology are described in

-

K. Hayashi, S. Aksoy, C. Park, H. Park, “Hypergraph random walks, Laplacians, and clustering”, -Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020. -https://doi.org/10.1145/3340531.3412034

-

Please direct any inquiries concerning the clustering module to Sinan Aksoy, sinan.aksoy@pnnl.gov

-
-
-algorithms.laplacians_clustering.get_pi(P)[source]
-

Returns the eigenvector corresponding to the largest eigenvalue (in magnitude), -normalized so its entries sum to 1. Intended for the probability transition matrix -of a random walk on a (connected) hypergraph, in which case the output can -be interpreted as the stationary distribution.

-
-
Parameters
-

P (csr matrix) – Probability transition matrix

-
-
Returns
-

pi – Stationary distribution of random walk defined by P

-
-
Return type
-

numpy.ndarray

-
-
-
- -
-
-algorithms.laplacians_clustering.norm_lap(H, weights=False, index=True)[source]
-

Normalized Laplacian matrix of the hypergraph. Symmetrizes the probability transition -matrix of a hypergraph random walk using the stationary distribution, using the digraph -Laplacian defined in:

-

Chung, Fan. “Laplacians and the Cheeger inequality for directed graphs.” -Annals of Combinatorics 9.1 (2005): 1-19.

-

and studied in the context of hypergraphs in:

-

Hayashi, K., Aksoy, S. G., Park, C. H., & Park, H. -Hypergraph random walks, laplacians, and clustering. -In Proceedings of CIKM 2020, (2020): 495-504.

-
-
Parameters
-
    -

  • H (hnx.Hypergraph) – The hypergraph must be connected, meaning there is a path linking any two -vertices

    • -

  • weight (bool, optional, default : False) – Uses cell_weights, if False, uniform weights are utilized.

    • -

  • index (bool, optional) – Whether to return matrix-index to vertex-label mapping

    • -
-
-
Returns
-

    -

  • P (scipy.sparse.csr.csr_matrix) – Probability transition matrix of the random walk on the hypergraph

    • -

  • index (dict) – mapping from row and column indices to corresponding vertex label

    • -
-

-
-
-
- -
-
-algorithms.laplacians_clustering.prob_trans(H, weights=False, index=True, check_connected=True)[source]
-

The probability transition matrix of a random walk on the vertices of a hypergraph. -At each step in the walk, the next vertex is chosen by:

-
    -

  1. Selecting a hyperedge e containing the vertex with probability proportional to w(e)

    2. -

  2. Selecting a vertex v within e with probability proportional to a gamma(v,e)

    3. -
-

If weights are not specified, then all weights are uniform and the walk is equivalent -to a simple random walk. -If weights are specified, the hyperedge weights w(e) are determined from the weights -gamma(v,e).

-
-
Parameters
-
    -

  • H (hnx.Hypergraph) – The hypergraph must be connected, meaning there is a path linking any two -vertices

    • -

  • weights (bool, optional, default : False) – Use the cell_weights associated with the hypergraph -If False, uniform weights are utilized.

    • -

  • index (bool, optional) – Whether to return matrix index to vertex label mapping

    • -
-
-
Returns
-

    -

  • P (scipy.sparse.csr.csr_matrix) – Probability transition matrix of the random walk on the hypergraph

    • -

  • index (dict) – mapping from row and column indices to corresponding vertex label

    • -
-

-
-
-
- -
-
-algorithms.laplacians_clustering.spec_clus(H, k, existing_lap=None, weights=False)[source]
-

Hypergraph spectral clustering of the vertex set into k disjoint clusters -using the normalized hypergraph Laplacian. Equivalent to the “RDC-Spec” -Algorithm 1 in:

-

Hayashi, K., Aksoy, S. G., Park, C. H., & Park, H. -Hypergraph random walks, laplacians, and clustering. -In Proceedings of CIKM 2020, (2020): 495-504.

-
-
Parameters
-
    -

  • H (hnx.Hypergraph) – The hypergraph must be connected, meaning there is a path linking any two -vertices

    • -

  • k (int) – Number of clusters

    • -

  • existing_lap (csr matrix, optional) – Whether to use an existing Laplacian; otherwise, normalized hypergraph Laplacian -will be utilized

    • -

  • weights (bool, optional) – Use the cell_weights of the hypergraph. If False uniform weights are used.

    • -
-
-
Returns
-

clusters – Vertex cluster dictionary, keyed by integers 0,…,k-1, with lists of -vertices as values.

-
-
Return type
-

dict

-
-
-
- -
-
-
-

algorithms.s_centrality_measures module

-
-

S-Centrality Measures

-

We generalize graph metrics to s-metrics for a hypergraph by using its s-connected -components. This is accomplished by computing the s edge-adjacency matrix and -constructing the corresponding graph of the matrix. We then use existing graph metrics -on this representation of the hypergraph. In essence we construct an s-line graph -corresponding to the hypergraph on which to apply our methods.

-

S-Metrics for hypergraphs are discussed in depth in: -Aksoy, S.G., Joslyn, C., Ortiz Marrero, C. et al. Hypernetwork science via high-order hypergraph walks. -EPJ Data Sci. 9, 16 (2020). https://doi.org/10.1140/epjds/s13688-020-00231-0

-
-
-algorithms.s_centrality_measures.s_betweenness_centrality(H, s=1, edges=True, normalized=True, return_singletons=True, use_nwhy=True)[source]
-

A centrality measure for an s-edge(node) subgraph of H based on shortest paths. -Equals the betweenness centrality of vertices in the edge(node) s-linegraph.

-

In a graph (2-uniform hypergraph) the betweenness centrality of a vertex \(v\) -is the ratio of the number of non-trivial shortest paths between any pair of -vertices in the graph that pass through \(v\) divided by the total number of -non-trivial shortest paths in the graph.

-

The centrality of edge to all shortest s-edge paths -\(V\) = the set of vertices in the linegraph. -\(\sigma(s,t)\) = the number of shortest paths between vertices \(s\) and \(t\). -\(\sigma(s,t|v)\) = the number of those paths that pass through vertex \(v\).

-
-\[c_B(v) = \sum_{s \neq t \in V} \frac{\sigma(s, t|v)}{\sigma(s,t)}\]
-
-
Parameters
-
    -

  • H (hnx.Hypergraph) –

    • -

  • s (int) – s connectedness requirement

    • -

  • edges (bool, optional) – determines if edge or node linegraph

    • -

  • normalized – bool, default=False, -If true the betweenness values are normalized by 2/((n-1)(n-2)), -where n is the number of edges in H

    • -

  • return_singletons (bool, optional) – if False will ignore singleton components of linegraph

    • -
-
-
Returns
-

A dictionary of s-betweenness centrality value of the edges.

-
-
Return type
-

dict

-
-
-
- -
-
-algorithms.s_centrality_measures.s_closeness_centrality(H, s=1, edges=True, return_singletons=True, source=None, use_nwhy=True)[source]
-

In a connected component the reciprocal of the sum of the distance between an -edge(node) and all other edges(nodes) in the component times the number of edges(nodes) -in the component minus 1.

-

\(V\) = the set of vertices in the linegraph. -\(n = |V|\) -\(d\) = shortest path distance

-
-\[C(u) = \frac{n - 1}{\sum_{v \neq u \in V} d(v, u)}\]
-
-
Parameters
-
    -

  • H (hnx.Hypergraph) –

    • -

  • s (int, optional) –

    • -

  • edges (bool, optional) – Indicates if method should compute edge linegraph (default) or node linegraph.

    • -

  • return_singletons (bool, optional) – Indicates if method should return values for singleton components.

    • -

  • source (str, optional) – Identifier of node or edge of interest for computing centrality

    • -

  • use_nwhy (bool, optional) – If true will use the NWHy library if available.

    • -
-
-
Returns
-

returns the s-closeness centrality value of the edges(nodes). -If source=None a dictionary of values for each s-edge in H is returned. -If source then a single value is returned.

-
-
Return type
-

dict or float

-
-
-
- -
-
-algorithms.s_centrality_measures.s_eccentricity(H, s=1, edges=True, source=None, return_singletons=True, use_nwhy=True)[source]
-

The length of the longest shortest path from a vertex \(u\) to every other vertex in the linegraph. -\(V\) = set of vertices in the linegraph -\(d\) = shortest path distance

-
-\[\text{s-ecc}(u) = \text{max}\{d(u,v): v \in V\}\]
-
-
Parameters
-
    -

  • H (hnx.Hypergraph) –

    • -

  • s (int, optional) –

    • -

  • edges (bool, optional) – Indicates if method should compute edge linegraph (default) or node linegraph.

    • -

  • return_singletons (bool, optional) – Indicates if method should return values for singleton components.

    • -

  • source (str, optional) – Identifier of node or edge of interest for computing centrality

    • -

  • use_nwhy (bool, optional) – If true will use the NWHy library if available.

    • -
-
-
Returns
-

returns the s-eccentricity value of the edges(nodes). -If source=None a dictionary of values for each s-edge in H is returned. -If source then a single value is returned.

-
-
Return type
-

dict or float

-
-
-
- -
-
-algorithms.s_centrality_measures.s_harmonic_centrality(H, s=1, edges=True, source=None, normalized=False, return_singletons=True, use_nwhy=True)[source]
-

A centrality measure for an s-edge subgraph of H. A value equal to 1 means the s-edge -intersects every other s-edge in H. All values range between 0 and 1. -Edges of size less than s return 0. If H contains only one s-edge a 0 is returned.

-

The denormalized reciprocal of the harmonic mean of all distances from \(u\) to all other vertices. -\(V\) = the set of vertices in the linegraph. -\(d\) = shortest path distance

-
-\[C(u) = \sum_{v \neq u \in V} \frac{1}{d(v, u)}\]
-

Normalized this becomes: -$$C(u) = sum_{v neq u in V} frac{1}{d(v, u)}cdotfrac{2}{(n-1)(n-2)}$$ -where \(n\) is the number vertices.

-
-
Parameters
-
    -

  • H (hnx.Hypergraph) –

    • -

  • s (int, optional) –

    • -

  • edges (bool, optional) – Indicates if method should compute edge linegraph (default) or node linegraph.

    • -

  • return_singletons (bool, optional) – Indicates if method should return values for singleton components.

    • -

  • source (str, optional) – Identifier of node or edge of interest for computing centrality

    • -

  • use_nwhy (bool, optional) – If true will use the NWHy library if available.

    • -
-
-
Returns
-

returns the s-harmonic closeness centrality value of the edges, a number between 0 and 1 inclusive. -If source=None a dictionary of values for each s-edge in H is returned. -If source then a single value is returned.

-
-
Return type
-

dict or float

-
-
-
- -
-
-algorithms.s_centrality_measures.s_harmonic_closeness_centrality(H, s=1, edge=None, use_nwhy=True)[source]
-
- -
-
-
-

Module contents

-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/algorithms/modules.html b/docs/build/algorithms/modules.html deleted file mode 100644 index 4eada88a..00000000 --- a/docs/build/algorithms/modules.html +++ /dev/null @@ -1,167 +0,0 @@ - - - - - - - algorithms — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
- -
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/classes/classes.html b/docs/build/classes/classes.html deleted file mode 100644 index 8f62af74..00000000 --- a/docs/build/classes/classes.html +++ /dev/null @@ -1,2833 +0,0 @@ - - - - - - - classes package — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

classes package

-
-

Submodules

-
-
-

classes.entity module

-
-
-class classes.entity.Entity(uid, elements=[], entity=None, weight=1.0, **props)[source]
-

Bases: object

-

Base class for objects used in building network-like objects including -Hypergraphs, Posets, Cell Complexes.

-
-
Parameters
-
    -

  • uid (hashable) – a unique identifier

    • -

  • elements (list or dict, optional, default: None) – a list of entities with identifiers different than uid and/or -hashables different than uid, see Honor System

    • -

  • entity (Entity) – an Entity object to be cloned into a new Entity with uid. If the uid is the same as -Entity.uid then the entities will not be distinguishable and error will be raised. -The elements in the signature will be added to the cloned entity.

    • -

  • weight (float, optional, default : 1) –

    • -

  • props (keyword arguments, optional, default: {}) – properties belonging to the entity added as key=value pairs. -Both key and value must be hashable.

    • -
-
-
-

Notes

-

An Entity is a container-like object, which has a unique identifier and -may contain elements and have properties. -The Entity class was created as a generic object providing structure for -Hypergraph nodes and edges.

- -

Honor System

-

HyperNetX has an Honor System that applies to Entity uid values. -Two entities are equal if their __dict__ objects match. -For performance reasons many methods distinguish entities by their uids. -It is, therefore, up to the user to ensure entities with the same uids are indeed the same. -Not doing so may cause undesirable side effects. -In particular, the methods in the Hypergraph class assume distinct nodes and edges -have distinct uids.

-

Examples

-
>>> x = Entity('x')
->>> y = Entity('y',[x])
->>> z = Entity('z',[x,y],weight=1)
->>> z
-Entity(z,['y', 'x'],{'weight': 1})
->>> z.uid
-'z'
->>> z.elements
-{'x': Entity(x,[],{}), 'y': Entity(y,['x'],{})}
->>> z.properties
-{'weight': 1}
->>> z.children
-{'x'}
->>> x.memberships
-{'y': Entity(y,['x'],{}), 'z': Entity(z,['y', 'x'],{'weight': 1})}
->>> z.fullregistry()
-{'x': Entity(x,[],{}), 'y': Entity(y,['x'],{})}
-
-
-
-

See also

-

EntitySet

-
-
-
-add(*args)[source]
-

Adds unpacked args to entity elements. Depends on add_element()

-
-
Parameters
-

args (One or more entities or hashables) –

-
-
Returns
-

self

-
-
Return type
-

Entity

-
-
-
-

Note

-

Adding an element to an object in a hypergraph will not add the -element to the hypergraph and will cause an error. Use Hypergraph.add_edge -or Hypergraph.add_node_to_edge instead.

-
-
- -
-
-add_element(item)[source]
-

Adds item to entity elements and adds entity to item.memberships.

-
-
Parameters
-

item (hashable or Entity) – If hashable, will be replaced with empty Entity using hashable as uid

-
-
Returns
-

self

-
-
Return type
-

Entity

-
-
-

Notes

-

If item is in entity elements, no new element is added but properties -will be updated. -If item is in complete_registry(), only the item already known to self will be added. -This method employs the Honor System since membership in complete_registry is checked -using the item’s uid. It is assumed that the user will only use the same uid -for identical instances within the entities registry.

-
- -
-
-add_elements_from(arg_set)[source]
-

Similar to add() it allows for adding from an interable.

-
-
Parameters
-

arg_set (Iterable of hashables or entities) –

-
-
Returns
-

self

-
-
Return type
-

Entity

-
-
-
- -
-
-property children
-

Set of uids of the elements of elements of entity.

-

To return set of ids for deeper level use: -Entity.levelset(level).keys() -see: Entity.levelset()

-
- -
-
-clone(newuid)[source]
-

Returns shallow copy of entity with newuid. Entity’s elements will -belong to two distinct Entities.

-
-
Parameters
-

newuid (hashable) – Name of the new entity

-
-
Returns
-

clone

-
-
Return type
-

Entity

-
-
-
- -
-
-complete_registry()[source]
-

A dictionary of all entities appearing in any level of -entity

-
-
Returns
-

complete_registry

-
-
Return type
-

dict

-
-
-
- -
-
-depth(max_depth=10)[source]
-

Returns the number of nonempty level sets of level <= max_depth

-
-
Parameters
-

max_depth (int, optional, default: 10) – If full depth is desired set max_depth to number of entities in -system + 1.

-
-
Returns
-

depth – If max_depth is exceeded output will be numpy infinity. -If there is a cycle output will be numpy infinity.

-
-
Return type
-

int

-
-
-
- -
-
-property elements
-

Dictionary of elements belonging to entity.

-
- -
-
-fullregistry(lastlevel=10, firstlevel=1)[source]
-

A dictionary of all entities appearing in levels firstlevel -to lastlevel.

-
-
Parameters
-
    -

  • lastlevel (int, optional, default: 10) –

    • -

  • firstlevel (int, optional, default: 1) –

    • -
-
-
Returns
-

fullregistry

-
-
Return type
-

dict

-
-
-
- -
-
-property incidence_dict
-

element.uidset for each element in entity

-

To return an incidence dictionary of all nested entities in entity -use nested_incidence_dict

-
-
Type
-

Dictionary of element.uid

-
-
-
- -
-
-intersection(other)[source]
-

A dictionary of elements belonging to entity and other.

-
-
Parameters
-

other (Entity) –

-
-
Returns
-

Dictionary of elements

-
-
Return type
-

dict

-
-
-
- -
-
-property is_bipartite
-

Returns boolean indicating if the entity satisfies the Bipartite Condition

-
- -
-
-property is_empty
-

Boolean indicating if entity.elements is empty

-
- -
-
-level(item, max_depth=10)[source]
-

The first level where item appears in self.

-
-
Parameters
-
    -

  • item (hashable) – uid for an entity

    • -

  • max_depth (int, default: 10) – last level to check for entity

    • -
-
-
Returns
-

level

-
-
Return type
-

int

-
-
-
-

Note

-

Item must be the uid of an entity listed -in fullregistry()

-
-
- -
-
-levelset(k=1)[source]
-

A dictionary of level k of self.

-
-
Parameters
-

k (int, optional, default: 1) –

-
-
Returns
-

levelset

-
-
Return type
-

dict

-
-
-
-

Note

-

An Entity contains other entities, hence the relationships between entities -and their elements may be represented in a directed graph with entity as root. -The levelsets are sets of entities which make up the elements appearing at -a certain level.

-
-
- -
-
-property memberships
-

Dictionary of elements to which entity belongs.

-

This assignment is done on construction and controlled by -Entity.add_element() -and Entity.remove_element() methods.

-
- -
-
-static merge_entities(name, ent1, ent2)[source]
-

Merge two entities making sure they do not conflict.

-
-
Parameters
-
    -

  • name (hashable) –

    • -

  • ent1 (Entity) – First entity to have elements and properties added to new -entity

    • -

  • ent2 (Entity) – elements of ent2 will be checked against ent1.complete_registry() -and only nonexisting elements will be added using add() method. -Properties of ent2 will update properties of ent1 in new entity.

    • -
-
-
Returns
-

a new entity

-
-
Return type
-

Entity

-
-
-
- -
-
-nested_incidence_dict(level=10)[source]
-

Returns a nested dictionary with keys up to level

-
-
Parameters
-

level (int, optional, default: 10) – If level<=1, returns the incidence_dict.

-
-
Returns
-

nested_incidence_dict

-
-
Return type
-

dict

-
-
-
- -
-
-property properties
-

Dictionary of properties of entity

-
- -
-
-property registry
-

Entity pairs for children entity.

-

To return a dictionary of all entities at all depths -Entity.complete_registry()

-
-
Type
-

Dictionary of uid

-
-
-
- -
-
-remove(*args)[source]
-

Removes args from entitie’s elements if they belong. -Does nothing with args not in entity.

-
-
Parameters
-

args (One or more hashables or entities) –

-
-
Returns
-

self

-
-
Return type
-

Entity

-
-
-
- -
-
-remove_element(item)[source]
-

Removes item from entity and reference to entity from -item.memberships

-
-
Parameters
-

item (Hashable or Entity) –

-
-
Returns
-

self

-
-
Return type
-

Entity

-
-
-
- -
-
-remove_elements_from(arg_set)[source]
-

Similar to remove(). Removes elements in arg_set.

-
-
Parameters
-

arg_set (Iterable of hashables or entities) –

-
-
Returns
-

self

-
-
Return type
-

Entity

-
-
-
- -
-
-restrict_to(element_subset, name=None)[source]
-

Shallow copy of entity removing elements not in element_subset.

-
-
Parameters
-
    -

  • element_subset (iterable) – A subset of entities elements

    • -

  • name (hashable, optional) – If not given, a name is generated to reflect entity uid

    • -
-
-
Returns
-

New Entity – Could be empty.

-
-
Return type
-

Entity

-
-
-
- -
-
-size()[source]
-

Returns the number of elements in entity

-
- -
-
-property uid
-

String identifier for entity

-
- -
-
-property uidset
-

Set of uids of elements of entity.

-
- -
- -
-
-class classes.entity.EntitySet(uid, elements=[], **props)[source]
-

Bases: Entity

-
-
Parameters
-
    -

  • uid (hashable) – a unique identifier

    • -

  • elements (list or dict, optional, default: None) – a list of entities with identifiers different than uid and/or -hashables different than uid, see Honor System

    • -

  • props (keyword arguments, optional, default: {}) – properties belonging to the entity added as key=value pairs. -Both key and value must be hashable.

    • -
-
-
-

Notes

-

The EntitySet class was created to distinguish Entities satifying the Bipartite Condition.

-

Bipartite Condition

-

Entities that are elements of the same EntitySet, may not contain each other as elements. -The elements and children of an EntitySet generate a specific partition for a bipartite graph. -The partition is isomorphic to a Hypergraph where the elements correspond to hyperedges and -the children correspond to the nodes. EntitySets are the basic objects used to construct hypergraphs -in HNX.

-

Example:

-
>>> y = Entity('y')
->>> x = Entity('x')
->>> x.add(y)
->>> y.add(x)
->>> w = EntitySet('w',[x,y])
-HyperNetXError: Error: Fails the Bipartite Condition for EntitySet.
-y references a child of an existing Entity in the EntitySet.
-
-
-
-
-add(*args)[source]
-

Adds args to entityset’s elements, checking to make sure no self references are -made to element ids. -Ensures Bipartite Condition of EntitySet.

-
-
Parameters
-

args (One or more entities or hashables) –

-
-
Returns
-

self

-
-
Return type
-

EntitySet

-
-
-
- -
-
-clone(newuid)[source]
-

Returns shallow copy of entityset with newuid. Entityset’s -elements will belong to two distinct entitysets.

-
-
Parameters
-

newuid (hashable) – Name of the new entityset

-
-
Returns
-

clone

-
-
Return type
-

EntitySet

-
-
-
- -
-
-collapse_identical_elements(newuid, return_equivalence_classes=False)[source]
-

Returns a deduped copy of the entityset, using representatives of equivalence classes as element keys. -Two elements of an EntitySet are collapsed if they share the same children.

-
-
Parameters
-
    -

  • newuid (hashable) –

    • -

  • return_equivalence_classes (boolean, default=False) – If True, return a dictionary of equivalence classes keyed by new edge names

    • -
-
-
Return type
-

EntitySet

-
-
-
-
eq_classesdict

if return_equivalence_classes = True

-
-
-

Notes

-

Treats elements of the entityset as equal if they have the same uidsets. Using this -as an equivalence relation, the entityset’s uidset is partitioned into equivalence classes. -The equivalent elements are identified using a single entity by using the -frozenset of uids associated to these elements as the uid for the new element -and dropping the properties. -If use_reps is set to True a representative element of the equivalence class is -used as identifier instead of the frozenset.

-

Example:

-
>>> E = EntitySet('E',elements=[Entity('E1', ['a','b']),Entity('E2',['a','b'])])
->>> E.incidence_dict
-{'E1': {'a', 'b'}, 'E2': {'a', 'b'}}
->>> E.collapse_identical_elements('_',).incidence_dict
-{'E2': {'a', 'b'}}
-
-
-
- -
-
-incidence_matrix(sparse=True, index=False, weights=None)[source]
-

An incidence matrix for the EntitySet indexed by children x uidset.

-
-
Parameters
-
    -

  • sparse (boolean, optional, default: True) –

    • -

  • index (boolean, optional, default : False) – If True return will include a dictionary of children uid : row number -and element uid : column number

    • -

  • weights (bdict, optional, default : None) – cell weight dictionary keyed by (edge.uid, node.uid)

    • -
-
-
Returns
-

    -

  • incidence_matrix (scipy.sparse.csr.csr_matrix or np.ndarray)

    • -

  • row dictionary (dict) – Dictionary identifying row with item in entityset’s children

    • -

  • column dictionary (dict) – Dictionary identifying column with item in entityset’s uidset

    • -
-

-
-
-

Notes

-

Example:

-
>>> E = EntitySet('E',{'a':{1,2,3},'b':{2,3},'c':{1,4}})
->>> E.incidence_matrix(sparse=False, index=True)
-(array([[0, 1, 1],
-        [1, 1, 0],
-        [1, 1, 0],
-        [0, 0, 1]]), {0: 1, 1: 2, 2: 3, 3: 4}, {0: 'b', 1: 'a', 2: 'c'})
-
-
-
- -
-
-restrict_to(element_subset, name=None)[source]
-

Shallow copy of entityset removing elements not in element_subset.

-
-
Parameters
-
    -

  • element_subset (iterable) – A subset of the entityset’s elements

    • -

  • name (hashable, optional) – If not given, a name is generated to reflect entity uid

    • -
-
-
Returns
-

new entityset – Could be empty.

-
-
Return type
-

EntitySet

-
-
-
-

See also

-

Entity.restrict_to

-
-
- -
- -
-
-

classes.hypergraph module

-
-
-class classes.hypergraph.Hypergraph(setsystem=None, name=None, static=False, weights=None, aggregateby='sum', use_nwhy=False, filepath=None)[source]
-

Bases: object

-

Hypergraph H = (V,E) references a pair of disjoint sets: -V = nodes (vertices) and E = (hyper)edges.

-

An HNX Hypergraph is either dynamic or static. -Dynamic hypergraphs can change by adding or subtracting objects -from them. Static hypergraphs require that all of the nodes and edges -be known at creation. A hypergraph is dynamic by default.

-

Dynamic hypergraphs require the user to keep track of its objects, -by using a unique names for each node and edge. This allows for multi-edge graphs and -inseperable nodes.

-

For example: Let V = {1,2,3} and E = {e1,e2,e3}, -where e1 = {1,2}, e2 = {1,2}, and e3 = {1,2,3}. -The edges e1 and e2 contain the same set of nodes and yet -are distinct and must be distinguishable within H.

-

In a dynamic hypergraph each node and edge is -instantiated as an Entity and given an identifier or uid. Entities -keep track of inclusion relationships and can be nested. Since -hypergraphs can be quite large, only the entity identifiers will be used -for computation intensive methods, this means the user must take care -to keep a one to one correspondence between their set of uids and -the objects in their hypergraph. See Honor System -Dynamic hypergraphs are most practical for small to modestly sized -hypergraphs (<1000 objects).

-

Static hypergraphs store node and edge information in numpy arrays and -are immutable. Each node and edge receives a class generated internal -identifier used for computations so do not require the user to create -different ids for nodes and edges. To create a static hypergraph set -static = True in the signature.

-

We will create hypergraphs in multiple ways:

-
    -

  1. As an empty instance:

    -
    >>> H = hnx.Hypergraph()
->>> H.nodes, H.edges
-({}, {})
-
    -
    -
    2. -

  2. From a dictionary of iterables (elements of iterables must be of type hypernetx.Entity or hashable):

    -
    >>> H = Hypergraph({'a':[1,2,3],'b':[4,5,6]})
->>> H.nodes, H.edges
-# output: (EntitySet(_:Nodes,[1, 2, 3, 4, 5, 6],{}), EntitySet(_:Edges,['b', 'a'],{}))
-
    -
    -
    3. -

  3. From an iterable of iterables: (elements of iterables must be of type hypernetx.Entity or hashable):

    -
    >>> H = Hypergraph([{'a','b'},{'b','c'},{'a','c','d'}])
->>> H.nodes, H.edges
-# output: (EntitySet(_:Nodes,['d', 'b', 'c', 'a'],{}), EntitySet(_:Edges,['_1', '_2', '_0'],{}))
-
    -
    -
    4. -

  4. From a hypernetx.EntitySet or StaticEntitySet:

    -
    >>> a = Entity('a',{1,2}); b = Entity('b',{2,3})
->>> E = EntitySet('sample',elements=[a,b])
->>> H = Hypergraph(E)
->>> H.nodes, H.edges.
-# output: (EntitySet(_:Nodes,[1, 2, 3],{}), EntitySet(_:Edges,['b', 'a'],{}))
-
    -
    -
    5. -
-

All of these constructions apply for both dynamic and static hypergraphs. To -create a static hypergraph set the parameter static=True. In addition a static -hypergraph is automatically created if a StaticEntity, StaticEntitySet, or pandas.DataFrame object -is passed to the Hypergraph constructor.

-
    -
  1. -
    From a pandas.DataFrame. The dataframe must have at least two columns with headers and there can be no nans.
    -
    By default the first column corresponds to the edge names and the second column to the node names.
    -
    You can specify the columns by restricting the dataframe to the columns of interest in the order:
    -
    hnx.Hypergraph(df[[edge_column_name,node_column_name]])
    -
    See Colab Tutorials Tutorial 6 - Static Hypergraphs and Entities for additional information.
    -
    -
    2. -
-
-
Parameters
-
    -

  • setsystem ((optional) EntitySet, StaticEntitySet, dict, iterable, pandas.dataframe, default: None) – See notes above for setsystem requirements.

    • -

  • name (hashable, optional, default: None) – If None then a placeholder ‘_’ will be inserted as name

    • -

  • static (boolean, optional, default: False) – If True the hypergraph will be immutable, edges and nodes may not be changed.

    • -

  • weights (array-like, optional, default : None) – User specified weights corresponding to setsytem of type pandas.DataFrame, -length must equal number of rows in dataframe. -If None, weight for all rows is assumed to be 1.

    • -

  • keep_weights (bool, optional, default : True) – Whether or not to use existing weights when input is StaticEntity, or StaticEntitySet.

    • -

  • aggregateby (str, optional, {'count', 'sum', 'mean', 'median', max', 'min', 'first','last', None}, default : 'sum') – Method to aggregate cell_weights of duplicate rows if setsystem is of type pandas.DataFrame or -StaticEntity. If None all cell weights will be set to 1.

    • -

  • use_nwhy (boolean, optional, default : False) – If True hypergraph will be static and computations will be done using -C++ backend offered by NWHypergraph. This requires installation of the -NWHypergraph C++ library. Please see the NWHy documentation for more information.

    • -

  • filepath (str, optional, default : None) –

    • -
-
-
-
-
-add_edge(edge)[source]
-

Adds a single edge to hypergraph.

-
-
Parameters
-

edge (hashable or Entity) – If hashable the edge returned will be empty.

-
-
Returns
-

hypergraph

-
-
Return type
-

Hypergraph

-
-
-

Notes

-

When adding an edge to a hypergraph children must be removed -so that nodes do not have elements. -Each node (element of edge) must be instantiated as a node, -making sure its uid isn’t already present in the self. -If an added edge contains nodes that cannot be added to hypergraph -then an error will be raised.

-
- -
-
-add_edges_from(edge_set)[source]
-

Add edges to hypergraph.

-
-
Parameters
-

edge_set (iterable of hashables or Entities) – For hashables the edges returned will be empty.

-
-
Returns
-

hypergraph

-
-
Return type
-

Hypergraph

-
-
-
- -
-
-add_node_to_edge(node, edge)[source]
-

Adds node to an edge in hypergraph edges

-
-
Parameters
-
    -

  • node (hashable or Entity) – If Entity, only uid and properties will be used. -If uid is already in nodes then the known node will -be used

    • -

  • edge (uid of edge or edge, must belong to self.edges) –

    • -
-
-
Returns
-

hypergraph

-
-
Return type
-

Hypergraph

-
-
-
- -
-
-classmethod add_nwhy(h, fpath=None)[source]
-

Add nwhy functionality to a hypergraph.

-
-
Parameters
-
    -

  • h (hnx.Hypergraph) –

    • -

  • fpath (file path for storage of hypergraph state dictionary) –

    • -
-
-
Returns
-

Returns a copy of h with static set to true and nwhy set to True -if it is available.

-
-
Return type
-

hnx.Hypergraph

-
-
-
- -
-
-adjacency_matrix(index=False, s=1)[source]
-

The sparse weighted s-adjacency matrix

-
-
Parameters
-
    -

  • s (int, optional, default: 1) –

    • -

  • index (boolean, optional, default: False) – if True, will return a rowdict of row to node uid

    • -

  • weights (bool, default=True) – If False all nonzero entries are 1. -If True adjacency matrix will depend on weighted incidence matrix,

    • -
-
-
Returns
-

    -

  • adjacency_matrix (scipy.sparse.csr.csr_matrix)

    • -

  • row dictionary (dict)

    • -
-

-
-
-
- -
-
-auxiliary_matrix(s=1, index=False)[source]
-

The unweighted s-auxiliary matrix for hypergraph

-
-
Parameters
-
    -

  • s (int) –

    • -

  • index (bool, optional, default: False) – return a dictionary of labels for the rows of the matrix

    • -
-
-
Returns
-

auxiliary_matrix – Will return the same type of matrix as self.arr

-
-
Return type
-

scipy.sparse.csr.csr_matrix or numpy.ndarray

-
-
-

Notes

-

Creates subgraph by restricting to edges of cardinality at least s. -Returns the unweighted s-edge adjacency matrix for the subgraph.

-
- -
-
-bipartite()[source]
-

Constructs the networkX bipartite graph associated to hypergraph.

-
-
Returns
-

bipartite

-
-
Return type
-

nx.Graph()

-
-
-

Notes

-

Creates a bipartite networkx graph from hypergraph. -The nodes and (hyper)edges of hypergraph become the nodes of bipartite graph. -For every (hyper)edge e in the hypergraph and node n in e there is an edge (n,e) -in the graph.

-
- -
-
-collapse_edges(name=None, use_reps=None, return_counts=None, return_equivalence_classes=False)[source]
-

Constructs a new hypergraph gotten by identifying edges containing the same nodes

-
-
Parameters
-
    -

  • name (hashable, optional, default: None) –

    • -

  • return_equivalence_classes (boolean, optional, default: False) – Returns a dictionary of edge equivalence classes keyed by frozen sets of nodes

    • -
-
-
Returns
-

    -

  • new hypergraph (Hypergraph) – Equivalent edges are collapsed to a single edge named by a representative of the equivalent -edges followed by a colon and the number of edges it represents.

    • -

  • equivalence_classes (dict) – A dictionary keyed by representative edge names with values equal to the edges in -its equivalence class

    • -
-

-
-
-

Notes

-

Two edges are identified if their respective elements are the same. -Using this as an equivalence relation, the uids of the edges are partitioned into -equivalence classes.

-

A single edge from the collapsed edges followed by a colon and the number of elements -in its equivalence class as uid for the new edge

-
- -
-
-collapse_nodes(name=None, use_reps=True, return_counts=True, return_equivalence_classes=False)[source]
-

Constructs a new hypergraph gotten by identifying nodes contained by the same edges

-
-
Parameters
-
    -

  • name (str, optional, default: None) –

    • -

  • return_equivalence_classes (boolean, optional, default: False) – Returns a dictionary of node equivalence classes keyed by frozen sets of edges

    • -

  • use_reps (boolean, optional, default: False - Deprecated, this no longer works and will be removed) – Choose a single element from the collapsed nodes as uid for the new node, otherwise uses -a frozen set of the uids of nodes in the equivalence class

    • -

  • return_counts (boolean, - Deprecated, this no longer works and will be removed) – if use_reps is True the new nodes have uids given by a tuple of the rep -and the count

    • -
-
-
Returns
-

new hypergraph

-
-
Return type
-

Hypergraph

-
-
-

Notes

-

Two nodes are identified if their respective memberships are the same. -Using this as an equivalence relation, the uids of the nodes are partitioned into -equivalence classes. A single member of the equivalence class is chosen to represent -the class followed by the number of members of the class.

-

Example

-
>>> h = Hypergraph(EntitySet('example',elements=[Entity('E1', ['a','b']),Entity('E2',['a','b'])]))
->>> h.incidence_dict
-{'E1': {'a', 'b'}, 'E2': {'a', 'b'}}
->>> h.collapse_nodes().incidence_dict
-{'E1': {frozenset({'a', 'b'})}, 'E2': {frozenset({'a', 'b'})}} ### Fix this
->>> h.collapse_nodes(use_reps=True).incidence_dict
-{'E1': {('a', 2)}, 'E2': {('a', 2)}}
-
-
-
- -
-
-collapse_nodes_and_edges(name=None, use_reps=True, return_counts=True, return_equivalence_classes=False)[source]
-

Returns a new hypergraph by collapsing nodes and edges.

-
-
Parameters
-
    -

  • name (str, optional, default: None) –

    • -

  • use_reps (boolean, optional, default: False) – Choose a single element from the collapsed elements as a representative

    • -

  • return_counts (boolean, optional, default: True) – if use_reps is True the new elements are keyed by a tuple of the rep -and the count

    • -

  • return_equivalence_classes (boolean, optional, default: False) – Returns a dictionary of edge equivalence classes keyed by frozen sets of nodes

    • -
-
-
Returns
-

new hypergraph

-
-
Return type
-

Hypergraph

-
-
-

Notes

-

Collapses the Nodes and Edges EntitySets. Two nodes(edges) are duplicates -if their respective memberships(elements) are the same. Using this as an -equivalence relation, the uids of the nodes(edges) are partitioned into -equivalence classes. A single member of the equivalence class is chosen to represent -the class followed by the number of members of the class.

-

Example

-
>>> h = Hypergraph(EntitySet('example',elements=[Entity('E1', ['a','b']),Entity('E2',['a','b'])]))
->>> h.incidence_dict
-{'E1': {'a', 'b'}, 'E2': {'a', 'b'}}
->>> h.collapse_nodes_and_edges().incidence_dict   ### Fix this
-{('E1', 2): {('a', 2)}}
-
-
-
- -
-
-component_subgraphs(return_singletons=False)[source]
-

Same as s_components_subgraphs() with s=1. Returns iterator.

-
-

See also

-

s_component_subgraphs

-
-
- -
-
-components(edges=False, return_singletons=True)[source]
-

Same as s_connected_components() with s=1, but nodes are returned -by default. Return iterator.

-
-

See also

-

s_connected_components

-
-
- -
-
-connected_component_subgraphs(return_singletons=True)[source]
-

Same as s_component_subgraphs() with s=1. Returns iterator

-
-

See also

-

s_component_subgraphs

-
-
- -
-
-connected_components(edges=False, return_singletons=True)[source]
-

Same as s_connected_components() with s=1, but nodes are returned -by default. Return iterator.

-
-

See also

-

s_connected_components

-
-
- -
-
-convert_to_static(name=None, use_nwhy=False, filepath=None)[source]
-

Returns new static hypergraph with the same dictionary as original hypergraph

-
-
Parameters
-
    -

  • name (None, optional) – Name

    • -

  • use_nwhy (bool, optional, default : False) – Description

    • -

  • filepath (None, optional, default : False) – Description

    • -

  • Returned –

    • -

  • ------------------ –

    • -

  • hnx.Hypergraph – Will have attribute static = True

    • -
-
-
-
-

Note

-

Static hypergraphs store the user defined node and edge names in -a dictionary of labeled lists. The order of the lists provides an -index, which the hypergraph uses in place of the node and edge names -for faster processing.

-
-
- -
-
-dataframe(sort_rows=False, sort_columns=False, cell_weights=True)[source]
-

Returns a pandas dataframe for hypergraph indexed by the nodes and -with column headers given by the edge names.

-
-
Parameters
-
    -

  • sort_rows (bool, optional, default=True) – sort rows based on hashable node names

    • -

  • sort_columns (bool, optional, default=True) – sort columns based on hashable edge names

    • -

  • cell_weights (bool, optional, default=True) – if self.isstatic then include cell weights

    • -
-
-
-
- -
-
-degree(node, s=1, max_size=None)[source]
-

The number of edges of size s that contain node.

-
-
Parameters
-
    -

  • node (hashable) – identifier for the node.

    • -

  • s (positive integer, optional, default: 1) – smallest size of edge to consider in degree

    • -

  • max_size (positive integer or None, optional, default: None) – largest size of edge to consider in degree

    • -
-
-
Return type
-

int

-
-
-
- -
-
-diameter(s=1)[source]
-

Returns the length of the longest shortest s-walk between nodes in hypergraph

-
-
Parameters
-

s (int, optional, default: 1) –

-
-
Returns
-

diameter

-
-
Return type
-

int

-
-
Raises
-

HyperNetXError – If hypergraph is not s-edge-connected

-
-
-

Notes

-

Two nodes are s-adjacent if they share s edges. -Two nodes v_start and v_end are s-walk connected if there is a sequence of -nodes v_start, v_1, v_2, … v_n-1, v_end such that consecutive nodes -are s-adjacent. If the graph is not connected, an error will be raised.

-
- -
-
-dim(edge)[source]
-

Same as size(edge)-1.

-
- -
-
-distance(source, target, s=1)[source]
-

Returns the shortest s-walk distance between two nodes in the hypergraph.

-
-
Parameters
-
    -

  • source (node.uid or node) – a node in the hypergraph

    • -

  • target (node.uid or node) – a node in the hypergraph

    • -

  • s (positive integer) – the number of edges

    • -
-
-
Returns
-

s-walk distance

-
-
Return type
-

int

-
-
-
-

See also

-

edge_distance

-
-

Notes

-

The s-distance is the shortest s-walk length between the nodes. -An s-walk between nodes is a sequence of nodes that pairwise share -at least s edges. The length of the shortest s-walk is 1 less than -the number of nodes in the path sequence.

-

Uses the networkx shortest_path_length method on the graph -generated by the s-adjacency matrix.

-
- -
-
-dual(name=None)[source]
-

Constructs a new hypergraph with roles of edges and nodes of hypergraph reversed.

-
-
Parameters
-

name (hashable) –

-
-
Returns
-

dual

-
-
Return type
-

hypergraph

-
-
-
- -
-
-edge_adjacency_matrix(index=False, s=1, weights=False)[source]
-

The weighted s-adjacency matrix for the dual hypergraph.

-
-
Parameters
-
    -

  • s (int, optional, default: 1) –

    • -

  • index (boolean, optional, default: False) – if True, will return a coldict of column to edge uid

    • -

  • sparse (boolean, optional, default: True) –

    • -

  • weighted (boolean, optional, default: True) –

    • -
-
-
Returns
-

    -

  • edge_adjacency_matrix (scipy.sparse.csr.csr_matrix or numpy.ndarray)

    • -

  • column dictionary (dict)

    • -
-

-
-
-

Notes

-

This is also the adjacency matrix for the line graph. -Two edges are s-adjacent if they share at least s nodes. -If index=True, returns a dictionary column_index:edge_uid

-
- -
-
-edge_diameter(s=1)[source]
-

Returns the length of the longest shortest s-walk between edges in hypergraph

-
-
Parameters
-

s (int, optional, default: 1) –

-
-
Returns
-

edge_diameter

-
-
Return type
-

int

-
-
Raises
-

HyperNetXError – If hypergraph is not s-edge-connected

-
-
-

Notes

-

Two edges are s-adjacent if they share s nodes. -Two nodes e_start and e_end are s-walk connected if there is a sequence of -edges e_start, e_1, e_2, … e_n-1, e_end such that consecutive edges -are s-adjacent. If the graph is not connected, an error will be raised.

-
- -
-
-edge_diameters(s=1)[source]
-

Returns the edge diameters of the s_edge_connected component subgraphs -in hypergraph.

-
-
Parameters
-

s (int, optional, default: 1) –

-
-
Returns
-

    -

  • maximum diameter (int)

    • -

  • list of diameters (list) – List of edge_diameters for s-edge component subgraphs in hypergraph

    • -

  • list of component (list) – List of the edge uids in the s-edge component subgraphs.

    • -
-

-
-
-
- -
-
-edge_distance(source, target, s=1)[source]
-

XX TODO: still need to return path and translate into user defined nodes and edges -Returns the shortest s-walk distance between two edges in the hypergraph.

-
-
Parameters
-
    -

  • source (edge.uid or edge) – an edge in the hypergraph

    • -

  • target (edge.uid or edge) – an edge in the hypergraph

    • -

  • s (positive integer) – the number of intersections between pairwise consecutive edges

    • -

  • TODO (add edge weights) –

    • -

  • weight (None or string, optional, default: None) – if None then all edges have weight 1. If string then edge attribute -string is used if available.

    • -
-
-
Returns
-

s- walk distance – A shortest s-walk is computed as a sequence of edges, -the s-walk distance is the number of edges in the sequence -minus 1. If no such path exists returns np.inf.

-
-
Return type
-

the shortest s-walk edge distance

-
-
-
-

See also

-

distance

-
-

Notes

-

The s-distance is the shortest s-walk length between the edges. -An s-walk between edges is a sequence of edges such that consecutive pairwise -edges intersect in at least s nodes. The length of the shortest s-walk is 1 less than -the number of edges in the path sequence.

-

Uses the networkx shortest_path_length method on the graph -generated by the s-edge_adjacency matrix.

-
- -
-
-edge_neighbors(edge, s=1)[source]
-

The edges in hypergraph which share s nodes(s) with edge.

-
-
Parameters
-
    -

  • edge (hashable or Entity) – uid for a edge in hypergraph or the edge Entity

    • -

  • s (int, list, optional, default : 1) – Minimum number of nodes shared by neighbors edge node.

    • -
-
-
Returns
-

List of edge neighbors

-
-
Return type
-

list

-
-
-
- -
-
-edge_size_dist()[source]
-

Returns the size for each edge

-
-
Return type
-

np.array

-
-
-
- -
-
-property edges
-

Object associated with self._edges.

-
-
Returns
-

If self.isstatic the StaticEntitySet, otherwise EntitySet.

-
-
Return type
-

StaticEntitySet or EntitySet

-
-
-
- -
-
-classmethod from_bipartite(B, set_names=('edges', 'nodes'), name=None, static=False, use_nwhy=False)[source]
-

Static method creates a Hypergraph from a bipartite graph.

-
-
Parameters
-
    -

  • B (nx.Graph()) – A networkx bipartite graph. Each node in the graph has a property -‘bipartite’ taking the value of 0 or 1 indicating a 2-coloring of the graph.

    • -

  • set_names (iterable of length 2, optional, default = ['nodes','edges']) – Category names assigned to the graph nodes associated to each bipartite set

    • -

  • name (hashable) –

    • -

  • static (bool) –

    • -
-
-
Return type
-

Hypergraph

-
-
-

Notes

-

A partition for the nodes in a bipartite graph generates a hypergraph.

-
>>> import networkx as nx
->>> B = nx.Graph()
->>> B.add_nodes_from([1, 2, 3, 4], bipartite=0)
->>> B.add_nodes_from(['a', 'b', 'c'], bipartite=1)
->>> B.add_edges_from([(1, 'a'), (1, 'b'), (2, 'b'), (2, 'c'), (3, 'c'), (4, 'a')])
->>> H = Hypergraph.from_bipartite(B)
->>> H.nodes, H.edges
-# output: (EntitySet(_:Nodes,[1, 2, 3, 4],{}), EntitySet(_:Edges,['b', 'c', 'a'],{}))
-
-
-
- -
-
-classmethod from_dataframe(df, columns=None, rows=None, name=None, fillna=0, transpose=False, transforms=[], key=None, node_label='nodes', edge_label='edges', static=False, use_nwhy=False)[source]
-

Create a hypergraph from a Pandas Dataframe object using index to label vertices -and Columns to label edges. The values of the dataframe are transformed into an -incidence matrix. -Note this is different than passing a dataframe directly -into the Hypergraph constructor. The latter automatically generates a static hypergraph -with edge and node labels given by the cell values.

-
-
Parameters
-
    -

  • df (Pandas.Dataframe) – a real valued dataframe with a single index

    • -

  • columns ((optional) list, default = None) – restricts df to the columns with headers in this list.

    • -

  • rows ((optional) list, default = None) – restricts df to the rows indexed by the elements in this list.

    • -

  • name ((optional) string, default = None) –

    • -

  • fillna (float, default = 0) – a real value to place in empty cell, all-zero columns will not generate -an edge.

    • -

  • transpose ((optional) bool, default = False) – option to transpose the dataframe, in this case df.Index will label the edges -and df.columns will label the nodes, transpose is applied before transforms and -key

    • -

  • transforms ((optional) list, default = []) – optional list of transformations to apply to each column, -of the dataframe using pd.DataFrame.apply(). -Transformations are applied in the order they are -given (ex. abs). To apply transforms to rows or for additional -functionality, consider transforming df using pandas.DataFrame methods -prior to generating the hypergraph.

    • -

  • key ((optional) function, default = None) – boolean function to be applied to dataframe. Must be defined on numpy -arrays.

    • -
-
-
-
-

See also

-

from_numpy_array

-
-
-
Return type
-

Hypergraph

-
-
-

Notes

-

The from_dataframe constructor does not generate empty edges. -All-zero columns in df are removed and the names corresponding to these -edges are discarded. -Restrictions and data processing will occur in this order:

-
-
    -

  1. column and row restrictions

    2. -

  2. fillna replace NaNs in dataframe

    3. -

  3. transpose the dataframe

    4. -

  4. transforms in the order listed

    5. -

  5. boolean key

    6. -
-
-

This method offers the above options for wrangling a dataframe into an incidence -matrix for a hypergraph. For more flexibility we recommend you use the Pandas -library to format the values of your dataframe before submitting it to this -constructor.

-
- -
-
-classmethod from_numpy_array(M, node_names=None, edge_names=None, node_label='nodes', edge_label='edges', name=None, key=None, static=False, use_nwhy=False)[source]
-

Create a hypergraph from a real valued matrix represented as a 2 dimensionsl numpy array. -The matrix is converted to a matrix of 0’s and 1’s so that any truthy cells are converted to 1’s and -all others to 0’s.

-
-
Parameters
-
    -

  • M (real valued array-like object, 2 dimensions) – representing a real valued matrix with rows corresponding to nodes and columns to edges

    • -

  • node_names (object, array-like, default=None) – List of node names must be the same length as M.shape[0]. -If None then the node names correspond to row indices with ‘v’ prepended.

    • -

  • edge_names (object, array-like, default=None) – List of edge names must have the same length as M.shape[1]. -If None then the edge names correspond to column indices with ‘e’ prepended.

    • -

  • name (hashable) –

    • -

  • key ((optional) function) – boolean function to be evaluated on each cell of the array, -must be applicable to numpy.array

    • -
-
-
Return type
-

Hypergraph

-
-
-
-

Note

-

The constructor does not generate empty edges. -All zero columns in M are removed and the names corresponding to these -edges are discarded.

-
-
- -
-
-get_id(uid, edges=False)[source]
-

Return the internally assigned id associated with a label.

-
-
Parameters
-
    -

  • uid (string) – User provided name/id/label for hypergraph object

    • -

  • edges (bool, optional) – Determines if uid is an edge or node name

    • -
-
-
Returns
-

internal id assigned at construction

-
-
Return type
-

int

-
-
-
- -
-
-get_linegraph(s, edges=True, use_nwhy=True)[source]
-

Creates an ::term::s-linegraph for the Hypergraph. -If edges=True (default)then the edges will be the vertices of the line graph. -Two vertices are connected by an s-line-graph edge if the corresponding -hypergraphedges intersect in at least s hypergraph nodes. -If edges=False, the hypergraph nodes will be the vertices of the line graph. -Two vertices are connected if the nodes they correspond to share at least s -incident hyper edges.

-
-
Parameters
-
    -

  • s (int) – The width of the connections.

    • -

  • edges (bool, optional) – Determine if edges or nodes will be the vertices in the linegraph.

    • -

  • use_nwhy (bool, optional) – Requests that nwhy be used to construct the linegraph. If NWHy is not available this is ignored.

    • -
-
-
Returns
-

A NetworkX graph.

-
-
Return type
-

nx.Graph

-
-
-
- -
-
-get_name(id, edges=False)[source]
-

Return the user defined name/id/label associated to an -internally assigned id.

-
-
Parameters
-
    -

  • id (int) – Internally assigned id

    • -

  • edges (bool, optional) – Determines if id references an edge or node

    • -
-
-
Returns
-

User provided name/id/label for hypergraph object

-
-
Return type
-

str

-
-
-
- -
-
-property incidence_dict
-

Dictionary keyed by edge uids with values the uids of nodes in each edge

-
-
Return type
-

dict

-
-
-
- -
-
-incidence_matrix(weights=False, index=False)[source]
-

An incidence matrix for the hypergraph indexed by nodes x edges.

-
-
Parameters
-
    -

  • weights (bool, default=False) – If False all nonzero entries are 1. -If True and self.static all nonzero entries are filled by -self.edges.cell_weights dictionary values.

    • -

  • index (boolean, optional, default False) – If True return will include a dictionary of node uid : row number -and edge uid : column number

    • -
-
-
Returns
-

    -

  • incidence_matrix (scipy.sparse.csr.csr_matrix or np.ndarray)

    • -

  • row dictionary (dict) – Dictionary identifying rows with nodes

    • -

  • column dictionary (dict) – Dictionary identifying columns with edges

    • -
-

-
-
-
- -
-
-is_connected(s=1, edges=False)[source]
-

Determines if hypergraph is s-connected.

-
-
Parameters
-
    -

  • s (int, optional, default: 1) –

    • -

  • edges (boolean, optional, default: False) – If True, will determine if s-edge-connected. -For s=1 s-edge-connected is the same as s-connected.

    • -
-
-
Returns
-

is_connected

-
-
Return type
-

boolean

-
-
-

Notes

-

A hypergraph is s node connected if for any two nodes v0,vn -there exists a sequence of nodes v0,v1,v2,…,v(n-1),vn -such that every consecutive pair of nodes v(i),v(i+1) -share at least s edges.

-

A hypergraph is s edge connected if for any two edges e0,en -there exists a sequence of edges e0,e1,e2,…,e(n-1),en -such that every consecutive pair of edges e(i),e(i+1) -share at least s nodes.

-
- -
-
-property isstatic
-

Checks whether nodes and edges are immutable

-
-
Return type
-

Boolean

-
-
-
- -
-
-neighbors(node, s=1)[source]
-

The nodes in hypergraph which share s edge(s) with node.

-
-
Parameters
-
    -

  • node (hashable or Entity) – uid for a node in hypergraph or the node Entity

    • -

  • s (int, list, optional, default : 1) – Minimum number of edges shared by neighbors with node.

    • -
-
-
Returns
-

List of neighbors

-
-
Return type
-

list

-
-
-
- -
-
-node_diameters(s=1)[source]
-

Returns the node diameters of the connected components in hypergraph.

-
-
Parameters
-
    -

  • and (list of the diameters of the s-components) –

    • -

  • nodes (list of the s-component) –

    • -
-
-
-
- -
-
-property nodes
-

Object associated with self._nodes.

-
-
Returns
-

If self.isstatic the StaticEntitySet, otherwise EntitySet.

-
-
Return type
-

StaticEntitySet or EntitySet

-
-
-
- -
-
-number_of_edges(edgeset=None)[source]
-

The number of edges in edgeset belonging to hypergraph.

-
-
Parameters
-

edgeset (an interable of Entities, optional, default: None) – If None, then return the number of edges in hypergraph.

-
-
Returns
-

number_of_edges

-
-
Return type
-

int

-
-
-
- -
-
-number_of_nodes(nodeset=None)[source]
-

The number of nodes in nodeset belonging to hypergraph.

-
-
Parameters
-

nodeset (an interable of Entities, optional, default: None) – If None, then return the number of nodes in hypergraph.

-
-
Returns
-

number_of_nodes

-
-
Return type
-

int

-
-
-
- -
-
-order()[source]
-

The number of nodes in hypergraph.

-
-
Returns
-

order

-
-
Return type
-

int

-
-
-
- -
-
-classmethod recover_from_state(fpath='current_state.p', newfpath=None, use_nwhy=True)[source]
-

Recover a static hypergraph pickled using save_state.

-
-
Parameters
-

fpath (str) – Full path to pickle file containing state_dict and labels -of hypergraph

-
-
Returns
-

H – static hypergraph with state dictionary prefilled

-
-
Return type
-

Hypergraph

-
-
-
- -
-
-remove_edge(edge)[source]
-

Removes a single edge from hypergraph.

-
-
Parameters
-

edge (hashable or Entity) –

-
-
Returns
-

hypergraph

-
-
Return type
-

Hypergraph

-
-
-

Notes

-

Deletes reference to edge from all of its nodes. -If any of its nodes do not belong to any other edges -the node is dropped from self.

-
- -
-
-remove_edges(edge_set)[source]
-

Removes edges from hypergraph.

-
-
Parameters
-

edge_set (iterable of hashables or Entities) –

-
-
Returns
-

hypergraph

-
-
Return type
-

Hypergraph

-
-
-
- -
-
-remove_node(node)[source]
-

Removes node from edges and deletes reference in hypergraph nodes

-
-
Parameters
-

node (hashable or Entity) – a node in hypergraph

-
-
Returns
-

hypergraph

-
-
Return type
-

Hypergraph

-
-
-
- -
-
-remove_nodes(node_set)[source]
-

Removes nodes from edges and deletes references in hypergraph nodes

-
-
Parameters
-

node_set (an iterable of hashables or Entities) – Nodes in hypergraph

-
-
Returns
-

hypergraph

-
-
Return type
-

Hypergraph

-
-
-
- -
-
-remove_singletons(name=None)[source]
-

Constructs clone of hypergraph with singleton edges removed.

-
-
Parameters
-

name (str, optional, default: None) –

-
-
Returns
-

new hypergraph

-
-
Return type
-

Hypergraph

-
-
-
- -
-
-remove_static(name=None)[source]
-

Returns dynamic hypergraph

-
-
Parameters
-

name (None, optional) – User defined namae of hypergraph

-
-
Returns
-

A new hypergraph with the same dictionary as self but allowing dynamic -changes to nodes and edges. -If hypergraph is not static, returns self.

-
-
Return type
-

hnx.Hypergraph

-
-
-
- -
-
-restrict_to_edges(edgeset, name=None)[source]
-

Constructs a hypergraph using a subset of the edges in hypergraph

-
-
Parameters
-
    -

  • edgeset (iterable of hashables or Entities) – A subset of elements of the hypergraph edges

    • -

  • name (str, optional) –

    • -
-
-
Returns
-

new hypergraph

-
-
Return type
-

Hypergraph

-
-
-
- -
-
-restrict_to_nodes(nodeset, name=None)[source]
-

Constructs a new hypergraph by restricting the edges in the hypergraph to -the nodes referenced by nodeset.

-
-
Parameters
-
    -

  • nodeset (iterable of hashables) – References a subset of elements of self.nodes

    • -

  • name (string, optional, default: None) –

    • -
-
-
Returns
-

new hypergraph

-
-
Return type
-

Hypergraph

-
-
-
- -
-
-s_component_subgraphs(s=1, edges=True, return_singletons=False)[source]
-

Returns a generator for the induced subgraphs of s_connected components. -Removes singletons unless return_singletons is set to True. Computed using -s-linegraph generated either by the hypergraph (edges=True) or its dual -(edges = False)

-
-
Parameters
-
    -

  • s (int, optional, default: 1) –

    • -

  • edges (boolean, optional, edges=False) – Determines if edge or node components are desired. Returns -subgraphs equal to the hypergraph restricted to each set of nodes(edges) in the -s-connected components or s-edge-connected components

    • -

  • return_singletons (bool, optional) –

    • -
-
-
Yields
-

s_component_subgraphs (iterator) – Iterator returns subgraphs generated by the edges (or nodes) in the -s-edge(node) components of hypergraph.

-
-
-
- -
-
-s_components(s=1, edges=True, return_singletons=True)[source]
-

Same as s_connected_components

-
-

See also

-

s_connected_components

-
-
- -
-
-s_connected_components(s=1, edges=True, return_singletons=False)[source]
-

Returns a generator for the s-edge-connected components -or the s-node-connected components -of the hypergraph.

-
-
Parameters
-
    -

  • s (int, optional, default: 1) –

    • -

  • edges (boolean, optional, default: True) – If True will return edge components, if False will return node components

    • -

  • return_singletons (bool, optional, default : False) –

    • -
-
-
-

Notes

-

If edges=True, this method returns the s-edge-connected components as -lists of lists of edge uids. -An s-edge-component has the property that for any two edges e1 and e2 -there is a sequence of edges starting with e1 and ending with e2 -such that pairwise adjacent edges in the sequence intersect in at least -s nodes. If s=1 these are the path components of the hypergraph.

-

If edges=False this method returns s-node-connected components. -A list of sets of uids of the nodes which are s-walk connected. -Two nodes v1 and v2 are s-walk-connected if there is a -sequence of nodes starting with v1 and ending with v2 such that pairwise -adjacent nodes in the sequence share s edges. If s=1 these are the -path components of the hypergraph.

-

Example

-
>>> S = {'A':{1,2,3},'B':{2,3,4},'C':{5,6},'D':{6}}
->>> H = Hypergraph(S)
-
-
-
>>> list(H.s_components(edges=True))
-[{'C', 'D'}, {'A', 'B'}]
->>> list(H.s_components(edges=False))
-[{1, 2, 3, 4}, {5, 6}]
-
-
-
-
Yields
-

s_connected_components (iterator) – Iterator returns sets of uids of the edges (or nodes) in the s-edge(node) -components of hypergraph.

-
-
-
- -
-
-s_degree(node, s=1)[source]
-

Same as degree

-
-
Parameters
-
    -

  • node (Entity or hashable) – If hashable, then must be uid of node in hypergraph

    • -

  • s (positive integer, optional, default: 1) –

    • -
-
-
Returns
-

s_degree – The degree of a node in the subgraph induced by edges -of size s

-
-
Return type
-

int

-
-
-
-

Note

-

The s-degree of a node is the number of edges of size -at least s that contain the node.

-
-
- -
-
-save_state(fpath=None)[source]
-

Save the hypergraph as an ordered pair: [state_dict,labels] -The hypergraph can be recovered using the command:

-
>>> H = hnx.Hypergraph.recover_from_state(fpath)
-
-
-
-
Parameters
-

fpath (str, optional) –

-
-
-
- -
-
-set_state(**kwargs)[source]
-

Allow state_dict updates from outside of class. Use with caution.

-
-
Parameters
-

**kwargs – key=value pairs to save in state dictionary

-
-
-
- -
-
-property shape
-

(number of nodes, number of edges)

-
-
Return type
-

tuple

-
-
-
- -
-
-singletons()[source]
-

Returns a list of singleton edges. A singleton edge is an edge of -size 1 with a node of degree 1.

-
-
Returns
-

singles – A list of edge uids.

-
-
Return type
-

list

-
-
-
- -
-
-size(edge, nodeset=None)[source]
-

The number of nodes in nodeset that belong to edge. -If nodeset is None then returns the size of edge

-
-
Parameters
-

edge (hashable) – The uid of an edge in the hypergraph

-
-
Returns
-

size

-
-
Return type
-

int

-
-
-
- -
-
-toplexes(name=None, collapse=False, use_reps=False, return_counts=True)[source]
-

Returns a simple hypergraph corresponding to self.

-
-

Warning

-

Collapsing is no longer supported inside the toplexes method. Instead generate a new -collapsed hypergraph and compute the toplexes of the new hypergraph.

-
-
-
Parameters
-
    -

  • name (str, optional, default: None) –

    • -

  • collapse (#) –

    • -

  • sets. (# Should the hypergraph be collapsed? This would preserve a link between duplicate maximal) –

    • -

  • size. (# If False then only one of these sets will be used and uniqueness will be up to sets of equal) –

    • -

  • use_reps (#) –

    • -

  • of (# If collapse=True then each toplex will be named by a representative of the set) –

    • -

  • edges (# equivalent) –

    • -

  • collapse_edges). (default is False (see) –

    • -

  • return_counts (boolean, optional, default: True) – # If collapse=True then each toplex will be named by a tuple of the representative -# of the set of equivalent edges and their count

    • -
-
-
-
- -
-
-translate(idx, edges=False)[source]
-

Returns the translation of numeric values associated with hypergraph. -Only needed if exposing the static identifiers assigned by the class. -If not static then the idx is returned.

-
-
Parameters
-
    -

  • idx (int) – class assigned integer for internal manipulation of Hypergraph data

    • -

  • edges (bool, optional, default: True) – If True then translates from edge index. Otherwise will translate from -node index, default=False

    • -
-
-
Returns
-

User assigned identifier corresponding to idx

-
-
Return type
-

int or string

-
-
-
- -
- -
-
-

classes.staticentity module

-
-
-class classes.staticentity.StaticEntity(entity=None, data=None, arr=None, labels=None, uid=None, weights=None, keep_weights=True, aggregateby='sum', **props)[source]
-

Bases: object

-
-
Parameters
-
    -

  • entity (StaticEntity, StaticEntitySet, Entity, EntitySet, pandas.DataFrame, dict, or list of lists) – If a pandas.DataFrame, an error will be raised if there are nans.

    • -

  • data (array or array-like) – Two dimensional array of integers. Provides sparse tensor indices for incidence -tensor.

    • -

  • arr (numpy.ndarray or scip.sparse.matrix, optional, default=None) – Incidence tensor of data.

    • -

  • labels (OrderedDict of lists, optional, default=None) – User defined labels corresponding to integers in data.

    • -

  • uid (hashable, optional, default=None) –

    • -

  • weights (array-like, optional, default : None) – User specified weights corresponding to data, length must equal number -of rows in data. If None, weight for all rows is assumed to be 1.

    • -

  • keep_weights (bool, optional, default : True) – Whether or not to use existing weights when input is StaticEntity, or StaticEntitySet.

    • -

  • aggregateby (str, optional, {'count', 'sum', 'mean', 'median', max', 'min', 'first', 'last', None}, default : 'count') – Method to aggregate cell_weights of duplicate rows if setsystem is of type pandas.DataFrame of -StaticEntity. If None all cell weights will be set to 1.

    • -

  • props (user defined keyword arguments to be added to a properties dictionary, optional) –

    • -
-
-
-
-
-properties
-

Description

-
-
Type
-

dict

-
-
-
- -
-
-property arr
-

Tensor like representation of data indexed by labels with values given by incidence or cell weight.

-
-
Returns
-

A Numpy ndarray with dimensions equal dimensions of static entity. Entries are cell_weights. -self.data gives a list of nonzero coordinates aligned with cell_weights.

-
-
Return type
-

numpy.ndarray

-
-
-
- -
-
-property cell_weights
-

User defined weights corresponding to unique rows in data.

-
-
Returns
-

One dimensional array of values aligned to data.

-
-
Return type
-

numpy.array

-
-
-
- -
-
-property children
-

Labels of keys of first index

-
-
Returns
-

One dimensional array of labels in the second level.

-
-
Return type
-

numpy.array

-
-
-
- -
-
-property data
-

Data array or tensor array of Static Entity

-
-
Returns
-

Two dimensional array. Each row has system ids of objects in the static entity. -Each column corresponds to one level of the static entity.

-
-
Return type
-

numpy.ndarray

-
-
-
- -
-
-property dataframe
-

Returns the entity data in DataFrame format

-
-
Returns
-

Dataframe of user defined labels and keys as columns.

-
-
Return type
-

pandas.core.frame.DataFrame

-
-
-
- -
-
-property dimensions
-

Dimension of Static Entity data

-
-
Returns
-

Tuple of number of distinct labels in each level, ordered by level.

-
-
Return type
-

tuple

-
-
-
- -
-
-property dimsize
-

Number of categories in the data

-
-
Returns
-

Number of levels in static entity, equals length of self.dimensions

-
-
Return type
-

int

-
-
-
- -
-
-property elements
-

Keys and values in the order of insertion

-
-
Returns
-

Same as elements_by_level with level1 = 0, level2 = 1. -Compare with EntitySet with level1 = elements, level2 = children.

-
-
Return type
-

collections.OrderedDict

-
-
-
- -
-
-elements_by_level(level1=0, level2=None, translate=False)[source]
-

Elements of staticentity by specified column

-
-
Parameters
-
    -

  • level1 (int, optional) – edges

    • -

  • level2 (int, optional) – nodes

    • -

  • translate (bool, optional) – whether to replace indices with labels

    • -
-
-
Returns
-

    -

  • collections.defaultdict

    • -

  • think (level1 = edges, level2 = nodes)

    • -
-

-
-
-
- -
-
-property incidence_dict
-

Same as elements.

-
-
Returns
-

Same as elements_by_level with level1 = 0, level2 = 1. -Compare with EntitySet with level1 = elements, level2 = children.

-
-
Return type
-

collections.OrderedDict

-
-
-
- -
-
-incidence_matrix(level1=0, level2=1, weights=False, aggregateby=None, index=False)[source]
-

Convenience method to navigate large tensor

-
-
Parameters
-
    -

  • level1 (int, optional) – indexes columns

    • -

  • level2 (int, optional) – indexes rows

    • -

  • weights (bool, dict optional, default=False) – If False all nonzero entries are 1. -If True all nonzero entries are filled by self.cell_weight -dictionary values, use aggregateby to specify how duplicate -entries should have weights aggregated. -If dict, keys must be in (edge.uid, node.uid) form; only nonzero cells -in the incidence matrix will be updated by dictionary.

    • -

  • aggregateby (str, optional, {None, 'last', count', 'sum', 'mean', 'median', max', 'min', 'first', 'last'}, default : 'count') – Method to aggregate weights of duplicate rows in data. If None, then all cell weights -will be set to 1.

    • -

  • index (bool, optional) –

    • -
-
-
Returns
-

Sparse matrix representation of incidence matrix for two levels of static entity.

-
-
Return type
-

scipy.sparse.csr.csr_matrix

-
-
-
-

Note

-

In the context of hypergraphs think level1 = edges, level2 = nodes

-
-
- -
-
-index(category, value=None)[source]
-

Returns dimension of category and index of value

-
-
Parameters
-
    -

  • category (string) –

    • -

  • value (string, optional) –

    • -
-
-
Return type
-

int or tuple of ints

-
-
-
- -
-
-indices(category, values)[source]
-

Returns dimension of category and index of values (array)

-
-
Parameters
-
    -

  • category (string) –

    • -

  • values (single string or array of strings) –

    • -
-
-
Return type
-

list

-
-
-
- -
-
-is_empty(level=0)[source]
-

Boolean indicating if entity.elements is empty

-
-
Parameters
-

level (int, optional) –

-
-
Return type
-

bool

-
-
-
- -
-
-keyindex(category)[source]
-

Returns the index of a category in keys array

-
-
Returns
-

Index osition of particular label in keys equal to the level of the -category.

-
-
Return type
-

int

-
-
-
- -
-
-property keys
-

Array of keys of labels

-
-
Returns
-

Array of label keys, ordered by level.

-
-
Return type
-

np.ndarray

-
-
-
- -
-
-property labels
-

Ordered dictionary of labels

-
-
Returns
-

User defined identifiers for objects in static entity. Ordered keys correspond -levels. Ordered values correspond to integer representation of values in data.

-
-
Return type
-

collections.OrderedDict

-
-
-
- -
-
-labs(kdx)[source]
-

Retrieve labels by index in keys

-
-
Parameters
-

kdx (int) – index of key in E.keys

-
-
Return type
-

np.ndarray

-
-
-
- -
-
-level(item, min_level=0, max_level=None, return_index=True)[source]
-

Returns first level item appears by order of keys from minlevel to maxlevel -inclusive

-
-
Parameters
-
    -

  • item (string) –

    • -

  • min_level (int, optional) –

    • -

  • max_level (int, optional) –

    • -

  • return_index (bool, optional) –

    • -
-
-
Return type
-

tuple

-
-
-
- -
-
-property memberships
-

Reverses the elements dictionary

-
-
Returns
-

Same as elements_by_level with level1 = 1, level2 = 0.

-
-
Return type
-

collections.OrderedDict

-
-
-
- -
-
-restrict_to_indices(indices, level=0, uid=None)[source]
-

Limit Static Entity data to specific indices of keys

-
-
Parameters
-
    -

  • indices (array) – array of category indices

    • -

  • level (int, optional) – index of label

    • -

  • uid (None, optional) –

    • -
-
-
Returns
-

hnx.classes.staticentity.StaticEntity

-
-
Return type
-

Static Entity class

-
-
-
- -
-
-restrict_to_levels(levels, weights=False, aggregateby='count', uid=None)[source]
-

Limit Static Entity data to specific levels

-
-
Parameters
-
    -

  • levels (array) – index of labels in data

    • -

  • weights (bool, optional, default : False) – Whether or not to aggregate existing weights in self when restricting to levels. -If False then weights will be assigned 1.

    • -

  • aggregateby (str, optional, {None, 'last', count', 'sum', 'mean', 'median', max', 'min', 'first', 'last'}, default : 'count') – Method to aggregate cell_weights of duplicate rows in setsystem of type pandas.DataFrame. -If None then all cell_weights will be set to 1.

    • -

  • uid (None, optional) –

    • -
-
-
Returns
-

hnx.classes.staticentity.StaticEntity

-
-
Return type
-

Static Entity class

-
-
-
- -
-
-size()[source]
-

The number of elements in E, the size of dimension 0 in the E.arr

-
-
Return type
-

int

-
-
-
- -
-
-translate(level, index)[source]
-

Replaces a category index and value index with label

-
-
Parameters
-
    -

  • level (int) – category index of label

    • -

  • index (int) – value index of label

    • -
-
-
Return type
-

numpy.array(str)

-
-
-
- -
-
-translate_arr(coords)[source]
-

Translates a single cell in the entity array

-
-
Parameters
-

coords (tuple of ints) –

-
-
Return type
-

list

-
-
-
- -
-
-turn_entity_data_into_dataframe(data_subset)[source]
-

Convert rows of original data in StaticEntity to dataframe

-
-
Parameters
-

data (numpy.ndarray) – Subset of the rows in the original data held in the StaticEntity

-
-
Returns
-

Columns and cell entries are derived from data and self.labels

-
-
Return type
-

pandas.core.frame.DataFrame

-
-
-
- -
-
-property uid
-

User defined identifier for each object in static entity.

-
-
Returns
-

Identifiers, which distinguish objects within each level.

-
-
Return type
-

str, int

-
-
-
- -
-
-property uidset
-

Returns a set of the string identifiers for Static Entity

-
-
Returns
-

Hashable set of keys.

-
-
Return type
-

frozenset

-
-
-
- -
-
-uidset_by_level(level=0)[source]
-

The labels found in columns = level

-
-
Parameters
-

level (int, optional) –

-
-
Return type
-

frozenset

-
-
-
- -
- -
-
-class classes.staticentity.StaticEntitySet(entity=None, data=None, arr=None, labels=None, uid=None, level1=0, level2=1, weights=None, keep_weights=True, aggregateby=None, **props)[source]
-

Bases: StaticEntity

-
-
-collapse_identical_elements(uid=None, return_equivalence_classes=False)[source]
-

Returns StaticEntitySet after collapsing elements if they have same children -If no elements share same children, a copy of the original StaticEntitySet is returned

-
-
Parameters
-
    -

  • uid (None, optional) –

    • -

  • return_equivalence_classes (bool, optional) – If True, return a dictionary of equivalence classes keyed by new edge names

    • -
-
-
Returns
-

hnx.classes.staticentity.StaticEntitySet

-
-
Return type
-

StaticEntitySet

-
-
-
- -
-
-convert_to_entityset(uid)[source]
-

Convert Static EntitySet into EntitySet with given uid.

-
-
Parameters
-

uid (string) –

-
-
Returns
-

hnx.classes.entity.EntitySet

-
-
Return type
-

EntitySet

-
-
-
- -
-
-incidence_matrix(index=False, weights=False)[source]
-

Incidence matrix of StaticEntitySet

-
-
Parameters
-
    -

  • index (bool, optional) –

    • -

  • weight (bool, dict optional, default=False) – If False all nonzero entries are 1. -If True all nonzero entries are filled by self.cell_weight -dictionary values. -If dict, keys must be in self.cell_weight keys; nonzero cells -will be updated by dictionary.

    • -
-
-
Returns
-

Sparse matrix representation of incidence matrix for static entity set.

-
-
Return type
-

scipy.sparse.csr.csr_matrix

-
-
-
- -
-
-restrict_to(indices, uid=None)[source]
-

Limit Static Entityset data to specific indices of keys

-
-
Parameters
-
    -

  • indices (array) – array of indices in keys

    • -

  • uid (None, optional) –

    • -
-
-
Returns
-

hnx.classes.staticentity.StaticEntitySet

-
-
Return type
-

StaticEntitySet

-
-
-
- -
- -
-
-

Module contents

-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/classes/modules.html b/docs/build/classes/modules.html deleted file mode 100644 index 537b950f..00000000 --- a/docs/build/classes/modules.html +++ /dev/null @@ -1,140 +0,0 @@ - - - - - - - classes — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
- -
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/core.html b/docs/build/core.html deleted file mode 100644 index e774ba2f..00000000 --- a/docs/build/core.html +++ /dev/null @@ -1,197 +0,0 @@ - - - - - - - HyperNetX Packages — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

HyperNetX Packages

-
- -
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/build/drawing/drawing.html b/docs/build/drawing/drawing.html deleted file mode 100644 index b81cf29f..00000000 --- a/docs/build/drawing/drawing.html +++ /dev/null @@ -1,573 +0,0 @@ - - - - - - - drawing package — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

drawing package

-
-

Submodules

-
-
-

drawing.rubber_band module

-
-
-drawing.rubber_band.draw(H, pos=None, with_color=True, with_node_counts=False, with_edge_counts=False, layout=<function spring_layout>, layout_kwargs={}, ax=None, node_radius=None, edges_kwargs={}, nodes_kwargs={}, edge_labels={}, edge_labels_kwargs={}, node_labels={}, node_labels_kwargs={}, with_edge_labels=True, with_node_labels=True, label_alpha=0.35, return_pos=False)[source]
-

Draw a hypergraph as a Matplotlib figure

-

By default this will draw a colorful “rubber band” like hypergraph, where -convex hulls represent edges and are drawn around the nodes they contain.

-

This is a convenience function that wraps calls with sensible parameters to -the following lower-level drawing functions:

-
    -

  • draw_hyper_edges,

    • -

  • draw_hyper_edge_labels,

    • -

  • draw_hyper_labels, and

    • -

  • draw_hyper_nodes

    • -
-

The default layout algorithm is nx.spring_layout, but other layouts can be -passed in. The Hypergraph is converted to a bipartite graph, and the layout -algorithm is passed the bipartite graph.

-

If you have a pre-determined layout, you can pass in a “pos” dictionary. -This is a dictionary mapping from node id’s to x-y coordinates. For example:

-
>>> pos = {
->>> 'A': (0, 0),
->>> 'B': (1, 2),
->>> 'C': (5, -3)
->>> }
-
-
-

will position the nodes {A, B, C} manually at the locations specified. The -coordinate system is in Matplotlib “data coordinates”, and the figure will -be centered within the figure.

-

By default, this will draw in a new figure, but the axis to render in can be -specified using ax.

-

This approach works well for small hypergraphs, and does not guarantee -a rigorously “correct” drawing. Overlapping of sets in the drawing generally -implies that the sets intersect, but sometimes sets overlap if there is no -intersection. It is not possible, in general, to draw a “correct” hypergraph -this way for an arbitrary hypergraph, in the same way that not all graphs -have planar drawings.

-
-
Parameters
-
    -

  • H (Hypergraph) – the entity to be drawn

    • -

  • pos (dict) – mapping of node and edge positions to R^2

    • -

  • with_color (bool) – set to False to disable color cycling of edges

    • -

  • with_node_counts (bool) – set to True to replace the label for collapsed nodes with the number of elements

    • -

  • with_edge_counts (bool) – set to True to label collapsed edges with number of elements

    • -

  • layout (function) – layout algorithm to compute

    • -

  • layout_kwargs (dict) – keyword arguments passed to layout function

    • -

  • ax (Axis) – matplotlib axis on which the plot is rendered

    • -

  • edges_kwargs (dict) – keyword arguments passed to matplotlib.collections.PolyCollection for edges

    • -

  • node_radius (None, int, float, or dict) – radius of all nodes, or dictionary of node:value; the default (None) calculates radius based on number of collapsed nodes; reasonable values range between 1 and 3

    • -

  • nodes_kwargs (dict) – keyword arguments passed to matplotlib.collections.PolyCollection for nodes

    • -

  • edge_labels_kwargs (dict) – keyword arguments passed to matplotlib.annotate for edge labels

    • -

  • node_labels_kwargs (dict) – keyword argumetns passed to matplotlib.annotate for node labels

    • -

  • with_edge_labels (bool) – set to False to make edge labels invisible

    • -

  • with_node_labels (bool) – set to False to make node labels invisible

    • -

  • label_alpha (float) – the transparency (alpha) of the box behind text drawn in the figure

    • -
-
-
-
- -
-
-drawing.rubber_band.draw_hyper_edge_labels(H, polys, labels={}, ax=None, **kwargs)[source]
-

Draws a label on the hyper edge boundary.

-

Should be passed Matplotlib PolyCollection representing the hyper-edges, see -the return value of draw_hyper_edges.

-

The label will be draw on the least curvy part of the polygon, and will be -aligned parallel to the orientation of the polygon where it is drawn.

-
-
Parameters
-
    -

  • H (Hypergraph) – the entity to be drawn

    • -

  • polys (PolyCollection) – collection of polygons returned by draw_hyper_edges

    • -

  • labels (dict) – mapping of node id to string label

    • -

  • ax (Axis) – matplotlib axis on which the plot is rendered

    • -

  • kwargs (dict) – Keyword arguments are passed through to Matplotlib’s annotate function.

    • -
-
-
-
- -
-
-drawing.rubber_band.draw_hyper_edges(H, pos, ax=None, node_radius={}, dr=None, **kwargs)[source]
-

Draws a convex hull around the nodes contained within each edge in H

-
-
Parameters
-
    -

  • H (Hypergraph) – the entity to be drawn

    • -

  • pos (dict) – mapping of node and edge positions to R^2

    • -

  • node_radius (dict) – mapping of node to R^1 (radius of each node)

    • -

  • dr (float) – the spacing between concentric rings

    • -

  • ax (Axis) – matplotlib axis on which the plot is rendered

    • -

  • kwargs (dict) – keyword arguments, e.g., linewidth, facecolors, are passed through to the PolyCollection constructor

    • -
-
-
Returns
-

a Matplotlib PolyCollection that can be further styled

-
-
Return type
-

PolyCollection

-
-
-
- -
-
-drawing.rubber_band.draw_hyper_labels(H, pos, node_radius={}, ax=None, labels={}, **kwargs)[source]
-

Draws text labels for the hypergraph nodes.

-

The label is drawn to the right of the node. The node radius is needed (see -draw_hyper_nodes) so the text can be offset appropriately as the node size -changes.

-

The text label can be customized by passing in a dictionary, labels, mapping -a node to its custom label. By default, the label is the string -representation of the node.

-

Keyword arguments are passed through to Matplotlib’s annotate function.

-
-
Parameters
-
    -

  • H (Hypergraph) – the entity to be drawn

    • -

  • pos (dict) – mapping of node and edge positions to R^2

    • -

  • node_radius (dict) – mapping of node to R^1 (radius of each node)

    • -

  • ax (Axis) – matplotlib axis on which the plot is rendered

    • -

  • labels (dict) – mapping of node to text label

    • -

  • kwargs (dict) – keyword arguments passed to matplotlib.annotate

    • -
-
-
-
- -
-
-drawing.rubber_band.draw_hyper_nodes(H, pos, node_radius={}, r0=None, ax=None, **kwargs)[source]
-

Draws a circle for each node in H.

-

The position of each node is specified by the a dictionary/list-like, pos, -where pos[v] is the xy-coordinate for the vertex. The radius of each node -can be specified as a dictionary where node_radius[v] is the radius. If a -node is missing from this dictionary, or the node_radius is not specified at -all, a sensible default radius is chosen based on distances between nodes -given by pos.

-
-
Parameters
-
    -

  • H (Hypergraph) – the entity to be drawn

    • -

  • pos (dict) – mapping of node and edge positions to R^2

    • -

  • node_radius (dict) – mapping of node to R^1 (radius of each node)

    • -

  • r0 (float) – minimum distance that concentric rings start from the node position

    • -

  • ax (Axis) – matplotlib axis on which the plot is rendered

    • -

  • kwargs (dict) – keyword arguments, e.g., linewidth, facecolors, are passed through to the PolyCollection constructor

    • -
-
-
Returns
-

a Matplotlib PolyCollection that can be further styled

-
-
Return type
-

PolyCollection

-
-
-
- -
-
-drawing.rubber_band.get_default_radius(H, pos)[source]
-

Calculate a reasonable default node radius

-

This function iterates over the hyper edges and finds the most distant -pair of points given the positions provided. Then, the node radius is a fraction -of the median of this distance take across all hyper-edges.

-
-
Parameters
-
    -

  • H (Hypergraph) – the entity to be drawn

    • -

  • pos (dict) – mapping of node and edge positions to R^2

    • -
-
-
Returns
-

the recommended radius

-
-
Return type
-

float

-
-
-
- -
-
-drawing.rubber_band.layout_hyper_edges(H, pos, node_radius={}, dr=None)[source]
-

Draws a convex hull for each edge in H.

-

Position of the nodes in the graph is specified by the position dictionary, -pos. Convex hulls are spaced out such that if one set contains another, the -convex hull will surround the contained set. The amount of spacing added -between hulls is specified by the parameter, dr.

-
-
Parameters
-
    -

  • H (Hypergraph) – the entity to be drawn

    • -

  • pos (dict) – mapping of node and edge positions to R^2

    • -

  • node_radius (dict) – mapping of node to R^1 (radius of each node)

    • -

  • dr (float) – the spacing between concentric rings

    • -

  • ax (Axis) – matplotlib axis on which the plot is rendered

    • -
-
-
Returns
-

A mapping from hyper edge ids to paths (Nx2 numpy matrices)

-
-
Return type
-

dict

-
-
-
- -
- -

Helper function to use a NetwrokX-like graph layout algorithm on a Hypergraph

-

The hypergraph is converted to a bipartite graph, allowing the usual graph layout -techniques to be applied.

-
-
Parameters
-
    -

  • H (Hypergraph) – the entity to be drawn

    • -

  • layout (function) – the layout algorithm which accepts a NetworkX graph and keyword arguments

    • -

  • kwargs (dict) – Keyword arguments are passed through to the layout algorithm

    • -
-
-
Returns
-

mapping of node and edge positions to R^2

-
-
Return type
-

dict

-
-
-
- -
-
-

drawing.two_column module

-
-
-drawing.two_column.draw(H, with_node_labels=True, with_edge_labels=True, with_node_counts=False, with_edge_counts=False, with_color=True, edge_kwargs=None, ax=None)[source]
-

Draw a hypergraph using a two-collumn layout.

-

This is intended reproduce an illustrative technique for bipartite graphs -and hypergraphs that is typically used in papers and textbooks.

-

The left column is reserved for nodes and the right column is reserved for -edges. A line is drawn between a node an an edge

-

The order of nodes and edges is optimized to reduce line crossings between -the two columns. Spacing between disconnected components is adjusted to make -the diagram easier to read, by reducing the angle of the lines.

-
-
Parameters
-
    -

  • H (Hypergraph) – the entity to be drawn

    • -

  • with_node_labels (bool) – False to disable node labels

    • -

  • with_edge_labels (bool) – False to disable edge labels

    • -

  • with_node_counts (bool) – set to True to label collapsed nodes with number of elements

    • -

  • with_edge_counts (bool) – set to True to label collapsed edges with number of elements

    • -

  • with_color (bool) – set to False to disable color cycling of hyper edges

    • -

  • edge_kwargs (dict) – keyword arguments to pass to matplotlib.LineCollection

    • -

  • ax (Axis) – matplotlib axis on which the plot is rendered

    • -
-
-
-
- -
-
-drawing.two_column.draw_hyper_edges(H, pos, ax=None, **kwargs)[source]
-

Renders hyper edges for the two column layout.

-

Each node-hyper edge membership is rendered as a line connecting the node -in the left column to the edge in the right column.

-
-
Parameters
-
    -

  • H (Hypergraph) – the entity to be drawn

    • -

  • pos (dict) – mapping of node and edge positions to R^2

    • -

  • ax (Axis) – matplotlib axis on which the plot is rendered

    • -

  • kwargs (dict) – keyword arguments passed to matplotlib.LineCollection

    • -
-
-
Returns
-

the hyper edges

-
-
Return type
-

LineCollection

-
-
-
- -
-
-drawing.two_column.draw_hyper_labels(H, pos, labels={}, with_node_labels=True, with_edge_labels=True, ax=None)[source]
-

Renders hyper labels (nodes and edges) for the two column layout.

-
-
Parameters
-
    -

  • H (Hypergraph) – the entity to be drawn

    • -

  • pos (dict) – mapping of node and edge positions to R^2

    • -

  • labels (dict) – custom labels for nodes and edges can be supplied

    • -

  • with_node_labels (bool) – False to disable node labels

    • -

  • with_edge_labels (bool) – False to disable edge labels

    • -

  • ax (Axis) – matplotlib axis on which the plot is rendered

    • -

  • kwargs (dict) – keyword arguments passed to matplotlib.LineCollection

    • -
-
-
-
- -
-
-drawing.two_column.layout_two_column(H, spacing=2)[source]
-

Two column (bipartite) layout algorithm.

-

This algorithm first converts the hypergraph into a bipartite graph and -then computes connected components. Disonneccted components are handled -independently and then stacked together.

-

Within a connected component, the spectral ordering of the bipartite graph -provides a quick and dirty ordering that minimizes edge crossings in the -diagram.

-
-
Parameters
-
    -

  • H (Hypergraph) – the entity to be drawn

    • -

  • spacing (float) – amount of whitespace between disconnected components

    • -
-
-
-
- -
-
-

drawing.util module

-
-
-drawing.util.get_collapsed_size(v)[source]
-
- -
-
-drawing.util.get_frozenset_label(S, count=False, override={})[source]
-

Helper function for rendering the labels of possibly collapsed nodes and edges

-
-
Parameters
-
    -

  • S (iterable) – list of entities to be labeled

    • -

  • count (bool) – True if labels should be counts of entities instead of list

    • -
-
-
Returns
-

mapping of entity to its string representation

-
-
Return type
-

dict

-
-
-
- -
-
-drawing.util.get_line_graph(H, collapse=True)[source]
-

Computes the line graph, a directed graph, where a directed edge (u, v) -exists if the edge u is a subset of the edge v in the hypergraph.

-
-
Parameters
-
    -

  • H (Hypergraph) – the entity to be drawn

    • -

  • collapse (bool) – True if edges should be added if hyper edges are identical

    • -
-
-
Returns
-

A directed graph

-
-
Return type
-

networkx.DiGraph

-
-
-
- -
-
-drawing.util.get_set_layering(H, collapse=True)[source]
-

Computes a layering of the edges in the hyper graph.

-

In this layering, each edge is assigned a level. An edge u will be above -(e.g., have a smaller level value) another edge v if v is a subset of u.

-
-
Parameters
-
    -

  • H (Hypergraph) – the entity to be drawn

    • -

  • collapse (bool) – True if edges should be added if hyper edges are identical

    • -
-
-
Returns
-

a mapping of vertices in H to integer levels

-
-
Return type
-

dict

-
-
-
- -
-
-drawing.util.inflate(items, v)[source]
-
- -
-
-drawing.util.inflate_kwargs(items, kwargs)[source]
-

Helper function to expand keyword arguments.

-
-
Parameters
-
    -

  • n (int) – length of resulting list if argument is expanded

    • -

  • kwargs (dict) – keyword arguments to be expanded

    • -
-
-
Returns
-

dictionary with same keys as kwargs and whose values are lists of length n

-
-
Return type
-

dict

-
-
-
- -
-
-drawing.util.transpose_inflated_kwargs(inflated)[source]
-
- -
-
-

Module contents

-
-
- - -
-
- -
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-
- - - - \ No newline at end of file diff --git a/docs/build/drawing/modules.html b/docs/build/drawing/modules.html deleted file mode 100644 index 4dd44936..00000000 --- a/docs/build/drawing/modules.html +++ /dev/null @@ -1,140 +0,0 @@ - - - - - - - drawing — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - - - -
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  • Index
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Index

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- A - | B - | C - | D - | E - | F - | G - | H - | I - | K - | L - | M - | N - | O - | P - | R - | S - | T - | U - -
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E

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K

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L

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© Copyright 2021 Battelle Memorial Institute.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
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- - - - \ No newline at end of file diff --git a/docs/build/glossary.html b/docs/build/glossary.html deleted file mode 100644 index 489708e3..00000000 --- a/docs/build/glossary.html +++ /dev/null @@ -1,212 +0,0 @@ - - - - - - - Glossary of HNX terms — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - - - -
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Glossary of HNX terms

-
-
Bipartite Condition

Condition imposed on instances of the class EntitySet. -ities that are elements of the same EntitySet, may not contain each other as elements.* -elements and children of an EntitySet generate a specific partition for a bipartite graph. -partition is isomorphic to a Hypergraph where the elements correspond to hyperedges and -children correspond to the nodes. EntitySets are the basic objects used to construct dynamic hypergraphs -NX. See methods classes.hypergraph.Hypergraph.bipartite() and classes.hypergraph.Hypergraph.from_bipartite().

-
-
degree

Given a hypergraph (Nodes,Edges), the degree of a node in Nodes is the number of edges in Edges to which the node belongs. -See also: s-degree

-
-
dual

For a hypergraph (Nodes,Edges), its dual is the hypergraph constructed by switching the roles of Nodes and Edges. More precisely, if node i belongs to edge j in the hypergraph, then node j belongs to edge i in the dual hypergraph.

-
-
Entity

Class in entity.py. -The base class for nodes, edges, and other HNX structures. An entity has a unique id, a set of properties, and a set of other entities belonging to it called its elements (an entity may not contain itself). -If an entity A belongs to another entity B then A has membership in B and A is an element of B. For any entity A access a dictionary of its elements (keyed by uid) using A.elements and a dictionary of its memberships using A.memberships.

-
-
Entity.children

Attribute in class Entity. Returns a set of uids for the elements of the elements of entity. -For any entity A, the set of entities which belong to some entity belonging to A. Use A.children to access the set of uids belonging to the children of A and A.registry to access a dictionary of uid,entity key value pairs of the children of A. -See also Entity.levelset.

-
-
Entity.depth

Method in class Entity. -The number of non empty Level sets belonging to an entity. -For any entity A, if A.elements is empty then it has depth 0 and no non-empty Levels. -If A.elements contains only Entities of depth 0 then A has depth 1. -If A.elements contains only Entities of depth 0 and depth 1 then A has depth 2. -If A.elements contains an entity of depth n and no Entities of depth more than n then it has depth n+1.

-
-
Entity.elements

Attribute in class Entity. Returns a dictionary of elements of the entity. -For any entity A, the elements equal the set of entities belonging to A. Use A.uidset to access the set of uids belonging to the elements of A and A.elements to access a dictionary of uid,entity key value pairs of elements of A.

-
-
Entity.levelset

Method in class Entity. -For any entity A, Level 1 of A is the set of elements of A. -The elements of entities in Level 1 of A belong to Level 2 of A. The elements of entities in Level k of A belong to Level k+1 of A. -The entities in Level 2 of A are called A’s children. -A single entity may occupy multiple Level sets of an entity. An entity may belong to any of its own Level sets except Level 1 as no entity may contain itself as an element. -Note that if Level n of A is nonempty then Level k of A is nonempty for all k<n. -Use A.levelset(k) to access a dictionary of uid,entity key value pairs for the entities in Level k of A.

-
-
Entity.memberships

Attribute in class Entity. -A dictionary of uid,entity key value pairs of entities to which the entity belongs.

-
-
Entity.registry

Attribute in class Entity. -A dictionary of uid,entity key value pairs of the children of an entity.

-
-
entityset

An entity A satisfying the Bipartite Condition, the property that the set of entities in Level 1 of A is disjoint from the set of entities in Level 2 of A, i.e. the elements of A are disjoint from the children of A. An entityset is instantiated in the class EntitySet.

-
-
hypergraph

A pair of entitysets (Nodes,Edges) such that Edges has depth 2, Nodes have depth 1, and the children of Edges is exactly the set of elements of Nodes. Intuitively, every element of Edges is a (hyper)edge, which is either empty or contains elements of Nodes. Every node in Nodes has membership in some edge in Edges. Since a node has depth 0 it is distinguished by its uid, properties, and memberships. A hypergraph is instantiated in the class Hypergraph.

-
-
incidence matrix

A rectangular matrix constructed from a hypergraph (Nodes,Edges) where the elements of Nodes index the matrix rows, and the elements of Edges index the matrix columns. Entry (i,j) in the incidence matrix is 1 if the node corresponding to i in Nodes belongs to the edge corresponding to j in Edges, and is 0 otherwise.

-
-
s-adjacency matrix

For a hypergraph (Nodes,Edges) and positive integer s, a square matrix where the elements of Nodes index both rows and columns. The matrix can be weighted or unweighted. Entry (i,j) is nonzero if and only if node i and node j belong to at least s shared edges, and is equal to the number of shared edges (if weighted) or 1 (if unweighted).

-
-
s-auxiliary matrix

For a hypergraph (Nodes,Edges) and positive integer s, the submatrix of the s-edge-adjacency matrix obtained by restricting to rows and columns corresponding to edges of size at least s.

-
-
s-connected component, s-node-connected component

For a hypergraph (Nodes,Edges) and positive integer s, an s-connected component is a subhypergraph induced by a subset of Nodes with the property that there exists an s-walk between every pair of nodes in this subset. An s-connected component is the maximal such subset in the sense that it is not properly contained in any other subset satisfying this property.

-
-
s-connected, s-node-connected

A hypergraph is s-connected if it has one s-connected component.

-
-
s-degree

For a hypergraph (Nodes, Edges) and positive integer s, the s-degree of a node is the number of edges in Edges of size at least s to which node belongs. See also: degree

-
-
s-diameter

For a hypergraph (Nodes,Edges) and positive integer s, the s-diameter is the maximum s-Distance over all pairs of nodes in Nodes.

-
-
s-distance

For a hypergraph (Nodes,Edges) and positive integer s, the s-distances between two nodes in Nodes is the length of the shortest s-node-walk between them. If no s-node-walks between the pair of nodes exists, the s-distance between them is infinite. The s-distance -between edges is the length of the shortest s-edge-walk between them. If no s-edge-walks between the pair of edges exist, then s-distance between them is infinite.

-
-
s-edge

For a hypergraph (Nodes, Edges) and positive integer s, an s-edge is any edge of size at least s.

-
-
s-edge-adjacency matrix

For a hypergraph (Nodes,Edges) and positive integer s, a square matrix where the elements of Edges index both rows and columns. The matrix can be weighted or unweighted. Entry (i,j) is nonzero if and only if edge i and edge j share to at least s nodes, and is equal to the number of shared nodes (if weighted) or 1 (if unweighted).

-
-
s-edge-connected

A hypergraph is s-edge-connected if it has one s-edge-connected component.

-
-
s-edge-connected component

For a hypergraph (Nodes,Edges) and positive integer s, an s-edge-connected component is a subhypergraph induced by a subset of Edges with the property that there exists an s-edge-walk between every pair of edges in this subset. An s-edge-connected component is the maximal such subset in the sense that it is not properly contained in any other subset satisfying this property.

-
-
s-edge-walk

For a hypergraph (Nodes,Edges) and positive integer s, a sequence of edges in Edges such that each successive pair of edges intersects in at least s nodes in Nodes.

-
-
s-linegraph

For a hypergraph (Nodes, Edges) and positive integer s, an s-linegraph is a graph representing -the node to node or edge to edge connections according to the width s of the connections. -The node s-linegraph is a graph on the set Nodes. Two nodes in Nodes are incident in the node s-linegraph if they -share at lease s incident edges in Edges; that is, there are at least s elements of Edges to which they both belong. -The edge s-linegraph is a graph on the set Edges. Two edges in Edges are incident in the edge s-linegraph if they -share at least s incident nodes in Nodes; that is, the edges intersect in at least s nodes in Nodes.

-
-
s-node-walk

For a hypergraph (Nodes,Edges) and positive integer s, a sequence of nodes in Nodes such that each successive pair of nodes share at least s edges in Edges.

-
-
s-walk

Either an s-node-walk or an s-edge-walk.

-
-
simple hypergraph

A hypergraph for which no edge is completely contained in another.

-
-
subhypergraph

Given a hypergraph (Nodes,Edges), a subhypergraph is a pair of subsets of (Nodes,Edges).

-
-
toplex

For a hypergraph (Nodes,Edges), a toplex is an edge in Edges whose elements (i.e. nodes) do not all belong to any other edge in Edge.

-
-
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- - - - \ No newline at end of file diff --git a/docs/build/index.html b/docs/build/index.html deleted file mode 100644 index 6e9677b4..00000000 --- a/docs/build/index.html +++ /dev/null @@ -1,293 +0,0 @@ - - - - - - - HyperNetX (HNX) — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - - -
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HyperNetX (HNX)

-_images/hnxbasics.png -
-

Description

-

The HNX library provides classes and methods for modeling the entities and relationships -found in complex networks as hypergraphs, the natural models for multi-dimensional network data. -As strict generalizations of graphs, hyperedges can represent arbitrary multi-way relations -among entities, and in particular can distinguish cliques and simplices, and admit singleton edges. -As both vertex adjacency and edge -incidence are generalized to be quantities, -hypergraph paths and walks thereby have both length and width because of these multiway connections. -Most graph metrics have natural generalizations to hypergraphs, but since -hypergraphs are basically set systems, they also admit to the powerful tools of algebraic topology, -including simplicial complexes and simplicial homology, to study their structure.

-

This library serves as a repository of the methods and algorithms we find most useful -as we explore what hypergraphs can tell us. We have a growing community of users and contributors. -To learn more about some of our research check out our Publications.

-
-
For comments and questions you may contact the developers directly at:

hypernetx@pnnl.gov

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Contents

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Indices and tables

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- - - - \ No newline at end of file diff --git a/docs/build/install.html b/docs/build/install.html deleted file mode 100644 index bd011b5c..00000000 --- a/docs/build/install.html +++ /dev/null @@ -1,199 +0,0 @@ - - - - - - - Installing HyperNetX — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - - - -
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Installing HyperNetX

-

HyperNetX may be cloned or forked from: https://github.com/pnnl/HyperNetX .

-
-

To install in an Anaconda environment

-
>>> conda create -n <env name> python=3.7
->>> source activate <env name>
->>> pip install hypernetx
-
-
-

Mac Users: If you wish to build the documentation you will need -the conda version of matplotlib:

-
>>> conda create -n <env name> python=3.7 matplotlib
->>> source activate <env name>
->>> pip install hypernetx
-
-
-

To use NWHy use python=3.9 and the conda version of tbb in your environment. -Note that NWHy only works on Linux and some OSX systems. See NWHy docs for more.:

-
>>> conda create -n <env name> python=3.9 tbb
->>> source activate <env name>
->>> pip install hypernetx
->>> pip install nwhy
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-
-
-
-

To install in a virtualenv environment

-
>>> virtualenv --python=<path to python 3.7 executable> <path to env name>
-
-
-

This will create a virtual environment in the specified location using -the specified python executable. For example:

-
>>> virtualenv --python=C:\Anaconda3\python.exe hnx
-
-
-

This will create a virtual environment in .hnx using the python -that comes with Anaconda3.

-
>>> <path to env name>\Scripts\activate<file extension>
-
-
-

If you are running in Windows PowerShell use <file extension>=.ps1

-

If you are running in Windows Command Prompt use <file extension>=.bat

-

Otherwise use <file extension>=NULL (no file extension).

-

Once activated continue to follow the installation instructions below.

-
-
-

Install using Pip options

-

For a minimal installation:

-
>>> pip install hypernetx
-
-
-

For an editable installation with access to jupyter notebooks:

-
>>> pip install [-e] .
-
-
-

To install with the tutorials:

-
>>> pip install -e .['tutorials']
-
-
-

To install with the documentation:

-
>>> pip install -e .['documentation']
->>> chmod 755 build_docs.sh
->>> sh build_docs.sh
-## This will generate the documentation in /docs/build/
-## Open them in your browser with /docs/index.html
-
-
-

To install and test using pytest:

-
>>> pip install -e .['testing']
->>> pytest
-
-
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To install the whole shabang:

-
>>> pip install -e .['all']
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License

-

HyperNetX

-

Copyright © 2018, Battelle Memorial Institute

-

Battelle Memorial Institute (hereinafter Battelle) hereby grants permission -to any person or entity lawfully obtaining a copy of this software and associated -documentation files (hereinafter “the Software”) to redistribute and use the -Software in source and binary forms, with or without modification. Such person -or entity may use, copy, modify, merge, publish, distribute, sublicense, and/or -sell copies of the Software, and may permit others to do so, subject to the -following conditions:

-
    -

  • Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimers.

    • -

  • Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other

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  • Other than as used herein, neither the name Battelle Memorial Institute or Battelle may be used in any form whatsoever without the express written consent of Battelle.

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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. -IN NO EVENT SHALL BATTELLE OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, -INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR -PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, -WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) -ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -POSSIBILITY OF SUCH DAMAGE.

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Modularity and Clustering

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Overview

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The hypergraph_modularity submodule in HNX provides functions to compute hypergraph modularity for a -given partition of the vertices in a hypergraph. In general, higher modularity indicates a better -partitioning of the vertices into dense communities.

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Two functions to generate such hypergraph -partitions are provided: Kumar’s algorithm, and the simple Last-Step refinement algorithm.

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The submodule also provides a function to generate the two-section graph for a given hypergraph which can then be used to find -vertex partitions via graph-based algorithms.

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Installation

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Since it is part of HNX, no extra installation is required. -The submodule can be imported as follows:

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import hypernetx.algorithms.hypergraph_modularity as hmod
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Using the Tool

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Precomputation

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In order to make the computation of hypergraph modularity more efficient, some quantities need to be pre-computed. -Given hypergraph H, calling:

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HG = hmod.precompute_attributes(H)
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will pre-compute quantities such as node strength (weighted degree), d-weights (total weight for each edge cardinality) and binomial coefficients.

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Modularity

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Given hypergraph HG and a partition A of its vertices, hypergraph modularity is a measure of the quality of this partition. -Random partitions typically yield modularity near zero (it can be negative) while positive modularity is indicative of the presence -of dense communities, or modules. There are several variations for the definition of hypergraph modularity, and the main difference lies in the -weight given to different edges. Modularity is computed via:

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q = hmod.modularity(HG, A, wdc=linear)
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In a graph, an edge only links 2 nodes, so given partition A, an edge is either within a community (which increases the modularity) -or between communities.

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With hypergraphs, we consider edges of size d=2 or more. Given some vertex partition A and some d-edge e, let c be the number of nodes -that belong to the most represented part in e; if c > d/2, we consider this edge to be within the part. -Hyper-parameters 0 <= w(d,c) <= 1 control the weight -given to such edges. Three functions are supplied in this submodule, namely:

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linear

\(w(d,c) = c/d\) if \(c > d/2\), else \(0\).

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majority

\(w(d,c) = 1\) if \(c > d/2\), else \(0\).

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strict

\(w(d,c) = 1\) if \(c == d\), else \(0\).

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The ‘linear’ function is used by default. More details in [2].

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Two-section graph

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There are several good partitioning algorithms for graphs such as the Louvain algorithm and ECG, a consensus clustering algorithm. -One way to obtain a partition for hypergraph HG is to build its corresponding two-section graph G and run a graph clustering algorithm. -Code is provided to build such graph via:

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G = hmod.two_section(HG)
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which returns an igraph.Graph object.

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Clustering Algorithms

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Two clustering (vertex partitioning) algorithms are supplied. The first one is a hybrid method proposed by Kumar et al. (see [1]) -that uses the Louvain algorithm on the two-section graph, but re-weights the edges according to the distibution of vertices -from each part inside each edge. Given hypergraph HG, this is called as:

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K = hmod.kumar(HG)
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The other supplied algorithm is a simple method to improve hypergraph modularity directely. Given some -initial partition of the vertices (for example via Louvain on the two-section graph), move vertices between parts in order -to improve hypergraph modularity. Given hypergraph HG and initial partition A, this is called as:

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L = hmod.last_step(HG, A, wdc=linear)
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where the ‘wdc’ parameter is the same as in the modularity function.

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Other Features

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We represent a vertex partition A as a list of sets, but another conveninent representation is via a dictionary. -We provide two utility functions to switch representation, namely A = dict2part(D) and D = part2dict(A).

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References

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[1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S. and Ravindran B. “A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering”. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24

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[2] Kamiński B., Prałat P. and Théberge F. “Community Detection Algorithm Using Hypergraph Modularity”. In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13

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NWHy

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Description

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NWHy is an addon for HNX providing optimized C++ implementations of many of the hypergraph methods. -NWHy is a scalable, high-performance hypergraph library. It has three dependencies.

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  1. NWGraph library: provides graph data structures, a rich set of adaptors over the graph data structures, and various high-performance graph algorithms implementations.

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  2. Intel OneAPI Threading Building Blocks (oneTBB): provides parallelism.

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  3. Pybind11: encapsulate NWHy as a python module.

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The goal of the NWHy python API is to share an ID space between NWHy and its user for hypergraph processing, instead of copying the sparse matrix of the hypergraph back and forth between NWHy and its user. -NWHy was developed by Xu Tony Liu. The current version is preliminary and under active development.

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Installing NWHy

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The NWHy library provides Pybind11 APIs for analysis of complex data sets interpreted as hypergraphs.

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To install in an Anaconda environment

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>>> conda create -n <env name> python=3.9
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Then activate the environment

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>>> conda activate <env name>
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Install Intel Threading Building Blocks(TBB)

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To install TBB:

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>>> conda install tbb
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If a local TBB has been installed, we can specify TBBROOT

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>>> export TBBROOT=/opt/tbb/
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Install using Pip

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For installation:

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>>> pip install nwhy
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For upgrade:

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>>> pip install nwhy --upgrade
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or

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>>> pip install nwhy -U
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Quick test with import

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For quick test:

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>>> python -c "import nwhy"
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If there is no import error, then installation is done.

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NWHy APIs

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nwhy module

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_version

Attribute in nwhy module. -Return the version number of nwhy module.

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NWHypergraph class

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NWHypergraph

Class in nwhy module. -The base class for hypergraph representation in nwhy. It accepts a directed edge list format of hypergraph, either weighted or unweighted, then construct the NWHypergraph object.

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NWHypergraph class attributes

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NWHypergraph.row

Attribute in class NWHypergraph. -Return a Numpy array of IDs, row of sparse matrix of the hypergraph. Note the number of entries in the Numpy lists, row, col and data must be equal. The row stores hyperedges.

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NWHypergraph.col

Attribute in class NWHypergraph. -Return a Numpy array of IDs, columns of sparse matrix of the hypergraph. The col stores vertices.

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NWHypergraph.data

Attribute in class NWHypergraph. -Return a Numpy array of IDs, weights of sparse matrix of the hypergraph.

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NWHypergraph class methods

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NWHypergraph.NWHypergraph(x, y)

Constructor of class NWHypergraph. -Return a NWHypergraph object. Here the hypergraph is unweighted. X is a Numpy array of hyperedges, and y is a Numpy array of vertices.

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NWHypergraph.NWHypergraph(x, y, data)

Constructor of class NWHypergraph. -Return a NWHypergraph object. Here the hypergraph is weighted. X is a Numpy array of hyperedges, y is a Numpy array of vertices, data is a Numpy array of weights associated with the pairs from hyperedges to vertices.

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NWHypergraph.collapse_edges(return_equal_class=False)

Method in class NWHypergraph. -Return a dictionary, where the key is a new ID of a hyperedge after collapsing the hyperedges if the hyperedges have the same vertices, and the value is the number of such hyperedges when return_equal_class=False, otherwise, the set of such hyperedges when return_equal_class=True. Note the weights associated with the pairs from hyperedges to vertices are not collapsed or combined.

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NWHypergraph.collapse_nodes(return_equal_class=False)

Method in class NWHypergraph. -Return a dictionary, where the key is a new ID of a vertex after collapsing the vertices if the vertices share the same hyperedges, and the value is the number of such vertices when return_equal_class=False, otherwise, the set of such vertices when return_equal_class=True. Note the weights associated with the pairs from hyperedges to vertices are not collapsed or combined.

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NWHypergraph.collapse_nodes_and_edges(return_equal_class=False)

Method in class NWHypergraph. -Return a dictionary, where the key is a new ID of a hyperedge after collapsing the hyperedges if the hyperedges share the same vertices, and the value is the number of such hyperedges when return_equal_class=False, otherwise, the set of such hyperedges when return_equal_class=True. This method is not equivalent to call NWHypergraph.collapse_nodes() then NWHypergraph.collapse_edges(). Note the weights associated with the pairs from hyperedges to vertices are not collapsed or combined.

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NWHypergraph.edge_size_dist()

Method in class NWHypergraph. -Return a list of edge size distribution of the hypergraph.

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NWHypergraph.node_size_dist()

Method in class NWHypergraph. -Return a list of vertex size distribution of the hypergraph.

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NWHypergraph.edge_incidence(edge)

Method in class NWHypergraph. -Return a list of vertices that are incident to hyperedge edge.

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NWHypergraph.node_incidence(node)

Method in class NWHypergraph. -Return a list of hyperedges that are incident to vertex node.

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NWHypergraph.degree(node, min_size=1, max_size=None)

Method in class NWHypergraph. -Return the degree of the vertex node in the hypergraph. For the hyperedges node incident to, if min_size or/and max_size are specified, then either/both criteria are used to filter the hyperedges.

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NWHypergraph.size(edge, min_degree=1, max_degree=None)

Method in class NWHypergraph. -Return the size of the hyperedge edge in the hypergraph. For the vertices edge incident to, if min_degree or/and max_degree are specified, then either/both criteria are used to filter the vertices.

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NWHypergraph.dim(edge)

Method in class NWHypergraph. -Return the dimension of the hyperedge edge in the hypergraph.

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NWHypergraph.number_of_nodes()

Method in class NWHypergraph. -Return the number of vertices in the hypergraph.

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NWHypergraph.number_of_edges()

Method in class NWHypergraph. -Return the number of edges in the hypergraph.

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NWHypergraph.singletons()

Method in class NWHypergraph. -Return a list of singleton hyperedges in the hypergraph. A singleton hyperedge is incident to only one vertex.

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NWHypergraph.toplexes()

Method in class NWHypergraph. -Return a list of toplexes in the hypergraph. For a hypergraph (Edges, Nodes), a toplex is a hyperedge in Edges whose elements (i.e. nodes) do not all belong to any other hyperedge in Edge.

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NWHypergraph.s_linegraph(s=1, edges=True)

Method in class NWHypergraph. -Return a Slinegraph object. Construct a s-line graph from the hypergraph for a positive integer s. In this s-line graph, the vertices are the hyperedges in the original hypergraph if edges=True; otherwise, the vertices are the vertices in the original hypergraph. Note this method create s-line graph on the fly, therefore it requires less memory compared with NWHypergraph.s_linegraphs(l, edges=True). It is slower to construct multiple s-line graphs for different s compared with NWHypergraph.s_linegraphs(l, edges=True).

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NWHypergraph.s_linegraphs(l, edges=True)

Method in class NWHypergraph. -Return a list of Slinegraph objects. For each positive integer in list l, construct a Slinegraph object from the hypergraph. In each s-line graph, the vertices are the hyperedges in the original hypergraph if edges=True; otherwise, the vertices are the vertices in the original hypergraph. Note this method creates multiple s-line graphs for one run, therefore it is significantly faster compared with NWHypergraph.s_linegraph(s=1, edges=True), but it requires much more memory.

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Slinegraph class

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Slinegraph

Class in nwhy module. -The base class for s-line graph representation in nwhy. It store an undirected graph, called an s-line graph of a hypergraph given a positive integer s. Slinegraph can be an ‘edge’ line graph, where the vertices in Slinegraph are the hyperedges in the original hypergraph; Slinegraph can also be a ‘vertex’ line graph, where the vertices in Slinegraph are the vertices in the original hypergraph.

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Slinegraph class attributes

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Slinegraph.row

Attribute in class Slinegraph. -Return a Numpy array of IDs, row of sparse matrix of the s-line graph. Note the number of entries in the Numpy lists, row, col and data must be equal.

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Slinegraph.col

Attribute in class Slinegraph. -Return a Numpy array of IDs, columns of sparse matrix of the s-line graph.

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Slinegraph.data

Attribute in class Slinegraph. -Return a Numpy array of IDs, weights of sparse matrix of the s-line graph. The weights are not the hyperedge-vertex pair weights. Currently, if Slinegraph is an edge line graph, the weights are the number of overlapping vertices between two hyperedges in the original hypergraph. If the Slinegraph is a vertex line graph, the weights are the number of overlapping hyperedges between two vertices in the original hypergraph.

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Slinegraph.s

Attribute in class Slinegraph. -Return s value of the s-line graph.

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Slinegraph class methods

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Slinegraph.Slinegraph(g, s=1, edges=True)

Constructor of class Slinegraph. -Return a new Slinegraph object. Given a positive integer s, construct a s-line graph from the hypergraph g. The vertices in the s-line graph are the hyperedges in g if edges=True, otherwise, the vertices in the s-line graph are the vertices in g.

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Slinegraph.Slinegraph(x, y, data, s=1, edges=True)

Constructor of class Slinegraph. -Return a new Slinegraph object. Given an edge list format of a s-line graph stored in three Numpy arrays, construct a s-line graph from the edge list. A positive integer s and a boolean edges are required to indicate the properties of the s-line graph.

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Slinegraph.get_singletons()

Method in class Slinegraph. -Return a list of singletons in the s-line graph.

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Slinegraph.s_connected_components()

Method in class Slinegraph. -Return a list of sets, where each set contains the vertices sharing the same component.

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Slinegraph.is_s_connected()

Method in class Slinegraph. -Return True or False. Check whether s-line graph is connected.

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Slinegraph.s_distance(src, dest)

Method in class Slinegraph. -Return the distance from src to dest. Return -1 if it is unreachable from src to dest.

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Slinegraph.s_diameter(src, dest)

Method in class Slinegraph. -Return the diameter of the s-line graph. Return 0 if every vertex is a singleton.

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Slinegraph.s_path(src, dest)

Method in class Slinegraph. -Return a list of vertices. The vertices are the vertices on the shortest path from src to dest in the s-line graph. The list will be empty if it is unreachable from src to dest.

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Slinegraph.s_betweenness_centrality(normalized=True)

Method in class Slinegraph. -Return a list of betweenness centrality score of every vertices in the s-line graph. The betweenness centrality score will be normalized by 2/((n-1)(n-2)) if normalized=True where n the number of vertices in s-line graph. Betweenness centrality of a vertex v is the sum of the fraction of all-pairs shortest paths that pass through v:

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Slinegraph.s_closeness_centrality(v=None)

Method in class Slinegraph. -Return a list of closeness centrality scores of every vertices in the s-line graph. If v is specified, then the list returned contains only v’s score. Closeness centrality of a vertex v is the reciprocal of the average shortest path distance to v over all n-1 reachable nodes:

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Slinegraph.s_harmonic_closeness_centrality(v=None)

Method in class Slinegraph. -Return a list of harmonic closeness centrality scores of every vertices in the s-line graph. If v is specified, then the list returned contains only v’s score. Harmonic centrality of a vertex v is the sum of the reciprocal of the shortest path distances from all other nodes to v:

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Slinegraph.s_eccentricity(v=None)

Method in class Slinegraph. -Return a list of eccentricity of every vertices in the s-line graph. If v is specified, then the list returned contains only eccentricity of v.

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Method in class Slinegraph. -Return a list of neighboring vertices of v in the s-line graph.

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Method in class Slinegraph. -Return the degree of vertex v in the s-line graph.

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Overview

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The HyperNetX (HNX) library was developed to support researchers modeling data -as hypergraphs. We have a growing community of users and contributors. -For questions and comments you may contact the developers directly at: hypernetx@pnnl.gov

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HyperNetX was developed by the Pacific Northwest National Laboratory for the -Hypernets project as part of its High Performance Data Analytics (HPDA) program. -PNNL is operated by Battelle Memorial Institute under Contract DE-ACO5-76RL01830.

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  • Principle Developer and Designer: Brenda Praggastis

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  • Visualization: Dustin Arendt, Ji Young Yun

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  • High Performance Computing: Tony Liu, Andrew Lumsdaine

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  • Principal Investigator: Cliff Joslyn

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  • Program Manager: Brian Kritzstein

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  • Mathematics, methods, and algorithms: Sinan Aksoy, Dustin Arendt, Cliff Joslyn, Nicholas Landry, Tony Liu, Andrew Lumsdaine, Brenda Praggastis, and Emilie Purvine, François Théberge

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New Features in Version 1.0

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  1. Hypergraph construction can be sped up by reading in all of the data at once. In particular the hypergraph constructor may read a Pandas dataframe object and create edges and nodes based on column headers.

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  2. The C++ addon NWHy can be used in Linux environments to support optimized hypergraph methods such as s-centrality measures.

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  3. The JavaScript addon Hypernetx-Widget can be used to interactively inspect hypergraphs in a Jupyter Notebook.

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  4. We’ve added four new tutorials highlighting the s-centrality metrics, static Hypergraphs, NWHy, and Hypernetx-Widget.

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New Features in Version 1.1

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  1. Cell weights for incidence matrices.

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  2. Support for edge and node properties in static hypergraphs.

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  3. Three new algorithms modules and their corresponding tutorials

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    1. Contagion module for studying SIS and SIR contagion networks using hypergraphs.

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New Features in Version 1.2

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  1. Added algorithm module and tutorial for Modularity and Clustering

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COLAB Tutorials

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The following tutorials may be run in your browser using Google Colab. Additional tutorials are -available on GitHub.

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Notice

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This material was prepared as an account of work sponsored by an agency of the United States Government. -Neither the United States Government nor the United States Department of Energy, nor Battelle, -nor any of their employees, nor any jurisdiction or organization that has cooperated in the development of -these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility -for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process -disclosed, or represents that its use would not infringe privately owned rights. -Reference herein to any specific commercial product, process, or service by trade name, -trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, -or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. -The views and opinions of authors expressed herein do not necessarily state or reflect -those of the United States Government or any agency thereof.

-
- 
-      PACIFIC NORTHWEST NATIONAL LABORATORY
-      operated by
-      BATTELLE
-      for the
-      UNITED STATES DEPARTMENT OF ENERGY
-      under Contract DE-AC05-76RL01830
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License

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HyperNetX is released under the 3-Clause BSD license (see License)

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Publications

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Joslyn, Cliff A; Aksoy, Sinan; Callahan, Tiffany J; Hunter, LE; Jefferson, Brett ; Praggastis, Brenda ; Purvine, Emilie AH ; Tripodi, Ignacio J: (2020) “Hypernetwork Science: From Multidimensional Networks to Computational Topology”, in: Int. Conf. Complex Systems (ICCS 2020), https://arxiv.org/abs/2003.11782, (in press)

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Feng, Song; Heath, Emily; Jefferson, Brett; Joslyn, CA; Kvinge, Henry; McDermott, Jason E ; Mitchell, Hugh D ; Praggastis, Brenda ; Eisfeld, Amie J; Sims, Amy C ; Thackray, Larissa B ; Fan, Shufang ; Walters, Kevin B; Halfmann, Peter J ; Westhoff-Smith, Danielle ; Tan, Qing ; Menachery, Vineet D ; Sheahan, Timothy P ; Cockrell, Adam S ; Kocher, Jacob F ; Stratton, Kelly G ; Heller, Natalie C ; Bramer, Lisa M ; Diamond, Michael S ; Baric, Ralph S ; Waters, Katrina M ; Kawaoka, Yoshihiro ; Purvine, Emilie: (2020) “Hypergraph Models of Biological Networks to Identify Genes Critical to Pathogenic Viral Response”, in: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04197-2, BMC Bioinformatics, 22:287, doi: 10.1186/s12859-021-04197-2

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Aksoy, Sinan G; Joslyn, Cliff A; Marrero, Carlos O; Praggastis, B; Purvine, Emilie AH: (2020) “Hypernetwork Science via High-Order Hypergraph Walks”, EPJ Data Science, v. 9:16, https://doi.org/10.1140/epjds/s13688-020-00231-0

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Joslyn, Cliff A; Aksoy, Sinan; Arendt, Dustin; Firoz, J; Jenkins, Louis ; Praggastis, Brenda ; Purvine, Emilie AH ; Zalewski, Marcin: (2020) “Hypergraph Analytics of Domain Name System Relationships”, in: 17th Wshop. on Algorithms and Models for the Web Graph (WAW 2020), Lecture Notes in Computer Science, v. 12901, ed. Kaminski, B et al., pp. 1-15, Springer, https://doi.org/10.1007/978-3-030-48478-1_1

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Joslyn, Cliff A; Aksoy, Sinan; Arendt, Dustin; Jenkins, L; Praggastis, Brenda; Purvine, Emilie; Zalewski, Marcin: (2019) “High Performance Hypergraph Analytics of Domain Name System Relationships”, in: Proc. HICSS Symp. on Cybersecurity Big Data Analytics, http://www.azsecure-hicss.org/

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    - algorithms.generative_models -
    - algorithms.homology_mod2 -
    - algorithms.hypergraph_modularity -
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    - algorithms.s_centrality_measures -
 
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    - classes.hypergraph -
    - classes.staticentity -
 
- d
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    - drawing.rubber_band -
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© Copyright 2021 Battelle Memorial Institute.

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reports package

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Submodules

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reports.descriptive_stats module

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This module contains methods which compute various distributions for hypergraphs:
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  • Edge size distribution

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  • Node degree distribution

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  • Component size distribution

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  • Toplex size distribution

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  • Diameter

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Also computes general hypergraph information: number of nodes, edges, cells, aspect ratio, incidence matrix density

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-reports.descriptive_stats.centrality_stats(X)[source]
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Computes basic centrality statistics for X

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Parameters
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X – an iterable of numbers

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Returns
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[min, max, mean, median, standard deviation] – List of centrality statistics for X

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Return type
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list

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-reports.descriptive_stats.comp_dist(H, aggregated=False)[source]
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Computes component sizes, number of nodes.

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Parameters
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    -

  • H (Hypergraph) –

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  • aggregated – If aggregated is True, returns a dictionary of -component sizes (number of nodes) and counts. If aggregated -is False, returns a list of components sizes in H.

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Returns
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comp_dist – List of component sizes or dictionary of component size distribution

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Return type
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list or dictionary

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See also

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s_comp_dist

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-reports.descriptive_stats.degree_dist(H, aggregated=False)[source]
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Computes degrees of nodes of a hypergraph.

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Parameters
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  • H (Hypergraph) –

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  • aggregated – If aggregated is True, returns a dictionary of -degrees and counts. If aggregated is False, returns a -list of degrees in H.

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Returns
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degree_dist – List of degrees or dictionary of degree distribution

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Return type
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list or dict

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-reports.descriptive_stats.dist_stats(H)[source]
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Computes many basic hypergraph stats and puts them all into a single dictionary object

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  • nrows = number of nodes (rows in the incidence matrix)

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  • ncols = number of edges (columns in the incidence matrix)

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  • aspect ratio = nrows/ncols

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  • ncells = number of filled cells in incidence matrix

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  • density = ncells/(nrows*ncols)

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  • node degree list = degree_dist(H)

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  • node degree dist = centrality_stats(degree_dist(H))

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  • node degree hist = Counter(degree_dist(H))

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  • max node degree = max(degree_dist(H))

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  • edge size list = edge_size_dist(H)

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  • edge size dist = centrality_stats(edge_size_dist(H))

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  • edge size hist = Counter(edge_size_dist(H))

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  • max edge size = max(edge_size_dist(H))

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  • comp nodes list = s_comp_dist(H, s=1, edges=False)

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  • comp nodes dist = centrality_stats(s_comp_dist(H, s=1, edges=False))

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  • comp nodes hist = Counter(s_comp_dist(H, s=1, edges=False))

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  • comp edges list = s_comp_dist(H, s=1, edges=True)

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  • comp edges dist = centrality_stats(s_comp_dist(H, s=1, edges=True))

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  • comp edges hist = Counter(s_comp_dist(H, s=1, edges=True))

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  • num comps = len(s_comp_dist(H))

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Parameters
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H (Hypergraph) –

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Returns
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dist_stats – Dictionary which keeps track of each of the above items (e.g., basic[‘nrows’] = the number of nodes in H)

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Return type
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dict

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-reports.descriptive_stats.edge_size_dist(H, aggregated=False)[source]
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Computes edge sizes of a hypergraph.

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Parameters
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    -

  • H (Hypergraph) –

    • -

  • aggregated – If aggregated is True, returns a dictionary of -edge sizes and counts. If aggregated is False, returns a -list of edge sizes in H.

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Returns
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edge_size_dist – List of edge sizes or dictionary of edge size distribution.

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Return type
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list or dict

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-reports.descriptive_stats.info(H, node=None, edge=None)[source]
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Print a summary of simple statistics for H

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Parameters
-
    -

  • H (Hypergraph) –

    • -

  • obj (optional) – either a node or edge uid from the hypergraph

    • -

  • dictionary (optional) – If True then returns the info as a dictionary rather -than a string -If False (default) returns the info as a string

    • -
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Returns
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info – Returns a string of statistics of the size, -aspect ratio, and density of the hypergraph. -Print the string to see it formatted.

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Return type
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string

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-reports.descriptive_stats.info_dict(H, node=None, edge=None)[source]
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Create a summary of simple statistics for H

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Parameters
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    -

  • H (Hypergraph) –

    • -

  • obj (optional) – either a node or edge uid from the hypergraph

    • -
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Returns
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info_dict – Returns a dictionary of statistics of the size, -aspect ratio, and density of the hypergraph.

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Return type
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dict

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-reports.descriptive_stats.s_comp_dist(H, s=1, aggregated=False, edges=True, return_singletons=True)[source]
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Computes s-component sizes, counting nodes or edges.

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Parameters
-
    -

  • H (Hypergraph) –

    • -

  • s (positive integer, default is 1) –

    • -

  • aggregated – If aggregated is True, returns a dictionary of -s-component sizes and counts in H. If aggregated is -False, returns a list of s-component sizes in H.

    • -

  • edges – If edges is True, the component size is number of edges. -If edges is False, the component size is number of nodes.

    • -

  • return_singletons (bool, optional, default=True) –

    • -
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Returns
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s_comp_dist – List of component sizes or dictionary of component size distribution in H

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Return type
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list or dictionary

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See also

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comp_dist

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- -
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-reports.descriptive_stats.s_edge_diameter_dist(H)[source]
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Parameters
-

H (Hypergraph) –

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Returns
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s_edge_diameter_dist – List of s-edge-diameters for hypergraph H starting with s=1 -and going up as long as the hypergraph is s-edge-connected

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Return type
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list

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-reports.descriptive_stats.s_node_diameter_dist(H)[source]
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Parameters
-

H (Hypergraph) –

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Returns
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s_node_diameter_dist – List of s-node-diameters for hypergraph H starting with s=1 -and going up as long as the hypergraph is s-node-connected

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Return type
-

list

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-reports.descriptive_stats.toplex_dist(H, aggregated=False)[source]
-

Computes toplex sizes for hypergraph H.

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-
Parameters
-
    -

  • H (Hypergraph) –

    • -

  • aggregated – If aggregated is True, returns a dictionary of -toplex sizes and counts in H. If aggregated -is False, returns a list of toplex sizes in H.

    • -
-
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Returns
-

toplex_dist – List of toplex sizes or dictionary of toplex size distribution in H

-
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Return type
-

list or dictionary

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- -
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Module contents

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package", "algorithms.contagion package", "algorithms", "classes package", "classes", "HyperNetX Packages", "drawing package", "drawing", "Glossary of HNX terms", "HyperNetX (HNX)", "Installing HyperNetX", "License", "Modularity and Clustering", "NWHy", "Overview", "Publications", "reports", "reports package", "Hypernetx-Widget"], "terms": {"contagion": [0, 2, 5, 9, 14], "anim": [0, 2, 5, 9], "epidem": [0, 2, 5, 9], "chung_lu_hypergraph": 0, "k1": 0, "k2": 0, "sourc": [0, 1, 3, 6, 10, 11, 17], "A": [0, 1, 3, 6, 8, 11, 12, 13, 15], "function": [0, 1, 3, 6, 12], "gener": [0, 3, 6, 8, 9, 10, 12, 14, 17], "an": [0, 1, 3, 6, 8, 9, 12, 14, 17, 18], "extens": [0, 10], "chung": 0, "lu": 0, "implement": [0, 1, 13], "mirah": 0, "shi": 0, "describ": [0, 1], "bipartit": [0, 3, 6, 8, 18], "network": [0, 1, 3, 9, 12, 14, 15], "aksoi": [0, 14, 15], "et": [0, 1, 12, 15], "al": [0, 1, 12, 15], "http": [0, 1, 10, 12, 15], "doi": [0, 1, 12, 15], "org": [0, 1, 12, 15], "10": [0, 1, 3, 12, 15], "1093": 0, 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"refer": 12, "nwhy": 13, "Then": 13, "activ": 13, "intel": 13, "thread": 13, "build": 13, "block": 13, "tbb": 13, "quick": 13, "test": 13, "import": 13, "api": 13, "nwhypergraph": 13, "attribut": 13, "method": 13, "slinegraph": 13, "new": 14, "version": 14, "1": 14, "0": 14, "2": 14, "colab": 14, "tutori": 14, "notic": 14, "public": 15, "report": [16, 17], "descriptive_stat": 17, "widget": 18, "layout": 18, "select": 18, "side": 18, "panel": 18}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 6, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1, "sphinx.ext.todo": 2, "sphinx.ext.viewcode": 1, "sphinx": 56}}) \ No newline at end of file diff --git a/docs/build/widget.html b/docs/build/widget.html deleted file mode 100644 index 7f72f997..00000000 --- a/docs/build/widget.html +++ /dev/null @@ -1,185 +0,0 @@ - - - - - - - Hypernetx-Widget — HyperNetX 1.2.5 documentation - - - - - - - - - - - - - - - - - - -
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Hypernetx-Widget

-_images/WidgetScreenShot.png -
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Overview

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The HyperNetXWidget is an addon for HNX, which extends the built in visualization -capabilities of HNX to a JavaScript based interactive visualization. The tool has two main interfaces, -the hypergraph visualization and the nodes & edges panel. -You may demo the widget here

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Installation

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The HypernetxWidget is available on GitHub and may be -installed using pip:

-
>>> pip install hnxwidget
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Using the Tool

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-

Layout

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The hypergraph visualization is an Euler diagram that shows nodes as circles and hyper edges as outlines -containing the nodes/circles they contain. The visualization uses a force directed optimization to perform -the layout. This algorithm is not perfect and sometimes gives results that the user might want to improve upon. -The visualization allows the user to drag nodes and position them directly at any time. The algorithm will -re-position any nodes that are not specified by the user. Ctrl (Windows) or Command (Mac) clicking a node -will release a pinned node it to be re-positioned by the algorithm.

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Selection

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Nodes and edges can be selected by clicking them. Nodes and edges can be selected independently of each other, -i.e., it is possible to select an edge without selecting the nodes it contains. Multiple nodes and edges can -be selected, by holding down Shift while clicking. Shift clicking an already selected node will de-select it. -Clicking the background will de-select all nodes and edges. Dragging a selected node will drag all selected -nodes, keeping their relative placement. -Selected nodes can be hidden (having their appearance minimized) or removed completely from the visualization. -Hiding a node or edge will not cause a change in the layout, wheras removing a node or edge will. -The selection can also be expanded. Buttons in the toolbar allow for selecting all nodes contained within selected edges, -and selecting all edges containing any selected nodes. -The toolbar also contains buttons to select all nodes (or edges), un-select all nodes (or edges), -or reverse the selected nodes (or edges). An advanced user might:

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  • Select all nodes not in an edge by: select an edge, select all nodes in that edge, then reverse the selected nodes to select every node not in that edge.

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  • Traverse the graph by: selecting a start node, then alternating select all edges containing selected nodes and selecting all nodes within selected edges

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  • Pin Everything by: hitting the button to select all nodes, then drag any node slightly to activate the pinning for all nodes.

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Side Panel

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Details on nodes and edges are visible in the side panel. For both nodes and edges, a table shows the node name, degree (or size for edges), its selection state, removed state, and color. These properties can also be controlled directly from this panel. The color of nodes and edges can be set in bulk here as well, for example, coloring by degree.

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Other Features

-

Nodes with identical edge membership can be collapsed into a super node, which can be helpful for larger hypergraphs. Dragging any node in a super node will drag the entire super node. This feature is available as a toggle in the nodes panel.

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The hypergraph can also be visualized as a bipartite graph (similar to a traditional node-link diagram). Toggling this feature will preserve the locations of the nodes between the bipartite and the Euler diagrams.

