pax_global_header00006660000000000000000000000064145526164700014524gustar00rootroot0000000000000052 comment=375e216634007a9f40abf0b1b44bd6c7d2f3f55c sidpy-0.12.3/000077500000000000000000000000001455261647000127375ustar00rootroot00000000000000sidpy-0.12.3/.github/000077500000000000000000000000001455261647000142775ustar00rootroot00000000000000sidpy-0.12.3/.github/workflows/000077500000000000000000000000001455261647000163345ustar00rootroot00000000000000sidpy-0.12.3/.github/workflows/actions.yml000066400000000000000000000056761455261647000205350ustar00rootroot00000000000000name: build env: PYTHON_MAIN_VERSION: 3.9 on: pull_request: branches: - '*' push: branches: - '*' tags: - '*' jobs: build-linux: runs-on: ubuntu-latest strategy: max-parallel: 5 matrix: python-version: [3.8, 3.9, "3.10"] steps: - uses: actions/checkout@v3 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v3 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | sudo apt-get update -qq python -m pip install --upgrade pip python -m pip install flake8 pytest coveralls if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - name: install package run: | pip install . pip list - name: Lint with flake8 run: | # stop the build if there are Python syntax errors or undefined names flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: Generate coverage report run: | pip install pytest pip install pytest-cov pytest --cov=./ --cov-report=xml - name: Upload coverage to Codecov if: ${{ matrix.python-version == env.PYTHON_MAIN_VERSION }} uses: codecov/codecov-action@v3 with: token: ${{ secrets.CODECOV_TOKEN }} files: ./coverage.xml # directory: ./coverage/reports/ flags: unittests env_vars: OS,PYTHON name: codecov-umbrella fail_ci_if_error: true # path_to_write_report: ./coverage/codecov_report.txt verbose: true - name: Documentation build if: ${{ matrix.python-version == env.PYTHON_MAIN_VERSION && github.ref == 'refs/heads/main'}} run: | pip install sphinx>=3.1.1 sphinx-gallery sphinx-rtd-theme>=0.5.0 sphinx-autodoc-typehints numpydoc wget pysptools cvxopt scipy nbsphinx sudo apt-get install pandoc sphinx-build -b html -aET docs/source docs/_build/html touch docs/_build/html/.nojekyll - name: Deploy to GitHub Pages if: ${{ matrix.python-version == env.PYTHON_MAIN_VERSION && github.ref == 'refs/heads/main'}} uses: crazy-max/ghaction-github-pages@v3 with: target_branch: gh-pages build_dir: docs/_build/html env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - name: Upload to PyPi if: startsWith( github.ref, 'refs/tags') && matrix.python-version == env.PYTHON_MAIN_VERSION env: PYPI_TOKEN_PASSWORD: ${{ secrets.PYPI_API_TOKEN }} run: | pip install wheel twine python setup.py sdist bdist_wheel twine upload --username "__token__" --password $PYPI_TOKEN_PASSWORD dist/* sidpy-0.12.3/.gitignore000066400000000000000000000024411455261647000147300ustar00rootroot00000000000000# Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # OS Files .DS_Store # Spyder Files from devEnv .spyproject/ # Distribution / packaging .Python env/ build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ *.egg-info/ .installed.cfg *.egg # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *,cover .hypothesis/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_static docs/_build # PyBuilder target/ # IPython Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # dotenv .env # virtualenv venv/ ENV/ # Spyder project settings .spyderproject .spyderworkspace # Rope project settings .ropeproject # PyCharm project settings .idea/ # pypi config file .pypirc # Folder for testing scripts test_scripts/ \.pytest_cache/ /docs/source/_autosummary/ /docs/source/notebooks/ sidpy-0.12.3/.travis.yml000066400000000000000000000025421455261647000150530ustar00rootroot00000000000000language: python python: - '3.7' - '3.8' - '3.6' - '3.5' before_install: - sudo apt-get update -qq - pip install coveralls dist: xenial sudo: true install: pip install . script: coverage run --source sidpy setup.py test stages: - name: after_success if: env(python) = "3.7" after_success: - pip list - pip install sphinx>=3.1.1 sphinx-gallery sphinx-rtd-theme>=0.5.0 sphinx-autodoc-typehints numpydoc wget pysptools cvxopt scipy nbsphinx - sudo apt-get install pandoc - sphinx-build -b html -aET docs/source docs/_build/html - touch docs/_build/html/.nojekyll - coverage report -m - coveralls before_deploy: - python setup.py sdist - python setup.py bdist_wheel deploy: # Github pages deployment - provider: pages skip_cleanup: true github-token: $GITHUBTOKEN local_dir: docs/_build/html on: branch: master python: 3.7 # Github releases deployment - provider: releases skip_cleanup: true api_key: $GITHUBTOKEN file_glob: true file: dist/* on: tags: true branch: master # PyPi deployment - provider: pypi skip_cleanup: true user: secure: $PYPIUSER2 password: secure: $PYPIPASS2 distributions: "sdist bdist_wheel" file_glob: true file: - dist/*.whl - dist/*.tar.gz on: tags: true branch: master python: 3.7 sidpy-0.12.3/ALL_CAPS.RST000066400000000000000000000000071455261647000145440ustar00rootroot00000000000000Nothingsidpy-0.12.3/LICENSE000066400000000000000000000020531455261647000137440ustar00rootroot00000000000000MIT License Copyright (c) 2020 pycroscopy Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. sidpy-0.12.3/MANIFEST.in000066400000000000000000000000741455261647000144760ustar00rootroot00000000000000include README.rst include LICENSE recursive-exclude tests *sidpy-0.12.3/README.rst000066400000000000000000000022141455261647000144250ustar00rootroot00000000000000sidpy ===== .. image:: https://github.com/pycroscopy/sidpy/workflows/build/badge.svg?branch=main :target: https://github.com/pycroscopy/sidpy/actions?query=workflow%3Abuild :alt: GiHub Actions .. image:: https://img.shields.io/pypi/v/sidpy.svg :target: https://pypi.org/project/sidpy/ :alt: PyPI .. image:: https://img.shields.io/conda/vn/conda-forge/sidpy.svg :target: https://github.com/conda-forge/sidpy-feedstock :alt: conda-forge .. image:: https://codecov.io/gh/pycroscopy/sidpy/branch/master/graph/badge.svg?token=BCFR4FR6AL :target: https://codecov.io/gh/pycroscopy/sidpy :alt: coverage .. image:: https://img.shields.io/pypi/l/sidpy.svg :target: https://pypi.org/project/sidpy/ :alt: License .. image:: http://pepy.tech/badge/sidpy :target: http://pepy.tech/project/sidpy :alt: Downloads .. image:: https://zenodo.org/badge/138171750.svg :target: https://zenodo.org/badge/latestdoi/138171750 :alt: USID DOI Python utilities for storing and visualizing Spectroscopic and Imaging Data (SID) Please see our `website `_ for more information. sidpy-0.12.3/blah.TXT000066400000000000000000000000071455261647000142430ustar00rootroot00000000000000Nothingsidpy-0.12.3/blah.png000066400000000000000000000000071455261647000143500ustar00rootroot00000000000000Nothingsidpy-0.12.3/blah.rst000066400000000000000000000000071455261647000143740ustar00rootroot00000000000000Nothingsidpy-0.12.3/dask-worker-space/000077500000000000000000000000001455261647000162615ustar00rootroot00000000000000sidpy-0.12.3/dask-worker-space/global.lock000066400000000000000000000000001455261647000203610ustar00rootroot00000000000000sidpy-0.12.3/dask-worker-space/purge.lock000066400000000000000000000000001455261647000202430ustar00rootroot00000000000000sidpy-0.12.3/docs/000077500000000000000000000000001455261647000136675ustar00rootroot00000000000000sidpy-0.12.3/docs/Makefile000066400000000000000000000020211455261647000153220ustar00rootroot00000000000000# Minimal makefile for Sphinx documentation # # You can set these variables from the command line, and also # from the environment for the first two. SPHINXOPTS ?= SPHINXBUILD ?= sphinx-build SPHINXPROJ = sidpy SOURCEDIR = source BUILDDIR = build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) .PHONY: help Makefile # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). # Create a .py (percent format with multiline comments) from an .ipynb in the first place: # jupytext --update-metadata '{"jupytext": {"cell_markers": "\"\"\""}}' --to py:percent .ipynb # Convert .py to .ipynb (don't seem to need to --execute? perhaps sphinx-build does it...), then build html: %: Makefile rm -rf build rm -rf source/_autosummary rm -rf source/notebooks @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)sidpy-0.12.3/docs/Using PyCharm to manage repository.pdf000066400000000000000000016106461455261647000230250ustar00rootroot00000000000000%PDF-1.5 % 1 0 obj <>>> endobj 2 0 obj <> endobj 3 0 obj <>/Font<>/ProcSet[/PDF/Text/ImageB/ImageC/ImageI] >>/MediaBox[ 0 0 720 540] /Contents 4 0 R/Group<>/Tabs/S/StructParents 0>> endobj 4 0 obj <> stream xuMk@Er d|o d tpaljW%)*d9Zy Z&@ o M.GJ(D/ )NR`wHoMڱꮙ^z`G^ : &8Zp>Hh=0`8o5僖gGK]L?QfU,*H}s0mHsz 8lUU=aģ|3X?mej endstream endobj 5 0 obj <> endobj 6 0 obj <> endobj 7 0 obj <> endobj 8 0 obj <> endobj 9 0 obj <>/Font<>/ProcSet[/PDF/Text/ImageB/ImageC/ImageI] >>/Annots[ 16 0 R 17 0 R] /MediaBox[ 0 0 720 540] /Contents 10 0 R/Group<>/Tabs/S/StructParents 1>> endobj 10 0 obj <> stream xTɎ@[?Ա{$ڽq`h"4 R(Mh>Հ!sjy@<;{8p9Rr Vr0C|/x|70љPp}=}W߃s$L!b}5㡆!"R0.A+Gzy6TpRR!x ?|oxQʲ0 mٛՍ(G,3\#ʑU9Z1G)Glsy _Ē~N5Ȗ8 sÊW 1WuzD͡Z̪4r\ӐlaBAA["OwA$ m 7qQv?1sRӀ*YKSV0FJfZŔl8LgXQu^ރ+f.-\)`8$,]VLE tpp2N2fnբ^Y7Zt-nZ-~D)89mQ͚PWNQ3jNNh4dB/_5U~Ji@EHE^<>g SԁZ(yfPbyn]r;զ$y$[`͛a%w@ 1\s9"y(O endstream endobj 11 0 obj <> endobj 12 0 obj [ 13 0 R] endobj 13 0 obj <> endobj 14 0 obj <> endobj 15 0 obj <> endobj 16 0 obj <>/F 4/A<>/StructParent 2>> endobj 17 0 obj <>/F 4/A<>/StructParent 3>> endobj 18 0 obj <>/XObject<>/Font<>/ProcSet[/PDF/Text/ImageB/ImageC/ImageI] >>/MediaBox[ 0 0 720 540] /Contents 19 0 R/Group<>/Tabs/S/StructParents 4>> endobj 19 0 obj <> stream xVMo@#渮fזBRJBIYl713o>ޛ=\^^ 7f=2Ƹ̂ b~Hу<3f8S5Ň~{wCJ$ip˜CPrT !Oa%5fLinRB^O(3 & _j!L6Q7%.~Kw9Wdf[]m֡H@>hl%2{U#Fz#@p(I;JKB{UOײ7dNf"e4IE|. !o->+BEC^c^!-T<6i}I!_f-εQSzkA=x<$y.~ƪ2u0BӰI9'RD7 1+%3q >^ H! 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For a # full list see the documentation: # http://www.fsphinx-doc.org/en/master/config # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # import os import sys import shutil import matplotlib matplotlib.use('agg') import sphinx_rtd_theme sys.path.insert(0, os.path.abspath('../..')) from sidpy import __version__ as sidpy_version # - Copy over examples folder to docs/source # This makes it so that nbsphinx properly loads the notebook images examples_source = os.path.abspath(os.path.join( os.path.dirname(__file__), "..", "..", "notebooks")) examples_dest = os.path.abspath( os.path.join(os.path.dirname(__file__), "notebooks")) if os.path.exists(examples_dest): shutil.rmtree(examples_dest) os.mkdir(examples_dest) for root, dirs, files in os.walk(examples_source): for dr in dirs: os.mkdir(os.path.join(root.replace(examples_source, examples_dest), dr)) for fil in files: if os.path.splitext(fil)[1] in [".ipynb", ".md", ".rst"]: source_filename = os.path.join(root, fil) dest_filename = source_filename.replace(examples_source, examples_dest) shutil.copyfile(source_filename, dest_filename) # -- Project information ----------------------------------------------------- project = 'sidpy' copyright = '2020, Suhas Somnath, Gerd Duscher, and contributors' author = 'Pycroscopy contributors' # The short X.Y version version = sidpy_version # The full version, including alpha/beta/rc tags release = sidpy_version # -- General configuration --------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. # # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.intersphinx', 'sphinx.ext.todo', 'sphinx.ext.autosummary', 'sphinx.ext.mathjax', 'nbsphinx', 'sphinx.ext.viewcode', 'sphinx.ext.autosummary', 'sphinx.ext.autosectionlabel', 'sphinx.ext.napoleon', # Use either napoleon or numpydoc not both. ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # source_suffix = ['.rst', '.md'] #source_suffix = '.rst' # The master toctree document. master_doc = 'index' # Ignore errors during notebook execution (for the time being...) nbsphinx_allow_errors = True # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path . exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] # The reST default role (used for this markup: `text`) to use for all # documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. #keep_warnings = False # Napoleon settings # https://www.sphinx-doc.org/en/master/usage/extensions/napoleon.html napoleon_google_docstring = True napoleon_numpy_docstring = True napoleon_include_init_with_doc = False 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 = True napoleon_use_rtype = True napoleon_type_aliases = None # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = True # Generate autosummary even if no references autosummary_generate = True autoclass_content = 'both' autodoc_default_flags = ['members', 'inherited-members', # 'private-members', # 'show-inheritance' ] autodoc_inherit_docstrings = True # If no class summary, inherit base class summary # -- Options for HTML output ------------------------------------------------- # on_rtd is whether on readthedocs.org, this line of code grabbed from docs.readthedocs.org... on_rtd = os.environ.get("READTHEDOCS", None) == "True" # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = 'sphinx_rtd_theme' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # # html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] # The name for this set of Sphinx documents. # " v documentation" by default. #html_title = u'sidpy ' + sidpy_version # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. # html_logo = 'logo_v01.png' # The name of an image file (relative to this directory) to use as a favicon of # the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # 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'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. #html_extra_path = [] # If not None, a 'Last updated on:' timestamp is inserted at every page # bottom, using the given strftime format. # The empty string is equivalent to '%b %d, %Y'. #html_last_updated_fmt = None # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, must be a dictionary that maps document names # to template names. # # The default sidebars (for documents that don't match any pattern) are # defined by theme itself. Builtin themes are using these templates by # default: ``['localtoc.html', 'relations.html', 'sourcelink.html', # 'searchbox.html']``. # # html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Language to be used for generating the HTML full-text search index. # Sphinx supports the following languages: # 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' # 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr', 'zh' html_search_language = 'en' # A dictionary with options for the search language support, empty by default. # 'ja' uses this config value. # 'zh' user can custom change `jieba` dictionary path. #html_search_options = {'type': 'default'} # The name of a javascript file (relative to the configuration directory) that # implements a search results scorer. If empty, the default will be used. #html_search_scorer = 'scorer.js' # -- Options for HTMLHelp output --------------------------------------------- # Output file base name for HTML help builder. htmlhelp_basename = 'sidpydoc' # -- Options for LaTeX output ------------------------------------------------ latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'sidpy.tex', 'sidpy Documentation', 'Pycroscopy contributors', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output ------------------------------------------ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'sidpy', 'sidpy Documentation', [author], 1) ] # -- Options for Texinfo output ---------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'sidpy', 'sidpy Documentation', author, 'sidpy', 'Utilities for storing, visualizing, and processing Spectroscopic and ' 'Imaging Data USID)', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. #texinfo_no_detailmenu = False # -- Options for Epub output ------------------------------------------------- # Bibliographic Dublin Core info. epub_title = project epub_author = author epub_publisher = author epub_copyright = copyright # The basename for the epub file. It defaults to the project name. #epub_basename = project # The HTML theme for the epub output. Since the default themes are not # optimized for small screen space, using the same theme for HTML and epub # output is usually not wise. This defaults to 'epub', a theme designed to save # visual space. #epub_theme = 'epub' # The language of the text. It defaults to the language option # or 'en' if the language is not set. #epub_language = '' # The scheme of the identifier. Typical schemes are ISBN or URL. #epub_scheme = '' # The unique identifier of the text. This can be a ISBN number # or the project homepage. # # epub_identifier = '' # A unique identification for the text. # # epub_uid = '' # A tuple containing the cover image and cover page html template filenames. #epub_cover = () # A sequence of (type, uri, title) tuples for the guide element of content.opf. #epub_guide = () # HTML files that should be inserted before the pages created by sphinx. # The format is a list of tuples containing the path and title. #epub_pre_files = [] # HTML files that should be inserted after the pages created by sphinx. # The format is a list of tuples containing the path and title. #epub_post_files = [] # A list of files that should not be packed into the epub file. epub_exclude_files = ['search.html'] # The depth of the table of contents in toc.ncx. #epub_tocdepth = 3 # Allow duplicate toc entries. #epub_tocdup = True # Choose between 'default' and 'includehidden'. #epub_tocscope = 'default' # Fix unsupported image types using the Pillow. #epub_fix_images = False # Scale large images. #epub_max_image_width = 0 # How to display URL addresses: 'footnote', 'no', or 'inline'. #epub_show_urls = 'inline' # If false, no index is generated. #epub_use_index = True # -- Extension configuration ------------------------------------------------- # -- Options for intersphinx extension --------------------------------------- # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = {'python': ('https://docs.python.org/{.major}'.format(sys.version_info), None), 'numpy': ('https://numpy.org/doc/stable/', None), 'scipy': ('https://docs.scipy.org/doc/scipy/reference', None), 'matplotlib': ('https://matplotlib.org/', None), 'h5py': ('https://docs.h5py.org/en/latest/', None), 'sphinx': ('https://www.sphinx-doc.org/en/master/', None), 'dask': ('https://docs.dask.org/en/latest/', None), } # ------------------------------------------------- sidpy-0.12.3/docs/source/contact.rst000066400000000000000000000016601455261647000173570ustar00rootroot00000000000000Contact us ========== If you find any bugs or if you want a feature added to sidpy, please raise an `issue `_. You will need a (free) Github account to do this. When reporting bugs, please provide a (minimal) script / snippet that reproduces the error(s) you are facing, the full description of the error(s), details regarding your computer, operating system, python, sidpy and other related package versions etc. These details will help us solve your problem a lot faster. Credits ------- The core sidpy team consists of: * `@ssomnath `_ (Suhas Somnath) * `@gduscher `_ (Prof. Gerd Duscher) Substantial contributions from many developers including: * `@CompPhysChris `_ (Chris R. Smith) * `@JamesALeedham `_ for helping us out with Sphinx documentation * and many moresidpy-0.12.3/docs/source/contribute.rst000066400000000000000000000140571455261647000201060ustar00rootroot00000000000000Guidelines for Contribution ============================ We would like to thank you and several others who have offered / are willing to contribute their code. We are more than happy to add your code to this project. Just as we strive to ensure that you get the best possible software from us, we ask that you do the same for others. We do **NOT** ask that your code be as efficient as possible. Instead, we have some simpler and easier requests. We have compiled a list of best practices below with links to additional information. If you are confused or need more help, please feel free to `contact us <./contact.html>`_. Before you begin ---------------- Please consider familiarizing yourself with the `examples <./auto_examples/index.html>`_ and `documentation <./api.html>`_ on functionality available in sidpy so that you can use the available functionality to simplify your code in addition to avoiding the development of duplicate code. Structuring code ---------------- General guidelines ~~~~~~~~~~~~~~~~~~ * Encapsulate independent sections of your code into functions that can be used individually if required. * Ensure that your code (functions) is well documented (`numpy format `_) - expected inputs and outputs, examples, notes, purpose of functions * Please avoid very short names for variables like ``i`` or ``k``. This makes it challenging to follow code, find and fix bugs. * Please consider using packages that are easy to install on Windows, Mac, and Linux. It is quite likely that packages included within Anaconda (which has a comprehensive list packages for science and data analysis + visualization) can handle most needs. If this is not possible, try to use packages that are easy to to install (pip install). If even this is not possible, try to use packages that at least have conda installers. * Follow best practices for `PEP8 compatibility `_. The easiest way to ensure compatibility is to set it up in your code editor. `PyCharm `_ does this by default. So, as long as PyCharm does not raise many warning, your code is beautiful! sidpy-specific guidelines ~~~~~~~~~~~~~~~~~~~~~~~~~ * Recall that sidpy is a general collection of tools that can help store, analyze, visualize, and process spectroscopy and imaging data. Any code specific to the Universal Spectroscopic and Imaging Data (USID) or N-Dimensional Spectroscopic and Imaging Data (NSID) should go into pyUSID or pyNSID respectively. Code that provides scientific functionality goes into pycroscopy. * Please ensure that your code files fit into our package structure (``base``, ``hdf``, ``io``, ``proc``, ``sid`` and ``viz``) * Once you decide where your code will sit, please use relative import statements instead of absolute / external paths. For example, if you are contributing code for a new submodule within ``sidpy.hdf``, you will need to turn your import statements and code from something like: .. code-block:: python import sidpy ... sidpy.hdf.hdf_utils.print_tree(hdf_file_handle) x_dim = sidpy.sid.Dimension(...) to: .. code-block:: python from sidpy.hdf.hdf_utils import print_tree from sidpy.sid import Dimension ... print_tree(hdf_file_handle) x_dim = Dimension(...) You can look at our code in our `GitHub project `_ to get an idea of how we organize, document, and submit our code. Contributing code ----------------- Please read this `beginner's guide to contributing `_ to open source projects. We recommend that you follow the steps below. Again, if you are ever need help, please contact us: 1. Learn ``git`` if you are not already familiar with it. See our compilation of tutorials and guides, especially `this one `_. 2. Create a ``fork`` of sidpy - this creates a separate copy of the entire sidpy repository under your user ID. For more information see `instructions here `_. 3. Once inside your own fork, you can either work directly off ``master`` or create a new branch. 4. Add / modify code 5. ``Commit`` your changes (equivalent to saving locally on your laptop). Do this regularly. 6. Repeat steps 4-5. 7. After you reach a certain milestone, ``push`` your commits to your ``remote branch``. This synchronizes your changes with the GitHub website and is similar to the Dropbox website /service making note of changes in your documents. To avoid losing work due to problems with your computer, consider ``pushing commits`` once at least every day / every few days. 8. Repeat steps 4-7 till you are ready to have your code added to the parent sidpy repository. At this point, `create a pull request `_. Someone on the development team will review your ``pull request``. If any changes are req and then ``merge`` these changes to ``master``. Writing tests ------------- Software can become complicated very quickly through a complex interconnected web of dependencies, etc. Adding or modifying code at one location may break some use case or code in a different location. Unit tests are short functions that test to see if functions / classes respond in the expected way given some known inputs. Unit tests are a good start for ensuring that you spend more time using code than fixing it. New functions / classes must be accompanied with unit tests. Writing examples ---------------- Additionally, examples on how to use the new code must also be added so others are aware about how to use the code. You can now do it by simply adding a Jupyter notebook with your tutorial/example to the `notebooks `_ folder. sidpy-0.12.3/docs/source/external_guides.rst000066400000000000000000000177031455261647000211130ustar00rootroot00000000000000Tutorials on Basics ==================== For those who are new to python and data analytics, we highly encourage you to go through `Prof. Josh Agar's tutorials `_ for a throrough primer on all the basic concepts. Here are a list of other tutorials from other websites and sources that describe some of the many important topics on reading, using / running and writing code: .. contents:: :local: Python and packages -------------------- There are several concepts such as file operations, parallel computing, etc. that are heavily used and applied in pyUSID. Most of these concepts are realized using add-ons or packages in python. Here is a compilation of useful tutorials: Python ~~~~~~ The following tutorials go over the basics of python programming: * `Official Python tutorial `_ * The `Hitchhiker guide to Python `_ * Introduction to programming in `Python 3 `_ * Tutorials on a broad spectrum of `real-world use topics `_ HDF5 and h5py ~~~~~~~~~~~~~ Our software packages - ``sidpy``, ``pyUSID``, ``pyNSID`` are all designed to be file-centric, we highly recommend learning more about HDF5 and h5py: * `Basics of HDF5 `_ (especially the last three tutorials) * `Quick start `_ to h5py * Another `tutorial on HDF5 and h5py `_ Installing software ------------------- python ~~~~~~~ `Anaconda `_ is a popular source for python which also comes with a large number of popular scientific python packages that are all correctly compiled and installed in one go. Tutorial for `installing Anaconda `_ (Python + all necessary packages) python packages ~~~~~~~~~~~~~~~~ Two popular methods for installing packages in python are: * `pip `_: * included with basic python and standard on Linux and Mac OS * Works great for installing pure python and other simple packages * `conda `_ * included with Anaconda installation * Ideally suited for installing packages that have complex dependencies * Here's a nice tutorial on `installing packages using both pip and conda `_ Updating packages ~~~~~~~~~~~~~~~~~ Following `these instructions `_, open a terminal or the command prompt (Windows) and type: .. code:: bash conda update conda conda update anaconda Note that you could use the following line instead of or in addition to ``conda update anaconda`` but it can lead to incompatible package versions .. code:: bash conda update --all Note that this does **not** update python itself. Upgrading python ~~~~~~~~~~~~~~~~ Follow these instructions to `upgrade python using conda `_ to the latest or specific version Writing code ------------ Text Editors ~~~~~~~~~~~~ These software often do not have any advanced features found in IDEs such as syntax highlighting, real-time code-checking etc. but are simple, and most importantly, open files quickly. Here are some excellent text editors for each class of operating system: * Mac OS - `Atom `_ * Linux - `gEdit `_, `vim `_, `neovim `_ * Windows - `Notepad++ `_ Integrated Development Environments (IDE) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ These applications often come with a built-in text editor, code management capabilities, a python console, a terminal, integration with software repositories, etc. that make them ideal for executing and developing code. We only recommend two IDEs at this point: Spyder for users, PyCharm for developers. Both of these work in Linux, Mac OS, and Windows. * `Spyder `_ is a great IDE that is simple and will be immediately familiar for users of Matlab. * `Basics of Spyder `_ * `Python with Spyder `_ - this was written with Python 2.7 in mind, but most concepts will still apply * `Pycharm `_ * Official `PyCharm Tutorial `_ from Jetbrains * `VS Code `_ * Completely free and open-source editor by Microsoft. Much faster and extremely lightweight compared to Pycharm. Jupyter Notebooks ~~~~~~~~~~~~~~~~~ These are `interactive documents `_ containing live cells with code, equations, visualizations, and narrative text. The interactive nature of the document makes Jupyter notebooks an ideal medium for conveying information and a narrative. These documents are neither text editors nor IDEs and are a separate category. * Notebook `basics `_ * `Video `_ tutorial * Another `video overview `_. Software development basics --------------------------- This section is mainly focused on the other tools that are mainly necessary for those interested in developing their own code and possibly contributing back to sidpy. Environments ~~~~~~~~~~~~ Environments allow users to set up and segregate software sandboxes. For example, one could set up separate environments in python 2 and 3 to ensure that a certain desired code works in both python 2 and 3. For python users, there are two main and popular modes of creating and managing environments - **virtual environments** and **conda environments**. * `Virtual environment `_ * Basic python ships with virtual enviroments. Anaconda is not required for this * How to `use venv `_ * Conda environments * `Basics `_ of Conda * How to `manage environments in conda `_ * `Managing Python Environments `_ with Conda Version control ~~~~~~~~~~~~~~~ `Version control `_ is a tool used for managing changes in code over time. It lifts the burden of having to check for changes line-by-line when multiple people are working on the same project. For example, sidpy uses `Git `_, the most popular version control software (VCS) for tracking changes etc. By default, git typically only comes with a command-line interface. However, there are several software packages that provide a graphical user interface on top of git. One other major benefit of using an IDE over jupyter or a text editor is that (some) IDEs come with excellent integration with VCS like Git. Here are a collection of useful resources to get you started on git: * Tutorial on the `basics of git `_ * Our favorite git client - `GitKraken `_ * Our favorite IDE with `excellent integration with Git: PyCharm `_ * Our own guide to `setting up and using git with PyCharm `_ sidpy-0.12.3/docs/source/getting_started.rst000066400000000000000000000077031455261647000211170ustar00rootroot00000000000000Getting Started =============== * Follow these :ref:`instructions ` to install SIDpy * We have compiled a list of :ref:`handy tutorials ` on basic / prerequisite topics such as programming in python, hdf5 handling, etc. * See our `examples <./notebooks/00_basic_usage/index.html>`_ to get started on creating and using your own SIDpy datasets. * Please see this `pyUSID tutorial for beginners `_ based on the examples on this project. * Details regarding the definition, implementation, and guidelines for N-Dimensional Spectroscopy and Imaging Data (NSID) are available in `this document `_. * If you are interested in contributing your code to SIDpy, please look at our :ref:`guidelines ` * We also have a handy document for converting your :ref:`matlab code to python `. * If you need detailed documentation on what is where and why, all our classes, functions, etc., please visit our :ref:`API ` * For a concise change-log, please see the `release history `_. * Please :ref:`get in touch ` if you would like to use SIDpy and pyNSID for other new or mature scientific packages. * Have questions? See our `FAQ <./faq.html>`_ to see if we have already answered them.dd * Need more information? Please see our `Arxiv `_ paper. * Need help or need to get in touch with us? See our :ref:`contact ` information. Guide for python novices ~~~~~~~~~~~~~~~~~~~~~~~~ For the python novices by a python novice - **Nick Mostovych, Brown University** #. Watch the video on `installing Anaconda `_ #. Follow instructions on the :ref:`installation ` page to install Anaconda. #. Watch the `video tutorial `_ on the Jupyter Notebooks #. Read the whole :ref:`Tutorial on Basics page `. Do NOT proceed unless you are familiar with basic python programming and usage. #. Read `the document on the pyNSID data format `_. This is very important and highlights the advantages of using NSID. New users should not jump to the examples until they have a good understanding of the data format. #. Depending on your needs, go through the recommended sequence of tutorials and examples (see 'EXAMPLES' on the side panel on the left) Tips and pitfalls ~~~~~~~~~~~~~~~~~ For the python novices by a python novice - **Nick Mostovych, Brown University** * Documentation and examples on this website are for the **latest** version of SIDpy. If something does not work as shown on this website, chances are that you may be using an older version of pyUSID. Follow the instructions to :ref:`update SIDpy to the latest version ` * pyUSID has excellent documentation (+ examples too) for all functions. If you are ever confused with the usage of a function or class, you can get help in numerous ways: * If you are using jupyter notebooks, just hit the ``Shift+Tab`` keys after typing the name of your function. See `this quick video `_ for a demo. E.g. - type ``sidpy.Dataset(``. Hit ``Shift+Tab`` twice or four times. You should be able to see the documentation for the class / function to learn how to supply inputs / extract outputs * Use the search function and reference the source code in the :ref:`API section ` for detailed comments. Most detailed questions are answered there. * Many functions in SIDpy have a ``verbose`` keyword argument that can be set to ``True`` to get detailed print logs of intermediate steps in the function. This is **very** handy for debugging code If there are tips or pitfalls you would like to add to this list, please :ref:`get in touch to us ` sidpy-0.12.3/docs/source/index.rst000066400000000000000000000024371455261647000170360ustar00rootroot00000000000000.. SIDpy documentation master file, created by sphinx-quickstart on Sun Jul 12 16:57:01 2020. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. sidpy ===== **Python utilities for storing, visualizing, and processing Spectroscopic and Imaging Data (SID)** This utilities package supports other packages such as: * Data formatting: * `pyNSID `_ * `pyUSID `_ * Scientific analysis: * `pycroscopy `_ * `ScopeReaders `_ * `BGlib `_ Please use the side panel on the left for other documentation pages on ``sidpy`` Jump to our `GitHub project page `_ .. toctree:: :glob: :maxdepth: 1 :caption: SIDpy install getting_started external_guides contribute matlab contact Source code API --------------- .. autosummary:: :toctree: _autosummary :template: custom-module-template.rst :recursive: sidpy * :ref:`modindex` .. toctree:: :glob: :maxdepth: 2 :caption: Examples notebooks/**/index sidpy-0.12.3/docs/source/install.rst000066400000000000000000000125231455261647000173720ustar00rootroot00000000000000Installation ============ Preparing for sidpy ------------------- `sidpy `_ requires many commonly used scientific and numeric python packages such as numpy, h5py etc. To simplify the installation process, we recommend the installation of `Anaconda `_ which contains most of the prerequisite packages, `conda `_ - a package / environment manager, as well as an `interactive development environment `_ - `Spyder `_. Do you already have Anaconda installed? - No? - `Download and install Anaconda `_ for Python 3.6 - Yes? - Is your Anaconda based on python 2.7, 3.4+? - No? - Uninstall existing Python / Anaconda distribution(s). - Restart computer - Yes? - Proceed to install sidpy Compatibility ~~~~~~~~~~~~~ * sidpy is compatible with python 2.7, and 3.4 onwards. Please raise an issue if you find a bug. * We do not support 32 bit architectures * We only support text that is UTF-8 compliant due to restrictions posed by HDF5 Terminal -------- Installing, uninstalling, or updating sidpy (or any other python package for that matter) can be performed using the ``Terminal`` application. You will need to open the Terminal to type any command shown on this page. Here is how you can access the Terminal on your computer: * Windows - Open ``Command Prompt`` by clicking on the Start button on the bottom left and typing ``cmd`` in the search box. You can either click on the ``Command Prompt`` that appears in the search result or just hit the Enter button on your keyboard. * Note - be sure to install in a location where you have write access. Do not install as administrator unless you are required to do so. * MacOS - Click on the ``Launchpad``. You will be presented a screen with a list of all your applications with a search box at the top. Alternatively, simultaneously hold down the ``Command`` and ``Space`` keys on the keyboard to launch the ``Spotlight search``. Type ``terminal`` in the search box and click on the ``Terminal`` application. * Linux (e.g - Ubuntu) - Open the Dash by clicking the Ubuntu (or equivalent) icon in the upper-left, type "terminal". Select the Terminal application from the results that appear. Installing sidpy ----------------- 1. Ensure that a compatible Anaconda distribution has been successfully installed 2. Open a `terminal <#terminal>`_ window. 3. You can now install sidpy via **either** the ``pip`` or ``conda`` methods shown below. Type the following commands into the terminal / command prompt and hit the Return / Enter key: * pip: .. code:: bash pip install sidpy * conda: .. code:: bash conda config --add channels conda-forge conda install sidpy Offline installation ~~~~~~~~~~~~~~~~~~~~ In certain cases, you may need your python packages to work on a computer (typically the computer that controls a scientific instrument) that is not connected to the internet. In such cases, the aforementioned routes will not work. Please follow these instructions instead: #. Recall that sidpy requires python and several other packages. Therefore, you will need to: #. Download the `Anaconda installer `_ from a computer is online #. Copy the installer onto the target computer via a USB pen drive #. Install Anaconda #. Download the sidpy repository from GitHub via `this link `_ #. Copy the resultant zip file to the offline computer via a portable storage device like a USB pen drive #. Unzip the zip file in the offline computer. #. Open a `terminal <#terminal>`_ window #. Navigate to the folder where you unzipped the contents of the zip file via ``cd`` commands #. Type the following command: .. code:: bash python setup.py install Installing from a specific branch (advanced users **ONLY**) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Note that we do not recommend installing sidpy this way since branches other than the master branch may contain bugs. .. note:: Windows users will need to install ``git`` before proceeding. Please type the following command in the Command Prompt: .. code:: bash conda install git Install a specific branch of sidpy (``dev`` in this case): .. code:: bash pip install -U git+https://github.com/pycroscopy/sidpy@dev Updating sidpy -------------- We recommend periodically updating your conda / anaconda distribution. Please see :ref:`these instructions to update anaconda `. If you already have sidpy installed and want to update to the latest version, use the following command in a terminal window: * If you originally installed sidpy via ``pip``: .. code:: bash pip install -U --no-deps sidpy If it does not work try reinstalling the package: .. code:: bash pip uninstall sidpy pip install sidpy * If you originally installed sidpy via ``conda``: .. code:: bash conda update sidpy Other software -------------- We recommend `HDF View `_ for exploring HDF5 files generated by and used in sidpy. sidpy-0.12.3/docs/source/matlab.rst000066400000000000000000000655261455261647000171770ustar00rootroot00000000000000Upgrading from Matlab ===================== **Chris R. Smith** Here are some one-to-one translations for many popular functions in Matlab and python that should make it easier to switch from Matlab to Python System functions ---------------- +------------------+-------------------+-------------+ | Matlab Function | Python Equivalent | Description | +==================+===================+=============+ | addpath | sys.path.append | Add to path | +------------------+-------------------+-------------+ File I/O -------- +-----------------+--------------------------------------------+-------------------------------------------------------+ | Matlab Function | Python Equivalent | Description | +=================+============================================+=======================================================+ | dlmread | either read and parse or skimage.io.imread | Read ASCII-delimited file of numeric data into matrix | +-----------------+--------------------------------------------+-------------------------------------------------------+ | imread | pyplot.imread | read image file; N is number of files used | +-----------------+--------------------------------------------+-------------------------------------------------------+ Data Type --------- +-----------------+-------------------+-----------------------------------------------+ | Matlab Function | Python Equivalent | Description | +=================+===================+===============================================+ | int | numpy.int | Convert data to signed integer | +-----------------+-------------------+-----------------------------------------------+ | double | numpy.float | Convert data to double | +-----------------+-------------------+-----------------------------------------------+ | real | numpy.real | Return the real part of a complex number | +-----------------+-------------------+-----------------------------------------------+ | imag | numpy.imag | Return the imaginary part of a complex number | +-----------------+-------------------+-----------------------------------------------+ Mathematics ----------- +------------------+-------------------------------+-------------------------------+ | Matlab Function | Python Equivalent | Description | +==================+===============================+===============================+ | sqrt | math.sqrt or numpy.sqrt | Square root | +------------------+-------------------------------+-------------------------------+ | erf | math.erf or scipy.special.erf | Error function | +------------------+-------------------------------+-------------------------------+ | atan2 | math.erf or numpy.atan2 | Four-quadrant inverse tangent | +------------------+-------------------------------+-------------------------------+ | abs | abs or numpy.abs | Absolute value | +------------------+-------------------------------+-------------------------------+ | exp | exp or numpy.exp | Exponential function | +------------------+-------------------------------+-------------------------------+ | sin | sin or numpy.sin | Sine function | +------------------+-------------------------------+-------------------------------+ Array Creation -------------- +-----------------+----------------------------+-------------------------------------------------+ | Matlab Function | Python Equivalent | Description | +=================+============================+=================================================+ | zeros | numpy.zeros | Create an array of zeros | +-----------------+----------------------------+-------------------------------------------------+ | meshgrid | numpy.meshgrid | Create grid of coordinates in 2 or 3 dimensions | +-----------------+----------------------------+-------------------------------------------------+ | ndgrid | numpy.mgrid or numpy.ogrid | Rectangular grid in N-D space | +-----------------+----------------------------+-------------------------------------------------+ Advanced functions ------------------ +-----------------+------------------------------------------------------+------------------------------------------------------------------------------------------------------+ | Matlab Function | Python Equivalent | Description | +=================+======================================================+======================================================================================================+ | permute | numpy.transpose | Rearrange dimensions of N-dimensional array | +-----------------+------------------------------------------------------+------------------------------------------------------------------------------------------------------+ | angle | numpy.angle | Phase angles for elements in complex array | +-----------------+------------------------------------------------------+------------------------------------------------------------------------------------------------------+ | max | numpy.max | Return the maximum element in an array | +-----------------+------------------------------------------------------+------------------------------------------------------------------------------------------------------+ | min | numpy.min | Return the minimum element in an array | +-----------------+------------------------------------------------------+------------------------------------------------------------------------------------------------------+ | reshape | numpy.reshape | Reshape array | +-----------------+------------------------------------------------------+------------------------------------------------------------------------------------------------------+ | mean | numpy.mean | Take mean along specified dimension | +-----------------+------------------------------------------------------+------------------------------------------------------------------------------------------------------+ | size | numpy.size | get the total number of entries in an array | +-----------------+------------------------------------------------------+------------------------------------------------------------------------------------------------------+ | cell2mat | numpy.vstack([numpy.hstack(cell) for cell in cells]) | converts data structure from cell to mat; joins multiple arrays of different sizes into single array | +-----------------+------------------------------------------------------+------------------------------------------------------------------------------------------------------+ | repmat | numpy.tile | Repeat copies of an array | +-----------------+------------------------------------------------------+------------------------------------------------------------------------------------------------------+ | unwrap | np.unwrap | Shift the phase of an array so that there are no jumps of more than the desired angle (default pi) | +-----------------+------------------------------------------------------+------------------------------------------------------------------------------------------------------+ Array Indexing -------------- +-----------------+-------------------+--------------------------------------------------------------------+ | Matlab Function | Python Equivalent | Description | +=================+===================+====================================================================+ | find | numpy.where | Find all indices of a matrix for which a logical statement is true | +-----------------+-------------------+--------------------------------------------------------------------+ | isnan | numpy.isnan | checks each array entry to see if it is NaN | +-----------------+-------------------+--------------------------------------------------------------------+ | isinf | numpy.isinf | checks each array entry to see if it is Inf | +-----------------+-------------------+--------------------------------------------------------------------+ | ischar | numpy.ischar | checks each array entry to see if it is a character | +-----------------+-------------------+--------------------------------------------------------------------+ Advanced functions ------------------ +-----------------+------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Matlab Function | Python Equivalent | Description | +=================+==============================================================================+======================================================================================================================================================================================================================================+ | fft2 | numpy.fft.fft2 | 2D fast Fourier transform | +-----------------+------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | fftshift | numpy.fft.fftshift | shift zero-frequency component to the center of the spectrum | +-----------------+------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | ifftshift | numpy.fft.ifftshift | inverse fftshift | +-----------------+------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | ifft2 | numpy.fft.fifft2 | inverse 2d fft | +-----------------+------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | interp2 | scipy.interpolate.RectBivariateSpline or scipy.interpolate.interp2 | Interpolation for 2-D gridded data in meshgrid format | +-----------------+------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | imshowpair | skimage.measure.structural_similarity | Compare differences between 2 images | +-----------------+------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | imregconfig | | Creates configurations to perform intensity-based image registration | +-----------------+------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | imregister | | Intensity-based image registration | +-----------------+------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | imregtform | skimage.feature.register_translation or skimage.transform.estimate_transform | Estimate geometric transfomation to align two images | +-----------------+------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | imwarp | skimage.transform.warp | Apply geometric transformation to an image | +-----------------+------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | imref2d | | Reference 2d image to xy-coordinates | +-----------------+------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | corr2 | scipy.signal.correlate2d | 2d correlation coefficient | +-----------------+------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | optimset | | Create of edit optimizations options for passing to fminbnd, fminsearch, fzero, or lsqnonneg | +-----------------+------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | lsqcurvefit | scipy.optimize.curve_fit | Solve nonlinear curve-fitting problems | +-----------------+------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | fastica | sklearn.decomposition.FastICA | fast fixed-point algorithm for independent component analysis and projection pursuit | +-----------------+------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | kmeans | sklearn.cluster.Kmeans | kmeans clustering | +-----------------+------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | fsolve | scipy.optimize.root(func, x0, method='anderson') | Root finding. Scipy does not have a trust-region dogleg method that functions exactly like Matlab's fsolve. The 'anderson' method reproduces the results in many cases. Other methods may need to be explored for other problems. | +-----------------+------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ Basic Plotting -------------- +-----------------+--------------------------------------------+-------------------------------------------------------------------------------------------------------+ | Matlab Function | Python Equivalent | Description | +=================+============================================+=======================================================================================================+ | figure | matplotlib.pyplot.figure | Create a new figure object | +-----------------+--------------------------------------------+-------------------------------------------------------------------------------------------------------+ | clf | figure.clf | clear figure; shouldn't be needed in Python since each figure will be a unique object | +-----------------+--------------------------------------------+-------------------------------------------------------------------------------------------------------+ | subplot | figure.subplots or figure.add_subplot | 1st creates a set of subplots in the figure, 2nd creates one subplot and adds it to the figure | +-----------------+--------------------------------------------+-------------------------------------------------------------------------------------------------------+ | plot | figure.plot or axes.plot | Add lineplot to current figure | +-----------------+--------------------------------------------+-------------------------------------------------------------------------------------------------------+ | title | object.title | Title of plot; better to define on object creation if possible | +-----------------+--------------------------------------------+-------------------------------------------------------------------------------------------------------+ | xlabel | axes.xlabel | Label for the x-axis of plot | +-----------------+--------------------------------------------+-------------------------------------------------------------------------------------------------------+ | ylabel | axes.ylabel | Label for the y-axis of plot | +-----------------+--------------------------------------------+-------------------------------------------------------------------------------------------------------+ | imagesc | pyplot.imshow or pyplot.matshow | Scale image data to full range of colormap and display | +-----------------+--------------------------------------------+-------------------------------------------------------------------------------------------------------+ | axis | axes.axis | Axis properties | +-----------------+--------------------------------------------+-------------------------------------------------------------------------------------------------------+ | surf | axes3d.plot_surface or axes3d.plot_trisurf | Plot a 3d surface, need to uses mpl_toolkits.mplot3d and Axes3d; which you use depends on data format | +-----------------+--------------------------------------------+-------------------------------------------------------------------------------------------------------+ | shading | | Set during plot creation as argument | +-----------------+--------------------------------------------+-------------------------------------------------------------------------------------------------------+ | view | axes3d.view_init | Change the viewing angle for a 3d plot | +-----------------+--------------------------------------------+-------------------------------------------------------------------------------------------------------+ | colormap | plot.colormap | Set the colormap; better to do so at plot creation if possible | +-----------------+--------------------------------------------+-------------------------------------------------------------------------------------------------------+ | colorbar | figure.add_colorbar(axes) | Add colorbar to selected axes | +-----------------+--------------------------------------------+-------------------------------------------------------------------------------------------------------+ sidpy-0.12.3/examples/000077500000000000000000000000001455261647000145555ustar00rootroot00000000000000sidpy-0.12.3/examples/README.txt000066400000000000000000000001251455261647000162510ustar00rootroot00000000000000==================== Examples & Tutorials ==================== **Under construction**sidpy-0.12.3/examples/hdf/000077500000000000000000000000001455261647000153165ustar00rootroot00000000000000sidpy-0.12.3/examples/hdf/README.txt000066400000000000000000000000351455261647000170120ustar00rootroot00000000000000HDF5 Utilities --------------sidpy-0.12.3/examples/hdf/plot_dtype_utils.py000066400000000000000000000344321455261647000213010ustar00rootroot00000000000000""" ================================================================================ Utilities for handling data types and transformations ================================================================================ **Suhas Somnath** 4/18/2018 """ ################################################################################ # Introduction # ------------- # The general nature of the **Universal Spectroscopy and Imaging Data (USID)** model facilitates the representation of # any kind of measurement data. # This includes: # # #. Conventional data represented using floating point numbers such as ``1.2345`` # #. Integer data (with or without sign) such as ``137`` # #. Complex-valued data such as ``1.23 + 4.5i`` # #. Multi-valued or compound valued data cells such as (``'Frequency'``: ``301.2``, ``'Amplitude'``: ``1.553E-3``, ``'Phase'``: ``2.14``) # where a single value or measurement is represented by multiple elements, each with their own names, and data types # # While HDF5 datasets are capable of storing all of these kinds of data, many conventional data analysis techniques # such as decomposition, clustering, etc. are either unable to handle complicated data types such as complex-valued # datasets and compound valued datasets, or the results from these techniques do not produce physically meaningful # results. For example, most singular value decomposition algorithms are capable of processing complex-valued datasets. # However, while the eigenvectors can have complex values, the resultant complex-valued abundance maps are meaningless. # These algorithms would not even work if the original data was compound valued! # # To avoid such problems, we need functions that transform the data to and from the necessary type (integer, real-value # etc.) # # The ``pyUSID.dtype_utils`` module facilitates comparisons, validations, and most importantly, transformations of one # data-type to another. We will be going over the many useful functions in this module and explaining how, when and why # one would use them. # # Recommended pre-requisite reading # ----------------------------------- # * `Universal Spectroscopic and Imaging Data (USID) model `_ # * `Crash course on HDF5 and h5py <../beginner/plot_h5py.html>`_ # # .. tip:: # You can download and run this document as a Jupyter notebook using the link at the bottom of this page. # # Import all necessary packages # ------------------------------- # Before we begin demonstrating the numerous functions in ``pyUSID.dtype_utils``, we need to import the necessary # packages. Here are a list of packages besides pyUSID that will be used in this example: # # * ``h5py`` - to manipulate HDF5 files # * ``numpy`` - for numerical operations on arrays in memory from __future__ import print_function, division, unicode_literals import os import subprocess import sys def install(package): subprocess.call([sys.executable, "-m", "pip", "install", package]) import h5py import numpy as np # Finally import pyUSID. try: import pyUSID as usid except ImportError: # Warning package in case something goes wrong from warnings import warn warn('pyUSID not found. Will install with pip.') import pip install('pyUSID') import pyUSID as usid ################################################################################ # Utilities for validating data types # ===================================== # pyUSID.dtype_utils contains some handy functions that make it easy to write robust and safe code by simplifying # common data type checking and validation. # # contains_integers() # --------------------- # The ``contains_integers()`` function checks to make sure that each item in a list is indeed an integer. Additionally, it # can be configured to ensure that all the values are above a minimum value. This is particularly useful when building # indices matrices based on the size of dimensions - specified as a list of integers for example. item = [1, 2, -3, 4] print('{} : contains integers? : {}'.format(item, usid.dtype_utils.contains_integers(item))) item = [1, 4.5, 2.2, -1] print('{} : contains integers? : {}'.format(item, usid.dtype_utils.contains_integers(item))) item = [1, 5, 8, 3] min_val = 2 print('{} : contains integers >= {} ? : {}'.format(item, min_val, usid.dtype_utils.contains_integers(item, min_val=min_val))) ################################################################################ # validate_dtype() # ----------------- # The ``validate_dtype()`` function ensure that a provided object is indeed a valid h5py or numpy data type. When writing # a main dataset along with all ancillary datasets, pyUSID meticulously ensures that all inputs are valid before # writing data to the file. This comes in very handy when we want to follow the 'measure twice, cut once' ethos. for item in [np.float16, np.complex64, np.uint8, np.int16]: print('Is {} a valid dtype? : {}'.format(item, usid.dtype_utils.validate_dtype(item))) # This function is especially useful on compound or structured data types: struct_dtype = np.dtype({'names': ['r', 'g', 'b'], 'formats': [np.float32, np.uint16, np.float64]}) print('Is {} a valid dtype? : {}'.format(struct_dtype, usid.dtype_utils.validate_dtype(struct_dtype))) ################################################################################ # get_compound_sub_dtypes() # -------------------------- # One common hassle when dealing with compound / structured array dtypes is that it can be a little challenging to # quickly get the individual datatypes of each field in such a data type. The ``get_compound_sub_dtypes()`` makes this a # lot easier: sub_dtypes = usid.dtype_utils.get_compound_sub_dtypes(struct_dtype) for key, val in sub_dtypes.items(): print('{} : {}'.format(key, val)) ################################################################################ # is_complex_dtype() # ------------------- # Quite often, we need to treat complex datasets different from compound datasets which themselves need to be treated # different from real valued datasets. ``is_complex_dtype()`` makes it easier to check if a numpy or HDF5 dataset has a # complex data type: for dtype in [np.float32, np.float16, np.uint8, np.int16, struct_dtype, bool]: print('Is {} a complex dtype?: {}'.format(dtype, (usid.dtype_utils.is_complex_dtype(dtype)))) for dtype in [complex, np.complex64, np.complex128, np.complex256]: print('Is {} a complex dtype?: {}'.format(dtype, (usid.dtype_utils.is_complex_dtype(dtype)))) ################################################################################ # Data transformation # ==================== # Perhaps the biggest benefit of ``dtype_utils`` is the ability to flatten complex, compound datasets to real-valued # datasets and vice versa. As mentioned in the introduction, this is particularly important when attempting to use # machine learning algorithms on complex or compound-valued datasets. In order to enable such pipelines, we need # functions to transform: # # * complex / compound valued datasets to real-valued datasets # * real-valued datasets back to complex / compound valued datasets # # flatten_complex_to_real() # -------------------------- # As the name suggests, this function stacks the imaginary values of a N-dimensional numpy / HDF5 dataset below its # real-values. Thus, applying this function to a complex valued dataset of size ``(a, b, c)`` would result in a # real-valued dataset of shape ``(a, b, 2 * c)``: length = 3 complex_array = np.random.randint(-5, high=5, size=length) + 1j * np.random.randint(-5, high=5, size=length) stacked_real_array = usid.dtype_utils.flatten_complex_to_real(complex_array) print('Complex value: {} has shape: {}'.format(complex_array, complex_array.shape)) print('Stacked real value: {} has shape: ' '{}'.format(stacked_real_array, stacked_real_array.shape)) ################################################################################ # flatten_compound_to_real() # ---------------------------- # This function flattens a compound-valued dataset of shape ``(a, b, c)`` into a real-valued dataset of shape # ``(a, b, k * c)`` where ``k`` is the number of fields within the structured array / compound dtype. Here we will # demonstrate this on a 1D array of 5 elements each containing 'r', 'g', 'b' fields: num_elems = 5 structured_array = np.zeros(shape=num_elems, dtype=struct_dtype) structured_array['r'] = np.random.random(size=num_elems) * 1024 structured_array['g'] = np.random.randint(0, high=1024, size=num_elems) structured_array['b'] = np.random.random(size=num_elems) * 1024 real_array = usid.dtype_utils.flatten_compound_to_real(structured_array) print('Structured array is of shape {} and have values:'.format(structured_array.shape)) print(structured_array) print('\nThis array converted to regular scalar matrix has shape: {} and values:'.format(real_array.shape)) print(real_array) ################################################################################ # flatten_to_real() # ----------------- # This function checks the data type of the provided dataset and then uses either of the above functions to # (if necessary) flatten the dataset into a real-valued matrix. By checking the data type of the dataset, it obviates # the need to explicitly call the aforementioned functions (that still do the work). Here is an example of the function # being applied to the compound valued numpy array again: real_array = usid.dtype_utils.flatten_to_real(structured_array) print('Structured array is of shape {} and have values:'.format(structured_array.shape)) print(structured_array) print('\nThis array converted to regular scalar matrix has shape: {} and values:'.format(real_array.shape)) print(real_array) ################################################################################ # The next three functions perform the inverse operation of taking real-valued matrices or datasets and converting them # to complex or compound-valued datasets. # # stack_real_to_complex() # ------------------------ # As the name suggests, this function collapses a N dimensional real-valued array of size ``(a, b, 2 * c)`` to a # complex-valued array of shape ``(a, b, c)``. It assumes that the first c values in real-valued dataset are the real # components and the following c values are the imaginary components of the complex value. This will become clearer # with an example: real_val = np.hstack([5 * np.random.rand(6), 7 * np.random.rand(6)]) print('Real valued dataset of shape {}:'.format(real_val.shape)) print(real_val) comp_val = usid.dtype_utils.stack_real_to_complex(real_val) print('\nComplex-valued array of shape: {}'.format(comp_val.shape)) print(comp_val) ################################################################################ # stack_real_to_compound() # -------------------------- # Similar to the above function, this function shrinks the last axis of a real valued dataset to create the desired # compound valued dataset. Here we will demonstrate it on the same 3-field ``(r,g,b)`` compound datatype: num_elems = 5 real_val = np.concatenate((np.random.random(size=num_elems) * 1024, np.random.randint(0, high=1024, size=num_elems), np.random.random(size=num_elems) * 1024)) print('Real valued dataset of shape {}:'.format(real_val.shape)) print(real_val) comp_val = usid.dtype_utils.stack_real_to_compound(real_val, struct_dtype) print('\nStructured array of shape: {}'.format(comp_val.shape)) print(comp_val) ################################################################################ # stack_real_to_target_dtype() # ----------------------------- # This function performs the inverse of ``flatten_to_real()`` - stacks the provided real-valued dataset into a complex or # compound valued dataset using the two above functions. Note that unlike ``flatten_to_real()``, the target data type must # be supplied to the function for this to work: print('Real valued dataset of shape {}:'.format(real_val.shape)) print(real_val) comp_val = usid.dtype_utils.stack_real_to_target_dtype(real_val, struct_dtype) print('\nStructured array of shape: {}'.format(comp_val.shape)) print(comp_val) ################################################################################ # check_dtype() # -------------- # ``check_dtype()`` is a master function that figures out the data type, necessary function to transform a HDF5 dataset to # a real-valued array, expected data shape, etc. Before we demonstrate this function, we need to quickly create an # example HDF5 dataset. file_path = 'dtype_utils_example.h5' if os.path.exists(file_path): os.remove(file_path) with h5py.File(file_path) as h5_f: num_elems = (5, 7) structured_array = np.zeros(shape=num_elems, dtype=struct_dtype) structured_array['r'] = 450 * np.random.random(size=num_elems) structured_array['g'] = np.random.randint(0, high=1024, size=num_elems) structured_array['b'] = 3178 * np.random.random(size=num_elems) _ = h5_f.create_dataset('compound', data=structured_array) _ = h5_f.create_dataset('real', data=450 * np.random.random(size=num_elems), dtype=np.float16) _ = h5_f.create_dataset('complex', data=np.random.random(size=num_elems) + 1j * np.random.random(size=num_elems), dtype=np.complex64) h5_f.flush() ################################################################################ # Now, lets test the the function on compound-, complex-, and real-valued HDF5 datasets: def check_dataset(h5_dset): print('\tDataset being tested: {}'.format(h5_dset)) func, is_complex, is_compound, n_features, type_mult = usid.dtype_utils.check_dtype(h5_dset) print('\tFunction to transform to real: %s' % func) print('\tis_complex? %s' % is_complex) print('\tis_compound? %s' % is_compound) print('\tShape of dataset in its current form: {}'.format(h5_dset.shape)) print('\tAfter flattening to real, shape is expected to be: ({}, {})'.format(h5_dset.shape[0], n_features)) print('\tByte-size of a single element in its current form: {}'.format(type_mult)) with h5py.File(file_path, mode='r') as h5_f: print('Checking a compound-valued dataset:') check_dataset(h5_f['compound']) print('') print('Checking a complex-valued dataset:') check_dataset(h5_f['complex']) print('') print('Checking a real-valued dataset:') check_dataset(h5_f['real']) os.remove(file_path) sidpy-0.12.3/examples/hdf/plot_h5py.py000066400000000000000000000504641455261647000176240ustar00rootroot00000000000000""" =============================================================================== Primer to HDF5 and h5py =============================================================================== **Suhas Somnath** 4/18/2018 **This document serves as a quick primer to HDF5 files and the h5py package used for reading and writing to such files** """ ######################################################################################################################## # Introduction # ------------- # We create and consume digital information stored in various file formats on a daily basis such as news presented in # HTML files, scientific journal articles in PDF files, tabular data in XLSX spreadsheets and so on. Commercially # available scientific instruments generate data in a variety of, typically proprietary, file formats. The proprietary # nature of the data impedes scientific research of individual researchers and the collaboration within the scientific # community at large. Hence, pycroscopy stores all relevant information including the measurement data, metadata etc. # in the most popular file format for scientific data - Hierarchical Data Format (HDF5) files. # # HDF5 is a remarkably straightforward file format to understand since it mimics the familiar folders and files paradigm # exposed to users by all operating systems such as Windows, Mac OS, Linux, etc. HDF5 files can contain: # # * ``Datasets`` - similar to spreadsheets and text files with tabular data. # * ``Groups`` - similar to folders in a regular file system # * ``Attributes`` - small metadata that provide additional information about the Group or Dataset they are attached to. # * other advanced features such as hard links, soft links, object and region references, etc. # # h5py is the official software package for reading and writing to HDF5 files in python. Consequently, Pycroscopy relies # entirely on h5py for all file related operations. While there are several high-level functions that simplify the # reading and writing of Pycroscopy stylized data, it is still crucial that the users of Pycroscopy understand the # basics of HDF5 files and are familiar with the basic functions in h5py. There are several tutorials available # elsewhere to explain h5py in great detail. This document serves as a quick primer to the basics of interacting with # HDF5 files via h5py. # # .. tip:: # You can download and run this document as a Jupyter notebook using the link at the bottom of this page. # # Import all necessary packages # ------------------------------- # For this primer, we only need some very basic packages, all of which come with the standard Anaconda distribution: # # * ``os`` - to manipulate and remove files # * ``numpy`` - for basic numerical work # * ``h5py`` - the package that will be the focus of this primer from __future__ import print_function, division, unicode_literals import os import numpy as np import h5py ######################################################################################################################## # Creating a HDF5 files using h5py is similar to the process of creating a conventional text file using python. The File # class of h5py requires the path for the desired file with a .h5, .hdf5, or similar extension. h5_path = 'hdf5_primer.h5' h5_file = h5py.File('hdf5_primer.h5') print(h5_file) ######################################################################################################################## # At this point, a file in the path specified by h5_path has been created and is now open for modification. The returned # value - h5_file is necessary to perform other operations on the file including creating groups and datasets. # # Groups # =========== # create_group() # ---------------- # We can use the ``create_group()`` function on an existing object such as the open file handle (``h5_file``) to create a # group: h5_group_1 = h5_file.create_group('Group_1') print(h5_group_1) ######################################################################################################################## # The output of the above print statement reveals that a group named ``Group_1`` was successfully created at location: '/' # (which stands for the root of the file). Furthermore, this group contains 0 objects or members. # .name # ------- # One can find the full / absolute path where this object is located from its ``name`` property: print(h5_group_1.name) ######################################################################################################################## # Groups in Groups # ---------------- # Much like folders in a computer, these groups can themselves contain more groups and datasets. # # Let us create a few more groups the same way. Except, let us create these groups within the newly created. To do this, # we would need to call the ``create_group()`` function on the h5_group_1 object and not the h5_file object. Doing the # latter would result in groups created under the file at the same level as ``Group_1`` instead of inside ``Group_1``. h5_group_1_1 = h5_group_1.create_group('Group_1_1') h5_group_1_2 = h5_group_1.create_group('Group_1_2') ######################################################################################################################## # Now, when we print h5_group, it will reveal that we have two objects - the two groups we just created: print(h5_group_1) ######################################################################################################################## # Lets see what a similar print of one of the newly created groups looks like: print(h5_group_1_1) ######################################################################################################################## # The above print statement shows that this group named ``Group_1_1`` exists at a path: ``"/Group_1/Group_1_1"``. In other # words, this is similar to a folder contained inside another folder. # # .parent # --------- # The hierarchical nature of HDF5 allows us to access datasets and groups using relationships or paths. For example, # every HDF5 object has a parent. In the case of 'Group_1' - its parent is the root or h5_file itself. Similarly, the # parent object of 'Group_1_1' is 'Group_1': print('Parent of "Group_1" is {}'.format(h5_group_1.parent)) print('Parent of "Group_1_1" is {}'.format(h5_group_1_1.parent)) ######################################################################################################################## # In fact the .parent of an object is an HDF5 object (either a HDF5 group or HDF5 File object). So we can check if the # parent of the h5_group_1_1 variable is indeed the h5_group_1 variable: print(h5_group_1_1.parent == h5_group_1) ######################################################################################################################## # Accessing H5 objects # ---------------------- # Imagine a file or a folder on a computer that is several folders deep from where one is (e.g. - # /Users/Joe/Documents/Projects/2018/pycroscopy).One could either reach the desired file or folder by opening one folder # after another or directly by using a long path string. If you were at root (/), you would need to paste the entire # path (absolute path) of the desired file - ``/Users/Joe/Documents/Projects/2018/pycroscopy``. Alternatively, if you # were in an intermediate directory (e.g. - ``/Users/Joe/Documents/``), you would need to paste what is called the # relative path (in this case - ``Projects/2018/pycroscopy``) to get to the desired file. # # In the same way, we can also access HDF5 objects either through ``relative paths``, or ``absolute paths``. Here are a few # ways one could get to the group ``Group_1_2``: print(h5_file['/Group_1/Group_1_2']) print(h5_group_1['Group_1_2']) print(h5_group_1_1.parent['Group_1_2']) print(h5_group_1_1.parent.parent['Group_1/Group_1_2']) ######################################################################################################################## # Now let us look at how one can iterate through the datasets and Groups present within a HDF5 group: for item in h5_group_1: print(item) ######################################################################################################################## # .items() # ---------- # Essentially, h5py group objects contain a dictionary of key-value pairs where they key is the name of the object and # the value is a reference to the object itself. # # What the above for loop does is it iterates only over the keys in this dictionary which are all strings. In order to # get the actual dataset object itself, we would need to use the aforementioned addressing techniques to get the actual # Group objects. # # Let us see how we would then try to find the object for the group named 'Group_1_2': for key, value in h5_group_1.items(): if key == 'Group_1_2': print('Found the desired object: {}'.format(value)) ######################################################################################################################## # Datasets # =========== # create_dataset() # ---------------- # We can create a dataset within ``Group_1`` using a function that is similar to ``create_group()``, called # ``create_dataset()``. Unlike create_group() which just takes the path of the desired group as an input, # ``create_dataset()`` is highly customizable and flexible. # # In our experience, there are three modes of creating datasets that are highly relevant for scientific applications: # # * dataset with data at time of creation - where the data is already available at the time of creating the dataset # * empty dataset - when one knows the size of data but the entire data is not available # * resizable dataset - when one does not even know how large the data can be. *This case is rare* # # Creating Dataset with available data: # ------------------------------------- # Let as assume we want to store a simple greyscale (floating point values) image with 256 x 256 pixels. We would create # and store the data as shown below. As the size of the dataset becomes very large, the precision with which the data is # stored can significantly affect the size of the dataset and the file. Therefore, we recommend purposefully specifying # the data-type (via the ``dtype`` keyword argument) during creation. h5_simple_dataset = h5_group_1.create_dataset('Simple_Dataset', data=np.random.rand(256, 256), dtype=np.float32) print(h5_simple_dataset) ######################################################################################################################## # Accessing data # ---------------- # We can access data contained in the dataset just like accessing a numpy array. For example, if we want the value at # row ``29`` and column ``167``, we would read it as: print(h5_simple_dataset[29, 167]) ######################################################################################################################## # Again, just as before, we can address this dataset in many ways: print(h5_group_1['Simple_Dataset']) print(h5_file['/Group_1/Simple_Dataset']) ######################################################################################################################## # Creating (potentially large) empty datasets: # -------------------------------------------- # In certain situations, we know how much space to allocate for the final dataset but we may not have all the data at # once. Alternatively, the dataset is so large that we cannot fit the entire data in the computer memory before writing # to the HDF5 file. Another possible circumstance is when we have to read N files, each containing a small portion of # the data and then write the contents into each slot in the HDF5 dataset. # # For example, assume that we have 128 files each having 1D spectra (amplitude + phase or complex value) of length 1024. # Here is how one may create the HDF5 dataset to hold the data: h5_empty_dataset = h5_group_1.create_dataset('Empty_Dataset', shape=(128, 1024), dtype=np.complex64) print(h5_empty_dataset) ######################################################################################################################## # Note that unlike before, this particular dataset is empty since we only allocated space, so we would be reading zeros # when attempting to access data: print(h5_empty_dataset[5, 102]) ######################################################################################################################## # populating with data # ---------------------- # One could populate each chunk of the dataset just like filling in a numpy array: h5_empty_dataset[0] = np.random.rand(1024) + 1j * np.random.rand(1024) ######################################################################################################################## # flush() # -------- # It is a good idea to ensure that this data is indeed committed to the file using regular flush() operations. There are # chances where the data is still in the memory / buffer and not yet in the file if one does not flush(): h5_file.flush() ######################################################################################################################## # Creating resizeable datasets: # ----------------------------- # This solution is relevant to those situations where we only know how large each unit of data would be but we don't # know the number of units. This is especially relevant when acquiring data from an instrument. # # For example, if we were acquiring spectra of length 128 on a 1D grid of 256 locations, we may have created an empty 2D # dataset of shape (265, 128) using the aforementioned function. The data was being collected ordinarily over the first # 13 positions but a change in parameters resulted in spectra of length 175 instead. The data from the 14th positon # cannot be stored in the empty array due to a size mismatch. Therefore, we would need to create another empty 256 x 175 # dataset to hold the data. If changes in parameters cause 157 changes in spectra length, that would result in the # creation of 157 datasets each with a whole lot of wasted space since datasets cannot be shrunk easily. # # In such cases, it is easier just to create datasets that can expand one pixel at a time. For this specific example, # one may want to create a 2D dataset of shape (1, 128) that could grow up to a maxshape of (256, 128) as shown below: h5_expandable_dset = h5_group_1.create_dataset('Expandable_Dataset', shape=(1, 128), maxshape=(256, 128), dtype=np.float32) print(h5_expandable_dset) ######################################################################################################################## # Space has been allocated for the first pixel, so the data could be written in as: h5_expandable_dset[0] = np.random.rand(128) ######################################################################################################################## # For the next pixel, we would need to expand the dataset before filling it in: h5_expandable_dset.resize(h5_expandable_dset.shape[0] + 1, axis=0) print(h5_expandable_dset) ######################################################################################################################## # Notice how the dataset has increased in size in the first dimension allowing the second pixel to be stored. The second # pixel's data would be stored in the same way as in the first pixel and the cycle of expand and populate-with-data # would continue. # # It is very important to note that there is a non-trivial storage overhead associated with each resize operation. In # other words, a file containing this resizeable dataset that has been resized 255 times will certainly be larger than # a similar file where the dataset space was pre-allocated and never expanded. Therefore this mode of creating datasets # should used sparingly. # # Attributes # =========== # * are metadata that can convey information that cannot be efficiently conveyed using Group or Dataset objects. # * are almost exactly like python dictionaries in that they have a key-value pairs. # * can be stored in either Group or Dataset objects. # * are not appropriate for storing large amounts of information. Consider datasets instead # * are best suited for things like experimental parameter such as beam intensity, scan rate, scan width, etc. # # Writing # --------- # Storing attributes in objects is identical to appending to python dictionaries. Lets store some simple attributes in # the group named 'Group_1': h5_simple_dataset.attrs['single_num'] = 36.23 h5_simple_dataset.attrs.update({'list_of_nums': [1, 6.534, -65], 'single_string': 'hello'}) ######################################################################################################################## # Reading # ---------- # We would read the attributes just like we would treat a dictionary in python: for key, val in h5_simple_dataset.attrs.items(): print('{} : {}'.format(key, val)) ######################################################################################################################## # Lets read the attributes one by one and verify that we read what we wrote: print('single_num: {}'.format(h5_simple_dataset.attrs['single_num'] == 36.23)) print('list_of_nums: {}'.format(np.all(h5_simple_dataset.attrs['list_of_nums'] == [1, 6.534, -65]))) print('single_string: {}'.format(h5_simple_dataset.attrs['single_string'] == 'hello')) ######################################################################################################################## # Caveat # -------- # While the low-level attribute writing and reading does appear to work and is simple, it does not work for a list of # strings in python 3. Hence the following line will not work and will cause problems. # # .. code-block:: python # # h5_simple_dataset.attrs['list_of_strings'] = ['a', 'bc', 'def'] # # Instead, we recommend writing lists of strings by casting them as numpy arrays: h5_simple_dataset.attrs['list_of_strings'] = np.array(['a', 'bc', 'def'], dtype='S') ######################################################################################################################## # In the same way, reading attributes that are lists of strings is also not straightforward: print('list_of_strings: {}'.format(h5_simple_dataset.attrs['list_of_strings'] == ['a', 'bc', 'def'])) ######################################################################################################################## # A similar decoding step needs to be taken to extract the actual string values. # # To avoid manual encoding and decoding of attributes (different strategies for different versions of python), we # recommend: # # * writing attributes using: ``pycroscopy.hdf_utils.write_simple_attrs()`` # * reading attributes using: ``pycroscopy.hdf_utils.get_attr() or get_attributes()`` # # Both these functions work reliably and consistently across all python versions and fix this problem in h5py. # # Besides strings and numbers, we tend to store references to datasets as attributes. Here is how one would link the # empty dataset to the simple dataset: h5_simple_dataset.attrs['Dataset_Reference'] = h5_empty_dataset.ref print(h5_simple_dataset.attrs['Dataset_Reference']) ######################################################################################################################## # Here is how one would get a handle to the actual dataset from the reference: # Read the attribute how you normally would h5_ref = h5_simple_dataset.attrs['Dataset_Reference'] # Get the handle to the actual dataset: h5_dset = h5_file[h5_ref] # Check if this object is indeed the empty dataset: print(h5_empty_dataset == h5_dset) ######################################################################################################################## # Once we are done reading or manipulating an HDF5 file, we need to close it to avoid and potential damage: h5_file.close() os.remove(h5_path) ######################################################################################################################## # As mentioned in the beginning this is not meant to be a comprehensive overview of HDF5 or h5py, but rather just a # quick overview of the important functionality we recommend everyone to be familiar with. We encourage you to read more # about h5py and HDF5 if you are interested. sidpy-0.12.3/examples/viz_dataset/000077500000000000000000000000001455261647000170725ustar00rootroot00000000000000sidpy-0.12.3/examples/viz_dataset/README.txt000066400000000000000000000000671455261647000205730ustar00rootroot00000000000000Visualizing Dataset objects ===========================sidpy-0.12.3/examples/viz_dataset/plot_visualizers.py000066400000000000000000000157751455261647000231010ustar00rootroot00000000000000""" ================================================================================ Plotting Datasets ================================================================================ **Gerd Duscher** 08/25/2020 **Please download this example and run it as a notebook by scrolling to the bottom of this page** """ # Ensure python 3 compatibility: from __future__ import division, print_function, absolute_import, unicode_literals import numpy as np import matplotlib.pyplot as plt import sys sys.path.append('../../') import sidpy print(sidpy.__version__) ############################################################################### # Plotting an Image # ----------------- # First, we make a sidpy dataset from a numpy array x = np.random.normal(loc=3, scale=2.5, size=(128, 128)) dset = sidpy.Dataset.from_array(x) ############################################################################### # Next, we add some information about this dataset dset.data_type = 'image' dset.units = 'counts' dset.quantity = 'intensity' ############################################################################### # For plotting it is important to set the dimensions correctly. dset.set_dimension(0, sidpy.Dimension('x', np.arange(dset.shape[0])*.02)) dset.x.dimension_type = 'spatial' dset.x.units = 'nm' dset.x.quantity = 'distance' dset.set_dimension(1, sidpy.Dimension('y', np.arange(dset.shape[1])*.02)) dset.y.dimension_type = 'spatial' dset.yunits = 'nm' dset.y.quantity = 'distance' ############################################################################### # Now we plot the dataset: dset.plot() ############################################################################### # Creating an Image-Stack DataSet # ------------------------------- # In the following we will make a numpy which resembles a stack of images # # In the ``sidpy Dataset`` will set the ``data_type`` to ``image_stack`` for the plotting routine to know how to plot this dataset. # # The dimensions have to contain at least two ``spatial`` dimensions and one that is identifiable as a stack dimension ('stack, 'frame', 'time'). # First we make a stack of images x = np.random.normal(loc=3, scale=2.5, size=(25, 128, 128)) dset = sidpy.Dataset.from_array(x) dset.data_type = 'image_stack' dset.units = 'counts' dset.quantity = 'intensity' dset.set_dimension(0, sidpy.Dimension('frame', np.arange(dset.shape[0]))) dset.frame.dimension_type = 'time' dset.set_dimension(1, sidpy.Dimension('x', np.arange(dset.shape[1])*.02)) dset.x.dimension_type = 'spatial' dset.x.units = 'nm' dset.x.quantity = 'distance' dset.set_dimension(2, sidpy.Dimension('y', np.arange(dset.shape[2])*.02)) dset.y.dimension_type = 'spatial' dset.yunits = 'nm' dset.y.quantity = 'distance' ############################################################################### # Plotting the Dataset # -------------------- # Please note that the scroll wheel will move you through the stack. # # Zoom to an area and let it play! # # Click on the ``Average`` button and then click on it again. dset.plot() ############################################################################### # The kwargs dictionary is used to plot the image stack in TEM style with scale bar kwargs = {'scale_bar': True, 'cmap': 'hot'} # or maybe 'cmap': 'gray' dset.plot(verbose=True, **kwargs) ############################################################################### # Plot Dataset as Spectral Image # ------------------------------ # We need to change the data_type of the dataset to ``spectrum_image`` and the dimension_type of one dimension to ``spectral``. # # Now the plot function plots it as a spectrum image. # # Select the spectrum with the mouse (left click). dset.data_type = 'spectrum_image' dset.set_dimension(0, sidpy.Dimension('spectrum',np.arange(dset.shape[0]))) dset.spectrum.dimension_type = 'spectral' dset.plot() ############################################################################### # We make the selection more visible by setting the binning of the spectra selection. # # The binning averages over the binning box. # Run the code-cell below and look in the plot above. # While you can make the modifications in a jupyter noteboook in a code-cell after the # dset.plot() command is executed, that does not work in a script. # Here we use the explicit visualization command followed by a plt.show() command. dset.view.set_bin([20, 20]) plt.show() ############################################################################### # The axes (and figure) instances of matplotlib can be accessed through the ``view`` # attribute of the sidpy dataset. For example ``dset.view``. # Again that does not work in a prgram and we use the explicit command. # Note that you always have to keep a reference for an interactive plot (here view) """ <<<<<<< HEAD:examples/viz/dataset/plot_visualizers.py view = sidpy.viz.dataset_viz.SpectralImageVisualizer(dset) view.set_bin([40,40]) x, y = np.mgrid[0:501:100, 0:501:100] + 5 view.axes[0].scatter(x, y, color='red'); ======= ############################################################################### # kwargs = {'scale_bar': True, 'cmap': 'hot'} view = sid.viz.dataset_viz.ImageStackVisualizer(dset, **kwargs) <<<<<<< Updated upstream:examples/viz_dataset/plot_visualizers.py ======= >>>>>>> 608507c4c878dbbaaf7968979bd27d058695deed:examples/viz_dataset/plot_visualizers.py >>>>>>> Stashed changes:examples/viz/dataset/plot_visualizers.py plt.show() ############################################################################### <<<<<<< HEAD:examples/viz/dataset/plot_visualizers.py ======= print(dset.shape) kwargs = {'scale_bar': True, 'cmap': 'hot'} view = sid.dataset_viz.ImageVisualizer(dset, image_number=5, **kwargs) <<<<<<< Updated upstream:examples/viz_dataset/plot_visualizers.py ======= >>>>>>> 608507c4c878dbbaaf7968979bd27d058695deed:examples/viz_dataset/plot_visualizers.py >>>>>>> Stashed changes:examples/viz/dataset/plot_visualizers.py ############################################################################### # The generic plot command of a dispy dataset looks for the ``data_type`` to # decide how to plot the data. # We cn force any plot with the expliit plot command, but we need to provide the # ``dimension_type`` as information what axis to be used for the plot. <<<<<<< HEAD:examples/viz/dataset/plot_visualizers.py print(dset.shape) kwargs = {'scale_bar': True, 'cmap': 'hot'} view = sidpy.viz.dataset_viz.ImageVisualizer(dset, image_number = 5, **kwargs) plt.show() ############################################################################### ======= dset.data_type = 'spectrum_image' dset.set_dimension(0, sidpy.Dimension('spectrum',np.arange(dset.shape[0]))) dset.spectrum.dimension_type = 'spectral' view = sidpy.viz.dataset_viz.SpectralImageVisualizer(dset) view.set_bin([30, 40]) plt.show() ############################################################################### # dset.data_type = 'spectrum_image' dset.set_dimension(0, sidpy.Dimension('spectrum',np.arange(dset.shape[0]))) dset.spectrum.dimension_type = 'spectral' # view = SpectralImageVisualizer(dset) # dset.plot() """sidpy-0.12.3/extras_require.txt000066400000000000000000000000151455261647000165360ustar00rootroot00000000000000mpi4py pyqt5 sidpy-0.12.3/notebooks/000077500000000000000000000000001455261647000147425ustar00rootroot00000000000000sidpy-0.12.3/notebooks/00_basic_usage/000077500000000000000000000000001455261647000175065ustar00rootroot00000000000000sidpy-0.12.3/notebooks/00_basic_usage/create_dataset.ipynb000066400000000000000000005310411455261647000235250ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Creating and Manipulating Datasets\n", "\n", "**Gerd Duscher and Suhas Somnath**\n", "\n", "08/25/2020\n", "\n", "**This document is a simple example of how to create and manipulate Dataset\n", "objects**\n", "\n", "**UNDER CONSTRUCTION**\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n", "Populating the interactive namespace from numpy and matplotlib\n", "sidpy version: 0.12.1\n" ] } ], "source": [ "\n", "# Ensure python 3 compatibility:\n", "from __future__ import division, print_function, absolute_import, unicode_literals\n", "\n", "%pylab notebook\n", "\n", "import sys\n", "\n", "sys.path.insert(0, '../../')\n", "import sidpy\n", "print('sidpy version: ', sidpy.__version__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating a ``sipy.Dataset`` object\n", "We can create a simple sidpy Dataset from any array like object\n", "Here we just use a numpy array filled with zeros\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sidpy.Dataset of type UNKNOWN with:\n", " dask.array\n", " data contains: generic (generic)\n", " and Dimensions: \n", "a: generic (generic) of size (4,)\n", "b: generic (generic) of size (5,)\n", "c: generic (generic) of size (10,)\n" ] }, { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", "
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" ], "text/plain": [ "sidpy.Dataset of type UNKNOWN with:\n", " dask.array\n", " data contains: generic (generic)\n", " and Dimensions: \n", "a: generic (generic) of size (4,)\n", "b: generic (generic) of size (5,)\n", "c: generic (generic) of size (10,)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_set = sidpy.Dataset.from_array(np.random.random([4, 5, 10]), name='random')\n", "\n", "print(data_set)\n", "data_set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that ``data_set`` is a dask array....\n", "We will be improving upon the information that will be displayed when printing ``sidpy.Dataset`` objects\n", "\n", "Accessing data within a ``Dataset``:\n", "Indexing of the dataset works like in numpy\n", "Note, that we first index and then we make a numpy array for printing reasons\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.41955319 0.59615527 0.8109613 0.34605858]\n" ] } ], "source": [ "print(np.array(data_set[:,0,2]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Slicing and dicing:\n", "\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "data_dictionary = {\"main_dataset\": data_set, \n", " 'new_dataset': data_set, \n", " 'metadata': {'atoms': blobs}, \n", " 'structure': {'SrTiO3': ase.build.SrTiO3()}}\n", "\n", "data_dictionary['new_dataset'].metadata = {\"origin_dataset\": 'main_dataset'}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Metadata\n", "``sidpy`` automatically assigns generic top-level metadata regarding the\n", "``Dataset``. Users are encouraged to capture the context regarding the dataset.\n", "The attributes included in the sidpy dataset are \n", "Required Attributes:\n", "\n", "- ``quantity``: string: Physical quantity that is contained in this dataset\n", "\n", "- ``units``: string: Units for this physical quantity\n", "\n", "- ``data_type``: string : What kind of data this is. Example - image, image stack, video, hyperspectral image, etc.\n", "\n", "- ``modality``: string : Experimental / simulation modality - scientific meaning of data. Example - photograph, TEM micrograph, SPM Force-Distance spectroscopy.\n", "\n", "- ``source``: string : Source for dataset like the kind of instrument. One could go very deep here into either the algorithmic details if this is a result from analysis or the exact configurations for the instrument that generated this dataset.\n", "\n", "Those attributes are set to ``generic`` originally but one would want to set them\n", "for the specific dataset. The attributes ``data_type``, ``quantity`` and ``units`` will be important for plotting the data.\n", "\n", "Here's how one could do that, but with the wrong key word:\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "ename": "Warning", "evalue": "('Supported data_types for plotting are only: ', ['UNKNOWN', 'SPECTRUM', 'LINE_PLOT', 'LINE_PLOT_FAMILY', 'IMAGE', 'IMAGE_MAP', 'IMAGE_STACK', 'SPECTRAL_IMAGE', 'IMAGE_4D'])", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mWarning\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata_set\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'spectrum_image'\u001b[0m \u001b[0;31m# not supported\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/Dropbox (ORNL)/Python_scripts/sidpy/sidpy/sid/dataset.py\u001b[0m in \u001b[0;36mdata_type\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m 598\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 599\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataType\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUNKNOWN\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 600\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mWarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Supported data_types for plotting are only: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataType\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_member_names_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 601\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 602\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataType\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mWarning\u001b[0m: ('Supported data_types for plotting are only: ', ['UNKNOWN', 'SPECTRUM', 'LINE_PLOT', 'LINE_PLOT_FAMILY', 'IMAGE', 'IMAGE_MAP', 'IMAGE_STACK', 'SPECTRAL_IMAGE', 'IMAGE_4D'])" ] } ], "source": [ "data_set.data_type = 'spectrum_image' # not supported" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's how one could do that sucessfully:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data_set.data_type = 'spectral_image' # supported\n", "\n", "data_set.units = 'nA'\n", "data_set.quantity = 'Current'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Scientific metadata\n", "These ``Dataset`` objects can also capture rich scientific metadata such as\n", "acquisition parameters, etc. as well:\n", "We would want to add those parameters as attributes.\n", "These attributes could be lists, numpy arrays or simple dictionaries.\n", "It is encouraged to add any parameters of data analysis to the datasets,\n", "to keep track of input parameters. Here I made some up as an illustration:\n", "\n", " These ``Dataset`` objects can also capture rich scientific metadata such as acquisition parameters, etc. as well:\n", "\n", "We would want to add those parameters as attributes. These attributes could be lists, numpy arrays or simple dictionaries. It is encouraged to add any parameters of data analysis to the datasets, to keep track of input parameters.\n", "\n", "It is recommended to add any parameters to the (nested) metadata dictionary.\n", "These metadata can then be viewed in dataset.view_metadata and dataset.view_original_metadata. It is encouraged to add any parameters of data analysis to the datasets, to keep track of input parameters.\n", "\n", "There is a size limit of 64kB for the storage of dictionaries in h5py. Therefore, large data such as reference data should be added directly as attributes. All attributes that you add to a dataset will be stored within the pyNSID file. \n", " \n", "Please note, that the dictionary ``original_metadata`` should not be changed so that information provided by the acquisition device stays pristine, but relevant inforamtion should be copied over to the ``metadata`` attribute/dictionary.\n", "\n", "Here I made up some metadata as an illustration:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1 2 3 4]\n", "nothing : \n", "value : 6.8\n", "instrument :\n", "\tmicroscope : Nion\n", "\tacceleration_voltage : 60000\n", "acquired : nowhere\n" ] } ], "source": [ "data_set.calibration = np.arange(5)\n", "data_set.metadata = {'nothing': ' ', 'value': 6.8, 'instrument': {'microscope': 'Nion', 'acceleration_voltage':60000}}\n", "data_set.metadata['acquired'] = 'nowhere'\n", "\n", "print(data_set.calibration)\n", "sidpy.dict_utils.print_nested_dict(data_set.metadata)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another set of metadata in these Datasets is the Dimension ones:\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dimensions\n", "The ``Dataset`` is automatically populated with generic information about\n", "each dimension of the ``Dataset``. It is a good idea to capture context\n", "regarding each of these dimensions using ``sidpy.Dimension``.\n", "As a minimum we need a name and values (of the same length as the dimensions of the data).\n", "One can provide as much or as little information about each dimension.\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "data_set.set_dimension(0, sidpy.Dimension(np.arange(data_set.shape[0]), \n", " name='x', units='um', quantity='Length',\n", " dimension_type='spatial'))\n", "data_set.set_dimension(1, sidpy.Dimension(np.linspace(-2, 2, num=data_set.shape[1], endpoint=True),\n", " 'y', units='um', quantity='Length',\n", " dimension_type='spatial'))\n", "data_set.set_dimension(2, sidpy.Dimension(np.sin(np.linspace(0, 2 * np.pi, num=data_set.shape[2])),\n", " 'bias' ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One could also manually add information regarding specific components of\n", "dimensions associated with Datasets via:\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "data_set.bias.dimension_type = 'spectral'\n", "data_set.bias.units = 'V'\n", "data_set.bias.quantity = 'Bias'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at what the dataset looks like with the additional information\n", "regarding the dimensions. \n", "\n", "We can access a dimension by its name or by the dimension number.\n", "\n", "Also the print function now provides a little more information about our dataset." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bias: Bias (V) of size (10,)\n", "y: Length (um) of size (5,)\n", "sidpy.Dataset of type SPECTRAL_IMAGE with:\n", " dask.array\n", " data contains: Current (nA)\n", " and Dimensions: \n", "x: Length (um) of size (4,)\n", "y: Length (um) of size (5,)\n", "bias: Bias (V) of size (10,)\n", " with metadata: ['nothing', 'value', 'instrument', 'acquired']\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
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" ], "text/plain": [ "sidpy.Dataset of type SPECTRAL_IMAGE with:\n", " dask.array\n", " data contains: Current (nA)\n", " and Dimensions: \n", "x: Length (um) of size (4,)\n", "y: Length (um) of size (5,)\n", "bias: Bias (V) of size (10,)\n", " with metadata: ['nothing', 'value', 'instrument', 'acquired']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(data_set.bias)\n", "print(data_set.dim_1)\n", "print(data_set)\n", "data_set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting\n", "The ``Dataset`` object also comes with the ability to visualize its contents\n", "using the ``plot()`` function. Here we only show a simple application, but a more\n", "detailed description can be found in the plotting section.\n", "Here we plot a spectral image you can click in the image part of the plot on the\n", "left and the spectrum on the right will update.\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "scrolled": true }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "/* global mpl */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function () {\n", " if (typeof WebSocket !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof MozWebSocket !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert(\n", " 'Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.'\n", " );\n", " }\n", "};\n", "\n", "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = this.ws.binaryType !== undefined;\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById('mpl-warnings');\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent =\n", " 'This browser does not support binary websocket messages. ' +\n", " 'Performance may be slow.';\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = document.createElement('div');\n", " this.root.setAttribute('style', 'display: inline-block');\n", " this._root_extra_style(this.root);\n", "\n", " parent_element.appendChild(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message('supports_binary', { value: fig.supports_binary });\n", " fig.send_message('send_image_mode', {});\n", " if (fig.ratio !== 1) {\n", " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", " }\n", " fig.send_message('refresh', {});\n", " };\n", "\n", " this.imageObj.onload = function () {\n", " if (fig.image_mode === 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function () {\n", " fig.ws.close();\n", " };\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "};\n", "\n", "mpl.figure.prototype._init_header = function () {\n", " var titlebar = document.createElement('div');\n", " titlebar.classList =\n", " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", " var titletext = document.createElement('div');\n", " titletext.classList = 'ui-dialog-title';\n", " titletext.setAttribute(\n", " 'style',\n", " 'width: 100%; 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position: absolute; left: 0; top: 0; z-index: 1;'\n", " );\n", "\n", " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", " if (this.ResizeObserver === undefined) {\n", " if (window.ResizeObserver !== undefined) {\n", " this.ResizeObserver = window.ResizeObserver;\n", " } else {\n", " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", " this.ResizeObserver = obs.ResizeObserver;\n", " }\n", " }\n", "\n", " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", " var nentries = entries.length;\n", " for (var i = 0; i < nentries; i++) {\n", " var entry = entries[i];\n", " var width, height;\n", " if (entry.contentBoxSize) {\n", " if (entry.contentBoxSize instanceof Array) {\n", " // Chrome 84 implements new version of spec.\n", " width = entry.contentBoxSize[0].inlineSize;\n", " height = entry.contentBoxSize[0].blockSize;\n", " } else {\n", " // Firefox implements old version of spec.\n", " width = entry.contentBoxSize.inlineSize;\n", " height = entry.contentBoxSize.blockSize;\n", " }\n", " } else {\n", " // Chrome <84 implements even older version of spec.\n", " width = entry.contentRect.width;\n", " height = entry.contentRect.height;\n", " }\n", "\n", " // Keep the size of the canvas and rubber band canvas in sync with\n", " // the canvas container.\n", " if (entry.devicePixelContentBoxSize) {\n", " // Chrome 84 implements new version of spec.\n", " canvas.setAttribute(\n", " 'width',\n", " entry.devicePixelContentBoxSize[0].inlineSize\n", " );\n", " canvas.setAttribute(\n", " 'height',\n", " entry.devicePixelContentBoxSize[0].blockSize\n", " );\n", " } else {\n", " canvas.setAttribute('width', width * fig.ratio);\n", " canvas.setAttribute('height', height * fig.ratio);\n", " }\n", " canvas.setAttribute(\n", " 'style',\n", " 'width: ' + width + 'px; height: ' + height + 'px;'\n", " );\n", "\n", " rubberband_canvas.setAttribute('width', width);\n", " rubberband_canvas.setAttribute('height', height);\n", "\n", " // And update the size in Python. We ignore the initial 0/0 size\n", " // that occurs as the element is placed into the DOM, which should\n", " // otherwise not happen due to the minimum size styling.\n", " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", " fig.request_resize(width, height);\n", " }\n", " }\n", " });\n", " this.resizeObserverInstance.observe(canvas_div);\n", "\n", " function on_mouse_event_closure(name) {\n", " return function (event) {\n", " return fig.mouse_event(event, name);\n", " };\n", " }\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mousedown',\n", " on_mouse_event_closure('button_press')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseup',\n", " on_mouse_event_closure('button_release')\n", " );\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband_canvas.addEventListener(\n", " 'mousemove',\n", " on_mouse_event_closure('motion_notify')\n", " );\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mouseenter',\n", " on_mouse_event_closure('figure_enter')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseleave',\n", " on_mouse_event_closure('figure_leave')\n", " );\n", "\n", " canvas_div.addEventListener('wheel', function (event) {\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " on_mouse_event_closure('scroll')(event);\n", " });\n", "\n", " canvas_div.appendChild(canvas);\n", " canvas_div.appendChild(rubberband_canvas);\n", "\n", " this.rubberband_context = rubberband_canvas.getContext('2d');\n", " this.rubberband_context.strokeStyle = '#000000';\n", "\n", " this._resize_canvas = function (width, height, forward) {\n", " if (forward) {\n", " canvas_div.style.width = width + 'px';\n", " canvas_div.style.height = height + 'px';\n", " }\n", " };\n", "\n", " // Disable right mouse context menu.\n", " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", " event.preventDefault();\n", " return false;\n", " });\n", "\n", " function set_focus() {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'mpl-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " continue;\n", " }\n", "\n", " var button = (fig.buttons[name] = document.createElement('button'));\n", " button.classList = 'mpl-widget';\n", " button.setAttribute('role', 'button');\n", " button.setAttribute('aria-disabled', 'false');\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", "\n", " var icon_img = document.createElement('img');\n", " icon_img.src = '_images/' + image + '.png';\n", " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", " icon_img.alt = tooltip;\n", " button.appendChild(icon_img);\n", "\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " var fmt_picker = document.createElement('select');\n", " fmt_picker.classList = 'mpl-widget';\n", " toolbar.appendChild(fmt_picker);\n", " this.format_dropdown = fmt_picker;\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = document.createElement('option');\n", " option.selected = fmt === mpl.default_extension;\n", " option.innerHTML = fmt;\n", " fmt_picker.appendChild(option);\n", " }\n", "\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "};\n", "\n", "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", "};\n", "\n", "mpl.figure.prototype.send_message = function (type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "};\n", "\n", "mpl.figure.prototype.send_draw_message = function () {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "};\n", "\n", "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1], msg['forward']);\n", " fig.send_message('refresh', {});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", " var x0 = msg['x0'] / fig.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", " var x1 = msg['x1'] / fig.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0,\n", " 0,\n", " fig.canvas.width / fig.ratio,\n", " fig.canvas.height / fig.ratio\n", " );\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "};\n", "\n", "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "};\n", "\n", "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch (cursor) {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "};\n", "\n", "mpl.figure.prototype.handle_message = function (fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "};\n", "\n", "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "};\n", "\n", "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "};\n", "\n", "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", " for (var key in msg) {\n", " if (!(key in fig.buttons)) {\n", " continue;\n", " }\n", " fig.buttons[key].disabled = !msg[key];\n", " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", " if (msg['mode'] === 'PAN') {\n", " fig.buttons['Pan'].classList.add('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " } else if (msg['mode'] === 'ZOOM') {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.add('active');\n", " } else {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " }\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Called whenever the canvas gets updated.\n", " this.send_message('ack', {});\n", "};\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function (fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = 'image/png';\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src\n", " );\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data\n", " );\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " } else if (\n", " typeof evt.data === 'string' &&\n", " evt.data.slice(0, 21) === 'data:image/png;base64'\n", " ) {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig['handle_' + msg_type];\n", " } catch (e) {\n", " console.log(\n", " \"No handler for the '\" + msg_type + \"' message type: \",\n", " msg\n", " );\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\n", " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", " e,\n", " e.stack,\n", " msg\n", " );\n", " }\n", " }\n", " };\n", "};\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function (e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e) {\n", " e = window.event;\n", " }\n", " if (e.target) {\n", " targ = e.target;\n", " } else if (e.srcElement) {\n", " targ = e.srcElement;\n", " }\n", " if (targ.nodeType === 3) {\n", " // defeat Safari bug\n", " targ = targ.parentNode;\n", " }\n", "\n", " // pageX,Y are the mouse positions relative to the document\n", " var boundingRect = targ.getBoundingClientRect();\n", " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", "\n", " return { x: x, y: y };\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys(original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object') {\n", " obj[key] = original[key];\n", " }\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function (event, name) {\n", " var canvas_pos = mpl.findpos(event);\n", "\n", " if (name === 'button_press') {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * this.ratio;\n", " var y = canvas_pos.y * this.ratio;\n", "\n", " this.send_message(name, {\n", " x: x,\n", " y: y,\n", " button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event),\n", " });\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", " // Handle any extra behaviour associated with a key event\n", "};\n", "\n", "mpl.figure.prototype.key_event = function (event, name) {\n", " // Prevent repeat events\n", " if (name === 'key_press') {\n", " if (event.which === this._key) {\n", " return;\n", " } else {\n", " this._key = event.which;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which !== 17) {\n", " value += 'ctrl+';\n", " }\n", " if (event.altKey && event.which !== 18) {\n", " value += 'alt+';\n", " }\n", " if (event.shiftKey && event.which !== 16) {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", " if (name === 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message('toolbar_button', { name: name });\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "\n", "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", "// prettier-ignore\n", "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* global mpl */\n", "\n", "var comm_websocket_adapter = function (comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function () {\n", " comm.close();\n", " };\n", " ws.send = function (m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function (msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data']);\n", " });\n", " return ws;\n", "};\n", "\n", "mpl.mpl_figure_comm = function (comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = document.getElementById(id);\n", " var ws_proxy = comm_websocket_adapter(comm);\n", "\n", " function ondownload(figure, _format) {\n", " window.open(figure.canvas.toDataURL());\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element;\n", " fig.cell_info = mpl.find_output_cell(\"
