From a67aa3743abf2061c6daa2fa53546c9d8300a2ed Mon Sep 17 00:00:00 2001 From: Amirhessam Tahmassebi Date: Sun, 7 Aug 2022 01:28:20 -0500 Subject: [PATCH] Fix API Docs Configurations and Add Pages (#115) Fix API docs with myst-parser and sphinx configs + more cleanups ... Co-authored-by: Amirhessam Tahmassebi --- CHANGELOG.md | 98 +-- CONTRIBUTING.md | 5 +- README.md | 22 +- docs/conf.py | 74 +- docs/index.rst | 39 +- docs/pages/{citation.rst => citation.md} | 12 +- docs/pages/code_of_conduct.md | 129 +++ docs/pages/contact_us.md | 4 + .../{contributing.rst => contributing.md} | 34 +- docs/pages/examples.md | 4 + docs/pages/installation.md | 26 + docs/pages/installation.rst | 100 --- docs/pages/{license.rst => license.md} | 6 +- docs/pages/need_help.rst | 4 - .../pages/{quick_start.rst => quick_start.md} | 0 docs/pages/releases.md | 1 + .../metrics/BinaryClassificationMetrics.ipynb | 794 +++++++++--------- src/slickml/metrics/_base.py | 4 +- src/slickml/metrics/_classification.py | 4 +- src/slickml/metrics/_regression.py | 14 +- 20 files changed, 751 insertions(+), 623 deletions(-) rename docs/pages/{citation.rst => citation.md} (72%) create mode 100644 docs/pages/code_of_conduct.md create mode 100644 docs/pages/contact_us.md rename docs/pages/{contributing.rst => contributing.md} (92%) create mode 100644 docs/pages/examples.md create mode 100644 docs/pages/installation.md delete mode 100644 docs/pages/installation.rst rename docs/pages/{license.rst => license.md} (94%) delete mode 100644 docs/pages/need_help.rst rename docs/pages/{quick_start.rst => quick_start.md} (100%) create mode 120000 docs/pages/releases.md diff --git a/CHANGELOG.md b/CHANGELOG.md index 120661f..9fcdea6 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,125 +1,123 @@ -# Changelog -All notable changes to this project will be documented in this file. -This project adheres to [Semantic Versioning](http://semver.org/). +πŸ“£ πŸ₯ Changelog & Releases +========================= -## Unreleased Version X.X.X - XXXX-XX-XX +- We follow [Semantic Versioning](http://semver.org/) to document any notable changes. +- Please checkout [SlickML Official Releases](https://github.com/slickml/slick-ml/releases) for more details. -### Fixed +--- -### Updated +## πŸ“ Unreleased Version X.X.X - XXXX-XX-XX -### Added +### πŸ›  Fixed -## Version 0.2.0-beta - 2022-05-29 +### πŸ”₯ Added -### Fixed +--- + +## πŸ“ Version 0.2.0-beta - 2022-05-29 + +### πŸ›  Fixed - [#78](https://github.com/slickml/slick-ml/pull/78) `build` badge using GitHub actions and removed the `travis-ci` badge and dependencies. -- -### Updated - [#77](https://github.com/slickml/slick-ml/pull/77) updated `.flake8`, `.gitingore` entries, `ISSUE_TEMPLATES`, `README.md`, `CONTRIBUTING.md`, `assets/`, `examples/` formats, and `src/` style, `ci.yml` workflow. -### Added +### πŸ”₯ Added - [#77](https://github.com/slickml/slick-ml/pull/77) added poetry essentials and essentials based on [#72](https://github.com/slickml/slick-ml/pull/72) and removed all `setup.py` essentials. - [#77](https://github.com/slickml/slick-ml/pull/77) added `tox`, `mypy`, `pytest-cov`. - [#77](https://github.com/slickml/slick-ml/pull/77) added `sphinx-auto-api-doc` based on [#32](https://github.com/slickml/slick-ml/pull/32). +--- -## Version 0.1.5 - 2021-09-06 +## πŸ“ Version 0.1.5 - 2021-09-06 -### Updated +### πŸ›  Fixed - [#74](https://github.com/slickml/slick-ml/pull/74) updated `requirements.txt` to the latest versions. - [#71](https://github.com/slickml/slick-ml/pull/71) updated optimization examples. -### Added +### πŸ”₯ Added - [#71](https://github.com/slickml/slick-ml/pull/71) added `XGBoostRegressorBayesianOpt` and `XGBoostRegressorHyperOpt` classes in optimization. -## Version 0.1.4 - 2021-05-31 +--- -### Fixed +## πŸ“ Version 0.1.4 - 2021-05-31 + +### πŸ›  Fixed - [#70](https://github.com/slickml/slick-ml/pull/70) fixed bugs in `plot_xgb_cv_results`. - [#70](https://github.com/slickml/slick-ml/pull/70) fixed bugs in `plot_regression_metrics`. - -### Updated - [#70](https://github.com/slickml/slick-ml/pull/70) updated metrics initialization in `XGBoostClassifier` and `XGBoostCVClassifier`. - [#70](https://github.com/slickml/slick-ml/pull/70) updated notebook examples to go over each class separetely. -### Added +### πŸ”₯ Added - [#70](https://github.com/slickml/slick-ml/pull/70) added `XGBoostRegressor` and `XGBoostCVRegressor` classes. - [#70](https://github.com/slickml/slick-ml/pull/70) added `NeurIPS 2021` submission pdf. +--- -## Version 0.1.3 - 2021-05-15 +## πŸ“ Version 0.1.3 - 2021-05-15 -### Fixed +### πŸ›  Fixed - [#66](https://github.com/slickml/slick-ml/pull/66) fixed bugs in feature selection algorithm. - [#67](https://github.com/slickml/slick-ml/pull/67) fixed bugs in metrics. - -### Updated - [#66](https://github.com/slickml/slick-ml/pull/66) updated the order of the functions inside each class. - [#68](https://github.com/slickml/slick-ml/pull/68) updated `save_path` in plotting functions. - [#68](https://github.com/slickml/slick-ml/pull/68) updated `bibtex` citations to software. -### Added +### πŸ”₯ Added - [#68](https://github.com/slickml/slick-ml/pull/68) added directories for `JOSS` and `NeurIPS` papers. +--- -## Version 0.1.2 - 2021-04-17 +## πŸ“ Version 0.1.2 - 2021-04-17 -### Fixed +### πŸ›  Fixed - [#63](https://github.com/slickml/slick-ml/pull/63) fixed bugs in RegressionMetrics plotting. Now, the text label positions are dynamic and invariat of the data. Additionally, fixed the bug in coef. shapes in `GLMNet` classes. - -### Updated - [#61](https://github.com/slickml/slick-ml/pull/61) updated `metrics.py` attributes API to end with under-score - [#63](https://github.com/slickml/slick-ml/pull/63) updated all docstrings based on Scikit-Learn API - [#64](https://github.com/slickml/slick-ml/pull/64) updated `setup.py` with dynamic version and install requirements -### Added +### πŸ”₯ Added - [#60](https://github.com/slickml/slick-ml/pull/60) added `CHANGELOG.md` - [#63](https://github.com/slickml/slick-ml/pull/63) added `GLMNetCVRegressor` class -## Version 0.1.1 - 2021-03-18 +--- + +## πŸ“ Version 0.1.1 - 2021-03-18 -### Fixed +### πŸ›  Fixed - [#54](https://github.com/slickml/slick-ml/pull/54) fixed bug in XGBoostClassifer. dtest has `y_test` as required parameter while it should be optional, since you wont have the `y_true` in production. - [#56](https://github.com/slickml/slick-ml/pull/56) fixed bugs in plotting - -### Updated - [#57](https://github.com/slickml/slick-ml/pull/57) updated `requirements.txt` - [#59](https://github.com/slickml/slick-ml/pull/59) updated docstrings -### Added +### πŸ”₯ Added - [#44](https://github.com/slickml/slick-ml/pull/44) added XGBoostClassifierHyperOpt - [#57](https://github.com/slickml/slick-ml/pull/57) added GLMNetCVClassifier class, plotting, and examples, `CODE_OF_CONDUCT.md` -## Version 0.0.8 - 2021-02-17 +--- -### Fixed -- [#47](https://github.com/slickml/slick-ml/pull/47) fixed bugs in HyperOpt `__init__` +## πŸ“ Version 0.0.8 - 2021-02-17 -### Updated +### πŸ›  Fixed +- [#47](https://github.com/slickml/slick-ml/pull/47) fixed bugs in HyperOpt `__init__` - [#52](https://github.com/slickml/slick-ml/pull/52) updated xgboost version to 1.0.0 to remove the conflict with shap version -### Added +### πŸ”₯ Added - [#44](https://github.com/slickml/slick-ml/pull/44) added XGBoostClassifierHyperOpt - [#49](https://github.com/slickml/slick-ml/pull/49) added Google Colab links to notebooks - [#51](https://github.com/slickml/slick-ml/pull/51) added regression metrics - [#52](https://github.com/slickml/slick-ml/pull/52) added SHAP waterfall plot +--- -## Version 0.0.7 - 2020-09-27 +## πŸ“ Version 0.0.7 - 2020-09-27 -### API Changes +### πŸ›  Fixed - [#28](https://github.com/slickml/slick-ml/pull/28) updated feature selection method from run to fit and removed X, y from init and added to fit to be similar to sklearn API. - [#17](https://github.com/slickml/slick-ml/pull/17) updated plotting to Matplotlib object oriented API - -## Fixed - [#34](https://github.com/slickml/slick-ml/pull/34) fixed formatting and import bugs in source code - [#38](https://github.com/slickml/slick-ml/pull/38) fixed typos in README and bug in `df_to_csr` function - -### Updated - [#41](https://github.com/slickml/slick-ml/pull/41) updated requirements for bayesian optimization, design pattern, classification examples -### Added +### πŸ”₯ Added - [#4](https://github.com/slickml/slick-ml/pull/4) added `metrics.py` - [#6](https://github.com/slickml/slick-ml/pull/6) added logo design - [#9](https://github.com/slickml/slick-ml/pull/9) added plots for metrics and `utilities.py` @@ -132,7 +130,9 @@ This project adheres to [Semantic Versioning](http://semver.org/). - [#38](https://github.com/slickml/slick-ml/pull/38) added unit tests for classification - [#43](https://github.com/slickml/slick-ml/pull/43) added BayesianOpt class +--- + +## πŸ“ Version 0.0.1 - 2020-08-31 -## Version 0.0.1 - 2020-08-31 -### Added +### πŸ”₯ Added - [#2](https://github.com/slickml/slick-ml/pull/2) initial ideas diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index b406007..66be9c6 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -150,9 +150,10 @@ Please note that before starting any major work, open an issue describing what y poe sphinx ``` - The generated API documentation file can be found at `docs/_build/index.html`. +- You can also add a new page in `.rst` or `md` formats under `docs/page`. +- All `sphinx` configurations that we are using are available in `docs/conf.py`. - -### πŸ”₯ Pull Requests +## πŸ”₯ Pull Requests - We currently have `bug-report` and `feature-request` as [*issue-templates*](https://github.com/slickml/slick-ml/issues). As laid out above, please make sure to open-up an issue before start working on a major work and get the core team feedback. - Try to fix one bug or add one new feature per PR. This would minimize the amount of code changes and it is easier for code-review. Hefty PRs usually do not get merged so fast while it could have been if the work was splitted into multiple PRs clearly laid out in an issue before hand. Therefore, the code reviewer would not be surprised by the work. - We recommend to follow [*Fork and Pull Request Workflow*](https://github.com/susam/gitpr). diff --git a/README.md b/README.md index d01561f..96eda23 100644 --- a/README.md +++ b/README.md @@ -10,13 +10,25 @@

- - + + +

-

- SlickML🧞: Slick Machine Learning in Python -

+
+

SlickML🧞: Slick Machine Learning in Python

+

+ Explore Releases + 🟣 + Be a Contributor + 🟣 + Explore API Docs + 🟣 + Join our Slack + 🟣 + Tweet Us +

+
**SlickML** is an open-source machine learning library written in Python aimed diff --git a/docs/conf.py b/docs/conf.py index 45142e6..cb9669c 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -28,12 +28,16 @@ # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. +# Reference: https://www.sphinx-doc.org/en/master/usage/extensions/index.html extensions = [ "sphinx.ext.autodoc", "sphinx.ext.intersphinx", "sphinx.ext.napoleon", "sphinx.ext.doctest", "autoapi.extension", + "sphinx.ext.viewcode", + "sphinx.ext.todo", + "myst_parser", ] # Auto-API directories @@ -42,7 +46,9 @@ ] # Add any paths that contain templates here, relative to this directory. -templates_path = ["_templates"] +templates_path = [ + "_templates", +] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. @@ -57,21 +63,19 @@ ".ipynb_checkpoints", ] -# numpydoc configuration -numpydoc_show_class_members = False # Otherwise Sphinx emits thousands of warnings -numpydoc_class_members_toctree = False - # The name of the Pygments (syntax highlighting) style to use. # pygments_style = "sphinx" - -# If true, `todo` and `todoList` produce output, else they produce nothing. -todo_include_todos = False - +numpydoc_show_class_members = False # Otherwise Sphinx emits thousands of warnings +numpydoc_class_members_toctree = False # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. -# +# References: +# - https://sphinx-rtd-theme.readthedocs.io/en/stable/configuring.html +# - https://www.sphinx-doc.org/en/master/usage/theming.html#builtin-themes +# - https://github.com/BusKill/buskill-app/blob/b604e23535bb27f1ecb0c3311a8c55bf34f88ef8/docs/conf.py#L99-L106 + html_theme = "sphinx_rtd_theme" html_logo = "../assets/designs/logo_clear.png" html_favicon = "../assets/designs/logo_clear.png" @@ -79,17 +83,33 @@ html_show_search_summary = True html_show_sphinx = True html_output_encoding = "utf-8" +github_url = "https://github.com/slickml/slick-ml" # 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", +] + +# Replace "view page source" with "edit on github" in Read The Docs theme +# Reference: https://github.com/readthedocs/sphinx_rtd_theme/issues/529 +html_context = { + "display_github": True, + "github_user": "slickml", + "github_repo": "slick-ml", + "github_version": "master/docs/", +} + # -- Options for Auto-API-Docs ------------------------------------------------- # Reference: https://sphinx-autoapi.readthedocs.io/en/latest/reference/config.html autoapi_type = "python" autoapi_template_dir = "" -autoapi_file_patterns = ["*.py", "*.pyi"] +autoapi_file_patterns = [ + "*.py", + "*.pyi", +] autoapi_generate_api_docs = True autoapi_options = [ "members", @@ -100,7 +120,9 @@ "special-members", "imported-members", ] -autoapi_ignore = ["*migrations*"] +autoapi_ignore = [ + "*migrations*", +] autoapi_add_toctree_entry = True autoapi_python_class_content = "class" autoapi_member_order = "alphabetical" @@ -108,3 +130,29 @@ autoapi_prepare_jinja_env = None autoapi_keep_files = False suppress_warnings = [] + +# -- Options for View-Code ------------------------------------------------- +viewcode_follow_imported_members = True + + +# -- Options for Napoleon ------------------------------------------------- +# Reference: https://www.sphinx-doc.org/en/master/usage/extensions/napoleon.html +napoleon_google_docstring = False +napoleon_numpy_docstring = True +napoleon_include_init_with_doc = True +napoleon_include_private_with_doc = False +napoleon_include_special_with_doc = True +napoleon_use_admonition_for_examples = False +napoleon_use_admonition_for_notes = False +napoleon_use_admonition_for_references = False +napoleon_use_ivar = False +napoleon_use_param = False +napoleon_use_rtype = False +napoleon_preprocess_types = False +napoleon_type_aliases = None +napoleon_attr_annotations = True + + +# -- Options for TODOs ------------------------------------------------- +# Reference: https://www.sphinx-doc.org/en/master/usage/extensions/todo.html +todo_include_todos = False diff --git a/docs/index.rst b/docs/index.rst index f197785..1c19e8c 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -3,35 +3,42 @@ - https://sphinx-tutorial.readthedocs.io/ - https://www.sphinx-doc.org/en/master/usage/restructuredtext/directives.html +.. NOTES: + 1) You can create a new page under `docs/pages/` in `.md` or `.rst` format + 2) You can add any required sphinx extension to `docs/conf.py` + 3) `docs/pages/releases.md` is a symlink to `CHANGELOG.md` in the root + +.. TODOS: + 1) Once we add example notebooks, we can populate them below. I am not still sure on the format + maybe we can have one examples pages and add the examples there as table with a permalink to + github project repo. Anything we put in the doc, should be easy to maintain. So, we should + maximize the use of permalinks and symlinks. + Welcome to SlickML🧞 Documentation! =================================== -**SlickML** is an open-source machine learning library written in Python aimed -at accelerating the experimentation time for a ML application. Data Scientists' -tasks can often be repetitive such as feature selection, model tuning, or -evaluating metrics for classification and regression problems. SlickML provides -Data Scientists with a toolbox to quickly prototype solutions for a given problem with minimal code. +**SlickML** is an open-source machine learning library written in Python aimed at accelerating the +experimentation time for a ML application. Data Scientists' tasks can often be repetitive such as +feature selection, model tuning, or evaluating metrics for classification and regression problems. +SlickML provides Data Scientists with a toolbox to quickly prototype solutions for a given problem +with minimal code. .. toctree:: :maxdepth: 1 :caption: Contents: - License -.. TODO(amir): update this pages one-by-one and add to the main doc - 1) Each page needs more attention to change the markdowns into sphinx codes - 2) We might need to add more `.rst` files under `pages/` such as Examples or Gallery - 3) We need to do this as simple as possible cuz writing Sphinx has