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- - - - \ No newline at end of file diff --git a/docs/index.html b/docs/index.html deleted file mode 100644 index adf374c2..00000000 --- a/docs/index.html +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 00000000..1279219f --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,2 @@ +nb2plots==0.6.1 +texext==0.6.7 \ No newline at end of file diff --git a/docs/source/_static/copybutton.js b/docs/source/_static/copybutton.js deleted file mode 100644 index d0580c42..00000000 --- a/docs/source/_static/copybutton.js +++ /dev/null @@ -1,62 +0,0 @@ -/* This script from Doc/tools/static/copybutton.js in CPython distribution */ -$(document).ready(function() { - /* Add a [>>>] button on the top-right corner of code samples to hide - * the >>> and ... prompts and the output and thus make the code - * copyable. */ - var div = $('.highlight-python .highlight,' + - '.highlight-default .highlight') - var pre = div.find('pre'); - - // get the styles from the current theme - pre.parent().parent().css('position', 'relative'); - var hide_text = 'Hide the prompts and output'; - var show_text = 'Show the prompts and output'; - var border_width = pre.css('border-top-width'); - var border_style = pre.css('border-top-style'); - var border_color = pre.css('border-top-color'); - var button_styles = { - 'cursor':'pointer', 'position': 'absolute', 'top': '0', 'right': '0', - 'border-color': border_color, 'border-style': border_style, - 'border-width': border_width, 'color': border_color, 'text-size': '75%', - 'font-family': 'monospace', 'padding-left': '0.2em', 'padding-right': '0.2em', - 'border-radius': '0 3px 0 0' - } - - // create and add the button to all the code blocks that contain >>> - div.each(function(index) { - var jthis = $(this); - if (jthis.find('.gp').length > 0) { - var button = $('>>>'); - button.css(button_styles) - button.attr('title', hide_text); - button.data('hidden', 'false'); - jthis.prepend(button); - } - // tracebacks (.gt) contain bare text elements that need to be - // wrapped in a span to work with .nextUntil() (see later) - jthis.find('pre:has(.gt)').contents().filter(function() { - return ((this.nodeType == 3) && (this.data.trim().length > 0)); - }).wrap(''); - }); - - // define the behavior of the button when it's clicked - $('.copybutton').click(function(e){ - e.preventDefault(); - var button = $(this); - if (button.data('hidden') === 'false') { - // hide the code output - button.parent().find('.go, .gp, .gt').hide(); - button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden'); - button.css('text-decoration', 'line-through'); - button.attr('title', show_text); - button.data('hidden', 'true'); - } else { - // show the code output - button.parent().find('.go, .gp, .gt').show(); - button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible'); - button.css('text-decoration', 'none'); - button.attr('title', hide_text); - button.data('hidden', 'false'); - } - }); -}); diff --git a/docs/source/algorithms/algorithms.contagion.rst b/docs/source/algorithms/algorithms.contagion.rst deleted file mode 100644 index aaf64fec..00000000 --- a/docs/source/algorithms/algorithms.contagion.rst +++ /dev/null @@ -1,29 +0,0 @@ -algorithms.contagion package -============================ - -Submodules ----------- - -algorithms.contagion.animation module -------------------------------------- - -.. automodule:: algorithms.contagion.animation - :members: - :undoc-members: - :show-inheritance: - -algorithms.contagion.epidemics module -------------------------------------- - -.. automodule:: algorithms.contagion.epidemics - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: algorithms.contagion - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/algorithms/algorithms.rst b/docs/source/algorithms/algorithms.rst index 5a819963..7e160c16 100644 --- a/docs/source/algorithms/algorithms.rst +++ b/docs/source/algorithms/algorithms.rst @@ -1,17 +1,17 @@ algorithms package ================== -Subpackages ------------ - -.. toctree:: - :maxdepth: 4 - - algorithms.contagion - Submodules ---------- +algorithms.contagion module +--------------------------- + +.. automodule:: algorithms.contagion + :members: + :undoc-members: + :show-inheritance: + algorithms.generative\_models module ------------------------------------ diff --git a/docs/source/classes/classes.rst b/docs/source/classes/classes.rst index e6463304..75542ea7 100644 --- a/docs/source/classes/classes.rst +++ b/docs/source/classes/classes.rst @@ -12,18 +12,26 @@ classes.entity module :undoc-members: :show-inheritance: -classes.hypergraph module -------------------------- +classes.entityset module +------------------------ -.. automodule:: classes.hypergraph +.. automodule:: classes.entityset :members: :undoc-members: :show-inheritance: -classes.staticentity module ---------------------------- +classes.helpers module +---------------------- -.. automodule:: classes.staticentity +.. automodule:: classes.helpers + :members: + :undoc-members: + :show-inheritance: + +classes.hypergraph module +------------------------- + +.. automodule:: classes.hypergraph :members: :undoc-members: :show-inheritance: diff --git a/docs/source/conf.py b/docs/source/conf.py index 7daaca27..60546391 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -17,22 +17,22 @@ import sys import os -import shlex -__version__ = "1.2.5" + +__version__ = "2.0.0.post1" # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. -sys.path.insert(0, os.path.abspath(".")) -sys.path.append(os.path.join(os.path.dirname(__name__), "hypernetx")) +sys.path.insert(0 , os.path.abspath("../../hypernetx")) + # -- Project information ----------------------------------------------------- project = "HyperNetX" -copyright = "2021 Battelle Memorial Institute" -author = "Brenda Praggastis, Dustin Arendt, Emilie Purvine, Cliff Joslyn, Sinan Aksoy, Tony Liu, Andrew Lumsdaine, Nicholas Landry" +copyright = "2023 Battelle Memorial Institute" +author = "Brenda Praggastis, Dustin Arendt, Emilie Purvine, Cliff Joslyn, Sinan Aksoy" # The short X.Y version version = ".".join(__version__.split(".")[:2]) @@ -59,6 +59,7 @@ "sphinx.ext.viewcode", "nb2plots", "texext", + 'sphinx_copybutton', ] # Add any paths that contain templates here, relative to this directory. @@ -80,7 +81,7 @@ # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. -language = None +language = 'en' # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: @@ -134,7 +135,7 @@ # html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. -html_theme_path = ["_static"] +# html_theme_path = ["_static"] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". @@ -155,7 +156,7 @@ # 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"] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied @@ -376,6 +377,10 @@ # If false, no index is generated. # epub_use_index = True +# Remove the command prompts such as >>> when copying code snippets from copybutton +# see https://sphinx-copybutton.readthedocs.io/en/latest/use.html +copybutton_exclude = '.linenos, .gp' -def setup(app): - app.add_js_file("copybutton.js") +# tables and code-blocks are automatically numbered if they have a caption. +# See https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-numfig +numfig = True diff --git a/docs/source/glossary.rst b/docs/source/glossary.rst index dcced646..c927c49b 100644 --- a/docs/source/glossary.rst +++ b/docs/source/glossary.rst @@ -4,93 +4,85 @@ Glossary of HNX terms ===================== + +The HNX library centers around the idea of a :term:`hypergraph`. This glossary provides a few key terms and definitions. + + .. glossary:: :sorted: - Entity + + .. // scan hypergraph.py + + Entity and Entity set Class in entity.py. - The base class for nodes, edges, and other HNX structures. An entity has a unique id, a set of properties, and a set of other entities belonging to it called its :term:`elements ` (an entity may not contain itself). - If an entity A belongs to another entity B then A has membership in B and A is an element of B. For any entity A access a dictionary of its elements (keyed by uid) using ``A.elements`` and a dictionary of its memberships using ``A.memberships``. - - Entity.elements - Attribute in class Entity. Returns a dictionary of elements of the entity. - For any entity A, the elements equal the set of entities belonging to A. Use ``A.uidset`` to access the set of uids belonging to the elements of A and ``A.elements`` to access a dictionary of uid,entity key value pairs of elements of A. - - Entity.children - Attribute in class Entity. Returns a set of uids for the elements of the elements of entity. - For any entity A, the set of entities which belong to some entity belonging to A. Use ``A.children`` to access the set of uids belonging to the children of A and ``A.registry`` to access a dictionary of uid,entity key value pairs of the children of A. - See also :term:`Entity.levelset`. - - Entity.registry - Attribute in class Entity. - A dictionary of uid,entity key value pairs of the :term:`children ` of an entity. - - Entity.memberships - Attribute in class Entity. - A dictionary of uid,entity key value pairs of entities to which the entity belongs. - - Entity.levelset - Method in class Entity. - For any entity A, Level 1 of A is the set of :term:`elements ` of A. - The elements of entities in Level 1 of A belong to Level 2 of A. The elements of entities in Level k of A belong to Level k+1 of A. - The entities in Level 2 of A are called A's children. - A single entity may occupy multiple Level sets of an entity. An entity may belong to any of its own Level sets except Level 1 as no entity may contain itself as an element. - Note that if Level n of A is nonempty then Level k of A is nonempty for all k` belonging to an entity. - For any entity A, if A.elements is empty then it has depth 0 and no non-empty Levels. - If A.elements contains only Entities of depth 0 then A has depth 1. - If A.elements contains only Entities of depth 0 and depth 1 then A has depth 2. - If A.elements contains an entity of depth n and no Entities of depth more than n then it has depth n+1. - - entityset - An entity A satisfying the :term:`Bipartite Condition`, the property that the set of entities in Level 1 of A is disjoint from the set of entities in Level 2 of A, i.e. the elements of A are disjoint from the children of A. An entityset is instantiated in the class EntitySet. + HNX stores many of its data structures inside objects of type Entity. Entities help to insure safe behavior, but their use is primarily technical, not mathematical. hypergraph - A pair of entitysets (Nodes,Edges) such that Edges has :term:`depth ` 2, Nodes have depth 1, and the children of Edges is exactly the set of elements of Nodes. Intuitively, every element of Edges is a (hyper)edge, which is either empty or contains elements of Nodes. Every node in Nodes has :term:`membership ` in some edge in Edges. Since a node has :term:`depth ` 0 it is distinguished by its uid, properties, and memberships. A hypergraph is instantiated in the class Hypergraph. + The term *hypergraph* can have many different meanings. In HNX, it means a tuple (Nodes, Edges, Incidence), where Nodes and Edges are sets, and Incidence is a function that assigns a value of True or False to every pair (n,e) in the Cartesian product Nodes x Edges. We call + - Nodes the set of nodes + - Edges the set of edges + - Incidence the incidence function + *Note* Another term for this type of object is a *multihypergraph*. The ability to work with multihypergraphs efficiently is a distinguishing feature of HNX! + + incidence + A node n is incident to an edge e in a hypergraph (Nodes, Edges, Incidence) if Incidence(n,e) = True. + !!! -- give the line of code that would allow you to evaluate + + incidence matrix + A rectangular matrix constructed from a hypergraph (Nodes, Edges, Incidence) where the elements of Nodes index the matrix rows, and the elements of Edges index the matrix columns. Entry (n,e) in the incidence matrix is 1 if n and e are incident, and is 0 otherwise. + + edge nodes (aka edge elements) + The nodes (or elements) of an edge e in a hypergraph (Nodes, Edges, Incidence) are the nodes that are incident to e. subhypergraph - Given a hypergraph (Nodes,Edges), a subhypergraph is a pair of subsets of (Nodes,Edges). + A subhypergraph of a hypergraph (Nodes, Edges, Incidence) is a hypergraph (Nodes', Edges', Incidence') such that Nodes' is a subset of Nodes, Edges' is a subset of Edges, and every incident pair (n,e) in (Nodes', Edges', Incidence') is also incident in (Nodes, Edges, Incidence) + + subhypergraph induced by a set of nodes + An induced subhypergraph of a hypergraph (Nodes, Edges, Incidence) is a subhypergraph (Nodes', Edges', Incidence') where a pair (n,e) is incident if and only if it is incident in (Nodes, Edges, Incidence) degree - Given a hypergraph (Nodes,Edges), the degree of a node in Nodes is the number of edges in Edges to which the node belongs. - See also: :term:`s-degree` + Given a hypergraph (Nodes, Edges, Incidence), the degree of a node in Nodes is the number of edges in Edges to which the node is incident. + See also: :term:`s-degree` - incidence matrix - A rectangular matrix constructed from a hypergraph (Nodes,Edges) where the elements of Nodes index the matrix rows, and the elements of Edges index the matrix columns. Entry (i,j) in the incidence matrix is 1 if the node corresponding to i in Nodes belongs to the edge corresponding to j in Edges, and is 0 otherwise. + dual + The dual of a hypergraph (Nodes, Edges, Incidence) switches the roles of Nodes and Edges. More precisely, it is the hypergraph (Edges, Nodes, Incidence'), where Incidence' is the function that assigns Incidence(n,e) to each pair (e,n). The :term:`incidence matrix` of the dual hypergraph is the transpose of the incidence matrix of (Nodes, Edges, Incidence). + + toplex + A toplex in a hypergraph (Nodes, Edges, Incidence ) is an edge e whose node set isn't properly contained in the node set of any other edge. That is, if f is another edge and ever node incident to e is also incident to f, then the node sets of e and f are identical. + + simple hypergraph + A hypergraph for which no edge is completely contained in another. + +------------- +S-line graphs +------------- + +HNX offers a variety of tool sets for network analysis, including s-line graphs. s-adjacency matrix - For a hypergraph (Nodes,Edges) and positive integer s, a square matrix where the elements of Nodes index both rows and columns. The matrix can be weighted or unweighted. Entry (i,j) is nonzero if and only if node i and node j belong to at least s shared edges, and is equal to the number of shared edges (if weighted) or 1 (if unweighted). + For a hypergraph (Nodes, Edges, Incidence) and positive integer s, a square matrix where the elements of Nodes index both rows and columns. The matrix can be weighted or unweighted. Entry (i,j) is nonzero if and only if node i and node j are incident to at least s edges in common. If it is nonzero, then it is equal to the number of shared edges (if weighted) or 1 (if unweighted). s-edge-adjacency matrix - For a hypergraph (Nodes,Edges) and positive integer s, a square matrix where the elements of Edges index both rows and columns. The matrix can be weighted or unweighted. Entry (i,j) is nonzero if and only if edge i and edge j share to at least s nodes, and is equal to the number of shared nodes (if weighted) or 1 (if unweighted). + For a hypergraph (Nodes, Edges, Incidence) and positive integer s, a square matrix where the elements of Edges index both rows and columns. The matrix can be weighted or unweighted. Entry (i,j) is nonzero if and only if edge i and edge j share to at least s nodes, and is equal to the number of shared nodes (if weighted) or 1 (if unweighted). s-auxiliary matrix - For a hypergraph (Nodes,Edges) and positive integer s, the submatrix of the :term:`s-edge-adjacency matrix ` obtained by restricting to rows and columns corresponding to edges of size at least s. - - toplex - For a hypergraph (Nodes,Edges), a toplex is an edge in Edges whose elements (i.e. nodes) do not all belong to any other edge in Edge. - - dual - For a hypergraph (Nodes,Edges), its dual is the hypergraph constructed by switching the roles of Nodes and Edges. More precisely, if node i belongs to edge j in the hypergraph, then node j belongs to edge i in the dual hypergraph. + For a hypergraph (Nodes, Edges, Incidence) and positive integer s, the submatrix of the :term:`s-edge-adjacency matrix ` obtained by restricting to rows and columns corresponding to edges of size at least s. s-node-walk - For a hypergraph (Nodes,Edges) and positive integer s, a sequence of nodes in Nodes such that each successive pair of nodes share at least s edges in Edges. + For a hypergraph (Nodes, Edges, Incidence) and positive integer s, a sequence of nodes in Nodes such that each successive pair of nodes share at least s edges in Edges. s-edge-walk - For a hypergraph (Nodes,Edges) and positive integer s, a sequence of edges in Edges such that each successive pair of edges intersects in at least s nodes in Nodes. + For a hypergraph (Nodes, Edges, Incidence) and positive integer s, a sequence of edges in Edges such that each successive pair of edges intersects in at least s nodes in Nodes. s-walk Either an s-node-walk or an s-edge-walk. s-connected component, s-node-connected component - For a hypergraph (Nodes,Edges) and positive integer s, an s-connected component is a :term:`subhypergraph` induced by a subset of Nodes with the property that there exists an s-walk between every pair of nodes in this subset. An s-connected component is the maximal such subset in the sense that it is not properly contained in any other subset satisfying this property. + For a hypergraph (Nodes, Edges, Incidence) and positive integer s, an s-connected component is a :term:`subhypergraph` induced by a subset of Nodes with the property that there exists an s-walk between every pair of nodes in this subset. An s-connected component is the maximal such subset in the sense that it is not properly contained in any other subset satisfying this property. s-edge-connected component - For a hypergraph (Nodes,Edges) and positive integer s, an s-edge-connected component is a :term:`subhypergraph` induced by a subset of Edges with the property that there exists an s-edge-walk between every pair of edges in this subset. An s-edge-connected component is the maximal such subset in the sense that it is not properly contained in any other subset satisfying this property. + For a hypergraph (Nodes, Edges, Incidence) and positive integer s, an s-edge-connected component is a :term:`subhypergraph` induced by a subset of Edges with the property that there exists an s-edge-walk between every pair of edges in this subset. An s-edge-connected component is the maximal such subset in the sense that it is not properly contained in any other subset satisfying this property. s-connected, s-node-connected A hypergraph is s-connected if it has one s-connected component. @@ -99,36 +91,35 @@ Glossary of HNX terms A hypergraph is s-edge-connected if it has one s-edge-connected component. s-distance - For a hypergraph (Nodes,Edges) and positive integer s, the s-distances between two nodes in Nodes is the length of the shortest :term:`s-node-walk` between them. If no s-node-walks between the pair of nodes exists, the s-distance between them is infinite. The s-distance + For a hypergraph (Nodes, Edges, Incidence) and positive integer s, the s-distances between two nodes in Nodes is the length of the shortest :term:`s-node-walk` between them. If no s-node-walks between the pair of nodes exists, the s-distance between them is infinite. The s-distance between edges is the length of the shortest :term:`s-edge-walk` between them. If no s-edge-walks between the pair of edges exist, then s-distance between them is infinite. s-diameter - For a hypergraph (Nodes,Edges) and positive integer s, the s-diameter is the maximum s-Distance over all pairs of nodes in Nodes. + For a hypergraph (Nodes, Edges, Incidence) and positive integer s, the s-diameter is the maximum s-Distance over all pairs of nodes in Nodes. s-degree - For a hypergraph (Nodes, Edges) and positive integer s, the s-degree of a node is the number of edges in Edges of size at least s to which node belongs. See also: :term:`degree` + For a hypergraph (Nodes, Edges, Incidence) and positive integer s, the s-degree of a node is the number of edges in Edges of size at least s to which node belongs. See also: :term:`degree` s-edge - For a hypergraph (Nodes, Edges) and positive integer s, an s-edge is any edge of size at least s. + For a hypergraph (Nodes, Edges, Incidence) and positive integer s, an s-edge is any edge of size at least s. s-linegraph - For a hypergraph (Nodes, Edges) and positive integer s, an s-linegraph is a graph representing + For a hypergraph (Nodes, Edges, Incidence) and positive integer s, an s-linegraph is a graph representing the node to node or edge to edge connections according to the *width* s of the connections. The node s-linegraph is a graph on the set Nodes. Two nodes in Nodes are incident in the node s-linegraph if they share at lease s incident edges in Edges; that is, there are at least s elements of Edges to which they both belong. The edge s-linegraph is a graph on the set Edges. Two edges in Edges are incident in the edge s-linegraph if they share at least s incident nodes in Nodes; that is, the edges intersect in at least s nodes in Nodes. - Bipartite Condition - Condition imposed on instances of the class EntitySet. - *Entities that are elements of the same EntitySet, may not contain each other as elements.* - The elements and children of an EntitySet generate a specific partition for a bipartite graph. - The partition is isomorphic to a Hypergraph where the elements correspond to hyperedges and - the children correspond to the nodes. EntitySets are the basic objects used to construct dynamic hypergraphs - in HNX. See methods :py:meth:`classes.hypergraph.Hypergraph.bipartite` and :py:meth:`classes.hypergraph.Hypergraph.from_bipartite`. + .. Bipartite Condition + .. Condition imposed on instances of the class EntitySet. + .. *Entities that are elements of the same EntitySet, may not contain each other as elements.* + .. The elements and children of an EntitySet generate a specific partition for a bipartite graph. + .. The partition is isomorphic to a Hypergraph where the elements correspond to hyperedges and + .. the children correspond to the nodes. EntitySets are the basic objects used to construct dynamic hypergraphs + .. in HNX. See methods :py:meth:`classes.hypergraph.Hypergraph.bipartite` and :py:meth:`classes.hypergraph.Hypergraph.from_bipartite`. + - simple hypergraph - A hypergraph for which no edge is completely contained in another. diff --git a/docs/source/hypconstructors.rst b/docs/source/hypconstructors.rst new file mode 100644 index 00000000..7596fcc7 --- /dev/null +++ b/docs/source/hypconstructors.rst @@ -0,0 +1,159 @@ + +.. _hypconstructors: + +======================= +Hypergraph Constructors +======================= + +An hnx.Hypergraph H = (V,E) references a pair of disjoint sets: +V = nodes (vertices) and E = (hyper)edges. + +HNX allows for multi-edges by distinguishing edges by +their identifiers instead of their contents. For example, if +V = {1,2,3} and E = {e1,e2,e3}, +where e1 = {1,2}, e2 = {1,2}, and e3 = {1,2,3}, +the edges e1 and e2 contain the same set of nodes and yet +are distinct and are distinguishable within H = (V,E). + +HNX provides methods to easily store and +access additional metadata such as cell, edge, and node weights. +Metadata associated with (edge,node) incidences +are referenced as **cell_properties**. +Metadata associated with a single edge or node is referenced +as its **properties**. + +The fundamental object needed to create a hypergraph is a **setsystem**. The +setsystem defines the many-to-many relationships between edges and nodes in +the hypergraph. Cell properties for the incidence pairs can be defined within +the setsystem or in a separate pandas.Dataframe or dict. +Edge and node properties are defined with a pandas.DataFrame or dict. + +SetSystems +---------- +There are five types of setsystems currently accepted by the library. + +1. **iterable of iterables** : Barebones hypergraph, which uses Pandas default + indexing to generate hyperedge ids. Elements must be hashable.: :: + + >>> H = Hypergraph([{1,2},{1,2},{1,2,3}]) + +2. **dictionary of iterables** : The most basic way to express many-to-many + relationships providing edge ids. The elements of the iterables must be + hashable): :: + + >>> H = Hypergraph({'e1':[1,2],'e2':[1,2],'e3':[1,2,3]}) + +3. **dictionary of dictionaries** : allows cell properties to be assigned + to a specific (edge, node) incidence. This is particularly useful when + there are variable length dictionaries assigned to each pair: :: + + >>> d = {'e1':{ 1: {'w':0.5, 'name': 'related_to'}, + >>> 2: {'w':0.1, 'name': 'related_to', + >>> 'startdate': '05.13.2020'}}, + >>> 'e2':{ 1: {'w':0.52, 'name': 'owned_by'}, + >>> 2: {'w':0.2}}, + >>> 'e3':{ 1: {'w':0.5, 'name': 'related_to'}, + >>> 2: {'w':0.2, 'name': 'owner_of'}, + >>> 3: {'w':1, 'type': 'relationship'}} + + >>> H = Hypergraph(d, cell_weight_col='w') + +4. **pandas.DataFrame** For large datasets and for datasets with cell + properties it is most efficient to construct a hypergraph directly from + a pandas.DataFrame. Incidence pairs are in the first two columns. + Cell properties shared by all incidence pairs can be placed in their own + column of the dataframe. Variable length dictionaries of cell properties + particular to only some of the incidence pairs may be placed in a single + column of the dataframe. Representing the data above as a dataframe df: + + +-----------+-----------+-----------+-----------------------------------+ + | col1 | col2 | w | col3 | + +-----------+-----------+-----------+-----------------------------------+ + | e1 | 1 | 0.5 | {'name':'related_to'} | + +-----------+-----------+-----------+-----------------------------------+ + | e1 | 2 | 0.1 | {"name":"related_to", | + | | | | "startdate":"05.13.2020"} | + +-----------+-----------+-----------+-----------------------------------+ + | e2 | 1 | 0.52 | {"name":"owned_by"} | + +-----------+-----------+-----------+-----------------------------------+ + | e2 | 2 | 0.2 | | + +-----------+-----------+-----------+-----------------------------------+ + | ... | ... | ... | {...} | + +-----------+-----------+-----------+-----------------------------------+ + + The first row of the dataframe is used to reference each column. :: + + >>> H = Hypergraph(df,edge_col="col1",node_col="col2", + >>> cell_weight_col="w",misc_cell_properties="col3") + +5. **numpy.ndarray** For homogeneous datasets given in a *n x 2* ndarray a + pandas dataframe is generated and column names are added from the + edge_col and node_col arguments. Cell properties containing multiple data + types are added with a separate dataframe or dict and passed through the + cell_properties keyword. :: + + >>> arr = np.array([['e1','1'],['e1','2'], + >>> ['e2','1'],['e2','2'], + >>> ['e3','1'],['e3','2'],['e3','3']]) + >>> H = hnx.Hypergraph(arr, column_names=['col1','col2']) + + +Edge and Node Properties +------------------------ +Properties specific to edges and/or node can be passed through the +keywords: **edge_properties, node_properties, properties**. +Properties may be passed as dataframes or dicts. +The first column or index of the dataframe or keys of the dict keys +correspond to the edge and/or node identifiers. +If properties are specific to an id, they may be stored in a single +object and passed to the **properties** keyword. For example: + ++-----------+-----------+---------------------------------------+ +| id | weight | properties | ++-----------+-----------+---------------------------------------+ +| e1 | 5.0 | {'type':'event'} | ++-----------+-----------+---------------------------------------+ +| e2 | 0.52 | {"name":"owned_by"} | ++-----------+-----------+---------------------------------------+ +| ... | ... | {...} | ++-----------+-----------+---------------------------------------+ +| 1 | 1.2 | {'color':'red'} | ++-----------+-----------+---------------------------------------+ +| 2 | .003 | {'name':'Fido','color':'brown'} | ++-----------+-----------+---------------------------------------+ +| 3 | 1.0 | {} | ++-----------+-----------+---------------------------------------+ + +A properties dictionary should have the format: :: + + dp = {id1 : {prop1:val1, prop2,val2,...}, id2 : ... } + +A properties dataframe may be used for nodes and edges sharing ids +but differing in cell properties by adding a level index using 0 +for edges and 1 for nodes: + ++-----------+-----------+-----------+---------------------------+ +| level | id | weight | properties | ++-----------+-----------+-----------+---------------------------+ +| 0 | e1 | 5.0 | {'type':'event'} | ++-----------+-----------+-----------+---------------------------+ +| 0 | e2 | 0.52 | {"name":"owned_by"} | ++-----------+-----------+-----------+---------------------------+ +| ... | ... | ... | {...} | ++-----------+-----------+-----------+---------------------------+ +| 1 | 1.2 | {'color':'red'} | ++-----------+-----------+-----------+---------------------------+ +| 2 | .003 | {'name':'Fido','color':'brown'} | ++-----------+-----------+-----------+---------------------------+ +| ... | ... | ... | {...} | ++-----------+-----------+-----------+---------------------------+ + + + +Weights +------- +The default key for cell and object weights is "weight". The default value +is 1. Weights may be assigned and/or a new default prescribed in the +constructor using **cell_weight_col** and **cell_weights** for incidence pairs, +and using **edge_weight_prop, node_weight_prop, weight_prop, +default_edge_weight,** and **default_node_weight** for node and edge weights. \ No newline at end of file diff --git a/docs/source/hypergraph101.rst b/docs/source/hypergraph101.rst new file mode 100644 index 00000000..fd5ff15d --- /dev/null +++ b/docs/source/hypergraph101.rst @@ -0,0 +1,465 @@ +.. _hypergraph101: + +=============================================== +A Gentle Introduction to Hypergraph Mathematics +=============================================== + + +Here we gently introduce some of the basic concepts in hypergraph +modeling. We note that in order to maintain this “gentleness”, we will +be mostly avoiding the very important and legitimate issues in the +proper mathematical foundations of hypergraphs and closely related +structures, which can be very complicated. Rather we will be focusing on +only the most common cases used in most real modeling, and call a graph +or hypergraph **gentle** when they are loopless, simple, finite, +connected, and lacking empty hyperedges, isolated vertices, labels, +weights, or attributes. Additionally, the deep connections between +hypergraphs and other critical mathematical objects like partial orders, +finite topologies, and topological complexes will also be treated +elsewhere. When it comes up, below we will sometimes refer to the added +complexities which would attend if we weren’t being so “gentle”. In +general the reader is referred to [1,2] for a less gentle and more +comprehensive treatment. + +Graphs and Hypergraphs +====================== + +Network science is based on the concept of a **graph** +:math:`G=\langle V,E\rangle` as a system of connections between +entities. :math:`V` is a (typically finite) set of elements, nodes, or +objects, which we formally call **“vertices”**, and :math:`E` is a set +of pairs of vertices. Given that, then for two vertices +:math:`u,v \in V`, an **edge** is a set :math:`e=\{u,v\}` in :math:`E`, +indicating that there is a connection between :math:`u` and :math:`v`. +It is then common to represent :math:`G` as either a Boolean **adjacency +matrix** :math:`A_{n \times n}` where :math:`n=|V|`, where an +:math:`i,j` entry in :math:`A` is 1 if :math:`v_i,v_j` are connected in +:math:`G`; or as an **incidence matrix** :math:`I_{n \times m}`, where +now also :math:`m=|E|`, and an :math:`i,j` entry in :math:`I` is now 1 +if the vertex :math:`v_i` is in edge :math:`e_j`. + +.. _f1: +.. figure:: images/exgraph.png + :class: with-border + :width: 300 + :align: center + + An example graph, where the numbers are edge IDs. + +.. _t1: +.. list-table:: Adjacency matrix :math:`A` of a graph. + :header-rows: 1 + :align: center + + * - + - Andrews + - Bailey + - Carter + - Davis + * - Andrews + - 0 + - 1 + - 1 + - 1 + * - Bailey + - 1 + - 0 + - 1 + - 0 + * - Carter + - 1 + - 1 + - 0 + - 1 + * - Davis + - 1 + - 0 + - 1 + - 1 + +.. _t2: +.. list-table:: Incidence matrix :math:`I` of a graph. + :header-rows: 1 + :align: center + + * - + - 1 + - 2 + - 3 + - 4 + - 5 + * - Andrews + - 1 + - 1 + - 0 + - 1 + - 0 + * - Bailey + - 0 + - 0 + - 0 + - 1 + - 1 + * - Carter + - 0 + - 1 + - 1 + - 0 + - 1 + * - Davis + - 1 + - 0 + - 1 + - 0 + - 0 + + +.. _label3: +.. figure:: images/biblio_hg.png + :class: with-border + :width: 400 + :align: center + + An example hypergraph, where similarly now the hyperedges are shown with numeric IDs. + +.. _t3: +.. list-table:: Incidence matrix I of a hypergraph. + :header-rows: 1 + :align: center + + * - + - 1 + - 2 + - 3 + - 4 + - 5 + * - Andrews + - 1 + - 1 + - 0 + - 1 + - 0 + * - Bailey + - 0 + - 0 + - 0 + - 1 + - 1 + * - Carter + - 0 + - 1 + - 0 + - 0 + - 1 + * - Davis + - 1 + - 1 + - 1 + - 0 + - 0 + + + +Notice that in the incidence matrix :math:`I` of a gentle graph +:math:`G`, it is necessarily the case that every column must have +precisely two 1 entries, reflecting that every edge connects exactly two +vertices. The move to a **hypergraph** :math:`H=\langle V,E\rangle` +relaxes this requirement, in that now a **hyperedge** (although we will +still say edge when clear from context) :math:`e \in E` is a subset +:math:`e = \{ v_1, v_2, \ldots, v_k\} \subseteq V` of vertices of +arbitrary size. We call :math:`e` a :math:`k`-edge when :math:`|e|=k`. +Note that thereby a 2-edge is a graph edge, while both a singleton +:math:`e=\{v\}` and a 3-edge :math:`e=\{v_1,v_2,v_3\}`, 4-edge +:math:`e=\{v_1,v_2,v_3,v_4\}`, etc., are all hypergraph edges. In this +way, if every edge in a hypergraph :math:`H` happens to be a 2-edge, +then :math:`H` is a graph. We call such a hypergraph **2-uniform**. + +Our incidence matrix :math:`I` is now very much like that for a graph, +but the requirement that each column have exactly two 1 entries is +relaxed: the column for edge :math:`e` with size :math:`k` will have +:math:`k` 1’s. Thus :math:`I` is now a general Boolean matrix (although +with some restrictions when :math:`H` is gentle). + +Notice also that in the examples we’re showing in the figures, the graph +is closely related to the hypergraph. In fact, this particular graph is +the **2-section** or **underlying graph** of the hypergraph. It is the +graph :math:`G` recorded when only the pairwise connections in the +hypergraph :math:`H` are recognized. Note that while the 2-section is +always determined by the hypergraph, and is frequently used as a +simplified representation, it almost never has enough information to be +able to recover the hypergraph from it. + +Important Things About Hypergraphs +================================== + +While all graphs :math:`G` are (2-uniform) hypergraphs :math:`H`, since +they’re very special cases, general hypergraphs have some important +properties which really stand out in distinction, especially to those +already conversant with graphs. The following issues are critical for +hypergraphs, but “disappear” when considering the special case of +2-uniform hypergraphs which are graphs. + +All Hypergraphs Come in Dual Pairs +---------------------------------- + +If our incidence matrix :math:`I` is a general :math:`n \times m` +Boolean matrix, then its transpose :math:`I^T` is an :math:`m \times n` +Boolean matrix. In fact, :math:`I^T` is also the incidence matrix of a +different hypergraph called the **dual** hypergraph :math:`H^*` of +:math:`H`. In the dual :math:`H^*`, it’s just that vertices and edges +are swapped: we now have :math:`H^* = \langle E, V \rangle` where it’s +:math:`E` that is a set of vertices, and the now edges +:math:`v \in V, v \subseteq E` are subsets of those vertices. + + +.. _f3: +.. figure:: images/dual.png + :class: with-border + :width: 400 + :align: center + + The dual hypergraph :math:`H^*`. + + +Just like the “primal” hypergraph :math:`H` has a 2-section, so does the +dual. This is called the **line graph**, and it is an important +structure which records all of the incident hyperedges. Line graphs are +also used extensively in graph theory. + +Note that it follows that since every graph :math:`G` is a (2-uniform) +hypergraph :math:`H`, so therefore we can form the dual hypergraph +:math:`G^*` of :math:`G`. If a graph :math:`G` is a 2-uniform +hypergraph, is its dual :math:`G^*` also a 2-uniform hypergraph? In +general, no, only in the case where :math:`G` is a single cycle or a +union of cycles would that be true. Also note that in order to calculate +the line graph of a graph :math:`G`, one needs to work through its dual +hypergraph :math:`G^*`. + + +.. _f4: +.. figure:: images/dual2.png + :class: with-border + :width: 400 + :align: center + + The line graph of :math:`H`, which is the 2-section of the dual :math:`H^*`. + + + +Edge Intersections Have Size +---------------------------- + +As we’ve already seen, in a graph all the edges are size 2, whereas in a +hypergarph edges can be arbitrary size :math:`1, 2, \ldots, n`. Our +example shows a singleton, three “graph edge” pairs, and a 2-edge. + +In a gentle graph :math:`G` consider two edges +:math:`e = \{ u, v \},f=\{w,z\} \in E` and their intersection +:math:`g = e \cap f`. If :math:`g \neq \emptyset` then :math:`e` and +:math:`f` are non-disjoint, and we call them **incident**. Let +:math:`s(e,f)=|g|` be the size of that intersection. If :math:`G` is +gentle and :math:`e` and :math:`f` are incident, then :math:`s(e,f)=1`, +in that one of :math:`u,v` must be equal to one of :math:`w,z`, and +:math:`g` will be that singleton. But in a hypergraph, the intersection +:math:`g=e \cap f` of two incident edges can be any size +:math:`s(e,f) \in [1,\min(|e|,|f|)]`. This aspect, the size of the +intersection of two incident edges, is critical to understanding +hypergraph structure and properties. + +Edges Can Be Nested +------------------- + +While in a gentle graph :math:`G` two edges :math:`e` and :math:`f` can +be incident or not, in a hypergraph :math:`H` there’s another case: two +edges :math:`e` and :math:`f` may be **nested** or **included**, in that +:math:`e \subseteq f` or :math:`f \subseteq e`. That’s exactly the +condition above where :math:`s(e,f) = \min(|e|,|f|)`, which is the size +of the edge included within the including edge. In our example, we have +that edge 1 is included in edge 2 is included in edge 3. + +Walks Have Length and Width +--------------------------- + +A **walk** is a sequence +:math:`W = \langle { e_0, e_1, \ldots, e_N } \rangle` of edges where +each pair :math:`e_i,e_{i+1}, 0 \le i \le N-1` in the sequence are +incident. We call :math:`N` the **length** of the walk. Walks are the +*raison d’être* of both graphs and hypergraphs, in that in a graph +:math:`G` a walk :math:`W` establishes the connectivity of all the +:math:`e_i` to each other, and a way to “travel” between the ends +:math:`e_0` and :math:`e_N`. Naturally in a walk for each such pair we +can also measure the size of the intersection +:math:`s_i=s(e_i,e_{i+1}), 0 \le i \le N`. While in a gentle graph +:math:`G`, all the :math:`s_i=1`, as we’ve seen in a hypergraph +:math:`H` all these :math:`s_i` can vary widely. So for any walk +:math:`W` we can not only talk about its length :math:`N`, but also +define its **width** :math:`s(W) = \min_{0 \le i \le N} s_i` as the size +of the smallest such intersection. When a walk :math:`W` has width +:math:`s`, we call it an **:math:`s`-walk**. It follows that all walks +in a graph are 1-walks with width 1. In Fig. `5 <#swalks>`__ we see two +walks in a hypergraph. While both have length 2 (counting edgewise, and +recalling origin zero), the one on the left has width 1, and that on the +right width 3. + + +.. _f5: +.. figure:: images/swalks.png + :class: with-border + :width: 600 + :align: center + + Two hypergraph walks of length 2: (Left) A 1-walk. (Right) A 3-walk. + + +Towards Less Gentle Things +========================== + +We close with just brief mentions of more advanced issues. + +:math:`s`-Walks and Hypernetwork Science +---------------------------------------- + +Network science has become a dominant force in data analytics in recent +years, including a range of methods measuring distance, connectivity, +reachability, centrality, modularity, and related things. Most all of +these concepts generalize to hypergraphs using “:math:`s`-versions” of +them. For example, the :math:`s`-distance between two vertices or +hyperedges is the length of the shortest :math:`s`-walk between them, so +that as :math:`s` goes up, requiring wider connections, the distance +will also tend to grow, so that ultimately perhaps vertices may not be +:math:`s`-reachable at all. See [2] for more details. + +Hypergraphs in Mathematics +-------------------------- + +Hypergraphs are very general objects mathematically, and are deeply +connected to a range of other essential objects and structures mostly in +discrete science. + +Most obviously, perhaps, is that there is a one-to-one relationship +between a hypergraph :math:`H = \langle V, E \rangle` and a +corresponding bipartite graph :math:`B=\langle V \sqcup E, I \rangle`. +:math:`B` is a new graph (not a hypergraph) with vertices being both the +vertices and the hyperedges from the hypergraph :math:`H`, and a +connection being a pair :math:`\{ v, e \} \in I` if and only if +:math:`v \in e` in :math:`H`. That you can go the other way to define a +hypergraph :math:`H` for every bipartite graph :math:`G` is evident, but +not all operations carry over unambiguously between hypergraphs and +their bipartite versions. + +.. _f6: +.. figure:: images/bicolored1.png + :class: with-border + :width: 200 + :align: center + + Bipartite graph. + + +Even more generally, the Boolean incidence matrix :math:`I` of a +hypergraph :math:`H` can be taken as the characteristic matrix of a +binary relation. When :math:`H` is gentle this is somewhat restricted, +but in general we can see that there are one-to-one relations now +between hypergraphs, binary relations, as well as bipartite graphs from +above. + +Additionally, we know that every hypergraph implies a hierarchical +structure via the fact that for every pair of incident hyperedges either +one is included in the other, or their intersection is included in both. +This creates a partial order, establishing a further one-to-one mapping +to a variety of lattice structures and dual lattice structures relating +how groups of vertices are included in groups of edges, and vice versa. +Fig. refex shows the **concept lattice** [3], perhaps the most important +of these structures, determined by our example. + +.. _f7: +.. figure:: images/ex.png + :class: with-border + :width: 450 + :align: center + + The concept lattice of the example hypergraph :math:`H`. + + +Finally, the strength of hypergraphs is their ability to model multi-way +interactions. Similarly, mathematical topology is concerned with how +multi-dimensional objects can be attached to each other, not only in +continuous spaces but also with discrete objects. In fact, a finite +topological space is a special kind of gentle hypergraph closed under +both union and intersection, and there are deep connections between +these structures and the lattices referred to above. + +In this context also an **abstract simplicial complex (ASC)** is a kind +of hypergraph where all possible included edges are present. Each +hypergraph determines such an ASC by “closing it down” by subset. ASCs +have a natural topological structure which can reveal hidden structures +measurable by homology, and are used extensively as the workhorse of +topological methods such as persistent homology. In this way hypergraphs +form a perfect bridge from network science to computational topology in +general. + +.. _f8: +.. figure:: images/simplicial.png + :class: with-border + :width: 400 + :align: center + + A diagram of the ASC implied by our example. Numbers here indicate the actual hyper-edges in the original hypergraph :math:`H`, where now additionally all sub-edges, including singletons, are in the ASC. + + +Non-Gentle Graphs and Hypergraphs +--------------------------------- + +Above we described our use of “gentle” graphs and hypergraphs as finite, +loopless, simple, connected, and lacking empty hyperedges, isolated +vertices, labels, weights, or attributes. But at a higher level of +generality we can also have: + +Empty Hyperedges: + If a column of :math:`I` has all zero entries. + +Isolated Vertices: + If a row of :math:`I` has all zero entries. + +Multihypergraphs: + We may choose to allow duplicated hyperedges, resulting in duplicate + columns in the incidence matrix :math:`I`. + +Self-Loops: + In a graph allowing an edge to connect to itself. + +Direction: + In an edge, where some vertices are recognized as “inputs” which + point to others recognized as “outputs”. + +Order: + In a hyperedge, where the vertices carry a particular (total) order. + In a graph, this is equivalent to being directed, but not in a + hypergraph. + +Attributes: + In general we use graphs and hypergraphs to model data, and thus + carrying attributes of different types, including weights, labels, + identifiers, types, strings, or really in principle any data object. + These attributes could be on vertices (rows of :math:`I`), edges + (columns of :math:`I`) or what we call “incidences”, related to a + particular appearnace of a particular vertex in a particular edge + (cells of :math:`I`). + +[1] Joslyn, Cliff A; Aksoy, Sinan; Callahan, Tiffany J; Hunter, LE; +Jefferson, Brett; Praggastis, Brenda; Purvine, Emilie AH; Tripodi, +Ignacio J: (2021) “Hypernetwork Science: From Multidimensional +Networks to Computational Topology”, in: *Unifying Themes in Complex +systems X: Proc. 10th Int. Conf. Complex Systems*, ed. D. Braha et +al., pp. 377-392, Springer, +``https://doi.org/10.1007/978-3-030-67318-5_25`` + +[2] Aksoy, Sinan G; Joslyn, Cliff A; Marrero, Carlos O; Praggastis, B; +Purvine, Emilie AH: (2020) “Hypernetwork Science via High-Order +Hypergraph Walks”, *EPJ Data Science*, v. **9**:16, +``https://doi.org/10.1140/epjds/s13688-020-00231-0`` + +[3] Ganter, Bernhard and Wille, Rudolf: (1999) *Formal Concept +Analysis*, Springer-Verlag + + diff --git a/docs/source/images/biblio_hg.png b/docs/source/images/biblio_hg.png new file mode 100644 index 00000000..8b301423 Binary files /dev/null and b/docs/source/images/biblio_hg.png differ diff --git a/docs/source/images/biblio_two_sec.png b/docs/source/images/biblio_two_sec.png new file mode 100644 index 00000000..130be396 Binary files /dev/null and b/docs/source/images/biblio_two_sec.png differ diff --git a/docs/source/images/bicolored1.png b/docs/source/images/bicolored1.png new file mode 100644 index 00000000..8b9dae77 Binary files /dev/null and b/docs/source/images/bicolored1.png differ diff --git a/docs/source/images/bicolored2.png b/docs/source/images/bicolored2.png new file mode 100644 index 00000000..d9d86024 Binary files /dev/null and b/docs/source/images/bicolored2.png differ diff --git a/docs/source/images/dual.png b/docs/source/images/dual.png new file mode 100644 index 00000000..06dac85d Binary files /dev/null and b/docs/source/images/dual.png differ diff --git a/docs/source/images/dual2.png b/docs/source/images/dual2.png new file mode 100644 index 00000000..2b61434f Binary files /dev/null and b/docs/source/images/dual2.png differ diff --git a/docs/source/images/ex.png b/docs/source/images/ex.png new file mode 100644 index 00000000..1318c4a0 Binary files /dev/null and b/docs/source/images/ex.png differ diff --git a/docs/source/images/exgraph.png b/docs/source/images/exgraph.png new file mode 100644 index 00000000..2c4680d1 Binary files /dev/null and b/docs/source/images/exgraph.png differ diff --git a/docs/source/images/simplicial.png b/docs/source/images/simplicial.png new file mode 100644 index 00000000..87371590 Binary files /dev/null and b/docs/source/images/simplicial.png differ diff --git a/docs/source/images/swalks.png b/docs/source/images/swalks.png new file mode 100644 index 00000000..cd8d7a8a Binary files /dev/null and b/docs/source/images/swalks.png differ diff --git a/docs/source/index.rst b/docs/source/index.rst index 7e437e0e..e3f8019c 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -6,43 +6,58 @@ HyperNetX (HNX) :width: 300px :align: right -Description ------------ -The `HNX`_ library provides classes and methods for modeling the entities and relationships -found in complex networks as hypergraphs, the natural models for multi-dimensional network data. -As strict generalizations of graphs, hyperedges can represent arbitrary multi-way relations -among entities, and in particular can distinguish cliques and simplices, and admit singleton edges. +`HNX`_ is a Python library for hypergraphs, the natural models for multi-dimensional network data. + +To get started, try the `interactive COLAB tutorials `_. For a primer on hypergraphs, try this :ref:`gentle introduction`. To see hypergraphs at work in cutting-edge research, see our list of recent :ref:`publications`. + +Why hypergraphs? +---------------- + +Like graphs, hypergraphs capture important information about networks and relationships. But hypergraphs do more -- they model *multi-way* relationships, where ordinary graphs only capture two-way relationships. This library serves as a repository of methods and algorithms that have proven useful over years of exploration into what hypergraphs can tell us. + As both vertex adjacency and edge incidence are generalized to be quantities, -hypergraph paths and walks thereby have both length and *width* because of these multiway connections. +hypergraph paths and walks have both length and *width* because of these multiway connections. Most graph metrics have natural generalizations to hypergraphs, but since hypergraphs are basically set systems, they also admit to the powerful tools of algebraic topology, including simplicial complexes and simplicial homology, to study their structure. -This library serves as a repository of the methods and algorithms we find most useful -as we explore what hypergraphs can tell us. We have a growing community of users and contributors. -To learn more about some of our research check out our :ref:`publications`. +Our community +------------- + +We have a growing community of users and contributors. For the latest software updates, and to learn about the development team, see the :ref:`library overview`. Have ideas to share? We'd love to hear from you! Our `orientation for contributors `_ can help you get started. + +Our values +------------- + +Our shared values as software developers guide us in our day-to-day interactions and decision-making. Our open source projects are no exception. Trust, respect, collaboration and transparency are core values we believe should live and breathe within our projects. Our community welcomes participants from around the world with different experiences, unique perspectives, and great ideas to share. See our `code of conduct `_ to learn more. + +Contact us +---------- -For comments and questions you may contact the developers directly at: +Questions and comments are welcome! Contact us at hypernetx@pnnl.gov Contents -------- .. toctree:: + :maxdepth: 1 Home overview/index install Glossary core - NWHypergraph C++ Optimization - HyperNetX Visualization Widget + A Gentle Introduction to Hypergraph Mathematics + Hypergraph Constructors + Visualization Widget Algorithms: Modularity and Clustering Publications license + long_description Indices and tables diff --git a/docs/source/install.rst b/docs/source/install.rst index b8e059f4..ca103f5d 100644 --- a/docs/source/install.rst +++ b/docs/source/install.rst @@ -1,87 +1,129 @@ +******************** Installing HyperNetX -==================== +******************** -HyperNetX may be cloned or forked from: https://github.com/pnnl/HyperNetX . -To install in an Anaconda environment -------------------------------------- +Installation +############ - >>> conda create -n python=3.7 - >>> source activate - >>> pip install hypernetx +The recommended installation method for most users is to create a virtual environment +and install HyperNetX from PyPi. -Mac Users: If you wish to build the documentation you will need -the conda version of matplotlib: +.. _Github: https://github.com/pnnl/HyperNetX - >>> conda create -n python=3.7 matplotlib - >>> source activate - >>> pip install hypernetx +HyperNetX may be cloned or forked from Github_. -To use :ref:`NWHy ` use python=3.9 and the conda version of tbb in your environment. -**Note** that :ref:`NWHy ` only works on Linux and some OSX systems. See NWHy docs for more.: - >>> conda create -n python=3.9 tbb - >>> source activate - >>> pip install hypernetx - >>> pip install nwhy +Prerequisites +###################### -To install in a virtualenv environment --------------------------------------- +HyperNetX officially supports Python 3.8, 3.9, 3.10 and 3.11. - >>> virtualenv --python= -This will create a virtual environment in the specified location using -the specified python executable. For example: +Create a virtual environment +############################ - >>> virtualenv --python=C:\Anaconda3\python.exe hnx +Using Anaconda +************************* -This will create a virtual environment in .\hnx using the python -that comes with Anaconda3. + >>> conda create -n env-hnx python=3.8 -y + >>> conda activate env-hnx - >>> \Scripts\activate +Using venv +************************* -If you are running in Windows PowerShell use =.ps1 + >>> python -m venv venv-hnx + >>> source env-hnx/bin/activate -If you are running in Windows Command Prompt use =.bat -Otherwise use =NULL (no file extension). +Using virtualenv +************************* -Once activated continue to follow the installation instructions below. + >>> virtualenv env-hnx + >>> source env-hnx/bin/activate -Install using Pip options -------------------------- -For a minimal installation: +For Windows Users +****************** - >>> pip install hypernetx +On both Windows PowerShell or Command Prompt, you can use the following command to activate your virtual environment: -For an editable installation with access to jupyter notebooks: + >>> .\env-hnx\Scripts\activate - >>> pip install [-e] . -To install with the tutorials: +To deactivate your environment, use: - >>> pip install -e .['tutorials'] + >>> .\env-hnx\Scripts\deactivate -To install with the documentation: - >>> pip install -e .['documentation'] - >>> chmod 755 build_docs.sh - >>> sh build_docs.sh - ## This will generate the documentation in /docs/build/ - ## Open them in your browser with /docs/index.html +Installing Hypernetx +#################### -To install and test using pytest: +Regardless of how you install HyperNetX, ensure that your environment is activated and that you are running Python >=3.8. - >>> pip install -e .['testing'] - >>> pytest +Installing from PyPi +************************* -To install the whole shabang: + >>> pip install hypernetx - >>> pip install -e .['all'] +Installing from Source +************************* +Ensure that you have ``git`` installed. + >>> git clone https://github.com/pnnl/HyperNetX.git + >>> cd HyperNetX + >>> pip install -e .['all'] +If you are using zsh as your shell, ensure that the single quotation marks are placed outside the square brackets: + + >>> pip install -e .'[all]' + + +Post-Installation Actions +########################## + +Running Tests +************** + + >>> python -m pytest + +Interact with HyperNetX in a REPL +******************************************** + +Ensure that your environment is activated and that you run ``python`` on your terminal to open a REPL: + + >>> import hypernetx as hnx + >>> data = { 0: ('A', 'B'), 1: ('B', 'C'), 2: ('D', 'A', 'E'), 3: ('F', 'G', 'H', 'D') } + >>> H = hnx.Hypergraph(data) + >>> list(H.nodes) + ['G', 'F', 'D', 'A', 'B', 'H', 'C', 'E'] + >>> list(H.edges) + [0, 1, 2, 3] + >>> H.shape + (8, 4) + + +Other Actions if installed from source +******************************************** + +Ensure that you are at the root of the source directory before running any of the following commands: + +Viewing jupyter notebooks +-------------------------- + +The following command will automatically open the notebooks in a browser. + + >>> jupyter-notebook tutorials + + +Building documentation +----------------------- + +The following commands will build and open a local version of the documentation in a browser: + + >>> make build-docs + >>> open docs/build/index.html diff --git a/docs/source/nwhy.rst b/docs/source/nwhy.rst deleted file mode 100644 index e918be96..00000000 --- a/docs/source/nwhy.rst +++ /dev/null @@ -1,279 +0,0 @@ -.. _nwhy: - -==== -NWHy -==== - -Description ------------ -NWHy is an addon for HNX providing optimized C++ implementations of many of the hypergraph methods. -NWHy is a scalable, high-performance hypergraph library. It has three dependencies. - - 1. NWGraph library: provides graph data structures, a rich set of adaptors over the graph data structures, and various high-performance graph algorithms implementations. - 2. Intel OneAPI Threading Building Blocks (oneTBB): provides parallelism. - 3. Pybind11: encapsulate NWHy as a python module. - -The goal of the NWHy python API is to share an ID space between NWHy and its user for hypergraph processing, instead of copying the sparse matrix of the hypergraph back and forth between NWHy and its user. -NWHy was developed by Xu Tony Liu. The current version is preliminary and under active development. - -Installing NWHy ---------------- - -The NWHy library provides Pybind11_ APIs for analysis of complex data sets interpreted as hypergraphs. - -.. _Pybind11: https://github.com/pybind/pybind11 - -To install in an Anaconda environment -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - - >>> conda create -n python=3.9 - -Then activate the environment -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - - >>> conda activate - -Install Intel Threading Building Blocks(TBB) -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -To install TBB_: - -.. _TBB: https://github.com/oneapi-src/oneTBB - - >>> conda install tbb - -If a local TBB has been installed, we can specify TBBROOT - - >>> export TBBROOT=/opt/tbb/ - -Install using Pip -^^^^^^^^^^^^^^^^^ - -For installation: - - >>> pip install nwhy - -For upgrade: - - >>> pip install nwhy --upgrade - -or - - >>> pip install nwhy -U - - -Quick test with import -^^^^^^^^^^^^^^^^^^^^^^ - -For quick test: - - >>> python -c "import nwhy" - -If there is no import error, then installation is done. - -NWHy APIs ---------- - -.. _nwhy:: - :sorted: - - -nwhy module -^^^^^^^^^^^ - - _version - Attribute in nwhy module. - Return the version number of nwhy module. - - -NWHypergraph class -^^^^^^^^^^^^^^^^^^ - - NWHypergraph - Class in nwhy module. - The base class for hypergraph representation in nwhy. It accepts a directed edge list format of hypergraph, either weighted or unweighted, then construct the NWHypergraph object. - -NWHypergraph class attributes -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - - NWHypergraph.row - Attribute in class NWHypergraph. - Return a Numpy array of IDs, row of sparse matrix of the hypergraph. Note the number of entries in the Numpy lists, row, col and data must be equal. The row stores hyperedges. - NWHypergraph.col - Attribute in class NWHypergraph. - Return a Numpy array of IDs, columns of sparse matrix of the hypergraph. The col stores vertices. - NWHypergraph.data - Attribute in class NWHypergraph. - Return a Numpy array of IDs, weights of sparse matrix of the hypergraph. - -NWHypergraph class methods -^^^^^^^^^^^^^^^^^^^^^^^^^^ - - NWHypergraph.NWHypergraph(x, y) - Constructor of class NWHypergraph. - Return a NWHypergraph object. Here the hypergraph is unweighted. X is a Numpy array of hyperedges, and y is a Numpy array of vertices. - - NWHypergraph.NWHypergraph(x, y, data) - Constructor of class NWHypergraph. - Return a NWHypergraph object. Here the hypergraph is weighted. X is a Numpy array of hyperedges, y is a Numpy array of vertices, data is a Numpy array of weights associated with the pairs from hyperedges to vertices. - - NWHypergraph.collapse_edges(return_equal_class=False) - Method in class NWHypergraph. - Return a dictionary, where the key is a new ID of a hyperedge after collapsing the hyperedges if the hyperedges have the same vertices, and the value is the number of such hyperedges when `return_equal_class=False`, otherwise, the set of such hyperedges when `return_equal_class=True`. Note the weights associated with the pairs from hyperedges to vertices are not collapsed or combined. - - NWHypergraph.collapse_nodes(return_equal_class=False) - Method in class NWHypergraph. - Return a dictionary, where the key is a new ID of a vertex after collapsing the vertices if the vertices share the same hyperedges, and the value is the number of such vertices when `return_equal_class=False`, otherwise, the set of such vertices when `return_equal_class=True`. Note the weights associated with the pairs from hyperedges to vertices are not collapsed or combined. - - NWHypergraph.collapse_nodes_and_edges(return_equal_class=False) - Method in class NWHypergraph. - Return a dictionary, where the key is a new ID of a hyperedge after collapsing the hyperedges if the hyperedges share the same vertices, and the value is the number of such hyperedges when `return_equal_class=False`, otherwise, the set of such hyperedges when `return_equal_class=True`. This method is not equivalent to call `NWHypergraph.collapse_nodes()` then `NWHypergraph.collapse_edges()`. Note the weights associated with the pairs from hyperedges to vertices are not collapsed or combined. - - NWHypergraph.edge_size_dist() - Method in class NWHypergraph. - Return a list of edge size distribution of the hypergraph. - - NWHypergraph.node_size_dist() - Method in class NWHypergraph. - Return a list of vertex size distribution of the hypergraph. - - NWHypergraph.edge_incidence(edge) - Method in class NWHypergraph. - Return a list of vertices that are incident to hyperedge `edge`. - - NWHypergraph.node_incidence(node) - Method in class NWHypergraph. - Return a list of hyperedges that are incident to vertex `node`. - - NWHypergraph.degree(node, min_size=1, max_size=None) - Method in class NWHypergraph. - Return the degree of the vertex `node` in the hypergraph. For the hyperedges `node` incident to, if `min_size` or/and `max_size` are specified, then either/both criteria are used to filter the hyperedges. - - NWHypergraph.size(edge, min_degree=1, max_degree=None) - Method in class NWHypergraph. - Return the size of the hyperedge `edge` in the hypergraph. For the vertices `edge` incident to, if `min_degree` or/and `max_degree` are specified, then either/both criteria are used to filter the vertices. - - NWHypergraph.dim(edge) - Method in class NWHypergraph. - Return the dimension of the hyperedge `edge` in the hypergraph. - - NWHypergraph.number_of_nodes() - Method in class NWHypergraph. - Return the number of vertices in the hypergraph. - - NWHypergraph.number_of_edges() - Method in class NWHypergraph. - Return the number of edges in the hypergraph. - - NWHypergraph.singletons() - Method in class NWHypergraph. - Return a list of singleton hyperedges in the hypergraph. A singleton hyperedge is incident to only one vertex. - - NWHypergraph.toplexes() - Method in class NWHypergraph. - Return a list of toplexes in the hypergraph. For a hypergraph (Edges, Nodes), a toplex is a hyperedge in Edges whose elements (i.e. nodes) do not all belong to any other hyperedge in Edge. - - NWHypergraph.s_linegraph(s=1, edges=True) - Method in class NWHypergraph. - Return a Slinegraph object. Construct a s-line graph from the hypergraph for a positive integer `s`. In this s-line graph, the vertices are the hyperedges in the original hypergraph if `edges=True`; otherwise, the vertices are the vertices in the original hypergraph. Note this method create s-line graph on the fly, therefore it requires less memory compared with `NWHypergraph.s_linegraphs(l, edges=True)`. It is slower to construct multiple s-line graphs for different `s` compared with `NWHypergraph.s_linegraphs(l, edges=True)`. - - NWHypergraph.s_linegraphs(l, edges=True) - Method in class NWHypergraph. - Return a list of Slinegraph objects. For each positive integer in list `l`, construct a Slinegraph object from the hypergraph. In each s-line graph, the vertices are the hyperedges in the original hypergraph if `edges=True`; otherwise, the vertices are the vertices in the original hypergraph. Note this method creates multiple s-line graphs for one run, therefore it is significantly faster compared with `NWHypergraph.s_linegraph(s=1, edges=True)`, but it requires much more memory. - - -Slinegraph class -^^^^^^^^^^^^^^^^ - - Slinegraph - Class in nwhy module. - The base class for s-line graph representation in nwhy. It store an undirected graph, called an s-line graph of a hypergraph given a positive integer s. Slinegraph can be an 'edge' line graph, where the vertices in Slinegraph are the hyperedges in the original hypergraph; Slinegraph can also be a 'vertex' line graph, where the vertices in Slinegraph are the vertices in the original hypergraph. - -Slinegraph class attributes -^^^^^^^^^^^^^^^^^^^^^^^^^^^ - - Slinegraph.row - Attribute in class Slinegraph. - Return a Numpy array of IDs, row of sparse matrix of the s-line graph. Note the number of entries in the Numpy lists, row, col and data must be equal. - Slinegraph.col - Attribute in class Slinegraph. - Return a Numpy array of IDs, columns of sparse matrix of the s-line graph. - Slinegraph.data - Attribute in class Slinegraph. - Return a Numpy array of IDs, weights of sparse matrix of the s-line graph. The weights are not the hyperedge-vertex pair weights. Currently, if Slinegraph is an edge line graph, the weights are the number of overlapping vertices between two hyperedges in the original hypergraph. If the Slinegraph is a vertex line graph, the weights are the number of overlapping hyperedges between two vertices in the original hypergraph. - Slinegraph.s - Attribute in class Slinegraph. - Return s value of the s-line graph. - -Slinegraph class methods -^^^^^^^^^^^^^^^^^^^^^^^^ - - Slinegraph.Slinegraph(g, s=1, edges=True) - Constructor of class Slinegraph. - Return a new Slinegraph object. Given a positive integer `s`, construct a s-line graph from the hypergraph `g`. The vertices in the s-line graph are the hyperedges in `g` if `edges=True`, otherwise, the vertices in the s-line graph are the vertices in `g`. - - Slinegraph.Slinegraph(x, y, data, s=1, edges=True) - Constructor of class Slinegraph. - Return a new Slinegraph object. Given an edge list format of a s-line graph stored in three Numpy arrays, construct a s-line graph from the edge list. A positive integer `s` and a boolean `edges` are required to indicate the properties of the s-line graph. - - Slinegraph.get_singletons() - Method in class Slinegraph. - Return a list of singletons in the s-line graph. - - Slinegraph.s_connected_components() - Method in class Slinegraph. - Return a list of sets, where each set contains the vertices sharing the same component. - - Slinegraph.is_s_connected() - Method in class Slinegraph. - Return True or False. Check whether s-line graph is connected. - - Slinegraph.s_distance(src, dest) - Method in class Slinegraph. - Return the distance from `src` to `dest`. Return -1 if it is unreachable from `src` to `dest`. - - Slinegraph.s_diameter(src, dest) - Method in class Slinegraph. - Return the diameter of the s-line graph. Return 0 if every vertex is a singleton. - - Slinegraph.s_path(src, dest) - Method in class Slinegraph. - Return a list of vertices. The vertices are the vertices on the shortest path from `src` to `dest` in the s-line graph. The list will be empty if it is unreachable from `src` to `dest`. - - Slinegraph.s_betweenness_centrality(normalized=True) - Method in class Slinegraph. - Return a list of betweenness centrality score of every vertices in the s-line graph. The betweenness centrality score will be normalized by 2/((n-1)(n-2)) if `normalized=True` where n the number of vertices in s-line graph. Betweenness centrality of a vertex `v` is the sum of the fraction of all-pairs shortest paths that pass through `v`: - - .. math:: - - c_B(v) =\sum_{s,t \in V} \frac{\sigma(s, t|v)}{\sigma(s, t)} - - Slinegraph.s_closeness_centrality(v=None) - Method in class Slinegraph. - Return a list of closeness centrality scores of every vertices in the s-line graph. If `v` is specified, then the list returned contains only `v`'s score. Closeness centrality of a vertex `v` is the reciprocal of the average shortest path distance to `v` over all `n-1` reachable nodes: - - .. math:: - - C(v) = \frac{n - 1}{\sum_{v=1}^{n-1} d(u, v)}, - - - Slinegraph.s_harmonic_closeness_centrality(v=None) - Method in class Slinegraph. - Return a list of harmonic closeness centrality scores of every vertices in the s-line graph. If `v` is specified, then the list returned contains only `v`'s score. Harmonic centrality of a vertex `v` is the sum of the reciprocal of the shortest path distances from all other nodes to `v`: - - .. math:: - - C(v) = \sum_{v \neq u} \frac{1}{d(v, u)} - - Slinegraph.s_eccentricity(v=None) - Method in class Slinegraph. - Return a list of eccentricity of every vertices in the s-line graph. If `v` is specified, then the list returned contains only eccentricity of `v`. - - Slinegraph.s_neighbors(v) - Method in class Slinegraph. - Return a list of neighboring vertices of `v` in the s-line graph. - - Slinegraph.s_degree(v) - Method in class Slinegraph. - Return the degree of vertex `v` in the s-line graph. - diff --git a/docs/source/overview/index.rst b/docs/source/overview/index.rst index 30cb329a..3f62dd9a 100644 --- a/docs/source/overview/index.rst +++ b/docs/source/overview/index.rst @@ -1,4 +1,4 @@ -.. overview: +.. _overview: ======== Overview @@ -8,51 +8,12 @@ Overview :width: 300px :align: right -The `HyperNetX`_ (`HNX`_) library was developed to support researchers modeling data -as hypergraphs. We have a growing community of users and contributors. -For questions and comments you may contact the developers directly at: hypernetx@pnnl.gov - -`HyperNetX`_ was developed by the `Pacific Northwest National Laboratory `_ for the -Hypernets project as part of its High Performance Data Analytics (HPDA) program. -PNNL is operated by Battelle Memorial Institute under Contract DE-ACO5-76RL01830. - -* Principle Developer and Designer: Brenda Praggastis -* Visualization: Dustin Arendt, Ji Young Yun -* High Performance Computing: Tony Liu, Andrew Lumsdaine -* Principal Investigator: Cliff Joslyn -* Program Manager: Brian Kritzstein -* Mathematics, methods, and algorithms: Sinan Aksoy, Dustin Arendt, Cliff Joslyn, Nicholas Landry, Tony Liu, Andrew Lumsdaine, Brenda Praggastis, and Emilie Purvine, François Théberge - - - -New Features in Version 1.0 ---------------------------- - -#. Hypergraph construction can be sped up by reading in all of the data at once. In particular the hypergraph constructor may read a Pandas dataframe object and create edges and nodes based on column headers. -#. The C++ addon :ref:`nwhy` can be used in Linux environments to support optimized hypergraph methods such as s-centrality measures. -#. The JavaScript addon :ref:`widget` can be used to interactively inspect hypergraphs in a Jupyter Notebook. -#. We've added four new tutorials highlighting the s-centrality metrics, static Hypergraphs, :ref:`nwhy`, and :ref:`widget`. - -New Features in Version 1.1 ---------------------------- - -#. Cell weights for incidence matrices. -#. Support for edge and node properties in static hypergraphs. -#. Three new algorithms modules and their corresponding tutorials - - #. Contagion module for studying SIS and SIR contagion networks using hypergraphs. - #. Clustering module for clustering vertices based on hyperedge incidence and weighting. - #. Generator module for synthetic generation of ChungLu and DCSBM hypergraphs. - -New Features in Version 1.2 ---------------------------- -#. Added algorithm module and tutorial for Modularity and Clustering - +.. include:: ../../../LONG_DESCRIPTION.rst .. _colab: COLAB Tutorials ---------------- +================ The following tutorials may be run in your browser using Google Colab. Additional tutorials are available on `GitHub `_. @@ -60,43 +21,46 @@ available on `GitHub `_. + Notice ------ This material was prepared as an account of work sponsored by an agency of the United States Government. @@ -129,6 +93,8 @@ License ------- HyperNetX is released under the 3-Clause BSD license (see :ref:`license`) + + .. toctree:: :maxdepth: 2 diff --git a/docs/source/publications.rst b/docs/source/publications.rst old mode 100644 new mode 100755 index 6632d5b4..ef9ad873 --- a/docs/source/publications.rst +++ b/docs/source/publications.rst @@ -1,19 +1,37 @@ -.. _publications: - -============ -Publications -============ - -Joslyn, Cliff A; Aksoy, Sinan; Callahan, Tiffany J; Hunter, LE; Jefferson, Brett ; Praggastis, Brenda ; Purvine, Emilie AH ; Tripodi, Ignacio J: (2020) **"Hypernetwork Science: From Multidimensional Networks to Computational Topology"**, in: Int. Conf. Complex Systems (ICCS 2020), https://arxiv.org/abs/2003.11782, (in press) - - -Feng, Song; Heath, Emily; Jefferson, Brett; Joslyn, CA; Kvinge, Henry; McDermott, Jason E ; Mitchell, Hugh D ; Praggastis, Brenda ; Eisfeld, Amie J; Sims, Amy C ; Thackray, Larissa B ; Fan, Shufang ; Walters, Kevin B; Halfmann, Peter J ; Westhoff-Smith, Danielle ; Tan, Qing ; Menachery, Vineet D ; Sheahan, Timothy P ; Cockrell, Adam S ; Kocher, Jacob F ; Stratton, Kelly G ; Heller, Natalie C ; Bramer, Lisa M ; Diamond, Michael S ; Baric, Ralph S ; Waters, Katrina M ; Kawaoka, Yoshihiro ; Purvine, Emilie: (2020) **"Hypergraph Models of Biological Networks to Identify Genes Critical to Pathogenic Viral Response"**, in: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04197-2, BMC Bioinformatics, 22:287, doi: 10.1186/s12859-021-04197-2 - - -Aksoy, Sinan G; Joslyn, Cliff A; Marrero, Carlos O; Praggastis, B; Purvine, Emilie AH: (2020) **"Hypernetwork Science via High-Order Hypergraph Walks"**, EPJ Data Science, v. 9:16, https://doi.org/10.1140/epjds/s13688-020-00231-0 - - -Joslyn, Cliff A; Aksoy, Sinan; Arendt, Dustin; Firoz, J; Jenkins, Louis ; Praggastis, Brenda ; Purvine, Emilie AH ; Zalewski, Marcin: (2020) **"Hypergraph Analytics of Domain Name System Relationships"**, in: 17th Wshop. on Algorithms and Models for the Web Graph (WAW 2020), Lecture Notes in Computer Science, v. 12901, ed. Kaminski, B et al., pp. 1-15, Springer, https://doi.org/10.1007/978-3-030-48478-1_1 - - -Joslyn, Cliff A; Aksoy, Sinan; Arendt, Dustin; Jenkins, L; Praggastis, Brenda; Purvine, Emilie; Zalewski, Marcin: (2019) **"High Performance Hypergraph Analytics of Domain Name System Relationships"**, in: Proc. HICSS Symp. on Cybersecurity Big Data Analytics, http://www.azsecure-hicss.org/ +.. _publications: + +============ +Publications +============ + + +**Joslyn, Cliff A; Aksoy, Sinan; Callahan, Tiffany J; Hunter, LE; Jefferson, Brett; Praggastis, Brenda; Purvine, Emilie AH; Tripodi, Ignacio J: (2021)** `Hypernetwork Science: From Multidimensional Networks to Computational Topology `_, in: `Unifying Themes in Complex systems X: Proc. 10th Int. Conf. Complex Systems*, ed. D. Braha et al., pp. 377-392, Springer, https://doi.org/10.1007/978-3-030-67318-5_25 + + +**Aksoy, Sinan G; Joslyn, Cliff A; Marrero, Carlos O; Praggastis, B; Purvine, Emilie AH: (2020)** "Hypernetwork Science via High-Order Hypergraph Walks" , *EPJ Data Science*, v. **9**:16, +https://doi.org/10.1140/epjds/s13688-020-00231-0 + +**Aksoy, Sinan G; Hagberg, Aric; Joslyn, Cliff A; Kay, Bill; Purvine, Emilie; Young, Stephen J: (2022)** "Models and Methods for Sparse (Hyper)Network Science in Business, Industry, and Government", *Notices of the AMS*, v. **69**:2, pp. 287-291, +https://doi.org/10.1090/noti2424 + +**Feng, Song; Heath, Emily; Jefferson, Brett; Joslyn, CA; Kvinge, Henry; McDermott, Jason E; Mitchell, Hugh D; Praggastis, Brenda; Eisfeld, Amie J; Sims, Amy C; Thackray, Larissa B; Fan, Shufang; Walters, Kevin B; Halfmann, Peter J; Westhoff-Smith, Danielle; Tan, Qing; Menachery, Vineet D; Sheahan, Timothy P; Cockrell, Adam S; Kocher, Jacob F; Stratton, Kelly G; Heller, Natalie C; Bramer, Lisa M; Diamond, Michael S; Baric, Ralph S; Waters, Katrina M; Kawaoka, Yoshihiro; Purvine, Emilie: (2021)** "Hypergraph Models of Biological Networks to Identify Genes Critical to Pathogenic Viral Response", in: *BMC Bioinformatics*, v. **22**:287, +https://doi.org/10.1186/s12859-021-04197-2 + +**Myers, Audun; Joslyn, Cliff A; Kay, Bill; Purvine, EAH; Roek, Gregory; Shapiro, Madelyn: (2023)** "Topological Analysis of Temporal Hypergraphs", in: *Proc. Wshop. on Analysis of the Web Graph (WAW 2023)* https://arxiv.org/abs/2302.02857 and +*2022 SIAM Conf. on Mathematics of Data Science*, https://www.siam.org/Portals/0/Conferences/MDS/MDS22/MDS22_ABSTRACTS.pdf + +**Joslyn, Cliff A; Aksoy, Sinan; Arendt, Dustin; Firoz, J; Jenkins, Louis; Praggastis, Brenda; Purvine, Emilie AH; Zalewski, Marcin: (2020)** "Hypergraph Analytics of Domain Name System Relationships", in: *17th Wshop. on Algorithms and Models for the Web Graph (WAW 2020), Lecture Notes in Computer Science*, v. **12901**, ed. Kaminski, B et al., pp. 1-15, Springer, +https://doi.org/10.1007/978-3-030-48478-1_1 + +**Hayashi, Koby; Aksoy, Sinan G; Park, CH; and Park, Haesun: (2020) "Hypergraph Random Walks, Laplacians, and Clustering"**, in:**Proc. 29th ACM Int. Conf. Information and Knowledge Management (CIKM 2020)**, pp. 495-504, ACM, New York,** +https://doi.org/10.1145/3340531.3412034 + +**Kay, WW; Aksoy, Sinan G; Baird, Molly; Best, DM; Jenne, Helen; Joslyn, CA; Potvin, CD; Roek, Greg; Seppala, Garrett; Young, Stephen; Purvine, Emilie: (2022)** "Hypergraph Topological Features for Autoencoder-Based Intrusion Detection for Cybersecurity Data", *ML4Cyber Wshop., Int. Conf. Machine Learning 2022*, +https://icml.cc/Conferences/2022/ScheduleMultitrack?event=13458#collapse20252 + +**Liu, Xu T; Firoz, Jesun; Lumsdaine, Andrew; Joslyn, CA; Aksoy, Sinan; Amburg, Ilya; Praggastis, Brenda; Gebremedhin, Assefaw: (2022)** "High-Order Line Graphs of Non-Uniform Hypergraphs: Algorithms, Applications, and Experimental Analysis", *36th IEEE Int. Parallel and Distributed Processing Symp. (IPDPS 22)*, +https://ieeexplore.ieee.org/document/9820632 + +**Liu, Xu T; Firoz, Jesun; Lumsdaine, Andrew; Joslyn, CA; Aksoy, Sinan; Praggastis, Brenda; Gebremedhin, Assefaw: (2021)** "Parallel Algorithms for Efficient Computation of High-Order Line Graphs of Hypergraphs", in: *2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC 2021)*, +https://doi.ieeecomputersociety.org/10.1109/HiPC53243.2021.00045 + diff --git a/hypernetx/__init__.py b/hypernetx/__init__.py index 5da6a336..084e7d9b 100644 --- a/hypernetx/__init__.py +++ b/hypernetx/__init__.py @@ -8,6 +8,7 @@ from hypernetx.reports import * from hypernetx.drawing import * from hypernetx.algorithms import * -from hypernetx.algorithms.contagion import * from hypernetx.utils import * from hypernetx.utils.toys import * + +__version__ = "2.0.0.post1" diff --git a/hypernetx/algorithms/__init__.py b/hypernetx/algorithms/__init__.py index a3b6fd6a..61ff8749 100644 --- a/hypernetx/algorithms/__init__.py +++ b/hypernetx/algorithms/__init__.py @@ -1,6 +1,121 @@ -from .homology_mod2 import * -from .s_centrality_measures import * -from .contagion import * -from .laplacians_clustering import * -from .generative_models import * -from .hypergraph_modularity import * +from hypernetx.algorithms.homology_mod2 import ( + kchainbasis, + bkMatrix, + swap_rows, + swap_columns, + add_to_row, + add_to_column, + logical_dot, + logical_matmul, + matmulreduce, + logical_matadd, + smith_normal_form_mod2, + reduced_row_echelon_form_mod2, + boundary_group, + chain_complex, + betti, + betti_numbers, + homology_basis, + hypergraph_homology_basis, + interpret, +) +from hypernetx.algorithms.s_centrality_measures import ( + s_betweenness_centrality, + s_harmonic_closeness_centrality, + s_harmonic_centrality, + s_closeness_centrality, + s_eccentricity, +) +from hypernetx.algorithms.contagion import ( + contagion_animation, + collective_contagion, + individual_contagion, + threshold, + majority_vote, + discrete_SIR, + discrete_SIS, + Gillespie_SIR, + Gillespie_SIS, +) +from hypernetx.algorithms.laplacians_clustering import ( + prob_trans, + get_pi, + norm_lap, + spec_clus, +) +from hypernetx.algorithms.generative_models import ( + erdos_renyi_hypergraph, + chung_lu_hypergraph, + dcsbm_hypergraph, +) +from hypernetx.algorithms.hypergraph_modularity import ( + dict2part, + part2dict, + precompute_attributes, + linear, + majority, + strict, + modularity, + two_section, + kumar, + last_step, +) + +__all__ = [ + # homology_mod2 API's + "kchainbasis", + "bkMatrix", + "swap_rows", + "swap_columns", + "add_to_row", + "add_to_column", + "logical_dot", + "logical_matmul", + "matmulreduce", + "logical_matadd", + "smith_normal_form_mod2", + "reduced_row_echelon_form_mod2", + "boundary_group", + "chain_complex", + "betti", + "betti_numbers", + "homology_basis", + "hypergraph_homology_basis", + "interpret", + # contagion API's + "contagion_animation", + "collective_contagion", + "individual_contagion", + "threshold", + "majority_vote", + "discrete_SIR", + "discrete_SIS", + "Gillespie_SIR", + "Gillespie_SIS", + # laplacians_clustering API's + "prob_trans", + "get_pi", + "norm_lap", + "spec_clus", + # generative_models API's + "erdos_renyi_hypergraph", + "chung_lu_hypergraph", + "dcsbm_hypergraph", + # s_centreality_measures API's + "s_betweenness_centrality", + "s_harmonic_closeness_centrality", + "s_harmonic_centrality", + "s_closeness_centrality", + "s_eccentricity", + # hypergraph_modularity API's + "dict2part", + "part2dict", + "precompute_attributes", + "linear", + "majority", + "strict", + "modularity", + "two_section", + "kumar", + "last_step", +] diff --git a/hypernetx/algorithms/contagion/epidemics.py b/hypernetx/algorithms/contagion.py similarity index 89% rename from hypernetx/algorithms/contagion/epidemics.py rename to hypernetx/algorithms/contagion.py index 3ab1c82d..11df3d5f 100644 --- a/hypernetx/algorithms/contagion/epidemics.py +++ b/hypernetx/algorithms/contagion.py @@ -1,8 +1,124 @@ import random -import heapq import numpy as np from collections import defaultdict from collections import Counter +import hypernetx as hnx + +try: + from celluloid import Camera +except Exception as e: + print( + f" {e}. If you need to use {__name__}, please install additional packages by running the following command: pip install .['all']" + ) + + +def contagion_animation( + fig, + H, + transition_events, + node_state_color_dict, + edge_state_color_dict, + node_radius=1, + fps=1, +): + """ + A function to animate discrete-time contagion models for hypergraphs. Currently only supports a circular layout. + + Parameters + ---------- + fig : matplotlib Figure object + H : HyperNetX Hypergraph object + transition_events : dictionary + The dictionary that is output from the discrete_SIS and discrete_SIR functions with return_full_data=True + node_state_color_dict : dictionary + Dictionary which specifies the colors of each node state. All node states must be specified. + edge_state_color_dict : dictionary + Dictionary with keys that are edge states and values which specify the colors of each edge state + (can specify an alpha parameter). All edge-dependent transition states must be specified + (most common is "I") and there must be a a default "OFF" setting. + node_radius : float, default: 1 + The radius of the nodes to draw + fps : int > 0, default: 1 + Frames per second of the animation + + Returns + ------- + matplotlib Animation object + + Notes + ----- + + Example:: + + >>> import hypernetx.algorithms.contagion as contagion + >>> import random + >>> import hypernetx as hnx + >>> import matplotlib.pyplot as plt + >>> from IPython.display import HTML + >>> n = 1000 + >>> m = 10000 + >>> hyperedgeList = [random.sample(range(n), k=random.choice([2,3])) for i in range(m)] + >>> H = hnx.Hypergraph(hyperedgeList) + >>> tau = {2:0.1, 3:0.1} + >>> gamma = 0.1 + >>> tmax = 100 + >>> dt = 0.1 + >>> transition_events = contagion.discrete_SIS(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax, dt=dt, return_full_data=True) + >>> node_state_color_dict = {"S":"green", "I":"red", "R":"blue"} + >>> edge_state_color_dict = {"S":(0, 1, 0, 0.3), "I":(1, 0, 0, 0.3), "R":(0, 0, 1, 0.3), "OFF": (1, 1, 1, 0)} + >>> fps = 1 + >>> fig = plt.figure() + >>> animation = contagion.contagion_animation(fig, H, transition_events, node_state_color_dict, edge_state_color_dict, node_radius=1, fps=fps) + >>> HTML(animation.to_jshtml()) + """ + + nodeState = defaultdict(lambda: "S") + + camera = Camera(fig) + + for t in sorted(list(transition_events.keys())): + edgeState = defaultdict(lambda: "OFF") + + # update edge and node states + for event in transition_events[t]: + status = event[0] + node = event[1] + + # update node states + nodeState[node] = status + + try: + # update the edge transmitters list if they are neighbor-dependent transitions + edgeID = event[2] + if edgeID is not None: + edgeState[edgeID] = status + except: + pass + + kwargs = {"layout_kwargs": {"seed": 39}} + + nodeStyle = { + "facecolors": [node_state_color_dict[nodeState[node]] for node in H.nodes] + } + edgeStyle = { + "facecolors": [edge_state_color_dict[edgeState[edge]] for edge in H.edges], + "edgecolors": "black", + } + + # draw hypergraph + hnx.draw( + H, + node_radius=node_radius, + nodes_kwargs=nodeStyle, + edges_kwargs=edgeStyle, + with_edge_labels=False, + with_node_labels=False, + **kwargs, + ) + camera.snap() + + return camera.animate(interval=1000 / fps) + # Canned Contagion Functions def collective_contagion(node, status, edge): @@ -350,7 +466,7 @@ def discrete_SIR( tmax=float("Inf"), dt=1.0, return_full_data=False, - **args + **args, ): """ A discrete-time SIR model for hypergraphs similar to the construction described in @@ -456,10 +572,7 @@ def discrete_SIR( times = [t] newStatus = status.copy() - if H.isstatic: - edge_neighbors = lambda node: H.edges.memberships[node] - else: - edge_neighbors = lambda node: H.nodes[node].memberships + edge_neighbors = lambda node: H.edges.memberships[node] while t < tmax and I[-1] != 0: # Initialize the next step with the same numbers of S, I, and R as the last step before computing the changes @@ -517,7 +630,7 @@ def discrete_SIS( tmax=100, dt=1.0, return_full_data=False, - **args + **args, ): """ A discrete-time SIS model for hypergraphs as implemented in @@ -609,10 +722,7 @@ def discrete_SIS( times = [t] newStatus = status.copy() - if H.isstatic: - edge_neighbors = lambda node: H.edges.memberships[node] - else: - edge_neighbors = lambda node: H.nodes[node].memberships + edge_neighbors = lambda node: H.edges.memberships[node] while t < tmax and I[-1] != 0: # Initialize the next step with the same numbers of S, I, and R as the last step before computing the changes @@ -668,7 +778,7 @@ def Gillespie_SIR( rho=None, tmin=0, tmax=float("Inf"), - **args + **args, ): """ A continuous-time SIR model for hypergraphs similar to the model in @@ -758,10 +868,7 @@ def Gillespie_SIR( R = [len(initial_recovereds)] S = [H.number_of_nodes() - I[-1] - R[-1]] - if H.isstatic: - edge_neighbors = lambda node: H.edges.memberships[node] - else: - edge_neighbors = lambda node: H.nodes[node].memberships + edge_neighbors = lambda node: H.edges.memberships[node] t = tmin times = [t] @@ -872,7 +979,7 @@ def Gillespie_SIS( tmax=float("Inf"), return_full_data=False, sim_kwargs=None, - **args + **args, ): """ A continuous-time SIS model for hypergraphs similar to the model in @@ -950,10 +1057,7 @@ def Gillespie_SIS( I = [len(initial_infecteds)] S = [H.number_of_nodes() - I[-1]] - if H.isstatic: - edge_neighbors = lambda node: H.edges.memberships[node] - else: - edge_neighbors = lambda node: H.nodes[node].memberships + edge_neighbors = lambda node: H.edges.memberships[node] t = tmin times = [t] diff --git a/hypernetx/algorithms/contagion/__init__.py b/hypernetx/algorithms/contagion/__init__.py deleted file mode 100644 index 1bb13033..00000000 --- a/hypernetx/algorithms/contagion/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .epidemics import * -from .animation import * diff --git a/hypernetx/algorithms/contagion/animation.py b/hypernetx/algorithms/contagion/animation.py deleted file mode 100644 index 2404aee3..00000000 --- a/hypernetx/algorithms/contagion/animation.py +++ /dev/null @@ -1,111 +0,0 @@ -from collections import defaultdict -import hypernetx as hnx -from celluloid import Camera - - -def contagion_animation( - fig, - H, - transition_events, - node_state_color_dict, - edge_state_color_dict, - node_radius=1, - fps=1, -): - """ - A function to animate discrete-time contagion models for hypergraphs. Currently only supports a circular layout. - - Parameters - ---------- - fig : matplotlib Figure object - H : HyperNetX Hypergraph object - transition_events : dictionary - The dictionary that is output from the discrete_SIS and discrete_SIR functions with return_full_data=True - node_state_color_dict : dictionary - Dictionary which specifies the colors of each node state. All node states must be specified. - edge_state_color_dict : dictionary - Dictionary with keys that are edge states and values which specify the colors of each edge state - (can specify an alpha parameter). All edge-dependent transition states must be specified - (most common is "I") and there must be a a default "OFF" setting. - node_radius : float, default: 1 - The radius of the nodes to draw - fps : int > 0, default: 1 - Frames per second of the animation - - Returns - ------- - matplotlib Animation object - - Notes - ----- - - Example:: - - >>> import hypernetx.algorithms.contagion as contagion - >>> import random - >>> import hypernetx as hnx - >>> import matplotlib.pyplot as plt - >>> from IPython.display import HTML - >>> n = 1000 - >>> m = 10000 - >>> hyperedgeList = [random.sample(range(n), k=random.choice([2,3])) for i in range(m)] - >>> H = hnx.Hypergraph(hyperedgeList) - >>> tau = {2:0.1, 3:0.1} - >>> gamma = 0.1 - >>> tmax = 100 - >>> dt = 0.1 - >>> transition_events = contagion.discrete_SIS(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax, dt=dt, return_full_data=True) - >>> node_state_color_dict = {"S":"green", "I":"red", "R":"blue"} - >>> edge_state_color_dict = {"S":(0, 1, 0, 0.3), "I":(1, 0, 0, 0.3), "R":(0, 0, 1, 0.3), "OFF": (1, 1, 1, 0)} - >>> fps = 1 - >>> fig = plt.figure() - >>> animation = contagion.contagion_animation(fig, H, transition_events, node_state_color_dict, edge_state_color_dict, node_radius=1, fps=fps) - >>> HTML(animation.to_jshtml()) - """ - - nodeState = defaultdict(lambda: "S") - - camera = Camera(fig) - - for t in sorted(list(transition_events.keys())): - edgeState = defaultdict(lambda: "OFF") - - # update edge and node states - for event in transition_events[t]: - status = event[0] - node = event[1] - - # update node states - nodeState[node] = status - - try: - # update the edge transmitters list if they are neighbor-dependent transitions - edgeID = event[2] - if edgeID is not None: - edgeState[edgeID] = status - except: - pass - - kwargs = {"layout_kwargs": {"seed": 39}} - - nodeStyle = { - "facecolors": [node_state_color_dict[nodeState[node]] for node in H.nodes] - } - edgeStyle = { - "facecolors": [edge_state_color_dict[edgeState[edge]] for edge in H.edges], - "edgecolors": "black", - } - - # draw hypergraph - hnx.draw( - H, - node_radius=node_radius, - nodes_kwargs=nodeStyle, - edges_kwargs=edgeStyle, - with_edge_labels=False, - with_node_labels=False, - **kwargs - ) - camera.snap() - - return camera.animate(interval=1000 / fps) diff --git a/hypernetx/algorithms/generative_models.py b/hypernetx/algorithms/generative_models.py index fe74c81a..931ef03f 100644 --- a/hypernetx/algorithms/generative_models.py +++ b/hypernetx/algorithms/generative_models.py @@ -31,7 +31,7 @@ def erdos_renyi_hypergraph(n, m, p, node_labels=None, edge_labels=None): Example:: - + >>> import hypernetx.algorithms.generative_models as gm >>> n = 1000 >>> m = n diff --git a/hypernetx/algorithms/homology_mod2.py b/hypernetx/algorithms/homology_mod2.py index 363b3677..a987d922 100644 --- a/hypernetx/algorithms/homology_mod2.py +++ b/hypernetx/algorithms/homology_mod2.py @@ -37,31 +37,8 @@ from hypernetx import HyperNetXError from collections import defaultdict import itertools as it -import pickle from scipy.sparse import csr_matrix -__all__ = [ - "kchainbasis", - "bkMatrix", - "swap_rows", - "swap_columns", - "add_to_row", - "add_to_column", - "logical_dot", - "logical_matmul", - "matmulreduce", - "logical_matadd", - "smith_normal_form_mod2", - "reduced_row_echelon_form_mod2", - "boundary_group", - "chain_complex", - "betti", - "betti_numbers", - "homology_basis", - "hypergraph_homology_basis", - "interpret", -] - def kchainbasis(h, k): """ diff --git a/hypernetx/algorithms/hypergraph_modularity.py b/hypernetx/algorithms/hypergraph_modularity.py index bfe63f97..21009323 100644 --- a/hypernetx/algorithms/hypergraph_modularity.py +++ b/hypernetx/algorithms/hypergraph_modularity.py @@ -2,11 +2,11 @@ Hypergraph_Modularity --------------------- Modularity and clustering for hypergraphs using HyperNetX. -Adapted from F. Théberge's GitHub repository: `Hypergraph Clustering `_ +Adapted from F. Théberge's GitHub repository: `Hypergraph Clustering `_ See Tutorial 13 in the tutorials folder for library usage. References ----------- +---------- .. [1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S. and Ravindran B. "A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering". In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24 .. [2] Kamiński B., Prałat P. and Théberge F. "Community Detection Algorithm Using Hypergraph Modularity". In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13 .. [3] Kamiński B., Poulin V., Prałat P., Szufel P. and Théberge F. "Clustering via hypergraph modularity", Plos ONE 2019, https://doi.org/10.1371/journal.pone.0224307 @@ -14,11 +14,15 @@ from collections import Counter import numpy as np -from functools import reduce -import igraph as ig import itertools from scipy.special import comb +try: + import igraph as ig +except Exception as e: + print( + f" {e}. If you need to use {__name__}, please install additional packages by running the following command: pip install .['all']" + ) ################################################################################ # we use 2 representations for partitions (0-based part ids): @@ -68,18 +72,19 @@ def part2dict(A): x.extend([(a, i) for a in A[i]]) return {k: v for k, v in x} + ################################################################################ -def precompute_attributes(HG): +def precompute_attributes(H): """ - Precompute some values on hypergraph HG for faster computing of hypergraph modularity. + Precompute some values on hypergraph HG for faster computing of hypergraph modularity. This needs to be run before calling either modularity() or last_step(). Note ---- - If HG is unweighted, v.weight is set to 1 for each vertex v in HG. + If HG is unweighted, v.weight is set to 1 for each vertex v in HG. The weighted degree for each vertex v is stored in v.strength. The total edge weigths for each edge cardinality is stored in HG.d_weights. Binomial coefficients to speed-up modularity computation are stored in HG.bin_coef. @@ -92,10 +97,9 @@ def precompute_attributes(HG): Returns ------- H : Hypergraph - New hypergraph with added attributes + New hypergraph with added attributes """ - H = HG.remove_singletons() # 1. compute node strenghts (weighted degrees) for v in H.nodes: H.nodes[v].strength = 0 @@ -124,6 +128,7 @@ def precompute_attributes(HG): H.bin_coef = bin_coef return H + ################################################################################ @@ -146,11 +151,12 @@ def linear(d, c): """ return c / d if c > d / 2 else 0 + # majority def majority(d, c): - """ + """ Hyperparameter for hypergraph modularity [2]_ for d-edge with c vertices in the majority class. This corresponds to the majority rule [3]_ @@ -169,6 +175,7 @@ def majority(d, c): """ return 1 if c > d / 2 else 0 + # strict @@ -191,6 +198,7 @@ def strict(d, c): """ return 1 if c == d else 0 + ######################################### @@ -220,7 +228,7 @@ def _compute_partition_probas(HG, A): def _degree_tax(HG, Pr, wdc): """ - Computes the expected fraction of edges falling in + Computes the expected fraction of edges falling in the partition as per [2]_ Parameters @@ -242,7 +250,7 @@ def _degree_tax(HG, Pr, wdc): tax = 0 for c in np.arange(d // 2 + 1, d + 1): for p in Pr: - tax += p**c * (1 - p)**(d - c) * HG.bin_coef[(d, c)] * wdc(d, c) + tax += p**c * (1 - p) ** (d - c) * HG.bin_coef[(d, c)] * wdc(d, c) tax *= HG.d_weights[d] DT += tax DT /= HG.total_weight @@ -272,11 +280,14 @@ def _edge_contribution(HG, A, wdc): for e in HG.edges: d = HG.size(e) for part in A: - if HG.size(e, part) > d / 2: - EC += wdc(d, HG.size(e, part)) * HG.edges[e].weight + hgs = HG.size(e, part) + if hgs > d / 2: + EC += wdc(d, hgs) * HG.edges[e].weight + break EC /= HG.total_weight return EC + # HG: HNX hypergraph # A: partition (list of sets) # wcd: weight function (ex: strict, majority, linear) @@ -293,7 +304,7 @@ def modularity(HG, A, wdc=linear): A : list of sets Partition of the vertices in HG wdc : func, optional - Hyperparameter for hypergraph modularity [2]_ + Hyperparameter for hypergraph modularity [2]_ Note ---- @@ -308,6 +319,7 @@ def modularity(HG, A, wdc=linear): Pr = _compute_partition_probas(HG, A) return _edge_contribution(HG, A, wdc) - _degree_tax(HG, Pr, wdc) + ################################################################################ @@ -335,13 +347,14 @@ def two_section(HG): except: w = 1 / (len(E) - 1) s.extend([(k[0], k[1], w) for k in itertools.combinations(E, 2)]) - G = ig.Graph.TupleList(s, weights=True).simplify(combine_edges='sum') + G = ig.Graph.TupleList(s, weights=True).simplify(combine_edges="sum") return G + ################################################################################ -def kumar(HG, delta=.01): +def kumar(HG, delta=0.01): """ Compute a partition of the vertices in hypergraph HG as per Kumar's algorithm [1]_ @@ -359,14 +372,16 @@ def kumar(HG, delta=.01): """ # weights will be modified -- store initial weights - W = {e: HG.edges[e].weight for e in HG.edges} # uses edge id for reference instead of int + W = { + e: HG.edges[e].weight for e in HG.edges + } # uses edge id for reference instead of int # build graph G = two_section(HG) # apply clustering - CG = G.community_multilevel(weights='weight') + CG = G.community_multilevel(weights="weight") CH = [] for comm in CG.as_cover(): - CH.append(set([G.vs[x]['name'] for x in comm])) + CH.append(set([G.vs[x]["name"] for x in comm])) # LOOP diff = 1 @@ -376,24 +391,29 @@ def kumar(HG, delta=.01): diff = 0 for e in HG.edges: edge = HG.edges[e] - reweight = sum([1 / (1 + HG.size(e, c)) for c in CH]) * (HG.size(e) + len(CH)) / HG.number_of_edges() + reweight = ( + sum([1 / (1 + HG.size(e, c)) for c in CH]) + * (HG.size(e) + len(CH)) + / HG.number_of_edges() + ) diff = max(diff, 0.5 * abs(edge.weight - reweight)) edge.weight = 0.5 * edge.weight + 0.5 * reweight # re-run louvain # build graph G = two_section(HG) # apply clustering - CG = G.community_multilevel(weights='weight') + CG = G.community_multilevel(weights="weight") CH = [] for comm in CG.as_cover(): - CH.append(set([G.vs[x]['name'] for x in comm])) + CH.append(set([G.vs[x]["name"] for x in comm])) ctr += 1 if ctr > 50: # this process sometimes gets stuck -- set limit break - G.vs['part'] = CG.membership + G.vs["part"] = CG.membership for e in HG.edges: HG.edges[e].weight = W[e] - return dict2part({v['name']: v['part'] for v in G.vs}) + return dict2part({v["name"]: v["part"] for v in G.vs}) + ################################################################################ @@ -425,11 +445,17 @@ def _delta_ec(HG, P, v, a, b, wdc): Pm = P[a] - {v} Pn = P[b].union({v}) ec = 0 - for e in list(HG.nodes[v].memberships): + + # TODO: Verify the data shape of `memberships` (ie. what are the keys and values) + for e in list(HG.nodes.memberships[v]): d = HG.size(e) w = HG.edges[e].weight - ec += w * (wdc(d, HG.size(e, Pm)) + wdc(d, HG.size(e, Pn)) - - wdc(d, HG.size(e, P[a])) - wdc(d, HG.size(e, P[b]))) + ec += w * ( + wdc(d, HG.size(e, Pm)) + + wdc(d, HG.size(e, Pn)) + - wdc(d, HG.size(e, P[a])) + - wdc(d, HG.size(e, P[b])) + ) return ec / HG.total_weight @@ -451,7 +477,7 @@ def _bin_ppmf(d, c, p): : float """ - return p**c * (1 - p)**(d - c) + return p**c * (1 - p) ** (d - c) def _delta_dt(HG, P, v, a, b, wdc): @@ -492,13 +518,21 @@ def _delta_dt(HG, P, v, a, b, wdc): for d in HG.d_weights.keys(): x = 0 for c in np.arange(int(np.floor(d / 2)) + 1, d + 1): - x += HG.bin_coef[(d, c)] * wdc(d, c) * (_bin_ppmf(d, c, voln) + _bin_ppmf(d, c, volm) - - _bin_ppmf(d, c, vola) - _bin_ppmf(d, c, volb)) + x += ( + HG.bin_coef[(d, c)] + * wdc(d, c) + * ( + _bin_ppmf(d, c, voln) + + _bin_ppmf(d, c, volm) + - _bin_ppmf(d, c, vola) + - _bin_ppmf(d, c, volb) + ) + ) DT += x * HG.d_weights[d] return DT / HG.total_weight -def last_step(HG, L, wdc=linear, delta=.01): +def last_step(HG, L, wdc=linear, delta=0.01): """ Given some initial partition L, compute a new partition of the vertices in HG as per Last-Step algorithm [2]_ @@ -516,10 +550,10 @@ def last_step(HG, L, wdc=linear, delta=.01): some initial partition of the vertices in HG wdc : func, optional - Hyperparameter for hypergraph modularity [2]_ + Hyperparameter for hypergraph modularity [2]_ delta : float, optional - convergence stopping criterion + convergence stopping criterion Returns ------- @@ -539,7 +573,10 @@ def last_step(HG, L, wdc=linear, delta=.01): if c == i: M.append(0) else: - M.append(_delta_ec(HG, A, v, c, i, wdc) - _delta_dt(HG, A, v, c, i, wdc)) + M.append( + _delta_ec(HG, A, v, c, i, wdc) + - _delta_dt(HG, A, v, c, i, wdc) + ) i = s[np.argmax(M)] if c != i: A[c] = A[c] - {v} @@ -552,4 +589,5 @@ def last_step(HG, L, wdc=linear, delta=.01): qH = q2 return [a for a in A if len(a) > 0] + ################################################################################ diff --git a/hypernetx/algorithms/laplacians_clustering.py b/hypernetx/algorithms/laplacians_clustering.py index f4c8f073..8ff66b49 100644 --- a/hypernetx/algorithms/laplacians_clustering.py +++ b/hypernetx/algorithms/laplacians_clustering.py @@ -5,14 +5,14 @@ Hypergraph Probability Transition Matrices, Laplacians, and Clustering ====================================================================== -We contruct hypergraph random walks utilizing optional "edge-dependent vertex weights", which are +We contruct hypergraph random walks utilizing optional "edge-dependent vertex weights", which are weights associated with each vertex-hyperedge pair (i.e. cell weights on the incidence matrix). -The probability transition matrix of this random walk is used to construct a normalized Laplacian +The probability transition matrix of this random walk is used to construct a normalized Laplacian matrix for the hypergraph. That normalized Laplacian then serves as the input for a spectral clustering algorithm. This spectral clustering algorithm, as well as the normalized Laplacian and other details of -this methodology are described in +this methodology are described in -K. Hayashi, S. Aksoy, C. Park, H. Park, "Hypergraph random walks, Laplacians, and clustering", +K. Hayashi, S. Aksoy, C. Park, H. Park, "Hypergraph random walks, Laplacians, and clustering", Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020. https://doi.org/10.1145/3340531.3412034 @@ -21,15 +21,11 @@ """ import numpy as np -from collections import defaultdict -import networkx as nx -import warnings import sys -from scipy.sparse import csr_matrix, coo_matrix, diags, find, identity +from scipy.sparse import csr_matrix, diags, identity from scipy.sparse.linalg import eigs -from sklearn.cluster import SpectralClustering, KMeans +from sklearn.cluster import KMeans from sklearn import preprocessing -from functools import partial from hypernetx import HyperNetXError try: @@ -41,13 +37,6 @@ sys.setrecursionlimit(10000) -__all__ = [ - "prob_trans", - "get_pi", - "norm_lap", - "spec_clus", -] - def prob_trans(H, weights=False, index=True, check_connected=True): """ @@ -78,19 +67,17 @@ def prob_trans(H, weights=False, index=True, check_connected=True): ------- P : scipy.sparse.csr.csr_matrix Probability transition matrix of the random walk on the hypergraph - index: dict - mapping from row and column indices to corresponding vertex label + index: list + contains list of index of node ids for rows """ # hypergraph must be connected if check_connected: if not H.is_connected(): raise HyperNetXError("hypergraph must be connected") - # if no weighting function, each step in the random walk is chosen uniformly at random. - if weights == False: - R, index, _ = H.incidence_matrix(index=True) - else: - R, index, _ = H.incidence_matrix(index=True, weights=True) + R = H.incidence_matrix(index=index, weights=weights) + if index: + R, rdx, _ = R # transpose incidence matrix for notational convenience R = R.transpose() @@ -115,10 +102,9 @@ def prob_trans(H, weights=False, index=True, check_connected=True): # probability transition matrix P P = D_V * W * D_E * R - if index == False: - return P - else: - return P, index + if index: + return P, rdx + return P def get_pi(P): @@ -174,21 +160,20 @@ def norm_lap(H, weights=False, index=True): ------- P : scipy.sparse.csr.csr_matrix Probability transition matrix of the random walk on the hypergraph - index: dict - mapping from row and column indices to corresponding vertex label + id: list + contains list of index of node ids for rows """ - if weights == None: - P, index = prob_trans(H) - else: - P, index = prob_trans(H, weights=weights) + P = prob_trans(H, weights=weights, index=index) + if index: + P, idx = P + pi = get_pi(P) gamma = diags(np.power(pi, 1 / 2)) * P * diags(np.power(pi, -1 / 2)) - L = identity(gamma.shape[0]) - (1 / 2) * gamma + gamma.transpose() + L = identity(gamma.shape[0]) - (1 / 2) * (gamma + gamma.transpose()) if index: - return L, index - else: - return L + return L, idx + return L def spec_clus(H, k, existing_lap=None, weights=False): diff --git a/hypernetx/algorithms/s_centrality_measures.py b/hypernetx/algorithms/s_centrality_measures.py index 31565d0d..5722a242 100644 --- a/hypernetx/algorithms/s_centrality_measures.py +++ b/hypernetx/algorithms/s_centrality_measures.py @@ -11,14 +11,12 @@ on this representation of the hypergraph. In essence we construct an *s*-line graph corresponding to the hypergraph on which to apply our methods. -S-Metrics for hypergraphs are discussed in depth in: +S-Metrics for hypergraphs are discussed in depth in: *Aksoy, S.G., Joslyn, C., Ortiz Marrero, C. et al. Hypernetwork science via high-order hypergraph walks. EPJ Data Sci. 9, 16 (2020). https://doi.org/10.1140/epjds/s13688-020-00231-0* """ -import numpy as np -from collections import defaultdict import networkx as nx import warnings import sys @@ -33,18 +31,8 @@ sys.setrecursionlimit(10000) -__all__ = [ - "s_betweenness_centrality", - "s_harmonic_closeness_centrality", - "s_harmonic_centrality", - "s_closeness_centrality", - "s_eccentricity", -] - -def _s_centrality( - func, H, s=1, edges=True, f=None, return_singletons=True, use_nwhy=True, **kwargs -): +def _s_centrality(func, H, s=1, edges=True, f=None, return_singletons=True, **kwargs): """ Wrapper for computing s-centrality either in NetworkX or in NWHy @@ -62,8 +50,6 @@ def _s_centrality( Identifier of node or edge of interest for computing centrality return_singletons : bool, optional If True will return 0 value for each singleton in the s-linegraph - use_nwhy : bool, optional - If True will attempt to use nwhy centrality methods if availaable **kwargs Centrality metric specific keyword arguments to be passed to func @@ -84,44 +70,25 @@ def _s_centrality( return {f: 0} stats = dict() - if H.isstatic: - for h in comps: - if edges: - vertices = h.edges - else: - vertices = h.nodes - - if h.shape[edges * 1] == 1: - stats.update({v: 0 for v in vertices}) - elif use_nwhy and nwhy_available and h.nwhy: - g = h.get_linegraph(s=s, edges=edges, use_nwhy=True) - stats.update(dict(zip(vertices, func(g, **kwargs)))) - else: - g = h.get_linegraph(s=s, edges=edges, use_nwhy=False) - stats.update( - { - h.get_name(k, edges=edges): v - for k, v in func(g, **kwargs).items() - } - ) - if f: - return {f: stats[f]} - else: - for h in comps: - if edges: - A, Adict = h.edge_adjacency_matrix(s=s, index=True) - else: - A, Adict = h.adjacency_matrix(s=s, index=True) - g = nx.from_scipy_sparse_matrix(A) - stats.update({Adict[k]: v for k, v in func(g, **kwargs).items()}) - if f: - return {f: stats[f]} + for h in comps: + if edges: + vertices = h.edges + else: + vertices = h.nodes + + if h.shape[edges * 1] == 1: + stats = {v: 0 for v in vertices} + else: + g = h.get_linegraph(s=s, edges=edges) + stats.update({k: v for k, v in func(g, **kwargs).items()}) + if f: + return {f: stats[f]} return stats def s_betweenness_centrality( - H, s=1, edges=True, normalized=True, return_singletons=True, use_nwhy=True + H, s=1, edges=True, normalized=True, return_singletons=True ): r""" A centrality measure for an s-edge(node) subgraph of H based on shortest paths. @@ -161,18 +128,13 @@ def s_betweenness_centrality( A dictionary of s-betweenness centrality value of the edges. """ - if use_nwhy and nwhy_available and H.nwhy: - func = partial(nwhy.Slinegraph.s_betweenness_centrality, normalized=False) - else: - use_nwhy = False - func = partial(nx.betweenness_centrality, normalized=False) + func = partial(nx.betweenness_centrality, normalized=False) result = _s_centrality( func, H, s=s, edges=edges, return_singletons=return_singletons, - use_nwhy=use_nwhy, ) if normalized and H.shape[edges * 1] > 2: @@ -182,9 +144,7 @@ def s_betweenness_centrality( return result -def s_closeness_centrality( - H, s=1, edges=True, return_singletons=True, source=None, use_nwhy=True -): +def s_closeness_centrality(H, s=1, edges=True, return_singletons=True, source=None): r""" In a connected component the reciprocal of the sum of the distance between an edge(node) and all other edges(nodes) in the component times the number of edges(nodes) @@ -211,8 +171,6 @@ def s_closeness_centrality( Indicates if method should return values for singleton components. source : str, optional Identifier of node or edge of interest for computing centrality - use_nwhy : bool, optional - If true will use the :ref:`NWHy library ` if available. Returns ------- @@ -221,11 +179,7 @@ def s_closeness_centrality( If source=None a dictionary of values for each s-edge in H is returned. If source then a single value is returned. """ - if use_nwhy and nwhy_available and H.nwhy: - func = partial(nwhy.Slinegraph.s_closeness_centrality) - else: - use_nwhy = False - func = partial(nx.closeness_centrality) + func = partial(nx.closeness_centrality) return _s_centrality( func, H, @@ -233,14 +187,13 @@ def s_closeness_centrality( edges=edges, return_singletons=return_singletons, f=source, - use_nwhy=use_nwhy, ) -def s_harmonic_closeness_centrality(H, s=1, edge=None, use_nwhy=True): +def s_harmonic_closeness_centrality(H, s=1, edge=None): msg = """ - s_harmonic_closeness_centrality is being replaced with s_harmonic_centrality - and will not be available in future releases. + s_harmonic_closeness_centrality is being replaced with s_harmonic_centrality + and will not be available in future releases. """ warnings.warn(msg) return s_harmonic_centrality(H, s=s, edges=True, normalized=True, source=edge) @@ -253,7 +206,6 @@ def s_harmonic_centrality( source=None, normalized=False, return_singletons=True, - use_nwhy=True, ): r""" A centrality measure for an s-edge subgraph of H. A value equal to 1 means the s-edge @@ -284,8 +236,6 @@ def s_harmonic_centrality( Indicates if method should return values for singleton components. source : str, optional Identifier of node or edge of interest for computing centrality - use_nwhy : bool, optional - If true will use the :ref:`NWHy library ` if available. Returns ------- @@ -296,34 +246,41 @@ def s_harmonic_centrality( """ - if use_nwhy and nwhy_available and H.nwhy: - func = partial(nwhy.Slinegraph.s_harmonic_closeness_centrality) - else: - use_nwhy = False - func = partial(nx.harmonic_centrality) - result = _s_centrality( - func, - H, - s=s, - edges=edges, - return_singletons=return_singletons, - f=source, - use_nwhy=use_nwhy, - ) + # func = partial(nx.harmonic_centrality) + # result = _s_centrality( + # func, + # H, + # s=s, + # edges=edges, + # return_singletons=return_singletons, + # f=source, + # ) + g = H.get_linegraph(s=s, edges=edges) + result = nx.harmonic_centrality(g) if normalized and H.shape[edges * 1] > 2: n = H.shape[edges * 1] - return {k: v * 2 / ((n - 1) * (n - 2)) for k, v in result.items()} + factor = 2 / ((n - 1) * (n - 2)) else: - return result + factor = 1 + + if source: + return result[source] * factor + else: + return {k: v * factor for k, v in result.items()} + # if normalized and H.shape[edges * 1] > 2: + # n = H.shape[edges * 1] + # result = {k: v * 2 / ((n - 1) * (n - 2)) for k, v in result.items()} + # else: + # return result -def s_eccentricity( - H, s=1, edges=True, source=None, return_singletons=True, use_nwhy=True -): + +def s_eccentricity(H, s=1, edges=True, source=None, return_singletons=True): r""" - The length of the longest shortest path from a vertex $u$ to every other vertex in the linegraph. - $V$ = set of vertices in the linegraph + The length of the longest shortest path from a vertex $u$ to every other vertex in + the s-linegraph. + $V$ = set of vertices in the s-linegraph $d$ = shortest path distance .. math:: @@ -342,8 +299,6 @@ def s_eccentricity( Indicates if method should return values for singleton components. source : str, optional Identifier of node or edge of interest for computing centrality - use_nwhy : bool, optional - If true will use the :ref:`NWHy library ` if available. Returns ------- @@ -351,30 +306,33 @@ def s_eccentricity( returns the s-eccentricity value of the edges(nodes). If source=None a dictionary of values for each s-edge in H is returned. If source then a single value is returned. + If the s-linegraph is disconnected, np.inf is returned. """ - if use_nwhy and nwhy_available and H.nwhy: - func = nwhy.Slinegraph.s_eccentricity - else: - use_nwhy = False - func = nx.eccentricity - - if source is not None: - return _s_centrality( - func, - H, - s=s, - edges=edges, - f=source, - return_singletons=return_singletons, - use_nwhy=use_nwhy, - ) + + g = H.get_linegraph(s=s, edges=edges) + result = nx.eccentricity(g) + if source: + return result[source] else: - return _s_centrality( - func, - H, - s=s, - edges=edges, - return_singletons=return_singletons, - use_nwhy=use_nwhy, - ) + return result + + # func = nx.eccentricity + + # if source is not None: + # return _s_centrality( + # func, + # H, + # s=s, + # edges=edges, + # f=source, + # return_singletons=return_singletons, + # ) + # else: + # return _s_centrality( + # func, + # H, + # s=s, + # edges=edges, + # return_singletons=return_singletons, + # ) diff --git a/hypernetx/algorithms/tests/conftest.py b/hypernetx/algorithms/tests/conftest.py index 895bbb6a..df70f7c6 100644 --- a/hypernetx/algorithms/tests/conftest.py +++ b/hypernetx/algorithms/tests/conftest.py @@ -133,13 +133,20 @@ class ModularityExample: """ def __init__(self): - E = [{'A', 'B'}, {'A', 'C'}, {'A', 'B', 'C'}, {'A', 'D', 'E', 'F'}, {'D', 'F'}, {'E', 'F'}] + E = [ + {"A", "B"}, + {"A", "C"}, + {"A", "B", "C"}, + {"A", "D", "E", "F"}, + {"D", "F"}, + {"E", "F"}, + ] self.E = E - self.HG = hnx.Hypergraph(E, static=True) - A1 = [{'A', 'B', 'C'}, {'D', 'E', 'F'}] - A2 = [{'B', 'C'}, {'A', 'D', 'E', 'F'}] - A3 = [{'A', 'B', 'C', 'D', 'E', 'F'}] - A4 = [{'A'}, {'B'}, {'C'}, {'D'}, {'E'}, {'F'}] + self.HG = hnx.Hypergraph(E) + A1 = [{"A", "B", "C"}, {"D", "E", "F"}] + A2 = [{"B", "C"}, {"A", "D", "E", "F"}] + A3 = [{"A", "B", "C", "D", "E", "F"}] + A4 = [{"A"}, {"B"}, {"C"}, {"D"}, {"E"}, {"F"}] self.partitions = [A1, A2, A3, A4] diff --git a/hypernetx/algorithms/tests/test_contagion.py b/hypernetx/algorithms/tests/test_contagion.py index fe45d7c8..184639bc 100644 --- a/hypernetx/algorithms/tests/test_contagion.py +++ b/hypernetx/algorithms/tests/test_contagion.py @@ -1,39 +1,45 @@ import numpy as np -import pytest -import warnings -import hypernetx.algorithms.contagion as contagion +from hypernetx.algorithms.contagion import ( + Gillespie_SIR, + Gillespie_SIS, + discrete_SIR, + discrete_SIS, + majority_vote, + collective_contagion, + individual_contagion, + threshold, +) import hypernetx as hnx -import sys import random # Test the contagion functions def test_collective_contagion(): status = {0: "S", 1: "I", 2: "I", 3: "S", 4: "R"} - assert contagion.collective_contagion(0, status, (0, 1, 2)) == True - assert contagion.collective_contagion(1, status, (0, 1, 2)) == False - assert contagion.collective_contagion(3, status, (0, 1, 2)) == False + assert collective_contagion(0, status, (0, 1, 2)) == True + assert collective_contagion(1, status, (0, 1, 2)) == False + assert collective_contagion(3, status, (0, 1, 2)) == False def test_individual_contagion(): status = {0: "S", 1: "I", 2: "I", 3: "S", 4: "R"} - assert contagion.individual_contagion(0, status, (0, 1, 3)) == True - assert contagion.individual_contagion(1, status, (0, 1, 2)) == False - assert contagion.individual_contagion(3, status, (0, 3, 4)) == False + assert individual_contagion(0, status, (0, 1, 3)) == True + assert individual_contagion(1, status, (0, 1, 2)) == False + assert individual_contagion(3, status, (0, 3, 4)) == False def test_threshold(): status = {0: "S", 1: "I", 2: "I", 3: "S", 4: "R"} - assert contagion.threshold(0, status, (0, 2, 3, 4), tau=0.2) == True - assert contagion.threshold(0, status, (0, 2, 3, 4), tau=0.5) == False - assert contagion.threshold(1, status, (1, 2, 3), tau=1) == False + assert threshold(0, status, (0, 2, 3, 4), tau=0.2) == True + assert threshold(0, status, (0, 2, 3, 4), tau=0.5) == False + assert threshold(1, status, (1, 2, 3), tau=1) == False def test_majority_vote(): status = {0: "S", 1: "I", 2: "I", 3: "S", 4: "R"} - assert contagion.majority_vote(0, status, (0, 1, 2)) == True - assert contagion.majority_vote(0, status, (0, 1, 2, 3)) == True - assert contagion.majority_vote(1, status, (0, 1, 2)) == False - assert contagion.majority_vote(3, status, (0, 1, 2)) == False + assert majority_vote(0, status, (0, 1, 2)) == True + assert majority_vote(0, status, (0, 1, 2, 3)) == True + assert majority_vote(1, status, (0, 1, 2)) == False + assert majority_vote(3, status, (0, 1, 2)) == False # Test the epidemic simulations @@ -47,9 +53,7 @@ def test_discrete_SIR(): gamma = 0.1 tmax = 100 dt = 0.1 - t, S, I, R = contagion.discrete_SIR( - H, tau, gamma, rho=0.1, tmin=0, tmax=tmax, dt=dt - ) + t, S, I, R = discrete_SIR(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax, dt=dt) assert max(t) < tmax + dt assert t[1] - t[0] == dt # checks population conservation over all time @@ -66,7 +70,7 @@ def test_discrete_SIS(): gamma = 0.1 tmax = 100 dt = 0.1 - t, S, I = contagion.discrete_SIS(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax, dt=dt) + t, S, I = discrete_SIS(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax, dt=dt) assert max(t) < tmax + dt assert t[1] - t[0] == dt # checks population conservation over all time @@ -82,7 +86,7 @@ def test_Gillespie_SIR(): tau = {2: 0.1, 3: 0.1} gamma = 0.1 tmax = 100 - t, S, I, R = contagion.Gillespie_SIR(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax) + t, S, I, R = Gillespie_SIR(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax) assert max(t) < tmax # checks population conservation over all time assert np.array_equal(S + I + R, n * np.ones(len(t))) == True @@ -97,7 +101,7 @@ def test_Gillespie_SIS(): tau = {2: 0.1, 3: 0.1} gamma = 0.1 tmax = 100 - t, S, I = contagion.Gillespie_SIS(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax) + t, S, I = Gillespie_SIS(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax) assert max(t) < tmax # checks population conservation over all time assert np.array_equal(S + I, n * np.ones(len(t))) == True diff --git a/hypernetx/algorithms/tests/test_laplacians_clustering.py b/hypernetx/algorithms/tests/test_laplacians_clustering.py index 60e84ca0..c8976bf1 100644 --- a/hypernetx/algorithms/tests/test_laplacians_clustering.py +++ b/hypernetx/algorithms/tests/test_laplacians_clustering.py @@ -9,7 +9,7 @@ def test_prob_trans(fish): - h = fish.hypergraph.convert_to_static() + h = fish.hypergraph P, _ = prob_trans(h) assert P[0, 0] - (4 / 9) < 10e-5 assert P[0, 2] - (1 / 6) < 10e-5 @@ -18,6 +18,6 @@ def test_prob_trans(fish): def test_norm_lap(fish): - h = fish.hypergraph.convert_to_static() + h = fish.hypergraph L, _ = norm_lap(h) assert L.shape == (8, 8) diff --git a/hypernetx/algorithms/tests/test_modularity.py b/hypernetx/algorithms/tests/test_modularity.py index c4f9540a..c689d247 100644 --- a/hypernetx/algorithms/tests/test_modularity.py +++ b/hypernetx/algorithms/tests/test_modularity.py @@ -1,9 +1,5 @@ -import numpy as np -import pytest import warnings from hypernetx.algorithms.hypergraph_modularity import * -import random -import hypernetx as hnx warnings.simplefilter("ignore") @@ -11,9 +7,9 @@ def test_precompute(modularityexample): HG = modularityexample.HG HG = precompute_attributes(HG) - assert HG.nodes['F'].strength == 3 + assert HG.nodes["F"].strength == 3 assert HG.total_weight == 6 - assert HG.edges['e2'].weight == 1 + assert HG.edges[2].weight == 1 def test_modularity(modularityexample): @@ -29,5 +25,5 @@ def test_clustering(modularityexample): HG = modularityexample.HG A1, A2, A3, A4 = modularityexample.partitions HG = precompute_attributes(HG) - assert {'A', 'B', 'C'} in kumar(HG) - assert {'C', 'A', 'B'} in last_step(HG, A4) + assert {"A", "B", "C"} in kumar(HG) + assert {"C", "A", "B"} in last_step(HG, A4) diff --git a/hypernetx/algorithms/tests/test_s_centrality_measures.py b/hypernetx/algorithms/tests/test_s_centrality_measures.py index d9cb2d80..faea9da1 100644 --- a/hypernetx/algorithms/tests/test_s_centrality_measures.py +++ b/hypernetx/algorithms/tests/test_s_centrality_measures.py @@ -44,7 +44,6 @@ def test_s_eccentricity(sixbyfive): s2 = {"e0": 1, "e1": 2, "e2": 2, "e3": 2, "e4": 1} for e in h.edges: assert shcc[e] == s2[e] - shcc = s_eccentricity(h, s=3) - s3 = {"e0": 2, "e1": 0, "e2": 0, "e3": 2, "e4": 1} - for e in h.edges: - assert shcc[e] == s3[e] + with pytest.raises(Exception) as excinfo: + s_eccentricity(h, s=3) + assert "Found infinite path" in str(excinfo.value) diff --git a/hypernetx/classes/__init__.py b/hypernetx/classes/__init__.py index d70dacf5..feccbb40 100644 --- a/hypernetx/classes/__init__.py +++ b/hypernetx/classes/__init__.py @@ -1,3 +1,5 @@ -from .entity import Entity, EntitySet -from .hypergraph import Hypergraph -from .staticentity import StaticEntity, StaticEntitySet +from hypernetx.classes.entity import Entity +from hypernetx.classes.entityset import EntitySet +from hypernetx.classes.hypergraph import Hypergraph + +__all__ = ["Entity", "EntitySet", "Hypergraph"] diff --git a/hypernetx/classes/entity.py b/hypernetx/classes/entity.py index deeaac97..81a80be6 100644 --- a/hypernetx/classes/entity.py +++ b/hypernetx/classes/entity.py @@ -1,615 +1,950 @@ -# Copyright © 2018 Battelle Memorial Institute -# All rights reserved. +from __future__ import annotations -from collections import defaultdict import warnings -from copy import copy +from ast import literal_eval +from collections import OrderedDict, defaultdict +from collections.abc import Hashable, Mapping, Sequence, Iterable +from typing import Union, TypeVar, Optional, Any + import numpy as np -import networkx as nx -from hypernetx import HyperNetXError +import pandas as pd +from scipy.sparse import csr_matrix -__all__ = ["Entity", "EntitySet"] +from hypernetx.classes.helpers import ( + AttrList, + assign_weights, + remove_row_duplicates, + dict_depth, +) +T = TypeVar("T", bound=Union[str, int]) -class Entity(object): - """ - Base class for objects used in building network-like objects including - Hypergraphs, Posets, Cell Complexes. + +class Entity: + """Base class for handling N-dimensional data when building network-like models, + i.e., :class:`Hypergraph` Parameters ---------- - uid : hashable - a unique identifier - - elements : list or dict, optional, default: None - a list of entities with identifiers different than uid and/or - hashables different than uid, see `Honor System`_ - - entity : Entity - an Entity object to be cloned into a new Entity with uid. If the uid is the same as - Entity.uid then the entities will not be distinguishable and error will be raised. - The `elements` in the signature will be added to the cloned entity. - - weight : float, optional, default : 1 - props : keyword arguments, optional, default: {} - properties belonging to the entity added as key=value pairs. - Both key and value must be hashable. + entity : pandas.DataFrame, dict of lists or sets, list of lists or sets, optional + If a ``DataFrame`` with N columns, + represents N-dimensional entity data (data table). + Otherwise, represents 2-dimensional entity data (system of sets). + TODO: Test for compatibility with list of Entities and update docs + data : numpy.ndarray, optional + 2D M x N ``ndarray`` of ``ints`` (data table); + sparse representation of an N-dimensional incidence tensor with M nonzero cells. + Ignored if `entity` is provided. + static : bool, default=True + If ``True``, entity data may not be altered, + and the :attr:`state_dict <_state_dict>` will never be cleared. + Otherwise, rows may be added to and removed from the data table, + and updates will clear the :attr:`state_dict <_state_dict>`. + labels : collections.OrderedDict of lists, optional + User-specified labels in corresponding order to ``ints`` in `data`. + Ignored if `entity` is provided or `data` is not provided. + uid : hashable, optional + A unique identifier for the object + weights : str or sequence of float, optional + User-specified cell weights corresponding to entity data. + If sequence of ``floats`` and `entity` or `data` defines a data table, + length must equal the number of rows. + If sequence of ``floats`` and `entity` defines a system of sets, + length must equal the total sum of the sizes of all sets. + If ``str`` and `entity` is a ``DataFrame``, + must be the name of a column in `entity`. + Otherwise, weight for all cells is assumed to be 1. + aggregateby : {'sum', 'last', count', 'mean','median', max', 'min', 'first', None} + Name of function to use for aggregating cell weights of duplicate rows when + `entity` or `data` defines a data table, default is "sum". + If None, duplicate rows will be dropped without aggregating cell weights. + Effectively ignored if `entity` defines a system of sets. + properties : pandas.DataFrame or doubly-nested dict, optional + User-specified properties to be assigned to individual items in the data, i.e., + cell entries in a data table; sets or set elements in a system of sets. + See Notes for detailed explanation. + If ``DataFrame``, each row gives + ``[optional item level, item label, optional named properties, + {property name: property value}]`` + (order of columns does not matter; see note for an example). + If doubly-nested dict, + ``{item level: {item label: {property name: property value}}}``. + misc_props_col, level_col, id_col : str, default="properties", "level, "id" + Column names for miscellaneous properties, level index, and item name in + :attr:`properties`; see Notes for explanation. Notes ----- - - An Entity is a container-like object, which has a unique identifier and - may contain elements and have properties. - The Entity class was created as a generic object providing structure for - Hypergraph nodes and edges. - - - An Entity is distinguished by its identifier (sortable,hashable) :func:`Entity.uid` - - An Entity is a container for other entities but may not contain itself, :func:`Entity.elements` - - An Entity has properties :func:`Entity.properties` - - An Entity has memberships to other entities, :func:`Entity.memberships`. - - An Entity has children, :func:`Entity.children`, which are the elements of its elements. - - :func:`Entity.children` are registered in the :func:`Entity.registry`. - - All descendents of Entity are registered in :func:`Entity.fullregistry()`. - - .. _Honor System: - - **Honor System** - - HyperNetX has an Honor System that applies to Entity uid values. - Two entities are equal if their __dict__ objects match. - For performance reasons many methods distinguish entities by their uids. - It is, therefore, up to the user to ensure entities with the same uids are indeed the same. - Not doing so may cause undesirable side effects. - In particular, the methods in the Hypergraph class assume distinct nodes and edges - have distinct uids. - - Examples - -------- - - >>> x = Entity('x') - >>> y = Entity('y',[x]) - >>> z = Entity('z',[x,y],weight=1) - >>> z - Entity(z,['y', 'x'],{'weight': 1}) - >>> z.uid - 'z' - >>> z.elements - {'x': Entity(x,[],{}), 'y': Entity(y,['x'],{})} - >>> z.properties - {'weight': 1} - >>> z.children - {'x'} - >>> x.memberships - {'y': Entity(y,['x'],{}), 'z': Entity(z,['y', 'x'],{'weight': 1})} - >>> z.fullregistry() - {'x': Entity(x,[],{}), 'y': Entity(y,['x'],{})} - - - See Also - -------- - EntitySet + A property is a named attribute assigned to a single item in the data. + + You can pass a **table of properties** to `properties` as a ``DataFrame``: + + +------------+---------+----------------+-------+------------------+ + | Level | ID | [explicit | [...] | misc. properties | + | (optional) | | property type] | | | + +============+=========+================+=======+==================+ + | 0 | level 0 | property value | ... | {property name: | + | | item | | | property value} | + +------------+---------+----------------+-------+------------------+ + | 1 | level 1 | property value | ... | {property name: | + | | item | | | property value} | + +------------+---------+----------------+-------+------------------+ + | ... | ... | ... | ... | ... | + +------------+---------+----------------+-------+------------------+ + | N | level N | property value | ... | {property name: | + | | item | | | property value} | + +------------+---------+----------------+-------+------------------+ + + The Level column is optional. If not provided, properties will be assigned by ID + (i.e., if an ID appears at multiple levels, the same properties will be assigned to + all occurrences). + + The names of the Level (if provided) and ID columns must be specified by `level_col` + and `id_col`. `misc_props_col` can be used to specify the name of the column to be used + for miscellaneous properties; if no column by that name is found, + a new column will be created and populated with empty ``dicts``. + All other columns will be considered explicit property types. + The order of the columns does not matter. + + This method assumes that there are no rows with the same (Level, ID); + if duplicates are found, all but the first occurrence will be dropped. """ - def __init__(self, uid, elements=[], entity=None, weight=1.0, **props): - super().__init__() - - self._uid = uid - self.weight = weight + def __init__( + self, + entity: Optional[ + pd.DataFrame | Mapping[T, Iterable[T]] | Iterable[Iterable[T]] + ] = None, + data_cols: Sequence[T] = [0, 1], + data: Optional[np.ndarray] = None, + static: bool = False, + labels: Optional[OrderedDict[T, Sequence[T]]] = None, + uid: Optional[Hashable] = None, + weight_col: Optional[str | int] = "cell_weights", + weights: Optional[Sequence[float] | float | int | str] = 1, + aggregateby: Optional[str | dict] = "sum", + properties: Optional[pd.DataFrame | dict[int, dict[T, dict[Any, Any]]]] = None, + misc_props_col: str = "properties", + level_col: str = "level", + id_col: str = "id", + ): + # set unique identifier + self._uid = uid or None + + # if static, the original data cannot be altered + # the state dict stores all computed values that may need to be updated + # if the data is altered - the dict will be cleared when data is added + # or removed + self._static = static + self._state_dict = {} + + # entity data is stored in a DataFrame for basic access without the + # need for any label encoding lookups + if isinstance(entity, pd.DataFrame): + self._dataframe = entity.copy() + + # if the entity data is passed as a dict of lists or a list of lists, + # we convert it to a 2-column dataframe by exploding each list to cover + # one row per element for a dict of lists, the first level/column will + # be filled in with dict keys for a list of N lists, 0,1,...,N will be + # used to fill the first level/column + elif isinstance(entity, (dict, list)): + # convert dict of lists to 2-column dataframe + entity = pd.Series(entity).explode() + self._dataframe = pd.DataFrame( + {data_cols[0]: entity.index.to_list(), data_cols[1]: entity.values} + ) - if entity is not None: - if isinstance(entity, Entity): - if uid == entity.uid: - raise HyperNetXError( - "The new entity will be indistinguishable from the original with the same uid. Use a differen uid." + # if a 2d numpy ndarray is passed, store it as both a DataFrame and an + # ndarray in the state dict + elif isinstance(data, np.ndarray) and data.ndim == 2: + self._state_dict["data"] = data + self._dataframe = pd.DataFrame(data) + # if a dict of labels was passed, use keys as column names in the + # DataFrame, translate the dataframe, and store the dict of labels + # in the state dict + if isinstance(labels, dict) and len(labels) == len(self._dataframe.columns): + self._dataframe.columns = labels.keys() + self._state_dict["labels"] = labels + + for col in self._dataframe: + self._dataframe[col] = pd.Categorical.from_codes( + self._dataframe[col], categories=labels[col] ) - self._elements = entity.elements - self._memberships = entity.memberships - self.__dict__.update(entity.properties) + + # create an empty Entity else: - self._elements = dict() - self._memberships = dict() - self._registry = dict() - - self.__dict__.update(props) - self._registry = self.registry - - if isinstance(elements, dict): - for k, v in elements.items(): - if isinstance(v, Entity): - self.add_element(v) - else: - self.add_element(Entity(k, v)) - elif elements is not None: - self.add(*elements) + self._dataframe = pd.DataFrame() + + # assign a new or existing column of the dataframe to hold cell weights + self._dataframe, self._cell_weight_col = assign_weights( + self._dataframe, weights=weights, weight_col=weight_col + ) + # import ipdb; ipdb.set_trace() + # store a list of columns that hold entity data (not properties or + # weights) + # self._data_cols = list(self._dataframe.columns.drop(self._cell_weight_col)) + self._data_cols = [] + for col in data_cols: + # TODO: default arguments fail for empty Entity; data_cols has two elements but _dataframe has only one element + if isinstance(col, int): + self._data_cols.append(self._dataframe.columns[col]) + else: + self._data_cols.append(col) + + # each entity data column represents one dimension of the data + # (data updates can only add or remove rows, so this isn't stored in + # state dict) + self._dimsize = len(self._data_cols) + + # remove duplicate rows and aggregate cell weights as needed + # import ipdb; ipdb.set_trace() + self._dataframe, _ = remove_row_duplicates( + self._dataframe, + self._data_cols, + weight_col=self._cell_weight_col, + aggregateby=aggregateby, + ) + + # set the dtype of entity data columns to categorical (simplifies + # encoding, etc.) + ### This is automatically done in remove_row_duplicates + # self._dataframe[self._data_cols] = self._dataframe[self._data_cols].astype( + # "category" + # ) + + # create properties + item_levels = [ + (level, item) + for level, col in enumerate(self._data_cols) + for item in self.dataframe[col].cat.categories + ] + index = pd.MultiIndex.from_tuples(item_levels, names=[level_col, id_col]) + data = [(i, 1, {}) for i in range(len(index))] + self._properties = pd.DataFrame( + data=data, index=index, columns=["uid", "weight", misc_props_col] + ).sort_index() + self._misc_props_col = misc_props_col + if properties is not None: + self.assign_properties(properties) @property - def properties(self): - """Dictionary of properties of entity""" - temp = self.__dict__.copy() - del temp["_elements"] - del temp["_memberships"] - del temp["_registry"] - del temp["_uid"] - return temp + def data(self): + """Sparse representation of the data table as an incidence tensor - @property - def uid(self): - """String identifier for entity""" - return self._uid + This can also be thought of as an encoding of `dataframe`, where items in each column of + the data table are translated to their int position in the `self.labels[column]` list + Returns + ------- + numpy.ndarray + 2D array of ints representing rows of the underlying data table as indices in an incidence tensor + + See Also + -------- + labels, dataframe - @property - def elements(self): - """ - Dictionary of elements belonging to entity. """ - return dict(self._elements) + # generate if not already stored in state dict + if "data" not in self._state_dict: + if self.empty: + self._state_dict["data"] = np.zeros((0, 0), dtype=int) + else: + # assumes dtype of data cols is already converted to categorical + # and state dict has been properly cleared after updates + self._state_dict["data"] = ( + self._dataframe[self._data_cols] + .apply(lambda x: x.cat.codes) + .to_numpy() + ) + + return self._state_dict["data"] @property - def memberships(self): - """ - Dictionary of elements to which entity belongs. + def labels(self): + """Labels of all items in each column of the underlying data table - This assignment is done on construction and controlled by - :func:`Entity.add_element()` - and :func:`Entity.remove_element()` methods. - """ + Returns + ------- + dict of lists + dict of {column name: [item labels]} + The order of [item labels] corresponds to the int encoding of each item in `self.data`. - return { - k: self._memberships[k] - for k in self._memberships - if not isinstance(self._memberships[k], EntitySet) - } + See Also + -------- + data, dataframe + """ + # generate if not already stored in state dict + if "labels" not in self._state_dict: + # assumes dtype of data cols is already converted to categorical + # and state dict has been properly cleared after updates + self._state_dict["labels"] = { + col: self._dataframe[col].cat.categories.to_list() + for col in self._data_cols + } + + return self._state_dict["labels"] @property - def children(self): - """ - Set of uids of the elements of elements of entity. + def cell_weights(self): + """Cell weights corresponding to each row of the underlying data table - To return set of ids for deeper level use: - :code:`Entity.levelset(level).keys()` - see: :func:`Entity.levelset` + Returns + ------- + dict of {tuple: int or float} + Keyed by row of data table (as a tuple) """ - return set(self.levelset(2).keys()) + # generate if not already stored in state dict + if "cell_weights" not in self._state_dict: + if self.empty: + self._state_dict["cell_weights"] = {} + else: + self._state_dict["cell_weights"] = self._dataframe.set_index( + self._data_cols + )[self._cell_weight_col].to_dict() + + return self._state_dict["cell_weights"] @property - def registry(self): - """ - Dictionary of uid:Entity pairs for children entity. + def dimensions(self): + """Dimensions of data i.e., the number of distinct items in each level (column) of the underlying data table - To return a dictionary of all entities at all depths - :func:`Entity.complete_registry()` + Returns + ------- + tuple of ints + Length and order corresponds to columns of `self.dataframe` (excluding cell weight column) """ - return self.levelset(2) + # generate if not already stored in state dict + if "dimensions" not in self._state_dict: + if self.empty: + self._state_dict["dimensions"] = tuple() + else: + self._state_dict["dimensions"] = tuple( + self._dataframe[self._data_cols].nunique() + ) + + return self._state_dict["dimensions"] @property - def uidset(self): - """ - Set of uids of elements of entity. + def dimsize(self): + """Number of levels (columns) in the underlying data table + + Returns + ------- + int + Equal to length of `self.dimensions` """ - return frozenset(self._elements.keys()) + return self._dimsize @property - def incidence_dict(self): - """ - Dictionary of element.uid:element.uidset for each element in entity + def properties(self) -> pd.DataFrame: + # Dev Note: Not sure what this contains, when running tests it contained an empty pandas series + """Properties assigned to items in the underlying data table - To return an incidence dictionary of all nested entities in entity - use nested_incidence_dict + Returns + ------- + pandas.DataFrame """ - temp = dict() - for ent in self.elements.values(): - temp[ent.uid] = {item for item in ent.elements} - return temp - @property - def is_empty(self): - """Boolean indicating if entity.elements is empty""" - return len(self) == 0 + return self._properties @property - def is_bipartite(self): - """ - Returns boolean indicating if the entity satisfies the `Bipartite Condition`_ + def uid(self): + # Dev Note: This also returned nothing in my harry potter dataset, not sure if it was supposed to contain anything + """User-defined unique identifier for the `Entity` + + Returns + ------- + hashable """ - if self.uidset.isdisjoint(self.children): - return True - else: - return False + return self._uid + + @property + def uidset(self): + """Labels of all items in level 0 (first column) of the underlying data table - def __eq__(self, other): + Returns + ------- + frozenset + + See Also + -------- + children : Labels of all items in level 1 (second column) + uidset_by_level, uidset_by_column : + Labels of all items in any level (column); specified by level index or column name """ - Defines equality for Entities based on equivalence of their __dict__ objects. + return self.uidset_by_level(0) - Checks all levels of self and other to verify they are - referencing the same uids and that they have the same set of properties. - If at any point we get duplicate addresses we stop checking that branch - because we are guaranteed equality from there on. + @property + def children(self): + """Labels of all items in level 1 (second column) of the underlying data table + + Returns + ------- + frozenset - May cause a recursion error if depth is too great. + See Also + -------- + uidset : Labels of all items in level 0 (first column) + uidset_by_level, uidset_by_column : + Labels of all items in any level (column); specified by level index or column name """ - seen = set() - # Define a compare method to call recursively on each level of self and other - - def _comp(a, b, seen): - # Compare top level properties: same class? same ids? same children? same parents? same attributes? - if ( - (a.__class__ != b.__class__) - or (a.uid != b.uid) - or (a.uidset != b.uidset) - or (a.properties != b.properties) - or (a.memberships != b.memberships) - ): - return False - # If all agree then look at the next level down since a and b share uidsets. - for uid, elt in a.elements.items(): - if isinstance(elt, Entity): - if uid in seen: - continue - seen.add(uid) - if not _comp(elt, b[uid], seen): - return False - # if not an Entity then elt is hashable so we usual equality - elif elt != b[uid]: - return False - return True - - return _comp(self, other, seen) + return self.uidset_by_level(1) - def __len__(self): - """Returns the number of elements in entity""" - return len(self._elements) + def uidset_by_level(self, level): + """Labels of all items in a particular level (column) of the underlying data table - def __str__(self): - """Return the entity uid.""" - return f"{self.uid}" + Parameters + ---------- + level : int - def __repr__(self): - """Returns a string resembling the constructor for entity without any - children""" - return f"Entity({self._uid},{list(self.uidset)},{self.properties})" + Returns + ------- + frozenset - def __contains__(self, item): + See Also + -------- + uidset : Labels of all items in level 0 (first column) + children : Labels of all items in level 1 (second column) + uidset_by_column : Same functionality, takes the column name instead of level index """ - Defines containment for Entities. + if self.is_empty(level): + return {} + col = self._data_cols[level] + return self.uidset_by_column(col) + + def uidset_by_column(self, column): + # Dev Note: This threw an error when trying it on the harry potter dataset, + # when trying 0, or 1 for column. I'm not sure how this should be used + """Labels of all items in a particular column (level) of the underlying data table Parameters ---------- - item : hashable or Entity + column : Hashable + Name of a column in `self.dataframe` Returns ------- - Boolean + frozenset + + See Also + -------- + uidset : Labels of all items in level 0 (first column) + children : Labels of all items in level 1 (second column) + uidset_by_level : Same functionality, takes the level index instead of column name + """ + # generate if not already stored in state dict + if "uidset" not in self._state_dict: + self._state_dict["uidset"] = {} + if column not in self._state_dict["uidset"]: + self._state_dict["uidset"][column] = set( + self._dataframe[column].dropna().unique() + ) + + return self._state_dict["uidset"][column] + + @property + def elements(self): + """System of sets representation of the first two levels (columns) of the underlying data table + + Each item in level 0 (first column) defines a set containing all the level 1 + (second column) items with which it appears in the same row of the underlying + data table + + Returns + ------- + dict of `AttrList` + System of sets representation as dict of {level 0 item : AttrList(level 1 items)} + + See Also + -------- + incidence_dict : same data as dict of list + memberships : + dual of this representation, + i.e., each item in level 1 (second column) defines a set + elements_by_level, elements_by_column : + system of sets representation of any two levels (columns); specified by level index or column name - Depends on the `Honor System`_ . Allows for uids to be used as shorthand for their entity. - This is done for performance reasons, but will fail if uids are - not unique to their entities. - Is not transitive. """ - if isinstance(item, Entity): - return item.uid in self._elements - else: - return item in self._elements + if self._dimsize < 2: + return {k: AttrList(entity=self, key=(0, k)) for k in self.uidset} + + return self.elements_by_level(0, 1) + + @property + def incidence_dict(self) -> dict[T, list[T]]: + """System of sets representation of the first two levels (columns) of the underlying data table + + Returns + ------- + dict of list + System of sets representation as dict of {level 0 item : AttrList(level 1 items)} + + See Also + -------- + elements : same data as dict of AttrList - def __getitem__(self, item): """ - Returns Entity element by uid. Use :func:`E[uid]`. + return {item: elements.data for item, elements in self.elements.items()} - Parameters - ---------- - item : hashable or Entity + @property + def memberships(self): + """System of sets representation of the first two levels (columns) of the + underlying data table + + Each item in level 1 (second column) defines a set containing all the level 0 + (first column) items with which it appears in the same row of the underlying + data table Returns ------- - Entity or None + dict of `AttrList` + System of sets representation as dict of {level 1 item : AttrList(level 0 items)} + + See Also + -------- + elements : dual of this representation i.e., each item in level 0 (first column) defines a set + elements_by_level, elements_by_column : + system of sets representation of any two levels (columns); specified by level index or column name - If item not in entity, returns None. """ - if isinstance(item, Entity): - return self._elements.get(item.uid, "") - else: - return self._elements.get(item) - def __iter__(self): - """Returns iterator on element ids.""" - return iter(self.elements) + return self.elements_by_level(1, 0) + + def elements_by_level(self, level1, level2): + """System of sets representation of two levels (columns) of the underlying data table - def __call__(self): - """Returns an iterator on elements""" - for e in self.elements.values(): - yield e + Each item in level1 defines a set containing all the level2 items + with which it appears in the same row of the underlying data table - def __setattr__(self, k, v): - """Sets entity property. + Properties can be accessed and assigned to items in level1 Parameters ---------- - k : hashable, property key - v : hashable, property value - Will not set uid or change elements or memberships. + level1 : int + index of level whose items define sets + level2 : int + index of level whose items are elements in the system of sets Returns ------- - None + dict of `AttrList` + System of sets representation as dict of {level1 item : AttrList(level2 items)} - """ - if k == "uid": - raise HyperNetXError( - "Cannot reassign uid to Entity once it" - " has been created. Create a clone instead." - ) - elif k == "elements": - raise HyperNetXError("To add elements to Entity use self.add().") - elif k == "memberships": - raise HyperNetXError( - "Can't choose your own memberships, " "they are like parents!" - ) - else: - self.__dict__[k] = v + See Also + -------- + elements, memberships : dual system of sets representations of the first two levels (columns) + elements_by_column : same functionality, takes column names instead of level indices - def _depth_finder(self, entset=None): """ - Helper method when working with levels. + col1 = self._data_cols[level1] + col2 = self._data_cols[level2] + return self.elements_by_column(col1, col2) + + def elements_by_column(self, col1, col2): + + """System of sets representation of two columns (levels) of the underlying data table + + Each item in col1 defines a set containing all the col2 items + with which it appears in the same row of the underlying data table + + Properties can be accessed and assigned to items in col1 Parameters ---------- - entset : dict, optional - a dictionary of entities keyed by uid + col1 : Hashable + name of column whose items define sets + col2 : Hashable + name of column whose items are elements in the system of sets Returns ------- - Dictionary extending entset + dict of `AttrList` + System of sets representation as dict of {col1 item : AttrList(col2 items)} + + See Also + -------- + elements, memberships : dual system of sets representations of the first two columns (levels) + elements_by_level : same functionality, takes level indices instead of column names + + """ + if "elements" not in self._state_dict: + self._state_dict["elements"] = defaultdict(dict) + if col2 not in self._state_dict["elements"][col1]: + level = self.index(col1) + elements = self._dataframe.groupby(col1)[col2].unique().to_dict() + self._state_dict["elements"][col1][col2] = { + item: AttrList(entity=self, key=(level, item), initlist=elem) + for item, elem in elements.items() + } + + return self._state_dict["elements"][col1][col2] + + @property + def dataframe(self): + """The underlying data table stored by the Entity + + Returns + ------- + pandas.DataFrame """ - temp = dict() - for uid, item in entset.items(): - temp.update(item.elements) - return temp + return self._dataframe - def level(self, item, max_depth=10): + @property + def isstatic(self): + # Dev Note: I'm guessing this is no longer necessary? + """Whether to treat the underlying data as static or not + + If True, the underlying data may not be altered, and the state_dict will never be cleared + Otherwise, rows may be added to and removed from the data table, and updates will clear the state_dict + + Returns + ------- + bool """ - The first level where item appears in self. + return self._static + + def size(self, level=0): + """The number of items in a level of the underlying data table + + Equivalent to ``self.dimensions[level]`` Parameters ---------- - item : hashable - uid for an entity - - max_depth : int, default: 10 - last level to check for entity + level : int, default=0 Returns ------- - level : int + int - Note - ---- - Item must be the uid of an entity listed - in :func:`fullregistry()` + See Also + -------- + dimensions """ - d = 1 - currentlevel = self.levelset(1) - while d <= max_depth + 1: - if item in currentlevel: - return d - else: - d += 1 - currentlevel = self._depth_finder(currentlevel) - return None + # TODO: Since `level` is not validated, we assume that self.dimensions should be an array large enough to access index `level` + return self.dimensions[level] - def levelset(self, k=1): + @property + def empty(self): + """Whether the underlying data table is empty or not + + Returns + ------- + bool + + See Also + -------- + is_empty : for checking whether a specified level (column) is empty + dimsize : 0 if empty """ - A dictionary of level k of self. + return self._dimsize == 0 - Parameters - ---------- - k : int, optional, default: 1 + def is_empty(self, level=0): + """Whether a specified level (column) of the underlying data table is empty or not Returns ------- - levelset : dict + bool - Note - ---- - An Entity contains other entities, hence the relationships between entities - and their elements may be represented in a directed graph with entity as root. - The levelsets are sets of entities which make up the elements appearing at - a certain level. + See Also + -------- + empty : for checking whether the underlying data table is empty + size : number of items in a level (columns); 0 if level is empty """ - if k <= 0: - return None - currentlevel = self.elements - if k > 1: - for idx in range(k - 1): - currentlevel = self._depth_finder(currentlevel) - return currentlevel + return self.empty or self.size(level) == 0 + + def __len__(self): + """Number of items in level 0 (first column) - def depth(self, max_depth=10): + Returns + ------- + int """ - Returns the number of nonempty level sets of level <= max_depth + return self.dimensions[0] + + def __contains__(self, item): + """Whether an item is contained within any level of the data Parameters ---------- - max_depth : int, optional, default: 10 - If full depth is desired set max_depth to number of entities in - system + 1. + item : str Returns ------- - depth : int - If max_depth is exceeded output will be numpy infinity. - If there is a cycle output will be numpy infinity. - + bool """ - if max_depth < 0: - return 0 - currentlevel = self.elements - if not currentlevel: - return 0 - else: - depth = 1 - while depth < max_depth + 1: - currentlevel = self._depth_finder(currentlevel) - if not currentlevel: - return depth - depth += 1 - return np.inf - - def fullregistry(self, lastlevel=10, firstlevel=1): - """ - A dictionary of all entities appearing in levels firstlevel - to lastlevel. + for labels in self.labels.values(): + if item in labels: + return True + return False + + def __getitem__(self, item): + """Access into the system of sets representation of the first two levels (columns) given by `elements` + + Can be used to access and assign properties to an ``item`` in level 0 (first column) Parameters ---------- - lastlevel : int, optional, default: 10 - - firstlevel : int, optional, default: 1 + item : str + label of an item in level 0 (first column) Returns ------- - fullregistry : dict + AttrList : + list of level 1 items in the set defined by ``item`` + See Also + -------- + uidset, elements """ - currentlevel = self.levelset(firstlevel) - accumulater = dict(currentlevel) - for idx in range(firstlevel, lastlevel): - currentlevel = self._depth_finder(currentlevel) - accumulater.update(currentlevel) - return accumulater - - def complete_registry(self): - """ - A dictionary of all entities appearing in any level of - entity + return self.elements[item] + + def __iter__(self): + """Iterates over items in level 0 (first column) of the underlying data table Returns ------- - complete_registry : dict - """ - results = dict() - Entity._complete_registry(self, results) - return results + Iterator - @staticmethod - def _complete_registry(entity, results): - """ - Helper method for complete_registry + See Also + -------- + uidset, elements """ - for uid, e in entity.elements.items(): - if uid not in results: - results[uid] = e - Entity._complete_registry(e, results) + return iter(self.elements) - def nested_incidence_dict(self, level=10): - """ - Returns a nested dictionary with keys up to level + def __call__(self, label_index=0): + # Dev Note (Madelyn) : I don't think this is the intended use of __call__, can we change/deprecate? + """Iterates over items labels in a specified level (column) of the underlying data table Parameters ---------- - level : int, optional, default: 10 - If level<=1, returns the incidence_dict. + label_index : int + level index Returns ------- - nested_incidence_dict : dict + Iterator + See Also + -------- + labels """ - if level > 1: - return {ent.uid: ent.nested_incidence_dict(level - 1) for ent in self()} - else: - return self.incidence_dict + return iter(self.labels[self._data_cols[label_index]]) - def size(self): - """ - Returns the number of elements in entity - """ - return len(self) + # def __repr__(self): + # """String representation of the Entity - def clone(self, newuid): - """ - Returns shallow copy of entity with newuid. Entity's elements will - belong to two distinct Entities. + # e.g., "Entity(uid, [level 0 items], {item: {property name: property value}})" + + # Returns + # ------- + # str + # """ + # return "hypernetx.classes.entity.Entity" + + # def __str__(self): + # return "" + + def index(self, column, value=None): + """Get level index corresponding to a column and (optionally) the index of a value in that column + + The index of ``value`` is its position in the list given by ``self.labels[column]``, which is used + in the integer encoding of the data table ``self.data`` Parameters ---------- - newuid : hashable - Name of the new entity + column: str + name of a column in self.dataframe + value : str, optional + label of an item in the specified column Returns ------- - clone : Entity + int or (int, int) + level index corresponding to column, index of value if provided - """ - return Entity(newuid, entity=self) + See Also + -------- + indices : for finding indices of multiple values in a column + level : same functionality, search for the value without specifying column + """ + if "keyindex" not in self._state_dict: + self._state_dict["keyindex"] = {} + if column not in self._state_dict["keyindex"]: + self._state_dict["keyindex"][column] = self._dataframe[ + self._data_cols + ].columns.get_loc(column) + + if value is None: + return self._state_dict["keyindex"][column] + + if "index" not in self._state_dict: + self._state_dict["index"] = defaultdict(dict) + if value not in self._state_dict["index"][column]: + self._state_dict["index"][column][value] = self._dataframe[ + column + ].cat.categories.get_loc(value) + + return ( + self._state_dict["keyindex"][column], + self._state_dict["index"][column][value], + ) + + def indices(self, column, values): + """Get indices of one or more value(s) in a column + + Parameters + ---------- + column : str + values : str or iterable of str - def intersection(self, other): + Returns + ------- + list of int + indices of values + + See Also + -------- + index : for finding level index of a column and index of a single value """ - A dictionary of elements belonging to entity and other. + if isinstance(values, Hashable): + values = [values] + + if "index" not in self._state_dict: + self._state_dict["index"] = defaultdict(dict) + for v in values: + if v not in self._state_dict["index"][column]: + self._state_dict["index"][column][v] = self._dataframe[ + column + ].cat.categories.get_loc(v) + + return [self._state_dict["index"][column][v] for v in values] + + def translate(self, level, index): + """Given indices of a level and value(s), return the corresponding value label(s) Parameters ---------- - other : Entity + level : int + level index + index : int or list of int + value index or indices Returns ------- - Dictionary of elements : dict + str or list of str + label(s) corresponding to value index or indices + See Also + -------- + translate_arr : translate a full row of value indices across all levels (columns) """ - return {e: self[e] for e in self if e in other} + column = self._data_cols[level] - def restrict_to(self, element_subset, name=None): - """ - Shallow copy of entity removing elements not in element_subset. + if isinstance(index, (int, np.integer)): + return self.labels[column][index] + + return [self.labels[column][i] for i in index] + + def translate_arr(self, coords): + """Translate a full encoded row of the data table e.g., a row of ``self.data`` Parameters ---------- - element_subset : iterable - A subset of entities elements + coords : tuple of ints + encoded value indices, with one value index for each level of the data + + Returns + ------- + list of str + full row of translated value labels + """ + assert len(coords) == self._dimsize + translation = [] + for level, index in enumerate(coords): + translation.append(self.translate(level, index)) - name: hashable, optional - If not given, a name is generated to reflect entity uid + return translation + + def level(self, item, min_level=0, max_level=None, return_index=True): + """First level containing the given item label + + Order of levels corresponds to order of columns in `self.dataframe` + + Parameters + ---------- + item : str + min_level, max_level : int, optional + inclusive bounds on range of levels to search for item + return_index : bool, default=True + If True, return index of item within the level Returns ------- - New Entity : Entity - Could be empty. + int, (int, int), or None + index of first level containing the item, index of item if `return_index=True` + returns None if item is not found + See Also + -------- + index, indices : for finding level and/or value indices when the column is known """ - newelements = [self[e] for e in element_subset if e in self] - name = name or f"{self.uid}_r" - return Entity(name, newelements, **self.properties) + if max_level is None or max_level >= self._dimsize: + max_level = self._dimsize - 1 + + columns = self._data_cols[min_level : max_level + 1] + levels = range(min_level, max_level + 1) + + for col, lev in zip(columns, levels): + if item in self.labels[col]: + if return_index: + return self.index(col, item) + + return lev + + print(f'"{item}" not found.') + return None def add(self, *args): - """ - Adds unpacked args to entity elements. Depends on add_element() + """Updates the underlying data table with new entity data from multiple sources Parameters ---------- - args : One or more entities or hashables + *args + variable length argument list of Entity and/or representations of entity data Returns ------- self : Entity - Note - ---- - Adding an element to an object in a hypergraph will not add the - element to the hypergraph and will cause an error. Use :func:`Hypergraph.add_edge ` - or :func:`Hypergraph.add_node_to_edge ` instead. + Warnings + -------- + Adding an element directly to an Entity will not add the + element to any Hypergraphs constructed from that Entity, and will cause an error. Use + :func:`Hypergraph.add_edge ` or + :func:`Hypergraph.add_node_to_edge ` instead. + + See Also + -------- + add_element : update from a single source + Hypergraph.add_edge, Hypergraph.add_node_to_edge : for adding elements to a Hypergraph """ for item in args: self.add_element(item) - return self def add_elements_from(self, arg_set): - """ - Similar to :func:`add()` it allows for adding from an interable. + """Adds arguments from an iterable to the data table one at a time + + ..deprecated:: 2.0.0 + Duplicates `add` Parameters ---------- - arg_set : Iterable of hashables or entities + arg_set : iterable + list of Entity and/or representations of entity data Returns ------- @@ -618,100 +953,107 @@ def add_elements_from(self, arg_set): """ for item in arg_set: self.add_element(item) - return self - def add_element(self, item): - """ - Adds item to entity elements and adds entity to item.memberships. + def add_element(self, data): + """Updates the underlying data table with new entity data + + Supports adding from either an existing Entity or a representation of entity + (data table or labeled system of sets are both supported representations) Parameters ---------- - item : hashable or Entity - If hashable, will be replaced with empty Entity using hashable as uid + data : Entity, `pandas.DataFrame`, or dict of lists or sets + new entity data Returns ------- self : Entity - Notes - ----- - If item is in entity elements, no new element is added but properties - will be updated. - If item is in complete_registry(), only the item already known to self will be added. - This method employs the `Honor System`_ since membership in complete_registry is checked - using the item's uid. It is assumed that the user will only use the same uid - for identical instances within the entities registry. + Warnings + -------- + Adding an element directly to an Entity will not add the + element to any Hypergraphs constructed from that Entity, and will cause an error. Use + `Hypergraph.add_edge` or `Hypergraph.add_node_to_edge` instead. + + See Also + -------- + add : takes multiple sources of new entity data as variable length argument list + Hypergraph.add_edge, Hypergraph.add_node_to_edge : for adding elements to a Hypergraph """ - checkelts = self.complete_registry() - if isinstance(item, Entity): - # if item is an Entity, descendents will be compared to avoid collisions - if item.uid == self.uid: - raise HyperNetXError( - f"Error: Self reference in submitted elements." - f" Entity {self.uid} may not contain itself. " - ) - elif item in self: - # item is already an element so only the properties will be updated - checkelts[item.uid].__dict__.update(item.properties) - elif item.uid in checkelts: - # if item belongs to an element or a descendent of an element - # then the existing descendent becomes an element - # and properties are updated. - checkelts[item.uid]._memberships[self.uid] = self - checkelts[item.uid].__dict__.update(item.properties) - self._elements[item.uid] = checkelts[item.uid] - else: - # if item's uid doesn't appear in complete_registry - # then it is added as something new - item._memberships[self.uid] = self - self._elements[item.uid] = item - else: - # item must be a hashable. - # if it appears as a uid in checkelts then - # the corresponding Entity will become an element of entity. - # Otherwise, at most it will be added as an empty Entity. - if self.uid == item: - raise HyperNetXError( - f"Error: Self reference in submitted elements." - f" Entity {self.uid} may not contain itself." - ) - elif item not in self._elements: - if item in checkelts: - self._elements[item] = checkelts[item] - checkelts[item]._memberships[self.uid] = self - else: - self._elements[item] = Entity(item, _memberships={self.uid: self}) + if isinstance(data, Entity): + df = data.dataframe + self.__add_from_dataframe(df) + + if isinstance(data, dict): + df = pd.DataFrame.from_dict(data) + self.__add_from_dataframe(df) + + if isinstance(data, pd.DataFrame): + self.__add_from_dataframe(data) return self - def remove(self, *args): + def __add_from_dataframe(self, df): + """Helper function to append rows to `self.dataframe` + + Parameters + ---------- + data : pd.DataFrame + + Returns + ------- + self : Entity + """ - Removes args from entitie's elements if they belong. - Does nothing with args not in entity. + if all(col in df for col in self._data_cols): + new_data = pd.concat((self._dataframe, df), ignore_index=True) + new_data[self._cell_weight_col] = new_data[self._cell_weight_col].fillna(1) + + self._dataframe, _ = remove_row_duplicates( + new_data, + self._data_cols, + weights=self._cell_weight_col, + ) + + self._dataframe[self._data_cols] = self._dataframe[self._data_cols].astype( + "category" + ) + + self._state_dict.clear() + + def remove(self, *args): + """Removes all rows containing specified item(s) from the underlying data table Parameters ---------- - args : One or more hashables or entities + *args + variable length argument list of item labels Returns ------- self : Entity + See Also + -------- + remove_element : remove all rows containing a single specified item """ for item in args: - Entity.remove_element(self, item) + self.remove_element(item) return self def remove_elements_from(self, arg_set): - """ - Similar to :func:`remove()`. Removes elements in arg_set. + """Removes all rows containing specified item(s) from the underlying data table + + ..deprecated: 2.0.0 + Duplicates `remove` Parameters ---------- - arg_set : Iterable of hashables or entities + arg_set : iterable + list of item labels Returns ------- @@ -719,335 +1061,562 @@ def remove_elements_from(self, arg_set): """ for item in arg_set: - Entity.remove_element(self, item) + self.remove_element(item) return self def remove_element(self, item): - """ - Removes item from entity and reference to entity from - item.memberships + """Removes all rows containing a specified item from the underlying data table Parameters ---------- - item : Hashable or Entity + item + item label Returns ------- self : Entity + See Also + -------- + remove : same functionality, accepts variable length argument list of item labels """ - if isinstance(item, Entity): - del item._memberships[self.uid] - del self._elements[item.uid] - else: - del self[item]._memberships[self.uid] - del self._elements[item] + updated_dataframe = self._dataframe - return self + for column in self._dataframe: + updated_dataframe = updated_dataframe[updated_dataframe[column] != item] + + self._dataframe, _ = remove_row_duplicates( + updated_dataframe, + self._data_cols, + weights=self._cell_weight_col, + ) + self._dataframe[self._data_cols] = self._dataframe[self._data_cols].astype( + "category" + ) + + self._state_dict.clear() + for col in self._data_cols: + self._dataframe[col] = self._dataframe[col].cat.remove_unused_categories() - @staticmethod - def merge_entities(name, ent1, ent2): + def encode(self, data): """ - Merge two entities making sure they do not conflict. + Encode dataframe to numpy array Parameters ---------- - name : hashable - - ent1 : Entity - First entity to have elements and properties added to new - entity - - ent2 : Entity - elements of ent2 will be checked against ent1.complete_registry() - and only nonexisting elements will be added using add() method. - Properties of ent2 will update properties of ent1 in new entity. + data : dataframe Returns ------- - a new entity : Entity + numpy.array """ - newent = ent1.clone(name) - newent.add_elements_from(ent2.elements.values()) - for k, v in ent2.properties.items(): - newent.__setattr__(k, v) - return newent + encoded_array = data.apply(lambda x: x.cat.codes).to_numpy() + return encoded_array + def incidence_matrix( + self, level1=0, level2=1, weights=False, aggregateby=None, index=False + ) -> csr_matrix | None: + """Incidence matrix representation for two levels (columns) of the underlying data table -class EntitySet(Entity): - """ - .. _entityset: + If `level1` and `level2` contain N and M distinct items, respectively, the incidence matrix will be M x N. + In other words, the items in `level1` and `level2` correspond to the columns and rows of the incidence matrix, + respectively, in the order in which they appear in `self.labels[column1]` and `self.labels[column2]` + (`column1` and `column2` are the column labels of `level1` and `level2`) - Parameters - ---------- - uid : hashable - a unique identifier + Parameters + ---------- + level1 : int, default=0 + index of first level (column) + level2 : int, default=1 + index of second level + weights : bool or dict, default=False + If False all nonzero entries are 1. + If True all nonzero entries are filled by self.cell_weight + dictionary values, use :code:`aggregateby` to specify how duplicate + entries should have weights aggregated. + If dict of {(level1 item, level2 item): weight value} form; + only nonzero cells in the incidence matrix will be updated by dictionary, + i.e., `level1 item` and `level2 item` must appear in the same row at least once in the underlying data table + aggregateby : {'last', count', 'sum', 'mean','median', max', 'min', 'first', 'last', None}, default='count' + Method to aggregate weights of duplicate rows in data table. + If None, then all cell weights will be set to 1. - elements : list or dict, optional, default: None - a list of entities with identifiers different than uid and/or - hashables different than uid, see `Honor System`_ + Returns + ------- + scipy.sparse.csr.csr_matrix + sparse representation of incidence matrix (i.e. Compressed Sparse Row matrix) - props : keyword arguments, optional, default: {} - properties belonging to the entity added as key=value pairs. - Both key and value must be hashable. + Other Parameters + ---------------- + index : bool, optional + Not used - Notes - ----- - The EntitySet class was created to distinguish Entities satifying the Bipartite Condition. + Note + ---- + In the context of Hypergraphs, think `level1 = edges, level2 = nodes` + """ + if self.dimsize < 2: + warnings.warn("Incidence matrix requires two levels of data.") + return None - .. _Bipartite Condition: + data_cols = [self._data_cols[level2], self._data_cols[level1]] + weights = self._cell_weight_col if weights else None + + df, weight_col = remove_row_duplicates( + self._dataframe, + data_cols, + weights=weights, + aggregateby=aggregateby, + ) + + return csr_matrix( + (df[weight_col], tuple(df[col].cat.codes for col in data_cols)) + ) + + def restrict_to_levels( + self, + levels: int | Iterable[int], + weights: bool = False, + aggregateby: str | None = "sum", + **kwargs, + ) -> Entity: + """Create a new Entity by restricting to a subset of levels (columns) in the + underlying data table - **Bipartite Condition** + Parameters + ---------- + levels : array-like of int + indices of a subset of levels (columns) of data + weights : bool, default=False + If True, aggregate existing cell weights to get new cell weights + Otherwise, all new cell weights will be 1 + aggregateby : {'sum', 'first', 'last', 'count', 'mean', 'median', 'max', \ + 'min', None}, optional + Method to aggregate weights of duplicate rows in data table + If None or `weights`=False then all new cell weights will be 1 + **kwargs + Extra arguments to `Entity` constructor - *Entities that are elements of the same EntitySet, may not contain each other as elements.* - The elements and children of an EntitySet generate a specific partition for a bipartite graph. - The partition is isomorphic to a Hypergraph where the elements correspond to hyperedges and - the children correspond to the nodes. EntitySets are the basic objects used to construct hypergraphs - in HNX. + Returns + ------- + Entity - Example: :: + Raises + ------ + KeyError + If `levels` contains any invalid values - >>> y = Entity('y') - >>> x = Entity('x') - >>> x.add(y) - >>> y.add(x) - >>> w = EntitySet('w',[x,y]) - HyperNetXError: Error: Fails the Bipartite Condition for EntitySet. - y references a child of an existing Entity in the EntitySet. + See Also + -------- + EntitySet + """ + + levels = np.asarray(levels) + invalid_levels = (levels < 0) | (levels >= self.dimsize) + if invalid_levels.any(): + raise KeyError(f"Invalid levels: {levels[invalid_levels]}") + + cols = [self._data_cols[lev] for lev in levels] + + if weights: + weights = self._cell_weight_col + cols.append(weights) + kwargs.update(weights=weights) + + properties = self.properties.loc[levels] + properties.index = properties.index.remove_unused_levels() + level_map = {old: new for new, old in enumerate(levels)} + new_levels = properties.index.levels[0].map(level_map) + properties.index = properties.index.set_levels(new_levels, level=0) + level_col, id_col = properties.index.names + + return self.__class__( + entity=self.dataframe[cols], + data_cols=cols, + aggregateby=aggregateby, + properties=properties, + misc_props_col=self._misc_props_col, + level_col=level_col, + id_col=id_col, + **kwargs, + ) + + def restrict_to_indices(self, indices, level=0, **kwargs): + """Create a new Entity by restricting the data table to rows containing specific items in a given level - """ + Parameters + ---------- + indices : int or iterable of int + indices of item label(s) in `level` to restrict to + level : int, default=0 + level index + **kwargs + Extra arguments to `Entity` constructor - def __init__(self, uid, elements=[], **props): - super().__init__(uid, elements, **props) - if not self.is_bipartite: - raise HyperNetXError( - "Entity does not satisfy the Bipartite Condition, elements and children are not disjoint." - ) + Returns + ------- + Entity + """ + column = self._dataframe[self._data_cols[level]] + values = self.translate(level, indices) + entity = self._dataframe.loc[column.isin(values)].copy() + + for col in self._data_cols: + entity[col] = entity[col].cat.remove_unused_categories() + restricted = self.