\");\n", " if (!fig.cell_info) {\n", " console.error('Failed to find cell for figure', id, fig);\n", " return;\n", " }\n", " fig.cell_info[0].output_area.element.on(\n", " 'cleared',\n", " { fig: fig },\n", " fig._remove_fig_handler\n", " );\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function (fig, msg) {\n", " var width = fig.canvas.width / fig.ratio;\n", " fig.cell_info[0].output_area.element.off(\n", " 'cleared',\n", " fig._remove_fig_handler\n", " );\n", " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable();\n", " fig.parent_element.innerHTML =\n", " '';\n", " fig.close_ws(fig, msg);\n", "};\n", "\n", "mpl.figure.prototype.close_ws = function (fig, msg) {\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "};\n", "\n", "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width / this.ratio;\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] =\n", " '';\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message('ack', {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () {\n", " fig.push_to_output();\n", " }, 1000);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'btn-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " var button;\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " continue;\n", " }\n", "\n", " button = fig.buttons[name] = document.createElement('button');\n", " button.classList = 'btn btn-default';\n", " button.href = '#';\n", " button.title = name;\n", " button.innerHTML = '';\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message pull-right';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "\n", " // Add the close button to the window.\n", " var buttongrp = document.createElement('div');\n", " buttongrp.classList = 'btn-group inline pull-right';\n", " button = document.createElement('button');\n", " button.classList = 'btn btn-mini btn-primary';\n", " button.href = '#';\n", " button.title = 'Stop Interaction';\n", " button.innerHTML = '';\n", " button.addEventListener('click', function (_evt) {\n", " fig.handle_close(fig, {});\n", " });\n", " button.addEventListener(\n", " 'mouseover',\n", " on_mouseover_closure('Stop Interaction')\n", " );\n", " buttongrp.appendChild(button);\n", " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", "};\n", "\n", "mpl.figure.prototype._remove_fig_handler = function (event) {\n", " var fig = event.data.fig;\n", " if (event.target !== this) {\n", " // Ignore bubbled events from children.\n", " return;\n", " }\n", " fig.close_ws(fig, {});\n", "};\n", "\n", "mpl.figure.prototype._root_extra_style = function (el) {\n", " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (el) {\n", " // this is important to make the div 'focusable\n", " el.setAttribute('tabindex', 0);\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " } else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager) {\n", " manager = IPython.keyboard_manager;\n", " }\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which === 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " fig.ondownload(fig, null);\n", "};\n", "\n", "mpl.find_output_cell = function (html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i = 0; i < ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code') {\n", " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] === html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "};\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel !== null) {\n", " IPython.notebook.kernel.comm_manager.register_target(\n", " 'matplotlib',\n", " mpl.mpl_figure_comm\n", " );\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_set.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The plotting depends on the data_type of the dataset and the dimension_types\n", "of it's dimension datasets. Above, we set the first two dimension_type types to\n", "``spatial`` and the third one to ``spectral``.\n", "\n", "The data_type was ``spectral_image``.\n", "So the spatial dimensions are recognized as relevant for an image and the third dimension is recognized as a spectrum, conducive to plotting as shown above.\n", "If we change the data_type to image, the default plotting behavoir is to plot the first slice in the dataset (i.e. data_set[:,:,0]).\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "/* global mpl */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function () {\n", " if (typeof WebSocket !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof MozWebSocket !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert(\n", " 'Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.'\n", " );\n", " }\n", "};\n", "\n", "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = this.ws.binaryType !== undefined;\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById('mpl-warnings');\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent =\n", " 'This browser does not support binary websocket messages. ' +\n", " 'Performance may be slow.';\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = document.createElement('div');\n", " this.root.setAttribute('style', 'display: inline-block');\n", " this._root_extra_style(this.root);\n", "\n", " parent_element.appendChild(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message('supports_binary', { value: fig.supports_binary });\n", " fig.send_message('send_image_mode', {});\n", " if (fig.ratio !== 1) {\n", " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", " }\n", " fig.send_message('refresh', {});\n", " };\n", "\n", " this.imageObj.onload = function () {\n", " if (fig.image_mode === 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function () {\n", " fig.ws.close();\n", " };\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "};\n", "\n", "mpl.figure.prototype._init_header = function () {\n", " var titlebar = document.createElement('div');\n", " titlebar.classList =\n", " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", " var titletext = document.createElement('div');\n", " titletext.classList = 'ui-dialog-title';\n", " titletext.setAttribute(\n", " 'style',\n", " 'width: 100%; text-align: center; padding: 3px;'\n", " );\n", " titlebar.appendChild(titletext);\n", " this.root.appendChild(titlebar);\n", " this.header = titletext;\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._init_canvas = function () {\n", " var fig = this;\n", "\n", " var canvas_div = (this.canvas_div = document.createElement('div'));\n", " canvas_div.setAttribute(\n", " 'style',\n", " 'border: 1px solid #ddd;' +\n", " 'box-sizing: content-box;' +\n", " 'clear: both;' +\n", " 'min-height: 1px;' +\n", " 'min-width: 1px;' +\n", " 'outline: 0;' +\n", " 'overflow: hidden;' +\n", " 'position: relative;' +\n", " 'resize: both;'\n", " );\n", "\n", " function on_keyboard_event_closure(name) {\n", " return function (event) {\n", " return fig.key_event(event, name);\n", " };\n", " }\n", "\n", " canvas_div.addEventListener(\n", " 'keydown',\n", " on_keyboard_event_closure('key_press')\n", " );\n", " canvas_div.addEventListener(\n", " 'keyup',\n", " on_keyboard_event_closure('key_release')\n", " );\n", "\n", " this._canvas_extra_style(canvas_div);\n", " this.root.appendChild(canvas_div);\n", "\n", " var canvas = (this.canvas = document.createElement('canvas'));\n", " canvas.classList.add('mpl-canvas');\n", " canvas.setAttribute('style', 'box-sizing: content-box;');\n", "\n", " this.context = canvas.getContext('2d');\n", "\n", " var backingStore =\n", " this.context.backingStorePixelRatio ||\n", " this.context.webkitBackingStorePixelRatio ||\n", " this.context.mozBackingStorePixelRatio ||\n", " this.context.msBackingStorePixelRatio ||\n", " this.context.oBackingStorePixelRatio ||\n", " this.context.backingStorePixelRatio ||\n", " 1;\n", "\n", " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", " 'canvas'\n", " ));\n", " rubberband_canvas.setAttribute(\n", " 'style',\n", " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", " );\n", "\n", " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", " if (this.ResizeObserver === undefined) {\n", " if (window.ResizeObserver !== undefined) {\n", " this.ResizeObserver = window.ResizeObserver;\n", " } else {\n", " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", " this.ResizeObserver = obs.ResizeObserver;\n", " }\n", " }\n", "\n", " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", " var nentries = entries.length;\n", " for (var i = 0; i < nentries; i++) {\n", " var entry = entries[i];\n", " var width, height;\n", " if (entry.contentBoxSize) {\n", " if (entry.contentBoxSize instanceof Array) {\n", " // Chrome 84 implements new version of spec.\n", " width = entry.contentBoxSize[0].inlineSize;\n", " height = entry.contentBoxSize[0].blockSize;\n", " } else {\n", " // Firefox implements old version of spec.\n", " width = entry.contentBoxSize.inlineSize;\n", " height = entry.contentBoxSize.blockSize;\n", " }\n", " } else {\n", " // Chrome <84 implements even older version of spec.\n", " width = entry.contentRect.width;\n", " height = entry.contentRect.height;\n", " }\n", "\n", " // Keep the size of the canvas and rubber band canvas in sync with\n", " // the canvas container.\n", " if (entry.devicePixelContentBoxSize) {\n", " // Chrome 84 implements new version of spec.\n", " canvas.setAttribute(\n", " 'width',\n", " entry.devicePixelContentBoxSize[0].inlineSize\n", " );\n", " canvas.setAttribute(\n", " 'height',\n", " entry.devicePixelContentBoxSize[0].blockSize\n", " );\n", " } else {\n", " canvas.setAttribute('width', width * fig.ratio);\n", " canvas.setAttribute('height', height * fig.ratio);\n", " }\n", " canvas.setAttribute(\n", " 'style',\n", " 'width: ' + width + 'px; height: ' + height + 'px;'\n", " );\n", "\n", " rubberband_canvas.setAttribute('width', width);\n", " rubberband_canvas.setAttribute('height', height);\n", "\n", " // And update the size in Python. We ignore the initial 0/0 size\n", " // that occurs as the element is placed into the DOM, which should\n", " // otherwise not happen due to the minimum size styling.\n", " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", " fig.request_resize(width, height);\n", " }\n", " }\n", " });\n", " this.resizeObserverInstance.observe(canvas_div);\n", "\n", " function on_mouse_event_closure(name) {\n", " return function (event) {\n", " return fig.mouse_event(event, name);\n", " };\n", " }\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mousedown',\n", " on_mouse_event_closure('button_press')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseup',\n", " on_mouse_event_closure('button_release')\n", " );\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband_canvas.addEventListener(\n", " 'mousemove',\n", " on_mouse_event_closure('motion_notify')\n", " );\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mouseenter',\n", " on_mouse_event_closure('figure_enter')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseleave',\n", " on_mouse_event_closure('figure_leave')\n", " );\n", "\n", " canvas_div.addEventListener('wheel', function (event) {\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " on_mouse_event_closure('scroll')(event);\n", " });\n", "\n", " canvas_div.appendChild(canvas);\n", " canvas_div.appendChild(rubberband_canvas);\n", "\n", " this.rubberband_context = rubberband_canvas.getContext('2d');\n", " this.rubberband_context.strokeStyle = '#000000';\n", "\n", " this._resize_canvas = function (width, height, forward) {\n", " if (forward) {\n", " canvas_div.style.width = width + 'px';\n", " canvas_div.style.height = height + 'px';\n", " }\n", " };\n", "\n", " // Disable right mouse context menu.\n", " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", " event.preventDefault();\n", " return false;\n", " });\n", "\n", " function set_focus() {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'mpl-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " continue;\n", " }\n", "\n", " var button = (fig.buttons[name] = document.createElement('button'));\n", " button.classList = 'mpl-widget';\n", " button.setAttribute('role', 'button');\n", " button.setAttribute('aria-disabled', 'false');\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", "\n", " var icon_img = document.createElement('img');\n", " icon_img.src = '_images/' + image + '.png';\n", " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", " icon_img.alt = tooltip;\n", " button.appendChild(icon_img);\n", "\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " var fmt_picker = document.createElement('select');\n", " fmt_picker.classList = 'mpl-widget';\n", " toolbar.appendChild(fmt_picker);\n", " this.format_dropdown = fmt_picker;\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = document.createElement('option');\n", " option.selected = fmt === mpl.default_extension;\n", " option.innerHTML = fmt;\n", " fmt_picker.appendChild(option);\n", " }\n", "\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "};\n", "\n", "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", "};\n", "\n", "mpl.figure.prototype.send_message = function (type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "};\n", "\n", "mpl.figure.prototype.send_draw_message = function () {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "};\n", "\n", "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1], msg['forward']);\n", " fig.send_message('refresh', {});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", " var x0 = msg['x0'] / fig.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", " var x1 = msg['x1'] / fig.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0,\n", " 0,\n", " fig.canvas.width / fig.ratio,\n", " fig.canvas.height / fig.ratio\n", " );\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "};\n", "\n", "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "};\n", "\n", "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch (cursor) {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "};\n", "\n", "mpl.figure.prototype.handle_message = function (fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "};\n", "\n", "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "};\n", "\n", "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "};\n", "\n", "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", " for (var key in msg) {\n", " if (!(key in fig.buttons)) {\n", " continue;\n", " }\n", " fig.buttons[key].disabled = !msg[key];\n", " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", " if (msg['mode'] === 'PAN') {\n", " fig.buttons['Pan'].classList.add('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " } else if (msg['mode'] === 'ZOOM') {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.add('active');\n", " } else {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " }\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Called whenever the canvas gets updated.\n", " this.send_message('ack', {});\n", "};\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function (fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = 'image/png';\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src\n", " );\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data\n", " );\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " } else if (\n", " typeof evt.data === 'string' &&\n", " evt.data.slice(0, 21) === 'data:image/png;base64'\n", " ) {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig['handle_' + msg_type];\n", " } catch (e) {\n", " console.log(\n", " \"No handler for the '\" + msg_type + \"' message type: \",\n", " msg\n", " );\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\n", " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", " e,\n", " e.stack,\n", " msg\n", " );\n", " }\n", " }\n", " };\n", "};\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function (e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e) {\n", " e = window.event;\n", " }\n", " if (e.target) {\n", " targ = e.target;\n", " } else if (e.srcElement) {\n", " targ = e.srcElement;\n", " }\n", " if (targ.nodeType === 3) {\n", " // defeat Safari bug\n", " targ = targ.parentNode;\n", " }\n", "\n", " // pageX,Y are the mouse positions relative to the document\n", " var boundingRect = targ.getBoundingClientRect();\n", " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", "\n", " return { x: x, y: y };\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys(original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object') {\n", " obj[key] = original[key];\n", " }\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function (event, name) {\n", " var canvas_pos = mpl.findpos(event);\n", "\n", " if (name === 'button_press') {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * this.ratio;\n", " var y = canvas_pos.y * this.ratio;\n", "\n", " this.send_message(name, {\n", " x: x,\n", " y: y,\n", " button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event),\n", " });\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", " // Handle any extra behaviour associated with a key event\n", "};\n", "\n", "mpl.figure.prototype.key_event = function (event, name) {\n", " // Prevent repeat events\n", " if (name === 'key_press') {\n", " if (event.which === this._key) {\n", " return;\n", " } else {\n", " this._key = event.which;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which !== 17) {\n", " value += 'ctrl+';\n", " }\n", " if (event.altKey && event.which !== 18) {\n", " value += 'alt+';\n", " }\n", " if (event.shiftKey && event.which !== 16) {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", " if (name === 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message('toolbar_button', { name: name });\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "\n", "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", "// prettier-ignore\n", "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* global mpl */\n", "\n", "var comm_websocket_adapter = function (comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function () {\n", " comm.close();\n", " };\n", " ws.send = function (m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function (msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data']);\n", " });\n", " return ws;\n", "};\n", "\n", "mpl.mpl_figure_comm = function (comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = document.getElementById(id);\n", " var ws_proxy = comm_websocket_adapter(comm);\n", "\n", " function ondownload(figure, _format) {\n", " window.open(figure.canvas.toDataURL());\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element;\n", " fig.cell_info = mpl.find_output_cell(\"
\");\n", " if (!fig.cell_info) {\n", " console.error('Failed to find cell for figure', id, fig);\n", " return;\n", " }\n", " fig.cell_info[0].output_area.element.on(\n", " 'cleared',\n", " { fig: fig },\n", " fig._remove_fig_handler\n", " );\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function (fig, msg) {\n", " var width = fig.canvas.width / fig.ratio;\n", " fig.cell_info[0].output_area.element.off(\n", " 'cleared',\n", " fig._remove_fig_handler\n", " );\n", " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable();\n", " fig.parent_element.innerHTML =\n", " '';\n", " fig.close_ws(fig, msg);\n", "};\n", "\n", "mpl.figure.prototype.close_ws = function (fig, msg) {\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "};\n", "\n", "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width / this.ratio;\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] =\n", " '';\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message('ack', {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () {\n", " fig.push_to_output();\n", " }, 1000);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'btn-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " var button;\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " continue;\n", " }\n", "\n", " button = fig.buttons[name] = document.createElement('button');\n", " button.classList = 'btn btn-default';\n", " button.href = '#';\n", " button.title = name;\n", " button.innerHTML = '';\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message pull-right';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "\n", " // Add the close button to the window.\n", " var buttongrp = document.createElement('div');\n", " buttongrp.classList = 'btn-group inline pull-right';\n", " button = document.createElement('button');\n", " button.classList = 'btn btn-mini btn-primary';\n", " button.href = '#';\n", " button.title = 'Stop Interaction';\n", " button.innerHTML = '';\n", " button.addEventListener('click', function (_evt) {\n", " fig.handle_close(fig, {});\n", " });\n", " button.addEventListener(\n", " 'mouseover',\n", " on_mouseover_closure('Stop Interaction')\n", " );\n", " buttongrp.appendChild(button);\n", " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", "};\n", "\n", "mpl.figure.prototype._remove_fig_handler = function (event) {\n", " var fig = event.data.fig;\n", " if (event.target !== this) {\n", " // Ignore bubbled events from children.\n", " return;\n", " }\n", " fig.close_ws(fig, {});\n", "};\n", "\n", "mpl.figure.prototype._root_extra_style = function (el) {\n", " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (el) {\n", " // this is important to make the div 'focusable\n", " el.setAttribute('tabindex', 0);\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " } else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager) {\n", " manager = IPython.keyboard_manager;\n", " }\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which === 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " fig.ondownload(fig, null);\n", "};\n", "\n", "mpl.find_output_cell = function (html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i = 0; i < ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code') {\n", " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] === html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "};\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel !== null) {\n", " IPython.notebook.kernel.comm_manager.register_target(\n", " 'matplotlib',\n", " mpl.mpl_figure_comm\n", " );\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_set.data_type = 'image'\n", "data_set.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Saving\n", "\n", "These ``Dataset`` objects will be deleted from memory once the python script\n", "completes or when a notebook is closed. The information collected in a\n", "``Dataset`` can reliably be stored to files using functions in sister\n", "packages - ``pyUSID`` and ``pyNSID`` that write the dataset according to the\n", "**Universal Spectroscopy and Imaging Data (USID)** or **N-dimensional\n", "Spectrocsopy and Imaging Data (NSID)** formats.\n", "Here are links to how one could save such Datasets for each package:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.16" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 4 } sidpy-0.12.3/notebooks/00_basic_usage/hyperspy.ipynb000066400000000000000000017270421455261647000224510ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Working with Hyperspy Data\n", "\n", "In this notebook we will load a dataset with hyperspy and convert it to a sidpy dataset.\n", "\n", "The sidpy dataset will enable you to use any package in the pycroscopy eco-system.\n", "\n", "Using hyperspy may be especially usefull for utilization if its io and/or anlysisis capabilities.\n", "\n", "\n", "## First we load the necessary packages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:hyperspy_gui_traitsui:The nbAgg matplotlib backend is not compatible with the traitsui GUI elements. For more information, read http://hyperspy.readthedocs.io/en/stable/user_guide/getting_started.html#possible-warnings-when-importing-hyperspy.\n", "WARNING:hyperspy_gui_traitsui:The traitsui GUI elements are not available.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0.0.6a\n" ] } ], "source": [ "%pylab --no-import-all notebook\n", "%gui qt\n", "\n", "import sys\n", "\n", "sys.path.insert(0, '../..')\n", "import sidpy\n", "\n", "import hyperspy.api as hs\n", "import pyTEMlib.file_tools as ft\n", "\n", "print(sidpy.__version__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Select a file and load with hyperspy\n", "\n", "We utilize here the open file dialog from pyTEMlib but this is not strictly necessary. Any convenient way to supply a filename will do.\n", "\n", "The file will be loaded and plotted with hyperspy." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "/* global mpl */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function () {\n", " if (typeof WebSocket !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof MozWebSocket !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert(\n", " 'Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.'\n", " );\n", " }\n", "};\n", "\n", "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = this.ws.binaryType !== undefined;\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById('mpl-warnings');\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent =\n", " 'This browser does not support binary websocket messages. ' +\n", " 'Performance may be slow.';\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = document.createElement('div');\n", " this.root.setAttribute('style', 'display: inline-block');\n", " this._root_extra_style(this.root);\n", "\n", " parent_element.appendChild(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message('supports_binary', { value: fig.supports_binary });\n", " fig.send_message('send_image_mode', {});\n", " if (fig.ratio !== 1) {\n", " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", " }\n", " fig.send_message('refresh', {});\n", " };\n", "\n", " this.imageObj.onload = function () {\n", " if (fig.image_mode === 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function () {\n", " fig.ws.close();\n", " };\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "};\n", "\n", "mpl.figure.prototype._init_header = function () {\n", " var titlebar = document.createElement('div');\n", " titlebar.classList =\n", " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", " var titletext = document.createElement('div');\n", " titletext.classList = 'ui-dialog-title';\n", " titletext.setAttribute(\n", " 'style',\n", " 'width: 100%; text-align: center; padding: 3px;'\n", " );\n", " titlebar.appendChild(titletext);\n", " this.root.appendChild(titlebar);\n", " this.header = titletext;\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._init_canvas = function () {\n", " var fig = this;\n", "\n", " var canvas_div = (this.canvas_div = document.createElement('div'));\n", " canvas_div.setAttribute(\n", " 'style',\n", " 'border: 1px solid #ddd;' +\n", " 'box-sizing: content-box;' +\n", " 'clear: both;' +\n", " 'min-height: 1px;' +\n", " 'min-width: 1px;' +\n", " 'outline: 0;' +\n", " 'overflow: hidden;' +\n", " 'position: relative;' +\n", " 'resize: both;'\n", " );\n", "\n", " function on_keyboard_event_closure(name) {\n", " return function (event) {\n", " return fig.key_event(event, name);\n", " };\n", " }\n", "\n", " canvas_div.addEventListener(\n", " 'keydown',\n", " on_keyboard_event_closure('key_press')\n", " );\n", " canvas_div.addEventListener(\n", " 'keyup',\n", " on_keyboard_event_closure('key_release')\n", " );\n", "\n", " this._canvas_extra_style(canvas_div);\n", " this.root.appendChild(canvas_div);\n", "\n", " var canvas = (this.canvas = document.createElement('canvas'));\n", " canvas.classList.add('mpl-canvas');\n", " canvas.setAttribute('style', 'box-sizing: content-box;');\n", "\n", " this.context = canvas.getContext('2d');\n", "\n", " var backingStore =\n", " this.context.backingStorePixelRatio ||\n", " this.context.webkitBackingStorePixelRatio ||\n", " this.context.mozBackingStorePixelRatio ||\n", " this.context.msBackingStorePixelRatio ||\n", " this.context.oBackingStorePixelRatio ||\n", " this.context.backingStorePixelRatio ||\n", " 1;\n", "\n", " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", " 'canvas'\n", " ));\n", " rubberband_canvas.setAttribute(\n", " 'style',\n", " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", " );\n", "\n", " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", " if (this.ResizeObserver === undefined) {\n", " if (window.ResizeObserver !== undefined) {\n", " this.ResizeObserver = window.ResizeObserver;\n", " } else {\n", " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", " this.ResizeObserver = obs.ResizeObserver;\n", " }\n", " }\n", "\n", " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", " var nentries = entries.length;\n", " for (var i = 0; i < nentries; i++) {\n", " var entry = entries[i];\n", " var width, height;\n", " if (entry.contentBoxSize) {\n", " if (entry.contentBoxSize instanceof Array) {\n", " // Chrome 84 implements new version of spec.\n", " width = entry.contentBoxSize[0].inlineSize;\n", " height = entry.contentBoxSize[0].blockSize;\n", " } else {\n", " // Firefox implements old version of spec.\n", " width = entry.contentBoxSize.inlineSize;\n", " height = entry.contentBoxSize.blockSize;\n", " }\n", " } else {\n", " // Chrome <84 implements even older version of spec.\n", " width = entry.contentRect.width;\n", " height = entry.contentRect.height;\n", " }\n", "\n", " // Keep the size of the canvas and rubber band canvas in sync with\n", " // the canvas container.\n", " if (entry.devicePixelContentBoxSize) {\n", " // Chrome 84 implements new version of spec.\n", " canvas.setAttribute(\n", " 'width',\n", " entry.devicePixelContentBoxSize[0].inlineSize\n", " );\n", " canvas.setAttribute(\n", " 'height',\n", " entry.devicePixelContentBoxSize[0].blockSize\n", " );\n", " } else {\n", " canvas.setAttribute('width', width * fig.ratio);\n", " canvas.setAttribute('height', height * fig.ratio);\n", " }\n", " canvas.setAttribute(\n", " 'style',\n", " 'width: ' + width + 'px; height: ' + height + 'px;'\n", " );\n", "\n", " rubberband_canvas.setAttribute('width', width);\n", " rubberband_canvas.setAttribute('height', height);\n", "\n", " // And update the size in Python. We ignore the initial 0/0 size\n", " // that occurs as the element is placed into the DOM, which should\n", " // otherwise not happen due to the minimum size styling.\n", " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", " fig.request_resize(width, height);\n", " }\n", " }\n", " });\n", " this.resizeObserverInstance.observe(canvas_div);\n", "\n", " function on_mouse_event_closure(name) {\n", " return function (event) {\n", " return fig.mouse_event(event, name);\n", " };\n", " }\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mousedown',\n", " on_mouse_event_closure('button_press')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseup',\n", " on_mouse_event_closure('button_release')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'dblclick',\n", " on_mouse_event_closure('dblclick')\n", " );\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband_canvas.addEventListener(\n", " 'mousemove',\n", " on_mouse_event_closure('motion_notify')\n", " );\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mouseenter',\n", " on_mouse_event_closure('figure_enter')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseleave',\n", " on_mouse_event_closure('figure_leave')\n", " );\n", "\n", " canvas_div.addEventListener('wheel', function (event) {\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " on_mouse_event_closure('scroll')(event);\n", " });\n", "\n", " canvas_div.appendChild(canvas);\n", " canvas_div.appendChild(rubberband_canvas);\n", "\n", " this.rubberband_context = rubberband_canvas.getContext('2d');\n", " this.rubberband_context.strokeStyle = '#000000';\n", "\n", " this._resize_canvas = function (width, height, forward) {\n", " if (forward) {\n", " canvas_div.style.width = width + 'px';\n", " canvas_div.style.height = height + 'px';\n", " }\n", " };\n", "\n", " // Disable right mouse context menu.\n", " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", " event.preventDefault();\n", " return false;\n", " });\n", "\n", " function set_focus() {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'mpl-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " continue;\n", " }\n", "\n", " var button = (fig.buttons[name] = document.createElement('button'));\n", " button.classList = 'mpl-widget';\n", " button.setAttribute('role', 'button');\n", " button.setAttribute('aria-disabled', 'false');\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", "\n", " var icon_img = document.createElement('img');\n", " icon_img.src = '_images/' + image + '.png';\n", " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", " icon_img.alt = tooltip;\n", " button.appendChild(icon_img);\n", "\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " var fmt_picker = document.createElement('select');\n", " fmt_picker.classList = 'mpl-widget';\n", " toolbar.appendChild(fmt_picker);\n", " this.format_dropdown = fmt_picker;\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = document.createElement('option');\n", " option.selected = fmt === mpl.default_extension;\n", " option.innerHTML = fmt;\n", " fmt_picker.appendChild(option);\n", " }\n", "\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "};\n", "\n", "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", "};\n", "\n", "mpl.figure.prototype.send_message = function (type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "};\n", "\n", "mpl.figure.prototype.send_draw_message = function () {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "};\n", "\n", "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1], msg['forward']);\n", " fig.send_message('refresh', {});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", " var x0 = msg['x0'] / fig.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", " var x1 = msg['x1'] / fig.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0,\n", " 0,\n", " fig.canvas.width / fig.ratio,\n", " fig.canvas.height / fig.ratio\n", " );\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "};\n", "\n", "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "};\n", "\n", "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch (cursor) {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "};\n", "\n", "mpl.figure.prototype.handle_message = function (fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "};\n", "\n", "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "};\n", "\n", "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "};\n", "\n", "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", " for (var key in msg) {\n", " if (!(key in fig.buttons)) {\n", " continue;\n", " }\n", " fig.buttons[key].disabled = !msg[key];\n", " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", " if (msg['mode'] === 'PAN') {\n", " fig.buttons['Pan'].classList.add('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " } else if (msg['mode'] === 'ZOOM') {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.add('active');\n", " } else {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " }\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Called whenever the canvas gets updated.\n", " this.send_message('ack', {});\n", "};\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function (fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " var img = evt.data;\n", " if (img.type !== 'image/png') {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " img.type = 'image/png';\n", " }\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src\n", " );\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " img\n", " );\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " } else if (\n", " typeof evt.data === 'string' &&\n", " evt.data.slice(0, 21) === 'data:image/png;base64'\n", " ) {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig['handle_' + msg_type];\n", " } catch (e) {\n", " console.log(\n", " \"No handler for the '\" + msg_type + \"' message type: \",\n", " msg\n", " );\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\n", " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", " e,\n", " e.stack,\n", " msg\n", " );\n", " }\n", " }\n", " };\n", "};\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function (e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e) {\n", " e = window.event;\n", " }\n", " if (e.target) {\n", " targ = e.target;\n", " } else if (e.srcElement) {\n", " targ = e.srcElement;\n", " }\n", " if (targ.nodeType === 3) {\n", " // defeat Safari bug\n", " targ = targ.parentNode;\n", " }\n", "\n", " // pageX,Y are the mouse positions relative to the document\n", " var boundingRect = targ.getBoundingClientRect();\n", " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", "\n", " return { x: x, y: y };\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys(original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object') {\n", " obj[key] = original[key];\n", " }\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function (event, name) {\n", " var canvas_pos = mpl.findpos(event);\n", "\n", " if (name === 'button_press') {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * this.ratio;\n", " var y = canvas_pos.y * this.ratio;\n", "\n", " this.send_message(name, {\n", " x: x,\n", " y: y,\n", " button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event),\n", " });\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", " // Handle any extra behaviour associated with a key event\n", "};\n", "\n", "mpl.figure.prototype.key_event = function (event, name) {\n", " // Prevent repeat events\n", " if (name === 'key_press') {\n", " if (event.key === this._key) {\n", " return;\n", " } else {\n", " this._key = event.key;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.key !== 'Control') {\n", " value += 'ctrl+';\n", " }\n", " else if (event.altKey && event.key !== 'Alt') {\n", " value += 'alt+';\n", " }\n", " else if (event.shiftKey && event.key !== 'Shift') {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k' + event.key;\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", " if (name === 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message('toolbar_button', { name: name });\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "\n", "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", "// prettier-ignore\n", "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* global mpl */\n", "\n", "var comm_websocket_adapter = function (comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.binaryType = comm.kernel.ws.binaryType;\n", " ws.readyState = comm.kernel.ws.readyState;\n", " function updateReadyState(_event) {\n", " if (comm.kernel.ws) {\n", " ws.readyState = comm.kernel.ws.readyState;\n", " } else {\n", " ws.readyState = 3; // Closed state.\n", " }\n", " }\n", " comm.kernel.ws.addEventListener('open', updateReadyState);\n", " comm.kernel.ws.addEventListener('close', updateReadyState);\n", " comm.kernel.ws.addEventListener('error', updateReadyState);\n", "\n", " ws.close = function () {\n", " comm.close();\n", " };\n", " ws.send = function (m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function (msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " var data = msg['content']['data'];\n", " if (data['blob'] !== undefined) {\n", " data = {\n", " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", " };\n", " }\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(data);\n", " });\n", " return ws;\n", "};\n", "\n", "mpl.mpl_figure_comm = function (comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = document.getElementById(id);\n", " var ws_proxy = comm_websocket_adapter(comm);\n", "\n", " function ondownload(figure, _format) {\n", " window.open(figure.canvas.toDataURL());\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element;\n", " fig.cell_info = mpl.find_output_cell(\"
\");\n", " if (!fig.cell_info) {\n", " console.error('Failed to find cell for figure', id, fig);\n", " return;\n", " }\n", " fig.cell_info[0].output_area.element.on(\n", " 'cleared',\n", " { fig: fig },\n", " fig._remove_fig_handler\n", " );\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function (fig, msg) {\n", " var width = fig.canvas.width / fig.ratio;\n", " fig.cell_info[0].output_area.element.off(\n", " 'cleared',\n", " fig._remove_fig_handler\n", " );\n", " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable();\n", " fig.parent_element.innerHTML =\n", " '';\n", " fig.close_ws(fig, msg);\n", "};\n", "\n", "mpl.figure.prototype.close_ws = function (fig, msg) {\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "};\n", "\n", "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width / this.ratio;\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] =\n", " '';\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message('ack', {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () {\n", " fig.push_to_output();\n", " }, 1000);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'btn-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " var button;\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " continue;\n", " }\n", "\n", " button = fig.buttons[name] = document.createElement('button');\n", " button.classList = 'btn btn-default';\n", " button.href = '#';\n", " button.title = name;\n", " button.innerHTML = '';\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message pull-right';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "\n", " // Add the close button to the window.\n", " var buttongrp = document.createElement('div');\n", " buttongrp.classList = 'btn-group inline pull-right';\n", " button = document.createElement('button');\n", " button.classList = 'btn btn-mini btn-primary';\n", " button.href = '#';\n", " button.title = 'Stop Interaction';\n", " button.innerHTML = '';\n", " button.addEventListener('click', function (_evt) {\n", " fig.handle_close(fig, {});\n", " });\n", " button.addEventListener(\n", " 'mouseover',\n", " on_mouseover_closure('Stop Interaction')\n", " );\n", " buttongrp.appendChild(button);\n", " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", "};\n", "\n", "mpl.figure.prototype._remove_fig_handler = function (event) {\n", " var fig = event.data.fig;\n", " if (event.target !== this) {\n", " // Ignore bubbled events from children.\n", " return;\n", " }\n", " fig.close_ws(fig, {});\n", "};\n", "\n", "mpl.figure.prototype._root_extra_style = function (el) {\n", " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (el) {\n", " // this is important to make the div 'focusable\n", " el.setAttribute('tabindex', 0);\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " } else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager) {\n", " manager = IPython.keyboard_manager;\n", " }\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which === 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " fig.ondownload(fig, null);\n", "};\n", "\n", "mpl.find_output_cell = function (html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i = 0; i < ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code') {\n", " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] === html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "};\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel !== null) {\n", " IPython.notebook.kernel.comm_manager.register_target(\n", " 'matplotlib',\n", " mpl.mpl_figure_comm\n", " );\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "/* global mpl */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function () {\n", " if (typeof WebSocket !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof MozWebSocket !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert(\n", " 'Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.'\n", " );\n", " }\n", "};\n", "\n", "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = this.ws.binaryType !== undefined;\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById('mpl-warnings');\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent =\n", " 'This browser does not support binary websocket messages. ' +\n", " 'Performance may be slow.';\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = document.createElement('div');\n", " this.root.setAttribute('style', 'display: inline-block');\n", " this._root_extra_style(this.root);\n", "\n", " parent_element.appendChild(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message('supports_binary', { value: fig.supports_binary });\n", " fig.send_message('send_image_mode', {});\n", " if (fig.ratio !== 1) {\n", " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", " }\n", " fig.send_message('refresh', {});\n", " };\n", "\n", " this.imageObj.onload = function () {\n", " if (fig.image_mode === 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function () {\n", " fig.ws.close();\n", " };\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "};\n", "\n", "mpl.figure.prototype._init_header = function () {\n", " var titlebar = document.createElement('div');\n", " titlebar.classList =\n", " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", " var titletext = document.createElement('div');\n", " titletext.classList = 'ui-dialog-title';\n", " titletext.setAttribute(\n", " 'style',\n", " 'width: 100%; text-align: center; padding: 3px;'\n", " );\n", " titlebar.appendChild(titletext);\n", " this.root.appendChild(titlebar);\n", " this.header = titletext;\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._init_canvas = function () {\n", " var fig = this;\n", "\n", " var canvas_div = (this.canvas_div = document.createElement('div'));\n", " canvas_div.setAttribute(\n", " 'style',\n", " 'border: 1px solid #ddd;' +\n", " 'box-sizing: content-box;' +\n", " 'clear: both;' +\n", " 'min-height: 1px;' +\n", " 'min-width: 1px;' +\n", " 'outline: 0;' +\n", " 'overflow: hidden;' +\n", " 'position: relative;' +\n", " 'resize: both;'\n", " );\n", "\n", " function on_keyboard_event_closure(name) {\n", " return function (event) {\n", " return fig.key_event(event, name);\n", " };\n", " }\n", "\n", " canvas_div.addEventListener(\n", " 'keydown',\n", " on_keyboard_event_closure('key_press')\n", " );\n", " canvas_div.addEventListener(\n", " 'keyup',\n", " on_keyboard_event_closure('key_release')\n", " );\n", "\n", " this._canvas_extra_style(canvas_div);\n", " this.root.appendChild(canvas_div);\n", "\n", " var canvas = (this.canvas = document.createElement('canvas'));\n", " canvas.classList.add('mpl-canvas');\n", " canvas.setAttribute('style', 'box-sizing: content-box;');\n", "\n", " this.context = canvas.getContext('2d');\n", "\n", " var backingStore =\n", " this.context.backingStorePixelRatio ||\n", " this.context.webkitBackingStorePixelRatio ||\n", " this.context.mozBackingStorePixelRatio ||\n", " this.context.msBackingStorePixelRatio ||\n", " this.context.oBackingStorePixelRatio ||\n", " this.context.backingStorePixelRatio ||\n", " 1;\n", "\n", " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", " 'canvas'\n", " ));\n", " rubberband_canvas.setAttribute(\n", " 'style',\n", " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", " );\n", "\n", " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", " if (this.ResizeObserver === undefined) {\n", " if (window.ResizeObserver !== undefined) {\n", " this.ResizeObserver = window.ResizeObserver;\n", " } else {\n", " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", " this.ResizeObserver = obs.ResizeObserver;\n", " }\n", " }\n", "\n", " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", " var nentries = entries.length;\n", " for (var i = 0; i < nentries; i++) {\n", " var entry = entries[i];\n", " var width, height;\n", " if (entry.contentBoxSize) {\n", " if (entry.contentBoxSize instanceof Array) {\n", " // Chrome 84 implements new version of spec.\n", " width = entry.contentBoxSize[0].inlineSize;\n", " height = entry.contentBoxSize[0].blockSize;\n", " } else {\n", " // Firefox implements old version of spec.\n", " width = entry.contentBoxSize.inlineSize;\n", " height = entry.contentBoxSize.blockSize;\n", " }\n", " } else {\n", " // Chrome <84 implements even older version of spec.\n", " width = entry.contentRect.width;\n", " height = entry.contentRect.height;\n", " }\n", "\n", " // Keep the size of the canvas and rubber band canvas in sync with\n", " // the canvas container.\n", " if (entry.devicePixelContentBoxSize) {\n", " // Chrome 84 implements new version of spec.\n", " canvas.setAttribute(\n", " 'width',\n", " entry.devicePixelContentBoxSize[0].inlineSize\n", " );\n", " canvas.setAttribute(\n", " 'height',\n", " entry.devicePixelContentBoxSize[0].blockSize\n", " );\n", " } else {\n", " canvas.setAttribute('width', width * fig.ratio);\n", " canvas.setAttribute('height', height * fig.ratio);\n", " }\n", " canvas.setAttribute(\n", " 'style',\n", " 'width: ' + width + 'px; height: ' + height + 'px;'\n", " );\n", "\n", " rubberband_canvas.setAttribute('width', width);\n", " rubberband_canvas.setAttribute('height', height);\n", "\n", " // And update the size in Python. We ignore the initial 0/0 size\n", " // that occurs as the element is placed into the DOM, which should\n", " // otherwise not happen due to the minimum size styling.\n", " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", " fig.request_resize(width, height);\n", " }\n", " }\n", " });\n", " this.resizeObserverInstance.observe(canvas_div);\n", "\n", " function on_mouse_event_closure(name) {\n", " return function (event) {\n", " return fig.mouse_event(event, name);\n", " };\n", " }\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mousedown',\n", " on_mouse_event_closure('button_press')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseup',\n", " on_mouse_event_closure('button_release')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'dblclick',\n", " on_mouse_event_closure('dblclick')\n", " );\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband_canvas.addEventListener(\n", " 'mousemove',\n", " on_mouse_event_closure('motion_notify')\n", " );\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mouseenter',\n", " on_mouse_event_closure('figure_enter')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseleave',\n", " on_mouse_event_closure('figure_leave')\n", " );\n", "\n", " canvas_div.addEventListener('wheel', function (event) {\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " on_mouse_event_closure('scroll')(event);\n", " });\n", "\n", " canvas_div.appendChild(canvas);\n", " canvas_div.appendChild(rubberband_canvas);\n", "\n", " this.rubberband_context = rubberband_canvas.getContext('2d');\n", " this.rubberband_context.strokeStyle = '#000000';\n", "\n", " this._resize_canvas = function (width, height, forward) {\n", " if (forward) {\n", " canvas_div.style.width = width + 'px';\n", " canvas_div.style.height = height + 'px';\n", " }\n", " };\n", "\n", " // Disable right mouse context menu.\n", " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", " event.preventDefault();\n", " return false;\n", " });\n", "\n", " function set_focus() {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'mpl-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " continue;\n", " }\n", "\n", " var button = (fig.buttons[name] = document.createElement('button'));\n", " button.classList = 'mpl-widget';\n", " button.setAttribute('role', 'button');\n", " button.setAttribute('aria-disabled', 'false');\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", "\n", " var icon_img = document.createElement('img');\n", " icon_img.src = '_images/' + image + '.png';\n", " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", " icon_img.alt = tooltip;\n", " button.appendChild(icon_img);\n", "\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " var fmt_picker = document.createElement('select');\n", " fmt_picker.classList = 'mpl-widget';\n", " toolbar.appendChild(fmt_picker);\n", " this.format_dropdown = fmt_picker;\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = document.createElement('option');\n", " option.selected = fmt === mpl.default_extension;\n", " option.innerHTML = fmt;\n", " fmt_picker.appendChild(option);\n", " }\n", "\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "};\n", "\n", "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", "};\n", "\n", "mpl.figure.prototype.send_message = function (type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "};\n", "\n", "mpl.figure.prototype.send_draw_message = function () {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "};\n", "\n", "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1], msg['forward']);\n", " fig.send_message('refresh', {});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", " var x0 = msg['x0'] / fig.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", " var x1 = msg['x1'] / fig.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0,\n", " 0,\n", " fig.canvas.width / fig.ratio,\n", " fig.canvas.height / fig.ratio\n", " );\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "};\n", "\n", "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "};\n", "\n", "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch (cursor) {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "};\n", "\n", "mpl.figure.prototype.handle_message = function (fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "};\n", "\n", "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "};\n", "\n", "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "};\n", "\n", "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", " for (var key in msg) {\n", " if (!(key in fig.buttons)) {\n", " continue;\n", " }\n", " fig.buttons[key].disabled = !msg[key];\n", " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", " if (msg['mode'] === 'PAN') {\n", " fig.buttons['Pan'].classList.add('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " } else if (msg['mode'] === 'ZOOM') {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.add('active');\n", " } else {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " }\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Called whenever the canvas gets updated.\n", " this.send_message('ack', {});\n", "};\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function (fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " var img = evt.data;\n", " if (img.type !== 'image/png') {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " img.type = 'image/png';\n", " }\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src\n", " );\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " img\n", " );\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " } else if (\n", " typeof evt.data === 'string' &&\n", " evt.data.slice(0, 21) === 'data:image/png;base64'\n", " ) {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig['handle_' + msg_type];\n", " } catch (e) {\n", " console.log(\n", " \"No handler for the '\" + msg_type + \"' message type: \",\n", " msg\n", " );\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\n", " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", " e,\n", " e.stack,\n", " msg\n", " );\n", " }\n", " }\n", " };\n", "};\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function (e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e) {\n", " e = window.event;\n", " }\n", " if (e.target) {\n", " targ = e.target;\n", " } else if (e.srcElement) {\n", " targ = e.srcElement;\n", " }\n", " if (targ.nodeType === 3) {\n", " // defeat Safari bug\n", " targ = targ.parentNode;\n", " }\n", "\n", " // pageX,Y are the mouse positions relative to the document\n", " var boundingRect = targ.getBoundingClientRect();\n", " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", "\n", " return { x: x, y: y };\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys(original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object') {\n", " obj[key] = original[key];\n", " }\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function (event, name) {\n", " var canvas_pos = mpl.findpos(event);\n", "\n", " if (name === 'button_press') {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * this.ratio;\n", " var y = canvas_pos.y * this.ratio;\n", "\n", " this.send_message(name, {\n", " x: x,\n", " y: y,\n", " button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event),\n", " });\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", " // Handle any extra behaviour associated with a key event\n", "};\n", "\n", "mpl.figure.prototype.key_event = function (event, name) {\n", " // Prevent repeat events\n", " if (name === 'key_press') {\n", " if (event.key === this._key) {\n", " return;\n", " } else {\n", " this._key = event.key;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.key !== 'Control') {\n", " value += 'ctrl+';\n", " }\n", " else if (event.altKey && event.key !== 'Alt') {\n", " value += 'alt+';\n", " }\n", " else if (event.shiftKey && event.key !== 'Shift') {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k' + event.key;\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", " if (name === 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message('toolbar_button', { name: name });\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "\n", "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", "// prettier-ignore\n", "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* global mpl */\n", "\n", "var comm_websocket_adapter = function (comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.binaryType = comm.kernel.ws.binaryType;\n", " ws.readyState = comm.kernel.ws.readyState;\n", " function updateReadyState(_event) {\n", " if (comm.kernel.ws) {\n", " ws.readyState = comm.kernel.ws.readyState;\n", " } else {\n", " ws.readyState = 3; // Closed state.\n", " }\n", " }\n", " comm.kernel.ws.addEventListener('open', updateReadyState);\n", " comm.kernel.ws.addEventListener('close', updateReadyState);\n", " comm.kernel.ws.addEventListener('error', updateReadyState);\n", "\n", " ws.close = function () {\n", " comm.close();\n", " };\n", " ws.send = function (m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function (msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " var data = msg['content']['data'];\n", " if (data['blob'] !== undefined) {\n", " data = {\n", " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", " };\n", " }\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(data);\n", " });\n", " return ws;\n", "};\n", "\n", "mpl.mpl_figure_comm = function (comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = document.getElementById(id);\n", " var ws_proxy = comm_websocket_adapter(comm);\n", "\n", " function ondownload(figure, _format) {\n", " window.open(figure.canvas.toDataURL());\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element;\n", " fig.cell_info = mpl.find_output_cell(\"
\");\n", " if (!fig.cell_info) {\n", " console.error('Failed to find cell for figure', id, fig);\n", " return;\n", " }\n", " fig.cell_info[0].output_area.element.on(\n", " 'cleared',\n", " { fig: fig },\n", " fig._remove_fig_handler\n", " );\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function (fig, msg) {\n", " var width = fig.canvas.width / fig.ratio;\n", " fig.cell_info[0].output_area.element.off(\n", " 'cleared',\n", " fig._remove_fig_handler\n", " );\n", " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable();\n", " fig.parent_element.innerHTML =\n", " '';\n", " fig.close_ws(fig, msg);\n", "};\n", "\n", "mpl.figure.prototype.close_ws = function (fig, msg) {\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "};\n", "\n", "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width / this.ratio;\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] =\n", " '';\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message('ack', {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () {\n", " fig.push_to_output();\n", " }, 1000);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'btn-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " var button;\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " continue;\n", " }\n", "\n", " button = fig.buttons[name] = document.createElement('button');\n", " button.classList = 'btn btn-default';\n", " button.href = '#';\n", " button.title = name;\n", " button.innerHTML = '';\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message pull-right';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "\n", " // Add the close button to the window.\n", " var buttongrp = document.createElement('div');\n", " buttongrp.classList = 'btn-group inline pull-right';\n", " button = document.createElement('button');\n", " button.classList = 'btn btn-mini btn-primary';\n", " button.href = '#';\n", " button.title = 'Stop Interaction';\n", " button.innerHTML = '';\n", " button.addEventListener('click', function (_evt) {\n", " fig.handle_close(fig, {});\n", " });\n", " button.addEventListener(\n", " 'mouseover',\n", " on_mouseover_closure('Stop Interaction')\n", " );\n", " buttongrp.appendChild(button);\n", " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", "};\n", "\n", "mpl.figure.prototype._remove_fig_handler = function (event) {\n", " var fig = event.data.fig;\n", " if (event.target !== this) {\n", " // Ignore bubbled events from children.\n", " return;\n", " }\n", " fig.close_ws(fig, {});\n", "};\n", "\n", "mpl.figure.prototype._root_extra_style = function (el) {\n", " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (el) {\n", " // this is important to make the div 'focusable\n", " el.setAttribute('tabindex', 0);\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " } else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager) {\n", " manager = IPython.keyboard_manager;\n", " }\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which === 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " fig.ondownload(fig, null);\n", "};\n", "\n", "mpl.find_output_cell = function (html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i = 0; i < ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code') {\n", " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] === html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "};\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel !== null) {\n", " IPython.notebook.kernel.comm_manager.register_target(\n", " 'matplotlib',\n", " mpl.mpl_figure_comm\n", " );\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "filename = ft.openfile_dialog()\n", "s = hs.load(filename)\n", "s.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conversion to sipy\n", "We convert the hyperspy object to a sidpy Dataset. To ensure that all information is retained some information is printed and the dataset is plotted" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Acquisition_instrument : └── TEM\n", " ├── Detector\n", " │ └── EELS\n", " │ ├── collection_angle = 48.0\n", " │ ├── dwell_time = 0.019999999552965164\n", " │ └── frame_number = 1\n", " ├── acquisition_mode = STEM\n", " ├── beam_current = 0.0\n", " ├── beam_energy = 60.0\n", " ├── camera_length = 1.0\n", " ├── convergence_angle = 30.0\n", " ├── magnification = 256.0\n", " └── microscope = UltraSTEM 100 kV\n", "\n", "General : ├── date = 2020-11-19\n", "├── original_filename = Room-SI1-LL-SI (dark ref corrected).dm3\n", "├── time = 12:22:48\n", "└── title = Room-SI-LL-SI (dark ref corrected)\n", "\n", "Signal : ├── Noise_properties\n", "│ └── Variance_linear_model\n", "│ ├── gain_factor = 1.0\n", "│ └── gain_offset = 0.0\n", "├── binned = True\n", "├── quantity = Intensity (Counts)\n", "└── signal_type = EELS\n", "\n", "sidpy.Dataset of type SPECTRAL_IMAGE with:\n", " dask.array\n", " data contains: Intensity (Counts)\n", " and Dimensions: \n", "x: x (frame) of size (200,)\n", "Energy loss: Energy loss (eV) of size (1340,)\n", "y: distance (pixel) of size (1,)\n", " with metadata: ['Acquisition_instrument', 'General', 'Signal']\n" ] }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "/* global mpl */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function () {\n", " if (typeof WebSocket !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof MozWebSocket !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert(\n", " 'Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.'\n", " );\n", " }\n", "};\n", "\n", "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = this.ws.binaryType !== undefined;\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById('mpl-warnings');\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent =\n", " 'This browser does not support binary websocket messages. ' +\n", " 'Performance may be slow.';\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = document.createElement('div');\n", " this.root.setAttribute('style', 'display: inline-block');\n", " this._root_extra_style(this.root);\n", "\n", " parent_element.appendChild(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message('supports_binary', { value: fig.supports_binary });\n", " fig.send_message('send_image_mode', {});\n", " if (fig.ratio !== 1) {\n", " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", " }\n", " fig.send_message('refresh', {});\n", " };\n", "\n", " this.imageObj.onload = function () {\n", " if (fig.image_mode === 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function () {\n", " fig.ws.close();\n", " };\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "};\n", "\n", "mpl.figure.prototype._init_header = function () {\n", " var titlebar = document.createElement('div');\n", " titlebar.classList =\n", " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", " var titletext = document.createElement('div');\n", " titletext.classList = 'ui-dialog-title';\n", " titletext.setAttribute(\n", " 'style',\n", " 'width: 100%; text-align: center; padding: 3px;'\n", " );\n", " titlebar.appendChild(titletext);\n", " this.root.appendChild(titlebar);\n", " this.header = titletext;\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._init_canvas = function () {\n", " var fig = this;\n", "\n", " var canvas_div = (this.canvas_div = document.createElement('div'));\n", " canvas_div.setAttribute(\n", " 'style',\n", " 'border: 1px solid #ddd;' +\n", " 'box-sizing: content-box;' +\n", " 'clear: both;' +\n", " 'min-height: 1px;' +\n", " 'min-width: 1px;' +\n", " 'outline: 0;' +\n", " 'overflow: hidden;' +\n", " 'position: relative;' +\n", " 'resize: both;'\n", " );\n", "\n", " function on_keyboard_event_closure(name) {\n", " return function (event) {\n", " return fig.key_event(event, name);\n", " };\n", " }\n", "\n", " canvas_div.addEventListener(\n", " 'keydown',\n", " on_keyboard_event_closure('key_press')\n", " );\n", " canvas_div.addEventListener(\n", " 'keyup',\n", " on_keyboard_event_closure('key_release')\n", " );\n", "\n", " this._canvas_extra_style(canvas_div);\n", " this.root.appendChild(canvas_div);\n", "\n", " var canvas = (this.canvas = document.createElement('canvas'));\n", " canvas.classList.add('mpl-canvas');\n", " canvas.setAttribute('style', 'box-sizing: content-box;');\n", "\n", " this.context = canvas.getContext('2d');\n", "\n", " var backingStore =\n", " this.context.backingStorePixelRatio ||\n", " this.context.webkitBackingStorePixelRatio ||\n", " this.context.mozBackingStorePixelRatio ||\n", " this.context.msBackingStorePixelRatio ||\n", " this.context.oBackingStorePixelRatio ||\n", " this.context.backingStorePixelRatio ||\n", " 1;\n", "\n", " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", " 'canvas'\n", " ));\n", " rubberband_canvas.setAttribute(\n", " 'style',\n", " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", " );\n", "\n", " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", " if (this.ResizeObserver === undefined) {\n", " if (window.ResizeObserver !== undefined) {\n", " this.ResizeObserver = window.ResizeObserver;\n", " } else {\n", " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", " this.ResizeObserver = obs.ResizeObserver;\n", " }\n", " }\n", "\n", " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", " var nentries = entries.length;\n", " for (var i = 0; i < nentries; i++) {\n", " var entry = entries[i];\n", " var width, height;\n", " if (entry.contentBoxSize) {\n", " if (entry.contentBoxSize instanceof Array) {\n", " // Chrome 84 implements new version of spec.\n", " width = entry.contentBoxSize[0].inlineSize;\n", " height = entry.contentBoxSize[0].blockSize;\n", " } else {\n", " // Firefox implements old version of spec.\n", " width = entry.contentBoxSize.inlineSize;\n", " height = entry.contentBoxSize.blockSize;\n", " }\n", " } else {\n", " // Chrome <84 implements even older version of spec.\n", " width = entry.contentRect.width;\n", " height = entry.contentRect.height;\n", " }\n", "\n", " // Keep the size of the canvas and rubber band canvas in sync with\n", " // the canvas container.\n", " if (entry.devicePixelContentBoxSize) {\n", " // Chrome 84 implements new version of spec.\n", " canvas.setAttribute(\n", " 'width',\n", " entry.devicePixelContentBoxSize[0].inlineSize\n", " );\n", " canvas.setAttribute(\n", " 'height',\n", " entry.devicePixelContentBoxSize[0].blockSize\n", " );\n", " } else {\n", " canvas.setAttribute('width', width * fig.ratio);\n", " canvas.setAttribute('height', height * fig.ratio);\n", " }\n", " canvas.setAttribute(\n", " 'style',\n", " 'width: ' + width + 'px; height: ' + height + 'px;'\n", " );\n", "\n", " rubberband_canvas.setAttribute('width', width);\n", " rubberband_canvas.setAttribute('height', height);\n", "\n", " // And update the size in Python. We ignore the initial 0/0 size\n", " // that occurs as the element is placed into the DOM, which should\n", " // otherwise not happen due to the minimum size styling.\n", " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", " fig.request_resize(width, height);\n", " }\n", " }\n", " });\n", " this.resizeObserverInstance.observe(canvas_div);\n", "\n", " function on_mouse_event_closure(name) {\n", " return function (event) {\n", " return fig.mouse_event(event, name);\n", " };\n", " }\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mousedown',\n", " on_mouse_event_closure('button_press')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseup',\n", " on_mouse_event_closure('button_release')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'dblclick',\n", " on_mouse_event_closure('dblclick')\n", " );\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband_canvas.addEventListener(\n", " 'mousemove',\n", " on_mouse_event_closure('motion_notify')\n", " );\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mouseenter',\n", " on_mouse_event_closure('figure_enter')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseleave',\n", " on_mouse_event_closure('figure_leave')\n", " );\n", "\n", " canvas_div.addEventListener('wheel', function (event) {\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " on_mouse_event_closure('scroll')(event);\n", " });\n", "\n", " canvas_div.appendChild(canvas);\n", " canvas_div.appendChild(rubberband_canvas);\n", "\n", " this.rubberband_context = rubberband_canvas.getContext('2d');\n", " this.rubberband_context.strokeStyle = '#000000';\n", "\n", " this._resize_canvas = function (width, height, forward) {\n", " if (forward) {\n", " canvas_div.style.width = width + 'px';\n", " canvas_div.style.height = height + 'px';\n", " }\n", " };\n", "\n", " // Disable right mouse context menu.\n", " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", " event.preventDefault();\n", " return false;\n", " });\n", "\n", " function set_focus() {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'mpl-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " continue;\n", " }\n", "\n", " var button = (fig.buttons[name] = document.createElement('button'));\n", " button.classList = 'mpl-widget';\n", " button.setAttribute('role', 'button');\n", " button.setAttribute('aria-disabled', 'false');\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", "\n", " var icon_img = document.createElement('img');\n", " icon_img.src = '_images/' + image + '.png';\n", " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", " icon_img.alt = tooltip;\n", " button.appendChild(icon_img);\n", "\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " var fmt_picker = document.createElement('select');\n", " fmt_picker.classList = 'mpl-widget';\n", " toolbar.appendChild(fmt_picker);\n", " this.format_dropdown = fmt_picker;\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = document.createElement('option');\n", " option.selected = fmt === mpl.default_extension;\n", " option.innerHTML = fmt;\n", " fmt_picker.appendChild(option);\n", " }\n", "\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "};\n", "\n", "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", "};\n", "\n", "mpl.figure.prototype.send_message = function (type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "};\n", "\n", "mpl.figure.prototype.send_draw_message = function () {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "};\n", "\n", "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1], msg['forward']);\n", " fig.send_message('refresh', {});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", " var x0 = msg['x0'] / fig.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", " var x1 = msg['x1'] / fig.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0,\n", " 0,\n", " fig.canvas.width / fig.ratio,\n", " fig.canvas.height / fig.ratio\n", " );\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "};\n", "\n", "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "};\n", "\n", "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch (cursor) {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "};\n", "\n", "mpl.figure.prototype.handle_message = function (fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "};\n", "\n", "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "};\n", "\n", "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "};\n", "\n", "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", " for (var key in msg) {\n", " if (!(key in fig.buttons)) {\n", " continue;\n", " }\n", " fig.buttons[key].disabled = !msg[key];\n", " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", " if (msg['mode'] === 'PAN') {\n", " fig.buttons['Pan'].classList.add('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " } else if (msg['mode'] === 'ZOOM') {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.add('active');\n", " } else {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " }\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Called whenever the canvas gets updated.\n", " this.send_message('ack', {});\n", "};\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function (fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " var img = evt.data;\n", " if (img.type !== 'image/png') {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " img.type = 'image/png';\n", " }\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src\n", " );\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " img\n", " );\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " } else if (\n", " typeof evt.data === 'string' &&\n", " evt.data.slice(0, 21) === 'data:image/png;base64'\n", " ) {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig['handle_' + msg_type];\n", " } catch (e) {\n", " console.log(\n", " \"No handler for the '\" + msg_type + \"' message type: \",\n", " msg\n", " );\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\n", " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", " e,\n", " e.stack,\n", " msg\n", " );\n", " }\n", " }\n", " };\n", "};\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function (e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e) {\n", " e = window.event;\n", " }\n", " if (e.target) {\n", " targ = e.target;\n", " } else if (e.srcElement) {\n", " targ = e.srcElement;\n", " }\n", " if (targ.nodeType === 3) {\n", " // defeat Safari bug\n", " targ = targ.parentNode;\n", " }\n", "\n", " // pageX,Y are the mouse positions relative to the document\n", " var boundingRect = targ.getBoundingClientRect();\n", " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", "\n", " return { x: x, y: y };\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys(original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object') {\n", " obj[key] = original[key];\n", " }\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function (event, name) {\n", " var canvas_pos = mpl.findpos(event);\n", "\n", " if (name === 'button_press') {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * this.ratio;\n", " var y = canvas_pos.y * this.ratio;\n", "\n", " this.send_message(name, {\n", " x: x,\n", " y: y,\n", " button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event),\n", " });\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", " // Handle any extra behaviour associated with a key event\n", "};\n", "\n", "mpl.figure.prototype.key_event = function (event, name) {\n", " // Prevent repeat events\n", " if (name === 'key_press') {\n", " if (event.key === this._key) {\n", " return;\n", " } else {\n", " this._key = event.key;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.key !== 'Control') {\n", " value += 'ctrl+';\n", " }\n", " else if (event.altKey && event.key !== 'Alt') {\n", " value += 'alt+';\n", " }\n", " else if (event.shiftKey && event.key !== 'Shift') {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k' + event.key;\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", " if (name === 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message('toolbar_button', { name: name });\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "\n", "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", "// prettier-ignore\n", "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* global mpl */\n", "\n", "var comm_websocket_adapter = function (comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.binaryType = comm.kernel.ws.binaryType;\n", " ws.readyState = comm.kernel.ws.readyState;\n", " function updateReadyState(_event) {\n", " if (comm.kernel.ws) {\n", " ws.readyState = comm.kernel.ws.readyState;\n", " } else {\n", " ws.readyState = 3; // Closed state.\n", " }\n", " }\n", " comm.kernel.ws.addEventListener('open', updateReadyState);\n", " comm.kernel.ws.addEventListener('close', updateReadyState);\n", " comm.kernel.ws.addEventListener('error', updateReadyState);\n", "\n", " ws.close = function () {\n", " comm.close();\n", " };\n", " ws.send = function (m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function (msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " var data = msg['content']['data'];\n", " if (data['blob'] !== undefined) {\n", " data = {\n", " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", " };\n", " }\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(data);\n", " });\n", " return ws;\n", "};\n", "\n", "mpl.mpl_figure_comm = function (comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = document.getElementById(id);\n", " var ws_proxy = comm_websocket_adapter(comm);\n", "\n", " function ondownload(figure, _format) {\n", " window.open(figure.canvas.toDataURL());\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element;\n", " fig.cell_info = mpl.find_output_cell(\"
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" ], "text/plain": [ "sidpy.Dataset of type SPECTRAL_IMAGE with:\n", " dask.array\n", " data contains: Intensity (Counts)\n", " and Dimensions: \n", "x: x (frame) of size (200,)\n", "Energy loss: Energy loss (eV) of size (1340,)\n", "y: distance (pixel) of size (1,)\n", " with metadata: ['Acquisition_instrument', 'General', 'Signal']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset = sidpy.convert_hyperspy(s)\n", "\n", "dataset.view_metadata()\n", "print(dataset)\n", "dataset.plot()\n", "dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Appendix\n", "Code for conversion" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def convert_hyperspy(s):\n", " \"\"\" \n", " Imports a hyperspy signal object into sidpy.Dataset\n", " \n", " Parameters\n", " ----------\n", " s: hyperspy dataset\n", " \n", " Return\n", " ------\n", " dataset: sidpy.Dataset\n", " \"\"\"\n", " import hyperspy.api as hs\n", "\n", " if not isinstance(s, (hs.signals.Signal1D, hs.signals.Signal2D)):\n", " raise TypeError('This is not a hyperspy signal object')\n", " dataset = sidpy.Dataset.from_array(s, name=s.metadata.General.title)\n", " # Add dimension info\n", " axes = s.axes_manager.as_dictionary()\n", "\n", " if isinstance(s, hs.signals.Signal1D):\n", " if s.data.ndim < 2:\n", " dataset.data_type = 'spectrum'\n", " elif s._data.ndim > 1:\n", " if s.data.ndim == 2:\n", " dataset = sidpy.Dataset.from_array(np.expand_dims(s,2), name=s.metadata.General.title)\n", " dataset.set_dimension(2,sidpy.Dimension([0], name='y' ,units='pixel', \n", " quantity='distance', dimension_type='spatial'))\n", " dataset.data_type = sidpy.DataType.SPECTRAL_IMAGE \n", " for key, axis in axes.items():\n", " if axis['navigate']:\n", " dimension_type = 'spatial'\n", " else:\n", " dimension_type = 'spectral'\n", " dim_array = np.arange(axis['size'])* axis['scale']+axis['offset']\n", " if axis['units'] == '':\n", " axis['units'] = 'frame'\n", " dataset.set_dimension(int(key[-1]), sidpy.Dimension(dim_array, name=axis['name'] ,units=axis['units'], \n", " quantity=axis['name'], dimension_type=dimension_type))\n", "\n", " elif isinstance(s, hs.signals.Signal2D):\n", " if s.data.ndim < 4:\n", " if s.data.ndim == 2:\n", " dataset.data_type = 'image'\n", " elif s._data.ndim == 3:\n", " dataset.data_type = 'image_stack'\n", " for key, axis in axes.items():\n", " if axis['navigate']:\n", " dimension_type = 'temporal'\n", " else:\n", " dimension_type = 'spatial'\n", " dim_array = np.arange(axis['size'])* axis['scale']+axis['offset']\n", " if axis['units'] == '':\n", " axis['units'] = 'pixel'\n", " dataset.set_dimension(int(key[-1]), sidpy.Dimension(dim_array, name=axis['name'] ,units=axis['units'], \n", " quantity=axis['name'], dimension_type=dimension_type)) \n", " elif s.data.ndim == 4:\n", " dataset.data_type = 'IMAGE_4D'\n", " for key, axis in axes.items():\n", " if axis['navigate']:\n", " dimension_type = 'spatial'\n", " else:\n", " dimension_type = 'reciprocal'\n", " dim_array = np.arange(axis['size'])* axis['scale']+axis['offset']\n", " if axis['units'] == '':\n", " axis['units'] = 'pixel'\n", " dataset.set_dimension(int(key[-1]), sidpy.Dimension(dim_array, name=axis['name'] ,units=axis['units'], \n", " quantity=axis['name'], dimension_type=dimension_type))\n", " dataset.metadata = dict(s.metadata)\n", " dataset.original_metadata = dict(s.original_metadata)\n", " dataset.title = dataset.metadata['General']['title']\n", " dataset.units = dataset.metadata['Signal']['quantity '].split('(')[-1][:-1]\n", " dataset.quantity = dataset.metadata['Signal']['quantity '].split('(')[0]\n", " dataset.source = 'hyperspy'\n", " return dataset\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 4 } sidpy-0.12.3/notebooks/00_basic_usage/index.rst000066400000000000000000000006661455261647000213570ustar00rootroot00000000000000Basic usage ============ | This folder contains notebooks for creating, manipulating and visualizing Datasets. | `Create Dataset <./create_dataset.ipynb>`_ | `Visualize Dataset <./plot_dataset.ipynb>`_ | `Basic Mathematical Operations <./math_dataset.ipynb>`_ | `Working with Hyperspy <./hyperspy.ipynb>`_ .. toctree:: :maxdepth: 1 :hidden: create_dataset.ipynb plot_dataset.ipynb math_dataset.ipynb hyperspy.ipynb sidpy-0.12.3/notebooks/00_basic_usage/math_dataset.ipynb000066400000000000000000410013011455261647000232070ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Mathematic Operations on Datasets\n", "\n", "**Gerd Duscher**\n", "\n", "04/16/2020\n", "\n", "**Please download this example and run it as a notebook by scrolling to the\n", "bottom of this page**\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n", "0.0.5g\n" ] } ], "source": [ "# Ensure python 3 compatibility:\n", "from __future__ import division, print_function, absolute_import, unicode_literals\n", "%pylab notebook\n", "\n", "import sys\n", "sys.path.insert(0, '../../')\n", "import sidpy\n", "\n", "print(sidpy.__version__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating an Image Datset\n", "First, we make a sidpy dataset from a numpy array, with all the information to plot it. " ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "/* global mpl */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function () {\n", " if (typeof WebSocket !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof MozWebSocket !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert(\n", " 'Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.'\n", " );\n", " }\n", "};\n", "\n", "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = this.ws.binaryType !== undefined;\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById('mpl-warnings');\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent =\n", " 'This browser does not support binary websocket messages. ' +\n", " 'Performance may be slow.';\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = document.createElement('div');\n", " this.root.setAttribute('style', 'display: inline-block');\n", " this._root_extra_style(this.root);\n", "\n", " parent_element.appendChild(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message('supports_binary', { value: fig.supports_binary });\n", " fig.send_message('send_image_mode', {});\n", " if (fig.ratio !== 1) {\n", " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", " }\n", " fig.send_message('refresh', {});\n", " };\n", "\n", " this.imageObj.onload = function () {\n", " if (fig.image_mode === 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function () {\n", " fig.ws.close();\n", " };\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "};\n", "\n", "mpl.figure.prototype._init_header = function () {\n", " var titlebar = document.createElement('div');\n", " titlebar.classList =\n", " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", " var titletext = document.createElement('div');\n", " titletext.classList = 'ui-dialog-title';\n", " titletext.setAttribute(\n", " 'style',\n", " 'width: 100%; text-align: center; padding: 3px;'\n", " );\n", " titlebar.appendChild(titletext);\n", " this.root.appendChild(titlebar);\n", " this.header = titletext;\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._init_canvas = function () {\n", " var fig = this;\n", "\n", " var canvas_div = (this.canvas_div = document.createElement('div'));\n", " canvas_div.setAttribute(\n", " 'style',\n", " 'border: 1px solid #ddd;' +\n", " 'box-sizing: content-box;' +\n", " 'clear: both;' +\n", " 'min-height: 1px;' +\n", " 'min-width: 1px;' +\n", " 'outline: 0;' +\n", " 'overflow: hidden;' +\n", " 'position: relative;' +\n", " 'resize: both;'\n", " );\n", "\n", " function on_keyboard_event_closure(name) {\n", " return function (event) {\n", " return fig.key_event(event, name);\n", " };\n", " }\n", "\n", " canvas_div.addEventListener(\n", " 'keydown',\n", " on_keyboard_event_closure('key_press')\n", " );\n", " canvas_div.addEventListener(\n", " 'keyup',\n", " on_keyboard_event_closure('key_release')\n", " );\n", "\n", " this._canvas_extra_style(canvas_div);\n", " this.root.appendChild(canvas_div);\n", "\n", " var canvas = (this.canvas = document.createElement('canvas'));\n", " canvas.classList.add('mpl-canvas');\n", " canvas.setAttribute('style', 'box-sizing: content-box;');\n", "\n", " this.context = canvas.getContext('2d');\n", "\n", " var backingStore =\n", " this.context.backingStorePixelRatio ||\n", " this.context.webkitBackingStorePixelRatio ||\n", " this.context.mozBackingStorePixelRatio ||\n", " this.context.msBackingStorePixelRatio ||\n", " this.context.oBackingStorePixelRatio ||\n", " this.context.backingStorePixelRatio ||\n", " 1;\n", "\n", " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", " 'canvas'\n", " ));\n", " rubberband_canvas.setAttribute(\n", " 'style',\n", " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", " );\n", "\n", " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", " if (this.ResizeObserver === undefined) {\n", " if (window.ResizeObserver !== undefined) {\n", " this.ResizeObserver = window.ResizeObserver;\n", " } else {\n", " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", " this.ResizeObserver = obs.ResizeObserver;\n", " }\n", " }\n", "\n", " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", " var nentries = entries.length;\n", " for (var i = 0; i < nentries; i++) {\n", " var entry = entries[i];\n", " var width, height;\n", " if (entry.contentBoxSize) {\n", " if (entry.contentBoxSize instanceof Array) {\n", " // Chrome 84 implements new version of spec.\n", " width = entry.contentBoxSize[0].inlineSize;\n", " height = entry.contentBoxSize[0].blockSize;\n", " } else {\n", " // Firefox implements old version of spec.\n", " width = entry.contentBoxSize.inlineSize;\n", " height = entry.contentBoxSize.blockSize;\n", " }\n", " } else {\n", " // Chrome <84 implements even older version of spec.\n", " width = entry.contentRect.width;\n", " height = entry.contentRect.height;\n", " }\n", "\n", " // Keep the size of the canvas and rubber band canvas in sync with\n", " // the canvas container.\n", " if (entry.devicePixelContentBoxSize) {\n", " // Chrome 84 implements new version of spec.\n", " canvas.setAttribute(\n", " 'width',\n", " entry.devicePixelContentBoxSize[0].inlineSize\n", " );\n", " canvas.setAttribute(\n", " 'height',\n", " entry.devicePixelContentBoxSize[0].blockSize\n", " );\n", " } else {\n", " canvas.setAttribute('width', width * fig.ratio);\n", " canvas.setAttribute('height', height * fig.ratio);\n", " }\n", " canvas.setAttribute(\n", " 'style',\n", " 'width: ' + width + 'px; height: ' + height + 'px;'\n", " );\n", "\n", " rubberband_canvas.setAttribute('width', width);\n", " rubberband_canvas.setAttribute('height', height);\n", "\n", " // And update the size in Python. We ignore the initial 0/0 size\n", " // that occurs as the element is placed into the DOM, which should\n", " // otherwise not happen due to the minimum size styling.\n", " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", " fig.request_resize(width, height);\n", " }\n", " }\n", " });\n", " this.resizeObserverInstance.observe(canvas_div);\n", "\n", " function on_mouse_event_closure(name) {\n", " return function (event) {\n", " return fig.mouse_event(event, name);\n", " };\n", " }\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mousedown',\n", " on_mouse_event_closure('button_press')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseup',\n", " on_mouse_event_closure('button_release')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'dblclick',\n", " on_mouse_event_closure('dblclick')\n", " );\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband_canvas.addEventListener(\n", " 'mousemove',\n", " on_mouse_event_closure('motion_notify')\n", " );\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mouseenter',\n", " on_mouse_event_closure('figure_enter')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseleave',\n", " on_mouse_event_closure('figure_leave')\n", " );\n", "\n", " canvas_div.addEventListener('wheel', function (event) {\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " on_mouse_event_closure('scroll')(event);\n", " });\n", "\n", " canvas_div.appendChild(canvas);\n", " canvas_div.appendChild(rubberband_canvas);\n", "\n", " this.rubberband_context = rubberband_canvas.getContext('2d');\n", " this.rubberband_context.strokeStyle = '#000000';\n", "\n", " this._resize_canvas = function (width, height, forward) {\n", " if (forward) {\n", " canvas_div.style.width = width + 'px';\n", " canvas_div.style.height = height + 'px';\n", " }\n", " };\n", "\n", " // Disable right mouse context menu.\n", " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", " event.preventDefault();\n", " return false;\n", " });\n", "\n", " function set_focus() {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'mpl-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " continue;\n", " }\n", "\n", " var button = (fig.buttons[name] = document.createElement('button'));\n", " button.classList = 'mpl-widget';\n", " button.setAttribute('role', 'button');\n", " button.setAttribute('aria-disabled', 'false');\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", "\n", " var icon_img = document.createElement('img');\n", " icon_img.src = '_images/' + image + '.png';\n", " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", " icon_img.alt = tooltip;\n", " button.appendChild(icon_img);\n", "\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " var fmt_picker = document.createElement('select');\n", " fmt_picker.classList = 'mpl-widget';\n", " toolbar.appendChild(fmt_picker);\n", " this.format_dropdown = fmt_picker;\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = document.createElement('option');\n", " option.selected = fmt === mpl.default_extension;\n", " option.innerHTML = fmt;\n", " fmt_picker.appendChild(option);\n", " }\n", "\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "};\n", "\n", "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", "};\n", "\n", "mpl.figure.prototype.send_message = function (type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "};\n", "\n", "mpl.figure.prototype.send_draw_message = function () {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "};\n", "\n", "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1], msg['forward']);\n", " fig.send_message('refresh', {});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", " var x0 = msg['x0'] / fig.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", " var x1 = msg['x1'] / fig.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0,\n", " 0,\n", " fig.canvas.width / fig.ratio,\n", " fig.canvas.height / fig.ratio\n", " );\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "};\n", "\n", "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "};\n", "\n", "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch (cursor) {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "};\n", "\n", "mpl.figure.prototype.handle_message = function (fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "};\n", "\n", "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "};\n", "\n", "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "};\n", "\n", "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", " for (var key in msg) {\n", " if (!(key in fig.buttons)) {\n", " continue;\n", " }\n", " fig.buttons[key].disabled = !msg[key];\n", " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", " if (msg['mode'] === 'PAN') {\n", " fig.buttons['Pan'].classList.add('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " } else if (msg['mode'] === 'ZOOM') {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.add('active');\n", " } else {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " }\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Called whenever the canvas gets updated.\n", " this.send_message('ack', {});\n", "};\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function (fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " var img = evt.data;\n", " if (img.type !== 'image/png') {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " img.type = 'image/png';\n", " }\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src\n", " );\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " img\n", " );\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " } else if (\n", " typeof evt.data === 'string' &&\n", " evt.data.slice(0, 21) === 'data:image/png;base64'\n", " ) {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig['handle_' + msg_type];\n", " } catch (e) {\n", " console.log(\n", " \"No handler for the '\" + msg_type + \"' message type: \",\n", " msg\n", " );\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\n", " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", " e,\n", " e.stack,\n", " msg\n", " );\n", " }\n", " }\n", " };\n", "};\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function (e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e) {\n", " e = window.event;\n", " }\n", " if (e.target) {\n", " targ = e.target;\n", " } else if (e.srcElement) {\n", " targ = e.srcElement;\n", " }\n", " if (targ.nodeType === 3) {\n", " // defeat Safari bug\n", " targ = targ.parentNode;\n", " }\n", "\n", " // pageX,Y are the mouse positions relative to the document\n", " var boundingRect = targ.getBoundingClientRect();\n", " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", "\n", " return { x: x, y: y };\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys(original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object') {\n", " obj[key] = original[key];\n", " }\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function (event, name) {\n", " var canvas_pos = mpl.findpos(event);\n", "\n", " if (name === 'button_press') {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * this.ratio;\n", " var y = canvas_pos.y * this.ratio;\n", "\n", " this.send_message(name, {\n", " x: x,\n", " y: y,\n", " button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event),\n", " });\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", " // Handle any extra behaviour associated with a key event\n", "};\n", "\n", "mpl.figure.prototype.key_event = function (event, name) {\n", " // Prevent repeat events\n", " if (name === 'key_press') {\n", " if (event.key === this._key) {\n", " return;\n", " } else {\n", " this._key = event.key;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.key !== 'Control') {\n", " value += 'ctrl+';\n", " }\n", " else if (event.altKey && event.key !== 'Alt') {\n", " value += 'alt+';\n", " }\n", " else if (event.shiftKey && event.key !== 'Shift') {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k' + event.key;\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", " if (name === 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message('toolbar_button', { name: name });\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "\n", "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", "// prettier-ignore\n", "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* global mpl */\n", "\n", "var comm_websocket_adapter = function (comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.binaryType = comm.kernel.ws.binaryType;\n", " ws.readyState = comm.kernel.ws.readyState;\n", " function updateReadyState(_event) {\n", " if (comm.kernel.ws) {\n", " ws.readyState = comm.kernel.ws.readyState;\n", " } else {\n", " ws.readyState = 3; // Closed state.\n", " }\n", " }\n", " comm.kernel.ws.addEventListener('open', updateReadyState);\n", " comm.kernel.ws.addEventListener('close', updateReadyState);\n", " comm.kernel.ws.addEventListener('error', updateReadyState);\n", "\n", " ws.close = function () {\n", " comm.close();\n", " };\n", " ws.send = function (m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function (msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " var data = msg['content']['data'];\n", " if (data['blob'] !== undefined) {\n", " data = {\n", " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", " };\n", " }\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(data);\n", " });\n", " return ws;\n", "};\n", "\n", "mpl.mpl_figure_comm = function (comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = document.getElementById(id);\n", " var ws_proxy = comm_websocket_adapter(comm);\n", "\n", " function ondownload(figure, _format) {\n", " window.open(figure.canvas.toDataURL());\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element;\n", " fig.cell_info = mpl.find_output_cell(\"
\");\n", " if (!fig.cell_info) {\n", " console.error('Failed to find cell for figure', id, fig);\n", " return;\n", " }\n", " fig.cell_info[0].output_area.element.on(\n", " 'cleared',\n", " { fig: fig },\n", " fig._remove_fig_handler\n", " );\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function (fig, msg) {\n", " var width = fig.canvas.width / fig.ratio;\n", " fig.cell_info[0].output_area.element.off(\n", " 'cleared',\n", " fig._remove_fig_handler\n", " );\n", " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable();\n", " fig.parent_element.innerHTML =\n", " '';\n", " fig.close_ws(fig, msg);\n", "};\n", "\n", "mpl.figure.prototype.close_ws = function (fig, msg) {\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "};\n", "\n", "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width / this.ratio;\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] =\n", " '';\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message('ack', {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () {\n", " fig.push_to_output();\n", " }, 1000);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'btn-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " var button;\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " continue;\n", " }\n", "\n", " button = fig.buttons[name] = document.createElement('button');\n", " button.classList = 'btn btn-default';\n", " button.href = '#';\n", " button.title = name;\n", " button.innerHTML = '';\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message pull-right';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "\n", " // Add the close button to the window.\n", " var buttongrp = document.createElement('div');\n", " buttongrp.classList = 'btn-group inline pull-right';\n", " button = document.createElement('button');\n", " button.classList = 'btn btn-mini btn-primary';\n", " button.href = '#';\n", " button.title = 'Stop Interaction';\n", " button.innerHTML = '';\n", " button.addEventListener('click', function (_evt) {\n", " fig.handle_close(fig, {});\n", " });\n", " button.addEventListener(\n", " 'mouseover',\n", " on_mouseover_closure('Stop Interaction')\n", " );\n", " buttongrp.appendChild(button);\n", " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", "};\n", "\n", "mpl.figure.prototype._remove_fig_handler = function (event) {\n", " var fig = event.data.fig;\n", " if (event.target !== this) {\n", " // Ignore bubbled events from children.\n", " return;\n", " }\n", " fig.close_ws(fig, {});\n", "};\n", "\n", "mpl.figure.prototype._root_extra_style = function (el) {\n", " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (el) {\n", " // this is important to make the div 'focusable\n", " el.setAttribute('tabindex', 0);\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " } else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager) {\n", " manager = IPython.keyboard_manager;\n", " }\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which === 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " fig.ondownload(fig, null);\n", "};\n", "\n", "mpl.find_output_cell = function (html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i = 0; i < ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code') {\n", " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] === html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "};\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel !== null) {\n", " IPython.notebook.kernel.comm_manager.register_target(\n", " 'matplotlib',\n", " mpl.mpl_figure_comm\n", " );\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.random.normal(3, 2.5, size=(512, 512))\n", "dset = sidpy.Dataset.from_array(x)\n", "dset.data_type = 'image'\n", "dset.units = 'counts'\n", "dset.quantity = 'intensity'\n", "dset.title = 'random'\n", "dset.set_dimension(0, sidpy.Dimension(np.arange(dset.shape[0])*.02, 'x'))\n", "dset.x.dimension_type = 'spatial'\n", "dset.x.units = 'nm'\n", "dset.x.quantity = 'distance'\n", "dset.set_dimension(1, sidpy.Dimension(np.arange(dset.shape[1])*.02, 'y'))\n", "dset.y.dimension_type = 'spatial'\n", "dset.y.units = 'nm'\n", "dset.y.quantity = 'distance'\n", "dset.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simple Arithmetic \n", "\n", "First we subtract the min of this image, and we want to have the rest of the information unchanged\n", "\n", "So we use the minimum function and do a subtraction.\n" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "/* global mpl */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function () {\n", " if (typeof WebSocket !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof MozWebSocket !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert(\n", " 'Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.'\n", " );\n", " }\n", "};\n", "\n", "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = this.ws.binaryType !== undefined;\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById('mpl-warnings');\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent =\n", " 'This browser does not support binary websocket messages. ' +\n", " 'Performance may be slow.';\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = document.createElement('div');\n", " this.root.setAttribute('style', 'display: inline-block');\n", " this._root_extra_style(this.root);\n", "\n", " parent_element.appendChild(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message('supports_binary', { value: fig.supports_binary });\n", " fig.send_message('send_image_mode', {});\n", " if (fig.ratio !== 1) {\n", " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", " }\n", " fig.send_message('refresh', {});\n", " };\n", "\n", " this.imageObj.onload = function () {\n", " if (fig.image_mode === 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function () {\n", " fig.ws.close();\n", " };\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "};\n", "\n", "mpl.figure.prototype._init_header = function () {\n", " var titlebar = document.createElement('div');\n", " titlebar.classList =\n", " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", " var titletext = document.createElement('div');\n", " titletext.classList = 'ui-dialog-title';\n", " titletext.setAttribute(\n", " 'style',\n", " 'width: 100%; text-align: center; padding: 3px;'\n", " );\n", " titlebar.appendChild(titletext);\n", " this.root.appendChild(titlebar);\n", " this.header = titletext;\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._init_canvas = function () {\n", " var fig = this;\n", "\n", " var canvas_div = (this.canvas_div = document.createElement('div'));\n", " canvas_div.setAttribute(\n", " 'style',\n", " 'border: 1px solid #ddd;' +\n", " 'box-sizing: content-box;' +\n", " 'clear: both;' +\n", " 'min-height: 1px;' +\n", " 'min-width: 1px;' +\n", " 'outline: 0;' +\n", " 'overflow: hidden;' +\n", " 'position: relative;' +\n", " 'resize: both;'\n", " );\n", "\n", " function on_keyboard_event_closure(name) {\n", " return function (event) {\n", " return fig.key_event(event, name);\n", " };\n", " }\n", "\n", " canvas_div.addEventListener(\n", " 'keydown',\n", " on_keyboard_event_closure('key_press')\n", " );\n", " canvas_div.addEventListener(\n", " 'keyup',\n", " on_keyboard_event_closure('key_release')\n", " );\n", "\n", " this._canvas_extra_style(canvas_div);\n", " this.root.appendChild(canvas_div);\n", "\n", " var canvas = (this.canvas = document.createElement('canvas'));\n", " canvas.classList.add('mpl-canvas');\n", " canvas.setAttribute('style', 'box-sizing: content-box;');\n", "\n", " this.context = canvas.getContext('2d');\n", "\n", " var backingStore =\n", " this.context.backingStorePixelRatio ||\n", " this.context.webkitBackingStorePixelRatio ||\n", " this.context.mozBackingStorePixelRatio ||\n", " this.context.msBackingStorePixelRatio ||\n", " this.context.oBackingStorePixelRatio ||\n", " this.context.backingStorePixelRatio ||\n", " 1;\n", "\n", " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", " 'canvas'\n", " ));\n", " rubberband_canvas.setAttribute(\n", " 'style',\n", " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", " );\n", "\n", " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", " if (this.ResizeObserver === undefined) {\n", " if (window.ResizeObserver !== undefined) {\n", " this.ResizeObserver = window.ResizeObserver;\n", " } else {\n", " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", " this.ResizeObserver = obs.ResizeObserver;\n", " }\n", " }\n", "\n", " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", " var nentries = entries.length;\n", " for (var i = 0; i < nentries; i++) {\n", " var entry = entries[i];\n", " var width, height;\n", " if (entry.contentBoxSize) {\n", " if (entry.contentBoxSize instanceof Array) {\n", " // Chrome 84 implements new version of spec.\n", " width = entry.contentBoxSize[0].inlineSize;\n", " height = entry.contentBoxSize[0].blockSize;\n", " } else {\n", " // Firefox implements old version of spec.\n", " width = entry.contentBoxSize.inlineSize;\n", " height = entry.contentBoxSize.blockSize;\n", " }\n", " } else {\n", " // Chrome <84 implements even older version of spec.\n", " width = entry.contentRect.width;\n", " height = entry.contentRect.height;\n", " }\n", "\n", " // Keep the size of the canvas and rubber band canvas in sync with\n", " // the canvas container.\n", " if (entry.devicePixelContentBoxSize) {\n", " // Chrome 84 implements new version of spec.\n", " canvas.setAttribute(\n", " 'width',\n", " entry.devicePixelContentBoxSize[0].inlineSize\n", " );\n", " canvas.setAttribute(\n", " 'height',\n", " entry.devicePixelContentBoxSize[0].blockSize\n", " );\n", " } else {\n", " canvas.setAttribute('width', width * fig.ratio);\n", " canvas.setAttribute('height', height * fig.ratio);\n", " }\n", " canvas.setAttribute(\n", " 'style',\n", " 'width: ' + width + 'px; height: ' + height + 'px;'\n", " );\n", "\n", " rubberband_canvas.setAttribute('width', width);\n", " rubberband_canvas.setAttribute('height', height);\n", "\n", " // And update the size in Python. We ignore the initial 0/0 size\n", " // that occurs as the element is placed into the DOM, which should\n", " // otherwise not happen due to the minimum size styling.\n", " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", " fig.request_resize(width, height);\n", " }\n", " }\n", " });\n", " this.resizeObserverInstance.observe(canvas_div);\n", "\n", " function on_mouse_event_closure(name) {\n", " return function (event) {\n", " return fig.mouse_event(event, name);\n", " };\n", " }\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mousedown',\n", " on_mouse_event_closure('button_press')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseup',\n", " on_mouse_event_closure('button_release')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'dblclick',\n", " on_mouse_event_closure('dblclick')\n", " );\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband_canvas.addEventListener(\n", " 'mousemove',\n", " on_mouse_event_closure('motion_notify')\n", " );\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mouseenter',\n", " on_mouse_event_closure('figure_enter')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseleave',\n", " on_mouse_event_closure('figure_leave')\n", " );\n", "\n", " canvas_div.addEventListener('wheel', function (event) {\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " on_mouse_event_closure('scroll')(event);\n", " });\n", "\n", " canvas_div.appendChild(canvas);\n", " canvas_div.appendChild(rubberband_canvas);\n", "\n", " this.rubberband_context = rubberband_canvas.getContext('2d');\n", " this.rubberband_context.strokeStyle = '#000000';\n", "\n", " this._resize_canvas = function (width, height, forward) {\n", " if (forward) {\n", " canvas_div.style.width = width + 'px';\n", " canvas_div.style.height = height + 'px';\n", " }\n", " };\n", "\n", " // Disable right mouse context menu.\n", " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", " event.preventDefault();\n", " return false;\n", " });\n", "\n", " function set_focus() {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'mpl-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " continue;\n", " }\n", "\n", " var button = (fig.buttons[name] = document.createElement('button'));\n", " button.classList = 'mpl-widget';\n", " button.setAttribute('role', 'button');\n", " button.setAttribute('aria-disabled', 'false');\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", "\n", " var icon_img = document.createElement('img');\n", " icon_img.src = '_images/' + image + '.png';\n", " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", " icon_img.alt = tooltip;\n", " button.appendChild(icon_img);\n", "\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " var fmt_picker = document.createElement('select');\n", " fmt_picker.classList = 'mpl-widget';\n", " toolbar.appendChild(fmt_picker);\n", " this.format_dropdown = fmt_picker;\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = document.createElement('option');\n", " option.selected = fmt === mpl.default_extension;\n", " option.innerHTML = fmt;\n", " fmt_picker.appendChild(option);\n", " }\n", "\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "};\n", "\n", "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", "};\n", "\n", "mpl.figure.prototype.send_message = function (type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "};\n", "\n", "mpl.figure.prototype.send_draw_message = function () {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "};\n", "\n", "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1], msg['forward']);\n", " fig.send_message('refresh', {});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", " var x0 = msg['x0'] / fig.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", " var x1 = msg['x1'] / fig.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0,\n", " 0,\n", " fig.canvas.width / fig.ratio,\n", " fig.canvas.height / fig.ratio\n", " );\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "};\n", "\n", "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "};\n", "\n", "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch (cursor) {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "};\n", "\n", "mpl.figure.prototype.handle_message = function (fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "};\n", "\n", "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "};\n", "\n", "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "};\n", "\n", "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", " for (var key in msg) {\n", " if (!(key in fig.buttons)) {\n", " continue;\n", " }\n", " fig.buttons[key].disabled = !msg[key];\n", " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", " if (msg['mode'] === 'PAN') {\n", " fig.buttons['Pan'].classList.add('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " } else if (msg['mode'] === 'ZOOM') {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.add('active');\n", " } else {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " }\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Called whenever the canvas gets updated.\n", " this.send_message('ack', {});\n", "};\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function (fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " var img = evt.data;\n", " if (img.type !== 'image/png') {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " img.type = 'image/png';\n", " }\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src\n", " );\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " img\n", " );\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " } else if (\n", " typeof evt.data === 'string' &&\n", " evt.data.slice(0, 21) === 'data:image/png;base64'\n", " ) {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig['handle_' + msg_type];\n", " } catch (e) {\n", " console.log(\n", " \"No handler for the '\" + msg_type + \"' message type: \",\n", " msg\n", " );\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\n", " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", " e,\n", " e.stack,\n", " msg\n", " );\n", " }\n", " }\n", " };\n", "};\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function (e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e) {\n", " e = window.event;\n", " }\n", " if (e.target) {\n", " targ = e.target;\n", " } else if (e.srcElement) {\n", " targ = e.srcElement;\n", " }\n", " if (targ.nodeType === 3) {\n", " // defeat Safari bug\n", " targ = targ.parentNode;\n", " }\n", "\n", " // pageX,Y are the mouse positions relative to the document\n", " var boundingRect = targ.getBoundingClientRect();\n", " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", "\n", " return { x: x, y: y };\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys(original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object') {\n", " obj[key] = original[key];\n", " }\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function (event, name) {\n", " var canvas_pos = mpl.findpos(event);\n", "\n", " if (name === 'button_press') {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * this.ratio;\n", " var y = canvas_pos.y * this.ratio;\n", "\n", " this.send_message(name, {\n", " x: x,\n", " y: y,\n", " button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event),\n", " });\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", " // Handle any extra behaviour associated with a key event\n", "};\n", "\n", "mpl.figure.prototype.key_event = function (event, name) {\n", " // Prevent repeat events\n", " if (name === 'key_press') {\n", " if (event.key === this._key) {\n", " return;\n", " } else {\n", " this._key = event.key;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.key !== 'Control') {\n", " value += 'ctrl+';\n", " }\n", " else if (event.altKey && event.key !== 'Alt') {\n", " value += 'alt+';\n", " }\n", " else if (event.shiftKey && event.key !== 'Shift') {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k' + event.key;\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", " if (name === 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message('toolbar_button', { name: name });\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "\n", "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", "// prettier-ignore\n", "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* global mpl */\n", "\n", "var comm_websocket_adapter = function (comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.binaryType = comm.kernel.ws.binaryType;\n", " ws.readyState = comm.kernel.ws.readyState;\n", " function updateReadyState(_event) {\n", " if (comm.kernel.ws) {\n", " ws.readyState = comm.kernel.ws.readyState;\n", " } else {\n", " ws.readyState = 3; // Closed state.\n", " }\n", " }\n", " comm.kernel.ws.addEventListener('open', updateReadyState);\n", " comm.kernel.ws.addEventListener('close', updateReadyState);\n", " comm.kernel.ws.addEventListener('error', updateReadyState);\n", "\n", " ws.close = function () {\n", " comm.close();\n", " };\n", " ws.send = function (m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function (msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " var data = msg['content']['data'];\n", " if (data['blob'] !== undefined) {\n", " data = {\n", " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", " };\n", " }\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(data);\n", " });\n", " return ws;\n", "};\n", "\n", "mpl.mpl_figure_comm = function (comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = document.getElementById(id);\n", " var ws_proxy = comm_websocket_adapter(comm);\n", "\n", " function ondownload(figure, _format) {\n", " window.open(figure.canvas.toDataURL());\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element;\n", " fig.cell_info = mpl.find_output_cell(\"