a huge learning curve. So, - maintenance would be hard down the road. - 4) We might need to add multiple sphinx - 5) Altair has great docs that can be a good example for sphinx syntaxes - Installation Quick Start + Releases Contributing - Need Help? - Citing SlickML + Citation + License + Code of Conduct + Contact Us +.. FUTURE PAGES: + Examples Indices and tables ================== diff --git a/docs/pages/citation.rst b/docs/pages/citation.md similarity index 72% rename from docs/pages/citation.rst rename to docs/pages/citation.md index fddf795..d93cb70 100644 --- a/docs/pages/citation.rst +++ b/docs/pages/citation.md @@ -1,7 +1,8 @@ -## πŸ“š Citing SlickML🧞 -====================== +πŸ“š Citing SlickML🧞 +=================== If you use SlickML in an academic work πŸ“ƒ πŸ§ͺ 🧬 , please consider citing it πŸ™ . + ### Bibtex Entry: ```bib @software{slickml2020, @@ -18,4 +19,9 @@ If you use SlickML in an academic work πŸ“ƒ πŸ§ͺ 🧬 , please consider citing i journal={URL available at: https://github. com/slickml/slick-ml}, year={2021} } -``` \ No newline at end of file +``` + +### APA Entry: +```bib +Tahmassebi, A., & Smith, T. (2021). Slickml: Slick machine learning in python. URL available at: https://github. com/slickml/slick-ml. +``` diff --git a/docs/pages/code_of_conduct.md b/docs/pages/code_of_conduct.md new file mode 100644 index 0000000..3327a0d --- /dev/null +++ b/docs/pages/code_of_conduct.md @@ -0,0 +1,129 @@ +πŸ‘¨β€πŸ’»πŸ‘©β€πŸ’» Contributor Covenant Code of Conduct +======================================== + +## βœ‹ 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. + +## πŸ“ 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 + +## πŸ”– 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. + +## β­• 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. + +## πŸ‘¨β€βš–οΈ Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +admin@slickml.com. +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. + +## πŸ“ 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. πŸŸ₯ 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. + +## πŸ“š 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. diff --git a/docs/pages/contact_us.md b/docs/pages/contact_us.md new file mode 100644 index 0000000..7170ecd --- /dev/null +++ b/docs/pages/contact_us.md @@ -0,0 +1,4 @@ +❓ πŸ†˜ πŸ“² Contact Us +================== + +Please join our [Slack Channel](https://www.slickml.com/slack-invite) to interact directly with the core team and our small community. This is a good place to discuss your questions and ideas or in general ask for help πŸ‘¨β€πŸ‘©β€πŸ‘§ πŸ‘« πŸ‘¨β€πŸ‘©β€πŸ‘¦ . You can also check out our πŸ”— Website [https://www.slickml.com](https://www.slickml.com) or πŸ“¬ Email us at [admin@slickml.com](mailto:admin@slickml.com). \ No newline at end of file diff --git a/docs/pages/contributing.rst b/docs/pages/contributing.md similarity index 92% rename from docs/pages/contributing.rst rename to docs/pages/contributing.md index 9599ef2..c7043e1 100644 --- a/docs/pages/contributing.rst +++ b/docs/pages/contributing.md @@ -1,25 +1,13 @@ πŸ§‘β€πŸ’»πŸ€ Contributing to SlickML🧞 ============================= +## πŸ‘‹ Introduction Hello from SlickML🧞 Team πŸ‘‹ and welcome to our contributing guidelines πŸ€— . Here we laid out the details of the development process based on our coding standards, and we hope these guidelines would ease the process for you. Please feel free to apply your revisions if you did not find these guidelines useful. -## πŸ”— Quick Links - * [Code of Conduct](#️code-of-conduct) - * [Getting Started](#getting-started) - * [Coding Standards](#coding-standards) - * [Environment Management](#environment-management) - * [Formatting](#formatting) - * [Linting](#linting) - * [Testing](#testing) - * [Documentation](#documentation) - * [Pull Requests](#pull-requests) - * [Need Help?](#need-help) - - ## πŸ‘©β€βš–οΈ Code of Conduct 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. By participating and contributing to this project, you agree to uphold -our [Code of Conduct](https://github.com/slickml/slick-ml/blob/master/CODE_OF_CONDUCT.md) πŸ™ . +our [Code of Conduct](code_of_conduct.md) πŸ™ . ## πŸš€πŸŒ™ Getting Started @@ -27,7 +15,7 @@ Please note that before starting any major work, open an issue describing what y [*good-first-issue*](https://github.com/slickml/slick-ml/labels/good%20first%20issue) label🏷 on the issue board. In this way, the SlickML team members and other interested parties can give you feedback on the opened *`issue`* πŸ™‹β€β™€οΈ regarding the possible *`idea`* πŸ’‘, *`bug`* πŸͺ², or *`feature`* 🧬. Additionally, it will reduce the chance of duplicated work and it would help us to manage the tasks in a parallel fashion; so your pull request would get merged faster 🏎 🏁 . Whether the contributions consists of adding new features, optimizing the code-base, or assisting with the documentation, we welcome new contributors of all experience levels. The SlickML🧞 community goals are to be helpful and effective πŸ™Œ . -### πŸ“ Coding Standards +## πŸ“ Coding Standards - Long time Pythoneer 🐍 *Tim Peters* succinctly channels the BDFL’s guiding principles for Python’s design into 20 aphorisms, only 19 of which have been written down as [*Zen of Python*](https://peps.python.org/pep-0020/) πŸ§˜β€β™€οΈ . 1. Beautiful is better than ugly. 2. Explicit is better than implicit. @@ -52,7 +40,7 @@ Please note that before starting any major work, open an issue describing what y - We try to maximize the use of [*Data Classes*](https://peps.python.org/pep-0557/) in our source codes and unit-tests. -### 🐍 πŸ₯· Environment Management +## 🐍 πŸ₯· Environment Management - To begin with, install a [Python version >=3.8,<3.11](https://www.python.org). - A working [Fortran Compiler](https://gcc.gnu.org/install/) (`gfortran`) is also required. If you do not have `gcc` installed, the following commands depending on your operating system will take care of this requirement. Please note that installing `gcc` sometimes might take couple minutes ⏳ πŸ€¦β€β™‚οΈ. @@ -83,7 +71,7 @@ Please note that before starting any major work, open an issue describing what y ``` -### πŸ›  Formatting +## πŸ›  Formatting - To ease the process and reduce headache πŸ’†β€β™€οΈ , we have serialized the required formatting commands to save more time ⏰. To apply all the required `formatting` steps, simply run πŸƒβ€β™€οΈ : ```console poe format @@ -103,7 +91,7 @@ Please note that before starting any major work, open an issue describing what y ``` -### πŸͺ“ Linting +## πŸͺ“ Linting - Similar to formatting, to ease the process and reduce headache πŸ’†β€β™‚οΈ , we have serialized the required linting commands to save more time ⏰. To apply all the required `linting` steps, simply run πŸƒβ€β™€οΈ : ```console poe check @@ -127,7 +115,7 @@ Please note that before starting any major work, open an issue describing what y ``` -### πŸ§ͺ Testing +## πŸ§ͺ Testing - We believe in [Modern Test Driven Development (TDD)](https://testdriven.io/blog/modern-tdd/) and mainly use [*pydantic*](https://pydantic-docs.helpmanual.io/), [*pytest*](https://docs.pytest.org/en/7.1.x/), [*assertpy*](https://github.com/assertpy/assertpy) along with [*pytest-cov*](https://github.com/pytest-dev/pytest-cov) with more specification laid out in [*.coveragerc*](https://github.com/slickml/slick-ml/blob/master/.coveragerc) to develop our unit-tests. - All unit-tests live in `tests/` directory separted from the source code. - All unit-test files should begin with the word `test` i.e. `test_foo.py`. @@ -143,7 +131,7 @@ Please note that before starting any major work, open an issue describing what y ``` -### πŸ“– Documentation +## πŸ“– Documentation - We follow [*numpydoc*](https://numpydoc.readthedocs.io/en/latest/format.html) style guidelines for docstrings syntax, and best practices πŸ‘Œ . - We use [*Sphinx Auto API*](https://sphinx-autoapi.readthedocs.io/en/latest/tutorials.html) πŸ€– for generating our API documentation πŸ’ͺ . - In order to generate the API documentation πŸ”– from source 🌲 , simply run πŸƒβ€β™€οΈ : @@ -151,9 +139,11 @@ Please note that before starting any major work, open an issue describing what y poe sphinx ``` - The generated API documentation file can be found at `docs/_build/index.html`. +- You can also add a new page in `.rst` or `md` formats under `docs/page`. +- All `sphinx` configurations that we are using are available in `docs/conf.py`. -### πŸ”₯ Pull Requests +## πŸ”₯ Pull Requests - We currently have `bug-report` and `feature-request` as [*issue-templates*](https://github.com/slickml/slick-ml/issues). As laid out above, please make sure to open-up an issue before start working on a major work and get the core team feedback. - Try to fix one bug or add one new feature per PR. This would minimize the amount of code changes and it is easier for code-review. Hefty PRs usually do not get merged so fast while it could have been if the work was splitted into multiple PRs clearly laid out in an issue before hand. Therefore, the code reviewer would not be surprised by the work. - We recommend to follow [*Fork and Pull Request Workflow*](https://github.com/susam/gitpr). @@ -174,4 +164,4 @@ Please note that before starting any major work, open an issue describing what y ## ❓ πŸ†˜ πŸ“² Need Help? -Please join our [Slack Channel](https://join.slack.com/t/slickml/shared_invite/zt-19taay0zn-V7R4jKNsO3n76HZM5mQfZA) to interact directly with the core team and our small community. This is a good place to discuss your questions and ideas or in general ask for help πŸ‘¨β€πŸ‘©β€πŸ‘§ πŸ‘« πŸ‘¨β€πŸ‘©β€πŸ‘¦ . +Please join our [Slack Channel](https://www.slickml.com/slack-invite) to interact directly with the core team and our small community. This is a good place to discuss your questions and ideas or in general ask for help πŸ‘¨β€πŸ‘©β€πŸ‘§ πŸ‘« πŸ‘¨β€πŸ‘©β€πŸ‘¦ . diff --git a/docs/pages/examples.md b/docs/pages/examples.md new file mode 100644 index 0000000..de5b6db --- /dev/null +++ b/docs/pages/examples.md @@ -0,0 +1,4 @@ +πŸ† Examples +=========== + +# TODO(trace/amir) diff --git a/docs/pages/installation.md b/docs/pages/installation.md new file mode 100644 index 0000000..173d8d0 --- /dev/null +++ b/docs/pages/installation.md @@ -0,0 +1,26 @@ +πŸ›  Installation +=============== + +- To begin with, install a [Python version >=3.8,<3.11](https://www.python.org). +- A working [Fortran Compiler](https://gcc.gnu.org/install/) (`gfortran`) is also required. +If you do not have `gcc` installed, the following commands depending on your operating +system will take care of this requirement. Please note that installing `gcc` sometimes might +take couple minutes ⏳ πŸ€¦β€β™‚οΈ. + + ```console + # Mac Users + brew install gcc + + # Linux Users + sudo apt install build-essential gfortran + ``` +- Now, simply run πŸƒβ€β™€οΈ : + + ```console + pip install slickml + ``` +- We highly recommend to manage your projects in your favorite `python environment`. For instance, all **SlickML** developments are done via [*python-poetry*](https://python-poetry.org/). To begin with, first install `poetry` following the [*installation documentation*](https://python-poetry.org/docs/#installation) depending on your operating system. +- You can also easily [*manage your Python environments*](https://python-poetry.org/docs/managing-environments#managing-environments) and easily switch between environments via `poetry`. To set the `poetry` environment using your preferred `python` version (i.e. `3.9.9`), simply run πŸƒβ€β™€οΈ : + ```console + poetry env use 3.9.9 + ``` diff --git a/docs/pages/installation.rst b/docs/pages/installation.rst deleted file mode 100644 index 0d77147..0000000 --- a/docs/pages/installation.rst +++ /dev/null @@ -1,100 +0,0 @@ -Installation -============ - -Altair can be installed, along with the example datasets in vega_datasets_, using:: - - $ pip install altair vega_datasets - -If you are using the conda_ package manager, the equivalent is:: - - $ conda install -c conda-forge altair vega_datasets - -At this point, you should be able to open `Jupyter Notebook`_ or `JupyterLab`_ -and execute any of the code from the :ref:`example-gallery`. -For more information on how to display charts in various notebook environments -and non-notebook IDEs, see :ref:`displaying-charts`. - -Dependencies -============ - -Altair has the following dependencies, all of which are installed automatically -with the above installation commands: - -- python 3.6 or newer -- entrypoints_ -- jsonschema_ -- NumPy_ -- Pandas_ -- Toolz_ - -To run Altair's full test suite and build Altair's documentation requires a few -additional dependencies: - -- flake8 -- pytest -- jinja2 -- sphinx -- m2r -- docutils -- vega_datasets_ -- ipython - -Development Install -=================== - -The `Altair source repository`_ is available on GitHub. Once you have cloned the -repository and installed all the above dependencies, run the following command -from the root of the repository to install the master version of Altair: - -.. code-block:: bash - - $ pip install -e . - -To install development dependencies as well, run - -.. code-block:: bash - - $ pip install -e .[dev] - -If you do not wish to clone the source repository, you can install the -development version directly from GitHub using: - -.. code-block:: bash - - $ pip install -e git+https://github.com/altair-viz/altair.git - - -.. _conda: https://docs.conda.io/ -.. _Vega-Lite: http://vega.github.io/vega-lite -.. _JupyterLab: http://jupyterlab.readthedocs.io/ -.. _Jupyter Notebook: https://jupyter-notebook.readthedocs.io/ -.. _Zeppelin: https://zeppelin.apache.org/ -.. _IPython: https://github.com/ipython/ipython - -.. _entrypoints: https://github.com/takluyver/entrypoints -.. _jsonschema: https://github.com/Julian/jsonschema -.. _NumPy: http://www.numpy.org/ -.. _Pandas: http://pandas.pydata.org -.. _Toolz: https://github.com/pytoolz/toolz -.. _vega_datasets: https://github.com/altair-viz/vega_datasets -.. _Altair source repository: http://github.com/altair-viz/altair -.. _nteract: https://nteract.io -.. _vega: https://pypi.python.org/pypi/vega/ - -.. To begin with, install [Python version >=3.8,<3.11](https://www.python.org) and simply run πŸƒβ€β™€οΈ : -.. ```console -.. pip install slickml -.. ``` -.. πŸ“£ Please note that a working [Fortran Compiler](https://gcc.gnu.org/install/) (`gfortran`) is also required to build the package. If you do not have `gcc` installed, the following commands depending on your operating system will take care of this requirement. -.. ```console -.. # Mac Users -.. brew install gcc - -.. # Linux Users -.. sudo apt install build-essential gfortran -.. ``` - -.. ### 🐍 Python Virtual Environments -.. In order to avoid any potential conflicts with other installed Python packages, it is -.. recommended to use a virtual environment, e.g. [python poetry](https://python-poetry.org/), [python virtualenv](https://docs.python.org/3/library/venv.html), [pyenv virtualenv](https://github.com/pyenv/pyenv-virtualenv), or [conda environment](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html). Our recommendation is to use `python-poetry` πŸ₯° for everything 😁. - diff --git a/docs/pages/license.rst b/docs/pages/license.md similarity index 94% rename from docs/pages/license.rst rename to docs/pages/license.md index 51d37ce..ae61736 100644 --- a/docs/pages/license.rst +++ b/docs/pages/license.md @@ -1,9 +1,9 @@ -License -======= +Β© License +========= MIT License -Copyright (c) 2020, Amirhessam Tahmassebi +Copyright Β© 2020, Amirhessam Tahmassebi Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/docs/pages/need_help.rst b/docs/pages/need_help.rst deleted file mode 100644 index 1680eac..0000000 --- a/docs/pages/need_help.rst +++ /dev/null @@ -1,4 +0,0 @@ -❓ πŸ†˜ πŸ“² Need Help? -================= - -Please join our [Slack Channel](https://join.slack.com/t/slickml/shared_invite/zt-19taay0zn-V7R4jKNsO3n76HZM5mQfZA) to interact directly with the core team and our small community. This is a good place to discuss your questions and ideas or in general ask for help πŸ‘¨β€πŸ‘©β€πŸ‘§ πŸ‘« πŸ‘¨β€πŸ‘©β€πŸ‘¦ . \ No newline at end of file diff --git a/docs/pages/quick_start.rst b/docs/pages/quick_start.md similarity index 100% rename from docs/pages/quick_start.rst rename to docs/pages/quick_start.md diff --git a/docs/pages/releases.md b/docs/pages/releases.md new file mode 120000 index 0000000..699cc9e --- /dev/null +++ b/docs/pages/releases.md @@ -0,0 +1 @@ +../../CHANGELOG.md \ No newline at end of file diff --git a/examples/quick-starts/metrics/BinaryClassificationMetrics.ipynb b/examples/quick-starts/metrics/BinaryClassificationMetrics.ipynb index 13414bb..3f179d2 100644 --- a/examples/quick-starts/metrics/BinaryClassificationMetrics.ipynb +++ b/examples/quick-starts/metrics/BinaryClassificationMetrics.ipynb @@ -62,10 +62,6 @@ " | 2) Maximizing Precision-Recall.