__class__( + entity=entity, misc_props_col=self._misc_props_col, **kwargs + ) + + if not self.properties.empty: + prop_idx = [ + (lv, uid) + for lv in range(restricted.dimsize) + for uid in restricted.uidset_by_level(lv) + ] + properties = self.properties.loc[prop_idx] + restricted.assign_properties(properties) + return restricted + + def assign_properties( + self, + props: pd.DataFrame | dict[int, dict[T, dict[Any, Any]]], + misc_col: Optional[str] = None, + level_col=0, + id_col=1, + ) -> None: + """Assign new properties to items in the data table, update :attr:`properties` - def __str__(self): - """Return the entityset uid.""" - return f"{self.uid}" + Parameters + ---------- + props : pandas.DataFrame or doubly-nested dict + See documentation of the `properties` parameter in :class:`Entity` + level_col, id_col, misc_col : str, optional + column names corresponding to the levels, items, and misc. properties; + if None, default to :attr:`_level_col`, :attr:`_id_col`, :attr:`_misc_props_col`, + respectively. + + See Also + -------- + properties + """ + # mapping from user-specified level, id, misc column names to internal names + ### This will fail if there isn't a level column + + if isinstance(props, pd.DataFrame): + ### Fix to check the shape of properties or redo properties format + column_map = { + old: new + for old, new in zip( + (level_col, id_col, misc_col), + (*self.properties.index.names, self._misc_props_col), + ) + if old is not None + } + props = props.rename(columns=column_map) + props = props.rename_axis(index=column_map) + self._properties_from_dataframe(props) - def __repr__(self): - """Returns a string resembling the constructor for entityset without any - children""" - return f"EntitySet({self._uid},{list(self.uidset)},{self.properties})" + if isinstance(props, dict): + ### Expects nested dictionary with keys corresponding to level and id + self._properties_from_dict(props) - def add(self, *args): - """ - Adds args to entityset's elements, checking to make sure no self references are - made to element ids. - Ensures Bipartite Condition of EntitySet. + def _properties_from_dataframe(self, props: pd.DataFrame) -> None: + """Private handler for updating :attr:`properties` from a DataFrame Parameters ---------- - args : One or more entities or hashables + props - Returns - ------- - self : EntitySet + Notes + ----- + For clarity in in-line developer comments: + + idx-level + refers generally to a level of a MultiIndex + level + refers specifically to the idx-level in the MultiIndex of :attr:`properties` + that stores the level/column id for the item + """ + # names of property table idx-levels for level and item id, respectively + # ``item`` used instead of ``id`` to avoid redefining python built-in func `id` + level, item = self.properties.index.names + if props.index.nlevels > 1: # props has MultiIndex + # drop all idx-levels from props other than level and id (if present) + extra_levels = [ + idx_lev for idx_lev in props.index.names if idx_lev not in (level, item) + ] + props = props.reset_index(level=extra_levels) + + try: + # if props index is already in the correct format, + # enforce the correct idx-level ordering + props.index = props.index.reorder_levels((level, item)) + except AttributeError: # props is not in (level, id) MultiIndex format + # if the index matches level or id, drop index to column + if props.index.name in (level, item): + props = props.reset_index() + index_cols = [item] + if level in props: + index_cols.insert(0, level) + try: + props = props.set_index(index_cols, verify_integrity=True) + except ValueError: + warnings.warn( + "duplicate (level, ID) rows will be dropped after first occurrence" + ) + props = props.drop_duplicates(index_cols) + props = props.set_index(index_cols) - """ - for item in args: - if isinstance(item, Entity): - if item.uid in self.children: - raise HyperNetXError( - f"Error: Fails the Bipartite Condition for EntitySet. {item.uid} references a child of an existing Entity in the EntitySet." - ) - elif not self.uidset.isdisjoint(item.uidset): - raise HyperNetXError( - f"Error: Fails the bipartite condition for EntitySet." - ) - else: - Entity.add_element(self, item) + if self._misc_props_col in props: + try: + props[self._misc_props_col] = props[self._misc_props_col].apply( + literal_eval + ) + except ValueError: + pass # data already parsed, no literal eval needed else: - if not item in self.children: - Entity.add_element(self, item) - else: - raise HyperNetXError( - f"Error: {item} references a child of an existing Entity in the EntitySet." - ) - return self + warnings.warn("parsed property dict column from string literal") + + if props.index.nlevels == 1: + props = props.reindex(self.properties.index, level=1) + + # combine with existing properties + # non-null values in new props override existing value + properties = props.combine_first(self.properties) + # update misc. column to combine existing and new misc. property dicts + # new props override existing value for overlapping misc. property dict keys + properties[self._misc_props_col] = self.properties[ + self._misc_props_col + ].combine( + properties[self._misc_props_col], + lambda x, y: {**(x if pd.notna(x) else {}), **(y if pd.notna(y) else {})}, + fill_value={}, + ) + self._properties = properties.sort_index() + + def _properties_from_dict(self, props: dict[int, dict[T, dict[Any, Any]]]) -> None: + """Private handler for updating :attr:`properties` from a doubly-nested dict - def clone(self, newuid): - """ - Returns shallow copy of entityset with newuid. Entityset's - elements will belong to two distinct entitysets. + Parameters + ---------- + props + """ + # TODO: there may be a more efficient way to convert this to a dataframe instead + # of updating one-by-one via nested loop, but checking whether each prop_name + # belongs in a designated existing column or the misc. property dict column + # makes it more challenging + # For now: only use nested loop update if non-misc. columns currently exist + if len(self.properties.columns) > 1: + for level in props: + for item in props[level]: + for prop_name, prop_val in props[level][item].items(): + self.set_property(item, prop_name, prop_val, level) + else: + item_keys = pd.MultiIndex.from_tuples( + [(level, item) for level in props for item in props[level]], + names=self.properties.index.names, + ) + props_data = [props[level][item] for level, item in item_keys] + props = pd.DataFrame({self._misc_props_col: props_data}, index=item_keys) + self._properties_from_dataframe(props) + def _property_loc(self, item: T) -> tuple[int, T]: + """Get index in :attr:`properties` of an item of unspecified level Parameters ---------- - newuid : hashable - Name of the new entityset + item : hashable + name of an item Returns ------- - clone : EntitySet + item_key : tuple of (int, hashable) + ``(level, item)`` - """ - return EntitySet(newuid, elements=self.elements.values(), **self.properties) + Raises + ------ + KeyError + If `item` is not in :attr:`properties` - def collapse_identical_elements(self, newuid, return_equivalence_classes=False): - """ - Returns a deduped copy of the entityset, using representatives of equivalence classes as element keys. - Two elements of an EntitySet are collapsed if they share the same children. + Warns + ----- + UserWarning + If `item` appears in multiple levels, returns the first (closest to 0) + + """ + try: + item_loc = self.properties.xs(item, level=1, drop_level=False).index + except KeyError as ex: # item not in df + raise KeyError(f"no properties initialized for 'item': {item}") from ex + + try: + item_key = item_loc.item() + except ValueError: + item_loc, _ = item_loc.sortlevel(sort_remaining=False) + item_key = item_loc[0] + warnings.warn(f"item found in multiple levels: {tuple(item_loc)}") + return item_key + + def set_property( + self, + item: T, + prop_name: Any, + prop_val: Any, + level: Optional[int] = None, + ) -> None: + """Set a property of an item Parameters ---------- - newuid : hashable - - return_equivalence_classes : boolean, default=False - If True, return a dictionary of equivalence classes keyed by new edge names - - Returns - ------- - : EntitySet - eq_classes : dict - if return_equivalence_classes = True - - Notes + item : hashable + name of an item + prop_name : hashable + name of the property to set + prop_val : any + value of the property to set + level : int, optional + level index of the item; + required if `item` is not already in :attr:`properties` + + Raises + ------ + ValueError + If `level` is not provided and `item` is not in :attr:`properties` + + Warns ----- - Treats elements of the entityset as equal if they have the same uidsets. Using this - as an equivalence relation, the entityset's uidset is partitioned into equivalence classes. - The equivalent elements are identified using a single entity by using the - frozenset of uids associated to these elements as the uid for the new element - and dropping the properties. - If use_reps is set to True a representative element of the equivalence class is - used as identifier instead of the frozenset. - - Example: :: - - >>> E = EntitySet('E',elements=[Entity('E1', ['a','b']),Entity('E2',['a','b'])]) - >>> E.incidence_dict - {'E1': {'a', 'b'}, 'E2': {'a', 'b'}} - >>> E.collapse_identical_elements('_',).incidence_dict - {'E2': {'a', 'b'}} + UserWarning + If `level` is not provided and `item` appears in multiple levels, + assumes the first (closest to 0) + See Also + -------- + get_property, get_properties """ - - shared_children = defaultdict(set) - for e in self.__call__(): - shared_children[frozenset(e.uidset)].add(e.uid) - new_entity_dict = { - f"{next(iter(v))}:{len(v)}": set(k) for k, v in shared_children.items() - } - if return_equivalence_classes: - eq_classes = { - f"{next(iter(v))}:{len(v)}": v for k, v in shared_children.items() - } - return EntitySet(newuid, new_entity_dict), dict(eq_classes) + if level is not None: + item_key = (level, item) else: - return EntitySet(newuid, new_entity_dict) + try: + item_key = self._property_loc(item) + except KeyError as ex: + raise ValueError( + "cannot infer 'level' when initializing 'item' properties" + ) from ex + + if prop_name in self.properties: + self._properties.loc[item_key, prop_name] = prop_val + else: + try: + self._properties.loc[item_key, self._misc_props_col].update( + {prop_name: prop_val} + ) + except KeyError: + self._properties.loc[item_key, :] = { + self._misc_props_col: {prop_name: prop_val} + } - def incidence_matrix(self, sparse=True, index=False, weights=None): - """ - An incidence matrix for the EntitySet indexed by children x uidset. + def get_property(self, item: T, prop_name: Any, level: Optional[int] = None) -> Any: + """Get a property of an item Parameters ---------- - sparse : boolean, optional, default: True - - index : boolean, optional, default : False - If True return will include a dictionary of children uid : row number - and element uid : column number - - weights : bdict, optional, default : None - cell weight dictionary keyed by (edge.uid, node.uid) + item : hashable + name of an item + prop_name : hashable + name of the property to get + level : int, optional + level index of the item Returns ------- - incidence_matrix : scipy.sparse.csr.csr_matrix or np.ndarray + prop_val : any + value of the property - row dictionary : dict - Dictionary identifying row with item in entityset's children + Raises + ------ + KeyError + if (`level`, `item`) is not in :attr:`properties`, + or if `level` is not provided and `item` is not in :attr:`properties` - column dictionary : dict - Dictionary identifying column with item in entityset's uidset - - Notes + Warns ----- + UserWarning + If `level` is not provided and `item` appears in multiple levels, + assumes the first (closest to 0) - Example: :: - - >>> E = EntitySet('E',{'a':{1,2,3},'b':{2,3},'c':{1,4}}) - >>> E.incidence_matrix(sparse=False, index=True) - (array([[0, 1, 1], - [1, 1, 0], - [1, 1, 0], - [0, 0, 1]]), {0: 1, 1: 2, 2: 3, 3: 4}, {0: 'b', 1: 'a', 2: 'c'}) + See Also + -------- + get_properties, set_property """ - if sparse: - from scipy.sparse import csr_matrix - - nchildren = len(self.children) - nuidset = len(self.uidset) - - ndict = dict(zip(self.children, range(nchildren))) - edict = dict(zip(self.uidset, range(nuidset))) - - if len(ndict) != 0: - - if index: - rowdict = {v: k for k, v in ndict.items()} - coldict = {v: k for k, v in edict.items()} - - if sparse: - # Create csr sparse matrix - rows = list() - cols = list() - data = list() - for e in self: - for n in self[e].elements: - if weights is not None: - try: - data.append(weights[(e, n)]) - except: - data.append(1) - else: - data.append(1) - rows.append(ndict[n]) - cols.append(edict[e]) - MP = csr_matrix((data, (rows, cols))) - else: - # Create an np.matrix - MP = np.zeros((nchildren, nuidset), dtype=int) - for e in self: - for n in self[e].elements: - MP[ndict[n], edict[e]] = 1 - if index: - return MP, rowdict, coldict - else: - return MP + if level is not None: + item_key = (level, item) else: - if index: - return np.zeros(1), {}, {} + try: + item_key = self._property_loc(item) + except KeyError: + raise # item not in properties + + try: + prop_val = self.properties.loc[item_key, prop_name] + except KeyError as ex: + if ex.args[0] == prop_name: + prop_val = self.properties.loc[item_key, self._misc_props_col].get( + prop_name + ) else: - return np.zeros(1) + raise KeyError( + f"no properties initialized for ('level','item'): {item_key}" + ) from ex - def restrict_to(self, element_subset, name=None): - """ - Shallow copy of entityset removing elements not in element_subset. + return prop_val + + def get_properties(self, item: T, level: Optional[int] = None) -> dict[Any, Any]: + """Get all properties of an item Parameters ---------- - element_subset : iterable - A subset of the entityset's elements - - name: hashable, optional - If not given, a name is generated to reflect entity uid + item : hashable + name of an item + level : int, optional + level index of the item Returns ------- - new entityset : EntitySet - Could be empty. + prop_vals : dict + ``{named property: property value, ..., + misc. property column name: {property name: property value}}`` - See also - -------- - Entity.restrict_to + Raises + ------ + KeyError + if (`level`, `item`) is not in :attr:`properties`, + or if `level` is not provided and `item` is not in :attr:`properties` + + Warns + ----- + UserWarning + If `level` is not provided and `item` appears in multiple levels, + assumes the first (closest to 0) + See Also + -------- + get_property, set_property """ - newelements = [self[e] for e in element_subset if e in self] - name = name or f"{self.uid}_r" - return EntitySet(name, newelements, **self.properties) + if level is not None: + item_key = (level, item) + else: + try: + item_key = self._property_loc(item) + except KeyError: + raise + + try: + prop_vals = self.properties.loc[item_key].to_dict() + except KeyError as ex: + raise KeyError( + f"no properties initialized for ('level','item'): {item_key}" + ) from ex + + return prop_vals diff --git a/hypernetx/classes/entityset.py b/hypernetx/classes/entityset.py new file mode 100644 index 00000000..c0c1b97d --- /dev/null +++ b/hypernetx/classes/entityset.py @@ -0,0 +1,656 @@ +from __future__ import annotations + +import warnings +from ast import literal_eval +from collections import OrderedDict +from collections.abc import Iterable, Sequence +from typing import Mapping +from typing import Optional, Any, TypeVar, Union +from pprint import pformat + +import numpy as np +import pandas as pd + +from hypernetx.classes import Entity +from hypernetx.classes.helpers import AttrList + +# from hypernetx.utils.log import get_logger + +# _log = get_logger("entity_set") + +T = TypeVar("T", bound=Union[str, int]) + + +class EntitySet(Entity): + """Class for handling 2-dimensional (i.e., system of sets, bipartite) data when + building network-like models, i.e., :class:`Hypergraph` + + Parameters + ---------- + entity : Entity, pandas.DataFrame, dict of lists or sets, or list of lists or sets, optional + If an ``Entity`` with N levels or a ``DataFrame`` with N columns, + represents N-dimensional entity data (data table). + If N > 2, only considers levels (columns) `level1` and `level2`. + Otherwise, represents 2-dimensional entity data (system of sets). + data : numpy.ndarray, optional + 2D M x N ``ndarray`` of ``ints`` (data table); + sparse representation of an N-dimensional incidence tensor with M nonzero cells. + If N > 2, only considers levels (columns) `level1` and `level2`. + Ignored if `entity` is provided. + labels : collections.OrderedDict of lists, optional + User-specified labels in corresponding order to ``ints`` in `data`. + For M x N `data`, N > 2, `labels` must contain either 2 or N keys. + If N keys, only considers labels for levels (columns) `level1` and `level2`. + Ignored if `entity` is provided or `data` is not provided. + level1, level2 : str or int, default=0,1 + Each item in `level1` defines a set containing all the `level2` items with which + it appears in the same row of the underlying data table. + If ``int``, gives the index of a level; + if ``str``, gives the name of a column in `entity`. + Ignored if `entity`, `data` (if `entity` not provided), and `labels` all (if + provided) represent 1- or 2-dimensional data (set or system of sets). + weights : str or sequence of float, optional + User-specified cell weights corresponding to entity data. + If sequence of ``floats`` and `entity` or `data` defines a data table, + length must equal the number of rows. + If sequence of ``floats`` and `entity` defines a system of sets, + length must equal the total sum of the sizes of all sets. + If ``str`` and `entity` is a ``DataFrame``, + must be the name of a column in `entity`. + Otherwise, weight for all cells is assumed to be 1. + Ignored if `entity` is an ``Entity`` and `keep_weights`=True. + keep_weights : bool, default=True + Whether to preserve any existing cell weights; + ignored if `entity` is not an ``Entity``. + cell_properties : str, list of str, pandas.DataFrame, or doubly-nested dict, optional + User-specified properties to be assigned to cells of the incidence matrix, i.e., + rows in a data table; pairs of (set, element of set) in a system of sets. + See Notes for detailed explanation. + Ignored if underlying data is 1-dimensional (set). + If doubly-nested dict, + ``{level1 item: {level2 item: {cell property name: cell property value}}}``. + misc_cell_props_col : str, default='cell_properties' + Column name for miscellaneous cell properties; see Notes for explanation. + kwargs + Keyword arguments passed to the ``Entity`` constructor, e.g., `static`, + `uid`, `aggregateby`, `properties`, etc. See :class:`Entity` for documentation + of these parameters. + + Notes + ----- + A **cell property** is a named attribute assigned jointly to a set and one of its + elements, i.e, a cell of the incidence matrix. + + When an ``Entity`` or ``DataFrame`` is passed to the `entity` parameter of the + constructor, it should represent a data table: + + +--------------+--------------+--------------+-------+--------------+ + | Column_1 | Column_2 | Column_3 | [...] | Column_N | + +==============+==============+==============+=======+==============+ + | level 1 item | level 2 item | level 3 item | ... | level N item | + +--------------+--------------+--------------+-------+--------------+ + | ... | ... | ... | ... | ... | + +--------------+--------------+--------------+-------+--------------+ + + Assuming the default values for parameters `level1`, `level2`, the data table will + be restricted to the set system defined by Column 1 and Column 2. + Since each row of the data table represents an incidence or cell, values from other + columns may contain data that should be converted to cell properties. + + By passing a **column name or list of column names** as `cell_properties`, each + given column will be preserved in the :attr:`cell_properties` as an explicit cell + property type. An additional column in :attr:`cell_properties` will be created to + store a ``dict`` of miscellaneous cell properties, which will store cell properties + of types that have not been explicitly defined and do not have a dedicated column + (which may be assigned after construction). The name of the miscellaneous column is + determined by `misc_cell_props_col`. + + You can also pass a **pre-constructed table** to `cell_properties` as a + ``DataFrame``: + + +----------+----------+----------------------------+-------+-----------------------+ + | Column_1 | Column_2 | [explicit cell prop. type] | [...] | misc. cell properties | + +==========+==========+============================+=======+=======================+ + | level 1 | level 2 | cell property value | ... | {cell property name: | + | item | item | | | cell property value} | + +----------+----------+----------------------------+-------+-----------------------+ + | ... | ... | ... | ... | ... | + +----------+----------+----------------------------+-------+-----------------------+ + + Column 1 and Column 2 must have the same names as the corresponding columns in the + `entity` data table, and `misc_cell_props_col` can be used to specify the name of the + column to be used for miscellaneous cell properties. If no column by that name is + found, a new column will be created and populated with empty ``dicts``. All other + columns will be considered explicit cell property types. The order of the columns + does not matter. + + Both of these methods assume that there are no row duplicates in the tables passed + to `entity` and/or `cell_properties`; if duplicates are found, all but the first + occurrence will be dropped. + + """ + + def __init__( + self, + entity: Optional[ + pd.DataFrame + | np.ndarray + | Mapping[T, Iterable[T]] + | Iterable[Iterable[T]] + | Mapping[T, Mapping[T, Mapping[T, Any]]] + ] = None, + data: Optional[np.ndarray] = None, + labels: Optional[OrderedDict[T, Sequence[T]]] = None, + level1: str | int = 0, + level2: str | int = 1, + weight_col: str | int = "cell_weights", + weights: Sequence[float] | float | int | str = 1, + # keep_weights: bool = True, + cell_properties: Optional[ + Sequence[T] | pd.DataFrame | dict[T, dict[T, dict[Any, Any]]] + ] = None, + misc_cell_props_col: str = "cell_properties", + uid: Optional[Hashable] = None, + aggregateby: Optional[str] = "sum", + properties: Optional[pd.DataFrame | dict[int, dict[T, dict[Any, Any]]]] = None, + misc_props_col: str = "properties", + # level_col: str = "level", + # id_col: str = "id", + **kwargs, + ): + self._misc_cell_props_col = misc_cell_props_col + + # if the entity data is passed as an Entity, get its underlying data table and + # proceed to the case for entity data passed as a DataFrame + # if isinstance(entity, Entity): + # # _log.info(f"Changing entity from type {Entity} to {type(entity.dataframe)}") + # if keep_weights: + # # preserve original weights + # weights = entity._cell_weight_col + # entity = entity.dataframe + + # if the entity data is passed as a DataFrame, restrict to two columns if needed + if isinstance(entity, pd.DataFrame) and len(entity.columns) > 2: + # _log.info(f"Processing parameter of 'entity' of type {type(entity)}...") + # metadata columns are not considered levels of data, + # remove them before indexing by level + # if isinstance(cell_properties, str): + # cell_properties = [cell_properties] + + prop_cols = [] + if isinstance(cell_properties, Sequence): + for col in {*cell_properties, self._misc_cell_props_col}: + if col in entity: + # _log.debug(f"Adding column to prop_cols: {col}") + prop_cols.append(col) + + # meta_cols = prop_cols + # if weights in entity and weights not in meta_cols: + # meta_cols.append(weights) + # # _log.debug(f"meta_cols: {meta_cols}") + if weight_col in prop_cols: + prop_cols.remove(weight_col) + if not weight_col in entity: + entity[weight_col] = weights + + # if both levels are column names, no need to index by level + if isinstance(level1, int): + level1 = entity.columns[level1] + if isinstance(level2, int): + level2 = entity.columns[level2] + # if isinstance(level1, str) and isinstance(level2, str): + columns = [level1, level2, weight_col] + prop_cols + # if one or both of the levels are given by index, get column name + # else: + # all_columns = entity.columns.drop(meta_cols) + # columns = [ + # all_columns[lev] if isinstance(lev, int) else lev + # for lev in (level1, level2) + # ] + + # if there is a column for cell properties, convert to separate DataFrame + # if len(prop_cols) > 0: + # cell_properties = entity[[*columns, *prop_cols]] + + # if there is a column for weights, preserve it + # if weights in entity and weights not in prop_cols: + # columns.append(weights) + # _log.debug(f"columns: {columns}") + + # pass level1, level2, and weights (optional) to Entity constructor + entity = entity[columns] + + # if a 2D ndarray is passed, restrict to two columns if needed + elif isinstance(data, np.ndarray) and data.ndim == 2 and data.shape[1] > 2: + # _log.info(f"Processing parameter 'data' of type {type(data)}...") + data = data[:, (level1, level2)] + + # if a dict of labels is provided, restrict to labels for two columns if needed + if isinstance(labels, dict) and len(labels) > 2: + label_keys = list(labels) + columns = (label_keys[level1], label_keys[level2]) + labels = {col: labels[col] for col in columns} + # _log.debug(f"Restricted labels to columns:\n{pformat(labels)}") + + # _log.info( + # f"Creating instance of {Entity} using reformatted params: \n\tentity: {type(entity)} \n\tdata: {type(data)} \n\tlabels: {type(labels)}, \n\tweights: {weights}, \n\tkwargs: {kwargs}" + # ) + # _log.debug(f"entity:\n{pformat(entity)}") + # _log.debug(f"data: {pformat(data)}") + super().__init__( + entity=entity, + data=data, + labels=labels, + uid=uid, + weight_col=weight_col, + weights=weights, + aggregateby=aggregateby, + properties=properties, + misc_props_col=misc_props_col, + **kwargs, + ) + + # if underlying data is 2D (system of sets), create and assign cell properties + if self.dimsize == 2: + # self._cell_properties = pd.DataFrame( + # columns=[*self._data_cols, self._misc_cell_props_col] + # ) + self._cell_properties = pd.DataFrame(self._dataframe) + self._cell_properties.set_index(self._data_cols, inplace=True) + if isinstance(cell_properties, (dict, pd.DataFrame)): + self.assign_cell_properties(cell_properties) + else: + self._cell_properties = None + + @property + def cell_properties(self) -> Optional[pd.DataFrame]: + """Properties assigned to cells of the incidence matrix + + Returns + ------- + pandas.Series, optional + Returns None if :attr:`dimsize` < 2 + """ + return self._cell_properties + + @property + def memberships(self) -> dict[str, AttrList[str]]: + """Extends :attr:`Entity.memberships` + + Each item in level 1 (second column) defines a set containing all the level 0 + (first column) items with which it appears in the same row of the underlying + data table. + + Returns + ------- + dict of AttrList + System of sets representation as dict of + ``{level 1 item: AttrList(level 0 items)}``. + + See Also + -------- + elements : dual of this representation, + i.e., each item in level 0 (first column) defines a set + restrict_to_levels : for more information on how memberships work for + 1-dimensional (set) data + """ + if self._dimsize == 1: + return self._state_dict.get("memberships") + + return super().memberships + + def restrict_to_levels( + self, + levels: int | Iterable[int], + weights: bool = False, + aggregateby: Optional[str] = "sum", + keep_memberships: bool = True, + **kwargs, + ) -> EntitySet: + """Extends :meth:`Entity.restrict_to_levels` + + Parameters + ---------- + levels : array-like of int + indices of a subset of levels (columns) of data + weights : bool, default=False + If True, aggregate existing cell weights to get new cell weights. + Otherwise, all new cell weights will be 1. + aggregateby : {'sum', 'first', 'last', 'count', 'mean', 'median', 'max', \ + 'min', None}, optional + Method to aggregate weights of duplicate rows in data table + If None or `weights`=False then all new cell weights will be 1 + keep_memberships : bool, default=True + Whether to preserve membership information for the discarded level when + the new ``EntitySet`` is restricted to a single level + **kwargs + Extra arguments to :class:`EntitySet` constructor + + Returns + ------- + EntitySet + + Raises + ------ + KeyError + If `levels` contains any invalid values + """ + restricted = super().restrict_to_levels( + levels, + weights, + aggregateby, + misc_cell_props_col=self._misc_cell_props_col, + **kwargs, + ) + + if keep_memberships: + # use original memberships to set memberships for the new EntitySet + # TODO: This assumes levels=[1], add explicit checks for other cases + restricted._state_dict["memberships"] = self.memberships + + return restricted + + def restrict_to(self, indices: int | Iterable[int], **kwargs) -> EntitySet: + """Alias of :meth:`restrict_to_indices` with default parameter `level`=0 + + Parameters + ---------- + indices : array_like of int + indices of item label(s) in `level` to restrict to + **kwargs + Extra arguments to :class:`EntitySet` constructor + + Returns + ------- + EntitySet + + See Also + -------- + restrict_to_indices + """ + restricted = self.restrict_to_indices( + indices, misc_cell_props_col=self._misc_cell_props_col, **kwargs + ) + if not self.cell_properties.empty: + cell_properties = self.cell_properties.loc[ + list(restricted.uidset) + ].reset_index() + restricted.assign_cell_properties(cell_properties) + return restricted + + def assign_cell_properties( + self, + cell_props: pd.DataFrame | dict[T, dict[T, dict[Any, Any]]], + misc_col: Optional[str] = None, + replace: bool = False, + ) -> None: + """Assign new properties to cells of the incidence matrix and update + :attr:`properties` + + Parameters + ---------- + cell_props : pandas.DataFrame, dict of iterables, or doubly-nested dict, optional + See documentation of the `cell_properties` parameter in :class:`EntitySet` + misc_col: str, optional + name of column to be used for miscellaneous cell property dicts + replace: bool, default=False + If True, replace existing :attr:`cell_properties` with result; + otherwise update with new values from result + + Raises + ----- + AttributeError + Not supported for :attr:`dimsize`=1 + """ + if self.dimsize < 2: + raise AttributeError( + f"cell properties are not supported for 'dimsize'={self.dimsize}" + ) + + misc_col = misc_col or self._misc_cell_props_col + try: + cell_props = cell_props.rename( + columns={misc_col: self._misc_cell_props_col} + ) + except AttributeError: # handle cell props in nested dict format + self._cell_properties_from_dict(cell_props) + else: # handle cell props in DataFrame format + self._cell_properties_from_dataframe(cell_props) + + def _cell_properties_from_dataframe(self, cell_props: pd.DataFrame) -> None: + """Private handler for updating :attr:`properties` from a DataFrame + + Parameters + ---------- + props + + Parameters + ---------- + cell_props : DataFrame + """ + if cell_props.index.nlevels > 1: + extra_levels = [ + idx_lev + for idx_lev in cell_props.index.names + if idx_lev not in self._data_cols + ] + cell_props = cell_props.reset_index(level=extra_levels) + + misc_col = self._misc_cell_props_col + + try: + cell_props.index = cell_props.index.reorder_levels(self._data_cols) + except AttributeError: + if cell_props.index.name in self._data_cols: + cell_props = cell_props.reset_index() + + try: + cell_props = cell_props.set_index( + self._data_cols, verify_integrity=True + ) + except ValueError: + warnings.warn( + "duplicate cell rows will be dropped after first occurrence" + ) + cell_props = cell_props.drop_duplicates(self._data_cols) + cell_props = cell_props.set_index(self._data_cols) + + if misc_col in cell_props: + try: + cell_props[misc_col] = cell_props[misc_col].apply(literal_eval) + except ValueError: + pass # data already parsed, no literal eval needed + else: + warnings.warn("parsed cell property dict column from string literal") + + cell_properties = cell_props.combine_first(self.cell_properties) + # import ipdb; ipdb.set_trace() + # cell_properties[misc_col] = self.cell_properties[misc_col].combine( + # cell_properties[misc_col], + # lambda x, y: {**(x if pd.notna(x) else {}), **(y if pd.notna(y) else {})}, + # fill_value={}, + # ) + + self._cell_properties = cell_properties.sort_index() + + def _cell_properties_from_dict( + self, cell_props: dict[T, dict[T, dict[Any, Any]]] + ) -> None: + """Private handler for updating :attr:`cell_properties` from a doubly-nested dict + + Parameters + ---------- + cell_props + """ + # TODO: there may be a more efficient way to convert this to a dataframe instead + # of updating one-by-one via nested loop, but checking whether each prop_name + # belongs in a designated existing column or the misc. property dict column + # makes it more challenging. + # For now: only use nested loop update if non-misc. columns currently exist + if len(self.cell_properties.columns) > 1: + for item1 in cell_props: + for item2 in cell_props[item1]: + for prop_name, prop_val in cell_props[item1][item2].items(): + self.set_cell_property(item1, item2, prop_name, prop_val) + else: + cells = pd.MultiIndex.from_tuples( + [(item1, item2) for item1 in cell_props for item2 in cell_props[item1]], + names=self._data_cols, + ) + props_data = [cell_props[item1][item2] for item1, item2 in cells] + cell_props = pd.DataFrame( + {self._misc_cell_props_col: props_data}, index=cells + ) + self._cell_properties_from_dataframe(cell_props) + + def collapse_identical_elements( + self, return_equivalence_classes: bool = False, **kwargs + ) -> EntitySet | tuple[EntitySet, dict[str, list[str]]]: + """Create a new :class:`EntitySet` by collapsing sets with the same set elements + + Each item in level 0 (first column) defines a set containing all the level 1 + (second column) items with which it appears in the same row of the underlying + data table. + + Parameters + ---------- + return_equivalence_classes : bool, default=False + If True, return a dictionary of equivalence classes keyed by new edge names + **kwargs + Extra arguments to :class:`EntitySet` constructor + + Returns + ------- + new_entity : EntitySet + new :class:`EntitySet` with identical sets collapsed; + if all sets are unique, the system of sets will be the same as the original. + equivalence_classes : dict of lists, optional + if `return_equivalence_classes`=True, + ``{collapsed set label: [level 0 item labels]}``. + """ + # group by level 0 (set), aggregate level 1 (set elements) as frozenset + collapse = ( + self._dataframe[self._data_cols] + .groupby(self._data_cols[0], as_index=False) + .agg(frozenset) + ) + + # aggregation method to rename equivalence classes as [first item]: [# items] + agg_kwargs = {"name": (self._data_cols[0], lambda x: f"{x.iloc[0]}: {len(x)}")} + if return_equivalence_classes: + # aggregation method to list all items in each equivalence class + agg_kwargs.update(equivalence_class=(self._data_cols[0], list)) + # group by frozenset of level 1 items (set elements), aggregate to get names of + # equivalence classes and (optionally) list of level 0 items (sets) in each + collapse = collapse.groupby(self._data_cols[1], as_index=False).agg( + **agg_kwargs + ) + # convert to nested dict representation of collapsed system of sets + collapse = collapse.set_index("name") + new_entity_dict = collapse[self._data_cols[1]].to_dict() + # construct new EntitySet from system of sets + new_entity = EntitySet(new_entity_dict, **kwargs) + + if return_equivalence_classes: + # lists of equivalent sets, keyed by equivalence class name + equivalence_classes = collapse.equivalence_class.to_dict() + return new_entity, equivalence_classes + return new_entity + + def set_cell_property( + self, item1: T, item2: T, prop_name: Any, prop_val: Any + ) -> None: + """Set a property of a cell i.e., incidence between items of different levels + + Parameters + ---------- + item1 : hashable + name of an item in level 0 + item2 : hashable + name of an item in level 1 + prop_name : hashable + name of the cell property to set + prop_val : any + value of the cell property to set + + See Also + -------- + get_cell_property, get_cell_properties + """ + if item2 in self.elements[item1]: + if prop_name in self.properties: + self._cell_properties.loc[(item1, item2), prop_name] = pd.Series( + [prop_val] + ) + else: + try: + self._cell_properties.loc[ + (item1, item2), self._misc_cell_props_col + ].update({prop_name: prop_val}) + except KeyError: + self._cell_properties.loc[(item1, item2), :] = { + self._misc_cell_props_col: {prop_name: prop_val} + } + + def get_cell_property(self, item1: T, item2: T, prop_name: Any) -> Any: + """Get a property of a cell i.e., incidence between items of different levels + + Parameters + ---------- + item1 : hashable + name of an item in level 0 + item2 : hashable + name of an item in level 1 + prop_name : hashable + name of the cell property to get + + Returns + ------- + prop_val : any + value of the cell property + + See Also + -------- + get_cell_properties, set_cell_property + """ + try: + cell_props = self.cell_properties.loc[(item1, item2)] + except KeyError: + raise + # TODO: raise informative exception + + try: + prop_val = cell_props.loc[prop_name] + except KeyError: + prop_val = cell_props.loc[self._misc_cell_props_col].get(prop_name) + + return prop_val + + def get_cell_properties(self, item1: T, item2: T) -> dict[Any, Any]: + """Get all properties of a cell, i.e., incidence between items of different + levels + + Parameters + ---------- + item1 : hashable + name of an item in level 0 + item2 : hashable + name of an item in level 1 + + Returns + ------- + dict + ``{named cell property: cell property value, ..., misc. cell property column + name: {cell property name: cell property value}}`` + + See Also + -------- + get_cell_property, set_cell_property + """ + try: + cell_props = self.cell_properties.loc[(item1, item2)] + except KeyError: + raise + # TODO: raise informative exception + + return cell_props.to_dict() diff --git a/hypernetx/classes/helpers.py b/hypernetx/classes/helpers.py new file mode 100644 index 00000000..332bd4b5 --- /dev/null +++ b/hypernetx/classes/helpers.py @@ -0,0 +1,270 @@ +from __future__ import annotations + +from typing import Any, Optional +import numpy as np +import pandas as pd +from collections import UserList +from collections.abc import Hashable, Iterable +from pandas.api.types import CategoricalDtype +from ast import literal_eval + +from hypernetx.classes.entity import * + + +class AttrList(UserList): + """Custom list wrapper for integrated property storage in :class:`Entity` + + Parameters + ---------- + entity : hypernetx.Entity + key : tuple of (int, str or int) + ``(level, item)`` + initlist : list, optional + list of elements, passed to ``UserList`` constructor + """ + + def __init__( + self, + entity: Entity, + key: tuple[int, str | int], + initlist: Optional[list] = None, + ): + self._entity = entity + self._key = key + super().__init__(initlist) + + def __getattr__(self, attr: str) -> Any: + """Get attribute value from properties of :attr:`entity` + + Parameters + ---------- + attr : str + + Returns + ------- + any + attribute value; None if not found + """ + if attr == "uidset": + return frozenset(self.data) + if attr in ["memberships", "elements"]: + return self._entity.__getattribute__(attr).get(self._key[1]) + return self._entity.get_property(self._key[1], attr, self._key[0]) + + def __setattr__(self, attr: str, val: Any) -> None: + """Set attribute value in properties of :attr:`entity` + + Parameters + ---------- + attr : str + val : any + """ + if attr in ["_entity", "_key", "data"]: + object.__setattr__(self, attr, val) + else: + self._entity.set_property(self._key[1], attr, val, level=self._key[0]) + + +def encode(data: pd.DataFrame): + """ + Encode dataframe to numpy array + + Parameters + ---------- + data : dataframe + + Returns + ------- + numpy.array + + """ + encoded_array = data.apply(lambda x: x.cat.codes).to_numpy() + return encoded_array + + +def assign_weights(df, weights=1, weight_col="cell_weights"): + """ + Parameters + ---------- + df : pandas.DataFrame + A DataFrame to assign a weight column to + weights : array-like or Hashable, optional + If numpy.ndarray with the same length as df, create a new weight column with + these values. + If Hashable, must be the name of a column of df to assign as the weight column + Otherwise, create a new weight column assigning a weight of 1 to every row + weight_col : Hashable + Name for new column if one is created (not used if the name of an existing + column is passed as weights) + + Returns + ------- + df : pandas.DataFrame + The original DataFrame with a new column added if needed + weight_col : str + Name of the column assigned to hold weights + + Note + ---- + TODO: move logic for default weights inside this method + """ + + if isinstance(weights, (list, np.ndarray)): + df[weight_col] = weights + else: + if not weight_col in df: + df[weight_col] = weights + # import ipdb; ipdb.set_trace() + return df, weight_col + + +def create_properties( + props: pd.DataFrame + | dict[str | int, Iterable[str | int]] + | dict[str | int, dict[str | int, dict[Any, Any]]] + | None, + index_cols: list[str], + misc_col: str, +) -> pd.DataFrame: + """Helper function for initializing properties and cell properties + + Parameters + ---------- + props : pandas.DataFrame, dict of iterables, doubly-nested dict, or None + See documentation of the `properties` parameter in :class:`Entity`, + `cell_properties` parameter in :class:`EntitySet` + index_cols : list of str + names of columns to be used as levels of the MultiIndex + misc_col : str + name of column to be used for miscellaneous property dicts + + Returns + ------- + pandas.DataFrame + with ``MultiIndex`` on `index_cols`; + each entry of the miscellaneous column holds dict of + ``{property name: property value}`` + """ + + if isinstance(props, pd.DataFrame) and not props.empty: + try: + data = props.set_index(index_cols, verify_integrity=True) + except ValueError: + warnings.warn( + "duplicate (level, ID) rows will be dropped after first occurrence" + ) + props = props.drop_duplicates(index_cols) + data = props.set_index(index_cols) + + if misc_col not in data: + data[misc_col] = [{} for _ in range(len(data))] + try: + data[misc_col] = data[misc_col].apply(literal_eval) + except ValueError: + pass # data already parsed, no literal eval needed + else: + warnings.warn("parsed property dict column from string literal") + + return data.sort_index() + + # build MultiIndex from dict of {level: iterable of items} + try: + item_levels = [(level, item) for level in props for item in props[level]] + index = pd.MultiIndex.from_tuples(item_levels, names=index_cols) + # empty MultiIndex if props is None or other unexpected type + except TypeError: + index = pd.MultiIndex(levels=([], []), codes=([], []), names=index_cols) + + # get inner data from doubly-nested dict of {level: {item: {prop: val}}} + try: + data = [props[level][item] for level, item in index] + # empty prop dict for each (level, ID) if iterable of items is not a dict + except (TypeError, IndexError): + data = [{} for _ in index] + + return pd.DataFrame({misc_col: data}, index=index).sort_index() + + +def remove_row_duplicates( + df, data_cols, weights=1, weight_col="cell_weights", aggregateby=None +): + """ + Removes and aggregates duplicate rows of a DataFrame using groupby + + Parameters + ---------- + df : pandas.DataFrame + A DataFrame to remove or aggregate duplicate rows from + data_cols : list + A list of column names in df to perform the groupby on / remove duplicates from + weights : array-like or Hashable, optional + Argument passed to assign_weights + aggregateby : str, optional, default='sum' + A valid aggregation method for pandas groupby + If None, drop duplicates without aggregating weights + + Returns + ------- + df : pandas.DataFrame + The DataFrame with duplicate rows removed or aggregated + weight_col : Hashable + The name of the column holding aggregated weights, or None if aggregateby=None + """ + df = df.copy() + categories = {} + for col in data_cols: + if df[col].dtype.name == "category": + categories[col] = df[col].cat.categories + df[col] = df[col].astype(categories[col].dtype) + df, weight_col = assign_weights( + df, weights=weights, weight_col=weight_col + ) ### reconcile this with defaults weights. + if not aggregateby: + df = df.drop_duplicates(subset=data_cols) + df[data_cols] = df[data_cols].astype("category") + return df, weight_col + + else: + aggby = {col: "first" for col in df.columns} + if isinstance(aggregateby, str): + aggby[weight_col] = aggregateby + else: + aggby.update(aggregateby) + # import ipdb; ipdb.set_trace(context=8) + df = df.groupby(data_cols, as_index=False, sort=False).agg(aggby) + + # for col in categories: + # df[col] = df[col].astype(CategoricalDtype(categories=categories[col])) + df[data_cols] = df[data_cols].astype("category") + + return df, weight_col + + +# https://stackoverflow.com/a/7205107 +def merge_nested_dicts(a, b, path=None): + "merges b into a" + if path is None: + path = [] + for key in b: + if key in a: + if isinstance(a[key], dict) and isinstance(b[key], dict): + merge_nested_dicts(a[key], b[key], path + [str(key)]) + elif a[key] == b[key]: + pass # same leaf value + else: + warnings.warn( + f'Conflict at {",".join(path + [str(key)])}, keys ignored' + ) + else: + a[key] = b[key] + return a + + +## https://www.geeksforgeeks.org/python-find-depth-of-a-dictionary/ +def dict_depth(dic, level=0): + ### checks if there is a nested dict, quits once level > 2 + if level > 2: + return level + if not isinstance(dic, dict) or not dic: + return level + return min(dict_depth(dic[key], level + 1) for key in dic) diff --git a/hypernetx/classes/hypergraph.py b/hypernetx/classes/hypergraph.py index 5c9d09fc..930785bd 100644 --- a/hypernetx/classes/hypergraph.py +++ b/hypernetx/classes/hypergraph.py @@ -1,217 +1,559 @@ # Copyright © 2018 Battelle Memorial Institute # All rights reserved. +from __future__ import annotations -import warnings import pickle +import warnings +from collections import defaultdict +from collections.abc import Sequence, Iterable +from typing import Optional, Any, TypeVar, Union, Mapping + import networkx as nx -from networkx.algorithms import bipartite import numpy as np import pandas as pd -from scipy.sparse import issparse, coo_matrix, dok_matrix, csr_matrix -from collections import OrderedDict, defaultdict -from hypernetx.classes.entity import Entity, EntitySet -from hypernetx.classes.staticentity import StaticEntity, StaticEntitySet -from hypernetx.exception import HyperNetXError -from hypernetx.utils.decorators import not_implemented_for +from networkx.algorithms import bipartite +from scipy.sparse import coo_matrix, csr_matrix +from hypernetx.classes import Entity, EntitySet +from hypernetx.exception import HyperNetXError +from hypernetx.utils.decorators import warn_nwhy +from hypernetx.classes.helpers import merge_nested_dicts, dict_depth __all__ = ["Hypergraph"] +T = TypeVar("T", bound=Union[str, int]) + class Hypergraph: """ - Hypergraph H = (V,E) references a pair of disjoint sets: - V = nodes (vertices) and E = (hyper)edges. - - An HNX Hypergraph is either dynamic or static. - Dynamic hypergraphs can change by adding or subtracting objects - from them. Static hypergraphs require that all of the nodes and edges - be known at creation. A hypergraph is dynamic by default. - - *Dynamic hypergraphs* require the user to keep track of its objects, - by using a unique names for each node and edge. This allows for multi-edge graphs and - inseperable nodes. - - For example: Let V = {1,2,3} and E = {e1,e2,e3}, - where e1 = {1,2}, e2 = {1,2}, and e3 = {1,2,3}. - The edges e1 and e2 contain the same set of nodes and yet - are distinct and must be distinguishable within H. - - In a dynamic hypergraph each node and edge is - instantiated as an Entity and given an identifier or uid. Entities - keep track of inclusion relationships and can be nested. Since - hypergraphs can be quite large, only the entity identifiers will be used - for computation intensive methods, this means the user must take care - to keep a one to one correspondence between their set of uids and - the objects in their hypergraph. See `Honor System`_ - Dynamic hypergraphs are most practical for small to modestly sized - hypergraphs (<1000 objects). - - *Static hypergraphs* store node and edge information in numpy arrays and - are immutable. Each node and edge receives a class generated internal - identifier used for computations so do not require the user to create - different ids for nodes and edges. To create a static hypergraph set - `static = True` in the signature. - - We will create hypergraphs in multiple ways: + Parameters + ---------- - 1. As an empty instance: :: + setsystem : (optional) dict of iterables, dict of dicts,iterable of iterables, + pandas.DataFrame, numpy.ndarray, default = None + See SetSystem above for additional setsystem requirements. + + edge_col : (optional) str | int, default = 0 + column index (or name) in pandas.dataframe or numpy.ndarray, + used for (hyper)edge ids. Will be used to reference edgeids for + all set systems. + + node_col : (optional) str | int, default = 1 + column index (or name) in pandas.dataframe or numpy.ndarray, + used for node ids. Will be used to reference nodeids for all set systems. + + cell_weight_col : (optional) str | int, default = None + column index (or name) in pandas.dataframe or numpy.ndarray used for + referencing cell weights. For a dict of dicts references key in cell + property dicts. + + cell_weights : (optional) Sequence[float,int] | int | float , default = 1.0 + User specified cell_weights or default cell weight. + Sequential values are only used if setsystem is a + dataframe or ndarray in which case the sequence must + have the same length and order as these objects. + Sequential values are ignored for dataframes if cell_weight_col is already + a column in the data frame. + If cell_weights is assigned a single value + then it will be used as default for missing values or when no cell_weight_col + is given. + + cell_properties : (optional) Sequence[int | str] | Mapping[T,Mapping[T,Mapping[str,Any]]], + default = None + Column names from pd.DataFrame to use as cell properties + or a dict assigning cell_property to incidence pairs of edges and + nodes. Will generate a misc_cell_properties, which may have variable lengths per cell. + + misc_cell_properties : (optional) str | int, default = None + Column name of dataframe corresponding to a column of variable + length property dictionaries for the cell. Ignored for other setsystem + types. + + aggregateby : (optional) str, dict, default = 'first' + By default duplicate edge,node incidences will be dropped unless + specified with `aggregateby`. + See pandas.DataFrame.agg() methods for additional syntax and usage + information. + + edge_properties : (optional) pd.DataFrame | dict, default = None + Properties associated with edge ids. + First column of dataframe or keys of dict link to edge ids in + setsystem. + + node_properties : (optional) pd.DataFrame | dict, default = None + Properties associated with node ids. + First column of dataframe or keys of dict link to node ids in + setsystem. + + properties : (optional) pd.DataFrame | dict, default = None + Concatenation/union of edge_properties and node_properties. + By default, the object id is used and should be the first column of + the dataframe, or key in the dict. If there are nodes and edges + with the same ids and different properties then use the edge_properties + and node_properties keywords. + + misc_properties : (optional) int | str, default = None + Column of property dataframes with dtype=dict. Intended for variable + length property dictionaries for the objects. + + edge_weight_prop : (optional) str, default = None, + Name of property in edge_properties to use for weight. + + node_weight_prop : (optional) str, default = None, + Name of property in node_properties to use for weight. + + weight_prop : (optional) str, default = None + Name of property in properties to use for 'weight' + + default_edge_weight : (optional) int | float, default = 1 + Used when edge weight property is missing or undefined. + + default_node_weight : (optional) int | float, default = 1 + Used when node weight property is missing or undefined + + name : (optional) str, default = None + Name assigned to hypergraph + + + ====================== + Hypergraphs in HNX 2.0 + ====================== + + An hnx.Hypergraph H = (V,E) references a pair of disjoint sets: + V = nodes (vertices) and E = (hyper)edges. - >>> H = hnx.Hypergraph() - >>> H.nodes, H.edges - ({}, {}) + HNX allows for multi-edges by distinguishing edges by + their identifiers instead of their contents. For example, if + V = {1,2,3} and E = {e1,e2,e3}, + where e1 = {1,2}, e2 = {1,2}, and e3 = {1,2,3}, + the edges e1 and e2 contain the same set of nodes and yet + are distinct and are distinguishable within H = (V,E). + + New as of version 2.0, HNX provides methods to easily store and + access additional metadata such as cell, edge, and node weights. + Metadata associated with (edge,node) incidences + are referenced as **cell_properties**. + Metadata associated with a single edge or node is referenced + as its **properties**. + + The fundamental object needed to create a hypergraph is a **setsystem**. The + setsystem defines the many-to-many relationships between edges and nodes in + the hypergraph. Cell properties for the incidence pairs can be defined within + the setsystem or in a separate pandas.Dataframe or dict. + Edge and node properties are defined with a pandas.DataFrame or dict. + + SetSystems + ---------- + There are five types of setsystems currently accepted by the library. + + 1. **iterable of iterables** : Barebones hypergraph uses Pandas default + indexing to generate hyperedge ids. Elements must be hashable.: :: + + >>> H = Hypergraph([{1,2},{1,2},{1,2,3}]) + + 2. **dictionary of iterables** : the most basic way to express many-to-many + relationships providing edge ids. The elements of the iterables must be + hashable): :: + + >>> H = Hypergraph({'e1':[1,2],'e2':[1,2],'e3':[1,2,3]}) + + 3. **dictionary of dictionaries** : allows cell properties to be assigned + to a specific (edge, node) incidence. This is particularly useful when + there are variable length dictionaries assigned to each pair: :: + + >>> d = {'e1':{ 1: {'w':0.5, 'name': 'related_to'}, + >>> 2: {'w':0.1, 'name': 'related_to', + >>> 'startdate': '05.13.2020'}}, + >>> 'e2':{ 1: {'w':0.52, 'name': 'owned_by'}, + >>> 2: {'w':0.2}}, + >>> 'e3':{ 1: {'w':0.5, 'name': 'related_to'}, + >>> 2: {'w':0.2, 'name': 'owner_of'}, + >>> 3: {'w':1, 'type': 'relationship'}} + + >>> H = Hypergraph(d, cell_weight_col='w') + + 4. **pandas.DataFrame** For large datasets and for datasets with cell + properties it is most efficient to construct a hypergraph directly from + a pandas.DataFrame. Incidence pairs are in the first two columns. + Cell properties shared by all incidence pairs can be placed in their own + column of the dataframe. Variable length dictionaries of cell properties + particular to only some of the incidence pairs may be placed in a single + column of the dataframe. Representing the data above as a dataframe df: + + +-----------+-----------+-----------+-----------------------------------+ + | col1 | col2 | w | col3 | + +-----------+-----------+-----------+-----------------------------------+ + | e1 | 1 | 0.5 | {'name':'related_to'} | + +-----------+-----------+-----------+-----------------------------------+ + | e1 | 2 | 0.1 | {"name":"related_to", | + | | | | "startdate":"05.13.2020"} | + +-----------+-----------+-----------+-----------------------------------+ + | e2 | 1 | 0.52 | {"name":"owned_by"} | + +-----------+-----------+-----------+-----------------------------------+ + | e2 | 2 | 0.2 | | + +-----------+-----------+-----------+-----------------------------------+ + | ... | ... | ... | {...} | + +-----------+-----------+-----------+-----------------------------------+ + + The first row of the dataframe is used to reference each column. :: + + >>> H = Hypergraph(df,edge_col="col1",node_col="col2", + >>> cell_weight_col="w",misc_cell_properties="col3") + + 5. **numpy.ndarray** For homogeneous datasets given in an ndarray a + pandas dataframe is generated and column names are added from the + edge_col and node_col arguments. Cell properties containing multiple data + types are added with a separate dataframe or dict and passed through the + cell_properties keyword. :: + + >>> arr = np.array([['e1','1'],['e1','2'], + >>> ['e2','1'],['e2','2'], + >>> ['e3','1'],['e3','2'],['e3','3']]) + >>> H = hnx.Hypergraph(arr, column_names=['col1','col2']) + + + Edge and Node Properties + ------------------------ + Properties specific to a single edge or node are passed through the + keywords: **edge_properties, node_properties, properties**. + Properties may be passed as dataframes or dicts. + The first column or index of the dataframe or keys of the dict keys + correspond to the edge and/or node identifiers. + If identifiers are shared among edges and nodes, or are distinct + for edges and nodes, properties may be combined into a single + object and passed to the **properties** keyword. For example: + + +-----------+-----------+---------------------------------------+ + | id | weight | properties | + +-----------+-----------+---------------------------------------+ + | e1 | 5.0 | {'type':'event'} | + +-----------+-----------+---------------------------------------+ + | e2 | 0.52 | {"name":"owned_by"} | + +-----------+-----------+---------------------------------------+ + | ... | ... | {...} | + +-----------+-----------+---------------------------------------+ + | 1 | 1.2 | {'color':'red'} | + +-----------+-----------+---------------------------------------+ + | 2 | .003 | {'name':'Fido','color':'brown'} | + +-----------+-----------+---------------------------------------+ + | 3 | 1.0 | {} | + +-----------+-----------+---------------------------------------+ + + A properties dictionary should have the format: :: + + dp = {id1 : {prop1:val1, prop2,val2,...}, id2 : ... } + + A properties dataframe may be used for nodes and edges sharing ids + but differing in cell properties by adding a level index using 0 + for edges and 1 for nodes: + + +-----------+-----------+-----------+---------------------------+ + | level | id | weight | properties | + +-----------+-----------+-----------+---------------------------+ + | 0 | e1 | 5.0 | {'type':'event'} | + +-----------+-----------+-----------+---------------------------+ + | 0 | e2 | 0.52 | {"name":"owned_by"} | + +-----------+-----------+-----------+---------------------------+ + | ... | ... | ... | {...} | + +-----------+-----------+-----------+---------------------------+ + | 1 | 1.2 | {'color':'red'} | + +-----------+-----------+-----------+---------------------------+ + | 2 | .003 | {'name':'Fido','color':'brown'} | + +-----------+-----------+-----------+---------------------------+ + | ... | ... | ... | {...} | + +-----------+-----------+-----------+---------------------------+ + + + + Weights + ------- + The default key for cell and object weights is "weight". The default value + is 1. Weights may be assigned and/or a new default prescribed in the + constructor using **cell_weight_col** and **cell_weights** for incidence pairs, + and using **edge_weight_prop, node_weight_prop, weight_prop, + default_edge_weight,** and **default_node_weight** for node and edge weights. - 2. From a dictionary of iterables (elements of iterables must be of type hypernetx.Entity or hashable): :: + """ - >>> H = Hypergraph({'a':[1,2,3],'b':[4,5,6]}) - >>> H.nodes, H.edges - # output: (EntitySet(_:Nodes,[1, 2, 3, 4, 5, 6],{}), EntitySet(_:Edges,['b', 'a'],{})) + @warn_nwhy + def __init__( + self, + setsystem: Optional[ + pd.DataFrame + | np.ndarray + | Mapping[T, Iterable[T]] + | Iterable[Iterable[T]] + | Mapping[T, Mapping[T, Mapping[str, Any]]] + ] = None, + edge_col: str | int = 0, + node_col: str | int = 1, + cell_weight_col: Optional[str | int] = "cell_weights", + cell_weights: Sequence[float] | float = 1.0, + cell_properties: Optional[ + Sequence[str | int] | Mapping[T, Mapping[T, Mapping[str, Any]]] + ] = None, + misc_cell_properties_col: Optional[str | int] = None, + aggregateby: str | dict[str, str] = "first", + edge_properties: Optional[pd.DataFrame | dict[T, dict[Any, Any]]] = None, + node_properties: Optional[pd.DataFrame | dict[T, dict[Any, Any]]] = None, + properties: Optional[ + pd.DataFrame | dict[T, dict[Any, Any]] | dict[T, dict[T, dict[Any, Any]]] + ] = None, + misc_properties_col: Optional[str | int] = None, + edge_weight_prop_col: str | int = "weight", + node_weight_prop_col: str | int = "weight", + weight_prop_col: str | int = "weight", + default_edge_weight: Optional[float | None] = None, + default_node_weight: Optional[float | None] = None, + default_weight: float = 1.0, + name: Optional[str] = None, + **kwargs, + ): + self.name = name or "" + self.misc_cell_properties_col = misc_cell_properties = ( + misc_cell_properties_col or "cell_properties" + ) + self.misc_properties_col = misc_properties_col = ( + misc_properties_col or "properties" + ) + self.default_edge_weight = default_edge_weight = ( + default_edge_weight or default_weight + ) + self.default_node_weight = default_node_weight = ( + default_node_weight or default_weight + ) + ### cell properties - 3. From an iterable of iterables: (elements of iterables must be of type hypernetx.Entity or hashable): :: + if setsystem is None: #### Empty Case - >>> H = Hypergraph([{'a','b'},{'b','c'},{'a','c','d'}]) - >>> H.nodes, H.edges - # output: (EntitySet(_:Nodes,['d', 'b', 'c', 'a'],{}), EntitySet(_:Edges,['_1', '_2', '_0'],{})) + self._edges = EntitySet({}) + self._nodes = EntitySet({}) + self._state_dict = {} - 4. From a hypernetx.EntitySet or StaticEntitySet: :: + else: #### DataFrame case + if isinstance(setsystem, pd.DataFrame): + if isinstance(edge_col, int): + self._edge_col = edge_col = setsystem.columns[edge_col] + if isinstance(edge_col, int): + setsystem = setsystem.rename(columns={edge_col: "edges"}) + self._edge_col = edge_col = "edges" + else: + self._edge_col = edge_col - >>> a = Entity('a',{1,2}); b = Entity('b',{2,3}) - >>> E = EntitySet('sample',elements=[a,b]) - >>> H = Hypergraph(E) - >>> H.nodes, H.edges. - # output: (EntitySet(_:Nodes,[1, 2, 3],{}), EntitySet(_:Edges,['b', 'a'],{})) + if isinstance(node_col, int): + self._node_col = node_col = setsystem.columns[node_col] + if isinstance(node_col, int): + setsystem = setsystem.rename(columns={node_col: "nodes"}) + self._node_col = node_col = "nodes" + else: + self._node_col = node_col - All of these constructions apply for both dynamic and static hypergraphs. To - create a static hypergraph set the parameter `static=True`. In addition a static - hypergraph is automatically created if a StaticEntity, StaticEntitySet, or pandas.DataFrame object - is passed to the Hypergraph constructor. + entity = setsystem.copy() - 5. | From a pandas.DataFrame. The dataframe must have at least two columns with headers and there can be no nans. - | By default the first column corresponds to the edge names and the second column to the node names. - | You can specify the columns by restricting the dataframe to the columns of interest in the order: - | :code:`hnx.Hypergraph(df[[edge_column_name,node_column_name]])` - | See :ref:`Colab Tutorials ` Tutorial 6 - Static Hypergraphs and Entities for additional information. + if isinstance(cell_weight_col, int): + self._cell_weight_col = setsystem.columns[cell_weight_col] + else: + self._cell_weight_col = cell_weight_col + if cell_weight_col in entity: + entity = entity.fillna({cell_weight_col: cell_weights}) + else: + entity[cell_weight_col] = cell_weights - Parameters - ---------- - setsystem : (optional) EntitySet, StaticEntitySet, dict, iterable, pandas.dataframe, default: None - See notes above for setsystem requirements. - name : hashable, optional, default: None - If None then a placeholder '_' will be inserted as name - static : boolean, optional, default: False - If True the hypergraph will be immutable, edges and nodes may not be changed. - weights : array-like, optional, default : None - User specified weights corresponding to setsytem of type pandas.DataFrame, - length must equal number of rows in dataframe. - If None, weight for all rows is assumed to be 1. - keep_weights : bool, optional, default : True - Whether or not to use existing weights when input is StaticEntity, or StaticEntitySet. - aggregateby : str, optional, {'count', 'sum', 'mean', 'median', max', 'min', 'first','last', None}, default : 'sum' - Method to aggregate cell_weights of duplicate rows if setsystem is of type pandas.DataFrame or - StaticEntity. If None all cell weights will be set to 1. - use_nwhy : boolean, optional, default : False - If True hypergraph will be static and computations will be done using - C++ backend offered by NWHypergraph. This requires installation of the - NWHypergraph C++ library. Please see the :ref:`NWHy documentation ` for more information. - filepath : str, optional, default : None + if isinstance(cell_properties, Sequence): + cell_properties = [ + c + for c in cell_properties + if not c in [edge_col, node_col, cell_weight_col] + ] + cols = [edge_col, node_col, cell_weight_col] + cell_properties + entity = entity[cols] + elif isinstance(cell_properties, dict): + cp = [] + for idx in entity.index: + edge, node = entity.iloc[idx][[edge_col, node_col]].values + cp.append(cell_properties[edge][node]) + entity["cell_properties"] = cp + + else: ### Cases Other than DataFrame + self._edge_col = edge_col = edge_col or "edges" + if node_col == 1: + self._node_col = node_col = "nodes" + else: + self._node_col = node_col + self._cell_weight_col = cell_weight_col + + if isinstance(setsystem, np.ndarray): + if setsystem.shape[1] != 2: + raise HyperNetXError("Numpy array must have exactly 2 columns.") + entity = pd.DataFrame(setsystem, columns=[edge_col, node_col]) + entity[cell_weight_col] = cell_weights + + elif isinstance(setsystem, dict): + ## check if it is a dict of iterables or a nested dict. if the latter then pull + ## out the nested dicts as cell properties. + ## cell properties must be of the same type as setsystem + + entity = pd.Series(setsystem).explode() + entity = pd.DataFrame( + {edge_col: entity.index.to_list(), node_col: entity.values} + ) - """ + if dict_depth(setsystem) > 2: + cell_props = dict(setsystem) + if isinstance(cell_properties, dict): + ## if setsystem is a dict then cell properties must be a dict + cell_properties = merge_nested_dicts( + cell_props, cell_properties + ) + else: + cell_properties = cell_props + + df = setsystem + cp = [] + wt = [] + for idx in entity.index: + edge, node = entity.values[idx][[0, 1]] + wt.append(df[edge][node].get(cell_weight_col, cell_weights)) + cp.append(df[edge][node]) + entity[self._cell_weight_col] = wt + entity["cell_properties"] = cp - # TODO: remove lambda functions from constructor in H and E. + else: + entity[self._cell_weight_col] = cell_weights - def __init__( - self, - setsystem=None, - name=None, - static=False, - weights=None, - aggregateby="sum", - use_nwhy=False, - filepath=None, - ): - self.filepath = filepath - if use_nwhy: - static = True - try: - import nwhy - - self.nwhy = True - - except: - self.nwhy = False - print("NWHypergraph is not available. Will continue with static=True.") - use_nwhy = False - else: - self.nwhy = False - if not name: - self.name = "" - else: - self.name = name + elif isinstance(setsystem, Iterable): + entity = pd.Series(setsystem).explode() + entity = pd.DataFrame( + {edge_col: entity.index.to_list(), node_col: entity.values} + ) + entity["cell_weights"] = cell_weights - if static == True or ( - isinstance(setsystem, StaticEntitySet) - or isinstance(setsystem, StaticEntity) - or isinstance(setsystem, pd.DataFrame) - ): - self._static = True - if setsystem is None: - self._edges = StaticEntitySet() - self._nodes = StaticEntitySet() - else: - if weights is not None: - E = StaticEntitySet( - entity=setsystem, weights=weights, aggregateby=aggregateby + else: + raise HyperNetXError( + "setsystem is not supported or is in the wrong format." ) + + def props2dict(df=None): + if df is None: + return {} + elif isinstance(df, pd.DataFrame): + return df.set_index(df.columns[0]).to_dict(orient="index") else: - E = StaticEntitySet(entity=setsystem) - self._edges = E - self._nodes = E.restrict_to_levels([1], weights=False, aggregateby=None) - self._nodes._memberships = E.memberships - for n in self._nodes: - self._nodes[n].memberships = self._nodes._memberships[n] ### a bit of a hack to get same functionality from static as dynamic - ### we will have to see if it slows things down too much - else: - self._static = False - if setsystem is None: - setsystem = EntitySet("_", elements=[]) - elif isinstance(setsystem, Entity): - setsystem = EntitySet("_", setsystem.incidence_dict) - elif isinstance(setsystem, dict): - # Must be a dictionary with values equal to iterables of Entities and hashables. - # Keys will be uids for new edges and values of the dictionary will generate the nodes. - setsystem = EntitySet("_", setsystem) - elif not isinstance(setsystem, EntitySet): - # If no ids are given, return default ids indexed by position in iterator - # This should be an iterable of sets - edge_labels = [self.name + str(x) for x in range(len(setsystem))] - setsystem = EntitySet("_", dict(zip(edge_labels, setsystem))) - - _reg = setsystem.registry - _nodes = {k: Entity(k, **_reg[k].properties) for k in _reg} - _elements = {j: {k: _nodes[k] for k in setsystem[j]} for j in setsystem} - _edges = { - j: Entity(j, elements=_elements[j].values(), **setsystem[j].properties) - for j in setsystem - } - - self._edges = EntitySet( - f"{self.name}:Edges", elements=_edges.values(), **setsystem.properties + return dict(df) + + if properties is None: + if edge_properties is not None or node_properties is not None: + if edge_properties is not None: + edge_properties = props2dict(edge_properties) + for e in entity[edge_col].unique(): + if not e in edge_properties: + edge_properties[e] = {} + for v in edge_properties.values(): + v.setdefault(edge_weight_prop_col, default_edge_weight) + else: + edge_properties = {} + if node_properties is not None: + node_properties = props2dict(node_properties) + for nd in entity[node_col].unique(): + if not nd in node_properties: + node_properties[nd] = {} + for v in node_properties.values(): + v.setdefault(node_weight_prop_col, default_node_weight) + else: + node_properties = {} + properties = {0: edge_properties, 1: node_properties} + else: + if isinstance(properties, pd.DataFrame): + if weight_prop_col in properties.columns: + properties = properties.fillna( + {weight_prop_col: default_weight} + ) + elif misc_properties_col in properties.columns: + for idx in properties.index: + if not isinstance( + properties[misc_properties_col][idx], dict + ): + properties[misc_properties_col][idx] = { + weight_prop_col: default_weight + } + else: + properties[misc_properties_col][idx].setdefault( + weight_prop_col, default_weight + ) + else: + properties[weight_prop_col] = default_weight + if isinstance(properties, dict): + if dict_depth(properties) <= 2: + properties = pd.DataFrame( + [ + {"id": k, misc_properties_col: v} + for k, v in properties.items() + ] + ) + for idx in properties.index: + if isinstance(properties[misc_properties_col][idx], dict): + properties[misc_properties_col][idx].setdefault( + weight_prop_col, default_weight + ) + else: + properties[misc_properties_col][idx] = { + weight_prop_col: default_weight + } + elif set(properties.keys()) == {0, 1}: + edge_properties = properties[0] + for e in entity[edge_col].unique(): + if not e in edge_properties: + edge_properties[e] = { + edge_weight_prop_col: default_edge_weight + } + else: + edge_properties[e].setdefault( + edge_weight_prop_col, default_edge_weight + ) + node_properties = properties[1] + for nd in entity[node_col].unique(): + if not nd in node_properties: + node_properties[nd] = { + node_weight_prop_col: default_node_weight + } + else: + node_properties[nd].setdefault( + node_weight_prop_col, default_node_weight + ) + for idx in properties.index: + if not isinstance( + properties[misc_properties_col][idx], dict + ): + properties[misc_properties_col][idx] = { + weight_prop_col: default_weight + } + else: + properties[misc_properties_col][idx].setdefault( + weight_prop_col, default_weight + ) + + self.E = EntitySet( + entity=entity, + level1=edge_col, + level2=node_col, + weight_col=cell_weight_col, + weights=cell_weights, + cell_properties=cell_properties, + misc_cell_props_col=misc_cell_properties_col or "cell_properties", + aggregateby=aggregateby or "sum", + properties=properties, + misc_props_col=misc_properties_col, ) - self._nodes = EntitySet(f"{self.name}:Nodes", elements=_nodes.values()) - if self._static: - temprows, tempcols = self.edges.data.T - tempdata = np.ones(len(temprows), dtype=int) - self.state_dict = { - "data": (temprows, tempcols, tempdata) - } # how can we incorporate the counts into the nwhy hypergraph? - if self.nwhy: - self.g = nwhy.NWHypergraph(*self.state_dict["data"]) - self.nwhy_dict = {"snodelg": dict(), "sedgelg": dict()} - self.state_dict["snodelg"] = dict() - self.state_dict["sedgelg"] = dict() - if self.filepath is not None: - self.save_state(fpath=self.filepath) + + self._edges = self.E + self._nodes = self.E.restrict_to_levels([1]) + self._dataframe = self.E.cell_properties.reset_index() + self._data_cols = data_cols = [self._edge_col, self._node_col] + self._dataframe[data_cols] = self._dataframe[data_cols].astype("category") + + self.__dict__.update(locals()) + self._set_default_state() @property def edges(self): @@ -220,8 +562,7 @@ def edges(self): Returns ------- - StaticEntitySet or EntitySet - If self.isstatic the StaticEntitySet, otherwise EntitySet. + EntitySet """ return self._edges @@ -232,35 +573,64 @@ def nodes(self): Returns ------- - StaticEntitySet or EntitySet - If self.isstatic the StaticEntitySet, otherwise EntitySet. - + EntitySet """ return self._nodes @property - def isstatic(self): + def dataframe(self): + """Returns dataframe of incidence pairs and their properties. + + Returns + ------- + pd.DataFrame + """ + return self._dataframe + + @property + def properties(self): + """Returns dataframe of edge and node properties. + + Returns + ------- + pd.DataFrame """ - Checks whether nodes and edges are immutable + return self.E.properties + + @property + def edge_props(self): + """Dataframe of edge properties + indexed on edge ids Returns ------- - Boolean + pd.DataFrame + """ + return self.E.properties.loc[0] + + @property + def node_props(self): + """Dataframe of node properties + indexed on node ids + Returns + ------- + pd.DataFrame """ - return self._static + return self.E.properties.loc[1] @property def incidence_dict(self): """ - Dictionary keyed by edge uids with values the uids of nodes in each edge + Dictionary keyed by edge uids with values the uids of nodes in each + edge Returns ------- dict """ - return self._edges.incidence_dict + return self.E.incidence_dict @property def shape(self): @@ -272,10 +642,7 @@ def shape(self): tuple """ - if self.nwhy: - return (self.g.number_of_nodes(), self.g.number_of_edges()) - else: - return (len(self._nodes.elements), len(self._edges.elements)) + return len(self._nodes.elements), len(self._edges.elements) def __str__(self): """ @@ -286,7 +653,7 @@ def __str__(self): str """ - return f"Hypergraph({self.edges.elements},name={self.name})" + return f"{self.name}, " def __repr__(self): """ @@ -297,7 +664,7 @@ def __repr__(self): str """ - return f"Hypergraph({self.edges.elements},name={self.name})" + return f"{self.name}, hypernetx.classes.hypergraph.Hypergraph" def __len__(self): """ @@ -308,10 +675,7 @@ def __len__(self): int """ - if self.nwhy: - return self.g.number_of_nodes() - else: - return len(self._nodes) + return len(self._nodes) def __iter__(self): """ @@ -333,10 +697,7 @@ def __contains__(self, item): item : hashable or Entity """ - if isinstance(item, Entity): - return item.uid in self.nodes - else: - return item in self.nodes + return item in self.nodes def __getitem__(self, node): """ @@ -354,94 +715,102 @@ def __getitem__(self, node): """ return self.neighbors(node) - @not_implemented_for("dynamic") - def get_id(self, uid, edges=False): - """ - Return the internally assigned id associated with a label. + def get_cell_properties( + self, edge: str, node: str, prop_name: Optional[str] = None + ) -> Any | dict[str, Any]: + """Get cell properties on a specified edge and node Parameters ---------- - uid : string - User provided name/id/label for hypergraph object - edges : bool, optional - Determines if uid is an edge or node name + edge : str + edgeid + node : str + nodeid + prop_name : str, optional + name of a cell property; if None, all cell properties will be returned Returns ------- - : int - internal id assigned at construction + : int or str or dict of {str: any} + cell property value if `prop_name` is provided, otherwise ``dict`` of all + cell properties and values """ - kdx = (edges + 1) % 2 - # return list(self.edges.labs(kdx)).index(uid) - return int(np.argwhere(self.edges.labs(kdx) == uid)[0]) + if prop_name is None: + return self.edges.get_cell_properties(edge, node) - @not_implemented_for("dynamic") - def get_name(self, id, edges=False): - """ - Return the user defined name/id/label associated to an - internally assigned id. + return self.edges.get_cell_property(edge, node, prop_name) + + def get_properties(self, id, level=None, prop_name=None): + """Returns an object's specific property or all properties Parameters ---------- - id : int - Internally assigned id - edges : bool, optional - Determines if id references an edge or node + id : hashable + edge or node id + level : int | None , optional, default = None + if separate edge and node properties then enter 0 for edges + and 1 for nodes. + prop_name : str | None, optional, default = None + if None then all properties associated with the object will be + returned. Returns ------- - str - User provided name/id/label for hypergraph object + : str or dict + single property or dictionary of properties """ - kdx = (edges + 1) % 2 - return self.edges.labs(kdx)[id] + if prop_name == None: + return self.E.get_properties(id, level=level) + else: + return self.E.get_property(id, prop_name, level=level) - @not_implemented_for("dynamic") - def get_linegraph(self, s, edges=True, use_nwhy=True): + @warn_nwhy + def get_linegraph(self, s=1, edges=True): """ Creates an ::term::s-linegraph for the Hypergraph. - If edges=True (default)then the edges will be the vertices of the line graph. - Two vertices are connected by an s-line-graph edge if the corresponding - hypergraphedges intersect in at least s hypergraph nodes. - If edges=False, the hypergraph nodes will be the vertices of the line graph. - Two vertices are connected if the nodes they correspond to share at least s - incident hyper edges. + If edges=True (default)then the edges will be the vertices of the line + graph. Two vertices are connected by an s-line-graph edge if the + corresponding hypergraph edges intersect in at least s hypergraph nodes. + If edges=False, the hypergraph nodes will be the vertices of the line + graph. Two vertices are connected if the nodes they correspond to share + at least s incident hyper edges. Parameters ---------- s : int The width of the connections. - edges : bool, optional + edges : bool, optional, default = True Determine if edges or nodes will be the vertices in the linegraph. - use_nwhy : bool, optional - Requests that nwhy be used to construct the linegraph. If NWHy is not available this is ignored. Returns ------- nx.Graph A NetworkX graph. """ - if use_nwhy and self.nwhy: - d = self.nwhy_dict - else: - d = self.state_dict + d = self._state_dict key = "sedgelg" if edges else "snodelg" if s in d[key]: return d[key][s] + + if edges: + A, Amap = self.edge_adjacency_matrix(s=s, index=True) + Amaplst = [(k, self.edge_props.loc[k].to_dict()) for k in Amap] else: - if use_nwhy and self.nwhy: - d[key][s] = self.g.s_linegraph(s=s, edges=edges) - else: - if edges: - A = self.edge_adjacency_matrix(s=s) - else: - A = self.adjacency_matrix(s=s) - d[key][s] = nx.from_scipy_sparse_matrix(A) - if self.filepath is not None: - self.save_state(fpath=self.filepath) - return d[key][s] + A, Amap = self.adjacency_matrix(s=s, index=True) + Amaplst = [(k, self.node_props.loc[k].to_dict()) for k in Amap] + + ### TODO: add key function to compute weights lambda x,y : funcval + + A = np.array(np.nonzero(A)) + e1 = np.array([Amap[idx] for idx in A[0]]) + e2 = np.array([Amap[idx] for idx in A[1]]) + A = np.array([e1, e2]).T + g = nx.Graph() + g.add_edges_from(A) + g.add_nodes_from(Amaplst) + d[key][s] = g + return g - @not_implemented_for("dynamic") def set_state(self, **kwargs): """ Allow state_dict updates from outside of class. Use with caution. @@ -451,83 +820,28 @@ def set_state(self, **kwargs): **kwargs key=value pairs to save in state dictionary """ - self.state_dict.update(kwargs) - if self.filepath is not None: - self.save_state(fpath=self.filepath) - - @not_implemented_for("dynamic") - def save_state(self, fpath=None): - """ - Save the hypergraph as an ordered pair: [state_dict,labels] - The hypergraph can be recovered using the command: - - >>> H = hnx.Hypergraph.recover_from_state(fpath) - - Parameters - ---------- - fpath : str, optional - """ - if fpath is None: - fpath = self.filepath or "current_state.p" - pickle.dump([self.state_dict, self.edges.labels], open(fpath, "wb")) + self._state_dict.update(kwargs) - @classmethod - def recover_from_state(cls, fpath="current_state.p", newfpath=None, use_nwhy=True): - """ - Recover a static hypergraph pickled using save_state. - - Parameters - ---------- - fpath : str - Full path to pickle file containing state_dict and labels - of hypergraph - - Returns - ------- - H : Hypergraph - static hypergraph with state dictionary prefilled - """ - temp, labels = pickle.load(open(fpath, "rb")) - recovered_data = np.array(temp["data"])[[0, 1]].T # need to save counts as well - recovered_counts = np.array(temp["data"])[ - [2] - ] # ammend this to store cell weights - E = StaticEntitySet(data=recovered_data, labels=labels) - E.properties["counts"] = recovered_counts - H = Hypergraph(E, use_nwhy=use_nwhy) - H.state_dict.update(temp) - if newfpath == "same": - newfpath = fpath - if newfpath is not None: - H.filepath = newfpath - H.save_state() - return H - - @classmethod - def add_nwhy(cls, h, fpath=None): - """ - Add nwhy functionality to a hypergraph. - - Parameters - ---------- - h : hnx.Hypergraph - fpath : file path for storage of hypergraph state dictionary - - Returns - ------- - hnx.Hypergraph - Returns a copy of h with static set to true and nwhy set to True - if it is available. - - """ + def _set_default_state(self): + """Populate state_dict with default values""" + self._state_dict = {} - if h.isstatic: - sd = h.state_dict - H = Hypergraph(h.edges, use_nwhy=True, filepath=fpath) - H.state_dict.update(sd) - return H - else: - return Hypergraph(StaticEntitySet(h.edges), use_nwhy=True, filepath=fpath) + self._state_dict["dataframe"] = df = self.dataframe + self._state_dict["labels"] = { + "edges": np.array(df[self._edge_col].cat.categories), + "nodes": np.array(df[self._node_col].cat.categories), + } + self._state_dict["data"] = np.array( + [df[self._edge_col].cat.codes, df[self._node_col].cat.codes], dtype=int + ).T + self._state_dict["snodelg"] = dict() ### s: nx.graph + self._state_dict["sedgelg"] = dict() + self._state_dict["neighbors"] = defaultdict(dict) ### s: {node: neighbors} + self._state_dict["edge_neighbors"] = defaultdict( + dict + ) ### s: {edge: edge_neighbors} + self._state_dict["adjacency_matrix"] = dict() ### s: scipy.sparse.csr_matrix + self._state_dict["edge_adjacency_matrix"] = dict() def edge_size_dist(self): """ @@ -538,134 +852,13 @@ def edge_size_dist(self): np.array """ - if self.isstatic: - dist = self.state_dict.get("edge_size_dist", None) - if dist: - return dist - else: - if self.nwhy: - dist = self.g.edge_size_dist() - else: - dist = list(np.array(np.sum(self.incidence_matrix(), axis=0))[0]) - - self.set_state(edge_size_dist=dist) - return dist - else: - return list(np.array(np.sum(self.incidence_matrix(), axis=0))[0]) - - def convert_to_static( - self, - name=None, - use_nwhy=False, - filepath=None, - ): - """ - Returns new static hypergraph with the same dictionary as original hypergraph - - Parameters - ---------- - name : None, optional - Name - use_nwhy : bool, optional, default : False - Description - filepath : None, optional, default : False - Description - - Returned - ------------------ - hnx.Hypergraph - Will have attribute static = True - - Note - ---- - Static hypergraphs store the user defined node and edge names in - a dictionary of labeled lists. The order of the lists provides an - index, which the hypergraph uses in place of the node and edge names - for faster processing. - - """ - if self.isstatic: - return self - else: - edict = self.incidence_dict - E = StaticEntitySet(edict) - return Hypergraph(E, use_nwhy=use_nwhy, filepath=filepath, name=name) - - def remove_static(self, name=None): - """ - Returns dynamic hypergraph - - Parameters - ---------- - name : None, optional - User defined namae of hypergraph - - Returns - ------- - hnx.Hypergraph - A new hypergraph with the same dictionary as self but allowing dynamic - changes to nodes and edges. - If hypergraph is not static, returns self. - """ - if not self.isstatic: - return self - else: - return Hypergraph(self.edges.incidence_dict, name=name) - - def translate(self, idx, edges=False): - """ - Returns the translation of numeric values associated with hypergraph. - Only needed if exposing the static identifiers assigned by the class. - If not static then the idx is returned. - - Parameters - ---------- - idx : int - class assigned integer for internal manipulation of Hypergraph data - edges : bool, optional, default: True - If True then translates from edge index. Otherwise will translate from - node index, default=False - Returns - ------- - : int or string - User assigned identifier corresponding to idx - """ - if self.isstatic: - return self.get_name(idx, edges=edges) + if "edge_size_dist" not in self._state_dict: + dist = np.array(np.sum(self.incidence_matrix(), axis=0))[0].tolist() + self.set_state(edge_size_dist=dist) + return dist else: - return idx - - def s_degree(self, node, s=1): # deprecate this to degree - """ - Same as `degree` - - Parameters - ---------- - node : Entity or hashable - If hashable, then must be uid of node in hypergraph - - s : positive integer, optional, default: 1 - - Returns - ------- - s_degree : int - The degree of a node in the subgraph induced by edges - of size s - - Note - ---- - The :term:`s-degree` of a node is the number of edges of size - at least s that contain the node. - - """ - msg = ( - "s-degree is deprecated and will be removed in" - " release 1.0.0. Use degree(node,s=int) instead." - ) - - warnings.warn(msg, DeprecationWarning) - return self.degree(node, s) + return self._state_dict["edge_size_dist"] def degree(self, node, s=1, max_size=None): """ @@ -675,9 +868,9 @@ def degree(self, node, s=1, max_size=None): ---------- node : hashable identifier for the node. - s : positive integer, optional, default: 1 + s : positive integer, optional, default 1 smallest size of edge to consider in degree - max_size : positive integer or None, optional, default: None + max_size : positive integer or None, optional, default = None largest size of edge to consider in degree Returns @@ -685,26 +878,15 @@ def degree(self, node, s=1, max_size=None): : int """ - if self.isstatic: - ndx = self.get_id(node) - if self.nwhy: - return self.g.degree(ndx, min_size=s, max_size=None) - else: - memberships = set(self.nodes.memberships[node]) - else: - memberships = set(self.nodes[node].memberships) - - if max_size is not None: - return len( - set( - e - for e in memberships - if len(self.edges[e]) in range(s, max_size + 1) - ) - ) - elif s > 1: - return len(set(e for e in memberships if len(self.edges[e]) >= s)) + if s == 1 and max_size == None: + return len(self.E.memberships[node]) else: + memberships = set() + for edge in self.E.memberships[node]: + size = len(self.edges[edge]) + if size >= s and (max_size is None or size <= max_size): + memberships.add(edge) + return len(memberships) def size(self, edge, nodeset=None): @@ -724,12 +906,8 @@ def size(self, edge, nodeset=None): """ if nodeset is not None: return len(set(nodeset).intersection(set(self.edges[edge]))) - else: - if self.nwhy: - edx = self.get_id(edge,edges=True) - return self.g.size(edx) - else: - return len(self.edges[edge]) + + return len(self.edges[edge]) def number_of_nodes(self, nodeset=None): """ @@ -737,7 +915,7 @@ def number_of_nodes(self, nodeset=None): Parameters ---------- - nodeset : an interable of Entities, optional, default: None + nodeset : an interable of Entities, optional, default = None If None, then return the number of nodes in hypergraph. Returns @@ -745,13 +923,10 @@ def number_of_nodes(self, nodeset=None): number_of_nodes : int """ - if nodeset: + if nodeset is not None: return len([n for n in self.nodes if n in nodeset]) - else: - if self.nwhy == True: - return self.g.number_of_nodes() - else: - return len(self.nodes) + + return len(self.nodes) def number_of_edges(self, edgeset=None): """ @@ -759,7 +934,7 @@ def number_of_edges(self, edgeset=None): Parameters ---------- - edgeset : an interable of Entities, optional, default: None + edgeset : an iterable of Entities, optional, default = None If None, then return the number of edges in hypergraph. Returns @@ -768,11 +943,8 @@ def number_of_edges(self, edgeset=None): """ if edgeset: return len([e for e in self.edges if e in edgeset]) - else: - if self.nwhy == True: - return self.g.number_of_edges() - else: - return len(self.edges) + + return len(self.edges) def order(self): """ @@ -782,10 +954,7 @@ def order(self): ------- order : int """ - if self.nwhy: - return self.g.number_of_nodes() - else: - return len(self.nodes) + return len(self.nodes) def dim(self, edge): """ @@ -802,42 +971,33 @@ def neighbors(self, node, s=1): node : hashable or Entity uid for a node in hypergraph or the node Entity - s : int, list, optional, default : 1 + s : int, list, optional, default = 1 Minimum number of edges shared by neighbors with node. Returns ------- - : list - List of neighbors + neighbors : list + s-neighbors share at least s edges in the hypergraph """ - if not node in self.nodes: - print(f"Node is not in hypergraph {self.name}.") - return - - if self.isstatic: - g = self.get_linegraph(s=s, edges=False) - ndx = self.get_id(node) - if self.nwhy == True: - nbrs = g.s_neighbors(ndx) - else: - nbrs = list(g.neighbors(ndx)) - return [self.translate(nb, edges=False) for nb in nbrs] - + if node not in self.nodes: + print(f"{node} is not in hypergraph {self.name}.") + return None + if node in self._state_dict["neighbors"][s]: + return self._state_dict["neighbors"][s][node] else: - node = self.nodes[ - node - ].uid # this allows node to be an Entity instead of a string - memberships = set(self.nodes[node].memberships).intersection( - self.edges.uidset - ) - edgeset = {e for e in memberships if len(self.edges[e]) >= s} - - neighborlist = set() - for e in edgeset: - neighborlist.update(self.edges[e].uidset) - neighborlist.discard(node) - return list(neighborlist) + M = self.incidence_matrix() + rdx = self._state_dict["labels"]["nodes"] + jdx = np.where(rdx == node) + idx = (M[jdx].dot(M.T) >= s) * 1 + idx = np.nonzero(idx)[1] + neighbors = list(rdx[idx]) + if len(neighbors) > 0: + neighbors.remove(node) + self._state_dict["neighbors"][s][node] = neighbors + else: + self._state_dict["neighbors"][s][node] = [] + return neighbors def edge_neighbors(self, edge, s=1): """ @@ -848,7 +1008,7 @@ def edge_neighbors(self, edge, s=1): edge : hashable or Entity uid for a edge in hypergraph or the edge Entity - s : int, list, optional, default : 1 + s : int, list, optional, default = 1 Minimum number of nodes shared by neighbors edge node. Returns @@ -857,237 +1017,25 @@ def edge_neighbors(self, edge, s=1): List of edge neighbors """ - if not edge in self.edges: - print(f"Edge is not in hypergraph {self.name}.") - return - - if self.isstatic: - g = self.get_linegraph(s=s, edges=True) - edx = self.get_id(edge, edges=True) - if self.nwhy == True: - nbrs = g.s_neighbors(edx) - else: - nbrs = list(g.neighbors(edx)) - return [self.translate(nb, edges=True) for nb in nbrs] - - else: - node = self.edges[edge].uid - return self.dual().neighbors(node, s=s) - - @not_implemented_for("static") - def remove_node(self, node): - """ - Removes node from edges and deletes reference in hypergraph nodes - - Parameters - ---------- - node : hashable or Entity - a node in hypergraph - - Returns - ------- - hypergraph : Hypergraph - - """ - if not node in self._nodes: - return self - else: - if not isinstance(node, Entity): - node = self._nodes[node] - for edge in node.memberships: - self._edges[edge].remove(node) - self._nodes.remove(node) - return self - - @not_implemented_for("static") - def remove_nodes(self, node_set): - """ - Removes nodes from edges and deletes references in hypergraph nodes - - Parameters - ---------- - node_set : an iterable of hashables or Entities - Nodes in hypergraph - - Returns - ------- - hypergraph : Hypergraph - - """ - for node in node_set: - self.remove_node(node) - return self - - @not_implemented_for("static") - def _add_nodes_from(self, nodes): - """ - Private helper method instantiates new nodes when edges added to hypergraph. - - Parameters - ---------- - nodes : iterable of hashables or Entities - - """ - for node in nodes: - if node in self._edges: - raise HyperNetXError("Node already an edge.") - elif node in self._nodes and isinstance(node, Entity): - self._nodes[node].__dict__.update(node.properties) - elif node not in self._nodes: - if isinstance(node, Entity): - self._nodes.add(Entity(node.uid, **node.properties)) - else: - self._nodes.add(Entity(node)) - - @not_implemented_for("static") - def add_edge(self, edge): - """ - - Adds a single edge to hypergraph. - - Parameters - ---------- - edge : hashable or Entity - If hashable the edge returned will be empty. - Returns - ------- - hypergraph : Hypergraph - - Notes - ----- - When adding an edge to a hypergraph children must be removed - so that nodes do not have elements. - Each node (element of edge) must be instantiated as a node, - making sure its uid isn't already present in the self. - If an added edge contains nodes that cannot be added to hypergraph - then an error will be raised. - - """ - if edge in self._edges: - warnings.warn("Cannot add edge. Edge already in hypergraph") - elif edge in self._nodes: - warnings.warn("Cannot add edge. Edge is already a Node") - elif isinstance(edge, Entity): - if len(edge) > 0: - self._add_nodes_from(edge.elements.values()) - self._edges.add( - Entity( - edge.uid, - elements=[self._nodes[k] for k in edge], - **edge.properties, - ) - ) - for n in edge.elements: - self._nodes[n].memberships[edge.uid] = self._edges[edge.uid] - else: - self._edges.add(Entity(edge.uid, **edge.properties)) + if edge not in self.edges: + print(f"Edge is not in hypergraph {self.name}.") + return None + if edge in self._state_dict["edge_neighbors"][s]: + return self._state_dict["edge_neighbors"][s][edge] else: - self._edges.add(Entity(edge)) # this generates an empty edge - return self - - @not_implemented_for("static") - def add_edges_from(self, edge_set): - """ - Add edges to hypergraph. - - Parameters - ---------- - edge_set : iterable of hashables or Entities - For hashables the edges returned will be empty. - - Returns - ------- - hypergraph : Hypergraph - - """ - for edge in edge_set: - self.add_edge(edge) - return self - - @not_implemented_for("static") - def add_node_to_edge(self, node, edge): - """ - - Adds node to an edge in hypergraph edges - - Parameters - ---------- - node: hashable or Entity - If Entity, only uid and properties will be used. - If uid is already in nodes then the known node will - be used - - edge: uid of edge or edge, must belong to self.edges - - Returns - ------- - hypergraph : Hypergraph - - """ - if edge in self._edges: - if not isinstance(edge, Entity): - edge = self._edges[edge] - if node in self._nodes: - self._edges[edge].add(self._nodes[node]) + M = self.incidence_matrix() + cdx = self._state_dict["labels"]["edges"] + jdx = np.where(cdx == edge) + idx = (M.T[jdx].dot(M) >= s) * 1 + idx = np.nonzero(idx)[1] + edge_neighbors = list(cdx[idx]) + if len(edge_neighbors) > 0: + edge_neighbors.remove(edge) + self._state_dict["edge_neighbors"][s][edge] = edge_neighbors else: - if not isinstance(node, Entity): - node = Entity(node) - else: - node = Entity(node.uid, **node.properties) - self._edges[edge].add(node) - self._nodes.add(node) - - return self - - @not_implemented_for("static") - def remove_edge(self, edge): - """ - Removes a single edge from hypergraph. - - Parameters - ---------- - edge : hashable or Entity - - Returns - ------- - hypergraph : Hypergraph - - Notes - ----- - - Deletes reference to edge from all of its nodes. - If any of its nodes do not belong to any other edges - the node is dropped from self. - - """ - if edge in self._edges: - if not isinstance(edge, Entity): - edge = self._edges[edge] - for node in edge.uidset: - edge.remove(node) - if len(self._nodes[node]._memberships) == 1: - self._nodes.remove(node) - self._edges.remove(edge) - return self - - @not_implemented_for("static") - def remove_edges(self, edge_set): - """ - Removes edges from hypergraph. - - Parameters - ---------- - edge_set : iterable of hashables or Entities - - Returns - ------- - hypergraph : Hypergraph - - """ - for edge in edge_set: - self.remove_edge(edge) - return self + self._state_dict["edge_neighbors"][s][edge] = [] + return edge_neighbors def incidence_matrix(self, weights=False, index=False): """ @@ -1095,12 +1043,12 @@ def incidence_matrix(self, weights=False, index=False): Parameters ---------- - weights : bool, default=False + weights : bool, default =False If False all nonzero entries are 1. If True and self.static all nonzero entries are filled by self.edges.cell_weights dictionary values. - index : boolean, optional, default False + index : boolean, optional, default = False If True return will include a dictionary of node uid : row number and edge uid : column number @@ -1108,171 +1056,149 @@ def incidence_matrix(self, weights=False, index=False): ------- incidence_matrix : scipy.sparse.csr.csr_matrix or np.ndarray - row dictionary : dict - Dictionary identifying rows with nodes + row_index : list + index of node ids for rows - column dictionary : dict - Dictionary identifying columns with edges - - """ - if self.isstatic: - if weights == False: - mat = self.state_dict.get("incidence_matrix", None) - if mat is None: - mat = self.edges.incidence_matrix() - self.state_dict["incidence_matrix"] = mat - if index: - rdict = dict(enumerate(self.edges.labs(1))) - cdict = dict(enumerate(self.edges.labs(0))) - return mat, rdict, cdict - else: - return mat - if weights == True: - mat = self.state_dict.get("weighted_incidence_matrix", None) - if mat is None: - mat = self.edges.incidence_matrix(weights=True) - self.state_dict["weighted_incidence_matrix"] = mat - if index: - rdict = dict(enumerate(self.edges.labs(1))) - cdict = dict(enumerate(self.edges.labs(0))) - return mat, rdict, cdict - else: - return mat - else: - return self.edges.incidence_matrix(index=index) + col_index : list + index of edge ids for columns - @staticmethod - def _incidence_to_adjacency(M, s=1, weights=False): """ - Helper method to obtain adjacency matrix from - boolean incidence matrix for s-metrics. - Self loops are not supported. - The adjacency matrix will define an s-linegraph. + sdkey = "incidence_matrix" + if weights: + sdkey = "weighted_" + sdkey - Parameters - ---------- - M : scipy.sparse.csr.csr_matrix - incidence matrix of 0's and 1's - - s : int, optional, default: 1 - - # weights : bool, dict optional, default=True - # If False all nonzero entries are 1. - # Otherwise, weights will be as in product. - - Returns - ------- - a matrix : scipy.sparse.csr.csr_matrix - - """ - M = csr_matrix(M) - weights = False ## currently weighting is not supported + if sdkey in self._state_dict: + M = self._state_dict[sdkey] + else: + df = self.dataframe + data_cols = [self._node_col, self._edge_col] + if weights == True: + data = df[self._cell_weight_col].values + M = csr_matrix( + (data, tuple(np.array(df[col].cat.codes) for col in data_cols)) + ) + else: + M = csr_matrix( + ( + [1] * len(df), + tuple(np.array(df[col].cat.codes) for col in data_cols), + ) + ) + self._state_dict[sdkey] = M - if weights == False: - A = M.dot(M.transpose()) - A.setdiag(0) - A = (A >= s) * 1 - return A + if index == True: + rdx = self.dataframe[self._node_col].cat.categories + cdx = self.dataframe[self._edge_col].cat.categories + return M, rdx, cdx + else: + return M - def adjacency_matrix(self, index=False, s=1):## , weights=False): + def adjacency_matrix(self, s=1, index=False, remove_empty_rows=False): """ - The sparse weighted :term:`s-adjacency matrix` + The :term:`s-adjacency matrix` for the hypergraph. Parameters ---------- - s : int, optional, default: 1 + s : int, optional, default = 1 - index: boolean, optional, default: False - if True, will return a rowdict of row to node uid + index: boolean, optional, default = False + if True, will return the index of ids for rows and columns - weights: bool, default=True - If False all nonzero entries are 1. - If True adjacency matrix will depend on weighted incidence matrix, + remove_empty_rows: boolean, optional, default = False Returns ------- adjacency_matrix : scipy.sparse.csr.csr_matrix - row dictionary : dict + node_index : list + index of ids for rows and columns """ - weights = False ## Currently default weights are not supported. - M = self.incidence_matrix(index=index, weights=weights) - if index: - return Hypergraph._incidence_to_adjacency(M[0], s=s, weights=weights), M[1] + try: + A = self._state_dict["adjacency_matrix"][s] + except: + M = self.incidence_matrix() + A = M @ (M.T) + A.setdiag(0) + A = (A >= s) * 1 + self._state_dict["adjacency_matrix"][s] = A + if index == True: + return A, self._state_dict["labels"]["nodes"] else: - return Hypergraph._incidence_to_adjacency(M, s=s, weights=weights) + return A - def edge_adjacency_matrix(self, index=False, s=1, weights=False): + def edge_adjacency_matrix(self, s=1, index=False): """ - The weighted :term:`s-adjacency matrix` for the dual hypergraph. + The :term:`s-adjacency matrix` for the dual hypergraph. Parameters ---------- - s : int, optional, default: 1 - - index: boolean, optional, default: False - if True, will return a coldict of column to edge uid - - sparse: boolean, optional, default: True + s : int, optional, default 1 - weighted: boolean, optional, default: True + index: boolean, optional, default = False + if True, will return the index of ids for rows and columns Returns ------- - edge_adjacency_matrix : scipy.sparse.csr.csr_matrix or numpy.ndarray + edge_adjacency_matrix : scipy.sparse.csr.csr_matrix - column dictionary : dict + edge_index : list + index of ids for rows and columns Notes ----- This is also the adjacency matrix for the line graph. Two edges are s-adjacent if they share at least s nodes. - If index=True, returns a dictionary column_index:edge_uid + If remove_zeros is True will return the auxillary matrix """ - weights=False ## Currently default weights are not supported - - M = self.incidence_matrix(index=index, weights=weights) - if index: - return ( - Hypergraph._incidence_to_adjacency( - M[0].transpose(), s=s, weights=weights - ), - M[2], - ) + try: + A = self._state_dict["edge_adjacency_matrix"][s] + except: + M = self.incidence_matrix() + A = (M.T) @ (M) + A.setdiag(0) + A = (A >= s) * 1 + self._state_dict["edge_adjacency_matrix"][s] = A + if index == True: + return A, self._state_dict["labels"]["edges"] else: - return Hypergraph._incidence_to_adjacency( - M.transpose(), s=s, weights=weights - ) + return A - def auxiliary_matrix(self, s=1, index=False): + def auxiliary_matrix(self, s=1, node=True, index=False): """ - The unweighted :term:`s-auxiliary matrix` for hypergraph + The unweighted :term:`s-edge or node auxiliary matrix` for hypergraph Parameters ---------- - s : int - index : bool, optional, default: False - return a dictionary of labels for the rows of the matrix - + s : int, optional, default = 1 + node : bool, optional, default = True + whether to return based on node or edge adjacencies Returns ------- - auxiliary_matrix : scipy.sparse.csr.csr_matrix or numpy.ndarray - Will return the same type of matrix as self.arr - - Notes - ----- - Creates subgraph by restricting to edges of cardinality at least s. - Returns the unweighted s-edge adjacency matrix for the subgraph. + auxiliary_matrix : scipy.sparse.csr.csr_matrix + Node/Edge adjacency matrix with empty rows and columns + removed + index : np.array + row and column index of userids """ + if node == True: + A, Amap = self.adjacency_matrix(s, index=True) + else: + A, Amap = self.edge_adjacency_matrix(s, index=True) - edges = [e for e in self.edges if len(self.edges[e]) >= s] - H = self.restrict_to_edges(edges) - return H.edge_adjacency_matrix(s=s, index=index, weights=False) + idx = np.nonzero(np.sum(A, axis=1))[0] + if len(idx) < A.shape[0]: + B = A[idx][:, idx] + else: + B = A + if index: + return B, Amap[idx] + else: + return B def bipartite(self): """ @@ -1285,169 +1211,149 @@ def bipartite(self): Notes ----- Creates a bipartite networkx graph from hypergraph. - The nodes and (hyper)edges of hypergraph become the nodes of bipartite graph. - For every (hyper)edge e in the hypergraph and node n in e there is an edge (n,e) - in the graph. + The nodes and (hyper)edges of hypergraph become the nodes of bipartite + graph. For every (hyper)edge e in the hypergraph and node n in e there + is an edge (n,e) in the graph. """ B = nx.Graph() - E = self.edges - V = self.nodes - B.add_nodes_from(E, bipartite=1) - B.add_nodes_from(V, bipartite=0) - B.add_edges_from([(v, e) for e in E for v in self.edges[e]]) + nodes = self._state_dict["labels"]["nodes"] + edges = self._state_dict["labels"]["edges"] + B.add_nodes_from(self.edges, bipartite=0) + B.add_nodes_from(self.nodes, bipartite=1) + B.add_edges_from([(v, e) for e in self.edges for v in self.edges[e]]) return B - def dual(self, name=None): + def dual(self, name=None, switch_names=True): """ - Constructs a new hypergraph with roles of edges and nodes of hypergraph reversed. + Constructs a new hypergraph with roles of edges and nodes of hypergraph + reversed. Parameters ---------- - name : hashable + name : hashable, optional + + switch_names : bool, optional, default = True + reverses edge_col and node_col names + unless edge_col = 'edges' and node_col = 'nodes' Returns ------- - dual : hypergraph - """ - if self.isstatic: - E = self.edges.restrict_to_levels((1, 0)) - return Hypergraph(E, name=name, use_nwhy=self.nwhy) - else: - E = defaultdict(list) - for k, v in self.edges.incidence_dict.items(): - for n in v: - E[n].append(k) - return Hypergraph(E, name=name) - - def _collapse_nwhy(self, edges, rec): - """ - Helper method for collapsing nodes and edges when hypergraph - is static and using nwhy - - Parameters - ---------- - edges : bool - Collapse the edges if True, otherwise the nodes - rec : bool - return the equivalence classes - """ - - if edges: - d = self.g.collapse_edges(return_equivalence_class=rec) - else: - d = self.g.collapse_nodes(return_equivalence_class=rec) - - if rec: - en = { - self.get_name( - k, edges=edges - ): f"{self.get_name(k,edges=edges)}:{len(v)}" - for k, v in d.items() - } - ec = { - f"{self.get_name(k,edges=edges)}:{len(v)}": { - self.get_name(vd, edges=edges) for vd in v - } - for k, v in d.items() - } - else: - en = { - self.get_name( - k, edges=edges - ): f"{self.get_name(k,edges=edges)}:{v.pop()}" - for k, v in d.items() - } - ec = {} - lev = self.edges.keys[1 - 1 * edges] - E = self.edges.restrict_to_indices(sorted(d.keys()), level=1 - 1 * edges) - E.labels[str(lev)] = np.array([en[k] for k in E.labels[lev]]) - if rec: - return E, ec - else: - return E + : hypergraph + + """ + dfp = self.edges.properties.copy() + if "level" in dfp.columns: + dfp = dfp.reset_index() + dfp.level = dfp.level.apply(lambda x: 1 * (x == 0)) + dfp = dfp.set_index(["level", "id"]) + + edge, node, wt = self._edge_col, self._node_col, self._cell_weight_col + df = self.dataframe.copy() + cprops = [col for col in df.columns if not col in [edge, node, wt]] + + df[[edge, node]] = df[[node, edge]] + if edge != "edges" or node != "nodes": + df = df.rename(columns={edge: self._node_col, node: self._edge_col}) + node = self._edge_col + edge = self._node_col + + return Hypergraph( + df, + edge_col=edge, + node_col=node, + cell_weight_col=wt, + cell_properties=cprops, + properties=dfp, + name=name, + ) def collapse_edges( self, name=None, + return_equivalence_classes=False, use_reps=None, return_counts=None, - return_equivalence_classes=False, ): """ - Constructs a new hypergraph gotten by identifying edges containing the same nodes + Constructs a new hypergraph gotten by identifying edges containing the + same nodes Parameters ---------- - name : hashable, optional, default: None + name : hashable, optional, default = None - return_equivalence_classes: boolean, optional, default: False - Returns a dictionary of edge equivalence classes keyed by frozen sets of nodes + return_equivalence_classes: boolean, optional, default = False + Returns a dictionary of edge equivalence classes keyed by frozen + sets of nodes Returns ------- new hypergraph : Hypergraph - Equivalent edges are collapsed to a single edge named by a representative of the equivalent - edges followed by a colon and the number of edges it represents. + Equivalent edges are collapsed to a single edge named by a + representative of the equivalent edges followed by a colon and the + number of edges it represents. equivalence_classes : dict - A dictionary keyed by representative edge names with values equal to the edges in - its equivalence class + A dictionary keyed by representative edge names with values equal + to the edges in its equivalence class Notes ----- Two edges are identified if their respective elements are the same. - Using this as an equivalence relation, the uids of the edges are partitioned into - equivalence classes. + Using this as an equivalence relation, the uids of the edges are + partitioned into equivalence classes. - A single edge from the collapsed edges followed by a colon and the number of elements - in its equivalence class as uid for the new edge + A single edge from the collapsed edges followed by a colon and the + number of elements in its equivalence class as uid for the new edge """ if use_reps is not None or return_counts is not None: msg = """ - use_reps ane return_counts are no longer supported keyword arguments and will throw - an error in the next release. - collapsed hypergraph automatically names collapsed objects by a string "rep:count" + use_reps ane return_counts are no longer supported keyword + arguments and will throw an error in the next release. + collapsed hypergraph automatically names collapsed objects by a + string "rep:count" """ warnings.warn(msg, DeprecationWarning) - if self.nwhy: - temp = self._collapse_nwhy(True, return_equivalence_classes) - else: - temp = self.edges.collapse_identical_elements( - "_", return_equivalence_classes=return_equivalence_classes - ) + temp = self.edges.collapse_identical_elements( + return_equivalence_classes=return_equivalence_classes + ) + if return_equivalence_classes: - return Hypergraph(temp[0], name, use_nwhy=self.nwhy), temp[1] - else: - return Hypergraph(temp, name, use_nwhy=self.nwhy) + return Hypergraph(temp[0].incidence_dict, name), temp[1] + + return Hypergraph(temp.incidence_dict, name) def collapse_nodes( self, name=None, - use_reps=True, - return_counts=True, return_equivalence_classes=False, + use_reps=None, + return_counts=None, ): """ - Constructs a new hypergraph gotten by identifying nodes contained by the same edges + Constructs a new hypergraph gotten by identifying nodes contained by + the same edges Parameters ---------- - name: str, optional, default: None + name: str, optional, default = None - return_equivalence_classes: boolean, optional, default: False - Returns a dictionary of node equivalence classes keyed by frozen sets of edges + return_equivalence_classes: boolean, optional, default = False + Returns a dictionary of node equivalence classes keyed by frozen + sets of edges - use_reps : boolean, optional, default: False - Deprecated, this no longer works and will be removed - Choose a single element from the collapsed nodes as uid for the new node, otherwise uses - a frozen set of the uids of nodes in the equivalence class + use_reps : boolean, optional, default = False - Deprecated, this no + longer works and will be removed. Choose a single element from the + collapsed nodes as uid for the new node, otherwise uses a frozen + set of the uids of nodes in the equivalence class - return_counts: boolean, - Deprecated, this no longer works and will be removed - if use_reps is True the new nodes have uids given by a tuple of the rep - and the count + return_counts: boolean, - Deprecated, this no longer works and will be + removed if use_reps is True the new nodes have uids given by a + tuple of the rep and the count Returns ------- @@ -1456,52 +1362,48 @@ def collapse_nodes( Notes ----- Two nodes are identified if their respective memberships are the same. - Using this as an equivalence relation, the uids of the nodes are partitioned into - equivalence classes. A single member of the equivalence class is chosen to represent - the class followed by the number of members of the class. + Using this as an equivalence relation, the uids of the nodes are + partitioned into equivalence classes. A single member of the + equivalence class is chosen to represent the class followed by the + number of members of the class. Example ------- - >>> h = Hypergraph(EntitySet('example',elements=[Entity('E1', ['a','b']),Entity('E2',['a','b'])])) + >>> h = Hypergraph(EntitySet('example',elements=[Entity('E1', / + ['a','b']),Entity('E2',['a','b'])])) >>> h.incidence_dict {'E1': {'a', 'b'}, 'E2': {'a', 'b'}} >>> h.collapse_nodes().incidence_dict - {'E1': {frozenset({'a', 'b'})}, 'E2': {frozenset({'a', 'b'})}} ### Fix this + {'E1': {frozenset({'a', 'b'})}, 'E2': {frozenset({'a', 'b'})}} + ### Fix this >>> h.collapse_nodes(use_reps=True).incidence_dict {'E1': {('a', 2)}, 'E2': {('a', 2)}} """ if use_reps is not None or return_counts is not None: msg = """ - use_reps ane return_counts are no longer supported keyword arguments and will throw + use_reps and return_counts are no longer supported keyword arguments and will throw an error in the next release. collapsed hypergraph automatically names collapsed objects by a string "rep:count" """ warnings.warn(msg, DeprecationWarning) - if self.nwhy: - temp = self._collapse_nwhy(False, return_equivalence_classes) - if return_equivalence_classes: - return Hypergraph(temp[0], name, use_nwhy=self.nwhy), temp[1] - else: - return Hypergraph(temp, name, use_nwhy=self.nwhy) - else: - temp = self.dual().edges.collapse_identical_elements( - "_", return_equivalence_classes=return_equivalence_classes - ) + temp = self.dual().edges.collapse_identical_elements( + return_equivalence_classes=return_equivalence_classes + ) - if return_equivalence_classes: - return Hypergraph(temp[0], name, use_nwhy=self.nwhy).dual(), temp[1] - else: - return Hypergraph(temp, name, use_nwhy=self.nwhy).dual() + if return_equivalence_classes: + return Hypergraph(temp[0].incidence_dict).dual(), temp[1] + + return Hypergraph(temp.incidence_dict, name).dual() def collapse_nodes_and_edges( self, name=None, - use_reps=True, - return_counts=True, return_equivalence_classes=False, + use_reps=None, + return_counts=None, ): """ Returns a new hypergraph by collapsing nodes and edges. @@ -1509,17 +1411,19 @@ def collapse_nodes_and_edges( Parameters ---------- - name: str, optional, default: None + name: str, optional, default = None - use_reps: boolean, optional, default: False - Choose a single element from the collapsed elements as a representative + use_reps: boolean, optional, default = False + Choose a single element from the collapsed elements as a + representative - return_counts: boolean, optional, default: True - if use_reps is True the new elements are keyed by a tuple of the rep - and the count + return_counts: boolean, optional, default = True + if use_reps is True the new elements are keyed by a tuple of the + rep and the count - return_equivalence_classes: boolean, optional, default: False - Returns a dictionary of edge equivalence classes keyed by frozen sets of nodes + return_equivalence_classes: boolean, optional, default = False + Returns a dictionary of edge equivalence classes keyed by frozen + sets of nodes Returns ------- @@ -1527,16 +1431,18 @@ def collapse_nodes_and_edges( Notes ----- - Collapses the Nodes and Edges EntitySets. Two nodes(edges) are duplicates - if their respective memberships(elements) are the same. Using this as an - equivalence relation, the uids of the nodes(edges) are partitioned into - equivalence classes. A single member of the equivalence class is chosen to represent - the class followed by the number of members of the class. + Collapses the Nodes and Edges EntitySets. Two nodes(edges) are + duplicates if their respective memberships(elements) are the same. + Using this as an equivalence relation, the uids of the nodes(edges) + are partitioned into equivalence classes. A single member of the + equivalence class is chosen to represent the class followed by the + number of members of the class. Example ------- - >>> h = Hypergraph(EntitySet('example',elements=[Entity('E1', ['a','b']),Entity('E2',['a','b'])])) + >>> h = Hypergraph(EntitySet('example',elements=[Entity('E1', / + ['a','b']),Entity('E2',['a','b'])])) >>> h.incidence_dict {'E1': {'a', 'b'}, 'E2': {'a', 'b'}} >>> h.collapse_nodes_and_edges().incidence_dict ### Fix this @@ -1545,9 +1451,10 @@ def collapse_nodes_and_edges( """ if use_reps is not None or return_counts is not None: msg = """ - use_reps ane return_counts are no longer supported keyword arguments and will throw - an error in the next release. - collapsed hypergraph automatically names collapsed objects by a string "rep:count" + use_reps and return_counts are no longer supported keyword + arguments and will throw an error in the next release. + collapsed hypergraph automatically names collapsed objects by a + string "rep:count" """ warnings.warn(msg, DeprecationWarning) @@ -1557,153 +1464,144 @@ def collapse_nodes_and_edges( ) ntemp, eeq = temp.collapse_edges(name=name, return_equivalence_classes=True) return ntemp, neq, eeq - else: - temp = self.collapse_nodes(name="temp") - return temp.collapse_edges(name=name) - def restrict_to_edges(self, edgeset, name=None): - """ - Constructs a hypergraph using a subset of the edges in hypergraph + temp = self.collapse_nodes(name="temp") + return temp.collapse_edges(name=name) + + def restrict_to_nodes(self, nodes, name=None): + """New hypergraph gotten by restricting to nodes Parameters ---------- - edgeset: iterable of hashables or Entities - A subset of elements of the hypergraph edges - - name: str, optional + nodes : Iterable + nodeids to restrict to Returns ------- - new hypergraph : Hypergraph - """ - if self._static: - E = self._edges - setsystem = E.restrict_to(sorted(E.indices(E.keys[0], list(edgeset)))) - return Hypergraph(setsystem, name=name, use_nwhy=self.nwhy) - else: - inneredges = set() - for e in edgeset: - if isinstance(e, Entity): - inneredges.add(e.uid) - else: - inneredges.add(e) - return Hypergraph({e: self.edges[e] for e in inneredges}, name=name) + : hnx. Hypergraph - def restrict_to_nodes(self, nodeset, name=None): """ - Constructs a new hypergraph by restricting the edges in the hypergraph to - the nodes referenced by nodeset. + keys = set(self._state_dict["labels"]["nodes"]).difference(nodes) + return self.remove(keys, level=1) + + def restrict_to_edges(self, edges, name=None): + """New hypergraph gotten by restricting to edges Parameters ---------- - nodeset: iterable of hashables - References a subset of elements of self.nodes - - name: string, optional, default: None + edges : Iterable + edgeids to restrict to Returns ------- - new hypergraph : Hypergraph + hnx.Hypergraph + """ - if self.isstatic: - E = self.edges.restrict_to_levels((1, 0)) - setsystem = E.restrict_to(sorted(E.indices(E.keys[0], list(nodeset)))) - return Hypergraph( - setsystem.restrict_to_levels((1, 0)), name=name, use_nwhy=self.nwhy - ) + keys = set(self._state_dict["labels"]["edges"]).difference(edges) + return self.remove(keys, level=0) + + def remove_edges(self, keys, name=None): + return self.remove(keys, level=0, name=name) + + def remove_nodes(self, keys, name=None): + return self.remove(keys, level=1, name=name) + + def remove(self, keys, level=None, name=None): + """Creates a new hypergraph with nodes and/or edges indexed by keys + removed. More efficient for creating a restricted hypergraph if the + restricted set is greater than what is being removed. + + Parameters + ---------- + keys : list | tuple | set + node and/or edge id to restrict to + level : None, optional + Enter 0 to remove edges with ids in keys. + Enter 1 to remove nodes with ids in keys. + If None then all objects in nodes and edges with the id will + be removed. + + Returns + ------- + : hnx.Hypergraph + + """ + rdfprop = self.properties.copy() + rdf = self.dataframe.copy() + if not isinstance(keys, (list, tuple, set)): + keys = list(keys) + if level == 0: + kdx = set(keys).intersection(set(self._state_dict["labels"]["edges"])) + for k in kdx: + rdfprop = rdfprop.drop((0, k)) + rdf = rdf.loc[~rdf[self._edge_col].isin(kdx)] + elif level == 1: + kdx = set(keys).intersection(set(self._state_dict["labels"]["nodes"])) + for k in kdx: + rdfprop = rdfprop.drop((1, k)) + rdf = rdf.loc[~rdf[self._node_col].isin(kdx)] else: - memberships = set() - innernodes = set() - for node in nodeset: - innernodes.add(node) - if node in self.nodes: - memberships.update(set(self.nodes[node].memberships)) - newedgeset = dict() - for e in memberships: - if e in self.edges: - temp = self.edges[e].uidset.intersection(innernodes) - if temp: - newedgeset[e] = Entity(e, temp, **self.edges[e].properties) - return Hypergraph(newedgeset, name=name) - - def toplexes(self, name=None, collapse=False, use_reps=False, return_counts=True): + rdfprop = rdfprop.reset_index() + kdx = set(keys).intersection(rdfprop.id.unique()) + rdfprop = rdfprop.set_index("id") + rdfprop = rdfprop.drop(index=kdx) + rdf = rdf.loc[~rdf[self._edge_col].isin(kdx)] + rdf = rdf.loc[~rdf[self._node_col].isin(kdx)] + + return Hypergraph( + setsystem=rdf, + edge_col=self._edge_col, + node_col=self._node_col, + cell_weight_col=self._cell_weight_col, + misc_cell_properties_col=self.edges._misc_cell_props_col, + properties=rdfprop, + misc_properties_col=self.edges._misc_props_col, + ) + + def toplexes(self, name=None): """ Returns a :term:`simple hypergraph` corresponding to self. Warning ------- - Collapsing is no longer supported inside the toplexes method. Instead generate a new - collapsed hypergraph and compute the toplexes of the new hypergraph. + Collapsing is no longer supported inside the toplexes method. Instead + generate a new collapsed hypergraph and compute the toplexes of the + new hypergraph. Parameters ---------- - name: str, optional, default: None - - # collapse: boolean, optional, default: False - # Should the hypergraph be collapsed? This would preserve a link between duplicate maximal sets. - # If False then only one of these sets will be used and uniqueness will be up to sets of equal size. - - # use_reps: boolean, optional, default: False - # If collapse=True then each toplex will be named by a representative of the set of - # equivalent edges, default is False (see collapse_edges). - - return_counts: boolean, optional, default: True - # If collapse=True then each toplex will be named by a tuple of the representative - # of the set of equivalent edges and their count - + name: str, optional, default = None """ - # TODO: There is a better way to do this....need to refactor - if collapse: - if len(self.edges) > 20: # TODO: Determine how big is too big. - warnings.warn( - "Collapsing a hypergraph can take a long time. It may be preferable to collapse the graph first and pickle it then apply the toplex method separately." - ) - temp = self.collapse_edges() - else: - temp = self - if collapse: - msg = """ - collapse, return_counts, and use_reps are no longer supported keyword arguments - and will throw an error in the next release. - """ - warnings.warn(msg, DeprecationWarning) + thdict = {} + for e in self.edges: + thdict[e] = self.edges[e] - thdict = dict() - if self.nwhy: - tops = self.g.toplexes() - E = self.edges.restrict_to(tops) - return Hypergraph(E, use_nwhy=True) - else: - if self.isstatic: - for e in temp.edges: - thdict[e] = temp.edges[e] - else: - for e in temp.edges: - thdict[e] = temp.edges[e].uidset - tops = list() - for e in temp.edges: - flag = True - old_tops = list(tops) - for top in old_tops: - if set(thdict[e]).issubset(thdict[top]): - flag = False - break - elif set(thdict[top]).issubset(thdict[e]): - tops.remove(top) - if flag: - tops += [e] - return self.restrict_to_edges(tops, name=name) + tops = [] + for e in self.edges: + flag = True + old_tops = list(tops) + for top in old_tops: + if set(thdict[e]).issubset(thdict[top]): + flag = False + break + + if set(thdict[top]).issubset(thdict[e]): + tops.remove(top) + if flag: + tops += [e] + return self.restrict_to_edges(tops, name=name) def is_connected(self, s=1, edges=False): """ - Determines if hypergraph is :term:`s-connected `. + Determines if hypergraph is :term:`s-connected `. Parameters ---------- - s: int, optional, default: 1 + s: int, optional, default 1 - edges: boolean, optional, default: False + edges: boolean, optional, default = False If True, will determine if s-edge-connected. For s=1 s-edge-connected is the same as s-connected. @@ -1726,19 +1624,16 @@ def is_connected(self, s=1, edges=False): """ - if self.isstatic: - g = self.get_linegraph(s=s, edges=edges) - if self.nwhy: - return g.is_s_connected() - else: - return nx.is_connected(g) - else: - if edges: - A = self.edge_adjacency_matrix(s=s) - else: - A = self.adjacency_matrix(s=s) - g = nx.from_scipy_sparse_matrix(A) - return nx.is_connected(g) + g = self.get_linegraph(s=s, edges=edges) + is_connected = None + + try: + is_connected = nx.is_connected(g) + except nx.NetworkXPointlessConcept: + warnings.warn("Graph is null; ") + is_connected = False + + return is_connected def singletons(self): """ @@ -1750,62 +1645,65 @@ def singletons(self): singles : list A list of edge uids. """ - if self.nwhy: - return self.edges.translate(0, self.g.singletons()) - else: - M, rdict, cdict = self.incidence_matrix(index=True) - idx = np.argmax(M.shape) # which axis has fewest members? if 1 then columns - cols = M.sum(idx) # we add down the row index if there are fewer columns - singles = list() - for c in range(cols.shape[(idx + 1) % 2]): # index along opposite axis - if cols[idx * c, c * ((idx + 1) % 2)] == 1: - # then see if the singleton entry in that column is also singleton in its row - # find the entry - if idx == 0: - r = np.argmax(M.getcol(c)) - # and get its sum - s = np.sum(M.getrow(r)) - # if this is also 1 then the entry in r,c represents a singleton - # so we want to change that entry to 0 and remove the row. - # this means we want to remove the edge corresponding to c - if s == 1: - singles.append(cdict[c]) - else: # switch the role of r and c - r = np.argmax(M.getrow(c)) - s = np.sum(M.getcol(r)) - if s == 1: - singles.append(cdict[r]) - return singles + + M, _, cdict = self.incidence_matrix(index=True) + # which axis has fewest members? if 1 then columns + idx = np.argmax(M.shape).tolist() + # we add down the row index if there are fewer columns + cols = M.sum(idx) + singles = [] + # index along opposite axis with one entry each + for c in np.nonzero((cols - 1 == 0))[(idx + 1) % 2]: + # if the singleton entry in that column is also + # singleton in its row find the entry + if idx == 0: + r = np.argmax(M.getcol(c)) + # and get its sum + s = np.sum(M.getrow(r)) + # if this is also 1 then the entry in r,c represents a + # singleton so we want to change that entry to 0 and + # remove the row. this means we want to remove the + # edge corresponding to c + if s == 1: + singles.append(cdict[c]) + else: # switch the role of r and c + r = np.argmax(M.getrow(c)) + s = np.sum(M.getcol(r)) + if s == 1: + singles.append(cdict[r]) + return singles def remove_singletons(self, name=None): """ Constructs clone of hypergraph with singleton edges removed. - Parameters - ---------- - name: str, optional, default: None - Returns ------- new hypergraph : Hypergraph """ - E = [e for e in self.edges if e not in self.singletons()] - return self.restrict_to_edges(E) + singletons = self.singletons() + if len(singletons) > len(self.edges): + E = [e for e in self.edges if e not in singletons] + return self.restrict_to_edges(E, name=name) + else: + return self.remove(singletons, level=0, name=name) def s_connected_components(self, s=1, edges=True, return_singletons=False): """ - Returns a generator for the :term:`s-edge-connected components ` - or the :term:`s-node-connected components ` - of the hypergraph. + Returns a generator for the :term:`s-edge-connected components + ` + or the :term:`s-node-connected components ` of the hypergraph. Parameters ---------- - s : int, optional, default: 1 + s : int, optional, default 1 - edges : boolean, optional, default: True - If True will return edge components, if False will return node components - return_singletons : bool, optional, default : False + edges : boolean, optional, default = True + If True will return edge components, if False will return node + components + return_singletons : bool, optional, default = False Notes ----- @@ -1819,9 +1717,9 @@ def s_connected_components(self, s=1, edges=True, return_singletons=False): If edges=False this method returns s-node-connected components. A list of sets of uids of the nodes which are s-walk connected. Two nodes v1 and v2 are s-walk-connected if there is a - sequence of nodes starting with v1 and ending with v2 such that pairwise - adjacent nodes in the sequence share s edges. If s=1 these are the - path components of the hypergraph. + sequence of nodes starting with v1 and ending with v2 such that + pairwise adjacent nodes in the sequence share s edges. If s=1 these + are the path components of the hypergraph. Example ------- @@ -1836,71 +1734,35 @@ def s_connected_components(self, s=1, edges=True, return_singletons=False): Yields ------ s_connected_components : iterator - Iterator returns sets of uids of the edges (or nodes) in the s-edge(node) - components of hypergraph. - - """ - components = list() - - if self.nwhy: - g = self.get_linegraph(s, edges=edges) - if return_singletons: - allobjects = set(self.edges) if edges == True else set(self.nodes) - for c in g.s_connected_components(): - comp = {self.get_name(nd, edges=edges) for nd in c} - allobjects.difference_update(comp) - for c in g.s_connected_components(): - yield {self.get_name(nd, edges=edges) for nd in c} - for obj in allobjects: - yield {obj} - else: - for c in g.s_connected_components(): - comp = {self.get_name(nd, edges=edges) for nd in c} - yield comp - - elif self.isstatic: - g = self.get_linegraph(s, edges=edges) - for c in nx.connected_components(g): - if not return_singletons and len(c) == 1: - continue - yield {self.get_name(n, edges=edges) for n in c} - else: - if edges: - A, coldict = self.edge_adjacency_matrix(s=s, index=True) - G = nx.from_scipy_sparse_matrix(A) - # if not return_singletons: - # temp = [c for c in nx.connected_components(G) if len(c) > 1] - # else: - # temp = nx.connected_components(G) - for c in nx.connected_components(G): - if not return_singletons and len(c) == 1: - continue - yield {coldict[n] for n in c} - else: - A, rowdict = self.adjacency_matrix(s=s, index=True) - G = nx.from_scipy_sparse_matrix(A) - for c in nx.connected_components(G): - if not return_singletons: - if len(c) == 1: - continue - yield {rowdict[n] for n in c} + Iterator returns sets of uids of the edges (or nodes) in the + s-edge(node) components of hypergraph. + + """ + g = self.get_linegraph(s, edges=edges) + for c in nx.connected_components(g): + if not return_singletons and len(c) == 1: + continue + yield c - def s_component_subgraphs(self, s=1, edges=True, return_singletons=False): + def s_component_subgraphs( + self, s=1, edges=True, return_singletons=False, name=None + ): """ - Returns a generator for the induced subgraphs of s_connected components. - Removes singletons unless return_singletons is set to True. Computed using - s-linegraph generated either by the hypergraph (edges=True) or its dual - (edges = False) + Returns a generator for the induced subgraphs of s_connected + components. Removes singletons unless return_singletons is set to True. + Computed using s-linegraph generated either by the hypergraph + (edges=True) or its dual (edges = False) Parameters ---------- - s : int, optional, default: 1 + s : int, optional, default 1 edges : boolean, optional, edges=False Determines if edge or node components are desired. Returns - subgraphs equal to the hypergraph restricted to each set of nodes(edges) in the - s-connected components or s-edge-connected components + subgraphs equal to the hypergraph restricted to each set of + nodes(edges) in the s-connected components or s-edge-connected + components return_singletons : bool, optional Yields @@ -1914,9 +1776,9 @@ def s_component_subgraphs(self, s=1, edges=True, return_singletons=False): self.s_components(s=s, edges=edges, return_singletons=return_singletons) ): if edges: - yield self.restrict_to_edges(c, name=f"{self.name}:{idx}") + yield self.restrict_to_edges(c, name=f"{name or self.name}:{idx}") else: - yield self.restrict_to_nodes(c, name=f"{self.name}:{idx}") + yield self.restrict_to_nodes(c, name=f"{name or self.name}:{idx}") def s_components(self, s=1, edges=True, return_singletons=True): """ @@ -1930,7 +1792,7 @@ def s_components(self, s=1, edges=True, return_singletons=True): s=s, edges=edges, return_singletons=return_singletons ) - def connected_components(self, edges=False, return_singletons=True): + def connected_components(self, edges=False): """ Same as :meth:`s_connected_components` with s=1, but nodes are returned by default. Return iterator. @@ -1941,7 +1803,7 @@ def connected_components(self, edges=False, return_singletons=True): """ return self.s_connected_components(edges=edges, return_singletons=True) - def connected_component_subgraphs(self, return_singletons=True): + def connected_component_subgraphs(self, return_singletons=True, name=None): """ Same as :meth:`s_component_subgraphs` with s=1. Returns iterator @@ -1949,9 +1811,11 @@ def connected_component_subgraphs(self, return_singletons=True): -------- s_component_subgraphs """ - return self.s_component_subgraphs(return_singletons=return_singletons) + return self.s_component_subgraphs( + return_singletons=return_singletons, name=name + ) - def components(self, edges=False, return_singletons=True): + def components(self, edges=False): """ Same as :meth:`s_connected_components` with s=1, but nodes are returned by default. Return iterator. @@ -1962,7 +1826,7 @@ def components(self, edges=False, return_singletons=True): """ return self.s_connected_components(s=1, edges=edges) - def component_subgraphs(self, return_singletons=False): + def component_subgraphs(self, return_singletons=False, name=None): """ Same as :meth:`s_components_subgraphs` with s=1. Returns iterator. @@ -1970,7 +1834,9 @@ def component_subgraphs(self, return_singletons=False): -------- s_component_subgraphs """ - return self.s_component_subgraphs(return_singletons=return_singletons) + return self.s_component_subgraphs( + return_singletons=return_singletons, name=name + ) def node_diameters(self, s=1): """ @@ -1981,31 +1847,19 @@ def node_diameters(self, s=1): list of the diameters of the s-components and list of the s-component nodes """ - if self.nwhy: - g = self.get_linegraph(s, edges=False) - if g.is_s_connected(): - return g.s_diameter() - else: - diameters = list() - nodelists = list() - for c in g.s_connected_components(): - tc = self.edges.labs(1)[c] - nodelists.append(tc) - diameters.append(self.restrict_to_nodes(tc).node_diameters(s=s)) - else: - A, coldict = self.adjacency_matrix(s=s, index=True) - G = nx.from_scipy_sparse_matrix(A) - diams = [] - comps = [] - for c in nx.connected_components(G): - diamc = nx.diameter(G.subgraph(c)) - temp = set() - for e in c: - temp.add(coldict[e]) - comps.append(temp) - diams.append(diamc) - loc = np.argmax(diams) - return diams[loc], diams, comps + A, coldict = self.adjacency_matrix(s=s, index=True) + G = nx.from_scipy_sparse_matrix(A) + diams = [] + comps = [] + for c in nx.connected_components(G): + diamc = nx.diameter(G.subgraph(c)) + temp = set() + for e in c: + temp.add(coldict[e]) + comps.append(temp) + diams.append(diamc) + loc = np.argmax(diams).tolist() + return diams[loc], diams, comps def edge_diameters(self, s=1): """ @@ -2014,7 +1868,7 @@ def edge_diameters(self, s=1): Parameters ---------- - s : int, optional, default: 1 + s : int, optional, default 1 Returns ------- @@ -2027,39 +1881,28 @@ def edge_diameters(self, s=1): List of the edge uids in the s-edge component subgraphs. """ - if self.nwhy: - g = self.get_linegraph(s, edges=True) - if g.is_s_connected(): - return g.s_diameter() - else: - diameters = list() - edgelists = list() - for c in g.s_connected_components(): - tc = self.edges.labs(0)[c] - edgelists.append(tc) - diameters.append(self.restrict_to_edges(tc).edge_diameters(s=s)) - else: - A, coldict = self.edge_adjacency_matrix(s=s, index=True) - G = nx.from_scipy_sparse_matrix(A) - diams = [] - comps = [] - for c in nx.connected_components(G): - diamc = nx.diameter(G.subgraph(c)) - temp = set() - for e in c: - temp.add(coldict[e]) - comps.append(temp) - diams.append(diamc) - loc = np.argmax(diams) - return diams[loc], diams, comps + A, coldict = self.edge_adjacency_matrix(s=s, index=True) + G = nx.from_scipy_sparse_matrix(A) + diams = [] + comps = [] + for c in nx.connected_components(G): + diamc = nx.diameter(G.subgraph(c)) + temp = set() + for e in c: + temp.add(coldict[e]) + comps.append(temp) + diams.append(diamc) + loc = np.argmax(diams).tolist() + return diams[loc], diams, comps def diameter(self, s=1): """ - Returns the length of the longest shortest s-walk between nodes in hypergraph + Returns the length of the longest shortest s-walk between nodes in + hypergraph Parameters ---------- - s : int, optional, default: 1 + s : int, optional, default 1 Returns ------- @@ -2073,32 +1916,27 @@ def diameter(self, s=1): Notes ----- Two nodes are s-adjacent if they share s edges. - Two nodes v_start and v_end are s-walk connected if there is a sequence of - nodes v_start, v_1, v_2, ... v_n-1, v_end such that consecutive nodes - are s-adjacent. If the graph is not connected, an error will be raised. + Two nodes v_start and v_end are s-walk connected if there is a + sequence of nodes v_start, v_1, v_2, ... v_n-1, v_end such that + consecutive nodes are s-adjacent. If the graph is not connected, + an error will be raised. """ - if self.nwhy: - g = self.get_linegraph(s, edges=False) - if g.is_s_connected(): - return g.s_diameter() - else: - raise HyperNetXError(f"Hypergraph is not s-connected. s={s}") - else: - A = self.adjacency_matrix(s=s) - G = nx.from_scipy_sparse_matrix(A) - if nx.is_connected(G): - return nx.diameter(G) - else: - raise HyperNetXError(f"Hypergraph is not s-connected. s={s}") + A = self.adjacency_matrix(s=s) + G = nx.from_scipy_sparse_matrix(A) + if nx.is_connected(G): + return nx.diameter(G) + + raise HyperNetXError(f"Hypergraph is not s-connected. s={s}") def edge_diameter(self, s=1): """ - Returns the length of the longest shortest s-walk between edges in hypergraph + Returns the length of the longest shortest s-walk between edges in + hypergraph Parameters ---------- - s : int, optional, default: 1 + s : int, optional, default 1 Return ------ @@ -2112,28 +1950,23 @@ def edge_diameter(self, s=1): Notes ----- Two edges are s-adjacent if they share s nodes. - Two nodes e_start and e_end are s-walk connected if there is a sequence of - edges e_start, e_1, e_2, ... e_n-1, e_end such that consecutive edges - are s-adjacent. If the graph is not connected, an error will be raised. + Two nodes e_start and e_end are s-walk connected if there is a + sequence of edges e_start, e_1, e_2, ... e_n-1, e_end such that + consecutive edges are s-adjacent. If the graph is not connected, an + error will be raised. """ - if self.nwhy: - g = self.get_linegraph(s, edges=True) - if g.is_s_connected(): - return g.s_diameter() - else: - raise HyperNetXError(f"Hypergraph is not s-connected. s={s}") - else: - A = self.edge_adjacency_matrix(s=s) - G = nx.from_scipy_sparse_matrix(A) - if nx.is_connected(G): - return nx.diameter(G) - else: - raise HyperNetXError(f"Hypergraph is not s-connected. s={s}") + A = self.edge_adjacency_matrix(s=s) + G = nx.from_scipy_sparse_matrix(A) + if nx.is_connected(G): + return nx.diameter(G) + + raise HyperNetXError(f"Hypergraph is not s-connected. s={s}") def distance(self, source, target, s=1): """ - Returns the shortest s-walk distance between two nodes in the hypergraph. + Returns the shortest s-walk distance between two nodes in the + hypergraph. Parameters ---------- @@ -2165,41 +1998,19 @@ def distance(self, source, target, s=1): generated by the s-adjacency matrix. """ - if self.isstatic: - g = self.get_linegraph(s=s, edges=False) - src = self.get_id(source, edges=False) - tgt = self.get_id(target, edges=False) - try: - if self.nwhy: - d = g.s_distance(src, tgt) - if d == -1: - warnings.warn(f"No {s}-path between {source} and {target}") - return np.inf - else: - return d - else: - return nx.shortest_path(g, src, tgt) - except: - warnings.warn(f"No {s}-path between {source} and {target}") - return np.inf - else: - if isinstance(source, Entity): - source = source.uid - if isinstance(target, Entity): - target = target.uid - A, rowdict = self.adjacency_matrix(s=s, index=True) - g = nx.from_scipy_sparse_matrix(A) - rkey = {v: k for k, v in rowdict.items()} - try: - path = nx.shortest_path_length(g, rkey[source], rkey[target]) - return path - except: - warnings.warn(f"No {s}-path between {source} and {target}") - return np.inf + g = self.get_linegraph(s=s, edges=False) + try: + dist = nx.shortest_path_length(g, source, target) + except (nx.NetworkXNoPath, nx.NodeNotFound): + warnings.warn(f"No {s}-path between {source} and {target}") + dist = np.inf + + return dist def edge_distance(self, source, target, s=1): - """XX TODO: still need to return path and translate into user defined nodes and edges - Returns the shortest s-walk distance between two edges in the hypergraph. + """XX TODO: still need to return path and translate into user defined + nodes and edges Returns the shortest s-walk distance between two edges + in the hypergraph. Parameters ---------- @@ -2213,7 +2024,7 @@ def edge_distance(self, source, target, s=1): the number of intersections between pairwise consecutive edges TODO: add edge weights - weight : None or string, optional, default: None + weight : None or string, optional, default = None if None then all edges have weight 1. If string then edge attribute string is used if available. @@ -2232,78 +2043,60 @@ def edge_distance(self, source, target, s=1): Notes ----- The s-distance is the shortest s-walk length between the edges. - An s-walk between edges is a sequence of edges such that consecutive pairwise - edges intersect in at least s nodes. The length of the shortest s-walk is 1 less than - the number of edges in the path sequence. + An s-walk between edges is a sequence of edges such that + consecutive pairwise edges intersect in at least s nodes. The + length of the shortest s-walk is 1 less than the number of edges + in the path sequence. Uses the networkx shortest_path_length method on the graph generated by the s-edge_adjacency matrix. """ - if self.isstatic: - g = self.get_linegraph(s=s, edges=True) - src = self.get_id(source, edges=True) - tgt = self.get_id(target, edges=True) - try: - if self.nwhy: - d = g.s_distance(src, tgt) - if d == -1: - warnings.warn(f"No {s}-path between {source} and {target}") - return np.inf - else: - return d - else: - return nx.shortest_path(g, src, tgt) - except: - warnings.warn(f"No {s}-path between {source} and {target}") - return np.inf - else: - if isinstance(source, Entity): - source = source.uid - if isinstance(target, Entity): - target = target.uid - A, coldict = self.edge_adjacency_matrix(s=s, index=True) - g = nx.from_scipy_sparse_matrix(A) - ckey = {v: k for k, v in coldict.items()} - try: - path = nx.shortest_path_length(g, ckey[source], ckey[target]) - return path - except: - warnings.warn(f"No {s}-path between {source} and {target}") - return np.inf - - def dataframe(self, sort_rows=False, sort_columns=False, cell_weights=True): + g = self.get_linegraph(s=s, edges=True) + try: + edge_dist = nx.shortest_path_length(g, source, target) + except (nx.NetworkXNoPath, nx.NodeNotFound): + warnings.warn(f"No {s}-path between {source} and {target}") + edge_dist = np.inf + + return edge_dist + + def incidence_dataframe( + self, sort_rows=False, sort_columns=False, cell_weights=True + ): """ Returns a pandas dataframe for hypergraph indexed by the nodes and with column headers given by the edge names. Parameters ---------- - sort_rows : bool, optional, default=True + sort_rows : bool, optional, default =True sort rows based on hashable node names - sort_columns : bool, optional, default=True + sort_columns : bool, optional, default =True sort columns based on hashable edge names - cell_weights : bool, optional, default=True - if self.isstatic then include cell weights + cell_weights : bool, optional, default =True """ - if self.isstatic: - mat, rdx, cdx = self.edges.incidence_matrix(index=True, weights=True) - else: - mat, rdx, cdx = self.edges.incidence_matrix(index=True) - index = [rdx[i] for i in rdx] - columns = [cdx[j] for j in cdx] - df = pd.DataFrame(mat.todense(), index=index, columns=columns) + + ## An entity dataframe is already an incidence dataframe. + df = self.E.dataframe.pivot( + index=self.E._data_cols[1], + columns=self.E._data_cols[0], + values=self.E._cell_weight_col, + ).fillna(0) + if sort_rows: - df = df.sort_index() + df = df.sort_index("index") if sort_columns: - df = df[sorted(columns)] + df = df.sort_index("columns") + if not cell_weights: + df[df > 0] = 1 + return df @classmethod - def from_bipartite( - cls, B, set_names=("edges", "nodes"), name=None, static=False, use_nwhy=False - ): + @warn_nwhy + def from_bipartite(cls, B, set_names=("edges", "nodes"), name=None, **kwargs): """ Static method creates a Hypergraph from a bipartite graph. @@ -2312,14 +2105,14 @@ def from_bipartite( B: nx.Graph() A networkx bipartite graph. Each node in the graph has a property - 'bipartite' taking the value of 0 or 1 indicating a 2-coloring of the graph. + 'bipartite' taking the value of 0 or 1 indicating a 2-coloring of + the graph. - set_names: iterable of length 2, optional, default = ['nodes','edges'] - Category names assigned to the graph nodes associated to each bipartite set + set_names: iterable of length 2, optional, default = ['edges','nodes'] + Category names assigned to the graph nodes associated to each + bipartite set - name: hashable - - static: bool + name: hashable, optional Returns ------- @@ -2333,17 +2126,19 @@ def from_bipartite( >>> B = nx.Graph() >>> B.add_nodes_from([1, 2, 3, 4], bipartite=0) >>> B.add_nodes_from(['a', 'b', 'c'], bipartite=1) - >>> B.add_edges_from([(1, 'a'), (1, 'b'), (2, 'b'), (2, 'c'), (3, 'c'), (4, 'a')]) + >>> B.add_edges_from([(1, 'a'), (1, 'b'), (2, 'b'), (2, 'c'), / + (3, 'c'), (4, 'a')]) >>> H = Hypergraph.from_bipartite(B) >>> H.nodes, H.edges - # output: (EntitySet(_:Nodes,[1, 2, 3, 4],{}), EntitySet(_:Edges,['b', 'c', 'a'],{})) + # output: (EntitySet(_:Nodes,[1, 2, 3, 4],{}), / + # EntitySet(_:Edges,['b', 'c', 'a'],{})) """ - # TODO: Add filepath keyword to signatures here and with dataframe and numpy array + edges = [] nodes = [] for n, d in B.nodes(data=True): - if d["bipartite"] == 0: + if d["bipartite"] == 1: nodes.append(n) else: edges.append(n) @@ -2353,28 +2148,42 @@ def from_bipartite( "Error: Method requires a 2-coloring of a bipartite graph." ) - if static: - elist = [] - for e in list(B.edges): - if e[0] in edges: - elist.append([e[0], e[1]]) - else: - elist.append([e[1], e[0]]) - df = pd.DataFrame(elist, columns=set_names) - E = StaticEntitySet(entity=df) - name = name or "_" - return Hypergraph(E, name=name, use_nwhy=use_nwhy) - else: - node_entities = { - n: Entity(n, [], properties=B.nodes(data=True)[n]) for n in nodes - } - edge_dict = { - e: [node_entities[n] for n in list(B.neighbors(e))] for e in edges - } - name = name or "_" - return Hypergraph(setsystem=edge_dict, name=name) + elist = [] + for e in list(B.edges): + if e[0] in edges: + elist.append([e[0], e[1]]) + else: + elist.append([e[1], e[0]]) + df = pd.DataFrame(elist, columns=set_names) + return Hypergraph(df, name=name, **kwargs) + + @classmethod + def from_incidence_matrix( + cls, + M, + node_names=None, + edge_names=None, + node_label="nodes", + edge_label="edges", + name=None, + key=None, + **kwargs, + ): + """ + Same as from_numpy_array. + """ + return Hypergraph.from_numpy_array( + M, + node_names=node_names, + edge_names=edge_names, + node_label=node_label, + edge_label=edge_label, + name=name, + key=key, + ) @classmethod + @warn_nwhy def from_numpy_array( cls, M, @@ -2384,8 +2193,7 @@ def from_numpy_array( edge_label="edges", name=None, key=None, - static=False, - use_nwhy=False, + **kwargs, ): """ Create a hypergraph from a real valued matrix represented as a 2 dimensionsl numpy array. @@ -2430,7 +2238,7 @@ def from_numpy_array( if len(M.shape) != (2): raise HyperNetXError("Input requires a 2 dimensional numpy array") # apply boolean key if available - if key: + if key is not None: M = key(M) if node_names is not None: @@ -2451,55 +2259,30 @@ def from_numpy_array( else: edgenames = np.array([f"e{jdx}" for jdx in range(M.shape[1])]) - if static or use_nwhy: - arr = np.array(M) - if key: - arr = key(arr) * 1 - arr = arr.transpose() - labels = OrderedDict([(edge_label, edgenames), (node_label, nodenames)]) - E = StaticEntitySet(arr=arr, labels=labels) - return Hypergraph(E, name=name, use_nwhy=use_nwhy) - - else: - # Remove empty column indices from M columns and edgenames - colidx = np.array([jdx for jdx in range(M.shape[1]) if any(M[:, jdx])]) - colidxsum = np.sum(colidx) - if not colidxsum: - return Hypergraph() - else: - M = M[:, colidx] - edgenames = edgenames[colidx] - edict = dict() - # Create an EntitySet of edges from M - for jdx, e in enumerate(edgenames): - edict[e] = nodenames[ - [idx for idx in range(M.shape[0]) if M[idx, jdx]] - ] - return Hypergraph(edict, name=name) + df = pd.DataFrame(M, columns=edgenames, index=nodenames) + return Hypergraph.from_incidence_dataframe(df, name=name) @classmethod - def from_dataframe( + @warn_nwhy + def from_incidence_dataframe( cls, df, columns=None, rows=None, + edge_col: str = "edges", + node_col: str = "nodes", name=None, fillna=0, transpose=False, transforms=[], key=None, - node_label="nodes", - edge_label="edges", - static=False, - use_nwhy=False, + return_only_dataframe=False, + **kwargs, ): """ - Create a hypergraph from a Pandas Dataframe object using index to label vertices - and Columns to label edges. The values of the dataframe are transformed into an - incidence matrix. - Note this is different than passing a dataframe directly - into the Hypergraph constructor. The latter automatically generates a static hypergraph - with edge and node labels given by the cell values. + Create a hypergraph from a Pandas Dataframe object, which has values equal + to the incidence matrix of a hypergraph. Its index will identify the nodes + and its columns will identify its edges. Parameters ---------- @@ -2515,56 +2298,42 @@ def from_dataframe( name : (optional) string, default = None fillna : float, default = 0 - a real value to place in empty cell, all-zero columns will not generate - an edge. + a real value to place in empty cell, all-zero columns will not + generate an edge. transpose : (optional) bool, default = False - option to transpose the dataframe, in this case df.Index will label the edges - and df.columns will label the nodes, transpose is applied before transforms and - key + option to transpose the dataframe, in this case df.Index will + identify the edges and df.columns will identify the nodes, transpose is + applied before transforms and key transforms : (optional) list, default = [] optional list of transformations to apply to each column, of the dataframe using pd.DataFrame.apply(). Transformations are applied in the order they are given (ex. abs). To apply transforms to rows or for additional - functionality, consider transforming df using pandas.DataFrame methods - prior to generating the hypergraph. + functionality, consider transforming df using pandas.DataFrame + methods prior to generating the hypergraph. key : (optional) function, default = None - boolean function to be applied to dataframe. Must be defined on numpy - arrays. + boolean function to be applied to dataframe. will be applied to + entire dataframe. + + return_only_dataframe : (optional) bool, default = False + to use the incidence_dataframe with cell_properties or properties, set this + to true and use it as the setsystem in the Hypergraph constructor. See also -------- - from_numpy_array()) + from_numpy_array Returns ------- : Hypergraph - Notes - ----- - The `from_dataframe` constructor does not generate empty edges. - All-zero columns in df are removed and the names corresponding to these - edges are discarded. - Restrictions and data processing will occur in this order: - - 1. column and row restrictions - 2. fillna replace NaNs in dataframe - 3. transpose the dataframe - 4. transforms in the order listed - 5. boolean key - - This method offers the above options for wrangling a dataframe into an incidence - matrix for a hypergraph. For more flexibility we recommend you use the Pandas - library to format the values of your dataframe before submitting it to this - constructor. - """ - if type(df) != pd.core.frame.DataFrame: + if not isinstance(df, pd.DataFrame): raise HyperNetXError("Error: Input object must be a pandas dataframe.") if columns: @@ -2576,9 +2345,6 @@ def from_dataframe( if transpose: df = df.transpose() - # node_names = np.array(df.index) - # edge_names = np.array(df.columns) - for t in transforms: df = df.apply(t) if key: @@ -2586,21 +2352,23 @@ def from_dataframe( else: mat = df.values * 1 - params = { - "node_names": np.array(df.index), - "edge_names": np.array(df.columns), - "name": name, - "node_label": node_label, - "edge_label": edge_label, - "static": static, - "use_nwhy": use_nwhy, - } - return cls.from_numpy_array(mat, **params) - - -# end of Hypergraph class - - -def _make_3_arrays(mat): - arr = coo_matrix(mat) - return arr.row, arr.col, arr.data + cols = df.columns + rows = df.index + CM = coo_matrix(mat) + c1 = CM.row + c1 = [rows[c1[idx]] for idx in range(len(c1))] + c2 = CM.col + c2 = [cols[c2[idx]] for idx in range(len(c2))] + c3 = CM.data + + dfnew = pd.DataFrame({edge_col: c2, node_col: c1, "cell_weights": c3}) + if return_only_dataframe == True: + return dfnew + else: + return Hypergraph( + dfnew, + edge_col=edge_col, + node_col=node_col, + weights="cell_weights", + name=None, + ) diff --git a/hypernetx/classes/staticentity.py b/hypernetx/classes/staticentity.py deleted file mode 100644 index 8e8f59ea..00000000 --- a/hypernetx/classes/staticentity.py +++ /dev/null @@ -1,1290 +0,0 @@ -from collections import OrderedDict, defaultdict, UserList -from collections.abc import Iterable -import warnings -from copy import copy -import numpy as np -import networkx as nx -from hypernetx import * -from hypernetx.exception import HyperNetXError -from hypernetx.classes.entity import Entity, EntitySet -from hypernetx.utils import ( - HNXCount, - DefaultOrderedDict, - remove_row_duplicates, - reverse_dictionary, -) -from scipy.sparse import coo_matrix, csr_matrix, issparse -import itertools as it -import pandas as pd - -__all__ = ["StaticEntity", "StaticEntitySet"] - - -class StaticEntity(object): - - """ - .. _staticentity: - - Parameters - ---------- - entity : StaticEntity, StaticEntitySet, Entity, EntitySet, pandas.DataFrame, dict, or list of lists - If a pandas.DataFrame, an error will be raised if there are nans. - data : array or array-like - Two dimensional array of integers. Provides sparse tensor indices for incidence - tensor. - arr : numpy.ndarray or scip.sparse.matrix, optional, default=None - Incidence tensor of data. - labels : OrderedDict of lists, optional, default=None - User defined labels corresponding to integers in data. - uid : hashable, optional, default=None - weights : array-like, optional, default : None - User specified weights corresponding to data, length must equal number - of rows in data. If None, weight for all rows is assumed to be 1. - keep_weights : bool, optional, default : True - Whether or not to use existing weights when input is StaticEntity, or StaticEntitySet. - aggregateby : str, optional, {'count', 'sum', 'mean', 'median', max', 'min', 'first', 'last', None}, default : 'count' - Method to aggregate cell_weights of duplicate rows if setsystem is of type pandas.DataFrame of - StaticEntity. If None all cell weights will be set to 1. - - props : user defined keyword arguments to be added to a properties dictionary, optional - - Attributes - ---------- - properties : dict - Description - - """ - - def __init__( - self, - entity=None, - data=None, - arr=None, - labels=None, - uid=None, - weights=None, ### in this context weights is just a column of values corresponding to the rows in data. - keep_weights=True, - aggregateby="sum", - **props, - ): - self._uid = uid - self.properties = {} - if entity is not None: - if isinstance(entity, StaticEntity) or isinstance(entity, StaticEntitySet): - self.properties.update(entity.properties) - self.properties.update(props) - self.__dict__.update(self.properties) - self.__dict__.update(props) - self._data = entity._data.copy() - if keep_weights: - self._weights = entity._weights - self._cell_weights = dict(entity._cell_weights) - else: - self._data, self._cell_weights = remove_row_duplicates( - entity.data, weights=weights, aggregateby=aggregateby - ) - self._dimensions = entity._dimensions - self._dimsize = entity._dimsize - self._labels = OrderedDict( - (category, np.array(values)) - for category, values in entity._labels.items() - ) - self._keys = np.array(list(self._labels.keys())) - # returns the index of the category (column) - self._keyindex = dict( - zip(self._labels.keys(), np.arange(self._dimsize)) - ) - self._index = { - cat: dict(zip(self._labels[cat], np.arange(len(self._labels[cat])))) - for cat in self._keys - } - self._arr = None - elif isinstance(entity, pd.DataFrame): - self.properties.update(props) - ( - self._data, - self._labels, - self._cell_weights, - ) = _turn_dataframe_into_entity( - entity, weights=weights, aggregateby=aggregateby - ) - self.__dict__.update(self.properties) - self._arr = None - self._dimensions = tuple([max(x) + 1 for x in self._data.transpose()]) - self._dimsize = len(self._dimensions) - self._keys = np.array(list(self._labels.keys())) - self._keyindex = dict( - zip(self._labels.keys(), np.arange(self._dimsize)) - ) - self._index = { - cat: dict(zip(self._labels[cat], np.arange(len(self._labels[cat])))) - for cat in self._keys - } - - else: # For these cases we cannot yet add cell_weights directly, cell_weights default to duplicate counts - if isinstance(entity, Entity) or isinstance(entity, EntitySet): - d = entity.incidence_dict - ( - self._data, - self._labels, - self._cell_weights, - ) = _turn_dict_to_staticentity( - d - ) # For now duplicate entries will be removed. - elif isinstance(entity, dict): # returns only 2 levels - ( - self._data, - self._labels, - self._cell_weights, - ) = _turn_dict_to_staticentity( - entity - ) # For now duplicate entries will be removed. - else: # returns only 2 levels - ( - self._data, - self._labels, - self._cell_weights, - ) = _turn_iterable_to_staticentity(entity) - self._dimensions = tuple([len(self._labels[k]) for k in self._labels]) - self._dimsize = len(self._dimensions) # number of columns - self._keys = np.array( - list(self._labels.keys()) - ) # These are the column headers from the dataframe - self._keyindex = dict( - zip(self._labels.keys(), np.arange(self._dimsize)) - ) - self._index = { - cat: dict(zip(self._labels[cat], np.arange(len(self._labels[cat])))) - for cat in self._keys - } - self.properties.update(props) - self.__dict__.update( - self.properties - ) # Add function to set attributes ###########!!!!!!!!!!!!! - self._arr = None - elif data is not None: - self._arr = None - self._data, self._cell_weights = remove_row_duplicates( - data, weights=weights, aggregateby=aggregateby - ) - self._dimensions = tuple([max(x) + 1 for x in self._data.transpose()]) - self._dimsize = len(self._dimensions) - self.properties.update(props) - self.__dict__.update(props) - if labels is not None: - self._labels = OrderedDict( - (category, np.array(values)) for category, values in labels.items() - ) # OrderedDict(category,np.array([categorical values ....])) is aligned to arr - self._keyindex = dict( - zip(self._labels.keys(), np.arange(self._dimsize)) - ) - self._keys = np.array(list(labels.keys())) - self._index = { - cat: dict(zip(self._labels[cat], np.arange(len(self._labels[cat])))) - for cat in self._keys - } - else: - self._labels = OrderedDict( - [ - (int(dim), np.arange(ct)) - for dim, ct in enumerate(self.dimensions) - ] - ) - self._keyindex = defaultdict(_fd) - self._keys = np.arange(self._dimsize) - self._index = { - cat: dict(zip(self._labels[cat], np.arange(len(self._labels[cat])))) - for cat in self._keys - } - - elif arr is not None: - self._arr = arr - self.properties.update(props) - self.__dict__.update(props) - self._state_dict = {"arr": arr * 1} - self._dimensions = arr.shape - self._dimsize = len(arr.shape) - self._data, self._cell_weights = _turn_tensor_to_data(arr * 1) - if labels is not None: - self._labels = OrderedDict( - (category, np.array(values)) for category, values in labels.items() - ) - self._keyindex = dict( - zip(self._labels.keys(), np.arange(self._dimsize)) - ) - self._keys = np.array(list(labels.keys())) - self._index = { - cat: dict(zip(self._labels[cat], np.arange(len(self._labels[cat])))) - for cat in self._keys - } - - else: - self._labels = OrderedDict( - [ - (int(dim), np.arange(ct)) - for dim, ct in enumerate(self.dimensions) - ] - ) - self._keyindex = defaultdict(_fd) - self._keys = np.arange(self._dimsize) - self._index = { - cat: dict(zip(self._labels[cat], np.arange(len(self._labels[cat])))) - for cat in self._keys - } - else: # no entity, data or arr is given - - if labels is not None: - self._labels = OrderedDict( - (category, np.array(values)) for category, values in labels.items() - ) - self._dimensions = tuple([len(labels[k]) for k in labels]) - self._data = np.zeros((0, len(labels)), dtype=int) - self._cell_weights = {} - self._arr = np.empty(self._dimensions, dtype=int) - self._state_dict = {"arr": np.empty(self.dimensions, dtype=int)} - self._dimsize = len(self._dimensions) - self._keyindex = dict( - zip(self._labels.keys(), np.arange(self._dimsize)) - ) - self._keys = np.array(list(labels.keys())) - self._index = { - cat: dict(zip(self._labels[cat], np.arange(len(self._labels[cat])))) - for cat in self._keys - } - else: - self._data = np.zeros((0, 0), dtype=int) - self._cell_weights = {} - self._arr = np.array([], dtype=int) - self._labels = OrderedDict([]) - self._dimensions = tuple([]) - self._dimsize = 0 - self._keyindex = defaultdict(_fd) - self._keys = np.array([]) - # self._index = lambda category, value: None - self._index = { - cat: dict( - zip(self._labels[cat], [None for i in len(self._labels[cat])]) - ) - for cat in self._keys - } - - # if labels is a list of categorical values, then change it into an - # ordered dictionary? - self.properties = props - self.__dict__.update(props) # keyed by the method name and signature - - if len(self._labels) > 0: - self._labs = { - kdx: self._labels.get(self._keys[kdx], {}) - for kdx in range(self._dimsize) - } - else: - self._labs = {} - - self._weights = [self._cell_weights[tuple(t)] for t in self._data] - self._memberships = None - - @property - def arr(self): - """ - Tensor like representation of data indexed by labels with values given by incidence or cell weight. - - Returns - ------- - numpy.ndarray - A Numpy ndarray with dimensions equal dimensions of static entity. Entries are cell_weights. - self.data gives a list of nonzero coordinates aligned with cell_weights. - """ - if self._arr is not None: - if type(self._arr) == int and self._arr == 0: - print("arr cannot be computed") - else: - try: - imat = np.zeros(self.dimensions, dtype=int) - for d in self._data: - imat[tuple(d)] = self._cell_weights[tuple(d)] - self._arr = imat - except Exception as ex: - print(ex) - print("arr cannot be computed") - self._arr = 0 - return self._arr # Do we need to return anything here - - @property - def data(self): - """ - Data array or tensor array of Static Entity - - Returns - ------- - numpy.ndarray - Two dimensional array. Each row has system ids of objects in the static entity. - Each column corresponds to one level of the static entity. - - """ - - return np.array(self._data) - - @property - def cell_weights(self): - """ - User defined weights corresponding to unique rows in data. - - Returns - ------- - numpy.array - One dimensional array of values aligned to data. - """ - return dict(self._cell_weights) - - @property - def labels(self): - """ - Ordered dictionary of labels - - Returns - ------- - collections.OrderedDict - User defined identifiers for objects in static entity. Ordered keys correspond - levels. Ordered values correspond to integer representation of values in data. - """ - return dict(self._labels) - - @property - def dimensions(self): - """ - Dimension of Static Entity data - - Returns - ------- - tuple - Tuple of number of distinct labels in each level, ordered by level. - """ - return self._dimensions - - @property - def dimsize(self): - """ - Number of categories in the data - - Returns - ------- - int - Number of levels in static entity, equals length of self.dimensions - """ - return self._dimsize - - @property - def keys(self): - """ - Array of keys of labels - - Returns - ------- - np.ndarray - Array of label keys, ordered by level. - """ - return self._keys - - def keyindex(self, category): - """ - Returns the index of a category in keys array - - Returns - ------- - int - Index osition of particular label in keys equal to the level of the - category. - """ - return self._keyindex[category] - - @property - def uid(self): - """ - User defined identifier for each object in static entity. - - Returns - ------- - str, int - Identifiers, which distinguish objects within each level. - """ - return self._uid - - @property - def uidset(self): - """ - Returns a set of the string identifiers for Static Entity - - Returns - ------- - frozenset - Hashable set of keys. - """ - return self.uidset_by_level(0) - - @property - def elements(self): - """ - Keys and values in the order of insertion - - Returns - ------- - collections.OrderedDict - Same as elements_by_level with level1 = 0, level2 = 1. - Compare with EntitySet with level1 = elements, level2 = children. - - """ - try: - return dict(self._elements) - except: - if len(self._keys) == 1: - self._elements = {k: UserList() for k in self._labels[self._keys[0]]} - return dict(self._elements) - else: - self._elements = self.elements_by_level(0, translate=True) - return dict(self._elements) - - @property - def memberships(self): - """ - Reverses the elements dictionary - - Returns - ------- - collections.OrderedDict - Same as elements_by_level with level1 = 1, level2 = 0. - """ - try: - return dict(self._memberships) - except: - # self._memberships = reverse_dictionary(self.elements) - # return self._memberships - if len(self._keys) == 1: - return None - else: - self._memberships = self.elements_by_level(1, 0, translate=True) - return dict(self._memberships) - - @property - def children(self): - """ - Labels of keys of first index - - Returns - ------- - numpy.array - One dimensional array of labels in the second level. - - """ - try: - return set(self._labs[1]) - except: - return - - @property - def incidence_dict(self): - """ - Same as elements. - - Returns - ------- - collections.OrderedDict - Same as elements_by_level with level1 = 0, level2 = 1. - Compare with EntitySet with level1 = elements, level2 = children. - """ - return self.elements_by_level(0, translate=True) - - @property - def dataframe(self): - """ - Returns the entity data in DataFrame format - - Returns - ------- - pandas.core.frame.DataFrame - Dataframe of user defined labels and keys as columns. - """ - return self.turn_entity_data_into_dataframe(self.data) - - def __len__(self): - """ - Returns the number of elements in Static Entity - - Returns - ------- - int - Number of distinct labels in level 0. - """ - return self._dimensions[0] - - def __str__(self): - """ - Return the Static Entity uid - - Returns - ------- - string - """ - return f"{self.uid}" - - def __repr__(self): - """ - Returns a string resembling the constructor for staticentity without any - children - - Returns - ------- - string - """ - return f"StaticEntity({self._uid},{list(self.uidset)},{self.properties})" - - def __contains__(self, item): - """ - Defines containment for StaticEntity based on labels/categories. - - Parameters - ---------- - item : string - - Returns - ------- - bool - """ - return item in np.concatenate(list(self._labels.values())) - - def __getitem__(self, item): - """ - Get value of key in E.elements - - Parameters - ---------- - item : string - - Returns - ------- - list - """ - # return self.elements_by_level(0, 1, translate=True)[item] - return self.elements[item] - - def __iter__(self): - """ - Create iterator from E.elements - - Returns - ------- - odict_iterator - """ - return iter(self.elements) - - def __call__(self, label_index=0): - return iter(self._labs[label_index]) - - def size(self): - """ - The number of elements in E, the size of dimension 0 in the E.arr - - Returns - ------- - int - """ - return len(self) - - def labs(self, kdx): - """ - Retrieve labels by index in keys - - Parameters - ---------- - kdx : int - index of key in E.keys - - Returns - ------- - np.ndarray - """ - return self._labs[kdx] - - def is_empty(self, level=0): - """ - Boolean indicating if entity.elements is empty - - Parameters - ---------- - level : int, optional - - Returns - ------- - bool - """ - return len(self._labs[level]) == 0 - - def uidset_by_level(self, level=0): - """ - The labels found in columns = level - - Parameters - ---------- - level : int, optional - - Returns - ------- - frozenset - """ - return frozenset(self._labs[level]) # should be update this to tuples? - - def elements_by_level(self, level1=0, level2=None, translate=False): - """ - Elements of staticentity by specified column - - Parameters - ---------- - level1 : int, optional - edges - level2 : int, optional - nodes - translate : bool, optional - whether to replace indices with labels - - Returns - ------- - collections.defaultdict - - think: level1 = edges, level2 = nodes - """ - # Is there a better way to view a slice of self._arr? - if level1 > self.dimsize - 1 or level1 < 0: - print(f"This StaticEntity has no level {level1}.") - return - if level2 is None: - level2 = level1 + 1 - - if level2 > self.dimsize - 1 or level2 < 0: - print(f"This StaticEntity has no level {level2}.") - return - # elts = OrderedDict([[k, UserList()] for k in self._labs[level1]]) - elif level1 == level2: - print(f"level1 equals level2") - return - # elts = OrderedDict([[k, UserList()] for k in self._labs[level1]]) - - temp, _ = remove_row_duplicates(self.data[:, [level1, level2]]) - elts = DefaultOrderedDict(UserList) - for row in temp: - elts[row[0]].append(row[1]) - - if translate: - telts = DefaultOrderedDict(UserList) - for kdx, vec in elts.items(): - k = self._labs[level1][kdx] - for vdx in vec: - telts[k].append(self._labs[level2][vdx]) - return telts - else: - return elts - - def incidence_matrix( - self, level1=0, level2=1, weights=False, aggregateby=None, index=False - ): - """ - Convenience method to navigate large tensor - - Parameters - ---------- - level1 : int, optional - indexes columns - level2 : int, optional - indexes rows - weights : bool, dict optional, default=False - If False all nonzero entries are 1. - If True all nonzero entries are filled by self.cell_weight - dictionary values, use :code:`aggregateby` to specify how duplicate - entries should have weights aggregated. - If dict, keys must be in (edge.uid, node.uid) form; only nonzero cells - in the incidence matrix will be updated by dictionary. - aggregateby : str, optional, {None, 'last', count', 'sum', 'mean', 'median', max', 'min', 'first', 'last'}, default : 'count' - Method to aggregate weights of duplicate rows in data. If None, then all cell weights - will be set to 1. - index : bool, optional - - Returns - ------- - scipy.sparse.csr.csr_matrix - Sparse matrix representation of incidence matrix for two levels of static entity. - - Note - ---- - In the context of hypergraphs think level1 = edges, level2 = nodes - """ - if self.dimsize < 2: - warnings.warn("Incidence matrix requires two levels of data.") - return None - if not weights: # transpose from the beginning - if self.dimsize > 2: - temp, _ = remove_row_duplicates(self.data[:, [level2, level1]]) - else: - temp = self.data[:, [level2, level1]] - result = csr_matrix((np.ones(len(temp)), temp.transpose()), dtype=int) - else: # transpose after cell weights are added - if self.dimsize > 2: - temp, temp_weights = remove_row_duplicates( - self.data[:, [level1, level2]], - weights=self._weights, - aggregateby=aggregateby, - ) - else: - temp, temp_weights = self.data[:, [level1, level2]], self.cell_weights - - if isinstance(weights, dict): - cat1 = self.keys[level1] - cat2 = self.keys[level2] - for k, v in weights: - try: - tdx = (self.index(cat1, k[0]), self.index(cat2, k[1])) - except: - HyperNetXError( - f"{k} is not recognized as belonging to this system." - ) - if temp_weights[tdx] != 0: - temp_weights[tdx] = v - # weights = {(self.index(cat1, k[0]), self.index(cat2, k[1])): v for k, v in weights.items()} - # for k in weights: - # if temp_weights[k] != 0:: - # temp_weights[k]=weights[k] - temp_weights = [temp_weights[tuple(t)] for t in temp] - dtype = int if aggregateby == "count" else float - result = csr_matrix( - (temp_weights, temp.transpose()), dtype=dtype - ).transpose() - - if index: # give index of rows then columns - return ( - result, - {k: v for k, v in enumerate(self._labs[level2])}, - {k: v for k, v in enumerate(self._labs[level1])}, - ) - else: - return result - - def restrict_to_levels(self, levels, weights=False, aggregateby="count", uid=None): - """ - Limit Static Entity data to specific levels - - Parameters - ---------- - levels : array - index of labels in data - weights : bool, optional, default : False - Whether or not to aggregate existing weights in self when restricting to levels. - If False then weights will be assigned 1. - aggregateby : str, optional, {None, 'last', count', 'sum', 'mean', 'median', max', 'min', 'first', 'last'}, default : 'count' - Method to aggregate cell_weights of duplicate rows in setsystem of type pandas.DataFrame. - If None then all cell_weights will be set to 1. - uid : None, optional - - Returns - ------- - Static Entity class - hnx.classes.staticentity.StaticEntity - """ - if levels[0] >= self.dimsize: - return self.__class__() - # if len(levels) == 1: - # if levels[0] >= self.dimsize: - # return self.__class__() - # else: - # newlabels = OrderedDict( - # [(self.keys[lev], self._labs[lev]) for lev in levels] - # ) - # return self.__class__(labels=newlabels) - else: - if weights: - weights = self._weights - else: - weights = None - if len(levels) == 1: - lev = levels[0] - newlabels = OrderedDict([(self._keys[lev], self._labs[lev])]) - data = self.data[:, lev] - data = np.reshape(data, (len(data), 1)) - return StaticEntity( - data=data, - weights=weights, - aggregateby=aggregateby, - labels=newlabels, - uid=uid, - ) - else: - data = self.data[:, levels] - newlabels = OrderedDict( - [(self.keys[lev], self._labs[lev]) for lev in levels] - ) - return self.__class__( - data=data, - weights=weights, - aggregateby=aggregateby, - labels=newlabels, - uid=uid, - ) - - def turn_entity_data_into_dataframe( - self, data_subset - ): # add option to include multiplicities stored in properties - """ - Convert rows of original data in StaticEntity to dataframe - - Parameters - ---------- - data : numpy.ndarray - Subset of the rows in the original data held in the StaticEntity - - Returns - ------- - pandas.core.frame.DataFrame - Columns and cell entries are derived from data and self.labels - """ - df = pd.DataFrame(data=data_subset, columns=self.keys) - width = data_subset.shape[1] - for ddx, row in enumerate(data_subset): - nrow = [self.labs(idx)[row[idx]] for idx in range(width)] - df.iloc[ddx] = nrow - return df - - def restrict_to_indices( - self, indices, level=0, uid=None - ): # restricting to indices requires renumbering the labels. - """ - Limit Static Entity data to specific indices of keys - - Parameters - ---------- - indices : array - array of category indices - level : int, optional - index of label - uid : None, optional - - Returns - ------- - Static Entity class - hnx.classes.staticentity.StaticEntity - """ - indices = list(indices) - idx = np.concatenate( - [np.argwhere(self.data[:, level] == k) for k in indices], axis=0 - ).transpose()[0] - temp = self.data[idx] - df = self.turn_entity_data_into_dataframe(temp) - return self.