\");\n", " if (!fig.cell_info) {\n", " console.error('Failed to find cell for figure', id, fig);\n", " return;\n", " }\n", " fig.cell_info[0].output_area.element.on(\n", " 'cleared',\n", " { fig: fig },\n", " fig._remove_fig_handler\n", " );\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function (fig, msg) {\n", " var width = fig.canvas.width / fig.ratio;\n", " fig.cell_info[0].output_area.element.off(\n", " 'cleared',\n", " fig._remove_fig_handler\n", " );\n", " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable();\n", " fig.parent_element.innerHTML =\n", " '';\n", " fig.close_ws(fig, msg);\n", "};\n", "\n", "mpl.figure.prototype.close_ws = function (fig, msg) {\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "};\n", "\n", "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width / this.ratio;\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] =\n", " '';\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message('ack', {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () {\n", " fig.push_to_output();\n", " }, 1000);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'btn-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " var button;\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " continue;\n", " }\n", "\n", " button = fig.buttons[name] = document.createElement('button');\n", " button.classList = 'btn btn-default';\n", " button.href = '#';\n", " button.title = name;\n", " button.innerHTML = '';\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message pull-right';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "\n", " // Add the close button to the window.\n", " var buttongrp = document.createElement('div');\n", " buttongrp.classList = 'btn-group inline pull-right';\n", " button = document.createElement('button');\n", " button.classList = 'btn btn-mini btn-primary';\n", " button.href = '#';\n", " button.title = 'Stop Interaction';\n", " button.innerHTML = '';\n", " button.addEventListener('click', function (_evt) {\n", " fig.handle_close(fig, {});\n", " });\n", " button.addEventListener(\n", " 'mouseover',\n", " on_mouseover_closure('Stop Interaction')\n", " );\n", " buttongrp.appendChild(button);\n", " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", "};\n", "\n", "mpl.figure.prototype._remove_fig_handler = function (event) {\n", " var fig = event.data.fig;\n", " if (event.target !== this) {\n", " // Ignore bubbled events from children.\n", " return;\n", " }\n", " fig.close_ws(fig, {});\n", "};\n", "\n", "mpl.figure.prototype._root_extra_style = function (el) {\n", " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (el) {\n", " // this is important to make the div 'focusable\n", " el.setAttribute('tabindex', 0);\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " } else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager) {\n", " manager = IPython.keyboard_manager;\n", " }\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which === 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " fig.ondownload(fig, null);\n", "};\n", "\n", "mpl.find_output_cell = function (html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i = 0; 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" ], "text/plain": [ "sidpy.Dataset of type IMAGE with:\n", " dask.array\n", " data contains: intensity (counts)\n", " and Dimensions: \n", "x: distance (nm) of size (512,)\n", "y: distance (nm) of size (512,)" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dset = dset - dset.min()\n", "dset.plot()\n", "print(dset)\n", "dset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Operations on 3 Dimensional Datasets\n", "\n", "### First we make a stack of images" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(20, 512, 512)\n" ] }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "/* global mpl */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function () {\n", " if (typeof WebSocket !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof MozWebSocket !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert(\n", " 'Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.'\n", " );\n", " }\n", "};\n", "\n", "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = this.ws.binaryType !== undefined;\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById('mpl-warnings');\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent =\n", " 'This browser does not support binary websocket messages. ' +\n", " 'Performance may be slow.';\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = document.createElement('div');\n", " this.root.setAttribute('style', 'display: inline-block');\n", " this._root_extra_style(this.root);\n", "\n", " parent_element.appendChild(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message('supports_binary', { value: fig.supports_binary });\n", " fig.send_message('send_image_mode', {});\n", " if (fig.ratio !== 1) {\n", " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", " }\n", " fig.send_message('refresh', {});\n", " };\n", "\n", " this.imageObj.onload = function () {\n", " if (fig.image_mode === 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function () {\n", " fig.ws.close();\n", " };\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "};\n", "\n", "mpl.figure.prototype._init_header = function () {\n", " var titlebar = document.createElement('div');\n", " titlebar.classList =\n", " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", " var titletext = document.createElement('div');\n", " titletext.classList = 'ui-dialog-title';\n", " titletext.setAttribute(\n", " 'style',\n", " 'width: 100%; text-align: center; padding: 3px;'\n", " );\n", " titlebar.appendChild(titletext);\n", " this.root.appendChild(titlebar);\n", " this.header = titletext;\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._init_canvas = function () {\n", " var fig = this;\n", "\n", " var canvas_div = (this.canvas_div = document.createElement('div'));\n", " canvas_div.setAttribute(\n", " 'style',\n", " 'border: 1px solid #ddd;' +\n", " 'box-sizing: content-box;' +\n", " 'clear: both;' +\n", " 'min-height: 1px;' +\n", " 'min-width: 1px;' +\n", " 'outline: 0;' +\n", " 'overflow: hidden;' +\n", " 'position: relative;' +\n", " 'resize: both;'\n", " );\n", "\n", " function on_keyboard_event_closure(name) {\n", " return function (event) {\n", " return fig.key_event(event, name);\n", " };\n", " }\n", "\n", " canvas_div.addEventListener(\n", " 'keydown',\n", " on_keyboard_event_closure('key_press')\n", " );\n", " canvas_div.addEventListener(\n", " 'keyup',\n", " on_keyboard_event_closure('key_release')\n", " );\n", "\n", " this._canvas_extra_style(canvas_div);\n", " this.root.appendChild(canvas_div);\n", "\n", " var canvas = (this.canvas = document.createElement('canvas'));\n", " canvas.classList.add('mpl-canvas');\n", " canvas.setAttribute('style', 'box-sizing: content-box;');\n", "\n", " this.context = canvas.getContext('2d');\n", "\n", " var backingStore =\n", " this.context.backingStorePixelRatio ||\n", " this.context.webkitBackingStorePixelRatio ||\n", " this.context.mozBackingStorePixelRatio ||\n", " this.context.msBackingStorePixelRatio ||\n", " this.context.oBackingStorePixelRatio ||\n", " this.context.backingStorePixelRatio ||\n", " 1;\n", "\n", " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", " 'canvas'\n", " ));\n", " rubberband_canvas.setAttribute(\n", " 'style',\n", " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", " );\n", "\n", " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", " if (this.ResizeObserver === undefined) {\n", " if (window.ResizeObserver !== undefined) {\n", " this.ResizeObserver = window.ResizeObserver;\n", " } else {\n", " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", " this.ResizeObserver = obs.ResizeObserver;\n", " }\n", " }\n", "\n", " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", " var nentries = entries.length;\n", " for (var i = 0; i < nentries; i++) {\n", " var entry = entries[i];\n", " var width, height;\n", " if (entry.contentBoxSize) {\n", " if (entry.contentBoxSize instanceof Array) {\n", " // Chrome 84 implements new version of spec.\n", " width = entry.contentBoxSize[0].inlineSize;\n", " height = entry.contentBoxSize[0].blockSize;\n", " } else {\n", " // Firefox implements old version of spec.\n", " width = entry.contentBoxSize.inlineSize;\n", " height = entry.contentBoxSize.blockSize;\n", " }\n", " } else {\n", " // Chrome <84 implements even older version of spec.\n", " width = entry.contentRect.width;\n", " height = entry.contentRect.height;\n", " }\n", "\n", " // Keep the size of the canvas and rubber band canvas in sync with\n", " // the canvas container.\n", " if (entry.devicePixelContentBoxSize) {\n", " // Chrome 84 implements new version of spec.\n", " canvas.setAttribute(\n", " 'width',\n", " entry.devicePixelContentBoxSize[0].inlineSize\n", " );\n", " canvas.setAttribute(\n", " 'height',\n", " entry.devicePixelContentBoxSize[0].blockSize\n", " );\n", " } else {\n", " canvas.setAttribute('width', width * fig.ratio);\n", " canvas.setAttribute('height', height * fig.ratio);\n", " }\n", " canvas.setAttribute(\n", " 'style',\n", " 'width: ' + width + 'px; height: ' + height + 'px;'\n", " );\n", "\n", " rubberband_canvas.setAttribute('width', width);\n", " rubberband_canvas.setAttribute('height', height);\n", "\n", " // And update the size in Python. We ignore the initial 0/0 size\n", " // that occurs as the element is placed into the DOM, which should\n", " // otherwise not happen due to the minimum size styling.\n", " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", " fig.request_resize(width, height);\n", " }\n", " }\n", " });\n", " this.resizeObserverInstance.observe(canvas_div);\n", "\n", " function on_mouse_event_closure(name) {\n", " return function (event) {\n", " return fig.mouse_event(event, name);\n", " };\n", " }\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mousedown',\n", " on_mouse_event_closure('button_press')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseup',\n", " on_mouse_event_closure('button_release')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'dblclick',\n", " on_mouse_event_closure('dblclick')\n", " );\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband_canvas.addEventListener(\n", " 'mousemove',\n", " on_mouse_event_closure('motion_notify')\n", " );\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mouseenter',\n", " on_mouse_event_closure('figure_enter')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseleave',\n", " on_mouse_event_closure('figure_leave')\n", " );\n", "\n", " canvas_div.addEventListener('wheel', function (event) {\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " on_mouse_event_closure('scroll')(event);\n", " });\n", "\n", " canvas_div.appendChild(canvas);\n", " canvas_div.appendChild(rubberband_canvas);\n", "\n", " this.rubberband_context = rubberband_canvas.getContext('2d');\n", " this.rubberband_context.strokeStyle = '#000000';\n", "\n", " this._resize_canvas = function (width, height, forward) {\n", " if (forward) {\n", " canvas_div.style.width = width + 'px';\n", " canvas_div.style.height = height + 'px';\n", " }\n", " };\n", "\n", " // Disable right mouse context menu.\n", " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", " event.preventDefault();\n", " return false;\n", " });\n", "\n", " function set_focus() {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'mpl-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " continue;\n", " }\n", "\n", " var button = (fig.buttons[name] = document.createElement('button'));\n", " button.classList = 'mpl-widget';\n", " button.setAttribute('role', 'button');\n", " button.setAttribute('aria-disabled', 'false');\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", "\n", " var icon_img = document.createElement('img');\n", " icon_img.src = '_images/' + image + '.png';\n", " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", " icon_img.alt = tooltip;\n", " button.appendChild(icon_img);\n", "\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " var fmt_picker = document.createElement('select');\n", " fmt_picker.classList = 'mpl-widget';\n", " toolbar.appendChild(fmt_picker);\n", " this.format_dropdown = fmt_picker;\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = document.createElement('option');\n", " option.selected = fmt === mpl.default_extension;\n", " option.innerHTML = fmt;\n", " fmt_picker.appendChild(option);\n", " }\n", "\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "};\n", "\n", "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", "};\n", "\n", "mpl.figure.prototype.send_message = function (type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "};\n", "\n", "mpl.figure.prototype.send_draw_message = function () {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "};\n", "\n", "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1], msg['forward']);\n", " fig.send_message('refresh', {});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", " var x0 = msg['x0'] / fig.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", " var x1 = msg['x1'] / fig.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0,\n", " 0,\n", " fig.canvas.width / fig.ratio,\n", " fig.canvas.height / fig.ratio\n", " );\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "};\n", "\n", "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "};\n", "\n", "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch (cursor) {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "};\n", "\n", "mpl.figure.prototype.handle_message = function (fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "};\n", "\n", "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "};\n", "\n", "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "};\n", "\n", "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", " for (var key in msg) {\n", " if (!(key in fig.buttons)) {\n", " continue;\n", " }\n", " fig.buttons[key].disabled = !msg[key];\n", " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", " if (msg['mode'] === 'PAN') {\n", " fig.buttons['Pan'].classList.add('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " } else if (msg['mode'] === 'ZOOM') {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.add('active');\n", " } else {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " }\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Called whenever the canvas gets updated.\n", " this.send_message('ack', {});\n", "};\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function (fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " var img = evt.data;\n", " if (img.type !== 'image/png') {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " img.type = 'image/png';\n", " }\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src\n", " );\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " img\n", " );\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " } else if (\n", " typeof evt.data === 'string' &&\n", " evt.data.slice(0, 21) === 'data:image/png;base64'\n", " ) {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig['handle_' + msg_type];\n", " } catch (e) {\n", " console.log(\n", " \"No handler for the '\" + msg_type + \"' message type: \",\n", " msg\n", " );\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\n", " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", " e,\n", " e.stack,\n", " msg\n", " );\n", " }\n", " }\n", " };\n", "};\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function (e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e) {\n", " e = window.event;\n", " }\n", " if (e.target) {\n", " targ = e.target;\n", " } else if (e.srcElement) {\n", " targ = e.srcElement;\n", " }\n", " if (targ.nodeType === 3) {\n", " // defeat Safari bug\n", " targ = targ.parentNode;\n", " }\n", "\n", " // pageX,Y are the mouse positions relative to the document\n", " var boundingRect = targ.getBoundingClientRect();\n", " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", "\n", " return { x: x, y: y };\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys(original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object') {\n", " obj[key] = original[key];\n", " }\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function (event, name) {\n", " var canvas_pos = mpl.findpos(event);\n", "\n", " if (name === 'button_press') {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * this.ratio;\n", " var y = canvas_pos.y * this.ratio;\n", "\n", " this.send_message(name, {\n", " x: x,\n", " y: y,\n", " button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event),\n", " });\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", " // Handle any extra behaviour associated with a key event\n", "};\n", "\n", "mpl.figure.prototype.key_event = function (event, name) {\n", " // Prevent repeat events\n", " if (name === 'key_press') {\n", " if (event.key === this._key) {\n", " return;\n", " } else {\n", " this._key = event.key;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.key !== 'Control') {\n", " value += 'ctrl+';\n", " }\n", " else if (event.altKey && event.key !== 'Alt') {\n", " value += 'alt+';\n", " }\n", " else if (event.shiftKey && event.key !== 'Shift') {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k' + event.key;\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", " if (name === 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message('toolbar_button', { name: name });\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "\n", "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", "// prettier-ignore\n", "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* global mpl */\n", "\n", "var comm_websocket_adapter = function (comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.binaryType = comm.kernel.ws.binaryType;\n", " ws.readyState = comm.kernel.ws.readyState;\n", " function updateReadyState(_event) {\n", " if (comm.kernel.ws) {\n", " ws.readyState = comm.kernel.ws.readyState;\n", " } else {\n", " ws.readyState = 3; // Closed state.\n", " }\n", " }\n", " comm.kernel.ws.addEventListener('open', updateReadyState);\n", " comm.kernel.ws.addEventListener('close', updateReadyState);\n", " comm.kernel.ws.addEventListener('error', updateReadyState);\n", "\n", " ws.close = function () {\n", " comm.close();\n", " };\n", " ws.send = function (m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function (msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " var data = msg['content']['data'];\n", " if (data['blob'] !== undefined) {\n", " data = {\n", " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", " };\n", " }\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(data);\n", " });\n", " return ws;\n", "};\n", "\n", "mpl.mpl_figure_comm = function (comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = document.getElementById(id);\n", " var ws_proxy = comm_websocket_adapter(comm);\n", "\n", " function ondownload(figure, _format) {\n", " window.open(figure.canvas.toDataURL());\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element;\n", " fig.cell_info = mpl.find_output_cell(\"
\");\n", " if (!fig.cell_info) {\n", " console.error('Failed to find cell for figure', id, fig);\n", " return;\n", " }\n", " fig.cell_info[0].output_area.element.on(\n", " 'cleared',\n", " { fig: fig },\n", " fig._remove_fig_handler\n", " );\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function (fig, msg) {\n", " var width = fig.canvas.width / fig.ratio;\n", " fig.cell_info[0].output_area.element.off(\n", " 'cleared',\n", " fig._remove_fig_handler\n", " );\n", " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable();\n", " fig.parent_element.innerHTML =\n", " '';\n", " fig.close_ws(fig, msg);\n", "};\n", "\n", "mpl.figure.prototype.close_ws = function (fig, msg) {\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "};\n", "\n", "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width / this.ratio;\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] =\n", " '';\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message('ack', {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () {\n", " fig.push_to_output();\n", " }, 1000);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'btn-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " var button;\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " continue;\n", " }\n", "\n", " button = fig.buttons[name] = document.createElement('button');\n", " button.classList = 'btn btn-default';\n", " button.href = '#';\n", " button.title = name;\n", " button.innerHTML = '';\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message pull-right';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "\n", " // Add the close button to the window.\n", " var buttongrp = document.createElement('div');\n", " buttongrp.classList = 'btn-group inline pull-right';\n", " button = document.createElement('button');\n", " button.classList = 'btn btn-mini btn-primary';\n", " button.href = '#';\n", " button.title = 'Stop Interaction';\n", " button.innerHTML = '';\n", " button.addEventListener('click', function (_evt) {\n", " fig.handle_close(fig, {});\n", " });\n", " button.addEventListener(\n", " 'mouseover',\n", " on_mouseover_closure('Stop Interaction')\n", " );\n", " buttongrp.appendChild(button);\n", " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", "};\n", "\n", "mpl.figure.prototype._remove_fig_handler = function (event) {\n", " var fig = event.data.fig;\n", " if (event.target !== this) {\n", " // Ignore bubbled events from children.\n", " return;\n", " }\n", " fig.close_ws(fig, {});\n", "};\n", "\n", "mpl.figure.prototype._root_extra_style = function (el) {\n", " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (el) {\n", " // this is important to make the div 'focusable\n", " el.setAttribute('tabindex', 0);\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " } else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager) {\n", " manager = IPython.keyboard_manager;\n", " }\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which === 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " fig.ondownload(fig, null);\n", "};\n", "\n", "mpl.find_output_cell = function (html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i = 0; i < ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code') {\n", " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] === html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "};\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel !== null) {\n", " IPython.notebook.kernel.comm_manager.register_target(\n", " 'matplotlib',\n", " mpl.mpl_figure_comm\n", " );\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "daadc89b9a6f47cf8e1ae1127b4c1f42", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Play(value=0, description='Press play', interval=500, max=20), IntSlider(value=0, continuous_up…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "imput_stack = np.random.normal(3, 2.5, size=(20, 512, 512))\n", "x, y = np.mgrid[0:16, 0:16] * 32\n", "imput_stack[:, x, y] = 55.\n", "dset = sidpy.Dataset.from_array(imput_stack)\n", "print(dset.shape)\n", "dset.data_type = 'image_stack'\n", "dset.units = 'counts'\n", "dset.quantity = 'intensity'\n", "dset.title = 'random'\n", "dset.set_dimension(0, sidpy.Dimension(np.arange(dset.shape[0])*.02, 'frames'))\n", "dset.frames.dimension_type = 'temporal'\n", "dset.set_dimension(1, sidpy.Dimension(np.arange(dset.shape[1])*.02, 'x'))\n", "dset.x.dimension_type = 'spatial'\n", "dset.x.units = 'nm'\n", "dset.x.quantity = 'distance'\n", "dset.set_dimension(2, sidpy.Dimension(np.arange(dset.shape[2])*.02, 'y'))\n", "dset.y.dimension_type = 'spatial'\n", "dset.y.units = 'nm'\n", "dset.y.quantity = 'distance'\n", "dset.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plottting a summed image\n", "\n", "If we sum over an axis, we reduce the dimensionality and so we only have to supply the ``data_type`` of the new dataset." ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "/* global mpl */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function () {\n", " if (typeof WebSocket !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof MozWebSocket !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert(\n", " 'Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.'\n", " );\n", " }\n", "};\n", "\n", "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = this.ws.binaryType !== undefined;\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById('mpl-warnings');\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent =\n", " 'This browser does not support binary websocket messages. ' +\n", " 'Performance may be slow.';\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = document.createElement('div');\n", " this.root.setAttribute('style', 'display: inline-block');\n", " this._root_extra_style(this.root);\n", "\n", " parent_element.appendChild(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message('supports_binary', { value: fig.supports_binary });\n", " fig.send_message('send_image_mode', {});\n", " if (fig.ratio !== 1) {\n", " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", " }\n", " fig.send_message('refresh', {});\n", " };\n", "\n", " this.imageObj.onload = function () {\n", " if (fig.image_mode === 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function () {\n", " fig.ws.close();\n", " };\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "};\n", "\n", "mpl.figure.prototype._init_header = function () {\n", " var titlebar = document.createElement('div');\n", " titlebar.classList =\n", " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", " var titletext = document.createElement('div');\n", " titletext.classList = 'ui-dialog-title';\n", " titletext.setAttribute(\n", " 'style',\n", " 'width: 100%; text-align: center; padding: 3px;'\n", " );\n", " titlebar.appendChild(titletext);\n", " this.root.appendChild(titlebar);\n", " this.header = titletext;\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._init_canvas = function () {\n", " var fig = this;\n", "\n", " var canvas_div = (this.canvas_div = document.createElement('div'));\n", " canvas_div.setAttribute(\n", " 'style',\n", " 'border: 1px solid #ddd;' +\n", " 'box-sizing: content-box;' +\n", " 'clear: both;' +\n", " 'min-height: 1px;' +\n", " 'min-width: 1px;' +\n", " 'outline: 0;' +\n", " 'overflow: hidden;' +\n", " 'position: relative;' +\n", " 'resize: both;'\n", " );\n", "\n", " function on_keyboard_event_closure(name) {\n", " return function (event) {\n", " return fig.key_event(event, name);\n", " };\n", " }\n", "\n", " canvas_div.addEventListener(\n", " 'keydown',\n", " on_keyboard_event_closure('key_press')\n", " );\n", " canvas_div.addEventListener(\n", " 'keyup',\n", " on_keyboard_event_closure('key_release')\n", " );\n", "\n", " this._canvas_extra_style(canvas_div);\n", " this.root.appendChild(canvas_div);\n", "\n", " var canvas = (this.canvas = document.createElement('canvas'));\n", " canvas.classList.add('mpl-canvas');\n", " canvas.setAttribute('style', 'box-sizing: content-box;');\n", "\n", " this.context = canvas.getContext('2d');\n", "\n", " var backingStore =\n", " this.context.backingStorePixelRatio ||\n", " this.context.webkitBackingStorePixelRatio ||\n", " this.context.mozBackingStorePixelRatio ||\n", " this.context.msBackingStorePixelRatio ||\n", " this.context.oBackingStorePixelRatio ||\n", " this.context.backingStorePixelRatio ||\n", " 1;\n", "\n", " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", " 'canvas'\n", " ));\n", " rubberband_canvas.setAttribute(\n", " 'style',\n", " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", " );\n", "\n", " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", " if (this.ResizeObserver === undefined) {\n", " if (window.ResizeObserver !== undefined) {\n", " this.ResizeObserver = window.ResizeObserver;\n", " } else {\n", " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", " this.ResizeObserver = obs.ResizeObserver;\n", " }\n", " }\n", "\n", " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", " var nentries = entries.length;\n", " for (var i = 0; i < nentries; i++) {\n", " var entry = entries[i];\n", " var width, height;\n", " if (entry.contentBoxSize) {\n", " if (entry.contentBoxSize instanceof Array) {\n", " // Chrome 84 implements new version of spec.\n", " width = entry.contentBoxSize[0].inlineSize;\n", " height = entry.contentBoxSize[0].blockSize;\n", " } else {\n", " // Firefox implements old version of spec.\n", " width = entry.contentBoxSize.inlineSize;\n", " height = entry.contentBoxSize.blockSize;\n", " }\n", " } else {\n", " // Chrome <84 implements even older version of spec.\n", " width = entry.contentRect.width;\n", " height = entry.contentRect.height;\n", " }\n", "\n", " // Keep the size of the canvas and rubber band canvas in sync with\n", " // the canvas container.\n", " if (entry.devicePixelContentBoxSize) {\n", " // Chrome 84 implements new version of spec.\n", " canvas.setAttribute(\n", " 'width',\n", " entry.devicePixelContentBoxSize[0].inlineSize\n", " );\n", " canvas.setAttribute(\n", " 'height',\n", " entry.devicePixelContentBoxSize[0].blockSize\n", " );\n", " } else {\n", " canvas.setAttribute('width', width * fig.ratio);\n", " canvas.setAttribute('height', height * fig.ratio);\n", " }\n", " canvas.setAttribute(\n", " 'style',\n", " 'width: ' + width + 'px; height: ' + height + 'px;'\n", " );\n", "\n", " rubberband_canvas.setAttribute('width', width);\n", " rubberband_canvas.setAttribute('height', height);\n", "\n", " // And update the size in Python. We ignore the initial 0/0 size\n", " // that occurs as the element is placed into the DOM, which should\n", " // otherwise not happen due to the minimum size styling.\n", " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", " fig.request_resize(width, height);\n", " }\n", " }\n", " });\n", " this.resizeObserverInstance.observe(canvas_div);\n", "\n", " function on_mouse_event_closure(name) {\n", " return function (event) {\n", " return fig.mouse_event(event, name);\n", " };\n", " }\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mousedown',\n", " on_mouse_event_closure('button_press')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseup',\n", " on_mouse_event_closure('button_release')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'dblclick',\n", " on_mouse_event_closure('dblclick')\n", " );\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband_canvas.addEventListener(\n", " 'mousemove',\n", " on_mouse_event_closure('motion_notify')\n", " );\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mouseenter',\n", " on_mouse_event_closure('figure_enter')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseleave',\n", " on_mouse_event_closure('figure_leave')\n", " );\n", "\n", " canvas_div.addEventListener('wheel', function (event) {\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " on_mouse_event_closure('scroll')(event);\n", " });\n", "\n", " canvas_div.appendChild(canvas);\n", " canvas_div.appendChild(rubberband_canvas);\n", "\n", " this.rubberband_context = rubberband_canvas.getContext('2d');\n", " this.rubberband_context.strokeStyle = '#000000';\n", "\n", " this._resize_canvas = function (width, height, forward) {\n", " if (forward) {\n", " canvas_div.style.width = width + 'px';\n", " canvas_div.style.height = height + 'px';\n", " }\n", " };\n", "\n", " // Disable right mouse context menu.\n", " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", " event.preventDefault();\n", " return false;\n", " });\n", "\n", " function set_focus() {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'mpl-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " continue;\n", " }\n", "\n", " var button = (fig.buttons[name] = document.createElement('button'));\n", " button.classList = 'mpl-widget';\n", " button.setAttribute('role', 'button');\n", " button.setAttribute('aria-disabled', 'false');\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", "\n", " var icon_img = document.createElement('img');\n", " icon_img.src = '_images/' + image + '.png';\n", " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", " icon_img.alt = tooltip;\n", " button.appendChild(icon_img);\n", "\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " var fmt_picker = document.createElement('select');\n", " fmt_picker.classList = 'mpl-widget';\n", " toolbar.appendChild(fmt_picker);\n", " this.format_dropdown = fmt_picker;\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = document.createElement('option');\n", " option.selected = fmt === mpl.default_extension;\n", " option.innerHTML = fmt;\n", " fmt_picker.appendChild(option);\n", " }\n", "\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "};\n", "\n", "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", "};\n", "\n", "mpl.figure.prototype.send_message = function (type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "};\n", "\n", "mpl.figure.prototype.send_draw_message = function () {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "};\n", "\n", "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1], msg['forward']);\n", " fig.send_message('refresh', {});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", " var x0 = msg['x0'] / fig.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", " var x1 = msg['x1'] / fig.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0,\n", " 0,\n", " fig.canvas.width / fig.ratio,\n", " fig.canvas.height / fig.ratio\n", " );\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "};\n", "\n", "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "};\n", "\n", "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch (cursor) {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "};\n", "\n", "mpl.figure.prototype.handle_message = function (fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "};\n", "\n", "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "};\n", "\n", "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "};\n", "\n", "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", " for (var key in msg) {\n", " if (!(key in fig.buttons)) {\n", " continue;\n", " }\n", " fig.buttons[key].disabled = !msg[key];\n", " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", " if (msg['mode'] === 'PAN') {\n", " fig.buttons['Pan'].classList.add('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " } else if (msg['mode'] === 'ZOOM') {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.add('active');\n", " } else {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " }\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Called whenever the canvas gets updated.\n", " this.send_message('ack', {});\n", "};\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function (fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " var img = evt.data;\n", " if (img.type !== 'image/png') {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " img.type = 'image/png';\n", " }\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src\n", " );\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " img\n", " );\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " } else if (\n", " typeof evt.data === 'string' &&\n", " evt.data.slice(0, 21) === 'data:image/png;base64'\n", " ) {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig['handle_' + msg_type];\n", " } catch (e) {\n", " console.log(\n", " \"No handler for the '\" + msg_type + \"' message type: \",\n", " msg\n", " );\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\n", " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", " e,\n", " e.stack,\n", " msg\n", " );\n", " }\n", " }\n", " };\n", "};\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function (e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e) {\n", " e = window.event;\n", " }\n", " if (e.target) {\n", " targ = e.target;\n", " } else if (e.srcElement) {\n", " targ = e.srcElement;\n", " }\n", " if (targ.nodeType === 3) {\n", " // defeat Safari bug\n", " targ = targ.parentNode;\n", " }\n", "\n", " // pageX,Y are the mouse positions relative to the document\n", " var boundingRect = targ.getBoundingClientRect();\n", " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", "\n", " return { x: x, y: y };\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys(original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object') {\n", " obj[key] = original[key];\n", " }\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function (event, name) {\n", " var canvas_pos = mpl.findpos(event);\n", "\n", " if (name === 'button_press') {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * this.ratio;\n", " var y = canvas_pos.y * this.ratio;\n", "\n", " this.send_message(name, {\n", " x: x,\n", " y: y,\n", " button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event),\n", " });\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", " // Handle any extra behaviour associated with a key event\n", "};\n", "\n", "mpl.figure.prototype.key_event = function (event, name) {\n", " // Prevent repeat events\n", " if (name === 'key_press') {\n", " if (event.key === this._key) {\n", " return;\n", " } else {\n", " this._key = event.key;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.key !== 'Control') {\n", " value += 'ctrl+';\n", " }\n", " else if (event.altKey && event.key !== 'Alt') {\n", " value += 'alt+';\n", " }\n", " else if (event.shiftKey && event.key !== 'Shift') {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k' + event.key;\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", " if (name === 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message('toolbar_button', { name: name });\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "\n", "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", "// prettier-ignore\n", "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* global mpl */\n", "\n", "var comm_websocket_adapter = function (comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.binaryType = comm.kernel.ws.binaryType;\n", " ws.readyState = comm.kernel.ws.readyState;\n", " function updateReadyState(_event) {\n", " if (comm.kernel.ws) {\n", " ws.readyState = comm.kernel.ws.readyState;\n", " } else {\n", " ws.readyState = 3; // Closed state.\n", " }\n", " }\n", " comm.kernel.ws.addEventListener('open', updateReadyState);\n", " comm.kernel.ws.addEventListener('close', updateReadyState);\n", " comm.kernel.ws.addEventListener('error', updateReadyState);\n", "\n", " ws.close = function () {\n", " comm.close();\n", " };\n", " ws.send = function (m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function (msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " var data = msg['content']['data'];\n", " if (data['blob'] !== undefined) {\n", " data = {\n", " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", " };\n", " }\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(data);\n", " });\n", " return ws;\n", "};\n", "\n", "mpl.mpl_figure_comm = function (comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = document.getElementById(id);\n", " var ws_proxy = comm_websocket_adapter(comm);\n", "\n", " function ondownload(figure, _format) {\n", " window.open(figure.canvas.toDataURL());\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element;\n", " fig.cell_info = mpl.find_output_cell(\"
\");\n", " if (!fig.cell_info) {\n", " console.error('Failed to find cell for figure', id, fig);\n", " return;\n", " }\n", " fig.cell_info[0].output_area.element.on(\n", " 'cleared',\n", " { fig: fig },\n", " fig._remove_fig_handler\n", " );\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function (fig, msg) {\n", " var width = fig.canvas.width / fig.ratio;\n", " fig.cell_info[0].output_area.element.off(\n", " 'cleared',\n", " fig._remove_fig_handler\n", " );\n", " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable();\n", " fig.parent_element.innerHTML =\n", " '';\n", " fig.close_ws(fig, msg);\n", "};\n", "\n", "mpl.figure.prototype.close_ws = function (fig, msg) {\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "};\n", "\n", "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width / this.ratio;\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] =\n", " '';\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message('ack', {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () {\n", " fig.push_to_output();\n", " }, 1000);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'btn-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " var button;\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " continue;\n", " }\n", "\n", " button = fig.buttons[name] = document.createElement('button');\n", " button.classList = 'btn btn-default';\n", " button.href = '#';\n", " button.title = name;\n", " button.innerHTML = '';\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message pull-right';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "\n", " // Add the close button to the window.\n", " var buttongrp = document.createElement('div');\n", " buttongrp.classList = 'btn-group inline pull-right';\n", " button = document.createElement('button');\n", " button.classList = 'btn btn-mini btn-primary';\n", " button.href = '#';\n", " button.title = 'Stop Interaction';\n", " button.innerHTML = '';\n", " button.addEventListener('click', function (_evt) {\n", " fig.handle_close(fig, {});\n", " });\n", " button.addEventListener(\n", " 'mouseover',\n", " on_mouseover_closure('Stop Interaction')\n", " );\n", " buttongrp.appendChild(button);\n", " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", "};\n", "\n", "mpl.figure.prototype._remove_fig_handler = function (event) {\n", " var fig = event.data.fig;\n", " if (event.target !== this) {\n", " // Ignore bubbled events from children.\n", " return;\n", " }\n", " fig.close_ws(fig, {});\n", "};\n", "\n", "mpl.figure.prototype._root_extra_style = function (el) {\n", " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (el) {\n", " // this is important to make the div 'focusable\n", " el.setAttribute('tabindex', 0);\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " } else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager) {\n", " manager = IPython.keyboard_manager;\n", " }\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which === 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " fig.ondownload(fig, null);\n", "};\n", "\n", "mpl.find_output_cell = function (html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i = 0; i < ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code') {\n", " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] === html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "};\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel !== null) {\n", " IPython.notebook.kernel.comm_manager.register_target(\n", " 'matplotlib',\n", " mpl.mpl_figure_comm\n", " );\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summed_image = dset.sum(axis=0)\n", "summed_image.data_type = 'image'\n", "summed_image.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Power Spectrum of Dataset\n", "\n", "The ``power spectrum`` is the magnitude of the Fourier transform of a dataset.\n", "\n", "The magnitude of a complex number is just it's absolute and so we just dasiy-chain the ``fft`` and ``abs`` function to obtain a power spectrum.\n", "\n", ">\n", "> Please note that the Fourier Transform is dependent on the data_type of the dataset.\n", ">" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "/* global mpl */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function () {\n", " if (typeof WebSocket !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof MozWebSocket !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert(\n", " 'Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.'\n", " );\n", " }\n", "};\n", "\n", "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = this.ws.binaryType !== undefined;\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById('mpl-warnings');\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent =\n", " 'This browser does not support binary websocket messages. ' +\n", " 'Performance may be slow.';\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = document.createElement('div');\n", " this.root.setAttribute('style', 'display: inline-block');\n", " this._root_extra_style(this.root);\n", "\n", " parent_element.appendChild(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message('supports_binary', { value: fig.supports_binary });\n", " fig.send_message('send_image_mode', {});\n", " if (fig.ratio !== 1) {\n", " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", " }\n", " fig.send_message('refresh', {});\n", " };\n", "\n", " this.imageObj.onload = function () {\n", " if (fig.image_mode === 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function () {\n", " fig.ws.close();\n", " };\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "};\n", "\n", "mpl.figure.prototype._init_header = function () {\n", " var titlebar = document.createElement('div');\n", " titlebar.classList =\n", " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", " var titletext = document.createElement('div');\n", " titletext.classList = 'ui-dialog-title';\n", " titletext.setAttribute(\n", " 'style',\n", " 'width: 100%; text-align: center; padding: 3px;'\n", " );\n", " titlebar.appendChild(titletext);\n", " this.root.appendChild(titlebar);\n", " this.header = titletext;\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._init_canvas = function () {\n", " var fig = this;\n", "\n", " var canvas_div = (this.canvas_div = document.createElement('div'));\n", " canvas_div.setAttribute(\n", " 'style',\n", " 'border: 1px solid #ddd;' +\n", " 'box-sizing: content-box;' +\n", " 'clear: both;' +\n", " 'min-height: 1px;' +\n", " 'min-width: 1px;' +\n", " 'outline: 0;' +\n", " 'overflow: hidden;' +\n", " 'position: relative;' +\n", " 'resize: both;'\n", " );\n", "\n", " function on_keyboard_event_closure(name) {\n", " return function (event) {\n", " return fig.key_event(event, name);\n", " };\n", " }\n", "\n", " canvas_div.addEventListener(\n", " 'keydown',\n", " on_keyboard_event_closure('key_press')\n", " );\n", " canvas_div.addEventListener(\n", " 'keyup',\n", " on_keyboard_event_closure('key_release')\n", " );\n", "\n", " this._canvas_extra_style(canvas_div);\n", " this.root.appendChild(canvas_div);\n", "\n", " var canvas = (this.canvas = document.createElement('canvas'));\n", " canvas.classList.add('mpl-canvas');\n", " canvas.setAttribute('style', 'box-sizing: content-box;');\n", "\n", " this.context = canvas.getContext('2d');\n", "\n", " var backingStore =\n", " this.context.backingStorePixelRatio ||\n", " this.context.webkitBackingStorePixelRatio ||\n", " this.context.mozBackingStorePixelRatio ||\n", " this.context.msBackingStorePixelRatio ||\n", " this.context.oBackingStorePixelRatio ||\n", " this.context.backingStorePixelRatio ||\n", " 1;\n", "\n", " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", " 'canvas'\n", " ));\n", " rubberband_canvas.setAttribute(\n", " 'style',\n", " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", " );\n", "\n", " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", " if (this.ResizeObserver === undefined) {\n", " if (window.ResizeObserver !== undefined) {\n", " this.ResizeObserver = window.ResizeObserver;\n", " } else {\n", " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", " this.ResizeObserver = obs.ResizeObserver;\n", " }\n", " }\n", "\n", " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", " var nentries = entries.length;\n", " for (var i = 0; i < nentries; i++) {\n", " var entry = entries[i];\n", " var width, height;\n", " if (entry.contentBoxSize) {\n", " if (entry.contentBoxSize instanceof Array) {\n", " // Chrome 84 implements new version of spec.\n", " width = entry.contentBoxSize[0].inlineSize;\n", " height = entry.contentBoxSize[0].blockSize;\n", " } else {\n", " // Firefox implements old version of spec.\n", " width = entry.contentBoxSize.inlineSize;\n", " height = entry.contentBoxSize.blockSize;\n", " }\n", " } else {\n", " // Chrome <84 implements even older version of spec.\n", " width = entry.contentRect.width;\n", " height = entry.contentRect.height;\n", " }\n", "\n", " // Keep the size of the canvas and rubber band canvas in sync with\n", " // the canvas container.\n", " if (entry.devicePixelContentBoxSize) {\n", " // Chrome 84 implements new version of spec.\n", " canvas.setAttribute(\n", " 'width',\n", " entry.devicePixelContentBoxSize[0].inlineSize\n", " );\n", " canvas.setAttribute(\n", " 'height',\n", " entry.devicePixelContentBoxSize[0].blockSize\n", " );\n", " } else {\n", " canvas.setAttribute('width', width * fig.ratio);\n", " canvas.setAttribute('height', height * fig.ratio);\n", " }\n", " canvas.setAttribute(\n", " 'style',\n", " 'width: ' + width + 'px; height: ' + height + 'px;'\n", " );\n", "\n", " rubberband_canvas.setAttribute('width', width);\n", " rubberband_canvas.setAttribute('height', height);\n", "\n", " // And update the size in Python. We ignore the initial 0/0 size\n", " // that occurs as the element is placed into the DOM, which should\n", " // otherwise not happen due to the minimum size styling.\n", " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", " fig.request_resize(width, height);\n", " }\n", " }\n", " });\n", " this.resizeObserverInstance.observe(canvas_div);\n", "\n", " function on_mouse_event_closure(name) {\n", " return function (event) {\n", " return fig.mouse_event(event, name);\n", " };\n", " }\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mousedown',\n", " on_mouse_event_closure('button_press')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseup',\n", " on_mouse_event_closure('button_release')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'dblclick',\n", " on_mouse_event_closure('dblclick')\n", " );\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband_canvas.addEventListener(\n", " 'mousemove',\n", " on_mouse_event_closure('motion_notify')\n", " );\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mouseenter',\n", " on_mouse_event_closure('figure_enter')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseleave',\n", " on_mouse_event_closure('figure_leave')\n", " );\n", "\n", " canvas_div.addEventListener('wheel', function (event) {\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " on_mouse_event_closure('scroll')(event);\n", " });\n", "\n", " canvas_div.appendChild(canvas);\n", " canvas_div.appendChild(rubberband_canvas);\n", "\n", " this.rubberband_context = rubberband_canvas.getContext('2d');\n", " this.rubberband_context.strokeStyle = '#000000';\n", "\n", " this._resize_canvas = function (width, height, forward) {\n", " if (forward) {\n", " canvas_div.style.width = width + 'px';\n", " canvas_div.style.height = height + 'px';\n", " }\n", " };\n", "\n", " // Disable right mouse context menu.\n", " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", " event.preventDefault();\n", " return false;\n", " });\n", "\n", " function set_focus() {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'mpl-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " continue;\n", " }\n", "\n", " var button = (fig.buttons[name] = document.createElement('button'));\n", " button.classList = 'mpl-widget';\n", " button.setAttribute('role', 'button');\n", " button.setAttribute('aria-disabled', 'false');\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", "\n", " var icon_img = document.createElement('img');\n", " icon_img.src = '_images/' + image + '.png';\n", " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", " icon_img.alt = tooltip;\n", " button.appendChild(icon_img);\n", "\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " var fmt_picker = document.createElement('select');\n", " fmt_picker.classList = 'mpl-widget';\n", " toolbar.appendChild(fmt_picker);\n", " this.format_dropdown = fmt_picker;\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = document.createElement('option');\n", " option.selected = fmt === mpl.default_extension;\n", " option.innerHTML = fmt;\n", " fmt_picker.appendChild(option);\n", " }\n", "\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "};\n", "\n", "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", "};\n", "\n", "mpl.figure.prototype.send_message = function (type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "};\n", "\n", "mpl.figure.prototype.send_draw_message = function () {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "};\n", "\n", "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1], msg['forward']);\n", " fig.send_message('refresh', {});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", " var x0 = msg['x0'] / fig.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", " var x1 = msg['x1'] / fig.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0,\n", " 0,\n", " fig.canvas.width / fig.ratio,\n", " fig.canvas.height / fig.ratio\n", " );\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "};\n", "\n", "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "};\n", "\n", "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch (cursor) {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "};\n", "\n", "mpl.figure.prototype.handle_message = function (fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "};\n", "\n", "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "};\n", "\n", "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "};\n", "\n", "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", " for (var key in msg) {\n", " if (!(key in fig.buttons)) {\n", " continue;\n", " }\n", " fig.buttons[key].disabled = !msg[key];\n", " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", " if (msg['mode'] === 'PAN') {\n", " fig.buttons['Pan'].classList.add('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " } else if (msg['mode'] === 'ZOOM') {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.add('active');\n", " } else {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " }\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Called whenever the canvas gets updated.\n", " this.send_message('ack', {});\n", "};\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function (fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " var img = evt.data;\n", " if (img.type !== 'image/png') {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " img.type = 'image/png';\n", " }\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src\n", " );\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " img\n", " );\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " } else if (\n", " typeof evt.data === 'string' &&\n", " evt.data.slice(0, 21) === 'data:image/png;base64'\n", " ) {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig['handle_' + msg_type];\n", " } catch (e) {\n", " console.log(\n", " \"No handler for the '\" + msg_type + \"' message type: \",\n", " msg\n", " );\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\n", " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", " e,\n", " e.stack,\n", " msg\n", " );\n", " }\n", " }\n", " };\n", "};\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function (e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e) {\n", " e = window.event;\n", " }\n", " if (e.target) {\n", " targ = e.target;\n", " } else if (e.srcElement) {\n", " targ = e.srcElement;\n", " }\n", " if (targ.nodeType === 3) {\n", " // defeat Safari bug\n", " targ = targ.parentNode;\n", " }\n", "\n", " // pageX,Y are the mouse positions relative to the document\n", " var boundingRect = targ.getBoundingClientRect();\n", " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", "\n", " return { x: x, y: y };\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys(original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object') {\n", " obj[key] = original[key];\n", " }\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function (event, name) {\n", " var canvas_pos = mpl.findpos(event);\n", "\n", " if (name === 'button_press') {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * this.ratio;\n", " var y = canvas_pos.y * this.ratio;\n", "\n", " this.send_message(name, {\n", " x: x,\n", " y: y,\n", " button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event),\n", " });\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", " // Handle any extra behaviour associated with a key event\n", "};\n", "\n", "mpl.figure.prototype.key_event = function (event, name) {\n", " // Prevent repeat events\n", " if (name === 'key_press') {\n", " if (event.key === this._key) {\n", " return;\n", " } else {\n", " this._key = event.key;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.key !== 'Control') {\n", " value += 'ctrl+';\n", " }\n", " else if (event.altKey && event.key !== 'Alt') {\n", " value += 'alt+';\n", " }\n", " else if (event.shiftKey && event.key !== 'Shift') {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k' + event.key;\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", " if (name === 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message('toolbar_button', { name: name });\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "\n", "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", "// prettier-ignore\n", "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* global mpl */\n", "\n", "var comm_websocket_adapter = function (comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.binaryType = comm.kernel.ws.binaryType;\n", " ws.readyState = comm.kernel.ws.readyState;\n", " function updateReadyState(_event) {\n", " if (comm.kernel.ws) {\n", " ws.readyState = comm.kernel.ws.readyState;\n", " } else {\n", " ws.readyState = 3; // Closed state.\n", " }\n", " }\n", " comm.kernel.ws.addEventListener('open', updateReadyState);\n", " comm.kernel.ws.addEventListener('close', updateReadyState);\n", " comm.kernel.ws.addEventListener('error', updateReadyState);\n", "\n", " ws.close = function () {\n", " comm.close();\n", " };\n", " ws.send = function (m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function (msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " var data = msg['content']['data'];\n", " if (data['blob'] !== undefined) {\n", " data = {\n", " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", " };\n", " }\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(data);\n", " });\n", " return ws;\n", "};\n", "\n", "mpl.mpl_figure_comm = function (comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = document.getElementById(id);\n", " var ws_proxy = comm_websocket_adapter(comm);\n", "\n", " function ondownload(figure, _format) {\n", " window.open(figure.canvas.toDataURL());\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element;\n", " fig.cell_info = mpl.find_output_cell(\"
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" ], "text/plain": [ "sidpy.Dataset of type IMAGE_STACK with:\n", " dask.array\n", " data contains: intensity (a.u.)\n", " and Dimensions: \n", "frames: generic (generic) of size (20,)\n", "u: reciprocal (1/nm) of size (512,)\n", "v: reciprocal_length (1/nm) of size (512,)" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fft_image = dset.fft().abs()\n", "fft_image.plot()\n", "fft_image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Numpy Functionality\n", "\n", "Also numpy functions on a sidpy datset will return a sidpy dataset.\n", "\n", "Let's use the log function of numpy to make the power spectrum better visible." ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "/* global mpl */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function () {\n", " if (typeof WebSocket !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof MozWebSocket !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert(\n", " 'Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.'\n", " );\n", " }\n", "};\n", "\n", "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = this.ws.binaryType !== undefined;\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById('mpl-warnings');\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent =\n", " 'This browser does not support binary websocket messages. ' +\n", " 'Performance may be slow.';\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = document.createElement('div');\n", " this.root.setAttribute('style', 'display: inline-block');\n", " this._root_extra_style(this.root);\n", "\n", " parent_element.appendChild(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message('supports_binary', { value: fig.supports_binary });\n", " fig.send_message('send_image_mode', {});\n", " if (fig.ratio !== 1) {\n", " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", " }\n", " fig.send_message('refresh', {});\n", " };\n", "\n", " this.imageObj.onload = function () {\n", " if (fig.image_mode === 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function () {\n", " fig.ws.close();\n", " };\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "};\n", "\n", "mpl.figure.prototype._init_header = function () {\n", " var titlebar = document.createElement('div');\n", " titlebar.classList =\n", " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", " var titletext = document.createElement('div');\n", " titletext.classList = 'ui-dialog-title';\n", " titletext.setAttribute(\n", " 'style',\n", " 'width: 100%; text-align: center; padding: 3px;'\n", " );\n", " titlebar.appendChild(titletext);\n", " this.root.appendChild(titlebar);\n", " this.header = titletext;\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._init_canvas = function () {\n", " var fig = this;\n", "\n", " var canvas_div = (this.canvas_div = document.createElement('div'));\n", " canvas_div.setAttribute(\n", " 'style',\n", " 'border: 1px solid #ddd;' +\n", " 'box-sizing: content-box;' +\n", " 'clear: both;' +\n", " 'min-height: 1px;' +\n", " 'min-width: 1px;' +\n", " 'outline: 0;' +\n", " 'overflow: hidden;' +\n", " 'position: relative;' +\n", " 'resize: both;'\n", " );\n", "\n", " function on_keyboard_event_closure(name) {\n", " return function (event) {\n", " return fig.key_event(event, name);\n", " };\n", " }\n", "\n", " canvas_div.addEventListener(\n", " 'keydown',\n", " on_keyboard_event_closure('key_press')\n", " );\n", " canvas_div.addEventListener(\n", " 'keyup',\n", " on_keyboard_event_closure('key_release')\n", " );\n", "\n", " this._canvas_extra_style(canvas_div);\n", " this.root.appendChild(canvas_div);\n", "\n", " var canvas = (this.canvas = document.createElement('canvas'));\n", " canvas.classList.add('mpl-canvas');\n", " canvas.setAttribute('style', 'box-sizing: content-box;');\n", "\n", " this.context = canvas.getContext('2d');\n", "\n", " var backingStore =\n", " this.context.backingStorePixelRatio ||\n", " this.context.webkitBackingStorePixelRatio ||\n", " this.context.mozBackingStorePixelRatio ||\n", " this.context.msBackingStorePixelRatio ||\n", " this.context.oBackingStorePixelRatio ||\n", " this.context.backingStorePixelRatio ||\n", " 1;\n", "\n", " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", " 'canvas'\n", " ));\n", " rubberband_canvas.setAttribute(\n", " 'style',\n", " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", " );\n", "\n", " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", " if (this.ResizeObserver === undefined) {\n", " if (window.ResizeObserver !== undefined) {\n", " this.ResizeObserver = window.ResizeObserver;\n", " } else {\n", " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", " this.ResizeObserver = obs.ResizeObserver;\n", " }\n", " }\n", "\n", " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", " var nentries = entries.length;\n", " for (var i = 0; i < nentries; i++) {\n", " var entry = entries[i];\n", " var width, height;\n", " if (entry.contentBoxSize) {\n", " if (entry.contentBoxSize instanceof Array) {\n", " // Chrome 84 implements new version of spec.\n", " width = entry.contentBoxSize[0].inlineSize;\n", " height = entry.contentBoxSize[0].blockSize;\n", " } else {\n", " // Firefox implements old version of spec.\n", " width = entry.contentBoxSize.inlineSize;\n", " height = entry.contentBoxSize.blockSize;\n", " }\n", " } else {\n", " // Chrome <84 implements even older version of spec.\n", " width = entry.contentRect.width;\n", " height = entry.contentRect.height;\n", " }\n", "\n", " // Keep the size of the canvas and rubber band canvas in sync with\n", " // the canvas container.\n", " if (entry.devicePixelContentBoxSize) {\n", " // Chrome 84 implements new version of spec.\n", " canvas.setAttribute(\n", " 'width',\n", " entry.devicePixelContentBoxSize[0].inlineSize\n", " );\n", " canvas.setAttribute(\n", " 'height',\n", " entry.devicePixelContentBoxSize[0].blockSize\n", " );\n", " } else {\n", " canvas.setAttribute('width', width * fig.ratio);\n", " canvas.setAttribute('height', height * fig.ratio);\n", " }\n", " canvas.setAttribute(\n", " 'style',\n", " 'width: ' + width + 'px; height: ' + height + 'px;'\n", " );\n", "\n", " rubberband_canvas.setAttribute('width', width);\n", " rubberband_canvas.setAttribute('height', height);\n", "\n", " // And update the size in Python. We ignore the initial 0/0 size\n", " // that occurs as the element is placed into the DOM, which should\n", " // otherwise not happen due to the minimum size styling.\n", " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", " fig.request_resize(width, height);\n", " }\n", " }\n", " });\n", " this.resizeObserverInstance.observe(canvas_div);\n", "\n", " function on_mouse_event_closure(name) {\n", " return function (event) {\n", " return fig.mouse_event(event, name);\n", " };\n", " }\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mousedown',\n", " on_mouse_event_closure('button_press')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseup',\n", " on_mouse_event_closure('button_release')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'dblclick',\n", " on_mouse_event_closure('dblclick')\n", " );\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband_canvas.addEventListener(\n", " 'mousemove',\n", " on_mouse_event_closure('motion_notify')\n", " );\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mouseenter',\n", " on_mouse_event_closure('figure_enter')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseleave',\n", " on_mouse_event_closure('figure_leave')\n", " );\n", "\n", " canvas_div.addEventListener('wheel', function (event) {\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " on_mouse_event_closure('scroll')(event);\n", " });\n", "\n", " canvas_div.appendChild(canvas);\n", " canvas_div.appendChild(rubberband_canvas);\n", "\n", " this.rubberband_context = rubberband_canvas.getContext('2d');\n", " this.rubberband_context.strokeStyle = '#000000';\n", "\n", " this._resize_canvas = function (width, height, forward) {\n", " if (forward) {\n", " canvas_div.style.width = width + 'px';\n", " canvas_div.style.height = height + 'px';\n", " }\n", " };\n", "\n", " // Disable right mouse context menu.\n", " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", " event.preventDefault();\n", " return false;\n", " });\n", "\n", " function set_focus() {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'mpl-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " continue;\n", " }\n", "\n", " var button = (fig.buttons[name] = document.createElement('button'));\n", " button.classList = 'mpl-widget';\n", " button.setAttribute('role', 'button');\n", " button.setAttribute('aria-disabled', 'false');\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", "\n", " var icon_img = document.createElement('img');\n", " icon_img.src = '_images/' + image + '.png';\n", " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", " icon_img.alt = tooltip;\n", " button.appendChild(icon_img);\n", "\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " var fmt_picker = document.createElement('select');\n", " fmt_picker.classList = 'mpl-widget';\n", " toolbar.appendChild(fmt_picker);\n", " this.format_dropdown = fmt_picker;\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = document.createElement('option');\n", " option.selected = fmt === mpl.default_extension;\n", " option.innerHTML = fmt;\n", " fmt_picker.appendChild(option);\n", " }\n", "\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "};\n", "\n", "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", "};\n", "\n", "mpl.figure.prototype.send_message = function (type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "};\n", "\n", "mpl.figure.prototype.send_draw_message = function () {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "};\n", "\n", "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1], msg['forward']);\n", " fig.send_message('refresh', {});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", " var x0 = msg['x0'] / fig.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", " var x1 = msg['x1'] / fig.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0,\n", " 0,\n", " fig.canvas.width / fig.ratio,\n", " fig.canvas.height / fig.ratio\n", " );\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "};\n", "\n", "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "};\n", "\n", "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch (cursor) {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "};\n", "\n", "mpl.figure.prototype.handle_message = function (fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "};\n", "\n", "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "};\n", "\n", "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "};\n", "\n", "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", " for (var key in msg) {\n", " if (!(key in fig.buttons)) {\n", " continue;\n", " }\n", " fig.buttons[key].disabled = !msg[key];\n", " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", " if (msg['mode'] === 'PAN') {\n", " fig.buttons['Pan'].classList.add('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " } else if (msg['mode'] === 'ZOOM') {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.add('active');\n", " } else {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " }\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Called whenever the canvas gets updated.\n", " this.send_message('ack', {});\n", "};\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function (fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " var img = evt.data;\n", " if (img.type !== 'image/png') {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " img.type = 'image/png';\n", " }\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src\n", " );\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " img\n", " );\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " } else if (\n", " typeof evt.data === 'string' &&\n", " evt.data.slice(0, 21) === 'data:image/png;base64'\n", " ) {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig['handle_' + msg_type];\n", " } catch (e) {\n", " console.log(\n", " \"No handler for the '\" + msg_type + \"' message type: \",\n", " msg\n", " );\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\n", " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", " e,\n", " e.stack,\n", " msg\n", " );\n", " }\n", " }\n", " };\n", "};\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function (e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e) {\n", " e = window.event;\n", " }\n", " if (e.target) {\n", " targ = e.target;\n", " } else if (e.srcElement) {\n", " targ = e.srcElement;\n", " }\n", " if (targ.nodeType === 3) {\n", " // defeat Safari bug\n", " targ = targ.parentNode;\n", " }\n", "\n", " // pageX,Y are the mouse positions relative to the document\n", " var boundingRect = targ.getBoundingClientRect();\n", " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", "\n", " return { x: x, y: y };\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys(original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object') {\n", " obj[key] = original[key];\n", " }\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function (event, name) {\n", " var canvas_pos = mpl.findpos(event);\n", "\n", " if (name === 'button_press') {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * this.ratio;\n", " var y = canvas_pos.y * this.ratio;\n", "\n", " this.send_message(name, {\n", " x: x,\n", " y: y,\n", " button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event),\n", " });\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", " // Handle any extra behaviour associated with a key event\n", "};\n", "\n", "mpl.figure.prototype.key_event = function (event, name) {\n", " // Prevent repeat events\n", " if (name === 'key_press') {\n", " if (event.key === this._key) {\n", " return;\n", " } else {\n", " this._key = event.key;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.key !== 'Control') {\n", " value += 'ctrl+';\n", " }\n", " else if (event.altKey && event.key !== 'Alt') {\n", " value += 'alt+';\n", " }\n", " else if (event.shiftKey && event.key !== 'Shift') {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k' + event.key;\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", " if (name === 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message('toolbar_button', { name: name });\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "\n", "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", "// prettier-ignore\n", "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* global mpl */\n", "\n", "var comm_websocket_adapter = function (comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.binaryType = comm.kernel.ws.binaryType;\n", " ws.readyState = comm.kernel.ws.readyState;\n", " function updateReadyState(_event) {\n", " if (comm.kernel.ws) {\n", " ws.readyState = comm.kernel.ws.readyState;\n", " } else {\n", " ws.readyState = 3; // Closed state.\n", " }\n", " }\n", " comm.kernel.ws.addEventListener('open', updateReadyState);\n", " comm.kernel.ws.addEventListener('close', updateReadyState);\n", " comm.kernel.ws.addEventListener('error', updateReadyState);\n", "\n", " ws.close = function () {\n", " comm.close();\n", " };\n", " ws.send = function (m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function (msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " var data = msg['content']['data'];\n", " if (data['blob'] !== undefined) {\n", " data = {\n", " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", " };\n", " }\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(data);\n", " });\n", " return ws;\n", "};\n", "\n", "mpl.mpl_figure_comm = function (comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = document.getElementById(id);\n", " var ws_proxy = comm_websocket_adapter(comm);\n", "\n", " function ondownload(figure, _format) {\n", " window.open(figure.canvas.toDataURL());\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element;\n", " fig.cell_info = mpl.find_output_cell(\"
\");\n", " if (!fig.cell_info) {\n", " console.error('Failed to find cell for figure', id, fig);\n", " return;\n", " }\n", " fig.cell_info[0].output_area.element.on(\n", " 'cleared',\n", " { fig: fig },\n", " fig._remove_fig_handler\n", " );\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function (fig, msg) {\n", " var width = fig.canvas.width / fig.ratio;\n", " fig.cell_info[0].output_area.element.off(\n", " 'cleared',\n", " fig._remove_fig_handler\n", " );\n", " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable();\n", " fig.parent_element.innerHTML =\n", " '';\n", " fig.close_ws(fig, msg);\n", "};\n", "\n", "mpl.figure.prototype.close_ws = function (fig, msg) {\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "};\n", "\n", "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width / this.ratio;\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] =\n", " '';\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message('ack', {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () {\n", " fig.push_to_output();\n", " }, 1000);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'btn-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " var button;\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " continue;\n", " }\n", "\n", " button = fig.buttons[name] = document.createElement('button');\n", " button.classList = 'btn btn-default';\n", " button.href = '#';\n", " button.title = name;\n", " button.innerHTML = '';\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message pull-right';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "\n", " // Add the close button to the window.\n", " var buttongrp = document.createElement('div');\n", " buttongrp.classList = 'btn-group inline pull-right';\n", " button = document.createElement('button');\n", " button.classList = 'btn btn-mini btn-primary';\n", " button.href = '#';\n", " button.title = 'Stop Interaction';\n", " button.innerHTML = '';\n", " button.addEventListener('click', function (_evt) {\n", " fig.handle_close(fig, {});\n", " });\n", " button.addEventListener(\n", " 'mouseover',\n", " on_mouseover_closure('Stop Interaction')\n", " );\n", " buttongrp.appendChild(button);\n", " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", "};\n", "\n", "mpl.figure.prototype._remove_fig_handler = function (event) {\n", " var fig = event.data.fig;\n", " if (event.target !== this) {\n", " // Ignore bubbled events from children.\n", " return;\n", " }\n", " fig.close_ws(fig, {});\n", "};\n", "\n", "mpl.figure.prototype._root_extra_style = function (el) {\n", " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (el) {\n", " // this is important to make the div 'focusable\n", " el.setAttribute('tabindex', 0);\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " } else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager) {\n", " manager = IPython.keyboard_manager;\n", " }\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which === 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " fig.ondownload(fig, null);\n", "};\n", "\n", "mpl.find_output_cell = function (html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i = 0; i < ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code') {\n", " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] === html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "};\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel !== null) {\n", " IPython.notebook.kernel.comm_manager.register_target(\n", " 'matplotlib',\n", " mpl.mpl_figure_comm\n", " );\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4809f0e8d1dc47cc847178a2ff93cefe", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Play(value=0, description='Press play', interval=500, max=20), IntSlider(value=0, continuous_up…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fft_image = dset.fft().abs()\n", "fft_image = np.log(1+fft_image)\n", "\n", "fft_image.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Works also in daisy-chain.\n" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "/* global mpl */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function () {\n", " if (typeof WebSocket !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof MozWebSocket !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert(\n", " 'Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.'\n", " );\n", " }\n", "};\n", "\n", "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = this.ws.binaryType !== undefined;\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById('mpl-warnings');\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent =\n", " 'This browser does not support binary websocket messages. ' +\n", " 'Performance may be slow.';\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = document.createElement('div');\n", " this.root.setAttribute('style', 'display: inline-block');\n", " this._root_extra_style(this.root);\n", "\n", " parent_element.appendChild(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message('supports_binary', { value: fig.supports_binary });\n", " fig.send_message('send_image_mode', {});\n", " if (fig.ratio !== 1) {\n", " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", " }\n", " fig.send_message('refresh', {});\n", " };\n", "\n", " this.imageObj.onload = function () {\n", " if (fig.image_mode === 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function () {\n", " fig.ws.close();\n", " };\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "};\n", "\n", "mpl.figure.prototype._init_header = function () {\n", " var titlebar = document.createElement('div');\n", " titlebar.classList =\n", " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", " var titletext = document.createElement('div');\n", " titletext.classList = 'ui-dialog-title';\n", " titletext.setAttribute(\n", " 'style',\n", " 'width: 100%; text-align: center; padding: 3px;'\n", " );\n", " titlebar.appendChild(titletext);\n", " this.root.appendChild(titlebar);\n", " this.header = titletext;\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._init_canvas = function () {\n", " var fig = this;\n", "\n", " var canvas_div = (this.canvas_div = document.createElement('div'));\n", " canvas_div.setAttribute(\n", " 'style',\n", " 'border: 1px solid #ddd;' +\n", " 'box-sizing: content-box;' +\n", " 'clear: both;' +\n", " 'min-height: 1px;' +\n", " 'min-width: 1px;' +\n", " 'outline: 0;' +\n", " 'overflow: hidden;' +\n", " 'position: relative;' +\n", " 'resize: both;'\n", " );\n", "\n", " function on_keyboard_event_closure(name) {\n", " return function (event) {\n", " return fig.key_event(event, name);\n", " };\n", " }\n", "\n", " canvas_div.addEventListener(\n", " 'keydown',\n", " on_keyboard_event_closure('key_press')\n", " );\n", " canvas_div.addEventListener(\n", " 'keyup',\n", " on_keyboard_event_closure('key_release')\n", " );\n", "\n", " this._canvas_extra_style(canvas_div);\n", " this.root.appendChild(canvas_div);\n", "\n", " var canvas = (this.canvas = document.createElement('canvas'));\n", " canvas.classList.add('mpl-canvas');\n", " canvas.setAttribute('style', 'box-sizing: content-box;');\n", "\n", " this.context = canvas.getContext('2d');\n", "\n", " var backingStore =\n", " this.context.backingStorePixelRatio ||\n", " this.context.webkitBackingStorePixelRatio ||\n", " this.context.mozBackingStorePixelRatio ||\n", " this.context.msBackingStorePixelRatio ||\n", " this.context.oBackingStorePixelRatio ||\n", " this.context.backingStorePixelRatio ||\n", " 1;\n", "\n", " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", " 'canvas'\n", " ));\n", " rubberband_canvas.setAttribute(\n", " 'style',\n", " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", " );\n", "\n", " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", " if (this.ResizeObserver === undefined) {\n", " if (window.ResizeObserver !== undefined) {\n", " this.ResizeObserver = window.ResizeObserver;\n", " } else {\n", " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", " this.ResizeObserver = obs.ResizeObserver;\n", " }\n", " }\n", "\n", " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", " var nentries = entries.length;\n", " for (var i = 0; i < nentries; i++) {\n", " var entry = entries[i];\n", " var width, height;\n", " if (entry.contentBoxSize) {\n", " if (entry.contentBoxSize instanceof Array) {\n", " // Chrome 84 implements new version of spec.\n", " width = entry.contentBoxSize[0].inlineSize;\n", " height = entry.contentBoxSize[0].blockSize;\n", " } else {\n", " // Firefox implements old version of spec.\n", " width = entry.contentBoxSize.inlineSize;\n", " height = entry.contentBoxSize.blockSize;\n", " }\n", " } else {\n", " // Chrome <84 implements even older version of spec.\n", " width = entry.contentRect.width;\n", " height = entry.contentRect.height;\n", " }\n", "\n", " // Keep the size of the canvas and rubber band canvas in sync with\n", " // the canvas container.\n", " if (entry.devicePixelContentBoxSize) {\n", " // Chrome 84 implements new version of spec.\n", " canvas.setAttribute(\n", " 'width',\n", " entry.devicePixelContentBoxSize[0].inlineSize\n", " );\n", " canvas.setAttribute(\n", " 'height',\n", " entry.devicePixelContentBoxSize[0].blockSize\n", " );\n", " } else {\n", " canvas.setAttribute('width', width * fig.ratio);\n", " canvas.setAttribute('height', height * fig.ratio);\n", " }\n", " canvas.setAttribute(\n", " 'style',\n", " 'width: ' + width + 'px; height: ' + height + 'px;'\n", " );\n", "\n", " rubberband_canvas.setAttribute('width', width);\n", " rubberband_canvas.setAttribute('height', height);\n", "\n", " // And update the size in Python. We ignore the initial 0/0 size\n", " // that occurs as the element is placed into the DOM, which should\n", " // otherwise not happen due to the minimum size styling.\n", " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", " fig.request_resize(width, height);\n", " }\n", " }\n", " });\n", " this.resizeObserverInstance.observe(canvas_div);\n", "\n", " function on_mouse_event_closure(name) {\n", " return function (event) {\n", " return fig.mouse_event(event, name);\n", " };\n", " }\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mousedown',\n", " on_mouse_event_closure('button_press')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseup',\n", " on_mouse_event_closure('button_release')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'dblclick',\n", " on_mouse_event_closure('dblclick')\n", " );\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband_canvas.addEventListener(\n", " 'mousemove',\n", " on_mouse_event_closure('motion_notify')\n", " );\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mouseenter',\n", " on_mouse_event_closure('figure_enter')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseleave',\n", " on_mouse_event_closure('figure_leave')\n", " );\n", "\n", " canvas_div.addEventListener('wheel', function (event) {\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " on_mouse_event_closure('scroll')(event);\n", " });\n", "\n", " canvas_div.appendChild(canvas);\n", " canvas_div.appendChild(rubberband_canvas);\n", "\n", " this.rubberband_context = rubberband_canvas.getContext('2d');\n", " this.rubberband_context.strokeStyle = '#000000';\n", "\n", " this._resize_canvas = function (width, height, forward) {\n", " if (forward) {\n", " canvas_div.style.width = width + 'px';\n", " canvas_div.style.height = height + 'px';\n", " }\n", " };\n", "\n", " // Disable right mouse context menu.\n", " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", " event.preventDefault();\n", " return false;\n", " });\n", "\n", " function set_focus() {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'mpl-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " continue;\n", " }\n", "\n", " var button = (fig.buttons[name] = document.createElement('button'));\n", " button.classList = 'mpl-widget';\n", " button.setAttribute('role', 'button');\n", " button.setAttribute('aria-disabled', 'false');\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", "\n", " var icon_img = document.createElement('img');\n", " icon_img.src = '_images/' + image + '.png';\n", " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", " icon_img.alt = tooltip;\n", " button.appendChild(icon_img);\n", "\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " var fmt_picker = document.createElement('select');\n", " fmt_picker.classList = 'mpl-widget';\n", " toolbar.appendChild(fmt_picker);\n", " this.format_dropdown = fmt_picker;\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = document.createElement('option');\n", " option.selected = fmt === mpl.default_extension;\n", " option.innerHTML = fmt;\n", " fmt_picker.appendChild(option);\n", " }\n", "\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "};\n", "\n", "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", "};\n", "\n", "mpl.figure.prototype.send_message = function (type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "};\n", "\n", "mpl.figure.prototype.send_draw_message = function () {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "};\n", "\n", "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1], msg['forward']);\n", " fig.send_message('refresh', {});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", " var x0 = msg['x0'] / fig.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", " var x1 = msg['x1'] / fig.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0,\n", " 0,\n", " fig.canvas.width / fig.ratio,\n", " fig.canvas.height / fig.ratio\n", " );\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "};\n", "\n", "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "};\n", "\n", "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch (cursor) {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "};\n", "\n", "mpl.figure.prototype.handle_message = function (fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "};\n", "\n", "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "};\n", "\n", "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "};\n", "\n", "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", " for (var key in msg) {\n", " if (!(key in fig.buttons)) {\n", " continue;\n", " }\n", " fig.buttons[key].disabled = !msg[key];\n", " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", " if (msg['mode'] === 'PAN') {\n", " fig.buttons['Pan'].classList.add('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " } else if (msg['mode'] === 'ZOOM') {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.add('active');\n", " } else {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " }\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Called whenever the canvas gets updated.\n", " this.send_message('ack', {});\n", "};\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function (fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " var img = evt.data;\n", " if (img.type !== 'image/png') {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " img.type = 'image/png';\n", " }\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src\n", " );\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " img\n", " );\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " } else if (\n", " typeof evt.data === 'string' &&\n", " evt.data.slice(0, 21) === 'data:image/png;base64'\n", " ) {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig['handle_' + msg_type];\n", " } catch (e) {\n", " console.log(\n", " \"No handler for the '\" + msg_type + \"' message type: \",\n", " msg\n", " );\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\n", " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", " e,\n", " e.stack,\n", " msg\n", " );\n", " }\n", " }\n", " };\n", "};\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function (e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e) {\n", " e = window.event;\n", " }\n", " if (e.target) {\n", " targ = e.target;\n", " } else if (e.srcElement) {\n", " targ = e.srcElement;\n", " }\n", " if (targ.nodeType === 3) {\n", " // defeat Safari bug\n", " targ = targ.parentNode;\n", " }\n", "\n", " // pageX,Y are the mouse positions relative to the document\n", " var boundingRect = targ.getBoundingClientRect();\n", " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", "\n", " return { x: x, y: y };\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys(original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object') {\n", " obj[key] = original[key];\n", " }\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function (event, name) {\n", " var canvas_pos = mpl.findpos(event);\n", "\n", " if (name === 'button_press') {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * this.ratio;\n", " var y = canvas_pos.y * this.ratio;\n", "\n", " this.send_message(name, {\n", " x: x,\n", " y: y,\n", " button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event),\n", " });\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", " // Handle any extra behaviour associated with a key event\n", "};\n", "\n", "mpl.figure.prototype.key_event = function (event, name) {\n", " // Prevent repeat events\n", " if (name === 'key_press') {\n", " if (event.key === this._key) {\n", " return;\n", " } else {\n", " this._key = event.key;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.key !== 'Control') {\n", " value += 'ctrl+';\n", " }\n", " else if (event.altKey && event.key !== 'Alt') {\n", " value += 'alt+';\n", " }\n", " else if (event.shiftKey && event.key !== 'Shift') {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k' + event.key;\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", " if (name === 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message('toolbar_button', { name: name });\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "\n", "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", "// prettier-ignore\n", "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* global mpl */\n", "\n", "var comm_websocket_adapter = function (comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.binaryType = comm.kernel.ws.binaryType;\n", " ws.readyState = comm.kernel.ws.readyState;\n", " function updateReadyState(_event) {\n", " if (comm.kernel.ws) {\n", " ws.readyState = comm.kernel.ws.readyState;\n", " } else {\n", " ws.readyState = 3; // Closed state.\n", " }\n", " }\n", " comm.kernel.ws.addEventListener('open', updateReadyState);\n", " comm.kernel.ws.addEventListener('close', updateReadyState);\n", " comm.kernel.ws.addEventListener('error', updateReadyState);\n", "\n", " ws.close = function () {\n", " comm.close();\n", " };\n", " ws.send = function (m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function (msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " var data = msg['content']['data'];\n", " if (data['blob'] !== undefined) {\n", " data = {\n", " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", " };\n", " }\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(data);\n", " });\n", " return ws;\n", "};\n", "\n", "mpl.mpl_figure_comm = function (comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = document.getElementById(id);\n", " var ws_proxy = comm_websocket_adapter(comm);\n", "\n", " function ondownload(figure, _format) {\n", " window.open(figure.canvas.toDataURL());\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element;\n", " fig.cell_info = mpl.find_output_cell(\"
\");\n", " if (!fig.cell_info) {\n", " console.error('Failed to find cell for figure', id, fig);\n", " return;\n", " }\n", " fig.cell_info[0].output_area.element.on(\n", " 'cleared',\n", " { fig: fig },\n", " fig._remove_fig_handler\n", " );\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function (fig, msg) {\n", " var width = fig.canvas.width / fig.ratio;\n", " fig.cell_info[0].output_area.element.off(\n", " 'cleared',\n", " fig._remove_fig_handler\n", " );\n", " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable();\n", " fig.parent_element.innerHTML =\n", " '';\n", " fig.close_ws(fig, msg);\n", "};\n", "\n", "mpl.figure.prototype.close_ws = function (fig, msg) {\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "};\n", "\n", "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width / this.ratio;\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] =\n", " '';\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message('ack', {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () {\n", " fig.push_to_output();\n", " }, 1000);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'btn-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " var button;\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " continue;\n", " }\n", "\n", " button = fig.buttons[name] = document.createElement('button');\n", " button.classList = 'btn btn-default';\n", " button.href = '#';\n", " button.title = name;\n", " button.innerHTML = '';\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message pull-right';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "\n", " // Add the close button to the window.\n", " var buttongrp = document.createElement('div');\n", " buttongrp.classList = 'btn-group inline pull-right';\n", " button = document.createElement('button');\n", " button.classList = 'btn btn-mini btn-primary';\n", " button.href = '#';\n", " button.title = 'Stop Interaction';\n", " button.innerHTML = '';\n", " button.addEventListener('click', function (_evt) {\n", " fig.handle_close(fig, {});\n", " });\n", " button.addEventListener(\n", " 'mouseover',\n", " on_mouseover_closure('Stop Interaction')\n", " );\n", " buttongrp.appendChild(button);\n", " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", "};\n", "\n", "mpl.figure.prototype._remove_fig_handler = function (event) {\n", " var fig = event.data.fig;\n", " if (event.target !== this) {\n", " // Ignore bubbled events from children.\n", " return;\n", " }\n", " fig.close_ws(fig, {});\n", "};\n", "\n", "mpl.figure.prototype._root_extra_style = function (el) {\n", " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (el) {\n", " // this is important to make the div 'focusable\n", " el.setAttribute('tabindex', 0);\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " } else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager) {\n", " manager = IPython.keyboard_manager;\n", " }\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which === 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " fig.ondownload(fig, null);\n", "};\n", "\n", "mpl.find_output_cell = function (html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i = 0; i < ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code') {\n", " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] === html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "};\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel !== null) {\n", " IPython.notebook.kernel.comm_manager.register_target(\n", " 'matplotlib',\n", " mpl.mpl_figure_comm\n", " );\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4b968a3b7cc94750a8ebbf64178bfe39", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Play(value=0, description='Press play', interval=500, max=20), IntSlider(value=0, continuous_up…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fft_image = np.log2(1+dset.fft().abs())\n", "\n", "fft_image.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## All together\n", "\n", "Please not that you need to specify the data_type, whenever you change the number of dimensions." ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "/* global mpl */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function () {\n", " if (typeof WebSocket !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof MozWebSocket !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert(\n", " 'Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.'\n", " );\n", " }\n", "};\n", "\n", "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = this.ws.binaryType !== undefined;\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById('mpl-warnings');\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent =\n", " 'This browser does not support binary websocket messages. ' +\n", " 'Performance may be slow.';\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = document.createElement('div');\n", " this.root.setAttribute('style', 'display: inline-block');\n", " this._root_extra_style(this.root);\n", "\n", " parent_element.appendChild(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message('supports_binary', { value: fig.supports_binary });\n", " fig.send_message('send_image_mode', {});\n", " if (fig.ratio !== 1) {\n", " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", " }\n", " fig.send_message('refresh', {});\n", " };\n", "\n", " this.imageObj.onload = function () {\n", " if (fig.image_mode === 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function () {\n", " fig.ws.close();\n", " };\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "};\n", "\n", "mpl.figure.prototype._init_header = function () {\n", " var titlebar = document.createElement('div');\n", " titlebar.classList =\n", " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", " var titletext = document.createElement('div');\n", " titletext.classList = 'ui-dialog-title';\n", " titletext.setAttribute(\n", " 'style',\n", " 'width: 100%; text-align: center; padding: 3px;'\n", " );\n", " titlebar.appendChild(titletext);\n", " this.root.appendChild(titlebar);\n", " this.header = titletext;\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._init_canvas = function () {\n", " var fig = this;\n", "\n", " var canvas_div = (this.canvas_div = document.createElement('div'));\n", " canvas_div.setAttribute(\n", " 'style',\n", " 'border: 1px solid #ddd;' +\n", " 'box-sizing: content-box;' +\n", " 'clear: both;' +\n", " 'min-height: 1px;' +\n", " 'min-width: 1px;' +\n", " 'outline: 0;' +\n", " 'overflow: hidden;' +\n", " 'position: relative;' +\n", " 'resize: both;'\n", " );\n", "\n", " function on_keyboard_event_closure(name) {\n", " return function (event) {\n", " return fig.key_event(event, name);\n", " };\n", " }\n", "\n", " canvas_div.addEventListener(\n", " 'keydown',\n", " on_keyboard_event_closure('key_press')\n", " );\n", " canvas_div.addEventListener(\n", " 'keyup',\n", " on_keyboard_event_closure('key_release')\n", " );\n", "\n", " this._canvas_extra_style(canvas_div);\n", " this.root.appendChild(canvas_div);\n", "\n", " var canvas = (this.canvas = document.createElement('canvas'));\n", " canvas.classList.add('mpl-canvas');\n", " canvas.setAttribute('style', 'box-sizing: content-box;');\n", "\n", " this.context = canvas.getContext('2d');\n", "\n", " var backingStore =\n", " this.context.backingStorePixelRatio ||\n", " this.context.webkitBackingStorePixelRatio ||\n", " this.context.mozBackingStorePixelRatio ||\n", " this.context.msBackingStorePixelRatio ||\n", " this.context.oBackingStorePixelRatio ||\n", " this.context.backingStorePixelRatio ||\n", " 1;\n", "\n", " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", " 'canvas'\n", " ));\n", " rubberband_canvas.setAttribute(\n", " 'style',\n", " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", " );\n", "\n", " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", " if (this.ResizeObserver === undefined) {\n", " if (window.ResizeObserver !== undefined) {\n", " this.ResizeObserver = window.ResizeObserver;\n", " } else {\n", " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", " this.ResizeObserver = obs.ResizeObserver;\n", " }\n", " }\n", "\n", " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", " var nentries = entries.length;\n", " for (var i = 0; i < nentries; i++) {\n", " var entry = entries[i];\n", " var width, height;\n", " if (entry.contentBoxSize) {\n", " if (entry.contentBoxSize instanceof Array) {\n", " // Chrome 84 implements new version of spec.\n", " width = entry.contentBoxSize[0].inlineSize;\n", " height = entry.contentBoxSize[0].blockSize;\n", " } else {\n", " // Firefox implements old version of spec.\n", " width = entry.contentBoxSize.inlineSize;\n", " height = entry.contentBoxSize.blockSize;\n", " }\n", " } else {\n", " // Chrome <84 implements even older version of spec.\n", " width = entry.contentRect.width;\n", " height = entry.contentRect.height;\n", " }\n", "\n", " // Keep the size of the canvas and rubber band canvas in sync with\n", " // the canvas container.\n", " if (entry.devicePixelContentBoxSize) {\n", " // Chrome 84 implements new version of spec.\n", " canvas.setAttribute(\n", " 'width',\n", " entry.devicePixelContentBoxSize[0].inlineSize\n", " );\n", " canvas.setAttribute(\n", " 'height',\n", " entry.devicePixelContentBoxSize[0].blockSize\n", " );\n", " } else {\n", " canvas.setAttribute('width', width * fig.ratio);\n", " canvas.setAttribute('height', height * fig.ratio);\n", " }\n", " canvas.setAttribute(\n", " 'style',\n", " 'width: ' + width + 'px; height: ' + height + 'px;'\n", " );\n", "\n", " rubberband_canvas.setAttribute('width', width);\n", " rubberband_canvas.setAttribute('height', height);\n", "\n", " // And update the size in Python. We ignore the initial 0/0 size\n", " // that occurs as the element is placed into the DOM, which should\n", " // otherwise not happen due to the minimum size styling.\n", " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", " fig.request_resize(width, height);\n", " }\n", " }\n", " });\n", " this.resizeObserverInstance.observe(canvas_div);\n", "\n", " function on_mouse_event_closure(name) {\n", " return function (event) {\n", " return fig.mouse_event(event, name);\n", " };\n", " }\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mousedown',\n", " on_mouse_event_closure('button_press')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseup',\n", " on_mouse_event_closure('button_release')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'dblclick',\n", " on_mouse_event_closure('dblclick')\n", " );\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband_canvas.addEventListener(\n", " 'mousemove',\n", " on_mouse_event_closure('motion_notify')\n", " );\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mouseenter',\n", " on_mouse_event_closure('figure_enter')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseleave',\n", " on_mouse_event_closure('figure_leave')\n", " );\n", "\n", " canvas_div.addEventListener('wheel', function (event) {\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " on_mouse_event_closure('scroll')(event);\n", " });\n", "\n", " canvas_div.appendChild(canvas);\n", " canvas_div.appendChild(rubberband_canvas);\n", "\n", " this.rubberband_context = rubberband_canvas.getContext('2d');\n", " this.rubberband_context.strokeStyle = '#000000';\n", "\n", " this._resize_canvas = function (width, height, forward) {\n", " if (forward) {\n", " canvas_div.style.width = width + 'px';\n", " canvas_div.style.height = height + 'px';\n", " }\n", " };\n", "\n", " // Disable right mouse context menu.\n", " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", " event.preventDefault();\n", " return false;\n", " });\n", "\n", " function set_focus() {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'mpl-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " continue;\n", " }\n", "\n", " var button = (fig.buttons[name] = document.createElement('button'));\n", " button.classList = 'mpl-widget';\n", " button.setAttribute('role', 'button');\n", " button.setAttribute('aria-disabled', 'false');\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", "\n", " var icon_img = document.createElement('img');\n", " icon_img.src = '_images/' + image + '.png';\n", " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", " icon_img.alt = tooltip;\n", " button.appendChild(icon_img);\n", "\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " var fmt_picker = document.createElement('select');\n", " fmt_picker.classList = 'mpl-widget';\n", " toolbar.appendChild(fmt_picker);\n", " this.format_dropdown = fmt_picker;\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = document.createElement('option');\n", " option.selected = fmt === mpl.default_extension;\n", " option.innerHTML = fmt;\n", " fmt_picker.appendChild(option);\n", " }\n", "\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "};\n", "\n", "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", "};\n", "\n", "mpl.figure.prototype.send_message = function (type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "};\n", "\n", "mpl.figure.prototype.send_draw_message = function () {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "};\n", "\n", "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1], msg['forward']);\n", " fig.send_message('refresh', {});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", " var x0 = msg['x0'] / fig.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", " var x1 = msg['x1'] / fig.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0,\n", " 0,\n", " fig.canvas.width / fig.ratio,\n", " fig.canvas.height / fig.ratio\n", " );\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "};\n", "\n", "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "};\n", "\n", "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch (cursor) {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "};\n", "\n", "mpl.figure.prototype.handle_message = function (fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "};\n", "\n", "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "};\n", "\n", "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "};\n", "\n", "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", " for (var key in msg) {\n", " if (!(key in fig.buttons)) {\n", " continue;\n", " }\n", " fig.buttons[key].disabled = !msg[key];\n", " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", " if (msg['mode'] === 'PAN') {\n", " fig.buttons['Pan'].classList.add('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " } else if (msg['mode'] === 'ZOOM') {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.add('active');\n", " } else {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " }\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Called whenever the canvas gets updated.\n", " this.send_message('ack', {});\n", "};\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function (fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " var img = evt.data;\n", " if (img.type !== 'image/png') {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " img.type = 'image/png';\n", " }\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src\n", " );\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " img\n", " );\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " } else if (\n", " typeof evt.data === 'string' &&\n", " evt.data.slice(0, 21) === 'data:image/png;base64'\n", " ) {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig['handle_' + msg_type];\n", " } catch (e) {\n", " console.log(\n", " \"No handler for the '\" + msg_type + \"' message type: \",\n", " msg\n", " );\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\n", " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", " e,\n", " e.stack,\n", " msg\n", " );\n", " }\n", " }\n", " };\n", "};\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function (e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e) {\n", " e = window.event;\n", " }\n", " if (e.target) {\n", " targ = e.target;\n", " } else if (e.srcElement) {\n", " targ = e.srcElement;\n", " }\n", " if (targ.nodeType === 3) {\n", " // defeat Safari bug\n", " targ = targ.parentNode;\n", " }\n", "\n", " // pageX,Y are the mouse positions relative to the document\n", " var boundingRect = targ.getBoundingClientRect();\n", " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", "\n", " return { x: x, y: y };\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys(original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object') {\n", " obj[key] = original[key];\n", " }\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function (event, name) {\n", " var canvas_pos = mpl.findpos(event);\n", "\n", " if (name === 'button_press') {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * this.ratio;\n", " var y = canvas_pos.y * this.ratio;\n", "\n", " this.send_message(name, {\n", " x: x,\n", " y: y,\n", " button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event),\n", " });\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", " // Handle any extra behaviour associated with a key event\n", "};\n", "\n", "mpl.figure.prototype.key_event = function (event, name) {\n", " // Prevent repeat events\n", " if (name === 'key_press') {\n", " if (event.key === this._key) {\n", " return;\n", " } else {\n", " this._key = event.key;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.key !== 'Control') {\n", " value += 'ctrl+';\n", " }\n", " else if (event.altKey && event.key !== 'Alt') {\n", " value += 'alt+';\n", " }\n", " else if (event.shiftKey && event.key !== 'Shift') {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k' + event.key;\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", " if (name === 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message('toolbar_button', { name: name });\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "\n", "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", "// prettier-ignore\n", "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* global mpl */\n", "\n", "var comm_websocket_adapter = function (comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.binaryType = comm.kernel.ws.binaryType;\n", " ws.readyState = comm.kernel.ws.readyState;\n", " function updateReadyState(_event) {\n", " if (comm.kernel.ws) {\n", " ws.readyState = comm.kernel.ws.readyState;\n", " } else {\n", " ws.readyState = 3; // Closed state.\n", " }\n", " }\n", " comm.kernel.ws.addEventListener('open', updateReadyState);\n", " comm.kernel.ws.addEventListener('close', updateReadyState);\n", " comm.kernel.ws.addEventListener('error', updateReadyState);\n", "\n", " ws.close = function () {\n", " comm.close();\n", " };\n", " ws.send = function (m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function (msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " var data = msg['content']['data'];\n", " if (data['blob'] !== undefined) {\n", " data = {\n", " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", " };\n", " }\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(data);\n", " });\n", " return ws;\n", "};\n", "\n", "mpl.mpl_figure_comm = function (comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = document.getElementById(id);\n", " var ws_proxy = comm_websocket_adapter(comm);\n", "\n", " function ondownload(figure, _format) {\n", " window.open(figure.canvas.toDataURL());\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element;\n", " fig.cell_info = mpl.find_output_cell(\"