\n", " | 3) Maximizing Sensitivity-Specificity.\n", " | \n", - " | References\n", - " | ----------\n", - " | .. [1] https://en.wikipedia.org/wiki/Youden%27s_J_statistic\n", - " | \n", " | Parameters\n", " | ----------\n", " | y_true : Union[List[int], np.ndarray, pd.Series]\n", @@ -73,16 +69,16 @@ " | \n", " | y_pred_proba : Union[List[float], np.ndarray, pd.Series]\n", " | List of predicted probabilities for the positive class (class=1) in binary problems\n", - " | or y_pred_proba[:, 1] in scikit-learn API\n", + " | or ``y_pred_proba[:, 1]`` in scikit-learn API\n", " | \n", " | threshold : float, optional\n", - " | Inclusive threshold value to binarize y_pred_prob to y_pred where any value that satisfies\n", - " | y_pred_prob >= threshold will set to class=1 (positive class). Note that for \">=\" is used\n", - " | instead of \">\", by default 0.5\n", + " | Inclusive threshold value to binarize ``y_pred_prob`` to ``y_pred`` where any value that\n", + " | satisfies ``y_pred_prob >= threshold`` will set to ``class=1 (positive class)``. Note that\n", + " | for ``\">=\"`` is used instead of ``\">\"``, by default 0.5\n", " | \n", " | average_method : str, optional\n", - " | Method to calculate the average of any metric. Possible values are \"micro\", \"macro\",\n", - " | \"weighted\", \"binary\", by default \"binary\"\n", + " | Method to calculate the average of any metric. Possible values are ``\"micro\"``, ``\"macro\"``,\n", + " | ``\"weighted\"``, ``\"binary\"``, by default \"binary\"\n", " | \n", " | precision_digits : int, optional\n", " | The number of precision digits to format the scores dataframe, by default 3\n", @@ -93,75 +89,75 @@ " | Attributes\n", " | ----------\n", " | y_pred_ : np.ndarray\n", - " | Predicted class based on the `threshold`. The threshold value inclusively binarizes\n", - " | `y_pred_prob` to `y_pred` where any value that satisfies `y_pred_prob >= threshold` will\n", - " | set to class=1 (positive class). Note that for \">=\" is used instead of \">\"\n", + " | Predicted class based on the ``threshold``. The threshold value inclusively binarizes\n", + " | ``y_pred_prob`` to ``y_pred`` where any value that satisfies ``y_pred_prob >= threshold``\n", + " | will set to ``class=1 (positive class)``. Note that for ``\">=\"`` is used instead of ``\">\"``\n", " | \n", " | accuracy_ : float\n", - " | Accuracy based on the initial `threshold` value with a possible value between 0.0 and 1.0\n", + " | Accuracy based on the initial ``threshold`` value with a possible value between 0.0 and 1.0\n", " | \n", " | balanced_accuracy_ : float\n", - " | Balanced accuracy based on the initial `threshold` value considering the prevalence of the\n", + " | Balanced accuracy based on the initial ``threshold`` value considering the prevalence of the\n", " | classes with a possible value between 0.0 and 1.0\n", " | \n", " | fpr_list_ : np.ndarray\n", - " | List of calculated false-positive-rates based on `roc_thresholds_`\n", + " | List of calculated false-positive-rates based on ``roc_thresholds_``\n", " | \n", " | tpr_list_ : np.ndarray\n", - " | List of calculated true-positive-rates based on `roc_thresholds_`\n", + " | List of calculated true-positive-rates based on ``roc_thresholds_``\n", " | \n", " | roc_thresholds_ : np.ndarray\n", - " | List of thresholds value to calculate `fpr_list_` and `tpr_list_`\n", + " | List of thresholds value to calculate ``fpr_list_`` and ``tpr_list_``\n", " | \n", " | auc_roc_ : float\n", " | Area under ROC curve with a possible value between 0.0 and 1.0\n", " | \n", " | precision_list_ : np.ndarray\n", - " | List of calculated precision based on `pr_thresholds_`\n", + " | List of calculated precision based on ``pr_thresholds_``\n", " | \n", " | recall_list_ : np.ndarray\n", - " | List of calculated recall based on pr_thresholds\n", + " | List of calculated recall based on ``pr_thresholds_``\n", " | \n", " | pr_thresholds_ : numpy.ndarray\n", - " | List of precision-recall thresholds value to calculate `precision_list_` and `recall_list_`\n", + " | List of precision-recall thresholds value to calculate ``precision_list_`` and ``recall_list_``\n", " | \n", " | auc_pr_ : float\n", " | Area under Precision-Recall curve with a possible value between 0.0 and 1.0\n", " | \n", " | precision_ : float\n", - " | Precision based on the `threshold` value with a possible value between 0.0 and 1.0\n", + " | Precision based on the ``threshold`` value with a possible value between 0.0 and 1.0\n", " | \n", " | recall_ : float\n", - " | Recall based on the `threshold` value with a possible value between 0.0 and 1.0\n", + " | Recall based on the ``threshold`` value with a possible value between 0.0 and 1.0\n", " | \n", " | f1_ : float\n", - " | F1-score based on the `threshold` value (beta=1.0) with a possible value between 0.0 and 1.0\n", + " | F1-score based on the ``threshold`` value (beta=1.0) with a possible value between 0.0 and 1.0\n", " | \n", " | f2_ : float\n", - " | F2-score based on the `threshold` value (beta=2.0) with a possible value between 0.0 and 1.0\n", + " | F2-score based on the ``threshold`` value (beta=2.0) with a possible value between 0.0 and 1.0\n", " | \n", " | f05_ : float\n", - " | F(1/2)-score based on the `threshold` value (beta=0.5) with a possible value between 0.0 and\n", + " | F(1/2)-score based on the ``threshold`` value (beta=0.5) with a possible value between 0.0 and\n", " | 1.0\n", " | \n", " | average_precision_ : float\n", - " | Avearge precision based on the `threshold` value and class prevalence with a possible value\n", + " | Avearge precision based on the ``threshold`` value and class prevalence with a possible value\n", " | between 0.0 and 1.0\n", " | \n", " | tn_ : np.int64\n", - " | True negative counts based on the `threshold` value\n", + " | True negative counts based on the ``threshold`` value\n", " | \n", " | fp_ : np.int64\n", - " | False positive counts based on the `threshold` valuee\n", + " | False positive counts based on the ``threshold`` valuee\n", " | \n", " | fn_ : np.int64\n", - " | False negative counts based on the `threshold` value\n", + " | False negative counts based on the ``threshold`` value\n", " | \n", " | tp_ : np.int64\n", - " | True positive counts based on the `threshold` value\n", + " | True positive counts based on the ``threshold`` value\n", " | \n", " | threat_score_ : float\n", - " | Threat score based on the `threshold` value with a possible value between 0.0 and 1.0\n", + " | Threat score based on the ``threshold`` value with a possible value between 0.0 and 1.0\n", " | \n", " | youden_index_ : np.int64\n", " | Index of the calculated Youden index threshold\n", @@ -179,15 +175,15 @@ " | \n", " | thresholds_dict_ : Dict[str, float]\n", " | Calculated thresholds based on different algorithms including Youden Index\n", - " | `youden_threshold_`, maximizing the area under sensitivity-specificity curve\n", - " | `sens_spec_threshold_`, and maximizing the area under precision-recall curver\n", - " | `prec_rec_threshold_`\n", + " | ``youden_threshold_``, maximizing the area under sensitivity-specificity curve\n", + " | ``sens_spec_threshold_``, and maximizing the area under precision-recall curver\n", + " | ``prec_rec_threshold_``\n", " | \n", " | metrics_dict_ : Dict[str, float]\n", " | Rounded metrics based on the number of precision digits\n", " | \n", " | metrics_df_ : pd.DataFrame\n", - " | Pandas DataFrame of all calculated metrics with `threshold` set as index\n", + " | Pandas DataFrame of all calculated metrics with ``threshold`` set as index\n", " | \n", " | average_methods_: List[str]\n", " | List of all possible average methods\n", @@ -203,6 +199,10 @@ " | get_metrics(dtype=\"dataframe\")\n", " | Returns calculated classification metrics\n", " | \n", + " | References\n", + " | ----------\n", + " | .. [1] https://en.wikipedia.org/wiki/Youden%27s_J_statistic\n", + " | \n", " | Examples\n", " | --------\n", " | >>> from slickml.metrics import BinaryClassificationMetrics\n", @@ -210,8 +210,8 @@ " | ... y_true=[1, 1, 0, 0],\n", " | ... y_pred_proba=[0.95, 0.3, 0.1, 0.9]\n", " | ... )\n", - " | ... f = cm.plot()\n", - " | ... m = cm.get_metrics()\n", + " | >>> f = cm.plot()\n", + " | >>> m = cm.get_metrics()\n", " | \n", " | Methods defined here:\n", " | \n", @@ -236,7 +236,7 @@ " | \n", " | Returns\n", " | -------\n", - " | Union[pd.DataFrame, Dict[str, Union[float, None]]]\n", + " | Union[pd.DataFrame, Dict[str, Optional[float]]]\n", " | \n", " | plot(self, figsize: Optional[Tuple[float, float]] = (12, 12), save_path: Optional[str] = None, display_plot: Optional[bool] = False) -> matplotlib.figure.Figure\n", " | Plots classification metrics.\n", @@ -299,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, "source": [ "from sklearn.datasets import load_breast_cancer\n", "from sklearn.model_selection import train_test_split\n", @@ -324,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "source": [ "bcm = BinaryClassificationMetrics(y_test, y_pred_proba)" ], @@ -334,66 +334,66 @@ "data": { "text/html": [ "\n", - "\n", + "
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 AccuracyBalanced AccuracyROC AUCPR AUCPrecisionRecallAverage PrecisionF-1 ScoreF-2 ScoreF-0.50 ScoreThreat ScoreTPTNFPFNAccuracyBalanced AccuracyROC AUCPR AUCPrecisionRecallAverage PrecisionF-1 ScoreF-2 ScoreF-0.50 ScoreThreat ScoreTPTNFPFN
Threshold = 0.500 | Average =\n", + " Threshold = 0.500 | Average =\n", " Binary0.9360000.9230000.9850000.9900000.9290000.9720000.9890000.9500000.9630000.9370000.90400010456830.9240000.9110000.9680000.9750000.9200000.9630000.9670000.9410000.9540000.9280000.8880001035594
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {} @@ -403,7 +403,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 6, "source": [ "f = bcm.plot()" ], @@ -411,7 +411,7 @@ { "output_type": "display_data", "data": { - "image/png": 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wSjentqdZT4grw+e7lFv/+WYJW/dlknRSVT4ZfBbhYcpbKooKkehMmrSCq676H9nZuWRnO3PO7NuXxZgxc3n3nfmMHxRH75OzT/xCXi988QX89puz3KkTDB8OZ5xx5ONERESkdKLiwPRyvvLyYPM8WD7Jqe3ZsgiWfOF8ecKg4ZnQpLPTxyeQImIgsR5Urg+VG0JcDQgraSBD/+nQoUOgQ/C5H5dv43+pG4mKCOP5q05TklPBhHyis2rVTq666n9kZByeyGRn55GdncdVI7ezgJE0ZVfZXLR2bRg6FK6/PigKMhERkZAUFgYNkpyvbo/C7vVgJzu1PWt/hvW/Ol/BJjzKSXwSGzjJT2J9Z2TCyg3cn+tDTBWf9z1KTU0t+PmzeRtZviXNp9cLhM/nbQLgvh7NaVozIcDRiL+FfKLz0ku/kZ192DQPh8gmgpfb3c5rPbJO/IK1a8Ott0Ji4omfSySIZWdn07VrV4wxvPnmmwXrjTH89ttvVKt2sMnI5MmT+eCDD3j//fcB2Lt3L6+++iqzZs0iLCwMj8dD//79ufrqq08opj/++IN///vf7N+/n1q1avHCCy9Qq1atw/abOHEiI0aMIDw8nDp16vD4449Tv359AIYPH87EiRMJDw+ndevWPPnkk0RHR2OtpW/fvjRqdHAI0pdffpkmGvVQJHhUaQRnJDtfB/bCqqlOjU+g+6BkpcPeTbBnk9PUbv8uZ0S5XWtLPiYy3q0BanAwEcpPgvITpDIacvv7JVu49+M/yuRcwei0BpW55VzNN1gRhXyiM3bsgoLmaiXJJoz3V8fy2vMP+ykqkfLv+++/xxjD4sWLWbVqFU2bNi3VcZmZmQwYMICLL76Yzz77jIiICDZt2sSgQYMAjjvZycrK4u6772bYsGEkJSXx4Ycf8sgjjxRMiJdv7dq1PP7444wdOxZjDL///jt33303n376KbNmzeKbb77h888/Jzo6mjvvvJP333+fW265hXnz5nHRRRfx1FNPHVd8IuJnMYnQ+nLnK9hkpR9MevZscpOgjYWSoU2QlQbblztfJYmt6iQ9dU6FNlc78w2FhR9TKHv2Z/PIZwsBuLJDA5rUDK35iiLCPFzarj4RRWcDlQoh5BOdtLTS1dKUdj+RgLjwQpg40bfX6NMHvvmm1Lt/9NFH9OnTh5NOOol3332XJ598slTHTZw4kbi4OG699daCdfXr1+eVV14hO/vwJqYpKSl8U0xc77zzDlWrVi1YXrhwIQkJCSS5Q7dfddVVPPPMM+zateuQ/ZYtW0aLFi0KRtPp1KkTmzZtYuPGjeTl5ZGVlcWBAwcICwsjMzOTaHeUxHnz5rFhwwauuuoqAJKTk+nZs2ep7llEgsuMjTP42H5Mjjcn0KEcFA/E14a6tcHbAfJyIOeA85V9AHIyDy7nuMvePOAvmm7YwM0LP6ZaXC1ocxW0vc5Jfo4if9S1kx76mg6NqqgPi4SckE90EhKi2Lfv6ElMQkKUH6IRCQ0rV65k/vz5DB8+nNatWzNw4EDuvffeQxKKkixatKjYDrCtW7cudv/k5GSSk5OPet6//vqLOnXqFCxHRUVRrVo1tmzZckhcrVq1Yvny5SxdupSWLVsybdo0du/ezbZt2zjrrLM4++yz6dq1K5GRkTRu3Jhrr70WgNjYWC666CL69evHqlWrGDhwIPXq1ePUU4/+YUJEgsesP2dx9/S7yckLoiTnWHiASCDy4DDavxDLhMREhuzcxbUzRxD522tQuzW0ucZJfCodOupreJFan6iIMJ67UkmOhJ6QT3QGDDiNMWPmHrH5WmRkGAMHnubHqESO0THUtPjDRx99RJcuXahSpQpVqlShQYMGfPzxxwwZMqTYidfy8vIIcwfm8Hg8xzRnQ2lrdPLyiv8/Hh5+6AO9UaNGPPPMMzz++ONkZWVxwQUX0KJFCyIjIxk/fjwbN25kxowZREVF8Y9//IPnnnuOxx57jCeeeKLgHE2bNqV3795MmzZNiY5IObJi1wrumX4POXk5XN38aro27BrokI6J1+tly95M1u3MYP3ODNbtyGD9zn0ciJ1BWsJyXqxelRer55eL+2DVm85XEXkHapO1rU/Bcp8zN3PnjKtIjE7kvqT7OKveWX66IxHfCvlE5/77z+Ldd/84SqITzr33nunHqETKr4yMjII+LN26dQMgLS2NDz74gJtvvpmqVauye/fuQwYj2LFjB1WqVAGgXbt2fPDBB4edd+rUqcyZM4eHHnrokPWlrdGpW7cu27ZtK1jOzs5m165d1K5d+5D9srKyOOmkk/jkk08AyMnJ4d1336VBgwYMHz6ciy++mIQEZ2Sea665hqeeeorc3FxSUlIYOHBgwTav10tERMgXoSIhY0v6Fm6bchtp2Wn0OKkHj575KGGe4O23kZmTy/K/0ljy5x4Wb97L4s17WfrnXjKycnE+viW6X3WAZoTHW6JrTSIsegsAHrx43O+F5eEhLGYLMQ3fLlg3dccrAGxO30zy98l0adCFS065pNjfT1KtJKrEVPHBHYuUvdB+Su/dS9MlvzD+vgZc9cJasnO8FB6ALTIyjMjIcMaPv7psJw0VCWFfffUVVatW5dtvvy2oLdm7dy9du3Zl0qRJnH/++bz//vs89thjhIWFsWfPHj777DMGDBgAQM+ePRk1ahSjR4/mpptuIjw8nA0bNjB06FAGDx583HG1bduW3bt3M3fuXDp06MCnn35Ku3btSCwyAmJWVhbXXXcdX375JXXr1uWdd94hKSmJKlWq0KpVK77//nsuvfRSwsPD+f7772nbti3h4eFMmzaN6OhobrrpJjZt2sR3333Hu+++e/y/SBHxm7SsNG6fejtbMrbQvlZ7nj3v2aBKcvYeyGaJm8w43/ewcmsaOXmH137XrRxDq7qJtK6XSKt6lWldL5G6lWPwePoA9x5+8oydeJZ8hmfBx3g2ziYL+CixEm9UrVKwS83IStzVoCc7ouMYvfJTftj4Az9s/KHYWKvFVOODPh/QoFKDMrl3EV/yHEsTkkAzxngBrLWlO6BfP/joIwBWUZWXOYv3OY00onnyP51p1qwGSUn1leSIHIPLLruMq6++mv79+x+y/pVXXmHGjBm8/fbbDB06lPnz5xckQpdeeik333xzQbO27du388ILL7Bw4ULCw8MJDw/n+uuv54orrjih2BYsWMCTTz7J/v37qVKlCs899xwNGjRgy5YtJCcnk5KSQu3atZk8eTKvvfYaubm5NG3alCeffJJq1aqRmZnJs88+y2+//UZUVBTGGB5//HEqVarEunXrePzxx9mxYwe5ubnceeed9OnT5+hBufIHP7DWVphG8MdcZov4QHZeNndMuYPf/vyNkxNP5v3e7wesRiK/6dmSP/eweJOb2Py5l/U7Mw7b1+OBJjXiae0mM63qJdKqbiLVE6KLOXMp7VgFCz6BBePYuWc91R/fDUD6E5WJ83rBE8b2VhfzVq36bMxNP+zwDfs2sHL3yoD/Hv2lIpbboSa0E51u3WD6dDj7bKhZ8+D6K69k98UXExERQVRUFFFRGohARHyrIj4wlehIoHm9Xh775TG+WPUF1WKqMbbPWBpWaujXGFLX7eL7JVtYvHkPSzbvZUf64QMkRUWE0aJOpUNqaVrUqURclI8a3ni9sGEWnpOcvjjeD6+D3ExY/SPkZQMeOPUKOP9BqNWi4LC0rDRumHwDy3ctp32t9ozuOZro8BNIvIJcRSy3Q01oN13L99RTTtJTSERaGt26deOjjz6iQYMGBUPIioiIiO+lZaUxZuEYvlr9lc9GQMv15rIncw+xEbGMuGCEX5Oc1dvSGDppGd8t2XLI+sSYiENqaVrXq0zTmvH+nefF44FGZzJq1Chn+Tq3H+TuDfDzMJj7Piz6FBZNgNaXOQlP7VYkRCUw4oIR9J/Yn3lb53HHlDt45MxHaFxZk3FKcKoYNTpTpx6W6OTk5BAbG0tiYiIzZsygSZMmxMTElHnMIiJQMd8MqkZH8uV581i+azlZuU5tht1lGTFvBDsO7PD5teMj4xl63lC6NOzi82sB7EjL5NWpK/hg1npy8rzERobT/4xGdDy5Gq3rJdKgamyxo1MGlT0b4eeXYe574P7NaHUZdH4Qardm+a7l3Dj5RvZm7SXCE0HfFn3p1bgXHg69Lw8emlZpSlxknP/voQxUxHI71FSMGp1i7Nmzh5ycHHbu3MlZZ53FtGnTaNWqFbGxsYEOTUREJGTM+WsOz//+PEt3Lj1s22k1T+P+pPs5KfEkn10/LjKO2AjfP9sPZOfy1i9rGDl9FfsycwjzQN9ODbm3R3NqJ5azF6mVG8CFL8G597kJz7uw5HPnq+UlNO/8EF9c9gWvzXuNCSsmMHbpWMYuHVvsqarFVOPO9ndyxSlXHDZ/j4ivVdhEZ9OmTQU/7927l/PPP5/vvvuO9u3bExdXPt88iIiI+MqW9C18tfor9ufsL/UxK3etZNqGaQBUj6lOvYR6AESHR3N186vp3bh38NduHEVenpfP52/ixW8tm/ccAKCLqck/erfE1KkU4OiOLCUlBaDkIfwr14cLX4Tz7oOfX4HUd2Dpl7D0S2q0uIgnOj9E3xZ9GfXHKLZkbDns8H1Z+1i7dy1P/vYk45aNo3ODzof9vT14OLve2XSoffhE0iInqsI2Xfviiy+47LLLDlkXHR3N119/zVlnnUV8fHyZxCwiAhWzCYSaroWG/Tn7eWfxO7y96O1jSnLyxUbEcuOpNzKo9SC/1Kz406+rtvPMxKUs2rQXgBZ1KvHIhS05r1nNoxwZHPKTjlJ/Ftz7J/zyX0h9G3KcpA5zIXR5COq2PWx3r9fL9+u+Z1jqMDalbTpse2HdG3Xnvo73+X2wiCOpiOV2qKmQNTp5eXksW7bssPWZmZn07t2b8ePH0717dyU7IiLiE/O2zmPUH6PYvn+7z64R5gmja6OuBQmG1+tl6vqpvL/kfdKzDx86uCTb9m9j54GdAHRr2I2W1VuW+tjo8Gh6N+5Nnfg6xxx/MFuxZR9DJy1j6rKtANRJjOH+ns25okMDwsNC+DNxYl3oPRTOvQd+eRXmvAX2G+fL9HH68NRrX7C7x+Oh58k96dywM1+t+qrYf+97Mvfw6YpPmbJ+Cj9u/JF/nvFPrmp+lR9vSkJZhUx09u/fz+rVq4vdlpOTwxVXXMHYsWO55JJLlOyIiEiZ2Zy2mZdTX2by2sl+ud7SnUv5dPmn3HjqjUxZN4U5W+Yc13laVmvJg50epGOdjmUcYfmybV8mL09ZzrjZ68nzQnxUOLd1acrN5zYhNqoC9T+pVAd6PQPn/A1+fRV+fxPsROereS/o/BDUP9gULTo8+ojJy6DWg3h13qt8uepLhs4eytn1zi5o5ihyIipkopOdnc26detK3J6Xl8f111/PDz/8QLt27ZTsiIjICVu3dx3Xfn0t6dnpRIdHM6j1IC5odIHP+qhszdjKa/NeY+nOpQydPRSAKtFVuK3tbcfUHyIyLJLGlRsT5vHj8MdBJiMrhzEz1jDqx1WkZ+USHuah/+kNuad7c2pWqsDTU1SqDf/39KEJz/LJzlezntD5YWiQdNTT1I6vzdPnPk12XjaT1kzixTkvMqzLMD/cgIS6CpnohIWFsX79+mK3NW/enLvuuosBAwYQFhamUdhERKRMPP/786Rnp3Nm3TN58uwnqZtQ16fXa1GtBefUO4cvV33JB0s/4PS6pzP4tMFUjq7s0+uGktw8L5/O3chL31m27M0EoHvLWjzcuwWn1ArugQb8KqEW9PwPnP03+G04zB4DK75zvk7p7iQ8DTsd9TT3Jd3HDxt+4Pt13zP7z9mcXvd038cuIa1CJjqxsbEl1ug88cQTXHPNNYSHV6AqaBER8amfNv7ETxt/IiEygWfPe5YasTX8ct3wsHAub3Y5lze73C/XCyU/Ld/GMxOXsuyvfQC0qV+Zf/ZpyVlNqwc4siCWUBN6PAln3w2/vQazR8PKKc5X0wugy8PQsOTkpU58HW5pcwvD5w3n2dnP8r+L/0dEWIX8qCplpELWQ2dmZhITE8PgwYOZP38+Dz74YMG2Dz/8kLS0tABGJyIioSQ7N5vnf38egCFth/gtyZHjs+yvvVz/1myuf2s2y/7aR/0qsbxybTu+uOMcJTmlFV8Duj8Bf1vgzMUTlQCrpsKbPeC9y2D9zBIPvaH1DdRPqM/K3St5bd5rZOdl+y1sCT0VMk2OiYlh06ZN5OTkkJCQQN26dXn+eech9O233xIWViHzPxGRCsnr9ZLnzfPZ+d9f+j7r9q6jceXG9GvRz2fXkROzZe8BXvrOMj51I3leqBQdwe1dT+HGc04mJjI0W3n4fIqR+OrQ/XE4+y74bQTMGgWrpztfjTs7NTwnnX3IIdHh0TzU6SHunn43by56k2kbpvH3jn/nvAbn+TZWCUkVMtGJiIggIuLgrcfGxnL22Wfz66+/kp2dzSeffMINN9xwyD4iIhJavF4v3677llfnvsqGfRt8fr2HOz1MZHikz68jxyY9M4dRP61m9E+r2Z+dS0SYh+vPOom7L2hGtfioQIcXGuKqwQWPwVl3wMyRMOsNWPOj83XyeU7Cc/K5Bbt3bdSVV7u+ygtzXmDNnjXcPvV2hrQdwh3t7gjgTUh5pKoLnETnlltuKVgeM2YM+/cf+6RoIiJSPizevpgbJt/A33/8Oxv2bcCDhzBPmE++wj3hXH7K5Zxd/+yjByZ+k5Obx4ez1tP5hR94deoK9mfn8n+ta/PdvefzxCWtleT4Qlw16PYI3LPAGaAgujKsnQHvXAjvXARrZhTs2rVRV7649AvuT7ofgHcXv8uezD2BilzKKVVZ4NTwXH311QwePJjs7GxmzpxJeno6lSppRBURkVCyJX1LwXwdANViqnFn+zu54pQrCA8LzeZJciiv18sP1hloYMVWp09u24ZVePTClnQ6uVqAo/OvpCRn6OfU1FT/Xji2KnT9B5x5m1O7M/N1J+FZOwPOe8Cp/QEiwyMZdOogZv45k182/8L45eO5uc3N/o1VyjXV6Lhyc3Pp1atXwfJbb71FZmZmACMSEZGy9O7id7n484v5ctWXRIZFcuOpN/L15V9zdfOrleRUEIs27aH/mFnc+M7vrNiaRsNqsQy/rj2f3352hUtyAObOncvcuXMDF0BsFafZ2j0Locs/nXUzR0JW+iG7DWg1AICPln2kwQnkmCjRcSUmJjJ48OCC5XfffZfc3NwARiQiImXlvcXv8eKcF9mfs5/ujbrzxaVfcF/SfVSKUs19RbB5937u+2Q+F7/2M7+u2kFiTASPXtiSKfd15uK29Xw2aauUUkxl6PIQNDwDstNh6deHbD6n3jk0qdyELRlbmLJuSoCClPJIiY7L4/FwwQUXkJiYCMDy5ctLnFRURETKj+/WfseLc14E4Jlzn+Hlri/TMLFhgKMSf9h7IJvnJy+j64s/MGHuJiLCPNxybmN+erArt5zXhOgI1eQFlbZ9ne9/fHTIao/HQ/+W/QEYu2Ssv6OSckyJTiHZ2dlceeWVBcsjR44kPT39CEeIiEiw2XVgFwu2LWDBtgV8t/Y7/jHjH3jx8rcOf+PiphcHOjzxg7TMHEZMX8l5z03n9R9WkZmTx4Wn1WXKfZ159KJWVInTQANBqfXlEB7ljMa2d/Mhmy5uejGJUYks2L6AP7b9EaAApbxRolNIpUqVuO222wqWx40bR3i43vaIiJQHGdkZvD7/dXqO70n/if3pP7E/9/94P1l5WVxrruXmU9WJOdRlZOXwxo+rOO+5abzwrWXP/mxOP7kaE24/mxH9OnBS9fhAhyhHElsVmvcCbx4s/N+hmyJiubr51QAMmzOMrNysQEQo5YxGXSuiTZs21K9fn02bNrF161bmzJnDueeee/QDRUQkIPK8eXyz+htemfsKWzO2AtCiWgsiw5w5azrU6sA9SfeoH0YI25+Vy9iZ63jjx1XsSHc+ACedVJX7ejTn7KbV9bcvT9peB0u/hPkfwdl3Q6G/3YBWA/hy1ZfM3TqXR395lKHnDSXMo3f2UjIlOkXk5eXRv39/nn/+ecBpvnbaaacV9N0REZHgMX/rfJ7//XkWbl8IQKvqrXiw04Mk1U4KcGTiDweyc/lw1npe/2EV29OckVLbNazCvT2ac36zGkpwjuLWW28NdAiHO6U7xFWHbUvhrwVQt23BphqxNXi9++vcMOkGJq2ZRL34etyTdE/gYpWgp0SniLi4OAYPHlyQ6Hz++eeMGTMmwFGJiAjA8HnD+XHDjwDkenNZuXslADVjaxb0wdEb3tCXmZPLx79vYMT0lWzZ6yQ4bepX5r4ezeliairBKaWUlJRAh3C4iCg49UqYneIMNX3ZyENqdVpUa8GwLsO4Y+odvLnoTQAGtx1MbERsoCKWIKanQTFq165NmzZtAMjIyGDixInk5eUFOCoRkYpt1e5VpCxIwe6y2F2WlbtXEh0eTfJpyXx9+ddcesqlSnJCXFZOHh/MWkeXF37gX18sZsveTFrWTWT09R358s5z6NqilpKcUNDhBvCEO6OvfX0P5B063cc59c/h8bMeB+DNRW9y8WcX89Wqr8jz6rOaHEo1OsWIjIxk0KBB3H///YDzxqNHjx5qviYiEkAfLP0AgIubXMz1ra8HoE5cHarEVAlgVOIP2bl5TJi7kVenrmTT7v0AmNqVuLdHM3q2qkNYmJKb45GamgpAUlKQNfWscyr0/QD+NwhS34H07XDlmxAZU7DL5c0up1FiI56b/RxLdy7lnz//k8U7FvPw6Q8HLGwJPnr1VYyoqChuuOGGgrdCU6dOxev1BjgqEZGKa/eB3Xy16isAbjntFlpUa0GLai2U5IS4nNw8xqdu5IKXfuShTxeyafd+TqmVwIh+HZj0t/PodWpdJTknoGPHjnTs2DHQYRTP9IaBnzuTiS77Gr79x2G7JNVOYtxF43jy7CeJ8ETw4dIPWbpjqf9jlaClRKcEkZGRnH/++QDk5ubywQcfkJOTE+CoREQqpvErxnMg9wDn1j+XJpWbBDoc8bGc3Dw+n7eJHi//xAP/+4P1OzNoUiOe//Ztx7f3nM+FpynBqRBOOgsGTHB+XvIFFNONIMwTxuXNLue6ltfhxcuzs5/Vy2kpoESnBPHx8YeMRvLWW29x4MCBAEYkIlIxZedm89FSZ6b0ga0GBjga8aW/9hzglSnLOfe56dzz8XzWbE/npOpxDLumLd/dez6XtqtPuBKciqV+ElRuBBk74K+SJwq9re1tVIupxryt85i0ZpIfA5Rgpj46JQgPD+eyyy4jOjqazMxMUlNT2b17NwkJCYEOTUSkQvlu3Xds3b+VU6qcwll1zwp0OFLG8vK8zFi5nQ9mrmPqsq3k5jlv4xvXiOe2Lk25on19IsL1XrbC8nigaVeY+y6smgb12he7W6WoSvytw994/NfHeSn1Jc5rcB6xEbF48BAepsnfKyqVHEeQm5vLRRddVLA8evRo1eqIiPjRzD9nMnzecAAGtBygEbVCyPa0TEb+sIrOL07nhrdm892SLXiAC9vU5cNbzmDa/Z25pmNDJTkCTbs531dOO+Jul51yGa2rt2ZrxlbO/uhs2r/fng5jOxQMZCIVj2p0jqBSpUoMGTKETz/9FID333+fhx/WaB4iIr62bu86XpzzIj9s+AGAU6qcwoVNLgxoTHLivF4vs9bsZOzMdXy7+C+yc53am/pVYul3RiOu7tiAWpVijnIWqXCadAZPGGyYBZn7ILpSsbuFecJ47MzHGDxlMGlZaXjxkufNY9Qfo7iq+VVEh0f7OXAJNCU6R+DxeDj33HOpWrUqu3btYs2aNaxYsYLTTjst0KGJiISkPZl7GLVgFB8t+4icvBziIuK49bRbGdhqoD6klGO7M7L4dO4mPpy1jlXb0gEI80D3lrXpf2Yjzm9WU31vpGSxVZ2+Oht/h7W/gOlV4q6ta7Tm574/A05ife3X17J051K+Wf0NVzS7wl8RS5BQonMUOTk5XH311QWzB48cOZIXXnhBfXVERMrY5LWTeXrm0+zO3I0HD1c0u4K72t9FjdgagQ5NjoPX62Xeht18MHM9Xy/YTGaOM2JWrUrR9D29EX07NaReFc1mH0hz5swJdAil17Sbk+ismnbERKcwj8fDwFYD+efP/+T9Je9z+SmXq/lrBaNE5ygSEhK4/fbbCxKdTz75hJdffjnAUYmIhJYNezfwzxn/JDsvm461O/JgpwdpWb1loMOS45CWmcPn8zbxwaz1LP1zb8H685rVoP8ZJ3FBy1pEqt9NUAi6iUKPpGk3+PE5WDX1mA7rdXIvhqUOY+Xulcz8cyZn1dOAJhWJEp1SaN68OSeffDJr165l586d/PLLL3Tr1k1vBUREysjzc54nOy+bi5pcxDPnPqPytRxatGkPH85ezxfzNpGelQtAtfgorunYkOtOb8hJ1eMDHKGUa/U7QnQi7FgJu9ZB1ZNKdVhkeCR9TV9em/8aY5eOVaJTweiVSil4PB4GDBhQsPzGG2+wb9++AEYkIhI6ft70Mz9s+IG4iDjuS7pPSU45sj8rl0/mbODSEb9w0fCf+XDWetKzcjmjcTVeva49v/2jGw/3bqEkJ0glJyeTnJwc6DBKJzwCGjsTubN6+jEderW5mujwaH7a+BNr9qzxQXASrJTolEJMTMwhBcHXX39NRIQqw0RETlR2bjbPzX4OgCFth1AzrmaAI5LSWLFlH098uZjTn5nCg+MX8MeG3STGRHDjOScz5b7z+XjwWVzSth7REZq/JJiNHj2a0aNHBzqM0ssfZnrxZ8d0WLWYalzUxJkuZNyycWUdlQQxn31aN8ZcCDwLRAMLgJuttXuL7HM58G8gD9gF3GKtXeWrmE5E1apV6dChA3PnzuXAgQN88cUXXHPNNYSHqxAXkfIvUGX2h8s+ZO3etZyceDIDWg44+gESMJk5uUxe9BcfzFrP7DU7C9a3a1iF/mc04qLT6hEbpWei+FCry2DKE7D6B1g13ZlItJSuNlfz6YpPmbx2Mn/v9HciwvTCuiLwSY2OMaYm8DZwpbXWAKuBoUX2iQXGAldYa9sBXwKv+iKeshAdHc2NN95YsDx69GjS09MDGJGISNkIVJnt9Xp5b/F7APy909+JDI88kdOJj6zbkc6zk5Zy1rPT+Nu4+cxes5P4qHD6ndGIr+86l8/vOIerOzZUkiO+F18dzr3H+fn7f0FeXqkPbVWtFScnnszOAzuZ9ecs38QnQcdXTdd6Ar9ba1e4yyOB/saYwg2vwwEPUNldTgAO+CieExYZGcmAAQMKanB+/PFHcnJyAhyViEiZCEiZvW7vOrbu30q1mGqcV/+8EzmVlLHs3DwmL/qTgW/OovMLPzDqx9XsTM+iZd1Enr78VGY90p1nLm/DqfUrH/1kImXpjNugUj34awEs/F+pD/N4PPRp3AeAiWsm+io6CTK+SnQaAhsKLW8EEoGCqWyttWnAEOBXY8xm4E7gIR/FUybCwsLo1s1pH5qXl8e7775LVlZWgKMSETlhASmzZ/81G4COtTtqAIIgsXn3foZ9Zzln6DSGjJ3LjBXbiY4I46qkBnx2+9lMvPtc+p9xEgnRavYjARIVB90edX6e9hRkl/59S+/GvQGYun4qB3KC9t26lCFfJTolnTc3/wdjTBvgX0Ara2094Gng0yJvEPP3TTbGBHxWq4SEhEMGJXj77bfJzs4OYEQiImUiIGX2nL+cXTrV6XQ8MUsZyc3zMn3ZVm5593fOfW4ar05bydZ9mTStGc+/LmrF7H9258Wr29K+UVUlpBIc2vaFWq1hzwb44g7YtbZUh51c+WRaV29NenY6P238ybcxSlDw1SuZ9cAZhZbrA7ustYU7tfwf8EuhjqwjgJeB6sD2wiez1qYAKcYYr4/iLZWwsDAuvPBC4uLiyMjIYOHChWzdupXGjRsHMiwRkRPl9zLb6/Xy+5bfATi9zuknfANy7LbuO8D/5mzkw1nr2bR7PwCR4R4uPLUu/c9oxBmNqymxCXEdOnQIdAjHJywcej0L718Oi8bD4gnQ6lI4+y6of+RJUPs07sPiHYuZuGYiPU/u6aeAJVB8VaPzHXCmMaaZuzwE+KLIPnOBzsaY2u7yZcAaa+12glh2djaXXnppwXJKSgr79+8PYEQiIifM72X22r1r2b5/O9ViqtG4sl4W+UtenpdfVm7n9g9SOfvZabzwrWXT7v00qhbHw71b8Ns/LmD4de05s0l1JTkVQGpqKqmpqYEO4/g06QxDZkDb68AT5gw5PbobvN0H7OQSByro1bgXHjz8tPEn9mbtLXYfCR0+SXSstVuBG4HxxpilQBvgfmNMR2PMfHefacALwA/GmD9w2ntfWsIpg0ZiYiJDhgwpWB47dmwAoxEROXGBKLN//8upzelUp5M+UPtIbp6XrJw8snLy2J6WyeifVnPBsB/pP2YWExf+hRf4v9a1ee+m0/nhgS4M6dyUGgnRgQ5bpPRqt4bL34C/LYCz74boRFj3C3x0Lfz4XLGH1IqrRac6ncjOy2bKuil+Dlj8zWe9Ca21E4Giw1rsBNoV2mcETvOHcuX000+nZs2abNu2jY0bN7JkyRKSko5cVSoiEsz8XWYX9M+prf45Jyo7N4+129OxW/axfEsaK7bsY/mWfazdkUFu3uGtB+tWjqFvp0Zc26khdSrHBCBikTJWuT70fArO/zvMTnEGKZidAufdDxFRh+3e46QezP5rNrP/ms0Vza4IQMDiLxo25Tjk5ubSt29fhg8fDsDrr7/Of//7XxISEgIcmYhI8PN6vQUjrnWqq0SntHJy81i3M8NNZNJY7iY0a7ank51bfHeoyHCntizM4+HMJtUZcOZJdDU1iQj3Vct1KU/ya1O93oB2gS47MYlOcrPoU9i6BFZ+Dy0uPGy3ZlWdVrrr9673d4TiZ0p0jkN8fDy33XZbQaLz6aefMmJEuauYEhEJiDV717DjwA6qx1SncaL65xSVl+dlw66MQ5KZ5VvSWLUtjayc4vsdNKoWR/PaCTSrXYnmtRNoXrsSTWsmEBOpSTylgvF44LRrYcrj8Me4YhOdkxJPApy+gl6vV81nQ5gSneN00kkn0axZM1asWMGePXuYPn06vXr10n8WEZGjKDysdEUuM71eL5t27y9IZJZv2ceKLWms2LqPA9nFJzT1q8TSzE1kmrtJzSm1EoiL0uNcpMBp18CUJ2D5ZMjYCXHVDtlcPaY68ZHx7Mvax+7M3VSNqRqYOMXnVDIep/DwcG644QYefdSZtGrUqFGcc845JCYmBjgyEZHgVngggopmwtyN/LZqB8u3prFyyz7Ss3KL3a92YvQhyUyz2pVoViuBSjGRfo5YpBxKrAdNusDq6c5obJ1uPmSzx+OhUaVGLN25lHV71ynRCWFKdI5TdHQ0N998c0GiM2nSJMLC1OZZRORoluxYAkC7Wu0CG4ifLd68h/s++eOQdTUSoguamhUkNbUqUTlOCY3ICWl7nZPoLPj4sEQHnOZr+YlORSuLKhIlOicgPj6eM888k5kzZ5KVlcX48eMZOHAg4eFqEy0iUpJdmbsAqBVbK8CR+Nfva3YCcM4p1bmrWzOa165EtfjDR4QSkTLQ8iL4Oh42zIIdq6B600M25/fTWbd3XSCiEz8J7SoIH48iEhsby803H3xLMGbMGDIyMnx6TRGR8iwnL4d9Wfvw4KFSVKVAh+NX8zbsBqBPm7qc2aS6khwRX4qKh1aXOD8v+OSwzUp0KobQTXQyM2HuXOfnRo18comIiAiuvfZaIiKcirFff/2V/fv3++RaIiKhIH8m8kpRlQgPq1i13/PW7wagfUP1B5DgMGrUKEaNGhXoMHyn1WXO9zU/HbYpP9FZv09DTIey0E10Jk+GvXuhfXs45RSfXcbr9fJ///d/BT+/9dZbZGZm+ux6IiLl2Z7MPQBUia4S2ED8bHtaJut3ZhAXFU7z2ppzTYJDcnIyycnJgQ7Ddxqe7nzfPA9ysw/ZVLhGJ2TmEZLDhG6iM26c8/3aa316mYSEBAYPHlyw/O6775KbW/woOiIiFV1FTXTmu7U5pzWorMk6RfwlrhpUawo5+2HL4kM2VY6uTJXoKuzP2c+2/dsCFKD4WmiWtunp8OWXzs8+TnTCwsLo0aMHlSo5bc2XLVvGxo0bfXpNEZHyanfmbsD5kFGRzNvgDMDQvpGarUnwSElJISUlJdBh+FYDdxj7jb8ftqlRotO1Qf10QldoJjrffAMZGXDmmXDyyT6/XHZ2NpdffnnB8htvvKFBCUREipFfo1PhEp2C/jlVAhqHSGGDBw8+pFVKSGrQ0fleTKJzcuLJgBKdUBaaiU5+s7W+ff1yuUqVKnH77bcXLH/00UeaU0dEpBj5NToVqelabp6XP9wR19o1qhLQWEQqnPx+OsXV6FRyanTW79WABKEq9D6N79kDEyeCxwNXX+23y7Zt25a6desC8NdffzE3f8Q3EREpUBFrdFZs3Ud6Vi4NqsZSq1JMoMMRqVhqtYaIWNi5GtJ3HLLppMrOgARr964NQGDiD6GX6HzxhTO09PnnQ716frtsXl4e/fr1K1geOXIk+/bt89v1RUTKg4rYR6eg2Zr654j4X3gE1O/g/LxpziGbTqrkDjGtGp2QFXqJjp+breWLi4tjyJAhBcuff/45kZGRfo1BRCTYVcRR1+atdwciUP8ckcDI76ezYfYhq/MHI1i/bz25eRoxNxSFVqKzYwd8/z2Eh8OVV/r98nXr1qVVq1YApKWl8e2335KXl+f3OEREglVFbLp2sEanSkDjEKmwShh5LT4ynpqxNcnOy+avjL8CEJj4WmglOhMmQE4OdO8ONWv6/fKRkZEMGjSoYHnUqFFqviYiUkhFGYzA6/WyaNMeXpmynJXb0ogKD6NVvcRAhyVSMeUnOpvmQpGam4Ihpvdo5LVQFFqJToCareWLiopi0KBBeDweAL7//vuCn0VEJLT76GTm5PLj8m089vkizh46jYuG/8wrU1bg9cJ5zWoQHREe6BBFDuH1evF6vYEOw/cq1YHKjSBrH2yzh2wqGGJ6nxKdUBQR6ADKzJ9/wvTpEBUFl10WsDCio6M599xzmTFjBjk5OXz00UfcdNNN6q8jIgLszdoLhE6Nzs70LKYv28qUpVv4afk20rMOvi2uVSma7q1q06Nlbc45pUYAoxQRGnSEPeth42yo3apgtSYNDW2hk+h8/z14vdCzJ1SpErAw4uPjueWWW5gxYwYAb775Jv369VOiIyIVXmZuJvtz9hMRFkFcRFygwzluq7elMWXpFqYs2cqcdTvJK/RCvGXdRHq0rEX3VrU5tV5lwsJUqy8SFBp0gsUTYMPvkDTo4OqEBgBsStsUoMDEl0In0dm/3/nuxyGlixMeHs6VV15JcnIymZmZ/P7776SlpVGpUqWAxiUiEmi7D+wGoHJU5XLVrDc3z8vc9buYsmQL3y/dwupt6QXbIsM9nNOkOt1b1uaClrVoULX8JnBS8SQlJQGQmpoa4Ej8IH/i0CJDTNdPqA/A5rTN/o5I/CB0Ep0gkpubS58+ffjss88AGD16NA8++CAxMZooTkQqrj1Z5Wdo6fTMHGas2Mb3S7Yy3W5lZ3pWwbbKsZF0a1GL7i1rc37zGlSKUY29lE8VanLzOm0gPAq2LYP9uyG2CnBoouP1esvVSxg5OiU6PlCpUiUGDx5ckOi89957PPDAAwGOSkQksIJ9aOk/9+xn6lKnv82vK3eQlXtweoCTqsfRo2VtureqTceTqhIRHlpj+YiEvIhoqNvWGWJ681xo2g1wyqO4iDjSstPYm7U3aMsnOT5KdHzA4/HQuXNnqlSpwu7du1m1ahVr1qyhdevWgQ5NRCRggm1oaa/Xy+LNe5mydAtTl25l4aY9Bds8Hkg6qSrdW9amR6taNK2ZoDe9IuVdg05OorPh94JEx+PxUC+hHit3r2Rz2mYlOiFGiY6P5OTkcNVVVzFmzBgAXn/9dZ5//nni4+MDHJmISGAEw9DSmTm5zFy9kylLtjB16RY27zlQsC02MpzzmtWge6vadGtRixoJ0QGLU0R8oEFH53uRiUPrJ9QvSHRaVm8ZgMDEV5To+EhCQgK33357QaLzySef8NJLLwU4KhGRwMlvuhaoGp3/fL2Ej2avP2wI6AvcWpuzm9YgJlJz3YiErAbugAQbf3dG6nVraeslOANZaeS10KNEx4datmxJo0aNWL9+Pdu3b2fmzJl07txZzR9EpEIKZB+dVdvSGPPzGuDgENAXtKxNm/oaAlqkwqjcABLqQNpfsGMV1DgFKDQgQbpGXgs16k3pQ16vlwEDBhQsv/HGG+zbty+AEYmIBE4gm65NWvgnAFd0qM+kv53HfT0NbRtWUZIjFdqtt97KrbfeGugw/MfjKdR8bXbB6oIanX2q0Qk1SnR8KDY2lsGDBxcsf/nll0REqBJNRCqmQA5GMHHhXwBc2Kau368tEqxSUlJISUkJdBj+1aCT871QP52CRCddiU6oUaLjY9WqVaNt27YA7N+/n6+++oq8vLyjHCUiEnr2Zu4F/F+js3Z7Okv+3EtCdATnNqvh12uLSJBpWKifjqtBQgPg4Fw6EjqU6PhYdHQ0N910U8Hy6NGjSUtLC2BEIiKBEaganUmLnNqc7i1rER2hwQZE8qWmppKamhroMPyrbjvwhMOWxZCVDkBiVCLxkfGkZ6ezN2tvYOOTMqVEx8ciIyMZOHAgYWHOr3r69Onk5uYe5SgRkdATqD46kxY5/XN6q9mayCE6duxIx44dAx2Gf0XFQZ1TwZsHm+YCB+fSAY28FmqU6PhBeHg4Xbt2BSAvL4+xY8eSnZ0d4KhERPzH6/UWNF3zZ43Ohp0ZLNi4h7iocDo3r+m364pIECvop3NwQIL68e7Ia2kaeS2UhE6is9etaoyLC2wcxUhISDhkVJO33nqLzMzMAEYkIuJf6dnp5HhziI2IJSo8ym/Xnew2W+vWopbmyBERR53TnO/bVxSsUo1OaAqdRGeNMz8CjRsHNo5ihIWFcfHFFxMbGwvA/Pnz2blzZ4CjEhHxn0D0z8nL8/LVAuftbB81WxORfAm1nO/p2wtW5Sc6qtEJLaGT6Kxe7Xxv0iSwcZQgJyeHiy++uGB51KhR7N+/P4ARiYj4j78nC/V6vTz+5WIWbNxDpegIuhg1WxMRV7xbHmQcTHQKJg1VohNSlOj4SaVKlbjtttsKlseOHRvAaERE/MufiY7X6+XfXy3h/ZnriIoI47X+HYiL0hxmIuKKd4eZTz880dFcOqElNBKd3FxYu9b5+eSTAxlJiTweD2eeeSY1ajj/udavX8+yZcsCHJWIiH/4q+ma1+vlP98s5Z1f1xIVHsaoAUkahEBEDhWXn+hsA3fenMJN1zSXTugIjURn82bIzoY6dYJyMIJ8ubm5XHPNNQXLI0eO1Jw6IlIhFAwtHeW7Gh2v18vQSct48+c1RIZ7eL1/B7q2qOWz64mUd3PmzGHOnDmBDsP/ouIhIhZyDhwyl05CZILm0gkxoZHoBHmztXzx8fHcfvvtBcv/+9//iIhQcwoRCX17snzbdM3r9fLCt5ZRP60mIszDiH4d6N6qtk+uJRIqkpKSSEpKCnQY/ufxHOynk77NXaW5dEKREh0/a9y4MU3cOHfv3s1PP/2kKlIRCXn5fXR81XTt5e+X8/oPqwgP8/Bav/b0bF3HJ9cRkRARX935XszIa0p0QocSHT8LCwvj+uuvL1h+44032LdvXwAjEhHxvYI+OjFVyvzc/52yglenrSQ8zMOrfdvT61QNJS1SGsnJySQnJwc6jMDQyGsVghIdP4uJiTlk8tCJEycSHq5J7EQktPmqj85r01bw8pTlhHng5WvbceFpSnJESmv06NGMHj060GEERpGmawB1453y46/0vwIRkfhAaCQ6QTxZaHESExPp1KkTAJmZmXz66afk5uYGOCoREd/Zm+l07i3LPjpjZqzmxe+W4/HAsGvacUnbemV2bhEJcXH5TdcOJjrxkfEA7M/RPIehIjQSnXJUowMQHR3NzTffXLD8008/afQ1EQlpteNqExsRy8mJJ5fJ+fYeyOb5yRaAF65qy2Xt65fJeUWkgiio0dlRsComIgZQohNKyv+QX+npsGULREVBvfLxNi8yMpLrrruO9evXM3jwYKpVq0ZUVFSgwxIR8ZnnOz9PRnZGmdXoTF26hazcPM5sUo2rkhqUyTlFpAIppulabHgsAAdyDgQiIvGB8p/oFG62FlZ+KqjCw8N59NFHiY2NDXQoIiI+FxkWWabN1iYudNrQ92mjPjkichzi3UlDCw1GEBvhJjq5SnRCRflPdPKbrZWT/jn54uPjAx2CiEi5lJaZw4/Lt+HxwP9pGGkROR75iU6hGh01XQs9Pkt0jDEXAs8C0cAC4GZr7d4i+7QBhgOVgVxgsLU29ZgulF+jU07654iIBCO/ldllYNqyrWTl5NHp5KrUTozx9+VFQkaHDh0CHULgxOUnOgdrdPITHTVdCx0+aetljKkJvA1caa01wGpgaJF94oDvgOette2Bp4APjvli5WwgAhGRYOPXMrsMTFr4JwC9NV+OyAlJTU0lNdXv7yqCQ3yhRMeduF01OqHHV51aegK/W2tXuMsjgf7GGE+RfVZZaye6y18C1xzzlZToiIicKP+V2ScoIyuH6XYrAL3bqNmaiBynyFiIqgR52XBgD1BoMAL10QkZvmq61hDYUGh5I5AIVALym0I0B/4yxrwJtAV2Aw8e85WU6IiInCj/ldkn6Ae7jQPZeXRoVIW6lTWYi4icgPjqkLXPqdWJrVIwGIFqdEKHr2p0Sjpv4VkxI4E+QIq1tiNOu++JxpjoogcZY5KNMXMOO5vXW24HIxARCSL+KbPLwES32ZpGWxM5cR6PB4/Hc/QdQ1X+ENPuyGvqoxN6fJXorAcKP4XqA7ustemF1m0GlllrZwFYa78AwoHDqmastfkP1kP99RccOADVq0NiYlnGLyJSkfinzD5BB7JzmbbMabbW61Q1WxORE1RkLp3ocOe9TWZuJnnevEBFJWXIV4nOd8CZxphm7vIQ4Isi+0wCTjbGJAEYY84HvMCaUl8lxEZcGzp0KAMHDqRXr1506dKFgQMHcvfddzNr1izuvffeMrvOwIEDWbVqVan2ffjhh/npp58OWZeZmUm3bt2O+bp5eXn861//4tprr2XgwIGsW7fukO1Lly5l4MCBBV9t2rThp59+YvPmzQwaNIiBAwcyYMAAVru1eNOmTePKK6/k2muv5ZNPPjnmeESkgH/K7BP0g91GRlYubRtUpkHVOH9dVkRCVVx157s78prH4zk4l45qdUKCT/roWGu3GmNuBMYbY6KAVcD1xpiOwBhrbTtr7V/GmMuA140x8UAmcIW1tvT/skKsf87DDz8MwIQJE1i9ejUPPPAAALNmzQpkWGVmypQpZGVl8fHHHzN//nyGDh3KyJEjC7a3bNmS999/H4BJkyZRq1Ytzj//fB566CEGDBhA9+7dmTFjBsOGDePll1/m2WefZfz48cTGxnLdddfRrVs3atSoEajbEym3/FZmn6BJi9zR1tRsTUTKQkGNTqEhpsNj2J+zXwMShAifzaPjjswzscjqnUC7Qvv8BJxx3BepQP1z1q1bxy233MLOnTvp2rUrd911FwMHDqRatWrs2bOHlJQUnnjiCdatW0deXh733HMPZ5xxBi+//DKzZs0iJyeHnj17kpycDMCIESPYvn07+/fvZ9iwYTRs2JChQ4cWDDN50UUXccMNNxRcPz09nQceeIC9e/fSqFGjw+JLT09nyJAhh6w744wzuPPOOwuWU1NTOe+88wBo164dixYtKvZeMzIyGD58OGPHjgXgoYceolKlSgDk5uYSHR3NqlWraNSoEZUrOzOtJyUl8fvvv9O7d+/j+v2KVHR+KbNPwIHsXKYudUdbU7M1ESkLRZquAcRGxLIrc5cGJAgRPkt0/CLEanSOJDMzk9dff53c3Fy6dOnCXXfdBTgJSY8ePfjwww+pWrUqzzzzDLt27WLAgAF88803fPXVV7z33nvUqlWLCRMmFJyvc+fOXHrppQwfPpzJkydzyimnsHHjRj755BNycnLo168fZ555ZsH+48aNo3nz5tx777388ccfh9UyxcfHF9TGlCQtLY2EhISC5fDwcHJycoiIOPSf4fjx4+nVqxfVqlUDKPi+evVqnnvuOUaMGMHOnTsLkp/866elpR3Lr1REypGfV2wnLTOH1vUSOal6fKDDEZFQkD+XToYmDQ1VSnTKiWbNmhEVFQVwSGLQ2K3NWr58OampqSxYsACAnJwcdu7cyQsvvMBLL73E9u3bC2pTAE499VQAatSowfbt21m1ahUdO3bE4/EQGRlJ27ZtD+nHs3btWjp37gxA27ZtD0tOSlOjk5CQQHr6wb7NeXl5h50H4KuvvuLVV189ZN3MmTP597//zfPPP0+TJk3Iyso65Fzp6emHJD4iElomLtJoayJSxgomDT1Yo6NEJ7SU70QnxAYjOJKShn/MX9+kSRPq1KnDkCFDOHDgACNHjiQhIYHJkyczbNgwAPr06cOFF15Y7HmaNm3KhAkTGDRoENnZ2cybN4/LL7/8kO3z58+ne/fuLFmyhJycnEOOL02NTocOHZg+fTp9+vRh/vz5NG/e/LB99u3bR1ZWFnXrHvwwM3PmTJ5++mnGjBlD/fr1C+JZt24du3fvJi4ujjlz5nDzzTcf8foiUj5l5eTx/ZItgJqtiZSlUaNGBTqEwCqhjw5oLp1QUapExxhTx1r7l6+DOSYHDsCmTRAeDg0bBjqagOvbty+PPvooAwYMIC0tjX79+hEVFUXlypW55ppriImJ4ZxzzqFevXrFHt+1a1dmz57NtddeS3Z2Nr169aJ169YF26+77joefPBBrrvuOpo0aUJkZOQxx9ijRw9++eUX+vbti9fr5ZlnngHg7bffplGjRlxwwQWsWbOmIJnJ98wzz5CdnV0wWEPjxo158sknefjhh7n55pvxer1ceeWV1K5d+5hjEpHg98vK7ew7kEOLOpVoUjPh6AeISKnk99utsOLya3QOJjqaNDS0eLxe71F3Msb8DGwD3gQmWmsDMri4McYLYK0Fa6FFC2cggvwmbCIiQcoYA4C1tsLMzndImX0C/v6/P/hf6kbu69Gcuy9odvQDRERKIycL/lMTPOHw2HYIC+Pe6fcyZf0UhnUZxp09neb3FancDjWlmkfHWnsu8AjQGfjVGPO0MSaw7cUqUP8cEZGKauOuDL5Z6A4rrWZrImUqJSWFlJSUQIcROBFREFMFvLlwYDegPjqh5lj66GwCVgNJwKnAf40xi621D/sksqNRoiMiEtK8Xi//mLCQjKxcep9ah2a1NeCISFkaPHgwUMGbsMXXcJKc9G0QV60g0QnlpmvGmIeB7kAkkAc8YK1NPcFzvgIMA/YBU4Ed7vdp1trZxezfC2hkrU0xxiQDb1trs08khuKUto/OJzjJzVhggLV2s7t+TlkHVGoVaCACEZGK6H+pG5mxYjtV4iJ58tJTAx2OiISi+JqwY6WT6NQ0BYMRhGqNjjGmFXAJcI611muMaQe8C7Q9kfNaa+9xz38+sMZae+VR9p9caPGfwHtAYBId4Ctr7TX5C8YYY51G1+eWdUClphodEZGQ9deeAzz19RIAnri4NTUrRQc4IhEJSXHVne/ugAT+HIzg5Ie/+QboU8annbh2aAlD7Dr2AI2Am4wxk621840xpxtjfgCWAS0AD3CttfYvY8yzwHlAODDMWvs/Y8wZwCs4XWA2Af2BScDdwKtAPWPMv4GTgHHAj8Db7nIUcCdg3GutAOoA44wxS4BN1toRxpiqwBRrbdKJ/DKO2EfHGHOqMeb/gPuNMT2MMT2NMb2BjwGstYFLd/MTHXceGRERCQ1er5dHPlvIvgM5XNCiFpe2K360SBGRE1YwxLQzl05+onMgNzRrdKy1m3BrdIDfjDHLgIvczb9aa7vgfM7/p/uZv7HbV78r8IgxpgowCrjJWnsG8A3Q0j0+C7gHp7na44UuOwRYa609C+gLnFEonjeBv9z1Y4Dr3U39gA9O9H6PVqNT1b1wbfeC4LTle/1EL3xCvF7V6IiIhKgv/9jM1GVbqRQdwdOXtylxHjERkRNWZC4dfw5GcJSaF58wxpwC7LXW3uQud8SpjfkTmObu9itwKbARSHJre8Dp03MyUMdauxQKEpWCkUVLuqx7Day1K4BXjDGDiu5krV1tjNnnNq/rj5OQnZAjJjrW2hnADGNMB2vt3BO9WJnZt8/5iouDatUCHY2IiJSRbfsyefzLxQA8elFL6lSOCXBEIhLS4t25dDIOTXRCeDCC04BkY8wl1tosYDmwG8jFGXBsI05tz2KcpmzTrbXJxpgw4DFgFbDZGNPMWrvCGPOQe44jWQp0Ar5wR23+D/Bdoe15HGxlNtq9zkZr7XZO0NGarr3m/jjCGPNr4a8TvfAJyc11vkdFgd70iYiEjCe+XMzujGzOa1aDazpqMmgR8bH8PjoZOwAODkYQuk3XJgAzgN+NMb8A3wJ/x+m7M8gY8yNwIfA08BWQZoyZAaQCXmvtPmAw8Ja7b3tg4lEuOwpo4u7/Hs7obIXNACYaYzzAZzgjwr15wjfL0ZuuPeV+vx6n3Z2IiIhPTFr4J98s/JP4qHCevUJN1kR8rTSTxoe8IolOwWAE2SFbo4O19mmcRKaAMeYe4B/W2mVFdr+vmON/xxmgoLAu7vdlwA/ufoMKbe936O4UjNxsrb2hUBwRwFrg+yPdQ2kdrenaFvfHL3GyujHW2qNVT4mISAAZYyoBDwH1gK+BBdbalYGN6uie/9YC8HDvFjSoGhfgaESkQihIdHYCoT8YQTAzxpyNU/vzb2ttXlmcs7TDS7fF6RA0zBgTgzOpzwmPhCAiIj7xFk7Hz844o9m86f4ctLbuO8Ca7enER4XT74yTAh2OiFQURZuu+XEwgmDijrYW6Bh+BdqU5TmP2Een0IWzrLXjgaE4HZYeLcsgRESkTFW31r4FZLsPjlKV9YE0f/1uANo2rEJ4mJqsifhDUlISSUknNE1J+RfnDmqVsQO83oowGEGFUqoaHWPMv4BrgLnAq9ban3walYiInBBjTAv3ewMgJ8DhHNW8DbsBaN+oSkDjEKlI5s4NngF1AyYyFiLjITsdMvcRG+6/CUPF90rbdG0XcK61drcPYxERkbJxN84s1C2B8cDtgQ3n6Oat3wVA+4ZVAxyJiFQ4cdVhTzpk7CA2Sn10QskREx1jzC3W2jE4