__class__(entity=df, uid=uid) - - def translate(self, level, index): - """ - Replaces a category index and value index with label - - Parameters - ---------- - level : int - category index of label - index : int - value index of label - - Returns - ------- - : numpy.array(str) - """ - if isinstance(index, int): - return self._labs[level][index] - else: - return [self._labs[level][idx] for idx in index] - - def translate_arr(self, coords): - """ - Translates a single cell in the entity array - - Parameters - ---------- - coords : tuple of ints - - Returns - ------- - list - """ - assert len(coords) == self.dimsize - translation = list() - for idx in range(self.dimsize): - translation.append(self.translate(idx, coords[idx])) - return translation - - def index(self, category, value=None): - """ - Returns dimension of category and index of value - - Parameters - ---------- - category : string - value : string, optional - - Returns - ------- - int or tuple of ints - """ - if value is not None: - return self._keyindex[category], self._index[category][value] - else: - return self._keyindex[category] - - def indices(self, category, values): - """ - Returns dimension of category and index of values (array) - - Parameters - ---------- - category : string - values : single string or array of strings - - Returns - ------- - list - """ - return [self._index[category][value] for value in values] - - def level(self, item, min_level=0, max_level=None, return_index=True): - """ - Returns first level item appears by order of keys from minlevel to maxlevel - inclusive - - Parameters - ---------- - item : string - min_level : int, optional - max_level : int, optional - - return_index : bool, optional - - Returns - ------- - tuple - """ - n = len(self.dimensions) - if max_level is not None: - n = min([max_level + 1, n]) - for lev in range(min_level, n): - if item in self._labs[lev]: - if return_index: - return lev, self._index[self._keys[lev]][item] - else: - return lev - else: - print(f'"{item}" not found') - return None - - # note the depth and registry methods may or may not be useful. We can add these later. - - -class StaticEntitySet(StaticEntity): - - """ - .. _staticentityset: - """ - - def __init__( - self, - entity=None, - data=None, - arr=None, - labels=None, - uid=None, - level1=0, - level2=1, - weights=None, - keep_weights=True, - aggregateby=None, - **props, - ): - - if entity is None: - if data is not None: - data = data[:, [level1, level2]] - arr = None - elif arr is not None: - data, cell_weights = _turn_tensor_to_data(arr) - weights = [cell_weights[tuple(t)] for t in data] - data = data[:, [level1, level2]] - if labels is not None: - keys = np.array(list(labels.keys())) - temp = OrderedDict() - for lev in [level1, level2]: - if lev < len(keys): - temp[keys[lev]] = labels[keys[lev]] - labels = temp - super().__init__( - data=data, weights=weights, labels=labels, uid=uid, **props - ) - else: - if isinstance(entity, StaticEntity): - data = entity.data[:, [level1, level2]] - if keep_weights: - weights = entity._weights - labels = OrderedDict( - [(entity._keys[k], entity._labs[k]) for k in [level1, level2]] - ) - super().__init__( - data=data, - labels=labels, - uid=uid, - weights=weights, - aggregateby=aggregateby, - **props, - ) - elif isinstance(entity, StaticEntitySet): - if keep_weights: - aggregateby = "last" - super().__init__( - entity, - weights=weights, - keep_weights=keep_weights, - aggregateby=aggregateby, - **props, - ) - - elif isinstance(entity, pd.DataFrame): - cols = entity.columns[[level1, level2]] - super().__init__( - entity=entity[cols], - uid=uid, - weights=weights, - aggregateby=aggregateby, - **props, - ) - else: - # this presumes entity is an iterable of iterables or a dictionary - super().__init__(entity=entity, uid=uid, **props) - - def __repr__(self): - """ - Returns a string resembling the constructor for entityset without any - children - - Returns - ------- - string - """ - return f"StaticEntitySet({self._uid},{list(self.uidset)},{self.properties})" - - def incidence_matrix(self, index=False, weights=False): - """ - Incidence matrix of StaticEntitySet - - Parameters - ---------- - index : bool, optional - - weight: bool, dict optional, default=False - If False all nonzero entries are 1. - If True all nonzero entries are filled by self.cell_weight - dictionary values. - If dict, keys must be in self.cell_weight keys; nonzero cells - will be updated by dictionary. - - - Returns - ------- - scipy.sparse.csr.csr_matrix - Sparse matrix representation of incidence matrix for static entity set. - """ - return StaticEntity.incidence_matrix(self, weights=weights, index=index) - - def restrict_to(self, indices, uid=None): - """ - Limit Static Entityset data to specific indices of keys - - Parameters - ---------- - indices : array - array of indices in keys - uid : None, optional - - Returns - ------- - StaticEntitySet - hnx.classes.staticentity.StaticEntitySet - - """ - return self.restrict_to_indices(indices, level=0, uid=uid) - - def convert_to_entityset(self, uid): - """ - Convert Static EntitySet into EntitySet with given uid. - - Parameters - ---------- - uid : string - - Returns - ------- - EntitySet - hnx.classes.entity.EntitySet - """ - return EntitySet(uid, self.incidence_dict) - - def collapse_identical_elements( - self, - uid=None, - return_equivalence_classes=False, - ): - """ - Returns StaticEntitySet after collapsing elements if they have same children - If no elements share same children, a copy of the original StaticEntitySet is returned - - Parameters - ---------- - uid : None, optional - return_equivalence_classes : bool, optional - If True, return a dictionary of equivalence classes keyed by new edge names - - - Returns - ------- - StaticEntitySet - hnx.classes.staticentity.StaticEntitySet - """ - shared_children = DefaultOrderedDict(list) - for k, v in self.elements.items(): - shared_children[frozenset(v)].append(k) - new_entity_dict = OrderedDict( - [ - # ( - # f"{next(iter(v))}:{len(v)}", - # sorted(set(k), key=lambda x: list(self.labs(1)).index(x)), - # ) - ( - f"{next(iter(v))}:{len(v)}", - sorted(set(k), key=lambda x: self.index(self._keys[1], x)), - ) - for k, v in shared_children.items() - ] - ) - if return_equivalence_classes: - eq_classes = OrderedDict( - [ - ( - f"{next(iter(v))}:{len(v)}", - v - # sorted(v, key=lambda x: self.index(self._keys[0], x)), ## not sure why sorting is important here - ) - for k, v in shared_children.items() - ] - ) - return StaticEntitySet(uid=uid, entity=new_entity_dict), eq_classes - else: - return StaticEntitySet(uid=uid, entity=new_entity_dict) - - -def _turn_tensor_to_data(arr): - """ - Return list of nonzero coordinates in arr. - - Parameters - ---------- - arr : numpy.ndarray - Tensor corresponding to incidence of co-occurring labels. - """ - temp = np.array(arr.nonzero()).T - return temp, {tuple(t): arr[tuple(t)] for t in temp} - - -def _turn_dict_to_staticentity(dict_object): - """Create a static entity directly from a dictionary of hashables""" - d = OrderedDict(dict_object) - level2ctr = HNXCount() - level1ctr = HNXCount() - level2 = DefaultOrderedDict(level2ctr) - level1 = DefaultOrderedDict(level1ctr) - coords = list() - for k, val in d.items(): - level1[k] - for v in val: - level2[v] - coords.append((level1[k], level2[v])) - coords, counts = remove_row_duplicates(coords, aggregateby="count") - level1 = np.array(list(level1)) - level2 = np.array(list(level2)) - data = np.array(coords, dtype=int) - labels = OrderedDict({"0": level1, "1": level2}) - return data, labels, counts - - -def _turn_iterable_to_staticentity(iter_object): - for s in iter_object: - if not isinstance(s, Iterable): - raise HyperNetXError( - "The entity data type not recognized. Iterables must be iterable of iterables." - ) - else: - labels = [f"e{str(x)}" for x in range(len(iter_object))] - dict_object = dict(zip(labels, iter_object)) - return _turn_dict_to_staticentity(dict_object) - - -def _turn_dataframe_into_entity( - df, weights=None, aggregateby=None, include_unknowns=False -): - """ - Convenience method to reformat dataframe object into data,labels format - for construction of a static entity - - Parameters - ---------- - df : pandas.DataFrame - May not contain nans - weights : array-like, optional, default : None - User specified weights corresponding to data, length must equal number - of rows in data. If None, weight for all rows is assumed to be 1. - aggregateby : str, optional, {None, 'last', count', 'sum', 'mean', 'median', max', 'min', 'first', 'last'}, default : 'count' - Method to aggregate cell_weights of duplicate rows in data. - include_unknowns : bool, optional, default : False - If Unknown was used to fill in nans - - Returns - ------- - outputdata : numpy.ndarray - slabels : numpy.array of strings - cell_weights : dict - - """ - columns = df.columns - ctr = [HNXCount() for c in range(len(columns))] - ldict = OrderedDict() - rdict = OrderedDict() - for idx, c in enumerate(columns): - ldict[c] = defaultdict(ctr[idx]) # TODO make this an Ordered default dict - rdict[c] = OrderedDict() - if include_unknowns: - ldict[c][f"Unknown {c}"] - # TODO: update this to take a dict assign for each column - rdict[c][0] = f"Unknown {c}" - for k in df[c]: - ldict[c][k] - rdict[c][ldict[c][k]] = k - ldict[c] = dict(ldict[c]) - dims = tuple([len(ldict[c]) for c in columns]) - - m = len(df) - n = len(columns) - data = np.zeros((m, n), dtype=int) - for rid in range(m): - for cid in range(n): - c = columns[cid] - data[rid, cid] = ldict[c][df.iloc[rid][c]] - - output_data = remove_row_duplicates(data, weights=weights, aggregateby=aggregateby) - - slabels = OrderedDict() - for cdx, c in enumerate(columns): - slabels.update({c: np.array(list(ldict[c].keys()))}) - return output_data[0], slabels, output_data[1] - - -# helpers -def _fd(): - return None diff --git a/hypernetx/classes/tests/conftest.py b/hypernetx/classes/tests/conftest.py index abf503bf..2f5e7519 100644 --- a/hypernetx/classes/tests/conftest.py +++ b/hypernetx/classes/tests/conftest.py @@ -2,13 +2,11 @@ import os import itertools as it import networkx as nx -import hypernetx as hnx import pandas as pd import numpy as np -from collections import OrderedDict -from hypernetx.utils.toys import HarryPotter -# from harrypotter import HarryPotter +from hypernetx import Hypergraph, HarryPotter, Entity, LesMis as LM +from collections import OrderedDict, defaultdict class SevenBySix: @@ -47,23 +45,25 @@ def __init__(self, static=False): ] ) - self.data = [ - [0, 0], - [0, 1], - [0, 2], - [1, 2], - [1, 3], - [2, 0], - [2, 2], - [2, 4], - [2, 5], - [3, 1], - [3, 3], - [4, 5], - [4, 6], - [5, 0], - [5, 5], - ] + self.data = np.array( + [ + [0, 0], + [0, 1], + [0, 2], + [1, 2], + [1, 3], + [2, 0], + [2, 2], + [2, 4], + [2, 5], + [3, 1], + [3, 3], + [4, 5], + [4, 6], + [5, 0], + [5, 5], + ] + ) class TriLoop: @@ -73,7 +73,7 @@ def __init__(self): A, B, C, D = "A", "B", "C", "D" AB, BC, ACD = "AB", "BC", "ACD" self.edgedict = {AB: {A, B}, BC: {B, C}, ACD: {A, C, D}} - self.hypergraph = hnx.Hypergraph(self.edgedict, name="TriLoop") + self.hypergraph = Hypergraph(self.edgedict, name="TriLoop") class SBSDupes: @@ -121,7 +121,7 @@ def __init__(self): (8, {"FN", "JA", "JV", "PO", "SP", "SS"}), ] ) - self.hypergraph = hnx.Hypergraph(self.edgedict) + self.hypergraph = Hypergraph(self.edgedict) class Dataframe: @@ -130,25 +130,39 @@ def __init__(self): self.df = pd.read_csv(fname, index_col=0) +class CompleteBipartite: + def __init__(self, n1, n2): + self.g = nx.complete_bipartite_graph(n1, n2) + self.left, self.right = nx.bipartite.sets(self.g) + + @pytest.fixture -def seven_by_six(): +def sbs(): return SevenBySix() +@pytest.fixture +def ent_sbs(sbs): + return Entity(data=np.asarray(sbs.data), labels=sbs.labels) + + +@pytest.fixture +def sbs_edgedict(sbs): + return sbs.edgedict + + @pytest.fixture def triloop(): return TriLoop() @pytest.fixture -def sbs_hypergraph(): - sbs = SevenBySix() - return hnx.Hypergraph(sbs.edgedict, name="sbsh") +def sbs_hypergraph(sbs): + return Hypergraph(sbs.edgedict, name="sbsh") @pytest.fixture -def sbs_graph(): - sbs = SevenBySix() +def sbs_graph(sbs): edges = set() for _, e in sbs.edgedict.items(): edges.update(it.combinations(e, 2)) @@ -160,7 +174,7 @@ def sbs_graph(): @pytest.fixture def sbsd_hypergraph(): sbsd = SBSDupes() - return hnx.Hypergraph(sbsd.edgedict) + return Hypergraph(sbsd.edgedict) @pytest.fixture @@ -176,7 +190,7 @@ def G(): @pytest.fixture def H(): G = nx.karate_club_graph() - return hnx.Hypergraph({f"e{i}": e for i, e in enumerate(G.edges())}) + return Hypergraph({f"e{i}": e for i, e in enumerate(G.edges())}) @pytest.fixture @@ -186,11 +200,170 @@ def bipartite_example(): return bipartite.random_graph(10, 5, 0.4, 0) +@pytest.fixture +def complete_bipartite_example(): + return CompleteBipartite(2, 3).g + + @pytest.fixture def dataframe(): return Dataframe() +@pytest.fixture +def dataframe_example(): + M = np.array([[1, 1, 0, 0], [0, 1, 1, 0], [1, 0, 1, 0]]) + index = ["A", "B", "C"] + columns = ["a", "b", "c", "d"] + return pd.DataFrame(M, index=index, columns=columns) + + @pytest.fixture def harry_potter(): - return hnx.HarryPotter() + return HarryPotter() + + +@pytest.fixture +def array_example(): + return np.array( + [[0, 1, 1, 0, 1], [1, 1, 1, 1, 1], [1, 0, 0, 1, 0], [0, 0, 0, 0, 1]] + ) + + +@pytest.fixture +def ent_hp(harry_potter): + return Entity(data=np.asarray(harry_potter.data), labels=harry_potter.labels) + + +####################Fixtures suite for test_hypergraph.py#################### +####################These fixtures are modular and thus have inter-dependencies#################### +@pytest.fixture +def les_mis(): + return LM() + + +@pytest.fixture +def scenes(): + return { + "0": ("FN", "TH"), + "1": ("TH", "JV"), + "2": ("BM", "FN", "JA"), + "3": ("JV", "JU", "CH", "BM"), + "4": ("JU", "CH", "BR", "CN", "CC", "JV", "BM"), + "5": ("TH", "GP"), + "6": ("GP", "MP"), + "7": ("MA", "GP"), + } + + +@pytest.fixture +def edges(scenes): + return list(set(list(scenes.keys()))) + + +@pytest.fixture +def nodes(scenes): + return list(set(list(np.concatenate([v for v in scenes.values()])))) + + +@pytest.fixture +def edge_properties(edges): + edge_properties = defaultdict(dict) + edge_properties.update( + {str(ed): {"weight": np.random.randint(2, 10)} for ed in range(0, 8, 2)} + ) + for ed in edges: + edge_properties[ed].update({"color": np.random.choice(["red", "green"])}) + return edge_properties + + +@pytest.fixture +def node_properties(les_mis, nodes): + return { + ch: { + "FullName": les_mis.dnames.loc[ch].FullName, + "Description": les_mis.dnames.loc[ch].Description, + "color": np.random.choice(["pink", "blue"]), + } + for ch in nodes + } + + +@pytest.fixture +def scenes_dataframe(scenes): + scenes_dataframe = ( + pd.DataFrame(pd.Series(scenes).explode()) + .reset_index() + .rename(columns={"index": "Scenes", 0: "Characters"}) + ) + scenes_dataframe["color"] = np.random.choice( + ["red", "green"], len(scenes_dataframe) + ) + scenes_dataframe["heaviness"] = np.random.rand(len(scenes_dataframe)) + + return scenes_dataframe + + +@pytest.fixture +def hyp_no_props(): + return Hypergraph( + np.array( + [ + np.random.choice(list("ABCD"), 50), + np.random.choice(list("abcdefghijklmnopqrstuvwxyz"), 50), + ] + ).T, # creates a transposed ndarray + edge_col="Club", + node_col="Member", + ) + + +@pytest.fixture +def hyp_df_with_props(scenes_dataframe, node_properties, edge_properties): + return Hypergraph( + scenes_dataframe, + cell_properties=["color"], + cell_weight_col="heaviness", + node_properties=node_properties, + edge_properties=edge_properties, + ) + + +@pytest.fixture +def hyp_dict_with_props(scenes): + scenes_with_cellprops = { + ed: { + ch: { + "color": np.random.choice(["red", "green"]), + "cell_weight": np.random.rand(), + } + for ch in v + } + for ed, v in scenes.items() + } + + return Hypergraph( + scenes_with_cellprops, + edge_col="Scenes", + node_col="Characters", + cell_weight_col="cell_weight", + cell_properties=scenes_with_cellprops, + ) + + +@pytest.fixture +def hyp_props_on_edges_nodes(scenes_dataframe, edge_properties, node_properties): + return Hypergraph( + setsystem=scenes_dataframe, + edge_col="Scenes", + node_col="Characters", + cell_weight_col="cell_weight", + cell_properties=["color"], + edge_properties=edge_properties, + node_properties=node_properties, + default_edge_weight=2.5, + default_node_weight=6, + ) + + +####################Fixtures suite for test_hypergraph.py#################### diff --git a/hypernetx/classes/tests/sample.csv b/hypernetx/classes/tests/sample.csv index 8b460528..5f8451c6 100644 --- a/hypernetx/classes/tests/sample.csv +++ b/hypernetx/classes/tests/sample.csv @@ -1,4 +1,4 @@ ,a,b,c A,5,,2 B,3,2,0 -C,1,3,-1 \ No newline at end of file +C,1,3,-1 diff --git a/hypernetx/classes/tests/test_entity.py b/hypernetx/classes/tests/test_entity.py index 4df4df73..761fc261 100644 --- a/hypernetx/classes/tests/test_entity.py +++ b/hypernetx/classes/tests/test_entity.py @@ -1,142 +1,130 @@ import numpy as np import pytest -from hypernetx import Entity, EntitySet -from hypernetx import HyperNetXError - - -def test_entity_constructor(): - ent = Entity("edge", {"A", "C", "K"}) - assert ent.uid == "edge" - assert ent.size() == 3 - assert len(ent.uidset) == 3 - assert len(ent.children) == 0 - assert isinstance(ent.incidence_dict["A"], set) - assert "A" in ent - - -def test_entity_construct_from_entity(triloop): - e1 = Entity("e1", elements=triloop.edgedict) - e2 = Entity("e2", entity=e1) - e3 = Entity("e3", entity=e2, elements={"Z": "A"}) - assert e1 != e2 - assert e1.elements == e2.elements - assert e1.children == e3.children - assert e1.elements != e3.elements - with pytest.raises(HyperNetXError): - e4 = Entity("e1", entity=e1) - - -def test_entity_add(): - # add an element with property - ent = Entity("e", {"A", "C", "K"}) - ent.add(Entity("D", [1, 2, 3], color="red")) - assert ent.size() == 4 - assert len(ent.children) == 3 - assert 2 in ent["D"].elements - assert ent["D"].color == "red" - - -def test_entity_no_self_reference(): - # confirm entity may not add itself. - ent = Entity("e", {"A", "C", "K"}) - with pytest.raises(HyperNetXError) as excinfo: - ent.add(Entity("e")) - assert "Self reference" in str(excinfo.value) - with pytest.raises(HyperNetXError) as excinfo2: - ent.add(ent) - assert "Self reference" in str(excinfo2.value) - - -def test_add_and_merge_properties(): - # assert that adding an existing entity will not overwrite - e1 = Entity("e1", {"A", "C", "K"}) - e2 = Entity("C", w=5) - e1.add(e2) - assert e1["C"].w == 5 - ent3 = Entity("C") - e1.add(ent3) - assert e1["C"].w == 5 - - -def test_add_no_overwrite(): - # adding and entity of the same name as an existing entity - # only updates properties - e1 = Entity("e1", {"A", "C", "K"}) - e2 = Entity("C", ["X"], w=5) - assert "X" in e2.elements - e1.add(e2) - assert "X" not in e1["C"] - assert e1["C"].w == 5 - - -def test_entity_remove(): - ent = Entity("e", {"A", "C", "K"}) - assert ent.size() == 3 - ent.remove("A") - assert ent.size() == 2 - - -def test_entity_depth(): - e1 = Entity("e1") - e2 = Entity("e2", [e1]) - e3 = Entity("e3", [e2]) - e4 = Entity("e4", [e3, e1]) - assert e4.depth() == 3 - e3.remove(e2) - assert e4.depth() == 1 - - -def test_entity_set(): - eset = EntitySet("eset1", {"A", "C", "K"}) - eset2 = eset.clone("eset2") - assert eset.elements == eset2.elements - assert len(eset) == 3 - assert eset.uid == "eset1" - assert len(eset.children) == 0 - with pytest.raises(HyperNetXError) as excinfo: - eset2.add(Entity("Z", ["A", "C", "B"])) - assert "Fails the bipartite condition" in str(excinfo.value) - - -def test_entity_set_from_dict(seven_by_six): - sbs = seven_by_six - eset = EntitySet("sbs", sbs.edgedict) - M, rowdict, coldict = eset.incidence_matrix(index=True) - assert len(rowdict) == 7 - assert len(coldict) == 6 - x = np.ones(6).transpose() - assert np.max(M.dot(x)) == 3 - - -def test_entity_from_dict_mixed_types(): - e1 = Entity("e1", [2]) - d = {"e1": e1, "e2": [5]} - e3 = Entity("e3", d) - assert 2 in e3.children - - -def test_equality(): - # Different uids, elements generated differently - e1 = Entity("e1", [1, 2, 3]) - e2 = Entity("e2", [1, 2, 3]) - assert not e1 == e2 - assert not e1[1] == e2[1] - # Different uids, elements generated the same - elts = [Entity(uid) for uid in [1, 2, 3]] - e1 = Entity("e1", elts) - e2 = Entity("e2", elts) - assert not e1 == e2 - assert e1[1] == e2[1] - # Different properties only - e1 = Entity("e1", elts) - e2 = Entity("e1", elts, weight=2) - assert not e1 == e2 - - -def test_merge_entities(): - x = Entity("x", [1, 2, 3], weight=1, color="r") - y = Entity("y", [2, 3, 4], weight=3) - z = Entity.merge_entities("z", x, y) - assert z.uidset == set([1, 2, 3, 4]) - assert z.weight == 3 - assert z.color == "r" + +from collections.abc import Iterable +from collections import UserList +from hypernetx.classes import Entity + + +def test_constructor(ent_sbs): + assert ent_sbs.size() == 6 + assert len(ent_sbs.uidset) == 6 + assert len(ent_sbs.children) == 7 + assert isinstance(ent_sbs.incidence_dict["I"], list) + assert "I" in ent_sbs + assert "K" in ent_sbs + + +def test_property(ent_hp): + assert len(ent_hp.uidset) == 7 + assert len(ent_hp.elements) == 7 + assert isinstance(ent_hp.elements["Hufflepuff"], UserList) + assert not ent_hp.is_empty() + assert len(ent_hp.incidence_dict["Gryffindor"]) == 6 + + +@pytest.mark.xfail( + reason="Entity does not remove row duplicates from self._data if constructed from np.ndarray, defaults to first two cols as data cols" +) +def test_attributes(ent_hp): + assert isinstance(ent_hp.data, np.ndarray) + # TODO: Entity does not remove row duplicates from self._data if constructed from np.ndarray + assert ent_hp.data.shape == ent_hp.dataframe[ent_hp._data_cols].shape # fails + assert isinstance(ent_hp.labels, dict) + # TODO: Entity defaults to first two cols as data cols + assert ent_hp.dimensions == (7, 11, 10, 36, 26) # fails + assert ent_hp.dimsize == 5 # fails + df = ent_hp.dataframe[ent_hp._data_cols] + assert list(df.columns) == [ # fails + "House", + "Blood status", + "Species", + "Hair colour", + "Eye colour", + ] + assert ent_hp.dimensions == tuple(df.nunique()) + assert set(ent_hp.labels["House"]) == set(df["House"].unique()) + + +def test_custom_attributes(ent_hp): + assert ent_hp.__len__() == 7 + assert isinstance(ent_hp.__str__(), str) + assert isinstance(ent_hp.__repr__(), str) + assert isinstance(ent_hp.__contains__("Muggle"), bool) + assert ent_hp.__contains__("Muggle") is True + assert ent_hp.__getitem__("Slytherin") == [ + "Half-blood", + "Pure-blood", + "Pure-blood or half-blood", + ] + assert isinstance(ent_hp.__iter__(), Iterable) + assert isinstance(ent_hp.__call__(), Iterable) + assert ent_hp.__call__().__next__() == "Unknown House" + + +@pytest.mark.xfail( + reason="at some point we are casting out and back to categorical dtype without preserving categories ordering from `labels` provided to constructor" +) +def test_level(ent_sbs): + # TODO: at some point we are casting out and back to categorical dtype without + # preserving categories ordering from `labels` provided to constructor + assert ent_sbs.level("I") == (0, 5) # fails + assert ent_sbs.level("K") == (1, 3) + assert ent_sbs.level("K", max_level=0) is None + + +def test_uidset_by_level(ent_sbs): + assert ent_sbs.uidset_by_level(0) == {"I", "L", "O", "P", "R", "S"} + assert ent_sbs.uidset_by_level(1) == {"A", "C", "E", "K", "T1", "T2", "V"} + + +def test_elements_by_level(ent_sbs): + assert ent_sbs.elements_by_level(0, 1) + + +def test_incidence_matrix(ent_sbs): + assert ent_sbs.incidence_matrix(1, 0).todense().shape == (6, 7) + + +def test_indices(ent_sbs): + assert ent_sbs.indices("nodes", "K") == [3] + assert ent_sbs.indices("nodes", ["K", "T1"]) == [3, 4] + + +def test_translate(ent_sbs): + assert ent_sbs.translate(0, 0) == "P" + assert ent_sbs.translate(1, [3, 4]) == ["K", "T1"] + + +def test_translate_arr(ent_sbs): + assert ent_sbs.translate_arr((0, 0)) == ["P", "A"] + + +def test_index(ent_sbs): + assert ent_sbs.index("nodes") == 1 + assert ent_sbs.index("nodes", "K") == (1, 3) + + +def test_restrict_to_levels(ent_hp): + assert len(ent_hp.restrict_to_levels([0]).uidset) == 7 + + +def test_restrict_to_indices(ent_hp): + assert ent_hp.restrict_to_indices([1, 2]).uidset == { + "Gryffindor", + "Ravenclaw", + } + + +def test_construct_from_entity(sbs): + ent = Entity(entity=sbs.edgedict) + assert len(ent.elements) == 6 + + +@pytest.mark.xfail(reason="default arguments fail for empty Entity") +def test_construct_empty_entity(): + ent = Entity() + assert ent.empty + assert ent.is_empty() + assert len(ent.elements) == 0 + assert ent.dimsize == 0 diff --git a/hypernetx/classes/tests/test_entityset.py b/hypernetx/classes/tests/test_entityset.py new file mode 100644 index 00000000..ca373324 --- /dev/null +++ b/hypernetx/classes/tests/test_entityset.py @@ -0,0 +1,50 @@ +import numpy as np +import pytest + +from hypernetx import Entity, EntitySet + + +@pytest.mark.xfail(reason="default arguments fail for empty Entity") +def test_construct_empty_entityset(): + es = EntitySet() + assert es.empty + assert len(es.elements) == 0 + assert es.dimsize == 0 + + +@pytest.mark.xfail( + reason="at some point we are casting out and back to categorical dtype without preserving categories ordering from `labels` provided to constructor" +) +def test_construct_entityset_from_data(harry_potter): + es = EntitySet( + data=np.asarray(harry_potter.data), + labels=harry_potter.labels, + level1=1, + level2=3, + ) + # TODO: at some point we are casting out and back to categorical dtype without + # preserving categories ordering from `labels` provided to constructor + assert es.indices("Blood status", ["Pure-blood", "Half-blood"]) == [2, 1] # fails + assert es.incidence_matrix().shape == (36, 11) + assert len(es.collapse_identical_elements()) == 11 + + +@pytest.mark.skip(reason="EntitySet from Entity no longer supported") +def test_construct_entityset_from_entity_hp(harry_potter): + es = EntitySet( + entity=Entity(data=np.asarray(harry_potter.data), labels=harry_potter.labels), + level1="Blood status", + level2="House", + ) + assert es.indices("Blood status", ["Pure-blood", "Half-blood"]) == [2, 1] + assert es.incidence_matrix().shape == (7, 11) + assert len(es.collapse_identical_elements()) == 9 + + +@pytest.mark.skip(reason="EntitySet from Entity no longer supported") +def test_construct_entityset_from_entity(sbs): + es = EntitySet(entity=Entity(entity=sbs.edgedict)) + + assert not es.empty + assert es.dimsize == 2 + assert es.incidence_matrix().shape == (7, 6) diff --git a/hypernetx/classes/tests/test_hypergraph.py b/hypernetx/classes/tests/test_hypergraph.py index 45cc8b50..4f5ef0f3 100644 --- a/hypernetx/classes/tests/test_hypergraph.py +++ b/hypernetx/classes/tests/test_hypergraph.py @@ -1,12 +1,9 @@ import pytest import numpy as np -import networkx as nx -from hypernetx import Hypergraph, Entity, EntitySet -from hypernetx import HyperNetXError +from hypernetx.classes.hypergraph import Hypergraph -def test_hypergraph_from_iterable_of_sets(seven_by_six): - sbs = seven_by_six +def test_hypergraph_from_iterable_of_sets(sbs): H = Hypergraph(sbs.edges) assert len(H.edges) == 6 assert len(H.nodes) == 7 @@ -15,8 +12,7 @@ def test_hypergraph_from_iterable_of_sets(seven_by_six): assert H.number_of_nodes() == 7 -def test_hypergraph_from_dict(seven_by_six): - sbs = seven_by_six +def test_hypergraph_from_dict(sbs): H = Hypergraph(sbs.edgedict) assert len(H.edges) == 6 assert len(H.nodes) == 7 @@ -25,8 +21,7 @@ def test_hypergraph_from_dict(seven_by_six): assert H.order() == 7 -def test_hypergraph_custom_attributes(seven_by_six): - sbs = seven_by_six +def test_hypergraph_custom_attributes(sbs): H = Hypergraph(sbs.edges) assert isinstance(H.__str__(), str) assert isinstance(H.__repr__(), str) @@ -37,27 +32,22 @@ def test_hypergraph_custom_attributes(seven_by_six): assert sorted(H.__getitem__("C")) == ["A", "E", "K"] -def test_hypergraph_static(seven_by_six): - sbs = seven_by_six - H = Hypergraph(sbs.edges, static=True) +def test_get_linegraph(sbs): + H = Hypergraph(sbs.edges) assert len(H.edges) == 6 assert len(H.nodes) == 7 - assert H.get_id("E") == 3 - assert list(H.get_linegraph(s=1)) == [0, 1, 2, 3, 4, 5] - # H.get_name - # H.translate + assert len(set(H.get_linegraph(s=1)).difference(set([0, 1, 2, 3, 4, 5]))) == 0 -def test_hypergraph_from_dataframe(lesmis): - df = lesmis.hypergraph.dataframe() - H = Hypergraph.from_dataframe(df) +def test_hypergraph_from_incidence_dataframe(lesmis): + df = lesmis.hypergraph.incidence_dataframe() + H = Hypergraph.from_incidence_dataframe(df) assert H.shape == (40, 8) assert H.size(3) == 8 assert H.degree("JA") == 3 -def test_hypergraph_from_numpy_array(seven_by_six): - sbs = seven_by_six +def test_hypergraph_from_numpy_array(sbs): H = Hypergraph.from_numpy_array(sbs.arr) assert len(H.nodes) == 6 assert len(H.edges) == 7 @@ -70,58 +60,43 @@ def test_hypergraph_from_bipartite(sbsd_hypergraph): HB = Hypergraph.from_bipartite(H.bipartite()) assert len(HB.edges) == 7 assert len(HB.nodes) == 8 - assert HB.s_degree("T1") == 1 - - -def test_hypergraph_from_entity_set(seven_by_six): - sbs = seven_by_six - entityset = EntitySet("_", sbs.edgedict) - H = Hypergraph(entityset) - assert H.edges.incidence_dict == sbs.edgedict - assert H.s_degree("A") == 3 - assert H.dim("O") == 1 - assert len(H.edge_size_dist()) == 6 - assert len(H.edge_neighbors("S")) == 4 -def test_add_node_to_edge(seven_by_six): - sbs = seven_by_six +@pytest.mark.skip("Deprecated method; will support in later release") +def test_add_node_to_edge(sbs): H = Hypergraph(sbs.edgedict) assert H.shape == (7, 6) - # add node not already in hypergraph to edge - # alreadyin hypergraph - node = Entity("B") - edge = H.edges["P"] + node = "B" + edge = "P" H.add_node_to_edge(node, edge) assert H.shape == (8, 6) # add edge with nodes already in hypergraph - H.add_edge(Entity("Z", ["A", "B"])) + H.add_edge({"Z": ["A", "B"]}) assert H.shape == (8, 7) # add edge not in hypergraph with nodes not in hypergraph - H.add_edge(Entity("Y", ["M", "N"])) + H.add_edge({"Y": ["M", "N"]}) assert H.shape == (10, 8) -def test_remove_edge(seven_by_six): - sbs = seven_by_six +def test_remove_edges(sbs): H = Hypergraph(sbs.edgedict) assert H.shape == (7, 6) # remove an edge without removing any nodes - H.remove_edge("P") + H = H.remove_edges("P") assert H.shape == (7, 5) # remove an edge containing a singleton ear - H.remove_edge("O") + H = H.remove_edges("O") assert H.shape == (6, 4) -def test_remove_node(): +def test_remove_nodes(): a, b, c, d = "a", "b", "c", "d" hbug = Hypergraph({0: [a, b], 1: [a, c], 2: [a, d]}) assert a in hbug.nodes assert a in hbug.edges[0] assert a in hbug.edges[1] assert a in hbug.edges[2] - hbug.remove_node(a) + hbug = hbug.remove_nodes(a) assert a not in hbug.nodes assert a not in hbug.edges[0] assert a not in hbug.edges[1] @@ -133,7 +108,8 @@ def test_matrix(sbs_hypergraph): assert H.incidence_matrix().todense().shape == (7, 6) assert H.adjacency_matrix(s=2).todense().shape == (7, 7) assert H.edge_adjacency_matrix().todense().shape == (6, 6) - assert H.auxiliary_matrix().todense().shape == (6, 6) + aux_matrix = H.auxiliary_matrix(node=False) + assert aux_matrix.todense().shape == (6, 6) def test_collapse_edges(sbsd_hypergraph): @@ -174,12 +150,15 @@ def test_restrict_to_nodes(sbs_hypergraph): assert len(H1.nodes) == 3 assert len(H1.edges) == 5 assert "C" in H.edges["P"] - assert not "C" in H1.edges["P"] + assert "C" not in H1.edges["P"] +# @pytest.mark.skip("reason=Deprecated method") def test_remove_from_restriction(triloop): h = triloop.hypergraph - h1 = h.restrict_to_nodes(h.neighbors("A")).remove_node("A") + h1 = h.restrict_to_nodes(h.neighbors("A")).remove_nodes( + "A" + ) # Hypergraph does not have a remove_node method assert "A" not in h1 assert "A" not in h1.edges["ACD"] @@ -196,19 +175,21 @@ def test_toplexes(sbsd_hypergraph): def test_is_connected(): setsystem = [{1, 2, 3, 4}, {3, 4, 5, 6}, {5, 6, 7}, {5, 6, 8}] h = Hypergraph(setsystem) - assert h.is_connected() == True - assert h.is_connected(s=2) == False - assert h.is_connected(s=2, edges=True) == True - assert h.is_connected(s=3, edges=True) == False + assert h.is_connected() is True + assert h.is_connected(s=2) is False + assert h.is_connected(s=2, edges=True) is True + # test case below will raise nx.NetworkXPointlessConcept + assert h.is_connected(s=3, edges=True) is False +# @pytest.mark.skip("Deprecated methods") def test_singletons(): E = {1: {2, 3, 4, 5}, 6: {2, 5, 7, 8, 9}, 10: {11}, 12: {13}, 14: {7}} h = Hypergraph(E) assert h.shape == (9, 5) singles = h.singletons() assert len(singles) == 2 - h.remove_edges(singles) + h = h.remove_edges(singles) assert h.shape == (7, 3) @@ -232,7 +213,7 @@ def test_connected_components(): setsystem = [{1, 2, 3, 4}, {4, 5, 6}, {5, 6, 7}, {5, 6, 8}] h = Hypergraph(setsystem) assert len(list(h.connected_components())) == 1 - assert list(h.connected_components(edges=True)) == [{"0", "1", "2", "3"}] + assert list(h.connected_components(edges=True)) == [{0, 1, 2, 3}] assert [len(g) for g in h.connected_component_subgraphs()] == [8] @@ -249,8 +230,8 @@ def test_s_components(): def test_s_connected_components(): setsystem = [{1, 2, 3, 4}, {4, 5, 6}, {5, 6, 7}, {5, 6, 8}] h = Hypergraph(setsystem) - assert list(h.s_connected_components()) == [{"0", "1", "2", "3"}] - assert list(h.s_connected_components(s=2)) == [{"1", "2", "3"}] + assert list(h.s_connected_components()) == [{0, 1, 2, 3}] + assert list(h.s_connected_components(s=2)) == [{1, 2, 3}] assert list(h.s_connected_components(s=2, edges=False)) == [{5, 6}] @@ -264,18 +245,18 @@ def test_s_component_subgraphs(): [len(g) for g in h.s_component_subgraphs(s=3, return_singletons=True)] ) -def test_size(seven_by_six): - sbs = seven_by_six + +def test_size(sbs): h = Hypergraph(sbs.edgedict) - assert h.size('S') == 4 - assert h.size('S',{'T2','V'}) == 2 - assert h.size('S',{'T1','T2'}) == 1 - assert h.size('S',{'T2'}) == 1 - assert h.size('S',{'T1'}) == 0 - assert h.size('S',{}) == 0 - -def test_diameter(seven_by_six): - sbs = seven_by_six + assert h.size("S") == 4 + assert h.size("S", {"T2", "V"}) == 2 + assert h.size("S", {"T1", "T2"}) == 1 + assert h.size("S", {"T2"}) == 1 + assert h.size("S", {"T1"}) == 0 + assert h.size("S", {}) == 0 + + +def test_diameter(sbs): h = Hypergraph(sbs.edgedict) assert h.diameter() == 3 with pytest.raises(Exception) as excinfo: @@ -283,15 +264,13 @@ def test_diameter(seven_by_six): assert "Hypergraph is not s-connected." in str(excinfo.value) -def test_node_diameters(seven_by_six): - sbs = seven_by_six +def test_node_diameters(sbs): h = Hypergraph(sbs.edgedict) assert h.node_diameters()[0] == 3 assert h.node_diameters()[2] == [{"A", "C", "E", "K", "T1", "T2", "V"}] -def test_edge_diameter(seven_by_six): - sbs = seven_by_six +def test_edge_diameter(sbs): h = Hypergraph(sbs.edgedict) assert h.edge_diameter() == 3 assert h.edge_diameters()[2] == [{"I", "L", "O", "P", "R", "S"}] @@ -315,6 +294,7 @@ def test_dual(sbs_hypergraph): assert set(H.edges) == set(HD.nodes) +@pytest.mark.filterwarnings("ignore:No 3-path between ME and FN") def test_distance(lesmis): h = lesmis.hypergraph assert h.distance("ME", "FN") == 2 @@ -322,15 +302,29 @@ def test_distance(lesmis): assert h.distance("ME", "FN", s=3) == np.inf +# TODO: fix test once get_linegraph is fully tested +@pytest.mark.filterwarnings("ignore:No 2-path between 1 and 4") def test_edge_distance(lesmis): h = lesmis.hypergraph assert h.edge_distance(1, 4) == 2 - h.remove_edge(5) - assert h.edge_distance(1, 4) == 3 - assert h.edge_distance(1, 4, s=2) == np.inf + h2 = h.remove([5], 0) + assert h2.edge_distance(1, 4) == 3 + assert h2.edge_distance(1, 4, s=2) == np.inf def test_dataframe(lesmis): h = lesmis.hypergraph - df = h.dataframe() + df = h.incidence_dataframe() assert np.allclose(np.array(np.sum(df)), np.array([10, 9, 8, 4, 8, 3, 12, 6])) + + +def test_construct_empty_hypergraph(): + h = Hypergraph() + assert h.shape == (0, 0) + assert h.edges.is_empty() + assert h.nodes.is_empty() + + +def test_static_hypergraph_s_connected_components(lesmis): + H = Hypergraph(lesmis.edgedict) + assert {7, 8} in list(H.s_connected_components(edges=True, s=4)) diff --git a/hypernetx/classes/tests/test_hypergraph_by_setsystem.py b/hypernetx/classes/tests/test_hypergraph_by_setsystem.py new file mode 100644 index 00000000..7b4f0e81 --- /dev/null +++ b/hypernetx/classes/tests/test_hypergraph_by_setsystem.py @@ -0,0 +1,40 @@ +import pytest +from pytest_lazyfixture import lazy_fixture as lf + +from hypernetx import Hypergraph + +""" + This test suite runs all the tests on each hypergraph defined by `hyp` + Write a test based on the following pattern: + + @pytest.mark.parametrize( + "hyp, expected", + [ + (pytest.lazy_fixture("hyp_no_props"), ), + (pytest.lazy_fixture("hyp_df_with_props"), ), + (pytest.lazy_fixture("hyp_dict_with_props"), ), + (pytest.lazy_fixture("hyp_props_on_edges_nodes"), ) + ], + ) + def test_(hyp: Hypergraph, expected): + actual = hyp.() + assert actual == expected +""" + + +@pytest.mark.parametrize( + "hyp, expected", + [ + (lf("hyp_no_props"), None), + (lf("hyp_df_with_props"), None), + (lf("hyp_dict_with_props"), None), + (lf("hyp_props_on_edges_nodes"), None), + ], +) +def test_dual(hyp: Hypergraph, expected): + actual = hyp.dual() + # assertions on the hypergraph + assert isinstance(actual, Hypergraph) + + # assertions on the actual result compared to the expected result that was defined in the parameterize decorator + assert actual != expected diff --git a/hypernetx/classes/tests/test_hypergraph_constructors.py b/hypernetx/classes/tests/test_hypergraph_factory_methods.py similarity index 61% rename from hypernetx/classes/tests/test_hypergraph_constructors.py rename to hypernetx/classes/tests/test_hypergraph_factory_methods.py index 4a569d31..a72af049 100644 --- a/hypernetx/classes/tests/test_hypergraph_constructors.py +++ b/hypernetx/classes/tests/test_hypergraph_factory_methods.py @@ -7,53 +7,61 @@ from hypernetx import Hypergraph, EntitySet - def test_from_bipartite(): g = nx.complete_bipartite_graph(2, 3) left, right = nx.bipartite.sets(g) h = Hypergraph.from_bipartite(g) - assert left.issubset(h.nodes) - assert right.issubset(h.edges) + nodes = {*h.nodes} + edges = {*h.edges} + assert left.issubset(edges) + assert right.issubset(nodes) + with pytest.raises(Exception) as excinfo: h.edge_diameter(s=4) assert "Hypergraph is not s-connected." in str(excinfo.value) +@pytest.mark.skip(reason="Deprecated attribute and/or method") @pytest.mark.parametrize("static", [(True), (False)]) -def test_hypergraph_from_bipartite_and_from_constructor_should_be_equal(seven_by_six, static): - edgedict = OrderedDict(seven_by_six.edgedict) +def test_hypergraph_from_bipartite_and_from_constructor_should_be_equal(sbs, static): + edgedict = OrderedDict(sbs.edgedict) bipartite_graph = Hypergraph(edgedict).bipartite() hg_from_bipartite = Hypergraph.from_bipartite(bipartite_graph, static=static) - entityset = EntitySet("_", edgedict) - hg_from_constructor = Hypergraph(entityset, static=static) + hg_from_constructor = Hypergraph(EntitySet(edgedict), static=static) assert hg_from_bipartite.isstatic == hg_from_constructor.isstatic assert hg_from_bipartite.shape == hg_from_constructor.shape - incidence_dict_hg_from_bipartite = {key: sorted(value) for key, value in hg_from_bipartite.incidence_dict.items()} - incidence_dict_hg_from_constructor = {key: sorted(value) for key, value in hg_from_constructor.incidence_dict.items()} + incidence_dict_hg_from_bipartite = { + key: sorted(value) for key, value in hg_from_bipartite.incidence_dict.items() + } + incidence_dict_hg_from_constructor = { + key: sorted(value) for key, value in hg_from_constructor.incidence_dict.items() + } assert incidence_dict_hg_from_bipartite == incidence_dict_hg_from_constructor +@pytest.mark.skip(reason="Deprecated attribute and/or method") def test_from_numpy_array(): M = np.array([[0, 1, 1, 0, 1], [1, 1, 1, 1, 1], [1, 0, 0, 1, 0], [0, 0, 0, 0, 1]]) h = Hypergraph.from_numpy_array(M) assert "v1" in h.edges["e0"] - assert "e1" not in h.nodes["v2"].memberships + assert "e1" not in h.nodes.memberships["v2"] with pytest.raises(Exception) as excinfo: h = Hypergraph.from_numpy_array(M, node_names=["A"]) assert "Number of node names does not match number of rows" in str(excinfo.value) node_names = ["A", "B", "C", "D"] edge_names = ["a", "b", "c", "d", "e"] h = Hypergraph.from_numpy_array(M, node_names, edge_names) - assert "a" in h.edges + assert "a" in h.edges() assert "A" in h.nodes assert "B" in h.edges["a"] +@pytest.mark.skip(reason="Deprecated attribute and/or method") def test_from_numpy_array_with_key(): M = np.array([[5, 0, 7, 2], [6, 8, 1, 1], [2, 5, 1, 9]]) h = Hypergraph.from_numpy_array( @@ -66,50 +74,54 @@ def test_from_numpy_array_with_key(): assert "C" not in h.edges["a"] +@pytest.mark.skip(reason="Deprecated attribute and/or method") def test_from_dataframe(): M = np.array([[1, 1, 0, 0], [0, 1, 1, 0], [1, 0, 1, 0]]) index = ["A", "B", "C"] columns = ["a", "b", "c", "d"] df = pd.DataFrame(M, index=index, columns=columns) - h = Hypergraph.from_dataframe(df) - assert "b" in h.edges - assert "d" not in h.edges + h = Hypergraph.from_incidence_dataframe(df) + assert "b" in h.edges() + # assert "d" not in h.edges() assert "C" in h.edges["a"] +@pytest.mark.skip(reason="Deprecated attribute and/or method") def test_from_dataframe_with_key(): M = np.array([[5, 0, 7, 2], [6, 8, 1, 1], [2, 5, 1, 9]]) index = ["A", "B", "C"] columns = ["a", "b", "c", "d"] df = pd.DataFrame(M, index=index, columns=columns) - h = Hypergraph.from_dataframe(df, key=lambda x: x > 4) + h = Hypergraph.from_incidence_dataframe(df, key=lambda x: x > 4) assert "A" in h.edges["a"] assert "C" not in h.edges["a"] +@pytest.mark.skip(reason="Deprecated attribute and/or method") def test_from_dataframe_with_transforms_and_fillna(dataframe): df = dataframe.df - def key1(x): - return x ** 2 - - def key2(x): - return (x < 5) * x - - def key3(x): - return (x > 0) * x - - h = Hypergraph.from_dataframe(df) + # @pytest.mark.skip() + # def keymark.1(x): + # return x**2 + # @pytest.mark.skip() + # def keymark.2(x): + # return (x < 5) * x + # @pytest.mark.skip() + # def keymark.3(x): + # return (x > 0) * x + + h = Hypergraph.from_incidence_dataframe(df) assert "A" in h.edges["a"] assert "A" not in h.edges["b"] - h = Hypergraph.from_dataframe(df, fillna=1) + h = Hypergraph.from_incidence_dataframe(df, fillna=1) assert "A" in h.edges["b"] - h = Hypergraph.from_dataframe(df, transforms=[key1, key2]) + h = Hypergraph.from_incidence_dataframe(df, transforms=[key1, key2]) assert "A" in h.edges["c"] assert "C" not in h.edges["b"] - h = Hypergraph.from_dataframe(df, transforms=[key2, key3]) + h = Hypergraph.from_incidence_dataframe(df, transforms=[key2, key3]) assert "C" in h.edges["b"] - h = Hypergraph.from_dataframe(df, transforms=[key3, key1], key=key2) + h = Hypergraph.from_incidence_dataframe(df, transforms=[key3, key1], key=key2) assert "A" not in h.edges["a"] assert "B" in h.edges["b"] assert "C" not in h.edges["c"] diff --git a/hypernetx/classes/tests/test_hypergraph_nwhy.py b/hypernetx/classes/tests/test_hypergraph_nwhy.py deleted file mode 100644 index 4cc93276..00000000 --- a/hypernetx/classes/tests/test_hypergraph_nwhy.py +++ /dev/null @@ -1,34 +0,0 @@ -import pytest -import numpy as np -import networkx as nx -from hypernetx import Hypergraph, Entity, EntitySet, StaticEntity, StaticEntitySet -from hypernetx import HyperNetXError - -try: - import nwhy - - nwhy_available = True -except: - nwhy_available = False - - -def test_static_hypergraph_constructor_setsystem_nwhy(seven_by_six): - sbs = seven_by_six - edict = sbs.edgedict - H = Hypergraph(edict, use_nwhy=True) - assert isinstance(H.edges, StaticEntitySet) - assert H.isstatic == True - if nwhy_available: - assert H.nwhy == True - assert isinstance(H.g, nwhy.NWHypergraph) - else: - assert H.nwhy == False - - -if nwhy_available: - - def test_nwhy(seven_by_six): - sbs = seven_by_six - edict = sbs.edgedict - H = Hypergraph(edict, use_nwhy=True) - assert H.nwhy diff --git a/hypernetx/classes/tests/test_hypergraph_nwhy_deprecate.py b/hypernetx/classes/tests/test_hypergraph_nwhy_deprecate.py new file mode 100644 index 00000000..7e7fbdc6 --- /dev/null +++ b/hypernetx/classes/tests/test_hypergraph_nwhy_deprecate.py @@ -0,0 +1,56 @@ +import re + +import pytest + +from hypernetx import Hypergraph +from hypernetx.exception import NWHY_WARNING + +pytestmark = pytest.mark.skip(reason="Deprecated attribute and/or method") + + +def test_get_linegraph_warn_nwhy(sbs): + H = Hypergraph(sbs.edgedict) + lg = H.get_linegraph(s=1, use_nwhy=False) + with pytest.warns(FutureWarning, match=re.escape(NWHY_WARNING)): + lg_nwhy = H.get_linegraph(s=1, use_nwhy=True) + assert lg == lg_nwhy + + +def test_recover_from_state_warn_nwhy(): + with pytest.warns(FutureWarning, match=re.escape(NWHY_WARNING)): + with pytest.raises(FileNotFoundError): + Hypergraph.recover_from_state(use_nwhy=True) + + +def test_convert_to_static_warn_nwhy(sbs): + H = Hypergraph(sbs.edgedict, static=False) + H_static = H.convert_to_static(use_nwhy=False) + with pytest.warns(FutureWarning, match=re.escape(NWHY_WARNING)): + H_static_nwhy = H.convert_to_static(use_nwhy=True) + + assert not H_static_nwhy.nwhy + assert H_static_nwhy.isstatic + assert H_static.incidence_dict == H_static_nwhy.incidence_dict + + +@pytest.mark.parametrize( + "constructor, example", + [ + (Hypergraph, "sbs_edgedict"), + (Hypergraph.from_bipartite, "complete_bipartite_example"), + # (Hypergraph.from_numpy_array, "array_example"), + # (Hypergraph.from_dataframe, "dataframe_example"), + ], +) +def test_constructors_warn_nwhy(constructor, example, request): + example = request.getfixturevalue(example) + H = constructor(example, use_nwhy=False) + with pytest.warns(FutureWarning, match=re.escape(NWHY_WARNING)): + H_nwhy = constructor(example, use_nwhy=True) + assert not H_nwhy.nwhy + assert H.incidence_dict == H_nwhy.incidence_dict + + +def test_add_nwhy_deprecated(sbs_hypergraph): + with pytest.deprecated_call(): + Hypergraph.add_nwhy(sbs_hypergraph) diff --git a/hypernetx/classes/tests/test_hypergraph_static.py b/hypernetx/classes/tests/test_hypergraph_static_deprecate.py similarity index 54% rename from hypernetx/classes/tests/test_hypergraph_static.py rename to hypernetx/classes/tests/test_hypergraph_static_deprecate.py index 6d891344..7b839d55 100644 --- a/hypernetx/classes/tests/test_hypergraph_static.py +++ b/hypernetx/classes/tests/test_hypergraph_static_deprecate.py @@ -1,37 +1,33 @@ import pytest -import numpy as np -import networkx as nx -from hypernetx import Hypergraph, Entity, EntitySet, StaticEntity, StaticEntitySet -from hypernetx import HyperNetXError +from hypernetx import Hypergraph, Entity, EntitySet -def test_static_hypergraph_constructor_setsystem(seven_by_six): - sbs = seven_by_six +pytestmark = pytest.mark.skip(reason="Deprecated attribute and/or method") + + +def test_static_hypergraph_constructor_setsystem(sbs): H = Hypergraph(sbs.edgedict, static=True) - assert isinstance(H.edges, StaticEntitySet) + assert isinstance(H.edges, EntitySet) assert H.isstatic == True assert H.nwhy == False assert H.shape == (7, 6) -def test_static_hypergraph_constructor_entity(seven_by_six): - sbs = seven_by_six - E = Entity("sbs", sbs.edgedict) +def test_static_hypergraph_constructor_entity(sbs): + E = Entity(data=sbs.data, labels=sbs.labels) H = Hypergraph(E, static=True) assert H.isstatic assert "A" in H.edges.incidence_dict["P"] -def test_static_hypergraph_get_id(seven_by_six): - sbs = seven_by_six - H = Hypergraph(StaticEntity(arr=sbs.arr, labels=sbs.labels)) +def test_static_hypergraph_get_id(sbs): + H = Hypergraph(Entity(data=sbs.data, labels=sbs.labels)) assert H.get_id("V") == 6 assert H.get_id("S", edges=True) == 2 -def test_static_hypergraph_get_name(seven_by_six): - sbs = seven_by_six - H = Hypergraph(StaticEntity(arr=sbs.arr, labels=sbs.labels)) +def test_static_hypergraph_get_name(sbs): + H = Hypergraph(Entity(data=sbs.data, labels=sbs.labels)) assert H.get_name(1) == "C" assert H.get_name(1, edges=True) == "R" diff --git a/hypernetx/classes/tests/test_nx_hnx_agreement.py b/hypernetx/classes/tests/test_nx_hnx_agreement.py index 5d62d342..8f027923 100644 --- a/hypernetx/classes/tests/test_nx_hnx_agreement.py +++ b/hypernetx/classes/tests/test_nx_hnx_agreement.py @@ -54,8 +54,9 @@ def test_neighbors(G, H): assert_are_same_sets(G[v], H[v]) -def test_edges_iter(G, H): - """ - Confirm that the edges() function returns an iterator over the edges - """ - assert_are_same_set_of_sets(G.edges(), H.edges()) +# def test_edges_iter(G, H): +# """ +# Confirm that the edges() function returns an iterator over the edges +# """ +# breakpoint() +# assert_are_same_set_of_sets(G.edges(), H.edges()) diff --git a/hypernetx/classes/tests/test_staticentity.py b/hypernetx/classes/tests/test_staticentity.py deleted file mode 100644 index fe558a2c..00000000 --- a/hypernetx/classes/tests/test_staticentity.py +++ /dev/null @@ -1,168 +0,0 @@ -import numpy as np -import pandas as pd -import pytest -from collections.abc import Iterable -from collections import UserList -from hypernetx import Entity, EntitySet -from hypernetx import StaticEntity, StaticEntitySet -from hypernetx import HyperNetXError - - -def test_staticentity_constructor(seven_by_six): - sbs = seven_by_six - ent = StaticEntity(arr=sbs.arr, labels=sbs.labels) - assert ent.size() == 6 - assert len(ent.uidset) == 6 - assert len(ent.children) == 7 - assert isinstance(ent.incidence_dict["I"], UserList) - assert "I" in ent - assert "K" in ent - - -def test_staticentity_property(harry_potter): - arr = harry_potter.arr - labels = harry_potter.labels - ent = StaticEntity(arr=arr, labels=labels) - assert len(ent.keys) == 5 - assert len(ent.uidset) == 7 - assert len(ent.elements) == 7 - assert isinstance(ent.elements["Hufflepuff"], UserList) - assert ent.is_empty(2) == False - assert len(ent.incidence_dict["Gryffindor"]) == 6 - assert ent.keyindex("House") == 0 - - -def test_staticentity_attributes(harry_potter): - arr = harry_potter.arr - labels = harry_potter.labels - ent = StaticEntity(arr=arr, labels=labels) - assert isinstance(ent.arr, np.ndarray) - assert isinstance(ent.data, np.ndarray) - assert ent.data.shape == ent.dataframe.shape - assert isinstance(ent.labels, dict) - assert ent.dimensions == (7, 11, 10, 36, 26) - assert ent.dimsize == 5 - assert len(ent.labs(0)) == 7 - df = ent.dataframe - assert list(df.columns) == [ - "House", - "Blood status", - "Species", - "Hair colour", - "Eye colour", - ] - assert ent.dimensions == tuple(df.nunique()) - assert list(ent.labels["House"]) == list(df["House"].unique()) - - -def test_staticentity_custom_attributes(harry_potter): - arr = harry_potter.arr - labels = harry_potter.labels - ent = StaticEntity(arr=arr, labels=labels) - assert ent.__len__() == 7 - assert isinstance(ent.__str__(), str) - assert isinstance(ent.__repr__(), str) - assert isinstance(ent.__contains__("Muggle"), bool) - assert ent.__contains__("Muggle") == True - assert ent.__getitem__("Slytherin") == [ - "Half-blood", - "Pure-blood", - "Pure-blood or half-blood", - ] - assert isinstance(ent.__iter__(), Iterable) - assert isinstance(ent.__call__(), Iterable) - assert ent.__call__().__next__() == "Unknown House" - - -def test_staticentity_level(seven_by_six): - sbs = seven_by_six - ent = StaticEntity(arr=sbs.arr, labels=sbs.labels) - assert ent.level("I") == (0, 5) - assert ent.level("K") == (1, 3) - assert ent.level("K", max_level=0) == None - - -def test_staticentity_uidset_by_level(seven_by_six): - sbs = seven_by_six - ent = StaticEntity(arr=sbs.arr, labels=sbs.labels) - ent.uidset_by_level(0) == {"I", "L", "O", "P", "R", "S"} - ent.uidset_by_level(1) == {"A", "C", "E", "K", "T1", "T2", "V"} - - -def test_staticentity_elements_by_level(seven_by_six): - sbs = seven_by_six - ent = StaticEntity(arr=sbs.arr, labels=sbs.labels) - assert ent.elements_by_level(0) - - -def test_staticentity_incidence_matrix(seven_by_six): - sbs = seven_by_six - ent = StaticEntity(arr=sbs.arr, labels=sbs.labels) - assert ent.incidence_matrix(1, 0).todense().shape == (6, 7) - - -def test_staticentity_indices(seven_by_six): - sbs = seven_by_six - ent = StaticEntity(arr=sbs.arr, labels=sbs.labels) - assert ent.indices("nodes", "K") == [3] - assert ent.indices("nodes", ["K", "T1"]) == [3, 4] - - -def test_staticentity_translate(seven_by_six): - sbs = seven_by_six - ent = StaticEntity(arr=sbs.arr, labels=sbs.labels) - assert ent.translate(0, 0) == "P" - assert ent.translate(1, [3, 4]) == ["K", "T1"] - - -def test_staticentity_translate_arr(seven_by_six): - sbs = seven_by_six - ent = StaticEntity(arr=sbs.arr, labels=sbs.labels) - assert ent.translate_arr((0, 0)) == ["P", "A"] - - -def test_staticentity_index(seven_by_six): - sbs = seven_by_six - ent = StaticEntity(arr=sbs.arr, labels=sbs.labels) - assert ent.index("nodes") == 1 - assert ent.index("nodes", "K") == (1, 3) - - -def test_staticentity_turn_entity_data_into_dataframe(seven_by_six): - sbs = seven_by_six - ent = StaticEntity(arr=sbs.arr, labels=sbs.labels) - subset = ent.data[0:5] - assert ent.turn_entity_data_into_dataframe(subset).shape == (5, 2) - - -def test_restrict_to_levels(harry_potter): - arr = harry_potter.arr - labels = harry_potter.labels - ent = StaticEntity(arr=arr, labels=labels) - assert len(ent.restrict_to_levels([0]).uidset) == 7 - - -def test_restrict_to_indices(harry_potter): - arr = harry_potter.arr - labels = harry_potter.labels - ent = StaticEntity(arr=arr, labels=labels) - assert ent.restrict_to_indices([1, 2]).uidset == {"Gryffindor", "Ravenclaw"} - - -def test_staticentityset(harry_potter): - arr = harry_potter.arr - labels = harry_potter.labels - ent = StaticEntitySet(arr=arr, labels=labels, level1=1, level2=3) - assert ent.keys[0] == "Blood status" - assert len(ent.keys) == 2 - assert ent.indices("Blood status", ["Pure-blood", "Half-blood"]) == [2, 1] - assert ent.restrict_to([2, 1]).keys[1] == "Hair colour" - assert ent.incidence_matrix().shape == (36, 11) - assert len(ent.convert_to_entityset("Hair colour")) == 11 - assert len(ent.collapse_identical_elements("House")) == 11 - - -def test_staticentity_construct_from_entity(seven_by_six): - sbs = seven_by_six - ent = StaticEntity(entity=sbs.edgedict) - assert len(ent.elements) == 6 diff --git a/hypernetx/drawing/__init__.py b/hypernetx/drawing/__init__.py index d8043d38..fd7e8478 100644 --- a/hypernetx/drawing/__init__.py +++ b/hypernetx/drawing/__init__.py @@ -1,2 +1,4 @@ -from .rubber_band import draw -from .two_column import draw as draw_two_column +from hypernetx.drawing.rubber_band import draw +from hypernetx.drawing.two_column import draw as draw_two_column + +__all__ = ["draw", "draw_two_column"] diff --git a/hypernetx/drawing/rubber_band.py b/hypernetx/drawing/rubber_band.py index 55b32749..5a8e0323 100644 --- a/hypernetx/drawing/rubber_band.py +++ b/hypernetx/drawing/rubber_band.py @@ -2,7 +2,7 @@ # All rights reserved. from hypernetx import Hypergraph -from .util import ( +from hypernetx.drawing.util import ( get_frozenset_label, get_collapsed_size, get_set_layering, @@ -11,17 +11,14 @@ ) import matplotlib.pyplot as plt -from matplotlib.collections import PolyCollection, LineCollection, CircleCollection +from matplotlib.collections import PolyCollection import networkx as nx -from itertools import combinations -from collections import defaultdict import numpy as np from scipy.spatial.distance import pdist from scipy.spatial import ConvexHull -from scipy.spatial import Voronoi # increases the default figure size to 8in square. plt.rcParams["figure.figsize"] = (8, 8) @@ -326,6 +323,7 @@ def draw_hyper_labels(H, pos, node_radius={}, ax=None, labels={}, **kwargs): } ) + def draw( H, pos=None, @@ -430,9 +428,7 @@ def draw( pos = layout_node_link(H, layout=layout, **layout_kwargs) r0 = get_default_radius(H, pos) - a0 = np.pi * r0 ** 2 - - + a0 = np.pi * r0**2 def get_node_radius(v): if node_radius is None: diff --git a/hypernetx/drawing/two_column.py b/hypernetx/drawing/two_column.py index 771dc980..e60cb21d 100644 --- a/hypernetx/drawing/two_column.py +++ b/hypernetx/drawing/two_column.py @@ -6,7 +6,7 @@ import networkx as nx -from .util import get_frozenset_label +from hypernetx.drawing.util import get_frozenset_label def layout_two_column(H, spacing=2): @@ -79,7 +79,7 @@ def draw_hyper_edges(H, pos, ax=None, **kwargs): """ ax = ax or plt.gca() - pairs = [(v, e.uid) for e in H.edges() for v in e] + pairs = [(v, e) for e in H.edges() for v in H.edges[e]] kwargs = { k: v if type(v) != dict else [v.get(e) for _, e in pairs] @@ -120,18 +120,16 @@ def draw_hyper_labels( ax = ax or plt.gca() - edges = [e.uid for e in H.edges()] - to_draw = [] if with_node_labels: - to_draw.append((H.nodes(), "right")) + to_draw.append((list(H.nodes()), "right")) if with_edge_labels: - to_draw.append((H.edges(), "left")) + to_draw.append((list(H.edges()), "left")) for points, ha in to_draw: for p in points: - ax.annotate(labels.get(p.uid, p.uid), pos[p.uid], ha=ha, va="center") + ax.annotate(labels.get(p, p), pos[p], ha=ha, va="center") def draw( @@ -183,8 +181,8 @@ def draw( pos = layout_two_column(H) - V = [v.uid for v in H.nodes()] - E = [e.uid for e in H.edges()] + V = [v for v in H.nodes()] + E = [e for e in H.edges()] labels = {} labels.update(get_frozenset_label(V, count=with_node_counts)) @@ -192,7 +190,7 @@ def draw( if with_color: edge_kwargs["color"] = { - e.uid: plt.cm.tab10(i % 10) for i, e in enumerate(H.edges()) + e: plt.cm.tab10(i % 10) for i, e in enumerate(H.edges()) } draw_hyper_edges(H, pos, ax=ax, **edge_kwargs) diff --git a/hypernetx/drawing/util.py b/hypernetx/drawing/util.py index 67d16968..33dd6b88 100644 --- a/hypernetx/drawing/util.py +++ b/hypernetx/drawing/util.py @@ -44,13 +44,14 @@ def transpose_inflated_kwargs(inflated): def get_collapsed_size(v): try: - if type(v) == str and ':' in v: - return int(v.split(':')[-1]) + if type(v) == str and ":" in v: + return int(v.split(":")[-1]) except: pass - + return 1 + def get_frozenset_label(S, count=False, override={}): """ Helper function for rendering the labels of possibly collapsed nodes and edges diff --git a/hypernetx/exception.py b/hypernetx/exception.py index 3c28b5fb..02917a09 100644 --- a/hypernetx/exception.py +++ b/hypernetx/exception.py @@ -5,6 +5,12 @@ Base classes for HyperNetX exceptions """ +NWHY_WARNING = ( + "As of HyperNetX v2.0.0, NWHy C++ backend is no longer supported. " + "Public references to the deprecated NWHy add-on will be removed from the " + "Hypergraph API in a future release." +) + class HyperNetXException(Exception): """Base class for exceptions in HyperNetX.""" diff --git a/hypernetx/reports/__init__.py b/hypernetx/reports/__init__.py index 4dec6400..a476fb9f 100644 --- a/hypernetx/reports/__init__.py +++ b/hypernetx/reports/__init__.py @@ -1 +1,27 @@ -from .descriptive_stats import * +from hypernetx.reports.descriptive_stats import ( + centrality_stats, + edge_size_dist, + degree_dist, + comp_dist, + s_comp_dist, + toplex_dist, + s_node_diameter_dist, + s_edge_diameter_dist, + info, + info_dict, + dist_stats, +) + +__all__ = [ + "centrality_stats", + "edge_size_dist", + "degree_dist", + "comp_dist", + "s_comp_dist", + "toplex_dist", + "s_node_diameter_dist", + "s_edge_diameter_dist", + "info", + "info_dict", + "dist_stats", +] diff --git a/hypernetx/reports/descriptive_stats.py b/hypernetx/reports/descriptive_stats.py index 8fbbff92..d23cac11 100644 --- a/hypernetx/reports/descriptive_stats.py +++ b/hypernetx/reports/descriptive_stats.py @@ -10,24 +10,8 @@ """ from collections import Counter import numpy as np -import networkx as nx -from hypernetx import * from hypernetx.utils.decorators import not_implemented_for -__all__ = [ - "centrality_stats", - "edge_size_dist", - "degree_dist", - "comp_dist", - "s_comp_dist", - "toplex_dist", - "s_node_diameter_dist", - "s_edge_diameter_dist", - "info", - "info_dict", - "dist_stats", -] - def centrality_stats(X): """ @@ -43,7 +27,13 @@ def centrality_stats(X): [min, max, mean, median, standard deviation] : list List of centrality statistics for X """ - return [min(X), max(X), np.mean(X), np.median(X), np.std(X)] + return [ + min(X), + max(X), + np.mean(X).tolist(), + np.median(X).tolist(), + np.std(X).tolist(), + ] def edge_size_dist(H, aggregated=False): @@ -87,10 +77,7 @@ def degree_dist(H, aggregated=False): degree_dist : list or dict List of degrees or dictionary of degree distribution """ - if H.nwhy: - distr = H.g.node_size_dist() - else: - distr = [H.degree(n) for n in H.nodes] + distr = [H.degree(n) for n in H.nodes] if aggregated: return Counter(distr) else: @@ -354,10 +341,9 @@ def dist_stats(H): dist_stats : dict Dictionary which keeps track of each of the above items (e.g., basic['nrows'] = the number of nodes in H) """ - if H.isstatic: - stats = H.state_dict.get("dist_stats", None) - if stats is not None: - return H.state_dict["dist_stats"] + stats = H._state_dict.get("dist_stats", None) + if stats is not None: + return H._state_dict["dist_stats"] cstats = ["min", "max", "mean", "median", "std"] basic = dict() @@ -408,6 +394,5 @@ def dist_stats(H): # # Diameters # basic['s edge diam list'] = s_edge_diameter_dist(H) # basic['s node diam list'] = s_node_diameter_dist(H) - if H.isstatic: - H.set_state(dist_stats=basic) + H.set_state(dist_stats=basic) return basic diff --git a/hypernetx/utils/__init__.py b/hypernetx/utils/__init__.py index 344d9c0a..95c004e8 100644 --- a/hypernetx/utils/__init__.py +++ b/hypernetx/utils/__init__.py @@ -1,13 +1,27 @@ -from .extras import ( +from hypernetx.utils.extras import ( HNXCount, DefaultOrderedDict, remove_row_duplicates, create_labels, reverse_dictionary, ) -from .decorators import not_implemented_for -from .toys import * +from hypernetx.utils.decorators import not_implemented_for +from hypernetx.utils.toys.harrypotter import HarryPotter +from hypernetx.utils.toys.gene_data import GeneData +from hypernetx.utils.toys.lesmis import LesMis, lesmis_hypergraph_from_df, book_tour +from hypernetx.utils.toys.transmission_problem import TransmissionProblem -# from .toys.harrypotter import HarryPotter -# from .toys.lesmis import LesMis, lesmis_hypergraph_from_df, book_tour -# from .toys.transmission_problem import TransmissionProblem +__all__ = [ + "HNXCount", + "DefaultOrderedDict", + "remove_row_duplicates", + "create_labels", + "reverse_dictionary", + "not_implemented_for", + "HarryPotter", + "GeneData", + "LesMis", + "lesmis_hypergraph_from_df", + "book_tour", + "TransmissionProblem", +] diff --git a/hypernetx/utils/decorators.py b/hypernetx/utils/decorators.py index 22b5a95c..5652bf30 100644 --- a/hypernetx/utils/decorators.py +++ b/hypernetx/utils/decorators.py @@ -1,16 +1,14 @@ -import sys -from warnings import warn -import hypernetx as hnx +import warnings +from functools import wraps + from decorator import decorator -from hypernetx.exception import HyperNetXError, HyperNetXNotImplementedError -try: - import nwhy -except: - pass +import hypernetx as hnx +from hypernetx.exception import NWHY_WARNING __all__ = [ "not_implemented_for", + "warn_nwhy", ] @@ -65,3 +63,29 @@ def _not_implemented_for(not_implemented_for_func, *args, **kwargs): return not_implemented_for_func(*args, **kwargs) return _not_implemented_for + + +def warn_nwhy(func): + """Decorator for methods that allow the deprecated `use_nwhy` kwarg + + As of HyperNetX v2.0.0, NWHy C++ backend is no longer supported. + Public references to the deprecated NWHy add-on will be removed from the Hypergraph + API in a future release. + + Warns + ----- + FutureWarning + If kwargs contain ``use_nwhy=True`` + """ + + @wraps(func) + def wrapper(*args, **kwargs): + if kwargs.get("use_nwhy"): + kwargs.update(use_nwhy=False) + warnings.simplefilter("always", FutureWarning) + warnings.warn(NWHY_WARNING, FutureWarning, stacklevel=2) + warnings.simplefilter("default", FutureWarning) + + return func(*args, **kwargs) + + return wrapper diff --git a/hypernetx/utils/extras.py b/hypernetx/utils/extras.py index 21ae4dd4..b52abd4f 100644 --- a/hypernetx/utils/extras.py +++ b/hypernetx/utils/extras.py @@ -182,7 +182,7 @@ def create_labels( Returns ------- OrderedDict - used for labels in constructing a StaticEntitySet + used for labels in constructing a EntitySet """ enames = np.array([f"{edgeprefix}{idx}" for idx in range(num_edges)]) nnames = np.array([f"{nodeprefix}{jdx}" for jdx in range(num_nodes)]) diff --git a/hypernetx/utils/log.py b/hypernetx/utils/log.py new file mode 100644 index 00000000..378ff16d --- /dev/null +++ b/hypernetx/utils/log.py @@ -0,0 +1,48 @@ +from __future__ import annotations +import os +import sys +import math +import logging +from datetime import datetime +from logging.handlers import RotatingFileHandler +from pathlib import Path + +FORMATTER = logging.Formatter( + fmt="%(asctime)s %(levelname)s %(name)s %(funcName)s:%(lineno)d — %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", +) +MAX_LOG_FILE_SIZE = 2 * int(math.pow(10, 6)) # 2 MB +BACKUP_COUNT = 10 +LOGS_DIR = "logfiles" + + +# Stores logs in LOG_DIR +# Log files are named "logfile_name" +def get_logger(logger_name: str): + curr_date_time = datetime.utcnow().strftime("%Y%m%d-%H%M%S") + Path(LOGS_DIR).mkdir(exist_ok=True) + logfile = Path(f"{LOGS_DIR}/{logger_name}-{curr_date_time}.log") + + return _make_logger(logger_name=logger_name, logfile=logfile) + + +def _make_logger(logger_name: str, logfile: os.PathLike[str]): + logger = logging.getLogger(logger_name) + logger.addHandler(_get_console_handler()) + logger.addHandler(_get_file_handler(logfile)) + logger.propagate = False + return logger + + +def _get_console_handler(): + console_handler = logging.StreamHandler(sys.stdout) + console_handler.setFormatter(FORMATTER) + return console_handler + + +def _get_file_handler(logfile: os.PathLike[str]): + file_handler = RotatingFileHandler( + filename=logfile, maxBytes=MAX_LOG_FILE_SIZE, backupCount=BACKUP_COUNT + ) + file_handler.setFormatter(FORMATTER) + return file_handler diff --git a/hypernetx/utils/toys/ChungLuTransmissionData.csv b/hypernetx/utils/toys/ChungLuTransmissionData.csv index 46894f72..62af7e00 100644 --- a/hypernetx/utils/toys/ChungLuTransmissionData.csv +++ b/hypernetx/utils/toys/ChungLuTransmissionData.csv @@ -18756,4 +18756,4 @@ 13651,14 13651,15 13651,19 -13651,21 \ No newline at end of file +13651,21 diff --git a/hypernetx/utils/toys/__init__.py b/hypernetx/utils/toys/__init__.py index 1d01ddd5..0f744d98 100644 --- a/hypernetx/utils/toys/__init__.py +++ b/hypernetx/utils/toys/__init__.py @@ -1,4 +1,13 @@ -from .harrypotter import HarryPotter -from .lesmis import LesMis, lesmis_hypergraph_from_df, book_tour -from .transmission_problem import TransmissionProblem -from .gene_data import GeneData +from hypernetx.utils.toys.harrypotter import HarryPotter +from hypernetx.utils.toys.gene_data import GeneData +from hypernetx.utils.toys.lesmis import LesMis, lesmis_hypergraph_from_df, book_tour +from hypernetx.utils.toys.transmission_problem import TransmissionProblem + +__all__ = [ + "HarryPotter", + "GeneData", + "LesMis", + "lesmis_hypergraph_from_df", + "book_tour", + "TransmissionProblem", +] diff --git a/hypernetx/utils/toys/gene_data.py b/hypernetx/utils/toys/gene_data.py index 94a54934..43623d6a 100644 --- a/hypernetx/utils/toys/gene_data.py +++ b/hypernetx/utils/toys/gene_data.py @@ -1,8 +1,6 @@ from networkx import bipartite import os -__all__ = ["GeneData"] - class GeneData: def __init__(self): diff --git a/hypernetx/utils/toys/harrypotter.py b/hypernetx/utils/toys/harrypotter.py index dab31e2d..69eec2eb 100644 --- a/hypernetx/utils/toys/harrypotter.py +++ b/hypernetx/utils/toys/harrypotter.py @@ -1,13 +1,10 @@ import os from hypernetx.utils import HNXCount, remove_row_duplicates from collections import OrderedDict, defaultdict -import scipy -from scipy.sparse import coo_matrix, issparse + import pandas as pd import numpy as np -import itertools as it -__all__ = ["HarryPotter"] current_dir = os.path.dirname(os.path.abspath(__file__)) diff --git a/hypernetx/utils/toys/lesmis.py b/hypernetx/utils/toys/lesmis.py index e382e3fd..e86030f2 100644 --- a/hypernetx/utils/toys/lesmis.py +++ b/hypernetx/utils/toys/lesmis.py @@ -1,24 +1,26 @@ # Copyright © 2018 Battelle Memorial Institute # All rights reserved. -import numpy as np import pandas as pd from itertools import islice, chain, repeat -import networkx as nx - import matplotlib.pyplot as plt import hypernetx as hnx -__all__ = ["LesMis", "lesmis_hypergraph_from_df", "book_tour"] - class LesMis(object): def __init__(self): self.volumes = pd.DataFrame.from_dict(volume_names, orient="index") - accents = {"\`e": "è", "\\`e": "è", "'e": "é", "\\c{c}": "ç", "\^o": "ô"} + accents = { + r"\`e": "è", + r"\\'e": "è", + r"\\`e": "è", + r"'e": "é", + r"\\c{c}": "ç", + r"\^o": "ô", + } for k, v in accents.items(): self.names = names.replace(k, v) @@ -114,7 +116,7 @@ def get_scene_data(): # LesMis Data: -names = """AZ Anzelma, daughter of TH and TM +names = r"""AZ Anzelma, daughter of TH and TM BA Bahorel, `Friends of the ABC' cutup BB Babet, tooth-pulling bandit of Paris BJ Brujon, notorious criminal diff --git a/hypernetx/utils/toys/transmission_problem.py b/hypernetx/utils/toys/transmission_problem.py index 40bec55b..aee3d7eb 100644 --- a/hypernetx/utils/toys/transmission_problem.py +++ b/hypernetx/utils/toys/transmission_problem.py @@ -2,8 +2,6 @@ import os -__all__ = ["TransmissionProblem"] - current_dir = os.path.dirname(os.path.abspath(__file__)) diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..7f235f4b --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools >= 65.3.0" +] +build-backend = "setuptools.build_meta" diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 00000000..286a2cb1 --- /dev/null +++ b/pytest.ini @@ -0,0 +1,5 @@ +[pytest] +minversion = 6.0 +; addopts are a set of command line arguments given to pytest: +; '-r A' will show all extra test summary as indicated by 'a' +addopts = -r A diff --git a/setup.cfg b/setup.cfg index 1faf14c7..6a30605e 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,3 +1,123 @@ +[mypy-setup.py] +ignore_errors = True + +[mypy] +exclude = (?x)( + tests/ # test directory + ) +pretty = True + +# Per-module options: +[mypy-igraph] +ignore_missing_imports = True + +[mypy-decorator] +ignore_missing_imports = True + +[mypy-celluloid] +ignore_missing_imports = True + +[mypy-matplotlib.*] +ignore_missing_imports = True + +[mypy-networkx.*] +ignore_missing_imports = True + +[mypy-sklearn.*] +ignore_missing_imports = True + +[mypy-scipy.*] +ignore_missing_imports = True + +[mypy-nwhy] +ignore_missing_imports = True + +[mypy-pandas] +ignore_missing_imports = True + [metadata] -description_file=README.md +python_requires = >=3.8,<3.12 +description_file = README.md +name = hypernetx +author = Brenda Praggastis, Dustin Arendt, Sinan Aksoy, Emilie Purvine, Cliff Joslyn +author_email = hypernetx@pnnl.gov +description = HyperNetX is a Python library for the creation and study of hypergraphs. +url = https://github.com/pnnl/HyperNetX +long_description = file: LONG_DESCRIPTION.rst +long_description_content_type = text/x-rst +license = 3-Clause BSD license +license_files = + LICENSE.rst + +[options] +packages = + hypernetx + hypernetx.algorithms + hypernetx.classes + hypernetx.drawing + hypernetx.reports + hypernetx.utils + hypernetx.utils.toys +install_requires = + networkx>=2.2,<3.0 + numpy>=1.24.0,<2.0 + scipy>=1.1.0,<2.0 + matplotlib>3.0 + scikit-learn>=0.20.0 + pandas>=1.5.3 + decorator>=5.1.1 +[options.extras_require] +releases = + commitizen>=3.2.1 +linting = + pre-commit>=3.2.2 + pylint>=2.17.2 + pylint-exit>=1.2.0 + black>=23.3.0 +testing = + tox>=4.4.11 + pre-commit>=3.2.2 + pylint>=2.17.2 + pylint-exit>=1.2.0 + black>=23.3.0 + pytest>=7.2.2 + coverage>=7.2.2 + celluloid>=0.2.0 + igraph>=0.10.4 + nbmake>=1.4.1 + pytest-lazy-fixture>=0.6.3 + pytest-xdist>=3.2.1 +tutorials = + jupyter>=1.0 + python-igraph>=0.10.4 + partition-igraph>=0.0.6 + celluloid>=0.2.0 +widget = + hnxwidget>=0.1.1b3 + jupyter-contrib-nbextensions>=0.7.0 + jupyter-nbextensions-configurator>=0.6.2 +documentation = + sphinx>=6.2.1 + nb2plots>=0.6.1 + sphinx-rtd-theme>=1.2.0 + sphinx-autobuild>=2021.3.14 + sphinx-copybutton>=0.5.1 +packaging = + build>=0.10.0 + twine>=4.0.2 + setuptools>=67.6.1 + tox>=4.4.11 +all = + sphinx>=6.2.1 + nb2plots>=0.6.1 + sphinx-rtd-theme>=1.2.0 + sphinx-autobuild>=2021.3.14 + sphinx-copybutton>=0.5.1 + pytest>=7.2.2 + coverage>=7.2.2 + jupyter>=1.0 + python-igraph>=0.10.4 + partition-igraph>=0.0.6 + celluloid>=0.2.0 + igraph>=0.10.4 diff --git a/setup.py b/setup.py index 8c4384f0..c4d7f9f3 100644 --- a/setup.py +++ b/setup.py @@ -1,89 +1,5 @@ from setuptools import setup -import sys -__version__ = "1.2.5" +__version__ = "2.0.0.post1" -if sys.version_info < (3, 7): - sys.exit("HyperNetX requires Python 3.7 or later.") - -setup( - name="hypernetx", - packages=[ - "hypernetx", - "hypernetx.algorithms", - "hypernetx.algorithms.contagion", - "hypernetx.classes", - "hypernetx.drawing", - "hypernetx.reports", - "hypernetx.utils", - "hypernetx.utils.toys", - ], - version=__version__, - author="Brenda Praggastis, Dustin Arendt, Sinan Aksoy, Emilie Purvine, Cliff Joslyn", - author_email="hypernetx@pnnl.gov", - url="https://github.com/pnnl/HyperNetX", - description="HyperNetX is a Python library for the creation and study of hypergraphs.", - install_requires=[ - "networkx>=2.2,<3.0", - "numpy>=1.15.0,<2.0", - "scipy>=1.1.0,<2.0", - "matplotlib>3.0", - "scikit-learn>=0.20.0", - "pandas>=0.23", - "python-igraph>=0.9.6", - "celluloid>=0.2.0", - "decorator>=5.1.1" - ], - license="3-Clause BSD license", - long_description=""" - The HyperNetX library provides classes and methods for the analysis - and visualization of complex network data modeled as hypergraphs. - The library generalizes traditional graph metrics. - - HypernetX was developed by the Pacific Northwest National Laboratory for the - Hypernets project as part of its High Performance Data Analytics (HPDA) program. - PNNL is operated by Battelle Memorial Institute under Contract DE-ACO5-76RL01830. - - * Principle Developer and Designer: Brenda Praggastis - * Visualization: Dustin Arendt, Ji Young Yun - * High Performance Computing: Tony Liu, Andrew Lumsdaine - * Principal Investigator: Cliff Joslyn - * Program Manager: Brian Kritzstein - * Contributors: Sinan Aksoy, Dustin Arendt, Cliff Joslyn, Nicholas Landry, Andrew Lumsdaine, Tony Liu, Brenda Praggastis, Emilie Purvine, Mirah Shi, François Théberge - - The code in this repository is intended to support researchers modeling data - as hypergraphs. We have a growing community of users and contributors. - Documentation is available at: - - For questions and comments contact the developers directly at: - - **New Features of Version 1.0:** - - 1. Hypergraph construction can be sped up by reading in all of the data at once. In particular the hypergraph constructor may read a Pandas dataframe object and create edges and nodes based on column headers. The new hypergraphs are given an attribute `static=True`. - 2. A C++ addon called [NWHy](docs/build/nwhy.html) can be used in Linux environments to support optimized hypergraph methods such as s-centrality measures. - 3. A JavaScript addon called [Hypernetx-Widget](docs/build/widget.html) can be used to interactively inspect hypergraphs in a Jupyter Notebook. - 4. Four new tutorials highlighting the s-centrality metrics, static Hypergraphs, [NWHy](docs/build/nwhy.html), and [Hypernetx-Widget](docs/build/widget.html). - - **New Features of Version 1.1** - - 1. Static Hypergraph refactored to improve performance across all methods. - 2. Added modules and tutorials for Contagion Modeling, Community Detection, Clustering, and Hypergraph Generation. - 3. Cell weights for incidence matrices may be added to static hypergraphs on construction. - - **New Features of Version 1.2** - - 1. Added module and tutorial for Modularity and Clustering - """, - extras_require={ - "testing": ["pytest>=4.0"], - "tutorials": ["jupyter>=1.0", ], - "documentation": ["sphinx>=1.8.2", "nb2plots>=0.6", "sphinx-rtd-theme>=0.4.2"], - "all": [ - "sphinx>=1.8.2", - "nb2plots>=0.6", - "sphinx-rtd-theme>=0.4.2", - "pytest>=4.0", - "jupyter>=1.0", - ], - }, -) +setup(version=__version__) diff --git a/tox.ini b/tox.ini index d498200e..6995ce6d 100644 --- a/tox.ini +++ b/tox.ini @@ -3,11 +3,34 @@ # test suite on all supported python versions. To use it, "pip install tox" # and then run "tox" from this directory. + [tox] -envlist = py37 +min_version = 4.4.11 +envlist = py38-notebooks, py{38,39,310,311} +isolated_build = True +skip_missing_interpreters = true [testenv] deps = - pytest + pytest>=7.2.2 + coverage>=7.2.2 + celluloid>=0.2.0 + igraph>=0.10.4 + nbmake>=1.4.1 + pytest-lazy-fixture>=0.6.3 + pytest-xdist>=3.2.1 + partition-igraph>=0.0.6 +allowlist_externals = env +commands = + env + python --version + coverage run --source=hypernetx -m pytest + coverage report -m + +[testenv:py38-notebooks] +description = run tests on jupyter notebooks +allowlist_externals = env commands = - pytest hypernetx/ -v --disable-warnings + env + python --version + pytest --nbmake "tutorials/" --junitxml=pytest.xml -n=auto --nbmake-timeout=20 --nbmake-find-import-errors diff --git a/tutorials/Demo 1 - HNXWidget.ipynb b/tutorials/Demo 1 - HNXWidget.ipynb new file mode 100644 index 00000000..53d65bcd --- /dev/null +++ b/tutorials/Demo 1 - HNXWidget.ipynb @@ -0,0 +1,186 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Prerequisites\n", + "\n", + "This notebook requires the hnxwidget package; please install by running: `pip install hnxwidget jupyter_contrib_nbextensions jupyter_nbextensions_configurator`\n", + "\n", + "# HyperNetX Widgets\n", + "\n", + "Unlike the tutorials, this is an interactive demo to get you acquainted with the constructor options and how to use the widget. **Hover over the nodes and edges each time you run the widget to see how properties enhance the visual information.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import hypernetx as hnx\n", + "from hypernetx.utils.toys.lesmis import LesMis\n", + "try:\n", + " from hnxwidget import HypernetxWidget\n", + "except:\n", + " print(\"Required dependencies not installed. To install, please run: pip install hnxwidget jupyter_contrib_nbextensions jupyter_nbextensions_configurator\")\n", + "\n", + "scenes = {\n", + " 0: ('FN', 'TH'),\n", + " 1: ('TH', 'JV'),\n", + " 2: ('BM', 'FN', 'JA'),\n", + " 3: ('JV', 'JU', 'CH', 'BM'),\n", + " 4: ('JU', 'CH', 'BR', 'CN', 'CC', 'JV', 'BM'),\n", + " 5: ('TH', 'GP'),\n", + " 6: ('GP', 'MP'),\n", + " 7: ('MA', 'GP'),\n", + "}\n", + "H = hnx.Hypergraph(scenes)\n", + "dnames = LesMis().dnames\n", + "dnames" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## I. LesMis Hypergraph in the Hypernetx-Widget - Default Behavior\n", + "The widget allows you to interactively move, color, select, and hide objects in the hypergraph. Click on the question mark in the Navigation menu for a description of interactive features." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Default behavior\n", + "example1 = HypernetxWidget(H)\n", + "example1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## II. Preset attributes \n", + "Some of the visualization attributes of the hypergraph may be set using similar parameters as the hnx.draw function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# node_colors = {k:'r' if k in ['JV','TH','FN'] else 'b' for k in H.nodes}\n", + "example2 = HypernetxWidget(\n", + " H,\n", + "# nodes_kwargs={'color':node_colors},\n", + " edges_kwargs={'edgecolors':'g'}\n", + ")\n", + "example2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## III. Attributes of visualization:\n", + "The `get_state()` method returns the attributes available from a widget for reuse.\n", + "\n", + "**Note:** if you \"Run All\" this notebook, the following cells may produce an exception. Acquiring the widget state in python requires some time for the widget to initialize and render. Run the cells below individually for best results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "example2.get_state()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## IV. Reuse attributes\n", + "Once an attribute of a widget visualization has been set it may be reused in another visualization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "example3 = HypernetxWidget(\n", + " H,\n", + " nodes_kwargs={'color': example2.node_fill}\n", + ")\n", + "\n", + "example3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## V. Setting Labels and Callouts\n", + "We can also adjust specific labels and add call outs as node or edge data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "example4 = HypernetxWidget(\n", + " H,\n", + " collapse_nodes=True,\n", + " node_data=dnames,\n", + " node_labels={'JV': 'Valjean'},\n", + " edge_labels={0: 'scene 0'},\n", + " nodes_kwargs={'color':'pink'},\n", + ")\n", + "==\n", + "ex\\ample4" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials/Demo 2 - HNX Constructor and More Widget Examples.ipynb b/tutorials/Demo 2 - HNX Constructor and More Widget Examples.ipynb new file mode 100644 index 00000000..1b958f41 --- /dev/null +++ b/tutorials/Demo 2 - HNX Constructor and More Widget Examples.ipynb @@ -0,0 +1,715 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Prerequisites\n", + "\n", + "This notebook requires the hnxwidget package; please install by running:\n", + "\n", + "```pip install hnxwidget jupyter_contrib_nbextensions jupyter_nbextensions_configurator```\n", + "\n", + "# HNX Constructor and Widget Examples\n", + "\n", + "Unlike the tutorials, this is an interactive demo to get you acquainted with the constructor options and how to use the widget. **Hover over the nodes and edges each time you run the widget to see how properties enhance the visual information.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from collections import defaultdict\n", + "import matplotlib.pyplot as plt\n", + "import networkx as nx\n", + "import hypernetx as hnx\n", + "import pandas as pd\n", + "import numpy as np\n", + "import warnings\n", + "\n", + "warnings.simplefilter('ignore')\n", + "\n", + "try:\n", + " from hnxwidget import HypernetxWidget as HW\n", + "except:\n", + " print(\"Required dependencies not installed. To install, please run: pip install hnxwidget jupyter_contrib_nbextensions jupyter_nbextensions_configurator\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def checkplts(h):\n", + " fig,ax = plt.subplots(1,2,figsize=(15,6))\n", + " hnx.draw(h,ax=ax[0])\n", + " ax[0].set_title('Hypergraph',fontsize=15)\n", + " hnx.draw(h.dual(),ax=ax[1])\n", + " ax[1].set_title('Dual',fontsize=15)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set Systems \n", + "The next 2 cells construct the necessary dictionaries and dataframes to run the demo." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "### numpy array, single property dict - uncomment np.random.seed for consistent results\n", + "# np.random.seed(0)\n", + "npcol1 = np.random.choice(list(\"ABCD\"),50)\n", + "npcol2 = np.random.choice(list(\"abcdefghijklmnopqrstuvwxyz\"),50)\n", + "\n", + "npdata = np.array([npcol1,npcol2]).T\n", + "npedge_col = 'Club'\n", + "npnode_col = 'Member'\n", + "\n", + "npproperties = {k :{'affiliation': np.random.choice(['red','green'])} for k in np.concatenate([npcol1,npcol2])}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [], + "scrolled": false + }, + "outputs": [], + "source": [ + "## LesMis data as dictionaries and dataframes - uses LesMis class in utils.toys\n", + "## Uncomment np.random.seed for consistent results\n", + "# np.random.seed(0)\n", + "from hypernetx.utils.toys import lesmis as lm\n", + "np.random.seed(0)\n", + "LM = lm.LesMis()\n", + "## dict\n", + "scenes = {\n", + " 0: ('FN', 'TH'),\n", + " 1: ('TH', 'JV'),\n", + " 2: ('BM', 'FN', 'JA'),\n", + " 3: ('JV', 'JU', 'CH', 'BM'),\n", + " 4: ('JU', 'CH', 'BR', 'CN', 'CC', 'JV', 'BM'),\n", + " 5: ('TH', 'GP'),\n", + " 6: ('GP', 'MP'),\n", + " 7: ('MA', 'GP'),\n", + "}\n", + "\n", + "### Nested dict with cell_properties\n", + "scenes_with_cellprops = {ed: {ch: {'color':np.random.choice(['red','green']),'cell_weight':np.random.rand()} \n", + " for ch in v} for ed,v in scenes.items()}\n", + "\n", + "### Pandas dataframe\n", + "scenes_df = pd.DataFrame(pd.Series(scenes).explode()).reset_index().rename(columns={'index':'Scenes', \n", + " 0:'Characters'})\n", + "### Dataframe with cell properties\n", + "scenes_dataframe = scenes_df.copy()\n", + "scenes_dataframe['color'] = np.random.choice(['red','green'],len(scenes_dataframe))\n", + "scenes_dataframe['heaviness'] = np.random.rand(len(scenes_dataframe))\n", + "\n", + "\n", + "### Node and edge property data\n", + "nodes = list(set(list(np.concatenate([v for v in scenes.values()]))))\n", + "edges = list(set(list(scenes.keys())))\n", + "node_properties = {ch: {'FullName': LM.dnames.loc[ch].FullName, \n", + " 'Description': LM.dnames.loc[ch].Description,\n", + " 'color':np.random.choice(['pink','lightblue'])} for ch in nodes}\n", + "node_props_df = pd.DataFrame.from_dict(node_properties,orient='index')\n", + "default_node_weight = 10\n", + "\n", + "### These edge properties will have missing weights so \n", + "### will be filled by constructor with default_edge_weight\n", + "edge_properties = defaultdict(dict)\n", + "edge_properties.update({ed:{'weight':np.random.randint(2,10)} for ed in range(0,8,2)})\n", + "for ed in edges:\n", + " edge_properties[ed].update({'color':np.random.choice(['red','green'])})\n", + "default_edge_weight = 2\n", + "\n", + "properties = [{'id':nd,\n", + " 'color':np.random.choice(['red','blue','green','yellow']),\n", + " 'weight': np.round(np.random.rand(),3)}\n", + " for nd in nodes+edges]\n", + "properties = pd.DataFrame(properties)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hypergraphs without properties" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def test(H):\n", + " edge = list(H.edges)[0]\n", + " node = H.edges[edge][0]\n", + " pair = (edge,node)\n", + " return {'pair' : pair,\n", + " 'nodes' : list(H.nodes)[:5],\n", + " 'edges' : list(H.edges)[:5],\n", + " 'diameter': H.diameter(),\n", + " 'edge_diameter' : H.edge_diameter(),\n", + " 'linegraph': H.get_linegraph(1).edges(), \n", + " 'info' : hnx.info_dict(H),\n", + " 'get_cell_property' : H.edges.get_cell_properties(edge,node)}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Numpy Arrays" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "### With no data and dictionaries, constructor works as before but will now accept \n", + "### n x 2 dimensional Numpy ndarrays.\n", + "H1 = hnx.Hypergraph(npdata)\n", + "test(H1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "checkplts(H1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Pandas DataFrames" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "scenes_dataframe[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "## dataframes will by default use the first two columns for (edge,node) pairs \n", + "### but different columns may be specified using the edge_col and node_col keywords\n", + "H2 = hnx.Hypergraph(scenes_dataframe)\n", + "checkplts(H2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "H2 = hnx.Hypergraph(scenes_dataframe,edge_col='Characters',node_col='Scenes')\n", + "checkplts(H2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hypergraphs from setsytems with cell properties" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def testp(H):\n", + " edge = list(H.edges)[0]\n", + " node = H.edges[edge][0]\n", + " pair = (edge,node)\n", + " HD = H.dual()\n", + " return {\n", + " 'pair' : pair,\n", + " 'single_cell_property' : H.get_cell_properties(edge,node),\n", + " 'single_cell_weight' : H.get_cell_properties(edge,node,H._cell_weight_col),\n", + " 'single_dual_cell_property' : HD.get_cell_properties(node,edge),\n", + " 'neighbors' : H.neighbors(node),\n", + " 'edge_neighbors': H.edge_neighbors(edge),\n", + " 'line_graph': H.get_linegraph(edges=True)\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataframes with properties" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "H3 = hnx.Hypergraph(scenes_dataframe,\n", + " cell_properties=['color'],\n", + " cell_weight_col='heaviness',\n", + " node_properties=node_properties,\n", + " edge_properties=edge_properties)\n", + "testp(H3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "H3.incidence_matrix(weights=True).todense()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add object properties\n", + "Hover over nodes and edges in the widget to see their properties" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "H4 = hnx.Hypergraph(scenes_dataframe,\n", + " cell_properties=['color'],\n", + " cell_weight_col='heaviness',\n", + " properties=properties,\n", + " weight_col='weight')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "HW(H4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "H5 = H.remove(['JV',1,2,3])\n", + "HW(H5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### Line Graphs persist properties as well" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "h = H4 ## Try with H3\n", + "G1 = h.get_linegraph()\n", + "G2 = h.get_linegraph(edges=False)\n", + "nxncolors = [h.nodes[nd].color for nd in G2.nodes]\n", + "nxecolors = [h.edges[nd].color for nd in G1.nodes]\n", + "fig,ax = plt.subplots(1,2,figsize=(15,7))\n", + "nx.draw_networkx(G1,node_color = nxecolors,ax=ax[0])\n", + "ax[0].set_title('edge line graph',fontsize=15)\n", + "ax[0].axis('off')\n", + "nx.draw_networkx(G2,node_color = nxncolors,ax=ax[1])\n", + "ax[1].set_title('node line graph',fontsize=15)\n", + "ax[1].axis('off');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "G1.nodes(data=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "G2.nodes(data=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dictionaries with properties" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "H5 = hnx.Hypergraph(scenes_with_cellprops,\n", + " edge_col='Scenes',\n", + " node_col='Characters',\n", + " cell_weight_col = 'cell_weight', \n", + " cell_properties=scenes_with_cellprops)\n", + "testp(H.dual())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "checkplts(H5.collapse_nodes())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "H5.incidence_matrix(weights=True).todense()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "H5.adjacency_matrix(s=2).todense()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "H5.edge_adjacency_matrix().todense()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "H5.get_cell_properties(0,'FN','color')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hypergraphs with properties on edges and nodes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "H6 = hnx.Hypergraph(\n", + " setsystem=scenes_dataframe,\n", + " edge_col=\"Scenes\",\n", + " node_col=\"Characters\",\n", + " cell_weight_col='cell_weight',\n", + " cell_properties=['color'],\n", + " edge_properties=edge_properties,\n", + " node_properties=node_properties,\n", + " default_edge_weight=2.5,\n", + " default_node_weight=6,\n", + ")\n", + "H6.properties" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "plot = HW(H6,node_fill = {nd:H6.nodes[nd].color for nd in H6.nodes},\n", + " edge_stroke = {ed:H6.edges[ed].color for ed in H6.edges},\n", + " edge_stroke_width = {ed:12 for ed in H6.edges})\n", + "plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### Properties are preserved when removing or restricting edges or taking toplexes\n", + "tops = H6.toplexes()\n", + "HW(tops)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tops.edges.properties" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### np array with node and edge data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "npproperties" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "H7 = hnx.Hypergraph(npdata,\n", + " edge_col=npedge_col,\n", + " node_col=npnode_col,\n", + " properties = npproperties)\n", + "HW(H7)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "HW(H7,node_fill={nd: H7.nodes[nd].affiliation for nd in H7.nodes},\n", + " edge_stroke={ed: H7.edges[ed].affiliation for ed in H7.edges})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hypergraphs with multi-edges\n", + "HNX distinguishes between edges by their ids, not their contents. This allows for multi-edges\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df1 = scenes_df.copy()\n", + "df1['cell_weights'] = 1\n", + "df2 = scenes_df.copy()\n", + "df2.Scenes = df2.Scenes.apply(lambda x : str((x + 8)))\n", + "\n", + "## Duplicate edges\n", + "ndf = pd.concat([df1,df2])\n", + "## Change an attribute on duplicate edges to try aggregation methods\n", + "ndf['color'] = np.random.choice(['red','lightblue','green'],len(ndf))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "H8 = hnx.Hypergraph(ndf)\n", + "HW(H8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "H9,eclasses = H8.collapse_edges(return_equivalence_classes=True)\n", + "## equivalence classes for collapsed edges\n", + "eclasses" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "HW(H9)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Restrict_to and Remove \n", + "\n", + "The same restriction can be used for remove nodes and restrict_to methods. Depending on the number of objects being restricted to it could be faster to just remove the objects you don't want." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "H10 = hnx.Hypergraph(scenes_dataframe,node_properties=node_properties,edge_properties=edge_properties)\n", + "HW(H10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "HW(H10.restrict_to_nodes(['JV','TH','BM','FN']))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "HW(H10.restrict_to_edges([0,1]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "HW(H10.remove_edges([0,1]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### If nodes and edges have distinct id sets a single remove command cat remove both.\n", + "HW(H10.remove(['JV','TH',2,3]))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials/Tutorial 1 - HNX Basics.ipynb b/tutorials/Tutorial 1 - HNX Basics.ipynb index 4e25e4c6..50fa8900 100644 --- a/tutorials/Tutorial 1 - HNX Basics.ipynb +++ b/tutorials/Tutorial 1 - HNX Basics.ipynb @@ -1,23 +1,24 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, + "cell_type": "raw", "metadata": {}, - "outputs": [], "source": [ "# !pip install hypernetx" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import networkx as nx\n", - "import hypernetx as hnx" + "import hypernetx as hnx\n", + "\n", + "import warnings\n", + "warnings.simplefilter(action='ignore')" ] }, { @@ -26,12 +27,12 @@ "source": [ "# Data\n", "\n", - "The data in several of our notebooks are taken from the jean.dat dataset available from the Stanford GraphBase at https://www-cs-faculty.stanford.edu/~knuth/sgb.html. This data gives character scene incidence information from the novel **Les Miserables** by Victor Hugo.\n" + "The data in several of our notebooks are taken from the [jean.dat dataset](http://ftp.cs.stanford.edu/pub/sgb/jean.dat) available from the Stanford GraphBase at https://www-cs-faculty.stanford.edu/~knuth/sgb.html. This data gives character scene incidence information from the novel **Les Miserables** by Victor Hugo.\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -50,13 +51,31 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# Draw the hypergraph! For more on using all of the parameters\n", - "# of the draw function see the Visualization tutorial\n", + "# Draw the hypergraph!\n", + "For more on using all of the parameters of the draw function, see the Visualization tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplots(figsize=(5,5))\n", "hnx.draw(H)" ] }, @@ -69,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -84,10 +103,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplots(figsize=(4,4))\n", "hnx.draw(HB)" ] }, @@ -95,76 +126,111 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "All hypergraphs have a natural dual structure where the edges and nodes switch roles. Edges become nodes and nodes become edges. This can be constructed by callig the `dual` method for a given hypergraph." + "All hypergraphs have a natural dual structure where the edges and nodes switch roles. Edges become nodes and nodes become edges. This can be constructed by calling the `dual` method for a given hypergraph." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'H-dual')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax = plt.subplots(1,2,figsize=(15,5))\n", "HD = H.dual()\n", - "hnx.draw(HD)" + "hnx.draw(H,ax=ax[0])\n", + "ax[0].set_title(\"H\",fontsize=15)\n", + "hnx.draw(HD,ax=ax[1])\n", + "ax[1].set_title(\"H-dual\",fontsize=15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "# Hypergraph methods\n", "There are many simple methods to begin to familiarize yourself with the hypergraph." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# See the nodes as Entities within an EntitySet object\n", - "\n", - "H.nodes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Use list() to get just the names of the nodes\n", - "\n", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['CC', 'FN', 'JV', 'TH', 'MP', 'MA', 'BR', 'CH', 'BM', 'JU', 'GP', 'JA', 'CN']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Use list() to get just the names of the nodes and edges\n", "list(H.nodes)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Similarly for edges\n", - "\n", - "H.edges" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 2, 3, 4, 5, 6, 7]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "list(H.edges)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(13, 8)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# The number of nodes and edges is returned by the shape property\n", - "\n", "H.shape" ] }, @@ -172,16 +238,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The degree of a node is the number of edges it is contained within. The (optional) `s` parameter places a restriction on the size of the edges you consider. The degree function looks for all edges of size $\\geq s$. The (optional, not shown here) edges parameter allows you to restrict to a specific edge set.\n", + "The degree of a node is the number of edges it is contained within. The optional `s` parameter places a restriction on the size of the edges you consider (default `s=1`). The degree function looks for all edges of size $\\geq s$.\n", "\n", "Note: `H.s_degree(node)` is a wrapper for the degree method and returns the same thing." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "H.degree('JV', s=1)" ] @@ -190,14 +267,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The dim (dimension) and size methods return information about an edge. The size is the number of nodes contained in an edge and the dimension is one less than the size. The dimension is so named because if we consider a hypergraph as a simplicial complex then each edge is a simplex. The dimension of a simplex is one less than its number of nodes." + "The `dim` (dimension) and `size` methods return information about an edge. The size is the number of nodes contained in an edge and the dimension is one less than the size. The dimension is so named because if we consider a hypergraph as a [simplicial complex](https://en.wikipedia.org/wiki/Simplicial_complex), then each edge is a simplex. The dimension of a simplex is one less than its number of nodes." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 4)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "H.dim(3), H.size(3)" ] @@ -206,16 +294,40 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The neighbors method returns an interator that goes through all nodes which share an edge with the given node." + "The `neighbors` method returns an iterator that goes through all nodes which share an edge with the given node. This method also has an `s` keyword argument to return the list of neighbors that share `s` edges with the given node, the default is 1." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for nei in H.neighbors('JV'):\n", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BM\n", + "BR\n", + "CC\n", + "CH\n", + "CN\n", + "JU\n", + "TH\n" + ] + }, + { + "data": { + "text/plain": [ + "['BM', 'BR', 'CC', 'CH', 'CN', 'JU', 'TH']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for nei in H.neighbors('JV',s=1):\n", " print(nei)\n", " \n", "# As with any iterator you can get all of the values in a list\n", @@ -232,18 +344,59 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: ['FN', 'TH'],\n", + " 1: ['TH', 'JV'],\n", + " 2: ['BM', 'FN', 'JA'],\n", + " 3: ['JV', 'JU', 'CH', 'BM'],\n", + " 4: ['JU', 'CH', 'BR', 'CN', 'CC', 'JV', 'BM'],\n", + " 5: ['TH', 'GP'],\n", + " 6: ['GP', 'MP'],\n", + " 7: ['MA', 'GP']}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "H.incidence_dict" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 0, 1, 1, 1, 0, 0, 0],\n", + " [0, 0, 0, 0, 1, 0, 0, 0],\n", + " [0, 0, 0, 0, 1, 0, 0, 0],\n", + " [0, 0, 0, 1, 1, 0, 0, 0],\n", + " [0, 0, 0, 0, 1, 0, 0, 0],\n", + " [1, 0, 1, 0, 0, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 1, 1, 1],\n", + " [0, 0, 1, 0, 0, 0, 0, 0],\n", + " [0, 0, 0, 1, 1, 0, 0, 0],\n", + " [0, 1, 0, 1, 1, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 1],\n", + " [0, 0, 0, 0, 0, 0, 1, 0],\n", + " [1, 1, 0, 0, 0, 1, 0, 0]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "M_incidence = H.incidence_matrix()\n", "M_incidence.toarray()" @@ -251,16 +404,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "## Networkx and HyperNetX visualizations of the bipartite representation of the hypergraph\n", - "fig,ax = plt.subplots(1,2,figsize=(15,8))\n", + "fig,ax = plt.subplots(1,2,figsize=(15,6))\n", "BH = H.bipartite()\n", "top = nx.bipartite.sets(BH)[0]\n", "pos = nx.bipartite_layout(BH, top)\n", - "nx.draw(BH, with_labels=True,pos=pos,ax=ax[0])\n", + "nx.draw(BH, with_labels=True,pos=pos,ax=ax[0], node_color='lightgrey')\n", "hnx.drawing.two_column.draw(H,ax=ax[1])" ] }, @@ -269,18 +433,38 @@ "metadata": {}, "source": [ "# Connected components\n", - "Just as is done in graphs we can consider connected components of a hypergraph. But because edges can intersect in any number of nodes the connected components can be computed for different levels of connection strength. HNX has two main methods for exploring the components: `s_components` and `s_component_subgraphs`. To learn more about some of our research in this area check out our paper \n", - "\n", - "Hypernetwork science via high order hypergraph walks.\n", + "Just as is done in graphs, we can consider connected components of a hypergraph. But because edges can intersect in any number of nodes, the connected components can be computed for different levels of connection strength. HNX has two main methods for exploring the components:\n", + "* `s_components`\n", + "* `s_component_subgraphs`.\n", + "\n", + "To learn more about some of our research in this area, check out our paper, [Hypernetwork science via high order hypergraph walks](https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-020-00231-0\")\n", "\n", "`s_components` returns a generator object which iterates through the edge sets for each s-connected component. " ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1-component edge sets:\n", + "{0, 1, 2, 3, 4, 5, 6, 7}\n", + "\n", + "2-component edge sets:\n", + "{3, 4}\n", + "{0}\n", + "{1}\n", + "{2}\n", + "{5}\n", + "{6}\n", + "{7}\n" + ] + } + ], "source": [ "print('1-component edge sets:')\n", "for comp in H.s_components(s=1):\n", @@ -301,9 +485,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1-component sub-hypergraph incidence dictionaries:\n", + "{0: ['FN', 'TH'], 1: ['TH', 'JV'], 2: ['BM', 'FN', 'JA'], 3: ['JV', 'JU', 'CH', 'BM'], 4: ['JU', 'CH', 'BR', 'CN', 'CC', 'JV', 'BM'], 5: ['TH', 'GP'], 6: ['GP', 'MP'], 7: ['MA', 'GP']}\n", + "\n", + "2-component sub-hypergraph incidence dictionaries:\n", + "{3: ['JV', 'JU', 'CH', 'BM'], 4: ['JU', 'CH', 'BR', 'CN', 'CC', 'JV', 'BM']}\n" + ] + } + ], "source": [ "print('1-component sub-hypergraph incidence dictionaries:')\n", "for comp in H.s_component_subgraphs(s=1):\n", @@ -319,16 +515,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The methods `component_subgraphs` and `connected_component_subgraphs` are wrappers for `s_component_subgraphs`, and the methods `components` and `connected_components` are wrappers for `s_components`. \n", + "The methods `component_subgraphs` and `connected_component_subgraphs` are wrappers for `s_component_subgraphs`. The methods `components` and `connected_components` are wrappers for `s_connected_components`.\n", "\n", "The `is_connected` method with optional parameter `s` (default `s=1`) returns `True` or `False` depending on whether the hypergraph is s-connected." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(True, False)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# The Les Mis example hypergraph is 1-connected but not 2-connected\n", "\n", @@ -339,37 +546,101 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Adjacency matrices\n", - "In order to compute s-connected components we use a helper auxiliary matrix: the s-adjacency matrix of the edges of size >= s. Two edges are considered s-adjacent if they intersect in at least s nodes. This is computed using `auxillary_matrix`. The output is a scipy compressed sparse row matrix.\n", + "# s-Line Graphs and s-Adjacency matrices\n", + "\n", + "In graphs, two nodes are considered **s-adjacent** if they share s edges. In hypergraphs, two nodes are **s-adjacent** if they are co-incident with at least s hyperedges. Similarly, two hyperedges are considered **s-adjacent** if they are co-incident with at least s nodes.\n", + "\n", + "An **s-linegraph on the nodes** of a hypergraph is a graph on the nodes of the hypergraph where edges connect s-neighbors.\n", + "An **s-linegraph on the hyperedges** of a hypergraph is a graph using the hyperedges as nodes where edges connect s-adjacent hyperedges.\n", + "\n", + "The corresponding adjacency matrices are the **s-adjacency** and **s-edge adjacency matrices**.\n", + "An s-adjacency matrix with the zero rows and columns removed is called an **s-auxiliary matrix**.\n", "\n", "**Note**: At present these matrices are unweighted, we will introduce weighting in a near future release." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "M_aux1 = H.auxiliary_matrix(s=1)\n", - "M_aux1.toarray()" + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n", + " [1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## the adjacency and auxiliary methods return a matrix and has an optional keyword argument to return an\n", + "## index of the row and columns ids\n", + "\n", + "H.adjacency_matrix(s=2).todense()" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "M_aux3 = H.auxiliary_matrix(s=3)\n", - "M_aux3.toarray()" + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[0, 1, 1, 1],\n", + " [1, 0, 1, 1],\n", + " [1, 1, 0, 1],\n", + " [1, 1, 1, 0]])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "H.auxiliary_matrix(s=2).todense()" ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(<4x4 sparse matrix of type ''\n", + " \twith 12 stored elements in Compressed Sparse Row format>,\n", + " array(['BM', 'CH', 'JU', 'JV'], dtype=object))" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "There are two helper functions: `adjacency_matrix` and `edge_adjacency_matrix`. The former is the s-adjacency matrix of all nodes where two nodes are considered s-adjacent if they are in s edges together. The latter is the same as the auxiliary matrix but for all edges (not just those of size >= s). " + "H.auxiliary_matrix(s=2,index=True)" ] }, { @@ -377,30 +648,50 @@ "metadata": {}, "source": [ "# Distances and diameters\n", - "Just as connected components of graphs generalized to s-connected components in hypergraphs, the distance and diameter can be generalized to s-distance and s-diameter in hypergraphs. \n", + "Just as connected components of graphs can be generalized to s-connected components in hypergraphs, the distance and diameter can be generalized to s-distance and s-diameter in hypergraphs.\n", "\n", "We can compute s-distance between edges using `edge_distance` and between nodes using `distance`. See the glossary in the documentation for detailed definitions of distance between edges and between nodes." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Optional parameter s is not shown, defaults to s=1\n", - "\n", - "H.distance('MA', 'FN')" + "H.distance('MA', 'FN',s=1)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Optional parameter s is not shown, defaults to s=1\n", - "\n", "H.edge_distance(4, 6)" ] }, @@ -413,9 +704,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "inf" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "H.edge_distance(4, 6, s=2)" ] @@ -429,9 +731,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4, 3)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Optional parameter s is not shown, default is s=1\n", "\n", @@ -439,11 +752,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "Below is a drawing of the hypergraph to help confirm the outputs:\n", + "\n", + "" + ] }, { "cell_type": "markdown", @@ -454,7 +769,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -466,23 +781,56 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "If your hypergraph is not s-connected but you want to explore the s-diameters of all of the s-connected components (edge or node) use `node_diameters` and `edge_diameters`. This returns a tuple where the first element is the maximum diameter over all s-components, the second element is the list of all s-diameters, and the third is the list of the s-components (edges or nodes)." + "If your hypergraph is not s-connected but you want to explore the s-diameters of all of the s-connected components (edge or node), use `node_diameters` and `edge_diameters`. These methods return a tuple where the first element is the maximum diameter over all s-components, the second element is the list of all s-diameters, and the third is the list of the s-components (edges or nodes)." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1,\n", + " [1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [{'BM', 'CH', 'JU', 'JV'},\n", + " {'BR'},\n", + " {'CC'},\n", + " {'CN'},\n", + " {'FN'},\n", + " {'GP'},\n", + " {'JA'},\n", + " {'MA'},\n", + " {'MP'},\n", + " {'TH'}])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "H.node_diameters(s=2)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, [0, 0, 0, 1, 0, 0, 0], [{0}, {1}, {2}, {3, 4}, {5}, {6}, {7}])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "H.edge_diameters(s=2)" ] @@ -503,9 +851,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: ['FN: 1', 'TH: 1'],\n", + " 1: ['JV: 1', 'TH: 1'],\n", + " 2: ['BM: 1', 'FN: 1', 'JA: 1'],\n", + " 3: ['BM: 1', 'CH: 2', 'JV: 1'],\n", + " 4: ['BM: 1', 'BR: 3', 'CH: 2', 'JV: 1'],\n", + " 5: ['GP: 1', 'TH: 1'],\n", + " 6: ['GP: 1', 'MP: 1'],\n", + " 7: ['GP: 1', 'MA: 1']}" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "H_node_collapse = H.collapse_nodes()\n", "H_node_collapse.incidence_dict" @@ -520,30 +886,53 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplots(figsize=(5,5))\n", "hnx.draw(H_node_collapse)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "# There are no duplicate edges in H, but there are in the dual of H\n", - "\n", "HD_edge_collapse = HD.collapse_edges()" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplots(figsize=(5,5))\n", "hnx.draw(HD_edge_collapse, with_edge_counts=True)" ] }, @@ -559,184 +948,250 @@ "metadata": {}, "source": [ "# Updating the Hypergraph\n", - "You can add edges to and remove edges from an an existing hypergraph." + "You can remove nodes and edges from an existing hypergraph. (At this time the only way to add nodes and edges to a hypergraph is to start over.) " ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "edge1 = hnx.Entity('new1', elements=['JV', 'MA'])\n", - "edge2 = hnx.Entity('new2', elements=['GP', 'MA'])\n", - "edge3 = hnx.Entity('new3', elements=['FN', 'JV'])\n", - "\n", - "# add a single edge\n", - "H.add_edge(edge1)\n", - "\n", - "# add edges from a list of edges\n", - "H.add_edges_from([edge2, edge3])\n", - "\n", - "# display the incidence dictionary with new edges now added\n", - "H.incidence_dict" + "Recall the current state of our hypergraph:" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplots(figsize=(8,5))\n", "hnx.draw(H)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "H.remove_edge('new3')\n", - "H.remove_edges(['new1', 'new2'])\n", - "H.incidence_dict" + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: ['FN', 'TH'],\n", + " 2: ['BM', 'FN', 'JA'],\n", + " 3: ['JV', 'JU', 'CH', 'BM'],\n", + " 4: ['JU', 'CH', 'BR', 'CN', 'CC', 'JV', 'BM'],\n", + " 6: ['GP', 'MP'],\n", + " 7: ['MA', 'GP']}" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "H3 = H.remove_edges([1,5])\n", + "H3.incidence_dict" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "hnx.draw(H)" + "plt.subplots(figsize=(8,5))\n", + "hnx.draw(H3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You can also add nodes to already existing edges." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "H.add_node_to_edge('SantaClaus', 7)\n", - "H.incidence_dict" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hnx.draw(H)" + "# Building sub-hypergraphs\n", + "There are two methods that can be used to look at different kinds of sub-hypergraphs: `restrict_to_nodes` and `restrict_to_edges`. The method, `restrict_to_nodes`, builds a sub-hypergraph from a specific subset of nodes and all edges that they are included in. The method, `restict_to_edges`, builds a sub-hypergraph from a subset of edges and all nodes contained in the edges." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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yK7JJjArg6/tGEB7gxY1zNrFGDr0ULkDCRbgkV5jMb4peq1F0Ut1b6807F79DoFcg9/x6DwaTgchAb5beNYyhncO4/eNkFm/Jdlo9QpyOhItwSamGVLw13sQFxSldymmpVCoeujyRV2/ow7LkHG7/OJmq2nqnjR/sHcycy+Zgqjfxj9X/wGg24qfX8n+3DGTSkI48sWI3r/y4Tw69FIqRcBEuKdWQSmJoIlq1a6+AunFQLB9PV2ZSvYN/B96/xLbJ8pG1j1BvqUerUfPcdT157rqeLN5ymFnLd8mhl0IREi7CJaWVpLncfEtTRnS1TapXmMxOn1TvHtadN0a9wfaj2/k+43usVisqlYqpw+JYetcwIgP0LEvOwejEqyohQMJFuKBqczVZ5VkOPwnZnhKjAlgxY3jjpPra/UVOG3t4h+E8O/xZDpYdZEPehsbHk6IDuWlwDJnF1by35iDFFTVOq0kICRfhcvaV7MOK1W2uXI47Pql+Xucwpi/c6tRJ9dHxoxnRYQS/Zv/KlvwtjY93CvPn3lFdqDVbeP2X/WQUVTmtJtG2SbgIl5NmSEOv0dMluIvSpbSYn17L/00dxOShzp9UHxI1hMHtBrPiwArSDGmNj0cEePPPy7oRFeTNO78fZHt2qVPqEW2bhItwOamGVLqFdHP5yfymaNQq/nNtT566ujtz1h7igc+dc+ilSqXiqvirSApLYsm+JRQZT9ya89NrmXFRV/rGBLNgYxa/ph6VQy+FQ0m4CJeTakh1u1tif6dSqbjj/M58MHkAv6QeZcrcLZQ64dBLtVrNhMQJBHgF8FnaZ9Q1nBhTp1Ez9bxOXN6jHSt3HuGL5BwaLHLopXAMCRfhUoxmI5kVmW41mX8mo3tFs+Su88gsrmbcB5vIckInSW+tN1O6T6HYVMyPmT+e9JxareKavu2ZOCSWzYcMfLQuQ5YqC4eQcBEuZX/pfixWi9tfufzVgI4hrJgxApUKxn2wiZTDjj/0Mto/mgtjLuTPgj8x1ZuYNm0aY8eObXx+WJdw2LmCGRcncv1dj1FmdH4rAeHZJFyES9lr2ItOraNrcFelS7GrjmG+fHXvcLpG+jPxoy18tyv/nD6nqqqK7OxsqqqaX/U1NHoo9ZZ6th3ddtrnv/tyCTMe/Cd//ryc2b/sJ6/UeE41CXE67jljKjxWmiGNhJAEdBqd0qXYXbCvF5/ePoTHvtzFyz+mEeqr47wuYahUqmbfazQaWbhwIZs2baKhoQGNRsOwYcOYPn06Pj4+p31PkD6InuE9SS5IPuW5tWvXYjKZeON//+WrpUs4mr6dN+samD4ynu7Rga3+rkLIlYtwKakl7j+ZfyZ6rYY3b+7Hv6/pycZDhrOaVLdarcyePZv169fT0GCbH2loaGDDhg28/vrrZ1z1FR8YT6GpECsnv2bevHlMnDgRnU7HpEkTqd79C10i/Phw7SE2Hypu/RcVbZ6Ei3AZNfU1ZJRleHS4gG0l2SU92jGia3izk+o15gZ+3rSdTQeLqdaFnPL83r172bNnT5NjhfqEYraYMTeYGx+rqKhg+fLlTJkyBYApU6aw4qvl3NwvnGFdwljyZw6rdubJoZeiVSRchMvYX7qfBmuDSx6z7wjDuoRxz4VdyCiq5q1fD5wyqV5dW899i7bx5M9HOBg+nLTIi8gO7nvK5xw6dKjJMUL0tkD665LkxYsX07lzZ/r2tX1Wv3796Ny5M8u++IKbBsVyXb/2/JJayCd/HMbsxFYCwrNIuAiXkWpIRavWkhCSoHQpTpMUHciDlyVgrKs/aVK93Gjm+vc3UlBRw5h4NT0LfiaheANH/RMo9Ot80md4e3s3+fl1FluoqFUn/qjPnz+fvXv3otVqG3/t3buXefPm2a6qurfjthFx7Mwt4/3fD1JVI4deipaTcBEuI60kjYTgBLw0XkqX4lQdgn156LJu+Ou1vPnrAdLyK7j7s2SsVnjz5n7ce9UgfK0mgmoLCao5ikl3YsJdrVYzYMCAJj+7xGRb9qzX6AHYvXs3ycnJrFmzhh07djT+WrduHVu3bm28xda/Ywj3X9SVgopa3vx1P8WVcuilaBkJF+EyUg2pdA/zjM2TLRXk68UDlyTQJcKP+5dsY3deObNv6kdCuwAiIyOZPHkyAEZdENa/XIVMnjyZyMjIJj/XUGPAV+uLRq0BbBP5Q4YM4YILLqBXr16Nv0aOHMmwYcOYN29e43s7R/jz0KUJWIH31hwiR5YqixaQcBEuobahloOlB9vMfMvpeOs0XNk7iuraBjqF+pFRVNW4Euyqq65i+MQH8fP1pkugiiFDhvDEE09w1VVXNfl5VquVXcW7iA+Kx2KxoFar+eyzzxg/fvxpXz9+/Hg+++wz6upOzM9EBHrzz0u7ERmg5/Mt2fyyt8C+X1p4LNnnIlzCwdKD1FvrPX6lWHPMDVYqTGZuHhTL6n2FlJrMTB7akV25ZWwzqOkeH8O7k64lyKf5fUBZFVkUVBdwTedrWFK4hK5du1Jc3PQy44ceeoiHHnrolMf9vbXcPrIz3+/O55Evd/IPg5E7zo8/q/05ou2ScBEuYa9hLxqVpk1N5p+ORq2mY5gfF3eP5EKLlU//OMzevHKqa+s5XGLkvUkDzipYrFYra3PX4mv2JX1jOmvWrOGee+4557q8tGqu7duew4ZqXvw+jewSI8+O6YFWIzc/xOlJuAiXkGpIpUtwF7y1Ta98agviw/2IC/Nl5tId3HthF/RaNT/uKUCnUfPMmB5nvXt+W+E2Ug2pJL+ezNxdc3n44Ye57rrrWlWbWq3iwUu7ERnozVNf7yGvzMQ7E/vjp5cfI+JUKqs0dRAu4OZvb6ZbSDeeH/G80qW4hGe/2cP+o1UcKTdxde9ocktN+Om13HVBPPHh/md8r8Fo4M1tb9IrvBc3J93skPrW7i/ivkXb6BTmy/xpg2kX2Lb/UiBOJeEiFGduMDN08VAeGfQIk7pPUrocl1Fda9tf4qfXUlVTz9wNGWSXGJl6Xif6dTx1tz5AvaWeD3Z+QHVdNTMHznTolWDqkQqmL9yKWgXzbxtMUpScSSZOkBumQnEHyg5gtpjb/GT+3/nptY23nPy9tcwY1ZU+McF8kZLLtsMlpz1TbE3OGvIq85jUfZLDbzH2aB/I1/eNIMjXixs/2MyGA3ImmThBwkUoLs2QhlqlJjE0UelSXJqX1tZJ8po+0bzx6wGe/WYv9X85niWnIocNeRu4PO5yOgZ2dEpNUUHeLLtnGAM6hTBtwZ98sTXHKeMK1yfhIhSXakilc1BnfLSnPzpenKBWqxjeJZwxfaJZ9Gc2d32aQnVtPeW15fyY+SMJwQmMihnl1Jr89Vrm3TqIGwfF8tjyXbz+c/oZT2oWbYMs8xCKSzV49jH7jnDT4I60C/LhvkXbuPHDTST0XEmUfxj3Jd2HWu38vzNqNWpeur4XncJ8efmHfeSUGHnlhj7otRqn1yJcg1y5CEWZLWb2l+6ne2jbPPalNS7sFsGye4ZRaF3Pb7k/EePdm0C9cpPqKpWKey7swjsT+/P9ngJumfentE9uwyRchKIyyjKos9TJlcs58vY1oAr7Gr/aEfzvK51LTKqP6duexXcM5cDRSsZ9sIlsg5xJ1hZJuAhFpRpSUaEiKTRJ6VLcjrnBzKx1s4jyi2LV5FcZGHdsUj1Z+Un1QXGhfDVjBBaLlevf38j27FKlSxJOJuEiFJVqSCUuKA5fna/Spbidd7a/w4GyA7x8wctE+AUwd+ogbhocy2Nfusakeny4H1/NGEFcuB8T/u8Pftwjh162JRIuQlGpJTKZfy42H9nMgr0LeLD/g/QM6wnYJtVfHNuLf12ZxDu/HeSfS3dQW3/69snOEurnxaI7hnJp93bcuyiFueszFA894RyyWkwopt5Sz/6S/VzR6QqlS3ErJTUlPLnhSc6LPo+pPaee9JxKpeLuC7sQE+LLP7/YQX55Df93yyCCfJs/7NJRvHUa3pnYn9iffHnhuzRySow8M6YnGrWcquzJ5MpFKCazPJOahpo22yDsXFitVp7d+Cxmi5kXR754Uvviv7q6TzRL7hzK/qOVjPtgIzklyk6qq9UqHr8yiRev78VnW7K5+9NkjHXSPtmTSbgIxeRX5dMpsJMsQ26BL9K/YE3uGp4b/hyRvk13oAQY2CmUFTNG0OBCk+qTh3Zi7q2DSDmUz0Pvf0nJoRSoqVC6LOEAcnClUMzGvI0crjgsh1WepYOlB5nw3QTGdh3LU+c9ddbvK6mu465PktmdV85bE/ozuleUA6tsguEQpCyE3GQozYLKIyc/7xMKIXEQ2QP6T4aOw0Cakbk1CRehmPd3vE+wVzCTeki4NKe2oZaJ303EYrHw+TWft/hQyhpzA48s28l3u/N58qru3D7SCZ0kLQ2w/yfYOhcOrQafEOhyCYTGQ0gcBl0Uz/+YgVdVDvf109FJVQiHN0HJIVvIDL4d+twM+gDH1ikcQib0hSIsFgtHqo7Qs1NPpUtxC2+mvMnh8sMsvnrxOZ127K3T8PaE/sSG2ibVc0tNPH1ND8dNqpdlw7JpkJcCHQbC2A+g5/WgO3F+XBjwfFcz9y3eziV/FvPfcb25cUwHyFxrC6TvH4U1r8AN8yH+fMfUKRxGwkUooshURJ2ljg7+HZQuxeWty13HZ2mf8fiQx1t1crRarWLW6CRiQ3x5+ps95JYaeXtif3y97Pxj4MAv8NWd4BUAt/0InYY1+dIAbx3zbh3E01/v4dEvd5FTauKfl45C1eUiKM+F7x+BL26BCx6FofeCAuemiXMj/6aEInKrcgFoH9Be4UpcW7GpmKc3Ps3IDiOZlGSf24eThnZk3q2D2HzIwM0f/kFhZY1dPhewXWksuhFihsDda88YLMfpNGr+O643j41O5O3VB3h8+S5bK4GgGLh5MYyfC5VHYecSqK+1X63CoSRchCLyKvMI8w7DVys785tisVp4aqNt4v6FES/YdY5kVGIky+4ZTlFlLde/t4n9Rytb/6HbPoU1L8Gof8HEz8E39KzfqlKpmDGqK29P7M/qtEKWbs3BVFtvu1Lpeqlt7iVvG+xcCjJN7BYkXIQi8qrz5JZYMxanLWZj3kZeHPkiYT5hdv/8Hu0DWXHfcAK8tYz/YBObDrbi0Mv8XbZbWAOmwqhZ53z76tq+7flw6iAKq2p5b81BDFXHrlSiekKfGyBzjW1ORrg8CRfhNBarpfH3I1VHJFzOIL0kndkps5nSfQojO4x02DjRQT4su2cY/TuGMHX+n3yZktvyD6kphy+mQngCXPm/Vtc0sFMIU8/rhMVqZfYv+8kvN9me6DgMOo+CHYtty5mFS5NwEQ5VU1/Da1tfY8r3U3hm4zPsKd5DsamYmvoaOgTYwqWuQXp+/JWp3sRj6x4jPiiemQNnOny845PqNw6K4ZFlO3njl/0tO/9rzctgNMBNn5y0Gqw1wvz13DuqKwHeWuauz8R0fDd/nwkQGGPbMyO3x1yahItwqBf+eIF1eevoFd6LvKo8Zv4+k015m1CpVET7RgPwaeqnHK0+qnClruO1ra+RV5XH/y74H3qN3ilj6jRqXrq+N7NGJ/HW6gM8/MVO6uotzb+xrhq2L7LtSQntbNeaArx1TB8RT6XJzOI/s22Bp/WCXtfbljobDtl1PGFfEi7CYQqqC9h0ZBMvjHiBx4c8zoLRCxjZYSRvbnuTIK8gAvQBlNeW89a2t+TI/WN+y/6NL/Z/wWODH6NLcBenjq1Sqbh3lK2T5Le78pk6fwvlRvOZ37T7S6itgIG3OaSmyEBvJp/XkZ055ezKLTv2YE/wi4RDvztkTGEfEi7CYXYW7SRIH0SfiD7UW2y3NZ4c+iQ+Wh92F+8G4EDpAfy9/Anwkl3YR6uP8symZ7go9iJu7HajYnWM6dueRXcOZV9BJePnbGr60Eur1bbZsdsVENLJYfX0jQ0hPtyPfh1DUalUqDQaVFe9guq8u2z/fOzXtGnTAFtIfv3116d8zrRp0xg7dqzD6hQnk3ARDlNsKqZjQEcq6irQqrWYLWY0ag2dgzpTUF3Autx1pJemExcYp3SpirNYLTy58Um81F78Z/h/HH80SzMGx9kOvTQ3WLj+/Y3szCk79UVF+6BgFwyc1uLPz8/PZ+HChcyZM4ddu3Y1+/qRCeFMf/9ndqZnkp+fz5uvvUKgj478LV+Rn59Pfn4+b731VovrEI4j4SIcpmtwV4pNxeRU2tru6tQ6ioxF+Hn5cXHHi/k09VO+OfgNg6IGKVyp8j7e+zF/5v/JS+e/RIh3iNLlAMc6Sd47nI6hvtz8f5v5ee/fOklmrAGNF8Rf2KLPffvtt+nUqRO33XYb9957L3379mXKlCmYzU3fgusXG0x4RCRH6vRERUURFBaJSq0mysdCVFSU7bGgoHP4lsJRJFyEwwyNHsqnV3160pH6BUbbD6gZfWegUWnYV7KP8zu07XOj9hbv5e1tbzOt1zTOiz5P6XJOEuavZ/Gd53FJUjvu/iyF+RsyTzyZsRZih4LX2c+X/fDDDzz44IOnBMmiRYt46qmmT3rWadS0C/SmqPKvO/RVYGzF3hzhUBIuwqHUKvVJDa1yK3MJ1gcT7hvOv4f/m9t7396m2xwbzUZmrZ9Ft9Bu3N/vfqXLOa3jnSTvuqAzz32byr9X7qWh3gxZG6Bzy65aznTr6v333z/j1UuYvx5D9V+WratUUF102tdOnDgRf3//k34tWrSoRbWK1pGDK4VT5VWd2Jkf5RfFgwMeVLgiZb3858sUGgv54pov0GmUa0XcHLVaxb+u7E5siC/PfLMHfUEy/6qrhM4XtehzDhw40ORzVVVV5Ofn07Fjx9M+H+yrI9NQfeIBlQpMp2+A9sYbb3DppZee9NisWbNoaGhoUb3i3Em4CKexWq3kVeW1+dtgx/2U9RMrDq7gueHPERcUp3Q5Z2XKeZ3oEOzDrsVPUq3xxRjQnYgWvD8mJoaMjIzTPufl5UVkZNPdNStN9QTo//Ijy2oFfeBpXxsVFUXXrl1PeiwgIICysrIWVCtaQ26LCacpqSnBVG+SY1+wtXj+z+b/cHmnyxnbdazS5bTIRUmR3NEhmxR6cv2cLRwsPPtDL++8884mn7vlllvw9m66V42hupYw/79uKrWCX/hZjy2cS8JFOE1pTSnxgfF0DDz9bY+2osHSwOPrH8dP58czw55RfNlxi9VV41e4jb4XXoe/Xsu49zex6dDZTaxPmTKFf/7zn6c8fsEFF/DGG280+T6LxcrRilrC/bxOPGi1gl9LrpuEM8ltMeE06/PWs6toF346P6VLUdS8PfPYXrid+VfMJ0jvhstnszdDQx1BPS5j2XldmLFoG7fO/5OXx/Vh/MCYZt8+e/ZsJk6cyLfffktNTQ3Dhw/nmmuuQaPRNPmevUcqqKqtp0/Msf+/GuqPhYtcubgqCRfhNH/k/4GX2qv5F3qwnUU7eX/H+9zZ50733d+TsRb8oyAikQCVivnTBvP013t4eNlOcktNPHBJ12avxgYPHszgwYPPesgNB4voGOpDxzDbX0ymjR7ItI8nQ0TSKa9t6tDNhQsXnvV4ovXktphwCqvVSqohtU0vO66qq2LWuln0DO/JPX3vUbqcc5e51rYE+ViAHO8k+egVibzx634eWbbr7A69PEuHiqpIy69kRMJfboEd2QYB0RAonUxdlYSLcIqC6gLKasvoHta9+Rd7qBe3vEhZbRkvn/8yOrXrLjs+I2OJrTHY33blq1Qq7ruoK29N6MeqnUeYtuBPyk3NHHp5FipMZhZsyKRLhB9D4o6dXGCphyM7oMPAVn++cBwJF+EUqYZUgDZ75fJtxrd8m/EtTw59ktiAWKXLOXeZ6wBrk5snr+vXgc/uGMreIxXc8MEmDv91X0oL1ZgbWLgpEyswbXgcmuPdLYvSwVwNHQac82cLx5NwEU6x17CXcJ9wIn2b3sfgqXIqc3jhjxe4uvPVjOkyRulyWidzLYR1haCmJ+6HxIfy1YzhmBssXPPOBn76+5lkZ6Gosob3fjtATomJ24bHEeT7l7m6vBTbRH6w405iFq0n4SKcIq0k7aQzxtqKeks9j69/nGB9ME8OfVLpclovY81ZHVTZJcKflfePZESXcO7+NIV/fbWbQ0VVzb6vpLqOeRsy+XhTFioVPHx5Il3b/aUdg8UCeduh/cDGOR/hmmS1mHC445P5SvYoUcqcnXPYW7yXhaMXun/PmrJsKMmAS/9zVi8P9NbxwZQBLNiYxbu/H2TJn9mM6BrG6J5RdAzzIzbEB71OQ06JkZwSI5szDHy7K59AvZbnr+/FxUmR6LV/W55sOAi15XJLzA1IuAiHO2o8SklNSZubb0k5msJHuz/i3r730i+yn9LltF7GWkAFcSPP+i0qlYrpI+OZfF5HfthdwKd/HObfq1JpsJy6XDg+3I+HLuvGTQNjCPVvor3z0d0QEAWhzu3SKVpOwkU4XJohDWhbk/nlteU8vv5x+kX0487eTR954lYy10L7fuAb2uK36rUaxvbvwNj+HahvsJBfXkNOiZHaeguxoT7EhPjirWt6EyVg2zRZchhih4Fa7ui7OgkX4XCpJamEeofSzred0qU4hdVq5fk/nqe6rpqXR7+MRt3MD013YLXarlz6TWr1R2k1amJDfYkNPfs+MAAU7IZNb8HNn7W6BuF4Ei7C4VINqXQP6+5+Z2ido28OfcNPWT/x6oWvEu0frXQ59lGYBtWFLe7fYlepX9sm8Tu6VkM1cXpybSkcLs2QRo/QtnFL7HDFYV7a8hJju45ldNxopcuxn8y1oNFDx2HK1ZC2ChKvAhfueyNOkHARDlVkLKLIVNQm5lvMDWZmrZtFhE8E/xryL6XLsa+MtRA7BHQ+yoxfuA+K90N3N98n1IZIuAiHSiuxTea3hWNf3t3xLukl6bxywSv46lo4n+DKGurPqaWxXaWtAi//Fne+FMqRcBEOtdewlyB9EO39PPuAwS35W1iwZwH/6P8PeoX3Uroc+zqyDc6hpbFdpa2EhMtB13QzMeFaJFyEQ6UaUukR2sOjJ/PLasp4Yv0TDIkawm29blO6HPvLWGNrJxzdT5nxSzKhYJfcEnMzEi7CodIMaR59S8xqtfLspmeptdTy4sgXUas88I9UxlqIOx80Ci0u3fetbTFBwuXKjC/OiQf+SRCuwmAycNR41KMn87888CW/5fzGf4b9h3Z+HriPp64acv9Ufr6l6yWg91euBtFiEi7CYTz9mP2Msgz+9+f/uLHbjVzS6RKly3GMYy2Nz+awSoeoyIecLXJLzA1JuAiHSStJI8ArgBj/5vuqu5u6hjoeW/cY0f7RPDr4UaXLcZy/tDRWxL5vQa2Fbh60Z6iNkB36wmE8eTL/zW1vklGewaKrFuGjVWjvhzP8raWx06WttM33nMN5ZkJZcuUiHCbVkOqRt8Q25m3k09RPmTlgpkcvVmhsadx5lDLjVxsga6PcEnNTEi7CIUprSsmvzve4H74Gk4EnNzzJiPYjmNJjitLlONbxlsZKzbfs/wGsFki6RpnxRatIuAiH8MRj9q1WK09vfBorVl4Y+YJnLjv+q4w1x1oad1Bm/NSVtkMqAzxwFV4b4OF/OoRSUktS8df5ExsQq3QpdrNk3xLW563n+RHPE+4TrnQ5jpe5VrlbYjUVkPG73BJzYxIuwiFSDakkhSZ5zN/u95fu5/Xk15mUNIkLYi5QuhzHO97SWKlbYgd+ti2BlnBxW57xJ1+4HE+azK+pr2HWull0DOzIQ4MeUroc5ziHlsZ2lbbSdtxMcEdlxhetJuEi7K68tpy8qjyPCZfXk18nuyKb/13wP/SaJnq7e5pWtDRuNbMJDvwCPa51/tjCbiRchN150jH7a3PW8nn65zwy+BESQhKULsc5jrc0VuqW2KHfwGyE7hIu7kzCRdhdqiEVX60vcYFxSpfSKkXGIp7e+DQXxlzIhMQJSpfjPI0tjUcpM37qSohIgvA2EuYeSsJF2F2aIc3tJ/MtVgtPbngSjVrDcyOe88hTBprU2NJYgV719XW2/S1y1eL23PdPv3BZnjCZ/2nqp2zO38yLI14k1LuNHT2SsUa5lsZZ66GmXFaJeQAJF2FXlXWVZFdmu3W4pBnSeHPbm9za41aGdxiudDnO1VBvO3JFqVtiaSshuBNE9VZmfGE3Ei7CrvaV7AOge6h7TuYbzUYeW/cYCcEJPDDgAaXLcb7GlsajnD+2pQH2fWdbJdaWbkN6KDkVWdhVqiEVH60P8UHxSpdyTv639X8UVBewdMxSvDReSpfjfBlrQB+kTEvjnC1QXSTzLR5CrlyEXe017CUxJBGNWqN0KS326+FfWX5gObOGzKJzUGely1FGxlrbxkklWhqnroSAaOgwyPljC7uTcBF2lWZIc8v9LQXVBTy76Vku7Xgp4xPGK12OMuqqbVcPSrQ0tlpt7YyTrgG1/FjyBPJvUdhNtbmawxWH3W4yv8HSwBMbnsBb682/h/+7bS07/qvszWAxKzPfcmQ7VOTKKjEPInMuwm7SDGlYsbpduCzYu4DkgmTmXj6XIH2Q0uUo53hL4/Buzh87bSX4hEKnEc4fWziEXLkIu0krSUOv0bvVfMXuot28t/09bu99O0OihyhdjrIy1ijT0thqtc23JF2lzFyPcAgJF2E3qYZUEkMS0ard4wdEtbmaWetnkRSaxIx+M5QuR1nGEijYrcwtsaJ9UHJIVol5GAkXYTephlS3msx/actLGEwGXrngFXRqndLlKEvJlsapK8ErQLmNm8IhJFyEXRjNRjLLM91mvuWHzB9YeWglTwx9go6B0jPE1tI4QZmWxmmroNsVoG0j7QzaCAkXYRfppeluM5mfV5XH85uf58q4K7m2i9yKAY61NFbgqqUkA47ullViHkjCRdhFqiEVnVpHl6AuSpdyRvWWev61/l8EeAXw1LCn2u6y479SsqVx2irQekPCZc4fWziUe8y8CpeXakilW0g3dBrXnrv4aNdH7CzaycLRCwn0ClS6HNdwvKVx/PnOHzttFXS9FLz8nD+2cCi5chF24Q7H7G8v3M6cXXO4u8/d9I/sr3Q5ruN4S2OfEOeOW54HuVvllpiHknARrWaqN5FRnuHSK8Uq6yp5fN3j9Anvw1197lK6HNdxvKWxEiu19n0Hai10G+38sYXDyW0x0Wr7S/djsVpc9srFarXy/ObnqairYP7o+W6zD8cpjrc0VmS+ZaVtXJ9g548tHE6uXESrpRpS0aq1JAS7Zs/zVRmr+CHrB54+72k6+Cuw1NaVKdXSuLoYDm+UW2IeTMJFtFqaIY2E4ASX7H+SU5HDi3+8yLVdruWqzlcpXY7ryVgDHYc6v6Vx+ve2W3JJ1zh3XOE0Ei6i1Vx1Mt9sMTNr/SxCvUN5YugTSpfjehrMtpbGSi1B7jQc/COcP7ZwCgkX0Sq1DbUcKjvkkuHywY4PSDWk8soFr+Cnk6Wup8hTqKVxTTkc+l1uiXk4CRfRKgdKD1Bvrad7qGutFNtasJW5u+dyX7/76BPRR+lyXFPmWmVaGu//2dY3RsLFo0m4iFZJNaSiVWnpFqpAD5AmlNeW86/1/2Jgu4FM7zVd6XJcl1ItjdNWQvsBEBTj3HGFU0m4iFZJNaTSJbgLeo1rHDpotVr596Z/Y6o38d/z/4tGrVG6JNfU2NJ4lJPHNcLBX+WqpQ2QBf+iVVztmP2vDnzFr9m/8vqFrxPlF6V0Oa6rsaWxkyfzD60Gs1F6t7QBcuUizlldQx0Hyg40O5lvra93Sj2Z5Zm8svUVxieM5/K4y50ypttSqqVx2iqI7AHhXZ07rnA6uXIR5+xA2QHqLfUnhUtN+n4qf/6ZuuxszDk51OXm0lBcjCYoCF1sLLqYGLxiY/C/8EJ8Bg6026nEdQ11zFo3i3a+7Xhs8GN2+UyPlrHGdkvMmadC19dB+o9w3j3OG1MoRsJFnLM0QxpqlZoE/3jKv/uO0iVLMCWnoAkKwqtrV7w6dcJv5Ei07SJpMJRQl5uDOSeX8pQUDB/NRd+tGyGTJhJ4zRg0/q1bKvzO9nc4UHaARVctwlfna6dv6KGqDbaWxufd69xxM9dBbbncEmsjJFzEOUs1pDI6P5Lcy6+moagY3yFD6PDmGwRccgkqXdNH71stFqo3b6Z0yRIKnnuewldfI+LBBwmZMhmVuuV3ajcf2czCvQt5eODDLrnfxuVkKdTSOOdPiOwJ7Xo6d1yhCAkXcU6sDQ20W7SaC34txPuC82k3/1H0CWd3tphKrcZ/xAj8R4zAXFBAySefUPTuu1Rv+YOoZ55B167dWddRWlPKf7f8l4tjL2Zqz6nn+nXaloy1zm9pbLGAPgAufsq5t+KEYlRWq9WqdBHCvdSXlpL70ENU//EH+ZNGccmT753TFcdf1WZlUfHjj1jNZgIuuQTv7t2bnY+xWq18c/Ab8qvzmdJjCgFeAa2qoc14uz90uRiuft15Yxbtg7WvwiXPQEgn540rFCOrxUSLWM1mcu/7B6a0VF6YoCbs7rtaHSwA+rg4wqZORdeuHaWLF1O6dCmW6uozvufP/D/ZXrSdUbGjJFjO1vGWxs7e35K3zXa0flCsc8cVipFwES1S+PpsTLt2cfiJieyN05AYkmi3z1b7+hJy000Ejx9Pzd69HH31NWr27TvtawuqCliZsZJ+Ef1cap+Ny8tYCyq1bWe+s1gstnBpPxDs8BcR4R7k37Q4axU//0zJwoW0e/QRkiOriA+Kd8jKLN/+/Yl85BF00dEYPvqIsuXLsdTWNj5vtphZnL6YUH0oV3e+2u7je7TMtRDd17ktjUuzwFQCHQY4b0yhOAkXcVYaysrIf+JJAkaPJmTqVNIMaQ5dmaUNCiLsrjsJuv56qrcmUzT7DeoOHwbg+4zvKTYWM7H7RJfsIeOylGppfCTFNpkf7prN5IRjyGoxcVbKVnyNtbaWqKefosHaQHppOlfEXeHQMVUqFf4jR6JP6EbpkiUUvfsutcP7sjlkJ9ckXEd7//YOHd/jKNHS2Go9dkusP8g5b22KXLmIZlktFko/X0LAFVegDQsjozyD2oZap+0p0bWLJOIf9+F18QXoNm5n6iY9Q7Tyt+AWy1jj/JbGFblQddQ23yLaFAkX0azqTZsxH84mZNJEwLZ5EnDoRPq0adNQqVSNv8LbteOiF/7FnLhqQtR+FM1+o/G5P/7446T31tbWEhYWhkqlYs2aNQ6r0e1krnVsS+Oackj/AcpywFxjeyx3G+h8ITLJ9s+WBjCWOGZ84VIkXESzyr9ajr5bN3z69wdsx77EBcadVXfHgoICNmzYQGZmZovHHT16NPn5+eTn5/Pq4lcxWU0seHkl7R56GL+hQwHoEBLCvDlzTnrfihUr8Pf3b/F4Hs0ZLY23/B8smQifjYef/mULmkO/Q0QP0Bw7saEoHb66y1aP8GgSLuKMrFYr1Vv+xH/UqMZNjftK9jXbebKyspLJkyfToUMHzj//fDp37swll1xCbm7uWY+t1+uJiorC4m/hgO8B7rj/DvLz8jGUlxM87noAbuzdm6Wff45h0+bG982fP59bb731HL6tB2tsaXyR48bIXAsDp0Gv8XB4ky1odn0OmWtg79dQehhSFkJl/omwER5LwkWcUV1WFg0GA76DTtwzz67MJi4o7ozvmzBhAosXL8ZisTQ+9ttvv3HFFVdQU1Nz9uM31LE4bTFBBJG5JpOuXbsSFhbW+Pz5M2bQqV07Fj3/HCWffsrh9HTWrVvHLbfccvZfsi043tK4fT/HfH5dtW2TZHgCjJoF922BsXNst8Mq82HZNJh/Bfz5f7YAEh5PVouJMzKlpIBa3XhLzFRvothUTExA0y1qt27dyvfff3/a51JTU1m2bNlZ/fD/9ttvCQoMosHagNlkJjo6mm+//Rb1XzbiqfV67njoIZZ/9hk3pO/n3fkLuOL884mIiGjhN/Vwx1saO2rFlloHQ++xrQ4D222vkoPQ+0YYdh/UVsLGt2Hdq7bHhMeTKxdxRsatyeiTEtEE2I5Xya/OByDaL7rJ92zbtu2Mn9nc88cNHjmYKR9NYdEvi9iyZQuXX345V155JYeP7Xc5bsqUKfy5Zw9VN97Asj27GefvT/nKVWc1RpvgjJbGWi9beB3f+V9bAYZDtg2bxw+t1OohKMZ2hSM8noSLOCNjSgq+Awc1/rO/zjZRXm1u+tyv4ODgM35mc8+D7XZYqaWUEX1HcMPwGxgyZAjz5s2jurqajz766KTXhoWFcc0113D3zJnU6XSMnTmT6mMBZj56tNmxPJ4zWxofP2w0b7vtaqZ9f9uRLxYLmE0w4kHH1yBcgoSLaJK5oABzbi6+g06ES7hPOHqNntzKpifmR48eTUDA6Q+SVKvVjB8//ozjWqwWssqzUKHihoQbGhcSqFQq1Go1JpPplPdMnz6dNWvWMHXqVIIuvJCIGTMAKPvqKyp+/MlprZZdUsYaCIh2bkvjvG3Qrjt4HVtRqFbDhY/BoOnOq0EoSuZcRJOMySkA+A48cSaUWqWmvX97siqymnxfUFAQCxYsYNKkSdTV1Z303H//+1969ep1xnHX5a6j0lxJhFcEVSVVVFFFaWkp7777LlVVVYwZM+aU94wePZqioiICAwMB0EVGHKt9EJWrf6UmLY2QSRNb1CvGY2SstS1BdlYflZoKMOyH/n/rr6PVO2d84RLkykU0yZiSjFdcHNrw8JMeHxI1hF8P/4r5DHsVxo8fz9atW7nrrrsYNWoUU6ZM4ffff+exx87c3z6nIoefMn+inW871q9eT3R0NNHR0QwdOpStW7eybNkyRo0adcr7VCoV4eHheHmdfNaY35DBRNx/P9baGgpnv0HV+g1Y/7KCzeMdb2nsjFtixx3ZbvvdUSvThFuQKxfRJFNyCr6DB53y+M2JN7M0fSmrs1czOn50k+/v06cPH3744VmPV1tfy5L0JUT5R/Hjsh/Rqs/8n+eZ+twFBwef9HzEQw9R8e13lH+9gprUvQRPmIA2KOisa3NbSrQ0LjkEEd3BO9B5YwqXI1cu4rTqS0upPXAAn4GnngmVEJLAwHYDWbB3AWaLfXZaW61Wvj74NRW1FUxKmtRssLSU2suL4HHXE3bnXZgLjlL46msYd+6y6xguydktjWuroHAfxF/gnPGEy5JwEadl2m67tfHXyfy/mjlgJukl6byz7R27jLe1YCsphSlcn3A9Eb6O26PinZRIu0cexjsxkbJlX1C+ahUN5eUOG09xGWuce8T+vu8gZYF0nBQSLuL0jMkpaKOi0HU4/d94+0X2458D/8mCvQv45fAvrRorpzKHrw9+zZCoIQxs5/jTc9V+foTeMoXgG2+iNiuLrImTqNqw0eHjOl1ZNpRmOne+JW0lRPWBwCjnjSlckoSLOC1jcjK+Awc2LgM+nak9pnJ5p8t5ZO0jzNs9D4u15RPluwp38eHOD4n2i+a6rte1puQW8+3bh9Bbb8Wrc2dy7riDgueex3KaZc5uy9ktjeuq4eBq6H7qaj7R9ki4iFNYjEZqUlNPOk/sdFQqFa9c8ArTe03nzW1v8uBvD5JVnnVWYxQZi/j18K8sP7icnuE9uavPXejUzj/MUBsYSMzbb9HuqacoW76czOvHYdq92+l1OETGGoju57yWxgd/hXqThIsAJFzEaZh27oT6+ibnW/5Kq9by4IAHeffid9lVvIsxX4/hzp/v5MesH8kqz6KuwbbPpd5ST15VHuty1/HwmocZv3I8aSVpXB13NRMSJ6BXcA+ESq0mdMpk4ld8hdrfn6wJEyl6512sZjc+Ft5qhcx1Tr4ltgra9YKwLs4bU7gsWYosTmHcmowmKAivLmf/Q+LC2Av5+Yaf+TnrZ5amL+XRtY8CoEJFqHco5bXl1Fttu+Q7B3Xm/v73M6bLGLy13g75DudC37kzcUsWUzznQ4rnzKFq3Trav/IK+s7xSpfWcs5uaVxfC/t/sh1SKQQSLuI0jCkp+AwciErdsgtbvUbPmC5jGNNlDAXVBWRXZJNblcvR6qOE+YQR4x9DTEAMsQGxZ5zLUZJKpyPi/n/gf+EFHHlsFpnjxhH5yCOETJrY4v8/FOXslsYZa22HVXa/1jnjCZcn4SJOYq2rw7RzJxEPPNCqz4nyiyLKL4ohDLFTZc7l06cP8Su+ovDV1zj6wgtU/fYb0f99yX2Oj3F0S+O/S1sJoV0g0nGtr4V7caO/iglnMO3di7WmptnJ/LZA7eND1DNPEzt3LrUHD5Jx7XWUf/ed0mU1r8EMWRuct7+lod62v6X7GOedXyZcnoSLOIkpJQWVjw/e3eVvoMf5jxxB55Xf4D9iOEcefoS8hx6moaxM6bKalrcN6qogfpRzxsveBKYS6CG3xMQJEi7iJMatyfj274dKJz3O/0oTHEyH2bNp/9prVG3YQMa111G10UU3Xjq6pfHfpa2CwA7QfkDzrxVthoSLaGS1WDBu337a88SETdA1V9N55Tfou3Yl5/Y7KHj+BdfbeJmxBuLPd1xL47+yWGzhIrfExN9IuIhGtQcOYKmowHfQYKVLcWm6qChi535EuyefpOzLL8kcN951Nl7WVUPOn85bgpyXApX5skpMnELCRTQybk0GnQ6fvn2ULsXlqdRqQm+ZYtt46etr23j57nvKb7xsbGk8yjnjpa0E33DnLXkWbkPCRTQypiTj07Mnam/X2djo6vSdOxP3+RLC776b4g8+IGvSZGozMpUrqLGlcYLjx7JabeGSdLVzbsEJtyLhIgBbP5WmmoOJM1PpdEQ8cD9xSxZjqaggc9w4ShYtOmMzM4dxZkvjo3ugNEtWiYnTknARAJizs6kvKpLJ/FY4vvEyeNz1HH3+BXLuuBPz0aPOK6DaAAW7nHhLbJVtVVqcNAYTp5JwEYCtfwsqFb4DZDlpa6h9fYl65hliP/qI2v37ybj2Oiq+/945g2ets/3urMMqU1dC4pWg9XLOeMKtSLgIwHaemD4xEU2g9D23B//zR9J51Ur8hg8j76GHyXv4Ecd3vDze0jiwvWPHASg+AEVpcry+aJKEiwBONAcT9tO48fLVV6lav97xGy+d2dI4bRXofKHLxc4ZT7gdCReBubAQc3a2nCfmACqViqAx19g2XnbpbNt4+cKL9t946eyWxmkrIeEy8PJ1znjC7Ui4CEwpKQAyme9Ato2Xc20bL5cts//GS2e2NC7LgSPbZeOkOCMJF4FxazK6Th3RRUYqXYpHa9x4+dVy+2+8dGZL433fgsYLEi53/FjCbUm4CIwpKfgOlP0tzqLv0uXkjZeTp1Cb2YqNl1ar7bBKZ64S63wReMviD9E0CZc2rqG8nNr9+/EdJOHiTI0bLxcvwlJeTub1rdh4WZgK1UXOmcyvKrQdMSOrxEQzJFzaOOO2bWC1ymS+Qnz69m39xsuMtbaWxrFDHVPkX+37zja3k3iV48cSbk3CpY0zpaSgjYhAFxurdClt1omNl/93bhsvM9faDo50RkvjtJUQNwL8whw/lnBrEi5tnPHYeWIq6cWhOP/zzyd+5Tf4DWvBxsvGlsZOmG8xlULmOlklJs6KhEsbZjGZMO3ZI0uQXYg2JIQOb8ym/av/o2rdOjKuvY7qTZuafoMzWxrv/wks9bZTkIVohoRLG2bauQvq62Uy38XYNl6OofPKb/DqHE/29Nub3njpzJbGqSshZohzjpcRbk/CpQ0zpiSjDgxEn+CE3h+ixXTR0XScN492Tzxh23g5/gZMu/ec/CJntTSurYJDq2WVmDhrEi5tmDE5Gd8BA1Cp5T8DV6VSqwmdeott46WPD1kTJ1L03ntY6+ud29L44C9QXyPhIs6a/FRpo6xmM6YdO6U5mJto3Hh5150Uv3+s4+Wmb5zX0jhtFUT1htB4x48lPIKESxtVk5aG1WSSk5DdiG3j5QPELfqMhvIyMu9/iZKcaKxhXR07sLnGNpkvq8REC0i4tFHGrcmovL3x7tFD6VJEC/n060fnFSsI6q7j6EYVOXfdjflooeMGzFhjW5Em4SJaQMKljTKmpODTrx8qL+ki6I7UVhPRPTKIfXwStenpZFx7LRU//OCYwdJW2ZqQRSQ65vOFR5JwaYOsFgumlBS5JebOjrU09h93+4mNl/98iLxHHrVvx8uGekj/zjaRLxttRQtIuLRBtQcP0lBeLueJubOMtRDeDQLbn7zxcu3a5jdetsThDbad+T3klphoGQmXNsiUkgJaLT59+ypdijhXGWtOWoJ80sbL+GMbL198CUtNTevGSVsFQbG2XjFCtICESxtkTE7Bu2cP1L7SotYtlR5usqWxLjqajvPn0e6Jf1H2xRfHOl7uOc2HnAWLBdK+lVti4pxIuLQxVqvVtnlSmoO5r8wztzS2bbycatt46e1t23j5/vu2jZctkbsVqgpklZg4JxIubYw5L4/6o0flPDF3lrH2rFoaH994GXbnHRS/+x5Zkye3rONl2krwi4TYIa2rV7RJEi5tjDE5GQDfAf0VrkSck8aWxqPO6uUqLy8iH3yQuMWLaCgrI3PceEqXLGm+46XVaptvSbra8eeWCY8k4dLGGJOT0SckoAkOVroUcS4aWxq37Dyxxo2XY6+j4D/PkXPX3dSXlDT9hoJdUHZYVomJcybh0saYjjUHE26qFS2N1b6+RD/7LLHz51GXnc3hqbdi3LXr9C8+8AsEdYS481tZsGirJFzakPriYuqysqQ5mDvLWNPqlsb+w4cTt+wLQiZNpPKXXzGmpJx6m0yrh8v+DRpdq8oVbZeESxtiTE4BkMl8d9VghsMb7dLSWBsYSMhNN+HdvTtlK1ZQuX79iScrCqAoHUKlz484d1qlCxDOY0xJQRcbi65dO6VLEefieEtjOx2xr9JqCbrqSgAqV67Cq317vLt2hbxk2x6XyCS7jCPaJrlyaUOMKclynpg7y1gD3kF23y0fOPoKvLp0ofSzz6gvL7eFWFRv0MqhpuLcSbi0EQ2VldSm7ZPJfHeWudY2wW7npcEqtZrQyZMAFWWLPoWyLIgZYNcxRNsj4dJG1KSmotLr5crFXR1vaeygrpOawECCrh9L7aFM6oxeENXHIeOItkPCpY2wNjQQettt6Dp1UroUcS4Ob7a1NI5v/WR+U3x69ULtraa6vF2rVqMJARIubYZpx050HdqjkgMI3VPmGgiIhnDHreC6bdot3LlyOTe9tYhLL7rotK/ZvHkzKpWKbdu2OawO4RkkXNoAi9lMXVYWunZRSpcizlXGGtstsbP4y4HZbKasrKzlYxhL0OgtTOjVi9/WruXw4cOnvGT+/Pn069ePAQNkTkacmYRLG2DOzoaGerw6xytdijgXxhIo2N3sLbEjR45www034OvrS0hICF26dOGzzz5rwTgGVD7+jB42jIigIBYuXHjy00YjS5cu5fbbbz+HLyHaGgmXNqA2IwOVtw+66GilSxHnwnDQ9ntU7yZfUlZWxsiRI1m+fDn1x47Wz8jI4JZbbmHOnDnNj1FbBTXl4B2Ed0QkNw0ZwsKFC0/aub9s2TLq6uqYPHlyq76OaBskXNqAuoxMvOLjUKnlX7dbKs2y/R7S9GKMt99+m8wmjtN/8sknqWmuI2X+TttJyN7BaMNCublnT7KyslizZk3jS+bPn8+4ceMICTnzUf9CgISLx7M2NFCXlYVXfGelSxHnqvQw+IaBPqDJl6xbt67J50pKStizp5lulHkp4B0IGh2a0DDitTqGDx/O/PnzATh06BDr169n+vTp5/QVRNsj4eJh6ktKMO3cienYabfmvCNY62rRd5ZwcVsare1csTP0YPHyOvNu+jM+bzbB0b3gGwrY/kKCRs306dNZvnw5FRUVLFiwgE6dOnHJJZec01cQbY+Eiwcx7d7NkUcfI2vCRLKn3075d99h3LUTc2ERZV9+SfWWP5UuUZyL4E5QWwGm0iZfcuWVVzb5XGxsLD179mz68wt22/bQ+IQB0GAwoA0N4+abb0aj0bB48WI+/vhjbrvtNlnKLs6ahIsHKZw9G21EBJ2//47wGTOoWv0bxe+8S+2+fZhSUjj60ktU/ykB43ZC4my/lzbdovjuu+9m2LBhpzyu0+mYM2cOGs0ZjozJS7EFmM4bgPrSErRhofj7+3PzzTfzxBNPcOTIEaZNm9aKLyHaGgkXD2G1WqnZuYuwu+5EHx9P2PTbqPztN9R6PZGzHqPDW2+h9vam8sefsJrNSpcrWiIiEbz8If2HJl/i5eXF6tWrefrpp0lKSiIqKoqrr76adevWcdVVVzX92TWVcGQHxAzBYrGg1WppMJSgDbNdxdx+++2UlpZy6aWX0rFjRzt/MeHJJFw8RM3OnaiDg9BGRABgPnIEa20tug4d8OndG127SMLuvhvj9u3N908XrsXLD/pOgJSPob6uyZf5+Pjw3HPPkZaWRn5+Pt9++y3nnXfemT/78Abb73EjKSwsJDIoiPrCo+hiYgAYNmwYVquVn376yV7fRrQREi4eor64GN8BA7EYjbZ/LinF/6KLUPv7oTv2N05rbQ3WujrUzUz+Chc0+A6oLoQ9X9rvM+vrION3SoN68N3qdaxZs4YRke1Q+/rh3UcOrhStI83CPITveeehjYpGrdcD4N2zB77DhtFgMKDxsR1CWL1pE949eyhZpjhXkd2hx1j44XFbm+NQO6z+27kYaiqY/t4utu7Yw0MzZzJKo8Z36BDUOmlvLFpHrlw8hMbfH59ePdEEBwOgUqmoO5yFV1wcABU//4xp716Crr5auSJF61z7NviFwRdTbcuHWyNrA2Sug36TWfHtj+Tm5vLk9deDqQa/0ywMEKKlJFw8VH1JCZbSUvRdbH/D9R04kHaPzcJXfnC4L+8guOkTKD4IC6+BspyWf4bVCgd/hZSFEDcS4s8HbIebVv7yC969ezVO5gvRGhIuHqru2FEgXvG2wyq1YWH4nTdU5lvcXVRvmPYdVB2FDy+Afd+fcXPlSaqKYdfnsPdrSLgcBkxtfKp640YayssJvPIMK8uEaAEJFw9Vm5GBtl07NP7+Spci7C1mINy9DmIGwecT4b2hsOVDqC4+NWgazLZGY8vvgPeHgiETBt8JfW4CtW3K1WI0Url6Nb5Dh6JrF6nAFxKeSCb0PVRdRiZeXeTIF4/lGwqTvoCs9bB1Hvz0BPzwGOiDbAdc6gOhLBsqcsFqsS0AuPBx6D8ZvHxP+qjK9etBqyPw8ssV+jLCE0m4eCCL0Yja3w/vHmc48kO4P5UK4i+w/arIh+zNUHbYdopybSXEDrbt7g9PhNihcJpTsevLy6lNTSXw0kvRBAY6/SsIzyXh4oGM27dTvXETIZMmKV2KcJbAaOg1rsVvK3rzTao3/0HYnXc5oCjRlsmciweqXreO+qNH0UrfDXEGNfv2Ufb5UkKn3oLG30/pcoSHkXDxQMbkFHwHDVS6DOHiCl+fjVfHjoTceKPSpQgPJOHiYRqqqqlJS8NnoISLaFr15s1Ur19PxEMPoZLd+MIBJFw8jGn7drBY8B00WOlShIuyWiwUvvoaPn37EnD5ZUqXIzyUTOh7GGNKMpqwMLzi45QuRbioiu9/oCY1lU6LPpPmX8Jh5MrFw5iSU/AdMEB+aIjTstTVUfTGG/hffDG+cutUOJCEiwex1NVh2rUL38GDlC5FuKiyJUsw5+cT+fBDSpciPJyEiwep2b0ba12dTOaL02qoqKD4/Q8IHj8efZcuSpcjPJyEiwcxJqeg9vPDOylJ6VKECzJ8NBdLbS3h//iH0qWINkDCxYMYk5PxGTAAlUajdCnCxZgLCij55BNCb5smh1MKp5Bw8RDWhgZM27fLJK04raK330Ht60vY7bcrXYpoIyRcPERtejqWqirZmS9OUZO+n/Kvvyb8vvukBYNwGgkXD2FMTkbl5YV3795KlyJcTOHs19HFxBBykxzzIpxHwsVDGJNT8O7TG7Ver3QpwoVUb/mT6rXriPznTFTShVQ4kYSLB7BarRhTUvAdKPtbxAm2Y15exbt3bwJGj1a6HNHGyPEvHqAuM4sGgwHfQRIu4oTKH3+kZs8eOn7ysZzYIJxOrlw8gDElGdRqfPr3U7oU4SKsdXUUvvEm/qNG4TdkiNLliDZIrlw8gCk5Be/u3WUlkGhU+vlSzHl5xL7/ntKliDZKrlw8gDE5WZYgi0YNVVUUf/ABQdePRZ+QoHQ5oo2ScHFz5oICzHl5cp6YaGSYOxeLyUTE/fcrXYpowyRc3JwxOQVAJvMFAOajRylZ+DGhU6eii4pSuhzRhkm4uDlj8la8OndGGxqqdCnCBRS/+y5qb2/C7rxD6VJEGyfh4uZMKSlynpgAoPbgQcqWf0X4jHvRBAQoXY5o4yRc3Fh9aSm1Bw5KczABQOHrs9G1b0/whAlKlyKEhIs7M23bBiBXLgLj1q1U/f47Ef+ciVqOeREuQMLFjRmTU9BGR6Pr0EHpUoSCrFYrR197De+ePQm88kqlyxECkE2Ubs2YkiKrxASVP/1Mzc5ddFy4EJVa/r4oXIP8l+imLNXV1OzdK7fE2jir2UzhG7Pxu+B8/M4bqnQ5QjSSKxc3Zdq5ExoaZGd+G1f6xReYs3OIefttpUsR4iRy5eKmjMkpaEJC8OrSRelShEIaqqopfu99gsaOxTsxUelyhDiJhIubMiYn4zNwgByl3oaVzJ+HpbqaiAfkmBfheiRc3JC1rg7Tzp3SHKwNMxcWYliwkNCpt6CLjla6HCFOIeHihkx792KtrZWVYm1Y8bvvofbyIuzOO5UuRYjTknBxQ8bkZNS+vnh3T1K6FKGA2owMypYvJ+yee9AEBipdjhCnJeHihkzJKfj064dKK4v92qLC2bPRRUURMnmS0qUI0SQJFzdjbWjAuG2bnCfWRhm3baPq19VEzHxQjnkRLk3Cxc3UHjiApbJSmoO1QVarlcL/vYq+R3cCr75a6XKEOCO5r+JmjMkpqHQ6fPr0UboU4WSVv/yCaccOOs6fJ8e8CJcn/4W6GWNKMt69e6P29la6FOFEVrOZotlv4DdiBH7DhytdjhDNknBxI1arFWNyspwn1gaVLV9O3eHDRD7ysNKlCHFWJFzciDk7m4aiYpnMb2Ms1dUUvfseQdeOwbt7d6XLEeKsSLi4EWNyMqhU+PTvr3QpwokMCxZiKS8n4oEHlC5FiLMm4eJGjMkp6JOSpD96G1JfVIRh/nxCbrlFmsIJtyLh4kakOVjbU/T++6i0WsLvkmNehHuRcHET5qOFmLOzZTK/DanNyKTsi2WE3303muBgpcsRokUkXNyEKSUZQJqDtSFFb7yBtl0kIVMmK12KEC0m4eImjMkpeMXFoQ0PV7oU4QTG7dup/OUXIh54ALVer3Q5QrSYhIubMCYn4yNXLW2C1Wql8NXX0CclETRmjNLlCHFOJFzcQEN5ObUHDkhzsDai6rffMG3bRuTDD6PSaJQuR4hzIuHiBozbtoHVKpsn2wBrfT2Fr8/Gb/gw/EaOULocIc6ZhIsbMCYno23XTvY5tAFly7+iLiODiIcfRqVSKV2OEOdMwsUNmJJT8B04UH7YeDiL0UjRu+8QeM01+PTsqXQ5QrSKhIuLs5hMmPbulVtibYBh4UIsZeVEzHxQ6VKEaDUJFxdn2rkT6uulOZiHqzcYKJk7j5BJk/CKiVG6HCFaTcLFxRmTU1AHBaHv2lXpUoQDFb/3Pmg0hN1zt9KlCGEXEi4uzphi698inQc9V11WFqVffEHYXXeiDQlRuhwh7EJ+Yrkwq9mMacdOOU/MwxW++Rba8HBCb7lF6VKEsBsJFxdWk5qK1WSSyXwPZtq5k8off7Qd8yKtq4UHkXBxYcbkFFQ+Pg7tPjht2jTGjh0LwKhRo5g5c+Ypr/n6669lGbQDNB7z0q0bQdddq3Q5QtiVVukCRNOMycn49OuLSqdTuhThAFW/r8GYnEzs/30ox7wIjyNXLi7KarVi2rat2eZgdXV1PPvss3Tu3Bk/Pz+GDBnC8uXLnVSlOFfW+noKZ7+O79Ch+J1/vtLlCGF3Ei4uqr6oiIbycryTkpp8jcVi4dprr+W5554jMzMTo9HI1q1bueGGG3jnnXecWK1oqfKvv6bu4CEiH3lEbjkKjyTh4qLMuXkA6GJjm3zNl19+yU8//XTa52bNmkVpaalDahOtYzGZKHr7HQKvugqf3r2ULkcIh5BwcVHm3BwAvM5wWOUvv/zS5HMmk4mNGzfavS7ReiUff0J9aSkR/5ypdClCOIyEi4uyWiy2/3GGzZOW4685x+f/LjAwkPLy8lMeLysrIzAwsEWfJU6vvqQEw0cfETJxAl5nuCoVwt1JuLio4z94zHl5Tb7moosuavI5vV7PsGHDWjRmUlISycnJpzy+detWEhMTW/RZ4vSKP5gDKhXh996rdClCOJSEi4vSHTu8sC47p8nXTJgwgfObWGn0zDPPEBER0aIxZ8yYwaFDh7jvvvvYuXMn+/fv57333mPevHk8+uijLfoscaq6nBzKv/+O8Bkz5JgX4fEkXFyUNiICTXg41Rs2NP0arZYffviBBx98kJBjP6y6devGvHnzeOKJJ85qHIvFglZr2+4UFxfH+vXrOXToEJdffjmDBw9m4cKFLFy4kBtvvLH1X6qNM+3YQeiUWwiZMlnpUoRwOJXVarUqXYQ4vcK33qL0k0/punYtGn+/Zl9vNpvRtXDD5ejRo+natSvvvvvuuZYpzkJdTg7FH35I0Nix+DWzd0kITyBXLi4s5KabsJhMlK9YcVavb0mwlJaW8t1337FmzRouvfTScy1RnAWr1Ur5qm/RBAXh27+/0uUI4RQSLi5MFx1N0HXXUfTGG9RmZNr1s6dPn87dd9/Nww8/zHXXXWfXzxYnq01Lo+7QQQKvukqOeRFthtwWc3ENVdVk3XQTKo2auKVLUfv6Kl2SaAGrxULh67NR+/oQPmOG7MYXbYZcubg4jb8fMW+9SV1uHtl33oX5aKHSJYkWMG5Npr4gn6AxYyRYRJsi4eIG9AkJdJw3D3NODpnjxlG9aZPSJYlmFBQU8I8ZM+hx5Wg6v/02XUaMYMyYMaxevRqwrcx78803T3nfv//9b/r16+fcYoVwAAkXN+E7oD/xK75C3y2B7Om3kzVhIuUrV2KprVW6NPE3WVlZDBw4kNXff88TI89n+/r1/Pjjj1x00UXcd999SpcnhFNIPxc3og0Lo+PcuVT+9htlS5Zw5LFZqJ79N/qEBLwTE9EnJdp+T0xEExCgdLluy2KxsGrVKjZs2IBer+fKK69kxIgRZ/3+GTNmoAJW3XgT4cOGEXzeeQD07NmT6dOnO6hqIVyLhIubUWk0BF52GYGXXUZtRiZVa9ZQm56Oafduyr7+GsxmAHQdOqBPTMSnbx/8R12ENjwMTUgIqjOcVSagqqqKMWPGsGbNmsbHXnzxRe655x4++OCDZt9fUlLCjz/+yNOTJ+Or1RJw2cnLvIODg+1csRCuScLFjek7x6PvHN/4z9a6Omozs6hN30dNejq1+9Ip/WIZdTm5aIKDUGl1aMLD0EVEoo2MQBsejjYyErVer+C3cC2PPfbYScFy3Jw5cxg4cCB33HHHGd9/8OBBrFYrsZVV+F980RmvIGfNmsVTTz110mN1dXX06NHjnGoXwpVIuHgQlZcX3ond8E7sRtBfHq8vLqbm4EHqDh6i9uABKr//gdrDWWCuB2z7abwSEvBOSMCra1e8uyWg69ChzV3l1NfXs3Dhwiafnzt3brPhcnxlv9pbj/8FF5zxtY8++ijTpk076bG3336bdevWnVW9QrgyCZc2QBsejn94OBy79w/Hr3IyqU1Pp2ZfOrX79lHy6ac0FBcDoPL1xTshAX1SEvrEbngnJaHv1g2Nv79SX8PhSkpKMJlMTT6fm5vb7Gd08vVFBWSHhDR7RRgeHk7Xrl1Peiw0NPSsahXC1Um4tFG2qxzbAoCga088Xl9cbAub9HRq0vdh2rGDsuXLof7YVU5MjG3hQLdjCwiSktDFxHjEVU54eDghISFNdvDs1q3bGd9vtVrRbNzIqG7dmLtqFY++9hp+fiefCVdWVibzLqJNkHARJ9GGh+M/Mhz/kSdWR1nr6qjNyDhxlZOeTunSpTQYDACofX3Rd+uGPjER76RE9InHr3KaP2zTlajVah544AH+85//nPb5mTNnnvH9tenp1B04wHtvvcXFt93GkCFDeO655+jTpw/19fX88ssvfPDBB6SlpTmgeiFci4SLaJbKywvvpCS8k5II+ssxZPVFRdSk729cQGDavv3kq5zYWNsttcSkxmXSrn6V8/TTT5Obm8u8efMaH9PpdLzyyitce+21Tb7ParFQ/u13eMV3JumKK9i2bRsvvvgiDz/8MPn5+URERDBw4MCzWnEmhCeQs8WEXVnq6qjLyKBm3z5q96VTu992tdNQUgIcu8pJTPzLPE6iS17lpKWlsXHjRry8vLjsssuIjo4+4+urt26l7PPPCb//fvRxcc4pUggXJuEiHM5qtdLQOJezz3a1s28ftZmZJ65yOnbEO7Gb7ZbaseBxlxVrFrOZoy+/gldsDGF/W/0lRFsl4SIUY6mro+7QoZMWENTuS6fh2IS62s/PNpeTlGi7tZbYDe9u3VD7udZVTvk3K6nasIHIRx5B1y5S6XKEcAkSLsKlWK1W6ouKqE1P/8sCgn22fjYNDcDxq5y/HHdz/CpHgVOHjbt2UfrxxwRdd12z+1qEaEskXIRbsNTVUXfwYOMtNdsJBPtoKCsDjl3lHF+t1u3Y7wkJDr3KqcvKovijj9B3SyR06i1ypL4QfyHhItyW1WqlvrDo2KKBEwsIGq9yVCp0HWNP3FJLSkKfmISuQ/tWBYHVaqX6jz8oX/E1XjExhN11J2pvbzt+MyHcn4SL8DiW2lpqDx6ktnGZ9P6Tr3L8/W1XOccWEDRe5TTT5dNqsVC95U/MubmYdu7Ab/BgAq++GpVWVvQL8XcSLqJNsF3lFJ60EbQmfR91mVmNVzleHTuiT0zEKz4er9gYdDExWM1m6nJyMGfnUPnbbzSUlRE+YwY+gwfh27On0l9LCJcl4SLatMarnL/syanLzqa+oACO/9HQatF1aI9P376ETJ6CT5/eMr8iRDMkXIQ4DUtdHfVHjqDS6dBGRaHSaJQuSQi3IuEihBDC7lx/+7MQQgi3I+EihBDC7iRchBBC2J2EixBCCLuTcBFCCGF3Ei5CCCHsTsJFCCGE3Um4CCGEsDsJFyGEEHYn4SKEEMLuJFyEEELYnYSLEEIIu5NwEUIIYXcSLkIIIexOwkUIIYTdSbgIIYSwOwkXIYQQdifhIoQQwu4kXIQQQtidhIsQQgi7k3ARQghhdxIuQggh7E7CRQghhN1JuAghhLA7CRchhBB29/9vRbb/woh/fAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "H.remove_node('SantaClaus')" + "plt.subplots(figsize=(5,5))\n", + "hnx.draw(H.restrict_to_edges([0, 1, 2, 3]))" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hnx.draw(H)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Building sub-hypergraphs\n", - "There are two methods to look at different kinds of sub-hypergraphs formed from specific sets of nodes (and all edges that they are included in) or sets of edges (and all nodes contained in the edges): `restrict_to_nodes` and `restrict_to_edges`." + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplots(figsize=(15,5))\n", + "H_restrict_nodes = H.restrict_to_nodes(['JA', 'FN', 'JV', 'MA', 'BM', 'TH'])\n", + "hnx.draw(H_restrict_nodes)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 38, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: ['FN', 'TH'],\n", + " 1: ['TH', 'JV'],\n", + " 2: ['BM', 'FN', 'JA'],\n", + " 3: ['JV', 'BM'],\n", + " 4: ['JV', 'BM'],\n", + " 5: ['TH'],\n", + " 7: ['MA']}" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "H_restrict_nodes = H.restrict_to_nodes(['JA', 'FN', 'JV', 'MA', 'BM', 'TH'])\n", - "hnx.draw(H_restrict_nodes)" + "H_restrict_nodes.incidence_dict" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Notice that edges in these subgraphs restricted to nodes may be smaller than the edges in the original hypergraph. For example, in `H` edge 3 contains BM, CH, JU, and JV but in this sub-hypergraph it only contains BM and JV because CH and JU were not in our node set." + "When hypegraphs are very large and disconnected, often it is beneficial to isolate those components made of a single node and a single edge.\n", + "As in the above example we identify these **singleton** components using the `singletons` method and remove them with the `remove_singletons` method." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[7]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "H_restrict_nodes.incidence_dict" + "H_restrict_nodes.singletons()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "H_restrict_edges = H.restrict_to_edges([0, 1, 2, 3])\n", - "hnx.draw(H_restrict_edges)" + "plt.subplots(figsize=(5,5))\n", + "H2 = H_restrict_nodes_remove_singletons = H_restrict_nodes.remove_singletons()\n", + "hnx.draw(H2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Another way to get a sub-hypergraph is to remove \"uninteresting\" components. This may be different depending on what you are using as your data set to build a hypergraph. But one simple and almost universally uninteresting component is an \"isolated singleton\". These are edges of size 1 which are not duplicated. They can be removed from a hypergraph using `remove_singletons`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "H_restrict_nodes_no_singletons = H_restrict_nodes.remove_singletons()\n", - "hnx.draw(H_restrict_nodes_no_singletons)" + "Finally, sometimes we care only about top level hyperedges that are not contained in any other hyperedge, although they may intersect others. These top level hyperedges are called \"toplexes\" and can be found using the `toplexes` method, which returns a new hypergraph consisting only of the toplex hyperedges, note the toplex hypergraph we obtain need not be uniquely defined." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "If the isolated singletons are actually interesting to you then you can use the `singletons` method to find them. This returns a list of isolated singleton edges." + "The `toplexes` method on the above hypergraph removes 2 hyperedges. While HNX will pretty consistently remove the same two edges, there are actually 2 distinct hypergraphs made up of toplex covers of the above hypergraph." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "H_restrict_nodes.singletons()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, sometimes we care only about top level hyperedges, those which are not contained in any other hyperedge (though they may intersect others). We call these toplexes and they can be found using the `toplexes` method. This returns a new hypergraph consisting only of the toplex hyperedges." + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax = plt.subplots(1,3,figsize=(15,5))\n", + "hnx.draw(H2.toplexes(),ax=ax[0])\n", + "ax[0].set_title('toplex method')\n", + "hnx.draw(H2.remove_edges([3,5]),ax=ax[1])\n", + "hnx.draw(H2.remove_edges([4,5]),ax=ax[2])\n", + "fig.suptitle('Can you see the differences?',fontsize=15);" ] }, { @@ -744,12 +1199,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# The only non-toplex in our Les Mis example is edge 3\n", - "\n", - "H_top = H.toplexes()\n", - "hnx.draw(H_top)" - ] + "source": [] } ], "metadata": { @@ -768,7 +1218,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.8.16" } }, "nbformat": 4, diff --git a/tutorials/Tutorial 10 - Contagion on Hypergraphs.ipynb b/tutorials/Tutorial 10 - Contagion on Hypergraphs.ipynb deleted file mode 100644 index 3d7115dc..00000000 --- a/tutorials/Tutorial 10 - Contagion on Hypergraphs.ipynb +++ /dev/null @@ -1,109271 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "37301602", - "metadata": {}, - "source": [ - "## Modeling Contagion with Hypergraphs\n", - "This work is based on the paper [The effect of heterogeneity on hypergraph contagion models by Nicholas Landry](https://aip.scitation.org/doi/10.1063/5.0020034)\n", - "The SIS and SIR simulations will each take several minutes to run." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "7e629e88", - "metadata": {}, - "outputs": [], - "source": [ - "import hypernetx as hnx\n", - "import networkx as nx\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import random\n", - "import time\n", - "import hypernetx.algorithms.contagion as contagion\n", - "from hnxwidget import HypernetxWidget" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "4aaa036e", - "metadata": {}, - "outputs": [], - "source": [ - "n = 1000 \n", - "m = 10000 \n", - "\n", - "hyperedgeList = [random.sample(range(n), k=random.choice([2,3])) for i in range(m)]\n", - "H = hnx.Hypergraph(hyperedgeList, static=True)" - ] - }, - { - "cell_type": "markdown", - "id": "98025068", - "metadata": {}, - "source": [ - "## Initialize simulation variables\n", - "- $\\tau$ is a dictionary of the infection rate for each hyperedge size\n", - "- $\\gamma$ is the healing rate\n", - "- $t_{max}$ is the time at which to terminate the simulation if it hasn't already\n", - "- $\\Delta t$ is the time step size to use for the discrete time algorithm\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9d049e18", - "metadata": {}, - "outputs": [], - "source": [ - "tau = {2:0.01, 3:0.01}\n", - "gamma = 0.01\n", - "tmax = 100\n", - "dt = 1" - ] - }, - { - "cell_type": "markdown", - "id": "5f357f3b", - "metadata": {}, - "source": [ - "## Run the SIR epidemic simulations\n", - "- The discrete SIR takes fixed steps in time and multiple infection/healing events can happen at each time step.\n", - "- The Gillespie SIR algorithm takes steps in time exponentially distributed and at each step forward, a single event occurs\n", - "- As $\\Delta t\\to 0$, the discrete time algorithm converges to the Gillespie algorithm. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "26740308", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "512.8926649093628\n", - "161.48380184173584\n" - ] - } - ], - "source": [ - "start = time.time()\n", - "t1, S1, I1, R1 = contagion.discrete_SIR(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax, dt=dt)\n", - "print(time.time() - start)\n", - "## ~512.8926649093628 sec\n", - "\n", - "start = time.time()\n", - "t2, S2, I2, R2 = contagion.Gillespie_SIR(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax)\n", - "print(time.time() - start)\n", - "## ~161.48380184173584 sec" - ] - }, - { - "cell_type": "markdown", - "id": "4ec4465a", - "metadata": {}, - "source": [ - "The Gillespie algorithm is much faster in many cases (and more accurate) than discrete-time algorithms because it doesn't consider events that don't happen. Instead, it calculates when the next event will occur and what event (infection, recovery, etc.) it will be." - ] - }, - { - "cell_type": "markdown", - "id": "e755591e", - "metadata": {}, - "source": [ - "## Plot of the results\n", - "- Dashed lines are the results from the discrete time algorithm\n", - "- Solid lines are the results from the Gillespie algorithm\n", - "- Plots of the numbers susceptible, infected, and recovered over time\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "aa953ffd", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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buXLlADhx4kS27ezatYuwsDA8PT3T7itRogRXr1696z5XV9e0n4Wt0CN4E5nRbQa/Hv6VQ1GH8tsU25OUBAEBcPly5uO//QatWsHixRAUlC+maTRFmpxG1G5uOZ/39c35fLlyVhmx30mpUqUwDIOrV6+afM/Bgwfx8vKiVKlSWZ6fM2cO48aNY+XKlSxbtozXX3+d3377jc6dO5vUvru7e6b3ycnJdO3alY8++uiuawMCArJtJzk5mQYNGvDTTz/ddc7HxyfT+ytXruDn52eSfZZCO3gzqOFbo/iM4A8cgPvvh169wNEx3bmvXQvPPw97U5T9tm2DMWNk2hAgIQEiIqBaNXj0UTm2bx/UrWvzj6DRaPIfJycnatWqxcGDB+mS1ZLCHURERPDjjz/y+OOPY2eX/SRz/fr1qV+/Pq+88goPP/ww8+bNo3PnzjRs2JB//vnHLBsbNWrEzz//THBwMI6Ojtl+jqSkpLvuW7hwIb6+vjlG+Z84cYJbt27RqFEjs+zKK3qK3kSUUhyOPEzI+ZD8NsU6HDsm0+73ifY+depAdDR8+y189ZUc278f2reHkBDYuRMOHoSmTWH5cujbV7ann4ZXXoGlS+WeLVugXj3o1w8iI/Plo2k0mvylc+fObN68+a7jiYmJXLhwgYiICA4cOMA333xDixYt8PHx4b333suyrbCwMF599VX+/fdfTp06xbp169i7dy+1atUC4KWXXuK///5j+PDh7NmzhyNHjjBr1qxM0/h3MmrUKGJiYujTpw/bt2/n5MmTrFmzhuHDhxMbGwtAhQoV2L9/P0eOHCEqKoqEhAT69+9PQEAAPXr0YMOGDYSFhbFx40ZefPHFTJH0mzZtolKlSlStWjUvP0az0Q7eRAzDoIRLCW4n3WbOf3Py2xzLMm+ejLhBnHcW617s2gW1asGSJVC7tgT/+PtLkE9qdOqBAxK088QT8N13cuy+++Ctt+S+WrVg4UJZz9doNMWGYcOGsXLlSq5cuZLp+JEjRyhTpgxBQUHcf//9zJkzh+HDh7N79+5MwWwZcXNz4+jRo/Tq1Ytq1aoxYMAA+vfvn5ZW16BBA9asWcPhw4dp3rw5zZo146effsp2ZA5QtmxZtmzZgp2dHQ899BC1a9dm1KhRODs74+zsnPYZatasSZMmTfDz82PLli24ubmxceNGKlWqRK9evahRowYDBgzg6tWrlCxZMq39hQsXMmzYsLz+GM1HKVVktsaNGytr8vra1xWTUV1/6GrVfmzKlStKicu9e7t8WankZNlOnFCqQwc5Xru2UsuXy/EjR7K+t2fPzP3s26dU06Zyrm/f/PmsGk0B5uDBg/ltglXp06ePmjJlSn6bYXP27dun/P39VXR0dK7byOm7AYSobHyiHsGbQbdqIrSwK2JXPltiIXr0gIyBIIcPSzAdQHi4nDMMuHhRptl37JDp+j17oFs3OVetWvoIHmQKH+CXX+D27fTjderAv//Cxx9DqppV6uNAat9Tp8KTT+oRvkZTBJk2bRpeXl75bYbNOX/+PPPnz6dEiRI271sH2ZlB4zKNcbRz5OL1i8QnxeNk75TfJuUepWDZsszvAX7/HTZtguBgOWYYULq0OOauXbOOlre3l8C62FioUgVSA2NcXMDBAWbOhJIloUYNeOGF9PsWLIAZM+DaNZneB3j9dekzMRGefRb695flgKyWDTQaTaGhfPnyjB07Nr/NsDmdOnXKt771CN4MHO0d6V27NwrF1jO2VSSyON9/n/76YIYqec2awfjxIqLRpo04fIBnnsk5Fa50aahaVRxxcjKkCkYkJsKQIfD447IGf/Zs+j12diK24esLn38ur995R84dPSp9t28vEfo2zh/VaDSawo528GYys9tM3BzdWHRgUX6bkjf27JH9f//BHSpPKAWjRsHmzZASYGIWhiEPCVWyUPz79tv01/37i1Nfvx6eey7zA0StWnD6NLz7rkz3t2+vo/A1Go3GDLSDNxN3J3calm5YuEfwSUkybe7rCw0a3H1+9myYOxfefBMeeih3fdjZSepdxtC7++4TNTzDgA0b7t2Gqyu89ppE4IeGwqBBubNFo9FoiiHawZtJskpm5/mdHLl8JL9NyT3ffw83bsja+Z3s3g2jR0OnTjBxomX7rVQp/XXbtiKSYwqPPy6j/P/7P3kfFZU5sE+j0Wg0d6EdvJnYGXZUKlmJm4k3uZV4K7/NMZ/r19NHwjt23H3+998lv/2HH7J+AMgL06alO2mAYcMyR9rnRLNmUKGCvB4xQh4WPvjgbvlcjUaj0QDaweeKJmWk7OG/Z/7NZ0tywSefyL5tWyhf/u7zb70lo3hfX8v3Xb68rO0rBbNmiTLenev/pjBggAT0vfqq6GmPHavX5zUajeYOtIPPBan58F+HfJ3PluSCiRPB2xvWrct8/PhxcbhgHed+J4MHyz4sDMaNE+W7M2dMu7dbN9HE37tX8ua//BKmT7eaqRqNRlMY0Q4+F/Su3RsHOwc2nd6U36aYR6oWc3T03eeef17S4myVjmYYIpEL8NlnkkpXvjx8+CG89BJcuHDvNurWlQeD/fvh5Zfl2Pr16Tr4Go2mwNOuXTvmz5+fq3sNw2DJkiUWtsg2NG3alF9++cWqfWgHnwsMw+CRao8Qezs2rW5xgef0aRGvAfjzz8znNm6EFSvEsbq62s6mp5+WSPunn5apexBH/dFHMGmS6e3UqCFCOiAZAH36yGfSaDRWZ+DAgRiGgWEYODg4UL58eUaMGGFSedg//viDM2fO0L9//7RjFSpUSGvPxcWFcuXK8dhjj7F8+fK77o+IiKB79+4W/TzmktuHjDfffJNXX32V5ORkK1glaAefS1qVb8WNhBtE34rOb1NMI9W5A2SsmayUONWgIFnLtjVVqshI/v/+D44ckcC/Rx+VfPmMSnum8vnnULGiRN6fOGFxczUazd08+OCDREREEB4ezqxZs1i+fDkjR468532fffYZAwcOxP6OgN6JEycSERHB0aNH+emnn6hQoQKPPfYYo0ePznRd6dKl04rBWJqEhASrtJtKly5diI2N5a+//rJaH9rB5xIfV9FwX31ydT5bYibJyelSsiAiMtu3S366LUfvWVGtmuTKf/st1K8v6nnnz5vXRsmSMhuRnAzdu0NMjHVs1Wg0aTg7O1O6dGmCgoLo1KkTffr04e+//87xnsjISNasWZPlCNzT05PSpUtTvnx5WrVqxSeffMJXX33Fl19+yboM8UN3jp6nTJlCcHBwmj1PP/102jmlFB9//DFVq1bF2dmZoKAgXnvtNQDCw8MxDIOFCxfSvn17XF1dmTlzJgBz5syhVq1auLi4UK1aNT755JO0UXeFlMyeXr16YRhG2nuA5cuX07hxY1xcXKhYsSKvv/468fHxaeft7e3p0qULCxcuNPGnbD7aweeSmr4S/f3D3h/y2RITiI0FNzeZBr9T0/38eXGqGf4Q8h1fX3HyFy7AmDHmF5+pWhV+/VWm/6dNs46NGo0mS06ePMnKlStzLM8KsHnzZpydnalTp45J7Q4ZMoSSJUtmu279yy+/8NFHH/HVV19x7NgxVqxYQdOmTdPOT5gwgbfffpvXXnuNAwcOsHjxYsqVK5epjddee42RI0dy8OBBHn30Ub799lsmTJjAlClTOHToEB9//DEffPABX331FQA7d+4E4NtvvyUiIiLt/apVq+jfvz+jR4/mwIEDfPfddyxZsoQJEyZk6q9p06ZsMEX0K7dkV2bOGhvwPHAA2A8sBFyAisB24DiwCHBKudY55f3xlPMV7tW+tcvFZiQ5OVkxGVXnqzo26zPXzJ0rWnJbtmR9PjHRtvaYyssvi92BgUr995/592/cqFR8vLxOTraoaRqNpcmqJGibNndvX34p527cyPr8nDlyPjIy6/M//STnT5+++1xuGDBggLK3t1fu7u7KxcVFAQpQ06dPz/G+Tz75RJUvX/6u48HBwerDDz/M8p5mzZqphx9+OO09oBYvXqyUUurjjz9W1apVU/Gpf/MZiI2NVc7OzmrGjBlZthsWFqYA9dFHH2U6Xq5cOTV//vy77K5Zs2aWNqTywAMP3FUad+nSpcrd3V0lZ/hf9PvvvyvDMFRCQkKWdqVS4MvFGoYRCIwBmiil6gD2wJPAB8AnSqkqwFVgSMotQ4CrKcc/SbmuwGAYRlpluQLPb7+JSEyLFunHdu2SUS5YXtDGUrzxhhSxOXcOHnggPQvAVB54ABwd4coVaNr07uBCjUZjEVq3bk1oaCg7duzgueeeo0uXLowZMybHe27evImLi4tZ/SilMLKpLNmrVy9u3bpFxYoVGTJkCIsXL+Z2ipDWwYMHuX37Nh06dMix/SZNmqS9joyM5MyZMzzzzDN4eHikba+++ion7hHfs2vXLqZOnZrpvn79+nHjxg0uZMgQcnV1RSnFrVvWEU2zdblYB8DVMIwEwA2IANoD/VLOzwMmAzOAHimvAZYA/2cYhpHyxFIg8HL24uqtqzl+6QoEoaGiBJdq45Ur0LOnTH0//HD+r71nh6enBN5t2QJduki9+JR1MbO4dEnS/7p2lQI3n35qm1x/jSaPrF+f/Tk3t5zP+/rmfL5cuZzPm4ObmxtVUopLff7557Rr1463336byZMn52Cfr0mR9qkkJSVx9OjRTNPuGSlXrhxHjhxh7dq1rFmzhhdffJG33nqL7du3m9yHu7t72uvUdfavv/6ali1bmtxG6r2TJk2iV69ed53z8/NLe33lyhVcXFzw8PAwq31TsdkIXil1DvgIOI049hhgFxCtlEoVFj8LBKa8DgTOpNybmHJ9KVvZawr+7v4kJidy+WYBlkuNiYHwcKhXT94nJ8P//idr74sXF1znnoqXlzyEjB0L33wDzZtLAZqrV6WGvCnPezVqiDrfpEnw888ymzFmjPwsNBqNxZk0aRIffPAB53MIkm3YsCGRkZFERUWZ1OasWbOIjo7miSeeyPYaFxcXunbtyieffMLOnTs5cOAAW7ZsoWbNmjg7O7N27VqTP0NAQABly5blxIkTVKlS5a4tFUdHR5KSkjLd26hRIw4fPpzlfQ4O6ePq/fv306hRI5NtMhebjeANwyiJjMorAtHAYiCXpcoytTscGA5QPivpVSvSNLApV25ewdetAI8GU9Xp6teX/XvvyVT1l1/KtHVh4Y03JLd9+3bZ3n9fjrdrB8OHQ9++4OSUvba9kxNMniy15T/4ACIi0rMJDh3KnWSuRqPJkrZt21KrVi3eeeedtIC0O2nYsCH+/v5s3ryZRx99NNO52NhYLly4QEJCAmfOnGHx4sV88cUXjB49mjZt2mTZ3ty5c0lMTKRZs2Z4eHiwaNEiHB0dqVq1Kp6enowdO5bXXnsNZ2dnWrduzeXLl9m1axcjRozI9nO89dZbPPfcc3h7e9OlSxcSEhLYvXs3586dS4vAr1ChAmvXrqVNmzY4OztTsmRJJk6cSLdu3QgODqZ37944ODiwf/9+duzYwbQMgb+bNm3iodxW7DSF7BbnLb0BvYDZGd4/jUzFRwEOKcdaAKtSXq8CWqS8dki5zsipD1sG2Sml1MR/JipjsqESknIOkMhXfvhBAtUOHlTqwAGl7OyU6tu38AadHT8uQYOOjkoFBSnl5ZWxIK1SX30l1yUl5dxO6uc/cUIpe3ulXn9dqXsEumg01iSnQKqCzIABA1TXrl3vOv7DDz8oJycnFR4enu29r776qnriiScyHQsODk4L1HNyclKBgYGqR48e6vfff7/rfjIEuC1dulQ1b95clShRQrm5uakmTZqo5cuXp12blJSk3nvvPVWxYkXl6OiogoKC1IQJE5RS6UF2O3fuvKuPH3/8UTVs2FA5Ozsrb29v1apVK7Vw4cK088uWLVNVqlRRDg4OKjg4OO34qlWr1P33369cXV2Vp6enaty4sfriiy/Szp89e1Y5OjqqM2fOZPvzSSW3QXa2dPDNkAh6N8BA1tufQ0byT6Zc8zUwMuX1KODrlNdPAj/fqw9bO/ivd36tmIya/m/O0aL5Sr9+Srm6KhUbK+9/+in9dWHmxg3Znzql1NSpSp08qVS7dkq5uChVqpRSTk5Kff/9vduJi1Nq6FD5U2jTRqnz561qtkaTHYXVweeFixcvqlKlSqmTJ0/mtyk2Z/z48WrYsGEmXVvgo+iVUtuRYLndwD5k/f8b4BXgBcMwjiNr7LNTbpkNlEo5/gLwqq1sNZVALwkXWHq4gGqfnz8vUTRNm0JqEEefPumvCzNubrIvXx4mTBD1uoULJVPg8mWIj5dqc/eSrHV1lZz7efNg505o0EAK2Wg0Gqvj7+/Pd999x2lzM2SKAP7+/rz99ttW7cNQBScoPc80adJEhYSE2Ky/3RG7afxNY8p6luXcC+ds1q9J/PWXRJ4DvPuuVJA7elR03gtqWpwlUEqyBiIjpazs5cuSZpchcjVbDhyAXr0k0v71161uqkaTkUOHDlFTx4JosiCn74ZhGLuUUk2yOmfrNLkiRVnPsgBcuH6BhKQEHO1zVm6yKanOHeDFF6FhQ5FxLcrOHSQVsGFDeb1smcxerF0rZWXvRe3aEBKSnk64ZAmsWiXBeYGBOd6q0Wg0BQ0tVZsHAtwDcHd0J1klExYdlt/mpPPuu+mvly2Dgwdly1CxqVjQoAE4O0s6oKkzVW5u6amDJ0/K1H3dunD4sNXM1Gg0GmugHXweMAyDWn61cDAcuHTjUn6bk05q8YKICCm48sMP4OAg08/FCUdHePNNUexr3BgSE+99T0Zefhn27ZN2uneX6X6NRqMpJGgHn0fuL38/9nb2tCrXKr9NES5eTM99L10akpLE4T/0UPFUb5swQZzzf/+Jox42LF2y1pRRffXqsHSpyOQ+8QRYuYSkRqPRWArt4PNIkFcQt5NuF5y68CtWZH5//Tp06gRDhmR9fVHHMGSKPigIWraEWbNEstbHRwLveveWILycaNkSZs+Gjh1lJkSj0WgKAfq/VR4p41EGgCHLhvBrn1/z2RqkRKqjI9y4Ie9LlIDvvstfm/IbZ2c4c0ZeHz4sqnXXrsHjj0shnthYyTrIiaeeSn8dGys6+RqNRlOA0SP4PNK4bGMAVp9cnc+WpHDkCFSpIk7+1i2Zmi5CqZB5pkYNOH5cAuh+/lmm3FeuhAULTLs/NBQqVbp7pkSj0WgKGNrB55Fqparh7ujO9fjrxNyKyW9zZK04OFhe//knNGoEGzbkr00FjcqVRSAHRNwGpADPkiUSs5AT1arJz7dvX9i717p2ajRFnEGDBjFlypRc3WsYBkuWLDH5vbUJDw/HMAzM0WJ56aWXeO6556xmk3bwFqCcVzkAjlw+ks+WIFPR5cQefvwR/P3h/vvz16aCTJMmUk7Wx0eyDPz8YNQoqcKXFW5u8PvvUuWue3cJatRoijGRkZGMHDmSChUq4OzsTEBAAB06dGD16pxnNfft28dvv/3GuHHjMh0/efIkQ4cOJTg4GGdnZ8qWLUu7du2YN28e8fHxaddFRETQvXt3a3ykXFGuXDkiIiJo0KCByfe8/PLLzJs3j5MnT1rFJu3gLUD5EjIaPHr5aP4acvWqKLhVrizrxH/8IUFkOjAsZ/z8pATtI4+IQNDXX4uTz47AQNEXiIyExx6TpRCNppjSs2dPduzYwezZszl69CgrVqzg4Ycf5vI90kq/+OILevbsiZeXV9qxkJAQGjZsyP79+/niiy/Yt28fGzduZOTIkcybN4+dqTNuQOnSpXF2drba5zIXe3t7Spcunakc7L3w8/OjU6dOzJgxwzpGZSdSXxg3WxebSeXZFc8q+7fs1aL9i/Kl/zRGjJCiKcuXK7VggbzevDl/bSqM9O6tVLly9664t2SJUk8+KQVrNJo8UhiLzVy9elUBavXq1Wbdl5iYqEqUKKF+++23tGPJycmqVq1aqnHjxiopm2qQyRn+JslQSc6U92fPnlV9+vRR3t7eytvbW3Xp0kUdPXo07fzp06fVI488okqWLKlcXV1V9erV06rGpVab++GHH1SrVq2Us7Ozql69ulq1alXa/VlVpDtw4IDq0qWL8vDwUH5+furJJ59UERERmT7TvHnzVGBgYI4/rwJfbKYoE+QZRJJKokPFDvlnRHw8pD4FNmgg4i5BQVJ8RWMeHTvKUsfs2Tlf17OnaAy4uspa/pw5917D12iKEB4eHnh4eLBs2TJumTGTtXfvXmJiYmjSJF1CPTQ0lIMHDzJ+/Hjs7LJ2TUaqjLSZxMXF0a5dO1xcXNiwYQNbt26lTJkyPPjgg8TFxQEwcuRI4uLiWLduHQcOHODTTz/F29s7Uzsvv/wyY8aMITQ0lI4dO9KjRw/OZZNmGxERQevWralTpw47duxgzZo1XL9+nR49epCcnJx2XdOmTTl37hwnTpzI1WfLCT13awEerPQgb6x7g79P/E3fun3zx4jFi9NfBwXB/PkSLZ7NH4omB55+Gn76SabpGzdO17bPiXnz4Msv4eOP4YMPJNdeo8kD41aOI/RCqE37bFC6AZ8+9KnJ1zs4ODB37lyGDRvGN998Q8OGDWnVqhW9evWiWbNm2d536tQpDMOgTJkyaceOHpUlzurVq6cdi4mJITBDHYgJEyYwYcIEMz6R8NNPP6GUYs6cOWkPCTNnzsTf358VK1bQu3dvTp06Rc+ePalfvz4AFStWvKudESNG0Lt3bwA+++wzVq1axYwZM3jnnXfuunbGjBnUr1+fDz74IO3Y/Pnz8fHxISQkhKZNmwJQtqzUNAkPD6dy5cpmf7ac0P/9LUDjso1xtndm0O+DSFbJ977BGqTmac+dK3t3d0j5omrMxMlJAhT9/KBNG3jjDdi6Ned0wy++kIes+Hjo1k0U827etJ3NGk0+0bNnT86fP8/y5ct5+OGH+ffff2nevDnvZqyJcQc3b97E0dEx25F6Kp6enoSGhhIaGkrZsmUzBdmZw65duwgLC8PT0zNt1qFEiRJcvXo1beQ8duxY3nnnHVq0aMEbb7zBrl277mqnRYYZUTs7O5o1a8bBgwez7XPjxo1p/Xl4eFAuJQA642jdNaX2xU0r/L/QI3gL4GDngK+bL+diz3H22tm0oDubsWdP+uunn4bRo2WafuhQ29pRlPD3l7S5sWPh/fdh6lQRyBk7FoYPT684l4phiJRtjx5Sfe7dd6FWLXj++XwxX1P4MWcknd+4uLjQsWNHOnbsyMSJExk6dCiTJ09m/PjxODk53XW9r68v8fHxxMXF4ebmBkC1atUAOHz4MA1TZs3s7OyoUqUKQJbtmEpycjINGjTgp59+uuucj48PAEOGDKFz5878+eefrFmzhpYtW/Laa68xefLkXPfZtWtXPvroo7vOBQQEpL2+cuUKIAF3lkaP4C1EmwptADh4KeunOavSqFGKEW0ksnvGDAgrQNXtCivNm8P27XDlikjcenjAs89CFn+waTg6ysPApk2Qmt96+bIWG9IUK2rVqkViYmK26/KpqWQZR78NGjSgZs2aTJs2jSQLx7I0atSI48eP4+vrS5UqVTJtqQ4eICgoiOHDh/Pzzz8zZcoUvvnmm0ztbNu2Le21UoodO3ZkW6e9UaNGHDhwgODg4Lv69MyghLl//34cHR2pW7euRT8zaAdvMer51wNg5/md97jSwkRFyejRxQXWrYNffoHkZOjTx7Z2FGW8vETLf9s2yZV/+WXJgc9puvD++yU9MTIS6tWDZs1ELS+XU4waTUHk8uXLtG/fngULFrB3717CwsJYvHgx06ZNo0OHDplS4DLi5+dHo0aN2Lx5c9oxwzCYO3cuJ06coEWLFvz+++8cPXqUQ4cOMWvWLM6ePYu9vX2u7Ozfvz8BAQH06NGDDRs2EBYWxsaNG3nxxRc5duwYIFP0K1eu5OTJk4SGhrJy5Upq1aqVqZ0ZM2awZMkSjhw5wrhx4zh16hQjRozIss9Ro0YRExNDnz592L59OydPnmTNmjUMHz6c2NjYtOs2bdrEAw88kDaTYUm0g7cQVXxkGsnWQTFs3SqR26tXi6P/6SeZSrbC02Cxx85OghdfeUWkatetu/c93t6yhh8bK2p5wcEwZQpER1vbWo3G6nh4eNC8eXM+++wz2rRpQ+3atZkwYQL9+vVj0aJFOd47fPhwfvjhh0zHmjZtyu7du6lbty7PPfccderUoXnz5sybN4+pU6fy8ssv58pONzc3Nm7cSKVKlejVqxc1atRgwIABXL16lZIlSwIypf7cc89Rq1YtOnbsSEBAAPPmzcvUzvvvv8/06dOpX78+K1euZOnSpQQFBWXZZ9myZdmyZQt2dnY89NBD1K5dm1GjRuHs7Jwpf3/hwoUMGzYsV5/rnmSXP1cYt/zKg1dKqQ3hGxSTUWP/Gmvbjj/9VPLdL11S6uxZpQxDqbfesq0NxY2bN5Vyc1PqqadMvycpSamVK5V6+GH5fa1ZYz37NIWSwpgHnxdu3rypgoOD1caNG/PblHuSVY67JVixYoWqWbOmSkhIyPE6nQefz/i5SYBE08Cmtu04PFzysH19RV71oYf09Ly1cXGRFLoFC2SN/tSpe6+x29lB585SH+DoUeiQopkwbZrMCuj8eU0xw8XFhfnz56cFmRVHbty4wZw5c8xSvzMH7eAtRFnPlFzG6HBuJ962Xce//goBATI9X6uWOJAMeaQaK/HGG1KKt3lzqFABHn0Ubpv4e69aVfZJSaJrP2CABEquXKmD8TTFitatW9OjR4/8NiPf6N27d456AXlFO3gL4eXshaeTJ6//8zrrw9fbptPr16V6XHi4OIvISNv0q5HAu23b4NVXpQDNsmXpGgSmYm8v0fYLF8rv8uGHZWR/6JBVTNZoNLmjQoUKKKUyKe8VBrSDtxCGYdC+YnsADkQesE2nqalwo0fD4cOSu/3zz7bpWyO15d97T5xzzZqSQte4sYgO7dtnWht2dvDkk+LUP/9cfo+p4h9Xr0pGhEaj0eQC7eAtSKBnIAYGBy7ZyMGvWSP7/v0htQaxjp63PYYhKnbjxon63Q8/SEU6c3Bykrz58PD0JZYhQ2TZ5csvIUUvW6PRaExFO3gL4uUsOZ82G8F/8YXs3d2l2ImHh15/zy9q14ZPPpF19AcfFG36r74yf009o1pX796yzj96tKzbz5oFiYmWtVuj0RRZtIO3IL5uvigUByMPomwRLPXoo7KvW1ccfOPGurhMQWDOHLjvPom0f+653EfIP/mkROlv2CD588OGSTEbjUajMQHtDSyIn7ukyo1rNo7EZBuMtC5dkn/88fGiR3/ffdbvU3NvgoLgn38kOv7LL+Gtt/LWXuvWsGWLZEwMHy7HrlzREfcajSZHtIO3IKm58A9XfRhHe0frd3j8OFSqJIFYX32l898LEoYB330HrVrBp59mLgiU2/YeewxKlhR54iZNIBdlMzUaTfFBO3gLEuwdDMCWM1s4ceXEPa62AOfOyQjexQUGD5Z/+pqCg50dfP+97B99VARxLEGpUiKa8/77ORe+0WgKEPPmzaN9+/a5urdt27aMHj3a5Pe2oEKFCllWisuOP/74gwYNGpBsw8wY7eAtSLVS1XC2d2bSuklM3TTVup0pJSM5X1/4919Jr9IUPCpWFKW6c+dEEOeNN2TmJS8YBvzf/0kQ3ksvyUyBRpNPDBw4kG7duuV4TXx8PK+//jqTJk3KdDw2NpZJkyZRp04d3Nzc8PHxoXHjxrz77rtERUWlXffrr7/y3nvvWcX+3LJz505Gjhxp8vVdu3bF3t7+Lv19a6IdvAVxsHOgbkBdnBycrB9JHxcHt26Jgx81CsaMsW5/mtzzyCMQGiq/q6lTJSI+QxWtXGFvL7MDnTpJ8N3GjRYxVaOxBkuWLMHV1ZU2bdqkHbt69SotWrRg9uzZPP/882zdupX//vuPadOmceTIEb7L8ODq4+OTqcRqQcDPz8/sCnCDBg3i888/t5JFd6MdvIV5oPwDxN6O5cClA9aNpL98WfZeXiKqogPsCja1asHFi7B+vbx/6SWJjr95M/dtOjlJ4F2/fpBSX1sL42gKIj/++ONdo/wJEyYQHh7Ojh07GDJkCPXr1yc4OJgOHTowb948XnrppbRrzZ2Cj4+P55VXXiEoKAg3Nzfuu+8+Vq1alXY+ISGBMWPGULZsWZydnSlXrhyvvvpq2vkKFSowefJknnrqKTw8PChduvRd0/F3TtHHxMQwfPhw/P398fT0pE2bNoSk6pOk8MgjjxASEsLxvM7imYh28BamdXBrklQSNxJucDrmtPU6OnpU9rduSRqWdvAFHzs7aNNGJG0PHoS2bSXPfcwYKSebG9zdZSTv5SVa+C1bij6CdvSaAsTmzZszybwmJyfz008/8dRTT1G2bNks7zEMI9f9DRo0iA0bNvDjjz+yf/9+BgwYQPfu3dmTEuz6+eefs3TpUn766SeOHTvGokWLqH6Hhsj06dOpWbMmu3fv5q233mLChAn8+uuvWfanlKJr166cO3eOFStW8N9//9G6dWvat29PRERE2nXly5cnICCADRs25PqzmYN1StgUY0q5lkp7fSDyQFrgncVJfQK8fl32OsCu8DBgAPToITr0Tz0lDnnBAjhzRhx2bomNlSj7MWNg9WqRLXZxsZzdGtsybpws7diSBg0k68OCREdHExMTk8mRR0ZGEh0dfZdTbdmyJXv37gXggQce4K+//jK7vxMnTrBw4ULCw8MpX748AKNHj2bNmjXMnDmTr776ilOnTlGtWjUeeOABDMOgfPnytGzZMlM7zZo14/XXXwegWrVq7Ny5k+nTp/P444/f1ee6desIDQ0lMjISV1dXAN5++22WL1/O999/n6mOfdmyZQkPDzf7c+UGPYK3MB5OHgC83Opl7itrxVH1iRPg7Cwj+dKlITDQen1pLI+3N3TvLkstL78suvP9+kFCQu7b9PWVaoJffAHLl8tDRF6WADQaC3Az5TvoYsLD5qJFiwgNDeWxxx5Lu89cdu/ejVKKWrVq4eHhkbb98ccfnDgh2U0DBw4kNDSUatWqMWrUKP7444+7ottbtGhx1/uDBw9m2eeuXbuIi4vDz88vU5/79+9P6zMVV1fXXH82c9EjeAvj7eINQFWfqmnCN1bh9GkoW1aUzU6dkshqTeHDwQHefReio+Gbb2DsWPjww9yP5A1DpG3d3UXLfswY+PZbi5qssREWHknnF6VKlcIwDK5evZp2zM/PD29vbw7fkf1Trlw5AEqUKMGZM2dy1V9ycjKGYbBz504cHTPrkaSOrhs1akR4eDirVq1i7dq1DBgwgPr167N69WrscqEGmpycTEBAAJs2bbrrnJeXV6b3V65cwc/Pir4hA9rBW5hg72Cc7J3YfHoznk6e9KljJfGZTZtERMXfXzZN4cXeXoSKAGbMkAj7v/+WmZncMmiQrMs3b24ZGzWaXOLk5EStWrU4ePAgXbp0AcDOzo4+ffrw/fff88Ybb6Q5dkvQsGFDlFJcuHCBdu3aZXudp6cnTzzxBE888QQDBw6kefPmHD9+nGrVqgGwbdu2TNdv27aNmjVrZtlWo0aNuHjxInZ2dlSqVCnbPm/dusWJEydo1KhRLj6Z+egpegtjZ9jh5+bHzvM7GbxsMMnKCsFOFy5ARISo2H3wQXpEvabwYm8PM2fK6H3fPkmly2ukbc+esnSTlARTpkBMjGVs1WjMpHPnzmy+IzX03XffpXz58jRv3pxZs2axZ88eTpw4wbJly1i/fj329va56qtatWr079+fgQMHsmTJEk6ePElISAgfffRRWpDc9OnTWbhwIYcOHeL48eP8+OOPeHl5ERQUlNbOtm3beO+99zh27Bjffvst8+fP5/nnn8+yzwcffJBWrVrRo0cP/vrrL8LCwti6dSuTJk3KNKrftm0bzs7OtGrVKlefzVy0g7cCpdxK4WA4EJcQx6loC6mXZSRV9vTaNXj11dwXM9EUPMaPF+Gi69fh6adlTT2v7N4N77wjVe6uXMl7exqNmQwbNoyVK1dyJcP3z8fHh+3btzNo0CCmT59O8+bNqVOnDm+88QaPPPIICxcuzHV/c+bMYdCgQbz88svUqFGDbt26sXHjRoKDJejZ09OTDz/8kKZNm9KoUSNCQ0P566+/MuW1v/DCC+zdu5eGDRvyxhtvMGXKFJ544oks+zMMgz///JP27dszbNgwqlevTu/evTly5Eim4MKFCxfSv39/s/Pnc41SqshsjRs3VgWBhxY8pKp9UU0xGbX8yHLLd/Dee0qBUo89plT58pZvX5P/TJmilKen/J7HjlVqxw6l5sxR6vDh3LW3fLlSTk5KNWigVGSkJS3VWIiDBw/mtwlWpU+fPmrKlCn5bYZJBAcHqw8//NCibV68eFH5+PiokydPmn1vTt8NIERl4xP1CN4KVPKuxMXrFwEIuxpm+Q6OHYMyZWQqV6fHFU3efFOWYnr0gM8+g6ZNZV29eXO4Y23QJLp1k8j6w4ehXTsR3dFobMi0adPuCjgrToSHh/PVV19RsWJFm/WpHbwVqFiyIjG3Y3C2d+ZUjBWm6K9dkwCqEyegfn3Lt68pGLi5iVLd11/D4sUSWOnuDi1awIgRkud+44bp7XXqBH/8Ibr4GXS+NRpbUL58ecaOHZvfZuQbTZs2pY+NK37qKHorUNFbntB+ePwHOlTqYPkOwsPB1VVSrLSDL9rY2cEzz6S/37lT8uVnzxbHP3AgzJljenvt24t2gq+vvJ8xA3r1Sn+v0WhsJkRjbfQI3goEeAQA4OnsmZYXbzGUkmnW+++XQKyHH7Zs+5qCTZkysG6dqNY99pjI3v72m3ltpDrz48cl775mTfjpJ/luaTSaIoN28FbAz01EDDad2sT7m9+3bONnz4pjr1VLio04OVm2fU3hwNkZFi2CKlVEzCZDIQ2TqVJFIuwrVYK+fWW9/9w5y9uq0WjyBe3grYC/uwjP7Dy/k9fWvsatxFuWa/xAShnatWvBhmUHNQUQR0dZh1cKHnoInn/efAddp46k5X38MaxZIzNDiYnWsVej0dgU7eCtgLeLNw52DjjYSYjD2WtnLdf4/v2yX7VKpuo1xZuGDWWq/amnRNo0KAi6dDHPSdvbwwsvSFbG119LbAfoinQaTSFHO3grYBgGvm6+afXgLVo2NrVG8vXrOsBOIzg7S8nYQ4fk+/HXX+avywNUrgydO8vrd9+V4Ds9mtdoCi3awVsJf3d/bifdBrCOmh1AvXrWaVdTOKlRQxyziwtMmCAOP7ekpug9+6wOvtNoCinawVsJPzc/bsRLjvKZa7mrinQX167JvmNH2depY5l2NUUHBwf45Re4dAnatIHhwyWlztzylOPGwRtvyL2vvmoVUzUagHbt2jF//nyz7wsPD8cwDEJCQkx6bwvWr1+PYRhEmaEz0atXLz7++GOr2KMdvJXwc/cj6mYUUS9F8WbrNy3T6Pbtsvf3l0pynp6WaVdTtOjSBdavh8aNYckSGDpU1PDMZcoUGDkSpk2TGvMaTRYMHDgQwzAwDAMHBwfKly/PiBEjMpWHzY4//viDM2fO0L9//0zHQ0ND6du3L2XLlsXZ2Zny5cvTpUsXli5dmla3vVy5ckRERNCgQQNrfKxc0bJlSyIiIihVqpTJ90ycOJGpU6cSY4ViUNrBWwl/N38u3bhEKTephWwRfv1VnPqsWVJSVKPJjgYNZC3+8mVRvnvtNViwwLw2DEMc+1NPSSAeiALeF1/A+fMWN1lTeHnwwQeJiIggPDycWbNmsXz5ckaOHHnP+z777DMGDhyYqXLcihUraNasGTExMcyZM4dDhw7x999/07t3b6ZOncr5lO+evb09pUuXxsGh4Oi1OTk5Ubp0abP+59etW5dKlSqxwNy/TxPQDt5K+Ln7ce32NX7c+yOvrH7FMo1u2wbNmskaq0ZjCoYBq1eLhv0LL8hIPjLS9Pvt7GD+fJnqB0mlGzMGKlSQaXxz2tIUWZydnSldujRBQUF06tSJPn368Pfff+d4T2RkJGvWrKF79+5px27cuMGgQYPo2rUrf/75J507d6ZSpUrUqFGDgQMHEhISQmBgIJC7KfiDBw/StWtXPD098ff3p2/fvly4cCHt/L59++jQoQNeXl54eHhQv3591q1bB6RPv69YsYIGDRrg4uJC48aN2bVrV9r9WU3R//vvv7Rp0wY3NzcCAwMZMWIE11KXW1PIa/W87NAO3kqU9ZQSgevC1/HZ9s/SIupzzeXLksZUsSJUrw47dljASk2xwN1das2XKiVO+eGH4b//4PZt0+43jPTUuSeflOC9gQNlJF+5MkydqksWa9I4efIkK1euxNHRMcfrNm/ejLOzM3UyxBL9/fffREVF8fLLL2d7X25nRCMiImjdujV16tRhx44drFmzhuvXr9OjR4+0af9+/fpRpkwZduzYQWhoKJMnT8bljgHV+PHj+eCDDwgJCaFSpUp069aNuLi4LPvct28fnTp14pFHHmHPnj38+uuvhIaGMnjw4EzXNW3alB07dnDT3FiZe1Bw5jaKGOW8ygHg7ujO7aTbRMZFpgng5Iply+SfaOnSmbXENRpTqFdPHPN338GQIdCokaTXffON1J03hxo15L7nn5ep/w0b4PXXrWN3MWbcOAgNtW2fDRqInIK5rFy5Eg8PD5KSkrh1S4S9pk+fnuM9p06dwt/fP9P0/NGjRwGoXr162rF9+/bRokWLtPczZ868a83eFGbMmEH9+vX54IMP0o7Nnz8fHx8fQkJCaNq0KadOnWL8+PHUqFEDgCpVqtzVzptvvknnlHTSOXPmEBQUxI8//sjQoUPvuvbDDz+kT58+vPjii5nsaNiwIZcuXcLfX3xC2bJlSUhI4Pz581SuXNnsz5YdegRvJVI16D2dJRAuz7nwJ0/KdOnVq+DhIVOkGo25DB4s5YZ/+klKDQ8YAPfdl7s19Zo1Jd9+2TJ5f/q0jOwvXbKkxZpCQOvWrQkNDWXHjh0899xzdOnShTFjxuR4z82bN+8aHWdF9erVCQ0NJTQ0FKUUCQkJubJx165dbNy4EQ8Pj7StXDkZiJ04cQKAF154gaFDh9K+fXumTp3K4SzExDI+bHh4eFC3bl0OHjyYbZ8LFizI1GerVq0y9Qng6uoKoEfwhQVfNxlh29vJ0+mp6FM0KZuH2u0XL0r0/N69ULeuOHuNJjdUqSJb+/bynQoJgcBAmW7v10/S48ypcZD6T3rbNli4UOrOT5+eOThPYza5GUnnF25ubmmj3c8//5x27drx9ttvM3ny5Gzv8fX1vSvSvlq1agAcPnw4zZE6OTmltZ2XgOXk5GS6du3KRx99dNe5gAApEDZ58mT69+/PX3/9xapVq3jrrbf4+uuv75pSN6fPoUOH8vzzz991LjWWAODKlSsA+Pn55aqf7NBewkqkrsHHJ8Xj4uBCzO08pkBcuCDT83v3agU7jWXw84O4OJm2nzpVYjveflvy569fN7+93r1lbb9aNRnJV64MX31lcbM1BZ9JkybxwQcfpEW8Z0XDhg2JjIzMFJDWqVMnSpUqxXvvvWdxmxo1asSBAwcIDg6mSpUqmTbPDCnHVatWZcyYMfzxxx8MGTKEWbNmZWpn27Ztaa9v3LjB/v37qVmzZo593tlflSpV0kbtAPv37ycwMDDtQcNSaAdvJZwdnPFz8yMqLoq4CXEMbpi7J8A0LlyQdffHH08XutFo8oqrKwwaJMp3f/whzn7bNtnnJjC0Vi1J4fzlFwgOTi+OBJJipykWtG3bllq1avHOO+9ke03Dhg3x9/dnc4aUX3d3d2bPns3KlSt56KGHWLlyJSdOnGDfvn1Mnz6dW7duZVqzN4dRo0YRExNDnz592L59OydPnmTNmjUMHz6c2NhYbt68yahRo1i/fj3h4eFs376dzZs3U6tWrUztvPPOO6xevZoDBw4wePBgnJyc6NevX5Z9vvLKK+zYsYNnn32W//77j+PHj7NixQqeeeaZTNdt2rQpbV3fkmgHb0XKeJbhzLUzlsmDj4iQadTZs8XJazTWYMAAWZsfO1acf+pU/rffmt6Gvb18RzdsSJ9nXr8eypWToLzoaCsYrilovPjii8yePZtTp7KW6ra3t2fw4MH88MMPmY736NGDbdu2UaJECQYNGkSNGjVo27Ytf/31F3PmzMlVgB1IINuWLVuws7PjoYceonbt2owaNQpnZ2ecnZ2xt7fn6tWrDBw4kOrVq/PYY4/RokWLu4IF33//fV588UUaNWrEsWPHWLFiBe7u7ln2Wa9ePTZu3Eh4eDht2rShfv36vPbaa5lG6rdu3WLp0qUMGzYsV58rR5RSRWZr3LixKkiMWDFCuU91V9M2T1PPLn829w0lJSnl6KjUCy/Ia43Gmly4oNTnnyv10ktKPfmkUpUqKQVKHTiQ+zbDw5Xq31/a8fFRavp0peLjLWdzEeDgwYP5bYLNuXjxoipVqpQ6efJkfptyT9atW6cAFRkZadF2/+///k917Ngxx2ty+m4AISobn6hH8FakeVBzbiTcYPvZ7fxy6JfcN3TlCiQkwD//SB6LRmNNAgLguedEonbhwvQo+U8+yX3hmeBgUdLbvVtmCF54AR58UBeyKeb4+/vz3Xffcfq0BStuFjIcHR35wkpS0DqK3oq0Dm4NwK2kW0TGRRKXEIebo5v5DUVEyP7SJfnnqNHYklq1JFd+1iwpS1uxoky3V64MnTqJcI6p6ooNG8KqVZJeFxcnIjpKieiOVmgsljzyyCP5bUK+MjxVJdIK6BG8FQkuEYyTfXq60ZmYXFaVS326vXhR/tlqNLbEMGDOHAm8GzkSateWdfQff5S19rp14exZ89p89FFJyQOJK6lfHzJIfmo0BY22bduilMK3EImMaQdvRQzDoLRHaRKSRZjhVEwu68KvXg2OjqJkl0HhSaOxGXZ2Em0/fbpUqNuxQ6Lif/4ZwsKkoM3atblru1IlKWfbsiXMmKGn7TUaC6EdvJUp41GGuIQ4qvpUJSEpdwpMLFwoa/CgHbym4ODoCL16iWN3dZU8+NyojLVvL/nzDz4oMwT9+kFsrOXtLQQo/XCjuYO8fCe0g7cy/u7+XI+/ztHnjtK1WtfcNZIq/TlxosiDajQFiTZt4OOPJRj0f/9Ljxkxh1KlRAHv3XdlhmDrVsvbWcBxdHS0uFSppvCTkJCQ65K42sFbmdIepTkfm4fa2allBZs3h7feAm9vi9il0ViUrl1h0iQJnmvQAM6dM78NOzvJkz92TIL3oFiVo/X39+fcuXPExcXpkbwGEKnbixcvUqJEiVzdr6PorUz5EuW5dOMSo/8YTWxCLPMenWdeA8uXy97DQwrNlCxpeSM1mrxiZweTJ0PnzrKW3rYt9OghwXOp2vem6mynFlLasAG6dIGvv5aZgSKOl5cXAOfPn891QRVN0cPd3T3XgX3awVuZSiUrAXAy+iRHLh8xv4HU6c69e+HFFyWSWaMpqLRoId/R776TevHx8ennXntNpuBNpV49aNZMUvTCwuDNNyWivwjj5eWV5ug1mryip+itTLVSUh3Jyd6JMzFnSFbJ5jVw44bsL13SAXaawsGgQbBpkwTKHT4MK1ZAmTKSR39H9bAcKVkSVq4UBz9pkuTbZyN7qtFo7kY7eCtTL6AeLg4uXI+/TkJyAheuXzCvgZMnwcdHXmsHrylMODnJd7ZrV1i8WHLnGzSQtE9z2pg7V2YDNm+G33+3krEaTdFDO3gr42TvRIB7QNrI/XSMmZKM+/dLkRmQMpwaTWGkVStZU3dzg27dIDzc9HsNA0aPltmAUaPk2N9/y7KVRqPJFu3gbUApt1IopWhfsT12hhk/8uRk+afm6ipBTJUrW89IjcbatGgBf/0lQjY9e5rn5AGCgqRSXVKSaOU3aCDV74qxjrlGkxPawdsAH1cfbifdZu3Ta2ka2NT0G8+dE73uhx6SQh3OztYzUqOxBRUqwE8/yei7YkWprbBkiWjRm4q9veTJjx8PixbJzNZLL+kytBrNHWgHbwN8XH24cvOK+Tfu3y/79u2hb1/LGqXR5BePPy5StxMnis5Dr16i7/DaazJrZQo+PlLt7uhR+dv4+GPYssWqZms0hQ3t4G2Aj4s4+EcWPkK/X/qZfuPEibI/dcr8Yh4aTUGmYUMRbtqzR8rR2tvD+++LoNOBA6a3U768FMI5flyC+QB+/VUKM2k0xRzt4G1A6gg+MTmRw1GHTb8xJET2AwZIqpFGU9RwdYXu3UWxbsAA2LkT6tQx//teSfQmiI6GgQNF0nnmTEhMtLTFGk2hQTt4G+Dj6kOSSqKMRxnTo+j//lv2np6y1ylymqKMq6ukw507J8F0X3+du3a8vWX6v04dePZZ2S9dqivUaYolNnXwhmF4G4axxDCMw4ZhHDIMo4VhGD6GYaw2DONYyr5kyrWGYRifG4Zx3DCMvYZhNLKlrZaklFspAEq6lOTyzcvEJcTd+6ZUBbunnpK9dvCa4kDZsiJL+8cfUlnOHGGcVGrUkJS8pUslxa5XL1HC02iKGbYewX8GrFRK1QDqA4eAV4G1SqmqwNqU9wAPA1VTtuHADBvbajGCvIIAcHV0BeBMzJl733Q+pUBNcrLo0JcpYy3zNJqCxfjxEjg3Y0buS8caBjz6KOzbB+vWpU/hT5sGBw9a1FyNpqBiMwdvGEYJoDUwG0ApFa+UigZ6AKkVWOYBj6a87gHMV8I2wNswjELp5VIdvJujG0/XfxoHOxNKAEyYIPuwMEkDKuIa3BpNGj4+8OOPMk3/999SwMacNLqMODjAAw/I64sXRQu/bl0YMiT9IVqjKaLYcgRfEYgE5hiG8Z9hGLMMw3AHApRSqQWkLwABKa8DgYxD3bMpxzJhGMZwwzBCDMMIiSygpSVTHbydYce8R+dR2ecegjUZ1wu//VYXmNEUT555BhYulJz3bt1E4CYvBARItP2YMfD991LMZuVKy9iq0RRAbOngHYBGwAylVEPgBunT8QAoKYJsVjSMUuobpVQTpVQTP1PLUdoYDycPvF28OXvtLEopbifeYzSSmuLz/vuSBlS/vvWN1GgKIr17y1T9mjXQpo0o4eUFX1/45BOZui9bFvr3l1x8jcbKJCfDkVwUFM0LtnTwZ4GzSqntKe+XIA7/YurUe8r+Usr5c0C5DPcHpRwrlAR5BXHm2hlqfFmDZ1Y8k/PFR4/KPjAQpk/XU4ma4s0zz8BHH0ng6eOPS+58XqleHbZtkyUALy+ZNSugM4Caws/Vq6LOfN99kihiK2zm4JVSF4AzhmGkhoN3AA4Cy4ABKccGAKnlopYBT6dE0zcHYjJM5Rc6gryCOHvtLKVcS907VS71MS82VmrAXzCzAp1GU5QwDPk72LwZXFykfKypinc54eYGjRvL608/hXLlYPBgyzxAaIodGzbAN99ImMeLL4qsQ9eushJUqhT89htMniwTR7bChGgvi/Ic8INhGE7ASWAQ8pDxs2EYQ4BTQO+Ua/8EugDHgbiUawstgZ6B7Lmwh9bBrQk5H5LzxUePiu785cvyXleR02gkk2TSJHj+eQmSmzPHcm137y5/d/PnS7tt2sh/47ZtLdeHpshx6RJs3CgxoUuXph93dxen7usrz42NG8tXacCAbJuyCiaP4A3DCDAMY7xhGDMMw/BNOdbKMIyKprahlApNWS+vp5R6VCl1VSl1WSnVQSlVVSn1oFLqSsq1Sik1SilVWSlVVyl1D69YsPF28Sb6VjTlS5TnzLUzaeVjs+TIEahaFY4dk8c9Dw/bGarRFGTGjpU8+blzJbp+1iy4kos6D3dSpYqs9Z89Cx9+KNkrzz+f98A+TZEjNhb+/FOeMQMCRGZh/Xr5upw6JfXBrl+H7dth6FBZWZo7V8JIbI1JDt4wjMbAEaA/MATwSjnVEZhqHdOKFiWcS3Az8SaBnoHEJ8Vz6cal7C9etUrydo8c0QI3Gk1GDEPS5yZMkIj4YcOkQl3G4VNeKFlS8vD37ZMIe3t7rYKnSSMyUkbjXbvCvHni5LdulVXU6dMlJtrVVZ5By5YVrab4ePnK5lacMS+YOoL/CPgsJfo9Ywj4KqCVxa0qgvi6+QKyFv/6A69jb9hnfeHp0/KNWLtW/oHp6XmNJjNubjB1qvx9hIRI2dl+/WD2bPnbsQReXjI8S0iAPn1kCKYp1ty4IYFyp07JKk5MjEwgNW8Ojo7yjJk64dO0qawm7dsn2zPPyLS9rTHVwTcmXYwmIxGk561rcqBVeXkOSlbJvNP+Hfzcs0npO35c9lOnynThe+/ZyEKNppBhGDKc+vtvCU8eOlQyTz74wDLT9iDFaqKjYdAg+Oory7SpKXQoJc95W7bA559LPaNUhx0WBp06SYLHokVy7LnnxMHXqZO/GmWmOvibQMksjtcgPa1NkwNlPSV08nzsea7cvEJUXFTWF6amyHXvLhHDJbP6sWs0mjQCAkSO9q+/xOG/+qpM2//++z1vvSeurlLOtmtXGDVKouzjTKgloSkSKCVT8C++KOUR3n1XRuMgMdBTp4oT375dQjiefDJ/7b0TUx3878AkwzCcU94rwzAqAB8Av1jDsKKGj6sPDnYOXLh+geBPg5m6MZvQhUOHpIJcWBi88IIW4dBoTMHeHh56SNbN//sPgoNFi37ixLy37eIiDwsTJ8pUfc+eeW9TU6CJiRER0YcegpYtRRupXTsZmafyxBPwxhvQoQMcOCDFC+0KWH1WU9PkxiNpa5GAG7AZmZrfArxhHdOKFnaGHQHuAVy8cZHyJcpz+lo2ufBnzki5zHXrZC7ogw9sa6hGU9hp0ABWr5YR99tvS0jziBESKZ/b+VJ7e3jrLWjVKn1uNjZWnL+jo8VM19gepSA0VKLcT56UsVVIiIzQS5cWJz5mjAjU9O0roR6+viI06uYmpQ0KKiY5eKXUNeB+wzDaI+pzdsBupVQ+BP4XXsp4liHieoQ4+OzEbk6cgMqVJUWuYkX9z0OjyQ2lS8uC6OjRMvz65BMpOvPTT3lTGunUKf31O+9IAvTIkTB8uCQ+awoFUVGwY4eIGS5ZIhOnIHWOKlaUUfmIESKHcOqUpMD98IOcP3JEHHyzZvn7GUzBrAkFpdQ/SqmPlFLTtHM3n3Je5Qi7GkY5r3JZO3il5BGycmVZi9cR9BpN7nFwkNykI0ckh2n3blmj/7//kznYvPLgg1J7fsIEmXUbNgz27897uxqrsWULtGgB/v4SVvHOO/JcNmOGiNZcviyj90WLoHVrWXuvXh1++UVCO06ckEmcwkK2I3jDMF4wtRGl1HTLmFO0qVyyMn8e+5P+dftz6cYlbibcTKsRD8i36/p1kT46dkyraGk0lqBaNdk6dhSZ2+eeE03RBQtERzS3dOwo24EDspz2/feSS/Xjj3I+ObngLcoWY/74Q2oLeXiISGGbNtCokYQ8ZSQpSVZk7OxkxfSpp2R1JigoX8zOEzlN0T+Xw7mMKEA7eBPwdfPldtJtOlTsgL+7P+rOwnnbU+rwVKok63xVq9reSI2mqFKnjoziZ8+GceOkSmO3brJOX79+7tfna9eGmTMlxDo1wn7fPnj4YRnuNWsmydKNGsmavcbmTJ8uo/GKFeHXXyVM406UEqXiiRMl87J6dRnJF+ZntGwdvFLKZAlajWmkid2UCKJl+ZZ3X5BaVKZBAykZqxW0NBrLM2SIRNh/9RVMmQIrVkDDhvDFFxIynVtHX6pU+jr8rVvS1rZt8PPPcszFRTRNC8PibREhPFwc9vffy9T8P/9k/YwVGyvpbwsXyrPY7RQ5t8Ls3MH2xWaKNRVLyjPT/kv7ib4VTUmXkpQrkaEi7okTsm5YLuVYfiokaDRFmVKl4M035b/60qUyZ3v//eDtDbVqyeJsu3a5b/+++9Ide0SERHStXy8zBSCj/bVrJbI/KEi2KlXEBv13bxEiI8WpR0eLBtLbb2ft3Pftk5S348fl1/LKK4XfsadiTrGZRw3D2GgYRlTKtskwjMesaVxRI8hLFnEu3bhEg68b8N1/32W+4ORJyd/98ktZK9RoNNbF31+cfEiIrKP37Qv//ivR8lOmSO5UbGze+ihTBnr0kEj+VA/j7i6eZ+lSGWIOHixexhLBfxq2b5eReHS06B99+60kVmTFDz+I3MjatfDaa0XHuYOJI3jDMF4E3gXmA3NTDrcAfjQM402l1EfWMa9o4e3iDcD1+OuU9ih9dyT9iROy/v7PP/Jao9HYhsDAdBWTOnUk+n7yZFkms7OTv8vq1WV0P368PBjkhbFjZQOZzj9/XnT0vb1FHnfhQtHXt8+mZoUmS2JjRUJk8GCJV163Tkbxd3LmjKyI3nefjOxfeCHvv9KCiKnPKuOB0UqpYUqp71K2YcAY4EXrmVe08HXzxcHOgfOx57MWu8mYA69T5DSa/GHkSNi7F65eFWW8N9+UuJgzZ2QU/thjIl9rqcI2Li7yAFGjhrz/5ReZwXvggfQEbU22HD8uqyGTJsnqZo8e8oz05593O/fkZEmJq1VL9OSTk0VqpCg6dzDdwXsA67I4vi7lnMYE7Aw7yniU4VzsubvFbq5ela1iRXH0OoJeo8lfSpSQmvOTJ8PixbBnj4zsQ0PFi7RrJylylqZ3b4kKO3JEHiymTpWqdppMJCXJZErVqlII5u23JZTht98kRrl9+/Rrk5MllvKBB+T5rXlzeV+UpuOzwtSP9xvwRBbHewLLLGZNMSDQK5Cz186mid2o1Ej51Cl5Ly8ZGegRvEZT8BgyRCrVffWViNo0aiTHLLmkZhiSfH3okET7v/GGvNcQEyP57K+8IppFH38sGvChoSJBsH+/PHs5O2e+7++/pX7XqVOSJfn33zKWKuqYGkV/HHjVMIx2wNaUY81TtukZRXG06E3OBHoGsv/Sfj7t/CkdKnUgWSVLbfiTJ+WCUqXkm1urVv4aqtFossbZWXRMn3hCorJ++EG2BQtkaGgpRRR/f0nE7ttXAvVAZvlu3Up/Xww4dw4++0ziHffsSZ9Wb9pU6rEPGXL3PceOiWBh6dLyK+rUSfLfu3UrZurfSql7bkCYidtJU9qz1ta4cWNV0Hl+5fPK+W1nFX0zOvOJd99VCpS6di1/DNNoNLnjl1+UcnKSv19Q6tlnlUpOtk5fzz+vlJubUm+8oVRMjHX6KCDs26fUI48o5eCglKOjUu3aKTVpklL//KPUjRt3X5+QoNTKlUp17aqUYcg9Y8fa2mrbA4SobHyiqcVmisFkhm1oV6Edn2z7hJ3ndnIr6Ra1/GpRqWQlmeLz979bN1Gj0RRsHn9c8qz27pVIr6+/lrX7Rx+1fF8jR0rE/TvviHrec89Jml8RixK7fFmk/hMSJML92WeznlJPSBDpEMOQpISvvpIfxcSJck92qXHFBbNDDAzD8DAMw90axhQHgr2DAQiLDqP7wu4sP7JcTsyeLdG0ffvCoEH5aKFGozEbZ2fJufrpJxGseewxiQCLjrZsP1WqSB87d0KTJuLJXnrJsn3kE1evSobw8OHipC9eFOnYDz5Id+5Xrkhe++TJUqvdz0+eq0BS4xYvhtOn5Xxxd+5gntDNKMMwTgMxwDXDME4ZhjHSeqYVTSp6yzf18s3LuDm6ER4dnn7y9GlRvLp5M3+M02g0ecPLS+qPdukiEWBt2sDWrRLybUmaNJE8sEOHxMkDHDwI//ufSLgVIpYvF/VeHx8p0/rttxJTuGuXVHxLZc0acfxdukjEfESERM87Ocn5xo0lLOLOALvijEkO3jCMCcD7wGygU8o2B3jfMIxXrWde0cPT2RNne2eu3rxKRe+KhEWHpefTDhok4sk6gl6jKbzUry+h3i++KDqoLVtKAfGRIy3/8F6jhmhnQHqd05o1JeivANeyUEpG3m+9Bb16ielTpkh0e2QkzJsnCQqp14I8K735pojXxMRIwN3MmfJxNVlj6gj+WWC4UuotpdTalG0yMCJl05hBCZcSxNyOoWLJipy8ehKiouREcLCEiOoceI2m8PPRR+KtFi0SdbwZM0Sp7vnnreN8n34a/vtP/n889ZSEjBfA+vTffSdjmPr1xcE3by5yAm++KdV3fX3Tr920Sc5HRkr0+6RJUkXbQ6uvmISpDt4f2JnF8R1AgOXMKR6UcC7B2Wtn00bw6uJFOXHrluz1CF6jKRqUKiXCNRs3ypT6E0/Ap5/KFPsrr0hwniWpXRs2b5Y+1q+H33+X43FxorGfnGzZ/szkn3/S09pmzpR4wfXr00X8Utm5U55R2rWTdffLl21uapHAVAd/FOiXxfF+wBHLmVM8aB3cmo2nNjK22Vi2Dtkq03iQrrOoHbxGU7QwDKkP//33MG2aDEGnTZPR9syZlu3L3l5Cyk+flnx9kMi0Vq1kiJw6oLARYWEiJfvWWzBggDzz7NwpwXR3BsLFx0tBvaZNRQ34uedkLf7OBwCNaRjKhKkiwzAeB34G1gNbUg63AtoAvZRSv1nJPrNo0qSJCgkJyW8z7skX279gzMoxXBx/EX93fwn/XLZMqh846Aq+Gk2xYNMmma7ftUt0Vdu1k7r0FSvKcp27BZOVrl2TkPSXXpJlgp9+kkVtK3DhgsjFHj4sKwT//CMrEoYhSQCffSbPOhmJikqfmh8zRq4bOFBiFjU5YxjGLqVUkyzPmeLgUxppDDwPpIY0HAI+Vkr9ZxErLUBhcfB/HvuTrj92Zc3/1nDsyjH6j/sOT+Uo0SOpIaEajaboExcna/WLF8tCdMb/x6VLi6ds1sxy/e3dK1Ftx4+L1NsIy4ZQxcdL3Zxz58DNTUbenTtLtHu1auDqevf1774rkxmbN6cH1mlMJycHb/JwUSm1C9CCyBaglGspAKJvRTPijxH0CfOBNp3lsfXRR6UutUajKfq4uUma28SJkjN/6JBk0hw7JhFl7duL4kvTpiJfXb++eNDcUq+ehKyPGCGvQYqnb9ggDxItWpg9yDh7Vp5NDh+WeMJz50Rw5plnsi/mEhMj6XHvvy/39u8P5cvn/mNpssZkB28YRgDwP6ASMFEpFWUYRivgvFIqzFoGFkV83WQu6srNK5R29qXk+Sipb3jmTJFTpNJoNCbi7S0ONrXGaY8e4in/+UeW8FJ59NH09fvc4OkpuvmprF8Pr6ZkOzdpAqtWSVJ6DsTHw4QJ6cIyqfj5SYrb009nf69S8qxy/jxUqCBV3TLmu2ssh6l58I2RYLr+wFAgdWWkIzDVOqYVXSqVrISTvRMnrp7g4WspDn37dtmn5rRqNJriTf36EoB37BjExooI1pQpovhSq5YE0h09mvdSsq+8Inloc+dKwG/79jmK5SQlSUr/xx9LNdvPP5cJgIsXZbvTucfHw5dfiohNUpKsxX/yiQT1nzihnbs1MTWK/iPgM6VUQ+B2huOrkGA7jRkYhoG/uz+XblyiglOKgx88WPZ5mX7TaDRFEw8PkcJ9801ZPy9VSjxr9eoSjDdqVHpFytzg6ysh7suWyUNDanpdBk6cEMdes6Yoa48bJ5c99xy0bi2Tj4aRfr1SEj1fqxaMHi3OPTXdrXdvmago6vXY8xtTf7yNgXlZHI9A58HnCn93fy7euMgjmy4BkHwrReFKj+A1Gk1OBASIt/33Xxl19+4tU/lVq8KHH8Lt2/dsIls6dZI4gKFD5b1S3L4tM/pt24pWT8WK8M03MgrPjhs3ZIWhTx8JrPvjD4kh1iuQtsVUB38TKJnF8RrAJcuZU3wo7VGaC9cv0OCfgwDYtWsvZZNKlcpnyzQaTYHH3V2GwAMGiPc9fFgGBy+/LHoagwaJN75yxfy2g6Ug1q1/d7Oz9kDatbzN//4ntbD+/FOW6IcNy7kJw5DUt08+gdBQ0Y/POLrX2AZTg+x+ByYZhtEr5b0yDKMC8AHwizUMK+qU9SjLn8f+lMoIzZrJk3OnTvltlkajKYxUry5OfvVqmDVLhsxz54qwzqZNIn5jIgkJMimwfHlDkpLmYU8iI5+6xhfzvO45pX7oEAQGSv66md1qrICpI/jxgA8QCbgBm4HjQDTwhlUsK+I42jviGg/cvs3qSoptm39KLzqj0Wg05mJnJ0nnixdLtNuXX0olu8cfFyW7mTOlVn1ERLZN3LgBPXtK+v2oUQZLPjjBmRJ1+XJDHexOHs/2vsuXZdq+eXNZkwft3AsESimTN6A94uxfBh40515bbI0bN1aFhUX7F6lKY1AK1MBHUQkOdkq9/HJ+m6XRaIoKyclKTZumlIeHUhLzJpthKNWqlVLTpyt16ZJKSlJqwwalvvxSqUqV5JKPP87QTmioUr6+SpUpo1RYWKYuFi1SqkMHpezt5b5GjZQ6fdqmn7LYA4SobHyiWbqoSql/gH8s/pRRDAnyCqJCtLwu4V4Kh8TLOsBOo9FYDsMQadrnnpM8tlKlRFjnl1/gl1+4/cKrfPVxMt+XGsd/e2W47eEhgXEvvJChnfr1JULuk0+gbFn27oXariexr1KR3bsNzp6VTLuePUVpV6+1FxzMEbp5FHgBqJVy6BAwXSm11Ap2FXn83PyokVIltoR3AKAdvEajsQIuLjJ1n0qtWlwb+yavdj/AjI21qXd+L7PbhtKpnx+BrSpgVK0COGZuo04dNjw9m4kdpTDeMseX6F7vFFOGj+a9LY9glMpZGEeTP5gqdPMisAgRu3k5ZTsM/GgYxnjrmVd08XXzJfAaJNnbUVF5y0GdA6/RaKzAlStw8KCozHXvLopzMzbW5om2UewZ+n8M3jiIoOFdMGrXktr1d9QoWbpUCtGFh8OHU+O5/70ucOMGTs8Mwgjwl5O7d+fPh9NkizlBdqOVUsOUUt+lbMOAMcCL1jOv6FLCpQRNz0GCiyNVY+xJsIP4slpSQKPRWI4NG6RInZ+flIofOFDqzYwaJcVdFq31lei4mBjRqH/iCRG66dtXlGmQmL1evaBxY9izB8ZPcKLki0PkiSEkRJYBTp2S+X246+FAk3+YWi42FmiolDp+x/EqwH9KKU8r2WcWhaWaXBopi1WJWzZjv3cfxrPP5rNBGo2mqLBtm9RWDwoS+dhataSeVePGOayTJydL4fYpU6BlS7j/fvb6tOXVfzqxaIk9ntn9p0+tBxsfL1r5/frBU7o2mS2wRDW534AngPfvON4TWHbX1RqTOV/KmbItW0FLrfir0WjyRlSU6N5s2CDV2gIDZdRdooSJDdjZweTJ4OzMrR9+weWTT6iXMI0/6tXDuLEKPEtnfV/qE8OtW7L9738QFgZvvKGj7vIRU6fojwOvGoaxyjCMySnbSuA14IhhGC+kbtYztYiRmAjA4qbu3FqzkpFze/HroV/z2SiNRlMYSUoSddnAQHj+edi/H558En791Qznnoph8LXPBNwO7eLPRbEwciTG3r2icDdxoozys8PLC1auFAc/caKM4i9ezNNn0+QeUx38QOAqUA0YkLJVTzk2CHguZRtteROLKNHRAJy2i8W548N4LfqNLae35K9NGo2m0JCYKIXmZsyA0qWlAMzDD0tBuGPHZCTfuLH57X7+uZSL79IF2j/sLII5hw9LsZu335bCNMOGZS/M5eQk0XxvvSXVZnr2zNsH1eQak6bolVIVrW1IsSOlHON5lwSiXeBGudKcjc5DNSiNRlMsSE6GRx4RTfiUiUAaN4bPPpPYuLzMiH/0kcTMPfYY/PST+GpApHD/+guWLIG//xY53OPHpXRtVpJ1hiEj+L594do1OXb1KsyfL4n2pbOZ6tdYFLOEbjQW5PBhAKLc4JAfJFeqSNjVsHw2SqPRFFSSkmD9eqkns3u3BM299BJUqwZNm4JDLv+bx8eLI9+5U9rr00ck7B3vSIXH01OK2AwaBK1aiYBOcLCE6devDzVqSIpdhQrp91Stmv564UKpMfv883J/z54StR8UlDvDNfdEV+PNLx5/HACXRDhVAlyq1SIsOgxTsho0Gk3RJyZGZroHDoQGDaSA3IMPSiDdDz/IVPzAgRLsbq5zj4sTtbpq1SSFHWQGfvlymdq/y7nfyejRsGiRVLRbs0aeDLp3l1qyo0ZlXbJ2xAgxetIk+XDPPy9h/Tlo42vyhh7B5xd16sD+/aysAm0ve1C5XD0CIzYSlxCHu5N7flun0WjygcREOH9eyr337Svxab6+MkLv2BHq1ZNBr6tr7vs4dEjy2g8ehG7doH379HPdupnRUO/esoEo6Rw5IkP/r76SDzJmjDhwZ2e5xjDk/16dOuLkjx6FXbugTBk5f+pUWqlajWUwKQ++sFCo8uC7dIFLlyjV8zi9St7P18+uyG+LNBqNjVFKZN4XLYJ//5WVu9R1dZDib8OGcc8yraayeze0bi2S9AsWWKFCtVIyzfBPSskSe3uZwu/dG2rWlK1Uqbvv27RJrhs9WoTtU52+5p7klAef7dfGMIzvDMPwTHnd2jAMPdq3JJGR4O9PeZ+KrLy1P7+t0Wg0NiYyEoYPhw4dJKAtMFBmur/5RuLYTpyAZ57Ju3NPSkoL+aFuXXlg+O8/Kzh3kFH6qlWicPfDDxIwsH+/fNAHHhBJvenT776vfn0x7PPPZRT/1FOwY4cVDCxeZDuCNwwjHiivlLpgGEYSUEYpdcmm1plJoRrBV6gATZvydNNzLLixlQvjL9BnSR/61unL8MbD89s6jUZjJbZsgRUrxJFfuQL9+0tQuouLZfvZtAl+/FF05K9fh0uXZORuc5KT4fRpWRuYMkWmEUJDZTR/J8ePwxdfwJw5Eghw7pzlfzBFjFyN4IFw4DnDMNoABtAiZSR/12YFm4s28fGy3hQQwCNz/kWhiIiNYO/FvYReCM1v6zQajRW4elVSyu+/Hz78UKLgd+2SqXJL+rDISFGKbd1astIeeEBy5LPKZrMJdnYyoHn4YZg7V0b5jRqJMs8ff4jyXSpVqki+39mzsGyZ/GCSk2VKQ2M2OU27vwTMQtTqFJBdWVgF5NdXp3By4YLsDYPAlBTRc7HnqOBdgfDo8HwzS6PRWJ6zZ+H//k9yzJOSJPB81Sqy13XPI+fPw++/i+LsSy/l06g9O6pXlyo3n3wiIjizZ0PJkjKd//DD6dd5eUkqHchTyqBBMoX/2Wd5izAsZmTr4JVSvwO/G4bhDVwBagMFeoq+0HD+vOxv36b0dXl58fpFKnhX4GDkwfyzS6PRWIzvv4f335dodZAZ6ZkzoXlzE9LQzEApKSyzYweMHSvL2adPZx3LViBo0kQc+u3bEow3dKgEHXfvDs2ayei+Q4d0lZ2nnpII/fffh61bRQa3TRu5VpMj9wzfUEpFA+2AY0qpy1ltVreyqHHihOzj4vBFHq+j4qKoUEJG8EUps0GjKW5ERcHUqfDss+JoP/xQSrQeOCDT5ZZy7vHxMr3ftKnkwk+eDGfOyLkC69wz4uwso/a//5bo+UOHpDhNly6SlD9zpkx5ODjAe+9J4EJ8vETZf/BBejtffCFr+vr/5l2YKlW7wTAMZ8MwngZqIdPyB4EflVJZKBpocuTYMVmHunwZj/KVcbI/wuWbl2lUphGtg1tzI+EGHk4e+W2lRqMxk6QkyV9fs0ZG6t99l3UsWV7Ztk20siIiREDuq69kYOtRGP9t1K4tThpEAGfxYpg2TZ6Q4uNFMQ+ga1fZoqLS5W8vXBDBnKQkqFRJ1PGGDJGlAI3J9eBrAX8BJYB9KYfrAjHAQ0qpQ1az0AwKTRR969YSCbNhA1y+TPW1j1LFpwp/9Psjvy3TaDS5JDFR9FvefVdk2N96y7LtK5WWXcvVq1LjffRoEcCxVJ58gUEpaNhQat22bStrD927Zx0pGBkpQQe//CJPVomJUkbvscdsbnZ+kFMUvakOfjUQB/xPKXUt5ZgXsABwVkp1tqC9uabQOPjy5UU+au5cAHot7sW+i/s4PPpw/tql0WjMZtcu8T+7d8PNm7KEvHOnZZ1uaKgovd68Kf0UOYeeFdeupafMnTiRHnjXpo0Mkpo0uXu94+JF+PZbUdHz8pIcwYgIeRoqlNMb9ya3aXIZaQVMSHXuACmvXwfuz7uJxYyYGFl/mjoVjhyhondFTlw9QdjVMKp8XoXpW7MQgtBoNAWKpCTxPX36SG77M89IYPjWrZZzwLduweuviy8LCxO/VWyWmr285MMfPiw/2L59ITwcXn1Vgg68vdOn8VMJCJB1fC8veb90qWjjBwXBiy/KD7EYYerX8BbgncXxEinnNKaSlASxsTKN9MYbcOwYXap2ITE5kWNXjhF9K5pjl4/lt5UajSYHQkNlMDl4sAwi//xTMr969cpQYjWPhIfLLPW778oA9NAh6S/f8tnzCwcH+cF+/bWkJFy8KGVrn3xSAvHatpVR+6ZNohyUkXnzRAP4oYckxa5KFVlHKSaY6uCXA98ahtHKMAz7lO1+YCawzHrmFUHOn5dH8NS/0nLlKOMhusuRNyIJ9g4mPCY8/+zTaDTZ8u+/4k8aNRIHPG+e+JyMKdx5JXWEXrasVFtdtUqC9UqWtFwfhRp/fwmmmz1bNH6PHRMp3NatRcP+9delah1IMHOLFnJdeLgELTRJmc0uBlMhpjr4scAxYBMyYr8FbACOAuOsYllR5dQp2ad+ucqXx9/dH4DIuEgqeFfgVPSpfDJOo9FkJCYGVq+GTz+FkSMlcv3YMRg/XvLOn35afIilWLVKlO6io2UmYNkyK2nGFxX69JERfViYpNE1bixTHvXqSWpBRoKCZBTfvbu8f/ttybG/XHQzvU1Nk4sGehiGUQVITfo4pJQ6bi3Diiy7d8v+5k0J+vD2xhtwtHMUsZsSFfjr2F8opTAs+Z9Do9GYRKqG+7//ykAw9Vnc21vkZb/4QkbwliAxUVLezp6Vaf7vv5e0twsXpD+NCaRK4VaoIGl0ERGSWjBqlKyfDBuW9X0ODlLGb8MG2bdsaUurbYJZFeJSHLp26nkhNBRKl4a4OImmNwwMoIxnGc7GnqVnzZ7E3I7hVuItXB21JKNGYwsSEyUj69AhyScH6NxZZoJbthR1OF/fvI/Wz56VDC4fHxk8KiUzy0qJv3n9dQnN0fVV8kCZMjLtMmiQTN3fvi3O/s5f3oQJ8kvu1Usi8z/4QHLqi9DASteDtzWdO0sS69atMg+XIjnVbl47EpIS2Dx4c/7ap9EUM/bvl2DsLVvkvb29xHMNHWqZ9tesEUXWf/6B7dvlWK9eEhgOcjwgQJ73raVPXyxJTJSns5075Qlt3jzZ30l0tEQvLl8uA7DatW1taZ6wRJqcxlKcOyeFn+3tM+lJVvKuxMmrJwFIVsnEJ8Vn14JGo8kjSoljrV1baqT/95+ooR44IJNreXHup07BjBnp77/4QuRqk5MlMzY16yuV9u3FDu3cLYyDg6y3fP65TM907Zp1mpy3t4jkbN+e7tx/+SVzlbtCinbwtubcOXlczzhkACqVrETE9Qgib0TiNtWNT7d9mn82ajRFmNBQ0W/v0EHqpP/f/8n//VdflTX23KS5KQUbN8qUfqVKoq6aqgs/Y4ZotuzYIbPCWkXVhjg7yy9j1SqZOa1eXUbqd5JawhbkYeCJJyQY4vvv5cmskHJPB28YhoNhGCMNwyhrC4OKNHFxMh3k7i75mxmeJiuVrARIJL27k7suG6vRWJhr1+R/fePGEgk/Y4ZMz48aJZlXueXAAfENbdrA+vVSojUsDMqVk/Nly+oKp/lOp07yi6pUSVIhuneXaZX16+8eqdevL2v4Pj6SJtGokTwgFMLlbFOqySUCHwIWLHBYTImMzPy+fPm0l6kO/uTVk7ouvEZjQZKTZfa1WTNx6r17S0nyZ5/N/bR4ZGR6QkxgoATFffONjNrffz/duWsKEBUqiOMeOVKiKV9+Gdq1kxnVwYPhjz9EEjchAR58EEJCJJ3i2jVR0bt+Pb8/gdmYOkW/DbBQYkgxJiZG9nFxstcOXqOxKt9/L0krzZtLuvPatbBwIdSpk/s2N2yQNOvUdXpvb4mZHTYM3NwsYrbGWpQrJ7nwx4/DpUsyXf/YY6KM162bKN25uEDFivDxxyJXePiwfHE8PUWJdMQI+RIUghG9qQ7+W+BjwzDGGYbxgGEYjTJu1jSwSLF/v+yVkjWfwMC0U75uvng4eYiD13XhNZo8kZgoM7BPPy3LrnPniuJcmza5bzM5WQLx2rcXqfM5cyxmriY/8PMTpz53rojlrF8vv9Q33pD/zS+/DMHB8MgjIkwQGysPBj//LHKGDRvK9QU4GM/UPPgfU/ZZVUFRQHFTR84dqVE3jo7yJJmhEpJhGFQqKZH0Y5qNwcvZi4TkBJzsLSRsrdEUE27dklz2JUskkO7PP/OuD3/tmkif//WX7L/5Rke9FylcXeXpL/UJcPJk2LtXatN/+KGswZcuLRGaZ87I1P1nn8nU/ltvyQi/cuX8/ARZYmq52OCcziulCoS2aoHPg08VUFBKhgN3lJx6bNFjHL18lAMjD+SDcRpN4SYqStbYP/tMpuNfeUVUSy1R2S0xEbp0kfisZ54pUloomntx44ZoBvfrB0OGSGEbw5D/46tXS6GABQskLS91dtaG5JQHb6pUbYFw4EWKLP7rBJcIZu3JtSiliIyLxNHOkZKuusKERpMTiYmSfvbppxIf9cgj8MILeZuOT+XMGVGULllSBnHasRdD3N0lyC40FKZNk9S7L7+UL0OnTunFAq5ckSmjMWPkSbBEiXw1G8zIgzcM42HDMFYYhnHQMIxyKceGGobRwXrmFSFS12k6doRHHxUhhTso61mW2PhYzseeJ+CjAL7d/a1tbdRoChm3bsnA6sMPRfp17174/XfLOPewMJGRffJJea+dezHn/fdlBP/VV5JbuXmzPF2mcuWKfEkGD5a8y65dZXR/7Vq+mWySgzcMoz/wM1JRriLpKXP2wMvWMa2IEREh+27d5D/Q+fN3XVLWU6QGYuNj8XH10ZH0Gk0WKCXr6k8/LRlOixfDm2/K/9K6dS3Tx+HD4tyvXZNpfo0Gw5A1oB49xMk/8IAoJn3+uXxhqlSR1Lp//xXBhQMH5IHg4kW5P9726qSmjuBfBoYppZ4HMjyysA1oYGmjiiR79sg+NbAuQ4pcKvUC6gGw5fQWnSqn0WTDp5/K4GjFClGOW78epkyxTNtXrohOfJ06Mjvwzz8ijKPRAPL/+7ffJMVuxgxRxxs7FmrWFIe/ZImsEw0eLGUCd++GqlXl3v79Je8+1RfYAFOj6KsCW7M4fh3wspw5RZgjR2Sfui6ThYOv6VsTA4NTMaeo4F2BQ5GHbGigRlPw+eMPWV+/7z6ZIc1rdHwqUVFSLa5ECTh5UvoYM0ZKiGs0d+HnJ0pJzz4rxQcWLpTI+z59sr62SRP5soaFSVCHjTDVwZ8HqgF3Btu1Bk6Y06FhGPZACHBOKdXNMIyKwE9AKWAX8D+lVLxhGM7AfKAxcBnoo5QKN6evAkVMjBSYuXRJ3mfh4B3tHSlXohxh0WG6LrxGcwe3bonjLVNG0tUs4dxv35Y2lyyRgVXp0jLLqv/kNCYTHCyFDIYPh9On5Wnx8mXZR0XJsS1bZJD35JPyJbMRpjr4b4DPDcNIrbFUzjCMB4BpwGQz+xwLHCJ95P8B8IlS6ifDML4GhgAzUvZXlVJVDMN4MuW6LB6PCgnXrsnwwN5epmx8fLK8rHLJypy8epJR942itn9tklQSDoapvyaNpmiyZYvEOB09CosWZSrEmGvCwmQ6ftcuGD8+vU3t3DW5wscn2//rKCVf4DlzZC3e3d0mJpm0Bq+Umgb8CqwG3IF1wNfA10qpL03tzDCMIKArMCvlvQG0B5akXDIPeDTldY+U96Sc72AU5qHspUsyB/jcc/JfKpuPUtG7ImFXw2ge1JzBDQfjYKedu6Z48++/sry5YQO8845oyeeVxYtFiOzECVlS/fDDTLpTGo1lMQx47TWZJippu9Rnk72HUup1wzCmArWQB4ODSilz1fc/RQL2UjWgSgHRKQVtAM4CqfqtgcCZlL4TDcOISbk+KmODhmEMB4YDlM9i2rvAcOaMSRUoypUox8UbF7kSd4Ww6DACPAII8tILgZriSWgoPPSQ/Ons3Wu51OJffpGg58WLRXZco7EJNi4raK7GkwJuAXFAkjk3GobRDbiklNplZp85G6TUN0qpJkqpJn5+fpZs2rKcPSsRO+3bS1pFNjQq04hklUzohVCafNuEhfsW2tBIjabgsGULdO4suu+bN+fNuR86JNojqeUgZsyQIGft3DVFGVPz4J0Nw/gUuALsAfYCVwzD+MwwDBcT+2oFPGIYRjgSVNce+AzwNoy0ReYg4FzK63NAqqCOA1ACCbYrfCQmSh58mTKwbp3UhM+G1NH61VtXKelSkrDosGyv1WiKIklJkmbcubM49dWrc19+9fx5iX2qUwfWrBFHDzJL6qBXvzRFHFNH8DOAJ4ChSMpclZTXjwFfmdKAUuo1pVSQUqoC8CTwj1KqP7Ke/0TKZQOA31NeL0t5T8r5f1RhLa924YL810oNrMhhKaGGbw3sDDv2XNxDZZ/KnLhqVpKCRlPoSRUKq1xZNKFq1sxdO2+/LdPwc+fC6NGy3t6rl0VN1WgKNKY+w/YCHldKrc5w7KRhGJeAX4DBebDhFeAnwzDeAf4DZqccnw18bxjGcWTm4Mk89JG/nD0re/uUons5OHg3RzeqlarGvkv7qFyyMiHnC3DxHI3GwigFs2eLuExe60bFxYkq9DvvQKVKFjFPoylUmDqCv0H61HlGzgE3ze1UKbVeKdUt5fVJpVRTpVQVpVQvpdTtlOO3Ut5XSTl/0tx+CgypDj4pJWzhHsGAFb0rcir6FFV8qhAeHU5CUoKVDdRo8p/vv5cR9549MGiQ+fefPy8OfdUqef/uu1LVUzt3TXHFVAf/BTDJMIy0EMCU12+mnNPkRKoWcWAgtGhxT3msCt4VOBVziqfqPcVf/f/SQjeaIk1kpKy3P/206IJ8+60IhJlKUpLUZ69VS5z7uZShiP6z0RR3sp2iNwxj2R2H2gLnDMPYm/K+bsr9tsnYL8xcuSL7//3PpKFJcIlgrty8QpBXEDV8a1jZOI3G9ly/LlHyc+fCTz/JsZYtYelSKcRlKmvXwrhxEh3fpg3MmiWzABqNJuc1+Dsj1u+sb6rDu03lyhXJ9TExbDfQS6QATkef5uiVo1TwrkCD0g2saKBGYzuOHJGy2efOyZ/EuHEi4X3ffelhKqYSFgY3b4q6Xa9eetSu0WQkW4+jlMrFKpgmSy5fFgnDli1liv7jj3O8PNBTHPyF6xfo90s/RjQZoR28pkgQHQ333y+Zo99/L5U3PT3veVsasbEi+12vHjzzjEyIPf205YrOaDRFCXOFbjS54coVcfB790qY8D0I9g4G4MjlI1QqWUmnymmKBJcvw9Chss7+xx/w1FPmOfe//oLatUWk5swZOWZvr527RpMdJs0ZG4ZREikq0w7w544HA6WUGatmxZArV+Q/2Y0bJil2VPSuSBmPMmw4tYEqPlW0g9cUes6cgebNpSTDu+/KZJap3LgBI0fC/PkSSLdli0yEaTSanDE1D34+UBsp/nIRkazVmMqVK+mpccHB97zcMAxalW/FjnM7eKzGY/x94m9dNlZTaLl6Ffr3lz+D7duhUSPz7t+8WdLd3nwTXn8dnJ2tY6dGU9Qw1cG3BdoopXZb0Zaiy+XL6Y69QgWTbqnqU5XfDv9GxZIVuZl4k4jrEZT1LGs9GzUaCxEaKpHxW7aINOzllHDdvn3Nc+7Xr4OHh6TQHTtm8p+ORqNJwVQHfwK9Xp87Tp+WRcfq1cHb2+TqFhW8K5CYnMj95e7n0KhD+LvrVRBNwefsWQmiu3kTmjaFJ56QtLUqVaBdO9PbWbMGnnxSouM7dNDOXaPJDaY6+LHAe4ZhjAf2K6XMqiRXrEktX9W7t/znM5HgEjLivxZ/jQZlGljBMI3GsqxaJRryt27JVHyTJua3oRR8+im8/DLUqAFVq1rcTI2m2GCqgz8OuAK7gbvWgpVSZmavFiOOH5e9CWvvGalfuj52hh2rT6zmYORBypcoT5eqXaxgoEaTd3bulLrtVatK9bfcOPfoaBg8WMRuevSAefMsV/9doymOmOrgFyLlWsegg+zMIzwcXF2hWzcpi5Uq23UPSnuUpqxnWU5Gn+TH/T/SNLCpdvCaAklkpATAOTuLo8+tU164EJYvh+nTRfxGx5RqNHnDVAffBGiqlNpvTWOKJGfPSmrc6dPwwANm3Xpf2fvYdGoTNf1qcvzKcSsZqNHknrAwSX+7cgXef998556QIIF4qcI1rVtLrrtGo8k7pjr4g4CXNQ0pskRGisjN0aNmT9M3LN2Q3w7/RqfKndh5bqeVDNRoTOPwYQkpOX1a8toPHID168HOToLi2rQxr73//oMhQ2SS68QJKFlSO3eNxpKY6uDfAKYbhvEGsA/IVL9UKXXF0oYVGS5cSK8eZ2Yo8H2B96FQONo5cvXWVa7evEpJ15KWt1GjuQczZojYTCru7lKGdexYGDYMqlUzva24OKnRPm0a+PpKgZiS+mut0VgcUx38nyn7v8m8/m6kvNdBdtlx4QJUriyvzRzB1/StCaQHNYZFh2kHr7E5p0+Lc2/WDL78Uhy7t3fu1sgvX5bp+PPnRUf+44+1c9dorIWpDt6MDFZNGnFxEhpcqRK8+KLZdSyDvIJwsnfCxcGF2Ndi8XDysI6dGk0GkpJgzx6Zhj99WkqyggS/NW5sfntKwb594thLlZJI+U6dzA5J0Wg0ZmKSg1dKbbC2IUWS9etl/+CD8MgjZt9ub2dP5ZKVCYsO085dYzOeflqkYVNxdoYBAySYzhzi4mDBAvj8c1m/P3RI0ujeftuy9mo0mqwxtdhMjgKTWsI2G/bskX3lyvLfzs3N7CaqlqrK8SvHeX/z+3g6eTKq6SgLG6nRpLNzpzj34cNlK1cO/PzMm46PioKPPoJvv5Xo+gYNYPZsk0UcNRqNhTB1ij4EWWvP+GeecS1er8FnxZo1UL8+DBwoc5MrV5rdRJWSVfj7xN/8cfQPDMPQDl5jcWJjYfdu0Y5/7z1x6G++mR4bai7XrsFXX0HHjhKE98ADOqddo8kPTHXwdz57OwINgdeB1yxqUVHixAlJ7P3rr9wtXiIj+FuJtyjjWYYtZ7ZY2EBNcWfzZlGNu5KSB9O+Pcyda75zP3sWvv8eXn1VQk7OnNEqdBpNfmPqGvypLA4fNwwjBpgE/GVRq4oCSkFEhIzco6LMjqBPpaqPiHF7OHlwPvY8cQlxuDmaP9Wv0dzJxo1SyKVUKVGRa9cOAgLMa+P2bRmtT54sojU9e0rKnHbuGk3+Y+oIPjvCgAYWsKPoERUF8fHp6+65LIdVtZQ4eDtDivmdvHqSOv51LGGhphhy4wZ88QWEhMgKUsWKUhjG3FS15GR5KHjjDRGq6dhRHL2ZiSIajcaKmBpk53PnIaAMMBk4YmGbigYREbJXKaEKuRzBB3kF4WzvzI2EG3i7eBMVF2UhAzXFjf37ZaT9yy8S99mpk0S0m+Pc4+PByUnKwb70EpQuDd98Iw5eo9EULEwdwUdxd4EZAzgD9LGoRUWFmBjZ16wp9S9r1MhVM3aGHQEeARyJOsLVV65azj5NsWLePIn1BHjhBRGYMZXYWKmR9MsvIk8bHi5Kdps2yQyAnZ01LNZoNHklt0I3yUAkcFwplWhZk4oIFy/Kvl49iaTPA0FeQVy5qdWANbnj339ltN2iBfz8s3kBdNeuydr87t0SPNe3r2R8enqmCzRqNJqCiRa6sRanT8s+NlZKbuUhCbiuf10W7l/IxH8mcvnmZb7s+qWFjNQUZaKjZa29b18JBfn2W/Oc++3bos+0dy/8+is8+qhOd9NoChM5Tq4ZhuFjymYrYwsVly6Bo6NI1A4fnqemWpVrxbXb19h1YRe/H/ndQgZqijLTp0s+e8eOov/+1VfmV2pzchL1unnz4LHHtHPXaAob9xrBZ7X2fifKhHaKH5cugb+/jOS7ds1TUzX9pOhMKZdSnIs9R+ztWDydPS1hpaYIMmqUOPQuXWDcOGjSxLxAuuRkWWEqU0ZqvGs0msLJvRxzTkVmHgLGAnoNPisiIyXBeO/eXKfIpVLGowwAbk6Scnfk8hGalG2SVws1RZDoaHHuvXrJyNvV1fz7x44V0cXQUHHyGo2mcJKjg89q7d0wjIbAh8ADwExAl47IishI8EgpEJPLFLlU/N39MTBwsJNf1+Gow9rBazJx+bLUbP/5Z3n/yivmO/dly2DECKlw/MYbkgKn0WgKLyYnuBiGUdEwjB+BHcBloJZSaoxSKtJq1hVmLl0CFxd5nccRvKO9I75uvtxOvE2D0g2wN7T0vyadjRuhbl2YOBG8vGDWLPOUkW/fhiefFMnaUqVE+Oatt/Sau0ZT2Lnn2rlhGKWAicCzwBagpVJqp7UNK/RERkqE06hR8t83j5TxLENkXCT/PfOfBYzTFBW++kq+Yv7+4ujvv9/8NpydJZf97bfh5ZcluE6j0RR+cnTwhmG8DrwEhAM9lFLml0Mrjty8Cdevy9T8449bpEk/Nz8i4/RkiSadsDDJb3/oIUljM2dKPjkZPvxQvp5Vq8IPP+gRu0ZT1LjXFP3bSOW4s8BIwzCWZbVZ38xCRmSKI756VVRGLICvmy+RNyKZGTKT8p+UJzFZxzYWV86cgcWLoXp1ycT89FPznHtUlCR2vPoqLFggx7Rz12iKHveaop/PvdPkNHeS6uBXrBClkXXr8tykn5sfUXFRODs4c+baGcKjw6nioyt7FDd+/RX69ZN1cwcHmDZNHL2pnD4tJWHPnIGvv86zRINGoynA3CuKfqCN7ChapDr4qChRCrEAvm6+XL11lcolRR/0UOQh7eCLAUrBb79J+tq5c/DmmyIRO2uWSM86O5ve1unT0Lq1tLVhg8W+mhqNpoCiBWqswZkzss9DHfg7aVimIQAXrl8AJFWue/XuFmlbUzBJSpKc9C8zKBM/8ACsWmV+ChyAjw80aCDR9o0aWcxMjUZTQNEO3hqcPCnzp4mJFnPwnSt3xt3RnU2nNxHgHsDhqMMWaVdTMJk1C957T75Kjz4qAXGOjlCunPnV244dk5x2T0+ZDdBoNMUDXejRGpw8Cb6+8rpmTYs06ezgzH2B97Ht7DYG1B9Ag9INLNKupuBx/LisjTs5wcyZsu5epYo8K5rr3Pfvl9S5oUOtY6tGoym46BG8NTh9WiKffvtNysVaiJZBLZn27zRW9FuBv7u/xdrVFBy+/16csb291GDPS6Xh//4TKQZnZ5gyxXI2ajSawoEewVuDyEgoWxaaNcvdYmk29Krdi8TkRFYdX8WtxFskJCVYrG1N/qKUCM0MGSJV4EJD8+bcd+6UaHl3dxHAMSfSXqPRFA20g7cGly9LoN2Gu6T880RN35oYGKwJW4P7u+5sO7vNou1r8gelRBp24kTJT9+92/zSrhlJSoIBA6SC3IYNEnWv0WiKH9rBW5orVyQP6d9/4e+/Ldq0s4Mz1UpVIyI2gmSVrAPtighffy0O/r77YOFCkZ3NDfHxEBcn0/tLl4pzz2MZBI1GU4jRDt7SRETIPjkZ6tSxePMNyzTk2JVjuDi4aAdfiElMlJF6zZowcqSsk69Zk16fyFx27pS676+8Iu+rV5eIe41GU3zRDt7SXL+e/toCRWbupI5fHVGxK1mFQ1GHLN6+xvqsXAmVKknFt8OHpYrbkSNSCc5clJLSrs2by8pQx46Wt1ej0RROdBS9pYmNlb29PVSrZvHma/vL4mxpj9J6BF8ICQuDhx+WEfb334twTV6kEl57DT74AAYOFE36EiUsZalGoynsaAdvafbvl33lylapu1nHX6b9a/rVpGu1riilMHSlkALPxYvQqRPs3SvvP/sMOnfOW5vnzsn6/bPPStlY/TXQaDQZ0Q7e0qxdKyP37dut0nxF74q4OLjgZO/EuObjrNKHxrJs2iTR8bGxEt0+eLCM3PNKYKCs4wcHa+eu0WjuRjt4SxMSIgW6vb2t0ry9nT2VS1Ym9EIo4dHhuDi4UNqjtFX60uSdixfhyScleG7NGmjaNO9tLl0q8rMvvyxr+RqNRpMVOsjOkly8CBcuyDT9iRNW66a6b3XOx56n0meV+GbXN1brR5N3RoyQ4Leff867cz91SmYAevYUkcT4eIuYqNFoiijawVuSwylBbyEh5ouGm4GnkyfX468T7B2sI+kLMBMmyGj7mWegbdvct3P1KowfL4F5ixbJ65UrrRLiodFoihDawVuSS5dk7+pqsSpyWVHOqxznYs9Ro1QN9l/ab7V+NLkjOhr+9z+pBvfEE7LPDQkpSsSxsTBjBvTrJ1Pz06blLqVOo9EUL/QavCWJjJR9zZpWHcHX8a9DskqmrGdZ1oSt4XbibZwdnK3Wn8Y8Bg+G5ctlBD9pknkjbaVg/XpJebt+XWI2y5eX6fnUAoUajUZjCnoEb0lSR/ANGli1m9RUOTdHNxKTE/U0fQHg3Dn46CMpFrN0qayVT51qnnMPCYEHH5QiMf/+Cy1biq48aOeu0WjMR4/gLUlkpIzcGze2ajdVS1XF0c4RwzBY9MQigktYbzlAYxqPPioOOiAA2rSBQYPMu//XXyV4ztdXRu/PPJN72VqNRqMB7eAty+3bULq0iItbESd7J+oF1GPn+Z18/vDnVu1LkzPJyVIgJiQE3nkHXn/d9HsvXZKig40bi+jNO+/Ac8/p9XWNRmMZ9BS9JQkJgYoVbdJV/YD6nIo+xZ4Le1h+ZLlN+tRk5pVXpHb7U0+JFvxzz5l23/XrMGWKiB327y8PCe7u8nCgnbtGo7EU2sFbivh42LNHQqhtQMWSFYm4HsFH/37E0OVDbdKnJp3t2yWavXlz+PFH2Ljx3s45IUGkZatUkeC7jh0ln92K8ZgajaYYo6foLcWFC7JPTrZJd+VLlAcgyCuISzcucfH6RQI8AmzStwbGjIEyZcS5m1rg5fffRfjm/vslEK9FC+vaqNFoijd67GApzp2TfdWqNumuXkA9ABzs5Bltz8U9NulXA+HhsGMHvPjivZ37gQOyRg/w+OOwerWM9rVz12g01kY7eEtx8KDs69SxSXdlPcsComoHsPfiXpv0W9zZulUKxdjZSR33nPj+e5Gnfestmdixs5M0OF0YRqPR2ALt4C3Fvn2yb9jQJt35uPoAcDPxJoGegXoEb2WUgg0boHt3cHSEv/+WtfSsuHkThg2Dp5+G++6Ddev0OrtGo7E9eg3eUly7JvtmzWzSnYOdA94u3kTFRbHqqVUEeQXZpN/iRHKyCM6sWydr7YcPS577qlXZr8Tcvi0CNaGhEhU/eTI46L8yjUaTD+h/PZZizhzZlytnsy593Xy5FHeJ2v61bdZncWL8ePjkE5lSb9ECZs+G3r3Bw+Pua5WS65yd4eGH4d13Za/RaDT5hZ44tASJifnSbaWSlTh+5ThnYs4wcd1ETl49mS92FEV+/12ce69eEBUFW7aIxnxWzn3nTlmZ2blT3mvnrtFoCgLawVuCkBDZ3yvqysJU9K7I6ZjTXLt9jbc3vs2/Z/61af9FlWPHoG9fcdoLFoCPT/bXzp0rufBRUSJgo9FoNAUFPUVvCZanKMlZWYP+ToK8goiKi6J8ifI42TvpSHoL8c03UuRl2bKci8Vs2CDBdO3bw5IlpufDazQajS3QI3hLcPiw7Bs1smm3gZ6BAETGRVLbr7Z28Bbg/HkJoqtdG4JyiFs8c0by2qtUgcWLtXPXaDQFD+3gLcGpU7KvXt2m3aZGzp+9dpZ6AfV0qlweeOcdqFBBnPrBgyJikxNly0pNoRUrwNvbFhZqNBqNeWgHbwkiIiSEukIFm3Yb6CUj+NMxp6kXUI/r8deJvhVtUxuKAhcvwsyZ8pw2eTIcOiRFYLIiMVFUie3t4e23pWCMRqPRFES0g7cE169D/fo2T3gu5yUpeXND5zLqvlHEvBqDt4u3TW0o7Fy+DA0ayDPa0qUwcWLOasMvvSShFleu2MxEjUajyRXaweeVI0dE5GbgQJt37ensSZBXEDcSbuDs4IydoX+d5vLRRzIiX74cHn0052sXL4ZPP4Unnsg5sl6j0WgKAtoj5JXU5Od27fKl+46VOnIqWmIAXln9ClM2TMkXOwoTSsGNG7Brl+jFd+wIDz2U8z1HjkgefIsW8OGHtrFTo9Fo8oJ28HnlwAHZb9mSL91X8K5AxPUIbifeZt+lfSw6sChf7Cgs3LgBrVuLYE2TJhI1P2pUzgVgbtyAnj3BxQV+/jnn1DmNRqMpKGgHn1dSq8jZqMjMnaRWlbt44yIty7XkYORBrt68mi+2FGSOHIHPP5dQic2b4bXXRMTm3Ll76xMlJEgw3Y8/5pw6p9FoNAUJLXSTV1IdfI0a+dK9n5sfAOHR4bQq1wqArWe30qVql3yxpyBy6ZJIFMTFgb8/jBsncrL3IjlZgul8fUW6VqPRaAoTegSfF5SC06fB1TXfkqHbVGiDt4s3X+z4gqaBTbE37NlyOn+WCwoisbEiSHPzplSFu3hRNObvRWSk6Ml36gTx8da3U6PRaCyNzRy8YRjlDMNYZxjGQcMwDhiGMTbluI9hGKsNwziWsi+ZctwwDONzwzCOG4ax1zAM28rEmUJ4uPz3DwzMNxO8XbzpUb0HSw4uIeZ2DI9UfwR3J/d8s6egMWQIbNsGixZB27am3bN5s6y4bNgAI0ZI/XeNRqMpbNhyBJ8IvKiUqgU0B0YZhlELeBVYq5SqCqxNeQ/wMFA1ZRsOzLChraZx7JjsBw/OVzOebfIsAM+seIZf+/zKhAcm5Ks9BYUbNyS17fnnpSqcKXz+uTwIuLjIg8GwYTkH4Gk0Gk1BxWYOXikVoZTanfI6FjgEBAI9gHkpl80DHk153QOYr4RtgLdhGGVsZa9JRETI3lTvYSWaBzWnUZlGbDu7DQClFInJ+VPCtiCxYoXsO3Qw7fqbN6XQTLdukkLXoIHVTNNoNBqrky9r8IZhVAAaAtuBAKVUiqfkAhCQ8joQOJPhtrMpxwoOR4/KvmTJ/LUDeLzG40TFRXHpxiUCpwfy+fbP89ukfGXWLHjySdGMb90652tv3BDn7uoK69fDL7/o4jEajabwY3MHbxiGB/ALME4pdS3jOaWUApSZ7Q03DCPEMIyQyMhIC1pqAtu3yz462rb9ZkHFkhUBWHV8FS4OLmw5UzwD7Y4eheHDJbe9SRPYvx/c3LK//tw5aNMGBg2S976+ojOv0Wg0hR2bOnjDMBwR5/6DUurXlMMXU6feU/aXUo6fA8pluD0o5VgmlFLfKKWaKKWa+Pn5Wc/4rDh7VvY2LjKTFR0rdQRg9cnVtCrfii2ntyDPS8WHlSslz/3HH+G+++Drr7OfXPnvP1EXrlRJiss89ZRNTdVoNBqrY8soegOYDRxSSk3PcGoZMCDl9QDg9wzHn06Jpm8OxGSYyi8YREbK8LAADPn83P14qMpDfL/3e1oEteDijYucvHoyv82yGSdOiMOuXBmOH5dI+MaNs772228lL37JEgmi27NH1t01Go2mKGFLoZtWwP+AfYZhhKYcmwC8D/xsGMYQ4BTQO+Xcn0AX4DgQBwyyoa2mERsLpUvntxVpVPSWafqSLjJs3XJmC5V9inY9U6VkWr5PH3neWrMm619JYqKcL1NGnPnHH0vyg67lrtFoiio2c/BKqc1AdglHd8U5p6zHj7KqUXkhIUG2AuTgRzcdzYyQGcQnx/NC8xeo6Vszv02yKpcuSZ76rymLPR9+CHXq3H1dRAT06yfX794tTv6FF2xrq0aj0dgaLVWbW+LiZN+iRf7akYFqparhZO/EwUsH+bjzx/ltjtU4fBgGDJCpdaVgyhRZc+/U6e5rN22SLMbYWJgxA5ydbW+vRqPR5AfaweeWbZJzfs86ozbEwc6BIK8gTl87TWJyIqEXQqnhWwMPJ4/8Ns0ihIfDn3/CO++Iwx4yBMaOhWrVsr4+KgoeewxKlYK1a6F2bZuaq9FoNPmKdvC5JXVeuGbBmgYv51WOMzFn2Hx6M+3mtWNF3xV0rdY1v83KM7t2yShdKYmUX7UK6tbN+Z733oOYGMlt185do9EUN3Sxmdzy88+yL1OwxPUqlqzIiasnaBrYFAc7hyKRD5+cDC++CA4O8O+//9/efcdHVeX/H3+dVGroEgKEIiVEkRCq0lG/AmtBERdERQFXURd1rdhQV0VXV9dVVlwRZJX1h9LEDiIgWCguoPQSepMQUgmp5/fHiaGkkjaZ4f18PHzcuffcmflwvfCZc+4pbohbUckd4OmnYe7c/J/Li4j4OiX4koiJcZPb1KtX6R7qRtaP5FDyIVIzUoluFM3yPcs9HVKpZGfDhAlu4ZeXX3ZdHoqaGz4jA9LSoGZNDX8TkXOXEnxJfPqp215yiWfjyEenMDf4e+qaqfRv3p8f9/1IYlpiEe+qnNLSoGtX98y9a1c3Q11xvPaam0c+Lq5cwxMRqdSU4Eti9my3vf12z8aRj77N+wLw2bbPGNR6EJnZmSzcsdCzQZXQW2+5Z++33OJmqatatej37N4NzzwDbdtC3brlH6OISGWlTnYlEZMzQ1wl62AH4Gf86Bnek0PJh7i46cV8NeIr+jTv4+mwztr27W6ses+eMHVq8SYLTEk5uXLvP8/ttXZERFSDL5Gbb3Y9vlq08HQk+erfvD874naQlJbEFa2uoEpAFU+HdFbS0uCOO6B6dbcqXHGS+5Ejbh33JUtg0iQIDy/vKEVEKjcl+JLYvx8aNqwUc9DnZ1DrQWTZLD7b+hmHkw/zxLdPsOnIJk+HVWx33QXffguvvOKa2oujenUICYF589yc9CIi5zol+LM1ejS8/z5ERno6kgJ1adyFxjUb85cFf8FieX7Z88zdPNfTYRVpwgTo1cs1yV9/vavFF2XpUkhMdGv+fPMNXHVV+ccpIuINlODPRloafPCBe52Z6dlYCuFn/OjSuAuxx2PZeWwnnRp14ottX3g6rEK9956bcvbYMZekX3ih8PO3bIEhQ1yz/Pjx7lhRw+dERM4lSvBn46efID3dvX7nHc/GUoTpg6dTPbA6/1n3Hwa1HsSP+34kLrXyjRv76Sdo2hRuu801inz/PcyfD61b53/+wYNw551uZroFC9yPgr/9rWJjFhHxBkrwZ2PRIrcND3cLj1diIcEhDGw9kOnrptOyTkuybTYLdizwdFinWbPG9ZJPToZHHnHTC9SqVfh77rvPNeHfdZdbA/7JJ93zdxEROZ2GyZ2NTz5x27Awz8ZRTK9c/goLdyxkwY4FNKvVjMPJhz0d0mleesk9O9+2DerXL/xca10T/LRp8Pzz0KpVxcQoIuKtVIMvLmtP9pq/4QbPxlJMzWo3o0vjLvzv4P/YMW4H93a/19Mh5UpPh5kz3bKvRSX35cvhiitOdqZTchcRKZoSfHEZAxER0KCBayf2En2a9WHL0S0cTnG192yb7eGInGeecdv+/Qs/b/Fil9z37HET2YiISPEowRdXUhJs3gydOnlVd+229dxA8kPJh4h+O5q/Lv2rhyNyTewvv+wS97XXFnzerFkwcKCbT2jp0kq3cJ+ISKWmBF9co0bBunWVcnrawjSt1RSAvQl7qRJQhXlb5mGt9Vg8CQnwxBNunZ5p0wo+b9o0GDrU/Z5assTNKyQiIsWnBF9cP//s1i6NiPB0JGel/XntMRjWHlrLyA4jWXtoLZ9t/cxj8dx9t9uOGVN4jbxvXzccbtGiop/Ri4hIXkrwxXH8OOzc6V57WYKvHlSdNvXasPbwWkZ1HEWbem14dNGjZGZX/EQ9kybBjBkucd90U97ymBg3pj072zXLv/UWVPGuafRFRCoNJfjiWL/+5Gsva6IHiAqNYu2htQT6BzLx0olsPLKRjzZ8VOFxrF3rtv/4x+nHjxyBe+91v52efto1loiISOkowRfHL7+4batWrhe9l4kKjWJX/C5ij8dybcS1fDz0Y264oGKH+p04AbNnw9VXQ3CwO5aV5aakPf98V7u/7Ta3TGyXLhUamoiIT1KCL46OHd10aRde6OlISmRgq4EAfPDLBxhjuD7yegL8AipsyNzChS6xHzvmauq/i4tzs9JdeqlrJHn7ba+ZQ0hEpNJTgi+O2rXdIOxBgzwdSYl0CO1Au/rtWLRzUe6xL7d9Sds32xJ7PLZcv3vxYnfZlixx09H26wcZGa723qCBa46fO9frujaIiFR6SvBFsda1LYPX1uABIupHsO3ottz9ZrWbEXMshue+e67cvnPNGjeRTa1abpGYF190yf2Pf3SjDq0teu55EREpGSX4ouzb56qe/v5eneA7hnZky9EtLN65GIDIBpHc2uFWJq+eTPyJ+DL/vowMN4Y9OBjmzIF69dxz+CFDXI09Otqr5gsSEfE6SvBFWbfObaOioGZNj4ZSGqOjR9Oidgtu//T23GfvY7uMJS0rrcx71Gdnu4VkrHWrvfXuDampcM018NlnbvjbvZVnWnwREZ+kBF+U3xN8586ejaOUwmqG8Vz/59hxbAdLdy0FoFOjTkQ2iGT6uull+l1vv+0Se3Q0PPCAOzZsmOts9+67bhy8iIiULy0XW5QVK9zWC8e/n+mattcQ4BfAwpiF9GvRD2MML132EsH+wWX6PfPmQePGsGoV+OX8hLzvPjf1bH4T3IiISNlTgi/KqlVu26+fZ+MoA9WDqhPdKJrle5bnHruyzZVl+h179sCCBW6++eRkV2u/7jo39ayeuYuIVBw10RflggvcZOjt23s6kjLRK7wX3+/9ntUHVuce23Z0G88ufbZU4+I3bYLBg6FNG7c/fLjbHz4c9u5VchcRqWhK8IVZvtytdnLllT6ToR685EEa1WjEPV/ck3tsxf4VTFgygWW7l5XoM7OyoGdP+OQTN7b91Vfh8cfdGPhp0yA8vKyiFxGR4lKCL0zfvm7bqZNHwyhLoTVCGdVxFCv2r+DXw78CcG3EtdQIqlHiznZ//7uble6hh1wT/a+/uufw//wnjBhRhsGLiEixKcEXplkztx0zxrNxlLHhFw4H4InFTwDu2fzQyKF8vPFjjmccP6vPWrPGTRPQubMbGrdokau1T5gAf/5zmYcuIiLFpARfmKNH3dh3H1uztF2Ddtx00U2nPYcf2WEkyenJzN0096w+a+ZMt/3b39xTjMsug6VLXYIXERHPUYIvyNGjkJBwshbvY9qf154DSQdyZ7Hr1awXFzS4gIPJB8/qc777zq3+9vbb8NNP7ljv3j7TZUFExGtpmFxBlucMJevVy7NxlJPIBpEAbPhtAz3Ce+Bn/Phl7C/4meL95ouPd10TYmLcCnA//+xq7927l2PQIiJSbKrBF2TlSrft2tWzcZSTzmFuZr6V+1fmHvs9uW89urXI90+d6pJ71apuGdi5c32uq4KIiFdTgi9I+/ZuOrbLLvN0JOUitEYotavU5pmlz5w2/v3NlW8SOSmSHXE7CnxvXJwbCgcuwX/3nVvvXUREKg8l+IIMG+ZWkmvSxNORlJvoRtEkpCXwxoo3co8NaTeEAL8AXlz+Yr7vSU+Hiy+G/fvhkkvcrHVePk2/iIhPUoI/h824bgYAr694nbTMNAAa1WzEmOgxTF83nT0Je3LPtdaNcf/DH2DrVujWDb7/3qemCBAR8SlK8Oew0BqhvPp/r7IzficPLXwo9/jDPR4G4OXvXyYlxS3x2qSJG1DwzTdu5OCSJR4KWkREikUJ/hw3rts4wmuF88bKN/hh7w8AhNcKZ2SHkczbMo/B12Zz1VVw4ACEhLj3vPCCz00NICLic5Tgz3H+fv7MGjoLgG9ivsk9PvGyicztv4VvFvpRrx7ceCMkJsLLL8M99xT0aSIiUlkowQtdGnchon4EE5ZMyK3F16tan1dfqgbA6D+l8t//wqOPwoMPejJSEREpLiV4AU52uOv79jXc9/hhIiLgww/hvAHvsqvNw0yc6JrmRUTEOyjBC+CGzA1pN4SMxQ/z+gsN2boV+vWDm+/exUe736T/zSs1/ayIiBdRgpdcM66bwYUB10CdHVw4dA6LF0OzPY8TWiOUcV+OO21CHBERqdyU4CXX5/OD2bCsNabRWta3voXH/76de8ZWYeKlE1mxfwX//fW/ng5RRESKSQleANi5E4YMAWsN59kOVKtqmBN8NcbALR1uoXNYZz5c/6GnwxQRkWJSgheSkqBlS/e6d2/YuLwVV0YMYlPsJuZtnoef8WP+sPnMHzbfs4GKiEixKcGf46ZOdXPLA4weDUuXQt26ML7neACunXktm2M306hmI/z9/IlLjWN/4n4PRiwiIsWhBH8OW7TIJfUNG2DgQJg8+WRZVGgUn9/4OQADPhhAZnYmmdmZdHmnC2M+HYO11kNRi4hIcSjBn4NSU+GBB06uhPvmm/DFFxAQcPp5g1oP4l+D/sXuhN10facrfsaPcV3H8dX2r5i9aXbFBy4iIsWmBH+Oycx0K8D9vp77+PFw990Fnz+2y1iGRg5lzaE1/LD3B+7uejdRoVHc+9W9JKUlVUzQIiJy1pTgzxHr1sHMmW7BmE2b3LHHHive7HQTL50IwAvLXsDP+DH5D5M5mHSQpxY/VY4Ri4hIaSjB+7gTJ2DUKIiKgmHDXPP8TTfBli3w/PPF+4zz657PYz0f48vtX/LHWX8k9ngsY6LHsDdxrya/ERGppAKKPkW81SefwK23Qnz8yWO//goXXnj2n/V036fZGreVWRtnMWvjLGZcN4Mb299YVqGKiEgZUw3eRy1eDIMHn0zu770HR46ULLkDBPoH8vHQj9l9324AHlv0GN/EfEPMsRhGzBlBYlpiWYQtIiJlRAneB2Vnw8MPu9f33++a5UeOhPr1S//Z4bXC+faWb9mdsJvL37+cH/b+wEcbPqLn1J7sTdhb+i8QEZEyoQTvg4YPh9WroVs311u+SpWy/fx+LfrxeK/HARj7+Vja1W9HzLEYuk3pxpqDa8r2y0REpESU4H1IUpKruX/0kdufO7f8vuu5/s+x9Nal3BB5A9vitpGSkUJiWiI9p/Zk9YHV5ffFIiJSLOpk5yOys9188rGxbn/yZGjUqHy/s3ez3vRu1puRUSP598//ZsavMwirEUZojdDy/WIRESmSavA+IDvbTTX7e3KfNw/uuKPivr93s958cN0HvHPVOxxIPkDEmxF8tvUzUtJTKi4IERE5jRK8l5s8GapWhQUL3P748XDNNZ6JZUz0GKYPnk5KRgpXfXgVjV9tzNfbv/ZMMCIi5zgleC+TnQ2HDsG0aXDzzTB2LKSnQ506bvGY4sxMV55u6XALC25aQHRoNAlpCQyYMYATmSc8G5SIyDnI+NKqYJ07d7arV/teB6/ERPj3v13P+IULIS7uZFmtWvDDDxARAX6V6OeatZauU7rmdrgb23kskwZNwhjj4chERHyHMeZna23n/MrUya6S27wZ2rU7ud+mjUvma9ZA9epuZrrQStinzRjDj6N/pM97ffhh7w+8tfotQoJDGH7hcFbuX0mnsE50DO2ohC8iUk5Ug6/EHn/8ZJP7VVfB66/D99+7ueVbtYLPP4cWLTwbY3Es272MftP7kWWzAAjwCyAzO5MWtVswpN0QHurxEOdVP8/DUYqIeJ/CavCVqFFXTnXq8/QPPnDzys+Z45679+jhEr03JHeAXs16cfCBg3w54ksAsrKzeOnSl4ioH8HrK16n25RubD261cNRioj4FiX4SujYMbjsMvd63jwYMQKMgaZN3eIxX3/tOtV5kwbVGzCg1QBW374ai+WRRY9gsTzb71nCaoZRu0ptT4coIuJTlOArkQUL4N13oW5dt3/HHbBiBbz2mtu/4QbXez4oyHMxllansE480uMROoZ25KvtXzF+0XiOpR4jMS2RjKwMDasTESkjegZfCcyaBQ88AHv2nDwWFQXWwrp1cNddMGmSx8IrNx9t+Ii/fP0X9iftB6BqQFVSM1O5vOXl3N/9fro36Y4xhlrBtdQZT0QkH4U9g1eC96CkJFdjv/9+tx8dDf/6F4SFwe23u+fwc+a4Dna+bEfcDt5Y+Qavr3g93/KI+hF0a9wtd797k+70a96PNvXaKPGLyDlNCb4SOnLEzRWf5TqWs2wZ9OzpXo8bB2+8AVOmwOjRnouxollr2R63nSW7lrAwZiHf7f6OkOAQ6lStw+HkwxxMPkh6Vnru+ZENIhlx4QgubnoxfZr3wc/oiZOInFuU4CuJ48fh/fdh1SpXcwcYNgwmToTmzU+eN2UKbN8OL77okTArDWstx04co25V1ynhjk/vYHPsZmLiYzicfJiM7Izcc69uezVRDaMYesFQLmhwgWr2InJOUIKvJB56CF555eT+gw/Cyy+f3D9+HKpVq/i4vJG1llX7V5GSkcKyPcuYsGRCblmDag148JIHGddtHFUCqngwShGR8qUEX0lcdJGbeS421iXyqlXh4EG3bvvs2a7H/KxZMGCApyP1PqM+GcXHGz8mPSs9txk/yD+IPs36EBIcQlpmGrdH3069avUAaN+wPSHBIZ4MWUSk1Lw2wRtjBgCvA/7AFGttoY3Wnkzwx4+7meUyMvKW/fYb/PQTzJwJF1/s5o7fv98t8bp+vestHxEBQ4bAn/8MDRtWfPy+ZN7meTy26DEOJh+kcc3GxB6P5XDK4dPOCfIPok3dNozvNZ46VeqwM34nu+J30SSkCS3rtKRr466aXU9EKj2vnIveGOMPTAIuB/YBq4wx8621Gz0ZV1YWvPOOm4xm/36YPh2qVDm5FntRoqLc1s8PwsPh+uvdf5GR5RbyOWdwxGAGRww+7dgrP7zCin0riE2NJfZ4LNuObmP9kfWMmDMi9xyDwXLyB2+L2i24Pfp2ALbFbSPQP5CQoBBqBNXAGIOf8cPf+NO2flsGthpYbn8eP+NHcEBwuX2+iPimSluDN8ZcDDxtrb0iZ388gLV2YkHvKesafGoqPPWUW80NIDPTTRubnn76eVFREBjoEn2zZvDkk+74wIFutbcrroDLL4eWLV25+n953vGM4+xL3EdKegpv//w2AAt2LGBn/E4PR5a/86qfx/l1zqdxSGP8cuanqhpYlWD/YLJsFklpSXneUy2wGkH+QWRmZ5KcnnxaWaB/INUCq2EwBPoHcleXuzQKwceF1wqnWqA6+fgar6zBA42Bvafs7wO6FXBumfv6axg61I1Vh5OzxwUGwjPPuMlnhgyBb76BtWtdWcOGrlbepo3b37DBJX2pfKoFVqNNPfc/avKVkwHXce/UYXin2hS7ib0JezmQdIDY4665pk29NgxoNYAZv87gvbXvkXAigYS0BJLTk7FYOoV2YmDrgaRnpfPUkqfyfGa/5v244vwrSE5P5rllz+UpH3D+APo278vhlMO89tNr/Jby22nlIcEhVAusRmZ2Zm5Mp6oVXIuqgVVJz0onLjUuT3ntKrWJPxEPwKRVPjiTkpwmwC+AWsG1AAirGUZYzTDSs9L55fAvec5tWqspDas35ETmCdb/tj5PefPazalfrT4pGSlsOrIpT3nLOi2pW7UuSWlJbDm6JU9563qtqRVci/gT8WyP256nvG39ttQMqsnR1KPsPJb3R3dkg0iqBVbjSMoRdifszlPe/rz2BAcEcyj5EPsS9+Up7xDagUC/QA4kHeBA0oE85dGNovEzfuxN3Mvh5NMf7xlj6NSoEwC74nfl+bvn7+dPx9COAMQci8nzd+/H0T/Sul7rPN9ZHipzgi8WY8yfgD8BhIeHl9nnpqdDSIhL6K1auVp3QICrgT/6qDvnuedcDb9JEzc5TfAZrahK7t7FGFNgU3hUaBRRoVH5lt3Z+U7u7HxnoZ/9cI+HOZB0gH2J+ziUfIgsm0Vkg0guangRJzJPcMF5F+R5T4eGHWjXoB3J6cl0bdw1T3mnRp1oXa81x1KP8fWOvFP8dmvcjRZ1WnAk5QiLdi7KPW6tJTEtkQGtBhBWM4yJyyfy/rr387x/dMfRNK/TnLWH1jJ74+w85WO7jCWsZhgr96/k0y2f5im/r/t91KtWj2V7lrFg+4J8r0nN4JosilnEkl1L8pQ/2ftJggKC+GLbF/y498fTCw38td9fAdfn4ucDP59WHBwQzBO9nwBg5vqZeZJUzeCaPNzjYQDeX/d+nsWO6levz73d7gVgyv+msDv+9CQSFhLG2M5jAffj6FDSodPKW9RpwaiOowB47cfX8vwjH1E/ghEXucdDLy5/kZT0lNPKL2p4EUMvGArAs0ufJSPr9M49XcK6cHXE1WTbbCYsnsCZeoT3YECrAZzIPMHz3z2PxRJ/Ip4TmScASElPIf5EPBnZGbnHTpWclkywfzBpWWn5lielJRHgF0BqZmr+5elJ+Bk/UjJS8i1PTEvEWktSWlKB5VnZWYWWp2elk5Sef3lCWgJBmUEkpyfnX34igQC/gALL40/Eu/jT88ZvjMn9YXw843iecn8//9zy/P78mdmZeb6vvKiJXkRExEt563Kxq4DWxpgWxpggYBgw38MxiYiIeIVK20Rvrc00xtwDfI0bJjfVWrvBw2GJiIh4hUqb4AGstV8AX3g6DhEREW9TmZvoRUREpISU4EVERHyQEryIiIgPUoIXERHxQUrwIiIiPkgJXkRExAcpwYuIiPggJXgREREfpAQvIiLig5TgRUREfJASvIiIiA9SghcREfFBSvAiIiI+SAleRETEBynBi4iI+CBjrfV0DGXGGHME2F2GH1kfiC3DzztX6TqWnq5h6ekalp6uYemV9TVsZq1tkF+BTyX4smaMWW2t7ezpOLydrmPp6RqWnq5h6ekall5FXkM10YuIiPggJXgREREfpARfuH97OgAfoetYerqGpadrWHq6hqVXYddQz+BFRER8kGrwIiIiPkgJvgDGmAHGmC3GmO3GmEc9HY83MMY0NcYsNsZsNMZsMMbcm3O8rjFmoTFmW862jqdjreyMMf7GmDXGmM9y9lsYY1bk3I8zjTFBno6xMjPG1DbGzDLGbDbGbDLGXKz78OwYY+7P+Xu83hjzoTGmiu7DohljphpjfjPGrD/lWL73nnH+mXM9fzHGRJdlLErw+TDG+AOTgIFAJDDcGBPp2ai8QibwgLU2EugO3J1z3R4FFllrWwOLcvalcPcCm07Zfwl4zVrbCjgGjPZIVN7jdeAra20E0AF3LXUfFpMxpjEwDuhsrb0Q8AeGofuwON4DBpxxrKB7byDQOue/PwFvlWUgSvD56wpst9bGWGvTgf8HXOPhmCo9a+1Ba+3/cl4n4f5RbYy7dtNzTpsODPZIgF7CGNME+AMwJWffAP2BWTmn6BoWwhhTC+gNvAtgrU231saj+/BsBQBVjTEBQDXgILoPi2St/Q6IO+NwQffeNcB/rPMTUNsY06isYlGCz19jYO8p+/tyjkkxGWOaAx2BFUBDa+3BnKJDQENPxeUl/gE8DGTn7NcD4q21mTn7uh8L1wI4AkzLecwxxRhTHd2HxWat3Q+8AuzBJfYE4Gd0H5ZUQfdeueYaJXgpc8aYGsBs4D5rbeKpZdYN29DQjQIYY64EfrPW/uzpWLxYABANvGWt7QikcEZzvO7DwuU8I74G92MpDKhO3mZnKYGKvPeU4PO3H2h6yn6TnGNSBGNMIC65z7DWzsk5fPj3Zqec7W+eis8L9ACuNsbswj0a6o97nlw7p6kUdD8WZR+wz1q7Imd/Fi7h6z4svsuAndbaI9baDGAO7t7UfVgyBd175ZprlODztwpondNjNAjXuWS+h2Oq9HKeFb8LbLLWvnpK0XxgZM7rkcAnFR2bt7DWjrfWNrHWNsfdd99aa0cAi4Hrc07TNSyEtfYQsNcY0zbn0KXARnQfno09QHdjTLWcv9e/X0PdhyVT0L03H7glpzd9dyDhlKb8UtNENwUwxgzCPQv1B6Zaa5/3bESVnzGmJ7AM+JWTz48fwz2H/wgIx632d4O19sxOKHIGY0xf4EFr7ZXGmJa4Gn1dYA1wk7U2zYPhVWrGmChcJ8UgIAa4DVeh0X1YTMaYZ4A/4kbHrAHG4J4P6z4shDHmQ6AvbtW4w8AEYB753Hs5P57exD3+OA7cZq1dXWaxKMGLiIj4HjXRi4iI+CAleBERER+kBC8iIuKDlOBFRER8kBK8iIiID1KCF5EiGWNuNcYkezoOESk+DZMTOccZY4r6R2A6MBaoaa3V7G8iXiKg6FNExMedunrVlcA7ZxxLtdamAqkVGpWIlIqa6EXOcdbaQ7//B8Sfecxam3BmE70x5mljzHpjzEhjzC5jTIoxZpoxJsgYc5cxZq8x5qgx5lVjjN8p7wsyxrxkjNlnjDlujFlljLmi4v/UIr5PNXgRKanmuBXHrsRNYTobV/M/CPwfEIGbnvP7nDKAacD5wI24RWEGAZ8aY7pYa9dVZPAivk4JXkRKyh83d3YCsN4Y8xXQB2hsrU0HNhljvgf6AbONMecDw4Hm1to9OZ/xpjHmMuAO4K6K/yOI+C4leBEpqT05yf13h4GtOcn91GPn5byOBgyw0a2xkSsY+LY8AxU5FynBi0hJZZyxbws45p/z2i9nv0s+56kDn0gZU4IXkYqyBleDD7XWLvZ0MCK+TgleRCqEtXarMWYG8J4x5gHgf7h1xfsCMdbaOZ6MT8TXKMGLSEW6DXgc+BvQBIgDVgKq0YuUMc1kJyIi4oM00Y2IiIgPUoIXERHxQUrwIiIiPkgJXkRExAcpwYuIiPggJXgREREfpAQvIiLig5TgRUREfJASvIiIiA/6/5SstJ0f1+OrAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.plot(t1, S1, 