\");\n", " if (!fig.cell_info) {\n", " console.error('Failed to find cell for figure', id, fig);\n", " return;\n", " }\n", " fig.cell_info[0].output_area.element.on(\n", " 'cleared',\n", " { fig: fig },\n", " fig._remove_fig_handler\n", " );\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function (fig, msg) {\n", " var width = fig.canvas.width / fig.ratio;\n", " fig.cell_info[0].output_area.element.off(\n", " 'cleared',\n", " fig._remove_fig_handler\n", " );\n", " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable();\n", " fig.parent_element.innerHTML =\n", " '';\n", " fig.close_ws(fig, msg);\n", "};\n", "\n", "mpl.figure.prototype.close_ws = function (fig, msg) {\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "};\n", "\n", "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width / this.ratio;\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] =\n", " '';\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message('ack', {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () {\n", " fig.push_to_output();\n", " }, 1000);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'btn-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " var button;\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " continue;\n", " }\n", "\n", " button = fig.buttons[name] = document.createElement('button');\n", " button.classList = 'btn btn-default';\n", " button.href = '#';\n", " button.title = name;\n", " button.innerHTML = '';\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message pull-right';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "\n", " // Add the close button to the window.\n", " var buttongrp = document.createElement('div');\n", " buttongrp.classList = 'btn-group inline pull-right';\n", " button = document.createElement('button');\n", " button.classList = 'btn btn-mini btn-primary';\n", " button.href = '#';\n", " button.title = 'Stop Interaction';\n", " button.innerHTML = '';\n", " button.addEventListener('click', function (_evt) {\n", " fig.handle_close(fig, {});\n", " });\n", " button.addEventListener(\n", " 'mouseover',\n", " on_mouseover_closure('Stop Interaction')\n", " );\n", " buttongrp.appendChild(button);\n", " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", "};\n", "\n", "mpl.figure.prototype._remove_fig_handler = function (event) {\n", " var fig = event.data.fig;\n", " if (event.target !== this) {\n", " // Ignore bubbled events from children.\n", " return;\n", " }\n", " fig.close_ws(fig, {});\n", "};\n", "\n", "mpl.figure.prototype._root_extra_style = function (el) {\n", " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (el) {\n", " // this is important to make the div 'focusable\n", " el.setAttribute('tabindex', 0);\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " } else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager) {\n", " manager = IPython.keyboard_manager;\n", " }\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which === 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " fig.ondownload(fig, null);\n", "};\n", "\n", "mpl.find_output_cell = function (html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i = 0; i < ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code') {\n", " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] === html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "};\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel !== null) {\n", " IPython.notebook.kernel.comm_manager.register_target(\n", " 'matplotlib',\n", " mpl.mpl_figure_comm\n", " );\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fft_image = np.power((dset-dset.min()).fft().sum(axis=0).abs(), 0.1)\n", "fft_image.data_type = 'image'\n", "fft_image.plot()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "/* global mpl */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function () {\n", " if (typeof WebSocket !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof MozWebSocket !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert(\n", " 'Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.'\n", " );\n", " }\n", "};\n", "\n", "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = this.ws.binaryType !== undefined;\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById('mpl-warnings');\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent =\n", " 'This browser does not support binary websocket messages. ' +\n", " 'Performance may be slow.';\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = document.createElement('div');\n", " this.root.setAttribute('style', 'display: inline-block');\n", " this._root_extra_style(this.root);\n", "\n", " parent_element.appendChild(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message('supports_binary', { value: fig.supports_binary });\n", " fig.send_message('send_image_mode', {});\n", " if (fig.ratio !== 1) {\n", " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", " }\n", " fig.send_message('refresh', {});\n", " };\n", "\n", " this.imageObj.onload = function () {\n", " if (fig.image_mode === 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function () {\n", " fig.ws.close();\n", " };\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "};\n", "\n", "mpl.figure.prototype._init_header = function () {\n", " var titlebar = document.createElement('div');\n", " titlebar.classList =\n", " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", " var titletext = document.createElement('div');\n", " titletext.classList = 'ui-dialog-title';\n", " titletext.setAttribute(\n", " 'style',\n", " 'width: 100%; text-align: center; padding: 3px;'\n", " );\n", " titlebar.appendChild(titletext);\n", " this.root.appendChild(titlebar);\n", " this.header = titletext;\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._init_canvas = function () {\n", " var fig = this;\n", "\n", " var canvas_div = (this.canvas_div = document.createElement('div'));\n", " canvas_div.setAttribute(\n", " 'style',\n", " 'border: 1px solid #ddd;' +\n", " 'box-sizing: content-box;' +\n", " 'clear: both;' +\n", " 'min-height: 1px;' +\n", " 'min-width: 1px;' +\n", " 'outline: 0;' +\n", " 'overflow: hidden;' +\n", " 'position: relative;' +\n", " 'resize: both;'\n", " );\n", "\n", " function on_keyboard_event_closure(name) {\n", " return function (event) {\n", " return fig.key_event(event, name);\n", " };\n", " }\n", "\n", " canvas_div.addEventListener(\n", " 'keydown',\n", " on_keyboard_event_closure('key_press')\n", " );\n", " canvas_div.addEventListener(\n", " 'keyup',\n", " on_keyboard_event_closure('key_release')\n", " );\n", "\n", " this._canvas_extra_style(canvas_div);\n", " this.root.appendChild(canvas_div);\n", "\n", " var canvas = (this.canvas = document.createElement('canvas'));\n", " canvas.classList.add('mpl-canvas');\n", " canvas.setAttribute('style', 'box-sizing: content-box;');\n", "\n", " this.context = canvas.getContext('2d');\n", "\n", " var backingStore =\n", " this.context.backingStorePixelRatio ||\n", " this.context.webkitBackingStorePixelRatio ||\n", " this.context.mozBackingStorePixelRatio ||\n", " this.context.msBackingStorePixelRatio ||\n", " this.context.oBackingStorePixelRatio ||\n", " this.context.backingStorePixelRatio ||\n", " 1;\n", "\n", " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", " 'canvas'\n", " ));\n", " rubberband_canvas.setAttribute(\n", " 'style',\n", " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", " );\n", "\n", " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", " if (this.ResizeObserver === undefined) {\n", " if (window.ResizeObserver !== undefined) {\n", " this.ResizeObserver = window.ResizeObserver;\n", " } else {\n", " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", " this.ResizeObserver = obs.ResizeObserver;\n", " }\n", " }\n", "\n", " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", " var nentries = entries.length;\n", " for (var i = 0; i < nentries; i++) {\n", " var entry = entries[i];\n", " var width, height;\n", " if (entry.contentBoxSize) {\n", " if (entry.contentBoxSize instanceof Array) {\n", " // Chrome 84 implements new version of spec.\n", " width = entry.contentBoxSize[0].inlineSize;\n", " height = entry.contentBoxSize[0].blockSize;\n", " } else {\n", " // Firefox implements old version of spec.\n", " width = entry.contentBoxSize.inlineSize;\n", " height = entry.contentBoxSize.blockSize;\n", " }\n", " } else {\n", " // Chrome <84 implements even older version of spec.\n", " width = entry.contentRect.width;\n", " height = entry.contentRect.height;\n", " }\n", "\n", " // Keep the size of the canvas and rubber band canvas in sync with\n", " // the canvas container.\n", " if (entry.devicePixelContentBoxSize) {\n", " // Chrome 84 implements new version of spec.\n", " canvas.setAttribute(\n", " 'width',\n", " entry.devicePixelContentBoxSize[0].inlineSize\n", " );\n", " canvas.setAttribute(\n", " 'height',\n", " entry.devicePixelContentBoxSize[0].blockSize\n", " );\n", " } else {\n", " canvas.setAttribute('width', width * fig.ratio);\n", " canvas.setAttribute('height', height * fig.ratio);\n", " }\n", " canvas.setAttribute(\n", " 'style',\n", " 'width: ' + width + 'px; height: ' + height + 'px;'\n", " );\n", "\n", " rubberband_canvas.setAttribute('width', width);\n", " rubberband_canvas.setAttribute('height', height);\n", "\n", " // And update the size in Python. We ignore the initial 0/0 size\n", " // that occurs as the element is placed into the DOM, which should\n", " // otherwise not happen due to the minimum size styling.\n", " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", " fig.request_resize(width, height);\n", " }\n", " }\n", " });\n", " this.resizeObserverInstance.observe(canvas_div);\n", "\n", " function on_mouse_event_closure(name) {\n", " return function (event) {\n", " return fig.mouse_event(event, name);\n", " };\n", " }\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mousedown',\n", " on_mouse_event_closure('button_press')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseup',\n", " on_mouse_event_closure('button_release')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'dblclick',\n", " on_mouse_event_closure('dblclick')\n", " );\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband_canvas.addEventListener(\n", " 'mousemove',\n", " on_mouse_event_closure('motion_notify')\n", " );\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mouseenter',\n", " on_mouse_event_closure('figure_enter')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseleave',\n", " on_mouse_event_closure('figure_leave')\n", " );\n", "\n", " canvas_div.addEventListener('wheel', function (event) {\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " on_mouse_event_closure('scroll')(event);\n", " });\n", "\n", " canvas_div.appendChild(canvas);\n", " canvas_div.appendChild(rubberband_canvas);\n", "\n", " this.rubberband_context = rubberband_canvas.getContext('2d');\n", " this.rubberband_context.strokeStyle = '#000000';\n", "\n", " this._resize_canvas = function (width, height, forward) {\n", " if (forward) {\n", " canvas_div.style.width = width + 'px';\n", " canvas_div.style.height = height + 'px';\n", " }\n", " };\n", "\n", " // Disable right mouse context menu.\n", " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", " event.preventDefault();\n", " return false;\n", " });\n", "\n", " function set_focus() {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'mpl-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " continue;\n", " }\n", "\n", " var button = (fig.buttons[name] = document.createElement('button'));\n", " button.classList = 'mpl-widget';\n", " button.setAttribute('role', 'button');\n", " button.setAttribute('aria-disabled', 'false');\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", "\n", " var icon_img = document.createElement('img');\n", " icon_img.src = '_images/' + image + '.png';\n", " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", " icon_img.alt = tooltip;\n", " button.appendChild(icon_img);\n", "\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " var fmt_picker = document.createElement('select');\n", " fmt_picker.classList = 'mpl-widget';\n", " toolbar.appendChild(fmt_picker);\n", " this.format_dropdown = fmt_picker;\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = document.createElement('option');\n", " option.selected = fmt === mpl.default_extension;\n", " option.innerHTML = fmt;\n", " fmt_picker.appendChild(option);\n", " }\n", "\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "};\n", "\n", "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", "};\n", "\n", "mpl.figure.prototype.send_message = function (type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "};\n", "\n", "mpl.figure.prototype.send_draw_message = function () {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "};\n", "\n", "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1], msg['forward']);\n", " fig.send_message('refresh', {});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", " var x0 = msg['x0'] / fig.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", " var x1 = msg['x1'] / fig.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0,\n", " 0,\n", " fig.canvas.width / fig.ratio,\n", " fig.canvas.height / fig.ratio\n", " );\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "};\n", "\n", "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "};\n", "\n", "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch (cursor) {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "};\n", "\n", "mpl.figure.prototype.handle_message = function (fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "};\n", "\n", "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "};\n", "\n", "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "};\n", "\n", "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", " for (var key in msg) {\n", " if (!(key in fig.buttons)) {\n", " continue;\n", " }\n", " fig.buttons[key].disabled = !msg[key];\n", " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", " if (msg['mode'] === 'PAN') {\n", " fig.buttons['Pan'].classList.add('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " } else if (msg['mode'] === 'ZOOM') {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.add('active');\n", " } else {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " }\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Called whenever the canvas gets updated.\n", " this.send_message('ack', {});\n", "};\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function (fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " var img = evt.data;\n", " if (img.type !== 'image/png') {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " img.type = 'image/png';\n", " }\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src\n", " );\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " img\n", " );\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " } else if (\n", " typeof evt.data === 'string' &&\n", " evt.data.slice(0, 21) === 'data:image/png;base64'\n", " ) {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig['handle_' + msg_type];\n", " } catch (e) {\n", " console.log(\n", " \"No handler for the '\" + msg_type + \"' message type: \",\n", " msg\n", " );\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\n", " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", " e,\n", " e.stack,\n", " msg\n", " );\n", " }\n", " }\n", " };\n", "};\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function (e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e) {\n", " e = window.event;\n", " }\n", " if (e.target) {\n", " targ = e.target;\n", " } else if (e.srcElement) {\n", " targ = e.srcElement;\n", " }\n", " if (targ.nodeType === 3) {\n", " // defeat Safari bug\n", " targ = targ.parentNode;\n", " }\n", "\n", " // pageX,Y are the mouse positions relative to the document\n", " var boundingRect = targ.getBoundingClientRect();\n", " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", "\n", " return { x: x, y: y };\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys(original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object') {\n", " obj[key] = original[key];\n", " }\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function (event, name) {\n", " var canvas_pos = mpl.findpos(event);\n", "\n", " if (name === 'button_press') {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * this.ratio;\n", " var y = canvas_pos.y * this.ratio;\n", "\n", " this.send_message(name, {\n", " x: x,\n", " y: y,\n", " button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event),\n", " });\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", " // Handle any extra behaviour associated with a key event\n", "};\n", "\n", "mpl.figure.prototype.key_event = function (event, name) {\n", " // Prevent repeat events\n", " if (name === 'key_press') {\n", " if (event.key === this._key) {\n", " return;\n", " } else {\n", " this._key = event.key;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.key !== 'Control') {\n", " value += 'ctrl+';\n", " }\n", " else if (event.altKey && event.key !== 'Alt') {\n", " value += 'alt+';\n", " }\n", " else if (event.shiftKey && event.key !== 'Shift') {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k' + event.key;\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", " if (name === 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message('toolbar_button', { name: name });\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "\n", "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", "// prettier-ignore\n", "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* global mpl */\n", "\n", "var comm_websocket_adapter = function (comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.binaryType = comm.kernel.ws.binaryType;\n", " ws.readyState = comm.kernel.ws.readyState;\n", " function updateReadyState(_event) {\n", " if (comm.kernel.ws) {\n", " ws.readyState = comm.kernel.ws.readyState;\n", " } else {\n", " ws.readyState = 3; // Closed state.\n", " }\n", " }\n", " comm.kernel.ws.addEventListener('open', updateReadyState);\n", " comm.kernel.ws.addEventListener('close', updateReadyState);\n", " comm.kernel.ws.addEventListener('error', updateReadyState);\n", "\n", " ws.close = function () {\n", " comm.close();\n", " };\n", " ws.send = function (m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function (msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " var data = msg['content']['data'];\n", " if (data['blob'] !== undefined) {\n", " data = {\n", " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", " };\n", " }\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(data);\n", " });\n", " return ws;\n", "};\n", "\n", "mpl.mpl_figure_comm = function (comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = document.getElementById(id);\n", " var ws_proxy = comm_websocket_adapter(comm);\n", "\n", " function ondownload(figure, _format) {\n", " window.open(figure.canvas.toDataURL());\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element;\n", " fig.cell_info = mpl.find_output_cell(\"
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" ], "text/plain": [ "sidpy.Dataset of type IMAGE with:\n", " dask.array\n", " data contains: intensity (a.u.)\n", " and Dimensions: \n", "u: reciprocal (1/nm) of size (512,)\n", "v: reciprocal_length (1/nm) of size (512,)" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fft_image = np.power(dset.fft().sum(axis=0), 0.1)\n", "fft_image.data_type = 'image'\n", "fft_image.plot()\n", "\n", "print(fft_image)\n", "fft_image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fourier Transform of Different data_types\n", "\n", "### Spectrum" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "/* global mpl */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function () {\n", " if (typeof WebSocket !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof MozWebSocket !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert(\n", " 'Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.'\n", " );\n", " }\n", "};\n", "\n", "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = this.ws.binaryType !== undefined;\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById('mpl-warnings');\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent =\n", " 'This browser does not support binary websocket messages. ' +\n", " 'Performance may be slow.';\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = document.createElement('div');\n", " this.root.setAttribute('style', 'display: inline-block');\n", " this._root_extra_style(this.root);\n", "\n", " parent_element.appendChild(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message('supports_binary', { value: fig.supports_binary });\n", " fig.send_message('send_image_mode', {});\n", " if (fig.ratio !== 1) {\n", " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", " }\n", " fig.send_message('refresh', {});\n", " };\n", "\n", " this.imageObj.onload = function () {\n", " if (fig.image_mode === 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function () {\n", " fig.ws.close();\n", " };\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "};\n", "\n", "mpl.figure.prototype._init_header = function () {\n", " var titlebar = document.createElement('div');\n", " titlebar.classList =\n", " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", " var titletext = document.createElement('div');\n", " titletext.classList = 'ui-dialog-title';\n", " titletext.setAttribute(\n", " 'style',\n", " 'width: 100%; text-align: center; padding: 3px;'\n", " );\n", " titlebar.appendChild(titletext);\n", " this.root.appendChild(titlebar);\n", " this.header = titletext;\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._init_canvas = function () {\n", " var fig = this;\n", "\n", " var canvas_div = (this.canvas_div = document.createElement('div'));\n", " canvas_div.setAttribute(\n", " 'style',\n", " 'border: 1px solid #ddd;' +\n", " 'box-sizing: content-box;' +\n", " 'clear: both;' +\n", " 'min-height: 1px;' +\n", " 'min-width: 1px;' +\n", " 'outline: 0;' +\n", " 'overflow: hidden;' +\n", " 'position: relative;' +\n", " 'resize: both;'\n", " );\n", "\n", " function on_keyboard_event_closure(name) {\n", " return function (event) {\n", " return fig.key_event(event, name);\n", " };\n", " }\n", "\n", " canvas_div.addEventListener(\n", " 'keydown',\n", " on_keyboard_event_closure('key_press')\n", " );\n", " canvas_div.addEventListener(\n", " 'keyup',\n", " on_keyboard_event_closure('key_release')\n", " );\n", "\n", " this._canvas_extra_style(canvas_div);\n", " this.root.appendChild(canvas_div);\n", "\n", " var canvas = (this.canvas = document.createElement('canvas'));\n", " canvas.classList.add('mpl-canvas');\n", " canvas.setAttribute('style', 'box-sizing: content-box;');\n", "\n", " this.context = canvas.getContext('2d');\n", "\n", " var backingStore =\n", " this.context.backingStorePixelRatio ||\n", " this.context.webkitBackingStorePixelRatio ||\n", " this.context.mozBackingStorePixelRatio ||\n", " this.context.msBackingStorePixelRatio ||\n", " this.context.oBackingStorePixelRatio ||\n", " this.context.backingStorePixelRatio ||\n", " 1;\n", "\n", " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", " 'canvas'\n", " ));\n", " rubberband_canvas.setAttribute(\n", " 'style',\n", " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", " );\n", "\n", " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", " if (this.ResizeObserver === undefined) {\n", " if (window.ResizeObserver !== undefined) {\n", " this.ResizeObserver = window.ResizeObserver;\n", " } else {\n", " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", " this.ResizeObserver = obs.ResizeObserver;\n", " }\n", " }\n", "\n", " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", " var nentries = entries.length;\n", " for (var i = 0; i < nentries; i++) {\n", " var entry = entries[i];\n", " var width, height;\n", " if (entry.contentBoxSize) {\n", " if (entry.contentBoxSize instanceof Array) {\n", " // Chrome 84 implements new version of spec.\n", " width = entry.contentBoxSize[0].inlineSize;\n", " height = entry.contentBoxSize[0].blockSize;\n", " } else {\n", " // Firefox implements old version of spec.\n", " width = entry.contentBoxSize.inlineSize;\n", " height = entry.contentBoxSize.blockSize;\n", " }\n", " } else {\n", " // Chrome <84 implements even older version of spec.\n", " width = entry.contentRect.width;\n", " height = entry.contentRect.height;\n", " }\n", "\n", " // Keep the size of the canvas and rubber band canvas in sync with\n", " // the canvas container.\n", " if (entry.devicePixelContentBoxSize) {\n", " // Chrome 84 implements new version of spec.\n", " canvas.setAttribute(\n", " 'width',\n", " entry.devicePixelContentBoxSize[0].inlineSize\n", " );\n", " canvas.setAttribute(\n", " 'height',\n", " entry.devicePixelContentBoxSize[0].blockSize\n", " );\n", " } else {\n", " canvas.setAttribute('width', width * fig.ratio);\n", " canvas.setAttribute('height', height * fig.ratio);\n", " }\n", " canvas.setAttribute(\n", " 'style',\n", " 'width: ' + width + 'px; height: ' + height + 'px;'\n", " );\n", "\n", " rubberband_canvas.setAttribute('width', width);\n", " rubberband_canvas.setAttribute('height', height);\n", "\n", " // And update the size in Python. We ignore the initial 0/0 size\n", " // that occurs as the element is placed into the DOM, which should\n", " // otherwise not happen due to the minimum size styling.\n", " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", " fig.request_resize(width, height);\n", " }\n", " }\n", " });\n", " this.resizeObserverInstance.observe(canvas_div);\n", "\n", " function on_mouse_event_closure(name) {\n", " return function (event) {\n", " return fig.mouse_event(event, name);\n", " };\n", " }\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mousedown',\n", " on_mouse_event_closure('button_press')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseup',\n", " on_mouse_event_closure('button_release')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'dblclick',\n", " on_mouse_event_closure('dblclick')\n", " );\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband_canvas.addEventListener(\n", " 'mousemove',\n", " on_mouse_event_closure('motion_notify')\n", " );\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mouseenter',\n", " on_mouse_event_closure('figure_enter')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseleave',\n", " on_mouse_event_closure('figure_leave')\n", " );\n", "\n", " canvas_div.addEventListener('wheel', function (event) {\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " on_mouse_event_closure('scroll')(event);\n", " });\n", "\n", " canvas_div.appendChild(canvas);\n", " canvas_div.appendChild(rubberband_canvas);\n", "\n", " this.rubberband_context = rubberband_canvas.getContext('2d');\n", " this.rubberband_context.strokeStyle = '#000000';\n", "\n", " this._resize_canvas = function (width, height, forward) {\n", " if (forward) {\n", " canvas_div.style.width = width + 'px';\n", " canvas_div.style.height = height + 'px';\n", " }\n", " };\n", "\n", " // Disable right mouse context menu.\n", " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", " event.preventDefault();\n", " return false;\n", " });\n", "\n", " function set_focus() {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'mpl-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " continue;\n", " }\n", "\n", " var button = (fig.buttons[name] = document.createElement('button'));\n", " button.classList = 'mpl-widget';\n", " button.setAttribute('role', 'button');\n", " button.setAttribute('aria-disabled', 'false');\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", "\n", " var icon_img = document.createElement('img');\n", " icon_img.src = '_images/' + image + '.png';\n", " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", " icon_img.alt = tooltip;\n", " button.appendChild(icon_img);\n", "\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " var fmt_picker = document.createElement('select');\n", " fmt_picker.classList = 'mpl-widget';\n", " toolbar.appendChild(fmt_picker);\n", " this.format_dropdown = fmt_picker;\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = document.createElement('option');\n", " option.selected = fmt === mpl.default_extension;\n", " option.innerHTML = fmt;\n", " fmt_picker.appendChild(option);\n", " }\n", "\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "};\n", "\n", "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", "};\n", "\n", "mpl.figure.prototype.send_message = function (type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "};\n", "\n", "mpl.figure.prototype.send_draw_message = function () {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "};\n", "\n", "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1], msg['forward']);\n", " fig.send_message('refresh', {});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", " var x0 = msg['x0'] / fig.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", " var x1 = msg['x1'] / fig.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0,\n", " 0,\n", " fig.canvas.width / fig.ratio,\n", " fig.canvas.height / fig.ratio\n", " );\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "};\n", "\n", "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "};\n", "\n", "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch (cursor) {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "};\n", "\n", "mpl.figure.prototype.handle_message = function (fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "};\n", "\n", "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "};\n", "\n", "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "};\n", "\n", "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", " for (var key in msg) {\n", " if (!(key in fig.buttons)) {\n", " continue;\n", " }\n", " fig.buttons[key].disabled = !msg[key];\n", " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", " if (msg['mode'] === 'PAN') {\n", " fig.buttons['Pan'].classList.add('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " } else if (msg['mode'] === 'ZOOM') {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.add('active');\n", " } else {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " }\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Called whenever the canvas gets updated.\n", " this.send_message('ack', {});\n", "};\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function (fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " var img = evt.data;\n", " if (img.type !== 'image/png') {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " img.type = 'image/png';\n", " }\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src\n", " );\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " img\n", " );\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " } else if (\n", " typeof evt.data === 'string' &&\n", " evt.data.slice(0, 21) === 'data:image/png;base64'\n", " ) {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig['handle_' + msg_type];\n", " } catch (e) {\n", " console.log(\n", " \"No handler for the '\" + msg_type + \"' message type: \",\n", " msg\n", " );\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\n", " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", " e,\n", " e.stack,\n", " msg\n", " );\n", " }\n", " }\n", " };\n", "};\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function (e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e) {\n", " e = window.event;\n", " }\n", " if (e.target) {\n", " targ = e.target;\n", " } else if (e.srcElement) {\n", " targ = e.srcElement;\n", " }\n", " if (targ.nodeType === 3) {\n", " // defeat Safari bug\n", " targ = targ.parentNode;\n", " }\n", "\n", " // pageX,Y are the mouse positions relative to the document\n", " var boundingRect = targ.getBoundingClientRect();\n", " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", "\n", " return { x: x, y: y };\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys(original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object') {\n", " obj[key] = original[key];\n", " }\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function (event, name) {\n", " var canvas_pos = mpl.findpos(event);\n", "\n", " if (name === 'button_press') {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * this.ratio;\n", " var y = canvas_pos.y * this.ratio;\n", "\n", " this.send_message(name, {\n", " x: x,\n", " y: y,\n", " button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event),\n", " });\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", " // Handle any extra behaviour associated with a key event\n", "};\n", "\n", "mpl.figure.prototype.key_event = function (event, name) {\n", " // Prevent repeat events\n", " if (name === 'key_press') {\n", " if (event.key === this._key) {\n", " return;\n", " } else {\n", " this._key = event.key;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.key !== 'Control') {\n", " value += 'ctrl+';\n", " }\n", " else if (event.altKey && event.key !== 'Alt') {\n", " value += 'alt+';\n", " }\n", " else if (event.shiftKey && event.key !== 'Shift') {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k' + event.key;\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", " if (name === 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message('toolbar_button', { name: name });\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "\n", "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", "// prettier-ignore\n", "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* global mpl */\n", "\n", "var comm_websocket_adapter = function (comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.binaryType = comm.kernel.ws.binaryType;\n", " ws.readyState = comm.kernel.ws.readyState;\n", " function updateReadyState(_event) {\n", " if (comm.kernel.ws) {\n", " ws.readyState = comm.kernel.ws.readyState;\n", " } else {\n", " ws.readyState = 3; // Closed state.\n", " }\n", " }\n", " comm.kernel.ws.addEventListener('open', updateReadyState);\n", " comm.kernel.ws.addEventListener('close', updateReadyState);\n", " comm.kernel.ws.addEventListener('error', updateReadyState);\n", "\n", " ws.close = function () {\n", " comm.close();\n", " };\n", " ws.send = function (m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function (msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " var data = msg['content']['data'];\n", " if (data['blob'] !== undefined) {\n", " data = {\n", " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", " };\n", " }\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(data);\n", " });\n", " return ws;\n", "};\n", "\n", "mpl.mpl_figure_comm = function (comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = document.getElementById(id);\n", " var ws_proxy = comm_websocket_adapter(comm);\n", "\n", " function ondownload(figure, _format) {\n", " window.open(figure.canvas.toDataURL());\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element;\n", " fig.cell_info = mpl.find_output_cell(\"
\");\n", " if (!fig.cell_info) {\n", " console.error('Failed to find cell for figure', id, fig);\n", " return;\n", " }\n", " fig.cell_info[0].output_area.element.on(\n", " 'cleared',\n", " { fig: fig },\n", " fig._remove_fig_handler\n", " );\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function (fig, msg) {\n", " var width = fig.canvas.width / fig.ratio;\n", " fig.cell_info[0].output_area.element.off(\n", " 'cleared',\n", " fig._remove_fig_handler\n", " );\n", " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable();\n", " fig.parent_element.innerHTML =\n", " '';\n", " fig.close_ws(fig, msg);\n", "};\n", "\n", "mpl.figure.prototype.close_ws = function (fig, msg) {\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "};\n", "\n", "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width / this.ratio;\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] =\n", " '';\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message('ack', {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () {\n", " fig.push_to_output();\n", " }, 1000);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'btn-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " var button;\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " continue;\n", " }\n", "\n", " button = fig.buttons[name] = document.createElement('button');\n", " button.classList = 'btn btn-default';\n", " button.href = '#';\n", " button.title = name;\n", " button.innerHTML = '';\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message pull-right';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "\n", " // Add the close button to the window.\n", " var buttongrp = document.createElement('div');\n", " buttongrp.classList = 'btn-group inline pull-right';\n", " button = document.createElement('button');\n", " button.classList = 'btn btn-mini btn-primary';\n", " button.href = '#';\n", " button.title = 'Stop Interaction';\n", " button.innerHTML = '';\n", " button.addEventListener('click', function (_evt) {\n", " fig.handle_close(fig, {});\n", " });\n", " button.addEventListener(\n", " 'mouseover',\n", " on_mouseover_closure('Stop Interaction')\n", " );\n", " buttongrp.appendChild(button);\n", " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", "};\n", "\n", "mpl.figure.prototype._remove_fig_handler = function (event) {\n", " var fig = event.data.fig;\n", " if (event.target !== this) {\n", " // Ignore bubbled events from children.\n", " return;\n", " }\n", " fig.close_ws(fig, {});\n", "};\n", "\n", "mpl.figure.prototype._root_extra_style = function (el) {\n", " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (el) {\n", " // this is important to make the div 'focusable\n", " el.setAttribute('tabindex', 0);\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " } else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager) {\n", " manager = IPython.keyboard_manager;\n", " }\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which === 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " fig.ondownload(fig, null);\n", "};\n", "\n", "mpl.find_output_cell = function (html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i = 0; i < ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code') {\n", " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] === html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "};\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel !== null) {\n", " IPython.notebook.kernel.comm_manager.register_target(\n", " 'matplotlib',\n", " mpl.mpl_figure_comm\n", " );\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "input_spectrum = np.zeros([512])\n", "x = np.mgrid[0:32] * 16\n", "input_spectrum[x] = 1\n", "\n", "dataset = sidpy.Dataset.from_array(input_spectrum)\n", "dataset.data_type = 'spectrum'\n", "dataset.units = 'counts'\n", "dataset.quantity = 'intensity'\n", "dataset.title = 'periodic'\n", "\n", "dataset.set_dimension(0, sidpy.Dimension(np.arange(dataset.shape[0]) * .02, 'x'))\n", "dataset.x.dimension_type = 'spectral'\n", "dataset.x.units = 'A'\n", "dataset.x.quantity = 'current'\n", "\n", "dataset.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Power Spectrum" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "/* global mpl */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function () {\n", " if (typeof WebSocket !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof MozWebSocket !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert(\n", " 'Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.'\n", " );\n", " }\n", "};\n", "\n", "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = this.ws.binaryType !== undefined;\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById('mpl-warnings');\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent =\n", " 'This browser does not support binary websocket messages. ' +\n", " 'Performance may be slow.';\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = document.createElement('div');\n", " this.root.setAttribute('style', 'display: inline-block');\n", " this._root_extra_style(this.root);\n", "\n", " parent_element.appendChild(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message('supports_binary', { value: fig.supports_binary });\n", " fig.send_message('send_image_mode', {});\n", " if (fig.ratio !== 1) {\n", " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", " }\n", " fig.send_message('refresh', {});\n", " };\n", "\n", " this.imageObj.onload = function () {\n", " if (fig.image_mode === 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function () {\n", " fig.ws.close();\n", " };\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "};\n", "\n", "mpl.figure.prototype._init_header = function () {\n", " var titlebar = document.createElement('div');\n", " titlebar.classList =\n", " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", " var titletext = document.createElement('div');\n", " titletext.classList = 'ui-dialog-title';\n", " titletext.setAttribute(\n", " 'style',\n", " 'width: 100%; text-align: center; padding: 3px;'\n", " );\n", " titlebar.appendChild(titletext);\n", " this.root.appendChild(titlebar);\n", " this.header = titletext;\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._init_canvas = function () {\n", " var fig = this;\n", "\n", " var canvas_div = (this.canvas_div = document.createElement('div'));\n", " canvas_div.setAttribute(\n", " 'style',\n", " 'border: 1px solid #ddd;' +\n", " 'box-sizing: content-box;' +\n", " 'clear: both;' +\n", " 'min-height: 1px;' +\n", " 'min-width: 1px;' +\n", " 'outline: 0;' +\n", " 'overflow: hidden;' +\n", " 'position: relative;' +\n", " 'resize: both;'\n", " );\n", "\n", " function on_keyboard_event_closure(name) {\n", " return function (event) {\n", " return fig.key_event(event, name);\n", " };\n", " }\n", "\n", " canvas_div.addEventListener(\n", " 'keydown',\n", " on_keyboard_event_closure('key_press')\n", " );\n", " canvas_div.addEventListener(\n", " 'keyup',\n", " on_keyboard_event_closure('key_release')\n", " );\n", "\n", " this._canvas_extra_style(canvas_div);\n", " this.root.appendChild(canvas_div);\n", "\n", " var canvas = (this.canvas = document.createElement('canvas'));\n", " canvas.classList.add('mpl-canvas');\n", " canvas.setAttribute('style', 'box-sizing: content-box;');\n", "\n", " this.context = canvas.getContext('2d');\n", "\n", " var backingStore =\n", " this.context.backingStorePixelRatio ||\n", " this.context.webkitBackingStorePixelRatio ||\n", " this.context.mozBackingStorePixelRatio ||\n", " this.context.msBackingStorePixelRatio ||\n", " this.context.oBackingStorePixelRatio ||\n", " this.context.backingStorePixelRatio ||\n", " 1;\n", "\n", " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", " 'canvas'\n", " ));\n", " rubberband_canvas.setAttribute(\n", " 'style',\n", " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", " );\n", "\n", " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", " if (this.ResizeObserver === undefined) {\n", " if (window.ResizeObserver !== undefined) {\n", " this.ResizeObserver = window.ResizeObserver;\n", " } else {\n", " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", " this.ResizeObserver = obs.ResizeObserver;\n", " }\n", " }\n", "\n", " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", " var nentries = entries.length;\n", " for (var i = 0; i < nentries; i++) {\n", " var entry = entries[i];\n", " var width, height;\n", " if (entry.contentBoxSize) {\n", " if (entry.contentBoxSize instanceof Array) {\n", " // Chrome 84 implements new version of spec.\n", " width = entry.contentBoxSize[0].inlineSize;\n", " height = entry.contentBoxSize[0].blockSize;\n", " } else {\n", " // Firefox implements old version of spec.\n", " width = entry.contentBoxSize.inlineSize;\n", " height = entry.contentBoxSize.blockSize;\n", " }\n", " } else {\n", " // Chrome <84 implements even older version of spec.\n", " width = entry.contentRect.width;\n", " height = entry.contentRect.height;\n", " }\n", "\n", " // Keep the size of the canvas and rubber band canvas in sync with\n", " // the canvas container.\n", " if (entry.devicePixelContentBoxSize) {\n", " // Chrome 84 implements new version of spec.\n", " canvas.setAttribute(\n", " 'width',\n", " entry.devicePixelContentBoxSize[0].inlineSize\n", " );\n", " canvas.setAttribute(\n", " 'height',\n", " entry.devicePixelContentBoxSize[0].blockSize\n", " );\n", " } else {\n", " canvas.setAttribute('width', width * fig.ratio);\n", " canvas.setAttribute('height', height * fig.ratio);\n", " }\n", " canvas.setAttribute(\n", " 'style',\n", " 'width: ' + width + 'px; height: ' + height + 'px;'\n", " );\n", "\n", " rubberband_canvas.setAttribute('width', width);\n", " rubberband_canvas.setAttribute('height', height);\n", "\n", " // And update the size in Python. We ignore the initial 0/0 size\n", " // that occurs as the element is placed into the DOM, which should\n", " // otherwise not happen due to the minimum size styling.\n", " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", " fig.request_resize(width, height);\n", " }\n", " }\n", " });\n", " this.resizeObserverInstance.observe(canvas_div);\n", "\n", " function on_mouse_event_closure(name) {\n", " return function (event) {\n", " return fig.mouse_event(event, name);\n", " };\n", " }\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mousedown',\n", " on_mouse_event_closure('button_press')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseup',\n", " on_mouse_event_closure('button_release')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'dblclick',\n", " on_mouse_event_closure('dblclick')\n", " );\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband_canvas.addEventListener(\n", " 'mousemove',\n", " on_mouse_event_closure('motion_notify')\n", " );\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mouseenter',\n", " on_mouse_event_closure('figure_enter')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseleave',\n", " on_mouse_event_closure('figure_leave')\n", " );\n", "\n", " canvas_div.addEventListener('wheel', function (event) {\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " on_mouse_event_closure('scroll')(event);\n", " });\n", "\n", " canvas_div.appendChild(canvas);\n", " canvas_div.appendChild(rubberband_canvas);\n", "\n", " this.rubberband_context = rubberband_canvas.getContext('2d');\n", " this.rubberband_context.strokeStyle = '#000000';\n", "\n", " this._resize_canvas = function (width, height, forward) {\n", " if (forward) {\n", " canvas_div.style.width = width + 'px';\n", " canvas_div.style.height = height + 'px';\n", " }\n", " };\n", "\n", " // Disable right mouse context menu.\n", " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", " event.preventDefault();\n", " return false;\n", " });\n", "\n", " function set_focus() {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'mpl-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " continue;\n", " }\n", "\n", " var button = (fig.buttons[name] = document.createElement('button'));\n", " button.classList = 'mpl-widget';\n", " button.setAttribute('role', 'button');\n", " button.setAttribute('aria-disabled', 'false');\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", "\n", " var icon_img = document.createElement('img');\n", " icon_img.src = '_images/' + image + '.png';\n", " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", " icon_img.alt = tooltip;\n", " button.appendChild(icon_img);\n", "\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " var fmt_picker = document.createElement('select');\n", " fmt_picker.classList = 'mpl-widget';\n", " toolbar.appendChild(fmt_picker);\n", " this.format_dropdown = fmt_picker;\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = document.createElement('option');\n", " option.selected = fmt === mpl.default_extension;\n", " option.innerHTML = fmt;\n", " fmt_picker.appendChild(option);\n", " }\n", "\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "};\n", "\n", "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", "};\n", "\n", "mpl.figure.prototype.send_message = function (type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "};\n", "\n", "mpl.figure.prototype.send_draw_message = function () {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "};\n", "\n", "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1], msg['forward']);\n", " fig.send_message('refresh', {});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", " var x0 = msg['x0'] / fig.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", " var x1 = msg['x1'] / fig.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0,\n", " 0,\n", " fig.canvas.width / fig.ratio,\n", " fig.canvas.height / fig.ratio\n", " );\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "};\n", "\n", "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "};\n", "\n", "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch (cursor) {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "};\n", "\n", "mpl.figure.prototype.handle_message = function (fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "};\n", "\n", "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "};\n", "\n", "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "};\n", "\n", "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", " for (var key in msg) {\n", " if (!(key in fig.buttons)) {\n", " continue;\n", " }\n", " fig.buttons[key].disabled = !msg[key];\n", " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", " if (msg['mode'] === 'PAN') {\n", " fig.buttons['Pan'].classList.add('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " } else if (msg['mode'] === 'ZOOM') {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.add('active');\n", " } else {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " }\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Called whenever the canvas gets updated.\n", " this.send_message('ack', {});\n", "};\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function (fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " var img = evt.data;\n", " if (img.type !== 'image/png') {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " img.type = 'image/png';\n", " }\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src\n", " );\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " img\n", " );\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " } else if (\n", " typeof evt.data === 'string' &&\n", " evt.data.slice(0, 21) === 'data:image/png;base64'\n", " ) {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig['handle_' + msg_type];\n", " } catch (e) {\n", " console.log(\n", " \"No handler for the '\" + msg_type + \"' message type: \",\n", " msg\n", " );\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\n", " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", " e,\n", " e.stack,\n", " msg\n", " );\n", " }\n", " }\n", " };\n", "};\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function (e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e) {\n", " e = window.event;\n", " }\n", " if (e.target) {\n", " targ = e.target;\n", " } else if (e.srcElement) {\n", " targ = e.srcElement;\n", " }\n", " if (targ.nodeType === 3) {\n", " // defeat Safari bug\n", " targ = targ.parentNode;\n", " }\n", "\n", " // pageX,Y are the mouse positions relative to the document\n", " var boundingRect = targ.getBoundingClientRect();\n", " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", "\n", " return { x: x, y: y };\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys(original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object') {\n", " obj[key] = original[key];\n", " }\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function (event, name) {\n", " var canvas_pos = mpl.findpos(event);\n", "\n", " if (name === 'button_press') {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * this.ratio;\n", " var y = canvas_pos.y * this.ratio;\n", "\n", " this.send_message(name, {\n", " x: x,\n", " y: y,\n", " button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event),\n", " });\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", " // Handle any extra behaviour associated with a key event\n", "};\n", "\n", "mpl.figure.prototype.key_event = function (event, name) {\n", " // Prevent repeat events\n", " if (name === 'key_press') {\n", " if (event.key === this._key) {\n", " return;\n", " } else {\n", " this._key = event.key;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.key !== 'Control') {\n", " value += 'ctrl+';\n", " }\n", " else if (event.altKey && event.key !== 'Alt') {\n", " value += 'alt+';\n", " }\n", " else if (event.shiftKey && event.key !== 'Shift') {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k' + event.key;\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", " if (name === 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message('toolbar_button', { name: name });\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "\n", "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", "// prettier-ignore\n", "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* global mpl */\n", "\n", "var comm_websocket_adapter = function (comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.binaryType = comm.kernel.ws.binaryType;\n", " ws.readyState = comm.kernel.ws.readyState;\n", " function updateReadyState(_event) {\n", " if (comm.kernel.ws) {\n", " ws.readyState = comm.kernel.ws.readyState;\n", " } else {\n", " ws.readyState = 3; // Closed state.\n", " }\n", " }\n", " comm.kernel.ws.addEventListener('open', updateReadyState);\n", " comm.kernel.ws.addEventListener('close', updateReadyState);\n", " comm.kernel.ws.addEventListener('error', updateReadyState);\n", "\n", " ws.close = function () {\n", " comm.close();\n", " };\n", " ws.send = function (m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function (msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " var data = msg['content']['data'];\n", " if (data['blob'] !== undefined) {\n", " data = {\n", " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", " };\n", " }\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(data);\n", " });\n", " return ws;\n", "};\n", "\n", "mpl.mpl_figure_comm = function (comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = document.getElementById(id);\n", " var ws_proxy = comm_websocket_adapter(comm);\n", "\n", " function ondownload(figure, _format) {\n", " window.open(figure.canvas.toDataURL());\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element;\n", " fig.cell_info = mpl.find_output_cell(\"
\");\n", " if (!fig.cell_info) {\n", " console.error('Failed to find cell for figure', id, fig);\n", " return;\n", " }\n", " fig.cell_info[0].output_area.element.on(\n", " 'cleared',\n", " { fig: fig },\n", " fig._remove_fig_handler\n", " );\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function (fig, msg) {\n", " var width = fig.canvas.width / fig.ratio;\n", " fig.cell_info[0].output_area.element.off(\n", " 'cleared',\n", " fig._remove_fig_handler\n", " );\n", " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable();\n", " fig.parent_element.innerHTML =\n", " '';\n", " fig.close_ws(fig, msg);\n", "};\n", "\n", "mpl.figure.prototype.close_ws = function (fig, msg) {\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "};\n", "\n", "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width / this.ratio;\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] =\n", " '';\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message('ack', {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () {\n", " fig.push_to_output();\n", " }, 1000);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'btn-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " var button;\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " continue;\n", " }\n", "\n", " button = fig.buttons[name] = document.createElement('button');\n", " button.classList = 'btn btn-default';\n", " button.href = '#';\n", " button.title = name;\n", " button.innerHTML = '';\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message pull-right';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "\n", " // Add the close button to the window.\n", " var buttongrp = document.createElement('div');\n", " buttongrp.classList = 'btn-group inline pull-right';\n", " button = document.createElement('button');\n", " button.classList = 'btn btn-mini btn-primary';\n", " button.href = '#';\n", " button.title = 'Stop Interaction';\n", " button.innerHTML = '';\n", " button.addEventListener('click', function (_evt) {\n", " fig.handle_close(fig, {});\n", " });\n", " button.addEventListener(\n", " 'mouseover',\n", " on_mouseover_closure('Stop Interaction')\n", " );\n", " buttongrp.appendChild(button);\n", " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", "};\n", "\n", "mpl.figure.prototype._remove_fig_handler = function (event) {\n", " var fig = event.data.fig;\n", " if (event.target !== this) {\n", " // Ignore bubbled events from children.\n", " return;\n", " }\n", " fig.close_ws(fig, {});\n", "};\n", "\n", "mpl.figure.prototype._root_extra_style = function (el) {\n", " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (el) {\n", " // this is important to make the div 'focusable\n", " el.setAttribute('tabindex', 0);\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " } else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager) {\n", " manager = IPython.keyboard_manager;\n", " }\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which === 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " fig.ondownload(fig, null);\n", "};\n", "\n", "mpl.find_output_cell = function (html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i = 0; i < ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code') {\n", " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] === html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "};\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel !== null) {\n", " IPython.notebook.kernel.comm_manager.register_target(\n", " 'matplotlib',\n", " mpl.mpl_figure_comm\n", " );\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fft_dataset = dataset.fft().abs()/5\n", "fft_dataset.title= 'Fourier transform of ' + dataset.title\n", "fft_dataset.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Spectrum Image" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "/* global mpl */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function () {\n", " if (typeof WebSocket !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof MozWebSocket !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert(\n", " 'Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.'\n", " );\n", " }\n", "};\n", "\n", "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = this.ws.binaryType !== undefined;\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById('mpl-warnings');\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent =\n", " 'This browser does not support binary websocket messages. ' +\n", " 'Performance may be slow.';\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = document.createElement('div');\n", " this.root.setAttribute('style', 'display: inline-block');\n", " this._root_extra_style(this.root);\n", "\n", " parent_element.appendChild(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message('supports_binary', { value: fig.supports_binary });\n", " fig.send_message('send_image_mode', {});\n", " if (fig.ratio !== 1) {\n", " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", " }\n", " fig.send_message('refresh', {});\n", " };\n", "\n", " this.imageObj.onload = function () {\n", " if (fig.image_mode === 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function () {\n", " fig.ws.close();\n", " };\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "};\n", "\n", "mpl.figure.prototype._init_header = function () {\n", " var titlebar = document.createElement('div');\n", " titlebar.classList =\n", " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", " var titletext = document.createElement('div');\n", " titletext.classList = 'ui-dialog-title';\n", " titletext.setAttribute(\n", " 'style',\n", " 'width: 100%; text-align: center; padding: 3px;'\n", " );\n", " titlebar.appendChild(titletext);\n", " this.root.appendChild(titlebar);\n", " this.header = titletext;\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._init_canvas = function () {\n", " var fig = this;\n", "\n", " var canvas_div = (this.canvas_div = document.createElement('div'));\n", " canvas_div.setAttribute(\n", " 'style',\n", " 'border: 1px solid #ddd;' +\n", " 'box-sizing: content-box;' +\n", " 'clear: both;' +\n", " 'min-height: 1px;' +\n", " 'min-width: 1px;' +\n", " 'outline: 0;' +\n", " 'overflow: hidden;' +\n", " 'position: relative;' +\n", " 'resize: both;'\n", " );\n", "\n", " function on_keyboard_event_closure(name) {\n", " return function (event) {\n", " return fig.key_event(event, name);\n", " };\n", " }\n", "\n", " canvas_div.addEventListener(\n", " 'keydown',\n", " on_keyboard_event_closure('key_press')\n", " );\n", " canvas_div.addEventListener(\n", " 'keyup',\n", " on_keyboard_event_closure('key_release')\n", " );\n", "\n", " this._canvas_extra_style(canvas_div);\n", " this.root.appendChild(canvas_div);\n", "\n", " var canvas = (this.canvas = document.createElement('canvas'));\n", " canvas.classList.add('mpl-canvas');\n", " canvas.setAttribute('style', 'box-sizing: content-box;');\n", "\n", " this.context = canvas.getContext('2d');\n", "\n", " var backingStore =\n", " this.context.backingStorePixelRatio ||\n", " this.context.webkitBackingStorePixelRatio ||\n", " this.context.mozBackingStorePixelRatio ||\n", " this.context.msBackingStorePixelRatio ||\n", " this.context.oBackingStorePixelRatio ||\n", " this.context.backingStorePixelRatio ||\n", " 1;\n", "\n", " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", " 'canvas'\n", " ));\n", " rubberband_canvas.setAttribute(\n", " 'style',\n", " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", " );\n", "\n", " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", " if (this.ResizeObserver === undefined) {\n", " if (window.ResizeObserver !== undefined) {\n", " this.ResizeObserver = window.ResizeObserver;\n", " } else {\n", " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", " this.ResizeObserver = obs.ResizeObserver;\n", " }\n", " }\n", "\n", " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", " var nentries = entries.length;\n", " for (var i = 0; i < nentries; i++) {\n", " var entry = entries[i];\n", " var width, height;\n", " if (entry.contentBoxSize) {\n", " if (entry.contentBoxSize instanceof Array) {\n", " // Chrome 84 implements new version of spec.\n", " width = entry.contentBoxSize[0].inlineSize;\n", " height = entry.contentBoxSize[0].blockSize;\n", " } else {\n", " // Firefox implements old version of spec.\n", " width = entry.contentBoxSize.inlineSize;\n", " height = entry.contentBoxSize.blockSize;\n", " }\n", " } else {\n", " // Chrome <84 implements even older version of spec.\n", " width = entry.contentRect.width;\n", " height = entry.contentRect.height;\n", " }\n", "\n", " // Keep the size of the canvas and rubber band canvas in sync with\n", " // the canvas container.\n", " if (entry.devicePixelContentBoxSize) {\n", " // Chrome 84 implements new version of spec.\n", " canvas.setAttribute(\n", " 'width',\n", " entry.devicePixelContentBoxSize[0].inlineSize\n", " );\n", " canvas.setAttribute(\n", " 'height',\n", " entry.devicePixelContentBoxSize[0].blockSize\n", " );\n", " } else {\n", " canvas.setAttribute('width', width * fig.ratio);\n", " canvas.setAttribute('height', height * fig.ratio);\n", " }\n", " canvas.setAttribute(\n", " 'style',\n", " 'width: ' + width + 'px; height: ' + height + 'px;'\n", " );\n", "\n", " rubberband_canvas.setAttribute('width', width);\n", " rubberband_canvas.setAttribute('height', height);\n", "\n", " // And update the size in Python. We ignore the initial 0/0 size\n", " // that occurs as the element is placed into the DOM, which should\n", " // otherwise not happen due to the minimum size styling.\n", " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", " fig.request_resize(width, height);\n", " }\n", " }\n", " });\n", " this.resizeObserverInstance.observe(canvas_div);\n", "\n", " function on_mouse_event_closure(name) {\n", " return function (event) {\n", " return fig.mouse_event(event, name);\n", " };\n", " }\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mousedown',\n", " on_mouse_event_closure('button_press')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseup',\n", " on_mouse_event_closure('button_release')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'dblclick',\n", " on_mouse_event_closure('dblclick')\n", " );\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband_canvas.addEventListener(\n", " 'mousemove',\n", " on_mouse_event_closure('motion_notify')\n", " );\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mouseenter',\n", " on_mouse_event_closure('figure_enter')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseleave',\n", " on_mouse_event_closure('figure_leave')\n", " );\n", "\n", " canvas_div.addEventListener('wheel', function (event) {\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " on_mouse_event_closure('scroll')(event);\n", " });\n", "\n", " canvas_div.appendChild(canvas);\n", " canvas_div.appendChild(rubberband_canvas);\n", "\n", " this.rubberband_context = rubberband_canvas.getContext('2d');\n", " this.rubberband_context.strokeStyle = '#000000';\n", "\n", " this._resize_canvas = function (width, height, forward) {\n", " if (forward) {\n", " canvas_div.style.width = width + 'px';\n", " canvas_div.style.height = height + 'px';\n", " }\n", " };\n", "\n", " // Disable right mouse context menu.\n", " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", " event.preventDefault();\n", " return false;\n", " });\n", "\n", " function set_focus() {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'mpl-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " continue;\n", " }\n", "\n", " var button = (fig.buttons[name] = document.createElement('button'));\n", " button.classList = 'mpl-widget';\n", " button.setAttribute('role', 'button');\n", " button.setAttribute('aria-disabled', 'false');\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", "\n", " var icon_img = document.createElement('img');\n", " icon_img.src = '_images/' + image + '.png';\n", " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", " icon_img.alt = tooltip;\n", " button.appendChild(icon_img);\n", "\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " var fmt_picker = document.createElement('select');\n", " fmt_picker.classList = 'mpl-widget';\n", " toolbar.appendChild(fmt_picker);\n", " this.format_dropdown = fmt_picker;\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = document.createElement('option');\n", " option.selected = fmt === mpl.default_extension;\n", " option.innerHTML = fmt;\n", " fmt_picker.appendChild(option);\n", " }\n", "\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "};\n", "\n", "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", "};\n", "\n", "mpl.figure.prototype.send_message = function (type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "};\n", "\n", "mpl.figure.prototype.send_draw_message = function () {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "};\n", "\n", "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1], msg['forward']);\n", " fig.send_message('refresh', {});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", " var x0 = msg['x0'] / fig.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", " var x1 = msg['x1'] / fig.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0,\n", " 0,\n", " fig.canvas.width / fig.ratio,\n", " fig.canvas.height / fig.ratio\n", " );\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "};\n", "\n", "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "};\n", "\n", "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch (cursor) {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "};\n", "\n", "mpl.figure.prototype.handle_message = function (fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "};\n", "\n", "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "};\n", "\n", "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "};\n", "\n", "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", " for (var key in msg) {\n", " if (!(key in fig.buttons)) {\n", " continue;\n", " }\n", " fig.buttons[key].disabled = !msg[key];\n", " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", " if (msg['mode'] === 'PAN') {\n", " fig.buttons['Pan'].classList.add('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " } else if (msg['mode'] === 'ZOOM') {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.add('active');\n", " } else {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " }\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Called whenever the canvas gets updated.\n", " this.send_message('ack', {});\n", "};\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function (fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " var img = evt.data;\n", " if (img.type !== 'image/png') {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " img.type = 'image/png';\n", " }\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src\n", " );\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " img\n", " );\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " } else if (\n", " typeof evt.data === 'string' &&\n", " evt.data.slice(0, 21) === 'data:image/png;base64'\n", " ) {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig['handle_' + msg_type];\n", " } catch (e) {\n", " console.log(\n", " \"No handler for the '\" + msg_type + \"' message type: \",\n", " msg\n", " );\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\n", " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", " e,\n", " e.stack,\n", " msg\n", " );\n", " }\n", " }\n", " };\n", "};\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function (e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e) {\n", " e = window.event;\n", " }\n", " if (e.target) {\n", " targ = e.target;\n", " } else if (e.srcElement) {\n", " targ = e.srcElement;\n", " }\n", " if (targ.nodeType === 3) {\n", " // defeat Safari bug\n", " targ = targ.parentNode;\n", " }\n", "\n", " // pageX,Y are the mouse positions relative to the document\n", " var boundingRect = targ.getBoundingClientRect();\n", " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", "\n", " return { x: x, y: y };\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys(original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object') {\n", " obj[key] = original[key];\n", " }\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function (event, name) {\n", " var canvas_pos = mpl.findpos(event);\n", "\n", " if (name === 'button_press') {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * this.ratio;\n", " var y = canvas_pos.y * this.ratio;\n", "\n", " this.send_message(name, {\n", " x: x,\n", " y: y,\n", " button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event),\n", " });\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", " // Handle any extra behaviour associated with a key event\n", "};\n", "\n", "mpl.figure.prototype.key_event = function (event, name) {\n", " // Prevent repeat events\n", " if (name === 'key_press') {\n", " if (event.key === this._key) {\n", " return;\n", " } else {\n", " this._key = event.key;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.key !== 'Control') {\n", " value += 'ctrl+';\n", " }\n", " else if (event.altKey && event.key !== 'Alt') {\n", " value += 'alt+';\n", " }\n", " else if (event.shiftKey && event.key !== 'Shift') {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k' + event.key;\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", " if (name === 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message('toolbar_button', { name: name });\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "\n", "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", "// prettier-ignore\n", "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* global mpl */\n", "\n", "var comm_websocket_adapter = function (comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. 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\");\n", " if (!fig.cell_info) {\n", " console.error('Failed to find cell for figure', id, fig);\n", " return;\n", " }\n", " fig.cell_info[0].output_area.element.on(\n", " 'cleared',\n", " { fig: fig },\n", " fig._remove_fig_handler\n", " );\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function (fig, msg) {\n", " var width = fig.canvas.width / fig.ratio;\n", " fig.cell_info[0].output_area.element.off(\n", " 'cleared',\n", " fig._remove_fig_handler\n", " );\n", " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable();\n", " fig.parent_element.innerHTML =\n", " '';\n", " fig.close_ws(fig, msg);\n", "};\n", "\n", "mpl.figure.prototype.close_ws = function (fig, msg) {\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "};\n", "\n", "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width / this.ratio;\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] =\n", " '';\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message('ack', {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () {\n", " fig.push_to_output();\n", " }, 1000);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'btn-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " var button;\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " continue;\n", " }\n", "\n", " button = fig.buttons[name] = document.createElement('button');\n", " button.classList = 'btn btn-default';\n", " button.href = '#';\n", " button.title = name;\n", " button.innerHTML = '';\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message pull-right';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "\n", " // Add the close button to the window.\n", " var buttongrp = document.createElement('div');\n", " buttongrp.classList = 'btn-group inline pull-right';\n", " button = document.createElement('button');\n", " button.classList = 'btn btn-mini btn-primary';\n", " button.href = '#';\n", " button.title = 'Stop Interaction';\n", " button.innerHTML = '';\n", " button.addEventListener('click', function (_evt) {\n", " fig.handle_close(fig, {});\n", " });\n", " button.addEventListener(\n", " 'mouseover',\n", " on_mouseover_closure('Stop Interaction')\n", " );\n", " buttongrp.appendChild(button);\n", " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", "};\n", "\n", "mpl.figure.prototype._remove_fig_handler = function (event) {\n", " var fig = event.data.fig;\n", " if (event.target !== this) {\n", " // Ignore bubbled events from children.\n", " return;\n", " }\n", " fig.close_ws(fig, {});\n", "};\n", "\n", "mpl.figure.prototype._root_extra_style = function (el) {\n", " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (el) {\n", " // this is important to make the div 'focusable\n", " el.setAttribute('tabindex', 0);\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " } else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager) {\n", " manager = IPython.keyboard_manager;\n", " }\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which === 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " fig.ondownload(fig, null);\n", "};\n", "\n", "mpl.find_output_cell = function (html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i = 0; i < ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code') {\n", " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] === html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "};\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel !== null) {\n", " IPython.notebook.kernel.comm_manager.register_target(\n", " 'matplotlib',\n", " mpl.mpl_figure_comm\n", " );\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "../..\\sidpy\\viz\\dataset_viz.py:484: MatplotlibDeprecationWarning: \n", "The set_window_title function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use manager.set_window_title or GUI-specific methods instead.\n", " self.fig.canvas.set_window_title(self.dset.title)\n" ] } ], "source": [ "input_si = np.random.normal(3, 2.5, size=( 5, 5, 512,))\n", "\n", "x = np.mgrid[0:32] * 16\n", "input_si[ :, :, x] = 20.\n", "\n", "dset = sidpy.Dataset.from_array(input_si)\n", "dset.data_type = 'spectral_image'\n", "dset.units = 'counts'\n", "dset.quantity = 'intensity'\n", "\n", "dset.set_dimension(0, sidpy.Dimension(np.arange(dset.shape[0])*.02, 'x'))\n", "dset.x.dimension_type = 'spatial'\n", "dset.x.units = 'nm'\n", "dset.x.quantity = 'distance'\n", "dset.set_dimension(1, sidpy.Dimension(np.arange(dset.shape[1])*.02, 'y'))\n", "dset.y.dimension_type = 'spatial'\n", "dset.y.units = 'nm'\n", "dset.y.quantity = 'distance'\n", "\n", "dset.set_dimension(2, sidpy.Dimension(np.arange(dset.shape[2]),'spectrum'))\n", "dset.spectrum.dimension_type = 'spectral'\n", "dset.spectrum.units = 'A'\n", "dset.spectrum.quantity = 'current'\n", "\n", "dset.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the Fourier Transform\n", "\n", "please note that we need to define the datset here or we will not have a good reference for the plot and loose interactivity immediately." ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "/* global mpl */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function () {\n", " if (typeof WebSocket !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof MozWebSocket !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert(\n", " 'Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.'\n", " );\n", " }\n", "};\n", "\n", "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = this.ws.binaryType !== undefined;\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById('mpl-warnings');\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent =\n", " 'This browser does not support binary websocket messages. ' +\n", " 'Performance may be slow.';\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = document.createElement('div');\n", " this.root.setAttribute('style', 'display: inline-block');\n", " this._root_extra_style(this.root);\n", "\n", " parent_element.appendChild(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message('supports_binary', { value: fig.supports_binary });\n", " fig.send_message('send_image_mode', {});\n", " if (fig.ratio !== 1) {\n", " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", " }\n", " fig.send_message('refresh', {});\n", " };\n", "\n", " this.imageObj.onload = function () {\n", " if (fig.image_mode === 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function () {\n", " fig.ws.close();\n", " };\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "};\n", "\n", "mpl.figure.prototype._init_header = function () {\n", " var titlebar = document.createElement('div');\n", " titlebar.classList =\n", " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", " var titletext = document.createElement('div');\n", " titletext.classList = 'ui-dialog-title';\n", " titletext.setAttribute(\n", " 'style',\n", " 'width: 100%; text-align: center; padding: 3px;'\n", " );\n", " titlebar.appendChild(titletext);\n", " this.root.appendChild(titlebar);\n", " this.header = titletext;\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", "\n", "mpl.figure.prototype._init_canvas = function () {\n", " var fig = this;\n", "\n", " var canvas_div = (this.canvas_div = document.createElement('div'));\n", " canvas_div.setAttribute(\n", " 'style',\n", " 'border: 1px solid #ddd;' +\n", " 'box-sizing: content-box;' +\n", " 'clear: both;' +\n", " 'min-height: 1px;' +\n", " 'min-width: 1px;' +\n", " 'outline: 0;' +\n", " 'overflow: hidden;' +\n", " 'position: relative;' +\n", " 'resize: both;'\n", " );\n", "\n", " function on_keyboard_event_closure(name) {\n", " return function (event) {\n", " return fig.key_event(event, name);\n", " };\n", " }\n", "\n", " canvas_div.addEventListener(\n", " 'keydown',\n", " on_keyboard_event_closure('key_press')\n", " );\n", " canvas_div.addEventListener(\n", " 'keyup',\n", " on_keyboard_event_closure('key_release')\n", " );\n", "\n", " this._canvas_extra_style(canvas_div);\n", " this.root.appendChild(canvas_div);\n", "\n", " var canvas = (this.canvas = document.createElement('canvas'));\n", " canvas.classList.add('mpl-canvas');\n", " canvas.setAttribute('style', 'box-sizing: content-box;');\n", "\n", " this.context = canvas.getContext('2d');\n", "\n", " var backingStore =\n", " this.context.backingStorePixelRatio ||\n", " this.context.webkitBackingStorePixelRatio ||\n", " this.context.mozBackingStorePixelRatio ||\n", " this.context.msBackingStorePixelRatio ||\n", " this.context.oBackingStorePixelRatio ||\n", " this.context.backingStorePixelRatio ||\n", " 1;\n", "\n", " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", " 'canvas'\n", " ));\n", " rubberband_canvas.setAttribute(\n", " 'style',\n", " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", " );\n", "\n", " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", " if (this.ResizeObserver === undefined) {\n", " if (window.ResizeObserver !== undefined) {\n", " this.ResizeObserver = window.ResizeObserver;\n", " } else {\n", " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", " this.ResizeObserver = obs.ResizeObserver;\n", " }\n", " }\n", "\n", " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", " var nentries = entries.length;\n", " for (var i = 0; i < nentries; i++) {\n", " var entry = entries[i];\n", " var width, height;\n", " if (entry.contentBoxSize) {\n", " if (entry.contentBoxSize instanceof Array) {\n", " // Chrome 84 implements new version of spec.\n", " width = entry.contentBoxSize[0].inlineSize;\n", " height = entry.contentBoxSize[0].blockSize;\n", " } else {\n", " // Firefox implements old version of spec.\n", " width = entry.contentBoxSize.inlineSize;\n", " height = entry.contentBoxSize.blockSize;\n", " }\n", " } else {\n", " // Chrome <84 implements even older version of spec.\n", " width = entry.contentRect.width;\n", " height = entry.contentRect.height;\n", " }\n", "\n", " // Keep the size of the canvas and rubber band canvas in sync with\n", " // the canvas container.\n", " if (entry.devicePixelContentBoxSize) {\n", " // Chrome 84 implements new version of spec.\n", " canvas.setAttribute(\n", " 'width',\n", " entry.devicePixelContentBoxSize[0].inlineSize\n", " );\n", " canvas.setAttribute(\n", " 'height',\n", " entry.devicePixelContentBoxSize[0].blockSize\n", " );\n", " } else {\n", " canvas.setAttribute('width', width * fig.ratio);\n", " canvas.setAttribute('height', height * fig.ratio);\n", " }\n", " canvas.setAttribute(\n", " 'style',\n", " 'width: ' + width + 'px; height: ' + height + 'px;'\n", " );\n", "\n", " rubberband_canvas.setAttribute('width', width);\n", " rubberband_canvas.setAttribute('height', height);\n", "\n", " // And update the size in Python. We ignore the initial 0/0 size\n", " // that occurs as the element is placed into the DOM, which should\n", " // otherwise not happen due to the minimum size styling.\n", " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", " fig.request_resize(width, height);\n", " }\n", " }\n", " });\n", " this.resizeObserverInstance.observe(canvas_div);\n", "\n", " function on_mouse_event_closure(name) {\n", " return function (event) {\n", " return fig.mouse_event(event, name);\n", " };\n", " }\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mousedown',\n", " on_mouse_event_closure('button_press')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseup',\n", " on_mouse_event_closure('button_release')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'dblclick',\n", " on_mouse_event_closure('dblclick')\n", " );\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband_canvas.addEventListener(\n", " 'mousemove',\n", " on_mouse_event_closure('motion_notify')\n", " );\n", "\n", " rubberband_canvas.addEventListener(\n", " 'mouseenter',\n", " on_mouse_event_closure('figure_enter')\n", " );\n", " rubberband_canvas.addEventListener(\n", " 'mouseleave',\n", " on_mouse_event_closure('figure_leave')\n", " );\n", "\n", " canvas_div.addEventListener('wheel', function (event) {\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " on_mouse_event_closure('scroll')(event);\n", " });\n", "\n", " canvas_div.appendChild(canvas);\n", " canvas_div.appendChild(rubberband_canvas);\n", "\n", " this.rubberband_context = rubberband_canvas.getContext('2d');\n", " this.rubberband_context.strokeStyle = '#000000';\n", "\n", " this._resize_canvas = function (width, height, forward) {\n", " if (forward) {\n", " canvas_div.style.width = width + 'px';\n", " canvas_div.style.height = height + 'px';\n", " }\n", " };\n", "\n", " // Disable right mouse context menu.\n", " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", " event.preventDefault();\n", " return false;\n", " });\n", "\n", " function set_focus() {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'mpl-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'mpl-button-group';\n", " continue;\n", " }\n", "\n", " var button = (fig.buttons[name] = document.createElement('button'));\n", " button.classList = 'mpl-widget';\n", " button.setAttribute('role', 'button');\n", " button.setAttribute('aria-disabled', 'false');\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", "\n", " var icon_img = document.createElement('img');\n", " icon_img.src = '_images/' + image + '.png';\n", " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", " icon_img.alt = tooltip;\n", " button.appendChild(icon_img);\n", "\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " var fmt_picker = document.createElement('select');\n", " fmt_picker.classList = 'mpl-widget';\n", " toolbar.appendChild(fmt_picker);\n", " this.format_dropdown = fmt_picker;\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = document.createElement('option');\n", " option.selected = fmt === mpl.default_extension;\n", " option.innerHTML = fmt;\n", " fmt_picker.appendChild(option);\n", " }\n", "\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "};\n", "\n", "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", "};\n", "\n", "mpl.figure.prototype.send_message = function (type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "};\n", "\n", "mpl.figure.prototype.send_draw_message = function () {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "};\n", "\n", "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1], msg['forward']);\n", " fig.send_message('refresh', {});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", " var x0 = msg['x0'] / fig.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", " var x1 = msg['x1'] / fig.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0,\n", " 0,\n", " fig.canvas.width / fig.ratio,\n", " fig.canvas.height / fig.ratio\n", " );\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "};\n", "\n", "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "};\n", "\n", "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch (cursor) {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "};\n", "\n", "mpl.figure.prototype.handle_message = function (fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "};\n", "\n", "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "};\n", "\n", "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "};\n", "\n", "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", " for (var key in msg) {\n", " if (!(key in fig.buttons)) {\n", " continue;\n", " }\n", " fig.buttons[key].disabled = !msg[key];\n", " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", " if (msg['mode'] === 'PAN') {\n", " fig.buttons['Pan'].classList.add('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " } else if (msg['mode'] === 'ZOOM') {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.add('active');\n", " } else {\n", " fig.buttons['Pan'].classList.remove('active');\n", " fig.buttons['Zoom'].classList.remove('active');\n", " }\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Called whenever the canvas gets updated.\n", " this.send_message('ack', {});\n", "};\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function (fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " var img = evt.data;\n", " if (img.type !== 'image/png') {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " img.type = 'image/png';\n", " }\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src\n", " );\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " img\n", " );\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " } else if (\n", " typeof evt.data === 'string' &&\n", " evt.data.slice(0, 21) === 'data:image/png;base64'\n", " ) {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig['handle_' + msg_type];\n", " } catch (e) {\n", " console.log(\n", " \"No handler for the '\" + msg_type + \"' message type: \",\n", " msg\n", " );\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\n", " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", " e,\n", " e.stack,\n", " msg\n", " );\n", " }\n", " }\n", " };\n", "};\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function (e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e) {\n", " e = window.event;\n", " }\n", " if (e.target) {\n", " targ = e.target;\n", " } else if (e.srcElement) {\n", " targ = e.srcElement;\n", " }\n", " if (targ.nodeType === 3) {\n", " // defeat Safari bug\n", " targ = targ.parentNode;\n", " }\n", "\n", " // pageX,Y are the mouse positions relative to the document\n", " var boundingRect = targ.getBoundingClientRect();\n", " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", "\n", " return { x: x, y: y };\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys(original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object') {\n", " obj[key] = original[key];\n", " }\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function (event, name) {\n", " var canvas_pos = mpl.findpos(event);\n", "\n", " if (name === 'button_press') {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * this.ratio;\n", " var y = canvas_pos.y * this.ratio;\n", "\n", " this.send_message(name, {\n", " x: x,\n", " y: y,\n", " button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event),\n", " });\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", " // Handle any extra behaviour associated with a key event\n", "};\n", "\n", "mpl.figure.prototype.key_event = function (event, name) {\n", " // Prevent repeat events\n", " if (name === 'key_press') {\n", " if (event.key === this._key) {\n", " return;\n", " } else {\n", " this._key = event.key;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.key !== 'Control') {\n", " value += 'ctrl+';\n", " }\n", " else if (event.altKey && event.key !== 'Alt') {\n", " value += 'alt+';\n", " }\n", " else if (event.shiftKey && event.key !== 'Shift') {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k' + event.key;\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", " if (name === 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message('toolbar_button', { name: name });\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "\n", "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", "// prettier-ignore\n", "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* global mpl */\n", "\n", "var comm_websocket_adapter = function (comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.binaryType = comm.kernel.ws.binaryType;\n", " ws.readyState = comm.kernel.ws.readyState;\n", " function updateReadyState(_event) {\n", " if (comm.kernel.ws) {\n", " ws.readyState = comm.kernel.ws.readyState;\n", " } else {\n", " ws.readyState = 3; // Closed state.\n", " }\n", " }\n", " comm.kernel.ws.addEventListener('open', updateReadyState);\n", " comm.kernel.ws.addEventListener('close', updateReadyState);\n", " comm.kernel.ws.addEventListener('error', updateReadyState);\n", "\n", " ws.close = function () {\n", " comm.close();\n", " };\n", " ws.send = function (m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function (msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " var data = msg['content']['data'];\n", " if (data['blob'] !== undefined) {\n", " data = {\n", " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", " };\n", " }\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(data);\n", " });\n", " return ws;\n", "};\n", "\n", "mpl.mpl_figure_comm = function (comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = document.getElementById(id);\n", " var ws_proxy = comm_websocket_adapter(comm);\n", "\n", " function ondownload(figure, _format) {\n", " window.open(figure.canvas.toDataURL());\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element;\n", " fig.cell_info = mpl.find_output_cell(\"
\");\n", " if (!fig.cell_info) {\n", " console.error('Failed to find cell for figure', id, fig);\n", " return;\n", " }\n", " fig.cell_info[0].output_area.element.on(\n", " 'cleared',\n", " { fig: fig },\n", " fig._remove_fig_handler\n", " );\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function (fig, msg) {\n", " var width = fig.canvas.width / fig.ratio;\n", " fig.cell_info[0].output_area.element.off(\n", " 'cleared',\n", " fig._remove_fig_handler\n", " );\n", " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable();\n", " fig.parent_element.innerHTML =\n", " '';\n", " fig.close_ws(fig, msg);\n", "};\n", "\n", "mpl.figure.prototype.close_ws = function (fig, msg) {\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "};\n", "\n", "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width / this.ratio;\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] =\n", " '';\n", "};\n", "\n", "mpl.figure.prototype.updated_canvas_event = function () {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message('ack', {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () {\n", " fig.push_to_output();\n", " }, 1000);\n", "};\n", "\n", "mpl.figure.prototype._init_toolbar = function () {\n", " var fig = this;\n", "\n", " var toolbar = document.createElement('div');\n", " toolbar.classList = 'btn-toolbar';\n", " this.root.appendChild(toolbar);\n", "\n", " function on_click_closure(name) {\n", " return function (_event) {\n", " return fig.toolbar_button_onclick(name);\n", " };\n", " }\n", "\n", " function on_mouseover_closure(tooltip) {\n", " return function (event) {\n", " if (!event.currentTarget.disabled) {\n", " return fig.toolbar_button_onmouseover(tooltip);\n", " }\n", " };\n", " }\n", "\n", " fig.buttons = {};\n", " var buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " var button;\n", " for (var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " /* Instead of a spacer, we start a new button group. */\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", " buttonGroup = document.createElement('div');\n", " buttonGroup.classList = 'btn-group';\n", " continue;\n", " }\n", "\n", " button = fig.buttons[name] = document.createElement('button');\n", " button.classList = 'btn btn-default';\n", " button.href = '#';\n", " button.title = name;\n", " button.innerHTML = '';\n", " button.addEventListener('click', on_click_closure(method_name));\n", " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", " buttonGroup.appendChild(button);\n", " }\n", "\n", " if (buttonGroup.hasChildNodes()) {\n", " toolbar.appendChild(buttonGroup);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = document.createElement('span');\n", " status_bar.classList = 'mpl-message pull-right';\n", " toolbar.appendChild(status_bar);\n", " this.message = status_bar;\n", "\n", " // Add the close button to the window.\n", " var buttongrp = document.createElement('div');\n", " buttongrp.classList = 'btn-group inline pull-right';\n", " button = document.createElement('button');\n", " button.classList = 'btn btn-mini btn-primary';\n", " button.href = '#';\n", " button.title = 'Stop Interaction';\n", " button.innerHTML = '';\n", " button.addEventListener('click', function (_evt) {\n", " fig.handle_close(fig, {});\n", " });\n", " button.addEventListener(\n", " 'mouseover',\n", " on_mouseover_closure('Stop Interaction')\n", " );\n", " buttongrp.appendChild(button);\n", " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", "};\n", "\n", "mpl.figure.prototype._remove_fig_handler = function (event) {\n", " var fig = event.data.fig;\n", " if (event.target !== this) {\n", " // Ignore bubbled events from children.\n", " return;\n", " }\n", " fig.close_ws(fig, {});\n", "};\n", "\n", "mpl.figure.prototype._root_extra_style = function (el) {\n", " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", "};\n", "\n", "mpl.figure.prototype._canvas_extra_style = function (el) {\n", " // this is important to make the div 'focusable\n", " el.setAttribute('tabindex', 0);\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " } else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "};\n", "\n", "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager) {\n", " manager = IPython.keyboard_manager;\n", " }\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which === 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "};\n", "\n", "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", " fig.ondownload(fig, null);\n", "};\n", "\n", "mpl.find_output_cell = function (html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i = 0; i < ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code') {\n", " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] === html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "};\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel !== null) {\n", " IPython.notebook.kernel.comm_manager.register_target(\n", " 'matplotlib',\n", " mpl.mpl_figure_comm\n", " );\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "../..\\sidpy\\viz\\dataset_viz.py:484: MatplotlibDeprecationWarning: \n", "The set_window_title function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use manager.set_window_title or GUI-specific methods instead.\n", " self.fig.canvas.set_window_title(self.dset.title)\n" ] } ], "source": [ "fft_si = dset.fft().abs()\n", "fft_si.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary\n", "\n", "Basic operators and numpy operators will return a sidpy dataset.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 } sidpy-0.12.3/notebooks/00_basic_usage/plot_dataset.ipynb000066400000000000000000365516721455261647000232650ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Mathematics and Datasets\n", "\n", "**Gerd Duscher**\n", "\n", "04/16/2020\n", "\n", "**Please download this example and run it as a notebook by scrolling to the\n", "bottom of this page**\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.12.1\n" ] } ], "source": [ "# Ensure python 3 compatibility:\n", "from __future__ import division, print_function, absolute_import, unicode_literals\n", "%matplotlib widget\n", "\n", "import numpy as np\n", "import sys\n", "sys.path.insert(0,'../../')\n", "import sidpy\n", "\n", "print(sidpy.__version__)" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Creating an Image Dataset\n", "First, we make a sidpy dataset from a numpy array, with all the information to plot it. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0529d4c485f54d42a7880dded518ee4d", "version_major": 2, "version_minor": 0 }, "image/png": 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\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = np.linspace(0, 4*np.pi, 512)\n", "x = np.array(np.zeros((512,512)) + 4*abs(np.cos(t)[:, np.newaxis]) + np.random.normal(0, 0.2, size=(512, 512)))\n", "\n", "dset = sidpy.Dataset.from_array(x)\n", "dset.data_type = 'image'\n", "dset.units = 'counts'\n", "dset.quantity = 'intensity'\n", "dset.title = 'random'\n", "dset.set_dimension(0, sidpy.Dimension(np.arange(dset.shape[0])*.02, 'x'))\n", "dset.x.dimension_type = 'spatial'\n", "dset.x.units = 'nm'\n", "dset.x.quantity = 'distance'\n", "dset.set_dimension(1, sidpy.Dimension(np.arange(dset.shape[1])*.02, 'y'))\n", "dset.y.dimension_type = 'spatial'\n", "dset.y.units = 'nm'\n", "dset.y.quantity = 'distance'\n", "view = dset.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have the capability to store the variance of our data within the sidpy.Dataset by either adding an additional attribute variance using ``sidpy.Dataset.from_array(x, variance=var)`` or by directly saving our variance array in ``sidpy.Dataset.variance``" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d13dae0792ae4e1f931acdb09fe43585", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Dropdown(description='Image', layout=Layout(height='40px', width='20%'), options=(('z', 1), ('σ…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "64eb25d6cff84d0fb6b38945176e204a", "version_major": 2, "version_minor": 0 }, "image/png": 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\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_var = np.array(np.random.normal(1, 0.1, size=(512, 512))+ abs(np.sin(t)[np.newaxis,:]))\n", "\n", "dset.variance = x_var\n", "dset.plot();" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Simple Arithmetic \n", "\n", "First we subtract the min of this image, and we want to have the rest of the information unchanged\n", "\n", "So we use the minimum function and do a subtraction.\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e7fefe8f31d24e59bf95850a8ff237d9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Dropdown(description='Image', layout=Layout(height='40px', width='20%'), options=(('z', 1), ('σ…" ] }, "metadata": 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\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dset = dset - dset.min()\n", "view = dset.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting a Complex Image\n", "What if the data form a complex image?\n", "\n", "Because, we do not want to start all over again, we will use the ``like_data`` function, \n", "which copies all metadata onto the new dataset.\n", "\n", "The resulting plot consists of two images, which share however the axes, try it out and zoom into one image.\n", "\n", "Another feature of the ``plot`` function is that you can add any matplotlib keywords and values to the plot. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d75d2654db1947a3bbdf0b0b9ac494fe", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Dropdown(description='Image', layout=Layout(height='40px', width='20%'), options=(('z', 1), ('σ…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ebbedd36f14b46068e52ffe83cbb8ca7", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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\n", " Figure\n", "
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\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.random.normal(3, 2.5, size=(1024))\n", "x_var = np.random.normal(10, 0.2, size=(1024))\n", "\n", "dset = sidpy.Dataset.from_array(x, variance=x_var)\n", "\n", "# dataset metadata\n", "dset.data_type = 'spectrum'\n", "dset.title = 'random'\n", "dset.quantity = 'intensity'\n", "dset.units = 'a.u.'\n", "\n", "# dimension with metadata\n", "scale = .5\n", "offset = 390\n", "dset.set_dimension(0, sidpy.Dimension(np.arange(dset.shape[0])*scale+offset, 'energy'))\n", "dset.dim_0.dimension_type = 'spectral'\n", "dset.energy.units = 'eV'\n", "dset.energy.quantity = 'energy'\n", "\n", "view = dset.plot()" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Creating an Image-Stack DataSet\n", "In the following we will make a numpy which resembles a stack of images\n", "\n", "In the ``sidpy Dataset`` will set the ``data_type`` to ``image_stack`` for the plotting routine to know how to plot this dataset.\n", "\n", "The dimensions have to contain at least two ``spatial`` dimensions and one that is identifiable as a stack dimension ('stack, 'frame', 'time').\n", "First we make a stack of images\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "x = np.random.normal(3, 2.5, size=(25, 512, 512))\n", "x_var = np.random.normal(10, 2.5, size=(25, 512, 512))\n", "\n", "dset = sidpy.Dataset.from_array(x)\n", "dset.data_type = 'image_stack'\n", "dset.units = 'counts'\n", "dset.quantity = 'intensity'\n", "\n", "dset.variance = x_var\n", "\n", "dset.set_dimension(0, sidpy.Dimension(np.arange(dset.shape[0]), 'frame'))\n", "dset.frame.dimension_type = 'temporal'\n", "dset.set_dimension(1, sidpy.Dimension(np.arange(dset.shape[1])*.02, 'x'))\n", "dset.x.dimension_type = 'spatial'\n", "dset.x.units = 'nm'\n", "dset.x.quantity = 'distance'\n", "dset.set_dimension(2, sidpy.Dimension(np.arange(dset.shape[2])*.02, 'y'))\n", "dset.y.dimension_type = 'spatial'\n", "dset.y.units = 'nm'\n", "dset.y.quantity = 'distance'\n" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Plotting the Dataset\n", "Please note that the scroll wheel will move you through the stack, also the slider and the play button will let you navigate through this image stack.\n", "\n", "Zoom to an area and let it play!\n", "\n", "Click on the ``Average`` button and then click on it again.