HVofLDwZkLX2nz6OTUREjk8vdwbqciEnN48FG/cA0E41OiLib3HVYM96yNhJTJwzEX1F66MTqo7WbnuD+30ZYAt9FR16TkREgkcfY0x4oIMoreVb0sjIyqVRtThqJEQHOhwRqWgKDUhQEQYjMMY8bIyZYoz50Rgz3Rhzwh21jDGDjDGXuD/f6X7vZYxJLmH/OsaY192fzzfGnHaiMRTnaMNLf+v+2Mlae2eh4N7DmfBHRESCT02cmavXAF6cSd7ODnBMJZqb32xNtTkiEgjxNZzvGTuIDndetoRq0zVjTCuckZTPsdZ6jTHtgHdxRlg+btbadwotPgq8Zq2dfIT9/+Jgs+qbgHHAghOJoThHa7p2B06w1YwxV7irPcCSsg5ERETKzEWBDuBYzHNHXGvfsEpA4xCRCqpQjU6YJ4yY8Bj/JDpPVP4G6FPGZ53IE3suPML2PUAj4CZjzGRr7XxjzOnGmDbAqzif83fgJB/tceZkywKaAOOstU+7OcFDQDawGegL/AtnOoPqOHnD68BsoIW7T1Vr7b+NMdHAHzjJ1nvAHUAvoIMxphZwsbX2agBjzC/A1dbazcf7yzhi0zVr7QhrbV2ciXvquV91rbUXHO8FRUTE53KBF4GJwCs4D66gNW9Dfo2OBiIQ8adbb72VW2+9NdBhBF7hIaY5OGloKLLWbsKt0QF+M8Ysw3k5Nhq4w51PZyLwoHvIScCVwJmF1l0HvGCtPRdnUurEQud/GthprS08CM77wDXGGI977a9xkiestanAZPfc7wNtjDFVjTGtge0nkuTA0Wt0LrLWfg3sKNrGzlqbciIXFhERnxkNjAR+ArrgTBgalC+odmdksXpbOlERYbSsm3j0A0SkzKSk6KMcUPykoZl+uO6Ra158whhzCrDXWnuTu9wRZ4LpGOB1d+CxSGCFe8hCa20OkGOMyR9z+z7gH8aYu4ClwOdHuqa1dpcxZh5wLjAIuL+E/bzGmLE4iVQTnGfXCTnaYATuX546QN0iXyIiEpxirLVfWmt3W2s/x3loBaX57vw5bepXJioi6Oc1FZFQVFyiE7pOA14zxkS5y8uB3cBK4Hq3RudBnFoXcPp5FpUMPGGt7YzTYuDyItuLa0UwGrgHiLXWFh3ULI+DOcnbwNXA+Tg1SyfkaE3X3nW//xun+cMwYDVOGz4REQlOEW57a9zvxT2ogoL654gETmpqKqmpqYEOI/AKEp2dAMSEh26iY62dAMwAfnf7wHwL/B24BXjPGPMzMJQjDwwwG/jaGDMVpzLk6yLbl7g1M4Wv+yNwKvBOMeebBQw1xrR0m9btA6a6NUknpFQThhpjxuHcxNk4ydEVHJ69iYhIcLgbeMsYUxenE2ixw3sGg3lujY7654j4X8eOHQHweoP2XYh/FCQ624HQ7qMDBf1oni5mU5ciy8uBHwodV8f9/hXwVZF9nyi0X9cSrmsK/bwWp98P1tpRwKhCu4ZRBs3W8k9UGvWstWOBltbaIUClsri4iIj4xBIg2VrbAHgGWBzgeEq0Yss+AE6tr/45IhIgRZquhXqiE6yMMbHGmFRgqbV2ZVmcs1Q1OkCUO5TcEmNMDZToiIgEsw+Ab4B5QHPgGqBfQCMqRl6el+1pTo/f2omh21RERIJcrFujvH8X5OWGeh+doGWt3Q+c8OSlhZW2Rud5nDGyn8VpEvFUWQYhIiJlqr619m0Aa+3zBOkAMnv2Z5Od6yUxJoKYyPBAhyMiFVV4JMRUBm8eHNijRCeElKpGx+24NMFd/JfvwhERkTLgNcY0t9Yud4cSDcosYptbm1OzUnSAIxGRCi+uOhzYAxk7QnowgoqmtIMR/BNnqLkMnCHjvNbaer4MTEREjts9wMfGmJY4/XOCcjCCbfuU6IhIkIirATtXQ8YO9dEJIaXto3MtzoAEGb4MRkREjp8xpgPOSDWn4zQxfgOnT2V9IOjGkD2Y6OjtqYgEWKEBCZTohI7SJjprgP1H3UtERALpBeAGa222MeY/QC+cSeAmAV8GNLJiFCQ6CarREQmEOXPmBDqE4FEo0akofXSMMQ8C9wKNrbUHAh2PL5R61DVgoTFmIe7Ec9baoBvBR0Skggu31i4wxtQD4q21cwGMMXkBjqtY6qMjElhJSWU6wFX5FlfN+Z6xg5iq1QMbi/8MAMbhDDj2TmBD8Y3SJjrP+TQKEREpC9nu917AFABjTCRBOiWA+uiISNAoXKNTs75fLtnm3TbfAH3K+LQTF96w8MKj7WSM6QKswmniPBZ4xxhzBvAKzqjMm4D+wGnFrJsEDLHWLjPGDAHq4CRKXwE7gInALOBx97gEoJ87QM6jwGU4OchInAqUZtbavxtjwoH5QKeyqmEq7fDSc4EewA1AdZwbFRGR4DLFGPMLzgzVw40xTXGarH0c0KhKoERHJLCSk5NJTg7KsUr8ryDR2VlR+ujcAoyx1log001yRgE3WWvPwJmLrWUJ60pSB+jpTmvQGhhgre2CM3Lz1caY9kBv4AycvqTNgY+Ay9wkpxcwvSyb0ZW2RuctnOytM/AXTmfXzmUVhIiInDhr7XPGmC+BPdbazW6ik2Kt/SzQsRUnP9GppURHJCBGjx4NQEpKSoAjCQL5iU76dr8lOqWpefEFY0xVnJqkWsaYu4DKwJ1AHWvtUgBr7ZvuvsWtK3w6T6Gf11hrs9yfNwGvGmPScAbE+QUwwGxrbS6QC9zvnu9H4P+AG4Eny/JeS5voVLfWvmWMGWCt/dUYU9qaIBER8aP8B5L78yqcpglBaes+56WdanREJOAq1mAEA4A3rbV/BzDGxOEMPJZhjGlmrV1hjHkIWA5sLmbdAZyJqJcBHTjY0qtwf9DRQFNr7T5jzLs4CdEy4DY3jwjHaeJ2kbvvQ0ANa+2CsrzR0iY6GGNauN8bADllGYSIiFQsWTl57MrIJjzMQ9W4qECHIyIVXeFEJ/QnDL0FGJi/YK3NMMZ8CmwB3nIHsPkTp2/OxmLWZQKvG2PWU3J3lrHADGNMunveetba+caYyTi1O2HASGttJjDLndx6RFnfaGkTnbuBt4FWwOfArWUdiIiIVBw70p1ma9XjowgP8xxlbxERHysYdS30++hYa9sWs+5298d/F9n0O3BekXUT3a+izix0vvtKuPazwLOF17k1POk4/XXK1BGboBljOhhj5gEWZ36GTCARaFjWgYiISMWhgQhEJKjEVAFPGGTuIcZT6gZPcoKMMY1xBj0bZ63dW9bnP9pfslxNPiciIuWDEh0RCSphYRBbDTK2E5uTGehoKgxr7Rqgna/Of7REp1xNPiciIuVDQaKToERHJFA6dOgQ6BCCS3wNyNhOTFZGoCORMnK0RKdcTT4nIiLlg2p0RAIvNTU10CEEF3dAgpjMtAAHImXlaIlO/uRzDYFL3DkZXiNIJ58TEZHyYVuaEh0RCTLugAQxB8q8q4gEyBEHI7DWPoczBN2Z1tr57uoUd8QEERGR46IaHREJOm6NTtj+XRVhiOkK4ajDSpSnyedERKR8UB8dkcDzeJyh3b1eb4AjCRL5c+ns3UxMRAx7Uc1OeXfEGh0RERFfUNM1EQk6CXWc7zNeJObAvsDGImXCZwOFG2MuxJkQKBpYANxc0vjYxpjLgPestYm+ikdERErm7zJbTddEJOi0vRa2LYUFnxCTfSDQ0UgZ8EmNjjGmJvA2cKW11gCrgaEl7NsMeNFXsYiIyJH5u8xOz8whIyuXmMgwEqI1MZ+IBImYynDRy3D/MmIT6wc6GikDvkouegK/W2tXuMsjgf7GGE/hnYwxccBY4D4fxSEiIkfn1zK7cG1Ofh8BEZGgEV2J2MQGgY5CyoCvXqU1BDYUWt4IJOLMv1O4KcQo92vBkU5mjEkGkss4RhERcfi1zC7on6OBCEQkSMVEaNS1UOCrGp2Szpub/4Mx5nYgx1r71tFOZq1NsdZ2LKvgRETkEH4ts9U/R0SCnYaXDg2+qtFZD5xRaLk+sMtam15o3SAgzhgzH4gCYt2f+1hrN/soLhEROZxfy2wlOiLBYdSoUYEOIWipRic0+CrR+Q54yRjTzG3zPQT4ovAO1trT8382xpwMLLLWtvNRPCIiUjK/ltkH59DRBwmRQEpOVq+AksRGxAY6BCkDPmm6Zq3dCtwIjDfGLAXaAPcbYzq6bwBFRCRI+LvMVo2OiAQ7JTqhwWfjelprJwITi6zeCbQrZt+1QIKvYhERkSPzZ5mtyUJFgkNKSgqgmp3iqOlaaNAEBiIi4leq0REJDoMHDwaU6BRHgxGEBk3SKSIifrX3QDYAlWMjAxyJiEjxGlTSPDqhQImOiIj4VVZOHgDREXoEiUhw6t24d6BDkDKgp4yIiPhVfqITpURHRIJUmEflUyjQX1FERPxKiY6IiPiDnjIiIuJXmbluohOuR5CIiPiOnjIiIuI3Xq/3YI2OEh0REfEhDS8tIiJ+k53rBSAy3ENYmCfA0YhUbF6vN9AhiPiUXqeJiIjfZKnZmoiI+ImeNCIi4jcaiEBERPxFTxoREfEbJToiwSMpKYmkpKRAhyHiM+qjIyIifqNERyR4zJ07N9AhiPiUnjQiIuI3Wbm5gProiIiI7+lJIyIifpNZUKMTHuBIREQk1CnRERERv8lU0zUREfETPWlERMRv8vvoRKvpmoiI+JieNCIi4jcajEBERPxFo66JiIjfKNERCR633nproEMQ8SklOiIi4jdZuW6io6ZrIgGXkpIS6BBEfEpPGhER8RvV6IiIiL/oSSMiIn6jREckeKSmppKamhroMER8Rk3XRETEbzJzleiIBIuOHTsC4PV6AxyJiG/oSSMiIn5TUKOjPjoiIuJjetKIiIjfFMyjoxodERHxMT1pRETEb9RHR0RE/EVPGhER8Zus3FxATddERMT39KQRERG/UY2OiIj4i540IiLiN0p0RETEXzS8tIiI+E2WhpcWCRpz5swJdAgiPqVER0RE/CZTw0uLBI2kpKRAhyDiU3rSiIiI36jpmoiI+IueNCIi4jeaR0ckeCQnJ5OcnBzoMER8Rk8aERHxG/XREQkeo0ePZvTo0YEOQ8Rn9KQRERG/KWi6Fh4e4EhERCTUKdERERG/UR8dERHxFz1pRETEb9R0TURE/EVPGhER8ZssDS8tIiJ+oieNiIj4jZquiYiIv2jCUBER8ZtMDS8tEjQ6dOgQ6BBEfEqJjoiI+I366IgEj9TU1ECHIOJTetKIiIjfqI+OiIj4i540IiLiN+qjIyIi/qInjYiI+I2arokED4/Hg8fjCXQYIj6jJ42IiPhFbp6X3DwvHg9EhOnDlYiI+JYSHRER8YvC/XP0FllERHxNiY6IiPiF+ueIiIg/6WkjIiJ+kZmbC2gOHRER8Q89bURExC80tLSIiPiTnjYiIuIXaromIiL+FOGrExtjLgSeBaKBBcDN1tq9RfYZAPwd8AIZwN3W2jm+iklERIrnjzJbQ0uLBJdRo0YFOgQRn/LJ08YYUxN4G7jSWmuA1cDQIvsY4AWgl7W2HfAfYIIv4hERkZL5q8xWjY5IcElOTiY5OTnQYYj4jK+eNj2B3621K9zlkUB/Y0zh8UQzgVustX+6y3OAOsaYKB/FJCIixfNLma0+OiIi4k++arrWENhQaHkjkAhUAvYCWGvXAmsB3IfpMOBLa21W0ZMZY5IBvXIQEfENv5TZqtERCS4pKSkAqtWRkOWrp01J580tusIYEw98ApwC3FLcQdbaFGttx7ILT0RECvFLmZ1Z0Ecn/LgDFZGyM3jwYAYPHhzoMER8xleJznqgbqHl+sAua2164Z2MMY2AX3Eepl2ttbt9FI+IiJTML2W2mq6JiIg/+epp8x1wpjGmmbs8BPii8A7GmGrAj8AEa21fa+1+H8UiIiJH5pcyOz/R0YShIiLiDz7po2Ot3WqMuREY73ZUXQVcb4zpCIxxR+y5DWgEXG6MubzQ4RdYa3f4Ii4RETmcv8ps9dERERF/8tk8OtbaicDEIqt3Au3c7U8DT/vq+iIiUnr+KLML5tFR0zUREfEDPW1ERMQvVKMjIiL+pKeNiIj4hRIdERHxJ581XRMRESmsoOmaEh2RoOD1egMdgohP6WkjIiJ+kanhpUVExI/0tBEREb9Q0zUREfEnPW1ERMQvNI+OSHBJSkoiKSkp0GGI+Iz66IiIiF9k5eYCqtERCRZz584NdAgiPqWnjYiI+EWW+uiIiIgf6WkjIiJ+oT46IiLiT3raiIiIX2h4aRER8Sc9bURExC/UdE1ERPxJTxsREfGLTDVdExERP9KoayIi4hfqoyMSXG699dZAhyDiU0p0RETEL/L76GgeHZHgkJKSEugQRHxKTxsREfGLg310wgMciYiIVARKdERExC/UdE0kuKSmppKamhroMER8Rk3XRETELzS8tEhw6dixIwBerzfAkYj4hp42IiLiF6rRERERf9LTRkRE/ELz6IiIiD/paSMiIn6hGh0REfEnPW1ERMQvMjW8tIiI+JGeNiIi4nNer1dN10RExK/0tBEREZ/LznVGdYoI8xAW5glwNCIiUhFoeGkREfE5DS0tEnzmzJkT6BBEfEqJjoiI+JwGIhAJPklJSYEOQcSn9MQRERGfU/8cERHxNz1xRETE51SjIxJ8kpOTSU5ODnQYIj6jJ46IiPhcVm4uoERHJJiMHj2a0aNHBzoMEZ/RE0dERHwuU03XRETEzzQYgYiI+Fx+0zVNFlo+DR06lMWLF7Nt2zYOHDhAw4YNqVq1Kv3792fcuHG8/PLLZXKdgQMH8sQTT9C0adOj7vvwww/Tp08fzj///IJ1mZmZ9O7dm2nTph3TdfPy8njiiSew1hIVFcV//vMfTjrppILtS5cu5ZlnnilYnj9/PiNGjGDGjBksW7YMgG3btpGYmMgnn3zCJ598wrhx44iIiOC2226ja9euxxSPiJQNJToiIuJz6qNTvj388MMATJgwgdWrV/PAAw8AMGvWrECGVWamTJlCVlYWH3/8MfPnz2fo0KGMHDmyYHvLli15//33AZg0aRK1atXi/PPPL0iysrOz6devH0899RTbtm3j/fff59NPPyUzM5N+/fpxzjnnEBUVFZB7E6nIlOiIiIjPaR6d0LVu3TpuueUWdu7cSdeuXbnrrrsYOHAg1apVY8+ePaSkpPDEE0+wbt068vLyuOeeezjjjDN4+eWXmTVrFjk5OfTs2bOgU/yIESPYvn07+/fvZ9iwYTRs2JChQ4eSmpoKwEUXXcQNN9xQcP309HQeeOAB9u7dS6NGjQ6LLz09nSFDhhyy7owzzuDOO+8sWE5NTeW8884DoF27dixatKjYe83IyGD48OGMHTv2kPVjx47lnHPOwRjD1KlTad++PVFRUURFRdGoUSOWLVvGaaeddhy/3dLbu3cviYmJPr2GSHmjREdERHxOw0uHrszMTF5//XVyc3Pp0qULd911F+AkJD169ODDDz+katWqPPPMM+zatYsBAwbwzTff8NVXX/Hee+9Rq1YtJkyYUHC+zp07c+mllzJ8+HAmT57MKaecwsaNG/nkk0/IycmhX79+nHnmmQX7jxs3jubNm3Pvvffyxx9/HFbLFB8fX1AbU5K0tDQSEhIKlsPDw8nJySEi4tCPSePHj6dXr15Uq1atYF1WVhbjxo1j/PjxBeeqVKnSIddPS0sr7a/zuM2ZM4fmzZtTuXLlQ64vUpEp0REREZ9T07XQ1axZs4JmWYUTg8aNGwOwfPlyUlNTWbBgAQA5OTns3LmTF154gZdeeont27cX1KYAnHrqqQDUqFGD7du3s2rVKjp27IjH4yEyMpK2bduyatWqgv3Xrl1L586dAWjbtu1hyUlpanQSEhJIT08vWM7LyzvsPABfffUVr7766iHrfvvtNzp16lSQXBQ9V3p6ul8Sj59++okePXrQt29fnnvuuVIlPB06dPB5XCKBpERHRER87mDTtfAARyJlzePxHHF9kyZNqFOnDkOGDOHAgQOMHDmShIQEJk+ezLBhwwDo06cPF154YbHnadq0KRMmTGDQoEFkZ2czb948Lr/88kO2z58/n+7du7NkyRJycnIOOb40NTodOnRg+vTp9OnTh/nz59O8efPD9tm3bx9ZWVnUrVv3kPW//vrrIQMinHbaabzyyitkZmaSlZXFqlWrij2fL+Tl5fHhhx8ybty4UiU8+c0BRUKVEh0REfE5DS9dcfXt25dHH32UAQMGkJaWRr9+/YiKiqJy5cpcc801xMTEcM4551CvXr1ij+/atSuzZ8/m2muvJTs7m169etG6deuC7ddddx0PPvgg1113HU2aNCEyMvKYY+zRowe//PILffv2xev1Foyw9vbbb9OoUSMuuOAC1qxZQ/369Q87ds2aNVx22WUFyzVr1mTgwIH069cPr9fLvffeS3R09CHHpKenH1ecJQkPD2ffvn0Fy8ea8IiEKo/X6w10DKVmjPEC2JkzoVo1qFIFdu0KcFQiIkdnjAHAWlv86+8QVFBmW8vYmet49PNFXHd6I569ok2gQxMJqN27d9OvXz9++umnMjtn4eZyRYWFhSnhOQ4VsdwONarRERERn9M8OiKH2r9//xGTk7JUuIZn1KhR3HLLLXi9XsLCnP+P5emlt8ix0BNHRER8TsNLiwRWt27dmDNnTkHzvJL6VomEEtXoiIiIz2l4aZGDIiMj+e6778jLyyuzc/7tb39j1KhRh63v1q0bL774Is2aNSM+Pl4JjlQoSnRERMTnNLy0yEHx8fFlfs46deocsqwER0SJjoiI+IGaron4hxIckYOU6IiIiM+p6ZqIb3Xo0IG5c+cqwREpRImOiIj4XKaaron4VLdu3ZTgiBShREdERHxOfXREfCshIeGYjylu8AKRUKJER0REfC6/j47m0REJHsnJyYEOQcSn9MQRERGfy8rJBdRHR0RE/EdPHBER8Tk1XRMJPikpKaSkpAQ6DBGfUdM1ERHxOQ0vLRJ8Bg8eDKgJm4QunyU6xpgLgWeBaGABcLO1du+x7iMiIr7n6zJbw0uLiIi/+eSJY4ypCbwNXGmtNcBqYOix7iMiIr7njzJbTddERMTffPXE6Qn8bq1d4S6PBPobYzzHuI+IiPiez8tszaMjIiL+5qumaw2BDYWWNwKJQCVg7zHsA4AxJhk42IA0MRFatICTTy7ruEVEKiLfltlA2wZV2J6WRaNqcWUbuYiISAl8leiU9Mou9xj3AcBamwKkGGO8AISHw8KFzncRETlRvi2zgaFXtiEnz0uk+uiIiIif+CrRWQ+cUWi5PrDLWpt+jPsUyxhTJkGKiAigMlukQmrevDmg/6MSunz1au074ExjTDN3eQjwxXHsIyIivqcyW0REQo7H6/Uefa/jYIzpgzMMaRSwCrgeaAKMsda2K2kfa+3OY7jGHGttxzIOPWhVpPutSPcKut9QVl7uVWV22dP9hq6KdK+g+5Xyy2fz6FhrJwITi6zeCbQ7yj4iIuJnKrNFRCTUqFeoiIiIiIiEnPKe6KQEOgA/q0j3W5HuFXS/oawi3evRVLTfhe43dFWkewXdr5RTPuujIyIiIiIiEijlvUZHRERERETkMEp0REREREQk5Phs1LWyYoy5EGc402hgAXCztXbvse5TXpTyfgcAfwe8QAZwt7V2jr9jPVHH8nczxlwGvGetTfRfhGWrlH/bNsBwoDLOjPODrbWp/o61LJTyfi8H/g3kAbuAW6y1q/wda1kwxniAt4FF1toXi9keMuXUkajMDt0yGypWua0yO7TLbFC5XREEdY2OMaYmzj/AK621BlgNDD3WfcqLUt6vAV4AerlzW/wHmODnUE/Ysfzd3AkKXyTI/70eSSn/tnE4kzI+b61tDzwFfODvWMtCKe83FhgLXOH+W/4SeNXPoZYJY0xLYCpwTQnbQ6acOhKV2aFbZkPFKrdVZod2mQ0qtyuKYC+AegK/W2tXuMsjgf5uBn4s+5QXpbmXTJw3KH+6y3OAOsaYKD/GWRZK9XdzHyRjgfv8HF9ZK+2/5VXuXCXgPESKLYDLgdLcbzjgwXkTCpAAHPBfiGXqDpwH4iclbA+lcupIVGaHbpkNFavcVpkd2mU2qNyuEIK96VpDYEOh5Y1AIlAJ2HsM+5QXR70Xa+1aYC0UVLkOA7601mb5M9AyUNq/2yj3a4H/QvOJ0txvc+AvY8ybQFtgN/CgH2MsS6X5t5xmjBkC/GqM2YHzED3H34GWBWvtnQDGmAtK2CWUyqkjUZkdumU2VKxyW2V2CJfZoHK7ogj2Gp2S4ss9xn3Ki1LfizEmHuctxCnALb4MykeOeq/GmNuBHGvtW/4JyadK87eNBPoAKdbajjjtvicaY6J9HZwPlObv2wb4F9DKWlsPeBr4NETfloVSOXUkKrMdoVhmQ8Uqt1VmOypqmQ2hVVZVWMGe6KwH6hZarg/sstamH+M+5UWp7sUY0wj4Fec/W1dr7W6/RVh2SnOvg4BOxpj5wEQg1hgz3xhTz29Rlp3S3O9mYJm1dhaAtfYLnDdmTfwWZdkpzf3+H/BLoY6sI4BTger+CdGvQqmcOhKV2aFbZkPFKrdVZlfsMhtCq6yqsII90fkOONPt1AgwBPjiOPYpL456L8aYasCPwARrbV9r7X4/x1hWjnqv1trTrbWnup0e+wD7rbXtrLWb/RtqmSjNv9NJwMnGmCQAY8z5OKM0rfFblGWnNPc7F+hsjKntLl8GrLHWbvdPiH4VSuXUkajMDt0yGypWua0yu2KX2RBaZVWF5fF6vYGO4YiMMX1whvaLAlYB1+O8LRnjFqTF7mOt3RmQgE/Q0e7XGPMI8CSwsMihF1hrd/g12BNUmr9toX1Pxhn+McHPYZaZUv5bPh9nhKZ4nE7Mf7PW/hyQgE9QKe/3DuBOIAvYCdxprV0ckIDLgDHmHdxhSo0xHQnRcupIVGaHbpkNFavcVpkd+mU2qNwOdUGf6IiIiIiIiByrYG+6JiIiIiIicsyU6IiIiIiISMhRoiMiIiIiIiFHiY6IiIiIiIQcJToiIiIiIhJyIgIdgMiRGGPOAJ6z1nY5hmNOAf6LM2t1Is4cFv+w1uadYCztgEustU8aY+4E7gCGAmdYa28v4ZhxOEN01gHaWmu/OpEYRETKI2PMS0ASTlkYB6wGWgNTrbV9y+gaPwBDrLXLSrHvO8A4a+3kQuticCYAPbks4hGRwFOiI0HLGPMgMBA41lmInwGGW2snG2M8wATgUuCzE4nHWjsfmO8uXgFcY61dCLx7hGP6AhhjugEtACU6IlLhWGvvBzDGDAJaWGsfNsZ0wZmEUUTEJ5ToSDBbhZNQvH+Mx20BBhlj9gGzgWuAHPeh+giQh/NWMcVaO8IY0wZ4FfAAO4CbgL3AcOB0nInCHgf24DyUpwEdgDeNMdcCH1lrzzTGXOTu58GZQXoIB99aPgzEGWN+A4YBza21ucaY54BUa+0nx/rLEREJAc2MMZOAWsBX1ton3JqZrUA14ELgdaAZTnP7R621Pxhjnga64nyO+dRa+5x7vseNMbVxJvC8zlq72q1NOtfd/qG19r/5FzfGJAAfAFWBlT6+VxHxM/XRkaBlrf0UyD6OQx8AZuLMZrwVeBuo7G6rD1wCnAnca4ypBYwG7nCbx00EHgQuA2pYa0/HeZh2LBRXCk7NzvWAF8AYEwG8Blxore2I88Bs4B6Si9PE7UNr7RfAz8D/GWPCgd7A58dxjyIioSAGp7w9D7iz0PqPrLXdcV48bbfWno9TMz/C3d4f6Ocet7vQcd9Ya7sBk4Cr3BdQjXHK/HOBfu7LrXxDgEXu+UeV7a2JSKCpRkfKJWPMucB/3MUXrLXfFNrc1Vr7CvCK+7buReAxnGZjv1prM91zLAKaAi2B140x4PTrWQHsA34DsNbuAh5za4RKUgPYZa3d6h7zvHuN4vYdDdyN86JhirU261juXUQkhCwqVCbnFFpv3e9tgPPc/poAEcaYGjiJzlCc2vlJhY5Ldb//5W5rCcyw1nqBbGPMTKBVof2bA98AWGtnGWOO5+WaiAQp1ehIuWSt/dla28X9+qbI5ueNMZ3d/dKA5UCmu62dMSbcGBOH06RsBc4D9Xq3RudB4GtgKdAJwBhT2Rjz7VFC2gpUMcZUc4951RhzeqHtebj/36y1P+MkWDcDbx773YuIhAxvCevzB49ZhlO70wWnBvx/OC+irgauw6lxH2SMOamE8y3FbbZmjIkEzsYp9/MtAc5yt7fHedklIiFCiY6EomuBR40xc4wxv+L0p3nW3RaJ8/ZvBvAfa+124DbgPWPMzzhvCBcAXwK73HXfAq8c6YLuiG63A9+4x3iA3wvtshC41BiTP7rQB0Ada+3iE71ZEZEQNgpoYYz5EfgVWOfWAO3EaaI8HfgOWF/cwdbar4E1bv/ImcB4a+3cQru8ATRxy+07OPhSTERCgMfrLellikhoyR/hp6yGMj3BWP4O7LDWvhXoWERERERCkfroiPiZO39DPeDiAIciIiIiErJUoyMiIiIiIiFHfXRERERERCTkKNEREREREZGQo0RHRERERERCjhIdEREREREJOUp0REREREQk5CjRERERERGRkKNER0REREREQo4SHRERERERCTlKdEREREREJOQo0RERERERkZCjREdEREREREKOEh0REREREQk5SnRERERERCTkKNEREREREZGQExHoACR4GWNOBlYBCwut9gD/tda+VUbXeBJYaa197wj7zAe6WGt3l9E11wKZwH7AC0QDecAD1trJZXGNItfzAjWBi4CrrLUXFbNPDPCIu48HCAfGAs9ba71lHZOIBEZZlqvGmIbAJCAXGGKt/a2s4jzCNRcBd1prfyiyPhJ4GuiFU656gHHAs+W9DDPGdAdedBfr4JTPm9zlZ4HewCJr7YvFHH6i1+4CvGatPfUYj/MCNa2124usfwA41Vo7qMyCFAliSnTkaPZba9vlLxhj6gOLjDFzrLULTvTk1tp/lWKfdkfb5zj0t9bOyV8wxlwFvA3U9cG1jsgY4wE+B5YDZ1lrDxhjqgPfAAnAY/6OSUR8qqzK1a7AX9ba7mUd4HG4B2gCdLDW5hhjKgPTgO1ASiADO1HW2ilAOwBjzBNADWvtnfnbjTG9AxOZiByNEh05JtbaTcaYFUBzY0wH4GYgHthjre1qjLkZuB2nWeQOnDd/y4wxCcBw4BwgB+eD/SM4ycUia+2Lxph/A5cDWe6xg6y1fxZ+M2WMeQy4zj3Hcvf8fxljfgB+c8/fCJgB3GCtzTvaPbmJRmNgZ6F1x3ofzYAROIlJPWA+cK219kApfq3nAy2BC621ue7veYcxZiBwshvPDzhv9cYXXTbGZAJfAG2BN4Hz82uNjDEtgKnu76Q58F+gOs4byVfLqmZORI7f8ZSrOC9l/gNUNsZMd/e7GHgUiAIycGqpf3M/nJ/lHrMAWFl42Vo7wBjzCHCle421wO3W2s3GmFbAW0AcsMyNqzh1gUicGvIca+0etwwLAzDG1AHeAFrg1KC/Ya191RjTABiJU9Z5gHettS+4NV8zgKXuts445fRzbgx5wBPW2q/dc78H1HBj+cZae8gLImNMMnBJCWXjvyjm2VPS36sEZxtjfgVqA4uAftba9CLlc38gnWLKYffZ8jbOsyQPSAUGu+dOMMaMc393McCt1toZbjI5AicJ8+LU7v3TWptT6L4jgVeBHsBWYAuwx912Bc6/lzycWsG/W2t/Osb7Fglq6qMjx8QYcxZwCjDLXdUap1lZV2NMZ+AG4DxrbXvgeWCCu9+TOAV0S5xC+RycB1f+eRvivBHsZK3tCHwHnFHk2jfiNBHoZK09Dedh8k6hXZoCXYA2QLfC5y/GB8aY+caYDcAGnAfIxe51juc+bsV5QOf/fhoDFx7h+oV1BGblJzn5rLUrrLXfl+L4KOAra63B+cBwrvvgB7gR5+HpAcYDD1trk9yYHzDGnFnKGEXER46nXLXWTsf5gD7D3a8Z8AzQx90vGZhgjMlPTE7CqW0ZUHTZGHM9Trl5ulvTNBEY4+73ATDaLXP/6x5XnGFAfWC7MeYHY8zTQLS1dpG7/XVgubW2BU6SlWyMOcU9/3RrbRuc8nSAMaave0wD4ClrbXPgAE5ZNtBa2wG4BBhpjGmEU/6udtefBzRzk4DCPqL4srEeR3n2lFJ9oDvOC6UGwBXu+sLl83xKLocvByq5v/9O7rFNCv0eXna3jQKecNe/ipOYtcF5jrQFHigS1+1uTK1wkp1Ghba9gJPQdsRpOdDlOO5bJKipRkeOJtbtIwPOv5ftOM2+NhhjwHkbuNfdfiHOw/pXdxtANWNMNZwHwH3uh/lc3CTEGDPI3W8T8Acw1xgzCZhkrZ1aJJbewNvW2nR3+b/AI8aYKHf5K7cGZ58xZiVQ7Qj31d9aO8cY0xiYAiy11q4+gfv4CehhjHkQ56FSD6d2pzTyOPGXDjMArLX7jDHjcT4svAwMAM51Y2oKvFXonmKB9sDME7y2iBybsipXC+uBU6sytdB+ee6xADMLv+kvsnwRcDowxz02HIhzm9CehlNbgrX2F7ePzmGstRuBjm4NUFf36zdjzH3W2tdxys4H3X33AKe6Sdg5QM/89caYd3DK+pk4teb5/Y7ya6A+L3R/Xje+ycBEN+mZgpNI7CkSX0llY2mePaXxubU2Awr6MdUqtG2G+/1I5fBk4Bm3tv574BVr7Uq3xmuVtTY/CZ4P3OT+3Bs4x+0DlWmMeQMnaRta6NrdgQ+ttVlAljHmA5zfGTh9qD4zxnzjXvP547hvkaCmREeO5pC25MVIK/RzOPC+tfYhAGNMGM4H/l04D6yCDqluDU5G/rK1Ns99c9kRp2B+2W2O8bdC5y+aDITh/Bv25MdaaJsX8BhjhgBD3HVzrLW3FD6BtXaN27ziR2PMDPdhcjz38bobyyc4fWsaFYrraGYC9xhjwgvX6hhjOgF3W2sHcrBzb76oIuco/HcYg9MmfimwxL3HNsDuIv0CauM2YRARvyqrcpUi+0211l6bv8Itnzbj1BakFdm/6DWes9aOdI+LBqpysKwrXPYUTpYKGGOeB8ZYa5cAS4ARxpgBwMM45WPRsrMJTm1E0XIyDKcJHEBmoWQsHOeFVEFtizGmHrDNWpvtvrTqjlObP9sYc5m19tci5z6sbHTPc7RnT2lkF/q5aHmd/7sOp4Ry2O2beQpOrUo3YIox5i6cJLikcxf3TIwssq5oLAV/P2vtI8aYN3ESzUHAw8aYpNI0+RYpL9R0TcrSd8B1xpj8Dv1DcNpAg/OW7QZjTJj7EB3PoU3X2uI0RVtqrX0WeBmnGr6wb4EbCzXFuBv4yVqbWVJA1to3rLXt3K9bStjnV5wmcK+7HyKO5z7+D3jSWvsxzoPlDJyH2lFZZ6SkZcAw44y+lv/wGw6scXfbhvMgxhjTlINv5Io730ycB9u/gNH5q4ED7geP/A9Ai4Ck0sQoIgFzpPKosGlAT7fvCcaYPjj9cWJKcY1vgVuMMYnu8pM4ydVOnL4it7jn7IDTTKo4tYCnjDFx7r4ewABz3e1TcJqL4TYrm4pT2zQTuKPQ+utxaheKmonTJO18d992wAqgnjFmKPCYtfZz4G/AYpzak0MUVzaW8tlTVkosh40xt+E0pfvOTWq/BY420tq3wB3GGI/7PErm8N/dZOB6Y0yM+3y51r12hHFGII231r6B08StJYcnSiLlmhIdKTPW2m9xOop+b4xZAPQDrnCr1f+N09HzD2AeMNFaO6HQsX/g1IbMMcbMwamav7fIJd7EeVjONsYsBTrgdO4sC//A6fCafJz38U+cJgBzcDrc/sjBJiOlcSXOAzjVGPMHzoeAT4HH3e3/wfkQs8iN7WgdRkfjtO/+HMBttnApzoeZBTgfnh6z1v5yDDGKiJ8dpTwqvN9inA+649wy5CmczvfpRc9ZjDHA18BMY8xinBcpg9xt1wF9jTELcfpxLC3hHLfj1B4tcM+xDGdwgDvc7XcCLd17+AVn2OlUnDL8Avf8s3HKvXeK+T1swyknX3Dv732c/jrrgFeAdm75OAfnBdFHJcRZtGwszbOnTBylHH4P5+XYEjeO/2/vvuOjqtI/jn+mpYcUepV+UEQQUFkbKhYE17Uiotgp1t1V1/Lbou5acF111w7Yde1dAdvaG1IEC3DQIEiRmkB6mfL7406GBAIkkMkkM9/36zWvmXvn3jvPkHAyz5xzntMKZ3j2zlyBk2B+F75ZnBLfNU3F+Tf5Hufv0s/hWPw4w9yeMcbMB14ELtjZF4ciLZErFGrR5e1FRERERES2ox4dERERERGJO0p0REREREQk7ijRERERERGRuNOiyksbYzShSERaNGttfcuOt3hqs0UkHiRSux1voprohMtLPgZ8b639Vx3PjwZuA5JxymBeWGORNBERaUJqs0VEJJ5ELdExxuwN3A8MwylruO3zbXH+oB5irf3RGHM7zmq+l+zq2tbaRo5WRCS6aqyE3iypzRYRqa25t9uya9Hs0bkU54/iLzt4/lhgjrX2x/D2g8BCY8yl264PUKcrroDPtQSINANt2kDXrtClS+37rl0hMzPW0YnUV1Tb7II7r6T00w/odFwmLneCjwJJyYKMDpDZATI7bnPfAXypsY5QJK79tL6Yf7y1iE0l2y8blOL18JcT9mFQ1+ymD0waXdQSHWvtZQDGmBE7OKQrsLLG9iqcBbIygVpDIYwxE3EWQnMEAvDCC7BuXWOGLNL4WrWqOwmqea9kSJqBqLbZwOa3PqL81wpa77WSlBx/o8Udl1Kyw4lP+zoSofB9RnvwJsc6UqmHiROd/wrTpk2LcSQCUFrpZ/LT8/hpffEOj/l21WYlOnEilsUIdlTxLbDtDmvtNGBaZGKrxwNLlkBeXhTDE6mHYNBJuFetgpUrt78vLIQffnBuO5KVtfNEqGtXyMhouvckUrfdb7MBd9d94dc5+EfcDUMHRCvG5i8UgrICKF4HRb9C0doa9+Fb+WbntmHxzq+V1rqOnqHqx+FEydNMkyGPD1KzYx1Fk5g+fTqgRKe5uOmNRfy0vphebdO5a8wg3K7aPcypSW56tdXf3HgRy0TnF+CgGtudgQJrbUm9zs7OhiFDohCWSCMJhSA/v+4EqOb9li3ObVfJ0I6Gx1U/Tk/f/Tj9fvB6wZXgQ4pkZ/aozfbk5AIQ9LaBTvs3fnTxIhiEsvxw8lNXMhS+L14HpZuc2/qdtB3NWbv+YI4HM8r5nXBrxQtpfKFQCFf4b9vrC1bz/NyVJHvd3H/WYPp1aBXj6CTaYpnovAvcaYzpEx7zPRl4PYbxiDQulwtat3ZugwbVfUwoBJs27TwRWrlyazL0/XZzxLfKzt6a8FRVbX/z++veHww657drB4ccsvU2eDAkJTX2v4q0XHvUZnuyswHwb94cjdjih9sN6W2cW4ed9HwFA1CyEYrX1p0IVSdKwWY6TLCyxEnQ1v8An/7LGYrX9zgn6ekxHJLSYh2hxIF3fljL9a98R1llgDaZSWwocubk3PDb/kpyEkSTJjrGmKHAw9baQdba9caY84GXjDFJQB5wTlPGIxJzLpdTzKBNm10nQ3UlQNWPV62CzZud2+5wu2H9enj1VecGkJICBx7oJD2HHgq/+Q3k5Oze9Zur6t6s6qSvsrJ2EujzwV57xTrKmGnMNtuTlQVAQIlO43B7wnN42kPHgbGOpuH8FbD8M7CzYOnbsGUlzH/SuXlTodeR0G807HeGM8xNoqK00s/aLeV43W68HhcetwuXC1y4cLvA5dp673KB2+XC43Lh9bjwul2RnpLm6Iu8jVz+zDdUBpwv81bmlwHw24GdOPPArrEMTZqQKxRqOeu5VY/3VqlSkW2EQrBxo5P4VFQ4w9B8vtq3bfdVb3vD33f89JNTyfCzz5z7JUu2f53+/bcmPoccAj167Hi4WyjkJA4VFbu+VVZuPXZn99s+rk5Mam7v6LmaiUzNhGZX/v1v+P3vd/tHU626TGkiLTxXs83e9OhjrP/nP8k99xzaX399rEOT5iQUgnXfO0mPnQVr5m99rtcIOOMpSNrNobnNQHUy0Nw+b339cz4TnpzLlrJ6tIM74HU7SY/P7cbjceF2Vd/A4w4/dm9NnNw1Eqat9y7Skzy0SvWRleoj2evG53FuOWk+2mYm0zYzmVapPtKTvGQke0n2uUnyuPF53aR43Xg9tYc9frtqM2dO+4qSygDn/GYv/nScYVNxJUXlfvp1zMTnqd8wyURst+NNLIeuiUhjcbmgbVvntrv69HFu553nbG/aBF98sTX5mTNna2GF6km1HTo4r1le7iQg2963BB7P9kmhzwdlZc6/waJF252Sl5fPnXd+yYMPzgUgMzOJs8/ej6uu+g29euU29TtoEaqHrqlHR7bjcjnD9DoMgOHXQOGvsHQWfHAL5P0PnvgtjHsR0lvHOtK48f6idVz6zHwq/EE6tErB63HhD4QIhEKEQk5SFgKC4e2a94FgCH9w670/GKKcYEzfT5LHTWqSh1Sfh7QkD+uLKiipDHDiwE7c+Nv+uN0uMlPUM5iIlOiISN1at4bf/ta5gZO4zJtXu9dn7VrntiM+HyQn7/yWlLT946Sk2o/r2ld97er91Tefb/vna+7b9vGOJkBPnQqTJ8PChXDbbZHkbdaSAKfNyqAqtPW8oqJKHn54Pk88sZCXXjqd44/v04g/iPigOTpSb606wtALoPvh8NTJsHoePHwUdG6EAkRuL6S3dW4Z7SGjLaS3cx6ntQbP7n8sqgoEyS+pZFNxJZtKKthUXMnG4go6996HYDDEDa9/H+ndqR4etvVx+N5VvRcIH1PzeWf31n2Ej6/uHdl2mJm7xjWqtzeVVPLAR3kEgiHOPLAbN5+0L57dWNsqGEl0glQFQgSDTqIUDIUIBrcmRZFEieqEKUSwRvIUCIYoqfBTWO6nsKyKCn+QqkCQSn+QgtJKNhRVsKG4gqJyPyUVfoor/FT6g1SGjymrCjiPy4K1eqeO6teOf50+EHeir9uV4JToiEj9JCfDwQc7tz/9yRlu8tNPUFrqzOdJTt7+viVXUUoNL9o4e7ZzA/LI4TQuphRgm28wq6qCVFUFOe20F/n228nq2dmGJ1tzdKSB2vSGC9+Fp091ihYULN+jyxW6XXydkoLf5cIVHkbmqnEDF67kTEjJwpWSBSlZVPlaUe7JoNSVSZErjS3BdDYF09hUlUJheZCi8q0f0EsqA86VIiPUnKsmj58EIQ9PzZ9NKJBCKJgCwSR2XLG9aVx2ZG+uOrbvbs+zcbtdJLldJMX4fYRCISr8QcoqA5RWBSirDBAKhejdLqNZzyGSpqFEJ8pWrVrFMcccQ9++fSP7QqEQ55xzDqeddlqdz5eWltKhQwduvfVWunate8LclClTePrpp3n//ffp0KFDZP91111Hnz59uPDCC2sdv//++/Pmm2/SpUsXAF599VWee+45ysvLqaqqYsiQIfzpT3+iVauGVSFZuHAhN910E2VlZbRr14477riDdu3abXfczJkzuf/++/F4PHTo0IEbbriBzp07U1lZyc0338xXX31FWloaRx55JJdffjlut5v8/HyuueYa1qxZg9vt5u9//zuDBw9uUHwSRS6XM9QtXp10Evz1r1BUFEnc7nzbQ9Wc4LY5Ti1VVQHuvvsr7rtvVJOF2hJU9+gEN2+JbSDSsrTqCBe9Bz/9DwKVu32Zn8rWc2neM6yprM/vX7lzK1/nPNyZ5PAtCxpWJ85FkjuNZHcaye50klxpJLnTSHKnO/eu6ntnv88Vfs6Vhs+dho80PK5kqpOpmkPNgiEiw8+2DjlzniN8zIE9cjlhv04Niri5crlcpPg8pPg8xFm5HGkESnSaQEpKCq+/vrUK67p16zjhhBPYd999ycjI2O75UCjEzTffzN13381dd9213fUqKip47bXXOO6443j66ae5+uqrGxTPQw89xCeffML9999PmzZtqKqq4tZbb2Xy5Mk888wz9b5OZWUlV1xxBXfddRdDhgzhmWee4c9//nNkcbRqy5cv54YbbuDpp5/GGMOcOXO44oorePnll3nooYdYvXo1b775Jj6fj7/97W8888wznH322dx0000MHTqUyZMns3jxYiZOnMi7775LavU37SLR1KoV/P3vtXY9fddtVAV3/mGrqirIU099q0RnG5qjI7stKR32OXG3T//616/5w4d3UVRVRKC8PcHK6i/jQtTofsHlCpKEn2SqSKKKJFcVKW4/KS4/yS4/Pqrw4scbqsQd8keuABByVV9taw9CzatXepMo9vgocrspdgUpDQWoDJZQGSyhiA279b48Lg8ZSRlk+DLITcmlY3pHOmV0olN6Bzqld6JjRkc6pneiVVKrOns2Fm9azMs/vkyaL41x/cbRIb1DHa8i0rLFb6IzejTMnBnd1xg1CmbMaPBp7du3Z6+99mL58uXsu+++2z1fUVHBhg0baN267omXM2bMoFu3bpx33nlceOGFXHrppfX+8F9aWsrUqVN59dVXadOmDQA+n49rrrmG9957j8rKSpJqrJ3y008/cdVVV213nXPOOYfu3buTkZHBkPDCraeddhq33norBQUF5NQoQ7xkyRL69esXqV5ywAEHsHr1alatWsUPP/zA6NGjSU52Vu8++uijeeSRRxg7diwfffQRN9xwAwB777033bt359NPP+XYY4+t13sVaWzFxfX7Rrm+xyUST7i3OFBYSCgQwOXxxDgiiUehUIh1pev4afNP/FTwEz9u/pGZP8/EH/TjL+pP+ZqxdMluRev0ZNpkJNE6PZnWGUnkpifRJsN5XP1cTnrSjqtzBfxQutFZuLV4A5Ssh+LwbZvHrmt/dmK7YeuICT9Q4nZT5HZR7HZT5PFRlN6a4rQcilJbUZyUTnFSCkUer3MMQYoDFRRVFVNUWURxZTHlgXK2VGxhS8UWVhev5ruN39UZarovnY7pHSO39unt+erXr5izdk7kmKcWPcVJvU/ilN6n0Cu7F2k+rWMk8SF+E51m7JtvvuGXX35h4MCBBAIBysvL+d3vfkcwGGTTpk1kZWVx7LHHMnHixDrPf/bZZ/nd737HgAEDaNu2La+++irjxo2r12svW7aMlJQUunfvXmt/amoqJ564/TdmvXv3rtXbVNOMGTNqDZtLSkoiNzeXdevW1Up09tlnH5YuXcrixYvZe++9+eCDD9i8eTMbNmxgv/32Y+bMmRx33HH4fD7efPNN1q9fT0FBAcFgkNzcrfMc2rdvz9qdTXwXibKMjCSKinadxGRkaKHVbbm8XtyZmQSLiggWFUV6eER216ayTU5CU30r+Im8zXkUVRVtd2xu1TGsWHUk5x/Skxt+23/PX9zjhcwOzm1Xrg33plz4nrNe0JbVeAtXk7VlFVmFq2HLaic5KisFVu74Ot4UaNUJWnWGLENVZgeKM9pRlJbNprQs1rhC/Fq6ll+Lf2VNyZrIfUlVSeTfqKY0bxon9zmZTWWbeGf5O7y09CVeWvoSAB3TO9Izuye9snrRM6snvbJ70SOrB1nJWbv5DyYSG/Gb6OxGT0u0VCcyAIFAgJycHO644w46duzIqlWrag1d+/TTT/nTn/7EIYccQnr69usG/PDDDyxevJhp4fK+J510Ek8++SRnnnlmuNJK3RPvgsEgHo8Ht9tNMFj/MpA769Gp2fNTk2ebb2q7devGrbfeyg033EBlZSUjRoygX79++Hw+JkyYwN13383YsWNp1aoVo0aNYunSpTuMcdtrizSls8/ej4cfnk9V1Y7/D/l8bsaP368Jo2o5PNnZBIuKCGzerEQnQeWX5/P9xu/5eYvTy+FxeXC73Hjd3u0ee91eUrwppHnTCBHi5y0/10pqCioK6nyN7ORs+uT0oVdWL/rk9GFTfntuf72Y3PQk/nB03zrPaRJdD3RudfFXQHXSU7gatqzaur1lFRSugvItkL/MuQE+ICd86wbsn97OuX63YWCOg44DCXmSKKwsZE3xGn4t+dW5Ff9Kx4yOnNjrRDKTMgG4ZNAlPPHDEyzcsJAVhSsix36++vNaYbZJbcPBnQ7mogEX0SOrR9T+qUQaS/wmOs3ItnNwduawww7j/PPP58orr2TWrFlkZmbWev6ZZ57B6/Vy6qmnAuD3+1m/fj2ffPIJw4cPJycnh83bjIEvLi6moqKCVq1a0bp1a/x+PytWrGCvGiu+V1RUcNlll3HzzTfTvn37yP6d9ejMnTuXDRu2ji2uqqqioKCg1vngzOXZa6+9eOGFFyIxP/HEE3Tp0oUtW7Zw/vnnc+211wJO0YJu3bpFhu1t2bKFrPCK6uvWrdvu2iJN6aqrfsMTTyzcRaLj4Y9/HNaEUbUcnuxsqlau1DydOBcMBSmsKCS/Ip91JetYnL+Y7zd+zw8bf2BNyZpGe50MXwa9s3vTK9tJaKoft05pHfnSr7TSz1EvfwzANccZslKb6Voq3mTI7encdqSiuO4kaMtKZ8HVkvWw5C3nBuBJwtVpf7K6HkRW14PYu+uB0O2oOi/dI6sHNx58IwD+oJ9VRavI25LHz1t+Jm9zHnmb81heuJyNZRt5I+8N3lr2FiO7j2TSfpPomb2TmEViTIlOM3TBBRfw+uuvc8899/DnP/85sr+wsJAZM2bw0EMPMWzY1g9S11xzDY8//jjDhw/n8MMP5y9/+Qtnn3027du3JxQK8cQTT3DAAQdEeogmTJjA//3f//Gf//yHNm3aUFlZya233kpZWVmDEomBAweyefNm5s+fz+DBg3n55ZcZNGjQdpXbKisrOfPMM3njjTfo2LEjjz/+OEOGDCE7O5vnn3+eDz/8kAcffJDS0lIee+wxLrzwQrxeL0cccQTPP/88EydOZMmSJeTl5XHQQQft4b+uyO7r1SuXl146nTPPfJlgMFRrGJvP58bn8/DSS6ertPQOeMJfWmgtnZbFH/SzuWIzBeUFFJQXkF+Rv/VxefhxxdbtLRVbCIQCdV4r1ZvK3rl7Y3INPrePQCiAP+gnGApGHgdCAYLBIFXBKsoCZZRVlREIBdir1V70ye4TSWzap7Xf4SiGjcUVvPPDWl6et4q1heUM6JzF6UPrrmLaYiRnQFvj3LYVCjk9PStnO7dfZsOGxVu3q+X0cHp8srtRXbGN7odAj8Mjh3jdXrpndad7VvdaLxEMBVm+ZTlPLX6K1356jZk/z2TWz7M4rvtxTNpvEr1zejf+exbZQ0p0miGfz8df//pXLrroIk4//fRI6elXX32VXr161UpyAC6++GJGjx7N0qVLGTZsGBMmTIjM7ykvL2efffbhzjvvjBw/efJkUlNTIyWoKyoqOPDAA3nggQcaHOd9993H3//+d8rKysjOzub2228HnN6XiRMnMm3aNNq3b88//vEPJkyYQCAQoFevXtx2220AnHrqqSxcuJATTjiBQCDAmDFjGDlyJAA33HADf/nLXzjhhBNwuVz885//3K6HS6SpHX98H37++WK+nr2GMcc/STHJZLRKYfz4/fjjH4cpydmJSInpLSoxHUvl/nI2V2yOJCnV9zX3VScuBRUFFFYUEqpRnaw+Mn2Z5KTk0Dq1NX1z+tK/dX/2bbMvPbN64nFHZwjy+sJy3v5hLTO/+5Wvf84nGA45Pcmz24tithguF7Tu5dwGhefslhXAqnnhZOcr53HBz86tpo+BkbfDsMk7fQm3y03P7J7c8JsbmDBgAo989wiv/PQKby9/m3eWv8MBHQ7gwA4HckCHAxjQZgA+TzPtPZOE4gqFGtZ4xZIxJgRgrY11KCKSoEpKSvjhhx8wvXuT1bq1s8ZOWdkuz6uuOmitjeNPW7Vt22avvfkWCp5+mvbXX0fuuefGNLam9mvxr7z848u8tewtSqpKSPelk+ZLI92bvvWxL/zYu/VxredqHFt97q6ShuLKYmYsm8HMn2eyrnQd+eX5lPl3/ftakwsXWclZ5KTkkJOcQ25KrvM4Jfw4ucbj8DFN9SH31y1lvP39WmZ9t5Y5K/Kp/kjj87g4rE9bjt+3A8fs057stNgVCKnudYr5562A31l4deXXULLR2Ve8FuY97jw+7Co46q9O0lRPa0vW8vB3D/PKj69QFayK7E/xpDCo3SAO6HAA+7bZlz7ZfWiT2qbFLeCZiO12vFGPjohIPZWUlHD33Xfzyiuv8OH//hfrcFqc6h6deB26FgqFIqV+VxavZHXRalYVr2L5luXMWzevVq/I5orNjfKaKZ6UHSZJIUJ8suqT7RIbr9sbSU5yUnLITc4lOyU78rh6f/UxWclZeN3N5+PC6s1lzPruV2Z9v5Z5K7YWJEjyujm8T1tG79eBo/q1bzbzcaZOnRrrEBweL3Qc6Nxq6nIgvHE5fHonrP0OBoyBPkdD6q6X3+yQ3oG/DPsLlw66lDlr5/D12q+Zu3YueVvy+OrXr/jq168ix7ZKakXv7N6RuVS9s3vTO6c3uSnqBZfoaT4tl4hIM1ZSUsLNN9/MlClTyM3NJVkL1zZY9Rydljx0rSJQweri1awqWlXnfXFVcZ3n+dw+jt7raE7rcxq9sntRWlVKib+EkqqSyOPSqlJKqmrsqyqpfUzV9seXB8opD5STX56/w5iHth/KaX1PY0CbAeSk5JDhy2hx36yvzC9l5ne/MvP7tSxcuTmyP9nr5kjTjuMHdOCofu3ITGkeyU1NO1oqotnY/yxIbwMvnAs/vuvcXB7Y62Awxzu3nRVJAHJScji2+7Ec291Z525j2UbmrpvL3LVzWVqwlJ82/0RhZSHz189n/vr5tc7NTcnl0kGXMsaMidpblMSlREdEZBdKSkq4/vrruffeewHIz893PihmZkJpKSxeDHvvHeMom7/qHp3mXHUtGAqyvnQ9q4pWsarYSV6qe2ZWF61mfdn6nZ6f7kunS0YXOmd0pkvm1vvqJKNa69S6F4RuaKzl/vJIAlQzWSqtKqUiUMHAdgPpmdVyq2KVVPi58Y0feHHeqsi+VJ+Ho/o5yc2Rph3pyfoos8f6HgeXz4XFb4KdCcs/h+WfOrd3/g/a9oO+I8GMgi5DYRdDJtuktmFk95GM7O7MuQ2FQmwo28BPBU558Lwtec795jzyy/O5dfatDGgzgL1bqx2VxqXWQURkJ0pLS7niiit49NFHa+1fv349XU89FR5/HA480LkPl32XujWnRGdV0SoWbVpUu1emeBVritfUmmuwLa/LS8eMjk4yk9k5ksh0yXBuWclZTdZb4na5I/N12tK2SV6zKf2wZguXP/sNyzaUkOR1c1z/DozatwNHmHakJrWcNdWq171r9j07WV1g2MXOrawAfvof2Fnw43uwYYlz+/zf0HkInPk8ZNT/d87lctEurR3t0tpxcOeDI/tDoRC3z7md/y7+Lzd8cQPPjH6mWQ2TlJZPv00iIjtQWlrK+eefH1kDqqaVK1fS9YEHnEIEzz8Pp50GV10Ft90GvuY3fKY58GTHtrx0mb+M91a8xys/vsK8dfN2eFzrlNa1emO6ZHSJbLdLa6cPYlESCIZY/Gshc5bnM3d5Ae8tWkdlIEjf9hncN24wfdu3zKqbkyZNAlpAolNTag4MOM25BapgxRdO0vPDK7B6Hjx6LIx/FXK679HLuFwurtj/Cj785UMW5y/myUVPcsG+FzTOexBBiY6ISJ1KS0sZO3Ysb775Zp3P//jjjxx88MHw7LMwbBhcfTXceSd8+aWT+HTp0sQRN3+R8tKbm26OTigU4ruN3/HqT68y6+dZlFSVAM56Lgd2OJCumV0jyUznjM50yuhEmi+tyeJLZGWVARas3Myc5fnMWZ7PN79sprjCX+uYcQd1428n7EOKr+X04MQdjw96Dnduh10JT5/iFC145Fg4+xXosO8eXT7Nl8bffvM3Jr8/mQcWPMCIbiPYq9Veuz5RpB6U6IiI1BAKhSgtLeXkk0/mvffe2+FxS5YsoaqqCp/PB3/4AwwdCmPHwhdfwKBB8OmnmrezjaYcurahdANvLXuL1396nbwteZH9+7Xdj1N6n8LIHiNJ96VHPQ6pbUNRBa99s5qZ3//Kd6u24A/WLrm8V+s0hu6VywHdczioZ2t6tNHPqFnJaAfnzYTnxjnzdx4bBWc+6yw6ugcO6XwIv+35W95c9ibnzjqXiftN5PS+p2stHtljSnRERGoIBoN88sknrFq1aqfHrVixgrKyMifRATj0UPjmG2eezqefwlNPwa23NkHELYc7IwM8HoKlpYQqK3ElNe7aJpWBSj5a+RGv573O56s/JxAKAM5QtBN7nchJvU+iZ3bLnZjfUlUFgny4ZD0vzlvFh0vWR5Ibtwv27dwqnNjkMrR7Du1bpcQ4WtmllFZw1kvwygRY/AY8dTKc/hj0G71Hl732wGtZWbSSBRsWcNvXt/Hkoie5+ZCbGdphaCMFLolIiY6ISA0ej4ejjjqKuXPnsnr1aq644grefvvt7Y5bsWIFwWCw9s62beGEE5xEx+/f7pxE53K58GRlEcjPJ7BlC962ez6BPhQKsSh/Ea//9Dozf57JlgpnWJzX5WVEtxGc1PskDul8CD63vhluKqWVfn7eWMLPG0tY8MtmXluwmo3FlQB43C6O3rs9pw3pwiG9WzfLctBSD74UOP1xmHk1zH0Unj/bWWz0wImQnLFbl8xKzuLJ45/kg18+4N5v7iVvSx5//uzPvHXKW/r/K7tNiY6IyDaSk5MB6Nq1KwMGDIgkOtnZ2QQCAYqKilixYsXW3hypN092tpPobN68W4lOMBRkVdEqbIFlSf4SPvjlA37a/FPk+b45fTmp90mM7jlaCxFGkT8QZFVBGT9vLCFvQ3Eksfl5Ywm/binf7vg+7TI4fWgXTtq/M+0y1WsTF9weGH0XpLeDj6fA/25yqrINvRAOmgSZHRp8SZfLxYi9RjC863BOfv1klhcu593l7zK65571FkniUqIjIrITzzzzTOTx66+/zrBhw3j33Xd5+OGHSUnRB7aG2p15Ot9u+JY38t7A5luWFiyl1F9a6/mc5BxG9RzFib1OZO/cvVvcYpjNVSgUYkNRBT9vLGFZOIlZtqGEZRuLWZlfSlUgVOd5Po+LvVqn07NNOj3bZnBc//YM6pqtn0s8crngyOuh82D49C5Y+RV8dhd8eR/sNwZ+czm069fgy3rdXs7rfx43fnkjj33/GKN6jNLvj+wWJToiIjvw3XffsXr1agDatWvHgQceSFJSEqNHj+awww7D41ElqIbyZDWsxPSyzcu44J0LqAhURPa1TW2LyTWYHMOgdoM4pNMhmrS8B7aUVrFsYzHLN5Xw8wYnqal+XFIZ2OF5nbJS6NE2nZ5tMujRJp0ebdPp1SaDTtkpeD3uJnwHzVsoVHdCGFf6HufcVn4NX9wDi9+Cb552bt0Ohn6joO/x0KZ3vS95Qq8TuG/BfdgCy5drvqy1/o5IfSnRERGpQ1FREQ8++GBke+zYsQQCzoc+l8tFVvgDuzRMpMT0ll2XmK4KVnH9Z9dTEajgiK5HcGa/MzE5htapraMcZfwpqwxEhpYt3+T0zPy8sZjlm0rJL6nc4XlZqT66t0mnV5t0eoR7aHq0Sad7mzTSkvQRQrbR9UA442nYlAdf3g8L/gu/fOHc3v0LtO4D5ngwo5xj3Tv+sijZk8xZe5/Ff+b/h0d/eFSJjuwWtVIiInXw+Xy8/PLLke3JkyeTnq5St3uqIUPXpn87nUWbFtExvSO3HnormUktc8HIWAoGQzz6+c/c8Y6lwh+s85hUn4fubZyhZk4SE05q2qSTk964lfEkQbTuBSfcBUffAD/9z1ls9Md3YdOP8MWPTq9Paq7TC2SOd3p7vNv/ro0xY5j+7XRm/zqbHzb9QP/W/WPwZqQlU6IjIrKNUCjEBx98QGFhIQB9+vRhr70auIBdIABr14LXC23aRCHKlqm+ic53G75j2rfTcOHilkNviZskp6Ckko3FFbjdLtwuFx6XC7cb53F4n9vlVCer8xiXs78+NhRVcNWLC/lk6QYAerbdmsz0aJNB9zZp9GyTQftWyZr/EEVDhgwBYN68eTGOJAZSsmDfU5xbwO/M4bGzwM6E/GWw8FnnNuB0OPXh7U5vldSK0/uezhOLnuCOOXcw/ZjpGqYqDaJER0RkG0VFRUybNi2yfc455+B2N3DOwV13OTe321lfR4Ctc3QCdQxdqwxU8tnqz5ixbAYfr/qYQCjAOfucwwEdDmjqMBtdIBhi2ifLuOs9u8NJ/A0RSYZcrhpJUo19bhfF5X7KqgLkpPn452kDOWaf9o3wTqSh5s+fH+sQmgePF7of6tyOvRk2/ugkPB/cDN+/DMfeApnb/46et+95zPh5BvPWzePPn/+ZKYdNwe3SHDCpHyU6IiLbcLvdzJo1K7J90UUX1b/C2sCB4PE4PTpuNwSDsHRplCJtebbt0QmGgsxbN48Zy2bw3or3KKwsjBx7ZNcjuWLwFTGIsnGt3lzGlc8vYPbP+QD0bJNOCAiGQgSCIYLBEMEQBELVj8P7QzWOCYWPCS+2GQxBMBACdp40HdK7NXeePogOWaoQKM2IywVt+zq3VXNgyVvw3Ytw8GXbHdomtQ0PjHiA8985n1k/z6Jtalv+dMCfYhC0tERKdEREaggEArz88stUVjoTtA866KCGzc057jgoKIDkZBg3DmrM85EaiU7BZkqqSrjk/UuYv37rN959c/oyuudoRvUYRYf0hq/D0RysKyxn3ooC5i4vYN4vBfywegv+YIg2Gcnccfp+HGna7dH1I8lQKEQwSI3HW5OhYCiEy4XWrJHmb+BYJ9FZ+FydiQ7A3q335u4j7uaS/13Ck4ueJBAKcNmgy8hI2r3FSSVxKNEREamhtLSUhx/eOlb8wgsvJDU1tWEXyYyP+STR4MnJBqBqc0EkyWmd0ppT+pzCqB6j6J1T//KzzYE/EGTJ2iLm/xJObFYUsHpzWa1j3C44ft8O3HzSvrTOSN7j13S7Xbhx6Q+4xIc+x0JKNqz7DtZ+Dx32rfOw33T6DTcfcjPXf3o9/138X2b9PIvL9r+MU3qfgmcn1dsksamdFBGpoby8nM8//xwAr9fLGWecgderprKxVM/RKVi/kvnrV9AurR2Pj3ycrpldYxxZ/Wwpq+KbXwqYv8LprVnwy+bt1prJTPYyqFs2Q/bKYeheuQzsmkVmiiZQi9TJmwz7ngpzH4Fvn4MON+/w0NE9R9Mtsxt3zL2Db9Z/w9+//Dvv/PwOdx15F62SWjVh0NJS6K+3iEhYZWUljz/+eGSBv2OPPTYxFvtrQtVD15JLKmmd3I6Hj3242SY5oVCIFZtKnWFoK5zkZun6Irb9leiWm8bQvXIYvFcOQ7vn0KddJp56VkYTEWDgmeFE5wUYcaNTuGAHBrQdwBMjn+DdFe9y2+zbmL12NufMPIcHjn6AThmdmi5maRGU6IiIhFVVVfH4449HtidOnEimhqE1qpWV66jwQrIfph12Lz2yesQ6pIjyqgDfr95SK7HZtM1imkkeN/t2bsXQ7rkM7pbD4L2yNQ9GdmrChAmxDqH56zIUcntBfh78/BH0Pnqnh7tcLo7rfhwD2gzgkvcvIW9LHmfNPIv7RtyntXakFiU6IiJhv/76K4sWLQIgIyOD4447ruFlpWWnnl3yLIemQnIRdA+1jmks64vKnSFo4dv3qwupDNReVLN1ehJD9spxhqF1z6F/pyxSfJoPIPVXs1S97IDL5RQl+PAW+PRu6HYwJKXt8rROGZ14ctSTXPnhlcxeO5vz3z6fOw6/g+FdhzdB0NISKNEREcEpQvDQQw9Ftk866SSqqqrqX1Zadqm4spjXfnqN3jnQuggq7BKSunRu8jh+WLOF/3v1exau3Fxrv8sFpn0mQ7rnMKSbk9zs1TpNi2mKNIX9x8NXD8KKz+Cpk2Hcc5Cas8vTWiW14sGjH+TGL2/kjbw3uOLDK7j+wOsZ229sEwQtzZ0SHRERnLVznn322cj2xRdfrGFrjez1vNcpqSph896d4ZfVlH79NZkjRjTZ61f6g9z3wY888FEe/mCItCQP+3fLZkg3Z37N/t1yyEpV0QBpXPPmzQNgyJAhMY6kmWvVES54G546BVZ+BY8eD+NfgVa7nnfj8/i4+ZCb6ZLRhQcWPsAts28hGAoybu9xTRC4NGdKdEREgIULF7JmzRoA2rdvz+DBg2McUXwJhoI8u8RJJPsedQq8cy8lX89pstdfuHIz1778LUvWFgFw3sHd+dNxhvRk/RmU6Bo6dCiACpvUR1sDF77jJDsbFsOMq+DMZ3d9Hs68nYsHXUybtDb8/cu/8+j3j3KGOUOlpxOcBp+LSMIrKiqqNWztzDPPJBAI7OQMaajPV3/OisIVdEzvyLBjz8WVlETFkiUENm+O6uuWVwW4beZiTn7gc5asLWKv1mk8P3EYN57YX0mOSHOU1QXOec15nPcBVJU36PTT+pxG18yurCtdxxdrvmj8+KRFUaIjIgnP5/PxyiuvRLYnTZpEenp6DCOKPy//+DIAY/uNJSk1ndSBAyEUonTu3Ki95hd5Gxn570+Y+skyACYe3pO3f384B/WMbREEEdmFVp2gwwDwl8MvXzboVJfLxcm9Twbg1Z9ejUZ00oIo0RGRhBYKhfjf//5HYWEhAH379qVbt24xjir+dGvVjb1z9+bUPqcCkHbggQCUfP11o7/W5tJK/vTiQsZNn83yTaX0bZ/BK5ccwv+N2pvUJA1jEWkReh3l3Od90OBTT+x1Im6Xmw9Xfkh+eX4jByYtiRIdEUlohYWFTJ06NbJ97rnn4vHow3Bju3LIlbzw2xfISs4CtiY6pY04TycUCvHGwjUcfdfHvDhvFUkeN1cd05e3Lj+MQV2zG+11RKQJ9AoXKtmNRKd9ensO7Xwo/qCfN/PebOTApCVRoiMiCc3j8fD2229Hti+44AKSk5NjGFFiSB00MDJPx19QsMfXW1VQyvmPz+GKZ79hY3ElB/bIZdYfDuPyEX1I8upPnUiL020YeFNh3fdQtLbBp5/S+xQAXv3xVRWCSGBq/UUkYfn9fl588UWqqqoAGDZsmObmNBF3cjKpgwYB7NE8HX8gyPRPlnHMXZ/wkd1AZoqX204ZwHMThtGrbUYjRSsiTc6bDN0PdR4v+6jBpx/e9XByU3LJ25LHtxu/bdzYpMVQoiMiCausrIxHHnkksn3RRReRmpoaw4gSy54MX/MHgnz64wZ+e9/n3DJzMWVVAUYP6Mj/rhzOmQd2w+3WIp/SPMydO5e5USy6Edeq5+n89L8Gn+pz+zix14kAvP7T640ZlbQgqq0pIgmrrKyML75wyo96vV7GjBmD16tmsamkHXgAAKX1LEhQXOHnY7uB9xev44Ml69lS5vTEdclJ5R+/25cj+7WLWqwiu0sLhe6B3iPgHWDZhxAMgrth38+f0PMEHv/hcT745QP+MuwvuF36fj/R6C+6iCSkiooKHn300cjY7eOOO45gMBjjqBJL6sDwPB1rqVqzBl+nuldA/zJvEw9+nMdXeZuoDGz9GfVsm86JAzsx6fBeqqYmEo/a9IVWnaFwtTNXp+N+DTq9b05fOmd0ZnXxar7d8C2D2g2KTpzSbCm1FZGEFAgEeOKJJyLbkyZNIjMzM4YRJR53cjKZRzuVldbfeVedx2wpreKCx+fwydIN+INBDuiew/XH9+N/Vw3ng6uO4A9H91WSI83axIkTmThxYqzDaJlcLuh1pPM4r+HD11wuF0d2dc7/YGXDq7dJy6dER0QS0qpVq1iyZAkAmZmZHHPMMbgbOCxC9lzbK6/ClZxM4YwZdRYleHHeSsqqAhzQPYc5fz6aFycfzKThvVRoQFqM6dOnM3369FiH0XJVl5le+DwEAw0+/ahuzjyfD375QNXXElDUhq4ZY0YDtwHJwLfAhdbawm2OORm4CQgCBcBF1tq8aMUkIgJQWlpaa+2ck08+maqqKlJSUmIYVWzFqs1O6tKZ1hddxMb772ftzbfQ4+WXcIXXMQoGQzz11QoAJhzWk9YZKvstknDMKMjqBhsWw4JnYPD4Bp2+f7v9yU7OZkXhCpZtWUav7F5RClSao6h8fWmMaQs8BpxqrTXAMmDKNsekAk8Dp1hrBwFvAPdEIx4RkZrcbjfPPvtsZHvy5MkJPWwt1m1264suxNupIxVLlrD5xZci+z9aup4Vm0rpnJ3KiL3bN8ZLiUhL40uBEX91Hn94C1SWNuh0r9vLEV2PAJxeHUks0RqncSwwx1r7Y3j7QeAsY0zNep8ewAVkhbczgPIoxSMiErFgwQJ+/fVXADp06MD+++8f44hiLqZttjs1lfbXXAvA+jvvZPOrrxEKBnniC6c3Z/xv9sKjctEiiWvf06DjQCj6Fb66v8GnH9V16/A1SSzRSnS6AitrbK8CWgGRr0yttcXAZOALY8wa4DLg2rouZoyZaIxREXoR2WNFRUU8+OCDke0zzzxT1daaQZudedyxZBw9gmBREb9efz32lNPY+OXXJHvdnDG0a0Pfj4jEE7cbjvmH8/iz/0Dxhgad/ptOvyHVm8r3m75nbcnaKAQozVW0Ep0dXTcyi8wYMwD4G7CPtbYTcAvw8jbfIAJgrZ1mrR0alUhFJKH4fD5effXVyPbkyZNJS0uLYUTNQszbbJfLRZd77qHT7VPwtmtHaMli/vXp/dy95EXSCxr2oUZE4lDP4dDnWKgsgrf+AP7Kep+a4k3h4E4HA/Dhyg+jFKA0R9FKdH4BOtbY7gwUWGtLauw7Dvi8xkTW+4F9gdZRiklEElwoFOK9996jqKgIgH79+tGlS5cYR9UsNIs22+V2s3LoEdw05ib+a46h3OOjx/dfsmzUKDY+NFUVk6RFGjx4MIMHD451GPHh2JshKQOWvAXPngEVxfU+9dDOhwLwzfpvohWdNEPRSnTeBYYZY/qEtycDr29zzHxguDGmeobpScDP1tqNUYpJRBJcUVER06ZNi2yfe+65eDxag4Vm0GavKyznTy8u5Lf3fcZnq0qYMeQElt/1GK1++1tClZVs+Pe/KVuwoDFeSqRJzZs3j3nz5sU6jPjQ1sB5b0FaG8j7AJ48EUo21evUrpnOENh1JeuiGaE0M1FJdKy164HzgZeMMYuBAcBVxpihxpgF4WM+AO4APjLGLMQZ7/27aMQjIgLO8Kh33nknsn3BBReQnKySxbFss0sr/dz93lKOuOMjXpy3Cq/bxUWH9uCjPx3JyccNofMd/6T1hAkA5D/x5J6+nIi0dJ32hwvfhexusHoePDcOAv5dntYhvQOA5ugkmKito2OtnQnM3GZ3PjCoxjH34wx/EBGJKr/fzwsvvEBVVRUABx98MKmpqTGOqvmIRZvtDwQ5c/psFq7cDMDI/h247vh+dG+TXuu4nLPPYtNjj1H07rtUrV6Nr3PnxgpBRFqi1r3ggndh+lGw8iv4eAoc9ZedntI+zemMXl+6nkAwgMet3vxEoGXARSQhlJWV8cgjj0S2L7zwQiU6MfbklytYuHIzHbNSeGHSb3ho/JDtkhwAX/v2tDr+eAgGyf/vMzGIVGT3uVwuXC6VR290rTrCqdPB5YZP/gXLPt7p4SneFHJTcvGH/Gwqr99wN2n5lOiISEIoLS3lyy+/BJzKa2PGjMHrjVqntuzC2i3l3PmuBeAfv9uXA3vk7vT43HPOAWDziy8SLCnZ6bEikiC6HwqH/wkIwSsToWTnUware3U0fC1xKNERkbhXUVHBo48+Gtk+7rjjtHZOjP3jrUWUVAY4Zp/2HL1P+10enzpgX1KHDCFYVMTmV1+LfoAi0jIcfg10OxiK18Krk2Enbbvm6SQeJToiEvcCgQBPPPFEZHvSpElkZmbu5AyJpo+XbmDGd7+S6vNw44n9631eda/Ohn//m2WnnMKyU05h9ZVXUrFsWbRCFZHmzuN1hrClZMNP78FXD+zwUCU6iUeJjojEvZUrV2KtM0wqMzOTo48+WmPmY+j2WUsA+P3RfeicXf95UpkjjiKpZ0+CxcVULFpMxaLFFM6cxbITf8faW28lsHlzlCIWkWYtqwucFE5w3r8RVs+v87BIolOqRCdRKNERkbhWUlLCQw89FNk+5ZRTIpXXpOmt2VzGol8LSU/ycP4h3Rt0rsvrpfsLL9D95Zec2wvPkz1mDASDFDz5FHnHjST/qacJ6ecrknj6jYYDJ0KwCl66AMoLtzukQ5p6dBKNZuKKSFzzeDw8++yzke2LL75Yw9Zi6JOlGwA4uHcbkr0NL+/qyUgntf/W4W6p++1HzrgzWXfbFEpnz2bdLbdQ8Oyz5IwdiyspyTlm0EBS+vVrnDcgIs3XMf+AFV/Cuu/gk3/CsTfXelpD1xKPEh0RiWvz589n3TpnJeyOHTsycODAGEeU2D4OJzrD+7ZttGum9OtHt8cfo/iDD1h3+z+pXLaMdbfeuvUAj4euDz1ExmGHNtpritTX1KlTYx1C4vClwMjb4IkTIO+j7Z5WopN4lOiISNwqKiqqNWxt3LhxqrYWQ1WBIJ/96JR/bcxEB5y1SjJHjCDjsMMoeOklKpY4c7Kq1q2l5ONPWP3737PXf58mZe+9G/V1RXZl4sSJsQ4hsXQZCm4vrP8BKoohOSPyVNu0trhwsbFsI1WBKnweXwwDlaagREdE4pbX6+XVV1+NbE+aNIm0tLQYRpTYFqzcTFGFn55t0+maG52fgyspidxx4yLboWCQNX+6hsIZM1g5cRLdn38OX6dOUXltEWkGfKnQYQCs+QbWzIceh299yu2jbWpb1petZ33ZejpndI5hoNIUVIxAROJSMBjkvffeo7i4GIC9996bzp31Ry2WPrLrgcbvzdkZl9tNx9tuJe2AA/Bv2MCKc86l6MMPCYVCTRaDJLZp06Yxbdq0WIeRWLoc4NyvmrPdUxq+lliU6IhIXCoqKqo1Nv68887D61UndixVz885wrRr0td1JyXR5b57Sd57b6pWrWLVxZew8sKLKF+6tEnjkMQ0adIkJk2aFOswEksk0Zm73VNKdBKLEh0RiUsul4t333038vj8888nKVyFS5rehqIKvl9dSLLXzUE9cpv89T1ZWfR4/jnaX38d7latKPniC34+6WR+vekm/Pn5TR6PiERRl6HO/ao5sE3vrRKdxKKvN0Uk7vj9fp5//nn8fj8ABx98MMnJyTGOKrF9+qPTmzOsZ2tSfA0vK90YXElJ5J57Lq1OPJGN995HwfPPs/nZ5yh8awaZxxyDyxO9uDKPPYaMww6L2vVFpIacHpDWGko2QMFyyO0ReUqJTmJRoiMicaesrIxHHnkksn3RRReRnp4ew4jkI9v4ZaV3lzcnhw5/+6uz/s6U2yn57DO2vPJKVF9zy4wZ9Jo1C1/7ph22J5KQXC7ociAsneUMX6sr0SlVopMIlOiISNwpKSlh9uzZAPh8Pk477TQ8Ufy2XnZtS1kVSR43R+/dPtahRCT37k23h6dTOmcOFct+jtrrFM6aRelXX7H+zn/R+Z//jNrriEgNXYaGE505sN/pkd0d0pxEZ13JulhFJk1IiY6IxJWKiopavTnHH3+81s5pBv4zdhCbSirp1rr5lfdOO+AA0g44IGrXTz/kYJaNGk3hG2+SM/ZM0gbvH7XXEpGwHVReq+7R+bXk16aOSGJAxQhEJK4EAgGeeOKJyPakSZPIzMyMYUQCkJ2WRK+2Gbs+MA4ldelC7oUXALDullsIBQIxjkgkAXQeDC43rP0Wqsoiu1untsbr9rK5YjNl/rKdXEDigRIdEYkrv/zyCz/++CMArVq14qijjsLlcsU4Kkl0bSZMwNuhA+U//EDBM8/GOhxpQqFQSOs2xUJyJrTbB4J++HVhZLfb5aZ9mjOEVsPX4p8SHRGJGyUlJTz00EOR7VNPPZWqqqoYRiTicKel0f6aPwFOr87KyRdHdV6QiFC7zHQN1YmOChLEP83REZG44fF4ePbZrd+WX3zxxRq2Js1G5vHH027dejbeey/FH31E8WefkdynD0Spw9Hl9pB59AhyzzsPd0pKdF5EpDnrcgDMexxWfl1rt0pMJw4lOiISN+bNm8f69esB6NSpEwMGDIhxRCJbuVwuWp9/HlknjGbDPfew+aWXqVi8OKqvWf799xQ8/wLtrr6KVqNGaRhnDAwZMgRw2idpYpGCBHNr7VaikziU6IhIXCgsLOTBBx+MbI8bN07V1qRZ8rZtS8d//IM2l16Kf9OmqL2Of/16NvznHiqWLGHNVVdT8NTTtP+/60ndb7+ovaZsb/78+bEOIXG17gMpWVC0BrashqzOwNZEZ12p5ujEOyU6IhIXfD4fr732WmR78uTJpKU1v1LGItV8HTrg69Ahei/Qvz8Zhx/OlldfZf3d/6ZswQKWjzmDVif+lrTBQ+p9GZfPS8YRR+Bt3Tp6sYpEg9sNnYdC3v+ceTrhRKdNahsA8svyYxmdNAElOiLS4gWDQd5++21KSkoA6N+/Px2i+QFSpIVweTxkn3YamSNHsmnqNPIff5zCN96k8I03G3Qdd3o6bS65mJzx43EnJUUpWpEo6HLA1kSn/0kA5CTnAJBfrkQn3inREZEWr7i4mOnTp0e2zzvvPHw+XwwjEmlePBkZtLvqSrLHnE7Bs88RLC6u97mVK3+h9MuvWH/Hvyh45lmSe/eu97mu5GSyTzuVjMMP352wRfZcHfN0clNzASU6iUCJjojEhffeew9wJnyfe+65JOlbZ5HtJHXtGilz3RDFn33O+tunUPHjT1StXt2gc4vefZf0ww6j/bXXkNSrV2S/CiNIk+gSHqa55hvwV4I3idYpzjBMJTrxT4mOiLRofr+f5557Dr/fD8Chhx5KcnJyjKMSiS8Zhx5C+rBXKZ07j2BZab3Pq/jxJzZNm0bJp5+y7NNPI/tdyclkjxlD20svwZOdHYWIRcJSc6BNX9i4FNZ9B52HkJmUidflpbiqmMpAJUkefTEWr5ToiEiLVlZWxiOPPBLZvvDCC0lPT49hRCLxyeX1kj7soAadk3nkkWSfdiob7rmHLa+8SqiyEoBQRQUFTz3FljfeoM3EiST16A6AJyuL1MGD46q3Z8KECbEOQboc4CQ6q+ZC5yG4XW5yUnLYULaB/PL8SBU2iT9KdESkRSsuLubrr53F4JKSkjjttNPweDwxjkpEqnlzc+l44410vPHGyL7yJUtYd9sUSmfPZv0dd9Q6PvuMM+hw4w1xk+xMmzYt1iFIl6Gw4L9OQYKDJgGQm5KrRCcBKNERkRaroqKChx9+OLI9atQoAoFADCMSkfpI6dePbo8/RvEHH7Dl9TcIVVVBKETJF1+w+fnn8XXuTJuJ6gmRRtLlQOd+5deRXbkpKkiQCJToiEiLFQgEePLJJyPbkyZNIjMzM4YRiUh9uVwuMkeMIHPEiMi+wnfeZfUf/sCGu+7C17EjWb89IYYRNo558+YBMGRI/dcukkbWbm/wpcPmFVC8HjLakZOiEtOJwB3rAEREdtfy5cv56aefAMjKyuKII46Im+EuIomo1XHH0u7aawBYc911LD3sMJYedhjLfnsipfPnxzi63TN06FCGDh0a6zASm9sDnQc7j8NlpiM9Olo0NK4p0RGRFqmkpIQHH3wwsn3qqadSVVUVw4hEpDG0Pu88Wk+4CIJBAhs2EtiwkYoff2TlxZdQsWxZrMOTlqprePjaKmf4WutUlZhOBEp0RKRF8ng8PP/885Htiy++WMPWROJEu6uuou+XX9D7k4/p/fHHZIwYQXDLFlZOmIh/w4ZYhyctUdu9nfuCFYDm6CQKzdERkRZpzpw5bAh/4OnSpQv9+/ePcUQi0pg82dlU10/s/K87WHHueZR/+y2/TJhI5lFH1fs63nbtyDrlZNxaRDixpbdx7kucvxtKdBKDEh0RaXEKCwt56KGHIttnnXUWoVAohhGJSDS5U1Pp+uADLB97JhVLllCxZEmDzi+dM4dOd/wTl1sDWRJWelvnvmQjoEQnUSjREZEWx+fz8dprr0W2J06cSFpaWuwCEpGo87ZuzV5PP82WN14nVFFZv5OCAfKfeJLCGTPwdepEu6uujG6Q0nxVJzqlSnQSiRIdEWlRgsEgs2bNorS0FIB9992X9u3bxzgqEWkKvvbtaDOhYevrpA4ewspJk9g0fTq+zp3IGTs2StFJs5bmJDaUboJgsFaiEwqFVLEzTqkPV0RalOLi4lorjZ933nn4fL4YRiQizVnGoYfQ8e9/B2Dt3/9B0QcfNunrz507l7lz5zbpa0odPD5IzYFQEMoKSPWmkuJJoSJQQam/NNbRSZQo0RGRFuf9998HnAUHzzvvPJI0yVhEdiL71FNoc+mlEAyy+qqrKPvuuyZ77SFDhmix0OYiMk9nAy6XS2vpJAAlOiLSYlRVVfHf//6XQCAAwOGHH67eHBGplzaXXUrWyScTKitj5eSLqVy5MtYhSVNL20HltQolOvFKc3REpMWoqKjgsccei2xfeOGFpKenxzAiEWkpXC4XHf9+E/516yj54gtWjD+HFGOi/rrXfvIxnqwsnvjkE5W4jrXqEtPVBQlS1aMT75ToiEiLUVhYyJw5cwBITk7mlFNOwePx7OIsERGHy+ej8z3/YcXZ46lYsoTitWuj/prPWqcU9pTr/08lrmMtspaOKq8lCiU6ItIilJeX8/DDD0e2R40ahd/vj2FEItISeTIy6P78c5R+/TWhpmhDwoubOiWuO9Luqqui/5pStxpzdECJTiJQoiMiLUIoFCI3N5e99tqLFStWMHnyZFq1ahXrsHbumGPg669BE5FFmhV3cjIZhx3WtC/q8bBp+sO4kpJJ6b9Po146qXsPknv2aNRrxiUtGppwlOiISIuQmprKhRdeyIUXXsjSpUsxxjT/dQ8mTXJuIpLwOv797/z65z+z8f77G//iLhdZp5xMuz/8AW/bto1//XiR1tq536ZHZ1P5plhFJFGmREdEWozU1FQABg4cSDAYjHE0IiL1l33qKeBxU/TOu4163ZDfT8mXX7Ll5VcomvU2mcccgytp59Uok/fem5zTTsOVaMURqnt0Sp3ERj068U+Jjoi0SG5N6BWRFib7pJPIPumkRr9uxc8/s/6Of1H8wQdsef31ep1T8MSTtLv2GjKOPLL59443lvS6y0sXlBfEKiKJsqglOsaY0cBtQDLwLXChtbZwm2MGAPcCWUAAmGStnRetmEREpG5qs0WiY/DgwVF/jeQePej6wP2Uzv+Gip9+3OmxoYpKCp59lsply1h1yaWk/WYY7a+7rklKbcecihEknKgkOsaYtsBjwCHW2h+NMbcDU4BLahyTBryL88d0pjHmd8B/gX7RiElEROqmNlskeubNa7rvAtIG70/a4P13eVzO2DMoeO55Ntx3H6VffsXPJ59C9mmn0fb3V+Bt3boJIo2R1BxwuaGsAAL+Wj06wVAQt0sjBeJNtH6ixwJzrLXVXys8CJxljHFtc0yetXZmePsNYEyU4hERkR1Tmy2SQFw+H7njz6b3O2+TM348uFxsfuEF8o4byaZHHiFYWRnrEKPD7YHwIqGUbsLn8ZGZlEkgFKCwonDn50qLFK2ha12BlTW2VwGtgEyg+jepL7DWGPMIMBDYDFxT18WMMROBiVGKVUQk0anNFklAnuxsOvz5/8gZewbr/vlPSj7+hPV3/IuC556n1ehRuNzbL8icceSRpA7YNwbRNpL0tlC60Rm+ltme3JRciiqLyC/PJzslO9bRSSOLVqKzo56iQI3HPmAUcKS1dnZ4GMRMY8xe1tqKmidZa6cB04wxoeiEKyKS0NRmi0RJ9UT/UKj5/ndI7tWLblOnUvzpp6ybcjuVeXlsemhqncduevhhuj32KGktdX2w9DawASfZwZmns6JwBZvKN9GTnrGNTRpdtBKdX4CDamx3BgqstSU19q0BllhrZwNYa183xjwM9AQWRykuERHZntpsESHjsMNI/81vKJwxg8qVq7Z7vsIuoei991l5yaV0f/YZknu2wMQgUnlNi4YmgmjN0XkXGGaM6RPengxsW+9wFtDdGDMEwBhzOBACfo5STCIxM2XKFMaPH8/IkSM54ogjGD9+PFdccQWzZ8/mj3/8Y6O9zvjx48nLy6vXsddddx2ffPJJrX0VFRUcddRRDX7dYDDI3/72N8444wzGjx/PihUrtjvm448/ZsyYMZx++unceOONtb7dzMvLY8iQIVRUOB0DCxYs4PTTT2fs2LHcd999DY5HGkxttogA4PJ6yfrd72h72aXb3Tr/+99kHHkkwS1bWDlhIv4NG2IdbsPtoPKaSkzHp6gkOtba9cD5wEvGmMXAAOAqY8xQY8yC8DFrgZOAB4wx3wN3A6dYa8ujEZNILF133XU89dRTTJw4kRNOOIGnnnqKe+65J9ZhNZr333+fyspKnn/+ea666iqmTJlS6/ni4mLuuOMOHnroIV588UU6d+5MQUFB5Lnbb7+dpBoL191www3ceeedPPvssyxcuJBFixY16ftJNGqzRaQ+XB4Pne/8FykDBlC1ejXLx55J4bvvNuthedtJU49OIonaOjrhyjwzt9mdDwyqccwn1B4uIZJwVqxYwUUXXUR+fj5HHnkkl19+OePHjyc3N5ctW7Ywbdo0brzxRlasWEEwGOQPf/gDBx10EHfffTezZ8/G7/dz7LHHMnGiM/f7/vvvZ+PGjZSVlXHXXXfRtWtXpkyZEilxesIJJ3DuuedGXr+kpISrr76awsJCunXrtl18JSUlTJ48uda+gw46iMsuuyyyPW/ePA477DAABg0axPfff1/r+G+++Ya+ffty++23s3LlSk4//XRyc3MJhUL89a9/5corr+SSS5xKxsXFxVRWVkZiOfTQQ/niiy/YZ5999vSfWnZCbbaI1Ic7LY2uDz3ILxdcSIW1rL7i96QdcACtRo8Ctll41OUibegQknv1ikmsddpm0dCclBwgfnt0jDFHAC8Ai3B64VOB/1pr793N6z0HnGOt3a40nzHmPCDfWvvGbgfcyKKW6IhI/VRUVPDAAw8QCAQ44ogjuPzyywEnITnmmGN45plnyMnJ4dZbb6WgoICzzz6bGTNm8Oabb/Lkk0/Srl07Xnnllcj1hg8fzu9+9zvuvfde3n77bXr37s2qVat44YUX8Pv9jBs3jmHDhkWOf+655+jbty9//OMfWbhwIbNnz64VX3p6Ok899dRO30NxcTEZGRmRbY/Hg9/vx+t1mpiCggJmz57Na6+9RlpaGmeddRaDBg3irbfeYvjw4fTr12+H10pPT2flypoFwUREJJa8rVvT4+WXKHjhBTbecy+lc+ZQOmdO3Qe73WSPOZ22V1yBNze3aQOtS/XQtdJNAPjcPgCqglWxiqgpfGCtHQtgjEkGrDHmKWvt5oZeqPo6O3ju8d2OMEqU6IjEWJ8+fSLDtqoTA4AePXoAsHTpUubNm8e3334LgN/vJz8/nzvuuIM777yTjRs3RnpTAPbd1yn72aZNGzZu3EheXh5Dhw7F5XLh8/kYOHBgrXk8y5cvZ/jw4QAMHDiwVgxQvx6djIwMSkq2zlsPBoO1rpOdnc2AAQNo29b5AzN06FAWL17MG2+8QYcOHXj55ZfZsGEDF1xwAVOnTq11rZKSElq1alXvf08REYk+l9dL7rhxZI0eTf5//4t/7brtjgkUFVL07ntsfu55Ct+aQeqQwbjq6PVJP+QQcs4Yg6vGEOao2aZHx+t2/lYFQoEdndFoul83YwZO9crGNHP5lNGjG3B8Jk5FzfeNMcuAXGA08ADQB2day1+stR8ZY04AbsDpqpuPM39zGc5C0aOAa4EqnGI1Y4G/AWuttQ8ZY+4EDg2/5jPW2v8YYx4HKoDuQEfgPGvt/N194/WhREckxqpLj+5of8+ePenQoQOTJ0+mvLycBx98kIyMDN5++23uuusuAEaNGsXoHbRzvXr14pVXXuG8886jqqqKb775hpNPPrnW8wsWLODoo49m0aJF+P3+WufXp0dn8ODBfPjhh4waNYoFCxbQt2/fWs/379+fpUuXkp+fT6tWrVi4cCFjxozhvffeixxz1FFH8eijj5KcnIzP5+OXX36ha9eufPbZZ7WSKhGRlmTq1LrLNMcLT1YWbcNDj+tSkZfHuttvp+STTyn5+JM6jyn+6CMK/vtf2l17DRlHHLHDv4uNYptiBNWJjj/o39EZ8eAoY8xHQBAnMbkcZx20Z621rxpjLgY2WmsvNMa0Bj4xxgwE7gMOtNauN8ZcA3Spcc0zgTustS8ZY87BWXsNgHCC1AMYhpNrfGaM+SD89Apr7SRjzASc9dZqf5PayJToiDRzY8eO5S9/+Qtnn302xcXFjBs3jqSkJLKyshgzZgwpKSkccsghdOrUqc7zjzzySL7++mvOOOMMqqqqGDlyJP379488f+aZZ3LNNddw5pln0rNnT3w+X4NjPOaYY/j8888ZO3YsoVCIW2+9FYDHHnuMbt26MWLECK666iouuugiAEaOHLldMlTTTTfdxNVXX00gEODQQw9l4MCBDY5JRKQ5qJ4/maiSe/Wi27RplH33Pf6N21dpC2zewqaHHqJy+XJWXXwJueeeS/vrr4teQGmtnfsSZ+iax+UsihoIRr9Hp4E9L43pg22HnIUTFxveHAAcZoypnoPpBTrgLDOwHsBa+8/wedWXuBK43hhzOc4SA6/VuPzewKfW2hBQZYz5CqieaPtN+H4lcEijvLudcLWkShnVi89Za3d1qIhIs1L9x8FaG8WvKpsXtdkiUh+hykoKnn2Wdf+6E/x+ur/wAqkD9o3OiwWDcHNbCPrhL+t5Z9VHXP3x1Ryz1zHcdcRdtQ6Nh3Y7XIxgch2Jzkfh/UuMMVcAGdbaW40xqcCfcYah5QFDrLX5xph7gKdxChv0Cx9zb7i3ZyrwBU4vzlpgFXC+tfZUY4wv/Nwk4ArgOWvt28aYkcBYa+150Xz/0VpHR0RERCThTZs2jWnTpsU6jGbNlZRE7rnnknvOeAiFWHfLLYSCwei8mNtdq8S015UQQ9d2ZSrQzxjzMU5SssJaGwQuAWYYYz7DmadTs+LE18Bbxpj/4fT+vFX9hLX2LeBnY8yXwFfAS9Gei7Mj9erRMcYMwhlHl1K9z1p7QfTC2mEc+nZQRFqkePhmsKHUZotsnW/ZkkbQxEqguJi8448nsGEjnW6fQtbvfhedF3rwEFj3PUz6hI/9m7nsg8s4rPNhPHD0A7UOS8R2O97Ut0fncZxqC8/XuImIiIiINApPRgbtrroKgHX/+heB4pJdnLGbalRe87jDc3SaoOqaNL36FiNYa619OKqRiIiIiEhCyzrxRAqefZbyhd+y5rpr6fC3v+Fr165xXyRSeW0T3vS9gIQfuha36tujs9wYc50x5jhjzLHGmGOjGpWIiIiIJByX202Hv/4NV3Iyxe//j7yRx7PxoYcIVlQ03ouk1ejRCVddU6ITn+qb6CQDBmcxoDPD9yIiIiIijSp13/70fP01MkaMIFRayoZ//4df//rXxnuBGkPXfG5nSQV/SIlOPKpXomOtPR+4E5gF/CcWhQhEREREJDEkde9O1/vvo9tjj4LPR+FbM6hctbpxLl6d6JRubNJ1dKTp1SvRCS8GNB04GJhmjLk6qlGJiIiISMJL/81vyBp1PASDFPz3v41z0epFQ0vz8bpVXjqe1bcYwTjgMGutv8bCP/+KXlgiIrK7jDGZwLVAJ5y1Db611v4U26hEEpPKSu+5nPHnsOX1N9j84ou0ufRSPBnpe3bB6jk6pZvivupaeMHQF4BFQAhoBSwDzrLWVu7BdbvjLP45zBizHOhnrS3f44AbWX3n6ListX4Aa20VUBW9kEREZA89ivOHrA/OKtWPxDYcEZHdl7pvf1KHDiFYXMyWV1/d8wtGenQ2JUqPzgfW2iOstUdaa4fgfI4/MdZBNYX69uh8Zox5CfgUOAz4PHohiYjIHmptrX3UGHO2tfYLY0x9v9QSEWmWcs85h9Vz55H/1FPknDUOl3sPmrWaiY6rCROdG7NmAKMa+aozuXHL6PoebIxJAjoCBcaY23A+13uAu6y1LxpjDgL+jdMZsho4CzgQuCG8LwNnpNdu9wY1pfoWI7gaeAzwAY9aa/8U1ahERGSPGGP6he+7AHH9VaVIczZkyBCGDBkS6zBavMwRI/B17kzVL79Q/NFHe3ax1GzABWWb8YZHFsZ51bWjjDEfGWMWAfOBV4EkoIe19lDgSODPxphsYCpwgbX2IGAGsDfQHzjbWnsE8ApwetO/hd2z0x4dY8wJ1tq3jDETw7sKgS7GmInW2mnRD09ERHbDFThfTu0NvARcEttwRBLX/PnzYx1CXHB5POSMP5v1U25n3W1TSB04EG/r1rt3MbcHUnOgLB9PVQnQRFXXGtDz0sg+sNaONca0Bt4DfgYGAEOMMR+Fj/EB3YEO1trFANbaRwCMMV2Be4wxxUBnWtDIrl316FT/BnWs4yYiIs3TSGvtb6y12dbaYdZafdISkRYv54wzSNlnH6pWrmTlJZcQLCvb/YuFh695y4uAuJ+jA4C1dhNwNvAwsA74MNxLcxROwYI8YI0xpg+AMeZaY8zJOJWXz7fWngesAVxNH/3u2WmiY619Inx/E854vbtwJrjeE/XIRERkd40yxnhiHYSISGNyp6bSdepD+Dp1onzht6y++k+EArvZE1Od6JRtBuJ+6FqEtXYRzuf4E4BiY8ynwDwgZK0tAiYBjxpjPgb2B2YCTwOfGmM+BzJxKnq2CPUqRmCMeQ6nROnBOMnRKcDJUYxLRER2X1ucb+V+xiknGrLWHhzjmERE9pi3bVu6Tp/G8jPHUfy//7HykkvocP31JHXv3rALRXp0tgDx26Njrf0I+Gibfbfs5Pg5OAUKarpyB4cPC5/TfbcDjLL6lqzoZK19GtjbWjsZJ5sTEZHm6QScKjlnAGOBM2MbjohI40nu1Yuu99+HOz2dko8/Ie+3J7Lu9n8SKC6p/0XScgHwlBUATTRHR5pcfROdJGPMKcAiY0wblOiIiDRnAZxFnWfiDDtuMeOpRUTqI+2AA+g5ayZZp54Cfj/5jz3GmquvbsAFEnPoWqKpb6LzT5xvBW/Dqebzj6hFJCIie2o68BRwCPAEWjBUJGYmTJjAhAkTYh1GXPK1a0enW26h+4sv4s7IoPijjyiqb+npcKLjKXV6dIKhIMFQMEqRSqzsNNExxlTP4XkLp0rDeuBm4N0oxyUiIrsvxVr7hrV2s7X2NZyyoSISA9OmTWPaNK3IEU2p+/anzaWXArD+tikEK+uxlmU40XGV5UcWDdXwtfizqx6dJ8P3FliyzU1ERJonrzFmAED4PhTjeEREoir3rHEk9exJ5YoVFDz55K5PCCc6lG7C43aKVGr4WvzZVXnpceGHPYHh1tqewBnhexERaZ6uwCkPugpn2NrvYxyPSMKaN28e8+bNi3UYcc+VlET7//s/ADY+8CAlX39N2XffU75kSd0lqNPbOPelm/C6nR6deK28lsjqVV4aeBD4CWdy69nGmLOstX+IWlQiIrInFgETrbXfGGNOAn6IcTwiCWvo0KEAhELqWI22jEMPIeOooyj+4AN+OefcyP7WEy6i3VVX1T44XHWN0k14Mp0aW/E4dM0Y0x34Fqi5cPQH1tq/G2N6A69aawfs4NxzgXNxCtokATdZa1vU9JX6JjqDw2Wlsdb+3hjzSRRjEhGRPfNfYAbwDdAXGAOM2+kZIiJxoMNf/syaslKChUWE/H4qrGXLjBm0vfJKXK4aBSgjQ9fy8bpzgLgeurbIWntEzR3GmPE4vf1t6zrBGJMF/BXYx1pbaYzpBHxtjOlmrW0xVRvqm+hgjGltrd1kjMluyHkiItLkOltrHwOw1v7TGPNhrAMSEWkKvk6d2OuxxwAIBYP8eNjh+Nf8SsWPP5LSt+/WA5NbgdsLlUV4XeE5OlEeujbgiQEzgFGNfNmZ35373ejdOK8AGA7k7eD5CpxenIuNMW9Za/OMMb2stUFjTB/g4fDzpTiVmdOBR3FyhBBwhbV2oTFmBc7c/kXAXcA0IBUowxl5sHI3Yq+3+paX/jsw1xgzH5gH3BS9kEREZA+FjDF9AcJDEzwxjkdEpMm53G4yDjsMgOKPP97mSdfWtXRczsfhOJ6js48x5qMat87W2restTtcYdVaWw4cBfQB3g4nLBeEn/4XcJu19jfAf4D9w/v+Y609HKenqHpZg67AOGvtH8PH3BPuXfoXMKXR3+k26tUzY619yxgzC+gArLHWaqCpiEjz9QfgeWPM3jjzcybGNhwRkdjIOGI4W157jZKPP6HNtusZpbWG4nV4wp9qA6HoztHZzZ6XxrDd0LW6GGPeAjKA73DWzky11l4Wfq4vTsLzGWCALwGstW+En78b+CS8b4Expmv4shuttZvCjwcA/2eMuRZn3k9V47y9HatXj44xZjiwEHgfuMkYc2FUoxIRkQYzxgw2xnyDMzfnH0AhkAl0jmlgIiIxkn7wweDxUPrNNwS2bKn9ZHWPTngzjnt06sVae4K19ghr7eU4nRtPG2Myw0+vADYClcBi4AAAY8xZxpjLw/sOC+8bBKwNn1dzPs8S4Npw0jUJeDGqb4j6D137B3A4TtC3ApdELSIREdlddwDnWmurcBZ3HgkMBa6NaVQiIjHiadWKtMGDIRCg5PPPaz8ZrrzmCVfES/REpyZr7XzgXuATY8wXOL01D1trLfAn4HpjzEfAWTgFcK4GLg8XLHsQqKtT5GrgBmPMxzhrdX4b7fdR36ICQWttvjEmZK0tN8YURTUqERHZHR5r7bfh6jjp4T9UGGNaTIUckXgzd+7cWIeQ8DKOGE7pnDkUf/wxrUbVqAUQ7tHxVSc6cVh1zVq7HBi2k+c77OS5h3GKDmy7/ydgxDa784FjdnZ9a+0y4LhdBt2I6tuj85Mx5jagtTHmOpzuKxERaV6qxzuPxBlqjDHGhzN8TURiYMiQIQwZMiTWYSS0jOHDASj+5NPai4eGEx1PyPkuKB7X0Ul09U10LsFJbj4DSoAJOz9cRERi4H1jzOfAjcC9xphewBvA8zGNSkQkhpJ69cLXuTOBggLKv/tu6xPVc3TCCY6GrsWf+g5de8tae2xUIxERkT1irb3dGPMGsMVauyac6Eyz1r4a69hEEtXEiU7Rw2nTpsU4ksTlcrnIGD6cgmeeoeijj0gdNMh5YptEJ9pV16Tp1TfRKTDG/A6whKsnWGuXRi0qERHZLdbaxTUe57HjxeBEpAlMnz4dUKITaxkjjnISnVlv0/b3v8flcm0tRhDuyakKRr3asTSxXSY6xphWQE+cdRmqhXAWERIRERERadbSDzoIT+vWVK5YQfkPi0jdtz+ktQHAG6gCj+boxKOdztExxlyGs35ODnC7tfbI8E1JjoiIiIi0CC6vl1bHHw9A4VtvOTurh64FKgHN0YlHu+rRGYez+mkr4Cng7ahHJCIiIiLSyLJOGE3B009TOHMm7f50Na7qRMdfCUkpcTtHxxgzBLgNSMPp5PgQuMlaW9lEr/84MBinBLULaA3caa19bCfnnAzMttau2ZPX3lXVtXJrbaW1diOQtCcvJCIiIiISKykDB+Lr0gX/+vWUzpkLSWngTcUTx1XXjDFdgKeBy6y1hwKHABXA3U0cyjXW2iOstcOBw4FbjTGunRz/e5yOlj1S32IE4GRgIiIiIiItjsvlotUJo9n00FQKZ7xF+rCDIK01XsqA6C8Yurjf3jOAUbs8sGFm7r1k8eidPH8O8HB1ETFrbcgY8w9gmTEmFZgFTLbWLjHGTAY6WGtvNMZcjjOyKwQ8Z629J9wz85y19m1jzEhgrLX2PGPM6cCVQAD4zFp73S5i7oDTmRIyxuwL3AV4gDbAxThTZgYBTxpjDgUmbRtLff9xdtWj098Y84wx5tkaj58xxjxT3xcQERERSVSDBw9m8ODBsQ5DwrJOOAGAwnfeJVhZCWm5eEMhID57dIC9gGU1d1hrQ8A6nIRjO8aYfYAzgEOBw4CTjDFmB8fmAjcBI8I9Rp2NMcfUceg/jTGfGmN+wUlsTg/v7w9cZa0dAdwOnG+tnQEswEnSetc3lrrsqkdnTI3HD9X3oiIiIiIC8+bNi3UIUkNy794k9+tHxZIllHz6KZlprfEWrgKiX3VtFz0v0bICp3pyhDHGDXQD1m9zbPXorX1xEqT/hbdzgD47OLY30BaYGc4/MoFewHvbHH9NuCdoFE5CU730wWrgr8aYsvC5hduct6NYbN1vt7adJjrW2o/rcxERERERkZag1ahRbFiyhOKPPyFzv9Z4tsR1j85TwLvhxaQ3AC8Aq4C3rLUlxphyoCOwBKdgwGqcJOIH4Pjw8LI/At8CJ4SPJXwswM/ASuAYa22VMeY8nN6YOllrZxpjfgNMw+nVuQc4y1q72BhzE9A9fGgQZ+TZjmKpl10NXRMRERERiRsp/ZyRT1WrVjpzdJw8J+pzdGLBWrsSOBu4D5iB0/vSEUgKDzu7B3jAGPMOzjwZrLULcXpQPjPGzMXpQVkNPAz80RjzPtA5fOwGnKFoHxtjZgPHA0t3EdY/gH2MMaNxCiW8aIz5FOgLdAof8wXwJE4SVVcs9eIKhccltgTGmBCAtfXqrRIRaTaqhxRbaxOmsIvabBFnAjxAS/q8Fe8q8vJYNvoEfHt1o/f1R3H7tw/wdFYrrh56Nef2PzdyXDy328aY/YBl1triWMcSTQ2putYg4SztNiAZp4vpQmvttuPuqo89CXjSWrvHZeRERKTh1GaLSKLwdXI6DarW/EooORtfdY9OfA5dq5O1tt7Dv1qyqAxdM8a0BR4DTrXWGpxqD1N2cGwf4F/RikVERHZObbaIJBJ3aiqeNm2gqgp/cSUenEwnXhcMTWTR+kN1LDDHWvtjePtB4KxtFwYyxqThjM27MkpxiIjIrqnNFpGE4usc7tXJL906RyeBenQSRbQSna44k4eqrcJZ3TRzm+Omhm8J0X0mItJMqc0WkYSS1LkzAFWbiiM9Okp04k+0Ep0dXTfSJ2iMuQTwW2sf3dXFjDETw5UWRESk8anNFpGE4qtOdDYWxXXVtUQXrUTnF7bW2QanBF2Btbakxr7zgAOMMQuAmUCqMWaBMaYT27DWTrPWDo1SrCIiiU5ttogklK2Jzha84Yp40V4wVJpetKquvQvcaYzpEx7zPRl4veYB1toDqx8bY7oD31trB0UpHhER2TG12SJRMnXq1FiHIHWoTnQq1xXg7e7s09C1+BOVRMdau94Ycz7wkjEmCcgDzjHGDAUe1h9HEZHmQ222SPRMnDgx1iFIHSI9Ouvz8YRUdS1eRW0dHWvtTJzhDTXlA4PqOHY5kBGtWEREZOfUZotIIomspbMhH29QxQjildZBEBEREYmSadOmMW3atFiHIdtwp6Q4a+n4A6SUOpX0lejEHyU6IiIiIlEyadIkJk2aFOswpA7Va+mkFIUTHVVdiztKdEREREQk4VSvpZNU5HwcVtW1+KNER0REREQSTnVBguRwoqOha/FHiY6IiIiIJJzqRMdbHE50NHQt7ijREREREZGE4+vcxblXj07cUqIjIiIiIgmnukfHU6w5OvFKiY6IiIiIJBxfp44AuEvcuIIhDV2LQ0p0RERERKIkFAoRCoViHYbUoXotHVfQRW4xBDR0Le4o0RERERGRhFRdYrrtFvAHqmIcjTQ2JToiIiIikpCq5+m03RzCH1SiE2+U6IiIiIhEyZAhQxgyZEisw5AdqE502m1BiU4c8sY6ABEREZF4NX/+/FiHIDuR0r8/AAG35ujEI/XoiIiIiEhCyjz2GJJO9/DGMJfW0YlDSnREREREJCG53G6S2icTdCvRiUdKdEREREQkYXk9PgCtoxOHlOiIiIiISMLyeJIACAQDMY5EGpsSHRERERFJWJEeHQ1dizuquiYiIiISJRMmTIh1CLILkR6dUDDGkUhjU6IjIiIiEiXTpk2LdQiyCz53MoTAH9LQtXijoWsiIiIikrCqe3SU6MQfJToiIiIiUTJv3jzmzZsX6zBkJ7zeZAD8GroWdzR0TURERCRKhg4dCkAoFIpxJLIj7nCPTogQwVAQt0v9APFCP0kRERERSVgubzLecCKqymvxRYmOiIiIiCQuT5ISnTilREdEREREEpcnKTKXwx9SohNPlOiIiIiISOLybu3RCQRVeS2eKNERERERkcTlScITrhWhoWvxRYmOiIiIiCQuTxJewj06Wksnrqi8tIiIiEiUzJ07N9YhyK54kvCGe3SqglWxjUUalRIdERERkSgZMmRIrEOQXanZo6M5OnFFQ9dEREREJHF5kzVHJ04p0RERERGJkokTJzJx4sRYhyE74/FtrbqmOTpxRYmOiIiISJRMnz6d6dOnxzoM2RlPEp7wQ/XoxBclOiIiIiKSuDxJ+MI9OlowNL4o0RERERGRxOVJwhMuRqAenfiiREdEREREEpc3OVJeWlXX4osSHRERERFJXB6fenTilBIdEREREUlcNRYM1Ryd+KIFQ0VERESiZPDgwbEOQXbFkxwpL60enfiiREdEREQkSubNmxfrEGRXPL7IB2KtoxNfNHRNRERERBKXNxmPenTikhIdEREREUlcnqRIj44SnfiiREdEREQkSlwuFy6XK9ZhyM54fOrRiVNKdEREREQkcXmS8YUfao5OfFGiIyIiIiKJSz06cUuJjoiIiIgkLm9yZB0d9ejEFyU6IiIiIpK4PEl4UI9OPFKiIyIiIiKJy+2N9OhUBSpjG4s0qqgtGGqMGQ3cBiQD3wIXWmsLtznmbOBPQAgoBa6w1s6NVkwiIlI3tdkikrBcLrwu57v/QKAixsFIY4pKj44xpi3wGHCqtdYAy4Ap2xxjgDuAkdbaQcDNwCvRiEdERHZMbbZI9EydOpWpU6fGOgzZBa/LA4BfPTpxJVpD144F5lhrfwxvPwicZYypWUi+ArjIWvtreHsu0MEYkxSlmEREpG5qs0WiZOLEiUycODHWYcgueCI9Okp04km0hq51BVbW2F4FtAIygUIAa+1yYDlA+I/pXcAb1lr9homINC212SKS0Lxu5yOx36+ha/EkWonOjnqKtqvZZ4xJBx7H+UM7sq6TjDETAX0dIiISHWqzRaJk2rRpAOrVaeYiQ9eCVTGORBpTtIau/QJ0rLHdGSiw1pbUPMgY0w34AueP6ZHW2s11XcxaO81aOzRKsYqIJDq12SJRMmnSJCZNmhTrMGQXvG7N0YlH0Up03gWGGWP6hLcnA6/XPMAYkwt8DLxirR1rrS2LUiwiIrJzarNFJKF5XM4gp0BQiU48icrQNWvtemPM+cBL4YmqecA5xpihwMPhij0XA92Ak40xJ9c4fYS1dlM04hIRke2pzRaRRLe1R0dD1+JJ1NbRsdbOBGZuszsfGBR+/hbglmi9voiI1J/abBFJZB63D1CiE2+iNXRNRERERKRFiFRdUzGCuKJER0REREQSmjfcoxMI+mMciTQmJToiIiIiktCqEx316MQXJToiIiIiURIKhQiFQrEOQ3bB466uuqYenXiiREdEREREEprXkwRAlRKduKJER0REREQSWmSOTkiJTjxRoiMiIiISJUOGDGHIkCGxDkN2weupnqOjRCeeRG0dHREREZFEN3/+/FiHIPXgcTtD1wKhQIwjkcakHh0RERERSWhbe3SU6MQTJToiIiIiktC83mQA/OrRiStKdEREREQkoXk9SnTikRIdEREREUloHk/1HJ1gjCORxqRER0REREQSmnp04pOqromIiIhEyYQJE2IdgtSDJ5zoqEcnvijREREREYmSadOmxToEqQdfuBhBlRKduKKhayIiIiKS0LzeFAAChGIciTQmJToiIiIiUTJv3jzmzZsX6zBkFzzhRMevHp24oqFrIiIiIlEydOhQAEIh9RQ0Z16PenTikXp0RERERCShRXp0lOjEFSU6IiIiIpLQfD4lOvFIiY6IiIiIJLStPToaZhhPlOiIiIiISEJze1NwhxOcoAoSxA0lOiIiIiKS2DxJeMIP/SF/TEORxqNER0REREQSmycJb7hHJxAMxDgYaSwqLy0iIiISJXPnzo11CFIf3mS84ak5VcGq2MYijUaJjoiIiEiUDBkyJNYhSH14fHjDFdcCIfXoxAsNXRMRERGRxOZJwhPu0fEHNUcnXijREREREYmSiRMnMnHixFiHIbviSdrao6M5OnFDiY6IiIhIlEyfPp3p06fHOgzZFfXoxCUlOiIiIiKS2LzJ+MJV11ReOn4o0RERERGRxOb24gkPXfP7K2McjDQWJToiIiIikthcLry4AAgEymMcjDQWJToiIiIikvA84UTHX1UW40iksSjREREREZGEV92j4w9UxDgSaSxaMFREREQkSgYPHhzrEKSevK5wouPX0LV4oURHREREJErmzZsX6xCknrzhgU4Bv4auxQsNXRMREWkGpkyZwvjx4xk5ciRHHHEE48eP54orrmD27Nn88Y9/bLTXGT9+PHl5efU69rrrruOTTz6pta+iooKjjjqqwa8bDAb529/+xhlnnMH48eNZsWLFdsd8/PHHjBkzhtNPP50bb7yRULjcL0BeXh5DhgyhosIZVrRgwQJOP/10xo4dy3333dfgeES25Yn06GjoWrxQj46IiEgzcN111wHwyiuvsGzZMq6++moAZs+eHcuwGs37779PZWUlzz//PAsWLGDKlCk8+OCDkeeLi4u54447ePLJJ8nNzWX69OkUFBSQm5tLcXExt99+O0lJSZHjb7jhBu699166du3KxIkTWbRoEfvss08s3prECadHJ0BAc3TihhIdERGRZm7FihVcdNFF5Ofnc+SRR3L55Zczfvx4cnNz2bJlC9OmTePGG29kxYoVBINB/vCHP3DQQQdx9913M3v2bPx+P8ceeywTJ04E4P7772fjxo2UlZVx11130bVrV6ZMmRIZZnXCCSdw7rnnRl6/pKSEq6++msLCQrp167ZdfCUlJUyePLnWvoMOOojLLrsssj1v3jwOO+wwAAYNGsT3339f6/hvvvmGvn37cvvtt7Ny5UpOP/10cnNzCYVC/PWvf+XKK6/kkksuAZykqLKyMhLLoYceyhdffNEkiU5RURGZmZn1Pt4V7iWo2TslzZPH5SQ6VZqjEzeU6IiIiDRzFRUVPPDAAwQCAY444gguv/xywElIjjnmGJ555hlycnK49dZbKSgo4Oyzz2bGjBm8+eabPPnkk7Rr145XXnklcr3hw4fzu9/9jnvvvZe3336b3r17s2rVKl544QX8fj/jxo1j2LBhkeOfe+45+vbtyx//+EcWLly4XS9Teno6Tz311E7fQ3FxMRkZGZFtj8eD3+/H63U+ihQUFDB79mxee+010tLSOOussxg0aBBvvfUWw4cPp1+/fju8Vnp6OitXrtyNf9mGmT9/Pv/+97+ZMmUKmZmZDUp4pPnzuTxAFYFAVaxDkUaiREdERKSZ69OnT2TYVnViANCjRw8Ali5dyrx58/j2228B8Pv95Ofnc8cdd3DnnXeycePGSG8KwL777gtAmzZt2LhxI3l5eQwdOhSXy4XP52PgwIG15vEsX76c4cOHAzBw4MBaMUD9enQyMjIoKSmJbAeDwVrXyc7OZsCAAbRt2xaAoUOHsnjxYt544w06dOjAyy+/zIYNG7jggguYOnVqrWuVlJTQqlWrev977q5AIMBTTz3FM888w7hx45TwxBmnR0dV1+KJEh0REZFmrnr404729+zZkw4dOjB58mTKy8t58MEHycjI4O233+auu+4CYNSoUYwePbrO6/Tq1YtXXnmF8847j6qqKr755htOPvnkWs8vWLCAo48+mkWLFuH3+2udX58encGDB/Phhx8yatQoFixYQN++fWs9379/f5YuXUp+fj6tWrVi4cKFjBkzhvfeey9yzFFHHcWjjz5KcnIyPp+PX375ha5du/LZZ5/VSqqiTQlPfPK6PAAEApUxjkQaixIdERGRFm7s2LH85S9/4eyzz6a4uJhx48aRlJREVlYWY8aMISUlhUMOOYROnTrVef6RRx7J119/zRlnnEFVVRUjR46kf//+kefPPPNMrrnmGs4880x69uyJz+drcIzHHHMMn3/+OWPHjiUUCnHrrbcC8Nhjj9GtWzdGjBjBVVddxUUXXQTAyJEjt0uGarrpppu4+uqrCQQCHHrooQwcOHC7YzZv3szXX3/d4Fh3ZNasWbW2lfDEF4/LAyHwK9GJG66WNDnOGBMCsNbGOhQRkQYxxgBgra37q/k4pDZbYu3TTz/l8MMPb7LX83g82yU8KkbQctz49BG8HNjE3/Y6kdOPuCUh2+14o3V0RERERBpBIBBg5syZPP/886Smpm43xE+at+qha+rRiR8auiYiIiJxqU2bNpx66qmNdr0lS5bwww8/1Plc69atufbaa7nkkktwu92RQgtTp05ttNeX6PK6nZ+ZEp34oURHRERE4lKXLl145JFHGuVaXq+Xb7/9loMPPrjW/m0TnNTU1FrPV69dJM2fx11djEDlpeOFEh0RERGJS41dFKBmOexdJTjS8nhdTpENf1CJTrxQoiMiIiJSTw1NcKZNmwaoZ6clqO7RUaITP5ToiIiIiNRD3759WbFiRYN6cCZNmgQo0WkJfG716MSbqCU6xpjRwG1AMvAtcKG1trChx4iISPSpzRbZtfT09FrD1yS+eDxOoqM5OvEjKuWljTFtgceAU621BlgGTGnoMSIiEn1qs0XqR0lOfItUXQuqLHi8iNY6OscCc6y1P4a3HwTOMsa4GniMiIhEn9psEUl4XncSAP6QEp14Ea1Epyuwssb2KqAVkNnAYwAwxkw0xsyNQpwiIqI2W0SEg/udRv+glyN7nxjrUKSRRKsPdkcJVKCBxwBgrZ0GTDPGhPY0MBER2Y7abBFJeL17HctzvY6NdRjSiKKV6PwCHFRjuzNQYK0taeAxdTLGNEqQIiICqM0WiZq+ffsC+n8gEgvRGrr2LjDMGNMnvD0ZeH03jhERkehTmy0iInHHFQpFZ2SBMWYUThnSJCAPOAfoCTxsrR20o2OstfkNeI251tqhjRx6s5VI7zeR3ivo/cazlvJe1WY3Pr3f+JVI7xX0fqXlilqdRGvtTGDmNrvzgUG7OEZERJqY2mwREYk30Rq6JiIiIiIiEjMtPdGZFusAmlgivd9Eeq+g9xvPEum97kqi/Vvo/cavRHqvoPcrLVTU5uiIiIiIiIjESkvv0REREREREdmOEh0REREREYk7Uau61liMMaNxypkmA98CF1prCxt6TEtRz/d7NvAnIASUAldYa+c2dax7qiE/N2PMScCT1tpWTRdh46rnz3YAcC+QhbPi/CRr7bymjrUx1PP9ngzcBASBAuAia21eU8faGIwxLuAx4Htr7b/qeD5u2qmdUZsdv202JFa7rTY7vttsULudCJp1j44xpi3OL+Cp1loDLAOmNPSYlqKe79cAdwAjw2tb3Ay80sSh7rGG/NzCCxT+i2b++7oz9fzZpuEsyvhPa+3+wD+A/zZ1rI2hnu83FXgaOCX8u/wGcE8Th9oojDF7A/8Dxuzg+bhpp3ZGbXb8ttmQWO222uz4brNB7XaiaO4N0LHAHGvtj+HtB4Gzwhl4Q45pKerzXipwvkH5Nbw9F+hgjElqwjgbQ71+buE/JE8DVzZxfI2tvr/LeeG1SsD5I1JnA9wC1Of9egAXzjehABlAedOF2KguxfmD+MIOno+ndmpn1GbHb5sNidVuq82O7zYb1G4nhOY+dK0rsLLG9iqgFZAJFDbgmJZil+/FWrscWA6RLte7gDestZVNGWgjqO/PbWr49m3ThRYV9Xm/fYG1xphHgIHAZuCaJoyxMdXnd7nYGDMZ+MIYswnnj+ghTR1oY7DWXgZgjBmxg0PiqZ3aGbXZ8dtmQ2K122qz47jNBrXbiaK59+jsKL5AA49pKer9Xowx6TjfQvQGLopmUFGyy/dqjLkE8FtrH22akKKqPj9bHzAKmGatHYoz7numMSY52sFFQX1+vgOAvwH7WGs7AbcAL8fpt2Xx1E7tjNpsRzy22ZBY7bbabEeittkQX21Vwmruic4vQMca252BAmttSQOPaSnq9V6MMd2AL3D+sx1prd3cZBE2nvq81/OAA4wxC4CZQKoxZoExplOTRdl46vN+1wBLrLWzAay1r+N8Y9azyaJsPPV5v8cBn9eYyHo/sC/QumlCbFLx1E7tjNrs+G2zIbHabbXZid1mQ3y1VQmruSc67wLDwpMaASYDr+/GMS3FLt+LMSYX+Bh4xVo71lpb1sQxNpZdvldr7YHW2n3Dkx5HAWXW2kHW2jVNG2qjqM/v6SyguzFmCIAx5nCcKk0/N1mUjac+73c+MNwY0z68fRLws7V2Y9OE2KTiqZ3aGbXZ8dtmQ2K122qzE7vNhvhqqxKWKxQKxTqGnTLGjMIp7ZcE5AHn4Hxb8nC4Ia3zGGttfkwC3kO7er/GmD8Dfwe+2+bUEdbaTU0a7B6qz8+2xrHdcco/ZjRxmI2mnr/Lh+NUaErHmcT8e2vtZzEJeA/V8/1eClwGVAL5wGXW2h9iEnAjMMY8TrhMqTFmKHHaTu2M2uz4bbMhsdpttdnx32aD2u141+wTHRERERERkYZq7kPXREREREREGkyJjoiIiIiIxB0lOiIiIiIiEneU6IiIiIiISNxRoiMiIiIiInHHG+sARKLFGHMEzkrki3DWNmgFLAPOstZW7sF1uwPPWWuHGWOWA/2steV7HLCISJwyxtwJDAE6AGk4bXF/4H/W2rGN9BofAZOttUvqcezjOO342zX2peAsANq9MeIRkdhTj47Euw+stUdYa4+01g4BqoATYx2UiEgisdZeZa09ApgCPBN+fHpMgxKRuKceHUkYxpgkoCNQYIy5DTgM8AB3WWtfNMYcBPwb5wuA1cBZwIHADeF9GcA4nIXSRERkz/UxxswC2gFvWmtvDPfMrAdygdHAA0AfnHb4L9baj4wxtwBH4nyOedlae3v4ejcYY9rjLOB5prV2Wbg36dDw889Ya/9T/eLGmAzgv0AO8FOU36uINDH16Ei8O8oY85ExZhEwH3gVZ4XjHtbaQ3H+UP7ZGJMNTAUusNYeBMwA9sYZWnF2+NvHV9A3kCIijSkFOAnni6fLaux/1lp7NHABsNFaezjwO+D+8PNn4XzxdBiwucZ5M6y1RwGzgNOMMScAPYBhOMnOOGPMgBrHTwa+D19/auO+NRGJNSU6Eu8+CCcph+H0xPwMDACGhL81fBvwAd2BDtbaxQDW2kestfNxenbuCY/nPjJ8rIiINI7vrbUV1tpSwF9jvw3fDwBGhdvrlwGvMaYNTqIzBXgHyK5x3rzw/VqcuUB7A59aa0PW2irgK2CfGsf3Bb4GsNbOxhneLCJxQomOJARr7SbgbOBhYB3wYTgBOgqnYEEesMYY0wfAGHOtMeZkYDpwvrX2PGAN4Gr66EVE4lZoB/uD4fslOL07RwDHAy8CRTi962fifAF1njFmrx1cbzHhYWvGGB9wMPBjjecXAb8JP78/+jJLJK4o0ZGEYa1dBNwDnAAUG2M+xfn2L2StLQImAY8aYz4G9gdmAk8DnxpjPgcygU4xCV5EJDFNBfqF2+UvgBXW2gogH6d35kPgXeCXuk621r4F/GyM+TJ8/Evh3vpqDwE9jTGfAZcCFVF7JyLS5Fyh0I6+TBEREREREWmZ1KMjIiIiIiJxR4mOiIiIiIjEHSU6IiIiIiISd5ToiIiIiIhI3FGiIyIiIiIicUeJjoiIiIiIxB0lOiIiIiIiEnf+HyvfSKJsveccAAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -425,7 +425,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "source": [ "print(f\"Accuracy = {bcm.accuracy_}\")\n", "print(f\"Balanced Accuracy = {bcm.balanced_accuracy_}\")\n", @@ -446,19 +446,19 @@ "output_type": "stream", "name": "stdout", "text": [ - "Accuracy = 0.935672514619883\n", - "Balanced Accuracy = 0.923481308411215\n", - "AUC ROC = 0.9853241822429907\n", - "AUC PR = 0.990226375695269\n", - "Precision = 0.9285714285714286\n", - "Recall = 0.9719626168224299\n", - "F1-Score = 0.949771689497717\n", - "F2-Score = 0.9629629629629629\n", - "F0.5-Score = 0.9369369369369369\n", - "Average Precision = 0.9890931713411104\n", - "Threat Score = 0.9043478260869565\n", - "Metrics Dict = {'Accuracy': 0.936, 'Balanced Accuracy': 0.923, 'ROC AUC': 0.985, 'PR AUC': 0.99, 'Precision': 0.929, 'Recall': 0.972, 'F-1 Score': 0.95, 'F-2 Score': 0.963, 'F-0.50 Score': 0.937, 'Threat Score': 0.904, 'Average Precision': 0.989, 'TP': 104, 'TN': 56, 'FP': 8, 'FN': 3}\n", - "Thresholds Dict = {'Youden': 0.72, 'Sensitivity-Specificity': 0.77, 'Precision-Recall-F1': 0.64}\n" + "Accuracy = 0.9239766081871345\n", + "Balanced Accuracy = 0.9109959112149533\n", + "AUC ROC = 0.9684579439252337\n", + "AUC PR = 0.9751682328758773\n", + "Precision = 0.9196428571428571\n", + "Recall = 0.9626168224299065\n", + "F1-Score = 0.9406392694063926\n", + "F2-Score = 0.9537037037037037\n", + "F0.5-Score = 0.927927927927928\n", + "Average Precision = 0.9665598940893543\n", + "Threat Score = 0.8879310344827587\n", + "Metrics Dict = {'Accuracy': 0.924, 'Balanced Accuracy': 0.911, 'ROC AUC': 0.968, 'PR AUC': 0.975, 'Precision': 0.92, 'Recall': 0.963, 'F-1 Score': 0.941, 'F-2 Score': 0.954, 'F-0.50 Score': 0.928, 'Threat Score': 0.888, 'Average Precision': 0.967, 'TP': 103, 'TN': 55, 'FP': 9, 'FN': 4}\n", + "Thresholds Dict = {'Youden': 0.63, 'Sensitivity-Specificity': 0.72, 'Precision-Recall-F1': 0.66}\n" ] } ], @@ -473,7 +473,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "source": [ "import pandas as pd\n", "import seaborn as sns\n", @@ -565,7 +565,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "source": [ "cmap = create_colormap()\n", "styles = create_df_styles()\n", @@ -583,7 +583,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, "source": [ "display(\n", " df_to_show.style.background_gradient(\n", @@ -597,439 +597,443 @@ "data": { "text/html": [ "\n", - "\n", + "
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 AccuracyBalanced AccuracyROC AUCPR AUCPrecisionRecallAverage PrecisionF-1 ScoreF-2 ScoreF-0.50 ScoreThreat ScoreTPTNFPFNAccuracyBalanced AccuracyROC AUCPR AUCPrecisionRecallAverage PrecisionF-1 ScoreF-2 ScoreF-0.50 ScoreThreat ScoreTPTNFPFN
Threshold = 0.720 | Average =\n", + " Threshold = 0.630 | Average =\n", " Binary0.9710000.9730000.9960000.9980000.9900000.9630000.9980000.9760000.9680000.9850000.95400010363140.9470000.9420000.9680000.9750000.9540000.9630000.9670000.9580000.9610000.9550000.9200001035954
Threshold = 0.770 | Average =\n", + " Threshold = 0.720 | Average =\n", " Binary0.9530000.9590000.9960000.9980000.9900000.9350000.9980000.9620000.9450000.9780000.92600010063170.9240000.9240000.9680000.9750000.9520000.9250000.9670000.9380000.9300000.9460000.884000995958
Threshold = 0.640 | Average =\n", + " Threshold = 0.660 | Average =\n", " Binary0.9770000.9780000.9960000.9980000.9900000.9720000.9980000.9810000.9760000.9870000.96300010463130.9420000.9380000.9680000.9750000.9530000.9530000.9670000.9530000.9530000.9530000.9110001025955
Threshold = 0.720 | Average =\n", + " Threshold = 0.630 | Average =\n", " Weighted0.9710000.9730000.9960000.9980000.9720000.9710000.9980000.9710000.9710000.9710000.94300010363140.9470000.9420000.9680000.9750000.9470000.9470000.9670000.9470000.9470000.9470000.9010001035954
Threshold = 0.770 | Average =\n", + " Threshold = 0.720 | Average =\n", " Weighted0.9530000.9590000.9960000.9980000.9560000.9530000.9980000.9540000.9530000.9550000.91100010063170.9240000.9240000.9680000.9750000.9250000.9240000.9670000.9240000.9240000.9250000.860000995958
Threshold = 0.640 | Average =\n", + " Threshold = 0.660 | Average =\n", " Weighted0.9770000.9780000.9960000.9980000.9770000.9770000.9980000.9770000.9770000.9770000.95400010463130.9420000.9380000.9680000.9750000.9420000.9420000.9670000.9420000.9420000.9420000.8900001025955
Threshold = 0.720 | Average =\n", + " Threshold = 0.630 | Average =\n", " Macro0.9710000.9730000.9960000.9980000.9650000.9730000.9980000.9690000.9720000.9670000.94000010363140.9470000.9420000.9680000.9750000.9450000.9420000.9670000.9440000.9430000.9450000.8940001035954
Threshold = 0.770 | Average =\n", + " Threshold = 0.720 | Average =\n", " Macro0.9530000.9590000.9960000.9980000.9450000.9590000.9980000.9510000.9560000.9470000.90700010063170.9240000.9240000.9680000.9750000.9160000.9240000.9670000.9200000.9220000.9180000.852000995958
Threshold = 0.640 | Average =\n", + " Threshold = 0.660 | Average =\n", " Macro0.9770000.9780000.9960000.9980000.9730000.9780000.9980000.9750000.9770000.9740000.95200010463130.9420000.9380000.9680000.9750000.9380000.9380000.9670000.9380000.9380000.9380000.8830001025955
Threshold = 0.720 | Average =\n", + " Threshold = 0.630 | Average =\n", " Micro0.9710000.9730000.9960000.9980000.9710000.9710000.9980000.9710000.9710000.9710000.95400010363140.9470000.9420000.9680000.9750000.9470000.9470000.9670000.9470000.9470000.9470000.9200001035954
Threshold = 0.770 | Average =\n", + " Threshold = 0.720 | Average =\n", " Micro0.9530000.9590000.9960000.9980000.9530000.9530000.9980000.9530000.9530000.9530000.92600010063170.9240000.9240000.9680000.9750000.9240000.9240000.9670000.9240000.9240000.9240000.884000995958
Threshold = 0.640 | Average =\n", + " Threshold = 0.660 | Average =\n", " Micro0.9770000.9780000.9960000.9980000.9770000.9770000.9980000.9770000.9770000.9770000.96300010463130.9420000.9380000.9680000.9750000.9420000.9420000.9670000.9420000.9420000.9420000.9110001025955
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {} diff --git a/src/slickml/metrics/_base.py b/src/slickml/metrics/_base.py index ac6fb48..1daeaae 100644 --- a/src/slickml/metrics/_base.py +++ b/src/slickml/metrics/_base.py @@ -29,7 +29,7 @@ def plot(self) -> Figure: def get_metrics( self, dtype: Optional[str], - ) -> Union[pd.DataFrame, Dict[str, Union[float, None]]]: + ) -> Union[pd.DataFrame, Dict[str, Optional[float]]]: """Returns calculated metrics in a desired output dtype. Parameters @@ -39,6 +39,6 @@ def get_metrics( Returns ------- - Union[pd.DataFrame, Dict[str, Union[float, None]]] + Union[pd.DataFrame, Dict[str, Optional[float]]] """ ... diff --git a/src/slickml/metrics/_classification.py b/src/slickml/metrics/_classification.py index fb60a37..d063104 100644 --- a/src/slickml/metrics/_classification.py +++ b/src/slickml/metrics/_classification.py @@ -323,7 +323,7 @@ def plot( def get_metrics( self, dtype: Optional[str] = "dataframe", - ) -> Union[pd.DataFrame, Dict[str, Union[float, None]]]: + ) -> Union[pd.DataFrame, Dict[str, Optional[float]]]: """Returns calculated metrics with desired dtypes. Currently, available output types are "dataframe" and "dict". @@ -335,7 +335,7 @@ def get_metrics( Returns ------- - Union[pd.DataFrame, Dict[str, Union[float, None]]] + Union[pd.DataFrame, Dict[str, Optional[float]]] """ check_var( dtype, diff --git a/src/slickml/metrics/_regression.py b/src/slickml/metrics/_regression.py index 56330c8..aee48cf 100644 --- a/src/slickml/metrics/_regression.py +++ b/src/slickml/metrics/_regression.py @@ -103,7 +103,7 @@ class RegressionMetrics: cv_y_ratio_ : float Coefficient of variation calculated as ``std_y_ratio/mean_y_ratio`` - metrics_dict_ : Dict[str, Union[float, None]] + metrics_dict_ : Dict[str, Optional[float]] Rounded metrics based on the number of precision digits metrics_df_ : pd.DataFrame @@ -226,7 +226,7 @@ def plot( def get_metrics( self, dtype: Optional[str] = "dataframe", - ) -> Union[pd.DataFrame, Dict[str, Union[float, None]]]: + ) -> Union[pd.DataFrame, Dict[str, Optional[float]]]: """Returns calculated metrics with desired dtypes. Currently, available output types are ``"dataframe"`` and ``"dict"``. @@ -238,7 +238,7 @@ def get_metrics( Returns ------- - Union[pd.DataFrame, Dict[str, Union[float, None]]] + Union[pd.DataFrame, Dict[str, Optional[float]]] """ check_var( dtype, @@ -305,12 +305,12 @@ def _mse(self) -> float: ) # TODO(amir): double check the return type here with mypy - def _msle(self) -> Union[float, None]: + def _msle(self) -> Optional[float]: """Calculates mean-squared-log-error. Returns ------- - Union[float, None] + Optional[float] """ if min(self.y_true) < 0 or min(self.y_pred) < 0: msle = None @@ -403,12 +403,12 @@ def _ratio_hist(self) -> Tuple[np.ndarray, float, float, float]: return (y_ratio, mean_y_ratio, std_y_ratio, cv_y_ratio) # TODO(amir): refactor this into a dataclass with dependency injection - def _metrics_dict(self) -> Dict[str, Union[float, None]]: + def _metrics_dict(self) -> Dict[str, Optional[float]]: """Rounded calculated metrics based on the number of precision digits. Returns ------- - Dict[str, Union[float, None]] + Dict[str, Optional[float]] """ return { "R2 Score": round(