'g--', label='S (Discrete)')\n", - "plt.plot(t1, I1, 'r--', label='I (Discrete)')\n", - "plt.plot(t1, R1, 'b--', label='R (Discrete)')\n", - "plt.plot(t2, S2, 'g-', label='S (Gillespie)')\n", - "plt.plot(t2, I2, 'r-', label='I (Gillespie)')\n", - "plt.plot(t2, R2, 'b-', label='R (Gillespie)')\n", - "plt.xlabel(\"Time\", fontsize=14)\n", - "plt.ylabel(\"Number of people\", fontsize=14)\n", - "plt.legend(fontsize=14)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "d4f22471", - "metadata": {}, - "source": [ - "## SIS Model\n", - "In this model, once individuals heal, they may become re-infected." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "770b9a5c", - "metadata": {}, - "outputs": [], - "source": [ - "tau = {2:0.01, 3:0.01}\n", - "gamma = 0.01\n", - "tmax = 100\n", - "dt = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "583539ce", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "680.240907907486\n", - "236.78710913658142\n" - ] - } - ], - "source": [ - "tau = {2:0.01, 3:0.01}\n", - "gamma = 0.01\n", - "start = time.time()\n", - "t1, S1, I1 = contagion.discrete_SIS(H, tau, gamma, rho = 0.1, tmin = 0, tmax=tmax, dt=dt)\n", - "print(time.time() - start)\n", - "# ~680.240907907486 sec\n", - "\n", - "start = time.time()\n", - "t2, S2, I2 = contagion.Gillespie_SIS(H, tau, gamma, rho = 0.1, tmin = 0, tmax=tmax)\n", - "print(time.time() - start)\n", - "\n", - "\n", - "# ~236.78710913658142 sec" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "3ae298ba", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.plot(t1, S1, 'g--', label='S (Discrete)')\n", - "plt.plot(t1, I1, 'r--', label='I (Discrete)')\n", - "plt.plot(t2, S2, 'g-', label='S (Gillespie)')\n", - "plt.plot(t2, I2, 'r-', label='I (Gillespie)')\n", - "plt.xlabel(\"Time\", fontsize=14)\n", - "plt.ylabel(\"Number of people\", fontsize=14)\n", - "plt.legend(fontsize=14)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "4317879a", - "metadata": {}, - "source": [ - "## Animation of SIR model" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "5cbd4054", - "metadata": {}, - "outputs": [], - "source": [ - "import hypernetx as hnx\n", - "import matplotlib.pyplot as plt\n", - "import random\n", - "import time\n", - "import hypernetx.algorithms.contagion as contagion\n", - "import numpy as np\n", - "from IPython.display import HTML" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "7b68fa87", - "metadata": {}, - "outputs": [], - "source": [ - "n = 100\n", - "m = 40\n", - "\n", - "hyperedgeList = [random.sample(range(n), k=random.choice([2,3])) for i in range(m)]\n", - "H = hnx.Hypergraph(hyperedgeList, static=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "7afb2d28", - "metadata": {}, - "outputs": [], - "source": [ - "tau = {2:2, 3:1}\n", - "gamma = 0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "19570770", - "metadata": {}, - "outputs": [], - "source": [ - "transition_events = contagion.discrete_SIR(H, tau, gamma, rho=0.2, tmin=0, tmax=50, dt=1, return_full_data=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "1f61777c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "At time 1, 37 was infected by e33\n", - "At time 1, 85 was infected by e1\n", - "At time 1, 47 recovered\n", - "At time 1, 36 was infected by e10\n", - "At time 1, 22 was infected by e21\n", - "At time 1, 11 recovered\n", - "At time 1, 92 was infected by e5\n", - "At time 1, 29 was infected by e6\n", - "At time 1, 35 was infected by e6\n", - "At time 1, 13 was infected by e7\n", - "At time 1, 87 was infected by e7\n", - "At time 1, 19 was infected by e8\n", - "At time 1, 84 was infected by e8\n", - "At time 1, 6 was infected by e15\n", - "At time 1, 72 was infected by e27\n", - "At time 1, 82 was infected by e20\n", - "At time 1, 66 was infected by e21\n", - "At time 1, 30 was infected by e26\n", - "At time 1, 38 was infected by e26\n", - "At time 1, 45 was infected by e31\n", - "At time 1, 95 was infected by e39\n", - "At time 1, 16 was infected by e33\n", - "At time 1, 14 was infected by e35\n", - "At time 1, 74 was infected by e35\n", - "At time 2, 60 was infected by e0\n", - "At time 2, 42 was infected by e2\n", - "At time 2, 61 was infected by e25\n", - "At time 2, 68 was infected by e4\n", - "At time 2, 0 was infected by e9\n", - "At time 2, 93 was infected by e9\n", - "At time 2, 51 was infected by e11\n", - "At time 2, 91 was infected by e11\n", - "At time 2, 25 was infected by e14\n", - "At time 2, 31 was infected by e14\n", - "At time 2, 26 was infected by e17\n", - "At time 2, 10 was infected by e24\n", - "At time 2, 1 was infected by e25\n", - "At time 2, 38 recovered\n", - "At time 2, 54 was infected by e36\n", - "At time 2, 9 was infected by e32\n", - "At time 2, 48 was infected by e32\n", - "At time 3, 21 was infected by e3\n", - "At time 3, 44 was infected by e3\n", - "At time 3, 91 recovered\n", - "At time 3, 65 was infected by e18\n", - "At time 3, 73 was infected by e18\n", - "At time 3, 28 was infected by e19\n", - "At time 3, 76 was infected by e19\n", - "At time 3, 30 recovered\n", - "At time 3, 5 was infected by e28\n", - "At time 3, 55 was infected by e30\n", - "At time 3, 45 recovered\n", - "At time 3, 18 recovered\n", - "At time 3, 52 was infected by e37\n", - "At time 3, 94 was infected by e38\n", - "At time 4, 46 recovered\n", - "At time 4, 25 recovered\n", - "At time 4, 34 was infected by e16\n", - "At time 4, 76 recovered\n", - "At time 4, 77 was infected by e23\n", - "At time 4, 88 was infected by e23\n", - "At time 4, 59 recovered\n", - "At time 5, 68 recovered\n", - "At time 5, 51 recovered\n", - "At time 5, 26 recovered\n", - "At time 5, 65 recovered\n", - "At time 5, 66 recovered\n", - "At time 5, 39 was infected by e22\n", - "At time 5, 54 recovered\n", - "At time 6, 84 recovered\n", - "At time 6, 12 recovered\n", - "At time 7, 29 recovered\n", - "At time 7, 13 recovered\n", - "At time 7, 6 recovered\n", - "At time 7, 97 recovered\n", - "At time 7, 55 recovered\n", - "At time 8, 75 recovered\n", - "At time 8, 48 recovered\n", - "At time 8, 94 recovered\n", - "At time 9, 35 recovered\n", - "At time 9, 14 recovered\n", - "At time 10, 60 recovered\n", - "At time 10, 31 recovered\n", - "At time 10, 9 recovered\n", - "At time 10, 52 recovered\n", - "At time 11, 85 recovered\n", - "At time 11, 21 recovered\n", - "At time 11, 22 recovered\n", - "At time 11, 1 recovered\n", - "At time 11, 74 recovered\n", - "At time 12, 42 recovered\n", - "At time 12, 10 recovered\n", - "At time 12, 16 recovered\n", - "At time 14, 70 recovered\n", - "At time 14, 87 recovered\n", - "At time 15, 34 recovered\n", - "At time 15, 73 recovered\n", - "At time 16, 93 recovered\n", - "At time 17, 36 recovered\n", - "At time 17, 92 recovered\n", - "At time 17, 82 recovered\n", - "At time 17, 88 recovered\n", - "At time 18, 5 recovered\n", - "At time 19, 27 recovered\n", - "At time 20, 28 recovered\n", - "At time 21, 41 recovered\n", - "At time 21, 79 recovered\n", - "At time 23, 77 recovered\n", - "At time 23, 95 recovered\n", - "At time 23, 15 recovered\n", - "At time 24, 19 recovered\n", - "At time 27, 39 recovered\n", - "At time 29, 37 recovered\n", - "At time 33, 44 recovered\n", - "At time 33, 0 recovered\n", - "At time 45, 61 recovered\n", - "At time 49, 72 recovered\n" - ] - } - ], - "source": [ - "for time, events in transition_events.items():\n", - " if events != []:\n", - " for event in events:\n", - " if event[0] == 'R':\n", - " print(f\"At time {time}, {event[1]} recovered\")\n", - " elif event[0] == 'I' and event[2] is not None:\n", - " print(f\"At time {time}, {event[1]} was infected by {event[2]}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "5ca81437", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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time 1, 35 was infected by e29\n", - "At time 1, 70 was infected by e29\n", - "At time 1, 87 was infected by e7\n", - "At time 1, 19 was infected by e17\n", - "At time 1, 6 was infected by e15\n", - "At time 1, 72 was infected by e11\n", - "At time 1, 31 was infected by e14\n", - "At time 1, 26 was infected by e17\n", - "At time 1, 65 was infected by e18\n", - "At time 1, 73 was infected by e18\n", - "At time 1, 28 was infected by e19\n", - "At time 1, 76 was infected by e19\n", - "At time 1, 79 was infected by e21\n", - "At time 1, 88 was infected by e23\n", - "At time 1, 38 was infected by e26\n", - "At time 1, 16 was infected by e33\n", - "At time 2, 37 recovered\n", - "At time 2, 47 was infected by e12\n", - "At time 2, 42 was infected by e2\n", - "At time 2, 21 was infected by e3\n", - "At time 2, 44 was infected by e3\n", - "At time 2, 68 was infected by e4\n", - "At time 2, 29 was infected by e6\n", - "At time 2, 19 recovered\n", - "At time 2, 84 was infected by e8\n", - "At time 2, 75 was infected by e8\n", - "At time 2, 0 was infected by e9\n", - "At time 2, 93 was infected by e9\n", - "At time 2, 12 was infected by e10\n", - "At time 2, 82 was infected by e20\n", - "At time 2, 38 recovered\n", - "At time 2, 27 was infected by e27\n", - "At time 2, 54 was infected by e30\n", - "At time 2, 55 was infected by e30\n", - "At time 2, 52 was infected by e37\n", - "At time 3, 37 was infected by e0\n", - "At time 3, 60 recovered\n", - "At time 3, 85 was infected by e1\n", - "At time 3, 42 recovered\n", - "At time 3, 11 recovered\n", - "At time 3, 19 was infected by e8\n", - "At time 3, 41 was infected by e13\n", - "At time 3, 34 was infected by e16\n", - "At time 3, 28 recovered\n", - "At time 3, 38 was infected by e26\n", - "At time 3, 5 was infected by e28\n", - "At time 3, 45 was infected by e31\n", - "At time 3, 59 was infected by e31\n", - "At time 4, 60 was infected by e0\n", - "At time 4, 42 was infected by e2\n", - "At time 4, 44 recovered\n", - "At time 4, 11 was infected by e5\n", - "At time 4, 87 recovered\n", - "At time 4, 72 recovered\n", - "At time 4, 41 recovered\n", - "At time 4, 34 recovered\n", - "At time 4, 28 was infected by e19\n", - "At time 4, 66 recovered\n", - "At time 4, 39 was infected by e22\n", - "At time 5, 47 recovered\n", - "At time 5, 44 was infected by e3\n", - "At time 5, 87 was infected by e7\n", - "At time 5, 72 was infected by e11\n", - "At time 5, 41 was infected by e13\n", - "At time 5, 34 was infected by e16\n", - "At time 5, 26 recovered\n", - "At time 5, 66 was infected by e21\n", - "At time 5, 88 recovered\n", - "At time 5, 1 recovered\n", - "At time 5, 54 recovered\n", - "At time 6, 47 was infected by e1\n", - "At time 6, 61 recovered\n", - "At time 6, 11 recovered\n", - "At time 6, 84 recovered\n", - "At time 6, 51 recovered\n", - "At time 6, 26 was infected by e17\n", - "At time 6, 76 recovered\n", - "At time 6, 77 recovered\n", - "At time 6, 88 was infected by e23\n", - "At time 6, 1 was infected by e25\n", - "At time 6, 27 recovered\n", - "At time 6, 54 was infected by e28\n", - "At time 7, 61 was infected by e3\n", - "At time 7, 11 was infected by e5\n", - "At time 7, 13 recovered\n", - "At time 7, 84 was infected by e8\n", - "At time 7, 12 recovered\n", - "At time 7, 51 was infected by e11\n", - "At time 7, 34 recovered\n", - "At time 7, 76 was infected by e19\n", - "At time 7, 77 was infected by e23\n", - "At time 7, 27 was infected by e27\n", - "At time 7, 55 recovered\n", - "At time 8, 47 recovered\n", - "At time 8, 46 recovered\n", - "At time 8, 13 was infected by e7\n", - "At time 8, 19 recovered\n", - "At time 8, 12 was infected by e10\n", - "At time 8, 31 recovered\n", - "At time 8, 34 was infected by e16\n", - "At time 8, 54 recovered\n", - "At time 8, 55 was infected by e30\n", - "At time 9, 47 was infected by e1\n", - "At time 9, 68 recovered\n", - "At time 9, 11 recovered\n", - "At time 9, 46 was infected by e7\n", - "At time 9, 19 was infected by e8\n", - "At time 9, 31 was infected by e14\n", - "At time 9, 39 recovered\n", - "At time 9, 54 was infected by e28\n", - "At time 10, 60 recovered\n", - "At time 10, 22 recovered\n", - "At time 10, 68 was infected by e4\n", - "At time 10, 11 was infected by e5\n", - "At time 10, 13 recovered\n", - "At time 10, 12 recovered\n", - "At time 10, 72 recovered\n", - "At time 10, 97 recovered\n", - "At time 10, 28 recovered\n", - "At time 10, 39 was infected by e22\n", - "At time 10, 52 recovered\n", - "At time 11, 60 was infected by e0\n", - "At time 11, 22 was infected by e4\n", - "At time 11, 13 was infected by e7\n", - "At time 11, 87 recovered\n", - "At time 11, 12 was infected by e10\n", - "At time 11, 72 was infected by e11\n", - "At time 11, 97 was infected by e15\n", - "At time 11, 34 recovered\n", - "At time 11, 28 was infected by e19\n", - "At time 11, 45 recovered\n", - "At time 12, 47 recovered\n", - "At time 12, 87 was infected by e7\n", - "At time 12, 34 was infected by e16\n", - "At time 12, 79 recovered\n", - "At time 12, 30 recovered\n", - "At time 12, 45 was infected by e31\n", - "At time 12, 52 was infected by e37\n", - "At time 13, 85 recovered\n", - "At time 13, 47 was infected by e1\n", - "At time 13, 68 recovered\n", - "At time 13, 11 recovered\n", - "At time 13, 75 recovered\n", - "At time 13, 79 was infected by e20\n", - "At time 13, 10 recovered\n", - "At time 13, 30 was infected by e25\n", - "At time 14, 85 was infected by e1\n", - "At time 14, 68 was infected by e4\n", - "At time 14, 11 was infected by e5\n", - "At time 14, 35 recovered\n", - "At time 14, 75 was infected by e8\n", - "At time 14, 0 recovered\n", - "At time 14, 25 recovered\n", - "At time 14, 88 recovered\n", - "At time 14, 10 was infected by e24\n", - "At time 14, 27 recovered\n", - "At time 14, 59 recovered\n", - "At time 15, 37 recovered\n", - "At time 15, 61 recovered\n", - "At time 15, 35 was infected by e6\n", - "At time 15, 0 was infected by e9\n", - "At time 15, 25 was infected by e14\n", - "At time 15, 88 was infected by e23\n", - "At time 15, 1 recovered\n", - "At time 15, 27 was infected by e27\n", - "At time 15, 59 was infected by e31\n", - "At time 16, 37 was infected by e0\n", - "At time 16, 60 recovered\n", - "At time 16, 36 recovered\n", - "At time 16, 61 was infected by e3\n", - "At time 16, 22 recovered\n", - "At time 16, 29 recovered\n", - "At time 16, 12 recovered\n", - "At time 16, 66 recovered\n", - "At time 16, 30 recovered\n", - "At time 16, 1 was infected by e25\n", - "At time 16, 5 recovered\n", - "At time 17, 60 was infected by e0\n", - "At time 17, 36 was infected by e2\n", - "At time 17, 44 recovered\n", - "At time 17, 22 was infected by e4\n", - "At time 17, 29 was infected by e6\n", - "At time 17, 84 recovered\n", - "At time 17, 51 recovered\n", - "At time 17, 66 was infected by e21\n", - "At time 17, 30 was infected by e25\n", - "At time 17, 38 recovered\n", - "At time 17, 27 recovered\n", - "At time 17, 5 was infected by e28\n", - "At time 17, 94 recovered\n", - "At time 18, 85 recovered\n", - "At time 18, 44 was infected by e3\n", - "At time 18, 84 was infected by e8\n", - "At time 18, 12 was infected by e10\n", - "At time 18, 51 was infected by e11\n", - "At time 18, 91 recovered\n", - "At time 18, 28 recovered\n", - "At time 18, 76 recovered\n", - "At time 18, 38 was infected by e26\n", - "At time 18, 27 was infected by e27\n", - "At time 18, 94 was infected by e38\n", - "At time 19, 85 was infected by e1\n", - "At time 19, 91 was infected by e11\n", - "At time 19, 26 recovered\n", - "At time 19, 28 was infected by e23\n", - "At time 19, 66 recovered\n", - "At time 19, 16 recovered\n", - "At time 19, 52 recovered\n", - "At time 20, 22 recovered\n", - "At time 20, 84 recovered\n", - "At time 20, 75 recovered\n", - "At time 20, 26 was infected by e17\n", - "At time 20, 65 recovered\n", - "At time 20, 76 was infected by e19\n", - "At time 20, 82 recovered\n", - "At time 20, 66 was infected by e21\n", - "At time 20, 16 was infected by e33\n", - "At time 20, 52 was infected by e37\n", - "At time 21, 22 was infected by e4\n", - "At time 21, 68 recovered\n", - "At time 21, 11 recovered\n", - "At time 21, 29 recovered\n", - "At time 21, 13 recovered\n", - "At time 21, 84 was infected by e8\n", - "At time 21, 75 was infected by e8\n", - "At time 21, 65 was infected by e18\n", - "At time 21, 82 was infected by e20\n", - "At time 21, 94 recovered\n", - "At time 22, 42 recovered\n", - "At time 22, 44 recovered\n", - "At time 22, 68 was infected by e4\n", - "At time 22, 11 was infected by e5\n", - "At time 22, 29 was infected by e6\n", - "At time 22, 13 was infected by e7\n", - "At time 22, 65 recovered\n", - "At time 22, 5 recovered\n", - "At time 22, 94 was infected by e38\n", - "At time 23, 42 was infected by e2\n", - "At time 23, 44 was infected by e3\n", - "At time 23, 51 recovered\n", - "At time 23, 31 recovered\n", - "At time 23, 65 was infected by e18\n", - "At time 23, 5 was infected by e28\n", - "At time 23, 52 recovered\n", - "At time 24, 60 recovered\n", - "At time 24, 51 was infected by e11\n", - "At time 24, 31 was infected by e14\n", - "At time 24, 28 recovered\n", - "At time 24, 76 recovered\n", - "At time 24, 77 recovered\n", - "At time 24, 16 recovered\n", - "At time 24, 52 was infected by e37\n", - "At time 25, 60 was infected by e0\n", - "At time 25, 68 recovered\n", - "At time 25, 29 recovered\n", - "At time 25, 84 recovered\n", - "At time 25, 12 recovered\n", - "At time 25, 28 was infected by e19\n", - "At time 25, 76 was infected by e19\n", - "At time 25, 77 was infected by e23\n", - "At time 25, 16 was infected by e33\n", - "At time 25, 94 recovered\n", - "At time 26, 42 recovered\n", - "At time 26, 68 was infected by e4\n", - "At time 26, 29 was infected by e6\n", - "At time 26, 13 recovered\n", - "At time 26, 84 was infected by e8\n", - "At time 26, 6 recovered\n", - "At time 26, 12 was infected by e10\n", - "At time 26, 97 recovered\n", - "At time 26, 39 recovered\n", - "At time 26, 94 was infected by e38\n", - "At time 27, 42 was infected by e2\n", - "At time 27, 13 was infected by e7\n", - "At time 27, 6 was infected by e9\n", - "At time 27, 65 recovered\n", - "At time 27, 39 was infected by e22\n", - "At time 27, 88 recovered\n", - "At time 28, 61 recovered\n", - "At time 28, 70 recovered\n", - "At time 28, 25 recovered\n", - "At time 28, 97 was infected by e15\n", - "At time 28, 65 was infected by e18\n", - "At time 28, 77 recovered\n", - "At time 28, 88 was infected by e23\n", - "At time 28, 1 recovered\n", - "At time 28, 59 recovered\n", - "At time 29, 61 was infected by e3\n", - "At time 29, 92 recovered\n", - "At time 29, 70 was infected by e6\n", - "At time 29, 25 was infected by e14\n", - "At time 29, 26 recovered\n", - "At time 29, 77 was infected by e23\n", - "At time 29, 1 was infected by e25\n", - "At time 29, 59 was infected by e31\n", - "At time 30, 92 was infected by e5\n", - "At time 30, 87 recovered\n", - "At time 30, 97 recovered\n", - "At time 30, 26 was infected by e17\n", - "At time 30, 66 recovered\n", - "At time 30, 10 recovered\n", - "At time 31, 60 recovered\n", - "At time 31, 42 recovered\n", - "At time 31, 35 recovered\n", - "At time 31, 87 was infected by e7\n", - "At time 31, 84 recovered\n", - "At time 31, 97 was infected by e15\n", - "At time 31, 76 recovered\n", - "At time 31, 82 recovered\n", - "At time 31, 66 was infected by e21\n", - "At time 31, 10 was infected by e24\n", - "At time 31, 27 recovered\n", - "At time 31, 54 recovered\n", - "At time 31, 5 recovered\n", - "At time 32, 60 was infected by e0\n", - "At time 32, 85 recovered\n", - "At time 32, 42 was infected by e2\n", - "At time 32, 35 was infected by e6\n", - "At time 32, 84 was infected by e8\n", - "At time 32, 73 recovered\n", - "At time 32, 76 was infected by e19\n", - "At time 32, 82 was infected by e20\n", - "At time 32, 88 recovered\n", - "At time 32, 27 was infected by e27\n", - "At time 32, 54 was infected by e30\n", - "At time 33, 85 was infected by e1\n", - "At time 33, 46 recovered\n", - "At time 33, 73 was infected by e18\n", - "At time 33, 77 recovered\n", - "At time 33, 88 was infected by e23\n", - "At time 33, 5 was infected by e28\n", - "At time 34, 92 recovered\n", - "At time 34, 46 was infected by e7\n", - "At time 34, 34 recovered\n", - "At time 34, 79 recovered\n", - "At time 34, 77 was infected by e23\n", - "At time 34, 16 recovered\n", - "At time 34, 94 recovered\n", - "At time 35, 92 was infected by e5\n", - "At time 35, 70 recovered\n", - "At time 35, 46 recovered\n", - "At time 35, 12 recovered\n", - "At time 35, 72 recovered\n", - "At time 35, 34 was infected by e16\n", - "At time 35, 65 recovered\n", - "At time 35, 79 was infected by e20\n", - "At time 35, 88 recovered\n", - "At time 35, 27 recovered\n", - "At time 35, 16 was infected by e33\n", - "At time 35, 94 was infected by e38\n", - "At time 36, 70 was infected by e6\n", - "At time 36, 46 was infected by e7\n", - "At time 36, 12 was infected by e10\n", - "At time 36, 72 was infected by e11\n", - "At time 36, 65 was infected by e18\n", - "At time 36, 39 recovered\n", - "At time 36, 88 was infected by e23\n", - "At time 36, 27 was infected by e27\n", - "At time 36, 5 recovered\n", - "At time 36, 55 recovered\n", - "At time 37, 42 recovered\n", - "At time 37, 44 recovered\n", - "At time 37, 68 recovered\n", - "At time 37, 41 recovered\n", - "At time 37, 79 recovered\n", - "At time 37, 39 was infected by e22\n", - "At time 37, 5 was infected by e28\n", - "At time 37, 55 was infected by e30\n", - "At time 38, 85 recovered\n", - "At time 38, 42 was infected by e2\n", - "At time 38, 36 recovered\n", - "At time 38, 44 was infected by e3\n", - "At time 38, 68 was infected by e4\n", - "At time 38, 41 was infected by e13\n", - "At time 38, 79 was infected by e20\n", - "At time 39, 85 was infected by e1\n", - "At time 39, 36 was infected by e2\n", - "At time 39, 11 recovered\n", - "At time 39, 19 recovered\n", - "At time 39, 12 recovered\n", - "At time 39, 5 recovered\n", - "At time 39, 94 recovered\n", - "At time 40, 37 recovered\n", - "At time 40, 60 recovered\n", - "At time 40, 11 was infected by e5\n", - "At time 40, 29 recovered\n", - "At time 40, 70 recovered\n", - "At time 40, 19 was infected by e8\n", - "At time 40, 6 recovered\n", - "At time 40, 12 was infected by e10\n", - "At time 40, 41 recovered\n", - "At time 40, 76 recovered\n", - "At time 40, 77 recovered\n", - "At time 40, 5 was infected by e28\n", - "At time 40, 16 recovered\n", - "At time 40, 94 was infected by e38\n", - "At time 41, 37 was infected by e33\n", - "At time 41, 60 was infected by e37\n", - "At time 41, 85 recovered\n", - "At time 41, 29 was infected by e6\n", - "At time 41, 70 was infected by e6\n", - "At time 41, 75 recovered\n", - "At time 41, 0 recovered\n", - "At time 41, 6 was infected by e9\n", - "At time 41, 41 was infected by e13\n", - "At time 41, 26 recovered\n", - "At time 41, 76 was infected by e19\n", - "At time 41, 77 was infected by e23\n", - "At time 41, 38 recovered\n", - "At time 41, 16 was infected by e33\n", - "At time 41, 52 recovered\n", - "At time 42, 85 was infected by e1\n", - "At time 42, 47 recovered\n", - "At time 42, 61 recovered\n", - "At time 42, 29 recovered\n", - "At time 42, 84 recovered\n", - "At time 42, 75 was infected by e8\n", - "At time 42, 0 was infected by e9\n", - "At time 42, 72 recovered\n", - "At time 42, 41 recovered\n", - "At time 42, 26 was infected by e17\n", - "At time 42, 79 recovered\n", - "At time 42, 38 was infected by e26\n", - "At time 42, 54 recovered\n", - "At time 42, 52 was infected by e37\n", - "At time 43, 37 recovered\n", - "At time 43, 47 was infected by e1\n", - "At time 43, 36 recovered\n", - "At time 43, 61 was infected by e3\n", - "At time 43, 92 recovered\n", - "At time 43, 29 was infected by e6\n", - "At time 43, 84 was infected by e8\n", - "At time 43, 72 was infected by e11\n", - "At time 43, 41 was infected by e13\n", - "At time 43, 28 recovered\n", - "At time 43, 79 was infected by e20\n", - "At time 43, 30 recovered\n", - "At time 43, 54 was infected by e28\n", - "At time 43, 94 recovered\n", - "At time 44, 37 was infected by e0\n", - "At time 44, 36 was infected by e2\n", - "At time 44, 61 recovered\n", - "At time 44, 92 was infected by e5\n", - "At time 44, 29 recovered\n", - "At time 44, 84 recovered\n", - "At time 44, 0 recovered\n", - "At time 44, 93 recovered\n", - "At time 44, 41 recovered\n", - "At time 44, 28 was infected by e19\n", - "At time 44, 66 recovered\n", - "At time 44, 88 recovered\n", - "At time 44, 30 was infected by e25\n", - "At time 44, 94 was infected by e38\n", - "At time 45, 61 was infected by e3\n", - "At time 45, 29 was infected by e6\n", - "At time 45, 84 was infected by e8\n", - "At time 45, 0 was infected by e9\n", - "At time 45, 93 was infected by e9\n", - "At time 45, 91 recovered\n", - "At time 45, 41 was infected by e13\n", - "At time 45, 97 recovered\n", - "At time 45, 28 recovered\n", - "At time 45, 79 recovered\n", - "At time 45, 66 was infected by e21\n", - "At time 45, 88 was infected by e23\n", - "At time 45, 30 recovered\n", - "At time 45, 54 recovered\n", - "At time 46, 42 recovered\n", - "At time 46, 22 recovered\n", - "At time 46, 84 recovered\n", - "At time 46, 93 recovered\n", - "At time 46, 91 was infected by e11\n", - "At time 46, 97 was infected by e15\n", - "At time 46, 34 recovered\n", - "At time 46, 28 was infected by e19\n", - "At time 46, 79 was infected by e20\n", - "At time 46, 66 recovered\n", - "At time 46, 30 was infected by e25\n", - "At time 46, 54 was infected by e28\n", - "At time 47, 47 recovered\n", - "At time 47, 42 was infected by e2\n", - "At time 47, 21 recovered\n", - "At time 47, 22 was infected by e4\n", - "At time 47, 35 recovered\n", - "At time 47, 84 was infected by e8\n", - "At time 47, 0 recovered\n", - "At time 47, 93 was infected by e9\n", - "At time 47, 31 recovered\n", - "At time 47, 34 was infected by e16\n", - "At time 47, 26 recovered\n", - "At time 47, 82 recovered\n", - "At time 47, 66 was infected by e21\n", - "At time 48, 85 recovered\n", - "At time 48, 47 was infected by e1\n", - "At time 48, 21 was infected by e3\n", - "At time 48, 35 was infected by e6\n", - "At time 48, 0 was infected by e9\n", - "At time 48, 6 recovered\n", - "At time 48, 72 recovered\n", - "At time 48, 51 recovered\n", - "At time 48, 31 was infected by e14\n", - "At time 48, 26 was infected by e17\n", - "At time 48, 82 was infected by e20\n", - "At time 48, 77 recovered\n", - "At time 49, 85 was infected by e1\n", - "At time 49, 92 recovered\n", - "At time 49, 6 was infected by e9\n", - "At time 49, 72 was infected by e11\n", - "At time 49, 51 was infected by e11\n", - "At time 49, 91 recovered\n", - "At time 49, 28 recovered\n", - "At time 49, 39 recovered\n", - "At time 49, 77 was infected by e23\n", - "At time 50, 37 recovered\n", - "At time 50, 61 recovered\n", - "At time 50, 92 was infected by e5\n", - "At time 50, 91 was infected by e11\n", - "At time 50, 28 was infected by e19\n", - "At time 50, 82 recovered\n", - "At time 50, 79 recovered\n", - "At time 50, 66 recovered\n", - "At time 50, 39 was infected by e22\n" - ] - } - ], - "source": [ - "for time, events in transition_events2.items():\n", - " if events != []:\n", - " for event in events:\n", - " if event[0] == 'S':\n", - " print(f\"At time {time}, {event[1]} recovered\")\n", - " elif event[0] == 'I' and event[2] is not None:\n", - " print(f\"At time {time}, {event[1]} was infected by {event[2]}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "b0471efc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Other choices are 'strict'\n", + "and 'majority', or any user-supplied function with the following format:\n", + "\n", + "```python\n", + "def linear(d,c):\n", + " return c/d if c>d/2 else 0\n", + "```\n", + "\n", + "where $d$ is the edge size, and $c$ is the number of nodes in the majority class, $d \\geq c > \\frac{d}{2}$\n", + "\n", + "### Two-section graph\n", + "\n", + "Build the random-walk based $2$-section graph given some hypergraph HG:\n", + "\n", + "```python\n", + "G = hmod.two_section(HG)\n", + "```\n", + "\n", + "where G is an igraph Graph.\n", + "\n", + "### Clustering: Kumar algorithm\n", + "\n", + "Given hypergraph HG, compute a partition of the vertices as per Kumar's algorithm described in [1].\n", + "\n", + "```python\n", + "K = hmod.kumar(HG, delta=.01)\n", + "```\n", + "\n", + "where delta is the convergence stopping criterion. Partition is returned as a list of sets.\n", + "\n", + "[1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S., Ravindran B. (2020) *A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering*. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24\n", + "\n", + "\n", + "### Clustering: Simple qH-based algorithm\n", + "\n", + "Given hypergraph HG and initial partition L, \n", + "compute a partition of the vertices as per Last-Step algorithm described in [2].\n", + "\n", + "```python\n", + "A = hmod.last_step(HG, L, wdc=linear, delta = .01)\n", + "```\n", + "\n", + "where 'wcd' is the the weight function (default = 'linear') and delta is the convergence stopping criterion.\n", + "Returned partition is a list of sets.\n", + "\n", + "[2] Kamiński B., Prałat P. and Théberge F. “Community Detection Algorithm Using Hypergraph Modularity”. In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13\n", + "\n", + "### Utility functions\n", + "\n", + "We use two representations for partitions: list of sets (the parts) or dictionary.\n", + "Those functions are used to map from one to the other:\n", + "\n", + "```python\n", + "dict2part(D)\n", + "part2dict(A)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# Toy example" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/prag717/Library/CloudStorage/OneDrive-PNNL/Documents/tdm/hnx/hypernetx/classes/entity.py:1387: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.\n", + " properties = props.combine_first(self.properties)\n", + "/Users/prag717/Library/CloudStorage/OneDrive-PNNL/Documents/tdm/hnx/hypernetx/classes/entity.py:1390: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.\n", + " properties[self._misc_props_col] = self.properties[\n", + "/Users/prag717/Library/CloudStorage/OneDrive-PNNL/Documents/tdm/hnx/hypernetx/classes/entity.py:1387: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.\n", + " properties = props.combine_first(self.properties)\n", + "/Users/prag717/Library/CloudStorage/OneDrive-PNNL/Documents/tdm/hnx/hypernetx/classes/entity.py:1390: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.\n", + " properties[self._misc_props_col] = self.properties[\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## build a hypergraph from a list of sets (the hyperedges)\n", + "E = [{'A','B'},{'A','C'},{'A','B','C'},{'A','D','E','F'},{'D','F'},{'E','F'}]\n", + "HG = hnx.Hypergraph(E,static=True)\n", + "hnx.draw(HG)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "## compute node strength (add unit weight if unweighted), d-degrees, binomial coefficients\n", + "HG = hmod.precompute_attributes(HG)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 has weight 1\n", + "1 has weight 1\n", + "2 has weight 1\n", + "3 has weight 1\n", + "4 has weight 1\n", + "5 has weight 1\n" + ] + } + ], + "source": [ + "## list the edges (unit weights added by default)\n", + "for e in HG.edges:\n", + " print(e,'has weight',HG.edges[e].weight)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "F has strength 3\n", + "A has strength 4\n", + "E has strength 2\n", + "B has strength 2\n", + "D has strength 2\n", + "C has strength 2\n" + ] + } + ], + "source": [ + "## list the nodes (here strength = degree since all weights are 1)\n", + "for v in HG.nodes:\n", + " print(v,'has strength',HG.nodes[v].strength) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Counter({2: 4, 3: 1, 4: 1})" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## total edge weight for each edge cardinality\n", + "HG.d_weights\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "linear edge contribution:\n", + "qH(A1): 0.414445267489712 qH(A2): -0.03746831275720153 qH(A3): 0.0 qH(A4): -0.19173004115226341\n", + "strict edge contribution:\n", + "qH(A1): 0.43490699588477366 qH(A2): -0.02385843621399164 qH(A3): 0.0 qH(A4): -0.12887572016460908\n", + "majority edge contribution:\n", + "qH(A1): 0.39379753086419755 qH(A2): -0.0343506172839505 qH(A3): 0.0 qH(A4): -0.22078024691358022\n" + ] + } + ], + "source": [ + "## compute hypergraph modularity (qH) for the following partitions:\n", + "A1 = [{'A','B','C'},{'D','E','F'}]\n", + "A2 = [{'B','C'},{'A','D','E','F'}]\n", + "A3 = [{'A','B','C','D','E','F'}]\n", + "A4 = [{'A'},{'B'},{'C'},{'D'},{'E'},{'F'}]\n", + "\n", + "## we compute with 3 different choices of functions for the edge contribution: linear (default), strict and majority\n", + "strict = hmod.strict\n", + "majority = hmod.majority\n", + "\n", + "print('linear edge contribution:')\n", + "print('qH(A1):',hmod.modularity(HG,A1),\n", + " 'qH(A2):',hmod.modularity(HG,A2),\n", + " 'qH(A3):',hmod.modularity(HG,A3),\n", + " 'qH(A4):',hmod.modularity(HG,A4))\n", + "print('strict edge contribution:')\n", + "print('qH(A1):',hmod.modularity(HG,A1,strict),\n", + " 'qH(A2):',hmod.modularity(HG,A2,strict),\n", + " 'qH(A3):',hmod.modularity(HG,A3,strict),\n", + " 'qH(A4):',hmod.modularity(HG,A4,strict))\n", + "print('majority edge contribution:')\n", + "print('qH(A1):',hmod.modularity(HG,A1,majority),\n", + " 'qH(A2):',hmod.modularity(HG,A2,majority),\n", + " 'qH(A3):',hmod.modularity(HG,A3,majority),\n", + " 'qH(A4):',hmod.modularity(HG,A4,majority))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "Plotting not available; please install pycairo or cairocffi", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m G \u001b[38;5;241m=\u001b[39m hmod\u001b[38;5;241m.\u001b[39mtwo_section(HG)\n\u001b[1;32m 3\u001b[0m G\u001b[38;5;241m.\u001b[39mvs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m G\u001b[38;5;241m.\u001b[39mvs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m----> 4\u001b[0m \u001b[43mig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mG\u001b[49m\u001b[43m,\u001b[49m\u001b[43mbbox\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m250\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m250\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/hnx/lib/python3.8/site-packages/igraph/drawing/__init__.py:284\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(obj, target, bbox, *args, **kwds)\u001b[0m\n\u001b[1;32m 282\u001b[0m background \u001b[38;5;241m=\u001b[39m kwds\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbackground\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwhite\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 283\u001b[0m margin \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(kwds\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmargin\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m20\u001b[39m))\n\u001b[0;32m--> 284\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mCairoPlot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 285\u001b[0m \u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 286\u001b[0m \u001b[43m \u001b[49m\u001b[43mbbox\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbbox\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 287\u001b[0m \u001b[43m \u001b[49m\u001b[43mpalette\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpalette\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 288\u001b[0m \u001b[43m \u001b[49m\u001b[43mbackground\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbackground\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 289\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 290\u001b[0m item_bbox \u001b[38;5;241m=\u001b[39m result\u001b[38;5;241m.\u001b[39mbbox\u001b[38;5;241m.\u001b[39mcontract(margin)\n\u001b[1;32m 291\u001b[0m result\u001b[38;5;241m.\u001b[39madd(obj, item_bbox, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/hnx/lib/python3.8/site-packages/igraph/drawing/cairo/plot.py:148\u001b[0m, in \u001b[0;36mCairoPlot.__init__\u001b[0;34m(self, target, bbox, palette, background)\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m target \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 147\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_need_tmpfile \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 148\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_surface \u001b[38;5;241m=\u001b[39m \u001b[43mcairo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mImageSurface\u001b[49m(\n\u001b[1;32m 149\u001b[0m cairo\u001b[38;5;241m.\u001b[39mFORMAT_ARGB32, \u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbbox\u001b[38;5;241m.\u001b[39mwidth), \u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbbox\u001b[38;5;241m.\u001b[39mheight)\n\u001b[1;32m 150\u001b[0m )\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(target, cairo\u001b[38;5;241m.\u001b[39mSurface):\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_surface \u001b[38;5;241m=\u001b[39m target\n", + "File \u001b[0;32m~/opt/anaconda3/envs/hnx/lib/python3.8/site-packages/igraph/drawing/utils.py:428\u001b[0m, in \u001b[0;36mFakeModule.__getattr__\u001b[0;34m(self, _)\u001b[0m\n\u001b[1;32m 427\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getattr__\u001b[39m(\u001b[38;5;28mself\u001b[39m, _):\n\u001b[0;32m--> 428\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_message)\n", + "\u001b[0;31mAttributeError\u001b[0m: Plotting not available; please install pycairo or cairocffi" + ] + } + ], + "source": [ + "## 2-section graph\n", + "G = hmod.two_section(HG)\n", + "G.vs['label'] = G.vs['name']\n", + "ig.plot(G,bbox=(0,0,250,250))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "## 2-section clustering with ECG\n", + "G.vs['community'] = G.community_ecg().membership\n", + "hmod.dict2part({v['name']:v['community'] for v in G.vs})\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "## Clustering with Kumar's algorithm\n", + "hmod.kumar(HG)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "## hypergraph clustering -- start from trivial partition A4 defined above\n", + "print('start from:',A4)\n", + "A = hmod.last_step(HG,A4)\n", + "print('final partition:',A)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# Chung-Lu hypergraph example\n", + "\n", + "We build a Chung-Lu hypergraph and compute modularity for partitions from 3 algorithms:\n", + "* Louvain, on the 2-section graph\n", + "* Kumar algorithm\n", + "* LastStep algorithm\n", + "\n", + "We use the **strict** modularity, so only edges where all vertices are in the same part will add to the modularity.\n", + "For each algorithm, we compute the modularity qH and compare with the number of edges where all vertices are in the same part.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "## Chung-Lu hypergraph\n", + "n = 200\n", + "k1 = {i : random.randint(2, 10) for i in range(n)} ## node degrees\n", + "k2 = {i : sorted(k1.values())[i] for i in range(n)} ## edge sizes\n", + "H = gm.chung_lu_hypergraph(k1, k2)\n", + "\n", + "## pre-compute required quantities\n", + "HG = hmod.precompute_attributes(H)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "## Louvain algorithm on the 2-section graph\n", + "G = hmod.two_section(HG)\n", + "G.vs['louvain'] = G.community_multilevel().membership\n", + "D = {v['name']:v['louvain'] for v in G.vs}\n", + "ML = hmod.dict2part(D)\n", + "\n", + "## Compute qH\n", + "print('qH =',hmod.modularity(HG, ML, strict))\n", + "\n", + "## number of edges where all vertices belong to the same community\n", + "print('edges with all vertices in same part:',\n", + " sum([len(set([D[v] for v in HG.edges[e]]))==1 for e in HG.edges()]))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "## Kumar algorithm\n", + "KU = hmod.kumar(HG)\n", + "\n", + "## Compute qH\n", + "print('qH =',hmod.modularity(HG, KU, strict))\n", + "\n", + "## number of edges where all vertices belong to the same community\n", + "print('edges with all vertices in same part:',\n", + " sum([len(set([hmod.part2dict(KU)[v] for v in HG.edges[e]]))==1 for e in HG.edges()]))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "## Last-step algorithm using previous result as initial partition\n", + "LS = hmod.last_step(HG, KU, strict)\n", + "\n", + "## Compute qH\n", + "print('qH =',hmod.modularity(HG, LS, strict))\n", + "\n", + "## number of edges where all vertices belong to the same community\n", + "print('edges with all vertices in same part:',\n", + " sum([len(set([hmod.part2dict(LS)[v] for v in HG.edges[e]]))==1 for e in HG.edges()]))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Game of Thrones scenes hypergraph\n", + "\n", + "REF: https://github.com/jeffreylancaster/game-of-thrones\n", + "\n", + "We built an hypergraph from the game of thrones scenes with he following elements:\n", + "\n", + "* **Nodes** are characters in the series\n", + "* **Hyperedges** are groups of character appearing in the same scene(s)\n", + "* **Hyperedge weights** are total scene(s) duration in seconds involving those characters\n", + "\n", + "We kept hyperedges with at least 2 characters.\n", + "Moreover, we discarded characters with degree below 5.\n", + "\n", + "We saved the following:\n", + "\n", + "* *Edges*: list of sets where the nodes are 0-based integers represents as strings\n", + "* *Names*: dictionary; mapping of nodes to character names\n", + "* *Weights*: list; hyperedge weights (in same order as Edges)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## load the GoT dataset\n", + "Edges, Names, Weights = pickle.load(open( \"../hypernetx/utils/toys/GoT.pkl\", \"rb\" ))\n", + "print(len(Names),'nodes and',len(Edges),'edges')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Build weighted GoT hypergraph " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Nodes are represented as strings from '0' to 'n-1'\n", + "H = hnx.Hypergraph(dict(enumerate(Edges)))\n", + "## add edge weights\n", + "for e in H.edges:\n", + " H.edges[e].weight = Weights[e]\n", + "## add full names\n", + "for v in H.nodes:\n", + " H.nodes[v].name = Names[v]\n", + "## pre-compute required quantities for modularity and clustering\n", + "GoT = hmod.precompute_attributes(H)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Modularity (qH) on a random partition\n", + "\n", + "We use the default choice for the modularity (**linear** weights).\n", + "Result for the random partition should be close to 0 and can be negative." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## generate a random partition into K parts to compare results\n", + "K = 5\n", + "V = list(GoT.nodes)\n", + "p = np.random.choice(K, size=len(V))\n", + "RandPart = hmod.dict2part({V[i]:p[i] for i in range(len(V))})\n", + "## compute qH\n", + "hmod.modularity(GoT, RandPart)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate the 2-section igraph Graph and cluster with Louvain Algorithm\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## build 2-section\n", + "G = hmod.two_section(GoT)\n", + "## Louvain algorithm\n", + "G.vs['louvain'] = G.community_multilevel(weights='weight').membership\n", + "ML = hmod.dict2part({v['name']:v['louvain'] for v in G.vs})\n", + "\n", + "## Compute qH\n", + "print('qH =',hmod.modularity(GoT, ML))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cluster hypergraph with Kumar's algorithm\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## run Kumar's algorithm, get partition\n", + "KU = hmod.kumar(GoT)\n", + "## Compute qH\n", + "print('qH =',hmod.modularity(GoT, KU))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cluster with simple H-based (Last Step) Algorithm\n", + "\n", + "We use Louvain on the 2-section or Kumar algorithm for the initial partition" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## H-based last step with Louvain parition already computed\n", + "LS = hmod.last_step(GoT, ML)\n", + "## Compute qH\n", + "print('qH =',hmod.modularity(GoT, LS))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example: show top nodes in same cluster as Daenerys Targaryen\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Index for \n", + "inv_map = {v: k for k, v in Names.items()}\n", + "DT = inv_map['Daenerys Targaryen']\n", + "## DT's cluster\n", + "DT_part = hmod.part2dict(LS)[DT]\n", + "## Build dataframe: all nodes in DT_part\n", + "L = []\n", + "for n in LS[DT_part]:\n", + " L.append([Names[n],GoT.nodes[n].strength])\n", + "D = pd.DataFrame(L, columns=['character','strength'])\n", + "D.sort_values(by='strength',ascending=False).head(5)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/Tutorial 11 - Laplacians and Clustering.ipynb b/tutorials/Tutorial 11 - Laplacians and Clustering.ipynb deleted file mode 100644 index c6690e51..00000000 --- a/tutorials/Tutorial 11 - Laplacians and Clustering.ipynb +++ /dev/null @@ -1,364 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Laplacians and Clustering\n", - "\n", - "\n", - "\n", - "Tutorial for hypergraph clustering utilizing random-walk based Laplacians. The hypergraph may be weighted or unweighted. \n", - "The optional weights are associated with each **vertex-hyperedge pair**, sometimes referred to as \"edge-dependent vertex weights\" or \"cell weights\" of the incidence matrix. If unweighted, the underlying random walk is equivalent to a weighted random walk on the clique expansion (i.e. 2-section, one-mode projection) of the hypergraph. If weights are specified, the random walk isn't necessarily reversible, which implies it cannot be characterized as any random walk on an undirected graph. For more background on Laplacian-based hypergraph clustering, see\n", - "\n", - "Hayashi, K., Aksoy, S. G., Park, C. H., & Park, H. \n", - "Hypergraph random walks, laplacians, and clustering. \n", - "In Proceedings of CIKM 2020, (2020): 495-504.\n", - "\n", - "and the references contained therein. Feel free to direct inquries concerning this tutorial to Sinan Aksoy, sinan.aksoy@pnnl.gov" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import hypernetx as hnx\n", - "import networkx as nx\n", - "from hypernetx import Entity\n", - "from sklearn.datasets import fetch_20newsgroups\n", - "from sklearn.feature_extraction.text import TfidfVectorizer\n", - "from scipy.sparse import csr_matrix, coo_matrix\n", - "from pprint import pprint\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.patches as mpatches\n", - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Unweighted hypergraph clustering on LesMis\n", - "\n", - "A toy example of unweighted hypergraph clustering characters in the LesMis example based on scene co-occurance." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: ['MA', 'MP', 'GP'],\n", - " 1: ['JV', 'CH', 'CC', 'JU', 'CN', 'BR', 'BM'],\n", - " 2: ['TH', 'JA', 'FN']}" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scenes = {\n", - " 0: ('FN', 'TH'),\n", - " 1: ('TH', 'JV'),\n", - " 2: ('BM', 'FN', 'JA'),\n", - " 3: ('JV', 'JU', 'CH', 'BM'),\n", - " 4: ('JU', 'CH', 'BR', 'CN', 'CC', 'JV', 'BM'),\n", - " 5: ('TH', 'GP'),\n", - " 6: ('GP', 'MP'),\n", - " 7: ('MA', 'GP')\n", - "}\n", - "\n", - "H = hnx.Hypergraph(scenes)\n", - "hnx.spec_clus(H,3) #3 clusters" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "hnx.draw(H)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Weighted hypergraph clustering\n", - "\n", - "Hypergraph clustering on term-document data using tf-idf as cell weights. In this example, we use the 20newsgroups dataset:\n", - "\n", - "https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html\n", - "\n", - "and consider documents falling into two subcategories. We form a static hypergraph with 787 documents as vertices, 20,868 terms as hyperedges, and tf-idf as vertex-hyperedge (i.e. cell) weights. We then form the normalized Laplacian and apply the spectral clustering algorithm as defined by \"RDC-Spec\" (Algorithm 1) in:\n", - "\n", - "Hayashi, K., Aksoy, S. G., Park, C. H., & Park, H. \n", - "Hypergraph random walks, laplacians, and clustering. \n", - "In Proceedings of CIKM 2020, (2020): 495-504.\n", - "\n", - "We plot the proportions of the document subcategories within each output cluster." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[(0, 'alt.atheism'),\n", - " (1, 'comp.graphics'),\n", - " (2, 'comp.os.ms-windows.misc'),\n", - " (3, 'comp.sys.ibm.pc.hardware'),\n", - " (4, 'comp.sys.mac.hardware'),\n", - " (5, 'comp.windows.x'),\n", - " (6, 'misc.forsale'),\n", - " (7, 'rec.autos'),\n", - " (8, 'rec.motorcycles'),\n", - " (9, 'rec.sport.baseball'),\n", - " (10, 'rec.sport.hockey'),\n", - " (11, 'sci.crypt'),\n", - " (12, 'sci.electronics'),\n", - " (13, 'sci.med'),\n", - " (14, 'sci.space'),\n", - " (15, 'soc.religion.christian'),\n", - " (16, 'talk.politics.guns'),\n", - " (17, 'talk.politics.mideast'),\n", - " (18, 'talk.politics.misc'),\n", - " (19, 'talk.religion.misc')]\n" - ] - } - ], - "source": [ - "#list possible categories to choose from\n", - "all_categories = np.array((fetch_20newsgroups(subset='test').target_names))\n", - "pprint(list(enumerate(all_categories)))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "#select categories of documents to be clustered\n", - "categories = all_categories[[1,15]]\n", - "twenty_train = fetch_20newsgroups(subset='test',\n", - " categories=categories, shuffle=True, random_state=42)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "#record categories of documents\n", - "doc_types=dict()\n", - "for i,x in enumerate(twenty_train.filenames):\n", - " doc_types[i]=x.split('/')[-2]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<787x20868 sparse matrix of type ''\n", - "\twith 136994 stored elements in Compressed Sparse Row format>" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#form TF-IDF term-document matrix\n", - "tfidf_vect = TfidfVectorizer()\n", - "X_tfidf = tfidf_vect.fit_transform(twenty_train.data)\n", - "X_tfidf" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "#extract vertex-hyperedge incidences and weights from TFIDF matrix\n", - "mat = coo_matrix(X_tfidf)\n", - "edges = mat.col\n", - "nodes = mat.row\n", - "data = np.array([edges,nodes]).T\n", - "weights = mat.data\n", - "\n", - "h = hnx.Hypergraph(hnx.StaticEntitySet(data=data,weights=weights))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#check the hypergraph is connected, as this is required by spectral clustering\n", - "#if not, restrict to largest connected component or modify hypergraph as necessary\n", - "h.is_connected()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<787x20868 sparse matrix of type ''\n", - "\twith 136994 stored elements in Compressed Sparse Column format>" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# # outputs the cell weight of a selected node in a selected edge\n", - "# weight = lambda self, node, edge: self.elements[edge].cellweights[node]\n", - "\n", - "#the weighted incidence matrix which contain the cell weights\n", - "I,verMap,edgeMap = h.incidence_matrix(weights=True,index=True)\n", - "I" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[381, 406]\n" - ] - } - ], - "source": [ - "#cluster the vertices (documents)\n", - "num_clus=len(categories)\n", - "clusters=hnx.spec_clus(h,num_clus,weights=True)\n", - "print([len(v) for v in clusters.values()])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#visualize document category composition within each cluster\n", - "fig = plt.figure(figsize=(22, 10))\n", - "labels=sorted(list(set(doc_types.values())))\n", - "colors=['#e41a1c','#377eb8']\n", - "\n", - "#pie charts\n", - "for clus in range(num_clus):\n", - " ax=plt.subplot(131+clus)\n", - " counts=[[doc_types[y] for y in clusters[clus]].count(z) for z in labels]\n", - " ax.pie(counts, colors=colors, shadow=True, startangle=90)\n", - " ax.set_title(\"Cluster \"+repr(clus)+ \"\\n\"+ repr(sum(counts))+ ' Documents',fontsize=20)\n", - "\n", - "#legend\n", - "ax=plt.subplot(131+num_clus)\n", - "patches = [mpatches.Patch(color=color, label=label)\n", - " for label, color in zip(labels, colors)]\n", - "ax.legend(patches, labels, loc='center',fontsize=20, frameon=False)\n", - "ax.axis('off');" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "interpreter": { - "hash": "4b11832e3fb1d317fabfdf226ff96dd8761e1caa77b8bb75a64cd45c858a9356" - }, - "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": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/tutorials/Tutorial 12 - Generative Models.ipynb b/tutorials/Tutorial 12 - Generative Models.ipynb deleted file mode 100644 index 2d1ef075..00000000 --- a/tutorials/Tutorial 12 - Generative Models.ipynb +++ /dev/null @@ -1,284 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "## Generating hypergraphs using random models\n", - "\n", - "This tutorial and all supporting code were developed by Mirah Shi, Sinan Aksoy, and Nicholas Landry.\n", - "\n", - "Implementation of and tutorial using two hypergraph generative models: \n", - "1. [Erdös–Rényi](#erdosrenyi)\n", - "2. [Chung-Lu](#chunglu)\n", - "\n", - "Hypergraph Erdös–Rényi and Chung-Lu implementations are described in\n", - "\n", - "> S. Aksoy, T.G. Kolda, and A. Pinar. Measuring and modeling bipartite graphs with community struc-ture. In:Journal of Complex Networks 5.4 (Mar. 2017), pp. 581–603.\n", - "\n", - "and adapt the algorithm in\n", - "\n", - "> J. C. Miller and A. Hagberg. Efficient generation of networks with given expected degrees. In 8th International\n", - "Conference on Algorithms and Models for the Web Graph (2011), pp. 115–126." - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "\n", - "\n", - "Generative models are useful tools in network science for their ability to approximate real data. Datasets are typically of a fixed size and generative models allow us to create networks with similar properties, but of arbitrary size. These models can be used as a proxy when the real data may too sensitive to reveal. Lastly, we can use generative models for *inference*, where given a real network and a generative model, we can calculate which parameters best match the given data. We can extend these network science ideas to networks where interactions can happen between greater than two entities." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": null, - "source": [ - "import hypernetx.algorithms.generative_models as gm\n", - "import hypernetx as hnx\n", - "import random\n", - "import matplotlib.pyplot as plt\n", - "import time\n", - "from collections import Counter" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## Erdös–Rényi Hypergraphs \n", - "\n", - "\n", - "\n", - "\n", - "In the article [Measuring and modeling bipartite graphs with community structure](https://doi.org/10.1093/comnet/cnx001) by Aksoy et al., they define the bipartite version of the network Erdös–Rényi model. Any bipartite network can be expressed as a hypergraph if one layer is defined as the nodes and the other layer is defined as the edges. We developed an efficient algorithm based on the [Miller-Hagberg approach](https://doi.org/10.1007/978-3-642-21286-4_10) that runs in $O(N+M)$ complexity by drawing from a geometric distribution instead of the naive algorithm that runs in $O(NM)$ time by iterating through every combination and performing a weighted coin-flip." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": null, - "source": [ - "n = 1000\n", - "m = n\n", - "p = 0.01\n", - "\n", - "# generate ER hypergraph\n", - "H = gm.erdos_renyi_hypergraph(n, m, p)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "Calculate the number of expected and generated vertex-hyperedge pairs" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": null, - "source": [ - "print('Expected # pairs: ', int(n*m*p))\n", - "print('Output # pairs: ', H.incidence_matrix().count_nonzero())" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "## Chung-Lu Hypergraph \n", - "\n", - "\n", - "\n", - "Also in the article [Measuring and modeling bipartite graphs with community structure](https://doi.org/10.1093/comnet/cnx001) by Aksoy et al., they define the bipartite version of the network Chung-Lu model. Like before, we can generate a bipartite network and define one layer as the nodes and the other layer as the edges. We developed an efficient algorithm based on the [Miller-Hagberg approach](https://doi.org/10.1007/978-3-642-21286-4_10) that runs in $O(N+M)$ complexity instead of the naive algorithm that runs in $O(NM)$ time. Unlike the Erdös–Rényi case, in the Chung-Lu model, the probabilities vary by degree, so in addition to drawing from a geometric distribution, we sort the degrees in reverse order and perform rejection sampling.\n", - "\n", - "The Chung-Lu model fulfills a degree distribution in expectation. Given degree distributions $W_n=\\{w_1^v,...,w_n^v\\}, W_m=\\{w_1^e,...,w_m^e\\}$ for vertices and hyperedges respectively, the hypergraph Chung-Lu model assigns vertex $i$ to hyperedge $j$ with probability $$p_{ij}=\\frac{w_i^v w_j^e}{S},$$ where $$S=\\sum_{i=1}^n w_i^v=\\sum_{j=1}^m w_j^e$$" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "### Example hypergraph\n", - "\n", - "We use a preprocessed disease-gene dataset (available from https://www.disgenet.org/downloads) and create a hypergraph with genes as vertices and diseases as hyperedges. Then we extract the degree sequences as input to ``chung_lu_hypergraph``." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": null, - "source": [ - "gene_data = hnx.utils.toys.GeneData()\n", - "genes = gene_data.genes\n", - "diseases = gene_data.diseases\n", - "disease_gene_network = gene_data.disease_gene_network\n", - "print('Number of vertices: ', len(genes))\n", - "print('Number of hyperedges: ', len(diseases))" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "### Construct degree sequences\n", - "\n", - "Label vertices and hyperedges with their desired degree:" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": null, - "source": [ - "k1 = {n: d for n, d in disease_gene_network.degree() if n in genes}\n", - "k2 = {n: d for n, d in disease_gene_network.degree() if n in diseases}" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "### Create Chung-Lu hypergraph\n", - "\n", - "``chung_lu_hypergraph`` generates a bipartite edge list, or equivalently, a list of vertex-hyperedge pairs and outputs it as a HyperNetX object." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": null, - "source": [ - "H = gm.chung_lu_hypergraph(k1, k2)" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "### Visualize results" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": null, - "source": [ - "\n", - "# plot desired vs output degree distribution\n", - "node_degrees = [H.degree(node) for node in H.nodes]\n", - "edge_degrees = H.edge_size_dist()\n", - "\n", - "fig, ax = plt.subplots(1, 2, figsize=(14,5))\n", - "ax[0].scatter(Counter(k1.values()).keys(), Counter(k1.values()).values(), color='orange', s=8, label='DisGene')\n", - "ax[0].scatter(Counter(node_degrees).keys(), Counter(node_degrees).values(), color='blue', s=8, label='Chung-Lu hypergraph')\n", - "ax[0].set_xscale('log')\n", - "ax[0].set_yscale('log')\n", - "ax[0].set_xlabel('Degree')\n", - "ax[0].set_ylabel('Count')\n", - "ax[0].set_title('Vertex degree distribution')\n", - "ax[0].legend(loc='best')\n", - "\n", - "ax[1].scatter(Counter(k2.values()).keys(), Counter(k2.values()).values(), color='orange', s=8, label='DisGene')\n", - "ax[1].scatter(Counter(edge_degrees).keys(), Counter(edge_degrees).values(), color='blue', s=8, label='Chung-Lu hypergraph')\n", - "ax[1].set_xscale('log')\n", - "ax[1].set_yscale('log')\n", - "ax[1].set_xlabel('Degree')\n", - "ax[1].set_ylabel('Count')\n", - "ax[1].set_title('Hyperedge degree distribution')\n", - "ax[1].legend(loc='best')\n", - "plt.show()" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "As we can see, the Chung-Lu model does not match the degree distribution exactly (notice the small tail of the distribution of actual degrees in contrast to the desired degree distribution)" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "This algorithm, as mentioned before, has linear time complexity $O(N+M)$. We can test this out by plotting the hypergraph generation time with respect to $N+M$." - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": null, - "source": [ - "\n", - "n = [500, 500, 500, 1000, 1000, 1000]\n", - "m = [100, 500, 1000, 1000, 5000, 10000]\n", - "m_and_n = list()\n", - "generation_time = list()\n", - "\n", - "for i in range(len(n)):\n", - " k1 = {j : random.randint(1, 10) for j in range(n[i])}\n", - " k2 = {j : random.randint(1, 10) for j in range(m[i])}\n", - "\n", - " m_and_n.append(n[i] + m[i])\n", - "\n", - " start = time.time() \n", - " H = gm.chung_lu_hypergraph(k1, k2)\n", - " generation_time.append(time.time() - start)\n", - "\n" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": null, - "source": [ - "plt.plot(m_and_n, generation_time, 'ko-')\n", - "plt.xlabel(r\"$M+N$\")\n", - "plt.ylabel(\"Generation time (s)\")\n", - "plt.show()" - ], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "From the plot, we can see (sans artifacts for small $M+N$) that there is a roughly linear relationship as we predicted." - ], - "metadata": {} - } - ], - "metadata": { - "interpreter": { - "hash": "4b11832e3fb1d317fabfdf226ff96dd8761e1caa77b8bb75a64cd45c858a9356" - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3.9.4 64-bit ('hypergraph': conda)" - }, - "language_info": { - "name": "python", - "version": "3.9.4", - "mimetype": "text/x-python", - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "pygments_lexer": "ipython3", - "nbconvert_exporter": "python", - "file_extension": ".py" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb b/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb deleted file mode 100644 index 200eb688..00000000 --- a/tutorials/Tutorial 13 - Hypergraph Modularity and Clustering.ipynb +++ /dev/null @@ -1,836 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import pickle\n", - "import random\n", - "import igraph as ig ## pip install python-igraph\n", - "import partition_igraph ## pip install partition-igraph\n", - "import hypernetx as hnx\n", - "import hypernetx.algorithms.hypergraph_modularity as hmod\n", - "import hypernetx.algorithms.generative_models as gm\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Main functions for Hypergraph Modularity using HyperNetX\n", - "\n", - "### Pre-computing key hypergraph quantities\n", - "\n", - "Given some hnx hypergraph HG, the following function needs to be called first\n", - "to pre-compute node strengths (weighted degrees), d-degrees and binomial coefficients\n", - "and add these as attributes to HG:\n", - "\n", - "```python\n", - "hmod.precompute_attributes(HG)\n", - "```\n", - "\n", - "### H-modularity (qH)\n", - "\n", - "The function to compute H-modularity for HG w.r.t. partition A (list of sets covering the vertices):\n", - "\n", - "```python\n", - "hmod.hypergraph_modularity(HG, A, wcd=linear)\n", - "```\n", - "\n", - "where 'wcd' is the weight function (default = 'linear'). Other choices are 'strict'\n", - "and 'majority', or any user-supplied function with the following format:\n", - "\n", - "```python\n", - "def linear(d,c):\n", - " return c/d if c>d/2 else 0\n", - "```\n", - "\n", - "where $d$ is the edge size, and $c$ is the number of nodes in the majority class, $d \\geq c > \\frac{d}{2}$\n", - "\n", - "### Two-section graph\n", - "\n", - "Build the random-walk based $2$-section graph given some hypergraph HG:\n", - "\n", - "```python\n", - "G = hmod.two_section(HG)\n", - "```\n", - "\n", - "where G is an igraph Graph.\n", - "\n", - "### Clustering: Kumar algorithm\n", - "\n", - "Given hypergraph HG, compute a partition of the vertices as per Kumar's algorithm described in [1].\n", - "\n", - "```python\n", - "K = hmod.kumar(HG, delta=.01)\n", - "```\n", - "\n", - "where delta is the convergence stopping criterion. Partition is returned as a list of sets.\n", - "\n", - "[1] Kumar T., Vaidyanathan S., Ananthapadmanabhan H., Parthasarathy S., Ravindran B. (2020) *A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering*. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_24\n", - "\n", - "\n", - "### Clustering: Simple qH-based algorithm\n", - "\n", - "Given hypergraph HG and initial partition L, \n", - "compute a partition of the vertices as per Last-Step algorithm described in [2].\n", - "\n", - "```python\n", - "A = hmod.last_step(HG, L, wdc=linear, delta = .01)\n", - "```\n", - "\n", - "where 'wcd' is the the weight function (default = 'linear') and delta is the convergence stopping criterion.\n", - "Returned partition is a list of sets.\n", - "\n", - "[2] Kamiński B., Prałat P. and Théberge F. “Community Detection Algorithm Using Hypergraph Modularity”. In: Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13\n", - "\n", - "### Utility functions\n", - "\n", - "We use two representations for partitions: list of sets (the parts) or dictionary.\n", - "Those functions are used to map from one to the other:\n", - "\n", - "```python\n", - "dict2part(D)\n", - "part2dict(A)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Toy example" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "## build a hypergraph from a list of sets (the hyperedges)\n", - "E = [{'A','B'},{'A','C'},{'A','B','C'},{'A','D','E','F'},{'D','F'},{'E','F'}]\n", - "HG = hnx.Hypergraph(E,static=True)\n", - "hnx.draw(HG)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "## compute node strength (add unit weight if unweighted), d-degrees, binomial coefficients\n", - "HG = hmod.precompute_attributes(HG)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "e0 has weight 1\n", - "e1 has weight 1\n", - "e2 has weight 1\n", - "e3 has weight 1\n", - "e4 has weight 1\n", - "e5 has weight 1\n" - ] - } - ], - "source": [ - "## list the edges (unit weights added by default)\n", - "for e in HG.edges:\n", - " print(e,'has weight',HG.edges[e].weight)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "B has strength 2\n", - "A has strength 4\n", - "C has strength 2\n", - "E has strength 2\n", - "D has strength 2\n", - "F has strength 3\n" - ] - } - ], - "source": [ - "## list the nodes (here strength = degree since all weights are 1)\n", - "for v in HG.nodes:\n", - " print(v,'has strength',HG.nodes[v].strength) \n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Counter({2: 4, 3: 1, 4: 1})" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "## total edge weight for each edge cardinality\n", - "HG.d_weights\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "linear edge contribution:\n", - "qH(A1): 0.414445267489712 qH(A2): -0.03746831275720153 qH(A3): 0.0 qH(A4): -0.19173004115226341\n", - "strict edge contribution:\n", - "qH(A1): 0.43490699588477366 qH(A2): -0.02385843621399164 qH(A3): 0.0 qH(A4): -0.12887572016460908\n", - "majority edge contribution:\n", - "qH(A1): 0.39379753086419755 qH(A2): -0.0343506172839505 qH(A3): 0.0 qH(A4): -0.22078024691358022\n" - ] - } - ], - "source": [ - "## compute hypergraph modularity (qH) for the following partitions:\n", - "A1 = [{'A','B','C'},{'D','E','F'}]\n", - "A2 = [{'B','C'},{'A','D','E','F'}]\n", - "A3 = [{'A','B','C','D','E','F'}]\n", - "A4 = [{'A'},{'B'},{'C'},{'D'},{'E'},{'F'}]\n", - "\n", - "## we compute with 3 different choices of functions for the edge contribution: linear (default), strict and majority\n", - "strict = hmod.strict\n", - "majority = hmod.majority\n", - "\n", - "print('linear edge contribution:')\n", - "print('qH(A1):',hmod.modularity(HG,A1),\n", - " 'qH(A2):',hmod.modularity(HG,A2),\n", - " 'qH(A3):',hmod.modularity(HG,A3),\n", - " 'qH(A4):',hmod.modularity(HG,A4))\n", - "print('strict edge contribution:')\n", - "print('qH(A1):',hmod.modularity(HG,A1,strict),\n", - " 'qH(A2):',hmod.modularity(HG,A2,strict),\n", - " 'qH(A3):',hmod.modularity(HG,A3,strict),\n", - " 'qH(A4):',hmod.modularity(HG,A4,strict))\n", - "print('majority edge contribution:')\n", - "print('qH(A1):',hmod.modularity(HG,A1,majority),\n", - " 'qH(A2):',hmod.modularity(HG,A2,majority),\n", - " 'qH(A3):',hmod.modularity(HG,A3,majority),\n", - " 'qH(A4):',hmod.modularity(HG,A4,majority))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\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" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": { - "image/svg+xml": { - "isolated": true - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "## 2-section graph\n", - "G = hmod.two_section(HG)\n", - "G.vs['label'] = G.vs['name']\n", - "ig.plot(G,bbox=(0,0,250,250))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'A', 'B', 'C'}, {'D', 'E', 'F'}]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "## 2-section clustering with ECG\n", - "G.vs['community'] = G.community_ecg().membership\n", - "hmod.dict2part({v['name']:v['community'] for v in G.vs})\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'A', 'B', 'C'}, {'D', 'E', 'F'}]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "## Clustering with Kumar's algorithm\n", - "hmod.kumar(HG)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "start from: [{'A'}, {'B'}, {'C'}, {'D'}, {'E'}, {'F'}]\n", - "final partition: [{'B', 'A', 'C'}, {'E', 'D', 'F'}]\n" - ] - } - ], - "source": [ - "## hypergraph clustering -- start from trivial partition A4 defined above\n", - "print('start from:',A4)\n", - "A = hmod.last_step(HG,A4)\n", - "print('final partition:',A)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Chung-Lu hypergraph example\n", - "\n", - "We build a Chung-Lu hypergraph and compute modularity for partitions from 3 algorithms:\n", - "* Louvain, on the 2-section graph\n", - "* Kumar algorithm\n", - "* LastStep algorithm\n", - "\n", - "We use the **strict** modularity, so only edges where all vertices are in the same part will add to the modularity.\n", - "For each algorithm, we compute the modularity qH and compare with the number of edges where all vertices are in the same part.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "## Chung-Lu hypergraph\n", - "n = 200\n", - "k1 = {i : random.randint(2, 10) for i in range(n)} ## node degrees\n", - "k2 = {i : sorted(k1.values())[i] for i in range(n)} ## edge sizes\n", - "H = gm.chung_lu_hypergraph(k1, k2)\n", - "\n", - "## pre-compute required quantities\n", - "HG = hmod.precompute_attributes(H)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "qH = 0.0569711443530809\n", - "edges with all vertices in same part: 28\n" - ] - } - ], - "source": [ - "## Louvain algorithm on the 2-section graph\n", - "G = hmod.two_section(HG)\n", - "G.vs['louvain'] = G.community_multilevel().membership\n", - "D = {v['name']:v['louvain'] for v in G.vs}\n", - "ML = hmod.dict2part(D)\n", - "\n", - "## Compute qH\n", - "print('qH =',hmod.modularity(HG, ML, strict))\n", - "\n", - "## number of edges where all vertices belong to the same community\n", - "print('edges with all vertices in same part:',\n", - " sum([len(set([D[v] for v in HG.edges[e]]))==1 for e in HG.edges()]))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "qH = 0.19310099840187803\n", - "edges with all vertices in same part: 54\n" - ] - } - ], - "source": [ - "## Kumar algorithm\n", - "KU = hmod.kumar(HG)\n", - "\n", - "## Compute qH\n", - "print('qH =',hmod.modularity(HG, KU, strict))\n", - "\n", - "## number of edges where all vertices belong to the same community\n", - "print('edges with all vertices in same part:',\n", - " sum([len(set([hmod.part2dict(KU)[v] for v in HG.edges[e]]))==1 for e in HG.edges()]))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "qH = 0.21300020106295142\n", - "edges with all vertices in same part: 57\n" - ] - } - ], - "source": [ - "## Last-step algorithm using previous result as initial partition\n", - "LS = hmod.last_step(HG, KU, strict)\n", - "\n", - "## Compute qH\n", - "print('qH =',hmod.modularity(HG, LS, strict))\n", - "\n", - "## number of edges where all vertices belong to the same community\n", - "print('edges with all vertices in same part:',\n", - " sum([len(set([hmod.part2dict(LS)[v] for v in HG.edges[e]]))==1 for e in HG.edges()]))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Game of Thrones scenes hypergraph\n", - "\n", - "REF: https://github.com/jeffreylancaster/game-of-thrones\n", - "\n", - "We built an hypergraph from the game of thrones scenes with he following elements:\n", - "\n", - "* **Nodes** are characters in the series\n", - "* **Hyperedges** are groups of character appearing in the same scene(s)\n", - "* **Hyperedge weights** are total scene(s) duration in seconds involving those characters\n", - "\n", - "We kept hyperedges with at least 2 characters.\n", - "Moreover, we discarded characters with degree below 5.\n", - "\n", - "We saved the following:\n", - "\n", - "* *Edges*: list of sets where the nodes are 0-based integers represents as strings\n", - "* *Names*: dictionary; mapping of nodes to character names\n", - "* *Weights*: list; hyperedge weights (in same order as Edges)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "198 nodes and 1492 edges\n" - ] - } - ], - "source": [ - "## load the GoT dataset\n", - "Edges, Names, Weights = pickle.load(open( \"../hypernetx/utils/toys/GoT.pkl\", \"rb\" ))\n", - "print(len(Names),'nodes and',len(Edges),'edges')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Build weighted GoT hypergraph " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "## Nodes are represented as strings from '0' to 'n-1'\n", - "H = hnx.Hypergraph(dict(enumerate(Edges)))\n", - "## add edge weights\n", - "for e in H.edges:\n", - " H.edges[e].weight = Weights[e]\n", - "## add full names\n", - "for v in H.nodes:\n", - " H.nodes[v].name = Names[v]\n", - "## pre-compute required quantities for modularity and clustering\n", - "GoT = hmod.precompute_attributes(H)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Modularity (qH) on a random partition\n", - "\n", - "We use the default choice for the modularity (**linear** weights).\n", - "Result for the random partition should be close to 0 and can be negative." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.0054328760823038336" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "## generate a random partition into K parts to compare results\n", - "K = 5\n", - "V = list(GoT.nodes)\n", - "p = np.random.choice(K, size=len(V))\n", - "RandPart = hmod.dict2part({V[i]:p[i] for i in range(len(V))})\n", - "## compute qH\n", - "hmod.modularity(GoT, RandPart)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate the 2-section igraph Graph and cluster with Louvain Algorithm\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "qH = 0.5372359319251633\n" - ] - } - ], - "source": [ - "## build 2-section\n", - "G = hmod.two_section(GoT)\n", - "## Louvain algorithm\n", - "G.vs['louvain'] = G.community_multilevel(weights='weight').membership\n", - "ML = hmod.dict2part({v['name']:v['louvain'] for v in G.vs})\n", - "\n", - "## Compute qH\n", - "print('qH =',hmod.modularity(GoT, ML))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Cluster hypergraph with Kumar's algorithm\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "qH = 0.5382594158646983\n" - ] - } - ], - "source": [ - "## run Kumar's algorithm, get partition\n", - "KU = hmod.kumar(GoT)\n", - "## Compute qH\n", - "print('qH =',hmod.modularity(GoT, KU))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Cluster with simple H-based (Last Step) Algorithm\n", - "\n", - "We use Louvain on the 2-section or Kumar algorithm for the initial partition" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "qH = 0.5460841945299417\n" - ] - } - ], - "source": [ - "## H-based last step with Louvain parition already computed\n", - "LS = hmod.last_step(GoT, ML)\n", - "## Compute qH\n", - "print('qH =',hmod.modularity(GoT, LS))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Example: show top nodes in same cluster as Daenerys Targaryen\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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characterstrength
16Daenerys Targaryen31103
23Jorah Mormont19344
7Missandei13683
24Grey Worm10497
8Barristan Selmy6514
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" - ], - "text/plain": [ - " character strength\n", - "16 Daenerys Targaryen 31103\n", - "23 Jorah Mormont 19344\n", - "7 Missandei 13683\n", - "24 Grey Worm 10497\n", - "8 Barristan Selmy 6514" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "## Index for \n", - "inv_map = {v: k for k, v in Names.items()}\n", - "DT = inv_map['Daenerys Targaryen']\n", - "## DT's cluster\n", - "DT_part = hmod.part2dict(LS)[DT]\n", - "## Build dataframe: all nodes in DT_part\n", - "L = []\n", - "for n in LS[DT_part]:\n", - " L.append([Names[n],GoT.nodes[n].strength])\n", - "D = pd.DataFrame(L, columns=['character','strength'])\n", - "D.sort_values(by='strength',ascending=False).head(5)" - ] - } - ], - "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": "3.7.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/tutorials/Tutorial 2 - Visualization Methods.ipynb b/tutorials/Tutorial 2 - Visualization Methods.ipynb index a09b4577..591bb8f8 100644 --- a/tutorials/Tutorial 2 - Visualization Methods.ipynb +++ b/tutorials/Tutorial 2 - Visualization Methods.ipynb @@ -40,6 +40,9 @@ "import hypernetx as hnx\n", "from networkx import fruchterman_reingold_layout as layout\n", "\n", + "import warnings\n", + "warnings.simplefilter('ignore')\n", + "\n", "### GraphViz is arguably the best graph drawing tool, but it is old and tricky to install.\n", "### Uncommenting the line below will get you slightly better layouts, if you can get it working...\n", "\n", @@ -90,14 +93,12 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", 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", 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\n", 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BATz44IPs3bvXZ+fWXkn4tcNwW9to3uNdq5r+ICcnh7CwMGbMmKF2KX5z/vx5FEXx6hSRvtB/5yaFEEKIbtCFBWIcG0Xz3grMMxK8flduypQpHDt2jF27dvHOO+9QUFCA1WolNTWVzMxM7r77bp+2fbkaQ5wZ8/QEbFuKCZoSiy7Yu56HaikoKODcuXPccsstfp01VZvFYiE2NrbfNoC+SAKgEEKIAS94ZgK1z+bhKLQROMw333jnzp3L3LlzfTKWt0KXpdFyrBrb5mIiVvfvmaXL8Xg8bNq0ieTkZMaPH692OX7j8XiwWCxMnjxZ7VKuSpaAhRBCDHiBw8PRR5to3lehdil+oTMbCF2Sin1/Bc5Ku9rl9Fhubi6VlZWsWLFi0OxmvpyKigpaWlr6/ft/IAFQCCHEIKDRajDPTKA1r3bQnp4RPCsRfaSRhg8LvNrx3NccDgdbtmxh/PjxpKSkqF2OX1ksFgIDAwfE1ykBUAghxKBgnhKLRqvBfqBS7VL8QqPXEnZNBu2WBtrOWtUup9t2795Na2srS5cuVbsUv7NYLAwfPrzfnv97KQmAQgghBgVtkIGgSbHY91eguAd+y5TLMY6LJDAjjMYPCwbE12iz2dizZw8zZ87s1+fi+oLdbqesrGxALP+CBEAhhBCDiHlWAm6bg9ZB1Dj5UhqNhrDrMnDVtmIfAO87bt26FYPBwLx589Quxe/Onz8P0O/bv1wkAVAIIcSgEZAYTEB66KDomdeVgMRgzNPisW0pxtPiXfNrf6qoqODYsWMsWrQIo9Godjl+Z7FYiI+PJyQkRO1SukUCoBBCiEEleHYijgs2HBUDb7dsd4UuT0NxKdi2FKtdymUpisLGjRuJjo5mypQpapfjdx6Ph/z8/AGz/AsSAIUQQgwypvFRaEMDsO8dvLOAupAAQhan0Ly3AmdNi9rlfM7Zs2cpLCxk+fLlA2JDhLfKyspobW2VACiEEEKoRaPTEjwjgZaj1f16idRbIXOS0IUF0PjhBbVL+RS3201OTg4ZGRkDKhB5w2KxYDKZSE5OVruUbpMAKIQQYtAxT49H8SjYD1epXYrfaAxawlYNo+1MPW2W/tMW5tChQ9TV1bF8+fJB3fT5Uhfbv2i1AydWDZxKhRBCiG7ShQQQNDGa5r0VKJ6B0zS5p0wTowlID6XhgwIUt/pfZ2trKx999BFTpkwhPj5e7XL6RFNTExUVFQNutlMCoBBCiEHJPDsRd30bbef6z+yYr2k0GsKvzcBV1YL9kPoNsHfs2IHL5WLRokVql9Jn8vPzARg+fLjKlfSMBEAhhBCDUkBKCIbk4EHdEgY6vs6gKbHYNhXhaXOpVkd9fT379+9n7ty5A6YVii/k5+eTlJREcHCw2qX0iARAIYQQg5JGoyF4ViLt56z9cqesL4WtSEdxuLFtLVGthpycHIKDg5k1a5ZqNfQ1t9tNfn7+gGn+fCkJgEIIIQatoMwYtGY99r39/9QMb+jCAglZkEzz7jJcda19/vyioiJOnz7NkiVLCAgI6PPnq6W0tJT29vYB9/4fSAAUQggxiGkMWszZCdgPV+FpV295tC8Ez09GF2ygcX3ftoXxeDxs3LiRhIQEJk6c2KfPVpvFYiEoKIjExES1S+kxCYBCCCEGNfPMBBSHm5aj1WqX4lfaAB2hK4fRmldHe0FDnz03Ly+P8vJyVqxYMaDaoPiCxWJhxIgRA/LrHngVCyGEED2gDw/ENC6K5j0VKIr6rVL8KSgrBkNKSEdbmD5of+N0Otm8eTNjxowhPT3d78/rT2w2G1VVVQNy+RckAAohhBgCzLMTcVW30H6+Ue1S/Eqj1RB+XQbOcjstR/zfBHvv3r00NzezbNkyvz+rv7FYLGg0mgHX/uUiCYBCCCEGvcCMMPRxQYO+JQxAYFoopqwYGjcW4ml3++05zc3N7Nq1i+nTpxMVFeW35/RX+fn5JCcnExQUpHYpvSIBUAghxKB3sSVM2+k6XNY2tcvxu7CV6Xha3TR95L+2MNu2bUOr1TJ//ny/PaO/crlcnD9/fsAu/4IEQCGEEENE0ORYNIE67PsGd0sYAH2EkZB5STTtLMPV4PvAW1VVxZEjR1i4cOGAnQHzRklJCQ6HY0D2/7tIAqAQQoghQRuowzw1DvvBShSn/5ZG+4uQhcloTToa1xf6fOxNmzYRERHBtGnTfD72QGCxWAgODh7Q5x1LABRCCDFkBM9KxNPioiW3Vu1S/E4bqCdseTqtuTW0F9l8Nq7FYuH8+fMsW7YMvV7vs3EHkoHc/uWigVu5EEII0UP6aBPG0RE07y0f9C1hAIKmxmFINNPoo7YwbrebTZs2kZaWxpgxY3xQ4cDT0NBATU3NgH7/DyQACiGEGGLMsxJxljXjKG5SuxS/u9gWxlHSRGtujdfjnTt3joaGBlasWIFGo/FBhQPPxfYvGRkZapfiFQmAQgghhhTjqAh0UcYh0RIGIDAjHOP4KBo3XMDj6P27jw6HA6vVyqpVqwbk0We+YrFYSE1NxWQyqV2KVyQACiGEGFI0Wg3BMxNpPVGL2+ZQu5w+EX7NMNzNTpp3lvV6jLNnz1JdXc3YsWN9WNnA4nK5uHDhwoBf/gUJgEIIIYYg87Q4NDoN9gODvyUMgD7KRPCcRFpP1eJpc/X4frvdjsViYeTIkQN+5ssbRUVFOJ1OCYBCCCHEQKQ16QmaEkvz/goUl0ftcvpE6OJUAtLDaDtb3+N78/LyCAgIYNSoUX6obOCwWCyEhIQQGxurdilekwAohBBiSAqelYinyUnryTq1S+kTWqOeoMmxtJ6px1XX+rnPt7a20t7e/rmP19XVUVJSwvjx4zEYDH1Rar91cRZ0MGyAkQAohBBiSDLEmwnMCBsym0EAAhKD0YUEYD9a/bk2OBs2bGDfvn2d/6woCoqicPz4ccLCwkhLS+vrcvuV+vp66urqBsXyL0gAFEIIMYSZZyXiKLLhKGtWu5Q+odFqCJoci6umFUfJJ21wTp48SW5uLjt37uTYsWMd12o0lJaWUldXR2Zm5oBueuwLFosFrVY74Nu/XDS0/2sKIYQY0kzjotCFBdC8dwjNAiYEY0g003KsuvP9x/Xr17Ny5Urmzp3Lzp07KS0txe12c+LECeLj44mLi1O5avVZLBbS0tIIDAxUuxSfkAAohBBiyNLoNJhnJtByrAa33al2OX0maFIsnhYXrees7NixA4PBwMyZMzvf83vvvfc4duwYra2tZGZmql2u6pxOJ4WFhYNm+RckAAohhBjizNnxoCi0HKpSu5Q+ow8LxDgyguYT1ezYsYMVK1YAEBMTwxe/+EX0ej07d+4kKSmJ0NBQPJ6hsVO6K4WFhbhcLgmAQgghxGChCw4gKCuG5n3lPjkvd6AwTYiiqr2O5PB4xowZg6IoeDweQkJCSEpKoqmpidraWgB5/89iITw8nOjoaLVL8Zmh/V9UCCGEoKMljNvaTtuZnvfIG6i0gXpGzBjHdVFzcFnb0Gg0aLVabDYb7e3tzJw5k9OnT7Nr1y61S1WVoihYLBZGjBgxKNq/XCQBUAghxJAXkBJCQErIkGoJA2AcGY4+NJCWS9rCHD9+HJPJxNy5c5k4cSLbt2+nqKhI5UrVU1dXh9VqHVTLvyABUAghhADAPDuR9vwGnNUtapfSZzRaLUGTYnFWteAsa6aqqorKykomTpyIyWRi5cqVDBs2bEi/A2ixWNDpdAwbNkztUnxKAqAQQggBBE2MRhtsGFItYQAMiWYM8UHYj1Zx/PhxIiMjSU5O7gx9N91006ALPz1hsVhIT08nICBA7VJ8SgKgEEIIAWj0WszT42k5XI2nzaV2OX1Go+loDl3eUkNjYyNZWVmd7wMCGI1GlStUT3t7O0VFRYNu+RckAAohhBCdgmckoLjctBypVruUPqWYdRQE1BBHOBHmMLXL6TcuXLiA2+2WACiEEEIMZrqwQEzjo2neO7RawhQVFREWFc4IQxItebVql9Nv5OfnExkZSVRUlNql+JwEQCGEEOISwbMScdW00n6+Qe1S+oTNZuP9998nJCSEiMyOdjiuxja1y1LdxfYvg3H2DyQACiGEEJ8SMCwUQ7x5yLSE2bJlC06nk7FjxxKYEoKn3UV7QaPaZamupqbjncgRI0aoXYpfSAAUQgghLqHRaDDPTqDtTD2u+sE9E1ZeXk5ubi6LFi3CaDSi0WsxpofR9FEpjspmtctTlcViQa/Xk56ernYpfiEBUAghhPiMoEmxaAL1NO8bvLOAiqKwadMmoqOjmTJlSufHjeOj0AYZqF97DsU9tPv/DRs2DIPBoHYpfiEBUAghhPgMbYAOc3Yc9oNVeBxutcvxi7Nnz1JYWMiKFSvQ6XSdH9doNIRfl4Grwo79QKWKFaqnra2N4uLiQfv+H0gAFEIIIS4reGYCSpuL1twatUvxOZfLxaZNm8jIyLjsO24BScEETY3DllOEp8WpQoXqKigowOPxSAAUQgghhhp9lAnj6Eia95R3npM7WBw6dAir1cqKFSvQaDSXvSZseTqKy4Nta0kfV6c+i8VCdHQ0ERERapfiNxIAhRBCiC4Ez07EWWHHUWRTuxSfaW1tZfv27UyePJm4uLgur9OFBhCyKIXmPeU4a4bO+ciKopCfnz+oZ/9AAqAQQgjRpcAR4eijTYOqJcyOHTtwu90sWrToqteGzE1CFxpA47oLfVBZ/1BVVUVTU5MEQCGEEGKo0mg1mGcl0JpXh7uxXe1yvFZXV8f+/fuZO3cuISEhV71eY9ARtiqdttP1tOU3+L/AfsBisWAwGEhNTVW7FL+SACiEEEJcgXlqHBq9lub9FWqX4rXNmzcTHBzMzJkzu32PKTOGgNQQGj8oGBLH41ksFjIyMtDr9WqX4lcSAIUQQogr0Br1BE2JxX6gEsU1cPviFRUVcfr0aZYsWUJAQEC379NoNIRfPxxnpR37ocHdFqa1tZWSkpJBv/wLEgCFEEKIqwqenYin2UlrXq3apfSKx+Nh48aNJCYmMnHixB7fH5ASQtDkWGybivC0ufxQYf9w/vx5FEWRACiEEEIIMMQGETgifMBuBsnLy6O8vJwVK1ag1fbuW3/oynSUdjdN2wZvWxiLxUJsbCxhYWFql+J3EgCFEEKIbgielYC72TngWqI4HA42b97M2LFjSUtL6/U4+rBAgucn07SrbFCekezxeIZE+5eLJAAKIYQQ3WAcG4V5ZjzOqoEVAPft20dzczNLly71eqyQBclozQYa1w++tjCVlZXY7XYJgEIIIYT4hEarQR8bROup2gHzHlxTUxO7du1i+vTpREVFeT2eNkBH2Ip0Wk/U0n6h0QcV9h8Wi4XAwEBSUlLULqVPSAAUQgghusmYHgZoaDs/MMLPtm3b0Ol0LFiwwGdjBk2OxZAcTMMgawtzsf2LTqdTu5Q+IQFQCCGE6CatUU9gaijt+dZ+H36qqqo4evQoCxYswGQy+WxcjVZD+HUZOMuaaTla7bNx1WS32yktLR0yy78gAVAIIYToEeOoCDwtLpxlTZ/6uOJWPhUKFUW9gKgoChs3biQiIoJp06b5fPzA9DBME6Np3FiIx+H2+fh97fz58wCMGDFC5Ur6jgRAIYQQogf0kUb00SbazjUA4La10/BBATX/yqX2mRPUvnQKZ3ULGo1GtRrz8/MpKChg+fLlfjvRImzVMDwtTpq2l/pl/L5ksViIj48nNDRU7VL6jARAIYQQooeMI8NpL7JR/eQxKh4/QPPuMgLSQzFNjEYbpKd+7VnaztarUpvb7WbTpk2kpaUxevRovz1HH2kkZG4SzTtKcTUM3HOSh1r7l4skAAohhBC94CxtwtPqInhuEkFT43CW29GFBRJ5yygCM8Jp3FSkSl1HjhyhpqaGFStW+H0WMmRhCppAHbYNA7ctTFlZGa2trRIAhRBCCNE1xe3BtrkYrdmAPjaI0GVpRN4yiqBJMTT8t+NdspC5ibjqWvu8aXRbWxvbtm0jKyuLxMREvz9Pa9QTtjydlmM1tBfb/P48f8jPz8doNJKUlKR2KX1KAqAQQgjRA+4mJx67k7BrMtDotbQXdLSEMSQGow024KxtRROoJ3hOEkp7326Q2LVrFw6HgyVLlvTZM4OmxWFIMNP4QYGqG196y2KxMGLEiCHT/uUiCYBCCCFED+jDA/G0uPC0OAlIDaHdYsVtd9K0oxQU0IUGoNFrCJ6VgCExuM/qslqt7N27lzlz5vTpZgaNVkPYdRk4iptoza3ps+f6QnNzM+Xl5UNq9+9FEgCFEEKIHgqel0TzvgochTbai2xY37bgrLATMi8JbYAOjU6LLjgAjbbvdgJv2bIFk8nE7Nmz++yZFxmHh2McF0Xj+kIU58BpC5Ofnw8MrfYvF0kAFEIIIXooeHYiwbMTcdscuBsdOEubCJmXjCkzRpV6SkpKyMvLY/HixQQGBqpSQ9g1w3A3O2jaWabK83vDYrGQmJhIcHDfzdT2FxIAhRBCiB7SaDWYJ8cS89WJhF2fQcCwMIyjIvp0xu+ii02f4+LimDRpUp8//yJDtIngWYk0fVSC29b/28K43W7Onz8/5Hb/XiQBUAghhOgljV5L0LgotAYtbflWVWo4deoUpaWlrFixAq1W3W/roUtS0ei1NG5UpwVOT5SWltLW1iYBUAghhBA9p9FrCRgeTntBIx6Xp0+f7XQ6ycnJYdSoUWRkZPTpsy9Ha9ITuiyNliNVOMqa1S7niiwWC0FBQX3SLqc/kgAohBBCeMk4IhzF5cFR2Ninzz1w4ACNjY0sW7asT597JebpCehjgmj44Hy/bguTn5/PiBEjVJ81VcvQ/KqFEEIIH9IFB2BIDKYtv6HPQo/dbmfHjh1MmzaNmBh1Np9cjkanIfy6DBwXbLSdrFO7nMuy2WxUVlYO2eVfkAAohBBC+IRpXCQavRZXbWufPO+jjz4CYOHChX3yvJ4wjorAODqChnUXUPp4Wbw78vPz0Wg0DB8+XO1SVCMBUAghhPABfZQJR0kTtpxCvz+rpqaGQ4cOMX/+fMxms9+f1xth12bgbmijeXe52qV8jsViISkpiaCgILVLUY0EQCGEEMIHNBoNpjGRtObV4WrwbxuUnJwcwsLCmD59ul+f4w1DbBDmGQnYthbjbnaoXU6nod7+5SIJgEIIIYSPBE2JRWPQYd9f4bdnFBQUcO7cOZYtW4bBYPDbc3whdGkaaDTYcvpPW5ji4mIcDocEQLULEEIIIQYLbaAe89Q47AcqUJy+f/fN4/GwceNGUlJSGDdunM/H9zWd2UDo0lTsBypxVtrVLgfoWP41m83Ex8erXYqqJAAKIYQQPmSelYDH7qLlRI3Pxz527BhVVVUsX74cjabvTx3pjeCZCeijTDR8UNAv2sJYLBZGjhw5ZNu/XDS0v3ohhBDCxwwxQQSODKd5j283P7S3t7N161YmTJhASkqKT8f2J41eS9g1w2jPb6DtTL2qtTQ0NFBTUzPkl39BAqAQQgjhc8GzEnGWNuMoafLZmHv27KG1tZWlS5f6bMy+YhwbSeCIcBo/VLctzMX2L/3h1BS1SQAUQgghfMw4JhJdpNFns4CNjY3s3r2bWbNmER4e7pMx+5JGoyHs2gxcda007/PfBpmrsVgspKSkYDKZVKuhv5AAKIQQQviYRqsheGYCLcdrcDd53wJl69atBAQEMHfuXB9Up46ABDPm7HhsW4px2519/nyXy0VBQYEs/35MAqAQQgjhB+ZpcWi0GuwHKr0ap7y8nNzcXBYtWoTRaPRRdeoIXZYGHoWmLcV9/uyioiKcTqcEwI9JABRCCCH8QBtkIGhSLPb9FSju3r33pigKGzduJCYmhilTpvi4wr6nCwkgZFEKzfvKcVa39OmzLRYLISEhxMXF9elz+ysJgEIIIYSfmGcl4LY5aD1V16v7z549S1FREcuXL0en0/m4OnWEzElCF26k8cOCPn3uxfYvA6V9jr9JABRCCCH8JCAxmID00F5tBnG5XGzatInhw4czYsQIP1SnDo1BS9iqdNrOWmk7Z+2TZ9bX11NXVyfLv5eQACiEEEL4UfDsRBwXbDgqenYSxqFDh7BarQOq6XN3mSZEE5AeSsOHBShu/zeHzs/PR6vVMmzYML8/a6DQq12AEEIIMZiZxkehDQ3AvrecgJu6NwPV0tLCRx99xOTJk/vknTW3y0VTbQ0N1ZXYqqtoqK6ksaqSxuoqNFoNYbHxhMfFExoTR3hcPGGx8YRERaPt5bK0RqMh/LoMqv9+DPvBCoJnJvr4K/o0i8VCWlragN9E40sSAIUQQgg/0ui0BM9IoOmjEsJWpqMNMlz1nh07duDxeFi0aJHf6lIUhcrz58jdtI6ze3bicna0q9FotIRExxAeF0dM2jAUxUNjdSXl507TVFcLHx/nZjCaGDdvIVnLryUmNb3Hzw9IDiFociy2nCKCsmLRmvwTSZxOJxcuXPDrv8uBSAKgEEII4Wfm6fHYthZjP1RFyPzkK15bV1fHgQMHWLhwISEhIT6vxdnexpk9O8jdtI6qgnxCY2KZefPtxA8fRVhcx8yeTn/5eOByOmmqraaxqpKyc6c5sXUTuTnrSRozjqzl1zJqxmx0+qsH3IvCVqbTeqIW29Ziwq/1z+kchYWFuFwuef/vMyQACiGEEH6mCwkgaGI0zfsqCJ6bhEbb9Tt9mzdvJjg4mFmzZvm0BmdbG3vefJUTWzfS3tLCsKwprP7BTxk2eSpabfeWcvUGAxEJSUQkJJE+aSozb7qd/IP7yN30Iev+8js+Cgsna9kqpq9eg95w9SCoCw0kZGEKtq3FBM9IQB/t+xM6LBYLYWFhxMTE+HzsgUwCoBBCCNEHzLMTaTmWS9s5K6YxkZe9pri4mNOnT3PTTTdh6EaA6q66shLe/+PjNNZUMWn5tWQtXUV4fILX4+r0ekbPmsvoWXOpKy3m2KYPOfDuG1w4dpjrv/0YoTGxVx0jeF4S9gOVNKy7QPSXxnld06UURZH2L12QXcBCCCFEHwhICcGQHHzFljB79+4lJiaGCRMm+Oy5Z3Zv55UffgdFUbjr139iwV33+ST8fVZUcipL7nuQ2//fb7E3WPn3Y49w4djhq96nDdB1tIU5VUfb+Qaf1lRXV4fVapXl38uQACiEEEL0AY1GQ/CsRNrPWXHWfP4UDJvNxpkzZ8jOzkar9f7bs9vlZMtz/+TDv/yO4dNm8MVf/5Go5FSvx72a+BGjuPs3fyZh5Gje/s3P2b32ZTwe9xXvMWXFEJASQuMHBSge37WFsVgs6HQ6af9yGRIAhRBCiD4SlBmD1qzHvrfic587fPgwBoOBzMxMr5/T1tzM6z97jOObN7Dkvge55pv/Q4DR9+/XdcUUEsqNP/gpc9bcxf631/L24z/H2d7W5fUajYaw6zNwVthpOVzlszry8/NJT08nICDAZ2MOFhIAhRBCiD6iMWgxZydgP1yFp93V+XG3283hw4fJzMz0uled4vGw/u9/wFpZzu2/+D8mrbhWlfffNFotM2+6jZt//AvKzpxiy7NPoihdz+4FpoZiyoqhcWMhnvYrzxh2h8PhoLCwUJZ/uyABUAghhOhD5pkJKA43LUerOz925swZmpubyc7O9nr8A++9ScGRg1zzje+RMGK01+N5K23iJJbd/zAnt2/hxNZNV7w2bGU6nhYnLUe9nwW8cOECbrd7UB2j50sSAIUQQog+pA8PxDQuiuY9FZ0zYgcPHiQ1NdXrUz+K846z+/WXmXnTbQybPM0X5frEuPmLyVy6kq3P/5OqC+e7vE4fYcQ4NormvRVXnC3sDovFQkREBFFRUV6NM1hJABRCCCH6mHl2Iq7qFtrPN1JdXU1hYaHXs39N9bV8+JffkjJ+IrNuvdNHlfrOonu+RlRyKu//6XHa7M1dXhc8MwFXVQuOQluvnyXtX65OAqAQQgjRxwIzwtDHBdG8p5xDhw5hNpsZO3Zsr8fzuN18+OffotVqufZb3+92Y+e+pA8I4Ibv/pC25iY2/OOJLmf4AoeHo4820bzv8xtluqumpobGxkZ5/+8KJAAKIYQQfexiS5im01UcO3aMKVOmoO/i+LXuOLbpQ8rOnubabz9KUFi47wr1sbDYeJbd/03OH9pH+dnTl71Go9VgnplAa14t7iZHr55jsVjQ6/Wkp6d7Ue3gJgFQCCGEUEHQ5FgKAmtwOpxMnTq11+PYG6zsfv1lMpesIHnMeB9W6B+jZswmPC6BY5s+7PIa85RYNFoN9oOVvXqGxWJh2LBhPj1NZbCRACiEEEKoQBuooyHaSSTBhJlDej3OjleeR6vXM/f2L/mwOv/RaLVkLVvFuX27aWlsuOw12iADpqwY7PsrUdw92wzS1tZGcXGxLP9ehQRAIYQQQiXNJichbiMtubW9ur/0dB6ndmxl3h33YAoJ9XF1/jN+4VK0Wu0V28IEz0rE3dhO25n6Ho194cIFPB6PtH+5it6/cCCEEEIIrzTabaRFRNO8t5ygqbE92rHqcbvZ8tw/iR8xiomLlvmsJkVR2Lp1K7t37+bChQtotVoyMjJYuHAhc+bM8ckzTCGhjJ49n9zN68n+ws2X3bQSkBSMLtJIe5EN0/jut3KxWCxERUURGRnpk1oHKwmAQgghhAo8Hg8NDQ1Mm5KJc1czjuImAtO6P4t3bOMH1JYUcdev/4TGB2cHA7z77rs89thjnD179rKfz8zM5I9//CNLlizx+lmTll/Dye2buXD0EMOnzrjsNfpII25r10fIfdbF9i/jx/f/dyHVJkvAQgghhApsNhsej4eYUUnooow07ynv9r3N1np2r32FrKWriMvwzVLn97//fW688cYuwx/A8ePHWb58OY8//rjXz4sfMYrw+ASKjh/r8hp9pBFXffcDYFVVFU1NTfL+XzdIABRCCCFUYLVaAYiIjCB4ZiKtJ2px27rX9mTHK8+j0+uZc/vdPqnld7/7Hb///e+7da3H4+FHP/oRL774otfPjUxMprG6652+ukgjrrruB0CLxYLBYCAtLc3r2gY7CYBCCCGECi4GwPDwcMzT4tDoNNgPXL35cempPE7v3Ma8L96LKbj3u4cvys3N5Uc/+lGP73vwwQcpKiry6tmhMXE0Vnd97q8+0ojS5sLT4uzWeBaLhYyMDK96Kg4VEgCFEEIIFVitVkJDQ9Hr9WhNeoKmxNK8vwLF5enyHrfLxZbnniRh5GgmLFjqkzr+/Oc/43K5enxfa2srTz75pFfPDo+Lp7G6qstTQfSRRgBc1vZu1VNSUiLLv90kAVAIIYRQgdVqJSIiovOfg2cl4mly0nqyrst7jm38gLrSEpbc96BPNn44nU5ee+21Xt//0ksvefX8sNh4XI72LvsB6iI+DoD1rVcdq6CgAEVRpP1LN0kAFEIIIVTQ0NDwqQBoiDcTmBHW5WaQ5vo69rzxClnLfbfxo7i4mNbWq4errlRUVGCz2Xp9f1hcPECX7wFqg/RoAnW4668+A2ixWIiNjSU8PLzX9QwlEgCFEEIIFdhsNkJCPv0OX/DsRBxFNhxlzZ+7fvvLz6EzBDBnjW82fgCUl3d/53FXysrKen1vcGRHf7+musvPemo0GnRhAbgbrxwAPR4PFotFZv96QAKgEEIIoYLg4GCamz8d9Ixjo9CFBdC899PBrOTkcc7s3s78O+/FGBzssxpiY2O9HiM+Pr7X97Y0dGyEMV9h1s7d5EQbEnDFcSorK7Hb7fL+Xw9IABRCCCFUEBERQUNDw6c+ptFpMM9MoOVYDW57x87Xjo0f/yRh1BjGL/C+AfOl0tPTMRgMvb4/KirqU8vYPXVxB/DFpeDP8rS6UFpdnZtBumKxWAgICCA1NbXXtQw1EgCFEEIIFURERHS2grmUOTseFIWWQx3vxR1d/1/qy0p9tvHjUoGBgdx44429vv+OO+7w6vkNVZXoDAaCwy9/bNvFJtDdCYDDhw9Hp/v8kXLi8iQACiGEECoIDw+nsbERt9v9qY/rggMIyoqheW8Fttoa9rz5H7KWX0PcsOF+qeORRx7p0RnEF+n1eh5++GGvnt1YXUloTFyXwfZiANRdIQC2tLRQWloqy789JAFQCCGEUEFERASKotDY2Pi5zwXPTsTd0M6xZ99FHxDAnNvu8lsds2fP5vvf/36P7/vNb37DmDFjvHp2Y3Ul4bFxXX7ebW1DE6hDG9R1Y+fz588DyAaQHpIAKIQQQqjg4rtzl1sGDkgOQYnWEVRuZMFd92E0+27jx+X8+te/5s477+z29d/4xjf43ve+5/VzGyorCI3tehOJq74NfaTxijOUFouF+Ph4QkNDva5nKJEAKIQQQqggLCwMjUZz2QDodjnJq9xJvCmdkaNn+L0WnU7HK6+8wt///neioqK6vC4hIYGXX36Zv/71r14/s660mLrSYpLHjOvyGld9W2cz6MvxeDzk5+fL8m8vyGF5QgghhAp0Oh1hYWGf2wkMcGTdfzlbuo+JY+di319JwBf6ZnnzoYce4r777uONN95g9+7dFBQUoNPpyMjIYOHChaxevdqrXcOXyt28HlNoGCOmz+7yGnd9G8Yxl98gAh19DFtaWmT5txckAAohhBAqudxO4Ka6Wva++R8yV64kJDGJ5l3lhK1IR2vsm2/ZRqORu+++m7vv9l3D6c9ytrVxavtWspatQt9FoHRWt+CqbSUgpevlb4vFgtFoJDk52V+lDlqyBCyEEEKoJDw8/HMB8KN/P4vBaGT2rV8keEYCistNy+EqlSr0jzN7dtDe2kLm0pVdXmPfV4HWbMA0PrrLa6T9S+9JABRCCCFUEh8fT2VlZeeJIEXHj3Fu787OjR+6sEBM46Np3luB4lFUrtZ3cnPWMyxrCmFdbADxONzYD1dhzo5Ho798VKmtraW8vJzRo0f7s9RBSwKgEEIIoZKJEyei1Wo5evQobpeTrc//k6Qx4xg7b1HnNcGzE3HVttJ+vkG9Qn2o8ryFqgILWcuv6fKalmPVKA435hld7xA+dOgQQUFBjB071h9lDnryDqAQQgihkqCgICZMmMChQ4cIqKvEWlnOdd9+9FNtTwLSQzHEm2neU45xZO+PXesvcnPWERIVw7DJ0y77eUVRsO+twDgmEn0XO4AdDgdHjx5l2rRpPtuUMtTIDKAQQgihouzsbBobG9m5YR2TV15PTNqwT31eo9Fgnp1A25n6zpMxBqo2ezNndu8gc8kKtNrLv7fnKG7CWWEneGZCl+Pk5eXR3t7OtGmXD5Hi6iQACiGEECpKSkoiSKfBER7D7Fsv34w5aFIsmkA9zfvK+7g63zq1Yyset4sJi5d3eY19XwW6SCOBXcx2KorCgQMHGDlyZGczbdFzEgCFEEIIFRUeP4qnpACHMQh7W/tlr9EG6DBnx2E/WIWnzdXHFfqGoijk5qxnxLSZBEdcvref29ZOy/EagmcmoNFe/vSPsrIyKisryc7O9me5g54EQCGEEEIlLqeTrc/9k7TEBIxGI4cOHery2uA5SeBRsL5xDkUZeDuCS0/nUV9W0uXmD8WtUP/aWbRBeszTuj4f+ODBg4SHh0vzZy9JABRCCCFUcvjDd2moqmDZfQ8wefJkjh49itPpvOy1+vBAIm8dRevJOpp3Dbyl4NxN64hISCJlfOZlP2/LKaS9sJGoO8aiDbr8xg673U5eXh7Tpk1Dq5UI4w35tyeEEEKowFZbzb63X2PKqhuITk1n2rRptLa2cvLkyS7vMU2IJnh+Eo3rL9Be2NiH1XrH3mDFcmAvWctWfWqH80Wtp+po+qiUsBXDCMwI63KcY8eOATB58mR/lTpkSAAUQgghVPDRS88QGGRm1i0dGz+ioqIYPnw4Bw4cuOISb9iKdAJSQ6h79QzuZkdfleuVvG05aLVaxi1Y8rnPuerbqF97DuO4KILnJ3U5htvt5tChQ4wfPx6z2ezPcocECYBCCCFEHys8dhjL/j0suPsrBAYFdX58zpw5lJeXs3fv3i7v1ei0RN05Bjwd78z19xNCPB43x7dsYPTs+ZiCQz71OcXpoe6V02iD9ETeOuqys4MXbd26lYaGBmbNmuXvkocECYBCCCFEH3I5nWx94V+kjJvImNnzP/W5jIwM5syZQ05ODkVFRV2OoQsNJPKOMbSfb8C2uevr+oPCY0ew1VSTtWzV5z7X8MF5nFV2or44Fq2p67Mpzpw5w+7du1m2bBkJCV33BxTdJwFQCCGE6EOHP3iHxuoqFt/3wGVnvBYvXkxqaipvvPFG5xnBl2McHk7o8nSatpbQvL+i3+4Mzs1ZR2z6cOJHjOr8mKIo2LaVYN9fScQXRhCQFNzl/fX19bzzzjuMGTNGZv98SAKgEEII0UdsNdXse/t1Jq+6geiUtMteo9PpuOWWW1AUhTfffBO3293leCELkjHPTKDhnXysb5zD4+j6WjXYaqopOHqIrOWfbP7wtLqo+/dpbBsLCVmcQtAVWr44nU7Wrl2L2Wxm9erVV1wiFj0jAVAIIYToI9tefBqj2czsW+644nUhISHceuutFBUV8dFHH3V5nUarIWL1CCLWjKL1RC01/ziGs6bFx1X33vEtGwgwGhkzZwEAjrJmqv56lPaCRqK+NI6w5elXDHXr16+ntraWNWvWYDRe/lxg0TsSAIUQQog+cOHoIfIP7mXB3V8hwBR01evT09NZsmQJO3fu5OzZs1e81jwljtiHJ6G4Far/doyWEzW+KrvX3C4nJ7ZuYtz8xRgCjdgPVlL95DG0Jj1x35yEaVzUFe8/evQoR44c4dprryU+Pr6Pqh46JAAKIYQQfuZyONj6/L9IGZ/J6M9s/LiS2bNnM3r0aN555x2sVusVrzXEm4n9xiSMoyOof+UMDR8UoLg93pbea/kH99HS2EDmghVY37RgfcuCeWocsQ9koY8yXfHeyspKPvzwQyZPniw9//xEAqAQQgjhZ4fefxtbbTVLutj40RWtVsvq1asxGo2sXbu2y1NCOq8P1BN5xxjCr8+geW85Nf86Ttv5BlU2iORuXMeEkQvxvG+l9XgNEWtGEXHjSDSGK0ePtrY21q5dS1RUFNdcc/lj44T3JAAKIYQQftRYXcX+d9Yy5ZovEJWc2uP7TSYTa9asobq6mtdff52Wliu/46fRaAiek0TM1zPxtLmpffoEVX88TPPuMjxtrt5+Gd3mtjup/G8emU2zGe+aAXotsQ9Pwjyl680eFzU1NfHKK69gt9tZs2YNBsPlj4QT3tMo/XXfuBBCCDEIvPu7X1FVYOHLf3yyW+/+dSU/P5+33nqLwMBAbr31VpKSuj414yJFUWgvaMS+r4LWk3VodBqCJsdinplAQGLXrVd6SlEUHCVN2PdW0HKiBo/bTVmrhaxv3YRpWES3Zj0vXLjAm2++iUajYc2aNaSm9jwsi+6TACiEEEL4ScGRg7zzf/+P6779KKNnzfN6vIaGBtauXUtVVRWrVq1i6tSp3V5SdtvasR+opPlAJR6bg4C0UMzZcRgSgtFHGdEau27E/FmKoqC0unDVt+Eoa8a+vwJnuR1dpBHT1GjWvvpzxixeyPwvfvmqY3k8Hnbv3s3WrVtJT0/n5ptvJjjYd+FUXJ4EQCGEEMIPXA4HL/zPQ4TFxHHLT37lsx52LpeLjRs3cvDgQTIzM7nuuusICAjo9v2K20Pb6Xqa91XQnt/Q+XFtkB5dhBF9pBFdZMdf9ZFG8Ci4rG246ttw13X81WVtQ2n7uOegBoyjIzHPSsA4MoKT2zez8V9/4St/fprwuCvv3m1tbeWdd97h3LlzzJs3j0WLFqHVyttpfaH7cV8IIYQQ3Xbwv2/RVFvLjY/+zKcNjPV6Pddeey0pKSm8//77VFRUcNtttxEdHd2t+zU6LaYJ0ZgmROO2O3HXfxzq6ts6/t7ahuNELe6GNri4iVirQR8RiC7SSEBqCEGTYjpCYoQRfZTpU8e45easIz1rylXDX3l5OWvXrqWtrY0777yTUaNGXfF64VsSAIUQQggfa6yu5MC7bzD1utVEJaX45RmZmZnEx8ezdu1annrqKW644QYmTJjQozF0ZgM6s4GAlJDPfU5xK7gb20EDurBANNqrh9iqgnwqz1v4wv/8pMtrFEXh8OHDrF+/nri4OO655x4iIiJ6VLfwngRAIYQQwse2vfg0xtBQZt50m1+fExsby/3338/777/Pm2++SVFREfPnzyck5POBrqc0Ok3HEnAP5OasIzgqmowp2Zf9fENDA1u3buX48eNMmzaNlStXotdLFFGD/FsXQgghfOj84QOcP7Sf67/zGAHGKzc89oXAwEBuvvlmUlNTycnJ4fDhw4wdO5bs7GzS0tL67Pzc9hY7p3dvZ/oXbkGr03V+3OPxcP78eQ4ePIjFYiEgIICbbrqJzMzMPqlLXJ4EQCGEEMJHnI52tr3wL9IyJzNyxpw+e65Go2H69OlMnDiR3NxcDh48yAsvvEBMTAzZ2dlkZmb6/Szdk9u34nY6mbhoOQAtLS0cPXqUQ4cOYbVaiY+P57rrrmPixIk92rQi/EN2AQshhBA+sueNV9n/zlru+f3fiExMVq0ORVG4cOECBw8e5MyZM+j1erKyssjOziYu7uoNmXvzvBf/52EiEpOYets9HDx4kLy8PADGjx9PdnY2ycnJfTYbKa5OAqAQQgjhAw1VlbzwvQeZeu1q5t1xj9rldLLZbBw+fJjDhw/T3NxMamoqkyZNIiYmhoiICMxmc6+Dmcfjobm5mXPHjpDzyosEj82irqGB8PBwpk2bxuTJkzGbzT7+ioQvSAAUQgghfOCd//t/1BQV8uU/PonBz8utveF2uzlz5gwHDx6ksLCw8+MGg4GIiAjCw8OJiIj41K/w8HAURcFqtdLQ0IDVav3Ur4aGBlyuj4+XUxRGjhxJ9vTpjBgxQvr59XMSAIUQQggvnT+8n3d/+0tu+O6PGDljttrlXFV7e/unQtxng53b7b7sfQaD4XNBMSgggE1/+S1zb7yFmV+4pY+/EtFbsglECCGE8ILT0c7W558iPWsKI6bPUrucbgkMDCQ+Pp74+M83a764rHsxHGo0ms6wd7nl4v3vvoHe1U7WkhV9Vb7wAQmAQgghhBcOvPsmdmsdt/z4F4Nik4NWqyU0NJTQ0FDS0tKueK3H4+b45g2MnjUPU7D3vQdF35EFeiGEEKKXrJXlHPzvm0y7/mYiEpLULqfPFeUexVZTRdaya9QuRfSQBEAhhBCiFxRFYdvz/yIoLJwZN96qdjmqOJazjpj0DBJGjla7FNFDEgCFEEKIXjh/aD8Xjh1m0T33Ywjsf7t+/c1WW82FI4fIWrpqUCx9DzUSAIUQQogecra3se3Fp0ifNJUR2QNj44evndiyEYMxkLHzFqpdiugFCYBCCCFEDx149w3s1noW3/u1ITn75Xa5OLF1E2PnLe6T846F70kAFEIIIXrAWlHGwf++RfYNQ3PjB0D+wX3YG6xkLVuldimilyQACiGEEN2kKApbn/8X5ohIpq8emhs/AI5vXkfi6HHEpKarXYroJQmAQgghRDflH9xLYe4RFt3ztSG58QOgvryU4rzjTJLZvwFNAqAQQgjRDc62Nra9+DTDJk9j+LQZapejmtyc9RhDQhk5Y47apQgvSAAUQgghumH/u2tpaWxg8b1fH5IbP6Dj2LuT2zczYeFS9AEBapcjvCABUAghhLiK+vIyDv73bbJvuIXw+AS1y1HN2T07abfbyVoqy78DnQRAIYQQ4go6Nn78k+DIKKavvkXtclSVm7OOtMzJQzoEDxYSAIUQQogryD+wl6LjR1n85a9hCAhUuxzVVBXkU5l/jqzlcu7vYCABUAghhOjCxY0fGVOyGT516G78AMjdvJ7gyCiGT5mudinCByQACiGEEF3Y987rtNgaWHTv19UuRVXtLXZO7/qIiYtXoNXp1C5H+IAEQCGEEOIy6stLOfT+O0z/wq2Ex8WrXY6qTu3chtvpZOKS5WqXInxEAqAQQgjxGRdP/AiJjib7CzerXY6qFEUhd9M6RkybSUhktNrlCB+RACiEEEJ8hmX/7o6NH/d+fUhv/AAoO3OSutJiMuXkj0FFAqAQQghxCUdbK9teeobh02aQMSVb7XJUl5uznvD4BNImZKldivAhCYBCCCHEJfa9/TptNhuL7rlf7VJU12JrxLJ/N1lLV6HRSmQYTOS/phBCCPGxutISDn/wDtNvvJWw2KG98QMgb1sOaDSMX7hU7VKEj0kAFEIIIfjkxI/Q6Fiyrx/aGz8AFI+H45vXM3rmXEwhoWqXI3xMAqAQQggBnNu3i+K8XBZ/+evoAwLULkd1hceP0lhdJSd/DFISAIUQQgx5jtYWPnrxaYZPm8mwydPULqdfyM1ZR0zaMBJGjlG7FOEHEgCFEEIMeXvfeo225mbZ+PExW20NBYcPkrVsFRqNRu1yhB9IABRCCDGk1ZUWc2Tde8y4cQ1hsXFql9MvnNi6EX1gIGPnLlS7FOEnEgCFEEIMWYqicHTDB4THJzLt+pvULqdfcLtcnNi6iXHzFhFgClK7HOEnEgCFEEIMWVUXzmOOiOS6R34gGz8+dv7wfuzWerLk5I9BTQKgEEKIIcnlaCdv6yYCTCZi0oapXU6/kbtpHYmjxsq/k0FOAqAQQogh6dz+PTjb2+U9t0vUl5dRnJcrrV+GAAmAQgghhhxbbQ0Xjh1m5PSZBIWGqV1Ov3F883qMIaGMmjFH7VKEn0kAFEIIMaQoisLJ7Zsxh4UxbHK22uX0G05HOyc/2syEhUvlfcghQAKgEEKIQWvfW69RmX/uUx8rP3ua+tISxs9fgk6vV6my/ufc3l202ZvJXLpS7VJEH5AAKIQQYlCqLSli9xuvoL0k5Dnb2zm9axvxI0YRk56hYnX9T27OOtIyJxMRn6h2KaIPSAAUQggxKK3/2x+ZuGgZsekZtNmbqcg/y/q//YGa4iLCEyTkXKq6sIAKy1lp/TKEyNy3EEKIQSdvWw62uhru/r8/A7D5mX9QkX+WlgYrgUFmDn/wLs62NsbMnq9ypf1D7qZ1BEdEMnzqDLVLEX1EZgCFEEIMOkc3foDRbKa9xU5uznqsFWUkjx5P1rJrWP2D/8UQGMju1/+NvcGqdqmqa29p4fSuj5i4ZAVanU7tckQfkQAohBBiUPG43UxeeT1Ryam8/MNvs+3FpxgxbQYet4vxC5cSP3wUi+79Om1NTTRb69UuV3Wnd27D5XQwcckKtUsRfUiWgIUQQgwqWp2OCQuXMmzSVI5tWoetpor6slLiR44mJjUdj8eNVqcjJCYWu7Uehg1Xu2TVKIpCbs46hk+dQUhktNrliD4kM4BCCCEGJXN4BLNvvZPotGFodDrGzVsEgFaro/jEMdrtzQybPE3lKtVVfvY0tSVFsvljCJIAKIQQYtBqqq2h+sJ5Rs2ciykkFI/bTcmpE+x+4xVm3nw7Go1G7RJVlZuzjvC4BNImTlK7FNHHJAAKIYQYlBRFIe+jzZjDwsmYPBWANnszhz98l5RxE5m4aLnKFaqrxdbIuX27yFy2Co1W4sBQI+8ACiGEGJTKzpzCWl7K9BvXoNV1fLsLCg3jhu/9CLfDqXJ16jv50WbQaBi/YInapQgVSAAUQggx6Dgd7RQcOUjCqDHEpKZ/6nNarQ6tcWi3O1E8Ho5v3sComXMJCg1TuxyhApnzFUIIMeic2LYJrU4ns1tdKDp+lIaqCrKWXaN2KUIlEgCFEEIMKtWFBXz0/NOEREYRGGRWu5x+KXfzeqJT00kcNUbtUoRKJAAKIYQYNBSPhy3P/ZOIxCQmLF6mdjn9UlNdLecPHSBr2TVDfhf0UCYBUAghxKBxauc2ys+eYsl9D6DTG9Qup186vmUj+sBAxs1bqHYpQkUSAIUQQgwKbfZmtr/8HKNnzyd1Qpba5fRLbpeLE1s3MnbuAgJMQWqXI1QkAVAIIcSgsPv1l3E5HCy4+z61S+m3Cg4fwG6tl80fQgKgEEKIga/qwnlyN61j9i13yJm2V3AsZx0Jo8YQm56hdilCZRIAhRBCDGgdGz+eJDIpmcmrblC7nH7LWlFG8YljZC2Vc3+FBEAhhBAD3MntW6g4d+bjjR9yvkFXcjdvwBgcwuhZ89QuRfQDEgCFEEIMWG3Nzex45XnGzFlAyvhMtcvpt5yOdk5+tJnxC5eiDwhQuxzRD0gAFEIIMWDtev3fuF1OFtwlGz+uxLJvN23NTWQtXal2KaKfkAAohBBiQKoqyCc3Zx2zb/0iwZFRapfTrx3LWUfqxElEJCSpXYroJyQACiGEGHAubvyITk5l0orr1C6nX6suLKDi3BkmSesXcQkJgEIIIQacvO2bqbCcZcl9D8rGj6s4vnk95ohIMqZOV7sU0Y9IABRCCDGgtDY3sfOVFxg7bxHJ4yaoXU6/5mht4dTOj5i4eIUEZfEpEgCFEEIMKLtf+zdul0s2fnTDqZ0f4WpvJ3PJCrVLEf2MBEAhhBADRlVBPrmb1zNnzRcxh0eoXU6/pigKuTnryJg6nZAoOR1FfJoEQCGEEAOC4vGw+dl/EJ2SJhs/uqH83BlqiwuZtExO/hCfJwFQCCHEgHBiWw6V+edYct8DaHU6tcvp93Jz1hEWF09a5mS1SxH9kARAIYQQ/V5rk42d/3mRcfMXkzxWNn5cTYutkXP7dpG5ZCUarXyrF58nvyuEEEL0e7teewmPy8X8L35Z7VIGhJPbt4CiMGHRMrVLEf2UBEAhhBD9WmX+OY5v2cic2+6WjR/doHg8HM9Zz6iZcwkKDVO7HNFPSQAUQgjRb3k8bjY/+yQxqelMWi4nWXRHUV4uDVUVZMrmD3EFEgCFEEL0W3lbc6gqsLDkvgdl40c35W5aR3RKGkmjx6ldiujHJAAKIYTol1psjez8z4uMX7CEpDESZrqjqb6W84f3k7XsGjQajdrliH5MAqAQQoh+addrL6F4PMy78161SxkwTmzZhN4QwNh5i9QuRfRzEgCFEEL0OxWWs5zYuok5t90lGz+6yeN2c2LrRsbOXUhgUJDa5Yh+TgKgEEKIfsXjcbPluSeJSRtG1jLZ+NFd5w/vp7m+TjZ/iG6RACiEEKJfObFlI1UF+bLxo4dyc9aTMGI0ccOGq12KGAAkAAohhOg3WmyN7PrPS4xfuJSk0WPVLmfAsFaWU3T8KFnSKkd0kwRAIYQQ/cbOV19EQWG+bPzokeObN2A0BzNq1ly1SxEDhARAIYQQ/UL5uTPkbdvE3Nu+RFBYuNrlDBguh4O8jzYzfuESDAGBapcjBggJgEIIIVTn8bjZ8uyTxKYPJ3PZSrXLGVDO7d9NW5ONzKWy/Cu6TwKgEEII1R3P2UB14XmWfOVBtFrZ+NETuZvWkTohi8jEJLVLEQOIBEAhhBCqarE1suv1l5iwaDmJo8aoXc6AUlN0gfJzp8mS1i+ihyQACiGEUNXOV19Ag4Z5d96jdikDTm7OeswRkQyfNlPtUsQAIwFQCCGEasrPnSZvWw5z7/gSQaFhapczoDhaWzi1cxsTFy9Hp9erXY4YYCQACiGEUIXH42bzs08SlzGCiUtWqF3OgHN613Zc7e1MXCz/7kTPSQAUQgihityc9dQUXZCNH72gKAq5OevImJpNaHSM2uWIAUgCoBBCiD7X0tjA7tf+zcTFy0kYMVrtcgacCssZaoouyFnJotckAAohhOhzO155AY1Wy9zbv6R2KQNSbs56wmLjSM+crHYpYoCSACiEEKJPlZ05xcntm5l3xz2y8aMXWptsnN27k8ylq9Bo5du46B35nSOEEKLPeNxutjz3JPHDRzJh8TK1yxmQTn60GcWjMGGR/PsTvScBUAghRJ85tmkdNcWFLLlPNn70huLxcHzLBkbNnCOzp8IrEgCFEEL0CXuDld2v/5vMJSuIHzFK7XIGpOK841gryuXkD+E1CYBCCCH6xI5Xnker08nGDy/k5qwjKjmVpDHj1S5FDHASAIUQQvhd6ek8Tu3Yyrw778EUEqp2OQNSc30d+Yf2kbVsFRqNRu1yxAAnAVAIIYRfdWz8+CfxI0YxcdFytcsZsE5s3YTOYGDc/MVqlyIGATk8UFyRoii0213Y6lpprGnFVttKc307AUF6wqJNhEQbCYs2ERwRiFYnP08IIT7v2MYPqC0p4ov/3x+lbUkvedxujm/ZwNi5CwkMMqtdjhgEJACKTra6Vorz6j4Oem3Y6lqx1bTiaHN3XhNg0hMSaaS91UmztR2Ujo9rtBpCIgMJjTZ9/MtIZGIwSSPDCTDJbzMhhqpmaz27175C1tKVxA8fqXY5A1bBkYM019eRtVQ2fwjfkO/MQ5ziUSg5Xc+J7WUUnahFo9EQHGUkLNpIXHooI6fFdQa60GgTRrOh8163y0NTfRu22o8DY23HDGFNcRPnj1TT3uJCq9UQlxFKythIUsZGEpsWIjOFQgwhO155Hq1ezxzZ+OGV3Jx1xI8YRVzGCLVLEYOEBMAhqq3Zyem9FeTtKMNW00pUUjAL7hzNyOw4Aozd+22h02sJjw0iPDbosp9vrGmh5LSVklP1HNtcwoH3LxAYpCdpdERnIAyLMfnyyxJC9COlp/I4vXMby7/+LUzBIWqXM2A1VFZQmHuEFQ9+W+1SxCAiAXCIqSq0kbe9FMuhahRFYcSUWJbeO474jFCf7yoLiwkiLCaICfOT8Lg9VBc1UXK6npJT9ex47RyKRyE0xkTK2EhSx0aSNDqcwCDD1QcWQvR7bpeLLc89ScKI0UxYuFTtcga041s2EGg2M3rWXLVLEYOIBMAhoq6sma3/PkN1oY2QSCPZ16YzdnYiQaEBffJ8rU5LfEYY8RlhZF87jPZWF2VnrR2B8HQ9J3eUodFA3LBLlouHhaKT5WIhBqRjGz+gtrSYu379J9n44QWX00nethzGL1iKIdCodjliEJEAOASc2VfB9lfOEhZr4pqHMkmbEIVWq24PqUCTnoxJMWRMigHAVtvaGQaPbyvl4IeFBBh1n14ujjVJ7yshBoDm+jr2vPEKWcuukXfWvGTZt4vWJpuc/CF8TgLgIOZyutn5uoVTu8oZMyue+XeMxhDQP8/eDI02MX5eEuPnJeHxKNRcXC4+Xc+utRY8HoWQKGNnGEweE/GpDSlCiP5j+8vPodMbmHvb3WqXMuDlbl5PyvhMIhOT1S5FDDISAAepxppWNjx1AmtlC4vuHsO4OYlejacoCq6aGpylpTjLK9CFhmBITsGQlIg2MNBHVXfQajXEDQslblgo065Jx9HmovxcA8Wn6yk9Xc+pXeVoNBCTFkrquEhSxkYQNywMnV6WmYRQW8mpE5zZvZ0VDzyCMThY7XIGtJriQsrOnOK6bz+mdiliEJIAOAhdOF7LlhdOEWg2cPMPphKT0rPdd87KSpo25eAoLsZZUoKjtBRnWRlKW9tlr9fHxWFITiYgORlDSgqBo0dhnjEDXahvjnsKMOpJz4wmPTMagKb6ts7ZwbztZRxaV4ghUEfSqHBSxnXMEIbHBclysRB9zO1yseXZJ0kYNYbxC5aoXc6Al5uzHnN4BCOyZ6pdihiEJAAOIh63h/3/LeDIxmKGZUWz5J6x3d5Vq3g8tOzbR/2rr9K8dRsavZ6AtFQMySmYZ88mICW5Y8YvOQlDYhKeJhuOkhKcJaU4y0pxlJTiKCqiedcu3HV1oNNhmjgR85w5mOfMxpSZiUbvm99uIZFGxs1JZNycRBSPQk3JJ8vFu9/Mx+NWCI4I7FguHtexXGwK7pvNLkIMZUc3vE99WSlffFw2fnjL0dbK6Z1bmbzyBnQ++n+nEJfSKIqiqF2E8F6LzcGmZ/Ioz29k1urhTFqW0q0ZMHdjI43vvov1P6/hKCwkcNQoIu68g9DrrkcX3Lvjhhylpdh378G+ezf2ffvw2Gxog4MJmjkD8+zZBM+ZgyE11S8zdM52N+WWBkpO1VNypp76cjtoICYlpDMQJmSEoTPINychfKm5vo7nvvMA4xcsYcl9D6hdzoB3fPMGNj/zD776t2cIjY5VuxwxCEkAHAQqzjey8akTKAqsuH88iSMjrnqPu6mJ6t//gcb33kNxuwldvpyIO+/ANGWKT4OZ4nbTlpeHfc8emnfvpvVYLrhcGJKSOmYHZ8/GPGsmurAwnz3zUs3WdkrP1FN8qp7SM/W0NjnRB2hJHBlB6rhIksdGEJlgluViIbz04V9+R9GJY9z3xL8wmuXdP28oisK/H3uEkKhobvzBT9UuRwxSEgAHMEVROPFRKbvfyCcuI5QV90/AHHb1DRltZ85Q+sgjuOutRH3lPsJvuQV9dHQfVAzuZjstBw50zA7u2YPjwgXQajFOmIB5zmyCZ8/GlJWFJsD3S7aKR6G2rLmzGXVFfiNulwdzWMAly8WRfdYbUYjBojjvOG/88kesfOg78u6fD1RYzvLqT77HjY/9jIzJ2WqXIwYpCYADlLPdzbaXz2A5WEXWkhRm3TS8W02TG956m8pf/IKAjAyS//wEAampfVBt15zl5Z2zgy179uJubEQbFETQjI7lYvOcOQQMS/fLDJ3L4aY8/+Pl4tNW6sqaAYhOCSZlzMfLxSPC0Bv6Z+scIfoDt8vFSz/4JsbgEG7/+W/k3T8f2PCPP1FyKo+v/OUptFr5/4/wDwmAA1BDVQvr/3UCW10bi+8ew8hpcVe9x9PWRuUvf0njW28TfustxP34x2iN/aurvOJ203bqNPY9He8Pthw9Ck4n+oSEztnBoFmz0EdcfYm7N+yN7ZSesX4cCOtpsTnQGbQkjgzv7D8YlSTLxUJc6uD7b7PzlRe46zdPEJueoXY5A15rcxNPPXAPM2+5gxmrb1W7HDGISQAcYAqO1bDlhVMEhQWy6usTiUy8+kYNR3ExpY98G0dBAfE/+xnhN93YB5V6z2O303LoUOcMoSP/PGg0GMeN63x/0DRlMlp/LBcrCvXl9s7l4nJLAy6nh6DQAJLHRpA6NpLksZHdWnIXYrBqqqvl+e88wITFy1h879fVLmdQOPzhu+x45QW+/uQLBIWFq12OGMQkAA4Ql7Z4GT45hsVfGkuA6eqtAZxV1Vy4+Wa05iCS//IXjKNH90G1/uGsrMS+Z2/H+4N79+Kur0djMhE0PZvgi8vFw4f7Z7nY6abifCOlpzs2lNSWdCwXRyWZSR4bSerYSBJGhvfbk1aE8IcPnvg/Sk6d4Mt/+qds/PABRVF4/jsPEDtsONc98gO1yxGDnATAAaDF5mDTsycptzT0qMWL4nRSdO+XcZaWMuztt9BHRfVBtX1D8XhoP3Pmk93Fh4+gOBzoY2M/2V08e5bfvuYWm4PSs/Wd7w/aG9rR6bUkjAjrXC6OTg5Go/KZy0L4S9GJY7z5q5+w6uHvMm7+YrXLGRQu/ju97We/IXncBLXLEYOcBMB+rrKgkQ1P5eFxe1hx/wSSRnX//beq//st9f/+N2kvvUjQlCl+rFJ9ntZWWg4d7nx/sP3cOQACx43tnB00TZni82ProOOndmtFS2cz6rJzVlwOD6YQA8ljIjsDYXCELBeLwcHtcvLS97+JKTSU237+f/JerI+8/8fHqSsr4Z7f/13+nQq/kwDYTymKQt72Mna9YSE2raPFS08ChG3TJsq+9Qixjz1K1L33+q/QfspZXU3L3r00796Nfc9e3LW1aIxGgqZN69xdHDhqpF/+J+t2eqgsaOwMhNXFTaBARIKZlLERpIyNJGlUBIZAWS4WA9OB995k12svcfdv/kxM2jC1yxkUmq31PP3wl1lw91eZsup6tcsRQ4AEwH7I6XDz0StnOLe/isxFycy+eQQ6ffdbKzgKC7lwy62YZ88m6c9PDPmfJBVFof3cuc7TSVoOHUJpb0cXE90xO/jxL31MjF+e39bspORMfeeGkmZrO1qdhoThYR3vD46LJDolBK0sF4sBwFZbwwvffZCJi5ez6N6vqV3OoLH3rf9w4L03eeCfLxEY1LtTmIToCQmA/UxDdQsb/pVHY00Li+4ew6js+B7d72lro/C221Ha2kh/6010wfJi9md52ttpPXz44/cH99B++jQAgaNHd84OBk2b6pc2OYqi0FDVQslpa8dy8VkrznY3gWZ9Z+/BlLGRhET2rxY9Qlz0/p9+Q+npPO574l++DyqKAi31YCuFxjKwlUFjKWi0EJEGEekdv0KTQTd4zsf1uN08/c2vkJ45hRUPfEvtcsQQMXj+BA0CF3Jr2PzCaUwhBm55dBpRST0Pb9bXXqO9oIBhb74h4a8L2sDAzlm/2P8BV20t9r37sO/eje3DD6l//nk0AQEETZv6yXLx6NE+aXCr0WiIiDcTEW8mc1EybreHqgJb53LxRy+fQVEgPC6o83SSpFHhBBjlj6pQX+Hxo5zbt4tV3/he78Jfm+3jUFf2+ZDXWAq2cnC1fnK91gChCR3B0FYGiqfj4xodhCV/HAjTIHI4jF/d8c8DUMHRQzTX1TJp+TVqlyKGEJkB7Ac8HoUD7xdweH0RGZNiWHzPWAK70eLlsxSPh4JV12CcMIGkP/zeD5UOfoqi4MjP/+R0koOHUFpb0UVGdoZG85zZGOKu3ny7N9rsTsrOWin+eLm4qa4NrVZDXEZoZyCMTQuV5WLR51xOJy99/xsEhYVz289/8/lXS5xtn4S5y4a8MmhvvOQGDYTEQ2gShCVBWMonfx+a3PFXcyxc/MHL5YDGEmgoAmvhx78+/vtaCzhbYOQyyP4qjFgKA+gEjbce/xltTTa++Os/qV2KGEIkAKqstdlBzrMnKT1jZebq4Uxentrrd/aad+2m5KtfJe2VlwmaOtXHlXZobGykqKiI8PBwkpOT0Q7yY588DgetR452nl3cduoUKAqBI0dcslw8DW1QkM+frSgKjTWtnb0Hy85acbS5CQzSkzw6onO5ODTa5PNnC/Epbhf733iB3e+9x91fvYGYQPvnQ15L7afvCYr6ONAlXz7khSSAzuCb+hwtkPcWHHwaKnIhPBWm3QeT7wZz35xz3lsNVZU8+8j9LP/6N5m4aLna5YghRAKgiqou2Njw1AncLg/LvzKe5DGRXo1X8vA3cJaUMOy9d3228cPlcvHuu+/y1FNPcejQIaxWa+fnAgICyMjIYM2aNXzta18jKSnJJ8/sz1xW66d2F7sqKtAYDJimTOkMhMZxY/1yHqrH7aGqsKlzM0lVoQ3FoxAWY/pkuXh0RK9mj8UQ5vGAvaaLJdmOkGez1vN8/hSyIipYGHcBAkMvma27NOQlf/z3iWBQ4QcTRYGyI3DwmY5AiALjb4Ts+yElu+/r6YYdr77A8c3r+fqTL2IIlHd/Rd+RAKgCRVE4ubOcnWvPEZMSwsqvTSA4wrs/+M7ycvKXLiP+p/9LxO23+6TOV155hR/84AeUl5df9Vq9Xs/tt9/O3/72N8LCwnzy/P5OURQcFy58srv4wAE8LS3owsMxz57V2ZDakJDgl+e3t7ooO/vJ2cWNNa1otBri0kM72s2MiyIuPQStbnDP0vYJey3UF0B7EyROhiDvfljrM4oCrdYrv3fXVAFuxyf36I2fW4r977YSysutfPl/HyMwfjgYB8Cf8ZZ6OPpvOPhsx7LxjAdg2S9B7/ujI3vL5XTy1IP3MHbuQtlRLfqcBMA+5nK42f7qWc7sq2TCgiTm3jISncH7b9DVTzyB9d8vM2L7dnTB3u3Ma29v55FHHuFf//pXj+8dPnw4b775JpMmTfKqhoFIcThozc2lec8e7Lv30JaXBx4PARkZne8OBmVP9/q/T1dsta2ds4OlZ620t7gIMOpIGh1B2oQoRmbHyWaSnlIUOPUe7Pkr1OVDWyPctwFSZ6pdWYf25qu/d+e0f3K9RtcxO3fpsuzF9+0uzuAFRcElKwiFxw7z1uM/45pv/g9j5y7s+6/RWx4PHHoWNvwQErJgzYsdX2c/cHr3dtb95Xfc+4cniUpOUbscMcRIAOxDjTWtbHjqBA2VLSy8awyjZ/SsxUtXFIcDy6LFhK5YQfxP/9ersdxuNytXrmTz5s29HsNkMrF9+3ays/vnkktfcTc0YN+3v+P9wd27cZaXg15P0KRJmOd8vFw8fjwane9fVvd4FKqLbJ3vD1YW2NAbtIyeGc+E+Um92mE+ZJ35sCNgJU2F51fBF9+AjIX+f66rvWNX7GWWZDs/1tbw6XuC4z6zFPuZkBcc16PNER0bPx7GHBHJmp8+PrB7ipYehjfuAYcdbn66Y6OIyl772aNotVrW/OxxtUsRQ5AEwD5SeKKWzc+fItBsYNXXJxKd7LtvwG2nTnHhpptJe/UVr498+9GPfsTjj3v/P6O0tDSOHDlCZOQAWSrzM0VRcBYVdc4Otuzbh8duRxsWhnnmzI5AOHsOAcn+eY+y2drGyV3lnNpZTovNQeLIcCYsSCJjUkyPmowPeb+MgS/8HTLXeDeOxw1NlZ+ZvftMyLNXf/oeU8TnZ+suDXkhiT5f3tz/zlp2r32ZL/32r0SnpPl07Ktpc7optbZSUt8CQEqkieSIIIwGL35gaqmHt78G+ZthwaOw4Aeq7RauLS7kxe9/g+u+/SijZ81TpQYxtEkA9DOPR+HgBxc4tK6Q9Mxolt47lsAgH+18+5ht4ybKHnmEkXv3oI/o/lnBn7V582aWL1+Or35L3HDDDbz33ns+GetShYWF/PKXv2Tr1q1UVlaSmJjIXXfdxY9//GMCAvrP+z1XojidtJ440fn+YOvx4x3LxWlpnbODQTNm+LyXo9vt4cKxWvK2l1J2rgFTaADj5yYybm6iNJ++Erero/Hw70bCrIdh9rc+aU/yWYrSMcvUUAj1hZcJeWUd790p7k/uCQj+/KaKSzdWhCZCQN+eDmGrreb57zxI1vJrWHj3V/z6rLKGVt49Wsb5mmZK6lsorm+hytZ+2WvjQgNJiQgiNTKI4bHB3JCVSEpkD3bhezyw8w+w7f+D4YvgpmfAHOWjr6T7tjz3T87t28XX/vE8Or1vvycI0R3yQpAftTU72fTcSUpP1zNzdQZTlqeh8UP/NmdpCVqzGV14uFfj/PrXv/ZZ+AP473//y8mTJxk/frzPxgQ4c+YMHo+Hf/3rX4wYMYK8vDzuv/9+7HY7v//9wOh/qDEYCJoyhaApU4j55jdw22zY93csFzfv2o311f+ATocpK6vz/UHTxIlo9N79kdXptIyYGsuIqbHUl9vJ21FG7tYSDq/v+AFl0tIUEkf2/oeIQevi0mdIHDQUd4S79mZore+YVer8q7Xjr5HDIO/Njv50uoBPglzEMEif+/mQZwz71Ht3/cFHLz6D0Wxm9i13+GV8j0dhV34t/95XxJbTVRgNOsbEh5AaGcSsjCiSIztCXurH4e5iMCz5eFawqL6FTaeq+P2msywZE8tdM9OYPzLm6j0ytVpY8H1IngZvfQWeWgj3b4Vg/xwFeTmOtlZO7djK5JXXSfgTqpEZQD+pLrKx4V95OB1uln9lPClj/bcUWvHzn9N6LJeMd9/p9RinT59m3LhxPqyqw0MPPcTf//73Lj9fU1PDxIkT+da3vsWPfvQjAPbv38+8efP44IMPWL68e32xfve73/Hkk09SUFDgk7rV5igp6ZwdtO/bh6epCW1ICOaZMzp3FwekpvrmWW0uzh2oIm97KXVldqasSGXGDRlDc/ewqx1arNBa90mYa62H5tqOJsZ5b3ecRpGSDVp9xxFlxggIigBTZMfuYFMkhKd0XBcU3dGHrp+Fu6u5cOwwbz/+M6791vcZM2eBT8duaHHw5uFSXt5XRGFdC2PiQ7h7VhqrJyVhDuzZDzgtDhfvHSvnpb1FnK6wkRoZxF0zU7l1agoR5m6sBjSUwNOLIHYc3P1Ony0HH9+ygZyn/879f32W0JjYPnmmEJ8lAdAPTu0qZ/trZ4lOCmbl1yf6fWmt+Kv3ozEGkvK3v/V6jB//+Mf8+te/9mFVHUJDQ2loaLjiy+Pr1q1j9erV7NmzhzFjxjB58mSuvfZannjiiW4/5yc/+QkbNmzg0KFDPqi6f1FcLtry8jrfH2zNzQWXC0NKSufsoHnmTHShod49R1E4trmEve+cJ2F4GMu/Oh5zWKCPvop+wO28ZFNFWcepEhf/Pno4BIZBzZlP3xMY2hHoTOEdQe7Mhx2zeqv/CZFpYAwfUCdOdIfL6eTF/3mIkKgYbv3f/8+nGz8+OF7OY2+doN3lZtWEBO6elca0tAivn6EoCkeKG3h5XxEfHq9Ar9Pw+E0T+cKkbrxTe2EHvPQFmPc9WPwTr+robq0vP/ZtgiMjufHRn/n9eUJ0RZaAfcjlcLPjtXOc3lPB+PlJzLvVNy1ersZZUkLwokVejXH69GkfVfNpNpuN8vLyKzaJvuaaa7j//vv54he/SHZ2Nkajkd/85jfdfsb58+f561//yh/+8AdflNzvaPR6TJMmYZo0iZiHHsLd3EzLgQPYd3WcTtLw+uug1WKaOLHz/UFTZiYaQ8+WljQaDZOXpRKXHsrGZ/JY+/8dZPlXx5M0agAsCXs80Fz1+ZYol4a85irgkp93jWGfbKoISepoEZI+9+OZvIiOmb2Lmyo8no6lQ7cTqk52nE8bFNXxzwyuAJi3dRON1VWs/v7/+iz8OVwefr3uNC/sKeS6zAR+dv14YkJ898OFRqNhaloEU9Mi+Mm1Y/nlB6d45LVjHCq08pPrxhKov8J/o2HzO4Lfll9A8nQY5d/TOCrPn6O68DxzbrvLr88R4mpkBtBHbLWtbHgqj/oKOwvvHM2YWf5p/vtZitvNmUmTiXvsUSK/+MVejzNp0iRyc3N9WNkndu7cydy5c694TWtrKxMmTKCkpIRDhw6RmZnZrbHLy8tZsGABCxYs4JlnnvFFuQOOs6zsk93Fe/fibmxEazYTNGMGwQsXEHbttWjNPdtAYG9sJ+e5k5RbGpn5hQyvjij0mqJ0LMV+tolxZ8grhaZy8Lg+uccQ9PkWKJ/aVJEEgT3YYNNQDCUHwJIDx1+HhExAA5PuhBlf9/mXrBZFUXjxfx4mMjGZG773I5+MWd7QysOvHiGvrJGfXDuOL81K8/vvJUVReGV/Mb94/xRjE0L4+xenkBxxhY0iHg+8dgcU74MHdnYcJecnG/7xBCWnjvOVvzyNdpDNHouBRWYAfaAor46c504SGKTn5h9MJSYlpM+e7WlqAqcTfZR3u9iqq6uvflEvVVVVXfWagoICysvL8Xg8FBUVdSsAlpeXs2jRImbNmsVTTz3li1IHJENSEhG33krErbeiuN20nTrV+f5g5c//H9X/91vCVq8m4o7bCRwxoltjmsMCueFbk9j//gX2vnOeivONftnBDnScrnExyHUV8lytn1yvNXzSzDgsGVJnfH5ThSnCt+/dFe2FD74DsWNg6j0d40cO75gxHERKT+dRV1rss1Mpdpyr4duvH8Oo17L267OYnNo3s8kajYa7ZqaRmRzGgy8f4dq/7OKJ2yaxaEwX79tptbD6SXhqAaz9Ety3EfS+f/2hrbmZs3t2MPPm2yX8CdXJDKAXFI/CwXWFHPzwAmkTolh67ziM5r7d0aUoCmcnTyH2O98m8p57ej1Odna2396f27dvHzNmzOjy8w6Hg+nTpzNp0iTGjBnDH//4R06cOEFcXFyX95SVlbFo0SKmTp3Kyy+/jM4PzZQHA2d5Oda1a2l4403cdXUETZ9OxJ13ELJkSbeXiAuP17L5hVMEBulZ+bWJxKT24AccZ1tHiLt0tu6zJ1W0N15ygwZCEi5zxuzFmbxkMMd03YJFeOX9J/6PmqILfPmPT3o9S7f9XA1ffv4A80bG8MRtk7q3KcMPGlocfHdtLtvOVvPMl6axZGzX/1+h7Ag8twKmfAmu9f0rJYc/fI8drzzP1/7xPObwAfBqhRjUJAD2UpvdSc5zpyg+VceM64cxdWW6X1q8dMf5667DPHMW8T/5ca/HuO2221i7dq0Pq/pEVVUVsbFd73T7/ve/z5tvvklubi7BwcEsWrSIkJAQPvjgg8tef3HZNzU1lZdeeulT4S8+3jenqww2isOBLScH63/+Q+uhw+hioom49VbCb7sdQ9zVdyF2vuJQbmfF1yYwLDO6ozdeU8Xl+9xdDHkttZ8eKCjq41CXfPmQF5IAOmmLoYZmaz1PP/xlFtx1H1Ou+YJXY5U1tHLtX3YyKSWcZ+/JRqfS/xsv8ngUvvbvwxy4UMeH35p35b6Bh57rmO2990OfzvAqisLz332Q2LRhXPftR302rhC9JUvAvVBT3MT6f53A0ebi+m9kkTq+75uIXiogOQVnaalXY2RmZvolAMbFxV0x/H300Uc88cQTbNu2jdCPd7H++9//JjMzkyeffJIHH3zwc/ds2rSJ/Px88vPzSU7+9Jme8vPM5WkCAgi79lrCrr2WtrPnsL72H+pfeJH6f79M4uO/JmTpZ47F8njAXtMZ5EJtZdw0qZxNLcPZ/K8Wbk3+DeGOEx2tTi4KDP0kyCVOhjHXfybkJYLB1LdfuOi2E1s3otXpGbdgiVfjOFweHnrlCOYAPX9aM0n18Aeg1Wr4w5osrvvrTh585TBvPjC76xNFpn4Z9v8LDjzl0wBYcvIE1vJSln31IZ+NKYQ3ZAawh07vKWf7q+eITDSz8msTCI1W/xta5a/+P+z79jK8ixmz7igtLSU9PR232331i3vghz/8oV/aywgvKAq0NeAuPUvFr/9E097jRC4ZQ+zCKDTNFR2hz1YObscn9+iNEJqEwzyMtSe/hN6g5Zab6tFHXnJ6hdG7NjRCPR63m6e/cR/DJk1l+de/5dVYP//vSV7ZX8SbD8wmKyXcNwX6SF5ZIzc9uYdbpibz6xsndn3hgadh/aPwnY93fPvA+3/6DbUlRdz7h38M7DOVxaAhM4Dd5HK62fm6hVO7yhk3N5F5t41E782ZlD4UkJJMw5tlKIrS6/+xJCcnc/311/Puu+/6rC6tVssDDzzgs/FENznslyzDfmZJ9uJ7d047OiApFerbQqjeeobWs4Ek3TkRw7ipEJby6XfvgiJBoyEAWFXWzJu/OcT2syNY8qWxan+1wgfOH95Pc30dWcuu8Wqc93PLeWFPIb/8wvh+F/4AJiSF8YsbxvPY2yeYlhbBTVOSL39h5m2Q8zM48iIsfMzr5zZb68k/uJcFd39Fwp/oNyQAdoOtrpUN/+p4/2nR3WMYNydR7ZI+xZCSgtLWhru2Fn1M748z+t///V/WrVuHw+G4+sXdcP/995Pqo9MqxMcUpaOfnbUIGoo6+tw1fmbnbFvDJTdoIDjuk1m6EUs/CXZhKWhCk4gKjsV09Bhl3/kuF54rJOn338A8vetNO1FJwSy4czRbXjxNwvCwfvfnQfTcsU3rSBg5mriM7u0Sv5yS+hYee+s4N2QlctfMNB9W51u3ZadwsNDKj9/JY+6IaGJDL9Oo3xgKWbfB4Rc6GkR7+V5q3racjuX1+Yu9GkcIX5IAeBXFp+rY9OxJAowft3jpyQ7IPmL4+D249oILXgXAKVOm8Kc//YmHH37Y65omT57co5M8xGV4PFCwFc5tAmthR+CzFn26JYop8pNZutSZn9lUkdyxqUJ/9d2XQVOnMuydtyn7n/+h+L77iHnkEaLu/yqaLnbbjpmVQMX5Rna8do6Y1JA+bX0kfKu+vJTiE8dY9fB3ez2Goij86J0ThJkM/Pqmif16lkuj0fDT68ex7kQFrx0s4VtLRl7+wmlf6dgQcuZDGL+618/zeNwc37KBMXPmYzT3oPekEH4mAbALikfh0PpCDnxwgdRxkSy7b3yft3jprsDhw9HFRNO8dSvmGdO9Guuhhx7i8OHDPPfcc70eIy4ujjfffBOj0b9H4A1aLfVw7BU4+CxYL0DEMIgZDcMWwJR0iPj4V3hqz5oZX4U+KorUZ56h5m9/o+ZPf8JRXETCr37V5TfzebeN7Djz+qk81vxwmn96BAq/sxzYS4DJxKiZvd/w8NaRMnZaann+y9kE9/A8XzWEmQysnpzIq/uLeWjhcPSXO/c6fgKkzoKDz3gVAC8cPURTbY3Xy+tC+Jo007qMNruTD588zoEPLpB9TTrXPZzVb8MfgEanI3T5CmwbN6J4PFe/4SqeffZZ/vCHP6DX9/x/5PPnz+fo0aNkZGR4XceQU3YY3n0I/jj242OpsuG+TfCto3Dn63DNb2HWQzDmGogb59Pwd5FGpyP2kUdIePxxGt96m4a1b3R5rd6gY+XXJtLW7GTLi6dlB/YA1VBZQURCEvqA3vXpq2lq55cfnGL1pEQWjb56S6H+4q6ZaVTa2th8+gpN8LO/CoU7oeZsr5+Tm7OeuIwRxA/vYqZRCJVIAPyMmpIm3nj8IJXnG7nu4SymX5+hWn+/nghdtRJXZSWtx3xznNt3v/tdtm/fftUj3C6KjY3ll7/8JVu3biUhoW+OwRs0HHZ46354ejFc2AkLHoXvnIKbn+445UKF5bTwG1cTfvttVP3qV7TmnezyurAYE0vvHcuF3FqO5ZT0YYXCVxqrKwmL7X3/zJ+/fxKdVsNPrx/vw6o6eDweiouLKS4uxuODH24vNT4xjCmp4by8r6jri8Ze33Hqy4mufxC6ksbqSi4cOyyzf6JfkgB4iTN7K3jrt4cJMOlZ86Ns0iao29+vJ0xTpqCPicG2Yb3Pxpw9ezY7d+7k+PHjPPzww8yYMaOzp5/JZGLs2LHccMMNvPLKK5SUlPCTn/xETuToqZpzHcHvzAfwhX/AI8dg3nchuPfvcvpK3A9/SOCoUZQ98gjuxsYurxuWFcPkZanse/c89ob2PqxQ+EJjdRVhcb0LgDmnqvjweAU/u34ckT466cPtdvPGG2+wePFiTCYTaWlppKWlYTQaWbhwIa+//joul+vqA3XD3bPS2JVfy/ma5stfoA+EuAlQd75X4x/fvIFAUxBjZs/3okoh/EMCIOB2evjo1bNsefE0o7LjuPn7U/tFf7+e0Gi1hKxcSdMG3ywDX2rixIn87W9/Y9++fVRVVdHS0oLdbufUqVO899573HnnnQT0cvloSMt7G55e1NFM+f5tMPmL0I/OB9UGBpL05z/jbm6m/NHHrvj7auqqNLR6Dad2l/dhhcJbbpeLptoawnsxA2hrc/KTd0+waHQMN2T5Zid4eXk5CxcuZM2aNWzbtu1THQmcTifbt2/n9ttvZ/78+ZR62fweYNWEBCKCDLx95ApjRaR1bMLqIbfLyYltOYybvxiDvA8t+qEhHwCb6tt4+w9HOL2nnEV3jWHxl8aiD+g/34R7InTVKlzV1bQePerX55hMpn69y6/fczk6msy++WUYtaIj/MWOUbuqywpITiLxN4/T/NFH1D37bJfXBQYZGDU9npM7ynC7ffsDiPCfptoaFMVDaOwVzsftwm/Wn6G5zcWvbvTNrt+ysjKmTZvGrl27rnrt3r17mTp1KsXFxV4902jQkZkcjqWqixlAgPD0XgVAy/49tNoayVq2qtf1CeFPQzoAlpyuZ+2vD9Jia+fm709l3NyB3c/MNCkLfXw8tnW+WwYWPqYo8O4DHTt8r/k93PysXzZz+FLIokVEfe1r1PzpCez7D3R53YQFSdgbHRQer+3yGtG/NFZXAfR4BnBfQR2v7i/m0VVjSAr3frXE5XKxZs0aKioqun1PdXU1t956q9d9S1MiTRTXt3R9QUQ6tNZDm61H4+bmrCd53ASikqUXquifhmQAvNji5f2/HCMmNYQ1P8omNm3gH2Ol0WoJXbEC26aNKD4+0k34yIGnIe+tjg0e0+9XZYNHb8R865sEZWdT/sPHULp4/yomJYT4jDDytpf1cXWitxqrK9FotIREd/+d0zanmx9+fJLGXTN80/D59ddfZ8+ePT2+78CBA7z66qtePTs1MoiS+paud7FHpHf8teEKm0U+o660mNLTeWQtldk/0X8NuQDY3uJk3T9PsP+9AqauSue6b2RhCh4876+FrlqJu6aWlsOH1S5FfFbJQdj4I5jxIIy/Ue1qekSj1xP7g+/jKq+gefv2Lq+bsCCJ0jNWrJX2PqxO9FZDdSUh0dHoetDy6c9bLJRZW/nNzZlofdQh4cknn1TlXoCUiCDsDjfWFuflL4j4OOT2YBk4N2c9QWHhjJwx26vahPCnIRUAa0ubWfv4ISryG7j2oUxm3JDhs/+B9RfGrCz0iQk0bdigdiniUvY6eONeSJwMy36hdjW9Yho/HmNWJtZX/9PlNSOmxGIMNpC3Q2YBB4LG6qoetYDJK2vkqR0FfHPxCEbE+ubVhfb2dvbu3dvr+w8cOEBLyxWWcK8iJTIIoOtlYHMMGII6TuHpBmdbGye3b2HComXo9P23f6wQQyYAnt1fyVv/dwhDoI5bfziN9MxotUvyC41GQ+iKldg2bpJl4P7C44a37+84wu3WF7p1NFt/FXHHHdh378ZRWHjZz+sMWsbNSeTM3kqc7fL7r79rrOp+D0CX28Njbx9nZGwwX18w3Gc1FBYWet3jr6CgoNf3pkZ1BMCiui5mrTWajmVg64VujXdmzw4cba1kLlnZ65qE6AuDPgC6XR62/+csm58/xYipsdz8g6mExQSpXZZfhV6zCnddHS0HD6ldigA4/Dyc3wo3P9Nxbu8AFrpqFbrwcKyvvd7lNePnJeJoc2E5WNWHlYne8HjcaLs47/mzntl1gVPlNv7v5kwC9L771uGLBs9uL37Y1X38Hq7nSifZaHQdP8h1Q27OOoZNmkpYL3ZWC9GXBnUAbLa28c4fjnBqdzkL7hzN4nvGYhigLV56wjhhAoakJGzrZTew6hQF9v2z4yzR4YvVrsZr2sBAwm+5mYZ33sHT2nrZa0KjTaRPiOLE9lI5Hq6fC4+Np7Hm6kH9Qq2dP+Wc4745w8hKCfdpDenp6V6P4c3RkyXWjqXf1MguJgYUpWMDyMXNIFdQmX+OqoJ8OflDDAiDNgCWnulo8WJvaOem701lwvykIdO7TqPRELpqJU2bNnW5Y1P0kQs7oM4C2ferXYnPhN92Gx6b7YrthsbOTqS2pJmmurY+rEz0VFhcPI1VlVe8RlEUfvj2cWJDA/nu8lE+r8FkMjF58uRe3z9+/HhCQkJ6fX9xXUcATOkqALZaod3WrQCYu3k9IdExDJs8tdf1CNFXBl0AVBSFIxuL+O+fjxGVFMyaH2UTN2zgt3jpqZCVq3BbrbQc6Lpvm+gDB5+BmLGQNnh2AwakpGCePw/rf7reDBKZZAagsebys4SifwiLicNWW43nCkuorx0sYV9BPY/fmElQQPd3C/fEAw880Ot7H3zwQa+eXVzfgtGgJSY48PIXXHz3L+LKLW/amps5s3sHmUtWou1HJ/oI0ZVBFQDbW11s+Fcee985z5QVaVz/rUmYQgbuC/feMI4fhyElBdt62Q2sGls5nPkQsr/i835/NpuNvLw8KiuvPHvjLxFr1tCWl9flZpCQSCMaDdhqJQD2Z2Fx8XjcbprqLt+8u8rWxq/XnebWqcnMHem/jXN33303EyZM6PF9Y8aM4d577/Xq2aXWVlIjg7peIbq4+/cqM4CndmzB43YxcfFyr+oRoq8MmgBYV9bMG48fpPRMPasemMjM1cMHXYuXntBoNISuXElTTg6Ks4v+VsK/Dr8IBhNk3uaT4crLy/npT39KSkoKYWFhTJw4kYSEBEJCQrj33ns5dKjvNv0Yx48HwFF0+dYYOr2W4AgjtlpZAu7PLu4AvngiyKUUReF/380jUK/jJ9eO82sdJpOJN998k9DQ7q/WBAcH89Zbb2E2m716dnF9CykRV9gYaC0EYxiYIrq8RFEUcnPWMyJ7Fubwrq8Toj8ZFAHw3MFK3vy/Q+gNWm79UTYZk7rf1X4wC121EndDwxWP7xJ+lPsqTLwVjN6/gvDOO+8wbtw4fvnLX1Ja+umD65ubm3nxxReZPn06jz76qFc7IrtLHxuLxmDAUVLa5TWh0UaZAeznQmNiQaOhsfrzM8nr8yrZdKqKX3xhPGFB/u9nN3r0aPbt28fYsWO7de3evXsZN867YOpye8grayQj5goh0loI4Vde/i09dYL68lLZ/CEGlAEdAN0uDzteP0fOs6fImBzDzY9OIzx2cLd46YnAsWMJSEvDtkF2A/c5Rws0FEPKDK+Hevrpp7nppptobGy84nWKovDb3/6WNWvWeP3Mq9FotRiSknCWlHR5TWi0SQJgP6c3GAiOjPpcAGxocfDT906yfFwcqyb07Jxgb4wdO5aDBw/yxBNPMHr06M99fuTIkfzxj3/k0KFDvVoy/qwtZ6qpbmpn9eQrtGfqxg7gYznriUhMJmX8RK9rEqKvDNgAaG9s590/HuXkjjLm3z6KpfeOGxItXnpCo9EQsmolTTmbUbw8MF30UEP33hu6msOHD/PNb36zR/e8/fbb/O53v/Pqud1hSEnBUXqlGUCTLAEPAOGx8Z9bAv7Vh6dpd7n55eoJfd49wWw288gjj3DmzBmqqqrYu3cve/bsobKyknPnzvGd73yH4GDfnELy771FTEkNZ3xi2OUv8Lih+jREDutyDHuDlfwDe8haumrIdJoQg8OADIBup4d1/ziOra6VG783hYkLk+UPXhdCV63C09iIfd8+tUsZWrr54vjVPProo7S3t/f4vp///OdXnTH0VkBKMs4rBkAjbXYn7a3Siqg/C4v9dCuYnZYa3jxcyo+uGUtcqFHFyiA2NpaZM2cya9Ys4uJ821j5fE0zu/JruXvWFZZ3z22E5qornt2dty0HrU7P+AVLfFqfEP42IAPgrjct1JY1c+1DmcRndPGTmwAgcNQoAoYNk93Afc1aCLpACO79N61z586xdevWXt3b0tLCiy++2Otnd4chKRlnSUmXzZ5Do02A7ATu78Li4qivKMXtctHicPHDt08wMyOS27NT1C7Nr17ZV0ykOYBVExK6vujgM5A0teMM78vweNwc37KB0bPmYfTRrKQQfWXABcBzByrJ217GvDWjiE0bev39eqqzKfRmWQbuU9bCjr5h3Txm63J27Njh1Uka27dv7/W93WFIScbT0oLbar3s5y8GwCZZBu7Xhk+dQbvdTsHhA/xh0zlqmtr5zU2Zg3pVpcXh4o3DJdw6LRmjoYtXh+rOw/ktkP3VLse5cPQwtppqJi2XzR9i4BlQAbC+3M62l88wekY84+clql3OgBGyciWepiaa9+xRu5Shw1ro9fKvNwfc++L+qwlI6Zgh6mojiCnEgD5QR6PMAPZrsekZJIwaw8733+P53Rf4zrJRpEd711qlv3t25wVaHG6+OP0Ky7+Hnuto/XKF5d/cTR8SlzGS+BG+PyFFCH8bMAHQ0eZiw1MnCI02seDO0YP6p1NfM44aRcCI4TTJMnDfUTyAd79HvT1H1+/n8H78Z1DxeK54iZwH3P9NWLIKq+Uk08KdfHVu1xseBoP86ib+ujWfr83PIDWqi64RzlY4+jJMvqujl+dlNFRWcCH3iMz+iQFrQARARVH46OUzNFvbWfm1CRgCZbdvT4WuXEXTli14erGhQPRCRPonO4F7adgw774Re3v/1VzcAHJxJvCz2uxOnG1uwqIv/w1U9B/b2hNo1RpZE1yKXjcgvi30isej8NhbJ0iKMPHIkpFdX5j3NrQ1wrT7urwkd/N6jEFmRs+e54dKhfC/AfEn3XKoCsuhahZ/aSwR8YN7acJfQleuwNPcjH33brVLGRoi0jt2Ansx+zV37lyvSpgzZ45X91+No6QUjcmELirqsp+31XS8+xcqAbBfy69u4u87iggcP4vKQztxtg3edzZf3l/EoSIrv7lpYtfv/kHH5o8RSyEy47KfdjrayduWw/iFSzEEqrtTWojeGhABMHdLKSnjIhkxNdavz3E6nZw/f57i4mI8V1jWGogCR4wgcORI2Q3cVyLSwdUKzdW9HmLcuHHMm9e72QWj0ciXv/zlXj+7O5wlJQQkd92C6eLu39Bo+QbZX3k8Co++dYLkCBP3fPkO2ltbOL3bv5uH1FLW0Mr/rT/DnTNSmZFx+R9aADj1HpQfueLmj3N7d9HW3ETWslV+qFSIvtHvA2B1kY3qQhsTF1yhU3svNTc3889//pNly5aRnp6OyWRixIgRpKWlYTKZGDVqFDfccANr167FOQjO0w1ZtZLmLVvwDOKf8PuNiI9fLrcWejXM448/jl6v7/F9jz76KFFdzMz5iqOsFENycpeft9W1EhikJ7APjhETvfPvfUUcLrLy+E0TiU1KJGPyNI5t+nDQvbepKAo/eecEwUY9j60a0/WFtfnw7sMdGz9GrejysmObPiQtczIRCb7/viREX+n3ATBvexnBkYGkTYz22ZhFRUV84xvfICkpiQcffJDNmzdTVFT0qTNUHQ4HFouF999/n9tuu420tDR++tOf0tDQ4LM6+lroypV4Wlqw79qldimDX7hvAuCcOXP47W9/26N7VqxYwU9/+lOvntsdzpJSDClXCIA1rbL824+VNbTy2w1n+OIlM2JZy6+hprCACstZlavzrf/mlrPtbA2/Wj2RUGMXP5A4WmDtlyAkHm74a+cmp8+qPG+hMv8ck5Zf68eKhfC/fh0A2+xOzh2sYvy8JLRa3+z6ff/995k8eTJ///vfsdls3b6voqKCX/7yl0ydOpWjR4/6pJa+FpiRQeDo0bIM3BcCgyE0CcoOeT3Ud77zHV544QVMpquHqQceeID33nsPrRf9B7tD8XhwlpYSkNx1s+DG2jZZ/u2nFEXhx++cIMRo+NSMWHrWFMJi48jd9KGK1flWvd3B/3v/FNdmJrBsXBeN2RUFPvweWC/AmpcgMKTL8XJz1hESFUPGlGw/VSxE3+jXAfDM3goUj8K4Od73/HO73fzwhz/kC1/4AtYuGtd2R0FBAbNnz+bpp5/2uiY1hK5aRdO2bbIM3Bcyb4Pc18Fh93qoe+65hxMnTvDII48QHh7+qc/pdDpuvPFGtm7dypNPPklgYKDXz7saV00tisNxxRnApjqZAeyv3jtWzkdna/jV6gmEXDIjptXqyFy6irN7d1Jb4t0u9v7iF++fxO1R+Pn147u+6MhLkPsqXPcniBvX5WV1ZSWc2bWdzKUr0eqkG4UY2PptAFQ8Cnk7yhg+JZag0ACvx/vud7/Lb37zG5+829LW1sbXvvY1nnnmGa/H6muhK1egtLTQvH2H2qUMftO+DO02OPHm/9/efUdHVW9tHP9m0nvvPQRC7zWEHqqCdARFvZZrb3hV7Iq9YXvtHZQmRYqIEHrvHRJKIL33SZvJzHn/iHD1mlCSycxksj9ruXQxZ87ZEUie2b9mkNu1atWKDz/8kIKCAlJTU9m5cydJSUmUl5ezfPlyhgwZYpDnXIvqxNMA2IXXvZGuTqenrLBaAqAZKlBX88rqk9zYOZD4Ojpi3UbdiGdgMKvmvommssIEFRrO5qRcfj2SyQs3tsfXtZ4PRplHYO2T0ONf0OXmeu+lrapi9dw3cfP1o/uYcU1TsBBGZLYBMD2xiJLcSjoaYPHHkiVL+Pjjjw1Q1d89/PDDzW442C4iAvv27Shd97upS7F8HmHQZhTs/7pR28H8L5VKRWhoKLGxsbRp08YoHb//VfTLL9jHxGBXz16D6sJqFL0iQ8BmaM6aUyjAy+Pq7ojZ2jswdtazlBcV8MeXnzTbBSHq6hqeW36cAa19mNS9np8jFYW18/782sGot+q9l6IobPjmU0rychj3xLPYOcgHG9H8mW8ATCrC2d2OwFbujbrPmTNnuPvu+pfzN0ZVVRWTJ0+mpKSkSe7fVNxGjUa9ZSv6iub96b5Z6HU3ZB+H9MbPBTQX2qws1Js24zn95nq3gCnMqh32lg6gedmUmMPKI5m8cEN7fFzq/+DgFRTMyPse5czu7Rxet8aIFRrOu+sSKarQ8saETnX/Oc06Bl8Pre3ST/0RbOv/sHIsYR2nt29mxL8fxjskrAmrFsJ4zDYAlhZU4uHv1Ogj35599lnKysoMVNU/JScn8+GHHzbZ/ZuC26iRKJWVqLfJMHCTazW0dk/A/c1vukB9ipYsQeXoiNuNY+u9JnF3Fl5Bzrj7SgA0F+rqGp5fcYIBrX2YWF9H7C/a9I2j+5ib2Dr/WzLPJBqhQsM5mFLIvD0p/GdkDKFedRz3dmgefBNfu9jjns1XPLc7+/xZNv/wJV1G3EC7uMFNVrMQxma+ATCvEtdGdg8yMzNZuXKlgSqq39dff01NTU2T3HvcuHGEhYXh4OBAYGAgM2fOJDMzs1H3tAsLw6FDB1kNbAwqFfT+Nxz/BVJ2m7qaRlM0Gop/WYr7TTdh7VL3qTzqoiouHM2n48BgObPbjLyzLpHiyit0xOow8JY78G8VzZoP36aitHmMdFTX6Hh62XG6hHhwR2zE31/UVNTu87fq4dr5fndtAK/6j0ysVJex+oM38QmLZPBtTTOSJISpmG8AzK/CvZHzh7766qsmC2Z/lZGR0WRBc8iQISxZsoSkpCSWLVvG+fPnmTx5cqPv6zZ6FOqtW9GXN36FqriK3vdCWF/45Y5GnQxiDsoSEtDl5+M5vf7J8id3ZGJjqyKmT4ARKxNXcuBiIfP3pPCfEfV0xOphbWPL2MdmU6OpZu0n76HX667+JhP7dNM5UgrKeXtSZ6z/un1YwXn4djicWAbjP4dxH19x2FfR61n36Vw0FRWMfXw2NrayobmwLGYZADWVNVSVaxs9f+jnn382UEVX99NPP13x9by8PAICAnjjjTcu/9revXuxs7Nj/fr19b7v8ccfp2/fvoSHhxMbG8vs2bPZs2dPo08mcR01GqWqirItWxp1H3ENrG1g8neg6GHZXdAMfojWp2jBQpx69cK+des6X9fp9JzanklMnwDsHK//BBNheFVaHU8vO0aXEA9u/9+O2DVw9fZhzCNPknL8CHuXLzF8gQaUmF3KZ1vOc//gaGIC/rKX3+nV8NVg0FbC3QnQdcZV77Vv5VKSD+1n9MNP4O5Xz/6BQjRjZhkASwsunSHa8ACo1WpJTk42VElXlZR05Z3zfX19+e6773j55Zc5cOAAarWaW2+9lQceeIARI0Zc0zMKCwv5+eefiY2NxbaRn0btQoJx6NyZsnUyDGwUrgG1IfDiDtj8xtWvN0NVZ85QceAAnjOm13vNhSP5VJRqDLJ6XxjGp5vPkVpYwTuT/6cjdh0iOncjdvIMdi1dwPmDew1coWHo/jzXOMLHmQeHtIKaaji2BL4dAYtvhVZD4N9bIKDjVe914chBdi7+iT4TphHVTTZ8FpbJPANgXu0mxY0JgCkpKej1ekOVdFUXL1686nYJY8aM4Z577uGWW27hvvvuw8HBgbfeqn/rgUuefvppnJ2d8fb2JjU11WDDzW6jRqHeug2dWoaBjSJyAAx9Aba/B2f+MHU11y3//z7FxtcX12HD6r3mxNZ0AqPd8Q52MWJloj6ns0r5fMt5HhgcTRv/+k+3uBZ9J04jumcfVr77OnuWL0Yx4vfXa/H9zgscSy/mgxGe2G95Fea2h+X3gI09TJ0PU34EB7cr3kPR69m3cikr3nqFiK7diZ169U6hEM2VeQbAgkps7FQ4uja8y2XM7h9AZWUlWVlZV73uvffeo6amhiVLlvDzzz/j4HD1eY5PPvkkhw8fZv369VhbW3PbbbcZZG8ut5EjUDQa1Js3N/pe4hr1fwzajIYlt8PRRaau5pqVrltH2fr1+D8zGyu7ujdmL8wsJ+NMMZ0G1X86iDAenV5h9rJjRPo488CQVo2+n5VKxdhZz9Bn4jR2LvmJFe/MoVLddDssXI+0AjX71i9mne+ndFo6EPZ/C50mw4P74fbV0H5cvWf7XlKlVrPy/dfZvuAHet00ifFPvoBKJad9CMtllpN09Dql0Wf/GmPxx/+6lnl5ycnJZGZmotfrSUlJoXPnzld9j4+PDz4+PrRp04Z27doRGhrKnj176NevX6PqtQ0OxrFLF0rXrcN97I2Nupe4RipV7VDwb0/AinshdU/tBrRXmIxuajWFhWS/MgfXESNwHT263utObMvA0dWWqG6+RqxO1Of7nRc4llHC0vtisbcxTJBRqazpP/UWgtq0Ze0n7/HT7EcZ+/gzBLSqe06owWnKoSgFilOg6CIUXUQpuoDj+UN8ZZ2HzrETDPoQOk0Bu7pXqdclJ/kcqz94k6pyNeOfepFWPXo32ZcghLkwywDo6u2ApkpHdUUNDs4N6wJG1nNCQVOxtbUlNDT0itdoNBpuueUWpk2bRtu2bbnrrrs4fvw4/v7XPsH4Uuevurq6UfVe4jp6FHnvz0WnVmPtIsN2RmHnBOM/g7A+sPYpyDxcuxHtFfYiM6XsV18FRSHgxRfq3T5EU1VD4p4sOg8JwdrGLAcWWpTUggreW5/E7f0i6BHuafD7R3buxszX32X1R++y6MUnGTxtKl3698VKXwO6atBpoEZT++9L/9RUg07bsNfL82sDX/lfVtHbOIBHONkqP9ZVd6fL6LvoHjviqp2+v1IUheOb/mDT91/iHRLGlBdex91PVq+LlsFKMcNzfnJTSvnlzQNMeaYnfuFXnrNRn8rKSpydnY12jFF0dDRnz5694jVPPvkkS5cu5ejRo7i4uDBkyBBcXV1Zs6bunfb37dvHvn37iIuLw9PTk+TkZF588UWysrI4efKkQY4A02Znc27wEILefgv3m25q9P3Edco8UnsUVVUx3PQptL3xun6ANbXSdX+Q8dhjBL3/Hu433FDvdQfXXWTvymRmvh6Lq5f5djObFb3uz1BUX0jS1vm6otPw7ZYkStTlPDQoDHtqruP9l/77r6/XEcr0taMdNXortuZGcaQoiHZuuQwPPIut6hrmBqpswNoerG1r5+hZ2/33H5tL//2X1x09az8gXfrHIxxc/Mkr1xI/dytD2/rxwbSu1/W/V1tdxcZvP+fk1o10GT6awbfdg0090xuEsERm2QG8tPijNL+qwQHQ0dGRoKAgMjIyDFlavaKjo6/4+pYtW/jwww/ZvHkzbm61X9P8+fPp3Lkzn3/+Offff/8/3uPo6Mjy5ct56aWXKC8vJzAwkFGjRrFo0SKDnf9qGxCAY/fulP6+TgKgKQR1hXu3wq8P1K5U9G0Hve6CztOuOmG9qdUUFpI9Zw6uw4fjNmZMvdcVZZezf81FugwLbV7hT1FAX3ONIaipX//fkFVdu21QA1gBdwN6lS2q7Q5/CVm2f4aqv4asS6HLvvZUDKcrvH75/f8NbTbWdgyztiP41EXW/7qR3OL2jL3jZryDgusPeNZ2tVMhDODlVSexVlnxwo3tr+t9hZkZrP7gTYpzshj94CzaDxxqkHqEaE7MsgOoKArfPL6NHqMj6D4yvMH3efzxx412TNu3337LnXfeaZRnGVrhvPnkvPcebXbuwNq1cSsFRQMpCiRvgQPfQuJasHWsDYG97gL/DiYpKWPWLMp37iLqtzXY+PjUeY1er7D83YNUV9Qw7ble2Nj9z1wzRbnK8N5VOk2Nfr2OztdfX6Mx3/6s/gw39tcesuoIUVfsfF319b9fm1epMPKTPQxuG8Tcm7s14mu7fgXpaaya+walebm0HziELsPH4BcR1WTP++NkNvfOP8hHN3flpq7Xtu1QXupFjq5fy8ltG3H19mXc47PxCYtoshqFMGdmGQABFr++D78IN4bc0rbB9zhz5gxt27Zt8mFgT09PMjIycHRsnuee1hQWkvv++3jOmIFjB9OEDfEXJRlw6Ec4+AOocyCgM/jG1A57/XUYzC0IVNa1Q4WNnWP1P6+XHrxAxnd7CJrRGffO3vUOHx7O7MGuzHgmRnxKoP25eocKG8zK+u9BqElC1PW+funXzG8A5YGfD7I3uZCEWYPwdDb+cKamqpKDa37lWMLvqIsKCYppT9cRY2jdp79BT9IoqdQyfO5WOga78+3tPa94tJ2uRsvZvbs4sn4tGYkncfb0otPQkfQaOwE7x2s/FUUIS2O2AfD3L4+jraph3KON+xQ7fPhwEhISDFRV3R5//HHmzp3bpM9oaoULF6JoNHjffrupSxGX6LS1Jxic3VA7Ab44BUozudy1svqz26Y08mQR1d+7VzVaW5KX6HAMtCVknAdWtg51hqSiSm8W74qnY2QacV2Sr7HzVV/IqqdzJttwXLNLHbGPp3djXJcgk9aiq6kh+eA+jqz/jdQTR3F0c6fT0BF0iR+Nm69fo+//zPJjrD6axfrHBxLkUfcH79L8XI4l/MHxTX9QUVJMaIfOdB0xhlY9+2JtY37hXQhjM9sAuHPZOZIP5zLztdhG3WfPnj0MGDCgybaF8fHx4ciRIwQHN++TD9Q7dlCychWBr7yMykk+FZstbRWUpP13Kwy4Ssi6Wgiz+8eik4xZT1C+cydRa1Zj41v3li56vcKK9w5SVV7P0K8wqksdsU7B7nxzlY6YsRVkpHFsw++c3LqR6soKorr3ouuIG4jo3A2rBswF3H2+gOlf7+HVmzows1/E315T9HpSjh/hyPq1JB/ch62DAx0GDaPL8NF4h4QZ6CsSwjKYbQA8fyiXdV+dYNrzvfEJadz2JHPnzuWJJ54wUGX/pVKpWLt2LSNHjjT4vY2tpqSEnDmv4jF1Ks59ZA+slqp0/XoyHnmUoHffveLekEcSUtm57BwTn+hOYLSH8QoUdbrUEdswayCB7uY5FUVbVcXpnVs5sv438i4m4+LphXdoOO5+/rj7BeDuF4CHfwBufv44OLvUGWKrtDpGfbiNAAc9H4wOoyw/h+KcbEpysynJzaEgIw11QT6+YRF0HXkjbeMGYedgnv8/hDA1sw2AOp2eec/uIrKLL4NnxDT6fpMnT2bZsmUGqOy/XnrpJV5++WWD3tOU8j77HCsbG3z+fU+drys6HYpGg6qZznUUV1ZTVETyjWNx7NqVkP/7pN4uUlF2OYtf30/HgcHETTHSBsCiXrvO5zPj6728Or4jM/s2fNGcsSiKQtbZJM7u21Ub3HJyKMnNprriv0dS2js5/xkK/XH3r92XryQ3m6SzKWiL87HX/3cfVDtHJ9z9A/DwC8DdP4DoXv0IatPWrLqgQpgjs50IYW2tokNcEIcT0oid0Ao7x8aV+sMPP2BlZcXSpUsbXZuVlRX/+c9/ePHFFxt9L3Pi2LULJStW1LkptK60lJJfV1JTWIDfY4+ZpkDRpHJeex2lpobAl1+q94enXq+waV4iLp729Lmp6VZ4imtTqdHxzPLj9I7w4pbezWOI08rKiqA2bQlq8/cFflVqNSW52X/p6NV29c7t242Cgo27D6dr3Oncqxuj+3W8HA7r6xYKIa7MbAMgQPu4YA78nkLS3mw6DW7c+aIuLi788ssvfPjhhzz11FPXdGxbXTw8PPjhhx+4yQL3zHPs2JGS5SuoPHECl759//aatZsb9m1ak//kV3hOn4Gtf+MncgvzUbphA6W//UbQu+/UO+8P4NimNLIvlDDhie7Yyrw/k/sw4QxZJVV8d0evRh+faWoOLi44uETjH/XPPVW1Oj03/d9O9N4KXz4ch621nDYjRGOZ9d8iF097orr4cHxrhsG2cnnsscfYtm0bI0eOvK5PjXZ2dkyfPp0DBw5YZPgDqMkvQF9VReWRI5d/reLQYTTptZtpO/fti31kFCUrlpuoQtEUaoqKyH5lDi5Dh+J2Y/3z/opzKtizMpkuQ0IJknl/Jnc8vYSvtyfz6LDWtPK17GMcv96eTGJ2Ke9M7izhTwgDMfu/SR0HBVOUVU7m2WKD3bNv376sW7eOM2fOMGvWLMLCwlDVsRrNxsaGtm3b8tprr5GamsqCBQto1aqVweowN/lf1M4B1Jw/j06tBqBw/jxy33/v8jXO/fpSvnevqUoUTSDn9TdQtFoCrjr0exoXD3v6jJehX1PT6vQ8tewYMQFu/HugZf9+JOep+TDhLHfFRdI5xMPU5QhhMcw+AAbHeOLh78SJrYY/0i06Opr333+flJQUKisrSUpKYt26dWzcuJELFy5QVVXF6dOnee655/D39zf4882NQ0wMNdnZAFT82QV0HTKE8q3bqCkoAKDy2HEc2rdH0TVy7zlhFsoSEihds4aA557F1q/+Yf3jm9PJSi5h6G3tZOjXDHy1LZmk7FLentTJojtier3C7OXHCXBzYNbwxi8GFEL8l1nPAYTaCcOdBoew45ezZJ4tJqi1R5M8x87OjjZt2tCmTZsmuX9z4HbjWPK/+BKHLp2pOn4C17g49BWV2IaHk/7Ag1RfvAg6HV63zcTKWkJAc6crLibr5VdwGTIEt7Fj672uOKeCPb+ep/OQkCb7+yeu3fk8NR9tPMvdA6IsviO2cH8q+y4U8vPdfXCUDx5CGJTZB0CAjgODOH8olz++OcHUZ3vh7G5v6pIskq2/H069elGdmISi1WIb4E/+55/jfee/cOjYkeozZ7Bv3Rqnnj1NXaowgOzX30DRaAh4+eV6h34VvcKm+adx8rCn702WO/2hudDrFZ5ZdpxAdwcej7fsD6vZJVW8tTaRqT1D6B9d91nUQoiGaxZjByprFSPu7gAKbPjuJHqd3tQlWSy/J/+DjZ8flUePkv/Z57gMHIDHzTfj1KMHntOnS/izEGUbN1K6enXt0O8VVnQf25xO1rkSht3WFlt76cCY2oJ9qey7WMibEztZdEdMURSe//UEDnbWPDemvanLEcIiNYsACODsbs+IuzuQebaEvasvmLoci2UfGUng66/hOWMGjl274vvII6jspeNqSWqHfl/GZfBg3MaNq/e64ty/Dv16GrFCUZeskkre+j2Rm3uFEtvKsjtivx3PIuF0DnPGdcDdydbU5QhhkZpNAAQIbuNJ35uiOLQuhYvH8k1djsVS2dnhGj8M9HqsJPxZnJw330Sp1hDwyitXHvqddxondzv6jpehX1NTFIUXfj2Bk501z4xpZ+pymlRRuYaXV51kVIcARncKNHU5QlisZhUAAbqNCCOisw8JP5yiNL/S1OVYLMeOHUGlovLYMVOXIgyobNNmSlauwv+ZZ6489Lulduh36G3tZOjXDKw5lkXC6Vzm3NQRd0fL7oi99ttpNDV65tzUwdSlCGHRml0AtLKyIv6Odtg72fDbZ8ckBDYRlbMz9m1aU3n0qKlLEQaiKy4m66UXcRk0CPfx9W9mXpxbwZ4V5+k0OITgNjL0a2qXOmKjOwYwqmOAqctpUtvO5LHsUDrP3dAOPzcHU5cjhEVrdgEQwN7Jlhse6EKNRseSN/ZzQYaDm4Rj165oki9QU1Ji6lKEAeS8+RZKVTUBc6489Lt5fiJO7nb0myBDv+bg0y3n0Or0vGLhHbHy6hqeXXGc2FbeTO0ZaupyhLB4zTIAAngFOTP12V4EtfZg7WfH2L3inKwONjDHDh3A2pqqU6dNXYpopNqh35V/Dv3Wv6n58a3pZJ4tZuhMGfo1Bxfyy7GzVvHxzd3wc7Xsjtj768+Qr67mzYmdruuYTiFEwzTbAAi1ncDR93Wi38RWHN6QxsoPj1BeUm3qsiyGyskJl0ED0VxINnUpohF0JSVkv/QSzoMG4j5hfL3XleRVsHvFeToNCiY4RoZ+Ta1Kq+OHnRfxcbFjUIyvqctpUodTi/h+1wVmDW9DuLezqcsRokVo1gEQaucEdh8RzvjHu1KcU8GS1/eTebbI1GVZDJWzM/kff4L2zyPiRPOT8+Zb6KuqCJwz5yqrfhNxcrOjrwz9moXfjmWirtYyrkuwRXfENDV6Zi87Tscgd+7sH2nqcoRoMZp9ALwkqLUnU5/rhWeAE79+cITdv55Hp5Uh4cZy6tEDfXk5ZX/8YepSRAOUbdlCya+/4j979lWGfjPIPFvMkJntsHNoFgcEWbQL+Wq2ns3nhk6B+Lha9lZMn285z/k8NW9P6oyNBZ9rLIS5sai/bc7u9ox7tCu9bojgyIZUFr++j+wLsoChMaxdXXEeMIDS39eZuhRxnXQlJWS/+BLOAwfgPnFCvdeV5FWye8U5Og4KJkSGfo2qukbHhfxy8tXVlFRoAajR6Vm4L5UwT0eLH/o9m1PG/20+y72Domgf5GbqcoRoUSzuo77KWkWvGyKJ6urLpnmnWf7OQboMC6XPuChsLPjopKbkNnoUmU8+hTYzE9ugIFOXI65Rzltvo6+ouOrQ7+b5p3F0lVW/xpZwKoef96aw/Ww+ET7OBHs4Et/eHx9nO3JLq3lyZAzWKov6jP43Or3C08uOEerlxMNDW5u6HCFaHIv97uId7MKkp3rQd3wrjm/JYNFr+8g8V2zqspollyFDsbKzo3SdDAM3F+qtWylZsQL/Z2ZjG1D/3nEntmWQcaaYoTPbytCvEVVpdTzxy1FiAtxY9O++3BUXSZCHA19uPc/Lq0/SIciNYE8nU5fZpObvvsih1GLemtgZB1v5cC6EsVlsAITabmD3keFMfa4XDs62rHj/ENsXn0FbrTN1ac2KtYszLoMGUrpOhoGbA11pKVkvvoTzgAG4T5xY73Wl+ZXsWnGejgODCWnrZcQKxZIDaUT5OjN7dFt6RngxvXcYjw1rQ7sAV2xUKjYl5nEy03Knr6QXVfDOH0nc2jeM3pHyZ08IU7DoAHiJV6AzE5/sQf9J0ZzakcmiV/eSniQrha+H66hRVB07hiY9w9SliKvIeett9OXlBF5lw+dN807j6GxLv4ky9GtsLvY2ZBRVklH835OMTmeX4mhnwyPDoimq0PDLgXQTVth0FEXhuRUncHe05elRbU1djhAtVosIgAAqlRVd48OY9kJvXDwdWPnBYbYsSEJTVWPq0poF18GDsbK3p+wP6QKaM/W2bZQsX47/7KexDQys97qT22uHfofcJkO/ptA9zBMvZzvWHM2kvLqGAnU1a45lERftw7ReYdzZP5INp3IoLNeYulSD+/VIBlvP5PHa+I64Olj2ucZCmLMW953fw8+J8Y9348S2DHatOE/KiXyG3NKWsA7epi7NrKmcnXEZNIjS39fhfdddpi5H1EFXVkbWCy/i3L8/7pMm1XtdaX4lO5efp8PAYEJl6Nckwr2dGN7en/fXn6G4QoO1tRWOtirGdqkN7TEBrlirrCiq0ODlbGfiag2nQF3NnNWnGNsliGHt6t+WSAjR9FpMB/CvrFRWdBocwvQXeuPh58TqT46yad5pqv/chkHUzW30KKpOnECTlmbqUkQdct56C71aTeCrV9nwef5pHJxtiJWhX5OxsrLiiRExvDWpEz/uTmHerhT8XB04n1fOmZwyvt6eTICbA618XUxdqkG9svoUCvDS2PamLkWIFq9FBsBL3HwcGfdoVwbfEsO5Q7ksfGUvF4/lm7oss+UyaBBWjo6yJ6AZUm/fTsmy5fjNfvqKW/Wc3JFJRlLtWb8y9Gt68e39iW/nR5i3M19vT+aRRYeZ8sVucsuq+Xh6N1OXZ1CbEnNYdTSTF29sj4+LZW9uLURzYKUoimLqIsxBWWEVW35OJPVkIW36+DNgahscnGV+yv9Kf/xxNCkpRC1fbupSxJ90ZWUkjx2HfVQUod9+U2/3rzS/kkWv7qNNb38G3yKT783BD7sukpRVyrM3tCOntIrc0mqc7G0I9XTE24JCUlmVlhEfbKO1vys//quXRR9tJ0Rz0aI7gH/l6uXAjQ91Ydjt7Ug5XsCCV/Zy/nCuqcsyO26jRlN96jSaixdNXYr4U+4776AvKyPwtVfrH/pVFDbNT8Te2YbYidFGrlDU5URGCYdSipjYIxhXB1ui/VyJjfaha6iHRYU/gHfWJVFSqeWNCR0l/AlhJiQA/oWVlRVt+wUy/cU++Ee4se7LE/zx9QkqyyxvJV5DuQwcgJWTk9lvCq0oCnkVeRzOPcyB7ANkl2ejVyzvbGj19h0U/7IUv6efuvLQ7/ZMMpKKGHprO+wcZejX1Co1NSw5kEa7QFd6hlv2Qpz9FwuZvyeFJ0fGEGLhm1sL0ZzIEHA9FEXh7IEcti86C1YwcFobonv6yadXIGPWE1QnJxP16wpTlwJASXUJf1z8g/PF50lXp5Nelk6GOoNqXfXfrrNV2RLsEkywazAhLiGEu4UzIHgAEe4Rpim8kf479BtJ6Lff1j/0W1DJojn7aN3LnyG3ytCvOdh4OocNp3J4cmSMxXX7/qpKq2PMx9txd7Rl6X2xWKvk+6cQ5kIC4FVUlGrYtugM5w/lEtnFh0EzYnB2t9xv2NeidMMGMh5+hKi1a7GPijRZHSfzT7IoaRG/X/gdnaIjwi2CYJdgQlxDCHEJufzf1lbWl4NhujqdjLIM0tXppJSmUK2rJtojmuHhw4kPj6e1R+tmE/KzXniR0t9+I2r1KmyDg+u8RlEUVn10hOKcCqa/2Ee6f2bgSGox3++8wL/6R9A1zNPU5TSp9/5I4stt5/ntkQG08Xc1dTlCiL+QAHiNzh/KZevCJPQ6hbiprYnpE9BsgoKh6auqOBvbH6+778L3gQeM+uyqmirWXVzH4sTFnCg4QZBzEFNipjAhegLejte3l2NlTSW7MnaxIXUDW9O2otaqCXcLJz4snuHhw2nv3d5sf4/VO3aSdvfdBLz8Mp43T6v3upPbM9jycxJjH+lCWHvZ69LUqrQ6xny0HR8XOxb+u59Fd8ROZ5Uy9pMdPDQ0msfi25i6HCHE/5AAeB0q1Rp2LDnLmX05hHf0ZvAtMbh4Opi6LJPIePIpqhMTiVq9ymjP3JO1h9nbZlNYVUj/4P7cHHMzccFxWKsaf5C8RqdhT9YeNqZuZFPqJoqriwlyDmJY+DCGhw+ni28XVFbmMWVWp1bXDv1GRlzb0G9PP4bMbGfkKkVd3v0jka+3XeC3R+JobcEdsRqdnomf76JKq2PNwwOwszGPvztCiP+SANgAF47msWVBEjXVOvpPbk27/oFm2ylqKmWbNpH+wINErVmNfXTTrirVK3q+PvY1nx75lL6BfXmh7wuEuoU22fNq9DUczDnIhpQNbEzdSH5lPr6OvgwNG8rw8OH08O+Bjcp0Q6lZL75E6Zo1RK5ahV1I/UO/qz8+QlF2BTe/2Ad7Gfo1uZOZJYz7v508MrQ1j8a3NnU5Terrbcm88ftplt0fS3cLH+YWormSANhAVeVadi47R+KuLELaejLk1ra4+Tiauiyj0VdXc7Z/HF533IHvQw822XOKq4p5Zscz7MzYyX1d7uPezvcapON3rfSKnqN5R9mQsoGElASyyrPwtPdkSNgQ4sPi6RvYF1tr4+0XWb5rF6l33kXAyy/hefPN9V53eej34S5yzKEZqNHpGf/ZTrQ1CqsfjrPojlhKQTkjP9zG9N5hvDS2g6nLEULUQwJgI6WcLGDLT4lUV9TQb0IrOg4MxsqC5/X8VebTT1N58iSt1qxpkvsfzzvOE1ufoLKmkjcHvElccFyTPOdaKYrCqYJTtWEwNYGU0hRcbV0ZFDqI+PB4+gf1x8Gm6aYE6NRqkseNwy4snLDvv6u361xWWMXCOXuJ7uHHUBn6NQtfbj3PW+sSWX5/LN0suCOmKAq3fLOXlIIK1j8+EGd76TwLYa4kABqAprKGXcvPcXJ7JsFtPBgysy3uvpa/31XZ5s2k3/8AUatXYd/asENax/KOcce6O2jn1Y73Br1HoEugQe/fWIqicLb4LAkpCWxI2cC54nM42jgyIHgAw8OHMyBkAM62zgZ9ZtZLL1OyejVRVxv6/eQoRVnlMvRrJi7m13bEbukTzosWfgbukv1pPLXsGPPu7M3ANr6mLkcIcQUSAA0oLbGQzfMTqSzV0Hd8KzoNCUFlwd1AvUZTOww8cya+jzxssPsWVRUxdc1U/J38+W7kd9hZ2xns3k3lYslFElJrw+CpglPYqeyIDYolPjyewaGDcbd3b9T9Lw/9vvQintOn13vdqR2ZbP4pkRsf7kK4DP2anKIozPh6L2lFtR0xJzvLDeS5pVXEz91KfHt/5k7taupyhBBXIQHQwDRVNexZmczxzekERLkz9La2eAYYthNkTjJnP0Pl0aNErf3NIAthdHodD258kFMFp1gydgkBzgEGqNK4MtQZJKQkkJCSwJG8I9hY2dA7sDfx4fEMDR163dvV6NTlXBg3DtvQ0NqhX1Xd88cuD/1292PobTL0aw4W7Utl9vLjLaIjdv9PB9l3oZCEWYPwdDb/D21CtHQSAJtI5tliNs07jbqomt5jI+kaH4rK2vImfqu3bSPt3/cSufJXHGJiGn2/z498zudHP+eL4V8QGxRrgApNK7cil42pG0lISeBAzgEAuvt1Jz48nviwePyd/a96j6yXX6Zk1WqiVq3ELiSkzmsURWHNJ0cpyCxn+ou9sXcy3sIUUbecPztiI9oH8P7ULqYup0mtO5HFfT8d4pPp3Rjbpf4jCYUQ5kMCYBPSanTsXZXM0Y1p+IW5MvT2dngHuZi6LINSNBrODBiI5/Sb8XvssUbda1fGLu5LuI/7u97P/V3uN0yBZqSwqpDNqZvZkLqBvVl7qdHX0Nm3M8PDak8hCXH9Z7gr372b1H/dif+LL+A1Y0a99z61M5PN8xO58aEuhHeUoV9zcO/8AxxMKSJh1iA8nCy3I1ZSoSX+g610CXHn69t6trgtsYRoriQAGkF2cgmb5p2mJK+SXjdE0G1kONYW1A3MfO45Kg8cJGrd7w3+5l+uLWf0stG0927PZ/Gfmc2my02lVFPK1rStbEjZwK7MXVTrqmnn1a62MxgeT5R71H+HfkNCCPvh+ysO/S6as5eo7n4Mk6Ffs/D78Szu//kQn87ozg2dzWsBk6E9vfQYvx3PYsOsgQS6t5ytsIRo7iQAGkmNVsf+NRc5vCEV72Bnht7WDt9QyzgJQL19B2n33EPkiuU4tGtYAFmcuJg39r3BuonrzG7Fb1Or0FawLWMbCSkJbEvfRmVNJa3cW/Fggi0hO88RtWoV9qF1b3ytKApr/u8oBelqpr/UR4Z+zcCljljXUA++mtnDojtiu87lM+Obvbw2viO39g03dTlCiOsgAdDIclNK2TTvNEVZFXQfFU7PMRFYN/NNYRWtlrNxA/CYNg2/WY9f//sVhYmrJhLmGsZHQz9qggqbj6qaKnZl7uLEuoUMn7uDb0eoODU4nPjweIaHDaejT8e/BYrTuzLZNC+RGx7sTEQnHxNWLi55aulRfj+ezYZZgwhwt9yjIis1OkZ+uI0AdwcW3dPXonc8EMISNe/k0Qz5hbsx5ZledB8dzqF1KSx5Yz+5KaWmLqtRrGxtcR0xnNLff6chnycO5R7iXPE5prWd1gTVNS8ONg4M9u7DmMUXcOjVk/FPfk7vgN78evZXZqydwYhlI3h739sczDlISUEFO5acpW3fAAl/ZmLnuXyWHEjnmTHtLDr8AXyQcIbs0iremthJwp8QzZB0AE0oP72MjT+epiBdTbcRYfS6MRIbW+Mdc2ZIl/api1i6FMeO13f801Nbn+JU4SlWjV9l8Ll/paWl6HQ6PD2bz+kL2XPmULzi19pVv38O/dboazice7j2fOKUjeRW5HLTmYfwrwyj60Pu9I3sha1Khn9N6VJHLNDdgYUW3hE7ll7M+E938sSIGB4c0rRngQshmoZ0AE3IJ8SVybN70ntsFEc2prHk9f3kpTbPbqBT795Ye3pStu7363pffmU+G1I3MC1mmsHC38aNG5k8eTLe3t64u7vj5eWFh4cHY8aMYdWqVej1eoM8pymU79lL0YKF+D3xxOXwB2CjsqFXQC+e7fMsG6Zs4L3AbwgsbM3+mNU8sONehiwZwgs7X2Bb+jY0Oo0Jv4KWa+6GJHJKq3hrUmeLDn9anZ6nlh6jbYAb/x4YZepyhBANJB1AM1GQqWbLgiTcfRyJ7ulHSFsvbK4yN1DRK2Z17nDWSy9TvmMHrRI2XPPE9x9P/sgnhz9h45SNjT4to7i4mNtvv51Vq1Zd8bqBAweyaNEiAgPNa7GJvryc5JvGYxsQQNi8H+td9asuqmbhnL1EdvFh2O3tOF14+vKRdBdLL+Ji68LAkIEMDx9O/+D+ONrIysymdjStmAmf7eTJkW25f3ArU5fTpD7dfI731yex8sE4OoU07u+sEMJ0JACaEb1OT87FUk7tyMLe0ZoOA4LwDPz7voGXfrsuBSxdjZ6004XYOdoQFO1h7JL/pnzPHlLv+BcRvyzBsVOna3rP8zue50LJBX6+4edGPbuoqIg+ffpw9uzZa7o+MDCQvXv3ElrP6lpTyH71NYqXLydq5a/YhYXVeY2iKPz26THy0sqY/mIfHJxt//ba+eLzbEjdQEJKAmeKzuBo40hccBzxYfEMDBmIi51l7UNpDrQ6PWM/2YG1yoqVD/bHxoK2ePpf5/PUjP5oO/+KjeCZMbLlkBDNmeUeTNkMqaxVBLbywNXbgVPbM9n/Wwqh7b1o3dMPG7vauYFWVlYoeoXi3AoOrruIrkbh/KFc9HqFGx4w7UpQp549sfb2pvT3ddccANPV6QS7BjfquYqiMHPmzGsOfwBZWVlMmTKF7du3Y2tr+rlz5Xv3UfTzz/g/91y94Q8gaU82KScKGPNA57+FP6j9sxHtGU20ZzT3d7mflNKUy0fSPb39aWxVtvQL6kd8WDxDw4Y2uuMqan259Txnc9UWH/70eoXZy44R6O7AY/FtTF2OEKKRpANopvR6hbTThZzdn4Odow0d4oJw83Xk3IEczuzLAcA72IXoHn7sWZmMk5sdPUaF4x1s2g5P1iuvoN66leiNG69pGDj+l3jGtRrHI90fafAz16xZw9ixYxv03q+//pq77767wc82BH1FBcnjbsImwJ/wefOuPvTb2Yf4f7W/rmdkqbNISK0Ng4dzD6OyUtEroBfDw4czNGwoPo6yirghzuWqGfPRdu6Mi2T26LamLqdJzd+Twgu/nmDBPX2IbSV/XoRo7iQAmrmK0mpObs8kL7WMouwKVNZWxE1pTVAbD6ytVez/7QIpJwroMjSU1r2ufq5sUyvfu4/U228nYtFCHLt2veK1Gp2Gnj/15OXYl5nYemKDn3nDDTewdu3aBr23W7duHDp0qMHPNoTs116neOnS2qHf8Lo301UUhd8+O0ZeShnTX+rzj+7f9ciryGNT6iY2pG7gQPYB9Iqebn7dGB5eeyRdgHNAg+/dkuj1CtO+2k2+WsPvjw7AoZmu4L8WWSWVDJ+7jbFdAnlzYmdTlyOEMADLHa+wEE5u9vQcHUHbfoEoeoXCrHIyzhRhba0iI6mI5CN5BMd4EtnVPD6RO/XsgbWvD6W/r7vqtZnqTBQUQlz+eQbutVIUhY0bNzb4/YcPH6awsLDB72+s8n37KPrpJ/xmPV5v+ANI2ptNyvECBt/atlHhD8DXyZdpbafxzYhv2Dx1M6/EvoKzrTPvH3yf4UuHM+O3GXx34jvSStMa9RxL9/PeFPZfLOLNiZ0sOvwpisLzK07gZGfN7NEy708ISyFzAJsBK5UVUV198Q13ZcfiMxzZkMqZfTm4eNrjFehMm97+2NhaoyiKyY+dsrK2xm3ESEr/+AO/p5+qdzgTauf/AYS4NjwAZmRkUF1d3eD3AyQnJ+Pl5dWoezSEvqKCrOeex7FnDzxvvbXe68qLq9mx5Cxt+vgT2dmwQd/TwZMJrScwofUEyjRlbE3fSkJKAp8f+ZwPDn5AjGdM7Skk4cNp5WHZq1uvR2ZxJW/9nsj03mH0jfI2dTlNavWxLDYm5vLlzB64O5p+vqwQwjAkADYjrp4OjLq3ExeO5bPxh1OoC6tQWavwDqqd92fq8HeJ2+hRFP38M5VHjuLUvVu91+VV5AG1HamG0mgav+edIe7RELkffEhNXh5hX39Vb1BWFIUtPyeislExYGrTTrx3tXPlxqgbuTHqRiq0FezI2EFCSgLfn/ieT498SqR7JPFhtWGwrVdbs/nzZmyKovD8rydwcbDhmTGWPe+vqFzDK6tOMqZTACM7yNQAISyJBMBm5NK+f3YONrh5O1JToyfzTBE7l52l1w2R2DmYx2+nY/fu2Pj6Urru9ysGwHJtOY42jo06wSIsLAxra2t0Ol2D7xEZGdng9zZUxf79FM2fj/8zs7GLiKj3ujN7s7l4vIDR93Vq9NDv9XCydWJExAhGRIygWlfN7szdbEjZwOKkxXx9/GtCXEKID48nPjyeTj6dDH6CizlbdTSTTYm5fDWzB24Olt0Re3XNKWr0Ci+Pu77TfYQQ5s88EoO4JlYqK2o0OvauPI+Tuz2xk1pRWabhzL4cdi07R9t+gQREmX5rDyuVCtdRoyhb9wf+s2fX290q05bhYtu4Vcs2NjZ0796d/fv3N+j9YWFhBAQYt7Ohr6gg87nncezRA8+ZM+u9rrykmu1LztKmtz9RXRveJW0se2t7BocOZnDoYLR6Lfuz9rMhdQOrzq/ih5M/4OfkR3xYbRjs7tcda5XlzocrLNfwyupT3NApkBEW3hHbkpTL8sMZvDO5M36uln2usRAtkQTAZsbGzpouw8LQaXWXh359Ql1J3JXFsU3p5CSX0C4uyOTdQLfRoyiaP5/KQ4dw6tmzzmvUGjXOts6NftZ9993X4AB47733Gn0oM/fDD6nJySHsqy+vMvSbZJSh3+thq7IlNjiW2OBYnu/zPIdyD7ExdSMJKQksSFyAl4MXQ8OGMjxsOL0CLe984lfXnELXAjpi5dU1PLfiBHHRPkzp0fA5ukII8yXbwDRzlxZ+KIpC9oVSLhzJw8ZWhU+oC5FdfE02T0vR6zk3dBiuw4YR8MLzdV7z0q6XOFt0lgU3LGjUs6qqqujVqxcnTpy4rveFhoZy5MgRoy4AqThwgJSZt+H39FN433FHvdcl7c0m4ftTjL6vk0m7f9dKr+g5kX/i8pF06ep03OzcGBw6mOHhw+kX1A97a3tTl9koW5JyueP7/bw7uTNTeprPCTJN4eVVJ1m8P40/HhtImLeTqcsRQjQBCYAWRltdw6H1qRz47SIRnX0YPCMGZw/T/ODNefMtStb+RustW7Cy/uew4Kwts1Br1Hw14qtGPyspKYlevXpRVlZ2Tdfb2tqybds2+vbt2+hnXyt9ZSXJ48dj4+1D+Px5df4/gdqh34Wv7CWsgzcj7mp+nSZFUUgqSmJDSu2RdMklyTjZODEwZCDx4fEMCB6Ak23zChXq6hpGfrCNKF9n5t3Z26IXwBxMKWLyF7t4bkw77h4QZepyhBBNpOXM3G4hbO1t6DM2itH3diLnYikL5+zl9K4sTJHz3UaPQpeXT8XBg3W+Xq4tN9jZtDExMezcuZPWrVtf9drg4GA2b95s1PAHkPfhh9Rk5xD4+mv1hr/LQ7/WVgycZj5Dv9fDysqKtl5tebjbw6wcv5KVN63kzo53crH0Iv/Z+h8GLh7Io5seZU3yGso01xbYTe29P5IoLNfwxoROFh3+qmt0zF52jM7B7vyrv/EXRwkhjEcCoIWK6ubLjJf6ENHJh03zTrPm/45SVlhl1BocunTBJiiQsnV1bwqt1qgbvQjkrzp16sTBgwd54403CK9jU+WAgACef/55Dh8+TP/+/Q323GtRcfAghfPm4/vYY9hfYdXxmX05XDyWz+AZbXFwsYz5c1EeUdzb5V5+GfsLayes5cGuD5Jfmc8z259h4OKBPJDwAMvPLqeoqsjUpdbpYEoRP+6+yBMj2hDq1bw6l9frs83nuZBfzluTOmOtstygK4SQIeAW4eLxfLb8lIimWkf/SdG0jwsyWhcj5+13KFm5ktZbNmNlZ/e318b9Oo644Die6vWUwZ+r1+u5ePEiycnJ6HQ6oqKiiIyMxMbG+Itj9JWVXBg/AWsvL8J/mn/1od/2Xoy4u6ORqzS+7PJsNqZuZEPKBg7lHEJlpaKnf0/iw+MZFjasUftDGkp1jY4bPt6Bs70Ny++PtehQdCanjBs+3s59g1rxxIgYU5cjhGhiEgBbiOoKLTuXneP0ziyCYzwZOrMtbj6OTf/cc+dIvnEsQe+/h/sNN/zttaFLhjKlzRTu73p/k9dhSjlvvkXRokVErliBfVTd3T9FUfj9i+NkJ5cw/aU+OLrY1XmdpcqvzGdT6iYSUhLYl70PvaKnq1/Xy9vLBLkEmaSuuRvO8Nnmc6x5JI62AW4mqcEYdHqFSZ/voqxKy9pHB2BvY7lb+QghaskQcAth72TL0JntGPtIF0ryKlj46j6ObU5H0Tdt/rePjsapd2+KFi78x2tqrdpgcwDNVcWhQxTOm4fvo4/WG/4Azu7P4cLRfAbNiGlx4Q/Ax9GHqTFT+WrEV2ydtpU5/efgZufGh4c+ZOSykdy85ma+Of4NKaUpRqspMbuUz7ec44HBrSw6/AH8uOsiR9OLeXtSZwl/QrQQ0gFsgTRVNexefp4T2zIIjHZn6Mx2ePg33dym0nXryHjscSJXrsQhpnZhQ42+hm7zuzEndg4TWk9osmebkr6qigs3jcfaw4PwBT9feeh3zl5C23kxsgUM/V4PtUbNtvRtJKQmsCNjB5U1lbT2bM3wsOHEh8cT7RHdJNMZdHqFiZ/vory6ht8eibPoUJRWWMGID7YxpWcIc26SP39CtBSyEXQLZOdgw6AZMbTq4cfm+adZ/No++twUReehoaiaYI6T67BhWPv6ULx4EQEvvgjUrgAGLLoDmPfRx2izsgj5/LMrrvrduiAJlar5rvptSi52LoyJGsOYqDFU1lSyM2MnG1I28OOpH/ns6GdEuEVcPpKuvVd7g4XB73de4Fh6MUvv62fR4U9RFJ5dcRxPJ1ueGmXZ5xoLIf5OAmALFhLjyc0v9GHPyvPsXHaOcwdzGXpbO7wCG386x19Z2driOWUqhT/8gO+sJ7B2cb68/YchVwGbk4pDhyn84Qf8/vMf7KPq30vt7IHaod9R/+6Io2vLG/q9Ho42jpfDnkanYU/WHjakbOCXM7/wzfFvCHYJZljYMIaHD6ezb+cGn0+cVljB++vPcFvfcHqEG2+TcFNYfiiD7Wfz+f6OXrjYy48DIVoSGQIWAGSdK2bT/ETKCqrodWME3YaHobI23BRRbXY254bFE/D8c3hOn05iYSJTVk9h4Q0L6ehjWcNO+qqq2lW/7u5XHPqtKNWw4JU9hLb1YuQ9lvX/wJi0ei0Hsg+QkJLAxtSNFFQV4OfoV3skXfhwuvt3x0Z1beFGURRmfruP5Dw162cNsuhQlK+uJn7uVga18eWjm7uZuhwhhJFZ7nc3cV0Coz2Y9lwv9q2+wN6VyZw/lMew29vhHWyYDp1tQACuQ4dStGAhHjffjFqjBiyzA5j38SdoMzMJ+ezTaxv6vVmGfhvDVmVLv6B+9Avqx7N9nuVI3hESUhJISE1gUdIiPO09GRo2lPjwePoE9MHWuv79FZceTGfHuXy+/5fld8ReXnUSK+DFG9ubuhQhhAlY9nc4cV1s7KyJnRRNq+5+bJx3miVv7KfnmAi6jwrH2gDdQM9bZpB6x78o/W0t6i61w8yWNgewfM+e2qHfWY9fcej33MFcko/kMfIeGfo1JGuVNT38e9DDvwdP9XqKkwUnLx9Jt+zsMlxtXRkcOpj48Hhig2JxsHG4/N68smpe++0047sGMSTGz4RfRdNLOJXDmmNZfDitK94uzfuMZiFEw8gQsKiTTqtn/9oLHPojFa8gZ4bd1g7fMNdG3VNRFDKffIqyTZu4+P79PJn6Eftv2f+3H8LNmTYnhwsTJ+EQ04bQr7++4tDvwlf2Ehzjyah/y9CvMSiKwpmiMySkJpCQksC54nM42jjWnk8cFs/AkIE8uSSR3ckFJMwahJez5Yby0iotI+Zuo22gK9/f0cuij7YTQtRPAqC4orzUMjbOO01hZjndR4bRa0wk1rYN7wbqy8u5MHUaJZpSHri5lN13HraIH0CKVkvKHf9Cm55O5Irl2HjVvXhAURTWfXWCzLPFTH+xD05ulhs0zNmFkgskpCSwIWUDpwtPY2NlS2VpNDe3v4HH+4/H3d7d1CU2medWHOfXwxmsnzWIYI+m3wxeCGGeJACKq9LV6Dn0RwoH1l7E3c+JYbe1wz+y4RvjVp8/z9mJEzjQRsXtSywjAOa88y6F8+YRPm8eTt3rn1B/9kAO6785yYi7O9C6p78RKxT1Scy/yIwFX2PndpJyq/PYWNnQJ7AP8eHxDA0bipeD5awE3ptcwLSv9vDKuA7cHhth6nKEECYkJ4GIq7K2UdHrhkimPtsLG1sVy945wK5l56jR6Bp0P/tWrTh+zwD6HK+meNEiA1drfKUbNlD43Xf4P/mfK4Y/dVEV2xaeoVV3Xwl/ZuTHbWVU5w9g2fiFJExO4MleT6LRa3h1z6sMWTKEO/+4kwWnF5BTnmPqUhulSqvjmeXH6RHuycy+4aYuRwhhYtIBFNdFr9NzeEMq+9ZcwM3bkaEz2xIY7XHd93l196uEfr2OvvvLCF/wM46dOhm+WCPQpKRwYdJknPv3J/jDD+rtZupq9Pw69xDqomqmPterRR73Zo72JBdw81d7mHNTB27rF/G31woqC9ictpmElAT2Zu2lRqmhi28XhofXnkIS7BJsmqIb6J11iXyz/QJrH40j2q9x83mFEM2fBEDRIIVZ5Wyad5qci6V0HhJC35taYWt/7ScmPLXtKYrKcnn2x0p0+flELFuKjadnE1ZsePrKSi5On4FSWUnEsqVYu9S/onnHkrMc35LOhP90JyDKcueXNSdVWh2jPtyGj4s9S+7td8VTcEqqS9iavpUNKRvYlbELjV5DO692l8NgpHv95zybg5OZJYz7v508Oqw1jwxrbepyhBBmQAKgaDC9XuHYpjT2rEzG2d2OoTPbERxzbSHugYQHsFHZ8H7M01yYNBmVuxshH32EQ9vmcRyVJi2N9EcfRXPhIhGLFl0+47gu5w7m8sfXJ4ib2pouQ0ONWKW4krd+T+S7HRdY++gAov2ufTuicm0529O3syFlA9sztlNZU0k3v25Mi5nG8PDh2FmbV3e3Rqdn/Gc70dYorH44DjsbmfkjhJA5gKIRVCorusaHcfPzvXH2sOfXDw6zdUESmqqaq75XrVXjaueKbXAwEYsXoXJ04uK0mylevsIIlTdO2aZNXJg4Cb26nIgFP18x/BXnVLBp/mmie/jReUiIEasUV3Iio4SvtyfzyLDo6wp/AM62zoyKHMX7g99n27RtvDfoPexUdszePpvhS4fz0aGPyFRnNlHl1+/bHRc4lVnK25M7S/gTQlwmHUBhEIpe4fjWDHb/eh4HZxuG3NqWsPbe9V4/cdVEevr35Nk+zwK1x6dlv/YaJUuX4TFlMv7PPYfKwbz2B1Rqasj76CMKvv4Gl/hhBL3xBtZu9a+G1mp0LH3rAHqdwpRnemLnIPuum4ManZ6bPt2JTl/bEbM10JGHycXJLDmzhJXnVlKuLWdQyCCmtZ1GbFBsg88lbqyL+eWM/HAbt/YN5wU58UMI8RcSAIVBleZXsml+IhlJRbTrH0j/SdHYO/3z6K0RS0dwY9SNPNL9kb/9evGy5WTPmYNdqyhCPvwQu7AwY5V+RdrcXDJnPUHF4cP4PfEEXv+644rb1yiKwsYfT3P+UC6TZ/fEO8iyTjxpzj7fcp53/0jk1wf70znEw+D3r9BWsPbCWhYlLiKpKIlQ11BeiX2FXgG9DP6sK1EUhRlf7yW9uII/HhuIk518ABFC/JeMBwiDcvNx5KbHujL4lhjOHcxl4Zx9XDye/4/r1JraIeD/5TFpIhGLF9VuGD1pMmUbNxqj7Csq37ePCxMnoUlJIfzHH/C+819X3bvw1I5MkvZkM3hGjIQ/M3Ihv5wPE85wV1xkk4Q/ACdbJya3mcwvY39h/uj5BDoHcvf6u/nm+DfoFX2TPLMui/ensTu5gDcndJbwJ4T4BwmAwuCsrKzoMCCY6S/2wTvYmd8+PUbC96eoKtcCoFf0qLXqes8BdmjblsilS3Hu24f0Bx8i5Y5/UfrHehSt1mhfg6LXo962jbT77if19juwb9WKyOXLcOrZ86rvzb5QwvbFZ+kwIIiYvoFGqFZcC71eYfayY/i7OTBreEyTP8/Kyoqufl35avhX3N3pbj469BGPbnqUkuqSJn92TmkVr689zeQeIcS19mny5wkhmh8ZAhZNSlEUEndns3PpWVQ2KgZPj8G/oxN9F/Tl3YHvMipy1BXfW7p2LUU/L6Dy0CFs/PzwmDoVjylTsPX3a5J6a4qKKFm+nKJFi9GmpWHfrh2eM6bjMWECVjZX76Kc2pnJtkVn8A114abHu2Fje+1b44imtWBvKs+uOM7Pd/ehf7TxQ9G29G08s/0ZXO1ceX/w+3Tw7tBkz7p3/gEOphSTMGsgHk7mtSpZCGEeJAAKoygvrmbLgiQuHssnpKsbr9k8xNxR7zIgZMA1vb8qMZGihYsoWb0apboa1/h4PKdPx6lP70YfJacoClXHjlG0YCGlv/8OioLbmNF4Tp+OQ5cu13T/Go2OrYvOkLgri/ZxQQyY1lrCnxnJLqli+NytjO4UwDuTu5isjgx1Bk9seYIzRWeY3Xs2U9pMMfhRiL8fz+L+nw/x6Yzu3NBZOtBCiLpJABRGoygKZ/fnsGVhIqXaUrpM8Cd+6PUFOF1ZGSUrV1G0cCGa8+exCw/HoUMHbENDsQ0Jxi40FNuQUGwD/P/RsVN0Ompyc9GkpaFNz0CbnoYmLZ3qpCSqz5zBNjgYz+k34z5xIjZe137+a3FuBeu+OkFJTgWDZsTQtp/80DUniqLw7/kHOZJWTMLjg3CvY1GSMWl0Gt7Z/w6LkxYzrtU45sTOwVplmA8LxRUa4uduo1uYB1/N7GER52wLIZqGBEBhdPuSD7Homy20KuxKVFdfBk5vg7O7/XXdQ1EUKvbtp3TNajQXLqLJyKAmOxsu/XG2scE2MBC70BBQWaNNS0Obmfm3eYQ2vr7YhoZiFxaG2+hROMfFYWV9fT+Ikw/nsfHHUzi62THq353wCZEFH+bmt2NZPLjgEJ/f0p3RncwnnK8+v5rndz7PXR3v+sdq+IZ68pejrDuRzYZZgwhwN69tlIQQ5kWWhgmjq7YrZ0PM98xovYxjK3JY+MpeBkxtTZs+AdfcsbCyssK5T2+c+/S+/Gt6jQZtRsbfunvatDQUvR6XwYP/3iUMDm7UPoM6nZ49K85zJCGNVt18GXpbO+wc5a+TuSmu0PDSqhOM7OBvVuEPYGyrseRU5PDRoY/o6teVgSEDG3W/HWfz+eVgOm9M6CThTwhxVfITSxhdubYcgHa9gunQOYLti8+S8MNpzh3MZdCMtrh4Xl838BKVnR32kZHYRzbtuazlxdX88c0JcpJLiZvSms5DQ2SozUy99ttpqmv0zLmpo6lLqdOdHe/kSO4Rntn+DEvGLiHYJbhB96nQ1PDMimP0ifTi5l5y3KAQ4upkGxhhdGXaMqywwtHGEUcXO0bc1YHR93UiN7WMBa/sYceSsxTnVJi6zH8oLahkz6/nWfjqXkrzKhk/qxtdhoVK+DNT28/msfRgOs+NaYe/m3l2xFRWKl6Pex1XO1ee2PIEGp2mQfeZu/4MuaXVvDWpMyqV/HkUQlyddACF0ak1alxsXf52PFZUV1+CWntweH0Kp3ZkcXRTGiFtPek0KISIzt6oDHRc1/VS9Apppws5vjWDlOP52NpbE9M3kJ5jInByk+01zFWFpoZnlh+nX5Q308y8I+Zu7877g99n5tqZvLP/HZ7v+/x1vf9oWjHf7bzAkyPbEunj3ERVCiEsjQRAYXRlmrI6N4F2cLal34Roet0YyflDeZzYms7vXx7H2cOeDgOCaB8XdN2LRRqqSq3l9O4sTmzLoDSvEu9gFwbNiKF1L38507cZeH/9GfLKqvnprj7NokPbwbsDs3vP5tU9r9LVrys3Rt14Te/T1Oh5etkx2gW6cc+App36IISwLPKTTBhduba83lNAAGxsrYnpE0BMnwDyUss4sS2DQ3+kcOC3i0R186XjoGCCWns0yQ/2nIulnNiaztkDuSiKQnR3P+LvaE9AlFuzCBICjqQV8/3OCzw9qi0RzagjNqXNFI7kHmHO7jl09ulMmNvVz8H+cut5zuaqWflgf2xM1CUXQjRPsg2MMLrndjxHWlka80bPu+b3VFdoSdydzYltGRTnVODu64h3sAuuPg64+zji5uOIm48Drt4OV92AWVejp6ywitL8SkrzL/27ksKsCoqyynH1cqDDwCDaxQbJMG8zo6nRM/aTHdjZqFjxQGyzC0UV2gpGLx/NyIiRPNvn2Steey63jDEf7eCuAZE8PaqtkSoUQlgK6QAKoyvTlOFie3375dk72dJlWCidh4aQnlRE8qE8SvIruXg0n7LCKvS6/36Ocfawx83H4c9Q6IhKxeWgV5JfSXlR9eXtAq1UVrh62ePm40hgK3f6TWhFeEdvmUjfTH2x9Tzn8tSseqh5dsScbJ2Y1HoSCxIX8Fj3x3CydarzutpzjY8T7OnIo8NaG7lKIYQlkAAojE6tVePj2LCzWK2srAht60Vo2/+e1KHXK5QXV1OaVxvwygqqKMmrpDingtSTBSh6agOhryP+Ue64edf+t7uPIy6e9iZbYCIM61xuGf+36Rz3DoyiQ5C7qctpsCltpvDtiW9Zk7yGqTFT67zmp70pHEgpYvG/++IgRw4KIRpAAqAwOrVGTaSb4Sasq1RWuHo54OrlQHCMp8HuK5oPRVF4etlxQjwdeaSZd8QCXQIZFDKIxUmL6zwrOKO4krd/T2RGnzD6RHmbqEohRHMnrQ9hdGqt+oqLQIS4XnsvFHIwpYiXxnWwiI7YzTE3c6boDEfyjvzt1xVF4fkVx3FxsGH2aJn3J4RoOAmAwugu7QMohKHM35NCK19nBrZu2NQCc9M3qC9hrmEsSlz0t19fdTSTzUl5vDa+E24OtiaqTghhCSQACqNSFIUybd37AArRELmlVfxxIpuZfcMtZqselZWKqTFTWZ+ynoLKAgAKyzW8svoUN3QOZHh7fxNXKIRo7iQACqOq1lVTo6+RDqAwmEX707C1VjGxR4ipSzGo8dHjsbay5tdzvwIwZ/VJdHqFl8d2MG1hQgiLIItAhFGptWoAXO1cTVyJsAQ1Oj0L9qYyvluwwYZEtVoty5cvZ9OmTSQnJ1NTU0NkZCT9+vVjxowZODsbZ3Npd3t3uvp15WTBSTYn5fLrkUzem9IFX1fjnIYjhLBsEgCFUak1tQHQ2bb5nNAgzFfC6VyyS6u4te/VT824FgsXLmTWrFlkZ2f/7de3bNnC999/z3/+8x9eeOEFnnjiCaMMN4e4hHAy/xTP7T7OgNY+TOoe3OTPFEK0DDIELIxKOoDCkA5cLCTC28kg+/49+uijzJgx4x/h769KS0t58sknmTBhAhqNptHPvJoQ1xDOF6dSVKHljQmdLGaOoxDC9CQACqMq05QByBxAYRCphRWEeTe+m/zRRx/x8ccfX/P1K1euZNasWY1+7tVoqjzQKuU8FB9MqFfdp4IIIURDSAAURiUdQGFIqYUVhHk5Nuoe58+f58knn7zu93366ackJCQ06tlXUl2jY/GuCgAGtZdv1UIIw5LvKsKoZA6gMBRFUUgrrCCskZ2xzz77DK1W26D3Xk/X8Hp9uukcmfm14TazPKPJniOEaJkkAAqjUmvVONo4YqOS9UeicYoqtJRrdIR6Ni4ALl68uMHv/e233ygrK2vU8+uSmF3KZ1vOc9+AzjjbOpOhlgAohDAsCYDCqOQUEGEoqYW1w6ONmRtXUVFBRkbDw5Veryc5ObnB76+LTl97rnGEjzMPDY0mxCWE9LJ0gz5DCCGkDSOMSk4BEYaSX1YNgI9Lw/fFy83NbXQdOTk5jb7HX32/8wLH0otZel8s9jbWeDt6U1BVYNBnCCGEdACFUak1alxtZQGIaLwAdwcAskurGnyPwMBAVKrGfRsMCTHcCSRphRW8v/4Mt/eLoEe4JwA55Tn4OfkZ7BlCCAESAIWRqbVqWQAiDOLS0G/an0PBDWFvb09kZKTJ3v9XiqLwzPLjeDnb8eTImMu/lqHOIMTFso65E0KYngRAYVRqjVqGgIVBuDva4u5oe3kuYEPddtttDX7vlClTcHRs3DY0lyw9mM6Oc/m8PqEjzva1s3MKqgqo0lUR4ioBUAhhWBIAhVGptWrZA1AYTJiXU6M6gAD33HMPrq7X/2fS2tqaRx99tFHPviSvrJrXfjvNhG7BDI7573DvpcUfEgCFEIYmAVAYVZmmTFYBC4MJ9XIkrahxATAwMJDvvvvuut/36quv0rNnz0Y9+5KXV53EWmXFCze2/9uvp6v/DIAyBCyEMDAJgMKoyrXlMgQsDCbUy4nkvHIURWnUfSZPnsxHH32Ejc21bYwwa9YsZs+e3ahnXrL+ZDa/Hc/ipbHt8XK2+9tr6WXpeDl44WQrx8AJIQxLAqAwKrVW9gEUhjOwtS9ZJVXsv1jU6Hs98sgjbNmyhd69e9d7TUxMDMuWLeP999/Hysqq0c8srdLywsoTDG3rx7guQf94Pb0sXbp/QogmIfsACqPR6rVU1lRKABQGE9vKmyhfZ+bvSaF3pFej79e/f3/27t3LgQMH2Lx5M8nJyWi1WqKioujXrx9DhgwxQNX/9ebaRMqrdbw2vmOdgTKtLI1g12CDPlMIIUACoDCick05gCwCEQZjZWXFrX3CefP30+SWtcPP1cEg9+3Zs6fB5vfVZ09yAQv3pfLqTR0I8vjnSuKc8hyO5h1ldqRhhpqFEOKvZAhYGE2ZtvbMVJkDKAxpUo8QrFVWLNmfZupSrlmVVsfsZcfoFeHJLX3C67xm2dll2Fvbc2PUjUauTgjREkgAFEZTrv2zAygngQgDcne0ZXzXYH7em0qNTm/qcq7JhwlnySyu4s2JnVGp/jn0q9VrWXpmKWNbjZUPTEKIJiEBUBhNmaa2AygngQhDu7VvOFklVfx6JNPUpVzV4dQivt6ezCPDoon2qzvcbUrdRF5lHlNjphq5OiFESyEBUBiNWqMGZAhYGF7HYHdu6hrESytPcC5Xbepy6lVYruHBnw/RJcSdewe1qve6xUmL6e7XnTaebYxYnRCiJZEAKIxGra39wSyLQERTeGNCJwI9HHng54NUaGpMXc4/6PQKjy0+QlWNnv+b0R1b67q//Z4vPs/+7P3c3PZmI1cohGhJJAAKoynTlGGrssXe2t7UpQgL5Gxvwxe3die9qJJnlx9v9ObQhvZ/m86x/WweH93ctc5Vv5csTlqMl4MX8WHxRqxOCNHSSAAURlOuLZc9AEWTivZz5c2Jnfj1SCY/7001dTmXbTuTx4cbz/DYsDYMaO1b73WFVYWsOr+KSa0nYWtta8QKhRAtjewDKIymTFsm8/9Ek7upazAHLhYxZ/UpAt0dGNbO36T1HE0r5rHFRxjQ2peHh0bXe51Or+PpbU9jb23PjHYzjFihEKIlkg6gMBq1Ro6BE8bx/I3tGNjGl7t+PMA76xJNsj2MoijM35PClC92E+rlxIfTuta55cslXxz7gn3Z+3h74Nv4OPoYsVIhREskHUBhNGqNWhaACKOwt7Hmq5k9+Gp7Mu+sS+RwajEfT++Gr6tx5p+WV9fw3Irj/Hokk9v7hfPsDe2wt7Gu9/odGTv48uiXPNTtIfoG9jVKjUKIlk06gMJo1FrpAArjUamsuG9QKxbc05ezuWpu+Hg7+y4UNvlzz+WWMf7Tnaw/lcPH07vxyk0drxj+stRZzN4+m7jgOO7udHeT1yeEECABUBiRWquWOYDC6PpGebP2kTgifJyZ/vUeZi87xomMEoM/J6WgnDfWnmbc/+1EAVY91J9xXYKu+B6NTsMTW5/A2caZNwe8icpKviULIYxDhoCF0ZRpyqQDKEzCz82BBXf34ZsdF/hx10UW7U+jW5gHt/ULZ3THQBxs6+/QXYlOr7ApMZf5e1LYdiYPd0dbZvYN55FhrXG2v/q31/cOvEdiYSLzRs/D3d69QTUIIURDWCnmtlmWsFjDlw5nXKtxPNztYVOXIlqwGp2ehNO5/LQnhR3n8vFytmNKzxB6hXsR5u1EqKcTjnZ1B8LqGh0ZRZWkFlZwPL2ERfvTyCiupEuIO7f2DWdsl6BrCpN6Rc+XR7/ks6Of8Xyf55nWdpqhv0whhLgi6QAKo1Fr1LjayiIQYVo21ipGdQxgVMcAzuep+XlPKgv3pvLl1uTL1/i42BPm5UiYlxM21ipSCytIK6wgu7SKSx+ZHWxVjO0cxK19w+kS6nHNzy+qKuKZ7c+wK3MXD3Z9UM77FUKYhARAYRR6RU+5thxnO2dTlyLEZa18XXhxbHuev6EdeepqUgsrSC2oIK2o4nLoq9ErhHo60TvCizAvJ0L+DIYBbg7Y1HOcW32O5R3jia1PUF1TzRfDvyA2KLaJvjIhhLgyCYDCKCq0FSgo0gEUZkmlssLfzQF/Nwd6RXgZ/P6KorAwcSHvHniXDt4deG/QewQ4Bxj8OUIIca0kAAqjUGvVALIKWLQ4FdoKXt71Mr9f/J1b293KrB6z5Jg3IYTJSQAURlGmKQOQVcCixajWVbP+4nq+OvYVuRW5vDfoPUZGjDR1WUIIAUgAFEZyqQMoJ4EIS5dWlsYvZ35hxdkVFFcX0y+wHx8P/ZhI90hTlyaEEJdJABRGodbUBkBnW1kEIiyPTq9jZ+ZOFiUuYkfGDlzsXBgfPZ6pbaYS4R5h6vKEEOIfJAAKo5AOoLA0iqJwvvg8m9M2s+zsMjLUGbTzascrsa8wKnIUjjaOpi5RCCHqJQFQGEWZpgyVlQonGydTlyJEg6k1avZk7WFHxg52Zu4kuzwbe2t7RkaM5N2B79LRpyNWVlamLlMIIa5KAqAwCrVWjbOts/xwFM2KoigkFSWxI2MHOzJ2cDT3KDVKDRFuEcSHxdM/uD89/XviYONg6lKFEOK6SAAURiGngIjmoqS6hN2Zu9mesZ1dmbvIr8zH0caRPoF9mN17NrHBsYS6hpq6TCGEaBQJgMIo1Fq1nAIizJJOr+NUwSl2ZNZ2+U7kn0Cv6In2iGZs1Fj6B/enm1837KztTF2qEEIYjARAYRTSARTmJL8yn92Zu9mRsYNdmbsori7GxdaFfkH9mNRvErFBsXJShxDCokkAFEZRpi2TU0CEydToaziWd+zyXL7ThacBaOfVjiltphAXHEcn307YquSEDiFEyyABUBiFWqPGz8nP1GWIFiS7PJtdmbvYkbGDPZl7KNOW4WHvQb+gftza/lZig2LxcfQxdZlCCGESEgCFUZRry+UYONGktDoth3IPsTNjJzsyd3C26CxWWNHJtxMz288kLjiO9t7tsVZZm7pUIYQwOQmAwijKNDIELAwvQ53BjvQd7Mjcwd6svVTWVOLt4E3/4P7c0+ke+gX2w8PBw9RlCiGE2ZEAKIxCrVXLKSCi0apqqjiYc/DyXL6LpRextrKmi28X/t353/QP6k+MVwwqK5WpSxVCCLMmAVA0OUVRUGvUMgQsrpuiKKSUprAzcyfbM7ZzIPsA1bpq/J38iQuO49Huj9InsI98uBBCiOskAVA0uWpdNTVKjcGHgEtLS/npp5/YvXs3Fy5cwM7OjsjISOLj45k0aRJ2drJvW3NUoa1gX/a+y12+DHUGtipbuvt356GuDxEXHEcrj1ZyqowQQjSCBEDR5NRaNYDBOoB6vZ7XX3+dt99+m/Ly8r+9tnnzZr777jv8/PyYO3cut9xyi0GeKZqOoiicKz53efHGoZxDaPVagl2CiQuOIy44jt4BvXGylXOkhRDCUCQAiiZXpikDDBMAq6qqGD9+PH/88ccVr8vNzeXWW29l7969fPzxx41+rjCsMk0Ze7L21Ia+jB3kVORgb21Pr4BePNHzCfoH9SfcLVy6fEII0UQkAIomp9bUdgANMU/rwQcfvGr4+6tPPvmE6OhoHnnkkUY/WzScXtGTVJh0eVj3aN5RdIqOSPdIhocPJy44jh7+PXCwcTB1qUII0SJYKYqimLoIYdl2Ze7i3g33sm7SOoJdght8n7Vr13LDDTdc9/tsbW1JTEwkKiqqwc8W16+4qpjdWbXHre3M2ElBVQFONk70CexDXHAcsUGxhLiGmLpMIYRokaQDKJpcubZ2nl5jh4A/+uijBr1Pq9XyxRdf8M477zTq+ZZKp9exPWM729O342DjwJjIMXTw6dCg+5wsOHl5WPd4/nEUFFp7tmZc9DjiguLo5tcNW2s5bk0IIUxNOoCiya04u4IXd73I4ZmHsVE17DNHUVER3t7eNPSPa1hYGCkpKQ16ryXbmbGTL499SX5lPn0C+5Bfkc+erD28FPsSN0bdeNX351fm1x63lr6DXVm7KKkuwdXWlb5BfRkQPIDYoFj8nf2N8JUIIYS4HtIBFE2uTFOGo41jg8MfwLlz5xoc/gDS0tKorq7G3t6+wfewRHmVebTxbMPcwXPxcfRBURRe2/MaP5/6mfiw+H/MyavR15BalkpeeR6rklex6vwqANp7t2dqm6kMCBlAJ59Ojfq9FkII0fTku7Rochq9ptGBIC8vr1HvVxSFvLw8QkJkztlfjQgfwaiIUTjYOKDT67BWWdPDvwdb0rbgYOOAoiiUVJeQVJTEmaIznCs6R6Wukki3SDr7dKZvYF9ig2LxdvQ29ZcihBDiOkgAFE0uwDmAMk0Z5dpynG2dG3SP4OCGLx4BsLGxISAgoFH3sER/3Vvv0pYrW9O3Eu0ZzZrza0gqSiKnIgcrrAhzDSMuJI4YzxhCXEJQqeS4NSGEaK4kAIomF+JS23VLL0snxiumQfeIjo7Gzs4OjUbT4Pfb2Mgf9/oUVBZwpvAMOzN3sjF1I518OnE49zAxnjHEh8UT7RGNs13DwrsQQgjzIz8RRZO7tNVHhjqjwQHQ2dmZiRMnsmjRoga9//bbb2/Q+yyVRqchuSSZM4VnSCpKIq8yD2usOVN8hhjPGF6Lew1fR1/srOU4PSGEsEQSAEWT83bwxtHGkfSy9EbdZ9asWSxZsgS9Xn9d7/Pw8OCuu+5q1LObO0VRKKwq5FzROY4XHOdCyQW0ei0e9h608WjD6IjRFFYXsm/XPr4c/mWj9msUQghh/iQAiiZnZWVFsEsw6erGBcBevXoxZ84cnn/++et69rx58/D19W3Us5ujCm0Fe7P2sjOzdl++Tj6dCHMNw8HGgZHhI4nxisHPye/y3L9ZW2YxvvV4oj2jOVd0jvUp63Gzc2N89Hhc7AxzjrMQQgjzIAFQGEWISwgXSy42+j7PPvsshYWFzJ0796rX2tnZ8cknnzB27NhGP7c5UBSFc8XnLp+8cTD3IDX6GkJcQhgQPIDRkaPp5NOpzo2Y92fvJyElgbZebfn17K8UVxcT4hrC/V3vx9HG0QRfjRBCiKYkAVAYRWxwLG/ve5vcilz8nPwafB8rKyvef/994uLieP755zl16lSd18XFxTF37lx69erV4Gc1B6WaUvZm7b18xm5uRS4O1g70CujFf3r+hwHBAwhzC7vqfdzs3HC1c6VPYB/6Bvalf3B/I1QvhBDCVOQkEGEUao2aob8M5V8d/sX9Xe832H23bdvGnj17uHDhAnZ2dkRGRjJs2DA6depksGeYE72iJ7Ew8fJxa0fzjqJTdES5R9E/uD9xQXH0COiBvbVseC2EEKJ+EgCF0by6+1W2pG1h3eR12KrkPNhrVVRVxO7M3ezM3MnOjJ0UVBXgZON0uVPXP7i/LNoQQghxXWQIWBjN1JipLDmzhM2pmxkRMcLU5ZgtnV7HiYIT7MyoDXzH84+joNDGsw03Rd9EXHAcXX271jmXTwghhLgW0gEURnX777djo7Lh25HfmroUs5JfmX858O3K2kVJdQmudq70C+xHXHAc/YP7N2rupBBCCPFX0gEURjUtZhpPb3+aI7lH6OrX1dTlmIxWr+Vo7tHLw7qnC08D0MG7A9NipjEgeAAdfTo2+gxlIYQQoi7SARRGpdVruWPdHeRW5PLLjb/g4eBh6pKMJrs8+/IWLXuy9qDWqvG09yQ2OJb+Qf2JDYrF29Hb1GUKIYRoASQACqPLLs9myuopdPDpwGfDPkNlpTJ1SU1Co9NwKPcQO9J3sDNzJ+eKz6GyUtHZp3Ptit3gONp7t7fYr18IIYT5kgAoTGJXxi7uS7iPB7o+wH1d7jN1OQaTVpZ2eYuWfdn7qKypxNfR9/Jq3X6B/XC3dzd1mUIIIVo4CYDCZD4/8jmfH/2cL4Z/QWxQrKnLaZDKmkoOZB+4fNxaSmkKNlY2dPXrSlxwHHHBcbTxbHP5uDUhhBDCHEgAFCaj0+t4YOMDnC44zafDPqWTr/lv3qwoChdKL1xesXsg5wDVumoCnQMvD+v2CegjZ+cKIYQwaxIAhUkVVRXx0MaHOFV4iqd7Pc20mGlm1y0r15azN2tvbejL3EmGOgNblS09/XvSP7g/A4IHEOkeaXZ1CyGEEPWRAChMTqvT8t6B91iQuIDRkaN5ud/LONk6maweRVE4W3z28ordQ7mHqNHXEOYadrnL19O/p0lrFEIIIRpDAqAwG+surOOlXS8R4BzAB4M/IMojymjPLtWUsidzz+XQl1uZi4O1A70De9M/qDb0hbmFGa0eIYQQoilJABRmJbkkmSe2PEGGOoPJbSYztc1UItwjDP4cvaLndOHpyyt2j+UdQ6foaOXe6vKK3R7+PbC3tjf4s4UQQghTkwAozE6FtoKvjn3FsrPLKK4upl9gP6a1ncagkEGNOhmjqKqIXZm7Ls/lK6wqxNnWmb6BfWtDX1B/glyCDPiVCCGEEOZJAqAwW9W6atZfXM+ipEUcyzuGv5M/k1pPor13e0JcQwhyCcLRxrHO95ZpykgvSydDnUFiYSI7M3ZysuAkCgoxnjGXz9ft6tsVW2tbI39lQgghhGlJABTNwqmCUyxJWsLaC2uprKm8/Os+jj6EuIQQ7BqMVqclQ51BujqdkuqSy9e42bnRL6gfccFxxAbF4ufkZ4ovQQghhDAbEgBFs6JX9ORV5JGuru3upZel1/6jTsdWZUuIawghLiGEuIYQ7BJMiGsInvaeskWLEEII8RcSAIUQQgghWhg5hV4IIYQQooWRACiEEEII0cJIABRCCCGEaGEkAAohhBBCtDASAIUQQgghWhgJgEIIIYQQLYwEQCGEEEKIFkYCoBBCCCFECyMBUAghhBCihZEAKIQQQgjRwkgAFEIIIYRoYSQACiGEEEK0MBIAhRBCCCFaGAmAQgghhBAtjARAIYQQQogWRgKgEEIIIUQLIwFQCCGEEKKFkQAohBBCCNHCSAAUQgghhGhhJAAKIYQQQrQwEgCFEEIIIVoYCYBCCCGEEC2MBEAhhBBCiBZGAqAQQgghRAsjAVAIIYQQooWRACiEEEII0cJIABRCCCGEaGEkAAohhBBCtDASAIUQQgghWpj/B51ew9tf9TxKAAAAAElFTkSuQmCC", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -198,7 +195,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Similarly, hyper-edges can be collapsed and relabeled. We will use the dual to illustrate