\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c08ad38e2fe54cf990c72c61e4282b59", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(Play(value=0, description='Press play', interval=500, max=25), IntSlider(value=0…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "681fb456df4f4a4f9d6c434182cc6193", "version_major": 2, "version_minor": 0 }, "image/png": 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+67/O3Nwc73nPe7jwwgu5+eabGRoaus/v+K3f+i0+8YlP8Nu//ducfvrpLCws8NWvfpXbb799NYC69tprefazn83U1BS/8zu/w+TkJLfffjv/+I//yO/8zu+sbivPcy6++GKe+MQn8q53vYtyuYxzjuc+97lcf/31/Nqv/RrnnHMOn//853n1q1/N0aNHefe73/1jnaMzzzyToaEhvvzlL/Pc5z4XgK985Storfn2t79Ns9mkXq9jreVrX/sav/mbv7nm83fffTcveMEL+LVf+zUuv/xy/uIv/oIrrriCc889lzPOOAOAbrfLRRddxNGjR3npS1/K5s2b+drXvsZrX/tajh07xjXXXLO6vZe+9KX85V/+JVdeeSWvfOUr2b9/P+9973u5+eab+dd//Vd833/Ax/bhD3+YX//1X+e8885b3e8dO3YAcOutt3LBBRdQr9d5zWteg+/7fOADH+BJT3rSatDxg/DXf/3XtFotXvrSl6KU4h3veAeXXHIJ+/btW93Pa6+9ln379nHllVcyOTnJrbfeygc/+EFuvfVWvv71r6OU4lnPehbVapW/+7u/46KLLlrzHR/72Mc444wzOPPMM+9zP6644gr+7u/+jpe85CU89rGP5Utf+hLPetazTlrvm9/8Jl/72te47LLL2LhxIwcOHOD9738/T3rSk7jtttsol8tceOGFvPKVr+S///f/zu///u9z2mmnAaz+/8Mf/jCXX345F198MW9/+9vpdru8//3v54lPfCI333wzW7du/cEX5h5YWlri2c9+NpdddhkvfOELef/7389ll13GRz/6Ua666ip+67d+ixe/+MW8853v5AUveAGHDx+mVqs94GMCOHr06GpA+9rXvpZKpcKf//mfE4bhSfvzQI/x+c9/PrfeeiuveMUr2Lp1K7Ozs1x77bUcOnToRzoPAwzwkIUbYICHCD70oQ85wO3fv3/N8uuvv94B7vrrr3fOOXfzzTc7wH384x+/z20dOHDAGWPcW97yljXLv/vd7zrP805afk80Gg338pe//D7fz/Pcbdu2zW3ZssUtLS2tec9au/rvyy+/3AHu937v99as86lPfcoB7s1vfvOa5S94wQucUsrdfffdq8u2bNniLr/88tXX9zwf94VnPetZ7rzzzlt9fckll7hLLrnEGWPcZz/7WeecczfddJMD3Kc//ek13we4L3/5y6vLZmdnXRiG7lWvetXqsje96U2uUqm4u+66a833/t7v/Z4zxrhDhw4555z7yle+4gD30Y9+dM16n/vc505aftFFF7mLLrrofo/LOecqlcqac3ICz3ve81wQBG7v3r2ry6anp12tVnMXXnjh/W5z//79DnCjo6NucXFxdfmnP/1pB7h/+Id/WF3W7XZP+vzf/M3fnHTefvmXf9lNTEy4PM9Xlx07dsxprd0b3/jG1WVveMMb3Pc/jm+88UYHuKuuumrNd1xxxRUOcG94wxvud19uuOEGB7j//b//9+qyj3/84/d637RaLTc0NOR+4zd+Y83y48ePu0ajcdLye+Le7seLLrrIAe6v//qvV5fdcccdDnBaa/f1r399dfnnP/95B7gPfehDP/QxveIVr3BKKXfzzTevLltYWHAjIyNrniUP9BiXlpYc4N75znfe7zEPMMDDAYMS8AA/dziR4fv85z9Pt9u913X+z//5P1hrufTSS5mfn1/9m5ycZNeuXVx//fX3+x1DQ0N84xvfYHp6+l7fv/nmm9m/fz9XXXXVSZnEe7Pz+M//+T+vef1P//RPGGN45StfuWb5q171KpxzfPazn73f/XsguOCCC7jppptWOYdf/epXeeYzn8k555zDV77yFUCygkopnvjEJ6757Omnn84FF1yw+np8fJzdu3ezb9++1WUf//jHueCCCxgeHl5zjp/61KdSFAVf/vKXV9drNBo87WlPW7PeueeeS7Va/YHX4oGiKAq+8IUv8LznPY/t27evLp+amuLFL34xX/3qV2k2mz9wOy960YsYHh5efX3iPHz/sZdKpdV/x3HM/Pw8j33sYwHW0ARe9KIXMTs7u6Y8+olPfAJr7f223Pvc5z4HwMte9rI1y1/xilectO7370uWZSwsLLBz506GhoZOoizcG6699lqWl5f55V/+5TXXxxjD+eef/yNfn2q1ymWXXbb6evfu3QwNDXHaaaetycSe+Pd9nd/7O6bPfe5zPO5xj+Occ85ZXTYyMrJKs/hhj7FUKhEEAf/yL//yI9ETBhjg5wmDEvAAP3fYtm0bv/u7v8uf/Mmf8NGPfpQLLriA5z73ufzKr/zKanC4Z88enHPs2rXrXrfxg0qO73jHO7j88svZtGkT5557Ls985jP5T//pP60GFnv37gW43xLeCXiex8aNG9csO3jwIOvXr18teZ3AiZLcwYMHf+B2fxAuuOAC8jznhhtuYNOmTczOznLBBRdw6623rgkATz/9dEZGRtZ8dvPmzSdtb3h4eM2guGfPHr7zne8wPj5+r98/Ozu7ut7KygoTExP3u96Pi7m5ObrdLrt37z7pvdNOOw1rLYcPH14tYd8X7nnsJ4LB7z/2xcVFrr76av72b//2pP3/fh7qL/7iL9JoNPjYxz7GU57yFEDKv+eccw6nnHLKfe7DwYMH0Vqzbdu2Nct37tx50rq9Xo+3ve1tfOhDH+Lo0aNrOKT35MTeG/bs2QPAL/zCL9zr+/V6/Qdu496wcePGkyZDjUaDTZs2nbQM1p7fB3pMBw8e5HGPe9xJ333P8/RAjzEMQ97+9rfzqle9inXr1vHYxz6WZz/72fyn//SfmJyc/IHHPMAAP08YBIADPGRwX0a4RVGctOyP//iPueKKK/j0pz/NF77wBV75ylfytre9ja9//ets3LgRay1KKT772c9ijDnp89Vq9X735dJLL+WCCy7g7//+7/nCF77AO9/5Tt7+9rfzf/7P/+EZz3jGD3VcYRg+KArJRz/60URRxJe//GU2b97MxMQEp5xyChdccAHve9/7SJKEr3zlK/zSL/3SSZ+9t3MGrBmIrbU87WlP4zWvec29rnsiwLHWMjExwUc/+tF7Xe++AsgHCw/k2C+99FK+9rWv8epXv5pzzjmHarWKtZZf/MVfXCOACcOQ5z3vefz93/8973vf+5iZmeFf//Vfeetb3/oT299XvOIVfOhDH+Kqq67icY97HI1GA6UUl1122UlinHvDiXU+/OEP32uQ43k/2jBxX+fxgZzfH/eY7okf5hivuuoqnvOc5/CpT32Kz3/+87z+9a/nbW97G//8z//MIx/5yB/6uwcY4KGKQQA4wEMGJzIty8vLa5bfVzbsrLPO4qyzzuK//bf/xte+9jWe8IQn8D//5//kzW9+Mzt27MA5x7Zt2+4303J/mJqa4mUvexkve9nLmJ2d5VGPehRvectbeMYznrEqOrjlllt46lOf+kNve8uWLVx33XW0Wq01WcA77rhj9f0fF0EQcN555/GVr3yFzZs3r5YyL7jgApIk4aMf/SgzMzM/sn/bjh07aLfbP/D4d+zYwXXXXccTnvCENaW9Hwf3NlkYHx+nXC5z5513nvTeHXfcgdb6pOzTj4KlpSW++MUvcvXVV/MHf/AHq8tPZJnuiRe96EX81V/9FV/84he5/fbbcc7db/kX5Ppba9m/f/+aLPbdd9990rqf+MQnuPzyy/njP/7j1WVxHJ/0O7qvCdaJe3liYuJHupd/Gnigx7Rly5Z7PSf3XPbDHuOOHTt41atexate9Sr27NnDOeecwx//8R/zkY985Ec4mgEGeGhiwAEc4CGDEw/pE9wxkOzfBz/4wTXrNZtN8jxfs+yss85Ca02SJABccsklGGO4+uqrT7JVcc6tseu4J4qiOKl0NjExwfr161e3/6hHPYpt27ZxzTXXnDQo3fP77g3PfOYzKYqC9773vWuWv/vd70Yp9UNnGe8LF1xwAd/4xje4/vrrVwPAsbExTjvtNN7+9revrvOj4NJLL+WGG27g85///EnvLS8vr16jSy+9lKIoeNOb3nTSenmen3T+HggqlcpJnzPG8PSnP51Pf/rTayw7ZmZm+Ou//mue+MQn/sjlzHt+D5x8nb9f9fz9eOpTn8rIyAgf+9jH+NjHPsZ55513Umn3nrj44osBeN/73rdm+Xve85573Z977st73vOekzLnlUoFOHmCdfHFF1Ov13nrW99KlmUnbX9ubu5+9/WngQd6TBdffDE33HAD3/rWt1aXLS4unpRtfqDH2O12ieN4zXs7duygVqut/vYHGODhgkEGcICHDM444wwe+9jH8trXvpbFxUVGRkb427/925OCvX/+53/mt3/7t3nhC1/IKaecQp7nfPjDH8YYw/Of/3xAHtpvfvObee1rX8uBAwd43vOeR61WY//+/fz93/89v/mbv8n/9//9f/e6H61Wi40bN/KCF7yAs88+m2q1ynXXXcc3v/nN1YyE1pr3v//9POc5z+Gcc87hyiuvZGpqijvuuINbb731XoOi78dznvMcnvzkJ/O6172OAwcOcPbZZ/OFL3yBT3/601x11VWrwfCPiwsuuIC3vOUtHD58eE2gd+GFF/KBD3yArVu3nsRPfKB49atfzWc+8xme/exnr1rEdDodvvvd7/KJT3yCAwcOMDY2xkUXXcRLX/pS3va2t/Gtb32Lpz/96fi+z549e/j4xz/On/7pn/KCF7zgh/ruc889l+uuu44/+ZM/Yf369Wzbto3zzz+fN7/5zVx77bU88YlP5GUvexme5/GBD3yAJEl4xzve8SMd5z1Rr9e58MILecc73kGWZWzYsIEvfOEL7N+//17X932fSy65hL/927+l0+nwrne96wEd3/Of/3yuueYaFhYWVm1g7rrrLmBtNu/Zz342H/7wh2k0Gpx++unccMMNXHfddYyOjq7Z5jnnnIMxhre//e2srKwQhiG/8Au/wMTEBO9///t5yUtewqMe9Sguu+wyxsfHOXToEP/3//5fnvCEJ5w0Uflp44Ee02te8xo+8pGP8LSnPY1XvOIVqzYwmzdvZnFxcfU81ev1B3SMd911F095ylO49NJLOf300/E8j7//+79nZmZmjaBlgAEeFngQlMcDDHCf2Lt3r3vqU5/qwjB069atc7//+7/vrr322jU2E/v27XO/+qu/6nbs2OGiKHIjIyPuyU9+srvuuutO2t4nP/lJ98QnPtFVKhVXqVTcqaee6l7+8pe7O++88z73IUkS9+pXv9qdffbZrlaruUql4s4++2z3vve976R1v/rVr7qnPe1pq+s94hGPcO95z3tW37/88stdpVK51+9ptVruv/yX/+LWr1/vfN93u3btcu985zvX2Mg496PbwDjnXLPZdMYYV6vV1liRfOQjH3GAe8lLXnLSZ7Zs2eKe9axnnbT83ixaWq2We+1rX+t27tzpgiBwY2Nj7vGPf7x717ve5dI0XbPuBz/4QXfuuee6UqnkarWaO+uss9xrXvMaNz09fb/fcW+444473IUXXuhKpZID1pyfm266yV188cWuWq26crnsnvzkJ7uvfe1rP3CbJ2xg7s0ChHtYrxw5csT90i/9khsaGnKNRsO98IUvdNPT0yetdwIn7mGllDt8+PBJ79/TBsY55zqdjnv5y1/uRkZGXLVadc973vPcnXfe6QD3R3/0R6vrLS0tuSuvvNKNjY25arXqLr74YnfHHXecdN8459yf/dmfue3btztjzEn30PXXX+8uvvhi12g0XBRFbseOHe6KK65w//Zv/3a/5+2+bGDOOOOMk9a9r3sLWGO79MMc08033+wuuOACF4ah27hxo3vb297m/vt//+8OcMePHz9pX+/vGOfn593LX/5yd+qpp7pKpeIajYY7//zz3d/93d/d7zkYYICfRyjnfgJtBwYYYIABBvip41vf+haPfOQj+chHPnKS1ckA38NVV13FBz7wAdrt9n2KTgYY4N87BhzAAQYYYICHIHq93knLrrnmGrTWP7Jw5+GIe56nhYUFPvzhD/PEJz5xEPwNMMD9YMABHGCAAQZ4COId73gHN954I09+8pPxPI/PfvazfPazn+U3f/M3fyJq5ocLHve4x/GkJz2J0047jZmZGf7X//pfNJtNXv/61z/YuzbAAA9pDErAAwwwwAAPQVx77bVcffXV3HbbbbTbbTZv3sxLXvISXve61/3I3nwPR/z+7/8+n/jEJzhy5AhKKR71qEfxhje84SFjaTPAAA9VDALAhyD+x//4H7zzne/k+PHjnH322bznPe/hvPPOe7B3a4ABBhhggAEGeJhgwAF8iOFjH/sYv/u7v8sb3vAGbrrpJs4++2wuvvjin1i7rAEGGGCAAQYYYIBBBvAhhvPPP5/HPOYxq75b1lo2bdrEK17xCn7v937vQd67AQYYYIABBhjg4YABkeQhhDRNufHGG3nta1+7ukxrzVOf+lRuuOGGB7QNay3T09PUarX7bP00wAADDDDAww/OOVqtFuvXr/+Z9x+P45g0TX8i2wqCgCiKfiLbGuC+MQgAH0KYn5+nKArWrVu3Zvm6detWe8TeE0mSrGlRdPToUU4//fSf6n4OMMAAAwzw0MXhw4d/5A4/PwriOGbblirHZ4sfvPIDwOTkJPv37x8EgT9lDALAn3O87W1v4+qrrz5p+db/+nqKHQr/UIj1IVhW5FVHOlyAcZQP+mQNhzWg13fJFyO8tswYs/EMb94nbxSYjsZ0FdmIBQuuVKBbHowmuOUANZRi/IKsG+DP+pieIp7K8VqGomSp3W3QhWPlNItKFYwmeAcjsk0pLjagHH49Qe2v4DREc4rOOT3CO0rEuxL8KCOfKWN6inwqwTsWohOF34LepKOo5wSzHn5TkYw5SscU8ToHCor1Mbbjg29BO1TLAwWuUhDtD3AeJOtyUA4CC7HBXzbkGxL8AxHpxpTguE/WsFCAcgpbLiBTKKfQiaIoW3RPY0sWVc7BQVDKsPuq5OsTWAowsUKnEC4r4gmHSiEdLcB3qK4hWNYkEzk61igHXlOTbEoJDwfoDIqKI1ufYuYCdAo6lsxusjPGdTy8lsF5jqJkCRaNXOsSxJszgnpMfryC9S3hnEdecZSPKrIqZMMWlStQUD0IzV0OW88wiz46V1jPYT3QBRSRJZz1MGc16S5HmCWfYiQjmPbJ6hbnOyoHPPISJOszvBWPIrKoQqFyhYmhKDlMrCgiR1Et8GspyljSZkT1Tp/isU2MsfT21ylKFlXJcW0PfyghSzyCablmAMGSIh63mK7C7eiSz0egEFazBazCpIqiZHGek2WBRSWGcNaQ1WXfQI4tmjXE6wq8sR7ZSkTpsEcyZvHaCp0rsoalKFl0qrF16SWr2h6umhOUU/RtNeLJXN73LbqagQJzJCIbzYmGe8SLJUzHYA24/nk2PQW72qRLJTAOv5rg3VKlqDjysiOa1aTDjo1nT3Pg7nVExzyiJUd3EtLJDG/JR/cUugCnwfmOdCpFr/hyr2uH8x14FuU5zGxAXi/kPq9aKJRck6rFlQrIFSrV6EThPGT/lNxzyYYUNJT3BOgcOpsLlFV4TUV45gqt+QqmabAeVA9oklFHUXZgFTqDbF0GhYJC4a0YdAp5zaEKeQZlnQB/zoetHfLFEl5bYzr9739Ek96xCqar5V6vFHhNuceccaChdMQjnijAKcxYTN71Ke/36W7OUYUimjb0tqcE1YRsrgxAdZ9BFRCPO9LJDNP0KIYzzJKPDR3+igYLOlVYD1COrO7AOFypIDwSUJQcNnDYwGJ6GuuBsmDLBaZtsL6DRoZ/KERZRV5x2JLF9J+30bwiGXFynxzxsZGDXK6JWhejjpRwQBE58Bz+oiZdL/t44rpb3xFubNM5qpj+r2+jVqv9lEace0eaphyfLTh441bqtR8v89hsWbace4A0TQcB4E8ZgwDwIYSxsTGMMczMzKxZPjMzw+Tk5L1+5rWvfS2/+7u/u/q62WyyadMm8kkfE/oU6z2cccSbLdHBAGsstlRQTHgEu1rof6sTL0aokYzqoYAiBFWJsA2HrhYQaMKWQWUSVLkeuMBR2IDxM5aZn61jOwYdWFTVwzhFbQaSEYcpoHuawxlH0NHY0FFon2K9xmtHON8x8h1FZ6pGb0cKqaZwHjiDb3zcfEhRdqgSmExhy4ogjyQgqoMbzdHOxzunR68ZUr0zIJsCFTpMCjYL0I0C7Re4xRDXsHjLBps60l0F3opHkITkExnEGj81UAbTCYlyDa2QfH2BMo5gziMdy9HGQ/kWtRRQ2blC784h8qGC0Y0rtDsRWdcnvDWgO2nRWYDnNNnmDDPr09mSE037JFMFXq6wUYE3lpGtU5huCVex6KZHaBVZKOcgrzlc5DC9kOC0DvFShEo0rpqjsgqqBmiD31VEy4pk3JLWJCDQvkduQ8LtCclCiQAPfEeyrT+YADYqUFFBe5uDzMOPwJsvkY72g7d6jisU5SM+8a4Uq2p4WYgdt/g1sGlI2FVkkwnxKDirMNoHF6A9RzRj5LvKoC2kG2VSEaxo3FSbdDGiccCju92CruFMgQ4jXD3HrJTxm5p8SqNzH+oe0YKiszsli3z8DPLTYrQpoUY9KBQq1zjforsGP1UEW9vESxHRtE+8PgNfER7zQDkqxyEeUfQmM5KaQikPr2op4ghd9ii3IGs49I42bqFEuCzBQtEpES4prA+pZ8nDHH/Yxy9CvBSS4ZzyWEHveBVV1uiwIM1CgsTH5IpkOMfrRRilKEYc+niEnsjk/rchbpOHG02J9pUoJhx2MuXI3m34DtyIwmqFqjlMXeGcj9Ya64MNJdA1WYgOQGUSxDvjULlCpeC2J3jzZXw0/tYW3jdr9CYsYaIopmKKFR98qB316J7XJW/6Mnla8tAljV9NiX1D9S4fz1h0oci2Z+TtUcrzPvH6HF3KSQglOAkdzoMisBgXoIZT1PEIO2rJQ4sKLCiHWxphaOsKcbNGsRChx1Oc8Qk6mu6UxWYNwjggXVdQPuRBB6wHaWBRmaIoFdhHJ+jEQy35qJUI34GONOVlyOoWGprqdER3Y44uK/RQSlqEmK6i2BGj0xJqKCc8WgcDheegqlAO0tEc0zT4LY0NLI09ivZmKMWK5u6Eyp0hWcVhUkVvMsVb8FHruhSjCu00NCNUTeO29CDXqG6IHS3wSxnpwQq6gPKSQvuQVp0E88MZjiqB7+N2dLBLEaaRorKI8myJdMRiPUewqLEO0uWQqCUl2AeL/lOtKaq1H++7LQPq0s8KAxXwQwhBEHDuuefyxS9+cXWZtZYvfvGLPO5xj7vXz4RhSL1eX/MHoMo5atknmDeoco5e8Uh39bDVnHDGozyt8L5WJx6z2JJl6KaA9hZLMurQGUSzmuC4x9Atmt6URRWQTWTY9TFFxeI1UpZuH4VMgZUfrDOO3taU3qSliBxZo8D0FKXjBp0qimpBeMwjmpGBVBWwcK4lPaPLyHgTlWiS8QKVatqnyoPMb2l0Bun6DKUcybjF78j36Y4hnPGIF0qYRZ/ulKUIHTqHdMgSLGjq3wlgIcQFFhyYFMJFzbpNS6jNHfLxDCyoXOF1FHZzD8YT2jszTKLwlwy6bSgC0D2DN++jFiSr1znQIJpX+MuG5YND+N+p4M/5tHenRPMaHSus79DLnmRbjCMZK6CeEW1ugXEUC2H/QsvxuMmYZNQx+g2PrO4kczGv8XqK4u4q0VGf6n6DWfDx5z3qE22CFU2wovDbEM1oCX49QDuqtwekxyoEC4bi3BbZupRiMoFGBsqhMk2pmkimNNb4d5UpQgjnDdGmluxboUi2x2AV/l0lgmUt2dxjIcpKNtMVGmUcquthjod4HYXzHFk/k2V9iM5aJpzxwEpGLZuRrFfnUT10plCHI9KFCLOpiz/no3JFVrPUqj1wiiJ0mATMgk9eL0hHC7wDEfZ4BFahEo23Io80N5SRlx3ZwQoq0ZgYoqM+w982mATSkYLlxybkVSfB4rKhus+j/M9VUGB96E0VhPOKeCnCWzE4hQQLwxlpw5GMF0Rzmi2b5nEawiWF31TU1rXptUO8piYfycG3BMd9nOfQKZIFDBzBisIpUNs6mEUPb8WgekayzE7u4bxqUW0PO5RTlC3ZUIFTMH7ODGopwOtq0rECvbWDiRWbzjyOGY/RicJsbxNtaTG6fQkbWoJlTXhXCRTEW1L5PQ05/KYmqzqKnkHlivoeyZRHN5flvmwb8qGc8akV8tjHrHi0t+fYsZR8fcLYZBOVarKao36Hh54NyRsF6VQmmeSGZENdWOAdjCjNKbyOBt/iugZzLMSNpEzVm6SjBcG2Fq5niGY1nS0FXlvhHwvIhiyEBb1TY/KSI2tYbKXATcaYnlx3L8xRBeBArY9JG454a0JRK0jHCqwPKtPU7zC4mRB/RZHVLXo2pH6Lj3cgQmXybPJakrksNsSYSkawqElGCmy1oLMR8rqlO+UIjgR0NxTYHT3qj5ulOtqleuoS2ljCO0uE+yJcVOAMcKSENx0y/G0jmcrb5H4zu9rEG1O6m3LysYzSnCKY8RmeaGE9yOdLVPd7mP0lvK4CLfcitYwigGwqhVqOOqX9ExyRfngUzv5E/gb42WCQAXyI4Xd/93e5/PLLefSjH815553HNddcQ6fT4corr/yhtuMfCdGhoig5yrdFJEMOOxeiAkcRQW/ie6UMFLS2W2zgKCqW0lGPrCblm2RI43WgCIFUQ9MjaCsyG+LlMkt2UYFKNH5TU5Q0JlZkw5LpiRYUacNh+wFUNK8oQrCeQu1ok0xXsEshS/MhLirQPYOt5eiwwIY+uivB2fC/+cSjUnoqAiknqbaH0w4VFRT9Ep/X8cmrDlcpyBNNvtFKydRq0LKteMziOiXy2RJeT+N1FHnZES6Bu6tMMmwJ2nLu8jHJzKhC4bU0qoB8OMNahYoN8ZgjbxTgW7qb5VySaeJxi40spqsJljVBC5qPyHHG4R0PybMQVbe4cgFW4ao5NH1O8LaTIUX5KKw8MiVJtQRwkSNblxErB4khOuDRnKkSOTknnc0F1DKCAxF5I0dFBd0pA9phPQV3VKk2FZ0tBS6wlI8bihB6RQ3KBarQ5CUJUvw2ZN9pYOqWomJxqaF+u481kA47XLV/zlNN+aBH1nDkVQOFlK7CwwbT80iHZZBW1YzmTBVTsZhYYZY1WcNRudvQXe+RNQp0rIlmPdyYo/AkWC8MLE03Vu/r1s4C5zlUqvDaGudD0NTkqWQ9SzOKlTFwDkwi5TaAzo4MFRboNMJ5EM14FIGBftk0Kxco59Gb7Ge6jaN0zNDemRMNx2TNCo09iqVHFESHA/KKw5UL8qrm0PER3FiG1wvoTRZwuA6NjHx9gmr5eG2N11aoApyR4wqaht6ExZYt64ZazCpHEOS05iuU9gf0djjq+zQrZ2SoqMDMB9jAYbqaogRzN68j6CkJiLseScnDaJi+cYp8XUaYKezdVdINCfHB2vfuq1GLDSxjE03mDw1R6iiCFoBGzQb0Nmd0J4WKUERyLlShKB32mVNDUpocyiUruBgI5WFdjulorO9onpFDIZMiMGQjOV5YULQ9VK3Aej7xiKNYn0DSp1zULDR9Zlo1/CVDUvHxhxKSrpRfnZHsZjhvULOG3oacdDLHn/cotCO4u0QROcztFdJdPRRy7c2tZdJhiz8TgJXjSSYKnGdZOav/my0ZlFUE85reOkdet4TzhjxwVA8r4lFgPqR0VNM+JUO3Dc4q9CltSjfVSEYt6aiV0myQM3NsiOCYT7fihHqwTp6DgGRjRzJoeSyfqijVY7KqT7igsK0aXsWRT2QElZT2No2/Ylg6VkdNJXhHQ+JRyebmW2JcoTDHQmj5mBTcgo/1IetVftShZ4B/hxgEgA8xvOhFL2Jubo4/+IM/4Pjx45xzzjl87nOfO0kY8oOQVxxBfyIVj1mCFU1RAF1FVnXkVUs0I5wwpAqDHUsxsyHxuIWhlMZQl+VaFW/OR2fgT3ukI33uVDUj01IOpefjajl5rClNdOnpsmTbYuG2OANF6LChJR7VJOtyVK5wzZBwqkM2XSFY0sSbCmxkZQBeDlYDM6+jaG/ql7Ic5FXJqOQVRzqVERwOpEwzIrylaFbTDTXFaCaZgI4hnDcy007AbyuS2TIm1uSNAp0ZiqmEZhhQ36txSvbRXzKQakxbEy5IwKIzRTAdkDUseiSBVgnTMpjYSIZCQbCoySuSvXMaonkoIuRzG1KiAx5pXbZVWAW+RbU8avs19nCJzraCIlCoyRhtFea4T15yBEuarPDwOifOIUTH5NqYRL43xSff1WW42qOwmmamhbOJXOP2KRn+nEc2ZiXDNWfAOLwlD52B11F0N+bkVS3XYSjDzPtE85qsDPGExfkWb97HBsIvi0/v4QoNbQ+/rcjLEI86vB6oqZhgb4ks0ehcMrc2kGuIk0DT+o7qXo+8KvdrGvuELUW8VWgBKlPokZTCM5JtLhTR+g7pgSpeR5NHcl94bUV6YZPIKexdVQkcGplwP43DdT2cBhML50unkE5k+NWUbCkincgxKwZdzUg9SxZYyrdHJB0ZVHvjCq+pibcmmD5nVO1q45ohuimTET2a4hxEt5YASMYsTiPZ3ByKqsN6jqxmcYHDXzIcX5yUe7mRQX9dExY0dxf4Cx7ZOJSnpczb3VCQT+ToZR+nEJ5erCQYA5nIVBOSjVDZExA0IzqnpATHfFQh65quIT4whtpUkFcdWQ3KM4p41BEe90lHCpzvKB326G0pKBo5cVkTzHjkW2NYCCVD1tY47ZhZrOMCCbRNOccuBeSTKeU7QlThkVqF39JkoYfZ2iFrhtD1COYMRUloAvFUwfJiBVWzuK6HDSzBxi6q0GRBgO5qOW8jFr+RwP4yJlFkDrKqw+tKJi+8s9Q/Jks2BP6y/BZtYPHahuiYpr3DER73CJrQXW8pagU21BT9ADvekqCXfbpTkK7LINe0T8vRLbm/i8yQditweg/b9vGXhefX80O8BZ9iW0x0a4nuJgmU9bJMPqMtLUI/Z1lV0HMBxe01CCVbX0RSuRgabdO+YxhjhJ9d3evjPaFFvC8iq1qcgXX/ELK0W6/+PmwAtQOalVMLVP5jDD4/AVgclh/PWe7H/fwADxyDEvBDEL/927/NwYMHSZKEb3zjG5x//vk/9DaUlQERJzP4eLygCPpvaiFNp0MWvyWlOhMryrdF+B2F31KU7ohYPlbHLHvoAkyqiCfz1dIUKz7BgsF1PCoHDarrUYQOdWMdf0lm9kXkyDYlRHMKnSnCWU9EKFrI1co4kqUIG1niDRk6LCgd8YgOBVQ3Nok3ZFLOUcC2LsW6FLWtg46VlIWaChXIQzHdmqAyRV6SgFPlCr3sSQAxnIKCZLQgHpf1KSCvFZi2xpzawuuXM60BrwfBgqF8TBHMGfy2pjdpycuWYKm/TlPj31Emb/QzUrvbEtRGBcXOngQ3K5rSlhYruy2t3RleW+EyTTwqhHPbHzT1so9OZQCOx/uBbqFwMxG6f61QkKwr8LpKAuB5Q1aDeDKnuzmntTsjWFGYrsbbU4bPjtA62EDFJwQiDpwinPYpIgn4olkjGZFKjtvYQyeK7uZcBrRGjq3l1L4VUpSt7NuuBH+yC0A+muG0I5wzBHtLmOOSDSoih60UFNWCaE7h3VkW4YaGcF4C6bwfmOtMkdYV4zdCXgGVgdcG/3CISSRw95cNpWmD8QrMko9KNdFYj3R/Db8pjy+TSjnOGYjbIfqmGn5LycSm6xHNGbxZH69pyGqO9mkpRSil6dKBAHeogtcy+HMeRa2gdEsJU85hxad7SoK/ItntrCa/E9X0KRKDbnmkc2W8BR87lGN94FiIno7obstIRkTskg/noCCvOvJaIcFqVxPOeDgjk7XScY13TIRU+XiGXQok0O4q/HmP1g757VUOGvxSRrCkyYcK/JZCWcliWs9J1vNwhertAb11lmTY4c35WA8q045sXUo2mhOPOPndFpCP5jRP6f+2RwpUoaju8/C7oBKN6hmckpJrdFuJ8rQmWJRzHy4r7FJIfY/GRpZixcdf0njHA4oIytMywXEeUiZeivCqGdFxj3xbjPXl9xYdM/gzAU47EUd1PMx3quRdD3yLrRbYUHiN+VxENplRnNpBNz2KaiHczOGc3qZMrpFV6FgRLCsYSwjHevL70+CtiACms8FiI8fwzfLsMqmS8qpVBCfEHy0P09ZEh/sT0lL/+eqAuRDd0xSho363Qi8LNcFZyM9uo6wS6sKOHmbBx93cYGm2RvU7EUXVkk7m6BTi9QXF1hiTKAongrHKYY2/tU1ng6W5fwh91grKyrVePF0TT+WQaOq3e2SjOa0tjuiYEdHOgwj7E/pvgJ8NBhnAhylUoSjNO5RVZDWH9RV2XYJt+hIkzCkhjTuwEynxiEY3Pbx2Xy08maESybhkdSllosGNpKjFgHDRkA6JMtR6UDpq0Dn0xh0mRkpHmaiQu+st/oommZByiMuEzxUcDMmrFq+j0Kmit9URryuoHjB0DjTwUkVRdmQTGayE6EpG2vUx62NSWxJ1Xcsnr1nK1YTsYChl27IEVuGixmlNVjPku7u4ZoCzCn9BkQ1bKQkXit58GR04dK5ob7PYciGD7jbhR5qOhkaGnvdJh2DoTph/bI5b8qSEVXZkiSdqxraHbXrYqsWWFOlKRPm4Jh1SpEMOU8kwxzxyp3ClAtUzMmhXLTqXYFwlmqKeY1qGfDkgihVZvUCnUl5PtyYUbQ/TMXjLhnwiQ7UN7a0FpWOG3lSBDaSMp9uaomoZ27lA+4ZxrN8vj/Uku+p8i17yMb2AdFeP2rdK9NYJb1Gninjc4TUNOgN1PMBbCaHs8Lp9mkAg2TddQH3rEksLNRQQ7g/JqpAOW1QO/pImnipQqUL5lmBJkw5beutAWY0NHMpAb0OB1zYUkai1K3ca8gjcvgrFcEGwYEhsGTeU44zBhQ4dK5wSJSYdD2VFhOQ3RRYcj1mUk/uxGMtQHYPpBwc2gLxeEM4a0oZj09Z5jtUaFLGHHk4Jgpxkh8N1PFQ1h+lQBtnER+XgPBmwKYT2gAV/RbLNykF5rEu3GVEEksk28x7x7hiLBLo6E4VpEcn/85091EIgyt1CBvxsJMc0JVvvdRTZcoSfwdB3PJq7LLaWY1Y87HhKsRTgtxXpsASrTiP3VKboTSjMok9RsuRjGe26lmfBkkewo0lcC2AxoDyt6U6KsltlosL1Fw06U/TWF9DIGP7XkJVdDqcVKlEkI1A+5BGPW1H2jicUFtIdoJYCwp1NOjOSSVUHS+SRwzsYoRPVL70WolSODWbWp6hZ0iGHt+hLNaHvdJWPZ5Bpgmkfp32ilqKINMXpbczRsnD9GvJMUbkiGXW4hZBUB5iKw5k+X1nLBLlwEI+JSt0ZKAJ5JsaTOV7T4IzDDedkTqoM4aKiuylHZfJbzGsFKOiuN9hIBCkA9mAFP0UmOtNlknUFyWSK0dA+KyE4GuCUBMjLY1I+jicKOFLHbchIJxTe/iq1aUV3ytFdKcF4SlRNiVsh/nHJ6HanHOqEe8BjlujdHv4MR5kBft4xyAA+TBEsKdobFb1JKdmWtzXRXt/KxUiQpArJPpyycYbaSAcz2ZUsWGTxyjnkitKczIhXFYbHQ6I5TW99jtnYpdjVJS878oqjfUoq9gw9Re0AkmUcKxj9lkLnYNqafDinqFiKekE6ZDE9IcIXJeEb6UTRm3AEy0Lct6GFTKO7mkaji+p4uNmIvF5gEkV03OBKBf51DWwgXKF8fQIKelsysaFQDncsAi3b7a0XqwjlW7yeonxQgoYidPIQDyzhspIMk3bYsRTlWYqqJS87Fs9yqNiQj2fkGxL0WEJ0W0msNHxH0NTgySy2dkuIchAtyGCOU/htKWMGMz7UcvKSw2trig0xRU0ycqXDPnY8hciKvUgtw29K0MaKj2lk2HUJyoFZlCyX82Xwqh4wYg1RyymmEqhnzO8fAeQ8Z40CL5bSK0bOSdaw2NgjGXYUZYtJT6gfC/J1Kfm2GLSUgMVOqK8iDr8XaCwdHMZZhUs0Wc0Sj1uCRS2ZvpECf0XjAkf51ohkvJB93ByjU0dWt2LJE1lq+yGrWcgUzZ2WeEyCu9oejyIAZRX+gmTPwjkjN3zUz+w6KF0wL/SDUUtecav3mC1ZyvsCykcMXkfoCcoiE4shS/1uzeH94+SxB5nCFYr0eJnoroihW0Xc4vpZtnBBiPjlaUW4rNBtg+4JhzGvWvwFD+s5etNVGewDUcPmNYdaCIjuikhHCnSiSMcKyo9cINmYUsQGF1misR5e0xAuSuY2aIrqOB5z+IuGZNTS3iwCDhQUZYvL+wIYJRYxlSOS0SrqhXDu+nw6ArnHTSNDWREqFbc0qNxUEp5dIVQAGhnOd5jJHulURjzVtypaDFjZKeXn8nGF11PEkwW902Jc4NCpQh2J5F5YCqgcMSR31ykf9sBBNpnixYp0uMD6Dn9FoWMNTni2wYpi+Nsavy2ZfmWRSWwA5XoM2hHNC92jN2mpHHXksyWxW2qLkCWrOvKKRW9vEy5IoGvXx5gEiqolmtPkNVHQnjjexl3gtxTlvQGVA1LSd5GFtid0EgvpkFhGuXpGdMqK0GG0owilauBCS3B3ifIx2b90VH5rXlNjYw83E2KCgqwqv5u0AaajSY71aTNtLVY5ngTFrZ2S3SSTzHO8FKF6hqLscDu7hKc0Jbs8ntI+Uiecf3CH9MK5n8jfAD8bDALAhymSUYvOob59GRs48m8N4Y5HIkhYlPKRKiTztmd6gvZKSXhQWxIpkXR8dA7ds3q4sC+k0GDXx3Q35kTHPdSdFfS+EkFLysZmyae6z8PEsPQkUQvrrmbhbCez+Z48oIN5UdaaRKF3S6mkCEB5VgLNTT2cFpGKsorKug46U7RvGcEZKR+XDwqfqwgc1TsDVk6VwFMnUnZxgSUaislrltKsBCEq01jjiI4b8Cwu1egEogX5Hm9dF1XK0XPBaik4PO5R/XaEv7ckGQENOleEi1pKlAdDio7H6JOOEc54mOGExnmzoqI8bsjLwv/rbCpgaxd9MKI7ZVGJJl2fEhwKCBek3OTvKzH1RUN9rENxVhvX8wiO+gRzhvo3SpLN3ZSgY03R9GHZJ59K8TZ36G0QP0MbQutUGZzCIwF6PoBlESJk1X7AU89o706xY+LLFjQl8K6OdQjPXMZranQqWRKVKoKjAeXvlMTrrCL8sLyRY3a1V4O/vCzXRTc9ygd8SjOSlXBntvBPaVLd5/VLcI604ShNGxEarPi0L+qiR1KCeY/ynoD2ZglogjkZhL2efEdnkyVYlixpXhHifVazlI9qSDTZxgTT1SzuGRHvRsDVcmqnLxIuaVGpTwkNQJ+9QlEr5LwthISLhpVHpkTHPBo3B5LttpL9ys7osnyWqEv9LR2GHjNLb9KSjWU0T81pn5UwceoctlJIJqmaY7oKW7bokVQC/npGNlRQ9LPuRSjZtXhjhq5mLE03hE8L6HJOlgonc+lsoSk4I+e4tHOFvGFx6xJRQo8URIcCGrd5KC0+cdlwgVn06U30M2cW9FwgHMuxhPBoQLQngumIfCjHjaQk63LyChRlR3tLQVZ3uEyDA3dQytwq0RSNHH9F+LXlaU17k8NrCzfSxYbKAfH/dBrMTIhONJ3NBUXZ0ttQEM4bgumAeCrDdCSrmNX72Vot28orjuVTHcmIJdmeYHYKvaLYGNOdqaC6htYuUUPTyIjHFNGcIR3LZeJSCGeyGM5JFkqSmc+g+m8lklGLtyLCNr+piBYdvXWW0pxjZZdkjrtbcnrrxCPT9LOA4eGAomLxm4rqnQEkhvZsBVcqxDuwakknpNQfb0rpbLBkdTleryOqYuXbVSVweVrcDU5kgL2upnGXwesqSsM9lG/xF83qhMM0PRhKiYZjRm4WKxq1r0yWycSDTONCyZo+mDjBAfxx/wb42WBQAn6YQqcyC+4drVNuSklI5wpViLpVxzLId3elqLyv7m1pWJCMirfkCeclj1CeE/+1miIsZfQ6PvE64Yplw4WU4hQUYymdQEoTtu3LTNmTAc06hZsJqRzRtLcXkvEKHcl0BRM60A5zXMjlRRER9ks4Wc2R31KHCLLRHN2V/XMGvK4EFMmokLfzSIQVbskH44iXI5STIFZPR/hLQgg/YTOlu4a84uhECmcsHKpA30YmHrfYWsH4Vz3amyCvWIqyxV+WwDWeFHsP/6hBWZ+j3XWEZzRJ5svMpg30ikc8WRAsyOCtY02+EBHGwofUKaRjoiBUmRyH6WkWz1DYXkDW81GlnLwiKuV4BMYfd4zD+8exZYu3YrAbY9xSQLbk4/ckQ5RHDr+WkMQ+2UYh/9vAES4q2qeJ8lItBaL+zjSE/Qyb74h7ARwqUdSlZJ83ilUxT29csqO6nBPtLxGvUyQ9Hzfad/63kA1JOTCesKtGt3ZvlXR9gvfoNvl8CV3PyCJLNB+gJmKiO8oEd0m5PeiqftAPeH0Fat841w1n0PTo7UyIDoTEUznVfR6dzQWtM1PMkoctiW1HPpKLkflQgV726EyPkG4W02mcwuU++W11qk0RrCgLvakc5VniqZy8ZETdejSQ+6/bz1yN5dhDFWaGQlFNxyIuiqccK19bh6n3By77vd+YWhEFvloIcNVCLF4CS2F9/JYmreXYVJTanl9QaEd0a4msLllRr2lWzbN1Du3jVVSusLGHyvoK9pKj2ADR3pDeelEBON+RjlmKlmbo2z7xGGQNi7OKZFwmcMWIGFq7ngwD2WldgjAjP1KT71SS0XKJZMkaW5dZnq7j9aCY92lvlzJpUXJkI0JnAESYUxHaiLZQVHKxLWpq+d0YJ0FTraAYsZQOBGJkfFTMl7Na/zwOp7iOR9Is4xo5aiGEUgGFwoUFttKfrAXCH1WJxl+RLHk67AiqKfZgBZMBSlTiRbXA9DzaWyxuNCVYidCZo7mdfoZe+MM4JOt8QLNyRiETCge9HVINCA6HfRN14esmZXmeMJagj0aSeW0U5CVYOU2oAS42qKGUou2T98yqgbqOlXBjyyKK6qYeLtMUUX+SMJXjrRho+rgDkdg0zXkkUxk6NXgpFIUiWDDQ+sl04hjg3wcGGcCHKYqob09CPxuYiOghq1vKx6S0UppzEAth20VizxE0FfnWmKJsKUpiZWADR3tbjjcSkxyqEh3z0ImmclShyjlOyaAbHQgJljVeV6ErGeExD901eAcj1EJA9bDC6wm3KVuXEs1q/KZkdIqaiCl0jqglIykvFkM56ailiCz+okdpS4usJr6EedlJVqsQZa/fEl5XuKApb2yjO5KRqP5bCZ3KOTFxf3Buethajg1kebCkURbGdi7gt6SstWnzPMu7ZSCwYxle21A5qkgmCskOrAgPzobCzcrvqqF7Gtcz2LLFH+vhlAy8qhBvvaLkJAs2bKVMpvsl1L5H3NCj51D7yvgzPt7REJP2OZlDlsP7xiUjtyDn2K0Eq2VzKcVKeUvtrcBBKSlZD/RISlp3eLMBwVy/TLVgcFGBt2xIx6Qknvc8KVfPSABkWgbVz9DoDKp7PSrVmN5UAaMJlVsiCSQDKyXSfmcDM9nD1DLseCqBwEyI2lvBG+thDkV48z6VY5bwljJFydE8TcrbKPHSg7733JYYG1qcguBQgPMcfpTj9cBbNrR3ZFQ2tIgOBFQPiAq5iBy6LRkZb9lgS6LM9ksZLjGwIhOUIhKvP+vJsalcBDoElmIyldJ/ALX9muodAV7LCGVgkwgPdM/IpKPsKB/wiScKtMRTeKWcYmMsnL7+fR0si69mOOPhz/S7wyQKEgOJ8PCyVoBt+sJ/1GBi4W9mdbtKD9CJGF2bZY/ytCabSrGRI1xQ9Lamoijv+3IOb17CRk4CEKAYynGpxmtJoObP+eigEN6acej9JbI765RmpNsGWu4BZxzhkmLlwBDV/R7d9RLQnPBGtKd0JMso1pJkQ2JtlDcKKocU3lwg2bgJ4bH6Sx6lnSuUxzugID21J4F2RQyrXVmEMqU7IsJZD4bkxHo9RfmAL5ZJWgIqv6NItib0NubSUWfUCm2haskzUefmEcQ7E1Z2F4QzHkUIZkq8JbMK4BR2g4gw0FBe38ZWi/6Eme91VClZ/BnJvKtCBDM2QOgMnkzkKjeXxJOzBbXbfdxkjI4V0ayRcvGKTzhjJBg3DhfKpKS3UUR6yYjDFYraaAcXOvxlqXb4bYWLxF+1OyXPEHKF7YhDQGlc6DvF7s7PaIS5d1gcxY/5N8gA/uwwCAAfpgjntZhA58JlS8fF9worvJNgRdGdUuhEkw0VeNUMZ6C9PSeMRKXppJoqmYxKTq3aQ03GKAvhgmb57AztS6utaNonHbJib6GBuZB0RN5L1+UES5rOBkdnIyI8WPFJxizlGbGICOb6BsE1UfGmDUtecvhz/SS1kTJKekcdkypsKG3jytMau61HUZIyczzm6K3PsTc2iGY06ZCjeaYoA3WmJHO4qSMeff1WXPlYRm99TjivmD8wIuUvDYf3jVOUHenmFJoeOoGV3VKSq+6XQTRdn5HVC9LJjGw8R62LGb3JYDqatC1KSB0ronmF14ZsuCBoqdVBujTexd/VFCNbBQvfHScdy6Wsuj4lXFTYkvjFoWXASEdkIHCqPxBs7kk3gABqewzpSCHmtaWCfKgg+k6JdLTAb0rwW7vTR2cKf8mjqEnZsXxMU9oXSCu/8YJsc0K4qCgdM+hEsl/xuCO+YwiMZHjTumNoooXqGFZ2i0I5aGq824TsP/LVkNJx4dvhIE89oQr0FCs7pARmYilvpWPCi8qqjnQix28rbNOnsq4jxsvD4k9pj5bEe3FIMkHJnQ1s4Fg5W5TeNnLYUoHTbrXlWRE57HSJyj5fBnkA7ehuzHE+dE9N5EY/UfKMReGb1ws6G+SeqhxWqEWf6Cs1KdMd15SPKqJZRXZ2G2ecZAg9CL9ThrlwVc2tc0U6ZEnHJCNURI50rBBLpVwRHfcIFxWN70r7tvDsJZnA1QsqB0VcYFOD7hrcUEZ9fYtoR1NUx4Ui2NChvSMXn06rxKTbQrsb4U11oZGJ8nveX21xJqIYuY7RjBFPvW09sqmUeMJSDOcirun7JHZ2pdIOzSCqeoQ3pwvQ2lFb3yJYkvOHdrh6JgGeFsW4Mo7yYUP9bk02nDNZb9Gdq+DN+9IOsiUZ/7TfcjJY1Ogc/Ba4nlBG8pqUkU1g8WbFhUBlQMeDUPi5flNTv9NDZYpgTwmvLabjZka4n8qKkbc9WsLMirdiMZxhux7JeIENHJ2lEhTyXFg+K6d2h0952lDd44vRfKJRVvjHpRklzwInIrveuNxXeVmshsyRCGUVyalCa/FbmnTEEm1q4SoFqqf7kxxHelpPjmFfic7+BqovgovP6IkN1HGPfCgXn9X+vUNgYSIhv6smk7ilB7d12qAE/POFQQD4MEVvS0a2UR7aznMEwzHBMR9PJr50zoolUBrKpIyzGJLu6OHVU+Lpihgf9xRmfRfWx6iFgF4S4I5HYl0wIb1c7XwovD0kQLNlS96wVI5KQINVRIelRZRw+yCa9agc1oTzmu6UI57KSdenkqHoZ0v8tgRs1heLCGdcXxFZkA3neG3onJFQRFAshKQT+WqJBy3WCnlFggnlW+JTYpzqewAulihNdMWio2IJpn3Q0NlSUD5sCBeFeB4sGukhu9Iv140JpyeaE4GE39REhyQ75M/2OVItn2RYiPckmmIoF2XikBDFATobC/Ka8KKK22uiEi3b1XZe4Ywn2a6jAcmQw1sR3qS37BHMe+Jt1pDMFr7DzkYieClZ2lvEIia4u0Tp5hLlQx7RvMNfEQsUO5HSW+dIRqWrhGqkfWsfRzpimbzgKK5cULspojdpsaH49p3IgphYoRopuiZZmfTrIzIQNTLizSnxBplIFCs+y6dIJisvScBDy5e+qaFklfOScOH8jvSG9dsSKJkVI8bhsUb/awOz4uGN90BJ2d8GUhpTTpGX7WpJX/reWkzLEM55eEOpGIbnYhrd2dHvZFO16ExhOmKb4RIR+wQLBm/OB88SLor/o/Md+bqUtAH1fZq0AcW2HvqxS0Iv2GjJVkK8tsH0y6GdHRnBisb0JKhRuQTYpp5JJ5oFjd9IxLOtEBGG34a0DtExj+Tbw5SP9U2QNZhGSnQ4IJzThPtD8v83TByLDyZOobXwxbwVg0o0tizZqyzx4K4KZjokr7hVy6JgWUuWvewoH/LIyyLcsEshZsEXIZRnKRoFtpGLuCiTftfdUxL8MJcsfyb7l8yX6O4ZIhmzqAuXxBdzPiC7q05rq0UZx9h4k3TI4RRU9vsc+tpGoT4Yh5uJ5PeeQ/1uTdj3+OxOWtrbc3RHJlumrSkfNgR3lGSSOJbLhDGSjKT1RdgkYjBLOmzJNvVbo1nonhFjjYNU9x0CpEONbno0vuvjAuH4qX7vcG9OrIOKUIzWu1Ny36DlOJwPnQ2W7npZP4jk3g/HemR1Rz6VrrocMBtSmhYbrHBOU4lSSDQMZXjdvkglV9hqIV6lTY0bTQnnNW4lIK9a0vGC2l0+JoHSEYPX1qi2QWsx7behUGEGGOCBYnC3PEyhwgKUoxjORaV7rCz+eIUo9sx0KOWVTAYDFxXYto+bjmTQGk9Ihi156mGXAphISA6LuW5vnRUT5fmIcEF4LenOHtWDGtPRqHqK64sg/JYi2RmDceTNAOuJxUIR9IOO0QJdyQimA0xTWsbpR4jnlddTFA3x9vIXDZUjUnZSqaZ9umTlkt099IiU7LIhaU4f9HmMOhcFs+uXXoqyJW2IACPuBOhEiOJqd1sG+2ouAoEMwgVFtiHF9j3G/JYSsjWQjDqSISkfRrPyXjaSi5Bj0SNckjK31zSU9/uUDvuYWBHvjvGXhDfmL0kJsYiEa2Y6YivBaEIyISU0rGRZ8noh/VSrFttXuvpLGq8pYpbKYU39Np/qAUP1sMakwhlrbxdvt+6kIpqT7J9aDNAp6PFYujd0pZxvulKSPLBvAuVbWtsLSjMimslHcoJ6ImrhmsX2PGxsxNvN9QftciY8zpV+0IwYLqcNh9vck9J4JCUzVYiXpA2F5xevzyRD2gU1IiVN01O4wNHZYDFdsT6JJ+X6+iu6b7ItGbZ0VPh4pm0IZj10rvA7UOSS1aztF26VijW25UsZfVkyNnnFocs5KlOYWErpKpF2cToXOgE9g0lh+REZ8bhFHSrRPtigO9k3UQ9Fza4KyXT6C554ZToph7sd3dVWa+GionbAkWeGYignWJHoNW2IqjarC13AGjA9uT8UEtj0NuXEGzO6m3PsXEResXhLHkniy28eJADo+8Xp2YBs2EqWdygVz8ueBNkgQol0SNTJyVSG7knKX1cywlIGnpSah9evoKuZbDvVDFV7OE/EEt62NuUjfaPxAjqd/mSmJFnqomqp3BbS+ddxdKpIhvtdhU7p4AJLbb/GbynySJTfRSD3jddVmKmeXOdQqChOS4k0WEFaMC55FGULudAHdJ8uYfu+k244xcWG0jHhG7Psk44XmH5Aqayi6GfXuxscqqflfu16+AseRUmuofCNwVYLikr/5laOZKTAxIrS9qa0eZwvoXNIViIJbFONv6zlmVOIEr16wMOk0LxxTH4jx0LJUAeOqJKiYnEpKM2BWgxWvQ+Hty2hY01Wg87Wgt7GQn4/Bor5UDKvWzvo5Kc4qDwADFTAP18YBIAPU4x/MWDohhAKxchpC+hE+Cwmgdo+s5qFQUHQVDIrnvW+Z+sxG6JTJX0sfYvtegQrWjKK/c/6TU28LkfNhAR7SmRVyS4Gd5fEAqbez3qt+JimkZZtFfGC6+5MsRtidDXDNcVE+MRf3AvIKo5kW0xln0807ZNXLSu7RawQLPXNaQOHzTVKyQMjmDd4TS0WDNoRT+aYdr8TxlDG6PYlcIrRbylo+ehY2rnluRHVaTnDdBWd9Y7OJrF/qIx1UesSOtszxr7VD6JysXcoP2KJ1jYZmHQ5l/ZzdcvKTjkOXUB3i1iMxOMW70hIXrerCu2hO6Fxd78LQKpIJnJsZtAVMcDOGsLDNG3hMvlLWvrBbklI12fkEylkmtaZCb0JRzLiaG0vSEYsxcaYYNGQ1R3lGUdvXd90Oulbg3Q9iorFrHhEm1rk1RPn0MN1PPyWCGbymsOUc9zeimy3bAnqCWZJ/Nk6OzJWTrEkXR9S2b9kXbGaIXS+w2aSDdYtj7xiV/ltfqvvf9iWlmxFiAgiLMQb+2bRqcKkfZuQak5WtSTjOX5TYdqiylaR8NiKqohW8polPqeL6xl663Pam5ESqQJVEh5dEYHLNPmIlCfrd/eVz55DdzXxOVKaj8cLTE8Tjzm8RU/aHjoozQgP06SKyq3iZ2kDySiVjilsn7eWz0UUx0qkWxPIpd3Y3KMd2jiUL1kqE0t7RJQEncEKeDEUFZmwcLjU55EqVKwpHfXwuqIczUcyisSgdgj3y+spgnkjbceMqLj9RbFuMbHwZHUiXnatMxPh+oEIV8pWTJVnQ9KjFZRxbLjesjTdwKZG2iIWivaXJ0hHJOuYJtLVpjSjGLpDwXSESjV6JMFb38Xrt7zLqo5kPCfelhBvyMiOl0HDyikFdlcX5zkqhw3pkKN3WkyyLSHPZLLktCNcMARNRTGRsvKIlKzqCOelp3DpiC8Zy5FMjm1XSjjSwxwPKR31SBtOjJ2VnA9dSJ/nvFYQLhgJrMblepme4jFn7kVniqHbxbQ8nihon5bKfdoXiRSB+FQCdA7VMYs++NKqsbLXl2dCKV/lFaoC4qmc9ikp8ZicL3/ZkI9IqR0DvaZMvkvThvYmx6bTj5Od0sUGlnY3wpYLrHHU7jb4izJhjKYN4aJQfWwh+/dgwv6E/gb42WAQAD5M0RvTYo8w7bNy8xj5WEaxPSYdEtHECXFH6ZBPZ5u0oEo2pHhtKS/6TU1lWsmMsp/ZSIYtugCvpfudB6B8RMjo5uwVkjHbV7M5ulNW1ILVQgK2QqE8i78ifnWqa1DHQ2xiiI4bso0pyop3lu14FPUcPRuSDDvShsVf18Nra+n7Wnbizn/cg8SQ9zyiwwGlWQluupOO6JhHda8n3JxALF9AuI/LpyF8sQ2xlA2PS5qlOFSR8mW1wIZWVMO31FGHI3CwfIqULNMxCWJ73xqhGM6IN2T4YQ6ZpnTESBmpEPWqyjR+WwJn6VbRN/OtOrKqYuUUB+tjgqYM3ACVWozzHV5HU2yLpf9xxVKEkrUw06G0qJsP8BY9TCDlY5DjYixBGUewLNzDhcdnq5YT5ePC6wpmJBOmHCRxQDGRirBESelb5VJ+r+/ReHsjsaJY3xVBxU1VCbIV/Ywb6PkA3ZNALjpuMEciooV+Bg2w9Rx/fYf6XUbKvi1FZ0u/N3JP0d6R0ZsqiA4Gwkmb88S/ckaRjFhsqcAcDygaBSrTq2phVyr6LcckuCvKEsDlywEq1ZQPezLBcVDd2EQZCXKKU9tUR7r41RQ7F5EMQ2d7ht/3n3THIrI9dcpHDZXDYhfkxZJ1ckbENb0tGXnN0t6RUzRy4ZVua9PZLObaXleI+0FTY6ZDKhMdVC4lYH0owpsOcZEl2d0jr4rfY15y5BUxVMeIZYzT4iNXOSi2IMkpIi4qQulkotoe9mCFIhCxUFF2RPMakyhsTdq9BcuadFQyyTZwKKeI9od4HYXbEENYMHSLJ1llB6XjGtf2OPZ46cbiz/kiTGoaUTU3Ummx1hO6Qnt3RnM7FPWc0nGNPliSqoMSVbDfEqFJMB2AlWwrmQRw5s6KbMuT7jDh3RHhvhBzLCTd1YOoEKX2xgy16BNOC5ezvSPHbo5FCLYU0Ph2IBOmRY88N4RLYjiPEsGYKxXf8zkci+W3osTc3ix7YnMVOG45PkVekQmnSr73vLJDGa5U9FtKKvxyRjouxxsuKkoHfMgkOPSbkl23vojaTvhY6rZHafcyy+cIZzg47qGAaF2HsCrpu+5m+V0cvnUSpiPKhz2y2RK6lJNXpPqgHFTOWiSekEmC11V4e0V9PMAADxSDAPBhiu6jemI/kkjZN5j2KToepqdo7i5wgQhC4vFCBm7fMnyjWIboWKwUWlulX6yONaYlZH67uUc+JaayRdXSW2eJxx3pnXWCRU28OyYfy1ATiTyYUymrFBMpzIdSnkEGBVtyKE+yA/5hESBEMyIIMS1DUSnIG4WUQY+VpcPDduFwQV/VW0/Biq1K81EJ8Zglq0mQ25u0lI8LVwzjmD80JAax9QLd1XiHxBzab/XNdEczabO2YKjul8AhHZIuEihIJ3LssGSmgmVNtABYRTjjkR2toGMt5q/DFq8p5TbT0ZSPObFuGetJJkuBG01JRuTzHIuoHJVSHEDnQIOiXmB6iqInbasqB6QlH5b+wNWf6msolRPxnMsk0HHNgOC2fsnfIm2odN8aqAzJiCUb6vPgYvDuLlH7dkhvYy7XP5fz6a8oOhsdyZgMwFlTgpis6qgelYxXOikcO6cREYCFeEdCXnY0T8mJxyx6NqS0LyDtBrS3SsktHRKbkHCsJwN/x+Cv61Gc2qG3SaxMbLmQ/rSLGpVr8vEM3TF4E70+f8uhAkt3Q0HRKIjuDiltb+KmYrlnu5reVEFnS44/lNA+UqdaizFdTdbzyb81RJ56qJGEZNRSOuKTjVhsIEGX9R3d02PJbPuQn9KV1mJW2rqZFYO/rPGXDN6Cj441aVf84lwgGTjdMVLarFqSPXXplVyRLLLKRe1sO2KRorsaF4jqWTnwKhk6V5RmRbzU3ipGzOZIJNnGeo7OJGA/4SeHkwnMCZ9CPCkp46QVm+uLqbx+Z55kU4qb7U8oEinZeh1NPG4xPaETlGYUwZIim8jwO4ryMYXNDC6wqFgmN8FxD+dB6ZBP0GQ1E1WaU+LZiNiknPj950M5BJa8ZkmGLW4iIV5n6W3ISbYnxBszmawsSsAYj1lUaGE8IRkv8DvyHSNDbXAiemvuEO4mCmwm5e10rCBdn0mAtOgTLfYfkAfLmLq0BFQnusggGdjeXJlgSVrbUcj5NR2NavqonpFzH4r5NPS5h0G/h3PbiGp/S0IwlPQrBopoTlwGVA7tQ3XJkpZEhKQKcLfUSVaEC0m/U47fUhQjuajukd+1C92qVU78/0b7XExFsj4jbXzP4urBwo+rAD7xN8DPBoMA8GGK4I4SRejobZCHR151DK1rYc5ewSmHigqxXLGSySHTdCfV90rAkzGNuzReWxPNSRYrXFIEt5ep3BLh/L4C1ZOuIt6uFsrCyJdDRr/mU/lmabWbBMoR3R2KCrVSgOfQ29uoegotn3BRkY4Xq47/2UYhbpuOoX67J6WrFU1RcoR7I+m/WbWoDMLvlqVJfdmhjCVc38GMx/R2JsK7G3O4wOLNBELorlmGvutRO2VJBkbjCM9ewqun1L8d4Lfke5IRh3ucOP1nwwXkSlpmHQ36XBxHc3chwo9+wOB038PMKbIRsaIAaG8W0rY9UqY8rcmr/a4NZ7QwiWQHFs8UY1q94ok4wEG2W4QP5SOStQiWJXNSTCZC9o8kWIjvaqBj4U76TY1piW9buORob7JiBqyEY5af1ZYs3pKmckSjE0VWs7R2CpE+3NYi7ffV7W0QVatOpPMLVpHeWccZmH2MTCz8OTEMjza3SCYKohkPWjLRqO730OOxKJl9MLNShqyu6w/aC5qkFaK2dPB6inQ5JFsKqe4VOxoKRTZk6W1NxQC8nKFyyJrBqkFu6a4Q09MoXwKd+M4GzIXSpqshptVoR1RK8ZqadiuSwLHr4XUh3Behp6NVG55wVkqdXkfEGeZYSDxZkK1LybveKidPJ6I4zjfHDD1iHrdJeilXbhOlj0oV6bAEguG8cMFQ/XZjkSOdzPF6otD3VsSP0kYWr5HizmlJRvNASQyU11mKyQS/KbY2ykqA6M/4opoespSnFUXFYisyoQuWNGZFMuBFoyDekBEuiA+f9RFxQktTviukfFRT2e+zeFEi2fgdItYq6gXJSEHnMV3iR/RQXUNySo/e49rSwrAj94/XUwQtJd1VxizL56aSaQwtnTNjgiVNvM5S39MXhjmEK2IclQ0ted4shth6TjjjoX1LZZ9PZ6OFRiY83nomPnozIV7T0Do7oTTRZeHOUfIhUZDrsUSqBS2NS0T9PXSLEFWzU2TSsLJbOtDoRGHnIumKExSo4VQmvsPibxpPil+ji/pt5fp8XKwE2TrpP9uMQz2iSTJWSEvBrS05tJkA892qTFRC6cWdVUTk5coF1DJsW6gteclRRMInTcZF3Z7VHUWE2OuMJYRzBtPS+H2ltddR9HYkZMMF+VhGeZ+PKxV0N+c/w1HmZBTuJ/M3wM8GgwDwYYp4vM+1amuSdeKRlX59hG5LFIvBwZBsIpP2b6MJaEcyKSUOfUob2/alu0ZHEY9b4g0Z7e05vQ05Xkd82AgsY1sXcaElPl6hd1rM4lmOhfNz0iEwXYXfVJSOG+kDaxWVyQ6maUgWS7jloM/xQQYFJSpfWsJxcp4jHRYundeTjExpxlHbr/Fa0t1DF30OXabw95XIDlSxsxGkWrI4HmzYtCB3upLzsXxmTveWYZINGf6sT/qtYYr5kO6Uw27rYcelHFoOU1FNzhtMT2xLVKGo79PSisrIjPtEkKHHErGJOPEM7qtWTU/Mkd26hN46i04V45MrxAulvm2GHH93S055a1Ou24pH0fXwFnySUUdnqwTIfkvhHQvxOv2sZUUCeRMrikJLCbFRkA470rrCNnLS8ZxsSCwu8tSTklhVBqSsLkKKE/vbO17FNnKSUeGc2cgSzfbtPZR0mSgi4T2aVPqp6kzRXSjjPCljOk/Ks711lqIZkNVF7GOnYnSm6Byqi79dTdSl7KuQNSzRMV+CHE+2e0KZGx2S4N3cURHPuxkPnUognYxYgh1Nwn2h+BXm/YyKJ/1r0ZKVaS1UhG84HYmXnnF0z+4Rb06pn76A63evSEYsYS3pe0pqCQZbGhNYSDXxxpRiY0xRljZiNjEsf3cM/84yRc3S3plROuLhfEdRsVQ3NCke2SLfEvfV1hZXzSmPdOluT0nGxdbDeo5oxqNyQ5m4FZKuy8kmM1E7W0VQysjXJ9JtpquwkfTI1oVCjSSsnCLBUjDj4U11SYct4bzGLImgAStctNJhCYhKxwzZiFzndFi6s0R3RiJI6Hn4TY0/JwIhjkUUHUnduZUAdXcFf0n3J4oJ8XhBZ0tBvEWseIZuDAiWhOPmYuEbu+G0z5eVjBi+hcTQma5JxcAiitYU/D0l0iEnYqBjoUy+ZgJUVMD6GK+n8KYD0sQnXNLSD3uowM6Lb6bfkkxkaXNLVMgd+S1VjurVVnTFLrHH0akmmyuhtKOoy4SufExJwJ7Kb6y3XiZztbMWRPkcOJINKSpThNM+yaEqrprjtQzd5ZIYXFcly61z+f0XO8WqikaGWfbwD/YnCrnCjWQUgYiR/CWD6nNm82qBWfIpOj7psGTcTdLvo95U1L8j/dFVyyMZtUTTPuhB9DTAA8cgAHyYQvebkjtPjHpRMtijxBDa9BTenE8pyoSk7wBPetzGCyWCkVjsM2LwWxpv0QPj8JcN3Q1ijaGMo90LpTySCncHpNRkT2uTjBVYSUJQmmyTjBSkiXAG/SWDGU3IS/LAGr3RkGyL8RsJlYPCo2MsIe8HG3lJeEm9CfExzEZyikiCmBMP23RL8j1vubbBbo4phnJ6mfT6zRtivaJSUb2Sq9XzFM2JYrlYCvGmQ5JhS+v/jWM6Gv/0Jl5by6DcsLS2Scslf1kEC+G8qJ/tUiildYUMfqUCryMlYVsucCuSYQyWNSs3jREsGIoIvNEebnNPzI3nK9hGjt9SVEa75MM5diqWknsI2dnt1TZtVHP8FUM6XJCXHPq7NclSIGT2eMxJMLMoJs02slRuidCx+NglozKoON9BJac80cFf7gtsqrkoFwNL1hB7CZVo8K3wAWNFOpYTrytIhuWceiseaEdpWvhUXld4jX5TyqbOSdcKnYPuCZfTXzYiRFqUR1E2kdHdkhNPSPY0WmCVM6j6fayVVThfWs/5LU13pUS8Je23mgPWCw3B+vQNm0XkpFOxkLFl6bChZkLKjR7Le0ekNV/D4So5bk911ZSYXItXXKrRtYzguI9bkc4VyYYM5Un7smAFnBJT6N62FGpSrm0tVkg6ATY1VI6I0ELFhkqUCn8vVySbUslWb0ppnlpQvSOgepdPcFQMh/NaQZEblHJiIOxBdZ9BdTzSHT3UrGTXXSpekNlcSUQxJTlHwbJkY7ESGOtEstx+PaV6SKgKedXS2yzlfIyURE+Ui83GrmThStKn2usqsmEJbpgNQYPuSrDptRQruwuxHPIlqHEaStVE7E1aWix9tPB4XVTglzPJbOYKk/I9D1Lfko/mBItG+L3HA4yxmFg4eLaQ+ymfyCQwjhWmK91dehtzutNVKkc0paOG4LgnBsqVAtX2UIdKEpzWcryWxub9Z2A9o7m76HcMcvjzHuG8QSeaZquM19GEc4bS/kCM10OwwxnebCBtC+ckCDMtDU5K5/V94I6WyIYtruNR1EUN7y0bwnmNPtEC8FhEXreoRKyDvKZk/lWqsVXhHva2JzgjljStbSLY8XqKYElTPeQoHTxBBn5wMBCB/HxhEAA+TGESyOsFtuTQKx7Jtr4P3sFQfOVWEF7bzcP4MwHhjCeDW1QQjfZImyHlY/1S4iIUJUv9Nr/fOUFRuTOEFZ/egvBgTCqGqwynOO0oR6m0E6sKr6fbjCgfNah9ZZyWTFZwS5miZKX35rgiOBDB/jJ5FbwLFoVgbsRHTmd925ghKx6Eix75hkR8/OYleHNdQzZUkNUl8FRHInTLsHRwmHBRUd0nmRmU+ODpjrRjykuO/Iy2mLUaCThHdy+QDlm8WBHHYnKt2wZbLohmRQEaLCvUSEp2dht/ZwsdK6qTbbHnaCsoFJ2thWQWMg21TFSdFtIN0lwe7fC+W8XORRK8LnticLwjJr+lTuO7PsGeEs5z9LakRFFGsi6neuoS3rTY6pzIGPQ2ZYRL4qmX1WXwtHdXMf1WUwSW9q4MtykmePyCCIGGMsnYLfr0jonNj2mLv6EqwF/y8FdE1FI6bogOB0TzwktTqaZ0zFA9KN0plIXSYV+82RQkYwXZsCVacFQPGIIDYlehpmIJiAOLyqX0DRAsgzfrS+axZPFWPFZO7wf6NUfan8BgJeAotsVwZgvVNphFn2Tcko0UmH0lVM9Q1HNMT4yYVSWnqFrKW5ur3oemq6iVEhhNGN26RD5U4C34pMMF1oei5PCWRSFe/06IWwkkAPP7QX4lxT8i56l5dkKwIF6LaEel0cPWckr7A6KDIdXbA7qP74jqXTmSL4z3eWxC+HMeEhDWU9qnpCQjjnS86AtADOG3yxi/wDYygkcukYw44X9NRxQVK9fkYEC4pDBt6cNcRKIk7+yQe80ZsVgxsfhh5rMlVs7KyKpCkzBNj7xq8UvStzhakJKourNC+aBkAMt3haR1RzDe7QfmelVYUZROZLz0KjdNT0dUD0PvaFWC06ojW4owoWTJVNsjWwmlt7iF+JFd4nVyrcQcXhT3rZ0SSGZzJfKS2LLomVA6x6SaZEo8HnVGX3wlhth56URXFYXe0pGeub4jn0wpj3fwy5kIRGJDOBzDis/QpmUq21eE3lIXqklpRmFnI7n+CirHpO92ETm8OenIEyxLZcBfkOeM88VEfGUX6I1dqRgEluioL0bf46IwLn2rJOrvJdnn0qwmm8gIVtT3bG4KoQto36LHEmwtl9epwl+RYN974ewqf/bBgkVR/Jh/9sEmMv47wiAAfJgincjRqYZKTrigcbk461sx3qe9xRHuakqg0LfmCBcVJJrsQJVw2qd9WkLnvC7NU3P0WCKDXluCwu6pCV5H4S17Ii4YKoQPdDQEBSsHGzKAVcV3KzgqD8msKg/FdEhaP2HAH4rpbs0own6JI3LEN46IbcxIhg2lBJxOZuhUUTncLxu3JFhIx4r+cSm8ttg6hEuavFbgbeiCZXW2PrJhmWh9B50LsV7linwkxx2s4O+PpH2Y75g7PIwNLfH6DLO3JAGm7zAtsapIRsQv0C2E5PMl3C116dvZ63ME647wmI/X1KLmVOBS6SOcDjlGxps4wyrXztUzgpaUtE3L9HuBQmurtAKr7pVycGuxQumQT3P/EABFIxdF4GHd50L2B/qeZN2ydRk2cHS3ZkQHAyp3+9jFgDj1pQ/pso+/fGIgkWtblC3xurzvbSeBhAQUjvyULr1xR2ubJVwQw+b2Fou/5JGNZX0fNkc+lEv5dtrQWyccwGR9Rj5UkLd9vCWP2h7psbvxqYdIJgqyCvgdhSv3BQL1gmA4xoXSy1gVQFiQbE+k7L0/Ij0spuXKSrY7mBNz52jaSIA/3A+ijoToWGP/3xBuKSCfSMmGLLN3j0rwnIqtTbEuBSPZLxtIxxGdiA2MM07Md2PJFiklQWJvd4Je9lfLfcGRgPZMFQpFb4NY1/QmLd4tFbG5MdLOqzvpYDhFadfvGgLMhQTH/L7Jt8VvK4qphHDZwb4KwdGA9v4GlSNyPRp3QXTco7WjIK07ehN29ameV6VlYXmfL+bf63K660XkEo+KMli3DPlwTm1du9/+zlEqpZiOpjtlpbtE2dHdJO3XdCE8tmSxhMoh39kT5XHNUtvrUYT9wGZdQbExJq8VNLf1M39tyfD5y4aiGVC/3aNyRPwsnZLtqkMldKKp32lIJ/LvcQY12KkYZxylmX4GXDmSR3Wo7PcoHfZxiyFZQ/xAdaqkZ/eEWC4l45Z0IcJ0+tduySfuBRTHS3g9qQYkSxEusLRvHaF9tI5qe1QOGtJhSzLmiGb0qvp6ebf83+spisiSVwvxCuzzgXWmsA3xxlQO8vkIf9kQHQyIp0Q5HEz76ESTV0Sh392SUzsg3+HP+3R3pbiSJVzSBHMi9GE+RB2NxL6qLMrpZEQy5McPjBLOeD/7wWaAn1sMAsCHKapjHekIMBeQNhyqYyi2xZIVDKQM1ztYo7ZzGerigdXeWkBoRf2mnHQFmA9R5Zwi9kgb0j4qGbWcsvk42bjM1E1PTJIbj1jAJIpoxiNcMFLCaBt0IbN4k0hgYsPveemZtiZbCVfbkllfHuDx+pxqvYc/6+O0+PJ5cz5OnFIIdzbRier3Dla4LZK9y0fE0qY4tY2q5uTHynjtfseRdQWL8zWS2BfO0UiCDRxD3/Zldo6ULZU9wR8D+obUfqvfYsuJlQxjCa2dOY961N04z5FHwlmyMxEmlsEq3RZT3wt+OcVf0vgLHqVjCtbHNFtlTL9Li980lO8KaZ8m6mqdSunRaYcdkhl9Z4sYv6quET6hFjuZYNajtleCxXRCTL91DtGConTYF+HGeE543Cfb3SWrib1MnhnGNy3hN2VADJoiBtKJEnPvsgTV4YKUTYNlJT1Pj0V9bzNHvDGVVlxGyn1m2ROjaoVkk7bG9HakpP0slFmWXri6LYFwb8IRnLnC3pkxdE+yVvF4QXg4IDoYUL/TI20FAJjHLpHXC0r7QtSyL/uay7UB+c7Khlaf4vA9RbXpyMQnG89xQxm900QhrNoeejShvsfg7Y9I76zjLxlQfS5WU0QNqiQG31ndUjkoPoaVI4bSMUO6FElnlCWfcFETLEMyUUjgElhUTyxV/LamNKtBS//d6KiPSSWrVLsxwsyEhPOiWI7m9Kp1yPjkCsmmFJYCFh+dU2yKRXFasqQN8RpsbRcjbdPVFKPCpQuW+ybZmfTrzhpS9sazVLesUDttkWhOrisaomM++TeHV31BHaz+zlSiKUZyTCOTTNuIiFVMW5ONWJiOpK2ZceRl5DeTKep3GbxDEaYtdizp+pSsbklHhMuqMkU6DO1dGTqFdEzcBrKxDDuUEY/KfRU0EjCOoe94mMMR5UPCYS0iKBqF9E/2IVyQknHWb/9XmtFkw8Jh1lmfCqP61BgFbjgj2FOCsUT6Nida3BCAbFxMsb2uWLoUZSvuB2UJ+IIVERhF82Kij5HJZ/fRXeLTequCkeBIQDSryWviuJCXHPaMNrrfrcOGMH6TGFvnJSmJpw3xKCyCfjm5UP3ftCJtiEDPBm6VZ2sSKdMnw8JVttGDywG07ifzN8DPBsq5ge32wwnNZpNGo8HGd78R45eobWrS2TOELVkqh6StUTxZ4MKC4LgvdiCJEqI/0uUCZABSBauWIL2pAleyVMc6dA/W5b1UHq7JRL/PcK5Quaa2oUm7HeEKRbg3IhkriGakTVZ05jKt5TJBOaXYVyUfzUE5vAWfvC6K5bFNy8zP1lEdaQGnY83QnYp4VAxzlRNBSPURCyzO1SntD0iGLeVjmvZ2UfFloznRtC+dKyJHNG1wvih1rSeZm2BJmt4nUzkqEI6Niw1+LcW7pUI8VUhj+pYHwynRHulG4S9JyTU6Y5n41iGKfuupods0yYg8qDc88hjHvz5Fsl6EJtlwgen2hSklaRzvfIcq5SjjcAuhqK070hKsCKW9k9MOFzi8pmQAsiGLHkoJbymRDku2TTqeSFkvWIHmrgKcorSxRa8T4h0NyYYLSoc9ikharznNqurabyqSEUd9L3Q2yICDEvsVZ6C3XgjuykI+meISTX1dm/i7Q4AMZND3pZuR7CdWMlQudJSOGLo7UoJjvihQh3NMNce/s4RykgFVpZzgYAhODLGHbvHorhcfvGzIQqHQ4zGlm8vE406sSAx0N4iRst3Sw1nFyD9HdDYoyscdy6cgSunAoruG0nEtfW5LFl3PGBnq0OxEpK2A8t6A3qTF1XJqtwTE445sMsU/HvRbtWmSYSvZwZIYTqvYEM4a4vVC3tepovzIBdq3jJDXLcNbllg81oBcEc55JFMZ3rJH+ZjwWNOGdMxo3OrR2SCDelEriI76xJM5Xttg18fSE/mQR2dLLoHyZIx/h9j8JJMZtTt9nAfusSukd9QlAxZrws1ttHb0OgE29vDnPbLRfNUSxnRF5IICb94XmsVogT8UY26r9vsRCzfPRnbVpDgvSSBiUmjvzOQ3v8eIqKgmqtcTwZPe1CGfK+G1xAYlncgpH/BJhp1kNyNZN3rMAksLVYZGOrTulvtKpyJ4cKFD1VNsz5NnTKZl8tbx0V2NLVvKh/5/9v7sx7IsO+8Ef3s4053Mrs1mbuZjRHhkRo6cJUFVXcV+YQkSIEBggxAgNKD/QFCDjUJLENBA66FfKfBVL3oR0IIgoNECKKiLEsWhmEzmFBkR7hE+u83THc+0h35Yxy0ptEolKlMZZNA3YMgMH67f4dyz117r+36fJaRQr8l9avN3LO1QMbsXBD2jxH2rZ1a6h1ZiA9Fy70FJ0Vave2Lfk79MqfZaVCP65pjImPxNLF12pSn3W5JLi27l4Ljc8zfGKdVo+TcHXvKO90vCcd6N4eX7F/JATCL5ekk1T6EUrWH/laZejcR3FySJZ3nRE9RVJbnB+bFl8PPnXFwM0GcpfuhRTpOdGsr+gpf/l3/AZDJhNBr9tLadm33nDz7cYTD88fpK81ngFz84/qm/hr+I620H8Iu6IiRbJbOXI5KF6k6Sskn3nxrWtqc0t0QLFdZbAUQHcJsNfrsmmYvOq1kRFmAcOuyFpSxT4lqDbuWE/GaMai8ksSMmgenRkORJF0quIb0yNx2Z2esRySthwrl1x3Bzjloa3Ipn9UOB/zbOiEFjvYZC8kiXu0ryUu9WglzoR66v+yTHgsLILmRklR93SJhWY8oO11DLZlXdqSWj81YthcWKbGp6ZkifCZgXBe0ioVmTLgeVFmf0WUr1oKa/N6MdBdFWLXKabcl3RcH1lwP1WMZ5L0/GuAJUaWi3JW85vdLo7Upct0FhRw32MKP/xwVb755TvVMz/UA6JW+cvTGN0nlExudqpcHXYh4JNhLGLYPn6uY5+JybzWp50kedp0QjHc1mTYol1xNN3eK2uxnLEeHyLzXU+w2hFwhbDdVWED1Vo3ErTroSxymq1TR/PMb3Is1uS0giblOMDOUt6V6EOwKz1kspuggK7RVuVWLbkkcFrh8pb4lZgllCfiaHjezMsDiQdBSfS2cEHfHTlOVuYOUxlFvSFc5PxdAQj3M4z7j4WQHjVmsSI5ifSD5uftrxKNda+s8tYZpwfjqCx33spXTkVh6LASYkXfTfRMbCyiuqLUcyV2SnooMcfJxi1mpJUIng9ytUgMm0R7vboGtF8+83KJ4noj01kfTEEmxkuR2ptgT2m54byu1IPKjkc7qwVLsy+kxmiuL7BYNnkmaBiYQVh1aCDfGFYJhmX2qp1yLuByOJB0tESlGe9WgaQ5gnqC7GsHie0H+aSBzhTJGciiYkdmNvOzWYjwai59VQHIpRIbkyJBN5D7XvECh/ogheHATK+w1hvxJk0UThR462TKSwLaQrmx3La7FLRbXrSB9OKfdbrk6HZC9TZotc8EITGYWGQnA1oZWDWXZusNtLwkzMDrZUYMVRXu3LyJRWMXlH5C5EyE81ptSkJ8mNhrf3wqCXkuFtl4LQ0S3YuUZP5XNKLi2x5wnjVg6ChUS/Ka/kMJDIZ28qxXLfM3xqyM+6xJEgj6WsYGR8Y8guNNmFmHK0ky5ocmkInwwkSUSJyanaiPROFOkfD1he9Og/SUiujUw8Kom1u3y0xnCllI6mjeDeFNNvt/S36798vb1avqBL1xp32CP2PdVuSzIXPEI7jJgaLl+tgtMkWyXFI4koW9z2UBtiKZ2i+V3hwRGVdELSyHi0ZDye0243lPstzVh0RvFWRb3l0Us5Idd7bafRA+0gnSDOwXFNs+kwJylqaSgfraKC3FSn7wRiFjBaTtp+ltB7lKEzT7MaaPsQgxKjyqkmVJZ2q2XlEaQzGe/kZ7JxxswLSiKTDlm1HrGnEjnX/0EuXbMI1Z5Dt3LzjErim1QS8EMv346u28NuRWw089M+jBxmrSbLG7767qvOmSl6xpAHcfktEvzAUxwZdOYlizeP+LOcqCPDZ5oQRDvZjOD6W5soG9C5x87Fzel2aznt355KLqmNkmtca8J7C+HGZZ7FfqQdSTHVrIoAPj+22LkhP+/wNVFGWeXtlmCh3XAM92b4IuIKcUibNNBbLQEkpUJHkokSPdXc3Iy27FS6ObpWFE9lRJu9THEjSUjRThEuUgm47/Aq9lLycXUpxVhII/GgxMw1dqZg4Jg9CJQPGuEobkiEngoKc3+OWa9lHLtVc/2+yARi31GcddxKA3EsrD+/6lje9qAFbxLTSO84MngBo+9lkkccFeYiodl2BCtRecvtSHKtO7ZepHeoKU5/JEi3cxm/uVzSOvxFhht6hrsz4lVKve5FozWzZOcau4B2JVJtyuGqWfeSfTtX0g0eSMfbZ+AXlrje0K557MRALtd8SESv125JxFjvsxR3lt9odtPjhOHHiRiKohJTUCYaOBS4i0Jg512MY70hedLt4M1IFAgSoxe7LlizGiR5Jo/CqCsirh9oh4G4X1HttSQzyaSNfYd7uMSPHfY8IcwT/Ipn/qUaMxB2pCT8RNqR3CuKU8XgpXS1249GrO7MSI8S6n0hEkQdqfZb+q/FvOOKSHKa4BpJqBn8dp/82JKeCZxeVYZ2w2F7ckhVQV572+/uCRdScDZbTsbd/YgvpHDj/oLqVkvUkXosOky1VdMOI7qWe4S5SER+spTxc3bZmZTOEtCCiVFOIiSXd1u5TpXob0G0pLFLr6kO5NeKU0VxIqlLzbonvdQUh5b0WiQZs3vihVWlodoIN+y/lUciezCNov7uWIrNpRSXygu8/fNcP64B5M3P2/XTWW8LwC/osqVo2fLnKcXLhGZHNn4VBQWBieiZobnIWd5rwUuslnLiImzvV5JokARi7nG3alSAxR9s3BSPKguogZMT7tLKY3RwaD2zZGcG9+UFzUhMAzHz+IlEdOVnip3fFb0S6zX0HWqrQtWa65erMpY+FCF09kkhMNnNQP+TjNGH4n69f+8EtbBUf20qOJg8srwljCyVBNo1x+jdK9xGi9tsu26O5MIq96PkBLfWOecixOsUrlNULTdUnOAc/CTFXluy4wR1maBNYHnW5/s/vH2jL8qPDXahGXyaYGaGfKNE//w1oTLM78umkJ9pkmvD9Gu1dDESATU3KxH7LCf/sMC9UzL84IJYy7ht+Xi1G4UryVWeG+LLHsor/GXHc9yWws2niGA+ibgVKQibNY/rRYafCpPH7dfYS0v1w258ncpr99OE6sXwBkPRf2GoNwJ+5LAHC3xfuHt2KYYMu1DS9ckCvW9coitB3ORd0eT7UhjHLOBWvbyGRuGGnQHoMIconWR7mIrBZ7XElsJBDANP1JHmuEcI8pj6VY5fkfcyPUmY3RNdaswC9nVGNFA8TYlJkO7WrZLk0jD/n+ZcfynSrIhuSldKCoxKHKvJRImuLI8kH0xxK456TbqM0YoModwJDJ/TxfQBKy3vvXdInjjJyB5KHFz/haHcl8LerTr6r6WoSsY11YaM1JOpxlxKVJgfeuyVlUxrI8YTpUSHV20G+q8UvU/TLvYrkp8Z0oli8Z6gb3wqHSdfRLK9BVwnDH/2nPRC0jrM3OB7YkYIqy3Vrids1+z+969EH2zFxEOkAykrGa0OHc1+0yXGaAbPDelHBemZZfKVLqs59/iLjPQwIZkr7NRQPE/QVwnqZY5aGvIj6V6RCq5pftczfUcOJLpWXJ8PUA/nwt2birFLtZrJB47kZUZ6LcxB+zojZJHrD4RLCrA8cPReGgEhvy6ETTmTFI2QgLtVM3kvUt+vGDxKblJWlAN/q6KdZdKdHrbCGPzyFD+3DJ4LwSDvEopuDgJK+JfppREXsxfXfP+lRu8vwSl6H+X07kwlGvLjnGC6kXsNxTO5p7UDMW/YmUY5dWOKU0E00rH7d3QLfs3h9uouBk7B16e06456vxFCQqVJp6KvXj5oflpbzH9yhah+Ij9/mvXv/t2/46//9b/O3t4eSin+5b/8lze/17Ytv/7rv85Xv/pV+v0+e3t7/J2/83c4PDz8zz7mP/pH/wil1H/08/777//XvCV/ptfbAvALutq+uPx8Lt2H4llKtREJfc/0ocNMrIxxZwZzLdiC5DyRoPNeIF6mJNcCQDZzQ/RKmHq3RGOnGo1JPdmnuWxAtaZ/MJPYuWNL6HVdu2WCX2+JuUBNURAHDl/A+TfkxsdFRv44Qz8vpEhYSoeJCEpL0ogKiBbIwfRdhxtEnjzdJmaB+IcrhARiTyDFRNHy4RVXRyN6j1MpCmwULE6/JdypJPVkKU7IkEinQzWKOHA3LsLsMLnhKIY0CoT1WsPjPumFoffSohpFsiojvJBEyk3pJLQv+iwOh+iZZfDUEiyk086leyGF3OBjASWPPtM0G17Geoc5sw/XpXMz/1HiQFhvBQFRBCmyhhIkn79ISVKH6jnR99koY8wrS9irpEOVRvmcWkX+KMeNPO2ax/cFS/OG8Rh6XvAdTtGMZSyYHSe0h32SiSE/1cL+Ww/on50w/GFKfmop/2idZCZC98XXKohdDN5zK2P2UjN8Io7T/isxWIQi4kdOHMoXYlxYHg0IpnM3N5q4XaPGDYNvFXAtsgFVGYFzV6IDbMcBXcmGncxU9zlq2nVH+mFPcCafDcguNfXdmvl9h+8Hpl9uyS6EQ1hteyn4dyvCH6+gF4b8VLRavpAIMr1ZMbsDftVJYVgbHj3e4+oHG2x/5RRqiVScv9eSXOoOI2Op18QF285T6luNmJQKAXBHjWRAR4n6Kl4IRy6Wkvubn2vqtS7LVoO5N6e8X9OsRIpRhfr5Cct7Le1ImI5J4ohDx+zb66J965Iokpm+SZlIrmXk/uTTHUmyuJQubtivxGCyLokiamGEmdeKdGT+1Zpqx+PvVpKKc2ngNMN2UYrlrkN54V4mcwW3S+JA8C0h7XJ111ryU0npiEYMEIOPU5qqG0UnUZItXhpGn1jalSBThm6s7IpIfmKkkzeUz3m5F25IASpIN3hxTw515kgEqrHVN/gYIpIuc52SHVmyUxmT65c55fGA4lUiXdm1lnYYaLYc1Wb3/bCR+V0v0o+Fxi4UyVwx+0qN+agPGpYHXiYF6k2+sSTvpBPhqppLSfuJGtoVf4M9Ui0SG9gPN9prU4tDmXki13sGzbMB9tJizxMxpAHltmiG9fQvngt4sVjw9a9/nX/yT/7J/9/vLZdLvv3tb/MP/sE/4Nvf/jb/4l/8Cz755BP+xt/4G/+7j/vBBx9wdHR08/M7v/M7/y2e/ue6/uJdLX9BViw8LkaJI1NaRnkO7GEiqRoGql1P/4Vh/n6DmguKQXkoDi3lnRY/bgiNIem1+GsBPqcTTfWgJnmZ0iQJOpUipRpEls9GhJ5H3apQR33UVg2VQV9bQi8QjWBUfCr5v6sfw/lfdoLL2JciS3mFH7eEpaFdDwK/XXe0m5IaoSKs/tDS9iG7EDF5+/U57WWOmRh8PzC/F0hGDfpxj/hwwfJAYRbye8WhxU3zrhiTsayYHqB3JBqnlW8lVBtKMornCipNemFk7DIOtEZwGG9GRDEPuNrS7rX0nySizzOR5EqzfBPF14PBC0W1ISYIVYmuLRojzEAAG6j3BL0RGi1MutWAHzuBMweFOk6Jq575Ow4q0VZWuw5TdyOqAMlVh8kJEGqD+3iMHkumcUwibiBdA1NLTmuz2hW4XSav22p4eO+QDx/vo3OH85m4PPsR5bQULgYWJ330QZCIP6khSBYy/lWBDs3SaUSXiuVOxPQd0wdi7lGNwswt5t6ckCRkF5rySxXNqsZcW9K7c6z1zK97NCNIdxc0xz0pcFPZWN1YHM7JHFCw2PfSMZprUJryfkNyJt0p5WHwvYzlz3bd0klCO5D3W9fSCWyOc3FrDj3ze1E6PbnCKQTcXUTMldw20+eJoDjSyNFnm/RfGFwP6oOGZtuJZu27fRa3HbrWpMdWYMBGRqqx7ylWKuzvjmhHYDu0R3oqTLxmLcgYeQjNtsNMDXw0JHk4p91QuPMeqlH0jkR2sbzXMrvok/QbXF8KhrTTyFbbDkyk9ySlXgs377/w6gL5aYJqc5p14TPquYHNmrC0+J6md6hZaitszyYjDAO+L4e5OJX316y0uIEjekVxnFFXluRU9JXZhRR9fiUIgHzgUDayuC1xk8wSKDxqrtELS7XZRbr1HcMfSIZ4OxSjTLMiaTohC9x/95gnz7aojSE7k5ze4kRReWFhZpdaspwXVswppRJ+5DSR5/T+XCQuT3uiGaQrthWYS+lGosUZjAa8glZBqxm8UEze8+RnhuxlKmY4Jd8t1QoMvdpxZOdGHPTadPncSrKkazC1QTtFfb+iJRGd31LG4Nc/06IWRg4FUTA5dqaoDhzJWSKw911Hvl6ivz0kv4zU5vPt6fwkRrh/2r//K7/yK/zKr/zKf/L3VlZW+K3f+q3/6Nd+4zd+g1/4hV/gxYsX3L59+3/zca217Ozs/Kmey5+39bYD+AVd2aEgU9LtpWicbvkb9IMrkKSGjVI2/al0adzQC65gENn69xb7PGf4YYp+3BMR+KxzQqqIf1Bi+g6fyYl/8FS6fro0qB8MiWlg8AcFaiH5pZhIsr+g/+411NKZTP9PJxIef25QQaH7Lb3dOelxgm6EMRgXlmKthCAdpeVeYH4Qqb5Wsrjf4nca4nPpxoU80t9eoHqe+KIn6SBXOemVwa+3mLmhXhPUjO97dIMUTDMZQ7uebMzX70tnwlTScVj/tpENuJICNZmLO9HnkdX//phkVBODQi8M2ZX8ekxkfJdMNdm5RI/NDyLtMJAeWxi1JFcyVqwflszuychy+MNUEgiuNLrpnNYTK0aWlQY3EnROdipZrL1DLRtG5xLsH0XaFYmH007cneFrM+J2LXm8C3ncN9y5te+JNsxeWbSTfN7eo4wffvcOo805o9+T8XtMA3q9xmfcFKy60oSRExc10tWKpstZzYVN5rpOdLMh3SH7aSHgaBsJPelAto1s+OWeJytEr5pea/juiPbbYwbfy6jv17Sv+2TnpgP0duknS4EQzx82tAMRwbf3KvxQDC8EddNtKfcFbVN8p5DYsIW5+TyzSyU60k5Mb64t/RdSUIQ00n9mpTgykgGsghwQtJPull5Kse/fXUKtyY4Sit/vs7jj6L205KeaZCEmkmRiiANJwMj+vyOqX5hTHTS0Ayls4oMlzUokOzdS/L1Tkp7Zm8g+/ckAVUvMXXYhxZ+pRb+anCa0ZVe4RClm3mB7VKtZ3mnFIf7asvY9SbAZfZRQvl9hl4r+c3uDRYqXKao0NJuO6utL0ivpatJ1Lc1Skw8a4tDRbDn8LCFWhv4nmeRP19KVfXMIatcdnGWEnsdcW/R5QrCQff1K8snn4oIdPhEpgX9/TlK0zB64mxHp+EOJgGzXPGal4XLRIzlLiH0nUZHHinpVDr52b0l+HqXztlWKo30nEC5TMBGfRtpJRjgsyL40EQyOE5OUiiJhIEJyLgW/mlkObp+TXhq5HoaQXRrqsZilhp8ZSASUn10JRJsujjI/komL7wfcWkuz4cjPVZcRHOEqxSw0fujFNLPi0RNLLALNl0qqPU+963AD0fz5XO5R9trQVJb6q0tmd6Ha+nyhHh79E/kBcRb/yZ+6rn8iz3EymaCUYnV19T/75x4/fsze3h7379/nb//tv82LFy9+Iv/+n6X1tgD8gq5m3eOzSD3LpBvkFbMvN7TrTgqjUSQE2RDSay3j1ahIFmJMuPi63MhcH5r1QDqRkcSbw1k4ywlXKbaE9Er4YPbKolpFcSoC72ZFChg7MZipIT4esPhsRbRw55qT729jKtmc34yR2o9G0lXrsntVVJSzDHspN1CCjKj93GKvLUwS1O2liL2PDfFbKxIaH4XMr1opQqh1x0eT56trKSbblSBxTq10zaKVLsHsbiC7RG7SuULVmsWdjmX49QkxFdjx0aNN1OM+9jTFLhTXX5KOZTJR9F9rmk0nCRWDKJ2QS43rR5IXGc26J7vUZJ8Ij+xNB6L/OCVaGW22q17cmnNFuJLYLd+XMZ1uFc1Qnm+8FObY5TeCjKzDj3ATzVEfdZRL9NpUU+860eX1I81fu8atOdz6GxD3m3xgmJ4OuP6GwJ3ttZWoOxNp90X3l16LSxonqSjJTBGMZODqpXRTg43UazJ6VAHqTScQ4gsj8WJ96RjZhXx+1SwTPVMjOq1mLVCvRZhZBs8ES2MmFpJAOpXOYnW7EZd7ITy1UBvJwL5dc/fuKc1uS3WrJT8yHTpHrr+YyGfoi0gzFhOAbuDgK8doB/N7UlTkZyLgV31H6Itg35TSBW0HEaK64UgmH/UoXlmaDU+10RWKo0i1HfB5l46y7qDRGBuYvhPQXYSYbhX+3SV50aAduDzi318QWtGC9l8r6m1HOwqCojGxGxOK+7s3kA2y+DTDrcnnhgK34kiuLMUrg5kZ9MGCctdRbSj80FNuReLSUu56lrc80/uddjMV/SZRvu92KRGAUYHr0kfckwHFZynF84TipaBmfA7lZmR1byr8y6mm2muxl1YOAVeWsNnge4H8XDM9GUAeiEXAj1uaESRTeG/nTCLwohRKYeC5+is1UcHKhxb1smBZdRq9zzLacaDa6MbAWcC/7DF9J1JtBNxFjm6kGEsmWhBRqx6Ve8xSUX+0goqdLtjKuB0d8Ts17diLFthGTq6HuL4Uo804Uh00Eo1YyvVHIyzDckeMbHqpb8bdpobkWtP/VKD4i4MuinGnkn+rkE48qy16YYTdqCP6ZQ4eek8TSRLSor9th9A/VMSrlHic4/cr4XB+QdbBwQErKys3P//4H//jH/sxq6ri13/91/m1X/u1/yxi5hd/8Rf5p//0n/Kv//W/5jd/8zd5+vQpf/Wv/lVms9mP/Rz+LK23I+Av6LJzjWkU+lJuNtWOk5sYEEcttt9QTzOwkewC2r4WYOuqbGS6leKhXgvEnsP1dedk1dQDySVNrjTNWoCBE2NE07kJjUUFie7KLjTNWPhmIZPOj3KSkKBaxWJfWIJ+1ZE+z/F3K+nOlIp4uxRThlfS6SsNZtSijzJ8CnG/EiTGcUHaiFty9AR0Y8UBbDuWWMdWs5XwuppVuSEPnxrKLekWqNLQbHgGn3VavZ+fMksHN4xEs9RSKPUUzWlfXteaPH46UfhCANlxrUFNEppxpEG0kXGtIf8kp7zlKF5b8rMOpzFoad5vCV6RPJcNdnE7YGcKNwxi+ojSaVI7FcM/6KEiNEMpjEypqDe8jOZ3lpSmR293jv4PK5gSZvcixanCFZ2w/MGCapLReyLPz281zF+OSBa664oGmrGMSsPQM/gkodwJwmIbt5jTFDfwIvCPoBvovbQs70rurs+44cWFPBDzwOhDSaGICppbDeZSnLcocQb7VmGXmmY10H9poFWC0lGIpqlRNDvCqovWUG867NSQ9BuWt2XUq2wkOZHqeeODcxZ1SjO0qJOC13+0B2NH8VI4d6ZUAlw+McTVFnuciuZw1RGvLSELPH+1AaudIapU2FK+D8pn1AcN7SgSco8qHEnucIc98nNNuS3FknzJuo5oEmi3JGJOtTIKbYeSxbwoEnQA96JP6AW5Hu8FmkYOUr1jBSd92oeOzffPiX+8KYkQTuHLBN+TUfgbCYP+7iqxF29GlaHvxYTxOqHac/hU7gnBS6eqWYkCYM8749ZCINRs1qjrlKikUG9v12SHCfWqXCNmKU5V+X4JxxENdtTgKoufWgZPNdOnq6ieFDbZcUKz3hEDdCT7LKfakwLbzA1mf4l/1cOvSrevHcIPv38bVsShy7jFHmW4rYhbd7STRA4rRz1sJppXO9HUW/Ido3O9m4UhnXVdvaAY37/g8tUq+bElP7GE1NyM69OP+jKmTgK61fSeJrTDzqjTObb5tE9Y96SvLLUSMHs79h1wXN24sZOZkpznLUlIqjc8JJFk0NCoCBO5x/kiECtLfmaot2WEHBuN3qzJEoe1gWVliDbQjLrc5rWGxkon3Hwsbnu37lDXKSH5yXTJ/mtX/K8wcfynHgPg5cuX/1GRlmXZj/W4bdvyq7/6q8QY+c3f/M3/7J/9kyPlr33ta/ziL/4id+7c4Z//83/O3/27f/fHeh5/ltbbDuAXdKkg0Wn1pqd9b8nmwRWD7bnors5T4icD9MyCjszuB4p3JpTvV4RECjS3V9M+EK1U8WlGfLhgfvcNmqAr5CLYqcacSPfrjXFj9pdLoobxR/I8tr920uVjhk6jJpdddmaIoxY/diSDBne7wk8S1FqNG3n8IsGuNqilEVNH7lGvBeFiphb7WY4vZdOOGnrHiul9MVlEDe2OdDdVKaPf+R0vhZWDwXMp/nQjZoritYwDfQYoaD4eCe7EKcrtgNtoUUv5M8UrYQ0ybPFDz+K2J7sEO1Pos5Q4cLhVJ2kdc83oDwUA29teSK7tqDOcnGTYTwvs6wzdKqrNABs17UFDNDJ6fCNq1y8Kpt+oKbcjzZdL/IoTfIXpRq0v+qhWUT8eUW1Eym15/+uxpHQ0Y49+1AcTqTYDcb8kLo2kwNxZSlZqJaNaf79EpaIzDFuNJMPMrHSAc0mKCUPP8r2a5R0nzuK+jENZaUluLciPDdmRZfpQsnzzSwWlwc7FJZ2eyuPFVPAgthvvq0o4fO2dGr1XiqbreUpvtcT9pSl2bihOpYhRThA1xSeZpCU0ipOjVarHK+S/NyA/NYTOxPLGRFHtOhg5kgUkL8UYhI7YfovfaMhPDQTYu3dOcShpMLO7gkiqNz29R5noZDeWoit73kc5xfJ+gy3FFd0ORV/nh57NW9foiZUEnFQ6gNmFZnHbs/K/5qSXWjphZ4Z2AK6x9IsatyLj6mpN4u1OP1tnvi/dxHrdY5eKbH8u2kdNx5aUomf8V45JT6UbZ0spfoY7M/ROxf1feEE47zKZ7y/ov1KkU42eiz4NIx3UdHuJaqXjp85TmnFA3Vmi1xrRolUCDx+8lGi37NSgnhdQi9xh+r4c8MJ6i1kYkq5xojvdYTuM4oAvFfrWEms9v/BLnwj/8+4CgOFnhuSlsDljaXFbLaO1BXomxp03nWrRpgr3L70QHSVaUFjJQq7NeLuk2nbMvr8Ouafaa2nfK9G1ZPfyrHeDS8JG2lGg2gokD6eoAP1nUpS7nlT41c/PiQkQFL3nFl8Eqh0Z9ycXUjRW92qSazEygRx4wmGBPxKwc7PXisluaqjXA8Urg0oCykbspwXt6z7u+yvEzKNqjfaSaKMuUvl8TxK5h3nhO75xP3+e6yeJgRmNRv/Rz49TAL4p/p4/f85v/dZv/akB06urq7z33nt8+umn/9XP4c/ielsAfkGXbhTZa+mexdOci0fruO+uYucy+mrWPcmtBTs71+L8/aNVuE7ovdZkZwabeOKVjFfaYaS5zOVEXSGw1UxGWiqIGL/ZEFdpemkIFylu6Dn/OU89jhx+uknUkFxa0UNdqJvoL7UUur/5aIB5nWMWhtXVheT6Xlmy7/bovTbkRxa1sJiqi7qyojczlwnkgWojMP+6wHjL3YBbCVBp6ebtLtCuy4q9FLzL7KGErbt+QBlhnumlptyXgkW3CrfX3LC3krNERuVJJJnJRhsXluRKhPHzgyg8QA3FZ4KWQEG75pk+9AKqfT4kZEi6gpcRrV2KBrHalvGifp0zWF1irw3FoemSQORx1dJKKsZhDl4x3pmiep7ywEnayYmGg5LiS9eENDK7iyBiGoVu5L1QiZhBwmVGemXY+1cJ6klPujMe7LXFT1PsYUZvc4HSEbvSoNcaAQWfJ5IKUmv0JMHMNWYmLtZ2qyUuLcH/CU7goSW7EAemCor6do3u4quabUm2QIMb/ggZY0qFOc7wR4UktySR5UWP8rSHqeT6Vq9zYt9hGkX5sCYMHfWWJzlNCDay2A83OkgV5H+Xd5zoJSMsd+XzSuZairDzDDUVl3p/reTw6YZEf80VvUPpeJmVhtVPZWRYHfVvtIjoCE7j03gDNQ+5FLPX39/ANEq0nxNEw6ikOJn+pZLqvYrsXFzo9XogNprJtM/wU+lEj54KMy6ZSCddos26ouZ7I+Z3Pe1qEED6gwoiHJ+viJRjLDpXu1TMX47gVcGsydCNIn+VkP/egHITGaEr+X6oVoHTZL87xGzUVO9WJDORUoTDguDFfCGsQM/svhQlvUM5eK380JKdizs+bNei80sj9ZoYOLKLjq3XSCyeXUJbJiyvCn7vw3fIxhXhaR/dKha35D6FFsc0lWZ6OhAZRCYoH1MpwtAR7padHlPMEsmFZfwD0fAmc4W/lkmI68vIXi8M4TIlfmV249QtH9adbhSKW3Oyc037yQgOStExRggrjnRc4U56MjlYq9GtGOf6Ty3JQq7t/ivhTLp+JCwt/S7CLukYmoOdOfnzVOQStRQ8yztOUEZeuqShCNTbjvQkIdku5X3rCXZLO/k7oSdZ4e1QDuTJ/PMtAP8srjfF3+PHj/k3/+bfsL6+/qd+jPl8zmeffcbu7u5/g2f4+a23BeAXdCknyJHBnYnceM81wQo0F6Qb1Zz0OP/ulgiPjYy5yu1Ifa+mnWQwam9E39l6Ke61ngjkTe5FP5XJTTU7M6jcS2bqK3ODTomJoFaKE9EW2grqsTzH/utIeqGxE0sy68wFOnJ1OcCMpQsYEljckc1dNYp6v2X5fk3oC7jWD33HfYuCjNloCZmkeOhGUx801Fc5ulasfGzFsZpH9NywuNvx2BLfuf0i+ZG94dylL1LaUWB+BwEP20gYOcqdCLkUwa4XZIT0zoziyNB/pYXftdSYpZJOUc+Ls7kX8Ns1diaFpPLCKrMl6EYL0Bcolxl+IEWfblSXAiDObl2KS1ovDVeHK6jLBHst3TAUFL1aNFWdjjI9SURkvlHj9hpikK5MNBGzVFy/Y/BZl2Yx9LgVGdO5UcB9PCJep/hWo1/kuFEg2Chj4FrGvH4QxPHrwVzL6I2nPZpxpLzb0vYFnWNLyWrFacodAfPquaBOSIOMZAdSEDZrgfzdiTjHbRSZgRFHbrMSWdwO0o0E6k2PPUpJzhIYtiQzRX4mbnVXyPOKWooTXcmhw2Qet96KmaDTc5lSw0pLsxJYvhqAkU6tbsV9SlQkn/S4+EonstJ05iTBhahaY/aWMJBkE11LXBl3lrRb8tnPv1YLlqmDo6ujHJMEihMBOBdHmvxFSrhMMaU4qG0ZaQcybo1GRp361hJTS2fYlJJUg4LYCIcyXqdkV1FGsr03hZchvVIcfbZJOlVUB5IeooIU9epNMsVUTEXLnYh6VqBPsx/lzi4VsdGk5x14OCqhDESYvhPRNZ0Gjxsck18TvSkKSERvayqNKyQucPauwOez1yn5q4TmtCfGifcW0pmddlpRFbFzQ7pSY2dycHIrnmZTYNPhKsMu5d72pnNZbosRo9rxUMiYNiYBc2UZPdHotYZ6nmFuL8SpfpRS3m0wE0vzeET5QMDc/iJDjYW5OPhhSvqtAbYUzap5mVOvRco7LYv7LfPbAtqe/EIlGcI9geP7HJlq5HIInZ8MbrqJ/VeCelJeGJ20ohO2153BZ6lornJBXQW5lttRIN4t0aVgfWypaLZbuVY/x+Wj/on8/GnWfD7nO9/5Dt/5zncAePr0Kd/5znd48eIFbdvyt/7W3+Jb3/oW/+yf/TO89xwfH3N8fEzT/IiZ+Mu//Mv8xm/8xs1///2///f57d/+bZ49e8bv/u7v8jf/5t/EGMOv/dqv/UTepz8r620B+AVd5YFjcSswOxsQe7JB+Ew2RDtTtK2M/7SnK0QiarcimcsYMz03pM8zYk8KPfPHQ/DIzXmzYfD7kjBgGghDJ2L4haAP0km86RSZShH3S9EWZoHpu476di2Ghf+xor0vXTvXF82UWSpiq/EXGb3nIlE1pSZaEYwPxktMKnqc7Fr4e+W+RDXF6xQajWo0g5eK/ETTfyTjknbdMX0gjDbdSPJI77klnYJ3hnYopgqfRdpBJH5lRjTQf61xo0ByJUVW73FKuFNCpTFd9w8F1esB1QelFCgHUlD6HKrjPjrxmIlluDOj/4Ocdt11m7ZskslUOi+zdxxuxbO7PpHiuxdpx15cvV6h5jIeHXySorYrSdFYKLJrRf+lJE8sZjl2YmU8v5RukfYQr1KYWfofZzJOt1GukYcN2185RQ9bes8SsrVSRklX+sacY1MvnRvkKfdeSp6qLjV2YqS4P6hFJ2Ujaz+M+F6g/1mnFfSKZtTF8026btc40n8tSJn0dUK16dHXwqFUDprGgg3YuXQXR99LJUv6VADFvLvAnqYSk3dvQbvm0Gcp5Z5nedtRbXqSuaB+2gclzZoX9MhuhXlaQCtu0uyyK2IAc5ySn2swoFoNJjJ/x9GOJc6LCMmCLvkl4B4P6b+wpIepFJrP+tij9OY7mH1c4C5zkpMEOzHos/QGZ0J3eHGLhOsPHO2GY3FHDB7KKa6/It+pekVGrb6IXaoG+MOecD6/foXfrkUi4TT564RkKh2s5Y4i+bCHX29xhWTkSiSfMPBUl9jTDqRg8D0xFzSroXMNS6Hh+8KFJEJMIB02JB9Mad4vxXSVBpJL28WYQXOvotqRcTnzBHMphp03S6IjIa5JsR7TgMqFf1lveexUtMbm4z6D5932lHnyEys60ouc/FKx/FIthd7EUjxLKF4bytutaORGnupWiysEqTP62JAcpyRTfTMun92J+KXF5g4e96kfVOgWRj9IGTyXcevg47SDUivUsUhPfAbzd+Wxy4NWDB6dYUQ1mtWPO/cvUGwt0aVkRNd3a5JLgxsEyndqkQj0A+m4YnY/kJ9oMcJdCxxeeeGQ2lFD+8FSvnsezMtcroXdClcbQhbF/HKnRs+sGH8+xxVQBPSP+fOn62J+61vf4pvf/Cbf/OY3Afh7f+/v8c1vfpN/+A//Ia9fv+Zf/at/xatXr/jGN77B7u7uzc/v/u7v3jzGZ599xvn5+c1/v3r1il/7tV/j4cOH/Oqv/irr6+v8/u//Ppubmz+ZN+rPyFIxxs/3inm7fqLrTSj3/f/5/4EaZphaUe23pKcyUlJ3FhS/N8AXyMawJcBdFSGkgeTayCk0KGJpyI+FSWZKiX6yMw33JOBdUCKi6Rl8Zlnsdzd6HVGrDUqDnyXoQYt5KS7UdKII7y4xNlBdFOhS4sIIdBwwSRDRHbojmUO1GSU+bmKxd+ZkvzckJDC/78jWSpplSqw1vY0lo17F8dEYWoVeSgKBitKRiGkkuRQhfHmnQZU/0pG5FY+ZGbJLRfPVJep5QbvZopIA8wQ71bQbjvTEkk6VsBNNwBxJ56F9WMpYUgv2xDRQbkmUV8yC8A/7nuLQ4jNh8aXXYn6wc3E5JnNNvSbxca4vAn/fC6hGMXipmR9Ity291hLpV4n79E3RIKNF2ViyC4HWNrst9ixh+Bwmf7lCHeeETHAjEsEnjs6YBYoXCeW9htH3U6bvSydLBelCupGYUsxS4Ts3cTqF5bZ089TXpjinsd8f3OgBm1Vh3aEEoYGK2M2KtrKiK01k/KgnUlAOn0nnKbuU3Op62wmrbuhQM4n4Sq801d0aZd7E4hnMsMV+WoijsiswTCP5vbrShF6Qblhp0amHi0zGe8PI8BnM7iBZvKcZMY3YiSa/UCx3ZSN2K1Ks22sprN1eTayMJOx8aYkCwnFOdntOed4jP7LUG/7me4ESTE16JDw315exZL3tJKVjKF0i8+b9zSNuJWAnGvPunKayxMsMU4lpwC41bhhIz4V1pwRlyOonistfasifi1ZKOTlUuZ44aPVcihF1Z4G7KIhKOp9m2XEa35XCUDmFnaub7mFMI8lqhTsvxMShBMpcb4k2DRBHcqkxFTR7raSLvMhQoYtA7ByuyolBot70YjAZisQBJUgTu/wRyLxdl+8bSuQC9tKSXYpJSHvROOcvUvm8W3Ft917I/Wpxz5Efdiy+bYedSQc0JFLAh82GuLBgI+mxXFvNhqd4ZXADQTnJe6jwgwB9R/5pRrUtMXumktcVEvlzMQ+d7ENYp1FLGlOU8wvZtVxPcSAGKBp535JrwRjZuZiT3kChQQxkjBts4nGNQZ+nkp5SaUEzfWVGfVlAJl1UXWmKRw0f/ZP/mclk8qfWuf04682+8//+3n36wx/PiryYef7a15781F/DX8T1tgP4BV0xlbzUkEb0QmDDUYvexvVhuSddNDszguIYNx0LMKAuUpKixc4MzViQMYuv1OJoHHvMxwM5GTv5yddLSVGwkeFTjV1ITq95klO8ssTrlOJYNhlXgPeaepFip5IskVwb8hMrN7FGUfzcBeUt6QqU28LOM5OOxfXhkOVOpFqT2Cz/ZABTiy4N5esBs1Lip5ILKSpCFmXDTiLFK0Oz7ilvia6m/9JQnHRZuY3ceMtdL/DpVXFQxoUlFl5MLqmnWfPoGuxZIjq5Eym8wkUq4+ethvKWw/VElG63yptNMj8Vx2Wz5QgDT3Eqo/nNnz/ptJYCv3WDKC7oWxXJZilGhAeeuNGQzDXJDIkXW/eiBVptZSRooxQyVsZx7UqQkPgistgH+yzH9z1xICN/N5L3WLeK7MRS7Xj01FJtdc7MTps0+qwbqy0V0UJxrEmv5fXll1JE1mUiSRcdniZqRArQaGiVjH8VuIuctNdSHjjyUxh9PyWZKbILzXI74rYaFvdb6m1HfmwpbklEWH4m5oyQRuyZaBTNeYrKPTFI9nS70+A2G9xQBPnJuL5h57lJKoL9QS0SgUp0X1ffFAyMepUDMrpPJ4pqMwouqIXsxKKcFiTIiieWBpV5yv0WX4v5wZaK5tkAXWrqNemm6VJTrYsbPjlJ5DBghDXYrETywwTfCxTPpTDMLqQTn06FFxgSUN8ZooyMYN1654ZWiFnCyWEgbDSYvSVtX2FPUsE/rQuySHmk4OhSNlSEdpFK581Jd8sNI8t7LcoEsnNDdqZJpzJKzy4MqlX4IwFw60YKuOZ2Q++5lc57Fhg8MeQX0vV+Y9oylaLeaUV2omUEn0y7wmdcCVNyobArDcVLKx3E1a4bWwqOxefQbLlOBwiLu+I4JoAqBY8j+dESz1ZtBXwBgyeW9v0lzShI+s25otwJVHsOO9fo0xQzN9gLK+kaqzLKLneFL+n7gmjJzxWqVjCX78fgYHqjE1S7FbEIQlyY2A5sLsWfqUSnmZ13XdtUxv7jzRnZS8kkJwi2KCphVJoOzq6cEmj3oRbs1VEhxbRXkAXyUy16xycDei8s9ky6zmb5+ev/3mYB//labwvAL/DqvZQTs513KSBLRfFpRnnLk+0uJSN2xZEeJZijjOJI4K69Q01zleMGnuxcuiFxKegIe22o71cQwC4FIty+6FPuROlI9KEdScckWUj4vJ1rqs2uE7HZEJYWfSEB9ssHDcMPLlj7S8fkxxa7UCyrlORK3zCtVFDo/SV+3NIOpJuw9kNJrUjmit5rQ+h7IfZ/OJKC7E4lMU1TjT0UHVO1GW4QESGB+Tst5QclyUwxfCoOVbXWwPOepHR0+jS8otr1mCSQrFXC79oTx3Tbl+cYCwFkKx3Jjy3BRMxmRXuVEQeuY7VFtBNzS+9pwvWXxJBxMeuLq/NCE+6VDJ6LfrP4ToH3mtgJ9Hsf5jQrgXInMr8TSK417SCgzlPC7YrhgeSYJteGalM2PlVrimPh5zXrHtV3UGkG9ybYqRwM7FzT3BGnovIK/d4cbomT2/cEjJ1MxYHqbtXiiu4JAmRxICYU+yKn/ziVVIODRpA2+14239cJKkr3Tq/VhOd9ileW+e1I25PPt9pzpBPF6rcy8AL9rscB/wOJZQMwe0tJaBCvg8TFXSUkT4ob/Ia5SMiPDem4wnzUxw9FO4oSAf1ynoEWzMgbULTdKgXHoUWzWm1E0muF+8qi40QKIDh2XZbsRPiTZmbQ1wn9V4pwryRsNcRUutzRSGGdToRx6HuRwUv1I7e8kUQLKc6kUBMkUWS57yXtYn/J6K+ckiSedKKlg9h11OxSUb1bycj2KEN91mf25Ua6kYlICxZ3PCoo7ExJkkxP2KDmWpyqg2cGt9XKdR4U+ac5RKg3AvO7jpBBve5FF9hKUWoqMXxlTzJ8LrpP5RTtCEEv9eT7np1YmnHADluaNen2ZZeK5l5FfdBIB9JE0ZpOxaDxxl0e3lvQrAb0xOKHIiug1XBvCbm/6UCCXJ/5i7TjlXrCQCIox798RPKoR1xv0Bs1i1vSKTUzkXvE7VpYi2PXjbfl+xQLcbmv/sAy/EySXUB+X7WKxTyn2XI0m17yqbuoPRUgPzH0X2rCipOO+VZFuSuHgXpLDtyTaQ/9wRSz0qBHcnATA45MAkISRZozF91grLpEllQOP8XTVP5/Aq4XKG957L05qEi72zC/638qe8v/1vo8NIBv13/9essB/IIuX0ScFbizKZW4IjdqwlWKmWuqJEcBKx2nTTsE3LtXUWrZCOzMyBivQyoEIwwuGhmjmqWMOOotz3B3RvXxqnDElAjL63EkpAFTyWin2fBkL9IbgG4cOoYbCy5fr8poKBF2ny8T1EBij9pxID82uLJHoqQz4J1m8o5s1s1KpDhT9LoItv7ryOVWwJeGsBJILzTtVsva1pTL16uUudzIiR2QttPVRQPJRJFMC9qRuAhvRjw6MvjMUG0W2IWMxvxZRjIXblq97chXK1xrCF46bkQIJzlkspG1Q3mcdhxumHbJdkk1NtxenXLRDGkH4FvN5KGgXUwFfpKSX2lcrm7GvnahYK4oziKTdyV1oMwTFkcZhjeuV4XbbMBpqk0FmzVUluRFRkhgagcw8JiFIbsCc5zJhtiL+E8HJLVsRmalgZEi+eOCZjWQFS2Nlc9w+vWawUcZ9WrH7etSQvyWpBQsD2Q8ls6g2g/CoTMBv1+ispb0WytUW9LRyE4s+heumbwY0d9ZsDjrCUi3gd6hFhF9bUkiFMeK+e2ImQgCqL4lcW9iPJBEj8lVju1FVM+Rv85pRpHiicXlUO05mrEnmWh0qYkjdaNzU0HdvJbBb/eYPBS9XjpRtNEQBp44tzfSgXbNc/2VgD7JiSMHPUdbJ6L36kdC2hWflWK5K6NNIsS+Q695mMnYPR/VuNbgl5aV74lWMB73ObmXYZaawSUs9iGZynMNWYRJQsgCymuCliLP9YKM9scNve8WuB40e478dUKtIIwcyYm4U5d7keRYjEfRdBzLcUP0mqRD5+Snpsu/VlQbYsrBBrlOeoHYk055sN1nfW5u0obSiaYNBcmdBWHel7Ho60zSVTYaQmokvnEUqDcCxaGhHWnipSXmkcFzTTvUwsG8VeJqy2i8pDpMqdciZiH623rdUxwK/Ft54YoeXw0hi0Sn8UuLaWWc68ZO5BjzhGyhCKnBrbekJwnxwRJ1WoCKlJsIBus0I2ZyDYQskD7LZWw9dHCakc1lFJ5cGsoDR7PQUGt6rwxLMuiwWtAZeeYJ5TTBzo1gXcZSsJmpjLp9P2BOU8JGi7rIKF5Zqh2B3xOFyxqyQDIxsNqS5i3VVADx6WFKnbqf8k7zdv15Xm9L7S/o6r+QUaLPBLkQ+45wnXZwYQQPU3gW+5F2v0YF6J0oskfFzQ3GrXj8yBP6wh1LLoUbaK4SKejG4Qaw6r24jKOB1e9L7mbIJBfTDST/s//USlpDL5JdaYrnCeWjVVY+lED2ZrcV9+vCEtIgyBPf4VQ2HH5fUBfxjvC7YhLI7k/xmQjr6y3H5S+24BW9cYlqFc3dGp15ZvNCjBytJu2ycn0eWX94cdMRrNcExaF85x5NI8mwJuk3JP/DOfrBvNvUI/m5pr5XySahIs3LPm5pyX9YiJtZQSgCetjejOCSrZLiVdIVNorgNWFpefZyk9mXmhtBftSSJSyRfdIpUlFwMe2qp9xzlAeOy59zFO9MBG2BvAbl5PPVDSgdhZkGqNMMPZM8Y93C4FEi2bZDwVsMnnfMt85lGBU0ew33ti/Ii0YipjRUVx27rojYE4Eo5xcy4jNvGLRTS72GmHE+syTTSLpSywj4dY9wnrE86QuQO41dYkVkftand6hZXPSwV/amO/KmQ2YSSVSpNsB32Bi71DcFVuxGpT6Tkf7Kp6BPJAknuTun7Xfu1CCdTl9079uJjH/fjMX3vnZMteOYvCeb9/CZFIbJVGGm5keZsYNwg8ChG8smR6kkZtSd1m0tUI+7Q1QrBgXJzVPC40sDmzsT6kWKv8rARCZfcix3pajWw5aQSlcy7pe4YcT3IumVEk6hkucW0jcIHU3IA9nHBc1qpNoXh3J6DclmiZ5Zsit5boPnmpBG2p0WWyqycw3XKclpcoPPsaUAuctdT0wj+d6CdChIoP4rI2imUtOsefL3r0XfOgw317Hy4F/08fvVzfvSO9QkLzPslSWZaYqXCSGXKEDl5L2MAycdzdCZZa5T7HFK/HdjlId2tyHsVbi1FqLELgK4oZfi9LBHuyaHVTIRSbo1R+9ZgnYKVfgO2aJIziUv2R9J8XfnS8fUtxrs05zekRii9FKMU82aF1OdV12+OdipwY3CTZpHfmyxSwTflHb6Ry/vOZkAoaMSsoB0pyO+k4ToUuNXHaM/yqjXAtW7ldyzRy2x78nPNMVrMcQwSYiPhOeqGk27Kl3Mz3OJCeTH/3m7fjrrbQH4BV3zex42627DFFeuqkWb5dadnJ6bLm/2XDQk9Srixmyl0FA9h8o9w0cJ2aVsgL1PU0zFjUA8FIGoI8tzofgTYfnfz4lJJNufS0LATEaQywOP3xDrfX23ph1KFNf0fXHtpYcJ/acyhrbzrnCJkMwgPbGEyjD8o5zeH/QIqUSCLa8Lhs+6Ls2lICOKI0vz2YjiWIvmq7QYKzfrmMnzjTZKAfw7m5QHjnrT47uYr3SisKWi98qw+v/p017lLL69QXPWk8SMVSdZrb2WeJGhWtl0Rz9IKXcC5QPBfWQnltDIeNqUGvvdAcVJvImD41WBefM6F9KMT15lMlJbFQaYmRnSiXDofC5dWTsz6H7L2u6EGBW91wrSQBw66eRueAFaX6fYW0vC0FOcaJK5wi6g2W9Y3PH4nriPl7cdsweBcq8bh5lIfashPUx5+p1bNB+PSK86bVISbpyGSWcU2P6fXuJvVVSbgfxcpAFR9jWqrcjFz3uapTD2bCkxXeSBkEWyU0O8v6TedeSHCeV2ID2V4i92hV+zEqk+KEXHhxQldq0inWgBWE8s9Vrg3peP8Lu1dJ+uNdcPuyi/LNK86lPfrlEPFvRf2Jv4LlMJzia5vSCOpZi4+F92yc5EwzZ8rqhXFf79Oe0oMvxMk1+qG3lBdqnks50r7MTi9hpJ5yjEgGNKxehTjdtsCEkUaHAWUQtLemWgNpydjjAnqYDDz1L0QByyuu1QMaWm3vLE05zsQuNvVTcw8exUHk9X8tlE3ek1a3B3KsxADiDVZqQ9K2BdqvR0KlFiKijsaYIrIsWZTAIiUkS3K57FQcD3ZaxbHBqqoz7mewPiTkW1Fkl2lugW0OJAV3RGpIGnXvdSwHXf+XJffq1ejUQL2aUY1HwqUovsQgpatykGLaKi2vSkZwLkzi4Uy1tBzCfTBKWRk4qSaD/lhXepXQe37kk3LH2d4NdbVOZZ3nG43RpzmtIOZKLwxpUfs0h+Zjj5X24x/ChFe8XijqCtTKkYbsn9LH+VYC+sFHPvLBh96UK4prUmOzc0a4H5L5ZyeNVyQB4+7VJWagNWisXkWg6C6blFD1uqDSmQ9dQy+UC4oPYwI+SR4pMclQSW7zRU79ZyUDYRd6dCeYmYe3OP+DxX+AnkAIe3ZclPbb0dAX+B19rqggunJRvXK/RSw14FtTj98hMrGp/tEv+0jwoiFA9aOijjP86Y/48Lyu1IMlGUDxrUzBIzcSCGVjJqlRfBc9itsGXaMbs0g6LGbRsByA4camFJjlIB/04SGWMFBU5huwLIDZCotHGkOJLuSrkTCR2EeHlLOHQqKNRqQ5Y6rh8mqCAjFntpb4qYah3CqwHJUlO1Baw6VC3jREkk0CzvtuLEXGvJn2XSIdJSNIRE3IR6aahvyaYU0giNJKGYbw3JLVTvVRSfCg8sZgGbedS2o1EFemrFRbvmcCsKsDf8r/RKUa8hHZem23QHUYTgXpFdGOp1TzVwZKdGxvojT3puUC9zJqeZZPvekq5sdqUodyTObrkrmqf2vCA/NTRD6eC5HuirRLJE18Qk0tuaA1AtU9LDgnK/xV7KCLB3qJk/bFA+IerI8AcZdhFphxKpN3wKL/Q+Yewp7s6Z7Vn2N655dTaGk4yoILkyFKeWcrNzV5pI/iylWe2yUJ/3YE02a+UV6ZXCDSDcqqgyicwyL3LIJZd3uRcIk4z2oEEtjBSUXvHyfBWlI9WeAxvIX6b4QsZsyWrN5sqCs4shzbviyHV3xRVt5oa6ETFnNumSVzp4s/vlCeWrIXGawdBRj8Xoot50EXPpujabDuU0tkPCgJgUxl89Z1Ztkh6m8h2xkO0taF/0cX1xdIYg3TKSAMFItqsRl74tpcYZfWRpVsTZGyp7M6q2JdTbgbDWYo9y1O0FGlj6HmGRoFrRXkYlphQ9y6l/ZkE7kYNGMtE3KJOrL0vRTgd3JwnYtYrmIu9QTWKMqdcDYZGg/8Q+3XthaUeSvOIHHl3+iGGnrxMpZuZaHPNdx3e5GyleJDfmo9BJQMx5ih+5Lg/X00ZFemaYv9N2CTJSKMbXOflSpBIhES4ggNmuSf+gjzvKiT3pnhMhthozavBz4R66XiTeKWGWSFcuRMr7NeY8pY3IQbHvMJnHu5z4O2OSDTGlZReaZJ5RvQOXlznFVOEbTb3XoueWcJ5ha+n4qlYx/XKLyj3qOiG5EONTuSNFXHqlaWYJ9TjcuO57R1aKz3WpyMs9iEtB6iSvBE+TzKDc1fQeTFg8WZHowsXb7tnb9V++3pbaX9QV4eLJGHOSklwboor4cYt6nWMu7c3NEqBZJsSkSxkoNfmxIZlpZvcgeI0bu45dJvgKM5eOnqnEDQgiptZd4LlSMmo7/2yNthSnY3KWUBxLx4bYuWOdFDoo6QRmX7nGP1xgaiiOhOJfbQXaFY8feNSdJW7FQSp6IXWUUy9Smg15LX67IZ0omi+VwtGqEI5hP6AbTXYkr1N76QyZhZaNKoh7sn5Q8aX/7omAixeqGxdK17R4mpJc65vupB94mlV5rNhqqk0ZPeWHlhAU7jKXUPm1hnhQCnfNBtqRdCuLY0203XhrGG60Yb0jQa9EHbFLyE8NvRcSLRWVOCnjO4sfscAeLsnPxWEdUgm6d30ZN+paSWdICYaCiJgAaiXj/jMjwOPHI/wPVlDHuQDET8U81HutaUaRwSepOIpXWtoBTB9AtRFpdlou/kpDsy4JMPWLARzmvP7OrrhOs0jIA+26Y3ZPPqNk1kXzPeiYk7Vi8KKL4wqQdBtYfa8iLETjZpdy0DCVdBzTa429NiRn8nmqWvhu7TwlTtKb1JjqjrhDB08tW+MZZxdD7IscGi0svyhjYz9uYdyQTBXNWqA8aKk3BOL79e1DVu9cCzJobuXzQ7RaHJSygU8UvY2lQKEPKmFlZtJtPns5xuVigLK7S/xA8EfZuRRDMekkFEN/081SHe/PfXUuBVGj6J9IB7v+SimmmCjd0Tfa0tXVhRT8kwz1uE9+ogXj03bIlYkwD9utlnaWyphy1nWNRp643pDfmovGb68inSh05mkuc+zM0K4EmnWPK0TaYEYNUUdcI27xej0IQcCDqowUqkNHMw6EItw44U0txeWbEbnrd93LAM2mw220AOLuHjddBw3okoRCBr0jhU/FCFOPg3SItz35kUUvNf6koFqPuDXJEfa5uPaTswT1qkDV8h1TAfTTQsa7jZZ84KcZpoawW8lnoUB3Lu9ou7hHp8gvxJWcPc1IOralcqLDTC9/tK2q3OOHnuEnCcwS9t47w9+tqNZFipCdGWICZiSwcFN2xqFS8txJZHwds4Dui7yl2Wmpb9c0q6IJXi0krUU3inr/R3Djz2O9NYH8+Vpv3+kv6EovDb1Dgxt62pFHDcTt68aOUEQZOey1xDTSXxFqfXlPtD3l7VbihbZayba3AodVC0t7q2H4RNNutrSrHmZWOh4B3CJheeBpXko3JZlqcJKdK3FnEiHVO1SYT/rotnPXrTWoSrNcZqSZY/rQMb8buu6idFhGjyzmo77oEIMws7JLJRDeN87AFyntKJJ/vyAq6VjouSXbWWJKRXYJ+aElP1UsDgSRo7y460IaiZXh+390D+UVy7syhiOVU3l21Y081xz23pztgyvaNSdYChMFfZNG6gcVuxsTSUs4M8TS0v9fe2y9e07Sb2lXOsxEEanerVCtxD75TET1i9tOXNuNklGZgewy4voBt9mKfu1xXyCx1mM+LSi/KTFY9YYnPChvUBrhTQGroNyO7P7CEW4ogv52ANWdGrNWi/swjRSniuqDEhSU24HlrYB6OJfOZh7IH+WkE4mfyq4U2etE+HyNotly/K3/4ffRTqFr4HWBWWhGjwzpSYcLMfDBLz8i3V5iX2dkV2LYmL4nz1e3YJcyxso+y7tRGbgPFlRbkpihWxkbqjtL3F4tI3QjGbC9J6mkZXT5vr3HkmE7v+s4/nCLeJWSTGWUHfPA1saUaMVZKa9Nis/BZwmbfwi9Zwn/4fvvMv/hGrp5U2RH2KwxC416XpB/5RrXk5g/1Sp+/v5zwZYAdKgkPxThvn/Vk1GdEfOSbgXDoscNyZVgbvqvZSStG8G1uJHoHif35Fbta8PtD44kq3bP4W5XmKnh6nJAMhFXc7saaMaCElJe9G7Vzyyp1yIb21PSM0u142S8vuUwU4FUV4d97FQTLlNxrwL5icXcm0sxEsGvt2RnBj/pwMpnGboFP3bENKC9mHZiFzvoVx2jjwSY7geBxb1WnNBetKPKIwiXiab/WYJOPX7FwTxBXUmUZXFnhnKK7ELhBp7lnnSCdS33j2gQVuBKgM2aZCqF5egHKePvG9zA4/sBdX+BqSSOr9l2+BTUu3NiGhk+lvekGcthLCwtcbumN6rkM9gXyUq1FXB7Ndd/VcbwyomWr77VUm867GYpec9XP9paNw+umN+TFJ/T72zDsbxnca2Vz6EVXipGDG1uEFnsS7dcLSxm1NB7khAvM/TCsPLdlP4nmYzx92suFz1WPpX7h71I/hvvLP/59eNDoN+OgH+a6+0I+Au6lAdfgJ0b8lOFaSzzOzLy+8ZXn/DdP3pA0oFK0++MqO9CyLVAhp10ajgzhCRBGxmXqJUG8zpneUs0haaRIqu85UiuLO1mi5lrGeXkATU1mJmh2hXHpZ0r3N2KcpFLrNwwoEojaQEBnMlYaOFzhV4QlElQxJ5jsa9IZpJnq5cSIbY8cKJhOk7JLoXSHx/OWcxSBo9S6lWId0vaxqLp9DGqG63NNb4n3MLFvoy4lz3h8NW7rQTCH9QYG/FBUY8lokzVmraxnFyNMcOWGBT6dU67IwDeMNFcPtqh/VJJWFpUrZn9TMXs6bpENlkRqjerCqUiwYgOzzQKGoVqkQ7m65xkpli+09AOLXaucUqSSRa3Atop2td98krhX+XU40B+ZIinPdwg4jZbTObp9yuWn6yiPLz+9i7ZQjbHesdBZXC1Ia7KSKvcCYRKNsfRDxLaIVRrCeqgIn2ek86g7UNvd84yDsjOzc0oEuD/9W9/iTCUsaG/kqzhN5uzqg2+H/jef3hXPuu9hulmd0B4LR3OaksYgutfOeP0yTooAeXa132yEtqOCWumhtZnmLkmvVaEUlPvOpJPE8g8zQaYpXSpiVLE6EYwJYt7Mh7ODhNO4hjTyCg7WkkZIcByL6BbAUWTRPx+hTrO8JkYcRqdEdaFAWj/aExz0N7E2v3Rf3hI70p0lfmhvTENKUQXR7DYfovzChUNyURT9yyhH9B9R9tP6D+1LO46lIk3hZfrdwy+k4SXl7s30YVKi2SidhnNV5fooAilJTk1VNuSrJF+lonDd7um/PcbNO/J6Lz/CtqVrrDrkkBUBDVuCKWleFTQjCLxsI86qFBXqbAw7wSoDfnPXtJ8tircu0uLbhXN2NOMIV2vaGYpujMERQNb9y44PR/hc0t8d8HsvEBXCr/qSGapYE+uU0mbeW1Y3JFCevlqgE0jrqfIzizD55Hp/Ug7jKRXhnY1EHOPai3ppwUhkUnF4iBI1ONSk8w0bdPDb8uoHh1J781o6gS91Cz2xbzhejLFcN1FXT4bYhqF01aKxcsCawPm04J616Hu16Sf9jGllaL+vE9vKZxH3YCfJEyfb6I7zbQ6WBCOe/iRQ00tpF3UoRI8z7KLSWxWIqHw5IcJVSL32t6RZrnnWdyK5JfyZ5glLCcJ4Y4iOzcs/8Rk5+16u/731ttS+wu6fE/cgtFE2fj2peNglprvvtjHVAJHtQtFuaVucjSrO3WHfYFmHIgWyYBNI/EqvQEZS3KITK3M3NCOPPnzFLfZCm9rIVo7v9ZilppmU0LM43WKu1sRdcSs18Se5MqqBwv0UpMfWUIeGH4qIvBkpsDLJuGKyPC5Ir3UmLm+uXrNgznzd1vKOy3NPMWeJyxuBYn4elWgdSBYcflKlq3wx6KJXH9VupwhgZUPLfV+g54bktMEpgnhPJNN74OldMkODdkPCszMEM5yzItcdHmvU4HvNorywKG0jHrzUyMao0ErAOhM0DwrdyZSPDaSCxwSYe01Ow7faNTBEveNOWpuGT6RcWGxN2d+13cpHoCC5W1HdiX/HVLB/0QdKUYV+mVO+fEq2slIPj+X4s9niA7vxGKvpUgzjUB5s8OE0Q8TXA/i12cwS+gPKnwWWdySzlX9aMTwsaHe9NJRizKes3MtuqmXPcxSxqzz2+IUj5k4Hu1M0hiSlylqaWQM70TLZkq5Js8+3hCGYSVdsWYl4Aaik2vWOoZbqW9GpiEV7Wc9jqi5JT+2mKWiPGgJQyfojATSayVO8MpgSuExhkzyfvuv5Hq1CxmbLncEyGzPE7SOsFsTBl4A3UPRhdmpgIj10mBLRXFkiCayfE+MFqaWrnEYeKKJ2IklJhE3ky6NxMIF9m5fSIf0MsX1JINY9R3JSzG9RC2v8U3qR3qtxVkaFOEipdqIZFcKf5ajD3Mw8YYdGa2gQ/ITTWw01WZAp+Jkza8jrLSEzQY7NWRrpRSs84TkvOvsaxlVBidOVHWSoZOAGbRMXq2gIizfaQi5XL9oJFLuKhfn61yRXwARTp6toVSk2Wtx5wV2pglbDXpmSeai2+0/FxOYLwQ9lY0rsnNx2VZ7jmY1ML8t96rYk88DgFbTey2j3Xa/oXco71t10IjZ6KqbSJgIwxZaTXXUJ7QaW0JYbanXPG7gadaDpJI8zYlrrcQYr9bU00yoBtcp9Y7DXFva0wKQznXcqYgPF8LJ3G2kSB836LbLfvbQXOasfizXXkwi/adWsFSdFCFkXcTcUNzipgbdc5IH3WU/u1XfHVJUFwcp04dmJJnZn+fyUf1Eft6un856WwB+QVe7KY5GP/C04yAIi1Jgw+okkw39Z0qqg5ZmHESXsxQtXBg53EBSKkIqN8yYBEylJS7u3N4kEoReQDvpuFQ7Dps71J6Qev1Wg5omBBtJLq2gJGwkTkXkXvRqkgtLM/aCiliVVIbY8/hU9HFvBPX2LGHtQ5jdC/gvz/GDQLGxJCaBlX6JLg3JuSU9Shg9EW5bcWsuhdRlTtyt4KCk2ZQCSpeScjD6KMFti0Mzu470PkvFALDqYShjLa5T/HVKHDjK9yvKvQ6QfSZZvHrQSprEaoNPxd3npymjHybiPDYRc5KRXFjsTIGHyYsV4tISep5kYsjONcvbDpV58ucZNvG0ZwW9I831V2U0tZwUqFYx/ur5DQ9QFY7FvZZoIb+Ads1RnGiqE+GuqXsCMw65FEG6kUQJeyGmDu1FC9UOpQgO7y6Z3Q2SzXrUQ1eK8tEqvhdufpKFOEhjz0FU9F9oQfTstuhSk9yfEbYacf0upMtcPE8pTjv3s474ByX921NxqK5Hqo2A70XqVXFJp/sLGHR4kgtNtd9Qb4igXr+WODtbigbOZ6KFUneWAnI+aKSbVQgcPD+VwqDakkNB7HmqL5fy3ArP8JlcY/WmdJyika7dspMJrI6W5EWDagR/UzxNice5dHh6YpzyWaTcEylCcpxKvOIvXYs2rJR8Yd2IQza5EpySHznsUnH4ZANixxVcCZiHM8xhJgcGKzIFe0+MOs12S34RSWZaknROzE08Wswlsab3WApJ3W9xXSFRbUrXU3lFWCTgNCe/JK/bHGX4PSlwQs+jCocKkopRnGjiWktcWPlz/UDxnZ5EPC4FWYITmHg0QCoQ5fTMMPo4waew2JNMa7MwpI8L9MwyeGpIporio1xGxgbqHcf8HUe83Y1RD+a4F33q9YCdaol5i5JrrBsxj/lcOui61MzvOer1AHNLua0wu0vBwAwdPoPm3RKVBOLcsvJDMbMNvpdBVKz+Ycbg9pRiaynQb9uN6E0Qp/nj3s29VTdanP/bNbHvuwSTSJyk+Bd9dKtY+cOMdiWQPsso9x2m7u69QTG9j+igp4blvpe87JkFI/fcZCSpTOjI4l4LZ5kYkvpyqBw+7nKR3ylFFqI62cSlpvf08x0B/7gO4Dc/b9dPZ73NAv6CrTeZjAf/z/87JinkFLlUuJVA8VoSIrJzTfj6DP90QH6hhI9WiPEhP4f5QcRtN2QvMtxAcB0SVWWEsZXJiKUdSaeuXpNuA1HMB28ctLrLuGwHEq1EkJNddqkZfxw4+j96dOGIkxR858zVIjInl3zL7MQS3hPXollIwVXfam9YhrrniEGJQ05Hel2sVjQyNhO3ZufqHET8ToNOPeplgW7pjBOK6mFFnFsZF801ppSOoW5FnG6vxDhjutO2KUXTBGCn+sY1amolxXcrG7R2Au9tViODFzC9L92AkMrGFdKI2q7xs4T8yAp428YuE9Rjry2+6OC+DvwgkK+XxAjuZZ+QRemY5NIRkDGjdHp13ZkJejKS7+8saD8a0Yw9KiryI0P75SXp92Vs3K4KJDv0PbQK1Wp6BzOWrwYiDRhE1E5FuMrQqw3maS7mlYMS7zRcpRTH4uB2Kw5sxBaOcJwT8iDmnecp5a5ks7oVyZK1pUCn9a0l5uMB1V4rfD3k2m3WPXrYoo8EUB5txK840mFDc5ELBPfC0Ow10GhUo2HUwjQhudZoL6YBlOgMm1XJ+JWsacXaDyLn31A30WUrHybUa9I90jNLNJHY9/QfpTKWTAI0muLIUm16MNB/bqjWoiRLKMgPE+p7FbE2gjQx4iilkc51emJptp0kmGgkj7Yb9RYnYr5RATZ+4YTTb2+TXUr+NEbSZsxRRrJQNO+XmKc5oycwvSdpGGiJQosKFg9aCApdaQbPNLN35Rqm4z36FYe9thIZWSkp7vuduclA24GTQxHJD410/O60mKntIvVE8qFbhc+DwImfSia2bgVl04zlfVG1JpkIM1BXmnSiaVYCoe9Jzi22lA61G3bu2AsjUO5cDEXJtZY4y7GHJND7LKX92pzwuicd3YXo55p1j50YKY5qwcPkR4bynRq1tJJYtBLQtbrRfQYrGlq11pA+LrAlLHfCjfZTDkkCyo/XqXTtEDOLL+T7BZ15xSlwMlrvP7Noh6QH5QEyT/oqJZmrm8LRjeR7ENMg1263YuFJLi3tVisThall+NTQjOR+HawYosp9kRs0a4FQVrz4v/7fPrcs4H/6x1+n92NmAS9nnv/zN7/7Ngv4p7Deltpf0BX7njhupfOTRqKKLG87EaP3Iv1CopDKzUiz4XHrLdmVdNjcVkt6KOMn3w/iFtQSqs5+Kew0K92MckfgqO2qnFCr2w3+diUjr5mM4dyqdCG0E4F4veE5+1mF7bfE65T0XGLYQDZ3XWlB1eQSXXV/+xxVOMJGI5FKrThJ03OLfZGjbUQ1Gr2QcHnXj5QPatxuQ7saaIcyVkODukzo92rciqfZaQX4bCF/lEvU3VJ0eCGVTlmz5jFzQ7JQ4tYcBNKdJW6jxcw0UcXOxRtl1KdBZx6z1Lj1lvZWQ/X1pRhG9sCPnLx3rbAG06nGT5Ib4HNMI9mZIYyku+aGnvRad+5ADTZQH/apJzn5mSa91LihsBtN2QGZF/omjF43irDq0MOWprYCqdbSvSvvNvR6NeWedFTsRBOyADZgpwZWWqoyJfY9TZdgwlEu4/2l4HaSmUK9KOj9MEdFYb35nZp0tcbkHjdNCb2AarWkkmyLIcmtdJ2myE2KRDsRbAyRG3AyiA4weZpLodF10WgkVUQFGevm52JSGn6SyEGi1eS7ixsESLvWXYOtIqaRuN7AuMHnkYuvC66jHUmRutyNNA9K8ufSDY5ZgFbRfH0hsWQazEKKk/TKkJ53kWEKkgtL8SKhGQVs1hWLKy3Zubk50LzJ/lVJIBtX4pg2UO86Qt6l4RSRetPTTxrcXkO5E+g9SRl+PyM25kbfGC4lNWR2V7pAKiqSlVq0expwCjNqpKvfQ0aGS0VxJl0ue9XJwIMcLtqBPJd0Is5c3XP4oSfZKG/A8qrRrH4shyrl5cDmi66oXtob127/UOIXQxJlHLzZZYanUvSprvus54J1CUnsxvSa4SNLMJLlHdfam4I1pBE7MRRPUpqViD/u4VfkO9WMxYmbH8lr0lMr391r0fsOPsxIL7QcCOvOFZ9HQiopKNmlQb/OqfZaFh9UImWpOxqA+lEWs1kK9zM/12RXclDLj8QMZY9Sek9S7KghvZTPqdyW4trMNbTCKaw2xFCSTMSok046lmfuRZ5zaw5BSAjF0xQWFuUV5XYkuxQIuW6VSDcmktmuWvlMP88Vov6J/LxdP5319p3+oi6nKB5lkHkZ0+iOer9W0a4Grp6s0Ww42KsYfWKwFwnLXbmB6kSKozepCcEi3b1O/wLdjXgpaIniyEgXrFaYywT9OpfuQi4ietJALDwhiVRfX/IG9K5UR74vJKTer3jsVknoeRnveimSPnl0C32WUnySk6xWmIWm/9JgF4pmu5Xu2Zlo6dqxpJb0P84YfiejeC1A3+K9a+lyOEX43fFNPqw/qCQNYRTpPUmotx3l3ZZmRdA1g6eWlUcKU0pep+63NCc97HlC8s5MChsv0Vz2yjJ8BunjQjbEpSEf1JgnhWx6CTcn/KikoHa53LBN32EahWq6PFAAJYiQekOAugDZYUJ2KWr9ZCaF0vBTEeCHRLJZ/dihG0X/F8+p79eoubDl3Hku3MNRA6Wh/2nKYpGjxjJeDXfkvc2fZzJ67TfE0wxqLdDjCzHhRAXJmaUdd8X1isfn0HulKV4ZkpcZ7UkBh7nAxL1Cb9TS0UkCxce5OHGDYFDChujQUFDvCJcxFIF21ZPOVPcZdSkn7y3oHyrsTKDEsRD8znK3u1ZTyM416UlC+FC6BypIERmSznldOLJCusijd69w/SBaxFqRvpLrO8kcrhdxGy16YTBzg2sMtIrsVSKcydsN7TB00WcRt9XcXEsA4WUfnMYepRCl+Kr3WqodT7MjGj/14ZDieYrbr9GFIz03Mg5fa0FHHn22C1MBVzcrkXocoRVMSL0m8WA3WbN7FfmxwVg5jPlCusPj0bKLiZMuf7MSKDfl++t7AbuQVIveKynE/NBTrUmhm35aSHrOHw3ILgTZVBwZ/N+4Im7XtGNPOq5Q40YKzoC4adc9Fz/n6b+WLOreC4v+cIDPIT1OULWmvOXQe+I6zy4l6jBY+bxcIcaUZCLaRYYOdW8h7MV+oF0RzWFxrBn+MJVDJlBvOdGMrnh6rzTJTA6r7UqgdyJ/p9p1FKdy7TWrgepWe3MwdUNPfpSQPs8E0jwB7i0p9xzpSULx/QK/U9N7aW/SR5IrS/teScxFYqACmMc9fCrXY7AyMfGrwjasNwQN0zvU9I8k0s6UitU/7qQDEcKHo5tfVx4pIN/gckYydcguFOW2TAyyc03/laL//Mfrvv246+0I+M/XevtOf0FX/jphue9IchErYyJ6XOMPe6hRQ3qtKdZLsh8UTB90m1hfoKmhtPQ+TfED4ef5vojYTS1jwTedlOZ2w+LAU94S7ZP2iFljXULO7ZenmEtL+jph8JHAS8OljHJ9PxBf9Bg9kRFtdqEwE4OrEkgD1SJle/caVwjnLVqox5H2MsfsL2n7XfRZEkiuDdWWOI1Vpwtqh5HZA089lg5d871Vwa3s1LR9iEXAXFtGwxKfS7RWuSfdPpUE/MjTbjjKby6Z3pdEC1UZOMskJH7kiT8YSZEWpXsR0sjV171gSlqNXWrKaS4b70kqLLZzcYu6kUf1PPmFovfSEi5SKUQqGTnZswQ7sZi1Gj1qBXT75QXNOEgyylHC9EE3ch52/34WSRYKWziihounY+LSEAtB7aTbS7CRcJWRbZSyQV2nhHmCrrquHvJYPo9Ulzlqu7oxdLzp4obtWjiEoQMHK8ljbVal+PF5JDs3kp2at+AU4TrFp0AQZ3PsOwiCQUmKlvRZRnIuHRvdShdn9XvSBTKvc5KpdDfayjK7I6YZXSuSs4RkpkivZUMvt8S4ZBdSlDcrIllo1xzpnTnpswx9mlFkDXFpWXx/DUYtulWMnnRmi1VPvUgJtyoIYk5JLzXME2H8vbeQWLRXKdlF121dKCgFsO47+LBfcRQvEnwmusTsQqNsZHzniuJFQjuK1PdqynsNNvGEeUK95aSLUxqSC4teGnTdjd/vLHB3q5uMbr1TybW42aByf4MXqS5zijsz0QE6xfUP19GlZEaHLMrvrYp2zS7kAIYNlFtBCqBj2x2mHO0wML5zRTOKLPcDIYXl3Zbr1yOSpznZiaU97mFe5gxeaJJrQ3Eo11HxUrKXQyJ/R8UfSUJiIpIRd1pg95ZysEilaK03PNXDiuzSCHS7EcA2j/uYCtJLiZ2s3pWkI9cHey3fSVV00ZVTQ70u381kroi55+zn3jAFI/Pb0jFb/UgzeJRQbUqxmZ0bObgi1/bs/RZ/UpBMDPb9qWSGv8ikWC3kPWvHDv0yF+nFQgsrMZHfD4nEHRLBXiZELeYVey0519cPRSu9vO2Y3g/svXt2wyBVXhE6SDe7FcEKv7TeCLRrXoxRBpLbC3H2/5XZTUzh2/V2/ZestwXgF3SZGtDgTwoRMS8M+fd6AkhOZNxVHfXh5yYUx5r+Sy2anUbEx+WOIGPsTEYU/e2FjGEKLwyrW5VkUdpI8cqQXcsIcrw9RU8tdqlYXhcUJxo3jCzu+S6urUseMBG33nL1dU+7LZuDKRXmNKV4ksEs4eL7m1LQLN5oB5VgH457olksofgklwzigSc+WBKtbCDNumidooniKAZxaS4t9V6LSsVBOP9kjKkUy3utiMXXWugE2atbM8xnBcWpwq3KuLI4FR0VOlLtN6ixbOxmo75574vnCdneAldItyasOtyWjI/SCTJ+BJhaym0x4OimK3DGnnpNNio38oTQgbMXCncmOU/NaryJI4taxOB+1REGjmQK/iIjFIH00siG/CwhtJp6mmGuLVFHwqcD6bw2CjJP6AWSsy6N5U4lLsZWisJ0IigNonSHkhfy+CjQ98WcEDtgM3CTsxzTSD3LxJwwESMGqVxXBEGaxNWW8LyP60eSmeTRtmsO3cD8jmQzBwPtSLpXcWFBybjdr0hXudnwVFuB8a2J4IPerUSntlRkl1rGw6WhPJOuTNytWH5nDeVFX7iyusTUCldIIYkCtbTiqG017W5Due+ISSC5NBS/N6A4NLT9SLXncVvSQSUNuFVBr0QFdtBSbQRMI3mzPofeRxmX50PRemYBZQL2PMFd5KQXBpJAdi5OWDHtCCQ6mSr0JwOSJwVuTTq86kVBMwY1TYheNGWLuw4zNyxnmRwaOp2qH3ragWgMl68H9J7LCD+9VkTAFJ4wbiELKPdmRKpI5pqrqwHtuOs2Io5vXUuhE3W8kSgsDqQwKfdbKSgP3I1+OLmw5GeiqY09DzYI+3ClJT7ti0u2VjS7rcRAdk7pZCow8/6h8A3dO2UHRo7EVtPstd0YN7Kxf42yQZJ9egHuLsWBbuSgaGrpkmcnohcW6DY3AHafSUewXfWkX7uWmLVak0yVJCHNcvlcl8KjTK6MHLpGYiJTvmM+dofBNxpGvEDaVZewopYGPwii5esF7FGKncnB8PCTLZpRFPMO0m2vH5aoV+I2nj8QaU+2VlJveqKO1MtEDFSTXKY1n+MK/PhO4PD5voS/UOttAfgFXc1qRC81xZEYJ+5/9TXpX75Ae+BZXzbwoAjfXUEFMTwQuw0jKsxmhalEm9J7ZXA/GJHMFGYqJ2T1KpcbYS2i/+WBp9r1XL9Y7TI1JeJs+bWSMHDYidxIo4bhU+kU6Kll/F1DepRQjyPNpgBbm5VAdmrIriS7NqSy85Rfqhg+ERaeXSqWO4HN/8OhjFYmlnaSkaxWDJ5aileW/qFgKNqhjJRAQNB6YcieyM3S9yXhQKWBrN9AbbAblQj+lxnmSzOq9YgZtMQksrgvrENVCaYmLBKqjSCjcQW955Z2RbJnUdK9VAuDubKUO15YjBPJxY19T8gkqSCZabIzw2hnJp2DrmtQfL/Avsip10QfaDdL6ahsR2Iio7/QGPCK9CRhsS+dDTOXMbHbr6keVlAZstepZB1fSKRdu+Jho0YtrAB1tWyE5kVO8jqVceJVQv1eiXqDapka3N0KkPzRtraQBvTckE6hXXfYvSXFqSK5ND/akAsp+ouVit7ThP7jFH9QcefWuTiwz7V0l+6IKaHc86jbS8JWg+97VIsU0FcGu1QMPk0EjrslhXdca/D/dp30zKAuUuywpdpz2G9cE7VgXpTvcEfnGe096e5hIrNPV2lGgdl7nmo7oBpFfigbcvHaQqNJVmtUpQl3S8q/NKf6kqSAxDSgZpZkpknOE3Spadc8+ZlGvSjov+pykZ2ksNTrETVNBEzcymHMbcqBIJkq9NyKjnYgY2C34hg8sWivcO8I5Ds5t/hekOKgiw9TRqDEZiGvM9aGZKbovTQCPz6WvF+AmAbKXTnItcOIW3MM/qBgsNol1kThNCqvGLyIFB/nkoJTd/pYA2anRB8saDY89V4rnS4TGT6ykESUjdiRpKuEFLIrxfRdMYclF+LW779SxKWh/0pG2migVR3CJshYfSD8w9ldifsznxXSYT6TcTxe3Zi8Lp6PBVNTC7ImvuyJg3opGdvZpXQf6x0n+c2lYnFLNHrBwugzkbOsfdvgvSZ2cpf2XiUEg1q6pcs7Dr40w1SdhOF1IRONJDC8NZVx78jTP4y0ax69WdGseYpjJYSEoSM/kXG7Wa9p15zIKiZavn+3F+hazDVx1Iq7OJORPRHRA/5wiJ1ocV+XwlHNjj5/rO9bEPSfr/X5XzFv13+TFRUMXmqm77dgIs++tY9dKIyCateRnkmEU7Ujp0hda0FAtBozNYS5gTQSEkV+GQmp3MhDLl2j/pGAl7VTzB44dK2J4xZ7lNJ/LWLlZKrwZxn5VJPM6KLIJEbMFRG9UVNOC5qtVrQvhSPWBp8K6+yNJsj3AnrYUnxUcPHzDl1qcaoCLx5tk8w1Po2YmcEPDPMv16SvUyYPWpLThPDuAj9NqZUhbDUoFUmfFbg+rOxPuD4dos9S2rMUtV3TXmWErYB91KceBcKqZ/DHPRZ3vPC7TIfcuLRdpytgJ+ZHjsJUNi03cpgrK3ibRF6fOs3IDuZ4r+kXNan1nKRj8kMZD81ejygOpWO5XHUChE0jcehQNmA/HODTSEwhuZbMXFUazFzTHtQwTdCVwm03qBNJuPC5PB9bwWLfo8YN9lmObgzm1NKsBMxSXM6ztYieG7ILTb0ZhOs3T4jDwGzb03+cUocc9iritSF7VIhWbhio1kV6UFca97Ml8TrFHmX4/QrzKie9UlRxiOlLIcB5xtkPbqHH3ai2EA2qW2tRpaG9yjClvonHqjdkHOa2W5KPU9q1riUVBWVSbncdpnGLelaQBlj4IXq301kuBOYd+p5+v8F/VhCVOFujAu8ibrMh6zdUusfoE8P0nUDvWUJ5SxAe/jrFIzw23wtdF63LuB0KsiVZrSjrHtiIGyJYES2Rd4tbkdil8kQDrc1BCyTY59JRM1cJugU3Uui1GpDs2hglR1j/8RDzTkm4GBC1dOyjliQITCQ7TojG4O9VNI2Gytx8vwePExZ3I6YSA8PiViC5sJg6Mjntk11q2hVJxUnWKi6+kWM3K9qrjOLI0ozkegy1Je014tZvNc2mp/fCsvhmibpKiVkgXEpGdzTyuUKnVfSKbKlZHERGj62gndZais8yfC50geJVQnnLQeYhJhSnmsVdR3vbkT7Lqe7UFJ9m8M0pVdmXNJhz+d5kl7DIUnQjZhbXk4ztZssJe3IqvMbqoL357kQNVz/bQqMpH3r0syH5nTlaR6qnQ7RT+JGTKcVC09R94q7r/j1NmGnhTUbR5WVHCZN3A+mZwZU5vTsz3NEK6kWBTqLcd9PA3r/MOfuGoh17Bi8ty91IfNqHXGIkzVlKeq2px/Jd7N+aUZUplRHzHEoc3yCuZTd0/833lrfri7Peltpf0PVmtEESyQ4T3Kpoi+yCzi0mTkNdCSIirjdi2FAiol/9SGFqiVy7+pp0h6ptJ8VPElnudLFIPcQVWSuKTzKJZBp2sWkd/d7OodyKlNuR8v2KZt1L7uhZRrXbgorY9S713ilWt2a0Q8E5oGSzjZNURO/I68qOEtIzS3KlCUb0On7cEhYWauk4vXfnGJ9KXmn/SUJ2ockf58SrlHoMPg9MXqxgpgbTgKmAiejhlJfnb0rpckYjJ/SwV4mpoefQLbDSCvdvKF2VkIiTWfhvhmiRUZNX6LMUU0N11KeZpVy9WuHk5Rjo3lstm2U9lvG2nli4t5RukY5oG6i2PM2W6zqlkexUXI7ptYKrVBJOBgGTiiC9WYk/cjsaMVLESYp6OMeWgkeJWhh1+ZHFDFrhJDqwu0ti35Os1qLXM5Hlvkc3YJ/nuH6gXg/4g4rBU9lUm3GQrsQ0JT8xMmK8TnG9QLUVOoSOwtRyzZiqy3YtIrHwFK8N2WFCfmxQAyfXcITFHd/hfSI685S7MqpLnhQ3xffqx/I+x0ZLJvSmQ5cKe2eOaWT81jvUqFJTPR3SdGDlastJbrSO2LMUYwIrHxl8Ln+nXpdCL5kqBs8s+ak4Ms1CoypNvSXcSnHmKtx5QTJXYoyqFPmpFlZfl4iRHkoHuN2QGEFpeQno2Sx1VwxGkok4nZc7QcaAOuKeDij3PPVhn3Yt0DtSJFO5rrMzS29DZBD9BxPSHxakh6mMKocOdOySdDRu5JndFSdpMlXM7si9ot7yhL2K/NiSfK+PqWSL6L2UkbGKSLfYKepJTvo6lUPjTJBHcZKKE/d1Iq7wHRnR+6wzjs3FhVvdqyHC9GEHR9eS5+sz+f6Uu478yJIcp+RnmuWBp3hliZWYebQNpDOoXwqiCKdIvnZN+6Ck2pRrvtnwlDsSq5efaulugmgxW3nPe68MtpL7nEoDKkpBrWtF01jKF0MBOkdk5LwqBbBdiGHLzAzlw4resRw+ymUGKgpqqZMYKA+L0z4+k5E7QZFca7LDhNmBvsH2LHejpCnVCjZqopMuoKSTKNwwUn+ygm8Mut+y9rOnQjzYjFQbETZrei8+357O2yzgP1/r7Tv9BV3timd2L5A/F/F98UI0SIvbvhsTQbsquq1krig+zhl+mLJ+54rlg4bLn3X4fRn1vQGu5ieW3guLKTUhF15ZO4io3DP48uXNv/sGiKuCwi4183ccbrO94ebpSuMeDYlJRPcctudYGVQwtaSXhunTVdzIY+bSZdn8lqAXTKkksP1M06wGRp9Bfinssqi5GcvaqcGPHY+edJFZ1ymLe46QSqqEGjcC/dUImzB2WqsiYmdS7LlVR9wVFIRdCsevvdVwf++c7N0p8TolWjAnqRheJhpTaqo7DYxaBs9lpAtgzxMIyPu1KpFbZmIF2XFh0aVh9dZUnNq1OBd7J2IIcec5xWtL/jjDmIBdaEnd2Cgx9+c040DaJYGA1BLFS4t+nnfFoDh03UAKsJhEtn5PUV8WlHca8gthtfki0j4s0UoYcIs7jvCsT/EsxZ8UxNxLod7F8fk8otdkdJl9VEiBNFc3aQy6krFetSEjVd12msWhuKzrLSnoZu94SanpGhfljjhYk6WgefQtGXmPvy+boXIydtetAivAau0FWux6oj9UmWfxjnSVe0ea+Ggg8gPTsR1rMQvlZ6JrzE+t6C67cWJ5OKBah2YkwOU3DMtgETRKDYPnit47E5IOUKxbKE7EFR/TQHywICTyHal2POWuHGZ6x4rRUxmjqlqjC+lyqUbTO9RwuySZKbQTJl78rE92ey64oxeFYIEuDMp3aTuZpIn4TLqAzeMRzV7D4vEq5YEjvRLdm55YVr6bsrwjxS46go2sff2M4V89Fa1eo9GlRunIyi+csnynYfBcoR/1qdekuNcNFEeWwceS5JJOFXa9xA888y/VRBPZ+haCddkQVzRa4tWilcJKRcifZgJarkVfqq5SMaQMBIyuWi2g44EcrKKSLrFyGrdfkzzNWe52APCgSK8185MB+pUQCEIieBRTCRswGsjPZeyrYgciUAjs28rnHL2YReJCogmTT3qYSg4pMvnQ+BWHXmtwgyApP0B/VNGMFL1DzeD3C+nWe4XfqXn4/mtJoTkzuL4cpNNOL60b+b7aLvPcjQI4MVwN/6BAX3fu/tsV7XaLKUWLGJ0izBMmv7tNfqLpHUv+tn6dEz5fEzAB9RP5ebt+OuttAfgFXarvuk0vdrgDgfPGoTDYkrl8ydpRpPj6Fct77Y1zFAXFq4QwT7BbJWZTUCnpRFygbuAJPS/auBpiUFw/HQua4drQfyGjifpW86OCZGIF5OplXKZih1UxETdNOT8dydWoIDvTN2MNIhz/dwG31tKORaDui4jdXjJ5F2bflJiyOG5hKN04FQAvyArtxKCQrNSCfzjR6MO822QUar1G3xI9Vygi7aqc2NMzS7zIaDpDidutiV7x8mKVlV5JMtUCyb03px4HsmtB1hRPUmKjWf7CkvpeTViRNIvBU0O13gnDbcDsL0kvNO1Oi9qsmbxc6TYHTX2/5vxriuxadYiWQLXtactEOkNTBU/6JH84BKBZkQ5AzGSDaVcibiUIkFpHuFWCkeIomWpOfylSvLKo0lCvCaTbDzzxNKOdpbL5TbqOjhZDgJ1IEgQR3IrApn3ZbbD9yOw9R7Ue8ak4xIOFdiAfYX7eRZdFQaiozGNmIkGwM426VcrnMjeoNekMLbtxrn7c63Sc0unyu7WgcmxEpcKfCxbyY8PVNx3Nmid7mjP+tiWZaub3hFG5eNAKEDuJ9A8FqbHyNHQdZmHZFQ+vRRcZFO79JfHdhbzfV0ZSSzQsvlqxPHBcf8VRPlqlHQpf0/7iFfPbUmRjIvFZn7UfdJotJW7bZiWgPCy3RTvWf2YoflCQvkpl9DqE0W8XnXlDUk+ihuq4L1nXHY+u3pYCw220lPuiIw1WDioxiejrhJAHNg+umD9wDF5I1vTsXujAxNKF0qsNp483OPtkQ97DQytMv3nC9bc2SfrNTScN3aFt7osetNwJxKFjccdjPh5g5ob0VUp6Ybj4ihymdBJgvcZMLK6A8fcM9abrcEyCZskutIz8HSTvTkmuDG4YJELuo4KYSld09KjjC3bP/Y3bNdwRM4R20t12Wy3tiuBneocK5aS4qvYc1Y7H7cg9qd1uwInW0y7kz1CLVnfj4Fq+V+NAsJF2IIUrTslne5ZhlppyT6YA9UcrtMNItRWZ3w2SfpQH7FHGi397R/BJKWx/5ZTqyyX1eqDcd1Tv1vLd3QwsfqYkvei24wCLgygd+qEnXKSiV1Vyvx7+MBXywY6X797DlvCgFDf329rp7fpTrLcF4Bd0qcuUdiSn9rhdE4ogcWlHKWapqVcjdqpRmzXTJ6skg4bFXelSqNJIwgHgqgQ3FfJ9vSYgWDs1Ym7wHVvwVNh0rhdoN6QQcL2Iqg1u6PFfmxPXGtGoIXqcdiTmgHiUo0stXL0rTby/lJvpZmT0mSIOJFmBRnJm3SBS77XsjGfEOyV5T7oM9jhFn6XYuSK9UuSvEtKjRE7VJmI+6WOvDfP7MnJyQzFVRK9ol4nkGhdeRnJbtYx8Z+omp9McZSRnCf7ZgMNnG1IUXVlx6epIMovoWqLB0pOE21uXxMqgjLiFyx0ptNphID238Gn/BmicpE4KISf4iOxphht5ys1IqGS0ykrL/4+9/4q1dUvTMsFnmN9Nv7zZfh9vwqXtbLIQNKmiUasauOIyyQu4KCgplRcllYSRWimlmotSCgnBFUJcILgCWrQ6WyW6IVsFaQh/4ph9zvZmeTPt74bpi+/f61Q0mQVRGUREnt5DWorY++y11pz/nPMf3/i+933e/ofZ5+DqUyUdqplo0wBU03VDu7xTZSLF0wS/FG6d2apob9TYhThMsxMjHQMl3aioRaCvGyn8ghU9FTdKQfs00uUqnluS2wv0vPu3RpyV0YgsoNpvYdxSb3h6r0/lcV4a6uuNuCHPUvzEUd1o8L2IKy2L247eYRdTuOZQt5fUa5H6RkO166m2Is2bJeosRXUFQGxkVB8SKShUo68SabJL6Q7FVAxFqtHdRq+YveVoNj2LfY1uYHVN8qjbb61hF4Y4dESgnWVSrLSK8sEI5SHvN3K4mppODtClkLSWuN7IdT5PCHlkua9YvFtfuYJ1o/C55DebVWdO6nHV7avXArM7Miod7C0Em7ISBE21IR0+dJcgs1OLxEBFxp/ICDxZQthopSvWKi4+2EQFxfyudO7CyFFPpKBBR5JPC7IzfeX8b8dBGJFJgLclYWO1J2ayZC7Xz81Syt2A7wfMucV0qTnpyxxgwI0DdqEwDwuyewV2JVrj2WvCFtRNN1LtJCeDjRVhs6V6OsSNA8mlxt2qpMizkiISEvA7jcDTn6S02zIq9ZVFl4ryZguAPU6wk4ZyJ7C6FsnPxABmFprYc8TKUL9fkuQCWlcBbCX4n5cJQmeP16TwGjm23jshDD1uIiN0IoS+pzgRJFbIJFfcZ+CvVygnB0Gs5DHX6+Lyjyby4ukGodVXr0/0UpjrRqGOMuJbS7IjSYTxPeki654DI1ME+/qc4ljRjiTyMeYeN4jYSysmtHGLG/54QdCvRsB/vNarK/0FXSF7CWmD0IijMfTlpBy3a+LNUmLdpgmju5e0s5TYcxR7C3Go5gFdacxxSn5gCbkkbDTdCHPz7rl0m4ZySo66G/HMDW4iY0dJAzG4gx5ME8xhhiqNZL2eC+8tvdSkl9IFiAmE0GWKApfvBCjFlWiWBnIZdRWPUl58ZxeeFtTPBlJYbbVX3Lf6KyuqW410GY0AmqOOFG9fCog5iA7STrWkNTRSCGRPU0ikKHxZBCXbJdm4EiNKrUjmMnoMmbgnedRHtQrXkxFjVNBer3nx/7nO4IEleZyhKiP6ss1KsoNrAVjXG7Lhulac1e5ajWlk5LT+bcl41bkwGNNHOXbBVYRZO5BuQDsM9H/qFD/02JWYYQgKdZ4SV+L2fZkP6mqDPk5J5op6r6V5o8StOZILQzLvRuwn8jzc2OPWHPPXAvFFTnKSSNE5kQ5stUil42Q6bd2BZPhiIuMPEgbfzciPDMuHYymkvbAL2w0nOcpPE5QNjO9eoGxg7TuGxW2HX3f07qe4ox7ZhZLXxCvcfk28TAWMu1Vjl+rK1RzWWpavteRHwqCrNzzn70p0Xv48wQ0i/Scd363bH3WlWN4MNGuR7EzecG1for6UjqSpI3+e4MdO9F2NvGbheyO4TOUA4UUn2+y1uAcD4sqSTMV9bTpdWXIg7nDfl65W2xfwb7seRIbwzlwSRGbys0wtHa7F0YDBE4kKbAeRdrcl9jz7N8+giz6MSeCdd57RjESnWG1GmFkx03jRWeYHkjOt353Le2DsCVkQ80PJVVeUoNDXVvjSYo9S6sv8yjUdbaR5vZTr1ply+jtL/Niz/ZUjVu9WRCOxffG1FdFEyusO5RTl9RZTgZrIQa14KvcSX0SSc007DCxO++gzQQLpqivuz1PaUaD3WQppYHk9SJE68gJt/yyl3vKYcyucPC+MTVsp/EkuXe1+oBnLfclUirTXYi8N6llOeNajeC755OlUDoRm1ApDcK4pnlnMheX869tkLxJGHydMPtTkzwTWvPxaSba/vOqQu82WME+ka5dLgR0yKXhXrzXE3RqdO3HjXypMqdFTS/+5QKJVq2gucmI3jh5+ZuBBn9DK/WrwVFFeFJSdvjE9tcLXzAKmVAz3u9e3w8f8uNYrEPQfr/XqSn9Bl50LlHbwyKCWht5ji72wFEcKpSGcZWAl03a+FKCrWlpWJ32SS83wY4mr8oXgGEwlUNKQRvx2w8nTtW5T04StBvfGiuK5JabSMfC9wPCepf9ctDH5kUHXwvoKVgpJ+/qcaKC6XdP/NCXYiG80d37qGW6zFY2NlRFccazQqReB+L4jbDWkl4rsRKNXWjJbbcQ0oJ4UFA9T6TY10hEMFlarTDZYBfmJ8OGCN5ilpt51pDOJjyo+FvCwGwT0RwPaZ336T42Muw0kfWGP5c8TsnPRUPoMyuuOdq+BeYKpBVw9+toZjFpxTr4oSGdiLul/mjJ+/QJ/mRKfF9ilInmW4QpB6px/1VO9J05aPWxxg8D0HU9+rEgvBd9hl4rJJ4qLJ2vSuby9xI2DdC96XjplFpr3V9hLQ/Y0JRSR6t0SVWvCPKF4khBSYfD5QpyobiSMtuwgIZlq/EYrm1xPNJTNhkdbKYh9JiL/diSav6gj05+qWdzxVG9WDB6JBq4dS5fEXliqHU+152CasCwz4tIyez2i+g68FE4xC/iczmkOzBNiEmh3WuJRBhqadRl/rv/7VH7uthfDhhYNWLUVpAN+rWLxVkPMIuFaJdm2Gy3puXSVXxpNlIfRsKQ3qvCfDAU7c5Rw8ZVO01pLhmx+oknPNOYoE+d316mzc0Oz7a7Gk71rCxnFbzqyUyMO2kSKePqOZK2mOekRTKReD1fXMvRE0Lm8Hml2W0IRSIoWtTK8eLgJk4beYxnhf/Y7t+Q5K6j3W0wpuKXiRF77aivQTCJNnWAuEskBpkvb6Mvnpf9MTDPtZY6eWknVcEqujYLi1lxGwV0coALa740gwvF3dkieZJL/nUWK3+kLOsdIl7//IKHeDHCSkcxlFJodSXRZO4qkF5rBpwnJTKOTgF9zhGsVdqXJTsV0oZZWmImnKWSByUfyIKKRFJn0xhKQbrXv3sv5gRRIzViupetHmlkm4/I0XmX7Dh4bZnfk9Q8XKaoWTmk7EL5hs+Vo1jzL64GLr3h0Lb+3960C/Y2hdOSVYHjQkWYkOcaDj1LCyEmHdWEJiwR1nFE8s6JVVYCC05/zVDcaooHx96xE69nI8rowFZPDlOLAiMN9aa7iEXWtKJ4l3b2rpbw3IVY/ZgHgq/XHbr0qAL+gS7dy8q02Rbhf7ou2rdyJ6Kc56bkhPRMzh3pW0KwHTKe/atY8q/1AKMR08DJPldgZO16k6FLTf2ywC4Rr9yLHfWkhHbeVEmF3hHpNRr71RsANI/mJlsizAOVpj2rXQW1kYzjTUBoOpiOoNNWbFabv4M6KaiuSZiLAzk4NyXMRtA/+9ydS1EXINkrpBkREM9cBc+1KNndfWfxuTciEoWYaSD4VQKDuOeZvtkTFVaIFCqprLWHgWd7ykqwyA1fbK9emXcrzW73WYIYt1F231coo7+zhGnFhJZM3k/ZTcaxY3fBcPB9f4TFMqTonaSQ/MiRTQ3Qy0szuFehaOoflTmR1LXZQWYEX25k4HF2VCKMwl1F27Hna7RbfGOk+DSLJhab/zUJGx1F+t++HTrsnmyJ9hxsE2pE4iQniZlzt+6sYulBa6SAPAiQCa15ek64xQZEfGGJQuH4HuU4EkZIfy3sjOzHEnkepCJnIFPRpKg7MRiC7vQMZ//mRx8w1xbOE/ElKtBHVIhnVwOyuvL7KCUOuf22OG0umqvLAQYZOPf39OaEVk032KGP4RDplzZroE92aYzYvKJ8MaQfixG1HAVXLYSE/kHQT5aVzZipY7Ys+zheizcyfJwxuTSX7+bQnppGFoRkHybldKtLXZ0Sn8AcFeKSY2Wjo35zR359D311JLGjFqMLjnriCl2LSqN4rsaWYCcxKij17mkgXfC2w2hcAtV1JR1k9z8kuuo777oq41nbOXPmKHU7kpZlIBRmXl/ue5WWBOs6wc3E1u6F8llXXOX8Z+1hfa1ncCQJe7lBNq2uiZSyO9JUbffBUDp66lcJs0ekz/TxBzQXA3X+iqLZE06qaDq/UCyS9lrYvhyCSAHNLfdgjPTPMXpf3u27V53GTTjF4oq/uX6HvRXc49tIJX4udGUwOL3RyCrfd4t5bYC+t4H/6gev/T0W9EbCjhmYs94iQR8JaizpPSM7k+tmlSCzSg4Rq15NekwJVeYXvyfvKdP667NiQHCckS0UzlnuJEBwU2bEYR1xfcqFDFmjuVFR7ElPYjKX7lx0KNqj32DLYWfzQ95IfZIWofihfr9aPZr0qAL+gyw2jjASvV7RboqNLL5VAW63cgNKpAIMFvBoJQdF7Iu5UPwjolUbdWTL58intlnSBzEpuYlHD4p2GxesOxi3aKeLjvnRBdlt0LQ7Pl5FddAaHcjfgepHiUDYzO2rABhE09yKDR5bV0yHZaQf0TRztIsWNPNVljn6c09xsaPYbzNxwcjCm/0xu+EqBuru8yjA2U0mxWO1JUVrcT8nu55BKZ8hnUuApp+AkgzSQ7i3xe/UVODoZ1ty9e9ThPCzVVkSfpPgN+T77504JhYzU/FxAwAREkxSBUYuK6vNCr0Yyl8eNYC5mmlDI4zENhCJS3mrwWSR5njJ4YKWYTET7J6Nece6GN5fM3nb4XATj9iAl7FcUn2RMvp4y/lZK734qiSPXxTjQrgXmbzh8FtGlIiaQXAgGRx6fgqUYcNKpOFOTo0TQLmmk2vYMPkvIDi3JXGEvRTjvi0jcqcVc5JQkdxyllDuB/FiL83siKSfZpSRuDD9KaU56AjE+zgibDebCkp1q3CBw/lVPdmjBBtyGo7whY2vdKJotj9tvaPZEm6g8V5245YuhdFr6orsMCYSVZbXIBN6dSKfm/D1537+8nrqUzOS43gjHLpEOVUwCyy9VuPeW2Kmh3JVUCVMqMRgdpRRHUkj6XmTxdITpO1QqxbOppRCrtgPlNUf1dAhBOuN2oWl2HOosxX1rwurRiMFkRcykw2pnhsHegnarle5gGolew0kmmrxexL9WojtzVEwEp2MqRTsI8vkfCSNw9ZoYiup5hrqUNqVPI8ubTsaQDoYPNP1HgldyE4dZaNIDcV37QgryWMhhMjuRg5t2oi/sfZaSXmhJqEiEJxn7HjfxXeKNHPyqze56d9rH4llCSCLJuCaZybUKCWQ3F4ReYPKxIj0zpGeGdpGyuOMJGeRPU2LHA3VFpPfcoOeWel3uMWarIrtQlNuSHJM/Sygedx1JLTrl3qEYgvpPDKonmduDhxqdetqLHL/TXOmAT78spifvNG4QJb5yoUmfp2inaNflsSRTKV6DEe1h+2iAXmkZ51+vCQZCJge4+pqY76rr8gHsHWp8KofTZhzFPLTXinO97zA2iGa6cz83W/KeGN+TQn5x1vvRbDB/yAo/hPHvKxD0j269utJf0CX6O8EFqEoyLlWE4lFKSCPNWsTlIhMMPYm4Sj8tUEFOpapV6N2KL+2/4OTpGjrz+IHcIPV2JdFtlxYzl3g110Fx81OFmVqaLU9+KPq98oZDOWgHgexC046lM5DdnuMuU+x5gi4l6mnxRguThuRLU0koOSvQcyNjJSWnZ5aW29dPJe4uKqZve+mwfTIgeE1MJIv2Zc6wW3PUX1pR3m1QX5qR9EUvWG1KF8ONPWEiENi2scTSEHakG+pOCx5+uIfvBepNT3Ymmig1t7h1x8VHGySTmmSrpHgmLkqSSP++jLXsQSYpCg3YzZJ6IkVg9klBve1pNj12syTu1DT7jXQhGim2ZIOXronve8LAgQ3UdyrZ+A8LVCGsPNcP0u18nFO+UzF9z3e4Fnk/2JkkaOQvDNmRRPWFNNI7iHB7Rb0uRX297knPNHYu0VjlTuzSYegQKKLRTC+FS5afC+Kl2Qj0vlt0Iz7BooRcxmir9ypiEmUz3HLUX14xf91RbUXh/V0mkrU7T2CvFlxNHlCtxvWjIEMWhvTEYp7kMkYtHPo0keLz9VI2wl6QTXmuqXYk6m94z4px4NTCWYY5t13RIl3J3nNhKLbrUtSotQa64uilASQ7NWgbMR/3pZhbKtzNShIhgqL/VLH4qVIQNTcqGU2epzC3xDTQbkqHyy47UxHQe5SwutZx+E7tVZ5yGDrmh0OydcmoTuaKxcGA7HlCem7IzgzFsCKZy3hzfB/yb0t3sDiU0bSeWQFH79YU3yvQW5V0kOcW06VJhKEcCm0pZobVTc/8rZZqC8rtgL9boXtOeIobHjcI9J9Jdzd9npBeaAHKXyjqDohcvVtS3Wwwc40qPCpC7zPREOtGHq/bayT1Z9vRTjysNaK9XGvxXl91I5uJuJ/pO85/Srp1zW7LtRtnxDRQr0VMCTZ3kqgzcfIeNoK3UTdWRDqDT6MYfCqxldWuh42abFLh+oHZ3UD7zkqiKnUk5IH53YA6yMmOLenjDBWh/1lCdaMRNmkUfXC1GTu9s8RaEiG90KxuOmI3Zn6ZYpRdaOKkJeu1+KHHdxF56WGCm3iyUY3/0oJySwxNrhevspNF5yjdyc21ueR/9+V3qlrTbrWcfUXyptNhzav1av3nrlcF4Bd0qVaRXYpoGyWRUbpFmFg9uYnVG+Eqlzdq0Xf5rIMSF57kox7f+PZrvARn6b4Tt9tJTsgD6YUAbntP7BUvrdwWfl16JkHuplb0H4pQO2qo9kSnFItA8+kIXUmh4QcBs9SYnpNUh/sjzEpOuS9dg6o2YrQwkccf70okXa1h3OJ7gWbdo02QG/2+oxkpVjc7MX9t0FNL/WRAu0rQjaLddJJlemow5wl6ZbCPcwafJXCZUBwYGQ3lAd1oGLUs3mixK0V2ZsifJpKxWlnstyRb1yw09kJ0PvW2x6zk8Ucto+O2G9tU2x7lFNmRxVUJ+nkuUVcLQ++ZIHOClfgwt92Ajdy6eYqeW+xBRhg7krkiNh1rcCHXEQWxFtTM4o5kPqeXMm5rbleUt9orrV8y11QbinaaSezURiMdJCXZw+2GQ3nJUM4upAOazOW1qLY/R+a0a59z2pBpKm4QBbrsIXZGnmhBpR5/lpFMBfdRrwvaJ1nJc4hBukmq1MRM3mMgAOdmR5A6MQlwKcDh7FQi14gy7msmMpZDSQE6f6vFZ5H8WMbDIZEC3BfyPmmHsHjNYUaNxLmlUlDkGyW6VlRbUnzE44zqWksz6Uarj3L6jyyq0SxuRDjN8EXA3s9RTl7z/hNJ4FArOXzZN+ekF5pQeOo16S4mS3D7NWq7JvY9vbWS/FDc5e26p9qSQjgqYQdWe47VSR9XyKiRIJ0fN5AxdLCAhvZ6Q5gnLF9v8Jfpleu23WvQuUctjZinRoHiULqcqtW0b63ks248MShWrzVynU8M5VbEjhuBvh9Heo+tOJpnBnuSYB9LdrLbbomVoTgQZ7kr1hw3OQABAABJREFUIukbMzGKHKVyoPDSFdc24HYkgjGUn0srql0pKmNpZHT603OSk4T5/2OX4mlCsxGky3aYY48TzFlyVWypRkkxeZqJ3lLFztEuBXioDfVMoPXpTJN9u0d6prucacFG+bWWZl0KX1/IGFbPhVPKIgEr/ElVy7gXJ1OIZq27jy0lEjD0PLe+9lyeS2WoTgrStUrQSL1As9NCGqjPC1xj8Bst6aWS6MKtmmq/g8unEfsi5fwb2+gG2K+gFQe3PU8IA0lYaS6KH/FO8/0rRP1D+Xq1fjTr1ZX+gi5bKhY3JIkjmcpmurglmpr8iYwKYyojo7RzQWonGhflFaobE8csoJyG0wzzLJeoq7lCrzT1hozX3FcXBCsbTEjlZtveqGGjph0JGiT0AnYpN8mQRJIzS3HSccsaKSKjQTonWjZvu1LgZUwHovlau3sukXFWxsgMW/pD6XAk6xXtaSEswFxSAPqPZJyp5vbKXZwcicElG1dgJXEkGnFOB9OhOZpOg9S5ak2p6H9HxsdRiXYpJJHJdy36ImH1RkPvuSKZS8qCLyQuLp1Lhm6z6bEvJKe0HUUxlawU9Y4jlga3IWDquFuxekMwNCgproiK3qOExw+3UDuVjHxPEilEXySYRtF2rl395gJaRf+hoF/SS0mqEG2k4D56zxUEcLdEiKRaGbuljzMG9y1oQWPYc0u73dJ+ecnyupeDwftzlrdk7KyckpF4vyWZSrE8eP+c9P0pdm/Fai/QjiVKTTeKkAfMYQZDB3eWUrwMZITVjKRQMM9z6TJdSLxLebOl2nUs3mrQMysJEYeWwZNuA12TQlSXAnd+yZF8OaZUuXR3XU/ey3qzlgMFMp6rtz3pWgUHOb2fPsU96WNyj2uNbLyZdD8nHyvSE+lCV9da2kmgnki3yW040W1NRH82vtdF6a2J7tFUArEuzwvqGw1m4MShewblVmTwnZzgFJs7M1YXBfWmsPV0qcW1PxKJRcgj2IAetIReoN52zN4Q7abvSQdYvbYkDB3ZowwVxCmdH1uyM42Za/JHGZxkHTNSOrDpHIafSDoJBzm92zPaWYY+zug9EM1ldb1FBfCnmWhe92WkPXgoEoVooNlyJJNKgOFTg10JFslttTSfjQQErulepyhF4DPhIOpSk5xKt9auZOSKQvShJlIf9og3S5bXxWGsV9LxZKuW5+4g5IGYBQaPJM865t2ovpLko/ldT9xsBMzeauxSdwY3aNaDdPzODPbSkD+Rorm4OyO5lLFs2mGq+o9EP63fXMjPzj29Rwmkcvg1pcKNvWCZziwP7u9QbXb6WAXNNKO82dJ7bqAVTacetIRlgloYkdUA4SwVDmLZMVv3ZEwcUgiXqSCQiiDxhYcWlXv5HT/G5VE/lK9X60ezXhWAX9AVjSQzRBsZf/UUENSEGDlAv8hR40ZioQoZZegGBk9khGqOU1Z7QXAd3U3UFZGNL59Id2FNuFvtMOKe9wj7lbhxawGwpg9zwkLQIapVDB5Y2kmQLttS0048pobRp8K5K3c9fiwh7cGKRm51u2X0maBM6k0pXpff3IBFgp0aqq1IXFkWRwPiTk17kUvBqMEcp8QNGTe1Y3GGYiJ6txJt2JrD2oCeG9lk1ht6zyzZhepGb1q6bBfiEg6JgF6LBxntbovvB7IL1fECBemwuBWp79T0H1kpcrsEkOW1gJkbKeaAeKOk+toK35P8YhRgA/mjFPsoh9rQbrekMzqNUUK568lfJJgHBXYhLk+fdeaW65XoF08t9VEPEunwqSjGDwK0G47ik4x02FBtR8zdBfEiFQB44bFLRXhDuj9Rw+KWl4Kj0ZiP+iSd4aJZpSRTQ34szm43CITK4oaBZsuzWOasHo9Ivy4RXXG7lt+xXYuofeRJH2f4Fz161xbSPVoJDiT2HQRID+X6FU8T+g8ScbTXhjBx6FFL/tPnzF9zsoGGzrwQpfAb3+sc0qeCcFEm0h9XorkqxcASrYx6VUR0kA/72IXm4nwgI977OW6Wohsp+pbv11y+JYaQL3/lEebSku6s8MNAcqFJjy1RQZq3lHdrVruCTIoW/FjiE5OZxkytdKrOBMUzvxPY+kakXo8wTZh+sIEqDWrS0Izl8BE2WvRFguvLCNmeJoJ1yiXRp91qr1zHtoQsa0kHjRwSppp6x+HeXNGsCVi7WQv0nguuxyxlnL+43skhWjEqrRaZoIS8MAuV73Sduy3Ryr2i2nGYvZUkC5VKohRnlhg0axsLoo0sb8oIm85EERLRsIY1SWkxpSLsV/hevHLT1uvCEEzOBHNjzqUrqJsO7zL0YANxt5LP4/cKoo0Cfe45SCKzdxzhVsVoe0E78TSTLlruxND/bo52nX7RRtzEsbwp8YXtKMg9UomuUbeKxUlf0C79QHhvQfqtPupPXOBTaB/3xSAXRIuZP0qvkkd0pcn2VrhhgDSg1msZ/3ecSj03lO+X6GGLMoF4KXBnu12SHVmakRx8VaWvDsBKy3Xyueh3kwv5b0pH6j1HbLXorV+tV+s/c70qAL+gS1dyU7aThpPDscSqZeAHgfJ2Q3YuejHlweyVJOMad7ti/nZLcm6uwLXtMMopsxdg0nDyySb2RUr6PJXRsZUOHicZoSdjNVSXM1xrGQsigmblpQv0MkdzeS1il+IMfinmjt3JP16mmIVh+o4TcHEacGOHz+Xm7YcCt02mRrR3K4tywteKOuKGnuRpJtFVhxo7k26hP8lRG5JtWy7lFE2riZUh/tQMW8LkE7BLoNUM3jsnmWuyM3GPul7kZXZx9bNLVnuRestddZ9s3uKKrjBLI/U6VykKmEi75fCXKe4yRW1X+KGMgk1nTGlHUTR0l5Zm/LlQXjci0m/2Wyb3YHHbU28JCFbriEpC18mMpIeWaq8lOzb4vieZGfLnCe0wkmWCsAGwWyX23GLPEnwGr++esLzpaXdageS2SkZeL9NAZgZ7IAw8V4ieKpl2nZuZRg9a3HnO4LG+GoHHqCjWS0JtaEdR0DSZaK1Wiww/8IQbFSoIUNwXkfZmzfAx2BUdNgVwCrUwhMZweTQkP5LOk6kVy2ti2IgGFjfFaJAsZDTGaUb45lheT4DnhfzbqUDD405NOxYtpjnKJKoPRLawW0nHysh71s41n/7Wa6KLO+iTvzCEu6VkDjuFay2URuQU66LzMj2HKRXtDYlJC5k4YJuJHEJWOzLGNZXIKcxSw3EmBhMbYGGJmw1+u8GnXQpLo0X72AvgRE4QrWh65ycD2uNC8ppXkvPtLlN8Hmhv1VJkbQqk3a4U1Y1WuI+jIAWKVxI7V0mmdH4o+rNso0TPLOmFIT0VsHa7SNFV55rtRfxQXu/y6xsi1UA0eKrUXXEZsDOBZSYz6ZRHr/CFdC+zS0VxqPG5TAnWdmeELF4xQ2PS4Ymep4SljHxHjzpkzrVILK2A1S8MfpZQfTCRGMUTJYDvzuHfbDma9YAfeYE5d6FDuu6kGhstq5tdDrWWYl5XGv+0R0hhft6nOBaXdLhZSqd+EnADkUFkpxqzu8I7LWDw05T8QxnNZi8Stv6dxFkqHYkXKdm9ArPUuL2aLBPsTDSR4kC68VFLTndsjJACVEdMiJCcG3HPO0XxKP2x7+ivRsB/vNarK/0FXSpA9dUVo+EKOk3SzleOiDZgzxPKbjyn7yxxpznuJCfMEkxPWHZ+5MhP5eZu5wa90dlEt2rR8CiIhXTOXhaByonjcX7Xk150Y8aeOFRlTBQxo0ZyOhfSfSi3FeU1D7WWDlc3QrELDbs1dibg1NEnFrwI7nUNMfOk0y5Dsx8xM9lx3NhLh6cQnWNzrWXxbg13VsS26zosEoE5l5b8VNF7LDtAddBntR+5fEt0Ycm4pqxTYfPdadFfm0rcUqNpL3PaiwzXl6iq/jOF324wHw3oHUbClujpmtuVsPT6nuFHEoNWbK8Ek/OswE4NeIlVCwNP7HnqbSedqIVkK8ckyuPOpLN18TYMb8xE65QLqDmuLKpVbP2O6dzdhupGg51Jp0M3MrqvvzshvzOnXmT41hCuVcTrFfVey8ffvUF6buh/korhZuLY+ZlDqr0WN4hdd6PTODXigAxWiv3iRAlX8LlstACxc8FWB33So4TsTEazPu/QL06LPrM0bH7pWODKXb7w/Bai0ZrC4An0DjS6VZgLi8okAsvUEoumnbwX27dKqm1POutcpreW2IWi3HMsb3iJkht4tr8esDMtZpMLGT/ajZLsXFE8E51pO5IiMWoIy4R2Egi3KqrtLne35+VwdJiTnIvpwjzMSS5F25gfWtJLjXlYyGdlKRFs6YWkR7Q7LelhgnYweAZ+t2bxRoubeMxeiRsF7FQK61gb1jbm+PVWuqGtwi41dmGkgwyivUwEvxJzMSzVb5UyAfjI0n9qsM8yBk+EE6hahXu9JH+WoD1XWc/Kdzm6S0Vvf0E77D7rnw7IzsQw4wbxaixpaoWtBC+TH1p63yiu4ifdmphksr3VFZrFZ5HBx+Ka1sOW6DR62NLutixvO5FdWOmYzj9cR7UCX28nHrrkkGjkMKm3K87ek0OKm3gGn0kyCYDqO8ZfO0W3sNoLJFPVZRkrSCJq0pAMGyn6ugjFZCmHLRAdYbRI2k2tror3+q0SNRetYEgj8SiXA3MuI3U/EXON+qzP6P/VY+ObhtF9eT/pVliNZ1+NlHseP0vJdldEI6Dn9HHG4lwggRLXB+mZJr81x5aQP0tQgN6sCRm0awIXNyu5tuWdhuy12X/5zeV/ZXl+GGPgH2z99m//Nv/Nf/PfsL+/j1KKf/Ev/sX3/fcYI3/rb/0t9vb2KIqCX/qlX+LTTz/9T/7cv/f3/h63b98mz3N+/ud/nt/7vd/7AR/ZT/56VQB+QVe96bEf96j+3abEgRWR58/WGX8vuUrCSGYa892BsMWCnJLDWUpyJN23dhSlK1EJJJVFgjGBOJCP6OTbCeo8xXdMrdglgqx9IOLv0AuESUvzeokfyI3dftqj2fS4caD/TE7c0UR6z6y4XfMId1b4LJJ+WNB7ofAJ1OswvG/xhRQcvfspq31hjombVWLBzNygtitiafAZDD6SI3M7TzGXlt7bl2THhuxJRnosjtBmImkh6blBtchmYyOuNVSLFDf0mKmlejBk8FCTnEvBWLywMHIkM01+EUXL14tcvhOIraZ/fY59keEL6WKEX5ySPsypnw7w6w635iTIvUVGbiuDWhkZO1cSFaaC5AE3Wx7z3kwA1aPA7GiAXUrHM+QR1XOYlcYViJM6SAcsnSrCVkP9fkm9GbCloqoSaLSMEs8ywllK75GgXtq7pYwv84C5tBz/7i79Bwlhr6KZBJRT+J2G6g1JR8kuFKHwzL5cs/n78vxdIWYfAqRPBNVhVuJc7N9LMStFcpSQP0qprrfo3HP+9W1JuHCa5GlGO/HM33DM3vDM70Izko6Z2quItcFnkcWb4rz0fYn7U09z8mMj2sOfWtCeFNLBGwiqqB0GRp9YTr5iOm2ebP7pVBMf9Vm+0dCOI82adGXtTFiW6akRR/tBRv/OlPZmjZkacY/3hJUYX0acKdG4uUJSWnStaLYd+ZHFbTW0dyviWkP2LCEkwsmcvQbRi1M1PTXoT3tkx1K4+1zem9OPN1A24HMpyEL2/S7T9EyuScwC6bFl/JEl+7jALjTldrxKtlncDJhauuI8l06hasXR7ydOpABWMp2Xh335PY04cKvXapY3PCHvYIFOjDm6kUNYte1oxsIhVI2iv7WCtQb3cEDvuSI/MqSduWn1C0uURgp6DebcMrhvCQO59s0b0lkLvUAzErOXubSoFppth2oU6lFBuxbkUJYElrdF6+oLuSbTRUG1GQgTR3lbzFvpFHAy/XCnYtgRww80bwtHVGeSqpOeC6KpHUmhNXhoME8EndD2IYwddtWlA3n5nNEqyn3H6AGsthXVhuLy/UA6FWmJSuX5xUI0gdUipRkFVjtdylIXWVhveMo7Dc0kEL47pl6LVLtOMDRLi09lZJ+fxc85rbWmfDL80W40PwFruVzyla98hb/39/7eH/jf/87f+Tv83b/7d/kH/+Af8Lu/+7v0+33+7J/9s1RV9Yf+zH/2z/4Zv/Zrv8bf/tt/m2984xt85Stf4c/+2T/L8fHxf6mn8WNZrwrAn7D1G7/xG/zsz/4sw+GQ7e1t/sJf+At88sknP/DPiYncBMvrXSdAy2l2tS9sND/0V25NU4sZ42VMlhsGMShs+8/zSM8M+YHBe9G7JXNFtQE33jtktLWgvOYgDbj1ltW+jOHSUxn3xCjjm+KFpb4uXC3VKKbvOoHwJoJ1aEfStcvyFrSw9Fb7Alxt7lRXqBKJpIuEkcP1ZTNa3PHYRQc2LqUb9pL2j1cdWFcxfzqiGQvaxN2uxGU7Fv2jbrtT9aXpIuPkxp4fW/xA+HshAzcIokHc9eLsu9Fy9mVJLPG9gCk19jyhrgWHUhxomm3HcppLsbDRoHTE9FuW1+Txm7nBbFUyLtxw9J/JeD2aSPIgJz+wVGUqmJ4TIxop8SQQbaQ/qrClRNKlpxY/coREnKHqIoVDybVtRxHfdUDTg6QDCYsONGpQh7l0a6x0M3wuuIuwSIg9L6aYmSXWXYLKJKJLQ+9exvRNaMcBW8LgfkJ2ZKl3nWRL70jHFkSfyp3VVeEb5mJAUF6SWHSjSGaG7Fh+hxt52k0H3TgTHUmnivS4G/svNfrSkk7F6YuNeCdAbrvsMlePMtKpptqUAq/ZdvhCtJk+E0cnXU6rWYn5wvdi14VUmFb0a/OLHslTGdHmh1ZysSeNGBFaKbLCQLJio5EOeHZgqW42UBnUYUb2MKfeEpNKvSYuU3OeyGdgGKmvtx0TssurVsjjmyfoWuQM7WZLcSiP1S70lQNYVVKEl9uSm+0KuVYhlZ+RzAU4Xu4K6H31Zi2at1ZhLhJCBtm5xq+39J8ID7A4MOiZJXmaksw1m9cvcVstyYWBRrAnIRVTkM8gP5JuvP/OGHOQ4fuB2TuO7Fzeq8VxJD7uEYMU7/FE+IzNOJKcyj1Cv8hFq7zStLst2Zl0b11fEjeyM43vRfqPDYPHmuJRKiB7QDkoPs6JD/qEBIpRhVrJY6rXxdT1siNp7izQWg6SfmUxtUIdZ2Tn3fRjJoYeUwmSSXmZXjTrAXOWiCHm9QZdafJ7OXTF8cV7kWrHixykkVi+uNGgj7v0mLaLR3whDEG7kgM5mRS00Ub0whJ7nmYih4xkauQ+tTBXzMfpm5KNbjsts9r6w4uaH8X6cYyA/9yf+3P8+q//On/xL/7F/+i/xRj5zd/8Tf7G3/gb/Pk//+f58pe/zD/+x/+YFy9e/Eedwv/l+h//x/+Rv/JX/gq/8iu/wrvvvss/+Af/gF6vxz/8h//wB70kP9HrVQH4E7b+7b/9t/y1v/bX+J3f+R3+p//pf6JtW/7r//q/Zrlc/qe/+X+x0nODLbsi7CSTsUEhyA3dKHQpRoz0Uk7FuhSjh11pQXpselTuacaxgxCLqF2/yAljRzuQkcbjx1us7k0YPJTsTD0T0fbq9Uailj5OSR9lpEcWV0RUZcSlqeXmuv+lI3QuhhDSQNytqUpx4KnKiLO1QQTOraLcCRLjNZXibHBjdtVFbK61+M2GyeYCs9LduEi6G3EgSBOz0lcjZvMsl8KjVbTrgXLfoUtNvFUSMw/ThPxpil1Jty8aWF73EsN0pCheGOzUUDxJ6D/XZBfSkSPKSE/d76O8ot6I2HNL8ZmI62NtYJrgp9I99YOA2qtIv9ujd6RQuWf6VofPODG4fucmfZazut0S35ujnXSwklmnRQpS/Mi1ABSM78lrqrbqLutXS42vEFTIKBBvVPi9WkacXUKB8tKRwSkGjyXizc4NyaDBrlfkJwZaxeCZuB3NTilcyWEgOzO4AlwOwcKNW6dkRyIhqN4tWb1d4yaOdpWIJrBRTD4QPZ/qsnWrfTGD1Nda3HZLsblCZV6kCI2ggpr1IMV9FE1UOtOsbgvfUa+0JH6Ma4H+AmGvwn71kvjaSt7/CykQTSldaJ9FkRkEKI4FmxMG0nGp1yPL66IXU0tLSERa4N9e4vNw1RWv12XjVrVm/lbLzu9Ip644Voy+k0LusTeXEk/WpdHoVkwSyVQkEWq9hkZLtvFmSUyDjFP7MrbWrej1dOZpxqBurQAxbykvRq/0UhOtHNyShRQui7caXBHZ+dlDVjccIZWO/Fu3DuWz3nI1Om8mgeGHKavrcgAs36kIPRmtt9dramdIBw3BQvHcyvtv4KSIvLMQiPlCkczlUDX+nqH3xLK8IY776Rtd4ReUuF8Lj6m4Sp7Jn6YUx9IxC0X3+l2l6GjSw0QOtesN1ddWrHYj5fUWt0hEX7xfUe56wRQFaB4OBR0ENLfFjKEcECD5/SHNpyPadU96JJ397FRTbotERnTOUO4E1n/xEDS4/YYwdCS3FzIq/0wyqeutQP44ZfDI4tcc6aUWd7iC1U6k+DjHNHIoHe7PJZNdIR3UgTAn9cyS3Vig+g61JcW5nX9+SFvdagljRygCxXPDxreF70gQxisHP14MjI/6h/L1w1oPHz7k8PCQX/qlX7r6u/F4zM///M/z7//9v/8Dv6dpGr7+9a9/3/dorfmlX/qlP/R7/rgu++N+AK/W96/f+q3f+r4//6N/9I/Y3t7m61//On/yT/7J/+yfE4xoX0ytGD7STCeiHfJFF1LuFclU4rZOftGRjmqa2qJPUjDiwCxvCi0fZIM0d1e4RYqedpvgSE7sbhgot/XnKJWlwpwnhO2a9jLH9USgr5xorOqNIGjBAM8+3cYuZOzBQYJ5p6I8Kxg81yzeatFLIyzAqaXe8KQ7K5qjnhQqtab+7oRioVje9eRPUnweuWw1qi/6MzeRrqKaSw4yGumOdKDVdqdBXyREA/mFoR0G/FlGcWxkfJREVpPuRF6LzjFqSfMIuej/mpF0WdpJN5LbrEkf5JJDW3gG91LS/+qUxSpn4//e5+wrYhQYfGZpB52Gcd1TvStjr+yzAp9Gwpon7jX4WUq8uSQ86ZMMG8L9Ae2WR3lFLMQU4D4akS6gHcoIOzm31GuK6t2SeJYKVy1Asl6hD3skM0HQqGc5ar8SPeHQ44KRgsBJF7D+0zOaRUZcGdRJTnFopOMxtVx+tWFwL2W1r4k9Gfu5PJK8P6cpE9Lc8exoDT2McJgRE0k2oOMMVtuewY0Z1fkaIAVLdg6uL+9TVUpcXDnPwIn+LFkopiOLqRTFazPKVSbsvVZyhu1C4XYC0StC0OidiniSk55rWpMzfA7zO52TspIukmoV/aea2Zcb1MJSpUHGvs/kuqlGMXxoaEbI+6Pj2MUnPbQG+70+Fij3Rf+VH1qq/cDRLwoKaPqmIQ6djN0PBhgNXFrSmcgrfKaoN2W8njzMCW8uaVcp7eM+1ok5Ynm3pe15+msl7ukQk4jJxLWGuNuwsGLK8vsN+ijrYs4CYehRSYDKMHiqeTrZQEVF/5Fh8UbLJ5/uo0t9FafYrMn7anktXL3nY6vBKxavOVhZ2p7FfHdAfG9JWPXRK02spZDl4wExkaJl/laLmRum74jL1u015P2GuumR7y2pjvpEOiJMLzL5SDN7LV6BlnWj0LUBZa5MSMvXWrIDiz7IcWsO5zQ6Fzd93KlRIcGfZ9itivCsh6nl86VrMcGp0xS76nSMlWa1F2CrJrGBMO1BlEIsFFGiMhM5iGEix9/aoV332KMUt9Hi7stzrbY+1+JV11uSE4vJHfV1iYOLJhKNkkPSVkNykDI/7ctIu4t0rDe9RMgNHeVpTw6WR3INdduljETB9Sy/VhK6tJ3ju4702DL6DMod8Bvuh7MR/QSs2ez79YxZlpFl2Q/0Mw4PDwHY2dn5vr/f2dm5+m//v+v09BTv/R/4PR9//PEP9Pt/0terDuBP+JpOpwCsr6//QN+XzBRxsxEtWR/MWSJstG5klp/KSz99Uzpk3OtjXmTSPbKRak8C4Xs35oSbFaHvCS8K8ueJbLKbLX4o2icU9F+oLvIt0uy1JEsFc4l40p0JJSaR1RsNfuTldOykI6ccIq7Poa4SssME1xcOXchlIzKVYvSpIUsd2e5KdHsr6XS0I4mTasZBNpRSUDN6q8JMLfbC0nthaAdd8deXG3Z2piS7N+1E7Z2eLJlqkhnkx5p2TTZmXQnoOmZR3M6JjLxeCt6TubiDQxpEV5eCriQIvhlHlt/coD0tOP7feXwvEIaOalNGtL4fcI8HwsA7yQhW3MJ6aSQv1khMn64VbSVnNjszIngPEnyvvIz2molwHAVbEwgri11oiudWRtNWHK3t3Uqu81aLeZbjRx7dc4Re6DRKnvRQwNnRSeFrV5rVnZa036B2ZNTU9sVxnp0a4nqDCrA9WmAf58SPBsSVjMHTS83kI3FSByOPMz03lKuMet2ju32r3pCCTDpjMPlGSvYkQ88N7TAyvxPEATvylA9GxCivf3nN4caeeiOgtysmmwvCaYabJ2T7S1wHv754Pwh/MA/0n2uSucaWSqC+K3HGkoobO3bd4f5Tw3Jf+H9RC5rHXEoSzEv2JSDjPxOpduTJqFpMK+NbU9TCQlRd8SG6w3LXi4t8U7SCppZuZnjaBydMSe3Ap4BXkhrxwUgOG5e5oJReSHGsWzlQpQ9Fo+b6HUevVdi8ZfSxpdyK0Cqijixeb7Hn4jJlq5bn1UhHL7kU7WNyaXCDSHqYMPnAXmGe+N5QXL6Pe3KgTCP5qTxWu1Jwe0X5dkV/c8XmO6fke0vasYelpbrMBfvzYCju54Wm2FvgB57Ld4Qi7ruUnmi7a9vBofMz0cnWu+LQTY8tyghjNJlr4nkmTvieR93vYUuBj+tG3MUhC4SRI72U8avvB8LYkd4vaFcJ4VqFv1tRbwSikkOrqUTjp5xkICeTGjcUDbStlLxvFWRnina7xfQE0O5nKcoIVsas5F4bTSQpBJ+VPxVtcu9AEnl0Ld+fHiQSY7jVUm5H1r7Z5TN7uVbzdxuMDZiZFfC7jjT7DbO7UG0G0rMfb08nogh/xK/YcQBv3LjBeDy++vqN3/iNH+tz+yKuVx3An+AVQuBXf/VX+RN/4k/w/vvv/4H/pq5r6vrz+J+Xpyb3/oLohzRj2fCTpQJlhV93YsguZGNprskNSeUON0vpPba0lRhF3CDIOPYwgy5qLCSRcKfEPitwaw4VNHhY7cqYMrmw2KWiGckGpDppoXbQf65Z7if4YaC6VUOnI4tWbrK6UTTkuNdLjPW4i1zQKYOI3qxplgXl4RB7aeidyWj1JaZG1ZrY85jKErOAMwp1nGM78Xswshm0Ey+C/CKCEi2icl3O6U6DmiUSR2U7DVjf4ZURHeAkko5LmoO+4GpGLcFpzHnCxfsR9irUaQZrDew4wmlBur3CPxqgDOAUW9/QnPyCx54ltDst+aOUiIyK2u1WYvHeWlCoSDMdMHygmd+1cJSIIH9pGb5/xsXDNYYPDCsstpJu7/ob59SXfezjXLR4Cw0bDhUt5b4juTTwrREqjWysLTi5ZhitL7G/M2GaGuLckC6lO0JtaLZEBqB0pPep6A99bmh8Lkkz5wbtZFSrDxJYJLRrnhdf36N3JBo49U5J3fYwtaEdiMvSTxxmKu5V32ri0JE+yag3RFYQxo64lCK+WgdUxHTwZruUPGPVaLJTTfKoYPquQ/Ud6aOMettDZSk/Xce+uaS9zGhqS5h40jNDsBLllp8Ja625LaaSekOTnUkHmFbGqU2HhFl9pSSs5FapG0XvSDG/HVCVFkPE1Mq/DdDfXLGaZ9iDrEMBweXxkGSzIj7uSRKPjjRjOWiErIv6W3Pkj1L8IJJdKCpjqLtObNhvsSeJ4GW2uuLnxErebQN2UqEeDaEUTFGy0FT7LcmpxezWuKMeykPvUFG/UVGd56gs4Pdl3BydxvWiGKK635meS8qEL2IXCagIXdyjzxXcWmFMIEk8y6dD0eb2ojD8vtPHWtDLnOlInp9JOgROv6X1cqCyWyXtLMOd9UjOLW4U8H2PzwJqIWzHkAbiuoeF7dJPFE5pzJ0FMSo4LgTmPgnYlbw3+pMS7qXU6xFdg+vwQ3alYalZ3vQdMNxQXReNXXqYoHxCVOJsDgtNc6smVgY7aojnmXS4T3KSlcZnGrt8mderMHVk9EFKvSbxi9mxobnlRHPbFw6pnWvcWUGceFyHrJq+Kyk66ZkhTi3ppcIXoC8lX3t+R6FdJN1doT4cEqcWP7Pi3DaQPUtxfYnuS+4V+ObHywH8YYxwX37/06dPGY1GV3//g3b/AHZ3dwE4Ojpib2/v6u+Pjo746le/+gd+z+bmJsYYjo6Ovu/vj46Orn7eF2W96gD+BK+/9tf+Gh988AH/9J/+0z/03/zGb/zG952Sbty4AUB81gfkZuaLSHYGfk3C3Yc/d8Llu3JzsicJamngSUHxVIwFUUsCxfC+wTzMyc40w0+taGreWOAqS3am6D0Q8b4pZcN86aysXqsl+WOmaSdBMonvlMze9Pi9muRcQ6NldKQkV9bnUO16Bk8UHGa4xqA6rRYm4ucCw1XdpjR/08sGWCmSS0PvQDP5phQg+YEVHEnH32vXnWT/7gnYmc44Ue04MF3SiI3gBBTce67ELLDXwjTBzkR8rVaG9lmfOHDETFIt0oOEdCaFsV9Jp9E+ywhBSxLI7wyxC0W71WKXisu3pLPii8Dm9oz69UrwLitF/kwcx/UyxbXCPpu9Lto8f7uS2KeFZnpvnfGtKdUvLFBRir+YRqbf2YDjTMb2o5b+U+Ez+lzi7oL9vKiZfmOT/qcps+MB1YZoBU0lua96p8Is5PUBiI2m+eqSZiSmlN5jMWpEIxtv8TgVbt9M9I9EmN8N+D85pVqk6EpAzz7v9HrHFj8I8jgXCTgt6Qd3ltTbXpIaoqJd8xK/dqvGX6u4yq5tlOBy3i+ZvudEqH8/x64UdmpIjgWv0lZWsouDwl7K8w8jh+9FVvuB4gTMQSYJH+NWxu5bDb0n4kalS8qwiUdVGqxEms3ebcXwMXCkJ4Z2FAjbDcXjFO81xaDG5+ICVgF04QhRuJyqle5tKALR8jnvr0MmRR0p9z2mkQNRGHgZhVsxeeiVgMObXeH31VueprLUW+LubSeedhDoPRTnv77XRwUod8S5qr8xJD21JM9T0oc5qjQMvy2xaO3N7iAZpagvb7b4VEwhIYvkLyxmJdrh9jKjrS3630zIjw3V6/K9Ppeudr0ecH0ojiLJTDq6ZqVRTwsBcgP+Re/KbNV27Dtdi0QgP9aEnie5NMSVEY3dhid5awZRMezVmO8M5DB7YiQS0ANJoP1whE9FjuGGIvVYvNGSdCak9EKup08F1h7yQHujJljJMnc9QQzZFwJnTrNWCrT15mpykZ1r6p9eiH40jyxvCIsQIOpON90YMZMMBQYekkh2ZEguDRvf0ritFvrCEG2uN6J3VDJyzk9lLB8y6UTX5wXtIOC7g/hVmacifuTx04TqZpdV/AVZo9Ho+77+txSAd+7cYXd3l3/9r//11d/NZjN+93d/l1/4hV/4A78nTVN++qd/+vu+J4TAv/7X//oP/Z4/rutVB/AndP31v/7X+Vf/6l/x27/921y/fv0P/Xf/w//wP/Brv/ZrV3+ezWZXRaA9SslPFKtrkdW1KHm3Dk6eT1A9T/SB6OQtkJ+LWLy63ghSw0SisehWsbruJNPUK4LX9D5NadaioD06/pbri5jZFxF7kNKu+e5kbKSoPMixrYJ5RnGsME1CuSeaqdU1yUX1hYTAJzNNnaSooOgdCExaeG4etdYQmo6dl4I+s8SbJWWeS6A7wo5b3gikF5JUYRZiTLCXhnbd4zrAsamlMI0HOX7YkhxK+sPqmtx0zZnod0Ia8ZlEPiUnlnY9Ep2MjNu1QJNKBzI5kexVnEY5LQHvmZhIVPW5c0+1gsQ4zcbiUAbagXRI0lNDe8NjbKAeSOfALDQuN5BFgebWmunTsej0skgYO/qfpCxfb8FLTJ99ljF9R7o5quk6wDqinIz36l1He8Oj5jJiakeBfG+Jfzog+V6PaGTE1axSSXCYW1Qhrks3kCzdkCn6jw2r/UA7kM6aXVqq12tiUCyP+yTn5gqUGzpemWrlsYTtGqYJtIrR/pzlZ2NUB6nOTzT6p+a4745pbnvCyhIHUlQkM01ylFLuBuh57N0F1ZlEAA6eKBa3pMNGo8mPNG6ZYRdyyEjvp6zerLHHKcGCG3mSqSGW0rn2Z8J9VLmneJhcpWxkZ4YqiVRPh5AGwqa8X0yjaAoPndPZPBxIZ7w7GMXrFfZhTrPjCLdq9GkqBf/c0O7KewWvpCOqwI+l8HRerqcPkJzLQSrmXpJTLsVoFZKIXhjMmXxvvR6vGIHlnqc46N5zWjp7zbpILsJmS39csrjoMfpOStM1WbSJhEY6u2EmRq123VGm5oq5GY1Asl0/0jSaxZ1AcaAxRykhEedx/5mm3m8pewrXs4REZAWhIxNkZ5p6UzKVqRX+hsgJ8u8VlLuB5CjBVuJ2DamAs30OyVRTmgFx1HJ6MIbXGtTCiPGoL1gceyrMRl0r8iNNcRK5eFdMWKubDtVz6NMUOzeS6bwAt+uJpaTvtLtQPEiJRrR5ykN7b4QJwDyhOBYNcNuH+KSP9UgHriefVRWkuCQoQeukEGtNeibu7GQJKMVqB4qHqeg0xw5zJoeeuos2jFZSc5ILeZytiZgbK3jSv8K+6N2K5jyTPPQgCCwWP94CMERFiH+0LuQP+v2LxYLPPvvs6s8PHz7kW9/6Fuvr69y8eZNf/dVf5dd//dd54403uHPnDn/zb/5N9vf3+Qt/4S9cfc+f+TN/hr/4F/8if/2v/3UAfu3Xfo1f/uVf5md+5mf4uZ/7OX7zN3+T5XLJr/zKr/yRnttP2npVAP6ErRgj/91/99/xz//5P+ff/Jt/w507d/5X//0fJow1K4VbCyhvSKaaetMT80B+P4E3appZBl6E2/0nYuCotp2AT7sbHwrat1aoswxTKdIjzcoXtDckt9btOWg16Syy2pfcWVMpkgXoRrRv2bkinUF2oanXIf/aOfVinWYSSGaGduIpXsgGlh8Z2oEAZPXK0Hshm5BZKdI5tGON0ykK2Hv9hLPf2xEzB8gNsecIrWH+ur/qmPQOxKTRrntUz1F8mne5uOK8K77VY/l+hbqQhIvsxAiKZSQGmv4jzeXXGuq+4FWGj2FVZ5T7DrfdonSEyhD7jrhMSfot7TIhVob2LKWYi2EkPZNr3Ow5YqUpb8gYs9hfUD0Z4nuB4tqCukpJHuTUE0HZRCsdjPQgIbxWor1ClRnpsbnacNVKRrGq0qQXgqiwZ5p2z6HPZJy/uua74gDafuyyVqVgqLYD+ZGhtD3IAvXb0s2pZym9J91rMhGN4LJIRSPWamISqDZENG9qSf7QpWb47YzFT5fEuYzx2rHAo+ttL8kFrsPenFiadXlc7nfXCG9XaBvxISUkCv3ZiPHTyGUvE2xR4VFpoMkMKEF2mN2G5mkf+h7XD6z2JLbP1Ap1KWPS/Fgxf93L+2ssBijtYLUfUT2PPrFk6yX64VC6MAtxaZe7gZgEet8eYGpoSjkEkUJxL2N1tyGSQBLQc0voeZyVoiyuN8TjTNAiEfKnCfVGhwlaadzQkz/KqLe8vHdKiWDUc9GkAmx8EDn+OTlAje4Z5j/X4E3EXEj0XMwi0Uf8MEIa6H2WYheGZhLoPTcsb8nnND+w1JuR3nMjTuUIi/Mequr+bAQPU12kxDRiFxASMS2oWsvjmyWS/T1u8eeiFY65JHmEn12gWkP+nT71l1YkHxeSJFMZegeK2c/UxJUlGde004zyjpPiKIv0n2rcohBkSr9DUt0q8fMevQON68P408DF25KKE1VETxN6zzWLN1qUV7i3Vuj7xZX7PfYd3hr0kaYdiovdlArfVxQf56ITbeSwq1aGWBrszIg85lSun27Ekf4yj1udGpRTrPZEr5efQkjlf+d3FKoSXJEKMuo1FWQXkXIT6WaPAnHgqBepoIVmiuW+APSDl6K93nKYiy4aMshXu+5g1KAPC/ysR8gCptHw+pL2LJc4RCX3s9VrDWqj/GFtRf+blkfj/4iDxR/0+//Df/gP/Ok//aev/vyyIfLLv/zL/KN/9I/47//7/57lcslf/at/lcvLS37xF3+R3/qt3yLP86vvuX//Pqenp1d//kt/6S9xcnLC3/pbf4vDw0O++tWv8lu/9Vv/kTHkj/tSMcb4434Qr9bn67/9b/9b/sk/+Sf8y3/5L3nrrbeu/n48HlMU/2mL/2w2k1Hw//XX6S1E++N6gjJo17zc6HIZGb686alWYL720uI2WpJBQ3jeI+SRWHiKh6ngIUYR34voSkwgxeNUQKVZJLnQuH4k9D29x6JXihqqmw16Zgm9QHomAN6XLkedevSznMknsPw/zXGfDXG7DcmzlPZmTd5rqBYZsZW4MRSY1xZUJ4V0bRSEXJhgUUN1vRXR+6TBn+RM7lxw8WxMsl7RLlMZpZnI+ENLuSsbX++FIiQSrZZMNc1ey/r2jIuzIcnzlGa3Rc8tyUyRn0hhLGPZAIUnyR3tQiDTppbRXDaqqecZygaUiehnuXRQguAkBKkT6T+2rK57ScxQMt7LjzTtl5cyArYBXxvS56lov4x0FnqPElY3nOiolnJa1q0i/6lzLo+G3Lh1ysG3dgWqraI4MIfiimatkQipZ5mYPd5e0FzkmLkhPxPtpr9ZoV/k6FtL+FSkBLELnGi3hNGYD2vqZcpgsmIxLdCn6ZVz0409+QvpLNbrgeJYs7wp3V49aAmVdLDwiht3Tzid92nvDwlJxF5b0Z4UV/FobiQB97pRuImj/yDB9YR9Jt1OI1q1vRp9nEoSQ6Mlvm6liRsN9kXG+B5M3xCJQrMWpFBeC/SfiRPU9+RAIqYp0SkWayXN4wG+HzCjBj+VzlF6oVjdclKgDxyDjzJ8As27JTGCeSqvdzKH5XXR0A3uTlk8HJMfa7SXjm+9Kd1HSf2Q16j3TADm9abHblXoe32adY+dd6y/TDrAyUxTbztuv37E409El2RWYrIp7zQkxwnKy0hz9m6LmRqUV1fdo2QmCTxicJKs2+zMUF1vMFN71bEz+ytcbTEHGdwsUSoSngtgW7diYIhaNL4hixTPjXSY10UT7IZSROMVKipiJq5kfZx1sZAa3XBl/nDrkh5iLw1uU4Dmeimu+ZgEigMrndaVROjZUlG/VgleaWVILwxmJQiZ9pZ0mM1K48aiAYXP4wWVE9D66GdOmH5rE3ezEhd22r3XN1vWd2ZMP1kXI1stxaTP5LBj5tJNd8NAzAIq9USnodUUT2Usb5cyKUB19+GX4O5Tc4WZyU4N9ZboEkE6vv0XcPFzDXoqaBthSgbSS017tyJUBpUE8s9y2i6lJySRZmPK07/6f2E6nX6ffu6/9Hq57/zq//x/Jhskf6SfVS9afvNP/N9+5M/h/x/XKw3gT9j6+3//7zOdTvlTf+pPsbe3d/X1z/7ZP/vBfpCNlPtOxj/7Le2avzpVKq8onklRk1xqshMZWZpKiUjrQV90JuOG5DChvNlebeAhF2hq9jylXhcm30vmXjKXaK92IDd/NGAicbMBJQywYDuXYwCbeKKBs58KlFM5jZkjGcPGlaV5OISZxfQlv9asFNUiBRvJT6WLozryfrUn4ng9bHFTKVgvToYyAr3fJxt2/K9WMX3PCd5CwexNT9U5T+1KQaO5eLJGLA3NmodWE0YO5RWLm1E6AI1CNxp7ktJOM7IDgRC7nhRozXGP/HFKDAp1kGNq4astX28JQ0//mbnKg409L5DqVgwF9UbAfq9PXFh8l22qnUS7qaTTCPUjg4e2c+aqK4TH7P6E4nHC6byPG8sIPj8yDB9z5dCOK0t+TwoU3UB42Kd4ZilOFK7oNvKVHASaqegJ7bLDsgQBNdNo1LeH2BeyU8baQBD+nG7BDFvaYaS82RLTl8gdJW7mUoYOZmohDTw/ntA8HAqM2ylcYyiei/7RbbSiA22k0Bh+nLDaC9S7Th6Hkc007FcySlaglhbdaIa7c9lQ57IZTd+UA1CzFgQYriGmAkzWrcLODdmZjARfMibLi0KKv2ErOsLuwBQSQRABUBsWtyWOTqlImCe0E08zicxvi3RBBajqhN6Bpl6XvNj6RoNutHwuBy3RQnEgmJN6x9O7vpCkCo8UN1YwShSeMBJNWTI1PLq/Ix30gSNeqzAVpM/Ffd8OA7M3HPbcXhVpQCfNADVpiJNWnuNGTX2nkuKs7Yq/RuGOC7KHuRwGAXeeE5KX94FIGIoeVjAt8vxW1zxuvaXdEPQQJqJaLZ3j2qDOUkIqBxMihFQKsmQucO+N/yCHkWTQoJeCgIl9x/Az6ej2r81xE4dpEJD8RUr+aYbqedpJoHlvRbseiLUhZmJcGX0i16C9UaOcFG3tzZp2GDi9v06z3wgvtRE3brsu05D5oiA707i+MArbgZjSYuFx6w6fy+G3WJPOm8k9o9051XbAVKJ9zC6kuI2JaJjTU4OpYHhfMtB9ETFLTf+R6J4lIUah5kJB0I0SFmClqfcc2ccFqjQMxiX1WsCNPe1Qik19kv5v2nd+WOvlCPiP+vVq/WjWqwLwJ2zFGP/Ar7/8l//yD/Rz7Fx0J/VG6Ea90H/SIUS6rlE668DBtoMhDwP5s0SYUwrCQv6/SgIqSgQZNpDcXlzhGWJphIl1u6K+0VyBUuv1SDMKFPcz7IuM/sOE5MLQbgrEdLS5pDnLZbRnIuu/lxBuVGgH1Y2Wjd83qCC6nxhkFGMaSI5S9MpgSnkeuoM7D3YWqEzC6NMzQ9gSplvMgwi7Hw8ggt2qwAbqbcmkLfYWtFuOsNZSrwcBsI4aVE9cs8pLMdCMRdDvhpHk9TmjO5fE6yWqcIzuCycx5IHkTKCs9YYkBaggeI7lLY8qNcWTRGDSreQj509SMaIYEdmjoH67FKB0K8aDZhQkPP4sFV7jphS72YV0BsprjnYsXclmPVC96NhsGpr1wMWXAuzVkvaiRRi/2g/Cayu7zksPibgafh67lZ5Inm1xIl3f+NpSUCNnggcZPIXF4zHpuMbc/BxU7ucJvjNRFM8lUlCqB0U2qVCNJvSC8CSXibi4GylIi+8VlG9XUqwvDXQFivKw2pfXwA5k9Kdzj9quibNUCrqOLxh1ZH40oDjSMGxJZvIc7Vw6wLpV1OuemIhxIljwO410drrOVe+JRSVBItnOU9RBLgeMtZpqz5FOJRqMCOmGZASrJwXknmJrJRDjnZr67VL4mI/61BMpopv9htF3RJdoX2r/cilU9ELMS6uDAXYp4/yYBdJLBddK0l5L9jyh2ZQkHzuTTrg+STGPchZvijlk8EguiJk06Bb23j0mvrYkWMHOzF93qKMMc5QS8wDPCobjUoqmm6UU3TYy/kS6ZsrB5mRBeipFlRm3qP1KurCDlvTtGe1eg10J7ikdSqZzHHjUylAcakG05HIwoZNo+OsVyokRSzvREM/uIoeRe33sSrqTvQfi6nW9SP3RmPTYMngqjyuO5MDBNAEP8TBHV3LPU6W5SmlRAeLKCvT5XBO9JtpIdmYoHmT0DoT7R6NRZfe6HBaUb9VSvHpFs+mlCzvr1FNROqHJ/zyi91FO/p2CxYMxTBrcINCMw1WkYzLV5MfSyS13A/O7gbTfiAFoXT7TdiXsUp8L4Ty5NFS3a9qxvHd6D5Ir9Mz8oicSkcIRrXx20/Mf75Ye0D+Ur1frR7NeXekv6HrpgNNOir3sVMDGbs1Jrue2Z/am76C8MhoLXcavrjTBiFtVrzTMLL2ncjo1l5b40QB1d4lupNhSKwPHGebc0vYj+lQKH9Mo/JcWpG/OqL68ko6MjRBgdjwgO7GSVJJELt8SWLB03RSzX1rh1sSxyUlGdqZpB+IoBLj8kiOdi/EkZJH647FkFb/ICHdLYmWISUD3HIsbgpeIScQmHj23jD+WcbT7aMRgc4k9Sln/QAlH7SxDnwgwVlfSlQiFnOhDz8O3Ryy/t4Z5UJA+zjh/H5otGVW2ax5TabJzTbLQuIkjmSOsxGeG8pZsjLqBZKFoBxG7XsnI86tTCaW/XxC3azCRpNcSikD5Xol2iuSbA0FKrGQkH5V0N5KpZvKBZfhQxqV0iSBmJUy/JHUkkwqVSQRZGDrqDU8zDjTbjup6Q3OzIWQylgubzRUEfPaabFDtSsTx7Vg0VPH/eEEcOsGBfEMKbNeLKC8dNT23rF4XFqVZCkPOPe0Tk0DsOdKZFrfxWkPWjee2/sxzkqcZtotk6w1qdKnwaw4/lBQWfy5RXdlHBcm9gqgj/UeG4SNN8tbsKgYx+YVzGQH+/IUI8yPELFBvymukC4crwI8dxb0M1xcBfrUTqLYDOvWs/ewxyVQ0XW6zpf/7PbZ+10gXb02KymaeMnwgI0m8or0/JOrI8Js55mlOXGvl+u01+B0xfsy/VglyJcJwVBIHnpBIoTbcmxN1xPUkkjE5FwZi8nEPYwLtnQrddcWTqRKna5DXOhnWtGue1bXI8NYUDnKSueLFp1u0qxRTKsLQoQYyHQDEiQssHo7JJhXxRU6ylE7U9F1Ptevwg8DRk3XqmzWq1vhpgpumqMqgDnJWzwf0PxH0jVlo9IcD+bw7cWyvXm9IxjXJscTNDT8zAvZ+UIgD980lyzsyVm/3GtKZdPd8HkELbsVnQMfna3Zb6jVFeqEZf0NiDlWQgjNmkeJIk1xYtn8PikPD5ZeE4WinBoIcjJSO0DmpXT9KylAaGewsMFsVYdzKPXFuxV17vZR7YKkpbs5RqfAc21Fg+TMlIYHVDcmHtgcZdiHO5HrTS5zhKFBvSvQfVg6MzWVGuyYpJK4nn9nkXEwtm9+QIhzkgNCOPT6H5W0HoxZqyVaOjZH3850l1f4XxwX8av2XX68KwC/oSi41bNa49ZbRRwk+Bz/yFE8TMBGzWWNWGp9GXB98KuL4qLvooQoI0Huhocs0VU6hXQccvsivNlqGjnSqpesWpCCTeKMgY7FvjEkSj087npzrsnmD3MhVI1ots1IMHlr6TyztpcSmFQeaMGmJSkbZq9cbwkhOy66Aer9lcH2G26+JuRe0wrMCUonb4jSj/1yL3i2JlPNMCqotCZpvb9SsHo1wE8/5lwTDEfKAX3fUb5f4noxWVCvYE9Vqyj1H71B1ZhL5ucllFy02bAX03GUrp8eWei2iSy1ZorWIyaMSHZEfetxpLikHlwVxTTb2/MOCdFTjjkX3GS9SCNKxJcDsDelq+ELGmPVrFdUm1GMp5nUthbIvIjhFXSa00wx1kRJMFxvVLTu1DO6lZA8z7Eyc08wS8hNNM5GNJxk2kmSRSUeqvt4yfTQhfZ7g7g0FYmwln9nOND6TDVkl8v2iuVKy+QWFWlnaoRTV9rNChPq14ng2IH//knrD4yae8vGQkEWyp6nkBudyXUMmXdZ6w5MdW5L/6ozp247VLIckoArPfJljl5r54RCM6N6IivzYMHwAxQeFFJUrQ70WibdK0ZYaeR/E45zjj7ZoJ/I4TeFpB9D2FTEL9A6ls6hnlvmdgC2lW+wHwmA0ZaTddGQPMpIzS/oiRV0mkmRhxRnbjj1Z4lArQ7XvOPr2DvPDoRTRXSbxS5hxdauhvCgIy4Q4S7HbJW4QsXPpkoUsYm2AXIqA6oMJdqlY3nEUL4w4TTUkRwnqTJJDAPKdJdnrwg+NnwzEiGGkGxWNZFzrSnRt+iIRc9TjToJQKhnXTjXLW064gT2JnKRV5M8SVGnIn6SE0MVPXihmbzmqXY8bBPRpij/sCWOwiPI4FV1yjGL8kWgAbSkHWd8L4KTDXd5qCWnX+U0j9jTBLDTVeiTeLDn6P8h4vniakMzFVJUfWskrXljpCJda8r1XinbTsXw2RD0pBN5tg0wRtkrCcU56IQfH5WWBOk+wpbjS/dIKHubUgI60Gw77+pyQRpJLje63n2cub9SYuZZR8Ey6/s2mp317hevJ4whJZHFTpA/mLKF3fSH37VoYg8wTOZxHhZkZgo2YDwZy3/8xLh/VD+Xr1frRrFcF4Bd0VTcbQm0wUylAQtpR499doRT4swxTweiBptpzovlr5DTb3qpp18MVL83MZfzRO5DCxe9Jd0q3MhKJpaHac4wnK9zYw6gVAXYA9cFQTsFHPdHSDKQrabdK6i0ZD8UkUAw+Z5CZEvIjS/7CsnirZfTtjGrfUdxPUSuDMoHezpLVtYBaWpYPx2QPc8kiTj3FkUJPE3CSg7u8ESgODdgg2JNawLfJuZGCKBOXqqmUICQqjT1N0C8kK3j1eiM6xyijWjszlFvxymDzUk8YswAHGboUMT5RijE0+IGnea2U8XKEmEiCBlZ+XzqVHNC1jTnhWiU5uR8PWPtQQNVmpaUg2hc4s51LFBQK2SyXtnNyi0bN55HeYZcy0HfEDk2TH2lspXDvLol5IBTSFc7OhItoKyl2GbeUu55krq9GYXol5gq1MqiFuKVtKRBvn0siR/YikWI8kUJvbW0BuReUzsjRe9phhw4Ner+k2WsxFSzeqam2PHWVMjsYSpG2Oyc706JP23GoRkwvyaWWQr0nhohmErg4HZJuVFAb8qcpyeOM5F4PkJF17HXpHK3Erq32JONXV5q41uDXWvJv9ug/lQ2asQDSdZeck03BN5rymmP2WsBODbrtuk5GilfXj7KhD1oxm0yUFN+bnf4WpPs0jvjakFxq7Ljh5PkEvIyD81Ml6I/ucFbvt7iBv9Jfmqk4Vof3DPFpT7pCPSmKiyMlxfi56BDboQCQVS3aPFPLY3WDQNyQrquKUC0yyidD0qkmdgaJ7FzeV2bU0G63otmtoDgSl3W5FwSEbiSBQjt537iedOyEcaivYNb1tkc/zSU6cRJJxjXFgaH3XOMnTg5ZXqL+lJesXrMQ5/xqVw5lkp0s/MX8hcRR6sKxuBkImXyefB6uCAb+JEctDc1I9KjljugbbQmmArOUrq2+sSRa0WbaMykOd792yOTWpSCdKk3bWKKNHTA7opaGMHYsr0uOuek7yl3pWCbDhmxcwTdH0k1PIP+wYHLzkuLtS9SjAt8LNLsO3UL+QiYh6mlBTMSwR1SYks8P3V5LysyWXAc7FRlFSOVgDvKahR8z1+OVBvCP13pVAH5Bl7KSpUqEes8RJ61oyl7kqIsEvd7Q3KppB6B6YvBQXsTd5jBD1XL6F7aVcOKaMQI2vUxBRfpPleSIjhtu3T1mdn9CcmGIjUGdpsQkdt8XyU4MuiuYzO0F7TTr8og1+bOE6ukQnwuwdv6GpxkFql3BRbhCHJ3VvsesS6G4OuoTupt9GDoZlexX5B8VLG5LB0S1Gt91rVZ3G1TZdd9yj+9i8vpPNWbcsvbWOc1OK/qla0uBSL/0xytB04QM+o8s+bmSMajvjCNdYZgdWdKpxjTgC0kSqfZl9KOcIpSW4nsFUXEFpaXR0pG8JkXd/IMN7OMc5QTafPGuvAYh7zqwpYZZQnuzZvhIk14oXCEdSBUlci27UMRRK+YWr0g/Kxh9J0VXUvy0b5QM+hXru1OStQq1Mpz9iVbi7jqxujnISOZyIBDnqya70CQdP1G3CvvaAvUzU5o12ZiIYlBRF2LUyS4Uq/+wKZ0UABtZ3XIMHkhyhvlE8lDrTamOVFCoZznpuaEZB+ZHA5EyVJZ0rUJt1DTrXu5aUdF7bCmOxdmqdMQ7Q7pWyYHGS1HuRgF/p6T/cSb5r6W+0rsSIfQ8totTq7Yil1928LRAn3TZujs1yVyx/MUF5jSFzBOGEl13/tOO0Pes3T0nWBi/f4a9tSCuLNW+Y3XDCy6n53EjMUqEsSMOHcpEmnWPm6VikJhJd6reiIL+oDs8OI0eteJwV/K8g42Uu9Jpo/DiEB+1XP5MLY78qSY/sOjNGtNRQbQTs0W77iRCsUv/MCtF/3sZIQs0o0C7JfBn1xPOYDjPxAg18MzfauVg4hTZmQCvuVbiN1rMl6fdawgbv29o31rRe20qMpFLTXZscH0xkPl+wDvN6rpj/qa7io8kyj0oaqjXpQPZjgLNpu+iJAXdRCVyAVdE4iwVExQyAte1vnL3awexCASLUAR0pP/OBc0ksvpyKc78qHBnBcop8lNNvFFRvVHx7GCdGMWIEpOIX1iSCylW2avFUd9IBy5qMA9y0q77Fh/38A8HlHebK9B9++Ull08mrO5Nrh5vcmIxbyyodzzNhpdIwmGLe3MlBxAt9+N2u8U9GFA8SEkvNCGVTml6YnD7NX63Bi3wdbfZ/pfYTl6tL+h6VQB+QZeaJmKGyIT5ps8SVCOcuDBypFlLbGT0NRyVuLEnFIJqcZutjFkRkXW70xLSSL3jpKuwXqOSIHy7C41fWU7+3/sSWr7mRaeTREZ7c0IiZgVAYttGjnhvgF4ZsjONemtB+/aK0Bc9lKlkfJ1cX0q+7UD0OTHrKPvPC2JtUEF+Zuw0Ms2mx5eWZiKw4vRSBN75w4zs2GIuEsn4nWtU4TGpJFFUm5HQaqrW0lsrpdA8F9ejXSrIvNzsu3HZ8q2aZiibYH1bCpL0jRl+3dHcrimvi9bo5UY2eNAF2c9FWP7yGrRfXgruYm66sZSRzTEX56hddQWiip0AXZ6rHwquRp0JwHd1Q7pDxZEivYRmw7O87RhMSjHwBGhGgcWtLlGkF1DPcqrf3WD6yTruqId20BuXjF+/wA2lWPW5dDgF6+KuukzayXvCD71sSmmLW2+vcBe6kfGzXog5IT8Bs1GT353T+ywluTCs9kTb1g5lc9a1QhnhSOankhXsRx57YaUoLA3tUYF9lKO71BlfRFZ3WlZ7kqKhzxJ8bWiWqaRB9KNw1eZiInI/PWfra0eom0t831/pJO2gRb+2QPdbOdxYcVUSuwzXaUJ5zaHu9/FDT9oT/IzyUGyUqEpjdMTcWXB6OKI57pFcGJEMqIjZqtAzK6iPTU/xIIVGk93PMaWMAfGiIVWtwl+r5Hk7KaawgXgumKHYsRvDuNPvDZyMktOAPklJDlORSkwC1a0G/VgQIbZUuM5Io0ojcg8Egt0ORfs2eCjpJ9mwxk28cCOdGGrSMyPmJi3Il/5zRb0eWB33yT7oQW0oVyLtCGPHxZeEzVmuRJvXDiL1hkwGoo2ocUP6WYHyCjPsrufcotYa8pMOWv7+XLrilWxRulVEIL2Qv6v2HP5mRcy9dAW7Tngyl/Hp/K4U+MOPE+xKsfaxaJsvjySDOP20EINc36Fq1XVmA36WCCLpMmE2L8i+KYYqVYjD2F5Y/FJc/6pV6M2akEmxqp1ETrqdhmgjg49T7GsL7M9fSAdPQbARu6LrRCrqo55MShpNtePQz3NcabGZkwxoDXQ55O1IYPFuuyH0PCFD9LKpJ78xJzsV89aPc8WoCX/Er/hHjJJ7tf7z16sr/QVdZqWotxx2rhl8KIy24QNNO4rkT1L8R0M5cXeGDHS8ipjSqUcvLPv/Vm5sqhJdVDKpMJVi7bdzmCdXupX0MGH4NFAcy8259yAVLMmLIdu/Twd4FfNAOmhob9ZSXDgInw3wlUUvDclU0Wx3+r4nwp9Lzw3tjZqoO7OEjgw/ScgPDL3HMpZRqXSQ0kEj2iMgO0NMDwHqLcfgqeBElAf7IsNXRuLI9gRRszjvUa1ScdtWMp5DgT1OZYOqxZVsj1Ka/Zb+M40+TWDoWJ33xA15LAHv4VYpI+a5jLLsUksc21SKpOQ4wZ3lV53F4qmVUU43ulq9WUuCw6iluLYQR+ZmQ/HUMtqdi6u0F+j9zCm61IzvKRa3A7N3Wnm9H1ia742vNmy9WxGGos3LD+0Vb0x1aRPRgPt4xOWjCbHvMCvpqqVTGSH3HiXouwtJPhkJrDd/npBOFdNFTv5U0kLyE90lKIiZobzTMH3Pw9OC5VlPIMx0WaupaEXR0uHM7+X4rHMnG1BpwFbS7TILTezwQ5M3zmX0HWTsFpPI+FOJsZtsLKSrfCIJLipKlJY6yrA2MCtz4uM+w/uWkEfcegtPCtoXfdL7BfVEdLDpWiV6xkw6hnZucEVkbX+KfyIIo+Zmg3PyuE5eTKjnGfmTFLMUl6fyUmC6RUJ6KaNQFJQ3287IEEk6tl/vuRGsjIbQGPx+jRsHmg0v78WZxg+FfReySDasSS8Vg+9kxPNMit+Jw3eHJN0q1rdm8rr2RSP4Mtc3JuIWV3NLtlHihx4/CCyve1TmCZ8NsFNJ1/D9wObNS9HpOkmzcddqXCGHkfxIDlyq1oSVlWSPIIcf/bAg/W6P2PMy1g2K6DX5kSF5mFNda2UEfj8nPUyIaUDryOxNT327pm3loPCSNvBSSuH6CFqn0YRZwuAjKXrTqbjqQ6c9jUO5ESy/WtK+VXL8C56wU1M8TmjXAtW+I5lpYqMJfS/awoHA2Zv1zig1E4SQ3q1QKsJQRrbFUykqk6mYYerb8nktjiVKkChZys0o0jzvU30wIf2sQJeSp1xtRPx6S3mnIT0TfWWyEKlBSCPJcQKPe1Q7XqYQRiYo6aU8x8GHGSoLxOulJC990Kf5bIT2ktDz41we9UP5erV+NOtVAfgFXeruQthrd5dUW5HQ86z2I27sqTek25YedXywhcGeiw4lOUkkdiuJnHxNY96bQd+RnRrCsx4+76KV5prhPYufpUQLZ+8rFjcD6bmm2g4wbtG15viXWomNmggDS384oPhI8oXbkeSG2tOE4kCKJNVozDtz/EbL9mtngu94nonDdiodhcWXarILeZ7FC0vyWP47n/bxvUi9Hqk3wB/ITXTw0NIMkVHLWsANA8lxQswCxT3JQbVnCflHheAkbJewIc1MVKXJDw3hRiWmCh1ZvOaxK0X6OCN/npC9SIi2cxke5OjXFvgsUm2LISb0vbgBe/FqBJReaEYfJdRbMiZv10Xfkz/MaNYCOpGbefK1C5gnJCsoP5oQlXDoFivJv63WFcWRIGZcT5zB0QBWmIX93+lhLi3t2F+lDFR7nni9kqi5laLZdKL3izLeTa8tWXylwi4Uqzdr6mlOsy5dlWpPHMTVZiD7+oDsHNrdhmYtErprd/DhtlzjnkMFGH2YyEi2F0W3ttPI2PzM0K45YUomkbtffk52rjCHKcmMThsmxZ67VnPxeE0MGbUi/06BWWqmb0aCgcvDIYNPhIEX11vwMuINaaT+bMTq4Qi/0zB/uyWkgcHGSgqHnqeZBPx+TXZk0R8ORApwKXo3fWspWcv31+gdKpKpIXuY4U6Ex1Y8Sdj+t4lAkY10v8PAYeZGDCYbATvXxCyQnFuSc0tIEXdtTxzHdqlwQ48pHDZ16FJhx81VnrauRFcaC0+4P6Bej8zfaaUb2hdThG4VxbHG9wLl724SkngFf/brjuxUxsnKiYmlvshRuYe0AxmbiBsEwn5FtdcS+47zz9YZfq8zDEWwzzLKfY9diPbSLuQQgY64IjK4l+L78vftOGIuLc0bJaHv+drrj2mHEZ8j+b/rQTJ7E8gPLel3e+R7S9HBOo0fiNHJLCUH2JSip9RvL0RjauVn+ZvyuRQklBiF8idyGBv+foGfJ2AjSosWMD8wYhTZcag0oLKA64vZpf9ckx+JwSU9M5TbgXAkbu7eqOqwL5K/3WyIHCF6RbMRWNwQLJZaiovX9SNqqxZzSxrJ784hDfi9Gpt59MxSnEBx/Lle2NTyfg9JxKw0xQtLdphgKtFV9t66FF32YQoHgtEq7zT4PEo33L7KdXi1/vPXqyi4L+jyTwfE647wrIcCiicJ5WsNam5h0tCYBLNW4+YJKguow1TyRm3EXlh0I2OpeG9E4qSTkMy0pF1sekI/4gaa3mMr+i8lp25fJphSwSoTncrjDFsisVo2MvjZUy4+2iCdSo7wS91ZsybjEVNqymmOMpGzywEqiLiescSu9T7Mqdc0q70OApxIZq2dQ7JUuIF0vZIZuJ6CVlFuS9FW7ErsGsOW4lpJezCUZI5G43dryiIhOzWoczFlNBPBgpAGso8N9VGGcQo/t8Sep96K2Kmh/1wx/YqkLyQLRVSK2vTlXtw5G3VpCD3h/1W7YmiIBqoNSU/RuSNGRawNvpQxarhMWS0t6bHFWih/bok7LtCtFEbNZQZ7LcmpaOHqn1sRlwmrfdmklJHNfDZIMQvpMLki4jZbepMS7zX1WDpL6amlWZMRZXndkXw6QPVkfKieyGvpNlryJymuVRKtlUeqLXHMUkp2aXYsEOfyekt+ktBOOp5aBm67EQdwErCpo6011bZDVYbQC6jC8fBog9GFONO1E02ozyP2cY6JMiLXtaLedtRJQNWGtZsXTD/ewE4ty5uemEaSFym2VDRjJSkfc0V5t8EeCiS8nQTK+yMyheQSDxzmKBP92V5L/jwRPaSN6I8G5F16x2pPIgKrHYddGHwRaEaR069GsgvR1alWoSt7BUu2C3GO6pWkfuiyc3d2438/8oQkQGUIZxmsN8Sdml6vZr6VCAD8MsE0kB4lNDst5iKheJRQnEbOfz4w+CjF9STmLz0X7W8YO1QSiJf5VVxgvEyvtGrhjRXuJCdZCujYnAm0vFYpeq1B6Uh2lLG8EejdntHTgeVHawweGhZ3hemnTqX7m5zK9Vre6N4DTuLX2lst5iBHm8i3HtwkDiUuUOUee5h2vDtgqah2AurhkHKzYbK+pHy0JqNfDc53h8WFpnneRwPZQZeO0jnKe88Fz7N555zp5SYqwuytlwYcRVhZiqcJ1Y6Ym0xtaLSYTUIW6T3XLK9L0SnOXMfa1pzld9eJCsplSr5SNGPp5ru+JlhQpeh420kguTD4XIwc0UCYJqgM3MTRHvXJTsTRHFIwHua3xb0dbRT3ttMSv2dkIuM2A9mzlNV1j/KK8qwPWSAakSgkI4nXw0bMTOOGP94CMMQfPMv3D/oZr9aPZr3qAH5BV0wi5jS9cti9jAXLr4lIXbUKf9JlIc6kO6S7rNP0Upxn+aGkhbi+RA3ZpaBX1r5lIAnEnqfc84S1FjZrEeJnEbdf4zoyvV2JTi0qSM8155+tS+7otsTShZEjFr5LTBC92fDDVBJArCQKvNTf5fdyyu0ggOOtTjPYiG4QRGOoWoVbd9g/dUYyV+gawk6NHztWU4mwiqVh9fGE/MgSCy/Q2mlCfmwEDJzJqLO9UWNnBmUD9YaMy91YDDP2NCE/FOft/E4gHTa0ax73lQXlNUdcawiJPE77zoyQB/LnCaYWyHD/mSa8t+geuICuY6sFxrwhnaFoImbgcP2IbsHVFuVllP8yOs6eS95utRUJQYn70IpGMe83V/iYdKoYfGZlo5uUrE76NMc9MDKOCgnYueT+Fk9lnBdNZHWrpd5vv08K4NZboo74friK4+s9s5LvWwnbkES6vvmWuBBWN7x0ULxCqYhvDdlhQrJeSZHZKopBjV8kTN+ONNuO6XuC1Imj9vvG1ipCsbXCFB6z0Fw8XMO+NDu0imu3T2nXPNXrFWGzJT+26BbG38joP1c0W9K5CWkUN+hFAk5jby0YfOUMVWpcIaDimEaSuXQufREYPtIUXRdQN4CGzfdOUDtiTgoTJxihjZb0QqMbjS8C7SgQCo8aNYJZGbqrkX/2IiF6RbG5Qm/U+HlCWCb4312T4mVuSa8tKXeE2WjPE0IWyKZw/lWPubDU65L8ES3imI5QjCsptIdSYKyuyfVKp1JUtdOM0WdSnMYsoJ1E96XnhvReAS9yVrcccaOh+nTM9MmYdt0RUtHpFo9S6ZiriGplLNx7ZohvLXE/N78a32oPxe25gJqzQOh7JusL3CBgdleELHRsSYXve6gNPiriW0vaoWgIk9fmhP0Kf70i9D1s1DSTl9dD8subcWT4CJrf2iKdSnRj8VxSd+yoYW13RnmrJTuVQ0p8bSnJG12c5OorAsD2Q4/vBcbfTql+fwPXF0NTWEoeshsGmrXA8PVL4qjF7q8ExqzFIRwmjni9lAnDc9vFbWqyEzGvtKOA7wfcKKC8xAkCqIUlfybZ3aN7FuWVQL/XPLHvBTPzLMXOjIgeNbTLlOzISnpOo+h/+uNOAvmjawDDKw3gj2y9utJf0BVyj18TgXW16/A5mPOE8rRH74mld2smG5CNFNeE8J8sFHa9wufiaiUKa604lvHs8o6nvVuivWjj1MLCyEFtUMcZWV9AwuoiFRH860uaNYlNirmYOfJjTXVdxsM+DxQPU9FyGfDbDflziftKZpp6npEeJug1KdBiBzf2Q89kbyaF4CSwuu4FXTORTk/+PGHx4bpAjrc8+jhFzy3DD1IRb9ea0XtnIhx3SpiGqcQ8NZOI35ZUCHuQ4UYec5jh3luIu/CxFIp+v5bRbhFIZ5r83w8kqqo16FFLb1iL4aLWuI9H7N8+pd4S2O/qlmNx1+Gf9rpcUoV9nEOjpRjRUkDpQUsM0slpbjbESjauy58RNIduJJO5WZcoKI4zzDeH5C8kSqs66BN7kiMbvjan3OnQNR+OSNcqQbEsDNSG0TtnuJFwFNO57C8xD/QeJfTWV1euWd5eoDMvOJ9aSQG+kveHXSnKfRm5JoeSWGB/bygdERNp743oPUzIPywY/l5BfUMypwkSS1YuMlSrmXyo6N9PRBenIH2e0n/3QrSBQUan5VmBr7uA4u4uFnUkO9Ucfm8bs9QkzzIG382otzyrfU/7p6ZUm5KeoWpFHHjm7zWoa6WgUuYZs+9tiBnlRPiO2ED1s12hPmm5fN/RjMDfqGhv1hTPDMcnI8J5SrXryZ6mrH9LovHqbc/goYx+QZiQ8SLFrBTFsJaOXCKoF1UZnNPiujXSWQ4pJOdWOnrA5junJKdWMpqBZii4pPE90UACVzrAej1QXhS0F7mM+eeG628e43tBXPJdQT17QzpL6Ym47ZO5dCermw1+EEhPDPokJdhIfiTvl3Yg481qS5JZQholP1cJ4sa96BGCkti1hRwiyydD0IJPmezOcUHTe2Zo5xl6JGkv+RtTBnsLkktD/cEE74Rvl58q+PYI/Sxn9O8LQeY8zjGlIjmVAivkHbT8dVjeiFfd9dV1h19zDAcll08m6MJdxeQRBVIdbaS85jpXuET/JWsVs9fl4Jqdip4vuTT4iROQeRaYPh2TPkuJ9/vYg4x0QzTS2VMZz5q5vIbFMyNmlVreE7Hnr0xC0UT6H2f0H1m5B40CyVSja4g2UF9rKQ4MVFq6szsC5U4vJScbLdd++IvH6EZG06/Wq/Wfu14VgF/QZS8tyWlC/iJBReFrJVONvTT4IrJ8PhTRdqNpPxmRXUjnpp1mAv8dSHfErzmWd+UGHW0gXqac/2Ito7hGEZ0iO7KYSuHuD1CtFtG5U2TfGGBWCnNhSUaN5PbuBMzUYho67EXAV4bmjZLkWYrrR/y6dC3zUS2aoXlCureUPODOsXd5PBT8hUI2hBcZetJQbwTUl2a0I49fdzJO6XJfXSEdIjScn4xEZN+TrkhyZonXK1wvwDSRTcUIQ873AzzoY0px+rm+YFJWN+TUX+04Zl+p5eZ7nBFqw+pg0FkT5X9ePNpEV8LMU7WAqSWFRV3Fk5EFVAScaILCPME+zklOEkm0WAqLsPdpKp2AUjAh5NLRUl5R7nnqzYDrS5fGTK3oIg96mFp+XzsONOc55R2J7ksuDOdPJ4w+saiVYfquu8qNLfc9q9Meo88kFaWtLWHRFeNKroXvB+otj88k0xSkKGhv1FRfW5FMNemJoXcgY756XTKV7YnIBdo1T7xMURcpulRcviuxYPlR56BeKaaPJmSnoo8i6/KTV9KFG16f0awH9PUV6UzYaclC+GvL60HiwZLIqFcJ1ubOQjraJwn2NEE9LlDrNfYkwW0Lw89nEMataOvu9YlGOsfF5gr1U1Oi05iDjGYtMv79nJgFycLdbZnfhvxpivIK15fXMw4FI4SB5t2S8OFQMl5nmmxcoRqFO+pJJ25uyC5kzOzzjj13b8DJ2RCfRdoyEQnCRiCZwcW7or8LaSRuNCjfmV8yL8aCRAqN59/bIaaRya1LcaFedpnU65JHm8yVSDPWnTjfEYlFVBBGTjp62zXtjVoYkNul5P0qKcxfvvZEyZgejkpJAXmtIb8h2czKKaaPxwAs35QYtHiZQhqoPxmzOBzQbjh0q/CzhOJYU+7K4cMPA8sbEb2SWLl2TYodu9DgP3eoRy0HHT8IAk8+S5h9uib4mk8KgVwvNO48F7PRTBih6acFxXMDzwvCsx75ifBNy9sN7Xpg9KlohdMTkWUkHZKl3XD4NNJMM9qhuM9DFtn6ZiQ7F34iUWIkVSFj+ZAJXgmgmUSaNWFIhjzS3mjExdytcs+jGy2onMBVp9EsNNTC45z+3rYctHo/3iSQgPqhfL1aP5r1qgD8gq7s/POOgJlrcfFFibNqNgJ6vcb1A6NPLNmloh0IaT+5MPIPgXIvkD1PGGwt5WbTnZjNQUYsPNpD/iwlvLXA365QEbILTXujwa056rVIfiruUH9Q4PfrDjIrcONkKhu8nktRYUrF+DMBDZf7Hv/pgOxCxtL1TKK6fE9unPbCys7UOfOSmSL5tAAV2RnPyY+tIDc6I0R6Ki5en0uRYi6sAIWDJDegIB5n2JWGccvgoSE7Fy1e76khu5RQ96jAX6s6t6GS3/PcgtOYUtIIkqOEmHviwEnncxjY/nemM1mA3S4Z3JgRrOQem5XoIAcfprj1luJJIrq/hTD3oga1UQsQupHxdLPhUe/PIAmYc0lO+P+y9ycxlq3reSb2/M3qdhc7+siMjOxPf3kbkhJJCS4W5BJcogDLE8ETgRIIaSTAc8EjyzA090wDiRpw4BEtAYINCHTJVVKRotjd5tx72uwiMjP6iN2v7m88+FbGKVnFklik7tU9yh9I4Oa5kRE79l5r/d//fe/7vH5NMkFjEgkmor1EqIU8MHxmSDp4dBw68lNL//OU/FTi63SlacbSzSNA9bgSY9DuCpLI8rZs3umTgt6hFTfpeksoPPnuUiQFew3q3pJoIun9BVyn6KcFyQLQMP3QEQqP32wJmRSOrh+F3ZYG8lNNcSbcRoDmo9VNZyeZKRYPnUQF1gZGkm7RbHlWK2FKttc59brgd9pBwG22kogSRV5wer6GrhXNyz6mURRnSiC+pYLLTA4XtWb1oKVZD1AbdL+l3vL4boRflwmr8z6xMpgHC8nt3Y0QBLWSnSSdK1XAxOWeSB3UypJfKGIa4HVOMpPP0meRpkvBQIm7OT/X1OuiAUunmv5LKczSJwXFmSZWhvQ0QbdKNGvbFWG/IqYRFgnFS3EOZ1/mJHON3q0IRSC/NxfTzKwHH80B2PnmqRhXjGSDhwQIinRNRtrq3kqkFtcJzWZAnWREL4aUEDq9Wt9LrvWFojgXp2vII7PzgcRE6igYlAjrH8s9Mj8ZQqPJjxPWPjUUT1K5B55ZVKVFJ9vK/faGLxltoN2Q4tDODHaqhRW47tGbDfWdlvZWQ0gjy7ue0eeG0SeJoHOsfJ9qx9OOPfW2QzUKJokgfcYVvhcFoj70qCApL9m5gVYTC8/sHXADT3unoV0LtOtd+ksS5ZEZYXCoxYih4OR/E6g3I80IMMJ3jKUle5ZLx++gJJmLbtTdqjE1kl1dtJhSYRZSIOpNYZbiVcex7JzmQUEi5qn+S5Hp5C+TH8f28seut0kgP13rbQH4NV3Lx8KjezMaatYD7TBirhOyc4NfCnoh+8vnMhYcRJoN0aXEVNI60onGFdB8vEa00mEy1xa329ywykIS8Sc9wmWK63fZnUtL76k8WJd3u0iwgCAXeoHVgaMZB8LdSpiDPQ9JwA0j9a9MZTPU0O61BBtRo4bek5Q4cJiVpEG8cTjmTzLpeCRQ7Th0rTn84S2akYynsnNDOlXUey2r+y3+fiUg2o0W/Z6M9vLXgmExpaLdbik+z2j78p7UO57lXUc9FkTJ6sCTflnImKbUxA/nNONAdizJBNFE3FpALSwbOzPyjQrlFZfflO/l+pF42KP8fExxItyuUIjAvR1Ccp5QHjhCIsV6NOA2WkIlJov6tvy37MzA90YC9w6KaAN2YsnPrMSR9boxV6OxU8P8scdnXaF8kqI8rO562m8tMHMjncG+oE+UUwy+n+PWHe3rPnpq0V66n9XtVtJPhi32VDo3WkfsUqMvE5LEY7ZqyvOeuE9zMeWEBCnCCw9O+I4MhR8Yhg7VgYnLHSnwXQ/Uy0K6xvtecnOdwqy16FIQPKsDKRJs4qWw6q5zPxSRv70UVI1dSdeYKI7Z/kuNXUE6jdR7TtzNQPuoJL00kv2cS7cmTlJxmk9lzJ19WkAS6L2w1NNcQNWDKNGDBvw7K/ztWlAfIzH9RCuHsMU7LWZisSuF/8UZ9VhQMNmlFPqMWwFH66/YmfXDisV96Zrqn5lSbQeSiWgtB4dQnGnpWDVGkkSODT6XRIhmPVDdcqgXBbrSrE77Aj4/zKkuCkIWef18S/AyubAiq+3A2ieW9qQnessve4QkkvxPOtUmd7RrkXiWodYbud76HteH2XuOZK0WF/WpBRNRswT7B0P0Zk290aFiWkk+CSbS/qUp1Z5kddfrkfzMiD43C6zut4Q8kF0pisMEM7MkC+lwtmNPteMxc0P6ScH61lyu7aBIrwzzh8IqjUY636bqcrEnRnShtcIutTiZf2ckB4dhoHdoO+6ewLt1pSmepbRbLXYhBe3Oo0sBivcj4z9M8YUU/y5HUEKXktwRrJiHQBiMeqVpHpWsPqjxK0t1pxUEzHEGSJe5WaSiU70l4uY3z1ai6KixndZ5syU9SWh2HVc/I/KVt/K5t+tPst5eLl/Tlb1KCFZGaX4Y8IMgGbVZQNcI2DUqLj/bpO2mlcUrg666dI9SkZ/L6Ky5JbiJ2BM9jZ4mqCgPVd0K6045RexLzmdy3bnzEqHgV5sRP5DRL1pciulUwUkmHYJEOo3tbkP5ZMTG9zV2s0TNrbADvxQ8i56LmL+67SSnNA2UDyX9wBVdaomXLONQBJpxlNFkDum4FhPDaYZqFPY8JX4yYLy5kPHX3aVw3yaWZk0KoTexSsrJhmNXivxYEA/JUvRC9TyTuLBK8lh1pTFLyTm+fraO+v5Q3icnEWY+FzNBsPLAHhwpGVEq2XwAiZJbKJQNVLdEx5meJFL8Xia4rZbqgUSnqcJJxNpzwZ+kE8BG8gdzKSTODPFOha4U6RSaTcnmjVpGdq4WE0k0SFFcKdR6w/y9Fr0yhPQrMHR2LeNrWyr061wi0C5SlteSbmIqRfl6QPKjnnDaRo7i0Yzylhhami2PtgGcwl5bik9zKfZbcT36XMZ2bs3dMCoBgfV66RoHr0jvLEnuLUFBfmZonw+kIJ0a7EJYhyER48zqYSuHj8JjTjMxKRgB9k4+kE5N6HnJT36dY96fS5d4ocUVnAbK2w73oOrcmsJxrLaDmA8auQcIEDOPv8qIrXz+9trix066uEnE9FvYL4k60jwbSkd+EG5SPnTHe3vDsiMN6LNUChcF5cshxbGm3WppRpF6rCj3AsW5whYOt9NQPZZOUjqRwlpXMgLOLjTptWH2oRwM81NLfiZShJALAsUuRKIw/dCJUcTINZ9dGdEWpgG3X5NlLW4oZo5QWvTCoBtNvSGd5PiiR1TyeZtVp11bjxgTKHcD5EHSNfoBn0P1bCjpJafyc+qNQLXrSC4tZmFIrww+lQ6lbqHcd93XW0nhSaTImxyOJb1k5HD9wNpnUnz3XmlBxdypyF5LTF59vyYaaHdayrsty30Bipta3SQAFSeSrpNdSP43pmMMVobLH2xLfnkNizvde9MPVLseXWvqHUmBaTcEVE3HEdWtQmmIQUGrSQYNzaaX1zKU76OnFjJPnreYwnWoJk3+Wg6HZq5BR5LzhN5r0UEmM0XYbqi33Y91n/n/X29NID9d6+07/TVdIY+kU035nuiL8hOLWWpiInFc7W4jD+vO5ahbMXyEzZbsUi6LyUeOsNbS/0LYXtoGdCY8Lld0mZXdQ42dGjMRJEyyUDTj0EU6xZvAdT/yAlrd8hQnwq7rvTQoLcWWmiUopyi3FO08I9lbyfinJx2yqKDZcVLUaMEvEJW4LVuEM+g7Bl73sLazLkngk4EYQLyi/3AqDttKMbkcYBcKPh7K5rHd4NaE+p+9MyPfW5LdWgEQH66o3hUWGFHE3WplcIMgI7sgGjXdwt4HZ6AlJ7XZ8CQzEcWHPBLTSH4hbsx6HSlco2gidaMYHIoL1aYOOxF0BQrCVoMbO9TKYC4TdK3R5ynDzxIpuNecjEBtoHoykqJcQe8PC5KZZrUfyU4NqufFGJRFik9zzP0F/qDqcpkFuWInAtkuXlnRDi6kI2W6bFMVwa+39I4V6euE/FKK4MHzzlXa85jME/7t+KaITC8NoZXPw5aiB2zXJFc3P7HSFTSR9Eyuo6jl+opRCllTKbKnOd4rmld97KihvONIZ4roFbaSdJFmTf6t60X6myvcvQpznRC16FrtCjkUXMkIkyBjdl1DfThAtwr9YInPA3ppKF5Z7HPp9lV3WvJzxehLQSIVp3JYwkuWczLV4GW8rALQapodJ+zGlwW+6jJsWxmZEjojQyZa1+qWk67zfiA9SWRUnkc4KMnP5L0bbi3xI0e1HQhpoO1DOM0phjXMBc68uucoXqSsfSYQ6Pwqkk4UZird7sHPX7B62BILT+9IIMPtWiCkcuAxpRT7IOP0qCQtJitaVpc9hl8YVBrI1ip577pJQ3YhqRWmlH/re0HSRGaK9rTAj7zAuidGtGxZkHQiK11LtVsTNxshEnQdL59B+Nac+cNAu+1IL4xkS3fA7ZiIaeUNS0+V8v9P35Gu/vKbFXGSkn9S0KxFitcWc5bCbUlpeZMxraKM5NthJOlAzuWuTBKUl88qfXcmximAaYIbRNzYk5wlkofsBAKteo5H779m684EgGStFrRTFvFNt+1mgbbqEla6Yjs5SckvNL3PJZ85z0XGkF8o8jMhCGgv74srItN3hbbQbHqiUze6wp/UCvwZZAG/1QD+2NbbAvBruuxC4pqi16idWkLZIyRTI2HzmRdOmY7M33WYrpOhrhNx/+61JDODvk5oxpHeobTDktTBg6UAiPc89bYnu1LEqXQq0qmmHUbyc4PfbPE7DdmlJoxbedAuhAE2fxjwCSwftajr9CvI7X4lKRqDBveqh24V4XZ1E0NmptKBUw+X2KUmOU2o7jWEFOxK3xQqyaWVTTfIhs9Hc9pBFF7Xd9dRXrG66xj9IKX/WuKZTKWxiSe5llHN8qxPddzHfzkgnWjMZ31Ux+dafqOS0Z9GRjMTBUqi2lw/cvqDXXQpG4tZaVb3W7hORWSuIqu7jurDUpIaZlryhwcy6lzuB+y7c9STPqOnsim5foRZp8j3irDTkF3JmL5Zg3rbQ6skKzcoQbsUAfXegsW3Khm5J1JMrP9OyuCpGHdWjxrqZYo5yoXzWEkxNP6k6wh7aLoIwPljx+Y3z2nfW5FOFMW4Yv6Oo71Xs7zvMSXMP2jw35mjMg8nGeFn56iBww09/kGJPsswSyOg211ht4T9imYtQCZRW+1BA1ZMCTGLqKXga7STuC532iPaiJvKCNr1IuoqpR2II1v0dNIZWh0PiNMUP5Cx8PLPl8weC3sNBfmxfNbpRJHOFb0TGcH6w750XipFtB1iyEvncv7Y0YxltLe866UQPEoIQzHCoJBRYAQ7aigOE8Ja15lppECK90sWd4OMTnsydu+9sDd/+q/k2snOhekXo6JZD5S3AotJD1UZ6TyXcnqwK4X6gxExF8SInUrs4PyedLXKHcXqlpiMwnrL5Yt19NygFlIw1uty+GjHHr1RCxaolN/dDwL5lWJ13qd+3ae/taLaiujzlPqqYPhcOv79I+ng6UYOA7rUXdEtTE1dazE4tYpkqugdWdY+ldepvXRDQ6thlkjSyLmieqcmFIHivx9KukslZoiQRNp1R0wjvSND8VzwJ+5NZGEtYOx2HGCeEBPh+8U0UN5x+DwQLjIGhx3k/ZFci9HItaNiV6AjzL6Yy5Ri9Wognds1uc9CLu81CtwwMP5UUT+sYZ5wtSq4/nRDEo+CvomPUx1hIT1OGHyS0XuWCHapoxzwc1NWdx16p6JpDOpWRb0Rmb0TugOmonzQELNATARJYxcGM7Fv5Ntv19v1H7XegqC/pqt82JCUKb7WmH6D3ipxoaD/0lBvRMJFhho54sqgBy0+lbFsWCnJPnUaXYuw3XcJDgRFs0qF53WrkTHEUcZqP5BcyainHUXcmsNOreiuSks6h5XT9I6MiMxPUsJBRW0zcamuOcr3G+kADiLtOKC8dCejAn2WEbYauE6xC4kR8y97xIOK0BioJffX9QMRcTknCyWF5Llmed/BZYHuDpb1epCR4FxcpJP3xKnnewEaA/2AXWhCq1j7RPRozVoUXc4ikVzi2PHuPKx9ZljcjfTuLOhlDecna2QvU0lKKCXx4A0ou/LiIGw3ApyL7id5b4b7dAS3pYuhnCL8cERzq6WeJfhCTvnBivZRO4V6leF6MmZLpl32bCruRskiNrR7Lc0qRV0npNeabAL1BsweRUIa0JWieC4xge2ow/2kUD2uqT4ocR+vda9dNizVaM7O1ohOsbrt4aRP77Vh9TBAGlg8Fo1p/v0UtSEj7WqWoTOPnRjivJD3OChiAmGeioD+XNJY9MySnylKLakq5b6j/0zSaqrdgNqpUED6aY96R7qF+YuUetuLoF9B+GABr3qy2T4sSf5wwPKBFF8h6a77N5ukEp1c3GyoQypj+qkkW9TbMr6MWkDepure+xoG55ZyJzJ6qm/kEyGN5EcJugW/lK6VbhT+qJDxYZBcXb0StI2/zIijFrW0xL7DpxquLOVtT/Ha3OgFfRdTVucJrHnSUY163ifeqQgRet8vWN4RDYFuFOmJyCbcyMtY/HlOtStjRF1qYRdGRe/QUG1JNJxyChJILzVNULiQ4m879KBFPc8lH7wQDWe71skUnKLZbjELI7+rE9h3yKRT7b3GXiTEtIsaHHnsqCH7ogeA+/aCNHUsP1nDrUunm6DQlwncqvAry/whKBvQC83k52tx+18muEKcrr3nCavHDavbUuzrQcv25pyz8xFeW3H3eoVuIRTyWZF7ec/TSLJWUa56KIccJjYcemX+J25/QEWqWw4zM2Kg6nlCsOh+S/6iAKRTWe4GsnNDuY08H4GrkzVM7KYhK0v5QSWHuHkCqRRzywN/A4HWNhCjYnXZY+1HlumHnbQmiRgrTuxlTzqzOvOoiSWdyjWKkq52vd3+J91X/kMr/hm4eOPbDuCPbb3tAH5Nl55bcbfmXjQ7VSIU/VGkOFFggKVFRYU6zdBOGFLl/QY9FNBvvStZq+lUWGbZs5zhH2XEviN7momGK0BxIpua60f8Xo2dWNzAQ6Nh0LLcjySXltW+p9qTfEubOPJTTdhu6G2uyJ7l8nB1WjJ7TzLoTBymVMRGUhPM+3PpKqwUodXo1DP80gqWYir6rnQimIjewVzMK06RXBnSqXQg06mS+Lulwpbi4Ks3PP1DQ2wMGCjOOy3cA9HqhUTi1+xERpzqKkE3ItyffiAbUlWmnL8eS5xe2lUZ7y/IzozExR2lhDwQMtls00nnpvzeGulE0fsio3gthoCQRMbfS24wKyBdxvxCi3HGgu9JZ9asRMyuWoHfhjRK5/HKEruX4fPI8k4Uo8etijBymIcLGUc6MWks3mmptgP9tZLl6yHpTIn7OyiyiYTXZ88y8pepdHKdFPwoSM4txZElu5axdjuQTtTgk1SME0jXRLUKbCSstSSXlmS9kuirgRg6fNYZRq40ybWhWe8itRykn/QwTwuSpSBi0lNLtSeSgJBLYdlc5qQHS5pRpK0sy/caSL7SMcaBk25cFKdusMA0IbvSFKeSl1vti9EkKoEY+7RzeSpo9hzLb1SM3ruiXpOEGzeI1Lcc1W1BvShhI9MOu0Ks0iQXlvxccmcBMU9UBuVkZJmfWFYPWmIS5T31ivTBXF7341I694MGf9STzX5h4SKj2oyk10bSTYaBZlf0kgDbG3PRlYE4YYtAe7cmOUuk4+ckazu7VgQjOBJTKszSkFwZ6ZwWkWZdupztnYbhwYzVLBdjyJUVs8lYmJA+E2ZfDPI7xf1KtJ1ZJN8s8VdyaFER/FGP5SwXGYkVrIktofdaE85zbCdxMEe5yBLOUnpPUkG9lCIB6R9HMTmttBhZlgkX10PG60tB8KwL+kC62uKcp9WYhebWvxQyARHWnsDo068ysts1MarVm57hMyNyklaRnwo9wJSKOEmpNwOrB5KTbBdiCDG1sBTtSqGyrrgD8Iq4EoOWWQrCqBkHitdyeAPJgVaXKXplmD0OpBfS/bUTg19z2MuE7FR6Nuosk4zoDhMUdcT1AsOPf9Ig6D+DEfBbF/CPbb0tAL+m6w36JDlOWUx69NfKG0fm/JEnudYMvzCiC1TQO4bYd9JNm6T0DuUErfst9YOK3gtLdgWuB7bnZJPs4uLqjUiz7Sg+mKAuxGE6eG4xS0P6IoMHwvArjg1mrukdSydxdc+RPctYnfZpHpfQaJIXWfd6NPm56R5sEb0wIoT+/wwlxqlRsoGeZIS/OKXZ8jdj2PqgoXdoCX+wJikhSlhdgxeyGSmnOkZbZLUrY69krqk2JYs4OzMsDgL6tWRtRitJGpjI/s8eY5eiX0wniv4PcjFnlGJqyY4tPo3Ee6Wgcr43pL7d0r67orlXi0v3Tkm9I+Jw1xdmWfWdFat3asrdQP9VxPciiwMpBvovrMSQ3XM031hBqxg9kcK7vOXFvd2LpNeaxb0gI7cPS9FmAiEL+J4gdFSA/JMCc21RHwtXbnW/FQzLq4TsUrOc56hGsXinxY2le7W64wl3SxlxfjSnXfeEoZNu48qw+Z0zqj0viRyq06BeGtnUj6VIVI0wGHEKNROpgT/uEXfrbqxdSzGxXVE/qkinCn+nwn4g0O/yYU2wkkbjU/meg70FaLALiR9Lrw3ti77kSpuIubKYK+HmhZ0GPbXC/1sqZj9fiQNzpVkdOJZ/boUrpMAIO7WM8wuJw4ta8pz13GCOM65PRmIISCLNrhSzJIHmUUkwkfxCouEwUozbhRLjyMSy/QdgXub0Di1quyb2PdVBg+k7xh9b2i2Hyr04qZ0ivs5pxx53lePX3Q3iSe1UnRteUDZ+ENArQ34mBdHpcxk/2plh8/ct6aVBn2aS17zV4m4Lz7PciYTdGjfyNJ1pQXU4ObvQ2A7+HINi8XwNfZEQH8k9bUtFu9WKa/ndJa4H2ZHwHPWLHLPUMs7+3SF2Jhrk+KEwAfs/yOV+XBrSK+lkVdtd934o0pJ2R7KLk0dzqo9KSbXZXYGGi58P7N67kkSRlaBhOM64PhsSbaT/TPK+m9stydRg5xq9koPkyX8lmtl0qrj6CKotmXDoRhF365tUpLYPxYsEBdRbgfxCkywVdrsUB/HEUm3L+9B/pWiHcqjwWWT0e1K8xlwMJqrnKE4kq1m3IjmpPiqJfY+eJOi0izGcynvWbHqyK0N+oSRGcCgw6eJENI7Rxk7XLN3X4thQ3n47A367/uPX2wLwa7p8L4iGRYlrbXE8IAwlAcBuVTQbnuW+jFijAVco8sNUTt0rjc9lEwilhXlCSBCxeQLhLEdlgdV+INsocZstKgvMToY33Q+f0omboV2lXXSTFCGzdzyxlC5Dteckx9J6yMV8EY1kwBKEeRfTSO+1aKKiFndyMw6S9jDTVM+G9A4tqw6fkpzI640W6nGXVRsVk/cE09K8UxJ2xQXoc1B9R7MnsWPZqbj10omWsV4Xh1ftBuxmxeurkcBfR4L/CFYwLe1QjAd2pSQTtrLYa0u9FdALiz6ULlizFvCXGdlGSXnb0655yjsOV1moDbHvqDcUoe9votaqbUkeMKMGN01RrWb6vgjUdS3cPrVZyybdRYyZp7l0Oq8T7NxQHEvHTjeQX8hIt10LhP1KoOHXBldE2lEQxl2pJGYu96QXRoqH54XE3bVG2HYL6UbEwnP2ybYYTf7cCt3IfwsWqi24/ijiBoFYCMoijlpYb2i3HXG9JSwTVGVEq2UjvrQkRxnNKBJazepKNH/UBj8MlHdbTKPEXVpb6ocd2qcQN7gfBInjmqQkMxnR2YuEuLKkM40bS7xeciQj+JBF4amd56goh6dYG0lyKCSiLCTSxdZOfm7/aYKtJEc2e53gBl2qzcuc5k7D7HF3I0bp1KogTD839MzuadxQHLDppwXZy0SMHSvL9D3P8JMEZQKjW3NxLCeCTcnODYONFeVOQDUadVgQN1pMI8giXUmB5XrSUSuOLdmFRjuY/aXVTcxZKKRzFkvRViogfZ7Rf2ExK02722I+EGZgNpHr2w2kUNO1HJ7aq5ww8LheID9KBWZ8nXe6W3EW+0JQOIv7nmo7YhpFMlOoj+UQV94KZNdd7i0yVrZzQTqhJTFHLQ1uzVO/HBCvUtAQPx8QComuPD3cwC407XrAbTf0jzSqlo5qsya55jjpTtqVjFPfsCXzo1TSZbw8B7BBcFBW8D3pVEu3P8iBQzfSoS5vOdplSn3QfPW8zSPNGNw7K3Qr5iAVO0f/lWXju9LdWz1qSHdWhCKi7y0JiwS1sMT1FvMqx841phINqW40Po34QgDXdmqo3qlvIO2ugOUd0RyrCOV7NT77ySaBvHUB/3Stt+/013TpWjF733WnR42uNKrqRgbnOYPn4vLMrqSb5TN50IW+vxmZuH4gO05ILw2Dl9KBqjeDmEdazcY7VySJJzlNKIYVupQYr+yy02N13TYajX6dY1diWoh9j1kY3EjC07d/2xJe9lBauoSMGwbvXcums11jutF11FCPYXG3y7p9OCfaiLpVCRMuSpfO92QUU91tSJaK8Y8s6YV0iOxCE69T1EVKMhXtYHKYSTETO8TMz11T3XaMH11R7UlMXbQRm3j8SY9kpvCpaMpcL4KJ6NslKkjmbXnLYa5lHGpXIhT3dypYSgawKTVai/5K11r+TISZlpwn+BQZHyWRmHUaLRUxTwuwUbol6w3pRAwAYehlI4lgLhPsSuEGkWpTuk9+EEiW0iVyg8jVz3qySymeo5dIqWSu8LsNREX+OhE3ZYDYSKyczyNuFDALQzgVkLFupFOjZ1aciLstbpmINrKWArreFrORXWhoFYPnGp0EbOpQmcdmjr17l+itWiIDbUTPLNmVsAzNZYJKRQeXXBvSC4OZSje6WQu4KmH9f8wkUeNSPk/B/CToTrdnys5FeSyfsV6Ko7jZcVIsjZxAznOPqWQkLggcKZTfxNz5XHKQfS7vbfWLC1AIUHqhaUdS9NizFDfylAeikVv7XFHtBEH0ZIHyQPKvUYI88RksjkbopYyEFw88oTEYHQRLAgyfGqq7DUXaovcqGZf3A8mrlHojkl11bmREh4cW48rqwAljciqdvzcxeKonCKGQSnfKPy7lWj4osZcJ1eu+YFHKiLLiUlWtvI+m6kDuF4KSat8taQ9qkitDfbul3pT4wfRaY6aGWIgBzeWS62tXUtD4oSfqyOgLK2kZyGFU3V2KVjLz2JUmmYhcY/BCJB7tunQ6TQ3JlcTFxTTQ+zyTGMueE/7hwyVuw5GdyPVJVxv1jgzFKzGsxJ+d4cYeP/QMPhMjWzjq3zj9BdYtBR33V9IZ1XKg0BOLbsQolE4UzVogXGaSAT6OLPfFFJafKabvSgGJAnfYxyw07rjH4MvOBbwy9A8V7W4jetcI2bnghJr3S/qPp7JbTxMWD7+6fmIqhws3DLCw0m38Ca63I+CfrvW2APyaruETKRoWDzy915rhMwHO9o80vVemA65qqu1uY6Ib+wRxku7++RMYt9S3Wtzjkou/2AF4t2rWv2cwU8PF4Viim/Za6johnWguf8GxeOQp78joxvXktB2NFEvNpmw42bVi+ET0bhd/qcZvihu4HUX6P8ypf39DOHPPc7a+F8kmgjpA0TnuPN7Lptv/Nz36L6wYO5aa/qG+GU2Wd1omHzpxJXY8PwBzq8T1A7qVouhNVJ5qFfOTIapwTL7YIDszVDueZKox/3aEbhTNRug6A0rSAM5T3KWkLtjNUkZRdJ07Ayr3hHki3cdfmOP7gfKquGHQhTTATs1gaymcw/0WfZbesO9sqUjWappNT/5KBObKSPEQ0oi9lOLR9wXBE7XoHtV+SbJUbHxXM78XaXda0qlGrzSrRw3KQXKUYieG+hslsTSETDAq9a6n3XaoStP2ZJNLJlqyUDcbqncrkqUw0wTMC6oWvVS719LfW4qrsQPvEsHODe0Q1ElGmnqYJrSrhNOnW/hZgs48+YmhONXiTC8V2YM56iqlut/Q7jU0Xd5zNFLQ2pOUq+90ndS7raRhKPC7DXG3ljFyEVk8kCizN6zFYCP5q0Q2difFQXJpqfekKBx9oRk9EXZeuyXXj+mi93rH8t42s6zLX31z/SjCwAkyadCie46QR65+UTpFi7uRpNeiBy3ZQMbZw6fS2Rbzj4z1dS1mB/8vN8nPpOCafbOhGFWcv1jHftqj6Bh+7UCK1PJAWJ3ppUbdXdI7lJEhhccPPHv3LmnXxfVvaiW6xyc5xWtLu+7hqKDZkO70myLH9wPLO5A9y+i9kms6JFAfNBT7C4HGZwF9mNP7YQ4PVgJ/DnLf61aKPBSEA+nS2pUSych6ID210lHbjSzeb6ju1dS7Hp73RcfoNKqFtc+RdJrNSLvbQN+RTjXNXivJHWue5NKigmgy1Sxh7YeW8KKPXhiatYh/UFE+bLBzTb0pnWLdgv9kSPHSQhrwqTiR7UowQM3jknJbumvtIOIuc+qtQHZqUfeWhIG/0dCaCtJreX64kYf70nE1C/l5fuy+0i/aDtC/VbN4JFF6dmGYfiQGlfxUDur1dqDZdmQ/LFg+WWPwXGG2KymOGznwqFoKf12K6zo9e+vrfLv+49fbAvBrula3JHzerDSrO4HpR647gcu4o910uEEHUP1zNfVmkBzVtos/SxoRLQdFuExJzhM23rkiOMVqTwqp9ErwB8oG/EpYZnppoO9E9Kwjfqvjc5VK9HgelI2U+57Zu05Gp06THaUSSQU0Qxn/+n6g2W1Z3tKUu5H8pGPMmUh2bmkP+wLG7cvvnB53kWh/cUr5bk12KeaLZKMSfl+QDmB2aWinmZhFHpdEK87jkHZuwn6LtpF0psmulABq71esbgX6LwWXo1sZjcWelySBmQB33WkPtyEFg13IgzyWBuWFg+efDcCJpqc4TFCNpthZYY5yli+HNBuB4mWCX3eoNUlZqNcD7TRj698aesddtN3nhfAEz6RDl54J5zH9zjXNmnRA9bOCth+5+rYUitSaetPLazkWhpkvpGDShwJlzk+NfA6Zp/c0IT8zoKHZEFhtemZJn+UkRxn1hieZGUEMOWHBJb0GGs3yvIddKta+l0paxkg2VteLhJ2GqhQjiblKJIt5alCnGckSkjkUryzZpaKc5yRzRVK0qIUVWUIq3RWAduQ7kb6meJ4Il7JWrP1+RpylLB47kqlEwbmevHd2Jcy7esNL5vXckKzVuIEYSYoTzXI/Um6Lvk+VmmrXk04Vw705s3cd/jpD1Rr/oKLdb2g+KCnviwOzeGXgPCP/UUF+bBh8IteaH3raaUbxcUHwMgZe3ZZiKA4dvied2pBwE6VY7YhZw+SO+mggZpDua1SAmEn6TnJhGT6XGDye9qXTNjWkr1LSC8vJq3ViGiSOUNHdR13h2Ig72CxkhBysPAf6R4aQIjKNKCPa6oMSPbFUK7lfey8kEajcDfjT4gahohvRPG7cnmILh/2yoH9oJMN320kn2Emnx+cRNbdkh5l0J71AydMzixtGrv+3FSHrECm1oRhWVAcN2etECsyIFKNaYvQA6b7lnWZvLs+wNyu9lkPf4AWgoLzXohaGak80duHdJa41hEUiXNGuaM73lsRxS73jaFcpKveouyJ5mL7vad4tiVkkmRnaRSp6Sida4+xVik8h5r5LJUGmIlNDTEXqMXhq0OsN7VAOKtHItbm6I9nCq1uR5Ic9QiH5xeX7FTENtGNJyQnbzY2R5Se13mYB/3SttwXg13VFiAn43VoKn5kh7FdUe47+q8jg80TyZAMMxiV+TU6zMRG93JPfv0t2KsJxu9KEuyUXh2O0FXB0/1CyZUkCzBLsRUI6UwxeaOyxxDGZrRpzJUWOXYlDtTg2mJOUmASyM3Eqm2tLcSbJHtFK7FS57ySVIpGIKr/TUO47OeHfrog2dhozcYnW6xH/oIQIq4sezCzNIynuwqseXKfUu55mXdiFqtY0H5TEIFm/OAHB6pWGiwz9IqcdBNqBbJKcZBLrZaDdbYgJpCcJyZkUUgDNbgvjBntlaYeiSUumMn5Pr0Sr53uiKUuvBSRMgOpETAu6VhSvDM2apAEY6xk+1+TnoiOaPYSrP9dClPGzaoUN94bh5seOxdM1ccQCzW3hI0YrvDDSQP/QMP6sY/utSzGouo6sGTdSEBogSFatcl1CwahFPVzSrHvcINA7loSIqCDbKnH79Y3e004kqzmbiJ6r/1JTvDJdnivY1xl+1ekHbcSNPMF2bk2DuIiHUQrZUxnL6c/7JDvlTQKNqcWRa8YNgy8STC2dPt1oQhGZvueJueBhygcNuhTtnorQO1GMf6SxS02z4YVdeSWCfV1pidq7FsOK7QlrLppI/a0Vi1mBGjgYtCRTTWg19nVK8BJVZi8EYSNYjihO0T3JPE6ujETIbURCVOhNec90Ld2u9MqwuCPjzPTMSnTgyBMM2C8L0bBpMDU32t3ilaSRuNsN03fFgdrutrTjcINmadY9ZiLvt10B9yVFJZ0KvzC70oRHJSGLDJ5KZvHghcYnUmyjI8sHjvs/95LQGPJzjTrJsRNDyORa0q6TBPQioZDnimkUV2cjOCwwlXTnROZgWdyTyYPtxtbJTHXAaCnYzMQK7y8N+Gkih4CFwgxb6irF5HIfHzw4F6PNXEuRqOR7ubHICVwhSUastajK3Bx8AZb/7QLeWbK+O8NUIk3x/UDygz5hZSELAqhOoLzjqKtEYi4PLflzwWF5p+W6zoRhGHUXx6bF/OGGQdikSg5l+VqN7RBVfq+Wgs3JCDlY5Lmz0wrWp9L4DSc81krR7DkxWs3FuZ09yyEJJBsVbs2THKU3KSY/qfV2BPzTtd4WgF/T5QuJOVJXKdmVwGezz0RDdvXtQL0lbg1TKhYTwSGsbkdBahQyKgJ5mOqHCxHmzwz6KKe8Jdmk9WYUjlyHOlAB5t+uJXtVgXpeYFby4PY5TD9wZJOIrb5KP7ATQ3ahmfxMi51awlZL9c0VycyQTLWgZgBzkt5sdv4iw9+vyPcX+A3Z7Nx2i5+moKD3IiEWwtSyS8Xa55LcoNYaYhoZPhE9oDrKia1mte8x25U4CUtF/6U81P2ap3qnQmUBP3aCtonQ+zyTbhGw8QMxmSTvzTATCXoffy4mEr9bU+9I9ySk4pjOTwzZlZgxfAZmq4ahdI6SuWZ1T0aVxf05wRvm36mo9jztuqddlw6V68umFoaO4VON70nOcHKekF/IGEq9u4Ba8CD564T81GJ6jmorcvXNQP4ykQzgUrqZ5W5gY21JOwokEyk45++3lO9XgtNYJPiXPZKZIeSRakuSM7QTTZM9zshPBcydTSQJY7kfqMeRZiQmhexSdGs+lSLALpSMbK0ko4RExO+uJ+PsaBA0Sx6ptx3NdY5updBrNqVI8qWlGXXw31bRe6mJKgogOUKxv6B4lhIK+fnNpmf2iyXX35axMYBdSiFVbK9Ir7SgipDCLPt+D73SDG/NCV6RFS3Mku6+UqQvRPMVV5bybouKUO167FzR3K8p7s0JYwEPu0EQ+PfYEU9y0k96Ysq5VKSjmmZHNLv5icX1IuOPLfmJlTzjZcf5ezxj8s2WesvTe6V5/N88vcnZdmtO7slnKWapaW63hJ5cp9GKmzUkkP7RAIB6PVLeFtxJOMvpP5iyPPBU24HVXqS84/g//vJvgxdt7bM/2ic9SYg/N4PbFSiotzy653BrDrfVyqGwS7XoHyqSU8ljbtYknlDVmnbLkcw1rifXUHYpjtf2bn2TOhLSSHKSiJyiEF1tMw6knxaEq5TgFKrRvPx8Bz2z+P0KXcvv2Wx50T3XmmZbNMqxtJiFFlNIlFF7/GSAUpHVdzfE8PVcnjHyhkFy2sktPCTXQhywc83qcSPpIK3gYHh/wejThPSlFGA+iyRnCWZiyY+NxDp2B634yYBqJ6CCpNq4DUf/hZjF6m+t8L1IciE56slcdVBruW9QUmjm54rluw26kVhC3xr0sBVd662fLAfw7frpWm8LwK/p0k4eYMlcUd5tIQjMmKC68SygOwzJLEG14lBs1gN2rum91rQPS5b3HfUkB69Q0nySgPPdVvJJ7zeid3LiEo6tlozaDvnR3Gm6nFh5XT5VVPst7bbDbbe4DQmWl0g3hX2dknzao13zkm07F2dtyCLNOOCHgkZQRznlJAenSLZLlJUkifRaUe557KUlOs3yUUv7VyaUu1FO9TqyOAg3bD0aLcXiq4JopFuyui0jObzwB9WVdPr0sGVxL7C654h9R7Cw2pNbqH46ksI3wHJfUe+3JEcytlVeYR/PSdak47N6JJow04D9rCcd1KV08wjSFStfDPGlIbZaYtXmhpgEkiuLGwTS0wR7KSNvO1fEnsP1ZBwI4I765MfCaavuSmfPT1NJ8Dg2tO+VpBNFdtl1ko41sz/Ykli6LgrMTKwUOscWVSuyS01+JuNu0R9CcSKfjSkVqwMpdl0hOI/YF31TfiXZu+XPSNqCXUnmbEwAD/0niYwQs0j1Tk0yF+OS8rC47yVZ5sRiJ4Z27Gnu19B3tKMgLtFBvPm9owVdaSlaW031Ykj1Tt0dcIK8j1cpJIHh7TnYSLUdKe+2lFcFzTiQHwssXUWEmzhyzE+G5J8UtI3FLjT9lwIWVp0GcvClAL5NqYhZoN2QHN/lWZ/+uGRwdyaj9uOcwedi1HnjLF2819DMU9FbjuQ6N6Wi3InU6wHzC9e0w0i161id9yGRqLV6M/Lxi9v4fkC1GtW5t5txQN1bsbE9I2bSMVr/geoizTrNWqlo79boUUt+KhzFxayAoWP0RGL1zMLw//hXv4SuhVvnB3J/tU+H+HmCG3oxKZ1kKCe6tHpLoNTuvRXNmnRBVwcON/as5gL8fgNgb9YlEcenXefVa0bvXkvnfKZpd1r87Ro1Eye7XUq0YTLV9D/OQUs6i24UnGes3pXPuXdkxSVeKeyowQ0C+bGYstzAd45smRz4pwNB/OSBesdTPqpFF31mxVW+5jsIu6CN2g1xJpddHrlZapqTHlHLAU83+ibDOr8UoLTcE3KP6EbBdk2z7lGt8DOXj1riRoP9tHcjYSjONPW2x14k1LtOJjGXCXhJdNFTS/l+JQfZkxzOMpqxuLt/kuttB/Cna70tAL+mK7tUDA6h3nXk6xW60jeuOVN37s2FkdFLGiRrdSnAZxHLw/D3CuzUoCpN71mC9orBoYzSCCLwNteWOE1pdhx2hcRLrQuOIbtS0GrpXq1Jruz8kSc5tyTnFroc1uxS6ParRw35hxPaobDvTK2Y/UJJ22nqQF6/XYnDODlLGHyZ0JaJZNpeWVb3HIO7M9yaF2dtUMwv+5JW0HOoVtN7MJPxVR5RPUc6qgm7NfXdGrM0+CKgbCA/kaD70Pe02y3xKhMHZKtIX6b03p8wf+SIezV+o6VZF76X60VJAtnyXH0rCh/s6ZB2nhIy6ZpW24H+60C96bn73qkYKXoSJL9+ZyoFutMMN5eohaBpVCaMP11r2nEQPM2m4FyywwyzEsh1yLt80LGMoc1EYNG61tRbgfKWJ1yn1O+XVFvQjiVirB1G1Lih3hXnqN9ob4TuAOWeAIFNpW66E+1ANqxqz5FMxSjQjgLxXimw272W2cOA32uwz3PyE9GBFecCd9a1ZnUrwFYtY2qg2gmEIggH7VLyiavbDrcp/AvzOiP/UhAu6UQTxy2oKOaVDSkE0xPbQY415jRl+KQDiddgVpKkUH4+loSHsUflHl046dZEOeS4fkR7RfZKCsZ2GEm+KIhKumfxozlVZxop9wLpVFHddpiOOefHjsFTS1Nblou8KxzlYJTMxUDUDiP5YSoHM6Dec9KBN128HzC/6kue8bjpTmDSqVYtKC3jeYJ065stYVq21xnzH25KwVUp2pGiHUbKW4HVz61oN7yMEM8zyn0n9343lp98GHB5JCZRgNtTDVZ0uYvHjrhfdUkWHSrIfbVh27kBHbGf9ihviQZO1+LkjUuLnQuMPVnKuJbcS4712JEdpVyfjKi3/FfmMSc/Oz9XMhLvkDrLj2phO2ZBwOhFgNpQ3WmpPyqxS4UbdMilS9GpNuPI6HP5Had/scL3wo10ABuE53kmkoP+t65IrzX5cRevpiLDLw26cNJdziTHOmSR/sFcDgwdLFp5SY+pvrmi2ows7znpil/ELp5TzF3VLY8bBlTuyZ7kmEbcxu3DkuXjRqYdeWR8ayYcUgQrY5qukLxOCakUr9EAt6uffBbw2wLwp2q9LQC/pmt51zO/B5hI/bqPH7kbB206UTLSCOCGEqHk+nLqzk7szWY0fxgYHIqAfnXXyWn+duw6XaJR0U6RbJUQRPSdzjTqLGP+YSNg5dJQvLSic0Lgp3T6JbMSF9vyrqBnVGWoPhmjomQEl/cb9Kuc3osEu5CHXsgi1UbELhT5paLcDdi8cwP2A/lrS/iddTFEdI5XvMKWSOfHQ+//ucbwuXRB1FVKM8mIK8vDg3MGR4r02mBOMjFwJJHs1JIfpQxeSErJm7SO+eEIU2qS1GFyfwOXzt6f0qx7KVZvrZh9INDs4oWYFMr7Deb2itk9zfDujBdHW3J6Dwq9WzH9cp3sWpOeG+YX4oi0pRJ24rev8estZq5p17qYqkrjBqEb5UbsXDa8qGFwKK7SN9BiuxSQrJ1rzMuc8O5SOGl9EdGHyqBLLSifWtAtbzTZulVMfsYRjSSjtO+V1JtiLNj4rjh93ZqI19Xzgv6RQc+saBIXVrA+Xnhms4civme/JD/TxGkKeWDw/YzsQrPzO0pGY9ses1ELi/DEohqFvyVdmvxEEiHeRG+hReCvArQbskGXD2v8IDD7sCW9Mjf6L/0qRzkYPZHrGyXpCm8i/gZHimAj1Y4YlZLTBH+n+gqi3I80x33y1xbfC/hBYPFeQ/HSMnwuj9X0WJA47SIV+cX9FT4Tdtvqw0p0a0UQk4UWsK+9sqL9ulWj+k7QPrmT7nVnGsApXC7Fafp5QfZljq67yEEP9Y7HlJp204FT1NsdsDt22sFTATS7Qq5vlJhHslMLM4saNzz684eEgaPeDuga8tcGU8r96mcJ/bGAkFWQmLfkWmPmBrfdCi+whexCUDqmVrRr0omMBvhgTrkbKc4UaimZ1GphaLY8ZmpJrw1h3JIfptjcMT6YsPhIunvF+xOiBn2RkJ5IpBqAWRjB/1xZ9GGOqcWVa0otB5JUUEzzRx4VFMmzXIpaxLBCIxpdty4czcnzMc1awFSiR/XrTg6RxznJQjH8YUrse0IRcN8d02x4kpmWwvV2JRKQSYr2MHjaIaY2uvtwZUnuLCVdaW9145pe7Xt8AaGy4DQhl/u1bsUt7TZbkjtL2pFgdWLu0Q8X6EZkHPpFLvft2/V2/UeutwXg13SptYb0nRk4QbeYmWSEhjSifn6K60dBomzWqNMM5WRU2NyriXvVzQl8cVe+Jr2SDb7Za0W3cyIPtWAQbdZKE3dr6vs16ZUWNlyp6B1remcy1qq3ZbRlV/KQihYGL4RHZ0sl3LZUOkOq1qhETtixcxaixB0YUnGvzt9v0XdW8Kx/42at7jasDjy6kY5BGDt6zxLqHU9+btCN4vwXAuWOdDKLE03+OkGvNE+f7bK8LY5gn0lknllq0ZtN5DW4fqD8oEK3wLiFoKgvCtRR3nHnwP/+GOUVxbnC/GCAqjUhlSQB04gDt12mlAeO8tMxamFxRSR/bXHTVCK7hoGQgL1KCOOWdhDARlZfrkEjBYIvAsWRlaLHCYrFrTsR1qey6TVrkd6xotrxN3qiYEUf6kYB70R0b2eGtc81ZmJlIysETZFeG7i/lJFlP5CeS4fBjTzqZS4mAQWr/2ZBu+XQ67WM1O+XmEoKfV1pYi7Mt2YcpVCbiVYsXGWdOUSYf4t70rW8fr87UFxpXGlFQ7kj2jF9nhJzT70tRZ5dShSeXShWjxspZrqOtrlMRId1aan3WuxCMXwuY9B23TN9J1K8MiQvpKMYjSCH3hx0iteWZC46y/TLgpDSJZ0EAVprKTSSa4OeW8p9x+yhXOexa8aYiaUZB9qZAJOTqUZfiF4smWhUUMKVqwTaTIDikxx9Js7V5PMexK8AySRSVOla/l7dbzAHK8xCsDW6VORnmrUfJKQXlt7ukuWdcIPAASBIpzPaiCokgeKNTEOZyOdHu6hKRsPVbqDeCnK9nYipqXo2JBiZNPQPJa/5zWcoHbdA/biid28m+Jw0oO4tJRru6YB2GFh80IgDfz3IdeYRbmmt6H+ayfv3smD5/Q3hQQbF4tWo00pKAauWMhrNL0Vnp704gut