this." + "Similarly, hyperedges can be collapsed and relabeled. We will use the dual to illustrate this." ] }, { @@ -208,14 +205,12 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "hnx.drawing.draw(H, with_edge_labels=False, **kwargs)" + "hnx.drawing.draw(H, with_edge_labels=False, with_node_labels=False)" ] }, { @@ -282,14 +275,12 @@ "outputs": [ { "data": { - "image/png": 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W12szZyvKQ11Ps4xR0Uy9+S56jjwKIANtneaYIFzagLYutIxDwtJlt+sW3nnn2TqDwXHmDz+8NPPdd98FWPfcc5M2vPjik7bS0jHGqGiOOPE0eg4chj41CWLCc5JVU7bO+cUbllbgHAlLIUJPRpgh5Jk5OxeYkDZgMCfefX/YzpwF7Rbh4rdfJn/DWtBmp55iVtUVAb5sb7QlFHsBR+MXagoKouN79qwD2L9yZeqKf/zj7viePQfEpKcb47qlccSMU4lJ9GynpdeB0QD7S8DhJJw1mhELcJFZVb8McUlCCGSEGVJmVbWhLbzPKdmdzfKP3iGc/wFjMJmYfMPt3nWLycBsi6JMCvBl84HfgMO2DPGGpa2kJKpgyZILDDExQ8p37DB2H3YER595/oGwBHC5wQ2kpUAYT/gp35vHknde8YblPyQshQgfEpghZlbV/cBZQM2eVctQf/k+1CW1yGA0Mum6W+k/djxAItouJ9MDfNl1wEbgsM5DpZs3Z6x74YV/1uzdOyOpf39S+vQltUdv9IYmNnq2O8BkhOTwnPhzYEZsHWgzYh8PcUlCiEYkMMOAZwKNGXBv+P4rcteuDnVJLdIbDEy45mYGjp8EEA/8ZFGUEwN4STdaU/VytOUlAOz+8cdjtn7wwZPO2tqBMYmJKKedjSk6FltFC52U6uyQGAdxMQEst+2cdjuL3niRmpJi0GbEXmdW1fC93SBEFySBGSbMqvodcD/A0vdfpzRnd2gLaoVer+f4K29g8KRpALHADxZFOS2Al6wDvgViXXZ7zKbXXrswf/78e91OZ3xqn/4cffaFdOs3kJQBAynb1Up/BbsTUsNn60+3282qT97zLh/JR5vkI8tHhAgzMuknjHi2m3oXuDo2JZVT73sk7HfPcLtcrP78I7IWzgGoR5uk8l2grpe/YMH4yl27PijdvHkYbjf9jj2OPkeNRqfz8d9+RoM2AaikAmx1gSqzTbbM/om133wG2ozYyZ5dboQQYUYCM8xYFCUamANM6tZ/ICfe/SDGqPCdOQvaCGnNVxa2zf0NtJmslwVisopFUY4DvswcN65v92PH0veIUd5G8b6JNmnPMUvKw2am7CEzYi/07G4jhAhDcks2zJhVtQ44H9hTumcXKz56K6xnzgLodDrGXGBm5MlnABiBTz27gviFRVF0FkW5BW0z7r6u0nKGTphKSn8fmz3odNoazBorFJaGTViW5+c2nhH7sISlEOFNAjMMmVW1EG3mbPWe1SvY9HPA7nD6jU6n4+hzLuLI088BreHARxZFubqj57UoShzwPtpmyaZh009i5p/vJ8bm0IJQ38ofYYMeoozaqLKs0htOIWerqmTBa897Z8R+AkhDdSHCnNySDWMWRTkT+A7QTb7hNvqNOS7UJflk08/fsuH7r0Gb3XqTWVXfas95LIoyFPgKOMoQFcX4y69jwLgJBw6IjoLMbmCrb/oEUUZt/WVxmXYrNkxoPWKfpig7C7Tda6bLJB8hwp+MMMOYWVV/AP4GsOz9NynN2RXiinxz5GnncMx5l4C2d+WbFkW5ra3nsCjKecBq4KjEzB6ccu8/Dw5LgLp6KK9suuVdjEl7fX9JWIXlgRmxWQB5wLkSlkJEBgnM8Pcc8K7TXs+CV5+ntrws1PX4ZORJpzPmwsu9X75kUZS7fXmfRVGMFkV5BvgaSOo7ehyn3vdI85N7qmqhxgbRnkYFer0WoOXVUFwOLlcHfyX+teX3n9m5fDEc6BFbEOKShBA+kluyEcAzc3Y2MKVbv4Gc+JcHMEZFt/ymaBPExkBFdUif22UtnMuqT9/3fnm/WVWfbu5YzwbbnwLTdHo9o8+7hOEzTml970q9DjLTwGTw3IIt10aXYSZvw1oWvv4/mRErRISSwIwQFkXJAFYAA/uNGcek625F19yEl7gYSEsGdFBTC6WVQaz0cNlLF7Li43ca90d97NBjLIoyGfgc6BmbnMKk628lc0gb9qs2GSExXvsHgjM8ZsE2Vp6fy2/PPuad5POwWVWl7Z0QEUYCM4JYFEUBlgGJR55+LqPOPO/wg5ITIClBawHndntuT1ZBVU2wyz3IrhVLWPbBm97QfBwtON2eZg13Af8BDJlDRzDpulvCvmFDW9iqKvn16UepKS0GbUbs5dL2TojII4EZYSyKcjrwPaBv1ARdW2LRLVkbXR46azQmSpspag1tZ5s9q1ew9L3XcGvPFf8DPAa8A1wIcMSJp3P0OReiNxhCWKV/yYxYIToPCcwI5JlA85zBZOLEux8kbfBQyEgBo1EbWR5Kr9NuWYbBjNHctatZ/PbL3tAsA1KN0TFMuOpG77ZhnYbb7Wb5B2+ya8US0GbEHieTfISIXDJLNjI9D7zltNtZ+bWFuqQY0OmbDkvQJsI4nJCRqi3kD6G+o8cy/ISTvV+mJvfqw6n3P9rpwhK0HrGesKwFzpawFCKyyQgzQlkUJSpt1KjFmePGjTPqDYyccSoGo6nlN5kMWnAWlYVk5qzT4WDt15+yff5sAAYcN5HjLrsGY3QrM34jUN6GNSx8/QXv7/MFZlX9OtQ1CSE6RgIzMumBybayshM3vPDCTfbq6u5pAwYxfPpJre/aEW2CWmvQZ87WlpWy6K2XKNmVjd5g4NiLLmfIlBmtLxmJQGV5Ocx+9nEc9XUAfzerqrS9E6ITkFuykScGOBuYGJOaunXgWWf9R6fXW0t27zxs42l7bS0ArsbLLOrsEB+nLcEIkn1bVX7+9z8o2ZVNXGo3TrznIYZOndkpw7KhR6wWlhbgyRCXJITwExlhRpYU4DygG7DX+83dP/00Kn/evPsA3bBpM0nt3Z89Sxexe+E8kvv2w2G1cvxtdx18piDMnHW7XGz+7Uc2fP8VbrebHiMUJl53CzEJiQG7Zig57Xbm/O8pinfuAG3N7HSzqtpCXJYQwk8kMCNHb+ACtIbmxYe+uOW9904pVdWrXQ4Hhnonlbl7OPbaP5HYowdr3n8bl8PB5L/cd+ANOp3WnDxAM2fra2tY9v4b5G9cB8CRp5/Dkaefi7613UUi1CEzYnPRZsTuC3FZQgg/6pw/vTqf4cAVaP1HDwtLgBFXXfVrfK9ec4rXryd3+RISevaifPdOYpJTmHjnPSRkdsfauA+t263Nnu2W7Pdiy3L38MtT/yR/4zqi4uKZdutfGHXm+Z02LKHJGbESlkJ0MsZQFyB8MhRwAdXNHaDT6ynZuLFcbzRW9D7hhOSEtHR2LpxLfGZ3eo0+lpHnXYgprtFzS6NB++dSaZVfC925bBGrPn0fp91Oat/+TLnxDhLSM/x6jXCTt2EN6779wvvllWZVXRfCcoQQASKBGRnmAD3Rnl2WNneQy+EwDjObX6jYsePGqsL9PdxAVIIWklHxCQcOjDJqu3js998+kU57Pas//4jsJQsAGDxpGmMvvgKDqYmttzqRsrwclr7zmnf5yEOyfESIzqvz3iPrXKxoGymbgGant5oSEqq3fvDB+cb4+FnWwsI6a0U5RTuzDz4o2gT1dthf6rewrC4u4rdnHyd7yQIMJhPjr7ye8Zdf1+nD0lpZ0XhG7MfAv0NckhAigGTST2TpB5jRZsg22dbn92uvvdBeWZnscji6RSUlHR3Xo4d+6NQZZA4ers2Mra7VmrH76f97/qb1LHvvdepra0hIz2DyjXfQrW9/v5w7nDnt9cz539MyI1aILkQCM/KMAs4A9qDNmG2StajItOenn04o2bTpGmNsLMrZF5CgM2mB6Sebfv6WDd9rdyB7H3UME66+iai44K3vDBW3282y999g98qlIDNihegy5JZs5NmAtsVX35YOiu7WzT78yit/SxkxYoEpMZHFT/6Lmj05fiti5/LFbPj+a3Q6HUeffSFT//TnLhGWoM2I9YSlzIgVoguRwIxMi4BstIlATdIbDOj0+swRV175Zc4vvyws3baVBa89j93W8buG5fm5rPrkfQDGXXYNyqlnNb+ZdSeTt/6gGbFXyIxYIbqOrvFTrvNxAj8CFWgzZw+lQxuBFhqiot6r3bfvXGB7eX4uy9573bu1VrvYrVYWvfkiTns9g46fzOBJ09p9rkhTlpfD0ncbZsQ+aFbVb0JdkxAieCQwI5cV+JrDZ84agP7ARuBLoNqsqmXAWUB53oY1rP/uy3Zd0O12s/yjt6kq3E9Krz6MvfSqTtkPtinWygoWvPp/3hmxHwFPhbgkIUSQSWBGtjK05SZpaMEZgzaynAP8QqOZtGZV3Q5cBDg3//YjO5cvbvPFts2bTe7aVRhjYph84x0YozrftlxNcdrrWfT6C9SWlQIsB240q6rMlhOii5HAjHy5wE9oS066AZ8DK2liBq1ZVX8H7gBY+fG7FGVv9/kidpuVDT98BcDxV95AUvceHS48ErjdblZ8/C7Fu3aA9nt9riwfEaJrksDsHDYC3wAfok0GapZZVV8FXnI5HSx8/QWqS4p8usDulctw2GxkDBlGv9HjOlxwpNj824/eGbE1wFlmVd0f4pKEECEigdl5qEChj8feDfxWV13Fwlefx26ztniw2+0ma+EcAIZOndmhIiNJ7ro/Gj/vvcKsqutDWY8QIrQkMLsgs6o6gEuAbeV781jyzmu4Wpg5W5SdRfnePGISk+h7zNig1RlKZXk5LHvv9cYzYmeFuCQhRIhJYHZRZlUtR5s5W7Z30zrWz/qi2WO9o8vBk6ZhMHb+fv3WinKZESuEOIwEZhdmVtUstE2pHVt+/4mdyxYddoy1soLctavQ6XQMmXxC0GsMNqe9noVvyIxYIcThJDC7OLOqzgNuB1hheZfCHQfPnC3M2orL6aT7CIX4bmmhKDFovDNiS3Zlg8yIFUIcQgJTYFbV14EX3E4ni954geriAzNnvZ+n9Orj9+vW2mwsUzcwd80q9peW+P38bbX51x9kRqwQolmd/4GU8NU9wPC66qpTFrz6f5z814cxxcZS41l2kpCe4bcLud1unrK8x9OWD6ioqQbAoDdw4bQZvHL3fXRLSvbbtXyVu261d0asG5kRK4RogowwBXDQzNktFQX5LHnnVVwuV8MI05+BeecLz/Lgm680hCWA0+Xks3mzmXLnjdRYW17m4m9luXtY+t7r3i9lRqwQokkSmKKBWVUr0GbOlu5V17Pum8+oLtaWdsan+ScwV25Reembz5t9ffPuXTzt2QklGKwV5Sx47Xmc9fWgNX54OmgXF0JEFAlMcRCzqmYD5wOOrXN+obqkGICEtHS/nP/Tub+1eswnc1o/xh+c9noWHugRuwy4SWbECiGaI4EpDmNW1QXALQC43UTHJ2AwRfnl3LmFrc+jySkM/H7MbrebFR+9Q8nubIAc4DyZESuEaIkEpmiSWVXfAv4HUFdbg9Ph8Mt5e/owUu3lp9u/Ldn86w/sXrUMZEasEMJHEpiiJfcAVtxuakqL/XLC86e03vzg/KnT/XKt5hwyI9ZsVtUNAb2gEKJTkMAUzTKrqhNYBVBT7NuuJq2ZPvpYLpt5SrOv9+/ek4euuM4v12pK6cEzYh8wq+p3AbuYEKJTkcAUrdkBHNTMoKPeu/+f/PmCSzEd0pd22tFjWPjCGwFbh2mtKGfhqw0zYj8AngnIhYQQnZI0LhCt2Qn+Dcwok4nn77iHBy6/hmXqRqz1dRw1cDBHDhrit2scqmFGbHkpwFJkRqwQoo0kMEVrdgJU7Nvr9xN375bGuVOm+/28h3K73Sw/fEZsXcAvLIToVOSWrGjNYsC1b8tGrJUVoa6lXTb/+gN7Dp4R6+tG20II0UACU7TIrKq5wPcup5OdSxeGupw2y10rM2KFEP4hgSl88QpA1qK5uFyuUNfis9LcPSx9v2FG7P0yI1YI0RESmMIXvwM7astK2btpXahr8UkTM2L/E+KShBARTgJTtMqsqi7gVYCshXNDXE3rHPUHzYhdgsyIFUL4gQSm8NV7gK1g80b2bVVDXUuztB6xb3tnxO4BzpcZsUIIf5DAFD4xq2opnq2vlrzzqnf0FnbUX75nz+rlANXA2TIjVgjhLxKYoi0eA36vq65iyduv4HL6pyG7v+SuXc2G778CmRErhAgACUzhM09vWTOQX5SdxdoWNoIOtkNmxN5nVtXvQ1mPEKLzkcAUbWJW1SLgYsCxbe6v5KxZFeqSDp0RC/BWKOsRQnROEpiizcyquhT4G8DS915jx+L5uN2hmYRamrOb3559nNryUowxMd5vTw5JMUKITk0CU7TX/4CXXA4HKy3vsvzDt3DUB28yqtvtZsfi+fz27OPUlBSR2rc/g4+f6n15akvvFUKI9tCFamQgOgeLolwFvAbEpvTqw+Qb7yCpe4+AXtNRX8eqTz9g1/LFAAyZcgLHXmimMGsb8156FmCVWVWPC2gRQoguRwJTdJhFUY4CvgSGGaNjGHfpVfQfezx6g8Hv1yrN2cXyD96ifG8ehqgojrvsGgaOnwSA3Wbly7/eitvlcgKpZlWt8nsBQoguSwJT+IVFUZKALCATIC6lG0OmTGfwxGnEJqd06NxOez05a1axfeEcSnZlA5DUvSeTb7ydlF59Djr212cepWT3ToBTzKr6W4cuLIQQjUhgCr+xKMrnwEUxScnYPFuB6fQG+o4ey7CpM8gYMhydTufz+aqLi9ixeB7ZSxdSV60NFk2xcQyaMIVRZ56HKSb2sPes/fpTtvz+M8CTZlV9yA+/LCGEAGQDaeFfxQAjTz6TlF692b5gDvkb1pDzxwpy/liBKTaOhPQM7SMtk4T0DOLTMzBGRVFdXOT5KKSmpJjq4kKsFeUNJ07t04+h02YyYOwEjNHRzRaQMWS4NzBl4o8Qwq8kMIU/FQHU11bTY4RCjxEKNaUlZC9ZQPbSBVgryinL3UNZ7h6fTmYwmeg7ehzDps0kbcBgn0anmUOGgU4HbvdxFkWJNauqtWO/JCGE0EhgCn8qBqirOjDXJr5bGqPOOp+jzjyPuuqqhlFkdXERNSXaqNJRX+8ZdXpGn+na6DM2JbXNE4ei4uJJ6d2X8rycKGA8MN+Pvz4hRBcmgSn8qQigrubwyak6nY6YxCRiEpNIHzg4oEVkDhlOeV4OaLdl5wf0YkKILkMaFwh/KgawVYV2NUfm0OHeT6eFsg4hROcigSn8yTPCrA5pEZlDGgJzgkVRokJZixCi85DAFP6kPcOsDu0IMyYxiaQevQBigbEhLUYI0WlIYAp/agjMUK/vbTTKlOUlQgi/kMAUfmNW1Tqgyu1yYbfWhrSWRs8xJTCFEH4hgSn8rQjAFuLbso1GmJMtiiKzwYUQHSaBKfztsLWYoRCX2o2E9AyARODokBYjhOgUJDCFvzW7FjPYMoeO8H4qy0uEEB0mgSn8LSxGmCATf4QQ/iWBKfwtLNZiwkEjzCkWRZE/60KIDpEfIsLfPN1+KkNdB/Fp6cSldAPoBighLkcIEeEkMIW/abdkw2CEqdPpyJDlJUIIP5HAFP6m3ZINg2eYAN2lr6wQwk8kMIW/eUaY4RGYGY0m/lgUpfUNNYUQohkSmMLftBFmiBsXeCV170lMYhJAd2BoiMsRQkQwCUzhb9qknzAJTJ1O13iUKbdlhRDtJoEp/K0ccDpsNpx2e6hrAWQ9phDCPyQwhV+ZVdVNmD3HbLyhtDzHFEK0lwSmCATPc8zQLy0BSOnVh6i4eIC+QP8QlyOEiFASmCIQwmIjaS+dXk/GkGHeLw+9LasH0oGRwJnA5WgbTwshxEFk2yMRCGGxxVdjmUOGk79hLTqjcTrwC5AJDAEGov090AE1QALajNrdoalUCBGuJDBFIIRNA3YATEZ6jjuO4qoSkgYNOgvY4nmlBi3cnY2OjgL6IIEphDiE3JIVgRC6Lb50OogyQUIspKdCn+7QPY2kI0YQm5FJdX5+ekV2djWQB5RxcFgCVCLrNYUQTZDAFIEQ/BGmQQ8ZqdA7EzK7QUoSGA1QZ4c6O3q7k9j4BHC5KFq79ogWzlQLZCDPMYUQh5DAFIEQ/BGmTg8xUQ0BSZ0dnK6DDknq0ROA6ry8EU2dohE3WmgKIUQDCUwRCJ4tvoIYmA4HuNwtHpLcoxcAdaWlLY0wAVxAL/8UJoToLCQwRSCEZosvW712G7YZCemZ6A0GHFZrn6rc3MRDX68rL/dOgqtAnmMKIQ4hs2RFIISmAbvVBrHR4Dh0Ho9GrzeQmNGd8oJ8iv74Y0Ri376rNr322hF5c+ceV1NQMNheWdlr2quv3ttz4sRSoDcQDdQF85cghAhfMsIUgdDQuMDtbvk2qV/ZHa0eUl9WjqOmhqqcnBEup5Oszz67oK68PGPsQw+9Et+nz4Y9P//svV2rQ55jCiEakcAUfmdW1Tqgyu1yYbfWBu/Cdge0EtDVe/OpLSzEVlJyhN5goJui/BHXo0dO/1NP3ddt5MiNJRs2jPIc6kaeYwohGpHAFIESmm2+6urB0PxzzL7jJ2CvqsJRW9u/dv/+OL3B4HQ7nUaAfqeeqrqdTu+bK4HBQahYCBEhJDBFoISmAbu1DozN/7HuOWoMOjcUr1+vW3LPPVcXrlkzedB55y0G6DtzZuGZP/zwkufQarTnmKbAFy2EiAQSmCJQPM8xK4N71fqW9+CMSU5myOlnEJWcjMvpzDjmL395c8iFF+Y0cagbeY4phGhEZsmKQAnNCLOViT9ut5ueytGU7c3DlJBgHHzeebmHHBILJKH1lNWhzZQVQggJTBEwodviy7se85BOPwA6nY7E7j3QAfaamoF15eWp0Skp0Rz4u1AGrEXrNVsIhEkHeSFEqElgikAJzVpMAJtN6yXbRGCi12OMj6fb8COoLS81FK1dO7DPCSd8ixaQRWg7mAghxGHkGaYIlNDMkgWob3Rb1qCHaCNEm7QPtwsqq7Hn5rP9449ZePvtNmAx2nZeEpZCiGbJCFMESuhGmHZPX9lok9aEvaZW+2+jdZqp6d1x1dcDTAt+gUKISCQjTBEooXuG6XbDvmLIL4SiUqiq1WbPNmpqkDF4mLZ3Joy3KEpM8IsUQkQaCUwRKKEbYQK4XC12/YmOTyClVx/QZsOOD1ZZQojIJYEpAiV0zzB9lDlkuPfTqaGsQwgRGSQwRaBUAE6HzYbT3nIzgVDJHNqwj7QEphCiVRKYIiDMquqiYV/M8BxlZgwZ5v10okVRpAWeEKJFEpgikDwTf4Lc7cdHsUnJJHXvCRAHHBvicoQQYU4CUwRSaCf++CBzaMNzTFleIoRokQSmCKSwn/iTIRN/hBA+ksAUgaSNMKvCNzAbzZSdbFGU5jfSFEJ0eRKYIpDCetIPQHy3NOLTMkDboeToEJcjhAhjEpgikEKzxVcbNXqOKbdlhRDNksAUgRSaTaTbSCb+CCF8IYEpAikyRpgHnmNOsSiK/J0QQjRJfjiIQAr7WbIACemZxKakAqQBR4S4HCFEmJLAFIEU9uswAXQ6XeNRptyWFUI0SQJTBFJDpx93CzuHhANpxC6EaI0EpggYs6rWAVVulxO7tTbU5bSoUSP2aRZF0YWyFiFEeJLAFIEWEc8xk3r0JDohEaAHMCTE5QghwpAEpgi0sG7A7qXT6WQ9phCiRRKYItA8E3/Cey0mIBN/hBAtksAUgRYRI0yQiT9CiJZJYIpAi4ilJQDJvftiio0D6G9RlP6hrkcIEV4kMEWgeUaY4R+Yer2ejMHDvF/KKFMIcRAJTBFoRRD+s2S9usvEHyFEMyQwRaBFzAgTIEMasQshmiGBKQItIhqwe3Xr2x9jVDTAUIui9Ax1PUKI8CGBKQItIrb48tIbjKQPHur9Um7LCiEaSGCKQIuoESbI8hIhRNMkMEWgVQBOu82K024PdS0+adRXVgJTCNFAAlMElFlVXXhvy9ZExigzrf9A9EYjwJEWRUkPdT1CiPAggSmCIaJmyhpMJtIHNvRfnxzKWoQQ4UMCUwRDxHT78cqU5SVCiENIYIpgiIgtvhqTiT9CiENJYIpgiLgRZvqgIej0BoBjLIqSHOp6hBChJ4EpgiGinmECGKOiSes/ELS/I5NCXI4QIgxIYIpgiJgtvhqTDaWFEI1JYIpgiJhNpBuTiT9CiMYkMEUwROQIM2PQMHQ6HcBYi6LEh7oeIURoSWCKYIioLb68TLGxpPbtD2AEjg9xOUKIEJPAFMEQcZN+vDKGyG1ZIYRGAlMEQ8MtWbfbHepa2qS79JUVQnhIYIqAM6uqDah2u5zYrbWhLqdNMgYP8356vEVRYkJZixAitCQwRbBE3DZfANEJCST36gMQDYwLcTlCiBCSwBTBEnHt8bxkPaYQAiQwRfBEXHs8r0Z9Za+0KEpsKGsRQoSOBKYIloidKdsoMIcD2RZFuVOeZwrR9UhgimCJ2BFmbHIKiZk9vF/2BP6HFpy3S3AK0XVIYIpgidgRJhx4jjlw/GRS+/QD6AW8COywKMqtFkWJDmF5QoggkMAUwRKxI0w4cFu2rqaaU+9/lCk33UFK774AvYGX0YLzFglOITovCUwRLBE7SxYOBGbJrh3o9Hr6HjOW0x74F1NuvIMUbdlJH+AVIMuiKDdbFCUqhOUKIQJAAlMES1AasDscjoCcN65bGgZTFHU11dTX1gBowTl6LKc9+BiTb7jdu16zL/AqWnDeJMEpROchgSmCJWBbfBWVl3Hb80+Tee7JmE6cQM/zT+Xul56jrMp/19LpdCSkpwNQXVJ88Gt6Pf3GjOP0Bx9j8g23kdyzN0A/4HVgu0VRbrQoiskfZQDpnv8KIYJMAlMES0BGmHv2FTD6hit4ZdaXFJWXAbCvtITnv/yEY2+6koJDwq0j4tMyAagpLmrydS04j+P0hx5n0nW3ktSjF0B/4A204Ly+g8E5BPgTcIXnvBKcQgSRBKYIlnLAabdZcdrtfjvp1f9+hPziwiZf21Wwl5uefcJv10pIzwCgupnreen0evqPHc/pf3+CidfdQlL3ngADgLeAbRZFua4dwWkApgP7gXjgMuBytJGsBKcQQSCBKYLCrKouoAS0mab+sHn3ThasX9PiMT8uX0LO/n1+ud6BwGx6hHkovV7PgLHHc/rDTzLx2pu9wTkQeBvYalGUayyKYvTx8oOBNKAGqAD2AImA2fMhwSlEgElgimDSnmP66dmiuntnq8e43W7U3dl+uV5CmicwS3wLTC+9Xs+AcRM4/eEnmXDNn7xNEAYB76IF59WtBKd3dHno/eVytOBM4kBw9kWCU4iAkMAUwZQHUFNW6peTxcf41tY1ITbOL9ez19kAMEa3r7mPXq9n4HETOePhJ5lw9U0kZHQHbeT4HrDZoihXNhOcg4FUtNFlU8o5EJyXo92uleAUws8kMEUw7QTfb2m2ZoJyFLHRLfcJSI5P4NhhR/jlejWekaX31mx76Q0GBo6fxJn/+DfHX3UjCRmZAEOBDwDVoihXWBTF4DncO7os8eHU5WjBmYwWnJciwSmE30hgimDyBGbLk2Z8lZqYxD0XX97iMQ9cfg1xMf5p9+oN+oT0TL+cT28wMOj4yZz5j6c4/sobvEE8DPgQLTjN9traYTQzuqwrLzf+fMEF1696/PHjDnmpHC04U4GrgKP8UrAQXZwEpgimnXBgpOYPj1xzEzeffUGTr/3lYjP3XnaV3651IDA7NsI8lN5gYNCEKZz5z6cYf+X1xGvPSofr9PqPN7zwwtxdP/ww2GW3HzZKLN28OSm+d++8rE8/vWPxX/86o4lTlwFOPM+OhRAd4+sMPSH8YRf475YsgMFg4NW/3M+NZ57LlwvmkF9cRN+M7lwy4ySOGjTEb9eBA5N9vJN//E1vMDJ4wlQGHjeRXcuXsFtdiyEqqsfeBQtuLFyx4vS0Y475atA556zQG41ugJ4TJ5bqjcaVhatXTx90zjkbAFxOJ3qD924u6cB2oCAgBQvRxUhgimBqeIbpdrnQ6f13g2PMsBGMGTbCb+c7lNNup7asFJ1OR1y3tIBdBzzBOWkaA847j+KdWeRWVlNXU9V7//Lld5asX5+XfvTRXw/0BOfaZ589O7537+29pkwpdtntOr3J5G50qnhgaUCLFaILkcAUQWNW1QqLouxy2usHFmzZRC9lVKhL8ln+xrXgdpPcuy8GYxD+2sTGYIg20X3gUDL6D6Iwayu569dQX1PdZ9/y5XcWr1+fF5WUNLc6N3fkpGeffaaJM3hHl/5ZhCqEkGeYIujeBMhaOCfUdbTJ9gVavUMmTw/OBVMSoN4JgF5voMdwhWMvNDNo4lSi4uNxWK198ubOvSq6W7eU2sLCwQ6rVS+jSyECSwJTBNvbQH3+pvVtbgAQKhUF+RRmbcUYHc3A4yYF/oKxMWA0gMt10Lf1egMZA4aQlJhKRv/B1FdWkTx4cOK+pUvvWv3vfz+5c9ascS6Hw9ugfRsyuhTCryQwRVCZVbUQ+AK3mx2L54e6HJ9kLZwLwIDjJmKK9a1ZQoekJDaMLg/ldjopzd7B2rdex2gyMXTaTKLi4nFarf0Kliy5e9Vjjz25b9mysQ6rVUaXQviZzu12t36UEH5kUZSJwJLohETOfeL/MJj8sfNVYNhtNr558M84bDZOe/AxUvv0C+wF42IgLRlsLTeor96/jzUfvENy336MOONsSvL2kL9hDbroaGr37SNvzpx1wCPAd2ZV7cp/yXsAw9EGBzoODBJ0jT4af0/f6NitaCN1IQAJTBECFkXRAWuBo8ddehVDp84MdUnN2jz7J9Z98xkZg4dx0j0PBf6CPTO0W7Eu3/5e2q3WhlGvy+mgeF8+q5//L+W7d3kPWYMWnD900eA8ETgeqAQO/fW7D/meu9F/E4AsYFaA6xMRRG7JiqDz/OB+CmDNV59QlpcT4oqaVrxrBxu++xKAkSefEfgL6nRaWEaZwODbX83Gt4j1MTFk9uzDyXc9yJgLLycmKRlgDPAdsMqiKGd6/rHSlbjQdncpQmte3/ijBCht9FHm+SgHqpCWguIQEpgiJMyq+inwrtNuZ9GbL1FvrQ11SQexVVex+K2XcTmdDD/hZHofdUzgL+p2w/4SKNI2wiYmyufgBLRjK2swRkUxYsbJnP2vZxlzoZmYxCSAY4HvgRUWRTm9CwWnm/YHn/x8FAeRPxAilG4DNlQX7WfFh28TLo8HXC4XS999jdqyUtIHDuGY8y4JbgG2OthX3LbgNBmhxgp2R8O3tOA8hbMfe5bRF1zmDc5xwI/AcouinNYFgrPFwHTabDqnzdbU6+Hxh1GEFQlMETJmVbUCFwCVuetWs3Xur6EuCQD152/Zt2UT0QmJTLr+1uA0KmhKU8HZXHckgx6qmt79yxgVzREzT9WC8/xLiU5IBDgO+AlYZlGUUzpxcLqae6EqNzfm62nT/vvT+eff2cTLHRmZik5KAlOElFlVdwDXAKz75jOyly0MWS1ut5tt82ez8advQadj4rU3Ex/gNng+8QZncZn2I/zQ4GxidNkUY1Q0R5x4Guc89l+OOe8Sb3COB34BllgU5eROGJxNjhSdNptu9WOPnRybmbmjuWOQn4/iEPIHQoScWVW/AZ5wu1ys+PBtln/4No76+qDWYLfZWPruq/zx+UfgdnP02RfS84gjg1pDq6yHBGe0Z3KQvvnRZVOM0dGMPOl0zv7Xsxxz7sXe4JwA/AostijKiZ0sOA/7teQvXNi9YufOkf1PP32e2+Vq7hZCZ/o9EH4gy0pE2LAoyrXAK0BMSp9+TLnhdhIzuwf8uhUF+Sx68yUq9+3FGB3D+Muvo//Y8QG/bofFRmtNDursUFrR7tPYbTa2L/idLb//TH1Ntffbi9GWo8wNh+UongDXt/XjhDfeOC6hX79j7NXVxbhcerfLpbPX1hpW/vOfVw25+OJfavbuTdm7aNGk8Y8++pHb5dLhduvcLpdeHxMT76iurl14++3z2nPdQz7aVbvnox7YjbZxwS6zqpb7+/dW+E4CU4QVi6IcDXwJDDHFxHL8VTfS95hjA3a93auWsdLyLo66OpJ79mLyjXeQ3KNXwK4Xzuw2K9vn/86WOT9TX9MwYl0E/NOsqvOae59FUaKBAcAgYKDnv96PJPwTHO2SdswxpI8aha24uOF7tfv2YSspoZuiYC0spConh8yxYw96nzEuDkdtLXt++qm9lw6UMmAZ8Brwk1lVm24JJQJCAlOEHYuiJAMfA2cApA8cwtCpM+g3ZhwGU1SHz++or2PPHyvIWjCX0hxtgf+AcRM4znwtxujoDp8/0tmtVrbNn83WOb9QX9sQnAuAR8yqOh8aRnzHAbcClwAB/43T6XSg06PT6zyf69Dp9Ac+1x/ytU5Ht6OOJPXII6kv0W5j63Q6itavpzJnDzqdHpfTgdvhILF/f3pPnIQOHejAEBuHq66O/YsWHX4dnU7bms77uef1AzV46tTpDn5PO+p31NdTU1JEdUkx1cWFOA9+VJGDFpxve1pOigCTwBRhyaIoesBOo9FFdEIigydOZcjkE0hIb/smzpX795G1aC47ly3C7ln3GRUfz9FnXciQKSdoP6hEAy04f2PL7780/H4BC4HlaB10xgCg0xHfLZ2E9AztIy2DhPRMEtIziIqLR6c/PBRoFDQ6ve6grw+E0iHB0x6J8ZCUAPVNtxrcr25k6w/fMu2+vx/8gl4PbhcUlrbvugHgdruxVpSxZ/UKshbNpbqoISNtwF3AG+Fw+7wzk8AUYcuiKPlAr2POvZg9q5cf6Aik05E5ZDjJPXoR7/0hnZ5JQlo6UXHx1NfWUF1cSHVxUcNHRUE+RdnbG86dNmAQQ6fOpN+Y4zBGdXzU2pnVW2vZ8vtPbJn9My7HgZm4UfEJDJ44laFTTiAhPTOEFbYgMQ6SElsMzO0/f8+Uvz548AthGJiNuV0uCrZsYvuC39m7ab332x8Ct5hV1fcZYKJNJDBF2LIoymrg2JP/9g/SBgyiZHc22xfMIWfNyoN+cDdmMJlw2ltuXG6KiWXwpGkMnjStyz6vbIvK/ftY/OaLlO/NAyBt4GCGTZ3pt1vkAdVKYDZLrwdcsD88A7OxXSuWsPKT97y3a1XgArOqStP4AJDAFGHLoijfA2dOuenOgyb+2KqrKN6ZRXWx9lynpsQzkiwpwllfjzE6moS0jAOjz7RMohMTqS0rZfeqZZQ36l2bMWQYQyZNp+/ocTLSbELOmlUs/+gtHDYbiZk9mHD1TaQPHBzqsnyXEAvJSe0ITB3gjojABCjfm8fiN1+icn8BaL1wx5pVNTu0VXU+EpgibFkU5Q3gRl93NHG73ThsNowxMc0+83K73ZTu2cWOJfPZs3o5jro6AEyxcQwcP4khk6aR0ruvP38ZEcnldLD2m8/Z5um+1G/MOMZffn1w9gP1p/hYSGlvYKL19o0QdpuVxW+/QoG6AWAdMNHTTUv4iQSmCFsWRXkU+MeRp53DqLPO9/v57TYre1YvZ8fiBQ2zZUG75Thk8nT6jxnfJWfN2m1W5r/8HEXZ29HpDYy54FKGTT8pMidFdaHABO158y9PPUJ10X7QZs/eEOqaOhMJTBG2LIpyM/Dq4EnTGH/5dQG9VmnuHrIXz2fnisUNU/dNMbH0H3c8QyafQLe+/QN6/XDhdrtZ8vYr5KxZSWxKKpNvuJ2MQUNCXVb7xcdCapLW3KEtIjQwAcrycvjtP//yPsu/zqyq74a6ps5CAlOELYuinAt80+vIY5h+691BuWbRzh3MfvYxACvQcP+xW78BDJ40nQFjj4+825JtsG3ebP744iOMMTGcet+jJHXvEeqSOiY+FtJTwdnG9f06HdTVh+0s2dZkL1vIig/fBm1fz95mVa0KcUmdQoi2YRDCJwUAtsryoF0wMaNheYQVbWH+jcCVpTm7U0tz3mPtV5/Qf+x4Bk+eTlr/QZF5m7IZxbt2sPbrTwA4/oobIj8sAWptsN/T5afJsYG7+dbrjtA00Vmw7g9+WrGU7Pw8Kmqq6d+9J6MGD+HyE08lLTml6TdldtN6C3sM7nsRzh5p1JQUJ6aNGvUesKIDJRWgLVnp8iQwRTgrALBWlAftgtHxCej0BtwuZzcgy6yqf7Yoyv1o25Dd6Kivm5q9dCHZSxeS0rsvQyZPZ8C4CUTFxQetxkCwVVex+E3PhtkzTqbfmHGhLsk/3G6wBbeRf3st2rCWW557CnX3ziZfv+/1l7jhjHP4z813EnPos/VoE9gOvu2cmtGDInUT9RUVY/vOnPmlrrmt4Vons+A8ZLcSEc72AdiqKnG5mt3W0K90ej0xSUneL3uAtm+nWVU/MqvqNOAI4L9ASXl+Lqs/+5BvHriLZR+8SVH29rDZBLst3G43y957ndpyz4bZ5wZ5w2zBC199yoy7b2k2LAFs9XW89M3nTLjtOvIK97d6zrT+gzDFxOKwWvvlz58/3J/1dlUSmCJsmVW1Hihxu1zUVQfvEUxsUrL3055N1LTVrKp/BXoDlwJznPZ6di1fzOz/PsFPjz/I1rm/Ulddfehbw1ZR9nYKNm8kKj4+tBtmd1GzFs3nzy/+F4ePz1nX7djO+f+4l/pWGnTo9Qa6Dz8CgKI1a1pflyVaJYEpwp3nOWb7t69qq5ikFO+nzT7EM6tqnVlVPzOr6onAUOApYH9FwV7WfGnhmwf/zJJ3X2P/9i1hP+rMWjgHgKFTZ4bHhtldSFF5Gdc89Wib37dq62b+8c5rrR6XNmAQAPUVFQMPfa2wsNCUmZn5WFpa2lOpqanPzJgx48I2F9LFSGCKcBf055ixyc2PMJtiVtUdZlV9AO1ZzwXAry6Hw71n1TLmPP8UPzx6P5tn/4StqjJAFbeftaKc3LWr0el0DJk0PdTldDlv/TiLipr23Y147buvqbXZWjwmJlH7s+ysq8t0H/JYIy0tzb5y5crHS0pK7t+zZ88Dqqoe/dxzz0XwGqLAk8AU4W4fgDWIM2VjD8xE9Ckwvcyqajer6tdmVT0VbS/Ix4G9VYX7WPfNZ8x68C4Wv/USBVs2cegPr1DJXroQl9NJ71Gju97o0hT6W8/v//Jju99bUVPNrMXzm3xt1cYNjDrvdBwuN3adnsvfecf0wSuvHNH4GIPBwIABA+oAqqqqDE6n06DT6cL7dkiIhf5PjBAt027JVgTvlmzsgVuybQrMxsyquht42NOt6DTgJpfTeXrOmlX6nDWriE/LYMikaQyaMKVxQAeVy+lkx2JtX2hfWg92KonxWkODGiuUV4Iz+P+AcTgc7MjP69A5tuzZ1eT3xx01irOnz+TvL/yX3K2bOWXkSGYqymEPSa1Wq65Pnz5PVlZW9hg/fvxvd999t/SfbYGMMEW4C8Et2RTvp+0OTC+zqjrMqvq9WVXPAvoD/wRyakqKWP/dl8x66G4Wvv4/8jetD9pMYK+96npqy0pJzOxOj+Ejg3rtkDIZISUBbHUQEwU90iEuJuhl7C0pxunq2FrP3BZmy/7jljuYvXQJWwsKMI8bh62k5LBbCLGxse6SkpIH1q9ff9uuXbsGv/fee306VFAnJ4Epwp0WmEG8JRvTwizZjjCrap5ZVf+Fdrv2dOAbt8vlzFu/hgWvPMd3D9/Dxh+/oaY0OO3YinZo+4MOGDeBDqzRizypSeBwaQ0L6h1gd0JaCmSkgtEQtDLSDvw5a7f0Fu5OlFZUUG2tpdpqpd7hICoxsdmp5iNHjqwdMWLE5i+//PLoDhfViXWhvyUiQoVyhBmQVjdmVXWaVfVns6qejzZR6EFgZ21ZKRt/nMW3D9/D/FeeI2/DGlxtbenWBtXFRQAkdff/nqAbsrN49+fveP+XH9iWs9vv52+3+FiIjjq4i4+3uYHJpI02E+KCVEosPTr43HhQr97NvnbTIw/x2O13c7IyklcXLiShT5/Cxq+vWrUqcfPmzXEABQUFpi1bthw1YsSIvR0qqJOTZ5gi3IVgWUnDv/y7WxTFYFbVgKWWWVULgH9bFOVpYAZwI273eXs3rTft3bSe2OQUBk2YwuBJ00hIy/DrtauLtZ+f8enpfjvnvpJirnzyn/z+x8qDvn/elOm8fe/DpCYmNfPOIDAaWm7EbndoPWRTk7RbtGWV2vcC6Lwp03n126/a9V6jwcA5k6Y1+doH336N0Wjg0tNOp8/+XG62WHjhs8+6P/KvfzWE5oYNG1Lvu+++W9xut87tduvHjBmz/Nlnn13bvl9J1yDN10VYsyhKIlBpMEVx8fNvBK1361d/u406bbp/D7Oqtt5WxY8sipIBXI3Wx3YYADodPUYoDJk8nT6jRqM3dOzfum63my/vuQW7zcr5z7xETEJih+uusVoZf8s1zXarmXjkKOY//zqmUDVGyEjVnl/affj3j9EARj2UV0N1rTYKDQB1VzZHXXdZu9bqnj/1BL761zPaF327H9YaD6C2spy1X32CISqq6PgnnvhzO8vsCzzTzvd2KnJLVoQ1zy4LNU57PXZb8PbCjWl94o8eiAOi/H1ts6oWmVX1WWAEMB34GLe7bt+WTSx+8yVmPXg362Z9TpUP7dGaU19Tg91mxRgTQ3R8gl/qfnnWFy22dlu6aQMfz/7ZL9dqs7gYiIluNiwPW+bjcGoBlJIA3dMgytTk+zpKGTiY+y67qs3vS09O4fnb/9LqcTWeuwjGuLiCNl9EHEZuyYpIUAAMsVaUExUbhOdLBj1JffpgdzvpMX78WM93U4BkIMnzEQfogJ3Al4Eow6yqbmABsMCiKHcCVwI32aoqR27+7Uc2//Yj3YePZPCkafQ9+lgMJt9/qFeXaM8vE9Iy/DZq/3bJglaPmbV4AdecdpZfruczg77VPTHVb77AYbNxzOVXH/yCzQ4GgxaaZZXaaNPPHr9e6yH7/dJFPh0fHxPLJw8/Tt/M1h+xl+zZrb2nV6/1HalRaGSEKSKB/9dixkRDYhykJEJ6ivYDsVcG9OkOPTPoc+pp9D/lFHrPmHEycBJwNNALiEbbYzAPralCd/8V1TyzqpaaVfV/wJHAJOA9wLp/22aWvvMq3zx4F2u++oSKfb7N2fBOovLnGtD9pa3vHbm/LAT7SyZ7npu2cNtz4NQTqC0p5tcH/0pFfu7BLzqdzW8B5gcGg4FZjz/LI9fchEHf8izdI/oPZOVr73Hi2PGtntfldFCWlwNA9+OPX+WXYrs4GWGKSODfpSU6nfY8y+XWfoi63OB2eW7XabfsdHV11BYWUrlzp5uZM5tbXW5HG2nqgaAsovSMOpcCSy2KcjdgBm6qr6k+euucX9g65xcyhgxjyKTp9B09DmNU03eMvaNRf87CHdCjJ9l7W16IP6CHX1fqtC42GuJjWt3iKz4jk4l33sN6ywcUrFtDcu9GO1oZDWC3B2R06aXX6/nnNTdy7Wln8fr3X/PjssXsLNhLjc1K7/QMRg0ayvVnnM3ZE6diMPi29KW8IB+Xw44xLm5PtyOOKA5Y8V2IBKaIBJ7A9NMI0+0JyPrmZ0CaPLd+HbW1viyWiwEC99O0GWZVLQdesSjKq8BY4CbgsqId2+OLdmxn9ecfMXD8JIZMmkZK74O3NDRGawv1HXUt9yJti8tPOpU5a1oeyFx+4ql+u16r9Hrolgz1Le/qUbB+LTvnz2H0lddSsGEdQ0485eADjAbYVxbAQg/o170HT9xwK0/ccCugdQMytnOSVKmnC1B8r14yuvQTCUwRCTy3ZMv9d0anSxtpNnObzrshtMNmS23lTG4glhAEppdn1LkKWGVRlL8AlwE32q21Y7fPn832+bNJGziYIZOm0//Y8RijozF5NiB21NX5rY6rTzmTz+bO5tdVy5uu88RTOHPiFL9dr1XJCdr/Y9fh/4/dLldDs4bMkUeS9euPLHvp/+h97Dj6HHf8gQOjTVBRFfDlJc3xOSzr7BBz4Bm2y+XGWmclrkcP+syYkUvHNoGWCUMeEpgiEmgN2P0ZmA6nNpnD2UxgekaYzrq6FB/OFuu/wjrGM6v4DeANi6KMRluacnnJruykkl3Z/PHlxwwYN4FeyijAv4Gp1+v59on/8uBbL/P6d99Q45nVnByfwF0XXsbDV13vt2u1KjpKa0DQzK3Y2pJiYlO7oTcaMZhMHHXx5ajffMFRF1124CCDXnt+WRWyfwv5rvDgZ8Ml2dvZ+OorALu2vPPOPZ5/VIkOksAUkcC/t2RBC8wW/vVuijsQmNX5+dH26mpjVFKSI75nz0MTRod2SzbsmFV1LXCrRVH+BlwM3Oiw2SbsWDSPHYu0puuOev8FJkB0VBT/vfVuHr3mT2zN2Y3BYGBEv/7ERgfxt0in027FNjMqdLvd5P+xipyli5hwx1+Iz8hkv7qRpN59cLvdB2YNm4ywvyRgazADKW/9Gu+nsyQs/UcCU0QC/7fHczhB3/xyClN0DHXl5dSVlaUtve++81x2e5TTZouvr6xMG3rppV8e+ac/bW10eNiMMJtiVtUa4F3gXYuiHIk26rwSSPXnM8zGEuLiGDsiRA3dk+K10WEzy0h0Oh3DTj0DgHlPPEJyn764nE6Gn3bmgbCMMkFVTavPP8OR2+0md/0f3i+/CWUtnY0EpogE/g9Mp1MbiTSjaMtmyrZswZSYaEjs3780rnv3Yn1UlMNRUxO9/ZNPLk4aOPCNfiefvA9tpmzHu2gHiVlVNwF/tijK/UCN027XuVwu9J2p+bpOp33om35+6TXs1DPoO34i5Tm7SRs8lKgETwMHvV6bFFbRvo2dQ62iIJ/qokKAYrQZ1cJPJDBFJCgB7HZrrclpr8dg8kNznRZ+kAJs+uITMkePQR8TzTCzeVPG6NENCxxzf//95LItWzIaBWYIG6S2j1lVrRZFqQESnHV16GPDepDcNuVV2rPLbslg0rXYsCA2NZXY1EPmdUUZoag0Im/FAuQdGF1+F8g+yF2RBKYIe2ZVdVsUZR/Q11pZ4Z8m5K3sPZnYsxe1FeUYHPHsW7FiYM3evdQUFCTm/f77cXqj0Z48ZIh3D656ImiEeYhqIMFeZ8PUmQITtL0u9xVBUqLWoMLu8G2T6Cgj1NS2um4znDV+fhnCMjolCUwRKbTArAhOYB5x1nksf+MlqvLzsZWUHK/T6we53W5dVEJC1fArrvhowBlneEeckRyYNeDfmbJhxeWG8kqw2rTRZrSpxdFmwzPt8si8FQtQU1pCqbadWg3we2ir6XwkMEWk8O9azFYCM6X/AIadfS45K5ehMxhqkwYM2BjbvXtVr8mT9yYNHNi4C7wdSEebLRtp9/Cqwf8zZcNOXT3sK9YmAyUlaB2dmupwFGWE4vJW/2yEs0ajy1/Mqhq83Qq6CAlMESn82x6vlWeYoHX7MSUkkNi/f+Wo229f18xhbrTWeNFAYKacBo4WmAGaKRtW3G5tEo+1DrolHT7aNBm016yR/Y+HvA1yOzaQJDBFpAjATFnfuv247PZUDmzlFQWYOHhEWU9kbmTguSXbBQLTq94O+0u1pgbJCdqfAadLux1bVhXq6jqkrrqawqytAA7gxxCX0ylJYIpI4QlMPzYvcHqaF7jdB5YhNBKbkU5c9+7ojcY0tFuvxUCF56MWsKKNKmsIYWu8DvCMMCN3gku7uN3aGkvvaDM2GorKmr5NG0HyN63z7us536yqwWl+28VIYIpIoT3D9Ge3nxorxMWC06E913J5Rhsu7cOVV8CW994Dt9s16o47PvDfhcNG17kl2xSHQ2spF2WKyAYFh5LZsYEngSkihX+fYYIWmDXNz4uISUj03q4N8p5UQVMDYO+qgenVCcLSUV9HweaN3i+/DWUtnVkkPncRXZP/G7C3Ijo+Ab2292CKRVE62UJFwDPCdNZ3sVuyndC+LSpOez3AKrOqtrwpqWg3CUwRKfYD7rqqSlxBmvav0+mISWpYYtkjKBcNrmqQEWZn0Ki7z6wQltHpSWCKiGBWVTtQ7Ha7qauqDNp1GwVmZ7wt27kbF3QRLqeT/I3rvF9Ks/UAksAUkcT/S0taEZuc4v20044wu+ykn06iKHs7dTXVANuBra0cLjpAAlNEEv/vi9mK2KQU76edcYTpCUwZYUYy2fsyeCQwRSTxb3s8H8Qmd4Fbsp29NV4nJntfBpcEpogk2kxZfy4taUWjW7KdMTBlhBnhyvJyqC0tAe3vxsoQl9PpSWCKSOL/bj+tiOkSt2TlGWakajQ79luzqkZu1/gIIYEpIknwJ/3ILFkRxvLWye3YYJLAFJHE/91+WtE1ZslKYEaiqqL9lO/NA6gE5oW4nC5BAlNEEv/3k21FTFKS1pgdMi2KYgjahYNDbslGsLwNa72f/mRWVWnXFAQSmCKSNDzDdDezJZe/6Q1GouMTQPu7khmUiwaPzJKNYHI7Nvik+bqIGGZVrbUoSqXLYU+yW2sb9qsMtNjkFOqqq0B7jlkQlIsGhxVwO+12ncvp9PbNFcGS2U3byLod7LY60qdNJmP6FOfIG28cDtx7yCEFwIcdLVEcTEaYItIEvQl7Z11a4lnkrt2WlVGmX9Xb7TgcjpYPijaBzd6uj9Lt26jdtw+HzbYxKjFxB5B7yEen+rMaLmSEKSJNATDMWlFOcs/eQblgF5gpm+ioqyMqNi7UtUS0rLwcXvz6c2Ytnk9+cRE6dAzo0ZNLZpzEbedeRK/0DL9dq2TPTgAS+/df5beTilbJCFNEmqAvLekKO5bITNmOefvHbxl1nZkXv/6M3ML9uFwunC4n2XvzePKjdzny2kv5cdliv1zLYa/3zo5195w0aU1rxwv/kRGmiDRBnynbWW/JeshM2Q76ePbP3PCfx1s8pqyqknP//lfmPPcKU48e0+5r5Rbs5bK7bycvPxcX2MdkZU2YNWvWL+0+oWgTGWGKSBP8BuydOzBlpmwH7C8t4U///bdPxzqcTsyPPUy93d7u6xmNRv5yxpl8fN11fPKPf8yaN2/eyR9++GFwnk0ICUwRcUJwSzbF+2lnDEwZYXbAWz9+S43N6vPx+cWFfDH/9yZfW7VxA6POOx1bXR01tbUoZ5/KpqxtBx3TPS2NXnptSdXwE05Ynp6enr9t27Zu7f8ViLaQW7Ii0oRglmynnvTjCUxZ994ev61e3ub3/LpqOZefdNph3x931CjOnj6Tv7/wX6y2Oq446xyOHDr8oGMq9u3FWV+PMTY2b0VurmP//v0Drr766h3t/gWINpERpog0wX+G2WiEaVEUXdAuHByefrIywmyPXQV72/ye3fuaX8r7j1vuYPbSJaxWN3LvdTcd9nrpnl0A2JOS1l5zzTV3X3vttR8MHTrU9yGu6BAZYYpI065bso76OmpKiqkuLqK6uBBrRTmGqChMMbENH8aYGM/nnv/GxmKMjsEYHY0xJgaHzRYFpABlfv9VhU41gF0Cs10S49q+FKel95RWVFBtrcXucGCrqyO+0bFut4vSnF04nE6uf/HFERMmTFjy4osvyrKSIJLAFJGmDKiz26zRjvo6jFHRzR5YUZBP1sK55K5b3aFbuMboGJz2hluWCyyKUojW8Lq5j6qmvm9W1XCcWVMN4KyXW7LtcUS/gWzevatN7xnRr3+zr930yEM8dvvd7MrP5b7nnuGlvz/S8FpVSRF1NTU88euv9Wndu+/47rvvfmpv3aJ9JDBFRDGrqtuiKPuA/rbKChLSD27v6nI6yFu/hu0L5lCYtbXh+3qDgfi0dOLTMkhIzyAupRsuhwO7zXrgw2rFbrNht1lxeP5rr7MdervyqPbWblGUeloIVHwIXc9HjR/3PqwBGWG215Unn85XC+e26T1Xn3Jmk9//4NuvMRoNmM88G6fTycTLL2Lu8qXMOH4ioN2O3ZCfz+zNm6O6deumpKWl/Rvg+uuv/+yZZ55Z17FfifCFBKaIRAVAf2tF+UGBuX/7Fpa9/wa1ZaUAGKOjGXDcRIZMPoGU3n3R69v+yN7tcuGor/MEqu2ggG0I1SZe0z5sODz/tVtrcTmdUUC656Mj3BZFqaL14PXldZkl2wFnTpjMBOUolqkbfTr+4hNOZNTgoU2+dtU553PVOecDYDAYWPHp1we9XrpnF0f36cOeX399ot/JJ6sdq1y0hwSmiEQNu5aAFmqbZ//Ehu++xO12k9SjF8OmzWTgcZMwxcZ26EI6vb7hGScpHSvaabcfFKbaiNaKo+7A5w2vNYx4Dw1nG446mw5I8nx0dA2eA6TTT3sZDAY+/+e/mXj79eQW7m/x2KMHD+Wtv/29XdeprSjDWlGOzmCo6TV16tbW3yECQQJTRKKGpSX1tTUsfe8N9m5aB4ByylkcdeZ5YbnzhsFkwmAyEZOY1KHzuFyuQ0LWdviI96DA9b7eKIw9r7tdLiNIYHZEn8zurHnjI2589gm+XbLgsK3nDHoD15x6Ji/c+VfiYmLadY2i7O0AxKanrzHGxDg7XLRoFwlMEYkKACoLC/j53/+kpqQIU2wcE6/5E72POibEpQWeXq8nKjauw83S3W43OWtWseTtl+WWbAelp6TwzeP/YVvObr5dspCde/MxGgwM6d2HC6fNpE9m93af2+Vysn/bFgAyxoxp2wNT4VcSmCISFQDk/LGSuuoqUvv2Z8qNd5Dgx90gugKdTkd0vLanqENmyfrF8H4DuLffAL+es2TPTuw2K8bY2Nze06dva/0dIlAkMEUkKgCoq64iLqUbJ9z+1w7f5uyqjNHashwZYYavfVs2AZA8dOhsXTsmrgn/kcAUkUgBbULOpBtuk7DsAGO09kxNAjME6uwQY2rxkNqKChw6iO/du27QeeftAvr6ePbm2wmJdpPAFBHFoigDgIcBxlxwGRmDhoS2oAh3YIQpt2SDrrC0xZddTidL//c0hTu2Abx9zF13tbyHmAg4Gd+LSHMXkNDn6DEMm35SqGuJeN5OSTLCDD8bvv/KG5YFwGMhLkcggSkiiEVR4oFrAI46/Vx0us7WBz34vLdk7bKsJKzkbVjD5t9+BHACl5pVdV+ISxJIYIrIchmQnD5wCKl9m+/HKXxnMJnQ6XS4HHZcTlneFw6qiwtZ9v6b3i8fMKvqwlDWIw6QwBQRwbOt1m0AQ6fOCHE1nYdOp8PgfY5ZL6PMUKvcX8D8l5/Dbq0FmAU8G9qKRGMy6UdEitHAMdEJifQbMy7UtXQqpugYHDYbjrq6DjdDEO23548VrPjobW/XJRW41qyq7lbeJoJIAlNEilEAPY44EoMpql0nqKyp5plPPuTLBXPILy6ib2Z3LjnhJP56yRXEd7DnbCQ7MFPW/yPM75Ys4MPffmZnQT4ZySmcNXEK159+DjHRzW/L1tU4HQ7Wfv0p2+fP9n7rU+BGs6pWh7As0QQJTBEpBgHt7uazr6SYqX/+E1l5OQ3f27JnF4+89wZfzP+dBf97nbTkFL8UGmkCMVPW5XJx9b8f4aPZPx/0/V9XLeetH7/ll2deoHu3NL9dLxK53W72b9vM+u++omR3NoAd+Avwsowsw5MEpogUHQrM65557KCwbEzdvZObn3uKLx59qv3VRbCG5gV+fIb5lOX9w8LSa92O7VzxxD+Y/d+X/Xa9SFJfW8PO5UvYsWgulfsb+gvkAheZVXVFCEsTrZDAFJFCC8y0tgdmVl4OP69Y2uIxXy+aR35RIb0zMls8rjPyd3s8u8PBc59/3OIxv/+xkjXbtzJm2Ai/XDMSlObsImvRPHavWobzQO/efOB1tFFly50MRMhJYIpIMQDaF5gbsne0eozL5WLTruwuHpj+GWHuyM+lpLKi1eNWbNnU6QPTbrOxZ/VydiyeR2nO7sYvzQFeAb4zq6ojJMWJNpPAFJGiDsDpsLf5jTFRvk0S8vW4ziY6IRE4sCF3Rx26H2RHj4tEZbl7yFqsjSYdtoaRexnwPvC6WVVlE+gIJIEpIsVOYEB1cRFJ3Xu26Y3jjziSKJOJenvzYRsfE8voocM7WGJk8o7aq0sK/XK+Ib37kpqYRFlVZYvHjR0+0i/XCxeOujr2/LGCHYvnUbJ7Z+OXlqDddv3SrKrW0FQn/EECU0SKncCM6uKiNr8xPSWF2869iP/7wtLsMXdfdBlJ8QkdKC9yJaRrt6Friov9cr4ok4k7z7+ERw90qznMtKPHcNwRil+uF2rle/PYsWgeu1YuwW5tyMMK4APgDbOqbgpddcKfJDBFpNgJWtuw9nj6T3ewr7SET+b8ethr151+No9cc1PHqotg3pnH7f29bcrDV12PunsnXy6Yc9hrIwcMxPJwZG+84aivJ2fNSnYsnkfxzoOekS9HG01+blbV2tBUJwJFAlNEip0AVYXt60FtMhqxPPw4N5xxDl/Mn8PekiL6ZnTnkhknMWXUaL8WGmkaArOkCLfb7Zem9gaDgS8efYrP583mg19/Yte+fNKTUzh74lRuPvuCiG0UUVGQz47F89m1Ygn1tTXeb1cBH6KNJteHrjoRaBKYIlIsA9wFmzfpbFWV7d40esaYccyQ1noHiYqLxxQbh91aS111lV835L74hJO4+ITI3obNaa8nZ80qdiyeT1H29sYvrUIbTX5qVtWapt8tOhMJTBERzKqaY1GUH11Ox5k7ly1i5MlnhLqkTiW5Ry+Kd+1g31aVAeMmhLqcsFC5v4Adi+ezc/ki6msa8rAasKDNdF0TuupEKEhgikjyCnBm1qK5jDjxNPR62WzHXwaOn0Txrh1kLZzbpQPTabeTu/4PdiyaR2HWQSs/1qKNJi1mVa0KTXUi1CQwRST5FdhVU1I8sEDdQO+jjgl1PZ3GgOMmsHbWZxRlb6csL4fUPv1CXVJQVRXubxhN1lU35GEt8AlaUK6W/q5C15kXD4vOx6IofwOeyRg8lBP/8pBfJqgIzarPPiBrwRyGTDmB4y67JtTlBJzT4SB/wxqyFs1j/7bNjV/agBaSH5tV1T/dHESnICNMEWneAO4tys5Kz123mn6jZQKPvwydMoOsBXPYvXIpo8+9BFOEzmRtTXVxoTaaXLYI24HmClbgM7SgXCGjSdEUCUwRUcyqWmFRlH8Ar6yb9Tm9jzwGg8kU6rI6hZRefcgcOoLCrK1kL13AiJmnhrokv3E5HeRvWEfW4nns23JQHwEVLSQ/NKtqeUiKExFDAlNEojeBO6qLCo/YvnAOR3SiH+yhNvyEkynM2sr6776ixwiFlN59Q11Sh9SUFLNjyXyyly7EdqAhfB3wOVpQLpXRpPCVPMMUEcmiKKcBP5liYznr0f8Q42kgLjrG7Xaz/MO32LV8MYmZ3Tn1vkcj7tasy+lk76b1ZC2eR8HmjXDgZ9xWtJD8QLbSEu0hgSkikkVRdGizZk8aNv0kxl58RahL6jQc9XX89sy/KN+bR78x45h0/W0RMbmqprSE7KULyF66EGt5mffb9cCXaEG5SEaToiMkMEXEsijKUcA6nV6vP+PhJ9u8i4loXuX+ffzy9D9x2GyMufByRsw4OdQlNcnlclGgbiBr0Tz2qusbjya3o00Qe9+sqv7pKi+6PAlMEdEsivIGcGPvUaOZdvNdoS6nU8lZu4rFb76ETm9gyk130CeMeu7WlpeSvXQh2UsWUFvWcHfVDnyNNpqcL6NJ4W8SmCKiWRSlB5AFJMz483306GR7LIbamq8+YeucXwBQTj2Lo848P2QdllwuF/u2bGTHovnkb1qH2+XyvpSNNpp8z6yq/ttyRYhDSGCKiGdRlAeBJ1L79OOU+x+Vlnl+5Ha52Dz7JzZ89yVut5vuw0cy8dqbiU1KDloN1oryhtFkTWnD3VUHMAttNDnXrKqu5t4vhL9IYIqIZ1GUWGAb0Hf8ldczeMLUUJfU6ezfvoUl77yKrbKC2OQUJl1/K5lDhgfsem6Xi31bVXYsnk/ehjWNR5O70UaT75pVtX17vQnRThKYolOwKMrlwEexySmc+c+nMcXEhLqkTsdaUc7it1+maMd2dHo9A8ZNYOjUmaQNGOS3WbTWygp2LltE9pL5VBcXeb/tBL5DG03OltGkCBUJTNEpWBRFj7bb/bgjTz+HUWeeH+qSOiWX08n6b79ky5yfG2akpvbtz7CpM+k/7niMUdFtPqfb5WL/9i3sWDyPvPVrcDmd3pdy0JpUvGNW1b3++jUI0V4SmKLTsCjKZGCRwRTFWY8+TVxKt1CX1GlVFxeStWgeO5cupK6mGgBTbBwDxk0gtU8/EtIzSEjPIC61G3rDgYZibpcLa2UFNSVFVBcXUVm4j5w/VlBVuN97iAv4AW00+atZVZ0IESYkMEWnYlGUL4ALBx4/mQlX3Rjqcjo9p72ePX+sJGvhXEp2Zx/2uk6vJy61G3Gp3airrqampAin3d7UqfKBt4C3zaqaG+CyhWgXCUzRqVgUZTCwGYg69f5H6NZvYKhL6jJKc3aRv2kDNcWFVBdrI8ja8iY70BUDO4Fdnv8uA342q6ojiOUK0WYSmKLTsSjKf4C/Zg4dwcy77o+Itm6dkbWinJ+efJg6bQutT4GngF1mVa1s+Z1ChCfZrUR0Rk8A1xZmbU3LW7+GvsccG+p6uhyX08mSd171huUC4EoZQYpIJyu8Rafj2dfwEYB1sz7D6ZCf08G24YevKczaCrAPuFTCUnQGEpiis3od2FZVuJ+shXNDXUuXkrdhLZt//QG09ZOXSoMB0VlIYIpOyayqduCvAJt+mtWw9EEEVnVxEcvef8P75YNmVV0QynqE8CcJTNGZ/QjMqa+tYdPP34a6lk7Paa9n0ZsvYrfWgtaZ5z8hLkkIv5LAFJ2WZ3unewD39vm/U7lf7gwG0h9fWCjL3QPacpGrZXst0dlIYIpOzayq64F33S4X62Z9FupyOq1dK5awY/E8gDrgAs/EKyE6FQlM0RU8DNTkrV/D/u1bQ11Lp1Oen8tKy3veL283q+raEJYjRMBIYIpOz9O4+2mANV9ZGm8VJTrIbrWy6M2XcNrrAd4H3g5xSUIEjASm6Cr+C+SX5e5h18qloa4lvEWZIDqq1cPcbjcrPn6bqsJ9ABuBW+W5pejMJDBFl2BW1VrgAYD1332Jo64uxBWFKaMBMlIhsxskxbd46LZ5s8lZswqgCrjQ83ssRKclgSm6ko+BP6zlZdp+juJgOh2kpYAbsNVDciKkp4L+8B8TRTt3sPbrT7xfXmdW1e1BrFSIkJDAFF2GWVVdaMtM2Pzbj9SWl4W4ojCTnAAmI9g9Xexs9RBtgh5p2m1aD1tVJUveetn7LPj/zKr6ZUjqFSLIJDBFl+LpPPONs76eDd9/HepywkdsDCTGQ90he1XWO8Dlhu5pkBCHy+Vi6buvebftWgrcF4JqhQgJCUzRFd0H2HcuX+RdaN+1mYyQlnx4WHo5XVBXD6lJ7Nm9jcKdWQBFwMWeFoRCdAkSmKLLMatqFvASbjdrvv6ULr0nrPe5pdMFLf0+uKFsxw7Kivcx8Oyz3d3Hj7/VrKr5QatTiDAggSm6qseBsv3bNpO/cV2oawmd1CQwGMDhbPEwW00V2xfOwVpYSLcjjvh+5jvvDARGBqdIIcKDBKboksyqWgo8CrD2609xObvgdo3xsdpH/cF3VV1O50HNHVwuJ9vmzcZRZyM6NXXd4Asv/AwoBM4BTgFaX7QpRCcggSm6sleBrKrCfWQtmhfqWoIrygTdkpp8brn+kw9Z9N+n2PrDLOqqKtm9chnVRfvRR0UVj7j66lf0RqMbqAf2AKOAy4DU4P4ChAg+XZd+fiO6PIuinAPMiopP4OxHnyEqruXF+p2CXq8tFXG5tWeXjax661UcNhv9J08je85v6KNMkBAHOp1z4FlnPdJrypTsJs6YBpjQtvRq6nUhOgUZYYqu7jtgfn1NNZt+/i7UtQRHtyRtss8hYVm4RaUkazvH/el2eh0zhpHnX8R+dSPOujrSjjzyg2bCEqAEqAAuQRtxCtEpSWCKLq3xnpnb5s+mqmh/qEsKLJ1Oux3bxJ2llH79GX3lteiNRupqqslZtwq304nL6dw8/IorZu/+4YdeZVu3JjRz5nq0HkGVgSxfiFCSwBRdnllV1wAfuJ1O1s36PNTlBJbbDftLtG4+0aaDXoqKTyB9+BGggz2rl1NbXkZ0t27V8T16zKnctSvuj3//+/bq/Py4Zs7cB1gA7A7wr0CIkJHAFELzEGDNXbuawh2dvC2q0wVFZVBVAzFRoNc1vGQwmdi3VaUwezs6vb4udcSIb0s2bRo6/+abb88YM2Zh35kzC5s4Y3dgJ7AiWL8EIUJBAlMIwLMI/xnoQntmVlRrwWkyaLuUAFXF+9m1Ygk6nY7McePeSB4yJLt006ZTolJSiqa++OIvTZwlEbADPwEtL+YUIsJJYApxwH+AgtI9u9i9enmoawkOWx3sKwGXC7vOzbZ5s3G7XCT06fPbkAsvXDbyuuu29Zgw4YtTPv303SbebURbTjILqA5q3UKEgCwrEaIRi6JcA7wbl9KNMx95CmNUdKhLCgq3283GJXNwxUXjsFp3Hv3nPz9iio93ALjsdp3eZGrqB0V/4DdgTVCLFSJEZIQpxME+ANbWlpeydc6voa4laDb/+gObLB+yd9GiihFXX/2ZKT6+YUFqM2HZC9gMrA1akUKEmASmEI0ctGfmrz9grSgPbUFBsG+ryvrvvwJwl2/bdmlC797/87zUs5m3pABVaKNLuUUlugwJTCEOYVbVecB3jvo6NvzQuffMrC0vZcm7r3nXZT5uVtVfgP3A+2hde/oDhkZviQIS0J5b2oJbrRChJYEpRNPuBRzZSxdSlpcT6loCwuV0sOTtV6irqgT4HU8zeg8r8D0wB22NZTygQ7sV+yNa83UhuhQJTCGaYFbVbcAruN2s7aR7Zq6b9QVF2VkA+YDZrKqHLgtxAauAj4FYYAiwGu3ZpRBdjgSmEM37F1C+b6vKXnVDqGvxq9y1q9k65xcAB3CxWVWLWjoceA/4Ba2bjxBdkgSmEM0wq2oJ8Bh498zsHOvyKwv3sfzDt7xf/s2sqkt9eFsVsBKtZ6wQXZIEphAtexnIrty3l+wl80NdS4c56utY/OZL2G1WgC+B/7XyFiGEhwSmEC0wq2od2gQgNvzwDfXW2hBX1DGrP/uQ8vxcgCzges9uLUIIH0hgCtG6b4BFddVVqL98H+pa2i17yQJ2LlsE2gzYC8yqKltxCdEGEphCtMIzCvsLwLa5v1Fd3NL8mPBUlruHVZ994P3yZrOqbgxlPUJEIglMIXxgVtXVwIcupyPi9sysr61h0Vsv4XI4AN40q+oHrb1HCHE4CUwhfPcQYMtZs9K7fjHsud1uln/4FtVFhaD1fb0zxCUJEbEkMIXwkVlVc4FnAdZ89UlENDPY+vvP5K1fA1AOXGhWVWlnJ0Q7SWAK0TZPA/tKdmez548Voa6lRYVZW1n37RfeL682q+rOUNYjRKSTwBSiDcyqWg38HWDdrM9x1HdsHX9lTTU/r1jCp3N+Y13WNn+UCIC1opzFb7+C2+UCeNqsqt/57eRCdFHGUBcgRAR6D7iztrRk1LZ5v6GccmabT+B2u/n3x+/x5EfvUqM1EQBg3IiRfPDAo4zoP6DdxbmcTpa88yq2ygrQWtn9vd0nE0I0kBGmEG3kaVJ+D4D6y/fYqtq+nPHBN1/mobdeOSgsAVZt3cykO24gZ/++dte34YevKczaCrAPuNSsqo52n0wI0UACU4h2MKvq78CPjjpbm/fM3JGXy9OfNL+yo7SygvvfeKlddeVtWMvmX38AcAKXmFW1/ckrhDiIBKYQ7fc3wLlj8XzK9+b5/KavFs5tdYbtN4vm49KeP/qsuriIZe+/4f3yQbOqLmzTCYQQLZLAFKKdzKq6BXjNu2emrwpKils9xlZfR1kbbvU67fUsevNF7Fqv22+B//j8ZiGETyQwheiYR4GKgs0bfd4zs09GZqvHxMXE0C0p2eci/vjCQlnuHoCdwDXSVF0I/5PAFKIDPBsvPw6+75l50fQTMegNLR5zyQknodPpfKph14ol7Fg8D6AOrTlBuU9vFEK0iQSmEB33IrCroiCf7KWtPzbs36Mnj157U7Ov90xL58kbbvXpwuX5uay0vOf98nazqq716Y1CiDaTwBSigzx7Zt4H2pIOu9XayjvgoSuv4+W77iU9OeWg7580djzLX3mXHmnprZ7DbrOy6M2XcNrrAd4H3m579UIIX+kioR+mEOHOoig6YDEwceQpZ3LMORf59L66+nrWZG2lorqa4f36M7Bnb5/e53a7WfL2y+SsWQWwETjerKqRvbu1EGFOAlMIP7Eoynhgud5o5Kx/Pk28D6PE9to27zf++OJjgCpgrFlVtwfsYkIIQG7JCuE3ZlVdAVhcDgcrP33f28fV74p27mDNV594v7xWwlKI4JDAFMK/7gNKC9QNbP7tR7+f3FZVyZK3XvaG8f+ZVfUrv19ECNEkCUwh/MisqnnAFYB7/fdfsW+r6rdzu1wulr77GrXlpQBL8Uw0EkIEhwSmEH5mVtWfgcdxu1n0xovkrlvd4XPW19aw+M0XvQFcBFxsVlV7h08shPCZTPoRIgAsimIAPgYuATjixNM4+pwL0RvavqNeWe4eFr31EtVFhQAVwNnSJ1aI4JPAFCJAPEtN7gKeAYypffoxfMYp9BtzHMaoqFbfX1GQT9bCuWQvXYDTbgdYi9bJZ2cg6xZCNE0CU4gAsyjKJOAzoDdAVHw8g46fQlr/QcSnp5OQnokpJpaa0mJqiouoKiokZ81K756WXm8Bd5hV1RaCX4IQAglMIYLCoihxaLdnbwOO9fFtNcBHwCtm1cfO7kKIgJHAFCLILIoyDrgIGAwM8nwkALlou43sAlYDFrOqVoSqTiHEwSQwhQgxz7NOvVlVW9/qRAgRMhKYQgghhA9kHaYQQgjhAwlMIYQQwgcSmEIIIYQPJDCFEEIIH0hgCiGEED6QwBRCCCF8IIEphBBC+EACUwghhPCBBKYQQgjhAwlMIYQQwgcSmEIIIYQPJDCFEEIIH0hgCiGEED6QwBRCCCF8IIEphBBC+EACUwghhPCBBKYQQgjhAwlMIYQQwgcSmEIIIYQPJDCFEEIIH0hgCiGEED6QwBRCCCF8IIEphBBC+EACUwghhPDB/wPyYNOYdpFnJgAAAABJRU5ErkJggg==\n", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -300,7 +291,6 @@ " 'edgecolors': 'brown',\n", " 'facecolors': 'pink'\n", " },\n", - " **kwargs\n", ")" ] }, @@ -311,7 +301,7 @@ "## Node colors\n", "Pass an array of matplotlib colors to configure the individual colors of each node. The order of the array corresponds to the order returned by `H.__iter__()`.\n", "\n", - "In this example, we make the nodes that " + "In this example, we color the collapsed nodes red that are larger than 1 node:" ] }, { @@ -321,28 +311,33 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ + "from hypernetx.drawing.util import get_collapsed_size\n", "H_collapsed = H.collapse_nodes()\n", "\n", - "hnx.drawing.draw(H_collapsed,\n", - " edges_kwargs={\n", - " 'edgecolors': 'black'\n", - " },\n", - " nodes_kwargs={\n", - " 'facecolors': ['red' if len(v) > 1 else 'black' for v in H_collapsed]\n", - " },\n", - " **kwargs)" + "colors = [\n", + " 'red' if get_collapsed_size(v) > 1 else 'black'\n", + " for v in H_collapsed\n", + "]\n", + "\n", + "hnx.drawing.draw(\n", + " H_collapsed,\n", + " edges_kwargs={\n", + " 'edgecolors': 'black'\n", + " },\n", + " nodes_kwargs={\n", + " 'facecolors': colors\n", + " }\n", + ")" ] }, { @@ -360,32 +355,30 @@ "outputs": [ { "data": { - "image/png": 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ex985pofm/XBhM53HAAwMrWC0gdZxLz7XOCN3Degzt2utqzqJed4RX09rrW8Cfg94jnK5vN2AXZ2WiclIYAohJqW1zgOvBx60scLP8/i7xvVIrMrNmlSSsXaARlpH1qvrjq9l654GWo4COkv6xh6Ov62ki75qtc+Fe2KVbEwppbTWJ7TW/6m1/j3glcCearVNXJoEphBiSlrrLPAa4DGLUuQ5HntXQo9Fq92uS8mQageI0JCEcrGABtXS10b3HoVRKlFc0cvJX83qVEs12nf+mZj6gj0uWutsZThczDMSmEKIadFap4FXAE9alGLP8ei7Uno8XO12XUyeTDtAlMb0+a8HVSTRxbJn3LjTDnbDAD2/HNcjc14ByP3CiSUyV1lDJDCFENOmtU4CLweeLVFseJZH3pnWyWC123U+S1tmgUILQITGzIXve5S30Mny3X6CQ6A9cYbeNKh7XjqX85oeOROzJklgCiEui9Z6HLgDeL5EoflZHn5nVqcCVW7WOWMMtoA2fPhzbuW56OIZU5lOO4sPRmk6AZAheUsvJ+6xdGlOCgZ48UkPswZJYAohLpvWehS4HThQJN/6DD95R06nLzwppCrGGW0HCBBJT3adUoom1Xa2la69CmUVKazt5cQv53Rm1lcB+whID7MGSWAKIa6I1noIuA04WiDfvoufvD2vs95qtytDoh0gRHTSwJwQUtGxTpY968KdtbFbBjjzKwk9umI22+jBV6B8xFdYKeWazWeJmSOBKYS4YlrrfuBW4ESBXOcuHnp7QeeqWgc1W1nwEyE2rcAE8Cpfrotlz/oIjGq0b5TBtw3p3htnq0i7oQxtYBYqfxublYeIGSeBKYS4KlrrHsqheSZPtnsXD721qAtVORhZa02BbGWFbNO0AxPAVC67gyX7IjScBlSaxO19nLrL1vas9AANOeKr5khgCiGumtb6NOXQ7M2RWbKLB99S0sU5H2pMMBZzcLwu3CUfgeLlfl4pRbPqONVMx35QdoHcxh6Ov6egczNe23WyI77E/CSBKYSYEVrr48DLgMEs6WW7ePBNli6Zc9mGOMOVBT/h9NUc4RVRDSOdLNlt4srbWB19nPrVpI4vnrGGAuZ5R3zN5H3F7JHAFELMGK31YcqhOZIhtXIXD91taWvOQjPFeGXBz+QrZKfDpwKZLpY948Uf1+jgCP3vGtH926++lWXSw6w9EphCiBmltd5PefVsPE1izTM89AZb23PyXZMl1QYQvowFP5NxKbfVwZK9IaI9gJEk/so+fepVjnau+pcAE9e5erJXey8xNyQwhRAzTmu9h/I+zUSK8Wue4aHXO9qZ9Uo6+cqCn8gFJfGuhqEM3aq6jjfSdghw8mSv7eH4u4q6cFUVjs47sUR6mDVCAlMIMSu01s9QLqOXThLf8Aw/ec1shmZeZ30WpZjCcMLEclN/4vLEVNNgO0ueMzALFqVFfZz81bROdE79yYs778QSCcwaIYEphJg1WusngDuBbILRLbt55JWztbdxlIE2gAChjKGMWXlIQAVTXSx71oM36eBEhuj9xTE9uOlK7iU9zNojgSmEmFVa60eBVwP5OMPX7uaRO2cjNBPE2wGChGdsOPZi3MpT7GTZc0Ei/YBrnNHX9+szdzjauazv0/OP+Jr5VorZIIEphJh1WusfA68DimMM7XiOx+6Y6dDMkGyHmVvwMxlDGbpNdR+ZOJQ6R/qGXk68raSL066n65EjvmqOBKYQYk5orb8HvAEojTJww/M8futMhubEGZhhGmY9MCeUD6VetMcoH0q9vJeTv5LVqdbpfFaO+Ko9EphCiDmjtf4m8CbAHqbv5n08+dKZuG/lDMxWgOhFzsC8UlprZWvLO9k2kqAKJzpZ9owbz3mHUg9fM9W9PXLEV82RKvlCiDmltb5fKfU24POD9Nyi9FPWBrXjsau55xiDzaAN7yRnYE6HpUuBArkOGzvkYPs02vdCwykZmHkDM+vBO+TBOzZRTcijvIVOvWz3EL1rcqRb4wzfU9T5h1vpfkgpddFutA+/9DBrjASmEGLOaa2/qJTyAJ8e4MxthjbsdWr7E1d6v0TlDMzgFVT40VqrIvnmAvlOGyt24fsKZWu0CbgdbLeDHbYotuXI5D3a2+fD328o0zKV6bTrRQfHGEonGF2eIfWSIXp1G90PXey53hfOxIwqpUyt9RUHvZgbEphCiKrQWn+mEpof6+PULxjasNeqbU9fyb3S587AvLzAtLXlSZNc72BPFFfXHrxpN96cgVkyMS2lFFprHGzTwXFZlHwFciGN4yuQW14gtyioIwc9yhtXStFE21mP9maH6duQIfnShB49G1VNxy98tqEMbWij4OB4gSgwdiU/u5g7MocphKgarfXHgV8H6OHEKw7r57ZdyX1eOANz+gt+irrQkCS+3cGOKJTtIzgWpelsUEXGPMqbcymXNTHkqpTCVC7brTwFvwomojT1BgkPmbgKgDtDclNWp5dMLGIKq9hohMZTAGMMveFSp50YUk+2pkhgCiGqSmv9YeADAGc59qqj+vnNl/n5887AnF5JvJzOdmRIbgLcJu58hIY+vwqkplvwQCmFR/lyYWIDXvwJgAK5pWkS6ydCs4m205XC7f5Beu6+2MIh84UzMWPTea6oLglMIUTVaa3/BfjfgDrNkdce1/vXT/ezScaiDo6vfAZmcMozMEu6EMmTWQXgxZ8IEx00lOlcSbuVUgRUaDxIZAiUY1FqzpFZOvFeG90HzXIpve4R+nde+PnzCrBLD7MGSGAKIeYFrfX/A/4UUCc5eNdJfXDKrRkAY5dxBqajbXeG1DpAefClAio0fjXnZk7wKG8uSHgYoEBuSVHnGwBcyl1qpO0oQJbUdVrrFz1MzsSsLRKYQoj55C+BvwaM4+x/wyl9ePVUH7icMzDTJNdqtNfAVQgQmtFFNh7lzXvxjwNkSK2zte0utys6amLmHZxYgrGV539Gepi1RQJTCDFv6PIE4AeBfwTMY+y754w+umKyz2RJTaskXkkXQzZWI6BDhIdnomd5IT/BRCUEXQVynVAemg0R6wNIMX7d+de7pIdZUyQwhRDzSiU0fx/4d9DmUZ5/c48+vuxS1xfINwOEiU1a4WciwDz40qZyzcqeR6UUfgKVRUD5zokh2CiNA4AuUViV19nYxPVyxFdtkcAUQsw7ldD8LeC/Ndp1mOfe0qtPLr7wOkc7yqLYABAkkr/w/fOuc5UotgF48Sen1waHEd3vO6OPhvr1mUBRF6b1fenCk1cYJdCeIvkmKM9l+gkOA6QYX33etdLDrCFSuEAIMS9prbVS6n2AR6PffYjdbzO08ZkOtaRn4poMiZBGmy7cpclK4hXLvVDDxJV3KZc11bNH9aD3ALtaCuTObQUxMPRSvXZ8GdckJxvOVUrh1b5UnmxjkUKbF/8IlHu2OTKtFqVz4Sg9zNoiPUwhxLyltXaAXwY+r3E8B3nmbYO6p2Pi/STjDQDeF+qyXpRFKQLgfqFHd0njetTzHI+1nx+WAA6OOsGBhhPsj051j4mzLh3swIWv2VjnwlFOLKktEphCiHmtUmP1XcCXHRzffp56x5DubQPIkmoA8BGYNAht7BCAC9eU+zSPsKdRc+ltmac4HCvo3KTfnQamBeDg+CYKGUyEtY19LhzdciZmTZHAFELMe1prC3gr8ICD49/HU+8c0f0tObITgXnJHqbWWjnYQQAT96SBWdR5I8mYd9K2oBmiLzDZNeWKQcoBDAfHAy/0Jm3shokQ9eKTHmYNkcAUQtQErXUJuAf4joMd2MuT78yTaQTw4L1kENpYAcBQKMtQxqQVfQrkL3nu5fmK07jOQNkADra3/PemBWjQbofyHk2Tc/Op/uk8V1SXBKYQomZorQvAG4Af2lihFInVAAVyl+wVWpRCMHXvEsBP0IKp92eWr5su5QDYWG5AKVTeVK4SQIbkxHzomenfT1SLBKYQoqZorXPAa4GHdfloLHJkgpe63saqBKY5ZWC6lFs30Zad7BoTl26la9JrAHQleVUlMIsU/AAGZnzimgzJiaHYk1PdT1SfBKYQouZorbPAq4C9UF78U9KFi/YybewwgGsaPUyAtWwZc+O55NDtGraMupR7Gqea6EpgllcQlSj6AExc5wJzYg4WODGdtonqksAUQtQkrXUKeB1AiaLq4/Tmki56LrjmvB7m1CtkAfwqZF/Hrf0NtLxo5a2foLWR64c61dJJKwqde/a5wCzPZZYo+gFc5wVmgVys8kcJzBoghQuEELXsJJB2cEJFCv5+Tm/u1Eufcyl3CcDG9gGmQjmmMqddDi+gQta1vHQor3NmlpTLg9cOErEus/5spUNSHpItUD5E2oXnXNH3InnpYdYQ6WEKIWpWpYTeCQCLYtyiFOjj1GZLl1wANqUwgImrcCX39ym/3ahaCyEVvaywnNg2AuU5zKLO+wrkGgE7SsMhAFtbRoF8S+UymcOsARKYQoha912AFIl+A3PEohTs5/RmW1su6zIW/MykieFYQCulSDDWCeDFv9+tvFmA0xxZ42D7gUPA6blsn7gyEphCiFr3X4BOEl8To/k+A3OsRDHUx+lNFsVKD3N6C35mzrnAtB1tG2mS7QARGp6euGKAMxNHfX1Yn98lFfOWBKYQoqZprU8A3wFt9nJ8dQeLP21gjJcohMcZjWn0tErizWibzgWmcpLEWzWO28Q1ECLaAzCqB5qzpJcBWeB/5rJt4spJYAoh6sF/AAzTv92NN9XO4k8rjJSNpdIkoLJSda5M7MF0sHWc4ZUAIaJPTMyDnubo9sqln9VaJ+aybeLKSWAKIerB94CTFqXYWY6t9KnAeITYgwAWJQY42+po57KWuF4drTSaNOMejTY9+A400roHoKgL7nGGt1Qu/M+5a5O4WhKYQoiaVznR5CMApzl8e1EX3BZWuPK2UyDrG+Rsi6MnLSU7c+1BqywpbGxlYI62s+iBid7lfp661SlXKHpca/3cnDRIzAgJTCFEvfgwcLhIoWUPj72qSKGy0KbxtMKwcmT8g5xptbU1qz1NrTUJRsNFCgC6hc77XMpdADihD6wbZfB6wAJ+ZzbbIWaeBKYQoi5ordOUC7NnE4xtGmNgCUCIyGg7i/aUQzPr7+VkZ17nPJPf7crY2lIDnGlJEp+oLjQcVOEhgDE91HSSQ6+pXPq7WusnZqMNYvZIYAoh6obWej/wqwDDDAQK5BwPvqxfBdOdLH3GjTttUXL1cao9ocdCM/nsvM65eznZmSMToFI/1qwUWi/qgnsfT91TKRZ/H/DvM/lsMTckMIUQdUVr/TngAdAM0qtKFEwAr/Llu1i+O0C4H7QaZaBpQJ9pyemM52q2QZZ00RzVg7E+TnVYlFxu3OkYTScBFKqktaY8RJxvBQ4Dvyz7LmuTkn9vQoh6o5T6feDvALz48xvZuT+mmtMT74/r0fYxBldR6TS48RYjNKTCRDOGMqf8UtRakyPtSxAP50gHJl4PEB5opfNoknjbGEOrXbj39nHKnSaxlvKeyx2VXrCoQRKYQoi6o5T6LPA2F+6ERSmqUM4KNhxdqtYMTFxT1AVfgtHONMkOjeOCct1XD/6iG7flwm258ZRcuG0byyhRclmUXBZFd4mC28KaOLxC+wmORGjoDapIAmBMD3UN0rNykJ6CRdELJIC3aK2/M9f/LMTMkcAUQtQdpdR+YN01bP9EP6e2jDOyDaCFzoH1XHfUpdzn9pc42jZSjLckGe8sUYhM9xkGZjFEtC9KY79beV5USeiQ3r2uhxMtoAGeA96otT4+Iz+cqBoJTCFEXVFKBYAUwEt5zd+4lcc6qp/ffIajr9Jol59QZjWbjjXTMX7hCSQFnfcVKQQsir4SRb9FyWdj+QzMkgt3zoU778aTd+PJefFnDGW86As0rRO+Y+xbOkJ/W+Wl3cBNWusXna0papOchymEqDcbAcODb8itPBbAKrVpT0w3Dxxg1z050o17+OlmH4Fsp17a182KAY/y2lBeGOTFl7+chznaYZCexh6OdyUYbZx4mfL86JclLOuHBKYQot5sA/AT7D//xRbVObhT3/7fR9hzwygD1+bJhk5wYOUpDi1r0Z1D3azoi9CYMae56CdL2tvHydZ+znQWyfsAFMqO0LhPo1WSsU2AhGUdkcAUQtSbrQBBwgMXvuFT/sImrn/I1tbDpzmydoAz12VJLx2kp2OQng4Aj/YWvPjzPgI5H4G8n2C+SMGdJ+PPk/XlyfkK5H0a59y2PBfu8RY6n17GNbsDKpTbpR96deUtCcw6IoEphKg3WwGiNPZf6gJTuZzlrDuwnHUHRvRAyxmObE8SX2NRihQpeIsUvCnGo5M9xMDMBgj1dLDk6UWsPH7+fObEqlskMOuKBKYQom4opdzAJoAm2n+mh3kxzap9uJn27wDfsbRlpohHU4w3ZEnH8mQbiuSjLtxZH4FxP6F4iEg8QkPcq/yXPGPTwXFX/iiBWUckMIUQ9eQawOPGE/epQOFyP+xSLruBlrEGWsauphGO9DDrkpTGE0LUk60APgKXHI6dC1p6mHVJAlMIUU+2wcUX/Mwl6WHWJwlMIUQ92QoQmWTBz1yQHmZ9ksAUQtQFpZQBbAFopK2qgSk9zPokgSmEqBfLgbCJKx1SkUw1G6LR0sOsQxKYQoh6sQ2qv+AHpIdZryQwhRD1YitA+YDo6pIeZn2SwBRC1IvKgp9YVVfIaq2l0k+dksAUQtQ8VT6naxtAAy1V7WHa2CagAFtrXapmW8TMksAUQtSDTqDFwMhHaRqvZkMsitK7rFMSmEKIejBR4WfgwkOh55pFSeYv65QEphCiHlTOwAxVfcGPRUl6mHVKAlMIUQ+2AoSJzoPAtKSHWackMIUQ9WArQIyWqq6QBbCxpIdZpyQwhRA1TSnVCCxRqFIjrSPVbo8tPcy6JYEphKh1WwG8+IcMZehqN0Z6mPVLAlMIUeu2AvgJVn3+EqSHWc8kMIUQtW4rQGgeLPgBsLGlh1mnJDCFELVuG0CMpqov+AGwsaWHWackMIUQNUspFQTWAE4jbUPVbg+AIz3MuiWBKYSoZZsA5cE37FYeq9qNAXCkh1m3JDCFELWsUuEnOC+GY0HmMOuZBKYQopZVFvxE5sWCH5AeZj2TwBRC1LLKGZiN8ygw5SzMeiWBKYSoSUopD7ABoIn2wSo35xzpYdYvCUwhRK1aB3jceMZ8yl+odmMmSA+zfklgCiFqVeUMzPlR4WeCxpEeZp2SwBRC1KqtAEHC82aFLEgPs55JYAohatU2gAgN86qH6UgPs25JYAohao5SygA2AzTRNq96mFp6mHVLAlMIUYtWAiETVyqoIplqN+Z80sOsXxKYQohaVKnwE5hXw7EgPcx6JoEphKhFWwEC82zBD0gPs55JYAohatFWgPA8W/ADoNHSw6xTEphCiJqilFJUArORlqoH5pDubS/pogvA0Y5ysL2Vt+5SSoWq2DQxwyQwhRC1phtoNjDzERoT1W7MIZ59Q4ZkGOAIz23X6Ikh2VcAf1s5s1PUAQlMIUStqVT48feXO5vVZeLK+ghmAYbp33LeW68AbgKkl1knXFNfIoQQ80plwU+o6sOxABalcA/H1gR0OH7ecGxea62VUg6Qr2b7xMyRwBRC1JptACFi82KFbIDw6UF6t2kcw8SVLVFsAnKV01QsYN4UhhdXRwJTCFFrtgI0zIMFPwDXqZ//+sSfk3os8hQ//gDlFbIKeL3WWnqYdUICUwhRM5RSzcAihSo10DJa7fZcyMI6twdTa10A+qrZHjGzZNGPEKKWbAXw4h80lKGr3ZgLWZRkD2Ydk8AUQtSSrQD+ebLg50L2eT3MqjZEzAoJTCFELalU+InO18CUHmYdk8AUQtSSrQBRmubFCtkL2djSw6xjEphCiJpQKTO3GnCaaBuqdnsuRnqY9U0CUwhRKzYDyotvyKXcdrUbczGO9DDrmgSmEKJWVBb8BOflcCyAjS09zDomgSmEqBXbAIJE5uWCH5AeZr2TwBRC1IqJBT/zNjClh1nfJDCFEPOeUsoLrAd0E+2D1W7PpTg40sOsYxKYQohasB5wu/GMeZWvWO3GXIojPcy6JoEphKgF837BD8gcZr2TwBRC1IKtAEHC83b+EsDBkR5mHZPAFELUgq0AERrne2BKD7OOSWAKIeY1pZQJbAFoom1eD8lq6WHWNQlMIcR8twoIuHAnAyqcrXZjJiM9zPomgSmEmO+2AvgIzOvhWJA5zHongSmEmO+2AQQIz+vhWAAtPcy6JoEphJjvKgt+YtLDFFUlgSmEmLeUUopKYDbSOu8DU3qY9U0CUwgxny0GGg3MXJiGZLUbMxWNlh5mHZPAFELMZ+cW/JQ7m/ObrJKtbxKYQoj5bCtAgNC8H44F2YdZ7yQwhRDz2VaAMLH5v0JWazRaeph1TAJTCDGfbQNooHne9zBtLLPyR0trbVW1MWJWSGAKIeYlpVQL0KUwijFaxqrdnqlYlKR3WeckMIUQ81VlwY9/0FCGrnZjplKiJPOXdU4CUwgxX20D8BOc98OxALb0MOueBKYQYr7aChAiOu8X/ABY0sOsexKYQoj5aitArAYW/ADYWNLDrHMSmEKIeUcpFaF8rJfdSNtwtdszHRaW9DDrnASmEGI+2gzgxT/sUi672o2ZDulh1j8JTCHEfLQVamfBD4CDLT3MOieBKYSYj7YCBInUTGDaEph1TwJTCDEfbQOI0lgTK2QBbGwZkq1zEphCiHlFKeUF1gG6mY6aCUwZkq1/rqkvEUKIObUBcLnxjniUt3QlNyjqQqBArrFEsaFEscGm1GBjhw3MjAvXuAtP3I0n7sU35sGXmomjwxzpYdY9CUwhxHxTqfATuKzepaMdI8HomjSJ60oUl13qusIFf2/iGgjpyFMxmveZynVFAQ0yh7kQSGAKIeaby1rwU9T5UJyRbVnS2zVOuPKy48aTdeHOuXDnK/9btLHcJUo+i5LfouSzKAZsrPYEY69JEr/DpwPPxWje5VfB0ctttPQw658EphBivtkKEKFxysAc04Mbxxl9NeAGMHHlwsR6ozQOmso15RFbjraNFOMtScY7SxQiOTLX58jsDOrIg610PaqUmnbRd0cOj657EphCiHlDKWVSKVrQTNslh2QdbZsDnH15nux2AC/+sShNZ4OExy9nPtJQphOlaTBK02BOZ0IJRruypNszJG/tobCoXS+636080wpA6WHWPwlMIcR8sgbwu3An/Cp00eDJ62xskJ67baxOQDfQejRGU//5Qam1o4oUmmzskI3tc7B9GserMEoGRt7AzJmYWQ++EUMZNoBfBdN+godTOjE8Qv81JQqrejnx3mbdcV9IRfumarj0MOufBKYQYj6pnIEZuOhwbEKPLR9j8G6N9pmY+Va69/tVMD3xvq0tb55cZ5F8B5Vh2vPpcniGoby2J0fGcWvPoJdAr1u5MwBhFR3zat+uQXrWlyhEh+h9j6Odz0ZUw6nJGq5xpIdZ5yQwhRDzSWXBT/hFw7Faa0bovz7F+B2A8uIfa6P7oEu5LSivkM2SXlGi0DnxGQOj5MaTNTAtE9NSmJbGMR1sl43tsij5bCxfiWJHiWKHS7tGg0QOGcq0PMpb6NLLdg/RuypLqmOUwTd6tf8jXuVLcwnSw6x/EphCiPlkG0CYhnM9TFvbrgHOvLpAblPlvdPNtJ+aGIK1teVLk1zvYIcA3HgyXvwpF+7CReYzLV7YWZKwtOUukA0XKYQsrKYk8e1BHdnvVp6UoQzdpruP9HHSXyAfG+Ts3d16xacNZTgXa7gjPcy6J5V+hBDzgiqn21aAJlr7AQo6F+nh+C9WwtJppuNAi+o4F5ZFXYgliV/rYIcUygoT6w+p6IhbeS4Wlj/DpVyloIqMRWjoNTCLGu1Nk9ia19n2SptoZdEBA7NoUVo8RM+tl7qXlh5m3ZPAFELMF0uAmImZDRFLpfT4oj5O/6qN1Wli5jtZ+mxENZw7G9PSli9DcgPgcuHORWjsdyl38UoebCqXHaGh34M3BagcmTVFXYgBuJW71ELnAUBnSd+U1GMXLYogPcz6J4EphJgvtgF4CfSPMrhtmL53a5ygF994F8uf9alAZuJCRztGhsQGwHThzoaIDl1qqHS6lFIEVWTMiy8BkCG5zta2ByCowokIjWcAEsRvuNjnpYdZ/yQwhRDzxVYAL75gkrFXA0aIaG8HS593KfeLStZlSa10cIIKwwoSGZmJWrAT/ITGTVx5wJ0msU5rrQBiNPUCTonCqrzOxi78nPQw658EphBivtgJ4MbTDuhG2g63qq5jhjJeVG3H0qVAiWIHoENEhi98/2oppQgRGVYo28GOFsk3AbiUuxQgNAwwzsi1F35Oo6WHWeckMIUQVaeU2gbcCuAjUGpn8e6YarpopZ8CuU4AD97Mlc5ZTsVQpuPFnyg/L9818XqExj6AHJltjrbN8z8j+zDrnwSmEKKqlFJvAR4FTIAlrH4moEKpi13raMcsUmgH8OJPTvcZli6ptE64i7ow7e88L/4MoG2smKVLAQA/waQbd1qjA0niK1/UNulh1j3ZhymEqIpK3di/Bv5g4jUP3oJX+S88geucIvkWwDRxFS6c17yYnM6Yh9ndOMJgAMojt1HdWFjN5rGoapq0d2oow3Frb6ZEIVQg1+HCfVwphVcHEiUSoSKFpolrHe0o0Cblh1yy/aK2SQ9TCDHnlFINwDcph6XTQOvTAF4Ck/bObKyJ4gTZqZ6R1WnXU/yoc4SBc2EJkGDMu4ufdIzpQe9U9/DgzZafWy6KAODCnQOwKDVMvGZRmuh85LXWMzqnKuYPCUwhxJxSSl0DPAm83MDMrmXrZ4KEhwB8+POTfdbB9gEYmFMe3XWQZxpLFC/6Hadx2M+uZq0n34liYJbOfy6AG08ewMY6LzCLMn+5AEhgCiHmjFLq1ZTDcpUX3+A2XvLRbrXiVJ5sA4Cf4KSB4+D4AcwpAjOvc2acYf9k1xTIuUYZ8k12jUn5JBON9k1sL3FTPu7Lxr5YD1MCs45JYAohZp0q+yDwdSAcpmH/Tm7/eEw1jQMUyUcAfAQmnf9zcLwABsakgZklNa31GVNdp5ShFcouP9v2ArjxFCp/H524rkTJc+6Wom7Joh8hxKxSSoWATwFvAHQ7i3+0nusePb/YgFmZFyy9MLR58XuhLI02NdoALjme6sE7rao/7mlcVz7lxDJtbK+JK29XDopWGOfCMUNyIjx7p/NcUZskMIUQs0YptYxyr3KjwiisZP1Xlqg1Ry+8zoc/DpAjM+kQqcIoaGyvg22auC7ZywwSKfkIWHmyl/yOMzB0E22TzpkCmBiWA97KPGaiRMFXft2MT1yTJR2r/PHEVPcTtUuGZIUQs0Ip9TJgF7DRjXd0Kzd99GJhCRAgFAfIk500MA2MynCoM8VQqmIVG8cmu2YJaxIeNXUP06jMY04MB5coVgLTNT5xzcQcLHByqvuJ2iU9TCHEjKoc0/WbwD8CZpDI0a383Fd86tLzk0Ei4wB5cpMu1DEw8gAO9pTfXW1qUc7S1sgR9jTZWOfGfxWKJawZX866xLR+nsoCo4k5zBJFP4AL97keZoHcRGBKD7OOSWAKIWaMUsoHfAR4F0ALnY9s5PoHp6r3GqEhDlAk59Nac6li6gbmRA/TvOgFF+hSyzKtuis7RG8gT8blwWe30JXzKb893Z9pYoGRg+MDKFEIwosDs0hBepgLgASmEGJGKKU6gfuBHQpVWsrar69Q6/dP57M+FSgY2sw52P4caW+A8EV7o+cF5rS/u9zKo7tYlpn6yoszMStbSxxvSRc8eXKNgBMiegLKVX5KFBorl0sPs47JHKYQ4qoppW4AngF2uHAnNnL9J6YblhMCBM8C9HGq9VLXXM6Q7Ew5v4eZIN4JKA++Q17lSwGc5ejKyvzmCWBortol5p4EphDiqiil3gM8BLT7CZ26jlv/u1V1XfSkkcl0sGQXQD+nOy9VXe5yh2RngsJwAK3RZppEB0CEhqcn3u/j9HWVP35EyuLVNwlMIcQVUUq5lVL/Dnwc8DTS+tT13PaZoApf0eb9Raw65sI9XiDvG+Rs48WuMTBKlPdfGuWC57NPKYXCsEsUcbA9JuZImNgpgHE90pAhuZJywfVPzkV7RPVIYAohLptSqhn4PvB+UPZiVj2wTb3kO6ZyTatgwMUYytDNdOwC6OFE18WuqYTXZc9jXi0FdpY0AAEiT00sSjrF4WvLb/NFrfXIXLVHVIcEphDisiiltlDeX3mLiSu9nu2fWq02756Jey/jmt2g7HFGGtM6edE9mS/sxZybeUytNUniLo2DgRlvou0ZgJIuusYY2lq57MNz0RZRXRKYQohpU0rdAzwGLPER6N3OS/+7Qy3pman7B1U4G6VhP8Bx9i25aBteWPgzJ/OYYwzFShRNAC++I4YyHIAj7NnhYAeA3cBTc9EWUV0SmEKIKSmlTKXUXwNfBAJRmvbs5LZPhVVDaqaftYxrHlUoa5i+9tP6SPuF75tzOCSb1gl/gtFzRdYn9mL26VOL+jn9ssrLfyaLfRYGCUwhxKSUUlHgAeCPAKeTZd/dzi1fcyvPlGdSXolm1TG8iJXfBjjGvlXjeiT4ovacWyk7e0OyWmtG9WB0iN5WgAChwcozoxmdDB7muTdS/v78R631A7PVDjG/SGAKIS5JKbWW8nDjKwzM3Bq2fnaduvbJS1XimSmr1ebdMZqe0zjGXp5cX9SFc8Ov5rkh2dnZWmJpy+jndGuC0RhAhIbTMVrOAFhY0T08fpeNFaE8NP1Hs9EGMT9JYAohLkop9UrKhz2v9uAb2sbNH12kVsxZ6bdN3PhtD77BAjn/Xp5cOzHqeSXVfqYrpzPeXk505sn6FYbVStfeZtVxyo0nDzDKQCxLajnlAgVv0lqXZroNYv6SwBRCvEjlsOc/Br4BRMLEDu7kto/HVHN8qs/OJI/yljaw4z4DoxBnqPl5Hl9r6ZIxEZgaxzVTU4eOdhjVg7F+TrfbWKYbb6qLZbtCKjoGYGA4cUaccUYU5X2gb9Vay9mXC4ySuWohxASlVBD4BHAPQBuLHtzAjodnewh2Mqf14VXH2HePRrv8hDKbuH6/RWkb4IrSeNZQ5hXv/QQo6Lx7iN7mEgUPQJjo2SY6Tk4UjC/onPt5nrgmwehEgfV/0Fr//tX9VKIWSWAKIQBQSi0FvgZsVhiFFay7f6lae7i6rSob1n1tB9h1T4lio4lpL2VtKUazL0Ss363cxSu5p9aacUYicYZjgDIx8810Hgqq8Lljv8b0YHgfT60vUvCCskGbwF1a6/tn6EcTNUQCUwiBUuoW4MtAkxvP2AZ23Nuk2udV5Zq8znn38NhrUoyvA2ijmxVsGA6o0GWX4ivpgjlEX3OBnA8gQHighY5jpnKVD4vWjjrFoc6THFyh0cpHoMeDbzTJ2Gbgt7XW/zqjP5yoCRKYQixglcOe3w/8M2AGCR/bys1f8alAvspNuyitNQd55vo+Tt0BKBOXbmdxahErUyEVmXKbS6VqT2iMwUaNVgZGqYn2w2EVGwXI6YznDEc7BjjbOTFE20Tbkxu54QeHePb6Ac7cRnkrye/N7k8q5iM5D1OIBUop5aVc0u09AM10PLaJG3401WHP1aSUYh3bn/BoX1M/p7cXyKleTkR6ORGJ6eZ8N8tTrXRnDfWz6xktXTKG6WvKkQkA+AiOtNJ5xMRVGtZ90bMc7xpjqBm0AnDjGV3Eyh8vV+sOAPh0IFm51eK5+nnF/CKBKcQCpJTqAL4KXK9Q1hLWfH2l2rCv2u2arkZazhgY2zU6niGZH6G/bZwR3zgjPhfPOUEdKvkIlvwELD8hS6HMDMmoRcmwKDkG5miahNXD8WvyZALlOUoAdJjYwU6WPd3N8pPnL3YKEJqY21w05z+wmBckMIVYYJRSOymHZacLd+Iarv1im+rur3a7LocLTxLAg89codYfKer8ibMcb+vnVFeenD/BmDfBmPcSHzeAlvNfMHGlm2h7Zilrn42ohuTFPhTk3GIg6WEuUBKYQiwgSql3A/8FePwEz2zhpvuCKpKpcrMumwdvAsDG8gF4lM9awfre5Xpdb4akL0PKP0J/wyA9nZUi7VphFN14xj144178cR+B8QCheIhIPEbLmDnF9pQQ0RSggQ6llFuKFiw8EphCLABKKRfwD8BvATTQsmszN33XVVkVWms8+JIADrbH0Y6amHdVSuHXweJpjnT1c7obwIu/fx3XfvVqV/2ayuWY2pW2scJAF3DqKn8MUWMkMIWoc0qpJuA+4FbAXsTKb69RW56tcrOuiqEMR2kjpXHCFiWPB28BIK6HQ/vZdU2+vLBHt9L98Hq2P3w1B1ufz40nUQnMRUhgLjgSmELUMaXUJsrFCJaZuNJr2HJfp1p6tsrNmhEmZtLCCZco+lzaXTzG3sVnOLYEtHLjGV3Dlvvb1eIZLV/nxpvIk+1G5jEXJAlMIeqUUuqNwKeBgBd/32Zu/OKlFrTUIgMzAaWuFPHwXh5fniYZAWik7ckN7PiRR3lnfI7Rg3fin5+slF2AJDCFqDNKKQP4c+CDAFEan9/Cz31jts6vrBYDM5FgjJMcWq5xlAt3cgXrv75IrTwxW8/04ZeVsguYBKYQdUQpFQE+C7wa0J0s/f41XPtENYunz4aUjodPcXBVljSAitL4/Eau/85sVyjyEZC9mAuYBKYQdUIptZryfOU1BkZuFZu+PJu9rWo5rvdvOM3hVzg4fgAvvr7r1K1zUgw98MJeTAnMBUgCU4g6oJR6OfAFIOrBO7yR6+9tUC1zen7lbMvptH8vT74ySXx95aXHgJsAc67aECIi5fEWMAlMIWpYpXj67wN/C6gQsUNbuel+r/Jf0ZFX89UZfXTlcfa/prKlIwN8AHgAGChRisxVOwKEMwpla3SDUiqktU7P1bNF9UlgClGjlFIB4GPAWwDa6H5oPTsens/F0y9XURfce3nijjjD2ysvPQa8U2t9orK4qeBg+4u64J6NVbEXUkrh0u5EiWIj5WHZg7P9TDF/SGAKUYOUUkuA+4GtCqO4nHX3L1NrD1W7XTOpX5/uPsKeu0oUG4AS8P8B/6C1tgG01o5SqgdYkWI82kTbnJzf6cYjgblASWAKUWOUUi+lfNhzsxtPfD3X3dusOoar3a6ZYmnL3M9TLx2m7+cABTwPvENr/fxFLj8DrMiQnMPA9CYpr86VecwFRgJTiBpRma98H/CvgCtA+PhWbvqKX4VyVW7ajBnR/a0Heeb1BfLtlAud/z/gT7XWhUt85CxAnuyczWN68MlK2QVKAlOIGlA57PlDwC8DNNP+043c8KOpTtioFY521EGeuaGfM7eCNoETwLu01o9O8dEzAHmy0VlvZIUUL1i4JDCFmOeUUu3AV4Aby4c9r35gpdq4t9rtminjeiS2n6dflyOzpPLSfwO/p7VOTePjZwGK5OcsMP0EpTzeAiWBKcQ8ppS6jvLini4X7uQ1XPuFWjvs+VK01hzl+a1nOf5yjeMBBoFf0lp/6zJucwagSGHOAjMgB0kvWBKYQsxTSql3Uu5tef0Ez27mpi+GavCw54tJ62RwH0++Jk1ideWlLwPv01pf7sKdswAlinM2hxkmem4OUymltNZ1s41HTE4CU4h5pnLY899R3pxPAy3PbOam79TqYc8XOqkPrj3JoVc72AEgAfw68PkrDJ6zABalqNaauaiZ61X+oqGNvIPjA5qBulmhLCYngSnEPKKUagS+CNwGON2s+M5atXVXlZs1I/I6693Lk3cmGN1ceelHwC9qra/4fE6tdVIpldDoaI50IEA4OzOtnZwLT7JI3kd5HlMCc4GQwBRinlBKbQC+Diw3MTOr2XJfl1p2ptrtmgk9+viyY+x7nVUuY5cD/gD4sNZ6Jlb5ngWiKRLRuQpMN55EkXwr5cB8di6eKapPAlOIeUApdRfwP0DQi79/Ezd8MaoaE1N9br4r6aJrH0/eNsrgzspLT1MuQnB4Bh9zBtiQIRXN6Uzcoug2cdkBNXvh6cGbqEwmy8KfBUQCU9QNpVQMKGmta2ZhTKUe6p8C/wcgQuPeLdz0jbmoizrbBnVPx2F231Wk0AxYwF8Cf621numDrM8CDHJ2W4KRpQ6Oy6IUyOtcyyJWPLiUtQdnur6u94W9mLK1ZAGRwBR1QSm1Bfgx8Bngt5RSxgwN980apVSYcntfC+gOlvxwHdt/WiuHPdvaNkxlOo521PmBZGvLOMCumwfpeQlgAIco9ypnay7WC2BRCkbpfs5HIO3GU1AYziGefU2AUKKdxb0z+UAffjnmawGSwBQ1TynVQPnEju8wh2cjXg2l1ErK85XrDIz8SjZ+ebFadbza7Zqu3frRO0oUQzu49auGMvTECtUxPdh0gF135cl1Vi79F+CPtdazWb5vB4AX39hyte7A+W+4tSeVJR0FZjQw/YSkh7kASWCKerAJWA58HHh75bVLDsEppUzKvZKp/vJM87pLfdYAeoCTlEu9naj8eSflw55jHrwjG9h5b6NqHbv6fwyzT2vNHh67PUuqC+CA3nXDOrX9cRtbHdXPX9fHyds02k15mPTdWusfz0Gz9gPr8mRbRnR/q4NjFMgFBjm70cFxx2ganOkHBqV4wYKkZM+tmE0XhNPVBtDFXo8C1wKHgVagjfIikMk+Ny96oT4CZ67lls/7VeBShcXnpZSOhzVa5ckFDrH79ctY872znLgpS2p55ZL/AX5Taz0ni5aUUrcADypUqYm2p20cN4Cf4OhiVu2bjWIPlrbMh/jan1D+xcw7C/OyYh6SwBRTUkot54Ve3DJgCRBkekE3L8LpQi4XlstUtmliuVzKdk38r0tZbheV/1W2203lf5XlcmF73OU/e9zK9njK/+v1KMvjUbbXqyzfxP/6lK0dON1bip7psRr6B62GwWF7cXzc9pXO+2r14B1upevppVzzvE/5ayo4tdY8zY/fkCS+jnJvegR4r9b6q3PZjkph+hzAKjZ93o2n4COQDRNLuJVn1oLsQf2137WxQsASrXVdbP8Rk5MhWXFRSik38Drg14BbruZek4ST7TKx3G5lu1yVYHIpy+0uB9Vk4eTxYHvdWN/+UW7D6Z5Sp2li2zamZWvX1g2+Q//7Nxt+GvQbViCg7GDAsEJBww4HlRUOGbbHoxzDmLuFNX0DVuSLX0+9OZlyfEphR4zo8Uefznh/9NRoRzJTaOnhxCt6OXVbTDc9v4pNj0RUQ3Lqu1ZXVqcCe3nylSnG11VeOkz5v5PSXJeL01oXlFKDQHsDzUMR1Tgn//zceBKVwFxEpaatqG/SwxQ/Qyn1buCvgQ4At5vi6uWe0x1trvjiLld82WL3eEPMKPh8yg74DMvnU3bAb9jBgLKCAcMOBVU5oEKG5fPOXTj91/8klv73ZxI7nvnB4vscRzOXoXgpu/flF3/3R9l7iiUd9LjM/IbOtn1Rvy8DUCw56ns/HW3+5iPDXUfOlI+nMjGzK9n4lUVq5YnqtvzSTuvDq05w8DWVsEgDf0Z50dVxoED5pJGhuWyTUupJYMc6tn+iUy294spBl+NJ/cO7K78wvFVrfe9cPFNUl/QwxTlKqQDlMxd/EaCtxRx+7ctDu/74txv2LOp0z+vhQscp/+IX9KsCMKdhadtaHThS7BwesRvjSTuWTDkN6YxuyGSc5kJRhwC8LjPf3RA9rTVKa1AKPG5Dv/qlLcOvfmnL8MGT6cCHvnh25YETmYbDPPeOcT3y0Hp2PDzT+wevRkHnPft48o44w9dWXnoEeBdQBH4XSFI+w3JOw7LiLLAjRyZa+fOs8+CTY74WGOlhCuDcPOX9wCaXi9Jv/Urs23/7webn5kMvbb4aHrGCDz+Z23bsROnafEFP+3gpr8uVb4+Gertj0QGPyzw3x2bbmv/88tklX/3R0FINBAkf38yNX53NijXT1adPLTrC86+3KDZQDsg/Af4ZcIB/A85orf++Wu1TSv0T8IEOFv9wvdrx2Fw884DedX0fp34B+A+t9fvn4pmiuqSHKVBKtVEuhL20udEY+/g/t33xVXeEqtFLqAl79hcWPb07v6N/0FrnaAwAt8+dD4T9KY/PU8wWjUZbm35lunTQ7UoZjm2VCgVXqVhw5bMZb6FU8p0eHV9xdiyxrDHoH1rUEOuNBXxp01S8/02LT29cGUr+/f+cviaTS614ih+9d52+7t5W1TVQjZ/V0iVzH0/9/Aj9NwIK2AO8XWu9b+IapdRva62rfZLKWYAC+Tk75stHQPZiLjASmAtcZRj2AWDp4i5X76PfXPSZrnbXvB5+rZZiUZv3PZC6/fip0kRdVCLN4ZH2ZW19DR0N8WzaCp0+ld6Acrxuw7CbI5Ehr9tVPP8eWmtS43H/+PBgOJtK+kfS2faRdLa9uyFyemVL8yml4KXXNsZXdAee+T8fOb7uZG8ucoCn3xLSkf+a657msO5rO8izry+Sb6Pck/wb4M+11hf+TNUOS6juQdISmAuEDMkuYJU6pvcBb2iIGuMPf737Y+vWeGumDutc6h+0Ivc9kLp7POF0K6V0y5Lms50rOvp8IV8BYHQ439rfm12jtTbcLlehORIZdpnGpEFSyOVc8aGByPjIUBgg4vfGN3S2HfS6XCUoLwp6318f3HKiNxcJED5+Pbd/bi7mNB3tqAPsunGAMz9PeVvQceCdWuufzvazr5RSagfwpBf/wM3qlf81F89M60ToCX7wu8Co1rp5Lp4pqsuodgNEVf0t8AavV+U/++H2z0tYXtxTz+aXf/ILyfeOJ5xut9dVWHv96ueWb1520hfyFbTW9JxJL+/ryVyjtTYCXm+6LRYdmCosAbx+v9W+ZNlY94o1g6bpcpK5QsOuU73XjmVyESgvCvqL9604EPSbpSypFft46iWz/bOO65GGx/neLw5w5jbKYfmfwJb5HJYVZwBKFOeyh5kBZQNNlZEaUeckMBcopdR7gd83DJx//LPm+15+a1AOwb2IR5/Mrfnug5m3l0o6EGoMxTe+dMMz0dZoEsAqOa4TR5Ob4qOFRQCxYHCsKRIevdzi6aFYLL/kmg19vkCwULRt7/O9A1tGM9kIQFerr/D771xyUAFD9NxyRh9dMdM/I5SHig/r57Y9w0/+V47MIqAfuFNr/Wta6/RsPHOGDQElB9tf1AX3XDzQUIZ24ZaVsguIBOYCpJT6BeA/AH7zV2LffN+7Yyer3KR56dCxYttDP82+QWtU6+KWs+tvWvu8x+8pAWQzpcDRw4lrsxmrQRmG0xKNDIQD/tSVPsvj9dqL16wbiDQ2pbXW6lD/8LqCZbmhPKd518taTwEcY98bknpsRhe2VIYW33qWY6/WaA/lYfqNWuvvzuRzZlPlZJoegDTjc7bwx41H5jEXEAnMBUYptRH4EmC+9s7gI//4Zy27p/O5ZMox3/5rAze3bzjxfk/30Q82rD7+O7ff0/OKfYcKodltcXUMjVihr38n/Vbbxt3QFhtcvmXpCWWU/+8SHy00nTiW2maVHJ/LZRbbY9E+n8dz1QulDMOgfcny0Yme5r7ewWsmlhj82t2LTq9fHow72P6j7J2xodkT+sC6p/jx+zIkVwHjwFu01m/SWo/O1DPm0BmADKk5G5b14JFjvhYQCcwFRCnVCXwLCF+3xbvvvv/ueHA6nxsctjybfv70e+69P3Xr8KjdZNuYyZQT/vEjuetuetXZ9/7gJ9m6WvCQLziue7+aelO+oCOBSCC5avuKI1SGWQf6st09Z9IbtKNNv8eTbYvFBlymOWOrRA3DoGvFqmHT5XKS+ULDseHRpeXXFe9/8+JjAOOMbMrrrPdqnpPXWd9T+sd3neDA3Q52APg+sEFr/YWr/iGq5yxAjsyc9TA9+KSHuYBIYC4QSqkg8A1g0dLFrrPf/WLX110uNa0Vl29738DtZ3utzou9l87o0Ht+a+CNM9nWatJa87kvp14znnS63V53Ye3OVfsMl+kAjI8VGocHcysAIoFAvDkaGTbU9P4ZXg63x2t3LFkxDNATTyyJZ3NhgLVLg9nVSwLjGu0+yaHNV3r/s/rY8if4wfuSjG2kXLT814GXa61n9MzIKjgDkCc3Zz1ML3455msBkcBcACpHbH0e2NbUYMS//bmuL8Qi5rROcRgctjyPPJnbMtk1fYN22yfuTdTFF8b9387c3NNvbTRMw169Y+VeT8BbAijkLW9vT+YagEggMB4NBma1wHcoFsvHmltTAD3xRNfE66+6uaUPYJje6y53S1hRF9zP6ofvPMxz77AoRYAnKa+A/fBcFkufRWcBiuTnLDD9BGXRzwIigbkw/APwGr9P5e79SMfn1qz0THsD/OO78o2WNXWBi2f2FNquqoXzwMOP567Ze7BwK8CyTUsPhhvDGQDtaHX6ZHq9Y2uX1+3JRQOBOTnnsbGtPQkwmsm1Fi3bBfDyG5tGIkGzWKTQ3MvJpdO914A+2/k433/vGEM7AAv4IPBzWusjs9H2Kpnz4gVykPTCIoFZ55RS7wd+2zCw/+UvW774spcELmsxRzholKZzXTCgpnXdfHXgSKHj4SeyrwfoXNVxomVx87l/TvGxQlMhb4cNw7CbIqER5qi8rsfntwLhSE5rrXriifLJMS5Dv2xHUz9ALyeum+oetraM5/UTt+zjyV8qUWgCDgA7tdZ/VYeHHp8FKFGcsznMENFzc5jqcvcTiZojgVnHlFKvAv4V4Pd/reGBX3579PTl3uPm6/1jkbAx5XaJV98Ruux7zxeDw1boge9l3mLbuBvaY4OLr+l+0WkXY6OFLoCw358wDcOZy7bFWtpSAAPJdOfEoOkbXtbaD5Amsbqki5fs/Y/qgeaf8r1fHqLnpZTrwP4TcK3W+tlZb3h1nAGwKEbnaoTZpwIFA6MABIDGOXmoqBoJzDqllNoKfAEw7n516KG//pPm56/kPh6P0u+4O/yTya658Trfnpuv98ev5P7Vlss7rnvvT72lUNDhyorYw5zXUchlrUAua8VQSod8vmlXQjp1KunevXvId/Zs6qrqNYdjDTmX22MXLMs3limfmdnV6it0t3nTGu0a5OzPzJ052lEH9DM7n+Ox9xbIdQCngVu11r+rtc5fTXvmM611AkhptDtPxj9Xz5XiBQuHFF+vQ0qpbuCbQPD6a33Pf/4j7ZMG3lT+7a9an+kftCNf/Vb6Z/b/bVrnOXz/pzq/dTX3rxatNZ/9cup1iaTT6fa582uvX73PMM0XdU3GRvLtAAGPN2MYasre5Z49w75PfepA4+Bg9ly1me7uUPGXfmnD6Jo1DcXJPnsxSilC0Vh2fGQoPJbNNjSFyvOnG1eG4z2DhdAog8u7WXGu8ERCj0X389Rrs6SXVV76JPDbWutZXaQ0j5wB1qdIRP2EcnPxQDfeRJFCC+XAfG4unimqQ3qYdUYpFaYclp0rlrpPf+fergdm4kzLL32s48Gvf7rjP199R/Cxazd799/2ksBT//p/Wz6z+0dLvtDcaNbk/OVXvpl+ad+Atd4wDXvNdav2TVTxOV8+7wQB/B73lF++zz475Pv7v3+m7fywBOjpSXv+6q+e6jh8eMxzJe0MRKI5gPFsvmHite3rIuMAKeLLoRz+R/Xzm5/hofdVwnIYeJ3W+j0LKCyhMo+ZJT2HezG9svBngZAeZh1RSrmALwKbm5vM0e9+oeuLkfDURcCn61V3hIZedUfohzN1v2p66LHs+v2Hi7cALN+89ECoMXTR4dZSyfEBuFyT/1Jg25qPf3xfs+NcfO7Mshw+9rH9zX//9zf3XW5bg5FIHhTpQjFSsh3TbRr2zg3RcdNA551c55geajjK87enGL+m8pGvA7+qtV6IZ5qeAciRmcNqP1K8YKGQHmadqKzQ+zfgzoBfZe/7aPvnly+Zule0EO07VOh85Mnc6wC6Vnceb17UPHbRC7XGmghMY/J9qwcPjnrj8YI52TW9vWn36dPJyy4Mbpou7QsEyseIZbIxgKDfdFZ0l/eCPsejv1oJyxTwi8DrF2hYwrmDpOeueIEPv5THWyAkMOvHbwPvM02sD/1N6xdeekPg4iGwwPUPWuFvfj/zFsfB1djRMLBobVfPpa61LO3SWhsopQ1j8oo+w8O5aY3WTPe6CwXCkTxAPJNtALBsx1y+1GUCODg+4CHKBdM/VSdFCK5UZS/mXBYvCEkPc4GQIdk6oJR6HfCPAP/7Nxq//q43Rc5O/omFKZtz3F/4WuothaIOBaOBxMprX6gRezGmS1koNForR2s1WRm8hgbftIa+Gxt9V7T3MRiJ5sYG+6OJXL5hJJ2NHh4cXtvZha/y9jDwssqJHQvdFe/FLOmiK8lYLE2iIUc26saTCxGJh2mI+wnmLrXNUooXLBwSmDVOKbWdctk79ZbXh3/8F3/YtK/abZqPHEerz3459bpkyunw+Nz5NTtX7zdMY9KemFIKt8vIl0qO37Jtl8fluuQ85vr1Tflw2O2kUqVLjtq0tPitZcsiV7RAyh8KF5Rh6FzJCuztHdgCsGale8DrUY2Fom4Buqn0rha4aR8kndRjkR5OrI8zsrpIvtnGuuTJOwZGwa098QiNx5ayZldUNZ2r9hQmNjEk26WUMrXWM7ZuQMwvEpg1TCm1hPKKWP9NO3zP/c+H2h6pdpvmqy9/M31L/6C1zjQNe/WOVXsvtiL2Ytye6QWm223wtretHf3IR/a2XOx9peBd71p32YdLTyjZjsf0+rFy5bVJ16zyPPy6O0MPf+4rqbufP1BcA7yM8haSha4HwKIUdrSjDPXiX4pSOh4+y4l1YwxuyJPtfvFHlfa6ggWvO5zzukMFyy648qWUv1BK+RxteQvk24fpax+m76aQjh7tZOnT3aw47lJu29SujI0VBDom2iDqjwRmjVJKRSkf1dW2ern75Lc/3/XNmdg+Uo9+/Eh248EjxZcALN+y7ECoITTtWroer5nJZqyGfLHkD3i9ky6ieslLurNaM/K5zx1qTKdf6GlGox77Pe9ZP7ptW+tlFw3QGpLZbDSZzcYMnw9yGVqbzaP3vDb8IMDOa30nKoF5GxKYaK0LSqlBoC1DIhSmIZXWidBZjq8bY3B9jsy5YVOlTCcWXDTaGlk9HAstSvo9saKhzJ8ZddBaU7QyrmS2P9QX39MxljrVkiax+gh7Vp/k4NgKveEbbtyJSmAuQgKzbqmFvT6gNlVOH/km8PLWZnPkye8s+vjibnfdVnC5GnsPFrq+9p30LzoOZveazmPda7sv6wirTKoUOnEsea1SyulsauyZznFexaLNvn2jvng8bzY3++0NG5rypnn56+tKlu0aTaWaS5blBQj4nKHhIydbfV6V/IP3N/yzUorvP5RpvvMtfb8ODAIdC3zBDwBKqaeB7c20/zRDujNHeukL7xlOLNA91hJdM9wWu2bUbU5v7vl8+WLS3TP6bMfA+P7OopXxAtqDd7hIoRV4s9b6izP304j5RHqYtemDwMsDfpX9yic6PidheXF9A1bkmz/IvMVxMJs6G/u713Rd9nmPwbA77fWZqULeDmfzhWDI70tP9RmPx+RKepPnS2Vz4fFstgGtlekyil3dgUORmCceP3mmIV+wIyfPWE3Ll7hHb3tJYCQSMlLJtNMGrAcW7By2UqoZeD2VxTcjDNwIoDB0NNg11hpZPdQWWzfqdvmvao7R54mUVnbccmZ5+81nj/T9cGnf2J7FlbAEWH1VP4SY1yQwa4xS6g7gTwH9N3/S/JUbr/OPV7lJ81I643i+8LXUW4tFHQw1BMdXblt+dLIVsZNpbPL29vdm1yZzuWjA583MxqHREyzbMcdSqaZCqeQHCEc8Q92Lg0ddbsMCCDWE4uNDidaDR4rLly9xjxqGYt0az8knnslvojwsu6ACUynVCLwOuIfyz1/ZC6sIBVqz7dH1Z9pj60Y9rsCMn8xiKFOv7fqFk7FAd/JQ3/fXOU7JAH5PKfV5rfXxmX6eqD7Zh1lDKl8OnwPUPa8J/eT9vxQ7Ue02zUeOo9XnvpJ8fSrttHn8ntyaHav2qylWxE6msck37PaYOdu2XWOpdNNMtvV8mXwhMBCPdxZKJb9hKqtrUfDA0hXhgxNhCRBpicQBevpLyydeu3mnf+K/g9tmq23ziVIqppR6l1LqW8AA8HHgF0CpQLD1eFv7tv2LltzM0u6XxBc3bx+cjbA8X3vD+tF1S1593OMJA0SAryql5qz4u5g7Epi15T1A8/Il7jOf+Y/2h6vdmPnqS99I//zAkL3WdBnWmh2r9rp9nqv6wjRM5SxeGtynDOXkCoVgKpe75PaDK2E7jjGSSDaPpVItWmsjEHSNrVoTfbqx2Td84bUNbbE4wMiYs8yytAHwpteFJgLzpUqpy64kVAuUUhGl1NuVUg9Qnq/9FPAKwOUPNJ/sXnTjN3be+Dv/uPOGD3y2o+vaA6bpxnJK3rlqX8jfmmrv3IarPNy7CfjQXD1bzB0Zkq0RSikD+F8A731n9DGXa/aGBWvZjx7Objp0tHizUkqv2Lr8QDAWnJHygIGgO9veGTjS35NZO57ONgGE/f4p5zOnkisUfWPpdLPjOKYylNPa5j/W2ubrv9TwsT/sL3j8nlwxV/QfPFrs2HiNt3frRl+qtdkcGRqxm4EdwGNX2675QCkVAl4FvAm4E5gIQO33N51qbFq9v2vR9QeDwdYX1QF2u8ql6mxd8jFH3MpbMAwXre2bi/29T7m0dt6jlHpMa/2JuWqDmH0SmLXjdmBFLGokfvOXY0en84Gnduejn7w3uWZoxAp1d7oSv/bu2ME1Kz3T3lJRa/bsL3T/dFfuNQDdazqPNXY2zugZnc0tvsF8zgrGRwuLxtOZpkLJ8jWGQ6NXMqfpaK3G05mGTD4fBvD6XMnFS4OHfH7XlAEfbgzFR3vH/EdPlJZvvMbbC7B5vffED36SbaY8LFuzgamUClLuOd4DvBKYGNrUPn/jmcbGlfu7Ft1wIBRqv+QvKx5PuVSdreeuh+kyPEVQ2uMJ+rsW3fiNnjOPvhr4D6XU01rrvXPVDjG7JDBrx5sAXn5r4BmPZ/IvaMfRvOlXB37+/m+nb9aac12Vj3w68fJfeXv0ex/6m9Zds93YudbTb0W//cPMmx0Hs7m7qa9rTddlnwoyHd2LQyd8PjMz0JdbnSsUgoOW5Qn7/cnpLgZyHK0y+Xwolc9HbNt2odDNLb5T7Z2BM9MtahBtjoyP9o519g1Yy4FHAG65yX/iBz/J7qAcmH9+NT/jXKvM991JOSRfDQQm3vP5Gs42NK7c37Xo+gPhcGdqOvdze4JpUI6D7Xa0bRjKnPWSgQqFqVwFW5d83YtuOJVK9uxOjJ/aCvwJ8ObZfr6YGxKYtWMVwM07/VNuin7Xbwz+3MUOe7YsXP/5qcQrG6JG/i//d3PNrqbcvTcfXrHUk504uiyVdjxf/FrqrcWSDoYaQvEVW5cdm83nN7f6BwNBV/rMyfT6Usn2x9PppvFMpiHg9ab9Hk/eNA3LbZqWUko7WivLtl2WbbtyxZI/WyiE0FoBeDxmpntJ8FAw5L6sod2G9licPRBP2IuyOccd8Bult70hfOqDfzOqteZ6pVRYaz2tcKkWpZQP+AXKIfka4Ny8sNcX621oXLG/q2vngUh0UeJS97j0vQ1tGK6k45RiJV3welVgTk7tMZW7YOuSr1TKRletftVDu576j82g36CUatdaD8xFG8TsksCsHcsANq/3TjrMeKan5PvSN1I/E5bn+49PJm7//36naf9UPdX56OVv7r1zYMiKGQbOX/xB04N3viw4/LmvJO9KZ5xWr9+bW7Nz1QFlXPmK2OkKBN2Z1dfEdo2N5lvGRgtdhbwdzuTzkUw+f67ot1LK0Vr/zMI6n9+VaGr29jY0ekfUFKegXIzb57H8IX86l86F9h4sLt65zXd8Uae7sLjL1Xu6x+oGXkK5CtS8opTyUJ5aeBPwWsorSgHweqP9DY0r9nV27TwQjS0ev9pnTQSm5RS8XmNuAtOl3IUiYJVy0camVSdDofYj6XT/WuCXgf87F20Qs0sCswZUfhvvMgycrRu8ycmu/fz9qSWlEpOulEwknch3fpxpfe3LQ4Mz2tBZ9s73D/zc2d5S8zM/WPy5V7+j7857v5ZaMzbubB4Ysta43C5rzc5Ve91e96xuITifYSqnudU/2NzqH0ynSuH4aKGtULADpZLjtyzHWz4aDO12GXm3x8h7vWamsdk3EAi6LnpY9eUIN4XiuXQudOJ0adnObb7jANs2eU9UAvNlzHJgVhahvR64C3ga+LTW+md+maus2n0Z5Z7k64HYxHseb2SgoWH5/s6uHftjDctmdL7ZND0Jy8pR0sU5W/hjqnIBEcsq/9LU0XXd00cPP7AWeK9S6m+11nP236aYHRKYtaEZIBhQWZ/PmHQ+ZixuT2v/1+Dw9K6bL/YdKoSefDa/8pP/2vY1n89wWprN1OO78lue3l1oXNLt0q+4e/WRQHRuehIXEwq7U6Gw+9wwqHa0sizH5XIbpSstuD6ZWGs0PnR6eNHAkHVuP+bLfz544v5vZ17CLO/HrPQU/5tyofFPAm8AFiml/khrXVRKuYCfpxySdwGNE5/1eMJDsYZl+zu7duxvaFwxOlttNE1PZeFPce4W/ihPAcC2yyeldHbtOHnqxI9GS6VMN+U52m/MVVvE7JDArA2V/yNOVDG5tA1rvdP6Erpuy/Sumy82rPWmH/569+fbWlzFR5/MNXz/oez2t90VDuaLmoMnzcQTTyR967Z2VLuZ5yhDabfHvKKjvKYj1hpNKEM5yZTTMRq3A00NZvae14Z73v/HQ6VSiY2zPG9mA/8G7NVal5RSJ4E/Am5XSr2acoA2T1zs9oRGYrFl+zq7tu9vbFo9MkttehGXy5sAsJzLC8y+sT3NA+MH2vPFpN9leq2G0JLRpS3X906nnJ7b8OThhcA0DFNHY4sPjgwf/DngWiQwa54EZm3IAZQsPeW/r7e/MXz2D/9yZGJP3kVds8pzfOtG37xeFHIxbS2uIsCSbpf+1XdETa9HGc2Lmnq7N4SHv/PA2Y5s1jL8PtNRC+DUFsNlOsFoIJmOZ2J7DxaW3nJj4EAkbNgrl3rOHDxaXAHcSvmc1BmntbaVUrsBQyl1C/BWyvslXztxjdsdHI01LNvf3nnt/qam1UPlEdy547rMvZhaOzx/+v61o6njbedeLEE6PxQZShxq37L0nueDvqZJ6wO7lLcA4Dilc2dx+vyNY5U/Lrvcn0HMP1LppzaUA7OE23EmXyNiGIp/++uWr3o8FC/2fiioMp/417aa/U03mXa89z2QfqvHTSDcGBpbsWXZ8e9/u7c9HPGUAgHXggjLCZHGcBzg9NkXhmV3bPXOapk8pZShlLoJ+BfgLPAg8CuA6XIH4s0t6x7ZsOkdH7npJX/8oQ2b3vZgc/PaOQ9LALc7UO5hTnMv5onBRxa9KCzPUyil/HvPfG3dVAfBuIxKYNpWZOLaQKBlYm52+aU+J2qH9DBrQOU3+hLgTme0GQmrSYeH7n51uD8WMT/6e382fMf+w8WVWqNME3vbJu/B//r7tu9vXu+tud4lgG1r9bkvJ9+QzjgtvqAvu2LbigP/8nf7lyfiRc8H/+/Ww9Vu31yLtcXifccHlg2NvBCYr3l56MSn70sB3KaUUjNx3JcqT8LupDwneTdw7uBl0/SNK6V0e8e2R1aufsXuaoTjxbjPFS+wvBqN4tK/SGnt0Du2p/uSFwDZwmh4JHks1hJdNX6pa0zlshSGrXE8lpX3ud3+fCjUMXG9BGYdkMCsHTnAnUjZron9h5O5/aWBkT0PLvn82LjtOnqiFFy/xpMOBaf+3Hz2xa+lbh8asVeZLrO0ZueqvSXHYMXKSPpX3r/2VLXbVg3hplDKdBlWNuc0nO0txRZ1ucdfdXtwMOBX2WxOLwJWAtOqCnWhSkhupxyS91A5MgvA5fIlo9El+9s6tu5z7JI5MLB786o1r9p99syjKw1l2p3dO09WOzhdLl9eYRQ1jsfWlsulLr16Olcc91p23jPVPZO5vvBkgTlRvMDSxUCpmI663f58ONyRBOWA7lRK+bXWVVuYJq6eBGbtyAGRRNJxL+osLwKajsaYae3cZl725u/55rsPZrYdPVm6QSmlV167fH8gEsgDvOaNS2pqa8xMUoZBKBYaT4wkm/cfKS5f1OV+1uVSet1qz8ldewrrKQ/LTjswKyG5hfI+yXs4b97NNL2paGzJ/rb2zftb2zb3GoapAZ58/J/fXsiPtz/+6N+1KqWcRYt/7ifVDksApRSG6UrYdrGl5OR9LvPyikNc4q7TuKZclAKlLlzNrit/iRomgVk78gDJlLPg/p09sye/9Kln868EWLSu+0hDe0PN/wIwUyLN4XhiJNl8ttdaDjwLcMN1/hPnBeZ/Tvb5SkhupByQb6LcKwXAND3pSHTxgbb2zfvb2reenQjJCY5dMoLB1t5odPGxJUtv2eMPNM2r3pNpeuK2XWyxnIIPM3zJwPR7YgW3GSiU7Oyk853RQNeke6A1GktbPgCvNzIOkEr1RUAbQI/WWg56r3EL7su3huUAkimnLo9vupTTPaWG7z2YvUdrjJbFzT2dKzukxNh5Gtpi8bOHehketZc5jlaGofQ9rwmd+PePjQPcqpQytdY/MxSvlFrPC8OtaydeN0xPNhJZdKCtfdO+9o5tZwzDdclekWG6nQ2b3vbgzP9UM8M0vXFIU9KFSfccK2XQ3bT17Mmhx1Ze6pqQrzXZHFkx6S9qllPwglaGMtOm6SkBpFN9DZW3T172DyDmHQnM2pEDSGUWTg8zkbK9X3og/daSpf3hxvDY8s1L5RT7CwSigZzb6yqUClbgyIlS69qVnsEbr/OPN8aM+Ni40wBsBXYBKKXW8kJIrp+4h2G4c5Fo94HWtk37OzquPW2Y7lkvVj4XXC5fHKDo5ANTXbus7abeTGE0OJQ49DObef2eWGbD4tcemOoeBScbBDBMz/jEa9nsyERgymHvdWDBfPnWgRxAOrMwepiWpY3PfTl1dybrNHsD3mylRmy1mzX/KEWoMRyP98fbDx8rLl+70jMIsHGd98RPfpq7FniTUmqiyPmmiY8ZhisXjnQfam3duL+ja/tJ0/TURUieLxBsOZFK9ZKzky22to6ZyjXporcNi19zZHB89fDA+L72fDHhN02v1RRaNrqoeXu/y/RO+c8nZY20A3i94XO/2OXzcQnMOiKBWTtyANnc1MUL6sEXvpa6Y3jUXmG6zdLanav2ujyTf9ktZNHmSDzeH2/v7beWA4+f6Sk1rFnhdv/kpzmA35u4zjBc+XC461BL64b9nV3XnTRd3rr+ZxoINA+7XYHTJSu7JFkabmvwdEx55FtbbG28Lbb2suvalpyCJ2enmkHpaGzZMxOv57JjE3s7F9y2p3q0IL5860QOIJOt/yHZb/8os/34qdJOpZRede2K/f7KilhxcQ3tsfFTe08zGreX/fN/xX81mXI6IqEXeuPhSNfe1tZN+zq7dxx3uXx1HZIXCobanx4fP7EkZY10xTztfZPtx7waidJgJ6A8ntBBrzd8bp9zPh+fGOJ95uKfFLVExrhqRx4gm9V1PSS767n8sl3P5V8BsHjdoiOxtpisiL2EQt72DvZnu0+fzq033B4cBzOZcjoMw7C7u2JDXR2+PEB7x7XPLV76kiMLLSwBorHFhwzlSlm6GBgtnF06G8/I2clw0hpZDBCJdD818Xo61R+27WIISAAy/14H6r63UkdyALlc/fYwT54pNX3/oew9WqNal7Sc7VjZLitiL1As2J74WKElmSi15nPWufMkTV8Ap1SkIRYZ3rBu+SHTNJ3N64vLe/v7Fo2NHl3eveiGBTmHZhguu6Fp5f2jI4fekbSGl/jMUDLkOlff9apZuuQaKpxaD1r5fY1PhyNdpybeGx09MtG7fHYmKi6J6qvbL986VJ7DzNdnD3M8Yfu+/M30W0qW9kWawqPLNi1ZkF/wF1Mq2u74WLElMV5szeesc4W9UUr7fN6cP+DP2AEP/YfGW4qlktc0TQdg+9am+Ld/2Lconepb0IW/I5Huk4X8+IPp9MCtI4Uz17iU5zmfGbrqM0ltbRtD+ePrbF3yuly+vtb2Td87//1Usrez8kcZjq0TEpi1IweQz9ffop9SSRuf/UrqnmzWafIFfZnVO1YdXOgrYish2ZxMFFtzWSv2wjsKn8+b8wf9Gb/flzUMQwM4Po9CKTKZfKRUsky322Xv2NqcME2lC4VEZy435vf7G+dVYYG51Nyy7tFiIb2oWEqv6s8f2dbo6T4SdbdecZWogpP1D+aPb7B0MaCUmWtp3XifYbx4YVo2OyTzl3Wm7r5861g5MAv1F5j3fi318tExe5nLXa4Ru1BXxFolxxUfK7QkxostuazV8MI7Cq/PkwsEAhl/4IWQPJ9hurQvFCrkUynvaDwRa29tGg0EXM6KpeHEkePJ2GD/7qVLl7/s4Fz+PPOJUoZu79z2pcGBPa8qFBKbRotn1+btdLTJu+jEZHVmL6RxVLI03DpW7FulcUzT8Iw2t677os8X/Zm59nxuXHqYdabuvnzr2ERg1tWQ7Ld+kLnu5OnSdUopvWr7yn3+sH/adXLrgVVyXOPxQnNivNiSzVoN6BeWcXrLw63ZQCCQNYyfqU36MwLRWD6fSnnj8WRDe2vTKMDm9bH4kePJWDx+YvlSFm5gApimp9TRuf3+sdEjZ5LJnjszdrwjmx1v85vR4ai7pddvRi55ik/JKXiS1lBH2hrrsLXlBfB6I/tb2zY/4HJ5f+YovXSqP2TbhRCQRBb81A0JzNqRB8gX6mfRz5PP5lc883z+ToAlGxYdjrZGJ63VWTe0Jp0qhUdGCl2pZKkVrV8ISa837w/6M4GAP2sYxmUVEwjGGnJjPWejiWTmXO90x7bm+JceOLMskx6Q46UoF2Vval7zjM/f0BsfO/6yUimzMmuPt2Xt8TYDV9FluHMu5c27lDtva8tt66LP0iW/pYs+KtXXTcM9Fgx3PNrYuGp3uRTvzxobO3r+gp+6KwqxUNXNl+8CkAMo1EkP8/ipUvMPH87erTWqbWnrmfbl7Qvi1JHxeKFxqD+3rFCwQxOvebzevD9QDknTvLyQPJ8/Gi0ow9T5fCGQyxc8fp+3uHlDQ9rnM618PtuYSJyJRqOLZZsOEAy2DgSDrZ/L5+INicTp7blcfKujLX/RsTxFctGf/YTSHk/oUDjc+XQ40nVSqZ8dFj9fKtkjw7F1SAKzdpQDs1j7c5hj47b/q99KvdWytDfaHBlZumlJ3Rem1o5WfT3ZpWOj+cUAyjCcYDCQDoWDKZfLNe05tMkopfBHwvns+Lh/dCzR0N3ZOuhyGXrtqsj4c3vjzcODe5dHo4t3z8Sz6oXP3xD3+Rt+oB37R4ViOlIsphpKxWyDbeejhuHOutz+uMcTGvd6I/GJgurTkckMy4KfOlTzX74LyERg1nQPs1jU5ue+kronm9MNvpAvvXrHyoOXGtaqF6Wi7T59Mr1uYrVrOBoej0TCSaXUjO/NC0Rj+ez4uD8+nmzo7iyvAt2yoWH8ub3x5vHxU8sBCcyLUIbp+HzRcZ8vOs4MnCySz8Wlh1mHFvba/dqSAyjWcA9Ta82996fuHIvbS10eV3HtztX7TLerrud3HNsxThxLbc5lrZhhGnZza/NgNBpJzEZYAgQbyltHkufNY16/vSUOkMkMLXccu75/O5kHMunBoG0XwkAKOFbt9oiZI4FZO3IApVLtjgp88weZnafOlq5VhnJWbV+xzxfy1feKWK05czqzuliwg6bLZbW1t/b7fN5ZrYvrC4VKptvtlEqWJ5XOBABWLQ9nYxF30bGLgbHRw62z+XwBo6NHJnqXsuCnzkhg1o48QLFUm0OyTzyTW7l7b+EXAJZuWHw42hK95BL+ejE8lG9PJYptKKWbmxuHTNOck/2lgUg0BzA6lmyA8tzmurWxOMDI8MEFXfVnLqSSPTJ/WackMGtHzQ7JHjtZbPnRw7k3ao1qX9Z6um1Z21C12zTbSkXbPdifWwXQ0BAddXvc014wcrUCsVgeYHw8dW5YdtumxjhAMnFWtpfMsmxm+FwPs6oNETNOArN2lAOzxnqYo3E78NVvpd9i2dobbYkML9m45FS12zQXRofz7Vprw+vz5oKh4FXXLb0cE/OYqXQmpit7PG+8rjyPmc2OLJV5zNmVkyO96pYEZu2ozGFefg8znXHMx3flYiNj9pyGbbGozc9/JXVPLq8b/CF/evV1qw7V+4pYKC9uiseLnQChcGjOh57dPp/t9vks23bM+HgqDNDe5i+GQ66S1rY7kx4Iz3WbFopMZihoW/kIkAaOVLs9YmbV3PDeAlYOTItph96+Q4XQr/7u0B279uTX2TYmwNpV7uP/74PN33/VHaFZHRbVWvO5ryZfOTZuL3F7XMU116/aa7rNBbEAIhEvNlolx2eapuX3+6YseO44mod+eDr4+KM9wbHRvCsa9djX7uzI3n7n8pTHY15RG3yhUKGUz7symWygsSGSBGhq9OZTacudSvXHwpGuhVFVaY6NjZw70mu3LPipPxKYtSMHYFnT62Hu3psP33pX768k086LehOHjpZWvPGX+n/lo//U9ul33B3pmY2GAnzj+5nrz/RYWw1DOau2r9znC/p+pt5mvUqnSg0AgWBgyqHYYtHm7//v460H9o74J14b6MN9+OCY79GHzob+6M9vGoxEvJf9xev2+S2AXL547r6tzb7cqTOZcDY71ACcudx7iqklk2dl/2UdkyHZ2pEHKE0zMH/xtwZfdWFYTihZuH7n/wzfZVl6VsZHd+/NL35uX+EOgKUblxyKtFy6qHU9KhYdH4Db7Z7yl4TPfHxv4/lheb4zp5Ke//q3Z5uupA1un68EkM8XfBOvtbf6y/WIc/GGS31OXJ2sVPipaxKYtWNiH6bbcSbf837waDG471Bx1WTXjI07DR/7XGLpzDWvbGjECn3vwXKN2NYlLWdbl7YOz/Qz5rtSyfEDuFzmpCXvMumieuShM6HJrtnz7GCgryd12SNBnkoPs1AonQvjjjZ/pR5xUgJzluTzUuGnnklg1gittQ0UAcaTk59Ysuu5fIM+75ioSzlwpNg4Q80DyvOW9309fVehqEPBaCCxbAHUiL2YUqncw3S5J68Re+pkwlMqOpP+e9Iajh2Jey+3DW5/JTCLxXM9zK7OQHkvbzElgTkLspnhgFVe8JNBFvzUJQnM2tIH8Ny+wkVOU3hBe6trWtVkGmPmjFadefb5wtLRuL3MdJml1detPKAuctCxeMG0FwwrruCfo6589LxXzt1ldsryLXSjo4cnepe7K7/gijojgVlbTgDsO1iITXbRy272jzTGjPhk17hcWO+4O3xiBtvGs88XrgNoWdTU511Ai3wu5HYbeQCrZE06ErBkWazo8ZiThpdSsGp142X/syzlci4Aj9dzbpVub1/WD+DxhCb9b0NcmWRCKvzUOwnM2nIS4MiJ0qRDaoah+O1fbfjBZNe8/s7QoyuWeqbc8jBdg8NWqH/IWquU0u3L2/un8xntaPbtGQv98Du9Tbt3jYRtqz5W4bvdRmUL0OSBGQy69UtftnjSBVHbrmvPdnSFLvv4r2I+7wLweT3nRhH6h3I+AK83On659xNTy2aGZP6yzsm2ktpyFGD33sIiYNdkF/7JBxoPxpP21z/08fFXlEov7N1UCv2aXwg+9tkPtz88kw174pn8Rq0xoi3hkekUVd/1xHDkQ/90YM3QQC4w8VpDo6fwnvetOfqyX+gancm2zTWPx8hngFKp5AGyk137tl/cEB8cyLif3z30Mytll6+MFd77G9tGrqQNpXzeDeA9LzAHK4Hp9zdID3MW5KXCT92TwKwtXwb+5unn8uuOnyp+d6oe4j/8actzv/bu2OEPfWJ87ZmeUqylyZV595sjh3du8yVmumFjcbsVINIcGZvq2mefHon8+R8/u8W+YFtLfKzo/ae/3rvBtvT+O17ZfUVBMR+EIu54fKzQnc3kQrHY5L05t9vkD/6/G4Yefehs4PFHe4NjozlXJOq1t+/syL7sjqVp03Vlg0ATQ7J+n/fcfyNDI3k/QCDYIoE5w7LZkYBl5aOUF/wcrnZ7xOyQwKwhWuvjSqnv2jZ3/tU/j239xL+2/3Sqzyxf4s7905+3zPqhwemsjgH4Q74pFxJ9+J8OrL4wLCdoDR//8OHVt9zWMerxTj6/N19FY56xfreRt0q2L5vN+QMB/6S/2CiluPnnF2dv/vnFk/ZGp8uxbZUZj/sBotEXSvONjJb3ZIbCnRKYM2x05PBE7/I5WfBTv2QOs/Z8GODr38vsGBy2PNVuzIRs1mkA8AUnD8xjR5L+vt5scLJrUqmS+6nHh2Mz2Lw5pZSiodHbB5BJZSJz/fzE4EDQsW0jFPQnI+Fy4fe9B+KhdMZyG4YrHwy2pee6TfUuJRV+FgQJzNrzHWDfeMKJvvZdfa+eqojBXCkWdRjAG/BMOn852J+d1p7C4cH8Ze89nE+aW3z9ylBOoVDwpVOZSYsTzLTEQH8YoKO9uXfita9+q/yFHmtYtscwarPnPp9lpMLPgiCBWWMqwz13A5mndxc2/MYfD19X7TYBeDwqDVDIFSft9ba2+6e1RaK5beqFQ/OZy21Y7Z2BIwDj44nGYrE0J6MB2UTCW8hmPS6XWepobx4GSCSL5uNPDbcBLFrykqfnoh0LTT4nFX4WAgnMGqS1PgT8MsB/fybxCx/8m5GNVW4SgUB532c+nb9oXdQJq9ZEsx1dkxclD4VdpZ03tIzPYPOqornFNxiNefrRWo0Oj7bYln1lR49Ml9aMnD4ZA2htaeg3KoUjvvbts+3FkmP4A80nGxtX1vQK5Pkolx31W1YuSnlF9KFqt0fMHgnMGqW1/gLwj46D+Tf/Fr/r9nt6XpFMObP7hTyJUEBNBKZvqmvf91vXHDHNS1ebec/71hyp1QU/F+peHDrm9Zpp27ZdAwNDnbnc1P98rtTwqZOxXDLpc7vM0uJFHb1QLlf43R/3dQK0d2yV3uUsGB2VBT8LhQRmbft94H1A8ceP5K7bdMvp9/zHJ8eXV2NeszFmlof/RpJT1qfdfn1L8oN/tXVPS+uLz4qMxjzF3/7DDftf/qpFNbul5EKGqZzlqyJ7AkHXmHYcY3R4tG18PBHTemZPikmNjvjHenuiAGtWLz3g83qKAA8+Otg4OJQPmKY3tXjxzbLdYRYkE7LgZ6FQWtfFL/ILmlJqO+U9mksAOtrMwbfdFX78Tz7QtC8SNubkN97+QSv80c8mPgCKLS/b+MR0SuNpR/P87rFwf3/W29zsK265tinlctdn/VmtNQO92SUjw/mlAMownFAomAqFQ0nTNK6qxFEpl3Od2rO7w7EsY1F328kVy7rPAAwO59zv/Z0ntydTJU9b+5Yfrdvwpkdn4EcRF3j6yX+/O53qWwe8W2v96Wq3R8weCcw6oZSKAb8O/AbQBhAOqfSrbg899ed/0LhrJsvgXcp/fyZxT/+gdU378rZTSzcuOT3bz6tFyfFCbKA/t7yQt8tnlSqlAwF/JhwJJ9xTnG5yMblkwtN36GCrVSya0WhodMvG1fuUUti2w/v/8OnNh48lYz5/45md1//2pw3TXR+1B+eZRx76i9+yrFwM2Ki13lft9ojZI0OydUJrPa61/ivKvcxfBPam0jp07/2pW9fdfPoDt9/T84qHHsvO6HFeF9q20fs0wMjZka5CtjCne0S1o0mlSlWbw52uSMw7vnpt9Nkly8PPBULuUbRW2Uw2NNg/2DUyNNpayBemvZ0m3tsTPrP3+Q6rWDSDAX9q/TXLD6nKESj/9tHDyw4fS8ZM05PZuOntX5KwnB253Ji/EpY5ZMFP3ZMeZp1S5W/O24DfAV5eeVlvWuc58v73xB7/xbdEThvGjE6jobXmQ59IvHssbi8JxYLj62++5vnZPuLr6KFE4BMfObxs/954o1XSRjDkKt300raBX3n/2lPB4PwPiVzWCgwP5roTiVI7lXlNt8ddDIfDiUDQf9HKP45tqYEjR5pSoyNBgLbWxt41q5Ycn1gV+8Of9Df+9T/v2wjoNWtf/z+d3TtOzdXPs9D0nH18+dHDD7wDeEJrfUO12yNmlwTmAqCUWg98AHgH4AHo7nT1v/Pu8E//6LcaDwT8VzeHdr7BYSv0iXuT/6tY1MG2pa1nlm1eOmuHSO96cjjyVx/cvalQ+NnVwR1dgcw//9f1uyMRT02sWiwVbffQYL5rPF7ocmztAjBN0wqFQ8lgKJg2DKUL2YxrvK8vnBwZDjmWZRiGYa9c3n24s6NleOI+zzw3Gv4//2/PplzOdsm85ew7sO++mwYHdt8G/IfW+v3Vbo+YXRKYC4hSqg34tcpfzQDRiJF83cuDT/7Z7zc9u7jbPSMHSj+zJ7/0Wz/MvFNrVNuy1tNLNyw+pYyZHf0vFR317nse2hkfK15yCPPmn2/v/6M/33JkRh88yxzbMUaGC+2jI/lFVsnxaa1x8lntZNN2MZM+V/s5FPQn16xeejgcCmSh3Lv/zH0nuj7zxZMrbEerUKjj8LU7fv2LUtVndu168kN3p1K964D3aK0/We32iNklc5gLiNZ6UGv9p8Bi4L3AoUTSiXz6vtTta2489YE739L78p8+nYtd7XOu3ew7de1m33eUQg+eHFqy/9GDm4u5gnvqT07fww/2N04WlgBPPDrUls/bNfXfuGEaTjSsRqLe3BmSg6lszynywwNqIix9Xk9uzarF+7dvW7d7IizTmZL5R3+5+5pP3Xtipe1o1di06slt29/7JQnL2ZfLjUlJvAVEepgLmFLKoDy/+bvAreXX0Fs3eA/+5q/EHn/H3ZGeq7n/08/ll/3wJ9k3Fks64Pa4iss2Lz3Y2Nk4fvUth09+5PCiL33+5PKprvvXj97w9Ko10Rk5BWTWaE1yNBUa6483JYaTTblULnz+28GAMWoYWKm00zbxWjQSGlvU3XZ2ZEwV//zvn18/OJQPKGUWlyy95YFlK27bP/c/xMKTy8V9Tzz2d38I5IGw1vqyVzmL2iKBKQBQSm2hPM/5FigfOL10kavnXW+KPP6/f6PxoMdz6co8k+kftMJfeiB1dzzhLAJoXtTUu2zj0hOm27yqedMv/M/xzv/52NFVU133iS+85PH2zsC06tfOJceyjbH+eEN8YLwpOZJsKhVfOHnGNLCam8wTSxa5j2xZ7z3S0eZKAZw6W2p8+Inc9WfOlrb2DVquXc8V2HOggGWB2xMaXrf+nvsam1bVTdGH+a737OPLjhx+4J3Ak1rr66vdHjH7JDDFiyiluijv5/xfQANAQ8wYv+uVoSf+/Pebdne0uS47fCxLG1//bvrn9h8uvlRrDK/fm1uxddnBSEskNfWnL+7EsaT/N37ppzsm+8+3rcOf/eQXXzpvysHlM3nPWO9Y0/hQoikdTzc4jj43XOz1qlRHq+vIquXuI1s2eE8G/Ebpws8nU475dx8aW/eFr6V2njxjdZ3/nml6E61tG59YtuL2Z73eyLz7BaEeHdh/302D/btvAz6stf71ardHzD4JTHFRSqkg8G7Kvc4VAD6vyr/sJYFn/8/vND65fYsvebn33H+40PGdH2XvymSdZqWUblvWembJukWnlXllW0/+6oO71zz28GD7pd7/nT/auO+2O7uqVmxca01qJBUeG4g3JYYSTbl0/kXHfEXDRl93p+vI+jXew2tXuQcm9lCer1jU6otfT3V9+RvptQ8+lt2SyeqJs0STwGeBQeCtwBoAw3DlGxpXPrts+W1PhiNdl/3vSEzfric/9MZUqnc98Eta609Uuz1i9klgikkppUzg1ZT3c95cfg3n/2/vXoPbKs88gP9f3SXrYl18ke+WbTnkAjYQCNAQSCCQpXQLLV2mUKClQAnlsiXdsh+6MO3OsNvZD53ZabfsFthdSrZTSkPLpVx3G+LI1yR24sS27DhOYidxfJEs2bpLz344SqKAiZVEtuT4+c144pxzRn4VT/z3ed73vM/VDeoDzz5udt1zp+H4+bxeMJRQbHt3en3/oeh1AKDVa6drr6zuyTPrz3ueMRSKy158vrO+vXmsMPW4XCHo/u/U9v/N/TXnNbZMiEdjssnjXrNn1GP1jfutsdRSqxxRm0U+WFWhdDeuVLuLChSzNnIeORFTv7p1yvHBXwL1XfvDdTMB0qWc3gupifjrRDQNnJ6LvgPAFgA3SpeJhNFU3l1Ruc5VULh8dH7e7dK2Y/tPn4pFA2YADUTUle3xsPnHgcnSJoRYDSk47wEgB4CaKuXhh+8zNj/7PbNboUh/nnNXV6jq408DXw2FySRkIlFaZx8srS8dme0uay5duyYMrh2jVr8vqiwu0QVvu6P0ZJF94eYtQ9Mh9cSxSat3dMo67Z3Op5RSq0YtfPYihbvOoexrWKke0mpksy4McbUH8//rd776ppag0z0YrUwkkPps6SEAbwP4HQAXneM/bfJ79Cyk75EMAHS6gsHSsjWu0vI1B6VsZRcrFPJqmpv++UcAwpAW/HyuhM4uPRyY7LwJISoAPAXgEQBGALBZZJNfv9PQ8sIWS2eBTZHWDw/fdEL95tv+24+MxBoAQG/O89Y2Ono1Bm1ON48mIvjGfYZTq1pDnym15htlI2UlCveKZeq++hrl6BeVWv9nm79s23vT9W17Qs7RsXhByukEABekkHwHQM+5QnI2QohqAE9D6puaBwAqleFksb3RVVW9vluuUC+KDR1y1chwS7W7948PAGgjomuzPR62MDgw2QUTQhgBfAfAM0h2StFqRHDjzbqOF7ZY2y5frp615PhZO1qDy5pagndGoqSTy2XxssvK+u2OolFcwN3mfIlHY/LUVa2xaPz0c6VyOaIFVsXB6gpFX+NKdX+BTTFrg+yjx6Lql1/31X60PeDceyBcFwhSarPtKQDvQwrIPxNRRuZehRBmSM/cPgXALo1X7bcVLm911GzcpdHkZ2SziqWmZ/8b1584vvtWAP9GRJuzPR62MDgw2UUTQigA3AWpFHgtAMhkiK+5StO9ZbO5+a9v1885hzY2Ec978x3/naNj8XoAMBUYx2saHW6VVpW1UlfQH1RPHpu0ek5O2WY8M2f1sNSoxVRJscLtdCj7GlZqhtRqMesd246WoPm1N3z1O1qCzoGhaGUicdZmIQM4cxe5Yz7LekIINaRHhrYAWCEdk0fMlprdVY4NLSZTxdR8fe1LUUfbL77u9w2vAPBdIno52+NhC4MDk2WUEOJ6SPOcdyE5h1Zfoxx85Fum5icfzj94rnlOIsLHnwYa2naHN8XipFIo5dGqVZV9tnLbgqx0pUQCvnG/8XSpdSaUl3o+3yQbLi9R9q1cpnLXOZQnZyu1hkIJ2etv+sv/+MGMs31PyHlyPG5LOR0H0AQpIN8G4D7fUuvFSm7Kfxuk4NyQPJowGEsPVFTe6CosWrXgC6UWo6btP30yGg1YADQSUWe2x8MWBgcmmxdCCAekObSHkZxDK7TJx++9y9D8/BbL3nyj/At3RRk+HjNte2/6q5OeeBUAWOzmE46G6gGFSpHxebdYJCafPD5p9pzw2nwTfks8pdSqkItIgU0+UF2hcDeu0vTbLPJZV/IOHY1qXtnqq/1o+4xzX0+kLhgiTcppL4A/QwrJ94loMtPv4UIJIRohVQXuRXIRl1ZrHSopu8ZVVn7DAG+tN7tQaErd3PRPz4EX/Cw5HJhsXiUbWz8KaQ6tFADydGJm04a8jhd+aG2/rE4163xfIkHinY9mru3aH74lkYBcqVGGHFdU9ZqLzRddOgz6ApqJYx6r9+SUdcZ7dqlVqxHekmJFn9OhcjesVB9WqWYvtf5vU8C69U2/c0dr0HlwKFpBdFaptQ9n7iJduf4DVQhRDun78xgAAwAoVfrxouIrXNXVG/YplFre8i3FyHBrlbv3rQcBtBPRNdkeD1s4HJhsQQghlJAedXgWwJUAIJcjdsNq7d6/f9rcvPGmvFm3dBs4FCl4+8OZu3z+hB0AbGXWkdI6+7DWqEt7sQolEpga8xknj3tsU2M+azgQPv1coxCgfJP8aHmJwr3qMpW7pko5NlupNRBMyH7ze3/Fn96fdrZ3hZ3jE3FryukYgB1IzkcSUX+6Y8slQggTpFW1zwAoAwC5XDVttV3W5qi5tUOrswazOb5ckbLg51dE9Hi2x8MWDgcmW1DJObQbIQXnnaeOL69XDWx+yOR67AHToc82to5ESP7W+9M39vZH1hJBAIDBYpgsqioYyS80+RRq5Vl3QESESCCsmhrzmTwnvFbfhN8aj8VPt8ZSyEW4sEA+4KhQuhtWqQes5tlLrYOHo9pXtk7VfvxpwLmvJ1IbCp9VavUAeA9SSH5ARN6L+ofJIclfbr4BaZ6zQTomi+abHXuqqte35JurPdkcX7Z1tP3ia37f8EoAjxDRr7M9HrZwODBZ1gghnJDuZh4CoAUAe5F89L67Dc0/ftbarc+TnVUO7XFHil0dweuOnYitSH2wX66Qx1QaVUiukkejoag6EopoUjcPAACdVnhKihV99TUq9+XL1UdmK7UmEoRPdgRtW//gc+5sCzkHD0fLP1Nq7cGZUmvzpd6dIvnLzXpIwXl78jDpDSW95RVfchXbGy+qm81ilbLg50oi2pPt8bCFw4HJsk4IYYM0f/Z9AMUAYNCL6S/fqm/7yY+sHY5K5VmlwPHJuK6pNdg4eDi6KhBMWOJxfK7XplIpAga9bEwqtardjkrF+Gyl1umZhPy1N3wVb3844+zoDNVPeBLmlNMxANtxptR6MHPvenERQqyEVBW4D8luNhqt+WhJyWpXeeXaPplMsSR+kIRDPrWr6cXnAEQgLfjhje6XEA5MljNSnhX8AYBVAKBUILr2Om3n3z1habt1ne5z85xEhPGJeN7xk3Hz9ExCZzXLp0qKFR6DXvaFP8gODkW0v/6Nr+6TpoCzuydSG45QaiPqCZwptX5IRPx8YgohRAmAJyF1s8kHAKVSN1lYdEVzdc0tnUqlLqfvuqemjpg8kwftAoDFVj9iMJScV8ecYyNtlX092x4C0EFEq+dlkCxncWCynJMsBW6AdEdzqhQIp0N56L6vGdq3bDb3aTSytPtpJhKED/4vUPDbt/zOnW3B+qGjsbJTc6FJ+3FmA4EWIuJt4+YghNBD2uXpbwFUAYBMpgxYbcvaq2tuac/LK5x19XO2hEJeTffe17+c3GwgSZDJVLF35RX3v6tS6dNaydxz4PfXnTi2ayOAl4joe/M0XJajODBZThNCrID0yMP9AHQAYNTL/Dev1XauWqYeXe5Uea68XO2tqVIGZDKBSIRE5/6wsbM7bHYPRvK7eyL2jq6Q0+NN5Ke8bBTAX3Cm1Hpogd/WJSO5y9PdAH4I4GrpmCxmyq/qqqy+udliqc1ae7VT4rGwvLXl598Oh7yls53X6qxD16x55r/TKSvvavvl3T7f0VUAHiWi/8j4YFlO48Bki0LykYcHAWxGsvdjKrVKRHRaEZjyJwyf6fRxyhiAdyHdRX5IRBfcvJp9XrIqsBbSAqFTq59Jr7e7yyqudxXbrzySrU4pfb1vrT423PpX57qmomrdtpra2/fO9VpN2//x+9HojBXA1US0K2ODZIsCByZbVFJWbm4C4ABQnfzTmHLZCQCDyY9+AB9CesicS60LQAixDNI89AMA1ACg1uSP2EuuclVWruuVyZVpl9MzoWXnvzwYDE5Unesavd7et3rNU7891zXhsE/l2vHic5AWgxmIKKe76rDMU8x9CWO5I7n36ifJDwCnQ9QCwApgmIjOuxk1yxwi6gXwqBDixwCeAPBEOOQtHRr85J7hIy5vYdGq5mrHLZ0qtWFBVpjGYiHDXNdEY4E5r5kY77MDEAD2cVguTdxNli16JJkgIjeHZe4golEi+gcA5ZCC82AsFsw/NtK2qXnnz57Z1/Xa+mn/cf0cL3PRFEqtb65rlMq8OVdD+6aO2JOfcil2ieLAZIzNKyIKENEvIc093w3AlUjEtONjB9a2t/7rM7s7XvrKxHhfwRwvc8GsVmf3XNfYbJftn+uamenRkuSnHJhLFAcmY2xBEFGciLYR0Q0AbgDwB4BkU96hxr2d/7m5rfnn3xwZbq0iyuwUZ03dpj1arfXwF53Pyyvqr3KsnzMwg8FJvsNc4njRD2Msa4QQtZCe5fw2ktsjqtXG48X2q1yV1TcdkMtVGUnPaGRG2b339du93qEGQNo2UQhZPN9c07Hy8m9+pFBozrkgbHLCbeva8+oTAAIALDyHuTRxYDLGsi65PeLjkHYRKgAAhUIzZStc0epwbNyt1hgzElCBwLjOM9FfBCEji7VuVKu1pNWBpXP3y5s8kwPXAPh3InosE2Nhiw8HJmMsZwghNAC+BWmXp3oAEDJF2GKp3VXtuKXVYCydcwFPpkXCfpVr589+QImYGkADEXUt9BhYbuDAZIzlHCHtcnAHpI0QbkweTRhN5d0VletcBYXLRxdqLN17t940dnLfOgA7iehLC/V1We7hwGSM5TQhxGpId5z3ILlQUacrGCwtW+MqLV9zcD53EBo+2uzo7/vT/cm/biSij+fti7Gcx4HJGFsUhBDVAJ4G8F0AeQCgUhlOFtsbXVXV67vlCnVGd3Ly+0aMuzteeiyRiOoA/ISIns/k67PFhwOTMbaoCCHMkPqnPgXADgByudpvK1ze6qjZuEujyQ9d7NcYHztQ1HvgzW8kG0V/BGATb63IODAZY4tSSv/ULQBWSMfkEbOlZneVY0OLyVRxQb1MB/rfu2L4yM47iBJKAF0AbiWisYwNnC1aHJiMsUUtuZfwbZCCc0PycEJvKOktKb2mvaR09dBc85yJRFyMDLc4jo+0XzszM1qXPPwqgCeIKK1HT9iljwOTMXbJEEI0QFogdC+SzSWUKv24xVLXmacvHM/TF3uMxjIPUUL4fcPmmelR8/T0aMHkZH9jLBowJ18mBCkoX8nOu2C5igOTMXbJEUKUQloc9CiAkjkuP+UogF8BeJmIFuyxFbZ4cGAyxi5ZQgglgK9AKtVWp3wQgEOQeqYegtQz9V1e2MPOhQOTMbakJDdFAGV6l3d2yePAZIwxxtLA7b0YY4yxNHBgMsYYY2ngwGSMMcbSwIHJGGOMpYEDkzHGGEsDByZjjDGWBg5MxhhjLA0cmIwxxlgaODAZY4yxNHBgMsYYY2ngwGSMMcbSwIHJGGOMpYEDkzHGGEsDByZjjDGWBg5MxhhjLA0cmIwxxlgaODAZY4yxNHBgMsYYY2ngwGSMMcbSwIHJGGOMpYEDkzHGGEsDByZjjDGWBg5MxhhjLA0cmIwxxlga/h8MCpF+OJV1WwAAAABJRU5ErkJggg==\n", 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tBS6nvcH7TntNg9f0BiNdh5xD5oYfcDltfscUjGKTu5LU2dunef/+/bz++uuBDUgI0e5IAihEEKufABpC/EsAEzv1Q28wNXq8KSKOmA4ZxxzX55TLUBSFP797jaKsjVgqi6ku30/2ll9ZPe/1w34mvfdo9CGh7N+7vtHxBIrTXkNVSS7mssLDls5prB4jxqOpnVF98cUXKS4++vK6EEI0pcB3URdCHDffGcCwIw88DL3BSMbgs9i9ZkGjxncfNk5NWI4mLDKeURc9TOaGH9j557fYa6oIMUUQldCRPqdcdtjPaLU6ug87n83LPvXrO7SksoLdZG74gbKCXeprGq2ODl0G0m3oeY0++AKw5ddZFOxeoz43m82MGDGCefPmMXCgd2aw/hJ9eHg43bp148EHH+TGG2888S8jhGj3pBOIEEFs6dKlnHvuuYD3RG334eP8+ryiKGxe9gn7M48+89Z5wBn0OnnScccZ7DLXL2XPukVwhD8udQYjA067hqQuAxt1vS2/zsJuNdNzxAT+mPc/FLcTjUZDcnIyBQUFgDcBnD59Oueddx4Wi4U5c+bw5JNPsnjxYvXfuRBCHC9ZAhYiiPnMAPq5BAzeJGPgmTfQa+RFhJgiGrxvioil/2nXtOvkr3DPWvb89f0Rkz8At9POpmWfYi7Nb/R1tTo9kfHpdKlts6coCoWFhT5LwTExMSQnJ9OtWzeeeOIJ4uLiWLp06XF/FyGEOEiWgIUIYidyCKS+zv1Po2OfUygv3EtNVTEarZaw6CRik7s2atm3rfK4Xez445tGjnWy449vGTH+Hr/ukTHwb+RuW4nL4T0gk52dTWKi72Ebt9vN3LlzKSsrw2Aw+HV9IYQ4HEkAhQhiJ3II5FBanZ749F7E0+sEo2o7DmRvwWlrfImW8sLd1FSVEBaVcMyxJTnb+GnGowA+h0meeOIJdZbvqquuQqfTYbPZcLvdxMXFceutt/r5LYQQoiFJAIUIYk01AygOz1zW+CXdg6rLCxuVAMamdKdv7aEYu9XMusXv43bZ+eGHH/jhhx8AeO211zjrrLPIzc3loYce4sEHH6R79+5+xySEEIdqv2s7QrQB5eXl6uMTnQEUDbkc/tcldDmsjRqnM4QQFp1IWHQiscld6XPKpep7kydPBiA5OZnu3btzxhln8OWXX3LPPfewbds2v2MSQohDSQIoRBA70UMg4uhCI+L8/ozpOD4DkNxtCNSWflm/vuGp7O7du3PJJZfw+OOPH9f1hRCiPkkAhQhiPnsA/awDKI4toWMfv8YbjOFEJ3Zu1FiP24W9pgp7TRXV5fvZuerbBieNnU6nz/OHH36Y+fPns3btWr/iEkKIQ0kCKEQQ85kB9KOrh2iciNhkEjr2bfT4jn3HoNM37pRuad4Ofp09hV9nT+HPea9RWZLDoL/dRFxqT3XMkiVLfD4zYMAAzjrrLKZMmdLomIQQ4nCkELQQQax3797s3LkTvcHEmTe8EOhw2iR7jZk/vn0Ze03lUcfFJHdl+Lh70Gp1J3S/qpJc/vj2FQASEhLYu3cvUVFRJ3RNIYQ4lMwAChHEDs4Aygng5mMMi+TkSQ8etQ9ySo8RDDvvzhNO/gCiEjqS3G0oACUlJbz00ksnfE0hhDiUzAAKEcRMJhN2u53IuFRGXfxooMNp88oKdlO0byM1VSVotXrCY5JI6T6CyLiUJr1PTVUJK796HsXjJiwsjD179pCS0rT3EEK0b1IHUIggZbPZsNvtgJwAbilxqT2IS+3R7PcJi0qgY59TyNm6nJqaGp555hnee++9Zr+vEKL9kCVgIYJU/RqAsgTc9nQdfA46gxGAqVOnsnPnzgBHJIRoSyQBFCJINWUbONH6hIRGkDHwb4C3F/ATTzwR4IiEEG2JJIBCBCkpAt32dep/GiGh3hPAX3/9NatWrQpwREKItkISQCGClBSBbvv0BiPdhp6rPp88eTJybk8I0RQkARQiSPnsAZQZwDYrrddIwqITAfjtt99YsGBBgCMSQrQFkgAKEaRkCbh90Gp19Bg+QX3+2GOP4Xa7AxiREKItkARQiCDluwQsCWBbltRlINFJ3h7D27Zt4+OPPw5wREKIYCcJoBBByncGUPYAtmUajYYeIy5Qn0+ZMgWr1RrAiIQQwU4SQCGCVP09gFIGpu2LS+lGQsd+AOTn5/Pmm28GOCIhRDCTBFCIIOUzA9jMS8CKx4PVasViscj+swDqMWICaDQAPP/885SVlQU4IiFEsJJWcEIEqeYqBO1yuSgqKsJsNmO1WtWWc/XLj4SEhGAymQgNDSUsLIzo6Giio6PRauVnyuYUGZdCao8RFOxaTUVFBc8//zwvvfRSoMMSQgQhjSJFpYQISiNGjGDt2rWg0XD2za+iqZ0ZOl7V1dXk5+dTVFR0XLN8Wq2WqKgoYmNjiYmJISoqShLCZmCrLmfFl8/hcTsxGo3s2rWLTp06BTosIUSQkRlAIYLUwT2AhpDQE0r+SktLyc7OprKy8rDv6/UGQowmQkxGNBotDrsNh92O02H3GefxeKioqFBnJnVaLVHR0cTExBAbG0tUZCQaSQhPmCkilk79xrJv08/Y7XamTJnCjBkzAh2WECLIyAygEEEqISGB0tJSQiPjGXvFP/3+vKIoZGZmkpOT4/O6TqcjJiGJmPhEjKYwdHrdYT/v8Xhw2G3YLBaqzZVYqipx2G1HvJ9OpyP6YEIYE0tkVOQJz1q2V057Db998R9c9ho0Gg0bNmxg4MCBgQ5LCBFEJAEUIggpioLBYMDtdhMZn86oix7x6/N2u51t27b57CMMDQsnLimZmPhEtLrDJ33H4rDbsdQmgxZzJQ67/YhjDyaEB5eMIyMlIfTHvk0/s2v1PADGjRvHwoULAxyRECKYSAIoRBA4+Nv0YIJkNpuJiooCIC61J8PH3d3oa1VUVLB161YcDkftNbWkdOpCfFIKNGX+pYDDbsNirqydIaxqsGxcn16v90kIIyIiJCE8CrfLycovn8VmqQBg2bJlnH766QGNSQgRPCQBFCII5ebmqhv/k7oMYvBZNzXqc9XV1az76y/cHg8AISFGOnbvRVhEZLPFqlLAbreps4OWqkqcTscRh+v1emJiYtQ9hBHh4WoJlOPlcbsoL9xLSGgEkfFpJ3St1iB/12q2Lp8NeA8F/fnnn5I0CyEaRQ6BCNHKbdq0ie3btxMdHU1ERAQGg8HnfZ0+BLfLgVZnOOpf/i6Xiy1btqjJX2R0DOlde6I/5HrNRgNGkwmjyURcUgdvQmizepNBcyXVVZW4nE6feEtKSigpKQHAYDCoCWFMTMxxJYQ2SwWFe9dSVrAbl8NGTIcMug49l5ikLk35TVtMavfhZG9eRnV5IWvWrOGrr77isssuC3RYQoggIDOAQrRyN998Mz/++CO9e/emsrISvV6Px+Phjz/+ACA8Jpnw6ES6DDyTmA4ZR7zOli1bKC4uBiAsPIKufQa0rlO5CthsNViqqtSksH5CeKgQg4Ho2tnBmJgYwsPDj3kLp8OKy16DVh+Co8ZM9pZl2K1m+o29ElN4TBN+mZZTnLuN9Us+AKB79+5s27atwQ8JQghxKJkBFKKV69u3L7NmzWLSpEmcdtppZGdns3XrVjUB1Gp1OGyWo14jNzdXTf70ej0du/duXckfgAZMoWGYQsOI75CMoijYrVZ1drDGXIXLVZcQOpxOiouL1e8VEhLis2QcFtawP7IhJFQtmm0MjaTHiIn8OvtpqssKgzYBTEjvQ2xyN8r372XPnj18+OGH3H134/eECiHaJ5kBFKKVs1gsfPXVVyxZsoTrrruO888/n08++YQbbrgBgN6jL6FT37FH/LzVauXPP/9UD5J06dmXyJjYFom9KSmKgs1aU7eH0FyF2+U64nhjSAgxtbODsbGxhIZ6Ez9LRREGUwQhpnAO7NvExp8/ZsjZt5LQsU9LfZUmV3kgmz/nvQZAUlISe/fuJSIiIsBRCSFaM5kBFKKVCw8P54YbbkBRFF5//XX++OMP4uPj1fcNIaEoinLE/X8FBQVq8peQnBaUyR94T0CHhoUTGhZOQnKqNyGssWCpqqLaXEmNudKng4nd4aCoqIiioiIAjEYjMdHR2A9sonjvavTGUGI7ZNB92PlBnfwBRCd1pkPGIIqyNnLgwAFeeeUVnn766UCHJYRoxWQGUIgg8ssvv/Dss8/i8Xj4+eefARhyzm0kdup32PEej4fff/8dp9OJRqul96DhLXfoo4UpioLVYlH3D9aYq3wSwvpJsuKsRlOdg06x0XX4RSR0SMVkMgUq9CZhqSzm96+eR1E8REREsGfPHjp06BDosIQQrZQkgEIECY/Hg1arpbCwkDFjxpCZmQnASRPvP+Lhj/3797N9+3YAYhIS6di1Z4vFG2iKR8FaU11vydiMx1MvIXQ7cOX9gi6+H9qINEJNocTE1u0hNBqNAYz++Gxb+SV521cCcM899/DWW28FOCIhRGslS8BCBAmtVouiKKSkpHDqqaeqCaC+9lDD4eTn56uP45NSjuu+NWX5lO37C1vlATweN8bIeGLS+xOV0qtV15zTaDWERUQSFhFJrD0Oc9EeQmK7YDFXeZPCqlI0+jAUWzlEpGG1WbEWWiksLAQgNDRUPWEcExMTFAlhtyHnUrh7DW6Xg/fff58HHniA7t27BzosIUQrJAmgEEHkYMJVv4XbkRJAq9VKVVUVAKFhEYSF+1fs2e20k7NmLuXZG3xerz6wl9K9qwmL60jGKddgjIhr9DWdVjP7t/5EZcEOnNZK9KYIQmNSSeo1hqjkHmyZ9zxJPceQ1Nv3UEvB5qVU5m2lz/kP+vUdDrJVFrF/2zIiEjoTldKL0FgjrsKdoIPI9D54QqKpqTbjqa2RCN5fP6vVSkFBAQBhYWHExsSoB0tCQkKOK5bmZAyLovOAM8hcvwSXy8VTTz3F559/HuiwhBCtkCSAQgSh+gmgwXj4BNBiqSsNExkT61ebN8XjZs8vU7GUZB9xTE1ZLjuXvkXv8+4nJCz6mNe0V5ex68d30BlCSRs8jtCYFBTFTVXhLnLXfku/Cf9ofIB+CotPJ6X/WZTtW0fuX9+h1YcQFptKYo/RRKf1RaPR4PF4sFabqa5dMq6prkZR6hLCmpoaampqyK9NCMPDw73LxbVJYWupvddlwBnkbl+J01bNnDlzePjhhxkxYkSgwxJCtDKSAAoRhA4mgBqtDq3u8IlH/QTQGNqwJt7RFG758ajJ30EuezXZf3xBjzNvO+bY3LXfABp6nXsvOn3d7FlodDLxXZs3QdHqDMSk9yMm3XtYxu2wotHq0errfu20Wi3hUdGER3mTWY/bTU212btkbK6kptpM/S3TFosFi8WiLrNHhEeoewhjYmIClhDqQ0x0G3IuO1bNBWDy5Mn89NNPrXq5XgjR8iQBFCIIlZeXA94SMEf6i71+AmjyIwH0uJ0U717V6PHmot3UlBcSFnvkPYYuew1VhbtIHXiuT/J30NH2MTYHXSPup9XpiIiOISI6BqhLCA/OEFot1T4JYbWlmmpLNXl5eQBEREQQGxOrJoV6fcv9cZveexTZW3/FWlXCsmXLWLJkCeedd16L3V8I0fpJAihEEDo4A6g/wvIv1CWAGo0Gox8lTmpK83A7avyKp6pw51ETQHt1CaBgiko65rXyN35PweYlPq8pHnejPtucDpcQemcHvYdKrDWHJITV1VRXV5Obl4tGoyEiIkI9YRwdHd2sCaFWp6fHsHFsWvYJ4J0FPOecc9C2tu4vQoiAkQRQiCDj8XjUwx1HmjlTFIWaGm8SF2IK9avtm91S7ndMjppjfOZgXtSIVcgOfU4jLmO4z2vFu1ZSfSDT77iak1anIzImVi2s7Xa5fGYIbTUWNSFUFAWz2YzZbCY315sQRkZGqnsIo2Ni0Ol0TRpfh66Didq8jKqSXDZt2sSsWbO47rrrmvQeQojgJQmgEEGmqqpKTSwMRzkBfPBEqynUv+VVfYj/BZF1hqPfwxiZAGiwVR6A9GPdPxxTZILv9Vt4ifh46PT6Bgnhwf2D3hnCuiV5RVGoqqqiqqqKnJwcNSE8WHYmOjr6hBNCjUZLj5Mm8tf37wDw1FNPcdlllwV9wWshRNOQBFCIIHNw/x+AwXj4vX019Q+AmPw7AGKKScE7Vdf4GvFHW/4F0BvDiErpSfHuVST2GtNgH6DLYW3xfYDNTafXExUbR1Sst0yOy+mkprrKO0NYVYXNeviEMDs7G41GQ1RUVN2ScVQU2uNICONTexKf3pvSvB3k5OTwzjvv8NBDDzXZdxRCBC/ZECJEkGlMDUBLTd0ePn8OgAAYw2OJTO7R6PF6YzjRaYdvRVdfx+EXoSgedi55k/LczdjMxVgriziwcwU7f3jbrxiDkd5gICo2ntTOXekxYDB9hpxEp+69iU9KafDvSFEUKisryc7OZsOGDfy2YgXr16+noKDAp71dY/QcMZGDa+/PPvusz38/Qoj2S2YAhQgyjUoAT6AEDED60InsXPomHpfj2GOHTUKrO/YfJcaIOPqcdz/7t/5M/voFOK1V6I0RhMWl0Wn4RX7HGOz0BgPRcfFEx8UD3hlCS1Ul1bVLxnabVR3r8XioqKigoqKCvXv3kpycTFpaGmFhx/53GxmfRkr3YRTuWUtZWRkvvvgizz//fLN9LyFEcJBewEIEmW+++YaLL74YgB4jJpAx6KwGY9asWUN1dTUajYZ+w0b6dQjkoMqC7ez7/TPcTtvhB2g0pA48j+S+Z/h9bXFsTodD3T9oMVf5JIQHxcbGkpaWRkJCwlHr/FnNpaz48jnvaWqTiT179pCWltac4QshWjlZAhYiyBxrBrD+CWCjnyeA64tO7UPvc+8nLmM4mvrFpjUaolJ60fPMOyX5a0aGkBBi4hNJy+hOz4FD6d5vELGJHXxKuZSXl7NlyxbWrl2r/js/nNDIeDr1HQOAzWbj6aefbvb4hRCtmywBCxFkfA6BHCYBrH8C2OjnCeBDGSPj6TLycjqNuBhHTTmK201IRNxhizmL5hUaHkF6RndSOnahvOQAZQf2q7OC1dXV/PXXX/Tu3ZvExMTDfj5j8Dnk7/wTl9PG9OnTeeihh+jbt29LfgUhRCsiM4BCBBmfGcDDFII+kRPAR6LV6TFFJhIakyzJXzPyeDwca1eOTq8nITmVHgOGkNGrn3qAxOVysWXLFvbs2XPYa4SYwuky6G/qfR5//PGm/wJCiKAhM4BCBJljLQH7nABuxCEB0fI8bg8OpwOPx+Pzz8HETaPRoNVq6/2/Fq3W9zWtVktEdAzd+g4kf99eKkqLAcjNzaWqqop+/fphNBp97tu5/2nkbluBvaaSefPmsWLFCsaMGdNi3/uDDz7g+++/p6ioiC5dujBhwgSuueaaFru/EKKOJIBCBJn6CeDh6gBammEGUDQNp9OJ3W7H6XQedZyiKI0q96LRaNBoNMQkpaAPMVK6vwBF8VBZWcnq1avp1asX0dHRhISEoNFo0OlD6Db0PLatmAN4W8StWLHiqAdImoqiKMycOZPzzjuP8PBwsrOzefHFF6msrOTuu+9u9vsLIXxJAihEkKm/B/CwM4DH2QNYNA9FUbDb7djtdnVv5qHUmT6tFhQFRVEatRys1BtriogiKT2E0v25uJxOXC4XW7duVa9vMBgICQnBYDChD43BZa3g999/59FHH2XSpEmkpKSQkpLSqNIyx0Oj0bBw4UJMJhMGgwFFUfjXv/7FlClTuO222zAYDMe+iBCiyUgZGCGCzNixY1mxYgUAZ930sk8NPkVRWL58uTchCA2jx4AhgQpT4F3qtdRYcLlcPq9rtVpCQozo9Hp1WfewFPAo3kRQ8Xi8jz31E0SP+li9p8dNSV42DvsRyvcAHksh7v2rD/teZGSkmgwe/Cc5ObnBa7Gxscc9c+jxeNBqtfz8889cdNFFbNu2TcrSCNHCZAZQiCBzcAlYqzM0KMDsewJYln8Dyel0YrFYfGbx9AYDISFG9PpG/tGrAa2m9qzeMVrB1Z81NHTpTt6e7eoystFoqo3Ju+9QE5aMxhSHYitrcB2z2YzZbGbXrl1HvV9ISMhhE8NDX0tKSvL5vm63G51OR0VFBW+88QYjRoyQ5E+IAJAEUIggczABPNb+P39bwImmY7VasdnqZuC0Wh1hYWHH1c+3sTQaDbra6+sjo0jN6E7unp2AN/Hr238QEZGRuFwuHA4HZUkRZP0xG4CUtHSGDh9J8YEiig/s50DRfizV5qPez+FwkJOTQ05OzjHjSkpKIjk5meTkZFJTU4mNjWXZsmVUVVUxefJkMjMzSU5ObrblZyFEQ7IELESQiYiIwGKxEB6TzCmXPubz3r59+8jKygKgU/deRMclBCLEdq2mxoq93vKrwRCCKTS0RQ5aHKoobx+l+wsAMBqN9B80VN1rpyge1n77IjZzCQBLVqyna/ee6mdrLBY1GSw5UMSBA/spLvI+Lz5Q+1rRfspKS5os3ujo6EbNKsbExATk11OItkRmAIUIIgeXFeHwRaCbowagaDyHw6EmfxqNBqPRRMghpVhaUlJaZ6zV1dRUV2G328nPzaFL12618WlJ6TWarLXzAPjsk6k8+X//VT8bFh5O54xudM7odtR7OJ1OSooPUFybGBYfKPImirUJ48HHJQeKGuyFPFRlZSWVlZXs3LnzqOOMRuMR9ybWfy0pKUmdFRVC+JIZQCGCSElJidrpIaFjX4aee7vP+2tWr6HacmI9gMXxcbvdmM1mdc9faGgYhpDAF812OR3s2bwOj8eDTqdn6IiT1aTIaa9h9VfP4HG7iIyKZsX63YSFhzdLHD8uXsCUyfczftIlnHrmObUJo3fJubioyCdhrKmxHPuCjaDVatXl50MTxUOTRZOcmBftjMwAChFEfGoAHjIDqCgKNdYT7wEs/Kcois+BD0NISKtI/gD0hhCi4xMpLy7C7XZRUnyADskpgHcfaWKXIRTtXYO5qpIF337J5dfc2CxxfPj2a5QcKOK7r+aw6rdfiE9IJC4hkauuv5XhJ4/26XFssVTXLTfXm0H0mVk8UER5WelR7+nxeNi/fz/79+9nw4YNRx0bExPTqFnF6OhoWX4WbYIkgEIEEZ8agIe0gZMTwIFjs9nUE7c6nY5Q04n1YG5qsYnJlBcXAXBgf6GaAAKk9DqFor1rAJg5/X0uu/qGZklwPp27iIryUnL2ZZGXs4+C/Dyys/ZirqpscL/w8AjCu3anS9fuR72mw+GgtPiAui/xYMJ46FJ0afGBYy4/V1RUUFFRwfbt2486zmQyHbU8zsHXEhMTZflZtGqSAAoRRI7WBq4pTgC7HFZKM9dQVbADe3UZOoMRU3QH4ruOICq5x3Fds607WOgZvPv+QsPCoZXNEJnCwgmLiKSm2ozFUo25qorIqCgAIhM6EhHfkerSXLZv2cSGdWsYMuykJo8hJCSEpA4pJHVIYfjJo5vsmilp6aSkpR91nMfjoby0hOLiIp+DLAeKvM9LiovU5NFqrTnqtWw2G1lZWephqyPR6XQkJSWRkpJC586d6dq1q88/3bt395n1FKKlSQIoRBA52hJwTb0ewMZQ/2egqvbvZt/vn+GyV/u8bq0opDx7A9Hp/ehy8uXoDnP45HD2/TEHt8NGt1NvYNdP7xEak0rHYRf4jKnI20Lmb58w9Kr/HuEqrZ/D4ahb+jWEtNq/1GMTk6mpLe1StL9ATQABUnqOYveqXABWLPuxWRLAQNJqtcQnJhGfmETvvgOOOE5RFKqrzYfsT6xLGOteL6KivGENxfrcbjeFhYUUFhaybt26Bu937tyZO++8k5tvvpmkpKQT/o5C+EsSQCGCiM8M4CF1AK1Wq/o4xOhfAlhdvI+9v05D8Ry5/2xl3lb22mfQ48zb0Whlaeugg7N/4J2Raq2iYuMpyt2Hy+WkrLQEl8ulFmiOTOikjsvNPvrMVlum0WiIjIwiMjLKpyTO4djtdrU8zsGSOMX1l6JrXy8pPnDYvs7Z2dk8/vjjPP3001x22WXcfffdjBo1SvYXihYjCaAQQaT+HsBDZwDrFx72p/SI4nGzb9VnR03+DqouzqJox3KS+57R6Ou3ZS6Xq27vn15/XIWeFcVDVcEOqg/sxVlTgd4YTmhsOjGdBqEzNF0JGY1WS1RcPGUH9uPxeKixWIiKjgbAFBGnjsvNzW6ye7ZlRqORtI6dSOvY6ajjXC4X+wvyyc3JIjd7H7nZWWzeuI7fly9DURQcDgezZs1i1qxZXHHFFXz44YdERka20LcQ7ZkkgEIEkaPtATw4A6jXG9A1ttUYUJG3FYel/NgDaxXv/p0OvU+TU8Z4l38POp7Zv5qyPLJXfYaj+tBiyn9SsHEB6UMvJLbL0EZdK+fPLyjf95f6XBcSRlhcOimDxhEa4z30UbTqfe97aWNr6xV6E8D6M7p7d+3w+3uII9Pr9aR36kx6p86MGlP3eva+TD7/5CO++uwTdTl5zpw5bNiwgblz59KvX78ARSzaC/kTXIgg4rMHsN4yr+LxqEuR/hYeNh/Y69d4Z00l9gYJS/tUf2lPrzf49VlLyT72/PTuYZI/L4/TTs6fcziw49dGXzMyuSd9L3iKvhc8RbfTbwONlqzfpvsO0oeimHN8ZoxLcjarSWB5WSn2eu+J5tG5S1cmT3mWFet389yr7xAZ5U3Gd+7cyUknncTMmTMDHKFo6yQBFCKImM11/Vl1+roZJ3u9gwj6EP8SQGdNpd9xOI7jM23RwbI7Go3Wr71bbqed7N9noXiOXpoEoHDTYmpKcxt1XY1OjyE0EkNoJKGxqST1OR1nTSUuW93BHm1kRzzV+dhq6v5bKtqzmtDIRPV5ft7R+/uKpmM0mbjs6hv4ZukK+vQfCHgPdF133XX861//Cmxwok2TBFCIIFL/tKC9pkp9HGKom31yu5x+XVNv9L/zg94Y4dd4ncGI29lwVsntsKE1BGcHBkVRUDzepNvfk7/l2etxWquOPRBA8XBg53J/w8PttFORvZ6QiHh09Q4MaYwxoA/DXLQLALulnMqiTKI6dFXHtOeDIIHSuUtXvpj/s08h7meeeYbvvvsucEGJNk32AAoRRLp2rftLusZcShze2nxanY6QkBAcDgcOu3/Ld2HxHSnNXNPo8VqDCVNUgl/3MEYmUVXYsL+rpSwXU72Zp2Di8XhQOL4EsKpgm5/jj16cuG7cDjbP/ac3PpcDvSmSjLE3odHUxafT6VEiO2Et9iaA+/esIS69D2HRdT9c5Gbv8ys+0TRMoaE8+8rbdM7oxkv/8f57vOGGG1i3bp3P730hmoLMAAoRROr/JWCt8m2DFVpb+8/pcOA5TNmJI4nrPBitvvHLxvEZw9Dq/NvvlthjFPbqUnLWfkNNeQG2qmKKd/1O6d41dOhzml/Xai08nro26v4mgP4cugFQ3E6cVvMxx0UkdaXnOffT85z76XHW34lM7knW8mk+99MbDN5lYGspNZXFHNi7hg7dT8IUHquOyZOTwAF12z0Pct6EiwCorKzk0ksv9dmzKURTkARQiCCSkZGhPrZWH5IA1mtm73DYaSydwUTH4Rc1amxIRBwpA85p3IUVRT0pbIyIo9dZd+Ewl7Jn2VR2LH2T0qy1dB55ObGdBjY61tak/pa/g/svG0ur8//EsFZ/7M9o9SEYIxMwRiYQFt+RjiMuxeN2UJq5Wh3j8XjQ6ELQhiez548v8bhdxKX2xu2q+28mPNy/JX7RtDQaDc+99g4Z3bwz/OvXr+fhhx8OcFSirZElYCGCSJcuXdTHh84Amup1/7Baqv1qBxefMRSXvZr8Dd+D4jnsGGNUEt3GXt+g/MyRuGzVGCPrlorD4tLpfsatjY6ptas/63fwMEhjmaI7YC3Pa/R4Q1jM8dUE1Hj/R6m3L9TtcoIBjPHdqcxeTnq/M9BotdiqK9Qx6Z06+38v0aQiI6N4c+pMLh13OjarlQ8++IAnn3yS1NTUQIcm2giZARQiiJhMJtLS0gDvHsD64mLrivmWFxf5fe0OvU+l9zl/J7bzkLqDGRoNxshEUgedT+9z7sUUdeyWVS5HDZX52zEfyCQyubvfcQQLrbbu5K9yhKT5SOIyhjfLeMXtwmk147SasVUVkb9uHh6Xg6i0PuqYg8lqeEIGIy9/hs6DzwPAbqlrbdaxcwYi8Hr16c9Nd9wLeAtKT506NcARibZEZgCFCDI9e/YkPz8fp62aqpJcohI6AhAdE014eDgWiwWLuQprjYXQMP9O+IbFpZMx+irAm8hpdSFodf79MZH955fUlOaR1PtUotPadjFbrVaL2+32ewYwIqkr0en9qczbcsyxIeFxJPYcc8xxAOb9u9g27z/e2PRGjFGJdB59DRFJ3RqMNZnCMJjqlnpt9fYJSgLYelx57c28/8bLeDwe3n//fR5//HEMBv/24ApxOBrF380rQoiAev/997nzzjsBSOs1kn5jr1Tfy8/PZ9cu7+nOuKRk0ro0/ItfNJ3qagtOp7cbSERklF+HQdxOG1m/zcBSfOSSK4bQaDJOvUnt5HGiqspKyMv0/vfRqUtXUtPS1ff++u6/1FQWEWI0sjmrxO+DLcKXzWpl+9ZN7C/IJy4+gV59+xNTb5beH3fdeAU/Ll4AwNy5c7n44oubMlTRTsnvcCGCzDXXXKP2Ci3cuw6nvUZ9r0OHDuhr+9FWlhbjcvpXE1D4R6+va6HmrNcWrjF0BhPdTr+N5AHnoQ+N8nlPqw8hrutJ9Dz3/iZL/gAc9rqDHsZ6h4YURcFW7V0CTu/URZK/E2C32Xjx/57klMHduXzCmdx3+3Vce8n5jB7UjYfvuZkDRYWNvtaj993OXTdewdU33qa+NnnyZHQ6HS+88EJzhC/aEfldLkSQiYiI4IYbbgC8td4KdtfV8NPr9XRITga8bcoK9u0FmeNvNiEhIeo+wIMzgf7QaHV06HsGfSc8Rq/zHiTj1Jvpcfa99L3gKTqOuOS4inQfjdVSV0rGZKxLAJ02Mx6394eFjp26NOk925Oy0hIum3AGU995narKCp/3nA4H8+bOYdJZo9m6ab1f1z3l1DPpkOz9QSAzM5NHH32UadOmNVXYop2SBFCIIHTXXXepj3O3r/QpQ9K5c2d1j1BleSllxftbPL72QqvVqr/WHo8H53HOuGq0OkzRyUSl9CIsLv34Tvweg9Nhp7rSu8/PaDQSFl6XXFrrHSjqJPv/jouiKDx0901s37LpqONKig9wz81XU1nR+FqQWq2Wrt17At7/zh588EEsFgvLl/vfIUaIgyQBFCII9e3bl9NPPx2AmsoDlBXsVt8zGo307t1bfV6Yk4WtpubQS4gmYjTWJWtOP+ovtrTy4iL1B4WkDik+vYvLcreqjw8mGsI/v/60hJW//tyosfl5OXwy9V2/rp9eb2Y2Ly+Pq666io8++sivawhRnySAQgSpu+++W328849vfHoAJyQkkF67wd/j8ZC7dxcet38nVUXj6PV6dLX7Ll0u13HPAjYnRfFQUeItDaTRaEiq3SYA4HE72b/nTwAMBgPnT2xcUXDh65svZvs1/usvZvk1Pim5bi9oVlYW1157LV999RVVVY3sKS3EISQBFCJIXXjhhfTv3x+A6vJCdq2e5/N+t+7diIjwlvmwWS3sz93X0iG2G6Z6BypsVqvfnUGaW1V5qXogKC4+EYOhrqtI8b6NuGoPEp038SLiE49d61E0tGdX4/o1H5Sfm43Nam30+KL9BerjvXv3MnjwYLp27crnn3/u132FOEgSQCGClMFg4LPPPlOTj9xtv3Egu66unFarpW/fvursVOmBQqrKSw97LXFiQkJCCAnxJlWK4sFqbV1L7uUH6gqDd0jxPVVcuPN39fG1N93RYjG1NXa7f8v/iqJgtze+v++aVSvVx48//jh6vZ6tW7fKMrA4bpIAChHE+vfvz2uvvaY+37p8NjZLhfo8PDyc7t3runHkZ+3B6edfVKJxwsLC1PIpLqfT77IwzcVcUUpNtXeZMCwsnKioaPW96tI8zCXZAPTuN4Ahw08OSIxtQcfOXfwaHxUdQ3RMbKPG7ty+hex9merz//znP2zYsIHly5ezZs0atmw5dkFxIQ4lCaAQQe6OO+7goou8+7ac9ho2/zITpV5nitTUVBITEwHvHrXczN2tbomyLdBoNISF1fVfttmsfncIaWoOu42CrL3q85R6hZ/Bd/bvmhtv9zkYIvxz1nkT/Rp/9vmNH//l7I9JSEhUn48dO5b+/fszZswYRo0aJbOA4rhIAihEkNNoNEydOpX0dO9f7uWFe8ja+KPPmN69e6tLxRZzJcUFeS0eZ3tgMBjUU8GKomAL4FKw4vGQt3cnbrcLgPjEJBKTOqjvuxxWDmStA7xdTCZefHlA4mwrLrzsqgYJ9pGEGI3ccud9xxyneDxotFrmzZ1DVHTdbGHXrl3Vx5dccgkzZ87E0UpmnEXwkARQiDYgLi6OWbNmqUuQe9ctpqKorsWYXq+nb9++6gzPgYJcLGY5PdgcQkND65aCXS6f7hstqTAnE1uNxRtTWBhdu/XweT9/269q8eeLLr+G8PCIBtcQjRceHsEbH84kNDTsqOM0Gg3/ev41evTue8xrlpYW06FDCqu35ai/d00mE8n1TnE/9NBDFBcXq3tQhWgsSQCFaCNOPfVU/vnPfwLegwibln3i0yYuOjqaLl261L6vkLd3F26XKxChtmkajYbwekWW7XYbHre7RWOoKDlARckBAHQ6HT171R0GAqgo3E3O5h/VeK++4dYWja+tGjx0BF99/wu9+vQ77PsJiUm89/GXXHb1DUe9TmVFOct+WMyfv//G6FPPYO/unezdvQPwzuZLqz7RFPSBDkAI0XSeeuopfv75Z3777Tds1eVsW/EFA8+8QZ096Ny5M+Xl5VRUVOBw2Mnft4dO3XqDbP1qUnq9HpPJhM1mQ1EUrNYawiMiW+TelqoK9ufUHRjI6NaD0Hp7E+01lez4bSbU7gO995En6d6zd4PriOPTs08/Fixbze+/LWP17yvYX5hPXHwC/QcO4azzJzZqpu7xB+9i04a/uOXO+zjrvAn855//UN+7/vrrmzN80Y5oFNkNLkSbkpOTw+DBgykv97aa6jvmCtJ7j1Lft9vtrFmzRi1YnNalG3FJyYe9ljh+iqJgNptx187+GY0mjPXqBTaHqrIS8rP2oCjewycdklPJ6FZ3CtzjcbN56btUHfBuDxh7xllMnfWNzCi1YjUWC6cM7k61uYrQ0FDy8/OJjW3c6WEhjkZ+1wvRxnTq1ImpU6eqz3es+prq8rp+wNIqrmV4TwWHq7OvDoddTQabQ/mB/eRn7VaTv7j4BDpndPMZs2/dQjX5S0lL5+W3PpLkr5Wb/80XVNfu173qqqsk+RNNRn7nC9EGXXzxxdx5552At9XXpmWfNGgVl5aW5n3f4yE3U1rFNQe9XqeevlZPBTfDmktxQS6FOZlqeZ8OySn06NUXrbZubb8kexP5234FvKeV3/hwJnHxCU0fjGgyiqIwc9p76vP67R+FOFGSAArRRr366qv06+fdjF5dVsDuQ1rFde/enYjak5+2Ggv78/a1dIjtgsloQq/3brd2u93Y7I1v/3VMikJhdibFBbnqS2kdO5HRrQf1S/pZq4rZ9fsc9fnj/3qBwUNHNF0colks/3kpO7Z5izyPHDmSYcOGBTgi0ZZIAihEGxUaGsrnn3+uzkDlHK5VXL++6GqXAEuLpFVcs9B4u4SoS8F2e5OcvlYUD3mZuygvrlve79K1Gx07dfEZZ6nYz6al7+J2etuOjb/wUq69WVq+BYMP3qrr8vPoo48GMBLRFkkCKEQb1r9/f1599VX1+dblnzVsFdejrj6ctIprHjqdjtDQUPW51VpzQt1YPG43Obu3qwm7RqOhe8/eJKek+YyrKt7HpsVv46ipBKB7zz785+W3pONHEFj/12pWr/oNgF69ejFp0qQARyTaGkkAhWjj7rzzTi688EIAnHYLm3+ZFdSt4hRF8RZYdjiwWm1YLDWYzWbMZjMWiwWr1Yrd7sDpdAW8FVt9RqNRXQr2eDzYbce3FOxyOcneuRVLlTep0+l09OrTn4TEJJ9xZfnb2fzD+7gc3gM+/QYOZubc74looXI04sR8+FbdD26PPvqoHNYRTU7KwAjRDpSVlTFo0CDy8rwt4LoPG0fXIeeo7zudTtauXYvN5l0m7JDWiaS0jgGJ9UhcLhd2ux2n0+lXgmowhGA0hmAwGJoxusbxeDxUVVWp8YeFhaP3Iy6n3U727m04apNHg8FArz79iYj0TeoOZP7FrpWfqyeCR405jbenf05kZFQTfRPRnPbs2sH5p3r3+6WlpbF37161xaAQTUV+pBCiHThWqziDwUDfPq2vVZyiKNjtdqqqqjCbzTgcDr9nJ51OB9XV1VRVVamFmQNFq9X6tAqz2ayNjsdurWHfjs1q8mc0Gunbf1CD5C9/+3J2rpitJn/nTbiID2d9I8lfEJn6zuvq44ceekiSP9EsZAZQiHbkX//6F8888wwApohYRl30DwzGuoRk3759ZGV5E8MQo5Hu/Qaj0wemYZDL5cJisTRYxtVqtRgMBrRaHVqtFq1Oqx5k8Xg8eDwe3B4PHrcHp9PR8PMaLWHhYQGdEayutuB0OgDvDGX9Th2HU1NdRe6eHerhkdCwMHr3HeCTGCiKQvaGReRu/kl97arrb+Xp51/1aQMnWrfC/Dz+NrI/TqeTmJgYcnJyiIyUZXvR9GQGUIh25KmnnmLMmDEAaqu4+j8Ddu7cmZiYGMB7WjV/355mqVt3LDabnerqap/kTa/XEx4eTlRUFKFhYRhNRgwhBm9yo9GARoNWp0NvMGA0GgkNCyUqKprw8AifZM+jeKiursZqbcJyLH4KCwtVZ2OdToeaDB5OdWU5Obu2qclfRGQUffsP8k3+PG72rPrSJ/m79+EneObF1yX5CzLTP3hT7dLz97//XZI/0WxkBlCIdiYnJ4dBgwZRUVEBQN+xV5Lea6T6fsNWcd2JS+rQIrEpikJNTQ0OR11CpNfrCQ0NQ6c/sUTG43Zjtdp8ki2DwUBYWFhANtg7HE4slmrAe4o3IiISzSFxVJYWU7Bvj5qkx8TG0qNXX5+kzuN2smP5TEpzt6jX+ud/Xua6W+5soW8imkpFeRmnDetNTY0Fk8lEdnY2SUlJx/6gEMdBZgCFaGca1SquVy/1eWFOpreDRTM72Du3fvJnMpmIiIg84eQPQKvTER4eTmhoqLrX0el0YjabA3JaOCTEQEiIdxZPURSsh/wal+4vqG3t5k3+4hOT6Nmnv0/y53JY2fLjh2ryZzAYePXd6ZL8BamZ0z+gpsYCwC233CLJn2hWMgMoRDt155138v777wMQGZfKSRc8iE5ft1S6a9cu8vPzATCFhdOtz0C0uub7mdFisajJ38E+uoaQ5tmn53K6qKmp21+o1+uJiIho8fp4iqJQVVWlxmEKDSMkJISivGxK9+er45JT0ujctRv1o3NYq9jy44dYygsA74nit6bNZuzpZ7XkVxBNxFpTw2nD+1BeVoJOp2P37t1kZGQEOizRhskMoBDt1Kuvvkrfvn0BMJcVsHvNfJ/3u3fvTnh4OND8reJsNrtP8hcZGdlsyR+A3qAnMjJSXfp1uVxqCZyW5E10650KttaQn7XbJ/nr2KkLXQ5J/qzmEjYuektN/mLj4vnkq+8l+QtiX332CeVlJQBcccUVkvyJZicJoBDtVFhYGJ9//rl6mCBn6/IGreL69et3SKu4siaPw5t81R3ICAsLR9sCBxc0Wi3h4eHqrJ/NZsPhcDb7fQ9lqD20oigKpQW5VJYWe+PTaOjavSdpHTv5jK8uy2fjorewVXu7gKSkpfPZdz8waOjwFo9dNA2n08lH7/5PfT558uQARiPaC0kAhWjHBgwYENBWcYqiYLFY1H1uJpOpWWf+DqXT6zHVa9FWf1m4JYUYDJTm52C1mAFv8tejVx+SOiT7jKvYv5dNS97BafOO696zD3Pm/0y3Hr0aXFMEj+/nzSU/LweA888/n4EDBwY4ItEeSAIoRDt31113+dEqzkluVtO1irPbHT778Eym0GN8oukZQ4yEhIQAdYWnW5LT4SBr51ZsVotvXCaTz/OSnM1s+fED3E7vUvWQ4Sfz2XdLSUn17f8rgouiKD5t3x577LEARiPaE0kAhWjnNBoNH330Eenp6QCUF+4ma9NPPmN69eqFqTYhsVRVUlyY1yT3djjqkq2wsDBo2TMYXhp8TgY77P53GzledpuVzO2bsdWe/Dx4wldRFPbu2qkmx/t3/8n2Xz9G8XhrAZ525jnMmDOfmNi4FolTNJ9ff1rCzu1bARg5ciRjx44NcESivZAEUAhBXFwcM2fOrGsV99eio7eKyz/xVnFOpxO32w2AXm9okX1/R6Kp7S4C3kLRzhbYC2i1VJO5fTMOu3dGL9QUyrBhw9TCvzU1FnKz95G7+Sd2r/oCapPSSZdeybsff0FY7QEdEdzeP2T2r6VPoov2SxJAIQQAp512Gk899RQAiuJh07JPcNrratNFx0TTpUuX2vcV8jJ3qd0pjofdXlfv70R6ndaUF1Ces5GyfeuxlOYe9+xd/RjsjuZdBq6uqiRrxxZctcW2IyIiGDJ0COHh4fTp0wetVuv9Nd60mH3rv1c/d+Pt9/DfNz4MaBs70XTWrfmDtX+sBKBPnz5MnDgxwBGJ9kTqAAohVC6Xi9NPP52VK71/KSV3HcKAM65XZyUURWHDhg1qF5HouAQ6devl99Ktx+OhqrIKBQWtVktUVJS3nZsfKvO3UbBxMbbK/T6vh0TEkdzvLOK7Nv5UbPYfc3A7vF1CPG4nKSOuIjIyCn29AtTVJdns+uFtep97H2Fx6X7F6hN3WSm5mbvUfZYxMTEMGDAAfb2ey9n7stj1x1yU6rql9keefIbb//6wzBC1IXfecDk/LVkIwPTp07nxxhsDG5BoV2QGUAih0uv1zJo1S+0HvD9zPQW7/lTf12g09OnTR52BqiwroaykyO/7uN1ulNomwwZDiN/J3/4tP5K5fEaD5A/AUV1Gzp9fkLvmG7/jiu86HFvpPpzWygb9eUsz1xAak3pCyV/Zgf3k7t2pJn8JCQkMGjTIJ/lzOe2U7Fjqk/yNGDWGO+59RJK/NmT3jm1q8peens7VV18d4IhEeyMJoBDCR+fOnfnwww/V59tXfY2loi7JM5lM9KrfKi7b/1Zx9Uut6PzsLlKRu4XCzUuPOa5kzyqKd/3u17XjOvZHFxJOdf4mtRcygMfloDx7I/HdRvh1PZXi3TeZv2+vukSdkpJC//79ffoQO20W/lr0DqV5230+vmbVCn79acnx3Vu0Sh++87r6+KGHHlJPogvRUiQBFEI0cOmll3L77bcD3uRn088f43bVJUSJiYmkpXnLj3g8HnL37vKrfl79sVpt4w9/KIpCwcbvjz2wVuHmpXjcjT/QodUbiEofSHX+ZlwuFx63N87ynE0oHjdxXYY0+lo+MedkUpSfo77WqVMnevfu7TOjZ7NUsHrBm1QeyAYgKiqKhx56SH3/iYfupqIZCnGLlleQl8v8r+cAEBsby2233RbgiER7JAmgEOKwXnvtNf9axeXua/S1fRPAxv8xVFOag91c0ujxbkcNlfnbjz2wnriuw3FZK7GV5eConQUszVxDTMf+6EPCjvFpX4rHQ17mbkqLCtXXunfvTrdu3XzGWSqKWD3vdSwV3iXtDh068Ouvv/Lyyy9z3nnnAXCgaD//evxBv+4vWqfpH7yJq/YA1b333ktERESAIxLtkSSAQojDas5WcQfLv2g0Gr8SQFvlgUaPPcheVezX+PC4VIwxaZjzN+JyObGbS6ku3kd8V/+Wfz1uN9m7t1NRr7Vbnz596Nixo8+4ygPZrJ7/htqBpWvXrqxcuZLBgwerNRpjY2MBWPjtVyz89iu/4hCtS3lZKXM+nQ5460/ee++9AY5ItFeSAAohjujwreIq1efh4eF0695dfZ6ftQenw/fwRFM6WAi5OT+j0+qI6jiEmv07cVgtlGSuISQ8hsgO3Y/94Voup5OsHVsxV1Z4r6nTMWDAAJKTfVu7lebtZO33b+O0ewtBDxo0iJUrV/rMEKampvLOO++oz//1+IMcqDejKILLzGnvY63dM3vrrbeSkJAQ4IhEeyUJoBDiqO666y4mTZoEHGwVN9OnVVxaWppPq7i8zF3HrMV3cNZPURQUpfF7B0Mi4v0N3//PaCC200DQaDEXbKE0ay3xXYc3+gSuw24nc/tmamr7+hr0egYNGkR8vG8chXvXsW7pB7hd3oT51FNP5ddff22QJAJceeWVXH755QBUlJfxxEP3tFi3EtF0aiwWPp32HuD9oaD+Hk8hWpokgEKIozq4DHnw0McRW8XVLhVXV1VSXJh/1GvWX/Z1+3F4JCKpK3pj4ztgaHR6otP6NHr8QcbQCMJT+lC++1dcVjPxGY2rKWiz1pC5fTN2m9V7HaORIUOHEh0d7TMuZ+tvbF72KYrHuxR+4YUXsmTJkgbj6nvnnXfU5PDXn5bwxawZfn8vEVhffvYx5WWlAFx11VVqYXUhAkESQCHEMcXHxzNr1ix1FmzvX4uoOLBPfd9gMNCnb/1WcTnUHKVVXP2TvwdP2jaGVqcnqc/pjR6f0H1koxJGRVHQ1EtK9XodUemD8DhthCZ0wRAWc8xr1FSbydq+GWdtF5GwsDCGDh2qHpQ5eJ89fy1ix6q5UFsH8dZbb+XLL79Uey0fSXx8PB999JH6/Lkpk8nJzjrKJ0Rr4nQ6mfbuG+rzRx99NIDRCCEJoBCikRq0ivv5E5wOq/p+TEyMT6u43KO0itPVS7b8KR8DkNT7VKLT+x1zXHhCF1IHjWvUNV22avSmyLoXNBoikzLIOO9xkodfqZ7YPBJzRTlZO7aq4yIjIxkyZIhPUqd4PGxf+SWZ6+vq+T3xxBN88MEHPoWgj2bcuHFqyZCaGguT779DPVAjWreF335JQX4uAOPHj2fAgAEBjki0d5IACiEabcqUKYwePRoAW3UZ21d84bMXrXPnzmoXEYfdTv6+vQcnunxodfUTQP8SGI1GQ8aY60ju9zc0uoY9cTVaHQk9RtP9zNvR6o6eWLlqy8RUH8gkMtn3kEf9frv1i0IfqqK0mOzd29XvERsby5AhQ3wK+3rcLjYt+5i8HXWFqV977TWeffZZv7t7vPLKK2RkZACw9o+VTH//Tb8+L1qex+Phg7fqDlM99thjAYxGCC/pBSyE8Et2djaDBw9W+wH3O/Uq0nqerL5vs9lYu2YNztrZsLSM7sQldmhwnYqKChSlthfwUfa+HY3LXkNF3mbsVcUoioIxMp6Y9P4YQqMa9fnM3z6mpjSPuIxhpAw81zcZUxQqKyvVGA+3P69kfwGFOXXLsElJSfTp08dnj6PLYWPDjx9RVrAb8LbbmzFjBtdcc81xfWeA5cuXc/rpp6MoCoaQEL5dsoKefY49KyoC4+eli7jj+ksBGD16tNprW4hAkgRQCOG3r776issuuwwArT6EURc+THhMXZJXXFzMli3emoFarZZu/QZhCvUtomw2m9Ul0+joaJ89eK2Fpbpanf2LiopCp6vdu6jA/vxsigvq+vWmpaXRo0cPnyTSbjWzbvH7mEu940JDQ5k7dy7nn3/+Ccf2yCOP8MorrwDQd8Agvlz4i7QTa6WuvOAs/lq9CoB58+YxceLEAEckhCwBCyGOQ6NaxaWmet/3eMg7TKu4+vveXK7WuY/tcMvAiqKQv2+PT/KXkZFBz549fZK/GnMpa+b/T03+YmNj+emnn5ok+QP4z3/+o3Zq2bZ5I++89mKTXFc0rbV//q4mf3379mX8+PEBjkgIL0kAhRDH5bXXXqNPH2+JlWO1irMeplWcOpsGrfYgg0HvmwB6PB5y9uykrLgI8O5H7NmzZ4NyHuayAtbM/x81Vd62denp6axYsYJRo0Y1WWwmk4lPP/1UTaTfe+MlNq5b22TXF02j/t6/yZMn+9X5RojmJP8lCiGOy+FaxRXnbFXf1+p09OvXT/0L79BWcfVnAN1u/zt8tASNTqsmqk6Hg307t1JV7q3jptVq6du3r1of8aDy/ZmsWfAm9hpvGZxevXqxcuVKdbauKQ0dOpQpU6YA3iT60ftuw1pT0+T3Ecdn1/atLPthEQAdO3bkqquuCnBEQtSRBFAIcdwGDhyo7kMD2LJ8doNWcd2P0CpOq9WqyaHL5TrsaeHWwGAIweN2UZy3D0ttbUOdTsfAAQNJSkryGXsgewt/LXoXV215nBEjRrBixQo6derUbPE9/vjjjBjh7VOcuWcXLz83pdnuJfzz4duvqY8ffvhhny0FQgSaHAIRQpwQRVG48MILmTdvHgBxqT0Ydt5dPoc6tmzZQnFxMQDhUdFk9OqHRqOhutqC0+lNCKOiotDWWxZuLawWC9m7tuGqjdNgMDBo4CAioyJ9xuXvWs223z5XW9udffbZfP3110RERDR7jDt27GDIkCHYbDYAPvlqIaPGnN7s9xVHlp+bw99G9sftdhMXF0dOTo5PUXAhAk1mAIUQJ0Sj0TBt2jR1KbSsYDf7Nv/sM6Z+qzhLVSVFeTmAt+PGQa1xH6DNYiGnXvIHMGhQw+Qva9PPbF0+W03+rrjiChYsWNAiyR9A7969efHFukMgk++/A3NV5VE+IZrbtPffUP+bvvfeeyX5E62OJIBCiBMWHx/PzJkz1VOwe9Z+36BVXN9+/dT3iwvzqCov8zkI0tpOAluqKsnasUWdoTzo4CwbeGc/d/75HbtXz1Nf+/vf/87s2bNbvCTL3//+d8444wwACvPz+PdT/2jR+4s6ZaUlaq/msLAw/v73vwc2ICEOQxJAIUSTOP30031axW1e9ilOe92BhOjoaLp166Y+z8vajadei7XWdBDEXF5G9q5takz1Z29KSrwnez0eN1uXzyZ78zL1vf/7v//jjTfeCMhJT61Wy4wZM4iK8hbB/uaLWfywaP4xPiWaw8xp72OzeveB3nbbbSQkJAQ4IiEakj2AQogm43K5OO200/j9d2/Ls8TO/Rl81i0+9fHq7wcMDY8gMa0zHkVBo9F4u2342RqtqZUXF1Gwb6/a4i4+Pp7effry5x+rcLlcGAwGRp48gk0/f0JJrvfUs0aj4d133+WOO+4IZOgAzJgxg5tuugmAuPgEvv9lDfGJScf4lGgqFks1pw/vQ0V5GXq9nr179zbrISAhjpfMAAohmoxer2f27NnExcUBUJy9hX31ZsjAu18tLMzbFcRqqab8QCEoCoqi4HZ7GlyzxSgKxfm55GftUZO/Dh2S6dd/AAa9ntja7+SwWVi94G01+QsJCeGLL75oFckfwA033MCkSZMA71LkPyffj/yc33K+mDWDitpyR1dffbUkf6LVkgRQCNGkOnfuzMyZM9Xne9YsoKxwr/pcr9fTr18/df+fuaKM4rxs3C5XwJaB3S4X2bu3U5Sfo76Wnt6R3n16o62dkUyIT0BxWXEVrKS61DsuIiKCRYsWcemllwYk7sPRaDR88MEH6rLjD9/P49svZwc4qvbB4XAw/f031eePPvpoAKMR4ugkARRCNLnzzz/fZz/gpp8/Vgsjgzdx6tO7t7pXzm6r4UBOJubKihaP1Vpdzd6tGzBXlAPeBKprt250694dDXXL0Sa9G1f+CnB4v0diYiK//PILZ555ZovHfCxJSUl88MEH6vP/e/IRCvPzjvIJ0RQWfPOF+us8ceJE+vXrF+CIhDgy2QMohGgWbrebc889l59++gmA2JTuDDv/LrTaupO/ZrOZLVu21J2s1WjokNqRxNT05t8LqEDZgf0U5mah1PYpDjEY6NO3H7GxsT5Dq0pyWbfkfZy2avW177//vsn6+jaX66+/nk8//RSA0WNPZ/qc+dKKrJl4PB7Gn34Se3ZtB2DlypWMHj06wFEJcWTyJ4EQolnodDpmz55NamoqAOWFe9j71/c+YyIjIxk+fHhdWzhFoSg/h327tlFjrmq27iA2i4XcvTspyN6rJn/R0dEMHT6iQfJXVrCLtd+/5ZP8AWzatKl5gmtCb7zxBunp6QD8/tsvzJr+wdE/II7bsh8WqcnfmDFjJPkTrZ7MAAohmtXKlSs5/fTTve3egG5Dz6PrkHPQaOp+/ty5cycFBQUNPmsKCyc+KZno+MQT7hKieDxUlpVSdmA/NdVVPu917NiRjK7d1P1+4K3xl7d9JTv//AbF461ROHTYcNb9tRaAUaNGqaedW7Mff/yRs88+GwBTaCjzflxFRrceAY6qbVEUhSsm/o31a/8EYMGCBYwfPz7AUQlxdJIACiGa3WuvvcZDDz2kPo9P782A068lxOTtlFFYWMiOHTsA7yERl8v3MIhOpyMmIYnYxGRMoaGNXx5WwGG3Ul58gPKSIlxOp8/bBr2enr16k5iY6PO6y2ln+4o57M9cp742fsJEZn02h9EjR7Bt61Y0Gg2FhYV06NCh0b8OgXLvvffy1ltvATBo6Ag+n/dj3ayrOGFr/ljJ1ReeA0D//v3ZtGmTT+kjIVojSQCFEM1OURRefPFFnnzySTy1S66m8BgG/u1GYpK6YLFYWL16NeA9XBGfkEBBfgFVh2lnptFqCQkxEmI0YTB6/z/EaEKj0eCw22r/seOsfXzwfvVFRESQmppGUocO6A+ZWayu2M+mn6ZjqShSX7v3vgd44b8vodfr+eeTT/DfF58H4KOPPuLmm29usl+n5lJTU8PgwYPZvXs3AA8+9jR3PyAnVJvKrddczK8/LQHg008/5dprrw1wREIcmySAQogW8/PPP3PVVVdx4MABADRaHb1OnkR6nzGsWLECt9uNyRTKyJEjAe8hkYKCAg4cKDrhXsFarZbExERSU9OIio7mcPMzhXv/YvuKObhd3vZvkZGRfPjRdC66+BJ1zJ9//MGpY0YBcMEFF/Ddd9+dUFwt5Y8//uCUU07B4/Gg1+v5atGv9BswONBhBb0d2zYz8Uzvf6+dO3dm9+7dGAyGAEclxLFJAiiEaFEFBQVcccUVrFixQn0tuesQnFF9qDJbABh9yimEGOp66TpdLor276e8ohyb1YbNZj1mQqjTajGFhmIymYiOjiY5OeWI/Xk9bhc7//yWvO11MfUfMIDP5nxFz549fcd6PHTpmEpRURGhoaGUlJSoha1buyeffJLnnnsOgJ69+/L14t8wmkwBjqpxvIXCXej1rSu5eviem5k3dw7gPXRz7733BjgiIRpHEkAhRItzOp088cQTvPzyy+prupBwlPCOaKM6M3DIScTHxx/x8wrgdDqwWW1YbVZsVhuKomAKNRFqCsUUaiIkJMSnjt9h47BbKNi1mtwdK7FWlaivX3vd9bz59rtHTOzuuuM2pn00FYB58+YxceJEP7594DgcDk466SQ2btwIwK13P8DkKc8GOKqGKisrqKioxGa1YrXZsFmt2Ox2QEGn02EyhRIaasJkCiUsLJTEhEQMR0jum1NeTjZnjRqA2+0mISGB7OzsoPlhQAhJAIUQAfPNN99w4403UlVV/1SuhoikHvQefi4xyV2bZTN9VUkuudt+Y3/mejzuuoMhRqOR1994i5tuvuWo910wfz6XXHQBALfccgtTp05t8hiby+bNmxk+fDgOhwONRsPsb5cy/OTAlyxxu13s319Efn4+NTUWvz6r0WhITEwiLS2V6OiY5gnwMP7viYf5dNp7ADzzzDNMmTKlxe4txImSBFAIEVB79uzh4YcfZv78+Q161obHJNOxzxhSug9HH3JiS5Vul4OirA3kbl9BVXFOg/dPP/0MXnzpFQYPGXLMa1mtVlKS4rFarSQlJVFYWBhUBZb/+9//MnnyZAA6ds5g/s9/EB4eEZBY7DYb2Tk5FBXtP+Kyvl5vwGQyodfrsdtt2Gy2I/Y3Dg8PJz09nZTklGYtJl5aUszpI/pgs1oJDw8nOzv7qLPWQrQ2kgAKIVqFrKws+vXrh9VqbfCezmAkOrEzoZHxDf4xGMN8ZuucDivWqlKs5lKs1aXq48riHFyOGp/rRkVFcd0NN3Lb7XfSp08fv+K99OILmT/PewBk1apV6sGVYOB2uznttNNYuXIlAFdefwv//u8bLR5HaWkJ27dvb1D2JzoqmuSUFCIiIjCZQhuUrFFQsNvt2Kw2ysrK2L+/AOchJX7iYuPo07dvsx3IeP3Ff/P2ay8A8OCDD/Lqq682y32EaC6SAAohWo0JEyawcOFCAIYOHca6dX8d8zN6g4nQyHjQarGZS3Haa475mUGDB3PHnXdz5VVXEx4eflyxzpg+jTtuuwWAxx9/XD1cESz27t3LoEGDsFi8y61TZ33NaX87t0XurSgKWVmZ5OTUzcTqtDqSkpNJTU0lws/ZSI/iobi4mIL8fJ/SQUajkX79+hMVFdVksQNYLNWcNqw3lRXl6PV6MjMz6dixY5PeQ4jmFjxrFkKINu+kk05SHz/48COs+WsDt95+R4P2bPW5nDbMZfmYS3KPmvxFRkZy1dXX8Otvv/PnmnXccuttx538AYwbP0GdeQyWUjD1devWjVdeeUV9/sRDd1NRXtbs93U4HGzcuMEn+UtISGTkqFH07NHT7+QPQKvR0iGpA0OGDGXgwMEYak+Q2+121q9fR35eXpPFD/DFzOlUVpQDcO2110ryJ4KSzAAKIVqNRYsWMW7cOADuu/9BXnrFu6ymKArl5eVkZWaSlZXJvqwsMrMyycrMZN++LHKys/F4PN6WbhldyejalS5dMsjo2pWMjK50ycggMTGxyQ+UnD72FFat8raD2717N927d2/S6zc3RVEYN24cixcvBmD8hZfy+nsfN9v9nA4H69avU5f5NRoNXbt2Iz29aRMou8PO9q1bqaw3G9itWzc6dux0wtd2OByceXI/igq9rQu3bdvm9/YBIVoD6QUkhGg1RowYoT5eu2a1+lij0RAXF0dcXBzDhg9v8DmXy4WiKC1egHfCxAvUBHD+/Pk8+OCDLXr/E6XRaJg6dSoDBgygvLychd9+xdnnTWT8hZc2+b3cLhcbN21Uk7+QECN9+/YjOjq6ye9lDDEyaPAQsrIyyc31zjRm7s0kKjKK6JiYE7r2/K/nqMnfpEmTJPkTQUuWgIUQrUZCQgJdu3YFYP36dQ0OBxyJXq8PSPeF8RMvUB8H4zIwQFpaGm+//bb6/F+PP0jR/sImvYfH42Hzls1UV1cDYDSaGDp0aLMkfwcdnF3s1Kkz4D04snXbVhwOx3Ff0+Px8MHbr6nPD56kFiIYSQIohGhVDu4DtFqtbNu6NcDRHF3v3r3p3qMHACtWrKC0tDTAER2fK6+8kssvvxyAivIynnzo7iOWWfGXoihs27aNiooKAAx6AwMHDsJobJkOJF0yMoiJ8e4hdTgcbNu29bi/209LFpK5eycAp556KqNGjWqyOIVoaZIACiFalfoHQdbUWwZujTQaDRMmeGcB3W43ixYtCnBEx0ej0fDOO++QnJwMwK8/L+WLWTOa5Nq7du2ipKQY8J707T9wYIt2y9CgoU/fvoSEGAGoqKgg7zgOhSiKwvtv1h2aeeyxx5osRiECQRJAIUSrcqR9gK3VhHrLwPPmzQtgJCcmPj7ep6PJc1Mmk5OddULXzMzMpLB2v5xGo6Ff//5ERTZtSZbGCDGE0LdvP/V5Xl4e+DkLuHrVCjauWwPAwIEDOe+885o0RiFamiSAQohWZciQIeh0OqD1zwACjBo9Wu0AsWjRIux2e4AjOn7jx4/n1ltvBaCmxsLk++84YneOY8nLzSUnJ1t93rtPX2Jj45okzuMRHR1NXJz335PdbvN7uf7Dt+oKPU+ePLlZWhQK0ZIkARRCtCrh4eH0798fgG1bt6qFilsrvV7P+ePGA1BdXc0vv/wS2IBO0KuvvkqXLl0AWPvHSqa//6bf1ygq2s+evXvU5z169CQpMampQjxuqWlp6uP8gvxGf2771k38+vNSALp06aLulxQimEkCKIRodQ7uA3S73WxYvz7A0RxbW1kGBm/B7I8//lid4Xr1hWfYtb3xh3FKS0vZsX2H+rxz5y6kpqYd5RNHV22uYs7M6TzzxEM8cs/NvPLc06xZteK4rhUXF4fJFApAWVnZYdsOHs4H9Wb/HnnkkQat6YQIRlIIWgjR6kydOpXbbrsNgBdfeoUHHnwowBEdndlsJrVDAg6Hg/T0dHJycoJ+ifCRRx5RO4X0HTCILxf+QkhIyFE/U1VZyYaNG/F4vMvGqalp9OjR87hj+Ozjqfz3309isVQ3eG/gkOG8+u4MOnXOaNS1Jt9/O998MavB6z+u2sTbr73IN1/M4pEnn+GOex9R3/th0XzuvulKtFotHo+HxMRE9u3b16KHWIRoLjIDKIRodYLtIEhkZCRnnPk3wHvAYH0QzFoey3/+8x/69u0LwLbNG3nntRePOt5isbBp8yY1+UtMTFJL5ByP1154hqcfu/+wyR/ApvVrufjcMWTu2dXoa44942x+/GMLL7/3KS+/9ykzvlpEeqcuABhNJj546zW1xVt9Ho8HgPvuu0+SP9FmSAIohGh1+vXrR2iod6kuGA6CQNtaBgYwmUx8+umn6nLne2+8xMZ1aw871mazsXHjRrVwd2xMLL379EHD8c2Crvz1J95746VjjquqrOD+269t9EGVkBAj6Z06Ex0TS3RMLKGh4eqBo9FjzyAhqQPvvfGyOt5cWddKLiIignvuucfPbyJE6yUJoBCi1dHr9QwbNgyAfVlZlJSUBDiiYxs/YaL6uC0kgABDhw5lypQpgHc/5qP33Ya1psZnjNPpYOPGjTgc3tPPkZFR9Os/AK3m+P96ef+tVxpdrHnn9q388uPiRl9bq9FiNHprAlptNvV1nU7Hw4//i0+nvUdh7QGRZfWue/vttxMbG9vo+wjR2kkCKIRoleoXhF67Zk0AI2mctLQ0tU/x+vXrycnJCXBETePxxx9Xl+Qz9+zi5eemqO+53S42btyE1epNCkNDwxgwYKA6q3Y8qqvN/LHiV78+89OSBY0a98uPixjcLYnbrrqAv99wKW/+9xmf2cNzxl1An34DeeOl/1BdbebXn5cAYDAYgq7PsxDHIgmgEKJV8k0Ag2QZeELdMvD8+fMDGEnT0ev1fPLJJ5hM3tZtn0x9l99/W4bH42HL5i1UV5sBMIYYGTho0An3ZM7PzT72oEPkZu9r1LiTTzmV735cxf+mzmbKi29w5Y13YKs3Cwjwj6f+zTdfzOLtV59XZzuvu+460tPT/Y5LiNZMEkAhRKtU/yCI7AMMrN69e/PCCy+ozx+7/07WrllNee2BCb3ewMBBgzA1QX/fg2Va/PpMaOM+ExoaTueMbiSnpJKUnEpMbBw6re9fgyeNGsPoU8/k04/eU1/7xz/+4XdMQrR2kgAKIVqljIwMtcPG2jWrG70nLJAGDBxI586dAVi2bBmV9Q4RBLt7772XM844A4DCgjzeeuU5ALRaHQMGDCQsLLxJ7pOa3snva/Xo1cev8VbrwVk/DUZTw6R1yLCTfDq69O7d26/rCxEMJAEUQrRKGo1GXQYuKSkhO9v/pcGWptFoGF87C+h0OlmyZEmAI2o6Wq2W6dOnq7UAVy3/iQ1r/6Bfv/5ERTVdf1+DwcDEi6/w6zMXX36tX+NtNm8BaJPJ2KBeo8fjYcG3X/p1PSGCkSSAQohWK9j3AbalZWCA7777DofDoT6f9dG7KB5Xk9/n7gcmExMb36ixl119I939mAF0uZxquZrQwyw3/7h4gVpbMNiLeQtxNJIACiFarfoJ4JrVwZEAjj31VHVGbOHChTidzgBH1DRmz57N/fff7/NaRXkpUx69v8mX51PS0nlz6kyiomOOOm7MaX9jyrOvNOqaL/7vA96dMQdLvTI2plDv8u9/3/C+pygK779Zd71FixYFxdYDIY6HJIBCiFYr2DqCAISEhHDe+eMAqKioYMWK4+tb25osXryYG264QX1+330PkJCQAMAPi+bx7Zezm/yeJ48+lW+WruT8iRc3OFmcnJrG5KefY+rsbw+7h+9oivbvVx8funT958rlbFrvLXY9ePBgzjnnnOOMXojWT3oBCyFatYyMDLX/anFZpdqZojWb8/lnXH/t1QA88MADvPbaawGO6PitWrWKs846i5rambNbb7+Dt95+l+++/YYrLrsEgIjIKBYuW0NKWvOUSikvK2XPrh2YqypJTe9Ij159j6vWoMvlYtWq3/F43Oh0ekaPHu1znZuvmsRvy34E4LPPPuPKK69ssu8gRGsjCaAQolW74oor+OKLLwD4a/0m+g8YEOCIjq28vJz0lCRcLhcZGRns3bs3KPeTbd26lbFjx1Je7i33cvEllzJz9udq0nTzjdcza+anAIwaezrTP5+PVtt6F5by8/PYs2c34C3c3aNHT/W9rZs3cOHZpwDeHzp27doVFD9sCHG8Wu/vVCGE4JB9gEGyDBwbG8uYsacCkJWVxdatWwMckf+ys7M599xz1eTv9DPOZMYnM31mzF59/Q21QPKq335h1owPAhFqoygo5Ofnq8/TUtN83v/w7bpZ2n/84x+S/Ik2TxJAIUSrFowJIMDECyapj4PtNHBxcTHnnHOOmjANHTaMr77+Vu2he1BMTAzvfzhNff7Sv59ST9C2Nrk5OWrLupjoGMLC62oNZu/LZNG8rwFISkrixhtvDESIQrQoSQCFEK3a0KFD1WXFYDkIAjB+wkT1cTAlgGazmXHjxrFrlzeR69GzJ9/N/57IyMjDjj/r7LO58+57AG99vUfvu00ts9JaVFSUk5WVpT7vktHF5/1p776Bx+MB4P777ye0kZ1FhAhmsgdQCNHqDRw4kM2bN6PT6SgpryIsLCzQITXKsCED2bJ5MwCFhYUkJycHOKKjs9vtTJgwgR9/9B6ESE1NZdnylXTp0uWon7NYLJw0fAh7dnv31z04+WnueuDR5g63UewOO3+tXYvT6a1f2KVLhs/3KSku4rThfXDY7URGRpKTk0NMTExgghWiBckMoBCi1Tu4DOx2u9m4YUNgg/HDxIl1y8Dz588PYCTH5na7ue6669TkLzY2lgXfLzlm8gcQHh7OtOmfqDO1b77yLOvW/NGc4TaKR/Gwfds2NfmLi42jS22rPgBFUXjhmSdw1LZ9u+OOOyT5E+2GJIBCiFYvWPcBTpgYHF1BFEXh3nvv5csvvS3QQkND+ea7BfTr37/R1zh55Egenfw44C23cv8d11FWUtws8TaGgsLOHTuorKwAwGg00qdvX6h3GnvOzOl899XnAERERPDggw8GIlQhAkISQCFEqxeMBaHBe3giJSUFgB9//BGLxRLgiA7vmWee4d133wVAr9fz2ZyvGDV6tN/X+efT/2Js7ennosICHrrnJtxud5PG2lh79+zhwIEiwNvHuF+//j4FpbdsXM//Pfmw+vyjjz4iNTW1xeMUIlAkARRCtHr9+/fHVNvxIZhmALVaLeNqD4PYbDZ++OGHAEfU0FtvvcUzzzyjPv/wo+mcP27ccV1Lr9fz6ezP6dChAwC/L1/GW68+3yRx+iM7O5v8/DwANGjo16+fT9ePivIy7r31Gpy1fY3vu+8+Lr/88haPU4hAkgRQCNHqGQwGhg4dCkDm3r2UlpYGOKLGq78PsLUtA3/++efcd9996vOXXnmNq6+59oSumZKSwqezPlf3A77z2gss/3npCV3THwWFBezbl6k+79W7N/HxCepzj8fDo/fdTl5uNgAjR47kpZdearH4hGgtJAEUQgSF+vsA/1q7NoCR+OeMM89UTy0vWLAgYEuih1q6dCnXX389BwtBPDr5ce67/4EmufZpp5/Ov//zHODdX/jwPTezasUvTXLtoykuLmb3rp3q827duvmcvLbb7Tz92AMs+2ERAPHx8XzxxReEhIQ0e2xCtDaSAAohgkL9BDCY9gGaTCbOPudcwJug/PFH4E/H/vnnn1x88cU4nU4Abr7lVv7vP8826T0eeuQfai3EyopybrpiIu/+7yW13l5TK68oZ/v2berzTh070bFjJ/V5fm4OV194Dp9/8hEAGo2GWbNm0bFjx2aJR4jWThJAIURQqH8QJJj2AULr6gqyfft2xo8frx5ImXThRbz59rtN3qtYq9Uy/eNPOefc8wDv0utrL/yLO2+4jIrysia9l9lsZuuWzSiKN7lMTk6ha7du6vvLf17Kheecwqb13pljo9HI9OnTOffcc5s0DiGCiRSCFkIEBUVRiI+Pp7y8nKSkJHLy9zd50tJciouL6ZSWjMfjoXfv3mzfvj0gceTm5jJ69Gjy8rwHJE477XTmLVykHrBpDh6Phxeee5b/e+Zpdbk5Lb0Tb3w4iwGDh57w9WusNWxYv16t9ZcQn0C//v3RaDS43W7efPk53nn9RfXeXbt25auvvmLIkCEnfG8hgpnMAAohgoJGo1GXgQ8cOEBOTk6AI2q8xMRERo3yllXZsWOH2matJZWWlnLOOeeoyd/gIUP46pvvmjX5A+9M4BNP/ZMF3y8hIcF7GCM/L4crJ/2Nf/7jXrZt2Xjc17Y77GzeuFFN/mKiY+jbrx8Ou51vv5zNpeefxtuvvaAmf5MmTeKvv/6S5E8IJAEUQgQR34MgawIYif/GB7AodHV1NePGjWPHjh0AdOvenXkLFvmURmluZ519Nn+uXc/IkaMAcDoczJk5jQvPHs2VF/yNeXM/VztyNIbT5WTTxo3Y7DYAIsIjiImK4OVnpzB2aE/+ce9tbNm0HgCdTseLL77IN998I50+hKglS8BCiKCxYMECJk70Hix46OF/8PyL/z3sOEVRWt3y8K5duxjQtxcAY8eOZfny5S1yX4fDwcSJE1m61FuKJTk5mV9++52MjIwWuf/h4vn3M//i3Xfewmw2+7wXG5fApVdfz0kjx9CxcwbpHTsTYjQ2uIbb420JWJCfS0lxEWUlxWzbuJbflv3IoX+lDRw4kDfffJNTTz21Wb+XEMFGEkAhRNAoKipSy3qceupp/PDzLz7vu91uli5ZgsfjYfyECQGI8OgG9OvNrp070Wq1FBUVqUuizcXj8XDNNdfw+efedmfR0dH8tGw5AwYObNb7NobZbOaz2bN479232bply2HHaDQaOqSkkt6xCx07dyEqKpr8vBz27NrJ/sI8bFbrYT8XEhLCZZddxt13382oUaNa3Q8DQrQGkgAKIYJKp06dyM3NJTw8nOKySnQ6HR6PB7fbjcFg4JWXX+K7b77mi7nf+NSAaw0en/wor77iLTo8Y8YMbrjhhma7l6Io3Hfffbz11luAtxzN94t/4JQxY5rtnsdDURR+X7mS9997h6/nfqWWpjkenTt35s477+Tmm28mKSmpCaMUou2RBFAIEVQGDRrEpk2bAFi3YTP9+vf3eX/fvn387fSxPPfCf7niyqsCEeIR/b5yJWec5k3ALr74YubOndts9/r3v//NlClTAO8euC/nftsqZ0XrKyoqYtH3C8ncu5esrEz2ZWWRlZVJcXFxg7EajYZhw4YxbNgwunbtyqBBgzjrrLPQ6XQBiFyI4KMPdABCCOGP+snA2rVrSExKYtWq31n280+s+n0le/fsobq6msLCwgBGeXgnjxxJQkICJSUlLFmyBJvN1iyncN999101+QP4YOq0Vp/8AXTo0IEbb7q5wevV1dVMeepJ3n7rDcCb/H3zzTdMmjSpwVghROPIDKAQIqjcdtttTJ06FfDu9XI6nURFRdGzZy+GDBvG0KHD6Nq1Gxldu9KpU6djXK3l3XbLTXzy8QwAvv/+e84///wmvf6XX37JFVdcoR6GeOG/L/PgQw836T1a2rSPpnLXHbepzz/66CNuvrlhoiiEaDxJAIUQQeWXX37hjDPOACAtPZ0Pp06nY6dOJCQkEBER0er7un737TdcfunFANxxxx289957TXbtH3/8kXHjxqn76B75x2Seff6FJrt+IHz7zddcdcVlagu5F198kUcffTTAUQkR/CQBFEIEFbvdri6b6nQ6SivMhIaGBjiqxrNYLKQkxWO320lNTSU3Nxet9sRLsq5Zs4YzzjhDbfF24003894HU4P6BOwvy5Yxcfx5OBzeQs8PP/wwL730UlB/JyFaCykELYQIKkajkdjYWMBb9mXjhg2BDchP4eHhnPm3swAoKChg3bp1J3zNnTt3Mm7cODX5m3jBJN5+9/2gTpQ2rF/PpRdPUpO/66+/nv/+979B/Z2EaE0kARRCBJ0bb7xRfbw2yDqCAEyo1xXku+++O6Fr5eXlcc4551BSUgLA2LGn8umsz9Drg/eM3+7du5k4/jy1UPSECROYOnVqk8yUCiG85HeTECLoXHvtterjtWtWBzCS4zN+wkT18Ym0hSsrK+Pcc89V+yIPHDSIud/OC6ol8UMVFBQw/vxzOHDgAABjxoxhzpw5GAyGAEcmRNsiCaAQIugMGDAAY22LsDVBmACmpKQwYoS3r/GmTZvYt2+f39ewWCyMHz+ebdu2AZDRtSvzFy4mOjq6KUNtUeXl5Uwcfx7Ztb8eAwYMYN68eYSFhQU2MCHaIEkAhRBBx2AwMGTIEAD27N5NWVlZgCPyX/1l4Pnz5/v1WafTyaWXXsoff/wBeOvnLVy0tNV1PvFHTU0NF0+ayJbNmwHo0qULixcvVvd7CiGaliSAQoigdNJJJ6mP/1q7NoCRHJ+JF9QVMfZnH6DH4+HGG29k8eLFAERFRbHg+yV069atyWNsKU6nk2uuuoLff18JQFJSEkuXLiU1NTXAkQnRdkkCKIQISr4JYPAdBOnbrx9dMjIA+PXXX6moqDjmZxRF4cEHH2T27NmAt7/v19/OZ+CgQc0ZarPyeDzcefutfL9wAQCRkZEsWrSIHj16BDgyIdo2SQCFEEGpfgIYjPsANRqNugzscrnUGb2jef7553njDW87NJ1Ox8zP5jD21FObNc7m9sRjk5n56SeAt7PLd999x9ChQwMclRBtnySAQoig1L17d2JiYgBYs/pPgrGm/YQJjS8H88EHH/Dkk0+qz999/0Mm1ttHGIxefum/vPbqywBotVo+++wztcuLEKJ5SQIohAhKGo2GESNGAFBUVEReXl6AI/LfmLFj1SR20aJFatHjQ82dO5e77rpLff7s8y9yw403tUSIzWbG9Gk8+fhk9fl7773HxRdfHMCIhGhfJAEUQgSt+svAwVgP0GAwcN754wCorKzkt99+azDm559/5uqrr1Z74T740CM88o/g7oU7b9533HXHberz5557jttuu+0onxBCNDVJAIUQQcs3AQy+gyDgWw7m0KLQf/31F5Mm1bVDu+76G3j+xf+2aHxN7bfly7n2qivUhPaBBx7gscceC3BUQrQ/GiUYN84IIQRQWFiolgo5/fQzWPLjzwGOyH+VlZWkJSfidDrp3LkzWVlZaDQadu3axZgxYyguLgZg3PgJfDn3m6Bu8bZxwwbOOvM0qqqqAG9Hl48//lhavAkRAJIACiGCWseOHcnLyyMyMpKiknJ0Ol2gQ/LbuPPO4acffwBg48aNJCQkMHr0aLKzswE45ZQxLFy8NKhbvO3du5czTj2FoqIiAM4//3y+++47afEmRIDIj11CiKB2cBnYbDaza+fOAEdzfOovA8+ZM4dzzz1XTf76DxjA19/ND+rkb//+/Yw//xw1+Rs1ahRffvmlJH9CBJAkgEKIoBbs9QABxk+YqD5+44032LJlCwCdu3RhwfdL1JPCwaiiooKJ488jKzMTgH79+rFgwQLCw8MDHJkQ7ZskgEKIoOZzECQIO4IAdO7cmUGDBgNQXV0NeNuhfb/4B1JSUgIY2YmxWq1cetEkNm3cCHi/55IlS4iLiwtwZEIISQCFEEFt2LBhaDQaIDhLwYC3xdvJI0epz41GI/MXLqZ79+4BjOrEuFwurr36Sn77bTkACQkJLF26lLS0tABHJoQASQCFEEEuKiqK3r17A7Bp40ZsNluAI/KPoijs3buXAQPr+vkOHDSYwUOGBDCqE6MoCnffeTsL5nvL2kRERLB48WJ69uwZ4MiEEAdJAiiECHoHl4GdTqe63BgscnJyyM3NpXuPHiQkJACwccN6dSk4GD35+GN8PGM6UNffd9iwYQGOSghRnySAQoigF6wHQQoKCsisPRyh0Wg4t7YriMPh4McflgYytOP26isv88rL3mLVGo2GWbNmceaZZwY4KiHEoSQBFEIEvfoJ4F9BchCkuLiYnfXK1mR07crlV1ylPj+4fBpMPv3kYx6f/A/1+bvvvsull14awIiEEEcihaCFEEHP4XAQGRmJw+GgZ69ebN66I9AhHVV5eTmbNm1S26Glp3ekW/du2O0O0pMTqK6uJj4+npz8/a2y84eiKOrBm4MWzJ/P5ZdehNvtBuDf//43Tz31VCDCE0I0gswACiGCXkhICIMHDwZg186dVFRUBDSeozGbzWzevFlN/jp06EC3bt0ADUajkbPPPQ+A0tJS/li1KoCRHtmhyd/KFSu45qrL1eTv3nvv5cknnwxEaEKIRpIEUAjRJvguA68NYCRHVlNTw6ZNm9REKS4unl69ekO9hGrChLquIK1pGfiLOZ8z+R+PMGnieKZP+0g9bLN50yYumjRBPX191VVX8frrrzdIEoUQrYskgEKINsGnIHQrPAhit9vZuHEjDocDgKioaPr264dG6/vH8Lnnj1P7Gc+f/x2tYZfOp598zB233UJVVSUej4e33vwfd995O2/873UmjDuXyspKAM4991xmzJiBVit/tQjR2snvUiFEm9CaO4I4nU42bdqkzpKFh4czYMAANdGrLz4+ntGnjAFgz+7dPgdFAqG4uJi33vwfL7/6Ou++/yHzFy7itdffpEfPXjw++R/s378fgJNPPpm5c+cSEhIS0HiFEI0jCaAQok3o0aMH0dHRQGBnAC0WC1u3bOGPVasoKCjA6XSyefNmta6fyWRiwMBB6A2GI15jfCtaBna5XBwoKvLp3Tto8GDWrVuLy+UCIC0tjYULF0p/XyGCSOs7XiaEEMdBq9UyfPhwfvrpJwoKCsjPz2+2tmP79+9n544dZGVlsi8ri6ysTLIyM9m3L4uioiKfsSEhIXTokExySgqpqWkMHzGCoqIiMjK60qdv38Oe8h0/YSKPPfowAAvnz+ORfzzaLN+jMaKioujdpy+bNm7kwosuBuDSiyaxY/t2wJvQnnLKKcTHxwcsRiGE/yQBFEK0GSeddBI//fQT4J0FTEu7qMmu7Xa7+f77hbz/7jv8sHRJoz/ncDjIzc0hNzcHgO++/Vp9LzklhZtvuY2bb73dJ1nt3qMHvfv0Ycf27axa9TsHDhwgKSmpyb6LP8LDwxk79lT+9/qrjBw1ik8/+Zjly38FvMvVjz32GP/85z/JycmhU6dOAYlRCOE/SQCFEG2GT0eQ1auZdOGJJ4AHDhxgxrSP+PCD98jJyTnq2JTUVDIyutKlSwZhYWFkZ+8jO3sfOTk52KzWBuP3Fxby3H/+jxeff5aJky7kjjvv5rTTz0Cj0TBhoneWTVEUFn2/kBtuvOmEv8vxeuKpf5KZmcnVV16O0+kEvInh999/j9VqpV+/fq2yXqEQ4sjkd6wQos1oqoMgiqKw6vffef+9d5j71Zdq0nNQ5y5dmDBxEt26dSejqzfh69ylC6GhoQDYbDafhC80LIzy8nLvMnGWd6l43V9rWfT9QtxuN263m2+/nsu3X8+lZ69e3H7H3Zx22hm8/N8XAO8+wEAmgAApKSnqr4NWq+Uf//gHJpOJxx57jLi4OFJTUwManxDCP9IJRAjRpqSnp5Ofn09UVBRFJeV+lyTZsnkzt95yI+vXrfN5XaPRcO5553P7nXdzzrnnHfYEL4DDbqempkZ9HhoWhtFoPOzYvLw8pn/0IdOmfqCepj0oLCwMjUaDxWIhLCyMgqISNcFsaf97/TUefeQh9XnXrl0pLy8nNTWV+Ph4li1bJqVfhAgykgAKIdqUiy66iG+//RaAjVu207t370Z/duann/D3u+/EWm/2Lj4+nhtuuoVbb7uDjK5dj/p5p9OBxWKB2j9VTaEmTKZjJ21Op5N5333LB++9w/JffznsmK+/nc/4CRMa/V2ayqyZn3Lzjderz9955x0uvPBCzGYziqKQkZEhpV+ECELyI5sQok05noLQNpuNu+68nVtuukFN/vr268dH0z9hz748nn3+xWMmfy6XE4ulRk3+QoxGTCZTo+5vMBi45NLLWPLjMv7asIU77ry7wYzaRx992KhrNaXvFy7ktlvqlp7/9a9/cdddd5GSkkLPnj3p1auXJH9CBClJAIUQbYrPQZBGJICZmZmcNnY006bWJVg333IbK/9Yy9XXXteoJM7tdmGptkDtgoohJISw0FDA/3Zoffv14/U332bxDz+T1KGD+vrC+fP43+uvtVhnkN9XruTqKy9T29bdc889TJkypUXuLYRofrIELIRoUyorK4mJiQFgxIiTWLHqzyOOXTB/PrfcdD0VFRUAhIaG8sZb73Lt9Tc0+n4etxtzdTWKxwOA3mAgPDy8SXrhFhYWcsmFE1m/7i/1tYsvuZT3P/yIqKioE77+kWzdsoUzTx+r/rpcccUVzJ49W/b5CdGGyO9mIUSbEh0dre7727hxA3a7vcEYRVF45ukpXHLRBWqS071HD35d8Yd/yZ/HQ7WlLvnT6fVNlvyB9+TtnK++5rLLr1Rf+3ruV4w6eTi7d+9uknscat++fUwYd67663L22WfzySefSPInRBsjv6OFEG3OiBEjAG8R5s2bNjV4f+qHH/Dcs/9Wn1948SWsWLWGAQMHNvoeiuLBUl2Nx+1N/rQ6XZMmfwd16JDMLbfdztPP/JuIiAjA2yP4kosuwGw2N+m9Dhw4wPjzz6GgoADwLqd//fXXss9PiDZIEkAhRJtztH2A6/76i4ceuE99/tyLLzH78y/VPsKNoihYqi3q/jitVktERHizzJKFhIQQFRXFqNGn8OY779Gzl3d2c+eOHdx95+1NtiewqqqKCyacz57amcXevXuzcOFCNekUQrQtkgAKIdqcI50ELisr48orLsXhcABw99/v48GHHvFz1k7BYrHgcrkA0Gg1hEdEoNUevi5gU4hPSAAgJSWVt999X93/98Wcz3n3nbdP+Po2m43LLrlIrX2Ynp7OkiVLSKi9rxCi7ZEEUAjR5gwaNAiDwQDUdQTxeDzcctMNZO/bB8CIk07m+Rdf8vPKCjU1NXWdQTQawsMjjlgUuqnEx9clYpGRkbw/dbr6/NFHHmL1n0c+6HIsbrebG6+/ll+W/QxAXFwcS5culb6+QrRxkgAKIdoco9HIoEGDAO9SaWVlJS//90W+X7gA8BZ3nvnZF37vbbNabTjs3tlDNN5+uC3RAzcsLEztAlJZWcmECRO57wFvZw6n08nVV15GSUmJ39dVFIV7/34333w9V73P999/T58+fZoueCFEqyQJoBCiTTq4DKwoCtM/msrTU54CvC3dpn8yy+8ZLpvNht1mU5+HhYWrs4zNTaPRqLOAiqJQWlbKf557gVGjTwEgNzeXG6+/Fk/taeTGeubpKXz04QcA6PV6vv76a04++eSmDV4I0SpJAiiEaJPq7wP8v2f+pSZHTzw1hbPPOdevazkcdmz12sOFhoW1+MnY+Hr78UpLSjEYDHw6ew5JSUkA/LB0CS+9+EKjr/fWm2/w/HP/AbwJ5ieffMK55/r36yKECF6SAAoh2qT6CaDFUg3AWWefw+NP/tOv6zidDmpqatTnJpMJo9HYNEH6ISoqSl1uLisrxePxkJaWxoxP6wo0v/zSi95exMfw+WezefjB+9Xn//vf/7jqqquaJ3AhRKskCaAQok3q1auXTwkTrVbL2+996NeBDZfL1bC/b2jj+vs2Na1WS1x8POA9uFFZW6j5jDP/xrXXeYtXV1VV8flns496nSWLF3PLTXXFrv/5z39y7733Nk/QQohWSxJAIUSbpNVq6dy5s/r8/HET/Nr35/G4vTOHTdDft6kk1DsNXFJad+jjjrvuUR+//947R6wN+MeqVVx5+SVqCZs777yTZ555ppmiFUK0ZpIACiHarOrqavXx1dde2/gPKgoWSw2Kx5tI6Q16wsLCoIm7fPgrNi5OrVlYWlLCwanJocOGMXyEd8l744YN/PnHHw0+u23rVi68YLy6nH3ppZfy1ltvNXnnEiFEcJAEUAjRJm3fvp3s7GwA0tLSGTRoSKM/a7VacdfOkmm1WsLCmr7F2/HQ6/XExMYCYLfbqTbXJbh33Hm3+vj9997x+Vx2djYTxp1LeXk5AH/729+YOXNms9cvFEK0XpIACiHapHfffVd9PH7iRPUgyLE4HA7sdrv3iQbCwpunxdvxql8UurTeMvAll11OXFwcAF99+QXFxcUAFBcXM/78c8jPzwdg2LBhfPPNNwE5yCKEaD1az59qQgjRRKqrq/n4448Bb1Hos885D7PZrO7nOxJF8WCtd+I3NDSsRQo9+yO+9iAIQElpqfo4NDSU62+8GfAmsR9Pn4bZbGbSxHHs3rULgJ49e7Jo0SIiIyNbNmghRKsjCaAQos2ZPXs2VVVVAJx73jgiIyNxuVzU1KvldzgOh0M9QGEICcFobNlaf41hMpmIiPAmcNVmM3Z7XXHq226/U338wfvvctklF/HX2rUApKamsnTpUhITE1s2YCFEqyQJoBCiTVEUhffee099fv2NN6qPzeaqo30S+8E2b4DJaCSQJ36PJj6hbhawtKRuFrBrt26ccebfAO++v2U//wRAbGwsS5cu9TkVLYRo3yQBFEK0KWvWrGH9+vUADBs+nFNOGaO+Z64yH/FzLpcLj9sNgE6vR9fKln7rSzjCPkCAQYMH+zwPDQ1l4cKF9OvXryVCE0IEidb7J5wQQhyH999/X3182+13+ux3O9oMYP3Zv+M9ILFzxw4+njGNLZs3UVVVRecuXTjv/PFccullTdo6LjwiAr1e713WrrdnESAyKkp9rNVqmTt3LqNGjWqyewsh2gaNcqSKoUIIEWQqKipITU3FarUSFRXFvtwCwsPD+fPPP6mpqUGr1XLKmLENT/UqChWVFaCARqslKirKr7IvTqeTxx59hHfeeuOw73fJyGDWZ18ydNiwY14r1HD0+1573Q18OG0GoQYNU/71f4w+ZQxjTz0NrVZLfn4el108ifXr1gEwceJE5s2b1+jvIYRoP2QGUAjRZsycORNr7UGPq6+9jvDwcAAiIyOpqanB4/FgsVganIJ1ezxquze9Xud3zb8brruab+Z+dcT392Vl8bfTx7Bs+e8MHnL0eoRZuYXq46++mMO/n5nCxq071ddCQ0MbfMZut2E2m9mzezehoWHq6we/vxBCHEoSQCFEm3Do4Y/bbrtDfRwVFUVRUREA5qqqBgmgx+NRH2u1/hVH/nj6tKMmfwfZbDauvfpyNmzeftTSMsnJyerj6OhoNBqNz2uHU3ygmH37sgDvKeGDMjMzjxmXEKJ9kkMgQog24ffff2fr1q0AjBo1mv4DBqjvHWsfoG8C6N8fi/97/ZVGj927Zw8L5jf9kuy+fVlq+ZqIiAg1CZQEUAhxJDIDKIRoE+rP/t16+x0+70VGRKDRaFAUhSpzw5PAx5sA5uXlsX3bNr/i/HHpEi686GK/PnMkLzz3H594NRoNTqeT6OhobDYbJSUlmM1mKfwshGhAEkAhRNArLS3lyy+/BLw17y659DKf97U6HREREZjNZmosFtwul0+Zl+NNAHNqew37Izc3x+/PHMkdd97NkKHegyWRUZH06dWbp556grVrVtfFmJMjJWCEEA1IAiiECHoff/yx2r/32utuOOxBicjISG87OMBcbSYmJlZ9r/6hD38KI8TGxh570CFijuMzhzqYsMbGxZGalobBYGDkyJHo9XoiIyNxOJ0nFKMQou2TPYBCiKCmKIpP7b9Dl38PiqpXH+/QgtD1Z/3qzwYeS9du3fxeXh04aLBf4w+lKN6TzPWlpaX5HCw5WBvQZDId8wCJEKJ9kgRQCBHUfvnlF3bt2gXAqaeeRu/evQ87rn4CWHXIQZDjTQCNRiNXX3Ndo8cbDAauve6GRo8/lKIoWCw1uF0un9cj6pV7URSoqU0QMzIy/D7UIoRoH+RPBiFEUPMp/XLHnUccFxYWhk7nLfFirvJNAHU+CaDbr/s/OeVfpKalNWrs0//3Hzp06ODX9VWKQk1NDa56y7sHmeoteVutVjWJzcjIOL57CSHaPEkAhRBBq6ioiG+++QaAxMREJl140RHHajQadbnWbrfjcNjV9+rPkrnd/iWAiYmJzP1mPh07dTrquL/f9wAPPfwPv6593Q03sr+kAlCosVpxOmrb1Wk0rFq97v/bu++wqM60DeD3mRmm0AVEaSqIBbGhgLGlmWJ6MYmxxW4sMW7Kl7bZ9E3Z9E1ii7FFE2OMJpuYnmiKvReaIkUBRUWkTmFmzvfHwHFGERgYOAxz/64rV6bPg9eGvX3O+z4vhlSfc2y/5vGhuQ9Lt2NiYpz6PiLyHAyAROS2li1bhqrqjtgDEyfXe4bv5dYBCgoFFNXdQYvZAvNFl1jr0z8hAdt27sW8Rx5DUFCQw3PDr7wKG/63EW++/a7TJ4zUMOgNMFVvcoFgO+HDaDQAAFQqlcP6v5qB0AADIBFdHncBE5FbslqtWLx4sXR/6vQZ9b7H327DxtmiswgOCZHuazQa6Ks3T5hMpjpP66hNcHAwXv/PW3j19f+goKAApSUl6NylS5OPYzMajTAYDNJ9b29vmM1m6bGLdzxn2w1/ZgAkosthB5CI3NIvv/yC7Gxbt2vEddeja9eu9b6nXVAQVNWdvtOFhQ7r6dRqL6C6Q2cymZzaDGJPoVAgMjISveLjmxz+TCaTFEoBW9hTqzU4WVAgPda+fXuH9xw7lind5hpAIrocBkAickv2o19m1LH5w55KpULHsDAAtg7iqcJT0nOCoIBarbbdEUWYatbbyaSqqgqVlRfGvWi0Wmi0WlitFpw6dRKAbV1jWPXPA9jGv3y/8Tvb6zUaxMbGtmzRROQ2GACJyO0UFBTgf/+znanbsWNH3HLrbQ1+b3h4+IXPyc+3zU2pptGopdsGg8HpDSGuYjabbbP+qktTa9TQ6Wzn+545fUZa9xgaGnohtAL4cu0XKC4uBgCMHj0a3t7eLVs4EbkNBkAicjuffPKJFM4mT5kGLy+vBr/Xx8cHgYGBAGwjU4rPF0vPKZUqhy5gRUWFUyeDuILFYkFFRbkUTL28vKqDnO3ydEFBvvTaiIvGzyxaOF+6PXv27OYvlojcFgMgEbkVi8WCjz/+GIDtEujkqdOc/gz74FSQn+/wnM7bW9oRbLVYqk/VaJkQaLVaUFFeDtFq+z6VSlW9jtAW/srKylBaPcPQ19fXYVfz7l27sGf3bgDAgAEDkJyc3CI1E5F7YgAkIrfyww8/4MSJEwCAkTfdjM6dOzv9GSEhIdLImLNnz6L43DnpOUEQbKGrekNIlckEg8GI5g6BFosF5eUV0uYTpVIJH98LdYhWK45lHpVeHxER4TBW5uLuX2NHzhCRZ2AAJCK3Yn/yx7TptZ/7Wx+FQoGoqCjpflpaKox2o1aUSqXD+jmDXo+KikqIYuN2BtenqqoK5WVlsFZf1lYoFfDx9YUgXPgVnZWdhZKSEgC2M37tTxQpKirC2i/WAAACAgIwZsyYZqmTiNoOBkAichu5ubn4/vvvAQBRUVG46eabG/1ZkZGRCA4OBmALYKmpKQ6jX9RqNbRarXS/ymRCWVm5SzeGiKIIvV5vu+xbveZPoVTA18fX4XSSs2fOIK+66ykIAuLj46Vj7QBg5Yrl0lzAyZMnc/MHEdWLAZCI3MaSJUukoDRl6nSHEOQsQRAQFxcnhbzS0lJkZR1zeI1Wp3O4HGy1WFBWVgaDwdDoOYE2IqqqqlBRXu7QefTy8oKfr5+0BhEA9JWVyEhPk+7HxsY6rP3T6/VY8NEH0v1Zs2Y1oS4i8hQMgETkFqqqqrBkyRIAtku0k6ZMbfJnenl5oXfv3tJ6ufy8PJw5fdrxNWo1/Pz8LoRNUYRBr0dpaQkqKiqqj41r2PpAq9UKg8GA0tIyVJSXXzhyTqgOm74+EC46lzglNQXm6q5jaGjoJTt/33jtVeTm5gIARo4cie7duzv7x0BEHohHwRGRW/j2229x6pRtcPOtt93uMM+vKfz8/NCtWzccOXIEAJCengZRFBFqt8ZOqVTC188Pen0lTMbqAdGi7bJwlckEhVIJpVIBhUIJhUIBhUIBQRBgtVrt/rHAbLY4zB0EbOcQ+/h4Q6VyHGVjMpmQlpqKivJyALYj4Hr06OGwuSM9PR1vvfkGAFuYfeedd1zyZ0JEbR8DIBG5BVds/ric8PBwlJaU4FRhIaxWK9LSUlFSWoKuXWOltXiCIMDb2wdajRZGkxEmk0ka12K1WKo3cFTV8S2OVCoV1BoN1F4XjqCrUVJSgrTUFBiNRgC2TSu9e/d2OJ9YFEXMe2i2NBT6//7v/xAXF9eUPwYi8iCC2NJTTomInHTs2DHpWLMu0dFIy8h02CThClaLBRlHjkhdRsDWHewV39thM0gNURRRZTLBaDLBUnMptx6CQoCXlxoajeYy6xdF5J3IQ1bWMWmto1qtRnx8vDS8usZnq1dh8sQJAGxn/h4+fJibP4iowdgBJKJWb/HixdLtadNmuDz8AYBCqUTPnj0REBCAo0ePwmq1oqysDHt270JcXC8EVe8YriEIgq2Dp9EAogiLw+VeK0RRlC4HKxQKKBUKCAoBNUOdL2Y2m5GRno6zZ89IjwUGBqJXr17SzMIaxcXFeOLxR6X7H374IcMfETmFHUAiatWMRiOioqJw5swZeHl54VjOCYcZeM2hrKwMKSkp0Ov10mPtgoIQER6BoOBglw5ZNhgMOHmyAKdOnoTJZJIe79y5M6KjuzjMAqwxZ/ZMLFm8CAAwatQorFu3zmX1EJFnYAAkolZtzZo10mDje+69D6s//6JFvreqqgrp6ek4e/asw+NarRZhYeHoGBZ24dxgZ4kiiouLkV+Qj6KLPl+lUiEuLg4hISG1vnXH9u24avgQiKIIX19fpKWlITIysnF1EJHH4iVgImrVFi1aJN2ePmNmi31vzYiYgoICHD9+XBq0bDAYkJ2dhZycbLRv3x4BAYHQ6nTQarXQarW1XJ4WUWWqgt5ggEGvh15ficLCQofuImC7pNy+fXvExMRAp9PVWpPZbMZDc2ZK6wNffvllhj8iahR2AImo1UpPT5d2tnbr3h2HUtJlOeNWFEWcO3cO+fn5KCoqqvO1Go0GWq0OKpUSBoMBBoOhztNDNBoNwsPDERYWdslav4u9/9670tq//v37Y9euXQ47g4mIGoq/OYio1XLY/DH9QVnCH2DrzgUHByM4OBh6vR4FBQU4efKkNILFntFolMa31KVdu3aIiIhASEhwrev8LpaXl4eXXnhOqmfhwoUMf0TUaOwAElGrpNfrERERgeLiYmg0GmQfz5fO7m0NrBYLSkpLodfrYTAYHP5dEwwFQYCu+vKw/b99fX0ve5n3cu6/7x5sWP8VAGDmzJlYsGCBy38mIvIc/OsjEbVK69atQ3FxMQBg1D33tqrwB9jGxrRr1w7t2rW75Dmz2QyLxQK1Wu2SruX3GzdK4S80NBSvvvpqkz+TiDwbAyARtUr2J3+05OYPV1CpVC67PFtZWYl/zHtIuv/OO+/UGjqJiJzh+mmqRERNdOjQIWzduhUA0Cs+HoOHDJG5Ivm89u9XkJuTAwC49tprMXbsWHkLIqI2gQGQiFqdi0e/yLX5Q25pqal49523ANiOhJs/f77H/lkQkWsxABJRq1JRUYFPP/0UAKDT6TB23HiZK5KHKIqYO2eWtKHkySefRI8ePWSuiojaCgZAImpV1qxZg9LSUgDA6PvHIDAwUN6CZLLq05X4668/AQAxMTF4+umnZa6IiNoSjoEholYlKSkJu3fvBgD8vXUHkpKTZa6o5Z07dw59evWQjqH74YcfMHLkSJmrIqK2hB1AImo19uzZI4W//gkJSExKkrkiefzzmaek8Hffffcx/BGRyzEAElGrYb/5Q86TP+S0betWLF3yMQDAz88P7777rswVEVFbxEvARNQqlJaWIjw8HBUVFfD19UXOiQL4+fnJXVaLqqqqwhXJA3H40CEAwPvvv4+HH35Y5qqIqC1iB5CIWoXVq1ejoqICAHD/2HEeF/4A4MMP/iuFvwEDBmD27NkyV0REbRU7gEQkO1EUkZCQgAMHDgAAduzai/4JCTJX1bKOHz+O/n16oaKiAoIgYMeOHUjy0DWQRNT82AEkItnt2LFDCn/JyYM8LvwBwGOPzJM6oLNnz2b4I6JmxQBIRLJz2Pwx40EZK5HHd99+i/998zUAoEOHDnjllVfkLYiI2jxeAiYiWRUXFyM8PBwGgwEBAQHIOVEAb29vuctqMRUVFUjoG4/c3FwAwGeffYYxY8bIXBURtXXsABKRrD799FMYDAYAwLgJD3hU+AOAV195WQp/1113He6//36ZKyIiT8AOIBHJRhRFxMfHIy0tDQCw78Bh9IqPl7mqlpNy+DCSExNgNpuhVqtx6NAhdO/eXe6yiMgDsANIRLL5+++/pfA3dOgwjwp/VqsVc+fMgtlsBgA8/fTTDH9E1GIYAIlINgsXLpRuT39wpoyVtLxPV67Ali1/AwBiY2Px1FNPyVwREXkSXgImIlmcPXsWERERMJlMCA4ORlZuHrRardxltYizZ8+ib3xPFBUVAQB+/vlnXH/99TJXRUSehB1AIpLF8uXLYTKZAAATHpjkMeEPAP759JNS+Lv//vsZ/oioxbEDSEQtzmq1okePHsjMzAQAHErN8Jj1b1v+/hvXXj0cAODv74/09HSEhYXJXBUReRp2AImoxW3atEkKf1dfc63HhL+qqio8NOfCWsdXX32V4Y+IZMEASEQtzn7zxwwP2vzx/nvvIjUlBQCQmJiImTM952cnotaFl4CJqEWdOnUKUVFRMJvNCA0NxbGcE1Cr1XKX1exyc3PRv08vVFZWQqFQYOfOnRg4cKDcZRGRh2IHkIha1NKlS6XZd5MmT/WI8AcAj/7jYVRWVgIA5syZw/BHRLJiB5CIWozFYkFsbCxycnIgCALSjhxDdHS03GU1u//97xvce/edAICwsDCkpaUhICBA3qKIyKOxA0hELebnn39GTk4OAOD6G270iPBXXl6OR+fNle6/9957DH9EJDsGQCJqMYsWLZJuT5vxoIyVtJxXXnoRJ06cAADccMMNuPfee2WuiIiIl4CJqIXk5eWhc+fOsFqtCA8Px9GsXKhUKrnLalaHDx1CcmICLBYLNBoNDh8+jNjYWLnLIiJiB5CIWsYnn3wCq9UKAJg8ZVqbD39WqxUPzZ4Ji8UCAPjnP//J8EdErQY7gETU7MxmM7p06YL8/HwoFAocOZaDqKgouctqVks/WYJZD04HAHTv3h0HDx6ERqORuSoiIht2AImo2X3//ffIz88HANx08y1tPvydOXMGzzz1hHR//vz5DH9E1KowABJRs7M/+WP6jLZ/+sUzTz2B4uJiAMC4ceMwYsQImSsiInLES8BE1KxycnIQExMDURTRqVMnpB/NglKplLusZvPnH3/g+hFXAwACAgKQkZGBDh06yFsUEdFF2AEkomb18ccfo+bvmVOnzWjT4c9kMmHuQ7Ok+6+99hrDHxG1SuwAElGzqaqqQlRUFAoLC6FSqZCZfRxhYWFyl9Vs3nzjdTz7z6cBAMnJydi6dWubDrxE5L7YASSiZvPNN9+gsLAQAHDb7Xe06fCXnZ2Nf7/yEgBAoVBg4cKFDH9E1GoxABJRs/GUzR+iKOIfDz8EvV4PAHj44YeRkJAgc1VERJfHS8BE1CyOHj2K7t27AwBiunZFStoRKBRt8++cX29Yj9H3jgIAhIeHIz09HX5+fjJXRUR0eW3ztzERyW7x4sXS7WnTH2yz4a+srAyPPTJPuv/+++8z/BFRq8cOIBG5nNFoREREBIqKiqBWq5GVm4f27dvLXVazeOLxx/D+e+8AAG666SZs3LgRgiDIXBURUd3a5l/JiUhWX331FYqKigAAd909qs2GvwP79+PDD94HAGi1Wnz44YcMf0TkFhgAicjlFi1aJN2eNv1BGStpPlarFXPnzILFYgEAPPvss4iJiZG5KiKihuElYCJyqdTUVMTHxwMAevTsiQOHUttkV2zJx4sxZ5Yt3Pbs2RP79+/neb9E5DbYASQil7p480dbDH+nT5/GP59+Urq/YMEChj8icivsABKRy+j1eoSHh+P8+fPQarXIPp6PoKAguctyuSmTHsDqVZ8CACZMmICVK1fKXBERkXPYASQil1m7di3Onz8PALj3vtFtMvxt3rRJCn+BgYF46623ZK6IiMh5DIBE5DL2J3+0xc0fRqMRcx+aJd1/4403EBoaKmNFRESNwwBIRC5x4MABbN++HQDQp29fDLriCpkrcr13334LRzIyAABXXHEFpk2bJnNFRESNwwBIRC5x8eiXtrb5IysrC6+9+goAQKlUYuHChW32dBMiavv424uImqy8vByrVq0CAPj4+GDsuPEyV9Q0b77xOr7esF66L4oi5s2dA4PBAACYN28e+vXrJ1d5RERNxgBIRE32+eefo6ysDAAw+v4x8Pf3l7mixsvNzcW/nn0G7dpd2MCyYf1X+PmnHwEAkZGReOGFF2SqjojINTgGhoiabODAgdi7dy8AYNuO3RgwcKDMFTXe1cOHIqpTJ3y6+nOUlJRg3969GHXX7SgvLwcArF+/HnfddZfMVRIRNY1K7gKIyL3t3r1bCn8DBg506/C39os1OHjwADb/tQUAMG/uHPzww/dS+AsKCsLZs2flLJGIyCV4CZiImsR+9Mv0GTNlrKTpPvl4MXx9fZGakoIVy5dh166dKKmea6jRaDB06FC8+OKLyM3NlbdQIqIm4iVgImq0kpIShIeHo7KyEn5+fsg5UQBfX1+5y2oUURTx6y+/YPWqldi5cweO5+YiIiISOTnZAIDXXnsNU6ZMQVxcHL7++msMHz5c5oqJiBqPHUAiarRVq1ahsrISADB2/AS3DX8AIAgCrr/hBvz3w/mY89DDSEoeJIW/Xr16Yd68eQCAmJgYnDp1Ss5SiYiajAGQiBpFFEWH2X/T28jJH/7+/hh1z704dPCA9NiCBQug0+nw66+/oqCgALfddpuMFRIRNR0DIBE1yrZt23Do0CEAwBVXDEafvn1lrsh1nnricWmszaRJkzB06FBs3rwZzzzzDP75z39Cq9XKXCERUdNwDSARNcrEiROxcuVKAMAny1Zg/IQHZK7INX7/7TfcdON1AGy7ftPT06FQKDBjxgzodDpp4DURkTtjACQip507dw7h4eEwGo1o164dso/nQ6fTyV1WkxkMBgxM6IvMo0cBAB9//LF03q/VaoXBYIC3t7ecJRIRuQQvAROR01auXAmj0QgAGD9hYpsIfwDw9pv/kcLfkCFDMGXKFOk5hULB8EdEbQY7gETkFFEUERcXh4yMDADA/kOpiIuLk7mqpsvMzMSAfr1hNBqhVCqxb98+9OnTR+6yiIiaBTuAROSUP/74Qwp/V155VZsIf6IoYt7cOVJX89FHH2X4I6I2jQGQiJxiP/pl2oy2Mfpl3Zdr8esvPwMAoqKi8Nxzz8lcERFR8+IlYCJqsNOnTyMyMhJVVVUICQlBVm4eNBqN3GU1SUlJCfr1jsPJkycBAF9//TXuuOMOmasiImpe7AASUYMtX74cVVVVAIAHJk52+/AHAC88/y8p/N1+++0Mf0TkEdgBJKIGsVqt6NatG7KysgAAKelHERsbK3NVTbNn924MGzIIVqsV3t7eSE1NRefOneUui4io2bEDSEQN8ttvv0nh79oR17l9+LNYLHhozkxYrVYAwPPPP8/wR0QegwGQiBpk4cKF0u0ZD86UsRLXWLxoIfbu2QMAiI+PxyOPPCJzRURELYeXgImoXgUFBejUqRMsFgs6duyIzOzj8PLykrusRjt58iT6xvdEaWkpAOCvv/7CsGHDZK6KiKjlsANIRPVaunQpLBYLAGDS5KluHf4A4InHH5XC39SpUxn+iMjjsANIRHWyWCyIiYnB8ePHIQgCMjKz3Xqt3K+//IJbbroBABAcHIyMjAwEBwfLXBURUctiB5CI6vTjjz/i+PHjAIAbR97k1uHPYDDg4bmzpftvvvkmwx8ReSQGQCKqk/3JH9NnuPfmjzffeB3HMjMBAMOHD8fEiRNlroiISB68BExEl3X8+HFER0fDarUiMjISGZnZUKlU0vMVFRU4c+YMOnbsCK1WK2Ol9Tty5AgG9u8Dk8kElUqF/fv3Iz4+Xu6yiIhkoar/JUTkqRYvXizNyUsYMBAvv/gCcnKykZ2VhezsLJw+fVp6bUREBLp0iUZ0TAyio2PQJToa0dExiI6JQVhYGARBkOvHgCiKmDd3DkwmEwDgscceY/gjIo/GDiARAbCFpJycHOzcuRO7du3Cjh07sGXLFrjiV0SnTp0wddoMTJ46DR06dHBBtc75Ys3neGD8WABA586dkZKSAh8fnxavg4iotWAAJPJQZ86cwa5du7Bz504p9J09e7bB7w8LD0d0dAzah4biZEEBci7qCNbGy8sLd909Cg/OnI2hw4a1SFfw/Pnz6BvfE4WFhQCAb7/9Frfeemuzfy8RUWvGAEjkAcrLy7F3714p6O3cuRM5OTn1vi+0Qwd07RqL3n36IC4uXrq826lzZ+h0ulq/JzcnB9nZWdJl4rTUFPyxedMlncT43r0x48FZGDtuPPz9/V31o17i4blzsGjBfADAnXfeiQ0bNjTbdxERuQsGQKI2pqqqCocOHXLo7qWmpkpr+S4nODgYiUnJSExMQt9+/SCKQGC7dtBqtRg06AqgCd263JwcLPl4EZYvXXJJl9HX1xez58zFcy+86PIB07t37cKwIYMgiiJ8fHyQmpqKTp06ufQ7iIjcEQMgkRuzWq3IzMyUwt6uXbuwb98+GAyGOt/n7e2NhIQBSExKRlJyMgYmJiE6Olq6JHvs2DFp9l90TAw6dXLN7D+j0Yj1X63D4oXzsX3bVofnBg8egtVr1iIiIsIl32U2mzF0cDL279sHAHjrrbfw2GOPueSziYjcHQMgkRs5efKkw5q9Xbt24fz583W+R6lUonefPlJ3LykpGXG9ejmMc7FntVqxbds2mEwmCIKAKwYPgVqtBgCIVisMRiMMBj0MegMMBj1MVVXQqNXQanXQ6rTQanXQaDT1ru87sH8/Pl60ACtXLENVVRUAoH379ljx6WcYcd11zv/hXOTDD/6Lxx6ZBwDo27cvdu/e7fZH2BERuQoDIFErVVJSgj179kiBb+fOncjPz6/3fV1jY5GYmGTr7iUlo1///vD29m7w954+fRopKSkAgJD27REcHIzTp09DX1kJo9HYoF3BgiBAq9XC29sHHTp2QEhwCARF7XPnd+3ciXFj7sWJ6o6jIAh4/oWX8OTTz0BxmffUp6CgAH3je6KsrAwAsGXLFgwZMqRRn0VE1BYxABK1AkajEQcOHHDo7qWnp9f7vg4dOkhBLzEpGQMTExEUFNSkWvbt2yd1FZVKJSwWS5M+DwA0Gg3CwsLRMSwMGo3mkueLioowddIE/PTjD9JjN468CctWfNqoo9rGjRmNdV+uBQBMnz4dixcvbnzxRERtEAMgUQuzWCzIyMhw2JF74MAB6TLo5fj5+WHgwETbpdwkW4cvMjLSZaNURFFEQUEBjhw5UuvzKpVKusyr0+qg1Wqh1emg9vKC0WSCQa+HwWCA3u7y8MXhURAEhISEIDwiAoGBgQAu1G61WvGf11/FSy88J3UZO3XqhLXrNiBhwIAG/xw//fgjbr/1JgBASEgIMjIymhyKiYjaGgZAomYkiiJOnDjhsCN3z5490qXJy/Hy8kK/fv0xMClJ6u716NGj0ZdE62MwGJCSkoLS0lKHxwVBQPvQUISHR8Df39+psCmKIs4XFyO/IB9FtcwXDAoKRs+4uEvW5f3+26+YNGEszpw5AwAIDQ3Fjt37EB4eXu936vV6JPTrjeysLADA8uXLed4vEVEtGACJXOjcuXMOO3J37twpDSCuS4+ePaWgVzOGpbZLpc2hqKgIaWmpqKoyS4+p1WpERESiY1iYtAGkKQwGA06eLMCpkyel49gAQKvVolevePhdNAcwPz8fY+4bhV07dwAAhg4dhp9+/b3eTRwvPPcvvPbqKwCAq666Cps2bZL1CDoiotaKAZCokSorK7Fv3z6H7t6xY8fqfV9kZKQU9BKTkjFg4EAEBAS0QMWOao5+sx8IXRP8goKD4evr6/LvtFqtOHPmDI5lHpUueQuCgK6xsdXjXy6EtbNnz+KKpATk5+UBAB559HG8/p83L/vZ6enpSEzoi6qqKnh5eeHAgQOIi4tz+c9ARNQWMAASNYDZbEZqaqrDJo1Dhw7Vu0EiMDDwQthLtv07LCyshaq+PJPJhNTUVBQXF0uPBQQGIioyCkqlEr5+vlCpmm9kitFoRGpqCkpLSqTHQkND0b1HDyiVF8bT7Ni+Hddfe6UUFteuW4877rzrks8TRREjrx+BzZs3AQCeeeYZ/Pvf/262+omI3B0DINFFRFFEdna2w2XcvXv3orKyss73abVa9O+fgMRk267cgYlJiI2NbXWXIPV6Pfbt2wej0Sg91iU6Gv7+ARAAKJRK+Pv7wb4b1xysViuys7KQl3dCeszHxwf9+ic4XOqd/+EHeOyRhwEA/v7+2L5rL7p27erwWZ+tXoXJEyfYfpYuXZCSkuLU6BsiIk/DAEge7/Tp0w6XcXft2oWioqI636NQKNArPl4KeklJyYjv3bvVDxq2Wq3Yu3evtAlFrVYjrlc81Go1TNWBUKfTQaPVtlhNZ86cRkZ6utRNDQ4ORu/efaSj50RRxIRx9+Or6rEuffv1w59/b5POIi4uLkbf+J44ffo0AGDjxo24+eabW6x+IiJ3xABIHqW8vFwarlwT+nJzc+t9X5foaIfhyv0TEppljVxzy8jIQEFBAQBb0OvfPwFeajVKS0pso1cEAf7+/s222/hyKisrsX/fXulSb3R0DDp1vnD8XFlZGYYNTsKRjAwAwJRp07FgoW2230NzZuHjRQsBAKNGjcK6detatHYiInfEAEhtlslkwqFDhxy6e2lpabBarXW+LyQk5JLhyu3bt2+hqpvPqVOnkJaWBsDWwUwYMBC+vr4wGY3S5W21Wg1vHx9Z6jt37hwOHTwg3e/Xrz8C27WT7qempGD4kGSp1sNpR3CuqAhXDR8CURTh6+uLtLQ0REZGtnjtRETupvbDQIncjNVqxdGjRx1GsFy8zq02Pj4+GDBgoMNw5c6dO7e6dXtNVVFRgYzq7hkAdOvWXepgGu3GsqhbaPRMbYKCgtC5SxfkVu9KTktLxYCBidI4nF7x8Xj62efwr2eeAgAsWjgff2zeJA2Nfumllxj+iIgaiB1AcksFBQUOa/Z27dqFErsdpbVRqVTo07evtGYvKSkZPePioFQqW6hqeZjNZuzZs0fqnHUMC0OPHj0BABazWVoPqFQq4dcCmz/qIooiDh08iOLicwCAgIAA9OvXXzpH+OzZs4jtEgmj0Qidzht6ve1n6tevH3bv3g2Vin+nJSJqCP62pFbv/Pnz2L17t8Ol3Jp1bHWJ7dbNYbhyv/79pY0DnuTEiRNS+PP19UW32G7Sc5d2/+TtfAqCgJ5xcdi7ZzeMRiNKSkpwqvAUwsJsp4CEhITgnntHY/WqlVL4EwQBixYtYvgjInICf2NSq2IwGHDgwAGH7p79pcvL6dixI5KSB0kbNQYmJqKd3foxT2W1WqWwLAgCevWKh6K64ymK1guncggC1K1kB7NarUbPuF44sH8fAKAgP796dqItnM6YORurV62UXv/ggw9i0KBBcpRKROS2GABJVidPnsRPP/0kBb6DBw9KO0Evx9/fHwMGJtou4ybbxrBERES0uXV7rnD27Fkp5AWHhEBnNxvPZKoCqleAqNVq6TJraxAYGAg/Pz+UlZWhvLwcpaWl8Pe3nZbStWtXxHbrhsyjRwEA99xzj5ylEhG5JQZAanGiKGLz5s2YP38+NmzYUOdpGmq1Gv369ZdO0UhMSkb37t1bfEyJu8rPz5duR4RH2D0jSnP/ALjkvF9XCw+PQEZGOgBbF9DfPwAWiwWZmUdx2+134t23bcfCrVq1CiNGjJCzVCIit8NNINRi9Ho9lixZggULFkjjSOzVrP+yH67cp2/fVhlO3EFFRQV27twJAPD29kZScjJqLqOazWaU12z+UKng5+cnV5mXZbFYsH3bVpjNZgiCgMFDhiDvRB6OH8+FwWDAhLGjUVZWBq1Wi/z8fAQFBcldMhGR22AHkFrEkSNHMGrUKBw+fNjh8Q4dOmDS5KkYcd31SBgwAP7+/jJV2PbYd//CwyNgv8HDZGrd3T/Atiu5Y1gY8k6cgCiKOJ57HPn5eQBsQ6zHjBuPxQsXwGAw4M8//8Sdd94pb8FERG6E19Go2a1btw6JiYkO4W/48Cux6rM1yMw+jpde+Teuuvpqhj8XslosOHXqFADb0OcOHTtKz4lWq239H2xd19YaAIGa4GpTUJAvzfzr1KkTBg8eIj2XlZXV4rUREbkzdgCp2ZhMJjz55JN47733pMd6xsVhxcrV6J+QIF9hHqBSr5fWVoaEhDiMSDFVmaTNH15qdaM2z+j1enz15Vrs2rkDhacLERYWjmHDr8Ttd9zp0vOQdTodAgICUFJSIp3gotPp0LlzZxRWB1wAyM7Odtl3EhF5AgZAahaiKGLMmDFYv3699Nj9Y8biowWL3PIMXXdjMOil297ejke71XT/AECjcb77983XGzD7wWk4d+6cw+ML53+IyKgoLFu5GsOGDXfqM0+dOoU333gNP/6wEfl5eQgICEDX2G4YM3Y8kpJtI14eGD8GpwsLAdhCYFSnTtL72QEkInIOLwFTs3j77bel8KdWq/HfD+dj+cpVDH8tRK83SLe1Oq3Dc9bqzqBCoYBS6dzfAZcv/QT333v3JeGvRt6JE7jp+mvx048/NPgzs7OyMDgpAb/9+jNefPlVbN+1Dxt//BVz5z2C7zd+i927dkivnT5jJnLzTmL3voO46+5R0uMMgEREzuEuYHK5v/76C9dcc410CXLDN9/h5ltukbkqz3LkyBFpE0hCwgD4B9hm6IlWq3Rknkqlgq8Tu38z0tMxKLF/vecrA7ZzffcfTkf79u3rfe3tt4xEamoKDhxOh4+PY7fSbDbj+PFcnDh+HA+MH4MpU6fjxZdelp7389bAZDJBo9GgsrKS44GIiBqIvy3JpU6dOoXRo0dL4e/pZ55l+JOBwVB7B7BmHR0AKJTO/ef/4X/fa1D4A4Bz585h2Scf1/u6oqIi/PrLz3hw1pxLwp8oiqisrIRGc6H+i2dGajQaAIDRaJQ2vRARUf0YAMmlpk2bhpMnTwIArrl2BP71/AvyFuSh9HrbGkCFQuGwy9diHwAVSqc+c+PGb516/Xff/q/e1xzLzIQoiujevYfD45EdQ9C+nR+io8Lx1n9elx43m6uq/23GyhXLUVY9yxDgZWAiImcwAJLLpKSkYOPGjQCAsLAwrFz1GZRK50IGuUZNB1Cr1cJ+/p9DB1DR8N2/RqMRp6qDfUPl5jR8Z+7FO5H/+Gsbfvn9T/To2QNmc5X0v6MP//s+ggJ8EeCrw7y5c3DV1ddI78nJyXGqPiIiT8YASC6zYMEC6fbjTzyF0NBQGavxbDWB6eJLpvZBy5nVv15eXk7PC/RpwIafrrGxEARBOvKtRsewMERHR0Or1UGpVEqXeu+5bzR27t6Ho1m5OFdSjqvtAiA3GBERNRwDILlEWVkZVq5cCcB27Nj4CQ/IXJFn0+l0AGydO8eu34X/5O0fr49CoUDPnnFO1RAf37ve1wQHB2PEdddj4fwPUVFRAQCoMplQVXVhVI1KqZICoL+/Pzp36YLw8HAIgoDs7AuXfWNiYpyqj4jIkzEAkkusXr1aWo91/9hxCAwMbNTn/PXnn3jv3Xfw7DNPY8H8j5CWmurCKj2H7dKvjf2GEMcA6NgdrM+EiZOb5fXvfzAfFrMZQ69IxJdfrMGBA/uRmXkU675ci2OZmVCqLgRAwDZgvEa23bq/6Ohop+ojIvJkDIDkEsuWLZNuz5w52+n3b9+2Df37xuO6a6/Ck//3GN78z+v4x8MPoX/feNx04/U4fvx4gz9r2pRJ0KgEzJk985Ln5j40GxqVgGlTJjk8vm3rVujUStx680ina2+NajqAgGMAVDayAwgAU6ZNR58+fRv02mtHXIdbb7u9Qa+N6doV23btwzXXXod/Pfs0rr1qGEZePwLLln6Mfzz2GJ5/8WWo1RcCoP1O5JzqdYYhISHwc2KkDRGRp+McQGoyURTh4+MDvV6P2G7dkJJ2xKn3f71hPcaPvd/hst/FgoOD8dumPxHXq1e9nzdtyiRs3vQ7SktLkZt3UgpDBoMBnSPD4O/vj6uuvgZLli6X3jNzxjT4+Ppi2SdLsP9QKjrZnTLhjgoKCpCRkQEA6NatO8IjLpypW1p9rJogCPAPCHDqKLisY8dw+60jcSwz87KvGTBgIL7+7ocGzQC0Z7FYUFZWCogABMDPz19ay5ifn4fMo0cBAD179kRYWBiMRiMCfHUQRRHJycnYsWNHHZ9ORET22AGkJissLJTGjnTtGuvUe7OysjBtyqQ6wx9gmxc3+r5RDZ5D1z9hAKI6dcLXGy4cRff1hvWIjIpCv/6O5xBXVFRg3ZdrMePBWbj5llvx6YrlTv0MrZFjB1Dv8Jyy+lxgURTr/XO/WEzXrti6Yw/mzJ2HgOrh0jVCQ0PxzLPP4fc/tzgd/lA98w/Vfx3VarQOO8g16ksvAefm5qLm76+8/EtE5BwGQGqy7OwL4z6inVyI/967bzvMcqtLRno6vlz7RYM/+4GJk7Fi+YVL08uXLcXESVMued2Xa79A9x490KNHD4wZOx4rViyDuzfG7dcAVlZWOjynsdvN29BAbc/f3x9vvfMesk+cxI7d+/G/jT9i975DyMzJw7+ef9FhvV5DGU0mWMxmALYB1fb1A3D4zJqa7df/cQMIEZFzGACpybIcFuI793/E69d96dTr1325tsGvHTd+ArZu+Rs5OTnIzc3Ftq1bMHbc+Etet3zpJxgz1vb4jSNHoqK8HL//9ptTdbU2Wq0WXl62Tl9paSmk1hoAlZcKipoxMWYzLBZzo75Dp9Ohb79+uP6GGxHfuze8vLwa9TlWq9WhS6nTeQMXXZZWay4NrdwBTETUeAyA1GQOHUAnLsWVlpbizJkzTn3XsWOXX3t2sZCQENx08y1YtXIFVi5fhptuvgUhISEOr8nIyMCuXTtx3+j7AdjOx73nvtFYsXypU3W1NoIgwN/fdom2qqoKlZX2l4EFaBwClQlyMhj0EK22gOqlVtcaJNW1dC2//d830mM9e/Zs5iqJiNoWldwFkPuzX6t18eDhutiPJGnMdzXExElT8I95DwEA3v/vR5c8v3zpJzCbzYjudGGThCiK8PLyQnFxMdq1a+d0ja1FQEAAioqKANg2fnh7e0vPqdVq6PUGQBRhMhmh9vKCqpEdvKYwmUwwVQdQQRAc1i7aEwTbkXYmkwkmkwlHjx7Fr7/8DMD2l44hQ4a0WM1ERG0BO4DUZPZdP2fOY/X19UWE3e7Uhrj4zNj63DhypG2wsMmEG2680eE5s9mM1atW4o0338auPfulf3bvPYBOnTvj889WO/VdrY39Jo3S0hKH5wRBAW3NujoRqKisdHosTFNZLBaH9Ylana7OvxTUrAM0mUz4eNGFU2dmzZrVqL9MEBF5Mv7WpCazX39lvy6rIUbfP9ap148ZO86p1yuVShw4nIYDh9Mu6R5u3PgdiouLMXnKVMT37u3wz91334Plyz5x6rtaGz8/P2nES0lJySXPa3VaqKrXCYpWKyorKlps84soiraTP6q/T61WO1yWrk3NZWC9Xo8V1Tu1NRoNJk92bkA1ERExAJIL2AfAHLv1gA3x8D8eafCZwcnJg3Db7Xc49fmAbdeqv7//JY8vX/oJrh1x3SXjTADgrrtH4cD+/di3d6/T39daKJVKaThyZWVlLSNfBHh7+0Co7p6ZzWYY9HrnDgluBLF65Iu1ermAQqmEztsbQN3zCGs6gH9u3oTzxcUAgNGjR1+yrpOIiOrHQdDUZKIowt/fH+Xl5ejcuTMyMrOdGi78x+bNGHXX7XWOg+kSHY1ff/8DUVFRrijZYxw9ehR5eXkAgN59+iI4OPiS15jNZpSXl0kbhVVeXvD29m6Wy6pWqwUVFZXSyBcIAvz8/Bq0tjM3Jwc5OdmYO2cWjh6xDbnevn07Bg0a5PI6iYjaOnYAqckEQcCAAQMA2Ibzbt+2zan3X3X11diyfReuuXbEJc+p1WqMn/AAtu/cw/DXCA7rAGu5DAzYdj7rdBc2iJirqlBeVgazuXHjYS6nqqoKZWVlduEP8PH2bvDGHo1Gg4yMdCn8DRgwAMnJyS6tkYjIU3AXMLnEtGnT8OeffwIAFi74CIOd3JXZo0cP/Pjzr8jMzMS+vXtw5swZRERGYvDgIQ2+REyXqmsjiD2NRgOFQoHKygqIVhFWqxXl5eXQarVQq9VN6gZarVYYjUYY7c4kVigU8PbxgUrV8F9BXmovrLHbmDN79mynOs1ERHQBLwGTSxgMBkRGRqKoqAheXl7Iys1jcGsltm3bBoPBAIVCgWHDhktr/mpzySVaABAEqNVeUKs1TgQ2EVVVZphMRtvaQ7vfMiovL/h4e9dZR23efftNPPPUEwBsgfXcuXMOo22IiKjheAmYXEKr1WLq1KkAbJf6li917x20bUlNF7Cmq1cXhUIJP19fx+PcRBEmownlZWUoKytFZWUlDAYDTCYTzGYzrFYrzGYzTCYTDAYDKisrUVpahoryclSZ7MKfYNt57Ovr43T4275tG5579hnpflxcHMMfEVETsANILpOVlYXY2FiIoojAwEBs37XXqZNBqHnk5+fjyJEjAICusbGIjGzYWkqLxQKT0QiTydSk8TCCQiGNeVEonBvkDQBnzpzB4OQByK/ezAIAvXv3xqFDhxpdExGRp2MHkFwmJiYGY8fa5vqdP38eY0bfA4Pdui+Sh/0InMttBKmNsno8i39AAHRObNaQ3q9SwdvHBwH+/tDpdI0KfxaLBZMfGCeFP61WC8AWaomIqPEYAMmlPvroI8TGxgIA9u3di8ce/Ye8BRF8fX2k8FZaWgqHBXkNIAgCNBoN/Pz94R8QAF8/P3j7+ECr00KtUUPl5QW1RgOtTgdvHx/4+vkhICAAfn5+tuHNTdio8dq/X8Zvv/4CAOjQoQP69OkLACguLoZer6/rrUREVAcGQHKpgIAAfPXVV1KnZsniRVixfJnMVXk2QVBIXUCj0QiDwdjoz1IoFFCpVFCr1dBqdfD29oGvry+8vb2lHcMqlcrpNX61+enHH/DqKy9J3/vp6jXoYrekoKCgoMnfQUTkqRgAyeX69u2LBQsunNU6Y9oUPP+vZ2GpPvmBWl5D5gG2FqIoYuH8j3Dv3XdIaw9fevnfuOrqqxEWFi69jgGQiKjxGACpWUyaNAkzZ86U7r/+2r9x88gbcPr0aRmr8lz2AbCkjnmAcisvL8fECWPxyLyHpKPrbr/jTjz2f7bxL+EREdJruQ6QiKjxGACp2cyfPx9vvvmmtP5s86bfMSgxAd9v3Air1SpzdS1j7RdroPVSwN9Hi6AAX3RsH4TY6E54YPxYHD16tMXqaOxGkJaUlpqK4UOS8eUXa6TH5v3jUXy2Zq00iDo8nB1AIiJXYACkZiMIAh5//HFs2rQJYWFhAGz/p33XHbeid68eeO/dd3Du3DmZq2xet91+B9KOHMOO3fvw++a/sG79N9JYlZYclK1SqeDr6wvA1mWzuPiYt6b6Ys3nGD4kGelpaQAAPz8/fPHlV/jPW2/Dy8tLel14ODuARESuwABIzW748OHYt28frrnmGumxY5mZePL/HkN0pwhMnTwRH37wX2z87jukpaaisrJSxmpdS6fTITo6GnFxceifkAC9Xg+VSoUxY8c7XJZtCQ5dwLKyFv3uyykrK8M/5s7BpAljUVFRAQDo07cvtu3cgzvvuvuS14exA0hE5BIcBE0txmKx4JtvvsH8+fPx22+/1fna9u3bw6d6d6lOp4NOq7ONGam5r6u+r7twX2f3nO2fC/e9vb2htXvO29sbGo2mxc6StVgsUCqVGHXXHaioqMCyFZ9KXdGWcurUKaRVd9i6dIlG5y5dWvT77aUcPoyPFy3A6lUrHU4neWDiJLz/wUeXPeWjsrIS7fx9ANj+YlFz/jQRETmHAZBkkZ6ejoULF2L58uUokWlNmiAI0Gq1lwTEmlCprS08ai96rbfda7W1BFS7+9nZ2Rh6RRLeff8DjJ/wQIv/vHq9Htu3bwcAtAsKQt++/Vr0+00mE775egMWL5yPv/9yDG5arRbvf/ARJk2eUu/ndAhph/Pnz6Nr167IzMxsrnKJiNo0BkCSVWVlJXbv3o2srCxkZ2cjKysLWVlZyMvLQ2VlJfR6PfR6fZvYNCIIAgRBQLt27eDr63uhY1lLd1Pn7Q2dVtfo7qZWq5U2TtQQRRFbt26FyWSCSqnEkGHDm70DKooicrKzsXLFMiz75GMUFhY6PO/t7Y0x48bj4XmPoGfPng36zIR+vZGakgKtVovKysoW6+ISEbUlDIDU6omiiKqqKodAqNfrHe7X9Vx99y9+rq0cX6fRaC4JiIAAhUIBjUaDDh06wM/Pv/Zup/ZywfLSUCoIAk4cP47s7CxkZ2UhOzsLuTnZyM7OQk52dq1/nj3j4vDgzNkYN36C02shb7npRvz6y88AgKKiIgQFBbnij4uIyKOo5C6AqD6CIECtVkOtViMwMLDZv89qtcJgMDQqPF7ufk5ODo4dO4bo6GhYLBaH58zNtCPXaDTCaDTi/PnzzfL5zlKpVLjjzrvw4MzZuPKqqxrdubt4FAwDIBGR8xgAiS6iUCjg7e192Y0IjZGYmIhx48ZhyZIltvNx7ZjN5iZ3LZ2531JNf61Wi+iYGERHxyA5eRAmTp7iEN4a6+JRML17927yZxIReRoGQKJmtmXLFuzduxcffvjhJeEPsHXG/Pz84Ofn1+y1GI1G+Pv7w2QyIapTJ/z0y+/QXxQW9fqL7tsHT30lDBcFS7PZjMjISERHxyA6JgZdukQjOiYGHTt2bJb1eRwFQ0TUdAyARM1syJAhKC4ubvG5f7XRaDRITk7G33//jRPHj8PHxwddu3aVuyyn8Dg4IqKm4yBoomYmCEKrCH81hg4dKt3evm2rjJU0TngYO4BERE3FAEjkYYYMGSLd3rbVDQMgO4BERE3GAEjkYRwD4BYZK2mc0NBQacYhO4BERI3DAEjkYUJCQtC9e3cAwN69e9xu7qFKpULHjh0BsANIRNRYDIBEHqhmHWBVVRX27N4tczXOq9kJXFhY2GxzFImI2jIGQCIP5HAZ2B03glTPArRarZccL0dERPVjACTyQPY7gd1xHSBnARIRNQ0DIJEH6tGjB9q1awfANgrG3Y4EjwjnTmAioqZgACTyQAqFQroMfPbsWRw9elTmipxjPwqGHUAiIucxABJ5KPt1gO42EDrMbhg0O4BERM5jACTyUO68DjCCw6CJiJqEAZDIQyUlJUGlsh0H7m47gbkJhIioaRgAiTyUt7c3EhISAABpqak4d+6czBU1XGBgIHQ6HQB2AImIGoMBkMiD2a8D3LF9u4yVOEcQBKkLyA4gEZHzGACJPJhbrwOsHgVz/vx5VFZWylwNEZF7YQAk8mCDBw+WbrvdTmCuAyQiajQGQCIPFhkZiU6dOgEAdu7cgaqqKpkrarhwDoMmImo0BkAiD1dzGViv1+PggQMyV9Nw7AASETUeAyCRh7PfCLLVjdYBchYgEVHjqeQugIjkZb8RZPu2rXhg4iSkpaZi+/Zt8Pb2xrUjrkNsbKyMFdbO/jQQdgCJiJzDDiCRh+vTpw+8vb0BABvWf4UOIe1w9ZVD8cfmTfjyizUYekUSVq5YDoPBIHOljsLZASQiajR2AIkIgYGBqKyshMViwcxZc5CZeRRqtRr//XA+8vLy8Ny/noFarcb9Y8bKXaokLCxMus0OIBGRc9gBJPJwO3Y47v4dOmwYVn22Bmq1GitXLMf1N9yAoUOHY83nn8lY5aV0Oh2CgoIAsANIROQsBkAiD5eeng5/f3/p/tatW9CuXTsMTEzCvn17AQBDhg5FamqKXCVeVs1l4IKCAoiiKHM1RETugwGQyMMNHjwY58+fl+5v27oFhw4exKcrl+Oaa64FYNtwce+9o2EymWSqsnY1G0GMRqNbnWVMRCQ3BkAiD9erVy/cdtttUKvVAIAD+/fjySceh7+fP+67fwwAoEfPnnjy6Wek17QWHAVDRNQ4DIBEhLfffhsJCQkAAFEUYbVY8ebb7yI6OhoAEBQU5HCZuLXgKBgiosZhACTycKIoIigoCHPmzJEeu/Kqq5CYlCRjVQ3DUTBERI3DAEjk4QRBAOB4Isi2bVvlKscpPA6OiKhxGACJCAAQExODDh06AAB2bN8Gq9Uqc0X1iwhnB5CIqDEYAIkIgK0TWNMFLCkpQVpqqswV1Y8dQCKixmEAJCKJ/bnAW7dukbGShgkNDYVSqQTADiARkTMYAIlIYr8OcLsbrANUKpXo2LEjAHYAiYicwQBIRJIBAwZAo9EAcI8OIACEV68DLCwsdDjSjoiILo8BkIgkGo0GiYmJAICsY8dQWFgoc0X1q1kHKIqiW9RLRNQaMAASkQP7dYDucBmYswCJiJzHAEhEDhzmAW5t/QHQfhQM1wESETUMAyAROXAMgK1/HaD9KBh2AImIGoYBkIgctG/fHt26dQMA7N27BwaDQeaK6hbODiARkdMYAInoEjXrAE0mE/bu2SNzNXULZweQiMhpDIBEdAl3OheYm0CIiJzHAEhEl7DfCdza1wH6+/vD29sbAC8BExE1FAMgEV2iZ8+eCAwMBGAbBSOKorwF1UEQBKkLyA4gEVHDMAAS0SUUCoV0GfjMmTPIzMyUuaK6hYfZ1gGWlpaivLxc5mqIiFo/BkAiqpU7nQtsvw6Ql4GJiOrHAEhEtbJfB9jazwUOC7uwE5gBkIiofgyARFSrpKQkKJVKAO7VAeQ6QCKi+jEAElGtfHx8kJCQAABITUlBcXGxzBVdnv0sQHYAiYjqxwBIRJdlvw5wx/btMlZSN/vTQNgBJCKqHwMgEV2Wu5wLzE0gRETOYQAkosuy3wjSmtcBhoWFSbfZASQiqh8DIBFdVmRkJKKiogAAO3fuQFVVlcwV1U6j0SAkJAQAO4BERA3BAEhEdarpAlZWVuLQwYMyV3N5YdUbQQoKClr1ySVERK0BAyAR1cl+HWBrngdYsxHEZDKhqKhI5mqIiFo3BkAiqpO7rAO0HwXDdYBERHVjACSiOvXt2xc+Pj4AWvlOYI6CISJqMAZAIqqTSqXCoEGDAAB5eXk4ceKEzBXVLozDoImIGowBkIjq5Q7zANkBJCJqOAZAIqqX/TrAba10HSCPgyMiajgGQCKq1xVXXAFBEAC03o0g9qeBsANIRFQ3BkAiqldgYCDi4+MBAAf270d5ebnMFV2qffv2UKlUANgBJCKqDwMgETVIzTpAi8WC3bt2yVzNpRQKhXQkHDuARER1YwAkogZxWAfYSjeChIXZ1gGePn261R5bR0TUGjAAElGDOOwEdvE6QIvF4pLj2+zXAZ48ebLJn0dE1FYxABJRg3Tt2hWhoaEAgB3bt8FqtTbp8/Lz8/HsM0+jV89u8PfRItDPG4kD+uGdt99CWVmZU581bcokaFQCjhw5Ij1Wsw5w9uzZEAQBkyZNAgBMmjQJgiBAEAR4eXkhJiYGjz/+OCoqKpr08xARuRMGQCJqEEEQpC7g+fPnkZ6W1ujP+n7jRgzs3wdv/ud1HMvMhNlshsFgwKGDB/H0k/+HQUkDcOjgQac+MyoqClnHMqX7+fn5MBgM+Pzzz9GpUyeH144cORInT55EVlYWXnnlFcyfPx+PP/54o38eIiJ3wwBIRA1mvw5wayPXAe7csQOj770bxcXFl33NscxM3HrzjTh16lSDP7d/wgCEhLSX7hcUFGD9+vWIiopCQkKCw2s1Gg06duyIqKgojB07FuPGjcPXX3/t9M9CROSuGACJqMHs1wE2Zh6gKIqYOmUiTCZTva89deoUHntknlOff/Mtt0i38/PzsXTpUkyZMqXe9+l0Om4aISKPwgBIRA02cOBAqNVqAI3rAG76/Xccycho8Ou/3rDeqS7g2PEPSLePHDmCLVu2YPz48XW+Z+fOnfjss88wYsSIBn8PEZG7YwAkogbTaDRITEwEYLtMe/r0aafev3PHdqdebzabsW/v3ga/vmZYNQDs27cPt9xyC0JCQi553XfffQdfX19otVoMHjwYV155JT744AOnaiMicmcMgETklKacC1x4utDp7yssbHgH0M/PDwqF7ddaQUHBZS//XnPNNdi/fz8yMjJgMBiwfv16aYczEZEnYAAkIqc4rAPc6lwADK8e1OyMMCfeU1hYKI2nEQQBN954Y62v8/HxQWxsLDp37gwvLy+nayIicncMgETkFIeB0E6uAxwydJhTr9doNBhYfcm5IbKzsqTbEydOhFKpdOr7iIg8BQMgETklNDQUsbGxAIA9e3bDaDQ2+L1Dhw1Dv/79G/z60fePqXUN3+VkZ18IgD169Gjw+4iIPI0guuL8JSLyKJMmTcKKFSsAAJv/3ILBdl3B+qQcPoxhQwahsrKyztd17tIFO3btRbt27Rr82f9++SW89OLzAIANGzbgzjvvbPB7iYg8CTuAROQ0+8vA3337P6feG9+7N777/ieEh19+bV//hAT8+PNvToU/wHE0TdeuXZ16LxGRJ2EHkIicVlhYiKioKFRVVSEkJARZuXnQaDROfUZxcTGWL1uK9eu+RFbWMajVanTv3gPjJjyA0fePcfrzMjMzEd+zGwAgOjoaR48e5RpAIqLLYAAkokYZO3YsPv/8cwDA8pWrMGbsOFnrefL/Hsd7774NAHjjjTfwxBNPyFoPEVFrxgBIRI3y999/Y/jw4QCAwYOHYPNfjTsb2BX0ej2iO0WguLgYGo0GeXl5Tm0eISLyNFwDSESNMnToUPTp0weAbSD0NidnArrSiuXLUFxcDAAYPXo0wx8RUT0YAImoUQRBwJw5c6T7EyeMxblz51q8jsOHDuGpJx6X7s+ePbvFayAicjcMgETUaFOmTMHgwYMBALm5uZg8cYJ0EkdLKC0txej7RkGv1wMApk2bhkGDBrXY9xMRuSsGQCJqNC8vL6xdu1a65PrjD9/jzTdeb5HvFkURD06fisyjRwEACQkJ+OCDD1rku4mI3B0DIBE1SWRkJD777DMIggAAeOH5f2HJx4vRnPvLqqqq8H+PPYr1X60DAAQEBODLL7+EVqtttu8kImpLGACJqMmuv/56vPDCCwAAq9WKObMexLQpk+o97aMxCgoKMPL6Efjgv+9Jj61YsYKDn4mInMAASEQu8eyzz2Lu3LnS/VWfrsSwIYNw5MgRl33H5k2bcEXSAPz9918AbJeg58+fjzvuuMNl30FE5Ak4B5CIXGrNmjWYNm0aKioqAAC+vr6YPGUaZsyche7duzv9eaIoYsf27Vi0cD7WfP6ZtMkkMjISX375Ja644gqX1k9E5AkYAInI5dLS0jBq1CikpaU5PH7tiOswc9Zs3HLrbVCpVHV+RkVFBdZ8/hkWLZyPA/v3Ozx3ww03YPXq1Zz3R0TUSAyARNQsysvL8eSTT2Lp0qUwGAwOz3l5eaFzly7o0iUa0TEx6NIlGlUmE3JyspGdlYXs7Czk5eVdMlImKCgIjz/+OJ544gme80tE1AQMgETUrIqKirB8+XIsWLAAx44da9RnJCcnY/bs2bjvvvug0+lcXCERkedhACSiFmG1WvHLL79g+fLlSElJQVZWlrRO8GJBQUGIiYnBgAEDMH36dCQmJrZwtUREbRsDIBHJQhRFnD17FllZWcjOzoaXlxdiYmIQHR2NwMBAucsjImrTGACJiIiIPAznABIRERF5GAZAIiIiIg/DAEhERETkYRgAiYiIiDwMAyARERGRh2EAJCIiIvIwDIBEREREHoYBkIiIiMjDMAASEREReRgGQCIiIiIPwwBIRERE5GEYAImIiIg8DAMgERERkYdhACQiIiLyMAyARERERB6GAZCIiIjIwzAAEhEREXkYBkAiIiIiD8MASERERORhGACJiIiIPAwDIBEREZGHYQAkIiIi8jAMgEREREQehgGQiIiIyMMwABIRERF5GAZAIiIiIg/DAEhERETkYRgAiYiIiDwMAyARERGRh2EAJCIiIvIwDIBEREREHoYBkIiIiMjDMAASEREReRgGQCIiIiIPwwBIRERE5GH+Hzb50Seb6XXQAAAAAElFTkSuQmCC", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "cmap = plt.cm.viridis\n", - "alpha = .5\n", + "cmap = plt.cm.Blues\n", + "alpha = .75\n", "\n", - "sizes = np.array([len(e) for e in H.edges()])\n", + "sizes = np.array([H.size(e) for e in H.edges()])\n", "norm = plt.Normalize(sizes.min(), sizes.max())\n", "\n", "hnx.drawing.draw(H,\n", - " label_alpha=0,\n", - " edges_kwargs={\n", - " 'facecolors': cmap(norm(sizes))*(1, 1, 1, alpha),\n", - " 'edgecolors': 'black',\n", - " 'linewidths': 2\n", - " },\n", - " **kwargs)" + " label_alpha=0,\n", + " edges_kwargs={\n", + " 'facecolors': cmap(norm(sizes))*(1, 1, 1, alpha),\n", + " 'edgecolors': 'black',\n", + " 'linewidths': 2\n", + " }\n", + ")" ] }, { @@ -393,7 +386,7 @@ "metadata": {}, "source": [ "## Font\n", - "Fontsize and other attributes can be set with the `node_labels_kwargs` and `edge_labels_kwargs` parameters. Here we make the font size large for illustrative purposes." + "Fontsize and other attributes can be set with the `node_labels_kwargs` and `edge_labels_kwargs` parameters. Here we set the font size at 24 to make the nodes appear large for illustrative purposes." ] }, { @@ -403,14 +396,12 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -418,8 +409,7 @@ "hnx.drawing.draw(H.collapse_nodes(),\n", " node_labels_kwargs={\n", " 'fontsize': 24\n", - " },\n", - " **kwargs\n", + " }\n", ")" ] }, @@ -437,30 +427,36 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "hnx.drawing.draw(H,\n", " node_labels_kwargs={\n", - " 'fontsize': {v: 36 if v == 'JV' else 12 for v in H}\n", - " },\n", - " **kwargs\n", + " 'fontsize': {\n", + " v: 36 if v == 'JV' else 12 for v in H\n", + " }\n", + " }\n", ")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -474,7 +470,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.8.16" } }, "nbformat": 4, diff --git a/tutorials/Tutorial 3 - LesMis Case Study.ipynb b/tutorials/Tutorial 3 - LesMis Case Study.ipynb index 02904d5f..211d0542 100644 --- a/tutorials/Tutorial 3 - LesMis Case Study.ipynb +++ b/tutorials/Tutorial 3 - LesMis Case Study.ipynb @@ -36,7 +36,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As readers we are mesmerized by the prose and drama and are horrified by the meanness of the time. But, as mathematicians we delight in the opportunity to study these relationships and discover what the hypergraphs they generate tell us about the story. We use the data available from Stanford GraphBase: https://www-cs-faculty.stanford.edu/~knuth/sgb.html" + "As readers, we are mesmerized by the prose and drama and are horrified by the meanness of the time. But as mathematicians, we delight in the opportunity to study the relationships among the characters and discover what the hypergraphs they generate tell us about the story. We use the data available from the [Stanford GraphBase]( https://www-cs-faculty.stanford.edu/~knuth/sgb.html)." ] }, { @@ -51,7 +51,9 @@ "import itertools as itt\n", "from collections import defaultdict\n", "import matplotlib.pyplot as plt\n", - "import hypernetx as hnx" + "import hypernetx as hnx\n", + "import warnings\n", + "warnings.simplefilter('ignore')" ] }, { @@ -68,7 +70,7 @@ "metadata": {}, "source": [ "### Basic Structure of the Novel\n", - "The novel is broken into five parts, which we will here reference as volumes: **Fantine**, **Cosette**, **Marius**, **St. Denis**, and **Jean Valjean**. Each volume is subdivided into books, each book into chapters, and each chapter into scenes. By shifting the level of subdivision we are able to construct multiple hypergraphs modeling character interactions and relationships. " + "The novel is broken into five parts, which we will here reference as volumes: **Fantine**, **Cosette**, **Marius**, **St. Denis**, and **Jean Valjean**. Each volume is subdivided into books, each book into chapters, and each chapter into scenes. By shifting the level of subdivision, we are able to construct multiple hypergraphs modeling character interactions and relationships." ] }, { @@ -569,6 +571,161 @@ "scenes = lm.df_scenes;scenes" ] }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "h = hnx.Hypergraph(scenes,\n", + " edge_col='Volume',\n", + " node_col = 'Characters',\n", + " node_properties = lm.df_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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1TS801{'FullName': 'Toussaint', 'Description': ' ser...
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XA821{'FullName': 'Child 1', 'Description': ' son o...
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" + ], + "text/plain": [ + " uid weight properties\n", + "level id \n", + "0 1 0 1 {}\n", + " 2 1 1 {}\n", + " 3 2 1 {}\n", + " 4 3 1 {}\n", + " 5 4 1 {}\n", + "... ... ... ...\n", + "1 TS 80 1 {'FullName': 'Toussaint', 'Description': ' ser...\n", + " VI 81 1 {'FullName': 'Madame Victurnien', 'Description...\n", + " XA 82 1 {'FullName': 'Child 1', 'Description': ' son o...\n", + " XB 83 1 {'FullName': 'Child 2', 'Description': ' son o...\n", + " ZE 84 1 {'FullName': 'Zephine', 'Description': ' lover...\n", + "\n", + "[85 rows x 3 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h.properties" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -580,14 +737,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "### Construct the edges as a dictionary named by the name of the Volume\n", - "volume_edges = dict()\n", - "for v in range(1,6):\n", - " volume_edges[v] = set(scenes.loc[scenes.Volume == v]['Characters'])\n", + "volume_edges = {v : set(scenes.loc[scenes.Volume == v]['Characters']) for v in range(1, 6)}\n", + "\n", "### Construct a hypergraph made up of volume_edges\n", "HV = hnx.Hypergraph(volume_edges,name='Volumes')\n", "for node in HV.nodes:\n", @@ -597,7 +753,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": { "scrolled": true }, @@ -605,10 +761,49 @@ { "data": { "text/plain": [ - "Entity(1,['ZE', 'SC', 'FT', 'MR', 'CO', 'MT', 'GG', 'LI', 'SN', 'GE', 'BR', 'JU', 'CN', 'SP', 'MB', 'TM', 'IS', 'JL', 'PG', 'MY', 'BL', 'DA', 'FV', 'VI', 'PO', 'FF', 'JV', 'ME', 'CV', 'MC', 'BM', 'CH', 'TH', 'FA', 'JA', 'CC', 'SS', 'CL', 'NP', 'FN'],{})" + "AttrList(['CL',\n", + " 'DA',\n", + " 'LI',\n", + " 'JV',\n", + " 'CV',\n", + " 'FF',\n", + " 'ZE',\n", + " 'VI',\n", + " 'JU',\n", + " 'SC',\n", + " 'MT',\n", + " 'BR',\n", + " 'JA',\n", + " 'MY',\n", + " 'SN',\n", + " 'MR',\n", + " 'IS',\n", + " 'MC',\n", + " 'FN',\n", + " 'PO',\n", + " 'BM',\n", + " 'SS',\n", + " 'FA',\n", + " 'CN',\n", + " 'NP',\n", + " 'MB',\n", + " 'PG',\n", + " 'GG',\n", + " 'TH',\n", + " 'SP',\n", + " 'FV',\n", + " 'FT',\n", + " 'CC',\n", + " 'CH',\n", + " 'BL',\n", + " 'TM',\n", + " 'ME',\n", + " 'CO',\n", + " 'JL',\n", + " 'GE'])" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -620,54 +815,44 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "### Create a little helper method to nicely visualize the Hypergraph\n", - "def noborder(width=10,height=None):\n", - " if not height:\n", - " height = width\n", - " fig = plt.figure(figsize=[width,height])\n", - " ax = plt.gca()\n", - "noborder()\n", - "hnx.draw(HV)\n" + "plt.figure(figsize=[10,10])\n", + "hnx.draw(HV)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "### Collapse the nodes to understand the relationships (same as diagram in title)\n", - "noborder()\n", + "plt.figure(figsize=[10,10])\n", "hnx.draw(HV.collapse_nodes(),with_node_counts=True)" ] }, @@ -680,7 +865,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -720,20 +905,20 @@ " thief of bread\n", " \n", " \n", + " JA\n", + " Javert\n", + " police officer of M-- sur M--\n", + " \n", + " \n", " CO\n", " Cosette\n", " daughter of FN and FT\n", " \n", " \n", " TH\n", - " Th'enardier\n", + " Th\\'enardier\n", " sergeant of Waterloo and keeper of a chophouse\n", " \n", - " \n", - " JA\n", - " Javert\n", - " police officer of M-- sur M--\n", - " \n", " \n", "\n", "" @@ -742,12 +927,12 @@ " FullName Description\n", "Symbol \n", "JV Jean Valjean thief of bread\n", + "JA Javert police officer of M-- sur M--\n", "CO Cosette daughter of FN and FT\n", - "TH Th'enardier sergeant of Waterloo and keeper of a chophouse\n", - "JA Javert police officer of M-- sur M--" + "TH Th\\'enardier sergeant of Waterloo and keeper of a chophouse" ] }, - "execution_count": 13, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -755,13 +940,13 @@ "source": [ "### Who are the four characters belonging to all five volumes?\n", "volume_char_sets = list(set(HV.edges[idx]) for idx in range(1,6))\n", - "core_characters = set.intersection(*volume_char_sets)\n", + "core_characters = list(set.intersection(*volume_char_sets))\n", "names.loc[core_characters]" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": { "scrolled": true }, @@ -798,59 +983,54 @@ " \n", " \n", " \n", - " GA\n", - " Gavroche\n", - " young urchin living at Gorbeau House\n", - " \n", - " \n", - " QU\n", - " Claquesous\n", - " night-like bandit of Paris\n", + " JV\n", + " Jean Valjean\n", + " thief of bread\n", " \n", " \n", - " GI\n", - " Monsieur Luke Esprit Gillenormand\n", - " grand bourgeois\n", + " MN\n", + " Magnon\n", + " servant of GI\n", " \n", " \n", - " BA\n", - " Bahorel\n", - " `Friends of the ABC' cutup\n", + " JP\n", + " Jean Prouvaire\n", + " `Friends of the ABC' poet\n", " \n", " \n", - " GU\n", - " Gueulemer\n", - " Herculean bandit of Paris\n", + " BB\n", + " Babet\n", + " tooth-pulling bandit of Paris\n", " \n", " \n", - " CO\n", - " Cosette\n", - " daughter of FN and FT\n", + " GI\n", + " Monsieur Luke Esprit Gillenormand\n", + " grand bourgeois\n", " \n", " \n", - " CM\n", - " Combeferre\n", - " `Friends of the ABC' guide\n", + " EP\n", + " Eponine\n", + " daughter of TH and TM\n", " \n", " \n", - " CR\n", - " Courfeyrac\n", - " `Friends of the ABC' center\n", + " JA\n", + " Javert\n", + " police officer of M-- sur M--\n", " \n", " \n", - " MO\n", - " Montparnasse\n", - " genteel bandit of Paris\n", + " BO\n", + " Bossuet (Lesgle)\n", + " `Friends of the ABC' klutz\n", " \n", " \n", - " MG\n", - " Madamoiselle Gillenormand\n", - " unmarried daughter of GI\n", + " JO\n", + " Joly\n", + " `Friends of the ABC' medic\n", " \n", " \n", - " MN\n", - " Magnon\n", - " servant of GI\n", + " FE\n", + " Feuilly\n", + " `Friends of the ABC' political idealist\n", " \n", " \n", " EN\n", @@ -858,34 +1038,34 @@ " `Friends of the ABC' chief\n", " \n", " \n", - " TM\n", - " Madame Th'enardier\n", - " wife of TH\n", + " CM\n", + " Combeferre\n", + " `Friends of the ABC' guide\n", " \n", " \n", - " EP\n", - " Eponine\n", - " daughter of TH and TM\n", + " PL\n", + " Mother Plutarch\n", + " maid of MM\n", " \n", " \n", - " BO\n", - " Bossuet (Lesgle)\n", - " `Friends of the ABC' klutz\n", + " MA\n", + " Marius\n", + " grandson of GI\n", " \n", " \n", - " FE\n", - " Feuilly\n", - " `Friends of the ABC' political idealist\n", + " BA\n", + " Bahorel\n", + " `Friends of the ABC' cutup\n", " \n", " \n", - " JV\n", - " Jean Valjean\n", - " thief of bread\n", + " TH\n", + " Th\\'enardier\n", + " sergeant of Waterloo and keeper of a chophouse\n", " \n", " \n", - " PL\n", - " Mother Plutarch\n", - " maid of MM\n", + " GT\n", + " Grantaire\n", + " `Friends of the ABC' skeptic\n", " \n", " \n", " TG\n", @@ -893,44 +1073,49 @@ " soldier and grandnephew of GI\n", " \n", " \n", + " CR\n", + " Courfeyrac\n", + " `Friends of the ABC' center\n", + " \n", + " \n", + " QU\n", + " Claquesous\n", + " night-like bandit of Paris\n", + " \n", + " \n", " MM\n", " Monsieur Mabeuf\n", " prefect of church\n", " \n", " \n", - " GT\n", - " Grantaire\n", - " `Friends of the ABC' skeptic\n", - " \n", - " \n", - " TH\n", - " Th'enardier\n", - " sergeant of Waterloo and keeper of a chophouse\n", + " GU\n", + " Gueulemer\n", + " Herculean bandit of Paris\n", " \n", " \n", - " JA\n", - " Javert\n", - " police officer of M-- sur M--\n", + " TM\n", + " Madame Th\\'enardier\n", + " wife of TH\n", " \n", " \n", - " BB\n", - " Babet\n", - " tooth-pulling bandit of Paris\n", + " MG\n", + " Madamoiselle Gillenormand\n", + " unmarried daughter of GI\n", " \n", " \n", - " JP\n", - " Jean Prouvaire\n", - " `Friends of the ABC' poet\n", + " MO\n", + " Montparnasse\n", + " genteel bandit of Paris\n", " \n", " \n", - " JO\n", - " Joly\n", - " `Friends of the ABC' medic\n", + " GA\n", + " Gavroche\n", + " young urchin living at Gorbeau House\n", " \n", " \n", - " MA\n", - " Marius\n", - " grandson of GI\n", + " CO\n", + " Cosette\n", + " daughter of FN and FT\n", " \n", " \n", "\n", @@ -939,66 +1124,66 @@ "text/plain": [ " FullName \\\n", "Symbol \n", - "GA Gavroche \n", - "QU Claquesous \n", - "GI Monsieur Luke Esprit Gillenormand \n", - "BA Bahorel \n", - "GU Gueulemer \n", - "CO Cosette \n", - "CM Combeferre \n", - "CR Courfeyrac \n", - "MO Montparnasse \n", - "MG Madamoiselle Gillenormand \n", + "JV Jean Valjean \n", "MN Magnon \n", - "EN Enjolras \n", - "TM Madame Th'enardier \n", + "JP Jean Prouvaire \n", + "BB Babet \n", + "GI Monsieur Luke Esprit Gillenormand \n", "EP Eponine \n", + "JA Javert \n", "BO Bossuet (Lesgle) \n", + "JO Joly \n", "FE Feuilly \n", - "JV Jean Valjean \n", + "EN Enjolras \n", + "CM Combeferre \n", "PL Mother Plutarch \n", + "MA Marius \n", + "BA Bahorel \n", + "TH Th\\'enardier \n", + "GT Grantaire \n", "TG Lieutenant Theodule Gillenormand \n", + "CR Courfeyrac \n", + "QU Claquesous \n", "MM Monsieur Mabeuf \n", - "GT Grantaire \n", - "TH Th'enardier \n", - "JA Javert \n", - "BB Babet \n", - "JP Jean Prouvaire \n", - "JO Joly \n", - "MA Marius \n", + "GU Gueulemer \n", + "TM Madame Th\\'enardier \n", + "MG Madamoiselle Gillenormand \n", + "MO Montparnasse \n", + "GA Gavroche \n", + "CO Cosette \n", "\n", " Description \n", "Symbol \n", - "GA young urchin living at Gorbeau House \n", - "QU night-like bandit of Paris \n", - "GI grand bourgeois \n", - "BA `Friends of the ABC' cutup \n", - "GU Herculean bandit of Paris \n", - "CO daughter of FN and FT \n", - "CM `Friends of the ABC' guide \n", - "CR `Friends of the ABC' center \n", - "MO genteel bandit of Paris \n", - "MG unmarried daughter of GI \n", + "JV thief of bread \n", "MN servant of GI \n", - "EN `Friends of the ABC' chief \n", - "TM wife of TH \n", + "JP `Friends of the ABC' poet \n", + "BB tooth-pulling bandit of Paris \n", + "GI grand bourgeois \n", "EP daughter of TH and TM \n", + "JA police officer of M-- sur M-- \n", "BO `Friends of the ABC' klutz \n", + "JO `Friends of the ABC' medic \n", "FE `Friends of the ABC' political idealist \n", - "JV thief of bread \n", + "EN `Friends of the ABC' chief \n", + "CM `Friends of the ABC' guide \n", "PL maid of MM \n", + "MA grandson of GI \n", + "BA `Friends of the ABC' cutup \n", + "TH sergeant of Waterloo and keeper of a chophouse \n", + "GT `Friends of the ABC' skeptic \n", "TG soldier and grandnephew of GI \n", + "CR `Friends of the ABC' center \n", + "QU night-like bandit of Paris \n", "MM prefect of church \n", - "GT `Friends of the ABC' skeptic \n", - "TH sergeant of Waterloo and keeper of a chophouse \n", - "JA police officer of M-- sur M-- \n", - "BB tooth-pulling bandit of Paris \n", - "JP `Friends of the ABC' poet \n", - "JO `Friends of the ABC' medic \n", - "MA grandson of GI " + "GU Herculean bandit of Paris \n", + "TM wife of TH \n", + "MG unmarried daughter of GI \n", + "MO genteel bandit of Paris \n", + "GA young urchin living at Gorbeau House \n", + "CO daughter of FN and FT " ] }, - "execution_count": 14, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1008,13 +1193,13 @@ "## Replace the values in the array with the volumes of interest.\n", "vols = [3,4]\n", "volume_char_sets = list(set(HV.edges[idx].uidset) for idx in vols)\n", - "chars_of_interest = set.intersection(*volume_char_sets)\n", + "chars_of_interest = list(set.intersection(*volume_char_sets))\n", "names.loc[chars_of_interest]\n" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1107,7 +1292,7 @@ "(4, 5) 17" ] }, - "execution_count": 15, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1118,11 +1303,9 @@ "for pair in itt.combinations(range(1,6),2):\n", " volume_char_sets = list(set(HV.edges[idx].uidset) for idx in pair)\n", " titlepair = (volumes.title[pair[0]],volumes.title[pair[1]])\n", - "# volume_intersections[tuple(pair)] = {\"titles\":titlepair, \"intersection_sizes\":len(set.intersection(*volume_char_sets))}\n", " volume_intersections[tuple(pair)] = len(set.intersection(*volume_char_sets))\n", "print(\"Number of Characters shared by pairs of volumes:\")\n", - "pd.DataFrame.from_dict(volume_intersections,orient=\"index\") \n", - " " + "pd.DataFrame.from_dict(volume_intersections,orient=\"index\")" ] }, { @@ -1131,15 +1314,15 @@ "source": [ "### Highlights from each Volume (A very very very short description...)\n", "\n", - "Fantine: Volume 1 Lays the foundation for the novel. Most of the characters do not appear in subsequent volumes. Most important of these is Fantine. As mother of Cosette, she sacrifices her life in the support of her daughter and lays the charge on Jean Valjean to care for Cosette when she dies. In contrast to Fantine, Jean Valjean and Cosette appear in all of the volumes. A central story to the novel follows their travels as they flee from the unrelentless pursuit of Javert and the dogged and often comical abuses of Thenardier. Jean Valjean, originally convicted for stealing bread, begins as a hardened convict but through the mercy of a bishop is transformed into a philanthropist. \n", + "Fantine: Volume 1 Lays the foundation for the novel. Most of the characters do not appear in subsequent volumes. Most important of these is Fantine. As mother of Cosette, she sacrifices her life in the support of her daughter and lays the charge on Jean Valjean to care for Cosette when she dies. In contrast to Fantine, Jean Valjean and Cosette appear in all the volumes. A central story to the novel follows their travels as they flee from the relentless pursuit by Javert and the dogged and often comical abuses of Thenardier. Jean Valjean, originally convicted for stealing bread, begins as a hardened convict but through the mercy of a bishop is transformed into a philanthropist.\n", "\n", - "Cosette: Volume 2 Follow Cosette's liberation from her caretakers, the Thenardier's, by Jean Valjean. They flee into hiding from Javert and find refuge in a convent, where Valjean works as a gardener and Cosette is educated. Much character development is done, including a long description of Waterloo ending with the singular way in which Thenardier obtained a silver cross of the Legion of Honour while saving the life of one Pontmercy.\n", + "Cosette: Volume 2 follows Cosette's liberation from her caretakers, the Thenardiers, by Jean Valjean. They flee into hiding from Javert and find refuge in a convent, where Valjean works as a gardener and Cosette is educated. Much character development is done, including a long description of Waterloo ending with the singular way in which M. Thenardier obtained a silver cross of the Legion of Honour while saving the life of one Pontmercy.\n", "\n", - "Marius: Marius Pontmercy, son of an officer in Napoleon's army, and grandson of a Royalist, experiences conflicting loyalties and utlimately turns his back on friends and family and lives among the poor. He sees and eventually falls in love with Cosette. In honor of his father he attempts to help Thenardier but discovers his treachery when Thenardier attempts to murder one he takes to be Cosettes's father.\n", + "Marius: Marius Pontmercy, son of an officer in Napoleon's army and grandson of a Royalist, experiences conflicting loyalties and ultimately turns his back on friends and family and lives among the poor. He sees and eventually falls in love with Cosette. In honor of his father, he attempts to help M. Thenardier but discovers his treachery when M. Thenardier attempts to murder the person he takes to be Cosette's father.\n", "\n", - "St. Denis: With his love for Cosette thwarted, Marius joins a group of students, to participate in an uprising known as the June rebellion. They construct a barricade near the Rue Saint-Denis. \n", + "St. Denis: With his love for Cosette thwarted, Marius joins a group of students to participate in an uprising known as the June rebellion. They construct a barricade near the Rue Saint-Denis.\n", "\n", - "Jean Valjean: Jean Valjean saves Marius's life when soldiers overwhelm the baricades. Marius discovers Jean Valjean's true identity from Thenardier. Jean Valjean dies at peace with Cosette and Marius.\n", + "Jean Valjean: Jean Valjean saves Marius's life when soldiers overwhelm the barricades. Marius discovers Jean Valjean's true identity from Thenardier. Jean Valjean dies at peace with Cosette and Marius.\n", "\n" ] }, @@ -1152,7 +1335,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": { "scrolled": true }, @@ -1169,7 +1352,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -1189,65 +1372,60 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "noborder()\n", - "# draw(HB[1])\n", + "plt.figure(figsize=[10,10])\n", "hnx.draw(HB[1].collapse_nodes(),with_node_counts=True)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "### In the final book there is a long section on the Paris Sewers. \n", "### The only character in that section was the creator of the sewers.\n", - "noborder(10,10)\n", + "plt.figure(figsize=[10,10])\n", "hnx.draw(HB[5])" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Entity(BS,[],{'name': 'Bruneseau', 'description': ' explorer and mapper of the sewers of Paris'})" + "AttrList([])" ] }, - "execution_count": 20, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1258,16 +1436,16 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{2: Entity(2,['BS'],{})}" + "AttrList([2])" ] }, - "execution_count": 21, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1278,19 +1456,17 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1323,23 +1497,22 @@ "### Most of the students are killed\n", "### In the 4th book Javert dies alone\n", "### At the center, in the last scene (8), there is only JV and CO\n", - "noborder()\n", - "hnx.draw(c1)\n", - "# draw(c1.collapse_nodes(),with_node_counts=True)" + "plt.figure(figsize=[10,10])\n", + "hnx.draw(c1)" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Entity(8,['CO', 'JV'],{})" + "AttrList(['JV', 'CO'])" ] }, - "execution_count": 24, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1359,7 +1532,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1373,23 +1546,23 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{1: {'MY'},\n", - " 2: {'JV', 'MT', 'MY'},\n", - " 3: {'BL', 'DA', 'FA', 'FN', 'FT', 'FV', 'LI', 'ZE'},\n", - " 4: {'CO', 'FN', 'TH', 'TM'},\n", - " 5: {'BM', 'FF', 'FN', 'JA', 'JV', 'MT', 'MY', 'VI'},\n", - " 6: {'FN', 'JA', 'JV'},\n", - " 7: {'BM', 'BR', 'CC', 'CH', 'CN', 'FN', 'JU', 'JV', 'PO', 'SC', 'SP', 'SS'},\n", - " 8: {'FN', 'JA', 'JV', 'PO', 'SP', 'SS'}}" + "{1: ['MY'],\n", + " 2: ['MY', 'JV', 'MT'],\n", + " 3: ['FA', 'DA', 'LI', 'BL', 'ZE', 'FN', 'FV', 'FT'],\n", + " 4: ['TH', 'FN', 'TM', 'CO'],\n", + " 5: ['JA', 'MY', 'JV', 'FF', 'VI', 'FN', 'MT', 'BM'],\n", + " 6: ['JV', 'FN', 'JA'],\n", + " 7: ['SS', 'CC', 'CN', 'JV', 'CH', 'SP', 'JU', 'SC', 'BR', 'FN', 'PO', 'BM'],\n", + " 8: ['SS', 'JA', 'JV', 'SP', 'FN', 'PO']}" ] }, - "execution_count": 26, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1400,46 +1573,44 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "noborder()\n", + "plt.figure(figsize=[10,10])\n", "hnx.draw(FNNeighborhood)" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{1: {'MB', 'ME', 'MY'},\n", - " 2: {'IS', 'JL', 'JV', 'MB', 'ME', 'MR', 'MT', 'MY', 'PG'},\n", - " 3: {'FN'},\n", - " 4: {'FN'},\n", - " 5: {'BM', 'FF', 'FN', 'JA', 'JV', 'MT', 'MY', 'VI'},\n", - " 6: {'FN', 'JA', 'JV'},\n", - " 7: {'BM', 'BR', 'CC', 'CH', 'CN', 'FN', 'JU', 'JV', 'PO', 'SC', 'SP', 'SS'},\n", - " 8: {'FN', 'JA', 'JV', 'PO', 'SP', 'SS'}}" + "{1: ['MY', 'MB', 'ME'],\n", + " 2: ['MY', 'JV', 'MB', 'PG', 'MR', 'IS', 'JL', 'ME', 'MT'],\n", + " 3: ['FN'],\n", + " 4: ['FN'],\n", + " 5: ['JA', 'MY', 'JV', 'FF', 'VI', 'FN', 'MT', 'BM'],\n", + " 6: ['JV', 'FN', 'JA'],\n", + " 7: ['SS', 'CC', 'CN', 'JV', 'CH', 'SP', 'JU', 'SC', 'BR', 'FN', 'PO', 'BM'],\n", + " 8: ['SS', 'JA', 'JV', 'SP', 'FN', 'PO']}" ] }, - "execution_count": 28, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1454,59 +1625,54 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "noborder()\n", + "plt.figure(figsize=[10,10])\n", "hnx.draw(JVNeighborhood)" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "## Combine the subgraphs\n", "Fantine_edges = list(FNNeighborhood.edges.elements.values())\n", - "JVFNHypergraph = HB[1].restrict_to_nodes(JVnodes)\n", - "# JVFNHypergraph.add_edges_from(Fantine_edges)" + "JVFNHypergraph = HB[1].restrict_to_nodes(JVnodes)" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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0): ['MY', 'ME'],\n", + " (1, 4, 1): ['MY', 'CL'],\n", + " (1, 4, 2): ['MY', 'GE'],\n", + " (1, 4, 3): ['MY', 'MC'],\n", + " (1, 4, 4): ['MY', 'MB'],\n", + " (1, 5, 0): ['MY', 'MB', 'ME'],\n", + " (1, 6, 0): ['ME', 'MY'],\n", + " (1, 7, 0): ['MY', 'CV'],\n", + " (1, 7, 1): ['MY', 'MB', 'ME'],\n", + " (1, 8, 0): ['SN', 'MY'],\n", + " (1, 9, 0): ['MB'],\n", + " (1, 10, 0): ['MY', 'GG'],\n", + " (1, 11, 0): ['MY'],\n", + " (1, 12, 0): ['MY'],\n", + " (1, 13, 0): ['MY'],\n", + " (1, 14, 0): ['MY', 'SN'],\n", + " (2, 1, 0): ['JL', 'JV'],\n", + " (2, 1, 1): ['JV', 'MT'],\n", + " (2, 1, 2): ['MR', 'JV'],\n", + " (2, 2, 0): ['ME', 'MB', 'MY'],\n", + " (2, 3, 0): ['ME', 'MB', 'MY', 'JV'],\n", + " (2, 4, 0): ['MY', 'JV', 'MB'],\n", + " (2, 4, 1): ['MY', 'JV', 'MB', 'ME'],\n", + " (2, 5, 0): ['MY', 'ME', 'JV'],\n", + " (2, 6, 0): ['JV', 'IS'],\n", + " (2, 7, 0): ['JV'],\n", + " (2, 9, 0): ['JV'],\n", + " (2, 10, 0): ['JV'],\n", + " (2, 11, 0): ['JV'],\n", + " (2, 12, 0): ['MY', 'ME'],\n", + " (2, 12, 1): ['MY', 'JV'],\n", + " (2, 13, 0): ['PG', 'JV'],\n", + " (3, 2, 0): ['FT', 'LI', 'FA', 'BL'],\n", + " (3, 3, 0): ['FT', 'LI', 'FA', 'BL', 'FV', 'DA', 'ZE', 'FN'],\n", + " (3, 4, 0): ['FT', 'LI', 'FA', 'BL', 'FV', 'DA', 'ZE', 'FN'],\n", + " (3, 6, 0): ['BL', 'FV'],\n", + " (3, 6, 1): ['FV', 'DA'],\n", + " (3, 7, 0): ['FT'],\n", + " (3, 8, 0): ['FT', 'LI', 'FA', 'BL', 'FV', 'DA', 'ZE', 'FN'],\n", + " (3, 9, 0): ['FV', 'DA', 'ZE', 'FN'],\n", + " (4, 1, 0): ['TM', 'FN'],\n", + " (4, 1, 1): ['TH', 'TM', 'FN'],\n", + " (4, 3, 0): ['CO'],\n", + " (4, 3, 1): ['TH'],\n", + " (4, 3, 2): ['TM'],\n", + " (5, 1, 0): ['JV'],\n", + " (5, 2, 0): ['JV'],\n", + " (5, 3, 0): ['JV'],\n", + " (5, 4, 0): ['MY'],\n", + " (5, 5, 0): ['JA'],\n", + " (5, 6, 0): ['FF', 'JV', 'JA'],\n", + " (5, 7, 0): ['FF'],\n", + " (5, 8, 0): ['VI'],\n", + " (5, 8, 1): ['FN'],\n", + " (5, 9, 0): ['VI'],\n", + " (5, 9, 1): ['MT', 'FN'],\n", + " (5, 10, 0): ['MT', 'FN'],\n", + " (5, 12, 0): ['BM', 'FN', 'JA'],\n", + " (5, 13, 0): ['FN', 'JA'],\n", + " (5, 13, 1): ['FN', 'JA', 'JV'],\n", + " (5, 13, 2): ['JA', 'JV'],\n", + " (5, 13, 3): ['JV', 'FN'],\n", + " (6, 1, 0): ['JV', 'FN'],\n", + " (6, 2, 0): ['JV', 'JA'],\n", + " (7, 1, 0): ['SP', 'SS'],\n", + " (7, 1, 1): ['JV', 'SS'],\n", + " (7, 1, 2): ['JV', 'FN'],\n", + " (7, 2, 0): ['JV', 'SC'],\n", + " (7, 3, 0): ['JV'],\n", + " (7, 4, 0): ['JV', 'PO'],\n", + " (7, 5, 0): ['JV'],\n", + " (7, 6, 0): ['SS', 'FN'],\n", + " (7, 7, 0): ['JV'],\n", + " (7, 8, 0): ['JV'],\n", + " (7, 9, 0): ['JV', 'JU', 'CH', 'BM'],\n", + " (7, 10, 0): ['JU', 'CH', 'BR', 'CN', 'CC', 'JV', 'BM'],\n", + " (7, 11, 0): ['JV', 'BR', 'CN', 'CC', 'JU', 'CH'],\n", + " (8, 1, 0): ['SS', 'JV'],\n", + " (8, 1, 1): ['JV', 'FN'],\n", + " (8, 2, 0): ['JV', 'FN'],\n", + " (8, 3, 0): ['JV', 'FN', 'JA'],\n", + " (8, 4, 0): ['JV', 'FN'],\n", + " (8, 4, 1): ['JV', 'JA'],\n", + " (8, 4, 2): ['JA', 'FN', 'JV'],\n", + " (8, 4, 3): ['JA', 'JV'],\n", + " (8, 5, 0): ['JV', 'PO'],\n", + " (8, 5, 1): ['FN', 'SP', 'SS'],\n", + " (8, 5, 2): ['JV', 'SS'],\n", + " (8, 5, 3): ['PO', 'JA'],\n", + " (8, 5, 4): ['JA', 'SS']}" ] }, - "execution_count": 33, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1668,12 +1834,12 @@ "### Difference in relationships when drilling down into hierarchy of sets\n", "\n", "We consider the neighborhoods of Fantine and Jean Valjean in the Scenes hypergraph. \n", - "Starting with the subgraph generated by the neighbors of Fantine we restrict to neighbors of Jean Valjean." + "Starting with the subgraph generated by the neighbors of Fantine, we restrict to neighbors of Jean Valjean." ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 36, "metadata": { "scrolled": true }, @@ -1690,24 +1856,11 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "noborder()\n", + "plt.figure(figsize=[25,15])\n", "hnx.draw(FNJVNeighborhood)" ] }, @@ -1720,24 +1873,22 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "noborder()\n", + "plt.figure(figsize=[10,10])\n", "hnx.draw(FNJVNeighborhood.collapse_edges(),with_edge_counts=True)" ] }, @@ -1757,7 +1908,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -1770,47 +1921,43 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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i5qZi1sSehPp5urqkBic1R0e7GJk2JRoeCRpCCCGEOCud3sRt32yizGBm9sSeRAd5u7qkBsdotnIgv1z6M0SDJEFDCCGEEGeoMlqYOGMzWZoqZk3sQVK4LL1aGw4VlmO0WCVoiAZJgoYQQgghTmM0W7ln9lZSc3RMn5BCm2i5Ca4taTk6ANpEB7i4EiGcT4KGEEIIIU6yWBUmLdjB+kPFfH1rMt2ahLi6pAYtLVdHUpivNNiLBkmChhBCCCEAUBSFZ7/fza+7c/loVFf6tgh3dUkNXmqOVqZNiQZLgoYQQgghUBSFN3/dx3ebM3l7WGcGdYh2dUkNnqIopMmKU6IBk6AhhBBCCD5bfYgv1x7mxSHtGNY93tXlNArZpVXo9GYZ0RANlgQNIYQQopGbteEo76xIZ9KVrZjQt6mry2k0TjSCt4sJcnElQtQOCRpCCCFEI/bD9mxe+HEPt/dtykNXtHB1OY1KWq6OMD9PogK9XF2KELVCgoYQQgjRSP2Wls9jC3dyc7d4nru2LSqVytUlNSqpOTraxQbK5100WBI0hBBCiEZo/aFi7pu7jYFto3hzaEfc3ORm92KTRnDR0EnQEEIIIRqZnZml3PHtZno2DeXDUV1wV8vtwMWmrTSRXVoljeCiQZPvLEIIIUQjciC/jNumb6J1dABfjuuOl7va1SU1Smm5JxrBJWiIhkuChhBCCNFIZJZUMnbaRqIDvZk+vge+nu6uLqnRSsvV4e3hRrMIf1eXIkStkaAhhBBCNAIFOj1jpm7Ex0PNzIk9CPL1cHVJjVpqjpbW0YGopTdGNGASNIQQQogGrrTSyLhpmzCarcya2JPIAG9Xl9ToSSO4aAwkaAghhBANWIXBzPjpmyksNzD7jh4khPq6uqRGz2C2cLCgXBrBRYMnQUMIIYRooPQmC3fN2sLBgnK+ndCDFpEBri5JAAfyyzFbFRnREA2eBA0hhBCiATJbrDw0bztbMjRMuy2ZjvFBri5JHJeWq0OlgrYxEvxEwyZBQwghhGhgrFaF/y3exR/7Cvh8bDd6NgtzdUniFGk5OpqG+8mqX6LBk69wIYQQogFRFIWXf07j++3ZTBnZhcvbRLm6JPEf0gguGgsZ0RBCCCEakCm/HWDGPxm8emMHbugS5+pyxH9YrQppuTppBBeNggQNIYQQooGY9tcRPvz9AE8OasOYnomuLkecRZaminKDWUY0RKMgQUMIIYRoABZsyeSVn9O4p39z7h3Q3NXliHNIy9UC0D5WmvNFwydBQwghhKjnft2dy1OLdzG6ZxOeHNTa1eWI80jN0RER4EVEgJerSxGi1knQEEIIIeqxdQcKefi7HVzbKZZXbuiASqVydUniPKQRXDQmEjSEEEKIemrrUQ13zdxK3xZhvD+iM2o3CRl1nTSCi8ZEgoYQQghRD+3N1TFh+iY6xgfx2ZjueKjlR3pdV1JhJFerlxEN0WjIdyUhhBCinjlSVMG4aZtoEubLtNuS8fFUu7okUQ17c3UAtJcRDdFISNAQQggh6pFcbRVjp24kyMedbyf0IMDbw9UliWpKzdHi66kmMczP1aUIcVFI0BBCCCHqieJyA2OnbgRg9h09CfOXlYvqk7QcHW2iA6SXRjQaEjSEEEKIekCnN3Hb9E1oq8zMvqMnMUE+ri5J2EkawUVjI0FDCCGEqOP0Jgt3fLuFY8WVzJrYg6bhMvWmvtGbLBwqrKBdjGzUJxoPd1cXIIQQQohzM5qt3Dt7K7uztMy+oydtZcWieml/fhkWqyKN4KJRkRENIYQQoo6yWBUeW7iTvw4W8dWt3emeGOLqkoSDUnN0uKmgdXSAq0sR4qKRoCGEEELUQYqi8PyPe1i2K4ePbunKpS0jXF2SqIG0HB3NI/zx9pCliEXjIVOnhBBCiDro7RXpzN14jLeHdeKajjGuLkfUkDSCi8ZIRjSEEEKIOubz1Yf4fPUhnr+uHSOSE1xdjqghq1Vhb65OdgQXjY4EDSGEEKIOmbPxKG8t38dDV7Rk4iVNXV2OcIKjJZVUGi20j5UVp0TjIkFDCCGEqCN+3JHNcz/sYXyfJCZd2dLV5QgnScvRAdA2RhrBReMiQUMIIYSoA/7Yl89jC3ZyU9c4XriuHSqV7B7dUKTmaIkO9Jad3EWjI0FDCCGEcLGNh4u5d/Y2Lm8Tyds3d8LNTUJGQyKN4KKxkqAhhBBCuNDuLC0Tv91CclIIH43qirtafjQ3NGk50gguGif5biaEEEK4yMGCMm6bvokWkf58NS5Z9lhogArLDBSUGWRHcNEoSdAQQgghXCCzpJKxUzcR4e/FjAkp+HnJ1lYN0d5cWyO4TJ0SjZEEDSGEEOIiKyjTM27aRjzd3Zg1sQfBvp6uLknUktQcHf5e7iSE+Lq6FCEuOgkaQgghxEWkrTRx67RNVJkszLmjJ5GB3q4uSdSitFwdbWMCpMFfNEoSNIQQQoiLpNJoZsKMTeTr9Mye2JOEUHnK3dCl5WilEVw0WhI0hBBCiIvAYLZw96ytpOeVMWNCD1pGyeZtDV2l0czhogrpzxCNlnSeCSGEELXMbLHy8LwdbDxSwrcTetA5IdjVJYmLID2vDEWB9rFBri5FCJeQEQ0hhBCiFlmtCk8v2c2qvfl8NrobvZuHubokcZGk5uhwd1PRItLf1aUI4RISNIQQQohaoigKry7by6JtWbw3vDNXtotydUniIkrL1dEi0l/2RxGNlgQNIYQQopZ89PtBvvn7CC9f354bu8a5uhxxkcmO4KKxk6AhhBBC1ILpfx/hg9/288TVrRnXO8nV5YiLzGJV2Jenk0Zw0ahJ0BBCCCGcbNHWLCYvTeOufs24b0BzV5cjXOBIUQV6k1WChmjUJGgIIYQQTrQiNY8nF+/ilpQEnr6mDSqVbNTWGKXmaAFk6pRo1CRoCCGEEE7y98EiHpy7nUEdonntpo4SMhqxtFwdccE+BPt6uroUIVxGgoYQQgjhBNuOabhz5hZ6Nw/jgxFdULtJyGjM0nJ0tJXRDNHISdAQQgghamhfno4J0zfTPjaQL8Z2x9Ndfrw2Zoqi2Fackv4M0cjJd0IhhBCiBjKKKhg3bRNxwT5MvS0FH0/ZM6GxKywzUFxhpL0EDdHISdAQQgghHJSn1TN22kYCvNyZObEHQT4eri5J1AGpOTpAGsGFkKAhhBBCOKCkwsi4aRuxWhVm3dGTcH8vV5ck6oi0XB0B3u7Eh/i4uhQhXMrd1QUIIYQQ9U2Z3sT46ZsoqTCy8J7exAXLDaX414kdwWXVMdHYyYiGEEIIYQe9ycId327hSFEFMyf2oFmEv6tLEnVMWq40ggsBEjSEEEKIajNZrNw/Zxs7s0qZPj6F9rFBri5J1DHlBjMZxRXytSEEEjSEEEKIarFaFR5fuJO1Bwr5Ymx3kpNCXV2SqIP25epQFGkEFwIkaAghhBAXpCgKL/6UytKdOUwZ2ZUBrSNdXZKoo9JydXioVbSIlCl1QkgzuBBCCHEB765MZ9aGo7x1c0eu7RTj6nJEHZaWo6NlZIBs2igEMqIhhBBCnNeXaw7x6Z+HeHZwW0amNHF1OaKOk0ZwIf4lQUMIIYQ4h3mbjvHGr/t44LIW3NmvmavLEXWc2WJlX16Z7AguxHESNIQQQoiz+HlXDs98v5tbeyfy2FWtXF2OqAcOFVZgNFulEVyI4yRoCCGEEP/xZ3oBk+bv4MYucbw0pL1svCaqJS1XC0BbGdEQApCgIYQQQpxm05ES7p29lf6tInl7WCfc3CRkiOpJy9GREOpDoLeHq0sRok6QoCGEEEIctydby8QZm+maEMIno7vioZYfk6L60nJ1Mm1KiFPId1AhhBACOFRYzm3fbKJZhB9f35aMt4fa1SWJekRRFNJydLIjuBCnkKAhhBCi0csurWLc1I2E+nkyY0IP/L1kmylhn1ytHk2lSUY0hDiFBA0hhBCNWmGZgXFTN6JWq5h9R09C/DxdXZKoh9JydACyh4YQp5CgIYQQotHSVpm49ZtNlBvMzJnYi6hAb1eXJOqptFwdwb4exATJ15AQJ8jYsBBCiEap0mhm4ozN5JRWseDu3jQJ83V1SaIeS8uxNYLLUshC/EtGNIQQQjQ6RrOVe2ZvIy1Xx4wJKbSODnB1SaKeS8vVyY7gQvyHjGgIIYRoVCxWhUnzd7DhcDEzxqfQtUmIq0sS9ZxOb+JYSaX0ZwjxHzKiIYQQotFQFIVnluxmeWoen4zqSp8W4a4uSTQAe080gsfI0rZCnEqChhBCiEZBURRe/2Uv87dk8s6wTlzVPtrVJYkGIi1Xh6e7G80i/FxdihB1igQNIYQQjcKnfx7k63VHmHx9e4Z2i3d1OaIBScvR0ToqQHaSF+I/5F+EEEKIBu/bfzJ4d+V+Hh3Yitv6JLm6HNHASCO4EGcnQUMIIUSD9v32LF78KZU7LmnKg5e3cHU5ooExmq3szy+TRnAhzkKChhBCiAZrVVo+jy/cxYjkeJ69tq3scSCc7mBBOSaLQrsYCRpC/JcEDSGEEA3SP4eKuH/uNq5uH8UbQztJyBC1Ii3XtuJUGwkaQpxBgoYQQogGZ0dmKXd+u4WeTUP5YGQX1G4SMkTtSMvRkRTmi7+XbE0mxH9J0BBCCNGgpOeVMX76JtrEBPLluO54uatdXZJowNJytbSPlf0zhDgbCRpCCCEajGPFlYybtpGYIB++GZ+Cr6c8ZRa1R1EU0nJ00gguxDlI0BBCCNEglOtNvL8qnQBvD2be3oMgHw9XlyQauCxNFTq9WRrBhTgHedQjhBCi3ivXm5n212E6xQfx7LVtiQjwcnVJohE40QguIxpCnJ2MaAghhKjX9CYLX6w9RJ7OyLWdYokI8HZ1SaKRSMvREebnSaQEWyHOSoKGEEKIestotvL12sMUaPXcN6A5UYESMsTFk5Zr68+QpZOFODsJGkIIIeq0nZml/HOoCLPFetqfW6xWvv0ngyPFFdzVvzkJob4uqlA0VtIILsT5SdAQQghRZ23OKOHGz/7miYW72Hik5GTYsFoV5m48RmqOlol9m9Ii0t/FlYrGprTSSHZplTSCC3EeEjSEEELUSUXlBt5bmc5tvZNoGu7Hg/O282d6IQaThcXbsth8VMO43km0j5M9DMTFd6IRvL2MaAhxTrLqlBBCiDqpTG+mbUwg13WKoXtiKPfP2caj83cwrHs8BWUGxvRsQvfEEFeXKRqptBwd3h5uNA2X0TQhzkVGNIQQQtRJTcP9uL1vU7o1sYWJT8d0o0uTYL5dn0G4vyddj/+5ttLEkaIKV5YqGqG0XB1togNRu0kjuBDnIkFDCCFEnZUQ6otKpcJksbL+UDEhvp70bxXBnI3HWLAlk4yiCu6ds5XvNh1zdamikZFGcCEuTKZOCSGEqPP2ZGuZt+kol7QIZ3hyAh//cZBXl6Xx6Z+e+HqqmXNHT1eXKBoRvcnCwYJyxvZKdHUpQtRpMqIhhBCiTkvL1TFzfQbdE0MZ2jUelUrFQ1e0JCHEl1A/T355+FLZx0BcVAcLyjFbFRnREOICZERDCCFEnXW4sJxp6w7TJjqQMb2a4O7mhtFs5eklu8nSVPHP05fj6yk/ysTFlZajQ6WCNtEBri5FiDpNvjsLIYSokzI1lXyx5hBNQn2Z0Lcp7m62QXiVCjonBPHQFS0I9/dycZWiMUrL1dEs3E9CrhAXIP9ChBBC1DlF5Qa+WH2ICH8v7urXDE/3f2f6eqjdGNcrUaZLCZdJzdHSLlb2bxHiQqRHQwghRJ1SoNPz3aZjNA33494BLfA5y1NjCRnCVaxWhb25ZbIjuBDVIEFDCCFEnVFUbuCWrzfw98EiRiQn4O8tA++ibsnUVFJuMEsjuBDVIN/BhRBC1Ak6vYnbvtmErsrMN7elEOjj4eqShDhDWo4OQEY0hKgGGdEQQgjhclVGCxNnbCazpJJZE3uQFO7n6pKEOKu0XB2RAV5EBMhCBEJciIxoCCGEcCmj2cq9c7aSmqNj9h09aStPikUdlio7ggtRbTKiIYQQwmUsVoVHF+zgn4PFfDUumW5NQlxdkhDnlZajk2lTQlSTBA0hhBAuoSgKz/2wm1925/LRqK5c0jLc1SUJcV7F5QbydHoZ0RCimmTqlBBCiItOURTe/HUf8zZl8u7wzgzqEO3qkoS4oL25ZYA0ggtRXTKiIYQQ4qL7bPUhvlx7mBeua8ew7vGuLkeIaknL1eLrqSYpTBYrEKI6JGgIIYS4qGZtOMo7K9J55MqW3H5JU1eXI0S1peboaBsTiJubbBgpRHVI0BBCCHHR/Lgjmxd+3MOEvkk8fEVLV5cjhF2kEVwI+0jQEEIIcVH8vjefRxfs5OZu8Tx/bTtUKnkqLOoPvcnCocJyaQQXwg4SNIQQQtS69YeKuW/ONga2jeLNoR1l6omod9LzyrAq0gguhD0kaAghhKhVu7JKuePbzaQkhfLhqC64q+VHj6h/0nJ1qN1UtI4OcHUpQtQb8t1eCCFErTmQX8Zt32yiVXQAX47rjpe72tUlCeGQ1BwtzSP88PaQr2EhqkuChhBCiFqRWVLJ2GkbiQr0Zsb4Hvh5ydZNov6SRnAh7CdBQwghhNMV6PSMnbYRbw81Myf2IMjXw9UlCeEwi1VhX16ZNIILYScJGkIIIZyqtNLIuGmbMJiszJ7Yk8gAb1eXJESNHC2uoNJooV1MkKtLEaJekaAhhBDCaSoMZsZP30xBmZ7Zd/QgIdTX1SUJUWNpuToAGdEQwk4yYVYIIYRTGMwW7pq1hYMF5cy9syctImV1HtEwpOboiAnyJtTP09WlCFGvyIiGEEKIGjNbrDw0bztbMjRMvS2ZTvHBri5JCKeRRnAhHCNBQwghRI1YrQpPLt7N73sL+HxsN3o1C3N1SUI4VVquTqZNCeEAmTolhBDCYYqi8PLPaSzZnsWUkV24vE2Uq0sSwqkKyvQUlhlkREMIB8iIhhBCCIdN+e0AM/7J4JUbOnBDlzhXlyOE0+3NLQOgfaysOCWEvSRoCCGEcMg3fx3hw98P8L9BrRnbK9HV5QhRK1JztAR4uRMf4uPqUoSodyRoCCGEsNvCLZm8/HMad/dvxn0DWri6HCFqTVqOjrYxgbi5qVxdihD1jgQNIYQQdlm+J5cnF+9iVI8mPDWojavLEaJWSSO4EI6ToCGEEKLa1h0o5KF5OxjcMYZXb+yASiVPeUXDVWk0c6SoQhrBhXCQrDolhBCiWrYe1XDXzK30aRHG+yO6oJapJOIUBV/uwpih/fcPVCpQgW/XSEJubIHKvf4929yXV4aiyI7gQjhKgoYQQogL2purY8L0TXSMC+LzMd3xrIc3jaJ2hY9ri2JRQFFAAbNGT+GXu/BuHVovQwbYdgR3d1PRMsrf1aUIUS9J0BBCCHFeGUUVjJu2iSZhvkwdn4yPp9rVJYk6yM3X47TfV2zOQx3oiXerEBdVVHNpOTpaRPrj5S5f80I4QoKGEELUEsWqYNEZsZRUYS4xYC6pQjFZQaVCpeLk1BKOv3/yz9z+8/vjx6lUgJvq5PG2Pzv/71GpULn95/f/+fuTr3M78zxFFUaeWbST9h7uvHtdB3x0JkxlprOe56wf0wXqPflnosGp2JyHb9dIVJ71czQDpBFciJqSoCGEEDVg1ZsxF+sxl+ixaGy/mkv0WEr0mDV6sCgnj3UL9MTNSw0KoCgox3891+9RQLGeeP/47//z9yhnKcrJ3sb2pNrw5R7ya+MCpwSTM8PIiaDEeULNOcLTBUOO6ozwdtbzVjvc/ffaFwhhdTBYnutzh0qFm6cbuLuBVfn3vJzyMXG8XkB/QINFZ8S3ayRYFZQTf1+PgqXZYmVfro4hnWJcXYoQ9ZZKUZSL8GNKCCHqJ8VixVJqOD1AnPKmVJlPHqvyVOMe6o061Bv3428n3w/xQuXh/OkXyqmB4xxhRLEqpweTaoScCr2ZpxbvoqTMwNs3dyI2yOf081rPFnrOcl7r6b8/a33/PY/1PPVZz3ads/yZVTlrKKvRtc/x92ecp7qfmzoaLM/Fq1UwnvEBmIv1FzzWcEQLVgXPZkFnBovjv3WP8EW/vwRzTkWdDJblBjO/pxfSt0UYEYHetRgsz16bq4OljFgKZ5ARDSFEo6YoCtYK0xkh4sT7Fq3h35s7N1AH20KDZ6w/Ph3CcQ/1wj3UB3WoN26+7hf9h+rJH+y2353yX8fpTRbu+mYTe3WVzL+rN0kydaTOuGCwPCMIYUfIOX94UnmrUXmpUYyW48Ucf+P081orTVTtLiLg8gS8WwQfv9bpxwGovNzxiPTBqjPWjWB5/Bwnfl+hMxCGikCTgrlEX0vB8gL1uTBYOs1/Q5mzRiwvEJ5qdcRSpQI3FW4+7qc9UHLz95Bg9R8SNIQQDZ5ismDW2EYlLMVV/75fUmUblTBaTx7r5ut+8oeGb5MA2/shx0cngr1QqevvfPPqMFms3DdnG7uztMy+o4fMT69jaiNYOpvuz0zcfN0J7J+AyuMC/14SAi5OUQ6Y+stefjbo+PveLi6rwZXB8rzh6awB8OxhyekjltZzh7KaBMtqjViedm0Fa4UZa4Xp5P8vlYfbaSPaXs2D8W4Tahv5aaQkaAghGgyL1oD+YOkZoxPWMuO/B6lVJ38IeCYF4dst6vQnUt6N99uixarw2IKdrDtQyLTbUuieGOrqkkQ9VLHleBP4hUJGHVcXGsHrQ7Bs7KwGM+YSwyk/c6qwaAzo0zWU/52DOtgLv57R+CVHow7wdHW5F13j/YkqhGgQFEXBcEhLxYYcqtKKwQpuAZ7/PlFqEXxav4Q6wLNRP106F0VReOHHPfy8K4dPR3ejX6sIV5ck6iH9QQ2WEj1+KdGuLqVGFEUhLUfH2F6Jri5F1HFuXu54xrhDjN8Zf2fMKqN8Qy5lf2Si++0YPh3C8e8dg2diYKOZYiXN4EKIeslaZaZiWz4VG3IxF1bhHumLf+8YfLtE4uYjz1Ds9dbyfXy++hBv39yJESkJri5H1GOKotT7m6h8nZ6er//Ol+O6c3X7+h2ahOtZK01UbC2gYkMO5mI9HtG++PeNwzc5qt7/W7kQ+WkshKhXjDnlVGzIpXJ7AYpFwadDGCE3tcCz6VlWtxHV8sWaQ3y++hDPXdtWQoaosYbw7zA1RwtAe+lREk7g5utBwKVx+PeNxXColPL1uWgWH6BqXwmhw1s16Cm7DfcjE0I0GIrJSuWeIirW52A8VoY60JOAAQn4pUSjDmx8c16dae7GY7z56z4eurwFd1zazNXlCFEnpOXoCPR2Jy7Yx9WliAZE5abCu2UI3i1DqEotomTBfgo+3k7omLZ4xvq7urxaIUFDCFFnmUv0VGzMpWJLHtYKM14tggkb2xbvtmGo1PXnqanVYMCUnYMpKxOVhwceCQl4REejcnftt+Cfdubw7A+7Gd8niUkDW7m0FiHqkhON4A1hdEbUTT7tw4l6yI/i2Xsp+GwnITc2xy+54U3Tk6AhhKhTFKuC/oCGivW56NNLUHmp8esehV+vGDwifF1d3lkpVivmwiJMWZmYsrIwZmZhyszEmJ2FKTMLc/5Z9tNWq/GIicEjIR7P+AQ8EhLwjI+zhZD4eNTBwbV6k/PnvgIenb+Dm7rG8cJ17eSGSohTpOXouLxNlKvLEA2ce5gPkfd1pvSnw2gWHcCQoSPkhua1srmrq0jQEELUCZYKE5Vb8infmIulRI9HrB8hQ1vi0zkCN0/Xf9O1lFdgyj4eILJsAcKYlYkpMwtTdjaKwXDyWHV4OJ7x8XgkJODXowcecfHHA0U8isl08vWmrEyMmVno09LQrVyJVas9eQ43Pz9b+EiIxyM+AY/4ODwTEmzvx8Xi5uXl8Mey8XAx98zeymVtInn75k64ySpcQpxUbjCTUVzp8qVtReOg8lATcnNLPBMD0fxwEFN2OeET2qMOdPx7fF0iQUMI4TKKomDMLLM1d+8qBMC3UwR+t7TGMyHgoj5lV8xmTHn5x2/+MzFlZZ8SKjKxaDQnj1X5+NiCRHw8fpf0tY1IxMfbQkFcHG6+5x958Uw8+5KZFp3u3xGRrH8DTfkff2DMyQHTvxtDuUdF2cJLXPy/gSQhAY+4eNwjwlG5nX0Pgz3ZWu74dgvdE0P4eFRX3Bv4BoRC2Gtvrg6QRnBxcfklR+ER50/x9D0Uz9lHxF0dG8QGsbK8rRDiorMaLVTtLKR8Qy6m7HLUod7494zGNzkatZ9HrVxTURQspaW2AHF8JMGUmYkp+/iNfW4umM22g1Uq3GOiTw8Qp4wqqMPCLvpUI8ViwVxQYAtBmVn/1n18ipalsOjksSovLzzi4231xiecHE3J8wvjtl+ziIgKZc4dPfH3kmdNQvzXt/9k8NqyveyZfDWe7vX/Rk/UL4ajOgq/3IV/n1iCr6v/C3RI0BBCXDSmwkoqNuRSsTUfxWDBu3Uofr1i8G4V4tRN9KwGA2WrfkOfmnp8ZMA2OmEtLz95jFtg4MnpTadOS/JMiMcjJgaVZ/1azcpaWYkpO/v4aMgp07qysjBmZaFUVZ081i0kBM+EhJMfv29yMn59+5xzFESIxuTJRbvYk6Nl2UOXuroU0UiV/ZWN9ufDhI5pi2/HcFeXUyMSNIQQtUqxKOj3FlO+IRfDwVLc/NzxS47Gr2cM7qHeTr2WMSuL0vnzKV20GItGg0eTJrYAcfyJvscpT/fVQUFOvXZdVqDTM3HKCsJ0RbzSIxSf4rx/A8nRo5jz8/Fo0oSQkSMJGnoT7iEhri5ZCJcZ8vFftIkO4J3hnV1dimikFEWhZO4+9Ps1RD7YFY/w+rvMsgQNIUStsOiMVGzOo2JjLhadEc8mAfj1jsW3QzgqD+c9OVcsFir++gvN3HmUr12LW0AAwTfdRPAtI/Fq2tRp16mvtJUmRn61npIKI4vv7UNC6On9I4qioN+5E828eeh++RVUKgIHDyZk9Ch8OnVyUdVCuIbJYqX9iyt4+po2TOgr3z+E61j1Zgo+2YHK3Y2I+zrXiUVRHCFBQwjhNIqiYDyipXxDLlV7ilGpVfh2jcSvZwyecc7djMis0aBdvBjNd/MxZWXh3a4dIWNGEzh4MG4+9ffpjzNVGs2MnbqRw0UVLLi7N62iAs57vLmkBO2SJWjmfYcpOxvvDh0IGTWKwMHXyOdUNAr78nQMmrKOBXf3pkfTUFeXIxo5U14FBZ/uwKdTBCHDWtbLZcglaAghasyqN1O5vYDy9bmYCypxj/DBr1cMft2icPNxXsPxaU/ff10OQOA11xAyehTenTrVy2/CtcVgtnDHt1vYdlTD3Dt70TkhuNqvVSwWytetQzNvHhVr1+EWGEjwTTcRcstIPJOSaq1mIVxtybYsHl2wk10vXUWgd+0sTCGEPSq25qNZuJ/wOzvi3TzY1eXYTYKGEMJhprwKytfnULm9AMVsxaddGH69YvFqHuTUm35rZSXaZcvQzJuHIW0vHvHxhIy6haChQ+tVP4Gh0oSb2g0Pr9odAjdbrDw4bzu/7ytgxoQU+jR3vJnQmJn5b99LaSl+ffsSMuoW/AcMcPnO5kI426s/p7EyLZ+1/7vM1aUIAdgesOV/sBWPKD/CxrR1dTl2k6AhhLCb1Wih9MdDVG7Nxy3AE78e0fj3iEYd5NwNhgyHj6D5bh7a73/AWl6Of//+hIwehd8ll9TJFZIsZitlxXp0RVXH32zva4+/b6yyLZ/rE+BBYLjP8TdvAsN9CAr3ISDcG/8Q7xptoGe1Kjy5eBdLtmfz5djuXNnOObsbWw0GypYvRzN3HlU7d+IeHU3IyBEEDxuGe0SEU64hhKuN/noDgd4efDGuu6tLEeKk8o25aFdkEP1It3q3kZ8EDSGEXUxFVZTM3ou5uIqg65rhlxzl1E2FFLOZsj/+QDNvHpXrN6AOCSF42DCCR47EMz7OaddxqDZFoVJnpKxYj7bweJgo1qM7/n55qQGOf0dVuakICPU6I1BYzdaTr9EWVVFWrD/tdW5qFQGh3ieP/+/rvc+zz4iiKLy6bC/T/jrCByM7c1PX+Fr5PFSlplL63Xdol/6MYrEQOHCgrXm8e3eZvibqLUVR6PrKKm7v25SHrmjp6nKEOMlqslD2RyZeTYPwblV/RvFBgoYQwg5Ve4ooWbgfdYAnYWPb4hHt57RzmwoKKF24kNIFCzHn5+PTtSsho0cRcPXVuF3EPS1MBssZAUJ3yiiF2Wg9eaxPgAcBYT4EnRYKbO/7h3jhVs0AZjZZjo+E/Gc0pLgKXWEVRr3l5LFevu4EhHkT9J/rBYb7MHNXFu//foCXb2jPrb2TnP2pOYNFq0X7449o5s7DmJGBV8uWhIweReCQ61H7O+9rQ4iLIbu0ir5v/sG025K5oq1zRgKFcJbybXmYcysJuiapTo7on4sEDSHEBSkWK9pfMyj/KxufjuGE3NwSN++az89XFIXKTZvRzJtH2W+/ofLwIGjIEEJG3YJ329qZi2q1KlSUGmwhoth2Q3/q6ESVznjyWLWHG4Fh3gRG+BAY5vOfUQZvPJ3wObgQRVEwVJjRFVedfRSlxIDVavs2rqCArztxcQHHaz4+LSvCVrNPgEetjDgoikLlhg1o5s6l7Pc/cPP2JujGGwi+5Ra8W7Vy+vWEqA2r0vK5c+YW1j99OTFBssqaOLuCggIOHDjAkSNHcHd3p1mzZrRs2ZKQWu4XNGv0aJdn4N83Fq8mgbV6LWeSoCGEOC+L1kDx3H0YM8sIGtwU/76xNb5ZtZSVof3xJzTz5mE8dAjPZs0IGTWKoBtvQB1w/iVY7aEoCvlHdOzflE9pQSW6wirKSvRYLSfmN4FfkBeB4cdHCE65OQ+M8ME3wNOpO5bXBqvFysJ1R/l06T6GtIjkkphgyk4ZhakqM5081t3T7WRQCo31o23vGIKjfM9zdvuZ8vIoXbAAzcKFWAqL8E1Oto1MXXllvdttXTQuH/52gBn/HGHb8wNlCqA4jdls5ocffuCzzz7jzz//POPv1Wo11157Lffddx9XXXVVrX39aFcdRaVWEXh5k1o5f22QoCGEOCf9QQ0l89JRqVWEjmmLV2LNnqLo09PRzJ2HdulSFIOBgCuvJGTUKHx79nDqN2aTwcL+TXnsWZtNUWY5AaHeRDQJOGNEIiDMG3eP+rkJ0gkrUvO4b842hneP542hHc/4PBr15jN7SoqqyD+sQ19hIr5NCB0HxJPUMazaU72qQzEaKfv9dzRz51G5eTPq8HCChw8jZMQIPGJinHYdIZzl7llbKDeYmXNHL1eXIuqQrKwsRowYwfr166t1/JAhQ5g5cybBwcFOr8WQoaV8fS5B1zbDPbB+PLiRoCGEOINiVShbnYlu1VG8mgcTektr1P6OfVOzGo2UrViJZt48qrZtwz0iguARIwgeMRyPKOfOg9bkVbBnTTb7NuRh1JtJ6hhOh/5xNGkbWudHJhzx98EiJkzfzMB2UXw0qitqOz5Gs8nCoW2F7FmTRd5hHf4hXrS/NJa2fWPxc/bqYQcOoJn3Hdoff8RaVYX/5ZcRMmoUfr1716u5xqJhu/TtPxjUPppnr23n6lJEHbF+/Xquv/56ioqK7Hpd06ZNWbFiBS1bOndRAavBjGbJwXo1fUqChhDiNNZKEyXz09Hv1xBweRMCr2ji0E26KTsbzYKFlC5ahKW4GN+ePQkZNYqAKy5H5eG8jbCsFitHdhaxe0022ekafAI8aNs3lvaXxBIY3nDnWW8/pmHM1I2kJIXy9a3JeLo7fsNeeKyMPWuz2b8pD6tZoVm3CDr2jyOmRbBTR5os5RXofl6KZu48DPv345mYSPCoWwi+6SbUQUFOu44Q9tJWmeg8eSVTRnbhxq6uXd1O1A15eXl07dqVvLw8h17foUMHNm7ciK+vc6enlizaj3e7MHzbhTn1vLVFgoYQ4iRjZhnFc/aiGC2EjmyNd+tQu16vKAoVf/2NZt48ylevxs3Xl6AbbyRk1C14NW/u1ForSg2k/Z1D6tpsKrRGYpoH0aF/HM27RqL2aNhPydPzyhjx5XpaRvoza2JPfDydM/3LUGli33rblLPS/EpCY/3o0C+O1j2j8XTyDu9V27ahmTsP3cqVqNRqAq8dTMjo0fi0b++06whRXRsOF3PLVxtYOakfraKc1ycm6q/LL7/8rP0Y9hg/fjzTp0+v9vF5eXm89tprLFu2jOzsbCIjI+nSpQsPPPAAY8aM4ZFHHuHB5LGoQ73w7/HvFNQ33niD9957j5ycHDzrWC+cBA0hhC0gbMil9OfDeMT6EzamDe7B3nadw6LTkfPMM5T/9jterVsTMno0Qdddi5uf85Y5VRSFnP2l7F6TzZEdhbh5uNG6RxQd+scRHt84bg6OFlcw7Iv1RPh7Me+uXgT5OG906ARFUchK17BnTTZHdhbh7uFG657RdOgfR1icv1OvZS4qonTRYjTz52POzSXoxhuJfPJ/9WrHd1H/ffPXEd5cvo+0yVfj7sReJVE/rVmzhgEDBtT4PG5ubhw8eJCmTZte8NiMjAz69u1LcHAwkydPplOnTphMJlasWMFXX33FoEGD+Pnnn9k2/U8wKac1hLdq1Yprr72WDz74oMY1O5sEDSEaOavBgub7A1TtKMSvdwzB1zZDZec0HP3evWQ9/AgWjYaY114lYKBzV20xVJlJ35DHnjVZaPIqCYn2pUP/eFr3isbLiU/anUavBU0GaI4e/zUDSo+/r/aEkKR/34ITj//aBDzPP8Sep9Uz/Mt/8HBzY8E9vQn3r/0dYss1elL/yiFtXQ6VOiMxLYLo2D+eZl0jUNdgutZ/KWYz2h9+IP+dd1EBkf/7H0FDb5LVf8RF8fjCnezPL+OnBy5xdSmiDhg5ciQLFixwyrn+97//8dZbb13wuMGDB7Nr1y7S09Px+88DutLSUjIzM+nUqRO/fv09PaM6EjLENktg3bp19OvXj927d9OhQwen1OxMEjSEaMRMBZUUz96LpVRPyM0t8e0cafc5ShcvJu/lV/Bs1oz4D6fg2cR5y+4VZZWxZ0026ZvysZqsNO1i6x2IbeXc3gG7WUygzTx3mKjS/Hush98pwSIRLMZ/X1N61Pb7E/yjzgwgx9806jBGfLWRCoOZhff2IS744vafWCxWjuwoYs+aLLL3l+IT4EG7S2Jpf2kcAaH2jX6dj7m4mIK330b740/4JicTPfklp0+7E+K/Bn+4jk7xQbx5cydXlyJczGq14ufnh16vd8r5WrZsyf79+897TElJCeHh4bz22ms8/fTT5zyuR48etI5vwZQRLxA6ojUqNxUTJkwgLS2NjRs3OqVeZ5OgIUQjVbmzAM3iA6iDvQgb2w6PSPsa1qxVVeS98iraJUsIHj6MqGefxc275jecFpOVQ9sL2LMmm9xDWvyCPGnfL452fWPxC679J/gAKApUFv8bIE59Kz0K2ixQju8QrnKDoPgzgsHJN98wOFcoslqhLPffgPLf4FL+bxOiCXeyiSSySWt8I5v95zqJ4H3xmqlLcirYszabfRtyMRssJHWyre6V0MZ5q3tVbNhA3osvYczJIfzOOwi7+27cvC7S/3/RqBjNVtq/uJwXrmvHuN5Jri5HuFhWVhYJCQlOO5+npydVVVW4nWeFvU2bNtGzZ0+WLFnCTTfddM7jvvjiCx5/7HFSP1lD/PCOVGEkOjqa999/n7vuustpNTtTHZxzIISoTYrZSumyw1Ssz8WnSwQhN7XEzcu+ZmLj0aNkPfQwxowMYl5/neCh5/7GWF264ipS1+Ww9+8cqsps+zsMursDSZ3CUdfGnGlTFZQeO/sNfulRMJb/e6xPyL+jDHHd/725D0mCoARQO9gn4eYGQXG2t8Q+Z63RUHSEKQtXYi3O4M6OanxNuXBsA+z8DkwVZ6/xvyGkJjWeRWisH/1uaUWvG5uxf1M+e9ZksfSjnQRF+NChfxxtesfg7Vez6/n16kXTn36k+MuvKPr6a3TLfiH6pRfx693bSR+FEDYHCsowWRTaxdaP5UJF7crIyHDq+YxGI9nZ2ecNLyee+V9opH7UqFE8+uij/LD+V+4d3Ib5P89HURRuueUWp9bsTBI0hGhEzBo9xXP3YcopJ/jG5vj1jLF7CpJu1Spyn34GdVgoSQvm4926tcP1KFaFY3tL2LMmm4zdRXh6u9OmdzQd+sUREu28JnIAdLmw7Vs49OcZowWoPW09EiFJ0KQXdL7l35v04ETwCXZuLdVkcvPi/pUV/FXYmlkTbyU86ZRVwM4YdTnyb1jK3ga6s4y6hDSFtkOg00jwrvlNlae3Ox36xdH+0ljyDmnZvSab9d8fYsOPh2mZEkXH/nFE1mCTRzcvLyIeepDA664l74UXOTbhdgKvH0LUk0/iHlY/lnYUdV9ajg6VClpHS9AQnNEf4Qz+/udfRKNly5aoVCr27t3LjTfeeM7jgoKCGDrkJuat+Z773R9j+vTpDBs2jMDAuvu1K1OnhGgk9OkllMxPR+WpJmxMWzwT7FulSTGZKHj/A0qmTydg4EBiXn8NdYBjKz3py03s/SeXPWuz0BXpCU/wp2P/eFqmROFh5+jK+YtWIGMdbJ4Ke38Gd29odTWEtTj9qX9AjG10oQ6xWhUeXbCDZbtz+frWZAa0trN/5mx9JAVpcPB32+eh80hIuQOinLucbKXOeHLZ4XKNgcikQDr2j6NF90jca7AMr6IoaL//gYK330ZRFKKeeJygoUNlwz9RY5OXprI6vZA/Hx/g6lJEHaDVap26q3dwcDAajeaCx11zzTXs3r37nM3gJ2pateAXrhp5LT8u/oEbbr6RNWvW0K9fP6fV62wSNIRo4BSrgu73Y5T9cQzvViGEjGiN2s5pLab8ArIffZSqnTuJfPwxQm+7ze6REEVRKMgoY8+aLA5sKUBBoWV329K0UU0DndvcrdfaphZtngpF+yG8te2muvPIi9rL4ChFUXjhx1RmbzzKx6O6cl2nWOedXJcDW7+FrTNsozpNets+N22vB3fnrb9utSoc3V3EnjXZHEsrwcvPnbZ9YunQL5agCMc3sDJrNBS8/Q7a77/HJ7k7MS+9hFeLFk6rWzQ+I79cT7i/F5+O6ebqUkQd0aRJEzIzM51yrksuuYR169Zd8LgjR47Qp08fQkNDefnll+nUqRNms5lVq1bx+eefs3fvXgCq0orpcEV3NAYdYWFhHDhwwCl11hYJGkI0YJZyIyXfpWM4VErgwEQCBiTY3axbsWED2Y89jkqtJm7KB/h2s++HsdWqkL4hl92rsyk8VkZAmDcd+sXRtk8MPgFO3lgodxdsmQa7FthWc2pzne0mOumSczdk10Hvrkjnkz8P8ubQjtzSw3mreJ3GYoJ9y2xhLGMd+EVAt1uh+wQIdl4jJEBpQSWpa7PZ+08uhkozTdqH0nFAPIkdwhwOmBUbNpL30ksYs7MJm3g74ffc45TFCETjoigKnSav5J7+zbn/Mgmswuall15i8uTJTjnX9OnTGT9+fLWOzc3N5bXXXuPnn38mNzeXiIgIunfvzqRJk07u61G+OZd3Pn2fl799l9dff/28q1TVBRI0hGigDEd1lMzZi2JRCB3VGu8W9m2AplitFH/1NYUffYRvzx7Evfuu3fPiK3VGVk5LJXu/hsT2YXToH0eT9mG4OWllIgDMBkj70XbDnLnRNg2q+wTbTXNgzIVfX8d8tfYQr/+yj2cGt+GufhdpWdeCfbaAtmOercG81SBImQjNLnfqlDKz0cKBLQXsWZNFwdEymrQPo/+oVgSGO7ZUr9VopPirryn+8kvcY2KIfvEF/Pv2dVq9ouHLLKnk0rf/ZPqEFC6zd3qiaLBycnJISkrCZDLV6DxhYWFkZWXh7cSHILo/j4G7G4GXxjvtnLVJgoYQDYyiKJT/nYP2lyN4JgQQNroN6iD7lgW1lJaS8+RTlK9ZQ9i99xDxwAOo1PbNr885WMrKr/dgVeDqie2Ja+3knZ41R2HrdNg209YU3bS/bfSi9TVOXWHpYvpu0zGeWrKb+y9rzhNXt7n4BRjKYfcC2DwN8vdAaDNInghdRoNv6IVfb4fDOwpZN38/+nITKdc1pfOVCQ6vLmY4fIS8yZOp3LiRwOuuI+qpJ3EPD3dqvaJhWr4nj3tmb2XTM1cQGSgjYuJfb731Fk899VSNzjF//nxGjBjhpIpsSn8+jEecH35do5x63toiQUOIBsSqN6NZfICq3UX4XxJH0DVJqOy8eavavYfshx/GWlFB7Dtv429nk5miKOz4LZP13x8ipnkQV93RHj87g845Wa1w6Hfb6MX+FeAVaLsJTr4dIlo55xpOoigK5sJCTFlZmDIzMWZlYcrMQuXhgUdCAp4J8XjE297UwcEs253Lg/O2M7ZnIi/f0N61GxIqim10aPNUSP0B3NTQYZhtlCPOefPYjXozm5YeYdcfmYTG+jFgTBuimznWQ6MoCtoff6TgzbdQrFYiH3uM4OHDpFlcnNf7q/Yzd+NRNj97pexCL06jKAo33ngjP/30k0Ovf/DBB/noo4+cW5NVoWRhOn5do/Bu5eSHd7VEgoYQDYQpr8K2y3eZkdDhrfDpYN8TXUVRKJ0/n/zXXserTRvip3yAR1ycXecwVJn549u9HN5RSNeBTeh1YzPcnLEHRkUx7JgNW76xrZ4U3RFS7oSOw8DT+UsRVpelvAJTdhamrCyMmZmYMo+/n2X7VTEYTh6rDg/HMy4OxWTCmJ2NVas9+XdWXz8yPIJQxcTRrVd7PBPi8UxIwCM+AY+4WNduVFdeCNtnwpbptlWsYrvZRo46DAUP5+xOXnisjNVz9lFwrIz2l8TS68bmDu/DYdZoKHj3XbSLl+DTtSvRk1/Cu1XdCqGi7rjj2y0YzBZmTezp6lJEHVRZWck999zDrFmz7HrdE088weuvv467u3N3kTDrjGiXHSagfzyesedfMreukKAhRANQsTWf0h8O4h7mQ+jYtnjYOefdWllJ7osvoVu6lJDRo4l86kncPO1r1C7KKmP5l3uoKjdxxW1tadYlwq7Xn0FRIHur7an6niWAAu2H2m5y45MvSnO3YjZjysvHlJVpCxJZ2f+OTmRlYSkpOXmsytv7+ChFAh7x8Sff90yIxyMuDjff01dasmi1GLOy2LttHwuWbqCzupLePnos2dkYc3LglLnB7lFReCTE43nquRMS8IiLxz0i/OI8tbda4MBK2/+Pg7+BdzB0HWsbTQqreS+J1aqwZ00WG348jLunmkuHt6RFcqTjzeKbNpH30mSMx44RdvvthN97D24+zglGouHo88bvXN8ljqeuccFURVFvfPXVVzz//PMUFBSc97ikpCQ++OCD8+6FUROVuwrRp2sIvrEFbh71Y7RWgoYQ9ZhislK69BAVm/Lw7R5F8A3NcbNzrwLD4cNkPfQQpuwcYl5+maAh19ldx95/clgzbz8h0b4MuqtDjZYvxVgJexbZbmhzd9o20kueaLup9XPuvHtFUbCUltoCRFYmxuMjEiffz80Fs9l2sEqFe3Q0nvHHb/Lj446POthGH9Rh9q+gtCdby6ivNtA+LpAZE3rg7WH7f6dYLJjz80/WY8w6fbTEUlR08hwqL6/jU7DibEEkIf7fGuPiUfvXwohP8SFbf8z22VClgeZX2AJgq6tt06xqoFyjZ92CAxzeXkiTdqH0G9WaoAjHm8VLpk2j6PMvcI+MtDWLX3ppjeoTDYemwkjXV1bx0aiuXN/ZiUtIiwbJaDSyePFi5s+fz4EDBzhy5Aju7u40bdqUNm3aMG7cOAYPHoxbLT34USxWSn86hEdCAP7J0bVyjdogQUOIespSYaLomz2Y8isJuaE5fin2f+PR/fILuc89j3t0NPEffWj3fgRmo4W18/ez9+9c2vWN4dKRrRzflK3o4PGVj+aAXgctr7LdvLa4osY3r6eyGgyUrVhB6YKF6Pftw1pefvLv3AIC/h05OHHDfnxUwj021u5RnvM5VFjOiC/WExfiw9w7e+HvVf0hdmtl5fFRlVMCUmYmpuwsjJlZKHr9yWPVISH4dO1KyKhb8Ovb17mjH6YqSP3eFgqzt0JQAnQfb1vxy79mK/gc2VXE2u/SqSozkXJtEl0GNnG4WdyYkUHu5MlUrt9A4ODBRD39FO4RNRxxE/XePweLGD11I7892p8WkfVjGopovAxHdZT/k0PQ4CTcg+rPwgUSNISohxSrQtGMVExZZYRP7IhnnH0/JBWjkfy330EzezaBgwcT88rLuPnZ9+S7tKCS5V/toTS/kv6jWtO2jwNLyVrMsH85bP4aDq8Gn1DbTWryBNuO3U5kzMqidP58ShctxqLR4Nu7F/59+x4fDbCFCXXQxdnML7u0iuGf/4Oflzvz7+5NqJ/zAoyiKFiKik5O7zIePUbZqlUY0tPxaNKEkJEjCRp6E+4hTm4kzN5mC4q7F9mmWbW7wRYUm/RyeJqbUW9m87IMdv6eSUi0LwNGtyamRbBD51IUBd3SpeS/+RaKyUTkY48SPGKENIs3YlPXHebdlemkTh6E2plLbgtRC7SrM1GpILC/c/c5qm0SNISoh3S/HUX3+zHCb++Ad0v7bhhNOTlkTZqEPm0vUU89Scjo0XZP+Tm8o5DfZ6ThE+DJoLs7Eh5v59NAswHWf2JbRlWXDfE9bDel7W4AD+c9qVEsFir++gvN3HmUr12Lm78/wUNvInjkLXg1a+q069ijqNzAiC/WY7RYWXRPH6IvwpMpRVGo2r4Dzbx5lC1fDioVgYMHEzJ6FD6dOjn3YpUlsHOebZSj5DBEtode99pWB3NwZKows4zVc9IpyNDR7pJYet/keLO4pbSUgvfeo3ThIny6dCF68mS8W0uzeGP06PwdHC6q4If7Ze8VUbeZdQbK/8rGt2sknjH1a/RNgoYQ9Yz+gIaib/YQeEUTAq9MtOu15ev+IueJJ1D5+hA/ZYrdN5kWi5UNPxxmx6pjNOsaweW3tsXLx85VNUqPwYLbbPs0dB5lWzI1prN957gAs0aDdvFiNN/Nx5SVhVe7toSOHk3g4MFnNGVfTNoqE6O+2kBhuYFF9/QmMezir5hlLi6mdPESSr/7DlNODt4dOhAyahSBg69xbrO01QpHVsOmqZC+DOK6w3VTIMaxYGO1KqSuzWbDD4dQe7hxyfCWtEyJcrhZvHLLFnJffAljRgZhE8YTft99Lv3aEBffoClr6Z4Ywms3dXR1KUKcV+myw1TtKyb6ke52L1nvahI0hKhHzKUGCj7ehkdcAOHj26Oq5nC/YrFQ9OlnFH3+OX6XXELs22/ZPXWmotTAiql7yDuso8/Q5nS+IsH+m7z9K2HJneAdCCNmQmxX+15/HoqioN+1C83cueh+XQ5A4DXXEDJ6FN6dOrl8jfwqo4Vx0zZyoKCcBXf3pnV0gEvrUSwWyteuRTNvHhXr/sItMJDgm24i5JaReCYlOfdiR9fDz49A0QHofR8MeNrhZYkrSg2sW3CAQ9sKSGgbQr9RrQmOdCwgKEYjxd9Mp+jzz3EPD7c1i9u5b4yon/QmC+1fXMHLN7RnTE/7HtgIcTFZ9WZyX9+E/6VxBA2sf1+rEjSEqCcUs5XCr3Zh0RqJfKgr6mpOHTGXlJDzxP+o+Ocfwh98gPB77rF7XnpWuoaV01JxU8FVd3Yg1t558lYLrH4D1r4DLa+Gm75w2k7T1qoqdMuWoZk7D31aGh7x8YSMuoWgoUOd34fgIKPZyp0zt7A5o4Q5d/Ska5O6UdcJxmPH0Myfj3bRYixaLX59+xIyehT+/fujctY68GYjrP8Y1rwNfhEw+F1oPcjh02XsLmLtvP1U6owkD06i61VNULs72Cx+9Ch5k1+m4p9/CLhmEFFPP41HZM2a2UXdtjtLy5BP/uL7+/rUuX+PQpygKAolc/eh368h+rHuqANduKeSgyRoCFFPlC49RPmGXCLu7oRXk8BqvaZy+3ayJz2KYjAQ9967+PXpY9c1FavCtpVH2fjjYeJahzDw9vb4BtrZuFxeCIsnQsY6uPw56DsJnNCAazhyhNLvvqP0+x+wlpXh378/IaNH4XfJJXWqwbfKbOHuRTv5K1vD2Cua0yYqgERvTxJ9vIjx8kBdh3Yjtur16JYvRzNvHvqdu3CPiSFk5AiChw3DPdxJSwuXHIZlj8GhP6Dt9XDNWxDo2NKiJoOFzT8fYcfvmQRH+jBgTBtiWwY7dC5FUdAt+4X8N95AMRiIeHQSISNHolI7b8UzUXfM33yMp5fsJnXyIHwcXSlPiFpW9lc22p8PEza2rd2b8NYVEjSEqAcqdxVSMncfwUOa4d+3ert1l8yeQ/6bb+LTsSNxH7yPR7R9y9/qK0z8PiONjN3FJA9OIuW6prjZuzLLsQ2wcDxYzTDsG2has2kpitlM2Z9/UjpvHhX/rEcdEkLwsGEEjxyBZ3x8jc7tcE2KQpHJzNEqI8f0Ro5WGThaZeSo3sCxKiM5ehPK8U+bWgWWU77jeqhUxHt7kOjtRRMfT5ocDyCJPp4kensS5OHcXWXtUbUnFc1389D9vAzFYiFw4EBb83j37jWfhqYosGcxLH/atkTuFc/bFgNwsFm8KMvWLJ5/REe7vjH0HtrC8WZxrZaC996ndMECvDt3ImbyZLzbyGZuDc2LP+7hr4NF/P7YAFeXIsRZGY7qKPxyF/59Ygm+rpmry3GYBA0h6jhTYSUFH+/Au00IoaPaVOsmr3jGDArefIuQceOI+t8TqDzsu+kqOKpj+Vd7MFaZuXJCO5I62vkkRVFgw2ew6gWIT4Fh0yHQgeVvjzMVFFC6aBGl8xdgzs/Hp0sXQsaMJuDqq526t8W5VFqsHDseHE4NE7b3jVRZrSePDfVQ24KDtyfZOWXsSC/i3h6JjO0UR5yXJ2ZFIctge93RKgPH9EaOHQ8mR6uMlFv+PVeQu5pEb8/jIeR4APHxJNHbizhvDzwvwsiNRatF+8MPaObOw3j0KF6tWhEyehSB1w2p+WaAVRr4bbJt87/YrjDkQ4cXBlCsCql/5bD++0Oo3VX0HdaSVj1q0Cy+bRt5L76I4fARQm+7jYgH7pdm8QZk+Bf/EBPkw0ejnNcnJoSzWMqNFHy8HXWwNxF3dax3DeCnkqAhRB1mNVoo+HQHWBUiH+iCWzU2dStdvJjcZ58j7I6JRD7+uF3XUxSF1HU5rFuwn/A4f66+qwOBYXauRKTXwo/3w96l0OchuOIFUNv/dFlRFCo3b7YtybrqN1QeHgRddx0ho27Bu107u893PhZFIc9gOm0k4kSgOKY3UmA0nzzWy01FE2/bjX+T4yMPtgBgCxf+7ran8p/+eZB3VqTz0pB2jO9bvaV0FUVBY7acDCGZx4PMiZqyDMaTIyJuQIyXx8kRkJOjIceDSbiHu1Mb4BWrlcoNG2z/P37/AzcfH4JuuIGQUbfg1bJlzU5+bKOtWbxwH/S8Fy57BrwcW8KxQmvgr4UHOLilgPg2IfQf1ZrgqBo0i8/4lqJPP8U9LIyo558j4LLLHDqXqDusVoWOL63gwStack//5q4uR4jTKFaFoul7MOVUEPVQV9RB9a8v41QSNISooxRFQbNgP1V7ioh8oAseURd+eqxbsZLsSZMIHjaM6Mkv2XWjaTJYWD13H/s35tOhfxyXDGuJ2sPOpyh5e2DBOKgoghs/h7bX2fd6wFJejvannyidNw/DgYN4Nm1KyKhRBN14A+rA6vWmVIeiKGzTVTI9u4hlhaVUWf/9Vhjj5WELE8dHD/4NFF5EerrjdoHP68z1GbzwYyqTrmzFw1fW8Cb8FGarQrbB+G8AOR6EToSREpPl5LGhHmqGR4dyW2w4zXyd+4PKlJtL6cKFaBYsxFJUhG9KCiGjRxFwxRWoHB1hsphse6usfgt8w2DwO9BmsMM1Hk0tZu28dCpKjXS/JpFuVyXa//V8nDEz09Ys/tdfBFx1FVHPPoNHVJTDtQnXyiiqYMC7q5l5ew/6tZId4kXdol11lLI/jhE+sQPeLer/QgUSNISoo8o35lL6/UFCR7bGt+uFV8Ap/+tvMu+9l8CBVxL7zjt2NbFq8ipY/tUedEVVXDa2Da162NfPAcD2ObDsUQhrCSO+hTD7nhRaKyoomPIh2sWLsRoMBFxxBSGjR+Hbs6dTn8xXWCz8kF/KjOwidpdXkejtyeiYMDoE+NDE25MEb0+8azBM/cP2bB6Zv4OJlzTluWvbXtRldcvNlpMjMZu0FczPK6HEZGFASADj48K5MiwQdyfugKwYjZT99huaufOo3LIFdUQ4YePHEzp+vONN1CVH4JfH4eBv0OY6uOZtCKpeX9J/mYwWtizLYMeqYwRF+tB/dGviWjn2g1tRFHS//EL+G2+iVFURMWkSIaNukWbxeuiX3bncN2cbm5+9koiA+v20WDQsFZvz0Cw+QODARAKvaOLqcpxCgoYQdZAxq4yCz3filxJNyI0tLnh85bbtHJs4Ed8eKSR8/LFdT5UPbMnnz1n78A/xYtBdHQmNtXPevakKfv0fbJsJXcfali31sG+6leHQIbIeehhTbi5h48cTPHKE058YH6zU8212EfPzSigzWxkYFsj4uHAGhAZccISiun5Ly+fu2VsZ2jWOt4e5fu8OvcXK0kJbqNqqqyTOy4NxsWGMiQ0jwtOxZulzXmv/fjRz51I6fwF+ffoQ++47ji8vrCiQ+j0sfwqMFXD589DjToebxYuzy1k9Zx95h3W06RND36Et8PZ3sFlcp6Pg/fcp/W4+3h07EjP5JadP5RO1690V6SzYksmmZ690dSlCnFSxxRYy/HrGEHxDc5f//HAWCRpC1DHWShP5H2/Hzc+DyHs6o7rA3gD6ffs4eutteLVqSZOvv6727s4Ws5V/Fh9k159ZtEyOZMDYNnh627nKUclh2y7fRfttAaPbOPteD2iXLSP3+RfwiI0h/sMP8WruvDnTZqvCimItM7KLWKcpJ9RDzZiYMMbFhtHEx7lPMv85VMT46Zu5ok0kH4/qinsda97bVVbJt9lFLMnXYFbg2oggxseF0zPIz6k/0Mr//pucx59A5eVF/JQP8OnSxfGTVZXCH6/A5mm2JvEhUxze5PFEs/iGHw6hclPRd1gLWveMdrxZfPt28l58CcPBg4TeeisRDz6Am9/F3+ld2O/2GZtRFIXpE3q4uhQhAKjYko9m8X78ekQTfEOLam/GWx9I0BCiDlGsCsUz0zAc1RH1YFfcQ73Pe7wxI4OMMWPxiIqiybczUAdUb7fpshI9K77eQ+GxMi4Z3pIO/ePsv+Hatwy+vxf8wmy7fEd3tOvlVqORgjffQjN3LoFDhhAz+SWnreqTbzAxO6eY2bnF5BpMpAT6MT4ujOsig/GqhZWadmaWMvrrDXRLDGHqbcl4udfd6TSlJjML8zTMyC7iUJWBNn7ejI8LZ1hUyMlG9poy5eWR/cgkqlJTifrf/wgZO6ZmYSZzs61ZvCANetwNlz8LXo7trF6hNfD3ooMc2JxPXOtg+o9qTUi0YwFBMZko+fZbCj/5FHVICNHPP0/A5dIsXtf1fP03hnWP54mrZdli4XoVW/PRLGqYIQMkaAhRp+hWZ6JbnkHY+Pb4tDn/ztmm3FwyxozBzduHxNmzcA+t3k7bhkoTC17fjNWicPVdHYhuGmRfkRYz/D4Z/vnINof+xs/A275zmLKzyZr0KIa9e4l69hmCR46s8VN1RVH4p7ScGdnF/FpUiofKjWHRIdwWG0aHgNpblnR/fhkjvlxPs3A/Zt/RE19P1+19YQ9FUfhLU8707CKWF2nxUbsdbx4Po62/nSuNne38JhMF775LybczCbhmEDGvvFqz5XAtJtuSyavfBJ8QW++GA4sNnHAsrZg1c9MpLzXQfVAS3a+uQbN4VhZ5L79Mxdp1BAy8kqhnn7V73xpxcRSVG0h+9Tc+Hd2Nazs5vuS2EM5wMmSkRBN8Y8MLGSBBQ4g6Q3+olKKpuwkYkEDQ1UnnPdZcUsLRMWNRDAYS586p9k2Noij8+sVucg6UMuKZFALD7byhLMuDRbfbNuIbOBl6PwB2BoTydevIefwJ3Pz8iPtwCj4d7RsJ+S+d2cLCvBJmZBdxoNJAS18vbosLZ0R0KIG1PLKQWVLJsC/+IcTXk/l39SbI99zz/vV6PaWlpajVaoKDg/Gwc2+T2pSjNzIrp5g5ucUUGM30CvJjfFw4gyOCarxXh275CnKffRb3yEjiP/qw5kvhao7amsUPrITW18LgtyHIsc0azUYLW37JYPvKYwRG+DBgdGviWjveLF62YgV5r72GUlFJxCOPEDJmtDSL1zHrDhQybtom/nx8AE3DZaqbcJ3GEDJAgoYQdYJFZyT/o214RPkSPrHjeb/hWMrKOHbbeEwFBSTNnoVnUlK1r7Nt5VHWLznE4Ps60bSTnZvwHVlnCxkqNxg+AxJ72/VyxWKh6NNPKfr8C/z79SP2rTdRBwfbV8Mp0sqrmJFdxKJ8DQarlWvCbT0HfYP9L0oTXYFOz7Av1uOmgoX39CHU1x2tVktpaSkajeaMt6qqqtNeHxAQQHBwMCEhIWe8+fv743YRNuP7L6PVyq9Ftp6W9aUVRHi6MzYmjLGxYcR5O74xouHIEbIffgRjZiYxk18i6Prra1aookDaj/Drk2Aos02l6nE3qB0bTSrOKWfNnHRyD2lp0yuaPsNa4OPv2MdrKSuj8IMP0Mz7Du927YiePBmfDu0dOpdwvi/XHOKj3w+w+6WrcWugN3ai7qvYlo9m4X78kqMJvqnhhgyQoCGEyykWhcKpuzAX6W2b8wSc+wbHWlXFsTvvxLD/AImzZuLdunW1r5NzQMMPH+yg68Am9L7JjoZrqxX+nmJryk3sC8O+Af8LL7d7KnNJCTmPP07Fho1EPPQQYXfdicqBG2mD1cqyQtuN8CZtBVGe7oyNtd0Ix3jV7g7hiqJQWVmJRqMhO7+QL1buRm2qpEesJ5XlOrRaLSe+napUKgIDA88IEMHBwVgslrOGkfLy8pPXOjHqcbYQEhwcjLf3+Xt3nGFfRRXfZhezMK+ESouVq48HuUtD/B1apctaVUXeS5PR/vgjwbeMJOrpp3HzqmFDvl4Lf7wKm7629QgN+RDiujl0KsWqsPefXP5ZchCVSkWfm5vTpneMw6G1audOcl94EcOBA4SOG0v4gw/VfCd1UWMPf7edbE0Vi+7t4+pSRCNVsb0AzYL0RhEyQIKGEC5X+usRytdlEXFXJ7ySzt3roBiNZD74IJWbNtPkm2n4dq3+6jsVWgMLXttMSLQv1z/cBbfqrohUpbE1fO//FS593LZjs51LjFZu2072pEkoZjNx772LX69edr0eIFNvZHZOMXNyiikymekb7M/4uHAGhQfh4cRv0haLhZKSkpM3//8NBEaj8eSxRtyJjggjOiLsjDAQFBSE2s4pM0ajkdLS0nOOiJhMppPH+vj4nDWEnAgiTl1Fymxhcb6teXxvhZ5mPl7cFhfGyOhQgj3sG0FQFIXShQvJf/U1vFq2JO7DD/GMd2yPjNNkbYWlD0NBKqTcCZc/B96Obe5YqTPy96ID7N+UT2zLYAaMqWGz+MxZFH7yCeqgIKKfe5aAK2VJVVe68v019Gkexss3dHB1KaIRqtxeQMmCdHy7RxEytGWDDxkgQUMIl6pKLaZ4VhpBg5sS0O/c88wVi4WcJ55At+o3Er74HP++fat9DavFyo9TdlCaX8mIZ1PwC6rmU+Sc7bDgVtDrYOhX0Orqal8Tju9sPnMm+e+8i0+nTsR98L7de2NoTGae3p/FTwWl+KndGBEdym1x4bTyc+4TfZ1Ox7Zt29i6dStlZWXA6aMKJ371DwxiytocdhSYmHnnJXSMt7OR3kGnjqac7U2n050cTYmIiCAlJYVOnTo5deRDURQ2aSuYkV3Ez4Va3FUwIS6Cp5pF293HUZWaSvbDj2ApKyP2rTcJGDCg5gVazLDxC/jzNdviBNe8DW2H2N1DdEJmWglr5qVTVqKn26BEug9KxN3DsX4LU3Y2eS+/QvmaNfhfcQXRzz2LR4w0Il9sVUYL7V9czus3deSWHg1jMzRRf1TuKKBkfuMKGSBBQwiXMRdXkf/xdryaBxM29tw7SCuKQt6LL1G6aBFxH3xA4NVX2XWd9d8fYvuqY9w4qQuxLavR6KoosHWGbRO+qPYw/FsISbTrmpbycnKffY6yFSsInTCByEcnobKz+XmHrpI7UzMoN1t4qlkMw6JC8HNic7eiKGRkZLB582b27duHWq2mU6dOtG/fnrCwMAICAk7rkzBbrNw3Zxtr9hcy8/Ye9GwW5rRaaspisaDVaiksLGTHjh3s27cPDw8POnXqREpKClFO3vyw0GhiRnYRHx0toHOAD1+1TyLWzh4Oi1ZLzlNPU/7nn4TdfTcRDz6Ayt0JK3aVHoNf/mcbhWs1CAa/A8GO3VSajRa2Lj/KthVHCQjzpv/o1iRcYDW4c1EUhbKVq8h/7TUs5eVEPvwQIWPGOOdjFtWyI7OUGz/9m58e6Eun+GBXlyMakZMho1sUITc3npABEjSEcAnFZKHgs50oRguRD3bF7Twb5RW89x7FX08l5rXXCL55qF3XObKriF8+20Xvoc3pdlU1w8Ifr8HatyF5Igx6A9ztm0evT99P9kMPYS4uJub11wi8yr5gpCgKM3OKef5ANu38ffi6QxIJNWhEPqM+vZ6dO3eyefNmioqKCA8PJyUlhc6dO59zBMBqVXh80U5+2pHD17cmc1kb+3pULjatVntyhKa8vJwmTZqQkpJC27ZtcXfije1WbQV3pWZQZbXyebsk+ofat7eFYrVS8s03FLz/Ab4pKcS99y7u4XYuUnDWEyuw72db4NCX2qb89bzX4WbxktwKVs/ZR+5BLa17RtN3WAt8ztNLdT6W8nIKp3yIZs4cvNq2IWbyy/h0lGk8F8Pcjcd4/sc9pE6+Gm8HR6eEsFdjDhkgQUMIl9AsPkDF9gIi7+uMZ6z/OY8r+uprCt9/n6innyL0ttvsuoa2sIqFb2wmtmUw19zTsXrz9tOXw7yRcPnz0O9xu64HUPr9D+RNnoxnYiLxH06xa0UsgAqLhSfTs1iUr2FCXDgvtYh12gZ7eXl5bN68mV27dmE2m2nbti0pKSkkJSWd93OjKAqTl6bx7foMPrylK9d3jnVKPReDxWJh3759bN68mYyMDPz8/OjWrRvdu3cnuAYrfp2q2Gjmgb1HWV1SxuNJ0UxKirK7Wbxi0yayH30MlUpF3Afv45uc7JTa0OtsU6k2fglRHWw7i8c7dm7FqrB3va1ZHKDP0Ba07R3j8E1D1a5d5L74Eob0dELGjCHi4YdQ+5/7e4Goued+2M3mIxpWTOrn6lJEI1G5s4CS79Lx7RpJyLBWjS5kgAQNIS66ii22tbNDhrXEL/nc+19ovvuOvJcmE37ffUQ89KBd1zCbLCx5ZxuGShMjnknB6zz7O/x7wQz4sp9tZamRc8COG3yrwUD+q69RunAhQUOHEv3C87jZ2R9woELPHakZZOqNvNs6gaFRju1ncCqz2UxaWhqbN28mMzOTgIAAunfvTrdu3QgMrF6z8Pur9vPR7wd4/aaOjO5Zf+d1FxQUsGXLFnbs2IHJZKJVq1akpKTQrFmzGi+la1EUpmTk825GHgNCA/ikbSJhdm5caCooIOfRx6jcvp3IRx8l9PYJzmtqz95m21k8dxek3AFXPG/3JpMnVJUZ+XvxQdI35BHTIogBo9sQGutgs7jZTMms2RR+/DFqf3+innuWgIEDL8ryzI3RTZ/9TVKYHx+M7OLqUkQjULmzkJLv9jXqkAESNIS4qIw55RR8thPfLhGEDmt1zuO0Py8j54knCBk7lqhnnrb7xuPPOftIX5/Hzf/rTkSTakxnMenhm6ugqhTuXmPbebmajJmZZD38MMZDh4l+4XmCb77ZrloBfizQ8Oi+TGK9PJjaoSmta9jsXVpaypYtW9i2bRuVlZU0bdqUlJQUWrdubddqUFPXHebVZXt5clAb7h1gx5LAdZjBYGD37t1s2rSJgoICQkNDSU5OpkuXLvj61mwH9dUlOu5LO4qPmxtft0+iW5B9N+CK2UzhlCkUT52G/5VXEPv666irGQgvyGKGTV/ZlsP1CoBr3oJ2NzjcLJ61r4TVc9MpK9bT9aomJF+ThLung83iOTnkvfIq5X/+if9ll9maxeOcsBqXOMliVejw4goeHdiKO/s1c3U5ooGr3HU8ZHSOJGR44w0ZIEFDiIvGqjdT8PF2VJ5qIu/rjOocc4TL/vyTrAcfIui664h5/TW795vYtyGX32fs5bJxbWjXt5rTfH6eBNvnwMSVENul2tcq++MPcp58CnVICPEfTsG7bVu7ajVarbx8KIepWUXcFBnMu60THG74tlqtHDp0iM2bN7N//368vLzo0qULycnJRERE2H2+BZsz+d/iXdw7oDlPDmrjUE3/deo+G66mKAqZmZls3ryZ1NRU3Nzc6NChAz169CA21vHpYdl6I3elZrCrrIqXWsRye1y43R9v2e+/k/PU0w5/XZ2XNsvWu5G+DFpeBYPftXuxgxPMpuPN4suP4h/qzYBRrUloV4Nm8d9+I//V17DodEQ8+CCht46TZnEnOVRYzhXvrWHOHT3p28IJfUBCnIOEjNNJ0BDiIlAUhZLZe9EfLCXqwa64h/uc9biKTZvIvPMu/C69hPgpU+y+ySjOLmfRm1tokRzJ5beeeyWr0+ycD9/fZdvsrPv4al1HMZsp/PBDir+e6vCT56zjN6S7y6p4uWUc42PDHLoBr6ysZPv27WzZsgWNRkNUVBQ9evSgY8eOeHo61rD76+5c7p+7jVE9mvDqjR3sqsuor0JbkI82P8/2a0He8bd8tAX5uKnVBEVFExwZTWBkFMGR0QRFRRMUGU1gRCTudq7O5Qzl5eUnP4darZa4uDhSUlJo3749Hg7UY7RaeeVQDl9nFXFjZDDvORAgnTFSdl57f7atrFZZApc9Db3uA7Vjn3tNXgWr56STc6CUVj2i6DusJb6BjjaLV1D40YdoZs/Bq3VrYia/hE+nTg6dS/xr6c4cHpy3ne3PDyTEr3Y39xSNl4SMM0nQEOIiKFuXjXbZYcLGtcWn/dmfplXt3sOx8ePx7tSRhC++sHvXZGOVmQVvbMbdQ83NT3bHozrTOPLTYOoV0O5GuPGzak0jccZc+j+Lddy/9/gUmw5JdAu0c4qNopCdnc3mzZvZs2cPAO3btyclJYX4+PgajRis3V/IxG83c02HGD4Y2QX1f35QWK0WyouLKc3POz1E5OdRWpBHlU578lh3Ty+CIqNsb1HRBEVEYzGb0BXmn3y9rrAAq8Vie4FKhX9omC18REYRdCKERNhe7xccUqujIVarlQMHDrB582YOHjyIj48PXbt2JTk5mdBQ+5/U/1RQyqR9xxyeEndG78/zz+Hmc/aQ7hBDGfz5um3/jch2cN0USEhx6FSKorBvfR7/LD6Ioij0vqk57frGOt4svnsPeS++iH7vXkJGjSJi0iOoA+xb1Uv8663l+/hxezb/PH2Fq0sRDVTl7kJK5u3Dt1MEISNaS8g4ToKGELXMkKGl8Kvd+F8SS/Dgs88NNhw8yNGx4/BIbELiN9/g5mf/jfeKr1M5llbMiKdTCI6qxlx7Qxl8dRmoPeGO38Dzwq8xHDzI0fETHF4dyKIovJ+Rx/sZ+VwWGsAn7RIJtXN3aZ1Ox/fff8+RI0cICgoiJSWFrl274mfn5+xsth4tYezUTfRuHsYbl0dRmn3MFiTy89AW2sKEruj0YBAQGn5KKDj+a2Q0wVHR+AZdeJduq9VCeUnxyaCizT99FKRSW3ry2NOCy/HrBEVGEdWsBQFhzp0OUlxczJYtW9i+fTt6vZ4uXbowePBgu0eJDlbqmbgng2NVRt5r41iT/8nVzJo2JXHGdNRBTt4oMWeHbWfx3J2QfDtc8QL4BDt0qqpyI/8sPsi+9XnENA+i/5jWhJ1nZbnzUcxmNHPnUjjlQ9z8/Ih69lkCrr6qTky9q29u/WYTnmoVU29zLEgKcT6Vu4sombcXn04RhErIOI0EDSFqkaXcSP5H23EP9Sbizo6o1Gf2Wxizsjk6ZgzqwEASZ81E7cCyozt/z+SvhQcYdHcHmnetxh4PigKLJsCB3+Cu1RDeohofSwUZw4ejclfT5JtvcLez76HYaOb+tKOs0ZTxv6bRPJxo/zKoR44cYdGiRbi5uXHttdfSqlWrGq+YdEJajo5RX/xFH3U2l5jSyTuwDwBPH9+TU51OTHE6caN/MaY6GfVV6AryKT05Hev0URSzyYhK5Uaz7il0GTiYxE5d7e7rOR+TycT27dtZtWoVISEhjBgxgnA797o4ddni8XHhTHZg2WJ9ejpHx92Kb3Iy8Z987NSPEQCrBTZ9DX+8Ap5+tj1k2g91vFk8XcOauenoCqvoclUTUgbXoFk8N5e8116j/Lff8e/fn6jnn8czXprF7ZH86m+M7pHAo1e1dnUpooGp2lNE8dx9+HQMt4UMtYSMU0nQEKKWKFaFom/2YMqrIOqhrqgDz5wKZSoo4OjYcQAkzp6FR6T9G8HlHdby/bvb6Hh5PJcMa1m9F2380jY/ffi30P7GCx6uKArZjz5Kxdp1JC1ciFezpnbVuFVbwZ2pGRisCp+3S6SfnRu7Wa1W/v77b/744w+SkpK4+eab8XfingN70o/wwcff0kKTiqe5iiYdOtF54GASOnTG28+/zj5BVhSFCk0Jh7ZuYufKZRQeyyA4OobOAwfTfsCV+Pg7b6pNfn4+CxYsoKysjBtuuIH27dvbXevs3GKe3e/4Roxlf/5J1r33Efn4Y4TdcYddr602bbbt38a+n6HFlXDtexCS5NCpLCYr21YeZcuvGfgHe9F/VGuatHd8R/my338n75VXsWi1RDxwP6G33orKBT099U1BmZ4er/3OF2O7MahDjKvLEQ2IhIwLk6AhRC0pW5OFdvkRwu/oiHfz4DP+3lJaytFbb8NSWkri3Dl4xsfbfY2qMiMLXt9MQKg3NzzaFfVZRkzOkLkZpl8DPe60PbWthpJZs8l/7TXipnxA4KBB1a5PURSmZRfx0sFsugX68WX7RGK87Lu5rKys5Pvvv+fAgQP069ePAQMGOGUUQ7Faydi5jQ3LlpK9eytmtSedLhtIz8HXERaXUOPzX2yKopCTvpcdK5exf8PfuLm50bpvP7pcdS3RzasZQC/AYDDw008/kZqaSq9evRg4cKBdywUD7Cyr5I49GZSZLXzSLpErw+xbRKDg/Q8onjqVJjOm49ejh12vtcu+X+CXJ6CyGPr/D/o8WKNm8TXz0slOL6VlShR9h7XAL8i+HqwTLOUVFH38MSWzZuHVsqWtWbxLF4fO1VisTi9g/PTNrH3iMpqE1WwJZyFOkJBRPRI0hKgFisVK7lub8WkdSsjNZ97kWSsqOHb7RIxHj5I4exZeLS48demMc1gVfv54B0VZ5Yx4pgf+IdW4cakohi8vhaB4GL+sWjdOVTt2kDHuVkJG3UL0M89Uu75ys4VH0zP5qaCUuxMieK5ZLB52zlvNzs5m4cKF6PV6hg4dSqtW5957pLoqdVpSV//Gzt9+tU1F8o3kUFgn3vzfbSRE1nyTwLqgUlvK7j9WsvO3XykrKiS6eUs6X3UtrftcioenYze4JyiKwsaNG1m5ciVxcXEMGzaMIDt7JkpNZh7ce4xVxToeSYziiabRqKs5aqSYzRybeAeGQ4doumSxQ6OA1WYoh9VvwIbPIKKNrVm8SU+HTqUoCukb8/h70UGsFluzePtLatAsnppK3gsvok9LI/iWkUROmuS8PUcamM9WH+TzPw+x6yXpbxHOUZVaRPGcffh0CCN0ZBsJGechQUOIWlC1p4ji2XuJfKgrnv9pBLUajWTefTf6XbtpMmMGPh07OHSNjUsPs/WXDIY83IWENtVYEchqgTnDbA2vd6+DoAvP8TZrNBwZejMeUVEkzvwWVTUbgfeWV3FnagZ5BhNT2jThusjgar3uBEVR2Lp1K7/++itRUVGMGDGCYAd6V049X97B/exYuYz09etAUWjaoy/zyxPYbw1j0b19SAqveTN5XWO1WjiyfQs7Vv5Cxo6tePv50/6ygXQeeA0h0Y7vlQGQmZnJwoULMZvN3HzzzTRvbt+GhlZF4dNjBbxxOJc+wf583j6RCM/qjRiYCws5MvRmPBMTaTJjeu3vNZG7E5Y+AjnboPsEuPJFuza1PJW+3MQ/Sw6y959copsFMmBMG8LiHGwWt1jQzJ1H4ZQpqHx9iH7mGQIGDZKb6f+4f+42CssMLLi7t6tLEQ1AVWoxxXP2SsioJgkaQtSCwqm7UUxWIu/tfNqfK2Yz2ZMmUb5mLQlff41fT8emfhxNLebnT3bSc0gzkgcnVe9Fq9+0vY37HppfdsHDFYuFzLvuRp+WZntyHFO9uc0L80r4X3omTX28mNqhKc187Vym12jk559/ZteuXaSkpHD11Vfj7uCNpMmgZ9/fa9mxchkFRw4RGBFF54HX0KLv5dyzKJ19eTrm392btjEN/0mwJi+HXb8tZ8+fq9CXl5HUuRudr7qWZt2ScXNzrEm5oqKCJUuWcOjQIS677DIuvfRSu6e1/a0p4560o6hR8VX7RHoEV++mu3LLFo7eNp6w2ycQ+dhjjpRvH6sFNk+D318GD28Y9CZ0uNnhZvHs/bZmcW1BFV0GJpB8bdPqLUl9Fqa8PPJfe52yVavwu/RSol98waGpmA3V5e+upl+rCF663r6+IiH+qyrteMhoF0boLRIyqkOChhBOZiqoJP/9rYSObI3vKStAKVYruc88i/bnn4n/+CMCLrvwzf7ZlJXoWfDaZiKTArnu/k7Vm3px8HeYfTNc9oxtvnk1FH76KUWffErC1K/x79v3gsdbFYWn92fxbU4xI6NDeaNVPL7V6Rk5RVFREfPnz6e0tJQhQ4bQycGNykpystm56hdS1/yGobKSpl260+Wqa0nq0g2zVcXds7aw4XAJs+/oSffEhjFdqrpMRgP71//FjpXLyDu4n4DwCDpfeQ0dLhuIX7D9nwur1cqaNWtYs2YNLVq0YOjQofj62jcPPt9g4u7UDDbrKni5RRwT46u3olnxtG8oeOcd4j/7lIDLL7e7dofocmD5U5D2IzS/3NYsHnr2ZasvxGKysn3VUbb8chTfIE/6j2pNYocaNIv/8Sd5r76CpURD+P33ETZ+fKNvFq8wmOnw0greurkTI5LrX++VqDtODxmtz7qKpDiTBA0hnKx06SEqdxQQ83RPVO62b0SKopD/xhtoZs0m9u23CRpynUPntpitfP/eNiq0BkY+0wNv/2rcRGiz4ItLIa4bjF4I1XjiXP7X32TeeSfhD9xPxP33V6u29zPyeOdIHu+0TmBMTKjd0zf27NnDTz/9RGBgICNGjCDSzrn3VouFQ9s2sXPlLxzdtR3vgEA6XjaQTldeQ3BUNAAWq8LD321nZWo+08Ync2lL+5bobWjyDh2wTSf7ey1Wq5VWvfrS+arBxLVuZ/f/v4MHD7J48WI8PT0ZPnw48XY+UTdbFSYfyubrrCJmdmzKVeEX7vtQFIWsBx+kcuMmmi5ZjGfCRbyRTF8OvzwOFYXQ7wno8xC4O7bjdGl+JWvmpZO1T0OL7pFcMqKlw83i1ooKCj/5lJKZM/Fq1ozolyfj27WrQ+dqCLYe1XDz5//w84OX0CHOyfuviEbjZMhoG0roqDYSMuwgQUMIJ7IaLeS+vhH/XjEEDfp3CdjCTz6l6JNPiH7xBUJGjXL4/Gvn7yd1bTZDn+hOVFI1pvuYjTBjMOhy4Z514HvhXg5Tbi5HbhqKd4cOJHz1ZbX2K1hTUsYtOw/xWFI0jzeNrs6H8m+JZjOrVq1i48aNdOjQgSFDhuBl567opXm5LP3gTQoyDhHTsjVdrrqWVr0uwf2UnhJFUXjm+z3M33yMz8bIMpenqiovszXIr/qF0rxcmif3YtB9j+DtZ1/vgFarZeHCheTk5DBo0CBSUlLsCiyKojBhzxHWl1awMrkViT4X/jqw6HQcGTYcN38/kubNw83Or50aMVbYpiOu/xTCW9qaxRMd6wNQFIX9m/L5e9EBLCarrVn80jiHm8X1e/eS++JL6HftInjkSCIfneT8jQ7rgVkbjvLy0lRSJw/C011uDoX9qvYWUzxbQoajJGgI4UQVm/LQfH+A6CdScA/1BqBk5kzyX3+DiEmTCL/7LofPfWBzPiunpdLvllZ0HFDNp8W/PmmbV377CojvfsHDFaORo+NuxVRQQNMli3EPufBUmhy9kSu3pNPJ35c5nZtVe/UgqPmNKcDBzRtY/tkH+AQEcs0DjxLbqu1Zj3vz1318seYQ7wzrxHCZQnFWitXK/o3/sOqrj/EOCGDIpKeJampfk3dNg6PWZOaqLfsJclfzU7eWeFfjh7p+714ybhlF0PXXE/PKy3bV6xR5u23N4tlboNutcOXkaoX6s9FXmFi/5CBpf+cS1dTWLB4eX4Nm8e++o/D9D1D5+BD11FMEXju4UTWLP71kFzsytfz68KWuLkXUQ1X7SiielYZPm1BCR0vIcIQEDSGcRFEUCj7ajjrIi/DxtqbD0iXfk/vMM4ROvJ3Ixx93+Ad8SW4FC9/cQtNO4Qy8vZrTWvYsse3+Pfhd254Z1ZD3+uto5n1H0uxZ+HTufMHjjVYrQ7cfJMdgYlVya8I8q9+0ffDgQZYsWYKHh4dDU22sFgvr5n3LlqVLaJHSm0H3PYKX79lXjvps9UHeXp7O89e1Y+Il9m022BiV5uex9IM3KM46xhW330uHywbWaCrcyJEjibBjJ/ndZZVct+0AI6JDead19UJh6eLF5D77HDFvvEHwTTfaVatTWC2w5Rtbs7ja07ZHTcfhDjeL5xwoZfXcdErzK+lyRQIp1zXFw8vBZvH8AvJff52yFSvw69vX1izepIlD56pvbvjkL1pEBvDeiAt/PxPiVCdChnebUMIkZDhMgoYQTmI4pqPws52ETWiPT+tQdCtXkv3IJIJvvpnolyc7HDKMejOL3toKisKwp5Lx9K7GzXzRAfhqALS6Gm6eVq2bHd2vv5I96VGinnuO0LFjqlXbCweymZ5dxA9dW9A9qHrLw1qtVtauXcvq1asdbh4u15Tw85S3yNm/l36jx9P9upvO+fmdveEoz/2wh4evaMmkgTXfh6OxMBuN/DnjK3b9vpz2/a/kion34OHlbdc5CgsLWbBgAaWlpVx//fV07Nix2q+dk1PMY+mZfNS2CSOiqzc6kPPMs+h++YWk+fPxbu2i/9dlebZm8dTvodkAuPZ9CLNvVOgEi9nK9lXH2PJLBr4BnvQb1YqkjuGOl7Z6Nfkvv4K5uJjwe+8l7PYJ1V6yuj4yW6y0f3EF/xvURh4wCLucDBmtj4cMmXbnMAkaQjhJyfx0DEd1RD+eTMX6f8i6514CBl5J7DvvoLJz9+RT/f5tGge3FTL8qWRCY6pxM2+sgK+vAMUCd/4JXheedmE4fISMYcPwH9Cf2Pfeq1YoWlpQyp2pGbzaMo47qrlKkDOWQ81M3cXPH76Nys2N6x55kvg2516y8scd2Twyfwe39U7ixSH2NzgLSF3zO79N/YyQ6BiGPPo0ITEX3n/lVEajkaVLl7J79267litWFIVH9mXyU4GGX7q3oq2/zwVfY62qIuOWUSh6PUmLF6H2d2zKkVMcWAXLHoWyfFuzeN+HwN2x/pHSgkrWzksnc6+G5t0iuHREK/yCHWwWr6yk8NNPKZnxLZ5Nk4iZPBnf7heeVlkfHcgvY+AHa5l3Zy96N3d8NS/RuOjTSyiaKSHDWSRoCOEElgoTua9vJOiqJNSBhRy7fSK+KckkfPJJjZ4Y6oqqmPX8evqNrGZfhqLA93fD3qW2kBHZ5oIvsVZWkjFyJIrFStKCBaj9LxxmDlXquXrLfi4PC+TLdonVuoHPyspiwYIFDm/wplitbPppMX9/N4v4dh249qEnzrsc6x/78rlr5lZu6BLHO8M64eZgU62AwmMZLH3/dSpKNQy6dxIte/ax6/WnbsAYHR3N8OHDq7UBY6XFynVb92OwKixPbkWA+4UDuzEjgyPDhuPXty9xUz5wbbg0VsKat2D9JxDaHK77AJIuvFT02SiKwoEt+fy14ABmk5XeNzanfb84h7+u9fv2kfvii+h37iJ4+HAiH3sUdQ02xayLftyRzcPf7WDni1cR5NO4l/kV1aPfr6FoZireLUMIG9NWQoYTyGdQCCeo3JIHKlAHl5F59z14t29H/Icf1nhaQupfOXh6u9OmdzVXSNo6HXbNhyEfVStkKIpC3uTJGLOyif9wSrVCRqXFysQ9GUR7efB+64Rq3cgVFRUxc+ZMAgMDufvuu+0OGfrycn5491X+mvctPW4czrDnXjlvyNhwuJh7Z2/jiraRvHVzRwkZNRTRJIkxr08hqVM3fnr/dVbPnIrFbK7261UqFcnJyUycOJHy8nJmzZqFwWC44Ot81W5M7dCUfKOJR/dlUp3nYp5JScS8/hplK1agmTmz2jXWCk9fGDgZ7l4L3kG2FeB+vB8qS+w+lUqlolVKNKNf6kXLlCjWfrefxW9toTCzzKHSvNu0IWnuXKJffAHd8uUcuvY6tEt/rtbnuL5IzdERH+IjIUNUi4SM2iGfRSFqSLEqlG/Mw7uZL1n334VHfBwJn3+Om8+Fp3qcj8VkZe/fObTpHV29JtCc7bZVplLugE7Dq3WN0gUL0f74EzEvT8arZcsLHq8oCk/uz+RolZGpHZLwr84TZqORBQsWEBAQwNixYwmyc4nN/MMHmf30w+TsS+OmJ1/kklvGnXcn611Zpdzx7RaSk0L48JauuEsDn1N4+fpy3aSnuOy2O9m+fCkLXn6GspIiu84RGxvLuHHjKCsr46effqrWTW0zXy8+bNuEpYWlTM2q3vUCr7qK0AkTyH/nXSq3bberxloR1d628tt1H0DaUvgkGXbMs41A2snbz4PLxrRh6OPdMJusLHxjC38vOoBRX/3gd4JKrSZk1CiaLfsZv549yHniCTInTsR49Kjd56qL0nJ0tIupxjLgotE7GTJaSMhwNvlMClFD+v0aLCV6tN9PQR0QQJOpU1EHBNT4vIe2F1BVZqJDv2rMia/SwIJbbTc0V79erfNX7Ukl/9VXCR51C0FDhlTrNXNyS1iYp+Gd1vG08btwkFIUhWXLllFSUsKIESPw9q5+M7GiKOz6bTnzXngCb/9Axr75Ic26pZz3NQcLyrjtm020iPTnq3HJeHs43hsjzqRSqeg2+AZGvvQmuqICZj/1CEd377DrHOHh4dxwww2kpqaycePGar3m2ohg7k6IYPKhbLZoK6r1mshHJ+HTuTPZkyZhLi62q8Za4eYGybfDA5ttTeI/3AMzr4eigw6dLqZFMCOeSaHn9U3ZvSabeS9v5Mgu+4LfCR6RkcS9/z4JX32J8egxDg+5nqLPP0cxGh06X12gKAppuTraxUrQEOenP6Cx9WS0CCFsrIQMZ5MeDSFqqPCr7VTtOYhhz+ckzZmDR4xzNoJb8s5W1B5u3PDIBXb1VRT4bjQc/cc2RSMk8YLntmi1HBl6M+qQEBLnzsGtGlO8dpVVMmTbAUZGh/J2NZcc3bp1K0uXLuWmm26iczWWyz3BZNDz29efkrbuTzoPHMyA2+7E3eP80x8ySyoZ/sV6gnw8mH93L4J9Xb+ajtlqJq8ij6zyLLLLsskqz8LDzYP4gHji/eOJD4gn3CccN1X9+8FWqdPyy8fvcmz3TvqMGEPPG4dXa3PHE5YvX86mTZuYMGECCdXY0dtkVRi6/SDZBiMrk1sTXo2llE35BRwZOhTv1q1I+PrrGi3K4HQHf4OfH4WyXLj0cbjkEYebxbWFVaydl86xtBKadbU1i/uHONgsXlVF0WefUTx9Bp6JicRMfgnf5GSHzuVKeVo9vd74na/Gdeeq9vZtIioaD/0BDUXfpuHdIlhCRi2RoCFEDRgyiyn4JBXjgSUkvP8InklJTjlvUVYZ81/dzKC7O9C8a+T5D87aAlOvgOEzoP1NFzy3YrWSdd/9VG7fTtPFi/GMv/CISenxTdSCPdT81LV6m6jl5uYydepUunTpwpBqjpgAlORks/T91ykt+D975x0dVfW14WdqZtJ7r7TQe0cQRRFFEOmIShOxd8Xys2D77L0gHRFpUgRFBVF67yVAaOm9TZLJ9LnfHwMBhMy9CYMU77MWC0jOPfdMMuW8Z+/97jx6j3+MJt1vEr2moMLMkMlbAFg0oQvh/rWzYa0rgiBgsBjIqswiqyLrgr/zjHk4BAcASoWSCO8IbE4bRaazJ89apZYYvxhifWOJ8Y1xiZBzhIiPRppt8JXA6XSwdfF8tiyeT1Lrdtz+2LPofaVF8xwOB7NmzcJgMDBhwgR8fMQfZ67Fyi07Umnuq+dHic0hjVu3kjF2HKEPTSDsiSckre1fw1oF6z+EzV9AUJIrtSqpbo3lBEHg+K4CNiw8ht3qoPNd9Wh+Y2zdi8WPppL3+uuY9u4lYPAgwp99VlIDz6uFv47kM3bWTjZOvInYoNrZZ8v8N6gWGfUDCLmvqSwyLhOy0JCRqSNOs5msZ6ai0DcidEw8+uYX70hdF9bOPULa/iLuf7crSrFN/dKHXNGMJ/aAm9qFMxRNmUrhJ58QO/lb/Hr2FB3vFARGHzjFdoORVe0bEa8XPyk1mUxMmTIFnU7H2LFj0YhEI85wdMtGVn33OT5BIfR/5iVC48SjM4YqG8OmbKG0yspPD3UlLvjybCrsTjvrstaxJ3/PeYLCaDubyuOv9SfW7xzB4HtWNET5RKFRuX4OJruJnMqci4qT7MpsTHZT9ZxBXkHnzXlDzA20DW97VVn1ntq7i5VffYxWp6PfUy8S2UBaDwuDwcB3331HVFQUI0eOlGR1vKGkgqH7TvBMYgTPJ0mLHhZN/o7Czz4jbsp3+PboIemaf5X8FPjlacjcCq1Hwq1vgU/d7FgtVTa2LDvJofXZhCf40XNkY8Li65bKKTidlC1cRMHHH6NQq4l4cSL+/ftfVc+9mvhyzTGmbTzF3tdq32xS5vrHfLyUolmnRca9TVFoZJFxuZCFhoxMHRBsNrIeexJ8+qFrHkTY2M4em9tisjPrxU207R1Ph74iTaaMxfBJE7jpZVfqhQjGbdvJGDOGkPHjCX9afDzAl+n5vHMyl+9bJNE7VLyQWxAE5s+fT3p6OhMmTCBIwimow25j/Q8z2f3bcpK7dKf3hMfR6sUFg9Fi597p20grMrJwQhcaRlx6bcw/KawqZPGxxSxKXURBVQFxfnHE+8VfEH2I8YvBX3vp+eCCIFBiLjlPeJwRImmGNApNhTQIbMDw5OHcWf/OqybiUV5UwC+fvk9B2gl6jnqQVrfeLmmDd+LECebMmUPPnj3pKUH4AnyalscHp/KY27IeN4eI/8wFp5PMhx/GvHcfSUuXoImOlnSffxWnE/Z8D6tfA4UKbnsHWo2oc2fxvJMG1s49QkmOkZY3x9GxX5K0Zp8XwV5YSP5771P+6694d+5M5Ouv4ZV0dTfAe/iHXZRV2Zj3oOfem2WuD8zHyyiefQhtUgCh98ki43IjCw0ZmVoiOBzkPP8CVSll6FuPJuLZdmjCPHeKvv/vLDYtOsb973YVb8q18TP4+1145rDoCaitoIBTAwfh1aAB8dOnScpX31RawZC9J3gsPpyX60vbnG3cuJE///yTESNGkJycLDpeEASWf/wuJ3fvoOf942h9252SNqgWu4MHZu9kT0YZP47vRMvYQEnrk4IgCOzM38mCowtYk74GjUpD33p9GZY8jMbB4rbBlwun4GRb7jYWHF3A35l/o1Pp6Fe/H8OSh9EwSNw17HLjsNtYN2cGe35fQevb+nLz6AmS6jbWrVvH33//zahRo0iSsIF1CgL37j/J3ooqVrVPJlYnXo/jKCtz1SWFhpLwwxxJdUlXhMoC+OMVOLAQEru70qlC6/a7dTic7Pszkx2/nELnq6H7sEbUay2tueZFl7ZhI3lvvok9P5+QCQ8SMn78VftzvPHDv7mlSQSv3tn0Si9F5ipCFhn/PrLQkJGpBYIgkPfGJMoWLSLw3q9RR4QQNq6FR+efN2kbwdG+9HmwufvBTgd80Qbiu8DA79zP63CQPmoUtvQMkpYuQR0aKrqWfIuNW3YepZG3jgWt6qOWkOudlpbG7Nmz6datG7fccovoeIAdyxezfu5M7nrufzToIO300e5w8tiPe/jraAGzx3T0WNffSmslK06uYMGRBZwwnCApIIlhycPoX78/flrPR0suhTxjHj+l/sTiY4spMhXRLqIdw5OH0yu+V3WK1pVi/5rfWT31a1rcdCu3jn9MVGw4nU4WL16Mn58fPXv2lOROVmKzc+uOo0R4aVjWpgFaCYLGdOAg6ffcQ+DQoUS++j/Jj+eKcOIvV7F4eTbc8Azc8DRo6lZ7VF5kYt28VDIOFZPUKpTuwxrhF1y3uZxmM0XfTqZ4+nS0cXFETnoDn44d6zTX5aLCbKPFG6v4eEgrBrWT0OhU5j+B+UQZxbNkkfFvI/+UZWRqQeEnn1K2YAHhL76Lo1yFb2fPOEydITu1jNK8KlrcKMHS9vgaKEt39c0QoWLNGkw7dxH90UeSRIbdKTDhUBpKYHKzBEkio6Kigp9++omEhARuukm8gBsgK+UgG+bNpsNdgyWLDKdT4MUlB/jzcD7fjmzrEZGRWprKW1ve4uZFN/P+9vepF1iP6b2n8/NdPzOyycirTmQARPpE8libx1g1aBUf3vghChQ8v/55bv3pVr7c8yV5xrwrtraWvfrQ5+GnOPj3n/z+zac4HQ6345VKJf379ycgIIDNmzfjEBkPEKxRM615EgcqTEw6niNpXfoWzQl79hlKf/wRa0aGpGuuGPVvhke2QNcnYMPH8G1XOLmuTlP5h+q587GW3Da+Ofmnypk3aRv71mTidDhrPZdSpyP86aeot3QJqqAgMu4fRc5LL2MvLa3T2i4HR/JcTQxla1uZM1SLjER/WWT8y8gRDRkZiRRNnUrhx58Q/uJElH7dMKeWEPlCRxQqzxUa/j7lACU5Rka83kk8fWjuUJc15oT1onnc6WPGIJjMJM6fJ2kdb53IYXJmAYtbN6BzoK/oeIfDwffff09xcTETJkzAT0IfEWNZKXMmPkFQdAxD/vcOSgmpXIIg8OYvKczanMZnw1pzV2sJgqwGbA4bq9NXs+DoAnYX7CZcH87gRoMZ2HAgET4RdZ73SnK89DgLji5gxckVmOwmesb2ZHjj4XSK6nRFLHSPbF7Pyi8/olGnbtz+2LOo1O5rBIqLi1m7di3169endevWku4xM7uIl1KzmNw0gQER4vVATpOJYz1vInDwICKef17SPa44BUfgl6cgY4urbqP32+AjfmBwMSwmO9uWneDA+mzC4vzoOTKZ8IS6bcgFp5Oyn36i4KOPUSiVhE+cSMCAu6548fWsTad4d+URDr15Gxq5Yed/HsvJMopmnhYZ9zdFIfdX+leRX4EyMhIonb+Awo8/IfSRhwkadi9Vewvw6RjlUZFhLLNwcm8RzW+MFf+gLk2DY6tc0QyRsZaTJ6naspWge0ZIWsfvhQa+zijglXrRkkQGwN9//01GRgaDBw+WJDKcDge/fv4BAHc+OVGSyAD4fM0xZm5K4827mtdZZJSaS/li9xfc8tMtTNwwEY1Swyc9P+H3wb/zcOuHr1mRAdAgqAGvdH6FNUPW8EqnV8iszOTB1Q/Sf1l/5qTMwer4dxuwNe7ag35Pv8ix7Vv45bP3cdhtbseHhITQqlUrjh8/TmZmpqR7jI4OYWBEEM8czSTVaBYdr9TrCbz7bgw/LcZpFh9/VRDeGEavhP5fwtHfXJ3Fd8+pU2dxL72aHiOSGfRCO5wOgZ/e28mGhal16yyuVBI0dCj1V/6KT/fu5L70EhmjRmM5earWc3mSlNxyGkX6yiJDRhYZVwHyq1BGRgTDr7+SN2kSQffeS+jjj2PclY/gEPDp6NkmUIc25qDSKEnuLGHenTPByx9aDBYdWjp/PqqgIPxuu010bLrJwhNH0rk9NICH46QVjR45coSNGzdyyy23kCixj8imhT+QdeQQdz45EZ9Aad78Mzae4rM/j/H8bcnc11nc9vZi7C3Yy+AVg5l3ZB63J93Oz3f9zLTbpnFrwq1olFe2rsGT+Gh8GJo8lMX9FjO7z2yahjTlk52fcP9v95Ndmf2vrqVhx67c9dwrnNq7k+Ufv4tdpNt0/fr1iYuLY9euXZSXl4vOr1Ao+LBRLDFeGh44mIZRQtpV0PBhOAwGyn//XfLjuOIoldD2fnhsJzS8DZY/BrP6QuHROk0XmRTAkJfb0+XuBqRszOHHN7Zxck8hdUlyUIeGEvPhB8RNn4YtL49Td91F4Zdf4bRY6rS2SyUlt5ymUXLa1H8dy0mDS2QkyCLjSiILDRkZN1SsXUvOxBcJ6N+fiJdfAsC4NRd98xBUfp5zW3E4nKRsyCa5UyReehELSpsZ9syBNiNB697a1FlVhWHpMgIHD0Lp5d7Byuxw8sDBNII1aj5rHCcp/aGkpIRly5bRuHFjunbtKjoe4MSubWxftogbht9PbFORgvfT/LQrizd/SWFCj3o80rO+pGvORRAEfkj5gTG/jyHGN4Zldy3jxY4vUi+wXq3nqhNWo6tXwpGVcOxPKDoO9su/CVMoFLSNaMsHPT7gh74/UGYpY+iKoazPWn/Z730u9dp2YMDzr5JxYB/LPnwLm6XmSIJCoaBt27bo9Xq2bt2K3S5+0u6jVjG9eRJZFisvHM0S3SxrExPx6daN0nnSUgmvKnzDXOYP9//sSp38thv89Y7rfaGWqFRK2vSOZ8TrnQiN8+W37w6w8tsDVJTULdLj260b9Zb/TPAD4yiaMoVTdw3AuHVbneaqKzaHk9S8Sllo/MexnDJQNOsg2gR/QmSRcUWRazRkZGqgascOMh4Yj0/3G4j97DMUarWryc+0g4Q92BKveuI9JaRyYncBv085yLD/dSQ0ViRdad8CWPqg62RTxPaydNEi8l57nfqrV4t2AP/gVC5fpRewsl1DmvuJ2/XabDZmzJiB2WzmwQcfRK/Xi15jKMhjzotPEtukBXc994okMfP3kQLGzd7BsA5xvHt3i1rnf1daK3lt82usTl/N/U3v56l2T3k+euF0uNyBStNdaW2laa5C/TP/NhZe5CIF+MdAUAIEJbr+BJ7zb9/wOvdQqAmDxcArG19hXdY6xrcYz6OtH0Ulocmjp8g8tJ9fPv+A1r3vpMNdg1C7aeRoMBj466+/iImJoUOHDpJ+7z/llfDY4QyWt2lAR5G0v4o1a8h69DESf/oJffNmtX4sVwU2M2z8BDZ8AoFx0PcTqC/NiOGfCILAyT2FbFiQisXsoFO/JFreFCveMLQGLCdOkPv665h27iLgrrsIn/gC6uDgOs1VG47kldPnsw0snNCFjkmX/34yVx+WUwaKZh5EG+dHyKhmKLWyyLiSyEJDRuYimA4eImPUKHQtWhD33eTqaEDxnBRsRSYinvJsZ+Zln+7B6XAy8Ll24oOn3Qpab9eJphsEQeDUwEFoIiKIm/yt27HpJgs9th9hQmyY5H4ZK1asYO/evTzwwANERYm7b9mtVua99jwWYyX3vvc5Oh/x+o/Mkir6frGBDonBTLm/PSoJ7lfnklqayrNrn6XQVMjb3d7mlgRplrui5B2EvT9C4eHToiITnGfqDxTgH32+aDgjJgITwGE9X4ScK06qis7eQ60/e11Ua2hzr2szeYk4BSczDs7gyz1f0iGiA+/1eI9Qfd0Ki+tCSXYWJ3ZtxW610b7f3Wi8arZZzcjIYPv27bRt25Z69cSjT05BoNu2w7T19+Hrpu7T6wS7neO39sanW1ei33671o/jqqLwqKuzePomaDEUbnvXFfmoA1aTna3LT3JgbRahsb70HNmYiMS6F4sbli4l/4MPUQDhL7xAwMC7L2ux+OJdWTy7aB8H3uiNn+76SYeUkYYlzUDRDFlkXE3IQkNG5h9YTpwg/d770MTFET9jBipfV3qS3WAh7/3tBParj28Xz3UWLsk1Mm/SNm4d15RGHUTqM3L3wXc9YNgP0KSf26GmvXtJGz6CuCnf4dujh9ux4w6eYnd5FRs7NsZHLf7GvHfvXpYtW0b//v1p27at6HiA1VO/4tC6NYx46yMiksTTnyx2B4O/3UKZycovj3UnwLt2m4YVJ1bw5pY3ifeP55Oen5DgX7e6jmrsFkhZDjumQeZW8I2EuA7nRCKSTv87DtQijRZrwlJ5jgg5R4CkbwabEZLvgA7jIKmnK2f/EtiRt4Pn1z2PSqHiwxs/pG2EtN+jJyjNy2H7z4vwCQii44ChaC/SN8Nht1OclcGprGwyMjPpdVsfgiWciE/OKODdk7ns6tqUMK3750zR5MkUTf6OhuvWogrwXITyiiAILvG76hXXv299E9rcV+fnSf6pctb+eISirEpa3BhL57vqoRVL66wBe3ExBR98gOHn5Xi3b0/kpDfwql/7FEgpvPVLCn8ezmfd83WL7Mhcu7hExiG0sb6EjJZFxtWCLDRkZM7BmpVN+siRqPz9SZjzParAwOrvGVanU7khm6iXO6LU1e0D92JsWJDKsZ35jHq3Gyoxb+/lT8Cx1fDUAVC5X0POxIlU7dpN/VV/uG2Ytr6kgqH7TvBt0wTulmAPmp+fz9SpU2nevDkDBgwQHQ+Qsv4vfvv6E2598HFa9hIvSgd4ZekBFu3KYsnDXWkeI30TaHFYeG/7e/yU+hN31b+LVzq/gl4tntZVI2UZsGsW7P7elQKVdKPL7Sv5dvi3GuNZKl2dordPg4JDEFzfJTha3wN6acX0F6OwqpDn1j3HvsJ9PN3uae5vev+/Zk1qyM9j28+L0Pv603HAELy8z6brHd2ygYNr/yQ39QjBMbGUlZYS3qEb/Ubci5dIrVGpzU6bzYd4NjGSxxPcO4jZCws5dnMvIp57luBRozzyuK44xiJY9Srs+9HVzPPOTyG8SZ2mcjqc7P87i20rTuGlU7k6i7cJq/NzxLhlC3lvTMKak0Po+AcImTBBtHastoyYspVAbw3f3ishOixz3SCLjKsXuRhcRuY09sJCMsaNRaHVEjd92nkiQ3A4MW7PxbttuEdFhs3i4MiWXJp2ixYXGaYyOLAI2o8RFRn20lLKV/5G0IjhbkWGzSnwyrEsOgf4MCA8UHS9ZrOZBQsWEBISwh133CE6HqAoI43VU7+m2Y29aHFzb0nXLN2TxdxtGbzRr1mtREZWRRb3rbyP5ceXM6nrJN7q9lbdRIbTCcf/hHkj4PNWsH0qNB8Ej+6AUcuhaf9/T2QAePlC+7Hw8CYY+wfEtIXVr8PHTeDnRyFnT52mDfMOY/pt07m/6f18tPMjnl77NBXWCg8v/uIERETSeeBwzMZKti2Zj9lYCcChdWv4Y/IXhCckMeKtj+j/7Cs06XwDGat/4a+fl+B0um8yF6RRMyA8iNk5RThEztHUYWH433orpT/OQxCZ95rBJxTu/hZGrXAJ48k3wJo3wWaq9VRKlZLWt8Rzz+udCEvw5/cpB1n5zX7Ki2s/F4BPly4kLf+Z0PHjKZo6jZP9+2PcvLlOc10MQRBkx6n/IJb0cllkXMXIQkNGBnAYDGQ8MB7BZCZ+5gw04eHnfd90qBhnhc3jncBTt+dhszho2l1CKta++a78/rb3iw41LF4MCgUBAwe6HTczu5ATVRbebhgj6ZRy48aNVFZWMnToULRacdctq6mK5Z/8H4GRUfQa97Cke6TmV/DykoMMbBvDiI7SaxLWZq5l6C9DqbBW8MMdPzCw4cDan7xWlcDmL+HLtvDDIDBkuk6Enz0Ct78PYY1qN5+nUSggvjMMmgbPpMCNz7u6RU/pCVNvdqXO1HJDqVaqeab9M3x202dsz93O8F+Gc7SkbpaptcU/NIzOg4ZjNZvZung+B/5exe/ffobTbqMw/RTbly1ErdVy031jaXrTraTv3Er6yZOi846OCSXLbGNNsbg9btA9I7Cmp1O1dasnHtLVQ1IPeHgz9Hje9Zz+pjMcX1OnqfyCddzxcAtun9CCwsxK5k3axp5VGTjq0lncy4uwJx6n3s/L0ISFkzF2HNkvvIC9uLhOazuXHIMZg8kmdwT/D+ESGQfRxMgi42pFFhoy/3mcVVVkTngIe14e8dOnoY2NvWCMcWsu2kR/NJHu7WRrgyAIHFyfTUKLUPxDRE7dBcFVG9CkH/i5r+MQHA5K5y/A//bbUQfVnFZTaLXx4ak87osOkeQyZbfb2b17N23atCEkJER0vCAI/DH5C4xlJfR7+iW3Rb9nqLTYeeiHXcQHe/POAGkOU3annc92fcbjfz1O+4j2LOi3gCYhtUwVMWTBskfgkyau09+4jjBuNUzYAO1Gi9oIXxF8w6H7s/DkPhgx35VCtexh12NY/Zor3aoW9IrvxYI7F+Ct8WbkypEsO77s8qz7H/gFh9B58AgqSor5e+YUGnbsytDX36PL4HuwWSys+OT/cNht9BhyDwmdu5OelSU6Z2t/b1r7eTMzu0h0rL5dO7waNrw2rW7FUHtBzxddgiMgDn4YCD+Ng4r8Wk+lUCio1yaMe97oRNMbotmy9DiL/m8neacMdVqaV716xM/5nqh338W4fgMn7uiLYfnyOs11hkPZrrU0i77G621kJFEtMqJ9CB0ji4yrFVloyPyncVqtZD32OJbUVOKmTcWr4YV2sbZ8I5aTBny7eDaakX+qnKLMSlrcKKHD9an1UHzMVRsggnHjRmxZWaKdwN89mYtKoWBiPWmPKyUlhaqqKjp06CBp/J7fV5C6dSO3PfwUwdHij1EQBCYu3k9BuYVv722LXsKHRpWtigmrJzDr0Cyebvc0n9/0Of7aWp5mHl/jKrA/8ZdrU/bMYRg4xSU2/qV6hUtCqXLVi9y7GB7f7XKn2j7NFeEoOFKrqeL845hz+xz61uvLq5te5c0tb9apgVtt8Q0MQu/rh1avR+OlIzAikqiGyXQaOAxTZQWGgny8AwJp0qYdRUVFGAzim9vRMSH8XVJBmsl9vxKFQkHQPSOoWPMXtrw8Tz2kq4vQhq5UqgGTXc/zrzu4mn7WIV1Mq1PTfWgjBr/YHqVSweIPdrFu3lEspjp0FlcoCBx4N/V+W4lvjx7kvDCR3Nder3Ojv5TccoJ9tET4e7buQ+bqw5JxWmRE+RA6urksMq5iZKEh859FsNvJefY5qnbuJPbbb9G3aHHRcZVbc1H6atA386wF6IF1WfiH6YlrIsHrfcc0CGsMCd1Eh5b+OA9d06boWrascczuciPzckuYWC+KYI20mpMdO3aQlJREaKj4zyEn9TDr5kynXd+7aNRJfM0Aszen8ev+XD4Y3JJ6YeLWt4Ig8MbmNzhYdJCpvacytvnY2qVKOR2w9j1XilR0W9ep7w1Pu3Lcr1VC6kPvt+HBtaBQwtSbYP+iWk2hU+uY1HUSb3R5g0Wpi5h1aNZlWeq5OJ0OSvNySGzdDp2vL5sXz6OytITKkiIqi4twnu72HRcfj5eXFyclpE/dFR5EoFrFbAlRDf9+/VHq9ZQtXHjJj+WqRaGA1iNc/Xca94NfnoKZfVyNJOtAeII/gye244bBDTm6NY8f39jK8V0FdessHhRE9AfvE/X2WxiWLSN9xD1YJUSu/klKjqs+498yNJC5MlgyyimaflpkjGmO0ksWGVczstCQ+U8iOJ3kvvoaFX/9Rcxnn+HTqeNFxzktDqp2F+DTIRKF2nMvF1OFleO7CmjeIwaFWG+I8hw48qsrmiHyAWrNyqJy/XqC7hlR44etUxD437FsmvnquD9aPAUKIDc3l8zMTEnRjKpyAys+e5/I+o3ofs8YSfPvzijlnZWHGdstiTtaSIuwzD86n9/SfmNSt0l0iJQWZanGWAxzB7uExk2vwD0LwfvyNvcSBIGiSgsGk0188KUS1gjGr3Gl2i15AH59ttadyAc1GsS45uP4fPfn7MzbeZkW6kKhUBIcHYPNbKbLoBGoNVrWzZnG0S0bCYyKRufrB4BKpSIpKYn09HRsNvc/R71KyfCoYObnlmASqSVQ+foQcNddlC5ahGC1euxxXZX4hMCAr2H0r2Aqhe+6w59vgLWq1lMpVUpa9YpjxOudiEj054+pB/nlq/2UF9W+WFyhUBA4eDCJ8+fhqKjg1MBBVPz1d63mSMktl+szrnNkkXHt4Tn7HBmZawRBECh4/30My5YR/cH7+N1cs9961d4CBKsDn04i/S1qyeHNuSgUCppIScfaNRvUOmg5THRo2YIFKH198e/bt8YxC/NK2F1exdI2DVBJPPnbuXMnfn5+JCcni45d/8NMHFYrdz49EZVa/C2mxGjlsbm7aRETwIu3N5a0nv2F+/lgxweMbDKSPol9JF1TTeYOWDQK7Ga4b2mdOylfDJPVQVZpFRklVWSWVJFRYqr+d2ZpFVVW18l8gF5DfLA38cHexAbrq/8dH+xNdKAeTR27MZ+H1gfu/s5VPP7bRMjeDUNnQ2C85Ckea/MY+wr38fz651nUb9Fla+ynUCho1/du5r36HH/PnoLgdJJ34hiVpSX0vP8BfIPOisCkpCSOHj1KZmamaBO/UdGhTM4sZHlBGcOi3AvJoBHDKf3xRyrWrMH/9ts98riuahJvgIc2wqbPYf1HcHCJq7N4w9o3tXQVi7fk5F5XZ/F5k7bR4c4kWt0Sh6qWz2Vd06YkLf6JnJdeJuuRRwgZP56wJ59AIfJeYjDZyCo1yY5T1zHWzAqXyIg8XZMhi4xrArmPhsx/jsKvv6boy6+IeO1Vgu+5x+3Y/K/2oPLTEjqqmcfu73QK/PDqFmIaBdJrVFP3gx02+LQ5NL7D5X7kbl6LheM9byKgfz8iXnrpomPK7Q66bTtMt0BfJjdLlLRes9nMxx9/TLdu3ejZs6fbsVXlBqY8MppuQ++lQ/9BonM7nAJjZu3gYLaBX5+4gagAcSvaMnMZQ34ZQrh3OLNum4VGqs2sIMD2KfDHKy572MEzIUBCfYwbSo1WFu3KZNWhfDJKqiioOBs10KqU1SIiLsglIuKC9Vgdgkt4lJwWJKVV5JSZcThdb8VKBUQF6KkX5kP/VtH0axWNTnOJH6jZu2HhKLCUw8Cp0EiazTBAkamIISuGkBSQxJRbp6BWXr7zqYriInb9ugxDQR6BEVFUlBaj9dLTacAQAiLOiv0tW7ag1Wpp27ataJrMiH0nKLM5+K29uGNY+n0uR7eEOd9f2gO51ig+4eosfmodNBsIff5P1HSiJqxmO9tXnGL/X5kER/vQc2RjIuvVvjhbEARKZsyg4JNP8W7XjpiPP0IdVnO38y0nihkxdSurn+5Bwwi/Oq1d5urFmllB4fQDaCJ8CB3bDKWXfE5+rSD/pmT+U5R8/z1FX35F2FNPiYoMwSlgyzPi3Sbc7bjaknGomIpiM81vvNDd6gKO/AqVedB+nOjQij/+wFFaSuCw4TWO+SQtj0q7k9fqS+9svm/fPhwOh6QO4IfW/glAs57STkW/+us4G44V8v3YjpJEhlNw8uLGFzHbzXx848fSRYalApY/DoeWQudH4dZJde6DIQgC+7IMzNmSzor9OSBArybhdGsQelpMuERFuJ8XSrG0uNPYHE5yy8xkno6GZJRUcSDLwPM/7eftXw8ztH0sIzslkBhaR/ermLYwYR0sfQh+HOKyPO35kquQXIRQfSgf9viQB1Y9wFd7vuKpdk/VbQ0S8AsJ5cZ7x1b3frGazez4+Se2Ll1Ax7uGEBTlet42atSI9evXEx4eTlycewvk0TGhjDpwir3lVbT2d++uFnTPCLKffgbLsWMXNYa4bgmpD/f/DPsXwh8vwVcd4ZbXod2YWncW1+rU3DCkIcmdIlk79wiLP9xFs+4xdBlQDy9v6a85hUJByLhx6Fu2JOuZZzg5cCCxn3yCdw3pmym55XiplSTV9TUic9Uii4xrG/m3JfOfoWzpMvLf/T+Cx44lZMKDouOdFVawC6iDxW1Za8PBddmEJ/gRkSghxL9jmqu7b2Rz0aGlc3/Ep2sXvOolXfT7qUYz07IKeT4ximideA8McG2qd+zYQePGjfH3d79ewelk3+qVJHfpjre/+AnmhmOFfLYmlad6NaJ7w5pPKs9lyv4pbM7ezORbJhPpI/HEteAwLLgPKvJgyGxoNkDadf/AZHWwfF82P2zN4EC2gdggPc/c2ogh7WIJ8b00lxuNSkl8iDfxId6cWzqfXmxk7rYMFu7MZOqGU/RoFMZ9nRO4uXE4KokiphrvYJcN7qZP4a+3IXM7DJoOvuI/+/aR7Xmi7RN8uutTWoe3pmdcz9rduxacERmCIKDV6eg4YAg7li9m27KFdOg/iJCYOIKCgigoKGD79u2iQuOWEH9ivDTMzimitb/7tDG/Xr1QhYVSOm8eka+95rHHdE2gUECrYdDwVvjzdfj1Gdg3D+78TNL7zz8Ji/dj0MT2HFyXxdafT3JybyHdhzakQbvwWhVre3foQL0lS8h+9jnSR48h/JmnCR57ofFDSk45jSP9UHsi7VDmqsGadVpkhHufTpeSt63XGvIrUuY/Qfnq1eS+8gqBQwYT/vxz0vozlJgBPCo0DIUm0g8V01yKpW3hUUjbIMnS1nToEKZ9+wgccXFLW0EQeO1YNjFeWibESdvUA6SlpVFUVCSpCDxt324MBfm07l1zfcgZcspMPDl/L90bhvH4zQ0krWVLzha+2fsND7d6mK4xXSVdw4GfXDavKq3LiakOIuNkYSVvrkih07t/8uKSA4T6apk5ugPrnr+Jh26sf8kiwx0JIT68fEcTtr7Ui4+GtMJgsjH++530+OBvvv77OIUVtbQBVSpdvTfuWwYFKa5C4Mwdki4d02wMN8XdxMsbXyazIrP2D6aWnHmNary86HjXYALCozi2fTPFWRkolUpatmzJoUOHqKx03y9EpVBwf3QoS/NLKbO5t2BVaLUEDRmCYdnPOCqNHnss1xTewdD/SxjzG5jLXdbPq18Da+1/HkqlgpY3xXHP652Iqh/AqmmH+OXLfRgKa9lUMiyM+BnTCRk7loIPPyLrscdxlJ/fjFEuBL/+sGZVUDjtoEtkjG2OUieLjGsRWWjIXPcYN28m55ln8butN5FvvCH5NO2M0FAFeU5oHNqQjZdeTYP2EeKDd0wHnzBo0l90aNn8+agjIvC76eKFzX8UlbO2tII3G8agq8WJ344dOwgNDSUxMVF07N5VvxKeVJ/IBuK58C8vPYBOreSzYa0lpRflGfOYuH4iXaK78GBL8WgUACf+hsUPQOM74YE/IVSaoDnDycJK7pu+jZs/XsfSPVmM6BTPuuduYuaYjtxUl4jCJaDTqBjcLpafH+3G8se60a1BCF+sOUbX99bwzIK9lBpr6ZRU70ZXM8LAeJg7CErTRC9RKBS8fcPbBGgDeHbts1gcdet1UBfUWi0dBwxC46Vj8TuvkbZ3F61bt0apVLJnzx7R6++JDsYhwIK8EtGxgUOH4rRYKF9xac3jrnkSurqKxW96CbZOdnUWT11Vp6l8g3TcPqEFdzzSkpI8I/Pe3Ma+vzJrZYWrUKsJf/YZYr/5hqodOzg1aDDmFJc1r9Xu5HhBhVwIfh1hza6kcPpBNGF6WWRc48hCQ+a6xrR3L5mPPY53587EvP8+CpX0olp7iRmln8ZjjYAcNieHN+XSuGsUGrE5LZWutIW2o0DtPs3JUV6OYcUvBA4belFnFrPDyWvHs7kp2I/eIdI/iMvLyzl8+DAdOnQQFWeGgnxO7tlJ6959RceeKKxk7dFCnu+TTLCPeAqXzWnjuXXPoVVpea/7e6gk1BVQnuMSGfVvgrsng1a88/m5/Lo/l/5fbSK71MQnQ1ux5aVevHR7E+JDajfP5aBlbCAfDG7Ftpd7MbFPY/4+WsCdX25kX2ZZ7Sbyj4J7FoAu0FUobjOLX6L155Oen3Ci7ATvbX+vTuuvKyqVmla33E5YYhLLPnyLnJT9tGjRgp07d+IUaTwXptVwZ1gAs7OLcYpsbjWRkfjdfBOlP877V5oVXtWota56nke2QHB9V33PwlFQnlun6ZJahjLiNVdn8Y0Lj7Fq2iGs5to1+vO7+SaSFv+Eys+PtOEjKPvpJ1LzyrE5BJrKHcGvC6zZlRROO4AmVE/oOFlkXOvIQkPmusV8NJWMByega9KE2C8+R6GVVpdwBkeJGbUHoxkluUbMRhsN2kooLj+wCKyV0G606FDDsmUIdjuBgwdf9PvfZhaQY7HyVsOYWuVG7969G7VaTatWrUTH7v/zN7z03jTu1kN07NytGQT7aCX3y/h016ccKjrExz0/JkgXJH6BwwaLRoPay+WwJEWYnMZqdzJpxSEe/XE3PZPDWP74DQxsG3vprk+XgUBvLQ90r8cvT3QnzM+LIZO3MGdLWu02x/ogGPq9q47lj4s7lf2TJiFNeLnTy/yU+hPLT/y7p/5qrZb+z75MvbYdWf7xu0TotRgMBo4dOyZ67ZiYUE6aLGwodZ9qBRA0YgSWY8cw7drliWVf+4TUd1lBD5wG6Zvg646wfaqr6WUt0erU9BjWiNvGNyf9UDGL/m8nxdniv5Pz5oiLI2HejwQMGEDu/17FMOl1vJw2GkfKblPXOmdEhloWGdcNstCQuS6xpqeT8cA4NLExxE3+FqVe3NHon9hLzB6tzygvduUlB4SLrEUQXEXgjW6HQPeFroIgUPrjPPxuvQVN+IUCJtts5Yv0fMbHhtHAW/pjcTgc7Nq1i5YtW6LTub/ObrNx4K9VNOt5Cxov92OrrHYW7cpkWIc4vNTim/dVaauYkzKH5zo8R6swccEDwOrXIXsXDJlVqy7fuQYTw6ds4Yet6Uzq34wvR7TB9xooPIwJ1LNwQhfu6RTPqz8f4qkFezFaanFKHN0a7vgAds6AfQskXTKw4UD61+/PW1veIrU0tW4LryMqtYY7n5pIo843sHnmtwT7+7Fjh3idSYcAH5r66JgloVO4d+fOaBMTKf1xnieWfH2gUEDLIfDYDmg+EFY+B9Nvhdz9dZquQbtwhr7UAaVKwU/v7+TotrxaXa/08iLqzUlEvfd/+G9dy6sHfsLbQ9FnmSuDNaeSoukHUIfoCJPTpa4bZKEhc91hy88nY8xYVD6+xE+disqvbqdc9lIzKk8KjUIzGp0KnY+IxWPuPsg/CB3Gis5ZtXUr1rQ0gmooAn/zRA5+ahXPJNbOE//o0aNUVFRIKgI/tnUjpopyWt0q3uRsxb4cKi127uko3jQuzZDGa5tf47bE27insXsr4mpSfoatX0PvtyHu4t3eL8aGY4X0/WIjeQYzCyZ0YVTXxFpFf640WrWSN/o344sRbVidks9dX2/ieEGF9AnajoJW98AvT0F+iuhwhULB/zr/jzj/OJ5Z+wyV1tqdSF8qSpWK2x97hqbdb6LqyH6OHz9OSYn7+guFQsHomFD+KDKQbXZf06JQKgkaMZzy1auxFxZ6cunXPvog6Pc5jP3D1U18Sk9XbxpL7Z8DgRHeDH6xPfXbhvPnzBTWzj2C3Va7KEnggAEs7v0A7U7tovSHubVeg8zVgTWnkqJpB1AF6wgb1wKlXhYZ1wuy0JC5rrCXlpIxdhwCAvEzZ6AOCanTPILNgbPcijq49pGQmigvMuEfohffwBYcdv0dL+6sVPrjPLQN6l/UW35zaSU/F5Txv/rR+EmIHpzLjh07iIuLIzJSXKDsXbWS+BatCY523xdEEAS+35LOzcnhxAW7r3UQBIEX1r9AmD6MSV0nSdv0Fx2HZY9C0wHQ6SHx8biaJ37+5zHun7Gd5jEB/PJEd9rGS0jPEll7mbmMg0UH+f3U70w7MI03Nr/BA6se4PbFt9N/WX8e+fMR3t32LrMPzWZNxhqOlhzFaLt0l6P+raJZ/lg3FED/rzaxfF+OtAsVCuj7MQQlwcL7XX1HRNCr9Xxy4ycUmYr4cOeHl7bwOqBUqrjtoSdp1a4d2O38tniR6DWDIoLwVin5IadYdGzAgAEoVCrKFi/2xHKvP+I7w4T1cPMrrgjsN53h6O+1nkajVdFrVBN6jkzmyJY8lny4m/Ii6a5UgiCw2LcRGTf3J/+DDzDt3VvrNchcWWSRcX0jCw2Z6wZHZSWZ4x/EUVZG/PTpaKKk1QBcDHupy1FHHew569LyYhP+oRIiJGXp4BshWsBsy8uj4q+/CBox4oKNuN0p8MqxLNr5ezM4onYb58LCQk6dOiUpmlGQdpKc1MO07n2H6Ni9mWUcyinn3i4JomN35u/kcMlhXun8Cj4aCQ24rFWuDbJfhMuaU4IwKTFaGT1rR3Uvj5mjO0gqTr8YdqedNelrmLB6Al3mdaH7gu6M+HUEz69/nhkHZ5BSnIK/1p9bE26lW3Q3VEoVO/J28PXer3nq76cYvGIwnX/sTI/5PXhh3Qvsyt9V50LkBuF+LHu0G7c2jeCJeXt47eeDWOwSTom13q56jYo8V3NDCfdPDEjkoZYPseLECopN4pt3T6NQKunz4OPEBgdwLD2THb/+7Ha8j1rF0MhgfsgtxipSQK4KCMD/zr6ULliIYK9dwfJ/BrXWZZf8yFYIbQTzhrl61pRLFLinUSgUNOsew6AX2mGpsrHw3R2k7RdPcQPILDFRYbHj9cgT6Js1I+upp7GXltbl0chcAapFRtDpdClZZFx3yL9RmesCp9lM1sOPYE1PJ+H72XglXbxpnVSqrW09GtEwk9BCQoSlNA0CxTfjlWvXAhBw110XfO/7nCKOGM2sbNcIZS1TgHbu3Im3tzdNmzYVHbtv1Up8g0Oo366T6Ng5W9KJC9Zzo4TmfPOPzCcpIIlOkeLzIgjw67NQchLG/wU6cWctk9XByGnbyC838/3YjpIbBv6TwqpCFh9bzKLURRRUFdAyrCXjW4wnzi+OWL9YYnxjCPCq2QlHEASKzcVkVWSRXZlNWnkav536jdG/j6ZhUEOGJw+nb72+0sTWOfh4qflsWGvaJwbz1ooUSoxWvhzRRjwyFNoA7voKFo2CuM7QWTwyNKDBAL7a+xVLjy/lgRbiPV88jUKh4O5RY/nyyy9Z8/MSAsPCaNix5mjgqJhQZmQXsbLQwAARER40dCiGnxZTtXMnPp07e3rp1w/BSXDvYji4GH4/3Vm816uuHkC1MGMIi/djyEsdWDP7ML9+s5+2fRLo1C8JpRtL7pRcAwBNE0II+uxTTt09kJznniduyne1chmU+fex5hrPioxxzVHWonO8zLWDLDRkrnkEm43sp57GdOAA8TOmo2vS5JLndJSYQaVA5V+3E+5/IjgFV0QjRIJwKU2DoETRYdaMTDTR0ah8fc/7erHVzgen8hgRFUwb/9rZsVqtVvbu3UuHDh1QX8Qq91wsVUZSNv5Nx7sGoxT5QC8xWvllfy7P9m4k2jejoKqAvzL+4rkO0horsvt72Pcj3P0dRIiLI4DXfj7IqaJKlj3ajcaRtfPeFwSBnfk7WXB0AWvS16BRabgj6Q6GJQ+jSUjtnnsKhYJQfSih+lBah7cG4OFWD7M1dysLjizgnW3v8MmuT+hXrx/DkofRIEh6LxCFQsF9nRMI9tby6I+76ZAYzKiuieIXNhsAmY/Cqlcgpq1orUugLpA+iX1YdHQRY5qNkWY/7GFCQkJo0KABOUoFv3/zGaHxiQRFRl90bLKPjq6BvszKLhIVGrrmzVEFB2PctFkWGmIoFNBiMDS4BdZMgt9ecFl09/scoiQaOQA6Hw13PNSCPasz2LrsBPmnDPQe1xzvGt6LU3LKCfX1ItxPB36RRH/0IZkPjKfo28mEPfaopx6djIex5RkpmrZfFhn/AeTUKZlrGsHpJOell6nctInYL7/Eu21bj8xrP21tq/BQUzajwYLTLkhLnSpNlyQ0bFlZaOMurIt4/1QuTgReqlf71LEDBw5gtVpp37696NhD6/7CabfT4ubbRMcu3JkJChjS3r2LFsDi1MVoVBr61xdvVEjOXlj5vMsGuNVw8fHAgh0ZLNqVxTsDWtRaZKQUpzBoxSDG/jGWoyVHebb9s/w55E/e6PpGrUVGTSgVSrpGd+Xzmz/nj0F/MLLJSFanr+bu5XczYfUE8o35tZqvb8soxnRL5O1fU9idITGl5NZJENPOZRNsFE9hGd54ODnGHDZkb6jV2jxJhw4dqHIKqEPCWPHJ/2Gz1txQcHRMKFsNRo4Y3dcCKJRKfLp0wbh5s6eXe/2iD4Q7P4Vxq8FudRWLr30PRFLVzkWhVND2tgTueroNJblVLHhnOznHyi469p8dwX27dSP08cco+vprKjdsvLTHInNZsOUZKZy6H1WAlywy/gPIQkPmmkUQBPLeeovylSuJ+fADfLvf4LG57SUedpw6XdwYECYS0bCZoSJHWkQjKxNNzPlC40BFFXNyink+MYowbe3evAVBYMeOHTRq1IjAwEDRsftW/UqDjl3xDQp2O9bhFJi7LZ07W0aJ1kDYnDZ+Sv2JvvX64qcVcQuzW1wpPuGNoc/77see5lCOgVd/PsSIjnEMaue+eP1cBEFgUeoi7lt5H2qFmqm9p7J8wHLubXov/trL14040ieSx9s8zurBq/mgxwccLzvO0F+Gsi13W63meen2JrSICeCxubspkdJFXKVx2QPbLfDzY6LDm4c2p1lIM+YfnV+rdXmShg0bEhAQQFDbLpTm5vDXjMk1jr0t1B8lsMtQJTqvT7dumFNS5Lz/2hLXESasgxsnuoTG3MFgrF0dT0yjIIa90oHAcG+WfbqHzJQLncVScsov6Age+tBD+NxwAznPP48tt27NBWUuD+eJjAdayCLjP4AsNGSuWQo//YyyefOJenMS/n36eHRuh4d7aBgKXTUffiEic5ZluP4OEq/RsGVmoYk7GyEQBIFXjmXT0FvH6Bjp/SPOkJWVRV5enqQi8MxDByjJyZJUBL4+tZDMEhP3d0kUHbs2cy0FpgKGJQ8TX3DKz640s7u/A43478pgsvHwD7tpGO7L6/2aic9/GpPdxP82/Y83t7zJgAYDmHPHHDpHdf5X7W81Kg23J93OwjsX0iioEQ+ufpCp+6fiFKSdEmvVSr4e2Raz3clTC/bicEooNPePhj7/B6m/QcER0eHDGw9nU/YmMsszJa3J0yiVStq3b8/xU2n0GDWeg3+v5sDfqy461kupJMpLQ7qp5qjHGXy6dQVBoGrLFk8v+fpHpYGeL8J9SyB3L3zXA7J21moKnwAv7nqqNXGNg1g14xAVJWc72JcareQYzDSLPl9oKJRKoj94H4VeT9ZTTyFYJYhrmcuOS2QcQOUvi4z/ErLQkLkmKZ42jeIpUwifOLHGjth1RRCE6tQpT1FeZMInQItarMN0Wbrrb5GIhsNgwFlRcV7q1NKCMrYbjLzTMAZNHVK+9u7dS1BQEPXq1RMdu2/Vr4TExhPbpLno2Dlb02kRE0Cr2JqLos+w4MgCWoe1pnFwY/EF75gGST0gXDxlSRAEnlu0j7IqK9+ObCe503eaIY17fr2H1emrefeGd3m1y6t4qTznRFZbQvQhTL5lMuNbjOeLPV/w2JrHMFgMkq6NCtDz+fDWbDhWyFd/HZd2w6YDwCcMdk4XHdonsQ/+Wn8Wpi6UNvdloE2bNjidTiy+gbS4uTd/TZ9MQdrJi45N0HuRIdJPA0ATEYG2fn0q5fSpulP/ZpcVrn80zOgD276T5Gp2BqVKyS1jm6LWKFk17SAOu0tgp+SWA5yXOnUGdVAQsZ99ijnlMPkffuSZxyFTZ2z5Z0SGllBZZPynkIWGzDVH6YKFFHz0MSEPP0TImNEen99ptCFYHZ5NnSo24S+WNgWuE3qVFvzc11dYM7MA0MS6IhpGu4M3j+fQNyyA7sF1a1BYWFhIbGwsSqX7t4XKkmKO7dhCq953iJ7qZ5ZU8ffRAu7rnCA69mTZSbblbWNYYwnRjNz9kLnN5WojgSnrT7I6JZ+Ph7YmPkRagfyqtFUM/3U4dqeduXfMpV/9fpKuu9yolCoea/MY3/T6hv1F+xm6YiiHig5JurZ7wzCe6tWIz9aksj5VQiM6tdbVzG/vPNGGbDq1jrsb3M3S40sx281ux14ufH19qVevHkePHuXmMQ8RHBPHL5+9j9N5ob1vvE5LuknaSbdP164YN2+us+WwDBAQC6N/db1mf3sBfhorqV/LGfS+Wm57sDkF6RVsXuISyik55eg1KhJDLu7Kpm/VioiJEymdM4fylSs98jBkas8/RYZKrGmtzHWFLDRkrinKV64k7403CBo5krAnnrgs93BU99DwbFdw/1CJQiMgTtQS0pblSk/RxMYA8Fl6PmV2O6/Xv7jTjhRKS0sJChLvubF/zR+oNVqadr9ZdOzcbRn4eanp10p8XQtTFxLkFUTvhN7ii9053SXGkvuKDt12spgP/jjKQzfW59amEeJzAz+k/MCz657lhpgbmH/nfBoGNZR03b9J99juLLxzIcG6YO777T625EhL7Xn85gZ0bxjGk/P3kFMmoTFa+zFgM8IB8UjF0OShGCwG/kj7Q9JaLgeNGjUiPT0dm8PBrQ8+RmluNqf2XJiuk6DXkm4WT50C8OnaBXtOLta0NA+v9j+GWgu3v+eq/zm2CqbefLZBqQQikwLoNrgh+//K4viuAlJyy2kc5YfKTQQ3aOQ9+N9xB7n/exXLyYtHt2QuH9Uiw1cWGf9VZKEhc81QuW4d2S9MJKB/PyJeefmy5cjbS1ybL7VYPUUtcHUFl+I4lSatEDwzE6WvL6rAQE5WWfgus5BH48OJ19ctrcdms1FRUSFJaJzcvZ2GHbvg5e0+MmC2OVi4M5Mh7ePQa90LpypbFT8f/5mBDQeiVYlYCpsNsH8htBsDKvcWvGVVVh6ft4f2CUE817uR+3lPsyt/Fx/t/Ij7mt7Hhz0+rHUPi3+TaN9oZt8+mw6RHZi4fiJ5xjzRa5RKBZ8Na41eo+LxeXvET+kDYiH5DtgxXTTdJd4/nm7R3VhwdEFtHoZHSU5Oxul0cvz4cSLrNySyQSP2rrrwNDtB70WJzUGlhGaG3h06glotu095imZ3w4NrQaFyiY390tPtWvSMoWH7cP76/jDpaYYLCsH/iUKhIOqtN1FHRpL95JM4q8QNAGQ8g62g6qzIGC+LjP8qstCQuSao2rmTrCeexPfGG4l65x0UIuk9l4K9xIxCr0ap80ybGZvVQVW5VWLqlFRr22w0cXEoFApeP55NuJeax+KlndZfDIPBlecv5jYFUJafS3BsvOi43w7mUmK0MrKT+NiVp1ZitBkZkjxEdCz75oPDCm3vFx26YEcmZVU2vhjRBrWbpl9nKDIV8fy652kd3ppn2j3zrxZ81xWtSst73d9Dq9Ly3LrnsDltotcE+2j5cEgrdqWXsvmEBCegDuMg/6ArXU2EYcnDOFB0QHI6l6cJCAggPj6eU6dOAdD2jrvIPXYEQ/75IixJpyVArSJLQp2GytcH79atMW6ShYbHCG0I49dAk/6wZDz88rTL5UwEhUJBz3sb4x3oRasMG03DxFNFlT4+xH7xOdasbHJff0NOgfsXsBVUUThlPypfjSwy/uPIQkPmqsd06BCZDz2Mvk0bYj75GIVII7lLxVFi8WjaVEWRK19dNHVKEE5HNKQ4TmWijY3lz+JyVheX80b9GPQSNtI1UXraulMsomGurMRiNBIYESk659ytGXRvGEq9MF+34wRBYMHRBfSI7UGMb4z7SQXBVQTe+E7wd1/H4nQK/HDaVjfCX/z3aXfambh+Ik7ByYc9PkStvHb6mQbpgvi458ccKj7EJzs/kXRN1/ohJEf4MWdLuvjgpJ4QXN/1sxehR2wPonyirmhUo0ePHvj5+eF0OmnYqRttb7+L4uzz3bAa+ngxMioYkLbp9LmxB1XbtiHYxIWcjES0PnD3ZLjzM9jzA8y8A6ziEQetTk39AQn4OxRo9pVJEg5eDRoQ9eablK9YQdmCK2dY8F/gPJEhp0v955GFhsxVjeXkSTIfGI+2Xj1iv/oKpdfld/yxl5g8W59xuoeGaFfwqhKwVkjsoZGFMiaG145lc0OgL33DxB2d3FFaWopSqcTf330agqHAdSocEO5eaDidAvuzDPRqHC56732F+zhSckSapW3aBihKlVQEvu6Yy1b33i7iwg3gm73fsDN/Jx/e+CFh3mGSrrmaaBXWiufaP8cPh39gVdrFbV3PRaFQcG+XBFYfzifXIFKroVS6ohqHlkGl+yJylVLFkEZDWHlqZY2OWCUlJdjtdtE11pWQkBDKy8spLi5GrVYTHBtP2r492O1nRYKPSoXR7iTPIk04+HTpgtNoxHTgwOVa9n8ThcJVBzTmdyhIgV+fkeRIlW6zsdrHRuGBElI25ki6VUC/OwkYPIiCTz/Fab4yhgXXO650qf0ofU6LDF+RVFiZ6x5ZaMhctdiys8kYOw51aAjxU75D5fvv5Mp7ulmfociESq3EJ0DkDbcszfW3iNAQHA5sOTls8w0k3Wzh7UYxl5ziU1paSmBgoKjj1Fmh4T5NK7/CjNXhlOTwtODoAmJ9Y+kW0018oTumQWgyJIo3Z/xhSzrNov1pExcoOnZd5jqmHpjKE22eoEOkeB+Rq5V7Gt/DbYm38drm10gzpImOv7tNDDq1knnbMsQnb30PKNWw53vxeRvejUNw8PPxnwE4deoUEydOpFWrVvj5+RESEoJOpyMxMZGBAwfy22+/eTSdJSAgAJVKRU6OawMa16QZpvIyclOPVo9RKBT4qJXkWaUJHl2TJij9/eX0qctFbDtXZGPfPNg9W3R4Sm451hg9zXvEsH5BKgXp5ZJuEzp+PE6DgfKVv13igmX+ia3wtMjw1hA2XhYZMi5koSFzVWIvKiJ97FgUGg1x06ajklA74AkEhxOHwfOpU/6hOhRivS1K01x/B7o/gbfn5YHdzo9KPWNjQmnsI6H2QwSpjlOGgny0em90vu7zojNPF9THB7sXGlaHlT/S/mBwo8EoFSJvR+W5cPgXVzRDgq3uXxJtdXMrc3l548v0jOvJmOZj3K/hKkehUDCp6yTC9GE8vfZpLA73Oe++XmoGto1l3o5MrHaR5n/6IGgxCHbOhIvYxZ5LqD6UW+JvYenBpYwcOZIGDRrwwQcfsH//fiorXTa5DoeD9PR0li5dyh133EGjRo3YtGlTrR5vTSiVSqKiosjNzUUQBHwCgwhNTCJ9/+7z16lVUyRRaChUKnw6dZILwi8nrYZB+7Gw8gXI2et26JmO4DcMaUhojC+/TzmI2SgendImJOBzww2UzpvnoUXLwGmRMeWALDJkLkAWGjJXHQ6DgYwHxiNUmYifMR1NhHj6jcfuXWYBp2etbQ1FJunWtrpA0Ae6HXamh0ZpeATPJYrXSkhButDIIyAiUnTznlHiyrOODXIvNLIrs7E5bbQMaym+yN2zQa1zbUZE+HF7Br5eavq3FrfV/T7le1QKFW93e1tc7FwD+Gh8+OjGjzhedlySzey9nRMorLCwKkXcsYoOD4Ah02VNKkJQaRC/PfUbP/74I06neAfz48eP07NnTz75RFqNiRhRUVFUVlZSUeHq1RCT3BRDfh42y9mUmVCNmiKb9BQun27dMO3fj6NCev8HmVpy2/+5mnAuvB9MpRcd4nQKHM4tp1m0PyqNktvGN8dqsrNmVgqChK73QfeMwHzggJwG5yGqRYZeLYsMmQu49j9VZa4rnFVVZE54CHtuLvEzpqONi/tX728vcW1CPN0V3JPWtqmpx3EqFIxt34IAzaUXLAuCQFlZmUTHqTwCwsTdrTJKqgj38xLtwJ1V4RJNokXgDhvsmgUth4LOfT2Kxe5gwY5MBreLxVvr/udzrq1ugNel1blcTSQHJ9M1uisLjogXZCdH+tExKVhaUXh0G4hpB9unuh2Wl5fHxw9+jCVPWp+KM9jtdp599lmmTJlSq+suRnh4+HnpUz6BLiFdVX62biRUq6HEascpMW3Lp1tXcDio2ibuvnW1IggOTKZsSkq3UFT0N0bjcRyOq6heQaODod+7bKyXPgwXEanpJVUYrY7qjuD+oXpuGdOUtAPF7F4l/jz2vfFG1NFRlM6b7/Hl/9ewFZkonCqLDJmakYWGzFWD02ol6/EnsKSmEjd1Cl4N//0mafZSMyhAFeiZonNBECgvNtfC2tZ92pRDEFh38CiGoGCGJ7h3XZKKyWTCYrFIimiUF+YTIMFxKqukSjRtClwRDbVCTYS3iHjJPwQVudBSPJrx24E8SoxW7u0sXgT+26nfqLRVSrPVvcYY2XgkaeVpHC4Rb4g2tlsih/PKOV4g4aS+5XA4udYl/i6Cw+Fg+PDhlBSW1HLFZ3niiSfYvXu3+EBg9OjRKBQKHnroofO+rlarmTNnDk2aNGH06NF4+wfy6dyFBIa7InIKhYJeoQF82SyR3n36SLqXNi4OTVzcNZE+ZbUWk1+wkrT07zh85BX27BnF5i038ffapmze0oM9e+5l3/4H2LrtNtaua8aGjV3YuWsohw49y8mTn5Gbu5hK47Ers/igBBg4BVJ/g82fX/DtlBxXPUaTc3poJLYIpe1t8WxfcYqqcveWxQqViqBhwyn/9VccZWUeXfp/CVuRicIp+1HqVC6R4SeLDJkLuXb8G2WuawS7nZznnqdqxw7ipkxB31JCKs1lwFFiRhXghULtGQ1uqrBhtzikp05FD3A75MfcYtQ52fgnJKD0UI8Hqda2TqcDQ0EBgSKOU+CKaEgRGlkVWUT7RqMS6YReXb8SKt50b87WdLo1CKG+BFvd+UfnS7PVvQbpFtON0c1Gk1meSZPgJm7H3tIkgnHdkjhRWEmDcJG+BKENQHCAIQuCky749vz581m3bt2lLB2LxcJTTz3F+vXrJY2Pi4tj/vz5fPrpp+j1rtea2WxmzZo1hIaG4nA40Or1KFRKenTtwoLFSwDINVv5ND2fJ+vHSl6bT9euV21BuCAIlJfvIStrLvkFKxEEKyqVL3p9PHp9HGGht1b/W6eLQ6XSYTJlYTJnYDZlYTJlYjJnUFK6Gau1AIDAgA7ExI4kPOw2lMp/cSPZ6Dbo/iyseRNi2kNS9+pvpeQaiPD3ItT3/AOhNr0T2P9XFimbcmh/e6Lb6QMHD6Lwq68oW7qMkDGjL8MDuL6xF5komrIfpZeKsPEtZZEhUyOy0JC54giCQO7rr1OxZg2xX36BT+dOV2wt9hLz5bG2DRWZ02F3bdzcpE6V2ez838lcvi4vIbhpY4+tUarQqCwpxumwS4poZJRU0bVBqOi4rMosaZv8snTQ+oF3sNthR/Mq2JVeyuR724pOub9oP0dKjvBEmyfE738NolKqaBTUiPVZ67kh5ga8NTULP7VKSeMoPzYdK+am5Ai07oT2medoadpFhcbXX399aQs/zYYNGzh48CDNmzcXHdu2bVtOnjzJkiVLGDlyJABLliwhPj4evV6PyWRCoVCg1mhxOJxERrqew0FOJ95GJ1YRc4Nz8enalbIFC7BmZaONvToEqsNRRV7ecrKy51JZmYJOF0f9ek8TGTkArTbMbU2VThdNEB0vMqeJouK1ZGf9wKFDT5GqCSEmehgxMSPQ6cRrnzxCz5chczv8NBYe2gB+rt/bmULwf6Lz0dCwYwSH1mfT9rYElG4MONQhIfjfdhul8+cRPOr+y9oE9nrDfjqSofBSEfagLDJk3CO/smSuKIIgUPDe+xgWLyH6/97F7+abr+h6PG1tW14ssYdGeZbrlNiN49SHp/KwOgWiigrQxEk/gRWjtLQUnU5XfRJcE2c6K4tZ25ptDgoqLJJTp2L9JDyWM/UrIlGcg9mu/PsbG4kbCCw8upAY3xhptrqXQHp6Op988gkPP/wwffr0oX///jz55JN899131SLvctEmog1V9ip25e8SHdsw3A+1SkFplUin7IA4UChd4u8fHDt2jC1bttR1uRcwa9YsyWPHjBnDzJkzq/8/Y8YMxo0bh0ajwWRyvQ5VGi2Oc3ppeCmV+KtVFEt0ngJcByFKJcbNnnHIuhSqqtJITX2LjZu6cuTo/9DpomjdagZdu/xFQsKDeHmF19n6WqXSExF+O23bzqVTp9+JiLiDzKzv2bT5Rvbtn0BxycbL32FbpYbBM1zPt5/Gug5kgEM55TSLvnhNVYsbY6kstZB+oEh0+qB7RmBLz8C42XPP2esde7GJwqmnRYYcyZCRgCw0ZK4oRd9+S8ns2US8+j8C+ve/0svB4emIRqEZna8GrV4keHgmNaiGiMbhShOzcop4PsIfoaTEo0XytbG2RaHAP9T9Jj6r1OU4JSY0BEEgqyKrFkJDvOYio6SKMD8v9Fr3qVil5lJ+P/U7w5KHXTanqV27dtGvXz/q1avHs88+y+TJk/njjz9YsWIFX3zxBQ899BAxMTGMGTOGzMxM8QnrgL/WnxYhLdiSuwWn4N75KdRHS7Nof/FG2SoNxHYC44UbuZSUlEtY7YUcPixeX3KG++67j40bN5KWlkZ6ejqbNm3i3nvvRafTYTKZcDgcqLUaNu7cja+vb/Wfz9s2ZsaH70m+jyogAF2L5ld0cyoIAtnZ89m2/Xby8pcTEzOSrl3W0qrlFEJCbkTh4ee0r09Dkhu9wQ3dNpOcPAmzOYu9e0dx+PALOBwizR4v+ebhMGQmZGyFv96ksMJCQYWluhD8n4TF+xGR5M/BddmiU+vbtMErOVm2upWIvfh0JEN7WmT4yyJDRhw5dUrmilEy5weKvviSsKeeJPh0usOVxGm246yyezx1SnJ9hkLpOi3+B4Ig8MqxbBJ1XtwjmMkCNLGejWhItbb1DQ5BrXX/4XLG2lZMaBgsBiptldJSp0rTIfl20WGZEmtD1mSswSE4GNBggPi968A333zD008/jdXqPjpgMpmYNWsWv/zyC3PnzqV3794eX0vHqI7sPbCX7Ips4vxrFqh+eg3eWjXFRgsRASKvgZtfBe2FP2eFQkG3bt0oLCykoKCAsksstD158qTksaGhofTt25fZs2cjCAJ9+/YlNDQUnU6H0WiksLAQtUZLy4b1WbD8l+rmlItyi7HVInUKTqdP/TgPweFAoRKpL/IwDoeJo0dfIzdvCTEx99CwwSuoVJ57z3KHWu1DbMw9xESPIC9vGUeO/o+KikO0aPE13t4XptF5jISucMvrsPo10gNuA7ho6tQZmt8Yw5pZhykrqCIwvOb3A4VCQdCIEeS9+Sa2nBw00f9SStg1iEtkHEChkUWGTO2QhYbMFaFs2TLy33mH4DFjCJkw4UovBzhrbevR1Kkik3h9BriEhn8MqC98815RaGBzWSU/tqwHe3cAnhUaZWVlREWJO1hJtrYtrkKrVhLu5965K6vSZW0rGtFwOqAsQ5L1b2apNKGRXp5OjG8MQTpxgVVbXn75Zf7v//6vVtcUFRVx++23M3fuXIYPH+7R9cT5xRGmD8NgMRBHzUJDoVDg66WmwiwhjUiru2iPg9atW/Pmm29isVgwm81UVlaSm5tLVlYWxcXFFBcXk52dTWZmJpWVlTgc7hv/WSy1s8cdO3Ysjz32GHC2VkSj0aBWq8nNzUWhUKDTamlQvz7K0wKhmV8IWwyVtbqPb9euFH87GXPKYfQtxGtIPEVV1SkOHHiUKlMGTZt8RFTU3f/avc9FoVAQFXU3fn5NOXDwUbbvGEDTJu8THi7NvatOdHoYtnyNbt9MfLSD3b7OG7QLZ+OiYxxan023we7dCwP63UnBhx9SunAh4U895eFFXx+cFRlKwh5sIYsMmVohp07J/OtU/Pknua/8j4DBgwh/4fk65xB7GseZHhoeFRpmiRGN9ItupKscTiYdz6Z3iD83h/hjy8pC4eWFOizMI+tzOByUlZVJi2gU5hMooRA8s9REbJDebSEmnCM0fEWERnkOOG2ShEZGSRVxQeI/76wKiUXoteTnn3/mvfekp+Gci9PpZPz48ZLThWqydQV45JFHUCgUjBo1ir59+rLwuYXo1Oc/r7/55hsCAgLIyMio/pqvTqrQ8HE17vsHZrOZw4cPk5eXh8Viwd/fn1atWjFw4EDGjx/Piy++yNtvv81DDz3Eww8/zP33388dd9xBly5daNGiBfXq1SM8PBxvb9cmMimpdqfkffr0wWq1YrVaue2226q/7u3tTW5uLnabFaVKXS0ywNUdvNTmwCqhqeAZ9K1aofD2/ldtbvMLfmP7jgE4BRsd2i++YiLjXHx9k+nQfikhIT04cPBRUo+9g9Mp3p27Tqi10HYUDXJ+oU2E2n2ht0ZF067RHN6ci93qXswqfXwIGDCAskU/4RSJQP4XsZeYKZx6rsjwjPW7zH8HOaIh869i3LKF7Kefwe/WW4maNOmqERngekNVaJUofTQemc9hd1JZaiZAaupURNMLvvxlej6FVjtvNnRtiq2ZWWjiYj32cysvL0cQBGlCIz+PpFbtRMfVxtrWT+sn3ijvTNGxiNAw2xzkl1uIk3LvyixahLYQHVcbDAYDo0ePvqQC2crKSu6991527RIv3oaabV3nzZtHfHw8CoWCmTNn0qRZE6ZNncZ7L7hE0KlTp5g4cSJffvkl8fHx1fNFBuiQ9MzS+oLdDHYLqM9uPAIDA3E6nVRWVlJZeWGUwN/fH4VCQWlpKVqtlvDwcOLi4tBoXK85QRCqoyF5eXmSnpfnolKpqoWa6hwxodfrqaqqwmq14sTVUPAMgtGMqbSEYms0UTppJ7UKrRafjh0xbt5M6IQHa7XG2uJ02jl+4j0yM2cSHn4HTRr/H2q1e+vmfxO12o/mzb4gM6Atx4+/R3n5Xlo0/wovL/HoZ61pNxrN+o8Y7rUFuMXt0GY9otnzZwbHdhbQpKv7iG3QiOGUzp1LxarVBNzZ14MLvraxl5hdNRkqhatPhiwyZOqAHNGQ+dcw7dtH5qOP4d25MzEfvP+v5zaLYS8xowrSeWwTX1FiRhDAT2rqVGDieV9KN1n4JrOAh+PDSdS73uBtmZloYzxbnwHi1rY2s5kqQ5mo4xS46iTigqQJDdFoBpwtlL9I/cp5812OIvRaMHv27EuuSQDYvXs3GzZskDS2bdu2xMfHs2TJkuqvLVmyhLi4ONq0aQO4xMiTk57kize/4NSpUwiCwLhx4+jVqxejR48+bz61QoHN6RQXS2escs3nN/gLCwsjIKBm4ajRaCgvL6e8vJyioiKysrI4ceIER44c4ciRI6SmppKXl4darSYiIoIhQ2rfSNHf3x9///Pz93U6HWq1Gqvdwda9+4iKiqr+c0ODJJbfP4Qim3TnKXDVaVTt3o2zqqrWa6wNJ099RlbW9zRq+CrNm31xVYmMMygUCuLjxtCu7TzMpmz27R+Pw1G7tDcpVOkjWO1oxw2GZSDyHA0I8ya+aQgH12WJzuvVoAHeHTvKReHncEZkoFIQ+mBLVAGyyJCpG7LQkPlXMKemkvHgBHSNGxP7xecoRAqKrwSOUs86TlUUuVKxRCMa5nIwlVxwYj/peA7BGjVPxJ91ebJmZ6HxsOMU4HZzCK60KYAAkWZ9giBILsiulbWtXzRo3P9uMktc7jdiEY1ya7n0IvRaMHny5Csy18VsXceOHXvemCH3DKFFlxaMGTOGr776ioMHDzJlypQL5lIrlVRZnVjtImlEZ4SGtfy8LysUClq0qDlSpNFoqKioufu4IAiYzWbsdjsBAQHcdNNN7teBywJ32bJlNX5/2bJlzJ49m4iICB55aAKp2zYjCEL1H4fTychf/qqVxS2AT7euYLNRtXNnra6rDYVFa0hP/5b69Z4jLm70VRUBvhgBAW1p2WoKRuMxjh1/2+PzH82rYI7jFgIrjkOGuOtXixtjKEivID+tXHRs0D0jMO3ahfnoUU8s9ZrGXmqmcKpLZIQ92BK1LDJkLgFZaMhcdqwZGWSMG4cmOpq4yd+iFOnXcKXwdLM+Q5EJhVKBb5DIm/RFUoPWlVSwssjA6/Wj8VG7Ij+CIGDLzELr4R4aAQEBqNXusygNBad7aIjUaJQYrRitDmnpS5IjGhevX/knGSVVaFVKIvzd/w6zKiQWodeCsrKyWlmxilGbXhQ12bqei6/Wl9GvjyYlJYWnnnqK7777jvDwC22KtRrXR4LZ5j6vHbUXKDVguVA0NG3a9IKIwhmUSiVVEiIAOp2O228XdxmrDRFhYTjUWtTe5z83lQoFIRo1hbWMaGjr1UMdEXHZuoSbTJmkpDxHaOgtxMePvyz3uBz4+zWnUcPXyc7+kdy8ZR6dOyW3nG00xxncAHZMEx0f3zwEv2AdB9eLW9369eqFKiz0Px/VsJeejmQoFISNl0WGzKUjCw2Zy4otP5+MseNQ+fgSP20qqho2IFcawSl4vllfkQm/YC+UKpGX2T96aNicAv87lkXnAB/uCg+sHmYvLESwWDxubRsYGCg6zpCfh1qjxSfQfYrVGWvbuGD3YtLutJNrzJVobZsmuYdGbJAelUgRemalq4hZksiRSG1sWKWQmZmJ3S5t43uurevMmTOrbV3PxUfjQ0BIAKPGjqJJkybcfffFC4m9TncEN9tFhIZCAV5+FxUaXl5e3H777efVSJyL2WwWfUzdu3enc+fOouNqg59eB4JAle3CaE2IRl3riIZCocCna9fLUhDucFg4cPBRNOpAmjb58KqPZPyT6OhhREUO5MiR/1FZ6bkIQUpOOfXD/FF2GAcpy6Ei3+14pVJBsx7RHNuRj9novkhdodEQOHAQ5b/8evkbEV6l2Mtchd8oTkcyAmWRIXPpyEJD5rJhLy0lY9w4BIeD+BnTUYeEXOkl1YhgtoNDQOXruZQuU7kVvZSuqZUFgAK8XT+fGdmFnKiy8HbDmPM2GI7iYgCP/hyNRiO+vuI530ZDGfqAANENT1Gly7UlzNf9B5TBYsAhOAjRS3gsxgLwEXfZKqq0ECLh91dsKkatVOOv9ZzoLSgo8NhcAHa7neLTv28pjB07llmzZjF79uwL0qaAs45TKtxGr9QqJSoFWO0iGy1BALUOrBePToSHh9O7d+/qIm9wbcydTqeoZW3nzp3Pc4zyFHZTFUqbhdKLpG75a5SUi4mri+DTtSuWY8ewefj3f+zYWxiNx2jR4is0mqvzcMYdCoWC5OQ38dbHc+DgY9jttbMPrglXR3B/aD0ClGrY873oNU26RiMIAke25IqO1TVvhrOyEkdJiSeWe01hLzNTOOUAAGEPtpBFhozHkIWGzGXBUVlJ5vgHcZSUEj9j+lXfCEmhV6PQKHGUe87e0CfQC6NBQkGkfzQgQGU+hVYbH53K477oEJr7nZ/ioY5wFWJ7clPj5+fnNmf+DL7BIVSVleJ0ut+MRZ5OW8ovd/+4A70C0Sg1FFRJeCx+0VCRJzos0l8nel+ACO8I7E47pZYL+0DUlWgPP7+1Wi1htbAwrsnW9Qwmu6t+RaN076hmczhxCKBV1/DRYDK4aooUCrCZLtq07wwNGjRgyJAh6PxczwmNRoPNZqsxoqFWqxk1ahRPPvlkjdGQS6Hg5HG8BCeFxcUXRIvKbA4CNbW/p0/XLgAejWrk5i4lO2cejRq9gZ9fM4/N+2+jUulp0eJrLJYCDh956ZKjBA6nwJG8cldHcH0QtBgMO2eBw30kyttfS4O24RxYl43gdL8G7elosS3zQuvm6xl7meUfIuPfaQAp899AFhoyHsdpNpP1yKNY09OJnzYVr1p64V8JFAoFqmAd9tMFxZ7AP0xPZakFh1hh7Zn6g9I03j2Zi1qhYGK9C+0YVUFBKL29sWWKu6hIJSgoqLog3B2B4ZE47HYqRU76zhSBn0mhqgmVUkWMb0x1Lw33i0w4m17mhrhgb7LLTNgd7n/eZ2ozztRqeIJ69ep5bC6AhISE6s7VUjhj63r48GEsTgv7C/dTYi7BdrqngcFsoMJaUS04auJMbYb+n0LD6YDDK+CHgbBtMvz6PJSeBK8AONN/wlIJeQfPuywkJITEnokkdkgkLi4Ou91+QUQjKCiIQYMG8cUXX9Cnz+Vp+GY1VZFz7DCJ9erhcDguiEAVWe2Eamtva60OCcGrSROPCQ2LpYAjR/9HVOQgoqOGemTOK4m3dxJNm7xPQcFK8vOXX9Jcp4qMmG3Osx3BOzwA5Vlw7A/Raxt2iKC80ERFifu0vTNpqVYPvsde7bhExn4QBMLGyyJDxvPIfTRkPIpgs5H99DOY9u8nfsZ0dE0v7A1xtaIO0lU37fME/iE6EKCi2ExghJvi6EBXH4PdhXnMq9DzXqNYgjUXvjQVCgWa2FisWZ47bQsKCqKiogKbzXZemss/8T9ta2soyMM/tOaT9gBvDX46tajQAFxCQ8pmPygRjq8RHRYX7I3DKZBrMLstRj9TF5JdmU3LsJbi95eAr68v7dq1k9z/QoyePXvW+hp/f38qrZX8kfYHW3O3kl+Vj6XMQkZ5BstPLsdsN3O05ChVtpp/N2eEhk77j9P9rJ2w9j1oOQxaj4TU3yFtIxiyIeq0y1Tefji4BFuf/yOjIoMySxntItphtBtJrJeIfxN/vPDipptuoqioCB8fHyIiIkQdzzxBZspBEKB+qzZkGjaQm5tbHYVyOAVK7HZC6xDRAFdUw7B8OYIgXHItRU7OAkBBw4b/u+bqMmoiPLwPIcE9yMycRWTkXXWeJyXX5RzV5IzQiG4NMe1dReGN3fe+OPP+W15kcttAVeXnhyowEFv2f0No2A0Wl7uUILhqMoJkkSHjeeSIhozHEJxOcl56mcqNG4n98gu827a90kuqFepgHfZSDwqN0x9o5cUiURKtD06fCF6pCKKZr477omuuW9DExWHLEndQkcqZ/hli/R8Cws4IDffFl+CKamSWiguNWL9YsislPJagRFedRg31AOfeF1x9PNxxpkmgJyMa4OrG7Skefvhh0TEXs3U9VnqMtPI0nmz7JEfXH+WZD55hUeoivDXeNAluwv1P38/YGWPJr7r479Fsc6JSgOafBgan1rv6mHR5DHxCoP5N4BMK6RtcYgOgshCLTyg/HvmRu3++mzG/j+GBPx6gwFjA1P1TeXXjq7y/632yVFm0adOGRo0a/SsiQ3A6ST+wl8iGjdF5+xAVFUVubi7O05GYErsdp+DqEF4XfLp2xVFYhCX12CWt0+m0k50zn8jI/tdkXYY7YuNGU2VKo7z8UJ3nOFFQSVKIN0E+59RhdRwPJ/5yOdO5wS9YBwooLxJ/f9fExmL9D6RO2Q2nIxkOweUuJYsMmcuELDRkPIIgCOS//TblK1cS8+EH+HbvfqWXVGtcqVMW0TxeqfgGeaFUKiR9uC2MH8QeAnm7YSwqNyeZ2thYj+YPnxEaYulTaq0W3+CQaptbd8QHe4tu9sHl+pRVkSWeux142nGqzP1mIiZQj0IhnrZVfW8paVu1YMSIER6p1bjpppuqm+3VlsyKTKJ9oon2da3DW+ONTqWjnn89EgISuLvB3UT5RnGo6OIbPpPNgU6juvA0veQEBMbBmXQuSyX4x7jqi/YvcH2tMp/dShtLji3hgx4fsHrwarzUXvx84mfKLGX0TuxNhbWCSVsmSavN8RBFWRnYzCaSWrl+ptHR0TgcDgwGAwClVjveSiVhbiJ67vBu1w6FVnvJ6VNFxWuwWPKIjblXfPA1RkhwdxLiH8Rkcv8adkdUgI7R3f6Rhluvp+vvghS316rUSnyDvDAUiafGauJiPZqeejXiMFgoOiMyHmzpUVt3GZl/IgsNGY9Q+PnnlP44j8hJb+B/mfKsLzfqYB3YnTgrPVMQrlQp8Q32orzQ/Ydbud3B26H9uLtyL10C3TtAaWJjsWVnIzhq75BzMfz8/FCpVJLqNALCIzDkiwuNuGBvaalTfjFU2avEi7LPqWFxh1atJDpALz2aUuG5yBCAXq9n/vz5oj1J3BEaGsr334s76dSEwWrAT+tX/f9CUyFRPlFUOarwVnvjpfbCKdRcw2KxOdBfLIXIyw+sRrCefi6bDa6mfa1GgKkMDv8CFXnstZcT7x9Pn6Q+RPhEEOsbS6Wtkre6vcWoZqP4tOenJPon8uvJX+v8GGtL/snjhMYnEhDhqnsKCQnB39+/2tWr0uEkSKMiqI6pU0qdDu/27TFu2nRJ68zOmkuAfxv8/K6ddFOpKBRKfH0bU1K6BbvdWOvrBUEgo6SKpFCf87/hG+FyP5NQwxUQqqdCgtDQxsZhy7p+hYbjdCRDkEWGzL+ELDRkLpni6TMonvwd4S+8QNCQIVd6OXXmzBuu3ZN1GqF60dSpj9PyMCq0vHpqquh8mrhYBJsNu4ecp5RKJQEBAaKpU+DqCi4ldSou2JvsUhMOkcjQmT4Wohv+WmwmYoP0ZEgo6L8cEQ1w9X/45JNP6pRfr9PpmDdvHrGX0CfFT+uHTq3D7nQ58dwcdzOdojphc9jw93Kl45SYSmq0FTbbHHhpLvKx0LC3q2fG6eJyrJUu8eEdDK2GQ/omKEsnw2km2udsVCerIotI70gS/ROxOW0E6gIJ8AqgwirudOYJDIUFrJ0zDZ2Pb/XvRKlUUlFRwfr16wFYW1LOmuJy1LUovv8nPt26UrVzJ04R696aMBpPUlK6iZjYfyea4XQ6ycrK4tixY6J2w54iMLAdTkcVZWXba31tuclGcaWVcL9/WK4qFK6Ip4T3Br9QPQYpqVNxsdjy8hCsnnMgvFpwGCwUTj0giwyZfxVZaMhcEqULF1Lw4YeEPDSBkLFjrvRyLgnV5RAaYXq3qVOpRjPTswp5Sl9MdMlBsLm/tzYuDsCjJ25SnadcQkNa6pTdKZBrcL/hr3Z/EtvwK5WugnmRPOwz95YSTUkKSCKnMod8o7hwqi2PP/44y5Ytk9QI8Qz169dny5Yt3HLLLZd07x6xPUgOTkaBa1OdGJBY/T1/rT+pJak4cZ4nBs7gdAqYbE50FzvZb3grDJsDutO1A5Zy17+dTohoCvVuAq0PXl7++Gn9sDpcm7QWoS3oHN0ZpVJZba2bUZ5BlO+FrmqXgy2LfgRBoFGnbud9PSkpiYyMDMrKyjhaZSG4Do5T5+LTtSuC2Yxpz546XZ+d8yMaTTAR4Z7thn4uBoOBL774gjZt2uDt7U1cXByNGjXC29ubhIQEnnnmGY4du7Q6E3eo1f74+7eipGQTgpuo2sXIKnO9l0QGXGRjHJQoMaKho1xSRCMWnE5sueJ9N64lHOWnRYbN6XKXkkWGzL+ELDRk6kz5b7+R9/obBN1zD2FPPnmll3PJKLUqlL4ajzpPBYTqa/xwEwSB145lE6vTMiHidEpAWYbb+TQxLsckT9ovShcaERjLSrFZ3P984oJcRfBiG/5aFWVL3EzEB3uTJUFo9IrvhV6tZ/GxxeL3rgP9+/dnz549PPzww/j5+dU4LioqildffZVdu3bRunXrS75vsC6YGN8YVMqzYuF42XGCvIII9ApEpVRxQ8wNBOuCL7jW6nAiwMVTp/6JpQK8/F0i0OmERr3hlkncWu8O6gfWR8AVzWoc3JgWoS2qLztUfIhyazmNgxpf8mMVI23vLg6t+5MeI8ei0Z2/qWrQoAFKpZLU1FTSTVYS9JfWqNMrORlVcDDGTbWv03A4TOTm/kR09FCUysvTJO3bb78lJiaGJ598kr17954XxXA6nWRkZPDpp5+SnJzMqFGjMBprn94kheDgG7DaiqisTK3VddmlJnQaJQH6iwjCoARJhxD+oXrMlTasZvd9NzSnD3Os11H6lKPc1SdDsDldfTJCanbekpHxNLK9rUydqFy/nuwXJuLf704i/vfKdWPFqA7WeTSi4Reiw1Jlx2y0ofM5/0Py9yIDa0sr+L5FEjr16c1oWTqENapxPqVOhzosDJuHLW73798vas8ZEBEJQHlhASGx8TWOiwlyFWVnllRBfff3lt5LIxFObRAdFh/iTbHRSoXZhp+u5lNqX60v/er346fUnxjfcrxoI7u6kJiYyDfffMP777/Pb7/9xvHjxzl16hRarZakpCSaNGlyQfdsT1NprSS7MpuOkR1RKBTUD6xP/cCL/1JMZ6xt/yk0nE5wWEDl5RIWDrurXsPr9HNWqYT8Q5C2iW6dHjx/TruJYP1ZUZNSnEK7iHbUC/Rs35F/YqmqYtWUr4hv3ooWN/e+4Pt6vZ6EhARSU1PJSGzFbaGX5vKkUCrx6dLFVRD+7DO1uray8gh2ewUR4Xdc0houhtls5oEHHmDu3LmSxguCwPfff8/OnTtZtmwZDRs29Oh6vL2TUKsDMFYdx89PutjMKjURFaC7+PtTUCLsnuPqVu/m/avaBbDITGhszbVwmshIUKmum4JwR7n1tMhwuNKlZJEh8y8jCw2ZWlO1cydZTzyJb/fuRL/zDopLyG2+2lB5WGgEhLne1CuKzecJDZPDyWvHc7gp2I9bQ/xB8AWlRtKpvSYuzqOnbUFBQVitVqqqqvDx8alxXGC4S2iU5ee5FRpeahVR/joyJdZKSCrKDkyAsh9ENxOt4wIBWJ2Sz8C27msdhiYPZcHRBfyd8Te9Ey/cjHoKPz8/hg69Ms3XjpcdR6PUkOCfIDq2uMKCRqlA/88ajdz9sHcOxHeFyGaugnBblet3YTWC2guKUsFw4XOywlZBvP/Z58rghoPpV68fOvXlTdvY8OMszJUV9J7weI3iOTk5mV//+puSmOYk6C89kuDTrRvlK1diLy1FfdrNTQomk+vQQK8X/x3VlkcffVSyyDiXlJQU7rjjDnbu3OlRC2KFQkFwUDegdgdT2WUmWsXWIAaDEsFugsoC8IuocY6zQsPkVmgoNBo0kZEePcy5UjjKrRRO3S+LDJkryvWzQ5T5VzCnpJD50MPoW7Ui5tNPUFzG09grgTrYw037Tn+4Gf7hPDU5s4A8i423Gsa4NkJK1ek6hDTRObUetl+UanHrExiESqORVKch1Xkq1k9iUXZQomtzayx0OywhxIfuDUOZs1U8laJRUCPahrdlwdEF4ve/BnE4HWSUZ9AgsAEalfvXqd3ppMhoITpQd2FH8qO/wI7pkPIzHFoGW75yRTB2TodNn8OuWZCxFWdgHGa7udrVymK3YHfa8dWe3dRty9vGa5tf8/AjPZ/MlAPsW72S7veMIuC0OL4YjRo1olTjEhgJuktLnQJXQTiCQNWWLbW6zmTKQKMJRq127zhXW2bOnMmMGTPqfP3x48cZM0Z63d3o0aNRKBQ89NBDF3zvkUceQaFQMHr0aLReYTz55KcoFIrqPyEhIfTp04f9+/dfcK3Z5qDSYiM6sIYmnBLtr/V+GtRapaQ6DU1c3DXfHdxRcVpkWByuPhmyyJC5QshCQ0YylpOnyHhgPNqkJGK//hql1+XJJ76SqIN0OMqtCLbaFSvWhJe3Gq1Odd6HW7bZyhfp+YyPDaOB9zknu0HS3FM0MZ7vDg7iQkOhVBIQ5mGLW98Yco252M64GdVE8Gn/fBG/fIB7OyewJ6OMg9kG0bHDGw9ne952TpadFB17rVFiLiHSJ5Lk4GTRsYYqKyql8uLFtuYKaDcahn0PPV+E9mMgrAkk3w6Bia56DWMRh9QKPtr5EfOOzGN1+mq25G6hyFSEyWai3FqOzWljZ95OjpVevoJjm8XMqslfENO4Ka17u+8WHRwcjOK05W38JdZoAGgiItDWr09lLftpmMyZ6PVxl3z/c7FarUycOPGS51m6dGm1O5cU4uLimD9/PibT2fc7s9nMvHnziI93RbYUKHE4jPTp04fc3Fxyc3NZs2YNarWaO++884I5qyx2utQLoUF4DdHWoNNCQ+S9U6FQuFwAJThPaeNir2mLW0eF1WVhazkdyXDTDV1G5nIjp07JSMKWk0PGuHGoQ4KJm/IdKt+aU2yuZaqdp8rMaMJqOEGrBQqFwuU8VXz2w23SiRz81CqeTvxHmD8oETJ3iM6piYvDUViE02RCqb/0DxCdTodOp5NmcRsRiaFQWnfwtUfFLXhj/WJxCk7yjHnE+bnZbIU1cf189sw926SrBno1DicqQMcPW9N5b1BLt2Nvib+FYF0wC44u4KVOL4mu91piVdoqGgY2xFvj/nksCAIHsgwolQp0mot8JIQ2gPyDrn4ZugAoz4HIFtCg19kxvz7HqrIjLMxcRbAumDJLGU7BiVqp5peTvxDhHUG8fzy783fTNqKtZx/oOWxa8AOVJcXc/eIbklI6NTFxqB0Ogv/ZCb2O+HTtSsWaP0Xrnc7FZMpEr/Os0Fi0aBGFhe6jf1L55ptv6NGjh6Sxbdu25eTJkyxZsoSRI0cCsGTJEuLi4qhXz1WXo1b7IQhOtFoNkZGuiFNkZCQTJ06kR48eFBYWEhYWVj1nQYUFq91Zc82Vlx94h0o6pPF3Y85xLprYOMpXrRYddzVyJpLhlEWGzFWCHNGQEcVeVETG2HEoVCripk2vVf7xtYY65DL10jj94baptILlBWX8r340fup/FN2ecVYS6ZStjXPVHtiyPddwrjbOU1IiGvHB3hRVWjFa3Du8xPm6NliizlNKJbQfBynLoNL9BkqtUnJPx3iW7c3GYHIfKdGoNAxqOIifT/x8WaxurxSpJakYrAYCvMTz68tNNspMNpIja8iBbzkUujzu2tBV5oGpFMKbuIrEnU5XxEOlxaTWMLjRYNYOW8ve+/fyda+vGdF4BGOajaFpSFOKzcVkV2bTJLiJhx+ti5zUI+xeuZyuQ0cSHB0j6RpbYAj+pkqyPfRa8unaBXtOLta0NMnXmE2Z6Dwc0fjhhx88NteSJUuoqhKPTp5hzJgxzJw5s/r/M2bMYOzYsdX/P5MiJghn3xsqKyuZO3cuDRo0ICTk/B4vJUYrOo0KlTvhKDEa7C/R4lYTG4PTYMBRXi469mqiWmSYXSJDI4sMmasAWWjIuMVRXk7GA+NxGo3Ez5yBJiL8Si/psqLy9wKVwrN1GiE6ygtN2J0C/zuWTTt/bwZHXESsBSWCtcK1kXNDtf1ipmfTp6T20igryEMQEUNxwa4POLEu3ZG+kXirvdldsFt8kW3uBRSwZ47o0GEd47A7BH7aJZ7+cF/T+/DR+PD8+ufFU7iuASosFWzP3Y5OpaNBUAPR8dtOFXMkr4Iw3xpSIXUBrqiGUgX5h0EXCAGxLvGnVIKXL/R4lgZhLREQKKhyRbKcgpOWYS0Z0WQEL3V6icm3TCbcO7zGZoGXgt1m44/JnxOeVJ92fQdIvq5YrSXIZuHo0aMeWYdPx46g0bjcpyTgdFoxW3I9njp15MgRj81ls9k4deqU5PH33XcfGzduJC0tjfT0dDZt2sS9955tRKg6LTRWrlyFr68vvr6++Pn5sXz5chYsWHBBjVCJ0UKwj0hqW1CiZIvb8mIzgkgz0cvRr+hy4xIZB3CaHISNbyGLDJmrBlloyNSIs6qKzIcexpabS9z0adVvvtczCqUCdaCXxyMaFSVmZmUXccRo5p2GsSgvllZxpqix1P2HujosDIVW6/GCcElCIyISu8WCqdx9/UNcsCtdR8x5SqPUcGe9O1mculh8k+8dDM0Hwc6Z4HS4HRrup6NP80h+2JqOU2RTEaQL4uMbP+ZA4QE+3/W5+zVc5TidTv7O/Ju0ijQGNRp0Xj+Ni2Ew2UjNr6R9QjBKpUiqj80ExScgvOn5zl8KBXiH0Lf+ndzf9H6CdcHVtrr1/OvhcDpwOB2UmkvRq/U0DPSsZSrA1sXzKcvL5baHn0SpktAH5DSZZisJ3jpSU2vX16EmlD4+eLdqJbmfhtmcDQgeTZ2y2+1kevAQAuDkSek1TKGhofTt25fZs2czc+ZM+vbtS2hoaPX3VUo9oKB79w7s3buXvXv3sm3bNnr37s3tt99OevpZweB0OimpshFakwg+Q1Ci6PsmuPoaOWxOjAb3Xb+rD3Myrg3nKUflGZFhJ+zBFh5J+5WR8RSy0JC5KILVStYTT2I5coT4qVPQNaq5t8P1hqctbv3D9FSq4INTudwTFUxr/xo+BIISXX+LFTUqlWhiYrBle1ZoGAwGHA73G/hzLW7dEebrhU6jJL1YvPHX0OShFJoK+Tvjb/GFdngADBlwTDx/+r7OCZwqMrL5RLHo2NbhrXmm/TPMTpnNn+l/iq/jKmVT7iY25WxiePJwSWlTG1ILcQrQuZ6EKENZpiuCEXZxoeCn9SMpIAm1Us2JshNolBoSAxJRKVWolCoCvQKZ2ntqjX086kr+qRNs/3kRnQcOIyw+UfJ1TkEgw2ylaWgQhYWFlJSUeGQ9Pt26UrVtG4JNPDpWHRn0YB8isWjjvzHn2LFjmTVrFrNnzz4nber8OXx8vGnQoAENGjSgY8eOTJ8+HaPRyNSpU6vHGMx2nAIEiUU0UIimnJ4ZdrG11Iznf5aeRhYZMlc7stCQuQDB4SD7+Reo2r6d2G++Rt/SfUHt9YanLW4DQvX83UKP0ynwYr2omgfqA11pKRJSADRxsR7vDi4IAgaD+0hFQLirgF3M4lahUNAhMZiVB3JF750cnCzdZjamLUS1hh3TRId2TAqmUYQvc7amic8L3NvkXm5NuJVXN71Kern47+Bq41jJMVacWMFtibdJSplyOgU2nSiiSaQfvjoRXxCnw3ViHNIARHpgOJwOjpcdJykg6TxbXYVCQaRPpEebezrsdv6Y/DmhsfF0HDC4VtfmW21YnAJtY6JQqVSeS5/q2hWn0YjpwAHRsTpdDKDAbPLca1mj0RAb676HTG1JSkqq1fg+ffpgtVqxWq307n0rRuNJzJZ8jMZUMjOn43BU4nBUYbOdfb9RKBQolcrzHKtKjK7IQ5C3iI16WfrZgxo3lBeZUaoV+AS4j5DYslw1O5rYqzuKXy0yqmyyyJC5apGFhsx5CIJA7uuvU/Hnn8R8+gk+nTtf6SX965zpDu6pk8F0rcDu+l6MVvoQphX5wJRY1KiNjcXm4RoNQNR5Sqv3Ru/nj6FAvHD63s4J7JZoMzsseZg0m1mFwhXVOP4nlLgfq1AouK9zAqtT8jlZWCm6BoVCwZtd3yRUH8oza5/BZBcvGr1aKDWXMvfIXJoEN6FnXE9J16xPLeTXA7nEBkvYnJTngtbXJfJEyKzIxOKw0DDI8ylS/2TH8sUUZaRx28NPoVLXrqdPhsm1ia3v70tSUpLH0qd0zZuj9PeXlD6lUnnh5RWByZThkXufoZEHI9AqlaraMao21xw+fJhDhw5QZthEWvrXOOxGHA4L5RUHEQQHRmMxe/dNIy1tH4cPH+bxxx+nsrKSfv36Vc9TYrTg66VC+0/jjH9SmiZRaJjwD9GjEEkTPNOs74zxxtXIeSJjfEtZZMhctchCQ6YaQRAo+OBDDD8tJvr/3sWvVy/xi65DVME6BIsDZ5V7xyQpCILAq6dyiDAK3FQm4ST3jPOUCJpYV3dwT4mhgIAAFAqFpDqNwMgoCtPF86HP2MzO3SYeHbgl4azNrCjNB7kKlHfOFB06sG0sCSE+PDJ3N2ab+7QwAF+tLx/3/JiM8gwmbZmE3Xnpz4HLTZW9ijkpc9CpdAxLHoZSIf62bjDZWHeskO4Nwmgc6Sd+k/3zXZ2/9eLpWNmV2cT4xkhK3boUirMy2Lp4Hh36DSSinngE55+km11CI06nJTk5mfT09PNO0+uKQqXCp1MnyQXhen08JrNnawFGjBjhsbn69++Pj0/t7cz9/f3RasspLl5PQvyD+PjUx9c3GT/fZigUSjZsOEbHDs+RlNSaTp06sWPHDhYtWkTPnj2r5ygxWsULweG00BDvrF5eZMI/VLwrvTUzC6WfHyoPdkX3JI5KK0XTDuA0nhYZ4bLIkLl6kYWGTDXF331HycyZRPzvfwT073+ll3PFUAe5Pog8kT61JL+U7QYjw3PBWChhvqBE0Q634EqdEkwmHB7KK1epVPj7+0sSGg07dePEzq1UiRSEV9vM7skRtZnVqrQMajiI5SeWU2UTsdLUerscqPbMcRUou8HHS803I9uSVmzk1WUH3c97mkZBjZjUdRK/n/qdcX+Mo7DKM/0ILgfZFdl8sesLis3FjGw6UrRnBoDN4WTahpMkBHvz/qAW4qlMRcfgr7ck1RGUm8uxOWw0D2ku9SHUCafTwR/ffo5/eCRdBt9TpznSTRbCtWq8VUoaNWqE0+nk+PHjHlmfT7dumPbvx1FRITpWr4vF5MHUKYDhw4cTHBzskbkeeeQRSeNmzZrFsmXLzvua2ZyDRu2Pr28yS5cu4fvvf0StCeTdd/shCAIWSxHHjn3AyVPL2b59O4MGDaq+VhAESittBHuLCA2rEYyF0iMaEtyYbJmZaK7SaIbDaKNo2gEclTaXha0sMmSucmShIQNAyQ9zKfzsc8KefILge0de6eVcUdTBnumlUWl38OaJHPqGBdBRK82/ncAEV9Gtw/1JerX94hWwuG3e8xZQKDj4t3hB9rCOcdgcTpbsFt9IDW40mCp7Fb+e+lV8se3HumyADy0THdokyp+3B7Rg0a4sFu6Q9vO6o94dzLhtBlkVWQxZMYTtudslXfdvsj13O1/v/Rq9Ws8TbZ5w3/DwHJbtySar1ESvxuH41tQE7Vx2zgDvEGh6l+jQz/d8ztd7v8ZfV0NPDg+xe+Vyck+kcttDT6LW1q2rd4bZSoLOlasfEBBAZGSk5+o0unUFh4OqbdtEx+r08R5PndLr9bz55puXPE+fPn3odQmRbUFwolR6YbMZUJyOtFmtBSiVruedVhuCXh+HxXxhzZfR6sDscBLsI+I4daamTURoCIJAeZFZmtDIzkJ7FdZnOIw2iqaeFhnjW8giQ+aaQBYaMhiWLyf/iJZI0gABAABJREFU7bcJHj2akIceutLLueIovTUodGrspZcmND5Lz8dgd/BGg5gLuoPXSFAiCA4od78x15wu9vRkQXhERATp6emizlN6P38ad+3B/j9/wynRZnbO1nTRNK9o32h6xPZg/pH54ilhIfWh/s2SisIBBreLZXiHOF79+SCHcsRrRgDaRrRlQb8FNAhswPjV45l2YBpOwSnp2suJ1WFl4dGF/HTsJ9pFtOORNo9I7k2xK72UDceKGNg2hrgQCekwVqOrG3vb+0HtfsNXYa1gxckV3Bx/Mxpl7eolakNpXg6bFvxA2z79iEmue/O/DJOVeP1ZkZKcnMzx48dFn/9S0MbFoYmLk5Q+pdfHYbMVY7eLO7TVhkcfffSSUqji4+P54YcfLql438enIQ6niYKC3zCbcygs/BNTVQY6fTzg2vxbbcWoNYEXXFt6uhBcNHXqTKqpiNAwV9qwWRwESBAa1sys6vfYq4XqSEaF1SUyImqfziYjcyWQhcZ/nIo1a8h56WUCBg8ifOILHnWEuZZRh1ya89TJKgvfZRbyWHwEcTot/qF6TOVWbBaRTUy1xa379CmVry+qwECPWty2adOGyspKSae6rXrfgaEgn7S94o327uucwMlCI1sk2MwOTx5Oamkq+wr3iS+4wwOQvRMypUUb3ujfjAbhvjwyd7doKtcZQvWhfHfrd4xrPo7Pd3/OE389gcEiTahcDgqrCvl679fsK9zHsORhDGo06KKbepvjQkGUZzAxb3sG7ROCuKFB6AXfvyj75oGlHNqNER264sQKrA4rgxoNEh1bVwSnk1XffYFPYCA3DL//kuZKN1mJ153dxDZq1Aiz2UxGhmeiCz5du0oqCD/TrM/TUQ2AqVOnMmDAgFpfV79+fX799dcLunTXFp0ugpCQG6k0HuX4iQ8oKv4bL100vr6uYnWzJQcAb+/EC64tMVrwUinx8RIpBC9Ldzmh+Ua4HWY4HVH2E6nREOx2bDk5V1UheLXIKLe63KVkkSFzDSELjf8wxq1byX76GfxuuYWoSZNkkXEO6qBLa9r32vFsIrzUPBrv6qTuH+L6cBNNnwqIA4VSWkF4XJxHu4NHRkYSFxfHjh07xMfWb0REvQbsXSWe5nTWZla89qRLdBfi/OKYf3S++IIb3gaRLWDpBDCLb/51GhXfjmxHidHK84v2SS6kVylVPNH2Cb7u9TV7CvYw7JdhLDu+DLPdcxbIYhitRtZmruXLPV9ic9p4rPVjtItod8G4ggozA77exPSN5xfrm20Opm88RbC3hmEd46S91gtTYfXr0GqEaKGtIAgsOLqAm+NvJtw7vDYPrVbs+/N3slIO0nvCE2h04kW9NWF0OMiz2s6LaERFReHn5+dRm1trejrW01apNeHr0xiVypeCwt89ct/z1uDjw9KlS/nggw/QSkwxGzhwILt27aJ580uvsxEEgcCAdiQ3ep0mjf+P+LjxaDSB6Lxcm3gFasJCb8XH+0JXq2KjlSBfjfhztTTNlXIqMq6iyPV6FUudsuXlg8Nx1VjbOqtsFE0/LTLkSIbMNYgsNP6jmPbvJ/ORR/Hu2JHoDz9AUYtuuv8FVMH6OguN1UUG/iwu5436MehVrpeYf5jrw01UaKi14B8j0XkqxqPdwQE6dOjAqVOnKCx0XwCtUCho1fsOTu3dJdq874zN7KqUfPIM7n+mSoWSYcnDWJW2imKTSAREpYah34OxGJY9IqlhV3yIN58Mbc2qlHymbRB3zjqXHrE9WNhvIUkBSby66VVu+ekWPtrxERnlnj+JBtcmLd+Yz6Iji3hn2zv8kfYHLcJa8ESbJ4jyvbAfS2p+BeNm7SSnzMTnfx5j47Gi6nnm78iktMrG2Bvq4SVmFQqulKmF97mei3d8KDp8Z/5OThpOMjx5eK0fp1TKiwpYP3cmLXv1Ib55q0uaa3lBGQqga6Bv9deUSldR+NGjRz3i5ubTuRMolRg3b3I7Tq32ISrybnJyFuB0uu9YXVeef/55MjMzeeedd6hfv/4Fm/egoCDGjx/Pnj17WLx4MQEecls69z4qlR6zOQu12he9PgZwRTx8fRuhVF4ogkqNVkLECsFBsrWtociEzkeDl959z5irydrWWWWjcNoBHAaLS2REyiJD5tpDFhr/QcypqWSOfxBd48bEfvE5yjoWU17PqIN1OMrMCI7abTgsTievHc+me5AvfcPOflh7+2tRaZSUF0ms05DUSyMOa5ZnbTGbNm2Kj48PO3fuFB3buGsPvLy92f/nb6JjB7SJQadWMm+7+Kb8rvp3oVQoWXp8qfiCg+vBgG/gyC+w5Svx8cCtTSOYcGM93vv9CFtPiqdznUuMbwzf3vItv979KwPqD2Dp8aX0XdqXh1Y/xN8Zf+MQqVmRgsVhIaUohTkpc/h418ccNxzn1oRbeaXTKwxpNATdRRrmWe1OdqWX0iDcl7kPdKJP80gmLt5PZkkVG44VsTu9lBEd44kMkBAFEARY8ZTLlGDYHPDyFb1k/pH5JAUk0SGyQx0esZQlCaye8hVePj70uFc8jUuMWdlF3BTsR4L+/LqT5ORkSktLKSoquuR7qAIC0LVojnHzFtGxMTH3YLUWUli46pLvWxPh4eG8/PLLHD9+nKqqKg4fPsyuXbsoKSmhpKSEKVOm0Lp168t2f4fDTJUpDV+fRtWF4TVRZbFTYXEQ7CtFaEht1ifV2jYTFArU0dHi976MOKtsFE4/eFpktJRFhsw1i0g7WJnrDWtmJpnjHkAdHU3c5G9ResuuFRdDHawDJzgMlmoXKilMySwkw2xlZouk807zFAoF/qF6ac5TQQlQcFh0mDYxAXtePvaSEtQesrJUq9W0bduW7du306tXL7fpFhovHc173sKBv1fTdchIt+4/fjoNd7eNYd72DB67uQEaVc0bjUBdIH0S+7Dw6ELGNBuDSilyAt/kTuj6hCvNJ6Y9JHQRfZzP905mX2YZo2du5+0BLRjcrnanl/H+8TzX4Tkea/MYv6f9zoIjC3ji7yeI8onixtgbifOLI9Yv1vXHN/aitrOCIFBuLSerIovMykyKq4rRqrRU2irJrMgk1jeWMc3GkByUjFLpfmOmVStpGx9Ekyh/Gkb48e7dLej/1UYmzNlJYqgPNzcOp11CkLQHl7kVTq2F/l9AWLLo8IKqAv7K+IvnOjx32dIvD61bQ9q+3dz94ut4eV/ahmtPeRX7Kkx83+LCbtdJSUmo1WqOHj1KWFjYJd0HXOlTZT/OQ3A43EaNfX0bERjYiazsuURE3HnJ9xVDp9PRuHHjy36fczEaT4AAPj7iPU+OFVSgVkBskMjnkyCc7goutYeGBMeprGzUkZFX9ACuWmSUmgmVRYbMNY4sNP5D2PILyBgzFqW3N/FTp6Dyv7wWlNcyqnMsbqUKjTyLjU/T8xkbE0pjnws/0PxDpVrcJsJR8SiB7803o5j0JoYlSwh54AFJa5RCu3bt2LhxIwcOHKBduwvrAM6l5S13sOvXn0ndupGmPW52O/bezgn8sDWD1Sn53NHiwtSfcxneeDg/n/iZtVlr6RUvwV6z12uQtRMWjYaHNoCv+zoBtUrJrDEdee3ngzy3aB8700p4o38zdJrapRDq1DoGNBjAgAYDOFh0kIVHF7Ijb4erhsNxNnoVrAsm1jeWGN8YbE4bWZVZZFdkU2Fz9VlICkjixpgbifePJ9Inkv71+hPi7b4QN7/cjE6tIsDbVQyefE7jPZvDSZd6IczdnkFSqC8D20gUUiWn4NRG6PUmtBgs6ZKFRxeiUWnoX//y9N6pLC1h7fdTadr9Juq1ufSIyazsImJ1GnqFXPj+p9FoqF+/PkePHuWGG2645Hv5du1K8beTMf8/e+cd3mT1/uE7Sffee9NBoWWXPRTZIC6GKCqCDBU3uPWnX7c4UUGmKIJsUJApKhvKpqWlLXTQvXeSZr2/PwLIKE3SppTx3tfVq4W+7zlP2jQ5n3Oe5/kkJWMb23DNQ4D/oySefp6amhQcHAwLvFsJQdAhl6djb98Kmazhxb5OpyOjpJYIb0fDaX61xaCWG3miocQ7xHBKmDo7G6sW7Dh1tciw8hVFhsitjSg07hA05eVkPzUJQasl+JefsfAwsuvMHYqFizVITDPt+/BcHrZSKTNCfOr9vpOHLbkphn0qcA0BeSnUVYP19V2bLVxdcRo6lPIVK3F78kmz1dm4uLgQGRnJ4cOH6dSpU4O71G5+/gS368iJ7X8aFBqtfZzoGuLG0gNZBoVGjEcM3Xy68fHBj+ng2cFw+1aZJYxaDPP6wtpJ8NgGMHASYmMp4/NR7ekS7MY7vyeSkFvJnEc7EWxM29frxBzjoV9MCoJAqbKUnOoccmpy9J+rc8itycVSakmsRyxDQ4fi7+BPgGMAoU6hRpntXWThnnRmbUvh3XvbMLpzIFYWUgRBQCKRcLaohiX7MhCAlwdE8OWOVLYn+TIkxpfsMjnpJbX0i6xnt76uBg7OBVtXaDfaqDhOl5xmceJiHmn9CI5WRjiMm4ggCPy1cA4yC0vumjClyeOVqTX8XlTOKyE+yK7zvI6KimLjxo3U1tY2yhH7cmzbt0diZ0ft/v0GhYan50CsrDzJyV1O66j3mzTvzYZKVY6lpSuOjm0MXltQpUSC5ArhfF0ydus/ezXc5lir1VFTpjQudSonB+tWrQzP3QzoFBqKF18QGU/FiiJD5LZArNG4A9DW1JI9ZSqa0jKCFi3CsoVzT28FJBZSZE7Gd56Kr6hhTWE5b4X54mxZv3539rClqlhhuNDUJ1b/+dzfBud1fWQc6pwcavbsMSpOY4mLi6OgoICcHMPF5u0HDSM/LYXCdMOuyuN7BHMgvZSzRYYdkz/u8zFaQctre14zrvbByRdGLYLMvfDPx4avv8CYuEDWPdOTmjoNI77by/bTDRe3G4NEIsHD1oMOXh0YETaCae2n8WHvD/lpyE/MHzSfd3u8y8SYiQwOGUxb97YmiYwl+zJYcTibaF8nvvkrjePn/xOvO5ML+f7vNDwdrXl1cBTP9o9gfPdg3v39NIv3ZjDkm93sO1tP/YFOp/ck0Sih62R9ob0BKusqefnfl4lyjeL5Ts8bHb8ppBzYw7kjBxkw6RlsHZouZFbml6ET4GHf66caRkREIAgCaWlpTZ5PYmWFfdeu1O5ruCAcQCq1ws9vLAUF69Foapo8981EesY35OWvwcLC8Cn60awylGotLsYUgh9eBCF99LVaDVBTpkQQDHecgpZzBdcpNBQvSkBbdkFk+BmujRIRuRUQhcZtjq6ujpxnn0WVkUHQwgVYh12blyxSPzI3GzRlhlOdtILAW2m5tHe0bXAB4+hug0atQ15loLOMV2sI7KZ/EzWATbt22LRpQ/lvvxm81hTCwsJwdXU1qtVtq05dcXD34OSOzQavHdLWBw8HK349aLgo3MvOi8/7fs7hgsPMOTnHqLgJ7Qv934Y9X0DqNuPuAdr6ObPxud70bOXOlKVH+WRLMpp6vChaGqVai1Kj44GO/mx4thcBrrZ8vDmZ5PxKFu7J4PcTedzd2ovp/cNxvrBQe2VQFAq1lg/+TGJCrxDeHFbP7m/KZihIgLjJYG/4tFMn6Hhz75vUqGv48q4vsZKZP59dXlXJ34t/JLJ7byK69WzyeDpB4Oe8Eu71csHT6vqGgo6Ojvj7+5u1za38+HF0crnBa/39HkanU3P23GdmmftmoKTkH/LyluPu1stgDU9OuZxlh7IJ8TBiJ7/wNJzfr/fTMUBlkf513NCJhramFm15OVaBN7a1rSgyRG5nRKFxGyOo1eS+9DKKkycJnPcjNm0MH1uL/IeFmw2a8jqD1y3PLyWhRsHHEQFIG3gjdb7Q4rai0PCCg7jJkLFL72XQABKJBNdHxlG7e49ZPTWkUilxcXGcPn2ampqGd1elMhnt7xlC8t5dKGsbvtbKQsrDcUGsPZpDbZ3GYBxdfbvyXMfnmH9qPntyjDy16fUSRA6BdVMMGh9ejpONJT+O78xbw6JZuCeDRxYeoqjqxnllGIONpYz7OvgxrqveWXnJhK6cK67l+d9OkJhXwVO9Q7mvgz+yC8XjpTV1TFt6FIBVU3swc3A9BcCFSZC0QV9U7xtrVByLExezO2c3n/T5BD+H5jkh/WfJfARBoP+TU80y3q6yajIVKib4GxZSUVFRnDt3Do3G8HPUEPa9eoJajdyITm42Nr5ERr5Dbu5yCgp+b/LcLY1CkcPppFfw8LgHX1/DRo4/7csktbCG9oEuhgc/vAgcfKD1cIOXph0pxNHNBkd3Ax4aFwxQLf1v3ImGTqlPl9KUKvGYJIoMkdsPUWjcpgg6HXlvvUXN7t0EzP4WOwNFvSLXYuFmg9bAiUa5WsMn6fmM8XGls3PDu3CuPnbYu1iTdrjQ8ORtRoKdBxxZbPBSp+HDkTo4ULFypeFxTaBDhw5IJBKOHz9u8NrYewaj02pI2rXT4LXjugVRq9Kw/njDRmYXmRgzkX4B/Xhj7xvk1eQZvkEqhfvngo0TrH4CNIbF4kUkEgmT+4axYkp3MktqGfrtHn745yzF1caP0dz4OtviZm9FTrmcnWcK6RDoTFpRDa08HYnyvTK9KKtMjpeTNbtn3k1cSD2nbfJyiJ8Pnq0h+j6j5o/Pj+e7498xpd0U+gb0NcdDuoazhw9yZt8u+k+Ygr2Lkd2yDLAkr4QYB1u6OBlOU4uMjESlUpGZmdnkea3CwrDw9jbKJRzA328cPj73k3zmLWpqm56+1VLodHUkJD6LhYUTbaJnGWxpq1BpWX0kmzFdAg03ZVBWwamV0HmCvj6roUtr1KQdKSKmnz9SacMnKursG+uhoVNqKF6UiKZEgedTsVj5iyJD5PZDFBq3IYIgUPjhR1Rt3IT/rM9x6Ns8i4HbHQs3G3S1GnQN7LzPyihApRN4K8zwrq5UJqVtHz9S4gupUxjYKbWwhk6Pw4nlevO0hsa1tcXlwQeoWLMWXZ35FsR2dnbExMRw5MgRdLqG04jsXVyJ6NqTE9s3G6xB8XexZXg7P77YnkJuheHUNKlEyke9P8LB0oFX/n0FldYIUzM7Nxj9sz69YtloqDXNFyEuxI0/n+/DPdFezN6ZRs9Pd/L8b8c5nFlmFjO3xqLV6TiZXcEP/5zl860pxKeXMjYukOf6hzP333McTNfnt689msOOpEI6Bbny7cMdcbWvJ7WpMhd2f6Evmu86RS/QDFAkL2Lm7pnEecfxTPtnmuERgrK2hr8WzSGsUxyte99lljGzlSp2lFQxwd/DqBa83t7eODs7myV9SiKRYN+zJ7X7jRMaEomE1lEfYGsbQELCs7dsvUZq2kfU1qYSG/s9lpaGuz1tPJlHdZ2GR7sFGR781EpQK6DzEwYvTT6QjyAIRPdsuAEFgDL5DFI7O2Q3oFmKTqmhRBQZIncAotC4DSmePZvy5cvxef89nIYObelwbln+a3Fb/+I9uUbBktwSXg7xwdu64V21i7Tp5YdOrSPloBEFx12ehLoqSFhj8FKXsQ+jraigeutWo+Iwlri4OCorK40qjO0waDjl+bmcTzxp8NoP7muLvZUFzyw7hkpjuBbC2dqZL/t9SUp5CrMOG3aqBsC/Ezy6Ri82fuwD5w8Zd98FPB2t+XxUew69eQ+vDWnNqZwKRv94gKHf7mHZoSyjUr/MRaVCxdbEfN7fmMSivRko1VrGdw/ivZEx3Nven1cGRdEtzI0PNiUxcclh3lifgL1VA7vC5w/A3x/qxUWfGfrTHwOodWpm7pqJhcSCz/p+ZtjfpJHsWroItVLJgMnPms2X49e8UuxlUh7wdjHqeolEQlRUFKmpqeZxCe/Vi7q0NNRFRUZdL5PZERszh7q6As6ceatFxW1jKCj4ndzcZURGvIuTY8PdtkC/OfbLwUzujvIi0M0I74zDi/QpU04Nb/AIOoHE3bmEd/bC1rHhOiJBo6Fi3Tochw1tNj+Yi+iUGkoWJ6IuFkWGyO2PKDRuM0oX/0Tp3B/xmjkT1zFjWjqcW5qL/hn1pU8JFwrAw+yseSrA+N0vexdrQjt4krgrx/DiwSVIX2tweIH+zbUBrMNCse/Zg/Ll5i0K9/f3x8/Pz6iicP/otrgHBHFyu+GicBc7K+Y82onkvCo++jPJqFjaerTl9a6vsyJlBX+m/2nUPYT10/tquATBkmFwYI7Bn2V9sT7VJ4y/X7mLXyZ2JdDNjnc2JNLt45383++JpBUa7qDVGARBILdczpL9mbz3x2l2JBUS7ePEzMFRvDIoiq6h7thYydDq9I/nuf7hnCuuoaZOw86X+9EzvJ7npVYNx5ZC/ALw7wz939J36zKC2cdmc7L4JF/c9YXhdsONJPPkMRL/2UG/xybh6GaeXeU6nY5leaWM9XXD3oQW0FFRUVRWVlJYaESqowHse3QHMPpUA8DePozo6E8pLNpETu6vTY7hRlFTk0rymbfw8XkAP7+Hjbrnj5N5JOZW8VgPw8Z7ZO2H4mSjisCzk8uoKlYQ289wKlTNv/+iyc/Hddw4Y0JuNJdERpECz6diRJEhctsj+mjcRlSsWUPR55/jPnUq7pMmtnQ4tzxSB0skllI0pdcWBP9RXMH+ihqWtwvDyoiUk8uJ7efPhq+Pk5taQUCUgfzzuKdg2UN6M7rAhs3KXMaNI/e551GcPo1t27YmxdRgCHFx/P7775SVleHWgAO5RCKhw+AR/P3Tj1SXluDo3vBCsX2gC+/c24Z3NiTSOcSNke0Np5+NjhzN8aLjvH/gfVq7taaVixH97p38YMIm+Os92PaG3vl65PdG7eJfjlQqoW+kJ30jPcmtUPDbofOsOHyenw9k0SHQhUhvB4Lc7Ai88BHkZoe7vZXB3VGtTqCwSsn5MjnZFz7OX/ho4+tEiLs997X3p2uYG3ZW175ky6QSdiQVMmXpER7sGMCXY9rXP1FNMRyaC5U5+rS80H5g5M7tzqydLDm9hBldZtDRq6NR95iKSiFn+/zvCIppT2z/QWYbd3NxJSVqDU/4mSZcgoODsbKyIiUlBR+f+r1xjMXC3R3r6Ghq9+/H5f77jb7P22sYlQFHSUv7CJnUBj8/4/xNWorq6iROJTyNrW0graP+Z9TJQFphNa+vTeDBjv7cVZ+/y9UcXgjuEfoOcwZI2JWLR6AD3qGG/9bLl/+GTft2Zn3tvJprREaA+b1nRERuNkShcZtQtXUr+e/+H66PjMPzxRdaOpzbAolEgqW/A8rUchz7/rcjVqvV8r+zeQz2cKJ/Pe7ChvCLdMHVx47EXTmGhUar/noDv8MLDQoNx7vvxsLbm4oVK7D94AOT47oebdu2Zdu2bRw5coRBgxpeALbpcxe7l/3EqZ1b6TVmvMGxx3cL4khmGa+vPUUbX0fCvRp+45VIJLzT/R3OlJ3h5X9f5rfhvxnnQSGzhMEf6dsG//4sLLgbxvwC3o1bVPi72DJjcBTP3xPB1tMF7EgqJKWwhr+Siyir/a+GxNZSdkF82OoFiKsdaq2O7HI558sUZJfJyS1XoLqsla63kzVBbna08nTggU7+dApyNbhgc7C2YNao9ozqfJ2d2/yTEL8QrGzh7jeNclK+SHZVNm/ve5sBQQN4vM3jRt9nKnt++xlldTWD/u85s6auLMktobeLAxH2hs3aLsfCwoLw8HBSUlLo169fk+Ow79mDyj/+uGSsaCzh4a+h1cpJPvM6FZVHiYp8D5nMtMdyI8jLW0VK6v9hbxdBbOxcZDLDf5e1dRqm/XqUQDdbPnwgxvDPpboQkv+AQR8ZFMlVpQqyEkro90iUwXHrMjKo3b8f308/MRhzY9HVaSj56TTqIrk+XUoUGSJ3CKLQuA2o2bOH3Jmv4jR8ON5vv93s+aV3EvbdfClfmYK6SI6ll/6N8/usIkrUGt4P92/UmBKJhJh+AexdnUZtRR32LtbXv1gqhS6T4O8PYPDHYH/9lBWJhQUuY8dQOn8BXjNnInMyXQTVh5WVFR07duT48ePcfffdWFpevx7FytaOmLsHcPTP32ndqx/u/g33o5dIJHz8QCxJeVVM+/UYvz/bC3vrhl+W7Czt+PKuLxm3aRzvHXiPz/p8Zvxzvs1IvbhY9TgsuAdGfA0dGp8qYWUhZWR7vytOY6qVarLLFGSXX3k6sTu1mOxyBZZSyaUTj3tae136OtDNjgBXW8Mdd+qhR6vrPC90On3r2jObwLc9xE0CK+NTNZQaJS/9+xJuNm78r5dxO9SN4eyRQ5zY9if9n5yKs1fTTg8uJ6lGwaHKWha2DWnU/VFRUaxfv57q6mocHZu2MLTv2ZOyRYupS03DJirS6PukUiuioz/B2aUzKSnvUl2dSGzM99jZhTQpHnOh1SpISX2P/Pw1+PmNJTLi/5DJGnhNu4AgCLy+LoGCSiV/PNe73tO6azj2C8isoL3hlKykPXlYWsuI7Gr4+VSxYiUyZ+dmq2nU1WkoWXwadWEtnpNEkSFyZyHWaNziyI8eJee553Ho3Ru/jz9CYmIaj0jD2MV4ILW3oPZQPgBZijrmZBfxdKAXIbaG30yvR1R3H2SWUk7vNaJda8fxgASOLzV4qcuoUQgaDZUbNjQ6tvro0qULCoWC06dPG7y298OP4+juwcavPkGtNOxDYW9twdzxncirUPDm+gSjCl/DnMN4v+f7bMnYwsoUE9v6ureCp/6CmIdgwzTY+AKozeeX4WhjSRs/Jwa39eGpPmH8774YljzZlZ2v3EXKB0NIfH8wW1/sy/zHu/D2iDY80TOEu1t7Ee7l0CiRcV2UVbD3S70ZX8xD0OM5k0QGwCfxn5BZlclXd32Fo1XzLI4qCgvY+sNXhMf1oMPgEWYde0luCT5Wlgz2MNz1qD4iIiKQSCSkpjbsZ2MMdp07I7GyMqlO43L8fEfRpcs6tFo58Yfvo6jYeEPK5kIuz+TI0VEUFm6iTfTnRLf+2CiRAbD0YBYbT+bx2ah2tPI04nmp1cDRnyB2NNi6NHypWkfSvjxa9/DF0rrhvymdQkHF+vU4j3oIqXXjX9OvO/5FkVFwQWQEiiJD5M5CXJXewiiTk8me9jS27drh/83XSBrYaRZpHBJLKfZdfKg9WohOpeW9s3m4WVrwXLBXk8a1trUgqpsPSXty0RpyoLZz0y8UjywGnbbBSy29vHAcOIDy5b+ZtVONu7s7rVq1Mqoo3MrGlpEvv0lVcRE7FnxvVBzhXo58+lA7fj+Rx6+HDLuGAwwJHcIjrR/hs8OfsTXTxG5blrZw/w/6Wo2TK2DxICjPNG2MRiCRSG7MiWNxKux8Hyrz9F2lWg83qn3tRXSCjjkn5rAubR1vd3+bKLeoZglTo1Kx8atPsHV0YsgzL5r1Z1Ot0bKmsJzxfu5YGvBPuB52dnYEBgaapc2t1MYGuy5dqN23r9FjODq0pmvc77i59SYh4RnSzn6CTqducmyNoah4G/GH70OrVdKly1qjDPkucvx8OR9sSmJCzxBGtDPS8PHIYqjKNaoI/NyJIhTVamL6GT51rtq8GV11Na5jxxoXhwlcSpcqqNWnS4kiQ+QORBQatyh1GRmcn/QUViEhBMyZ0yw7MSJ67Lv5ItRp2X4khy0llfxfKz+Tutdcj5i+/tRWqsg8aYTHQ9xTUJEFZw0b4rmOG4cqMxP5wYNNjvGKEOLiyM3NJS/P8CmMe0AgA6c+R/Lefzn11xajxh/Z3o8negTzwcYkTmZXGHXPjC4zGBg8kJm7ZvJp/KeotSYuujo9BpN26Hf/5/aCP2dAUbJpY9wsCAIUn4GDP8Luz8HeEwb8H3jV4wbeABXKCp7d+Sw/nvyR6R2mc3/4/c0TL/D3knmU5WZz78tvYG3XsOGlqawqKKNOp2O8X9M6ZEVFRZGeno5KZYR/iwHse/VEfuRIk/xuLCwciY35nojwt8jOXsKBA/3JyPyBOpVpXjGNQaero6Dgd44cHU1CwjO4ufWma9wGHB2Mf46V16p4dtkxYvydeXNYtHE35R6F7W9B16ng287g5Ym7cvGPcsXVp+HnlCAIlC9bjn2f3lgFGeHfYQK6Oq1eZOTX4jEpRhQZIncsotC4BVHn5XF+4iRkbq4Ezp+HzMG8b9AiV2LhZoMsypX3K8vo7mzPfV4uZhnXI8AB33BnEnYZ4ZDt3wl8O+iLwg1gFxeHVXgrs7e6jYiIwMnJyahTDYDoXv3oMHg4/yyZT8E54xyO3xweTbSfE88sO0Z5reGFnaXMks/6fMZb3d5iZcpKJmydQEGtER4ll+PbDqb8C92fhqTfYU53+Gk4JK4DTdMXl82OSg5n/4Yd78Kuz6EiW5/D3neGwRSTq0koTmDMpjEkliQyd8Bcpraf2jwxA6d37SRh5zb6T5qGV0iYWccWBIEluSUM9XDGx0iPm+sRGRmJRqMhIyOjyXHZ9+yJoFSiOH68SeNIJBKCgiYSd+F0IzNzDvv29SYx8QXKKw6b3XdDocjh7LlZ7N3Xm9NJLyOV2hAbO4fYmO+xsDB+Aa3TCby48gQKtZYfHumElYURSxB5Gax6AnzawaAPDV5eklND/tlKYo04zVAmJKBMSjJ7S1u9yEi8JDKsg8xTLycicisiCo1bDE1pKecnTkIilRK0aBEWrga6FomYhTUxDmRZw/85upg1vSOmnz+5KeWU5Tfs/o1Eoj/VSNtuMMVHIpHgOm4c1X//jbrAxEV3A8hkMuLi4jh16pRRpxoA/R57Cs+QMDZ+/QmKGsN+E9YWMuY82olalYaXV51ApzO8YJJIJDzc+mF+GfILxYpiRm8czb5cE9NTbF2g/9vw0mkYtRgQYM2T8E0M/P2R3kX7ZqMyW++JsXkGnFwOjj76NKnBH0L4AJAa3+tDEARWnFnB41sfx9POk9X3rqaXf69mC734fCZ/LZxDzN0Dib3bfK1sL7K/ooY0eR0T/JvuxeHh4YGbm5tZ0qeso6KQublRu69xdRpX4+jQmujoT+jdaz/h4a9RVZ3IsWMPcyh+GDk5y9BoGu/xIghaSkr/5eTJyew/cBe5ucvw8bmP7t120KnjUrw8B5v0WqjVCXzwZxK704r59uGO+LnYGr5Jp4N1U0BVC6OXgEXDpnsAibtzsXe2IqS94d99+fLfsPTzw6Gv4Va5xqKr01KyRBQZIiIXkQi3muXoHYy2qoqsJyagKSkmZNkysx/1itRPUZ2anoeSGZan5j2JA25jzZevrlXr+PnNfUR08abPWAOdaFRy+Ko1dJ4AA//X8Lg1NaT17Yf7hAl4Pv+c2eLVaDQsWrQIpVLJ1KlTsbEx3GazqriIpa+/gF9ka+6f+Y5RDQv+TSniySWHeWVgJNP7RxgdX4Wygjf2vsG+3H1Maz+Nqe2mNt7BujAJjizS13CoFRA1VC/2wu4y2n/C7GhUcOZPKD0HOjXUFuv9MEL7gl3jNh3kajnvHXiPLRlbeDT6UV7p/AqWsuar96qTy1n25otYWFox7qMvsbQyf9rn5MRMztQq2N21tVk2BrZt20ZCQgIvv/wy0iY23Mh9ZQaqzExC165pclxXIwg6yssPkJP7K8XFfwE6rK28sbENxNY2EFubC59tg7CxDUAmtUWhzEahyEapyL70tUKRjVKZiyCocXRoS0DAeLy9RxjVsrY+iqvreGHFcQ6ml/LuiDZM6BVq3I27Z+mF/vg1evFsAJVCw0+v76PjwCC6jmh4Dk15OWf73YXH9Ol4TJlsXDwG0KkunGTk1eIxMQbrYFFkiIiI7W1vEXQKBdlPP4M6L4/gpb+IIuMG8nF6PpYSCTN8PJDvyMZ5RBgye/MsxGSWUtr08iPh3xy639+q4Q4pVnbQYbx+F/uuN8Hy+ot8mYMDziPvpXz1Kjyenma2RgEWFhaMHj2aefPmsXHjRkaNGmVwIefk6cWw6a+w7rP3if99Dd0eMOxYf1eUF8/1j+CrHal0DHKlV30u1/XgYuPCD/f8wIJTC/jhxA+cLD7Jp30+xdWmEYtw7zYw/EsY8B6cWgmHF8HS+8E9XN9yuMMjJqcmNZqKbDi6BI79rBcX0fdB35ngHW3SycXVpFek89K/L1FQW8CsfrMYEjLEfDHXgyAIbPvxG2orKhj/6TfNIjIK6tRsKangvXB/s50+RkVFceDAAfLz8/H3b1xb64vY9+pF1ebNaMrLzX4iLZFIcXPrhZtbL5TKPMrK9qNQXhAR8kzKyvaiUhXXe69M5nBBhATi6XEPtrZBODm1w9Extkk/x/iMMqYvP4ZOgGVPdb9+K+arSf8X/vkY+r1mlMgAOLEzG61aR9vehgvMK9etB0HAZZTxRewNoRcZp1HnXjjJEEWGiAggCo1bAkGlIuf5F1AmJxP802JsIo3vwS7SNI5V1bKioIxPIwPwd3Em/69s5EcKcOzXsD+EKbTp48exbVmkxhfQto+BRUyXiXDwB703goFe8q7jHqFixUqqd+7EaYj5FpBubm6MHDmS1atXExISQlxcw0aCAKEdu9D9wbHsW/krvhFRBMVcx736Ml64J4JjWeU8/9tx/ny+Dz7OxpmUSSVSprafSjvPdry+53VGbxzNl3d9SXtPw3PWi7Wj/iSjyyQ4f0BfJ7PjXdj5P/CK1pvfuQZf+HzhwykAZCa+vKqVUHFenxpXkaX/XJ4J5VlQdBos7fWeH10mmVzgXR+b0zfz3oH38Hfw57cRvxHmbN46ifo4tvkP0g7tZ+Qrb+LqY2S3IRNZlleKpUTKGJ/ru9ibSmBgIDY2NqSkpJhBaPQEQUB+4ABOw4aZKcJrsbHxw89v1DX/r9XKUShyUCiz0WkV2NoGYWsbiIWFedNCBUFg4Z4MPt16hs7Brnw/riNeTkYaDVblwZpJ+tPDfq8adUvOmTKO/JlB56EhDXsTAYJOR/mKFTgOHYKFW9OfJzqVltIlp1Hn1ogiQ0TkKsTUqZscQasl95UZ1OzcSeD8edj36NHSId0x6ASBYUfTUAs6tneJQiaRULYqhbrMKnxmdEHSyJaZ9fHnnFPUlCsZ82ac4Tf7X+4HVY3eC8IAmePHI5HKCP7lZ/MEehl//vknx44d46mnnsLX19fg9TqdlrUfvUtJdhaPffotDm6GdzZLa+oYPnsvvi42LJnQFWc7005mCmoLmLlrJoklicyIm8EjrR8xz2KquhASVuu7PF0UA1U5IFxoVSyRgXPAZeLjghBxCQGt6iohceH+6svqXqSW+ntcLtzn217f4tjaNB+M+lBpVXx++HNWpqxkeNhw3u3+rnHu6k0kNyWZVe+/TsehI7nrsUnNModaJxB3IImBHk7MijLfZgDA2rVrKSoq4umnn27yWOeGj8C2Ywf8PjRc3HwrUqVUM3P1SbadLmRq3zBmDo7CQmZkyplWDUuGQ2UOTN0N9oZPM2vK61j1cTzu/g7c+3wHpAZem2v27CF78hSCf1uOXceOxsV1HS6KDFVODR4T22Id0jjPFhGR2xXxROMmRhAECt57j+odOwj49htRZNxgVhaUcaJazoaO4cguLE7tu/siP1aEMrUc29bm2zGN7efPxu9OUphRhU+YgTequKdg5aOQdwL8OjR4qeu4ceS9MoO6s2exDg83W7wAgwYNIjs7m9WrVzNlyhSD9RpSqYzhz89k6esvsOnbzxj9zsfILBp+CXJ3sGbu+E48ueQww7/bw9xHOxMbYPwbuY+9D4uHLObro1/zafynHC86zvs938fesomd2hy9oef0K/9Po9KLjasFRP5JfTcrZcWV1zt4XxAfwRDS50pB4ugLja0taYC8mjxm7JrBmbIzvNP9HUZHjr4hvh7yygo2ffMpvhFR9Bn3RLPN81NuMQUqtVmKwK8mKiqKhIQEKioqcHFxadJY9j17Ur3zLwRBuDG+KjeQ03mVPLPsGGW1KuY91pnBbU10et/xf/p2tk9uMUpkaLU6ti9KRCqTMmhSW4MiA/RF4NbR0dh26GBabFfxn8io1tdkiCJDROQaxK5TNymCIFA06wsqVq/B96MPcRxgXI6qiHmo0mj56Fw+D3i50N3lv11kq0BHLP0dqD2Yb9b5AqPdcPK0JWFXjuGLI4eAk7++UNkATgMHInN3p/y3FWaI8kosLS0ZPXo0NTU1bNq0yaiWmnbOLox44TXy01LYu+IXo+bpGOTKpud642ZvxUNz97PsUJZJ7TstpZa8GvcqX931FXtz9/LwpodJKzeu3a5JWFiBWxi06q9PcRv4PxjzM0zdBa9nwWtZ+h3aZw7Cm3kwIxUmbYeHFkD/t6DjoxDSW38S0gwiY0/OHsZsGkOpopSlQ5cyJmrMDVnk6nRa/vzuC3RaLSNeeM2guGwsRypr+d+5PKYGeNLWwYiORiYSHh6OVCo1i0u4fc8eaPLyUWVmNj2wmwStTmBF/HkenLMfeysLNj3X23SRkfS7PjV00IcQ2NWoWw6uP0dhehWDJ8dg62i4K5UqJ5eaf//FddzDTXr+61RaSn8WRYaIiCFEoXGTUjpvPmWLF+P91lu43H9/S4dzx/FlZgFynY53w6/MI5dIJDh090WZUoamTGm2+SRSCTF9/Tl7tAhFtQHvBpkFdH4STq0GRUXD41pZ4TJ6FJUbNqCrNdBCtxG4u7szcuRIEhMTOXr0qFH3+LduQ99Hn+TIxnWkxRvX5jPA1Y7V03owNi6Qt9Yn8sqqk8hVGpNiHRg8kBXDV2Aps2T0xtG8+M+LHMg7gE4w4MxuLmxd9ClQXtFgdWO8b1RaFZvTN/PElid4ZucztPdsz6p7V9HWo+0NmR/gwJrfyE48xfDnZxqVLtcYSlUappzOpKOjPW+3ap7aDxsbG4KDg83S5ta+a1ewtDRbm9uWpKxWxY+7znHXF//w+roE7u/gz7pnehLsbuJzvPA0bHgW2twP3aYZdcu540Wc+Cubng+F49vKuIV+2eLFSO3tcR4xwrT4LkOn0lL6SxKq7Go8nhRFhohIQ4hC4yakbNkyir/5Bo/nn8PtsfEtHc4dR2qtkkU5xbwY7I2v9bU7ZLbtPZFYy6g9ZN5TjegevkgkEpL3GzFup8f17U1PGjblcx0zBp1CQeXGTWaI8lpiYmLo0qULW7ZsocBI345Ow+4joltPts75hvIC4zw5rC1kfHB/DN8+3IEtiQU88MN+zhXXmBRriHMIy4ct581ub3K++jxTdkzhvg33sTRpKZV1lSaNdTOTX5PP7GOzGbhmIK/teQ0LqQVf9vuS7/p/h7P1jVsUZRw/wsG1K+g1drxRDQAag1YQeDYpC6VOx7y2wViasXbqaqKiosjMzKSuCc7eAFJ7e+zat6d2/60pNARB4Nj5cl5eeYLun+zkqx2pxIW4se6Znnw2qh02liaeyJ1aDQsH6FMHR35nVPvoikI5f/+cTKtOnrTrH2DUNFU7dlC+fDke059Fate4uiRBfUFknK/CY0IM1qGiyBARaQixGPwmo3LjRvJmvorbE0/g9fprt13+7s2OIAiMPXmO80oV/8a1xuY6BYwVG88hP1GE7+vdkFiaT6/v/DmJ3NQKxn/Qw3Cu8eonoeAUTD9i8I05+9npqHNyCN2wvlmeU2q1mkWLFqFSqZg6dSrW1obbll7yU7CyZtyHX5jU6jStsJppvx6loFLJ56PaM7yd4WL0qxEEgeNFx1mRsoIdWTuwkFgwLGwYY6PG0sa9jcnjtTQ6QceBvAOsSFnB7pzd2FnYMbLVSMZGjSXMpfk7Sl3NRf8U34goHnj1XaP8UxrDFxkFfJlZwMr2rejrZrxLdWMoKytj9uzZjBkzhjZtmvYcKZk7l9KFi4g8eMBs7aebG4VKy+8ncll6MIvTeVUEutkyvlswo7sE4mZvOG3pGjR1sO1NfSe3dmNhxNdGnfZpVFrWfHYUjVrLmDfisLI1nI6nysoi46FR2Pfogf/sbxv1OiiotZT8koQqswqPJ9tiHeZi8hgiInca4onGTUT13/+Q9/obOD/0oCgyWoitJZXsLq/hf+H+1xUZoC8K19VqkCeWmHX+mH4BVJcqOX+61PDFcU9B6VnI2GXwUtdx46hLSaF6xw4zRHktl9drbNy40agaCms7O+596Q3K8/P4e/GPJs0X4e3IH9N70z/am2eXH+P9jadRaUxLgZJIJHTy7sTnfT9nx6gdTGk3hf15+xm7aSyP/vkof5z7gzpt03aubwSVdZX8fPpnRqwfwbS/ppFXk8fb3d9m5+idvNHtjRYRGRq1mo3ffIqVrS1Dp7/SbCLjn9IqvswsYGaoT7OLDNC3dvb09DRP+lTPnuhqa1EkJJghsuZDqdZyOq+S/21MouvHf/HG+gR8nGz46ck4ds24m6n9WjVOZJRnweLBcOwXvcB4YJ7RKYW7VqRSWSRn6NRYo0SGTqkk54UXkbm74fvxR00WGe4TRJEhImIs4onGTULtwUNkT5mCw1134f/1V0hk5i8GFWkYhVZH3/gzRNhZs6xdmME3o+KFCQgqLV7PdDBrHKs/OYytoxUjphtINREEmNMDPMJh7K8GLhXIee455IfiCV23FqtA87b+vEhCQgJr167l3nvvpXPnzkbdc3rXTrbO+ZqBk6fTboBpfh+CIPDLgSw+/DOJGH9nfnikE34ujS8E1ug07M7ZzcqUlezP24+LtQsPhD/A6KjRBDo2z8+ssSSWJLLizAq2Zm5FJ+gYFDKIh6Mepr1n+xbdpBAEgZ2L5pD4zw4e/t8sfFoZ7+xuCrlKFQOPpNDB0Y5f24UhvUGP+a+//uLYsWPMmDGjSS7hglZLao+euD32GJ7PTTd8QzOh0wkUVddxvkzO+TI52Rc+Lv67qFovtt3srRgbF8gjXYMIdGtiO+TU7bBuMtg4wZhfwM/4FrNJ+/L4Z+kZ7pkQTevuxp1k5r31FlWb/iRk1UpsoqJMDldQ6yhZmoQqoxL3CW2xaeVi8hgiIncqYnvbmwBFQgI5zzyDXVwcfl/MEkVGCzE3u4iCOjW/tTcsMgAcuvtS+msyqtwarPyb7m9wkZh+/vy99AyVxQqcPRtYNEskEDcJtrwGlbngfH0jMYlEgt/HH5MxajQ5L7xAyG+/ITUivclUYmNjyczMZMuWLfj7++PjY7jrTNt+95CflsKOBd9TUZhP74cfR2rk34BEIuGJniG0C3Dm2WXHGPHdXr59uAN9IjwbFb+F1IL+Qf3pH9SfrKosVqWsYm3aWpacXkIv/148HPUwvf17I2uGrlDGoNQo2ZKxhZUpKzldeho/ez+mtZ/GA+EP4G7bPIXWplAnr2XrnK85e/ggAydPbzaRodLpmHw6E1uplO/bBN8wkQH6Oo29e/eSk5NDUFBQo8eRyGTYd+9O7f79ZhcaKo2OcrmKstrrfMhVlNWoKKxWklOuuOI00MvRmkA3O4Lc7OgZ7kGQmx2BrrZ0CHLB2qKJz3udVu/2vecLiBgMD/wIdsa3CS/Ormb3ilTa9PYzWmRUrF1L5dp1+H78sSgyRERaAPFEo4WpS0sja/xjWIWFEbRoYaML1ESaRo5SRZ9DyTzp73lNp6nrIWgFCj6Lx6a1G64Pmm9BpVZp+fn1fbTp7UfPBw14Xyir4Kto6PEs3P2mwbGVyclkjn0Y5/vuw/eD/5kp4itRq9UsXLgQjUbDlClTjKrXEASBo5vWs3v5Evyj2jD8hVdxcDXNp6S8VsWLK0+wO62YF++J5Ln+4Ub11DeEQqNga8ZWVqSsIKk0CRuZDf4O/vg7+hPgEECAYwABDgGX/t1U87vKukpyanLIqdZ/5Nbk6r+uySG/Jh+NoLkkevr492kx0XM1RZnpbPzqExTVVQx59mXCu3RrtrneTsvh59xSfu8UTienG9PB6yI6nY4vvviCjh07MnDgwCaNVb5iJQUffEDkwQPIHOtP/RIEgeo6DWU1/wmEsgsiorxWRenlny98v7ru2o5sljIJbvZWuNpZ4e6g/+zpaE3QBVER5GZHgKsdtlbN9HyqKYa1EyFzL/R/G3q9BCacCNXJ1az65AhWNjIeerUzFkYUnCuTk8l8eBzOI+/F94MPTA75CpHxRFtswl1MHkNE5E5HFBotiCo7m6xHHkXm7k7wLz8jc3Jq6ZDuWKaczuRgRQ37ukXjaMKuXdXO81T/m43vm92QGpErbCx716SRcqCAJz7tafgNddPLcGYTvHQaZIaLSivWrCH/7Xfw/eQTXB643zwBX0VJSQnz588nKiqKBx980OhUnpwzp9n0zWcIOh0jXniVwLbtTJpXpxP4/p+zfP1XKn0iPPlmbIfG5Y9fh8SSRI4XHb+08M+tziWnJueKWg43GzcCHAPwd9ALj0DHQP3XjgF423mjE3Tk1eZdEhI5NZeJieocqtXVl8ZysHT4T8hcGKOnX0+CnBq/k94cJPyznb8X/YibfyD3vvwGLt4m+ieYwO9F5Uw9ncXHEf5MDGjcyVVT2bBhAzk5OUyfbvpJhFqro/zCqULF2UycnxpL2nPvcC6yC+Xy/4TDxdOHcrkKtfbat2lHGwvc7a1wtbfSf7azws3BCjc7K9zsr/1wsLZouZS6rAOw5knQaWDUYgjta9Ltgk5gy7wE8tIqGP1GXMMnvRfQVleT8dAopPb2hPy2HKkBQ9Fr5lTrKP01CeW5SjwmtMEm3NWk+0VERPSIQqOFUBcVkfXoeCRSKcHLfsXCw/xOtiLGsbe8mlEnzvFddBCjfUzbRddWqcj/NB7n4aE49rp+6pKpVBTKWfZ/BxkwIZooQykChadhbk+491voPMGo8fPefIuqzZsJWbmiUekExnCxXmPkyJF06tTJ6PtqK8r5c/YscpIS6T3uceLufdDkYuI9acW8sOIE1hZSvhnbgW5hzZdWJAgCJYoScmtyya7OvkKA5FTnUCQvQkD/MmshsUAraK/4t6+D7xUnIheFRYBjAE5WTjd1Uwh1nZKdi3/k9L9/EXvPYPpPmIqFlfmE3dWk1SoZcjSVge5OzG0T3GI/m+TkZFauXMlzzz2Hlb1Tg+lJl4uH0loV1corTxsWb/+EYz5RrOo1rl6BcOnjMiHhYmeFlcUt0MtFVQvx82HnB3oDvlE/gZNpHeKUNWp2/JTE+aRShk2LJbS9YXF5RU3a2jVYmZjiJmh0lC4VRYaIiDkQhUYLoCkv5/zjj6OtriFk2a9Y+ptvgSpiGhqdwIAjKTjIpPzRKaJRud6ly5NR59fi/XJnsy58/ph9ApVCw6jXuhi+eMOzkLgWJu8Eb8NmbDqFgsyHxyEolYSsXYPMwXw1Jpfzxx9/cOrUKSZPnoy3t7fR9+l0WvavWs6h9SsJ69yVoc+8jI2JMeZXKnh22TGOna+gfaALj3UPZkQ7X9N7/DcRlVZFXk3eJQFiKbO8JCS87LywkN6apXLlBXls/PJjygvyGfDUM7Ttd0+zzler1TLsaBo6QWBr50jsm1ovUA9q7bW1DfWlJ5VXy+lSuYfj2kAS1Nc+rx2tLXC7kJ50xanDVaLB3d4Kvv4M9eF4wrdtNfvjaTFK0uDwIjixHOqqoOd0uOf/jDpxvZzCjCq2LkhAU6djwMQ2BLc1bsOgdPFPFH3+OQE/fI/jPaY9L68QGU+0wSZCFBkiIk1BFBo3GG1NLecnTkSdnU3wsl+xDrvxrSdF/mNRTjFvp+WytUsk7R0bl1tfl15B8fwEPJ6KNWsOb/qJYrb8mMCYN+PwDDLQulMlh0UDQaOEyf/ou7kYQJWZScao0dj37In/t980m7/GggUL0Ol0TJ482ah6jctJP3aYLd9/ibW9Pfe+9AbeYQZqVq5CqxP4+0wRSw9msTu1GBc7S8Z0CeTRbkGmOxeLXCItfj9b53yDvYsL9778Jp5BIc06nyAIPJd8ns0llWzpHEmUveE0GEEQqFVpL6tpqKOsVk1Zbd016UkXP6qU19Y2WEgl9aYnWWftwwKBtneNvOLkwdXE04aq7dvJff4FWv31F1YBt/Cmk1YDKZv1nhgZu8DOXW8s2vlJvRGfCQiCQOKuXPauTsMzyJHBk2NwdDMu9Ul+5AhZT0zA/ckJeM2YYdq8Gh2lvyajPFshigwRETMhCo0biK6ujuyp01AmJhL08xJs2xreeRZpPkpVGnoeSuZeTxe+aN341qWCIFD49TEsvWxxH28+ozedVsfStw8Q2MaN/o9FG76h9BzMvwta9YfRS4xy1724yPF+43XcnniiyTHXR0lJCfPmzSM6OpoHHnjAZEFTWVTIxq8/pSQ7k/4TphJ7z+BGiaLMklqWHcpi1ZEcKhVq+kV68lj3YO5u7YWsGd2kbye0Gg17fvuZo5vWE9mtF4OmvYD1DWhg8UtuCa+m5vBpqB89bGwNpiddFBEq7bXeKo7WFv+dLNSXnnTVCYSTTf21DYcPH2bz5s28+uqr2No2vqWytqqK1O498Hnv/3AdM6bR47QYVfl6L4yjS6A6DwK76T1+2twHFqZ3tlMpNfy7LIW0w4XE3h1Ar4fCkRkp3DQlJWQ88CBWwcEELfkJiYXxp4X/iYxyPB5vi02kKDJERMyBKDRuEIJGQ84LL1K7dy9BixZi18WIdBiRZmVmSja/F5Wzv1sbPKyalr5ScyCPio3n8H2tKzJn87WNPbI5k6NbMnni017Y2BuRdpD0B6x6DIZ8Ct2fNmqOws8+p2zpUoJ/+Rk7E2opTOHUqVOsW7fO5HqNi2jUav79eT4nd2yhTZ+7GfDUs1iaWNx5EaVay8aTefx6MIuTOZX4u9jySLcgxsYF4uFg/pa/twvVZSVs+uZzCs6m0PfRiXQaNrLRp2AXTxuuSUuqJ00pV9CSGeWALKcWy+TKK8aRSSWXpSdZ4m5vjau9JW721rjZWeLmYH3pJMLdwQoXO8umt2i9QGVlJV9//TUPPfQQsbGxTRorY+xYLH39CPjma7PE1uwIgr571OGF+kYUMitoNwa6TAJf0xo4XE5Zfi1b5yVQU17H3Y+1JqKL8emWglbL+YmTqDt3jtB1a7H08jL+Xo2O0mXJKNNEkSEiYm5EoXEDEHQ68t94g8o/NxP4w/c49OvX0iHd8ZyqljP4SCofRPjzlBk61+iUGvI/PoRDnwCcB5qWJtAQ8ioVP7+xj7gRoXQZGmLcTdvegkM/wpNb9AWYBhDUarImPIk6J4fQdWuxcG+ewunG1mtcTtKef9ix4HtcvHy49+U3cPMLaFJMJ7Mr+PVgFn+czEMnCAyL9eWx7sF0Dna9qYuwbzTnE0/y5+xZSGUyRrz4Ov5RV56wabQ6KhTq6/s21FMoXZ+Tu4O1xRVCwd7Bim1u4CiV8oq9M14ONrhd+r4VTrYt2EkJ+PHHH/Hw8GDUqFFNGqfo22+pWP4bEfv33dw+SvIySFijFxglKeARqT+9aP8w2Dg3aejUwwX882sKTu42DJkSg6uPaamNRV9/Q+mCBQT99BP23Qy/7l1EFBkiIs2LKDSaGUEQKPzoY8qXLcP/yy9wGjaspUO64xEEgZHHzlKl1fJXlygszZQ2U74+DUVSGb6vxyGRma8jzL41aZz6J4cHXumET5gRb+ZaNSwZAZXZMHU32BvuaKYuLCLjwQexjowgaOHCZlnsqFQqFi5ciE6nY8qUKVg1sjtRSXYWf3z1CbXlpQya+gJRPXo3ObYKuYrVR3L49VAWWaVyWvs48liPYO7v4I+99a1ZrN1YBEFArtJSVquitFpJ8rYN5P29Hpl/BHW9x1Gus76yxkGuolKh5up3kounDXphcL30JOtLJxEudpZXFOrrBIEnEjI4UlnL9rgoAm2ar5tVY/nnn384dOgQM2fORNaEvxn54cNkPfY4IatXYxsbY8YIzYBaASlbIGE1pO0AQQfRI/QCI6SPUSmaDaFV69i7Jo3EXblEdvXmrkdbY2lt2s+y+t9/yZn2NJ6vvIzH5MlG33eFyHisDTZRpnUdFBERMYwoNJqZ4tmzKZkzF5//vX9r5t/ehqwpKGN68nnWdGhFb1cDRdYmoC6opfCbY7g92hq7WPP199dqdWz48jg15UrGvBWHrYMRC66qPPixD/jEwvi1YISpW+2BA5yf9BQe06bi+fzzZoj8WoqLi5k/fz5t2rThgQceaPQ4KoWcbfO+I/XAHjoNHUnf8U8iszCto0196HQCe86WsPRAFn+fKcTOyoKHOvkzJi6QCC/HW6Ol6FVodYK+U1ID6UlXnzjUaXRYa5UMKt5JiOI88S6dOe3VDRcHm3q7J10UD+6XOi1Z42hj0STDxNlZhXycns+ydmHc435zegzl5eUxf/58nnjiCUJDQxs9jqBSkdK9Bx5Tp+IxdYoZI2wkOi1k7NaLi6Q/QFUNfp2g3ViIeRAcjE9LaoiqUgXb5idSkltDnzGRtO3jZ/IJVc3efeS++CJ2cXEE/PC90a2wBY2O0uVnUKaU4fG4KDJERJoLUWg0I6U/LaHos8/wmjkD90mTWjocEaBGo6XXoWTinO1ZGNP4hcH1KPrxJBKpBM8pjc9Tro+aciUrPzqMZ5AjI6a3N24Bl/4vLH0A+r4Kd79h1DwlP/5I8TffEjh/Hg59TTPVMpaTJ0+yfv167rvvPjp27NjocQRB4MS2Tfz7yyK8W4Vz74uv4+huPj+anHI5v8WfZ0V8NqW1KqQS8HW2JdDN9pKTcuCFjyA3O9ztrW5IGo9cpbluSlK5XEVpzWWF0rX1nzZIJVzqkFRfUbRdVR5FG+YjqOroNel52nbrdkPbAu8tr2bMiXO8EOzNa2Gm+S7cSARB4KuvvqJt27YMGTKkSWNlT3sanVxO8C8/myk6ExEEyD8Bp1brW2XXFIBbGMSO0ddfuLcy63RZiaXs+Ok0VtYWDJkag1ew6WKybNkyCj/+BPtePfH/6iuj23RfLjLcH2+DrSgyRESaDVFoNBMVa9eS/9bbuE+ZgtfLL7V0OCIX+PBcHgtzitnTLbpZUjHkJ4so+y0F75c7Y+ll3m482cll/DH7BHHDQ+k6wkiRtHsW/P0RPLoGIgYYvFzQ6ch++mmUJ07qCyqbyePl999/JyEhgSlTpuBlQtFmfeSlnmHTN5+hUdVxz6RniOjWA6kRJzjGUqfRcjSznKwyOecvfORc+FwuV1+6zs5KRqDrf8IjyM320tcBrnbYWl0bk1YnUCH/TyDU1z3p0qnDhTatSvW1tQ12VjKD3ZMu/+xkY1mvWFXXKTn111b2LF+CZ3Ao9770Bk6e5tm9NpaCOjUDDqcQ7WDDivatkN3ktTIbN24kPT2d559/vklCs+yXpRTOmkXUoYNIb0Anr/8mztDXXZxaCaVpYO8JMQ/pBYZ/pyanRl1NbUUdJ/46z4m/sgmOdWfAhDbGNbq4DEGjofCTTylftgzXxx/D+9VXje4wJWgviIwzZbg/1gbb1qLIEBFpTkSh0QxUbd1G7ssv4zJ2DD7vvisWld4kpMvr6Bd/hheCvZkR6tMscwgaHfmfxmPXzhOXkebdAQQ4/GcG8ZsyuHd6e4KMMa/S6eC3sZBzRF+v4WK4ja+2ooKMBx9C5uFB8K9LkTaD07NKpWLBggUATJ48udH1GheRV1Wydc7XZBw/gqOHJ+0HDCXm7oHYuzRvYWe1Uk12mYLzZXKyL4iP7PKLYkRxRXtVT0drgtzskEq4dAJRcZ3TBle7+s3dLnZPuvokoqmnDWV5uZzcsZnTu/6iTi6nw6Dh9HtsEhaWTU9HMwW1TmDUibNkKVTsiIvE0+rGzt8YUlNTWb58Oc8++yyeno1Pmaw7d4704SOa9TTxErUlcHo9nFoFOfFgaa+vu2g3BkLvApl565IEQSA3tYLEXTmknyhBZiGhy7AQOg0KRmJiep22uprcl16m9sABfN55G9eHHzY+DlFkiIjccEShYWZq9uwl+5lncBo8GL/PPzM6X1Sk+Xn0ZDopcgV7ukZja8Zi7aup3JZJzf48fN/shtTEokZDCDqBTT+coiizijFvxRlnYiUvg3n99HnVT24BC8OLekVCIlmPPILL6NH4vPuOGSK/lov1Gm3btuX+++83y5gF59I4uWMzZ/buQqfTEdm9F+0HDcM/qs0NF/w6nUBRdd2lU5DsCx8CXGPw5n6Zi7Szbf2nDWaPT6vl3LF4Tm7fTNap49g4OhF790DaDRiKi3fzCPGGqNPpePmMvuX0ug7hdHVpHrd6c6NWq/nss8+466676N278Y0JBEHg7F134zRkCN5vvG7GCC+gqoUzmyFhFZz7W/9/re7Ri4uooWBlfgPLOoWGlIP5JO7KpbxAjquPHTH9/Inq7ou1reliRpWdTfa0p9EUFxPwzdfY9+xp9L2CVkfZ8jMozpThPj4a2+jm6a4nIiJyJaLQMCPyY8c5P2kS9t26EfDdbCQ3eDdQ5PrsKKnksYQMFsWEMNzTpVnn0lQoKfjsMC73h+PQzfz55coaNSs/isfexZoHXulknJlV7lFYNBi6TIRhnxs1T/lvv1Hw/v/w++ILnEcMb2LU9XPixAk2bNjA/fffT4cOHcw2rqKmmqRdOzmx/U8qCvLxCAqhw6BhRPe5Gyubxpur3Q7UVpSTsHMbJ3dupaa0BN+IKDoMGk5k995YNMPplTGUqjRMSszgeLWcb1sHcb/3rdVi9LfffkMulzOpibV4eW+8iTIxkbCNf5gnMK1GX6uVsAqSN4G6FgK66sVF2weM6kjXGEpyqknclUtKfCFatY6wDh7E9AvAP9Kl0YJffuQIOdOfQ+rsRODcH7EOM77GTtDqKPvtDIpkUWSIiNxoRKFhJpRnzpD12OPYtG5N4IL5SBtpJiZifup0Ou6KP0OAjRWr2re6ITvbJb8koS1T4vVCx2aZrzCjinVfHCWmrz99xkYad1P8Atg8Ax5aBLGG+/4LgkDeq69RvXMnoatXYd3K/KlgABs2bOD06dNMnjy5yfUaVyPodGQlnODE9s2kH43H0saaNn3vocOgYbgHBJl1rpsZQRDITT7Nie1/kha/H6nMguje/Wg/cBjeYeEtGttZuZLxp9Kp1uhYEhtKnLP5d9abm2PHjrFx40ZmzJiBvX3j46/c9Cd5M2YQvmsXlt6N/FsQBP3GwqlVcHod1Bbr/S5ix+j/7t3M3wQD9G1qzx0vInFXLvnnKrF3tqJNH3/a9PLDwbVpRpgV6zeQ/+672HXogP/sb7FwNV6IClodZStSUCSV4v5oNLZtRJEhInIjEYWGGajLyCBr/GNY+voStOQnoztfiNwYvssq5NOMfP6Oa02U/Y0RgMrUckoWJ+L5dHusG9FNxRgS/s1h94pUBj3V1jgHXUGAdZP16RNT/gHPKIO36ORyMsaMAQFCV61E2oRF1PUwd73G9agqKeLUX9tI+Hsb8soKAtvE0n7QcMLjuiMzspD0VkOlkJO0+x9ObP+T0pzzuPr602HQMNr0uwcb+5Z/ndpbXs2kxEy8rSxZ2i6UYNtb05m9pqaGL774osknc5rSUtJ69cb3009wMTWdsPScXlwkrIKydHDw0QuL2NHg297sRd0XqSpVcHpPHsn78lBUq/GPciW2nz8h7T2QNTFFVdDpKL5gxOf80IP4/t//ITHh9UGn1FC2MgVlarkoMkREWghRaDQRdX4+mY8+itTOjuClS03aaRFpfvLrVPQ6dIbxvu78L6J5OijVh6ATKPzyCJb+Drg/Em34hsbMIQjsWHSazIRSRr/RxTgn3boaWNBfv+h4aidYG15s1qWnkzFqNI53343fF7Oa5YSmqKiIBQsWEBMTw3333Wf28S9Hq1GTdmg/J7ZvJvfMaexd3YjtP5h29ww2a3vclqTkfCYndmwhafffaFR1hHfpTvtBwwiKaX/TNKdYnlfKq6nZ9HZxZH5MCE4WN7EjthEsWLAAJycnxo4d26Rx0h94EOuIcPw/NyLFsaZI34r21CrIOwZWjtDmPmg3Wm+mZ8bua5ej1erIPl3G6b15ZCWUYGkto3UPX9r29cfN1zybETq5nLzXXqP6r514zZiB28QnTXruqvJqKF2WjK5WjdvDrcXCbxGRFkIUGk1AU1pK1vjHEFQqgpcvw9LbiF1lkRvKs0lZ/FtWzf5urXG2vLG71rXxBZSvSzO7gd/lqJQa1nx6BIlUwqjXuhjnqFucAvPvhtbD4cH5Ru10Vv75J3mvzMD73Xdwe+QRM0R+LcePH+f333/ngQceoH379s0yx9UUn8/k5PbNJO3556ZdkBuLVqMmLf4AJ7dvJic5EXsXV2LvGXLTCSidIPDhuXzmZBfxuJ87H0cEYHEDit+bm927d7N3715effVVLJpwQlY4axaVf/xBxO7d9T8H66rhzJ/6drTp/4JEBhGD9OIicghYNk8NkiAIFJyrJDW+kLNHi1DWqnEPcCC2nz8Rcd5Y2Zjv9VVdWEj200+jyszC/4tZOPbvb9L9tUcKKN9wDktPW9zHR2PhfmfXZYmItCSi0Ggk2upqsp54Ak1xMSG//opVcHBLhyRyFfEVNYw8fpavogJ5xO/GH5kLgkDZb2dQppTjNb0Dlp7N0xu/LK+W1Z8dIayDBwMmGNldKWENrJ0Ew7+COOMKWAs++JDyVasIWfYrtu3Ma0h4kfXr15OUlMSUKVOa1CrUVOrkcpL3XJZi5BdAh4FDCWzbDmcvb6xsb6CvgZEIgoCypprKwgLOHT3EqZ03f0pYrVbLc0nn2VJSyfvhfkwO8LzlBN31KCwsZO7cuYwfP57w8MbXvdTs20f2pKcI/f13bKIu1F9p1XB2pz4t6sxm0CgguJc+LarNfWDXfLv1ZXm1pMYXkHq4kOpSJQ6u1kTEeRPZ1QePAPOn3ykSEsl55hmwsCBw7hxsWrc2+l5BraX893PIjxRi39UHl3vDkNxAo0kREZFrEYVGI9ApFJx/ajJ1Z88S/Msv/70ZiNw0aAWBwUdSkUlgS+dIpC20mNHVaSj6/gQSmQTPZzogrce0zRykxhewY3ES/R6JIqavkSlif86AYz/DxG16Yy4DCCoVmY89hqa4mNC1a5slTVClUjF//nykUilPPfVUs9VrXI+ri6Z1Wi0Ato5OOHv74Ozlg7OXN85ePrh46792dPdEKmue36tGpaKqpIjKwgIqiwqpKCq48LX+3yqFHAArW1va9L2H9gOH4hF4c256FNSpefxUOmcVdfzYJphBHs4tHZJZEQSBb775hsjISIYPb3yXNp1SSWq37ni++ALu90RfKOpeD4oy8GqjFxexo8Cl+ZoZ1JTXkXakkNT4Akqya7C2s6BVR08iu/rgF+FisveFsVRt207ea69hHRlJ4A/fY2HCZoOmREHpsmQ0JQpc7g/HvrOYYSAicjMgCg0TEbRacp55ltrDhwlevAhbM7bkFDEfv+SW8GpqDn92iqBzC3exURfWUvT9CWxjPXAdHdlsO7i7fkshaV8eD83sjJcxBeiaOvhpKNQUw9RdRu2KqvPyyHhoFNatowhasMBoN15TKCoqYv78+bRr146RI0eafXxjUdbUUJaXo1/UFxZQWVxIZWEBFUUF1JSWIgh6Mz6JVIqTpxfOnt6XxIiLt8+lf9s4OF73dy7odNRWlFNZVEhlUQEVhQVUFRdScUFM1JSXcdHRTyqzwNnL6wqx43xhHjf/ACytb95OdwnVch5PyEAC/BIbSozjzXdCZA42b97MmTNneOmllxr/d16cwvnJ06C2kKBeueDkf6Goewz4xJg34MuoU2g4d6yI1PhCclPLkcmkhMS6E9nVh+AYd2SWzec9JAgCpfPmUfzNtzgOHYLfJ5+Y1LlRkVhC2epUZI5WuD0ajZWZ6kRERESajig0TKT4+x8o+eEHAufPx6FP482ZRJqPcrWGXoeSGeDuxOzom2N3t/Z4EeUrU3B5MByHrub31gB9e8l1XxxFUa1mzFtx2Ngb4eNScR7m9YWAOBi3EowwmKw9eIjzkybhNuEJvGfONEPk19IS9RqmoNWoqSopvuJ04aIIqSoqRFlbc+laK1u7/4SBlzdajeaSmKgqKkSjVl261s7ZBWdvH1yuFhNe3ji4uSNtpuLe5mR7SSXTkrIIt7NmaWwY3ta3r7/QuXPnWLp0KdOmTcPHxwTTw6p8SFyjP70oOEXpWQ+KT9oQufZ7pOH9jPq7bAxatY6s06WkxheQeaoUrVaHf6QLkV19aNXRE2u75v9d6VQq8t9+m6o/NuLx7LN4TH/WaJEmaHV6g9TdudjGuOM6KhKpGWtFREREmo74F2kCNXv2UvLDD3g8N10UGTcxszIKUOkE3g7za+lQLmHf0QtVZiUVf5zDyt8RK3/z5zbLLKUMnhLDqo8O89eSJIY/3c5wioNLEDy4EJaNgr1fQd8ZBuex794Nr5kzKPr0M2xjYnAaOtRMj+A/OnToQEZGBps2bcLPz++G1msYg8zCElcfP1x96n+OKWtq9ALkwinIRTGSfixefzLh7UNIu456MXExJcvTG8vbyH9HEATmZRfz/rk8hnk6Mzs6CPtmSjG7WQgODsbKyoqUlBTDQkNZCUl/6OsuMvaAzAoiB0O/V7HXBlE06mEUZbbYm1lkCDqBvLMVpMYXcu5YEXVyDR6BDnQbGUZEnHeTPS9MQVNaSs7051CePm2yMai2qo7S5WdQna/GeUQYDr38bpt6HxGR2wnxRMNI1Pn5ZDzwIDbtYgn88UckzbTDJNI0kmoUDDicwjut/Hg6yLzmb01FUOso+vEkOoUG7+kdkDbTbmFmQgl//nCK7veH0XlIiHE3/f0R7PkCHlsPYXcZvFwQBPJmvkr1zp2ErFjRLHVKdXV1LFiwAKlUyuTJk7G0vH13wm831DqBt9Jy+CWvlOlBXrwZ5ttidVI3mlWrVlFRUcGUKVOu/aamDtJ26MVFylbQqiC0jz4tKvpesHUB9Cl1ab374PLQQ3i98rJZ4irNrSHlUAFphwupKa/D0d2GyAtF3W5+Nz7VqC4tjexpT6NTKgn84XuT0pCV5yoo++0MSCW4P9Ia65Dbq95HROR2QhQaRiCoVGQ99jjq4qJmK4IVaTqCIPDgibMUqzT8HReF1U0oBjVlSgq/O451iBPuj7VptqLKQ3+kc3RLJiNf7EhAlBHPV50Wfn0QCk/D1N3gZPg0SKdQkDnuEXS1tYSuWY3M2fxv9oWFhSxYsKDF6zVEjKdSrWHK6Sz2VVTzeVQgj/jeWSZpJ0+eZP369bzyyis4OjqCTgfn9+vTopI26E8yfGL/c+q+zt9a7oyZqDIyCF27ptGxVJcpSTusL+ouza3F2t6C8M7eRHX1xqeVc4udANTs2UPuiy9h6e9P4Nw5WPob18BC0AlU78qhansm1q1ccHs4CpnDjW0YISIiYhqi0DCCgo8+pnzFimZt6ynSdH4vKmfq6Sx+axfG3e7N48ZtDhTJpZT+nITz0BAc+wU2yxw6ncDG2Scoza1h7FtdsXcxIh2itgR+7KNPp5qwCWSGTxBUOTlkPjQKm/btCJw7F0kzpMYcO3aMP/74gwcffJB24t/fTU2Woo7xp9IpVmlYGBNCb1fHlg7phiOXy5k1axYj+nais+YwJKyFqhz931XsaL3A8DLcsrVi3Xry33qLiP37TNrcUtaqLxV156VVILOUEtreg8iuPgS1cUNm0XIbMIIgUL70Vwo//RSHvn3x++ILZA7Gnabo5GrKVqWiPFOGY/9AnAYEN9tGjYiIiPkQhYYBqrZuJffFl/B+523cHn20pcMRuQ61Wi19D50h1tGWJbFhLR2OQSq3ZlK9KxvPybFYh7k0yxzyKhWrPorHydOW+17qiExmxALj/CFYMgy6TIQhnxlVhFqzdx/ZU6bgPmUyXi++2PTAr0IQBNavX8+ZM2eYMmUKHh43j/mcyH/EV9QwITEDZwsZv7YLo5Xd7VNvYjQV2ZC4hsW7MrFRl/GI7V5o+wC0GwOB3Ywyx7yIurCQs/3uwv+rL3EaNqzBazUqLZkJ+qLurMRSBJ1AQLQbkV29CevgaVYzvcYiqNUUfPQRFStW4vbkk3jNeMXojQlVTjWly5IR6rS4jo3CNkp0+RYRuVUQhUYDCBoNZwcMxDY2Fv/Z34qFZjcxn6XrnYZ3d21NsO2NK2ZsLIJWoGRRAuoiOd7Pd0Lm1DzH//lnK9jw1XHa3RNIr4eMNBGLXwCbZ0DkUHhgLtga3k0tWbCA4i+/wv+72TgNHNjEqK+lrq6O+fPnY2FhwVNPPSXWa9xkrCss58Xk83RysmNxbChuli2/sDWa0nN6P5nKHAgfAKH9wNlILxoARTmc3gAJqyFrH1jYss99LP+UuPHqjBlY2Ta+/uHc8BHYduyA34cfXvM9nU4gN7Wc1PhC0o8VoVJq8Qp2JLKrD+FdvLB3vnleB7VVVeS++CK18YfxefcdXMeMMeo+QRCojS+g4o9zWPra4/5oNBaud6CAFRG5hRGFRgNU//UXOdOfI2TtGmzbtm3pcESuQ5aijr7xZ3g60IvXw5qndWxzoK1WUTj7OBYeNng+1Q6JrHmE7Im/zrNvzVmGTo0lrKOR3ZtSt8G6KWDjDGN+Ab8ODV4uCAK5L75E7Z49hKxehXWrVk0P/CoKCgpYuHAh7du359577zX7+CKmIwgCX2QW8GVmIWN8XJkVFYj1TVgbVS/yMvj3E0j6Hfy7gEugvguUWxg8shKsG+gMp6mDlC16cZG6DQStvolC7BiIHkFJdR3ff/8948aNIyoqqtEhFnz0MdU7/yJ8504kEgmCIFCSXUNKfAFnDxdSW6nCydOWyK7eRMZ54+pz8/lHqLKyyH76GTSlpQR8+y323bsZvEfQCSjTyqnZl0ddajn2PXxxGR6GpAXTvkRERBqHKDQa4PzESehqawlZuaKlQxFpgCcTMjhZLWdPt9a3XPvMuoxKihecwrFPAM5DQ5tlDkEQ2Do/kZzkMka/GYeLl5FmaeVZsPoJKEyCoZ9B5wkNpn7oamvJfPhhBLWGkNWrkDmaPz//6NGjbNy4kYceeojY2Fizjy9iPEqtjpfOnGd9UQVvhvnyXJDXrXXqW10IKx6Bgf+DkF76/ys9Bz8N03df825z5fU6HdQU6A0uk3+H/d+Bd1toNxbaPgiOVzpRz549m5CQkCY1Maj+5x9ynn4Gr+XrySiwITW+gPICObaOloR38SayqzfeIU437c+9Nj6e3OeeR+bqSsDcOViHNvwap61VIz9SSM2hfLRlSiz97HHqH4RtjJguKSJyqyIKjetQl5FB+tBh+H3+Gc5it5ubln/Lqnj4ZDo/tgnmfu9bsxtY9e4cKjdn4P54G2zbNE+HnjqFhtUfH8bCWsaoVztjYWWkINPUwdbX4chiaPcwjPgarK4vVFSZmWSMHoNdXBwB339n9jbQgiCwbt06UlJSmDp1Ku7ud1ZHo5uFYpWaiQmZJNTImR0dzEgvl5YOqXGcPwRBF3bYtRq9kN73jT5t0LuNXlzIS6H0LJSdA1UteESCjRPYeoDb9Q1Bt23bRkJCAi+//DLSRvwdKGpUnN2bhezlB0gNe5DCVvcQdqGoOyDa1biaqxakYu1a8t97H7vOnQn49pvrdqUTBAFVdjW1B/ORnyoGwK6dJ/bdfbEKdLxpRZSIiIhxiELjOhR+8imVf/xB+L//ILW+eXJdRf5DpdPR/3AKHpYWrO8Yfsu+IQmCQOnSZOrSK/B+riMW7rbNMk9JTg1rPjtCZJw3/R+PNu3mkyth04vgGgJjloLH9es9qv/9l5ynn8Fj+rN4Pvtsk2KuD7Feo2VJqVUy/lQ6Sp2On2ND6eR086XrmIxOp298UJ4FR36CDo+AqhpKzoGyHCxswb0VuIeDg5dRRd2ZmZksWbKEyZMn429k+1a1SkvGyWJS4wvJPl2GAHQ/8x223q6EL5mPpfXNf2IraLUUffUVZYsW4zJmDD7vvI2knr9RnUqL4mQxNQfzUefWIHOzwaGbL3ZdvJHZi3/TIiK3C7dQxd6NQ6dQULF+Pa5jxzS7yMjKymL//v2kp6eTn5+Pr68vYWFh9OzZk+Dg6++WicDinBLS5XXMiwu5ZUUGgEQiwW10JIXfH6d0WTJeT3dAYmn+3UqPAAf6jYvi71+S8WnlTJteJjintx8Lvu1g5WMw/y647zt9N516cLzrLjyem07J7O+wadMGx7vvNs8DuIC1tTWjR49mwYIFbNu2jREjRph1fJHrs6usmqcSMwiwsWJdx3ACbW4TDwOpFFQKSP4DNApI/weklnphHdRdXxwuNW2RHxgYiI2NDSkpKQ0KDZ1WR86ZC0XdJ4pR12nxDnWi95gIwjt7UfvrGUoXLsJCqgNubqGhq60l99XXqPn7b7xefw23J5645rVZXSyn9mA+tUeLEOo02ES54fRkW2wiXMV2tSIityGi0KiHqs2b0VVX4zJ2bLPNsXPnTr7++mu2bNmCTqe75vtSqZRhw4bx0ksv0b9//2aL41alqE7NF5kFPOHvQVuH5jkBuJFIbS1wfzSaojknqdh4DtcHI5plnuievhScq2D3ilQ8gxzxDDShjsIrGqb8A388D6sn6NNOBv4PLK5dbHpMm4bydBJ5M1/VF4cbyM02FR8fH4YOHcqmTZsICQkhJibGrOOLXMvPuSW8mZZDP1dH5rUNwdHi5l70GoVWrT/FKE3Tp0cVJkJQDwjuBS7B9T63jUUmkxEREUFKSso1r+GCIFCUWU1qfAFpR4tQVKlw8baj46AgIrt64+x5WXpiz54UfzsbRUICdp06NTqe5kadn0/208+gPn+egLlzcLzrrkvfE7QCyuRSag7mU3e2Aqm9BQ7dfbDv6ouFm9hFSkTkdkZMnaqHzEfHI7W3I2j+fLOPrVarmTlzJt9++63R97z00kt89tlnYorIZbyQfJ7tJZXs7x6N663UStMAtYcLKF+bhuvoSOw7exu+oRFoVFrWfXGMqhIF9zwRTWh7IztRXUQQ9C1wt70Jfh1h9JJ624Fqa2rIHD0GZFJCVqw02pjL+DAE1q5dS2pqqliv0YxoBYH3z+YxP6eYSf4evB/uj8WtvPOs0+kN9ErOQnkmaFX6Qu6qfKirgrte/+9atRIsG78QTkxMZM2aNbz44ou4uLhQUSgnNb6A1MOFVBYpsHOyIiJOX9TtGVR/PYKg1ZLaoydujz2G53PTGx1Lc6I4dYrsZ59FamlFwNy52ERFAqCtUlF7uIDaQ/loq1RYBTni0MMP21gPsYOUiMgdgig06iG1W3fcJk7EY+oUs46rUqkYOHAgu3fvNvnefv36sX37dqysbpNUhSZwrLKWYcfS+CwygCf8b79uJGWrU1GcKsbzmQ5Y+TZP/nudXM3On5PJOFlCp8FBdBsZhtTU4tLsw/qTDY0CHloIra49eatLTydz9Bjse/XC/9tvzJ7iVldXx7x587CysmLSpEmiGDcztRot05Ky2FlaxQcR/kwKMFGU3iwIAigqoChZf3KhUYCNi77mwiMcBODAhXRAey/I2guZexHCBiCJGgjSxm1mKJVKPv/8c2LDuqHJcacoswpLGxmtOnoS2dUH/yhXpEaItpznX0BTVETIit8aFUdzUrVlC3mvv4FN69YE/PA9Mnd3VBmV1BzMR5FYikQmwa6jF/bdfLHyb6BlsIiIyG2JKDSuQltVRWrXbka5sZrKM888w9y5cxt9/7PPPsv3339vxohuPXSCwLCjaWgEgW1dIpHdwrUZ10NQayn64SSCRofX9A5Im8nVVxAETuzI5sCGc/i2cmbQU21NN/mqLYV1T8G5f+CuN6DvzGvcxC/60Xi+/DIeUyab8RHoyc/PZ+HChXTq1Inhw4ebffw7lVyliscT0slSqJjXNoR73J1aOiTTKT0HiWv1QsMnFtQKcPIF9wiw9/ivqPvsX3DiN32hd20xuIZSbNsTzx6Nqy9SKTVknNAXdZ8q+BcJEtoH3E1kV29C23kY3/XtAuUrVlLwwQdEHjzQLG2jG4MgCJTMnUvJ7O9wGj4cr9f/D2VyJbVHCtEUybHwtMW+uy/2nbyR2t4+p84iIiKmIZ5dXoU6JwcAy4AAs477+++/N0lkAPzwww9s3LjRqGsnTJiARCJh2rRp13zvmWeeQSKRMGHChEv/V1BQwHPPPUdYWBjW1tYEBgZy7733snPnzibFbG5WFJRxolrORxH+t6XIAJBYynAfH422WkX5mlSaay9AIpHQcVAQ97/UkYoiOas+Okxuarlpg9i7w6Nr9CLj309g2Si9+LgMxwEDcH96GsVff03Nnj1mfAR6fH19GTJkCIcPHyYxMdHs49+JnKyWM+xoKhVqLRs7RdxaIqOmCA7+CAv6w3edYN9skEjBv5P+xCK4Jzh4Xtk5ys4THLwodx/ELsVLLPqzL+tWSKkslhs9rVarIzOhhO2LTvPTzL38tSQZtUpLm7bRaG2rGDApiogu3iaLDAD7Xj1Bq0V+6JDJ9zYHuro68ma+SsmchbhOfBfLqMcp/OoEldszsfS2w+OpWLxf7oxjL39RZIiI3OGIJxpXUbVtO7kvvEDEgf1YuJrPl6FXr17s37+/yeP07t2bPUYs1iZMmMDff/9NVVUV+fn52NrqC6aVSiW+vr44OTlx9913s2TJEjIzM+nVqxcuLi68//77tGvXDrVazbZt25g/fz5nzpxpctzmoFKtoeehM/Rzc2ROm9u/I5cisYTSX5NxHhGGY2/j2mM2ltrKOnYsOk1eWgXd729Fx0FBpqc5nd0Ja58CSzsY8zMEdLn0LUGrJfuZZ1CcOEnomtVYBQaaNX5BEFizZg1paWlivUYT+bO4gulJWUQ72LIkJhQv61sgHa2uBs78CQmr9KdrEilEDITY0RA1FCwbbhhRW67k719TOJ9USmg7vVdFq46eBrsgCYJATUUdCf/mkLwvH2WNGjc/eyK7ehMR542Tuy3l5eV8++23jBkzhjZt2jQ4XkOcHTgIhz698Xn33UaPYQ7UBUXkvfE1guCLhW8HQIJ1mDN2HbywjfVothNYERGRWxPxFeEq1DnZSO3tkbm4mG3MkydPmkVkAOzdu5eEhASjXJE7depEeno669at49FHHwVg3bp1BAYGEhYWdum6iycc8fHx2Nv/VxPQtm1bJk6caJa4zcFXmYUodDreaeXb0qHcEGxjPHDo40/l5gysAhywDqnf8Moc2DtbM/KFDhzamMGB9efIP1fJgAnRWNuZsMgMvwem7dHXbSweAoM/hq6TQSJBIpPhP2sWGaNHkzP9OUJ+W47UzkiHciOQSCTce++9zJ8/nzVr1jBp0iQsLMSXN1MQBIEfzhfxYXo+I71c+LZ1ELY3symcVg3n/oZTqyBlM6jlENQThn8Bbe4HOzejh7KwlqHVaIno4s2gSW31w2t1yKhfaChrVJQXKVDJ1Shq1ZTl1RLdw5fIbt64+ztcIdJdXV3x9PQkJSWlSULDvmdPaveZ533EVASdQF1GJdW7zqJMKkPmPQyZqxSHHsHYtfdEZmrKpYiIyB3DTfwu0jKocnKwDAgwa9GqudOPTBnvySef5Keffrr078WLF18hHsrKyti6dSvPPvvsFSLjIi5mFFxNIaVWyaLcYl4M9sbX+s4piHceEoJVkCNly8+grVE161xSmZQe97di+DPtyD9bwaqPD1N8vtq0QZwDYMJmiHsKtsyEtZOgTj+GzMmJgO++Q5WdTf7b75g9JczGxobRo0dTVFTE9u3bzTr27Y5Kp+PllGw+TM/npWBvfmwTfHOKDEHQt1X+8xX4MgqWj4HC09B3BryYABO3QJeJJokMAGs7SwKj3SjNrSF5fx5b5yeycfZJDqw/R2FGFaCvuyg+X0Xq4UJS4wspy6nB0taCoGh3hj/djp4PheMRUH/nqKioKNLS0uptZW4s9r16osrKQpWT2+gxTEWVX0vFlgwKPjtMyYIEFCdz0FWcwn18EL6v9cKxb4AoMkRERBrkJnwnaVnU2TlYBpq3PiM9Pb3FxnvsscfYu3cvmZmZZGVlsW/fPsaPH3/p+2fPnkUQBFq3bm3WGM2JIAi8k5ZDgI0VUwNv0a43jUQik+L+SGsEnUDZihQEXfNnOoa082DMm3FY21my9vOjnN6Ta5oosLCCoZ/q296mbtPnyhclA2ATGYnfxx9RtXkzZUt+NnvsF+s14uPjOX36tNnHvx2pUGsYdzKdNQXlzI4O4rUwX6Q3W/1TcSr8/SHM7gCLB8GZzdDhUZi2F545AH1eAZegJk0RGO2GoBM4ujULZy9b/CNdyDxVwpZ5p4jflMGZA/kUpFdiZSMjONad6F6++LVywc7JymCKVVRUFHK5nJwLNYCNwb57d5BKqd2/r9FjGIOmQknVv9kUfH2Uom+PIT9cAJIi5HtnIdH8ReC3T2Mbc/unroqIiJgHMbfgKtQF+dh3627WMZvy5tLU8Tw8PBg+fDg///wzgiAwfPhwPDz+awl7cQF5MztrbympZHd5Db/EhmItvfO0sczJGreHW1OyKIGqv7JwHhTS7HM6edjy4MxO7F19ln+XpVBwrpK+j0RhaUoha9sHwDtG7ya+oD/cOxvajcZpyBCUk09TNGsWNtGt9QsoM9KlSxcyMjL4448/8PX1xc3NtN3tO4kMeR3jT6VTrtGwukMrurvcRO1Hq/L1HaMSVkH+SbB2hjYjYeT3ekM9M78WuPnZ03FQMB5B9lhZW1BRqMDG3pITf50nN7WcHg+2wsPfAQtL04u5/f39sbe3JyUlhaCgxgkimZMTNrEx1O4/gOuYMY0a43ro5GrkCSXIjxehyqxCYinFpo07TgMDqVjxAxXLV+H+1CQ8X34ZyR34GiwiItJ4RKFxFRaubmjLy8w6pqeneXfhTR1v4sSJTJ+uN3r64YcfrvheREQEEomE5ORk7r//fnOFaDYUWh3/dzaP/m6ODLyVOt+YGZtwF5wGBVO1LQurYCdso5p/8WxhKeOuR6LwDXPi32UpFGdXM2RKLC7eJtRWeETA5J2w6WV9G9zzB2DIJ3i++CLK00nkvvgSoWvXYOlvvmJ3iUTCyJEjmTdvHqtXrxbrNa7DgYoaJiZk4GZpwZ+dIgm1uwlSYJRVkLxRLy4yduv9KyIHQ58ZEDGoSeZ5DSHoBJQ1amydLCnKqEar1mHtYIFPmBPhnb3ISanA1t6yUSIDQCqVXnIJHzhwYKPjtO/Zk4rlvyFotUhkTXNmF9RaFMllyE8Uo0wpA52AdbgLrqMjsY1xR1DUkPPiS8iPHsX3o49weejBJs0nIiJyZyJuTVyFZWCg2XNgQ0NDW3S8IUOGoFKpUKlUDB48+Irvubm5MXjwYH744Qdqa2uvubeioqIpoTaZudlFFNSp+SDC/6Y+dbkROPYLxKa1G+UrU9CUK2/YvFHdfRn1ehe0GoFVnxzm7NEi0wawsocHfoQR38DxpbB4MJKqHPy+/AKpvT05zz2PTmnexyPWazTMqoIyxpw4R1sHW/7sHNGyIkOj0neMWvUEfBEBvz8LOi3c+y3MSIOxv+pPMppBZChqVOSfq+DMwXzSjxejqFLj5udARFdvIjp74xnkhEalQ1Glwsmj4c5VhoiKiqKkpITS0lLDF18Hh5490VZWokxKbtT9gk5AmVZO2epU8j48pK/9qqrDeWgovm92w3NSLPadvVHnZZM59mHqkpMJWrRQFBkiIiKNRhQaV2EVGIA6O9usY3bt2rVFx5PJZCQnJ5OcnIysnl2wOXPmoNVq6dq1K2vXriUtLY3k5GRmz55Njx49zBW2yWQrVXyXVciUQE9a2TXPTuathEQqwW1MJBIrGaXLkhE0jS8sNRV3fwdGv9GF4LbubFuQyN7VaWi1JswvkUCXJ2HSdpCXwry+WBTHE/D9d9Slp1Pwf++ZvTjcz8+PwYMHEx8fT1JSklnHvlXRCQKfpufzfPJ5xvi48lv7VrhYtsBpj04HWfth4wt6cbHiEb253t1vwkunYcIm6PQ42LqYfWp5tYrywloyE0tIiy+kLL8WR3dbwjp50rq7Dz6hTtg6WCGVSclJKSfvbEXj2j1fRVhYGDKZjNTU1EaPYdu+PVI7O2pN6GIoCAKq3BoqNqWT/0k8JYsSqcusxKG3P96vdMZ7ekcce/sjc9Q32ag9eJDMh8eBTEbIqpXYm/n9S0RE5M5CzCe4CsuAALTl5WhrapE5XNuFqTHcc889REREkJaW1uSxIiMjuftu091qnZyun3YUGhrKsWPH+Oijj3jllVfIz8/H09OTzp07N9lksCn872wejhYyXgr2brEYbjakdpa4j4+maO5JKv5Mx/W+8Bs2t5WNBYOeaotPK2f2rzlLYUYVgye3xcHVBBHo1xGm7ob102D5GGzCB+D7zEPkfbUMm5gY3B4bb3gME4iLiyMzM5Pff/8dHx+fO7peQ6HV8XzyeTYVV/BuKz+eDvS88aeEhUn6tKiENVCZDc5B+i5R7caAV3SzTausVXPuWBGp8YXIq1V0HBiEm589rrF2OLrbIL2s7uDEX+epKlWSf7aC6jIlkV19iOza9Ncga2trQkNDSUlJafQGjsTKCruuXandtw+PqVMavFZTqkB+ohj5iSI0xQqk9pbYtffEtoMnVoH1d8cqX7WKgv99gH3Xrvh/8zWyBt43RERERIxBNOy7Cvnx42SNe4TQ3zdgExVltnFnz57NCy+80ORxvvvuu0v1Frcze8urGXXiHN9FBzHa585dHF6PmoP5VGw4i9vDUdh18Lrh8xekV7JtQSJajY6BE9sSGG3i70in0y844+dD7lEKT/tTlgTBc7/Frm/jc9jrQ6lUMm/ePGxtbZk4ceIdWa9RVKfmiYQMztQqmdMmiKGeLjdu8socvbBIWA2FiWDrqve5aDcWAruZvaj7Ihq1lsxTpaTGF5B1uhRBKxDQ2pWo7j606uh1XYfu4uxqjm3NwjfcmdbdfbEyo7P14cOH2bx5M6+++uolE1VTKftlKYWzZhF16OA1XjTaGhWKi0Xd56uRWEmxbeuBXQdPrMNdkcjqF5aCVkvRrC8oW7IEl3EP4/Pmm0gsbwGjRhERkZseUWhchaakhLTefQj4/jscBwww27harZaBAwfyzz//NHqMe+65h+3bt1+x+3Y7otEJDDiSgoNMysZOEXd8bUZ9CIJA+coUFEmluI9vg02k+VzsjUVRo2LH4iSyk8voOiKULkNDDLb5rJfcYwiHFnD+2x3UVcoIfb4rlgOe1S9CzfS7z8vLY9GiRXTp0oWhQ4eaZcxbheQaBeNPpaMV4Jd2obRzNJ9R4nVRVEDS73pxkbkXLKz1Dt2xYyB8gL4FcjOg0wnkpZaTGl/IuePFqBQavIIdiezqQ3gXL+xb2POhsrKSr7/+moceesgo09X6qDt3jvThIwicPw+Hvn3RqbQok0r1Rd2p5YCATYQrdh29sGnjjtRApzhtTS15M2ZQs3s33m+8gev4R8XXXBEREbMhCo2rEASBlE6d8XzhedwnTDDr2IWFhXTu3JncXNOLzQMCAjh69CheXjd+9/pGszCnmHfSctnaJZL2N2JRdIuiU2kp/TWZurRyHPsH4XRPUOMW+k2JQSdw5M8MDm/OJKiNOwOfbIONQ+N2QjU558gY8zAWFrUE98tF6hcDcZP0i1PrprddPXToEFu2bGHs2LFERzdfms7NxF+lVUw9nUmorTW/xIbiZ9OMZpdqJaRt0zt1p20HnQZC++nTolqPAJvmScMRBIGS7BpS4wtIO1xIbaUKJw+bSylPrj7mSYE1Fz/++CMeHh6MGjWqUfcLgsDZu+7Grttd2HYeh+J0CYJKh1WQI3YdvbCN9UDmYNzvWZ2bS/bTz6DOzcX/669w6Nu3UTGJiIiIXI87L4fAABKJBKuAAFRZWWYf29vbm/j4eMaOHcvevXuNvq9Pnz6sWLHijhAZJSoNszIKeNTXXRQZBpBayfCY0Jbqf7Kp+isL1fkq3B5ujcz+xqU8SKUSut4bhk+YMzsWJ7Hy43iGTI7FO9T0RaVFQCsC5v1E1qOPUlD5EL7RCiR/vgLb34UO46DLJPBqvLFk165dr6jXcHW98adAN5KFOcW8m5bLQA8n5kQHY2/RtHao9aLTQdZeOLUSkjZCXSX4doAB70HMQ+DoY/45L1BVorjk0l2eX4utoyXhnb2J7OqNd6jTTbsrHxUVxcGDB9FqtfU257gegiCgzqlBfrwIiX0ENf/uQeY9Asd+gdh18MTC3bRULMWJE2Q/Ox2pjQ0hK37DOiLC1IciIiIiYhDxRKMecmfMRJ2TQ8iK35plfI1Gw2effcb3339PQUHBda/z9fVl+vTpvPrqq3dMXvnMlGz+KKpgX7doPKzujMdsDpRp5ZStOIPEQorbo9FYB934Is7qMiXbFiRSfL6a3qMjiOnXuJbEFevWk//mm/i89x6uQ3rC0SVw7GeoLYaQPvpTjtYjQGa6oFIoFMybNw87O7vbtl5DoxN452wuP+WWMDXQk3db+SEz56JbEKAg4UJR91qozgPXEP3JU+xo8Iw031xXoahRce6ovqg7/1wlFlZSwjp4EtnVh4BoV2Symz+tNC8vj/nz5/PEE08Y1apcXaJAfrwIxYkiNKVKpI6WSHRJVCybRatd/2LlbXqheuWmP8l/801sYmII+P47LO7gJgkiIiLNiyg06qF08U8Uf/cdUUcON9kUqSHUajUbNmxg7969pKenk5+fj6+vL2FhYfTp04f777//tlwIXY+T1XKGHEnlgwh/ngowr8nhnYCmso6yZcmocmpwHh6KQ0+/G76rq9Xo2L/2LKf+ySEizpu7Ho3Cysb053DB/z6gfPVqgn/5GbuOHfVeC8l/wOFFcH4/OPhA5yeg0xPgbJrZX25uLosWLaJr164MGTLE5NhuZqo1WqaczmR3eTWfRgbwmJ+H+QYvz9LXXCSshuIzYOcObR/Up0YFxJmtnuZq1CotmSdLSI0v4PzpMgQgMNqNyK7ehLb3aNTzqyURBIGvvvqKtm3bXvf5p61WIT+p7xilzqlBYi3DNuZCUXcrF7TlZaT16o3vp5/gYoLRqiAIlHz3PSVz5uB830h8PvgAqVUzptOJiIjc8YhCox5qDx7k/IQnCftzE9atWrV0OHcEgiBw77E0qrU6dnaJwuIG1xrcLghaHZVbMqnZm4ttOw9cH4pAan3jF2JpRwr5Z+kZHFytGTI1Fjdf0/LkBZWKrCcnoj5/npC1a7C8PG2w8LRecJxaCWoFtB4GcU/p6wGMXOwePHiQrVu38vDDD9O6dePTsW4mspUqHjuVTl6digVtQ+nn5tj0QeVlcHq9XlycPwCWdtB6uP70otXdjTpVMgadVkdOSjmphwpJP1GMuk6Ld6iTvqi7sxd2Trf24njjxo2kp6fz/PPPX9oM0NVpUJwuRX68iLqzFSCVYBOpL+q2jXZDcpUrefoDD2IdEY7/558bNadOqSTvjTeo3rIVzxdfxH3qlJs2vUxEROT2QRQa9aCtrCS1W3f8Zs3C+d4RLR3OHcGagjKmJ59nTYdW9HY1wwLpDkeeUEz5mjRkTla4PxqNZQsUxJYX1LJ1fiJVpUrueiSKyK7eJi1sNMXFZDw0Ckt/f4J/XoLk6p1XZZVebBxeBMXJ4B6hT6tqP86g0ZsgCKxcuZLMzEymTp16y9drHKus5fGEDOxkUn5tF0akfRMMLlVySN0Cp1bD2R36VKlW/fUnF1HDzFKYXx+CIFCUVa0v6j5ShKJKhYu3HZFdvYmI88bF6/ap2UpNTWX58uU8M+1pHMst9B2jkkoR1DqsQpyw6+iFXawHUrvrC7nCWbOo/OMPInbvNvh3pS4qImf6c9SlpuL32Wc4DR5k7ockIiIiUi+i0LgOZ+8ZgOOgQXi/9mpLh3LbU6PR0utQMl2dHVgQE9LS4dw2qIvllC1LRlOqxOXBCOw73vhmAuo6Lf8uO0NqfCHu/vbE9Asgsqu30ekuihMnyHzscVxHj8Ln3Xfrv0gQ9C7Thxfq06ukltButP6Uw7f99ce+UK9hb2/Pk08+ecumKf5eVM4Lyedp52jH4pjQxtU2aTWQsUt/cpG8EVQ14N9FLy7aPggOzZfKWFEkJzW+kLTDhVQUyrFzsiKiizeR3bzxDKrfWO5WRhAE5OfK+Hr5HDoJYbRTBGHhbacXF+09sTDSALNm3z6yJz1F6O+/YxN1/boYZXIy2U8/AzodAXPmYBvT1lwPRURERMQgotC4DjnPPY+2uprgJT+1dCi3PR+cy2NxTjG7u0UT2JztN+9AdCotFRvOIj9WhH03H1xGtEJieWMLZgVBICe5nIRdOWSeKsHCWkbrbj607eePu5/h3fHyVasoePf/8P3oQ1weeqjhi6sL4dgvcPQnqMrV1w7EPaU3iLO8dgGXk5PD4sWL6datG4MHD27kI2wZBEHg26xCPs0o4EFvV76KCsTGlGJoQYC843pxkbgWagrBPfxCUfcocG++tFF5lYqzR/UdowozqrC0kdHqQlG3f2tXpLdh6qS6SI78eBHyk8Voy5T8ZZeAyg4mPPI4lr72JgsqnVJJarfueL7wAu4Tn6z3muq//yZ3xkysQ0IImDsHy0YUjouIiIg0BVFoXIeSH3+kdPFPRB46eNvtqN1MnJMruSs+hReDvXkltPlaYd7JCIJA7eECKv44h6W3Pe6PRmPh1oTUmiZQXaYkaW8ep/fmoahS4R/pQky/AEI7eDTYMSj/nXep3LCB4OXLsDXG6EyrgdSt+lOO9H/A1g06PQZdJuo7JF3GgQMH2LZt2y1Vr1Gn0zEjJZvVBeXMDPHh5RAT0tLK0vVpUQmroPQs2HvphUXsaPDr2GxF3SqlhowLRd3ZyeVIgKAYd31RdzuP6zp138poq+qQn7hQ1J1Xi8RGhl2sJ7YdPEmqSOePjX8wc+ZM7O0bl9p4fuIkkEoJWrjgiv8XBIGyxYsp+uJLHAcMwO+zT69xERcRERG5EYhC4zrU7NpF9tRptPprB1YBAS0dzm3LoyfTSZUr2d21Nba3QGvKWxlVbg2ly5LRyTW4jY3ENtq9xWLRanSknygmcVcueWkV2DlZ0aaPH217++FQT+qITqUi67HH0BQWEbp2DRbuJsRechaOLIYTv+rrOgK76nfuXUPANQTBOYgVu8+QlVvAtGnTcHFxMdvjbA5KVRomJWZwvFrON62DeMC7nvoSnRaq8qAiC8ozL3xk6btFFZwCKweIvlcvLkL7gax50sa0Wh3ZSWWkxheScbIYjUqHb7izvqi7k1ejzR1vZnRKDYrEEn1Rd3olSCXYtnbTO3W3dkNioX+dq6mp4YsvvuD++++nQ4cOjZqrdNEiir/7nshDB5Fa613PBZWK/Pffp3LtOtynTMHzxReQSMXXVhERkZZBFBrXQVNcTFqfvvjP/hanQWLhXHOwvaSSxxMyWBwTwjBPl5YO545AJ1dTtjoVZXIZjncF4jQwGImsZU/sSnNrSNydS8rBAjRqHaHtPYjp509AlOsVu/TqwkIyHhqFdWgoQYsXIbE0cZGqkkPiGjj3z4UFeBbISwBQYM2PPIajhZYnQ/ORuYXohYhL8AVBEgxWLe8wfVauZPypdKo1OpZEehCnKdCLiKsFRcV50Kn/u9HR75KwImIARA4Fq+bZ4RYEgcKMKlIPFZB2tAhljRpXX3uiunkT0cUbJw/TjOVuBQSNDmVKGfITxSiSS0ErYB3qrO8YFeOB1LZ+Ibdw4UIcHR0ZO3Zso+ZVJieT8cCDBC35Cfvu3dGUl5P7/AvIT5zA94P/mdT6VkRERKQ5EIVGA6T26YPLQw/h9eKLLR3KbUedTke/+DME2Vixsn0rMT3tBiLoBGr25FC5NRPrUGfcxrVG5tjytTEqpYbUQwUk7MqlLK8WF287Yvr607qHD9YXuu/Ijxwha8KTuD4yDp8332z6pHXV+oV5eSY5mWdZHF9Od+dSBlnE6xfvGuV/19p76QXHxQW7y2VfO/mB1MypPxoVVGZfEhB7K+VMksThrS5nafK7BFem/netleN/guhiTBc/nAPrrU8xN+UFtaTGF5IaX0BViRJ7F2si4vRO3R4BDrfd37igE1BlVupTo06VICg1WPra68VFe08snK0NjrF792727t3baFNWQacjrbf+fcr5gfvJnvY0uupqAr7/DrvOnRvzsERERETMiig0GuD81KkABM2b18KR3H58l1XIpxn5/B3XmqimtOIUaTR16RWULj8DEgnuj7TGOtS5pUMC9Dvi+WcrSdyVw7ljxUhlEiK7ehPTLwDPIEfKli2j8IMP8fv8M5xHjjTr3BfrNcaNG0dURIS+QPqaE4NMvTipzvvvRqkluATWL0JcQ+pvtysIerfz8qvGvjhXVS4IOgCW+47g1YiX6K3MZL7mAE6ul51QuIaCrWuz1VY0RG1lHWmH9UXdxeersbK1oFUnfVG3X4TL7VnUXVBL7fEiFCeK0VbWIXOxxq6DF3YdPbH0Nu3Uq7CwkLlz5zJ+/HjCw8MbFU/ujJkoEhLQlpVh4e1F4Ny5WAUGNmosEREREXNza/ZzvEHYREdTsXZtS4dx25Ffp+LrrEIm+XuKIqMFsQ5zwfv5TpT+lkzxglM4DwnFoY9/i+88SyQS/CJc8ItwobayjuR9+Zzek0vSvny8Q52I6Xs3jiMTyH/nXazDw7Fp08Zsc3fv3p3MzEw2bNjA1KlTcXHxBSdfCO5x7cVqpT5F6WoRkn1YX2ytqv7vWhvn/wSIVv3fPWr5f9fYuf8nUALiwDUYnUsIHyq9mVNUx+N+7nwc0RkL6SizPd7GoFJoOHe8mNT4AnJTypHIJITEetB5aDDBMe5YWN5+Rd2aCqU+LepEEeoCOVI7C2xjPbDr6IVVkBOSRgoqLy8vXFxcSElJabTQQCZDff48Dvfcg99nnyJrZGG5iIiISHMgnmg0QNX27eQ+/wLhu3dd6Uws0iSeScpid1k1+7tH42Rx+y1KbjUErUDV9kyqd+Vg08Ydt9GR180pbyl0Wh2ZCaUk7sohO7kcOxuBLgnfYK2tJWz9WizMaLgnl8uZN28ejo6OPPnkk8hkjXiOCoLeVbsi88pTkPJMkFlddhoR/J8AsXG6YoharZbnks6zpaSS98P9mBzg2WIiUKvRkZVYSmp8IZkJJWg1OvwjXIjs6kNYR09s7G/Dom65GnlCCfITRagyqsBCim0bN+w6eGET6XqpqLupbN68mTNnzvDSSy+Z9PsVNBpKFixEfmA/lsEhuI57GFszim4RERERcyAKjQZQ5eRybsAAAuf9iEO/fi0dzm3BoYoa7jt+lq9aB/KIb8t1PRK5FkVSKWWrUpDaW+L+aDRWRnhctAQVhXISd+eS/vdp2u/9CI13CG5ffEdwO2+zpepkZ2fz008/0b17dwa1QDOIgjo1j59K56yijh/bBDPI48antQk6gfxzlaTGF3D2WBF1tRrcAxyI7OpNZJx3vd3BbnUEtQ7FmVLkx4tRppSBTsA63AW7Dl7YtnVHaqTRpCmcO3eOpUuXMnXqVHx9fY26R6tQULVpE5riEhz69KY2Ph4LNzdcHnjA7PGJiIiINAVRaDSAIAikdu+B+5MT8Jg2raXDueXRCgKDj6RiIZGwuXME0tusOPR2QFOqoHRZMuoiOa73hWMfd/N6m6hVWtKWboUvXuV8QH+Kuo4jpq8/0T19sTVDcfv+/fvZvn07jzzyCJGR13deNjcJ1XIeT8hAAvwSG0qM4431PyjNrdEXdR8uoKasDgc3ayLjfIjs6o27/80pPpuCoBOoS69AfrwYRWIJQp0WS38HvVN3O09kTs3bKEGj0TBr1ix69uxJPyM2tDSVlVRu2IA6Lx+XBx7ApnUUFes3oDidiM9bb7V46qOIiIjI5dxc+RE3GRKJBJvoaJSnk1o6lNuCZXmlJNYo2NxJFBk3Kxbutng93YGKjecoX5tGXWYVLve1QnoTmqlZWsloM2k4pbJiJJ9+hmOndsRvVHFoYzoRnb2J6eePd6hToxdePXr0IDMzk/Xr1zNt2jScnZv/VGF7SSXTkrIIt7NmaWwY3tY3JiWpplxJ6uFCUg8VUppbg7WdBeGdvYjs6oNvK+dG1yDcrAiCgDqvFvkJvVO3rkqFzM0Gh15+2HX0wtLzxok7CwsLWrVqRUpKikGhUZeZSdnin5DYWOM+adIlp2/rqEhq9+5BU1wspvmKiIjcVIhCwwA20dFUb9/e0mHc8pSrNXyakc9YHzc6OYvFijczEksprg9GYBXsRMWGs6hza3AbH43lTep/4PbEEygTTyPZPpdHflrKuSJHEnfnkHKoAI9AB2L7BRAR542ltWliSSKRcP/99/Pjjz+yZs0aJkyY0Lh6DSMQBIF52cW8fy6PYZ7OzI4Owr6Z5rpInVzNuWMXirrTKpBZSAlt50G3kaEEtXVHZqYahJsJTZlSLy5OFKEpUiC1t8S23YWi7kDHFjsNiIqKYv369VRVVeHk5FTvNfKjRylfuQqr4CDcnngCmcN/p0vWYWEgs6AuNVUUGiIiIjcVYuqUASo3biJv5kwiDx1EdgN2NG9X3kjNYXVBGfu7ReN1g3ZpRZqOKr+WsmXJaKtVuI2OxDbGo6VDqhedQkHmuEfQ1dYSumY1UkcnzieXkbgrl8yEEiytZLj62OHkYXvhw+bS1w5u1sgacKW/WK/Ro0cPBg4caPbY1TqBt9Jy+CWvlOlBXrwZ5mu2Ez+NSktVqZKqEgVVJRc/678uL6xF0AoEtHbVF3V38MTqJmsCYA60tWoUp4qRnyhGlVWFxFKKbVt3bDt6YRPugqSB3/2NQi6XM2vWLEaMGEHnq/wvBJ2Oqm3bqPnrL+zi4nAeNQppPZ4bxT/8gNTWFveJE6/5niAIaMvLETQaUYiIiIjcUG6/dxUzY9MmGtA7sNp3797C0dyaJNUo+Dm3hHda+Yki4xbDytcer+kdKF+bRumvyTj09sd5aMhNsTi7HKmtLQHff0fmQ6PInTmTwLlzCW7rTnBbd6pKFJw9WkRFkZyqEgWFmVXUlCm5uMUikUpwcLXGycMWZw8bHD1scb5MkAQEBHDPPfewY8cOgoODzVqvUanWMOV0FvsqqhvVIEHQCdRW1l0SEZUlCqov+1peqfrvZ2Qhwcld/5h8w51p28ePsI6e2BthLPf/7N13eJPl18Dxb3badO+920DZQ9SiIk4UVLbiwC2KgqIi4hYRBw4EVFwIgrIRVMSJ8qogypJNW7ro3itt0ozn/QNB+clI2qTpuD/XxXUpfe47p4LNc/Lc55z2xtZkxXiggobdZRjTqwAJbbI/Adfr0aYGInfw6ZareXp6EhMTw+HDh09KNGxNTVQtW45xzx58hg7Fa/Dg0z510aSkUP/Tz0gWC7L/SUSkpiaqV6zAXFhE+AszXPq9CIIg/JtINM5CHReHzMMD4wGRaDSHJEk8kZ5PoqeGO6Pa5qfhwpnJtUoCbuxC/W+F1HydTdPROgJv7IKijd2gqqOiiHjjdY7efQ9l8+YR8tBDAPgEedD3ytiTrrVabdRXGk/6lL+mzEh5fj1HdpdhMlhOXKvSKPAO1OKrDmPVijVces5IwiIDjyUigVqUzaxfyW00cfOeLMqaLCzvlcgF/t6nvK6p0XIigaj51xOJ2vJG6iqMWC22E9d6+qqPJRPBWiK7+OMT6IFv8LGnNzpfTYertfg3ySphOlJNw65SGvdXIDVZUcd44zcsAY+eQSi8XFvU3VIpKSn89NNPNDU1oVarsdTWUfXpUpry8gi47VY8evQ443pNYiK1GzfSdPQo6rg4sNmOtVlWKJBrNOjOP5/CJ5+iYft2PPv3b51vShCETk8kGmchUyjQ6vUYD4iC8OZYX1rN7zUGlvdKQC1vW5+CC/aTyWR4XxCJOtqbyk8PUjJvFwE36NEmOW9+hTN4DRxI8JSHKHv9DbTduuFzmqNOCoUc32BPfE9T9GtqtPxzzKjs2E29tqwv+ys38eMvG/Ct6IWMYzftnr7qE09AvIO0Jz0NOd3N/R/V9dy2LxtfpYIveicRaoSjBytPecTJaDCfWKfUKI49dQn0ILZbID5/JxE+gcdeW9UGi/ZdSZIkzPn1NOwqpWFPGbZ6M8ogD7wvisSzdwjKNlpXdCp6vZ7vv/+e7OxsYiwWar74EoWfL8H33486Kuqs62s3bsRaVY3pcDqa+Hj4V42PMT2duh9+oCkri/rNm0WiIQhCqxGJhh20qakYtm1zdxjtjsFqZcaRQoYE+XBxwKkLHIX2RRPrQ8jkPlQuP0z5R/vwHhyN9wWRyD3bzpG4wLvuwrhvP0XTHkeTkIAmMdHhPTQeSoKjvQmOPvkpQ7+8MD7++GMSh1nopR9ATZmR2orjT0QaOXqwkobaf44rKZRyvAO1/9SEBHrwo2RkjqqBeIPEjTvq+OGT35Fsx85xyWTg5a/FJ1hLYKQX8b2CT0omPLxVon0pYClv/LuouwxLeSNybxWevYKPdYyK9GqX/42CgoIIDAxk76ZNSO9/gM/QoQRPeuCkou8zMe4/gLWyElN6OuY+van74Qfqf/yRht1/IZlMaBIT8bt+LJ4DznXxdyIIgvAPUQxuh+o1ayh66mn0O7Yj92zdnvbt2StZRbxztJT/G9CFWI+2dcxGaBnJJlH7Qy51m/NBJsOzVzBe54ejjjr18Z/WZjMYyLnhBiSzhbhVK1F4Oy+uX3/9lR9++IGbbrqJ5OTk/3zd3GQ9USdRW3HsiUjN3/+8Pkji5y5aBpTauLtWRVCQB96Bf9eEBGvx8td2yG5PzmCta6Lh76Ju89E6ZGoFHt0D8ewTgibBD5mi/SUX/yZJEutfe51D5WXcZGoi8uWXkGvtH4pY/8svHL1nAjKNBslkQhkSgkevXniedy4e3bqhiohA7uODTK1ul4mYIAjtk0g07GA8cIDskaOI/ewzPPv2cXc47UJuo4mL/jjExOgQpiXYN+1WaH+sdU0Ythdj+L0Ya40JVbQ3XueF49kzCJnKvcd4mnJyyB4zFs/+/Yl6ez4yJx3ds9lsfPbZZxQWFnLvvfeeth3pvxmtNqYcyuPz0mqeSAhnUkyIuNmzg81kpfFABQ27SjFlVgEytHp/PHuHoO0a0CbnuzSHramJ4qefIWPLFn669BLuuvNOoqKjHdpDslg41Ks3qogIAm6/Da+LLkLh549c5yn+rgmC4DbiozM7aJKSQKXCeFDUadjr2cwCAlVKHogVrRQ7MoW3Gp/BMYQ9dg6B41OReyipWpVO0Ut/UP11FpaKRrfFpo6LI2L2q9T//DPl777rtH3lcjkjRoxAoVCwevVqrFbrGa8vazIzZvcRNpbX8EG3OCbHhoobvzOQrDYaD1VSsewQRTN/p2rFYaQmK37XJhH+5LkE3doNz17BHSbJsFRVkXf7HdR+/TU9pz6KVqslPSPD4X1kSiXq6GgU/v7INRrUUVEovHQn/q5JZ/l7KgiC4AqiRsMOMrUaTXKSKAi3008VtXxTXsuC1FiXDx0T2gaZQoZHaiAeqYGYyxsxbCvC8GcJ9f9XgCbFH6/zwtF2CWj1rkfeF19M0KQHKJ87D21qKt6DBztlX51Ox+jRo1m0aBE///wzl1566SmvO2wwcvOeLIw2G2v7JNHXRwyrPBVJkmjKqzvWMWpvGTaDBWWIJ96XROPZKwRlgP1HiNoTU2YmR++9D1tDAzGLF+PZtw/JZjOHDx/mkksucXg/3xEjqP/1V0zpGdiMRizFxaiiopAplcgUCiSrFZn4mSwIQisSiYadtKmpItGwQ5PNxtOZBZzvp+O6ED93hyO4gSrIA7+hCfheEUvDX+XU/15IxScHUPhp0J0bju6c0FZtNRp0770Y9x+gcOpjxK1aeawjjxPExsZyySWX8OOPPxIbG0tSUtJJX99cWcdd+7KJ0qr5vE8SUdq23V7VHcylDcc6Rv1VhrXSiMJHjWe/UDx7h6AK13XoJz/1v/xKwZQpqMLDiVm0CHVUJHCs+9TevXuprq7Gz8/PoT39x92AKj6O+m+/xXg4ndJXX8XW0IDv0KvRpaWhTU11wXciCIJweuLolJ20XbtiyshEamo6+8Wd2Ef55WQ1mHgxOapD3yQIZydTKdD1DyX0gT6E3N8bTaIftT/mUfTSH1QsP4Qpp4bWKBGTyeVEvPIyyuBg8idNwlpvcNreAwcOJCkpibVr11JbW3vi9xcXlHPjniMM8PXii77JIsn4F2utibpf8imZt4uSN3ZQv7UQTYIvQXf3IOzxAfhdnYA6on12jrJX5dJPOTphAh79+hK77LMTSQZAUlIScrmc9PR0h/dV+PjgfcEFIJNjLirEdPgwPldegbWmhsJpj5M/aRKVixdjra1FsljOvqEgCEILiWJwOzXs2kXuuBuJX7tGfCp0GqUmM2nbDjImLICXUs7e913ofGwNZgw7SjD8XoSlwogqTIfu/HA8e4e4fFqzKSuLnDFj0aWlETn3LafdyBoMBhYsWEBAQAA33XILM7NLeD+/jLuignguMRJlBx6SZy+b0ULjvnIadpdhOlINchnaLgF49g7Bo0sAMlXn+MxLslgomfUSVZ99RsCt4wl57LFTHmX65JNPkMlk3HLLLY6/hiRRNncuCn9/6n/4Ec/+/QiePBlbUxP1mzdjOpxO3bff4H355QRPnuyMb0sQBOG0OsdPdyfQ6vUgl4vjU2fwYlYRarmMx+LD3B2K0EbJPVV4XxhF6CP9CbqjO4oALdXrMimatY2q9ZmYS5z3tOF/aRISiHjlZeq+/56K9z9w2r7H6zWO5Bdw3f/t5MP8MmYlRzIzOapTJxmSxUbj/goqPj1I4czfqVqTATYJ/xHJRDx5LkG3pOLZI6jTJBnWujqOTriXqhUrCHvuOUKnTz9tvURKSgo5OTmYTKZmvZY6KYmmjAy8h1xJ3fffA2ApKQFJoinrCJbSMqpWrGz29yIIgmAvUaNhJ7mnJ+r4eIwHDro7lDZpR42BFcWVvJoShb9K/LUSzkwml6FN8Ueb4o+l2ohhWzGGP4sxbC1Ck+CL7rxwPLoFIlM49ybU+7LLCJp4H2Vz5qBN7YrXhRc6ZV9laDjfX3AVhRYbs8M9uCkq2Cn7tjeSTaIpp/bYML295UiNFlRhOnwvj8OjVzBKv845T6cpL4+j903EUlZGzIcfoDv//DNer9fr+eabbzhy5AipDj5Bl8lkeHTpQv1332EpK8eUlU3WyJFYKyqRe3igjo8n4PbbUEVEYDMaHZrVIQiC4ChxR+gAURB+ajZJ4omMfLp7eXBTRKC7wxHaGaWfFt8r4/C5NIbGfeXU/15E5WeHkHur0Q0Iw2tAGApf592gBj3wAI3791Pw6FTiV69C7eC8gv/1V10D4/dkodJomVy8n4JdOdTG2Ddfo6MwFxv+KequNqHw0+B1btixou6wzt1pq2H7dvIfmITc14e45cvRJJy9GYG/vz8hISEcPnzY4UQDoPydd6j/6WfkO3YiU6lQhYYRNOFelIEBqKKiUAYEIFOLuiFBEFxPJBoO0HbtSt0PP4gWgf9jeXElf9U1sr5PEooOXMApuJZMKcezdwievUNoKjJg+L2Q+l/yqfspD4/UQHTnRaBJ9G1xbYVMLidy9myyx4wh/4FJxC37DLmnZ7P22lBWzQMHcunq5cHiHvF49IzlvffeY82aNYwfPx5FB/45Yak20fhXKQ27yjAXG5B5KPHsGYRn7xDUsT6t3sq4Lape+zlFzz6LZ58+RL41B6W/v91rU1JS2LFjBzabDbmDwya9LroIa20diuBgrMXFyD098bnyCkfDFwRBaDFRDO4Aw+/byLvtNhI2fIUmMdHd4bQJNWYLadsOMSjAm3dSY90djtDB2IwWGnaVUr+1CEtpA8pgD3TnhaPrG4rco2WfkxjT08m5YRzeF19MxOuvOZTASJLE23mlzMwq4toQP97qEoPH38e8cnJyWLx4MRdeeGGzZiG0ZbYGMw37ymnYVUZTTg0o5Hh0PVbUrdX7I1N2jnqLs5FsNsrenEPFBx/gO3oU4c884/AThKNHj/LRRx9x++23Exvr2M9WyWzGsPV3ar76isB77kYml6NJSDj2NUnq0B29BEFoW8QTDQdou3YBwHjggEg0/vZ6TgmNNhtPJ4a7OxShA5JrlXidH4HuvHCasmup/72Qmg3Z1H6Tg2efEHTnhaOO8GrW3tqUFCJmvUjBQ1PQdu9O4B2327WuyWZjWno+y4oqmRIbytT4MOT/unGLi4tj8ODBbNq0idjYWBLb+c8KyWyj8VAFDbvKMB6uBJuEJtEP/1EpeHQPRK4VbyP/ZmtooHDaNOp++JGQxx4j4PbbmnVjHxkZiU6nIz093eFEQ6ZSodGnwHoLmM1ounb952siyRAEoRWJdwgHKHx9UUVFYTxwEN9rrnF3OG532GDko4IyHo8PJ1wjzvsKriOTydAk+KJJ8MVa23SscHxbEYY/ilHHeKM7PwLP7o53MPIZMgTj3fspfe01tKld0Z133hmvrzZbuHNfDn/WGJjXNYYxYQGnvO6CCy4gJyeHtWvXcu+99+Lt7e1QXO4m2SRMWTU07C6lcV85ktGKKtIL3yFxePYKRuHTOYu6z8ZcXMzRiRNpyskl6u238b6k+ZPo5XI5KSkpHD58mMsvv9zh9cqQEOS+vpjS09H+K9EQBEFoTeLolIPyJz+ItaaG2MWL3B2KW0mSxNi/jpBvbOLnAV3QOHiGWBBaSrJKGA9WUP97EabMauQ6Jbr+YXj2DUEZ6GH3MR7JauXo3fdgPHCA+DWrUUVGnvK67AYTN+/JospiYWH3eM7zO/OTlPr6ehYsWEBQUBDjx493+Jx9a7M1WbGUNNCwp4yGv8qw1TahCNDi2Tv4WFF3SPPqWDqLxr37yJ84EZRKot99B22XLi3e8+DBg6xYsYJJkyYRGOh4o42q5StoOnqU0KmPtjgWQRCE5hBPNBykTe1KxcKPO/05143lNfxSVc+SHvEiyRDcQqaQ4dE9CI/uQZjLGjD8XkT9tiLqNueDDBS+GpQBWhT+WpQBWpSB//yz3Et14v9fmUJBxOuvkTN6DEcnTSLus8/+0/Jza3U9d+zNJlCtZEPfFOI9z/6JvpeXF6NGjeKTTz5h8+bNDB7c/E+3nUGySVhrTVgrjVj+9ev4v9vqzQDIdUo8eh5LLtQx3p3655y9ar/5lsLHH0eTkkL02/NRBjunvXFiYiIKhYL09HTOP0tL3FPRpKTQ8OcfWGpqUPr6OiUmQRAER4hEw0Ha1FRstbWYCwpQR3XO6deNVhvPZhZyaYAPlweJNy/B/VTBnvhdk4jPlXE05dUdu3muOnYDbS5twHioApvBcuJ6mUqOIuDvBCRAiyJAS9DDMyl+4l6Knn6WiFdfPnGDvaKokkcPH+VcXx0fdo/Dz4E5MfHx8Vx88cX89NNPxMbGkvB3Qa6r2Bot/5NANB775yoTliojWP95gK3wUR/7bxDkgTbF/9g/B3qgjvJy+vySjkqSJCree4+yOW/hc/VVhM+a5dS5FGq1moSEBA4fPty8RCM5CQBTejrKc85xWlyCIAj2EomGg46fdTXuP9BpE4138kopNplZ3su1N02C4Ci5WoE2ye+UX7OZLFgqTVgrG7FUmrBUNmKtNGJMrzp2E26R0HS/mdovP6KpyBuPtGG8E63gfS8rY1UezPIJRGuwIvkoHGrdeuGFF5Kbm8uaNWtaXK8hWWxYq03Hkomqk59IWCqNSI3/SqY0ihNJlLZLwLEnOseTKz9tp5nI7So2k4mip56m9ssvCbr/foIeuN8lT39SUlL4+uuvaWxsxMPDw6G1Cm9vlBGRNOXloROJhiAIbiASDQcpg4NRBgdjPHigU/YlP2psYl5eCfdEB5PoKSbKCu2HXKNEHa6E8P8OkJNsErb6JiyVPSl7q5a6b5fx4flJLNR1YUqOhRsPl1JN6bGLFTKU/n/ftPtrUAZ4nPR05H/b7srlckaOHMmCBQtYu3Ytt9xyy2nrNSRJwmYw/5NAVBxLKI4nE9YaExx/KCEHhd+x11RHeuHRPeikJzRyT6U49uQilooK8h+YhHH/fiJefw3foUNd9lopKSls2LCBjIwMevbs6fB63Xnn0rh7d6c/7isIgnuIRKMZNKldO+2E8OczC/BVKpgSG+ruUATBaWRyGQofDQofDaqZ08nMOcjQRXMYtOgTrrgkBcls++cJQtXfCUClkaa8Ohp2lSE1Wf/Zy0N50g2/MkCL0l/L8MuG8en6FWz+aTMXdB/wnxqJ4/8smW0n9pJ7Kk/s4RnjgyJA8/feHih8NcgU4saxtRnT08m/byI2k4nYTxbj0bu3S1/P19eX8PBw0tPTm5VoyDw8qPxkCd6XXY5Wn+KCCAVBEE5PJBrNoE1NpXr1aneH0ep+rarjq7Ia5neNwUvZcSceC53XwfpGbt6TheeEKbz30hOEP/80tiWfINdoUIV4nrLzkiRJ2Bos/0kYLJWNNBXUY602gg20QB9lHJv/bzO6H2uJsAWA8tjTEWWAFk2CL8p+oSclKGJGRdtSv3kzBQ8/gioqitgln6CKiGiV101JSeH333/HarU6PG3eIzUVyWjE8NtvItEQBKHViXexZtCmpmItK8dcWooqJMTd4bQKi03iyYwC+vt4MirU393hCILT/VBRy4T9OcR7aPjk4v74R84n96abKH7hBcJfeOG0x05kMhkKnQqFToU6+r/1F5L177qKKiMXVyRQ/ufXbDYc5p5b7sInzN+heg/BPSRJomrJEkpefgWviy4i4rXXUHj99wieq+j1ejZv3kxubq7DDQXkWi2e/fph2LLF7qGUgiAIziKqAZtB2zUVANPBg26OpPUsKiwn3WBkVkqUOOcrdDgf5pcxfk8WF/h7sb5PEhFaNR49uhP23HPUrF5D9YqVzd5bppCjDPRAm+SPz7mRjLn1BpDLWP/DV0iIMUZtnWQ2U/z885TMeomAW28l6u35rZpkAISHh+Pt7U16enqz1usGptGwfTs2k8nJkQmCIJyZSDSaQRUZgdzXt9PUaZQ3WZidXczNEYH09BZDu4SOw2KTmJ6ez1MZBdwdHczC7vHo/nUs0G/kCPxvvJHiF1+kYdcup7ymt7c3o0aNIisri19++cUpewquYa2pIe+ee6hevYawF2YQOu0xZA4eXXIGmUyGXq/n8OHDNGfGri4tDclopNFJf4cFQRDsJRKNZpDJZGi7dsV4oHM80Xg5qwiAafHhbo5EEJynzmLllr1ZfFJYzmx9FM8nRaI4xdO60Men4dGzJwWTH8RcWuqU105ISGDQoEH8/PPPZGdnO2VPwbmacnLIuWEcxgMHifnwQ/zHjHFrPCkpKVRVVVFWVubwWo1ejyIgAMNvW1wQmSAIwumJRKOZtKmpneKJxl91DXxaVMHU+DCC1KKkR+gYjhqbuGZnBjtqDXzWM5FbIoJOe61MrSZqzpsgk1Hw4ENITU1OiWHQoEHExsayZs0a6uvrnbKn4ByGbX+Qff0NIEnEr1iO7rxz3R0S8fHxqFSqZh2fksnl6NLSMPz2mwsiEwRBOD2RaDSTtmtXzAUFWKur3R2Ky9gkiSfT89HrtNx2hhsxQWhPdtYYuGp7Og1WG1/1TWFQwNkH6CmDg4ma+xaN+/ZR/NJLTolDLpczatQoJEli7dq12Gy2sy8SXK569Wry7rwTbdeuxK1Yjjouzt0hAaBSqUhMTOTw4cPNWq9LS8N48CCWqionRyYIgnB6ItFoJm23YwXhxkOH3ByJ66wpqWJ7bQMzkyNRis44QgewvrSKkbszSfDU8HW/FFJ09g+d9Ojdm7Cnn6J62XKq16xxSjze3t6MHDmSrKwsfv31V6fsKTSPZLVS8upsip56Gr+RI4n54H0Uvr7uDuskKSkpHD16FIPB4PBa3cA0kCQatm51QWSCIAinJhKNZlLHxiLz9MS4v2Men6q3WHnhSCHXBPtxgf/ZP/EVhLZMkiTm5BQzYX8uVwf7sbJXYrOOAvqPHYvfmDEUP/c8jXv2OCW2xMREBg0axE8//UROTo5T9hQcYzMYyJ80mcpFiwid/jhhzz+HTKVyd1j/kZJybA5Gc45PqUJDUScmUr9F1GkIgtB6RKLRTDKFAq1ej7GDtrh9M7eEOouVZ5NaZyCVILiKyWZj8qE8Xs4uZmpcGG93jUGraP6PvtCnn0KT2pX8yQ9iqahwSoyiXsN9zIWF5Nx0Mw3bthH1ztsE3Hprm23h7eXlRVRUVPPb3KalYdiypVmdqwRBEJpDJBot0FELwjMbjLx/tIxJsaFEadXuDkcQmq2iycL1u4/wRWk176bG8kh8WItvIuVqNVFz5yJZLBQ8NAXJbG5xnMfrNWw2G59//rmo12gljX/9RfbY67HV1hL72Wd4X3yxu0M6q5SUFDIzMzE34++dbmAalsIimrJznB+YIAjCKYhEowW0qV1pys7G1tDg7lCcRpIkns4oIEyj4r7ozjH1XOiYMhuMDN2ZTkaDidW9kxjhxIn2qtBQot6aQ8OuXZTMnu2UPY/Xaxw5coTfRHcgl6v9+mtyx9+KOiqKuFUr0epT3B2SXfR6PWazuVnH7HTnnAMqFQZxfEoQhFYiEo0W0KamgiRhPNS8LiBt0fcVtfxUWcfzSRF4tOB4iSC4069VdQzdkYFaJufrfsmc4+v8Sc6e/foROv1xqj5ZQs0XXzhlz8TERC666CI2bdpEbm6uU/YUTiZJEmXz36bg4UfwvvxyYhYvQhkY6O6w7BYSEoKfn1+zjk/JdTo8e/USiYYgCK1G3Em2gCYxEVSqDnN8ymSz8UxmARf5e3FVUNvqtiII9vqssIIb/jpCH29PvuqXTKyHxmWv5X/jjfiOGEHR08847efAoEGDiImJYfXq1c3qLiScns1opPDRqZTPn0/wg5OJmP0qco3r/n64QounhA9Mo2HbNqcc+RMEQTgbkWi0gEytRpucjPFgx0g03jtaRr6xiZnJUW22GFIQTscmSczILOThw0e5KTyQpT0T8FEqXPqaMpmMsOeeRZOURP4Dk5wyo0ChUDBq1CisVquo13AiS1kZubfeSt0PPxA5502C7ruv3f6cS0lJoba2luLiYofX6tLSsBkMNO7d64LIBEEQTiYSjRbSdkvFeKD9d54qNDbxZk4Jd0YGOzRbQBDaAoPVyl37cnj3aCkvJEXyckpUq81+kWs0RM2bi62xkYKHH0ayWFq8p4+PDyNHjiQzM1PUaziB8fBhsq+/HnNhIbFLl+AzZIi7Q2qR2NhYNBpNs45Pabt3R+7jg+E3cXxKEATXE4lGC2m6dsWUmYmtqcndobTIzKwidAo5j8SHuTsUQXBIscnMiJ2Z/FxVx+Ie8dwdHdzqn1SrIiKIfPNNGv74k9I333TKnklJSVx44YWiXqOF6jb9RO64G1H4+hG/ciUePXq4O6QWUyqVJCUlNWtKuEyhQHfeeRhEAisIQisQiUYLeaSmgtmMKSPD3aE02+/V9awtqeLJxHCXHzURBGfaW9fAVTvSKTdb+LJvMle4sbZId965hEx9lMqPFlL79ddO2fPiiy8mOjpa1Gs0gyRJVHy8iPz778cz7Xzili5BFR7u7rCcJiUlhcLCQmprax1eq0tLo3HvXqx1dS6ITBAE4R8i0WghjV4Pcjmmdjq4zypJPJmRT29vT64PC3B3OIJgt+/Ka7huVyYhaiUb+6XQzcvD3SERcOut+AwbRuGTT2E83Lyhav+mUCgYPXq0qNdwkNTURPEzz1D6yisE3nUnUXPnItc5v/OYOyUnJyOTyZp1fEo3MA2sVhq2bXNBZIIgCP8QiUYLyT08UCfEt9vOU0sLK9hfb2RWciTydloYKXQukiSxIK+UW/dmMzjAm8/7JBOqUbk7LOBYcXj4CzNQx8aS/8ADWKurW7ynj48PI0aMIDMzky2iLelZWaurybv7HqrXrSd81ixCHnkEmbzjvdV5enoSExPTrERDHR2NKjpatLkVBMHlOt5PXzfQdm2fBeFVZgsvZxVxfVgAfV0wZ0AQnM1sk5iWns9zRwq5PyaED7rF4dnG5r3IPTyImj8PW20tBVMfQ7JaW7xncnIyF1xwAT/++CN5eXlOiLJjMmVnk3P9DZgOHSJ24Uf4jRzh7pBcKiUlhaysLJqaUSOoS0sTBeGCILhc23qHbqe0qakYDx1yyg1Fa3oluxiLJPFUYsc5tyx0XDVmCzfvyeKzogre7BLNU4kRbfYpnDoqiog3Xsfw22+UzZvnlD0HDx58ol6joaHBKXt2JIatW8m5/gZQKIhbtRLPc85xd0gup9frsVgsZGVlObxWNzCNptxcmvILXBCZIAjCMSLRcAJtaiqS0UhTdra7Q7Hb/vpGPiko55G4MILVbePYiSCcTm6jiWE7M/irroEVvRIZF972Jzl7DRxI8JSHqFjwHrXffdfi/Y7P1zCbzaJe439UrVhJ3t334NG9O3HLl6GOiXF3SK0iKCiIwMDA5tVpnHceyOUYtojuU4IguI5INJxA27ULAMZ2UhAuSRJPpueT6Knhjqggd4cjCGf0R3U9V+1IxyJJbOiXzEB/b3eHZLfAu+7C+8orKXp8OqbMzBbv5+vry8iRI8nIyGDr1q1OiLB9k6xWSl56meJnn8V/7Bii338PhY+Pu8NqVcenhDuaeCp8fND26I5hi/h7JAiC64hEwwkUPj6ooqMx7m8fBeHrS6v5vcbAC8mRqDtgkaTQcawtqWL07iOkeGrZ0C+FRM/2NUxSJpMRMetFVJER5D8wySntRJOTkxk4cCA//PBDp67XsNbXkz/xfiqXLCH0qacIe+YZZEqlu8NqdSkpKRgMBgoLCx1eq0tLo2Hr1nZ37FcQhPZD3GU6ibZr13bxRMNgtTLjSCFXBflycUDn+uRPaD8kSWJ2dhETD+QyPNSPlb0TCVC1z5tIuU5H1Pz5WCoqKHxsGpITjjxdcsklREVFddp6jab8AnLH3UjDjh1Ev7eAgJtvcndIbhMdHY2Hh0ezjk95paVhralpl81MBEFoH0Si4STa1FSMBw8iSZK7QzmjebmlVJgtPJcU4e5QBOGUjFYbEw/k8npOCU8khPNWl5h2/+RNHRtLxOxXqf/5Z8rfebfF+x2fryGXy9m+fXub/7njTA27dpFz/fXYGhuJW74MrwsvdHdIbqVQKEhOTm7WlHCPXr2Qe3qKKeGCILhM+373bkO0qV2x1dZiLmi7HTxyGk28k1fKxOgQYj007g5HEP6jrMnMmN1H2Fhewwfd4pgcG4qsjXaWcpT3xRcTPHkS5fPnU/fTTy3ez9fXlxtuuAFJkjhy5IgTImz7ar78irxbb0MdG0vcyhVokpLcHVKbkJKSQklJCdUOzm2RqdV4Dhgg5mkIguAyItFwEm1qKkCbrtN4NrOAYLWSSbGh7g5FEP7jsMHI1TsyyDGaWNsniWtC/NwdktMFTpiA16WXUjj1MUxO6FIXFhZGQEAAf/31FxUVFU6IsG2SbDbK5s6lcOpUfK66iphFH6MMCHB3WG1GUlIScrm8WU81dGlpNOzaha0THsETBMH1RKLhJMqgIJTBwW12Qvimilq+La/lmaSINjfgTBA2V9YxbEc6Xgo5G/ul0NenYw6QlMnlRLzyMsrg4GPF4fWGFu/ZrVs3AgIC2LZtGyaT6T9fLy4uJjMzk+Li4ha/ljvYGhspePgRyt95l+ApUwh/+SXkarW7w2pTtFotcXFxzWtzOzANzGYatm93QWSCIHR24o7TiY7Vabg30aisrCQvLw/rv7qINNlsPJNZwPl+Oq4N9nNfcIJwCosLyrlxzxEG+HrxRd9korQd+yZS4eVF1NvzsRQXUzR9eovrK+RyOQMGDMBisbBjx46T9vvzzz9Zv349a9eu5fPPP2d7O7uZtNTXU7HwYxq2bydy7lsETbinwxylcza9Xk92djZGo9GhdeqEBJShoWJKuCAILiESDSfSdktt9e4dRqORRYsWcdFFF+Hv709gYCCxsbF4eHiQnJzM1KlTeWXLdrIaTLyYHCXepIU2wypJPJNRwLT0fG6PDGJxj3i8lQp3h9UqNAkJRLz6CnXff0/F+x+0eD+dTkf//v0pLCwkIyPjxEyFQ4cOER0dze23385FF13Et99+S0lJSYtfzxka/vyT+l9PX4TcVFBA2Zy3kHt5Efvpp/hccUUrRtf+pKSkYLPZHK7Xkclk6AYOFIP7BEFwifbZL7KN0nTtirW8HHNpKaqQEJe/3ooVK7j//vtPeTbbbDaTmZnJa6+9Bq+/TvfrRhG7dJHLYxIEe9RbrNx3IJcfK2qZlRzJHVHB7g6p1XlfeilBE++jbM4ctKldW9w9KSIigpSUFDZv3szWrVvx9PTEarVy7rnnEhgYSHBwMFlZWWzZsoURI0Y46btonoYdO8gdfyteF12E3NMDz759T/p64759VH76GargYHyHX4fS19dNkbYf/v7+hISEkJ6eTrdu3Rxaq0tLo2btWswlpahCXf/eJQhC5yGeaDiRx/GCcBfXaVitViZPnswNN9xgXwGoJLFv3WrOOeecZhULCoIzFRibuG5XBlur61naM6FTJhnHBT3wALqLLqTgkUdpauHwPZvNRmFhIZWVldhsNvz9/amvr+e77747cU19ff1JxyrdxVpdjUyrxWY0Ur1qNaasY4XxNpuNuk2bqPx4EVp9CkH3TxRJhgP0ej3p6ekOTwnXnX8eAIat4viUIAjOJRINJ1JGRCD39cXk4sF906dPZ968eQ6vO3jwIEOGDKGqqsoFUQnC2e2ubeDqHenUWKx82TeZSwI799BImVxO5OzZKPz9yH9gUos6/1gsFo4cOUKfPn0ICAjA09OTMWPGkJGRwbp161iyZAm1tbVERUU58TtoHnVcHH4jR+I/7gaMBw9StXwZ1ro6alauonbDBrwuvZSA8eORa0QbbkekpKTQ2NjI0aNHHVqnDAxE07WraHMrCILTiUTDiWQyGdrUri59ovHFF18cOw7VTDk5Odxyyy0OFaAWFxczadIkEhIS0Gg0REdHc8011/Djjz8CEBcXx5w5c5odk9A5bCirZsSuDCK1ajb2S6Grl4e7Q2oTFD4+RM2bR1N+PkVPPd3s4nCDwYBSqSQ+Pp5zzjmHwsJCqquriY+PJzAwkNDQUHr06EH37t2d/B04ztbQSMOff+IzZAg+Q6+mYfsO8idPpmzePDRduuJ79VXI2vmQRneIjIxEp9M1r/tU2vkYtm7tVMMfBUFwPfGT3Mm0XV1XEG6z2XjooYda/EawYcMGvv32W7uuzcnJoV+/fmzatIlXX32VvXv38s033zB48GDuv//+FsUhdA6SJDEvt4Q79+VweZAva3onEaxWuTusNkWbkkLErBep/fprKj9e1Kw9jp/R/+233wgNDSU5OZnt27dTXl5OREQEV1xxBWlpaXh5eTk3eAdJNhsKP18Uf8/B8L32WiSzmYbtO5CamvC5/DK3xteeyeVyUlJSmnVE1mvgQKxl5ZjSM1wQmSAInZVINJxMm5qKuaAAq4MTWu2xceNGsp0w5AvgnXfeseu6iRMnIpPJ+OOPPxg9ejQpKSl069aNhx9+mN9//90psQgdV5PNxsOHj/JiVhFTYkNZkBqLh5jjcko+Q4YQePddlL72GoatW5u1x4gRIzAYDKxcuZKDBw9SUVGByWRye3LxbzK5HIV/ANaKchp276Z45ouY8/JQ+vmhSUnB1EmmnLtKSkoK5eXlDg9w9OjXD5lGg+E30X1KEATnEe/4TnZiQrgL6jSWLFnitL02bNhAZWXlGa+prKzkm2++4f7770en++8ANT8/P6fFI3Q8VWYL4/7KYk1xFfO6xjAtIRy5aK98RsEPPYTu/PMpmPIw5oKCZu1x0003ERoaChybHB4XF8eRI0fa1JEYydwECgXFzz1P/U8/4X/jjcR8shi5hwcVH3xI45497g6x3UpMTEShUDj8VEOu0eDZr5+o0xAEwalEouFk6rhYZJ6eLjk+ddCJyYvNZiMj48yPyDMzM5EkiS5dujjtdYXOIavBxLAdGRw0NLKydyJjwgLcHVK7IFMoiHhtNnKdjqOTJmFzcPgaHPsA4JJLLuGGG25g7NixXHjhhRQWFpKZmemCiB0nWa3U//wz1opKTIcPEzptGiGPPoImPp6geyfg0asXWgfbswr/UKvVJCQkNHtKeMP27dhOMWFeEAShOUSi4WQyuRxtly4uKQh31rGp47Kyss749eOfgIohf4IjtlbXM3RHOjIZbOibwnl+befYTnug9Pcnav48mrKyKX722WY/iTi+LjIykuTkZPbs2XPWp5iuZmtspGLhQgxbthD4wP3ErVxJwC03I1McG9To2b8/odMfP/HvQvPo9Xpyc3NpbGx0aJ0uLQ3JaKRx1y4XRSYIQmcjEg0X0Hbt6pKjU87uf3+2/ZKTk5HJZE59kiJ0bCuKKhm7+wjdvDz4qm8y8Z6iPWlzaLt2JfyFF6hZ/wVVSz9t1h7//oCge/fu+Pv7s23bNpqampwVpkMsFRWUzZtHU04ugXfdRcD11+PRw/0dsDqilJQUJEk661Pr/6XR61EEBGD4TRyfEgTBOUSi4QLa1FSasrOxGQxO3TcuLs6p+8XHx5/x6wEBAVx55ZW8/fbbGE7xvVS7oOBdaJ9sksRLWUU8eCiPsWH+LOuViJ9K6e6w2jXfa4YRcOutlLz8MoY//mjRXgqFggEDBpy4+Wzteg1TdjZlb81FslgJnjwJrV7fqq/f2fj4+BAeHu7w8SmZXI4uLU0UhAuC4DQi0XABbWpXkCSMhx0/I3smKSkpTt0vKSnprNe88847WK1WBgwYwJo1a8jIyODgwYPMnTuX888//8R1BQUF7N69+6Rf7j6mIbSORquNe/bnMDe3hGcSI3hNH41KLo7bOUPI1Efx7NfvWHF4cXGL9vLy8uKcc87hr7/+Yvv27U6K8OwM27ZR9913KMPDCJ48CdXfheqCa+n1ejIyMhx+Eq5LS8N48CAWMdhVEAQnEImGC2gSE5GpVE6v07j++uudttfgwYNPdKY5k/j4eHbu3MngwYN55JFH6N69O5dffjk//vgj77777onrXnvtNfr06XPSry+++MJp8QptU6nJzMhdmfxYUcfC7nFMjAkRNT1OJFMqiZzzJjK1mvzJD7a4SDc4OBh/f3+++eYbCprZ1cpeks1G6RtvcvS+iSj8Awi6+24UbajNbkeXkpKCyWQiNzfXoXW6gWkgSTQ0s8WyIAjCv8mkttTzsAPJHjUajV5PxKwXnban2WwmJiaG4hZ+sgmwatUqRo8e7YSohM7qYH0jN+/JwirBJz3j6ent6e6QOqzGffvJvfFGfK4ZRvjMmS1K5iwWCwsXLqSxsZEJEyag1WqdGOkxtoYGCqc9Tt0PPxDy6CME3HGHSEBbmSRJvPHGG6SmpnLVVVc5tPbI0GF49OlNxMyZLopOEITOQjzRcBFtqvMLwlUqFTOd8IP//PPPZ/jw4S0PSOi0fqioZdjODAJUSjb2TxZJhot5dO9G2HPPUbNmLdUrVrRoL6VSyZgxY2hoaOCLL75wer2GuaSE3Jtvof7XX4maP4/AO+8USYYbyGQy9Ho96enpDv8Z69LSMGzZ0qZmrwiC0D6JRMNFtKmpmDIysDm5w8udd97Jrbfe2uz1QUFBrFy5EqVSFOoKzbOmuJLxe7K4wN+LdX2SCNeo3R1Sp+A3cgT+N91E8YuzaNjZsvaj/v7+XHfddRw4cIA///zTSRFC4/795IwZi6WykrhPl+J96aVO21twXEpKClVVVZSVlTm0TjcwDUthEU3ZOa4JTBCETkMkGi6i7doVLBZMTi4IB3j33XcZMWKEw+vCwsL44osviIqKcnpMQsdntUl8XVbNnrpGpsaHsbB7PDqlmHfQmkIfn4ZHz57kPzgZc2lpi/ZKTU1lwIABfPvttxQWFrY4ttrvviP3pptRhoYSt3IF2tTUFu8ptEx8fDwqlcrhKeG6c84BlUpMCRcEocVEouEimtRUFL6+1G7c6PS9PTw8eH/5SoLuexi5SmXXmsGDB7Nr166TOkUJgr0arTbeOVrKV2XVjAjxY0pcGApxHKbVyVQqoua8iUwmp+DBh5Ba+MT0iiuuICQkhFWrVmFsxhRyOFYLUP7e+xRMfhCvwRcT+8liVCEhLYpLcA6VSkViYqLDbW7lOh2evXqJREMQhBYTiYaLyNVqfEeNombNGmzNfAM/k5eyCgkYdxt7Mo4wY8YMYmNj/3ONVqvl+uuvZ/PmzWzatImwsDCnxyF0fBVNFmZnF5HVaGJSTCi9fXXuDqlTUwYHEzX3LRr37aP4pZdatlcL6zVsTU0UPT6dsjffJGjiRCJffx25h0eLYhKcS6/Xc/ToUerr6x1apxuYRsO2bUhms4siEwShMxCJhgv533A91poaajd+49R9d9c28FlRJY/Fh9EtNpqnn36anJwcamtr2b17N7/++iv5+fk0NDSwfPlyLrroIqe+vtDxrCyuZGVxJd+W12Cy2U78fnaDkZezimiySTwWF0ZXL3ET2RZ49O5N2NNPUb1sOdVr1rRor4CAgBP1Go7M17BUVpJ3+x3UbtxIxOxXCZ48CZlcvKW0NcnJyQAOTwnXpaVhMxho3LvXFWEJgtBJiIpgF1LHxKC78EKqli3Db8Rwp+xpkySeyshHr9Nya0TQSV/z9vamV69eTnkdofMYsSuDo8YmglUqdtc1cHWwLzeGB+KrlPNJQQUxHmruiQ7BR9RjtCn+Y8di3Lef4ueeR5OcjEfPns3e63i9xjfffENUVBTh4eFnvN6UkcHR+yZia2wkZvEiPPv0afZrC67l5eVFVFQU6enp9HHgz0nbvTtyHx8Mv/6GZ9++LoxQEISOTHz85GL+48Zh3LOHxr37nLLfmpIqttc28GJyJEoxfVlooTk5xTTZJL7tp2dj/xQ29E3GYLHxfGYBs44U0dvHk0kxIsloq0KfehJtair5kyZjKS9v0V721mvU//ILOeNuRO7hQfzKFSLJaAf0ej2ZmZmYHTgGJVMo0J13nqjTEAShRUSi4WJegy5CGRFO1fJlLd6rzmLlhSOFXBvix0B/bydEJ3R2mQ0mAlVKAtXHHm728Pagi5eGWouVaouVC/290ChEktFWydVqIue+hWS1UvDQlBadpz9er2EwGPjyyy9PWa9RufRTjk64F89+/YhdtgxVZGRLwhdaSUpKCmazmZycHIfW6dLSaNy7F2tdnWsCEwShwxOJhovJFAr8r7+B2q82YK2padFeb+aUUGex8kxihJOiEzormyRhsUl4KOSo5DKabDZqzBbm5pZQ0WTlzqggvJUK1pfWYLGJoV1tmSo0lKi35tCwezcls2e3aK+AgACuvfZa9u/ff1K9hmSxUDxjBiUzZxJwyy1EvfM2Ci/RFKC9CAkJwc/Pz/E2twPTwGqlYds2F0UmCEJHJxKNVuA3ehTIZC26CchsMPJBfhmTYkOJ0ooBaULzVJotAMhlMpRyGZcH+vBNeQ0byqp5LaeYQqOZKXGhTIoNo5uXB1+XVYsjeu2AZ79+hE5/nKpPllCzfn2L9urWrRvnnHMO33zzDUVFRVhrazl6zwSqVq4i7PnnCZ3+ODLxlKtdae6UcHV0NKroaHF8ShCEZhOJRitQBgYS9vTT1KxeQ/Xazx1eL0kST2cUEKZRcV+06E8vNM9DB/N4KqOAItM/sxeuCPLlsgAfphw6itFq4/HEcOI9NACMCvUnz9hEdoPJXSELDvC/8UZ8R4yg6Jlnady/v0V7XXHFFQQHB7Nq2TIybryJxn37iPnwA/yvH+ukaIXWlpKSQm1tLcXFxQ6t0w1Mw/CbSDQEQWgekWi0Er9RI/EdPYri55/HeOiQQ2u/r6jlp8o6ZiRF4KEQf2SCYyw2ickHc9lUWcu6kio+yi+n5u8nG79V1SGXQaBKybYaA9Vm64l1Bw1GUnRagtSiOV17IJPJCHvuWTTJyRRMmoylqqrZe6lUKq7p0oXaykp+j4sldvkydOed58RohdYWGxuLRqNx/PhUWhpNubk05Re4KDJBEDoycdfaisKeegp1QgL5Dz5od3Gd0Wrj6YwCBvl7MyTI18URCh3R3vpG6i023k2NZV7XGN7OK2VFcSXLCitYUljB5YG+fN47Ca1Czh37srltXzYvZRXxdEYBQ4J88RYdp9oNuUZD1Ny3sDU2UvDww0gWS7P2qV77ObWTJjOwspKcoCD2VVY6OVKhtSmVSpKSkhyeEq477zyQyzFs+c1FkQmC0JGJRKMVybVaot6ag7WyiqInnrTrrOz7+WUUmJp4ITkSmUyclRcc11WnZXxkIL19PBkVFsCDsaHMOFLIsqIKRob4My48gBhPDd/0S2FsWAAauZzcRhNvdIlmcmyou8MXHKSKiCDyzTdp+ONPSt9406G1ks1G6WuvUfTEE/gNv46L58yhf//+bNy40eEjN0Lbo9frKSwspLa21u41Ch8ftD26Y9iy1YWRCYLQUYlEo5WpY2KIePkl6r7/nspFi894baGxiTdzSrgzKpgUnbaVIhQ6Gq1CzsUBPugUCqrNFmySRKRGTXqDCa9/Pa2Qy2RMig3l/W5xzOkSw4hQfzdGLbSE7rxzCX1sKpULF1L79dd2rbEZDORPmkzFRwsJmTaNsBkzkKnVXHnllcfqNVatwmQS9TrtWVJSEjKZzPGnGmlpNGzdimS1nv1iQRCEfxGJhht4X3opAXfeQelrr1G9evVpr3vhSCE6hZxH4sJaMTqho8prNPFSViF1Viur+ySRotPyREY+6Q0mchpNPHIoj1+rjh3p04paoHbPf/x4fIYNo/DJpzCe5Vy+ubiYnJtvoWHrVqLefpvA22878QRVpVIxZswY6urq+OqrrxzqWiS0LZ6ensTExDicaHilpWGtqcF44KCLIhMEoaMSdxNuEjJlCn6jRlH01NMUPvEktsbGk77+e3U9n5dW82RiuJjKLLTYntoGXs8pwU+l5NG4MKK1atb0TsJos/HgwTyu3pHOkQYTA/283B2q4CQymYzwF2agjo0l/4FJWKurT3ld49695IwZi7Wmmthln+F9yeD/XBMYGMg111zD3r172blzp4sjF1xJr9eTlZVFU1PT2S/+m0evXsg9PTH8Juo0BEFwjEg03ESmVBI+43nCX36J2q+/JueGcTT9PbXVKkk8mZFPH29Prg8LcG+gQrsmSRI/lNfw7tFSUr20PBwXRqBahcUmoZLLuCcqmL/qGhge4s+6vsmiDqiDkXt4EDV/HrbaWgqmPvafoy+133xD7s23oIwIJ37FCrR6/Wn36tGjB/369RP1Gu1cSkoKFouFrKwsu9fI1Go8BwwQ8zQEQXCYSDTczG/4cOJWrEAyGskePYba775jSWEF++uNvJgSiVzc+AnNZLFJLCuqZHVJFVcE+XJ3VDAa+bH/5ZVyGUsLK3gyo4CnEiOYlRLl5mgFV1FHRRHxxusYfvuNsrnzgGMJaPm771Lw0BS8L7uM2MWLUQYHn3WvIUOGEBgYKOo12rGgoCACAwOb1ea2YdcubA0NLopMEISOSCQabYBWn0LcmtXoBg7kwPQneWl/FjeE+NHXR+fu0IR2ymCxsq6kii3VdYyPCGREqP9JSaskSYSolSzqEc/9MWIIZEfnNXAgIQ9PoeK996jZ8DWFj02j7K25BE16gIjXZiPX2tds4t/1Ghs2bBD1Gu3U8SnhNpvN7jW6gWlgNtOwfbsLIxMEoaMRiUYbofDyInLOm6x49iUsFgu3zJ5B09Gj7g5LaIdyG03c8NcR9tU3MDUujDR/7/9cI5PJuCLIlyvFbJZOI+DOO/G6ZDCFU6dS+803RL7xOsH33+/wcbmgoCCuueYa9uzZw65du1wUreBKer0eg8FAYWGh3WvUCQkoQ0PFlHBBEBwiEo025IDByArvIB4K8MQ3/yjZw0dQ8+WX7g5LaEf+qK7nqh3plJst3B4VTKynaIssHGPKyDjWNUgmQxkUiO7CC5u9V48ePejbty9ff/01JSUlToxSaA1RUVF4eHg4dHxKJpOhGzhQDO4TBMEhItFoIyRJ4sn0fBI9Ndw7oBfx6z7H69JLKJz6GAWPPYa1vt7dIQpt3NqSKkbvPkKKp5YN/VKI9dC4OyShjajfvJnccTei8PUlZtHH2AwNFD42DcmBozP/66qrriIgIEDUa7RDCoWC5OTkZs3TMGVkYi4pdVFkgiB0NCLRaCPWl1bze42BmclRqOSyY0epXn2ViFdfof7HTWQPH0Hj7t3uDlNogyRJYnZ2ERMP5DI81I+VvRMJUCndHZbQBkiSROXixRy9byKeAwYQ++mn6M45h8jZr1L/88+Uv/Nus/dWqVSMHTuWmpoaUa/RDun1ekpKSqg+TdvjU9Gdfx4Ahq3i+JQgCPYRiUYbYLBaef5IIVcF+TIo4OTz9L7XXkv8us9RBgaSc9PNlC9YIKazCicYrTYmHsjl9ZwSnkgI560uMajl4n9rASSzmeLnnqfkpZcJuO02oubPQ+F1rMGE16BBBE+eRPn8+dRt+qnZr/Hveo3d4oOQdiUxMRG5XO7Q8SllYCCarl1Fm1tBEOwm7kjagLm5pVSaLTyXFHHKr6ujo4lduoSgCfdQNnceebfehrmoqJWjFNqasiYzo3dnsrG8hg+6xTE5NlTMwRAAsNbUkHfPPVSvWUP4zBcIfWwqMsXJgz8DJ0zA67JLKXzsMUzZ2c1+rZ49e9K3b182bNhAaak4UtNeaLVa4uLimnF86nwMW7aKJ1iCINhFJBpultNo4t28UiZGh5zxTL1MpSJ48mRiFy+iKT+frOuGU/vNt60YqdCWHDYYuXpHBrnGJtb2SeKaED93hyS0EU05OeRcfwOmAweJ+egj/EaPPuV1MrmciJdfRhkScmxyeL2h2a85ZMgQAgICWLlypUMTpwX30uv1ZGdnYzQa7V7jNXAg1vJyTA4mKIIgdE4i0XCzZzMLCFYrmRQbatf1nuecQ8L6dejOP5+Chx6i6OmnxQClTubnylqG7UjHSyFnY78UMW9FOMGw7Q+yr78BgLgVy9GdO+CM1yu8vIiaPw9LcTFF06c3+1NqtVrNmDFjTtRrCO1DSkoKNpuNI0eO2L3Go18/ZBqNaHMrCIJdRKLhRpsqavm2vJZnkiLwVNj/R6Hw9SVyzpuEvziTmq82kD1yFI3797swUqGtWFxQzk17shjg68WXfZOJ0qrdHZLQRlStWkXenXeiTe1K3IrlqOPi7FqnSUgg4tVXqPv+eyre/6DZrx8cHMywYcP466+/xHyNdsLf35+QkBCH6jTkGg2e/fqJOg1BEOwiEg03abLZeDqjgDQ/L64N9nN4vUwmw2/UKOLXrEHu6UnODeOo+Ghhi9pVCm2XVZJ4JqOAaen53B4ZxOIe8XgpFWdfKHR4ktVKySuvUvz0M/iNGkXM+++j8HVsEKP3pZcSNPE+yubMof6XX5odS69evejTp4+o12hH9Ho9GRkZWB1oMqIbmEbD9u3YRFtjQRDOQiQabvJhfjnZjSZeTI5sUQGvJiGeuOXLCBh/C6WzZ3P0rrsxizf4DqXeYuW2vdl8mF/GrORIZiZHoZSLom8BrPUG8h+YROXixYQ+MZ2w555FplI1a6+gBx5Ad9GFFDzyKE15ec2O6aqrrsLf359Vq1aJeo12QK/X09jYSH5+vt1rdGlpSEYjjeLJlSAIZyESDTcoMZl5PaeY2yKD6Orl0eL9ZGo1oVOnEv3Rh5gyMsi+bjh1PzW/ZaXQdhQYm7huVwZbq+tZ2jOBO6KC3R2S0EaYCwvJvekmGv74g+h33yFg/PgWfWghk8uJnD0bhb8f+Q9Manbtl1qtZuzYsVRXV/P11183Ox6hdURERKDT6Rw6PqXR61EEBoo6DUEQzkokGm4wM6sQjVzG1Pgwp+7rNXAg8evX4dG7N/n3TaR4xgvYHOgmIrQtu2sbuHpHOjUWK1/2TeaSQB93hyS0EY1//UX22Oux1dURu+wzvAYNcsq+Ch8foubNoyk/n6Knnmp2cXhwcDBDhw5l9+7dYr5GGyeXy0lJSXGoza1MLkd3/vkYfvvNhZEJgtARiESjlW2vMbCquIrpCeH4u2B6szIggKh33ib0maepXrOGnDFjMB4WbQjbmw1l1YzYlUGkVs3GfilOefIldAw1GzaQe8t41NHRxK1aiTYlxan7a1NSiJg1i9qvN1L58aJm79O7d2969+7Nhg0bKCsrc16AgtPp9XrKy8upqKiwe40uLQ3jwYNYqqpcGJkgCO2dSDRakU2SeCIjnx5eHtwYHuiy15HJZATceCNxq1YCMnLGjKFy6adiwFI7IEkS83JLuHNfDlcE+bKmdxLB6uaduRc6FkmSKJs3n8JHHsX7yiuJWfQxykDX/BzxGXIlgXffRelrr7Wou9DVV1+Nn5+fmK/RxiUkJKBQKBw6PqUbmAaSRMPWrS6MTBCE9k4kGq1oeVEle+oaeTE5EkUrTHDWpqQQt2olfmPHUjJzJvn3TcRSWeny1xWap8lm4+HDR3kxq4gpsaG8mxqLhwNtj4WOy2Y0UvjIo5S//TbBDz1IxKuvINecfsCnMwQ/9NCxeT0PP0JTfkGz9jg+X6O6upqNGzc6OULBWdRqNQkJCQ4lGqrQUNSJidSLNreCIJyBuItpJTVmCy9mFTE61J8Bfl6t9rpyrZawp54kasG7NP71F1nXXUf9r+JcbVtTZbYw7q8s1hRXMa9rDNMSwpG3QjIqtH2WsjJyb72Vuk2biJwzh6B7721R0be9ZAoFka+/htzLi/zJk5pd7xUSEsLVV1/Nrl27+Ouvv5wcpeAser2evLw8Ghsb7V6jS0vDsGWLeFouCMJpiUSjlbyWU0yjzcZTiRFueX3viy8mfv06tCl6jt51FyWvvIpNHGVoE7IaTAzbkcFBQyMreycyJizA3SEJbYTx8GGyx16PpbCI2CVL8BlyZau+vsLPj6j582jKyqbomWeafUPZp08fevXqxVdffSXqNdqolJQUJEkiIyPD7jW6gWlYCotoys5xXWCCILRrItFoBYcMjSwsKGdKbChhGvedt1eFhBD9wfuETJtG5dKl5NxwA6asbLfFI8DW6nqG7khHJoOv+6VwXis+7RLatrpNP5Ez7kYUfn7ErVqJR4/ubolD26UL4S+8QO0XX1K1ZGmz9xk6dCi+vr5ivkYb5ePjQ3h4uGN1GuecAyqVmBIuCMJpiUTDxSRJ4umMAmK1Gu6Jdv8MBJlcTuDttxG/YjlSo5HsUaOoWrVKPPp2gxVFlYzdfYRuXh581TeZOA/XnrkX2gdJkqhY+DH599+PLu184j5diirMua2wHeV7zTACbruNkldewfDHH83a4/h8jcrKSr755hsnRyg4g16vJzMzE4vFYtf1cp0Oz169RKIhCMJpiUTDxTaU1fBLVT0zkiPRyNvOf25tairxa1bje801FD/9DAUPPoS1utrdYXUKNknipawiHjyUx9gwf5b1SsTPBa2OhfZHamqi6OmnKX31VQLvuououXORe3q6OywAQh59BM/+/Sl4aArmoqLm7RESwtChQ9m5cyd79uxxcoRCS+n1ekwmE3kOTIbXDUyjYds2JLPZhZEJgtBetZ073w6owWrj2cwCLgv04bI2OGxN7ulJ+IzniXzrLQzbtpE1fESzP60U7NNotXHP/hzm5pbwTGIEr+mjUclF0bcA1upq8u66m5r1XxA+axYhjzyMrA19OCFTKol88w1kGg35kx/EZjI1a5/evXvTs2dPvvzyS8rLy50cpdASYWFh+Pj4OHZ8Ki0Nm8FA4969LoxMEIT2qu28i3VA7+SVUtZkYUZSpLtDOSOfK68gYd3nqKOjybv1NkrnzBGfTrlAqcnMyF2Z/FhRx8LucUyMCWmV7kFC22fKyib7+usxpacTu/Aj/EaOcHdIp6QMCCBq3jxMhw9TPGNGs45cymSyE/UaK1euxCx+1rQZMpmMlJQUDh8+bPefrbZ7d+Q+PhhEN0NBEE5BJBouktdoYn5eCROig0nwbPtn71Xh4cQs+pjgBx+k4oMPybn5ZpoceHwunNnB+kau2pFOkcnM+r5JXBXs5+6QhDbCsHUrOTfcgEypIm7lCjzPOcfdIZ2RR/duhD3/PDVr1lK9YkWz9tBoNIwZM0bUa7RBer2e6upqu7uDyRQKdOedJ+o0BEE4JZFouMjzRwrxUyp5KDbU3aHYTaZQEHTvBOI++xRrZRXZw0dQs369u8Nq936oqGXYzgwCVEo29k+mp3fbOHMvuF/V8hXk3XU3Hj16ELd8GeqYGHeHZBe/EcPxv+kmil+cRcPOXc3aIzQ0lKuvvpodO3aIeo02JC4uDpVK5fDxqca9e7HW1bkwMkEQ2iORaLjAL5V1bCir4enEcHRKhbvDcZhHr17Ef74W78svp3Da4xRMfUy8gTTTh/lljN+TxQX+Xqzrk0S4Ru3ukIQ2QLJaKXnpJYqfew7/668n+r0FKLy93R2WQ0Ifn4ZHz57kPzgZc0lps/bo06cPPXv25KuvvhL1Gm2ESqUiMTHRsURjYBpYrTRs2+bCyARBaI9EouFkZpvEU5kFDPDVMTLU393hNJvCy4uIV14mYvZs6n/6iewRI2nY1bxPLjsji01ieno+T2UUcE90MAu7x7fLpFNwPmt9PUcnTqRyyVJCn3qKsGeeRqZsf13HZCoVUXPeRCaTU/DQQ0jNmI1xvF7D29ubVatWiXqNNkKv15Ofn099fb1d16ujo1FFR4vjU4Ig/IdINJxsUUE56QYjLyZHdohCX99rhhG/7nOUQUHk3nwLZe+8g2S1ujusNq3OYuWWvVl8UljObH0UzyVFougAfxeElmvKLyB33I007thJ9HvvEXDzTe4OqUWUwcFEzX0L4759FL/0UrP2OF6vUVFRIeo12ojk5GQAh6eE1/8mCsIFQTiZSDScqKzJzOycIm6JCKRHBzqHr46KInbpEoImTKB8/tvk3nor5sJCd4fVJh01NnHNzgx21BpY1jORWyKC3B2S0EY07NxFztix2BobiVu+DK8LL3B3SE7h0bs3oU8/RfWy5VSvXt2sPcLCwrjqqqvYsWMHe0WbVLfz8vIiKirK4ToNc24eTfkFLoxMEIT2RiQaTvRyVhEyZEyLD3d3KE4nUyoJnjyJ2E8WYy4oJGv4CGrFp48n2Vlj4Krt6TRYbXzVN4WLAtrXmXvBdWq+/JK8W29FHR9P3MoVaJKS3B2SU/mPHYvf2LEUPz+DxmYWdvft25cePXrw5ZdfUlFR4eQIBUfp9XqOHDli93E23XnngVyOYYt4qiEIwj9EouEku2sb+KyoksfiwwhUt7/z1vby7N+fhHWfoxuYRsFDUyh88klsBoO7w3K79aVVjNydSYKnhq/7pZCi07o7JKENkGw2St96i8Kpj+Fz9dXEfLwQZUCAu8NyidCnnkSbmkr+pMlYmlHYLZPJGDZsmKjXaCP0ej1ms5mcnBy7rlf4+KDt0R3Dlq2uDUwQhHZFJBpOYJMknszIp4tOy62d4KiMwteXyDfeIPzFF6nd+A3ZI0fRuG+/u8NyC0mSmJNTzIT9uQwN9mNlr0SCOnCiKdjP1thIwZSHqXh3AcEPP0z4yy8hV3fcrmNytZrIuXORrFYKHprSrKGfx+s1ysrK+Pbbb10QpWCv4OBg/Pz8HD4+1bB1q6jjEwThBJFoOMHqkip21DYwMzkSpbxzFP3KZDL8Ro0kfs1q5F5e5IwbR8VHHyHZbO4OrdWYbDYmH8rj5exipsaFMb9rDFqF+F9KAHNpKbm3jKf+//6PyHlzCbrn7g7RHOJsVKEhRL01h4bduyl5dXaz9jher7F9+3b27dvn5AgFe8lkMvR6Penp6XZPCfcaOBBrTQ3GAwddHJ0gCO2FuCtqoTqLlZlHCrk2xI+B/p3vTL4mPp64ZZ8ReOt4Sme/Rt6ddza7p357UtFk4frdR/iitJoFqbE8Eh/WKW4khbMzHjhAztjrsZSWErt0CT6XX+7ukFqVZ79+hD4xnaolS5o98LNfv350796dL774QtRruJFer6e2tpbi4mK7rvfo1Qu5pycG0X1KEIS/iUSjhd7IKabOYuXZxAh3h+I2MrWakEcfJWbhRzRlHiH7uuuo27TJ3WG5TGaDkaE708loMLGmdxLD2/G8FMG56n74gZybbkYZGEjcqpV4dOvm7pDcwn/cOHxHjKDomWdp3O/4scrj9RpeXl6iXsONYmJi0Gg0dh+fkqlUeA4YIOZpCIJwgkg0WiDDYOSD/DImx4YSqe24Z6/tpUtLI/6L9Xj07Uv+xPspnjEDm9Ho7rCc6teqOobuyEAtk/N1v2T6++rcHZLQBkiSRPkHH5A/aTJeF15I7NIlqEJD3R2W28hkMsKeexZNcjL5kyZhqapyeA+tVnuiXuO7775zQZTC2SiVSpKSkhyv09i1C1tDgwsjEwShvRCJRjNJksQzmQVEaNTcFx3i7nDaDKW/P1Fvzyfs2WeoXrOW7NGjMTrwJtWWfVZYwQ1/HaGPtydf9Usm1kPj7pCENkBqaqLoiScpe/0NAifcQ+ScN5F7eLg7LLeTazREzZuLZDRR8PDDSBaLw3uEh4czZMgQ/vzzT/Y348mI0HJ6vZ6ioiJqa2vtul43MA3MZhq2b3dxZIIgtAci0Wim7ytq+amyjueTIkQB8P+QyWT4jxtH/OpVyOQKcsaMpfKTJXYXFLY1NkliRmYhDx8+yk3hgSztmYCPUuHusIQ2wFJVRd4dd1L71VdEvPIyIQ89hEwufh4cpwoPJ/LNN2n4409K33izWXv079+fbt26sX79eiorK50coXA2SUlJyGQy0tPT7bpenZCAMjQUw2/i+JQgCCLRaBaj1cbTGQVc7O/NkCBfd4fTZmmSk4lbtRK/66+nZNYsjt57L5Z2VthpsFq5a18OC46W8kJSJC+nRHWazmLCmZmOHCFn7PWYjhwhZvEifK+7zt0htUm6cwcQ+thUKhcupPbrrx1eL5PJuOaaa07Ua1ia8WREaD5PT09iYmLsr9OQydANHCgG9wmCAIhEo1neO1pGgamJGcmRotPQWcg1GsKefILo9xZg3LuPrOuGU//Lr+4Oyy7FJjMjdmbyc1Udi3rEc3d0sPjzFgCo//U3cm4Yh1yrIW7VSjz79nV3SG2a//jx+AwbRuGTTzXrKOXxeo3S0lJRr+EGer2e7Oxsmpqa7Lpel5aGKSOzU3QgFAThzESi4aBCYxNzcku4MypYTH92gNegQSSsX4e2SxeO3n03JS+/gs3ONy132FvXwFU70ik3W/iybzJXiCdXwt8qP/uMoxMm4NGnN7HLlqGOinJ3SG2eTCYj/IUZqGNjyX9gEtbqaof3OF6v8ccff3DgwAHnBymcll6vx2KxkJWVZdf1uvPPA8CwVRyfEoTOTiQaDnrhSCFeSjmPxIW5O5R2RxkcTPT77xE6/XGqPv2UnOtvwHTkiLvD+o9vy2u4blcmIWolG/ul0M1LFPYKIFksFM98kZIZL+B/441Ev/MOCi8vd4fVbsg9PIiaPw9bbS0FUx9r1vTo/v37k5qaKuo1WllgYCCBgYF2H59SBgai6dpVtLkVBEEkGo7YWl3P56XVPJkQLoqBm0kmlxNw663ErVyBZDKRPWo0VStWtolCcUmSWJBXym17sxkc4M3nfZIJ1ajcHZbQBljr6jh6731ULVtG2LPPEPbkE8iUSneH1e6oo6KIeON1DL/9RtnceQ6vl8lkXHvttXh6eop6jVZ2fEq4zWaz63pd2vkYtmxtEz/bBUFwH5Fo2Mlik3gyPZ8+3p6MDQtwdzjtnrZrV+LXrMb3uusofvZZCiZPblavfWcx2yQeS8/nuSOFPBATwgfd4vAU3cQEoOnoUXLGjaPxr7+Ifv89/MeNc3dI7ZrXwIGEPDyFivfeo7YZ9Rb/rtf4/vvvXRChcCp6vR6DwUBhYaFd13sNHIi1vByTnd2qBEHomMSdlJ2WFlVwwGDkxZRI5KIg2CnkHh6EP/8ckfPm0vDHn2QPH4Hh922tHkeN2cJNe46wrKiCN7tE82RihPgzFgBo2LGDnLHXIzWZiVuxHK+BA90dUocQcOedeA8ZQtHj0zFlZjq8PiIigiuvvJJt27aJeo1WEhUVhYeHh93Hpzz69UOm0Yg2t4LQyYlEww6VZguvZBUxLjyAvj5iErSz+Vx+OfHr16GOiyPv9tspfeNNJLO5VV47t9HEsJ0Z7KlrZEWvRMaFB7bK6wptX/W6deTddjuaxETiVixHk5Dg7pA6DJlMRsSLM1FFRpJ//wNY6+oc3uOcc845Ua9R5canoZ2FQqEgOTnZ7kRDrtHg2a+fqNMQhE5OJBp2eCWrCIsk8URCuLtD6bBUYWHELPyI4ClTqFi4kJwbb6IpN9elr/lHdT1X7UjHIkls6JfMQH9vl76e0D5INhulb7xJ0ePT8bn2GmIWfoTS39/dYXU4cp2OqPnzsFRVUTj1MSQ7z/4fJ+o1Wp9er6e0tJRqO7uG6Qam0bB9OzaTybWBCYLQZolE4yz21TWwpLCCR+PDCFaLwmBXkikUBN1zN3GffYq1pobsESOpXrfOJcWEa4orGb37CCmeWjb0SyHRU7QqFsDW0EDBgw9R8cEHhEx9lPCZM5Gp1e4Oq8NSx8YSOftV6jdvpvyddx1ef7xeo6SkRNRrtILExETkcrndTzV0aWlIRiONu3a5ODJBENoqkWicgSRJPJVRQKKnhjsig90dTqfh0bMn8WvX4n3FFRQ9Pp3CR6c262jFqUiSxOzsIu4/mMeIUH9W9k4kQCW6BwlgLikh9+ZbqP/tN6Lenk/gnXeKAY2twGvQIIInT6J8/nzqNm1yeH1ERARXXHEF27Zt4+DBgy6IUDhOq9USFxdnd6Kh0etRBAZi+E1MCReEzkokGmewrrSa32sMzEyOQiUXNxytSeGlI+Lll4h4/TXqN28m+7rhNOxs2adiRquNiQdyeT2nhCcTwpnTJRq1XPwvIEDjvv3kjBmLpbKSuE+X4n3JJe4OqVMJnDABr8supfCxaZiysh1eP2DAALp27SrqNVqBXq8nJycHo9F41mtlcjm6888XBeGC0ImJu6zTMFiszDhSyNVBvgwKEGf33cV36FDi161DGRpK7s03U/b220jNOItd1mRm9O5MNpbX8EG3OCbFhopPqwUAar/9jtybb0YZGkrcyhVou3Z1d0idjkwuJ+Lll1GGhJA/aRLWeoNj6/+u19BqtaxevVrUa7hQSkoKNpuNI3YOW9WlpWE8eNCt7csFQXAfkWicxty8UirNFp5NinB3KJ2eOiqS2CWfEHTffZS//Q65t96GuaDA7vWHDUau3pFBrrGJtX2SuCbEz3XBCu2GJEmUL3iPggcfxGvwxcQu+QRVSIi7w+q0FF5eRM2fj6W4mKLp0x2uzfLw8GDMmDEUFRXxww8/uChKwd/fn5CQEPvrNAamgSTRsHWriyMTBKEtEonGKeQ0mng3r5SJ0SHEemjcHY4AyJRKgic9QOySTzAXFZI1fAS1X3991nU/V9YybEc6Xgo5G/uliPbEAgC2piaKHn+csjlzCJo4kcjXX0euFQ0B3E2TEE/Eq69Q9/33VLz/gcPrIyMjueKKK/j99985dOiQCyIU4NjxqYyMDKxW61mvVYWGok5MpF60uRWETkkkGqfwTEYBwWolk2JD3R2K8D88+/UjYd06vC68gIKHH6Fw+hOnPWaxuKCcm/ZkMcDXiy/7JhOlFd2DBLBUVpJ32+3UbvyGiNmzCZ48CZmo1WkzvC+9lKCJ91E2Zw71v/zi8Ppzzz2XLl26sG7dOlGv4SJ6vZ7Gxkby8/Ptul43MA3Db1tc0kFQEIS2Tby7/o8fK2r5rqKWZ5Mi8VSI/zxtkcLHh4jXXyf8pZeo/fZbskeNpHHv3hNft0oSz2QUMC09n9sjg1jcIx4vpcKNEQtthSkjg5wxY2nKzSVm8SJ8rxnm7pCEUwh64AF0F11IwSOP0pSX59BamUzGddddJ+o1XCgiIgKdTudQm1tLURFN2TmuDUwQhDZH3En/S5PNxjMZBQz08+KaYF93hyOcgUwmw2/EcBLWrkHh7UPOuBsp/+AD6prM3LY3mw/zy5iVHMnM5CiUomOYANT/8gs5N4xDrtMRv3IFnn36uDsk4TRkcjmRs2ej8Pcj/4FJ2BoaHFrv4eHB6NGjKSoq4scff3RRlJ2XXC4nJSXF/kTjnHNApRJTwgWhExKJxr98kF9OjtHEzORI0ZGonVDHxRH32acE3n47+z9ezNVf/szWqjqW9kzgjigx+0Q4VvRduWQpRyfci2f//sR+9hmqyEh3hyWchcLHh+j582nKz6foqaccPnYTFRXF5ZdfztatW0W9hgvo9XoqKiooLy8/67VynQ7PXr1EoiEInZBINP5WYjLzRk4xt0UE0dXLw93hCA6QqdUU3n0vk16cR51MzvzXn+ecXX+4OyyhDZDMZopnzKDkxRcJGD+eqHfeRuElGgK0F5rkZCJmzaL2641ULvzY4fXnnXceer2edevWUV1d7fwAO7GEhASUSiXp6el2Xa8bmEbDtm1IZrOLIxMEoS0RicbfZmYVopHLmBof5u5QBAdtKKtmxK4Monx0bLygF91iIsh/YBJFzz2HrbHR3eEJbmKtreXohAlUr1pN2IznCX18GjKFqNVpb3yGXEng3XdT+vrrDn8iLpPJGD58OBqNhtWrV9vVJUmwj1qtJj4+3qE6DZvBcFI9nSAIHZ9INIDtNQZWFVcxPSEcP5XS3eEIdpIkiXm5Jdy5L4crgnxZ0zuJ8JAgoubNI+y556hZt57s0WMwimMTnU5Tbi45N4yjcf8BYj78AP+xY90dktACwQ89iO788yl4+BGa8u2foQP/zNcoLCwU9RpOptfrycvLo8GOGhpt9+7IfXww/PpbK0QmCEJbIZM6eb85qyRx1Y50kGBj/xQUojajXWiy2ZiWns+yokqmxIYyNT4M+f/82ZkyM491rcnKImTqo/jfckunq705fPgwv/zyC9nZ2RQVFREWFkZCQgIDBw6kawedgG344w8KJk1G4edH1IJ30cTHuzskwQms1dVkjx6D3MebuM8+c3juydatW/n2228ZN24cer3eRVF2LrW1tbzxxhuMHDmSnj17nvX6/MkPYiktJW75slaIThCEtqDTJxqfFlbwyOGjfNEniQF+Xu4OR7BDldnCXfty+LPGwOtdohkTFnDaa20mE2VvvEHl4k/QXXQhEbNmoQwKasVo3eOrr77ijTfe4KeffjrtNYMGDWLKlClcd911rRiZa1WvWUvRc8/h2bcvUW/NQeHn5+6QBCcyHjpEzg3j8L7iciJeecWhDw4kSWL58uXk5eVx77334usrOgs6w3vvvUdAQABjxow567VVy1dQ/MILpPy+FYW3dytEJwiCu3Xqo1M1ZgsvZhUyOtRfJBntRFaDiWE7MjhoaGRl78QzJhkAco2G0OnTiX7/PYz7D5B13fBmDQFrL0wmExMmTOCaa645Y5IBsHnzZoYPH87dd9+N0WhspQhdQ7JaKZk9m6Inn8Rv+HBiPvxAJBkdkLZLF8JnzqT2iy+pWrLUobXH52uo1WpRr+FEer2ezMxMu+aV6AamgdVKw7ZtrRCZIAhtQadONF7LKcZkk3gqMcLdoQh22Fpdz9Ad6chk8HW/FM5zIDn0uugiEtavQ5uaytG776HkpZewmUwujLb1GY1GLr74Yt5//32H1n344YcMGjSIxnZaOG8zGMif/CCVCz8m5PFphM14HplK5e6wBBfxHTaUgNtuo+SVVzD84Vh3OU9PT0aPHk1BQQGbNm1yUYSdi16vx2QykWfHYEV1dDSq6GjR5lYQOpFOm2gcrG9kYUE5U2JDCdOIm5K2bkVRJWN3H6Gblwdf9U0mzkPj8B7KoCCi31tA6BPTqfpsGTnX34ApM9MF0brHAw88wO+//96stX/88QcTJ050ckSuZy4qIuemm2nYupWod94m8LbbOl0dTmcU8ugjeJ5zDgUPTcFcVOTQ2ujoaC677DJ+++03u1uzCqcXFhaGj4+P/d2nBqZR/5soCBeEzqJTJhqSJPF0RgGxWg13R4uhbm2ZTZJ4KauIBw/lcX1YAMt6JbaoM5hMLidg/HjiVq1EMpvJHj2GquUrHB4G1tasXbuWjz76qEV7LFq0iJUrV9p9fXFxMQ8++CBJSUlotVpCQ0O54IILWLBgwYkuNHFxcchksv/8evnll1sUK0Dj3r1kjx2LtbaG2GWf4T14cIv3FNoHmVJJ5BuvI9NqyJ/8oMNPJ88//3xSUlL4/PPPqampcVGUnYNMJjsxJdyen6O6tDTMuXkOdw8TBKF96pSJxoayGn6trmdGciQaeaf8T9AuNFpt3LM/h7m5JTybGMFsfRQquXM+rdZ26UL86lX4Dr+O4ueeI/+BSViqqpyytzu89tprrbpPVlYWffr04bvvvmPWrFns2rWLH374gSlTpvDll1/yww8/nLh2xowZFBUVnfRr0qRJLYqzduNGcm++BXVEJPErV6IVXYQ6HWVAAFFz52E6fJjiGTMc+rDg+HwNlUol6jWcQK/XU11dTVlZ2Vmv1Z13HsjlGLaIpxqC0Bl0urvsBquNZzMLuDzQh8sCfdwdjnAapSYzI3dl8mNFHQu7x3FfTIjTj8TIPTwIf+45oubPo3H7drKvG46hmUeP3Gn37t1s3brVKXv9+eefbN++/azXTZw4EaVSyfbt2xk7dixdu3alR48ejBo1ig0bNnDNNdecuNbb25uwsLCTful0zZvOLUkSZe+8Q8GUh/G+7DJiPlncKbqICafm0b0bYc8/T82atVSvWOHQWk9PT8aMGUNBQcFZGycIZxYXF4dKpbLr+JTCxwdtj+4YtjjnZ5YgCG1bp0s03s4roazJwoykSHeHIpzGwfpGrtqRTpHJzPq+SVwV7OfS1/O+7DLiv1iPOiGBvNvvoPT115Gamlz6ms7k7Juks+1XUVHBd999x/3333/ahMEVdRI2k4nCqY9RPnceQZMnEfHabOQax2t1hI7Fb8Rw/G+6ieIXZ9Gwc5dDa6Ojo7n00kv59ddfycjIcFGEHZ9KpSIxMdGhKeENW7ciiSdJgtDhdapEI6/RxNt5pUyIDibeU9ygtEU/VNQybGcGASolG/sn09Pbs1VeVxUaSszCjwh55GEqPl5Ezo030ZST0yqv3VLZ2dmtul9mZiaSJP1n6FlQUBBeXl54eXkxbdq0E78/bdq0E79//NfPP//sUEyW8nLybr2Nuu+/J/LNNwieOFEUfQsnhD4+DY9ePcl/cDLmklKH1p5//vkkJyezdu1aUa/RAnq9nvz8fOrr6896rdfAgVhrajAeONAKkQmC4E6dKtF4/kghfkolD8WGujsU4RQ+zC9j/J4sLvD3Yl2fJMI16lZ9fZlcTuBddxG37DOsdbVkjRxF9drP23yheEGBc4sq8/Pz7bruf2/0//jjD3bv3k23bt0w/as4d+rUqezevfukX+eee67d8RgPp5M9dixN+fnELvkEn6uusnut0DnIVCqi3nwTmUxOwYMPOvREUi6XM2LECFQqFWvWrBH1Gs2UnJwMYNeTIY9evZB7emL4TbS5FYSOrtMkGr9U1rGhrIZnkiLQKRXuDkf4F4tNYnp6Pk9lFHBPdDALu8e79c/Io0cPEtauxWfIEIqeeILCRx7BWlvrtnjOJiQkxKn7hYaeORFPSkpCJpNx6NChk34/ISGBpKQkPDw8Tvr9oKAgkpKSTvr1v9ecTt3PP5M7bhwKH1/iV63Eo2dPx74ZodNQBgcTNfctjPv3UzxrlkNrj8/XOHr0qKjXaCYvLy+ioqLsOj4lU6nwHDBAzNMQhE6gUyQaZpvEkxkFDPDVMSLEz93hCP9SZ7Fyy94sPiksZ7Y+iueSIlG0gSMxcp2OiFkvEvnG69T/8itZw4fTsGOHu8M6pYSEhFbdLzAwkMsvv5z58+djMBic+trHSZJE5WefkT/xfjzPO4+4T5eiCg93yWsJHYdH796EPvM01ctXUL16tUNrY2JiTtRrZHag+TqtSa/Xc+TIEcxm81mv1aWl0bBrF7a/W2ELgtAxdYpEY1FBORkNRl5MjhTnutuQo8YmrtmZwY5aA8t6JnJLRNvrHuRz9dUkrPscVVg4ubeMp2zefCSLxd1hneS8885r9f3eeecdLBYL/fv3Z8WKFRw8eJDDhw+zdOlSDh06hELxzxOpuro6iouLT/pVe4YnRJLFQt1331M25y0Cbr+NqHlzkTezS5XQ+fiPGYPf2LEUPz+Dxj17HFqblpZGUlISa9euPePfUeHU9Ho9ZrOZHDvq23QD08BspsGOLneCILRfMqmtH0BvobImMwO3HWR4iD+v6qPdHY7wt501BsbvzUankLOkZwIpOq27QzojyWKh/L33KH/7HTx69SJi9mzUUW2nc1mPHj3Yt29fi/fp0qULBw8etOvaoqIiZs2axYYNG8jPz0ej0ZCamsqYMWOYOHEinp6exMXFkZub+5+1EyZMYMGCBf/5fZvBQOXyFVirq9EmJ+F77bUt/p6EzsfW1ETeLeMxFxcTv2a1Qy2QDQYDCxYswN/fn1tvvfWkpFk4M0mSmDt3LomJiQwbNuys12YOvgSfK68gdPr0VopQEITW1uETjYcP5fF1WQ2/nduVQHXzJ0oLzrO+tIoHD+bR09uThd3jCWpHfy4NO3dS+OhUrLW1hD3/HL5Dh7o7JAA++OAD7rnnnhbv884773Dfffc5ISLHmUtLqfxoIZLNiv8tt6CJiXFLHELHYC4pJXv0KDSxccR8vBCZSmX32tzcXBYtWsQFF1zApZde6sIoO56NGzdy8OBBpkyZctYTBIVPPIlx7x4SvvyylaITBKG1deijU7tqG1hWVMlj8WEiyWgDJEliTk4xE/bnMjTYj1W9E9tVkgHg2bcv8es+x+uiiyh85FEKH5+Otd41dQqOuOuuuxjawqRnyJAh3HvvvU6KyDHGjEzK5s0DmYygCRNEkiG0mCo0hKg5c2jYvZuSV2c7tDY2NpZLLrmEX375RdRrOEiv11NbW0txcfFZr9WlpWHKyHS4JbEgCO1Hh000bJLEUxn5dNFpGd8Gz/53NiabjcmH8ng5u5jH4sOY3zUGjbx9/vVT+PgQ8fprhL/8EnXffUf2yJEOnwV3NplMxpIlS0hMTGzW+vj4eJYuXeqWGqb633+n4v33UUdFETx5kpj0LTiNZ79+hD4xnaolS6hZv96htQMHDhT1Gs0QGxuLRqOxq/uU7vxj9WCGraL7lCB0VO3zTs8Oq4qr2FHbwIvJUSjlogDcnSqaLFy/+whflFazIDWWh+PC2n1Rvkwmw2/4cOI/X4vC15ecG2+i/L333Trp1t/fn23btnHllVc6tO6yyy5j27ZtBAYGuiiyU5NsNmrWf0HNqlV4njuAwDvvRO7ZOgMahc7Df9w4fEeOpOiZZ2ncv9/udcfnaygUCjFfwwEKhYKkpCS7Eg1lYCCarl1Fm1tB6MA6ZKJRZ7EyM6uQ60L8SPP3cnc4nVpmg5GhO9PJaDCxpncSw0P93R2SU6ljY4n77FMC77iDsjlzyLvjTsx2HBlwlcDAQL7++mveeuuts7apjY+P58033+Tbb78lODi4lSI8xmY0UrnwY+p/+QXf64bjN2oUMmX7OkYntA8ymYywZ59Bk5xM/qRJWKqq7F6r0+kYPXo0eXl5bN682YVRdix6vZ6ioiK7ngTp0s7HsGVrmx+MKghC83TIYvDnMwtYVFDOr+d2JVLbutOlhX/8WlXHnftyCFWrWNIznlgPjbtDcinD79sonDYNyWgkbOYL+Fx+uVvjkSSJb7/9ll9++YXs7GwKCwsJDw8nISGBgQMHMmTIEORuOL5mqaykYuFCrJVVBNxyM9quXVs9BqHzMRcVkT1qNJqUFGI+/MChxPaXX37hxx9/5JZbbmn28cTOpLGxkVdffZWhQ4fSv3//M15r2LKFvDvuJH79OrR6fStFKAhCa+lwiUaGwcjgPw/xSFwYU+LC3B1Op/VpYQXT0o9ygZ8373ePw6eTTGO3VFVR/Mwz1H3/A35jxxL6+DRxHOhfmnJyqPh4ETKVioA770AthvAJrciw7Q/y7riDgFtvJfSxqXavs9lsfPrppxQVFXHffffh7e3twig7hk8//RS1Ws2YMWPOeJ3NZCLz0ssIuvdeAm6+qZWiEwShtXSoo1OSJPFMZgERGjX3RYe4O5xOySZJzMgs5JHDR7kpPJClPRM6TZIBoPT3J3LuXMKef56aL74ge/QYjHbOpejoGv7aQ/miRagiIwh+cLJIMoRWpzt3AKGPTaVy4UJqNmywe93/1mvYbDYXRtkxDBw4EH9/fyxnGXAq12gInjwZmaZjP/EWhM6qQyUa31XU8lNlHTOSItEqOtS31i4YrFbu3JfDgqOlvJAUycspnbMQXyaT4X/9WOLXrEam0ZAz9noqFi1C6qQ3J5IkUf/rb9R9/x26c84h4I47UIhPhAU38R8/Hp9rrqHoqacx2lGwfJyXlxejRo0iNzdX1GvYITAwEIPBQGnp2VvXKsPDaDywH9tZkhJBENqfDlN9abTaeCajgIv9vbkyyMfd4XQ6xSYz4/dkkdloYlGPeK4I8nV3SG6nSUwkbsVyyt54k9KXX8Hw629EvDQLZSsXXruTzWik+IUXqPv+B4LuvRefq69u9x3HhPZNJpMRPuN5TJmZ5D8wifhVK1H4+dm1Ni4ujsGDB7Np0yZiYmLafL1GcXEx69atIyMjg5ycHNRqNQkJCaSmpjJ8+HB0Op3LXtvb2xutVkthYSERERFnvFYVHo5UW4u1ogJ5aKjLYhIEofV1mBqNOTnFvJZTzE/ndCFZp3V3OJ3K3roGxu/NRgYs6ZlANy8Pd4fU5tT/8iuF06eDzUbES7PwGjTI3SG5nKWsjKP3P4ApPZ2Il1/GZ4hjbXcFwZWa8vPJGTUabY8eRL+3AJnCviOeNpuNpUuXUlJSwr333tsm6zX27dvHCy+8wOeff47ZbD7lNb6+vtx666089dRTLus6t2fPHnJzcxk6dOgZG09YqqoomTmTwLvuEs0hBKGD6RDniwqMTbyVW8pdUcEiyWhl35bXcN2uTELUSjb2SxFJxml4XXgBCevXoe3RnaMT7qX4xVnYTCZ3h+UyxkOHyB57PZaiImKXLBFJhtDmqKOiiHjjdQxbtlA2d57d6+RyOSNHjkQmk7XJeo2PP/6YAQMGsHLlytMmGQA1NTXMnTuXPn36sMVFcyzCw8MxmUzU1NSc8TqFry8olFgqKlwShyAI7tMhEo0XjhTipZTziOgy1WokSWJBXim37c1mcIA3n/dJJlSjcndYbZoyMJDoBQsIffJJqlesIGfMWEwZGe4Oy+nqNm0i58abUPj7EbdqJR49urs7JEE4Ja+BAwl5eAoV771H7bff2b/uX/Ua//d//+fCCB3z3HPPcccdd9DY2Gj3moKCAgYNGsSXX37p9Hh8fI4dY66vrz/jdTK5HKW/P9aKSqfHIAiCe7X7RGNrdT3rSqt5MiEc707U3cidzDaJx9Lzee5IIQ/EhPBBtzg8RfG9XWQyGQG33EzcqpVINivZo8dQtWxZhxhWJUkSFR8tJP/+B/AamEbc0qWowkTyL7RtAXfeifeQIRRNn44pM9PudfHx8Vx88cX8/PPPZGVluTBC+2zYsIEZM2Y0a63FYmH8+PFkZ2fbvaa0tJQJEyYQExODRqMhLCyMK6+8kq1btwLH6poPlx8AAB9lSURBVFm0Wi1ZWVkYDIYT6x566CEuvvji/+ynCAjAUimeaAhCR9Ou7w4tNokn0/Pp6+PJ2LAAd4fTKdSYLdy05wjLiip4s0s0TyZGIBfFvQ7T6vXEr16N36iRFD8/g/z7H3BoYnFbIzU1UfT005TOnk3g3XcT+dZbYn6I0C7IZDIiXpyJKjKS/PsfwFpXZ/faCy+8kISEBNasWUOdA+ucrba2lvHjx7foA4vq6mrGjx9v9/WjRo3ir7/+YvHixaSnp/PFF19w8cUXU1n5z1MJrVbL0qVLT0o0TkcRGIClUjzREISOpl0nGkuKKjhgMPJicpS42W0FuY0mhu3MYE9dIyt6JTIuPNDdIbVrcq2WsGeeIeqdt2ncuZPsa6/D8Penge2JpaqKvDvvomb9F4S/9BIhD09B5oaJ44LQXHKdjqj587BUVVE49TG7W1H/u15j7dq1bqvXWLx48Uk3+M3166+/sn379rNeV11dza+//sorr7zC4MGDiY2NZcCAAUyfPp2hQ4eeuG7ChAkcOnSIH3/88ax7KgMDsVZUdoinu4Ig/KPd3g1Umy28klXEuPAA+viIT05d7Y/qeq7akY5FktjQL5mB/m2v00p75X3JJcSvX486KZG8O+6k9LXXkJqa3B2WXUxHjpBz/Q2YMjKI/XghfiOGuzskQWgWdWwskbNfpX7zZsrffsfudcfrNXJyctxWr7FgwQKn7fXuu++e9RovLy+8vLxYt24dpjM0tYiLi2PMmDEsWLDgrEmYIiAQyWTEZsfTD0EQ2o92m2gsK6qkwWpjeryYLuxqa4orGb37CCmeWjb0SyHRU3T2cjZVaAgxH31EyKOPULFoMTnjbsTkwHlpd6j9+muyx4xFplIRt3IFnv37uzskQWgRr0GDCH5wMuVvv03dpk12r4uPj2fQoEFs3rzZoToHZ6iqquLAgQNO2+/XX3896zVKpZJFixaxePFi/Pz8GDhwIE888QR79uz5z7WTJ0+muLiYJUuWnHnPAH8ArO34CKkgCP/VLhMNmySxuLCca0L8CBGdjlxGkiRmZxdx/8E8RoT6s7J3IgGqDjPjsc2RyeUE3nknccuWYauvJ3vkKKrXrGlzRwmkpiaKZ75IwcOP4H3xxcSvXIE6JsbdYQmCUwTecw9el11K4WPTMGXZnzRcdNFFxMXFsWbNmrN2WXImZxei5+bm2nUEbNSoURQWFvLFF19w5ZVX8vPPP9O3b18WLVp00nUxMTFcc801PPvsszSd4UmtMvDYUdz2XKsmCMJ/tctEY3NlHTmNTdwWGeTuUDoso9XGxAO5vJ5TwpMJ4czpEo1anLtvFR49uhO/dg0+V19F0ZNPUfDww1jP0oe+tZgLC8m55RaqVqwg9JmniXj9NeQunC4sCK1NJpcT8fLLKENCyJ80CWu9fUd5jtdrSJLUqvUaxcXFTt3PZDLZXe+h1Wq5/PLLeeaZZ9iyZQu33XYbzz777EnXeHh4MGzYMBobG3nnndMfSZN7eoJS5VAxviAIbV+7vHNcVFhONy8t/UVthkuUNZkZvTuTjeU1fNAtjkmxochEsX2rkut0RLz4IpFvvoHh19/IGj6CBjuKNF2p/pdfyR45CktZGXGfLiXgxhvF3wuhQ1J4eRE1fz6W4mKKpj9ud3G4t7c3o0aNIisri19++cXFUR4THu7c48MajYbAwOY1+khNTf1Ph6nGxkY8PDyYNm0aL774IrW1tadca2toAIsZRRuctC4IQvO1u0TjqLGJ78truS0ySNzkuMBhg5Grd2SQZ2zi8z7JXBPi5+6QOjWfq64iYf06VJER5I6/lZJXZ2MuKWnVGMwlpZS8Opuj99yDtmcP4teswaNnz1aNQRBamyYhnohXX6Hu+x+oeP8Du9clJCQwaNAgfv75Z3JyclwX4N/i4+Odul9cXNxZ31srKiq45JJLWLp0KXv27CE7O5tVq1bx6quvct111510rcFgQCaTcf/99+Pr68uyZctOuefxqeBKf3/nfCOCILQJ7e7A/dLCCnQKOSNDXPvD6NChQyxdupSDBw+SnZ2NxWIhPj6elJQUbrjhBvr16+fS13eHnytruXtfDlFaNZ/3SSJKq3Z3SAKgioggdvFiKt5/n/IPPqRy8WK8L70U/xvH4XnuuS5JuCVJomHbH1QtW0bdDz8gU6sJfnAygffcI1rXCp2G96WXEjRxImVvvYW2WypeF15o17pBgwaRm5vL6tWruffee/Hy8nJZjP7+/nTr1o39+/c7Zb8L7fgevby8OPfcc3nzzTc5cuQIZrOZ6Oho7r77bp544omTrjUYDOh0OjQaDS+88AI33njjKfc8PkNDIRINQehQZFJbqzQ9A5PNRr8tB7guxI8XU6Jc8ho7d+7k0Ucf5aeffjrjdeeccw4vvfQSl156qUviaG2LC8p5IiOfwQE+LEiNxUtMWW+TrPX11KxfT9WyZTRlHkEdH4//uHH4Dr8OhY9Py/evq6Nm3Xqqli+n6cgR1ImJx/a/7lpxpEHolCSbjfz7JtKwaxfxq1fZ3figrq6OBQsWEBYWxk033YTchQn622+/zQMPPOCUvXbu3EmfPn2cshfAli1bsFqtZ01g6jZtou7HHwmfOVOcVhCEDqRdJRq/VNYx5q8j/HiOnm5eHk7f//3332fy5Mln7Av+b3K5nGeffZann3663f5gtEoSz2cW8n5+GXdHBfFcUiSKdvq9dCaSJNHw55/Hnjh8/wMylQqfq6/Co2cvVFGRqKOjUYWHI1OdviubZDZjLi7GfPQoTUfzMe7bS82Gr5FMJrwvuwz/cePwPHdAu/27LQjOYq2tJXvMGOQaLXHLl9k99f7IkSMsWbKESy65hIsuushl8dXV1ZGQkEB5eXmL9rnooovYvHmzk6I65ocffiAgIIC+ffue8bqq1atpys0l9JFHnPr6giC4V7s6OpXdaEIhgxQXzHGYPXs2jz32mENrbDYbzz77LKWlpcyfP9/pMblavcXKfQdy2VRZy0spUdwuuni1GzKZDN2AAegGDMBcWkr16tXUrv+Cms/XwfHCVbkcVXg4quhoVFGRqEJCsJSV0XQ0H/PRo5iLi8Fq/efa6CgC77gDvzFjUIWGuO17E4S2RuHjQ/T8+WRffwNFTz1FxOuv25WAJyYmctFFF/HTTz8RExNDXFycS+Lz9vZm6dKlXH311c3udhUQEMAnn3zi1LgkScJgMBAVdfYTCNaKyhMtbgVB6Dja1RONmUcK+aK0mj/OT3Xqvps3b+bSSy/Fevymqxk+++wzxo0bZ/f1xcXFvPTSS2zYsIH8/Hx8fX1JTk7m5ptvZvz48Xj+/YnZrl27ePnll/m///s/KisrCQsLo0ePHkyYMIFhw4Y1+9PmAmMT4/dmkdvYxPvd4rgksOXHbgT3k8xmzEVFmPPzjyUU+fk05R/FfDQfS2kpypAQVFFRqKOjUEVF2/30QxAEqP3mWwoeeoiQqVMJvPMOu9bYbDY++eQTKioquPfee9G5sB30rFmzePLJJx1ep1arWb9+PUOGDHFqPCaTiS+//JJzzz2X6OjoM15b/NJLeHTrju+11zg1BkEQ3KtdPdHIbWwixskFylarlVtvvbVFSQbAhAkTuOqqq/Dz8zvrtVlZWQwcOBA/Pz9mzZpFjx49sFgspKens3DhQiIiIrj22mtZv349Y8eO5bLLLmPx4sUkJiZSUVHBnj17eOqpp7jwwgvter3/tbu2gVv3ZqGSy/iybzJdXXAMTXAPmUqFOiYGdUwMYrqFIDiXz5ArMd59N6Wvv462axd0aWlnXSOXyxk1ahQLFixg7dq1Lq3XeOKJJ4iNjWXChAn/aTN7OjExMaxatYoBAwY4PZ7j8zh8zlI/JtlsWKuqUQQGOD0GQRDcq1090bhi+2F6eHnwehfnTSH+/PPPGTlypFP2mjNnDg8++OBZrxsyZAj79+/n0KFDp/x0S5IkGhoaiI2N5aKLLmLt2rWn3EeSJIefaGwoq+aBA7mkenmwqEc8wWrxKbYgCIK9JKuVo/dMwLh/P3GrV6OOirRr3fF6jUsvvdSuzk4tcejQIV588UVWrVp12prDgIAAbr/9dqZPn97suRlns2XLFgwGA5dddtkZ36ssVVWUzJxJ4F13oe3a1SWxCILgHu3qiUZeYxPDgv2cuud7773n1L3OlmhUVFTw3XffMWvWrNM+QpfJZHz33XdUVFScsW7EkSRDkiTm55XyYlYR14X4MadLDB4K0aZUEATBETKFgsjXXyN79BjyJ00i7rNPkXuc/anw8XqNTZs2ERMTQ2xsrMti7NKlC0uWLOHNN99k/fr1ZGZmkp2djUajISEhga5du3Lttdei1Tq/3vE4g8FAUVERffr0Oet71fEZGooAUaMhCB1Nu0k0aswWqi1Wpx+d2rFjh9P2OnjwIA0NDSfqK04lMzMTSZLQ6/Un/X5QUBBGoxGA+++/n4CAY4+Q/33dn3/+yeDBg0/8+/Llyxk2bNhZ42qy2ZiWns+yokqmxIYyNT4MuegkJAiC0CwKPz+i5s8j54ZxFD37LBGvvGLXBz//O1/DlfUacOx95c4773Tpa5xOdnY2SqXyrLUZAKb9B5B76lCKo1OC0OG0m4+084xNAMR4OC/RqKura3E7wP+VnZ1t13X/+6b0xx9/sHv3brp163baR909e/Zk9+7d7N69G4PBgMViOevrVJktjPsrizXFVczrGsO0hHCRZAiCILSQtksXwmfOpPaLL6lastSuNQqFglGjRmG1Wvn888+b3SGqrbNareTn5xMfH4/qLE0mbE1NNOzbi+cFA5Ep281nn4Ig2KndJBpV5mPF2v5O/EF0vFDNmSr+fgR8OklJSchkMg4dOnTS7yckJJCUlITH34/gk5OTATh8+PCJazQaDUlJSSQlJdkVS06DiWE7MjhoaGRV70TGhIlPiwRBEJzFd9hQAm67jZJXXsHwxx92rfHx8WHkyJFkZmayZcsWF0foHqWlpcjlchISEs56rfHwYeQaDbpzz22FyARBaG3tJtGI1B77VKTA1OS0PSMiIpze/SPmLFNjAwMDufzyy5k/f/4Zu4JcccUVBAQE8MorrzQrjtwGE9f/lYlcBl/3S+FcP69m7SMIgiCcXsijj+B5zjkUPDQFc1GRXWuSkpK48MIL+fHHH8nNzXVxhK3vhx9+oLy8HC+vM7/vSJJE2ezXaCooQNmMDoqCILR97SbRiNKqkfHPESpnUKlUdp0ftZe951HfeecdLBYL/fv3Z8WKFRw8eJDDhw+zdOlSDh06hEKhwMvLiw8//JANGzYwdOhQvv32W7KystizZw+vvvoqcOwx/Kn8UV3Pp0UVdNF58GXfZOI8NE77HgVBEIR/yJRKIt94HZlWQ/7kB7Gd5ujr/7r44ouJjo5m9erVdreibQ/y8vI4cuQIvXr1Ouu1xr17afjjD/yuu64VIhMEwR3aTaKhkcsJ16jIa3ReogHHnhw4y6WXXnram/9/S0xMZNeuXVx22WVMnz6dXr160b9/f+bNm8ejjz7KCy+8AMCIESPYsmULnp6ejB8/Hr1ezyWXXMKmTZtOWQhukyTWl1SxvKiSnt6efNg9Hj+VOPMqCILgSsqAAKLmzsN0+DDFM2ZgT9d4hULB6NGjsVqtrFmzpkPUaxgMBlavXk1UVBRdunQ56/VVny1DFRmJl4vb/QqC4D7tao7G8J0ZhGtUvNstzml7/vXXX/Tu3dspe33xxRdcc417ppo22WwsKihnV20DY8P8uTjAp9lTwwVBEATHVa9bR9Hj0wl77ln8b7jBrjVZWVl88sknDBo06KSugu2NzWbj008/paioiAkTJuDr63vG6y1VVWQOupigSQ8QdPfdrRSlIAitrd080QCI9dA49egUQK9evRgyZEiL9+nevTtDhw51QkSOqzFbeCOnhH11RiZEBzM40FckGYIgCK3Mb/hw/G+6ieIXZ9Gwc6ddaxISEhg8eDCbN28mIyPDxRG6zi+//MKRI0cYOXLkWZMMgJq1n4Mk4TdqVCtEJwiCu7SrRCNGqybXyUenABYvXkxERESz13t5ebF69WqnF5bbo8DYxCvZRVSbLTwaH0pvH9f2ZRcEQRBOL/TxaXj06kn+gw9iLim1a82FF15IUlISa9eupbq62rUBusCRI0f46aefuPjii+3qiijZbFQtX473VUNQBohuiILQkbWrRCPWQ0252YLBYnXqviEhIaxatcquT2H+l0aj4ZNPPvnPAL7WsLeugdnZxXgpFExLCCdGFH0LgiC4lUylImrOHGRyBQUPPojUdPYPx+RyOSNHjkStVrNq1Sq7ZiS1FTU1NaxZs+bE5HN71G/ahPnoUfzHjXNxdIIguFu7SjS6eh2bMbGlut7pe6elpbFjxw6H6jXi4+PZsmULI0aMcHo8Z7OpopZ380rR6zQ8HBeGvyj6FgRBaBOUQUFEzX0L4/79FM+aZdcaT09Pxo4dS1FRERs3bmwXxeEmk4lVq1ahVCoZOXKkXU/1zcXFFD39DLoLLsDDSfWRgiC0Xe0q0UjVaenp5cHiwjMPxWuuxMREtm7dyvz58+nWrdtpr4uLi+Pll19m165d9O3b1yWxnI7VJrG8qIKVxZVcEujDhOgQtIp29ccoCILQ4Xn06kXoM09TvXwF1atX27UmMjKSoUOHsmPHDj777DMaGhpcHGXzlZaW8sEHH1BaWsrYsWPR6c5+bFcymyl4aAoyjYaIV18RtYSC0Am0q65TAJ8VVvDI4aP8fl5XYl18VGjnzp0cOnSI7OxsLBYL8fHxJCcnc+6557qlHqPRauPD/DIOGv6/vXuNjau88zj+82VmbMeXGWec2J6J7SQQXHLpBtfaZZcULSsRiBIRbYLTZCENzRaprNrAVsCW2/YCBZoKlN3SipYWChtCcDYsBNomYqFiWdpiC+rgEIpT4sv4Mr7N+Dq3M+f0RZu0INHazrHHM/l+JL+yzuQ/eeHRd85zniei7RWlWucpnvMZAABT1/vvX9XI4cOq3v9fyl+zZkrXtLW16fDhw3I6nbr22mvl9/tnecrpOX78uI4cOSK3262GhgaVlZVN6brg/Q9oeP9+VT/1pArWrp3lKQHMB2kXGpNJU3/1Rqt2Vnp11/KZP8Cdbobihh7pDCpkJHWjv+zsMjIAwPxlxuPqvH6nEn19Wvrfh5Tr9U7punA4rMbGRvX29uqqq65SfX19yu8AGIaho0ePqqmpSWvWrNHGjRvldDqndO3o0WPq3rNHi++4Q6U7r5/lSQHMF2kXGpJ0T1u3DgWH9dalK8+LZUPtk1H9KDAoV3aWdvvLVJ43tT/sAIDUSwT7dXrrFjmrq1X9+OPKcjimdJ1hGDp27JjefPNNrV69Whs3bpTLlZpNP0KhkBobGxUMBnX11Verrq5uyuETO31a7Vuv1YJ16+R7+KGUBxOAuZOWoXFqMqrLfvWevvOJKm0tz+yt8U6MTer5gbAqnQ5tLS9VYe5fPnkcADC/TL71ljp2flae7dtVfucd07q2tbVVL7zwgoqLi3XFFVfooosuUk7O3HwWxGIxtbS06JVXXlFeXp4aGhqmtR28GYmofdtnZMXjqjnUqJzCwlmcFsB8k5ahIUkNvz6lyaSpF+tWpHqUWWFZlr7X1a99Hf3aVObWfSt8cqXguRAAgD2Gn35awa9/QxUP3C/35s3TunZgYEBHjhxRZ2enioqKVFdXp7q6OhUVFc3KrMFgUM3NzWppaVEikdDKlSu1YcMGFRQUTPk1LMtS71fu0OjPfqaaZw8qb0Vmfl4D+HhpGxo/HQjrhtZ2Hfzkcl1eOjt/aFMlZpr68ntdOhQM6bal5bqlejG3mgEgzVmWpd4779LoSy+pev9+5a/6+N0NP05fX5+ampp0/PhxJZNJ1dbWqr6+XjU1Nef8OWEYhk6ePKmmpiZ1dnaqsLBQdXV1uuSSS2Z0zlSosVF9d9+jygcfUMk115zTbADSU9qGhmlZ2tHygd4Zj+jl+hWqcGXGcwtDcUO7W0/r7bFJ7aut0ubFnlSPBACwiRmLqeO66xU/fVoV37xPxVdeOaPXiUajamlpUVNTkwYHB+X1elVbWyuPx3P2p7i4+GOXWCUSCYXDYYVCIYVCIQ0NDenEiROamJhQTU2N6uvrVVtbO6MlWpZpaujRRzXwH/8pd0ODKr721Rm9RwDpL21DQ5IG44aubP6NfC6nDq+9QI7s9P7Wv20iquvf+UBjhqkfr16qT5X85X3JAQDpJTk+rt4779LY0aMq3bVLi778r1N+QPyjLMtSe3u7mpubFQgENDo6qjMf61lZWSopKTkbHoZhnA2L8fE/Hnybk5Mjt9utZcuWqb6+XosWLZrxezNCIfXcfrsm/u91eW+6Sd6bvqCsOXqeBMD8k9ahIUnNIxPa/HabdvvK9LULfakeZ8ZeD41pd2u7yl0OPbV6qapm+YwQAEDqWJal0JNPKrj328pfs0a+hx+SY/Hic35dwzA0MjLyobsVZ35yc3Pl8Xjkdrs/dOejqKjIlrOhIsePK3DzzbImI6rc+y0Vrlt3zq8JIL2lfWhI0g+6BnT3qW49trJGGxe5Uz3OtO3vGdLt73fpMneRvr+qRsXsLAUA54XJt95W9y23yDIM+b69VwsuvTTVI02bZVkKPf20gg88qLyLPyH/ww/LMY2dqQBkrowIDcuydOOJDr06PKqjn1qh5QV5qR5pSkzL0r2/7dV3u/q1y+fVvRf4lJvmy78AANNjDA2p59ZbNfHLX6nsS1/UwhtvVFaa7DJoTkyo9+57NPqTn8hz3XVafNutypriIX4AMl9GhIYkjRlJXdX8vhzZWTr4yeVa7JrZete5MmYk9aWTnTo6OKKvX+jTbp+XnaUA4DxlJZMafOQRDX73e1pw+afle/BB5bjdqR7rz4qdOqXAnptl9Paq4t5vqHjDhlSPBGCeyZjQkKT3JiLa9uvfypT06MU1+lvP/DwY6N3xiP65tV0D8YQeubhaV3qnv20gACDzjL/2mnpuvU1ZBQVa+LnPqWTzNcqZpbMyZirR06PQwWc1/NRTcvoq5du3T65ly1I9FoB5KKNCQ5L6Ywl94d0O/SI8rq8sq9C/VC1S9jy6U/BM75D+7f2Alhe49MNVS1XDQ98AgD+R6OlR8Ft7Nfbyy8pyOFSyaZM8O7Yrr7Y2ZTNZpqmJ/39DoQMHNP7znys7P1/urVtUtmePsqdxiB+A80vGhYYkGaalve192tcR1HpvsfbVVsntyE3pTJGkqbvaAtrfO6wdFaW670K/8nPSYw0uAGDuJYL9Ch9qVPjgszL6+5W/dq08O7araP16Zc/RcxDJcFjh5/5HoWcOKNHRKdeKFfLs2KGSTRuVvYAt2AH8eRkZGmccGxzRF092qiQ3R4+tqtGaotR869Ieienzre1qm4zq/hV+ba9YmJI5AADpx0okNPbqqwodOKDJX/xSOaWlcm/ZIve2bXL6Z2db98g7rQodOKDRl16SZZoqXr9enh3blb92Lc8TApiyjA4NSeqIxPT5E+36zURUN/i82uXzztlypb5YQvt7hvRooF8LHbl6bNVSrSzMn5N/GwCQeWIffKDQgWc08txzMicmVPA3fy3X8gvkXOKXY8kSOfx+Of3+KS1nsixL5siI4l0BJboDind1KdEVULS1VdF331VuZYU82z4j95Z/VK7XOwfvDkCmyfjQkKRo0tRD7X16smdIYSOpvy8t0g0+r/5hYbFybP5mxrIsvREe1xPdQ/rpYFiOrGw1lHt05/JKzscAANjCnJzUyIsvavx/X1E8EFAiEJAVi539fc7ChXL6z8SHT06/X1Yi8fuo6OpSvDugRFdA5tjY2WuyCwvlqFoiZ3W1SjZtUuHll3OqN4Bzcl6ExhmRpKnn+0N6vHtQLWMR+fMc2lnp1faKUpU5z2073FEjqca+YT3RPai2yZguLHDpsz6vGspLCQwAwKyyTFPG4KASf4iOM3cnEoGA4oGAjGBQysmRo7LywwGyZIkc/iVy+n3KLilhWRQAW51XofGn3h6d1BPdg3q+P6SkJV3mKdTyApeq81yqyneqKs+pqnynFnzk25xo0lRXNK6OaFydkZg6onF1RGJ6LTSumGnqam+Jdvm8+jt3IX+wAQDzghmPKysnhzsUAObUeRsaZwwnDB3sHdbroXF1RmPqisYVNf/4X1LmzFVVnlO5WVnqjMbVG0uc/Z0jK0tL8pyqzneqrniB/qmyVBUuTkQFAAAAzvvQ+CjTsjQQN9Txh7sVnZG42qMxmZbO3uWoznOpOt+pcpfD9mc8AAAAgExAaAAAAACwHSfGAQAAALAdoQEAAADAdoQGAAAAANsRGgAAAABsR2gAAAAAsB2hAQAAAMB2hAYAAAAA2xEaAAAAAGxHaAAAAACwHaEBAAAAwHaEBgAAAADbERoAAAAAbEdoAAAAALAdoQEAAADAdoQGAAAAANsRGgAAAABsR2gAAAAAsB2hAQAAAMB2hAYAAAAA2xEaAAAAAGxHaAAAAACwHaEBAAAAwHaEBgAAAADbERoAAAAAbEdoAAAAALAdoQEAAADAdoQGAAAAANsRGgAAAABsR2gAAAAAsB2hAQAAAMB2hAYAAAAA2xEaAAAAAGxHaAAAAACwHaEBAAAAwHaEBgAAAADbERoAAAAAbEdoAAAAALAdoQEAAADAdoQGAAAAANsRGgAAAABsR2gAAAAAsB2hAQAAAMB2hAYAAAAA2/0OG9fzyJ2SvVYAAAAASUVORK5CYII=", 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\n", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "noborder()\n", + "plt.figure(figsize=[10,10])\n", "hnx.draw(Hdf.collapse_nodes_and_edges(),with_node_counts=True,with_edge_counts=True)" ] } @@ -1831,7 +1978,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.8.16" } }, "nbformat": 4, diff --git a/tutorials/Tutorial 4 - LesMis Visualizations-BookTour.ipynb b/tutorials/Tutorial 4 - LesMis Visualizations-BookTour.ipynb index b0db8309..f00d6ac2 100644 --- a/tutorials/Tutorial 4 - LesMis Visualizations-BookTour.ipynb +++ b/tutorials/Tutorial 4 - LesMis Visualizations-BookTour.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -45,9 +45,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{0: ['FN', 'TH'],\n", + " 1: ['TH', 'JV'],\n", + " 2: ['BM', 'FN', 'JA'],\n", + " 3: ['JV', 'JU', 'CH', 'BM'],\n", + " 4: ['JU', 'CH', 'BR', 'CN', 'CC', 'JV', 'BM'],\n", + " 5: ['TH', 'GP'],\n", + " 6: ['GP', 'MP'],\n", + " 7: ['MA', 'GP']}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "scenes = [\n", " ('FN', 'TH'),\n", @@ -66,9 +84,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/prag717/Library/CloudStorage/OneDrive-PNNL/Documents/tdm/hnx/hypernetx/classes/entity.py:1387: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.\n", + " properties = props.combine_first(self.properties)\n", + "/Users/prag717/Library/CloudStorage/OneDrive-PNNL/Documents/tdm/hnx/hypernetx/classes/entity.py:1390: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.\n", + " properties[self._misc_props_col] = self.properties[\n", + "/Users/prag717/Library/CloudStorage/OneDrive-PNNL/Documents/tdm/hnx/hypernetx/classes/entity.py:1387: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.\n", + " properties = props.combine_first(self.properties)\n", + "/Users/prag717/Library/CloudStorage/OneDrive-PNNL/Documents/tdm/hnx/hypernetx/classes/entity.py:1390: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.\n", + " properties[self._misc_props_col] = self.properties[\n" + ] + }, + { + "data": { + "text/plain": [ + "{'BM': [2, 3, 4],\n", + " 'BR': [4],\n", + " 'CC': [4],\n", + " 'CH': [3, 4],\n", + " 'CN': [4],\n", + " 'FN': [0, 2],\n", + " 'GP': [5, 6, 7],\n", + " 'JA': [2],\n", + " 'JU': [3, 4],\n", + " 'JV': [1, 3, 4],\n", + " 'MA': [7],\n", + " 'MP': [6],\n", + " 'TH': [0, 1, 5]}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "H.dual().edges.incidence_dict" ] @@ -83,9 +138,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/prag717/Library/CloudStorage/OneDrive-PNNL/Documents/tdm/hnx/hypernetx/classes/entity.py:1387: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.\n", + " properties = props.combine_first(self.properties)\n", + "/Users/prag717/Library/CloudStorage/OneDrive-PNNL/Documents/tdm/hnx/hypernetx/classes/entity.py:1390: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.\n", + " properties[self._misc_props_col] = self.properties[\n", + "/Users/prag717/Library/CloudStorage/OneDrive-PNNL/Documents/tdm/hnx/hypernetx/classes/entity.py:1387: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.\n", + " properties = props.combine_first(self.properties)\n", + "/Users/prag717/Library/CloudStorage/OneDrive-PNNL/Documents/tdm/hnx/hypernetx/classes/entity.py:1390: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.\n", + " properties[self._misc_props_col] = self.properties[\n", + "/Users/prag717/Library/CloudStorage/OneDrive-PNNL/Documents/tdm/hnx/hypernetx/classes/entity.py:1387: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.\n", + " properties = props.combine_first(self.properties)\n", + "/Users/prag717/Library/CloudStorage/OneDrive-PNNL/Documents/tdm/hnx/hypernetx/classes/entity.py:1390: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.\n", + " properties[self._misc_props_col] = self.properties[\n", + "/Users/prag717/Library/CloudStorage/OneDrive-PNNL/Documents/tdm/hnx/hypernetx/classes/entity.py:1387: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.\n", + " properties = props.combine_first(self.properties)\n", + "/Users/prag717/Library/CloudStorage/OneDrive-PNNL/Documents/tdm/hnx/hypernetx/classes/entity.py:1390: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.\n", + " properties[self._misc_props_col] = self.properties[\n", + "/Users/prag717/Library/CloudStorage/OneDrive-PNNL/Documents/tdm/hnx/hypernetx/classes/entity.py:1387: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.\n", + " properties = props.combine_first(self.properties)\n", + "/Users/prag717/Library/CloudStorage/OneDrive-PNNL/Documents/tdm/hnx/hypernetx/classes/entity.py:1390: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.\n", + " properties[self._misc_props_col] = self.properties[\n", + "/Users/prag717/Library/CloudStorage/OneDrive-PNNL/Documents/tdm/hnx/hypernetx/classes/entity.py:1387: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.\n", + " properties = props.combine_first(self.properties)\n", + "/Users/prag717/Library/CloudStorage/OneDrive-PNNL/Documents/tdm/hnx/hypernetx/classes/entity.py:1390: RuntimeWarning: The values in the array are unorderable. Pass `sort=False` to suppress this warning.\n", + " properties[self._misc_props_col] = self.properties[\n" + ] + }, + { + "data": { + "image/png": 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11MB
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862 rows × 2 columns

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" + ], + "text/plain": [ + " Chapter Characters\n", + "Volume Book \n", + "1 1 1 MY\n", + " 1 1 NP\n", + " 1 1 MY\n", + " 1 1 MB\n", + " 1 2 MY\n", + "... ... ...\n", + "5 9 4 MA\n", + " 9 4 CO\n", + " 9 5 JV\n", + " 9 5 CO\n", + " 9 5 MA\n", + "\n", + "[862 rows x 2 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "lesmis.df_scenes.set_index(['Volume','Book'])[['Chapter','Characters']]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Volume Book\n", + "1 1 (CL, CV, MB, SN, GE, ME, GG, MY, MC, NP)\n", + " 2 (MB, JL, ME, MT, MY, PG, IS, JV, MR)\n", + " 3 (DA, BL, FV, FT, FA, FN, ZE, LI)\n", + " 4 (TH, CO, TM, FN)\n", + " 5 (VI, JA, MT, MY, FN, FF, BM, JV)\n", + "dtype: object" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "book_tour_data = lesmis.book_tour_data\n", "book_tour_data.head()" @@ -178,7 +566,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.8.16" } }, "nbformat": 4, diff --git a/tutorials/Tutorial 7 - s-centrality.ipynb b/tutorials/Tutorial 5 - s-Centrality.ipynb similarity index 99% rename from tutorials/Tutorial 7 - s-centrality.ipynb rename to tutorials/Tutorial 5 - s-Centrality.ipynb index 840e08ec..34c7b677 100644 --- a/tutorials/Tutorial 7 - s-centrality.ipynb +++ b/tutorials/Tutorial 5 - s-Centrality.ipynb @@ -232,7 +232,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.8.16" } }, "nbformat": 4, diff --git a/tutorials/Tutorial 5 - Homology mod 2 for TriLoop Example.ipynb b/tutorials/Tutorial 6 - Homology mod 2 for TriLoop Example.ipynb similarity index 100% rename from tutorials/Tutorial 5 - Homology mod 2 for TriLoop Example.ipynb rename to tutorials/Tutorial 6 - Homology mod 2 for TriLoop Example.ipynb diff --git a/tutorials/Tutorial 6 - Static Hypergraphs and Entities.ipynb b/tutorials/Tutorial 6 - Static Hypergraphs and Entities.ipynb deleted file mode 100644 index 37fe0896..00000000 --- a/tutorials/Tutorial 6 - Static Hypergraphs and Entities.ipynb +++ /dev/null @@ -1,511 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#!pip install hypernetx" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#!pip install networkx" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Illustration of Static Hypergraphs using Kaggle's HarryPotter dataset.\n", - "\n", - "In this tutorial we introduce `hypernetx.StaticEntity` and `hypernetx.StaticEntitySet` and the new `static=True` attribute in the `hypernetx.Hypergraph` class. \n", - "\n", - "Harry Potter Data is available here: https://www.kaggle.com/gulsahdemiryurek/harry-potter-dataset.\n", - "\n", - "Python code for parsing the dataset is in `harrypotter.py` in the `hypernetx/utils/toys directory`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import hypernetx as hnx\n", - "import networkx as nx\n", - "import matplotlib.pyplot as plt\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The Harry Potter Dataset: \n", - "To use a csv file for a Static Hypergraph, we need every cell filled with a label. \n", - "We have edited the Harry Potter dataset so that it has 5 categories and every cell is filled. Where a value is unknown, we marked it as \"Unknown *category_name*\". " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hogwarts = hnx.HarryPotter()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "hogwarts.dataframe" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### We define a labeling based on the categories and store it in an Ordered Dictionary.\n", - "The ordering of labels is determined by their order of appearance in the table with the exception of Unknown labels, which are always listed first." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "hogwarts.labels" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Assign unique ids to the label values \n", - "We encode the data in each column of the dataframe using a sequence of integers and store the coded data along with a translator function to retrieve the original names as needed. Here we remove duplicate rows but counts could be collected for a weighting scheme. **Watch for a near future release.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## List of nonzero indices\n", - "hogwarts.data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "hogwarts.data.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## StaticEntity and StaticEntitySet\n", - "\n", - "The entire dataset has now been represented using a data array and a dictionary associating columns and integers with labels and values in the original data.\n", - "\n", - "The basic object in HyperNetX, which holds the data and label dictionary for a static hypergraph, is a `StaticEntity`. Similar to the `hnx.Entity` class, the data structure rests in the background to hold the data for flexibly switching between different orders of containment.\n", - "\n", - "Each column of the data is considered a **level** in the StaticEntity. A level's order corresponds to its column position in the datatable. The column header serves as a key to the label dictionary." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "E = hnx.StaticEntity(data = hogwarts.data, labels = hogwarts.labels)\n", - "E.keys" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### A StaticEntitySet is a StaticEntity restricted to two levels. \n", - "By default, a StaticEntity will grab the 1st two levels of the data and first two keys of the labels, but any pair of levels may be specified. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ES = hnx.StaticEntitySet(E)\n", - "ES.labels" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Static Hypergraph\n", - "A static hypergraph is one where all nodes and edges are known at the time of construction. This permits an internal ordering and uid structure for easy reference and faster computation of metrics.\n", - "\n", - "**Static Hypegraphs can be instantiated with a StaticEntitySet similar to the way a dynamic hypergrapah can be instantiated using an EntitySet.**\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "H = hnx.Hypergraph(ES,static=True,name='Hogwarts')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**But we can also pass the dataframe and specify which columns we want to use as edges and nodes. The default behavior is to use the first two columns.**\n", - "$$\\text{df}[\\text{edge_column},\\text{node_column}]$$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "H = hnx.Hypergraph(hogwarts.dataframe)\n", - "H.edges,H.nodes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.title('Hogwarts Hypergraph',fontsize=20)\n", - "hnx.draw(H)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## General construction of a static Hypergraph:\n", - "Set the parameter `static=True` inside the hypergraph constructor and input a set system just as you did before. If the set system is a pandas dataframe, a StaticEntity or a StaticEntitySet, the parameter is automaticaly set to True." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## example:\n", - "simple_data = {'A':{1,2,3},'B':{2,3,4},'C':{3,4,5}}\n", - "simple_static_hypergraph = SSH = hnx.Hypergraph(simple_data, static=True)\n", - "hnx.draw(SSH)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SSH.isstatic" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Static Hypergraphs are immutable. You can't add or remove nodes or edges. Uncomment the last line below and try it:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## Static Hypergraphs are immutable. You can't add or remove nodes or edges.\n", - "## Uncomment the last line and try it:\n", - "new_edge = hnx.Entity('D',[4,5,6])\n", - "# SSH.add_edge(new_edge)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## But you can remove the static property and create a new hypergraph \n", - "## This will also remove the benefits of an immutable datastructure and may slow things down.\n", - "SSH = SSH.remove_static()\n", - "SSH.add_edge(new_edge)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## State Dictionary\n", - "Since a static hypergraph does not lose nodes and edges, metrics computed on the hypergraph will persist. We store them in a state dictionary. \n", - "\n", - "Let $H$ be the Hogwarts hypergraph we constructed above." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "H.state_dict" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### The output of certain methods are automatically stored in the state dictionary once they are computed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "H.incidence_matrix().todense()\n", - "H.state_dict" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### They can be retrieved by their keys. But will automatically be retrieved when the method is called again to avoid duplicating the computation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "H.state_dict['incidence_matrix'].todense()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Most Hypergraph methods apply to Static Hypergraphs\n", - "Any method, which does not change the data and labels of the underlying StaticEntitySet, can be used by the static Hypergraph." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "H.dataframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Restrict to specific edges and nodes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "HF = H.restrict_to_edges(['Gryffindor','Ravenclaw','Slytherin','Hufflepuff'])\n", - "HF.dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig,ax = plt.subplots(1,2,figsize=(15,6))\n", - "hnx.draw(H,ax=ax[0]);\n", - "hnx.draw(H.dual())\n", - "H.edges" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Collapse identical elements\n", - "This method exists to collapse identical nodes and edges and is implemented for dynamic hypergraphs.\n", - "We wish to do the same for large unwieldy hypergraphs stored as static." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pos = {'Unknown House': [-0.11, 0.4 ],\n", - " 'Gryffindor': [-0.32, 0.27],\n", - " 'Ravenclaw': [0.57, 0.27],\n", - " 'Hufflepuff': [-0.02, 0.16],\n", - " 'Slytherin': [-0.02, -0.51],\n", - " 'Durmstrang Institute': [-0.09, -1. ],\n", - " 'Unknown Blood status': [0.15, 0.66],\n", - " 'Half-blood': [0.24, 0.04],\n", - " 'Pure-blood': [-0.45, -0.08],\n", - " 'Pure-blood or half-blood': [ 0.05, -0.21]}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "nodes = ['Pure-blood or half-blood', 'Unknown Blood status', 'Pure-blood', 'Half-blood', ]\n", - "Hn = H.restrict_to_nodes(nodes)\n", - "hnx.draw(Hn,pos=pos)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "Hc,clses = Hn.collapse_edges(return_equivalence_classes=True)\n", - "\n", - "fig,ax = plt.subplots(1,2,figsize=(15,6))\n", - "hnx.draw(Hn,ax=ax[0],pos=pos);\n", - "ax[0].set_title('original',fontsize=20,color='r')\n", - "hnx.draw(Hc,ax=ax[1],pos=pos);\n", - "ax[1].set_title('collapsed',fontsize=20,color='r');\n", - "clses" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### More hypergraph methods" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## bipartite\n", - "G = H.bipartite() ## this is a NetworkX graph\n", - "cmap = ['r' if G.nodes[n]['bipartite']==0 else 'cyan' for n in G.nodes ]\n", - "top = nx.bipartite.sets(G)[0]\n", - "pos = nx.bipartite_layout(G, top)\n", - "nx.draw(H.bipartite(),node_color=cmap,with_labels=True, pos=pos)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## reporting\n", - "print(hnx.info(H))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## Once the dist stats are computed, they are stored in the state dict for fast recall and reference\n", - "hnx.dist_stats(H)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "H.state_dict" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## toplexes\n", - "fig,ax = plt.subplots(1,2,figsize=(15,6))\n", - "pos = hnx.draw(H,ax=ax[0],return_pos=True)\n", - "hnx.draw(H.toplexes(),ax=ax[1],pos=pos)" - ] - } - ], - "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": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/Tutorial 7 - Laplacians and Clustering.ipynb b/tutorials/Tutorial 7 - Laplacians and Clustering.ipynb new file mode 100644 index 00000000..6e7949cc --- /dev/null +++ b/tutorials/Tutorial 7 - Laplacians and Clustering.ipynb @@ -0,0 +1,361 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Laplacians and Clustering\n", + "\n", + "\n", + "\n", + "Tutorial for hypergraph clustering utilizing random-walk based Laplacians. The hypergraph may be weighted or unweighted. \n", + "The optional weights are associated with each **vertex-hyperedge pair**, sometimes referred to as \"edge-dependent vertex weights\" or \"cell weights\" of the incidence matrix. If unweighted, the underlying random walk is equivalent to a weighted random walk on the clique expansion (i.e. 2-section, one-mode projection) of the hypergraph. If weights are specified, the random walk isn't necessarily reversible, which implies it cannot be characterized as any random walk on an undirected graph. For more background on Laplacian-based hypergraph clustering, see\n", + "\n", + "Hayashi, K., Aksoy, S. G., Park, C. H., & Park, H. \n", + "Hypergraph random walks, laplacians, and clustering. \n", + "In Proceedings of CIKM 2020, (2020): 495-504.\n", + "\n", + "and the references contained therein. Feel free to direct inquries concerning this tutorial to Sinan Aksoy, sinan.aksoy@pnnl.gov" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import hypernetx as hnx\n", + "import networkx as nx\n", + "from sklearn.datasets import fetch_20newsgroups\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from scipy.sparse import csr_matrix, coo_matrix\n", + "from pprint import pprint\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as mpatches\n", + "import numpy as np\n", + "import warnings\n", + "warnings.simplefilter('ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Unweighted hypergraph clustering on LesMis\n", + "\n", + "A toy example of unweighted hypergraph clustering characters in the LesMis example based on scene co-occurance." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: ['FN', 'JA', 'TH'],\n", + " 1: ['GP', 'MA', 'MP'],\n", + " 2: ['BM', 'BR', 'CC', 'CH', 'CN', 'JU', 'JV']}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scenes = {\n", + " 0: ('FN', 'TH'),\n", + " 1: ('TH', 'JV'),\n", + " 2: ('BM', 'FN', 'JA'),\n", + " 3: ('JV', 'JU', 'CH', 'BM'),\n", + " 4: ('JU', 'CH', 'BR', 'CN', 'CC', 'JV', 'BM'),\n", + " 5: ('TH', 'GP'),\n", + " 6: ('GP', 'MP'),\n", + " 7: ('MA', 'GP')\n", + "}\n", + "\n", + "H = hnx.Hypergraph(scenes)\n", + "hnx.spec_clus(H,3) #3 clusters" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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/KZBeSQFQRM6pqKiI5uZmcnJy2nV9aWkp5513Ho899hgul+u0zz0eD6+88gqjRo1i//79rb5veno6CxcupKGh4eT3GhsbWbBgwckZRavVyrhx4xg/fjyvvPIKs2bN4uOPP+bo0aPt+ll6s/pNxTTn1RI9awAmc3CHptpP8jGFWnGMVOsX6Z0UAEXknCoqKgCIiYlp87Ver5dbbrmFHTt2nHPssWPHuO66604JdGczevRo+vbty+uvv37ye6+//jrp6emMGjXqlLFxcXF89tlnXHXVVYwcOZI///nP6Cj01vM2eah6+xBhuX2w94v0dzkd4m3yULe+EOd4tX6R3ksBUETOqbKyEqfTSUhISJuvXbp0Ke+8806rx2/bto2nnnqq1ePvuOMOnnnmmZP/++mnn+bOO+88bdzLL7/MoEGDuP3227nxxhtZsGAB69ato6mpqdXP6s1qPjiGt8FDVJC3fQGo31yM0eQhfEKyv0sR8RsFQBE5p4qKinbN/gE88cQTbb7m8ccfb/XY2267jY8//pjDhw9z5MgR1q5dy6233nrauKeeeopbb70Vi8XCAw88gMfj4d1332X16tWUlpa2ucbexF3RSM2HeURckIo1JrjbpRiGQe26PMKGxAX9zyLSEQqAInJOHQmAmzZtavM1O3fupLm5uVVj4+PjmTlzJs899xzPPPMMM2fOJD4+/pQxX3zxBZ9//jk333wz4HsvcM6cOezYsQOHw8EHH3zA7t278Xq9ba61N6haeRhzmIWIi9P9XUqHNe2vxF3coNYv0uupDYyInFNtbS2pqaltvs4wDIqKitp8ndfrpaioiLS01vWZu/POO/n2t78NwD//+c/TPn/qqadwu92n/AyGYWCz2XjkkUcoKChg586dlJSUMG7cOMLCwtpcc0/VdKSahq0lxFyfhdke/O/L1a7Lx5bkJKS/Wr9I76YZQBE5p/Dw8Hadr2symUhKSmrzdRaLhcTExFaPnzZtGi6XC5fLddoZxW63m+eff54///nPbNmy5eTX1q1b6devHwsWLGDYsGFccMEFVFVVsXr16naF1p7I8BpULj2ILcWJY3Tr//8RqNxlDTTuKVfrFxEUAEWkFWJiYk7uBG6rMWPGtPma4cOHY7PZWj3eYrGwe/dudu/efVqfwqVLl1JRUcFdd93FsGHDTvm6/vrrT244SUxMZOrUqURFRfHRRx+xffv2Xr8k3LC1hOZjNURfGfxtX8A3+2cOs+IYGdw9DEU6gwKgiJxTRwLgvffe2y3XREZGEhl5enuSp556iilTphAVdfqS33XXXceWLVtOvqcYGhrK5MmTGTZsGHv37mXt2rXU19e3uZaewOvyULXiEGHD4nrESRneJjd1G4pwjk/GZAv+pWyRjjIZaoQlIuewdetW3njjDX76059it9vbdK1hGMyaNYtly5a1avyYMWNYu3Ztm5/T2UpLS9m0aRM2m42UlBQGDRrk13q6W/XqI1SvOUbSf4zBGhf870TWfpJP5ZIDJP1kPNZo//67JRIINAMoIud0Ygdwe2YBTSYTL7zwwmmNmVuSmZnJq6++6vfwB77dxRdddBExMTG88cYbLF++HLfb7e+yuoW7qomaD44TPjm1R4Q/w2tQuy6fsKHxCn8iX1IAFJFzSkxMxGazsXv37nZdHxMTw7p16/j+97+Pw+E47XObzcbtt9/Oxo0bycjI6GC1ncdut5Obm8uUKVPYuHEjTz75ZK/oGVi98jAmu4XIS4K/7Qt82fqlRK1fRL5KS8Ai0ipLly5lz549/OAHPzhto0VbVFZWsnz5cg4cOIDb7SYzM5Np06a1adevPxQUFLBo0SJqa2uZOXMmubm5/i6pS7iO1VD8zy1EXzuQ8PE946SM0md24KlxkfCdUdr9K/IlBUARaZXCwkIeffRRbrjhBoYOHervcvyiqamJZcuWsW3bNkaOHMmMGTPadTxeoDIMg5JHt2E0eUj47qgesfO3ubSBoj9tIOb6bJxjA/svGSLdSUvAItIqSUlJ9O3bl/Xr1/u7FL+x2+1ce+21XH311ezcuZPHH3+cwsJCf5fVaRq2leI6Uk3UlZk9IvwB1K3Lx+y04shV6xeRr1IAFJFWGzduHIcPH+71jZJHjhzJvffei8Vi4YknnmD9+vUE+2KK0exr+xI6JI7QgdH+LqdTeBvd1G080fpF/7kT+Sr9jhCRVhs8eDCxsbG88cYbrT6rt6fq06cPd999N6NGjWLZsmUsWrSIhoYGf5fVbjUf5+GpcRE1o7+/S+k0dRuLMJq9hE/oGe8yinQmBUARaTWr1coNN9xAaWkpK1as8Hc5fmez2bjyyiu58cYbOXDgAI899hjHjx/3d1lt5ql2UbPmGOHnp2CLD/62L+Br/VK3Lp+w4fFYotT6ReTrFABFpE2Sk5OZMWMGmzZtYsuWLf4uJyAMGTKE++67D6fTydNPP83atWuD6hi5qrcPY7Kaibysr79L6TSNeytwlzUSPlGtX0RaogAoIm02evRoRo4cydKlS3v9+4AnxMTEcOedd3L++eezatUq5s+fT11dnb/LOidXXi31m4qInNoPc5jV3+V0mtp1+djSwgnpG+HvUkQCkgKgiLTLzJkziYuLY+HChUERdLqDxWJh6tSp3HLLLeTn5/PII49w6NAhf5d1RoZhULn0ANY+Dpw9pOcfQHNxPU17KwifmKK+fyJnoAAoIu1is9m4+eabcblcLFy4sNdvCvmqrKws7rvvPuLj43nuuedYs2YNHo/H32WdpnFnGa5D1URfmYnJ0nOCUu0n+ZjDbThGqPWLyJkoAIpIu8XExDBnzhwKCgp48803g74VSmeKjIzk9ttv55JLLuHDDz/k+eefp6qqyt9lnWS4vVQuP0TooBhCs2P8XU6n8Ta6qd9YhPO8ZExW/SdO5Ez0u0NEOiQtLY1rrrmGHTt2sGbNGn+XE1DMZjMXXXQR8+bNo7y8nEcffZQvvvjC32UBULs2D09lI1EzM/1dSqeq21CE4TYIP6/nLGmLdAUFQBHpsKFDhzJlyhQ+/PBD7QxuQb9+/bj//vtJT09nwYIFrFy5Erfb7bd6PDUuqt87RviEFGwJDr/V0dkMr0HtunzCRsRjiew5R/SJdAUFQBHpFJMmTWLUqFG89dZbHD582N/lBByHw8GcOXO44oor+Pzzz3n66acpLy/3Sy3Vq46A2URED2r7AtC4pxxPeSMRk1L9XYpIwFMAFJFOYTKZuPLKK+nXrx8LFy6ktLTU3yUFHJPJxPnnn89dd91FQ0MDjz76KDt27OjWGlwFddStLyRySl8sTlu3Prur1a7LJyQ9gpB0tX4RORcFQBHpNBaLhRtvvJHw8PCg6YPnD6mpqXzzm98kOzubV199lbfeeguXy9XlzzUMg6plB7HGh/W449Gai+po2l9J+CQ1fhZpDQVAEelUYWFh3HLLLTQ2NvLyyy/79V23QBYaGsp1113HrFmz2LZtG0888QTFxcVd+szG3eU07a8kamYmJkvP+uO/dl0+5ggbYcPi/V2KSFDoWX8CiEhAONEeJi8vj7feekvtYc7AZDIxZswY7r33XgAef/xxNm7c2CW/XobbS9Wyg9izogkd1HPavgB465up31RMuFq/iLSafqeISJdIT0/nmmuuYdu2bXzwwQf+LiegJSQkcM899zBixAiWLFnCa6+9RmNjY6c+o/aTAtzljUTPzOxxp2PUbSjC8Bo41fpFpNUUAEWkywwbNoxLL72U999/n23btvm7nIAWEhLC7Nmzuf7669m7dy+PPfYYeXl5nXJvT10z1e8ewXleMrYkZ6fcM1AYXoPaT/JxjOiDJUKtX0RaSwFQRLrUBRdcwMiRI3nzzTc5cuSIv8sJeMOGDeO+++4jLCyMp556ik8++aTDS8LVq32/7pFTelbbF/C91+ipaCJ8ojZ/iLSFAqCIdKkT7WHS09NZuHAhZWVl/i4p4MXGxnLnnXcyfvx43n77bRYsWEB9fX277tVcVEfdZwVEXtoXS3jPmyGrXZdHSF+1fhFpKwVAEelyVquVm266CYfDwfz589sdZnoTq9XKtGnTmDNnDseOHeORRx5p1wxq5bJDWGNCe+QMWXNhHU0HqtT6RaQdFABFpFucaA/T0NCg9jBtMGjQIO677z5iY2N59tln+eCDD/B6va26tuGLcpr2VhA1o3+P3B1bv70Uc4xdrV9E2qHn/YkgIgErNjaWm2++mePHj7NkyRK1h2mlqKgobr/9di688ELWrFnDCy+8QE1NzVmvMTxeqpYexJ4ZReiQuG6qtPt4m9yYrCZirhrY43oainQH/a4RkW7Vt29frr76arZu3cqHH37o73KChsVi4ZJLLuEb3/gGJSUlPPLII+zbt++M4+s+LcBd2kDUlT2v7QtA04EqmsvqsfeP9HcpIkFJAVBEut3w4cO55JJLWLNmDdu3b/d3OUGlf//+3HfffaSkpPDSSy+xatUqPB7PKWO89c1Uv3sU59gkQlLC/VRp1zG8Bo37KrCnRWG2W/1djkhQUgAUEb+48MILyc3NZfHixRw9etTf5QSV8PBw5s6dy9SpU/nkk094+umnqaioOPl59btHMTwGkZf382OVXac5rwZvvZvQ7J51oolId1IAFBG/MJlMzJo1i7S0NBYuXEh5ebm/SwoqZrOZSZMmceedd1JXV8ejjz7Krl27aC6pp/aTAiIuSe+xjZEbvqjE2icMa2yov0sRCVomQ29hi4gf1dfX8+STT2Iymbj77rsJCwvzd0lBp6GhgSVLlrBr1y6GRQzgvOaBpP3wPEy24P47vrfJjclswmSznPyeu6KRqpWHCZ+Ugr2v3v8Taa/g/tNBRIKew+Hglltuob6+Xu1h2iksLIwbbriBK8Zdwu7qQ7xl30BpZfA33K5ccpCmw9UYzf9ue9O4twKzw0pIWs97t1GkOykAiojfxcXFcfPNN3Ps2DGWLl2q9jDt4YX+e8O5Lv5iDJuJxx9/nC1btvi7qnar31JM075KrPFhmGxmmksbqF2XT+0n+ZgjQjCZ9Z8vkY7Q7yARCQj9+vVj9uzZbNmyhY8++sjf5QSduvWFuIvrGXjNKO69916GDh3K4sWLef3112lqavJ3eW1Wuy4f57hErDGh1G0upuKVL6h65zDeumbq1uVT/soXeOqa/V2mSNDS/nkRCRi5ubmUl5fz3nvvERsby7Bhw/xdUlDwNripXnUYx+hEQtJ8Z+JeffXV9O/fn2XLlnH8+HFuuOEGkpOT/Vxp63gb3JhsZqx9HABUrzxM+IWpWPJqsfUJwxoTSvWqIzTsKCX8vOD4mUQCjWYARSSgXHzxxQwfPpw33niDY8eO+bucoFC95ihGs5eoK05t+5Kbm8u9995LSEgITz75JJ999lnAL68bhoE5zIolJpTGfRU0F9djjgzBEmMHj4FjZAKOkQmE5sTSdKgKo9lz7puKyGkUAEUkoJhMJq666ipSU1NZsGCB2sOcg7u0gdq1+URclI4l0n7a5/Hx8dx9992MGTOGFStW8PLLL9PQ0OCHSlvnxKklztEJNB+voX5zMSariYYdZVj7OLDG+Fq/WBMduEsbTtkhLCKtpwAoIgHHarVy0003YbfbmT9/fkAHFn+rXH4IS3gIERemnnGM1WplxowZ3HTTTRw+fJhHH3004Jtv2zOjCb8gjfpNRbgOVdO4qwwML+7yRpoOV1H7UR6O4X38XaZI0FIfQBEJWKWlpTz55JMkJydz6623YrFotuerGg9UUvrEdmJvHoRjZEKrrqmsrOS1117j+PHjXHrppUyaNAlzAO+o9bo8VLy+j4btpVjCfY2tvU1uHLl9iL5qICZzzzvnWKQ7KACKSEA7fPgwzz//PLm5ucyePfvkEmFvZ3gNiv++GZPNTJ/7c9v06+LxeHj//ff56KOPyMzM5NprryU8PDD76nkb3VS8uZ+wYfHY4sLw1Lgwh1mxJYdjsujfBZH2Cty/9omIABkZGcyePZvNmzezdu1af5cTMOo3FNFcUEfUlZltDsUWi4XLLruM2267jaKiIh555BEOHDjQRZV2TOOBSkwmE6EDo7ElOQnNiiEkLULhT6SDFABFJOCNHDmSCy+8kNWrV7Nz505/l+N33kY3Ve8cxjGyT4eOQxswYAD33XcfiYmJvPDCC7z77rt4PIGzq9bwemnaV0lIRiRmu7qWiXQmBUARCQqXXHIJw4YN44033uD48eP+Lsevat4/htHkIXJa/w7fKyIigltvvZXLLruMjz/+mGeffZbKysqOF9kJXMdr8Da4Cc2O8XcpIj2OAqCIBIUT7WGSk5NZsGABFRUV/i7JL9zljdR8lEf4hWlYo09v+9IeZrOZCy64gDvuuIOqqioeffRR9uzZ0yn37ojm/Drs2TFYo0P9XYpIj6MAKCJBw2azcfPNNxMSEsL8+fNpbGz0d0ndrmrFIcxOGxEXpXX6vfv27ct9991HRkYGCxcuZPny5bjd7k5/Tmu4jtVQ8/5xbPFhfnm+SE+nACgiQcXpdDJ37lxqamp45ZVXAuqdta7WdKiKhu2lRE3LwBzSNS1xHA4HN910E9OnT2fjxo08+eSTlJWVdcmzzqZ2XT6YICQ9otufLdIbKACKSNDp06cPN954I4cPH2b58uUBf7xZZzC8BpVLD2JLC291z7/2MplMnHfeedx99924XC4ee+wxtm3b1qXP/CpPjYv6bSWEn5+iPn8iXUQBUESCUmZmJrNmzWLjxo2sW7fO3+V0ufrNxTTn1RJ9ZWa3haLk5GS++c1vkpOTw+uvv87ixYtxuVxd/ty6zwowmU04xyZ2+bNEeivtqxeRoDVq1CjKyspYt24d/fv3JyUlxd8ldQlvk4eqlYcJGxGPPSOqW59tt9u55ppr6N+/P8uXLycvL4+bbrqJ+Pj4Lnme4fZS+1kBjtEJmB22LnmGiGgGUESC3KWXXsqll17K/v37KS8vP+d4r9fbDVV1rpoPjuFtaCZqesfbvrSHyWRi1KhR3HPPPRiGweOPP86OHTu65FkNO0rx1jQTPrFnhnmRQKEAKCJBzWw2k5ubi9vtZt26ddTV1Z1xbG1tLf/6179oamrqxgo7xl3ZSM2HeURckIY1xr/tUBISErjnnnvIzs7m1VdfZcWKFZ2+S7h2bT72gdHYEp2del8ROZUCoIgEPavVysSJE7FYLKxdu/aM76mFh4cTFxfH008/3c0Vtl/VisOYwyxEXNz5bV/aw263c9111zF9+nTWr1/Ps88+S1VVVafcu+loNa5jNZr9E+kGCoAi0iOEhoYyceJEGhoa+Pzzz8+41HvjjTdiNpuZP39+N1fYdk1HqmnYWkLU5RkBdRTaiV3Cd955J9XV1Tz22GPs37+/w/etXZePJTaU0JzYTqhSRM5GAVBEeoyoqCgmTJhAUVERW7ZsOa09jMfjwWKxcOutt1JUVMQ777zjp0rPzfAaVC09iC3FiWNMYO6GTUtL47777iMlJYUXX3yRtWvXtvtenuomGraVEj5RrV9EuoMCoIj0KImJiYwaNYqDBw+yb9++U2YCLRYLDQ0NlJeXk5yczLp16zpl5qorNGwtwXWsplvbvrSHw+Fg7ty5TJo0iVWrVrX7CLnazwoxWdX6RaS7BM6agohIJ8nMzKSuro5t27bhdDpJTU1l7969FBYWsmHDBkwmEzabjUsuuQSLpWtO1OgIr8tD1cpDhA2Nw54Z7e9yzslsNjNlyhRKS0tZvHgx9957L7GxrV/GNdxe6j4rwDEmEXOo/rMk0h1MRm9ooS8ivY7X62XlypUcP34cl8uF2+3GMAxGjBhBeno6AwcOxGwOzEWQ6tVHqF5zjKT/GIM1LnjOwm1oaODxxx8nNDSUO++8E5utdX386jYVUfHKXhL/Ywy2BEcXVykioBlAEemBGhoaWLBgAfn5+dhsNsLCwpg6dSppaWlERf27kbLX6w24EOipaqLmg+OET05tV/irLS+joiCPyuJCqouLqCwqpKqkCMPrJapPIlGJSUQlJBGVkEhMcgqR8Z13rFxYWBg33ngjTz75JCtXrmTWrFnnvMYwDF/rl+wYhT+RbqQAKCI9TlhYGOPHjycsLAy73c62bdvIz88nOzsbwzAwmXzv1AVa+AOoevswphALkZekt/oaj9vN/vWfsnXVco7t/PLMXpOJ8Ng4ovokEpucCiYT1cVFFOz/gprSUgzD925kctYgRl4+k+wJk7GGhHS4/uTkZGbOnMlbb71Feno6I0eOPOt419EamvNqiZs3tMPPFpHW0xKwiPR4VVVVrFmzhvj4eCZOnBiQwQ/AdayG4n9uIfqagYSfl3zO8TXlpWxb/Tbb33ubuopyUnOGMGLKdJIGZBEZn3DGQOdxN1NdWkLJ4YNse/dtjmzbTGhEJMMvmcqIKdOJTkzq0M9hGAZvvvkmO3bs4J577iEx8cwbO8oW7KH5eA2JPxwb0JtdRHoaBUAR6RUKCwtZu3YtmZmZjBo1yt/lnMYwDEoe3YbR5CHhu6POGoYMr5fP33qNda+8iMVqY8iFl5A7dQZ9+rXvqLjy/Dy2rV7OjvdX01Rfz7hZ1zL55tsxd2CDjMvl4qmnnqK5uZl7772X0NDTTzHxVDVR8D/riZrZn4hJqe1+loi0nQKgiPQaBw8eZNOmTeTm5pKVleXvck5Rv62E8vl7iL97GKEDY844rrG2lhX//DMHN61n/NU3MP6qG7A7OufdueamRjatWMLal18gddAQZn7vJ4THtL8pc1lZGY8++ijnn38+l1566WmfV719mNp1+ST/dLx2/4p0s8BcBxER6QKZmZlkZWWdfCcwUBjNXqqWHyJ0cOxZw1/hgX288J/fI/+L3Vzzn7/kgjnf6LTwB2Czh3Le1Tdw4y/+m4rCfF548Lv/fqewHeLi4sjNzWXTpk2nnRlsNHup+7wAp1q/iPiFAqCI9CrDhw8nOTmZDRs2UFNT4+9yAKj5OA9PjYuomZlnHLP74/dZ+IsfExYRyW3/8zCZo8Z1WT1pg4dx2x/+j7i0viz6zUNsWrGk3fcaN24ctbW1pzWIrt9agrfOjVPn/or4hQKgiPQqZrOZ8ePHk5qaypo1a6iurvZrPZ5aFzVrjhF+fgq2+JbbvhTs/4KV//ob2edfwM2//l8i+3Re65YzcUbHcP1Dv2HUtCtZ89zjHN6ysV33SUxMpF+/fqxfv/7k9wzDoHZdHqGDYs74M4tI11IAFJFex2q1MnjwYA4ePMj8+fNpamryWy116wvBMIi8tOW2Lw011Sz56x9I7D+AK+77LtZWNlfuDGazhYtvv5v+uaNZ9o8/U11a3K77jBs3jiNHjlBc7LvedaSa5vw6wrXxQ8RvFABFpFdyOBzcfPPNlJeX89prr51yZnB3MTwGdZ8WEpbbB7Pj9GBneL2s+MefaW5q4sofPIjF2n3h7wST2cz0b/8QZ3QMbz/yMB53c5vvkZOTQ2xsLJs3bwZ8odeWFo59YHQnVysiraUAKCK9VlJSEjfccAP79u3j7bff7vbnN+4px1PVRPj5Lb8H9+kbL3No6yZmfvuHnXpiR1uFRURy7X/+ksQBWRza3PalYKvVyowZM7DZbDTVNGIOtxE9K1N9/0T8SAFQRHq1rKwspk+fzmeffcbnn3/erc+u/TSfkPQIQlLDT/usqriQTxYtYMK1N5Excky31tWSyPgEMkaM5MDGz6gqLmzz9SkpKdTW1nJ4+36MJi+2lNN/ZhHpPgqAItLrjR8/ngkTJrBixQr27t3bLc9sLm2gaV8lzgktn/ixddUKQhxhjL/q+m6ppzXShgzHZg/l8NbNbb7W4XAQHxfPofzDhKRHYLa1v8m0iHScAqCICHD55ZeTnZ3Nq6++SmFh22e42qru0wLMDiuOEX1O+8ztcrF9zSqGXTwFm/30EzT8xWw203dYLvl7d+NqbGjz9X1s0dTSiG1ARBdUJyJtoQAoIoIv3Fx77bXExsYyf/78Lm0PY3gN6jYW4RibiMl2+h/Dez/9mMaaakZMmdFlNbRX+tDhYBgc37WjTdcZhoGt2NcMutHsPsdoEelqCoAiIl+y2+3MnTsXwzBYsGABLperS57jqWrCaHATOiC6xc+3vLOMfiNGEZvS/jYpFRUV/OUvf2HSpEkkJibSp08fzjvvPH73u99RVFTU7vvaHU6SBmZzZPsWjDbsnHYX12Or8W36qKura/fzRaRzKACKiHxFZGQkc+fOpbS0tMvaw7jLGwGwxJ6+vFtRkEfBvi/InTK93fd/5513yM7O5oc//CHr1q2juLiY0tJSPv/8cx566CGysrJ49dVX23zfwsJCvve973Hdfd/hirsfIDExkcmTJ/Poo49SX18PQEZGBiaTCZPJhMPhYNiwYTz22GM07q3AEenAZDIpAIoEAAVAEZGvSU5O5oYbbmDv3r2sWrWq0+/vKW8EE1ijTw+A5fnHfTVkDWrXvVeuXMn06dMpLS0945iamhpuuOEGXnzxxVbf9+DBg4waNYp33nmH3//hD/zjpz/i6f/7Kz/4wQ9YsmQJq1evPjn217/+NQUFBWzbto2rr76a++67j5dffYWw7FicTqcCoEgAUAAUEWlBdnY206ZN45NPPjnlGLPO4C5vxBIZ0uL7f1VFhVhtITijY9p835KSEm699dZWz1p+85vf5ODBg60a+8ADD2C1WtmwYQM33XwzuaNGkRgexnXXXceyZcuYNWvWybEREREkJSUxcOBAfvvb3zKwbyYrNr5HSP8oBUCRAKEAKCJyBueddx7jx49n+fLl7Nu3r9Pu6y5vbHH5F6CyuJDIhERM5rb/8fz4449TVlbW6vH19fU8/PDD5xxXVlbGO++8w7e+9S2cTicA0UkpVBTmYxgGACZTy02dvW4vIVjxhILZalYAFAkQCoAiImcxbdo0Bg4cyKJFizq0eeKrPOWNWGNaDoBVxUVEJSS2676vvPJKm695+eWXzzlm//79GIbBoEH/XpaOTU7hhv/4TyIiIggPD+fBBx887Tq3281Tf36EXUf3MvXKKwBOBsATwVFE/EMBUETkLMxmM9dffz0xMTG89NJL1NTUdPie7opGrGeYAawqKiQqIald992/f3+bryksLGz1jNxXZ/miklL4yw++zYpFrzB06FCamppOfvbggw8SHh5OWFgY3//lj/nuzfdx/3e/BfgCoNvt7rId1iLSOgqAIiLn0NntYQy3F6wt//HramzA7nC2+Z5er5fm5uZ21fPV8NaSgQMHYjKZ2LNnz8nvhYSGMnDQIKLtFsLCwk4Z/+Mf/5gtW7awb/1ujjy9kT/+9U+Yv1zSPvFPj8fTrlpFpHMoAIqItEJUVBRz5syhpKSE119/vUPtYawxob6dwC2wO5w01bf9HTmz2UxGRkabr4uMjCQ2NvasY+Li4pg6dSr/+Mc/TpktjE5KoaIg/7Tx8fHxDBw4kNjqUKzRdmyJjpOf1dXVYTabCQ0NnBNORHojBUARkVZKSUnhuuuuY8+ePae0PWkra2wo7oozBUAHrnYEQIDp09veO3DmzJmtGvevf/0Lt9vN2LFjefnll9m9ezfljS7eWvkOe/bswWI59WxfT62L5vxaQrNjTlk6rq+vx+l0npwJFBH/0O9AEZE2yMnJ4YorrmDdunVs2LChXfewxIWebAb9dXaHk6aG+nbd91vf+hY2m63V400mE9/97ndbNXbAgAFs3ryZKVOm8NOf/pTc3Fxm3zyXJR+u5b677uQ3v/nNKeMb91VgspkJyYg65ft1dXUndxKLiP8oAIqItNGECRMYN24cy5Yta9fGC2tMKJ6KJgzP6Tth7Q4nTe1sk5Kdnc3//u//tnr8T3/6UyZMmNDq8cnJyfz973/n4MGDuFwuqqurefhnP+SW2VficPiWeQ8fPsx3v/Vdmg5UYR8Qjflr7zrW1tYqAIoEAAVAEZE2MplMTJs2jQEDBrSrPYw1NhS8Bp6q0zdfhLTzHcATvv/97/OHP/zhtCXZrzKZTDz44IOnzdq1lclsJjoxhYrCvFO+7zpcheH2Ejow+pTvG4ahGUCRAKEAKCLSDhaLheuvv56oqCjmz59PbW1t66/9sgVMS+8B2h0OmurbtwR8woMPPsinn37KnDlzTtlsYbPZuPrqq1mzZg1/+MMfOuU9vJikFCoLCzC+3BRjGAaNeysISQ3HEh5yytimpiY8Ho8CoEgAUAAUEWmn0NBQ5s6di8fjaVN7GGtMKFhMuI6d3lPQtwu49WHyTMaOHcv8+fOpq6vj6NGjHDp0iIaGBt544w0uuuiiDt//hJjkFJobG6mtrACgubAOT7WL0OzTj7I7sYNYAVDE/xQARUQ6IDo6mjlz5lBUVMQbb7zRqvYwJqsZx4g+1H1eiOE99T1Au8OJq76h007KMJvNpKenk5GRcdZl4faKTkoGk4nKL9vBNO6rwBptx5rgOG3siQB44n1BEfEfBUARkQ5KTU3luuuuY/fu3bz77rutusY5IRlPeSNN+ypO+b7d4cAwvDQ3NnRFqZ3OZg8lPDaOisI8PDUumvPrsH+t9csJJSUlOBwOQkJCWriTiHQnBUARkU4wePBgLr/8ctauXcvGjRvPOT6kbwS2ZCe1nxac8n27MxyAxnbuBPaHmOQUKgvyadxbgdlmxt4v8rQxLpeLo0ePtqtZtYh0PgVAEZFOcv755zNmzBiWLVvGwYMHzzrWZDLhPD+Zxj3lp/QEtH+5PNreZtD+EJOUSl1FFU0HK7EPjMbUwjF3R48exev1KgCKBAgFQBGRTmIymZgxYwb9+/fn5Zdfpri4+KzjHSMTMNkt1H1eePJ7J84B7uhO4O4UnZJKUkQGmE3YB56++cMwDI4cOUJ6erre/xMJEAqAIiKdyGKxcMMNNxAZGXnO9jDmEAvOMYnUrS/EcPs2j/w7AAbPDKAzMpqU+Gw8sQYW5+knkZSXl+PxeBgwYIAfqhORligAioh0stDQUG655Raam5tZuHAhzc3NZxzrnJCMt9FN1YpDQHAGwNp1eTRvrGLbhlUtfv7hhx9y5MgRYmNju7kyETkTBUARkS5woj1MYWEhixcvPmN7GFsfB9Ez+lO7Np/67SVY7XZMZnPQLAE37q+kevlhqmIq2L37o5MNoU+orq5m27ZtjBgxosWdwSLiHwqAIiJdJC0tjWuvvZadO3eyZs2aM45zTkwhbEQ8Fa/uw13a8GUz6MCfAfRUNVG+cA/2AdGEX5hCU10d5fnHTxmzceNGrFYrubm5fqpSRFqiACgi0oWGDBnC1KlT+eijj9i8eXOLY0wmEzHXZWGJDKHsxd2EOSMDPgAaHi9lC/ZgMpuIvXkQSdmDwGQif++ek2MaGhrYuHEjI0aMOOVIOhHxPwVAEZEuNnHiREaPHs2SJUs4dOhQi2PMditxtw7GU97I8LDJuAK8D2DVysO4jtYQOzcHS3gIdoeD+PR+JwOg1+tl8eLFuN1uJk+e7OdqReTrFABFRLqYyWRi5syZZGRk8PLLL1NSUtLiOFuik5hrs0g2ZRBRcnoz5UBRv6WY2o/yiJreH3tG1Mnvp2TlULDPFwDXrVvHF198wTXXXENMzOmtYUTEvxQARUS6gcVi4cYbbyQiIoL58+efPBf36xyjEigKPUZqdQYVb+zDaD732cLdxfAYVK04RPnCL3CMSiB8csopnydn51B2/Ch79/iOxJs8eTKDBg3yU7UicjYKgCIi3SQ0NJS5c+ficrnO2h6mpE8Rey1bqNtYRPGjW085KcRfPNUuSp7cRs1Hx4ma0Z+YG7NP29Wbkp2D12rj9TcW069fPy655BI/VSsi56IAKCLSjWJiYpgzZw4FBQW8+eabGIZx2pgQp4OjjbtJuH8k3gY3RQ9vpmFXmR+q9Wk6WEnRw5twlzbS554RRFyY1mJLl6jEZFzpWRgeN9dddx0Wi8UP1YpIaygAioh0s7S0NK655hp27NjRYnsYuyOcpvo6QlLDSfzOKOyZUZQ9v4uqlYcwPKcHxq5ieA2q3z9GyRPbsSU4SPzuKOz9o844/r333qM51EGK4SIiIqLb6hSRtrP6uwARkd5o6NChlJeX8+677xIZGcnYsWNPfmZ3OE42gjaHWYm7bTC1H+ZR9fYhGnaU4TwvGeeYBMyO049d6wzeJjf1m0uo+zSf5sJ6Ii5OJ3JqP0yWMzdy3rFjB2vXrmVQcgJln36A4fViMmuOQSRQKQCKiPjJ5MmTqa6uZunSpRQVFXHFFVdgtVqxO5w0Nzbg9XgwWyyYTCYiLkrDPiCKmo/yqFp5iOp3DhOW24fwCcmEpHXObFtzUR21nxZQv6kYw+UhdHAc0bMHYs8886yf1+tlzZo1fPTRRwwbNoxxgwby6nvLKcs7Rnx6v06pS0Q6nwKgiIifmEwmZsyYQWJiIitWrCAvL48bb7wRu/PL84Ab6gkL/3e4C0mLIG5ODp4aF3UbCqn7tJD6DUXY0iMIPy8Ze2YUlij7WWfqvsrwGniqm3Adqab200Jch6owh9sIn5SCc3wy1mj7Wa+vra3ltdde4/Dhw0yZMoWJEyfidjVhMpnJ37tHAVAkgCkAioj4kclkYuzYsSQnJ/PKK6/w6KOPMnn0SABc9XWnBMATLBEhRF7Sl4iL0mncU07tpwVUvLrX96EZLNGhWGN9X5YY3z8xg6e8EfeXX57yRtyVTfDlO4Uh/aOInZtD2JA4TNZzL90eOXKEV199Fa/Xy+23307//v199wkNI75vPwr27WHEZVd0zi+SiHQ6BUARkQCQmprKN7/5Td544w1Wr/uUkD4pNNTWEpVw5mtMZhNhQ+IIGxKHu6oJd3H9KQHPlVeLe1spRqPbN95u8YXC2FBCh8SdDInWPg5fSGwFwzD45JNPWLVqFX379uX6668/bcNHSnYOx3Zub/evhYh0PQVAEZEA4XA4mDNnDqtWrOCTzz/nrZVvM/f2JMLDw895rTXKjjWq5SVbb30zhgFmh7XF9i2t1djYyOLFi9mzZw+TJk3i0ksvbbHVS3JWDltXraChtqbFGUwR8T8FQBGRAGI2m7lg8iS2vPoi5XY7jzzyCOPGjWP06NFERrbveLiO7hauq6tj8+bNfP755zQ1NXHzzTeTk5NzxvEp2b7PCvd9Qf9RY884TkT8RwFQRCTAhDicWOtruGjsSAqaPKxdu5YPPviAwYMHM27cODIyMjo0k9cahmFw/Phx1q9fz86dOwEYNmwYF110EbGxsWe9NjophbCISPL37VEAFAlQCoAiIgHGarNhsdkwezzMmjWLqVOnsm3bNtavX89zzz1HfHw848aNIzc3l9DQ1r2711oul4vt27ezfv16CgsLiYmJ4dJLL2XUqFE4HI5W3cNkMpGcNYj8vXs6tTYR6TwKgCIiAcjucNJUXwv4zhAeP34848aN48iRI6xfv563336b1atXM3z4cLKysoiJiSEmJga7/eytW77O5XJRWVlJRUUFBw8eZMuWLTQ1NZGdnc1ll13GgAEDMLejoXNK9mA+f3MRXq8Hs1lHwokEGgVAEZEA5AuA9ad8z2QykZGRQUZGBjU1NWzatIkNGzawadOmk2McDsfJMBgTE0N0dDQxMTGYzWYqKipO+6qrqzvl2nHjxjFmzBhiYmI6VH9Kdg6uhgbKjh+jT9+MDt1LRDqfAqCISACyOxy46uvO+HlERAQXXXQRF154IbW1tSdn8b76dfToUaqrq0+7LiYmhri4OAYMGHBKWAwPD2/XbF9LkgZkYzKbKdi7RwFQJAApAIqIBCC7M5ymujMHwBNMJhMRERFERESQnp5+2udut5vKykoMwyA6OhqbrWvOD/46W2goffr2J3/vHkZMmdYtzxSR1lMAFBEJQPYwB00N9eceeA5Wq5X4+PhOqKjtkrNzOLpjq1+eLSJn1zlz/SIi0qlCHE6azrIEHAxSsnOoyD9OQ22Nv0sRka9RABQRCUB2p7NVS8CBLCXL1xC6YJ/awYgEGgVAEZEAZHc4gn4GMCoxibDIKPK/UAAUCTQKgCIiAcjucOKq7/g7gP5kMplIyc6hYN9uf5ciIl+jACgiEoDsDifuZhfu5mZ/l9IhyVk5FOzfh9fr8XcpIvIVCoAiIgHI7nACnLUXYDBIyc6hubGB0qNH/F2KiHyFAqCISACyf3nubrC/B5iUmeVrCK2NICIBRQFQRCQAhXw5AxjsO4FtoaEkZGSSv1cBUCSQKACKiASgUOeXATDIN4LAl+8BagZQJKAoAIqIBKCTM4ANwT0DCF82hC7Ip766yt+liMiXFABFRAKQPaxnvAMIvgAIULDvCz9XIiInKACKiAQgs8WCLTQs6N8BBIjsk4gjKlrLwCIBRAFQRCRA+U4DCf53AE80hNZGEJHAoQAoIhKgfKeBBP8MIPg2ghTu34vXo4bQIoFAAVBEJEDZHc4e8Q4gfNkQuqmRkqOH/V2KiKAAKCISsHxLwD0jACYOyMJssVCgZWCRgKAAKCISoEIczh7xDiCALcROn36Z5GsjiEhAUAAUEQlQdoejR+wCPiElO0czgCIBQgFQRCRA2Z3hPaIR9AnJ2TlUFhVQX1Xp71JEej0FQBGRAGUP6xltYE5IzR4MQL4aQov4nQKgiEiAsjucNNXVYhiGv0vpFBHxfXDGxFKwd7e/SxHp9RQARUQClN3pxPB6cTc1+buUTmEymUjJytFGEJEAoAAoIhKg7A4n0DPOAz4hOTuHwgP71BBaxM8UAEVEAlSIwwHQo94DTMnKwd3URMmRQ/4uRaRXUwAUEQlQoSdnAGv9XEnnScwciNli1TKwiJ8pAIqIBKiQkwGw58wAWkNCSOifSf4X2ggi4k8KgCIiAaonvgMIvmXgAs0AiviVAqCISIAKCQ3FZDL3qNNAwLcRpKq4iLrKCn+XItJrKQCKiAQok9lMiCOs580AZucA6D1AET9SABQRCWB2hxNXQ895BxAgIq4P4TGxOhdYxI8UAEVEApg9zEFjD1sCNplMJGfnkK8AKOI3CoAiIgHM7gzH1cOWgAFSsgdTdHA/Hrfb36WI9EoKgCIiASzE4ehx7wAC9Bsxiv6jxlJXUe7vUkR6JQVAEZEAZnc4e2QAjE1NJy41jariIn+XItIrKQCKiAQwXwDsWZtAACwWC5hMlB477O9SRHolBUARkQDWU2cAAWKSUqgoyPd3GSK9kgKgiEgAszscPa4R9AnRSSk0VFfRWNtzzjoWCRYKgCIiAczudOJqbMDwev1dSqeLSU4BoKKw5VlAwzBwu1zdWZJIr2H1dwEiInJmdocTDANXY8PJs4F7irCISOwREVQU5pM8MBvwnXtcUZBPydFDFB86SEN1FVd+/0E/VyrS8ygAiogEMHuYA/AFo54WABtra7GF2Kn8ynuAR3dsZd0rL+FudhEeG8fx3TuZmH8LsSlpfqxUpOfRErCISAAL+TL09cT3AFf8889UFORRWZiH1+NrCJ2YORDDMBh/1Q3c9Ms/kDwwm8NbNvq5UpGeRwFQRCSA2Z1fBsAeuBPYbLHgiIzC8BpUlRQDEBmfQNKALCqLCgBfIDyyY6s/yxTpkRQARUQC2Ill357YCzAlezAVhQWYLZaTy8AedzONdbW4GhoA6DdiNEcVAEU6nQKgiEgAszv+/Q5gT5M5Zjx1FeU01FRTUZhPU309uz96n6PbtzLkwksASB8ynDEzrsLr9fi3WJEeRptAREQCmDXEjtli7ZEBMC41ndHTZ7PmuSfI27ubvZ9+THVpCbmXzyAhIxPwBeDJN9/u50pFeh4FQBGRAGYymbA7HLh64BIwwMgrZuJxN7NjzSr69Mtk5OUzGTTxQkwm08kxhmGc8r9FpOMUAEVEApzd4aSxrueeljH0osso2PcFQy68hOSBg077XOFPpPPpHUARkQBndzp77AwgQGh4BGGRUToXWKQbKQCKiAQ4u8PRI98B/KqY5JRTGkKLSNdSABQRCXAhYc6eHwCTUqgqLsTjdvu7FJFeQQFQRCTA2Z09PwBGJ6fg9XioLinydykivYICoIhIgLM7nD2yEfRXRfVJIDQyktrKCn+XItIrKACKiAQ4XxuYnj0DaLZYiUlKpezoEX+XItIrKACKiAQ4u8NJYw8PgAD11ZV89uYif5ch0isoAIqIBDi7w4m7qanHb5BI7D+A2rJSaspK/V2KSI+nACgiEuDsDicAroae/R5gSvZgAPL37vFzJSI9nwKgiEiAC3E4AGiq69nLwM7oGCL7JFKwb7e/SxHp8RQARUQCXKgzHKDHt4IBSMnO0QygSDdQABQRCXAnZwB7eCsYgOSsHIoPHcDd3OzvUkR6NAVAEZEAd+IdwKb6Wj9X0vVSsnPwuN0UH9rv71JEejQFQBGRAGfvRTOAffr1xxpiJ/8LvQco0pUUAEVEApzFasMaYu/xzaABLFYriZkDyd+n9wBFupICoIhIELA7HDT28F3AJ6Rk51Cwdw+GYfi7FJEeSwFQRCQI2B1OXA29IwAmZ+dQW1FOTVmJv0sR6bEUAEVEgoDd4ewV7wACpGTlAGoILdKVFABFRIJAiMPR4xtBn+CMjiEqIZECBUCRLqMAKCISBHwzgL0jAILvWDhtBBHpOgqAIiJBwO7sPUvA4HsPsPjQQdwul79LEemRFABFRIJAb9oEAr73AL0eN0UH1RBapCsoAIqIBAF7WO9pAwNfNoS227UMLNJFFABFRIKA3ensFY2gTzBbLCQNyNJGEJEuogAoIhIE7A4nHre7V70Tl5KVQ/7e3WoILdIFFABFRIJAiMMJ0Kt2AidnD6ausoLqkmJ/lyLS4ygAiogEgdBeGABTsgYB6D1AkS6gACgiEgRCHA6gdwVAR1Q00YnJeg9QpAsoAIqIBAH7iRnAXrQTGHz9AHUknEjnUwAUEQkCdueJJeDe0wwafBtBSo4cpNnV5O9SRHoUBUARkSAQEhYG9K4lYPDNAHo9HooO7PN3KSI9igKgiEgQMJsthISF9boA2KdvBjZ7qJaBRTqZAqCISJAIcfSuZtDwZUPogdkUaCewSKdSABQRCRKhDmevewcQIOXLjSBqCC3SeRQARUSCRIjDSVNdrb/L6HbJWTnUV1VSXVLk71JEegwFQBGRIGF3OGhq6H0zgMknGkLrPUCRTqMAKCISJOwOZ6/bBALgiIwiJjlFAVCkEykAiogECXsvfQcQfMvA+Xt3+7sMkR5DAVBEJEjYHY5edxLICSnZOZQcOURzY6O/SxHpERQARUSChN0Z3uvawJyQnJWD4fVSeFANoUU6gwKgiEiQsDscNNXX98p2KPF9+2ELDdN7gCKdRAFQRCRIhDicGIaX5sYGf5fS7cxmC8kDs9QQWqSTKACKiAQJu8MB0Is3ggxWQ2iRTqIAKCISJOyOcIBe2QoGIGVQDg3VVVQVFfq7FJGgpwAoIhIkTs4A9tKdwMlZOQDkaxlYpMMUAEVEgoTd4QSgqaF3BsCw8AhiUtK0EUSkEygAiogECbvzywDYS98BBEjJyqFAAVCkwxQARUSChM0eisls7rVLwPBlQ+ijaggt0lEKgCIiQcJkMmEPc/TaTSAAydlfNoQ+sNffpYgENQVAEZEgYnc6e+1pIABxaemEhKkhtEhHKQCKiASREIezV78DaDZbSBo4iPy9u/1dikhQUwAUEQkivuPgeu8MIPjeAyzY94UaQot0gAKgiEgQsTvCFQCzcmioqaayMN/fpYgELQVAEZEg4psB7L1LwPCVhtB6D1Ck3RQARUSCiN3hpKmu1t9l+FVoeDixKWkU6EQQkXZTABQRCSJ2h4Omht49AwiQMmiwZgBFOkABUEQkiNgdvbsNzAnJWTmUHj2CS2FYpF0UAEVEgkiIw4mroQGv1+PvUvwqJTsHw/BSeGCfv0sRCUoKgCIiQcTu8J0H7Kpv8HMl/hWXmo7d4dQysEg7KQCKiAQRu9MXAHt7KxiT2UzSwGxtBBFpJwVAEZEgYnc4AAVA8C0D5+/do4bQIu2gACgiEkROLAEHTAD0eqDyGBxZB0W7wNV9daVk5dBYW0NFQV63PVOkp7D6uwAREWm9fwfAbt792lgNB9+HikNQcfjfX5XHwNt86lhnH4jJ8H1F9/P9M20sJAzu1JKSsgYBvobQsSlpnXpvkZ5OAVBEJIicDIDd1Qy6aBesfxK2vQyuWgiJ+DLc9YNBM/4d9KLSoLHqK+HwiO+fh9dCzZdHtvWdCOPugsGzwRrS4dJCneHEpfWlYO8ehl08pcP3E+lNFABFRIKINSQEi9XatTOAbhfsWQLrn4IjayE8ESY8AKNuhei+YDKd+dq+E07/XnMD7F3pu99rd/lmCEd/A8bMg+j0DpWanJVDvjaCiLSZAqCISJCxO8O7phm0qx7W/g02PAN1xdBvMlz/DORc2bEZO1sYDL3G91W8BzY8BZ89Bh//BbKnw0U/gZSR7bp1SnYOO95fRVN9/ckNMiJybtoEIiISZLrkOLiyA/DUVFj7MAy5Ch74FO5YBsOu7ZTl2pMScmDGH+GHe2Dmn6Fsv++565+EduzmTckeDIZB4f69nVejSC+gACgiEmRCwpyd+w7grjfhsYt8S7X3vAsz/9TpGzZOYw+HsXfCfR/5loKX/RBevxea2vZzxaakYnc6yd+3u2vqFOmhFABFRIKM3eHonHcAPc2w8mfwyu0w8FK4931IHNrm2zS4POwvrqWkpqntPfmsdt+M4HVPwZ5l8ORlUPJFqy83mc0kZ+VQoBNBRNpE7wCKiAQZu9PZ8T6A1fmw6A7I2wDT/gDn3Xf2zR2AYRhsOlrJB3tLOFZez9Evv0pqmk6OCbNZ6BvrID02jPRYB0OSI5kxPBmn/Rz/uRl+PSQNh5dvg8cvgdkP+77XCilZOWxa/iaG14vJrHkNkdZQABQRCTJ2h5OastL236DsADw9DcxWuGMFpI8/6/B6l5s3t+TzwidH2FVQTZwzhMw+TvrFOpg8MJ6+sQ5SosOobmw+GQyPldfz4d4Snlt3mF8v2cV1Y9K4dUJfBiZEnPlBfQbBPe/B0u/7dgvXFsP5D5zzx0nOzqFx0UuUF+QRl9qxXcUivYUCoIhIkOnQEnBdKbx0PYRG+sJfeMIZhx6vqOfJjw7x2sbj1LncXJqTyIPTc7hgYDxm89lnC0/Iq2xgwWdHWbj+KM+uO8z5mXHMm5TB5UMSMbU042gPh2uf8LWeeechSBkF/c4/6zOSBw4Ck4mCvXsUAEVaSQFQRCTI2B3tbAPjqocFN0NTDdy16qzh7+2dhfxo0VZCLGZun9iPOeP7khbT9jYrqdFh/OiKQXz3sixW7izkhU8O880XNnLt6FR+d/VwwkIsp19kMsGU/wf5m2HRPN9GkbPUanc4iE/rS/7e3Qy7ZGqbaxTpjfSyhIhIkLE7HDTVtTEAej3w2t1QtBPmvgyx/Vsc5vZ4+f3y3XzzhY1MGhDPmh9fzI+vyGlX+PuqEKuZ2bkpLLpvIn+7aSQrthdyzb/WcrDkDLt+LVa4/mkwvPDqnb76zyI5O4d8bQQRaTUFQBGRIBPicOJuduFxN597MPj66638T9i7wtfYOXVMi8OKqxuZ++RnPPnxIR6aOZhHbh1NZKitEyv3uXpUKm9+exIuj5fZ/1jL8u0FLQ+MSPKFwCNrYc3vznrPlKwcyvKOdXxzjEgvoQAoIhJk7M4vzwNu7XuA6/4Onz/ua7w8aFqLQwqrGpn1j485XFrHwnsncPcFmS2/o9dJshMjeOvbk7loUB8eeGkTz6073PLA/hfApf8FH/0Z9r59xvslZ+eAYVCwr/UtZER6MwVAEZEgYw/7MgC2phn09ldh1X/BBT/0NV5uQbPHy3cWbMKEiaXfmcy4jNjOLPeMwu1W/jFnFHdMyuA3S3ex8UhFywMnfd93ZNzi+33NqlsQm5xKqDNcy8AiraQAKCISZE6ceXvOGcDDH/tC04ibfLNoZ/C/K/ew+Wgl/7xlFAmRoZ1Z6jmZTCZ+NmMwkwbE8dPXt1Fe23T6ILMZpv/B9x7g7qUt38dspu/wXEqPHe7agkV6CAVAEZEgY3eGA5z9fbfiPbBwLvSdALP/ccYmzyt3FPDER4f46YzBjOnXPTN/X2ezmPn73NHMHJ7MO7uK8HpbOE0kJgMu/w3Ul5/xPiOmTCc+rS+G19t1xYr0EAqAIiJB5sQMoOtMM4DVBb5ef5GpcNOLYA1pcdjRsnp+vGgb04clceekjC6qtnUiw2zMGJ7E5mOVvLu7qOVByaOgaDtUHG7xY2d0LDXlZdRWnmEpWUROUgAUEQkyIWG+ANhY38I7gE01MP8G33LpLYsgNOqM93nkgwOEhVj43+tHdOmGj9bKSoxk4oA4Vu4spN7lPn1A4lCwhcH+91q8PjoxEbermYqCvC6uVCT4KQCKiAQZi9WK1W4/fQbQ0wyvfAMqjvjCX1TaGe9R3djM4s15zD2vLxFd0OqlvS7O7oPHa/D5wRaWes1myLwYjn8OTaeHX2uInYi4OKoKz9BWRkROUgAUEQlCoQ7nqe8AGobvDN1DH8BNL0DSsLNe/8amPFweL3PG9+3aQtsoyhHCiPRoPj5QimG08C5gxgXg9fp6A7bAGRNLXZWWgEXORQFQRCQIhTicp54G8sH/wOYX4ap/+mbJzsIwDF749AhXDE0ksYO7ft1uN7t27WLFihV88sknlJefeZNGa10wMJ7i6ib2FdWc/mFoJKSMhOMbWrzWERVNfVVVh2sQ6ekUAEVEgpDd4fh3G5jNL8L7v4dLH4Lcm8957acHy9lfXMttEzLa/fz6+np++ctfkp6eztChQ5kxYwYTJ04kISGBq6++mo0bN7b73gMTwkmKtPPR/tKWB0SlQ11xix85IqNoqKnB6zn70XEivZ0CoIhIELI7w31LwPvfhSXfg9HfgAt+1Kpr13xRTEpUKBMy29f25eDBg0yYMIFf//rXFBYWnvKZx+PhzTffZOLEiTz22GOtup/JZDrly2w28/Mrh3L3BQMwmUzMmzfv5LjFixdDeB/fZheXLwDPmzePq6++GvDNAGJ4aahtYfZQRE5SABQRCUL2MAdNlUXwyu0w4FKY+Zcz9vr7uqNl9QxICG/Xzt+6ujpmzZrF9u3bzzrO5XJx//33s2TJknPes6Cg4OTX3/72NyIjI9l78Ch3PfIOi9dt5//+7/9OvcDZx/fP+tNnCB2Rvl3P9VWVrfp5RHorBUARkSBktxo05e2CuIFw/TNgsbb62qPl9fSNdbTrub/5zW/YtWtXq8YahsGdd95JQ0PLx7edkJSUdPIrKioKk8lEVv90Rg3qz5H6EKKivtbK5kQArDs9AIZFRILJTIPeAxQ5KwVAEZFg01CB/dA7uDwWmPsK2MNbfalhGBwrrye9HQGwqamJJ598sk3XlJaW8vLLL7f5WQCj+8awv6SOynrXqR/YI8Bih7qS064xWyyERURQV13ZrmeK9BYKgCIiwcTdBAtvxe6pptEaDRGJbbq8qqGZmiZ3u2YAt23bRllZWZuvW7NmTZuvAchNj8JiMrH5aOXJ782ZM4fwiAjC5z5F+MirCA8P56WXXjrlurDIKOqrNQMocjYKgCIiwcLrhcX3w/H12M/7Bq7GxpZ75Z3F8QrfcmxqdFibH3/kyJE2XwNw+PDhdl3nCLEyJDmSjUf/3dfvr3/9K1u2bGHLsz9hy9M/YsuWLcyePfvU6yKjaFAAFDkrBUARkWDx7q9gx+tw7eOEDxyH1+Ohro3n3obbfe8K1ja1cNTaOURGRrb5GuD0d/jaYHS/GI6W1VNa0wT43hccOHAgAxPDGdg/nYEDBxIREXHKNW5XE9YQe7ufKdIbKACKiASDz5+Atf8HV/wOhl5NVGISAFVFhee48FQp0WGYTXCsvP7cg78mJyenzdd05DqAYamR2CxmNh39WtCtK/33ZpCvqa+qwhnZ/tAp0hsoAIqIBLo9y2DFT2DCA3D+twCISvC9+1dVUtSmW4VYzSRHhXG0HQGwb9++XHjhhW2+7rbbbmvzNSfYrRaGp0WdGgDdLmioaDEAGoZBXVUlYVHR7X6mSG+gACgiEsiOb4BX74KcK+Hy3538dkhoGGGRUW2eAQRIj21fAAT4xS9+gdnc+v90XHvttQwfPrxdzzphTN8Y8isb//2N+jLAaDEANjc14nE1newHKCItUwAUEQlUZQdg/o2QPAKufRy+FryiE5KoKm57AOwb6+BYxdl7853JZZddxv/7f/+vVWOzs7N5+umn23T/efPmUVlZecr3cpIjCLOZWbo1z3fix4n+f18GwGeffdZ3Qgj/bgDt6MB7hyK9gQKgiEggqiuFF6+DsBiYsxBsp+/ajUpMoqq4bUvAANmJEXxRWE1dOzaCADz00EM888wzOJ3OM4656qqr+Oyzzzq0AeQEm8VMbno0G49U+HY915WAyer7tfma+i8bQDu0BCxyVgqAIiKBxlUP828CVy3c+ho4Wj6zNyohicp2zABeMTSJxmYvq3e3PTyeMG/ePI4fP87f/vY3rr32WsaMGcNll13Gd77zHTZs2MDixYuJjo5u9/2/bnTfGEprXb7NK3Ul4Iw7bUYUoL66Cqvdjs0e2mnPFumJWn92kIiIdD2vB167G4p3wbxlEJNxxqFRCYnUlpfhbm7GarO1+hHpsQ5G941mydZ8rhqZ2u5So6Oj+d73vsf3vve9dt+jtbISw4kItbLxcDl9S7ad8deloaqSsMiodp1zLNKbaAZQRCRQGAaseBD2roAbnoXU0WcdHpWQBIbRro0gs3JT+GBvyenHrAUoi9nMyPRoKo7ugNoCyLy4xXF1VRU4tfwrck4KgCIigWLdw7D+CZj5F8i+4pzDEzMHYrXb+eKTj9r8qJkjkvF4Dd7e2fbw6C9j+sUwqnkzTc5kiM8+7fPG2lrK844Tm5Lmh+pEgosCoIhIINj+Kqz6BVzwQxh7R6susTscDJl8CdvfXYnH3bYNHQkRoUzIjOOtrfntqdYvMhwuRpgPssM+GlpY4j26cxsms4XUwcP8UJ1IcFEAFBHxt8Mf+874HXETXPpfbbo09/IZ1FaUc2DjZ21+7OzcFD45UEZxTeO5BwcA89FP8YZEsao2E7fXe8pnXq+X/L276Tt0OCGh2gAici4KgCIi/lS8BxbOhb7nw+x/tDizdTYJGZmkZA9m6zvL2vzoacOSsJhNLN9W0OZru53XDcU7cfW7AMMaytGyUxtZlx0/itlkou/wUX4qUCS4KACKiPhLdQG8dD1EpsFNL4A1pF23GXnFTI7u2EbZ8WNtui7aEcKFWX2CYxl415vwyT9wJGXzyYEynv748Ckfr33lRYqOHCIiLs4/9YkEGQVAERF/aKqB+Tf42r7csghC298wOeu8SYRFRrF19fI2XzsrN4VNRyt9/fUCVflBWPIDyLwIU+IQLs5J4M0teTQ2e3wf5x/nwPpPGXrhpX4uVCR4KACKiHQ3TzO8cjtUHIFbX4Wo9vfiA7DabAy/9HJ2vv8uzY1te59v6pBEQm1mlgbqMnBzg+/XyhELs/8OwOzcZGqa3Lz/RQkAW1etICwikuzzJvmzUpGgogAoItKdDAOWfB8OfeRb9k0c2im3zZ0yHVdjA7vXvt+m65x2K5flJLIkUJeBl/8YSvf5fq2+nCUdmBDB4ORIlmzLp7mpkZ0frGbYpZdjDWnfErpIb6QAKCLSnT74H9jyIlz1jzM2M26PyD4JDBhzHp++/jINNdVtunZWbgq7CqrZX1zbafV0is0vwuYXYOafIWn4KR/Nzk3h3d1FfLRoAa6GBnKnTPNTkSLBSQFQRKS7bH4R3v+9r9VL7s2dfvtL77iX5sZGVvzzLxhfa5NyNhcP6kOE3RpYs4CF22HZD2HUrb6vr7lyRDJJVQfZvOQ1Jt98u+9UFBFpNQVAEZHusH81vPVdGDPP1+y5C0TGJzDjOz/i0JaNfLZ4UauvC7VZuHxoEku25mMYRpfU1iaVx+DlWyEuC2b8qcUhke5qppetoSZhEONmX9fNBYoEPwVAEZGuVrAVXvkGDJwCM/7c5l5/bdF/5BgmXHsz6155iSPbt7T6ulm5yRwsrWNnftuWjztd2QF4ZjoYXrj5RbCFnTbE7XLx1l9+T4jDySLnZKoamv1QqEhwUwAUEelKlUfhpRshbiBc/zRYrF3+yPOvv5n0YSNY9vAfqSkvbdU1kwbGE+sM8e8ycNEuX/izhsIdKyEmo8Vha557nLLjR5n2vQdpMIWwckfwnGcsEigUAEVEukpDBbx4PVjtvl5/9vBueazZbGHmd3+MxWpl6d/+t1XnBNssZqYPS2LptgK8Xj8sA+dvhmdngDMB7lhxxtY4uz58j22rV3LpHd9k8LAhnD8guM4zFgkUCoAiIl3B3QQLb4W6Yrj1NQhP6NbHOyKjuPL7/0nh/i/4aP6zrbpmdm4KeZUNbDpa0bXFfd2RT+C52RA7AOYtgfA+LQ4rPXqYVU/8k6EXXcbwS68AYNaIFD45WEZxdXCcZywSKBQARUQ6m9cLi++H4+thzkKIz/JLGamDBnPhLXeycdliNix5/ZwbPMZlxJIUGdq9y8AH3oMXroHkXLh9MYTFtDis7PhRFv/pt0QnJXPZXfdj+vI9ymnDkrCaTSzbHqCNrEUClAKgiEhnW/1L2PE6XPcE9J3g11JGz5jNuNnX8cGLT7Pkr7+nqf7MR76ZzSauHJHMsu0FuD2tbyPTbnuWwfyboP8FXy6RR7Q4bPfH7/Piz36A1RbCVT96CJs99ORnJ84zDqgWNiJBQAFQRKQzff4ErHsYrvhvGHKVv6vBZDJx4S13MPuHP+PIti289LPvU3Lk0BnHzx6ZQmmti08PlndtYdsWwcu3waAZcNNLLe/2bW5m9VOPsPzvfyJr/ERu+d1fiE48vd/f7JFBcJ6xSIBRABQR6Sx7lsGKn8CEB+D8B/xdzSmyxk/k1j/8DWuInfkP/YidH7zb4rjhqVH0i3Pw1ta8ritmwzPw+j2+ZtjXPQXW049wqy4p5uVf/oQd773NlLu/xfRv/Qe20NAWbgZTBgf4ecYiAUgBUESkMxxbD6/eBYNnweW/83c1LYpJSmHOb//EoIkXsPJff+Wdxx7G7XKdMsZkMjE7N4WVOwppcns6v4h1/4Cl34fx98Dsf7TYFufg5vW88OB3qa+u4uZf/5HcqdNPvvPXEqfdymWDE7UbWKQNFABFRDqq7AAsuMm3keGax8EcuH+02kLsTLv/+1x+33fZ/dH7LPivH1Oef/yUMbNyU6hudPPh3tb1EGwVw4D3/wfe+TlM/gFM/9/Tfp3cLhcfL3yBN/7w/0gZNJhb//B/JA1o3QaaWSNS2F1Qzf7ims6rWaQHMxkBce6PiEiQaqiExy8BswXuegccsf6uqNWKDx9kyV9+T2VxIf1HjmHk5TPJGDkas9nCtL99SHZiBA/PGdXxBxkGrPovWPd3uOwXpx2FV1VcyNbVK9nx3js01tYy6aZbGX/V9ZjaEKQbmz2M++1q7pjcn/+Ymt3xmkV6OAVAEZH2cjfBlgVwYDVc/tsznlwRyJqbGtmz7kO2vL2M4kMHiOyTSO7U6XxuG8g/Pylk439NwRHSgdNLvF5Y/kPY8LRv1u+8b375bQ+Ht25i6zvLObh5A/YwB0MvnkLu1OnEpqS161E/fGUrm49W8O4PLzrrkrGIKACKiLSP1wuf/hMqj8Pk70Nksr8r6hDDMCg8sJet7yxnz7oPMQyDXaGZTL3uWm6YPql9gcrjhjcfgO2LYNbDMPo26qur2LFmFdtWr6CquIiEjAGMvGImORMvPOMmj9b6YG8J33j6c5Z+ZzLDUqM6dC+Rnk4BUESkrQwDtrwEB9+Hid/xvfvXgzTUVLPj/dW8/errhDVW0qdvBokDsohKSCIqMYnohCSiEhIJi4w6YzBsrq+m+sV7qdq3nsrBd1BtTaSiIJ8j27cAMOj8Cxh5+UySBmZ32mxds8fLef/9LjeMSeOnMwZ3yj1FeioFQBGRtvpihW9Wa/TtkHmxv6vpMk9+dIAFr63iOxnV1BUXUFlcRGNN9cnPbfZQohKTiEpIIrJPH5pqa6ksLqKqqIC6yn8fJ2exWon8MjT2HTqCoRdPwRHZNTN0Dy3eznu7i/n4wUsxm7UMLHImHXixQ0SkB8vfDI54iE4/9ftHP/OFv5wre3T4A5iVm8rvlqfjmjyCW8b6fh2a6uupKi788qvI98+iQo5u34rdGU50XCx93TuIDj9K1IyfEjXyCsJjYtu0oaNDNY9I4cVPj7LpaAVjM4JnQ45Id1MAFBH5uh2v+Xr6jbwFLn4Qovv6vl+yBzY8BX0nwtBr/FtjN0iMDOW8/rEs2ZrPjV8GQLvDQUJGJgkZmadfUF8OL14LEQfh/lchfXw3V/zv84zf2pqvAChyFoHbrEpExB9K98MHf4ThN8DuJbD0B1C8B6ryfE2M47NhzDzoJbtMZ+WmsO5AGaW1TWcfWFMEz86EyqMwb4lfwh/8+zzj5d11nrFIkFIAFBE5wTCg/AAkDvW1dbnvQyja6Tu27O2fQWiM75i3Fk6v6KmmD0vGBKzYfpZj1iqPwTPToKEC7ljh900xJ84z/uRgmV/rEAlkCoAiIieYTJBxAUy4HyISfX397v0AqvPhwLuQNgYsNt/Y0n2+E0B6uFhnCJOz4s98zFrZAXhmOng9vvDXZ1D3FtiCE+cZL9HRcCJnpAAoIvJVIQ5IG+v7v5vrYcuLMGg6JA33nWG7Z5nv3N+Xroejn/i11O4yOzeF9YcryK9sOPWDol3w9DSwhcGdKyG2v38K/JoT5xmv6KrzjEV6AAVAEZGWGAZsWQhFu33Lvnes8O38feOb8PxV0GcwjLrV31V2i6lDErFbzSzd9pUZtbxN8OwM30zpvOUQmeK/AlswOzeFms4+z1ikB1EAFBFpya634MjHMO6ufy9rXvUP32xXxmSYu9C/9XWjiFAbl+YksGTrl+8BHlkHz82GuIHwjaUQ3se/BbYgKzGCnKSIMy9di/RyCoAiIl936CPY/SYMvRb6nQ9mCzQ3woK5YLLAnAX+rrDbzcpNYXteFYWblsEL10LqKLhtMYRF+7u0M5qVm8LqXUXUu9z+LkUk4CgAioh8VeF22PQc9L8Ycmb++/smE/SbCPev8wXCXubSnARmh2wkfsk3oP+FMHcR2MP9XdZZzRqRQkOzh9W7i/1dikjAUQAUETmhKg8+fcS34WPULaf2+rPa4fxv+d5564VCd73KX81/4yPLeIybXgBbqL9LOqe+cQ5Gpkfz1hYtA4t8nQKgiAhATQFseQlSRsJ532x5lq+XNH8+zYan4Y1vUtz/au6qvZ/dxedoCh1AZuWm8MHeYqrqm/1dikhAUQAUEWmogOeugkMfwoibwRr4s1vdZt3ffaehjL+X+FseJ9JhZ8m24JlRu3JEMm6vwds7C/1dikhAUQAUkd7N3QQLb4G6YrjyrxAa6e+KAoNhwJrfwzsPwQU/hOn/g81qZfqwZJZszccwDH9X2ConzzMOotAq0h0UAEWk9/J64Y374PgGmLMQ4rP8XVFgMAxf8PvgD3DZL3xfXy5/z85N4XhFA5uPVfq3xjaYnZvK2v2llNQEz9K1SFdTABSR3mv1L2HnG3DdE9B3gr+rCQxej+/Ek0/+AdP/6Jv9+4rx/WNJiLAH1caK6cOSMJtMrNhxlvOMRXoZBUAR6Z0+exzWPQzTfg9DrvJ3NYHB4/bNiG56Hq76F5x372lDLGYTV45IYdn2Ajze4FgGjnGGcEFWfFCFVpGupgAoIr3P7qWw4icw4Vsw4X5/VxMY3E2w6Buw83W4/mlfG5wzmJWbTElNE58dLOvGAjtmVm4K+ZUNWgYW+ZICoIj0LsfWw2t3wZDZcPlv/V1NYHDVwfybYN8quHk+DL3mrMNHpkeTHhsWVBsrpg9L4oax6Rwtq/N3KSIBQQFQRHqPsgOw4CZIGQXXPA5m/RFIYxW8eB0c+xxufRWyrzjnJSaTiVkjUli+vRCX29sNRXZcWIiVuHAb7+7RqSAioAAoIr1FbYkv6ITF+ma5guAkiy5XXw7PzYbiXXD7m74j3lpp9sgUqhqa+Xh/SRcW2LlGpEZzvKKBoupGf5ci4ncKgCLS87nqfTN/rjrfLJcj1t8V+V9NITwzA6qOwzeWQvq4Nl0+KDGCrITwoNpYMSQlCrvVzKYjFf4uRcTvFABFpGfzenzv/BXvhrkvQ0yGvyvyv8qj8Mx03/LvHSsgeUSbb2EymZidm8KqXUU0uDxdUGTnC7GaGZEWxcajFUHTyFqkqygAikjPZRi+3b5734YbnoPU0f6uyP9K98PT033B+M4V0Ce73bealZtCncvDe0H0Xt3ovjEUVzeRV9ng71JE/Mrq7wJERLrM2v+D9U/CrP+D7Mv9XY3/Fe2E56+GsBi4fTFEpnTodhnxTkakRbFkaz4zRyR3SoldbVByBI4QCxuPVJAW46Cx2cMnB8t4Z2cRJTWNDE2J4pKcBIanRmExm/xdrkiXUQAUkZ5p+6u+kz4u+BGMmefvavzv+EZ48VqI7gu3vQHO+E657awRKfzxnS+obmwmMtTWKffsShaTiZHp0Xy0t5Rtxyp5bXMejc0ehqVEMTAhnDc25/HOriLumJTBjWPT/V2uSJdRABSRnufQR7D4fsidA5c+5O9q/O/wWph/IyQMgVsWQVh0p936ytxkfrd8N6t2FnHdmLROu29XMZlM9Itz8NdVewkLsfDAxQOYOiSJxEg7IRYzXgNe+PQID7+7TwFQejS9AygiPUvxblh4C/SbCLMeBlMvX8bbt9rX/iZ1tG/mrxPDH0ByVBjjM2J5a2vw7Ab+YG8JVQ3NXDkimTsn9ad/vBNHiBWrxUyI1cz5mXE0uDzk6z1B6cEUAEWk56gugBevh6g0uPF5sIb4uyL/2vUWLLgZMi+GuYvAHt4lj5k1MoWP95dSXufqkvt3psZmD+sPV3DVyFQq6psxt/AXhFc2HCMnOYKIUC2SSc+lACgiPUNjNbx0A2D4ljlDo/xdkX9tXQiL5sHgWXDTC13a+HrGsCQAlm8v6LJndJZQm4UmtweH3UJNo5v9xTUAVNa7+OxgGQ8t3s6SrfnMGpFCRBC80yjSXiZDzZBEJNh5mn3hL28j3LkSEof6uyL/Wv8ULPsPGHWrbxncbOnyR97+9Oc0NXt4+Zvnd/mzOmrRhmP8c81+wkIsDEqMICc5koMltewqqMZsMnHd6DS+MTHD32WKdCnNb4tIcDMMWPI9OPwx3Pqawt/a/4NVv4Dz7oMrft9t5x3PGpHMT17bRmFVI0lRgX3M3rWj04gItfKrt3by/t4SDpXWERZiYcbwZGaNSCE91uHvEkW6nGYARSS4rfk9fPAHuPYJGHGjv6vxH8OA938PH/yPr/XNpQ916waY6sZmxv5mNT+ZNoi7L8jstud2RF5lPb96cxdzzkvn0pxEf5cj0q30DqCIBK9Nz/vC32W/UPh7++e+8DflV3DZf3X77ufIUBsXD+rDkiDaDZwa7aB/HwfHyrXbV3ofBUARCU77VsOS78OYO2Dyf/i7Gv/xemDJd+HTf8KMP8HkH/itlNkjU9h6vIojZXV+q6GtRveNYXteFS6319+liHQrBUARCT4FW2HRNyBrqi/09NZef55meP1e2PwiXP0IjL/Hr+VclpOII8QSVLOAY/rG4HJ72ZFf5e9SRLqVAqCIBJfKo74dv/FZcP3TYOmle9maG+GVb8CuN+H6Z2DkXH9XRFiIhalDElmyNfDbwZzQJzKUnKRwjpXV+7sUkW6lACgiwaOhwtfo2RoKc1+BEKe/K/IPVx0suAkOvAs3z4ehV/u7opNm56bwRVENXxTW+LuUVhvTL5Z3vyimtqnZ36WIdBsFQBEJDu4m3xFvdcW+di/hCf6uyD8aq+CFa+H4BrjlVci+3N8VneKCrD5EhdmCahm4X7yDJVvzWbmjyN+liHQbBUARCXxeL7xxn6/R85yXfcu/vVFdGTw3C0p2w+1vQv8L/F3RaUKsZqYPS+KtrfkES5expMgwxgXZecYiHaUAKCKBb/UvYOcbvl5/fc/zdzX+UVMIz86AqjyYtwzSxvq7ojOalZvC0fJ6th4Pno0Vs3JTWLu/lLLaJn+XItItFABFJLB99jis+ztM+z0Mme3vavyj8ig8PQ2aanxH3SUN93dFZzUhM474cHtQLQOfPM94R6GfKxHpHgqAIuJ3hmFQX+2i8GAVez8v5IvPCik4UEXdhqUYy38C538bJtzv7zL9o3S/L/xhwB0rgmL522I2ceWIZJZuy8fjDY5l4LhwO5MGxgdVaBXpiF7aP0FE/KG6rIGyvDqqSxu+/Go8+X+7XS014nVgNb9CxKeRRO3fSmR82JdfocSlhRMZF9btP0O3KtwBL1wNjji4bTFEJvu7olablZvCs+sOs/5wORMy4/xdTqvMzk3hx69upaCqgeSoHv7vlvR6CoAi0qVqKxrZv7GYfeuLKD7iaw1isZqJjA8lMj6M1KxoBk9MPhnsIuPCoOIQ1U/dSbUjl+ph/0F1hZvq0gaO7S6nuqwRT7MvLPYdEsuwi1LpNzwes7mHNYM+vhFevBZi+sGtb4AzOELUCaP7RpMaHcZbW/ODJgBePjSRn71hZtm2gqA5z1ikvUxGsGzTEpGgUVfVxIFNJezfUETBgSosVjN9h8aSNTaR5IHROKNCMJ0psNWWwFNTwWyFu94BR+wpHxte33Lx0V3l7Pgwj+LD1YTH2hl6QSpDJqXgiAzphp+wix3+GObfBIlD4ZZFEBrl74ra5Q8r9vDy+qN8/vMp2CzB8cbRN1/YQEFVI299e7K/SxHpUgqAItIpGmpdHNxcwr4NReTvrcRkMpE+JJassQn0z+1DSFgrFhxc9fDclVB5DO5eBTEZ57yk+Eg1Oz7IY+/6IgyvwYDRCQy7KJXkAVGYgvGIuH2r4OVbIf08mLMgqJtd78yvYubDH/PMHeO4ZFBw9G1cui2fb8/fzPs/upiM+OD9tRc5FwVAEWm3pvpmDm4pYf+GYo7tqQDDIC0nhoFjE8kc2YdQp631N/N6fMHn4AdwxzJIGdWmWhrrmtnzSQE7PsijqqSBuFQnk67PIn1w7LkvDhS73oRX7/KdcXz9M2AL9XdFHWIYBlP+8gG56dH85caR/i6nVRpcHsb8dhX3XzSA71wW+BtuRNpLAVBE2sTV6ObQ1lL2byzm6K4yvB6DlIHRZI1NIHNUQvuWYA0Dlv8INjwDcxZ26HQLw2twfE8FG1ceJm9fJeOv7M/Y6RlnXnIOFFsWwJsPwNBr4JrHwNKG8BzA/rZ6L09+dIgND00h1Gbxdzmt8r2Fm9ldUM07P7jI36WIdBltAhGRc2p2eTiyvYz9G4o4vKMMT7OXpMxIJl4zkIFjEnBG2zv2gLX/h/HZkzRP+BWuEicUryUkLRVbSgqmkLYFSpPZt/ScmhPDhuWH+XzpIQoPVjH1jqGEhgdoqPr8CV8AHn07XPk3MAdHUGqNWbkp/G31Pt7/ophpw4JjF/Ps3BTe3JLPnsJqcpIi/V2OSJfQDKCItMjT7OXIzjL2byzm0LZS3E0e+vSNYODYBAaOSWh3CxbD5aL2o49o2rcf1/FjNO/eRPOhfTQ32HwzgV9lNmNNSiQkLR1behoh6emEZGYSftFFmO2tC51Hd5ax6uldWO1mpt0znMT+AfYf9I//Bqt/CRMegCv+G4LxvcVzmPnwR/SLc/CvW8b4u5RWcbm9jPvdam6d0JcfX5Hj73JEuoQCoIic5PF4Ob67gv0biji4pQRXo4e4VCcDxyQycGwC0QmOdt+7OT+fildeoXLRq3jKyrBER2PrE4Wt6QtC+mdhu/QuQtLTsaWn+8YfP47r+HGajx2n+fgxXMfzaD52DE9FBZaYGKKvv47om24iJC3tnM+uKW/k7Sd2UHK0hsk3ZDHsolT/bxAxDFjzO/jwj3DhT+CSn/XI8Afw2AcH+MuqvWz8r6mE24Nj4ek/X9vGugNlfPDji/3/74pIF1AAFOnlvB4vefsq2b++iANbSmiqcxOd6CBrbAIDxyYSm9z+nZCG10vd2nVULFhA7fvvY3Y4iLr6amJuvgl7ZDM8dQWkjoK5i8DauqXepoOHqHx5IZWvv4G3tpbwCy8kZu4cnJMnY7KceenU4/ay7rX9bFtznKyxCVx8aw4hoX4KI4YBK38Knz0CU/4fTP6+f+roJnmVDUz6w3v89aZcrhl17sAeCNbtL2Xuk5+x+FuTGJke7e9yRDqdAqBIL2R4DQoOVLFvQxEHNhXTUNNMZHwoA8ckkjUugbjU8A7NehgeDxUvv0z5c8/RfOQo9kGDiJk7l6grZ2J2OqG6AJ6cAmHRvuPNQtu+LOutr6d6+XLK58+naddubGlpxM6bR8zcOZjMZ+45t29DEWte2ENsipNr/mM0Fls396fzemDJ92DzCzDjTzD+nu59vp9c/8g6IsNsPD1vnL9LaRWP12DC799l1ogUfjFriL/LEel0CoAivYRhGBQdrmb/+mL2byqmrrKJ8Bg7A8f4ZvoS+kV0ylJX07595D/0EI3bthM5YwYxt9xC2KiR/753YzU8MwMayuHu1RCZ0uGfq3HbNspfeonqt5YQfvHFpPzPH7BEnbl5ctHhal7/00aGTErhojmDOvT8NvE0wxvfhJ1vwFX/gpFzuu/ZfvbcusP8Zuku1v98CjHO4GjW/au3drJ8ewGf/PQyLIG+i1ykjRQARXowwzAoPVbLvg1F7N9YTE1ZI47IEAaMSSBrTAJJmVGd1h7FaG6m9IknKH3kUULS0kj+3W9xjB596iBPM7x0A+RtgjtXQmLnzqzUvP8++Q/+J5bwcFL/9jfChg8749gdH+bxwfwvmHrnELLHJ3VqHS1qboRF82D/arj+KRhyVdc/M4CU1DRx3n+v5nfXDGfO+L7+LqdVNh6p4LpH1rHgngmcPyA4jrMTaa3geBtXpBepLmugPK+OtJwYrCHtawdSllfrO393QxFVxQ2EhtsYMKoPA8cmkpIV3enn5jbs2EnBz39O0/79xN11F/HfeuD0XbqG4Vv6PPwx3PZ6p4c/gIiLL6b/a6+R9/3vc2TuXBJ//nOib7qxxZnNoRekUHCgkjUv7iE+LYLYlC489aGpFhbOhWOf+focZk3pumcFqD4RdiYOiOetLflBEwC/ep6xAqD0NJoBFAkgZXm1vP6nTYSG27js9sGkZEVjGEarlmYri+rZt6GIfRuKqSiow+6w0n9kH7LGJpA6KAZLF5zF6m1spPSf/6Ts6WewZ2eT/NvfEDZ0aMuD1/w3fPA/cO0TMOLGTq/llLpcLop+/3sqFywk6qrZJP3yl5gdp+9gbm7y8Or/bMDwGlz/n2O7ZlNIQyXMvxGKdsLclyGj954x+8r6Yzz4+jY+++llJEQGxyknf1ixh4Xrj7I+iM4zFmkNBUCRAHAi5O1bX8RHi/YRl+IkY3g8uZeln/W66tKGkzN9pcdqsdkt9M+NZ+DYRPoOju3SDQ71GzdS8POHaM7LI/5b3yLurjsx2c7QaHnT8/DWd+CyX8AFP+yymr6uaskSCn7xS0Jzcuj33LMtNpWuKKxj0e83kDE8jql3De3clh91pfDCNVB5FG59HdKCow9eV6mqb2bs71bx0+mDuXNyf3+X0yq78quZ8fBHPDNvHJfkBMd5xiKtoSVgkQBwInTs/byQqXcMYet7xyg9VkNTgxt72Km/TRtrXRQdqaa6rInNK4/QUOOi3/B4xs7IoN/QuHYvG7eWp7aOkr/+lYr58wnLzSXtn//APmDAmS/YtxqWfB/G3gmT/6NLa/u6qFmzCElP5/Btt1P0xz+R9POfnTYmJsnJJbfl8M6TO0keGM3wizupTUl1ATx/lW+zy7xlkHTm9xF7iyiHjYuyE1iyLT9oAuDg5AgG9HGyZGu+AqD0KAqAIgHAMAya6t001DYTlxpOanYMBzcXU3qshtTsGOprXZQeqaHwUBWVhQ2YLJAxPI5LbsshsX9kt/Wzq/14LQW/+C88FZUk/vQ/ibnllrP23iN/C7xyO2RNhel/9Euj47CRI0l88EGKfvtbHKNHETl9+mljssYmUnigio8X7SMpM4o+fSM69tCKI/D8bN+mlztWQvzAjt2vB5k9MoXvLtjMsfJ60mPb31i8u5hMJmbnpvL4hwdobPYEzXnGIueiFxpE/OzE8u/BLSXY7BYckSH0HRKL12NwcGsJH7+6j49f3scXnxVitVkYelEKF9+SQ9bYJNIHx3ZL+PNUVZH/059x7O67CenXj8y33iT29tvPHv4qjvjefeszCK5/Giz++/tmzC1ziZwxg4KfP0TTwYMtjpl43UAi4kLZ9PaRjj2sZC88PQ0w+XocKvydYsrgBMJsFpZsy/d3Ka02KzeZOpeHNXuK/V2KSKdRABTxM5PJhGEYVJc2kDU2gby9Fez5tICKwnp2vJ/H1tXHSB8cy0VzBzH6in6kZsVg6+Jl3q+qXrWKA1deSc2qVST95tf0ffppQtLP/m4i9eW+di+2MN/Gh5Au3GHbCiaTieTf/BprUhJ53/se3vr608ZYrGZGXJLGgc0l1FY0tu9Bhdvhmem+xtZ3roSYfh2svOdxhFiZMiSRt7YETwDM7BPOsNRI3toaPDWLnIsCoIifuV0eCvZXsu2943y+9BDL/rmNre8ex2a3EB5jZ+qdQxg8MRmbvXuXntylpRz//g/I+853CRs2nMxlS4m54YZzb5LwuGHRN6CuBG55DcID470ps9NJ2sP/h+t4HgW/+hUt7X/LmZCMNcTMjg/z2v6AY+vh2ZkQlQrzlkNEN/QWDFKzRiSzp7CGfUU1/i6l1WaNSOG9PcXUNDb7uxSRTqEAKOIHbreXwoNVbH33KO/P/4Jt7+UREReKPczG6Cv6Me8Pk5j9vZGERYRQW9EE0G0H0huGQdVbb3Fw5pXUf/YZKX/+E2n/+ie2xMTW3eD9//b1+rvx+YBb/rQPHEjyr39N9VtLqPvww9M+DwmzknN+Mjs/ysfd7Gn9jQ995Nvw0WcwfGMJONUz7mwuGtSHiFArS4JoRu3K3BSa3F5W7SrydykinUKbQES6iafZS2VxPUWHq8jfX4W32SAiPpQBo/qQ2D8Kq82MJcR8sl+fIzIEs8VEydEamuqbsTvO0GKlEzUXFFDwq19R98GHRM6cSeLPf4Y1Nrb1N/hiJXz0Z5jyK+h/QZfV2RGRV86k8tVXqXj1NcIvuui0z3MvSWPvZ4Xs31hMzoTkc99w7zvwym3QdwLcPN/vy93BwG61MG1oEku2FfCDqdnd9pebjkiNDmNsvxiWbM3n2tGdtFNcxI8UAEW6kMfj5fieCvavL+Lg1lLScmJIzYomc0Q8iZlROKPsLV7n9RqYzSYumjOIyPiwLl/+NbxeKl9ZRPEf/+hbKv3XP4m49NK23aTiMLxxL2RPh4nf65I6O4PJZCLhJz+mesUK3GVlWONOna2LSnAw8boBVJc1nLsJ987F8NrdkHW5b6OLLTiaGweC2SNTWLTxODvyqhmeduZzmwPJ7JEp/HrJLirqXEFznrHImWgJWKSTeb0Gx/eUs+bFPTz7k7Us/ftWCg9VM+LSNCZeM4ARl6aTOSrhjOEPOHlUW1xqeJeHP9eRIxyddweFv/oVkTOmk7l0SdvDn8ftO+c2NBqueQTMgf1Hi33QIIxmN3Vr17X4eWL/KNyNXooOVZ/5Jlvmw6t3wNCr4cbnFP7a6PzMOOLDQ4JqN/D0Ycl4DYMVOwr9XYpIh2kGUKQTGF6DggNV7N9QxP7NJTRUu4iMD2XI5BQGjk0gPi084Ja5DI+H8ueep+Thh7HGx9P3madxnn9++262dyXkb4a7VkFYTOcW2gXMViuOUSOp+eBDIqZOwRwWdsrnsUlOQsNt7Pw4j6TMFmanPn8Clv8IRn8DrvwrmNUbrq2sFjMzhiezZGs+/zktp9PPp+4KfSLsTBoYz1tb85h7XnCcZyxyJgqAIu1kGAZFh6vZv6GY/RuLqatswhltJ3t8IlljEknIiAi40HdC07595P/8IRq3byfmtltJ+P73Wzwrt9XWPwlp4yF9fOcV2cUc559PzbvvUb9+PeEXXnjKZyaziaQBUWx+5yi1FY2Ex3xldu/jv8LqX8GEb8EVv/NLc+ueYlZuCs9/coSNRysYl9GGd039aNaIFB58fRtF1Y0kBsl5xiItUQAUaQPDMCg9Vsv+jUXs21BMTVkjYZEhDBydwMCxCSRnRmEK4JkMw+Wi9IknKH30MULS0+n30ks4Ro/q2E1L98PBNXDN451TZDexRkURNmIEtWvX4pw8GdPXlq0T+kZgsflawky4agAYBrz3G98ml4sehIt/qvDXQWP6xpASFcpbW/KDJgBeMSyJhxbvYOm2Au4KkuPsRFqiACjSCmX5tezfUMy+DUVUFTcQ6rSROboPWWMSSMmOCYrlq4btOyj4+c9pOnCAuHvuJv7++zHbz/weYqtteBrCYmHIVR26jWEYbN++nf379wOQmZnJiBEjMHfh+4TOCybT8Pe/07RnD6FDhpzymTXEQva4RHZ9nM/YaX2xvvcQfPYoTP0NTPpul9XUm5jNJq7MTeG1jcf55awhWC2B/e4oQFSYjYsG9WHJ1nwFQAlqCoAiZ1BZVH9ypq88v46QMCuZI+O58KZsUnNiTrZrCXTexkZK//EPyp5+BvugQfRf9MppYafdXPWw5UUYM69DmyAeffRR/vznP58Mfyf079+fH/zgB3z729/ukuX0kH79sKWlU/vx2hZ/TYZMTmbzqqPsf/Iv5JQ8BjP/AuPu6vQ6erPZuSk8/uFB1h0o48LsPt3+fLfXTWFdIS6vi9TwVOyWc/+laFau7zzjo2X19I0L/POMRVqiACjyFdWlDezf6JvpKz1Wi81uIWNEPBOuHkDfwbFYbMER+k6o37CBgp8/RHNBAX2+9z3i7rwDk60T+wnmbYTGKhhxU7sub2xsZM6cOSxevLjFzw8dOsR3v/tdVq5cySuvvILTee4ee/PmzaOyshKPx0NDQwOrV68+bcwnn3zCxIkT2bhxI4MmT6Jy4UKai4qxJZ56akl0fAh9Y/PYtieGQXc9imnkze36OeXMhqZE0j/eyVtb87s0AB6sPMjeir0crz3O8ZrjJ/9ZWFeIx/h30++EsATSItJIDU8lLSKNtIg0MiIzGBY/DLPJ9/v/q+cZf+uSwGp2LtJaCoDS69VWNLF/YxH7NxZTdKgaq81Mv+HxjJ2eQb9hcVi78dzdzuKpraPkL3+hYv58wkaNIu2Rf2HPzOz8B1Uc9v0zdkC7Lv/Wt751xvD3VcuXL+eee+5h/vz5rb73XXfdxbXXXsuRI0fo1+/UM3mffvppRo4cyejRo/G63VQvXUbd2rVEX3vNvwe5m+GNbzKcYyxr/imFEWNoRVtoaSOTycSs3BSe+fgQv7tmGHZr5/1+c3lcvHPkHV7e8zJbSrYAEBkS6Qt24WkMzRh6MujZzDbyavPIq83jeM1xjtUc49OCTylpKAGgX2Q/bsy+kasGXkWUPYopQxJZslUBUIKXAqD0SvXVLg5s8s30FRyowmwx0W9oHFPvGkLG8HhCQoP3t0btRx9T8Mtf4KmoJPFnPyPmlrmYLF0UYiuP/P/27js8qmp7+Ph3ZtImvZEeUgihk0ASSkApIk1AQCkiVWyv6LVd5VoueFUsqHh/ilhQhCuICAgCIqIU6b2FHkogjYT0Nikzc94/IlFMgMyQZFLW53nyqDl7n7MGw2TNLmuDk59Z079btmxhwYIF1W6/dOlSxo8fz+DBg6vVfsiQIXh5ebFw4UJmzpxZ8f2ioiKWLVvGW2+9BfxREqZbVwq3bcd58CDUdnZQVgxHl8KlnQRN+QSX/2mJ25KIb4uGUbC4oRkW4ctHm+LZeuYqA9rd/hnKyQXJLD+znB/ifyC7JJuuvl2Z03sOXX274mzjfMN+McRU+p5Or+NExgm+P/s9Hx76kI8Pf8ygkEF0CrubtUfzOZuWT7i3023HLERda7i/5YQwUXFBGecPl5dsST6TjUqlIqCNO3dNbENIhGedHLVWmww5OaS98y65q1fjENsdn/+9jk1ALR9ZlZ0AbsFmdf3kk09M7jNv3rxqJ4BWVlZMnDiRhQsXMmPGjIo1hMuXL6e0tJQHH3ywoq1DbCwFm7dQtG8/jt06w57PQa+D0YtQNe9Gh96J7Fp5joLsEhzdamDjjLhOmJcTbXydWXs0xewEUFEUdqfs5tvT37ItaRuO1o7cG3Yvo1qNItTF/NFvrZWWaJ9oon2iydBlsCp+Fd+f/Z5VhatwCg3k//ZcYe6wh+ptySchbkSlKIpi6SCEqC0lOj0Xj1wl/kA6SaeyUBQF/1ZuhEV50aKTF3aODTvpuyZv40auvPEGSnEJ3v+ajsvIkXXzC+nLu8EjrPz0DxN5e3uTnp5uUh9nZ2dyc3Nv2ubaGsDVq1dz+vRp2rRpw+bNm+nTpw8AvXr1wt/fv9J0ctY3iylNvIx3TAmqMh30eApcAoHyn6NF/9pJxF2BdB1WC1Ppgnlbz/HRpngOvno3DrbVH5swKka2XN7CF3FfcDLzJK3dWzO21VgGhQzC3rp2NmgYjAa2JW3j9W3zyTDEMThkMDO7z6y15wlRG2QEUDQ6pcV6EuIyOHcgnUsnMjHqFXzDXOg5uiUtOnth79x4zvDUZ2Rw5Y03yf/lFxzvugufGTMqbWSoVbmJENrb5G7FxcUmJ38AeXl55Obm4uJSvanY1q1bExsby4IFC+jTpw/nz59n+/btbNy4sVJbh5gIdEcOU5JsxG7E8+Dy5+iprdaK1t18OLE9mehBwQ1uM1BDMLSjH7M3nOG3U2ncG+l/y/Z6o54NCRv4Ku4rzuWcI9o7ms/v/pzuvt1r/cOPRq2hT/M+WHVtz5Tl89mkWc3prNN82PtDQl3lA4JoGCQBFI2CvtTApeOZxB9I41JcJvoyI94hznQf3oKwKK/rT3JoBBRFIW/NGtLeehs0GvznfIDToEF1Pw1l5wrFOaZ3s7PD3t6eoqIik/pZW1vj6OhoUp+pU6fy5JNP8sknn/D1118TFBTEXXfddX2jgqvYXPgGayco0LXAzqXy1HmHPgHE/Z5M/ME0WneT7SA1LdDdnk7NXVl7NOWmCWCpoZQ159ew4PgCEvMT6enfkxndZ9DJ6zYLmpuhe6gHrsYY+jpGc5Z5jP1pLP+J/Q+DQgbVeSxCmEoSQNFgGcqMXD6VRfz+NBKOZVBWYsAz0JGYISGERXnh7Km99U0aoLLUVFJnzizftDBkCN6vvIyVm4XO33UL/nMnsInatGnDwYMHTeoTHh6OxsQNLaNHj+bpp5/m22+/ZdGiRTzyyCPXJ8p5qbD9fVQaGxz6DSJn1TrKrl7Futn1JUncfBxo3tadY5uTaNXVR9Z81YJhEX68tf4UuUVluPxtTa5Or2Pl2ZV8feJrrhZdpV9QP97v9T5tPWqopqUZrp1nvPFEGr89v4Q3973Bi9te5FDaIV6IeQEbTeOZbRCNjySAosHJSS/ixLZkTu1OpaRQj7ufA50HNCcsyhtX78a7BkcxGsn5/nvS33sftaMjAZ/Ow+mPdW0W4xYM5zeb1XXSpEkmJ4CTJk0y+TmOjo6MGTOGl19+mdzcXCZPnvznxezLsGMO2DrBHc+jtXIg79etFO3di8uQIZXu1aFPAD99coy0i3n4hMqO4Jp2Twdf3lh3kg0nUhkT0xyAgtICvjvzHd+c/IbcklwGhwxmaoeptHA1r/RQTRv2x3nGJ5KLebvn23T26sw7+97hXM45vrj7C6w1jWOdsWh8JAEUDYLRqHApLoPjvydz+WQWtg5WtIn1o3V3Hzz8TJsSbIhKL10i9dV/U7R/P66jRuH14gtonOpB6Qm3oPJSMEYjmHhk25QpU/joo48qnf5xI0FBQTzyyCO3bGc0GrGyuv6tberUqXz11Vf079+f5s3LEwsyz8OOD8HBC+54DmwdUQP23bpRtG8/TnffXemovKB2Hjg303JsS5IkgLXAy9mObqEerD2aSv8OTiw+tZilp5ZSbChmeNhwprSfQqBToKXDvE7nP84zXnssha6hHoxuNZoWri14eOPDzDk4h+ldpls6RCGqJAmgqPdSzuWwaeFJ8jKK8Q5x5q7JbQjr7NUgCzSbSjEYyFr0P65+9BFWzZrRfOHXOHTrZumw/uQWDPpiKEgDZ9PWxTk6OrJixQp69ep1y529jo6OLF++HFdX11veNz09nbCw64vzdu/enesKHqSdhF1zwa05xP4DbP4cOXaIjaVw926Kj5/APqrzdfdRqVV07B3ArpXnKLwvDAdXKQlT0/q0teODvV/Qf8UBQGFUq1FMajsJbwdvS4dWJbW6vJD18oNJzBzaDmuNmijvKP4Z/U/e2fcOkV6RDAgeYOkwhahEtrKJektRFA5vvMzqOYdxdLNj1EvR3D89mtbdfJtE8lealEzCuHGkv/cebmPGEPrj6vqV/AF4tyv/p5nTwBERERw4cICoqKgbtunYsSP79+8nJqZykd6/ys7O5qeffmLr1q3069fvxg1Tj8LO/wOPFtDzmeuSPwArFxe07dqhO3wIxWis1L11rC9qazXHtyXfNB5hmuSCZN7c8yafXZiKles+IpyH8Mv9v/BizIv1Nvm7ZmiEH1mFpew6n1nxvXGtxzEgeAAzds7gYu5FC0YnRNWkDqCol0qKyti06BQXj2bQeUBzug4LRa1pOp9X8rduJWX6v9A4OeH33mzsO9X9Dsdq+2Zk+U7gR8xLAq/ZsmULP/zwA+fPn0dRFFq0aMG9995Lv379qrXhYsSIEezfv59Jkybx5ptvVt0ncT/s+wJ8I6DrY3CD9Vklly6Rs3w5Dj164Ni9e6Xrvy89w/lD6Ux6q4eUhLlNF3Iv8FXcV/x04SecbZyZ0HYCOw6FU6CzZuX/i7V0eNWiKAp3ffA7nZq78cHoiIrvF5YVMnbdWKzUViwZvETqBIp6RRJAUe9kpRby07xjFBeU0W9yG0Iiau+A+PpG0eu5+vFcMj//HMe+ffF7+y001ax5ZzGn18N3D8AjW8C/863bW0rCDji4EAK6QswUUN94BYyiKGR98w1Fhw4R+N//VrqelVrI0v/spd/kNrSSkjBmOZ11mvnH5vPrpV9ppm3GpHaTuD/8fuyt7Vl9OJlnlh1hx/Q+BLg1jKRpzq9n+XrHRfa/2g876z9nKM5ln2Pc+nH0a96PWT1nye5xUW/IR1dRr5QUlfHTJ0fRWKkZ/XJ0k0r+9NnZXJ76MJnz5+P1z+cJ+GRu/U/+AMIHlJ+YceArS0dyY+d+gwMLIPgOiJl60+QPQKVSoe3UmaJduylNSKh03d3XgcC27hzbkoR8hjbNkfQjTNs0jVFrR3Ei8wSvdnuVn+/7mYntJlaMkN3d1hs7azXrjqVaONrqGxbhS36Jnq1nrl73/TC3MGZ0n8HaC2vZnHh7o+RC1CRJAEW9oSgKmxadoqRIz5BpHXFp1jA++dcERa8n+elnKDl7luYLv8bj4YcbzkiBWgNRkyFuJeiyLR1NZad/giPfQssB0HlitXcr27VpjcbVlawl31Z5vWPvANIv5ZN2Ma8mo22UFEVhT+oepv4ylQk/TyApP4m3er7FuhHrGN1qdKV6eQ62VtzV2pu1R1MsFLHpwryciAl257dTaZWuDQkdQk+/nqyOX133gQlxA5IAinrjyK+JXDyawV2T2jTaIs43cvX/PqLo4EECPvo/HLp0sXQ4pus8EVQq2PAS1JcRMUUpT0qPr4S290LH0eUxVpPaygqXEcPJ/eEHDAWFla4HtfegWZATl09kVtFbQHnitzVxK+PXj+eRjY+QX5rPnN5zWHXvKoa2GIrVTUZih0b4cSIlj/NXC+ou4Nv03N3h+DjbUqI3VLr2dOenCXEJIbNIfl5E/SAJoKgXUuJz2L36PJ0HNG9S074A+Zu3lE/7PvsM9rfY6VpvOXrBvZ/AmZ/h6FJLR1NelzBuBVzYApHjyhNAM0ZUXUeOxFhcTO7q1ZWuqdQqetwXhlqjpqSorAaCbjwMRgMbLm7g/rX389Tmp9CoNcy7ax7Lhizj7qC7Uatu/aund6tmONlaNahRwFbejlzNL+VkSuVR4TC3MPRGPXuv7LVAZEJUJgmgsDijwchvX5/Et4ULXYc1rYPUSxMTSfnXv3C86y7cp061dDi3p/1IGDYXMs5BrgVLpBiNcOpHuHIcOk+CsJuUhLkFa29vnPrfTfbixVWWhPEKciLnahGJp7JuJ+JGo8xYxqr4VQz/cTgvbHsBDzsPFgxYwKKBi7gj4A6TljXYWWvo386HNUdTGsw6S3dHW7ydbdl3sfLPg5Xaio7NOrIndQ8l+hILRCfE9SQBFBaXcCyT/Kxieo5u2aRKvQCkvvQyGmdn/N5+q+Gs+buZ8AFgZQf75kOpBabu9KXw4zRY9xz4RUDg7Y+ouk+ejMbNjZILlWu5Wdta4dfSlaQz2Rj1lRPEpqJYX8zS00u554d7mLFrBiEuISy9Zylf9P+CGJ8Ys3+2h0b4cuFqISdTG846y6ggN05fyaOwRF/pWlffrpQYSjh89bAFIhPiek3rt62ol+J+T8In1JlmgfXgaLM6VHzyJEUHDpQf6+bsbOlwaoaVDURPgaJM2Pkx6HLq7tmFGbDkfji+AoZ9VD4iWQO0HTviMnIERYeqPre4eRt3SosMXEloOElKTSksK+Tr418zcOXAilMvVg5byUd9P6K9Z/vbvn+PME/cHWxY04CmgSObu2JU4GhiTqVrbnZutPFow+6U3Q1mVFM0XpIACovKvlJI0uls2vcKsHQodS576XdYeXvj1LevpUOpWY7NoMfTUHgVfvsPXD1d+8+8vBc+uwPST8L4ldBmaI3dWqVSYdM8CN3eveivXq103dHNDg9/By6faDrTwLkluXx65FP6r+jPR4c/oldgL9YMX8PsO2cT7hZeY8+x1qgZ1N6HdUdTay1hKigo4NixYxw4cIDMzNvfoOGitaGllyOHLle9I76rT1dSC1NJL0q/7WcJcTskARQWdWJbCnaO1rTofHsbP7Zs2cLDDz/MnXfeSadOnRgxYgRz584lL69+jsoY8vLIXbcO1zGjUVk1wiO5PcPgrpnl5wNvex/Obixfm1fTFAV2z4OFg8EtCB7bBiF31vhjtJ07oXZwpGDnziqvB7ZzJ++qjpz0ohp/dn2SoctgzsE59F/Rn6+Of8WQ0CH8PPJn/hP7H4Kcg2rlmcMi/EjO0d0woTLXjh07GDp0KC4uLkRERBATE4OnpyddunRh6dLb28jUubkb8ekF5OpKK13zd/QHyv8shbAoRQgLMegNyvxnf1d2row3+x4pKSnKnXfeqQBVfjk7OyvLly83+b6pqanKk08+qYSEhCg2NjZKQECAMmTIEOW3335TFEVRgoKCFEDZvXv3df2efvpppVevXre8f9bS75ST7dorpWlpJsf2VxcuXFD+85//KGPGjFH69++vPPzww8qiRYsUnU53W/etMQa9osStUJTV0xTl0DeKknK05u6dekxRlo5TlJnOirLhZUXRl9bcvauQ89N6JfmllxVDFX+2BoNR2bbsjHJ006VajcFSUvJTlFl7ZilR30QpXRZ3UeYcmKNcLbpaJ882GIxK11m/KTN/PF5j93z99dcVtVp9w/cNQBk7dqxSWFhY7XtOmjSpyvvEx8dXXHv77bcVo9GovLz9ZWVb4jZl1apVivwaFpYiI4DCYnLSdJQU6Qlq52FW/5SUFKKjo9m2bdsN2+Tl5TFq1Cg++eSTat83ISGBqKgoNm/ezOzZs4mLi2PDhg306dOHadOmVbSzs7Nj+vTpZsWeu3o1Tnf3w9rLy6z+xcXFPProo4SFhTFz5kyWLVvGxo0b+fLLL5k0aRKBgYH8+OOPJt/3ypUrPPXUU4SGhmJra0tgYCBDhw5l06ZNAAQHB/PfKo5Ge+2114iMjKx8Q7UG2t8HXR+H/DRYNBTm3wVHlkJZscnxoS+BY8vhqwHwWU9IPgijv4EBs254rm9NcYjtjlJWStGBA5WuGXOy8b2widzftlDciErCXMq7xIydMxi8ajDrL65naoepbLx/I89GPYun1rNOYlCrVQzp6Mu6Y6noDbc/ivzRRx8xY8YMjLcYkf7uu+947LHHTLr3wIEDSU1NJTU1lae+3MySLUcICQkByt8v3n33XXJycnC3dSeruOksGRD1UyOcexINRWZK+S5RD39Hk/sqisLYsWNJSane4vBnn32Wrl27Eh0dfcu2TzzxBCqVin379uHg4FDx/Xbt2vHQQw9V/Pdjjz3Gp59+yvr16xk8eHC1Yzfq9ZRcvIjLfeZtUtDpdPTq1Yv9+/ffsE1GRgbDhw/n//7v//jHP/5RrfsmJCTQo0cPXF1dmT17Nh07dqSsrIxffvmFadOmcfr0bazl82kPXm3K/7n3M1j9OPzycnmNPt9IcAsu/3Lw/LNen6KUbybJTij/Sj1afqJHUUb5NO/o/0GrwbWe+F1j5eqKXYcOFO7YiUNsLKq/nCiisrHBxdOWonWLSTo1kLAo7zqJqbbEZ8czP24+vyT8gputG093eppRrUbhYO1w6861YGiEH1/uuMjei1n0CDM/8UxISOCf//xntdsvXryYkSNHMmLEiGq1t7W1xcfHB4DmgbkodnZoNOXnAvfr149z587x9ttv02ZiG7KKs3DDzfQXIUQNkQRQWExmUgEOLjbYOZr+C3zTpk1s37692u3Lysp46623+OGHH27aLisriw0bNjBr1qzrkr9rXF1dK/49ODiYxx9/nJdeeomBAweiruYRY8a8PFAUbAKbVzv+v3rqqadumvz91fPPP09UVBQ9evS4ZdvqJr5mU2vKy8SED4DM8+Vn88Yth91z/2xjbV+eCKIqT/rK/nICh0Mz6HA/RE+FZjW30cAUDj16kDlvHsVnzqJt0xqjTodaq0Xj6Ij7qPu4+r9vyVixmtDIRxpkSaPjGcf54tgXbEncgq+DL//q8i9GhI3AzsrOonF1DHAhyMOeNUdSbisB/OyzzygrM22E9uOPP652AvhXno62ZBT8We9Po9Hw1ltvMW7cOOYOnUuOXY4kgMKiJAEUFpOZXIBHgOmjf1D+ydxUa9asIS8vD+eblFw5d+4ciqLQunXrat3z1Vdf5euvv2bJkiVMmDChWn0MObkAWAeYvvP5/PnzLFiwoNrt9Xo9M2fO5LfffrtpO1MS3xrh0aJ82nbALCjJh+xLf470ZScACkQ+UJ4MugaVb/Cwc6nZGExUuGcvhbt3o/HxpWjnDrRtWpM+50Os/fzwmDIZjYsLLvcMpmTzZq5cHINfmKtF460uRVE4kHaA+cfmszt1N8HOwbwe+zpDWgzBWl03o6u3olKpGNrRj//tTuCN4e2xsTIvud64caPJfX7//XdKS0uxsbG5Zdt169bh6Fj+nmYwKoR06snT/X6puD5ixAgiIyP54ZMfiHwyklClaRW+F/WLJIDCYjKSC2gZbd5UmTnTkQaDgXPnztG5c+cbtlH+KDVR3cK1zZo145///CczZsxgzJgx1YsjNwesrLD2Mf21L1q0yORyGJs3byYxMZHAwMAbtjEl8Z0+fTqvvvrqdd8rLS2lbdu2JsVVwdapfGrY5/brxtWmvJ9/BrUK54GDyPn+e/RXr2Lt50fWwoV4TJkMgJWxFI23N5ePptX7BFBRFHYk72B+3HwOpx8m3C2c9+58j7uD7kaj1lg6vEqGRfoxd8s5tp29Sr+25r1vJCQkmNzHaDRy+fJlwsLCbtm2T58+fPrppwAcupTFL2dz0P9treG7775L37598R7sjU6rMzkeIWpKw5ujEI1CSVEZBVklZq3/A8jPz6+Vfi1btkSlUnHq1Klq3/O5555Dp9Mxb968arU35OZi7eNjVvmXEydOmNxHUZRbvh5TEt8XXniBI0eOXPf1+OOPmxxXQ+PUrx/5G3/FtnUrVFothbt2YR8dhSE7m+R/vkDi4/+PrP/9D5fojuRl6+ttSRijYuTXS78yZt0Yntj0BAajgY/7fsyKoSsYGDKwXiZ/AOHeTrTydrqtotD29va12s/BwYGwsDDCwsLw8g/GxcMLzd/+Tt155530uqsX2+dvp0hfP39GRNMgCaCwiMzk8rVdnmZOAQcHB9dKP3d3dwYMGMAnn3xCYWFhpes5OTmVvufo6Mi///1vZs2aVa26g8biYjRO5p16kp1tXi20rKyb7zg0JfH19PSs+CV37cvd3d2suBoSxzt6onFyInfVKuy7daNw3z7yN2/GPiYGxz69Qa3G+5VXCHhiClpnaxJP3n5R4ZqkN+pZe34tI34cwXNbn8PJxon5/eezePBiegf2bhBHEQ6L9OPXk2kUlVY+Zq06WrVqZXIfJycnfH19Te6XUViCp4NNlX+ub7z1Bud3nWf3rt0m31eImiIJoLCIzOQC1BoVrt7mfSLva8bpGS1atKB581tvvJg3bx4Gg4EuXbqwcuVK4uPjOXXqFB999BHdu3evss+jjz6Ki4tLtQrIapydKUtPMzl+oFrxm9PPnMS3KfKc9gT5G38l+5tvKNq3j7x1P+HYtw8u99xD4LxPcBs7Bo2VhsA27ly5kFcvSsKUGkr5/sz3DFk1hJd3vEyAUwDfDPqGrwZ8RTffbg0i8btmSEdfdGUGNp0y7xSNcePGmdzngQceMOvPKLOgFA9H2yqvde3Ulbb92rLkyyUm31eImiIJoLCIzOQC3Hwc0Ji5mHvKlClVbla4mWnTplXrjTwkJIRDhw7Rp08fnn/+edq3b8/dd9/Npk2bKtb3/J21tTVvvPEGxcW3rm2ncXHBkJmFscj06Z+ePXua3MfJyYmOHTvesp05iW9T4zJ0KN4vvoCVpydqewc0Li44DxlScf3atL5/uCsqtYqk0zV7eoUpisqK+N+J/zFo5SDe3PMm7TzasXzocj656xMivSItFtftCPJwICLQlbVmTgOPGzeOFi1aVLu9vb09zz33nFnPyiwswcOx6o0jGrWG/o/1v2UtQiFqlcVKUIsmbcW7+5WNC26vsv///ve/m1by/+tXz549lbKyshqK/vaUJCYqp2O6KLozZ0zuW1BQoLi6ulb7dQPKE088Ue37p6SkKNOmTVOCgoIUGxsbxd/fXxk2bJiyZcsWRVHKT0D58MMPK/WbOXOmEhERYfLraciKjh5Vkp57TtGdPl3l9RM7kpUtS04pBr2hTuPKLclVPj/6uXLH0juUiEURysvbX1bO55yv0xhq0/xt55WWL69XcorMO/nl8OHDir29/S3/3qhUKmXRokVmPcNoNCrPLTusbD5145N+5hyYo/xw9gez7i9ETZARQFHnFKNCZnIhHn7mrf+7ZsKECcyePbui0OqN9OjRgxUrVmBVT87cVbuUlzMpS0o2ua+Dg8MNRyGrEhwczBtvvFHt9r6+vsydO5eEhARKSkpISkrixx9/pHfv3kD5LspnnnmmUr/XXnuNI0eOVPs5jYFdhw5Y+flRuH1HldcD27pTWmQgI9G8DUumyirO4qNDHzFgxQA+P/o5/YP789PIn5jVcxahLo2n3MjQCD/0RqPZo4CRkZHs2bOH8PAb15J0dXVl9erVTJw40axnJGbpKDMo+LjcuH6ik40TBWUFZt1fiJpQP34jiiYlL7OYshKD2TUA/+qFF16gR48ezJo1iw0bNlw3pRISEsITTzzBM888U2+SPwCNoyMqGxtKL18yq//YsWO5fPkyL7300k2nkIKCglizZk2T2KBhCSqVCseePcn5fjn6jAysPK8vUOzkZkdQB3dSz+fiFVx7NQzTCtNYeGIhK+NXAjA6fDST2k2imX2zWnumJXk729GvjTeL91ziwa7NzVqf16FDB44fP87KlSv5/vvviY+Pp7S0lJCQEAYPHsykSZNwcTH//9mOcxm42VsT7n3j9zgnaycydBlmP0OI21V/fiuKJiMzufxTr6eZJWD+LjY2lp9++onMzEzOnTtHfn4+wcHBhIaGVvt0jrqkUqmwbdmS4pMnzb7Hiy++SExMDDNnzqx0IoqjoyPjx49n1qxZkvzVMm2nTuSt+4nCnTtxuffeStcd3ezYtvQsPqGueIfcuAC5ORLzE1lwfAE/nvsROys7JradyPg243G1c63R59RHE7oHMeGrfRy4lE1MsHk/49bW1owdO5axY8fWaGxFJXoOXsqifztvNDd5/3GyceJi7sUafbYQppAEUNS5zOQC7ByssXe5dWV9U3h4eODh4VGj96wtdu3bk//LL7dueBN9+vShT58+XLhwgVOnTpGdnU3z5s3p1KkTTmaWmRGmUdvYYN+tK8UnT+JUWor6b6dF+Ia5YutgzbGtidwd0q5Gnnk+5zxfxn3Jzxd/xsXWhScin2Bsq7E42tTMB6qGoEcLT0I8Hfhm9yWzE8DasvdiFkZFoVvozd+Lrk0BK4rSoHZii8ZDEkBR58qPgHNo0m96du3bkTV/PvqsLKxuc5QuNDSU0NDGs8aroXG88050cXEU7tiB09/KE6nVKjr0DmDP6vPEjgzDwaXqsiDVcTLzJF/Gfclvl37Dy96LF2JeYGTLkWittLf7EhoctVrFg12b8+6G01zNb0szJ/P/XGuS0aiw41wGEYFuuGhv/gHXycaJUmMpJYYSi5+1LJqm+jc/Jhq9zORCs08AaSy07TsAoDty1MKRiNulcXLCkJVN+gdzqjymr02sL2qNipM7zNu0cCjtEI//9jhj1o3hdNZpZnafyfqR63mwzYNNMvm7ZlRUINYaNe/8fNrk4xFry87zGVzNL+GOlp63bOtkUz5Kn19aN5uEhPg7SQBFnSorNZCTXtTkE0BrP180zTzRHZUEsDFwG3U/pefPU7S78skOdg7WtOrqw/FtyRj01av7pigKu1J2MWXDFCZtmERaYRrv3PEOa4av4b7w+7DR1OzyiYbIxd6aN4e3Z+WhJL4/kGjpcEjMKuTnuCv0bd2MFs1u/f7mbOOM1kpLYVnlwutC1AWZAhZ1KiulEBSafAKoUqnQRkSga2KlUxorbXQ0tq1akfXNYhxiYytd79A7gBPbUzh/OJ3wGJ8b3seoGNmauJX5x+ZzPPM47Tza8d8+/6VPYB/UKvm8/ncjOwewPyGbf/94gnZ+LrT3r73d1jdTVKJnzdFUwn2cGNLRr1p9nGyccLFxodhw6+LxQtQGeUcRdSozuQBU4O5n2ikejZF9ZCS6uDgUg8HSoYjbpFKpcJ8wnoKtWylNrDwa5eHviH8rN45tTqqyv8FoYP2F9dy35j6e3vI0NhobPuv3GUvvWcpdze+S5O8mZg5tS7i3I08sOUSuru6P3jMaFV5YcZTv9l+mfztvrDTV+39lq7Fl3YV1HEs/VssRClE1eVcRdSozqQBXL3usbW5evLkp0EZGohQVURIfb+lQRA1wHjIEjbMz2Uu+rfJ6xz4BpF3MIy0hr+J7ZYYyfoj/gWGrhzF9+3S8HbxZOHAhiwYtood/jya9Uaq67Kw1fPpgFDlFpfy/xQfJKSqts2eXGYy8tvYE649f4Y172+PnUv01mSqVChuNDSmF5q0NFeJ2SQIo6lRmSgEe/jL6B2DXrh1YWclGkEZCbWeH6+hR5KxcibGw8rqu4I6eOHnYEbcliWJ9MUtOLWHwqsHM3DWTlm4t+W7Id3zW7zOivKMsEH3DFuhuz2cTojiZmsc9H+3gWFJOrT8zLa+YcfP38O3ey7x+b3t6t/Iy+R6eWk8pBi0sRhJAUWcURSEzSXYAX6PWarFr1UrWATYibg88gLGwkNy1aytdU6tVhPdsxpn9qQxbOpLZ+2cT5R3FqmGr+G+f/9LOo2bqBDZVsS08WfdUTzwdbbj/090s3nOp1nYH7zqXwT0fbScxS8eyx7oxoVuQWfeRBFBYkiSAos4U5ZZSXFgmCeBfyEaQxsXazw+nu+4ia/Hi65KPnOIcPjnyCf+6+jh6yuhXeD/rhq/jnTveIcwtzIIRNy4BbvZ8/3h3xsQE8urq4zyz7AhJ2UU1dv9cXRkf/nqW8V/tpZWPE+v+0ZOoIPPreEoCKCxJdgGLOpNx7Qi4GjgDuLHQdook+9tv0WdnY+XmZulwRA1wmzCeyxMnUbRnD7rIliw6sYhlZ5ahKAr3t76flkYv0k454mfvb+lQGyVbKw1vDG9PdLAbr64+ztqjKfRt7c2E7kHcEeaJWm36usrjybks3nOJ1UeS0RsUnuwTxtP9wtGYca+/kgRQWJIkgKLOZCYVYG2rwcldqt5fo42IAKD42DEce/WycDSiJtjHxKAOC2HPRzN4cXAWNhobHmzzIOPbjMdD60GmfwHf7dnHhcNXaRnjbelwG617I/3p18abNUdT+N/uS0xasI8gD3vGdWlOp+ZuNHe3x8vJtlJCqCgK2UVlXM4q4syVPL7bn8jhyzn4utgxrXcYY7oE4uVUM+9hnlpPskuyMRgNaNSyMU7ULUkARZ3JTC7fAKK6zU/NjYl1YCAad3d0R49KAtgIJOQm8GXclxSGJ/Lwej3/mDKFEb0fx9nGuaJNeUkYV45tSZQEsJY52FrxQJfmjI0J5NDlHBbvucQHG89SaigvyG1jpSbATUtzd3usNWoSs4pIytZRUKKvuMcdLT35fEIUd7X2qnaJl+pqpm2GUTGSVZxFM/tmNXpvIW5FEkBRZzKTC/EJdb51wyZECkI3DmeyzjA/bj4bEzbiqfVkyvinsdoxn4GHwbl/5Z/5jn0C+fmzONIv5eEVJH8naptKpSIqyI2oIDfeHtmBxKwiLv/xlZil43JWESV6I1FBbozo5E9zd3sC3e1p7mGPs511rcXlqS0/Mi5DlyEJoKhzkgCKOmHQG8m+Uki7O6pXJb8p0UZGkvnFFygGAyqNTAM1JEevHmX+sfn8nvQ7/o7+vNrtVe4NuxdbjS3po3PI/n45zZ56ErW9/XX9gjt64uRux7EtSfSb3NZC0TdNdtYaWno70dLbydKh4KH1AJB1gMIiZBewqBM5aUUYDQoesgGkEm1EBMbCQkrOn7d0KKIaFEVhX+o+Hv7lYcavH8/l/MvM6jmLtSPWMrrVaGw1tsAfJWEKCshdU3VJmPa9/Yk/kEZRXt0VLhb1i4edJIDCciQBFHUiI6l8B7CUgKlM26E9qNXojkpB6PpMURS2JW1jws8TmLpxKrmluXzQ6wNWDVvFsBbDsFZfP1Vo7e+P0119yV6yuMp6dG17+KFWqTi5I7muXoKoZ6w11rjZukkCKCxCEkBRJzKTC3Byt8NWK6sO/k7t4IBteLisA6ynDEYDGxI2MGrtKKZtmoYKFZ/c9QnfD/me/sH9b7p70238BEriz1G0d2+la3YO1oR39eH478kY/tiUIJoeD62HJIDCIuS3sagT13YAi6ppIyMoOnDA0mGIvygzlrH+wnq+jPuShLwEuvl2Y8GABUR7R1f7jF77LjHYtmxJ1jeLcejWrdL1jn0COLkjpbwkTLTsCG6KPLWeXNVdtXQYogmSEUBRJzKT5Qi4m9FGRlJ67jyGvDxLh9LklRhKWHZ6GUNXDeXVna8S7BLMksFLmN9/PjE+MdVO/qB896nbhPEUbN5MaVJSpese/o74h7tybHPla6JpaKZtRqYu09JhiCZIEkBR64oLyijMKZENIDdxrSC07lichSNpuorKilh0YhGDVg5i1t5ZdPTsyIqhK/i478d0bNbR7Pu6DB2K2tmZ7G+XVnm9Y59ArlzIJf2SJP9NkZwGIixFEkBR6zKTZQPIrdgEB6NxcZF1gBaQV5rHZ0c/o//K/vz34H/p4d+DNcPXMLvXbFq5t7rt+6u1Wlzvv4+cFSswFlU+lza4oweO7rbEbZFRwKZI1gAKS5EEUNS6jOQCNFZqXL20lg6l3lKpVNhFSkHoupSpy+S/B/9L/xX9+TLuSwaHDGb9yPW80eMNgl2Ca/RZbg+MKy8Js3ZdpWtqjZoOvQI4KyVhmiRPrSdF+iKKyip/OBCiNkkCKGpdZnIB7n4OqGv4GKXGxj4yEt2xYyhG2RFam64UXuGdfe8wcOVAlp5eyujw0Wy4bwMvd30ZX0ffWnmmTYA/jn37kL34m6pLwvS8VhImpVaeL+qvZtryE0BkFFDUNfmNLGpdZpLsAK4ObUQExrw8Si9etHQojVJiXiKv7XqNQT8MYu35tUxpP4WN92/kuejnKo7kqk3uFSVh9lW6ZudgTXgXb47/niQlYZqYvx4HJ0RdkgRQ1CqjUSErVXYAV4ddx46gUqE7IgWha5KiKCw5tYRhPw5ja+JWnur0FBvv38gTkU/gYutSZ3HYd+1SXhJm8TdVXu/QJ5DC3FIuHJaSIE3JtePgpBSMqGuSAIpalXdVh77UKAlgNWgcHbENC5N1gDWosKyQF7a9wDv73mFsq7FsuG8DD7V/CAfruh+RVqlUuI0fT8HmLZQmVT79wzPAEb+WrsRtlc0gTYmzjTM2ahsZARR1ThJAUatkB7BptJGRciRcDTmXfY6x68ayPWk77/d6n+ldpmNnZWfRmFyGDkHt5ET20m+rvN6hdwCp53Ir/t6Ixk+lUuGp9ZRagKLOSQIoalVGcgFaZxvsnW0sHUqDoI2MoCQ+HkOBJAC3Y0fyDsatH4eV2orvhnzHgOABlg4JALW9Pa733UfOipUYdbpK10MiPbF3tuH473I+cFMitQCFJUgCKGpVZlIBnjW8AcTYiHfJaiMjQVEojpOC0OZKzE/kxW0vEuUdxZLBSwhxCbF0SNdxGzcOY34+uWvXVrqm0ahpe4cfZ/ZeoVSnt0B0whI8tB6yBlDUOUkARa0qPwP49qZ/s7Ozef/994mKisLJyQlbW1tCQ0N57LHHOHbsWA1FWj/YhISgdnaWdYBmKjGU8PzW53GxceHdO9/F3tre0iFVYhPgj9OgQeSuWVtlSZh2d/ihsVITfzDNAtEJS5Dj4IQlSAIoak1ZqYG8jGLc/cxPAHfs2EH79u154YUXOHToEAUFBej1ei5evMgXX3xBp06d+M9//mPyqOCVK1d46qmnCA0NxdbWlsDAQIYOHcqmTZsq2hw+fJhRo0bh7e2NnZ0d4eHhPPLII5w9e9bs13MrKrUabceOshPYTO/se4fzOeeZ03sOzjbOlg7nhppNm4Y2MoKylMp1/xxd7egxKozSYkOVCaJofGQKWFiCJICi1uRllK9xMvcEkP3793PXXXeRUsUvyWuMRiOvvfYa06dPr/Z9ExISiIqKYvPmzcyePZu4uDg2bNhAnz59mDZtGgDr1q2jW7dulJSUsGTJEk6dOsU333yDi4sL//73v816PdV1bSOI/PI3zZrza1hxdgWvdHuFNh5tLB3OTdkEB4FaQ+GuXVVe92ruRHZKIblphXUcmbAED60HmcWZGIwGS4cimhArSwcgGq+8jGIAnJuZngAWFxczatQoSkurdzTW+++/z913303//v1v2faJJ55ApVKxb98+HBz+XJ/Yrl07HnroIYqKipgyZQqDBw9m1apVFddDQkLo2rUrOTk5Jr8eU2gjIsiYO5fShARsQ+rX+rX6SqfX8e6+dxkSOoQRYSMsHc4tqdRq7Dt3Im/9zzgPHIjGyem6626+Dthorbh0MhtXH9lB39g10zbDqBjJLsmuk6LkQoCMAIpalHdVh8ZabdYO4GXLlnHp0iWT+rz33nu3bJOVlcWGDRuYNm3adcnfNa6urvzyyy9kZGTw4osvVnkPV1dXk+IylTaiI4CUgzHBhosbyCvN44mI8uS+IbCPjgaVisJ9+ytdU6lUBLZxJz0hj9Ji2QzS2F1L+mQdoKhLkgCKWpOXocPZw86sX8g//PCDyX02bdpEbm7uTducO3cORVFo3br1DdvEx8cD3LRNbdI4O2PTooVsBDHBsjPL6OHfg0DnQEuHUm0aR0fsO0VStHtXlec/e/g7oBihMLvYAtGJuiTHwQlLkARQ1Jq8zGKzpn+hPFEzlaIoXLhw4ZZtgJsmpfVh7Z02MgLd0ca1w7m2HM84zonMEzzQ6oEauZ/BYKCgjuowOsTGYsjOpuT8+UrXtE7lI+dF+WV1EouwHDkOTliCJICi1ihGxezpOHNr/RkMN19E3bJlS1QqFadOnbphm/DwcABOnz5tVgw1QRsRQcmZMxgLZRPArXx3+jv8HPzo6d/T7HtkZWXx9ttv065dO7RaLU5OTnh4eHD//fezdevWmgv2b6wDAkBjhT6tcskXjZUaG3sNuvzqrYMVDZeNxgYXWxcZARR1ShJAUWucPewqdgKbKjQ0tFb6ubu7M2DAAD755BMKq0iucnJy6N+/P56ensyePbvKe9T2JhD4oyC00Yju+Ilaf1ZDZjAa2HhpIyNajkCj1ph1jx07dtChQwdefvllTp48SVlZ+YhbVlYWK1eupE+fPjz22GOUlJRU+56TJ09GpVJVfHl4eDBw4MDr6laqVCrUGg1HC/IxZP659qukpAQPD4/yDyoJhyQBbCI87eQ4OFG3JAEUtca5mZa8DJ1ZU6rDhg0zuU/Pnj1xd3e/Zbt58+ZhMBjo0qULK1euJD4+nlOnTvHRRx/RvXt3HBwc+PLLL/npp58YNmwYv/32GwkJCRw4cIAXX3yRxx9/3OTYTGXbogVqBwdZB3gLV3VX0el1tPVoa1b/w4cPc/fdd9+01BDAF198wZQpU0y698CBA0lNTSU1NZVNmzZhZWXFkCFDrmsTGBjIsuPH0WdmVXxv1apVODqW7/y1tbeWKeAmwlPrKVPAok5JAihqjbOnFn2pEZ0Zv8DGjx+Pl5eXSX2ee+65arULCQnh0KFD9OnTh+eff5727dtz9913s2nTJj799FMA7r33Xnbt2oW1tTXjxo2jdevWPPDAA+Tm5vLmm2+a/HpMpdJo0EZ0lJ3At5CUnwRAgFOAyX0NBgNjxoyhuLh6myyWLl3KN998U+3729ra4uPjg4+PD5GRkUyfPp3ExESuXv3zl/ykSZNYdfAg+WlXKr63YMECJk2aVH4PrUwBNxWe9lIMWtQtSQBFrXH2tAMwaxrYwcGBpUuXotFUb1pv6tSpjBhR/fpvvr6+zJ07l4SEBEpKSkhKSuLHH3+kd+/eFW2io6NZuXIl6enpFBcXEx8fz+eff05YWJipL8csdhER6I4cqRebUuqrpILyBNDf0d/kvj/++GPFju/q+uCDD0x+DkBBQQFLliwhLCwMDw+Piu9HRUUR7OvL2l27URSFxMREtm3bxoQJEwCwsbemtMiAXt94z78W5WQKWNQ1SQBFrXH21IIKrl7ON6t/3759WbVq1S3r7k2bNo158+aZ9Yz6zD4yEkNWFmVJSZYOpd5Kyk/CS+uFrcbW5L6rV682uc/Ro0e5ePFitdquW7cOR0dHHB0dcXJyYs2aNSxbtgy1+vq33Qn33cd3R49gLCzk66+/ZvDgwTRr1gwAO/vyWv3FMgrY6MlxcKKuSQIoao2NnRXB7T04uTPF7FGsoUOHEhcXx/Tp0wkM/LPGm5OTEyNGjGDz5s3MnTsXGxvTi03Xd3Yd/ygILesAbyi5IBl/J9NH/wDOV1F6pSb79enThyNHjnDkyBH27t1L//79GTRoUKUC5+MnTOBgSgrxhw6xcOFCHnrooYprtvbWABRJAtjoeWg9KCgrQKc3b+OcEKaSBFDUqva9AshILCDtYp7Z9wgICOCdd97h8uXLFBUVkZGRQV5eHj/88AN9+vSpwWjrFys3N2yCg9EdPmLpUMRfVLe0kYODA2FhYYSFhdGlSxe++uorCgsLmT9//nXtPFxd6RcayuPTp1NcXMygQYP+cvWPupU1Fbyot5rZl4/6yiigqCuSAIpa1bytO86edhz/PblG7qfVaq9bQ9XYaSMiZCPITQQ4BZCcb97PVosWLeq0n0qlQq1Wo9NdP8Kjz8piTPv2/L5nDxMnTrxu3WtJUfkxcNeKQovGy9NOjoMTdUsSQFGrVGoV7e70J/5gGroCmcYylbZTJMVnzmDUybRQVQIcA0jXpVOsN/24NFM2DV3TqVMngoODq9W2pKSEK1eucOXKFU6dOsVTTz1FQUEBQ4cOva6dITOLvuGtSE9P5/XXX7/uWvEfCaCdJICN3rXj4KQUjKgrkgCKWtc21g8VKuK2yGYGU2kjI0Gvp/iEFISuyrXdvykFN6/jV5Vhw4ZVnPpSXc8//3y1227YsAFfX198fX3p2rUr+/fvZ/ny5dftNAfQZ2Vi5elJs2bNKq1lLS0qw8Zeg5WVvFU3di62LliprWQKWNQZeVcRtc7O0ZoOfQI4/OtlCrKrf5qCANuwMFT29rIR5Aau1f+7Vg7GFBqNhuXLl6PVVu+86vHjx/Pggw9Wq+3ChQtRFKXiKy8vj3379nHfffdVtFEUheHDh2PIzMLK4/oC5q6uriiKQsdWMTL920SoVCrZCSzqlCSAok5EDw7G2lbD7tXnLB1Kg6KyskLboYOsA7wBL3svtFZaTmSaN0LasWNHNm/eTEDAzQtJT5s2ja+++sqsZ9yKISsTK/eq17Xq8suwd7KuleeK+sfTThJAUXckARR1wlZrRddhoZzdm8aVi7mWDqdB0UZEUCQFoaukVqkZGDyQH+J/wGA0mHWPbt26ERcXx+zZs+nQoQO2tuU1Bb28vBg9ejTbtm2rtVJDitGIPisbjUfVRxgW5ZfKCGATIiOAoi5JAijqTJsefngEOLLj+3hJZkygjYzEcDUD/S3Oq22qxrQew5XCK2xL2mb2PVxdXXnhhRc4duwYOp2OoqIi0tLSWLZsGXfccUcNRnu9ssREMOix8vapdE2vN1JaZJAEsAmR4+BEXZIEUNQZtVrFHaNbknYxj7P70iwdToOhjSgvCF0k6wCr1M6jHR08O7DszLIauZ9Kpar2usDbVbhrNxp3D2xbhFa6du30D5kCbjpkBFDUJUkARZ3yD3ejRadm7F51nrIS86bsmhorDw+smzeXdYA3MabVGHam7ORy3mVLh1JthoICio4cxqF7N1Tqym/FGUkFqNTg4GZngeiEJXjaeZKly8KoyNnPovZJAijqXOx9YRQXlHFo46VbNxbAHwWhj0gCeCMDggfgYuvC3CNzG8zygqL9+wGw79Kl0jXFqJB0OgvvYGds7KzqOjRhIZ72nugVPTklOZYORTQBkgCKOufsqSWiXyCHN14mP8v0Ar5NkTYyguJTpzCWSBmdqthZ2fFSl5f4+eLPrIhfYelwbkkxGinctRv7iAg0jo6VrmelFFKUW0Zg26o3h4jG6VoxaJkGFnVBEkBhEVEDg7DVWrH7BykLUx3aiEgoK6P4xElLh1Jv3RN6D2NajeHtvW+bXRamrpTEx2PUFWHfs2eV15Pjs3Hx1uLqbV/HkQlLqkgAiyQBFLVPEkBhETZ2VnQbHkr8gXRSz+VYOpx6z65VOCo7OykIfQsvxrxIuFs4z299ntyS+ltuSHf8OLYtw7EJDKx0rbiojILsEpq380ClUlkgOmEpFQlgsSSAovZJAigspnU3X5o1d2L79/EoxoaxbstSVNbW2LVvJxtBbsFGY8MHvT8gvzSf6dumU1RWZOmQKsn9+WfS352Nlbd3lQne8d+TObs/DQ8/BwtEJyzJVmOLk42TTAGLOiEJoLAYlVpFz9EtuXo5n9N7rlg6nHrPPjJSRgCrwd/Rn/d6vceh9EOM+2kcF3IuWDqkCsVnz5L68is49emD84D+la4b9EbitiQR2tFTNn80UVIKRtQVSQCFRfmFuRIW7cWe1ecpLdZbOpx6TRsZiT4tjbIrNZcsK4rCxo0bmTFjBlOmTOH5559nwYIF5Ofn19gzLCHWL5bv7vkOBYWxP43l54s/WzokDAWFJD/9DDYBAfi8NrPK0b8LR65SlFdKu17+FohQ1AeeWk9ZAyjqhCSAwuJiR4ZRotNzcIOUhbkZbUQEQI2NAv7++++Eh4czYMAA3njjDRYuXMicOXOYOnUqfn5+vPXWWyaVVJk8eTLDhw8HoHfv3jzzzDOV2qxevbrO1rWFuoay9J6l9Answ4vbXuStvW9RWFZYJ8/+O0VRSP33q+jT0/H/6P9QV1Fo2qA3cuiXS/i1dMXDr/LOYNE0eGo9ZQ2gqBOSAAqLc3K3o9PdzTn6WyJ5GTpLh1NvWTVrhrW/f43UA1y4cCF33XUX585VvQu7oKCAV155hZEjR2I0NtyitPbW9rxzxzv8u9u/WXF2BXctv4tZe2ZxLrtud59nL15C/s8b8J01C9uQkCrb7FxxjqzUQmLvC6vT2ET9IlPAoq5IAijqhc4DgrBztGbXSikLczPlBaGP3NY9Dh06xOOPP47BcOuTWFavXs3rr79+W8+zNJVKxehWo1k/cj3j24zn10u/MmLNCKZsmMKGixso1tduLcqC7TtImz0b90kTcR44oMo28fvTiNuaxB2jWuId7Fyr8Yj6TaaARV2RBFDUC9a2GrqPaMH5w1dJPptt6XDqLW1kJMUnT2IsLTX7Hi+99BIlJhSUfuedd7h69arZz6svfBx8eLLTk/x6/6+81+s9AF7Y9gI9v+vJY78+xqITi4jPjq+xk0QUo5Gr8+aR+OijOHTvhtfzz1fZLiu1kM2LTxPexZt2d8rav6bOU+tJfll+rX8wEUISQFFvhMd44x3izPbv4zFKWZgqaSMjUEpLKTl1yqz+ly9f5tdffzWpT0lJCd98841Zz6uPrDXWDAweyNcDv2bN8DU81ekpAD4+/DEj14yk3/J+vLrjVX6++DPZxeZ9GNFnZ5P42ONkfDwXzyeeIPDTT1HZ2FRqV1qsZ8PncTi529FrXCup+ycqagFmFmdaOBLR2EmdAVFvXCsLs/Ldg5zamUK7O2Q05O/sWrdGZWOD7siRik0hpoiLizNrhOvYsWMm92kIQlxCCHEJYVK7SRTrizmUdoidKTvZlbKLH8//iAoVbT3aEusXS6xfLBFeEVirrW96T92xYyQ98wxKkY7AL77A8Y6qT/tQFIWtS86Qn13CqH9FS9kXAVx/HJy/o7wHitoj7ziiXvEJcSG8qzd711wgLNobW638iP6VysYGbadOZheETk9PN6tfWlqaSe2dnZ3Jza18EkdOTg7OzvVzjZudlR2x/rHE+scCkFaYxu7U3exK3sXys8uZHzcfB2sHYnxi6OHXgx5+PQh0/vMkD0VRyP72W9LeeRdt27b4//dDrH19b/i8478nE78/jf5T2+HuK0WfRTk5Dk7UFfntKuqd7sPDuHB4NwfWJ9BDdkRWou3Uidw1P5rV19/fvBGFgIAAk9q3bt2an3+uXHtv//79tGrVyqwY6pq3gzfDw4YzPGw4RsXIqcxTFaOD7+57F72iJ8AxgN6uMfQ6ruDx8370CZdwmzAB7xf+WeWU7zWJp7LYsTyeDr0DaBnjXYevStR3rrauWKmsZCewqHWSAIp6x9HNls4DgjiwPoF2Pf1w9ba3dEj1il379mR+9hllaelYe3uZ1DcyMhJra2vKyspM6telSxeT2j/xxBPMnTuXadOm8eijj6LVavn111/56quvGuR6QrVKTTvPdrTzbMejHR+loLSAw9tXkr9sOf67V6A2KGxvpeb8k21ofocHPfLO0sa9DRq15rr7KEaFQxsvsffHCwS0cafH/fIBR1xPrVLT3Lk5+WUNuxi7qP8kART1Uqe7m3NyZwo7V57jnic6WjqcekXbsQMAuqNHsO5f+Tixm/Hy8mLkyJEsW7as2n2cnZ154IEHbtnOaDRiZVX+lhIcHMz27dt55ZVX6N+/P8XFxYSHh7Nw4UJGjRplUsz1SVlaOoU7d5KzbBmeR4/i4+OD2xNPUTgwFoeS0xSk7OLrE18z98hcXG1d6ebbrWL9oAvubFp4koS4TKIHBxMzJAS1WjZ9iMpGhY/Cy960D3dCmEoSQFEvWdloiB0ZxsYvT5B4KovANu6WDqnesPbywsrHB93RozibmABCeVmXX3/9laysrGq1nzNnDo6Otz6ZIj09nbCwP0e0oqKi2LBhg8nx1ReKolCWnEzR/gMUHdhP0YEDlF26DIBDbCwBcz/GsXdvVFZWeAJBRDCm9RjKjGXEXY1jZ8pOdqfsZuaumXgWBDD43CPYGRxoMdaOiJ5+kvyJm5I1gKK2SQIo6q2wKC/itiaxY3k8Y16JQa2pvapFRUVFfP/99xw6dIgrV67g6+tLVFQUo0aNQlvFsV2Wpo2MRHfosFl9g4ODWb58Offddx85OTk3bTt9+nSmTp160zbZ2dns2rWLrVu38vjjj5sVU32gKAqlFy78kfCVf+n/OHfZNjwcxx49sX8mGm1UFNZeNx6dsVZb09m7M529O/Nk5JMc2HyOfT9cpsw1n19afUbCpXhsEm2I8o6ih38Puvt1p6VrSykBIyrYWdmRWpBq6TBEI6dSaqrqqRC1IP1SHsvfOUCvseG072XaRoTq+vzzz5k+fXqVu1bd3Nx47733bpkE/dXkyZNZtGhRxX+7u7sTExPD7Nmz6dixfDr7r7/sHRwcaNGiBc8++yyTJ0+u1jNyVq0m9aWXaPHLBmyCgqod219duHCBp556ig0bNlQ67q1Vq1a89dZbjBw58pb3GTFiBPv372fSpEm8+eabDSaRUQwGSs6evS7hM2RlgUaDXdu22EdHYx8TjX3nzmhcXU2+f1mJga3fnubs3jTa3+lPz1EtUVupuJB7gZ3J5ZtJDqQdoMRQgpfWi+5+3Yn1i6W7X3fc7Nxq/gWLBmNDwgYOpR3i5a4vWzoU0YhJAijqvU3/O0XC0QwefL0bdg43r8Fmqn/84x98/PHHt2z33HPP8cEHH1TrnpMnTyYtLY2vv/4agCtXrvDqq69y7NgxLl8un0JUqVR8/fXXDBw4kMLCQpYtW8Yrr7zChg0bGDCg6uPC/spYXMy5Xr1xGTkS7+kvViuuG7l48SJ79uzh8uXLeHh40LZtW2JjY2/rnvWRUlZG8YkT5cne/gMUHTqEMT8flbU1dhEdyxO+6Bi0kZFoHM0vy1KYW8LJHSmc2J5CSVEZvR9sTauuPlW2LTGUcDDtILtTdrMzZSfx2fGoUNHGow09/MpHByObRWKtqdmfe1G/7UrexdoLa3mr51sN5gOVaHgkART1XmFuCUtm7KFtDz96jm5ZY/dduHAhU6ZMqXb7xYsX8+CDD96y3eTJk8nJyWH16tUV39u+fTt33nkn6enpNGvWDJVKxapVqxg+fHhFGw8PDyZPnlztRDNt9nvkrFxJy9+3orazq/braCqMxcXojh6rWL+nO3IURadDpdVi3ykSbXQ09tHRaDt2vO0/P0VRSInP4fjvyVw4fBW1lYrwLj50uru5SbvY04vSK5LBPSl7yC7Jxt7Kni6+XYj1iy2vPegUKElBPWE0Gjlz5gwJCQk4ODgQHh6Oj0/Vyb4p4q7G8c2pb5jZbSYONlIjUtQOWQMo6j0HF1uiBgWxb81F2t3ph5vP7b8h6vV6Xn7ZtOmVl19+mbFjx6LRaG7d+C8KCgpYsmQJYWFheHh4VLpuMBhYuXIlWVlZWFtXf6THbewYshYsIG/9z7iOHGFSTI2RoaAQ3eHDFdO5xceOoZSVoXZywj4qimZPTsM+Ohq7tm1RmfDnfDOlOj1n9l4h7vdkslMLcfW2J/b+MFp388HW3vRneNl7cW/Yvdwbdm957cGsU+UJYfJOZu+bjV7R4+/oTw+/HsT6xdLFtwtONk418lpE9en1eubMmcO8efO4dOnSddf69u3LK6+8Qt++fc2+/7X/p/ml+ZIAilojI4CiQdCXGVj6n724+Tgw5EnTj0D7u7Vr1zJs2DCT+/38888MHDjwpm0mT57M4sWLsftjVKmwsBBfX1/WrVtH586dgfIpYDs7OzQaDcXFxRgMBtzd3dm7d+91O2lv5fIjj2LIzCR4xXJU6qZ1tLc+OxvdoUMVa/iKT54EoxGNh8cf07nla/hsW7ZEZWLSfjMlRWWkns8l4VgGZ/alYSgzEhrhSfte/vi3cqu10bnCskL2X9nPzuSd7E7dzaW8S2hUGiKaRdDdrzs9/HrQ1qNtpdqD5jiTdQYHawcCnGpn3W1Dlp6ezsiRI9m5c+cN26jVambMmMHMmTOrdc+/zxpk6jIZ8Y8R7Fywk1mzZvGvf/2rJkIX4joyAigaBCtrDbH3hbHh8+NcOpFJULvKI2mmiIuLM6vf8ePHb5kAAvTp04dPP/0UgKysLObNm8egQYPYt28fQX9s2vjwww/p168fiYmJPPfcczz77LMmJX8AHo88zOVJk8n47DOaPfGE6S+oASlLT0f3x+he0f4DlMTHA2Dl64t9TDSuo0dhHx2DTUhwjSZhRXmlpJ7LISU+h5RzOWQkFYBSXrA8sl8g7Xr64+hmW2PPuxEHawd6B/amd2BvAJLyk9iVsotdKbtYdGIRnxz5BBdbF7r5dqtYP+jjYPp05JXCK8zaO4tMXSYFZQXcGXAnU9pNIdQ1tIZfUcOj1+u5//77b5r8QfnU8GuvvYaPjw+PPfaYyc9xsnEi7uc4Jk6byIIFCyQBFLVCEkDRYIRGNsM/3JWdy+MJaO2G5jbKwly9erVW+zk4OFSqiefi4sL8+fN58803AfDx8SEsLIywsDCWL19Op06diI6Opm3bttWOx6FLFzynTSPj47nYR0bi0Eg2byiKQllKKkX791G0//oafDZBQWhjonGf+lB5whdg3vF2N5KfVVyR7KXG55B9pQgAZ087/Fq60rFPAH4t3XD2tLPoWrwApwBGtxrN6FajKTOWcTzjePno4B+1BxUUWri0KB8d9O9BlHcUWqtblzRSFIVxrcfRxqMN6UXpLDi+gK+Of8UrXV/B3rppn8rz6aefsn379mq3f/rppxk+fDje3qYd97d7x24MpQbGPzOejSs3sm3bNu68805TwxXipiQBFA2GSqWi5+iWfD9rP8d/Tyaib6DZ9zL1bNvb7adSqVCr1eh0uiqvh4WFcd999/HSSy/x44+mnfPr+f8eR3f4MMnP/5OQVT9gXQOL0OuaoigYsrIoTUqiLCmJ4pOnyF6yBKW42KQafOY8N/eqrjzh++MrP7MYADdfB/zC3Yi+Jxi/MFcc3ervRhtrtTWdvDrRyasTT3Z6ktySXHan7mZ3ym5+vfQri08txkZtQze/bkxsO5Eg5yC87b2rTGB9HX3xdfQFIMg5CK2VlgnrJ3BP6D3E+jWODxjm+uSTT0xqX1JSwvz583n11VdN6vfVV1/RaUAnipQiHnjgAb766itJAEWNkwRQNCieAU606enH/nUXadXFBztH8xbzd+/e3ax+3bp1q1a7kpISrvxRRDg7O5u5c+dSUFDA0KFDb9jn+eefJyIiggMHDhAdHV3tmFQaDX7vv8fFESNJfvY5ghYtRGVjU+3+lqAYDJScO4fu8BGKDh9Gd+QIhuzs8hp8rVph16ED/nM+MLsG3w2fa1TISi2sGOFLic+hKLcUlQo8A50IjWiGX0tXfMNc0DrV7z/Dm3GxdWFg8EAGBg9EURQu5l5kZ8pOzmaf5Uj6EVafW429lT3hbuGEu4UT5hZWsfHAqBhRoapIDsPdwjEoBjSqmltH2RClpKRw5swZk/tt3brVpAQwLy+PlStX8szXz5Bfms/48ePp0aMHH3/8Mc7OziY/X4gbkQRQNDhdh4Zybn8a+9Ze4M4HWpl1j9jYWDp27MixY8eq3ScqKoqYmJhqtd2wYQO+vuWjKE5OTrRu3Zrly5fTu3fvG/bp0KED/fr1Y8aMGaxfv77acQFYubkR8N8PSZgwkcsPTcVvzgc1OlJ2u25Vg8950MAaqcH3d0aDkYykgj9H+M7lUFKoR61W4RXsROtuvvi1dMWnhQu22sb5dqhSqQh1Da1Yw6c36LmQd4H47HjOZp/lYPpBAPwd/Al3D6eFawtCXUKxUllxtegqHxz8gCDnIDp4drDky7C4v+/2ra6EhAST2n/77beEhobSpn0bCsoKiIyMJDQ0lO+++45HH33UrBiEqIrsAhYN0uFfL7N71XnGvBqDh9+tz6mtyu7du+nVqxdlZWW3bGtjY8OOHTuqnQBaStHBgyQ/8yyKouD/wQc4dO1ikTiMxcXojh0rr7934ABFh4/UWg2+vzKUGUm7lEdKfPn6vdTzuZSVGNBYq/EJdcYvzBW/lq54h7pgbdO0R7SuySvJIz4nnjNZZ4jPjqdQX4iN2gajYuRg2kG87L2YFjmNbn7dUBSlydYgPHHiBO3btze5X+fOnTl48OBN2/x1F3CXLl04cOAAqj/Oilahwmg0EhMTw969e82KXYiqSAIoGiSD3sjS/+zF2dOOof+INPuX0jfffMMjjzxCSUnJDdvY2dmxYMECHnjgAXPDrVP6jAySn/8nRfv30+yZZ/B4eGqtl4i5VQ0++5joGq/BB+XHrV25mFuR8F25mIehzIi1nQbfFq74tXTBr6UbXkFOaKyaVpkccxgVI5sub2LhiYWczTpLiEsI/o7+eNt709KtZcV0cXU2kzQ2Op0OV1dXSktLTeo3btw4lixZctM21xLAN954g4iICLZu3coPST/Q0q0lvQJ7kZOTw5133smxY8fMSkKFqIokgKLBunj0Kus/jWPwEx0J6ehp9n0OHDjACy+8wNatWytd69u3L++//z6dOnW6jUjrnmIwcPXjj8n87HO0UVG4T5yIU98+NZZ8WboG37Up3auX8jEaFewcrPENc8E/3A2/lq54+Dugvo1d4k2RTq9j3pF57E3dS5BzEM9FPYe7nTvxOfGcyzlHfHY8V3VXUaOmuXNzWrq1pKNHRzztPWuk9mBDMG7cOJYuXWpSn+rWDs3JySE4OJg9e/awY9cOXt3xKveG3Ut3v/L1yj169KBLly58+OGHZscvxF9JAigaLEVRWPN/R8jPKuaBGV1ve4QnPj6ew4cPk5qaip+fH506dTK5Ll99U7BjJxmfforu4EGsvLxwHT0a11GjsPY2bX1gWXo6uoMHKxK+krNngT9r8F07R7euavDZu9jg37J8Ote3pSvuPg4VU2bCPGvPr+WVHa/QzqMdr8W+Riv3yutrs4qzOJt9lrNZZ7mYexFPe09+T/ydQKfA8qPq/HuYVXuwoYiLi6NLly4UFxdXq31sbCw7duy45d+JiRMnUlBQwLZt25g+fToPPfkQ7+5/l4fbP0y4ezgAc+bM4e233yY5ORmber7JSzQMkgCKBi0zuYBlb+6j+8gwOt3d3NLh1FvFp0+TvfQ7cteuRSkpwaFrF2yCg7EOCMQ6wB+bwECsfX0x5ORQmpRMWVIipYmJlCUlU3L6NKV/LIC/VoOvIuGrhRp8qedySI6vugbftS9nT22TXYtWW/JK8/g98XfWXVjHgSsHsNXY0tKtJU92epJo7+hKf94Go4HUwlR+uvgT25K2cTzjOEbFSKhLKLF+scT6xRLlHdXoagd+/fXXTJ06lVv96vT19WX//v34+9/678jAgQMJCwtj7ty5AMRnxzM/bj4vRr+Ip735sxtC3IwkgKLB+33pGc7uvcKDr3fH3lk+Gd+MIT+f3B/XULhzJ2VJSZQmJ6MUFVVuqNFg7euLdWAAtiGh2MfUbg2+1D9G+PIy/lKDr+Ufa/jqeQ2+xup4xnF+uvATrd1bc0/oPVipb75LOrcklz2pe8rPLk7ZyZXCK1irrens3bni7OJwt/BGkbj/9NNPTJo0iczMzCqv9+rVi6VLl1ZUAriR7Oxsdu3axX333cd3333H8OHDAdhwcQM7U3Yys/vMW/65C2EuSQBFg6crKGXJjD2EdmpG3wltLB1Og3KtAHNZUhJlKSloXF2xDgzE2scHlVXN/uK5UQ0+VOAZ4PjnCF+Ya4OuwSfKf64u5l1kV3L5UXUH0g6g0+vw1HrS3bc7sf6xdPftjof29o50tKSioiK+/fZbfv31VxISEnBwcCA8PJwHH3yQO+64o1r3GDFiBPv372fSpEm8+eabqFQq9EY9b+19i47NOjI8bHjtvgjRpEkCKBqFE9uT2brkDP2ntqNljGnHLonacasafOUFl13xbeGCrX3N7QwW9U+poZRD6YfKzy5O3sWZ7PKCym3c21RMF3fy6oS1Rn4OjqQf4dvT3/J81PN4O8h7mag9kgCKRkFRFH5dcJKLxzIY9a9o3H1rrpiwqB5DmZH0S3nlo3tnb1KDL8QFa9umsWtUVC1Dl1ExVbw7ZTdZxVlorbTE+MSUbybx60GQc1CjmC421adHPkWtUvNYxGOWDkU0cpIAikajtFjPincOgErF/dOjsLGTtTO16eY1+FwqpnO9gpzRWEtJFlE1o2LkTNaZ8tHBlF0cSj+E3qjHz8GPWP/y0cGuvl1xtmn8x6ClFqTy4aEPebDNg0Q0i7B0OKKRkwRQNCpZqYUsf+cAIR09ufuhtk1yBKG23KgGn62DVcXonl9LVzwDHKUGnzBbUVkRB9IOsDN5J7tSdpGQl4BGpaGDZ4fy6WL/WNp7tG90tQcNRgNfxn1Jhi6D6V2my+YPUeskARSNTvz+NDZ+dYIWnZrRd2IbbBrpGa+1oUSnJ++qjrwMHXkZxX/8U0deZjE56UVSg0/UueSCZHan7GZXyi72pOwhvywfJxsnuvl2q5gu9nW8+W7bhuC3S7+xM3knE9tNJMQlxNLhiCZAEkDRKF04fJVNi06idbZh0GMd8PA377zghkwxKpTo9BQXlKErKKO4oPSPf5Z/6Qqv/XspuvzyNqU6fUV/a1sNzp5anD3tcPbU4u5XXprFpZnU4BOWoTfqOZ5xvGK6OC4jDqNiJMQlpGIzSbR3dIOrPXgh5wKrz63mDv87iPGt3+eNi8ZDEkDRaOWkF7Hhi+PkphXRa1wrWndvuKMEiqJQVmIoT9gKy9DlX5/Q/ZnM/ZnsFRfqUYyV/3rb2lth52CNnaM1Wkdr7Jxs0P7x307u5cmeczM77BysJdET9VpuSS57U/dWJISphanltQe9OlesHwx3C0etqr9LEpLzk3nol4eI8IrgnTveqdexisZFEkDRqOlLDfz+3VlO70qleTt3OvQOoHk7D9QWnrI0lBnLE7XCv43K5Zdel9D99ZpBb6x0HytbTUXypnW89k8b7Byv/96179s6WKGR9XmiEVIUhYS8BHal7GJn8s6K2oMedh509+tOrF8s3f2646mtPydrJOYl8o8t/0Cn17FsyDJcbF0sHZJoQiQBFE1C/IE0Dv1yiYzEApw97Wh3hz9teviidbz9gsNGg5HiQv0fo3N/GZX7a2JXUHrd98pKDJXuo7ZS/ZHM2fwtoSv/3vXJnDV2DtZY2TSuhfBC1JRSQymH0w9XjA6ezjoNQGv31tfVHrTRWKbo+ObLm3l1x6u42bnxUd+PaOHawiJxiKZLEkDRZCiKQlpCHsd/T+bcgXQAfMNccGmm/WOtW/l6NwdX24rp1irXzl3798LyEbuSIn2lZ6lUlCdrDlWPyl1L6v6a6FnbamTKVYhacq324LWE8FrtwWjvaHr4lx9VF+wcXOt/B/VGPR8d+oivT3xNv+b9eL3H6zjZONXqM4WoiiSAoknSFZRyetcVrlzMLd/lelVHaXHlUblrKq2b+9uo3N9H6Wy1VrIzVoh6yqgYOZt9tuJkkkPphygzluHr4FsxOtjVt2uNTskqisLxjOO8f+B9jl49yrNRzzKx7UT50CcsRhJAISh/cy4p0pOXoaMwpwQbrZWsmxOiibhWe/Da6ODF3IuoVWrae7anh1/56GB7z/Zm1ebT6XVsuLiB7858x8nMkwQ4BjCr5yw6e3euhVciRPVJAiiEEEL8RUpBSsVRdXtS95Bf+mftwRifGIKcgwhwDMDXwbfS+cX5pfkkFySTlJ/EwbSD/Hj+RwpKC+jp35OxrcfSw69HoytiLRomSQCFEEKIGzAYDRzPPM6u5D9rDxqU8uUiapUaH3sf/Bz90Ol1JBUkkVuSW9HX3c6de8PuZVT4KAKdAi31EoSokiSAQgghRDWVGcu4UnilYpQvuSCZ5PxktNZaAhwDCHAKwN/RnwCnANxs3WSNn6i3JAEUQgghhGhiZGW7EEIIIUQTIwmgEEIIIUQTIwmgEEIIIUQTIwmgEEIIIUQTIwmgEEIIIUQTIwmgEEIIIUQTIwmgEEIIIUQTIwmgEEIIIUQTIwmgEEIIIUQTIwmgEEIIIUQTIwmgEEIIIUQTIwmgEEI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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hnx.draw(H)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Weighted hypergraph clustering\n", + "\n", + "Hypergraph clustering on term-document data using tf-idf as cell weights. In this example, we use the 20newsgroups dataset:\n", + "\n", + "https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html\n", + "\n", + "and consider documents falling into two subcategories. We form a static hypergraph with 787 documents as vertices, 20,868 terms as hyperedges, and tf-idf as vertex-hyperedge (i.e. cell) weights. We then form the normalized Laplacian and apply the spectral clustering algorithm as defined by \"RDC-Spec\" (Algorithm 1) in:\n", + "\n", + "Hayashi, K., Aksoy, S. G., Park, C. H., & Park, H. \n", + "Hypergraph random walks, laplacians, and clustering. \n", + "In Proceedings of CIKM 2020, (2020): 495-504.\n", + "\n", + "We plot the proportions of the document subcategories within each output cluster." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(0, 'alt.atheism'),\n", + " (1, 'comp.graphics'),\n", + " (2, 'comp.os.ms-windows.misc'),\n", + " (3, 'comp.sys.ibm.pc.hardware'),\n", + " (4, 'comp.sys.mac.hardware'),\n", + " (5, 'comp.windows.x'),\n", + " (6, 'misc.forsale'),\n", + " (7, 'rec.autos'),\n", + " (8, 'rec.motorcycles'),\n", + " (9, 'rec.sport.baseball'),\n", + " (10, 'rec.sport.hockey'),\n", + " (11, 'sci.crypt'),\n", + " (12, 'sci.electronics'),\n", + " (13, 'sci.med'),\n", + " (14, 'sci.space'),\n", + " (15, 'soc.religion.christian'),\n", + " (16, 'talk.politics.guns'),\n", + " (17, 'talk.politics.mideast'),\n", + " (18, 'talk.politics.misc'),\n", + " (19, 'talk.religion.misc')]\n" + ] + } + ], + "source": [ + "#list possible categories to choose from\n", + "all_categories = np.array((fetch_20newsgroups(subset='test').target_names))\n", + "pprint(list(enumerate(all_categories)))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#select categories of documents to be clustered\n", + "categories = all_categories[[1,15]]\n", + "twenty_train = fetch_20newsgroups(subset='test',\n", + " categories=categories, shuffle=True, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "#record categories of documents\n", + "doc_types=dict()\n", + "for i,x in enumerate(twenty_train.filenames):\n", + " doc_types[i]=x.split('/')[-2]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<787x20868 sparse matrix of type ''\n", + "\twith 136994 stored elements in Compressed Sparse Row format>" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#form TF-IDF term-document matrix\n", + "tfidf_vect = TfidfVectorizer()\n", + "X_tfidf = tfidf_vect.fit_transform(twenty_train.data)\n", + "X_tfidf" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#extract vertex-hyperedge incidences and weights from TFIDF matrix\n", + "mat = coo_matrix(X_tfidf)\n", + "edges = mat.col\n", + "nodes = mat.row\n", + "data = np.array([edges,nodes]).T\n", + "weights = mat.data\n", + "\n", + "h = hnx.Hypergraph(data,cell_weights=weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check the hypergraph is connected, as this is required by spectral clustering\n", + "#if not, restrict to largest connected component or modify hypergraph as necessary\n", + "h.is_connected()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<787x20868 sparse matrix of type ''\n", + "\twith 136994 stored elements in Compressed Sparse Row format>" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# # outputs the cell weight of a selected node in a selected edge\n", + "# weight = lambda self, node, edge: self.elements[edge].cellweights[node]\n", + "\n", + "#the weighted incidence matrix which contain the cell weights\n", + "I,verMap,edgeMap = h.incidence_matrix(weights=True,index=True)\n", + "I" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[383, 404]\n" + ] + } + ], + "source": [ + "#cluster the vertices (documents)\n", + "num_clus=len(categories)\n", + "clusters=hnx.spec_clus(h,num_clus,weights=True)\n", + "print([len(v) for v in clusters.values()])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#visualize document category composition within each cluster\n", + "fig = plt.figure(figsize=(22, 10))\n", + "labels=sorted(list(set(doc_types.values())))\n", + "colors=['#e41a1c','#377eb8']\n", + "\n", + "#pie charts\n", + "for clus in range(num_clus):\n", + " ax=plt.subplot(131+clus)\n", + " counts=[[doc_types[y] for y in clusters[clus]].count(z) for z in labels]\n", + " ax.pie(counts, colors=colors, shadow=True, startangle=90)\n", + " ax.set_title(\"Cluster \"+repr(clus)+ \"\\n\"+ repr(sum(counts))+ ' Documents',fontsize=20)\n", + "\n", + "#legend\n", + "ax=plt.subplot(131+num_clus)\n", + "patches = [mpatches.Patch(color=color, label=label)\n", + " for label, color in zip(labels, colors)]\n", + "ax.legend(patches, labels, loc='center',fontsize=20, frameon=False)\n", + "ax.axis('off');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "4b11832e3fb1d317fabfdf226ff96dd8761e1caa77b8bb75a64cd45c858a9356" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/Tutorial 8 - Generative Models.ipynb b/tutorials/Tutorial 8 - Generative Models.ipynb new file mode 100644 index 00000000..fb06016a --- /dev/null +++ b/tutorials/Tutorial 8 - Generative Models.ipynb @@ -0,0 +1,327 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generating hypergraphs using random models\n", + "\n", + "This tutorial and all supporting code were developed by Mirah Shi, Sinan Aksoy, and Nicholas Landry.\n", + "\n", + "Implementation of and tutorial using two hypergraph generative models: \n", + "1. [Erdös–Rényi](#erdosrenyi)\n", + "2. [Chung-Lu](#chunglu)\n", + "\n", + "Hypergraph Erdös–Rényi and Chung-Lu implementations are described in\n", + "\n", + "> S. Aksoy, T.G. Kolda, and A. Pinar. Measuring and modeling bipartite graphs with community struc-ture. In:Journal of Complex Networks 5.4 (Mar. 2017), pp. 581–603.\n", + "\n", + "and adapt the algorithm in\n", + "\n", + "> J. C. Miller and A. Hagberg. Efficient generation of networks with given expected degrees. In 8th International\n", + "Conference on Algorithms and Models for the Web Graph (2011), pp. 115–126." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "Generative models are useful tools in network science for their ability to approximate real data. Datasets are typically of a fixed size and generative models allow us to create networks with similar properties, but of arbitrary size. These models can be used as a proxy when the real data may too sensitive to reveal. Lastly, we can use generative models for *inference*, where given a real network and a generative model, we can calculate which parameters best match the given data. We can extend these network science ideas to networks where interactions can happen between greater than two entities." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import hypernetx.algorithms.generative_models as gm\n", + "import hypernetx as hnx\n", + "import random\n", + "import matplotlib.pyplot as plt\n", + "import time\n", + "from collections import Counter\n", + "import warnings\n", + "warnings.simplefilter('ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Erdös–Rényi Hypergraphs \n", + "\n", + "\n", + "\n", + "\n", + "In the article [Measuring and modeling bipartite graphs with community structure](https://doi.org/10.1093/comnet/cnx001) by Aksoy et al., they define the bipartite version of the network Erdös–Rényi model. Any bipartite network can be expressed as a hypergraph if one layer is defined as the nodes and the other layer is defined as the edges. We developed an efficient algorithm based on the [Miller-Hagberg approach](https://doi.org/10.1007/978-3-642-21286-4_10) that runs in $O(N+M)$ complexity by drawing from a geometric distribution instead of the naive algorithm that runs in $O(NM)$ time by iterating through every combination and performing a weighted coin-flip." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "n = 1000\n", + "m = n\n", + "p = 0.01\n", + "\n", + "# generate ER hypergraph\n", + "H = gm.erdos_renyi_hypergraph(n, m, p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the number of expected and generated vertex-hyperedge pairs" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected # pairs: 10000\n", + "Output # pairs: 9975\n" + ] + } + ], + "source": [ + "print('Expected # pairs: ', int(n*m*p))\n", + "print('Output # pairs: ', H.incidence_matrix().count_nonzero())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chung-Lu Hypergraph \n", + "\n", + "\n", + "\n", + "Also in the article [Measuring and modeling bipartite graphs with community structure](https://doi.org/10.1093/comnet/cnx001) by Aksoy et al., they define the bipartite version of the network Chung-Lu model. Like before, we can generate a bipartite network and define one layer as the nodes and the other layer as the edges. We developed an efficient algorithm based on the [Miller-Hagberg approach](https://doi.org/10.1007/978-3-642-21286-4_10) that runs in $O(N+M)$ complexity instead of the naive algorithm that runs in $O(NM)$ time. Unlike the Erdös–Rényi case, in the Chung-Lu model, the probabilities vary by degree, so in addition to drawing from a geometric distribution, we sort the degrees in reverse order and perform rejection sampling.\n", + "\n", + "The Chung-Lu model fulfills a degree distribution in expectation. Given degree distributions $W_n=\\{w_1^v,...,w_n^v\\}, W_m=\\{w_1^e,...,w_m^e\\}$ for vertices and hyperedges respectively, the hypergraph Chung-Lu model assigns vertex $i$ to hyperedge $j$ with probability $$p_{ij}=\\frac{w_i^v w_j^e}{S},$$ where $$S=\\sum_{i=1}^n w_i^v=\\sum_{j=1}^m w_j^e$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example hypergraph\n", + "\n", + "We use a preprocessed disease-gene dataset (available from https://www.disgenet.org/downloads) and create a hypergraph with genes as vertices and diseases as hyperedges. Then we extract the degree sequences as input to ``chung_lu_hypergraph``." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of vertices: 12368\n", + "Number of hyperedges: 2261\n" + ] + } + ], + "source": [ + "gene_data = hnx.utils.toys.GeneData()\n", + "genes = gene_data.genes\n", + "diseases = gene_data.diseases\n", + "disease_gene_network = gene_data.disease_gene_network\n", + "print('Number of vertices: ', len(genes))\n", + "print('Number of hyperedges: ', len(diseases))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Construct degree sequences\n", + "\n", + "Label vertices and hyperedges with their desired degree:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "k1 = {n: d for n, d in disease_gene_network.degree() if n in genes}\n", + "k2 = {n: d for n, d in disease_gene_network.degree() if n in diseases}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Chung-Lu hypergraph\n", + "\n", + "``chung_lu_hypergraph`` generates a bipartite edge list, or equivalently, a list of vertex-hyperedge pairs and outputs it as a HyperNetX object." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "H = gm.chung_lu_hypergraph(k1, k2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize results" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# plot desired vs output degree distribution\n", + "node_degrees = [H.degree(node) for node in H.nodes]\n", + "edge_degrees = H.edge_size_dist()\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(14,5))\n", + "ax[0].scatter(Counter(k1.values()).keys(), Counter(k1.values()).values(), color='orange', s=8, label='DisGene')\n", + "ax[0].scatter(Counter(node_degrees).keys(), Counter(node_degrees).values(), color='blue', s=8, label='Chung-Lu hypergraph')\n", + "ax[0].set_xscale('log')\n", + "ax[0].set_yscale('log')\n", + "ax[0].set_xlabel('Degree')\n", + "ax[0].set_ylabel('Count')\n", + "ax[0].set_title('Vertex degree distribution')\n", + "ax[0].legend(loc='best')\n", + "\n", + "ax[1].scatter(Counter(k2.values()).keys(), Counter(k2.values()).values(), color='orange', s=8, label='DisGene')\n", + "ax[1].scatter(Counter(edge_degrees).keys(), Counter(edge_degrees).values(), color='blue', s=8, label='Chung-Lu hypergraph')\n", + "ax[1].set_xscale('log')\n", + "ax[1].set_yscale('log')\n", + "ax[1].set_xlabel('Degree')\n", + "ax[1].set_ylabel('Count')\n", + "ax[1].set_title('Hyperedge degree distribution')\n", + "ax[1].legend(loc='best')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the Chung-Lu model does not match the degree distribution exactly (notice the small tail of the distribution of actual degrees in contrast to the desired degree distribution)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This algorithm, as mentioned before, has linear time complexity $O(N+M)$. We can test this out by plotting the hypergraph generation time with respect to $N+M$." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "n = [500, 500, 500, 1000, 1000, 1000]\n", + "m = [100, 500, 1000, 1000, 5000, 10000]\n", + "m_and_n = list()\n", + "generation_time = list()\n", + "\n", + "for i in range(len(n)):\n", + " k1 = {j : random.randint(1, 10) for j in range(n[i])}\n", + " k2 = {j : random.randint(1, 10) for j in range(m[i])}\n", + "\n", + " m_and_n.append(n[i] + m[i])\n", + "\n", + " start = time.time() \n", + " H = gm.chung_lu_hypergraph(k1, k2)\n", + " generation_time.append(time.time() - start)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(m_and_n, generation_time, 'ko-')\n", + "plt.xlabel(r\"$M+N$\")\n", + "plt.ylabel(\"Generation time (s)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the plot, we can see (sans artifacts for small $M+N$) that there is a roughly linear relationship as we predicted." + ] + } + ], + "metadata": { + "interpreter": { + "hash": "4b11832e3fb1d317fabfdf226ff96dd8761e1caa77b8bb75a64cd45c858a9356" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials/Tutorial 8 - NWHy API.ipynb b/tutorials/Tutorial 8 - NWHy API.ipynb deleted file mode 100644 index 9c938d82..00000000 --- a/tutorials/Tutorial 8 - NWHy API.ipynb +++ /dev/null @@ -1,1191 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# The NWHypergraph library for optimizing Static Hypergraph methods\n", - "\n", - "The Transmission Problem: In this tutorial we highlight the use of the NWHy library with HNX. We use a synthetically generated dataset to simulate the many to many relationship between a small group of **senders** of magazine or social media subscriptions and a large group of **receivers**. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NWHy is not available.\n" - ] - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import matplotlib.patches as mpatches\n", - "from matplotlib import cm\n", - "from collections import OrderedDict, defaultdict\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "import hypernetx as hnx\n", - "try:\n", - " import nwhy\n", - "except ImportError:\n", - " print('NWHy is not available.')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**To use this tutorial with NWHy you will need to install it in your environment. Please see the [documentation](https://pnnl.github.io/HyperNetX/build/nwhy.html) for installation instructions.**" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "## To compare with and without nwhy, set this variable. \n", - "## If nwhy is not available\n", - "USE_NWHY = True" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(18759, 2)\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " receivers senders\n", - "0 1 1\n", - "1 1 2\n", - "2 4 3\n", - "3 5 5\n", - "4 5 8" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = hnx.TransmissionProblem().df\n", - "print(df.shape)\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# NWHypergraph\n", - "\n", - "A `pandas.Dataframe`, `df`, may be passed to the hypergraph constructor if no cell contains a nan. \n", - "By default the first column will correspond to edges and the second column to nodes. \n", - "You may specify which columns to use by passing in `df[[edge_column_name,node_column_name]]` to the constructor.\n", - "\n", - "Because our example is large (34204 rows) we will use the NWHy api." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NWHypergraph is not available. Will continue with static=True.\n", - "CPU times: user 3.13 s, sys: 13.5 ms, total: 3.14 s\n", - "Wall time: 3.16 s\n" - ] - } - ], - "source": [ - "%%time\n", - "H = hnx.Hypergraph(df, use_nwhy=USE_NWHY)\n", - "\n", - "# CPU times: user 4.83 s, sys: 1.4 ms, total: 4.83 s\n", - "# Wall time: 4.82 s" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(24, 9623)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "H.shape ## (senders,receivers)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute the edge size and node degree distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "H.nwhy=False\n", - "CPU times: user 10.9 ms, sys: 2.53 ms, total: 13.4 ms\n", - "Wall time: 13.1 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "### NWHy generates a NWHy hypergraph object\n", - "if H.nwhy:\n", - " print('H.nwhy=True',H.g)\n", - " ed = H.g.edge_size_dist()\n", - " nd = H.g.node_size_dist()\n", - "else:\n", - " print('H.nwhy=False')\n", - " ed = H.edge_size_dist()\n", - " nd = hnx.degree_dist(H)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots(1,2,figsize=(15,6))\n", - "ax[0].hist(ed)\n", - "ax[0].set_title('Distribution of Receiver size\\n(edge size distribution)')\n", - "ax[1].hist(nd)\n", - "ax[1].set_title('Distribution of Sender size\\n(node degree distribution)');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Collapse the edges which contain the same nodes." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 238 ms, sys: 3.75 ms, total: 241 ms\n", - "Wall time: 241 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "Hc,equiv_classes = H.collapse_edges(return_equivalence_classes=True)\n", - "\n", - "# CPU times: user 16.1 s, sys: 72.8 ms, total: 16.1 s\n", - "# Wall time: 16.1 s" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((24, 9623), (24, 1875))" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "## This reduced the number of edges by about 75%\n", - "H.shape,Hc.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Identify The Largest Edges in the Collapsed Hypergraph" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "H.nwhy=False\n", - "CPU times: user 5.16 ms, sys: 1.04 ms, total: 6.19 ms\n", - "Wall time: 5.2 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "### NWHy generates a NWHy hypergraph object\n", - "if Hc.nwhy:\n", - " print('H.nwhy=True',H.g)\n", - " ed = Hc.g.edge_size_dist()\n", - " nd = Hc.g.node_size_dist()\n", - "else:\n", - " print('H.nwhy=False')\n", - " ed = Hc.edge_size_dist()\n", - " nd = hnx.degree_dist(Hc)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig,ax = plt.subplots(1,2,figsize=(15,6))\n", - "ax[0].hist(ed)\n", - "ax[0].set_title('Distribution of Receiver size\\n(edge size distribution)')\n", - "ax[1].hist(nd)\n", - "ax[1].set_title('Distribution of Sender size\\n(node degree distribution)');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Good Receivers: Restrict to Receivers connected to more than a fixed number of senders and compute their metrics" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "18" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "## restrict to receivers who are connected to more than K senders: |r|>K\n", - "K=10\n", - "good_receivers = [r for r in Hc.edges if Hc.size(r)>K]\n", - "len(good_receivers)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "Hgr = Hc.restrict_to_edges(good_receivers)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(24, 18)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Hgr.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "## Size Distribution\n", - "fig,ax = plt.subplots()\n", - "fig.suptitle('Good Receivers',fontsize=25)\n", - "ax.hist(hnx.dist_stats(Hgr)['edge size list'])\n", - "ax.set_xlabel('number of sender connections',fontsize=20)\n", - "ax.set_ylabel('count of receivers',fontsize=20);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# S-Metrics\n", - "\n", - "## Line Graphs \n", - "HNX uses s-linegraphs to compute graph-like statistics on hypergraphs. We use NetworkX (Python) and NWHypergraph (C++) to compute the statistics. The library chosen depends on the size of the hypergraph and whether or not NWHy is available.\n", - "\n", - "**Node Line Graphs** -> H-nodes become Nodes in the Graph \n", - "- s-connected if they commonly belong to s H-edges\n", - "\n", - "**Edge Line Graphs** -> H-edges become Nodes in the Graph \n", - "- s-connected if they intersect in s H-nodes \n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## S-Linegraph\n", - "\n", - "By default the `s_component_subgraphs` method defines s-connectivity using the edge s-linegraph. To define connectivity in terms of the node linegraph set edges=False in the signature. Here we will compute the s-connected component subgraphs and the centrality of the edges in the non-trivial components. \n", - "\n", - "Singleton components are only returned if return_singletons=True.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.3 s, sys: 10.6 ms, total: 1.31 s\n", - "Wall time: 1.31 s\n" - ] - } - ], - "source": [ - "%%time\n", - "comps = {s: list(Hgr.s_component_subgraphs(s=s, edges=True, return_singletons=False)) for s in range(1,25)}\n", - "\n", - "# CPU times: user 7.37 s, sys: 83.2 ms, total: 7.45 s\n", - "# Wall time: 7.3 s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Nontrivial s-connected components for each value of s" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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s#-non-trivialshape(s)
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121[(24, 18)]
231[(24, 18)]
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23240(0, 0)
\n", - "
" - ], - "text/plain": [ - " s #-non-trivial shape(s)\n", - "0 1 1 [(24, 18)]\n", - "1 2 1 [(24, 18)]\n", - "2 3 1 [(24, 18)]\n", - "3 4 1 [(24, 18)]\n", - "4 5 1 [(24, 18)]\n", - "5 6 1 [(24, 18)]\n", - "6 7 1 [(24, 18)]\n", - "7 8 1 [(24, 18)]\n", - "8 9 1 [(24, 15)]\n", - "9 10 1 [(23, 8)]\n", - "10 11 2 [(15, 2), (17, 2)]\n", - "11 12 0 (0, 0)\n", - "12 13 0 (0, 0)\n", - "13 14 0 (0, 0)\n", - "14 15 0 (0, 0)\n", - "15 16 0 (0, 0)\n", - "16 17 0 (0, 0)\n", - "17 18 0 (0, 0)\n", - "18 19 0 (0, 0)\n", - "19 20 0 (0, 0)\n", - "20 21 0 (0, 0)\n", - "21 22 0 (0, 0)\n", - "22 23 0 (0, 0)\n", - "23 24 0 (0, 0)" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "compdf = pd.DataFrame(columns=['s','#-non-trivial','shape(s)'])\n", - "for s,v in comps.items():\n", - " if len(v) !=0:\n", - " compdf = compdf.append([dict(zip(['s','#-non-trivial','shape(s)'],\n", - " [s,len(comps[s]),[cdx.shape for cdx in comps[s]]]))],ignore_index=True)\n", - " else:\n", - " compdf = compdf.append([dict(zip(['s','#-non-trivial','shape(s)'],\n", - " [s,0,(0,0)]))],ignore_index=True)\n", - "compdf" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "max_nontrivial = np.argmax(compdf['#-non-trivial'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Centrality Statistics:\n", - "\n", - "We compute the s-centrality edge statistics for $1 \\leq s < 20$. We then examine plots to compare the values across the edges." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### s-closeness centrality\n", - "If $u$ is a vertex in one of the s-line graphs above, the s-closeness centrality is computed on each of the connected components\n", - "\n", - "$V$ = the set of vertices in the linegraph. \n", - "$n = |V|$\n", - "$$C(u) = \\frac{n - 1}{\\sum_{v \\neq u \\in V} d(v, u)}$$" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.2 s, sys: 4.41 ms, total: 1.2 s\n", - "Wall time: 1.2 s\n" - ] - } - ], - "source": [ - "%%time\n", - "scc = dict()\n", - "for s in range(1,max_nontrivial):\n", - " scc[s] = hnx.s_closeness_centrality(comps[s][0],s=s,edges=True,use_nwhy=True)\n", - " \n", - "# CPU times: user 7.41 s, sys: 122 ms, total: 7.53 s\n", - "# Wall time: 7.18 s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### s-betweenness centrality\n", - "The centrality of edge to all shortest s-edge paths\n", - "$V$ = the set of vertices in the linegraph. \n", - "$\\sigma(s,t)$ = the number of shortest paths between vertices $s$ and $t$. \n", - "$\\sigma(s, t|v)$ = the number of those paths that pass through vertex $u$\n", - "$$c_B(u) =\\sum_{s \\neq t \\in V} \\frac{\\sigma(s, t|u)}{\\sigma(s, t)}$$" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.23 s, sys: 12.6 ms, total: 1.25 s\n", - "Wall time: 1.24 s\n" - ] - } - ], - "source": [ - "%%time\n", - "sbc = dict()\n", - "for s in range(1,max_nontrivial):\n", - " sbc[s] = hnx.s_betweenness_centrality(comps[s][0],s=s,edges=True,use_nwhy=True)\n", - " \n", - "# CPU times: user 7.43 s, sys: 48.1 ms, total: 7.48 s\n", - "# Wall time: 7.37 s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### s-harmonic centrality - \n", - "\n", - "The denormalized reciprocal of the harmonic mean of all distances from $u$ to all other vertices. \n", - "$V$ = the set of vertices in the linegraph.\n", - "$$C(u) = \\sum_{v \\neq u \\in V} \\frac{1}{d(v, u)}$$\n", - "\n", - "Normalized this becomes:\n", - "$$C(u) = \\sum_{v \\neq u \\in V} \\frac{1}{d(v, u)}\\cdot\\frac{2}{(n-1)(n-2)}$$\n", - "where $n$ is the number vertices." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.17 s, sys: 4.93 ms, total: 1.18 s\n", - "Wall time: 1.18 s\n" - ] - } - ], - "source": [ - "%%time\n", - "shc = dict()\n", - "for s in range(1,max_nontrivial):\n", - " shc[s] = hnx.s_harmonic_centrality(comps[s][0],s=s,edges=True,use_nwhy=True)\n", - " \n", - "# CPU times: user 7.53 s, sys: 98.5 ms, total: 7.63 s\n", - "# Wall time: 7.35 s" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.18 s, sys: 6.81 ms, total: 1.18 s\n", - "Wall time: 1.18 s\n" - ] - } - ], - "source": [ - "%%time\n", - "shcn = dict()\n", - "for s in range(1,max_nontrivial):\n", - " shcn[s] = hnx.s_harmonic_centrality(comps[s][0],s=s,edges=True,use_nwhy=True,normalized=True)\n", - " \n", - "# CPU times: user 7.68 s, sys: 50.1 ms, total: 7.73 s\n", - "# Wall time: 7.53 s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### s-eccentricity -\n", - "The length of the longest shortest path from a vertex $u$ to every other vertex in the linegraph. \n", - "$V$ = set of vertices in the linegraph\n", - "$$ \\text{s-ecc}(u) = \\text{max}\\{d(u,v): v \\in V\\} $$" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.18 s, sys: 7.06 ms, total: 1.19 s\n", - "Wall time: 1.19 s\n" - ] - } - ], - "source": [ - "%%time\n", - "sec = dict()\n", - "for s in range(1,max_nontrivial):\n", - " sec[s] = hnx.s_eccentricity(comps[s][0],s=s,edges=True,use_nwhy=True)\n", - " \n", - "# CPU times: user 7.66 s, sys: 50.7 ms, total: 7.71 s\n", - "# Wall time: 7.49 s" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot the centrality metrics" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "scex = [scc,sbc,shc,shcn,sec]\n", - "scnames = ['s-Closeness Centrality','s-Betweenness Centrality',\n", - " 's-Harmonic Centrality','s-Harmonic Centrality normalized','s-Eccentricity']" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "## specify which centrality to view from scnames by index\n", - "i = 0 ## index of centrality score type\n", - "ex = scex[i] ## dictionary of centrality scores keyed by s" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "## Collect points to plot\n", - "S = sorted(ex.keys()) ## all s values we are evaluating\n", - "indexdict = defaultdict(list) ## nd position in each s by centrality score\n", - "valdict = dict() ## s:{score:[nds]} groups nodes by their scores\n", - "vals = dict()\n", - "for s in S:\n", - " d = defaultdict(list) ## temporary score:[nds] dictionary\n", - " for nd,val in ex[s].items():\n", - " d[val].append(nd)\n", - " vals[s] = sorted(list(d.keys()),reverse=True) ## sort keys of scores large to small\n", - " for vdx,val in enumerate(vals[s]):\n", - " for nd in d[val]:\n", - " indexdict[nd].append(vdx) ## nd: position indexed by s\n", - " valdict[s] = OrderedDict([(vdx,d[val]) for vdx,val in enumerate(vals[s])]) ## organize scores by value\n", - "\n", - "yindex = defaultdict(list)\n", - "for sdx,s in enumerate(S): \n", - " for k,nds in valdict[s].items():\n", - " ## sorts elements with the same score by successive scores - lexi-like\n", - " yindex[s] += sorted(list(nds),key = lambda nd : sum(indexdict[nd][sdx:])) \n", - " \n", - "specpos = defaultdict(list) ## plots by relative position in ordering\n", - "specvals = defaultdict(list) ## plots by value\n", - "\n", - "## generate points\n", - "for s in S:\n", - " topval = Hgr.shape[1]\n", - " for ndx,nd in enumerate(yindex[s]):\n", - " specpos[nd].append(topval-ndx)\n", - " specvals[nd].append([s,vals[s][indexdict[nd][s-1]]])\n", - " \n", - "## set up for plotting\n", - "for nd in specpos:\n", - " specpos[nd] = np.array(specpos[nd]) ## an array for plotting sequential positions\n", - " specvals[nd] = np.array(specvals[nd]).T ## x,y arrays\n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "## Plot by value\n", - "\n", - "s = 1 ## limit to nodes in s-linegraph\n", - "N = len(yindex[s])\n", - "_cmap = cm.gist_ncar\n", - "\n", - "K1 = 20 ## constant spreads out colors so that they don't bunch up at the beginning\n", - "colors = [idx/100 for idx in K1*np.log(np.linspace(1,100,N))] \n", - "cmap = lambda idx : _cmap(colors[idx])\n", - "\n", - "K2 = 35 ## constant reduces size tapering off at the end\n", - "stops = K2*np.log(np.linspace(1,150,N+1)) ## reduce size to prevent occlusion\n", - "\n", - "nodes = list(yindex[s]) ## plot values only for nodes still around for this value of s\n", - "\n", - "fig,ax = plt.subplots(figsize=(15,10))\n", - "\n", - "patch = dict()\n", - "for idx,nd in enumerate(nodes):\n", - " ax.scatter(specvals[nd][0], specvals[nd][1], s=200-stops[idx], color=cmap(idx))\n", - " patch[nd] = mpatches.Patch(color=cmap(idx), label=nd)\n", - "# ax.scatter(specvals[nd][0],specvals[nd][1], s=200-15*idx, color=cmap(colors[idx]))\n", - "\n", - "ax.set_title(scnames[i],fontsize=20,color='r')\n", - "ax.set_xticks(range(1,20))\n", - "# ax.set_yticks(np.linspace(0,1,11))\n", - "ax.set_xlabel('s value',fontsize=15,color='r')\n", - "ax.set_ylabel('centrality score',fontsize=15,color='r')\n", - "\n", - "\n", - "fig.legend(handles=[patch[nd] for nd in nodes],loc=\"right\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Positional Plot - receivers ordered by centrality value" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "## Positional plot starting at specified s value\n", - "_cmap = cm.tab20\n", - "cmap = lambda idx : _cmap(idx%20)\n", - "\n", - "starts = 3 ## Limits first xtick to show in plot\n", - "starts -=1\n", - "fig,ax = plt.subplots(1,1,figsize=(15,20))\n", - "ax.set_yticks([v[0] for v in specpos.values()])\n", - "ax.set_yticklabels([k for k in specpos])\n", - "ax.set_xticks(range(20-starts))\n", - "ax.set_xticklabels([str(x+1) for x in range(starts,20)])\n", - "\n", - "ax.set_xlabel('s',fontsize=14,color='r')\n", - "\n", - "for idx,nd in enumerate(specpos):\n", - " if len(specpos[nd]) == 0:\n", - " continue\n", - " ax.plot(specpos[nd][starts:],marker='.', color = cmap(idx))\n", - "\n", - "plt.title(f'Positional Plot:\\n{scnames[i]} Good Receivers (|r|>{K})',fontsize=14,color='r');" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "## Positional plot starting at specified s value\n", - "fig,ax = plt.subplots(1,1,figsize=(15,20))\n", - "_cmap = cm.tab20\n", - "cmap = lambda idx : _cmap(idx%20)\n", - "\n", - "starts = 3 ## Limits first xtick to show in plot\n", - "starts -=1\n", - "ax.set_yticks([v[0] for v in specpos.values()])\n", - "ax.set_yticklabels([k for k in specpos])\n", - "ax.set_xticks(range(20-starts))\n", - "ax.set_xticklabels([str(x+1) for x in range(starts,20)])\n", - "\n", - "ax.set_xlabel('s',fontsize=14,color='r')\n", - "\n", - "for idx,nd in enumerate(specpos):\n", - " if len(specpos[nd]) == 0:\n", - " continue\n", - " ax.plot(specpos[nd][starts:],marker='.', color = cmap(idx))\n", - "\n", - "plt.title(f'Positional Plot:\\n{scnames[i]} Good Receivers (|r|>{K})',fontsize=14,color='r');" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "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": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/Tutorial 9 - Contagion on Hypergraphs.ipynb b/tutorials/Tutorial 9 - Contagion on Hypergraphs.ipynb new file mode 100644 index 00000000..69114d4a --- /dev/null +++ b/tutorials/Tutorial 9 - Contagion on Hypergraphs.ipynb @@ -0,0 +1,205178 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2d0f3716", + "metadata": {}, + "source": [ + "## Modeling Contagion with Hypergraphs\n", + "This work is based on the paper [The effect of heterogeneity on hypergraph contagion models by Nicholas Landry](https://aip.scitation.org/doi/10.1063/5.0020034)\n", + "The SIS and SIR simulations will each take several minutes to run." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6b7c2ac2", + "metadata": {}, + "outputs": [], + "source": [ + "import hypernetx as hnx\n", + "import matplotlib.pyplot as plt\n", + "import random\n", + "import time\n", + "import hypernetx.algorithms.contagion as contagion" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b5be46e0", + "metadata": {}, + "outputs": [], + "source": [ + "n = 1000 \n", + "m = 10000 \n", + "\n", + "hyperedgeList = [random.sample(range(n), k=random.choice([2,3])) for i in range(m)]\n", + "H = hnx.Hypergraph(hyperedgeList, static=True)" + ] + }, + { + "cell_type": "markdown", + "id": "631bee3a", + "metadata": {}, + "source": [ + "## Initialize simulation variables\n", + "- $\\tau$ is a dictionary of the infection rate for each hyperedge size\n", + "- $\\gamma$ is the healing rate\n", + "- $t_{max}$ is the time at which to terminate the simulation if it hasn't already\n", + "- $\\Delta t$ is the time step size to use for the discrete time algorithm\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d74c7a8e", + "metadata": {}, + "outputs": [], + "source": [ + "tau = {2:0.01, 3:0.01}\n", + "gamma = 0.01\n", + "tmax = 100\n", + "dt = 1" + ] + }, + { + "cell_type": "markdown", + "id": "b3f19f88", + "metadata": {}, + "source": [ + "## Run the SIR epidemic simulations\n", + "- The discrete SIR takes fixed steps in time and multiple infection/healing events can happen at each time step.\n", + "- The Gillespie SIR algorithm takes steps in time exponentially distributed and at each step forward, a single event occurs\n", + "- As $\\Delta t\\to 0$, the discrete time algorithm converges to the Gillespie algorithm. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4b5e18bd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.618687391281128\n", + "0.21742582321166992\n" + ] + } + ], + "source": [ + "start = time.time()\n", + "t1, S1, I1, R1 = contagion.discrete_SIR(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax, dt=dt)\n", + "print(time.time() - start)\n", + "## ~512.8926649093628 sec\n", + "\n", + "start = time.time()\n", + "t2, S2, I2, R2 = contagion.Gillespie_SIR(H, tau, gamma, rho=0.1, tmin=0, tmax=tmax)\n", + "print(time.time() - start)\n", + "## ~161.48380184173584 sec" + ] + }, + { + "cell_type": "markdown", + "id": "909ffee4", + "metadata": {}, + "source": [ + "The Gillespie algorithm is much faster in many cases (and more accurate) than discrete-time algorithms because it doesn't consider events that don't happen. Instead, it calculates when the next event will occur and what event (infection, recovery, etc.) it will be." + ] + }, + { + "cell_type": "markdown", + "id": "cbc76561", + "metadata": {}, + "source": [ + "## Plot of the results\n", + "- Dashed lines are the results from the discrete time algorithm\n", + "- Solid lines are the results from the Gillespie algorithm\n", + "- Plots of the numbers susceptible, infected, and recovered over time\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "476ece59", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(t1, S1, 'g--', label='S (Discrete)')\n", + "plt.plot(t1, I1, 'r--', label='I (Discrete)')\n", + "plt.plot(t1, R1, 'b--', label='R (Discrete)')\n", + "plt.plot(t2, S2, 'g-', label='S (Gillespie)')\n", + "plt.plot(t2, I2, 'r-', label='I (Gillespie)')\n", + "plt.plot(t2, R2, 'b-', label='R (Gillespie)')\n", + "plt.xlabel(\"Time\", fontsize=14)\n", + "plt.ylabel(\"Number of people\", fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5cfe7e23", + "metadata": {}, + "source": [ + "## SIS Model\n", + "In this model, once individuals heal, they may become re-infected." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "792d13a4", + "metadata": {}, + "outputs": [], + "source": [ + "tau = {2:0.01, 3:0.01}\n", + "gamma = 0.01\n", + "tmax = 100\n", + "dt = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "67999c75", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0778007507324219\n", + "0.37310004234313965\n" + ] + } + ], + "source": [ + "tau = {2:0.01, 3:0.01}\n", + "gamma = 0.01\n", + "start = time.time()\n", + "t1, S1, I1 = contagion.discrete_SIS(H, tau, gamma, rho = 0.1, tmin = 0, tmax=tmax, dt=dt)\n", + "print(time.time() - start)\n", + "# ~680.240907907486 sec\n", + "\n", + "start = time.time()\n", + "t2, S2, I2 = contagion.Gillespie_SIS(H, tau, gamma, rho = 0.1, tmin = 0, tmax=tmax)\n", + "print(time.time() - start)\n", + "\n", + "\n", + "# ~236.78710913658142 sec" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b1528139", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(t1, S1, 'g--', label='S (Discrete)')\n", + "plt.plot(t1, I1, 'r--', label='I (Discrete)')\n", + "plt.plot(t2, S2, 'g-', label='S (Gillespie)')\n", + "plt.plot(t2, I2, 'r-', label='I (Gillespie)')\n", + "plt.xlabel(\"Time\", fontsize=14)\n", + "plt.ylabel(\"Number of people\", fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b59bac25", + "metadata": {}, + "source": [ + "## Animation of SIR model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4efd9d76", + "metadata": {}, + "outputs": [], + "source": [ + "import hypernetx as hnx\n", + "import matplotlib.pyplot as plt\n", + "import random\n", + "import time\n", + "import hypernetx.algorithms.contagion as contagion\n", + "import numpy as np\n", + "from IPython.display import HTML" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "24962b8c", + "metadata": {}, + "outputs": [], + "source": [ + "n = 100\n", + "m = 40\n", + "\n", + "hyperedgeList = [random.sample(range(n), k=random.choice([2,3])) for i in range(m)]\n", + "H = hnx.Hypergraph(hyperedgeList, static=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3a83ee04", + "metadata": {}, + "outputs": [], + "source": [ + "tau = {2:2, 3:1}\n", + "gamma = 0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a7df1503", + "metadata": {}, + "outputs": [], + "source": [ + "transition_events = contagion.discrete_SIR(H, tau, gamma, \n", + " rho=0.2, tmin=0, \n", + " tmax=50, dt=1, return_full_data=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "12938dae", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "At time 1, 0 was infected by 4\n", + "At time 1, 6 recovered\n", + "At time 1, 10 was infected by 9\n", + "At time 1, 24 was infected by 3\n", + "At time 1, 25 was infected by 9\n", + "At time 1, 29 was infected by 39\n", + "At time 1, 51 was infected by 37\n", + "At time 1, 60 was infected by 39\n", + "At time 1, 68 was infected by 5\n", + "At time 1, 69 was infected by 0\n", + "At time 1, 70 was infected by 24\n", + "At time 1, 74 was infected by 27\n", + "At time 1, 81 was infected by 29\n", + "At time 1, 82 was infected by 11\n", + "At time 1, 86 was infected by 37\n", + "At time 1, 89 was infected by 15\n", + "At time 1, 91 was infected by 38\n", + "At time 1, 95 was infected by 0\n", + "At time 1, 96 was infected by 33\n", + "At time 1, 99 was infected by 15\n", + "At time 2, 0 recovered\n", + "At time 2, 1 was infected by 19\n", + "At time 2, 5 was infected by 2\n", + "At time 2, 7 was infected by 34\n", + "At time 2, 11 recovered\n", + "At time 2, 13 was infected by 18\n", + "At time 2, 14 recovered\n", + "At time 2, 42 was infected by 16\n", + "At time 2, 44 was infected by 31\n", + "At time 2, 54 was infected by 32\n", + "At time 2, 55 was infected by 28\n", + "At time 2, 64 recovered\n", + "At time 2, 71 was infected by 7\n", + "At time 2, 75 was infected by 7\n", + "At time 2, 77 was infected by 18\n", + "At time 2, 82 recovered\n", + "At time 2, 84 was infected by 34\n", + "At time 2, 87 recovered\n", + "At time 2, 91 recovered\n", + "At time 2, 98 was infected by 14\n", + "At time 3, 2 was infected by 35\n", + "At time 3, 7 recovered\n", + "At time 3, 21 was infected by 1\n", + "At time 3, 37 was infected by 1\n", + "At time 3, 38 was infected by 20\n", + "At time 3, 39 was infected by 8\n", + "At time 3, 51 recovered\n", + "At time 3, 56 was infected by 35\n", + "At time 3, 57 was infected by 12\n", + "At time 3, 63 was infected by 17\n", + "At time 3, 74 recovered\n", + "At time 3, 79 was infected by 6\n", + "At time 4, 2 recovered\n", + "At time 4, 5 recovered\n", + "At time 4, 31 was infected by 22\n", + "At time 4, 69 recovered\n", + "At time 4, 96 recovered\n", + "At time 5, 39 recovered\n", + "At time 5, 70 recovered\n", + "At time 6, 4 recovered\n", + "At time 6, 30 recovered\n", + "At time 6, 48 recovered\n", + "At time 6, 68 recovered\n", + "At time 6, 79 recovered\n", + "At time 6, 99 recovered\n", + "At time 7, 81 recovered\n", + "At time 9, 1 recovered\n", + "At time 9, 44 recovered\n", + "At time 9, 60 recovered\n", + "At time 9, 63 recovered\n", + "At time 9, 71 recovered\n", + "At time 9, 77 recovered\n", + "At time 10, 3 recovered\n", + "At time 10, 26 recovered\n", + "At time 10, 29 recovered\n", + "At time 10, 95 recovered\n", + "At time 11, 21 recovered\n", + "At time 11, 38 recovered\n", + "At time 11, 75 recovered\n", + "At time 11, 89 recovered\n", + "At time 12, 13 recovered\n", + "At time 12, 86 recovered\n", + "At time 13, 42 recovered\n", + "At time 14, 46 recovered\n", + "At time 14, 57 recovered\n", + "At time 15, 15 recovered\n", + "At time 16, 10 recovered\n", + "At time 16, 56 recovered\n", + "At time 17, 31 recovered\n", + "At time 21, 37 recovered\n", + "At time 22, 24 recovered\n", + "At time 23, 98 recovered\n", + "At time 25, 84 recovered\n", + "At time 27, 55 recovered\n", + "At time 32, 93 recovered\n", + "At time 38, 54 recovered\n", + "At time 39, 25 recovered\n" + ] + } + ], + "source": [ + "for time, events in transition_events.items():\n", + " if events != []:\n", + " for event in events:\n", + " if event[0] == 'R':\n", + " print(f\"At time {time}, {event[1]} recovered\")\n", + " elif event[0] == 'I' and event[2] is not None:\n", + " print(f\"At time {time}, {event[1]} was infected by {event[2]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "950570a6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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23\n", + "At time 1, 56 was infected by 35\n", + "At time 1, 60 was infected by 19\n", + "At time 1, 64 was infected by 36\n", + "At time 1, 68 was infected by 18\n", + "At time 1, 69 was infected by 0\n", + "At time 1, 71 was infected by 7\n", + "At time 1, 77 was infected by 18\n", + "At time 1, 82 was infected by 11\n", + "At time 1, 84 was infected by 34\n", + "At time 1, 86 was infected by 7\n", + "At time 1, 93 was infected by 11\n", + "At time 1, 95 was infected by 0\n", + "At time 1, 96 was infected by 33\n", + "At time 2, 10 was infected by 9\n", + "At time 2, 14 was infected by 37\n", + "At time 2, 15 was infected by 13\n", + "At time 2, 21 was infected by 1\n", + "At time 2, 25 was infected by 9\n", + "At time 2, 30 recovered\n", + "At time 2, 37 was infected by 1\n", + "At time 2, 38 recovered\n", + "At time 2, 39 was infected by 8\n", + "At time 2, 44 was infected by 31\n", + "At time 2, 51 was infected by 37\n", + "At time 2, 57 was infected by 12\n", + "At time 2, 79 was infected by 6\n", + "At time 2, 81 was infected by 29\n", + "At time 2, 87 was infected by 5\n", + "At time 2, 89 was infected by 2\n", + "At time 2, 99 was infected by 15\n", + "At time 3, 1 recovered\n", + "At time 3, 2 recovered\n", + "At time 3, 4 was infected by 38\n", + "At time 3, 5 recovered\n", + "At time 3, 7 recovered\n", + "At time 3, 13 recovered\n", + "At time 3, 14 recovered\n", + "At time 3, 15 recovered\n", + "At time 3, 29 recovered\n", + "At time 3, 30 was infected by 36\n", + "At time 3, 31 was infected by 22\n", + "At time 3, 38 was infected by 20\n", + "At time 3, 54 was infected by 32\n", + "At time 3, 55 was infected by 21\n", + "At time 3, 69 recovered\n", + "At time 3, 75 recovered\n", + "At time 3, 91 was infected by 25\n", + "At time 3, 98 was infected by 14\n", + "At time 4, 0 recovered\n", + "At time 4, 1 was infected by 19\n", + "At time 4, 2 was infected by 35\n", + "At time 4, 5 was infected by 2\n", + "At time 4, 7 was infected by 1\n", + "At time 4, 11 recovered\n", + "At time 4, 13 was infected by 18\n", + "At time 4, 14 was infected by 37\n", + "At time 4, 15 was infected by 13\n", + "At time 4, 29 was infected by 16\n", + "At time 4, 39 recovered\n", + "At time 4, 62 recovered\n", + "At time 4, 69 was infected by 0\n", + "At time 4, 75 was infected by 7\n", + "At time 4, 77 recovered\n", + "At time 4, 79 recovered\n", + "At time 4, 99 recovered\n", + "At time 5, 25 recovered\n", + "At time 5, 29 recovered\n", + "At time 5, 31 recovered\n", + "At time 5, 37 recovered\n", + "At time 5, 39 was infected by 8\n", + "At time 5, 51 recovered\n", + "At time 5, 52 recovered\n", + "At time 5, 60 recovered\n", + "At time 5, 62 was infected by 23\n", + "At time 5, 71 recovered\n", + "At time 5, 77 was infected by 18\n", + "At time 5, 79 was infected by 6\n", + "At time 5, 99 was infected by 15\n", + "At time 6, 15 recovered\n", + "At time 6, 25 was infected by 9\n", + "At time 6, 29 was infected by 16\n", + "At time 6, 31 was infected by 22\n", + "At time 6, 37 was infected by 1\n", + "At time 6, 51 was infected by 37\n", + "At time 6, 52 was infected by 23\n", + "At time 6, 55 recovered\n", + "At time 6, 60 was infected by 19\n", + "At time 6, 63 recovered\n", + "At time 6, 64 recovered\n", + "At time 6, 71 was infected by 6\n", + "At time 6, 99 recovered\n", + "At time 7, 15 was infected by 13\n", + "At time 7, 48 recovered\n", + "At time 7, 55 was infected by 21\n", + "At time 7, 63 was infected by 17\n", + "At time 7, 64 was infected by 36\n", + "At time 7, 87 recovered\n", + "At time 7, 99 was infected by 15\n", + "At time 8, 1 recovered\n", + "At time 8, 44 recovered\n", + "At time 8, 48 was infected by 3\n", + "At time 8, 63 recovered\n", + "At time 8, 87 was infected by 5\n", + "At time 9, 1 was infected by 19\n", + "At time 9, 44 was infected by 31\n", + "At time 9, 60 recovered\n", + "At time 9, 63 was infected by 17\n", + "At time 9, 68 recovered\n", + "At time 9, 71 recovered\n", + "At time 9, 77 recovered\n", + "At time 10, 25 recovered\n", + "At time 10, 42 recovered\n", + "At time 10, 55 recovered\n", + "At time 10, 60 was infected by 19\n", + "At time 10, 68 was infected by 5\n", + "At time 10, 71 was infected by 6\n", + "At time 10, 77 was infected by 18\n", + "At time 10, 98 recovered\n", + "At time 11, 6 recovered\n", + "At time 11, 24 recovered\n", + "At time 11, 25 was infected by 9\n", + "At time 11, 38 recovered\n", + "At time 11, 42 was infected by 16\n", + "At time 11, 55 was infected by 21\n", + "At time 11, 77 recovered\n", + "At time 11, 82 recovered\n", + "At time 11, 96 recovered\n", + "At time 11, 98 was infected by 14\n", + "At time 12, 3 recovered\n", + "At time 12, 6 was infected by 11\n", + "At time 12, 24 was infected by 3\n", + "At time 12, 38 was infected by 20\n", + "At time 12, 77 was infected by 18\n", + "At time 12, 82 was infected by 11\n", + "At time 12, 84 recovered\n", + "At time 12, 87 recovered\n", + "At time 12, 96 was infected by 33\n", + "At time 13, 3 was infected by 0\n", + "At time 13, 13 recovered\n", + "At time 13, 37 recovered\n", + "At time 13, 84 was infected by 8\n", + "At time 13, 87 was infected by 5\n", + "At time 13, 98 recovered\n", + "At time 14, 6 recovered\n", + "At time 14, 13 was infected by 18\n", + "At time 14, 29 recovered\n", + "At time 14, 37 was infected by 1\n", + "At time 14, 48 recovered\n", + "At time 14, 51 recovered\n", + "At time 14, 52 recovered\n", + "At time 14, 68 recovered\n", + "At time 14, 98 was infected by 14\n", + "At time 15, 6 was infected by 11\n", + "At time 15, 29 was infected by 16\n", + "At time 15, 48 was infected by 3\n", + "At time 15, 51 was infected by 37\n", + "At time 15, 52 was infected by 23\n", + "At time 15, 60 recovered\n", + "At time 15, 68 was infected by 5\n", + "At time 15, 69 recovered\n", + "At time 15, 77 recovered\n", + "At time 16, 2 recovered\n", + "At time 16, 44 recovered\n", + "At time 16, 48 recovered\n", + "At time 16, 51 recovered\n", + "At time 16, 57 recovered\n", + "At time 16, 60 was infected by 19\n", + "At time 16, 69 was infected by 0\n", + "At time 16, 75 recovered\n", + "At time 16, 77 was infected by 18\n", + "At time 17, 2 was infected by 35\n", + "At time 17, 15 recovered\n", + "At time 17, 24 recovered\n", + "At time 17, 44 was infected by 31\n", + "At time 17, 48 was infected by 3\n", + "At time 17, 51 was infected by 37\n", + "At time 17, 57 was infected by 12\n", + "At time 17, 75 was infected by 7\n", + "At time 17, 91 recovered\n", + "At time 17, 98 recovered\n", + "At time 18, 15 was infected by 13\n", + "At time 18, 24 was infected by 3\n", + "At time 18, 54 recovered\n", + "At time 18, 91 was infected by 25\n", + "At time 18, 98 was infected by 14\n", + "At time 19, 54 was infected by 32\n", + "At time 19, 56 recovered\n", + "At time 19, 69 recovered\n", + "At time 19, 81 recovered\n", + "At time 19, 93 recovered\n", + "At time 20, 52 recovered\n", + "At time 20, 55 recovered\n", + "At time 20, 56 was infected by 35\n", + "At time 20, 68 recovered\n", + "At time 20, 69 was infected by 0\n", + "At time 20, 81 was infected by 31\n", + "At time 20, 93 was infected by 11\n", + "At time 21, 24 recovered\n", + "At time 21, 48 recovered\n", + "At time 21, 52 was infected by 23\n", + "At time 21, 55 was infected by 21\n", + "At time 21, 57 recovered\n", + "At time 21, 68 was infected by 5\n", + "At time 21, 95 recovered\n", + "At time 22, 24 was infected by 34\n", + "At time 22, 44 recovered\n", + "At time 22, 48 was infected by 9\n", + "At time 22, 57 was infected by 12\n", + "At time 22, 60 recovered\n", + "At time 22, 79 recovered\n", + "At time 22, 95 was infected by 0\n", + "At time 23, 1 recovered\n", + "At time 23, 14 recovered\n", + "At time 23, 44 was infected by 31\n", + "At time 23, 55 recovered\n", + "At time 23, 57 recovered\n", + "At time 23, 60 was infected by 19\n", + "At time 23, 79 was infected by 6\n", + "At time 23, 82 recovered\n", + "At time 24, 1 was infected by 19\n", + "At time 24, 14 was infected by 37\n", + "At time 24, 21 recovered\n", + "At time 24, 55 was infected by 28\n", + "At time 24, 57 was infected by 12\n", + "At time 24, 82 was infected by 11\n", + "At time 25, 5 recovered\n", + "At time 25, 21 was infected by 1\n", + "At time 25, 39 recovered\n", + "At time 25, 42 recovered\n", + "At time 25, 64 recovered\n", + "At time 25, 79 recovered\n", + "At time 25, 93 recovered\n", + "At time 26, 5 was infected by 2\n", + "At time 26, 39 was infected by 8\n", + "At time 26, 42 was infected by 16\n", + "At time 26, 64 was infected by 36\n", + "At time 26, 79 was infected by 6\n", + "At time 26, 93 was infected by 11\n", + "At time 27, 13 recovered\n", + "At time 27, 54 recovered\n", + "At time 27, 60 recovered\n", + "At time 27, 64 recovered\n", + "At time 27, 79 recovered\n", + "At time 27, 95 recovered\n", + "At time 28, 1 recovered\n", + "At time 28, 13 was infected by 18\n", + "At time 28, 29 recovered\n", + "At time 28, 44 recovered\n", + "At time 28, 48 recovered\n", + "At time 28, 51 recovered\n", + "At time 28, 52 recovered\n", + "At time 28, 54 was infected by 32\n", + "At time 28, 60 was infected by 19\n", + "At time 28, 64 was infected by 36\n", + "At time 28, 79 was infected by 6\n", + "At time 28, 95 was infected by 0\n", + "At time 29, 1 was infected by 19\n", + "At time 29, 29 was infected by 16\n", + "At time 29, 44 was infected by 31\n", + "At time 29, 48 was infected by 3\n", + "At time 29, 51 was infected by 37\n", + "At time 29, 52 was infected by 23\n", + "At time 30, 4 recovered\n", + "At time 30, 57 recovered\n", + "At time 30, 64 recovered\n", + "At time 30, 75 recovered\n", + "At time 30, 77 recovered\n", + "At time 30, 81 recovered\n", + "At time 30, 84 recovered\n", + "At time 30, 86 recovered\n", + "At time 31, 4 was infected by 38\n", + "At time 31, 57 was infected by 12\n", + "At time 31, 64 was infected by 36\n", + "At time 31, 71 recovered\n", + "At time 31, 75 was infected by 7\n", + "At time 31, 77 was infected by 18\n", + "At time 31, 81 was infected by 29\n", + "At time 31, 84 was infected by 8\n", + "At time 31, 86 was infected by 7\n", + "At time 31, 87 recovered\n", + "At time 32, 1 recovered\n", + "At time 32, 3 recovered\n", + "At time 32, 10 recovered\n", + "At time 32, 13 recovered\n", + "At time 32, 29 recovered\n", + "At time 32, 30 recovered\n", + "At time 32, 48 recovered\n", + "At time 32, 63 recovered\n", + "At time 32, 71 was infected by 6\n", + "At time 32, 87 was infected by 5\n", + "At time 32, 96 recovered\n", + "At time 33, 1 was infected by 19\n", + "At time 33, 3 was infected by 0\n", + "At time 33, 4 recovered\n", + "At time 33, 10 was infected by 9\n", + "At time 33, 13 was infected by 18\n", + "At time 33, 29 was infected by 16\n", + "At time 33, 30 was infected by 36\n", + "At time 33, 42 recovered\n", + "At time 33, 48 was infected by 3\n", + "At time 33, 60 recovered\n", + "At time 33, 63 was infected by 17\n", + "At time 33, 87 recovered\n", + "At time 34, 4 was infected by 38\n", + "At time 34, 5 recovered\n", + "At time 34, 13 recovered\n", + "At time 34, 30 recovered\n", + "At time 34, 42 was infected by 16\n", + "At time 34, 60 was infected by 19\n", + "At time 34, 87 was infected by 5\n", + "At time 34, 96 was infected by 33\n", + "At time 35, 1 recovered\n", + "At time 35, 5 was infected by 2\n", + "At time 35, 13 was infected by 18\n", + "At time 35, 30 was infected by 36\n", + "At time 35, 52 recovered\n", + "At time 36, 1 was infected by 19\n", + "At time 36, 5 recovered\n", + "At time 36, 52 was infected by 23\n", + "At time 36, 68 recovered\n", + "At time 36, 69 recovered\n", + "At time 36, 75 recovered\n", + "At time 36, 79 recovered\n", + "At time 36, 82 recovered\n", + "At time 37, 5 was infected by 2\n", + "At time 37, 31 recovered\n", + "At time 37, 48 recovered\n", + "At time 37, 64 recovered\n", + "At time 37, 68 was infected by 5\n", + "At time 37, 69 was infected by 0\n", + "At time 37, 75 was infected by 7\n", + "At time 37, 79 was infected by 6\n", + "At time 37, 82 was infected by 11\n", + "At time 37, 91 recovered\n", + "At time 37, 99 recovered\n", + "At time 38, 31 was infected by 22\n", + "At time 38, 42 recovered\n", + "At time 38, 48 was infected by 3\n", + "At time 38, 60 recovered\n", + "At time 38, 64 was infected by 36\n", + "At time 38, 82 recovered\n", + "At time 38, 91 was infected by 38\n", + "At time 38, 98 recovered\n", + "At time 38, 99 was infected by 15\n", + "At time 39, 31 recovered\n", + "At time 39, 42 was infected by 16\n", + "At time 39, 44 recovered\n", + "At time 39, 52 recovered\n", + "At time 39, 54 recovered\n", + "At time 39, 60 was infected by 19\n", + "At time 39, 68 recovered\n", + "At time 39, 82 was infected by 11\n", + "At time 39, 98 was infected by 14\n", + "At time 40, 21 recovered\n", + "At time 40, 29 recovered\n", + "At time 40, 31 was infected by 22\n", + "At time 40, 44 was infected by 31\n", + "At time 40, 52 was infected by 23\n", + "At time 40, 54 was infected by 32\n", + "At time 40, 68 was infected by 5\n", + "At time 40, 91 recovered\n", + "At time 41, 15 recovered\n", + "At time 41, 21 was infected by 1\n", + "At time 41, 24 recovered\n", + "At time 41, 29 was infected by 16\n", + "At time 41, 71 recovered\n", + "At time 41, 75 recovered\n", + "At time 41, 77 recovered\n", + "At time 41, 86 recovered\n", + "At time 41, 87 recovered\n", + "At time 41, 91 was infected by 25\n", + "At time 41, 95 recovered\n", + "At time 42, 13 recovered\n", + "At time 42, 14 recovered\n", + "At time 42, 15 was infected by 13\n", + "At time 42, 24 was infected by 3\n", + "At time 42, 25 recovered\n", + "At time 42, 30 recovered\n", + "At time 42, 51 recovered\n", + "At time 42, 63 recovered\n", + "At time 42, 71 was infected by 6\n", + "At time 42, 75 was infected by 20\n", + "At time 42, 77 was infected by 18\n", + "At time 42, 81 recovered\n", + "At time 42, 86 was infected by 37\n", + "At time 42, 87 was infected by 5\n", + "At time 42, 95 was infected by 0\n", + "At time 43, 5 recovered\n", + "At time 43, 13 was infected by 18\n", + "At time 43, 14 was infected by 37\n", + "At time 43, 25 was infected by 9\n", + "At time 43, 30 was infected by 36\n", + "At time 43, 51 was infected by 37\n", + "At time 43, 63 was infected by 17\n", + "At time 43, 81 was infected by 29\n", + "At time 43, 96 recovered\n", + "At time 44, 3 recovered\n", + "At time 44, 5 was infected by 2\n", + "At time 44, 75 recovered\n", + "At time 44, 96 was infected by 33\n", + "At time 45, 3 was infected by 0\n", + "At time 45, 4 recovered\n", + "At time 45, 51 recovered\n", + "At time 45, 69 recovered\n", + "At time 45, 75 was infected by 7\n", + "At time 45, 81 recovered\n", + "At time 45, 86 recovered\n", + "At time 45, 89 recovered\n", + "At time 45, 96 recovered\n", + "At time 46, 4 was infected by 38\n", + "At time 46, 51 was infected by 37\n", + "At time 46, 69 was infected by 0\n", + "At time 46, 71 recovered\n", + "At time 46, 81 was infected by 29\n", + "At time 46, 86 was infected by 7\n", + "At time 46, 89 was infected by 2\n", + "At time 46, 91 recovered\n", + "At time 46, 96 was infected by 33\n", + "At time 47, 30 recovered\n", + "At time 47, 37 recovered\n", + "At time 47, 48 recovered\n", + "At time 47, 64 recovered\n", + "At time 47, 71 was infected by 6\n", + "At time 47, 79 recovered\n", + "At time 47, 91 was infected by 25\n", + "At time 47, 95 recovered\n", + "At time 47, 98 recovered\n", + "At time 48, 37 was infected by 1\n", + "At time 48, 48 was infected by 3\n", + "At time 48, 60 recovered\n", + "At time 48, 62 recovered\n", + "At time 48, 79 was infected by 6\n", + "At time 48, 95 was infected by 0\n", + "At time 48, 98 was infected by 14\n", + "At time 49, 39 recovered\n", + "At time 49, 51 recovered\n", + "At time 49, 60 was infected by 19\n", + "At time 49, 62 was infected by 23\n", + "At time 49, 89 recovered\n", + "At time 49, 91 recovered\n", + "At time 49, 93 recovered\n", + "At time 49, 96 recovered\n", + "At time 50, 39 was infected by 8\n", + "At time 50, 51 was infected by 37\n", + "At time 50, 89 was infected by 2\n", + "At time 50, 91 was infected by 25\n", + "At time 50, 93 was infected by 11\n", + "At time 50, 96 was infected by 33\n" + ] + } + ], + "source": [ + "for time, events in transition_events2.items():\n", + " if events != []:\n", + " for event in events:\n", + " if event[0] == 'S':\n", + " print(f\"At time {time}, {event[1]} recovered\")\n", + " elif event[0] == 'I' and event[2] is not None:\n", + " print(f\"At time {time}, {event[1]} was infected by {event[2]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "82bc96f9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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FullNameDescription
Symbol
AZAnzelmadaughter of TH and TM
BABahorel`Friends of the ABC' cutup
BBBabettooth-pulling bandit of Paris
BJBrujonnotorious criminal
BLBlachevilleParisian student from Montauban
.........
TSToussaintservant of JV at Rue Plumet
VIMadame Victurniensnoop in M-- sur M--
XAChild 1son of TH sold to MN
XBChild 2son of TH sold to MN
ZEZephinelover of FA
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\n", - "
" - ], - "text/plain": [ - " FullName Description\n", - "Symbol \n", - "AZ Anzelma daughter of TH and TM\n", - "BA Bahorel `Friends of the ABC' cutup\n", - "BB Babet tooth-pulling bandit of Paris\n", - "BJ Brujon notorious criminal\n", - "BL Blacheville Parisian student from Montauban\n", - "... ... ...\n", - "TS Toussaint servant of JV at Rue Plumet\n", - "VI Madame Victurnien snoop in M-- sur M--\n", - "XA Child 1 son of TH sold to MN\n", - "XB Child 2 son of TH sold to MN\n", - "ZE Zephine lover of FA\n", - "\n", - "[80 rows x 2 columns]" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import json\n", - "import hypernetx as hnx\n", - "from hypernetx.utils.toys.lesmis import LesMis\n", - "from hnxwidget import HypernetxWidget\n", - "\n", - "scenes = {\n", - " 0: ('FN', 'TH'),\n", - " 1: ('TH', 'JV'),\n", - " 2: ('BM', 'FN', 'JA'),\n", - " 3: ('JV', 'JU', 'CH', 'BM'),\n", - " 4: ('JU', 'CH', 'BR', 'CN', 'CC', 'JV', 'BM'),\n", - " 5: ('TH', 'GP'),\n", - " 6: ('GP', 'MP'),\n", - " 7: ('MA', 'GP'),\n", - "}\n", - "H = hnx.Hypergraph(scenes)\n", - "dnames = LesMis().dnames\n", - "dnames" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## I. LesMis Hypergraph in the Hypernetx-Widget - Default Behavior\n", - "The widget allows you to interactively move, color, select, and hide objects in the hypergraph. Click on the question mark in the Navigation menu for a description of interactive features." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e352155643ec495fa291518747e804e4", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HypernetxWidget(component='HypernetxWidget', props={'nodes': [{'uid': 'JU'}, {'uid': 'CC'}, {'uid': 'BM'}, {'u…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## Default behavior\n", - "example1 = HypernetxWidget(H)\n", - "example1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## II. Preset attributes \n", - "Some of the visualization attributes of the hypergraph may be set using similar parameters as the hnx.draw function" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f9f773978a754c77ad79ed9cad283cca", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HypernetxWidget(component='HypernetxWidget', props={'nodes': [{'uid': 'JU'}, {'uid': 'CC'}, {'uid': 'BM'}, {'u…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "node_colors = {k:'r' if k in ['JV','TH','FN'] else 'b' for k in H.nodes}\n", - "example2 = HypernetxWidget(\n", - " H,\n", - " nodes_kwargs={'color':node_colors},\n", - " edges_kwargs={'edgecolors':'g'}\n", - ")\n", - "example2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## III. Attributes of visualization:\n", - "The `get_state()` method returns the attributes available from a widget for reuse.\n", - "\n", - "**Note:** if you \"Run All\" this notebook, the following cells may produce an exception. Acquiring the widget state in python requires some time for the widget to initialize and render. Run the cells below individually for best results." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'_dom_classes': (),\n", - " '_model_module': 'hnx-widget',\n", - " '_model_module_version': '^0.1.0',\n", - " '_model_name': 'ReactModel',\n", - " '_view_count': None,\n", - " '_view_module': 'hnx-widget',\n", - " '_view_module_version': '^0.1.0',\n", - " '_view_name': 'ReactView',\n", - " 'component': 'HypernetxWidget',\n", - " 'edge_stroke': {'0': '#008000ff',\n", - " '1': '#008000ff',\n", - " '2': '#008000ff',\n", - " '3': '#008000ff',\n", - " '4': '#008000ff',\n", - " '5': '#008000ff',\n", - " '6': '#008000ff',\n", - " '7': '#008000ff'},\n", - " 'hidden_edges': {},\n", - " 'hidden_nodes': {},\n", - " 'layout': 'IPY_MODEL_a3df1d8dc25d4a86b1dfd39341c449d0',\n", - " 'node_fill': {'JU': '#0000ffff',\n", - " 'CC': '#0000ffff',\n", - " 'BM': '#0000ffff',\n", - " 'JV': '#ff0000ff',\n", - " 'CN': '#0000ffff',\n", - " 'FN': '#ff0000ff',\n", - " 'GP': '#0000ffff',\n", - " 'CH': '#0000ffff',\n", - " 'MA': '#0000ffff',\n", - " 'MP': '#0000ffff',\n", - " 'TH': '#ff0000ff',\n", - " 'BR': '#0000ffff',\n", - " 'JA': '#0000ffff'},\n", - " 'pos': {'JU': [167.25095746075195, 281.58263454123255],\n", - " 'CC': [135.75492160545446, 221.81829316643464],\n", - " 'BM': [278.61480696410683, 212.71528742142323],\n", - " 'JV': [249.92250491500195, 285.31995810389947],\n", - " 'CN': [162.53012168959805, 160.79651426243603],\n", - " 'FN': [454.1961016406691, 258.8662052248289],\n", - " 'GP': [424.8654480047309, 504.3322298621465],\n", - " 'CH': [229.60838922887152, 166.67980235536837],\n", - " 'MA': [489.50473371013675, 576.6717592487552],\n", - " 'MP': [344.83880096715603, 560.1598468493333],\n", - " 'TH': [421.5436288772694, 370.8729072562323],\n", - " 'BR': [201.4076830402482, 226.98341329000954],\n", - " 'JA': [427.60515300786, 194.81917087602267]},\n", - " 'props': {'nodes': [{'uid': 'JU'},\n", - " {'uid': 'CC'},\n", - " {'uid': 'BM'},\n", - " {'uid': 'JV'},\n", - " {'uid': 'CN'},\n", - " {'uid': 'FN'},\n", - " {'uid': 'GP'},\n", - " {'uid': 'CH'},\n", - " {'uid': 'MA'},\n", - " {'uid': 'MP'},\n", - " {'uid': 'TH'},\n", - " {'uid': 'BR'},\n", - " {'uid': 'JA'}],\n", - " 'edges': [{'uid': '0', 'elements': ['TH', 'FN']},\n", - " {'uid': '1', 'elements': ['TH', 'JV']},\n", - " {'uid': '2', 'elements': ['FN', 'JA', 'BM']},\n", - " {'uid': '3', 'elements': ['JU', 'BM', 'CH', 'JV']},\n", - " {'uid': '4', 'elements': ['JU', 'JV', 'BM', 'CN', 'CH', 'BR', 'CC']},\n", - " {'uid': '5', 'elements': ['TH', 'GP']},\n", - " {'uid': '6', 'elements': ['MP', 'GP']},\n", - " {'uid': '7', 'elements': ['MA', 'GP']}],\n", - " 'nodeFill': {'JU': '#0000ffff',\n", - " 'CC': '#0000ffff',\n", - " 'BM': '#0000ffff',\n", - " 'JV': '#ff0000ff',\n", - " 'CN': '#0000ffff',\n", - " 'FN': '#ff0000ff',\n", - " 'GP': '#0000ffff',\n", - " 'CH': '#0000ffff',\n", - " 'MA': '#0000ffff',\n", - " 'MP': '#0000ffff',\n", - " 'TH': '#ff0000ff',\n", - " 'BR': '#0000ffff',\n", - " 'JA': '#0000ffff'},\n", - " 'edgeStroke': {'0': '#008000ff',\n", - " '1': '#008000ff',\n", - " '2': '#008000ff',\n", - " '3': '#008000ff',\n", - " '4': '#008000ff',\n", - " '5': '#008000ff',\n", - " '6': '#008000ff',\n", - " '7': '#008000ff'},\n", - " 'edgeStrokeWidth': {'0': 2,\n", - " '1': 2,\n", - " '2': 2,\n", - " '3': 2,\n", - " '4': 2,\n", - " '5': 2,\n", - " '6': 2,\n", - " '7': 2},\n", - " 'edgeLabelColor': {'0': '#008000ff',\n", - " '1': '#008000ff',\n", - " '2': '#008000ff',\n", - " '3': '#008000ff',\n", - " '4': '#008000ff',\n", - " '5': '#008000ff',\n", - " '6': '#008000ff',\n", - " '7': '#008000ff'},\n", - " '_model': 'IPY_MODEL_f9f773978a754c77ad79ed9cad283cca'},\n", - " 'removed_edges': {},\n", - " 'removed_nodes': {},\n", - " 'selected_edges': {},\n", - " 'selected_nodes': {}}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "example2.get_state()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## IV. Reuse attributes\n", - "Once an attribute of a widget visualization has been set it may be reused in another visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ed794c6bf56d42008bb673c9a8d123c9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HypernetxWidget(component='HypernetxWidget', props={'nodes': [{'uid': 'JU'}, {'uid': 'CC'}, {'uid': 'BM'}, {'u…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "example3 = HypernetxWidget(\n", - " H,\n", - " nodes_kwargs={'color': example2.node_fill}\n", - ")\n", - "\n", - "example3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## V. Setting Labels and Callouts\n", - "We can also adjust specific labels and add call outs as node or edge data." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d1348669567a441586ccf9c17bdb20fe", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HypernetxWidget(component='HypernetxWidget', props={'nodes': [{'uid': 'JU'}, {'uid': 'CC'}, {'uid': 'BM'}, {'u…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "example4 = HypernetxWidget(\n", - " H,\n", - " collapse_nodes=True,\n", - " node_data=dnames,\n", - " node_labels={'JV': 'Valjean'},\n", - " edge_labels={0: 'scene 0'},\n", - " nodes_kwargs={'color':'pink'},\n", - ")\n", - "\n", - "example4" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/images/tutorial_1_hypergraph.png b/tutorials/images/tutorial_1_hypergraph.png new file mode 100644 index 00000000..8f69d7eb Binary files /dev/null and b/tutorials/images/tutorial_1_hypergraph.png differ