10cVGE/EDj+47OMtujCqmVPSeJhRnHXswCr5FpaFLQ1GEoaQc6VrMaPV6xK07ws/MqX9hQfo6YWN/QnVLOtOuiDT7LVqJLtjOhDe5eOxY3fViajpYgY60jXQi29YIv88IoaAdBJJLcfTrUj7z6nVfHOxzS7NKpUC2yKj9qC8TiWt5hvrsz34v+ZOstx3An671tgD8mq6wTFhNC9ILQ7hM5eRpI3G3xn93TfQzjUJpJDkhjeA0/ZHsAiGLhKGMAuN1KpoVLwWJ9p1rr8tI1f2WZK5JnxSs/V5GvdXx71pxHaazSL6/IKQSc2ZKuPXtE9isqbYjulU0I+miKK9IZ6KjSnLH+FMZXbm+FBpUhrDeUpxp0lOL+dGAdrul3W2kU3aZSGTcpRRvOEVIYfSZbFJ2qVGj5iYjtRnLRmEqGWuPv3GJahR6q6bekCLAzCUZZXEgG6h9mUl0FGDuLgVDEqXD5jalKMiuNLNvNCRLyC6NsMG2aikE1gPKRMhktJxda7LrLnWg7yD3sFeTPJ4TbEStZIMonouI384N2ZOc7NJIR3RHunc+D+jckV2Ke1V5KYKn32rE5Wgj5WNxHtoLwa+EZSKmiyJQ7ojOyf7MlGY9EBLJT20780Xxykr0XxetlU6EnZhOoKllXO5rQ+9Zgr/OmH4gLL9oxZigvBSRysPyYYsqDXYm3cvipZWieq0VeHUhRU695xj/YSou4YUhvVY3mjdTSrKDW+sKLg32PIHtmuxKrtGQyrWTvDNDOcHytAP52v7uEjSU79a4+xV6fwUKmp9b0I49PgX/MwuilvGeK8SxrHQkmRqywwxdy6i23W3FRDQVE1W9KcadaBA8R4T1WzNClz7jh552U3SMZiWFn3YK+8GMMPTYSjqu2Z2FxMQttWREpxF7lhCSSHnLUz+syI5SmmmGW5duZX6hWd5zVJsyym6+GBHTSP+lSD7sStF/qRg+BdKATgJ63LB6p5Gu1nVGrAzZmaF4ZYnjVly94wbtOx1hI/q2dhSptgRFksw02cMZPutMPLVhOS0kpvB1gnoi2CZTyzVhcgcBBvsz6l2H3mxY3Q5UtxyuB/V+w/CpFIWqy9DNzg3JhaXcCcQsordq3FBwProWGLlqFe2GFGVinoB4ncooO3Ljws2uxaBk7i8kdzeJJC9TiY1bKuEUduP4rc05bkeQOfGzAa4RV/vie5ugI2bUks7k+4eXPZSXazOdQP7aEnsyQUkSD0mUDnSt8bNUCAEdasb35L5Lphq/JnF4b6D6cbNBGXk9IY0kE4MfiNs5WklHcv2frA048qdHwfyEjcz/Ra23BeDXdCVTgzJBxh9WOiy61OiXuRSDRrIwfWWwU4Nfd+h+y+J0IKHkrYw5dKMEIH3YhaUHWDx0oklr5NScPC0k9eFhxfRDYWnFwlO9V1GP4fp9TX00oHhtmb4X6Z1Frn/rlojqx55qp/t+TuHGTly5UwNf9KnX5TTYjgPByO9lz1IWH9RSnNqISgKUhrUfSqGVzESfZh8syF8lNOsBlwvSxPUDYZGw9dE5rhCUjF5p2rHHTi2TH25Kdu7K0tyvqe426AdLym+UhPVu1NyLxFsV5nVGM8noba26rmRg+IlkIVfbHnuZMH/s0d+ckp9YuMgImWxUzC3Fk4z13++chiJjwxxn6ElC9nGB+d2RRF/pCNu1PNzXG+lq9OJNhy3kgvzoHxq4zISBdpndpBnka7U4E68MVIZ2JONin4su842+0g0CsedxTrqygxfyeLBz4T02awE7MfS/SGnHnnJfCud6IxIvM2ImxerqYSNO4pm4reNAzEX/9S99DIMWX0T6TztmXzdijp0hKXuWsTpw6H1JpkiuLLqNpBNNem9BtRMZfmlhIG5pVWsZCxdREksGAWOFe9l8tMKuNO2Go/18hJlr9H5J+Z1SOii/u0ZxrIm1Jqws6R8NpEP6rC+i+0rRTMS44NccbqclmRjyLzLyM0X/Zy9YPWiFt3csn6N23TW6FBeq60eSQrrWk6OxFD4NJBcW3fEh3XZDs+VRd5fE767Re5bgJDmQprHomSU/F1e5qQRx0u62qKAY/pHk6aanFtVqsrsLohLskxuIKcjvNeiVls8pkXt49jMNV78g49rQaszTHFpF78uU/jNDdpKII/znrmEuxpe9jRlRcQN0tgthYvoN0cXVuw79u2uYSlEcWZILizlL8X059DTr0lELaSTZqOTnrjTlZ2PSS0P2cUH/pSY7E4xS9iql3FXMH4Sb8bVuBMuSLBS9Q4knxMYbLiKNJm40kqrSCoj5TcZvvRGwd5e0Q/n6xV1YPWhpjvs0DyrSM0mgsUsxQaVT0WvW247UeIonKfmxEQ7o67wryLvsaCc62OxlSrJUNwWp/6+mtO+V0Gp6x8LHxCv8Xk12Ji3i7FKh91cycl6JzKS606JS0TzapRbt73lKaA2qhfxC02636NKIDta8Ae7/GDaX/8zW/fv3UUr9e3/+7t/9u/+zX/9P/sk/+fe+Ns/zH/Or/s9jvcWGf01Xu+Gw2ooGZ9TgC4M9Tzv3aweDXRnSa019y2EmlrAtnZj6vRK/Dss9j9IQn+RdJJVite/BhpsOmK5EYxPygLoWvh8K0nNLeyAC7+JEkZ9rVrek4Jo9UIQPF4TnfQZPLIt3W6KWcZgvFH6jxV6JBm/5Xk3xNCPkAogNBrIrhRsaeq81zVpEXaUkC0W5F8k2S+yXQ5o1aM565E4KqNWBk5P30jB4ZjlrtuhdKcpbgTB0sknUoonsHwpaxB840mFNc94T1MvEkJ8rYQ9ep7ixp9gsab4Y4bdbcekBzUj0lM3tFmUC5asBrH8VAO/60uks77ZUtwTbkl1qdN0x3ozojtzthvQwpd6CMEuI/Yg+E7DyG2MGILmwXpy3ykknMplq4qMVedEwPxuggxR4g2eWclfMB+iIXUJ54LBXVt7DMTRVQu5gtSeoIDf0+F2HTgLxOCdqJS5vI3gb3ShivyU5F+RHOtVMPhTeoW2UOLSj4r/74fv0vkxp1iM+l85oO5BNT0VxB+sW8hNLlQdYc9hXCYu7gIq0r/toHVk8CKjrhPRaU+14eq8Ni3da2nXRCqinPUweCXVO6EtubnGqaMZQTTOwgcQLhqd+WGNPUtKZXNu6VYQkQinjuHRcEyZWOj8EXC/get37Pu0z+CIRzeFAtHcx96gkoM+ls5NOYUGP4rRD/BTSce8/s6xGgZh7cdg3kq/bHrSdS7Zz6h/nRAvJL13B4RquJ9eOXtoO2s4NdokA7sshtnuqhzxilhq/tAyOFIt7QVJGVoq207/FgWOwVrKoDHpp8B2G581YfnY8xGzW1D3L8Y92CO806NxTveMEY1JEspeJFM5LRehiE11PDjTptWI1lHunXffYd+c0zweE2mJTj7m3xPz+kOU7Utn5HEImCT1vOol2LhzRkEqakL6zwh/2JF1oEMCLNpgA2bml8fJ+DJ4ZVrciw88ti4diqAlfDvDrHtWKbEAXjlhpOcxteEJi0LU4elWrsJsV/rjg9Rfb5N1BxXeYojd4FtNhnEKrqHdd5+TVtGue9qJHdpygU8Fm2Yk8k0PR6fZWmtk3G7jOMV66wK4X0T0ZWaug0BXUG15yvldyn7p+vEmwaT4oSb4oCHkguP88ouD+tN/jT7J+7/d+D+/9zd8//vhj/vJf/sv89b/+1//YfzMajfjss89u/q7Uf5lj57cdwK/pSq4t+jgHG/CzFDVLRCCsIexX0qWYa9GqHSb0XmuUFi6YPs7pPUlRpxl+3mXTOjlhp9fdJaPFRJHMpHhRrXT99EgMD66I7G5PcT1xFL+Jy4ppINhIs0rQjYyQAGIWabY8ft1hz1Lq24KIMZmn2ha3X7QRv95SbQfRPm1LkoldiQC+HYm2xlRd0oESx6BZaVTfoefiBF1+KLmzq48q8lPRvfU2VqTz7ncJ3IycQxBtIiYyfN6lqIz9TWJIOc+wlYjIdSOonWQubkydepLDTIwvcJPralaa/Ew6E2+6dPWGaIyShWL07jVuGNCXicTODRzYSHEikONkqolDR7vVEg4q9HYlKRtrXexW3ukYnWY5z1GVpjjRqI2G5V0vY600MDyYUe1EcXg7KPfFNKAuRZ9maoWZSyelGNT4ypJeiZM4ppHkWorZejOQnCe0G45mJE5bRi1+4Ekniv7HOVYIMKweN2SPZ8L22/A3juKoRZNXb3Ru25MENbfkl9JlAekQ+75kI0uiBzfdw/7TTkPXF1fum9efXRoYtZK4MQqMPrPYq0RyYA2YsxT7aIHyiFPYyLWtgkCK45M+8Y4kLuilkeizVkaS8TJjeSewPAhU9xpiZ0iITjo2ppbrJQzcVwX7m/SLAvITic5LzhJ0rbELQ3GUkF5p8ouOnzl2xNwz/2JMfmYkvznzpBPN6ClMvin53M2aFCTuVi2ZxVstdrPEDTx2pmlGMop+43q1JynmNIXKsDwaklxL4kuy6NBQsWNU5jISVoncc70nKcUPCvS5dLr9Xo1/XFJvyf1X7gVcAclSicY0h2Qi3cjBl5a6TOQeu05x5znVRYGKkB+lIreoRQuX3F2KKWYh92N5vxGMVQTXGhkxq87AkQq3VO1V1HutmD8qJQfOnZpqJ5KdG9IrQcv0Di2bf6TlmXaWEdOI223Izs1XUXlafna7kCmIXXQUhQVdh1Wi+FSjWPsfcsIkJaZBqAlRxr9mZshOLCGRGMaQCCQ6ZIJ7qnZFE2yuLbqWQ7nridN/9G8KkTcMguC3soC28u9cXzSMRJHhZD8saAeCLyqO/8tzAW9vb7O3t3fz55//83/Oo0eP+OVf/uU/9t8opf6df7O7u/un/dV/KtfbAvBruuyjOaMPLgVhoSX3stqW07Q6yXG3ahHtf3NKeduxeCgB9LZjdIHoTlStQQvEeXnPE95ZYa9kN/MHlbhBRxG7EsiqfpULf2ulmP7rXTY+7jb3g4ZqO9A7FB3Z4EcZplG4QgwfvUNDMpGxtVt3N8BWP01JZpritSW9FnPJm8QO5ekehAhN/0oTSsvsnXCTCmGaTi9zmJF1xau6Tug9T9BnKbYCc2dF+8UIn0rqx+JRS+w77IuceCg5c2ZuuP4oUO570iuDXmns8xwqSe7oHQlzr951lAcdruQop70n5P+YyQatW3CbjuYbK1TRQaLHLWFNCpfq2yuuT0ZsfE93WjdFrOShvjpwqFag2mphKF6kJF8UmGeFRH9NugLGiSmEiwx1JjFkphLna0zltdqpYXbRZ+0bl5R7gfagJj+xDL4Ux3X1TiV6s5UcGsqTAebKCurmdonygjFRQcnIvIioRjbe5n8/YXNzQXZqaYeweOBoH1QiKZhZqmdDMUDMOwPKCpp16T6Lm7sDMTeKxV1xFb8piAZPLL1XGvtwweK+R280LO9JgRWGjjCTfF9TqpvvbY4zki6lph2Im9LcWnWj9EC1SKm2JGqLzRpdKQYfXZG8MxNDyccFemEk0WSzxKwUcV2gzyoIh5FGozLBIqnS0NxqKb9R4gvAiTtUNUqMI8iBqtrxEm/XIZV8JiibZj0w/7BBf2dKcv5Vkk7UnXt+aql2PVe/1GBGDbHv2P5DKfDyz3NJX9koic/7Mha++srJLYiniE+/OhzZhSbcrUSbec9LYsqtCn+dkRzLYSB7mhOVPEOadeHvEUEnAbdIOtOEg3EjRXsSSS8M8f2F/O5e4fpgX+SUtzuZSBYoXlmBT99uWT1ucPcq3ChQnReEnQZ/UMnz5DKRvOsiYlMhGmjXafwaze13z+Wgmws3svhggl9z9H+Yd1IDKcZpNOV7NdcfRdyDiuJE03thSU5TSQhyiurdSoq3K0X+MpFCbNRhkiLCBiw7Z34amT+A4qUhubI3nUhdicmsGQfabYe5ljF6OuvyvWdGIPlTGTfnZ7qLzNSS2PTLc/Iz0fjVW4H+04Te9wuK16I99EWENJBcC5MzagGQ/6RNID/p1TQNv/Ebv8Gv/dqv/S929RaLBffu3ePg4IC/9tf+Gj/84Q9/jK/yP5/1tgD8mq7yvMf08w3qvZZk2OBHnvRaBPfprLsxFITvrZFeGXFKniTickuh3Pf87LefAAJxtguDGjWS9xkkiiyUXZB8RDh5Vk645YETUfJ6YPKePDTVteR2hhSStZrlgeTHFueCk2nWBXkQS4teGopTIf0nU3kQ1+uB6rYnDGU0k11rmnVP8+FKxsynmmZDAK5sShck5EHgv5Uiu5Z4OdVKcbp6p8ZvtpQ7kfasoB15fB7JjgWKrWcSReXGrhNkO8GFOCUh9ENJ5tCV4GaCBbfh0EvRVJKI/kqfZlQHLWhh2bUDGUOrwwJ9kUrWbmXQqRdjzqEUG7NHgIrkZ1oyR0uDWUokVCgCgxeGti8jXF0r2rHvPjsxAeha3ZgGooZmDObBAlUayn0vhV1UXLwci6DfRqr7tXTfFori01wAuAmklwZVK7RTgsM4LUgvpWjXDeSngnqJSaR+WFN/f8zF+ZDea+lsqqAItWH+jsPeXkkRe1CJQWEtUu4JjsSuID8T7SCA9hD6ct0uDgLtoNNuPXTUJz2KY4NSUXRkBy3F8xQ9aqk2I822p+1H2oEYN1wvSqdut4tFe94XJ22l0Ndd5msiTl9TKaZfrlM/H+IHgdXjhjDwNCNonwxptrwU5UHuIVMr0nNDLAWWnL826KmMOJtHpWRA94NE7JXipo8aGLc3rlR0pPdak11JsafnlnKZdh0gKSSCjTIW7QUxOFWGcJ6jKsP8QJO+yL6KOTvrS9FnohTg/Uh0oi8LPU/oS4a17wX8QYU20n1XjfDz4qsCveqKxwj1A4EO9040wUiHvR1Eso97YmbIhDdpX2e0A7mG6r0W8/EAUwkfz+dSuKj1Rlh7TxOqbZEKFJsl9kIOlmYhzmh9mhGvpNNoS9WlamjpfCWBZjNQvJa0lOv/fk+umbOM7FKzVlTowrHcF5KA68shYvjEoowA5UPdSQdGUQqxhcGtO2Kj8QPpyFe7Tu6lubymxd1AMjFUjwUJBNButVS7QUw/Gw3KKYbPtRyqbi/RS3nd7ShQ7Xj6R0YiIVtxrbf3alb3Wxl75wKsr65zyoOW/Di56ZBX27HTBIrbV88szY7kBIee3H/qJzsB/jPtAM5ms3/nT13X/8Gf/0//6T9lMpnwt/7W3/pjv+a9997jH//jf8w/+2f/jN/4jd8ghMBf+At/gZcvX/5ZvQ0/NettAfg1XboSEb+dWNpJRnGUUN0WjVJ5v0FdpqiVod7sTo+3PO2Wk6QEJUXFx8e3ZPyxVCJQj+Lec9stxcOZODHn4gZNjxPpCnRaOTcQoXhIoN4MZJea7CSRVIrTAt0oVvdbGccmktnpc+k4RhNZPPDcenzeQaBlA+i/MOjOkODTCEkkehmTuZ6cvO1SYZ/nFCf6BpqLhtV+xwurFO0osPU/pJirBJ9H8ltL9KjF5+J6TaYatVehFnKqVwGyV6noAPdWmOsE00XV6S683vUiqtXk9+ZEExl8lhIyeQ/MzKBaudXCWJyxIA5NU8PwS0uayUbTjjzs1Dz480dSiHpgkggouoN0m5VmeRDIroVvWO23UqC3msFziylFh2WXitiXMWxIu9SDTAqK6qDF9BzphcGPHMErTBpu9F/lBxXhm3PavYbRz58Ts4jbayCI89dn4qBuD2r8BwvsqKE4Eo4c7y8onmTMHtHl+0b6X0onqbnMJd+2luLR3W5utFCLR55qR6LiTK0YPgN7bYkWiTTTcjCxM4FDN+sRXsq11HueSJdTC/YDK27JdiCxcG4Y8CNxTCqn5IDhIGxJR65ddzeOUV9E1j9RmJUiPzXQySRCKskQKkjyR3atGTzTlPtyaDIzSzKuSOfSGXSNIdSGZl1G87Ebo5pVp8G9SMU5vL8iPRXhXr0ZaMaBuNHAZcbmH2j6r8RQ0q4HsmvN8Av5WrPsTDqbFdW2FNoqCOez/8xiahg8Nax9alFA78uUaKJ06ksjXaQIG+tL2qtceIO5vEbRq0rRYeeaWIvjvN6I4v6eCeamvCX8y2Quf1cehi9g8U5Ltl5JR1oJ8ikqyYXWr3Kilvzu7FIT0kh10scNPeZVLs3KAH5TMnHbtUiwUTrrAdp5CiYSe+4mFxsl0Wxqv8RnkdPf38Me5jJu7rRyi8etFE6vc8nazbyYkaJ0NkdPID9OpJtbC8BeN5riTDr3phQHsV3IQTU8LLETg15Yhk+0IJmeSwHajCDNW6qFdODrLS9dvqETaLQXDA86ElcWO5Aot5gEzNTS21wJE3EqXd/Vwxafy0hdtyL3iFYOk7Ev9wQ6Ut1tfjwbzB+z/iwLwIODA9bW1m7+/IN/8A/+gz//H/2jf8Rf+St/hdu3b/+xX/NLv/RL/Oqv/irf/va3+eVf/mV+8zd/k+3tbf7hP/yHf2bvw0/LemsC+ZquMG5p5+C2WmhlBGXnmtYG8uOE+kElqIcT0UOZpb55uIjTTuFeDNAOVgeeOJFRCj0viRvzkYxwBx5fClRaN6JtUk4MHcF2G0nHs4omUjxNhQdYK/ovE2bfbNA7Feo4l5GSg/6RYXE/MK8y8Irph55YeJRLiVsN8TzFZWJiCTqijGje7Ex3PweW7zSMfpBS7kXasad3aPELLSOkuWbyl0vCeY4pFe7LIRpwY4/qOZxLSb4sqO+0pK8M7TDSron2zB/2SScCDw4pkEaqXQ/DllhaypdD4rpjMRb3bVWk5C8y4jLiU9CtISbQ7jYkuWNpcwZPDE1lRXvWamziefZ7BxQzxeJRi14Z0g+n2H87BqD6sIQLiX5aPvCYYYtLBLXiu98/mWqSGZROEf/ClPjpSOC8GyX+2YDiy4TlfUUYBfn+GxXJvxnSjCC7AtdPGb63oK0tF883xPTRJiIncNB28W9xZbGf5/hepNx39A8tyadDph95ohWBvD0qWN3xqKi6zyiiNyp8z5IepvSOYXUL7KWh2grkD+dUVcLVPcvm/5gwfU/SEGZPx+QnhvJ+A43GjTz9Z5Z2GMV4s1Dkf9iTpIiDgN9q0NdJx5ZUDA8WhKhYhAH60NLuRuxxSrvpJPFl7AhWtIGXP+/EpXt7gTrviT6rLxqvaCOuD0SBpatGk04UzTgSXvXQjZiAQplLgke/pZ2lDHcWLJ+tYRea6n4NUeGjxc9TTCHdMXNnBYd9fC2w8ctvR/R2iToqiFo6gPW7FfZFjjeQXWv8skf2JgLOdQktQ/l+rugSe+ZGuIpTTTqDOFdUu5700jAZ96TT/qwnxYmTA5ddyv04+8USFRRm0OKbHKsVq313kzqRTPWNHlMFKLcUemGIZwOJ69utiZ8VqHsrFnlBfjAn/GAN/f6capGip4m4WE2k3WpRS8n8Hv5hSrktY+tgJQWout+guqLHXKRU71QwS2huSWya+aInvNO+l7zxD5bEVwWm0uSvkg63A/lmiXeaclf0rHajYu56RBNIZqaTlog5qdqOBCNweiLYqYXM4+YJWSPPzOWdSDSRze8pLr8pwHPz6YDMKar7Db0vUsr3K9RVSrvhuPfgnNPfvk12lArD89zQfx2p7nv0xKD+7Rq2B6vdjuCwELyQ77U02tJ7YVk9aGEQyHoNelQRnw8wi+R/dj/4aVxHR0eMRqObv2fZ//J8+8WLF/zWb/0Wv/mbv/kn+jlJkvCd73yHL7/88n/V6/xpXm8LwK/pSo8yqt0gG3Ypejm7UrRjJWiTqxTSSDsMxK2G5IWYFXwKPpUiwve9nMacIj83VLuO5CzB9QJr96YsPl1n848My1ty+u+/jJQ7mvpxxeC7OcvvlBKWHhV+oyHptVRXAkxuRwGfKRH7B+nENbuOZNDQTvoSc/bpmLXXitnPV5gz6cAxT7ALTTaB2bsOe57gNhwUHge0SRADzMzSjDtnaWPpv47U64oykQ3Fz1JsrfC3a8zrjJAKEkU5S3nL41NF/jxl+bAVl2YUnU47jCRLaNagHUqmsCo8NnO4qEjPDHGRkE4U9UZCcn+F66Vdx0SgzJOfbTBXCW47Yuaa1X4gNAaNbOjVIEXdqlFtxmh3wfxoxPLlEP2NEl8Z1Cwl5uLmVrXGV1bSIXoRd6uBpRV34LsN2ZOCqp+itWjVVosMnUZW++HGmakXivp1n7AuhXSzBQS4OB2hEnGqJpNExodpJLuGqDXV7Zbs1AqHMAmkl9I9nj+IZKcGFQ3wBvci+sB2IEkJIGaVJo3oVtzAzYY4glevBuRnBvfhkuk7CaZU1L+/gel1xbbrTDVLLYeTnodK4/vi7MwuO0TJdUZIojivh5Hy0zHtdku+XTJ73Jf4Oa+h1oS+HGyaTdEf9p4nkst71hOu3DASi4Bfa7tRqnSOTCVaMFNL8eOzyPXPO9KThHYtwCRFXxlsHvHP14l3vMQBPpHNTHkIs1R0sqNA8Bq1X5I+KSRbtt8ST3JsDSGR66/oNYT3HOHpgHpTDEktkB9b6h3PahghC/SepLSjiDmXQ0fQ0A5kJConHiU4oScFKFje9+iVxq97ODesDjx2rQGvYJbggyKuORqfYMcNxe2a+XUPrjUqijEnv4TZY+ky60ZhNCQfS852W1nShaI8HsC2hzJBXydEA7En2CS37sguNdHA5BsSpWcupcB6k2yS7q4ITwaEeyXJ00J0b5XG7zT4mRWTzsqgHi4JXpBG5f2W5NLSjCPZlUb/wZD6/Vqg2ksN8x7ZROFT0Us264HiWDA+7SjQPzQsRuombcatS8JQVOIYTq4Mbi0wv2vJrkTaUG970guDPUuoNwPqMhW242XC6+tb+DUZ28ZUIO+X2zLetqUim0R8rkiWinpdkDK6MfhcOprRIKPl4wTt5DqPDyt48uPcZf79FaMi/ildwG/+/Wg0+ncKwP/Q+vVf/3V2dnb4q3/1r/6Jfp73nh/84Af8yq/8yp/o330d1tsR8Nd0NXutgIUXlmgize0W7aB4aaHRDJ5r0ktNdqkZ/04mMOZxIJsoNn4Efr0l3awgCM6hHYUug1ZSNmbPxqBg8q48LJt1T7krWahKR+aPHXGSCh6ikEI0HvVEc9XKDW5Xgm8wtfy3/FVCO83w31gAMuadvu9QFynptZzG0wsZqS0ORECeTRTZcULSb0h3VhJnFSGstxLD1ZeuydX/rqQdclMIoyNuu+Hu3hXxbkkYOYqzKA5jBfFuKU7IYYvyShyb0HH5+HeBqzPRlNljcTJKrFMQx2Ij2cLlQUuz41jcjajKEHdqbCpjRz8SjV/ozA+qMsTG0K4H5kcjYhrITw1/4eETwdWsBBwbho6YBtTSkL8U80Z6KIVyursi+7QQ3VVlBGLcj9I17XvQAthNLw1+zaPbbkxnInhxe9vLhLi0pING/m2AdKqYfKslmUP+OpEub6MwS0PIoPpI3jc3EKNBdgmm7bqSP38tSQ8vCvTnfbIzQ/+FoV4PhKEnmegb00k7jLgLGRfXO45gu2J+sxaz0EDQPWqtIX+RYlYas9QkU+nExYSbNBsVEOjy2EOjaY76xDSiX+cUn2eiySzEWataxeCZpfqo4xBeawaH3WPShpvPJ5kpopUC0KeRdiTFn51pVCUZyLqS71fvOPxeLe7pJNzIHZo1+eOzSPOgwt5e4UqLqyRFxyw0vUFN3Gi/+v23K1bnfaqrHLfuYNjpWmdGnO9JoL+3JH+RUu4JvD0kiMZtpSgezQQxYwPJXDH6UoqM7EoRc0866yIevaJ3KP2B5Fku+t40oFOJIQteUX46xlwl4rjdEwjx4iCSXmn8QEwtri+ygupxLWaYfiQmQQwxlaE41cLt1IJWoVVUtxz1Xkvxygpoe6Mze2jpEprvDum/VPhJSkhEK6r2S5KjFBSSLLRStBcF5svi5jowjxZy7xaRehwxpynFKyNmoP2K5YOW+mGNe28lvMOu4WQ2Oyj8XNJl6g0ZnZMHgXdPjOh9S33D4SvOlYz6u3E6UaQ00Uin3OdCC4hJID23UGlU4cQkteuZPubma02tZJJzUBKHDrcm4GtAyAlZpHcM+Q+LG7PPT2r9aSHQb/78iX9uCPz6r/86f/Nv/k2s/Xf7Wr/6q7/K3/t7f+/m73//7/99/sW/+Bc8ffqUP/zDP+Rv/I2/wYsXL/jbf/tv/6l//5+29bYA/Jqu5ErGKMlEgwG1NKxuCarh4eMTFvdEVO+GkWBFx4YSzt7VN2C0tSQ+6TP+kcbOOzNBElG5oDvSa8Ez+L6YQLILQ70RwESBTfcFDptea8Yfa5JT0R9xqxKMwlyLsD2PlPcbQhKp7jRC5P+ij88hnYqmrXeiMS00mwK2VkERRpLZSewi2i4L/LOBGAXuldBoHvzsS0IeGXxwTQyKjb94QrMtGbk4hblKOP3t28TTHFUaZr9cEjN5uKuXhURNvSzEJZlE3EB0davbgd1vnoopREfW7k1lsxt1SSFLEb1rB9mz/IaLOPgiwVaK0WeGOElxlwWx50nPLLEneqSoI1FF8AqzUYt7uJTx3b/65F3SU0voBezUYq4TekeSsxxtpFkXjVB6ZWhf9smuZNxlJpY4koSN/EQ0bcMvDYuHXkZdXt0koZi1BkznNq0gO7PEZ9ItK+81LN5rMDMxdLh+pN3wDI4UfihRb+nnBT6LN+/Xm+/TrnkWz9ZY3RYdY/+lFEz1VkQ7RXoi12uwEs0Wtei67MKQXhp6J5HyXoM6KuD+kuKTnOxKoY9zqj0Rwt//cy+pvyGQZ+U7Hl2X+EKEzT8QA0Z2pW8ONj4TXaZ9mYmbfaFpf35OmCeSWGGg6jZ8tbKYYwFsV7sd8sVG0Rd2rto3kWjZqJbEDCe523FpBRtSSTe+XQtkV4K1aXdbzEmGe9WTn1MaQciseVYvRiRHKcW5uK7jSU4ykSqjOEo6xmKXgeyR7O8qoXmnRDlFsyGHivK2p10LtN8fExUMvkjwRaTcVfhUikRzLZF1RIlUrD4qCae5oFymBhUUve8VuKFHv8rxA9FZru63DI6+2kqqBzW9Q9FuKq/IJlLsqSRQnOpOr6lQuZfulpdMYDvVqIEjP7WYhbkBNtebYvzSM8vgs5R2GJm+GzClpv9K2IO+kS4nUQDnbyQvzWbA7zXs3bkifD4gjlrasaf/WoqM1YHDD70gs6Lkbac/6FGcaKrbjnrLw2EhbvBrhR940aNG6I/LLm5Ofu/0zpLqtqNZi0JVSKR76HtybTQ7ToSQCAqmGQfp4Kfc5I/nZ5r+C4Pvi5vZLLXQGnJP6ExoBCQxZCS53Noplvuwuut+4iaQn9T6rd/6LQ4PD/m1X/u1f+//Ozw85Pj4+Obv19fX/J2/83f44IMP+JVf+RVmsxm//du/zYcffvjjfMn/WSwVY3ybvPI1WrPZjLW1Ne7/X/9v5GUhmJZhxJbQPqzQr3LcToM9E3edip2b71JTHbSSrDBuyH9YUO7JaCJmHrUyxIEnP0zRDVQdC06XgjZoh4IMaQcBv+GwFwlu7FBpwJyluLEjPbc0Ww69Epp+fq5YPG6xU3vDyLJLhesyS3WjbkZ4IKNbtVNJVJX5Csxcb8gDP1oZbSVbJc11TnItOqrqjgij9cwS00jsO/Qkkc06DbK5Oely5mdGCuPtFnsukF9/tyIsLcm1pThVLO9I146I5HZWUrj4XN7P4lRR/fyS8FpGX6HnyV8nqBaajUAy19TrgfxcS2dkabFzjU8jvQczlvMcZgLXzS60OKpHkfzRDKUii9OBfNhG0CvJlaYdizNURek4NOsBvdHg5wl2Ygh3Koa/WzD9ULRbvhewC43rha7QkoK+2RPHspolxFFLdphJFymLtHdqbDcu90VnFqikg1PfcqiVASXRfmGzJf8iw/cizbajeJFQ7XnUuCEspHAt361hLh3bN0u34jRWcyuvY9wQpym6FA5be6chOikYVBT9WX1LMpnt1NxgOEylyC/Ftfnmf5c7Yspph1JcRiXvVTLXEjXXyOZa3pP7IKbyO6pKM3xiaEYCAdaduzpYyTSuN8JNEsPaZ4bVf7VAqUhz3CdmATsz0r3rxsSuJwVgtF3kVyoFqwoCOa92JFJQmUhcGbILQ+8YmjV1E6EXB5I6ETpWXHEiOtw3Gj5/txKO58ijl4b8VLN60KIXRlz5Xn6HdKLJL2D+4KvDi6S7dKPioYOlJZmIizUmXTdtqfFFQPUd6jpl+FQze7cbx3t1c1/Um150mx3CSHcszPzhnOWkgEaTnguAevhcsTyIqBY5jNquSC8iZm9FeNkTvM9C3gc7bHGzFNLAeHPB5GSImQkHtB3JfY7T5MeWZi2ITrlVuP0arlPJzO4A6ay19EcV7ntjkjnUY+GS2pm5uVb8eouZCu6JcUMsRWpBGsRZfrsiXGSMvjA0Y6QYzeQglJ9peZ6qSLG/YDXLSU5TXBGJWRBkTncrmEaefW4QiXdLttbnnD7dIr0wJHOodiJxv8IvLfnrhHYYsAdL6mmOOXI8+7/8n5lOp3+i8emfdr3Zd37hn/6fsP0/HYvGLWt+9//wf/+x/w7/Ja63HcCv6QqpjDOrW461LzojRts52iaiXwqpjJ/y/YV01hqNnWmSJwW+iGTXWpx2dDDllaBH6g0pXt7AUcs7LVFHgon4QSA5EQ2cvbKo60RGIF1ck1kY0WfVkF1HRp8kuI2W0POs/0hhGsX6x1o2mgiDFzKicRutOPCsmAtAoKnzd5w4KZdK4sueGPr/377Eu206gonQirYmjN0NPkM3iuJUo0t9g/NI91bywN4Ubpd2XdTa0mJmlqigXpcuJl4YYSAFdH6lKM5klNb2wV0KRiWZahnvmo6/ttHSjIK4cT3Y41T0e0UkDDzLaSFdpoCAlGfyeUYTaT4bsTwakp4bei8sxYuE4qUUFzEPuE0nCA8lhVmMgIkyTjRBRuBOEfcrYuGxj+fYlTgT2zV/k9k6+n4mG9Wwpt51uPdXEpF1nGFXb1JL9I0L1aeAld9HOYU9WJI9y0R3lkinyvWlmxFqQ3YuI+n8y0ziuiqNXWkGdyVqjEpGeGazJlQSQZhfdpFxK0PxXKC7ZrfEPS6l+Bs1+CIIW22u0a1i/o6D9YZwu6Jej+Ji3ZaxKMg4O1kIR629W+N6kfKebPL9O3MAisME1SpWt+Xf+lS+jykVfr2Vjtl2TRw6MDD9wNPMU9qXfVhr0f1WCiUnI+nqWyvaUSCdyHvX7jeELODGHjcIuAFycEoC9/YvyE8kw7rcEb2ZXkoCjloa6aCnglUp9zyrAy8u2S1HmCf0D+XaDnmgGUcGXySEQooF5SEUnmAj1YZ0LnWtaB9UrO46Qh4hKLJnGdmpuXHzqkYyoU2p0LUmLi0hlWSgWMihSDUat9dI0bXeCCtxIkVryKXoXL0aiLb4tSX5aIZfc8wfSKZzc0tkF26nZeNb52QXGn/SI5mJjjRqAd27Uiome5EwfT7uOHwyltUtsrtZgWRnV1q61AaRDgRuogizS8PgezmrZyP8e0sWdwPNtic/sWL48mKkwcloOyaB2GqyM4NyCjOxqKDwpQCd602Rr5R3W5o7Dcn+kmo3kCzk2ixfD6A0Eu/WCmO02ZLcX7cuXcJ6Q+5jN0s5ebYJXhKNQirdv3iWYa+tZKgXgeT3h+i5ubm2f1LrjQbwT/vn7frxrLcF4Nd0ZWcac3sFOjJ5P7J6p0GtLItvVsT1Vro3+yW+Hwg/HJHMtMCNS3k41vst1V6ns6plA7DzrsPklEClH89R7y7ARvILze1ffC2h7Y3qgL4ypmj2WkwmG+8bSHS0sLzdPcwvEnH7/nJJVDB5TwwZoy8F1aFvl9iewy4VzWkP5eThW5zIeFB3uj7/oMQVMHm/GyueyRgq2yyhVSgtTj1dC4C4Xpci1426LtG/GtLst5ipETyFRTY016UAzBX1lif5rIeZd+7gdUkpqceQX4qmq3pQo9cb1AcLmnGQ173tRau0suQXmuKVFQbYdkscOGLPi3Oy0bihoGj6zxKm70vREW2k3W5J91Y0e47VPUd5x1HuBVaPGtRKorw2vi+Gh2SuMC9z+l92iSvzFP/tObHvUS9z8qMU/XsjdANqkqAbTSgkc3V5R7q78eMRybVhNFwxf7+VdJJWOsoClxZdmR8EssNMuIMDT3gyoNp1VHuOqCPZFeIYH3nM1OJ6Ef+o7Dpn0jFNporqR2PC2N2kzPjz/CYmbflug3pnQXot+cK9I0t7nRFaDTYQTnPBEKnONZp05qfLVGDYU+k+DZ5aMR0o6cbqRn6WukxxtxqyE9HMrp6NUI2mfNAQU8GQEGRjckPRtem5Re+vCJWRjmXs7pGVODbjyshosQM4Dw8hXGVSaA0CdrNCGWE6qlrE/f69JaqVVJ6z/26f/FI609UtR/l+RUwFxVKcGPIzTfFoRigtsQjEnujF7LWFJLB4EMg3KpSTg4nyULy21Ne5oFNMxD8ssSXENErhWBuStZr81NA/0jTrXScQZGzuFM2aSDFCEcjOLcnUkMw7KUEHcI9BwNDxKqW85XFjD7U4qG0pnVuiIIxWxwN0z6GdaOTSk0Q6/1eW05fruEHE7JY3I/12JBpKM7GkV4bBC8HQmFoKLNeL+A1H74uU/peiy61ueYqXCX6nQU0SQibXerPuqXe8sCiNpIyojRoyKY7LO5Itnp2K85ZE8FNoaB9UqI2GUEjXdfijFFtCvS56SN2TDmQ9y4STabrO9Ikh3axoHpUSazfw6FJTfXtFcSgyENMI6kU5xegLS3otXdLylr8pXAeHXSGbBpbfkGvD3at+UlvO2/VTuN4WgF/TZSvgaR981x07S7BTLVocKxu5uxKtnv5oRnXbEXpBgMu3ZISrat0xseRB027Jhi68qkjzbIg76qNTz/Ku4+h7t8hPJKzc9SL5qSE9TkjOE/zCojw0t1rqDUkmSZYwf9dh52Ii6P9eD18I1DRYWNzvxh0verhZiusJAy9aecBW2/J1yUJ0P/p5QTOWjqYb+BvIbrNKKV5b2ZS2G+xCUW/JBlY9lpivdihJEGrZneobAQOH/YrYc+hGskn1sKXe8YRcNFzJtcQvJUtwhRS09jzFPslpXvYpTjTttpNZVgD8V+JyGomDS84TVCraSjO1kgaQCH7HLrWMJqMU4uHLATp35MeSBWt3Ssx1Qkwkj3n6qOv4rAfakWisqu1A8SJhf2OKPUtwWy31eqDcDdR7wlaTPGTp5ulGoXqOekNgwdPPNoTveCVj4mQuYfWAjEidImrRQJpBCw+WN2L09P6Cxd0ueaLn0bdLsgdzgtOE9ZZkoVjtCVrGlgpzmVAcWYmXW0jkml0q0tcJ9VVB71hhSmEQmpUmOkVykUhHq++/Gh16iBsN0cjGvrwvBcbyjoxe48BhVpp6M9xE9OVfZpIWoZHXv9aSvk7QlerkBWJYUqMGt92Sn2ncWUFyLmkRqlFiiqklYrF3ZPE9MTw0a4Grb4s71jQygvUnBVxmLO6LsUI3inaaoWpFKC2re45mJPnRZtjCXLqR2kknutx3LE4HZMdygNLTRJAh95fQpbLUxz0Gd2aUe0Eg7DPQfUfcrWWMfliwuh2ImZcCt9W48/zG9GNKRTsUzZtuFb0Tje9YiHZqaIeBdk2uM0nP6BItruxNNnYcOpIrQ/FSdJAuh2bboXKP7liU+nVOuyP3FnSd4xMtSRwawsse7YNKOtWDQHauCZst0cDsUSBaqLc8zV4ruKknKfWGdN2VF+NUO4wkLyXa7Y12NplqYuFxQw/jhvRZTnBaJCJWTGflbS+O/xz0xLL2/QSTedGJvswFd1QrmhHU255krijONOq0u9GdxnRyi/KuHL7biwLOM9pVSvHKyjP6kx4qihnL9QNmbiheicDQFxE/cuhK0z8SE1I7BL8uE5r0eUb/uSH7tPhPua38B9dPIgru7fpfv94WgF/TVe1EsmtFdmG6kQq0uy3ZScLavxam3+CpIf88p3rdF/p+qwhrDtvFIMXCi84sImOGc4k6ageiHzOVoDziVYYZN19FH60JB6sdCVaEIFgNFUBPLdGKfmr+rmitmnUh85c7kXC7IrmSLo9dKtr9hmQhTjvTKPytGntt6T+aysaQROb3RHdml6JrUn84utE46a2aWMumpCcWFgnpTApYlMSE3bt/jr63xDQST2VXisW3JIIqTlLS1yn144r2oOmwNhD7Xuj/Gw43iJTbsYugC7iNlvqWI/Q9IQMzNZJX2uEusutOC64QjaWCdw9OCfcqTAN+JKiQqCKRTqN5JV1P7aS7Ut2RMVm7lLFeMjVkZ0aipnqOkAURljeS3lDuO55+sdd1YjRmuxK95JV0xPTMkqzXolEbBKJXZFeGct+RPZiTvUwl+s2Li9JMLPm5ItwtiSYSHpVk55JwsjFaCei21pSz/EYvmByl2B/2qZ8PMScZ9iyVboaH5Uc1bV9yhMv7EsV3/xePYNDBe53CjBomP1ezvCMHiJBFaESD6McOM5AupV9zNJueuBRzjWoVuhSNperg4DrzqADFqaZdk8+83hKsT8i7gucsFXMMksUakyjxeIeSvtGuRemUaukyxyzeXFvSxYH81IjuTQkbMz/7Kic4FF16g1c3Y+DBU7k/0lNLslZT3vEMDhXpjwqSLuNWN13BemoZ7C6o9xvMUFJFmi1HeypFQLspRe78so9dKZpRpNqJmKOcsBQmngLSiSZ7naB6jmRqyM/lEBcNjJ5AdmW6DFtJVsF0HTktgG5dadoNL9rfpcgPJCIwkl4b7LkkmtSbAUYt9ZbITdRlSlgm2BX43ZrkTOLe4uMloRcEhVJE3NDjh570SSFu51aJm7jV+P1KzDoaekeG3pcpdimFvR962v1GnPADScoZPZV7b3W/lQjCPTmc6VpkIj6P4DqjzqYceOmMPfWudFinHzk2xgt6Ly3FsRzOfCFymtGn0gGt14U9mZxL7CLI91C1TBbMUtzlamVucqKrrYD589e425JVLnrRyHI/4HvhZnTdDsRl33xriSoNOhNXcLMWqXd+si6QtyPgn671tgD8mq52s5UHwkFD70lKsFA8TWmHgcmHgXYjMH/kGPzSuXRNdhqyc032KmHwQtGOItmJRTWa4lhjSnnIx1x0Rz6PtOud8cCDfl7ccPFcl64Q0ohqFcl7M1xPNIC9V1KMudsNemWwuyvR1wQ55UYvD+Fm1I3xEun2+ds1PotkT3OShWLxbI3+S31zBftvLIhJ5/BEUgH8w5J4mmFmlvLAEQYe3SEr7EwKXrfVcv4vb2O/O0DJYZpoYP1fZ+TnUgS3d2riyhJLc6NNVAtD6HnsxOIfSBFkb63o3Vmgllactp8m1F1erp4m9I4VvacpyzuBesdjlprsZYIbeV79v+6x9f/OZByZesytFb0jy/AF+EGgutfQvreiuSsAabwirAnk2yw07V5DsylZxVwLXLb/Uorw6k3yQKsgDVB4wmlOKDztupPu4bilXYnzNTs32LOU6qCBJFKeDOS9PxcneTpVmAqG/+2JOCcHjtFQ9JMKmK1yhl8a+bprKWjycymCqlsev+bwWWT4HGLSdeuCjJLVuwv6Xyb0nqQ8+7cHxEqSSqKO4tZWEHZqmt32JuWl2g7ohSWe5sKJO7Mi3ofua+JNIacbRX4uWkSfRhbvtMRxS7Siges/N+hSuIBhp4EIvWNxrqbnEunWbHrMXOPvVOSbJW7NU18VRBWptkTvl59ayh0ZDZqmY3Deqak+LFk9bGgHkf5zK0ifCMlEdJ3LOx5GjvSjKe0iZfDEMPlOQ/1BSbseaMdSGIRUXN/LWS5FhBdpRXpuJZd7Lqk5fk+Yk+0w4G43NBse83iByj39IymKm3Gg3vaYYxlPV1uB5KMZq/3A9UfCCg2p4F3SqSJ/kdL7nT7crlBekc7knktOEhb3pKjNz8Wp/6Yw6r02Qg64SCXlpCtm8s2S5eMWfSbdsuTSkuctdqOiWetG141muDen2nU30Xu+iJipYTCspBC1EZ+9QTSJpIMI5iyVg2ctRe70sWQm22uLrURTqRa2i6GUa1ZlXgou02VhA2arFkfuuqTMTL+7JUlGB0EKeBvpnSrKX1pI0bop0GjTiDY4mWn0qEVXimZL5DDNmJvkGSLo7YqNXklvWHeyAdH8vSkQk2sjk5tBoN1qsR/3pQs+Sdn5PcQl/p9yU3m7vnbrLQj6a7r0UrRWNPJwdpstuhWYb9ytUUc5brvl/Ggd7RWh0ZT3WvKXCdN3g4jsl1JcVFvS+UBHkktx7OpaQSUu3f8fe38aa1u63vWhv7cZ3WxXv9buu9rVnp7jYzu+IGPAcGVARAqxnHyISK4cokgJyhdLIYAhOE50lUiRP1w7XAEf6ARCsgBxkW0w8sHdsX3aOqdO1a7dd6ufa7aje5v74Rl7VXyPzT32OXaRol5pq2qvvdZcY8455hjP+zz//+8f04idaJK5odoNmHFNyEQcrivNapaTtwrVRUuFPIi2a6umXaRkB5b6UisSq9IS1xzmJKG9WZHczUnPFPMdTbPjyJ8llBcdZIG6Tiiey/E1yxS15QX9MI6kU41rC/zIY8YNycOCdrcl7caY9VbAzhUuCPMtlBbbb9FBYeoEV8i4UFWG6DQYee6uH+g9MTTjSKilIxMeFSRTRWl6tDbCmtzg5i8BScSeJrQm0g7FzSu5sBFKK6O2VjpCh5+JxKyFxuAWCdZC1Ar6juxRSrMm7ulmLIHyZuTxc9tFZymSrZJ2vyfFxl7L7KWE/EBylqONZIcWdSwRYKZSVGsStUVU2IO0y/KtiKWV8b+O5KOaprY4neCHCjwsr3rSieFwMiScpphaMVmuE7tcaLffp7rpRQLQKtpWUa+JU1s1iuRUIMmLK4LwCANxRyZnmtb1cdsBv+bIn6RwYmn3WtqhdHn9zGIqLdxCBZSG7FhwLaurXphurSaZa0wjnSvlFfWlhuQwoV3zhAst6jQjmWuaNBI7Q4Bb96wutsSzFOU0caVoNgJVJtpROxOtXswC+StT/G+s0awHeidaut07LWatwZ2lqKBwY4ctE5KPTqmrhLhKiF6JueJUU21LRGK9LkVAu+axc4M9znA6Q60H2hHkj1NMJVKH1V6kvCzOeTcUXZ0OCv08o1nr4MU9KYKSuUadZagI9YaCSYIO0D7qE/uBalO0ZspBa5UULT2HeVywPOyTLuV1dUU8d5bXO/I6VFdb1CSVVJNd17EpkeI6hXozkuwn0qVVkfQ/OGHx5gYAZj/runIBHgxgsyUmUR5DRcpVJu7eTbnuxFFL/eYaehDRNSQLRTuQorq8nKCuLcm+OiBqqF8tiZOUkEbyfUt9q4KzRHScHepod/eM44Mdqi0p+kMG/nqFOs5oxhH7LCOZKcpXKmGPnmjqNCFbaHrPNMtLkl+sl+IQzvcFgTX7SIN2BtMqvJcNjd9z1CqSPM4wT3KCjehS4foyKUk3K+zTAfVGwFeW5792AXe1Iu03uDMrhrNWo84S2nWPWRjpQp8kNN3mMvY8Z7dTonUSN/c+rvhtGOF+2AH8/VsfdgA/oGv95oTRrTOSUcPqZkv6PMFn4IcB9ThHXVuh5xYzlxHV2vZCyPKfmkj+78xIBwTRZr1AmZiVYCJeYCsAku0S86kzyovSYXPHueygNfRvTlFKhOzlzUbGW6eGdD/Bpp58XGNKRfZUsoTtqMFkHtMAR9IRa0eAk3gpXXfMrFNJJGm6cXP/bUGF+EIK0uqqZMySBPzK4i7WKB2pbstYzffFLRhGDo4z0gOLm6WYxzmr6y2+kM6PWWkB4y4NbZe/ubzZYledqP4A3IWa1TWH3qrZun5K762c/GGGajVrO3M5DoTTZW4uQEfsme3i8xRx6Jh+opFxpdMUjxLMQjpD8+tRjCG9SBzIcWdPU8HGtPo9gf/SwDuiOdQtIrjflePyuRTiKCkEo5GbuTlOpfCaSOcjGmCWgJXMWSqDd5pQWoYX5uiV7vRzXsDgRt5johJHsO66o07Re2aEwRfFbeuGoilLZgIZHl+b0u62hJ7o4mi0jHkrGd33302pdl03soUwcqRnSnJ915yMQp10fkJCB38OEvE1kA6rK6Swqvda7EkiSRJ5IHu7EHbk7ZXk80ZBD/V3l8RJKiPitUbGkQp6TwzpqcHeXAgUOg0sToXZ54vA6rIjmSmUDYQgsov+E7mJtf3I8vFQ9LaVEeivFR6nSBaEkxh6XsaQgfPzBRtpxgFTIeamTzVdJKGMvZOJIX8mpgFi1/XOoN526Fa6tNXltgNtCzPODzz9JyInSBbSuRo+RBJUniUkX+1JNnOpcdcqKUpL0WJGG0nXK9yaJPAAAoYvO/NYKxgan4tpxnaZ1yoozs5kDI0CdXUl7telxo8d9jAhjFtCJkW+WySYuYxG/SCgEym42KzPmXvRiFauPctplynljUZSjWYperMBBb3n8tmJQweZl0J3w7O/v0Z+oghGsp1DKq7aOGpJp5Ii04wj6iSlXo/UOx6VeupdJxrfUknHtFT4kae81pDMNEn3mkj0oGSaqzO5zsnnQsbofrMlmcutt5lJ59NvtugzS0ggzBNubJ+QnmniUQZTeY+L7RVhuxHH/9ATctlMqkrMNcKnfH9twBGI8Vv8874+g3+/1ocF4Ad0zd7c4OxwiDssIEJ7rZYuwkQT8kg7yVA7FdnNmYjKf32DdKoZ5jVxrxLx/EpYa/WmsMSIwi+zS0U6k1FtyCPxfp/l4yH5oSE/ljiu4qmMt1bvjomVwVxZoYw4KaOGYl8RHvSp5hmrl2vqiy1q3OCWCWEi2rD1N5WAWnPRzmQTjfbQbDlGdztsSx67nNYoiQYnUrRlTxOG96F4mKJqA/MEs5/CQswTqlVUVxr0wtJ72jl+Ec2NXhiWLzcCO94SxI1yom0yKxHk1xsBt91Sr0PyOIMkEFrN8f0Nltcc1WUpQN0vbki03FC0aPVp0em1xBGYninU3JI+T4RTmIghJTtVYm7ZasjXK+nmlGJQ8UUXizVNSGea1YXA4IEUXD4XMbw9k86lXmnsTinu0R0vHMKpwuyWhO2GwVvi3q22A6Hr+qrSkMxkjOkOC8zUYHXAXltg54akaAkjJ89lLu5h3SDasJWWruNapN1wVNuB1cu16N6urKgvtuL0Pu1jCweddu1FPrVqO4NEKk7fZl3wG2tfSEm/85R4pWS0syAkMvLrvzRldcXRbreQBNoHgy7+UM7RdKJJTizxcoXKPenzhPKSFGzJ2z3ssEXl4raMvzaWImeq8LXpIgDP2b1UJwXzj9T03k0p7qc0a50j1EbRt00TBr9RiKFoL55fXZVT9B6ZzmwUKB6k0lHbDFTXauFHlgY7lwJpdc3RvizFqV0pli81tDdLCKI9jZmXzGsLyScmoKXQDdsN1UUnju40do5RhdqtZRQ+cuL8zaDedaxer1BBcfo9DcW+aGib9UCzJQaqME9QTtzT0chGoT0ohM8YpODs35cRf7PXCkppJria/hMBvfuxmIvMs4xmXZiT+k4PP/SETcm5DnlET8WtXl5pUd3x959JGol6ngsT7yzFDcX41a57kjny2IUj2U8Y3j4jOzDYdwtJ0dhRJAPJgzbHKe1YjBXURuDXmSQjmVLTe6op3s3kWrgpzMCYRnGuLzT5nZzhHUvyB0+wr87E8JNA9tySP0qpLzW4i7VA8J2kqign5587zWkutDSbHrvQ4HTH2wR0pP3oUjZjyLlmF4a7v361g0x3r3VQ8MURLC35404r2RV9ZimmMZJ/TynQH67f9fqwAPyALhUhObZgRMuXPsgojsQVG3UXa3S/QH1uTHu5ph1Gql3H/td30M9zogVScfhlp6Zz5kZG74rTMXTUfbPQ+DwSe562H6m2pbOBEoG4XSnJyXzaw+yngjPZ8Mxe9ZgbC4p7KSYNDL+Wkt4pRHP4VEadi2tIx8B3Nz4jEF2iotpWNM/7IpQee9obFfWGpxnJBTNYOHsVypu1FLOJCKljKt2EmAZMzxHXGwmEv1KJGLwREX/6VHbz6bMEtd7ge8Io043CT1OJiZtYfCH5vjQafSQXcV1rlI3E3VoeeyDwa1NqkomBZ/L6hjRKEUEH5i0CZmrxeWB1xbO6GOVmdXeAK2REOr4yJTsV5EZ6puk/kRvB/NWWUHRpHKcCwNZHqeiwWiNO5ko4jyGVXFZzmErKRRBdFZmEMNmtinJXul92d0VIYHI0hLeGuJ0G1xjR1S1114mJhAyBhDvF6mrn7J4bYhIYfTkjmSqyXx2gMumiKhNl1DNNiJcr6m2PPswIKYIpQrp66alh8K5lfiOw+Po6YZIxfzrCvyTau+ora9iFITlM6N3J5LWJwkorrzWCK3p5jp8mZHdzQtIVmJmcW/p+QX4nI5krwfQ4RX25JX+QUjyxrH1dsXq5ph0G9KCluJtR7gbKWzUhC9hTi+k7YfTVinJboL/JQkErrvMwdjRrEVVpwa0MpNOlanE9qyCJLdrL5sgstTDlFsJ1U0tLercQXFKE9CCReLw0Mj+VSD3WBW9C5rEXViLRMIjT9nFO9jwhv5vJ5zKB3sOE4edzkptz0ofyuisv6KbkzJy3YaKVDmrYbjoItpYN1zgQBp7yQmB4dcbwa+l7WrZWPqfZiWbjc9349XJFeipdsZDA4G5Cdi8TXaCH/Mocn4KddMk2WcBnYozIb82IWqgC+YUlzYaAkxfXZQTqa8m0Xn5tXVJAbq+wCxnPJonHrhS2lIQc35fNUXnZ4TbfkxZUn1pR3mpwhbid2w0HoxZda/ygS00qYPb2hkhajgQl5VOoLopcIXs3JxoYvwvFg5RkqsU0EyU+cLC3ICrInyZQa+yZkc/MO338mmP0riZZSufWjR26Vti5JjuVRKXysmP8lrRA02NLvi+A7mBlClLcF+nB+7neryi4D9fvbn1YAH5AV7DI2LNR1HsSaVSvS7zai2UXinqzuxmbSP+hZfBQo2sRIPffSbFzQ73lZQRkoNxW1LsCKNZOuixxvUHnIpyORoTwL7Iq2xvV+U1XBUGrpEeSqekfDGjGEfYFhVHdqEk7jVq95WlHknTRbHryh6mAptNI76El+QMTYf9ttpiFJgYlN5o8EK5UIj5fc5hMAtmhw5tUWiLE+o5wnJE+yKWomyfovsM04tZsr8lr53oRtS8st3bLiVFEcW4qaK/UhFRuLtFKMaVaRfIkJbRGMA21/H43EgG/7wUGr5/iRmKoyA8NW9+5f45O6T0zXL59yOCViXTylOT2tsPA8q116Xw9tLSjyOnHwzmiRDkBKDebnnrbYWro3UtJModaWLkBaxnHq46X59bdOdJFdWPudiZpL/1nEe710dsVw80l9Y7DHKeYJNB/OyM904zuSRfF7wpI2fUCZIGYyI20eGrPo8aUF1xF9sYZea/B3s0l9molXVl0pDhQRK+EM2djJ5IHe2Up2qe5lvdlmnYQaYHnxo7ZWL1RCjKnS4GIo5bw1RHYKFzCy6VsXCphOvoU3FAgz6uPljCSc+YFZujku1uS/VQ0hY9y2pGc18W9DLvqxnxPcxlJX15JUdmKbk55RfF2JtrDLUcy1eT70mpu+5F0JnBiXwT8wMuoNo1kx5riXiqF40Y4P9eSqSLdF+SNGTckF5f07qRUV1qYyhjVHqU0K9E6ZkeGGBTtbiN5xB3cuV4PApfejZSnhejudgLlXpdaMu+yZ3VE90VsqSbifg+Z5AHbzRJVaUIeWN4dy6awyzeOFsrdgHZyveg9NiR3C+pXSxi1Alsv6DZqUZJx7g/RTrTKvh8wo4blVUFTlY+Gcu250VAe91AXKtr1QHZ5QTLR6EkCScBdaERC0IhOrt4INHdH8vnOZfy6fW1CvFJRbK0YvJ2SThXFvsJVFrUyNGMxsanci0t53MoGduSpXq6ISaS4l8k16mZ9DpSu1wW9pDxMb0F5tZW8cC2AbL3eUN0Z4/caOa+TiNuRz107FFd0tQXVnvBOJW9a9MrV7Uqg8WedpjXKFKTeFnRUspANt/aC7Xk/14cu4P9rrQ9NIB/UFSE6zfieZn5Noa8sCSd92nFgeNcwe73Fbcp8S01T0pnGFVBtRnHlJoF6XS5Aa29qJp9ymMpQ73r69w3T1zwMWnSdo20keAXjFnWQnY83k7mlvBoxWowOKCguLODZmLro0Aa2izM70/haughu7MgOrIB6axFTt2P5/3bN0+4G9IMxcRCwmaf3JGOeJmAjxTPDKrXkE01ViP5PW3H7+UGg91CinEJpoedxmw3xKCMZ1+g7PbmIt4reV3OqHYl8UysDJtLfXOHXNf1fH0rm7pmiJKXajOfQaOiK3ENNu2ZQjbgzfSFYCLNS6L5mdmedWASWVx2q73h6b4vkTDoZpdE8e3OXsNVggfxQUW1Lp4LrS+rGkt3N6T2RDly9GYnK4Lcaql0FNoIXt2E4M6i7A+z1FY0rzuOw/MJ2HVwjKTEp5wkaRKiuNSx0QnGkWF2H5kvrZF3MWxsLMbMUkahEn6iPpSuaHxnKLNAOJC1mddm/B/3dguxMsZgV0vm70lCXRhJjfKfX24zQains00D+uQJfQLVKiXlAOSNFSN0laYwi9kRQNqvbDVQSR6dOUnQED5ga1vdmrL6wyehXDUd/qGXtVxVnGNztFe4kk3SSpSYMAkFH9JWS9E6P1ksCjBuImWF8bcrZ0xHNGqy9JSyfs+8rcdMU86SH326BBOVArTWUMSX2PMlhIpuJEEknMiKvb1SoRSKfm1mC3q7QKtLUPfn5hUgCkpmieb0kHmZkx5psAmdbKcm+ZXXFkQxrXr59xFtfuIbvBfQkkei5BsxJQjqVcWW960gPLe2ayAzcupPP+a4w+ZLHGYPHMLsVhQMK2EcJtoL5Gw35w5T2lRXWepr9njjNHxuCESd27DaSbV+wRMvLoms0DfQfw2TPopeG0PdUhbyH2bGmvN6QPk8kkvA4EeD6pkZtSGEbE+ngJ/2GdpZhv94jq8GdjGQkPHbYE3nOKkJsNPnlBU1jCYc51bUWnXk4zphM+wDUTwaE2y3pvqXeFG5gO44SWdlviPf6hCRiD1L6b0yYnvVQxwKVNhVUO4EYQd1akqlIUyb0v5TTrEWaazWjL+a0Pbke+DIhmu75PUtptjz5E3Fm11udZs/JBln3W1ZvOOyzjHQmn5m4tJRXWym4rRhO9NKIhjmViUu1HST3efKhgu7ftVXXNb/6q7/Kw4cPWa1WbG9v88lPfpIbN26834f2YQH4QV2mVMSFpRkK9iAcFegcshPD4logf5oI8Hnc4FpxzTW7LXpm0f2WvNcQ70vw+uTTLTjhpTFoWdyKrH3VUm4b6l1HfqfAZ+LY870gyI6k6zQGhV2IHig90+hfHWNK0E6zvAS65PzfYypjNmsibWkwKxH865lFt9JtdENFfj+V79cywltelmD4ZCFuWpSIxLGBdD+RTM9RQ69ocM/W5Hl0K/9ywfJ2w7hfMc97FM8M9WbA/YE5fpIz3F6weDQiObIs2z7JmaG63ZAcJeexaO1QLrrZqYxpog40Y4VZadKJotqVWKfsUNJDkpm4eIvnmvJCIPQUqudpE+kghl6g/0AwIsFAOo2Uu6IPMm8NcFteOrobipgIhqJ4ZoizjPqVElpN/24mubFR3JLmC32Wr9eEIGgWs9LyXiGdGyKouabpMmrtcSLnkAK3svhNj90qUV8f4EYi/EdBdVUE97Sa5Jk4xPONinA0wPUg5oG2gwj7gadd09gnGaZR1BFMKV1Jn8vovlkXJFD2WNBF1baw9XpvZYREMDJmaTBXl6i3+9JxSqTrmBwmEm14qM8LbgAilHVKvddytKvQM8vB9wR0BWGannfF1cUK+7ggfWQpL3rqSw2qNvgkUjw3pFNYrtYxRjphJ9/t0HNLqCx2Ll2n5DiROMBhRD/N8XviCG/3BCrtepHyiozj41IkGr2v54QM4mlPXoco56/yoFthOA4/X1Cvi2TAf2yJvd+Xzm0USPJXz65gW4VeSsZ2tRNZXXVgA0yFw4eR8ys5k9FkciISBjtXpPOEeiOyuqAwlUTdaQ+Lmw6z0Oippd70qIOcOgvQ5QgHI5GObqAwtcSbNWMoDhWz2xFVQjASZadLQ/+ppl6XzWBzoaXdcWTW4wophJLU4d4dCAalMiRLRbsj3Vx3UkjHt9dthlQkuSwZuG6jRTkpdtNDSxl6Yk5ZKly0KG9Jp5pyaEn3Rd7hvTw+iHnCrjTZvqXJPCqLFIea5Y2W5lc2UDsBvVcRHxbnIHc9TWi8YrC5op7mzF9rSU4sTBPqNWg2PL0nhrofiYlglOqbFemTrINrS9dbjRviaSr53c9yilfOmFcGNxCzzmBvQfPVMe16YP5yF8251MRLFc0kxXYsQeUUvvf+mkBCVKhvsYP3QQFB/+Iv/iL/+//+v/NP/+k/pW1bxuMxRVFwenpKXdfcvHmTH/7hH+bP//k/z3A4fF+O8cMR8Ad06VZuIM1HV8QoTsZkLmkWxaEmWQpXKv1KD9UXUbw9kSSBsEyoVhJrFLJIsVZhZ0YE3YuE9S8Z6rXO7VY4mpdKXE9GObpSpBMZW6pWdGDNWjjXOS1eapm97Fm81BJ7Hj/y+EwC0ZVTxNOM3q/20LXClJK3m8yEV+Y+NRcHXF+eQ0wENN1/qoXPNg7YVSR/2kVJnVnaK7VopB4VrB6MqHeEf4aWKK9gRWM1OR6iXEfc7weaVUq6VrNa5oBo5HpPrCSO5J6QRBa3HOVFL+yx7VZE7pkXVEjXEay3AoMHwk+rLjr8nuiMULB4SXRc0SlsJmy/9NhiFtIhCosE7eH0E4GQyHi7uuCk2N0ppXvqhNu3utlSXnLoZzlqbmlGkeJxQrhaUV5wtH3I7mck+4kYUjq9Gwro/mtKidRKTw0+D9KNLUClgdh3tLMM1WWXKo8gdZKAPUnQgxbtOmPDNJPEl0sNem6wCxH126kUniogBXxfjiMaQX2EocPOZVzejCVmr/8UltdEDuCLeJ5tG+/2ZVMxVeeFXrstedEgzmPtkKi6ouuKpAGsdJLNuCHk0oUS7ZpCPSrE8W6RZI25hb7DbNby+AMBj+cnCt1Kuk4ylbQcc33RmYtk/ElUuDWP2U+xRwnJfiKu355o7GJnmDHd860uttR7rXTlrq1w2y1hKE5TU2oWN7zw4wK45z3SmRI00Uj0qcmZwd5YnGv8fBZJT8TIUl7uAJdeCUD4QkvMPfb2XFzwClZ7Mr70eed6UchGrRL92eCR5D+nk87gVWrC7RWrK46zNwLbf+CA9lIj6CnA5Z3mrito26EUmvPbjnYtEBKBwrt5Qr0UN7qvxXXuemK6slMjusMX3eplNzLvUjnaYZSfBUzhcRcbfCbP/9rNQwHJd2PZdCobtfRZIu7mgeh9le/kCUGmC8EKHD70pEs6uCOFsV0p9P2C9EzMb9GIljp7lrA47pMcWTEbwblLngiry57Q5QcD9EcVzZaTc1bHcwf34KER5EuExcMxquqy1hsYFwK7zg5Em2nmhrhTc3HrjGSiafuyqQTQlfm9uaF8k+tbdgB3f/6vvv70n/7T/OAP/iDXr1/nZ37mZ5jP55ycnPDkyRNWqxV37tzhf/gf/gf+5b/8l7z88sv87M/+7PtynB92AD+gyw3lAudPM7CR5XVHemroPxPh8upiYPhA3GP6KKXaDlz5+HOe/cpF0qlicVPGPtmTBHdnSChEP2SWmtntyIt5Z1wKdLfddISZotjXLK9JJFd2c85qUlA8kN1t+9oKdSzaMeWN0PxfOAqbDvGx7qi2RYPkl2IGeQGNDfcGsNPiLjREnZIdGSHgDyH2HbFNmN0C5TtuoYK4EtaeqRQ+jWQHhnInYnKPOkloXi8xKhKf5bQbHjOXzmNyYGkHiThlr85YqBHpqYHtWnheSRQDzCBgS4V7Eeu2L2OiaKTIUTsVzbRHvevI1ivCvQEh6VyjKpLultSnBUpLMdeuBZIzQXCY/UzMH9slwWvCLBU+H9CWCdmBFNfu1RX2UYHbbomXG8yjQgDJL1XYRzl+u+26StJN060UyNFGeo8N1VYgIAUqOmL6TvJzX5/ivcY6jWssyVOLG0bihQrzOJdNxklKdqpoXC4Q6mGLPkmJ6y3RKWE+Ks3wXUO1LfqwZsNjxi0srThHzzRqkoluarOlWKtwd4aYiyWTT2fouZUYPifdZN0iaI6eg0kuaJlTQzqRm3l5IdC7NaV8OpIYNwO6Nd14WJyq9bMcrbskkGEkmYN/Y476/Ih2JOeLG3v6b2WsrnpJjpgIl63UOXq3Qlsv2dSNwj3u4weekGp2Xz7i6HSEeZZ348xA2HL43KJyT8QIxqeImEo2RvbU4jYdZmZoTYKqDBtf0CyuKMyq6wiuNYRSo5zwJPOHKc2ahZ6k7fDmkNCLLG94cGIqUrVslKKCZFwzvlxyNusRT3PK0CM9NedFWzqVfOyQRbIzRbnbFRYKmjH0nitWFyStRbeSQoOGqCIHX9qFIhCvlYSTjPKVGrufMr6jWV6OknpihMMXC0/6TLqFy6sw+HqCyyGk4IuEtJENgL5YEhYJ2fMENxByQPqOpd5Q+ASyY02tEkmCySxay8ZXO3h4d4feUtFsRaIVXqkbiQEkVAZ6nv71Oe6zG+eRd8rJ5kS/tMAf9PApFMeRdijdxmYt0KxHzNKgWmGD1lseXTjsMqEdRXrPFEuT0NuXaLhoIk0qUpfqUot+MEJv1RRvJ9TrEKwmuVew2ouox4XIKoaO5CCh3W7RTcLTpxvoDYmuHLxrWX2iBODx003UmjirVaPIDwxl7/2423y4/n/XD/zAD/CP//E/JkmS3/Lfb968yc2bN/nP/rP/jK997Ws8f/789/kIZX1YAH5AV9Tg18QAEazo0tpBZPpaIKpIfijjYdeZNUIWefh8E51DlUSSM0P22oLqUNAYobuIiYtPRmLFUeT0oxq7ULjCiEBeQ3Ys47BVPxchvgG/5lCTDFNJTmo7EMxDtdXlfj43VDtyw0qninYkyBOCaOjqXU/20NLuQPYgwy5h8XKLSj2NTtFTCXingOTUSLZqpTCVkTD4WzX5u+KC1A6yLxYsbghKpb+3ZDlMMDNLslC4AlwexcCxtCxmI7ITQzMKqMOMYl+4aM0a2Oeaajt2UW+ath8IVhJD4lpLWFnihjyvcG8gXMNNRzauqI8LmsMeatTinvcEo7LUNBda0oc5UYEfeNTbfUafPGFynGFLwXX4LCGdg88VPCrEyTuxhMqQzxWmhWAyoob8SUp+BNWWFP/1nkTwxTSwuoigW6YK3Qg02j5OKK+2lPdGMqVyCpUI6iN9nkjGqYLVzVZG46/UojubahwyGveh6zQ6dW44yM7EJWvnBj9wcgxK8lNjElEr6VLXmScOAqGxqIWVIhJgobFL6RCZYUv21YKoobpRExtNtavJn1t8P1C+s4aJEK+UhHlCPMohCWRH0pUytZhlgtVdpCCUhz3MZugybxVL8wKiLTFc0Ub0o4LRu1Ad9Fi9VpGdGIKJtBsBO27wpxnHX9nBb7WkteRda6+popXzqdWELBIGnt79hNXtGrvM8MOAPbH4YaB3N6W86FleFklDvQ7ppSXVWQ5ZoPfECq5kTYpifWZw44A+ltdPN/KZazc9yhn0sCH2FSFoTh6so5xCAcVTYVFGBe5SjT/Nz3FIbV8KdUwkPRKofDNCNlVe2JNRW5EPvDB4pRE3Tc/B8m4YmN1S5yYWtVsLPLrfUK+L+5kI9XfPqU8l5s3nARu6wdSzHOvF/JQfadoR+EzAzXKMkdDz1JkwEU0N7biD1AOrl6VzG1TEV1be/zOLaeSmvHa95NHlcG5Aigbc9YowzVEa/NBz/OkuL1iLnCE9shK5N+6QVkVEPc+pu/Om3BMN9bwv3dP8WKO6jV/x6hllmdLOU8k4B9RGTT0wpM8Sho9g8jpkD1LJIjeJdCTPLKZUNH05RvNI0pBeSCfacSC5vKRKe+jjfzdMIN/qY/xfff2X/+V/+U1/7+uvv87rr7/+e3g0v/36cAT8AV1u3UHsdCgHUjT0nmuyA0N+aFHuxe42kMyEb7e+vpARy8Uac3vB4qQn4NKxRw0knUM5ham0BK0P5DF93mEI9sWxW28E1O0FREV6LF0qe5SQTDSjuzC6L5qYZixu5ajkwo2S4tFnYkgIY3e+o9elltHvPWEEillAYZ9mMhJMIr0ngkZwAxknK9+NwdcCdl9gseUldz4mSqZGOmX3hox2FzLyTiNuw3Ug60iyXhNTGedEK0VhuRPxBZTXmw6JE6HSAjX2SmCyF1YAbF+Y0r8+FcH7iXDWVCIjZjTYmYZJSn6gGVyZES5VpM8TigMpfM2okVSJf7mF2mhwRaS8IdDo+Q2PG4cOUC1cOF0qyiuOxWvCfXRrjupCy/KKJHFUV1rMzKLWG/Bdbm0i8N5qRxyn7SBSPEwIuXRukpk6N7j4rlukG9AL6fzipUtkVwo7leJDdZFsKiiKAynql1c8fiAjc/sko/fUdN0XLa7LINqxME/QtUI9zQUjNJEEDt0o6l1HMleE01Q2FnkU/t47Kflzex4/6DZlJJ1/uUfvQUKxL9Dsekecv+nrUwBs2QHNFaxdmjF86Yx204mxJ5POmco9pHKDjxqasWBkYmWorja0V2t0qcQI5RVu5MmeyMgXwKcCwDYniYywB1JYrS47qIwYKKyYooonhmYUsS/wSgaSueLqxgQ9N2THhtVlx+LlRpy+Jwq36c43ZuVlR7seWHsbdGmIhRcpgYmE0xQ7E+eybuXYXL/LN36aSUzfukDOQxpZe9Niz6zApyOwUwtapYsCNF3GMWsNIY2YucFOzbnbnm40H5JI7Dv8Upzofr9HfiyFYTrR1MsUvZJscbvUMvZtIRTyWbSlIlmKQ3b1UiNO3VyyllWtu9SVgH+pxKex450K768/roSZl4i5yPW67m4/cjAdwmYNFyt8IRnQNvGSc/3c0N9ZYlYyJUlmIkeJSlz0oZA/9kzyuXWrGN2T73vBwwy9IOPsN4e0m46yTHHHuXxuuq58WCbnxefpxzrgtZINfDLrxtKZaDKLRwk+lySUaKQr7PrijOfrA/lcut+f+8tvtz50AX/jevz4MU+ePDn/++c+9zn+wl/4C/wf/8f/8T4elawPC8AP6EoPLOmxId8umV9/j6nVbASqS4IoqMeK0buG6nKL8nD6dA07NaQPMqrTHJ13DK1TS1xaSSxIpNgLO4I/cb3ODZpE6o1I/kx2yPVpQf4oxS4V2bGGqyXJ6zPOXoscfo8jFP48e1c7uanoWvAf1XYgmSvUwuJ7chMOY4fyoutq1jtTxbGhOFKkR8K5W92Q52FWnSbwkojkpWMWwckIWwVY3a5ptyTCKhqYP5LRX7MhDj0M6ElCfNgjPbaiWet57FFC1LB8QyKaqi3BzQwvzsmfWwiK8kqLf9Qnv5tx8vYm8/0h8UJFO4yEgRMe21JaLdHI8y/3AlWZ0v9CIY7Wi93N/+0eqpUIsLDomHGJdI90LVF947csg0fdeM2Idkp1qJpkYtErgxsKwLt4lJAsFNnXC5KJIZkYes8kgcKvO/Jj6D8R3VhyqsUlXMHgkab/ruitkqkw0AYPNLrnRANXK9wbS4rXz9j42BEg2aTJxSXVTmB1SaC247cscU80TcsbrZgQFgpmltFdzexWpwW1oK8tpcC7vhIDgwLSIO9FLjfXZsujNxqWH6lwA9GEEiE5SMiPJeas3AtdkRVF0zZVlO+OwQbKnUizK4XF2dMR82VOciLn8NrXZMPCLEGZcK6zWn6ipLrgIInoM0sspUPDcUb/qRbs0KsLqt0gek8Nze2SsFOjlwZzZkn2E0Zvywh86/MKO7XkR4bVNUeyVMJNPFGUl0Qnev9zVzCVfE15MbLoRrO64qFV59IAe2boPTGcfDyev9+qcNDl/OpWoTdk4zL47iMpbhbv5WevfV7eY/9SyeJqxA29FDN57CDHsnlqxjB4JD+jbaQ40PSfSTFqKiB2BqUA6URz7dIJemWoLwn4u1mXzWd1vZEu75o7J26nk86N7pRoSJeKtg9m2JIcJoRCxp7NhiffN2QHBlUr0jd7Hd4n4AYBvdLU745QVgqo3sOuk7kmAPvyqId+luNLQ3ZkGb5j6f98n2ijnEtvjUQXnEVWtxuKQ9kUiJ7VsH5xSrJQFK+dyTVlT5HOBQ7de6axU0NzqaF9dcXg7QQe9tj8ghhTkgP5LI/estjDVIrIDcHYVFcbVtdamtdX6GGLLjX5iRyTLyKrGy0EwceAOOuziWhh20vN7/Gd5cP1O13/yX/yn/DzP//zAOzv7/PH/tgf43Of+xx/8S/+Rf7aX/tr7+uxfVgAfkCXXSrSuaI+7BFzj1t3qIsV0HVu9hoWrzfMv6OEoAhZxJ5J2L3PwJ5ZzOOc4qALM58Ymm3hAepW8jwXr0mCB0HSP7JT6RyYWgTb1aWW7Ew6Rv4oZ3nQl4JwX6KefCH6v3avEQBw12EDqNdjF0EF6acm2Fw4Zs3Hl5JO0OmEwh86o71aQ21QXZfQrXl0rcgep0SLMMCKgC6FrUVEMpK9Im41mFrRfyQcPz1oaV9dgZcgd7fu8DeEHaeSQN7lzub9Bj1oMTWolWG+P5Qs1n2NLiURpRlFwtCL49NJB8qeJrRd/i/dZFNfWhHTQHjSY/6qJGW4Qkb29Y2amEj81OhtS36oGf2qYFhCKt2M5eXI8pIUSMmlpRh3hpUw5Faq019FqouOZhxIp9C8XtJuOdylmtUfWNFuBJKipdqAxTUpoppNT8gCi5ccszdaOS8W0kVpxpH5bU9YJuTbJW4YcLVlMc+ZfGFbzB25p15kEAQPY0rN9FVHKC3NSyXpsSX0fZe8oJhfi2zeOiVmAbZqmqXkDPv9ArPQuH6QxJRGkT230n3MPPbdAubSXbMLjdmqcZdrfCrFH+sN6ZkmmUkxUV7whDxgCo9uIT206JkU5L60tNstze2SszcC8+tAUOR38q6YUcKFREbuyqvzJAblYfGqxLWFuwPpRA5EOxaWFqWjdIkvlSgvDufk1HD0XdL9rHY9vUeWZCEGh3IvdNSm7FAAAQAASURBVBFskB/Jf+cvedKJ8B5NqcgODcqLeceNg8TgbURxp48D+Yl09/xAGIMhixJ7ZiJHT9YgSGftxZh2+rpHN+DOpMNqSukYxg7g7QeB9FMTqguOclvA6NmbBYtbLYtPlaAj9Z4jPdNkWyXFvqZ6ueLRO7sSMXkkmr3slSnplSVqKVIPpSNuIMajYDt9cRYwF1eoT0vai5/LSBQlEW7pichGmtsloe8pX6lod1rpwk8MplH4ImCfpef5334QcNsNm9cm6ErjNpwkBSEyhdntSH6kRXPayIaz2Nck+wnLSzKZQEG8WnL2YE3wOl9ax1+paMaB6avCkCx3BCljD1PiQY6ppat3/B1eIviySPbGGasLYjBpB5HRcEWz6d+L6HxYEJYJfs2xuCpddztX6FyukSGPhL5ndSEwv+GJay02e5Ej+P6s0GUBf6t/PkjrzTff5DOf+QwA//Af/kM+8pGP8Eu/9Ev83b/7d/nbf/tvv6/H9mEB+AFd5QXP8lIg5p7e1orBnYQYpaDqPdNsfTbFHiesrS0hCfSeanEWdl22cKlCOag3RGuUTeRG59dbmo2ucgmKwduphLOPxKk5+PgJyeszsmONWhlWf3hBvSU3YbIgI+ILrcTRjWSnTtOhQPqB7HEqsWGV0PjtUjE77uMqy+3PPCT9Yp+QRnoHkuaxnOXoJFA8sdiVFAmqcOhLK5rrVaeTEZdk/7EmO5UbtZ1YEU73ZMdc7UR6Twz6eU44ybpRtIy10q/2MI0iNmIg0Q7KsxylI+1l+XnViA7K9SOh8NQbEXWpxMyMaIgqQ3XRiauzlrxRFSVii6gY7C3wa45k0ODzKI7tgSftNbiXV9Qbgfktz+pGy/yGwGrjwOH6Ab8jI7jsRJP8+pD0TOOcwY88biBdL72SFJLBQ838kxXhOMNOLCYJxIMc1Srs1/q0wyiw2UaL0F0jWJZnAiCOVlydzY7DzjXpkSSVRBvQJwnKRJoth7vYoGcWtTTELFJea/AjTzI12DOLTUUfmBxbccfmMoKbrzLMQIpEc2olKm+npvfqGcVzI0kfSop6vTLgJElCr6Sz5/Ya3EzyfJc3HCGB4RdygQz3JYotP5AbfjgVJls7FnSRHrbouWX05ZR4lqLXG9xQxnKuLwVZM5b3VTnRhqmAvM5rHn2pROmIXYq2UXkpou1SOrFhIeM+Hhf4WyXNXiujvqnBFZGYBuqNwOpCxPciZqXoPxSzxOpSkCKwkBF2Owq0m66DgEN7uUG1ivGVKVHHcy1cuSvFBshYfvBAjC/JQjHYWZJOFSThPFYs2ihu2YlkBRf7mrhbo1YCNU+PDcs7a+TPLeX1BnV1KcaIgSPME2zhMH2He6kkhE4rN5Ns66hkxN+OIuW7Y6qTQtiOHqJTxN1auvW9rtsbxey0O5pLlnWEuFehKiOA8TyiNmpCa+g9TEgf5OSPUwaPFPXVGrMSPFJIpPgiShfRHqUcP1kjnco1KmZeEFaFaD3rDr5dXWzFZNWTz8TgCZLNvNKY+4VMPS431FcaSUBpFMVTibZz6xK758bS+dauy8KeGlZXJBayqhLcmj8HwE8Oh8RUNmvQNUR1pHcvwV2qqXe88AeDvEbJRHdpO5LQEitD/uuD358bzG+zPnQBf+Nq25YsE+Hqz/3cz/Gn//SfBuDVV19938wfL9aHBeAHdOmNhv5TwZ00d0fiNLyb40eeZhxZXFWEixWTexuY04R2BKsrTm7um56wFLhuveFl7PCxkuz6nOHWkmS7ZPDqhMFbKcvrXvAnaSBcrZi+s8HyuCcO374jTR0bt09hkpIcJqiew04sse/EQWgiqtWSJZsG0fy1ou2yE4srJFYpf5Dy9Kevkx9L12v+3SW61GT3cpKv96h2vaRQKFCTlPYsJ3mckdyagxYWWfXpJdNP1aRzKcIYt1RPxZUbTaRek/8qp2TMaCOhiFQXunG1jiQLGSvmj1Lifg5zy/gtQ34kiIZmVy78tlS4WUr/kTAOCZCuV+hGbt7Npu8Kaoe+06NcZSTDGnecE4cOd03QO81+T7A4LRTPDBu/LjoqnXv01MIFMbfoRsbcy2sSyWdMoHiQ4Hdr6XZVoimc3wrElaX/WJOeKezbPSm++p7qtiQd6FZRXJ5LNF6l2PiSFMP1poxB0zNF8ViYa82mx+eCnNG1wtwrZFy7sIRMiq2YBsm6PbG0Y4/baHFPe/DaHDcKtBuewbtWDBrOYB7kmKkhWWiWr9TEo4yySlhdcxK1dqJwa9IFUauumMsD5asVvbcy0mODPu6KjlErLMxUxpVERX4qGrtkKt0cXSliHjBWitDZa3Juqic5di4IG+U7bJsDpbrow7FEHaqOxRgf9kgfZtiFwu81kit8bGh2RZqgS43KvLD7jnKKh9LhbEeemAnc11TqfGwK4tZ/kfiwvO7RuRfNXwf7jgahXU8TQt+zfGudUMh5bC6vzlN8VKNI5prlFWg/ucD1Isu5RJeN3kxpb8p0QDmFv1ATOg5hSBDpgVekzxOaDU8yVww/cyRFYWtw2w2h1eSHlrCfw/OM5J2C+ECgy+mpIZ1JHFu1EwkXKrkezQzFg4RkoWW02xiadX/Op8z3LaMvZzz6jUtyp1Jw/cIJxYGm2vMMH2i0icIofKMkvroQneBehFq0x9V2QF9Z4vrSvfRT6RSnx13R3fMoK/pYtd6ccwHD1QqVSWc63CqxK8VqV8F2TbEvr7sKiuHX5LoWlglu5Clfkxz10dcT7IkklMQiMPmEY/jGCbpBElpspJ1LUeB6cl1JnyeMvipoIT8ImJsL9MKwuurI+w2qUbDWkOSOYCDcKqVIHDiKA9ELz2+/zyLAD9c3rDfeeIOf/Mmf5LOf/Sw/+7M/y5/4E38CgGfPnrG5ufm+HtuHBeAHdJkHOc0wymhl7Fjtykgxf5pQHCrsCuwD6Yz4oZcA9weWZCoZlVjpQtmFJp1q4jRlddxjMStoJjlnT0eUuxFVS0HUu5fiF1ZGLmcCmDU9x+rumOP9kdwonUSB9Z4r9FmCrpTkuJ7J6DZ9JiBf15PxiW6lQHRvLKkuOilSL3TC+sqQH8np+yLiSnbCisFDzfCOFTfq14cyousF/EEBtWF5zeNeKomlZeulE0G6XF4Rr1TYhWb0rsaWEtGmutxg14+ohcUVohl7wTaMaWBxLVJebQWVoyJmakmnoseaveZgTbqEzTzFtMBWTfFUEiboROy+NBAVyUyTDWqi17iRl2KwB/mRvEbzG+LYHvxGQRg5wlkqjLws0mx60hMtGqY7Q9K5FMP2zEgxvx4onouub3klSCbwIEIimbbpg4zRHU1IIqvDvhgXSsXppxwxkfOhHUknLBpYXfKovkNt1535QPhn5qyLdssD5bVWMqmDmCOUV+A0YeAJdwckZ1pYkuvyvuu74uxFdRiduSXqiDsqJO5rw7H6aIkqDemZJhZeMC5nmv5Xc8rdIMdQSQ6vtvLe6Vo6NJjI5BNybgULZtRgrqzInifYr/Xl+AAz14LOsQL41k66b9lEoe72MCcJ2fW5dGuWFpwiWSrqiy3lRUcsjbyvhRRq7UCwO+Z5hmkEfQPQfyqOe91vCSMnhfxlGSeaWhHX2nNnuGoUcZKed2eTiRHd59BhakX+NKH/RLiQIY9sjJZEI1rAFxmxplLYLw5QXjH69RyfRebXA/nXCjGtBEE7JQtFedF16SWK5NKSZltSa6qrDcsqPe9cqaWl95YwIlUQx3+949FejEHNuqe84GVTBeRvFfSfqnMETrXjJGZxZeg9lrF6yCLV5ZZmLCYYkVZoHn7xIs0oYuaaZijSkqhBP87Jf0WeV7veaeMmmvxIo97tEzKJlyOTUXh7uRFO5NQwWFsRRy32YU467Qw793PMoWSX+9NMOoED0eFGIzGbDFqqrUi73XYOc4s6TSEJog2+UJP0G+n6esXiS5voWjR+fuRRpSY5M/idhqig2XMsrgWaLS+syKiIm9JdNJ8biS5yntBMMxTgnZyX6jRhccsRjnKy/fcX7CEdvG/VBPK+PoVv+/pf/pf/hZ/6qZ/ie7/3e/mhH/ohPv7xjwPwT/7JPzkfDb9f68MC8AO6QoYI4heCN/GDQH6sJXooCly32fKiuYuAkpD2diiOP2UD1esl7YbHdxmsBEWsjKRA6M4I0gskCylOhm8nqCD6vHSiCaeZjMr6LeFqJaO7FHwmBY1dCEJGt3LBb3Yd6VSyLc1A3IwxibiTHDsSB2x1uzqPjGrHwtbqP1FkE91BcCP1emR5JVDtBMLNkni5QleKMHDYmSGqSGg1queYr3Lp+M0ywlmKzyLVlgi/m7F0FPWiA/duv6ehjFaA0cpJWglW+H04TX6omb3a4i/W6EqjTlMZfZ4mLC97kvvC0LMrha5lhKlspF2k0h3c78HcitbxLJFs1T0BGIdUNEPFYUAvBBqtG85RKe1AgNHKw+xlf/4zIYtkE007ioTtBltKx9CvOVQaSF+aUV9smb4uSBZdyngzWYrBID3T9J7J48dE+I2m1MTGYO51I+SZodiXYjOmkd6dVFyx/cD6zVNUlOKEKO7NdsORHyn0YQZRINxr7yB5wDu1pCccG2Iq48n17TlEyArROfksgon49ZaQSWJKOtOi87y9JJaW6DX+Qk2zHsXdvtCsfSmhvtyCBj9LaSY50YrpZvS2HJ9b86JFC7LBcEUEG2mHUhQWB4pyf0C95Um2SsGyrEvahp2Jrs0XnSv82HLxlUOSmcateVwho9byekM7FLBwqCz2KBEw+CQVB+kgEitDuFAx3BDta0xkVBxzj+9Fycteyhjd5aIFzU6geGrYf7ouSRMe8gPdueMlPs/tNMw+VUsn3Mrm0Geiu7OLjg1oBZejS0V9XKBLgz7MKB6ktHdGmKUw7FQjlWC9HSRGstHE3J+bktBSfJvunKq2A9PXPPWVhmY9Ujy3JMcWvVFLtN1UNp3Fw4R6W2QMy5ea86IeJZrY8qJoONVmLaaPtuOJLrTkhc/kuSqvSE8M8TBHVaINjK1sbvzYU359DVpNe7Wm7SPcxpulTCMqRcw9YShjYntmWNx0ME+E3emlC61WRrSfeUDPLMGIS5wHffp3UvIDS7Ph0X9gKiagU4tdaoKNjD6fyTU385iLK7mWTBXNPJW0mMITjEDNo46oWsv/d88BLV1Wxh2M/n1cH7qAv3F97/d+L8fHxxwfH/M3/+bfPP/6D//wD/NTP/VT7+ORfVgAfmCXLyRpIZooqQiVwhUy+lpdkB20Xom42c7fSwnJjzTBgj7MCIuE3kNLc7HFDUVHlz9J6D8R7Vf6ICddr6hvCN1+/pGGCy8dEW6W1BuB9WsT/F6Nf15g7+Yy3ukHVm9U+E/Mqbc7w8emoEFwitVlGavFowzVis4qmWj8iYxLYmnhconvBdzFGpV5qm3h9q29aYUn99oCO1eELOBmKdmbBf0nmuxpil0o8u2S6BXqOCV+bUjoe1ACRc6Pu4IpjYzuymP0b06l4DvOzsdv+aHBzg1RR5KJRSkIN0ryZ5blSy3JxGL2ZRwY16VgKQ7EqRcVNGvSLYybDeX1BhYWlYrjMjsx8t516R/1ejgvksNWw+qy5+D7RE/o1p3oEFMpVkPfo4ctzaWG/gMBHyuvCIWnvC76wlgZXD/gLteiA3yesTruoUopdAcPDGq3pr7UsrzVUjw3KAfzG1IgMHC0Nyv8ToMqNe1GIJ3ISLlZi4SxI983RCtMxv4jw/ILm6hOVJ+sV5hSQLqz1wRh0mx4kplicVmJ43megFNU1xvszKBrzeKrGySnluqgDyrS7rTgFYOvpxQHiuUfWVCvS24070hsH3PLztZMxoorS36qmH6XFPJ2qdCVOKnbQaTe9sy/o8QsNetfEnNFfqxo1zt0zWFCfb2m3WlZvOToPTZQePRbA9JrC9ReJe7OTg9ml5p4uaTZ9Bx/bpdkoUjODINH8lnMH6csrnl0o0gPLG6rZfc79kl3V6ie6DsxksxTvr1GMlesvWnZ/FUpIFQrRpBkqiWjt1K0Oy3Tj7ZUO4FXbj4XlzZyvvWfaNR3TEXCcD+T8+AjC5JTTbvXSuG/lJSXkEpXvbpdwcUKs9YQRo7+MxnltrvNeXRfdnXB6vWKkAeSiSU5M6jOtR2SSP+RoTjQuK2Wdlc6nen2CjW3jN9GnLa5gJApRFLQ3i7PE0yyExm1umFAH4qhRdJClLyvJxnFM0O1RQcWV5SXPeWebJbqHUfvQD7LvceGdqtFdVGGqhRCQXpoiV5Rv1oKIH8/x12tpGN6nJAMpAB12y1mYdClQmnY/Y591q6dkV9YYhedIWggY2x7ahm8fsrl739IvS1msKqSiMWQSHFtV4rq/zbH9eX19vs9ksOE5qMr7GlCfmjZ+JWUajdgGmE8wntTjxeImMEjAfon0w9v6f+ure/7vu9jPp+zvr7+m76+sbHBD/7gD75PRyXrw7Plm1xt2/L48WPefvttTk9Pf19+5//8P//PKKX4C3/hL/yOfzbfW2KPE7INocZrp2guy6jBFzKO6j/VEmvVSETbC4TCy59+iB851r5iiRbMaYLdKs85covr0n1o1j3hYZ/8nRyCgkZz/Ou7uLkEuJ8+XSM6jd6rRGOkBfgcayPg14UR/d3Ak55p0lO5+dTrMraK6w3NhqfZczBu5YJda7zTXHt1n+RhRqwN7UhGks0QBvcs1WmOzyE9EX5cvRlYfKpERQFf+3cHIig33Y3ORLJRzepCoFmTUWTvmWG1J3m+q7tjcf5lAdVoYs9TXnb4PLB9dUK72xArgz/J5HW9m6DarpNlIb+TkZ5JYaMb6Xi22y31jieuxIFaPDUwFUCt60XS5wmoiB03Mr4uBI+S3c8lPkpLkapqjX2WUjxIGdy35M8SkntijGm+YyHZr3MxISRHCb2ngnyJqWjeVNcwUIXgVJRTzF9tyd4UjE/xKKG86OW1226xo4bsQUZoRHsXbcRuiOO43gykE0X+KJWulgV/tWJ5XSLNQiHj2Hae4bZahm+lDN61FAcyBm+vNKxuN+QnErunAqTPE0yFpI9U4kAnAEYAucWjBNeH1YVA8LqTGojGMxjR/S3/1Q7FUxnnL244mKSYie3yWKWjl8wFO5LeLeSmvClavNXHS/o7SzEJDESAr1ZSHC6vCcevHQTae0P8LEU3qnO1R7JjhX5UYIYt7Y1KYvVamL7mCANPtedQQWFvLWj2WuxpwuP729IF8YrROwYzNWBFj1pddJx9smF+XRh8L2LRooK4n+NfWaLngv0hwjtfuUJWtJQ3G7KJotqMrJ4NJIN31HUrvzogP5ZYO1MqYioFRbSgBy1xYUm/2iP5uoy9Z7cd1VZETxN8HskurKiei9ZPVxLZ2O61xNIw/uQx+vU51aboa4nSuc8fpjQHEllx/J2eZi2SnYrkQSmRpKjHOXZmMAvN4qZjccOj1hriXtXFsEHcaLALKS5DInF6diXGEzVqcBcaygsOs9KcfcSRH1iWN1qSQUPse6o9MWD404zsVNF7JyOeZPhUNk3pnUKmEAaRj0Sg1WQTRXGoGf5iweGv7jF5Nqa9NyRqMJdXrH1BNJNoWLy5wZ2nO6i1RrLDv9KTa2q3IW/Gkd7PDzArBQvZwLbrHvOuwN2jjlSbci0sL4hEIDsSrSgB2jWPv1wxuymazZi+v/PT+G3680Fa//pf/2ua5hvxPFVV8dnPfvZ9OKL31odJIP+WNZ/P+Tt/5+/wD/7BP+Bzn/scTdMQY0QpxeXLl/n+7/9+fviHf5jv+I7v+Lb/7l/7tV/jp37qp/jYxz72u/r5EBTxUkWSeEIHRKY0uDWHaiVRYbUnhWBxoIkv1VR78vWvf/kq2iNuxBxG9+AsK4hpEGPG0OPzQL5WUZ3m2GWCHraExuALfQ5CdZstykTsOz2qC6KTSWfgOp2KuyFuVJzQ/kMRsTPNpU8/4/lkRDwusHODLRXNWLpZdqnwMeXhdI+447BnVkawOsJpKsXjsCWUBr3QoALKa9KixWU5BOk4qFogxO2OOD9dT/KQ+8+gGWtWl6Qbl8w1UUcGDyyzVz16pQlOwNrtluPo4Tqm1GQnmnpd9FLlTiCsOUyV0m463AWPTgI8y0GJgNxM7TlYOJkpVtcc9sy8MD9CgN7DhOy7ZkwPc+xC0VyrZayEMAr9yKMqjRsFfCGO4mQuuJmwSPCLDD/yRCPJKMojInEbMVPJXe2fyjEs1w2xcGJGWBjyk8jZ65FkqiVr+LKXHOGrnmYs2jzW5QYc1iQlI2RQXvLkB4Z2PRITLxKEWsZ1KNERgqW91FBtSjpIvS6xes4a0iMjRVoaKB6mUkRmkTBLqC62KKeJSSA5lXxYn0W0V6RTTa0L4kj4b9WOF5iy6XKFR1GKxnEDz3OSuaa+UaGPUilOU4g9T/6OZnkJQIr1bFDTNJbBtSnzwwHrW3MWX9ugXfPYqWH42ilnD9e6UZwgX3QSSCaa+tML3EkBXdZzvesonlrIAvmjlHojEMYt4esD0s6lmm100YCFI/yRCckX1/HrEHdq1GkGrWL8yWOOHq+DDdCKyzWdaZj2ia+scGcp6UTGuOVpIZuFLiK299iwvCHOc7UyRC2f82Su0B5M2Zlq0oA6S0nmmmrXg0E68eNIOxYwdnKc4FpD8cywshHtRfrhVwmuHzmd9tkYL5k2Q0EU9Vv8wpIcKKpLQRJE6g7LpMQko2cGty6axhfMUdVzKB0x1hMf9dFBtIaxG8G2Ayk6lQk028impTUU91OSmeB2/Lpkhecbcs1K12oal5E/S2jGgWo7CnVgYqk3I8lSohHNonN654F0YnFOtIuLW17YgzNNemJoLrSClprkpC80u0WXX30k0wufQbPbyrWqES0uaw3za1lnkjHvfd+mp//QUG1GfBqJrYbCo2aGejMQR60Ymh4mhFQiMePAoZ6/zwXgh0kg5+vLX/7y+f9/7WtfY39///zv3nv+xb/4F1y6dOn9OLTz9WEB+Nus/+1/+9/4sR/7MW7dusWf+lN/iv/+v//vuXjxIkVRcHp6yptvvslnP/tZvv/7v5/v/M7v5Cd+4ie4ffv2t+V3LxYL/tP/9D/lb/yNv8Ff/+t//Xf1GNkvDXF7OU2a01yTC1n/kWV5q5VxZiZsPDvXrN3xTPSANEj3TW/VYujYaxkNSibDseBQLs4pbYGqOsyFipB0PLonuURvGaSgGoq7jpml9yxSHFjqdcX0NY+da1QLoTGojQZ9mpJONA2BkESe/vpFGX8lkdE9mL4csAtJ2Ki2BRzcXGrFlDHyUtDONeVlz9b1U06/tkXcbkRbttYSQkrzYEDMpQthGnFF9p5q9I0l7f114qk4l6cvR0LfkZxa2t2Gq1eOuHdvl2ZDblQv0kXcxQaqDiD7B445m/UoPt+jGYsOrfUCejVTg75c46qE3pFm+VKL6uKkAAhQHEaqXYWtlBTKQZHvW9pBxH1lg7DpaY0iedwF39eK8npDcpTQrnlU4bGPU9QrS+oil4SUqcFttqKRMxGzMLgNcbdmj1PpIiqotiJuIHmmcZIKO23DMb+ZUDwXOUA2AZ9r2g0vHatSYW4vaB8Mzh2t628p4v99wtnRAJ9rsomm3hKTUH6kacaRl7/7EV+NV8ifW9Z/OeX0uxpCJqL6/FDj1qQ7jZL4uuGDyOKyxACSBNRKWIKm0vgMKVhq6VqHPKBLGeeaGmItqTTYSGsMdqekXaT4aYptFfV6oP+VnGYc2f3MPk+eb1DczZhfF41j8MJdWz2UODznoX+gOavWiNstvbspq6uO9t9sUlgo9zzDryfMX2lZG60oXUF43MdcKkm+2qNZD5iLFe1MQNDtSKIV02eSCOOKLnXFaymuHmW0Lqf/JFJvGbJxRZkl5M8tq6fbmM2IH3dj0KHHjyLZfoJ6VGCVYHpoFWbQ4itL+VKNPUrFzJR7/AupWBTDk+9FkkNFNolUG0rMPSbiVErMPcmRmMeaDUV+aDElLK87zDMxkm1+3jC70XXUY2ekOOpxdNOiNgK6UqiHPdJbC9qJJO/MDgeYuRhl+k8VyUJTbktXu95x9B5Z6i0Z3dNo2sKTXF0SH/Zpd1uU1+RHggVq90B1juBmHOUc/9icxZM+ygsYHAXGBMzMEs4sthtR25Wi2fCkhwLm1o0A6VXnsk4WCuUNxUGk3oblp0qydwt891zbccCcJCRzKRqrrYjbdORPE6ih2XYkxzJNUStDNuk6pTstVOKQrrYkU7xdk2uyrqRjq1tI3pjBOyPSmWyk+g8N0WTU65FmPdB7IuBsv0xYf+05D7/1W9CH69uwPvGJT6CUQinF933f933DvxdFwU/8xE+8D0f23vqwAPxt1q/92q/xC7/wC7zxxhu/5b9/5jOf4T//z/9zfvInf5K/9bf+Fp/97Ge/bQXgf/1f/9f8wA/8AH/0j/7R33UBOL/tSZww21QtuJFwlpLuW9QrCzgcUA9Fh7L/HyjCVkXyJJOUhF7K8IGh2rZMdqwIySO0d4dYIJtIFFZdDiAP+BTyY4XPhZOlnIICzL7c3FYXFYNHXfRVFrDPDL4XyR+k1Lud1i2VMbXri6lENzIOnr4s5gd/q6R6LE7Fas9R3M1kJ5xJHJPr0BfHDzbQOrK+OWe6mZC9m8uo1SBIj6RjyLWSRhDLlPZKS3IiF//Ru5pqW9MOA/Yg5fHzSxQrKbiUjfhRFKizjqQnMn4+frQmN5ca+W8lUN/VEIZ3LOqlluXTHvV6JHtuaTaCsAENmAaqTSX8sU2PnQr7rh12ozgDZqHxa47RjQknJwP0/ZzkODmPjbLPxJEZ3h3Qe2VG+JKMrN0mUBt6Dy3lRY+ZSD7v1nfu8/T5ukT7BTqmmmQ5J3NNskhJ5rC47rFLTbUjI2ddacxpJky9uwOyqeSy+n5g8mkHh0OJhQtSPPWeGMrdgM+lc/vuL1wni4LYqS4EaHXH2AusXmror5VUyyF2bqh2HMEamg0x7ujW0A6F19esRWLfoyoDoduM1JpQBFTQhKsl5q0+yVT0XqZWtIsUlXki0mV5wdaLGp5/cQ92G5pRxPeFVejzSLvmSU8NzbaT6d9KRqT99ZJyu4NAV5A0Ee0Mixue9NgyXa2jB/KauUWCu9WQP0xpo+BrVO4p3srFdDUKcKFGP83JnllKJSgW7eS9nb4ixoZqmUqU3c2K6DWqG/VGIzDpdsuJEWVXunN7FyYcHo8Ik4xkrmlHXjr3PYU6S0iW4kz2vUjvqWJ+MzL/ZIMyAW3kVXLTFLte46YpETEVSeSfIHGStZrWpFROsboUsRdWmC8OqLYDppHEHpUG0mcJulViylgMqS546icjdGeU0Y1i+oaj99CSThXR6POsaVV2HbZNjx20xIcCk/e5RW3W4mZfa1EKel+SMW29GWmu1egHA3pHitWFgNfCyVw9HWA7UoopBdTtBsKkbNcDDFv0cSrv7Vyc3UTI50oMNoeGKrH4zljVbnj69y2r12rqzIrT+KzAzIzo/kpFcmIpXj9j/mREzAKuVPi+uOW164DxvYAPYp7xrRjWVGXwEfSXx6S1XCt0JZvgdKJwY0/+THBdzUjMe9Nf3P2W7z/f0vp2zHA/IDPg+/fvE2Pk5s2bfO5zn2N7e/v839I0ZWdnB2PM+3iEHxaAv+36+3//739T35dlGX/+z//5b9vv/Qf/4B/w+c9/nl/7tV/7pr6/rmvquj7/+2w2AyB/bgib0u1Cgd/wlBelA8Rpgb5ZdQJQSzrRVFYMEuVl3+WFyqjUHiYitFYSFaZSTx0FQ6BbhfIaP3ZU2M5woqk35Sbk1qD3SNAppx8RN56aW8obDenzhHqz4+NNNOHlJe6wIBae6kIg209ohxE/dvhWQ21gs8Wu5EZYXnYUWyvcnSFuKOMk3W+F5fasoKxT0lNNddET00D+OHkPmrvdolYGt+lhlkpHJBdn7+qCPHfdKri+xB0XqKgxk4SwW0s3AsCKEN0uxSSgTxJWF8RJrUJXHK5JFFi5P2T4TJMfR46/r4ZFIjqjSyX1TG42uhJGHUFGlsWhwqegS6ivOAhwOunDWUq9HsTR24+oRoTkbiBXTf/VEUpD24/07qYSgaYRgf8CYqk5+PIuxVzgxlEhWiikQG5HYujwqTgLXS9S7HeFc5fcEm6v8IcFYSXOUwIkR4lkIkfpJsUuGUM5RXEQJTWiVlQXvDiMvcLMDP5yhX2ckz41RJMSLnqyY43PlXRWlgZdi6M95gFvxbhBInq8dh2yw+4iqmVcX59l6Bfnb88RZlZydNdL3JMhyVxG7sM7Vm6mZ4rl0OD7ktErY2owtZHisjTEUSsZz0vLyg3ARHQp39cOFc1Q5BSCTQFdgxuCPbW4sRfW252CsOWh0kQr0HAuVmglj1VtyfhaeUW160BBf3eJ95rmSZ8wdAzHJe7X1ykvOlSjMaXE8pnbFebdRNJ8eorZL+wSbjWkp5p2LZDMpKgtrs6p7g/xuWwwTKUkk7bnSTJHeFbg1hzpoJFIxSAGC8me1eTHCtdDOlhTYS6aUtFcqXCNQeWSdRycGF5cTOXakUeqvUBUET1qMU8E3+ILAWfryytWKpOEoM2WYljjn/cxlSSaZEeGps1IKzEG6ZlFP8sJNpIcpJKDvBfwA08ysaC6TWtfusu6BV+I9jUq3bnipcBb+yU4fcWwvOGwz7NuQiLvY7nuKB4lIiPYCKTHBpVJ+zQ90ySP5LHNYfc8lwmMPNmxoe4F/CCiTw3VV9cYHisWV6SDHi41mIc5PgM/8PQfWJbXJVYxJpFirSK+OcKuwPUQPusIikNNtSE5yMmpIT8GFSMhFa2zz9/n6unb4eL9gIyAr127BkAI768z+9+2PiwA/x1ajx8/5r/9b/9bfvZnf5Y8z7+pn/nxH/9x/upf/avf8PWouyImICOZ+znBQnuhYeNXUuY3xAmaH3FuTigvS7GUzAwuF8PEuTB8Bl6LFsVWSqK87hQknzyjfGcNv1cTF5aqw5GYEwEFV1tBkDHjlvxBRnVdRlHtOGAqGTESFdzvQT+iUhn5NNcrYm3ARNIDQ9TiVF5dFWyNLjX+7SFuLRCTgC4cNhVTSjpVVJsZmQVdKoIXTA10WcZLI7/bSwLFcFwyB0wS0Mc96h0pgvU7fdiUAso0EI9TiiMtPLAzI67L2yXDzxVUW5Fm25PvW6pLLWYmmsXyssSVNWND21fEIM7NkAeYZAweGpYfqfBWDCvJlRXNSU7dGOmu7TT0RxXN10fYMqVZC2THmmpXXuc48NReiivfC/TORBOWLEXTFXqBMJd82pBICkS1Hah2A/0HhsUtR3oq/9ZueLIDSzuSDkMyFxBwvRHxQy/MvPsF7jjnwr+JtP3I9JZwiLOJIjuFZiSMyfl1aDc9e7+gOf6YwlZQXmtQJuILRf9OF8/lctwoAMLdS6ZS2IIUoO0oiJ4w9xT3U6pdiSSzzzJBs6w3tKWmd/uM+fMhyUQcw24QOj5cQRh7eg8tFQO0gXYcGdyz2JV0+UxlRR5xXbpz5SseziSRxvcj6ZmmQWLIXuB4hvc0Z284Vnti6rErRdux9vyaAyx2rmiv1uhJih/Icxy9I6/14qqM/PxRjl4pOd6eJ3uayGhx3BImKeWDoej71gLJUcK8HWLWA7oW3FD5WkV1MWLfGbB4qUUvxKBS7UjCjqkUzKSAB1jNM7BSvIQsUl+vqS53bMZnhZxb2uJzgU+HVDJoidDbVySLSOMV7a5wLqPIbMW00QjXjwiq51GnAgGPiWHto8ccPVonOzLUNqIRDR9Ix9w/6tM7U7QfW+DPcsq6h6k0yVQR0q5Q9eLozR6n1FdrQiN6Y30ine38aYKpRNMag8JttbhGzn03iMICnJtulBpY3HAkE8OzPUNIA7p6cW2riKVl/KWEs4/I58ynCA4m0SgTcJdqnIKm05D6nQZ9lJI9S7Ar6UJmR4bsRLG4JlzKxRUBrqsTzeA3pFvpetAE0foVT2wnIVE0qyHhRoX9So5u5BJpKilo3cgzXxOD0+K6JpkJs7H3yBLKD0bx9EFbd+7c4ed//uc5PDz8hoLwL//lv/w+HRWoGD9o2MVv/6qqip/4iZ/4bd/Az3/+89+W3/PTP/3T/If/4X/4m9rC3nuUUmitqev6G1rGv1UH8MqVK1z+f/0VkuUIPxAzQzTQf6RJlqLxWV0Vd5wUDrELYO+e71Yg9CTdQ9kgI8+lRJr5QiKq3DCyduuU1W9s0Q4DphSNnk/BtFDuymhHBWi2RNRPq8mfinswnUqhMLvdcef+T4YL06VaRCtIh9VlL1yxRtyi+b6hvNGIVq57vLaLXkqOEomX07Lb1wsjHcY00l6tSR9kDB7D5PVIGHj03KCbDrEy7piIOpI9krzUdhSIA09ykOAvV8TTjOxUU12vSZ+lNBdaBl9PWbxWo4wgVtIjianyQ8/oawnVdy9wjSEGdc71socp2USwKa5zo4o7N8pr5RV6YQhZIJmaTienqLdE0G/qzi18ZYm7O8CuRDTfbHpGb1uasYwXTSnjxGAj7nqFfpafj1h8L1DsG0LXjfJ7NWY/I1yqJLYsirMzP9RoD4tbIjwvtlbUVcLolwrOPtGiSnFirq632FPL2ttw8r01apKSHWvp5PbCe++bE61ediIRgK4vrk637li/MKP63CbNWmDwUFN2m5NmTV6jkIfzYi05kw6d322IUdAmzcsl9kFOux4w6zXJWz2Uk9fK9SNqryK0mlgbxl9JWF4WDVh1SbSXeiWMy+xY+IzlRS8OdxWxK4Fkm0phVko62EgBpJ0Ssb4V7AkKVh8rCY0hOUzIj0Q2EcQPgKnALqFeh+qiIzuwhJeXxEc93FA+s7qVjF8iEgGWiFnGljIKtk8z0WV6YfWFnYa4sNi5XCOyiRiSTP1eukU4S8+5fcW+Fqj3ZoPZz9CtaBG16zAlpRS2ysvrx8tLki8MJOJuJKafqERLHJPOIDYWc4xpEBj0oCVOUvRGg68M2ZO009dBfqxZXfFkR4bqSoPJPXnRsHo8PJd2KKR7qCuFaei67lpc5lkkubikOegxvCsxjdxY4U5y2SD2PSRieApZpPfUUK9H3HYrEYWJZHInM032xhn8wjrVVhfDWMg1av6RmuyxmJHaDS/Ra3sVYZmIaaVDuYB83nQjKB7bc/hJRu+xwWdyjruRJzk1hLTbVKwHeremzPeH2JlscKtdL8DvrYbxeMX0yVg63FFeT9tF6vlBIKYBWoGbq8oweKRZDUvu/5W/yHQ6ZTQafVvuS9/Mms1mjMdjbvytv4jufXPNi99uhVXF/T/3Y7/vz+H3av2Nv/E3+K/+q/+Kra0t9vb2UOq9Il0p9W2rH34368MO4Dex/ov/4r/gZ37mZ/iP/qP/iM985jO/6Q38dq4/8kf+CF/5yld+09f+3J/7c7z66qv8yI/8yG+pF8iy7Dxn8P+8tBEoq6o1vgjkBxafweITNebZC/BuwFSCFAg20tuXzo1pFHHDExsN8xQd4Xu/603+1VdeIz2wMuKzkdlbm/jrNenDjP6zyOKyjEkG9wy6Fv1a1MjIZJoQex5TQ7MeqQ34TJ0XXGHkaDakw6hWhtjzJEeWYCE/MFRbithllkr8lUI1ckNut9rzEWQyU/Kcaqi2BAmigqQ+ZPfkwnT6cekiDd5JWF4OknChwBwmuG7MqTqDQbPnZQxeRILTmK7DQd3BcmthyK1tLCWGy0a5YdUK5SztELzXEqfVFRIEhdtwaGdxF+X98P2A3SkxJtI87hPWHHahaK06L7qrrUi8WlJ8vke1HYkbLdU0oz+R59haQd+ERNJCTC1mGXuYSHB8YwjrjtFbCYurgZhG6g0BhPsCsrs5IYso6wnGkj+WMf2L4iM5sYw/dsL885voHM6+o4YORhss5E8SQhaZvBFh2mFFNF0xoQmpvG/F1ooyy4jTTDrEY3Fwskg4u7+OeqXEPsrPi7+o5b0Yf+SEk/vrhEQQJX6lpWBeWPS4odkIjEcreJpTlYZ2UXS8OGiv1sTS0vtKTwrSPDK/0Z1PVqF7DvMkp13zZIeG8pJskGLPY48T3DjQJp70RD4v9XYgZEH0k4UcZ0giMYq2tB0FktQzWFsy7xWUqofbdKSHluxExqjz7ywJKwteYNC+NdgAupEoMHvYaVsThCU5E71XO4gUg5rqSiR9kKOCuLB9x4hLzxT1RqTcDbBds/H/yTj+lHTpEgfpTLG46VjedvTWS1ZHfeEJzuXcbocCDa8utLTb0H83kZ9ZpGStxNPpVp3nHW9en3B8OMJWCruy55/DdqDRMxnT+mnC8OKcOUCXYpLMQdWKasehFhZ9kKBfr0B3rvDtVkxbk0Ti/wrB9UQtmlK1UJR5AWmg3tAixTgQWoHfalAK6Q5utuhpwvK6ZBl//NZjvvL5GzB02LGj1TntwzFDL1OTejOQTjXz2w57mFLvSHZv76GVOMT7gplyA4+pjcDAO01vu9vSezdldVMxvCeJO7pR+L1aXMJa+Jj1RqD3XDNf75Pvi6O92pPJQzJTrHqW/k7DcrOkSjNUKUa3ZKbE5HRk8IXwOVdXRf+5uhjI776/HcAPXcDfuP76X//r/NiP/Rg/8iM/8n4fyjesDwvAb2L9s3/2z/jn//yf8z3f8z2/p79nOBzykY985Dd9rd/vs7m5+Q1f//+3QmNIV4ZsolhdECfn8lLEpg5/KZI8zXHDINquLGJRUvxV0m1JHqf4mxUh9+Rv5fybn/0o6lpNezmIg/Pj4thNHqe4XmR6u0vGaDTVViQ7USyveUypSR9m1Jcb+u+klHtBRleXPG4YGb5rsSvN7FYk5KITtEuFG8uxKQ+ra470xLD76SOefnVX8k1bDaOWuOnRTsP1Fr9KqC94lIpUy0Q6aNs18Uh0g+tvBebXNNk7Bv+Hz2iOx9LheNERyyN2p8Qd5eSn8nphIgpJUlgNZHxbb8uoNOpILA3tMFI/G5F16Iqw1eBLi50ayps1658tKLcV1QUZOYXE4NecaLMeZwweweknI662uHlCf1/TrBLaUcRs1h3Ww0qKy3FGsxbPgc5o6Z7V65Heq2es3lljcbsVFMYVR9JrgITQjRd1q5i/5MGp86SGqLpYNOT9r44KlOo0ausN5llG6ODAp+9skJdi3DDHEpXVey4YFRSsNmJXVEXawuEXhjB0JM8S6ssNqjJUTwfY7UqMBDNFkxnIESbfwBHnMq9MpyKQTxaKchSZTPuiI9z2AgQfOZJD6QD3v1BQb0Qmz8b01hX2OybEL6zTjgJ+HMjv5bgiUn+kJP9yQevluaYzWFwPhMZgvRgu6h1PeiTGg3Q/Idwo6b3Zw5aCTIk2svHSKdM3N6n2RDahasHvYCLtWgQPzVnGWWsIswTWPHpp0K1icctLMs3SMthZsnw0EqzJwspmZFO62fXFgJ5biaLrNdROoUrpZpdzyaFWEZIluFyyZH0RqbfEweqLQJp4Tj6uCEkgqcTtaipL75FlddXh3xzTq6UjubjpsZsVxZf7VLsS6WhqSOZQbyBg7u2IH3lCEsjfzWjWA9Mvb5K9tCDYRNJ8RjWLswzdd8SDTLTCQTN/Jgaf4kAMOGcfl4LYXF1RnuXEOkH/q3XUjYDfcNhDQQg1a4HVxUhcbyneyVhdcdipwQ8DdtTgJ5I4lG2W+AcDshNL+5p8XV8sUQ8lS9ucWKo9z5feugZ9T/FuRjtMURelsz97o5XUm1Mj/MC56VA0CoaOck/iAKM1ZMcaN5J0DpDJiPJAq8VwNTUsrkt31o082YNMPk+RLq5SsbgWGX0pZf7JmuRJSv5MjFghk/Huya/s0V5uKB4m55sWFWB104l+dqMl68gAodP+rS79u6s3+/d1TSYT/uyf/bPv92H8lutDEPQ3sS5dusRwOHy/D+N3tMysG0OmAjCevewFS/K1AfZhx8NrFNmJ6GtehL9XVxsJs88hOHGnlq9WqFcW2ETMDtWm6HFiIpzAkEZx7taa7FSTThWLl1viwOGGXnhdS7m4FQeaZiS8v8E9y+KGZ/IJJyyzE0P/cQdafTvDDz3lJRmbmErx5N2d9xIWbCBWhnaSEc9SXG1FX7aweKexJ5bsRJO/I8y37Ezx/I96VhcD8+sB94U1ANpNR3nBo52YUdpFClEcf8WhQq3E8QeQP0pJz5SkdFhxUIYEek87jd0rC8JmS2wM2UYpXcTThOntSHVBWIXn8WqVRo0beGnJ/HtXJKcG+zwT4LQWDtj4+hnm3QLV8yJKX2vExDIK9K/OULknPbC0Yxmxzh+PsAslLLNXS1ThukJOCpQX3TQ8MG7RoxblxBARjRxbvR4wpab/yKAb6H8ll6I2lZQYFYX154sg8YEjx/yTNYuXW1yOjHVtZ/ipNHGjIXsqOqnkICHbWcFaQwiKZiPghpHeQ4vZT7EzGRH2dpc0uy2uoOPBiWPT3Mvx646Yy4gcrwjXS6KBcifirlWkx3KeLc56MlZcKbIje57Fm7xd0A4jzZbHfXTB6mK3cVkJS614btCVprnYkh9Kxy37Ug/Xi1L8mYhq4eTBOsrB+rUJKvds3phIokwEBq0Av3sO+yCXTYTvUjs+MSH2HH7g0aVh8XxA/3FXQPQdzVjSOPTUolLpTpdXW5ojSWpRAXrXZ5j9VNzziejI2lE450qGLOJ3GvJ9i3/Sw+802IWmOFBkx0bGkV2SRrMWRK7RStERgozyi33RD0YD7VA0sHYoGhE9NzCzlNcb0fBqpPCaiONVf3VA/lyA5GGzRTsobs3o7S7xuw2rT68EtHwkaJRylqNWBt8LLC/KhkZ1mCJbyjmlnCI60TaahYyG7Uxj3+kRczHZuNaia8mQ9scZ+XND8uW+yCMyqHY8+TMjGcpJB3jOI0qLNnZwJ2Hta1qK5KUh9AJxo3vOJwmDhxp71mVTdy5v/fEp7YYTFFalUK2YlKIVCUoYOFQrr6OuRSajIuRn0n2e3/LkdzPaKzVoGRDYFeT7BrsQ2HmyEAh7GDtxjbfScddWzHmq0WQHhuxQNhjv64rq2/Pnd7B+9Ed/9By38uLPq6+++m/9mX/0j/4Rr776Knme89GPfpR//s//+bfyrP+t68/+2T/Lz/zMz/yePf63sj7sAH4T63/9X/9XfuRHfoSf/MmfPHf2/H6tf/2v//Xv6ufMQqNjF0afeZKjlOZii3mc0uw6uTmngeUrIqpGiean8V0KgAO1n+FGclMLmaOdZtAP6IMONlyIlgcgnUB10VMNHKo0pIfCOkvmUjQQRSzfjCJupyU5SCh3A5uf15x8UvRI0SA5m2lg7YspYd9Sr0t4e7kmCArUey7eta9qolGcfapB6UjyKJPA+uMUt+HwfQFb24Wknaiqy91tZWSKguTUyvMeB+JYdEzFvqIddF2xhUZdXVGHgpBHqixgDxOadQlsRwu41ecQDnr0n2pWe4HaZJhUQMrVNckvpZL9VrUr48VwmuKCaKw0or+yc83yVgutYvXlddqrDbHRtLstLKWDp3crFqc9sicpzUZnihg3uJXF9aQAL4sMvV3RzDKKfUP9akmZWQhKOhs6ElaW8SNFSC2LqwHf5QX7VMamvhcJK4UfO7JnCa4Ho3cVqwuKbKKY3+o6iQtL0umgqls1amlJzmQ0bB4ayl0BMYeRwz/uYxqF220gD+TPLNp3/D8byc4MzZ0RqifH48ad2cWITpQgo3/RTGrR8+WeEAzmaU52KqL72GiSU4vPwW806MxjHuf09iOTjwQpdp720MhGKOaB/r0EVwhqQ1ciKciOZeRWb8pGJ4wcyiUkXa7y9J0N+s81060MNhxmqdFnmTAIW0OyUERrO61jYH4wQK8MoSdjRXqRxe0WM7PYRxnNlkfXGrNX0k4zwQH1W/Qw4FsDy5yqTLGVIi4l+aS83pAcJFR7jt5jS0ihWWtQrXQ57cOMdhSYvRQwK0GsuEGHV+qLE39+Exm33sshF7NLVDB4KMzF/Diy2s+JhejX6ost168d8eDxNuZiTfOkT9uXdIvzzrLmvEtY3hsRrGxGk3vSDQs3SuEWHifn73H28ozlcY/1jQVn8zUaL/xJOzPkjwR3lCzEDFFve0bvalyRiG51nhDGomtdXXVUL1eY5xluR1wUamEobzbYkwRtA9WefA6zNwtJoHmQdvnciuJIUa9rzFFG2xfXrevL2FcFdV54tW+P0FcqgjXElYahw0xT1ErjG0Vyc05V9XG9zmi3F+hdm3HqxuQ3ZqxmOW6hMc8zAW4rpIOcBkzu6P9aj3YIamFYe0szvS2bEFMr4kEmDunOLOV7gezd93sELH++1cf4na433niDn/u5nzv/u7W/fWnzS7/0S/zQD/0QP/7jP86f/JN/kr/39/4ef+bP/Bk+//nP/44nbd/Meumll/hLf+kv8Su/8it89KMfJUmS3/Tv/81/899823/nN7s+NIF8E+vo6Ij/+D/+j/mFX/gFer3eN7yBv1/RcN/MeiHGvfL//B9Rgww7M7g14VyZhRRZyVw4c369hS7jVHvJ3Jy/Ijm27XYLrRannxXBtYwvNH6zZbixZDnPCaV0KtRZQn5lzuqsgFajeo7eoGZ52IckyA34UU7/qWL6uufCrSOePdyUeLc0oFNPv1ezmOeYJ7nkkSaIU27TgQ0U72ZUOwG9XREPcvJDTd2NHNOpptp15Fsl9X7vXLhvVnKTbEcBu9QkS7l5tLdKwtLSv5ewuiIjORWUXOBbMShUF9x5xq4qDXhIdkvaykpW8rZEwJEG1MKgnGLwWLPajajrS7SOxHcG77mpjSQK6EahL5Wod3u0a4H+Y0P+h445fjomObFdyoEURbpVuG25gfXupJQXPHHozv++uimwZ3uYoB2So2xeiPmFxWaXUvjqRsbQANmBpblR0ftqTr3ZOaSt6Apjadj8DcPpxwKDB4bFNSnC+k+ke9t/Cs24c5peqIj7Of0nmsVNL+YVJ2zAbKMk+fUhy0uBmHuGuwsWD8cS6dZAs+UpnsrzXV17L+VDNfrc7OCKbgR8UUwfbhTIDwzZBNqB6NV2P73P47vbYg55WRAhyksBrzrjiFlo0jMxpLzgLI7uwclnHMO3ExYfrWFmURsNYSVZu6O7wmhcXRDeZPFEHNLxcol60rk4NxzJkaXdkpFhOpHkjBdGBeUgbIvJQgXwlys4yeT4thvskwzfFQfKSTKNsjLeT84M6US6PdUFz8aXNOW23OCbtSh534lkYE9flnEjEZK5xt9eoR4VtNst2RPBpLS7LfmopjopMEvZjGQnmnpDzhFUpL7gJCVmzUnSRxHpPdc0axE7F9NScaRY3GrRqy43uIhEFVE9j32ekr06ZfVwROiIAv0n0lHXrWyUVOjSXfIgo+ibLemhTCyI8rpkvZbss0Mp5pV06HwGuoX6oyvCJCPmHj21hKGneJAI8kmL+/aFsSk/0pS3apKDlPFHTph9YRNfiEELDem+pbnQojNPnKbEJDB6K0G3cPZR6dqbmSE964wnaXdNGklRqrrNSxg7dOIJi4Ts0OL6gWg7vM6pJZkqVrcb7KHcO5KFIJFi5lFLKzipbUdyYvGXK7SO+NMMu1WSf25AvS6pIsWhZnkpYJcKbqxQ93rdaxnZuXnCweOERz/8P75vJpBr/++/9G0xgTz8f3zzz+FHf/RH+emf/mm++MUvflOP/4M/+IMsl0v+2T/7Z+df+67v+i4+8YlP8JM/+ZO/28P+bdeNGzd+239TSnHv3r1v++/8ZteHHcBvYv3QD/0QT58+5X/6n/4ndnd3f89MIN/OZbdKmBS4nRacwqzECRfTSL3bkt/JqQpD7+KCZTdOK/atuNwU5A8F4lxvdsVjqXFrDjPThIVhNRmLW3Cjo6pu1VSPhjAQlhrzhOowpehuiGoioOXwxybE4z7Pnm1I8dlvcaXFz1Lmk0w6bh70KwviuwPasWdwRzhc5c1GclgfFcQ8CgolyniQCCRBcn774T2Ex0xRXvCo9Qb/JMfdLkm+3sPeF/F88h+c0v/FDQDhay3FaNNsdM5c18VVAXalaWYZKgn4kUNNE0k7MYHsNKXe9NRr4NY86Z0+EWj2ROdE7MaZVtF/qpltJKQakjNNvRZZvbuB8XLTUl5ukqHv0WcWNbfEVMbtyUzTGsvGFzSTjwSSYY2608cXUUabFz3m1Ao0+IITQHISIZFukK406ak4EJkl+EJuZgRINiqSLw9YXWs5+8MVOMX8lSDu2FpTb0bJPbYppur0otMUBp7VpyvUQU5MI+tf0syvG/TTIdWWuGzTKysWD8fnjkmAwd6ChRqQnhjssBXN1spw+V8Gzm6JkSGkkagVm79hqDcUvieGiWYoYn3fC+z/xh7mekmzXlO82RdX+KhjFo4dqjv3Q9JpDtcjfremXOSy+WgkOcTUGg4ysqWiWQ/Mr2vJIc6kgGzWxVilD3JJjLi6pP+FAe0wYidifgBhLr4ocPoPLKWWjjQmwspy5bUD9r+wh32QCablxgr9Th9TgRsJOsWeif6s2pKi3qw0s5vQrktkYDpRZPc19ZqiXhP3eLqxojno4W6vCKcZ5mpJer+g3nEUTyzpnRSfpuRK3LR+r6ZtxEDWDkU3SKDbrFjchVryqSOduUlyemtv0N2INDkzhH4LpUGddeinMiU906gOvzS/0RUsUeE2GuypMDlV3xG1hW507XsB1XPow4y4ymnG0q0fPFPMPlWTv5tJsXUsCT+sLKZRJM8t9XZg9xcVq13D/IbEEfqefCaS56KTW/3iFs0VyV8uHktcne9FkuOEkFjSqabZUMxeceTPRSdZ7cjmJ6RyLQlGiu/sSCQDYacVYsAqIdxwxFaRTSCkChzYhRS2q5fFlBK6Irccd3KBqCj2NatrUmy6XkAdSuSfUeCOChY333MGp+9klNtCLYgHBRZIpxpfRk6/tE1cn/4e3lW+ifVtBEG/YNq+WL+d4REEs3Lx4kXyPOe7v/u7+fEf/3GuXr36W37vL//yL/Pf/Xf/3W/62h//43+cn/7pn/4WD/y3Xvfv3/89edxvx/qwAPwm1i/90i/xy7/8y3z84x9/vw/lm17tNCcvFcolEkx/oli+LKPXNpesVF0pqvtD0krRbDnKvW5nHBS+UNiFJjuW8QJB0haaHUGl+FSKLlVqzKmkV5SvVVAZGdO1Cr/maJDiRS9E5zR/PJK82744lMM8p5hIfNfipsOtO4qHCeVURmp6ZVhe8egNiXYLQ0dYa7CPchmz6Ug0mqg1OCke80NDdcGjRg36UUHxzGDvFlSbkbYRIK6KwiE7OxyS51BdbjFz855ep1GY0tCuyUjO96VI6m2sWE0Lkkk3Ol4L9L+UM3+txZ5Y6gvCYmsuN6ilRTX6N2FwzNQye61FJx7Xk9Hg+PoZZ0cDdObJv17QjiK2lGgsn0vR/kKvpRvp5E5f6jAe9OifKWoN2almddMzennC5GAkiIiBw1uDGTVUWUZ6qsW9rGTUaVdyvuhGE5708BmiRXqak9TCggwDT1ABOzfkDzLqLY8v3mNMqtzjZynpQtHseiavi6603hCdk24U1b0hcexh1XU4DSznOfkzK1DeL/fwOVQ7juffY2g3W8kOPk4Ex7KjWL0kger2ICVkonUD6fL5ZULyOJGuShJIj62cq42MPAmiq9QvOHGtIGiYJqwuyuP09hXNGOpLLaZwtGNN8+J8vlhJgdpx9uJeJSPaV2uS5ylRw+CxZvYR6UCGLJCuV8SHgy432sM8Qa81PHlnB5VFgpGuX/GrfWavyDhSOUU0AihWTsw1zTiQncrIWXklG7MNGQOaGuZvNCS9lvZZn8FjTTUv0ApCneMz6YKjwScQjGhbiQqdO7zN0E7Rrjt0KY6gkIkeOFQG7RXLGw69El2kLRz2aU8kFVNLNoHpmsEstWxOBpJ+klWwuiwdYXTEBYVfbxhuLakGCcWXB6wKL67aM4MvItmhpR1r0Uc6YQxqJ6ic5GlKteNh5IhdsZmsNO2asDdD6tn/QwJOT+YaNxR0jIoy7iYqisNOA/lSSZlbkTWMori3085xnkR6jyU5p3gqHV3Xl+I3PYPeUcQNIKSSv1wNNfW2dKd9K69BuRtxF2tiY0j35fxWK0t6bARnlEg0Y9huKN7OROO61JJ2s+6IiSL0ACuTk/Ev5sxeiujnGdlM9NbtMBLGLS4mtGsB3UG9k8e/dYH0+7W+nS7gK1eu/Kav/5W/8lf40R/90W/4/u/8zu/kb//tv80rr7zC8+fP+at/9a/yB//gH+TNN9/8LbX7+/v77O7+5sSU3d3d35TV++/L+rAA/CbWq6++SlmW7/dh/I6WncnFu9luUa2MSkdvpsyvB4pxhX8+xN+oCJNUIsW8sPeKRwntG1IVeJ8LZ6xRqJ2a4q2CekNE+TGX2LL8WLF4vcaNzXnxl5xJkdW/kwrdPkK77s8D3+1S0fTlRq1LS1RQr4kZYPVSg2khOU5ot1viZkP0inCSEosgSI5NMa+0a4psXzovzY5jcCdh+UZFs5KOW6wN/jMzyklB/jQhWolNazscR0hAJSKAJwjc1/WFYag6MfXwwpz5wYDsQPRk1eMhtlJiBlF0+kEYvZkIGuL/y96fx2qWned96G8Ne/rmM1edmqt67maLFOlIsmRJ1zJgOYZ1A1wkiBNEDiQYSAJ4VBzBsRPnOrClBPAfggEbUQIFCBBDMhzEiK9zgSSOo4SUaJmiSDbZY81Vp848fdOe1nD/eHeftkJKpmKJxfD2Agqsriqes8/37W+vd73v8/yeVSnEypH8/6OWdAy7n1IcdtrC1NDbWFDt5KDh7KQPrYw/TQ3qXIn55VQ4Y2ZmCEXAbFQoHfB7PUCMD6ZUtENorzS0QLKXclZOwEYmb1umL2jiwJHnLfWyoFkN9J4Y2pEI4OevNORDGQv2HlnKyx57aiVdpEs6sdeXtO+NaLZFFqAaKf5DEVCFF4zN5Zp2rDB9hz6ytCuh+9nBzg311Qa8MNIABu+ktBNLtJH8SNH2xImqvECR80lFdZqjHNTrHW9vKXKEclu60rHw6NzjF7LRVldb6MwDwULvvrgn21XhwalGk92aoT8/Zt7T2M+cYt6bCMev75jf0PSvzQi1Rd3tEwcSOZecGtqYCpR8ZmhuVcR5AjZCKcw/rpRUTUHvrnSbyu1Ic5pjPj2lOehRvJ9jPn1GCIrKZWLgKALZiaUZgZkbJu/C9A8tCdMUQqfDSwJ2KugQNxLdaQRCP+BzMYXo1NPOUz7xqYe8lV5neE/MVTENAkqeifs1PRYg9OxOIL2yoF2kxPWG/N0MoqVd8STjGs56+H6UDn2XdBM+ZGwWlqwVwPngbsJiW1h6xUHn/j9R+MxK7OGhobraojJPIEFVhvl5IZKKoivet2rCnrwe7bA7aAZx0w92NcvtSD0MFFdnNId9zFFCdqzJTiPz64CKnb5OE3qeMPComSUYsKXAyWe3Ff0nmumrLaN3E+xXelTrgXYQ2bp9xN6jNXn2XHNcuX3EjlqXCMubLemRFW3dgSH+gTPO3hvjJh4yTzhJwCnszOCvy2i/2W5JdxPBWSWhQz+BWanhuBCMjRWOZLKXMXu1ucDiCLhfdRrDiJ5a0rOUZiLyHNePnP6/FqgvDcUos5CpCk4R1ls4SnDpd44L+MmTJ79pBPxbdf/+yB/5Ixe/f/PNN/me7/kebty4wd/9u3+Xn/zJn/w9v85/3vqJn/iJ3/bvf+EXfuFbdCVfvz4uAL+J9bM/+7P81E/9FH/tr/21byji/HaEVbqhJwxa0kPRQ7kXSmZFRn5oKLMerHuSxzl+xeMGkt8r6IhI8rUe7Uho/Sgtp+BlzvKqY7Q9YzYt0CcpbuypMCR7kppQr3liz+NTGZkubjqSc4O6tcA1Bo4yzOUlfNAnf5gS35gRDweoN6ckvz5ieasVVEmDjAFaTaw1FJ7Y91J8AKO3E+k+BRlNEcQtFwwQFWahBUx8JdCcDVDd25W9ck55d0y+m9BMAmqjRh2INivbt5y/6skvLQh7fXkQR5gdDsBGmnEkFh5sJIwgZILsULErpD5TERoDbdepWRp6zwz5UZRUjJXAvAe9HQFPz497DA8Fq5Hsp8J7i52AfxzJnxmqbY+qZQOxM41vcuJag4ofxvBJYR3mmvxeJqPRFU+vSxQ4+5SgdxY3Nc17I0IRMQuB765/QVOuaxbrgfqgB7lnecNR7EhXsnmxRO1nNBue5rAPnZRANd0GlUTIAtmDTBylJymmVtjDAuUhGunGunGgvtbI++UU2aSifdZneTWgRy21BtfTwkE8F1NNvmuIB0O42pLMBWTdvrQkHmVSjG1IZ0/PLXY3kcSUnphIPiwS7UK6m80EzLAlnMhBp344xA4ldWE5H8O1ksEvF8y0ALLLMmX4Kz1cLkVknEiiiO2kEfWmQx+lsNpSvCc/ezpTzCcpsRdptiXtRJ2k9B8bmsMhrHkpYh+N0I0i62L42lWHraDVUkyffkKRvd3HdLjIybuKqCzTFwPBgz0XvqYvpCMMoonTOzm2hff2b6FWJJs76ihmk8ITUhnvRi1FZHpsqGYZ6TPJgi63gkDQG03YKTAt6KXGnImbNXma4vod228kn29dGtqBjLujibi8Gx0r8Cstqjb4XGF6Dr+wMHAM3smY34zk+5Z6VZzX6cOU6sUKziXb2o1EF1qvBXQbiUB2qlkOCkASTFSE009K95/u8x/6ntVL55w8G4uWd6PBz608v87kWaCywOyOHKxCEek/MkyPtuB2Q0jkoHL4T7ewWafxO5dnStJ9z/lhH5NEBnct5SW5Z5PzriBvDNmZps4kek8tLJsvHnGyu0G6uST/P4aEHzllucjRj3PcIFIcynNLeUW7LlIa03fYeznNmvA5q005SOm1BvYymqMCO4rkRx/FdaKA/YxyK2DOvg3AHr9LroLRaPR/aW+dTCa89NJL3L179xv+/aVLl9jf3/9Nf7a/v8+lS5f+L13nP2+dnp7+pv9u25avfvWrnJ2d8Qf/4B/8Pfme3+z6uAD8JtaP/uiPAgJq/mdXjBGlFN7753FZv+0ypYY1T34onan6LCWdycOh2LGUNxratYhqNcm5pln3+BjxWy3mXobvybiRIAT67NDgxjA96jNaX1A9zrD7gskob7Rk7yX0djXnL3Uu4lrGV+3YM0gd7WEhAffA8DE0Q5gvU3QG7Twj3nDkzxLaobgTP+RvRR1R00QQKq2iXXWYKpGkkJ5HHxtx6dUiMI+1Rl8pWepC8nUVxJ4nJEqE6SOHLW2nBVNgo2A0ujzh9v4Q0xV1yVQJ8gLZ6EgC2WM5hX4oCHeXBTCM092GFHBr0qlsxvI1fCEjGj8IYr7piwB9fks6Wcm5xoI4WrtNOmqDqhWhFxjcF4xJ9sqU5u2xdGBNxE08em6wC40rItmZdEWXd6Twg4R0GinLLhrNRFQrY+5yQxyJ6jQVZ6uOqKmm3HbYqZEkkKLrJuiIWlhiX8wn6ZUF9bM+NGK80V3kmBsEVKtpNrwYjnTXpUs9HGSwUdPWlpAHVOHkNUsC6bmlXHHEay2x0dTrinxf07+XUF4SKG84ykSHOhT8jJlr/FZDspvhnRhCVGnQlSTbNJNAyJSkW+xnsN6Q3JMir1kNhHGDWlo0MLsF7VYjedPTlOkLgd6Oxq24Ln1GTAv5oaa50uBdRlwa6jU5OJVbAdV3BGOYrC442x9iy87d3UpHPGRi6Fl985D9pyvkzxJc5qlWBYVi55pgJIUnO5bPab2iKC9JUk5rpPDu7yimL0o2bnnJM7pr0LcWtIeFQIRPNcuXOugw4lRNu05ytSlj/Ggh3UloNnyn4etGtVmAuZFOb6MxJwY3CEy+pjl7XTqvybmMa3WlaNY82dYSdW9AveXFnbqQkXu2b8Sw0eSw2oJXzF8RDW8yh3pDDlTtSBNLi3YKtyZ0AteXAufsDTF1xETg8Mop2oGYm0jk3kyKFlOmqEPL8sk6RYD8OFKvGSEZeDo3t6ZpU3QrmkrtBLOiXQRSfCafqZBJ8ajP5RmjIlCr7rOjCWst874BG1BJgHPpwhb3UsqrAprWjSCODt9bJ15u0M6wvBxJg8YvLUoL6aTcUPiho/cwwfdkTB86sHrv8pyF6pOeGHwB7GUyolYaN/Yob+QgWKsLY5NarXHu+e5F3w4g6Pl8zr179/i3/q1/6xv+/fd93/fxj/7RP+LP/tk/e/Fn//P//D/zfd/3ff9C3/e3Wv/9f//ff92fhRD4d//df5c7d+78nnzPb3Z9XAB+E+sf/+N//Lwv4Xe8dCMPrfmnS+yjXHRAUULcQwr2JMFNHAxb0oc5PjPykEs95WXH6pcNZz9QEc9T7EwLfLnwJDspy5MJfhxwN8QxaaaGxScq6p2M9deO2N+dML49Y/GVVcnATHtigCgCo1/uc/x9LelOwuArGdVmJB3UNElCZSL2xFJebymeJugj6Ya1W6IHM4cpwSmCkQzXpN8QdSKw69uiveo/SGgHlnClEQ3NM3GkpafyUFFTKxFVS03yNMPOO1flmnQS0jMtY9C5lggtJU5TXWmSwwxTw+LFBj239J9o5tug+y2hNlLQZB5qQ3qmSBYybus/0bR94FwKtXzfSjTdtRp1kF5AZJO5Jj+KuJ7B1Ir8UHH23Y56EnGjQPtgRFor3PWSeJaKOeNSRbLXY3E14AZcGFeaccStt8x9IoXLJFK8fEb17oT0SEaBUYtDlgDqLOmixwzN9QYqQ35g0Y0kDfSfaBbXxaBQneQwaqUotJHeM025Gch3JUEDJKpK1VoKjSswfqCJjwvqFdFkeSz5M0vzYkl1yZM/ERONooN/v1STdvxA99KS7D1h8SmviEiHjJmlvBwYPBRZweLNFm8sxVPpMLXXJNouphGlI9XNmiR3hPMMasONl/Z49P4l4pUKfZwRChl/c71kXiSQBvRZgusH7EYFh318K2ihduTxKw57lDB4qDkvLMm5YX62QlZBbxfm12Qc7fqB7Eicy8d6A325wmeW9JEcJopdxeyWFJPJXLrKKsi9bz907s8V5RXH4oqkfjSrgWSqmd0M5F8aEIfi1NeNIt1JRSPWkwNCO4jkpWL1q4rjT3lIA+luIu7xuRwadKmJk5aYRNJBg3/So/dMMe0JID5mgdHGnNm0oHg3p7zSFWdRDBLrv6Y5/OEGdWZI9iVBBiXaunxU452mPc8YXJ0yywaYM4s90RQHiukdiYGs80CxPad8NqBdDwzuJmIE6sDi0UTM5YpmkZLtJBTfdcr0/oR2uxUT0cOCdrOl2jQMrk2ZHQywO5pys3Me9wM+gm4NzThQXROns88DcVWyfN3Io6bJRYylz4RLqBsY3DPMb4MaOJhZ0h0Boeta0Y4EGu87/eSHMXOxMqQfZIQssjjPSQ/E8Vtue9StBfrpgGpDJCfZqabaguWLDZeKimbV0PYSolNkO6mge5wiBIgW2oG8lyoJVJuQ3i8o9qtv5TbzbbH+/X//3+eP/bE/xo0bN3j27Bl/5a/8FYwx/PE//scB+PEf/3GuXLnCz/zMzwDwZ/7Mn+GHfuiH+Bt/42/wR//oH+UXf/EX+cIXvsDP//zPf8uuWWvNn//zf54f/uEf5j/4D/6Db9n3/T+vjwvAb2L90A/90PO+hN/x8kVEnYq2RnZM6VqZuhsdrnrsoMWdpzTjDmrqFOaDHqO9SLmuiKcpTFoG1xc0ztK8O8KnEDLBT0SnyZ8lNCtB9HZ5ZP/JCvbMcmb76EKAu8WDVEC8RcCnivSZhLYvP12S5Q3xi2P87Qa9MLg1QTK4wpK20gksHqTUK+L41MOWduAEmnzep71VEZ7mrGzOqB6uUb4hBS+1QaWBeLVi8BsFro9sdm8uUCc5cbWlXVhxR6dQPDMsb7S0IzG+VNcbdBcunx8rZp+q8C6j3gwM3k9p+7C81EXXpR5z3ukOl5p0quX7ZA5/3GP6cpDxZKmJWaDVSAXjuo1vNZCeSfRWfVMSFkIC9R+ckr43xKeQnGp6+5InWy0SigNJINFHBf1ngWAM1VZALxXJQgT0HCb0nypmtyUPtuz0bsvrAsRVmzW9rxZUr5UkV2ua3T5RK1hYFOIARwFJoBlLnBslcpC4XMsYcuAoNwwqdNm4qaRX2KUYi+LC0rubkiwi5y9Ae6ntIrGicNx+raBeA1NCdiTdpaihuJdRbjuUU6Qf9KjXfZcPa7H7Hwnf/cjTTDT1SkCdpFIo3W4wp5Y4SwiXKzjJCPOE4aWZjPQjkAUePt6g2DO48xztYPLSKQftGrHq3PBaumOqFYNMModlZbBzRf+pZX5NCvd6DYb3LK5DnABUa53+UyGpKKOA63Ud7f2MYk8x/0yJ2cmZ3QzYuaLZcngTJX97aaWAKIKAoU+kG+/WWtL9hGbDESoLmzWVyrDbS8wH/QuIuB03hL0cNmryd4RzV142qL4j67U0U0s2rqg2NfmeJT2F6SeUJG+c5DBxzH6/Zzxc4v/xGu08YRoHqEZTXhIHa75rqSgwXnH8qYg9SBk+hLNXP4xTVIxunnH6dEyxa0kctLsrcL3Bjx0oQ72iRK6x3WFsfn3MaA7TT7e0A9sd3BSLXqeFnPaZPFE0I6i+uAovltidHM4s7eWG9Y0ZtTOU708wJjJ7vaF4kAqvdNQSl7LlhV6QkXfScQu71zs76CgAE2EmmlIKQd0qlpe7zrOy2Jl0hpsrDWmvwc0zqMXA4oegeg51mEIqXX8VQJ8lhARBCdlI82BIOhfUUX4kn23VKoarC85+dQsDuC3P1q1jDuo1skODqUA3kg2czy3VzQbOUpKpOINnrzXf0n3m69bvogv4m11Pnz7lj//xP87x8TEbGxv8wA/8AJ///OfZ2NgA4PHjx2j90Wj89//+38/f+Tt/h7/8l/8y/+F/+B/y4osv8vf//t//PWEA/nbr3r17OOe+pd/z/7w+LgC/yXV2dsav/dqvcXBwQAi/WWj74z/+48/pqn7rFZLAh8nB7pbo09TCiA5spiVKqckYPTD4P3BO+2SIJhItVKsKNxCIcZMZpvur+IljvKeY3hG4c9TQjqG67NCDlt7bBSoIN81fqinezaXLpLjI1vQDyUA13TgqLi3uYU4YySc+39fonZRqQ4LYs7NI25eCNeQBsoBNPH63h7qxwLw9IOxnBAunO2P0WkDt5vg8Mtiay8g3Dxf8sOULDcwFep3upDQbjvKmcOfSY03vocSv6QbyJynNKGAqKG+2KJACz0Tmd1r69xOatYA5s+gmwc4V5VUpWOrbFb3MsdwZdGDlroNzvSVfqXAPpAhJz9UFHiZqSYAwp5ItS4TyuCCN4oTUraJaE81j8dRS3mroTUraD0acvCajTtvp5ZSD6pUKdZJy/op0lnStaMddmLwNgMFPE9pxxCSe+rggWUgBUKeK7ERfGDBUa3EDGWHapaK+WRNbTe/6jOXOAO2hXRWMRbqb0FxtaFcjdjfDjQKmNpy9JK5dNbMSy1XLOG/6snSSsFG6IzNFteXJDw3FroDAmzUvm/GpAIjVwjL8wOB6sFxx+Ffm9DPH/KiPC4bivsQTmlLR9jWYSO+xZRaH6EFL8bWC5bbkt9ZrgVB4VBZY1KkApxNPSMStTJT3xjSwuBrRpRFAda5way29lZL64RBd6wvUjG46x7aT3yfnhtF9mF3vnPSTwOJaIH+7QEVY3PS0qbpIa1FOXUgavJYDl2k0g4eWxXUvQQkK8iPFMs8IPU8zS8m8otlq0TND8YG4qksjnw8i2IUm3ckptwSo3O70KQ6lw1htilnJjcXgoKYJ3mnqr62x/ERzETlm5xrVQjuWn8/OjGjhTjThTsmMQhJiskh6ajh9OoY80PZldGsXCnssXTeA8prErxFkhFnGlKbSZPdzqkuOdqQZPOx0wAsjbMZ1uS91pdA70uE3t+aouwOO6gkq92Qd0DnMUvLjyNlrkkeedTw/e2awd+Y0O315SCaBkGncRJA/dtEVeOtidvkQjO0nHlWKJjC+PiOe5/DugMFceJGmlBjLrGjxbYbvWJF6LkYulKbY0/hcE3oBFzW6gXo9ws0FtiNaNZNAMlfku4bFdiojdSd6zd6Oprwc8AFM5vGtosmUaGjDtz+i7Hd7/eIv/uJv+/ffKEzhX/1X/9VvWTzb/xk5E2Nkd3eXf/gP/yF/4k/8iW/JNfxW6+MC8JtY/+Af/AP+zX/z32Q+nzMajX4TB1Ap9W1ZAKIhO5YSsNgzRK2YvhSISjazZhLAQLkRMV8eo7tcYBOR8HkjnDeVCcoj3be0fYh9R2sN2b7B9ZUgF0p9IbhPbs7hraGgTOaK3q6m3BQuGr7rEgFxIAHw+ZGi3IwMVpfoKhVnrpPxpOupC2RI74kYG/TTFNtAOUnQNuInjuJhSnul4eoLpzz94ja6UtS1JXQuSIBqQ078Zm4IaaRZ86jSiMtxVTZV3YpeyA0EpBwNuGHg2rVjdo/HxA8rahPxBfS259R3R8JNPInET5a0jcVPE5bnCXYuhhBdd7rIhaGKOTbA+lcie/8PR7ov8VUhFdciSAfHDFvMs5yopVCsJ/HCcNLbV6hJif78GFPIex11JFgBTadeUwxqqtOU7NjQrARJTxhqOVynAd+LFM8sdgGzlQyzEJ1gMjOk54rxvcBiW7RjCpEO+CKgb5TE0xwzM7idlCzKa4aOZDtSQKmlJRrh//meMOd0CwpF8coZi3tj3PUK+0Ri+pa3W3r3E3Hvpgj+5MOM6oWid+eMs+MBejchOUxo1x3tUFPelCK4uTeidjDeVfhC7h3lpYBiYWHUwo4hOzQkD+Rnzg8MrhfpP9HUq+LqrtdSkgB+0BKzgJ4Z8rUS+2tDFjfFPFDsi6M+nUXSvYSl0zB2mJ2UepWuswXJkaUde0xl8Dcr5k1Bs+7Qpaa3q1m+UrNcUSQHCfmulc9jt6IGV3Rj7gCmNNQrkXpTXLqtzUgOZfyd72tCountRxk560joBUIihUV6rqhu1Rcu/8VNT74raJL8UCQO1WrE3yrpvSXmr5B2nbGIyCGOEpqbFfpYgNJJqckPDMsXRWtoOl2hO0thrUXPxJ1qF4rJe4ZyQ7aZ+Q1PuwLJeonfL4gWcbhnESbi9icq7FxRXXJM3rIsr0TSaSTttbSF5fRN6USnpyJLSU8M7TiQfGUIiSCJ9GGKrqUbW+xHXE912CMthosOZbU5XLC3ZgmVJdkXCYQLIsVo7pTEqMjv5yyuiwba9yKx0sQ04q5XmPsD1FAOmD6NhCwAGjPT1KFHtlQkk5L4G2ORRgT5nNYrkagl9cdfqXDzRHS3z3qEviee9glbDb5Ncf2A3x0w+NQZ1XtjOTx/91JA+EeKeFZQbQbUWo1LAqNf+80GxW/96k4n/8Jf4ztn/cZv/MZv+m+tNRsbG/yNv/E3/rkO4d/r9XEB+E2sn/qpn+InfuIn+Ot//a/T6/We9+V8U6v/0DI4l6LAVpGjT0pWpRtGXEFHtpdRcX6sUF6TzGFxRXAMcdQSLgdWfzlncVV0d8vrDnNu0a2i3vT0nkiuaP+paNyqKy0cFZhxEN7cIFKuygl6/nKDnon2DZBj7rhlfkNAs8sHI8LL0g4vdqQLZhpFveWkY3cgm5YfBnCK/rsZ7SCiKsFwxGc5T59sE65URB3xZYKKit49K5mylQKlSRaKauJQmSdmivRZhm4tzYqnuRTInyZELdmnSQeR3j8bUvRqZqMUsoCaWVweaRYpXK7QuzmuUFRnOarU9HYN5cs1rTGShPBYUW1A/9Y588cjkhdmHKkhg/fFoFPe6ty6LzT07qc0q+Arg1Gi16xX5PpDKjFc5y974nEP7jiyAyPcupME1XUTy2st+ZfHMAn4Qoqo+W2PWWj8IDD4WobrS7xXuaFITm2ncyvp3yk5/mCN4zekC6ViF8+ZifBfvz1A3aqJpegI2xVxlQ7fkeK9XhW+oF91pFNLM1EyLn6W4a9UYiK6c870tEdY8ahg6N9NMA1Ua3K/9p9qZi8JxHrwSOP+j1WyISQzQaOgI6aC3t0UN0jo73Wj8XXR2/mB/BuVeQZfzqk2BLSb7Vvq754zP89QtSbZKqlUH93C/JaTbtZAwNeq1sQk4u4PCOMouspVTzRibjj5lOCRdKkxlcHUkrATeoLzQUG+LxrK1iuaGzV2LyWdKlwBd64dcPeDy7RjTztB3M0TT75vhdMYobevmF9X+JWWdJrS21hQ7gwEaRPArzgal+DWW+p1wYj0P0hZXpbDnO8yj9NnKcGA9tC7a2lHkizibzXEAzHGZKmj3O7gxCNH9jAjKjmMuJGHxkAq0wPddikrC8nFVUHJQeqS6F+jjQweGmavNdhlSrkhhqV8raQ6zWmmGclGBQ97Yl4IcjhxBRQHct9VVyPTFwJomN5WWOtJnhiWr1eY04x6Vcxiri/A9mpLrj02mpAHqk1FGHiqc4njszNNb1dx/qoU4dmR5vBsi9gTfWh+rJh/V8VgVNJ8eYWV/z3n5BMB99KSOE9oX6kJxxnRyHsTGkMYiSkmdJGPtrLU6571Vw7Ze7gmB593RzS3JQEkXqvQT/IL7mBbONKHOc2Gx/dl5BydRDGKPERRXhNkVX0+xnURmKn1hI2a5URj0kA8Tyi+VsjrNnnOGsDnMAL+dl/fzh6CbwPP+Lf/2tnZ4U//6T/9f5viD2B5OVBPFLaMnL2k8L2OpdaoCydcVBBXG8rNSHbWdV86hb3NPMYGzl6N1Lcr0VwNWoYPNHYmeiTp1kl3KibQv5eQnBpCHilvNTLqGzjqlY7NlkluZbFnyHZSsg+TELqV71l0pWn7grkIFsyoIT20tONAfqgvgKfLV2tWP3NAurmUzaAVDYzSkXCaoc8T7LmYE9LzrquYRKrLEp2WFS36NIHvOacdSZcz30kkzeNySzv2clJPhOe2eDCWLsLUkm0vcGNPWFr0s1zGwre9JBoMnYxVD1L6jwWpstwWrdjscIByimqvLz/Dd5Vy7ecWn0K6n2BLpJv2NEV5Rf+Fc9zYi5HglXNcX65p9La81oPHkO0khCLg80BzqYUgkW/FzZmMrbKIWa0FgD1ucH2oVwPTO5FkIQVeb8fQzlMOn6yQHWn8pYZoonQ3jwT/YheSmczUyjhLQ3okLshmJB1Uu1DoK0vsQUq13oGLZwnJVMFRJs7htyYUdzN0rdFOsXytotySjmvIokTPpYHhQ007RLKEh4H5y62YHHYTqvVI+XJNs+Y5f82xuC44o/RUY0cNJIHeOzntCHq7cv3NJDDsV9JlWmragwLzwpz6aoudG3zRbcK1wZYK3fEedaswGxXZkWivmkmQMeOJIXYHmumLgepaixoJ79DOFKaSLmTea8g/yPCDQL0aaNY9d+9eko1OS2cqJKCHrThgj6XrOn3ZiSO406rVVSpZ0JWMJ03u8IUYOuLAE7LIctujnXS8Xfc5ikoKK+VEgtGMBETspyl2LniYaq9PMtWS0GGDHHAmUsiqRonOdalJzzqEzTCSnmjIAm7sUAH672Xku5JCs7gSoNbUK+C3a5Izw9XVM5Jhw3BjjjuU3ObychAD2XaLH3oWVwNtH7I90ZHqRvBU5nNjmhWJyHO9SLa96Ipcz/CBRlea4X2Dnhtip1HOdxKUV6g3piLR2BJ0Ueh5qhdrSVipFAwdszuO9EFO+d6EZhI4fV04iOEgR5WGuJ/LeLWbKKilYXB5TnupIWSR+nYtNIKF5nxR0N9a4EZeinknxp7gVac5VOIWnkvnlFTuu5BGVCPXlD9N8LnIQlQQFqPOJeFEvT0kuVcIsH+a0NtaiJQhFbnIx+vbcx0eHvLZz36Wz372sxweHj7vywE+LgC/qfWH//Af5gtf+MLzvozf2dKR6ZsNp68qmjUBkvpMxpHuSs3ihiPkgbiwUswkYJfi6GPSkr7Vwx3mEurejfR6Xy6YviCiZkaO6rLHlsgJNpNNwV2tmXxVdzofCKcp3FrSu5tS7Fh4fSa8quqfGfHtinGCANmRprcnBczymmc0KAWQGmUsmMy0jD6CYu/hGvVJgdqsCamMnMfDJfZcEwaOduKJmYxcQiq0fTMzqKWhOsmJWWT5ZMjgsRg/ooL01DD+aoLudD4q99hz6Wqkx4Zke4F3Am5NDyxu6NGNdBh0rbGHKerWgmSmWL5a4cYybmpWAr37CWmH6ghJJC8aQrchhUw0a/NrgqPxecSNPLNHYwjgx57lo5FkFTddcVwpFleU8NesdGKKBynF+hJdajHt5PLnyTs97FIRFgnVlRa7tRSky3lE31wQFQzfTrtEAijey7CvTtEe0LDx8hHN9QZTdd+rVSRTxfChjCvrDcf8lqe+VRMf9kXkfrklbNWYufDM9FZFemo6fSeEkcP1IjEogo24KzVxpUsA6TVU61HMQyaSTjW6cPhrlej7bs0xBylmLoaS3tU56Y055TVH2MtRc8vipqOZBGY3Ox1kC6f3V6WAagWVVPyjAaMvi0kgPTX4S81FfJ6gW0TnytNCUlhenMsB5mEmsgYn2sqoJFdW7+YXHYx6NdLcqqie9WlWBcODArtWkq1UJCsVvY0FequSvO3W4HMBFBMEmh4S0dnVm56V8QK/4ohrDT4Dc68g3ze012rRknZJInYundNQBIrLc6KG+Y1AfaVh/oIjP5LitP/Q4m9VDG6cw6gVCci4hfhhJ06BUwzvG+xuhm5g8VIjeJQrJfWVFjW3qFZTbntxq3fw8HSqSaaG+npN9GJ0WPyXV/C7BfOnI2IRqL97Id3H+zl44XeqAOUrFa4b5fssdrpbLxBxLx3DZqeP8lA8Tjh/1WFqWP5LS0ylGL6boFoZI7uJVESuL507c2ohKvIPMsrLjvRMYQ5S8n3RfKoojmfdKAYPJAkkPVeEvscuJUIOEzFrNYnx6NNEJB6Vwa+0uLHHe81yZ4ApNXZboPpuGDDPRI+ZnUoqiW6lg2kPEpSD/hOBWbebLem5OH1NI0D2bN/CUUZIobrWCOJmaRjct5SLDFMrFi82uOFzbp/F36Vf30FrsVjwEz/xE1y+fJkf/MEf5Ad/8AfZ3t7mJ3/yJ1kul8/12j4eAX8T64/+0T/KX/gLf4G33377G4Kgf+zHfuw5XdlvszRSOGRIULsTgbmpFeZRRn3JEXMJIk/WSqavZCQnFrNUxCYT0XPPE1stzKvKMH+pJdtNaNY8+b0M4keux2Dl9/l7ObMbURy7taLYMbRryOndRPwswzQCs/WZjPySmXRnwsCBhrLS6EGLfZhT7a1RXWvRfUetRdNWbkbyRynqE1Oq/T6rkzmHs4TsyHJyMCLRQFTojoXmexF7bUG9SBl9JWP6RsONa0c8erSBbg2LqwE/drx8e5e7extMVzPpnOhI8V4miIcoPyPO4CqLScCvenQl3DNTKoYPoBkplmmPbAH5+7mMcK82ZDuicasuOfoPZLNxXxmjbtUkRYsrDclUSUep8OgTg+quHyMjcAKgDe0k4HpQb3hUK5tTcixfs7rksa0Izot9SXeITgPSRcBK5847Qxh6llsW/c6A8qqgcbIjLe9lhOaDEbZWNMPI4mgkkV79LnUhk87q8ac9yUoNOwXxUi3OcS26wPRZQrhTyshwuybNWmLZkzSKywFlA7qG3qhi2WhYWhnTbjqCN/hLMvKKJuJrKfpjo9Ea3KOBaMdWGvpfy2lORpIJfa1l8q5icUVTX2uwR4b2uuBgkql0m8trLa3R5JcXLKshrhCOZLPm0ceJXPupZP/OX5WOnu8FlNPEBwN0Xwqd/q1zZsd9dObRT3OaKy3JXkKxY6m2Pf1HhlJnmEbuj3LoxSV6kguC5dxS2S6WMBGDTHYixae7WjP8Yk4yj8xuyWf5+P4K2nW4kPXA4JUzpg8mMEuk2Fgo0qnCp9COpLNYPRqSNB0o20SSA3NxIPIOzMOcZZZhtivBoTS60wvK5zkOHK5IRSPrFaqUKLzsKz30p+bYu6mwQO/UeJ90Tm0jMpIskA8a+OqQ3meOONjokVgxHBHAHebkR5p6IzC4l0iMXC+SPMzRDZS3G/S5GJDMXJM+NfhcEmOWLzSkewnNayVmL6NdCeRv9+S9XVUUTyzZmWb6vSXmn4xoPlli7+Xitk8jPpcOn3bQe6aYXw9y2M0CZB7dGEFQ3aoI84T8WSLsvmutsEOXlrPTCUmpKPYUIdHymVwPNNOM/FhQM+FRn6LrBrcDyQJe3mrFPObFJJSdina1XhVJi6klB9vOxYwXjXTS8wP5bNKK6z09MczvSCZ5741T3LSQ2LvnuaIc1v+Fv8Z30Przf/7P88u//Mv8g3/wD/j+7/9+AD772c/yp//0n+anfuqn+Nt/+28/t2v7uAD8Jtaf/JN/EoC/+lf/6tf93bcrCLp4agh1QjsK0rlayKi2vtagSnGLaRsYXTsnsZ6j2pLMEhF2O43KPGY/I16uCJURJ2TfUW8odK1pRiLyn12TqDkVYPDKKeePxzKyWWrcyOMXGr9fYFsIKPJHIiTXtSKv5HRbr3tMpVFnVoqI0kjW76VuntEqQi2jkNH9yOFnAk2iUI8G2EZx+tV1UgfVlmi/2o2WpNfiyly4cQqurZ3x+P2rVGsRe5zwyG+AiYSRw5xazJnlvfeukB1Icor55DnurfGFGQEDvgj0i4bFYU5+rKgwIro3QBYpNwXKmh13RoFz0VzmjzvWXxDMw+KFlvRAtJRZv6He7xH7nvIyMnKbyvgnmckIzOfSLajXPPmBwZeKZkXGc6HvGX0tYXFVYLC9Z4Zqo0+76WT8uCd6ynpFRvj6ac7o9WNO9kcQxaEdVlrRbplItOIijkYKANcXZAWnKbaUEZapFN6JZgsN7XmGdQrzIBftZgVRC5BZPyokQ/ZZjr/lII0X70nyOEO/Mkf90zH6UiCMW0ImoGrfaNJBg340uOCxZXdz6ci9VqKf5jLyV5HldU9ypkkWUDea2U3RcKq5obkkDu6oBZRdHAUgkfuuHZCXCp9GmkstpufIv9KTz8lqgI0aNU2JPQFCJ/PuupcwfdVRvzXB3inJ8pbFJMEeJrRbrUSEDVqqNRnrhSwSjHQwOcoEvBzBbTTYw5TeS2csFrmMCOeZAKQPxQ1fryh0Ddmhpb7SEIGyp1Gt4ux4gN2sBPeiI9W1lqrRpKeG/FDhM4W/WtFkKXaqiUvRafoNR3Jkpehd6Rh7tSFZaLxPyI405RVxjCsTKS93ediFdMGbFU+YG8LDPtWWIIzyBxluEPFWifEki8TMEyPkJ3D6YEVQQeMWFRT5xpL2nuS02pmMoU0lWknvMpky1Bo2akk6KRXNJOKGHjsz6FkHR/9AEkKqvqPaCCQnVmDhQLUB8UQ6l742+E3P4L7FLDQxEX6nzyymQpJQgPxxSr2uGH3ymOPjAXhNemSothxm3GB2c+E/niS4ge+MV8JsbDYdemFIRzXNQuQCvh8gSHGoW9HHJlOLu1PRzgRw77rYxbDekjxNadY9BHnm+iwS8oDrJC7pmRhZAJpVySdPTzXN7iq8Wgrv9eP1bbX+u//uv+Pv/b2/xw//8A9f/Nm//C//yxRFwb/2r/1rz7UA/Phu+SZWCOG3/PXtWPyBOC+DAbUi0WH1SiQ7Vegzixo3ouvRMPtgwuHemDi3Agu+m9G/mzD4cg7blXSPouhw9KOc1WtnglfoBeEKzgy9pzIiPTvpM/maptjVjD/oio9MxlkfOkHtQsDK1UsVy9sN7fUagPRM4YcevZOT7wpNX5VyeyZTw+itlPXfUJy9DKbSpF2npF3xuLWW7ERR7GpW1uYUD1KSr/TpPzbywAxw791t6WjY7oCpI7Sa9FlC75nERalWUV9qaSaB5ZPhR5t334MHu1ky3x+QH2l8Bi995hFmqdDXFrSbLe6NOb4f0J8SrZ7PoP9UkU6lI1ZdbiWf1Sny188AaJ72pbg+s4Sex8w0cdTiJw6fiSauXXM040DvqYCWk6nGzmVkRlAsr4guEN05X1+fkR9YSVy4UxGul5Lr+ijHrThOdsfozGPGDb1bU1bW5iin6D/VZMdQHESqS552ImOv4T0tMOuJx15bXOS1NitRwNc64otIuyKj1osTfBSTkT1IyE4U6mvDC+MRCCeOt4ZUa5HRXQ21oV11EqH3MMN+eSDdsJ6MTqOB8qonnqe4UYCRg+OMqCLFvsCv7WolZqdeZP3OiThyn2ToVnH+iufo/1lS3mpQQVhwukMd6rnFLy3L1ypMIx250BqiDahpQnZgKC97ylsNbQ9Uq2mv1wz6FYujnkgGAmRPU9FhzRNsqeg90yRTOQzlbxckUw1OkR5ZqAUpM78/xk8T8vdymk13gU8ypYwIq22HXSJpHUC6UqG8OIjDQU52fU5Yb6DRZOslrh+ZX5ciKJSWZL0UR+yBkvu+K6hNKcVDsWOwhyluGOjtys+RX1pI+sndDFNp/KqMhv2LS3lNP30qZgYtGmEBUyuG74tV3i4V6bOUZq9HtSH3Z8gC9jBFl4ryuBAwc08OSotbLeUlL87+IhIGUqzF05RwtaK+VaMijD6w5C+di+GjLyP+dhAYbM2JA4epFcN7wpN0PbnP5rfCxYjZFTJxIABdR29+I3D1/2NI9xJCGsn3DdUvr8N5QgwKNAw/sNgPegQL+rwbF7cil2gH8kwjdsXYshsL2ygZwOojV3qyUNSXHOE4xZ4bsr0OTH+lhFmHHlJimkEJ2qnXaYmjjVTbLZu3jgm9gFpp0I2WzuRlT5gnuOHzTgL53fn1nbSWyyVbW1tf9+ebm5vPfQSsYvxOe7n//3tNp1PG4zFX/9Z/gk4LaBV2aroRlmS4xiygsm7nO0tk/NRoshMtXL9Sft9MAr6QmKv19RnHH6yJG9XI6CQ7/gi70awETNWJ3zusiUBy44XWL5nL37lCwuR1Jbq/ta8ozl7hwmSgvCJcqaQYPFQsrwQpwnTE9hzmgx71pRa9NOQH0pnQjaJ+pSTURhJCHvcJlyt5gEdFbDXJkTgs49BBqzBTS3YiY5aQwPonDjj+8uZHEhQtG1m9EtCtpFsUT60AmG8uUO8OqK80EsqObEjmNBFtpZGRG0pQF0Tov3BO+e5EdE2JCPTNpSXusMCs1cSdgmJfMX+tkeSToxQ/9OJILTzpuBbncaMvxsLtRArKD7l90XTFf96lPHQGF9Xz2GcpvhfJD7qM0SSiSwXbFenXegx2BNbcbDlU6rHPMuKNkrCXE/KAnRvJXlZIl3NNumT6WU56JhrNel3G0ul5hwYaR/pvnjD9YAW/2pI/yKguObCR5MTKIWKpUHcW4g4dNLiDgv6OZn7LgYmobjzu+gIrD1Yc4sHK+5YfatEKfvia1tCsBRi0xNqgF4a40pJ/IOPY+R2HamVjH9wzVBuSLJPvWZpxIKYRs9Qk50qQSB1ip91qSQ7Eta5b1XVqhK/nLtekD3Pa2yWhNegzS3J1QT3L0FOLbpR0ooqI2q5YX5lx8oVN3EA66e0kkJxq7KtTlic99MxcQNuTueqc7PJ6ArhNKchUaS6oGVFF8j2JZ7QltINIfwfOXpPPvl2Ik9mUGj/02DOLz4Xr2ayJtpSRIzrF6KspwUK13jl4dwXt4rOIv1oRTzJ5juSefFAT3h7S25Xv1X8ijMZmEghDh86kOFn/NcP8qpLurJOfRbeKekOiEc1MY0sl3VcbSU4lFi4MHcN3UkwllIJkplhek3vDDlvM3UI61h7SN89YPBgTVxuSp+JkDqkYjEwt70FIIDtRRCOGGDf8yIQh+CJhBX4YJakbLl7jZrsle5LK4TCVpJYPpyrJiZia6jUpwuxSHL1JlzbkenJ/+n7ALDT2zpz24YDQE/ag70uhyrgV6Q1IMk2Un007qK+26HPRZH9IcsgPZGScHytmG0se//Rf5vz8/FuaUX+x7/zN/ze6yP+FvlYoK57+qb/yLf8Zfq/Wj/zIj7C2tsZ/89/8N+S5vDZlWfIn/sSf4OTkhP/lf/lfntu1fdwB/C3WPw8u+c+uJ0+e8LnPfe738Gp+58ukHozotdzEo1Ya0msLookkR5boNMmjjDiQjS9mgXrNk+3Lw0U38uDUjUadppx9ZR27UJ0WSDpa9aqcepMZ5AcSc9ZMIs1KpBl1CJqFIjvWImYuxJDRTDqdTS2axJM3Im4grsWQCTA2HovGsBnHLq5MgdP4k0zilxYi0F6+2NAOI81KkPzaoDBvC2jZPszhLMXupthjS/+pIj/UKBOw5xY/8LIJXGpJzhVHb23SrrkuK1Q2u+pqQ3aqCZtNB0mUUU6zENiwnloZDfY8amnIThXpqYZUHub5TgKXK8JGQ1klhG4jyg4MrNe0pzlqIqkN2akSIGytMUfC1JMCQ5E/S9DvDGTD7/iGulHgIaZBCoWldAncqpPs001H/7FEuikTaDccZqtkeaMlvbKg/1AgvnFfxqlnL8m9kxxZmCcyej3KCCstyivc2BH6ntCT1w2F6PK6DseHxV9Yaak3PPWn5yRzxfl5j5BHqI289xpIAqMPYHRXM3kfmtMcVETd75HMFNV6ZHh5xpXrx+LWbCRKMJnKISZYSbXpP5UDgBsJ2DlqLowjykSSYYO5vITzRO6Tz8y7LqUUdvMbcnAhk462ulRJN+/akmozUG056dgkkByIDkzu1QDDVl4DoHg3l8/BOwXZk5S43lAvUnTqBUa+1dCuBOxCMxyU7D1aI2SS5dqsenQlrMj6wRBaKRiUk0JlcdNJkgXSmcyPJE1H/jtgzzVmoek9sRcd0/L1knYiJADllMCi1zxZl1VrTyxu7NCtor1aSzdZQ/I0pXiUUm5F5q+Jkz85k05lSOQ11zs5wxvn8jouLNVun2YlUK0p8n2DT0TrGvqe4dqCLG/Rg5bFFdH+Vm8uaUeRZsPTdK8JTjBH7UsldrMkmkjbFZCm55i9UXP2ZovbbigvS4a2Lg2X186l4fzCgpBC+f4E1muye7lkB6/6i8LOXa5xhaCLFq/XlJcC7ZoTnePQk0w1ulX4KxWh50lemF1oBaOGdhRQpaHecLSXGvJDKRSLBymqkYNkvenF6HQqyUhqq6a5UdMOIn5N7peVrwqOqnnSRztIjzXRRMbvGvnZS4OqtZiRumtX3bOQRsbiMRdjX/+JjMajQTitk+ecBPLx+rr1cz/3c3zuc5/j6tWr/MiP/Ag/8iM/wrVr1/iVX/kVfu7nfu65XtvHBeBvsf723/7bvPrqq/zn//l/zjvvvPN1f39+fs7/+D/+j/wb/8a/wXd/93dzfHz8HK7yt17hOEMZSTkgAIcZ8Z2BaPP6kd490aXlw1pO3F3bK2TSIVy82FxQ5UMWSF+eEiwMHikGjyCst2Sn0qEoNyPLm63EEW00+FxOo6aVjcD1ZVSkooxfdKvQ58kFYw4kik03yMn2tNvYewF3q4IkyMi6+QiUHPJAdUNE4mzU+GHHMOtMDvFaSfaJM8Fl1ApbKc4+4cQk8SyT9InuZJ0/SaleqkjOFTp3FJ85xg0ia3dOSHcT2hdL4tJKrupQupnmOGH8gYwRTank+/JRVwMTIQ3YEvKv9NBJoD3PUEHGOipA+n5BdmDhOLuAILebLXbckB/Ja1DsCjux2m5lhKw6btnjVNypk/aCg+YKLmDVzasl2NilPDhCY7D9Fr9fgBYXZb0mejwVpMtkl4rmcotdKomdWipi34vTc9KAgt6jBNWhOGISBbrrYHnFU2zPBWbdaJIzjTsuJOfUiIg/XyslWs0pqA3HP9Bw/mJgdkORnBiUjbgbFe3VBjdxzM8Lnu1P8EWkvNlgFprw5gy7UaKvlDTrghhqJ4H8wNJ/JmPG/EiwIBxkxCc94qM+MQu49Za2tgweGBmZJwHGLVVn3lAe4p4Uovr9vriVDyym74iXKrlfoBvpKQajkpgEAXWvyoFIeTkQxVZjTsT4owqPTj2x52mHgfKLa6RHRu4bBeReUDOlwg8Co+2ZHEyuNsxecozes5gGlrdaenfOxZQEjL6aYoetSBsUuE/OaS63FIdK3MijVjplwPA9cbbrDjOSThUERbGnsakU7rqWrpjPI+FGiZpZ0nMp8NtVfwFojgaW70+k+NWRG6/siV7QS2e/XhMjiF4YFg/GlAc9QmWkGzZVhFNheCZn0uWMCmIhbvqN1SntMoFMNIiTL6XwLEfNLGbgiJ3b2S7lZ3n6waZo8M47V3aEeJ5Kx76I5LsWu9QwbjH7GepKSXJmSJ6kjN7XJCeW7HGGCuoCY5S9XzB4P4HfGBELuV/tUp49ZiGmr8GkxHfYLFeIgz9kgZhLsk19TQoxP7cwTdCXKnqjimSqqFbFxX/tjT3cMMhoHpjdCmyuzEALV7B41n2YlXyPZNbJPiK8cGePZhRwfdHp+p5IBZg/ZxD0hyaQf9Ff30HrjTfe4IMPPuBnfuZn+OQnP8knP/lJfvZnf5YPPviA119//ble28cj4N9m/Q//w//A3/ybf5P/9X/9X+n3+2xtbZHnOaenp+zt7bG+vs6//W//2/y5P/fnvuGM/3msD1vxt//jv4bqSSyaMNtEr5eeaUbfd8CiTlk8HsmmZwX9EhuNnsqoJz/QVFvhgtMnUWIBtVGTvlNgGqgn3RhkoakviRnEzjqeVSvsq3ijxE1TklODGwXyZ0L+T+aKclPYfosXG8yZxffFsNKsCnMsZJE4cCR7KYPXTzi/tyIpDwF6N6e4L0+otltG7yS4HMpXK9RxSnYi2pzFnRa9MBL5teWw54awXZHeE+F4VFBvOYonlvJOg9KR3juZmDS6UVq8WRJ3JLKr99IZs50RatwQjwW9EK9UsJfRf6wxP3LM2aOJfG0TsVODW5MEAFXLJhQXFjNuMQ9zmhVxEQ8faqYvyYbnr1aEhRV49i1HcmbEvZsFkoOE9Ewi1JpVgdC6vhQxnCfkhwaC6JHcVgO1EaTKuCZJPM29EfbWnGqaoWeW7FBT3mrIn6aStZsGeg8TmnHETRz2zJK9OGWx35efZeyxowZ9X5Ao7Sjgh57kVNzj9aaXcXPhSIuW+MEAgOz1M2EgLg1x6EgOEtpVh5lJEktzs5aMViVFM5WYa1RlOq2ejAhVKRFdpuwSZZR0QbVTJOcygq6utgw+SATu/OmS9F5BfatCH6WkZ6Ibbcbx4vDxYdGlq473V3UjycgF5zBkUaLuunG0ahRh5GTsdy7X44ZR0iAKYRgmh4lo7bps48m7ivkNaMddQbFeUfyTPtOXHfm+perGiNmBoXmpJJRWxoGZZ/XzKaevRVbeVjRjxfzFFntsL9zWg8eSttOOxaxhKiUaSaRAG16e0Xx5hewMllsiDyB2P3suhbxZaMKVCg4zYQ+qTqsZBPOkGoUKSvJyT1XniHVSkFyuGP3vBbPb4CYO1UoahopyfdFIp3d+XTr8vR0pYha3PPme5Fb7QjTLftgVo43CTxy2cPijjPxQuse+HwRBtFli3hpIrq6R5xCKC/mB60Xc2DF8P2H2aiPGtlqK1METTTPupBKdbAAVu0hGGN2D4+9tJUt8bil2JHrRnHUCYi33XTJVlNseO9OSca0iam7l4GwjvceGxc3OyNbdT8mJISQdbmvcYvZTdPNRClA7kGmI8tK1VblHH0kMX3oi+sx6LeBXHWop9192LK7t5Us15mHgwX/yl57bCPjaz/3V35UR8JM/8x9/x4yAv53Xxy7g32b92I/9GD/2Yz/G0dERn/3sZ3n06BFlWbK+vs6nPvUpPvWpT/2mkOlvp6VLRbvRIQG06MV8JsXB4XvrUkit1SgF6QcFrtKSbXpriv/ChHYobs924uk9sjSfWKIfF+S9mvl16YaFXsDMDPUVEaDHvkOfpASrxDFqI+4kI99aUqlcotBerImNpgZUJVFpxaMU5aB1sgGbUhIrUILGcKPA6fGA3qGmHcpGt3wwoj+DCphfD4zuapZL2RSrzS5YXslm04y6DS6LqANxWZqyy9+MksSRP5J81GYcL3AnplLo+4Xo+CpF+faEuOKJjYEkyP8iXYPZS57si2uoF0qUAvU0FxTOQgwyMY2kTzKaDUc8yCj2xA1Yr0ZmtwN6paFNLToCRkbodib5n8WORIXpWpzcbqtBzUVXli8MTa0JGw1VtwGiQXXh9nHSEh/1qY1svn6ZoKeW/hPN4mogOUpohwFGLWnmqDYEbqxyj+9pFnt99KglDFuYJ8SdAlMpqk0v/85peZ83pIgKGehRED7jKGDWapb3xmQL2TBVTJlfD9hTAf0SYfxPcqYvhotRf7FjqV8uiYUnzK0gcLp4t3rD4zNh/6VbS+rTnNGXLLOb4oLGRuYvN9Bq7E5OMoV6nmBKhV3C/HYg9hyxFrexm3Td76hp1xx6Vwo3XWv82NHeaVD7GeZcHpWhCGIemVtYq1EnBTGR+6PYU7QDTfVSRbvZMng3ZTEMZEcGFboR6lI62+0iIc2RCLAkku0IBqW6VaMPM3qHmnIroOeaZiyjvumtDpLWKkIuqBrlFOWGdB5Vz8vn0IgsINu1UpxuGCnWc3Fx27kSx2ojh72YBtJnhuWqJekQTe0oYCpNfqBoNiNx6FGdvtX1RburfKcrPM44e1UKKFUa7FKc5M0lKVLElCOdRe24QBjRd1SXBDQd1xo4S8RUttsVNIXGVxn9azOWqxnp3YJiX8tn9KxPMoOQyNdGR6KK2EpTXe/GoE4xv9VpnY187/RM04wktSgd1bjTnGzfSvd9IpOG2S3F6Gspi6uB7FiKRnNmxczTj5LtPRWjkUgjAvrMyqHASQKJakSfN7xrWVwLAsE/EJ6juJADtJ05aE8SYxbXPclMDjmuH6Rg7TuK21Pm+wNco3CXGzhPZDIxCXLNXop+akO79vEI+Ntt/czP/AxbW1tfF/v2C7/wCxweHvLTP/3Tz+nKPi4Av6m1vr7Ov/Kv/CvP+zJ+R6tZD/R2DcMngXItodySsWy9Ei/GvePP5bQDJWL7JGJOLeXJmF4J/WewuKIIxggg+r0e1ZbDfXmC7boaxY6lerFCnSVkJ4bqaqBZlQ2vXg3YhbjtqnmKygL0HPZJjttuUDbAIkN30WXJicVdanBKtFv2WY4befpPDD6DdGpo+1IE2KlB3VxQNn1UI5FJ1Rqkx6YTgp+zeDimfzdl/nLTbUKauFkRTjKUQ7onRr5WMhdjRzIT1IItHM4rTCUJJDEPqFJLEsKJQXk5xbt+oPdWQT2J6KUmPwIoiK/NoFL4LUfyKJNx6YcJArXw2pqRjG7cULp7+QcFYcMTOptyuyIFX1NEil0ZF4ZUUDLmWMY87oUSP5fRmAoQCo9ZytgxttJBSfZS+TrrgeRMwzSTjWMEMY2EIKgKfZzSjDX0vHThlhbdOSezx5bFddd1l6B+pSR/t0B5WF5z2KWmHgSChnzP4M8KVCI5tCUZceQJiaZZBTvXFyk09a2aWGtUSNCbFeznMGiFtbZI5DpsJH3jnPL+SA4tjSY/1NSfWFLPM/TCMH3hwxEc0GjpVm40jK6ecTKeXESVlVtRkC61IVstcc6IszOLKAd2T0xCulEkM4UKFrtSUY0sOnf03ipYvN5ijlKKfc28by+6ZMlC4XMZwdvE409SuV9bGfPObkFyLgWEGbWkbxc0Y3HALl9ooBLdqDKR0PMsrwWSlQp1r8/ihmf4vhFe5kq44DDWt1ryu5ncF06hTpMLbaOZyj1TbnviSUHSjVqTmaYdy8EtagiTlmJYU08temGwcynKXU/A8dU6khrSV8TVlqzf0FYDfN+THYrmMDuUwsZfajDPMlw/SGf0XKL18qeW8lKQQ6GBZA6u0IRW7uPsSNO4DL/a4m2kXjNiSuk5xuMl03sT0nNNtd1ipha/Ihy9elVJRvFhyvhrhtkt6TYSYLS+YLYzggCD9ySOTgUYfO8hMSoSZ1jMcvI9KxGDG6BGjeiIl4ZGCDViKBpL19R50Ua7NUeVK/RCZBLtpoN+hFJ0yfkHmrPvanF9TUjlfa+vOkIi4OZqNcIoimb4RNOOAvWqvDe6FpOMbrV01EvLfGkZvWclsel+xvBRZHZTEQuPObUyMY3iDveL+lu4y3yD9bsBcv4Om0n+F//Ff8Hf+Tt/5+v+/PXXX+df/9f/9edaAH57tq8+Xv/CKyKb8/4PepqJ4E+C7TR+aw22VExvQ7Uhm0e6Z2G7Ql0pmb3acvoapFMxPMxe8NSrAdNhWXSLoD+ySPFOLqkg2y3ZToqdy/glP9IUh4rsxGAPUuxeijrIaC/JJpLlLXG1obrk0EuD6wXMQYo6TQmtxm/VDO7bC03V8nJk8FSK2N6ewn51gF1KAkc7isQ3ZgQL7k5F9e5EHLCDSLaTYkqNmzjCPCEOhY+3/ERJvFyRnquLkPZmEiSq6WkhmcBWxuN6Jp0+f7nGpwLXdkPpkDQjGT/51Rb9h4/weaRtLPX1Gn0oIiHtEZeuFzREOwnUr5a4KzVYwdF86DosHiekh0ZiyE4ET1O+UjH+7iNp/qx6Gd2ttZgHOemhxU8TtImYnkNdXwq+40ix9k+sjIzHAQQtRjoVfV94dS6dLqcu4qf01NJ7lGAPUlThSU+Ffbe8KqPFMPA0V1qUjrjXF9IZXRjcnUpSXoC2Hxk+hnClYnmzFXmBk/FhcqZpN1sZ/zYQa022l1CvBdzSYi4vGQ1L8p2E9FictOm+pbw/QreiN42FuCH8PEGfyZjej10ngo+SnpFGdBI4eTKRz4INVJuBkEZ672eQeYLXZG/1hH1I151qOvPPIOAGYqZpdvrS7V5I/FysxTgzf7lhZWsqXb0Gqg1P812CTkFF+jfPqTcES1LflE25HckIM3hFedVJ2ksRyZ4mJOcGU3j0fka+KnrD8FSwI/meodqMVC9X2OsLdKUwc4M6SWhHET/0+GGH4EEMC7pVF4w/e2px2zV2oXCDzpnbKmwJ6bOU8qiHqUT2ERIp8MPQSze+ayhl+5Z8UFMdF8QkCpdww8mouYPOx7klJpHJO5pmQzR9Zi4du+RcM3lXTC2z1xvUjaV0xk3E9wTEXYwrtm6e0Gw6eo8tnCWcPxqLw3/dC8zewdo/SWQ6MQrEUp5b85vyObNLhTm3lO9MULXgq+YvClbJTRyH+2MWVcpsd4jeyakuOebXA367Rh1kJBP5jMtBVjN/s5LXNYisw9+uGL6T0n+QoLZqQcpERfYgI12tqNc985sRe2ZxEycufSWaXTcMAuZPIvnThGJfnpXFvrif22EgncnzNT2XPGqViJls+lpLsOKsbkbSzVZdtno7Dvgc2oOCdpZ9C3aX32Z9rAH8urW3t8fly5e/7s83NjbY3d19Dlf00fq4APxOXTpiSi3F1ctL/CVxoiXnusvTlJGb7weKXUFJ+DPRmuCVcNVGnWmjEp2T78m4rLnUCpdKC70+1pp0X0C1H8JPRw8Cs1ue6rKYRXwqI1UqjbWe+skAs5tRPLPEleZCBB+ygJpZOE9YXPXMv6tidltG2dM7iuWlyOyOv9hAo5YHc3xvIJFNR9kFeiE/VMRX5sSrFabvMHNDuiMQYLWXE6YJ9WqgHX+oZRQEg88DtkJ+3k6gnx8a1GmKnzgZo83EufchXiJ/nHL+7poYSZ6KaN2vtqKn264lNzkKG1E5SbUAcdymx+YCVyGRYTJeq9cl41gfphw9WBU49KSClxbCNSsVzZoX96dX+HlCUTTEUSvFkpGRf+jJaEn7DnvRizQzKdZN2Y3cr1VELa+n8ogT8cNcUcXFWCvZT4heEaNieaOVgmlpKW802NXqQstkH+cCCG+li5ieadF2nkinqbkhfLR2LJ3OdD/BHRScPx5fjNlMLdo66ZzJ65LuW5qVyPDdBFMretdmFI9S2lUnI75zzfg91TH3DOmJoX8/IQzEdb68JfFl4WmP5TVHs+XkvV+qi8xoorz3qhajQXoqLtuoOxdIgOQw4fy8R7GvyU4VdqNC3e/hRgFjIvXXJuhKutOxNB3YW6PGDcYGVCNfOyRcuOX9eSJcwi8NMaUmmWnc2NMOZCTLeUL7tI8tFX69IZmLcYGgiLnHj8TBbpfiuM8PFYMH3ZAndliWDZFr2BdmtEN5v9Njg4pQbQbqLQeH0lnVbZdwc1/js0jzYCgj6zzQ3KhJzg12Ku5/lPA6Qx6Z3pZuVHEoyJOQABpO3pBiWS0N7XlGMtVyiNoQo1h50OP0ixugIssbLaaSeEU/kFQRXSnaSw0Eua6Yiq457uW4oacdeaqt7jCwJkVuyMPFz6+cxhwntI1kjsckkj+z2FIR55bsWON3C/zYEbJAtRFgnlw4gYkKrQOzFx3tIKKfiNateJxQXW9oFilqtfmoUz81mJUaFRTx9Rm6FHSSLwLtMFCtB9yKwxUweCQ4odkdz+JaoNoQjWBsNCqInMUPPWqrxtQR14P+UxkXx4EXp3CHtvp4fXuta9eufUNKyOc+9zm2t7efwxV9tD4uAL9DV7K5xA8EL+GPM6LTpFM5NSenomchQr4rkUfKi3bMvtsjPTQsbnhMIwWEdjLiKp4Kkyo5SCTDNhW4NEo6jOZMTqTJmebsRRGS64VhcauV3MsbFcUzy2JnSHYsGZu9/UjyVKLXTAVEuPHqHijoPzEMvpyjVusLJIvrRPT9h9L9CduVdG58x+MYt4Se5BVPX3Y004zgFfEgk824E5p/mHHrexJmn15aSo7prkCDg0VGpqXBrTrBYAzchZhbt2KMIYqwv75VSydKw+CpIg4deirRXnEuo+TJ+1JYhiygjlPiQjh4ulUXRWVyrqXw2WgJA49dysZAkOzcGDRt3XXbhpLM4HoR8zSn9yBhejCA2rC4Fjh7VboDo7cTmk3ZtNrVgLqxBNd99KMU8qExMGlF22eB3OMG0hWNmceeWNa+oCkOFGGWEPZyzNwweRfsicWcJsRHParLjpPvb2TUuZBUCruU99YspECImXTS0F3KCMJm040gS9x6K3DtiThPPwRHhyLgblboGwuWl0XLVt0b4d+YgxWWHwFOP+FZXnVSjDcdd66L1SueJAzvGsYfAHnghdt75EeawWOo/sBMRo9eCqqYe3weqO9UEDrWYq2xMzHfqIOMxXVBxExGS5oN4QtWs+xCQ7q806Br3XHvpBjxx9mFISU7lfG0mJu63ycyOlYB9FLjVp1gnLaWmKYzqTSaet0zeQ/pxJ5akmNLO5LupamVFPuF6NWUjuQ3ZuS9RgxZHwxRmzXcXNKOA/XVFru9JD0SzaraqGm2HNVmYHon4NYkN3fwqEMYTWVcbkslaKnNEn17LhKAvife67PYjmKiSeSzK+8vUsAtBGDss0h6KIciEvksmNME5aRrn06ly09EYNS14ewVSH7fqYxsUyms7bmheGbJ9y2h74UReqzkfdcRe2LpPxJTkT/OBLp+dSkxkQvFytXOXa1gsjWTkXqE3hMxYalGOqbtNEWXmvxYNM7RiAPfHib07qaovUw6602X3PGWdFfreYZ2knqinPydbhQ4YZ+2w+69zz1hKIdbnQRBMg1aMZboSPa1gmpNXnOfQnqqUHODG8gz3M6fc/cs/i79+g5af/JP/kn+7J/9s/zX//V/zaNHj3j06BG/8Au/wJ/7c3/uImXsea2Pjwvfocvt9kmilQxQQKceV3Qg0qsVyb2C5FxTXZaUi+zUYB6JFi5ayI4Ny20RQesGFrcc/YeW6rWS9ZUZp1/ckOinTIo8AKLCDx12Yam3W/RMDAX5s4R6TcZbyRyydw3LrUh+ophfk46/HwSqKw3po5wnB6vEJDK/7QTI+0CYXr76aPSjOhahsYH01ozw62OW1x15vyH90pDpJxqypynZd52y+GCCX2txjeS8gnQIBAOjIXc0hz2MF1CsahXtm3PSLw3wuSbkkeZKA1GRP03Qnzin/WBEsLEbbRr0oWTYVi9VqEGNqRN8ZqguB0bvWtohHH8i4q+W4ATsnD/MyI+gGYLaqmnPE9o8kOwn4gB1SnKPjxW6NaAMrpcQx57JlSnz4xVCiiShPE1xA+mM1d8zJ89apntDVOFYXFXY0w76e6YJ8x5WiZu62nbopaa4l1LebNFB4bdqbBLEmdkZC3QrOraoBCo9ujKl/dVVprdBxS6i69zQe2RZXoOYB6JVokP90X1O5j3CzuAjCPnSogsnxcY8xRWBdGsJ7w4wBym+SFDXG0xlqdc7p3NpUAcZTT+gNhqqzGKWmmaWojNP/+2U+W0vGsCJp3y5JnpFPbfkzwz93cjsBsxe9AzuG+xBwsOjq4R12YBTG2h6Hn2UoKfJRddWTzPSM4UbwPCuaFIrK0YMEvkMnLyzBmNPdqKxT1OWlyJuFNAzK2iREzErlHkirLlMukSLm67ToXZyhqE4sFGgW0t2fU550MMe5Kx/ep+dcUG+b6lXpFiY3lLYMyNdr1XP5C1LvSoOV1N1WbIa4klGYyLJ13qonhTP9gsFRJjfDFy/esT+57bZ+JLj7I5l2WaojYYA9O4n1BHKmxI1p8YNvV7DYr9PeV2K7visB8DwHMqr8QKNE410olUacOPAIpFkIUnjkXvQvjLFPxyCEo5ezD04Bb7LCM8ieEX/3Yx0Gjn9fS3T/YHIJwpP68E0iuqFmuxhRv4kEQ7lyj/z7FtVVNqSvTClKlPpmD/u41cc7RqUh0NM9xib3Z2gu4PR4oYTt/1AeIW1l0OFZJ/L66ucdGLLbS8SBSUabPMsQ/suhemJYLfsUuFbKQJVAAoPlaZaF33h6j8VB/vJGxE/t8Qiok5SsnMpmJc3W3oPEoKVz2J5RcDUykN1vcG78C3cZb7B+lgD+HXrL/yFv8Dx8TH/3r/379E0oqnI85yf/umf5i/+xb/4XK/t4wLwd7CapuHBgwfcuXMHa7+9Xzq7VPgVcQfmB4a6znC3KtR+Bkci1PZDL6LqtRbXE6dgeqbxShyLk7cV8+uR6lJgdGnGNA4xKnL66xvSsalUB4tWtOuCOxm+l9CMhWCfHwnqpbrspLOgIfzQGdVXxzQbHjfUJFOBtRIhTsUJqVVED1rsgxzTyPjYNnQjUTFglJvxIodWHfWgiPSeWKqyD2MZM9lPnLP4YIJ2MPxCyuK6REwlJ9LJ1K0kRiRPM0Ln5IxGumZqt0d6FlleE+NAspvSTjz1SsB8MLpIBghpRF+tiCaQBEU7S6lMKqDtM4NuFdPXW8y5AHjjaYpp1AXba3ZDwNr6cY6+ucTvFdJdSgL6PCGaSLUqY/VYeFRtyI4MZ4wwGqlQuo0yPVcsrgbiXg/fAiNPrCQFBivmH98I9iQ5E9xOvmvJToRBhu6SEFKPO8lJForWyBg3Gi7uD6aW+f4KaizxflF1uao9R5WIm/rDEdtyOzLdWUWZKODcTBEyibxqVYQg3c+YB5oqgVWJvGsHEWVEcmBnitYkqFGLOpeRuVlaqi0vqQqnCb6vL0wXukXG7EuBYFeXW+p18LnCvnZO3O9TbsUL3Z/pnK+L0wJa6Vwnc+nKiS5UNuvQwbfLzc5s4BS6NpgKdKGIc0O1KTif9ExRTgIo0ajqxtCshAtAcLgqOj8qS9iuaDSkbxfUa4HB3YR6It215SwTJIiBnZ1VVN9h3lygqwQ/Fwe38go/EHfpclsQQXrYEruEFunUaWqTEVcEvVSvRqpN4XPqWvPkq5fQReTwU2LsyA81TZNdxIyhQOeOoAxGR4q0pepyZ+NWzeithLNPNpgqQbWa8rKMbJWHYk9T6oTsWIDS2Yk4ZJsbNfooYXlakC1kjGCXivqqJzmWrOx6ywmCZugoL2kWL3TGByNaumgi6bkYJtRJIikzUe6/ds1hpgY/F0NRNJH4T8eEKx6GLaYCvxpJDhJJE7q2gId97ExR3m4oHqS0I0VExuTtSIq9qGH+Uov6kKu4VIQUYhIEdWQiMVEdIxDadXeBo/J57IweMqbHaUIvEAuHyhxz1Rf9Z09g78rLBMC/vCD9Up8mDfJ1xwE1amCWSIdxTSDVePeNN4SP13NbSin+s//sP+M/+o/+I9555x2KouDFF18ky56zXpOPR8Df1Foul/zkT/4kvV6P119/ncePHwPwp/7Un+Jnf/Znn/PVfePVTPzFGALktMpB1mlhILs1g6DwfU/ab4g2EsaO6rJD3V7giki5IUkdZqaZHvexM4O5X0jerAa3LlrAD09s+a64jdtRwNyc4zMxP9ipEbNBgPlJj2bNY+ZaxkwdhiPftehGcAbxIGdtZS6pARue5pKjnsiIw1TQe6YZPhBDxUUO7UQE1qYrSns7hvLhED9yuMsN1boYN/Tc0K63tGOPzyQKzPUj8WopMVddOoPeqlheVuR7Bm0D7UTYhLqF/EQwN+mZlof/Xo756gAe9aSgqQx2N8VtN2RnoltTQQlUt+/RV5f4fiB/6Rw/cahWitF2ngpM1krx4wfdr77kfuI0eMiPujHSRkN2IhF3MREheHJ1IWOuiWw6RDFg2FOLPbWkZ5riiZWiempI5jC7I2iT7GmKXWjaaYqqFc1WS+x7smPN4PUTkpkkbdi5voAFm1IKuPRuAdOEuCqpIXHSkj8THWHxMEWdJqhPncv4vIF24kmP5HUJiThp48ISC89yu9M6Pc2pXqrQTjF5WxNL6WK2YwFsDx4aSZ6YSBet3vCYrVL+fqZJzg3V1YZ8N8FcXmJenTHpl+haHnuDR8JVc0WXNtPFb4VUZA9ELsDas5cc2ivOPtXCrSV2JsgZu1DM7zjcMBA6LZZ0C0XoH9MASrAnykGzKkVr9k5BqA04RX9YoZ/kpDPpZC2uhIvuWPosJSaRZKEwJwnmWSZFTKvJdxIpDjvupp2LVo+kS8WJHaS4UugasgPTdR/BbbTk12YMHxhCKtF6Kn4UV1ZuBdoVMZfEvItKqyw687jKcv6VNRlTe0UoLeUG2KOEckNMRRKpJoxGEHizK6Qr2owFSZTdzxg+lDxrn4uMpLnekO6I7s4u6Z5XncxEg+23XREWYdB2hyz5d5N3RSunK00oAqN3ErJjTbFjSfYTwnpDecXTf2jofzVHe4VOPe2KJ5kqtBYZQb0WyIYfuWnXvyzPtHYosXmmgWzXEoci+XDD0D2LOg7hqRGQNPJ60sro2w2CjH3hQqPZe5CADaRFS1sKjNxngsbRc8H4RBPxu4V0VFtNMNL9i170gcs7jZjCFp1e8Hmuj0fAv+UaDAb8vt/3+3jjjTe+LYo/+LgA/KbWX/yLf5Evf/nL/G//2/92keUH8If+0B/il37pl57jlf3Wq/fUkp2piwD53o6SDdxAMtcszwvszICN6HcGskkkAh5tTnPRWw0iyRSyM0XvboobedprkrqhG9AzS7hSYW7P6d1PCEY0XbpR+PuDi4IKQNed03IqHRy/1tL79UKwIOcC241aiPYhDZx/aZ2w2YjQu98KhHiuOvyK4CnwgmJQXhH7jvpyS7hZ0l5pWNxwDB9oiqcJzCzFZ46l6IyyUaEEWjt7rSE71ORf6WFuz4VtWCnU40K6awGMDeQbpRQGrWyOzURMCf56Jfm7/Yh9YQZGuIa+iPRGFfqHT2ivNmQnHYLHK8LTHrrS1O+OoZUcWN0oAc0mkWRqsA9z9FIzuGuJA0c8S1E9QbHMr0WyIwNzS/MhjHsqG2F9mgva41GKXSpxUjuFG0skVnnV0bxWkk41brOlWheEii419dUG1UJyaoXLpiHZS6jXA4syo71W02w6ws0SdKT4Yk+cvmmkXg3ENIjzOQILS9mlUDQj6fosTwsx7ljQnaM8OdMwaUmOBbiLgt7VuTjBu/FXfbvi7Hsahu9bij1Demyov2cuMOfuAKJbuPPaM7zTEsvnpICiEXyIOygIQfHs6aqw7NZbFlc717eNqEaRHku1F3ue8pPLCy1ddiJaz3pFuI7Fr/VJ5qKFCynYc0kWUZlErbm+dAjTM6TzW4sRxl5fXADZq0see5Sga01iPK4XmL7o5XpURN1a0IxkfJsddP++lK7X8qWa8T/JscvOrOK5GEMqL/eRaqXTGxIprMornvaVJW4kiRHZ04Ryd8D0dYl7a9fFZZodK+oNR0wjut9KkZcL4BsbiNOU/GEqzO4XxQVdPJRZeUwi1WXhQzJuqW40uF4UrepZx/icSWc4gjjBNSTHFjfyMhZWUozGTGIlVSvay+x+LgVlx+VUK8LDc9s15RVP8cxw+qY4Zj/M4Z6+0bC81dJMAr09Rf9rObHvKC8Fys1I+slTQmVBCxy83elDIviqZrd/kZB09EkxnOi1RuDttRSsxb2M4kA65GFNRrP5oZhaFjc9408c48YSyRkK6fy1L5VicsqjsAdHkeH6Avekz42/+9EBRAV5vZpLTqL7lqKNpdNqr/26YfjljJgG8icpvfcz0nNFevq8C8CPXcAA/86/8+/w9OnTb+rf/tIv/RL/7X/73/4eX9E3Xt/ec8xvk/X3//7f55d+6Zf43u/9XpT66OZ8/fXXuXfv3nO8st96KQekYLeWNHUfoqK+1mC7hAJzanGrLabwtAMZT2ZFi3OacJzgJ452NRISQ7GvwQjFPhrBYLQrAVUr4nmK2nS0by5IvtJHBUV9vSZ/lHUpAFLYLa+JNsv3PA459c/frDH7KclcBOsxDxRPRN/SjgPMEgYPDIs3RF+mgiI/huUlBELcYR+abXkwJseW/N2E2UsObKTaiDQ3a4ajkvN7K2RzRTuEeE3QDrExqNJQXpMOUnzSJ/ZEQ6O8PJx1BWGvwJxqerUUFfM7Hl1KRyP/WkG9HgTx8HiA8cI1VLnHfXXEclU2pcWLwj5MnkmCSH6IuJs1pEcaU4Pve5JDK5zEk4Sw0rIkkUSPgSO7lxO6ToMbSLH9IdvN51J0ZHsJ4eU5bT8V8HMKIQ2YYYs+zVFbLfpJLh0pE2jHXg4CyD3RTmRUljyTaC27VLQrnni/T1zxmJkhWamogcVNL87qM3ktYm3FOVrLGNX1I+1mi7IRzjNUaQRZYiL5o5RoIz4H+zTDDQPtWqA/KVnuDrDnXRLMP3NEnb3conuOMEuwD/vYZaSeKEHhFJF7TzdQxylaIc7lRP5Xz6UDHbo86Xy9pDouCL1A8dTSjCOmkntOeQXOoI4S3DDAwGHKDHsuTvn0TMDL6bl0pZJZ5+TdbsneLwhWUi18FqnXYHRXsbgi1+8eDchvzml6luxuQXhtjnrQZ1Fm2KWm3WpI72dUd2qakxw1DiQn0tWJhSc7NiJZyB3nLycyIuwQOyGVLGM3lrE+c2hG0plyOfQfG5YuJz8WFmGwEHUkeyZaR28ivu9pKyOIl+0WvZfDdkV0EhPnnSbagGkMg6dwtNHDbFWUY4vJHb4zSEQT0Qdy/6HA9yLtuiN/luDWW4oHKfV6oJlAvSrFe75rqW/VcJZSrwQG90RrGZWmvCJj3eRcDntu6DAa4sgRvQITKa9IBJtqFUxaiXu8VmGsJ5z18Rkks4g9ENKBW29pf32FpC/FaLRRpBH7Ce1KIN831CvymmYnmpAofG3QtZhv7Fx3I+HuGdQT2YJdKsZvJSyuRRpn5P6aSCdf3VlggKAk93e5Lekxi4dj9OWK/X+pJ5rIRXfvBhi8n7B4vcaknkRF1L0erhdZXBXzSLpvqTc9ulT4kSfUH4Ogvx3WxsYGr7/+Ot///d/PH/tjf4zPfOYzbG9vXySJvf3223z2s5/lF3/xF9ne3ubnf/7nn8t1ftwB/CbW4eEhm5ubX/fni8XiNxWE31ZLSQHj9nvkL55TbQTSfoPfEufcytcU9iQhfacQMO/NEu815p0BfuShFrG27wUWrwtCRt1Z4LcaYt+RHgvQWJeKZqePOyi6035AnyXUq57QHS/8xIlOqRcwCwNRig37LCU967JAL1WoWjAJ+bEULZ/8xH3mL0pcUjpVNDdqmjHEBJrLrXQKtIi8B2tL2q1WBOeFA9dFk80ts2lBKAL1asBvNuhHOb13cvBd5u3M4FacFLo60myJ69dcWkqk2npNdcmxuN1SbUTMsCWMHKMHot8LFsZXzy/SKorVkriQ8SYBin2NKg2xNrQTT3Wt4fxVjy+CjGGnClcgOj3A7KXScbCCgUkmFTEIJ6ztukJuIJFZbFcAwmO7uRBQ7WFBtAK29mst6bHB3i3Qd+bYxJG8OMWuVnCe0HsiJoXY6duyw27meWsBQHVZirxggUR0o1oHYt/TeyLdrfRMfm4VuhxZK6PT7FCjFhYWkmUbrQCK0ZHqRkO96XEjj3ZI1nBUVFUio6wIvgiEkUMfpty6eogqPGYnJz8Qt/n0DtSX24sIt1gK0kP5zlEcIDmVws2tONTdPtm+pTrJJZrPCurDVF1BnQXSY0G+hFTyi9VJIg7vQSBerVhecxSvnbG82ulWLWgHoRRdWr3lCJcrwSZlML0jhWWzLVFh9UGP5H5BddkRHvTxaUTriL9SYY4TfCrj/+TckJwIl4+IdDI3Iumxpvi85BTHnic7lCiwD7Vp+TNLve6prgtaqfdMXst2IPdWvRpwA/k1uTIVZ/iKZ/RladWHFJI3zzBTI2Pn+znJkww3CjKu1FC+UXL0BxrSQyvu8UbjS0syrgljx+ChvOZmKU5nohQyqpVbK35yJgessbz/7UTGq/ZZSrGnMY0iPY/U66J3Q3du96Fctxk3krE7kxxfEkl3Ua2CkSOWBlsp1E5Oc5qTzBTz247ZDT46UDjVmeI+iutTdfe+LzTVC5WwEEt5jiRzxdalM+n0eoXvRbn2Vbl+5aQzFy3oNpKdKBaLLoozk66vdwb1Xp9i13D+vRXNumgro4mkWYt/ZSFF/DDgR45krsSU12r0g24iEYGbSyni+/4ipq//VJMcP/9+joq/O7/+777+0//0P+X999/n+7//+/lbf+tv8b3f+71cv36dzc1NXn75ZX78x3+c+/fv8/M///N8/vOf580333wu1/lxAfhNrM985jP8w3/4Dy/++8Oi77/6r/4rvu/7vu95XdZvu8rLEhNkS8V8dyB5n28NsM9S0S1dEz1dtelx/YhSkfxXB+TffYLKREQOgu5IduTUrL8mDk2l6NItIHRC+pgFgU33pGiJPX+B+FDLbkTmBKWAFtdvyCLtWEZE+dsFZqHRlaIZgp0ZvvrZF1Ad+Le87Cney0STFUFPrQjKTxW9LxcsngzRUysP8lO5XntmLsZ6w0szwtARK8mfrdYi9txQr3tCEVCVFh7bmcUeS8yaOyygNMSTjHzfUjxOcBNH8n6BLRzLLQGy6vWa89O+vF61Qn1xRHokYv98e4H7rjnFjqH3IMGeG+xRIq/FXNIa5i+I69MOWtpLLWtvHqJaxehXC9IzRfDSqcxuzeTNjfL6KCfsOJ9FiqeywWSnsPl51Wkh5XVK5opm1eOe9En+6ZD64ZB20W3mr1SE1+eiF70h42wVFG1nPtBLGdmFTATuxVNLfX+EmhsWNxztqme5LaNxf6VC1x3seuSpLnmRGCy16NqUHAYoDeluQrpSSZHlFHonR+UenuX4PHT8PYM+t0QLTz9/heSRaFh1I5tmSCLpuJbOj5eRbLPZFfIK+o/lc6prJdrPFeHc5btJh+DQjO51j8CNGl0a0nNJAYkrrYzUzjTZA8EU+VlCsWOZPhuSnGvaQcS/Mqf4l46wZxblYPKWhcNMeIhKwMcfGobs9cUFUNiOpFMT8kDzaIB9mONXnKRlLBJ5HTujgysig3sW7bqItMudHvbEErLI8qon9AKuL6ac5FzT+yAluTNj9on6wuwScmF/mqUmPzC0Xlz69twwvxHIDqWAdr8xEWh89/ltNpwA3vtRHKgLS/4wkwLKBFTPkT5LcEc5yUHC7JWWsN7g84jaqj56Ho0ig7Ul6itDopWxdXJmULXkMKvQPVeA85dFT2cqRXpkcf0Iw1bycB8XKKcvDFvDr6WopZhGkqKFJFJtOtzAX0gjiqdWRqhXShkht5Lf+yHKxlQKJg1xq6bYVySPMzF5yOMH3SpOvrSBWWqyY02+r7FnBjPXDB4YJl/TtBNPOwhMX4hSuO+KuSw7NAK6/lJBdtoZRpYyejaVIqaB5XlB/oU+bhDI9wyqMhfRnWZqaLYb4qM+7UoQOYONgvM6kWJ7dtuTHypM8ZxNIB9rAC/W1tYWf+kv/SXeeustjo6O+OIXv8jnPvc53nvvPU5PT/l7f+/v8aM/+qPP9Rqf/5Hh/wbrr//1v84f+SN/hLfffhvnHD/3cz/H22+/za/8yq/wy7/8y8/78r7hskuFG3efpMKjzhLKOzVqZjGV6JTcyItb04ibNs3BfX6VeMOhKk16eUmTpMTUy6jXKYnsOsnwKw7vFKoVRpdqBG2BjgQbMefyQPZ5JD+QIqy8U9OYhPxAo71mcd3hVwJqbjC18MfsUpEfR6qtgJ1riVK7VRGnCeG7Z9R7PXStSKb6outUbQgLjBcWxP0B6ammuhxEwD5pKPKG+ZMRSiNolZst+dNEtFoDL5tX70NkRRcsb+TB7VM5wVfXGulqFI7qZoDSwiBSR8VouGQ66xFWGhqToCvN6OUTlr+xRntvKOPdToyvANcL6NWG+CyXxBAbMLVFv9Wjt4T9apXsyoJ5PcBNBJ+DjfDrY5J+xF2XTVXt5fQOLMvbLdlZgn2Qc/b7asZfzEjPuXA2V+syGhPwLvSfaGbrga0rpxx9eZNARjpT1CYjWYAbKLId0f5lx5p6BWIeKZ4Zym0v4OGBx+SOcCJFeXVDTCrhUguVRldSWJlKNvd6RVhm+dOUdij6SbfXw4Qu0UFDbHR3zZpmHAgrLYO3M1xfeHFuELAbJVVrSO9Lxzk86pNWEnlm5pqVrSnL3VWa1YAv9AVuReI+EEPG7Qq9nzG4bwmJmGr8vMBUUF7qeIKNZnnDoXoOzhJiEcAGmlVN76lled0xuG9ZZAUnRSbw59VIs0KnG/xIl6drhTlP8McJ5IFm06EaA5dr8ge5xOJtOZKDhGo9kJwa2tsl+dsFzKUwWlyTSiQmUUbYZznpgWTYFruG8qUaVWqqrXCxgfoHQ3QiUgHfdaFQkXilwjc51TQXZ3jRaVAryE4j85viaI3G0I49xVpJqQvB9zzKKB4nNBPhOXImms/mcit4IyVw83YdlI5kWUvTSEfaLhTz0x7Jawuyt/visDZgtpe0ZUKyn9Lb6QD0CwSIPBLzVXaiqdakI13sGMrtFnOQXriy7azrNr7fJ9XSlXfrLS6JRGOImzW0hnCY0ztWVFqh1xvCqaUddTKIqFB7mdABNjzJiRAHsgNDvSbdaAC3BtlOQm9fIiijgemdKJnp44hbb1HHKclU43sCd88PJDXIDSTDuffI4nPpHrttz60rR9z3W2TPEtpxV5TWoE411Y2me0aAG8uzzg0CbS+Q71rclRp9kInc4WnxLdlfPl6/s7WyssLKysrzvoyvWx93AL+J9QM/8AN86UtfwjnHJz7xCf6n/+l/YnNzk1/91V/l05/+9PO+vG+4QgLpqQSxq5kl2sjgbRnz2AqKfRH+x5WG3o5GLwUNETJIzjpkwdOexIN9ucDMu8Dxp4Ww3OYWjIwkQhpJptJNUaXordLTThe2VYvxYBxZWZtLQfJyxeK6u2Bg6Vozv+UpDjQ+hfkPLYlJpF31slnuZSSrFe7BQODLQH2pvRDb27kie/0Maz3RQLMSSE4Mw7sG9SSnrpOL2CndiivXvbzEDSWZ4sOxUHnF4dfaj7pJk0B7qZUxWmmwxwnqacHG5hSdesLlimrDc3o4JLSa5GmGnWn8UP6suVmhby6E7daLLK852tFHndFrn3yGOUkIpaXakHGkbgVhEe4OUFE2H1MrBg/NhSO1+GpBqCzxciUj8LlheUncsToJLLcji5sygm9GoXO1imZouR1ZXJGNe/+DdfIjhVvpugbrNT7jQnuXH0iWqamAoSQs2IWge5QJpKmT0VcEe5xgjhOS/UTc0h1It5146sutjMcbTb3qSaYad6UWVFE/4FccYeh56c4u2bHGDTzpuZZEGESr1q478n1De5KTv1MQXlpcmJHq1UB6bMiPFKcHQ5JZF6P30hQ2aikuM9FO6koT/YcJGkCUQqO8FAipjLIH9y32zGJGDfZpRnZsGH01IXuc4XuB8pLkJQcDxZ6hfzfBDz3Fvsb3RDd2/rqTn38oCJ9gRetmlpLOY/ZTmCWEV+eYN84ByA8FBuyLKOYEulQWBXqtgZHDzowcgnKHT8WB2owj6eMUU4vkQLeK5kojo+lCwNYoLiIJQ6txN0QCEJPYjctl3Di7LdgdknhhRiiPeqT7lq3/b4rPoXyhRjuJqItWtLvJQQJJENPD7QVYeSboz49FitAPJHNQpcG1Bt2AvbaQ4nSvR/896Sgut7sD14bk72IiyVwOmTaVkakKwHkiWuTLjcCi50qMLz35rPmthqQn2mA715idHHuUkEw7zea6uG5Nqeg9kS45MyuJKH9AOu3tVotu5XATho5sUGNOLdQa8/qU2S2Pv1Uxv9OKVtKAurnAnFvaUaRZCQweQbErh1U3jIRJS3W1ZflSTbPqKa+16IOM+w+k+POFdLZdEVled+LKLg3pBwXtUOajIZWCVzWSDT0Yl0QTmV8PIln5eH28vsmlYozfIQ3XjxfAdDplPB5z66/8NXRPHMt2JqfJ+YstZmY6cXxEr9fYuzKWmN8IhJ64gONSNj/9uOg4VPLQbIcy5p3cOaGsU6rdvsSIFQFd6QsN2Pg9DRrOvqtFLwy9ZxqXy4O7HYo+plqPksX7foEKUN5oGb2dEDXMXpdIOXOSYK4vCF4To8Ley4U5OIiyoZyJU9SvOmhkk+g/sgQD9YZwyEIeL+Kg7FQYXX7sMKcJyUxR36kYfCVnfrtLTen+LhSB7MigazEzuJ50kkLSdVeHgagjyZlUZe2qF12ZEreg1oHwtIepYfAY2oGiHXVcuSxAEsBJsgSTBn2QUezL67y805A/Frire2OB3ysEyAziWLWBWMtIOaRQXW1JhrWMbVOPPsgImTD7lBcOnKBXPspzTm7PqPb64lrtyaYSTSS7tKR91CeZaaqrLSr1MEuwc8FPQKd5SzsW3npN8VVBVLiRdOFCHklPNM1qkOjBDxEepeoypcXVWG95kvWSUb9i9sU16k1h2fl+QOUeu5sSbpSoxwW2UlRbHZR32DHmLldkX+7hc6hu1pg0kH21IGpox1FGya0i6ThxREi3lrhWovOiV/TezySea+wvHPNJx+qTYjSSnCuW1xyqJ1xFkGzc+Moc96xHei4dS1Mr6R5qQcCEJNKudEiaqaLaDNi5wt2sCKWVrq5X0Cq5N5OIH3n03MBGTWiMHN5yT/E0wXz3GVWZ4mYJ2b4ULcrB/FYXBdbz2LzFHUkXqP/ICG9vEoTlaIWZF41AgwdvpxfPjWpdTBAfdtJCHol9R++DTCL/Svns6NUGPxP8jBo3hLl0vFWriIl0+Uwlut7mpZIwlX87eGioNuKFG1450SjHnufVF3Z49+1rXZdW5CSDrTnV+2Nx7b8wI741wr28RJtAs0hFo6wjvfUl5VMp+ps1McC4QXc/a5k+uH6X89zIIS/fEw5lM47ESxVmJxdjXKOIr82oZxnJgUDnAapLTow4aaB4mFC+UDN4OyM9j5SbgvxptxqoDXrQEr2MkH0vEjdrsvcKyluN8AazSFxvYJqgxg1qPxO377o8ewFiLfSCqKFe9wzvGuafKQmLpGNyRtTAwWkqsY/T/x97fx5rW5addaK/2axut6e/59xz+xt9k87IxjZOng3lR4IoUVSpVEggZBAC0dgUxhZ6VkGJsnkYUSCMkREWovGTkAWIpiwBLuMGm6KM7UynMzMio719c/pzdr9XO+d8f4wVJ0kybUeadN6sqpjSlSLuPc3ae6291phjfN/vi0jOtGic7ZxHf+p/YTKZMBgMflOeMV9qvfvcufpX/7/o/4SU8RtZvii4///5C1/11/D/xPV+B/A9rH/zb/4NP/ETP/FFf/8TP/ET/PiP//gTOKJffzWdQLNd0eyUlJuCg1CJjHqSkZZOxP1Muh4NxGca023QVrpnPJaHSO8+FJteiohGoKdnj1Zwb0peqb66IJpqfOqlg7FUTJ5zjF+uiY6tRC4ViN7skmicorl0I+K3M6qnc8EtqEB+ITC72Ui37Tj6fEzYUYpbSkek2BTnbP+WodqpUb7F13SkG1WtBNlp9xuarRo7k25eZ3NB03ZpqHXr8vWYyNN0AA+Dd5RoC7uO7u6Mct2xvFFTbTjRIo3bmLa+By9auXgqI7rkyAgbcGRwlcZGDrdW42MYveyZvVTh4kB6JHy66ETc2MpB9CjBlLC8KBF274JpfRJwBxnZoSY6s6hGS07zmykqcRQXpNsUHUu2LYA+EfyLaqSj03QFweFv5qxfH9EMG0yuyE+lkwvgLpRSUDUKd6t3zlJUlUYZKQTsTM6tqLQlhUVXwjws1sM5iy45bYsBLSPQphvgxoJmXboZyUjT3RMtqJ1qmtqglGg5dQvWValAfX0UYC9FN1DdzLEzgzcQn2r8ZoWNHMWmJJZE+1LMVK2m1HUlPzaatbDijgMN1XGHcJQQ5hY9iSg2PfbKguxBJOewTdXwqWCQgoJqNRCfGexBDDrQ3VrQPJUTfaonyJEAfqU+j6JDB6qnc2Hgedn4aAedx5r6cgUnCdGJlSJjKs5Sty36M5yMzIOTa7F/V8xYxaZjMclwZwnduyJfcF8/ZfZsTTTWmFyjxhH+YRddSAd0cdmJhCEN4nz3cj7qbkAtDE1PXtvyYpuzfCoGLdcVHZoeCUtQ54Z4JFpQpYJsElOHeSRmGtMCn6OJIFC6j2XDpB+nEAmyyEdgluL233zlkOpiTYg9dmR587XLcs1kTrKMx4aytDQrDfWKw73Rl8i41zoy8t8T85JeGPK9HrrhPNO4uFzLRuZUnzt1AVDCCzUrFfXQk1+uyQ4V4Syh+6hN5XhxJjGLXjZN1YroVtFiCKJlmUaHcYuCagHpK47oIJac5GlEyC31mnTefS4u4Oxe3BbW/nwTEYISGcCpIhoZzEGMOotkUwiU6w6GNflOwESiZ7SbuXTdxxH9Oxoqfe7Cx8q94/31/nqv6/0C8D2s7/me78E590V/H0Lge77ne57AEf36K/Qa4gcxGxsz4t2FwE3vJqAhPQHXb/lh1xcUm4Hico1bWPS9THbCkXQ+gpFRoG7kBh7NhGlWbTjZRc9i0iMBHC8vefovnYrhZG7oHCh0p2H6oZJyw5E9EuPG7FpA1WBz6Hwmw/ccKmpTH+aG7LHBXSzFyXzQweQKlcs4Mh5rgpYRkek0RNM2XWMvwS4kto4AJnWsrM+xy7ardthFVZrFJUfngcVtV2Q3pzTziOQjZyKkvgZqu4DMsXjYlxstnI/Q6p5oqULswQAKsiMpAKpVT71RC8R5afGfGxDvR7jM078lTsl6zQmrrW4zPLsNftBQbTYtHy9Qrzf4JNB0PfWNQlIT2kZNUAEVe+phQOkAGyWmlNfneh4aGSGCjDz9oMHbQH7R0UxjZssEoiAuO69IH0ZUQ0+ozDnjUFfCJazWnHSD5hHaekF5DGQj0XS8jA5riLo1PvPEI0223zLwokC1IZmm3WsT9Fs9KVJj4Z9NnmsoV6Uw7Xwm42zSRZeQHosLUx/HrL2m6N/RMs7cavCNphk43KChWvWEucU1ms5TE6KJAM/dUjA00UwKL5yi7nlxcR5E0G3oXpzBRgktfHrtmTOq/a7kuS7U+evSRZvVeybu9WrN4VIR5OfLGDeV4jE7MKSncOPyMfGxGHNUafCFmEJCJg7XxVXH/JkaphY/bKg3G5odGU8rB2ocofZTTL8mXMpRZzFrT50xfa4hPjZi3LiVYJaauisFbvG4h6o1zTXRhNpFmx9cS7EeXcip1h3d+wZ/FtP0HPV6w+C2dAebTqD7UK5jt1FR91sEUq1as41iebXh2a97QLkhrvMmt4QANpNrNpq1GbZGdJqYwPyKdN7DpdbZv+EwOZQ3SpRT7N3ZkPFsLJ/5EAXCWkXcq6Trvd6gbncxc3GMN33RxBUbXvijjUw0dCUgarwUoNVuhcqFTuAtbedP3t/kTMwXbmmJr84BKNdDCwIX9673Cl8a7Jk9N4a4JKAXkgeePRLNKMjmr1wRqYSgb6RwTk4MnQdWunSrFSp1+DSQX2yky/wutzLXaBPgICEZyTWnkHOh1irpmqeeTq9El4qmsngbiGMnn9Na4TLQnYZyt6JZcdj1HPXFj6mv7nqfA/h/qfV+Afge1jvvvMMLL7zwRX//3HPPcevWrSdwRO9hNYpyp+H4YEgcNVx8+ZByy9F53ObL7hns81P8/S7lxZqXn3mIXshOvVyVEVvTk9zN5Y1aiPS25XoNHCprpAs0s0xfFnq/KRSLX1ln+Lrsoicv1+hHKeYoJj00FBckncRtCGR4/nRNuRFIDiwht3QfKlbeBFNCcjttjQuK7EjRfWDQN+aCgSjkAcVeKjfkYS2dgzYIvRl4wmHC7NYK6qMT6s0aMkm0CJkUMcmdhPzOAFVrlq+tno+Tw2FKvBcTTeSj0bkbsfZpcfg1A0d6okkOLHaisRPN+DmodmvpjpTCAqORCKvqQiPF8grSoWyxNdWW6OnMfkJ8EAkiouMJcSA+sfK7gyJ5K6PYcpSbklaACUSPY/zlAvMwxd5NKXbltcenhmhiziO27FpB951Y4Lu5ZuOXDHVlsUdRe341xU4jzL9T6TS5rqceyNjWLLUU3k7ha9P+HElAMUtN746ls68IDzqEyFM9lYsEIAvQa4hGBmU98/tDQMbgyYkmIKN4lwbW3mqo+wH1KKNcbbNUteBizn57QfWtE1wSSB9HxI9jssdWjAwB6ci93iV/a4V81+GTgMo18UTJaLEFavvNCrtekL0wZn1jxnycyeuZiH7t7NYaIfJ07xuqDy5aDakUr27gcJl0D5ONHN8Vjal5mGInBtfz5LuO8YsNd+5cQDdSQNOT0Xl+uaH3TtvJDmC6DaHfQKGxpxZzmBDWpPPnh3IN6fspbhyjS8U8TyD2VNsNzVprQOg7iWFsjRGrr2o4TeTzqaD33Ah9c87iWkNdWPmc3ZDzDGDGlvGLnnxHNoDjFzzZgZYO1u6SZihj8/x6hb9UYGaGN968RHIsqSuUGnsc00xifK+h2qnlaxOPu1iiZ9Lx0pUiejtj+Ia4VOcfzekMCsrLFcSeepRgziJsrrATQ+8zKdzqikxhYmiuFuhKkjKiqXSzfSYdOVPA4oP5+dMrmqvz/w6pp1r1lLtSTLo2x3v6bEOz2rB76YzyUQ87kdg5n3gWVxx2pqhOU1Qs7MRwJZfu3mYtedBzQ7BQ7Mh5SE5Fb+iTcD4qDirgn17iLWQPLeZBSqg1yYkhGss1Z6diNunuKeI3M1zmOf5YI5zCqSK7OsMY6WoD5Pf6wkQdR4QkUL49OE8Byl/K0Y9T0kcxw9ct0Wd78rVPcr3vAv6i9Rf/4l/k/v37T/owvuR6vwB8D2s4HHLnzp0v+vtbt27R7XafwBH9+itbz4kHJemgZH5vyOEntzFzzeKSjAWz44D/7JB4JDzAzz3YwS6VEPMftmO6hZKRgmlBuT15kHXuRSR3U+G0dR1mbM9D55VXVENw67UgL6zswk0urtruXsCcxLgYyelMJADeTg0+geW2kqioSLoLyivmVzzVasDf6uH6jmBFz5aeaMoLDUwiyqcKmp447oJpnZ9eUb8+oPtOTHQo4GG9kOKjuFa10Wbh8x22xBNPNL37baRc5gkKinUZT5pcdDbKK3SD3MRHing/ktFh6giR3IBXXm2xMavShWMkv3/xXEl8IqYPt13R2ZOiIT41glwxUD5VSGLBpZrk1GDWSxklFkbAwyeJxGatO1SliRbS5Wh6XrowM4u61aHuSaqCXSpOPuLwC+lsYFvA8nlyC0xeqUiOWyzIQs69akCVCmU9xYY/x5kopHs7vxSIn5qiEo9fRGKkiAJ2P5b3fx6ddyRc1oKT55JKYheKk5cs5U5DM5D4raDlIW9KhdJQLGOUUxS7tRQal2rRfk413UfScXJdT+dhm7/ac8yfrnGpjGnr1YYobagnCdO9Pif7QzrvJKQPYuKRPJh9KjnJxXqgnsk5sjODGUWoWLAeJm2oljF2JJKGd2HPAPHmUtIySt12mjTMItQoxg4q8s1Atm8lNaSR7kb22JKMpGsV308k0msaSc4y0H0g3MS6ksxqVGDwRvT5Ma2WbmndCy3bTj6X0UxR/PIa1UEHVWr0USJyjlgKfrPU+Asl6aGYvOKJIvQblhc9XF9QH2fopZiY8ArzKD2PGKvWxKSUHrbdrUpjRhHUmvRB+wGaSna1KcT0Uew0ktjjwS8sVWXo3Iolcq91Jxe7Nc1GTb4VqDYbQfo0Cns3pR46MVOsSedPVZpqo2G569Am0GxWktqxKi5yNbfopRGWZaOJJyIpsEeST42H/TeF56oa0QPqUmMXol9Oji3qND6PdwxaNJquKz+/vF5ih4K3WdwQTJZdyMbIddr4vtsdVm55TC0dSGrRh9ZbNaZUhN2C9MCy3BZeZv+2JX0k0XfL6zX5vT5NKRrFdC8imunz8Xq6JzITc31OmFvCKKa7J9rG2Q0Z40fT9x/pX2vrx37sx7h58ybf+q3fyo/+6I9SluWv/01fpfX+1fIe1u/9vb+X7/zO7/yC1I9bt27x3d/93fw3/81/8wSP7Fdf5eMe1SymetglPZRuTtDA5RxdK5qOOk+P0CWowwRdizmj2JQxXTUMmKUivR9TrUhhsLxW4z44Ewr9mRgRXM+fu3MlPQLixxHJQcTaa9JJrFYkuWP8fMBvSvZw74EWQ4SXkej8qqd4Iae8XBHNJSNWBc7HpM3Ag5Z0CTvTFBsCUo63l4S87VItJFoumguKxUewuNqgK8nYxIuBIb0fn3POopkSB+1SE31wxHJHUb2QY+aa4plCug9dJ+M9B8WWODCDpsXVKNITRXwQEY0NIfIsdwL51QpVCH8OIxodtZR8z+REEwoj3UEHPgGfyYMufTtFlQZVaYorFRurM9Ijy9bTJ6KBG2vSI0V6aDELzfwpyUfN9gyhKyBe5RXRXFFsN1QDjwqKeKWkuFyJgzCAXhqyPUvTCWR3YjEG3BbkRbktLtZkpAmt41vdWOCjgLtUMHnW0aw2VLcH2L2YaGRIj6RIHr4D3oCdaXoPxBgzfbHCvTwnv9TQf0e6L+WGl9G/CcRT+V691Ni5IhwmqIMU13PorCHpl1J0DOWhP35eNjKqVpRrgeTIopaGdE+cncuHfcnq/XRProeJQcWO5Y1K2I8tHqazuRDN11qDXhqisRg2kjOFPkpEcuAVwbdRa40USCER+G8nrejsyYgWpIv5Ls9SPcjwqSeaiWYy5BaVS3xcuRbO86jtfkxyJoie5mLF8qKTjUwAexyhFpYmlaJTNwqXevIdifYD6XrZpSJYAVqrRs6970jhoidWjAdI17lcF8f48vmCeC9GbxY0pxl2pknO2nxaL4WFWhqSM008kvP47ui1d1/zzEfuQyK6x5B5sseGbE/QMXqpz7vM8ciQPo5oTjOabmD4hrA2lZOibvBqTNN34nY9M+efx2xPCuD+bUM0V3SvTLH9WrrTtzKig1hycSORFoTE0702obhcSweuTe4JGrAeO7HotpPt44DqNPhMtMs+gaYrxqlooTA7orWLj6zo/1YqQq2JXu+gCyUmDRPIdxr8izJS1oUmGSmWm6K/VAHJZe449EzYom4imyJdQ7Xh6P+OA8pNR3qsGbweceXlfYG+DzzlpqO80NC5OaHabsifKuGlGVUhPMpgAtUAynWP2hIigX/SEsD3O4BftD796U/ziU98ghdffJE/82f+DNvb2/zJP/kn+cQnPvGkD+39AvC9rP/1f/1f6Xa7PPfcc1y/fp3r16/z/PPPs76+zl//63/9SR/el1xBB6KTiGguYeHlhkc7sG938FFguS0FWflcjruZ41YayjXXaohkN5ycKcpNR7HTCK1/tR0vvNGXkVYuGiGMFJJNX/I8Z8/XVBcayislZy9L1mxztcB1AtvPHQHyEJne9JhhLWy0VTkeAkT7sSBMLjQUVyrivQh7ZqHXyBVbSqcsOdXEI4W/00MVhmrgSU8U6uqC4krF9K01caYOa3a/6TFuuwQN3acmkhKiof+OaNPexUvMHw9Ei3grE3fxPGL4wRPilRKfCNqC1Esn67dNYKOkGgYWV2Q8WO9UmKEAZtEQ+g1mrRRdlQ3gIL9RESIBVefbjmrDSZpD1RYRlxp5qEYBPbYc7K8Sj2DyS1vklxuariBLqlVPs1kRnQgQuBoIMmL2TE11vWD5XIkdVKgLJSFzuEcd0kcxqkEc37Uiv1pjF9JNym7HLC57/GZFf2tO3Q/ycPGK7u4M7zTZocbPInETt3KdpicFSP5cget4zl4WFlq4kjN9qaIeBOLDiPiXe6QHlvkNMdfYhaL7WsLgc6JF1JXCr3xeD4kK2GGFLyxVEdG/beh8ssPyZkU81ugLBemRxvXEpISXEd2741Jh3MmxmKfmMIlIHscCG1/35JcalqMM06+ly52L3rAehlZX6fAfmBHdztBTS3OlQA0qiei6Y1GVYnTaY3GtwWWecl2i41CSc2tnYqxpOqIxyx5Z9LJNOel4iKT7GhQUlyuWLxQo6+k8FoC5n0bSaX4oEOZ6raHYaug+kDEi9vPjx/z5gvxiQ9gtzg1ZwQTszbnIE85ievclcvDdcb6w6hRuaUkODfV6Q77rqIaBaGRIThXRqAWk58BKzfL5QnS8zzQ8+Ilr2KOYctNBo6g/sODS//sBqttgKsQhvrGkvNDQ9OT81CueyfONoHE2atSgYvqcdOJ0LeerWpMxrPLAjQXzy57FzZrFwz7NPJL3rzWa9u8JjkZ5WP0Vy+LeUCYS3cDiZi3O4BOFnlmiqXTu7dMzsptT9GFC9tDiEijXHK4j11HdE41ts9rgOgF1sSC+lxKdWPJrFeragmLDk+0Z0gNLXVjsRo7vOMqVwOwpJ7rGtoNIUOdOeF1oMZhcaMDB8bjH8A1Dvtswfb7m4acvQiXdabueo2rF4u4QtHQTy4MOjCOqNY+dGfLrlSQKvdMhORVu6ZNc7yeBfOn1yiuv8Lf+1t9ib2+Pv//3/z6PHj3iYx/7GB/4wAf4wR/8QSaTyRM5rvcLwPewhsMhP//zP8+//tf/mj/1p/4U3/3d381P//RP8zM/8zOsrKw86cP7kks0YW0nY25JD6VrhAe/Kd0AlwWSNzLUw4zoJGL4tkE/MxfhexrIdzzZviEayXjTTjVmZih3akIsnCtUQLUIGDMXXVVnbSldBCch6f2XTomSBl0rDt7YIr6fkJyJq9beljt503eC/zgUoGm14lFJC2nuBdHIFQYzskQjQ9OX8dDySiOjt1NNeqJpMmgq6bSoIA9q/TDl7us7xFlNcmnO9LAHXSk0Fq/kkkSyvUDv5oROQ3GtorxUCaNwqcmrCP25Hj6TiK20X2IKKG8NMI9Sedhp0QtRGDiQbqo5FRelqzUhDqxuzGQkdSxdANcVLIudGspV6WCUWw7drUmPxFCAkjSP5UVhg+mlJlhBxehCoea25c15Gc0uNN27EdkbKfYgloJ/FEOhRWtmQ1ssKaKFAttmyK415FdqsgONOo0pioimJ8kReJifdnCjhCaTQkY1SAbvUAqe6vklTNuEjSBcwPTTHaLjSBIoDIICcqALJUXfuqPuQzUQ160pFNndmGrDEXotEgMBC4fcMH2ukZznRlNdK8QdnkI01viOFKoCO0a6VaOI8lKNPYlobvfo7BkpZJRo6OTC02J+mlnqFck6Bs5zbL2TnGVTKOyDFHsvxSw182cr0V7uxZJmsVYxuCWpId1HMpZsuoHkRLcSCEV+pZaosQLwit7bopttBk6QQCrgZxHVqnyNrkSXurwoGw4zsZiF6Dy3nj45h5bbsSV6kIhxZRQTTQ11T8wc5b5oNE2umN1sTR6NbMDiqRb3bL+i3HR0N5Yiieh66s2aYkOSNuqhZ36tQekgKTsKdLcWQ9BQnKq61NSThNufukz/UynxRNH7ZEZ9q080NufQeTvVbP28kbg/4+E4QVVKpCS5yCHEYS7g5HoqxjVViqtXLwy+BVvXQ8f4RU+x7Yivzhl9UEbIwQbqzQZ0QPdr6r7gb4ot4YrmRx3mpx0ZmV90At+eaRm9F4r6cknwUlibpYIHGbqBeq2RbvFRRjyW2MpgA9lbKfVZStwmqdiZlo6/kvOsezXRvGVPKhkbYwIMGpqTjPErlXTYy89PRMo1wUjFF5ZkB+L+591M6J2lxEEOHOlD0caWWw3Fxfrc2f//pPVX/spf4aMf/Sj9fp+trS3+2//2v+Wtt976Nb/nR37kR1BKfcGf9L8QX/NeVgiBuq6pqooQAqurq/zQD/0Qly9f5p/8k3/ym/77//P1fgH4HpdSio9//OP8uT/35/iO7/gOvvmbv/lJH9KvuUwpo4G6L6Bkn8DsmYZ64NHHMc1ag1urKXacOHsLRd0D88t95h8scKkQ6wmSbNB/I6beEFBydGbBSRch3Y/OURgSZK5ZHnXJLzqikwh9GjF9fZ1ykgqqAIhmssurNqRrpiNH9tjSeSDj0XrV4Xoes5+gPKgt6dxlj+TfXSrMs/REEZ8Y6hWPe2EuO/mbBeZAIsNc6lnuesFzAHHcUBx0xTU4Es1VOEsIiSc/6aBud7CJwKlpFLoS3EhdG8mrLTXxYURVWIqtQHaoUDX0HkpRY48jkmMJrCdA95EiuxcTvJJOx9uruPWaZuiou4H+bUMzFGMOuhWOzzVR0lBstOy2tUo6OAG4nOM7HrO9lAeChtA+ONMj6eCEKzl1T1IpmhZK/K6b2d6TB7PeLsTR2QmYUzl/emnQuWH5fCkIjdd6krZSaqKJpnsrFh1bFCh2nKQPlAq9kKJf38+IRoKAkQIMltueaKpY+xUZ61UrnmJTjjeeBKIzQz0U7ZLO9fm/K6dQC0N0dUE4SInHorG7euOIeLUg3bMykt0S96tZKtI90xbVn+f5+VhwJwD+UsHi2ZLljjhPs0NFum9J1nLM1OBjSVVQTsb51WaDzjVR3Ai/crOmWnXwzEK6eXdjuVZ70jnkJGHyvCM5g+V2OAeZ51fafOp3MY7t9yjfYn8GATs3YAL2QUrvrqVebyheFLivX6/FXHVNjBzvMhiP39oAFWiuFUQzRf8ebPyKEmj2ekPxTCEO28zT3VxKMZ9IkVxtN2IWCDC846nGCSEKLGcJ2dZS3p97sXRjWpNKfGrgJBF9XMcRzhJMAaufMUQPEjFgxR63XrO8GMiOAtG8LSYHjs3LI1xf9JNH3ySTBnWYSBf4oUQdurSNzgsKH8uGBi9GMIBm6IWP2Bq/4pEhxJ5gPfk4JTmIiEcGu1R015eoucUXRpzt3UByZlBtSk3ndiw5ux0xZFXrMo5PTyHkluYkRdWaaqemWRc9rkpELxoSiRTUjaJ3X5BbqiMTBQJ0XxxhxpYQBdZ2x4TThP5d6diH9Yr8Zkl0EGGOJEUlHZby+avEYCVGlxZYfaeHSwWg3bsjModikpx3E3UlmxUzrCSVKfp/ngnk537u5/j2b/92fuEXfoGf/MmfpK5rPv7xj7NYLH7N7xsMBuzv75//+c00avzyL/8y3/Ed38HOzg5/9s/+WV555RXeeOMNfu7nfo533nmHv/yX/zL/4//4P/6m/f5fbb0Pgn6P66d/+qf56Z/+aY6OjvD+Cz9k/+Af/IMndFRfvN4Fcl76we9Fp5nkuRbiGsveTEVzlEP+Yk4Yi+g927NUA0ERpKeSk+vSgNnJiT/TxaWiYarW5CmW7kvgvF6v0A9Tmq4XNAqCJVBHCemxaPRcV26aplvjzxJULZ2/clO0Uumx4BmWT5d030pY3Kz/kxuBkhuaU6xcmDFfpLhRIsDmrscstGSDhv8km9iCv1ASCkN0ZqmHbczTpRx9O0M7eUiq5+eozwpfzOSK4pmCkIvLNFgxoFRrnmgkMGCfSLfOdeTBmR6J29EUUjxXQ4/vOTp3W4DrWhtkv10SvZXJsbXGlmoYiCeK/PkCpQLxOwLcdlnAx9C7p5h8Q0FoNPF+RD30RFs51VmKmcu4Kz1RuLRNwRhrios18bGIx3sPYfK0vIfNZo0eW9Jj2esVW57t54/Ye7COHVlcp2VDnspYvelKt8vbQLXuRDvnwMUI1qeG+RXRlvlMCgo70SI1uNIIq2ypaFKJ9St2GuJTKYpdGmjWRTyvylasD3T2BKhbDQO6Tb6I5kpE+oVGl0rG9xMRxdcr7TnYNzQvLej9+w6jlx3ZnsVlgXqlNQI18kB1mXDtfAz1UOLtXNwe3wXpuKCQkXJWsZilhEbydPMddz5uFW6jwg8aokOJD1y5PmL2+hrxRI7fZYH1TyuKdSVpFnGQ9I2o1etpKXJmT8v7Um02JAcR1fVCgNsTLS72xBOdWhmbrjl6tyzzZ2u6tyOWu6JBNL2a5NWOdMErTXag5bMaC2Jnca0R04YO1IO2UA1IIkop49D8eoU5jejsK5a7YnqKJ5rt3/qYe/e2wCk6D6xk0pYKXdImBolbPFooKaqHtfDtjHAp6zXRAYqWV2QC/TuG2TXZFLhOoH9HM78caIaO5MgKZqjrZFOhYXBbXs/iijtnBupaOmjlhlwDruMh9ejIEcYxarXCPEhRrSkm3/L4FXFep4eWuicAek4Elp49sjR9yR+OForOQaAaKBaXxCVtCtEj2oUmOxSItUug+1gxuy7GqOjiguZhFzdwmKlokdc+dMRo1qF50CVYMNtLullFZB2j1zZwFypCYaRrm8pxoCSbPZiAWRh5jy/kNHsdUDLlUA6iOUxeKUl7lXS2W4C8N7BYX/Dwu//nJwaCvvaX/vJXBAR973/+87/h13B8fMzW1hY/93M/96s2an7kR36E7/zO72Q8Hv8XHet7WS+//DJvvvkmH//4x/ljf+yP8Xt+z+/BGPMFX3NycsLW1tYX1Ra/2ev9LOD3sL73e7+X7/u+7+MjH/kIOzs7KPW1zylSmSQfRGdyE007FaZMJVf0akOSNLhleq57SW5MyQ96zLYd6f2Yes2x8rMdmkw6duMXHSpzRI9iKRI2SprSEC8ULlWQeAHlnlnsXLF4qoJai5ZopCg/XGI25WZWXBQGWCgiyg8vUXc6qGnE8pLDnom4We/mOKcJo5gQBRZvrEpE2UqN0/KgcT1HFaD7WOMShbueox+kuFqTPYiwSzCFxRSw3NY0OzUq8oTcoh91YV1GnGahMfsSk+d2pNCoVgL9S1NmvQ5RVhN9qkfdC9iFoRpKx9MPayDCzsRgY08lI9bMDNmRFFxVlRItpIDyiYCs43EbsbafnGud6oEU3Z19TTUEfRyLTipCuhy3u6hLJWoioOLyw3PcfgefenoPFcqLS1Q1sLwAQYvGinaMXK4FcWZXmr391XN3I0gx4K0UqHYmTD3lFOmBRVewvC4/Z/51DXFao2/1qNelcCl3K+zCkIwCdb/tEF6qZfy65cSI0fLpmg7ouXRx7VxhaihXAqOXpbh+l4NozizBiDPaR9ItDrmFrkONNcF6VKPJr9TEdzrMr4j73EfQeWlEXka4ez18+7t9pCh2HHa9AKfJhxo9joT91nHYsehdlyYhr1KUpc2Y9oTMC2/yrUykCJmn+1ZMuS48yMVn1kgWisX1+jxrd/SCwubhfLxbbMr7qRy4lYa6b0nWcyrfQaUOl1hC1WZbBwRg3HGo6wvqo0ySSRJQhWZxTa6vsFLh5hHafV56gZKNkKlEI2dnhuQU7BLGL8i1UV2qsMcx5UXRPWZ3Y8p1T7EpDtLyZkEx0Nx/c5v+5SmzkQgYm64nGI0N0iFtumIsK9YDrFTinm1EclKtCDjZx4H4AxOqz6yKAzmSUXi1IZuyYCCeKlxHyfHoQHwQ4Y0UsfNvXBIOUsni3ZJxs50aip5wKNWwQo1jkgcxdc/jOx7GMfVmg54Zqq1G9LFOMsvLDUfnsaHQCfFI4z+wgMd9zFK4gvkFeY26grBWYR7LBAIF/nLBIkqI22K+ydox+lixHMYwcMSHtk3bUYxmHaoiwlaKeqXGn6XM512CDvQeKeYmElA4spmM5rJ5zfZMWzALaL0uLemZJr/YoCvZpE1eqon2Y0Kd0OzUzHYcdj+hHjj02df+s+m9rul0+gX/nyQJSZL8ut/3rp5ubW3t1/y6+XzO1atX8d7zoQ99iO///u/nxRdf/I0f8K+yft/v+338kT/yR9jd3f1Vv2ZjY+OrXvzB+yPg97R++Id/mB/5kR/hF3/xF/nf/rf/jX/5L//lF/z5WlzyQBFum10q8knK/KoT7ppTVEcdXM8LZqRUVO8MsOs5Fy+e4RIZz5294pjdcMxueC7eOCEUBrsQjEec1GxvjwF5sKixxBw1O5XEsKUO5eXB763o8tyjDnYuY0O1sERzTXjQwVvOESr+osCP7Wtd9F7K6muSeemSgC5FV6e8IjqWZIxwoWRxMVBeaM7HdfZYtHc2D+1Y0Qs42avPd/lWa3zqUZWkm4Aga+zjhNATx+/0qEf0OKY+zFhecmgnbMX+XRHl63F03rkbvqXP0SA+8SRngcE9yYCdPd1QroXz0UZxsRF+XwusLbbceSG4vOSIv/EM3YjLt1lrUIl0GsPS4nZKzJUF9TiVuLcTw/R6yztb8dQvLCk2BaXSfT2h/0aM2ioZPHuGHrccPS9FoZ2Jtmjl7daVuOmoVj3dR1BeaCg3BM9iz6w8fOYW76XYzR5GxFOgltc9uwbNToVbbdCJk0i9pcaOLPUwkL+c4wbNOWS4HgaywyDj1D1Dsy7nQ48t3UeauuepNppzeDQ6SP5yDGZqCZmDKFBdrKg3Gq4+e0Dd90xnGeVZho8EgfMu3Jh+jfcaez8VCUAjnwtVaTGPbHrMhoCZ00MBUA9uaaITi7mXUq14mmFDtmcoNgLNULpuphAXMk6QRe8y2spN6eSgQK1W6EqRHWi6aznZoSb+hT7xiSHOatx2BZXoRKvNRpJmCk11mhKP3k2tEbAwWsbAcqEpqjYf1pQK8/Uj/IUSl0rnMZpIF3L61Oej+zpvy7g2uxehI9G/+WFNvdayAicRBMnyXtwbSqGspThsBk4Say6JS7l4IUc7UKexgMd3Cnkflwq30sCwZnbYo7xcoWtY7nhsLkil5ExRd2kj94QmoJYCp24uVPjVmviNDjZXJGPRNKtOg10o0kORaIQ2Gadc9QJCz5yYJ05Fy4cVeUH3dkR2qEmPDP4jU3Qp4Oobm6fkl2vKGyWL65JSlB5D83VzzH4i+sqlInQcrhRYet0P1KuOld9+gK7BtwQcVWmZkPRrmQCcZejjWGQg92NUTxJ4fCqUhWjacjYbPr9xuyjXarnhBIm0JV1C1SCSikti0LFnovstL9ZEp1Y2i2lAF0/+cf6VNIFcvnyZ4XB4/uev/JW/8uv+fu893/md38nHPvYxXnrppV/165599ln+wT/4B/zYj/0Y/+gf/SO893zTN30Tjx49+kq9FefrXa3ff77yPOf7vu/7vuK/78tZ74+A38NaX1/nl37pl7h58+aTPpRfd51nAf8vfxm3+3lwrq7UObTUxZLnGhS47UrMAnNF9cIS9uUBGV2b4xpNllVMz7qCgzgy2KXEbYGMTIr1gBu0zseJFdOAhv47RhAFG+KIQweiM3s+XvSRjGjKli+nNkviN7MW7dHugGuFyzwMaphHbFw74/StdQG12kDnoSV/rkCfxCQnmvzZkvR2QrHp6FyaEz4xpNiSUY7fqIkey936Xe6friXG6V2EBxcLglfY+6nkFW/KQ1R5KFcD6ali/lyFnsgN2FsImYx9dQOLa3LjVk5hp5KH3PQ9Zi5GAuXbkdJWjZpbVl9XjD4gGqb4xFKtOtJDS7Xq8ZkjOrU0fU/vnnSA3u0SJseiE8ov19ixdEw7e5rZczXJgWX96w85fG0Lu5SxZEi84DoqTXdzif/UEFPJa6pXHCufs9hFYHoDwtMLmqMMWoxL05XzaRaapudIjmVUR5DECRSkR0q6TK/UxAcW1wm4tRpKQ7pvqYbyM+Kxxtsg7EMXOPuGGnPWjqFbI1HQ4PsOrOfizoi9uxuoSstr8GBaTZ9bF6lAdBSRjOR1mmdn5NMUNTdgRJdplopqtyY6iKh3KvQkIijIDjR2CdOX6nOdo6SfGOkGXnSsvK4ZPx9abqLwAZUTTWyx1UDi0VNLdnWG+o9D6t67bEYlTtVhQ/I4plp32LkmHitJnTCB9EQx/bpSivFWM2onci59Inibdz+zqhZsSTxRlJdq1NK06ROB7FDwMcvdIEkRlxcUoxTTXqPNQEaxppTC3e8WkjhSIj/vw3PqeYyeW7J9jUukKIsnisX1Bt2r0Y9TiTFbaejcinGJjHRVo2i2KvqvJsyerwW1chxLGg/i4jctksmlwp50XY+ZiXNbr1a4SqPmUtArGwi1RkUe1WZ1u1SIBuqimH7iM01xvRSWoA3EexH1wBOPDKaUrHFdS0c4HsFyR4pwm8smEqTYqlc9nYtzlg/7cm1ZT3QcUa+6c4OQKUV/uLzaSDyh9URxQxw3zB8NpOtqPenDmPqZHIBwmOCHjWhEK4Fw61wTTTU2h+VzJZ03ElSQgi9YkSpkV2YUeYy5k+Ju5myvTdl7e1OkCYWGiwVuYc+Lv3rVMXjTsrjkcasNnduxaKwnJbf+2v/0xEbA17/3+78iI+C7f/F/4uHDh1/wGt5LB/BP/sk/yY//+I/zH/7Df+DSpUvv+XfWdc3zzz/P7//9v5+/9Jf+0m/42L/UMsawv7/P1tbWF/z96ekpW1tbXzJl7Ku1nvyW4f8C64/+0T/Kj/7ojz7pw/iyVn1BRMHm0lJ2rmvCPqtWHW69JjtSAoLNDdmBjMj8WYLaKbA5uNs9/F7GYpFiTyJBGgDzZ2q0k+6JrlvRfZARS3ImZoRoJEiH4qrw/iTSCdylQr6n1etVq166YKlHPUqpBgH71Ixqu8Gv1OcdM30cE2LPZJ5hc0WIpcgKFgafTPFpEBB1EDeqKTThk0OWV5vz2CTmov9pum0HRcvufusXDP17coMOXsFRQrXZUKy3sGQleanuYimvfSpanaBlnBWdie6s6QLdRroUM3HqNl1JVHE90dmppuWP5SJeL1ckp3hleybj5Zmh2JHcXVUYmp6nd9dQtgzFaCodg+KidOqiUxldmlKRXwjQKKorFXv7qwQLyQfGRNtLcRyWcg6Wez3RqiWcg7vzTRg/J50It9cR/EyhaC6K0aN739AMBcEjhUBNMtKsfQ6iiRTF4w9K+oXrtGiSRgwkxbWy7TYqohmfz4r+rYI80ZeWxGeimwq23f235/3obEA0MTCUIs3ODH6rFKekkvcRZCNTDzzFYVdYcmPN4C35mWjOY/hsIpsR3ciIdPqBCntqic90a2KSIkFG/IrZNSkCuvcN2SNLuJTjLhXiuG4U9iSSTcpnh5QfWmAKKe7QsjlQsafclDSYerOmWgnnRUjTAXMWMXgtlmKt1vQeKNzASbbvdk7n2TE+9ucdv/gDY+xxhFlqwk5BfHlB/aE5dU9Ynj7zFJOE7H6EXcrmSZUaUymarkddWaIfpaDE3bu46og+0yPui9GoWgmCPQkiGbBTg9pPZeNYKrK2yNC1vD5TgjmNpHOlg3QXl4rVN0Mb0QadPU08pnX4QuhI1GH3gcHlRuDNlcDUw7LtBJ5FhNUa91SOyySZxNzK8LGw8cxZhJ4b1MJQXWhElxkH8m1HvV1RrXqKrYb5NU8zdMJ4rORcrn/wiPDUEnoN9RsDUEEg7xPbbnYCIQ6EtZp6s2Z5sxIj3FEMRwl1aZnt9+m/IzIUPRc9tD9JUI9TfNeRPIppeoH58yWq0xA01Ddz8i0PM8vyhYJiQz5jdqrlHGuPb0QPzV7KwWtbxCNDcmKIJwr1KJX7xuVCTDHWM3ulAAXxXtTqZxXF5fqr8nz5aqzBYPAFf3694u87vuM7+Ff/6l/x7/7dv/uyij+AKIp45ZVXflOSvUIIX1I29pnPfObXHVP/Zq/3NYDvYRVFwd/9u3+Xn/qpn+IDH/gAUfSFtM2/8Tf+xhM6sl99RUcxYaioxgnpiaG7Hxi9EMCCThumz8roSzWKaoCMo6bQ7HcE2hwgHmlKneDWaszYUlxsiAYlZaFl13qoMcMaP45RpSKaw/Q5EWyvfU7yb0XrI3Fj3ljqlxf4kxSQzlTYblBjcRwGQP3ygGgoiAfRUAXiQ0MF1FVGuFjJuG1pqbuBfEfGtfWuaA45iyQzdstBIWMb32vI7sdUz+X4k0QwE21HZLktH8xqRRA00USz+eIhx6s9ol/oU64Iqy16mFDvVti5pdqtye7EUrC8NKV82EP5QCi1JHNMlXAN22gwXckD6l2BfTX06CC/3xQweTBENzB8G8p1S77ZdgtzTX4htOiQ1qhSGpL9iPJCg1ma865Z+syE/M6A6NBSXGgINjC/M8Svin7PLCRezq3WOBuolMangeTIiDsxCoLjWKlRC0tQYA7kvar6kvzAsKZWAXJJcDl5JZAdKJLHMf6ZBa7RxFfn1Lf64qxNAtFhjJ2JuD7YQLkG0UzjqgTTKFwc0xkrFlcddqGxM4UrxDBCHaOuL9CPOrhhQ9NRqKBIDw1VpWCnxBxl5NvSQQwmnOfDlmviDq4HgWiloF7E2MjhApJTm8vY1ieBYJRsZExAoamHnnQzp3rclQ2KgfxyA7OIaGSJnp5S3uvjLlSsb8w4eTyk96muwNKHlWhQ30pZ9jSdhzICV4Uk0NhZKzfQchz5pmxEyoFnekNBo4inCn65y+IVwYk0PU//8pTpaZc0VwL2Pkgo1jUmcbgXcsJxwupnDdXQUq2Ec7PHu/pUXSm08ZSrUsirQmM3coqqg32nS7jQENprwg8akkei4bRjg1tt8JnCXW6Ib2XYpRgxmgxBC5UGexydQ+XPXlA0PdF/5luBaKFoumKAwonhqMkg6lXUOkbPoza3V7SxPvOYyOOPUkypeOob7/PgJ66hDuQe5DpO4NaJyBOUl81W/8qU5VsrNFsVaa+imCQMNhZMRx3yXRnJHzxePe+6yiZOIhDtSMONkhBAj1Kci1DDmks7Zzy8uynnLAlwlhBPlWz4nPD9VC0bDbtQ6MpS38zpdEvqUQd9LLSBqpbiJTk0uK2cOo1IjwxNL2CWmqYxqJF0T5sugjVyok2USYnkIfu9lGJLOs+DW5rpb8mxr2dk9wKL3zUjevUJP9J/Ay7eL/kzvpwvD4E//af/NP/yX/5LfvZnf5br169/2b/SOcerr77K7/7dv/vL/t5fba2urp4jZp555pkvKAKdc8znc/7En/gTX7Hf9xtZ7xeA72F99rOf5YMf/CAAr7322hf829eqIcROFbbQ5C+VFNc99VB0cemhQT/qkIwDyx2FN6IdM6Wi7inqZ3LCUQJbJXk/YvPKiJPTPtFcIpPcuItRMlpaPNugGkWIPMGCSzXdewZdGcZPS3eIq0v8QUYTBVit8GcpIRXGH3OLn0UMbhvsMjB5SlyqyYkmXKmoKo3u1TQ9I12AQsE4Bg/VTk12K8aUMpLUh5Im4Ndr1EQYhCufM5T/1ZRir0txsyT7XCYOupdqwtLQXKwoKk32MMLuLqmmCW6lYe90iK815TXJLvabFfHdlO5rCYtrDjUXLEw5CDSnGaYRpp638qBtuuLCdIkQ/dkoCWdJG7EGzbMF0Rvi7PORcOySsWLxu2bU93qS4JA5lLe4lUZMOf1Aciel3GzB0YeWas2hgsRdVa8N6bbO4GhqJMXh+TnFaUb3nsXmUA0hH7YFyIWSUBuqpyrUQQrrJWEcS87rZoWdx/Tuw+ijDaGM0IXGJQZVa0K3wZSW3kMo1oRBGL/Vpd5uyGuDbUd9uluT3MtYfKAgmJRoLpGA2TecMLqzJjqn1Zr5ikLpgGsi8NC/D2cfFHNOuN3FdwPRcUTT9Qw+GzF92pEdGPw0E0frhQI3SojONNWGI5pbcb1em8OdHt5r4r2I+NWY0EpxygsNg605xWsrxBPFckfjVxrUUhGyQHnYIQwahk9PmC1TOtpT1wb7MMJ/dojqBHqvJoy3YlQamN9oxIW8lxJ6nuXlhvggorggEXeqaqMFC0O94ujes6ha3LDRoRKd56nFr3jmNxqSQ0v0ToZfd4Rew/SkiyoNxW5NdCzgb3KDK424mDPHckfMBJ3HimIDkqO2ozw2cj5u9ehOFMsrju4DQ76uiWZi1jBjS7NRo2KPmkbUvUA0NtSrDao0RGcaXUcUl2qKbUXSRsLZpCHEDn8SkV+rSB/GqJsLEh3wb/WoLjREczmv6XEgvw75ldYJfpRx4alTTpI+YRyjl5omlY4cS4k79HHg1n+8ysr/65izN9ch8iT9kmppMBN73h0NUWB+b0j3UBFOEooNOS/LkxWMEfNN91ZEuS5O9OhUzGpNNxBSR7XjMAcJupFNASaw8osxj5+9QDrSFLs1OEV2Ysgv15j7EWYhkOxopikvV5TKwrBm2M8Z7w+ElrDpSI8DTUfRDDyV8oTHHbSTzUkzaItsp3nlw7f41GdvygRhzeGXYuCrVoRTqCuFdVA+W8BpwnI3oI4S/Idm5J/qUy4jQvpkMTBfCZDzl/v93/7t386P/uiP8mM/9mP0+30ODg4A4fdmWQbAt33bt7G7u3uuI/y+7/s+vvEbv5GnnnqK8XjMX/trf4379+/zR//oH/0vO/j/ZP3Nv/k3CSHwR/7IH+F7v/d7GQ6H5/8WxzHXrl3jt/yW3/IV+32/kfV+Afge1r/7d//uSR/Cl72qdY+dAkGE3/VQHJHVUHbi0+vQrDREwxJzv0PT98QTgzMOe6bRhxlBw+LBJmYoYNrF1eb8oYBphe/TFgxby+/yEeRb4Vxfl7zdpb4i2YfKK0Ls6dyJgEgE1SuO6bMCbbWjdhwHhMcZYb0mzCP8hQqTOIb9JWcPV4jGhs6dmMVV0R6SODqvJ3BsKT9cEu8nrZYJQlDCMhvFgqVJBairHTSTiHgqnYBwt0sUJMpOnUZYL/o9u1CEIkE50eyoRtF9oCnXArqEaFDh+zXlSSIdvxqiF6aETw4ptwTD0dSiKfQWyvUAD+S9damgdezE4GIo97qwVaKPEuyJdNl0LKN71/P4WDR32WPD8mqNHVmqYUB56XwVW4F64FErFfbNjOKgQ3JmyHfkoRB0kAIu8vjWDGMvl8RPj5nfH7YcR4W+WlNdgHmI0a1DtuoHghFWWWPFwanbvFOfekobSPcluqvcEsSP85F0SsYRxcWGqhDTxPjWGtFcUV2qJHP2LJKfNfCEWjFtpbbdh5piK6DWStQsxW4WTEMmeJdSrrdgwZ1I51bXCl1oebCuNDTTBFalc6cSWOwGyblts2zn8xS/W2GKhGimqYyVzlbQpFdnLCcZk09viCtz4CD21LtNKz8IzLYc8aOYoBXZlTlz1SM+NXSemtH88ir5pZrsUSQmqE6r5buSwzQWPEks7uf8uQI1ivFXC7LXM/JrNeXliuRRBEOBi9uxIFDqAdQb4m51PRk3m36DfphSbjl8ZLCRpIBUKzLGdT1PtFJSJxGdg5jk2JBvBdwkJs2h6InucGN7ysmjFXr3DMuLXqISY0/3dsRi16OcZGn7fkO52dB5ZMkPOoQ4kC4Vfi7xbtU4gaAwacBMjCCRnGL0oUZSfRaC3/Fdx+H9NUil+xtNW5xJ/K7+Vz4bdqk4Phxitkv0YUIZpOgCqHcrzFF8jhSqhtJVj6YiUynXZHNLIzGX2aFiGQtrUTlLvdFgOw3dX+gwfdZBm7zivnHKyHQEQ/SBCaaMUA8zig2PnpvzHOF4IteCHlviicYtYhZpjaoFDZXtGco1SI/BTQ3LmxVf9/RDPvPGVVTqSO8luCQQbnd59e2nMbHcN+OHsRh/gnSL692KzpuJSEXup1SbDVUiTv3iLEVdcuiTmDpbftWeMV8r6+/8nb8DwG/7bb/tC/7+H/7Df8gf/sN/GIAHDx6g9ecVb6PRiD/2x/4YBwcHrK6u8uEPf5if//mf54UXXviKHdcf+kN/CIDr16/zTd/0TV80OfxaWO8XgP83XboWZpW9l4r7r+VW6e2C5kEmnKyVAu50qddEuF6tBIyRSDGJRxN4qs0Vi5s12YMI1UDT11AJhHhwfcz4uEeYW5a7UmjEk1ZEvSeC5f6rCfVAxkPFlif9phPyX9hotXgicg5rFVyuKQ9SYRc24Get27PR+IVlfJRiHCRjcRCa+bvCb838ZkP20FJPY1RfRrz5ViDc76KAtefOGL25RlCgLy/Qb/UwgKkEUOzWarJbiXDGlopyJRCNNeVuTfowploTJ66uoO6J5izbNyxHCfF6wcZTp/STkvu/dInFURduVkSdGn8qLr5yRXb7WDHDVC2PrjeSLoa3YkzIVXQO+23WZDze2VNUc8PymZLsdiKdxtzQrDWkjyQaq9hyNF0nbmfV6ixjj80NLhH3oZ1ropmi6Un0W9gqMb/SZ3a1QQGdfc3ikiNMEnpvS7ckPdbkmzK+jUdyXnVjaLLA/IqMtnUp588+Mudf6zqO7lsx0Uw6w74Ud7RZShH4bkeFpr0pK3Fh64p2rK3Jt4NoHHMxB9V5BKkj2Y+ou+KmFt0A6EZyjwHs3BCdWjHeOBg+e8a40yU4LbKEUysFdW5RhXAe7bzNiI2kuFged0Va0Bf9ZjQypMcSY4dT6EahBxWuI+PWxV5fwMxZIP75VZrVQHxizw0HIQ50HhoWHQuJQ48Nujb4iwWhkGzkutLC5RtbghWz1crPJ8yui/YSL2YRVbZdu7nBZR5zL5XjXimodUK9qujetVS7FennElCGhU8h8eRb0p1uRIXBctcJUDz2jN5cIy5VCznXwv57HLfsOUWTSbGfrRTkpxnluiCNqqslTc8IUqlq0zraZB8zN6jtHH0/w7X51OWKyCHsyBKPFcWmoFXKjYDvONABNYroPLTn3aD0fkxxuSI70xSxod6piPZj0OLIBpEtaBcoNwUXE81kOmHGFtcRDE/db6HcjcJlgejEEt2NJCe8USQjMcJY69C9mlAlLCcZ9jii2RDph4+hXBGDS3KmWHz9knCWiLktDuh7XeiJ8al8aUn2Kx3qHqQnUMwsn3nrCr07lnJFUk3EdNRyRO9EmNIwuA3zS5pqRZiLFK0W2MvnA6foXFgQHg1RTathdQKvf6LrCY2Af731sz/7s1/w/z/wAz/AD/zAD3x5v+jLWNPp9NzA8sorr5DnOXmef8mv/Wqadf7z9X4B+B7XJz/5Sf7pP/2nPHjwgKqqvuDf/sW/+BdP6Kh+9dVknrh9OBZPldSHsTzg1zUGKTCiN3qUNwqGv5ySXwi4GMKdHqaCaCHOPR+JcD19GFGuetKrM8Ikk6i1hWHs+sRH0jnhwxOq2wPqXostMbRQZPmABhuwC83Z/hB10YlpYhLJg/cwwRtJUIgvLyjGKdRisngXoosXfVSxIcdFALvU1JcqkvsJxbMFSVpj7vWFfXgq5gjlFePX1wVNoQzmVpd4Kh2hYqfEHCSYk5hoCfNWk+WTwNqvKE4yi7eB9FCT7zpxlhZSJFSrgl5wD7ocriRUF6aoGwv04w6+A/5xhrqR0yxkJJ09tORXalwsN3KBQ4NLoNhp4b5jiSTDBvRcHNyTFxw46K8umV3WbF4aM88TnNNUSyMPzUaBkpFcPYupLlZ03k6k83R1iZ8k+E4gGUdkB4qwC9EbmWSqxp4AlCuKkHrMRAoQAtKpST0qdTBOqIbSzQkmUPa8RARmDc0kZrEr5ozOfcvyakO+46n7iuz5MfkbK5hSU616KSRrRfY4EnDzULAnAO5GTvJah3KnIcwNwbTJGQG6KzmLcUbzzBJjHSxi4j3hUtYDcZMToL5c0V9ZsnxrBYDRnTV5P5cat1pLBJ7W+GGFPRBocN0TiLPeLKjOEnFudzyDSzPKz6xSrTuWkSI5lo6WnStqnRD6wn/TjXT33DSmXFVEM3XOdosnmrrvWdyoJdHh0pSF9aRvZORZzODKlMV0iDmNiKaaas0LHiQNTJ+WcboqNc1GTW99yfLugORMYtz8XDSL2YGmpCu86osFi6e95PSuSec/mmpQorEr1+VzpnoNw19M0XWg2BBZw7sokuUVcet2DhTlqqQJdfYV5W+bUzzusfKWptgUBJI6jensK+axptgIbD53wtGtdbqPDPMbDfFdKeAJkG97+nc0k1cq1GFM02knE+vuPLFm8FpMvhUotuR1VSuBersiOojJr9b03onIt6QbrlRALUVnbAox8STHwq8sdhzpkaV5Zok6SgVQ3g2CcUo186drdG0p171seAFVg/3oiNleX1zfAZoVL2aUiaW44DCFFjNOqZg95eBMuv/xVFEkMLgFoxcVZmdJvYxlQx3B5Hlxz/ffjKj70r021+fk93p0H8r5KT46J/50j2IDqjX5rOR9QL+7KRdup8k19TsD4kaIC8VmkMzm5ROWJH0FRsD/xQXk18BaXV09d/6urKx8SanYu+aQJ+kCfr8AfA/rH//jf8y3fdu38Tt/5+/k3/7bf8vHP/5x3n77bQ4PD/nv/rv/7kkf3pdc0VTTbMm41x7E1CuOMLIkd1O8DZiZpulLtufsukeXUkCkp3ITr4YCikVz3vEIUWB53GXwhmW5E4TyHwtglWAI7wzasaAUNqYU5IpqzSZuvcKveygNQUsMmbdSCAGkJ5L/WZxIgknQMiJODyzFxZqVz0TMbngp/ozkuZZrnlBJNqw5SGh0THlDNEbNVEwgIXaoWhMfWpSX17nYlR03pUB0fRooVzVBB+mK9BuOv96iGjEVNJ3WtbdUlJuOaLWgGaXnGZ5mbJl1M3iU4XuOeLVA7/coTxPRTu6UFE871MyKy9ApynUPqxVJVtO1jsUsxccGOxFzR73eYCeW+Fi6VLO9Pp2HlsVGTH6WoZei/4pOrOSabmrUIobdmrhTU60KTHrYKZkcZSImXw00Vz00iqbjSU6MoFTOpLsUT6XLWw2lM1Eq2jQJS4hAbRdo7bGf61GueuwsIhChWpad8orljZpkX8bTwUD+5orEpg2kmKzWwKxUhANLNBeQuM+keNT3M5aXGnTaYPolVd4VlEmpcZ8eMhyDj2KaFMxKAC+4F5BrzKyXuLOE6bJP70QJ3LfbCK9wtSZ9EEscXRu5BYLXCWsVYWHxo+Q88cPODPUvr9IMPWatxI0SfClifBTCi5wb3I0CP46hkgvZLlrkSgTz68J4NDODaiTVpPrsCkmtRNd5aqh2rCSEnMimKx5rqoHHzjXd50fM5hnME3S3Zn7aQcWBfFc2BdmBodKBxbWG6Mygbi5wxxnRXFJ2ivVAfrlBVQpdarSTolSXCr2XML8sn2PlOO8KhkhAziBjfpe1JqQ1qHLJL6770gUfPnPG9I11OR9L6aAe7q3Q2ZcxMtajnGxSkpUC9Xqfcg10yw1dvaUYPx/OE25c66B2HTFvlWVENFfUTiIi0YH5zRozN8JLHMdoLxo/sycFtClhca2ByFN3NeZORkjajrEJFMvovKMatExLlJOivekGiscDkhMx7TQdiaS0N+cUZynpXiQGrRIWl5xcf7UU2MqLI316U4wsHHToHEmhzjNzmrOU7LEl3w4EI11QXu1DP4jZKwlEb/Xk2FUg3Y8onirRZ9G5KS+/VhGMwS409VpDvSaZ3NVmQ3RmaeKv2iPm/fVrrJ/5mZ85d/h+LUvI3i8A38P6/u//fn7gB36Ab//2b6ff7/ODP/iDXL9+nT/+x/84Ozuw7wXyAAEAAElEQVQ7T/rwvuSqditslWBywWuYmznLSowHZqGpt0Qbkx0I+T7fCriBo5kLzkVdrvBB4eYR6EC92pA9lI5NfiHQbNQ0Kxo7MaCgviTQUrToq7yCZKRJRpBvQWdPMUvELZxNFcXTJWGrJHsjOy8A850W5LoUY0e4UOFrQ7nRjv364PpObngXapwJJIdW0jJMi0qZaOqLBd4pkjuRdEBGEj0XIojP5LWq7QJ9kIqzL0BIHOHZnPQ1SfzwK54rzx5w7+Em1UpAn0YS7F4L8Lc5yUiPDdFMdFnpCdQ3a8wzJfN7Q+xn5OeE1BMqdQ7LDV2HPY5ad7LDHiQUAysdNhUIvQZnDGap6d6JWD5fkmeWaKIhc+Q7ijBNSR9HwlYzwnSsh0F0a4ddooOIaguMEd3ZeH8Ag5oqlvMj1kVxH7pUttveQOcgsLikMI1o+4IVdEbTCTR9GUXpw5RwoaAa+hYFpCg3HL3PJsyvOdH0TQz6hRnJawOJ6NOBqFaE7RKjQBtHuN+l3Ghh4Q107xvyLQ3XlkT3O9Q2YCInMgEb8NYRZmKsWX5dji8M1Bq/Kek01U6NMgF/kGJq4fAV64HsSDPvaezUgJKHeug39N5KWLqUzoFinkCzsJilJj3SLK84XBqIpvLao5mmmMTEIzGZqEx0hfYowl8qcLOI9NgQTqXAyy+JPizPHN3NJYuzjGhhCUsj17VCogPTQDwxLKYJK7c1k+dkvKxC62auYTLpsLKyYLqfoO50YN2hVirCmWhv822HyTVN7KjXGzhL0Y3CxYFiQ3hxeNHfdg6EaTm/7KnXGqjEZQySW2tyRf+uFgf1ByZUlWEad/A9hy4t6Zki3MmoVjxBS9E0eXMd7SF/thSEihNYe7kmn0czimSDqEApiEewuOxJLs9RKlAcDkVWsVWLi18F5s94MJIKEox0Z9HQuW8F6r5doCZi4jgvtmNDsGAn8vN1v27fo0DTE3nFsqMIQLEjbD6da+KRksSXKNBsOsnG7jT4RApJl7WRcqMUWud+sd2gug2M4vb9g3LdUVxxqIUhuTGHtweYQrG8WZHej7m4NuHw1Z7co9o4SLQU2NFcrjPWS8xhdt59LjekMxSSgF5Ikoqey73AJ/L9dmTRz8wx93qizT59wu2zJzAC/lpc3/It3/Il//trbb3PAXwP6/bt2/zX//V/DYh7Z7FYoJTiz/7ZP8vf/bt/9wkf3ZdeUdKQHGuKC3ITaWpD6AkHrVkTrZSPA+7DMxZXJLNX5Ya6G1jueOpRgpvEdG9HmImVh3Ab5VRvNtjTiN5tS9OXh/jglxMGb1jiYysdj1JyOGfX5WE6ebkGL+Opph/I3krgOKEaBvKrFdVzOXYuiJRyywlipb06o7GmHniWFyW2KyiwpxHRmaG8Ig+e6FhGyfnlBnU/I5wl9B96fOap+4FoZNClpDb4CNTDDL9aE6yn+9SEeD+i2eugPjgRuPEbGfcebEpRFqRjopwURsM3TItVacfbL86YPOeoPzdgPktRjeQt15sNSSt6twuFqhXDX4nxFwuyQ03nbisKNgEWQvSn1OjViqDbEdhbCZ3H4oJOHsbYpaa/usRHgeRUEY+kCGs6nuVxl85Di48Dg1djdCmpCapS6MgTnxkICjsxgu/ZrM6zjOt+4PQVGT2W64F4IvBglwayIynSsj2L7zrMOx3ikaa5VkjcXq6p+y0YuhXouzf7lJtOJAQ9z/JmxebaTBzmd7s06zWm0LBR4nqe+c1GuselpffCGfGwxD/sCp4nN+i5obxUU2wGOEowHeHrMRcMSOd2DAsBkTdbFfVWTTJWuAjMXMv4byCGmuGnEmYfW3L9tzxg/JIw4nQhnZp4Kt2Z3gOJ3Xo3zzh7LBF2Tz29T6gMulDET0/Rj1K6dy26lK/VjXQP00NNcmhZzhNsp6G4Usm1ZALVhUau71qxfEFYbvW7WJFeQ4gkcs6UknYxmXbwqXQ7k0Mr8YhWOlidx6IjTA4tnXuSkuMTjx82uEuFMOxasPH85ZLp0w3RXBHvRSRHto1ehLXnTqnWHU0Hyk3H4qQDQHJlLj83QLkC2aFAlZeXG5q+o39P0XvhjFBqktsp5qxlEHblvuM7nvTI0nsnopgkFFvSbXdv9Cnu91ledjQD4e+hAslBJMihWsDczU5J8swUNZdxrF0o1KMMXUqdkO82NFk4L2DrgeCl0s9l0mnXcOPlxyx3HemBOYc86wsFIQkCkq+ko6unVvJ3s1ryvdfEpKMdwpzUIofpPLAwi0T+MG8ZooknfSCfz+Vxl6bvqVYF11JuOo5/alcwW12Rr6THcv8oXsgp1+W6jN/JWD5TSrH+0JIcGUIjWe7RVK6tEAXc00tJ/pgbKWBPs/PUmbD2hfKkr/oKX6E//zda//v//r/zH/7Dfzj//7/9t/82H/zgB/kDf+APMBqNnuCRvV8Avqe1urrKbDYDYHd39xwFMx6PWS6/Nl1XTW0kkq2U0YQ/TGXHr+TGoWuIzzTlYec8nks3krCQXZpB7InXClZ++4GIubOGatWJU7TV5S13PUQBO9WUq7C4FKgvlyKYP1L4niOaijsTKx2vcJYQruTn3SW3U4rm7Dhpc0xlLpecaNzS8tFn7lKvir7LFCIWV14czE3fY05iVK2oL1TUfblzJGfCGptdll2+2c7bFBQZU+lKRrnkBp0blm+uSMcrDhQP+sK+e2lJdBhhj2Mu/JQ8ABWiaXz3JlXeLGRkNE5JD4wgWO5k5xmp0bGl3GoodxqqdYdPPNNn5D1oOtIhiUcKakXI3Pmu3k0i4okgJgCKDQHgNh0xtxSvrxBuLik3AuVLOflO64a2Hh+DGzqmL9ZwY4HLAnazwOei01SNotmoid7JiO+lVKseU8jIDQPulZnEqJVSCMRjYeolp1qOuZQuWP5USbdXSIyVB7toR/laurE+hhCJ5ik+lvi5w8Oh5P0OW81LAF8ZgSpPDK4TyN5OGJ320G/0yA4V8ciIkL+U96nZqAlJwOWWzp6GgThmiw0P3YbuA0t6NyG7G+ONvC7lWq5g5ImuLJhfCbhpzK2DTbI9eV+6jzRhvaIaioGi7opGq9iQuD3lIL/UcP8Tl4iOBQZePOgTbGBxoyaaI5unS8KbW15tqK5UaCOub6UD5eWK0HOkqwVBt2PApYVKs9x1hH6DGVvBgESBcsMT70WESSxRiaZN4FitiNYKsIHF9YbkLUFduLRl0inOIdm60Li+kwjGyBOtFTSdQL3mBcydS7F2fH9VpBYbHrtWYCYW1xjqW33KVTmf9Y2c6dPSwTRLTfbIUg2h+j/X6W4uKS5X2MsL4olqUUSK6ExTrsrGwp5ExBNh8FUXGtlMrJYSSRgUZib3pWzPYMeyyYoeJCz3eqim7dRZ0djpWuH6ok1VIBnBu7UgU0aGaiWQHsh5Opz1QAtORTfiFHejhOyRgXk70l1q4pGgrtQ7XcprJcEEms2aui9Fpaok9tCU4qjXSxlDBw0q8ZRrHn8lR5WCSvLDmmiqic8MxZZn+qyjHkphWK5KEdj/RCbnfXtJeaPEnEV4K4VptJCNbnKiKbYd1VBShcJeiqpFY1i+lBOtFOhGYccWNXp/Bvy1tv7cn/tz57nGr776Kt/1Xd/F7/7dv5u7d+/yXd/1XU/02N4fAb+H9c3f/M385E/+JC+//DL/w//wP/Bn/syf4Wd+5mf4yZ/8Sb71W7/1SR/el1zmIIGnSvSDtKX3K0CTnErNXzxV4kvpJLiuo3/LMv+6QhIjACpNNUp5PJJYH18Z1GpFM0np7CmariK/6DATQ73eUHuFnRpCLsaF4qMLGCe4GzluHmFOIy593T73H24Q9lJcGjBPzcmihrpTUex16ewZFtdrsIFyXRGdWj7x9nXpnKTyEMIGWFr0Qh4wCmhSSSGoB4HQrSnWRfunnKJ/VzGJM5KpZnlVOp/xiaFcF4F43fPCPyvaQqNFWbjDlNAXw8P4GU2T+XP8h65BO4jfSmWUkzbUfelElduCZ3GZOCldR+ODx65UuGMxHKSH+pxftrws8WMN0HlsxCjgNPmuIHqWadzqDA1ZqydabnjCPCZbKMJbGd2JOJNdqkmPwS4j5i+UNEcZYcvBJJERKG1R1qg20F4E+MuLMkYMtaLe6xLi1o1biYbOztvs3IUiKOmIuCKC11apVwJut4DTjOREU614yssVNJIDHHSbvpI1+NatiAlQaZqVRjJdUwcLLb/HAV5RXGjo5vI+podWoMQjS7Mq36OnlnItYPYS0U7NNDURTUcKLbW0mBYV4tJAMtKo/YhyYbAOzLEhjGWcKQW9pOIIVFr0rm6lIbsT07y0IC+tsApzKc5QEG3m1KVFzSKmz7q2Kyed0vx6JQk2Jsg1O7Osvaapu4rlh6XYs4cx0ZGiej7HLazAlzccPlFEKwX+UactPhyqFKkFOwXhLKFuI8TQQVJhZho7B5tr6p6i2nCE3KI0qFoz+OApVWNYLBMBYZvA8oM5fhYRjSUPvLhWopaW0GgUEL+dUa1JLKFzCiqDigPhQoF9pyN4ohjJ4X3YJ9rKKRcxnaXEOVYbDiIPjWZxCbLDtt+wWqFmEdFYUylgrcEkDlNG5BcFL2MLGZNWO5K+kx1q8gselwTGz0HvoSIYQ3TfUA2CHO9KSfO4Q73qiE8Fo6QczA76IvcM4DJPNNYEo8h3HP1bhukzDcQeO5dIzM5RYBLHgrZC7jN2KkaT+OqcWS8lOorQlWplGIH1jRlnpou5nRH6AZ20CUkvzKhrg8styWNJ7AipI9pZYP+PAd19RzVIyC9Jskhyosl3HINPpiwvyudQXOpa7uOIRpQA+VrMjatHPDxexRSQHSpK/WR7Ok+CA/i1vu7evXuOl/nn//yf83t+z+/h+7//+/nUpz71FQVP/0bW+wXge1g/9EM/RFEUAPz5P//niaKIn//5n+e//+//e/7CX/gLT/jovvTSpcLcTqXTEMl46l0eWb3mMJGnulCT3RVn3eyZBht54jdTik6MWRj0pSVKB+qTjPh+QtP1NP1AHoG5OUcVFpfIGKlZlbBz19W4KwXbqzMOGo0/TqXLkAUef3oHGxD0wwUPpaUYpW16hpag84klmsgoxyViFOk+PWb2eCCg30uVOD4BVSoRgjvJ60QH4nsp9eWKuFNRjlLqfkRIPMVF4bjRaKrdiihtKKJEHlBOCY3fStZuSB16ITf87OKc3Pakk1iI3q8aSrSWa7OVzd1UCgkF0cjSdDx2IQ8svSlFtTtOxQzjYHlFMlZ9LiNzF4MKgphJ9iNQAZdCTQLWt8xFWG4LTiSYQPYgEqBuo3CZIWhhn02ecxLDFdS5NswMKtI7HelUlJoQB8mnnRqqDSdZpXN17m4NVlENBQujGpjfcPiF6I584kmOBG+yuOTp7GuqQhBAw7cUxXZATSN6D7Rk4z5V4AtL9DClc9aihgpD8VQJc8v6pzTTG6CemVMddzClYe2XImbXkM7amaXJAum+oVz36KVEvNVrDWoq4Fu1VVKrhN49zfTlSiLfziL6L5xR/sd1mhr0yxOKwy661DQXS0Ju5L3oN8SdGvVWl3gckV9uMIVB54rOnphN9Ntd0gaKpws6B3JeCVDNYqKjiO5jxeybpIgz/YaiUaIPTKXATs4Ui8ue6U3QdcDlRtBGScA1kHVKChUot1Sbd6wJiy5upcGOLNmDmNlLJbXVqFFC9550uKqLNen9GF6a4d/sUQ/k8+3WGvqvxfKZzhV4xcnDFfq3LGHXo52SbNlpJDDyOMC1JeQRqlKYxykuCTQd6N3XlCua8lJF760YF0HhEpqex/Ud0ZklRB6lNCEo0vsJs6siAdGlxCsGK5/XxXUxo4TcYGfS4VaVJj0wZEeW6U3a9Bxk47FUmNZ0snimwp5EZIeK2XM1s6+rYW4JRp9nJjd7HUypSE4teRvhpgrD7rUTHt/dAKXYefqY6U9tiwwkaOZXPCRyD6gHHjd05Luy4UlPDfEkML0pCKfOfUtV97n4wUMOJlvU6w3ZWk7xuMfJwxXiU4O/mWPvZjQHqXRxVSzTgXYTrptAyA36EwNcBvu/VaErqXj0wpDvODAwfcYRbeXUeUTwit7rMWxAcioTkmI9oGeW+4frMtUBqgEsNpsn8bh5f/0aK47j80nhT/3UT/Ft3/ZtAKytrZ13Bp/Uer8AfA/rP83r01rzPd/zPU/waN7bMiV0RoH8ggInXTLXazlsc405TYkLeSCvfCZi/KGKZhrjni+wj1KanieJG5ZnHYGZbvo2oF7C1IuTrAUfS+Zo/62I5Y4nGkkHYjTvYB+kYgKIApiAyWWEm297skNNMxN+met40e+8uKSZxLjtBnUWizbHw/SgLzq3fhCdVOrJ7kcCSh1WdF7NAE2xHvAJRI9jaBJMz7O4KkkIupERZbNbok5jsvsJei3graHeaCSubix8u3fNLuSGvJcQTMCt15h9GRctror2p7lY0vl0St0DgiI5EeF404N66GTUVFhM2hDWPMZ6uNfBKQjjmLTtUrhhg5kZYZGpd3NUxYnpNxtU6xhU2wVhlNC5b2Xc3Yiu0GXCT7N5hCnazlqtcB1PNDboNUfdC2x81hO0/B6XBcKlHLOX4rqeuiddL28V7mIJoxhda0wQDZ161x261FTXC0JhULlhccUR9UtqnzD6SJCsZMT93Ty7xKiAb0ewulHnpqTh6oJx6DJ6XkZW0et9dN8TTWF5QTAz0cieSxOCBbOd05ymZPc09ZbgY3yvofcrGfObDfOPlFzdGnH//iZqo2K0P4CrDXZiqG4PUJngifxhQnImx5EnnmS1plQQzSFHdGbFpsNlcrwAwSmS2ynzZyv0xMKFEpZWOmcbkH42Ewj6TQ820Kw2qMzRhJhqu4ZKY7YLqlmCijzmJGozojXVa0OatiNdXBBNYrUlpqv8YkPhDcmDRPSQXjSadiFmC//CnOokI/FifPGJRy0Ns2ckpeddF30de6pvmDFIamZvr+IKQ7RaUI9T8Ar7uS6RE0yL6EvFdDC/0nZIXcvGnCniscC29dLI1x5E8hnJU6qB8O+SkaK6XsE4IpoJSkmVGlYrjAk0CMcze2xYXqsJOsL1HGYuFILkVFNcrYiOpCuqJ5b0WNieemGwh5bqQkM9FHesKcSgUW9XhOOY7E4s2d8rjoPTIZ0HYiA5+vQFogyKTcfKtTHz19fwTpHdi6jWPN31JcXdPj6RdBcz//x4uu7Lz9t/e5OQeaIzS/zagOqyuJh1o6imMYNjhY8Vy532vtYLaAfFBUd6aFj72AEHbkvOYUsWCEbju042JSqg+o76MEN5hakk4Sc9lmsxvyDGNT9oSG5l50auwX1PkZiv6nPm/fXrr9/6W38r3/Vd38XHPvYxfumXfol/8k/+CQBvv/32l51Z/JVe72sA38MyxnB0dPRFf396eooxX5sfuOr5nMlzgWKnodhu5IFvA+mxwi70OaOvf09RbEJ0GKFqhTqJxQE50ZR3BqhcSw5qmzBkcukkqkZ0a6aUsWjdE01fPfCo0xhrHc2VQoqKVMZV1YaTEYGHJgtUWyL8z/alo+HOErABdRKLOaDT/tJI8CG6lND43tuRZMsq8IuIqh8oVwSj0KzX1JdL/DOLc62SLQTv4JKAPkxIzjTVUOLA7FK0M/GZFKeCtpGMWx8BY4EPUwnLzQ8aepem1ANHKNvEhChgt3KaXmBxtUHVghAJsYw6fa15eveI5lQMIsmRIcSeeNK+p1NDNGm5brE4A1VARn4KOE5kVD+NUZVieaWh3q5JN3JJV1mtJNbsSoXLJGZP1ZrOY4OdKaqjDiZXHH1ISxpKFlBXFvjThP5djapFZJ4cCVw6LARO7VJhi/kWqCxaLVBnsXQqkZFyv1ugEo+KBG4d4kC0EN2pfqcjjuOLS8pVyK/WFDsN48M+6l3n9kzybbsPBP5c9wNuq6LpiL5KPzen2G5oShkpT1+W7k5QoGIv43QFnW7Jo5MVdNYQKjG8xMcSi2evzzF5a2xZrym2PE0W6L8dUb62gm4EbRKfSMqJWYp2zmWS1uJj+SyhpBAOZ7FIHXakYFte9FTDQNyp6W4v5MROInzqsacCNW6KCNNpUDqQHYu4v7jYiC6t0kQzpHjsCSy82BF5gEtF+G9zRfehpl51VMOAW62pRqkAoZNAuSFj6GAD8WpBNNYySrYBak15kjF5OMRncq7cfqfVyYmu1sfyGTaFEl5l0porSik8XEd4jE0mWtqVNxRm2coiKnHH+2GDXYh0IEoaTK7pPlSsvCUmBvs4wZ/Gko08bFhebrh49ZRyw5E9NiQjhS3EWa5yKTBRwlIs1wLNhQqfeOoVyQFOjuV8+VZTrLRcU/nFhmghhVv8uiTvpCcivQga7FwzvrtKNGmLv5WA63gWY0FQmYUmGhvc0OE7HlNBvV23m+CWBJBJ1jFa7mXxB0fopWaxG4QAEAWaoSdqIePBBPJrFWUjGuryQnPOrkyPNarWotnuNcQPYhjW8n06UD6Xs9zxzK87uo/kvdRjSz30khDTC5y+rL4mOIDvm0C+cP3QD/0Q1lr+2T/7Z/ydv/N32N3dBeDHf/zH+V2/63c90WN7vwP4HtavRhovy5I4/toU3bppRLZQZLcs45eF6WcWWnAvfYdeiubKW855eySe4JTws6zcXF2txc06kpFNuSUIBH0cQ2VpdkvIDeWNQp7Ik4h4qqk+u4LfcKTHhuJqharbS81DWK1RuxXqoENzraCaRUQTg+v8J5y8paX70LRw0/g89cFHUK4K4iI9UTQtMmblDcX4BcjuyEibWEYhbrWBi410DkcxyomjVQewE0ux7QipwyUWvVHCXdE85tsCXu3fNUxf8Od8tORxxHw5IB6LINwNnOBFpjEqlXGSj6TrpYu2uDpKeHt8mXjSOnazgM4N0w+UEvM1M9RDfx5x1mTtKHq1RlvR7OlSCzdso2RtZcHyExugYxh4Qm5xr6/CU7U4PhcJKsDi2RI9jloMCNL9igP+YoEfp6y9pjl7RXhp5aYUgRd/ynD8QekqVhuOYA0+9jQ9jZ0pkqmm800nnOwN5eFVK0Z31ujsaZpuIL9a01lbMjFdBtszpqddoqMIsxdhcygjjzmLiM801WqrSzSSyDL7cEF0PyGdKEof06zXuGFNc5phx4ZmXT6H6aOY6qkclTnCccLshoz3F4/7MkLNNSEVoHe11aBzg32zR7xU5LsOtTTi9tzNKXRPCrntiug4Ipoq8ufFPaumgv7p3rdUr8xhnBI/jqh2RZfWuSMJEvlFyYx2fYd5u8tyEDA1Em+2gKofcG1nOp4oqkFoEzk05lCTb3vMhZxZN6Z7K2J5UeQFwzcMk+clVi9+tUN+ybG8XKFqjQO6azn5MkaPxYyQPRIpQNMLksyx2aD3OtANkEjsWPbhU+aLFHWnQ3qsWO6Ecx5iNJMs6+XTFcp4wuWGMBHcUHRiCddy3LgjzMnthrM1iEaiH9Q1qMtLmMU0Vwu6vYLlrSEhCcwvtw7pi0tJ6ikMYVCT3k2oh569OxvQb8gBvV5h7qSiQ+3WuMygIk9VK8JajZpb6NdEaUMUOaq3B9iZMDVVgODlOu7sWyYvt5m/Hx2Rv7mCXSq6Hzhj9voaPg0Cvr5Wi9zDSgaxj+W9j3cWNLUlvpNKsdfzdG7FLC81uDUnkPr1ksZ3UFulbBAbcSr71FOuynXWfWTIL8g9QdWKaK3i9PYaIfLEJxKzOP+AyIt05Ol8ssNSxax85JjTVzfl+5pWZrIpqKi6a7Bz6arbiUhTuo9g9KGG5OSr9ID5Vdb7GsAvXleuXOFf/at/9UV//5uZRPJe1/sF4K+x/tbf+lsAKKX4e3/v79Hr9c7/zTnHv//3/57nnnvuSR3er7lUralvFJTbFhV7so0l+UGPoD3EHnNm2qQPwR3YmaGxItYPI2GMiXkEVDu69RY6WwuaNwbSRWoUZj8hmirQEc0LC3wVn+/mSbzo9TyElRrbpjaocUTIagku90ocx88uqE86pCcSQdXEnqYDzVM5+mFKNDLnJP961aE6DdUVRz2PUZFneTEBLwUITqHe6WIiUGcRKkgnxuQafW1BnshoxQ8EuKqWVhhmR4kUdV0ZnQYdyDeDRHPtFMRvi9uye9+I4aR1BLuOF+PHTokKMppr1mp8JxAfWcodGck2HdHa1SuObM9SWIuqxJCha0U8VpLUsN1A4rEHsrlwfSkClYf4VsZoPcZ0xDEcbBAAcKCF7kpHrR4G9CjCDxpU5CmXojlTHjhJoNcwvyI6uOy+PU8lOf6gZPKahcYnollMTgzlxZomCHMxv7cququ+I5oaSWNrb9pqaahOB6iOZ7lMBMCcBMn5PWnZfbEYF5STiDq3U8IkIjSaEMHiqUrg4zMrm4VKvi46ttSrDv/8HD9O6NyPxLW61IRCi/s4Eudm/3Oim1NL0SvqRjq66YGh2PSQOerSEtprPH0o1+bymRJ9Ekvk2c6C/LhDdNdSPOpglBRXeio4nHy7Rd94RbavcYkmnor5oXPfUq4GltvCajTl593STaqohw7XaV27myV1HmG7NYsboDvSGVqU6flrWl512LEhfpxRrUiXqzkcEikx2WTbc8L+kGpVtHn6JEHTunyviayjuFZSHPXFoNANLCPZ/LlhQ1VHsCI6UgKY/UTQKJEU0vXAE+YRoSt5utmePe9emaKFvr/dha2G0GgW85TOzSnukyvklxqaDY8+zOhdnTI/7EEpfE8M2JnBF5rs2ozFURe34ejdsSx2NaYBv1Ghtwsxj/Uddj+hWjeYBxHNpqPpyXnNLzak9xKBZD82xIcCSJ/t9Ukq6WiPjvpYJZOMpiNtZN8XM1vvvmaxK7rI+Bf7VFc8OoJoDNW1Ck5TzFITSk3v+oTZJJMJyEFCVCvy0mC8gmElgPk4SNpI2ojbOyjqE4nhpOPEODc3IvE4iwlOUfdkQ3T81gZ+IB1gkGtKlVpQVzstP9O1KK8EihcL0lsZLnpyqRLvr199ee+5desWR0dHeO+/4N+++Zu/+Qkd1fsF4K+53q3QQwj88A//8BeMe+M45tq1a/zwD//wkzq8X3upQJrVLOcRTCLqNqA8ZF4cmEN5yJ8LkCswM0O2uWDeaJKHsWiIhgJJ1bXELC3POkQG7OUFdWXhJKEetqPhvQyfeELmyHcBLzq05HFMeaFBuXep++AedNHbBeZRiikVCy+xb01HGGjvMvLKaYQbOlS3oXaK7HZC01MEp6kLJTmfXUfdlQctXhGfGqoLjezsW3NEaKGC4W4Xdkq80+jYEd1JKXcaVK6JdhdkSc14f0DTlZFzMNL540g0WAJGlsIrGWmKSOLtfBQIuQXrCTpgTyPC5ZxmaSTzVsuN3bXj8GLHkRwbip1aiuCNGr/IsAsZr6vcnkOYgxGXq4sDdgEu09SD1rl9VVFv1dRrmrhTo6sEl4qOrV5viA8iqq2GZCQPl2q3ZvjpmNk1K+kvA48tRNeZb0scmmlfT3poZewJ6Jkw9tioCYuIoOW6MYWiuFKx6BiyfYPuitZUr1aEIK0l32oRm05A5Qa7ENF+sIHOI001S9DPzdHa4+/H1E6xfeWMg/vr0hFOPZSa7p4messwv9xDbTnKNTkfLhM8Tjxp5QqFZv5yibae0BZ+9bNLXCk4GrUwmNThz6QjV/cCNodit6EzKFjmhuTYUi17krpxQeLY0gMrecMKolML1xa4vY5oBrcCLvU0fS2bqQxC1HZdrxboeyn1wJNvGcrdSo4jF4epP00wtcJeK2l8jDpMUBcL6k2JjvM9J9eVMdgcuf60MPnGL4uZJ58nxB+Yoe70iE4t6saCZq9DteYwe6mM8I9imq1KUEyJJxiRcejE4WMLGoqnKnE79zz0a5K76TlImkKQUuWHF6jb4lCOpnJdNQMHUZB83IdWiukswXbkeuo+siwueeb7PdmIXcwJDztSsA8cvduWpe4Tz+SzUqwF7IUl+q0ejU+IxopyUx6czWYNpUgm0iPR5ikH/VtiRrIzcdLGY+lKD96xzF4p0IeJXI+JMAPjiaK61mD2EtxOycIlsqkDig1pZQWtREpymKA89B5oJs83TA/6mLmm2aoZvBpTrkA0Nlz96CMej4YUsy7pnqF8XvJf08fiUPe7Bd1+Qfg/VwXkvRnwS4sa1vjKUCSC1tKL1vXt5drDiDmtiQSpFXw7ll9zZHcSirMYO4fljfI386ny3tb/zTp4/6XrF37hF/gDf+APcP/+/S+aJj7pKLj3NYC/xrp79y53797lW77lW/jMZz5z/v93797lrbfe4id+4if4hm/4hq/o73z8+DF/8A/+QdbX18myjJdffplPfvKTX/bPCXFgedpB9wSr4h928R0nLsqZRIjpSvJyOw8N9eUKb2F5d0C0L128ak0A0XZqzvNrzURcmOn/0SdOGlQjaIVqXcLfO48NnVuCv+jciSR1pO0CVltNy4iTnxW/k1Fv1jRpIBqUqFo6TiETjZPygAl0HlqyN1KirBY21lyjJxa1tPiOR3mFGzTiADy0NFcLsoeR/KxeQ+cDI3zmaYYCJg5Oo3QgNJJNmz6K6Dw2bP//UspPrBGdGdJDQ+epCc1ag9oqsLnoa+pVJ+aKmXS21GpFQITowlNsx6yRsOp8C8SNTwxmO6f7QGKjdCFjtM69SHSK98QlnG97eTivV/KertYCV356SnoiAvl4JOPlakWKUHsakT2y6Dd64vLue4pth5ka4rEi2Y9arSLYtGF+1aMrRTKG7qUZ5Wpg+qwjeXGMXUqBHmI5T2ZQSdpKX7qSYRrL9WOEJ9n/6DHRUUSIAvXXzeXc5Bp/kuBPEuJj6TI2PX/eJaw3G9GR7hTMbzTUV0qKk4ytwZzw4ozsUcTB3uq5zlQlom2bPOcYPS8Zr9FUTAqq3UwHA8XTpUTjjQ3mMMbeT0nONGqtxE1iov0YPRGJgVtYdKHxzywYvq3wBuIjSz5Pzu+Kyamm+8ics+WKCw2DNy0bv2jEqT5OBbh8pcBcmwuKZrPGbVUECy4OdA4U4Tih7nsY1lTrDhot7tdjTTISfZgpwL3TI3tk4WKBepBhRhGdR/JZNYnDbVQ0qejd0hPF5BsL4lNDM3Qwi3B3e6LRG3jqI4kktFNDdiTjQnV1gT2OJR5yZtp8YIjThmAET5S9lYBXRCON3U9IjxDD0UaJWqtotircfkZ1sab7qDVfXC4gFfNJPNHUPTHoZIea3n3Z0CS/4xi30oipoVCEB1L8BRukG74Z6OwJsDto6Xq7vQ66AnTLRBxr0v2IuFeRtCxGH0H27JjihZzlrm9H4PIZKi/VMgVZAcYR3T2FKltJQ0c2jJ3XJSPY7ktx6FZElxw0xNtLoplwFU2uyF/MKTZkA7d6cUJnXzP8dMz8Q8IZ1aXi8c9epljGDN4ylM/lhFGMvZuycsuTnirUUcL88YDlRc/iknBV4yOLPolloxgFud5XK7JHFtcX4158bFGFQRcavTBEYwMtaia/UhO6DpeBOYv+C54+X4H1vgbwi9af+BN/go985CO89tprnJ2dMRqNzv+cnZ090WN7vwP4HtZ/nuXnnOPVV1/l6tWrrK6ufsV+z2g04mMf+xi//bf/dn78x3+czc1N3nnnnd/Q71CVQmOwR5IzqgKYY9mZh+2C9M2McsOj95TsXh/HhBtLuNuhXnECmr3bJTuwpMeB0w+3Ye2DhrBIJJN3HqMvlIRGCz/QSwanLjXpRs4ySlAL20KnhQ9YrwmHq7pQE4+FceeTAPsdOjNJD1GloftYRPmqbvlXTlFPErIA9YrD9GvRK45i7FZOnDQs0wQXFNH9lHLVy+htbGnWJKLLA27gSB/E4r5twcvRDGbP11QrlqYrubP5lZrOL61iVwJhYiSOqo2eKzcb6oEn29fUk1ikcL22GJxGJCdGNIttNq6Zi17Qn6USz9UP9K9O6CQVB6xjBhVBBfJRQv+2ZbZeo09i0mNBcDQrjuqdAXEQobuPRdBOkJEkQUZB3oLbLtEmyNh8ps4fitmhdEOqeUTadhPnVzzurIPpyoO4yGOangj98ZJaoB9m55Fe6amiySQDVi8NYb3i5LRPuFDReTthGcXoGznmYYrbqKE0KG9RIZDuGYqNFuhcWDGbPM5QmeS5kngevLojrMU1Lx26hSHuV9R5JEkuT81pim4b+6cI3Zoq0qiOwxzE6AMxMNWrDmyQrksWUIcJ1ilBogDp8ec/E3mW0XQEO6QcJJ0a/yCReL1BQDUIBHi7QBWW/IJh+kKDKgxmUOErg59HlJFBDSXRIr2VCM9QweR5MQXFY02YJVTrDlUJZqRc83QO9Lk+M99tSM4s/iSBKNC5OSH3K3TuReTPStBstSJPx3I1oI9iAQAPKjqdktnjgXT5E4dOhQPYDB3L3Zr09Yw8yggDR3xsCArqFU98EFGPLMrI+W46AaZW8o1PEuZXwW1UmIMEn7Xw8qmm0lYyv61ca0pBM3S4RDJ165UgRpVMMDCnb62jbOvcgXMkEijyHblnzK85yBzRYQzdBjuOKbY80Uxi57yVDrW+222ZedJ9XNwZEs2lW5++OGZ2f4iqNMEH1JmlWnOE1JNf0NiJFnhzx1FjqNYkpk4vNZ19zbwv3enOY81sK0bdLDGHsqG191Lp7NpA8Uvr1BcCugROE+lk9jzZYyM53ytgH6SiKb3gOfgdNeYkFlRWz2FnFl0pqmslab9gftYRvemtiGLT4DcrAVcXogt2qVwj6bFicc1Rr9R03kmoPzDHWk+xiClulqj7T9gE8v76ovXOO+/wz/7ZP+Opp5560ofyRev9DuB7WN/5nd/J3//7fx+Q4u+bv/mb+dCHPsTly5f52Z/92a/Y7/mrf/WvcvnyZf7hP/yHfP3Xfz3Xr1/n4x//ODdv3vyyf1Y01yRnbZxVroien0oaReqJ38mIPjoiaJhfk06bahTOaertGqJAPU6pN2uWu47p04gJoedgZmVXrkDNLL5qx+JtJ0ZV+vOYmNyQjDT9exrllHQ/utJtUEuDXYrQ3pSK3n3hq0VTLWy7RHRFa58SLVq55lrsBAw/Z4lfz2Acyc/v5cS2EfCtFdSHXSiG72h69zT+Eyu4jox09cJQ96UDJho0z+KyHHy91kgaxoqMjwkyfvFJoHd1cp7eYHJNuFAyf7YmPpFuUDRX2PspGHlIde5bdK8mGgsaRtUKerW4sSPP9LDH4dFQkDx3M5rayOhrXTh6bq1m8XRFMlbopcangXoozsF8t6Fcl46eSwPxVN6v8P9n789jLUvP8z709w1r2tOZp5qrq+eJpEiJtpULwYpzlcQwogRhnEhABCWBbnI9RUycRI7jOI4tGQiky8SxLEj5I0AAw4ZhxHZ8c3kT6Eq+jgaLIptDs4fq6q7x1JmHPa7pG+4f76rTYkjadESpdZn+gEJXna7ae+29117r/d73eX7PjZL0bo6vDclciR6y615kZ5H8WFE8SPBpJDvvWIFnViDCI+nsAJhtYVal5xrfD0QbCUXALqDecJ2WTRG9ongrJ32UsrjepVjcyxneQ3JdgXqnhbVaCrEgsNrsTLH0lYSQyfuuGyU6vyKIDjILMp7dXjD4//QxXWFXHxeEIkqkYBrJdlP0sEWdJ7ih6B+Vp3P4SHpKdqZIzzrmXirFk11AsxpYXHWkp5rFJdkstMuB6jyXq2KQTlpMYOWLhmac0f9KTrSi0YtDRzjKye5J52hlY4pdLyneFR1puSPGELq4sGZNEjT6m3NiLh3kmESBWS8UpgU0kq6DuGynZ5Ld7YuIzVsxZlyRbnR23iFEZuK2rr+0QtSRMJKYxlAZgaUvNPlXC8odT//KVL6HO61EtXlkI9S5rU0lRXLMAr4STZypFDgtZieguDyjGQXyQy36wUzOQb9TC7Dcy0ieNNBut1RbjrhVi1YueT9fOTtTJNMuJUgJ0gQbMacJw7sQSzGAxYGj2XDUW+4Crh6uVjTbLdNbvuP5iaM/GMR0NJEOeXoiEYoxEXyU6wfaZY+dala3x8Qs0NucX5yD0ch1TjdKUDuZRx+l8tqiks0Wcq0sr7b4rVrenwiDe5reI0NxJM5//8KcZtWzuObJzjTmJCE9VzTbLXrRubY3PUpH5uNCvjtnlvlNR/7UVCYJXfSmCqqDUgsCaPCegdqwuNWg7vRp7wzlnDlMLww9H9R6YgL57f76Tlqf/OQnuXPnzgd9GN9wfVgAfgvrb/2tv8VHPvIRAP6n/+l/4t69e7z11lv8+I//OP/pf/qfftue5+/9vb/HJz7xCT71qU+xubnJxz72MX7hF37h/9BjBdNBYbNIteklTqlVpCsV9ZZnctJHdWPg6nJLcSRsOiqNPbUiOD635PsGdXNOTCPpXiK5r0sCBGa5Jd1P0IkkXSgnNyU/eFINSnD6+astzaoAioevZeKwDIrJq41knyqYfpc4L6ONsNxQbUoH4eylSDJWYBD6/XagXoVmRXbx1U7L6dGIye0VgcwGRb0uN+75lUi5LZm6yZmMrpOxhqAk0D6J2FEj3aBSkitwCl24DmkTBXWz0FRvLWOWWumgKih6DaYvvBY3kHFps+nQCwM35+IMfZxLVy6TQlIfp6x+NdJ7ZOXvTSRNoFnx5L2GfK2EmwtBcZwk6LGl7Uf6T42xO5L/qQpJLVAduPZJ58+WinCY4/oBKi3Q5HmHl0ki7gfOmV+KZGdyo29GXeEfxISSv9a7uPmH3Z5gUHJ5/b096D001MuQHwibz+cRarlJJ3PpQmWnMlJsh4r0wApWKPekuegI8yPJDJ5fc1Trop1MxprsKYGhmoWGj03QoxaWWurznPllOY9Mpcg3StITzehtixsE6kstw88VEJDOTk+6ybaTB9i1kvLlknYUu3MLsnNxvg7flTFsdiY3WrfiYNiSHlrSc+nG6VKyls8+JrbxxZXA6F3ofaVAJ56YBBkLv2052xvhDnqYj5+jPKx8VaNb6N3OMM/MRAqx2dK8NUKloYuFc0SgeraiHUTyPStddiNa2aXXMqKW78HS/7sP5yn6gaTolBvSresdSnFe78hmhLYzFeWe9Ew2gOUlj90omT8eMnhX+I0kkTjwkjGcS5etXg+yOVqIjKNalw1HtmdFkxthMS7o3ZzQjMR8kk4UvUcadZpKYZ50iT2tJhvUojPe7c5LBf3dLuYxhepSi66h98CSHMrjK684f14cuer6nPRxIpIDFUkmGlOCn1kIitUvawYPBFmlWo3bakgfJ7TXa/QTWVU3UrTnhnzfQCE5480/XIcA5f4AM5SN2eJyl+YCNDstyRs9tIf+sMKvN4SBJx3LhiI7sBSDmvJaKxGPz3jKzcDJJwTB5PcL2SQ3iurpCt12nfsHKVHLiFk1iuS9HGVFahO1sEgXuwMxyvU8IeViXB1NJDuVPPP02NB/J5Xz5A7kt0VC4rOvNRj8rq8PR8Bft/7En/gT/Af/wX/Af//f//d8/vOf58tf/vLX/Pogl4rfjHHy4bpYeZ5z584drly5wo/92I/R6/X4zGc+w927d/nIRz7ybaN557kgSD796U/zqU99is997nP8qT/1p/i5n/s5fuRHfuQb/pu6rqnr94W/k8mEq1ev8vSf/kmapy35Y4EGRyPw2GbTdV066bgkZ4ZkIheVsNGQFC3+keQD56eSaauCXLDcsoOoGL5jmd30An0exe7GLjdZtyyj4pgG0gNxjSZTge5WVxvUwrD+Bc3sioKPTiiPe9hzw+g9KDeEbzV/tiF/mErH5uYCfbtPvSM7Z1N2cWHPzHGNIdRGxlhXa+xuRrTdcQaFPTeoG3P8fo+QdyDrWuFHnT7uTON7kWbFk54ZslOBtz4pOIiyA29W348LS+YKnwo2R9dS8BR70r0Myw61MOhSs/wWLC6pzmktk6/0XEZ4vi/uWVMKkqbaliITBbqWMW+9Jp3MdsWT71mqq/Ke2IV0Cd2akwSW9Zbh2pzyrWV6z59Tf2lFsnw7d3Gz8j5gmAiq7+iPKuaPhwxvGybPOdkGamHPxY49mJ5JWktcaSjezimfr0jv5tSbjnzf0jxdktwV0LcfSXe2t6uZvdCQD2uJv6rFADO4nbK4FMiuzKgfDCgOJPs2Gev3i+y+g8e5mGtOxCmsnKK63GImVmK4Jpr6KTmOaKUACbmcw7qRov7K8wfsvrEl3axnppTHPVThsI9l49FuOPLdhHYYsPPOpPLMAncijluiyBVCKt3EJ93xZkWON52oi+In9OV19x9qps+1KCdO5PRYzEdPcpJdtyGKRiQHutKEPJAdyjmwuOqwE0M6Eae3z0U/mB8rpi82FPdSmpUgLvw9KZ58LseAiRQ7MxaHfVSjGdzXTJ+WTORgIJ0KOLi9LGiX9E5BsFHe9yxIcYVEG7ZbDdlDea7+A8nlrdYibhgJA4c9lSLNLYkEA0A9lM/C1MIEDFmkvtyikoA6TRnc10xeblBJIDpNfi/FljC/6okDYf3FJKDSIB3O85Te5RnN7dGFDKHY1zQrHVw78+KoNRE9NfT2NcFAeUWuR812SzJoaE+lk6saMb4lg4b0i33m1+X8tQsxeoQskh/qi46tXSjqTU9y3nX25wLDDkWEviO9n1FfaUj3E8lXbmVTamqFnUkRbRZingNBAfk8Etca1ElKTCPZoaF5piTUgriJrZbv30FKei7RjLpRhCKSnMnGJpnJ5rraCISeJz2Sa2v/kZjS2pFsTPO7NV/9+T/DeDxmNBp9W+5L38qaTCYsLS3xzJ/+SUyW/7Yey9cV7/xXv/uv4Xdq6W8Qz6eUIsb4gZtAPtQAfgtra2uLN954g52dHT772c/y1/7aXwNgsVh8W0HQIQQ+8YlP8JM/+ZMAfOxjH+P111//xxaAP/VTP8V/8V/8F1//WGmUAigKg8vnMuIiCsjUlIqmL7V/1MKuSh6maC8wZteLLC7FjtcmEWRE6ejkJ4ZyU9NseFTuqZQgDpKpwo7FveaSroBZ9iRzcYBmjxPqqw2nr8g4xN0ZwmaLW4mUW0mnowE9kTFzM4rgJI0iORZUievSTNypjFv1XHQ89kDE+0+0VwDpWFHt92Q8/WRnqZAYtast9XZNmImBol4LXQxVh1fIuICq+hUnNyoTiIl+Pxt3JoVtsxxFP5TLFz30AosdQ7MkcVbFS+eUby/je5IIECstsNzrDXpmxSW5q6g2BCCbToUX2C55+ptzmtMRxd30YkRsS4WvNPmJYr4Vqb+yjG0Vk+M+at2Dk1Feux4oug1Avi9pDK7WzGcC/S53ohQjRcBMhC3WjuScCBnYnQXNIqHcCsRSovGUUxJK/zDvwNURey5dQbsAPbZUtSZZqYhnOaEXaIaRsNRSHvegF2hHElPXrgZUraRAu1OQTMH1VJfPq1AxohpN2GjQqcfu92hOUlSEtojoSqG87joqUgwd/eoORS0oGfP6iDwAWEwjn7+dJ5gammUptOxM0ZYJZqWRaLQ8oBtLSCPtksfMDdaJKSZamL1Yo+aW3kNDsyx8uNlTnuTMkj9/znyW46ei4YqVoXff4kYC0c6OZVRfr0tx0KwGeo8MyimW34b55W6cmtMVFhGVBNqB5BITFNWaFB0+j/QfiRGmWYzIF4p2GERHp6HcDKRjLekyQHE7ox0ILDo5l0xw11No300LthuKQU2bdgXY+Yh2RRz9yWGCqRIBas8VINiWMHLoLh7QVObCNEYEpSJ2qphfitjjRMZ6EfJTmF+OYCB7mJJOwH7fOWf7I6KR+LWlZ0r2hgPSY4PykcXNlv76Aj/JsY+yDkYdCUuOeSKxcqrrsNFq2rEgbNJjAUvn33/K0f4SfiuQHYuTutyKmArcpqMOFrMQSLRPkenHQoq5ek3O8WgDatbJX5DzxvVEm2yvzWmOevhckV6foT4/IjsXSPwTcLh5nOGWJR2ICHEs53FxJ6UdSgFdrwbitRKrA36/JyDvmaJdFoRVvS6PRxpoVj162DKPObaS4rBZCkxvfbAdwA85gF+/7t69+0EfwjddHxaA38L60R/9Uf61f+1fY2dnB6UUf+gP/SEA/tE/+kffVg7gzs7ORWj0k/XCCy/wt//23/6m/+YnfuIn+PSnP33x5ycdQHRX+KWRdiVg10viaX7BnfO9AIkUKG4oJP/sTIlbNO92nv1ItS3diXxP04wi7VAzflqcv88+85h7v35VRnCl6phsUgi6kTD7RrcNdhGZX1b4nuhjnsQjrX0lcvZcQnu1obwhsU++kG5D/mZCsJo4y2Us1SWOCE5FCoOYSRfHey3C86Ew/FTqyd7NqV4sSe8U2AX09hXTa4r6+ZKyn5As1WyvTNl7tN2FrYt2yQ096amRztz1GnuYsvIbCeN/pkIdZ+SHmsU1h6o1/lYJj3P8INCuR1Y/Z5lfkU7f7Iant2vE1fv6Mn4YcQMHraZ337K4Lnw+orx3k09UnSOzZTbIyfYtyhvm9GHFE2806PcKdItAt89Tys2AOUjxt0rigwJ7kohWSIEpITmT7oPvBexTC1xtUfsZeqXGzRLCMEIto1BJdBFRfygCujborw7QK6Kt02XHfXuiFZuLbi1YjRtJ11f9wDnhZEDvnQweDrAKWqdwNyuJTZuJFq9ZE7hxNJG45El2cwFP36xhmoDvXNUBTKWprcE3mvmLNUwtzZK8b34gYzNTim5UrzTUI0XyICM9EK2jLeV8n3fYnexEMb3lOwyKJhqFPrf4nhb8jRNNWtSRdK2C0764vXNPSDX2KGXr1QOOT7fo7SlmV6Xo91lk8e4SvX0puuJMCq5mJcroMUqXpt52ZHsJ9dARLcxviNP6/J8De7sHz8/wixSvI0wSOE+xlYKjhOE9iEZRr0K70+DGnflg6NHXK0a/1Gd6Q+ESj+nSNCoTKXYtIRX8CUstvspoVz3FI0P1Uol+nGP3Uhqbkj49ZTHJGZ4ofK5RczEwJWPN8F7k/Pkomr00CKJm6MBpcT33A8lSTTtPUCYSbpWYdwuilRiz6XMtzbML/CIlOo0KhtkrNf3/bY0Bgrdpn13w+NGqmGR2WsygxT4saE5G0A8XmBZTK/RJgt9sCFWKrhT5sWK2HqBVks6x7mmXFbN7q+QHRhikQTF/SYwdbhBRU0t6runtR05fFjwMpktV6Ukxrc8tvccJ5cslzWok0YFoxYBTXW5hLrGVulGUxz2SYYSZbOL0UzPUbh8/DORrJfV+T4qkViYM5Y44ed1SIN1Y0D7uy7WuH/C5IKzMTDY5w3c19SqUfY2dGVwaIIuozgCTzDT5/ges6vp2jHC/wwrA69evf9CH8E3XhxrAb2H9+T//5/nv/rv/jh/7sR/jV37lV8gy2VYbY76tucDf+73fy9tvv/01P7t9+/Y/9gTKsozRaPQ1v0AuME+cjPm+Id7vk54ZEZ/byNIblv5bWddxiNiZXMRDId8+08iukjygPbjvnkrcUikXG7Xa8O5rV0TcH6F+eSHGgFsL0dwt1SQz6XhUG6obbwrjKz2X0ejhP9dIhFKvIem1JJNuvAucvSIQadeLhLWW7FyJTsZEVl84ISYBM9O0K0HMLp3ZAadIHsmN0SRenK+jyOlL4IYRtZ/LzXieyqiwA8S2yxKpNLotLEJTdePBSxXTmwJgLq5NWVzrYvFUJP1qj3SsUbUmPbScvRSpNzzlZmT4nkRUKadoLrWwLHBjOzE0S9J5i6WFjRr//Jzsbo7Zy2j2ewDUl1vajZa1zxuyQ0v+ReHNpWPF4Es5dq5EEJ9EwlGOKRXFYVeYlQrTyOuNptNVfmVI/tWC/EjjSktxP6F3R5JRkpl0j6ICNmt0qbuRfYvaEv2SzyPBQL5nLkan9WoUw4cSLWH5hTXSRym6FeBxNob8UMwxqtEy0hp3PEYNZtiysjHFDSV9QamImWrQiGasuxHYscGeWWJ348xOtcRzzUTKEBW4axWh1UQv77frSQyX67/vPPUZLD65wM5EwxV7/kIjeenGMdGCWmmoLrViinrQxw0i/V1Fficj65zXB1/cEiD5QLo7o/fk9SRTzfxK6Do6dJ26QPV0RXmzEfbcoegXcZrscUJyagSX83oPO4P6LCc6Rf52ji71RRfarTnOXvXSye+LUUh5Ol2roT0qmN6A/q4iPTa4Jcnklc9GOI/JVGNswFSQnGvp2O7mon+8OccuFOU4R58kTF5uBJ5eKVafPsUXkZOPd7F7EUgF7ZQOG0kOWm9RtaadpuiJxU8T4m5Bs+7F+ZpL9m85zQmtITkQJ36sJCljft3TLAf8RMgAMQ2CPnlYdFm5cvy+O1cETB/RxwnLbwk2pl6LDN4QeLqZiJ4yGWuS8/ch6NW2g5m9mAagItWGZ3JTPq9wWSL0smPD4HZKcpAIEslAqCTxxx0XNKNIei4xjkpHRk+d0252501PRuw+iySfG14YZqpxJoXtSiA/0Azvaoo9IxIZEzFfHHbRbzC4K07talOiG8vLjsnTEkmXP0rIjxT5IwGWN0sCVm+HgWrzO6x6+g5Z/8P/8D/wvd/7vVy6dIn79+8D8JnPfIa/+3f/7gd6XB8WgN/i+lf/1X+VH//xH/+a8OYf+ZEf4V/6l/6lb9tz/PiP/zi//uu/zk/+5E9y584d/vpf/+v8/M//PH/sj/2xf+rHMg1UG6ET+guotV0ONCuiXavW5f8HA2HoaJfkQtJ7aIi5F/yFBqUjzVZLjDIKUVFGq6EyHY1eRi/hNKNdDrSTFF8E2nnC4oqjWZGc3mYpUG9LSkf98blkcraawTsJzUEP82YfW3XPMbfSodGdC+4sYX7FQxJIjwxHD1foPbL4QcCuVrQjuQC6fuegrBQhA+8MvgiCtMgjyTMT/FD0PTSaZCKFY3aiiXlAPzvDVBLBVm94iY87T7FPzXBFJP7mEnYibl1da0k4KCLLN88IFlDCNLMLxeSlVmLHIoKDMZHkxFIcSMEVCk92aEjey8nylmrbXSSCEEUs3ns3ZfwMaAf1cqTakAv8YieSjjv35FDioeJLU8qPLwS9kUUmn6iIVlIcsPHCNVytR0m9GMiYf3inG6HPNfaVMaGUoUC73aVx3CukYOlGO+WtmvKyOJEjML0BydgI3DsT3ZrrweSjNecvO+r1TqsWJQNZt1IEJxNN+lbB9M1V8LB04xwAtyqGEeUV6RTamxW+F8TFbQNhKCxHOzGkYyWaqlNNeqeAqSU5SMnvpeSHGjsRF2t9tYHcS8xe3UkUCsApmmdKzEKz/9Ymo9uaeJJhpoaQRUJPOoztUDZS1Ya8D8WRIjvW6AaSsebsVTEH1CuB4fUxuhHhPiDnShLQU8vi6UZcsr2APTfUO06MPUFRXhImnC4NyWFCsyIZsu1mS3xxij3rxpQtDO8LmqZZijTLAdcLpCcGBSwuPdH3ecxUUzy2lJe8dB9XPeZOQUzozGGR9FxhJgbz5gDtoHcnxQ87NEqjqLccZ+O+FCa5F36og8FtAcU3ZzlbO+fCgswCKvdyzpl4IRnwPTFi5TenJEVLMapoV+W1p0fyHY1KtH7piaH3TkZ6amgvN/idWjYfaZevm8oY1PeCALKB0+8KF67tcls2n77vyQ/MhV4ymC7hZiZaW7csryUmEV1L3GXYaAjzpMuFFnOVCrKB8kW8wMVEKzm/06cC/YdigBk/WJLrRV9Yp+2anMfzq57ysoxrzbmVTepmTbsUaUbQLAkzESPnl6kUoRfk3JpJV5HufQx9L3KMJ7DqbUd6YvBDLwaSiX7/vPug1ocmkK9bf+2v/TU+/elP8y/+i/8i5+fnF5q/5eVlPvOZz3ygx/bhCPibrP/mv/lv+LEf+zHyPL+IhPtm60/+yT/5bXnO7/7u7+Z//B//R37iJ36Cv/AX/gI3b97kM5/5DD/8wz/8T/1Yi5stxXZLPc1ws0Tili5V2Ae5FBTXavJ7HaA2iqbFzmWnjpGYM1sp/DghnSnYTVH97gL1iQlPLU+4m66TPMjIzhTzVHXJFarbsRvcWou73rK8NGf61TVi0o3Z5gUhjwxup/gCYhqob7Wo27nw3VYaVFTYuaVZ9yRnBp8jAvCnW7J3egQLut/SzhOGz58zO+8RgozzspNO33WnoLlVEjp3cvVgiNmq0Ac9iZYrRQtW7jSYc0tFTvNcRJmAPdfYx4bFdUd1UqD6Addq/FaNGieEIlyMRM8eL2GsjNGrDU/se/TECtIG8LnC3stJJ4p6OQoL8dRKOkplUF9cJusAtslUU+yLqUQ5hb01w90Z0G43qIUlPdIdr01GenpuCH1PkniUAkpFdaWFmUWNWupWsfylhHoZwqtT8i8MIUo3ttoMzIYRtHRHyntDtBYHaqiNdNi8ZOtK1xNMGlAHnQ5vs5W80wcG11Okk+59L8EepITLFW0U08PotpHcWaQhV1/uhPRzBVFzviud6/59YVXquoMzv1tQbTvyPYt9UDC/5qm3RZPp+qAHkhcbElDLDcu/mTO7IrpMN/KYPUt/ucR/eYm2H8nvZ1RXGvqrJYtpB/8dBOg7pk8l77PWbnqBUJeGZlkE9rpVuBXHrDD0rk6pb49wl2rMQYbPZCOxqJfRSriQ6VjDVMNMnMpRWdE3poFoNarUsFHT+3LB4lJgdlOc8u2Go7ifUG15zJmlnViyiaK84plflm6RLsU44PuB3sache2TnEvhCrD8xRT3z54zO+kxuJ0SLARrqG7WDN7MaAfA8zPa9wZS6K0KqLvYEwqAW3MkE03vsabaMMJ+zDymSWmWAvPrwrxrlhWnX9pAPz0juTMgDFtxw2ee0NMMb1uaJdEZLvYG6EoRnEInkbje0JqUYnOBe2coZikN7aszwm6P5GGKL2RT0Q46w9SlyOC+ZrGjBEPTF00wwJVX9jn8/14iv9NndkM0xL4vxWzvocUcGYmXdJF25IUTutCYBpKHCe1zjjhoaYeiO12MDPnjBJV7mjVQM0t+pClfaLBHct0rd2TTpueKZluwLmahcYUnLLekuyn+qZL15RmTtwvq1UCYJuhOP+tGvjOrCH4mpL8lA3qmLwxtutHEVCIRGxvQTlM8tPhc2KTNiqf3iWMWDz7YbPoPNYBfv/7KX/kr/MIv/AI/+IM/yF/+y3/54uef+MQn+A//w//wAzyyD13A33TdvHmT3/zN32RtbY2bN29+07+nlOK99977XTyyf/x64sa68lf/PFr3LtymAPW6Z3jHSHRTXzozysP2i4fsHSyTvZdf4FF0qUnmAl8NqVysiB0brohkJ4pyWzR49YZkd7oeZGed63AgBZFuIdwoUYB9q0e9KZ21/FBTXm/JlivaxhImCfmhRddc8O3aFdGK+RWHnljpMq2Ijm7pDcvk2a5LlwbyA0v7TIm5m9Mui3i9d0f4dLpz2ApT6303brsUWHrLMLsqbsC42sBM0kuSiaLakotzsT0jfGVJnJe9cIGkKXZlnButGARCHtCdwUM5uZhH1RXSmXQR8gONG0R8CiGTdIN6y9F/L2FxWdzIpoZkBvNLUToVaWD05ZT5ZRGu+1sl9p2eIGBSuQGFznHK1RI3T0iOLEvvwPHvc/TuJxePl8zlWJoNubGAjFXj0GGOJRqtvOJQXgkGZdw5vIM4RZUTMC1RxlNPHInRXkxaSaaK6umauDCYuZhHenuijavXwgX/r/fIUu74C31W1O9/Nm5JgMWqc19HHQkZbL56wOO765B71CQRDesgiIThCV7DiwPXrwk7L78jUWjljjD5XK9zwM5lpFscaGZPtwy3Zsx2R6haEZYc2W6CbrvzoEN6hFzOtfpmBbOEmAaJhbu+QL3bo1nz6FKTnmuqHUd2aGluVIJJUXKOmJlwHc1qjW8MyaOUZsuBF72XbmUzpTtzfzRdZ9wrqh3H8LZlfjXIaH6zgbklppJHnUwV9XMlRa+hKlPUw5zBQ0WwUO5E4SXWkn+rKo0phdv5xFGMl+/sk06lWWjRWhYeai0mL7goXLABe5wQUokrjM/PaA8L0c/VGmwk25fYwfIp2WjFrRr9SDaiAO56hd7LiVrO5XbZSxeoM3mFPJCeGppLLTZ3uHlC725nTso9+V7SRfRF0mNLe7khOoU9TjqGpUgL3MhT7FrqjSAGrlsLMYykgfxBSn2jRp8kMtW4VKEOsotuVNRi7gJ5zQRFMhI+oH29T/l0jZpZkrF0ndXHxigFi10psJ90930RGN2WvovPYbETiH0nZrBKYW7NqBcJxVu5XLv6LXo3l450IR3WZDclGBl7q0YRet352WqCmfLw//YXPjAX8HM//u1xAb/9//jOcQEXRcFbb73F9evXGQ6HfOlLX+Kpp57inXfe4dVXX6Usyw/s2D7sAH6T9VudO7+XXTzfbPXeTVF9S3nZQTDYUtG/b6jXRBc2eACzG4oIPH68yvCrKT6Tm2d6ZlBBEitCJmkIrNXEcUq95juRvDDcQhpJzqSorLccydQSrNxUF7ca1HFCr1fjvWaxIyOLaAR9go20u33CkkBgmy53eOlNQ7mpKHa7jkmjL/JwQyIZudOn5OJvzi1Bi3M0y1rKHYOaG/TYisjbK9R2hb0jkGMVFOWOjBTzfSvF8JqT/NRHkgX6RHfFUotSkcUkxwwCfuSxp+JG9kPP4pkGGi3xTI0iPRPcivca3/Ns3zjh8HhE1RiSA9FsVRsy2gtbgq2ptx3ZcoXrW+xcM3ovcv4cLG4I5sWMGvx5SkilAGuuNOgghawbBuxE467UF3mmsRKGY7vsaZYELhtS6B0EZlcldSGZGFTbfX5dcZE+yGhWPOX1gDm3MlbWUN5ooNWkx4Z2WW44PlcUB1KkxDWPL2QMNbiTUG7IRiGWRuLGpopmSTpyyiP6PasxFSyuOczM4HMR32sH6aRzvxaOdlXJBqDnL4qvvf0VVFDYfekOtVstyWFCu+pYvzzm7K1VfAGYiD1McKuOasOTH5kLvWDMPXouGKBo5Tw3U8M0DsFETKsx/QZXJIQVT/HYsLjRomojGs5bsqEJRtJGdA3BBqo1T29zTn13SHWlRfccdQS8hlTGsvY4kY5PK07ZuDDYhaIJMiJ/guGpLrUkxxbt5Jytt6UrWDyytEMxQaRnikoncl6eicu72ggwTql1JLQadaXifN2I3nCsOue1aCp1lzARelE2ak94i0eK2fMN5iyRXOdKEZzcKq58/DGn8x6T4z6991IZbzrwhXR1Ocuxc43reexyg363kM74tsecJmgHbizGlnolEEct6YNcxqBBNmnLX7G0Izm25laJOcgIKZiThGScol6e0YxEatH2u0SfzJM9yOR6dZaQXFrQaKQINKKHtFPJDc4POj7iOEMPWmziqa5H9FmCH0hBWZ+nJHPR4/X2FJOnA8Vux8DMRIIxuyX6y2rHk+yl0s3WiJbxqC/nWxYII0/vzZx6NaJHLe2wk1n0oxjmghUn/naFtZ563LsowgHUjTn9zw2oNhRu26NrRXPJyWi4lqjFmMnnxNLvEQ7gb/cxvoPWzZs3+eIXv/h1Wv7PfvazvPDCCx/QUcn6UAP4HbpcLhcq5URA3SwF6lXRDDUbjmpNdtrpRDRn02cczcsL0jNN+1xJtemFHTiVsYN+nIs7LRMjhht46nWPdor+Y0U6lhuYz0BdWwhWJREsxPTxEK0jo52pdGiCjH1pFWHoUDODSoO4T1drqjWka9KBinUtGr1opIDw27WkCvRbtJPjj0puqE9dP5QuWiMuvORcY62I55u1QLMU0JcX2KUG9cpE0jb2ZPednqmLbojPIsmDDPMwxx6kJNfn2HMrXdFekC5AlNxZ5SWb1fUjqi+dtd4Dy+GbG8SJiOLdQI4/7dh3nMkNY+vKGc1CgK7Bwumrwg1Mji35seLW1jF2IqOrdsmj5laix1YDg7uGZKZIHmYkU8XoN3OGX03RtUJFxeR5R34qBo/jjyj8Siud2u6mfusP3MetOUIhhpv0XLP05UTMO32BTauZReUi5E9PDPmRxm81TJ9vMQtFrAx+tUW1MrZOJ9JBHNy12I7ZqDwkL0wIKdQrYuypVyNmLiNmn4tGsRlFZtciSzfO6Y8qGZsacaSHIqBaLd2u3IsW7j0ZTasWkhPL6Tur5EeapdtaAOYRzNSQnRr0x8ai/Vry2HMrucpbosWpr7T4ZUf/rr3g3LUTwY2wJFnMemGkK9eCsYGwkCxhn0nRVS8S0JFqkUoHKPOESvSi9ijBTMX0oejSZYZO0l+s6ONAxsbJTBIndOEkf3oueI/ifkI2hvKSu0CR1CuR9MaM5ESKOVNJznS+ZzB3CsxBKsfQZV6HFPRMIvzSM0XvoDuXgzhI+w+N4JdWIsW9VGZxfYeppDjvX5vw4GCV8s1l0j3R+OKRhB8bhb05MbjNlmxYw6OC5nJLedmRHxhJ5EAK8LYfUas1/aWKZk3SOVhqCQnMrkdMKQW22s9Jz6Wrnk66rvr9Pm7Zk54rivsp7ZrDnCaCdekFBg80yWsD7KGgpdKVqjOvyfPXq5HyihMDziwhy1rUwtK/OUa1mt7jSOx1BrJ1z/g5Txj4C7aju1GxuBQY7UwJBjGjrTniR6dUm+LATs4kGpOgiLVh/kwj34N3C3GHLwe5liTQe6wxU4M76DHbHxAHnvl1L/nhuznBG+pliZpjnFBveVSthbXoO0h+IpzXcJb97t5o/vfrQw3g161Pf/rT/LE/9sf4m3/zbxJj5Dd+4zf4S3/pL/ETP/ET/Ef/0X/0gR7bhx3Ab7J+K1rln7R+5md+5nfwSP6PrfZGha7S7kLmsUcyLksnhsUViAlkB6Ih2b50xtHrm3if4a467KMcoyQlJCRgJxp/rULtZ2T7iWjXeg7OE6rLLfWqxlQaN/SYUYtWMu6IJ6kUZ/1A01j8wx5+RXAcxaNExs2dCaPSojVb/lzB/ArEyrB4SuDHrY707iW0w9gx4iT5wdcF6ULhChFKO2d47/4mqYmEqxVhL5NR270ByVjh1j1RaZQ3hPMU7zOKQyPQ5e0F9WFBTAMqKLIjg+uE39FEqpMCC1RbUhjr3BNPU+xM0axEZk85il2Lz1K0l5tMdipj7uHlGWWZot7tEWyHmlBS+Jx/bpNURcpLjuRctI5xrSGoSLtqefeLV8gmgs15wjfU/RYGMA+ZxOjdg3ITxq9IGyfft4SFIFDKDenkuYFoFqv1yOCuFJRv3r5MemhpR8JyqwcR5YT3tvxVC//cKdPbK8SzhGY1iP5pS25CpEEKlbkhWnFNVldayqGnuJ9IbNxqxzU815T3hhTPTpgf9yQv90FKuyQjbr3QsNLglyHOLZN3l4lZZOlt0UKFLFI8sphPnDM7GDB8I5WO8wqYsUU7hX9mRvaVAYurHl0pNj4PJ6+KSalZDpi3Rwz2FeWWYngfZlcidqOkHlmKOzm6hcVLgqsp1he0d4YiC1hYmhV5DekZzG4EwjTFnlncyGN3KuJBQfSa5MwSpgZ3o0IfZMJI3CiJkz5u6HEgI/GeQ58n9PY05WbAzjV1Hhg81LQ9QeC0LqMZdRF8My2ShZGYjxZXZUQfk0h4MMBEYXzGJIIN5A9T6u0uzlAj0o2tBfUwFdjyVL5Li6seOxHdoMtjBzHWLL94wvj1NQYPNOMV2UCGjYa6StCPcnwWWX1H4fYssxsRVziKuxbX17SXG0Zfypg+bcinimScUq8H+o8j7VCJOSLKqL/KUuazRCIT71tcPxHJg4pMc+kep+eK9qUFvtUXoPeQiCRhcVUKx+TEXgCZdauYX+70s6VCPzOh3B8wGANKtMCLpxv0udz6+vcss6Igmsh8mpMdiiREzQ1+4FGV6YwnkWSGxNOdpwyeGjPZG6JSOTeSRwn1QiYt7UjiE1XPY06kQ9u/nVJuB9oV0XkybCmXNOleQrURMY1AvtuR5HRrB+HZOX6/IJ6lGGB4xzB9yrPyZc3Zy4G4kE2OahT54xTTQJx9+7i0H65vz/p3/p1/h6Io+LN/9s+yWCz4oR/6IS5dusR//V//1/zr//q//oEe24cF4DdZr7322tf8+Qtf+ALOOZ577jlA8CzGGD7+8Y9/EIf3T1zJgwyWFGacUG96/HaNbjOSIyW5mEXEbTYoHTn54iZ+o2VjZ8zRwxXceostHEtLc85eXxfx+1kK2zXtYdaJjoXLJ4R/RX51zPzRkCR1NLt9wrIXh1omuiL11oCw2t1wUsFSPAEslzda9FTGiOPnDD73ZPsJ6US6RyGRAijqToOWOcwzLe1JQRtkHMaVirZM0KnH5xb2Miksu+6KG0bUwpAdG8KJwWi5aTZLUpCZt/qQRXwU4fvs2ebi4m8mRtAa/YiZafxOjXmQC5C2F/FLjvxBKtq45YY26Zh5Y4sZGxZnywzud25pKw7UetOTnmpQkXrHobpuUtRgH6cXSSO+H2i8IjtRuMsOpgkxKuxuhusF2qWA68u420zEDEGEZsPTdF1WUgEr06GBfCoC+d79hGapY+W1km9cXQF7ZlnccOhpQTaWLFL/0SluNpQuSBbElDFHxrvrguZY3xlz/GCZ6pKnerYleZQRM9FP9R9pynZE0qVrmBoaLdDd9FzjmgzdgnaKes2jlhrGz6Xo5QY/t7RDTXncBx2ZX5ZiNBrRL1bbDk5zfAfzTs811YpsYHwWYa2mnSW0G5IIsagGNBuO3ETUg4x2KB1tdS4duuqgj41caGHtXLR3bd9IUb/cYiuFWwb1dp8sKKqel7+3GSSpYkmykdtphlEyfvQ9KST0ucgBmqUoxd5QlPOLnY5HmYujU0VolIwhbUmnqVUsLkfi0JHfz6jXPC4RvW1sFSrIeVQ8lO5XnEtHd55lbGyNOR33ofC0hYegyJ6dMz/oQxRJQH6oOB4sY2wkJKKj81kkOo2LoHJx986uCdpEN4JCKS8JYLq/VDK9JV1a9dEZ1f0hulGcviyuarqou3YUibm4X8MsoXyuJpbSNUvPZGPajgJ2bvDHGcqLucyWUviH1hB2KkKZk50KwzIdd5m+FiloVwP63pB0JvrTZlWuBclBQn9XINXBgj5ORUu7m0vxpsXdnZ8Y5pcDQSn03LLYFg2tWm6o3lwmDdBebmisJNWYmcgxbOrwzhCmCfmRpm7lvAgDL/xSFWFuxQ0/VzCT70i9KozM9ESg2vE8Q3UO4GhhsRNRqw3zy7lsuK42EM1F5F+9GvHtB9s++9AE8o3XD//wD/PDP/zDLBYLZrMZm5ubH/QhAR+OgL/p+qVf+qWLX3/kj/wRvu/7vo9Hjx7xhS98gS984Qs8fPiQP/gH/yB/+A//4Q/6UL/hajYd+aFgHJIzI7DR6xWTF5wUAb1AcScju5vjRgE9sZy+ucbwHYs5s/izjKPHy2gHzarHrNXE85S43lBvtwzvGjb+kWH4WkZ+YAm/sSwXzV8eCqPuoZWx5kaDWW6Emt90CRUrjubZEjW14AEv+i7Vc/g8YCrBXaTnMg51A/mvnUvXoy0TvBP+HgqSp6a4SYp9nGIe5ehWMXxPEzJIJh1LLcjzmFfHmEZ26aqVBIbY5Sa7pUAYOFwB6YGkZRAUdq5p1j3Z1Rm2UuR38otxULveYUsCLC4FotfYiRHUTjdi9Hng/CXH9EagXhG9VX4gF/lmNVw4ALMz0fIoD8WeJR1rzEyOv9oMJLlDVwqzn9FuNTKe1JHFtnRE7LU5aq2mebqkeGhRC9H6mcxjDxOSE4vPumxRGykve4EI19JaTN8tRHjvZHSq9nKyMxlNxncGuH5geFfed59F5jc9ppGOY7vsOX60THJu0MsN6b2cZruVTOVOfpA+PZGIOiuFcBhKxm697gX3UUT6j8RFqXfFEZ7lDWgxu+DUhQazfL6i2gi4jRbVaHpbc+nWdoacerX7vQH9OBfTSG1oJ12hWWrqx31iIt0os9RKOsWpQTUKN+y6OE6Kv/49SzITiYA9TKkutyTLFc1ykIjDUjrJKFClQS0ke1rkBeLYtnNJ4MgPNfpyietFqh2PW3OMtmbYuSI/lg6XqWWTFI3AgOvLLfV6hzmZ/pY8axMFXN6KLtQuRIpQr8movR0F6uWIKg3Hd9ZI3u5BK5f95CiRyDUtjlzdKiYvt/QeWqKG2asV4VJFfqjJHiUCpl6rxR3tZBNDBLUjEHPdKuq3lsj3DXpiqUpxpGon/0+1imLPoucS/ahnBp1JOoY+TlB9J47zCP2H8t1eXJPvl6nEWLS4FGjWPW7kCfOE1RePKS97yiutpHgEiDpSHApCJW7WtEvC0osmku+JZKMdAErSa+xMY6dyzQlpJDvTaCcFYtSw/IYcS8gCwUTMo5x0LPGAsTRyXo5Fy6l0xDUCXE/ORNMZMmGZjt5IsBNNtl6KvCJC9UpJvRbFQX+zYvWLct4QZNOga42uFIOXTnGbDTEIqaHacZizBLfRXpiEkolIFD7Q9eEI+OvW93//93N+fg5Ar9e7KP4mkwnf//3f/wEe2YcF4Le0fvqnf5qf+qmfYmVl5eJnKysr/MW/+Bf56Z/+6Q/wyP4xK8iuUS8M/UeK1f8thWMBkQ7uafJDQ7skhYOZadR2hXZKblbrLcmp6KvaqzW6Vui7hQClFxbdd0w/WjF+FuZXg4xkQKKyrkX6r5zSvFhi52AfZ/jKErZq4Wq14laL5ynJRMFyi5lp8gMrTslumQpOPumY35BQe4DqZo2dKuxBSjsX7pjvB8IbQ0iEc+cuSSERLVQ7befcjbhLUkjMzwsZ49hIcW1KuaFYfumEkESyI0NynFBtOZoVDxs1RKi3W/p3Le6dIT6L1CtBuGQjT3poJa6ugdD3pHsJ6Vhi4nSlhZUY5PfJVBiAi+uOatOz9IbBLDTpiURPha4f7ws55uxEupvV5RZfBLjdl2J1vbvKTxLMwLH64jG+H/DOYO/nJHdz2lGk/8DAwIkD+9aMdt3Jjety2xWwET1sZaTvFP3dSPo4IRgpnH3fC8Iji5KduxCwt513YO9K0duTTgudvsqNAvEwE0dyq0nG6oLn6L86QtfSWSufrinupYLKUZC/k1Mcaqr1rks5kEJ4cV5Q3EtJzzXknnC5YnB9TKwNceDIhjX5sWZx2pPPdFduum4QL8w62oEyEh1nxlac22lk9XVxRNcvL/CNRpeK5umSMHTYiRS6+sqCfK1kcdVTr3naj8wEnGwD6p2+FCy1Znh1QkwiMQnELFwggFxfot3C0GHn8tnX6wF3LE7JmIh7d3LSJ9hIsyRdbtcTMHE0kfw4kg1rQs+jNmvRu5aGMHJkx4Z6y+GHnaN8U9iTMYnErZpoRVdYPJLCoh0GzFjOWbfdoBuRgiQPM8mr3rcX2baxtIRWMDiuL87p/M1CCsfOEBQyCPs56YnQAdoVT/NCiQoQpiI7aS+LaDGZaNoXFoQlx+lHvXTujzMZZeeR2Eg3GCX5u7HnsWMjIGYL2XsZfskxeM+SnBpIAuMvrKNX5PHTiWjq6nXP4pJAq8M8IaRBdLcmUl6Xc7/cDPirFYsdMbqhpCspko+OHViIYWuxoxjc7/iBfdmM+BRuPHMgQPc0XDA3wywhzsUS366K9tjOFbHVVOui03MP+iRj4UiGk5T8UDG/GvHjhLNXg0gWSi0M05GT7l5rwSuyd3PpVvccMREpRbMsk4DyihfZyofr99T65V/+ZZqm+bqfV1XFP/yH//ADOKL314cj4G9hTSYTjo6Ovu7nR0dHTKfTD+CI/slLOcXiiifayOy6iPGzE30BEX1yoajXpUulEVfp9KaCuYww7bBFPSjkQvXsnPZ+X0Y0twu52A87fdJ2S/6VlHSqOH/ZMZkVhBMp5twwMFydMz3tYxpF+XQNnXMtZBBrQ8gj1cCRHRqU15haUDKqFOF9SCVD13u5wLs1hz1JLvR5plSYcytFh4IwckSTonJPNILB8XMrUN6xJTw7x0ZFdXeIGUSOHyyjbKTtCxMPBcWuJW6UVI3BnCTUa9K5a5cD+WNDsoDZx2vazoRSboaLEWxIEKxH6IpdG0lOpUPQDiLZakm726fcFsdsdqJoRzIGQgs/rrpVU7pUklG8ovfQMtiNHP6BgFoYopUINJs6jt9bhQjGeryBdKZoVj3zXKGTgJslOJUwejOREXGlSXfmNIsU+0CwOSrC+fNRMmKJtM8vSO4XguQZinGiHbzPw3syipvckg5PnBuSK3PqWYbq/m5yKkgbO5NcVeUEbWJnGt2mVDuemIjBwRfi5HF9weXohWQdt4uk6wIH1NyCU4R+w/qvWcbPgj9KxNnaaMxCX+Ts+oGneJQQEn2BpyEoQiFIE7UwzK4ownIDpdxcfT8w+s2C2XXRkvV3FfMlSzs22JUaf5wRghaDRuqJJsVttOiJpW4s2YEBDCERxNDojqFZEsYeXl4bmUeNDclCJAHFgWgu51ZQOunvO6W+swy1YnbLgYLzlwPxtMBMDcWdhNlzDXpsMasO15NiyBdibCDzpPtWvpvHBfXVhnotENcajA348xQKD7XBnqQ0S1EMMcu+c1JFij0jxVASoBIMjF5o9MywuCXPjUYKs5WKot8wP5EEm+TIEicGt9GQ9RvsvSFhP0XRFU/TRJ5Hd2kePUE5qb5DnSeywel3GeTIdUiXmrhT0WxDspvjcjnnskdyjQmnqcgUMrqNnO0A25CeSMb14prILGIq04TQCwy+XNAMo+BljpLOWa3l/dquSe7lNKuB5Fyz2BaepamhudSSH6bsniy9P7lINBUWXWp6uzJyroouXnJDGJ7tKNAOxdgWHbRXanrDmmox6hiMlvJGgzciDbHnFtf3+IGnnGUU91KqbU+6VuEaIzFxr46Jh33R0YJMOj7A9eEI+P315S9/+eL3b7zxBvv7+xd/9t7z2c9+lsuXL38Qh3axPiwAv4X1L//L/zI/+qM/yk//9E/zPd/zPYDkAP/pP/2n+Vf+lX/lAz66b7z0ckNUCele0iVXiLsw2Ei9Fd+/UATh6LGXQxJZeu6U8zurIiqfpOSVQjfQ71dMfR+z0DTL0m1IpoZmFEkOEsrNSPLcBM4K/CwhnWiqNekaNF9aQa1KJ6b3eka9KlqnxVVHcmxpN1ryR6nofpYc6anoAYtdGfc1y5LIYE8TbKmI/RafeXqDmnKRUuYpZqWm16tZ3F6GIEUY4wS/5PE94fLpVnhcIWj83NI71iRzaNZl1FN0OkbVaMpbNflXhuQafBpBC8wWD+X1llLB8rBkqiN+IqkIfqdGmUh9nqKSQP5OJu7LzRZfygVaRYi3RQ/ptmRXOL8K7BYkU4UuHM2ywRxKWkdYbklyx+JWYPGspKRkR6KtdKNAvDcgXcjrah/3iYNAoyVRITuHWciwjeq6HlLAJWNDdnsIqwLZ1bUUEPmR5D2P7kL9ckPjxbHYu5dQv1RSfKVgcdnLObAaiANH+igl5qK9avZ7ZOfCQavWA95Ikaii8O8AwvWS+rQAFem/lzC/KYVL8chK4X9VHI5J2ckWR5FLzx2wd7REdJpYG9o3RvSidFZUgMVNh0o99jCn3gyYlZr0biERh1fnqNeH+OOUkAViEujdSWWk/uo54c0l4o0SHhTkz42Z5AV4RfI4pdqIMLXolQZ9t8A4RRwXwmH84oB6LbDyuYTJrUh9npPk0uExXtFc8kyellF/NJHBxpxqkFK82aPa8qRjw2LDERJNfiSdr2SOGGCsjCbD2IgWtFLkR4bFCzXz3Iqhq1I0GwostEtS3CivKG5nNEvSpWpuVOA1ulb4aYJTkXyjpC4T7LlsjFSLwMvTCDpgTyWnOt+3pLdTtBNWXXamKLci6X5CetZxRVcV5rHFnhcklyJuuyF5boJ7a0R+N6O6rLHSLCZ//hyA6f4QM9Nkp5rFjbaLY5ONYDKX7Gq1UVNlCemJJMf078DJx+Q6FlYdrqeFDZl5Vi+fw2trUuiOoP92hp3D+LvEjDJ53tFsRIabM6YHA9LjDjQ+aKlWZYOpSjn/MZHFMKJ6jv6XJO5NNUomI6VgaZIZ2HlKO4i0i1TA9oUifygRiNWmyB18T2Dmg/uKqU4I6y1LK3PO94dkewnBRPqjivkkxyJa53YY6d1JaYeChzE1zApDeq5pb7aixUwDzVmOHTW4p0uah0OUjvQfadEQftAzvQ8xMBfrox/9KEoplFLfcNRbFAV/5a/8lQ/gyN5fH/Tp8v8X6+d+7uf4F/6Ff4Ef+qEf4vr161y/fp0f+qEf4p//5/95fvZnf/aDPrxvuPSjXBh5qWj4snP5Xg3vaZZfF0zG4J6h2NeCWdmsiWlkOiuwM3HBCuzXUa0HqiZ5HzrcC1SXWxbXHG7J45YC7YbDOYM9TlCVplnzFEcSMxW1fKPbjZbpLUc7DDSvLEhWK+nOtJrqcitRbkpMCsmZodryEpE2kBGlz4QVqB8UxMowH+eEuSU50/iTjOnuCDcSfZDLZcyr54bRbUt20jHWDPA4F+3cNcfseqB/V2427kvLxIGjd3VK+jiVMVYihZa6OSdcqYhFEO1hhPG4R2hMx6iLZO/mxNNUCmqgvCodHKYJvV2NCvLa2mHEzA0bK1OIinCUX2Qgq4NM4uACF8DksFsIm682FI+NjKDyKPFPU3lf2jWJoGLgCFa6H+0Aerv6YkTvLtWSpVpEJi+26KYTwicRbiwoXyppN1um18B9aVlwFlOJKeNxTkggO+6iu2wkHzSkY0WxL8YYO9MCt70quq38SHJ9601PMhbkST3OIQ0ko4b5sw1mrtm6fipxYesipLelornWiLGlVuw+XsXez9FWuoi+iBz9ASdQaQv2REaYdg4r184IXvRfdqZojnq0oy4isNQkp5byedGslXeWGL0H9naPZK6YT3JJOkmC6MkcjN4x2Hs5zbrIAqIB+06P4f/lUBI0ljvc0IloBH0uGkoacUaXVx0xjcwfDTHv9CgvO/oPZKz6JJasfrmEV6bMLwdil+ThegiWyCv8smN+05G/m8HQ4XuS+6r3clSrGNxOyA8MZq6xZaeDSyLJowylI34ohW8yMdR7PWJtcH05D5KZPMfKpbFkVVei1asutcxvOqbPtYTNhvKyF6bjuqNei9RrkexMtLT5aaBddTC3lFPR4EYD+V7C4opE9033h8zGhRjDBoFyx6Myj+m3DG9bkhNLu90QkQSf9FRYpL6InL0g35fk1KBKI9/BtRp7bhi/vSqd1dqQ35Pit9yOJPsp+g+eiqZ1YajeXCZ/nBA1rHxVkd6RKYZ7WqIAWZFzMV2tiHPL7JkWd6MiLAl2J6RQ3aiZXw20S5GYgMmdbDQnFv/8XM6r9Voi/SrpKI+f85I0M06YTAt039E+U9JcaZjvDrF7GaZUIlMZyUUgaqi2As0SXS52pPhqIaanSrP6mmHpFwvifk5+LFnW86uB9lotCUX/J1x/9a/+VW7cuEGe53zyk5/kN37jN/6xf/9v/a2/xfPPP0+e57zyyiv8z//z//xtP6a7d+/y7rvvXqBf7t69e/Frd3eXyWTCv/Vv/Vvf9uf9p1kfFoDfwur1evzsz/4sJycnvPbaa7z22mucnp7ysz/7s/T7/Q/68L7hcoOAHwR8Ljvbti83BSJUa5JuML8iN4GQBUIj0V/tNIVn54QkkowF5huWHOXugLhVX6RzJGcWrLCwYs+hkkB7XODWW1RQjHamTF9sMKW6SOZIjhJ6D63kWz4saMuE4khMBMmwJjkX3ZWpRDOGV5JI0m/R2xX5sTgyg42kRxZ7mKLnhv6uuEHtRKDM+UYpzsokYipFuR0lyzeN2NUKU4GeyXgpbtbYRbcDHwTS3ZT5QZ92GCgvSSETC09zlqMf5STH0jS3Z5Y4s8RWE4dOQtlXgzzHoGXwlUxGQysOO+1255cbVET4eAGO3tjAvJfTfygXcdfFhw3uwepXBflBqyWRoVTYc8Pi6UbMAkfdyPOp+iISLppIrDqI90CKZTeAuC76x/ydHL0iukZVa9qBZDv7XqSdpcTTjGw3vdAgljcb6lvVhQC+WQq0zy1Qqw12bKiOCwEAr0ViafC9KNFVOlIcaMFeLAcZUeZRHL8HFhqNa8yF3urk9Q2JfssjYb3FziT/OSRSzOQPUpp1TzxLZVQ78BQPEjnRNbiNVsC8z7TMFrmMtYcB/7EpqpZiUAXR1LWrjuRhJok2I8f5C5FkIlBefZTiS4Pdy6h2PO2yF8f0urg3Tdm5LS+1HDxaoV31LK47XF/wHuWOp7nUSnHUpZjkayVmKjiW4gDs2DC7KUX60juK3mNNljdUE+G3xSRIobXi8RnoUmEKL5ijXpRc4VZhSkX+zLjLgeX97wzyeZJJsYqWbr+eG+xUHLjpqKa3K59JsBCWHLN5Lnm0VxuScw0mMtqego3YR5l04OdKxuodimVxWaIhT19SZPsW1SiUCQJDzwXZFPsO3xf3szpLaXZaQdjkgVhafG0I/4zkJw/eFHROvdPSDsPFpi8sO9Gf9iIsNxT3EvKiEabjtTm6ViKxmEvXePSuSE/GD5aEl+fkz82SfE71igDBiwNNPMpIzxXRK9T1uZxThRf5yDhFTy3puRRhyX5KeiZdSjsX57zy4hjW78gIPJ7JxtEte9ACr7fnHZrlULii+kEOTpMdGdolLxvrpiu8twJuxRF6Hv3SRDimV+c0I+EA6rWGyU2oNhR6u6IdCE8zPdMUb+YX8OgPbH0AJpC/+Tf/Jp/+9Kf5z//z/5wvfOELfOQjH+EHfuAHODw8/IZ//1d/9Vf5N/6Nf4N/+9/+t3nttdf4wR/8QX7wB3+Q119//Z/+9f5j1vXr17lx4wYhBD7xiU9cNI+uX7/Ozs4OxnzwyJ4Po+C+w9ZFFNzP/AV0Pyc9MmTniulLDclhQnquWFwVZINuRRvYPF8SD+Sm2D6/wI9TBtsz5g+HYMB0DjndKurO2RqN6LSyazOqhWhxBl/Kmb7YUNwV/l876mLStsRBnJ5I96pd9pIAMXIkp1bi5NZkrPwkMo0oY8XBC2ec741EOzVJ6D3u3IGXPabUuNX2Au/SPlfiS0O6n9CuBGH6ldKJcb0I10rSrCW+tkR5yUmixHrJ8mf7HH931yWaa3mPnmpIh40UfpWm/1AzvxrIjmRUV60JJDYZG8xCwuM3Xj7k+Eub9HcVs2sRPxSYa34sI9h2JDdqn0J1ybF9/YTzX98imcH0pQZzJpFZ7YbDTIzknM40ftmR7iW0yxJf9uQ9VQcZ2y8fsne8JIzGE4k5azYdpOLyrp8v4Sgj2kh61unTVj3LOxPKOiG8N0DXMm6q14TLGJPI0luGZiRsta1njzg4WCY6RTJocCcF9B3ZexnFYWTyjKBwVj6fcP686CiLx4Z0LKagdCLvDzcW0s0sJTHB51KgN9utxPZdFReoRMJJ+otuxZiEguwsMr/cYV+idIlBbu7JuZbiwkbsXorbaSTNxGvhzaQy+q1fXpC93hN8SS3sxOTUijQgjeJI9RCvlbjagH+yEZGRqRuKe/xJp0X3W8zDXIqpkaQzUBsGdyzlTiBkkfREtJDBQn4C7RDid49ZHPZJzoyYFyw0KxKt2FxriK1GNRrViC7N9eIFyFovNPmxho+PBTydeNpxhl4YwlBiE0MRIPcU72WU11qyx5LCUT9bEipL9jihWZfH8kPPcHvK9HBAtpfQXK9Z/kcpk1vCsnwCnq42pUuVrlQ0pzlmSVJqVBTgejKV4mP+dCPv2cJIFzuJ5MsVzaM++bUpzZ0Rvt+ZZY6sjEs3K5SKhN0CP/Ks7oyZvr52EaEWr5eEg5y41pC/kxOSKBuuLJAeWPm+m4idyORCRengoqAdysaMIDo7YVMKv9MuFOkZTJ8K3Hp5lzsPNkn2BMOkBo44s+8ncjjF6F05P5OZJPa4ZYdyGjwSf+eVXNMW76fuuEJScOJag32ciWv3UoW9n190tH0v0L9nmD0tOk5qg+63qP0cP5DrZTRyPdXr9cX7HpNAf33B4tEAXWuS3ZrbH0CM2pP7zov/929PFNwbP/utv4ZPfvKTfPd3fzf/7X/73wIQQuDq1av8iT/xJ/hP/pP/5Ov+/h/9o3+U+XzO3//7f//iZ7/v9/0+PvrRj/JzP/dzv61j/2brnXfe4Zd+6Zc4PDwkhK/t0v65P/fnfkee81tZH2oAv5NXGoiJYfaUR00trogkY3VR/DVLAd0ofGUgD7iBgt0CRo75JBft4ELj1lv676TMbwiAtvdQyPamNjT3BphLFb4xzK5JLmh2LmOMZjVKZ+7dAlNJQRgS6UiqgHDolqVTEIpAlQfQEd1zqP1cxnJvrqCzCP0WPCyuSAGadwBnVRnyQ0O16bEPcpbuwvi5SPHIUG2JQaO60dB/O2U+SnE6pee7EPhKYQ4GovGL0kUELoTucdAhSArP9FYgWakpixQeigEl78T2sQ/FkeLo9U3yc0WzLDcgvZCbhc/ounIBl0s3KjuwzN7botkO1BsRNTNdx0acmM2G4DGKA416nNIsSUHse4E48JiDjHSseXxng5gETFQsbrYUDxN69xPKbY/PI70vFcxvenQpiIiou26pCdSzjLwUZ6jyWjAdp4pmJGiZdknAwKeTPld2Tnl4dwP/uAc2os4T6rVAvRmxM83wtZTxs0GSMjq94/hFCbQvt4VpqO/3yMYKW8HkOYc9NzSbUrDUa0FSVJYiHGY0S+JEbQeBMPQkJ5ZmSQpoXWqSiRSIdq4wtaHakXG7skG0kbUm301I5mIociuRdhTxCysmACsFTf5IoMjpmRYTRSOj2fo4g55HWXFGt6POwTmQG7lWkXieYnZl5BmyCE5jTw3BykjOVIr+UxPqyRK9PZjeBD9TzG+1DH9lCZ51tKuedlWc+NmxprwkaJFkbC4wML6Q4vSJOF55KdgXd4dk426IU0gx7bdb7N2Ueg1i39H2I4P1Oc3BEuWWx2hJTWlH3QZprtGlpnpjmWIhG8LkUUq1Kikk1UaQEf+Te3oAt9vDtgqnUsgD+kyuLaYUNE3+IJXxfBEkGWTJUR0V9I40bT0iP1GUVkFlxMyxq1nEXLpgCvJHCad6RKKkq9muBsyjAh0hfVMycnv3LTERQ5S/WUnqi47oFXHD518qqL9rRjtLGbwlXbnZTS/xc1mklGRI3KqnXjMM3zXcK6+g00hvT9EsLKay1KvSectODdXllvFzkeRcM3laisr0SKIv49WKeJRhdxaoA0sziuhGXwDYowZzIMWfzyJ6P6NZdwLE7wXUcsPsKYFih4Uk0oSFhZEjPbTi3l+GkCr8Uc7KmwI1r7Yj5YMhMQ/4fosa/5+rn9M0DZ///Of5iZ/4iYufaa35Q3/oD/Frv/Zr3/Df/Nqv/drXBT38wA/8AH/n7/yd35Fj/IVf+AX+vX/v32N9fZ3t7W2Uer9Lq5T6sAD8cP0OLAWDN0VQ3NuZMT/qoUsjLkkxXBLyQG/PEk9TqldLnIJ8vcSfFDBN5EY7U+THKfPrDj1oiecp85tOMBJDiW6LQKwMl547ZO/OBpOnRCfYe2BpX5nT7hX4QuEG/iLNIlo5iGSiiFoRioidaPIjxeLjDt/31EYT1xqYJNhHuTxfrUivz2iqIaHv2bp8xkFvmeRAwLeLHY0pO3ZgT2C3qjTCTzs3LH/kmJNyTdyARaQxkcFDGX/HayWutKR74kYMj3rkE3H+zZ9taBcJ+Z44VnWjBLlRK7JTxfyKdCR9KiO4ZCzOXlt23S+EZRYNuKVAtAG1mxB6gWJ9Aa+NRC9m1UUh+mSkN32ulagtwJxZOJMiph0K9gQM5Y4XobsayJhaQ36iWOwImkSPrXz2XQzf4lfW0ZuBYAU6bEsIGfjucZ8E1PtBwJ3mPJxkLL1hmT4VYLlFHaWCX7nuaDda/FFKXGuIk4T+rhiAVKsIVyr5yA8ydKlolyK2EixOTKI4VpcDYRBQ0wSfBpJK0a46YqqxaxX6fg+zUB3LTd5H3YDvweK6FJKYKJrXrIvgSqHe8DQrijByJEeJjNAyT7XlyI7k0tesStfTLhRm1KDGBfWGwHjNvri6q0styYmw8Z5oOmMQN7MbBYqxob7i0NP3nejoiN9qmZ31MFmkXtFoF1lcEtdztREvWG9uyXP9o495+BuXiQNHspcSEoi55NYunpIud76vqbdrsgeW2U3ZCKlT+bwkOxbpBGZi6CmThNF7cNYbElc92YElnBqRccwMZmwkPm0QCAWYqnuNEcJHpvj3BsQ04jNony1JUod6ZyCbxzVhANLBys1UuvthvaVOLXHoSA5SXD9QPEholyKL6xIZ2G548vspaKgut4B9H4itI24AOve4vpGIuCAszZhEVJQ85notYMcWt+KIEUJj0McJLpVum+tBe56jCke1IYw8llriWUrsOWg1/XcTfGYudJO+LxvixU680DtnJ4p20+Nnmv6dhPl1R7PtGNxOuvSRKOD8RzlKgX/QR1t5vKAjDFvBwliJi5zdkC63HwTMRExuutSEgYZOokOnXQa5RmunmF8Tl7ZuFMn2gnNVYGea5Fw2jmqzIXu9h3PV7+Rd5Z+8vo0mkMlk8jU/zrKMLPvaqLvj42O892xtbX3Nz7e2tnjrrbe+4cPv7+9/w7//W1263871F//iX+Qv/aW/xH/8H//HvyOP/9tZH2oAv1NXhNktJyaBLy1RPEoEh1IIOy+ZKvr3LeWmwGXVw5x0taJeJJKmcCLYknpNtEgAyXsSlYaWfM2YBrSK+GlCcmqY/b+2sRNNGDlULY7SdpwRlh3hUiWjlDSQHMuuuV2SzN12FBi+YwgW0Q3ey8n3raAR3hJdS7siEWwhiSTW44ee3nsJJ1/egEaTjqU7Vu049HMzmlWP7XSKT0aJvoicjfuS1dpKNmixaxk/A+Fyhb5fkD9MJR2glQKsWQ3MbzjscYKeStGgG4Wddx2+RCKiTNUBm6+0FyNOkJ1/OpYUg2bL0W4JY9HMDM0zJXZscLeHMvbO/EVe6eA9S7xe4npgxxZzmkhXUnMxPtM1VC+VVC+KZXb2UMYl7bqDkWTYuiVPcipYjN6uuE3tWF5HyCQfNjvrXNNBxpAstZKCogSj0b8vIv1mBNmxhkkirvJ+BBtIeq2MNWsxxFTrEeUFxj34fEEM8n67vnSJyi3pqrJTSadztUF1ponBnYR21aEXBjvRtGcZbquhXQq02w3V9Zp2S/iOyilIROuqOoZctJLjqmqNXhNBftJrSZ+dsHbjTMwklTjZw05F6HkxQkR48fI+upHCzmeRcttLZ9FLhFm97lFOsXLtDEatwKJNZPFUi848YSSsP3FbiRYSLw5tn0O7FLDX5pK+slBkR2Ic0f2Wu2/skB8LSDhqGU/rWmLLljenxCywuO7wpWFxsyU91YxuGxbP1IBo1JoVSWghKvT3nMvPC0Uylve2HYp+ECMjez8QPp5ZalG1xn7snHrDy7m63+ug2kE2BIcZTSU50v3HkexY9MHKqc4pK3iT5GFKdizg+ahBLTe0Q/ne2mFLLLxsRrY89c2KZFjTfwSmQd6LVuGWHcZ0LLxcNG66VQzesygnYOxkptE19N5LCKUl2U+kO25il2Mt4/ms1xKvVNTrgeKtnDgSV73pO3wu3dX64zPGH22IqVwb22XP8Jlz2jXH7KWa4m7K4AHMb7Uop0iOLPPror0MNjJ4O8WNvHT2WuFmKg/Lb0p83eCelSShHqJLLpXoqG/MqG/UpGOFOk1IDy3mUY6aGYoDkbqkJzLSjnmgOJDNaHtQoNca0b8OhZDg55Lq4/ofbAfwCQbmt/sL4OrVqywtLV38+qmf+qkP9LX9H11nZ2d86lOf+qAP4xuuDzuA36krCzz37C63/RWyzQXV4z7Du5onnLJyR9APykkhlJ1a6t0etlboWzPMTmBx0MfODNUl0Ta5fiA5EYxCb0v0ce1MeHv66Yp51pfx1FlC3K5wcykmcYY4F+ixnmgZP2UBHwxJLcWb68HwnqKapVRbDl1rwtDh54ncVOdGnMX7hnmzhCoi1UaALk6MKDcgNWqIUZhdtlSofotrDOrUYGtFrAvR9/Qc+bu5jG80JHfl982SxIyFBNqVcPFe+kIixuKLU+ppRjas8eMcnGL8UitaMQzZviWdyNhRt5Afw+RWICw5iqUK9YURPgNf+G4kFFGxg0C3IgxXXlEvR2wiN5XkqSnN/QH9Xc30GeEL6krTXGoZ9GsWd0eoTdEE+n7EHiUk04TF5UjvgaVekfHh4nKn8cs9bi0KU+9yxeyq3EBUo+nfN8yWNHahCVbcxvWmAHl9HmlWPflGiZsPsKWiuJ/SLFvczYr0YSbdowySEsYvSma0PkplvI3osty2RBCGs4xkrFHHhjAKXc6zGIyedPxIIuY0Iaw3JKknPOoRsiBw5TRgCgfnVmL/1gNXbx7x8N0NYu7xpSWbK8K9HvPNlrnr0ZtLF9bOFGWSYCeGdikQrObB336KcEU6c0vvwuSmZGCbRznFnozu51ciZ/dXuk5qvGA9hlYwJa4QlmKVWlSryMbyPtYbjmLPonaHuFcWtL6QseFBhtrPpVbMIMkd2fMls4cjikszChOYLzJIA+bMErUI/n0Rmd4MpL0WdauhPc/Z+FXL9IYYNOaPhsKn3Ipk54rFpYByEJ6bwTyl925K+NiUprYk7xQ0y4FyIVDmakvGm2qhUZ3RISYC045RMb8sz6GCyAmSqRTU7WYLtYYskBwm2KmiJReGZxJx41SuAY3CnGvcXNMuG87+mbrrknWGlZlh+Vd7zHcUi6dbApAciumn2WkxpwKYbtY8IRGdZrvZUtxPO/C2VBBp7qgf99GNIu0c2nhFfNjDVvIaeo8VpetjbpWYvUIKZOA8G6BaDV3CS/1CDa1G9x3mWCYa06cddmIotwLZoRW5wUaEIMczNYmgrVY9g3dlLIxXtNdqlI7U+z2KSzPKpyE2mlhJpnEy1cyvS7a4RGBG+u8mzJ9qsV2GsZ8mKAXJRkm4L0bEkEVc4n937i+/C+vhw4dfowH833f/ANbX1zHGcHBw8DU/Pzg4YHt7+xs+7vb29j/V3//trk996lP8L//L/8K/++/+u78jj//bWR8WgN+hy+SO269fQTeK9n6f2AvMbkB+qKk2pUNjzgVN0i4LQX7wUFNuRuqzHDNwsvtUiCFjbgS2mss4xf3KKuqax54b3FDTTBJiTzQ/2bmivSIZwfhOe2ZlRNXstBIBlwTCUqTuKfLHCeW1lubUkp4rVCvsMr3l0HVKtOJqxcSuwIgwaMl3c7STi66pRKTNYUZdJJgA9c2KOE9QScAtBYpdQ7nc5REPW+l2qfdHreIUfaIbrElyR7tIhLvWRWy5kwLlFe68j4kIz+zpBjtscanFJ4GQZoLFMJF2qQuT94pynMMVJ91BHVl5Q9EOpCPmM8FVNGuB5EyTThTVvQFJrTC/MaLnpEDQ/ZY4SSWpwQZm+wMoAr2vCtYirEXcisMNFbrWtIMuJmomztGoIZ1qprfEze1zT6gNSTcSLbckmcJU4FciulQdd1BSSfJDCwdD4iBSXnYCx10oln414+yVgJ1o4uWKqic3e3uYYyr5/KuNQHnFoeYWXQrgW7qIkVh4mInOSheOuJfLKLdV2JmmNSkeiFmg/8gwvybdUn+eohUsrsix7H9+G7ZazJkV4K6Rjm3/nZRyUxAe7UaLXaT0HsprjnMxrCwuyegvmsjkKcHnrC7POTvIqNalM2HniqgFTIzTDL6cMb8i0gTlFOlYCUTZK3q7msUVMY2ogaMZGcJWjQqKsN2AU4I06TliZTCVpZmnuL2e8A2P+oInGomRQrUKv+RxPel2969OaRqLvzcguzHj5KN9lI/4ZYeqJL86ZBFdw+gdTbUG9VGB9sJpjPs9GcNuO0gCifEke4WMvJdq4kiLFnem8A6akUWpSL0ZKB5ZSaxopFBXqzX2oaCCgheX7ZPUjKifSD7Ar4rGrV2RIi05tvBUjT8RTmN2LFnh42dg9bsOKA+WYWZplzzusiN5lNGuO/wSrGxPGL+7QrFrSSYwfdZjVyvCLMXPFc1RwdXnD3j8+hb1WpDNxtzie4HermF6y5OdyQaAs4xsJh1XVRn02Eqm9LbDzg3uTGDWsVW0z5WEmZWYyFI+73rTUSOMTeUhTsUIEtJIvivEgdFdOO1r6It+NBlr2tkILtWYiWSNJxNxgOf7lnYUSc8Efq8dUrzaKNcPJedpM08xJpLtycbczj9gZ+m3cQQ8Go3+iSaQNE35+Mc/zi/+4i/ygz/4g4CYQH7xF3+RP/7H//g3/De///f/fn7xF3+Rf//f//cvfva//q//K7//9//+3+aBf+P19NNP85/9Z/8Zv/7rv84rr7xCkiRf8///5J/8k78jz/utrA8LwO/QpR4VxBV1Mfo0C425uqCp+hI7lkr8Uhg5qEWsvDASP6QajZ8kGOTGnZwbuWnuC88unUaOP9minER9KSfk+/4z57gHK5ga3J0ejISJlsw09aoHrdCJp399Tv2VZbIzxfyqaBIlM1WRn0R8Ibq5/Cs9yiseM5WCqLbi5jVzTfo4p9qSf5tMJGYpjBw0gl1QN+b4mXQ6bAmzT5SEo4J8Z476/IjaCG9PtzL+lOg1DXMZTZvjlHZZg+46HZ4LfZdykhGcnSnKbU9vqWRxVpA/SqlXvXRKjy3Fi+csjpalcOn0iMWe6H5cHqlXFIudwNJzp8xfW6NZDehKPrN6JWKuzXF3+xfpIj6PqJMMtDDYdJvRXGswhyn5aaTtK9qRuD2fjMFUhDh0tCuBsJcxeKAYv+ixEy3H+SCjXZXRoOtF+g81po7MrovTu1mVm1FIJcXAbzbokwTlBUsTO9ROOg2SsxrAvZfjNzx6qsnOweXSbWHksAcp6ViSGfRCsnGrLc/K5xKqdUW1ATHVuIFn8J5ltirHUDw21KuRmEVmTznMTKNbTbvZYk8N2SPL9MWGMFComSVsNthHGclUihGfQ3amJQHHdTm6GuJqg92TiMR2xWHPLabStF202uRLayTPzghBYb44kO6YidzaOebg/3lVzl/knFDA4qocq3KKZikyuK8ptyLJnRzfi8TTlGJfU20EcXefGNygJX8gZhRVGpKxprleo2Yi/jeVMDHVjTkmaPRxgZ5qqneWsDdnNBsNymtYq+n/ZsE0MxS7hvw0sthRTD9ekdzPKA4VyhtMizAHbSTft9SrgQi4KqHdcWAj5s2BjHAzifqzpaJ/O6XcCqiFonqmInkokX/tMKJ3c1xfgMSuZ2hHAVtqsnPp7j/JzjZTQ29PMX3KC/poGIjTDB0Rw0hfxtP91ZKD2xuSdtF0BauXeDUz7eQib61CInickIBdK0m+PIBRpNlqodFM/v4O2VAyhdvlcDEZqNalkKpXuinIqGXxlGSiE2Uj2C4FVM/T5gFzavEjD1pfQPTzfYtuYOsfwekLlupGTfauZf5KRawM+vD9OED30hy+MCA7U3BaUF528Mwc3uuL4/tShd6T9zA7EnNZyCO10nC5JLzbMRRvldj7BbrV3QhaDqZZEWyOG/we6AD+Lk+hP/3pT/MjP/IjfOITn+B7vud7+MxnPsN8PudHf/RHAfg3/81/k8uXL1+MkP/Un/pTfN/3fR8//dM/zR/+w3+Yv/E3/ga/+Zu/yc///M//jhzfz//8zzMYDPgH/+Af8A/+wT/4mv+nlPqwAPxwffuXbiEECNcq4mmKqTThbh9TC1ZjcVWSNjhKSWZQbQh+ZXhXtF+zV2uarShRZoW4iItDAQdPnw7oucHslJQ+R600hIVldneJzECzhIjEk0hIPVXfkp4Yoobh5wrmV3K0gtlTntjztN6SzLRcpAfSdVRBgMKDe5bZrRZbJhQHGvddU8xbA9EtrtWEhcV3Yu30zZTYXdRdLXmnUcPsekDtZ5gKmttD4rIULSDi/3pF4rDK7UD/ocbuy02r3BCAa29PMX5ZNHVPduGh8OKq3M9YnBfoiaXadsJCW25gntN8YQW/Fsj3pUvqtoWnoWtIa0U7BHN1wdnJkLjhpSvqZSefnivqhz1CL+C62LfY6cps3jLLMpIzi91LaVcdZ6+KK7a3Mce9NSLkCnWpojlPoVX038xZ7ARm31uiDnPsXNyVzbbk+DarYOe6SyRQhCQwujRl8lhGiTGNZNenLGaZoGpWPLrU2MsLatvjpFAQZfwW8sDgvU5fuimGI1spzKG4McvL3b8tFe1LCzjJmF2FwcNIsAa/EOzQ/KYXl2UiG5HiUGFLw/mLjmSmSSaQPFsSHqZMX6nJBw3tgz7sVGyvTjh6sIXPJRMaDemhIIdCYjuHqiYbVcyCwpSZbHx6ATeMFI8llsv2HMU/HDL5RIVfC9iZIh1rHh1dpb4pRoj0rMO8JOCvNIRD6cbauWJ6I5Cd6s7sIPGK1SaohwUqkZt3/mYhebtrLbZw1JlF24A90wQD5c0GVRq41ycMAkbJCLsdRbzT5IOG8PZAkts2BBXSLkXaJSBK563ZcsTE0o48S29ZVl9XzHcs9Yan2DO4QhGN6fSHFv/CDL/fIz2Xbny7JHDsJyxRs5eRThTlpnxnys1Isr1gluXSWR74jtmpcHmktycMxfmtFneeYGeaYEX35/qa9Ew2IOFaRaIii4dDuTZYTZhpkomiVVaK4w2P7rd4r4lOEXLN4K7Ff3XQaTgV7ZJGOWGAPll2KhsGvwKDtQWxtjS9FJ3I5sycWzEmddnjzUokey8TNNZIgY0khxr7qKAdRaotB4WnWUrRPpLfy6jXI4wTsAKaV41cg+KjHrPnGop74pBOV8WsEZKI6TmSN3pEK1OLdjOiDjIBVAfwZxnZoktyOs7Ipgr/0Snh3gB1mhIyMW3F0pA++uDZcr/b64/+0T/K0dERf+7P/Tn29/f56Ec/ymc/+9kLo8eDBw/Q+n27wx/4A3+Av/7X/zp/9s/+Wf7Mn/kzPPPMM/ydv/N3ePnll39Hju/u3bu/I4/77VgfFoDfocuUCm8j5mGO6rRJ2oFLIs16JJkY0fV4cP0u33WumDzrUIVHnYubc36ty+qMMHnWo1rF6B3D+AXH5vKMw4MCYwMBUFsVbVsQrIjEVVA0txwx87QD4YT5XNhZ9syilhv0fibAXyeQXO3AFZLlShKpVsUtXK957Fyj3xxKRy5E1H7G4NaEme5hKqH824UYMGIpGakxkdemaySCbtFx5EKXatEdj6ktdqqYXwmiL9p0ZA9SXC8yuxZQlSYZa1w/svT0GZPbK/g04Ldq0keZdJaSAK2h95VCwutPI9Fo2iXRA5pDKVDLZxrsUYLfaojTlOJuSkwk8s5OpWNbbUZCGkSD1SqJ82oVamFxxqK6iLpouyLdSITV4rygqKS45GGBTiNmZ8HslsLMNG6SwsDR1gm6NDJqbwWLIkkshnpZIuOyxKFHLRXQv2+Z2x7Fw4RowddaIt7u9okjD8qQHymmzzuyvQSfyqheXy7R7/VQTgrfZqdF1YbBA02zBP5xDl3UXjNSxOdnuJkAnwd3EmY3HGnH+7ML2VyoVtM+U6K/WrA47JNp0V3x1SFZA64q2I+KsCwdVeU0yak42qtN6RoXu5J3u5hnF+cXiLbSjxzVtqf3Xkq1YanWI9qKGzNqmD3Tkj9KLtIqTCUwbF1D9nZBeaVFzw3V1ZbkWMY9ZiIO4eLthNlTntGuYrwqukoUkrG9n+CueXrvpjQrkWiRrN4HKfVKoF0TMHmzFmnWuo7ZJCVMDX5dimrdiMkjJPKeRBvRh7L50U6hnGH6VCBkgWQsHVwpVBRmoXFrDucV+nGPkAfavuQ+66Yzt4T3DU/BiLyg2pTz2z/qoTpN7RPI9+JmS/5QcE+rr0fKTeneRSMd5upqS3KYoL10yuJpBsNWRqGZIurYsUs7ScVI4PCxSclODPWadBJnNx29B1YyeHdE5hG1HIsbCk8zmSqKo5RyIzKfS/6uDhBWBOGjIiy9oSg3JMVnsDWjrpdQDwtslztu54riKOL6ojnO7gpj02fiVA5WwNDtUiAWgWx9TjXNSPcSzONE4padopmnZI8S0kYRpz1MA013LhS9hrbMLxiGaccxFVyTTB3iLCO5uiAsElRpyB5kQidw7nf7VvM164PKAv7jf/yPf9OR7y//8i9/3c8+9alP/Z41Zvxurg8LwO/QVW8E1KjFu5S42hBLS/HQsrgiI6x2xdEmkeHrAnGuNoLElRWe7K6IbcutQOx5uTF1mJZoYLET0aVmf2+F7NKc5rBHb9dQ7mjiaosZW9nNZ4HsTkF9syYmgezlKe5zKwzuJMyveRQyvlUR3MszfJXgKrnZK6dRc4Vb8uSPLc1KJzJvdBfKLjek2VEfvZDRUf+hZnYjiEZxJV4IwqONDO4bxs970nNzAe8NzyxQ7wrXLjyzIARNqAw+WPTU4Aq5UaVjGQmGDBkzg2BrMo99u0dIouBqggSfzm6IHo2oiTrilhzFwwQ7l6xQNRM49LxnSSea4jgyvQlhqaXVlqW35FjNQtPf1cyuBoavnDCZ9tDHlmAlZir0AtgATgtH7oGMkIrvOaaZ9Mm/VFCtdTgMr/BrLcWwJnx1RPaRM4nOywJmZkCLTGD8oiM5M4Q8MP+VDcJVR7ZeMrcCgguvTgl3BuRXp4QvL4mOsvAMrow5HyxTPEionqlgnGDWatp5wspD6eDVy0rg2i6n9389YH42xC8smMjLH33Aa6/fRJUJ+agm3B7gcnFD+0I2CLPvXcjJfZYRjjPqtYAuNc2KYG5EB+bElLSXQQJhvSV5KFiVcku6cckcTBVpVoDDjOJUi5Z0O0ix4y1uu6FZEpd3e7MmsZ56U4oV6UpG2lW5ObcD0Wq5QaQdRoZvC+YlOI3rBdoND05JDm0hxWe52WniBgFTG8LlCrewpA9yAWLPZVTrc+jtRYp9xeKShednNHt9+V6OFXae0CxLPKHySHdubGCnxtzNaTPpaLlBpL8bOX85dBsKMU6N3rLUK6AbcEM5puWdCdNZAXNLdqZkRLxVE+YJ+WNLdbVF15bqsqN4kNCsBtKZxjSK+tkSAPtY4g1V5lGvlkSnOVF9yTjOojzfqiM5TgjXKxY9iURbe+GIo69u0KwJBzM70Z0jWhzeLDck9zOiEhSQnWvajRY9s5TbgfWnTygfrkh6zlxkDgwcepLKBvCZc9zDkXQSt1t07qExLH0lodyInL0cwEB2aFgsRqSl6J6zE4WpFeNXWxZPQfY4ofdQphVNT1zbVJr6akOT6QvTToVEcg7fg/GzCB6o79HnnVu7hWv/7D0OZgMWd1bhLMXlLclMjGD5kaF5YUFoNToJEBXp2wW1Aj+x9PckZajecPQfWKZbH2wB+GEWsKxPf/rT/Jf/5X9Jv9//Oubg/379zM/8zO/SUX39+rAA/A5ddqEw9zNBb+xm4gK9FLA7C/yj3gV3a/piQ3E/FfffeiS9n9GsCHPLzDRJr+XS9xxx7/4GoadBRYpHwlRLeg1tbUlPNSGVHXEMkg3rCqiuNdTroM4S+nua9nAFDJTLEbtW0S6Siw5IO87IHydU1xuUCajTFBWh90BO0WSiqYcIi9Cn9N/RAlz2gtIotwP1ypPIMklRiAOHPrP070uqhVlomuVuvFIp3IMeOiAayCqHviA/wnpDbAz2TG70uoawHiFGst2U6ckqbNe4aYK70pLtWfpfyak2xICh0kAoPItlRfYwxZ5ZQWl0QniAaj1iN0rciqEZF8RrCyzgJwnldtfZq8SskR0aTneX0YOW4SunnN9dQTnpCtq56YrtSHnFE5PA+biP1pHFqyVaR9SZdFV6b+ZUWwlh09HsjkhONW4Y0R7UtYWkO8y1cOKmhmozUDy2mLtD6qckXcTfG+BHgfa0B12nKzlIGY9XJK4rj6RFS72wJKnDn2biTM0CZmdBeNAnreCw3BAu4tUKdZDxlYOnYcWhTxL8XkpxpJhdC1Q7ATs24jj+zZ6M93dahu+l1Mtd53Dg0bXBlpHk3OCGCr/qSA4T2kzc42a5gf2c8oqj6jniwpI/ttQbgWpbgNWh53GtlTF75mjXFfYkgUUOpUIPA6orykIRUEsN8VwKnSfvPUlguqLRM9lojL5cMH5eOmjtcqA4MEye9Re8N1VKvKHfzzE64m9UqNOU/NaE+s4Iu1AsdiA7hWbZk741IG8U5XVP9WJJ8l5OWBYdb7AKXGeU+XJBuRNQKw01KXHoOM+ffBcizU5LsZ9SbkXazZak3+D3e+S7CeNmCTPXMPSUl7x06ndzTBDDiFoYmisN2gZMk+CXHMmtOdVXl9F7+UViRTOQiMCyKtALw9LLp0ymPfIvF7ieOHvbVU9qPfpM3NIH76xjryywOlLPMuJ5SjsSCUI0EbyMpFWnw2tXHflSTRVkQ3g27qMa2YipCNmpoXGCqQkJTA4GKC3ZvvkDcQ33jpUUwU4MR/G33BXrTU//ruhPfRHp3U1YXHVi9JDLoSR95NLhJnZcPxVFT13KOPzkk5FsTyD5urVUl1p8q6gvOd47WqOeZGgF/YeaZjYQ4H2AkCtiq2Ga4BOZCDxBINmFYnHNy3Wr1dQr3wGV03fIeu2112jb9uL332z9Vij0B7E+LAC/Q1ez6skXMm5wI4nK6u1rZkVOPhE9RHaqICbUK+JSdSsOfSSsLYKMu/xpzqO4IuJogM2a8lI3LjoWnH47ENiwmWuSSxWzZ6B3NyE5TERTNorML0uerVtvUaXBjzN69y2L67JjHWzOmbsh2cOUZk1YZNmpJBPMrovA2Z4muI1I/9KMiRuSHQsKpZ0Z7GaJa/sUj6yYUAbQ3GpIJiltH9JJB4ddDgzuGhY7ES5VuP1MRsapR58maK8IG5IwELVcUN0gYqedDmlJkCqxtNJpSgK6ESjsk4xdc27xmw2xM5n4TDosqlYkU8X8pZoQEsI0Q9Wa2Qs1iQ60ZYIOMr4vOuegG4rI3U4Mod9ydjCCUSv6MR3RX+3RO1eU3RiuXYbRlwvOX5ZIqRDEnco4ke6jA1sb7FMz6mHC0tKC8VkfGxTZoaW+1GL6LZ4UVcvIu7wk2ahZvyHsJpJZfGg7x2K8wH+EVAxDzXkuEOg3hmRBxu+L6x5zr09/TxINnugF40mGrcQ9O+9r7EyyoOtVKe5WLo+ZLhXUTuMfi/5QdwDdkIrZBeRm7HNFu9qCE7NRu6xJujFto1LSucJpjTmVRIZkDtWlyOBdi88hP06Y3+jc0XsFLDvcimN9Z8z0C2vELJI+1pR5ZHDXMLuWXcTZJWcaNxCHcDLWDB7B2Ssp9Yp8B00tRen0hjjjszON3yjxiaGZ5/i+l+Sdo4zevqZaS/BrLfZmTQiKyX5PJAs9cZyacwsTg24V+b2U6pJj6U3D+Xc1JAcJ9UrEThWxyikOFNMbcu65nnwf9dQKRgkkm7kqyI9kQ2BmooUkCyR7CWrT0QykcyjflSB6y1bYdoPbKe39FBO7vOVBFMNEUF1nUlBT4zsr9B5rFpe67rWOJMMGbvdpR+KkRkP62oB2FFGjQLXpUFGhlxv8wqInlvJGS3pgZaTdaurHfdKpkrzovVxA4pcbzFF2sfGKStHbV4TjpHO0d473uaJZjthZF0c5ltg9V0DoBey5YX5VEm6yY9lAYiP1lrjQdafNTfYSmksN9igVje9uwuJKkEzfhTiD6y2HLww+D+iZuRipx7cH2ATUjTmzLJcispakomAFco2Rkb5aaghVRsgCvXctzczQrMikwQ8C6d4HqwH8oEbAv9fWL/3SL33D3/9eWx+CoL9Dly67XN2+XFijoeuYSKFQXnKiW/Iy1oxJQC+68WuQ9ARTg64UvV6NLSX/NJ6ngl2YamLuScYyFs46ZIx72Ec1MnJqVyT5Iz3vtsoa8kcp/QcycnQ94dTle5b54yFmpmmfKlGNEs1iC5NnHWHgUV40NspEqndHmIWi2vSYU0vMA+FhX8TvI3EE6hr6Xyyorrb4XmRxSTJyk4m4Mv16g59Z7Eyz8kWDKo0gLYYek3phCDrpJLRLUrwWR1FGeksS9B4bjb6fU217Jq80JE9PQXUO3DPh6lU7nvxE4W5UNEtREkl2BXlhTy35gSHJHe64QCnpLLlepHqukrHdfSsxYFcXxLOU5MhiThI4zrB3CpIFpNOIaSRpIJrI+UsOMzVoGxh9RVy3yURuKNHK6K06LoiNZvGVFdRpAnd7NDcq9NTA45z+XYveqHBDQfvY/ZSmtrTXakgkJm5xVYT+yoq7Ml2rMLWSm5uB+kpLtekodzzJcoWuFZMXHM0zpTi+DzXZmaa9WjO/4klPxUzherFDuETqX1uj/2s94tziegK9DQNPsxThaokutWhUt2rZKKQdG1KLS9ONJJKNKMV0eqbJj6UjNHuqi6rblJHk7LmWaKWLqxuFHlvwiuP9kbhyW0W9JmiTto8UMGNNO4hSFJ9pdK3xvcjkpnx3tJfzv9mRjY9gPCIujwTffWdGHtXzcpOnK2bPU4qliuqowD/ow3IjXWEn48lk3oG2H0aSuQCDmyGo0tAue6KB5nKLK6LoeoNsZKrrDTH3xLUGs5DrAgoxL0VIzxTJTM57PbG46xX6y0NU8/9j7896LdvSNC3wGc3sVr/7bbatt2On8+PuEeERZCaVQBaoVKlS/QKUdykEfyG54R4hccEFcA1I9QuKuwIqKcjI6Nz9uJ/OjvVmu2/WXs1sR1MX37TtmSVIAlWEO35kQzK53H03a6815xzf+L73fV7F7GtFfmzZ/Z8N9s/GTP5pQUgl3cQNI+000mx5Ab6bKJDwvQq7UmAEoYOSbp5ZadITCy8HtPsObtWCBFKwftKKezuJTG4vUa3CLxIBYmv5ucGKuUe1kofbjSP5VkV6f0U3CSRvU/nPpRSV3Syw/KRj/WmDaeU9Hr3WolPe7yiftPiZI/TOZJTkQsd7FdjI4KNrtv/NI8kOPrVEHckPJbsb5N7Lhi3plbj4u0kkJhI1mJ1Ll1fXWoq/7Yaw0WHr/n3SEPKId8IAzbYrmdBMPX7mGD1NUIXkiodWtNW61Vx/5oWxmAf8hiPaIO7n3+WKf0P/PqzfyvrQAfyBLu0Uw8+vCf/TBt1Q8ifdhoc0UD5qSU8SwY3seOyFoEPcQJAbqtMiuJ4FzGbD8vWE6ZEianEId2MpLIsXKc2OnKJ1C7PXcPEPWtR5SlQwemYxLVx/5lBB4cYeN5biVNWakIFaQXPgGdxaUdcJ5nVBN/GQBda3E8gkW7R+0EBl0BeCdWnvteizFL8tztr8XNFsSfKFzzXZpSZdQv5OtHfaQd1a2tsd5tKi1lao/j+ZU4UZ6blBRehMhDcFbiBJBO+RD8kayj3ZwPITi1312cZpHwkWoR0I/BclKQ7mYC1jLGOYTkuW73LJAb4Qx2U38Shv6K4ytn6hmX+SwK0GV2vyQUvyaUX57YzsQtPoglh4wlZLPMkIY0eTaYiW1d1IuhC+4/h7y+qBbAzdKmH5UDaz4d0lzddTQd1cyOGg3YZ211G8kng75qk4nLNAjcBmB/srYlS0r0aYNznxftUH0UuBUz1sobTEoWeQt9RR5AftUEwVZtphnufE1ZBmT2LbQqklA3dHNnNajepF9lELW63ZiIzvLli5Ke1MocedpHg44UZ2wwiH8n7m2xWuM6QXmio30pWpMpotiXTzec9O60eY3USK4ezUSJG571CnCcm4Ib4a4rY6QhbJ7q7wXmN+NZLcbKf64kDkDWbgCNcFcdIRvaKNFtOI6zXOJD2mG0bcw5oi74ivJ9S3O8zQEboMdZhjWwgGbCmd62ARmUJQ1MdDiv01dZliDjN8xk3SBQp8KvFryVIiFU0jf6MaO0KnSE4SiU+sJVUibrSoeSo4nNzKGPRUE6ymvu1oftSQflMIiuTC0GwEkpe5/I6FZvFvlaRfDlg8Esfu+kcN2Ysc3UA3EePFqB+Xmivp0rZOo+43ME+ky/tAEETdbocficEpf5fQPBBsUUSBkU7bZH9JVScybi8c5Zcb6DyiTtIbAHwoxMyiOojXI5otSbKJ6wzlBXcVhw4agbQ3txzVFxXxKoVJR/YiJ3uX0N5viG3P3rswtJse3SrCmVxj65dT6v0EtxEkd3itabY9ulOYS4v9t89ZXQ9xmzKqDiN5jg4PI4tHgnCKSR/L+LJAazF42MsEd78mLBLy7wpJETkf4wdSDKvSUG9GsmFLfQf0lRhJdKNwI0ezoWHaYY8y4a+G/wNgYD6s35v1oQD8gS6/27CcD7CbAhlWezXZ04GMIIaBbhaImTwskr01IShoLFnmcJ3FXyfojRY3T1EG5j+VuKj0wqBbhME2lXixdhKpdwMh1USnGZxo1g8d3aaMBImgJi3Jq5yQgtsUM4eplAjozw1rMxRR/MSTXBvcUOF2O0YbJatOk5ykhLs1qIhzBbERcXjyKiMkkWYjEm7V6IuMMHK4JqHSoklrdgLTX4srUFVGRkdrRfZkQVVm+F7HpxuFvbAStZZE4lYN5xnaQTeC5lENC8kcrnekc6NHHeo0I9svad8MZQTilcSTfT9ieC1do/LVjLjhGGyXrMYj7EpLCsWmbJzdQMbd4TQjv7ekPBnSVhpb9xzHWqFrS1gbRm80S2P7rpAhu9Q0m9LpckXP/UvlfU9WinYjsDodwrYj/yaRHFkNw901dZ1QfRTQNhBLi+okiUN5sOuE7mKCu9MQxx6vQF0I+42dBvOswK+NxOptt8yPJuhZuEHsJKcJ3YbCwE1qRHEmndXFZ+KsjkWf4bvT4pwYkogwPFQs84lw3vIAy4TsQvht3aw/pMxa0UYdDbDrfphhImHWoUqLrhXNjrvJVs3PJOGk3YBmS7rX3UaAoKi3Ir6xJC03iRJ5KiN3Mw03EHS70rhbDfoyFTNRo1AKVC+R6LY7iArVj/18EeE8o8oTUiObuq8NKunlBUWvIxv2TmIHw7ea5U8d9jClMgP5u5Q4bgHIPaNZRTOfSkziJFDvSyEk0GyLDqKTS6402onzPc0d+nZHvAX+bAAeqgJGrwzdWuMTQ70rTn83CJhS3KzFwYrqaIQ+yummomNVDuxRxvBN5OJPhBVJFLNPe6u7AW/rWqNWKdopQorkH5eKzmnpOgYlEXtXCaZVMvZMFO3tlu54LM56BDtDBHW7xjwd0NySfOx83NAMEpI3Gc3t3iR2mYr0ZNuJwQkhDDT7DntpcZuI1KOyuI9L3CJlZ3tJ1SasDyWW0aw1btY7w5dGXMonA4m/XBkZz58mwjJ90tL+D9ukA3EyK69QCyMcv02FCuIeDp0iWiX5zF/EG9B09qygudfQ7IpTPyQy/SALZMcWn0Xqi0I6v0slBqMkokpDt+Wwxxl+FFhve+zz32377MMI+PdrfRgB/0CXmqegI912h+4U8UTE6m7s0RsNZq0ZPk2xFwlta2kWGaE1+OcjwlUqDrxfSepFTAPppCHdLWkOWpodyZB1251s7BHC0AviYGlpN0Sz9p79l14ajA1EQy+MNqIl0hH7+YLwyQqzNIyfWkFb3GqJY4cyQVy+laRNmBc5/jolWSmSSyvjzFzwCP5WQ/q0IOSBya9SkqWS/70IMOoob0XcKMi4aOqlI/hsQpinFC/lVN1sBiluohQrfpHArJWHdxqJrUFvtnQHrYwpskCMkvjgn44IA4G36g4ZfS8UzWZE3VvLab7R8BfTHicTCAc1BCmIQ1/7sNPgvplIwVEEml3Jl1W+T0BRsHwo+bXZ8xzl1E13wRcR88dzTBowS8PmnbkAclcGbKR4k0gcnJENrv1uQrjKSA9T0qcF2Yll8q1smOm1/NyQQOxTD7BBupADj36X09zuBHCtQZ9kqFYRhp5ut8MsrDjHr+2NjlE5KeDK27JJ6q0Geymd6OLbnOxcpAPFqaJ9HwCgID8SjmSzETFPVignRiN7mGHWmvzEiHHlj+aiOVskJFfCX1NeoVp9k6PqhgiUfCrveXYm+cV+EDAnAsWOBtTDNVfnY9n8+/8/ZAHul6L/dIruOsP9aAVri+4UYRBIRi26cGImMDJqnDyVYqc96CRxRYsuLb0Wl/f4hXToQL5+dTcQS0kAGe6tMZOO7Z+cShThRkd6mOJ+PpP0mBZ8n8DT7bU9bkcTRk6kH0l/cFHQnRfYfz6mPRugpi35iUE3mnI/olspWpO5dNWGrw35pbiFy4sBxaEhu9AyAdhyAmQuFddPYPS9pd51uKEYpmhlWxm+04RcDgR2JWP4mAW6z3s397RD79aYUnKcs3NFfdDR3m5RpWX0zPavSXLJQyJMPJ+JQap4nuKejTDHGe2eu/na7ELT3WnQa4MfBHQScJsOgoz+kwuJDhw/tfjzjNH3lvliwHqZM3htCLdq8k+u2bt3CZnH9LFxbLTC6txpSY6THoETMNeW9UN/QxyIRor44p1hfUe6hOmyv4eTSLmnCGm4QQ+FNJIeptLVm3iSpWL/n3nUew3yOLB//0L0uPsBN/bELFIciZTm/X2iM48Kv1tTwYcR8O/X+lAA/oCXOcpQieimhg+vqW859Lgj9Nw497OlMKwWwl1Ta0NxpsiPDab+TYKISj1p6mgWGWYurl9fCM6k+lmJ+mwlhcwosPHoknivQjkRv/thoJsE3FkuObSFZ/Pn0v0KKcS/msKLIYMjeTjrDpI3KYPvMpK3GbpwhIlsLt1GQI866lsOX0gnyE3loTf4SrhZs19Z6dZt9Bu5V0QnXRBTy+jZTkWrE23ELDXNtnR4wm6L8pCfGUkbuTDEIHqebiyidd9qspcZIY8Mvk9hkVDekjFc8cbC0BEeVuhKkawg3K5xdYLqlKBy9gLttie7VcpIc9yRHiVU+5JMkTzPyS4UdqvGrGWD9jm0+x22VEy+1wxfm94FKJ21dttjKkHjlC8nhKuUsNtw+WZG1FHSWmpzk3OcnVl0I12cqCLtlidZA1GxeOKxSwH8hiwSdhtIhZ2nlpbsMCE9t1JclQZ7kQhmaL8mnWvG3yWgpPM4ONQ30WqTx3OSg/XNA151inCZ4YcBNwkiQRhHdK3oRpJskJ+KEae+7Uh/dI3PoakSkqVG/2tzTKMYvVFUDzqUUzc6UlNJTqxyilj0sPFhZPmRXEcxjUQvRUnzoEFvtaiNFlMpxgcLmkc1rjNiBDLcbOz2Wsb12EBxphi+sHRzcU7n5wq7MOhvR4TaSIesVMQsUu2CxOr0RXxQ7D+4oL7Xsr4bWPzdCjd1xEknBdOtivTCMPnW0H47IZ5mXP3ZLu6TUsDfdxu6iRgWQiLswuJEsf1PU/wgUhxr0qP3so5A9iInuTQUbw3r+x57rQmNob4tB6HBsSIkkcl3YjLJjyyrRw5XQLJWJBeW8rHcG8N3ovFMN2pMK3rX/B+ck1wbdCPZw3YhxpvlZ1IwtVuebhRpZ5HsxMLbgmRupDCrpOPe7HpWn7Zgg4w5B04YpEhRXD9siDZSvDO43Q7divxAPVjjph69MsSBxx00Eqn2WnKNsRF9KMaK7FRGu91OR73rRRONcB3D2wHqQlAxobKULyZczEeopSUm4KeO/NucjT9PJLnj8VriNEvJCFeNIvlkgR9IwsviU0c3EYC78tANRYYyOJRs4ffaP3tt8Hdr3qePkAd8Fjn5E9Hdvk86OjmdYu+u8QNBD9lF/9yqRVdqN2v0u5x2+8MI+MP6668PI+Af6NLbNb7OKEYN3VGG/9MNRh3U2wZ/u5FN8mhIHApmww8DequluyjwuYyN7b0V+ucTBsc5yz+E0bfyQCpvhxsIbrshxoL8XYIbRC6Pp5iFQQHNXdHn0WryY4upQHcJzZYimnATIp8sNc1WZPQaRm+h2lGUD2SUlr3oGXZZJGYeYyIhQn6iiVpj/niO+8WMbhJptx3t1BBGPdl/aUivFa5OqG85shOJ+WonhtVjR75dUV9nmGsrNP2VmBy6RUJ6pQXCO2ppk0BsDUpH9FlKNwqEaUfjE/Jj6XK+f736MiG9X1POLPVminmX0008cehpZ4rhW836i5b6XIDN7Vqcx+1ARm+dFbcmLwf4qUdPOtRVgTLSWaj2pHvmhgE3lJg95WUDD3kgP7E0GwFtI8mpFa0cYDdruiqhzEy/8WjSa0V7K5AeJ7TTeGN6aTeCYHe2ArHTjLZKui+nJEtFO4kCvVb0+ciB2BhikPFXN4rQiZMSpQT/stZcv5yhNhviRPRro4MFq1dT4qTDpAF3UuBuNcROEzKLKeV3pReG7k5LucoIE4daCFi5rlLcnZZ6X6Myjz3rO8K7HfZMIM3dnQZ7kqE7eW261vgHNSwT9JW4me1xKn+PjigPy3cTdKnxUxkdDw4lB9m3cpjRrSZ2IntIrxXZiSX5yZz1YAz9dTruE2naaSS5EgNUMtfozjA4jFS7KYvv91C3PGHoYZFI6oMy+JHHXeQwiWJKyiVLt73XEmvLYKFwo4Tk3hqA+EqkE66AZks6TOv7Tsa3GwG7Et5ft+1wEy1g7CjFe3qlpYurEopTzfpubxrrFOmlsDXNg5WMxt/lNBvSvR0camo/hFEkOzOcj8aw7Ri8SGgnfdcqR0bhNhAjuJknuTJ0YzksuQL8dSrPkScrmuscvZSEFlMrzHGKG4WbcbOeJ/ixpzag1kZQLZ0mvh6S1XLQa5VocFUnU4nha0M7kzSV5EK2umKnpH01QvVduWg09vaauh0Sk8jgnaV1VmDVRzn5XEayMoKH6096o07oDWP9wSpMHeVVIXIIBSr3dLtCEzC1wZawftKRfJeKXreQv007cKtEfp8V7WQ3C9ildEQlAhJYJjSdRFOaRuGJpKWm/bTCLxP8OkHdaogXv+P22d9EB+9DB/C3tj50AH+gKx4VRBspz4Z95wHWd/tRyDzBfraQkUbmcTNJxoinGc2ul5zggYw3q7sd5V5k8FUu8Wi3AqEItBse3YJ5l9MeD8h/donbcGRHVnRJW47hrMJcJCSXhm4YqW4FSbnYivgNhxsILqIbyR0//yxy+Yee6kGLWVjhzmX00F0PTuM76R7qf20uoOe/mNFO5fUQFfmZxlxbiYra6MRB6yA9M2RXfZxY5iBCczRA1Yb8VFx6phdTz34tiQrJSuGfjrAvc8ylFfduIZ06tZRNot71dFOJxsv7nN/qcCSxUkbwL5PbS1QjwNr1fU9sDHYh+a75sUCzk2uDKTUhC8K6G4qbNcs7CXk/Tumm8l412x7tFH7maB40JJcyQkxmjXQqrURGNZseP/ZUdx3GBgZPxeShZy2+kHESraabBdrNQLMn49xkJQaamMl4q22tOF0HYi6Kmx3ptfDImHbgFcmVxQ/CjX4nJJFgIpNvZDwak4A2UTboacvycIzebkhyR/JdgWlg8hc52aEkurhRIOSBdtNjD1OybwpxXxae7k6DW6SYaysJFsfCuZx8doE9E10WAZK3GW7sCYl0JJOVQr3NSTdqwiDgc0nX0NsNBEX3aQlO3O7JhRW3+wraDU/+yTV2LZ3z8Uu5XlwBzY5jdTKS0d9IkEbVbpTcYis4JqAfp0eqPcm5trW47fHirA07rRiHliKdKO4s2f77RxJZt1sRK0PxQsw3k6eG+NWY+qwgFHKv+p+sMKXoK5OFwa414zsLkoUYvUY7a4kVGwtAOrmWce7geYofB1YfdbipkzSeVlyzwUb0l2P0YU631wrAuYP1xy35hWCamm1P+i4lO0yo9j26g3i3Il3C+OuEf/3jZ6gon4cbBoZvNc2ek4NBKTDmZp2SHVmSRT8ZSGQSMPtKE7NAdqXY+rmieJ0Qpo7iUEDl23fm+CKQXQo1IL2Uz6ebBobvYPXIERWEO7VoWktF93xMyCJup8PUMDhSdN+PbyLu1l808pkp6fpVt50cjtYGlDi9R08Tkl8OsdeG5EcLAPJXKaoxqM22Jx5EVK/57B5VtBuS87y+56nvN+hRJ6kfJ4rh7hq35QgJN5+hctBsC1g8aigODcXLlPwoIfuTS7p7DdWdjniSYZaG7DBhOKl/+xvN/896rwH8//ffh/XbWR8KwB/o8iMxFxRvhSfXTgP5qSY9E2xKdTjCvstITlI2/sqigmL48BpdarJzDTaQ//dj8iNh3FV7Ad1KzJFd9jFv04jvY7zm5yOK19IFdDOHLg0hKOJ+LZusQ1x+ptfTNJrxp5fYK0u6UOK4ywPJtCEZtSQrhZ5bik/mdHcbkitDemYovpFECv7nGdqJu1mFXme40pSPOkIRmPxpcQN6VVEQGNVepNyLdGWCXfSdsD6T1GcRv0ykMNyQzkuyVKQLEaa/12iZtWb2jYKoYNYyODQyKi0C3Uheiyk1xbFsRtFA9c2M7NygVxa71Dcw15hIUeAHstn6sQjelYc49JhJi/7zCW7sUR0kGzXFiXDmup6nyNLS3pLRYXwzwI88MQ3EsUSo5UcWs9I0RwPqnUB+ZEmeFnIo6LuOUUXR8iF5qe0ssLon4k4/DPB0iPKK5lEtkXzHKe1EIvPSlzmD15Zwt2bv/qW4GFt5s7pJZPXHlXAC84C/zLArRVjJ+1/8vCC8HWAaKO95qt1IsKI/tGXvblxKh1VirhT2PCFWlvTMYCpBbsQen3L5ZiaHnjuedsf/Ztz83uG+Je/vznQlWcRrze27F5gXObZUjP+nAem1bMBuKKO4dtJDnL+c0m048mPD6l6Q9IUtMV4QwCwNyaDDrsWRS5Tif/BG7q38TNNueYEGe9H5pVda3KS7nuRNRsiku9Y9rqiORlz+v27J+1jJWL3+qCYqWDzpP6uFQU1aAQ+/GEqRWQS6DUe74Vm+nvTZuJrulzN52nvF6hM5ALQ7otsdf29IT+XAlc7leo8mYg9KfBqFF9oIsiZZagbfp9TbgfKPSwFx7zoZQ5aie1Rv5PpaPnH8z3/6Kclco4aOOOjHrkGRXhkpRkctdJLmoiKMXopRKJ0rFo8jyYXFp3D+7zQ0n1YoHWm2xVxy9e0mdqVZfNGiu39Bizz0guFpRAeafV0wfikgaAKoaSvswwLWdyJu6uU5mQbMcUp6bkTH68SlnF4rTKXJfjLvpyNQftTi94XRGPJIuoDkUjP4ZYGujBiqrOSAj/6ioNvusGu5d4tnGcmLnFh4Vncj6/MBg+cJulW498B4EInLQYPbcLgi9oewiPpvN9BnKWZhKU5kC1dO4X4+Q7UftvQP66+/PoyAf8BLILl9IbLZwkUuHSKnyM4MzaOG/GnG4nEkjB3rlaQKlFNQqXS6gpVNmQjlPSdi41FHvMjIzxXl7Uh2auimMgpOrzRdULiRp7rOyd5Knq6MBEUTM34B6zuG+XAIo0C0kkM8+dqy+CKiC4d72MDSsnol6BI3jPhxvDmyVHvhJrQdJe5DohQek28NzYb8X6ZW+EwgrtVHDfoqIT1O6Ma9y3HT4Qaa9Mpg51JU2EoKkm5DyThw6CEP6JME3cLlzxx66DBvctYP+1H1iaXZ79CVjMabLTFlhMKjnKY4Es2Vqa0I7RMxsIReG5YsNK0N2P2KzhfopWWwtabcFv1SNw1oL/m1plbgNNNvDes7kVBr/FhYidlxIsXLTIqAaCPFbknbJOjXgrRwPWLCXon+Kr0yEORnBxsJI0kz0Sqij3pdUeZRy4Ts0hBMJOx1FC8yytvS6Xq8f8ar/89dEtODwQ1gIuZdTlr2JhoL7mFN9lyK+PV9LyNSYPKNYfGzBmUD6kWOKRXhLBUH9iTgpx350xy31VG8SkmWAvZud6Wba9d9xxgwa9FFhRRUJzrE7FJG+irA0dlUZAIYjr/ZJfS6qZXuHaNrRbsZaPckGUZ1ChcFOtxuShHpxx6SgI9WcEEHFfHNANVjknQnmq7yQUd+mEiH8xvpGq/veXETFxFTisGkvd2RnCQy7jvPiKkYklSjSc4N3d0GnQSq/cDkO8PqgcDRtYlET8+DQ1JM5vJYt5WiPBCtYH23xRYO7zSxEo1v/nBNWWSEopMi02uqOx5dK0gDd7bmnKxG+AKSWY3vDI1JsWu5fpPnBflSUd4VfI3P5f21K3Gq5ici++jGkqwSTodEK5rF5kCiE11nUJ3GLjU+jdS7gtF5ry/0I0nOSN5kkuiy1oQd+d7826y/x1PpaN/qJSdeHNAhDdi1od731Hv9c8JEVJTnX3lHOn12IQWfuUyEA5lDeqnRpVyb7f0G+y5j9WKKjXKYppNDsuvdzNWu3O/+UU10Cn2VMvi+6FFRiuRUUEvKy/2XXmnoFONXipAkrO4GuSa9vPe+VvK3DiCZtIRxh1sLW9RWuv98Jb4wJpGmEH21jr/jLuCHEfDv1fpwXPiBLl1pzEaD22kJeSSWlnpPHngUnmbPYzNH9UDMBXjF6M8KcaoGRVxbrn/aSipHJYaJZC5FUmhlRFUeBGylqG87GYfe7iRG7qCWDlsrGbp+6mScWMmJfPkQTKPInuXiwOy7Z90IzNxiX+ToCzkRJ0tFfUtGa3GzJT8Vs0g0olWr97zAh1vwM8fm/jUgD+l2UxiF8hrFVReNjH3iyAuAt9GiC9z2uJmnftDSbIg4O5oohZVT0EmiSkhg8DLBvsrJP5uDidhJixsGkkuLqaQD1G14cT72rrzlH9UMd0rpIDyoBenhFX6/Jb+1xn+2Ru3WuMYSR45koUj+nzOKU01xaGX8eJoRH0lXRuWecl/0eDGJqEJgv82dVqDPV1KsUXi6Vrp+3X4rhpaZJ2622Fo2QuWk8zB8p/FTj2oM5jBj+FcFhD6OrzF9NwT8IKLWltU9gX0HC98f7uAzYZslC0XMPdmZIdgIP17Sbov4Xh1nN0kLuu4hvSMpDmNtiE5AyvVBh5/4G9BuLCWWb/AsxWfiTq5ueXThGO2uSedSFBdHtuetSRFmS0m0IUqB4m61mLe5mA1aRRhIp9wsTc+Q7IHhV4Z8VgvX8T195Ujeg8GDheTpplJgZ+caDnPcxEtxDXT3G+o9T3IpyTrtBJoNGeuZWpMspUhRQQDBtFrkEbuB7FwzeGPFgZ3JNaeuUkInBhdfSAEabtfwukC38jfq/gAwOFb4Ww31QYe5VZKf8ptNdZGgGk2746hfjIm1gW9GDL4WmYNdSHSZLRzPX+1SHnhcLt9s3uUCXbeCq4laurwxlfssP++d15+s6KaSOlPdkkJ2WMhotNt25GeagzuXJOMW8ybvIwkD4X5N87Am5KJzJQpGyheShaxrkWaYowxlpAtb7Yd/Ad4smCeVecKDCrPuYwBLDVEJqLvWTKclzW6fVNLJexcN+K1OOpg7Dd1UCufhW02SO6KVOEN3v5YCNPOoy5Rumd0AromglNACAOrdSHu/YX0QpOO/IYdF+p8/+0XC/Kcd6z8pCRMn6Sa17q8rQWJxnRDeFfjLDHNtiDZw8X+pCYX87J1PzsErzGaDLRzqsPhb3ln+1UvF+Dfy78P67awPHcAf6CqONFQ5OpURqM8huZbAetYp+ali3RWYnYZkmWCrhOWjICHzhzJyUu59JwHqNKKWYFoF5wnuKMEY6UwVb2yPeJHCR12lxKkHK/yrdiyuT90KGmX1WYsfyeiMnmqfrKRIs0s5k2x+esF8WRCjQp8U2ErhGollUrdrwbccSVHQbEo3JH+TEP9yi9W/Ud4IzLuxjFmJwLUYCJYPA8lJgn2ypH05IqQS9WYqRX4h/392rgm5Jj+RzGTvFMlS000C1SctsdG0VwMZE70qiPdqurUVrd+eR60N7X6HSgPmMCM5zlFVjp1GXBSmWfHOop0l2AzGEftoRXxR3ETgVbuihYpaUBC60uRFSxsHxD6qb/hOU29HnBc9lSoNzUc1XCeoheT08mBNO5O4P1/Iz9FzeQ1u2I+ta4XLJcJPt9JdW98LqN2a/L8vKO9A3Ogom5SQiDvcZ7GPOQPvtehLO4X6ZEX+6zHdJDB8o1mrIdqA3ahR7xKUUzS7nlh4hl+mlPvSGbFzg9uWwszbiF5YwXZca5LPF3QbhsYZ0qyj6Qz25QAXU9aLlGwsxUI3jOjdmnCS003k0GFaSeTIzzW1snRTj1pb3FaHXloZ4XqwJxY3CqgrMQX5d0PYaNHnKaaGet9jdypWp0PS3Yr2KseWmuq2pzg0xGtNfh5ZPgT7NiNZKskNTsUQlZyKvlF3kM0j1WcN6kJyuLMTS7vpiXmgTsTtmiz7bvvSYJeKNrei15tEua4STzfzfaaxh7EjfZWxuivcRGzAd4b13YhqDGEpoGo/EPahXWl0J93OqCUD19tIvikuaHue9PKJgH05BCA5l4I2ZFJQpwuNLg3NthcN31qhfjVGjyPt5yXxPJP4tJ9vooqIvbTUu4HDpzuYSuO2HbpwxLUlnGfEgUf1z5xkYQhGunbtTEbT3aZ0ZSd/WVDuR/JzTXW3kxSjUcCuE7xL8XmAjY7sXUqz58gPLfUth+oUVZOQzA0RKM7kuk9WMHgqLmC/TFBZpNn1mMbQXuYwc6jKEFYJyZXB35FOm6o1ozcSQ7i6C/pZgX1coc6s6JYri7JilqlNQnapqHYkgaTZFMagndZQKHSXYirIflHgC2jocVRjjxo4YpeK67fVMjFIDfNVgWkUSeoIX43Ry+a3u9F8WL/X60MH8Ae6QiKFgy0Vw7f0OA7Z6LvdjnaKdEne5LSzSDMTfARAu+V/w4HLJPZq4xeCxNB90pCp5YEsTLuIH/Q/fyXpGbrSqEqyRfGKZN4XeTsRfS2dhvRKTvQhDbQbMlrrZtJxvPx6i26ey4NtKBiP4rUw47JfDtCtpnrYSnekF7d340i9qQgXmZzsjejJbliGVzLq8QPRr1VXhZyytxt8GsnPYP4jh+5UD7aWWLZwXx72UUfCwBNLQ3pusScpg1dWhOZekZzLeJdaOg0qCeRPc/JPrqn3PNWtQEwk63f0RmNr6TwAdLsdzbVw/QCWT2SkG1IZgaYXRtiFX80kx/Y4odvtWN8TTtn72LJYSNfArjSmkmsgBHmfMdIZsyuF2+lENxWg2+mkkP6oRnfyO7WTUZ56XVBvy/hTXyTo95SJINfCnSenNPsOFpb8bYKZdnStlZ/tBOkCEPJAd1JIPNzDhqjlOkCBfbyimf3mvXC3WmhEuvA+Gq5c5LQXOeEixX87Jr4Z0G079FYLXjSc3ZYjpBF/nokuMPSjZ2D1UBAw6Vxc07rS2POE9FqL+7JSUvRNHaNPrvBFEPbkSYYvAiFDkB5PhzKiey0mAOVk9BYyqPc89ZbIH0LWd6eyiK0AJ/eLXSvaDc/6jnQ8daPI9kq6ibhCR98mZKeG6tOa688d8UoKRO2kAzZ8KQai/GkGL4Z9zi7EPKD6EaYwJ4V/qI9y6XBl/qbTFfuuJyoyeKfIj+TeTq+Et+lejMh+OSCdK/Qj4S7ataLbEY2jf1zhJnIh5Oe98mLaialrIONbVMQ8LzC1Jg49zR3p3rupJGzYlYzm9VpQS8mVJT/X2EtLtJHVpy1uHBi+VRQnmviTJSERTaBdiC7UbTqqA4cZdfLeHmUk15K7nMwa0nErwOj+OtK1JuaBtk5wo4Db7gQsPo1c/6Slm0b8zN1gVuxSU37a3HQWtQO0SGK2NlcC2r7W1FtSzJkWuntiUAr3a8y4Qw+7f+mg1U0i2fNMHM27nrjdMhnKgbbZkuul/ZMVzSeC0tIdJFeG7JlIQeLY9aBuAaj7FyMBRa8k9zib/y1tKH/d9b+H9fev+vdh/VbWhwLwB7qilodOedexeCy6Jt2B/2wFtaApshPRAulOUZwqmm1xhepKs3rSCWz4XNAW8x8FfBrFOffxnHYacB+XtLc6gS5PvUCmW0UcO/JzgbuGocdeG9pbHeXnNX4oubrptTzEtn4JybUgKcYHC1Theg2VIj0zJP/9FFUJZ62dBfzdGp9KMZacJnSbTroqww7tofqoYfTCMHxt+sQOwUqkZ1a+f9MxeG1pJxFVaXAKTjOSZZ+tOZZiKAw82ZkYVIqfyykbhRhHlhKJNziWAHk/khSNZCVpB7rVpAslLtQiUr6cEIeSLxv6rujiJw3LTzuyKxmn5S9T0hNL97hifRBIrjTNF7IJoKTAiUZ+p25Fb4kTxEezLePm5MKCk4LGF1ESA2xEv5ZRbvYmRZeads+RvU0p7znJyr2WTGRtAz6H8nErhec04PdbygddP5bW0qXKws1h4vB8JgkxV0bGw7UhXon5xudS/MQ9YQnGkcfmDmpDemkEnmwg/R/HdGPZpPPXKcnbFN1qZg/mMlKMkI8a7MKQLOXAoUQVQPBS4KuIOEmXmvzMoDbam4g0kJF+ci3vXTKXEazba0W35hX5eZRovtKwXOfkx1Y0W/fWFLulvP8Kuo1A+i6V4qhFGIZLubaYdDTbYgyJew35uTDg1g87VC2av3YaMNOO7FIi7dyWoymFpag2W6r9QPOoFmfz0Ml49VZJfb8lu9BUewG31+KGMhZN5r2eTkeKU41dSYYu9BzOQZDiZ9l3U5eK4pW8fknikUOALeU+TxcKN3N0o0h1x9M1knv7vltf73rU2+KGY1feimBFYhKmjumjK+qdQNjuyL6Y4/OAubIMnqU0W5Hx/hI38eRnitELKfL1aYpPI+1MRsnZucFcJAxfiiPY1FBfFJhSkc0VpoXVA4+5NjLKf1ng9lu6LUf5pIX7JbsbS2KQmDkzt1RfVOi9muJVgn2bEYeO7FA6cqNXCjtw8vMPE7pxxE0D3cyTDoRlqDYbmSQEhamh/Kc76EbG+PVewK7FLW5OMjZuX+Nrg34tWlfVyUEk60fk7WaQhCQFxdf917QibfEbjrZMiBfyTBociSGp2QzYg7LvigvD0g+9FKELhT1Oyc4F5fO7XB9cwL9f68MI+Ae63CiS1OAm8tCv7jjanYhqLcnc0G4FzEqz8RWsb0tBURxpVk88YeIwV8lNwLtycmL3eWT10xr1Yiqjn8sB7p6jfNShKo3Zbuhyg+qZWdGAqgQabS8TCVA/l5Hq+oFj8Mpy9Vmkm3oGe2vK72aovkBywyjomoPI5HvD4iOPbjRaR5pHDTZzxNcDwY/kkRj6tIH7jtUDQdGg+tHmIBLSSH6mabY85ccN419lBGvEgXo70uwIO204qlmUFmzskxakyBu91rQz4ae5iWf40tJsiEMUACWCf5V5WCa004DvNYbZfklzOBS38pWmetCRvUlp77XM/04L1wnJSiK9uEqJg4BTmsEvClafttjcSVTfacb1px7TIylUp1AbLfo4o5sFiVp722ew9pFsIX2vEwyohUHtNWSJww2djB/TiB8F9EGJv8xpPq7Q5xnByOeuT1PcVkf2NiVdQLCG7l5DcijXh32W004jw3eR1T2kczfw2EOJQlt+2kGnMVfCaPNDw/CtALHHLzSrB9L1SC8FeC3ObukYAqweO8zSUL8bQRGxV5qQetK5xS8No68SlIPrHznIPMX3GdWDTlI9poH8xPSxdoJBiqm8T9FG0ncpwUqkXzuzUtyXmm6eEnYkLcR3hvh6iEFcvRw0xK2I/34gndhrTXe3IVbijA95IM48cW3xaSQ8rFBXGSrIoaf+vMYvE5Yfd6IP9YrB1znrRx1m1OGLyGDcUEZFXFmKI0vlFfmJpd734mBdJHR7HX4g41iCXOshEe2rrsWlHpNI1JH2QSNaPxPRjVxr5R3H8JUVuHY/vkWLqUA1IifAaey7nOJExqT2Uu41dW+NPhr0bmmBdw9fGxY/bll8t0HUYE5T4rQS93sHzbZcj+0vZ6j7Det7MikojgzBIkVqB8vPup6dp7BrWD4OvZkN2jst3VoiG1GikVOJFI3hIkEflHRXOfF0wMW3Q7qDjqQTePVinBALBxNBwCS5o5skNDseVXisFi0hWlz4bLXETuNfjoQ9mkp7WnlFNw24keqTlSIxCzSbinynom2HzF/OoAjkn80pn03FRb7hqdPI6O6C5eGY9MpC0FS3POXpBBNAH5TwZiid79sldZLjP22ItWzTrjU3zxufg4oyzdFLy+iVZvGjlngRfltbzIf1A1gfOoA/0KWCPNhRIrCffGtRaWA0qSTGaalJFtI50U42yGYz8ve++B5VS8i8228lpujJQlI8bJRRhIJmx9NsCeTXXlnSuSbNOpSJqKsEuxIGWnplmH5jyC6kGFk/kKSM7MRS7wXSRW8EaCXzMln2o6EOpk8hv1QCSU3jTeyRTj3dQhyiyUIRdURdpAxOIu48Jw5748ZWg9tv8VMnKJt7YkJI30ruWrMRhY04c6igxN0YFfbSQp9i4sbyO1cPAu3jSlyW1z3QdiXFdeydvLfvXRD7rtz0O2G6ZeeG7vWwx8JAfcujKi0xXZ2WjXnW0u51PWJHy1hZQ7Ur7D5XyQaQLjTJRkOyUhSnYtYJpcVPRdAeRl40nkpMNt1YYt+E+2Zw44B9ljMbCTxW9WPF7MTizgrhtSF/U7vrUF42ZVUbsrnAffMfzwEZSyeL/rMpPFc/Endydi68uvrziuZ2x2h7zWhWySHgsYwT691AdiFO0pBJvq7PQDc9Ly+VTtHy602SWY1puIkcdEUUg4SRcW43ECxKcildo3o3yPV4LQy591DzbhJlPI5cS2Zt6GZ9cZEEytuCAnHjAIX/DfbnbY7PA1H18XjXKe48J7/opRTbol/TpZHR+8KQFB3D58Ij9OeZjIgLT3nHMfiliPT10EEiyTbrBxJT5irJLy7fjmDdH9Sm4cYQEU1EnWQUhxadekwlhZ6dW+yVdLX1QXUj1TClJEvE0jL9MmH0bUo2V5T3HMWRpZ1G6j0nEOUNjxvJZ6prLYghIL2SWL7qQDr82e014d1AiiQV5TrWYnjA6ZuOrR8GqmcT6erVInuIRg6G9iiTQkZBvRtwI3kurO/KZzc41LjdlvkftfiRaBw3bl+jSktMAn4sWmF7ZTEnGdlcXPTvR+KmlevEXljKO/JzMWLOiHdqsjcpKJFCqIEj1gZXJ9IFTaE41ejDHLUWecfwjeBg1KbIDUytGRxpQiZaWHpzWfdqiG4Uo1cGe2VZr3L8UMwwk68t2X6J+/MN4X3aKKk0WcCkAt92JwNxl+80xJdDlFOEwwJzmUgyUuaIBzVu4oVMEEX6kSyU6CFfp9JZ/F2uDyPg36v1oQD8gS71aIVuNcpJKoNuIXmbsno9QQ0cbsOhf3bN5Y9g9XFHuS/cu28udmHckV4rYqPRjYY/n0pR1p+8Vdfr6jQ35HtTKeoqpfg6x64U5Z2AXRrcKFDvAArSuRZX32VGs+eISQ8jBvxRweBQjAU+F6xEN1A0f7gWd/Ba8lyzXwwkD/hK4MTtFyVMO2ISWd2RTuXg+5R0oci+LdBJuNFf6ZVkg7a7simoHr+SXAgrDyXjWu3BjDrJ2gyQXstobfSXBXanwg9EG7h65CjvO+JGh9/qOP5mV1IKBqLxsiuNqaVT0WwFSShwPeg1QPE6kWKvMSTD7qZ4Kx9JxnI61wLE/YuU5HlOOxM4dzcJrB45bKUwC4O9sIxfarb+VBI0oo6oh2vpwDolgOFGkVxK0XV6PsEujGS1jpwUEgp0qQXtca5QjcbPnLxvpWbx45bqtmdxOEbbiN/sqB43uIMGVWtMLV1mELNEWCakJwnNt1PWb8YkC0P7ZgjbDbqTTXr5eUtMhS+Zn/VjbaSrupoPcJuO+GpIu98Rxo7sXEa3UQviRLteW+UUbhwkH3i36ru9kWKzwg0j7nYrTmCvKA4t+ZFl+E717lBQa8n2re44opURb3Ktse8yAYeXmuFbJQk454b82Ii2sUfpCIZGQNPJQtFeZ4z/wQmr+4HiyJCcJGKImrU0mxGz0tikz4FdJRTv5PerUjbv4Vtxewo2RJzk9d2O4p1gZNwoorSkewyOFH5PxpN+o6NbpNL5TcBNA+lhwvCFZX0nsvqkxRXCLPS5OGdNqUkP1phZK7Dmgj5RRpzR5YGn2fbiMl0ZmqPBjbb4vSwijDzttmjnYh91Z7cq0msx4KzuBsKjSlh2Mylc0qveuVwr8vMeht2nY7TTyGBSQ1CMniY0W4H565kYyUqDajX+bi3FagvLBz0s+VQxeGtodkTDHNJIcWThVi3A7TcJvhZncDfPJX97lQj+6SQVY8vI007leTl8bciuBPgdhh77Mie7EMzT+iCIOWmhUGsr0ZhOxuc+FfZk6LRE4R10hASaMqE6cPiRSC3yc43KPfFUtIuT7zW2Hw2rALEI+M0OvyHRl/7liDBPIQmMbq0wa9GzdqN4o8m0v2MKzIcR8O/X+lAA/kBXe1WgPEy/kpi35b9e0d7usKVGXabYawv/fApAcmZp9x3lxw3d/3sLFsIjMyOH326p7ngZJxkIn6/Y//EJybUmP9X4ccC0UH9eES8ygUM/qvuvj2z9QjAuPv2Nzqh4J1DnZK6xy/7hPwi0f7hCIcWkH0RWDwPq+0EPkVbUf1Ay+rdOUZ0iuVZSwJzmTP48Z/DO0I2lMKt3A+1UdDMc5qRXBjeQEbCpBAlhaoUtFc1muDGLdGMZ+bZbHvOiEANF2neQZoF2DEQlo74iMHpuyQ8txbcZ+Yusdzor9LWl3ZB82/V9J52KSgqc2KcDoCBZyGiMoHBnObPHlww/v0LVmrjVYivwf3fB+m6UyC4dCSe5FHad6jd9J6PFrcjF32+xNTS3HO1VLt2FVoot3UJ7u6PbdJijDN3C+k5AryyD/9O54Hhmjqgji8eBzS81emWpn9T4qTggN3+hSS8Nfm1RK0tykjL8VS4pGHcr0eIFRXYq49BuHAgG8hPTA4U1XGQQJKXBXFtU7glPSsqfVnR7nXSWDipikII9arAXCYPn4sRttgLVHUd14AUD0ynChnRPy9uB8HZAslBc/dRL6sPEERuJvVOtGGOax7UgdCby/dpBdqVJrgy6NLiRZD+bRrE+COSnisVnjuzuCp8LczJZQv4mlQ6ijbSbgoBpPq1Qteb8y13GLwVM3s08+ZsU/bygm3j8MBC8Bie/sxtGBm8NTDuCRTp5Zc+kLBXZhaZ4lYjUofDSmb9IscOOZisSK0vc6EjOEkbfJ3RTT/54QbZb0t0RNl52qUjOEpotj4pi9IpZIO431KsU9bqQe0NLZF51xxH3a5KlxpQaNxdygArSjQxWzDl+GET2YETzqUvp4HfXGfW2wLTjZoebpwzfyHbjBpH680rG/eNAO400W9LpSz9aYGpF/WaMXhmqvR4zs5DvDUUQPe1hLlGFVu7RZs+x+FyMKPZamJaDIzmAxeOcbLOi/bjq0TLC7jM1xFQkCMlSUd9tMSuDmwS6jUAzi1S3Pd0kolpNSKG+2+Lv1MSBmGqqjxpiIRMH7cEsbC9fiWRvUvLXKfZcqArRaYZ7EuHnR556Ww6/YehpNyKLjwJuy6Hf5iRLxfCZUAsIED5e42YOu9Doa4v/yxmjV9KmVhHMVKY15YPut7K/fFg/jPWhAPyBrvTSkCw0y4d95uSbHLy6idgaHKleG9XreJYGVVo5FUdxyioVMRcJ+ZERBpoH88sRx1/u0Ry0og/sRBOT5kL6dzsST5QfCl9u/gmk57Kptn+wIhRy+m0POtr9Du0VzaYnO7HYX43ILsV9/B7y/H5s5EYe32lO3m2gO0X1SSNF5UrRjaE88GT3VujthjCUqLSQCyRVt4ow9pSPW/ymRAaYWrJmsRG/30jkUiopJmbSd0MGkVD0G1slfDp3IW68/NgyPBL2WLMl/2nvr2RkPhdxe73r0bUmP5OCttvrJDFhragOPO1MNIrZqaSSXF2MWTybAVB8k2NqqK4KQV+0ApNOlgrdj1pDImMfW8HoDRTPMppZZOMv+pHxhaHbdNLhvSvGj+TCCjNPIQVxqbh4tSGauwBdDzp2hSK91NjDjPG3CdFE1nfELFRsVtKl8OCGYKYd/jpFNX0Cxk+v5TVeGkwrondzu5QuYz8yJOm1ShHCYUFYJ9BouZa+HjD7C4nd8yOPLwL1ZxXpQty6ZmUYPRM9VLPrUDoKrFrB6I3qM3gl0iw9TEgurQBzR471fY99l+FmXjphw0BII/WOFxbdxBFnnTjB7zek15rlE+ECti8l8q3Z8tTbkXrXYZdaWH9ROrv2hUgk7Fp0c+mJRQ0czYbEGmZnltELi/12QHom7ls/lHtCqdh3tVR/DUvBYRrpqnZTQfmgxCSlXhVEBdmJ7WUXivUDj5l2VGVKfVHAyt6gZ5Jr0T7qBinSKzHsqNJKFFkWUbf6gr9VZEUnhU2LjIxnvRlEXoLcW6VGadHOhSL2zxZNem6Jmejz9HkCWvScyZXB7NSETpiKyXYlf5cWHWB5XVDf7QhFX9wPQj9mlfxvMi85ztsdMZV7KWaSZ26vrGCfski3I8WxzyCMPd27Ien3BYNnktMcRx5XIM75VO7x/HUqDuahI5lrui3H7Y/OCP1h1g96k9y7nOzI4gdRElK8PEvbLY+fOrqp6B19LoxLAthSQNPdryekF4bxUysSi/NUDEIzeX7qVX9NFJFmQ3ibyVmCPxxQvElI54qYiRmv2YB4qxZMzNue/6d/x+2zDyPg36v1oQD8gS67UjectmiijB8rEV6rgWN1L0i8WwfFiWb0RsZhs3/zGN1opl8bXCNdiMFJpLonkNRmJxCtPJhCLgXge21VemIl0H3oOfi33zA4UjfaH7fVCTR1IWDX/FUKjYyn48ALMLon5SerPttz/h6O6yRV4CQjObP4sScpOqqHba8Zkwiz6qKQ7lQnl7VupRBL/vCK7DCRh/dVgnIa1Qn4lVGHOcwIDyro8zn9IkV3im7TE410Dv2GuIPVTDoqpoarz4SBF/JIsoTgJerJDUQbpBvR5c2eiUYve5uga3HhDl4b3DAy+emFAKtXmvxZxvCtIFt0KxxAbKB4LdqydirjcdPKiCkmEg/VDaHcFxf3F3//e+Y/DiSzmvZWJw7GRou7OYskK+ka6EZJ5vAkMH5q0O8jpIYClHaFFLZuFFh+KjiLet+xuivIiZh72i0vXci15ObmZxq7hnKRM34hhptg5TCxv7GknQXJVy4iKgn4IsAqYfD4GjtpRbu41pgGTC2dKLs0ggxxmqufBLpbrUCIrUQKpheG8V/mbPxCMp3Xt2WG5EZSlMceE6Q6RXKSyGh3LTq3sNsIhFlDfiobOU6xvbMQHV0iznddadxE0lVCERi9lA5TvlOx9Yenco+8MQyOFe3tjmy/FA3tQIDVKBlZm17jWG+JxrWbRNTdkmS3otn2DH5Z0G2EfhxvJFrQRtYPHQ8/PUJNW4qdEmUi2YXcr6ZVvUM8iiEhDag3OfbVb9ylzaY4wsuPWrkOUilMTCVpE3ah0Ss54KlXBZOvEjlgfDVmeChoF9V34ZWX7rm7J10w04A6zihepIyeGZRXjN5IcW+W4uJVTkHmMe+Lm06TnCS4mSe+GJLtlahZy/qhw1xI2oUqBMqNk+eHXSvRDB8KFseeyWc5fCcdP1vJ+DUMPGHqyCeCdqr3PMNn4rKu78k0I6Q9guigwqyFBHD77xxS323p7koaTTRQbFWczkeEBEYv5FClOn0z7g15uIna9KNA8c6K0abWdCMpTv2WUBIWjwWI3+54xs97HE8qBTcadOGk+7pX40cydtcP1qi2H2WfamwpJh3GHesHkts9/ucFk68T0RTer0mPk9/SDvO/vD6MgH+/1ocC8Ae62o0oIvQiUry1FKeqd7hJAkdxrMkuxJGbLqCZiWbt6OkOxZlCd5H0rYTPd0N183OjEaJ9firCaH13jV5Y4tcjunuNnNwHjue/OGD1s4pmM9BteCm+1oLiiFqcqbrRgoUZiNaMXltDkFQDAoSxGDR8f+p1kyCjPBPQqcdvdbJRaBm/qMyjK0EnKI+Mhn++IVFcT610DJJAN43Y84TimxwVIU3FFTt6mqBLLZq9gQBktQOcRFApJa9p/dChG3EYJ3PN6qGnW6bSQdMwfdqPy2zk7f/NM3gyJ/vJnK1fCI+v/kkFwOXTTYYvDd1MdILVvuBG1nd7V2JjxGzTqJscZZ9KhyC5NvihF47jlkc5+KtfPZSubJmK89ZEGDlCbQj3xCCQLrnBrqSXuo8VA5UF1FzMIfVPS5KVFPfJmWXjfxA0SkyidHSiklQYSw/3lde/fuihNpS3IvmxmImSjZo3h5uYSkbuk+81sZKNM7nSlN9PcYtUMoXTSLUbuPxpIGRiYum2nej0OtFbBSsxcqYWB67P4fpJpPlxKfnEcw1KOjLdXisC+6nH1Ko3RwXirBN0yUCK/G4sXeHh84Tzl5viWP91QbvtiZudFErnCdmJpdqPZLfX1NcZJyczXBFZP2lZ/biBThOCkpFwXwRwLWNZ06gbk0v1SY0tFd11xmggGrV2Ehm+NjS7njDtBPdTG+y14dXPb5N9W9A9H5MXgoEJaaQbCncvZnK943ueYRYh89IFv1UL7mduJc1Dg9qv8fst2VzRbTuiiYTtFp+JMSqkkWbHc/2xaH+7XhcXBkHQN+9ylO6L3EwMDfWufO3p/7UlWBk7Rxvx+y23b11JtyqLxLWl228xa0038bTvhsSrVMazK3kOxN6EEtOAbhT1jieMBfmUXkkR5lNYPAQ39TQP5T1UrXxO9Twnu9DorYZ2KpSAwdOUWHj8WK4F86IgnYu2+OR6jF5Z1EVKWImz3H8zxv5qxPZfiv5TdXJtpXNNNofhSxn3qkZG524QxQTnIT/V6BbMpcWN5RCpMi9Slh1FsyVwaPN0IDrMpwVqbXDXKXbakp1Z/JtB/8yFZiYHh2gig5FwNJuPKxZPPItP+ojOo/xGR/thfVh/nfWhAPyBLrfd0mz0o86ZdMkGb8zNCCuk4vqNGsr9SLshqQJq1rL6rGH+b9RoLwLoxacOUhkdoyF5k9KNI/m5ovjTkbDo9hyjSSUjlctMOijvctR+jZ20ZGeG7ELTzkTYHq10MSbfGuJpjnagvYw3tJfRaH3LkR4nqB590OzJg44I7rsxxgZJdBj1ucWZJCBEA2jR2awfyPitvOso9yLtlpec2x6YX95zdLsd1SInbrV0I4hbwlzjOqGdBapdGXXF0gp+JQ1gQ9/RgfxCMXhnULUmvejTEEJvDDhJUGmg/eWM5dsJy3sKvbD4lQjHAVZPOil6G9F8iQBcYMLpRk3Y6Gj2ROv3XmsESKJFpxm+UwK5dr/p7tF39OLKMt1Yo9JAuJAYtvnPGtmkLsQgondrcTQeizs6WSv8MsGnEs0X0sjlHwbyP7yUojyPqNIQE7m+kqVCN70DdNKSXojmsjoQU0jyixH2JBVsysGa9YEUJ/mRHEz8QJyu6Vw2Tr8lo0e0dKnytwm6EqZkcWsl186JQJiTNxluGGG3wa8TgWtrOcwkc+nq1XuC+Gn2vGzYuzWxNGIAabT8rijXXDuNqFZhd2pCisTw5R1u7HGbjmbXSfRYZzBzuR5MozC5J3ZiJPKvh9JhHPibJBPVCr9Q79RynSLwbz3quHq1QUzEcV7tBvCgFgkhl8/fjcXxW93rcCNPOS9od3vzDlIg2LkVwPLC3PwttBoKT/q0wPTu+gj4jU5uo55plx5bdKckXWarlbF8inS2huGG8+kH0mGMSpzlLKTrSwS7VHQzz+i1oINsqW5c3rHRHH+9y2y6BhMl/u46ke9NpLto1hp7bWi2JCFmtLuWiMc+9zYOPfYiYfhGDlb5mcGPPf6xdPH0aQa571+7JI60GwGOBGOjFslNZ0lXcn36VDqy7SQSfj1h+FqTnWtUIYVmfFzKOLKXLqR31rSbHt/LZMpbQbiLIye6XQPZC8knDqkcslDQbTmZChxJ59wNItmldPZCGsku5CC5/fgSgPyvBrQb/gZYn1xr3E5/0J12VK/G0rk+y1AbLem5Yfxci7HmbvU3uo/8714fRsC/V+tDAfgDXUnmpLM1F8NCvS2jJFNqYg9djUnETT3ttmf0ysimVVtMGuA8o77fiAvy2mBPUrITi1lq0qV0VNqNSL0Zb6KsyqczuaKsCP7VgzX6dYGrxbHbjeWB57Y7kvtrySaOYvpQTtFseZJLgd3atejstBOzh10rxt8lojHbbOlutYSoKA4F+uw2O4pj2fz0Xg0e8s2azb80ZLfXgnvoI8zsoWxQukUeNkEx2igli9ZKh6LtuXppn40ahnJ6D+cZTw5Oyd6lNzFz1Y5s3OmVoTnoCJ+vuPi7HVt/75iQRilUg2Lz51pi1PZLkivJ/Yw2kh0n6EZLZvG+dDzRET/x+LcD6CRZpNkOjF8JuFsQIGD3SsrbYlpwQ0kMCT0IOL3U5DsVi2VB8kbSMdxBQ/4qQzsR8lePWux30oVQQXR97USMKdoJSmT8ZE6+t+b6eiCFxVjyo0MvwK9veTioCINA8iIXB3kSJfvZiCknfbKQjfDNUArY2lDfb1ndl2JITVrqXQh/tIRKPgc7t+QX6kZvNv4moXk+IZ1rmo9q4d7tyPsVGoNeGVaPHO2OFEfZhcJcJQz31uiDkuRSUxxp9KsC0kCyMDfJK/FuTXOrEx1gEUh/MSS9luLKnRfSWZpL1zIMPW6V4GfSNVYeku8K0hNL8SZh60v5msneiup2n8BRyLh8MqpILw2xMYIPucjQtZJYw0wQJ9HKiNhUirZMKI4ESE6Q+yQ9TMgOJRUnWgFChyRS3+6wK0ntSK8VJJHxrzKKU7nPlVeifSw87jqFTrG+G2h3PH7m2NldoK5SNn4tRfn4qSU9M5R3+/c4AZXItVzd8RD7DvhCy6i90XQjbtiTppFkoPTUkp9qluuc2OvrTC3uVTO3ooEciCtfeYWqDOvDMclS9WYp+Zv9IHD9I0nIUU50jKE1QhdYKvRVQiwC6cFaIOSlGJtW94X/WB547FkiWkL4lw4wPpMOZvWgY2NzhWkV9tdD3DBy/UQcxu65HHbdbocbynMOJdq+mEbswxXNlkf73xxWw057A81OFyIx6B7VVD+VbrzPIs1mRD1Yc342EQRPQMxJrWhi7Y+vMbnHHpTs7V5L1/JYi0HnbU676SkPhJuqX/1us4Dhw/j392l9aBj/QJc7K0gr6WgMX0umps96nd+vLN1IHKq6D6Zvp5HiSFP/tCae5iJQvk5YfdZCq0kuRbuXLBXr+x5dabqZx6xkFNzsOZSTB+vwlSRlNI1F362hMTIKo48Zu0xw5wnaRpoNKURSkdYJdsQpqv1I8mgJP5+weiAuO3WqGbw1tKucpO+UlfccydyQv06p9gVoG8oC8ki9yGg/jcTjAclCtHXtRsDlgW47YOcWs5ZOwmou6QbtfoeZWzl1j6RbalYyjtIOug3Pd9/fIo+ir8p644OKkC6grQ1+PsAExbu4SVop3HGOLeH6CVKgPh0SPyoJrWE4qVkzxi77bF3fb75fpaBg9dBz/8EZr95sC5swlUQJlUF2bnDrIWEoo1LBewi2pi5kzG2+HqEGUiAO3lqqW0rGhrWMUYsXKdUdhy413G7gPCMUAV2KCF6vDNdvpzDuiI2h2++gNriDBm0D3TzFLg3J8YBmK9AcdLQ29M7UFvNOHI1hPkVtexknjgPFoeBTohYNanZhmH8aSX41Rn9UYWzAnRZUu9yM/dZ3xdCUnynys5zl40B2nNBs9ny/Xn+la0U2h2ovkqwV1Tpl9JcFPhPdXUgiqjL4g5q7/w/Lu3/DkhwXonMdCjuzGxuaTYgK8iND9VGDq8xN0c6kY/SLnPWdQLPnGb4yrPY8k9tLlmyQv02oLmYMPllQhhHJpcU8XnH9fIM4CeSHotVyRcRtOlzPxRs9TcTNvN0RVERfpNQ7cv3nx1YSeIaRMOnE0LDRsb6TYhrFzqdXnK23UQFW2x5qkSakuyUsM1SpKd5aVBAGYNTS7R1/NGe5zpl/uY0JcP2xjF5XD6WTOuyTc7pNT/ouvcHeuKFoSuvdQGzFSd9sRKKNhDzSTqXoKe862giUKebS0k0DyW5Fu0wpZjXVZdHnVEfCyFG8Sn9zYJw6uj0HbwtU17MDP6oZfJ3TjSM276j3JYIxbrck7zKadkDMPaG06IWVyUUUnbBpBOweBx59baj3PTHzTH6d0o7B54blr7fQCjAwOFKs7slYPDvXRKMIKyNJQo1kq8ckcvfhGW+e7aA00jlfy7NW20A6aPGHCc0XJXniqc8KSCJ+W6DXdIrpqOb8OsN04jwPI+nu61nLremCZ29vExScdgY2AnapiAm43iTjD2riYY45UXxYH9Zfd33oAP5AV8wEkTA4kYdyO5VRVcgiq7uSS5meG0Ih0WyCaYFwmRGTiLpdY2+VpO8SzErMGD6LVLcdupLia/BawuVtJdy4mPSFw1DGy/o0I5xn2NOEmIAfiEvwvbPTVLKJ+JmMSIii/avvdLipo301knFoEgljR/lYOGbdpjgWmy0xOHQTLxmYsReMl4riWDP6NkU76RK2O450LmYA1SqyI+kocLsWR2qrodPYQsC43VgQF7rpRzW9xiq5ErehbqH5qBbd3oE4/5afdNhr3XPqIDmTtIz0WrN6LGNEPwpop1DfD8gGHc4JDic8EH6drhRMOupdyRxN5prDv7iFXlhUK45U13PB3jPbdCmjx/RaCV5iT5hsIRE8hrpdE1MxjDB2vXtXeHRuGEmmDXGzIywT1G6DajTDd8Ioez+OHn6Zk55aMbKsJZs0+W5wI4Cvn9Sk1/IeRi9uzbBKel2XgKPjQJy2ppJit96X/OZ2EulGCg6EFZc8LwjvpJORXSjSuXSLwsCTXWrKg8j6rnRI24c1TDvSUSuA51o6hvW2XE8+i4RGzAjVbqCbRJL7a3Gymsh618qYUsPgpbxenCady+8dHvZw7KMUMpFBJHNxRxdn0v0ya/nM82PL8vXkRl8ZDZTLTLS3DrzTMn5OpANfH3S4ne6mEMYGmi1pgdi8Q1tBmUQr/9v7dBZTy2tygwjXCW7i6TY8p6dTMU6NAmZlSK8MMQ24VyPMXADLKkD4Q4ljUxGaTc/ixYzQGsEd7XXCtqsMdkugcvW2xKLpuh9jt72LfdtT/7QkvZLDoBuKfCM7s+IgX//GaW9mLaONUkDHCtrrDFUZ6sOhFPUnRjiOFwnVbeme+SySjRpcKffb5peSG27fZYJwSSL+aEC6U9LuOLhOCH1LQxUeX0SJ2Bs67ErSRVwh0pP8TUIoIrsPL8Qg1mdbxyTeZKDXdzpW9wM7X5zCdiOSjGstDmwv5iKfS8F79It9snORHPhNSXnJLxTDPx3QvB3hM/DLhPbNUL4ugLlKiAN5Fp4fTnsEkWf4TrP1ZxYC+OuEZ9/cRm228hzs86Ntpeg2HHHkhRd6lMtBYvQ7bqHF+Dfz78P6rawPBeAPdN19cEaz6am3RESvnYwxsksRUK8PhHWVnhkJum8U1z/usAtN8dYQTnPadYp7WEsxeLtEbzciWO5F9vVOoJsKWDm7kKgpSQmRAi2kkeJEM/nRBW6/lRN81gvYVJ8Uci1jkXYaIcpGrxLBXYQ0Ur7vTrWawTNJ/yjeJEL6L0UzBxLnlp9qwt1adHv7gdUDR3Yh/D17JeL9wZEiP9ckK3En+pXFXiQkkxa7MITjHDfxjN4oSUtZyi1iSsnBtWuFul/SbAbUZUqYdqSbNUwkBi4kv0kHyT69lu+tIbm0DN5Ip6K+09HeaXHPR9g/HUuG8Hkmhphaoc5T6djm0M2CYEGmMr7Xo47hdsngUFOciStz8lx0TTeau4VgZUIeULXBfjsQ3eWDilgKmmVwpHEbjmSh6NbSjUovDPEsg6mAwfVZSrLSqFKST7ILRXapSK9llN08qlEDB0PHYNxQ3+pEU1ca9LUAdn0RWR/IpqsXlmRXNEohhcHtlej8GkV5O+LXCW6nI6RyLaAi9Xaf4pFE0lNL+MmSbibj7umXCeO/yFEm0jUWOzd0E09xrMRI8aii2xbe4PoLKYJNqagvCimqjnIu/kgK+5CKYzc9SqDf4NtppLwVpbt7rSiep4LW2ZCEkeU9gXJnl0qupx7eqx+sqW4F3E5L9iwnmki743CnBepNATqSn2hJhFlYBi8T3NSjMzGKZBcafzwg+1WBG8DgjSVuST5zcSIF4MbuEv+oho2WwWvL+DsLy95YcylRgd0sYBZGNJ1FYPnEiSv7+5HoNGeOjYdXUvBdJZhGoWrpIsck4I96buFEir8wcYxeaUwLxTvBuCiAP1gIy7PXs0Xd50DX4Gae9NTCu4Lq2QSi4IXSEytyi7FDDxzNZqC8Kxip9NzgtjpMpQjfjzADR0jE6W57Tt/yiQev+PEfvSA8G5EdJeSnRgDVa032LMeuFRubK+LKkl4p6oOO7OMF3VQKcDycHM+IGtZ3Is12QFc9SN3LSDtqOHm6DecZ7VagutdRnOh/QeMYiLknnQv2JWYBGo2fymtefOokAnHsMQvhnXaTSLYhCTc4TXEsh8q41UpmtoGLP3bYtUg/7GZNnKeYpTxjo5URO1Gxf0uyl+1KCZT8d9wA/OAC/v1aHwrAH+g6+nKf6f1rsisBiqbXim7AzZjR7NSklxr3QAomFQXb4A6aHm0SKF6II86Vlu60wC8SihPJBM7O5SFY3FoxeJ6gO8jeJQIw3V+TLKTYrA48l6cTWAlrbPA0Y+svDdOvhXhfPpYge9GNCStt+s8zcZymAdXpXq+nqT6tCX33ZHCob5hyutFs/fSU5tMK+31BGHjRD2UyLqvudZL8UCuWTxz15xXVroybVCexTN5p3FYnWr9O0WxIRmzzsMHUomkr3lkZg/9yiN9wZGeClcn+dIQ6TyUmT0fajYB6sqL+dopuFNUXFW4sr8UNI3plSN+lwrfbFg0QBmENFlI0g6A6hq9lM6cWV2G8zFhfFtTbkuxAgPrfWQp/7bWlOLQEI9o9e21QEYKN0k24yuRvyLkxQPhcQMuxlC5ZKDxoKXpCFknnYLcrip9eUd4ON3pDQHKLe2OM/+UUsxAMRnKtmX2lJHN1KZtfsxVQAdpSOlZ+t6X5fiIOz11/k4yiSikatYfxC8mRVU46t+PXEJ6PyM7k2lndC6zvROJlKo7UiWf3nxmWH3spcK5T7KW4ht/n7oakR8Jciv4vmYu7u91x1PtSIJm1FEGmERMEUWIDuz4W0Kz7x2ak34yFyVbd67DDjq5KZAz9JhX+3pWBRA4o+YVwDLtxxJYathqSFdilIawTurF0lJWD8qFkAzc/LrFHGdFGytsB/9ma+XyIfp3DVUq9E27MVdO/cypIIwUx82QPlzQPa5RTjJ7bnrcnGkF7ZVn9cguA7FILb3LohKmYRLIrSZqwlxZ2G7I3KeVepN4OVLc8TDqKvxhg/tmEZjMweGtww99s4tXPSmFc9pdLyKJ0S3uOoOoUyVGK0tIB1bMWak23EVBrQ8h73uHrguTx8ubQFweiFY5Z4JcvD37jrH7USJ52lI6zXSmunm+ChnovULxNKN+MIUqsYrpf9mzUyODJnJBJlN7oleBq7EK0imiJdtSVonidkCwi+UWUwu/cCAXByMEvmRtmv7KYhaHeDgJOH/df1wEBOQx9OSYkwnDsJqLRjKVl8CzFFZBt1BJVF0E9H9w819XKErY6TKtQuefkbCrGHBvJz+Tg8mF9WH/d9UED+ANdPg+sy4zu4w7di+qjjUy+tlQWumVCvlB0Z5mkcDxuZNOoDc1mxC6MdLkKhz5PMaUSePM0oj9e0T4bEfKIK1PCXSd8v0FAbbboIDBbN4jkR4ZkZVg+8tApysct9WcedZyja80nn77lusk5VFsk5yL6r/ZldGlGDh8V3mlC4clf5HR99Ff504p4lTK6tyAEzcl3O5JGkEXMyuAzeVg2u55k3HD9ubDfUJB+X6AcNFYyjAHqmUGvbM9PlBGuioLwcGMvxWSX0O10uIVl9HWKz2D0RrP4xKErTf2wYfA0k+7Rjrox4bimwPWB9tFGRi8l4aKbyZjQTxz5YSIjuA1Pebc/Ao8cjUvp9ltUacU92UXiUnKT0+OEbhSplhm6EpMI+w3hKkVdGYpTxepxoLvXkrzJKE7kb6t3W1YT048uJcVj8OVACpyo2NhYscoc8c1AEjOuM67PCigiupbiMqRI926t0dcZphLXYzIXreXVT6SQNLUiPZeCLT9VhKOM9RcNXMu4NRopqEIaaTIxjqgA3YbA/ibP4fInAcaOZZMKKDwBJh2+NiSTlumo4vLdjPTKUG9KdF1UfefsfTbtZiC90jQbYnCRHFfRnZkkEE8z/FaH7gze/CbpAicyBdNK/nV+ZGm3OyZfJSw/Em1es9+Rv0swC4PTEXsp4OPuYU3r5fqyx6K1tEspCAgWv9FhNFz/QYu5smSngoDRlRZHdx+lGFcJMRPZhmkU/rgQXtydmrhOwCvsqaadBU5ebmIqeT9pNeV1weBpSjuLlLdE0uAHgXYiBzQ3EnyJvb3GvhzK2HYzYFaC9okjh48WdZpJtnQq7MVmq2HwbU43kmLLVIpu0o+qrYxGDXJN2JUcArCB2EmXSrfSvXaTgDrNsLWC1zl+p4PSYHptcvWogdrg1ykmhXbiSI8Shu8gpAZ9Jvrcdsujr8UY42816JNMEFdzTbrQrP+gou3ym/u9nQbCKsNcG3SnWF4OKY4NVZHQPXGkGzVdY8mf5tjSMP+7NeY4oxtGopZuourE/BKKQOsV2ZU4oZuekcrI4WvplJpajCa60Tedel2LSUp5uWbD0FPvBUIaGJhAtedJ+teLF8f28J1h8XmQe+2NSFyaTeESttNIdvg7bgH+Tbh4P3QAf2vrQwfwB7qSa43rDERFmHbEoSM7sdQ7kexcoZxEZ9m1bNDqNCOUlqSPLbIr2SCGv86FrD8LMsJbKJpS9HMhDaRPCyg8W19G1GZLqCz+2zHVvY5ur6O64wj/1lxGqb1rTp1kMkqca75+dpvDF9ukZ8Lbq7ekSwagdUAPO9IrTXJlqQ860datxPVpas3yeEy5zGT0s9L4/VbyU1VEbTXYa4M/HmCWRrqGpb7RG4Yi0M6EeTZ4KgiUkCLjGwNqKs4U1QlbLL1SbP+Piegmm77LsRvFzbnZohYJ5eOW8q4nBI0bSUdTBTExtJte9FJD2Rhj4TG1jKebLU+338nIZyFGhtnmSoDGQREHgiCJBsJUoLFRycgpfZuKRimJmOc5yU5F+0VJtR9Rk5ZYiZt6+Siw+qyVcd9axtvuYU361YD0WnAVyaXh6vkm9hcjVBDXqCo86V6JruQza7c9+cfXJLuVwJHvCjLFVOIMbnY9xTvD4LuMwYmMeEc/uaAbiZHGHqXEoZM82q0Ot+EojjXsNJKzXESGOyU+j6zu9hiVy4Tsi7kYQhYw/DJn9H2CsYHVL7cYvpBkhXJfirVut6O6LSy+nb8IcJaJO9pGkv2S4li638WJJr4rZOzk+hjAFup7rYzjvRSCzbZn+MrSPK7JDxNCKmgS4WFKVzxZaUbfpPJ7IhTDlmJcY1JB+qjCYx+uwIk+TOeecJWSDEQe0Y0iyVwOa24saBw1bRntrOV1aukyhSKQfDsgLlJB1iy1RA6uNFt/ZQg7LclCY1eGfNxQ7QcZrQ+9RI11ivELkUF0m07MURfi3lZBkC7pXLSo2ZuU/NT0cPIex7TrKJ5lVLe88Ab760Q3PSR6txXcyUKuS/+oRo86GetrePL3X8q1u9UKTHq/uskILl6mkqSjkMKm7AvGRDp7o2c9B/IA/H5D/KjE1Ir8VHSH7zmDpgV3p8E/Kal2I+ok612/9GN7jT1N5HvPFXpuqW478tcpxRuLfz1ka3NFvR2odz3mJBO9phJNq2o1UUWCFVC4acSQhhZtK/RSNh3Rm624yR+IplI5eT6FPNzoOU0rnWEC0rGsUokHVAjbT0kUpysEEq5cH61ZxN/A9odiVvldLhX+Zv59WL+d9aEA/KGuCPo0xYw78hcZxfeZ4BmGkfUXzU2nRhIK+kzJTpOdy0O8G0eSn11R7QWyU0GtZOf6pjjLT8QRWd/psCcpp38vCsy1U7R7HWbUkb9OGT23rC4HtLsi7DZXlmjlYWlq0VwNX0kjup2K9mb8TJMdJ/BqQFgnVA9b3CDIBj0THY5eWGypGO8vQQkg2u+3jH+eyejkds3g5wXJQhEKz/jjK2GsIckU9a50N00tf2+zKX+XG3vMtaXZDpi3OdmlJj/T6A7WjzrO/56jeGtZPpSxW3GqJArsQqDZ5kqAxeE8w27XdNNA8bMLmu1AfipRe82up9n10GnaHY/b7th6dCUfm4kc/MkhplasvtpENwq1NpjzVJhoSuCyuhFMy+CVpbsrbtvptwY3juivR7jS4jYcWSHZoLaUMZs9kwJWN4p2GjBvcuo9z/wLhy6la5gsFPpP5vj9hsErceu2R0PBr2wGgd46g389hE7DcSZ4jGHE1jKiq3cC5T3Hxd/pqJ/UXL3Y6F+HMNBwWkb8taF4m0jX9XUuAvlpR9tYwRVtCAvN1Iry+ynlrcD1jxztRmT1pKO+zGn3Oto/XNFse7hXERVykOm7EZefGcJYkkvUpKU7L1jf96S5k+i0WrR1qjLYpSS0KBvxjyrhUrYKFaXDos9krNvOIulhIo7STNI7QhJZPXKkV9LFLA9H1GWKK60k8tSG9u0QuzTUW5FQWmISSFOP22vJz0SOoDZa7GZNfqpIXuSUryZUu1K0+ExGysq9H1FqfBqly/qw5PKngVgZuoOWeKfizuYcph20GtVoipcJUSFGmjsBvTK4TYeZdoQ/XJL9ySXDdyLNMMcZza6nutvhR4HxKxnb6kajvJiDUJJMoytB81SPBLBpr38zXIoRwiq5MaJ882cP4MdL9FnK6KuU7ioTxFIPlJbuIEyeapIrgSwH17+nB320pVfEyuKPC7nu1oCC4TstOjoF9m1G8pUYlexacsfLJy12Jdgct9fiMymqRDtoaGeBdlN0oVffbmJLxeaX0lGdbK4lQ/uu3D8oxK09CIJtqTRmacgPxdil5wkkAV8ZkZK8yQkDf+NmtpOWqAVk7W43wmlMBCyt3+WUdzztnutzzA3uTkM3jbDVEO7XNJvSYfe3GpFNdJJO8mF9WH/d9aEA/IEu5RXJSmOsdEGiBTfuY6YuEtGKaHH4tT0aI2ae9V3RIPmZY3ldiM7ORlaftaI1utdye29O9bjFrkUfpL1AppWTzcFeJoyHotdbPRCYc3Jm0UtD3G2wKwWJGDXedx3a/U4ci2NxeIZMuijpmSF7J2451SnS/VKC2aeOZAH1NzOy73NxIF8klLej/L2HOeVt0d0RFYtlQfZwKUkgqRQxMcgIyg2Fu5YshLMW0ijsv0TSEKoD6Zqk51YE61uiubGl6Cqjggc/ORRtUxBjysbDK+yvh2TnmsVywOBIY1fyGeQnhtFzi1lr0jNDcp6w/mfbkmSw2fL2r27TTQKqk6Jt/MwIO29XEgCyC92PHh3VJw0xCKg32PcRb0FelFfUx0MGrwT7obcb3MRD5nGTQJiJA1XNWigkHWH5Bw3d/YbV+ZD8aY4bRKazUoq+kXwm9tpSXxSkc8XgdQ+0jsgBIZeEFVtJCkp6lMAikeJxJ7B4DNVeYPjSMngtuB2fRukMrhS6kW6PNoH82FAcGdqZXL/KSYqKnbQStTW3ojsE1NMhFF70qrsdbhTITw3dhqf44wuJeYuwv3ONrqRbPB1W2IWw1qo9QRzFu5Wk3XSacJ4JoHnmBfkxCX1ub6S91dHe6ggJ5O+kqAq27xZXAulm5IhBoXrmH7Y39GRyfenSYBaW+vmY5Cil2g/4USDOU7pFyuoj11+Pgel3cihrNz26g/q2AIVNpRi+1agI7lLi37CR6BXqbcGLky3S1ynZaR+pl0Xyc9HP+pF0GYlQ/FVB1xnmRxPWd4BW4yYeOzdyMHhnmH8RULtS4JVPWoZPU9wk0G75mwPnxtaKja2VGJomLaExcJZh55Jj3cx6dMxZ0TMPRf9oVlo63UYMOSqALxDNZquZ/Jkk9oSBdFNd3usJ++SNdhKpwtoGbwABAABJREFU9x31ZpQDU19EosSEJUYfOQC1u06KvLXtxYj0BZR8fejj24KVUXW1rchPNNU3M0ZvYPhGnMAiKZGurfIwONTE3QY3jHTTQBgINsdeJuizlO5Wi50LlkYXDvW6ICRSPKv+dUhnXgkHcq2hE9B0/aQmri1RIaim80yug31HrKzIVAIMdsrf/mbzL674N/Tvb2G9fPmSf/yP/zEPHz6kKAoeP37Mf/Qf/Ue0bfuv/L5/8A/+AUqpf+nff/Af/Ad/Oy/yt7w+FIA/0BVNpJ0E2kVGsDLCirYPT1fCUlOuHycCTByT7TXT70T4TaeITkZbAONfpWAj9ijl3fNtkpMEP5COT3e3oZ3JBhoyyRe+vhqKTswKziQaYKchlJZ2y5OeJJLvuRspf1yhaoE4h7EjKimU/J2adlcgtOlRAtOO5lzwIPZMeGndpsN9WsqD1YrmjigFcH6upaN1ZRj8oqA8G+JGkeGzBDVrSXKHnrXoFrpt1zuRRWe1eftaROi1uApdIVqpeJkSBl5cwY8ruqkUq8+f7cnrKhX+YcXi6y1xGipBOehO0CSqUZgK6Vgg+8/wrbDJVGmIFxl+r5Gc35nwwMoDGQmm3xfoRt+MVbERZcV1aErN4jMnXbsjjb20qKYfJ3txE/u6d2q3WviNWjSHJvGolcTkJW9TkjcZ+btEoq2CYvXNBoODFT7tHYg7YtxpP68oH3WQSNKMJM5EQfhseFSrabc8+f4an8lmOv0eKUA6KO+7PtJM2GluILgiX1rcqxHVbUd14CWCrC8Sx8/E1ZyfawbvFH6jY/hUUkbsaSopLUEx/l6E/Mm0YT4fogYOszKc//meuCsdhKhk1Or6PF0NRdGiZi3ppIFph2o1+aHF1Ap7rTGTTrq3AF426m4sIGM39jCVnNb81KLPUqIXnaUfe+x5wuQZZGea9MIwfKMJRSDk4tTnvR6sVhRvJAGneVxjKs3ykSBxkt2K9n6DWWripKN7VLH8XEaM2akhudKSB9tJAefnKc3tTpBCS+Fgxj9a0BzIppdeK0mJScA8LyjeWbp7DYO9teh6h1LIxJ8t5N54mcN2QzZqSNYw+cYw2V8yeSwyj6t3U/hvN0kuNeEigz63ON6VA6EvJFJu/Mxy/8EZ+u9eoVtwmw4/8uJKn8jXlftyPZm1pt6WawTdP7OUPFOijZhW0W0G7LQlWSmGb6VD6geR9rMS97jGln00nYadO/PeQCafef1RTbDC+CSKRKHe88Q0YGrpcL5/3dWuotzvDRcBWCToVogIbgj5V8VvNI6tyE3cjuRXq5XFVnJo4jyjm/obUH+oLMODJW5LkFLJSuG2HCQyYo9Vr9m1ooFOFopsrsg2K9ILI0UoUC2z38r+8r+2/o/sAv7mm28IIfBf/pf/Jb/+9a/5T//T/5T/4r/4L/gP/8P/8H/ze/+9f+/f4+jo6Obff/wf/8d/Oy/yt7w+mEB+qEtLbiddHy0G2GuJQIobHQtrGbwzJMuUqMGcZrSDDPV/XsDREBXkRK6iaO7W9ySjFyU/myijVD/yJIkwt95rfMLDCi6zXs8iOi97kZC8yIWHVQS6vhPm80BYJdiV5AJnr2XEVg0l29LcLVGvE7qdIGPWXel4mYuM2CmK04Rm1vMIV6Kjcjsd5iIRQK0VY4L715bYFyMZf/2oFkZhlGKkmUVUrWGjJTYGlwWuXmwwfGNY35GHcTfpuyWzDmMiPu+TELY61NqQXFq6LUc9dhRfDajuSvFQ3fIUhwLe9uNA3ne0yj+oyb4pqO52tJua6TeGdgzlxw3pWxHcZ5eG9nHL3r0L3r3cJthIOldcfxRJPrmGsyGhlsizaGD2S8v154rwxwtMUCRfjgkWVp+2FM9S6v0eU9E7fNVFKiPb5ZDBhZgdogF30JC8ySRqbQnNnQ6+mxDHwhxU2wLP9ZXFXloZD/7snItnmzeXX3YuRhfdKaqkwFjZbK8+A3ZqQmVRacCeGdE4aeg2A2rSQmnxEyexgmmgvSUJDoJfUZI8YWD5KDDcrGg2E9yGQxcO+ybvx2NWRsLzTHSgtxr80BMy6fImS818Meh/RyQ7NyQLWJ6MhC2p5HpO9kvUfqA7Hcj7fJLB2JMcJ3S7HdFIZzKmcn1M/zwj9K/N1IrhrML9aiL30I9q6uuc6l6HygLtlnQGVafFBJRG/L0an1iqiSe5tLgp7P/olNO/2qPZiriLnGy7oh0k0GpGv8hYfN7R3WmIpbAiVZTXVNxaUb0do8Yd0fSmmzzgOkP2LhVGpAcWFvN3rgh/uSEGhdJSX6aErQ6deZJXOVUcSNf8UYl+M6CZOtx9iYPsvpyJRvjjCpYJ1Y7IE4ojQ3lPDk7DPy8k5zuKu9ksDMvnuySzGuMU018lLJ54mk8rzNucbrfj4PYl715vEY0mZHJwtVcWdW+NT6STZta/GUcrHag/rkle9VGUrcIf58Qssr4vEXODZynnF2PI4k02OhdipjBzi6mluwlI7GEiXfjsSlHtRLJLQPXPVA3D10Z0wCYSTMQEeZ2mUuSnmno3YM7lGRtNlFjCaUvyfYFvjBxIX1qqLypWxyPBKqWeUolLX1eC3SFAe7uDTpMfW+y6N9s8H6H6tJTySQsXH7b0/7X1D//hP+Qf/sN/ePPfHz16xLfffst//p//5/wn/8l/8q/83sFgwP7+/t/2S/ytrw8dwB/o0o1i9L1FRYWf+P7ELOBafSHRY9V+EMzEZxXrL2rqBw3tyxEEYZFFI9271ccCIU3mWkLtl0bGNK1Grw366QDdKrpNcTCG45y9x+e4TYfOPOmR5HAmS4XbcKT7Jdmtkvq2RFC9H+Uk8x4QvCuZw6NXAkxuZxG90aCdIss70WG1ivZWx+qJmE1QfVLHzIuhQ0fC0BN7FEz3eijvi4e4luxToqL8vJYi8Vqyf/XayCbqFKuPRD/XjSLplWb0UgDA+nUunZ0eHJ3dKiVHuRNeYUhkozJrTXF7RXW732jHHVFLfFm8TmWEtzTEQjR47TSiSklKiKlsnNm3BZf/VB487e2ObiLdovrVGDLP6NZKRpca6l1JO6jOB4RnIxlh3+5ITpO+QBfUD0Exeq3FTJNFRi9/w71zkyAZyFsC9vWFdFu1E3C4H4q4X+tAMmzlQBBg9c+3pQiKMP426Y040D6pGD1NyC40o9eawaFkmI6/SVDzhG5TRqrdSDbz9JnEtJmlgSygKk1yIow6u9T4DRnBg4B72+8mgoqpDGGZ4POIXyRy/b8QA0H6ZIE5yiCX9zUMAu2mRz0bMHwjXZV2FujGEl2mO4ipjCr92wHhqzHJwuDziHYKvZIR4/CpxMDFWzX5sSU7MVw/EQC7XYuUoPt6QnPQicbxSMZ2+WGCOZUEFZkdI13OBKLTJJOGwVZJsJHhlznHv9qVguOgxi4N8ekItSHMuGqvNyGtZaSOkq61XWq67yYyigfchqObSPQaQDsLmJWmfNDx+U9es7ouZDcIwoPUrWL4fUq8zEhWPTan07iLHDd1JFdWcFGppIlM710T++urnUkyTTuL2H48unwsPMEw8thLS7LSZKeGrhTTzPpAUlyybwtGr6B4lnL47S752+TmPYqTDt2APx5IspANhO2W6rbc8/q7IXFliYk8B9wgEsae8fcGu9SoxtBsB9R5Sn5iyA8toQjETcnibvY7mq2APRcwdHKe0OwJMLvakySUZgOqOx31tmgV623R7b3PTw5W3ut211HdkgNyGMgkwC7k2RJPckFL2Yh+vKK8JYWxKTXJ2wyuE9JLTXItQHKJXARlRB9Y7zlWTzrcoM9k7o2/qjSYjea3tsf8L67fMxD09fU1m5ub/5tf99/8N/8N29vbfPHFF/yTf/JPKMvf8aj9b2h9KAB/oKvdkLFOulPKCCGRTTNkcnOFQjbDZKEYjWpibUhOUlRQsvENBKeigmyuw1eG7p64aqOR9A6MONmShZKxYNonZwwCx8czsqOEsEpED5NEqn3Z2MOzEeqXY6KWLos+qEQMPRT+nKl6lIfpxd42Ei4yyQz+i4kI0R0Ur1LpCCRBMnQ9qIEXtEitsMMOc91rEw9K9P21FGpBSXRSErGJF61krYQVFyE7k9QAXRrUVgMm0ux66p3enRkgOU5IjxPcIqU+k9GZ6oXg2knXFA1Nk0AiaRf2bSZ/o9PEQvRPAMmgQw0cIY/E/j00k5bYw7ZtjaSVIJuL6nE2eEXbWinkx55uKJ/n4LWVcd9WoHj9G31aemXwhYzK2n99iU+lU1TtR9YPPHEo0NrhnaUUYDsNzccV2svfE21EDR3phcEfCpvMTTyzrwQMbRdSOLgC6lsOn0JoDNWecOPWB1KwmFZR/XEJ2w0x98R+TKgbGQPb05T8TJOcJthS023K13RTL3rSfXn4hgR4sJZ4wU6wGsO3msFrS7MTcAPpetdvxiRrRXKSiBzglZhoij42yywNybWmvttSHGuKU42dm75rIwB13RulohEGZn27o94N+IlHnYjMot0MpHPN8kcttpJMXp9F6DTZJWTnim4s4+zRa0U8qIULd6ukncn/ps9SvDPUZQpRsb7nJfNXg18LbLmbBuJ1SvpWMq3NwJFcG8oHnVzbRaTbdHQzySHmLBP5Rs+V88cFkhwjxpCvfnWPWFmaHU8yl6QJN5HrSW1KQaGidNT0ZiNYqXuSZduNxY2+/G6DsEwoToQe0Ow5YY7eL/GDCAFmv0gErmwjzabHPa6hlfs83qmxpaCjtAM0xFTu62ShGBxr9HWfrDPX8jOdxqaitSwfCyhbOUW325Fu1eL0HXbUW3LN6VKmDO+TWbpxFBf1kUTpzfaXhJ2W/FwypAE2/0o6jCGRQtsXkcHLRDqeDmwl7uj8RJ6V6VLuS4J0AO2VFYNOALcn0glzq5RicbujPRuQLBTJ80LMVlseZv1BcU8KUj+Qf6MvM2IW2L47Z/qrBFsq6j0BkbfTKIfdZfq3uq/8b62/yRHwYrH4l/41zd9scfv999/zn/1n/xn//r//7/8rv+7f/Xf/Xf7r//q/5r/77/47/sk/+Sf8V//Vf8U/+kf/6G/0tfyu1ocC8Ae6bB+z9mD7ks2DObbvPukW0rmWB1ajREf3ZxsQFfmZPPjf4y/SuWJwqEkvDatPW3QiyJSdT89RjcGUgppYPXKoRcLgadrr5jT5qKXZkfSKOHb4zY784ZKQiO7MZ5Hk2pC/SwjvColHC9ygJtCR9e1I96QipvJE6B5VoqmZeNYPHFFDuFWTPc/JTg3rxx02c1T3O9oNTzjNb6Lhkl+M6E4LSALTXxv0YS58rq+HtI8rms8qtBMg7ntRuLlVEjrRy5lVv1ENZGNLVoruQJAzw9eCIAljJ3mhSkTnbqvDz1MxwLyPX2ukiNCpl0K3U3RXGcxFE4mXEad9OsBPHXHsWD5xZONGeG9Xli9++kp0jpXBHQ1Q/jfi8fKBsBpdEcmPJR3EDwLa94kkC4NbJfinIykm75XiBl8KwsQuFdWzCSELhHVCvEqJj9c0G+LQjK2RwjFCd5kzeG25+gOHG/f60j4FAiDu15i5FZyJl060vb/CrhX6RYE+zMneptKV+6Ml2kvB5PebGzdoeqVucqhNKYW6OxzQTgNx0km6xt0+YkxDsyUFxOwrdaMdlINGHyGYRMqPpHN5/ZOObizFYzeR/OX1PQ9/b46biqxh+EDSXFSA7EqLVmwskoHiSKMq6ahmV/K3mwaSE3EHu/tS1JAErv9OLcDfQUQ3ivmPO3wp4OzwjUgTlo8DfrMjeVYQKgt9hF9IZPQ/eirYEjVtxdCR9Nrao1w0oV4xeqXJLjTZRg02StyhArWUazC9Ep1uslfRTqSYsgvN5GsrXcMdR/dJBTbQ3W+I85RgoTiW+zOcZ4Q04C8zuk3R63Zj0YaOXlhWP2qoP65Jz0XvFt4VJAvN8O4S00SKI0PYbTGVlnG6joR7Nb7VdJNAfq5Y3hPUClqeEe8LNl3La/BZH/nnhIkYS4NqJO88FgE9t7RXYohxjRTy9b1W0l9MJJr+GtD9c2XToTtYfj9DXaQC255JAd3+3+eC/FFIEb3u839VFDPJLOAmnnrfo1vF4hMPJjL9SuDQthJygR8E9DyhODSEtwPqOy2xNowPFjfXhfKK4p0le5ZLikvhiVn8/7L3Z6G6rWueF/h7m9F97ey71e+1dnvOPif6rIy0kgzKMhUVsVRQEjS90EsRREgLU4QgCYRE4qJAyBsJQSmEIsAibypNs0zTSCMyTt/tZq292tmt2X7daN+mLp6x5jHMpiI8EeecOK4XJnuvZs415/eNMd7nfZ7///fHLkWXXe7LAfrifEy9JZMSUyuWHzX4ccDMxMX+s7Lu3LnDdDq9+fiN3/iNf+Tf+yt/5a/8QyaN/+3HJ5988gc+5/DwkH/2n/1n+df+tX+Nf+ff+Xf+id/Hv/vv/rv8xb/4F/n444/5S3/pL/Ff/Vf/Fb/927/NkydP/th+1p/UeisY+Bld7abDxsjjb9zBDwL5qaW+2+KiJdjI+JmmXRPGWjaDds2IFq4XxHfjQLsd0KWRTlnuMI8LQab8zR3SXcEORAUh0QwONcsvNaRHIsivLwqKI0v1oMVcJhQnmvIgQQdQtWxePheOlh97OSnPNdFK944HK9xFAZcpyU6F1pH4/bEYK3LRNvo8Emcp7VQ2OUwkHBWoRDqLYejJpjVtOiAaGUeZK83qjhSRZqVlvHyUw50Kv92iz3LsCpoN0J+OmFxBuR+Zfq5Y3oHiiYCeq72AzTviiYwBkxU0V5Z2PeCnns3/xdJsicu1G0fyC0V70OKrFF1rzLXwFZWHpNdmun5Mq1tJLrDX0vEJ046utb2LFL7zxS1MhDBy2NwRoiLLOszvTJneueKs2gJkJI+KxJFHn2XUu0FMLQsZyxEV8eVAXM6NJlp1kwBiSo1XAQyYH4wk/SMixgcn3Tez1lKayPr+nOuXazf8rmikqxY6xcb7l1RtwupkKAaNH4zl+tx2FC8TQhJRneLB1gVP7AjtwJeWdjMQdSRcGXGvaykAXBGZPNYs70UZ96VRouzSAJVsfst3HLrWqBDF6ewV9XsdOC2w8krMDao0pHPwqaLbcvhcYyrN6nBMOtOkM8VybYB6GG7YbflrQSHFVMwrdiVFfLMuG3V1IAahmETiKpGuWeaJ19Jd92NH3OtQ8xRdSreY91aE8wJdatLjhGa/I5k08GxIulI3MOvVPQFPm1e5gMXXOlwl+bMAutWS9lNEwvMRqpBIxWgj+f6KeivBV+I670LB8LWi3pYO3vzLHarSmJlhvLNgcbQuh4Ye8Fy/2xA7TXqa3PAIy0ee/EJR3hHzxOq2F27fUEag9lJA3PWdFnU4Juz0nMFDMSq8SR8yry1+y1OcaHwq3LtmTRzV3V6LvkqIv1ziz3KRenSKdsuhh454nULhyccN9anEkNhSkDvdKFI8zkhK0F1Kc6dFlYbszOCGopUMSQQlMg/dvkFgBfJXCcFGykmGGkvBOflayvxhIFlIRFvs6KcODn2aSbRbkHtk/r4n5h5zZTGVIuvZhvW2jJyTY9E6L6spxWtNN4nkH16zejolDDx64Mi/U1AeSM6zrjVxtyHOU5KXqRSlScTZCI0h5p7kNCWUPzsg6JcvXzKZTG5+O8v+0QaX/+A/+A/4y3/5L/8Tv+Q777xz8/9HR0f82q/9Gr/6q7/K3/gbf+OP/O39mT/zZwDpID58+PCP/Pk/TettAfizurwibreoU2G0NdseaskBzl9buj83J9YJ9TChvhNJT6xs6hsN9nBAuxkpXiaYWjae9LuixbMryc1UTk7lMZHuSDeJJEWHfq+la/tT7CiCU5hK3WxeYbtld3uGUZGrv7tHsyGnWrfp8AdexqS3G8yLISqJTB9ecf1sDbvUxDzywVde8MXZJvvrc56ZbdTKSiqC06irfvwxcTJKyxTGBJJrfROR1Gz7G92an3pUoyXtYZGQb9RUuykhC5hSxprVrmgLy33RdLW3GmgM2UYFn4wEErwJUfdaSy1ZxqtbiTC5ZtKF8rmSjczKaxeVuBLdMDI4Uizvws7XImc/Z+h6HmJ2oaj25QEfgiIbNzTbhsGkpozC0vNOEypLHRTpEFZ/Z4dJBfNHgfFjw+qXWnxt6CaCIJHuZkB1/Vhq4lGFIw6h1Amm1IyeGhmfvlPSnA4YP4+c/ZlAfmLxda852m+w1qNmBcurDeJEOmZRi6M6nTbooLi4GIn+brsjNppkBas7ssGFRAqEmAV+8MUB4yvFYsOjho64FEODGweKYzG5pDPJfJ4/CsJL6xSDW0uaxxPSK9n4qg9rTN9ZShaa9CqTiLbU4ZUhOAWNEhabjiweObIzQ/FC4M4gSKNmI9KNBAczejCj+c4avkD0qVa63BK/JpnWMYkMnltMLd+rCppu4oVjmHjaJKC8mD5CJQeD/Fyx+KhFdwazEO1hOlO0W4quSjB3K7qXgtsB0ea1655uXdiczUBL8WeiGKhO5cCF0zejVnLhWlbXOcmwIy4yunUnBpOhXJvptSbuOdKXBd0kMJ8XIjtAum3BRmJpMdMW3YlmL1hQS0N525NsVdhvjUTDGoGZHAKjFpeuagRoLBo5MRr5PBBzwTHpVjR33USyhMt1j520mFcFoUtE//h8QNwUY5B+uCQDmutcpgazhHolY32vJUUl6oi/U6PPC1YHMr61rxPCrZpwnePGcp2lz3JBt6wpJnsLrPFcvlxj/EzjckX9ssCtOYG1r0GyEB5hOtP4D1bE5wPSpznNrqOcCmFg9X5LVBG1tKKN3u1w6+LaH36eUkc5+LhJlGttTYpW/qd14l1BOBXfLX44EWkFLZW8Kqh2I+lCUe4F4sCTDlv8qwGqM2gHzfTHp5/7R60/Dhfvm8+fTCZ/oAD8x63t7W22t7f/UF/78PCQX/u1X+MXf/EX+S//y/8Srf/oQ9BvfvObAOzv7/+RP/enbb0dAf+MrvTSYE5EdG4qTX4iD+HBK0uygPb5iLASo8L0O8mNWNx8PiAkMHghm4luIb3UMgLcEvFzVNDud2gP9a2OkAu+w50V1Fc5fmkpTpXk8UZF14vCo45sbiw5ebnB62/s0o0iYafFj2R8YY4z0XVdpbiNjlAEri9HmEpSNdJHcz7/B/eIn404/P0DTCFj1PS7A+yrfpyURlhZ/NgzeG4pz4a0617Glh8uMaWWE6aG0WcJcegI+zUq91jrGT3vXbV7Dd2Wo9sW1pvo6CL5swxdaprLnuHlFN1WR3nXM3iaYAaO6DTDo8jkU0Oz6xncWdCNQz/S1Ni7K3EEbktnaXVHRodn/7ey7wgqxk8k+SG91sIDazXt64E4VDtDNmrIjhLss5zBswTOM/wgsnzouP64g62G+Ucd8TQje5XiR6LD9KOAGvib/9pJS/Iqw77KyF+/6aB52rVAczzArDTzB4rRE8vgONLtdOhWUlrMN8aC6SjFcNHuChRXtZqusZhPh5jTDBURNuC1pVmXEag+Eke4u19j5gZzZeU9GnjW1lbyZLK9uF7kWJR78uviRCDSgyNN9XyMXQkjr3zUYo6ymy5m6F/jdNrA0yGcZ9hRx9r3RdYw/sKQrNfSsd7zNBseWypWD5yk30wiOncsXo/EJVopJk9Ev2mXohf1hYw/dSmGimYzMPmlM8nVNZGw1dI1luwkERTLucXMBLnTjRDTUGv6Q4KYDeykRa0swUnChBtGyq9UNDsOtd6iGi36uaCYfGpvoMshE+7iGzNIfmRRK0sy12SHKebToSSXOI0bBlweSQ5W1HdauMiEb7nXYF7lAhVO6RNOZDSZfDqQycBeR0jEUJZeGOy3RpKCgXQM3Sjgph4OaohSbEcbbjAwtlSS7tJqcJpuzZNu1gQD1Z5oSV1txYU7dcS9Hi590UPWH49oLguKFxK5Zyr5N+xKDifK9catxwXlbS/JPtstbuIJC4mLS2aGGBTpDNb+pxzVKrSKlHWGiorZh57lI9Efjj9NsLWSqEUVe1xRIPvGEBVEt1q8tJiFYfVei9IRapmcFGcKeyYXsLm2Nw5h5RTphYC9QV7n5X0vHWGkGH7zZ9mldEaX73aYFlYfNdJlv7KY744AUPdKynfatyDof8I6PDzkL/yFv8Ddu3f563/9r3N2dsbJyQknJyd/4O988MEH/N7v/R4AT5484dd//df52te+xrNnz/jv/rv/jn/z3/w3+fN//s/zla985Sf1o/yxrbcdwJ/VpWSU2K0JCiUsBR9QHcjoM5lrwnYLC8PqdsTvtMSgCIkneZoTtCRQhBTCeyvMkyHeCXeteJYSUivZogNHWCSYVhN2WsnrHQsTCy1JFeWDjuGThGo3cPHFOqYRPlYceIwN6CtDt+VQpYwm8zONWyXi7l1agb0GRft4gh8F3JZAT6kMahSoc4m7K5703Z5Rh1slNF+uyBJHezrADwL2u2PiekBXopPqRpDkDn9aYBrFSkE+hm7bcWf7mlef7GJXCtNKcsDgRFNvCki3Ooi4Ww35ZznBStSa9uCvUwa7K64+EpdkzDzh61NYF8e132+wXroU4W5FaAq6TYe5toSzAtvfkc06tBuSaDA40SwfyIM9f63xy6FseEtwQ3WTt5rOFfFui7/IyJ8V1F+qSI7EAZxcipYqdgo1S0UjutQkPYg7pNKxsaVCe00wMH33msWywB3ltJuBZqnJDhMxd5wU8NUlsbG4NsMPoHiaYloBPUeXEkzEjTzDvRXlPCd5mUrU2koE8/52jVIQtkUPVY496cuUZizj7qgV2YWl3nckVwJ1NiuNL0RX140g5IF2XQDByaCj25Y4sexCtF/trU70YFsOVWncLOX6Q0G22Erj6gRuN5hjQYfUtzvMsEPNCxm/LxLs0uCKiPb9aLIUTWLII9lrgxtKdFqYa4KJnD/ZYPTC0E0izY5isL2inqYMXmnadcmZdkMZPaIkDecN/84VYniKSWQyqZjf0tBpzGmG1hALKUD1yxylpGB0667vpMn92plIulfSNSOSpaLZ8WQ7JW1jCYuE5NpgGoXqoLoUriYRilNNHUTi0Gx7sgtDMwj4rL/+j8XEMvwsxRfQ3O/zEJcJ2amlXfe49cD0uwmr2xEXwG7XuMqSvE4wpSL70jXLlxPQULwyYgzbrmlXKUmE5EoJU/R1QrfhsGcJKiZ0Ew9JxMwEKO0Hfd5x7zZPbq3gkxF62NFODeagpGUgnd6oUSsj9+jtkiYo4lC6ieV+lMPqXHN9OkYXrk84kSKu3Qi4TlzIYa0jVCl+r4F5wvK+6xFAEd8YinfmrF5MpIu7Ll3q+Qcd+XGCS2TE3E4jtlJkX76ibS3+sRRwplXYE3PTOW3XPYOluNjbNWEQ6v75aE9SKSSbHnC91WKeDDAZdNlPiQv4R/0afwLrb/2tv8Xjx495/Pgxt2/f/t/8k70etOv49NNPb1y+aZry3//3/z2/+Zu/yWq14s6dO/wr/8q/wn/8H//HfyLf4497vS0Af0ZXSMA6cf+OHidEA81G73ore+abl5GsPkthZUk2K9zrgvFTaCdQ3/YEa+jmGeuvgGApzhT1Jqi9Go5z1GHO9Dks7oG2Ed1A1miW90VXFoqAqjXtVPAb2UZFWyUkuSM8HeI6RfJoSXdRwLTDvkpp14M46FYp9Y64mWPuIRjy3RXhexN8EckezVnNctIXGXGZUu/JuCvODYwCvrTEXBhhxfOUZiMwONS0fWh9u+7hLGf9+4ryQBHPUnwaUZnn1ec7sN5CmYk7tZDChghuINpJX2Wi6Sk8+tJS7gXSK0Oph9ge1mpXqRgbRh477hgNa64vh7DdkbwoZLPY71EehaezkWQuKAvVKfxuy/SjK1aPt4l9zmgcOWE09mDZblOKZzeKqMOCuN5hGoM+zGXUNPI33TSViZuw3o6MnoPPYXlXoq/UQS35rYC9NixWOfEsI72/pJ5nuEkn6JszcSDmecf8Mv+hbrGP2kuWiuCkS4xXlEcj6SLvO+ywI4wVbp4SSwtB9Sw3xei5dF66oxFq7NBnCc2O6J+6PemsBJ/Q7lZQWZpth2oFLbN8z+GPCxh76ocNsdGk5xZVGXSl0E6gz9t3rzh/siGu4nHEnsh7jurHV40m2+hoY9F3+Qzt7RZzntJOpHgFiStcPAzU+w7SwPiLhHJf0ET2PMG0UI4j6Zmh6kakM9H7xXdK7HeGNO+0gkfyoqNdPXDkx5aw25GeiM5u0U0EBNyv9PaK+qIgP7LipD3oGH8/xVYJ9Y5gT+Juw+A7Bf5bY+JmIDyooTXUVzl6ZRjdn7PMBiTTivJcYmxGX1hWtwLdRLSNDpkaNNueqOTQos5T3EDhh4EyjcShZ/BZJoiSJNJuemF5brSUB1YOXEuDui5Q40C31zL5nZSLo7H8TDbgcyM6wM8HwuVTkF9GUG90bJb0PbnHsxeZjLQDhExSZ4hiVGtut3y8e8Z3Zjlp6mmnDvNkSNopmi1P9toI1aBUNJc5TDpYiTs3WrA1tArS15budri5vttpgP0ad5bLxVEZ0pmi29XkeyVtbYlRoc9T2i1Pe13ApKNJDdlrK+lCKyh/uYS5jMWLE8XikSN+e41uEmEgJIW41NiVdGbfTCh8Ll3d9XuXzH+wSdxo0Rc5zZYTTey5aFjb40xQUDZSrFc/ph3mH73+OEfAf9zrL//lv/z/Vyt4//79m2IQxIjyP/6P/+OfzDf0U7DejoB/Rle8VeHfXwnr7quVdGW0nD7bacBtdCgTBaOAOFO76xxbaupNxeKhF7zKRxXJuKXeEDTM8m6gXQuES3n4c7fi6quiq1OvJDosWgEBm0rGZDGNdNsOXWni5yNiUPB4iJt60ivJR81OrQi31wPDVxqfixg8Tjo5rQeFOSipLguJdgpQP5lAnyEarGjJwiAwONYUryzZUYI+zFGVwdSw8e4ly3cc/mFF/X5Ndm6wK83qn17SvFOTzDTtlif7IiemgWLYECxUtz3pTFAyRBk1mTcj8+0aXRrcQIDRYq4At9HRbESqA0e6EONFOM25PpqgFNy6dYluFMsHTvSIw4C9SCheWerdgH9/xc7PnZIepVz/f/cojiR72Ww26OsEZQT5ErWMxuJIZke6A3uZUO31f26RxJDeLJCPGjH7dFBvKJZ3JenBNIp4WKAajWoVbqcjdMJnbKoEe5aQPsug08S7Fa6IzF+PUEHYjj4VvVJ6rTGVYvRCYS8SsJFst6TZ8SQXFnedYl/kmJVm7dsJutbirNzsuP5qR3KliVnAvE5JFgo7aQlDz8bfT2GRULwyhGWCqrUgf9JAO+4f2BrM3MAskesigeRaur2qj9Y6P5swem6IPYzcLiWJwa050acNHfEbU7kf1qRrS6v7lApzAx+e/UojRpKBAxW5/ijQ7DqKpymDY0mL0K0ctLJzKRoXjzzZ14ZU/UGF91bEgdw72anoIeldtW4i/w5Iwodfc3QvJNav+6AU968X84ncwNI5NzbgEykc8jONm6cMp5WMS4HV0ZjsVUL85lRGsEE0vsmy76RdCQPTrTlwivFjiz3KJJd5Ip143SmS1wnlO604qDfkEBRsvNEcM5aYwY3vS6c/OU25+LOdpJ3knnTcks4R/E8nblnVweKBxFOWdx0+j6xeD7GZpxsHTKlIVgq7EqNRNxVHb/4y5XvfuE92bOmqRHiFg4hdwuCVodnxuGGQNKTME1dW9JTbnuxKRvG6E/xV7LS4l60UZhwW2J0K1jr2/idNuy4a3/qsIJS2L0KFaQhAJ/ICFaDe9yzuB9wqYW1/jps6Fh83TA4WAuHuFNntJdmpJZ3J9yyJN6LrtB/PMBsNl0dTkplkHw//T+cyju+7uPUmFGeKal8cweP/9+hPdmN5u36m1tsC8Gd0xdAXKCtNKC0hC/gNybQ1rSI7TMk/zYkvBqJLmxuyU0swyEZaa1gk6OMcd5ETE9ECKq/QWw3pTkkcOdwyIT034rwzomMCIex3a55kvWb0RFh9YU0iqcxlIskaA0d3q2VwLIVGdmpFX7cmCRS6A7zCzARy2y0yVKepd3s90cShlxa336Dur0hfW6KWCKlmK+CGgexaTsbtJHJ+NIUkiH7tJOtHn5FmnsFVSvteBao3iuiI7jER2amhvOtQj4eELNCNo7iYt1r8VUYYeoaven7aIFIcWgZPUhnjfWrxCWIQ2W4g98TScHI5oV0LqE6RnCfoSt+MBHWjcGc5J2dTNr4n+sDqlpfuzELMJeo6od32+KFk1WaHCXFdEgx0K+5UWyGjcid5sdmZJX5nQrvnaPc7umlk8s41eiC/zi6luI0jSbqwRzKK1KcZ/qChncoo2TcGtdtgr63wJRMZd5kG3KMK5WH2QX8osIG6d7i6YYA0SEycA5/JCKsbR5SJmFGHGwtgPBrRi6lnBfbKUm8qhs8NtoKt3zXibP08I7kyUryYCFsNfhCIY0d6ZXBjgVk3G+LCjjqiz1Lhws2MaEsH0nmy11bypU9SqrudONwLL8aopQB87VL9kHl4kjL9nkVdpOgkoIIiey0pL/WORPUlc3GiN5uedtNTHBp8JjzGkETaMhFIdy7onGa7jzhLhAWpkC6wqSWlwg8D3TTilzIyzk4S3P1aIvoGnurjCu809YEnvFvSTiPDp5bVPKddCwIz/8LQ9QVzdmYgkalAu+7xt2thh86VmKNUZPHlFvtoId9TJz9jstBkF3JfRiuHR2Ul0s6vCz5FXab4YWD2jlzX2fsz0h4cnrxKGQ1qujG4QnSaPoskK9EP6lY4mGHg5dnxXAwR9YGn3hJ5ic9740QvdQl5oN0IFJ9mDJ8a4kZL9ysLVg87GPUds0Yz2iwxS0O7Ic7leiuyethR7zlxDT9PaDY87Y5jfLAQfNXvDxl8L2f2ULqO2kQZR487yeEdil6UTnSN7m5Ns+mxC0EzJWeW5SonPbPYs5TFi4nwOW2kej2Q9z/tiQC7lRSBuaM8HGE/G5BcWNo1gcZffbpBve+Ft2rlGRkSQVC1Dyvm937CW3r8Y/p4u34s620B+DO6sk8Lit8bCr7CiehZVb2W6Z0lPo9Utxz5hXTQilMlwOYiUB4E3HaL6ePZ7FzGQW4sPDL1Mqc9G8ioay6bWjTS+Ru96DEyk4BZGbpKxs+xNOhZgrq3kmIogj6XxI5qN7D/K8dEExkeKvy7Jcv7XkT4vZNORRHQJ9eaZK4wt0tIJPVA6Ui3TIU/p8S1m5+KRmj1sCPfqsRI4MQ5213luDXhBfpBIDlPpHP1Ime8t0C1suEuz4c3GqrBC0u74yQRZSHzwvQolfFi4Si/UlHddoRCis/yjsNUkhTQrgfy9RqtI8lJyuClRb8oZOzqFT6NgsDYaUU7ZAXqag5zTv9cxBWQXGp03Rc6SopxXWrU9H+V6XqRkp+Lpk95WN0O5JcKU2mSRZQCDIR/pgUqXdapFGo6Un1YowKsfS2l2/Dyc+q+0LaB4aEW3lyr8bME5SBda6A2qE50cOpQusB2JVm0sbQiuDdyjeB0j2iBxbt9F+Rckz/O8POU9e/0MO1OdGe6kw5juBHLw9WXI9lmRbMtEFw3kENF9Irh7orhp6mAeitNGHj8SIrR9Frjp040kUvpPHbrkl4TNdDK74EI+1VlyM+0XBu1ongdKfcUareh23Qs7/VShWfClOxGEb/e4e/XcqC5Bvr0nZh7qtviCu6mAbXeoq/F/ZpdaCZPJAnCniV9Qog4cOPI0Y3jTbexW3dyCNoWY5XqAerJazEEcZFhlppulmErhS8ge5qLAz+JLD5qyS5FBlEfOEafClvQLoyYF6IU5HYlnVhqjXMSC+lGQZ4fLZS3JeWinchYlqVkF7937wRda8LIk+6U1AcOPbP4r6/RLlPSU0u75an+ly2RJwwlzWL4pSvKDxqBkQPhMmPyg4RmI1KcSNcx5l5A9n0MW73nMKXo6opD6ep1UzGkxdLKAU5FlJEOpV/vyBO5h1FA7sVtf2xvio50Lu9Dem5ZXAwxV1YyiRNoP6ykq36aoTKPVuL8DrdqMaNNGxkvX2SYWuNvSZZ5SCP+MiO97g9YA091Szima9+zuO0O/cFSKANXuTAPj+QkXe9JXnaylK56MpOEkHbTiyRkO9CN5P1PvihEW/oTXD/NWcBv1z+83haAP0XLe89f/at/lQcPHlAUBQ8fPuTXf/3X/4Am4Q+7uklkea/f8G0/WqhkRNkdDUUP1mlWtz1RSdC6XSjyY0OY/HCciJdYJdUogpEcz2hkVBSNIB1MK58f0sDsI0e74fHrDr/eoZbi7jSVFq3L86EAjXcq1H5NrCQ94sXxBvHRivkH0lVUGy3BwOBI4yaB+uOKbqejmwZMpYhPhzLWBOKVkFp9Ia7aYEU4HQcydqyXKelckaw3PdTZkB0n2EtLuiG8i2ZXHtSLozHJQpAv61+TbNDBS0OzHhl9nrB2MKfZCjR7jvCgImy3FAMx0OhGBPoECbsPSST/+BoVFE2ZkGbSZUMh7tZBIGSBycNrVg87sqeZdDbXHcOnCfmZuuk+mg8XxN2G9EQE96aRkV/sGXCLDzpsqVj+XE17t6Xe9eRnmtWtQMgi1a4i5JHu/ZL0JBE8z9zQ1RZ1mpF/nhFLS3inYnlXoLSuiBKN12ji65zm51d0k0ByYUkvjSR0PB2SXhoBIZse2PuBHDCy1wY7M3RbHbqD4qgHH/dwYuhjs9alA0SA1W2FH3lcEUnvL+nGwuvr3q1Y3fWUtyTdIn46gq2G5Yct7f1aUlRKS9saylseO5frzSwMxZGl3I9Ut91N56W535CswC4MoQjc+/lDsjMpVEefJbDeCjOxldg0N/Vc/oLHjQLhIkWVoh/rpsKI0w7ufPkETCT7bkH3Tk29BdlhSrceUCsrY1HEUW4Oc7JLOaSYFhbvCJbIFwJbH76SIplOywGo0cTSShdpIWPabhrwC2EN+kFk+JVLAWVvdWADzUag3ukTOZDINrUyVAcOP3EMXtgbh7WbesyTAnWvlGLo/gqVe7Jzg3cGPe5uxqTVvr9h1YXtFhUVxStDeq357It94kYr6KlFhp20N3rL/GWKL6IUapPYH4Ckg1V+ex1zmmIupSM6/lxyuH0RmX/ocL1Ewqw09YGToqnWVLcdbs3RTiTOzxW9HnFhqFcpqjbE6xRzIbKAy89E/2lWgtDxuXweWmQk7bR/PYaR7JXoYf1GR/WwQb/M5ZmoIHYa9WSAacE+zyV5Z5GSPs1RO3KQCqXFDMUosv17mtVdcderxjD6wqIdzH5F0lDq84J0pln7riUU/fN+7FB5z0fsD7chffPzadz9mrjewbsrilOFqcGN/R95r3i7/o+73ppAforWf/af/Wf8F//Ff8Fv/dZv8aUvfYnf//3f59/+t/9tptMp/96/9+/9kb6WrhUUSsC3nWSo0mq8klEC0HfWFN16oL3lSQ8TycosDXHgaXcdybntwcAKNCRHCckcFh83JMcSct6NIqy16MtU+Fh5xJzKaMRtdeKOzATvIN+bxp0XxKHDjBxdDzPlKMMqKQriRUbYalmOBNwaL3MYSEeo2hemIJ5+9KyJ6y3BJ+DlgU1QqMaIwL8xNF+q8KsEE8UEY2oENvxiSDZXRGVwOy1qnkjqgTPMPgiETMj+40+kk7n8wToxjejS4GtNuldSHo8EdZFEqHWfUiA6rM4ZSaK4TilXluQXVsSoCF6x9vcLrr/iWHy+homK+lYnHZIsUO0Exs9kfJaeGZpyRBjLaEpVhnajTwjQEVVpilPJdo5ekR6ltBue8p2O9MSSX4h7ObnWtDaFXNyTfuwZTWqWjaHe7KAyBK9QPdakPRAdV3oqDs46ydH0+qvdTlI+nFxHq0fyd/XS4ErBcyivCGlAL2VTq0bidtZOulvDZ5bVu4IBApEhuKFEEEYbid+eYPIonMk1dcP+izbSTSB7XNzopkIqnMJa5aQLjSsiwxeW1aMWdZ4SbEQPO1QQBl8oU+pt2WiVU7z42i3ctpfUmwMnkWmjQMUP4xNVqwhbLXElP7epNMpJhnJ2qTj+vX3UWiBk0lGt7woEPb3oUzuqTDSFNhK1otp3qIFnOTRSkKiI3mwxT3MWDwJMO5IjyYyOiWT3tttOsC9R/n1zKYVgeqlZLTdQ765gkYrutZLkGN1IBrZuACOIp24iWb35uXT2sAFbWsInQ9ppYP1vDZm917uVzzPufumY59d7EDTD54Z2qlHv13SvhgyONas7nuzSYIYdw0HD4sUEu9R0m4pku6arLX5hMaUUtKYEP5D31heRuNahU0/+7QHlLc/iF2u4TgS6fG3pxkHYgSOPnltiqzB7JfF1gQoaUwuTFICBx5mImiWS0/18KN3SRjP9XHH9AbDfoC4kAo6goBQcjX1Y4eYZNJpmW56d5jIhWcgUojwI0jlOZfJRHgTyM021EzBXUryap2IgCokhfSE52t0wEvNADAo1cDRbWsbLg47aSd51s+1p15QAz7c79FVCyCRfW3cyrm63HclFn2X+Kqfb7WgvctiSCYzqtcA/sRWifPyoX+Pt+rGstx3An6L1O7/zO/xL/9K/xD//z//z3L9/n3/1X/1X+Wf+mX/mhkn0R1kqQNSR4e0F6YUmOUkgCSRXIjiWrNp4o7kwuaPdlJM9XjoFqtV06578taQi2KUSYwmQP8+IVhIB0muNupBi0BUCjiX2p9FOjCDZbokbeUYvBdkS0yAF6cpCJzDmbk2C15MrS3Gi0VeJ6IcuRPxNlO5JLAKDB3MJY3/Q4MeefND2cF/BgKhGxm5hIF2geJah5+LMy65k09aN6uPfRIOVnKaojYZ6W8YQxbFGBQG4lrdkNG4rJZ03E8lPDa4v8PJTTVSR/LVl9BK6sUBwq7OBjBeRDphrDW6VEINk8KYXhuJUvp7KRHOWXEl82vwXa9JZn+ywLkkNemmwu30QuUIiw7YaVvedFCXnKbqT6DX6jo0bQLvnBGB7bgT/0xdry2uJx4udFlfkdYpu5fU2VxaVBtF7daAmLew1uHUZ60VNryUM0ilMxcATO43abKj3nUT89RgNohTnPotwUOMzsBcyOswuRXyvGxG1xzSK7GASZGO7Shk+tcS0T+UA2mnAlkoSUZZGCq6VlvFvp2gnEXMtGCJbKeI8pV2LdHca4djNFG4kOcPKi9ZPdwqVBWIlLtHhoUb1r6fdrlEXKZODhcQb5pF2y6NuVZR3HN3dRgw0eUS9KBh/P5Xs3gc1plYMD+XaKV4Z0itFemVgaSkOZUNPTxKS1KFbwZToi4TBsSK/6P/9lYwmdSuHJDeSMaDeqanvtXTrAT4bCh/wc3GsNlteTCW5dLeyM3MTPRaNyC+KY4lSK+84bMlNd1Z3fab2SvHs812ikSxsn0lmbXs8RHeSV5xeyWGr+OaA+akYEWTqYODZgOQ4Ra23fVyffE/5uVAI4kZL7Lv51V4PbtdRdJw9cw/koDFcq6TwSyPdPCNZyM3VboqsJEw7xt/MSK4sceSpzwtUEKOPXWmqHbknQp/M4sZBcoJbMEuN/d0x2auU9e8Y0cxmkv2tOynikqW8H2a9IZ2JK9oVECaOZCVfR/dZ5YNDTUzkWbO8K6kn6YXppy9AVNSXOelxQkjkvRkcywFy+FlKcSpM0tGrIAftSWTt24ncD/1z255JCo4b9TnlP+n56VsN4J+q9bYA/Clav/qrv8rf/tt/m88++wyAb33rW/y9v/f3+Of+uX/uH/s5TdP8Q6HZgIwRFNSfT4W8X8T+QSOjDqI8qOw7S2IW8PMUuzQQlRRnQWF70KsbwOSfPhFMw+8PWd4LtBN5MKoIze2WUASShcKNPX6zE2RDKg9uN4y0L4eYUtMNgVZTbFRMdpfglYBrTWTr94zw7K7ktB03W/LNSv5fIXmvPa5hNc/F4VobipeW+sVYNGaRXiMU0TOLnYkDOL3WhImj2+uYf9jh1kRIrTzM3osipjZSUOkWilPpkBDgwwdHZOdaitJWDBnppaF61OCvU3QlxhQQJIZ2MjZUTqFrTXJ7hakV9Xs1W1sLCKJ/VF5ip0Iff2ePM9yap9vp8GMvuaR7osdSOsJUXtf4YigjoEI6kfowJ7k2+JGYMaTb4NHjTrqzQDatcQ9r2r0OFpL4ofZqzHnKYFKjKk2740h2Krp1T327FWDteUo3AZ+AeSlYGLMwfbGmoOkfIQHUdcLiPUd6nKBf5ahGS4GWB5K5Jj+15M9TwprDL4VPGBVsfFtA4/W+IyTQrEXSc+n85sfm5j1t16SgiKmYLEIuMGOVBPIzhaoMUYGdafILRbftJFbQyeusGpEJxJXgc6KGyWcWco+71aA7OcCYU4lps6Wi3oiSDLPQdPMUUytWj6cUX2TYhcLODPEoR0VFbMVxHO/UuN2W8iCgNho4z2jfrWj+/IJmS4wc9U4Qw9Wko971uLEUrfEHI9r1IPdqGql2ZXwaR47sEkwpHb/hVgka2l1HPMrlMDd0Mm5uNbMPHPWOmEn8QGDVAC6Xe0SMVxB3G1wRGewvUZ1m+VFDOtNc/pwUHe1eJzGANpIf93nB4yjmES9JMvmpmHPQ8j5lJ4K3kSQWiUDr1j2hkfdy/IWw/IKF5KW4hvWoI5zlNwgUzjKaOy12JfdoyOQhYP/OlG6WkVwb8ldJTx2QZ51dStrO4hdqskvF6BOJXXSb3c34VNzBiuFTg3KQnxjqew3tepAu9W4kP4frDyJuFEiupZBWsTeM3e7wGfjzjHrP33SkcfpG01jvOMpbUigH+6ZD3b/uRwp7mNFue5QJpOeWbi0weqGo9x3lfpDn5VdXlPuBeL/i6kMlWsIscP0loQYEG+k2nYCjW830U9FIZp8Wf3wb0tv1M7/eFoA/Reuv/JW/wr/+r//rfPDBByRJws///M/z7//7/z5/6S/9pX/s5/zGb/zGHwjMvnPnDkCfXCEP6DcC/O6uEOSbHRHCKw/1LEMXwjLzQ+mWmVKMFm4URNflYVlnlA86XAFx5AhrjohsmDSa7OyHDkdVWpotycFMzyxh4AlFgP2GkEJ6bqkuCxYvJiRrjZxkZ5bzP+uo7jhJiChAn6V0raUbSWcApPNhP5hjjzOSuWLw0lLdcYJsSKJgQdYiutXkZ7rvREoKSf4yZfA4RXUaVTjcrYZuTaDAfhQYv3eFvbK065HFAxlz6lbx5H++R7UX5HtfyCiwvddI5JZXqL0atd1gVmKymT2SNyAOJCKsPR4SkkisDLNvbMn4eOroJmL4aDb6Iu1CMf7MSuF6bYgbnYzOS8g/y4mVxdQy7lIe9HYtUVtIR2H4TBQdtlSYwhMqi7tfS6RZlLZONmnQ6w26VdjPB5gGwremZJcGVRu6kwGqVSRniXAFo+j62g3RY70Zw3a3Wtx2x3BPOsJ2pQhjh/KK9nYr71PZd8P6v68bqA8c02+K+ceN5BCxvCt6SOUUbiJpHKo3IzQfVnJ46TEZdqXIzowgR1qNHwfyYcvqrsdsNKAhv5COo5kJDNqsDMlMY1pFci2FaDeKuHFk8dCjZwnmNKPbdvihOMyzS8ln9TniXF0o0nMrMoiFlntrLK+PqSTpQmfS2Sq+WaBnCQRFaIykWVynNMcDQh5uHJ/pTBFL6TIBNO/U6EZJ3vBYXut2S7rCNIbqzy5pN6SYrsqU9Fz0mX7dQWPQ54l8rcCN3KI4lZGrT+U9dBNxRlc7AbYa1KmMpVevh2gHqrTUBx26eYMYStCNRlWG+kC6nioio+VODjhuKD+PXSlW9wW5ErJAfa8VE8O0P7iNW+EI7snot9qTcao+SxmOa9RmgwowfoYUS5+ntDsOP/ZM78wIOw3L++EmQ7re77ulJkp6T3+QSl5kBCMuc90q9NzK862/tuIvzlm+L4cs04B9nTJ83o/hdWT2oRwcwlYrcZC3POUdT1iT8Wp5yxNHnnxvJYWlBbR0ltOZZvzYEqYdbihpHhJNJ8Dz6692RCuxfsnLjOJE3OnVXpRIxs0Wthp4OiA/16TfGRAVbB3MpHMYREOcztWNFEB1mvmjiLmy1Ls/WQ2g4o/BBPIT/Qn+j7XeFoA/Reu//W//W/7r//q/5r/5b/4bvv71r/Nbv/Vb/PW//tf5rd/6rX/s5/xH/9F/xGw2u/l4+fIl0KMZImQblXS6NlqSl5lo+RQyLoqgFxYuMpJBC14MHcWJxr9TS3h9Lm7B1edrFM9Ez5KeJJhLS37+BsMgm4Df6ODRipgEtu9dkV5r2g1PcmnJtyr80gqDsIgkFzLm7KqE8r0Gt9Ux2VnyJmrJVr2G7LznDdqIcqKTKuc5g0NxnZa3HXYmnYXk3EIigu7hC+lw6E7MLW4QCUmk2Qgw7sgf58TWkL02krUa4fpshNtpSa8EvZEeJ5JBrMWp6b60ZP4osP4DRfY0w8z7wuJJAScZfuRF0zQM+KkjO0woDs3NGMleWdwoEqYOlQSyc42p9I1LtdkUHY/Zq3BrnvFayeiZFBvJCoqXlmDjDSPRfj5g8pkgRdyDmmZdCqTp40g8zciPEikCbzeoT4cU3yoY/u0R6mUho7g9Gd++6VrYSmG2awmZP2hIr8X9maxkcy/uLYhGEjD0ecLo05Tq2ZhoJNJNLa0YjZQAlbttGb2qCMlhKoXCwLG6HW9wNypIYWLnMmpNL6VjG5XIGJSC/M6CdN5rVp2Mid9MuuxMo74+Eddx1WOADoK8Rhq6DWG9mVY24W4tyKi8FlOT6kSn6IfSOYuFJww93UjMHYMTASW7YUQ5GYc2m57xL55jKkU39WinJEvYCdOu3paiN+w00EoKRXZmsKXutXYKe3dFtSe6RVVJp3P4vZyQSRdp7eElqhXXt1t3jPcWhFcDKTyHkfz7hZgBVppk2LJ+MCNqKJ4lZOfStYpJpFmLuIlEjEUbUQO5Rs1+RWgMdinO7vFnlvEXou+bfD8hu9S4HXHJ+7F8TnZqSRaa8VNBr7ipJ1rJ3C0PAvWujKSVVzf0ga3fMyJHSALdaYFdaLJrRXXLo1tFeU/MV+WTKeZlTsgjl18Wo1A0sPatBNVoVt9bJ32Wo2+V1PuCP8o2K9E4e7mOfCbRa/mForotha6KkMz7zGQbmD4JtE0CEdb2FnJv9QzE7NEcPwwMn0thZY/l2WNnGtUo1rcW8vvbFebS0rwa3VAHVKeJlaG+3eGGUlR2a6GPvZOi3pxkmKGD2xW+f5+bDUj6Yt2cZKiLlM2/lRM1NJuBZjNgGoXzGjRMPjPiwv64wm12hHcqeW+1FPvK/4TLpzdJID/qx9v1Y1lvTSA/Res//A//w5suIMDHH3/M8+fP+Y3f+A3+rX/r3/pHfk6WZWRZ9g/9fkgiKg+0ZwPirmPrf8hp1uTh0O0F1MzSbvqbWDS/GKJzwV90k0DxXRHYA6i5uGHTOVz/UoM9S/Fjj78WXIduwW12ctI+TuCg4+p7W7hbjrWDOdfJmG6esXVrxsUX62AgBAgjD21/BkkD3e+vU/zcnG5sWW1IB2X8mWX50KNLOZ3bcwmjb9Yhv0QyRBdDzMr0DluJSSpvCfds+gvnnL2eMPw0o7zjYeiIK0uwsHvrilO/wWh3CZcDVI8zCalsstFE8jNFuRFIFobmdQFJ5OojUEEKypCLYaJ4Z87y9fDm9bfXfdEQFPmJpV0L6Nsr1MuhjKidaK6UE1G48wqz1LgCzCdD3JZn9XhKsgb1riOcWdF1WuG25eeKxbuOkBh8ERh+pxAYtoWrjyLZ3SXtsxHZcUI3FfabG0SWH3ao0hKtuHm7NY9bc+iho8sD6RcFKRBnhvpezxV8mtLsePznEyw9Y7BGgLxWQMf1xGCcGCfs65SoI34s15Ife0LSd0svU/xOi7lI8FNH+jzFZwp3q5HR7Jk4nFd3PUTJDG59Trf9w1Ffu9aP9C1040C37bAXlsl3U8nXjVDdb0XzOexQ3lJvSaFQHAkqJNpI/lqzeuQkXmwu7tLsMPlhHNc0SA7rr10TP52CilR3HarWzL+5KU9PBc2OJ9oAlbkp8M1FQvIqE/1nlL/XbTiGn6e0P78kPBvBMFDfaaWQ3hGW4OBYwMTzH2wSx57t/9kyewT1xZpIdnVk/Ewzf9fL+BlYG9WUv7PF2kVk/kB+tuGjGRxORFdbODHRpAFt5eBRfDLAFYr5h+JMdgNDNJHxU0M37M0frSY/NVT7jjD0tIl00ruxjMexgRh7d/JBSXuRoxspgpXXOKCd9q/5xzXh2krW8n1HeiZ6S5AYyGA0upHuLshhqH6vploK9LjbdKRnluRbI4oaqu2IfjmGAy9u7Z5wEIy81nq9IS4KkaUMI8WzhHoncPllBccZaqsj/u0N4gSadXGlqzoBK1iVkEfCRLSuKkIYe65frjH5zNBsJiRfmstB9LNM/r3O4ApN8nCB2iiJdYp5mfdILOnm5seG5jQj7jRor+SwcKdFlYbi2FDd9iRXmqsPwW1Loozb7PBr0LyaQuZZ3pH3N/mioN3wuFZjonAQV7dBX73tn71df/j1tgP4U7TKskTrP/iWGGMI4Y8e8B2HHqyMg5JLQ7UjTLp6J6IWlvy18NgGx3/w34upnOCbrYAbC4y32e8o7zm6sfwdNxUnohuJrsVtdCSnCWq3llP75hJTAyYyezmV7txpwsUX6+IAXGnClrhIk2sDNjD8NBUR9/fH+MtMXLydEqjuQgtmZKe5yVGt77Usb0fUp8OeFwfNemT44RVuq5OoKAWzZUFynNJ8RfAWMQKZsNiuvrEtr/tSEi7MSjM4FOahHwZ8Eai3ArrWlHccsQjkR+KuDFbGMcml4DpWixx7ZSEqCZZv+1GhERF7/lrjjwb9C9hDn9Nwc2I3q15ztutwRcQuhD8HMHhpRUOkYHB7STfxVLtSCKoIybXoNNt1iSbb+4UTnNMkyx50HKXjNXwpjsbxY9PrtCB/bVGdZvj1QrpVXtGtifFDlQabOxYPxMHt90X43mwGyv3A/N2A2Wqob3ekhzKW06MOty2jPoIi7jZkJ5bBkcHsVUQF2VD+PBm10pmdOpLnspFW+556C/IzYfCFNArE10nh6fNezjCKco1pyF8lJCtFtSNsy2ZDsCtqryZepwLKHXqKU3mtlYe408jY8sLiR4FmR9zezZ1W8oob6WKtbkfa703Jrvsio9E3T82oxVQTkyAtSy0FMbXEvrXbDjcJ0kXfkezZaj+glHSr0nODPUtYPuzwo4CpuUnaiUre14v/S41/pyIkUcDqC8mwzs6N6Fg76P4/W9RbQXRrG46w1eF+b11GxFERW3FVD58kqOOc4jyyvA3L+4HsJBHQdUCKkJ0oXcg+iaQ6EO7g4GkCI8fGnWsZqzqFzj1q6KS7d1agOk0YO7rbLd2GRwXF/KNOOq/XGem1prrlKA6FqVnvOXEgd0o6xw9X/WhcXt/kuYDf3TCKMS1AvRtYPPJi8tkIxCRIF7OWQnz5Xkc7iTI56K8dNwriEFZ91nIuEPFyX7R5ptcCh05DKyai8RNN/iwlO9dkl3IvxNyzeNibapyB2qA7KO866lud4HcA94MJHOUCHzdy0FWtIrsW3TWvM+xKibO4Nqj1lubLFbrqOYEGssMEN/XohSWbNNJBvkhuUpBCJtpgO5P7RA0dMfeCi/kJrrccwD9d620B+FO0/sV/8V/kr/21v8bf/Jt/k2fPnvHbv/3b/Of/+X/Ov/wv/8t/5K+VniTYy4RYeOxKUW+KOSEqYNpRfSB6I91J18iNhUc2+cQIaLlW4ghWCEj6shfYn/bMvWGgm3i69ysIiuxaEZzG36mpvrmB7hT5ek3UkbXdBelcMX5i8BOP25AHZRj0/Denadci7YakACRXWhIeZoLZSFZK0jcqiytg6+si8HbbHSFFuitbok+qvrXO3sEV1V6g3vN0p4X8LEExvj2HxmALEVS3Ww7VKuJ1KogPG6l2pPgaPjMMXlrSaynMbr1zjrm28jo10k2JOoroPoDpUzNsqRgeatkgW0GmmKp/qF/JuDc/tqSn0nxXQRyuIRVNk4oK+3ApP08mRY5ppaNb7zpWZwOSmWH8wSUYpJvYuwL9ugMTKWwHXwxJr8BWsgFlF4pqP5Je6d7YEGlvdTJObSVSTOWeZlfSKKod6fasT1f4sRd342kmiSibjXRMdmrcdQo20m6LE1S/yklORXyPAs4ziIryjqdbJVJAHg9QuccfDSQB4nWCrSRlwpSadt1THTiqhw0xk2JIuqmiaU1nIrgvziSuS3nRKbpREJfz1BGHjnCVEbNAdUeut2ov0H1lRbvhiUHcq263Rdea4khc72rV66o8gmVpfzgufmOqiJmnvdXevL/5UULxUnRm6ZVm8NJKCs1xglmI9tCUGrswZOca/2qAbhXtpsfvNyRXlmStlsJPgx95wm4jhctVKpnbTonmMY+0Xy5p1wPFKytGr015/2MqukiWlmrfy73lQediHNCdGKnasVyP+anGlnJtERT1duhZeHIP+ELg07aSVJn0Rcr8e5syFs4i9llO+iwjPUyxy54s4DSjtVI66dOOZNxKIslzi88iW7evJVKu1z0m11pg2QuNfzGkuuVYHUTS3fKmMFNRHPS6T0XRlaLZEw2fvZaO8fKRE2TRmZgqkoUmZsIpVRutjP1Lzc6755hSk816Y1nOzfMuNgblFA8/OmLxjoxfo4HFRy0YcZRnF1JMt1c5m1+XAtBeG1St0XNLdTbgTa708oHDNKBSz60vnbJ40OtLrzTtWqD5cikawkVCfC1UBZBrzxWgS0kSaS4K0fgeynQimWsxyjnRArbrUWIao/rJV0/xj+nj7fqxLBX/91CG364/kbVYLPirf/Wv8tu//du8fv2ag4MD/o1/49/gP/lP/hPSNP1DfY35fM50OuX+r/81dJpjZIonxoqxl2zWtZr46Uh4ZT3o2SxNj0yRB3l1y4HtT95OMXxhWD7sGD1OWD4Q7IZuZWO+SU8YCVolpJJ+0E2DmDNUlK8VFMV6RX0ylFFPlE2p2ZCTeHalqfc7VKPZf++Mk092RL83FpG5nzqUFZdmOlOSb5wFikNLsx4II4+9sqJ/MjLaiVlk+NTgRvTZr9IV014KBrx0xqJFIqmKABpGu0uqLybyPbYKN5Xiwg0Dft0x+W5KtSvpGsopihNNNxJIK0mEIJsR/PA16qYBu1XTLVLM0KFf5nB/JRnMc4nTax7UxMbcuKObL1foF7nEdJUWPXSYl7l0wwrp7gUL/qDBJp74fIDb6shfyai8u9WSDVua04EgKJTgVQYv7c1osrzjGD0V93OzjnRTNkUXl59Y0f3ZHgjeaeLAoyqNLaVAzy6lEzn/Uisok4UkUAQjhWH22ojWaqGoDkSknp0bTCPvfXapKD+sYZmga2FXkgZUbWDoUJeJXJsrjbld4l8MBdC84UXz6RV23OGvMrKdkuZ0AF4Rs0B+bHGDSP7ejObTKRH5Ptr1gL27ornOUblghOJ3JuTnsLoj72t2Ltd4deBv/h/k9UquDcVrxfJeuBkVxq2W/POcdioFyM1oOA2YzONri7m0+DWHvUxuulZ20hJOckyrBHi8XeHOC/RGg3pVSOda9y54K5xO1jqG45rmU8mJbdcEUVI9aFGVwc403Ya81sWhJb4ZVZsoWbVeih/Xp/KgxWGdzHqn+zttPwK2dKNASKQL6zc61Mqy+/Cc+d/dpXzUx7udJmI8yyLptaa+25K/TMW9/W7Fu7deM/8bdzj9Fxr0K0m5cJuO5MzSrXvuPXzN8yc7TD6zLB5Ihy9/sKB+OhaJSo+eMSuNG3vpSI+liBy8NIQE6kc1yatMtJolrN5vyUYN8fOROKefGdpfXko6UURMXKUUcX4oI/ioImrk0GcpfionZnsho+v23Qp1mhF2hAU5/YGl3urTTC6lM9xsyCGg3hfDSL5VUZ8XJFdiSFJBzD8oeU9DCvW9BrWwArHPxXk8egFXP9ffK6eWZq8TjWpfONtRR/KDAab64fPTVAp70vDJ/+P/zmw2YzKZ/O/div7I682+80/92n+KtfmP9LWcq/l7f+c//bH/DP9HXG81gD9Fazwe85u/+Zv85m/+5o/8tfxuA2WGQk6KbrtDrQzjzyzz9xVse2HlFR6cIuw2cCVRYvVOJD+1NNsyughrHeX+D8dnetLhncLUWkTguWxO2XqN/fqYelOQCaHw0mP2Cp06wiqBb02wQxnFuE0BAw+OFYv3PN1IWFl+6rlcDDG1CLpdqah3RKcTewacKyIb31Vc/LIYO0IumjbVdzlD0qNCvGJ1z/c4DOkwht1GNtLTvI++Q772egdOUTxLaS6nhFGAiSOoKNq1IhJ2WszrlG4g5pKoNG7NUW8JXFq3mhhlbC2GASkQvEsk9uzrQ/ytAHODebikXghWJT9T+AyyJznhS0u8M5QPA/o0kwL0MpNOqFeoexXuRSEGhEYTtTiM/XmK1mLssUvIZtBcZXSTFD0UY40rek3iJN64M+2kZfl+QFWG5EpTnEG1358LlSQ1SCGsGbzSNFv9ezAIqI2WbpzJKO5CYsVW73ToUpIh0HIoiCbSflyij6Sb5d4v4dMBvgg0G5pi1FAt5fOj0lBJ56wzkldLEkivLJ0fykHg2gpf0BmGLw0+SfB7nnqWgYnYUpOeWKrbUvTUVYr2gkPyhWgVw8mA0SvD8hFU5QAOOrqRJZ0rdCNFUjeVUVt90JGeS6pNcdx3ne4HQhowhYMrS/Iskwg5r0hnon+zCyudvFnC2oNrZqsp6fEbBI7CLAwupoyONL4At+eIQWNKjVcZDKTAVVFMUcmFxMe11rLsBug0Ejb4YabyQqIT7UqhghFOoYNuvbdXKkG7qLLn0TmFXWjCvQqW8vejidBpxvsLFnZEcm7xE0fyKgES9EHFydE6ejcweJwKpkb3xaSJqEsZpapO2HXmZc7n53fI39GCWVLydwdPE7qJyAtefXuPfKGptiPFrSXldUHz2QSloN4KsN0Qa0uslYyasyjJKlepEAICxKpPHYqRervHQK1VrHYcZi5jbv39EcNSjDr0xZgbRUxlZJoAJEeScRy2A+Y4k4PQhaG5zIjbLfYog3sl5Z6h2+tQJtCYFN0o9j58zdGLTVRl0A70N8YMkHuovtv26UW6f7bB/JFoZv0oSKxlpXHjwPWvdlBa7KRFH1phg3bgB707+SSXQ3oUx3c6EyzU6iv1j7x3/ChLxYj6EXtKP+rnv11/+PW2APwZXeYkIz5o6RoZT9jc4RcWN0CMH5c9z6sSfZmZZ0TeoBk06QyaTTmx0hjYblCXKW4IWd7hFzlEwWrEBNTM4q9HNO90mFGH/lz0bvbSUpwqorEy2hj0Qu2DWpIwvuwIF6IFjP0GRYT2cIgByv0ozLY0Ys8S3IYjmSvqfU8zN+D6MVGn8HdqwlLiokwpGhvTiMDdVBBSTXmvk+7QZc72++ecf29bxqQpdI2GRHI/VRQdZfEko9mU0d/4GVwPEhH2a2EFql+Y414PMY383ui5aLSiaNnxm9IxeZNZ6wZ9Ed0qutaiU4+pE/mcD5a0swzjNevTFeeLNbIL0Ria3jGrG41fJSS1wo1El9btdSS5Q50PhCdYKkICLlfU21Lo6VLTDSNu1MeMeYWbAgHcMrlx5XbTwPX74k521uNyAfaWX60YjBrc9Zq8/juBbL+keS3jTL0yMoYuflg4mgbRAVrRvPnKohLIrxTVMiG8W6F1xDwuKOc5atyR3apoX49QuSfYgA7iKrZPctwoMnqm6MYJ9VdLlBe48+qul4NMqzFXifD8RoF2XWMWGrfpyIuW1VqGmrbkXyt6U0RkdUfGiG/GTr4IuKFGeUiuFc1mJIydMPe2HGbcUQ1TJp9ZdKMY3l/QNAntxBO1gKNdLu+xClIk6/UWX1rK76yTBtEytjuO/DxBxUhIteBkGoiV4f6d1zx7eRuz3qCeF8ReAxlMX3gPpWjSnbnhSzJPUOstcSFpFHEh1+nwqWX1QEwXbiKokfxxLt3hSbxhZyafDWjeqbGHmcgZPSz0CJV5orLkRxKh5yce9XwAU8/Go0uunMg9zDtL4oshw0czVvM1Bi8s6QJMJ+7r8qDFLVNUrRl9cIULmsmDmhAVWkVOLyd0UaFf5FRHI2wlOkrlxe1fqwyzW+O9wow7GZkuLesPrri+HhJXlvzI0ux6/KbHvk7wA0/5cgwDjx8EupHoCetdSRIaHBnK2x5GjlgZzNLArYp8VHF9PURpKWx1LYYj1lvB9gDxsIBMBGv2OBNIfhY5frJNslmhj0eCdznwmFLjRh5lYn9glq+7yqUAVxFi4ZncvWL22QbJTBO3PL5KcSrB73rJSF5Y4sBjj7Neiys642ZDcFLKKZi/iUP5Ca3Qf/yoX+Pt+rGstxrAn9E1eg7mOJM0iLnGfjJg+FIeFqYVtIfoqHqEQipOteJE498tWf1yCWMnMUkRsk8LdCvxVe3zEelM0a57ug3J5nUTESCr3BPOM6mg0kA4qHFDKXzcEKrboovhJGN5RzI5V/eFKajvr344kuqTI1SUUSLjjpBHkktLda8jDp2kGOyU3P7KCbrRmBc5utLy/fQGlfKgx5xsS3C6XhnMRYJdKC6+vY2fOpp9R7fhKF4lwh7bEnbiaKOkXeu7i0tFsyFjxTevw/IOVK/GAshdyUbXTuhxMzK+yUctulE3rD/18byPyHJwnMHrjG6t10I+HkpW7UnOxZMN1g9mVLedaJGUFIEAOCXGkhNxUFIbXGNptx3tVMZ17Vpk8aCPkRp1hDyw8QP5HhbvSZ5zfmIpjqywH68t6VWv29MRv9sy2F3h1rwYcV7mrA7HwgScROxC8o2jjgzevcZNRD9WnCrM3Ep03McVqEh9u0M7uY4wkWZburn2eU72nQHRwODzjOxxzuqLKSoNMEvwjSGsLPpFzuBIur7XvyRz2HiSE2cpgxcGAuQvUmHfqSjg44Hn7i8cilFiZag/m4IHzsSZO/pC9F4xC9hlHw/4YIkt5fpJFmKaMo0Apk0mHW71osD23SQULI7GtNcZyaQhFPLa2wrKW7LJM+0IlynFekU3FU2Z671A3bgf25WK5d3A6q4nuTa8/J9vC69vmZDMRUtKH5nWjeV6dNvdjUkr1pKnXXynYPjMYpY9juRWw+pRh8o9MYHBcwuzRN7DtSDShzTgxoH6VkdcWoaHSnSRrcJeW4bfyXFrntDnfqtGSxJMpbm4HAn42gCfjPCjwPxojLm7Qv+vcHTVXkBdJaQLMcpsDEuqJxOODjc4ebHB6fd2CJ3GzxK5D7TwPotT6aSGTFzfRCX39xc5ardGTzqun66Tf5ajvKI+cBSvDMUXqZh21lqyS42ZGdJLw+4/dUR2odCTDrPV4H9xAQryxzI2Dmkk+WzA1dEUc5wRVlai2ILIW5KXmaCpbtXYlXQhdQ8hr24J93L8mSG+kOg5P4iYjYZ4u6LYKVn73ZTJJ5bROzPcxJN9OBN97TtLzJVl9ukG6aXIUbpFSlzvGG6XpBcGe5rKc3zgRIO4Fm9A8nHoUPs14VYtMo+36+36Q663BeDP6Lr+qhOQ69z0mb+SJWkq4VLN33ek15r8XB4kMZUTZLUbcfOUeJrDLME04kYcnESSpXSOhDfV87WCYErsTCLN7GEGGsK7JdrKRl/d7qh3Hc16wExa3L36Jv2C/RpdaikgXw1ILuUBpiJ0e63EkDWK7HFOmDhxmq4ENq2dono94PkXO/gNYc690cRFE+m+sroB+/qRwGnRMH3/UgT4RcTMLMmZFQD0NFDd9uChOFaUzycy/jm2MgZFOpooJCbKvHGmRsmDvZKxjm4UdqmFb/jFSDaNbYnBap+PaHZEH+QHQeCyGmwl3bd6V/hoMQtcvZqSvZZxVNyvb1h0ZGIWiAaSC0t+YrEnKWu7C+xKbmnVgTkocYNAvJD35PUvIakWS9HfTZ5G2vVe63chnTrlRT9pUk95OhSH8YYkKZildCPTuRJX5TwhvTCUj6ck1xq31bG4H34oRD/OULnHXloxGji5XuJaBybSHrSY9o0YXzbM7FJj84449JjMi/GhiCzv91DjuZWu3NRRHBrqnUAyE82VLwJ+s0Pv1KDg5T+4hS77bOaAmIp2GhaPHM2GGB2g561te5pZjhsGmk3P8FB0m3YlrnWOM5plhgJsrZh9pcXnAT3ppDv2QpJuVo9alvdl7IqCJHeYzYbm1Qi71LR7HdFEBs8T2i1H+V5DspAUhxvMzTRSvLKiTbyU3wsPK4YvtQC7gzAqB6+EUUlQwtT7sGH1Tidu+UtLrKw4bBNxIkcrRpGQQnZpWD4Qh0Us5NCVnVlWf24llIAzSZuoduNN9J6p5LrWKyMRbIuE6v1G9HEbQVBNQHg2xKdiJOqG3Jij7BLsQvP0cEsOIC9SVCvdenMsLvDJY4lFxCtmHznaqRT+bhiJQRiZ6VwRX+eElQUtEhA71ySXRuQohRTfwYnZ4g3W5+TvH0jW9MucEBTx+2PsQt/cc0w7mm0POqLulgKWLwLJXLBLykk6EucZzUFHcqUxr3JWtwJ2bqj2PdWfXaKbHg4NhJMcv0iolxnlPqxuReZXAwYvLeXzCXZmCF9IdJ4fBclH9vKextLgvzWlOegYfXglB1PArzncxIsh5naJKTxulsLr7KfABRz/WD7erh/PelsA/owuO7PEvRq3JjqvNzmWyUpRPmrloa5heVuyMUHE/MWjGRhxV2JjD9/tU0UGshm4XJArKkgg/RtBs8/+V3/nPEcf5ejDnPTcYhdGYLwnueTXGhmF+GUiOZ0Lg9/ocNOAvbKEbWHFZVfiXkaDubJSNE0d48eGbiqmFnttUEZ0haMeAJ3tlyTfHpJcGsJWS7JVoWsxLVyeTKVb1GfAduteeFsjcfaiJTfVlIrhU0u962i3PK4QLmC8I50t0yiKU0W36Qi3a1a3Pcu78aYwzIYt0QrINzlMe3ep6ospgxp47HmCrhQhiazdvSYmkWShKF5JRmzUoqVUpwLxlrQGQ5xIUU0QvWVxorg+H+GGgq6xlcK9LmTD7mSMpTtFdibjztVdx+xfWBK2WoZPxV0djHSBo4myofRaymKtlvdqr6WbSH5sSCJ2q5aYrNAX6a9S4lYreIqVYu0HCnuUyfiyP2Aor1BXiRQms0TistJIeLek25XunjsdoOcWP0uJaeiB1FIk2pXCbbcMP0vFYblf40b9NeAU+jrBX0ruc7fTEVOBWisH/p0a32omn1naLTkAqD7Kzs7FjWoqwe8s7kmW8JtxaHGqUSbQrXmabY9eWoGEezHjuDWHKRXF85RkpukmEo3YXme4hZwekoXcZyHp3d+NBqdJ54CJDJ/aG/dxUkKsDVd/rhHe2zKh/GqFz4W76DPRsXXrnsn+AjcKjL+RoQeitXVrElNWHFnimYx1m80gKTZrAd30Bf3Kkh0lmIWh2XZ0q4T8zFDuR3SnKF4rYiIpMO26dKrVbiP8xzNDLA3tRO6b/N5C0nC2OolWtEhiDnIoqvaiONuPMwGAPxJIfcjkMDN4aZk/CuhKY+eG5Fp0mG7d4TY64ixFfTHAp+L6Jg3Eof8D6JB2Q7r35W2HOk8ZvtTCG41iIml2PN2GQ+lIskI68RHsUpN9kZFcata+leAuc3SftGFrRFtYQzf1MhZeGrr1INFuK0U6U2SXBt/Jc85NPX6jI7uQ68oei8DQVqBKcWnHjVbuzRZCH9HnH1S4gSSbkEj3HBW5vhxK6ssyoVirRf8KdGWKnyeMH1vRz96u/uQ3l3/SeusC/lO13haAP6NLV4qwSsheG5r7DX4cqG456o8rTO57PAxUdxzVgSM/FZfa6tUYOtmos9NeCL8RRNBeKkImzDRfCIst3q5QQ4etoLnVErNAcrBi+LJ3Go4k49TnUUwLnSKdCt7D5+IMJvf43Ra8wiw0fq+Vh/eh5urLkovZPqqkYxhBJ556MzLcXUlG6YWCuRX36RtO3PMh7bqkOaTPctpVSkzk18PHgiPRtUTAqVY2YulwKeLQ0W5It9C00gHVay3d3YbVXc97+69p7zcCVv7l/oF7npFdSioHCFbHvRwS8sjo00SK4mGPFVkY2RhmCem1vNamVlxfjsDK6+KLSHXbERNJEIlGxnbdmpdUA6+wCyP5pndrlvcDdFoK50rRTSLZhaHb6aQDmUrRJoBmL52+T0eSPzxAxvn3GsHiFJ7Bzgpda/zYU81zSTI5Thl9dImtlHQhXhe0a32R1EnnJXmRkSxEU1nuiz6zT6EjJkHylDtFdpKQn2rKd1q51r4xxJ6mgmxR/cGjU+QbNc16IHl/TrsRaDckuq3eDlR7kfGokiJ7w7Hx3uVNl0p5OTAMXhqmv5uLIehcgOnVdiQ/krG9corJY83kCaiVEbf2KFLd6XC7rYzQG0V5K8AiERySke+vW/Nsbi5xB41EgU2DHIAiYPoOd1Akl+Kk7j5eiTmg73rmZ+L2XtwPmMJLgXGrJfZoGxAtr6m0GJxOcnGuBwH/EmH0hWV+OSQWXlJkDnPpxKVBItJ+6UpcylsNBBi+FG5cN5YDXEylUCUo1MBDp6n2HcOvXpJdipZ09CRBO8Wjn39JyAPmaU43kszp/LUlP9OYlcH+3SnZpSIdtazebek2HUnRCdNyAc0t6ST7IjI40tjnueiPa7lnXSGd3TBx/e9Bs+spXiaoUlJ7sgs5uCqvMGmQIg35uyGBwaEhmF63VwS6kRTa3U4nSTPnBjO3+EVCO42Yodwf7YOa+OUFIYX5w4BZamISGTxLJKd6K1K+J3GPbyLrYu5Z3g1kV5I0pFvQhznVnQ5da7IXmYxqK9Fq6k6urWynFHrBsKG9JferZBMrfG17KUFk+p2E9MRSPEsZfpLJ1OXSMv1/jRh/YWh2HfZ1QnJleq3vW/Hc2/VHW28LwJ/R1e44UJHw3grmFl1LV8kvEtRhTnaY0GyKu9IsBdNRfVCTncvJO9muqO901Hu+z6oMtFNBrqhWSTeqVdKlqQ3qwyXJqBVH2u+OWd3xgjtAukgxCdDrafhsSDatMT37Tc0TmFtoNX4QGK+VxCzS/upCNvTvDdGHEo+kW419luMGkfrpmPQ4YXXHE9NI+W7L6l6fp3lQ47blhG0asLnDVDIyazYi5YMOohQ76f4K5RTpTAvYuewNK0Vk/kFHt+mIvcswOzd8/rv3UBcpKsBg2Ejm68jRbEv2b7PtpZsVIb3QuBG4d0vMfklIYXikSJb0jlfRzTVbAX0u7EbtVR+Tpui2OvyBbN7JUpIStEPGPhuSk5r/oEB1kiSiz1KigW4i7D7VSFFqVtKZ6O42pOcWsxLjQbfV8cFfeILaaBmMpLOTv0qpn48lTrDvkIXNDrffcnU8we034iI1kXZD3udmTTbAbj1QHgSag67PiJWRMkFhryzlLY/yclhQEbLeXLC6FVBIcSxjysja9zX1dQ7TDmtE8zh4Ke7fqMFtt1ydjyVl4Sjh/GjK8EjJmNmAn3hWDwRgXu94GS+fpjIS9tDdbgTQnUGzIYcL/8FKupS5VK5vsrDfFFw8KMmPLbqB/NRy8Wwd5gnb+zOSmWb4UvSDUYvmNN+Sjk4sBNEzfG7wg0h5z1Fve8zCoA5q/Moyf99hXqfYS0s7BlXL/aCcIrvodYrLvuOeRGytcEMpdCdbK7ohqAcreb29FEpVJZ1njjOySy1j2bHkMHd7HWZpcEPwWy0xipnKrAxXxxOqfRmf6laK4M+/eecGHTR6ofFDT7PpKR+2xCRSHkT8zy/oTgYUz1LslaWrhYm4uhXAiRscJQVVu99ht2uafdG1RQvDz1Js4ege1Pgsku+ucF9aokLf/Y5Q3XJimnkqB5PsXBzHfuwpb3nM3RXJUolhKo8k15rh5ykxla5mNJFiU7qpHBakV5rRN3O6wyFuq2P6ucKtuT71REbw2oE5T4kmEi8EMp0dpuhOsborhX83lsOfXsphurnVom+XMl6m/753G5rjAZMfJFQnI3Aicai3IqbUmCuLnQkvdXlHZBHVw0a67mNPt91x8bGYu9JzQ3YlRXq34Rg+N8SzHw3B8iOvt1Fwf6rW2wLwZ3Tt3L6S7toXQ9Irgx95qjsOPRRi/davnN4AYLNLRXu/JlaWZsvjxoHudYHtT8dE0f+onYb6/Zp0rnv0SyA/tRTPE5rTAerzISGNLO95ioMlbgDJlSa+U5KdWdRCHMcqKJpZLu7e3nRgev0QCtpOOibtq+HNhhNtxL67ID+VLNF0piXtI4LdrjGTluxliho41EXKdFySDjqqvUB5x2EeF7iRjG9sqW4Asqo0dEdDkist7LtBJL3QApIdeFQhXL+wkjGZG0bSd+eivbpf0raWZL3GDJyMZSadsLqiIr2/FI3g+yv8dUpXScev3ui1URqarUC5H1HbDSGXcXG7Fmi2POl2yWizJH2aS0G8KYWVH3v8LMHMDLoS7REHNX7qpbvXoyHKuw6z1AJZvpSO1e7OTCIAvYy17EXCt5/eJn1cUD0fk14aXBFJZ8I1s9sVxZNUCsva8IsfPUWfy5gzO5eCzA1ib26Q1y4MAsMnCa7XX0rWqsCnk4UWnmAtY8zsUq6BZK5QHWSX+qZjef2hwHf1eUL3++syah1ITqx20gVVRviM9Y5DryTGLJo+ZzgXjVu1JzrBZlOSM1CCJ2EhGBI3hNV9ydX1TjpH5jRDX1vSn7siWum0RRPxTsa73XqQyLM+2WZZZXR3G2YfeZbvdmAjIQu4zqC3a0HsLKSTmF5ruU6Dwo9E10WQIiGZC5y9G/f6s5nuTQhSiEYD9ueuUQM5cPClBSGNrMqMZtvTXuXE3GPmBl1q1OOB5E5faNppxK+J/jQmsU9+EQ2vOU/BSXc2WUpH0FSK6nZHuybjz8GJJtmo6dYCy7uB4d6K0Z25aDujoF2aVSq6yVpGt8mr7OYwQ5ADjKk17pbkJKeZY/g4EdTRfkuzKa+xOskF4n1R4M4KKbw0dENJX+nWPN1UEjt0r7e0M4NuFO1VTrseCIWn2/CEVKLldKVurp/qqiBsdviRp77TsXhPNNN6brn+KJCdWtILw+BIOvduHEhn0qkXZ5oYVIrXiuz2kjBxkixTKnHkbjTgNO5MmJ3Njic/sujnOabWlAeRaAPj7aVwOY+VyEuMSCLslfAi17+noDKE9Q57aSVuU0F8pxQJQ4TBu9eo3NMNuSk2f1LrbRLIn671FgPzM7ouPt1k/dBQbSuaXU92ktA+qFEa0mHL6+uRdIauBTZMJ7FCRHodGKgXBeZeyeBrQ9EL7ko0WPOgJn2WY95Z0qwn+FWCGTi6mIpgXivUP5jSfljDLIGLnLyjf3Bq1EcLzIsh9e3uxqFnGojGUJwq6tUIncs4SHlLtSkOXdeM6e450nNDu9cJ9qPUJJ8OcfcbmoOOtM9frX9nC1VEUsBnUmzkJ7YH4EYGx4pyt2fU7TV4n5FdSPSVH8rYyMwMfiQP1OKVpRtF3MQTXowxrYJnA4IW+G20kZgHis9ymk0ZxVTNgCGgvhhgsoi5NFTvNQynFauXY5Ldivh0KI7aPUFBHNy65PhkHZN6mqucJir0JKDXWvwskdSMUlzOrhCky/KjBvMsRyvoNno39huQ9GagXfN0a5piq+T08y2SUtNu+B7GrfALK+7SLBAy0TVGDaYxrExOUUK7Htn8B4bvnbyHTqUQi6mwy/JzLXq5vmOWH1tWd51oLh+uqK5yzFIzeqGZfbnH8BSe5p2OZp7Ipr0laA5bGcxSS6G8HdBOXLPtNDJ+ppi9F0guNe2+RAk++PlDHtd7pGeWdtdRf7Um+84AFaDzGTxasXVvydGLTXAyGi9ORaOlcg9L0RJmpwIEnq6vuAgKNgLxPGP1ZIq/26KsRNvlg5ZqU2Nep/hMor3cxDNOO9xnY9xEeIqmEobl6uM+5q7vEIUs0K5B8YlcJ7rSeJViJy3+POuLGelSmWFHuyfaMF4nN6kY4ffWiO81TD4z+JdjcgvqaEA3keu6OAuc/lOR4tBQvV/DIqG8J7nTyajFrNXEp2I8WP/4nKvvbeGLIAYnIHy0hEWKcgblNPWuQ486bOrprkW/p51EKOrTjMRDty5mpuJJRvjKguVFgZlLHnZMRNi1deea+ostfI5A4Y8szWzC+mnEDRXdVSLduR/kdONIcmTpxmKuKt9pcQPpWq99AuXMsnzUsbyrcWue9ExA6vW+JO00m57kymKXck36LBJTKbDdVmD4eUo6i9Qbck0IbaBHNJWKdjOQXmhmXxFmYnqpGR5FLj+Wez290ix/rkbbgPlkjLrdSirHODJ6bmjmOdmXr+k6iytHcqDdkHssOzNyWDq3+Ofr6O0g3M0oBVxIJdrSXlouf63GaMjylrI26EqL9nmVwMizHAbsp2vEqWgGzP3lj3GXebv+tK+3BeDP6LILTbMmztLilaG65VEXKX7i8NGQTxtiJSgD8yInf3/OyinMtSV/sGB1XZBep7THBc1mr/OaWdRJAkPR7dQvx+JcNBE/Tyj2V1RnA4rtknIwlIizsSM9SWSUPPCEqKivcqxXbO3POGeKH4oJJFn+EL2BBv06QXtQzlLtO7Jzw3hvwaqcMnycsnrYoZyi3nPQY0babUd+lOCGsuGmsx7p8W6N81oC1qeO9kBGO9QaNUsJGx3VSFAT+YmlvtWRHAofL6TCTAtpBBtJt1cCHA6K5NwS0iAxaL3A/o1+SwWBIqtOS06n0+jrhNUsQW/XhKd9osV6IF6nmFpzerkrhpaRxiwNwxeakMHKpJLtOw2AJlhxVgYL+joh/9I18+OxjI+KgO0kPs9vOPRckiC6J2MMCLMsFb0jiDvWF9KFVR6W9z35aQ/GzSR7VbWKi1/2qMKhLsUgkp8acXbX4Hci2onGKab+RqRurUe9FsZjN0ISYSKYwuHnqRhGqr7gbGUzFsyFRAVmVzJC9JsOdyrdJe0V6UlCNPD48R7pucR/DTZK/PcmknAxEqf04B+MOPmqwV5Z3FjGz9VuEAOQDYReO9a805AOWi6erWOWGr/foLzCF6IzU1FMQU2eovZq3MQTtb7pkM/mA9JW0Q08xRepYIgUDL+T040gfzCnPBrdjK+7kaSroCPFS0uzrUnnivKDBvM6lUJFRWLf8QFxijsVJR3mdUY7lc44SDe52+6oXEJ5oLDzXm/2NKe+3ZIdSjJMaAq6YYCJpGqcP9tgcKlYPQjEXjfnXxeooGi3BUsTDKiFwdmI6Q0T0UZ83afXbAsuKj0zAie+LLAL0QDHoDBzRTuNXM0H+PdbzMxi0v6gApz/smT66pVo5XzRsz+NHEabdbDnCerhinaVMu9S7FIi+HQLKCEHJEuwX1hcLrFvbhDotgRObc5T9O2Sxg4opjXNWoLP5L3Lz8QB340j3VoglhpdK4jqRurQHDjaewF1kTJ6Zqk3InFpiW2vp7xO6IaCi/E9DNx7jX8yEiRNY2nvNahZIjrjvqNZ7wRUp3DjQBw4Nv9eSr2lqbcE3O6jYm2y5OrZOqPnIv9Y3VIk2xXma2OChXq3T0YpIl39E+YA/nGMcN+OgH9s6+0I+Gd06U7GRcopmi9XDA6WMsYtDWvfTOlaS7xdk5+KLq6c56jKSDfnsz4CrZGxz/BQ4fcbyV3tKR/1jiRc2KUmvTAor/jy3jHZmaE+HFEcSaZwPmnweUTXCj23pPsrzMziRp7L6yEqCRIoj4yZoo24d0v5daPo1gLZtRgesivF8vmUaGQTV5XGNIr8WFzFZmYxc0M6E70c6y3pV65pNgPJq4zkdULYr8lOEmg0OvWyUbUKpSOqNtiZweWR0WcJ9e2W4kQL82u/lmxkwD0ZkZwlDLdKcRxvVbgiYldSTGQPFiiv0GstqtXo9ZbkXIwgphT9GcfCV2t2ZGybnRmyS+kamfqNcQDaddncx08M0Uo+qC9kg3GDiP6Va0IeWC5z8hPL2icCh42I07R4klKcaPJzTX4hOIt6W7Rd/gPpFrRbkhedn4mDOg497Vqk2XYkpwnFK8PwlSZ7bciHrRhrrsVRbUvF4qEnbrZSIGthO+peO5ha+RnbbXFimisrGyugBg633dFN+oQWI0WMn3jcdoedG7qRpHokg5bl+x3ZpRT8IZEizywlRSU/NpSvhzI+v93ihwG2GsqDQPqkwK050kvD9NEVplLo3AkeR0Vxx9pAs8gkVSWJ2KOMkAfhJO5X+CIQMsHocJGBDSRzLUxLI4cognTXQxrFLbzWj8aHgXKRYRcC2FZOwTuiU7MrzfiFbHjtZiB6JddaGhkOxEEbJg7tJJKuG0f0i5yYSEepG8UbRJHOPe2tDp9HJk96bmQEe5mw9nkgnUtBC5BeGEIWyY8Nq7uO7b9vifv1TbG5/TUFiRTRyUKyv8PI94Yl6aIf3L2gXfdkWxX5sZWxeKVFZrIp2lm34WjXA/mtJeE8Y/otkQ+4Vc/8u1eKIe1KzA+6lUNNuyZonjAUPI7uIMs6ktOEdhJwQ6hvdZT3RD/oRqKjq3bl2lZRDmDFs1SkAgGCl0KxOh/g1h3BRpodjxvA6kFHe9ASB8Ip9RvyOuZnWnBBRtKAwtjTjiNuTZ4dwxfyZ7pRNLfF0KEbcfVXJyPcXku9HXDDgE4C2cGqd2ErMfRsNXBQE9PA8JOMqy9HVvcdfuLggyWxMlS/t0Uy16zuBOYfODFcPRmJDnpfDlvBRsn1vvjDRYb+SS0V/ng+3q4fz3pbAP6MLp/LQzNkEb+0NJ9NBGrrFPOHAV9aks8L0RdNPcWnGdNPDMmy12Mt7Y1rFcAeZoyeWPILMX+MXgiTrZt44e7ZyNd/9126sZzeXdHjYJ6OCLm4Ju1S4TpL2GkExvoqh7nlYOda8mBveUIWUS8L7IPlTZh9eeAlEi6HOHLENBCGfTE2iHTvVSgj+iZ1UDN/39GsR8xRRvO9NdJrTXenEb50JVqu0RcWfZiL+1hB9Bq7U+HWZLNtp1GE9LEfH81TsuOEwTNxEHcbjtVr0Tw2lwXFqaY8kAK5WmUywvsiF1fiZYrqpNO29hk3XSNdapIrw+hxQn27k0L8wxo/eJOpHNAtlPuB+VfaG0Bxdqklxm3a4b6xJqPc84xuEpg/7B2Rb5zED8SMEpKIcrB8r6M40WAj6vFQuqeAf1QRf2WGerBiuF4xOFEUh4KWKB+1tNNIeq2I35mQzAVEXR14unV5WsdS4M/DF1KoptdSyNbf2AAguTQikg8K0487Y2PQPTTa1ork/hJzbZl8L2H0iWxk2oHba+lWKcMnCdWeZAJrB4yF/Tf/Uke965n+oI9BO08YPrWYV3lfWEZGT6RjeP1ijfxCEUo5jIRUoNbxOmWyuWJ8sMCPvTAoNyvqR42M8Fem32Q1IQ0krxPqW51oCY04MG0J5rsjKdJKzeBEUfbZx2v/S3ajiVQeuqsfivUvPxaTSUwC+YuUeJ0KtuX3Nshfa/TM0tzqSGaaZK7wt6VQixstYexxk8DoBaSfF+QvBH1y8audZNcGOVhdfiQd4fc/OJRUmHEgPzVUtx3kgdW+Ii/kZs/ODbOHSg5EpRQzk08N6akc3HwO40fXnH19V14Pr/GDSH5i0J0iVJbkLEG1mvEPErIzQ/NyJEzQPdGCqlZTvBaN4uDzFN1Cul5TH3SYVoqo9EJjLwRQbmpF9cka3Y78uS8i0+8Kh5IoLuJwr5Ix9wq6Lddn8goGizsVvtXoRqFHHSoLDE4VptS071XCxrxMGH8nI7tUDD/J8EVA/fwMNwrkT8VBbocd3Z1W3PrDQPw/X5Mf9zFzmZei+0Ep4/pJC0tBV+Vnmuy7BfVFweiZvknMCcuE9AcFZm6o9gN7H74muTak55bmokA1mvrdmuxCcDyD5xItqBtFs+eISp63ybKfmrifrAbw7frTtd4WgD+jq7sjm7ZdKJKenxe1jFTC2AkaYRSotwPFK+k2re5EuhHU2zLGDAnUmxFXwPCVohvD/H2BkDbrkZCKENytO/ITS8hEt+f2Wik2e2G43axuunkc5Yy+nRO9IiJYitff3KXcDxL3ti24l+ZkgNttSRYaU2vpdGTykMVGcErMHy3SEekj71xlGWyvIIpAu9121LuOrOgEnjp0pNdK8kORFICQS1uzW6SC2/AK/f4SM+6otyN2qciPDM1eR/1hJckGtYjs42aLnRnKj8QcE4rQpy0o2k0v2rgk0t1rCBbOfsXj1ztCAulcNnRXIPq0aT/OM5H2XoNu+kJHS+HUTYIAd1vBXDBL6PoEjuxcw14jo8ANR1xv8UXAXIuOKry3YvGeIz21woHsFO2uQ+Uy1s2LlupoRLtK6b4/oVmPN8kTw89TfA7LdzztusC9lVPEJKLXG7ILg24k3aTeFIZkdcvR7XZ071aC35gLBiNZKPw8FdfxRgkRurVA8nNXtI1cQ90IbAlu6uTPXqWoWjpVupNMZ+Uhe5qJdODcEseOxYNwYxiqdgPdTic8vPeXtBOBoGfnhuW7Hao28IOxaCD7Wmx+NGb1bCq/cJrmskAZSbqRQkq6X7Y3AyUXljAVqDWjjmpfnPLplWb4SjP7yIm5ScH1lwSHNH6mcduCQ3GDSNd3osxSo1pJ6qGPnnuDNglDj71Ibjp9WSGZ1TYVDmR6oan/4lzylRHNpO7j7apbjmbHo510kj97uYvabXgTuZgfWfCK8o6D358SMy/pN++tIEDxCxd0d1q6IYyfCyw6P4e6Teg2vDjhf2f4Q85ggLVvJ/0IXhIrml0vzmgTxaGtgXFH+4tL9v/MMdWeJKh0JwNQUN9p6TY87b0GN/XUtzuqR/L/dAKOjsDsK62YrCYiWeCwQDsxN6lW3xSGKNBfFOiZxOOFysLSMvvQ4/NA/v2CqCDuNjQbkcUjRzeSLnvzxYT8xFCcSeGefmdA7PPC7VzT1AntmjBN9VHO8FWkm0mxmH1WkO2WkHvKL9WS/LLSuIEgZJSTLmv1Totfc5hKcfzpjjynNZiVoGiSzAm028YbTWV7r0G1Grsw2KXcV66I6DurH8f28o9fb13Af6rWWw3gz+iKXuHv1tinMprSnZzko42SWfvUUO7JjVbveYoj0fqsHnSYkRN9z1FCNw3YyqBiZPQSfC46lPZui75KWF0MBBxtIhvf1CzviMD5Td6lnbR0lzluy6Nr4QiWBwoWCdmVZK362zXZ5wUqaJpqQBgGkmsNMwmaT+aqx4lEwiKRZI400toUPRRh/ejzRMY/3lLZDG0jiy/JRouTglIp8JWhPJBxljgvDXZuiG90RAslesnaYl/kBAs+h7XPI/W+JqwSBie9Y3gYyJ5ltNNIdJriNNINDX7ocesO0oC6lHlUPmjRVzm6M7iBOGWbrcjwuaHZCgxfWMrbnvR5JtBtZSWZZUeKGr0w4jRcSZ5zfqop7ziC6jVl+9JtSFpFpyPaRkJUhFw0lcOiZfFyKN3HoDB7JbGxmNMMt+5oGkssPPmzVPAwH5a42nJwcEn3/9ylPBBHajBQ3u9uRoUc5SQL6IYKpaW4Kb5IpbPUabhMoEeoNNsO3fZYmcpSXaWwIdy/xflQvp6Wa6c4i7jHibwWowhDh32dCjcxiLxgdVe4kq4IJEUHRYf5/oj6QKLKRj9IaaeRJslg0//QaR6FfxfeL+F0IMajbX3D94taXODplWJ1Tww3b6LfbKWghPbjCu9z0qOUdku60n4Q+mtIi+u0ln9Pdwo/dagyoVkX1Ek3FiONWYh21PemmuTC4kjIvnQNQPlkKg7bdScJFSuDqxKygxXxByMGlSCEujJFnWY0m57BoSR1dCpjdCToF0n/ULhxgtcR7aUjqFsYPE2obsl4OT1NaDc85vmAuOlYfXuDuCV/1k4FvdNNLPF0gF5rcZmHw0yeK1sNbpayvNegj3P8QEasdtKSPB/QbgiyJOTiwOZlxouR5FdrB96Ixpae16eC6Tvh0Ox1khpSiaNWdQo6jf7lazgeSZ6ujXRZlPfvWtNuiEO8uy2duWgRB3rmMReW/P0Z1RM57KjdBr+ytDuO8c6SerbG4EjjCqjfq6kqwQ8lK8Tk00J+Dqs4oPhohveaEBTXeigjYddHQ74corJIcqXpbrfE0lA+dAwep5TvdOzuX3PxnW0Z1S8V3W6HGwP9fWInLfr7I/QoovqUn8FLC1jaqTBQ22kU8PhMU5vsT2ZD+cOu2H/8qF/j7fqxrLcdwJ/V1WniLKVbC6iHK7L7C7qt3gl4Iifh9Frht1rSS02zLgkUqpAs3+JZSkwg5oHyfsfiHswfgltzdHca9EWCqRX2wrL2jZThoYB/m13H7a8e40aRcK8mnOZMP7GMnwj8OA48yUJjN2tW9xw+jaSfF5JdOpBu2+BI4x8KPy271LR7jvq2xDSZUot7uFIMniWCnCi1jJAuNWHoSV5kwkKLYlwxpWb6qSGZ9Qy1tB9FLgyDE4UbecHTeKh3hbsWZ6lkCk89w3evOf+/1sSBZ/TE0k0kWk5XgsMpTjWqNFS7kpzwRv9Gp4X7t11SXRbSVcukC6s6wUWs7njsUr4Ok47uQY1dKRh3orUaSNan2a9IXicSfbXreuCwIlloiZPzSnJai4heWOJpRvbaYLcqwoOK5afrDI+kiM5fJYLWaCTmLRm1dKVgQJQXMXx4nUOjOTpeZ3UgY//2oBUUxsyiWk3+SvAdy3c808+huLUkeZ0QLYz3F5hxx+T9S9I3418TyU81a9+VrpPyCnWdYA8lueMNu44IV+9r0fkNI+1uB0sLAVb3He1+x+rjmsGRYfqZmHa6WQZRYWtQhSNZKFZ3JMHCniWiZzPAXoMuhXXZLVNspVg+coSD+gahY7YalBcZQHpp6CZeOHwrKUZ8Dr6TxJDkwznJlWX8XcmTtQsR49/+M4eiFwS6nQ57nkgkYR57NqF0y20plbTeqUU/WStiGlgcj1lcDKX7WBsGXyTkL1N0o0kfFzTHA5odT3nX4acO8yoXw08jxqPmTkt+JtfV6KWgdtppZPSFIb0SlzJ3pDPfrAsgvNsUbeXghb3ZhAcnivHnYoJR90p5rqRRouuuUrKXKeWeJLSETqNaJSky0COUAm6VYJfSjVP3SrLbS/k+ZhJ5aBrILxTJtSZ5nZCdWro7De5RhfmlaxmXGtGX+pGMrtMrMUmtXo7Jziz1dhANYafoph6fItBxpzCnqXRsZwpsQJ9mqAirRU52qQlpZH26Il+vMTNL/cmagMOtSGhYJIw/tzB2LO4HYhZop5HyoI991AH1tQnJ749vDpzKw/Y3HclCk1xp2h2HPk+Eo2qiZCV3iuXf3YE7lZiKTN+5bTR61DF8ZuFFQTsVqHfIIu3/j70/idVty86ywWcWq/rKXVenvucW596oi7Qd1p/Aj0hlSkkLZ5MGiIYbCCQbCdFGAhshAR13kBAtJEtIdGhki98CEtvYRDjqW5262HXx1auaRTbGOjtw/hhIHI5rX50pbYVi37P3/or1rTnmGO/7vNuOekPuyfmZIKXMSjrbulVkR5+xCeTN+jO13nQAP6drsLugvChkk/1ogLMRdbMmJoHWGPqvLM1axGae9l5FnKWEWmMPM1wv4FPZAeyVdNv8MNB/amjHiriS7pRuFCGLTN/zpFdCpkdFzn7rgHDHwcqSTzW1yMCo1wPF01SyOl/0UHkQQ8muE5fmIBDTQHsroA9zkqXcnIcfJoQUlvdb+i80zVicd9lE+HbLW1E2+34gPbZiODCR7FDGZsWpou3LaCg9FuxIfb/CA6s9A3lAj1viYQ6bNXGWkh+Lric7saQ3PaEVrdFqP9B/pTGlJb+EyVdadCM6pXrTk8wM+akmJFoC5OeK5WlfhPFWRmD2Muk6GY7eobk2C3CV0nsh4Gh9ltLsOTG1HFrq9wL5XFGcGAEbv1OhLlKadS8ReRutjGTndC5DcReHy5zRJ5bljSgbvYVquwXATOW9DVcJiZd4rWpHOHXFiWJxG/Bd0dOXYqYZC9fPRXlvTKMIK83lNxzJxyMZG4897VIyUCeuTzxo0QuDnVrqrUCz3mkgdyr8ZUa750iOE5J5Qr3tCJlEbpmVjMBUKSYjtykjbLuU25bPYXkDoo4SfTixrPYCsez0q1s1wSliJbibZKYwJwWrdxrsIqH/MKXtR0jlejPdONq8KCQPuVSU92vyQUNzNZAINy8Z1foqpR0HmqdD8qlidRBYuzNhMumTPM94+v0Dsrmm3msZfJQKCHncUvxBRrsmyB9TONx9j36Wo54WsO4JtovMc4qQe5qDFnuaUG9IF7I4lc6S6iQQaSZaVJ9FdOLxQ0XxLMG0sLjn0LWmvOuglni1Zk2KTJ9FXGPQuRx88mNLdbemd6ikoKsUfm5Y/V8XtGeFJMSc5ygthAHfSKRhMlcCl952kv29XaOuOqNHm+LWPMmVRMvZmcGXBRysMBqaUUQ1inrfUW+LNm/0uwXVFqjLFD92LC8z0lLha4PdLkkSTzgfoxsxelWjQLMFRIFYhySSH1vqXc+qO2zRFeJRJ8I+jGI2MjZQvlsTg+Ly0w0pIK1060Iqnef41gpOCxZ3JCM4JpHkUlJkqm0Zc1fHQ7IiUq53sHAr3d3DP6cxNxe0ZYKaWekIrkUGayuqtZRkYqi+VOJLi2o17bjLBq8lJSUqkdDkJ5bqdkP+PKX39iXzZY6/zFne8dc6x2S7RH3c/8y7Zz+NLN83WcA/u/WmA/g5Xd5rMUxsNPgiCspBR5SCdKNi/lbAp+AWCXGaQufEzS6lM+X6UbRkNkrnLgksb3uJpbo09F8psokUDSqI3s0sNCSR1Vttp1sTN18yh3rLM7wzFeSJAbfRwliC63WjcVstREjPLHElG3a910rI/UFgeduRXFjsKpJdwdon4L4+p9yRDcz3AqqWm7suRf/TrEt02uwd0fT418aRgZzqzdzg84jJPEWvBhWJXhOVBMxHLY602Xc3SU4S6SQhRVB1p6EZSVqDG0SatYCda9E8bYpGzY085Q3H6GOLOs0EZVGZ61G8akWL6DYd+uaK4RNJpah2HHG3Ri8k6q0ZR8JCkDTzd7yMVxWENYddyOg6fZWiS02z6SX3tid4CRQsbgf80FPvOXwm8VbKSUYw643gJnLRSqkAzd2Kti9mIAJU+y3JeoW+SiQC6wtzAGEHWtkUzcJ00WJy8/YLS/HSkj7NSS4s2aWW/NlSsqWLQ4s/z2TjXhlQUO84iiOLCpBemc79KR2x/ESTHckmCqIRhO49Hgd6R3IY0a0iWavwRWB3cyqHlcpgeo79bx1S7Qb6n0hayvKOw+egU4/vB1xPjBgqSpazacBcJlTzjHZNcnDbdQ+VcOEA0qmWCLaV4upoRHSSPqGi5OCamcVnsP7WJb2RRPaZUYuZGTjMCVepZFHfqCXaL+n0lSZCaTCXVowYRcBttizeEpdqMtfoT/psDUTvGkaO9NOC0UcJaFjtBcj9taFLeQFM67brbKUw/F5Ge6vG9wP1RiA5Tpl90Moou9M8+hc9kWMoyM6MFEdWPnOmUuLGDyJRiFGhjnJJO8mlW4fqHLxXinCjEl3wWUE76GDXPU/xLBEt2/OcxZ1IsFHkDiuJ3Rt98ww707jakiWOYMT5XG97+o+TTj+psHPB8lQHjnR7hfKK9NLIFODS4tZkAqJryJ+ncJTD0pK9SBk91OhKEzcaVne6aUOpCK8KMJE48HCVivHoRsXqnZowELSV7rsuWlLTf25IT7u+SoTmKqf4OMNs1cSthlAEFi9G5OcK0yjSDwvyF6ncw4rA7t7kGr0TUtGbApiJHGqunq/jj3uoVlMcGgbPIT8ytKdy2P/M1xsN4J+p9aYA/JyuapViT1Pyj3MZPQ0C9lkuqQrHPVTbjX9PJC+1Py5ptiQpY/0HRk7yjcYutOThXlhiGlD7FWtfOWf6xVYE/xbsXMaw/Zcae5agMo+qNIOHkve6+FKNWWgWT8akVxqfCoBaXabEmcQr2XPhY7WjgFlrSObdiPerE0ypWPuxpR17FrdgeTNy8Rdq6stCQtd7AlfFSLEkxZWm98pgFoZkKvFbxaFFv7sgvT8TLt/Y0b89w1eG5eEQ3SrQkezcsPEDYfq5IuLuVCTvzvD7tYy/NPQepaQz6TDE9YbswpCfS1yXvlFS3nDolWwqi1tiDPBFkLinvZJmv0UFRRi3Evl1XDC/FyhvOezMEFpxYN5+74T19y9INyrK9yp5PxYaZgnKBopj0ZelM0XMImbYEt9eoWuF73uS9YrsUjN4bLGXlt4rTXnTY2eSj6pPUwmfn8mYMAwdLBLhGXa6KpUF2oWM9fztinqSi8vRK3wGaqci7tQ026KFM0sp4NuhGEmIEsVmV4KuSRZSjOQnhmSmYdii5Pwho1ED2RX0X4rgv3iedDBfaLcdq7sts/cd+dcuyfZWnctZxnF+vyZ4g240i//3HmapyW8s8EvLy+8c0DuU0XK7LUDxkEb0q5xsd4VdaOylxe01RC2Zwb4XGHyUMnxo6B1qVNtt9EfChUzmEpXWdEkkemIJRRSHaGXQN1eUNxzNf9iifD6UkeNlyvqDS5KZJmrpQJqjTBzoLYweaYojQ3ZuuPPVQ4pvXjD+SBzTWOl2u3dW+Dzy8gd7UkQvLb4Xmb3nREN5oklOOg3tXNF7KVzH167+165wdZVCkG5+VKByT36wpNmQTOqw1RLfXkp3zAkyyK85kpk4arNLTbxZSUJJ4um/UizveOFVDhzpWk2942m/NWdtvJROsu5yo2dKzDabQcaziA4upHIoeF3QTb+7hTuo0acZ7X/YJJ2KKSdaYX3q9vVzkiQZgPjxgND3NJuCkQkJZEcJG9/X2A9mVHcairdmJJcaU8HqQA5kw+/kwiNcaKpdTzrRmKUWHNKxJkYIywQ1SyTju9Low5zFXU/Yqam25G+ZWvSOvWfS9Y6vCpSO6FKTTAUiXa8HitNI++4KlByKzn+0jRsFQh7Z+99e4XcbMX4Y8DsN6bkhnWjsUtGsR8ptRXmnlUL0y1OSz9gD8mb92VpvCsDP65ok4jZEuhRmoXF3q+soKLcmjrpm1+F6UD4eYeaGaiswvwf2eS5dDhVZ3BVHp5lZ1LOC8ycbZEfSDfK9IL9j5GmHgu3Ie+LeXe0HydydJoRUOkXV2zWhO4kXJ5recyOjyxr6z4WRpl4W2EoyexcXPdq1wPxbJcmVYf+3HcMngqUR2F0U+HEqY7XsUroVdqZFQ3ilKI4V7379OeXtluqioDwWjl9yYak+GTP4MAUnUUzqMkUFxeSBuAJDFuE8Y3VVEEuLWpMbsutFVvsRbMAeZfgisvpGSbUpLsBkaqRDsC6YGYn26jpNjYFKTAfF44z81BCGsvlv3JiIs3lmaTY9zx7vsPydLXFIzhOSqbDj0gsDM9HbqaWl2pRxWrhMr12I6YXBXRTUGwI8titFtSOO0+zBFJD8VOVUV/zKpZOev87jBdVq1FUCXokB4jyj2CivGYDoSPGdHhv/Ry6stFrjhx1AOInCz0uj6ByLeO1+bUaR6sCLo3NlCQkMnliUE92Zz2H2QSu6zH1P88EKt92SHlv62yuwgeWP1mle9tGNaKHcTkP2MCdcpNiFohmL4aB+OZDucEDSH+7XEnP43uI6F7ia5BLjlUTUVUJ5uyWaKK9hDxZ3AqsbHjsX7V+5LbqreiNiatBLI2Pj/RLbfd70oIWnPbCiGVv7UNH2I2az5vx0RDsOJBMDtaRZqEpjGsXkA4frR+o9x5MfHXB1MkK3Av4unoruMlxlwvE7lWshu9IkUyXOdA3lXqBdd/IZzbvxfzeCVB/M0bVi9YtLzEqBjZhGjCax0ZRXgirJrjTZ4wxXiwFCO7k21NLSvlfSrHvqjYB+IbGOALMvtPRfGDmQAf5lDzsx+E8HXD1ZFxdwKjF6zaZn878YwlorB5+lHACjFSi5G4ixodn0aCsj8HojUu1Ihyg7sZhSXXdcQ9Z17RtFfHsFTsk0QP/EwT19F5o6QS0N8XfXiEaoB8WxXMvLWwF2anpHiuEjOSC47VYiK785xVwmFC8t+Zk49O1SOqLRRDFUbbfoO+KgBmi/vBQMzLmgh9AR+/6Mol/LqLhQ7G1ItKTyCP/PSTH+9NEuOvWoWysZZS8szZa/1pLqGkKG3O8KR7VKWR18xhC9iDz3P87Xmwbgz2y9KQA/pys7N6ilod6UoPJ4u0QpxEhAl5u5MjIGLaSTl10q7O2lGCqmEofkMxlJ4ZWwyhQkE+GI6VYyPu3EQiKMs3YccA+HgrAYiTmBcSsFBhArQ3qY4AeB5R2HLSHflI5YtSPdoexSNHsqQHaYEJPIvb1zAI5+0VKvCWNOFR5dykbjxh6deXlclaLdaal3vBgJevDhxzfJjhLSc9HyhTSSnyt6h0pC7wvP/EGLncvYU9cQg7pO9ehvlDIKmqSEkRgRYjdxcZ3uzZcyBu0/sYQ7JemZhYl0ON3QX7PtYmVQfYcppRtqV5CcJ/ihZzrvyViykmQFXUrhojYE3VG+X1Hvt+gGNr+tyS4jvSNJ0whFYPDUoJzCDz28uyT2HKHvO+wMpFcKt9WynBZkr1KSK4vbbin3nRSX5wm9Iykm203pkIY8oFeGdizgWq0j7bpICAQfFLj6QIpWO5Oup52LQUV1DthgoT5oCUMn11SU6yhkkbUfWNGSbUfcQUM7lG6Oqrru0ESzPlqhTKDZ9lRVIgzKTZE4ZBca7cGepIQskl3INVHveNILGSMmM2HJze+AukywH/Wolyl+v8YNAvbCCuImSrHQe5pg9ks2vnFK23HXWGsx7yyoDlrqL5QUp+L2XN4KmEqRXWh40qN/2HXULjLadU9yLm7my6941J0V6lkBrcCVTaNYuzGDCMPbM0yNGE9uNuhCNLXoSDNStOuBatfTbjjy3SUhE4xTe0vaesMXgd6hJp0qcbkr6Yrm5xoFHYoFmld93LrDneW0ey2qEQlHemZRpfD+2qFc19W+I64s2bm459t1ycQN5xnFjQVhpyEirMb4rEdyZrt0FSlYkqnGbTpMo6RrOJIingh61IrO1Mk1U+57/IbD3lriB53ZYtOjeo7sh4XoVRNwAy+JM1oKP929luneirqLFPRetrbsQktKkBKDl27AfNojnXRO7VYRN1pMI/FuIZPPeLUtOKL6oJUCfadlddFj/LF09qrtwPKGdLdVFDxOMlUkZwnNJCNaRE6g43WHutgoibs11YshVZkSbaD6xQWnkwGx5wUDFQWyHtNOwhHlueSHRrrrc427XUk2+K4ju4D01BJOc/rD6lqC8Vmt1xrAP+7Xn9S6e/cuSqk/9PXrv/7r/92fqaqKv/k3/yabm5sMBgN+6Zd+iZOTkz+xx/izXG8KwM/pigYYt7BTM3xwKR2Dq5R0Ki5bEI1XsN3G0POs7jjMHwxpbjQ0QwlPN7U4gfNXCXYuAOKoZdTSO+qMIiuFuUyob3YdxysRY2eHCXrYEksj49MsYOZSSCinMOOG5S+scI8GpMeJaAbv1DLGNfHakZqeGx692ibcqWh2HeV7NfW9mlhJdFTIRegdLzLR7eRRfp8Wjl2zJnpEObErEYunctpvxrLh64VBpaJP1A7cMDL8bn4NVrW/NcZMZTRkzxL5/UDxJMVUio3vGtBSiPocij/oia5OR3qvDNmZbKrJlSZbr4grS9TSZVsdBIoHE9AQGoO6vZQorARCT1I5bOrFeVkbCNL1uPxSpP5/SievHQaScc3ididE96Lf0lcJJJHxL5wy+6AVpElpSF6mAgN+MEEtLMlMcDRuu6VZU4ReEPDvH6xhFkacyQGSxznh22OKQyPOUI9EAW608vyHgbjeoL8x7YoxjblKpPPspcDUrYxMo5bsU9dHWGibLbFzULoPlphScDnRwPmrMf0f5fSeWfzKYmqF7juIivLAd4cRrlMk7Pszei/lMfafG/n+eiDeLUU/mUXSVzL+NCtJlNGdsSHYbqT8SZ/jw3X8yBNvlcTaCHrlWUL2w4LVvhRJplIUZwq7km7b7H7oUle0ODmRz5huNO48p91rSK4s2UZJvelZrjJ6LyyLRU65E2j2W1ha8h8XJLPOGXspnwPdSkpMedbDzmQcyMJy8L+95PT/IsaYkCBFFnS50OJErrc8bk2KZkDu/o0mvz0HFWm2PMlOKQXOjhfOXysHrWiisCaDmKlYb6jKlM2NBdm7M9H8Am4cBBy+7bj//iHVvmPtu4nICywYK27XdhRQRzk+l8xbXUvHTc8s7as+2amRkfpKExeW6gN537QHu9Zc33dWt52AsZMAHw1k3FsEwiJBDRz+SwviSPTFr2UL9Y2GelcOPKhI9jjj6suSqpLMtHx20k4XG5Tkmzcae2WZvicu/HQqB+J2uyUkkXrHUb1TE26X2Km9PjgGrzFrDe0o0j4corXEaqafFqgsoFTEnfTkQHCjQldacrEbSSCKZzLpqPb8tUteHwnaS2We5Y1Is+lJpprlkzGq+VOgA/xTvv7+3//7HB0dXX/9rb/1t/67//5XfuVX+Lf/9t/yr//1v+bf//t/z+HhIX/lr/yVn9Gj/ZNdbwrAz+myK+j9KCcsLYtVRrTS5WpHkapjy9UHLSqCTyNr30tkdGIAJ8J2dVDhM0hGNa7TqYCgOeqtwMXXPemlptqXwq//SUpMpbPnxzJqVscZpIH0Sc7wI2H/mVKyRPWzAv2kwG234robB5Lc0a55EaDnUgjGt5fiMDzK6D1J0OeJ6Kv6Le1uI1omE0SPlIgoOp0oek8Ed0MUcHJ5u71musVUouyqAzFGqAgsBERcbwSKY02wEnKfn2rRCDnpniULyVjWrWTX2pVi+k6EWssY/UZLvS5ojI3vaVw/4jPZOOxSUV/lJBPp1PXem+B7Afef1+lvrtAXCTzt0w7EhGMWRgqlDwfSyfBKsmqLKDFZ/3lNWIERsu/0SeYifM9OxXhhavmZix9uY6aWdtiZVbYdtlRUP16TDlEr8Vf2IpENHkkVaIfxWjbwGm/jhpKNbBeCRokmQisImZgGio+lSEwnCtNKZ/l1pqobC5T4dVdGeXl/zKV09dTKUL5V41oxhqQXBlNLl9n/3Ix6MzD6UUq74QjzBBaS7NK8XZLO5P3Ru9V1B0g5WNx3JAtxAfOiuGaVpRNxCCdLJYaWO6VwH/syZlNBouv00mAfFuQvJQVmddNJCsog4jYELD5/y1NtReKGjI7LXTEDLW873DBQHUgOsd0uUTNxoreHfdR6Q/5f+jKivRRziplY6cCOpMOoWyh3FO6dFdm5jHjtzHQj3UC6s+LJjw5QEcr7Na5LmRh+3BlCbgR5HpsNox8nMEnIjxKilRzi8mhAcmmvTQtu3ZGfShc1JhGdhE7vKaN8FaWLrV/lXDxdZ3nRI+81gkaq5H29c/eMw4lESs7eDoIu2m5pFyluFISzOJV0IF8E3Mgz+3JDdnNByAOuL5837aRwDqXoV6MCf5WJqSQRkHVYCTolJGJUsTNDcmngKqU9Kxj8WPSV0Ujhn5x1qJlxvD7w4cEPwvWBJlphQhLk7xDADT29V5ripe0yv+WGqLzCjhtirfHzBN9xL83SoHQgSTxRC0nBTVPyMy18xKOUGBVxKGksYZGgKyWGlqUiO5ciMziRtSQTcx35VhwZYlC4kRfzmhbdZ+juu5/ZivwUTCB/sg9xOByyt7d3/dXv9//IfzudTvkX/+Jf8E/+yT/hL/7Fv8g3vvEN/uW//Jf89m//Nr/7u7/7J/tAfwbrTQH4OV4+B7Mw+MMe6ZnFXsgG5jfE6Ze/SIXWv+mZ3wtkl4bywKFzhzuoCac56VTB0z68LGi3xQ0aM+FS5acyNlOtxvcFFqtS6e71HqfUO57iVAsQGGhG0jFJr5RAha0Uk3pucRstulQ0Vzn5iaW84QjrLfmpPP54kuMHQUwCuWizAEwaxG0YFboRQ0o2BdeX0UzoCR7Glop8rRI38JrDTi35c3HOhlw2m9h3ookCGWNZpCuXCEA2pJF0KNmeeCX5nt39tjiR/FsVEZ3bWDpXphY3n9ttBOy870Und7PC7zXMTgcSNr8RBRfTxWCFXgAb8WNHcSaU/2TUoHJpLehGCrt0KnmlfhCoNiP6vUXX9Qw/ydYsxfHbf6noHRopeAsvSRQ7LdX9WgrjlTglUZB1BcDwqSI9sfhc9JS66RAi/SDJJOMISUD1RNdp5ka+h5g2fCajt+xCY6bd6K9RJFMj+j8N9VZk+ESRXnWg21FFkjk2v3gmndtGirXwoxHJQjN7z7G+P4NUMBz95xb7NJfrrzK4RYJ7NmD5dkN54EiuDK4QTd1rl3s7jJR7QdAoIxkFZj/oycjQSPH3uosTNxsxoAwiq2+soBBjAUByZlncF/6eArLHGboVp6luRCJB3xGtjInj0764Zp2MZ9PM0Q7AbzXXd2PdKtH2TeT3RC1aL18bVnekmA2pJK7YpaY+L4gDJ1ndc4t28u9XBwGzEvj68Il0kxZ3A2sfavJzpCsWwCz0NTdTFU5SZwaR5Mqgeo5wkVJvBPqHMHwC+ZmgZnxfXn8A/mAkersgXd5nj3ZYnfXZ/I4hFIH8UNzYBOnAu37EVPI6qP+qy9Y04li29xb4NYfvBUIawClWN6RDnVxKMpC8WMjvjErShgYRv9tIznKpMBvCdBz/UEwcvUPRBIdEXLc+i6zeauTzhHzm7WZFKAL1juRXZ1eC0CENVNuSO2wXmuxMYy8SYhpxsxR7ZUmuLDGVA6UtoV2lVJc50crrmx9ZGV8bORS2ZwVqYSRxZmHwB7UcYArJNk7PTdeplnx3tVvT7Dqq7YCeJAI3j4pmy7N6v0LPP2Oy20/RBTybzf7QV13XP5WH+Ou//utsbm7yta99jX/8j/8xzv3RRfO3v/1t2rblL/2lv3T9vQcPHnD79m1+53d+56fyeD7L9aYA/Jyucl/wK1HL2HP8UL4fjJgG7ELTvFOSTDW60cSdmnrDyyh1msJCRrKrOxKKnl5JUecuCkA2N910G8hGTX9vSXuzRk0SkqlmdV/GND6V7luz7qlv17QdBqZ3JDq1/nONXXQdov1aOg2pZIVSGtTXpvRfaYpjEda36/5aLByWCbHbtPrjEr45RUWY35WTvP6FK9b2ZzIyvVfKC6AjaiH8u/ZdEVfHnpgz8mcZvWNxNvqe3OjTE4tupZAOI0d41v8JvLdV18VWuRsk1m3sUKXAX4kweQ8JfW81MQv0b82JA499lpM9yVBpYGN/Ko/LKdrtVswSwxY1t9jzRDpNY4/+tEdspNjLzxWjTy2LW90bbgNut6E+7hH6XsDNX13IZpNGekeKck+C5mMaxZhTBMbfT4mlga0au1FRvEgEcXO7od70TB94ilN5vs2auF6TmSCGTNW5eZ+ncCX6u2Qmjy8aSCc/uR7rbU9xpLELQ905TJuNQHmvwRWR2TuyGSdTzXq/pLnIufjhNu04UG8GygcV2klMmBk1TJ6ukQ4a7MKwvNdeZ02n5wZ7aQXLMbPkpzIu9kUkO7as3q8xpQj4w1YrhVQhMGJTQ/1W3cXuiaPc9wJMEhHde/DzBHuWUmyvxLSw34CKhEJctdVtidnzRddpziQakCjCfn8gm5gpO6j60wH1jsdcJNiFlo6PjZQ3PMs7jtVbLXalsEvIXqRkJ5bsSopTU2qyK0g2KtTKMNhdCJLpdiOd0NfGniwwvyconZAGFneg3BFDTnkgLnq35iAL6PMU0oDfq2n3GuxRJr/DRhZ3YH4Pqh0poNNLjV1ryA67OLSBly7suoPcYyeGZqykiMwEHp1cWolP3F+x+KCh2pJuu3JKzCLTVDidqwRaRbJdQiKHTSLs7V9JNN04SNZxK0VVVCL3SOaa2GpWd2U0+3o1Iyh3A80QVjdiF0fnpOu3Es4kA0e716I/6WNGYt7SpZjJVACVyOQk3BaAdsjkULRz74Lh3hy/17D9ne4wqaQzmL5KxEjVKsYfiVFIBeg/taQTTX5syE+MvO4qilymFZevXUk3tdn0YKDZCHCcoRqZoGx+TxEKyZ1WPUd0mjBqf/qbyWe0bt26xXg8vv76tV/7tT/27/zbf/tv85u/+Zv81m/9Fr/8y7/MP/yH/5C/+3f/7h/574+Pj0nTlLW1tT/0/d3dXY6Pj//Yj+ezXm9A0J/TZWrF/AsNOIVTguUISaTci2RnhmY9kH1cUJxEpu8Bxzm9E8Xyi46Ye9nUFTAVl1s7EuerrhS6lUiwejPgBx4WCf4ik3HJuMVvB9LHOe04dN2LIKf8S9HNVduB6p0GvMRUpTNFtBqnU5LtEnM8ILtStAPNKi8Y0nVjupEgQLseyDdLqrOC8UeW1Z50XurbDj1oqdKU9HvrNFteunOvegJOvTS4vsR2pY8Kirli8baMbIKJzO8JLLn/QlP/3AL/oke73o22Go3bbBn9IMWW4ArF4q44aJUH+8UZ9VVBjKKn6j+1Mp4NAl1WHvyzNfSmcOumDzzJy5TZ6SamlSSTZk0zegTxccbiLgQbqccB1YjxpfckEYfnCNQXZ7QXBT43orPbakTInoqD0k9ydIBoA5OvNRRPUtyoSwM5lq7e7G3pciSPC9zrEVitMVPh8KE6PSmi7WxH0Kx79CSh2XaQRKKxxFGLPZbEBSIkyw6tcWeJftYnJLC8K6Ow1yaH2GiRGySSR10eSGft7Nu72ERcqWZqZATdaqodgV6nTwXgHB/1UQpIIr4XseeiCdPDlvYwJww9vlK4QZA86bcqiSjLu2i3pSU7lw3eVAI0zj8Vd21U0p3GCsDYlNL5NAOHdxp3VcCmpzeqqFYpvUFNczmmeJbSjKJEsY2Egzd8rCn3FfmFog4ZzYZneUsc1jGJFC+NJDs8SVBORrt+ENi6OWH2vU1Wtx1mKZ3qmHvqm5CcJHIA6YtWMR60LE4GpPsrmkXKwY1Lzl/tks4VwVqihdUthwoyCn/NU0yvNOHdpcCKlxa/7kj7DdZ63EcjycRt1PXBLAxEi1Zsr+C7I9qTHJXFa41nebPFTA3ZpcgvmnEkLsTRnpwk+CJy7+Ccx5/skUwN7U5L0ySYRjSaw4eWZgStSlFAe1aAlffD1IrjJ5scvHXO2fEubSKA+PpWw2hjyfLhmGQB6mUiXMO5JpQF83elC6xrhfvSAvW0T3auWBaW3o0FzacjcX/bgD7OGLyMXGyn5Cemy6aOJI0ifZrLKHeREDYC/ReS63t6NhI5wyTh7OsK1QBvLXFP+6RTRTsGP/AsbosBLT+XbqZ0tzt3fVDiAO6QQD6DZsthFprR/pzVKiMe5tJRHjUUH+YsbkHvuWH5oIaVJTu1VPlnXAB2kYl/7N8BvHjxgtFodP3tLPtvx9z9vb/39/hH/+gf/Xd/5YcffsiDBw/41V/91evvffnLXyZNU375l3+ZX/u1X/sjf//neb0pAD+ny/UC/bWG9uMRIY2c/aIjPbVor6j2JFfU9RTltoyZfC9SbyLmgJsN/oMF5sOBmDmuFO2ww4i8U5F9lNOOIjEosuOEdihsr6jAXCTX2h27EEgyWcCcJbihRwWFWmsIi4T0QsT5Ucnj1aWmWaTYfqTtCzzWnyfXSRkhFexFtSU8rfCRZGSu9uWkX++1FC8SyjsRW2rq2zXZc/lQr75Ukn9ckMyh2RJOoDxGyF8Kpqa91RArg50YKQZ+f8DibXmtfCIsxHYNqi3BMCQLGSeqvRXBG/z3xthexO/XJM8l03fnv8D5VzrocoeDMbVicVuKq3a9yz1+e0l1XqArRbWpqbYC+bkmpOIE1K28TraGdCKw4eTbI5JRpN1uSU8SkqcZ1Z7w7WIhhd3okWV5kKAd3VhPE++vqLqbtF5p8lNDtePJTwQDRBJQGPrPRHtU7or70ucijE8n0rGpTcRedl3ZqKRjdJqS7a5oLgcior8oyBdSqCRXutPWifZOdQab7NywuuUw44Ysa+HZGN3CMjP0jjTzzSjZqFG6dlVuGdycMT8cUhxaOE5wo4ArFL3nFjcwNNuO7EjgucWxwZYwL1LyK3F9h4HMd8ubXSyfed251Pg1R/9hQjuSTnS76eDCCn6jNJCJs9ftNThnMK9ywjsCXI+JaAjjfos6ycArmnUxx5Ra4wZeimYlGrJkJiYWgqLaCgJ9TmUEf56O0IWwHb2SEergo5R2IJ+FdiTxZ8VLS3KekMwUfiNSjCuq1l7z4+JeLTFtc9tp20QikZ0KGzC87MGaEw1wqmhPC5xT0JeCkywQvEIthRoQskjzbEBiITvXNOsBMzP4XuDtd455+R9vYVcw+1LDcHNJfdnHnnapPCeaw8lNuN0AcrLQtZjN7FJ0pM1aYPypZvKVViIDW0nNQQmR4Ow7u/KeKIgPFpjnfdrH65giUm1LJzZmgZCIceng7jmHbIlRZZKjDmraNcvoQ8uyHRIzkVG0dU52qZj87ytGvZpytibpRDbitxv8LCW57F6DRGQbxaminkhsZEgj7VZLcprgTnrEdYfvaWyHtHFrkuzjenKYi1qkIfowxY+cvM+zlJh0piogFI7Z6QBVGrK5fAbc0l5rj9tRRE8S8lNNuRdQy8/WBPLTTAIZjUZ/qAD8o9bf+Tt/h7/21/7af/ffvPXWW//N7//8z/88zjmePn3Ke++993/673t7ezRNw2Qy+UNdwJOTE/b29v6Hj+1P+3pTAH5Ol24Uw6LmKsLguabty1hWrYyMELLOCLIp+ixU5xh0iuRVik9T/FrALhQul+6YH0T6388p90R3N9hdsDgZAF16QRZgZnA7Ld4GAT3bCJXGbUiHIDsxVD1zjZ5QhcP3Rcvii4C5TAg3KxF202nSgiBRlJfg87hXYZ9J2Hwy0zT3KuLSMvowYXE7oBLRv42+l7Haj9S7Hmsi1TsV4VlG8dKI/m0UqPbFmawimNMUv1vjC0fyMmP11RJzlAtSxURCEtn4ruHqS4Hs3FDtOVStJUe30RRL0QtyJFnEysPlBz9xp7o1j2okK3j0CMo9Q7Mm3cdmmlEcG5KZYFXiZkOY5dLpspF25HE3vBROnRs4GIu5vST9tC+on7XAcH+Oe7VO9lLwM7O3AmvvXFL99hYqSMoIs5T80FLdbSieWLJJxGdiLIlZZPBJSrUVmb7vSGZGxumNorlXicbraSbdMY04MFsDAXqPM9Q3p7Qfjmg3AmqtwT7JKe813cFAiu/sUtF7JgVKSKDekNi3fLelejRCjQVbxFkuyJi5JfY90XiGayvKNGP1cEy21DQfrAhdoggmwjentK8G2InFFVJw+ESK2OzMUO578mNDSA3u7ZLkWSrdQAW6FQdm/lJGmjETTp++MPD+nOAM6jIT0wtihPDnCX7D4Y4GGC8Rd3ZmyJ5J1KL3imrHMXhsWd0MYog6SaTruL+i1jlq4EQ6MdNUN1rWv2uZvB9IjhPaDU/2KEen0G61lF9zJB+Jwzyut2xszrlUY9JTK9rLxwPqNU+zGKCDIv9gQvnJGsaBv1Ux+IMCU3eJL2tcG0b6D1O8XMZkZ5ry7Zr8aYarFOlMCrT52478sOvADyPplUDZe0fSCeNK82Rxk7Dtcbm0GOsfrmHyiBt5dK1Z3nOo3KPoUoQWlnpfXLqDRwmzLzbohWX6fif1UFJUJ2MxBukzIRkobzGVolmXTOHeceTimx4zauj95x5urGj2W+xZwunlCF1psnOBO5vjDF8EFncC6VSTXcpIXEXRTYbS4r/Xo70j71u5Gwm5YfDIsvigJilaiu8MKHcCswee/MhiV1BvQogyEYiZFPp6WBNfFp1sRZEs5DBY7ck0IjlJRZObe3xlyU4N9Y0WpSObmwsunqxD4cnPRfdoaujtLCmLHPMkozloUCbSNJkUgcOf3R7zp2Vtb2+zvb39v/Sz3/3ud9Fas7Oz89/879/4xjdIkoR/9+/+Hb/0S78EwMcff8zz58/51re+9b/8mP+0rDcawM/p8muO00eb+CyyuBOIFnTXAbClbFTJVMwG7UgEycpJekQ7CvTemRD6gjRpx5FmT7AdxZm4zQAWZ30paPpyA8d1fL65JX2aoWtFLLzodxD2oOtFQaeUootJXmSCVllI5JepIV6l18iH4UcJutLoBwvh2aWRwX+RrNbsQtNsyA3dLDXz+x7lFfmnGclcMk1NLRrEeJQTl7JpvMaG+DUZuQyey6nZ70gHMHmZ4e+V9L5b0H+lWP+ewUwsdqmZvBexmxXVLdF65eea7IXYZp3UwoRMRopuIFxElBR/aWegQUcm36qluDMSu2dm0lVY3hKWW+/DnGrXEfcrTCXoD6Uje7cv0WcpqjT4zZZmnqKdIp1JYkH7nXUZvW1ICkJ+prl6tk47lNGs6nnyo4TqthRl9VdWTN6XTbbZ8uS7S5pRvO7WtuuSLJFfKEa/n2MPU9Ir0ZamOys4zyRfdWUoDzzL8x52IXqqeJWKU/NZSigC1W6gdyQbmevJl7qzxG5X2KViedwnnSlspeh9uyevYRHBiBtX2cjixQj9uCCZa0wtmjxTaWIu3Lj249F1lytkgeWNQLXvyS4V1d0GXWnqrYCpoNevyM9l9Ojzjqt2amiHgXa3RXlFu+Zp75c0Jz3U8wKz0iRXYmAxS429vRQY9qHBlp1JZq8hiOwPFZSYmnaFM1c8TfF3K9iuic/7FIeWuLJyGNvypOeWZqwojiS9Jrk0uEKMMKo0mCeiwY02UnyccXUxxMzk8+XWRGsHok8lQoyiyQtppPeDQuDqtyOLe144jqlcj+UXSklFqRTVvkfNkutC1+eRek3uD+VeoNwLNLst83c9IQ/M32tJ5lDecPjNlvzISAbwaUKz4whbLSjoHWqy40TSRxYJ7brM+uyVpfdM8o7NlXQy8UAq/121CvvjPuoypf9SU20HYWV66L8SacDsLy3RpcY8KmjGMHhkSE5kDO3PpTiqdj3ZhRHzSncf9EVkdSB633ZNINA0mnI3kJ9Ylvc8HFSY45TlHY89TQkve5LtHBXFK0t9r2b15VLG6kEmEYROs/nDHrqB4SNxJlcHLcP/7VRyhY2Y1qKF9GmOWhqRykwsTBPOT0cUh4bRD1NJJNpwtMPI6rKHTgLNeiB9mcJUHN3lLXc9Pv3M1p/iKLjf+Z3f4Z/9s3/G9773PR4/fsy/+lf/il/5lV/hr/7Vv8r6+joAr1694sGDB/ze7/0eAOPxmL/xN/4Gv/qrv8pv/dZv8e1vf5u//tf/Ot/61rf4hV/4hT+Rx/mzXG8KwM/pUsuOF7fuiKrDRVxpiiMrXZssUL5XowqHXSl23j/D9z2q54l9z/x4KCf1GyXxZonq0C1nv+DFXbjU5IeJbLyzFLvUkHuChfxUXIW2VCgbBCEDuM1W3Kr3a9lsakWz6wg3K9I517w55RQ6dzS7gqnRlSJ8NMBeWEIemT2Q1IzV3Ra7VQlPb6sV7lsQ1221I2Np1xexejIV2KzrR2IauPMXnmEKJ/DiUvJH1ZXk7TY7Dj9PpBOnoR0qbOchiVmkXSaMfiAaoWpH3LS61Nhlxw6LXdrFQYNdyrgWID7uk55bwa2kXvSTUeLXhk+1bPRth50A+s8snGbdWDCiTjJOP96WLkKjyJ+mpEcJ1Y6jfafEbbTXqAuCiO/rDekgRgPhgwWx1QTTRfFFie+K6w3tmnQy3KdDoobhpxa8QpeiwYtKXle0aPuijbhnA5L561myuElVraVL2+Ww+iJSv1Vd651WH1RoL51SWyr8UQ/9aY/w7hLVd1RbgfjBXEDgsWM0Voq4WxNb6VRHjYzyx5F0rRYmWsdpS2eK3kvJ32XkoEtiKbcj5iIhP5frK2SweD5idRAJ2+ICTYY19a1GDAERYVG2ingl77XbbXDbDdxbUd2r8QNPc9Yjf55SHnhcIY56pmLcsZUSaPpAjCbJQjo/fpagD3PcdkN5WwrN/FQ63O1IdnDXF4ai6vbCeteRTOT6rfYd6aWhWY+kzzJMg2CbWjGRkIqhhgghKMz0J1q2dl1c8dmFRA2apUS6cSYa2mjk86Y2a5q7NfF2Sb3jafZb9EbD4O5U9K6Xibw2Vq61+dsedCQ5TSjfakjGNe2Ww15aYmUgCaz2BewcTSQ/Esg5Vg5LzThKEk+Q525KjTlP0UvRzdWbgXSipcMfxNlc7gVcLkW2qxL0bkU7DvheZPHVinYsj0m3og2NSaS62VyjappNT3Yh2k7dyKg5XqYkV+YaRWOWmjBJ6R0p0gsjmtZWMC3JXDBJaMnq9nlAOS1frUTMtcNIeKu8lk+oWjP//0i3yS6UREZ2yKtsb0XbaWhj35McJ4QMqs2Iv1VJhxtITyzmaS7TmbsVZqnxY9dJGf6EGSr/o/WnuADMsozf/M3f5M//+T/PF77wBf7BP/gH/Mqv/Ar//J//8+t/07YtH3/8MavV6vp7//Sf/lP+8l/+y/zSL/0Sf+7P/Tn29vb4N//m3/yJPMaf9XozAv6crnRvRTzqQZ3ge4EYpCixK8Xg9wtm7wlEV7WK6qBlsuhJ7FWlhYs2ihTPcqAbE8869lwqG9Pq7QbdJKRnwpYLRpAsyUzRrAfysw4tMUtITgwMJLOz2W9Ji5Z4nMrUbmYIK8P8bqD/SrN4X3R7dV+6IvO3PdmphMu7kRdHXRCIdb2tUI969K4U7SJjeb8lPbXivlRgKmg2I22ULtzokWby5RZM5ONneygd6R/qLm1BCjF7Jqdp3YruTnlBmehWulF6vcY8KVjeiKLbGTiS1BF/PBSsyFI2MxVg+IOMqMV1qFeaduwFV3Ge0g6NdAES0JlncTMS9yswAfO7faqtKCOsi4S6c04nC4VXkXrDE/NAdpnge5Ih6iYGE8XJurrVSrfoOGPwTDH9gsJttvS/M6C539KuBeG5BYlAMzNDfqZJ5rC4LYW6LVOSvRXjQcnFp5uoIEWUiqDuLokXBcpDvSeJE/kLK8VDqyXzd6ypd8R0YI4zifcqFa40tGOPGTeY1IHXhKBolynpoCG9XbF8PiLebDFzg/viStyNK0t6anGDSMgii7uBWAS4ymHNY6cGdXvFclsK197jlNanhFsVrLrCSMHynkQHujwSi0AoNbEytKOAftLH3K4kL7fVqMKhBpEwl5Fteijdu/YqQ9daAOoKbAVV35G+ylhtBFStcdtipuJICvhYadqB6N2SqWgSl4XFbpekfzBAO4hGk8xEn+ffKonPCtxIkEvJpcW8P5fx9lQKa90gAOfdlvH3U+aFIrnS5OeW1c+v8OcZbWvEPGLEyBVNZPDIiuN5Q/Sk1Z6AhJt1wca4g5b0UUG96wit4G2iBY4ymjsOP5DPYbQRMk96KDD0cr9z17YKjnukUT7vyUkC0YgbftiirlLsCopTmL4jndx6zzHfALwS3p0R7Ik3EJNIcilmnWgiXgNRYVaK+ksr1FGOfZnS7DjSuUa9syBe5YKjOpSDVWXlY04QqcnaDy0ul8g8WyrsgxlNnWAeFvhMdKn5uaJeA9Asb4pZrTnpoW9XpB8WvPt/e8SH//Et9IuM9mbN+saCy1drcgNWkXYo1wfPC1a3PMOHhtV+pNyVQlEykA2jh3D51UBmAs1+RZglbO3OuMr7tJVEBVJatg6mnFdrpK9k3K9bRfSdvlhD7HmSozc9nT9qff3rX/8fsvvu3r1L/P8pQPM85zd+4zf4jd/4jT/Jh/eZrDdXy+d0uZfCG4s3S2IS0LXGjTz1jmd1ICdsOxUsRHJhCUE0M2HkaO/U7LxzLgYORJwt4FMk4/eejHxFg+NJ5hLF5RfdBp1HlvdlLPSaKZZOFc2NluQ8obnIaTcdppZuzNpHCrtT4nKJ83L9ACaSDBoJZb/Z4kaB8YfCODMriXEySyli5u+2uI7a396uMbUUS64AIvh1h19zNCPpMiZniYClZwnLW57FTdko9MyKrm3sccNAveWvu4n1RsANA6ExkmJhpGhMPimornJBTsSfpCS4fhCnn4K43l7rzF4nnGQnIuo3C0NYSmcq/1GB+ahPuSdO5MHHKW7s6d2bSTyURuLDKumcVF8oabfb66SHqLtOyVFCVJLDW+2AqhVqaak3I/2tleBGrOgS02MZi69uesqdLi3lXFqQ7XGP84ebxK7rlk6lQxRe9IlJ4Oe+9TFmajE9R/uFFfVbNdopqu2AHTeSzuC5xuakU8hfJSRTg18klOc9wtM+4VWBqgztSUEISrAgjTxGf1IQZyIJcH3p1CRzjd6q0XNDemaurwP1uIfNPO/eO2Z12xGSSAximEimmuzM0Nte4tbctQwiJJHRhwnppDOzHGUdyzFijzKM9fReiIvW3a7gOMPODdm5lk5QqVje9qiFpbwhjMfRxyIZ8AtB0PSOBJ7drAXCZov68oxyt7s2nveJGhZ3Pc1YdGlRQehi5GISRMKgoLwqREJAd113KB1CJ3eoFMkXZsweOHhZgAF3LmPr9NyQzBTF1opgYfoluW7Kmw4715hSulpuGNBnKc2GF61pFohpIDlNsLeXVPNMulsLTXJpKB5muDuVOLRXgi/BRNrdVnSiIEzRDvEDIt2IBmb3hZXn+tIZe50m0q47wm6NzwWmblaa4lgMMyooYiKvgS8iWdbii0CykPi+4kQRgiK5EB7f/EFLtS0MxtDzMqavNJOvNaxuBvxY4iOdM6hnUvzZUq7z2bseu4LsQjSQPpf7WDzL8F9a8IMXB4S3SprdFnOUcXkqWsPXK5vIIfL19Td/yxMORAaSXGn6L8UUdfHzDt0oyqdDlIL8xHL+aoyfJ+TPU4pDS3piWf2nLVCiU3T9SPNuiTISz1c8ThltLmlv/3RYef/L609xB/DN+j+vNwXg53QV92a4QaAoGvRSTrP5qaX/XDQwfuApPpiQb5ckc4V/3peN34nT7ORwjXh/yepeS73jpINTK0w3ClWVof8qSjpEQEZOXaB5NKJpa8Zys/eDwOp+Q9Jr8Gn37xrN8Km4aZc3gac9VJQiQzuFqgzxeQ+AbCxj3nJPukjJQl3HiIUUSZjYaiUdoRYuHMiNVzUyhk7OxIUYrBQk+iiXUUsaaNY9y9uSPuLWxaySTIVZtzqQjdquujHdJEGlEmdFgGrfoTKPGwTJfzWQFS0xFyC2T5ExVAea1ZXGrXnCewuKYxnd6JV0QZpxpLrV0G5KOkk0Yq4Z5LVkLPfkxphdyu+KQUErHQBVir5SeUXTdd7yE0O9LoklppbxWvujkYyiooyKm22PfndBzD3NusT8RQP1u6UI8NNATORvqwhuoyVkAub9nQ/vS5JMZSTRIEj3F8A7AfuCoGyi4ToDuF136J4jmRhMLbGCqpbntDrpC2g7SBEvTxR6TxLGn0ryQTsM6KcFyUKeV3GsKU5lJKeeFnz6akcwN/2AeZHDWLqePo9UL4aQBnGUHyakM0mQ8FmU63jD0b5VYaeS7Zr//kDScN6aE5YCE5ZrFJr3V/g8YlaCc0kvNVGLExVkJNuse1xPkZ1rQhGIpaG8KGSEf2Rw65JQERMxNNlSXkPdSIEKXD8/VerrNJV0oghbLcmVJj0TQ42/WdHLGuxcPu/FK3Fau6G/xjg1zwbipq016QT0oMVnwjy0C4Wda0ImRac9TLETixm2uEGg91sDaDSx7/D7UqBFK5rdaGQ6AKBzj0oCoe+xZwnJC3Hi21I6Vs0osni3lRQZJe+vXWnMxJKeGZJxjTZyv4kJnUNaOoLZuWZ0MKd3pIkmUq1Skplh+U6D+bkrmpFgcSSmUJG/SChuzvF9Adb3vnhFWHPozNO/OwWniGmkucxFl7rZolrhpQKsvloSf26KG0T0uwuJGVwqmssc8zLHPCzEQDcIKCvpQiJ3kPtiTOS9Dfe6CMJ5gu7L2H/+rrAaVaUJmy3aKfy80+fODaOPJOWm7EDt7TiSn9hrPFOYJ4SVRbWKZi1Q/XANJulPbQ/5X1rhp/T1Zv1M1psC8HO6qk/GpFeG1TNhVC3vOHwaWe0Hei8M6ZllucypT3vS6dPivLQTQ/Iiw14kqEd9uTl3TLhq17N8V8C3qlX4VLoGppIOWnYonK/R/hw/CJRvNegW7FzTe5TiGumIDPfm4gIeK1hv8Hcr3H5Deadhdb8h3KrIzgy+kI26OekJu3DLkc4U1ds16VT+bnYuxaK9SAg2kpwLV6b/pUtMLd0iJkLSRwsewqdSMJqVZv27Yu6wM+k4re3MyQ+tmA8ikq7gZXxe7Xj0ToU+T+nfnmEWRowsLzNBN/QiHFR4r8hfJpI6cB4pPsqleF4YwsBBEmhnGW4Q6T1JGD3UTL/ayI09KuygJdrI6kGNahTHLzZEGA+dGxqS04T+D3NUq1i832BWAjd+3W2LOopu0QjMWLRVgfjOktAXBMrgmSQc+IcD9FzAtFFJ4RUayZ01S03/maXdCNJJmXQbUCKZwuGgoniaYh7n6EScm2HgURepdGtSKTKzM83qjqPtR9JTS7zIcP0gWcj3G7KrjiNkO+juekOz15KfaNJL0cLVa4r+M0vcrUkWkuigW4k6W3xddFa6VeizVIrlKBDu2GoGjw3uRo1dKrSVDkq9Hmj7kdCN10kiwx+mMvJdc7ieRLr5IlKe9rCjhmBFe9oOI9Z66m0vcVxa5BXJtGNsXkimLMDyZqC827C2PyO9NOi+o133uGGkeCoMvOTK0N4vcYWAu/1GK/ikqSUksLgju2J6YtGlprwhDEkVRMOWXWqSxzlnJ2NMBfV6vO58JRO5ZuxKsfHggnJfeIv1OqSfFNiVIp3KOLXdkcNLNJF2Td7P5OMear1h8iCSTAzF44zYGOJWQ3W35nXUYv/WnObtklAZlIoC5J4I/zCZaeoviq4qmohKBKDuhp0ZyMthES1d0eRTKaxCInpTFWF5T4xos8Mhq/cr0okmOE12LtDv6sM13CBS32xpbzSijSsi8Q/G0n30MH26Bo0m+6hgfjIgOzOoWlJ8GLekR4lcf3eXcpB9mVMeDohrLfVJT9iLN+U5Z+eSaKIqgTnnn4qRw3RZxctbkhVslxpXC4dQBUX2SSGvQa0JtytiHkhepWK8yj068xT3Z1Rb3UHCRNiq5WD4YCH6zaUSZuBC3ltTy2FYvSme3qz/P9abAvBzunQtUWVRx+ubwmucx/J+S7Pfol6Kxk83kscZLjLcWLpAbiwuQVV40dxNDHamZWx5lTC6N2FxN153Acod0fipVhH+0zp2ZuivlYSvz2l3WlbvCPjXrhTLx2MZ3b7lYZLAcUb+KGO0tZSb/lVKvek780IkPzEkC4VeGsrdgD5PKPc9d/7CM3HpeXn8yssISznF1dkQgkCL7Uo6Nq6QDplAgxPc2NOMFe5GjRsFCXf/7Q3RtJ1r6QYq6TrkpxrlFfFVgSkV9cdjho9kw9KtIj+2hDwQI/jjHs07Jav9yPIGZL9wgfnKVGL1okKZiBm0hETGX66PREoZ0EuDeVjQf2kY/5eMMPDYQQsPFlLMFZHFBzXBRlZfLlFeMfphKq5XC7pGTBGtoDmSq58YJ0wpRdbG/lTySungzuueUARx5246TAVJryWZCe+vWYuYucZvtGLKqDXF00S6gxcZvUNh+3GRYWrF/v9hpEsY4Vtf+hTbFUXYgIrQbHvWPlSSwqIlYaG84WRE+TyRRJhTcRe3o0j7VkUw4kavtgLJIxnVLe4Ki+11pyxqyUT1G62YU7S83+mxZXkz0PtRLoYHJV01uxLESTsUdhxKundm2WWvLhTplRRY/WeWcJLTrEuiiKkU8ZOBFN6lRi8Nq/1Au9Ni5/K4d945lxzbNYE4Lj5eF2fwccbmtyWeLlkKV9KuFPZZTky6eLXKoCci0a7fLQVm3nW8w8CjN2p8EXGDQDLTJAuJy9MTK1nVW464KYVIdinPOZkrLj7elJF5LZ1jN5BrqtoOmMfFf3UDkf9xm+ICD/OE2HcEG6+lA+PxCnuSEtNIGHoWZ31Ca7A9R3Ayll/dcczec/RfRQa/00OVBu0UzCym64orrwhdh7j3SqDq7bsr4u0Sv9ESO3mFahQqwuhgDgvJU6Y2RCvPzVQKO1coHbF5K5rTnnQ3TSmvweihxk4N5e0WO2xpNmV8zFaNTj3tWiBkYD4aoJxCOTBLTdI5/UMuhzTV86xuBHkMAcp9RzaR1y37YU9yiwsBl7drnqzf4DdbYhJQX53Srolr3c8SkmFDs9+SH1vSZxn2aU7z4zHtSDKUuUrJihY9N/jjnph5hhF7JjDw8aeK0aOO6lB8thXgaw7gH/frzfrZrDcF4Od06Va0bKxJTFayUeGGUlCp0pCcJqQTRe+Fod4WfIryUsj4W5UUSn2PUpH0wUxE6haGnxi0h+m0JyM3ryTaa+QEfdFtEERYvRjSvOiLiLkRjV29GQTAvJSfjQNBsjRrgdnxsBvJyKYGsP4jEamXu9LFSuaa7FLGPx8/OhDgsZbxmWlEvK1L3W00gOEa2owCO9MMnsl45i9+7ccs77cwFXi1KTXVV1aS0TmM2CsLJmJ+/gqfS6Re6ElXpR15pl/w6FJRbwo6RzlF+mlB/7mGiwy3JoDkq8Mx9cMR5Z0WnEKfJ/iZnPiDlfeK2I2KBo76lnRO6zUE/6KkC6rXG5KZphjWqIOK/McFyUyxuCObFiqyutcy2l1gtitACky70ORnCt8P6E97VE1CdmlY7cnz7D236JUmDDzFc0FnhMNCCoSRx+dRovKeZsRuhG4rkQFkV5qLX5Aiwc4VbhQot7S8D5Xmd7/zLkQkE7rLa1U9x+QLAbPQMnqdJ5h55xjeCMSzjHQiWBs3DBgTaMcR7i67bNgO85JGGaFuNCSPpZMVMqA2lDdbMU50BV/MIss7ogvMf1SIOzOJ0g0eBxbvtPQepSgnnah0VFPuyug72VuJq1yBGzuSuSKdCjbHlAq9W0mSRLf3urFcIydPN+g/FwBzdpgw/lQKlWSumd+BmATmb3ncWFztzbonmRqSiSE/kY4sKhKWCav7jegQ1x16YfDLpCtQFNWdhvnbjtWBjPuXt+VQxCSR59fvDn43A71jkWQwbiVfuMvKZiz3ifxFSnJpJZc5D6THlnanhcJLxzeTUa8tFdOXYxQIBsp0zvO56Ilt5qWbfCUZutO3EUnIqYy9QVywphJHr+uytqcPRIrhZym+McLXfGWkuz/XlDdbZmcDog2Ub0snrtqUpJBmPeC/vCB6hautOKdnch8rb4gLeH4viHkmKNwkRa80zZY8nniay/vohXuq1hraYZTufg56o5au4ElK9F3R+PaCfH8JwOwtuQBCigCpWy2j564TXDzM0KVhddZHV4rBpwnbv2vwXu5X1Z1G7llRpB92u5IDd6VoGtEnRyuOctYbfD/geoHlgYzSy9stybj6k95a/vvrjQbwz9R6UwB+Tle9I/BifSaU+uR7A4kdWgmw2dRK8kbfbQTH0BUg1Z0GYwNmvSaZGIa/V1A9GtHcqXGbwv5qR57scU554HBjT/+VRvcdzd2K3ttTvvSXP5JNZa3pgtEV6XpFdil/hyi5lnGzIXuZ0H/RaYGSAB1WwvUjyUbF5S8014HzYhrpIrqyQP4iIX0lhoVmv6XZ8OhGkhiyU3M9BoxppNwJmLLrLvQBDb/1ybuYqSE/NbBZE5JIuMxo1zy+EPadmRrmZwO2fv5YCrxzI3qgYQteDCa9l4bxx4r+U0NII9WObBrK/QTL4MaiLezvLlFOsfn7RiKTFMy+IhtZ8cqQHiekL1N8JgJ9gHaWip5wJpia6qSPvxL4sV3KRuRGUkj3HyXU318jvioob7dkVyLwd31It1cC+300otpvibdK3DhQnMjmmp7Za/1NVFC9X8rrfKqxiw5EWymCheWNINrRLKJWhmSu0F66IZMvOtFm3igFdvv2nPabcwZbS6KO6JNMOmetcCJVq7q8W83aO5coB/lZJD+XDmQImsF7V1grRXZII+Vd0XkmlxZ9mknW9HoUTAuIEWGnJbvotJeljEL3f/6I1d0Wd7tCHVSCJck8ZmZJ51LU+SLSTDL0XkWz62hPC+JOTXzdyXvL4Qtk3J1A8mGP/nMjOr6zhKgj9aYnmRnmX2hIJxpbwtX7kZBCOwq0G4HiyDL+xIDpOqFBkZ9L57661VDtuQ7LokkPE+Jai5kZ4XBGKc7dULpMyglPTkXpwq9/T66v0Au0g0A6UWQXmsU7LbHnYWEFZO4UdmZkBNmXbqBddjiaKLo9dIRaMDXjD2X8WO550guNnXd62kYwS/3bMxmhzxPcbkNII+lhgttpcYNIsx4oniYUx931H+HO+/LZ8lkU40o/kh9aiWYrjRzGVh0eKSp6jwWyOPpuJnrGLiZSOWguc7LunoAV7EyyUfE61zcUcgAdfiIFVSgCybgmXmQyhj41NA/K6/hDtVGLs1lFzJNCNJO9gD1LCKUlPBxQPx+g+k7G8Fca1QpTNLnS9HeXmLmmvsrF9XuwFFOPgfLrK+Z3FfmPC+luzi3VO3LN1Qct/qiQz0CAcJ6JZGPR5YxPEuxWRTKTlJFyV6F77jqr/c16s/5n1psC8HO6oonY04TiSAwGIAL+3lcvMXODruWGSKWvxyy6EsNEfNYj+VEPu1KsbkRCHsie5NiLBL3SrP/AUJxE0nPDxo0J9dcXAgSOivlFn++8uAm+w38UnuRK4171RIfU8bNe//fX3cL8vOvaTS29ZxY7V7STHHuSohuN70l3w/VErJ6cJrQjAVyPP5bCidzTrEXSzQpbvXYbRvrPDOqgot510qmLMHjviugl0D0a6H2/EKxHFiAPUmgWXINZDz8V0nxIxCwQJ1KU5S9Squ3Aak+xuO9wfdFOZScGRp0TMhU2W/HCUlUJbqtl+f9YCLdLw2CtJFlIQd6OAu2dmuVdyS9t1x3FSxn19F4IcodBi1loiZVrwFgv7MRasbzraO9VhJ2a9MTSjGKX8AL1IpMN+UpckvpZAREuvumpd13XiZUuYhh44jQlOUsEvFtIgkd+2hWd27UUR+viFH3tqDUrfe1QVS8kX7g87dEc91g+H+Fz0VX6Qn5nemoJIyeFex6ZfLqBHwTmdxFQdhKJZxnVtzdongxJ5gIQNz0nxT0CBFZBsq6pDWbR6e+cOMHTmaQz6LcXnM37jD6UzTv5sCeGpKWVw8+muMR1q7BXFjdNydYqMTgsEmKt0StNdiopI4MnluxSDiU+g5h73F5DMjFEI6/X+LtSzJsa4mYjWKGhlzHxMFKPob8hEOFoBQSuW0XvoVz3vVdaNLZHiv4PJad4ec+RnliKV5bszGJKGaezWV/nOc/vQhy4a5gy35yy9q0TsIHep6kU3VOpctyaQ9eq64xBeUdYiPYikeIKWD+YEtda5veDdPFbRfGVK8qbTqQl3ftefzxGD1rslUVfJfiBSDDUwhARRmjvJFLtBKp3KtwocPIfbtB/LgVmTCSnGw2cZdeuedeTz52dGcoHFSSB2VflkBpuCEgcBfmWcAuZJgw2V9i1hvi0jyrc9XjU9SLtAPIji2oUSSLAdN3IZyhMUgFpn2WEssvw3pAutzrORDd4Z4WqNO5GfX1/bNbkmi7OIvmjTCIXHw9FmztsxXD0aCDvl5VM4XYgsob8VItmOXPcuXsGTqYNUUG7K93Zxa1AuF3S3GzAQHuV0TvsIPZfnZM+KkSf+1muEH86X2/Wz2S9KQA/p0v1PGgo9wNNJwjXc0v57U1Cr3OU6m7DrrVsWrtSKfpewDRQbwURYfc9UUfcmsMPPZP3A9N3pXBafXsLd1aQnWt6H2UMf5yiPu2jNuXkbM8TmhvSCWyHkWZLRj3pVG5cdiXdqXI3YOdyEy4fCNDVLPW1E7T3wspjGEqiRDpVhFzctcsb0m3rf5SRXimaVSLdoEHEzg3tIOLPM5IrQ34meberKiUfNGTnhvRKij2fgao0ab+RFIyBaMp052yu91q4t2LvG8cA4lb24pq1K0jPjaRg2MDoaUSdpyLQzjy0MsoOTaeRCjJyzi4V9Udj2g9WMGwF8RKUjGSTSHYihhSfB2F/OYU9zPC9QLvRdb3OC9F1RRm5hUrgu83NhpAF/M0K/6UFemrRTtGsy+sYlZhi7LyD8iIjymKjRLUaO9fkp0pMHa2MV5s1yCagTUTnDjuTUW9IJX0EBYPHlv4LLV2nc02yXsNaS0w7lNBE/lY60TQHLdQaN4z4vZpQBOzMdKNn0TCaUlFvijGh2fCkM4VS8Xrs2m618rpref7aSXKKMCojqwN5DeqLgvJYXL35YcL4YSC9MAyfdu7dOw1hp6bcd5IOkwSasx5m3ApG5cqSXQjfUJA0P4GON2tBCsmVwY0CZmm6jGtQt1fM3u26uYNI8TyRLNsA9XagfDqUjvelweyWpBNN+V6N3qmodsSkop3oHXWtSK4MvRPRLtbb0iVM1iriNGX8kSGddvDxWqQedqVwH404PlyHymArSKf6ujtt5oZ6x9PeanCjcD0Kd0PP8o5DXyVMnq6RP+pg0VZkBZOzAaSB5N2ZSCNKRX6mSB8WJF1BpnuOZhQlgWMQWN12XPxiS3amUZcpDFtcT55j2K0larCTBoQiEAcO0yiSpSK7UpgKbOrJXiXoK+n0xWlKyERLWB/2Sdcr7FKzOBrgSkt+qYillWLYyPi6HUaqmy2mUbRt50a/kvQeO5NIwJhE9MJgl5rkNOkA5Jr8HHjSI6YRnQSyrVJ0t07Ruz+VZB0D7iwnJDKdCKc5zTigWynoGIoTPr03J/QC9VbAbbU0FznPP9klO7WYpSCwkp6AypVX+JXFpAG7UZFMDfW6dOvdswH1eiBdL38W28sfvd6MgP9MrTcF4Od0mbO067IEkmlH7I/QvFOKgzeHZKZF1JwG7Dtz4iRl/J/za16X2q4JWw2jjSX1jVYyec+lQxLGTlyCEWIuBaXPYHHPE+6XmJe5JE2Uiux5hmo6IfpE4o6ilhFWecPhehCHDjcI5OdKoo2yIJFqtUCfVzclsWT42KArRdsXJ3I7CmRXUogFS2cKkM3C9QPBxi5vV3VYFYi7Nf5lj/DxgHojSNZnKn9LeYVrDftfOSZsS4qE7/AW6ZlFf9rj5dEGjFp0A+W9puPvRdFUFRJ1d/WBbBYqdEL3tNMCTRPCSU573JNM0K675pYJLBJJ82gEspufyfvge4LbiVY0ayERc4O4FxVrtycymk069E1XzJkLyZc1L3Las4LeoXRSiRJNVZwpVOrRtYz/B988FxiwCaQXAq6ev+Poba649/WXNCMBUk8/cLiVJQbB5uhGER8spHtWC1y33JGCJSTgjwqSF9n1+FY7hak0Lo8kvQY7btC1In2ai15PQbZVCqrGSscxP9Okt5bYmREjxEkupg8LZmJptsSx6vMooOlK0a53utGpJlQWMuESrm55Nn7hmNn/a47ysLgdwEB/rcS+ykgvDbN3AjSazW9r/Dyh2W9pR556Wwpm5WDxzZLqnUq0f0ERc9HSDj8V1/zynmP5ToM7lbGcukpxm45oob+5Ityo0LUiuxQGYTZR8KwnI9BHGTwvcAOP8rDaF/2kvbVE318w+XJLyCL5dokbBdplKszMTUkb0a0UdqYSOYXrRajlepzf81S3G+597RWDu1PijrjNzbFkd69uOdothyo8/eeW4RNNTCOmFN1edm5w/UDvcUrxJKX9eMT4RwJubwfS7ZTEnUj/uwUK6brp147klcF8cyLdxXlC/2Wn01xZzFwg0CERhFP6MqX/KkrU3L4cWvxJQb0t/NHBUzlQvD4kxTTQnhYyaZgZaDXLD2rSC0PxOBXnrZXPev6i410uUsqvrUQaEqHdlsNKdm7Y/T0E6rwlppJq3zH5sqPdaRl+YjFPChnv3q9IFjIBsQuF+uKMmAsyxl4kDJ5KN9dncihOn6eoo5z24ZD8yIpe8jTBLA3JRKM8cs2fWtpFKprpANmrlOSTgnaZ4PYbqp3A2kfqOjGmLT9jDMyb9WdqvSkAP6dL31pKPNfSsHq7wWzJqCJOUxmr2SjjRAXpUYL7dEgylQ2k2fQs3nKEi5QYFLPjIWohhgj/VimxQ0tDMhPhvpnIzb+6JXo9N5ObUDJTuFwKlrDedWmiCKzzCyWbRhKotz3F45TYZQ8PHxuwEq/m+tCORA9Yr0VW+2JICJl0kPRuRfmVn2h27Eox+kFKtRXJzwwxk+JJO0kGWb3TEIMUgz6P2F1huWknRfHgmSZepbw6WyN/KCNTu1LXMXKmUtjjlMH3c9pxkJQD2xVlrtPO7Xt0LcWpzwPJxwWDH2RkF8J5S2b6Wj+XzDX5iWTp5ocGvbBkx5ZkKnmkPu3c2w4IUgREK85kdKR+UDL7ZF3GZGN3/f6rWjN4Lt2zds3Tf2pY3HfEvug2lzcld5lZIhmowPnhmND3tD8YQ+w20DRQPxny5A9ukJ9pBi8iG98x9B6n5J/m6KXBDTzhmbDXmi0pvlBQnChM03WbOqRfdqE796rH7TaYDweEIKP9kEXWD6b4sePu1iU+E+ZdedNR3mkFuJ3KdRt6Hl3J6xjyiBk1hLFo2pIrQ7XnsFMR4de3GgYfJ+iZxaw0/eeGq/+0R/NwJLrGWhF1pPlkhBsF6VIXktJw8bUgbLrzhN4Li1kp/G4t2I3SwjwhmWvifoW9stiFptyR3N/s1JC/SIl9RzKVojG5sDTjIKPBVznayXXYe2ZZ3vG4fRnjlvca/H4tbEsD1d0au4J6mlMvU3TPoRxUM9HB9R6l6FZg26pW9F/Krb0ZyUEqrrfoSqPGDeneCnuWcPjvbrH6ZI2wTKTYvlGhCiefl4UhVgafwvTLLSSBajsKaHwGGz+Uz3LduWiF+Snj8OVdh2ohqkg7lGLODWXMet35/HgsGsOZZvrAX+fo+oG/Zvgpr+idKK6+EFnciZiVwOSJ0m3XDawOBEhvS0V2YaSj+9iI6/3+AuUVyYuUZq+lvN0SBw5fRJFaDKVqSvot5nEhnMu+5BHbK0vUcPTnO6evhvTBjOEnlvzQonPP8nagHQXW9uboJFAeOOx5F9921CcZCV1At0JJWN2QiUrvUPigtqSbYkAYSpRg6AWhA/SiRN31IrrTp77O+G6HgfxFSvpcCsPFbUQLYaPE7n2m66fR/XvTAfxZrTcF4Od0hWd9MSkMPDiF0jJa0ysZ/eSnmnSqyY+MRER1YGXtpEsn9j5Q3catgqQ4+EmKPU8wS9nUQxLxQy/5wkEQIdClFKyFjk8V0dOE5oNSsjeLKDfEgyji8Y1aOhYXiQTKZ2DPEvxmKzowBeFlj5DGa1ed32lkY5knhMu0+3uR+kYjzsruhto/mF9n2SqvKB5LEH1ck9xcnvQxtaJeF+1RM5SkkTBLZNw073KNVwrddDFvTpIblBPzSn4s4612wxO3GlSrSGfIvTCTbNK2L5o21wviqNYyMnID4eu97vYVx1JoumEkbrTXSQIqdKkJQ8GEVG/X4ghcSAGCArx0k8afGIn2GnaGGSuvjfJKxkdTgx95pj9XwaiVQ8CF8NBUI2BkNwi0ax61MqRX+lqM3owUs7eh2pIw+jByoKH37kTew4XE9tmFEg7aMFLvSBcrWkFytCOPGjjSQ8HX2Gf5Ndx6/tEGZmb45MOb6FZ0WmZu6D1JGDxMsEslhf9Scp+bB6UAj18W14xCX0SyzVKScHREp556M5LfmkuW8b7o2FDymHwuh412vQOezw3JlcHtNxRHghkhcP05SZ/mVHvuuoAJSYTTjHQqXWfbIUfagRQaqjTyvu3W14aFkIDbbml2HM2Nhmo3EHPPeG2FXm8wmcecZGQT6d6bzLP8Qo1qNLSa0bDE9+QAotdr2kGkdySvecgDbV+0kYMXwrg0xylh4Am1oV6mvM6rdpstqufIT+X60+epHKIeanrPLPWWRy8MOvX4vuTx+hQWN0RKEK1kDNdbgfLrK9wgMvrY4nMgFwOKH3hIA8t7DttviYWwOHUr139M5F6hnCJZq/GZpNjYhZID39jhhl66aWdd125dGJLtIFLeFH2ez8Vks7wpn7F6mVK8NDIh6Lh75jIh5LHrzgsbMxwWxPur60xgP5akn/pGg50a2nFktLEkRljc99Rv1cQrkXfEPDA5HeIXCfmxXH+6lve4nWWkl/oa6q4bKSSbMag7q2uo9Opei14aQRPNtEgh0kgYui6i0jN725PMBe4etlr8g6Uc7vYEIB2ySOw5sJ8xCPDNCPjP1HpTAH5Ol9tuxaRwLNmo7WVOspAUDXREO6h2PdXbNcXNObZzyLqe6PPSMyNMskZgwYOn3QbRZQW73eYa5UIahEhfyshJ9RzF7bkUhkUkvbuQyLmPC4rziLklcVTRQLJRSezVKOI3245qHxk9AbUyDJ8o0v2lOIg19J7LCTd2eITkQpAV6URJ96CLzkovpHOyfDFEpZ7QCzRvl7SjCH2HOU2vXcWmVvRfacYfmp90EueGxVuu6wqEazZiMpMO3GuxdbhRUW8K0kQ5RVyIqWG1J1oj0oDrS5dTRUk8aMaB7FKx+/syUo2bDeUNhwrQO5ZxdMgCyctUnJ0dJ+01z80uNGqSQER0QBsB85UpJJFmJCiNdC4FVf+ldGryC0V2YgitjIFN30liSduJ92/X6Epfmzhep6jEPNCOIuk0Um9GlgeR/FRcu8lc03so3d75tMCsFP2X0pHzeSS/sSAYKZDbtUB6bsgvFGrgyB7luH6kPPA0+y2re+11iozviR5094unhC8shAepBW7crgmvMHR61OyHBeWeoEzSyy4SzimKfz+kXQskM4PtRsvuw5HkwH4k4PLXui276OLlVqJn9CNHvFPCwlJvBNpNh7spLvHesZKCsdIUm5Lu4HPpcDfjSBz4TpsnsWMhjcQsoMYiJ2jWPcpBur+EWmP6LWomhS2tZnI0Ep3XkxzlBeAeskDyUY+4MhSvxDU8OR1iSi2Q5Y97ZBPF4nYkuztHd+ajds2zvCEHEz8M2EuLnlmKRxn+oO7G5orYaFZfqOAiE8f3UlHuCndRt2LailcpupKudbUlo/et70ayM+m2hZ7Hd4YJIvixw1xaOZzEn8g/iv/SE4ND39PuNQJBvjT4TTE6tJXFloriWCIX25HAx/tPLTEBvy86UYLcq+JWI11S/ROjSP+5ONZZWKrdwPwLHQ9xb4Xvfla3inQiyCnfD7gLSQYKRUBlnrhb01svsSvRHTbfXaec5yRbpbiTa/lc2StLcm4xU0u137m2nSSqDHcW5Ged3GVTRvaqFWxUeFVId3KuSEY1MRHN8vqHoM9StFOs/35C6Hv6myvsdtVllCOO7ctcDqNzS7XjCGkAp8UM92a9Wf+Ty37WD+DN+hNaUUak9WageNUxpDTkX7/E/2idxX1H75kFDPVWgrER1wFT22EUE8ZCCiI3diy1VEbaiSCf2ogwf+DQE0soAr1bc9wPR0SnKZcpxfME34vE74/IjHQRF39ZAu1792csz3v0vjfAbwfaGw1J3tLWOdxdcblnUQpWNzTx4YD6pmPwRNyXdq5waMo7LTp3OC+xVfmxJaSGZq8lTi3pTKO8xrsU0oh9ltMctJI3vOHQc0MsoDgWt6j7rwgKyktxlV0qXCGw6CT1qGeW/FRRHFvRFN6rmLUS2N573nU+kKLR3a5JXuTYUlHui8Yx5gEzNZS7kcUD4bXpyxSsuG/rLcPoU0MzFD1hu92iVoZwo2aRZfSfGFy/c9lGwa7svnvG2Y+3yZaKesujgiRkVJuRai9QvEhoe0jHqzSQBfSLnGpNsmvtUpEcZpQ3xbFtakU6M5R7ATU3hCRy9ZWA7reERcJiTVAjyVSMLcm4Zm20Yvo8o96K6FI6OyYq/EGNj6AuU9K5YnUQUFq6gtmJgS/NqSY5uudoh5r8zBCWinrXcfRom6givVNNedOjK4UupdiPtxzmqGD1bg21EbjvrS5Wz8Pkqy2qFkRGdmrwby9oJjn5kaXagGYtkl0pmlGXRRukyMzPNK6vSLdXtFcFzZYnubC0213G9Q1hSroI7eMhpu7wJU6TvD3Hn/RJrxT1zZr0aQ/dGKqbgXiVSrpNHqVofDygP1GYTy2rfekQvea/RSOFV91JNOzMdGk90H55gWos9jCj3W1YZHILbxolqTWfjkhXwtVMJgZTKtIrYWSWO2KscD1F9jhHf2lK21jaWYp9ldFuOpqvlrSLFNUdBnQj94Nkrmn25XMVBp7Q01xpQ32jQSUBSisa4y2P8hZVa/zIX3MftYlEnVDuRUk4ubKELU+0gf4rxcJ2SRg6ot9aEL83FDTPRNGORE+pnMIcZmgvkZHVVkRpOTDEwqNMJDapdEHTKASCw4RmJAfGalVg1xrcNMWnkuyTHyWkE9GaLt5ykEshBYFqlRIOHOmZEVxQZWhKQ3Eo0XtEUG8tcY0hBoU5SwkJ6EZ0mH6Ro2/J+1Y8Syn3HTrI62rvL6iPeuIA/u4A935FmGecfy1eR/HN7wktYXnRg1bBjRZ3mRB6QaYOWcQr6ezrWjTA44c/kYF8Jiv8FEa4b1zAP7P1pgP4eV21pr5fEUct5U0nEVM2Mn0+FhzLqKG8KYwHu9Ay5urSApqNLgS+G1nZqcRv2ZUiu+hC1adGNG1nwqbTtaZ8PqRZF0ZWDIryliMYaNYC9Y6jHUXKsx6oyPK0L6kPQMwiapLQTiTazF3kmDTIDXdXeG/KK8o9GTuKk1hO6vooR18IJsUVspG+dj5WnVszFOHaMKBK6RbgRVQd00C5F6g3ZUzZvlXiOuSDXSpJ01gosic57SRjdccxf69l/rZn/sWaurGih7wyrG7LCLE+aPFrDvtURptRgYqKOHDYC9uhZwTIXbxMyC41xaHBTiz5megwX4+M9Lzjlc3lOS3vSvyYdgpdyUjp4ns7+A6orStx7/ZORHuUXhpMJQUjUTqbZm5IrxTphbkeV/ocGLT4e5VEBt7whGHnMJ1rEefPE2HVHVv6z4zc503EnRacnw+l2zsVaUG0EfX9IenTjMH3cgZPNfWagK5DZYi5p9pzuCcD4eAFxeCZ6VI5FKrRZGcG3aWBvO7a+DWHG0TiSU47lGLSLDRuzYERZJHtMEPKSYFsahnH2Yngi6ptcSMrL47MMHb4kac41lI4KGg+GeF7kfzISlRZaVBrDb4vBovxI8Eq2fIn8XvlVYGda5r1SLjKCKkcurLDBLqcZDeWtBDfDxIrtyfXmc/jdXKJcO4EmUImrnlTKjCRNPVEpwm3S0nByKT7HNNIsSPpGfXbwr1rNx3tMIj2zXCtrQupjFzDD8a0V4JvcoNAcm5x5zn2wqJrhd+vRZtZSF42tcDCtw8m0E0L7GUiDtsg9wnVKOptR3FkyNYqgcVfpHCY44ZeOnBIUpE9ylBLy/yudLb0oEVdpTSnkgEuOuWI320EKB3FOBYNLG954s2SUIpbFqdQk6QDOSvMSmNyj7tboWzATgx2YXArK3resTi1gxWzUrUVxYBxnJANa5ikRC+pIdGILrn/VH42KnD7NebtBc0iFQ7iNCG7+En6SXJliFepIK9eawvXGtFl14r6pEdxJNzQYGX0/trIgYmks+4zVDjUytB7YRn9ML2+l4npRQrzZKoJWy0xC1y9/7PZXv7IFcNP5+vN+pmsNwXg53TpRsuNtxY9nwrS4bJLTXu7xrea5FL0Zq/1M7qW076dGYrnCf3vFMTdGrfREpz829VNT29z1QXMB2ylBDQ9U9cmD7fuiI3BTiQ5IKaCm0mvFMOHluzCoApH+VYjOI2VloKulpuZWWh8I3FVaiWuY7XWwH5FXJfIJOUl8/V1GklxCvF2ibq3JFjZnLJzA+uN6KZM11nQUdhk0+73BkXseclfzST7M38pp+yQRvqvOmRNEH1b/6klO07Ijw16mtDWMoLOJoINadeCbOCtIE6W9zzaCSdNde8DWly3MYn0jmQDWN1xuM2W1Q1J3rBl5/59jd2YSRdCV+I23f/KMX7oUS2i1asEZaE7tuD5VxTNtsP1ZYTr36oEkzJXjD9WVNuBZk+czOZ1xy6RzmC7HuQ5vUyJAwF8+1GXlmBDN5r0hERGYKgI0wS/5lj7+RMproKivOGodzzzBy2z98VZihbXbnooxaQKQK2JTrPai+g7S5EhXGkpUnoyMo2JjDT1Qgr4mETJLu6ub9IgRUAXXYcS04AK8v+TUU3IIpMvBnZ+H4ZPkZ9rNPY8oXieyEFgu3PQlp3JYdfDWiMGiqNcOHT7nsuvBOxFwupBTbPlWPuDBDMRfqXvCbJm9XZDfbOh3nESAeYUyaWWmL2dknojXBcIaqciP0oIeYBvTjvsDahZIqkotRh7qiqR7tlVJok3lcbttGI+mWeoZ4W4yDcbiufiAs+PEpZvtahbK2LhBQi/UNQbXrrIHVLHd6+7rTp4tomk+0vSM0P/maX33JKNK86Ox+itmv47E9xWK/BwBX63ZnxnKtfqONIc96g3ut898BTbK/JTw9Z/TGh3W+kTRTC3l7jbgrGJG40kblgp0HsPJjC3tJuiyyMNcGclBIPzDNruGkrlmrVzRTiocGNPuEoJi0S6j0ln1KoE2B1NFLTMpaLe8dhK4ddbfBEJjwcilZkmor9sJG2nWRNdns8j2kbJBp5akplcd+0wEjq9Y7xbEjMxhoS+JyQR/bQQgsFI8EOuF0l2S5q1IGPrsRy6dGloxjKaLj7K0a0cCKvNeC1b8EUkbLWYlRjaxr+XgY64jc+4A/hm/ZlabwrAz+mKJopAH0jPDO26sP18GolLS/9H+TX1HwWh8GSXinanlXB7BcuvlYRZQv48xZ6lrP9IkZ0ZVqf965OsKyLtbks7ijQ7jpgHVKPpPRFn4WvHcUgjzXpk/m5LtetQlym9h6nokLScvvuvNL1jhfZgOjG6cor6oEWdZKSpQ5+l+CIyfGjECRukEzO7H/ArSzMXl/Pmexe0b1Vw1Z2svaJf1NJJ0OLsfQ2w1UtDMq6v8TQhlYIrJLA8UCzuSFHgh57lbXERVu9Ugiv5qKDec9JB6InD0VbCLUzOEtZ+8No9DDHImGb40MgYaGKYvtN1UkYNemYZPhKIMErcjdFIR8wPAmbUXHebXrzcZPhRgrtXXT+nalNMMm7scSNPsVlKfqwFP02o9hztMFJuy/NURgT81b0as1K4RUK73TK+NaXe9tQ3G9E7nWYSOh+E0dKMA71XhpDHzqCisDslo60lxy82KG9IB7m3vaTYWmFmhuxUDhX2PEF5RbsRrlMTkitBgyQLRXMpcVz1jVbiyLyivdWQrFWgI8WJsBN5HZm3EoBw/jSDLi7MD2W07oYBu78SN/p5QejSWE6+FZm+J50+XUtHJz+Tz0L/qaUdB7Ir6cDGJKAuU1QAP3K4TTF/JBONG3nMqThyZ2+L+aJZi9h5d1sNct2RCVIkWhkHeqdRSrqVsRPhCBQ4oFea8niAqRSmhv4rTTsMrG7JtWUei9ZStQLjHn1iOtyPEmnDUCDKsdXiEr5XS3e71bjaQq1p9xv5rFrJeE4uZWQbcsGs1N0BoPcdQZygpIOVTSLx0wHFk5TRfyiYXfQZbSwxYzESxcaw+HhdnmMRxPSAvFaq0VRHfapdz/Qd0FNLyCP9FwZ31EObSO+lQZ+m8KqguimJG6uP1q4ninYubux2Lqav/FS6eqZW2LP0Gj8TlgkkQbLL+454KvzC0UN9nTyjS8EQlTsR3W+pbjXQSFJI28UfxkwwWtWtRhzcMwWNxt2syX8g6R3ZlSa/iKgohy97YUlOEuJhzuBhIn9rIZ+VqMEPg/yNt0o5lH3aBwWLu0Hi9paiowwDuS+2I5lq+EK6wGqjIaZyrQ3/IMO00K575vfkRdKLz9gF/MYE8mdqvSkAP68rKPRMbuwoGDwytLcEBaMrjU8gWQiSozhW0I1CXgu2m3EQaPHM0KwF3NBz8Q1J0VBOkZ2I5ocItFKIEZSIyrMgyQgaGEqHQNcKt91gJ5bi0BJNpHxQcePuOfrGirDdsHinZfa2tP9NJTqfZK7pP0wkuu6sJyNVDfN3PeVNTzuOpEcJ0YCZWtKjhDBwnL1awzzPSfeXhA52fHU5INwtSS4sKnRi7RLWf6SwPxaMCRHMSroI7W4rSJdWTtz5kXQefSHjaYnUk85MNN1rFxRtr0sbGHkmX5ROm6kl6imayPJGuHb6ubHHjxz+MiPkgtpIJxqXQzrpEBdz4f35aYqpZCycP09phxCvUkFerKQgIUB+KN0avjsinSna3ZZkIjo5n0uBEAae2EocG7HjiOmInlsmhyMZRy4syYtM4gPNf/2+ixtaVwqzW8oodZEyOxmgco9ea9CFY3Xap308xK85TCnFr+BqxJne7LfERDh/utRUBy261qQTjaq6DsmVIUZor3LJwM06rebCoq0UeNFIBzvtoNnJpSHvUDruVQ/tpOjWU0scOuk8ekW9KWNx3SIoDaAZR7buXzL9sozrkkuJWXPrTrh6Uyne3SBSvLIyMp6mhL4n9j1hp5ZOZyLuX6CTGyjCUCQJMSjqRYaKYghoNiQCMRSiL4y5dI2rrcji3QY/ECSNKTW9I0kFyc416ScF0y9IZ6xZE2C53awYrK/QUyujbh0FOlyK2UMVnvRlKrm9Xp5XNOJWB2j2ug6SgnpNrulm21Fve5Y3pcBqh2Ii0zPL7GhI0avJNkrpBkOnO+wKs6Wi2WtByYRBNQp/qxKDzMhRbkdC4dEmsDrw+KGXTO/DRGDkK8kLxynchqMde+yglcjFPU/29oxqX9AwbV8KbD1oUZVBRej/IMfslSQzRbUt+dWSeqJwax633Yqjd2VQraCVei+tsBFzjx62qKURhqUVt3z2MJdrcLum2vMs7sj9rd510oyeKooTLaagNBLGjp17F4QsglPYkcRtoiLFqXy2YhLJn2Ry0CyCaIN7Qi8IRSCZdR3G2qAqQzI1gqEZiLvZb7QkF/az39HfJIH8mVqf9eXyZv0JLRVl3JhdSUrD/B2HTZ3ohIw4/EBu1q+xK25LskZJAn7DoZIOaQHkJ5bkSlPdryUkfSwaJt8Pog/ryf9iIsmZvQ6MZ5LKTblVUBrJfC0FQp1kjsOH27RlQv/HGYNPkuu8XlMrVjc9qIjrdaaHVtGOBRERU3GVphOh99OBrqNFnMA9GTfq7w/JT+RUHStDv1fjbtRUO57qRte5HEnqRzsMhJsV5U0njDfXcQvzSLMZqA5asguDXWiy44Tihf3D4v3TTGLmfvEMd7fCzg2x50lOEur3SmLuxb3aD9ijjGgEc2In4tROpoZmw9OsS5FmV5H2hoCmswuJQHs9Ngw2Uu13EGYrr4mM+gVW26xHytuSspC/SGWkFSEOHXYlhY3pS+cGwKey6YaRpHv0nkoMWNTQ3i8Jhaf/QtJGXjugfS/gLvNrfZqqNeYww88S4jSl96I7JOj/Cnh9Q9Jm8hsL0qOEdrfB9wQlhJKYuHpTYNU+j7T7DZSy4eV35tSb4ixn2BKnKc1cimI3kCQK00ixrjw029LlsqvXgHCwuXTw3HaD224IfS8/k0cR9ms4Oxpz5/Y5rpAxMjpCkC6sbkRDByL0L460ZDifW7KXCfnDHFVqBh+nmJV0wvOXicgLrhLsQpE/zNnYnhF7rkt4AJzkIgMUT1KadSmGslcpxQsZLRNhdRBZ3O5kG53r1TTiZk8nCndaUH08JlkoBs816bPsutA3fQdLi7tboSuN3qxRtWb4BPLNkuzUYPvixq43AzEBM2ooXiQUhz9x+LqRpxlJvF7/qWX5fERz1pO4xrrTVo7E2FXvSFcrPxGcULJQxFkqjl3XvbZJxB31sAuNWRjaccC/syKdysFET6SwVKWmeGlZG63I95fke4JmyY4T3I44fVWrUCcZW78vndN6I+KPCgGSd8aQkMk4WFcac2WxS83giWCFVKVZ3XQyjq4MSeaIQ8fwg0uajYBdidGqvtHIeLmS9+U139AVkXpdrrVmr5V7Yqu5nPWI6y2x7/CNIbzsMfjdHslcrlVdK4qzSNyrybZKSbR5mWNq0dyWXyxxm06mMZ1kw2+0QkKYK1RlxIGfvNHPvVn/8+tNAfg5XbpRFMea4VPJJM3ODDzpU05zYk80KdWNlpBAeSCxR/kLQXqYqaV4lBKdEPbRUO05ggU1STCvBFERb1WYlcB4UZJLixPwcEgi9aYnWUjYvOuLK7F3HFm8JUJud9gjvdSY85S2L85M00inqB0Imy0/V9Q3JArJblfX46/8RYLrRVa3Hf17U0FU9AWcak4yQqtFbJ9FgpGTtqo1s/M+TFLsXJMMa7KbC6rNyOhxlzrxNBf348KQv0ywCzFVpOdi2GgHATcMtEMZ9zXrXecPxLE5V5xfDImXqeBWcke736COc4afJKi57QpZz+67Z9TbwiyMA8lUNQvpNlS7jur/PkNdJgI/Hsgo3Y+kY9RuCNfxNbIlDqVwiwru3D+FCNmxlaiyAykEUaCnCfXPLQhWNEm+CNjTBN0KZkbPLCGJrO43hI5ZmP+oYPAoYXHX0w4CseMSDm7PWP+edMP0VULse+nuKchOxSVuaq5d4uq1PElH2odDfA6j72dEBf2nAhM3c3O9qWIj9lQKqWSqaB6OZLypQJ9m6FrRe9wVE1p0Uv6m5MKqCL2nCay1TL7W0NypiVkkzVryU032PCN/lpFcWNF5ziR9IRjpbL340R79l1pc7ArMUkb22nW51ds1ZqWpv7ISPeV+S33Q4j5YEvsCUkdJRCBIgRDy0HUwI/MfbkIUpuFrnEj+VJ6LqcW8Y0cN8cGCaieIU/nukmDkc9fsOtx+Dbkw9aKRBJZ0IppAn8L8bblWehsr8nNF/oNCcmxPMskYflLQf2kotxXVWUGzEfDnmbhMT7X8rZOc8nZLtetxhXSjSAPtUFJB6q+s0Fs1Zq6vWYnVgeRKZ08yGLbYUnSYbtCN9rdXIkGoNGq/wuaOuCaxbOlEM3xk8K1m+PULqq1AOtPyeiw7k0WdUFcJ3mvMb49p1gK9TzKabU/YrfG9wNm3vBSUlbpm/plKYc87fFIXMamC6swyEDcksvLW/TPsZoWdGtzzPmphmTxfI4xbVndbSTj5NCU/sdfGj/TU8v9t77+DLcvO+n74s9bOJ90cO3dPTtIoGgmEXqNCxlgGFwUlGwzGNsI2SYDB4meEyTJQGFmAwWAV2BYyUIUlBBQGIaQBCZQZaUaTejqHm8OJO6613j+e3VcMCkhIM92a2d+qW9V97gnrnLvP3s96nm9QTvjQZjlnckg8Gk3H0Dnj07mvjb8eQqVxRixoRscsey/LhHbiw/7tls6HEuyZDuWMiH+cJ76CwbkYb+CRHc8pe2I0r7RjcnMuHUTtqNoQ7jcj4AafOZoC8GmKaroiXbH0b5axlq0TgvRQSPt2ukRFhux4QbwhnnpO1QkTIBevrQCFFDW95aEoF1uGZEORrGnU5ZhqpqK8bSIedHMirvDGHuHxEfGmFCStq4rZj8ooenAKSQiYMoR9sZkI+2JbYWLhyrWuKkzXYtqGfFZi1EzscBdbUlzsiPmu8xxEhvEwlk6cApcYknVFcibC2/OlwzcvCt9gWGd69kqCoaLcj8nTgOj2Pnv3WLpnNeWMmLO2rmqylQqvVj5f80js3rwvMXWJJCC4WjndvqJwswVeoWAvxCW1NcXlhPBKSLQrXEIXWSnW+h6xX6FTLebZDsppiS7DSZ7uZBBjZ2o/x+06maOOlsNKwZZsSnYwqSej9gounF8g3K9Nq0spLorFSsQTMwVL08OD8Rwa/EwSFrIvGmFji+0a/N1A4sEyeZ50ydI+PCQYavy+WMcMr3YZHpd4Pds29D4qxdqBGa2DfKkSD0dfikb2A1SmMR0LqxmjI2KUbSLQQ4/wxFBEGLWyU0bkRrJxS4i2PVrr6iBFIZ+Vokfl0gm2owBzMgVXb2xGUliqnVCUy0C2ZAlGYmLupYrREek2Ot+JIGK2lEQYD8qewUXSCaympaByHrAeSQKKlU6YF0n3zjoxG/dGmmpO1Le6FPV5sF/nNQ9r1a9RZAuGYKSxkXgHemntG9d2RHFJdbUlPEQnSmYbO/TFWGxBhoGMFA9NMLVoSVegRrUwplcSbXuUj/YY3lSRLlryWYPpWMbHjVifHK8kbaIQAZbORTBVzMhF2HkOFQlvzvSke6rGHvm8dKrtRoxzMgnQpWwEr+VYW9/hXY2E+9arN5Ceo6q8A1Pkaj+kqrOr8zlDtAfjVbF32b48LekhXfHNrOoN3mS3hdYOc7HN5LmTgxhEr1uKKEg7MTZ3UoArC+mxgqrjqKYqySXvieBl6jHphucnM6JzMd5Yc+n8PGYjEdP5a9+3TkWwHorv6EwmaSeVKMyjHblPclk2CNdEd+WsfG6jUxXFlJxDVD1VuMbpcxb0kvBbox2PwS3151Rn/1YdWaskAinUIKCzMgKraD0Us7A4QFXS+Uw26o719YTj81AAXt+38ExCUwA+TeH3fdqXNcW8cHywUljZXiU+V6XGlZqZ+aEE1K8riVY6UmGmK7JliWcqewbTchIHV0kXbfisXIoLC+2zASb1oRD1bjVfEp0cUJzrks9aSZTowO49cvJWRyZygmwJ0TqfFaWcl4qXlYnEryxe81CdSuLljIghzHJOPiudN+eL/Ya3G6C07O6DfY/2/ISyJ2OocE/TvqJJrvoMbjESLdaxkHpkd8iYJX44YXy1S/e0R7bgoFOSzxvCgRMLlYK6EJDxz+SBGVQqoyobOLrnZEw6uEk4ddHzdpk5uStJFWNfbCtOSUKCaxmJsNoLQMP6fg9b2+t0HwrRtZq6mjKMbi6J2gWz80M4lArfZ8rg1zv86WP7ZHNSmCXrmtYlX0bLYwWVIl0RErm+bSQGtJmQ/PVWyPqHlimnLOWUI9jTVLdMQDnMpRa6I1wis5Qf8J6uIX90SkyYI0cwAmKJGTNTBpxieLOko6Ag3pIxv78no8NruaudowNc24gB78THn4h/XT4vSQ/F+Q6dS8iaHSRXJAnE2wilg1lQd4ut+Ag66ZbaREbAKjaYOorQH2uiLZ+wU4hAo+dI1zqEu5p8Vi7AxYzE7Tk46LCiHINbKtIlKyKRWpQT7ItAJ5+x9B5XxJs+/vmYarEgjEr87QAzDLChUB6Ub6laYEPJxC6P5XjHR+TzcizpsQhDqkQSKIK+plisGJ+osB1DfrqH8x3JVZ9kXaFqC5JrqmfXrug8FlDsxeL1N5HUCZDvg9oOyecN5awhueoT7dacz8DWamCgztwOBqKItUs5vY8FnHzuJbSB9kWPMClxswX48p0I9zxUqWitaVzgCM4kMr6PRLDkT0RlXCyYegSv0KmHMpA8GqEuJQRj+b570wWz7w1on/NpHx4yuMkyc/sOUVKSzE8o75hgZirGhy0usRQLhuRiQDXxMbMl3fe0alslMMNAOHznA6pEPldlpRhrPx6KiMOreZ+Forx1wuCkjM79tYhspcR0DcnlgHBPM/0xTbKhCPY10dmIqic0hnJdNqL5bSmT504YH5YOrQ0k4cWl4r+oU30wHUgX5fjUmYJKi6H5tKX7/oQq9/DGklGMgmRLsrKd74g3tEwmTo1k09utqCoxzp4cMgzGMVXXkax5bH9ZDs8ePCXXlwZPDzRG0E9TVF3DqGNpXQgoOw5zJMNtJrROhzJ+bQv3ZHx1Ftt2DG4VVSY1GdwfaaqupXPOZ3zUEOxKJJbf1+S2tteYL7GBR3Q5pLopJVhP0FVA+OEQswTFvJU4uqQeP+aK8P42zDjCCz6Tw4Zo0yNblRg2L61zYhMHIwiiinKYCIF9KSd8PBH3/9rUWp0cY/Zi1E4oNK3A4Rzks4ZoV/Jgo9mUvB8TXwkO4ru6K0PG56cAyBZljdcSQLp/FVN2oH+zxS4WuCuRxNe1LLTmD1oAAG89SURBVOlsRXQppHvWY3jSYBdLyv0YXYldi9GO7EOzVB2H8sQ/TWca72qbYAROB5Q9S9DXKKvIJwEu9yhWSooFRbTukx4vCbaFWxguVow+Oof2pPCxvnQ11FAzfGQWM1MRdHNy2xYOXWDJY0l8qKYMwZ6HebyDnTGEMxn64Q7FlHQZda0wDu4YMrncIdkQk2J1NhbivYpwnvAMlRHyvqMuujNFPiPRfaZj8Hd8woEowquOQ/d9TALVbRPMToSqowWLeUPZT8TaZ7aAqzHlkZzksVi8Kk9k2FFA1fKxkUG1DGozItmQ8ezw5ooq0US74nuXn8rofDTGeT6jk2J544paGd4WE20AtZmAJ+IFb6zJFw10KoxniR5LyBYN4a5HRT02D3zKGXOQFlG1lajox5rOGZ9ixjE4CbpyVD2LtxtQ7AYEqcK06yJMg78WiY9d7FCz4i1Xake8rQ+4o1Vb135uitZVRXnE4CY+0/cHDI8L9zBbMWS3lPiBwX+wTXqsBKu4/dRVHhkfkfd7KsNZMFbsS7S8dcIdT4QFseRv987B5t+TcaouFUG7gI2AfKWimNbQDxjcVjE4vUpYQtWGYk3iEu1KRjntEa95BENNNu8I9jX5YiWioVLSeqqOCIQk6UIfiMVMy5J70nPI5w3BvsasxYwPQzCSDYY+mrJ9aRrdKXFO4QcGlXpEuxq1Jcb2JnRivL3nMVkVixdl5b0W85JXbFtyHqu+aIS1iuJim/D4CO9CBztX4uYM6mKCiR2TWjmtCi3q7rZ0L8uOorUmn2O2WqFKKW6tJ0bvwZmE6uaJpCGBPIc0gil7wheNdjTFDGKnVcikwuuW+I8nFCcyhidEwYwGfdsIfaUt9IHA0p5JSekS7mmMUailDLUbUe61UT6ouZx8EKE8R3rY4IwiHcdP4lXlM8DnY4TbjICfMjQF4NMUyfwE6yviR1uUXWA7Il2WGC8TSdara9fO+9sBNrA4X4vhbaWo6hPo6KYSnDoYZzilpXNyLEeN/ANlp0k97Kzs+PM5n/JUKorgUlPElvhqgPUdkyOGeF0sVlRVZ+YqH5O4Ayqdv5hi8xbVXiz2GbeNqEqPsmslLk6ByzVqKyHZ8EhP5rg0xLQt2YUu3nJGFgfoVkWRBRIyH4thcDDSjNOpg1GrFEKKyYrFdA1OSxRV+7Km3I9xGimABx5qRzo26bKobudv7rPfiSm7ovaLLkYySvTE209ZMc0uDZg7JlQbLZgqMfMWsx+i+sGBeCXYk5QTjKgCq5mK/MFpikMFauKjciXWEIo6qF4EAKb0ZLxaKlTqH4gKVCkfZrVSoJSj3EgIau+8bKWqDaPrlIFQCjenxR9v71kVrYs+1hdOVn5rih0GhANfzJetPAeeI9z08VLF5LgISlSh8edSqt0OzkmXJ5+tM5wLhQs10Z4mD33oGPzAkp4qDrwArwkd0OBtSievSiDZdLQXx2RnpbOs2yX6Sky6IOrXaFO6LnYoZs7FvFyJVerRvugxOlXBTEGVe+ixh96Qtl+2aET5nVP7EirMkRQKD6c8+TsaCPakaBvdWsr4NbTYiSdZw6XGhI5wADYQTuzkkPytnOeY/4DH9gsD4m2PKpVjykTSMYo3tXhAOs34CNAPCAdiBn4t8SE9XKK1w3+oDQhP0ssUj5RH6m6Wxox8Ua1uh2JdsiKK8KojOdNqrMhmRZHTuiy0kHzJoK62oOXEX3FG1LrephT/Zc/hj2VkrSxUwwA9VZAGPsnFgPxQKVneuca2DfRKuf4XHrY+lm1Yi7NmC5zReHWXM6k7wv5YEYyR3OquRTtFuO1RKIjXfbIjBUdu2mR9sHwgDgv7Cn/iMzlR0ns4oJz6+OfZXpiQpl1Uq8K/EpEVbVzLEI016V4CUxWqH+BMQLyvMLHC3Dqm2orrDqMjueRTdaGcLxkl8jeONj3ZONw0phpEBO2S/Eh9AXXiWlAlUrioSjYoqu7IWh+mexMGfoTtVgSXYtkIXopEeIQIa2zm42YKwrii3EywUzKxySJN+/4O2bwlGol9jbbgXYjFPzEQV4JgI6SYSZ/My8rfDmsRS4jP9TkaPBVoRsBPU2SjiOiv2uzf7rDHUmxkD/JU81mHN5dDqSRTNxc+TjlTF3mejBpcYvCG4uHmjTWuLcpEs1yIzNgo5m/aITtSMrM4RM2KYa7T4PZD1HaIt++DVQQDUejqXJEeLfHHwjcc3FaRHSqFK7gmnLpqMxF+zdCjOJZTDCK8izGqUui9AP9qiE49MBLDFV0KZQy3LZ2J8IEW/q6Pfy7G5R6qEDGL6Uj3INmQPNyqa1HIRbp9RRNuC1/MBqI8Vvf2KaYtcZ13eq1ISm7bR58Ys35hDnPbCC9XOCUcyGrKwnKOv5jK6GjdlzHq2TZ+qnC5hxmE6F6J69QjUyf+i97RsSgxxwqvTiDwt0J6j4hC0RvWxUsOnQvSbWn/VYJeyiT2aqjkNXzwhxp7NCM6F0nKwEwhytKo9hwsJBdahYZowxfPvFSx9ywZU6e35Ng7R6QnClTtRZgdLejetE8xJQIUNfGojkrqRLjlM/2RgHDbw51vkx4tUVdizJSMGl0s/EdKJYWDVXTO+lT9UOxSYoPeCkE7sapxYiycrhhJs1hUTNY66EOpRJEVHiZxdG7fo1guifaEL6WMcLJ0pkUxOdBibt73iM7EJOcDEXVkimhf0llw6uDibT2H2wvxYhHlZEeEgxkMRXmJL+89aIsYAE/4rLZXkc1J4Tk5YgiXJ3hzOd7Yo5hWtC6IMr6aLSVq8UghBeiSpbs8RC3klLMV0cpEhASJo/eIR9V29B4OsKOAYtqSHi+lg+VJkR/uyRg23PSJT8cywo7rbv6U3G+yYkkPGaEwLEs3PtpX+HMpNrHYxEje8BVJv7CRmGUzLUbtxXIpynSF2MogCmgVWFS7wl+ZiNWNFoPksLYKCnckA9pEEJ2TzlSxUhLs+WJfYoWjN7jJkM9bdKqwRmFDmPugRz5rSM6FXN6YIewrsVY6VJAtimI76OWkS5JZXR0WdXnxaI+pRxUMArGh0tA+HVJMi1F4cj4k2tO42ZJswZItVZSjkGTt48k26QlRFHt1sgmhUBSwUEwCwnUfs5agBz5cTISeEiGfjxHfQ90pqaYN5mSGLhTph+awAbTOhmK+vpxStZ2kvSD0Apv6uImPO9vGJYai8AnXg3q0XyfOuGuJLlJw2lGA3oxYOLxPOS0pMQ0afKZojpanKYKrIfm0Y+rkHuHDLVqXfOFPTVnCvpLxyr4vSlIL/q5YdgQDIYOTe2KMWvNaTMuiJkKwBojOxsSHRmxt9gi2fAaPzYjR8bQ45btYRmjXTnDjI1aKsJYl2PVJnyf8HlWpj4sCnPB1wn0x+zVTlWTX1oTqaqGO7YodajHDnyjKo8JVCy/LaLvsWiYnSsxyQbFgUBMPF1s6F7QYYk8bJkcNOtd441r5OV0xuKsQMn8mXatiypGf68qFYt6ijkzwcuHoDS/3KAYR4ZaH90iHcF/ROefXKmCNd15i44pDBd6dA0kKqDfG3lA+Q7UegVF4qUa3S9TQx7+/gwkhq02Qy9mKaEdRtaU7Fe5pSRI4UghfsVRMVhzVSLop5ckMfXdfukIVIlRoi8WKHYoBc7yjqHIPdygTu4p+QHkqxYWWsk5lKVakE1RuJ0RXAqIHWiSHRjzr1CX2r/QOUlK8XOH25OLqNKQLUMwaTMsSTuVU8yWtswFVxxFsBmCUmOOOfFqXfHkPDlxkUANJXfD7PnquIN7w6XxETKEBssMl3cc94g+0hds48NEVjD82gw4NgzsLKRRPFowPi4q1ShycHIuRuJFCP+pLMVqeTMmnxesw3hK/OTVV1HxZhd2KqaYkK7mYEeGHCy3eTkDYV5STQK7EDik2awX9wbj8dEfsPkLH4LZSFOPzFUFHXmPqgxF+Jh3i0eUebiNCVZoi91HP6eOOpfSfJYVIuujoPVpnT0cGfy2kWskldSQGb6QlHjGA5HxAfD5EVwpvPSJZ03XFAJObC8xKzvhYxehkRfvPO/h9sbDxxh7erUNaj4d48znb9yr8q5EU7Z4j3pA0EH0+YfaDvghm+gHeRkR1tYU30rAd4Z2XQs/f94j2FeGOxrYs2eGCub8MiC+HouI2Qr8Y3FOIcMjJmFRp6RbvviTHy7R0MgcBkxURJ7lU4ttsIN6QVdfSuqrrzuk1eoJEwQUjdZBS4kJLvOaTHq5w2qH2grorrQnXfbIlC1bRXRhBpVEnxpjpCjdXoHKPqYd94vU6d/lwgTLq4yb6sav5j7LJNZFDX42Z+piPd16SPKpbJygD6SEjcYdnW4T7mnTFYGKLPZrSWxzRPe0T7Ymti5n4lIeFI1y165z2nkQJ6lI6xNfSdPr3zxNMZwQbwVNzgflUaFTAX1BoCsCnKcqekLvHD8ySHqqY3JJjfcSsNnSYQlS5ugR7KsUdzoQgrUQxHM2mBLvi61culeKo70nHQ+3KOLd6rIsXyYUHBcHFiNbpUBR+pZzUs3knRQ9IksMVHxM5pnsTTCGmrO1LHsGuppiW7lsxbcnnhcwdr/mEWz7JZR/lObpzY5QFuxtJ59JJZ84GQrRHQ3w5kIvTULqbeuSRzziiPSm4gl3Jq/UzRXUskzipWoVofSeWF4sFZrbC2whxLUM5qTsKSi644YaPOZmRbDjKtiO9d4KeaMppI2bE+x5Umsl+QjljibdE3Rn0tRi8LucS3dQ1+BdjXCTilmteZMWMpX0uYHyTdCr8kXS4/IlwvPKVCn8onUECK/YWWxFFHmAjR3G0gOVcVMq9HBIRXgxvK6HUqEsx1VyFN59jRwHewMP0KlzkRCCUewdcOqchHUc8tLZE65JPtOkR7grXThfStXUa8tXyoKMWfLhDfDEku1O6z+W0Ac8xPmZQsznFXROUlc6HjmRt/kjRuiJ+ijao/6Z15rQ38MjmHOPDFh1JN8tGjnKhwrsU0zob0jnvEV8IsTPS+XIemMpDD/2aWyqHod+XY8nG0tkse4701hw3kMSPZF0SI9B1LrGByXExFzbTFTaUrlJyKcDf97FRrWDPFd5IY6crsUYaigBGFVKM+Hs+UVShK8kADgbQeSwgueJhe/L90ldjskmI2Ylonw5RlRTz+bTQFYKLEVXtI2liGaFWc7URcleSRaq2w7unT9UzVC1wgaSTeDsBwaVIOpr7HpMV2ZApw8e9HZ89xOxEJOsKGzjiuRRSiRlMFy3VoZzd5wuFwNV+iNGuiB2cL13wsC+WTONDYlTsjTTeXsDoiHRYbeAopy1upqD1eMjSBy3Jhqb3vgRqex83qX0HDWClaE9mUoLpHL2cie9fPWKfrArXtmw7qpYIy3TOQVqIDaRIL6YtM6t9oWzMSgfWHyvKqTqfONUMtzoooyj2YoKtgO5fxdApmay42q9R03k4on1VRtHBQGypgoEUhFXPEAxEPJIui7do1bFUg7B2Wqi5wYVC57JhdJGFKwmj81Ni57NUr6dWsIsCuJ6glAo19jAt6Xw6Xzi99kRKtZPgZddbBdwUgF9IaDiAT1f0StwkkVHfvoc/llFkVROWw6vSMQuGiuJ8gl8o7LwRnktgMZMQX4Fri/DBhmIaXd6S4gZyYgr7ivJcIsKKc4r9O0TNmC9VBFM5/pU2fgrDUxWd8xIYPzlkCQ6N2b48LapKJFeWToV/NcT0jNi+mIC4VgWbRHg00fmI0ZyPdgpvrIi3oZiWiyQnJhROoS8lmEQyTIv11oGazsTCRfPHwrHjtiFV6WEHIcpI+gK5Rt06Ini0Q+kCmC4IRgqzLKMVVcHkaIU30jgnFg6Dk2DaBrUd4XoV7XMB6ZKlnK8kU9gXgUi6JCPoYOSJhUWpodDEm8JzUmMZQbWvKAanhDt1zQj7Gslc53KxSjY02YIUy2jQ+7Lr14XCf6xF2bHYUuNdDrEBVLmPtx0SHB+R7cjfa+o07HY0le8RX5X4s2DPx8SO7qEBg522UAG0jOwpNFxpM7klRw0CvBzsSoZajw8SDTIrx5iu/f5M6Gh9JCGfcSRboijO5hxVy8OONJ2dWmV8JRYeYtcxThx6KcNeTBifKlGlpjyWYoYRNtR1zJl3MGpzWjh/+azDLeVEjyaosbwPM1vSfjDBS2FyyGFXM4Y6QhmFdy6h7FmiLY/WhmN3VhSiVcdRzBp0UmHHAZ1zmvFRGR+iHMoXUUfVgmq2IrkYwJGCykSUc1Z4eNoxPlaB79CzFZ62pF6EP/RI05BkVzE+YqhaCtuqDc3XAzGZDhzRgwnjm0qKGYnKK2ZFgIISKyKda3QBpuPovmAb3r8gSujpityTlJv88R6erlXv2lHOVKjI4nzLqZVtLv7lYXQhnTIbgpsryK50RKm7pSX3dr4k207orIwodqfEA9BoOo8Gom6OpDiOtxzpkYpw06dYKckWhAd7bcOo+z5mugJ8wr50oXEwuFPiKbfu1RRzctDEcyn5Wgt/IKIkb6IxKznxmZhyX4rCarmkdTokn7N0b96n32/RekDSObLVqrZN0qTLooq/xrPTlaJ/doZ4U5MRQuAoZwz+QDiVuoIqlwhBncn3fXjSEFyMCIfCE1apZnSqRBWaaEuyzsN9UWcnf2+bvN8mO27JMi3d/Vzjp6I0Hp0wRNueqK0DyBacfIeMdKC9uwaMorZ4jq5rTAhlHlHNiSl/+EiElyr8oYd91pB8J0EVIiKKHmoRAOl0/pRdYhp84aPpAD5N4erIo2hPMiyzZYO7bYS/5xOMRHGoSuh+6QbxjqLoWcJdjd+XjolzQspOzkTSzbBIh2wvlI5MpihmpLipWo792x1mqsLcMcLf9wg+2iY9UjI+JDFX+Yzs1IOBxv9wF28k3TIbO0nWOBtRzlVE6z5oh+0ahncUFMczwmMj1PGxjFhaBi8TVezwRRNcIGkR/kNtzEBc8oOhwpzrEO5r2peViE4WCoqVEhPVheyFNupCIry6VAlXUYPWTuwreiXshxRTkoXbmZ1IisW1EfWOwvVDdFnnySYyKjORGOSipWPgTxTtq46qa3GeI+yDPpfAIODQiW3UXQOSc9Jl9HJFPi0+h3P3a5KrHt358QHvp5iTzyufkSLpIGc5qe1YOpayY6XAaZfSdUoVySMx3fMK+1iH5JKPzjXjwwoXWnQ/wJ8gGbvbEpE1mUQ1n0lhEytWLoGlXCiJLooq1wYOdiIRT1RKjHQ9MaX2UhmNldOGqi3c0snzJ0xWhWeYnA3Bk6ItW7L4Q/HS05nC9AxmJ6JaKMEoog2P6EMdojWf5Kp0iuOLIeVJMUP2J+pgPB4+LsWtP9DiSbkdMD5aMbzFEAwUduwLhzWx6FtGkkPbcew+S+7rjTS2Zeic82k9kKCMYnxE8mB1t867nXiUU5INHW5KoakuJiK6CSwMAxZmh6h2JZ3lKzH2aoI3EW6s3Yuo2jD1mHRv/X3heupCiZ+mEkFK7yFJD9G3jXB+Hb1WSMdq6rYd8SMMHFuXZqQzVft2xtvC4RPhhsKbKIKpnM6ZALUb4PZCzjyySnU8k2PnUEa5WLKyuE+0J/6OJhGOXXQ+onPWZ3y5K4kyGz4q9RidrMhP5hRzsmEc///GhFs+TiOdaMBLxVEgWg+oekbycGufwsGdJaNjQgUZ3lxRLFQH/oHxfV1cIObHYV9cAVzmiWH6dEXVsejAUHXkeB8/PEPycMxk1ZIvCWfZ+Y7JbTnM5+QzMnrXSxkANrKkt2VyXI+kqDZLQh+4pv6delg63OURKaacJ124YKhwLSMCpVSTrVZSpHUt+aGC3SvTBGdjyDTJmi8Cl6EohyeHRGmcL1e4l+6RzYsllh0FBFdCbOiwD0zJpqFUjI8bshU5VlVkcJUiXbIHaTVFFuD3603jZYmdKzsf3yxeNzRRcF9QaDqAT1P4ez7ag3xWugbxukfmEjwFRc9hIukm7fzVIl5XTrZlTaK3vYrkTES0z4Eqs2qL3UK444mfXn0iKhdL9MCXxK+hT/RIyOSwJe1aCBxhX5N1TB3VpcXGxZfXyz0pxpxX20Ls+DgF/uVIRhwVmJmKbL0NvRLPR4QHHek02lI4cSZRZEsGPdFkixWqZUgejfAn0L+jonPGx1WKcD2gWKgI+3XHrE6/yOfEzFjVilndKeW553NsKfyiqhIRwdQZy+5dDpSMfkzssLGlfSZgfKyqizxFsaQOlInOVyRrMkYbH6r97dY8rvjzeCMPL3G0Hw+ZrIpy1YWOYlZjuhWsdWHKoEsPbyTiFec77E4ETrwOg7v75I9M4XKFjR3hnsL/UIuiB34K6V0p7ZkRxZVpvIEnSuBlSapQCzljFYEnhXg1XaE3Y3Ttn+ZMnRPcD/BTTbwN+Yy8rvPk2DIzJX4a4DyJ/5t9xLJ/SqOq2vDYgfd4QjVvKKbENFqFIioI9yRtwyQWf6LxOiXhpRbpyYqgL6MulIzPTQzFgoHQoiqNHnpki0ZyfReku+vXUXDJlfrUpjz0sTFmt028FpCfyAg2fPKpgHJBuoTBZiBd5m5FdCVgdFOdq4ocf8FmgMlljOvlivRkIc9bSCSf8+UiPIp86JZsbvdgL0R5Ned1X457XSJCh0DiB03LEu55dfEB+koi/LB6tG6nKvLNlvhjxiIYGN9csH9mFhdLt1EB6VEHVgoNVUEw8CQ3Nqzj4nIfZhzRnkaXkrhTRlJ4qo2IoFBczedR8wbXMkzdH5JXtX/dkqVzzmN4W0m5nBF/rEV6tETvBGJArh1FEMJSib9bez2eV4yPgM08PKm7pHsIjG8WKybbq3ChhVqkhQHbNUyW5XtVLNVKYc+hUw83W+CvRwCEDydMDleoXFP2DN7EFxummRImIZ0zPqObHa5SMF8QJCXlboznOfHvtAp/rMXip6VQHTF2D/s+tgODUzIWTx6JKabrguv2VCaTw+Ag/ahaMBjtpDsMhFuecANHHvms+P6ZkynOqvqcZlGZZnx2imioyHZiSScZy+MnR6QQNrHDhZZgy6dcLFF7IQSyeTaRUAfUw7EUr3mdBrSU48Y+NjNP2jXlM4FzFuc+NxXv5/r4Bp85mg7g0xTVTEU5a8lPyBnYeUC3RFVQdl1tf6FoX5ULkokcbjmjmqkg1+Szlv27KgY3W7EsGCn8qQJzMsXLpSiItjUYRXxkKCrVXJHPS0HkPIe/FZAdKrnllJhpWV+KSC9VpEckksvLxIA63PMoF0uKeUM4UERbsjufnh9JTuYjMVVLfMfCvow9vMBi5+rxpBVS9LUuTT5vGd5k8HqFdOV2JDpOp57wkjJRG1aJmF3rQlGNAtKrHdxOhNKO4HSCyzxcYDGnO9jQsfFiMfE1sYTbJxsaneoD/y8TwuimClWI1x9KclWLKcdkRcQENnCkK+LTBxxkliortivhtke0rQl2fbGJAVEZauFq2Zap+Uvy9U3P9Ii3pZDuXBDVa7okI7h00WIHAVuXZsQ+Zkm6mPG6JMIEjyUHZPmgr4gvBxKVVvMkg76mc0F4ZCgYnhS+m4mdiCsczHwowN06Fm5Xz7B7uyZbtLSueJhQeHQmBHxJ2YjWA6J2QdW2FNOWYlZiu8I9xamlbTlWcw9/JLGCB1ytXj2aG3uErQI7VREM9cc7oZ6Mn8O+XChVJYV9mfvMPHcLd8eQqekJLpDC1i1nNc9PChKAYs4S7PiSvLKnia4E4vWXSVGSHqrwt4ODTOCq66hmKkbHrJj8DgJcJpnX8ZqHqgtS25MM6HBfEwzqBJmZXLhwoXRu/LQ2cLZ1vnUmMYS6lJHp+CYx9wt3pbh2mYdLPVQmx38+bw7GndGOOkiIoB9Qte1BtrM/EjNifySvpeqGi0vksx4dFcNjNNiFgtFxgx57BIEhn7OSH3xWE+8I1y/u5SIY80XlvP/cAhM5wm0P74V7QhG45JNcy4Y2Cn9HeLrdRyRxBhAOMjVncd8nWzB4Ey1Cln6AWcqppiomJwvhJEeWaEvoC3YlI2wVuJpn2j4b0J0fi4XSmTbeWMa1NrKSGrNQMD5RSorMekQ8nwoH00DvjBangNpIOllTeOdjvEuxjKZn5DhQQx9/18ebaMKNgGKxEtHVci4bWx/8czGeL9/7cNfDJVJsehnE655skBeFxzf9gI+371OuFIQb/kGuOcj50d4yRh2bHHC3TSQ53aNjlqmpCeFsJqK+Bg0+QzQF4NMUeiI7Q1WfVLNDJWo3lJGVrUepqWL/Lgmkdz7i9bbpSwJEryLY90iODvFH4kbvPZ7gnUlwzxpiZkvpVMQVkx1xxrdHMszhTAobK2PLcMPn8QcO4zoVC7dtE+7LuMLf88lnRT1XzBuJyNoOSK74TG7LKWaEKD0YyHitasvIpDhUkh0qJej+gRbeZihjn9iKz9++JIiwnOPN5ShElee0qH11fbGxoRQHwRiiLZ980eC1K/x5sVRpdXOyJeH7BQNP8kxXSqY/5kvRUluPjI/LGHJ0cynZxTsKf98jueKRr5SY5RydKar5UixRKlH0ukBGPSARddVMJRe+egRX9mTN4br8PbxcupXhrocqNcFQ/ibBSDwLvUyUlcNTMiauOoZyytbKZY/2eR/Tsegr8YHptR7J2C4YKlximRytyJcMk+Ml6WpFtmRInr3L6BgyYjwxws0VkoW8UqACuZDl0wpOt8WQtxJKQHJsSLzt6FyUuD9dAYFF7YtIRX+4i5oupPCKLDYQb7qzHzxC2bH4fY/0cCXHkoLq2SMp2lti2jzdSWk/FlIslTjtcIEU/+OjBi8VsryNhMagtkP2PryA+liX6t2zUpRORAjSeVgyZJNzIWrsEW15VB1LuizpEsXRQlIaAkfndIDONVOnZTNTLJc4zxFfCVCzueTaDjR67NG+pIj2wU4JJ41C483mxDtCE8gXLGY9wR9qVFIRHh5TtUXkU9yUUs5JB7TqWqqewUwZWucCVOaRrVZiSr00grAWDPRK9FyOSRwmlNdwy7konEcaf6IJhhwU78lln2LK0TmvKOYrOmd9/J1A8mtbltaaIp83uLHP7Ee0JJNsttDLGV6qGR0T385g16O82JZkklmZBgStElsff6P9FkGvEKPvrsPb9/H7vhTBgWN8SApg1zG0Hg/lPDOWTjRautCTI4ZoeYIb+0TrgYiArkQQyOY03NXotZjychtbW+Bkc47x2SlJJ2pJp9cbaajH7d5uQPtcIDFrc6WYst86ppw2FF3pfOZHCsppw/CUpNSUM4ZqtqL1eEjncR96lRzLdX65Hks6kBsLnSToK6quw1R1x/Bohh55eCOP8VGxZDJ3jURs1XL0X5ihD0+Iz0eYExlBX8FINhtepnCXWtgrCYN7CsqupOGo9YhoVzM8PY0935aUmOsJ93kY/zYikKcMTQH4NEX7knQPbO5RdQyzy328VDJuZx5STH0kxCtEHZcvGkmFGNSdi02P1hnxq5oMI8ppc5AsUCwY7KMd2o9EtK861EaEyoXfFEYldljbENTJH07SulBDn83T8wdh9yYWexJ3csLC0T2CgXCcsgWLfzUUXtlshbPyHPGmggstyDR6JPypsuckISOWroTzxfh2ZXEftxFRDULMViw2NlMV3k5ANSeFa9WTbknRkwgrfz6l153gP9gm2PEZr7dRicG0LeV8SXZzhpdIaHzriuTvmkT4byaREZxywt/yUzG3RcvFoJoyBFsSU1V1rERwOTGPDcaSLOC3S+iWmFj4eDZ0kiUbgJouKFbFK1FG6HKC9FOJRRvebKTjp5HCuwJVSoTZNfsZp5HoNBALlFmLjS3llCVdtGCUGEoXClUKeV1Viv5+S3hG533yq23i0zHtCx5qEKC3A/HQa4tIh9CKohEYb7TrHGowHYkB9LdCbCT+i8WMw/VDUST70lWtOhZdSdfJxEI3uKZuLfZiUaNHhurmCdsfW5CLXaXRXRGLRHtSVFwzMDahdGntdCXdlEpEKCDqUTMQq6RrF+Bo1yMYSoFQzZUyikvFd9G0LZN7UmyvYryqKKfrbnPLkK1UREmJ2Y2wUZ0vfNTRv8WixuIhieeoBiHjVciWZSMS7ovQQG+HKCUcWi+DzocSoqvBwShQp3LMi7LZodsl1WJBerZHsBWI2EA7bCmZwtRZwt7FmHTZiDq+tkPxMtnIVB1HNW0Y3CJUjWxBRDLVlMUbS8KGP5LLw87zJe3F+RZT6TpjV0nWsgbTNQTbwjEMhhrtWdlIGtDbAWU/QmfSIfRy8RlFy3fHtizBriZYDyimHNV8KdzFgSc5xlOG9kWPbBgJl/hwga001WqBFxvcbEG+IB20qdNKrGoqsZBSDqqp2uh7zycYS1e3mhbaQLos/FN/K8SNfYq9WDiZPpgpg7cbEO560KtEnZ9rdFLVxyQw9LHdSo5LK9zdaEcmAtYXmowNLdHZmHzeYkcB13JuvVTjpwp7vo3tGKpEeNumEn9L1qK64y8iHn9CbTqu8PZ8/BRaVzVersiO59I1nq3EePt6olEBf0GhKQCfphgftpRL5QE/ZX+/TTVlqTqwd4cjnxVOlTdRJJcl29MfycX8WoB9vKXofSgm3PGIt2RkGm57VC1HMe3Y+uJSTJ8TKSDTYYTulpI+MJ9S3ZRij6e4TiW+e2ndfQtkXOzve5j1hJ3Tc1SJ7GjRf60zNpXjjByik1XpXBBZdK4Y3FJhjmRUxzO4JgKoTXE3dqZQRoxo/bGcGOOZTJSIWjhbREZ89UrESmYzoXz3HC4Q1XHrko/LNdGmEN/dxMf0QynA6g5aMW2ZvV/TuqLlvcxUjFfrAkODvysdo+SKj86lC9Q5J+a4RFIERbvyWfuBwRVSSGezjqnHFLrv400UdhTgbwZkC/bAesMG4vtXzJja1y6TOL1JPVafz4nmUzr37FDOyIW6mLa4oynRo4mYTY9EvahLhaoU0ZrP1KPi3bb4AbmgeWsR5kjGZNUQb2m8VOLBwn2x/jCJwyROsl37PgQOfXxcd3EM/hhUUmF7FdWUdK70tczabok/8NBbIWEf4k0Z+/qhqJ+Lxerj3oztOnViO8SkYr0yvqU2Hjey2TExJJfkM0tX7MHoNlwTCx9lwTspxt3Rhk+04WNajqDvEQwVZcdSJbWtST2+17nGBZJJbIcB/nYgnm6VEsGDVQS7HulugpouMD2DO55KkWGl26uNJL3ccvNVipWS1mXx5CymxD/RTFWkOwlUor51SqLGvFwRbElesvMc6aGKqYd91EYkuyon31G7mlHth3i1t2S8qWWDsyo+glMv2qBcKRielNfDyAjaG2vax/siqpiqCE8ntC55hPvCX3VK1h9tesTbCoxCb4UiaKq99WzsaJ2TEXO0K9/VIhWurVNynAT7HnZazhW6UDJCPVaIeXSl0LVwQTmIL4ZUHfGStJ4DLdQJ5YkS29vzCTZCXKHxgwp/LcKGlnBfiaVVIIU+Vkk8ohX177XCyMsU8VVJr3Ha4d0+lPPcoz7Brkc1Vwl/r+/hlnJR1q+FZPOyuXF7If5EFpuseSjfEXVz3NFUqBdHS7xM+J0g8XROibUQoSU4PMYFTrq6kZx7vKEIZOLZOgqxNq23dw/xBv6BIjjc1wec2mJaog2dEhcA5zn0sBn/fjq8613vQin1SX8+8IEPfMrHvfSlL/2E+/+bf/NvnsKVP3loRCBPU9jY0jofykkicNjcE/LyQKKjqsQR7SrGNxVEe6KezVYMrUvSsUiXLaZj8YYepmNwfq1+TDWtdU0264incvSZkDT0cKGTbNi2lfHkfofWvlgnMF1humK34BSEAznhZ8tGeHTrIijwMhm1+RPID5d4l1sEhaKcq/AGPrYjwoVq2oBvCR8T0jxBLVLRjqmP+fRvB+1JV65cqlChJb6vS/bcWtUHUErnUxXShfAHHuM7clyhiaYz0qAlxrzX0lF8Jx2I1ZIqMYTnY0zk2PmikuhqIMKOsyFlz5EvGJz2ROU3CahSERnoTolyPv5QU8biOTh8QQZbEeHHusRORovBUDE46Yh2NTNfvM7a6QUpWvYUk1VLsuaRz1nCxxPibSjbHpPDBjVT4LKIsuPwL8Q4B3udNnQqqpajta4ZTUlnJhgqskXJpVUV0PeYnCzw0hATObaeAzaS7kl0OsGGjvRwRXLFlyi5tObetSvIPLJYuofeno//eEhxRLziJocsYVKSl6IsDo+PyCYh/tUINYgo5gytSz6TFUcwUqTHC8LHW7T7imJKxpa6VAzmRaWrC4VN5ULn7UhXVe0EojAtITtZokZSNFlfCqTJIUc1XeGlAflOQmigmDHEGx+/YFaJbAxsIB1hSoVezOj+eUKVKPIZJ76LBqrCQyngcApjKS7DLZ9iXjwG2fHxj6aECyWTnRY2kY7sY6dXpduYiF9esC8d9yr38If6oEs3OWTxj4zJtxOCfY9i1tA5L3nHw+MW2zH4m2JIPTpmYD/ETzVmOZcYtlDTuuRTdjx0BdnvLREcldFwPmtlZF2BVyqGSVeUo6ElnzO42B6IO5SVWEgTgRfUkXNTFSoX4YpuV+i+LykjI+ENhwMNwwB/LN3IbEXEWcmZSMatkZwnbCjFnc4UxalUkoNmCrgUo3slC7NDNts9vM2IcKAwIxnVm57Bhha9F5ATExwfo7YSwhftUL5jjvSQbDajDf/AS7J1yz6DNYkQ9CciftOZ5BRnV9voJTlnKQtTDwQMnpuhfDFrv5Y7DtC66JMtWIbPyQguS+wj/UCy0VOPKFN46/7BOckeT7HbEf5EY6xC+ZZiq0WyoXGe+KQGYyWc6GWHfbRDXNXiscIj9yPCAka3lKAd8YWQeFOjnt9ncrVDcsUjvS2DUUCwo6WL+JRdYT4FbK0e+1zwJIlAXvSiF7G2tvaE21772tfyjne8g+c973mf9rHf8i3fwo/+6I8e/L/Vaj0pa3yq0RSAT1Mkl326u47du0U04O1J0sLouESbUauB0ZL3642EsJ7PiDmxstIh0gbCy77wwa74pEdLxodkzFleaMOsjKlU6mFmawM47ZGsK8aHhV+j9ySFoupJZyedRnbzk3qUGlN3B6TjYBJkNGglrqycVngF0BcbD2/oYSN1MEZWSUX8SIyJRaGqa5Pr4nCBvxFSzVWivh2JlUi6Iia/WGita8q2FHqtXkY6DslH0YE5rpfX9hW5JtwQM1m1G1DMS1GqJh75coW/W+ci25qk7yD4aJtqRTJhW1c1oyRgeJMh2NMk50OyRUPrgYTJEUO2JBcup6CctWIYG2jWt6bAKpIN2L+nFircmeEKj6oISRchXzAQWVypUaGjdckjvUvUh/QlgcMfKzFCLiV7NtqUYqbo1eruPQ2VJBPEGx7BWFJGrqUQ2EAKvKInJtcmdLjIohR4UwXqQiKijZ4jW7AHsVjOg/JyG1UnergHe6iemOO6UAQXJhb7mPKWjOBCTNVxlLMWfzYjv9CSomGssQsFwdmYCoi3PIqeiHlMJFxSfWwEewmuV1IpH9c2ZPOBpMrEtXBoJMbAOEU+L+kzqNpMPDHYVNWjY02V+oxXpUPraqNxG8hFP+hr8o6IGsxKTlV4+Hs+Vccw84DHyCaUJDBr8HuFRIYFdWazFc/Bcsbgj32Sy96BmXrZtaiVDHu2g1dvbHShGZ2o0BPhfha1IMeGIgQyscMs5dI5XYswoaPoidq8nLUUs5JuEw5E/a32tJhfz5W0Z1PGOy2S8wHp8ZKgU6DOtnAKUeKXta2SBHwQrgfSQQfKUYCLHP5chhtJYTM+UUFgqXwnog4H2miyJYkCDHZkfGmMrMfLYRREEj2Z+sRDRZl77H5kgcCKf95kxeKtTtBXW7LpcKAXc3zl8B/sUK0Y+o/PoI6Kp2KxUlIEHvFVOc+lmYxyrSejcYwSPmzpyWSiQMyjKyjbijApqUrxLgz3QkwErTUtfMXA4V+VjYueaLHd2Q9FyR074l3hTsbTGcWlNswVVC5EleAqTbirsRHEW/LdGB2tBUYzhmRNbIVsIFMGfzegmDOo0OCvyTnJxKCtkhQjC8GliHhbbJgCK2bW1xXO8TmXoU/SCDgMQ5aXlw/+X5Ylv/u7v8t3fMd3oNSnJ0+2Wq0nPPbpgmYE/BTiz/7sz3jFK17B6uoqSine+ta3PuH3zjl+6Id+iJWVFZIk4WUvexmnT5/+O71WOeUYHVLo1RSv70vXbT4X4nIgxGU0xBdCOqeli2F7kn+K52hd9uieFZuF9GiJP9GkJwra8xOiPTGobV0Vk2UCe5DzSiFeYvm0FENq4sNiTrKhmP0rj2g9INjySa762IVCeFtaFIxhX2w1yptTdKfEJJJh6o1lNHjNJDrZUkS7Hl4ho7z4dEy2JCrHsutw0yXOd7R6ooDuPRTgTxRBXx9cvLDS1ZusiOLRdSuSsMROfMiFNB9tialuMJJxkfUc7Yvy/vTYw3QN0a4n4oBKOHmdCzJmctd4Ox1D1bXkM5KI4Oqumg0dnfMeVQuJhBvJ5x3ua1buU5AY7FSFVzv9928T3pJul5ix8K1QolqNN3wRGewE4Ak30u1LrqsuJfIPJZ0nb+jJuDqsi+1ehZcqskMl8brP/KldslsyudAUNVczsSTHhzBVimAECAeaYMtH7QX4j7Woupb0UEXVM5LosTCWYn0in3Ny1SO56tG54AiGmtbqiN7HApINMRMWLzt3kMSBhXIU4qXSIfHHEk1W9iyLJ3ekEK+5ezgZJWZX2xJpWNTHZVnTB46IvUm0pwgHita68B2DgaRZmLDmSioZH3qFwnYkRrCctfiZ8FmVgWJJPgNdiu1RsOMTnYuJrgaEe/KdGh0RwY7Toux0gOnWtiC18rl1TriyTkN6ay7dsmVJiKn2Q8oZERjgyZjP65Vi6j6UY7eaEtm5rcVEejvEbCRUiRSWupDXj9d8SRrxoehZ4nXJ4TXLOUur++SP99Bjj/RYKTzF3VgU9YFwzuIdRTEjPncSMaiItxRBX757yeEh5SjEBU44dfuemJr74h8Z7PoHiTFYhTmSUfYkladKxNPyWsSevyOj8d6DIdGuopgzZPOOziWNfrRzoFZWlcbuhLgrifibhnIsBEfHlEdz4qmceN2j7EmqkHq8TbFYsfTsDdx0SbQ6RpWK3hmZRngTRbQ6BqvIlizVpTasRWI51RbvwuEtJTaxMjEIxWYo2ZSJRTEn3oBi1I1wYZ2Ya+vtUIQ9SxWUmmJBNnujo47JiqN9U5/qeCaG57dPsJ6Id7xMYZZyvG5J1Cop5yqyVUOyAcWFjmx4SigWKqoErA9LL7oKdwz/TteLGxGDweAJP3n++TW5ftvb3sbOzg7f/M3f/Lfe9zd+4zeYn5/nrrvu4gd+4AeYTCaf17VcLzQF4FOI8XjMs571LH7xF3/xk/7+p3/6p3nDG97AL//yL/O+972PdrvNy1/+crIs+6xfywaObNFS7cRC9u9KyL2beJhIVMHWQ05Gt4jJbetMKGpNq5jckbF/j4zxpj8aCFfwbIj3Z1Nki5VkjRbi4xefi+icEcuS9nmf7FSOaTmqxNE54+Gfj6k6MDwO+eECkzjyOUt8OhZCdcehM+Fwebmi894EtxNJ0kNoUU48svTII3ksYnxIwtnLjqWYMRTTtrZ1caAc/nrI1CMe2cUuwfjjBYbTkB4uxYqm7xNtywiw6hq8nYDRB+YJdv2DTlzVtnAklZNyYilWS3QlHEHlagHNgqHoSXyeU2BixeRIRTEl3dXW4yHtC5I04JZy/FYl1h8aJiuyXheIIGNwk/DG9m7WRJdCOo+GVFux5JFWCiqF2w+Fw+U7qinhXuZz0gXVhaJ1yaOYr3C+Y/a9AeGu8MG8XFTCpmvE0ieTDNGpBwOcgta5gGLK0r9/Hjf2mRwy2FMp5XIhCuGrHRgGUvjU9ip+KgbO2bIoXVuXfLyeHEuTDVEkmsQRLk9Il8XEdvuLKsIBFI/0yOYlni07XhBvadxGTDltSTalwzW9OJSiyBPRQdWSYmjj4qxwpraks2WmK+GUjuW9+n0ff7qgdcE/yPCl0GQLVjqZFdKBziBaFDPxcF8Tz2TS2SmQcWyvJNjV2Nq6p+g51MSjWKzIbs5q02ZZl0kcvZdsoGJDMFSM78qxoWN4wpLc36J9zqNzUWxZksND8llHtOkRjCA6J5nN+E66tgqi9QAXCN81GCjChxK658ErEMVzKGIi0zMEfRGUdI73xaKpJQIIXSiypUqK+VK4jfmsIdyXqLXNs3PYQLqo4YZPdDmASri+8bYow8dHK+E5bsoEIdyHfMaR3p6RbAAfmhKjYiUbyGq+lI3KUHZatjax9kdaLGmc8I6rtqNYqoi3Ff7AI+wr2lcUk2elDG6umPqydbH8cTB+XkrZs3gTebzqFfLdvab+9qRTrT7axRlN1o/IFkT8EvZFBdw6G7D9vmXaD0XkV8VXdHxY/EOVBfdwB9M22F6FP1Z1tq8ivHUg3/fMI9r0cfMSsZgtGEnqAaJtj2RN07oqGz818dAPdNGbEaa2mJp6MBCHglzTe8QXux8D5YdmpIueig2VPZRhB7Jh7UylAAfpPXRKxkdE2OR8R7Yo35PJyZLiREZuPPJh9FlfKz6fcNZ+Xn4Ajhw5wtTU1MHP6173us/rWt/4xjfy8pe/nMOHD3/a+/2zf/bPeNOb3sQ73/lOfuAHfoD//b//N9/wDd/weV3L9UIzAn4K8RVf8RV8xVd8xSf9nXOO17/+9fzgD/4gX/VVXwXA//pf/4ulpSXe+ta38spXvvKzei1dSeekWi3w1iJs6HBtA6WIBPwrgXTddjxsBCYWYrcNZYygeg7jYOoxxWRF1IcyjnGodkW2AtWtBdUoIDgfUHYg7BTgQvR2QLVcoHzLsOORnA3JlgzhvsbbFb6caYufnC6VkM5bwg8ysSOvY7nKHsSXAuJdKDsyYk2XDf5ChrUak3qoXIqrYt4QbfioCqqVnLSIUEsZdpgwPFl7whkhsqtSY+ZKoqkU9cAUdiJK3GgPhsflglit5rjcIwkrSkQB6E0041UhaLvI4u3L69kAzFKOHcUYB/7Qo5otSZfkc/CvhpRLJfHjCfmpDNcy2InsvfIFg2pX+GFFtZmQLwifSucSF+ZCSxSX6B1REoY7Yo5c9sRHsGo5XGxQg0DENSngFMlln9ERR9mzdJeHjNsxaj0W89yuo3VVMTxlqVqSl1xMiUKznLYH6sPgYy3crMUt5vjrMdooJidKvL6PiWuO6FhDZPB3ZLSu1yL0rSO80sMZBUOPfD+GbkURK8JNn7INzq+TQjoaNfRJVw2tKx7FlCNdEnrA/loPVdaxeEcs0eKE4kobb6wxiVjHVD3pqIqXo4bYoGYy7OUWNpQxZveRgOkvX2P9r5ZxCsZHpLN27cKvTmaUkxib+6hORXDexymf4M4JeRhhjXR10Q6rIb4SAJKgMlmxtNaE67h+afbA3shlHsFERDLCuXQMbpKiebKXoH1HvmDJFyG+6hPuarLQ4vAk17YnHTQbSzINDpzvS8fTd8QXQ5wPXqckPY6YYl/u4boGpRABzFCjjCLZhGxOk60Yoi2PckqEL1WrzpH1HWZB/A1RYhjtTyTD+JpHn67EtHt4Srz53MRndFwUz67QuNiK0fp8JYrzvZpbijrozOtC4Z9NxHc0BBUZBnc7WmdkzDo66uh8KGF8yLG+28ObKLxC4T+ckK0YUAqcwu2KybvzxApJpRKXF217ItKZqg5Mum0iBtPu6IhivY3zRIxWtTzyGaEAVB2HvmWEutpGT+Q5dabFJD7vycu2DFUHWg/FlB1HPFKki3LOrLpWDM4TsaJSlSJdrQhmMtx6i6ptGRwv6H44Jp+D0TER+JSLJdHlkOB0gokc3UcDRsc0qlZxq/fPEATge3KezG6WHHesQs8UuEmM1/+44fmW30VlN4AK+PM0Ar506RK9Xu/g5ij65MXta17zGn7qp37q0z7lww8/zG233Xbw/8uXL/NHf/RH/PZv//bfupxXvepVB/++++67WVlZ4cu+7Ms4c+YMp06d+lsffyOj6QDeIDh37hzr6+u87GUvO7htamqKF77whfzlX/7lp3xcnuef0CoHqGZLyp7BjXzsoUyMmXcCOmd88uWSbLki3vBwN00IBtJ1yFYN0Y4SG4U669IkqibYK8ZHa1HAmVjUwLmH1/eZHBH7inIjEef8lZzWw5GkaFgl3KLa0NbMlVRdAwbMiZTsmHj+qZtHgHTpsqUKr+bX5IuGyYqjmJKuYbLm4fmW3p/HUGnoiHm0iqUTCKB2QuwtY+xOJMpXLWNHP5Vc5HBljN73MQ9OSSKGE6uS/u3SbapWchgGqFy+HsFYiaWOX48oV0Z4fY94R9FaE7J7dyrFxI5sXnzboisyxorO1mTxUou34WZI63Gx2Il2lRB39kLc+TbxppDuXU1HiTc8VGQpsoB8wdI50Sc7VFIulGL10ZWYPb/vE+56zDxc5/D6lnJKCPDBUKLdnFVwKBUrmoVSfO460sUopmUM7qWKzlmP5JKM7sqp2lpmK8JGYluDUXhHx9h58VVbunOTYCMU1Wci3D/94S6tVo7ajIh2NK1zAd6edIi9XDqG1ayMHHVZi4ACRzYn48tiSvwQcaJ87J7VRJseRSa2GzZwtC77YqBbary+jz/0CPpa0jcqD10psqMFNrSMj1iubE7jTRTFaildpEKOeROCdynGtK0UrP2A8SFLsWCYXO5QzVQU0xZ/IsdFsPdxi4/RrQXKImPKC5r4sqgx420FkaGYlSiv4e0F40PSHXIKWmdDiWoz8pMdLYTHGVrURKLMRPkr3TKA5eM72DtGUlytBYR7smEx/RBKOQ79gUZnmvh0TLijJU4PGB92ZIdKvKHGadloWV9sXkzXYLqG+FJI2FcEA834loLxYYeLDK0rcp9i2goX7tAIs5KjU0nSKLuyESIxkixSatCOZMORHi9wdTZ0MFK132g9si7BjX2ScyGTk0Vtw6MYHxJer92N8ApFdiyn7AkH1PnSxVQGxkcs+s6BdDwN8rc3Qq2Ir0oHzQbCIWydC+AjPRGcrJaEfflMvVTROVdvIHMfXdUd3a6V9zzlcEckMm96dSB+qAGUU5bJqlAybCCcXxxMrwwwLSt+p9seZr11sIFJHo0pO2LebKcqMeN20iHPT2SShjTjwAPXlsi7sisdvnBfTL29tUisszyH24hExVxIdzwYafzTLbi+QSCfV/R6vSf8fKoC8Hu/93t5+OGHP+3PyZMnn/CYX/u1X2Nubo5//I//8We9rhe+8IUAPP7445/9m7rB0HQAbxCsr68DsLS09ITbl5aWDn73yfC6172OH/mRH/mE2/3tgNbIJ593mELTvaBJFxzZvCPc8g84SsXlFjZ0hLUH2vioBJZ3LkaMjjrGz0uJk4Lw7T2CoSfjpAr0UFHOCLfQDX1R4bYNes8juj9hdKoiuhSSL4r6N9qVk35aaPyBR9U1RA+0yO5IhdsUGCa1x53ullQarK8JFlKKJCS6EmBDmNySw3ZC9hwp3PyrEVXP4iqFt5BRjcSaw05CMcnVEGzX46hA4rGqjRZRv+5Q6JrD4zmJJyu02GnUxsfm0S6uTgSIdjSL95ds3iKdiPGxCn+qwA5Cyg/OUB4v8HcCyUIei8CimDeoXBPueFTHMux+KAbYYa0E3RYLDXsop1yLcYHjWpxnvmSI2wX5WgsbW0ajmGBX4vJcUHcCSuHpqQAGJ8Efgx6I0tccyVAXY8x+iJdqscFpGbxdn2RdYSIZabmRz+yRffrFnHSvQkvvUZ/BrZWIgxy0rkguLoGlGEQEWz7VlGXt0UXa2wobeMRbmvGtOW4/In9wmqAQI1wZwSvuvfkCf5WfIJzLcGfbVC1XR6nV9kCxJV6TLlc+K4R+EyFRfSX4F2LKniU5NKIa9CTzti/RhNdEGjjQl2PpLvf9gzGt93hCcXMKpYdJLN58zrgbML04ZPLRGVF6O/HATFckZszFBq/v42UwWbW47OOqYbOc0/1ITLogcW+jIwoXOXQq3ytvO5SO0FgTDH3SIyXhtry3cqFi6dAeG1dmoFIE25L7SypjTX8kNjnOrwvVyNB/zxJEDteWwn58VHwDle9whXQgg4GijC3l7RlmJyJcDyi7kgucbAYHGyld1BYpI018RfwGsxURgvkjje77VLMVKvUonj+CnZjW4RHj9Tb2YpdoX5MtVaSrTrJxK6GBmEQKNRzs327xEsntzmekuLNBrbSODa2FMTzUO+AccyglvNCimAZCS/tMyOjWAi+0RDcNyE/3Dkys7VSJ3g/w3teDkAPBUnG8ILos55zpBzTpsiK9NSc6H1HFDrtQ0J1KGR7ReK0KdqVbiwO1FkvmuarTU6ZKKsBD/ArzD87iYkcVyXfPG0q0YzFrqHpyn/3dNjrTbG73cAu1U8F6QDgQP8vWpmNv3hFdCYRzqqGaK2l3cyb9gGpa/BP1lsQ8Os9RtqVTHOyL6CPc8CmP5cSnY9JDFdGuT3Z7ih2K5ZRqFX+3C9DnC1ZoLZ8TPksRyMLCAgsLC5/F0zt+7dd+jW/8xm8kCILPdnXcf//9AKysrHzWj73R0HQAv8DxAz/wA/T7/YOfS5cuAbXnWQHlbIVtGQa3SEyRslAsl2TzdTZpbfBsQ8fk2SnKyom6f7uYILvNiDwL6N/qGN4q5rj+SCLUvKEmPVxKKPrJFG/fJ1+qGN2Rk1wWdadqVehjY0YnpJOnU3HFT65KEoEdB5RzFaNtSZKgV4plTVf8tIr9CBUIfyzc0+h9URSrTOPv+Xg3jaAt6li7E4kIILR4uz7ts4Fwm3aF7+U8ERi0L3gHgffBji8h97lGbwe4nnQIqpmK4kqbsK+I9hVhX1FOOdZe5JNf6mCmK1oXfdzVGJ3VI7OJ+MnZyJHN1icxJwWI0zLBCvZFoOAiC7GtOVGaxfmBXBQ6JX4q3oVEBuckKUTlGlfn/1az4rHmfEe5VGJblmhfuFuTY8L/wkH0cALISNpMCQnd2/dRRjG8SToYDAI6530mH5jHxrX33L7H8IRY47hALpCDewq8XIj8/q5P1ZXEhWRdky1Kp7BqOcLLIeXtEzHlrnmgxazw4k7//s0E+x481hbuV6qE+6clR9UfSRdHWQiWJxTz0kEru5ZwKLSGaNvDfKwnY7pcLor+WBHtaKrYoTcjbOxwMwW6EvFIOV+K2fluRNAqCAaaqh+K4fdDM4D8jdRuQLYkhH5/oiSD1hN+ZzDUYvWyIJ2u6ExMNu+wsfjBuUjytpVRpLdnRCcHMqY7koop89ATQc5YEa77bFycRU08wm0PVUo3yh9qpj+mSdbrbpknxVp4ORR7mlAK6XLWiFVLJafv5GJAtKPJ56VDb/YidC4iCmUUxbSTeLdYfDZb67XvnhEFddURHp0NHPG2vPeZpQEuspSZT7TlM94R2wtVQXa0QKeaaNuj9bgk8WSHy4OIyGAsSmc/qGpVu3ymdrFALWb4fY/JbgvTcqSHhZaiLiVUiWwuvT2f8WGL1/dxWxHqvVMoA/5Q0bqsIfOwiXQk83lL2UG4h5lHvlyCgcmqKKpbD0cH5ulhUjK62CPa9ghOSwRisVzibprg5VD25DzjQoeb+KAdZuILJzBwBwpdf18U9Paa571VdC54BBshdroiPh0TbXpEV4ODKEjnwf5t4sgQDOV8EGwGJGdDJlc7eKkUzk6DzhGrpTskccnFIqwysaOclmSb9FhBcq0Lvi4RdTpX2PFnX9B8XuGc2Lh8Tj9P7hj7T//0Tzl37hz/+l//60/43ZUrV7jtttt4//vfD8CZM2f4sR/7MT70oQ9x/vx53va2t/GN3/iNvOQlL+Gee+55Utf5VKApAG8QXJOYb2xsPOH2jY2NTys/j6LoE1rlIKOF0bG6wBh44Au3B0S9qEtFtC/2Gcmz9sTcdT/EtsW2xCWGeM0j2tHYjRjbrVCRiC8GL0pJVw2mY/H7PvGaz/wfxLCSMf2Aj78lfniqAm89YmVmgPOcdLqmS7JbMrKbM9TxsXQ4nGL1yM5BbmnrTAjrEd27dyBwqN1QRimBiDmSlRGuZYhv7TPXHZM8Gh1cDL1cCOumbUWY4YtpqrLCORvdWlK9cIg/VAdihnzeoKranqOorVCmM6ItTTZ/LVVC8lXLnqg58UT16LR0rsoeYs0R1GOqYxPKGUlxsKGMPk0unm4uFEVwdFnU18FIsfH4vKiLNyMZQRtJKKgutSmWS+Zv2kEXcvHXY4mm8zIxIw72xRJFWbH/CXc1pjbrtoEUSOFa8PGM4VOiFLw20s2nHcmmI6pNa6uuJVnXB2KIqmcgl65U72O1qXIlfmqTo5WMxitRi/oTRfKhllywupZoT7KNy7ZjdLKinDWUs5bJam0F48lFr1iqpHgOhYNZDEN0pgkGMoIc3GokTSSDYsbinRxhE4nks6HE/XknRzIimy7xo0rsjG4ZoSODa1W4Xok1Mh7WdS62lynKGfmb+nURBtRWHDKudJ4jP1QQjET5qowUC2XtEWkSEXTkc5Z4W6E3Q6pHetjtCDPxxSR5R1O2ZcxezogaNxjIv8tZKbxtAMW0OhiZXhNNXHt/1XwpHL0NKU5UroVrOIRgBL3HfBFO5KI2vtZFusaTVFaU8NmCo/eoz9RjongOBhqVeuii9qPzYe/yFJ3HApJHY0m46Uv3M1nXYsi9nJGfzDCxI9qRx5cdS3DzgPREIYksl9sibtGyIfLWIkzuUc1UxJdFoOSuWcXUJs4mFGPs+NBIOty5HJ9Vz2JOpZKZ3apQrerAWDp+wQ7T8yNwMLMio1pTi7LSZSl83VJOsdbmGr/CH4vKXQ98ykFY2+rIiD5cnMjGdeThtWTjXLWkg+hNFAoxFD/63CsHhVu65PAmqvZFrMiPFCJU69q6gJQNaLDjy+algnKpEN/CTLH4IUe8Jm4CkyMVx09t0GtlUCk6D4eUUw61JKb3/qEJVBp1bx83UxDtKsmuHisR4zX4tHjjG9/Ii170oidwAq+hLEseffTRA5VvGIb8yZ/8CV/+5V/Obbfdxvd+7/fyNV/zNfze7/3eU73sJwXN0XKD4MSJEywvL/OOd7yDZz/72YDI4N/3vvfxb//tv/2sny/Y16iuwmmPYCSRQVDbj5zoM55EDG0LVcH+RhcSQ9gpMJdaVC0Zn9l7hlijscMQ5VvUTog2Cm/Xo+o44g0RFGRHCvIFj1Zc0r/dEC+PSfcS/B2fcF9x5f4VfOQkX7oAF0nWa3JrytBLQEl6R9jXRLswuM0QLkzYPzNLOFaUHUc1U8G+CEGyC12UByPdYny5iztVyLhrSgKInQa/L0VRtKtkjXMOlVS0Ho5JlzzcqoFuSXQ2RmfCe7QeB5Yi9pEOruNkNz4vRYedKvF2xcTW25POQNmVC1x1ywSX+hRK+EiLM0OubsWwmMN2RNmxqKGPCyz+QIqp8kRG9GgCzx7AKIJN4fHtJNLlNLF4NqazDmOVmHtf8GmvObbvdSQbWuK4IovKNPGmJ+v0Qc3lxA8mjG8u0CaQC/N6h2LBokaRXLgUxNta1L+3ilpW9QqSVkFWdXEK4k1NtiIFnpkyDCMZUTqj8AYae8uY0kV4ExGuOA3Du3KUdgQXI7JDYpQ9d88mGxdmxfx2V+NuHsPllvhLrnnCFV0SNbdJDNeSLoqVUtIfBuLBODoutkBl4eONNMm6z/juDLMfEpzuoKYt0fkIE4Z4AdgZBVsRQa4ol0r0pVi6jGsxyaZivOo4dus6W6sdsu0WKtMHPoeqtjzJnpNC6jO+uSC+GBKMFWniWDiyx+CDC5Rd8TR02jE5hIymj6WozId+SLzlkS2JoreccvgDDXcOqTIf/0pMtCM8NnfniHEnofu4z+RYSXJROkjVTEVyKaCarc2Llyq82moFCyaRiDvbsnRPe9gQqpYmO1wQ7AaYsF7TQDP73E02H1kgn1akC1LcO+3wUo1NhHtoW+KDmC45TMcIv3Io3d7JYRlrhwslk3FLntuXaULY1+j3ThG/YEjRb8vIsq+o2h5mNaf1UIwuQlFRHyoJdn2hGtxSYUNIp+U7qRS4B3qYpUq614kj2vLwz7eks3wYGAaSb15q9tZ7qEKjZwr6Z2eg5Yi2NKYlDgPxpkeRx0QnhjinyJKYYBSIebpReAPJxPYnYpBvH+8QOLAemP0QFTlsbInOifl7MFKk2wnnL7cJRrq2fFJMTpaoifxd2lMZ47xNtOORrdT+qIFFWZ/JYoka+5BK3J3z4OrLKtAV/q505C/mK6Cgc2zAOOvhWobwTIIKoP3ONt6cotrq0d2RsfXwuAh6zNT1JQE663Cf4wjYPckdwDe/+c2f8nfHjx9/wusfOXKE++6770ldz/VE0wF8CjEajbj//vsPOATnzp3j/vvv5+LFiyilePWrX82P//iP87a3vY0HHniAb/zGb2R1dZWv/uqv/qxfq+pZ4dt0DNmykI6vRTMN91uYUYCXS4cg2PaJL4Z4D3Sk4+E7bGLJ92LK/UjsR4yM22woBH2nJLfXK5SczIyifFi6j+l2i+4j0oVTRnbazqvzYrXD73uYtmWw1iW+6tE6H8CajGrSRblA5mPxFivm6ixTJ52YqeP72JkSpgt03yfc0wSbAcWhAlVKx6CaqUi2RF1czEgXzCwXuFJC4tuXNDpTuNonrntWE/Sl+xRMiddUsVpSLkp3KVst8SYKfyPEm8hFOFmrO0VJHV93JkGHBhdYivmK9d0ewVCLgKJbYRPh1hFbyoUS/9AEZ6Q7mO7HqN0A50ExVUe0BRaXWNJVseHY2+lKRm3bMTwuI8J0SS7G/o6Pv5CSz1k6F7SMl7cj8mlHsBWgC2jH4uOWXBYPwWq2FI+zUgQwbq4Q65OtiEk/EU5crxTPtk2J1IvWfFxssUczbOwIRuAutKRjrIUjWXUcOhC+nA1ABRYTOYrKQxX16UaDWW+RbGqYktfAtzBdUHWN8EDH4vOoRj6mZcmXjMTCTWSEbjMJma5awCioo+nAH8hF3MROcoe3EmxLOmyUinKhJJ8Tkv/gJoNZzdn4s0NMrnTQEw8XS6GmlzNst8IkUA1C6aBlHtlSxfiwdL+Hk1gSXuooveSqj02kg15caYsIpu4M2thiD2VUMxXueIr//i6tBxLKKUM+48QCybPSSe2J6jyf+3gigr17KLfNWuFzdg1OOcI9D12In51rVRTTMDpRYUJHuBHUhY2qTaYd6+vTcnxPW6I9JbnO5uPCI33HUMyW67xpXafz5IuG0Z35gdBCv28Kv11SzZf4twwxLUt6ROLezOMd6dy2LOM7c+nwbUSYUNSteuzRPivcw8EtFd3TvggbYoPSDv9KRLLp6q48BAM5bqq2FHTeWgRGVMrOc8SXA7yJuAK4mUK6nEtGBBp96UwrC+luQr7eEuX0ghSV0U6dbTySD8D1SrEcSkRgEa9LTKY31oR9KcaLGeEWo2XMXLUd6YrkJU+d1uiRz3irRbQhvoYq13Qe9/FiodXMLg1k+mBkw4QDKkkTqWZLRicrsY0KHNX902LpMxZTazT0b3VkywYbwuDOknRREnmCgUZPf3698j5rfM7j3/qnwVOCpgB8CvHBD36Qe++9l3vvvReA7/me7+Hee+/lh37ohwD4/u//fr7jO76DV73qVTz/+c9nNBrx//7f/yOO48/6tcxM+fGT+kTc56Nn7ZEtGBj56InH+ERJMJJOUDCsC7pUChwAQkvrok9c5+Ha+RKmStJVQzAQD7iwD8mGdAeK1YJwp+5MAC6yjE+VhHf2sbGjc0GRrMn4kp4YD6c35RR3T8SaoStcoXJJOm3Bvqwj2lYEe7JT7l+ekk7a2Cc4NKZKxOoDIyT8YtoRXZVgeVurHYOxwtsIZU1BPRpdKKDQlDenYifScQQDhfdoW0xrjZKxZ8eiEiM8rgKqExnZasnopupgvJWcqy05LsVi0XE1wO5F4h/X+fiOXJUyivV3A+yFNsHlEBsCRnzcgiFMluTi6w8lWUKnmvBqQHhB+H/xjnCBXCAXTBuKoMRZDfO5JAsM61HutMFLFcmGYvJnC+hMkR6qJL1hJxAeVShjY7UT0n1cCr3W6VBU4xuiVr429s4XDF67hKsxtm0YH7bEtZLZeeJHpipgKyJaD4SLl3lUSwX7F6ZpHx1IbF/LoRZyRqdKtC+bBDRSkMdWTLBDK16DSxPxZUyMmB87Re+0Fs5abOVi3CnpXBT1rYkd7nCGTSz+SKx7gn2PKnaiUK3Hil63lM7pTki0K4ka/lCJjctKiT6XSFrLksQfqoknCsu6O5lc9ilyXxTBM8J1K+6Y0DktI3ld8yWjPeFqqlzjav5k+GCL0a2FFOh9j3K+kvHvmR7xyhiUFJV+quS4alfM9cbifaccnfNSUAR94aLlcw4vh9Zp2UR1lkcSnxaLitS/u0/3cU/sXTZEnHKtKESBi+uM6XZFupvQPe3JWNiIYrV7VuPN5CKu8gErtkHqYoKXGNLdBH/kSRFjZFydL0nXKzofyYbRd+SnMmzbEO1qxscrScxJDMW0w/SMfIetopw27N8l55ByVjit2WopnOVCUjzCffEVVJUiX5TvWHIuxI19uH3I1COyySy78l0PRvLZty57mJFYUWWHpGBVBvxMrISwiuym/KAoLnpynJm2ZXh3jp5IQdhbHtabW+E9um5FtVAyOiwj8eRiQDiAeFf4vuNjBj+ouPO2S+xemj6gURSLlWRp10IRPEew69G+JEWniWTSojNFvKOopmrx20hU2PG0jOG9vk8xb3Cbn/21osEzF8o92f3WBk8pBoMBU1NTnHztT9AaJQxvEv6PzrRwvpCkCLQILvwrkRRcscGMpdC7ZjQbrgeUMxa6Jd0PxdhQFH3KCWk9uynHGUWwEVAulVBqOmf8OqjcEq+OSbdb4le3UqC3A0zP0Hk8YHzMEOxqiuVKUgO2QoKBEmd/7eQE3JaOgHMQn4vExNeJ6z0nJqjHW3iF2GXgIHnuDnvrPYJdH1UiNh8nU9xGjJstcJXYhNAPiLdqW5ZDE6rtBIwICaI96TSkd6UEZxJ0AelNOXovoHVV+FMmscyd3KOoPEbDGO05rFXYXC6CaAg3fQm6LyG7JcONfdoXfBkzIwKd0W0FGIUeediWla5bpyJYk4LShnLxCbfF7y/Z1IT7jsmKcBFtYgn2JbvZRDKGumafUt01pkwDZt8bMFlVZEekA9g6E5IeEn5n2XOYlZzgUkT3POzdIYVHMFDksxYWcuzEJ9z2WXjuBmuPLIqVSSxJFpPjMoLzJnJsKauwt49w59p1VJx0WYIBYlrrwB7NMCMfFVncpOamrgsnNVuQLFoVGeiLqlEfnlAVHuH5WPiTSe1lWWfNuk7FzNyIvc0us+8P2H2uKHjRgBXxhj+STrCJZQxaHc+Ik4I8D0R8MGXonvYZ3lriDTz0oRSzlmC7RrqWtaem6YgaM7yzT/r4lEQFZoqqbaVrWUgXsn2iT/6xaYnfa7uDLNmqI1FiOhWvODNTim/dXK069iRhorUunoFV16JyKdT8iYg6dKqxsdjt+AOPZKOOb2xLdnS2WKdSOEiuCFWjffcuk/tnKWascHsvhyIomC+59eQap68uYkvx6DSzJaTSFYxXhMrh9T3MlMFvl5idSEzb25JPPD5mmDrcZ3+tJ2tLRFDi7fsiCBkq0qNiveMN5eB3WvKdg76i6ghXuFgwYmGzqUmXLK5doSYe7cse42NG1NVLOf7FWAyuD5Wo1GPmIUX/JjDzBe2pjMkoYnF+wN6HF9BFXRxa6bbpXIkZeii8XTRMfcxjcHNtuD1WZPMyzm9f8JmsGsk198EdSTGDEK9bYjKP9mnJWZeIRUdxuMDbDglqsUYwkOLdRCKCksLOP7Bwii6F5CslWEXvERlBT56bYndD2fgqOPz3rnD24iJq6MNUidqXc1AVy4Y13Kt5o7NSEOcLBr1RcO5H/iP9fv8JHnpP1XXnpeqf4KvPTYhSuZJ3ubc85e/hmYimA/g0hfPFnyxYSHGRJT41oFgtxVR2psAfanGqj8WWgPVIxgzaSUZvJSchfyhE89HzUsoOBxFNJna4iSeu93V6CIFldFshY55ckw4jsDJeCa6E2Ngxv9pHF5BcFm4PlYgdlEXMiEea7hlfxqVGifVGJmM05TjwiDOVPgiXz47lVC1Hv9+SiDclxUg5ZbE7kXTLMo/4Ykh4IcJF9uB5ylEo1he1utEkQkZXWkQA2bIhWAvFuLUr9h/+RNN/cI7BdpvkYwnVxEetxZBrwk2JB6va0oEsZizhuZh4w5dUBSA9WmIioBDyfLiv6T3s4zoVypOL+bVirrUwlk7NQk7ZcWRz8vnHR4fMHtqnc8euEP5nCsqlkvRwRXX3iCr3Udqx/yUZJnKEVwOSC6FE5pWK4o6UaloMqFWlGB4FN1fQvqQkWWUlxe2K71YxY7j6+ALJmhR7euwxuUmKVxeLUlfntWDhfJtquSDeFhPhfMEwvKminKkI+wp9MQariE9HxJs+wbaPCQELybocf8El+ZspB/ZKAoNACs/E4O8EKFurZAOH7geMH5hFD3z6L03pPRSIn2KhUDMF5ZQlW7DiB3dFChvvckx+vovdi+BIimpVpM+dSOc2cmjP4qeKaEr8M4OBdHmSKx7FcslorXOgPgZgqkRnUsD5I0V5/wxiTu0OlL/BQNYcTGcEh8aiuO5LkaQmnnTOI0u2bNj5eyXllGRGu1C6vcVCRXJZeKfhtnfgwze8s6A4VGBjK2rTlhHPvNCSz1uKOUP+3jmKWfkbqbCOTFwUQ+9Hz6yitPQATGLxN0PxovQdeRpI8dc1sknZiA9G8OGexMtRKfr7LaJNEZF4Y01yQegl/kRRth165EnnrBKaRbQrZswmceJlCRAZgtWxTCHq7qmXaqoWuMjgjzVuEKJLRT5niNYC8BzD41LM+ZEhO9cleSRmnIf4Y3Xg8YmTUbKN63NH2xDteLTPe+w/R9TtVc+gKoi3NO0LsomV6D0pwO2enMvUJUkvyuYt5eFcLH8KJQb3BoqbUqEZzMq5qVioUE6h25UUhpsB3q5Q75OLAfFcyviwZXhLhduMcC2DC6A6njEuQnTfJ9rVxI9HxBtSHFcdUSMXM46y64Snu1jhAvs5O7B8zmhGwF9QaEQgTzNca+jm0RgfB/0SZyuGVzRUhXDw1n383ZyyLWbMXE6w2sJyRvBIS/gpCWRLqZjO7oC5fUy6UFDoEL0OlSqxRYU39Kkq0GuABe/EiKJsCYeoL2spfB+Vgg0s++9rY6dTqimDalXErYJs3JZuSrdEKUe234Lda10VJZzAxQpTeDB0VJVGPwbeuJDuwp6idAZ1GWycYTOPKnS4sXQaUGIEPVnN6C6Mya52camm8Bz+WYUNDNElxfiYYThfig/efQGQk7Ur7GyF9qQTqS7G2DKTjFtlmHQqXFlS6gI1VrhxPcpJHM6ImtN60rX0rzrG8xmqr6gqA3sWN58zmQpIOwq361BOEW4VVIHD31dMdEC0k4OBUqdki4bwqs/IKbLdNtVciUozGINLCtTYpxgoktU+6UhGYtM3XWH74jR0EJFDX8M2+LpCP+qTrY5RvQLvTEyhMxg4cu1QJsVZR7DnEe4phsclmsofeNhAOjBemGPHMVkCdq4kiEvMXkxVOdzEMn9km0Eak621sWNDfrhAa0OwFjNZUqgUupcdO88xBAMPm5ZUlSE6rUiPZLQXx5SP9MgWKhiDt2HxNmr7ogH4ExmB2szBRRgsTQj3Ne2HYVBa9OwAvR7jRgpVQvuSw3zVLns7HdTIx6bgwhKbacp4AkFBuqUJxxnFhiO6UlF2CuxsQTnnpAjYjSgDh/Lrjuslh86Ee2WNOjDjLWcsZcyBnYttF8TvjzEth53OMJGBUPK4y57DbVtsksFQ07rgg6qwczlhq6DMfPROgk1E4W8CR7Cm4HhGGJcUOsCmEd6+wiqDPwBshTeA0aEJYavAbreJLivGx0boDY2aGKwy2NwSXtGUMzkMNVZbXOWwzuCPwLULbBqgFzPU6Ra+k899dLN4SPkXfJLTGTaE4RelVFst8k4JRhEMPbx9JcbG8wV6J6TQSvimDoKLYnIc70JxZ0lwqSBbNbixwxQV5IrkI5AtpqhtmHQNeBaThdjMUASgRgp/ZkRaQrlgUY+GBGS4sSFcGlNe6hCua/wM0gU5L1WmpGyDHVWYwmBzw/h4xezMmOFHZyniiqBbUF1tU2qDqyzRWoALSlTl0Ef6uAsdwisp/Vsd3lBR9AzqisYFBa3zlnzB4V+ActrS+ZhH0SvEn3ICecvhOUW5ZYkXtyl3E0g9wisaVI63pxj1e1T3DHFhIN9ZoLIGL9OoMRQ9+c5UhcLmFfFZn8nM+AnXgacaFdLt/Zyfo8FTgmYE/DTD2bNnv+DjaRo0aNCgwd8dly5d+lszbj+fyLKMEydOfNrQgs8Gy8vLnDt37u/Ef2/wmaMpAJ9m2N/fZ2ZmhosXLzI1NXW9l3PDYzAYcOTIkU/InWzwiWg+q88czWf1maP5rD5z/G2flXOO4XDI6uoqWj+1DK8syyiKz08SSRiGTfH3FKAZAT/NcO1LPzU11ZxMPwv8dRPtBp8ezWf1maP5rD5zNJ/VZ45P91ldr41/HMdN0fYFhkYE0qBBgwYNGjRo8AxDUwA2aNCgQYMGDRo8w9AUgE8zRFHEf/pP/4koiq73Ur4g0Hxenzmaz+ozR/NZfeZoPqvPHM1n1eDziUYE0qBBgwYNGjRo8AxD0wFs0KBBgwYNGjR4hqEpABs0aNCgQYMGDZ5haArABg0aNGjQoEGDZxiaAvBphl/8xV/k+PHjxHHMC1/4Qt7//vdf7yXdcHjd617H85//fLrdLouLi3z1V381jz766PVe1hcE/vN//s8opXj1q199vZdyQ+LKlSt8wzd8A3NzcyRJwt13380HP/jB672sGw7GGF772tdy4sQJkiTh1KlT/NiP/dh1izC70fBnf/ZnvOIVr2B1dRWlFG9961uf8HvnHD/0Qz/EysoKSZLwspe9jNOnT1+fxTb4gkVTAD6N8Fu/9Vt8z/d8D//pP/0nPvzhD/OsZz2Ll7/85Wxubl7vpd1QuO+++/i2b/s23vve9/L2t7+dsiz58i//csbj8fVe2g2ND3zgA/z3//7fueeee673Um5I7O3t8eIXv5ggCPjDP/xDHnroIX72Z3+WmZmZ6720Gw4/9VM/xS/90i/xC7/wCzz88MP81E/9FD/90z/Nz//8z1/vpd0QGI/HPOtZz+IXf/EXP+nvf/qnf5o3vOEN/PIv/zLve9/7aLfbvPzlLyfLsqd4pQ2+kNGogJ9GeOELX8jzn/98fuEXfgEAay1HjhzhO77jO3jNa15znVd342Jra4vFxUXuu+8+XvKSl1zv5dyQGI1GPOc5z+G//bf/xo//+I/z7Gc/m9e//vXXe1k3FF7zmtfwnve8hz//8z+/3ku54fGP/tE/YmlpiTe+8Y0Ht33N13wNSZLwpje96Tqu7MaDUoq3vOUtfPVXfzUg3b/V1VW+93u/l3//7/89AP1+n6WlJX7913+dV77ylddxtQ2+kNB0AJ8mKIqCD33oQ7zsZS87uE1rzcte9jL+8i//8jqu7MZHv98HYHZ29jqv5MbFt33bt/GVX/mVTzi+GjwRb3vb23je857H137t17K4uMi9997Lr/7qr17vZd2QeNGLXsQ73vEOHnvsMQA+8pGP8O53v5uv+IqvuM4ru/Fx7tw51tfXn/BdnJqa4oUvfGFzrm/wWaHJAn6aYHt7G2MMS0tLT7h9aWmJRx555Dqt6saHtZZXv/rVvPjFL+auu+663su5IfGbv/mbfPjDH+YDH/jA9V7KDY2zZ8/yS7/0S3zP93wP/9//9//xgQ98gO/8zu8kDEO+6Zu+6Xov74bCa17zGgaDAbfddhue52GM4Sd+4if4+q//+uu9tBse6+vrAJ/0XH/tdw0afCZoCsAGz2h827d9Gw8++CDvfve7r/dSbkhcunSJ7/qu7+Ltb397E/T+t8Bay/Oe9zx+8id/EoB7772XBx98kF/+5V9uCsC/gd/+7d/mN37jN3jzm9/MnXfeyf3338+rX/1qVldXm8+qQYOnCM0I+GmC+fl5PM9jY2PjCbdvbGywvLx8nVZ1Y+Pbv/3b+f3f/33e+c53cvjw4eu9nBsSH/rQh9jc3OQ5z3kOvu/j+z733Xcfb3jDG/B9H2PM9V7iDYOVlRXuuOOOJ9x2++23c/Hixeu0ohsX3/d938drXvMaXvnKV3L33Xfzz//5P+e7v/u7ed3rXne9l3bD49r5vDnXN/hc0RSATxOEYchzn/tc3vGOdxzcZq3lHe94B1/0RV90HVd248E5x7d/+7fzlre8hT/90z/lxIkT13tJNyy+7Mu+jAceeID777//4Od5z3seX//1X8/999+P53nXe4k3DF784hd/gp3QY489xrFjx67Tim5cTCYTtH7i5cfzPKy112lFXzg4ceIEy8vLTzjXDwYD3ve+9zXn+gafFZoR8NMI3/M938M3fdM38bznPY8XvOAFvP71r2c8HvPN3/zN13tpNxS+7du+jTe/+c387u/+Lt1u94A3MzU1RZIk13l1Nxa63e4ncCPb7TZzc3MNZ/Jv4Lu/+7t50YtexE/+5E/ydV/3dbz//e/nV37lV/iVX/mV6720Gw6veMUr+Imf+AmOHj3KnXfeyV/91V/xX/7Lf+Ff/st/eb2XdkNgNBrx+OOPH/z/3Llz3H///czOznL06FFe/epX8+M//uPcfPPNnDhxgte+9rWsrq4eKIUbNPiM4Bo8rfDzP//z7ujRoy4MQ/eCF7zAvfe9773eS7rhAHzSn1/7tV+73kv7gsCXfumXuu/6ru+63su4IfF7v/d77q677nJRFLnbbrvN/cqv/Mr1XtINicFg4L7ru77LHT161MVx7E6ePOn+43/8jy7P8+u9tBsC73znOz/pOeqbvumbnHPOWWvda1/7Wre0tOSiKHJf9mVf5h599NHru+gGX3BofAAbNGjQoEGDBg2eYWg4gA0aNGjQoEGDBs8wNAVggwYNGjRo0KDBMwxNAdigQYMGDRo0aPAMQ1MANmjQoEGDBg0aPMPQFIANGjRo0KBBgwbPMDQFYIMGDRo0aNCgwTMMTQHYoEGDBg0aNGjwDENTADZo0KBBgwYNGjzD0BSADRo0aNCgQYMGzzA0BWCDBg0+I7z0pS/l1a9+9cH/jx8/zutf//rrtp4nGy95yUt485vf/KS/zitf+Up+9md/9kl/nQYNGjT462gKwAYNGvyd8IEPfIBXvepVn9F9v9CKxbe97W1sbGzwyle+8kl/rR/8wR/kJ37iJ+j3+0/6azVo0KDBNTQFYIMGDf5OWFhYoNVqXe9lPCl4wxvewDd/8zej9ZN/irzrrrs4deoUb3rTm57012rQoEGDa2gKwAYNGnwCxuMx3/iN30in02FlZeWTjij/elfPOccP//APc/ToUaIoYnV1le/8zu8EZHR84cIFvvu7vxulFEopAHZ2dvin//SfcujQIVqtFnfffTf/5//8nye8xktf+lK+8zu/k+///u9ndnaW5eVlfviHf/gJ99nf3+dbv/VbWVpaIo5j7rrrLn7/93//4Pfvfve7+ZIv+RKSJOHIkSN853d+J+Px+FO+962tLf70T/+UV7ziFU+4XSnF//gf/4N/8k/+Ca1Wi5tvvpm3ve1tB79/17vehVKKP/qjP+Lee+8lSRL+/t//+2xubvKHf/iH3H777fR6Pf7ZP/tnTCaTJzz3K17xCn7zN3/zU66pQYMGDT7faArABg0afAK+7/u+j/vuu4/f/d3f5Y//+I9517vexYc//OFPef/f+Z3f4ed+7uf47//9v3P69Gne+ta3cvfddwPwf//v/+Xw4cP86I/+KGtra6ytrQGQZRnPfe5z+YM/+AMefPBBXvWqV/HP//k/5/3vf/8Tnvt//s//Sbvd5n3vex8//dM/zY/+6I/y9re/HQBrLV/xFV/Be97zHt70pjfx0EMP8Z//83/G8zwAzpw5wz/4B/+Ar/mar+GjH/0ov/Vbv8W73/1uvv3bv/1Tvpd3v/vdtFotbr/99k/43Y/8yI/wdV/3dXz0ox/lH/7Df8jXf/3Xs7u7+4T7/PAP/zC/8Au/wF/8xV9w6dIlvu7rvo7Xv/71vPnNb+YP/uAP+OM//mN+/ud//gmPecELXsD73/9+8jz/lOtq0KBBg88rXIMGDRr8NQyHQxeGofvt3/7tg9t2dnZckiTuu77ruw5uO3bsmPu5n/s555xzP/uzP+tuueUWVxTFJ33Ov37fT4ev/MqvdN/7vd978P8v/dIvdV/8xV/8hPs8//nPd//hP/wH55xzf/RHf+S01u7RRx/9pM/3r/7Vv3KvetWrnnDbn//5nzuttUvT9JM+5ud+7ufcyZMnP+F2wP3gD/7gwf9Ho5ED3B/+4R8655x75zvf6QD3J3/yJwf3ed3rXucAd+bMmYPbvvVbv9W9/OUvf8Jzf+QjH3GAO3/+/CddU4MGDRp8vtF0ABs0aPAEnDlzhqIoeOELX3hw2+zsLLfeeuunfMzXfu3XkqYpJ0+e5Fu+5Vt4y1veQlVVn/Z1jDH82I/9GHfffTezs7N0Oh3+6I/+iIsXLz7hfvfcc88T/r+yssLm5iYA999/P4cPH+aWW275pK/xkY98hF//9V+n0+kc/Lz85S/HWsu5c+c+6WPSNCWO40/6u7++lna7Ta/XO1jLJ7vP0tISrVaLkydPPuG2v/mYJEkAPmE03KBBgwZPFvzrvYAGDRp84ePIkSM8+uij/Mmf/Alvf/vb+Xf/7t/xMz/zM9x3330EQfBJH/MzP/Mz/Nf/+l95/etfz91330273ebVr341RVE84X5/8/FKKay1wMcLp0+F0WjEt37rtx7wEf86jh49+kkfMz8/z97e3if93adbyye7j1LqM3rMtTHywsLCp3gnDRo0aPD5RdMBbNCgwRNw6tQpgiDgfe9738Fte3t7PPbYY5/2cUmS8IpXvII3vOENvOtd7+Iv//IveeCBBwAIwxBjzBPu/573vIev+qqv4hu+4Rt41rOexcmTJ//W1/ibuOeee7h8+fKnfNxznvMcHnroIW666aZP+AnD8JM+5t5772V9ff1TFoFPBh588EEOHz7M/Pz8U/aaDRo0eGajKQAbNGjwBHQ6Hf7Vv/pXfN/3fR9/+qd/yoMPPsi/+Bf/4tNaovz6r/86b3zjG3nwwQc5e/Ysb3rTm0iShGPHjgGiGP6zP/szrly5wvb2NgA333wzb3/72/mLv/gLHn74Yb71W7+VjY2Nz2qtX/qlX8pLXvISvuZrvoa3v/3tnDt3jj/8wz/k//2//wfAf/gP/4G/+Iu/4Nu//du5//77OX36NL/7u7/7aUUg9957L/Pz87znPe/5rNbyueDP//zP+fIv//Kn7PUaNGjQoCkAGzRo8An4mZ/5Gb7kS76EV7ziFbzsZS/ji7/4i3nuc5/7Ke8/PT3Nr/7qr/LiF7+Ye+65hz/5kz/h937v95ibmwPgR3/0Rzl//jynTp06GHP+4A/+IM95znN4+ctfzktf+lKWl5f56q/+6s96rb/zO7/D85//fP7pP/2n3HHHHXz/93//Qbfxnnvu4b777uOxxx7jS77kS7j33nv5oR/6IVZXVz/l83mexzd/8zfzG7/xG5/1Wv4uyLKMt771rXzLt3zLU/J6DRo0aACgnHPuei+iQYMGDW4krK+vc+edd/LhD3/4oIv5ZOGXfumXeMtb3sIf//EfP6mv06BBgwZ/HU0HsEGDBg3+BpaXl3njG9/4CYrkJwNBEHyCL2CDBg0aPNloOoANGjRo0KBBgwbPMDQdwAYNGjRo0KBBg2cYmgKwQYMGDRo0aNDgGYamAGzQoEGDBg0aNHiGoSkAGzRo0KBBgwYNnmFoCsAGDRo0aNCgQYNnGJoCsEGDBg0aNGjQ4BmGpgBs0KBBgwYNGjR4hqEpABs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", 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\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "kwargs = {'scale_bar': True, 'cmap': 'hot'} # or maby 'cmap': 'gray'\n", " \n", "view = dset.plot(verbose=True, **kwargs)" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Plot Dataset as Spectral Image\n", "We need to change the data_type of the dataset to ``spectral_image`` and the dimension_type of one dimension to ``spectral``.\n", "\n", "Now the plot function plots it as a spectrum image.\n", "\n", "Select the spectrum with the mouse (left click).\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/dask/core.py:119: RuntimeWarning: divide by zero encountered in true_divide\n", " return func(*(_execute_task(a, cache) for a in args))\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2890c69383884023a8986474244fd9a9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Dropdown(description='Image', layout=Layout(height='50px', width='30%'), options=(('Pixel Wise'…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9b140e12dff349db95d07abfc9b881a2", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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"source": [ "The axes (and figure) instances of matplotlib can be accessed throught the ``view`` attribute of the sidpy dataset.\n", "\n", "The code cell below will draw a red square lattice on the plot ``above``\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "x, y = np.mgrid[0:501:100, 0:501:100] + 5\n", "dset.view.axes[0].scatter(x, y, color='red');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The plotting routine can also be used independently. \n", "\n", "Please note, that a reference (here the variable `view`) must be maintained for interactive plotting." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "baea767beaaa4717a961768ff22571bc", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(Play(value=0, description='Press play', interval=500, max=25), IntSlider(value=0…" ] }, 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pqalh9erVvPXWWyEDavny5YwZM4ZTTjmF73//+6SlpfHWW2+xePFivv/974fq+uCDDygsLCQ/P5+HHnqI2NhYgiBg7NixrFy5kquuuor+/fuzbNkybrrpJvbs2cMjjzzyD9GoT58+JCUl8eqrrzJ27FgAXnvtNSIjI9myZQsHDx4kISGB1tZW1qxZw7Rp09q8v2PHDiZOnMhVV13FFVdcwW9+8xuuvPJKzjjjDE4//XQAGhsbKSgoYM+ePVxzzTV0796dNWvWcOutt/L3v/+dWbNmheq75pprePrpp5k6dSrTp09n165dPPbYY2zatIm//OUvREdHf+q+/fa3v+Xqq6/mzDPPDOHdo0cPAN58802GDRtGQkICN998M9HR0Tz55JN8/etfDxkdnwS/+93vqK+v55prriEiIoIHHniACRMmUFZWFsJz+fLllJWVMXXqVNLS0njzzTeZPXs2b775Jq+//joRERGcd955dOzYkeeff56CgoI2bcyfP5/TTz+dPn36HBePK6+8kueff55vfetbnHXWWaxatYrzzjvvmHLr169nzZo1XHzxxXTr1o3y8nKeeOIJvv71r1NcXExsbCxnn30206dP59FHH+WHP/whp512GkDo729/+1uuuOIKCgsLuf/++2lsbOSJJ54gPz+fTZs2kZWV9ckD8yHYv38/Y8aM4eKLL+aiiy7iiSee4OKLL+a5555jxowZXHvttVxyySU8+OCDTJw4kYqKCuLj4z91nwD27NkTMmhvvfVW4uLi+PWvf0379u2PwefT9vHCCy/kzTff5IYbbiArK4t9+/axfPly3nnnnc9FhzCE4d8agjCE4VPAnDlzAiDYtWtXm+crV64MgGDlypVBEATBpk2bAiD4wx/+cNy6ysvLg6ioqOCee+5p83zr1q3BSSeddMzzD0NiYmJw/fXXH/f3Dz74IDj11FODzMzMYP/+/W1+a21tDf1/xRVXBEBwyy23tCnzxz/+MQCCn/zkJ22eT5w4MYiIiAh27NgRepaZmRlcccUVoe8fpsfx4LzzzgvOPPPM0PcJEyYEEyZMCKKiooKlS5cGQRAEGzduDIDgpZdeatMeELz66quhZ/v27Qvat28fzJw5M/Ts7rvvDuLi4oK33367Tbu33HJLEBUVFbzzzjtBEATBa6+9FgDBc88916bcyy+/fMzzgoKCoKCg4GP7FQRBEBcX14YmDi644IKgXbt2wc6dO0PPKisrg/j4+ODss8/+2Dp37doVAEFKSkpQW1sbev7SSy8FQPCnP/0p9KyxsfGY9+fNm3cM3aZMmRJ07tw5+OCDD0LP/v73vweRkZHBj3/849CzO+64IzhaVL7xxhsBEMyYMaNNG1deeWUABHfcccfH4rJ27doACJ599tnQsz/84Q8fyTf19fVBUlJS8J3vfKfN86qqqiAxMfGY5x+Gj+LHgoKCAAh+97vfhZ5t3749AILIyMjg9ddfDz1ftmxZAARz5sz5zH264YYbgoiIiGDTpk2hZzU1NUFycnIbWfJp+7h///4ACB588MGP7XMYwhCGTwfhLeAwfKHgPHzLli2jsbHxI8u8+OKLtLa2MmnSJN57773QJy0tjV69erFy5cqPbSMpKYl169ZRWVn5kb9v2rSJXbt2MWPGjGM8iR+VzuO6665r833JkiVERUUxffr0Ns9nzpxJEAQsXbr0Y/H7NDBs2DA2btwYijlcvXo1o0ePpn///rz22muAvIIRERHk5+e3eTc3N5dhw4aFvqemppKTk0NZWVno2R/+8AeGDRtGp06d2tB45MiRtLS08Oqrr4bKJSYmcs4557Qpd8YZZ9CxY8dPHItPCy0tLbzyyitccMEFZGdnh56fcsopXHLJJaxevZqDBw9+Yj2TJ0+mU6dOoe+ODkf3vUOHDqH/m5qaeO+99zjrrLMA2oQJTJ48mX379rXZHl2wYAGtra0fe+3eyy+/DMB3v/vdNs9vuOGGY8oejUtzczM1NTX07NmTpKSkY0IWPgqWL19OXV0dU6ZMaTM+UVFR5OXlfe7x6dixIxdffHHoe05ODklJSZx22mltPLHu/+PR9+P69PLLLzNkyBD69+8fepacnBwKs/isfezQoQPt2rXjf//3fz9XeEIYwhCGthDeAg7DFwqnnnoq//Ef/8HPfvYznnvuOYYNG8bYsWO57LLLQsZhaWkpQRDQq1evj6zjk7YcH3jgAa644goyMjI444wzGD16NJdffnnIsNi5cyfAx27hOTjppJPo1q1bm2e7d+8mPT09tOXlwG3J7d69+xPr/SQYNmwYH3zwAWvXriUjI4N9+/YxbNgw3nzzzTYGYG5uLsnJyW3e7d69+zH1derUqY1SLC0t5W9/+xupqakf2f6+fftC5Q4cOEDnzp0/ttw/CtXV1TQ2NpKTk3PMb6eddhqtra1UVFSEtrCPBx/uuzMGj+57bW0td911F7///e+Pwf/oONRvfvObJCYmMn/+fL7xjW8A2v7t378/X/nKV46Lw+7du4mMjOTUU09t87xnz57HlD106BD33nsvc+bMYc+ePW1iSD8cE/tRUFpaCsCIESM+8veEhIRPrOOjoFu3bscshhITE8nIyDjmGbSl76ft0+7duxkyZMgxbX+YTp+2j+3bt+f+++9n5syZdOnShbPOOosxY8Zw+eWXk5aW9ol9DkMYwtAWwgZgGD4VHC8RbktLyzHPHn74Ya688kpeeuklXnnlFaZPn869997L66+/Trdu3WhtbSUiIoKlS5cSFRV1zPsdO3b8WFwmTZrEsGHDWLhwIa+88goPPvgg999/Py+++CKjRo36TP1q3779CTkhOWjQIGJiYnj11Vfp3r07nTt35itf+QrDhg3jl7/8JYcPH+a1115j/Pjxx7z7UTQD2iji1tZWzjnnHG6++eaPLOsMnNbWVjp37sxzzz33keWOZ0CeKPg0fZ80aRJr1qzhpptuon///nTs2JHW1la++c1vtjkA0759ey644AIWLlzIL3/5S/bu3ctf/vIXfvrTn35h+N5www3MmTOHGTNmMGTIEBITE4mIiODiiy8+5jDOR4Er89vf/vYjjZyTTvp8Ivx4dPw09P1H+/Rh+Cx9nDFjBueffz5//OMfWbZsGbfffjv33nsvK1asYMCAAZ+57TCE4d8ZwgZgGD4VOE9LXV1dm+fH84b17duXvn378l//9V+sWbOGr33ta/zqV7/iJz/5CT169CAIAk499dSP9bR8HJxyyil897vf5bvf/S779u1j4MCB3HPPPYwaNSp06GDbtm2MHDnyM9edmZlJUVER9fX1bbyA27dvD/3+j0K7du0488wzee211+jevXtoK3PYsGEcPnyY5557jr17937u/G09evTg/fff/8T+9+jRg6KiIr72ta+12dr7R+CjFgupqanExsZSUlJyzG/bt28nMjLyGO/T54H9+/fzP//zP9x111386Ec/Cj13XqYPw+TJk3nmmWf4n//5H9566y2CIPjY7V/Q+Le2trJr1642XuwdO3YcU3bBggVcccUVPPzww6FnTU1Nx8yj4y2wHC937tz5c/Hy/wV82j5lZmZ+JE0+/Oyz9rFHjx7MnDmTmTNnUlpaSv/+/Xn44YeZO3fu5+hNGMLw7wvhGMAwfCpwQtrFjoG8f7Nnz25T7uDBg3zwwQdtnvXt25fIyEgOHz4MwIQJE4iKiuKuu+46Jq1KEARt0nV8GFpaWo7ZOuvcuTPp6emh+gcOHMipp57KrFmzjlFKH27vo2D06NG0tLTw2GOPtXn+yCOPEBER8Zm9jMeDYcOGsW7dOlauXBkyAE8++WROO+007r///lCZzwOTJk1i7dq1LFu27Jjf6urqQmM0adIkWlpauPvuu48p98EHHxxDv08DcXFxx7wXFRXFueeey0svvdQmZcfevXv53e9+R35+/ufezvxwO3DsOB996vloGDlyJMnJycyfP5/58+dz5plnHrO1+2EoLCwE4Je//GWb57/4xS8+Ep8P4/KLX/ziGM95XFwccOwCq7CwkISEBH7605/S3Nx8TP3V1dUfi+v/BXzaPhUWFrJ27Vo2b94celZbW3uMt/nT9rGxsZGmpqY2v/Xo0YP4+PjQ3A9DGMLw6SHsAQzDp4LTTz+ds846i1tvvZXa2lqSk5P5/e9/f4yxt2LFCr73ve9x0UUX8ZWvfIUPPviA3/72t0RFRXHhhRcCEto/+clPuPXWWykvL+eCCy4gPj6eXbt2sXDhQqZNm8YPfvCDj8Sjvr6ebt26MXHiRPr160fHjh0pKipi/fr1IY9EZGQkTzzxBOeffz79+/dn6tSpnHLKKWzfvp0333zzI42io+H8889n+PDh3HbbbZSXl9OvXz9eeeUVXnrpJWbMmBEyhv9RGDZsGPfccw8VFRVtDL2zzz6bJ598kqysrGPiEz8t3HTTTSxatIgxY8aEUsQ0NDSwdetWFixYQHl5OSeffDIFBQVcc8013HvvvWzevJlzzz2X6OhoSktL+cMf/sDPf/5zJk6c+JnaPuOMMygqKuJnP/sZ6enpnHrqqeTl5fGTn/yE5cuXk5+fz3e/+11OOukknnzySQ4fPswDDzzwufr5YUhISODss8/mgQceoLm5ma5du/LKK6+wa9eujywfHR3NhAkT+P3vf09DQwMPPfTQp+rfhRdeyKxZs6ipqQmlgXn77beBtt68MWPG8Nvf/pbExERyc3NZu3YtRUVFpKSktKmzf//+REVFcf/993PgwAHat2/PiBEj6Ny5M0888QTf+ta3GDhwIBdffDGpqam88847/PnPf+ZrX/vaMQuV/2v4tH26+eabmTt3Lueccw433HBDKA1M9+7dqa2tDdEpISHhU/Xx7bff5hvf+AaTJk0iNzeXk046iYULF7J37942B1rCEIYwfEo4ASePw/AvCjt37gxGjhwZtG/fPujSpUvwwx/+MFi+fHmbNBNlZWXBt7/97aBHjx5BTExMkJycHAwfPjwoKio6pr4XXnghyM/PD+Li4oK4uLigd+/ewfXXXx+UlJQcF4fDhw8HN910U9CvX78gPj4+iIuLC/r16xf88pe/PKbs6tWrg3POOSdU7qtf/Wrwi1/8IvT7FVdcEcTFxX1kO/X19cGNN94YpKenB9HR0UGvXr2CBx98sE0amSD4/GlggiAIDh48GERFRQXx8fFtUpHMnTs3AIJvfetbx7yTmZkZnHfeecc8/6gULfX19cGtt94a9OzZM2jXrl1w8sknB0OHDg0eeuih4MiRI23Kzp49OzjjjDOCDh06BPHx8UHfvn2Dm2++OaisrPzYNj4Ktm/fHpx99tlBhw4dAqANfTZu3BgUFhYGHTt2DGJjY4Phw4cHa9as+cQ6XRqYj0oBwodSr7z77rvB+PHjg6SkpCAxMTG46KKLgsrKymPKOXA8HBEREVRUVBzz+4fTwARBEDQ0NATXX399kJycHHTs2DG44IILgpKSkgAI7rvvvlC5/fv3B1OnTg1OPvnkoGPHjkFhYWGwffv2Y/gmCILgv//7v4Ps7OwgKirqGB5auXJlUFhYGCQmJgYxMTFBjx49giuvvDLYsGHDx9LteGlgTj/99GPKHo+3gDZplz5LnzZt2hQMGzYsaN++fdCtW7fg3nvvDR599NEACKqqqo7B9eP6+N577wXXX3990Lt37yAuLi5ITEwM8vLygueff/5jaRCGMIThoyEiCL6Aqw3CEIYwhOHfHDZv3syAAQOYO3fuMalOwuBhxowZPPnkk7z//vvHPXQShjCE4f8ewjGAYQhDGMLwGeHQoUPHPJs1axaRkZGf++DO/4/wYTrV1NTw29/+lvz8/LDxF4YwnGAIxwCGIQxhCMNnhAceeIA33niD4cOHc9JJJ7F06VKWLl3KtGnTvpDTzP+/wJAhQ/j617/Oaaedxt69e3nqqac4ePAgt99++4lGLQxh+LeH8BZwGMIQhjB8Rli+fDl33XUXxcXFvP/++3Tv3p1vfetb3HbbbZ87N9//j/DDH/6QBQsW8O677xIREcHAgQO54447/mlS2oQhDP/OEDYAwxCGMIQhDGEIQxj+zSAcAxiGMIQhDGEIQxjC8G8GYQMwDGEIQxjCEIYwhOHfDMLBKmEIQxjCEIYwhOEzQ1NTE0eOHPlC62zXrh0xMTFfaJ1h+GgIG4DHg9wIOAAUwJF50O4q2P8UdDoNqAeuAJKBVUA20AQsBqKASmA18LiVAVgIVbshzd0ith54E7gBaAYy7W++1fEAMAaIAcqBvUAuEAstt0HUtcAGqz8LmA+kAlXAtw2vTFixCEaMMhwB+gG/BtKAocAWIBFoB9QA8UCt9acWGA/UGW4xeqflXIgaAvQF1gClwGhgqn0vA1w/lwIZ9m606MkaIMn+1sL6pTD4eqABeBfoYu+WWLsNhvc260cFtGyDqCnQMA/iTgYK7Z1KYATwotEjxehXC1wG+2+HTldZPaVW/wggx/q+0erZZDRosDoHaQxJBgYCi4Bqe28aGvttRv+BQIvRrdb6nw00Wt+ybcyeMXrMs3qq7XsZEAekGw3K0fgnqw8UQ/mzkJUDjLV6lyHeKbI2p9u7m40GfdTXfc9B55HAcOAloKvhdBdwJZBnuFfCnqeg6wDDvxCYA1xvuM0zfLHyI+33dGtvO3C+/RZrfRqD51nHZ9FortwG3IP4ot7G5nqj1Vobg2XAJPvd5kPDoxA31n6vAMbDH3+lIU3oYmVbjG7V1vYWxPvZoiXNRoPdiG+fsPprgLPsnVobzzzDrZfR5A6jQV/16cnH4Zprja4b0bxuhEOzocPtaN6Vofnwmn57fQOclQ8tqyHqSqh5GlJONbxuBnYgvuoFDAEWGB5PGU5Rni/IBdqjefQ7YCLi42bRhh32vRC41/BugpaHIepOwzkPGAjloyCrp8akagOknWY07A9cCLxn4/coMMDGMd3oOMXGKw3NoSjrS5m1UXkULYca3c9GcrWPjVsFkmMTgBWIn4dbXcn2ewzQan9r0PzJtfH6ib273d4djeZStdHyJcMhyfDMA/7D8O1rNFpnNE6z8XrQ/j8PuFt11cyClPFHjZG7ra4W8WsumhsrrN4065c7LL4KL0+qgFfs+XrgF4gfRxp+2WjuFRuNeqEx/vqXH8rf1NTEqaeeSlVV1Rdab1paGrt27QobgV8ChA+BHA8mRrDvBeg8Gyl+N/GOIOVdiITqY0iwbkACbjZwO3AnEmpdkcFVgBTRBqQU4tDkTUSTuQCYa896ISG0FgnRq5Gg3Q70gD2PqumsSw2HdCTMJiCjpxIptCwkeKqRcByKBHAzEmSbkaFTAVwKPGs4boKgGSIeR0baBghKIOJCw6kb3qjpioRkNlLqW5EwHgE8iZTtZGTgFCNBtgEJ4mFISZ1ldC0yWu62PmUjhdUeCfAC6+dQZPS0t/bbA88jYVuElHc/JED3Gj2LkVHkFH4xUiYLDD9Xb7LRIRFIMHy2Gh6RSGGdjzce6o3mXQxHd8vcCGTArLPnkcBKZLAthpr3IOV2xFt9NRab34L+lyMFWIkUZarRPB8ZUpU2hn9G/FKNFOIypAgQXocWQYdTge8hxRxrv6XAoUehw+VWTypScJOMltEas5ZbIGomcBUy4jcgxdbPaJBn7z5rOPU0fA+gubEGKchipNRqjbb1yIDYjl/4NNrvg6xP8YZLnY1djfXvHGt3vdUfY2M6EynZGPu92uoC8UMGMA7YgxZlIw2vdOtDq9GiAbjO+ldseEXb+LyI5lOq1b0BGQ5NyJDvZd9fsrG6DCn6m4AucORVaDfK8Cy19kqMbmmG4xA01pE2prnWhzRkPDRZ+yvRWNcZHfpZ35OMNglovKvs/51WzznChXsg2AsRgwzXOuvPLDTWldbvJXDkPWg33truYjTpZfXXWx9WoDlUhsZ4nNGhBRmCq42e0cgwHY/kwWKrI82+b7E2ogynLdaH71s/R6L5/Jj1fyjwc+vbzWjhsdDenQsHWyGhI3CN4eIM/8XAuYZTHATPQ4Qbh3j8AjQDyfwC/CKh0mhQjeRHiT0bixbRC5BcPd/G8wkbs3bI0LvU6LfY6qy3un5kNHaGYbo9X48M12qj41g0565HfPkSmnfrvnw1fvDgQRITE6moqPhC7vF2dWZkZHDgwIEvrM4wHB/CHsDjwTrofBsSOOlIKI21vxVWJhlN5in4yXs9MA/2b4NOrswI+1uHlMwBq2cFEjhOqF5mzzciYdTFft+OhKspj67Drf1WJPRTkPFXb/U3QsujEHU5UjD9rI4mpHCXqX8ctPpdXc7LlwcRGVZXCjANIooN3zpkoDgvYRwSpuC9oduQwsxAgm8PEv7xSOiuMLxLkUJwHoLxR9WbZHSOJeTZYaXRJdXolYyM62prq8TaG4O8Es5QzUQGZrXRAqNLLfI4NFn7R3sTkoDeSLgXIuXvVtw9rd+D8Eqqyt6ZjIR2OlJaw+x5CzJsi0WjFPNkMgIp/Eugf7nRrx0h5USGvZ9vY9aK967U2vd++MVFpMavQxZeYWP4bNK/HU618vH2/AhaKFxGyJsY1RNv1DYaDZPwnuFm60+m9bcnUvwlyGCbhh/j/taPKDTWc5GXdBQyrEsQb7eg+ZaHDHrnRR2Kn0O5hvNexNNPofHOR3xXbXRtMBxdm3OMtjVofB3PjEBzq9zKOwXvvHSp1k5vYDnihSLEh+C92qlGj37W3ya8Ab8b2kVa/T2tT43Wlya8EVBp+B02uq5BsmKA0SLDytShRc8C6/duZBBVGq0eRkbCLCs3GC1iMLr3ggi3EGtE82o64i+3KOgDxEK7BdbHVKNRvNEFxPPJiOej0XhWI36ahBYAiw3vDGAVNOyAuFFo3qxGPPc75F2Lt/frkNGTinjI4V5mv8ci/k+2PuQjw7UCjfEgjVlCpdUfjZeTFfZeitENiJhhdHjN2klFfAtU7YC0KUbPPohfL0MybYv9X2nl3S7HjVbPFsSbWdbXkWgO7kW8MQbN3zzrM/Y3x34vFM0ot77nW53p1u9KNHcmcEIhISGWhITYTy74qeCDTy4Shi8MwodAjgdxSJhEIgEdjwRdCxKo8WhSVwG3IEMmHvihnnfqgxeUK5BCf9TqPYgE2SSrazhSKBuRMi1BQq8ZCZh4JEDT0ORPR4pjuT3figTQequ3HKJGIaF3I1IqzpvQFXkF/oQUSJO1n4oX4A1IELUiAZRk/UxFQmw3ElKgrefZSEAl4bdNy5CXcRDyYOZa32vt7ySrbwxSqBVIgW+1ZwvwxnaltbnG8FiHDL9H7Hmr9b3M+tqAlOZ1Rp94JGQLbCzrkVBuQIK8Ca3WlwG/QQZto9G3wmj7krXtaLwRCeStSCivsve32pjWG5734hcH45GwHo0MisuA1618IjIe3Ko/Go1/jtFjt/2Wi/hg7VG4OMMwEhleGch4azV8jiBejMRvZ4HGfRXyJPRGfBeLjI++Ni4r7dlQFJZQbjQZhPfWAPtfwBtDSYZvkfWtveGI4TTecCo22ixD4307bK205xNtzGKMlm7cL7GyB/Hb/qsNj1LEU+vs3aN5cTsww2gzB4K1iN/rkQfPGfeVaKyjjB7RiGeXIJ6Jt9/WI/6osj5mWNsZRq8Se6+Ljd9AZGQdsOfOkE0yetyIXxiOtfI/QDxWjOTLOqsvFxmuzuiMtOc7RYuDtWgHws37KBuXATZ2/fA8nwdcbuXcFqTzKlWiOYzRbzySiQet3VWI97ONTqX2aUX8k4eMYZDBkgNx19s7W9BCJtPGsRrJJregrrH3sg3fKryBm2s0H2G/ZxhNS9DCYRuav+69p5Bn8HXrh/PwZqAF+0h77haVFgIBkNbHyvYxPKdbv8YgHt1q9HIebrfI3QsHn0L8uxXNm1H2Tg5+t2Yz8jK6sBlnRJbiDfLnEX/mGN3zjQbj7FkRYQjD54KwAXg8qEOK4WFgDbx8D1ImO5Dy2oKEaT0SNG5LpjcyeCYiJdmKJn8T3oPRH03eWqRI5iKP2VpkHDmDrM7adNtbDUiYrMKvYn+NhFUxNGxCQnS0lc1HxtkOpECy7NmfkcC9DAm7xcATENwN75QgIZaLjNlE6+sw/GraGaRl1ocW/LbIKgheMZwWI0H2ANq2mY+EdDFaUbttpKFIEVjsEXvxRlO2Pa+wTzwy7PoigV2B4seybBxSkUFRjQTjcHu/FQlUZygvQ0pxEVIAPSDYrX40PI2U8CVIES6y8axGWzsVUPM83hjrZ3U8iBT6dmvXKTbnSXPjnYR44wnEC6VIQe5ARt9iG69V9v9i68tPrS+PwZ55yFM03vBwisfFyLlt4VR7vgjvBRljfzF6z7Wy8cjgS8B7Ht326xKjyVJ75ynEr/HqZ6dJNm4LDNcVyCCZZ/i7RY0zGkvwhnsBUqx3Q98Lrd0B9jwe8aDFiwXvif70Eu0bNgD3IV7aCpsfxocCpKj/Da8iY2E1ml+REHGV0bcK8baLQWvCz+sMxDPj4NC7yGDLQ/yXheKz9lofouDgbHunzvrQ3nCotX48A0duMZr+BvHUcKPDThsrrO1MG5d62PdnayPDaP8jtLUciTdkpqF5Gg8JmXBwA5o7q5CR4bbCF1mbGxDfrEPeUWdo9rK2E0Rv5iCenILm3CY015/Bx7CNMbpidW4FvpIs3k+0cs7gL0eyoRnJjwXW5n8Z3d3CZxXikVVoHtXa/09aW0fw8zza2rjMaJpNaCeEXrDfefErjca97d1yJIeutHHKQgZVb/x8x8ayBPFGi/V9LZLJbvdmmdXvHAQZQosrDN9mNLeKEX9Eo63pyWinwoUupBtddmss+QuSK6MRX3RBBuEqQgvg4IQbgB98wZ8wfFkQjgE8HhRGaOInA4VwZDy0uxwJt6FIwHS1v1vwXpJi++QhhZOGVo+pSNjZFiC98TFODUiRP4wEzwp8nEg2/qDANGSQzMN7z7KRIVEN9LDvlyIDYi4SyPlI4BQhIf6kyjYUQdxp8MZbcEYOEvz9kGFxmrXf354vwCvEZOSxKLV2s6HlOjuYEm9t5+E9Dc7T2Aokwju/gu6D8KvdErRdtB5tf75m9AEJxzhkNLgt3B9b/ZOQQttqz5vtUwE710KPTKNrM/z2cfjWSCtXYO1WoIMH9xlN441GufbeFsQDzgs8FQVou+2nKmAbBI1mUKQjT8NUpBiSkdd3jI31fKNtIlJ4L8Lbr8JXTkbenhYk/LOQt9Nth7Ug4+8B2LdJZEsYa8+jbMxTkTHitqObEC/tQMqiwWjmtuMc3zQb3pV653/XwtcjUezg63ieGoC8SmOM3mchZeS2Q1sQn8QDb9n3JUbLDDjyK2iXb+M8GynfMfit0K1w8D4NidsZ7RBtZTLxHk4Xa/qc2ghWQsQDSIEXGc4TbRxykFGwBUGytbcabcW6vkchvi6H5W/BOefivYkpsGIpjLgH7+F28Z99bZwetX49ivimr9G8xf4WIB6PB/pByyyIGoQMwb5QtQjSxiOP3I8Nn3HW3zRra7D9PYJ4ZR7wIrS8K4dcp0H4Q2POkMq2fm8wGrQgA3ISMuQ2I5m11cb7HmtzKt5gT8Iv8gYbXYbbs5VWdjGSKW6hNQbx20pkWKfiF4nRRv8G/Fwbht86LoQj04xXqvALEecBW403lOPtudtCbbCyrYZjNXAt3mO3Tu9WPQVpHa3dsUbLPBsr581dY7imWDsjjZ4L7Le91mY+mtPJVmaB/V2M9+4+j9+hiEKLv2uM/vU2rmWGYy/EuyONPg2IB9xu0na0mHFhKg3W1tYTFwN44MCeLzQGMDGxazgG8EuCsAF4PBgTIUW8DgmAcqS0b0cGXQmasLHI4NmIBIc71HEJUkZp+DjAWuTpq0aGjttKq8WvAPsgL0sOmuTxVnY7Pv5nIBLoLsZlGRIYMVauL1Jsq5GwuhoJlVwkkK9ECqbayl9i/7+OVqxOCc1EQngOodgh6tCWRwESlk6JOsO0l9Eqy2hTYPgNtX6OBF6DPU9D19PQynYFMvJcYLjzaK0iZEDQz+gy3Orri/dQVSOhn2y4zEMCegxSaElImBbiV9FNRtcnoeYwpIxExmYUWpVvR4LbeSnd1os70OEUUrX932hj2RUfYD4CnfIuRMZQjP3vAuIz8Cv9OgSr7e84pBAnABejIPeX8MrU8VMNPhj/KvwJ4ibkHZ5o/VyPeGyejescw9UFm/cC2sO+B6GzixmNxwew5yHPxy/QmD+APBPl9n6S9a8O7w0ZbN9L0OImHhnsC4yWo5AncaD9Voa3AJMILS7INLq4mK+eaGGSbnU/YuViEL82I4OsDm2rJtrvzyKFWYu2PaOAJ6BlE0Sdi3hxPv7EdizewC6x+soNtzjEy+U2Xm7h9ADi+SpgIxw6AB0eNdxW4sM/ipEB0guNcxx+rqwzOkxDnqbN1qcxer7zVegxHm8grkD8n454oQB4EQ7tgg6z8PGpa+y3x9ACqxkOFUGH4Vb3YutHmv1/HTrlXIbm282InydauST84i7TaFSGDMxWxKfR+G38iTYGmWgu90EGVjr+8FS2PVuDP/Sy1fBNRXN3EZpr2RqTlgctK0E6Xi4ORPySZjhl4reQNxt+LoQj1cZkB+KVvTa2znu+xca5wMbJLagfRgZwtPVrpLXpdha2W592IJ7di4z8RWgX5iL7/QrgITRfxqAF8BZ9/9NsOL8P8sCWAKOgZQpERVt7NRpHNoYNwDB8dggfAjkebEMCvQ5NsnZISA3Hxwu1IAXejITVESS42yHvWyISfsuQsGxEyscp1XOR8eg8EPV4r0GM1bkaKd8Ge5aFT5PRDwkoF7ScbeVb8cqqF1JkLmg8ByneAcgY6IuEViwSbOuRYNmIhOMIZLQMNByTrL0SZMA4RdbX8I02vNYgRR5tdWQggbhYuHVNRsLUBXZj9Ck0nFfg4/XqkYDNQ0K8v+GwAgn0ycjArbG2tqEtxU3WtyikAIZYH5rxMTsDISXDyvQy+r1kbTkc86z9ejT+i5GwjrT2W5HhUGb4bbXPBqSA6g3HOUjxxRodnTLsb207GkTiYwyP2PsrjJ4VSJE6g8TxYi3wK2RI9bFyV1sfXGxlrbXn+KjYyo9HxmIudI6Fg1sg4WSkdN22lIvhKwaKYM9u6FqC+K7BxsPxSBk+hcbVRtvVyAh+Fym5eqQQI62PaxBvDkR8cgTFZ+5BBky9jdt1Rv/t1s8FKAZ3keEwRPixGvFevZUdivcg1yHe2AhMhahso2E84p8ViJ/rbWxrjX7pNlZbrY/RRs8U/KngLmjeDVW/O+xEJ4+vQtu8udbv/viwkHJr4w4bVxcKsc1oHmV/8zRuPWqNtgMQL11iOI8zeqwDEqDDyTZeDYZfF8SDaYTSRHUYgIyNDNi/Ejq5uNNkGy+3eBlkdL/M6OQWQqX2rM764Tz6zfZOnNFxKZobafiUVrvt9yobFxcS0Ix4f4PRfCAcugU6jLeyB/Ge7HiISkb8PhQ/Hwai+NuB+G3VHHv3gNFzNZLRRdZ2k5XNsXa24Pl6o+E9ED8nRhi9Bxq+pYbHYaPvQPwifynig414L22W4VWK5ImLVx1odcTD+UOMVluM/pUQ9YDhHI30SDtOMHyRW7fhLeAvEyJPNAL/tJCLJn8tNOxCRtMaNDlHoEntVoep+EMi30Mr5Z74nFk34w94xOJjY5z3pwGv0FwA82r8KcJCJAxd3Ey9PduGlEoXJERS8CveA/j4pN14Q3CJ4fwsPuVMOVIyW/F5rNzWy3x8gHsJElznICPxgPVlAVJAbouxwfo1AH8wYz0+P1Y6UohjDO++yKg5ghTPRkIxXnu2IeGabzSKtvfjkcemBfjuUW2usTqfsHab8AZmmeEXrXElymgZY/W04g+RLDxqbOvxOf/S8IcPmvEHLfLxHrDL8aeVnRdkoL1TYXiut/r6I6XkPHZu6yfW2n8Kn2eyBG8ML0A8UWZ9jcSfcExDhlIm3gBKQfyVhLxKW43+bhHRiJTOKEiIxB8KyDBchhgeQ4Fy6Hoy4qV0fKqaDWhejEaKfYT14aC1m2F9LkNemMVICR7GH6CoJnTop+VVPGy0fmQiz1ia0eNSG4+xRn83BzbjFxfOc+22b6MQryXhDyU5L3+sfX/J2huL5ldf/EEcF26QYc/LjA7J+BRGLUAi7NxlfXtX+L79tLV1s/UzHs2jkTZ+l+EXZJE2ZuAXd5FocXOVPd+GD4eow3uE+yAeiERyoBs+xu82QidQg03II9wMnYZYebegzbXx+i/7P9LaLcV7wZ0H8jErM9nwutxwm2/lBxq9yw2P1+z5EOQhc4Zrnf1+AMmEvUA0dLgNzcvd+K332Ygv0o0+ldZGs9U1CL9dHIv4sx7lMHQL6KGGd4GNGdanA0aDQfZsEFr8ubn8Y8RD8faeLaC4wmh9KZLNa44alyqrOx+FC+TiD8OVw/5XrFwS4oExSJ9U27sFeE/tYKSTctE4hyEMnwPCBuDxIBedlO0CcVehidwLeUvctmNXJHg2I6XSz/72QUIiC28A9IKWV1BwdX8r14Im8xY00Uci4Txe7XIFmtzLgCaoWkno1Nsfn7L3E5DCcp68HHzwdzTe0zMUvx2zBX9gogYJ1ZH4NCVpVsdSQmlZyi83mmTab0n4pMgjjWYbkCFRgj/M0Qsp5WXW1z7IaAIJZXcwot5wzD+qD7nQ9XrDKQsZFv+FP7TwmtUdh1cwDxgOlXhlvdPaaYe2ddORcboKuBW/gp+LYt5GIGE/DcVCbkRKdiNeKTtv6Ub7tIq+r72Ftvr6Wd9HGs1+jRRTP41Vyy68IeIMsIKjcM5CyuUIWkxcgRT1CMN/pOGUbrg0G75LDa8io0GrjVs/q9t5iscgZbwaKdJxNibj0PjvQF41xzNPIb5dh7alJ4ruLfdZWWcwbrC/a6z+cwmlAOFm4Gn8CV4XmJ9oY7dSY7B1pWgRlW80MMW3Z5ON3zKj3RbkpbwAjtyshO2hQzatRtdqCJ6DI0/jkxBXIA9gjuFZiXjVhQ50MdyijQb3IgMgxnAdgZIg70HKvaeNxQbYavOFvaJLj0utPgsJ+MpVSJmXIeMnCs1Dt3OQDDUvWPneaOvPLVaG23j1s7GKUXmugT3XoUXLXhvjXKNBK4qDy7Ixi0TpYay+iCHwl9uQLMm3Ms5DvRpvND2C5u9Mo1kzkjk7bXwmokWAM7orCHnvdz6Pwkvm22/t8WmrzhH9grfs+Wh4fS/+VH2stV2O+KiPteX4MgnxbBaSw1vRfCv3ePxhkdGlUrGoPGfj6QzFLBuTLKuvJzJgrzccMuy3Z43uv4Y3dyF+TdKj/e/jeX8BfpGehufTDWheNuMzS9Sj8R0Dna5FsrPBaL8anTRPsj4/aH0cgXjmKaNTBScYWvjiDoC0EIYvD8IxgMeDWyM0YV0AcDrsGw+dz0XCswlN7OvQCrgFGSjNyLhwwqEUGTZLkHH3KBL0A5ASyrf31+HTc9TB/h3QaRRSWHX2241IwO7Eb1k04z1YTUgou63JR4U3l6PDC3lIOKXbe73QKvI/8HFRi/DbJc47tgcF/W9GgizP8HRbYNPw8Wrx+BQSLiB+DlSttsDrS5AwK7V+b7D+pYtewVpLOF2G32Z3BmsT8qRsQMI53fC7DAWhLzC8atHWI0hQvoiUhPMAvmj49VYf9i2Fzt2QcVKLhPQaIBaO/FWkinvUfiuzPjYZHXrZ2FUjYZyKtiRfwRuoqUZDt519NaF4HtbY+6sRL43Be0iP4LeQM5ECjkdb7rn4nHznqu3yBy05+F6893ExVDVC2lX2zo1I0UbjFyrLCN16E9qGdTGYcxEBCvGnTs9C/Ow8yM7rejNaRNgCpGoppF2JDMcyFEP4IJ4HcxEv5CNPSLX153obu1X4rfo5KK7vV7B/i506TkMeujn4HGnbkIFTYX2cjYz8/wR+ZvW+hLbjZhmd77Q2njIcXkDKfg+aq2473nlD09BC42eGYx+j0cPISHNhH1HIW3d0jBv2+0Kk6BPQvKtAc7oUeR6rjVYb8Dd81NE2frCfjVMF8gKPwd8YczPyUqXh82lGWZl78Mnrj+A9uDnIuPg1MpDc2GYZzjFH1bPK+jwK8dkCK+c8+D9AW9pZ+HjmJrTQXWz1OEM5Gp8DchKKUR6NjLKh+PQ3S61f1fjQmDnI43aW2t1XAp1zlM2g++Ww71noPA3lGvwR8jYvMZyi8DctmUzf/Ar0H4JPSF+AeCgdn1ezH1qI5CD+3Al/+St87WzDe6v1bTe8uQFOn2TjbVvYDa9C3KmGD9aXhYgfrzzqeSGsGAIjHkX8eg8yXrPxoS1rbHxPaAzg2yQkxH9BddaTmPiVcAzglwRhA/B4MC5Ck70cfyK0DE3wYiSkByMh8ASalFOQUHVKtQ4J4Dr8NWDDkIDNRsbRdLQyrrU6M5BxkITPCP8IMnKcUm/Cb2vtRsI3FR+0PdHKrMQbSfX4q+Jqre72+C2kTLxgde10RcLMxcJlWnsuti/JaFWGjMBLkXBstHbG4RPsrkdCOh6fuqHS/k6FP14OFyTjTy1XWLm9SAj3RAo21uq4An/zQV9k1DnPojNcE62OterjodXQ4TmkgJrwcZXVNh75+BQVFUgZH0YGwb1WdoWNoTOiV1jZCdbXgYbvCsP9RWSA3I4Uej2hHGOAjCoXX5eDDK2n8N6pKcgYmWx15SIlvRGOPK4rCkMJgodYX51ntdrGbigyapvwiXcvsfozjY4Ov0eVQiThUsQb2/HJbGsQ7/dHxtFAfPLjWrzXeQsy4lLwN25UCmfuVBv0RTy2Ec2jQfhbTtyJVbdl2YIWYVH4eMRo+74AzaE0/A0va/DJiavtmrMhNr6bkNGzDinahfjDW13RXHbGZTnyCNdZ+US856oMGY/P4uPWBuN3A2ptDBtszOrRgmMzPt53GD4rADZ+G9D8a4C/PA1fG4I/QOHCR56wMXSGRjreMHGLCWe8XW80ijM6bYVD11k8XU8ka6qQodvHcF+CeMbNVRevVogMrho09rmIR/bYbwvwibcnWF/dojYajflGo1WSjYPzDg+zcuvhyCLj6wo0hxw/5yJZvIZQCMvbW+ArfdCceB3JmDy0qB2ET+xejZebVarznT9D91tsDJJsvOKsfyvxoSNd0Bw4jM8teBP+tH8ZWtBH4W88As3FRKNRoZXbCg171aWIeUYLtzgYiubAekK3IO0pgq79kKEdb3jVWH/n4b3HNWEDMAyfHcJbwMeDKqQcx+BTVexGSqEbCkx2cWL1eO9fDFAJLSuRMMlFisHiCUP3g45Bk/zbhLYMDv4Zf4+sE74u+D0aGSTOED0qBuyd3WqTGCRklyHBPtI+8UgQtVidkUhI98Jv/e1AxoFTVpVIQLtYyCjk1apCK+IMK7cMCbwr8cl3U5EgdP/H4gXvYsNljP0eC4yHC041POPxW9irkBBvwR+M6Y0Mi9X2rBQfRzjN2uiCjNs4az9P+HS4HHnlcq3O8UaPPGQcvIJP1FuHTyLtDEa3/e2Mj3U2XhPwXuAGFA8VgxRzGj6B9zobt2hkWDUg3hpp9OmK99ass342W1tV9t5epHxuhXaTDJ+NeA9KFf7QURLivQqk7FPttwS0DZtq474UXtxLaCGR4Lwf+daPdUjppiLFE2t02o035JORIRCPDCPX1jL8yWanhJus/434mz/W4vOoxdvYxRnubsssBx83m4vnlYWIJxcg4/tua2ur3ouaYeVX42P/0pBR4zyuu/Epd2LQHNtrz1bgDeHt9j0Wfx9yvdW5k9DhD4bbs2F4g38Zmr+XAD+CYB7iqbH4ROrJiBd2w9em2fM8fC46t1PwIj6+dS1+u3Yp3rC+ERmLzlO0FHjOjD+3uFqH7sztg4xAtx1ehsa4K/5mmkp83GqavZuDvHLpaAGaY7R+BBk+cdZ+HFpkpKM5k4i/YnA8PlQmCtrdgsa80eg20D7Oi+iM3hz4ymwrs9N+74cMKKyO4fiUPuORt7VJ45QKPnQnyfq8BL89G4sMryjDcxnwHWAOvH0AGWMgfVCPvOXPovlVhubiBGQoxhMKmYk7EyK6IF5wuzalyEM+3/o9ENgOXQchHqwy2ragRWEymtPXIsP+hMIXtf3rPmH4siBsAB4P3kWC3k3y3fhtkTQgHjq0B9bDoVa0rbQGeQ8SISoRKaPhhALCQycwz0ETeBBSYM16N+Fc/C0VFUg4bLZ3tyKhV4cXWluBVdC9GxJUy5CQc/FPi/H3GLs4ur74tCqV+NsIqtC2Ri2SjM1Gg1IkjKqQIkpDnr4N+Bi4Llb3CvypvpFIWUYZ7bKQAOuDlFWT0cLF+/RHxmiB0TEKb5yutrIjDZdIfIqZFKT8K/G51tKQclmDDJxyw9MpdbeiXovPvVdvtD2IN3py8el54vHJrrfa90z7+5K1uQEpTWf05Vpdq5Axtcpo5eI+KwmlCyETbSsutfoj8dub2DhkIWWTjBSs20Yrpm2+tSjkDVmDDN58fP6yetg/D7/1Xqy+TojV/cFU4bdnXYxSto3FHPt/IlI+0WjMXYxbCuKlBKO7W0SlWn8GIp7qiw8t6IP32q0x+jajuTHIcB9pz13cXtpR5Qbi4z0bEM/di4+DnI887ovR1vsEo08ziieNsvd7Ge7RRtMqvKdvsPX7XXyM20KrOxdta2bg47xaDM9MKz8Sf7NJseFZDxEz8SlnMvH5CgegxUw//EGe9fhbSOrxPHl0XKpbPCTZGK7Cx7UNEy47V+O3+evxi6urkQeyAg41ogVtMv6e4Vq8ttiK9z7diE8wvdfaBS0mx1n/KoA5UF6LZIFb+OTirwgsFR4tC/FpXIYj3tqI+DnZxnE3flG6BX8NW1/kHas1PNzCOw8fflFNaFekw9nI4FuGYu3qrM4yqycVDs3Ch2AUWhtp8JV+iNe2I0M3xmhYaPTZiwzjLYiXXLv5aJHgwj9abGzH2TgVWttLkWEXixYeCSi/Zj/8qWo3Lic8BjAM/6oQNgCPB7ehFW1PpBDq8Dc4bMTHroyADtPx1/OsRKvuH6DJ/TxSFtuQkslGAmc0UlDt8HkAr0ZbV25ilyIjZ4SVW42EyBC00hyMT85rxs8SF4/XFwmV6wzf/vibGIbiLyN/yNrbiIRSOqHk10TiDbU8QslyeRcJnTj8CbQcZNjusX6/Z3g5A6oaCe4paiOYhhRBMtqmykbC7jW01TIGKcsFsHMLfrtyFf7kpcUHrncCPxIJ3XikQKLxiWAz8J6WOwy/JKNvKTIuGmxct9rzlXgFs86eNxpN5lqf041uXez7eggeR0bHHKAeamYjRdzLcO+DP408xWjYDp826Ps2to8QugGDIrwHNJe2welZSCFvOaqdVMQblUbHGKP1cug0x/qx0eieLdp2GGXjNB9tMUbhU2osxp++fByf0DZFY3TkKaT0M4yOO62N0TYmLpxhOT5Vx2Cj5Xwb+3GIp1qsv1F4PsszerkF2Wp84u/pKMHyJuA+2PxXtBD6heGchebnGhuTWBur61DsaC4+5KAdMl7H0fYkbhzinTI4uBb2lNh75fhr4Zrg5SJk7L2FFgZdUCzeQrQ4nGi0GIWMh2fx6Zpi0DzehBY16/AZBuKNJm47OArFjIEWMu2Mbj8wehRY+5nW1zxgD/QYi3guG2oWQtV71s/ZRtsm85RXWDvL8FfKZQG/HghN8E6lje9DUNPP2qgDDsNrLwCJUPUo4uFqCGoha5r1eSM+z+Rv7HuTaBV1n+G3mNChspa11t86GwMXbrPR6DXanlUbTWIQXzZAMBV/+KUUzdtxiOeGQc0mCP6KT1djeAV/VdsdYo0+GfiE03lW52D8In+ujeMD+Pvcc9Ehsiwbxw1WLsva24HCIfKQvNhguDt8n4LXX0W8vhV/ReFEQofk2IsPxTlhEPYA/qtC2AA8Hrjg9/5IOJfgt4q2oC3GXmhLwQVSl6CJX53g7+zMQMI1Ggn9fsgQeQwZea8hwd1L9W7uZmWH4OOaFuBPodYiIZeH3xZs1SEL+sDodGRADUCCewkyLC2GZM+v0FbDFPwpPBfj8jgSoo1W70ikWNyWy/XAb+3dmVb/FuRVmWY49zAa7cFfJ9UPf8n580BfiDjXFHYlXtGU4Lc7Uwld3dTjbCAL3r7ZfltFmxinwWNtzFah7dEW/Clktz0yAnkprsXn8OuG4gqvQUZLChLotXgPYLSNATYeg9CWmYv/cQLfeR7zIGI6oftrt5ZIfzAb/WPjxTQk7JeimL9lyOhOBr4f2XbLa6r1o9nGYYX9zbIx6GZK1ym5m4G7kAFYirySblvVecyajCZL8Ff7rTLaXA38FPbdg/d8DrJ+tsLb8xAvHiZkuLUbjxTUOiufhc/J923EJ82Gi/PwuG3IdsgYazAajMB7edMQL8xBxtp2G580NHcaCC1eooYL3/4z8CfBnRcyHvHzcDyP7TYaz8fnZuuJjFfz2jS8gIzueI0tvSFhOHSNRIu6udDyK3s/B745yMa+BngX9t2NvG+rbMxXGU2WosVeodGsBX+tZA2hUJJQ2xj+ZbDeGeA9Ec9mIhm0APHydxAfPW60a7R2xyFDeCmwGFLOg7QH9D8ZyICZgvJJVqCkz6vRnCjAsglshFzo3tP4YB6kLDZ8i6DmURj2ODDb7tHNB3pZvFs6itHLwMetZtnJXNtG33+L4VOAZMxYiHIxpv0Qbw5FhmWOfmeQjYUL/yhH8rnStlrno3mQgQxkF6O3BFKmQMRIq3MkIS92xFij73R8THcpkpOjkAzNwx+IycWnWsq1cV1pdHkG8fMoJG+dhz0bf1L7fHvXhVekQUsJnHUtWhiWG35T0A7MVsPpTxA8zAmGli/4E4YvC8KHQI4HV0f4e13PAk7GnzJswp/2fRdqiiBlFn4rsBs+7ikVCRe3hfcaEiLb8PE9R6x8ttXdHwmyG/C3V4xDW1ZOeE7G37Lg4qYGwqHLLWdWsj17HB8PdzUympKQULwMCeHXYOdblrKiHp+EdSpSBEnW92qgCvYtgs5T8KkYGvCGQjp+y6oAfyDkR0hBlVmfFiLFV4MOSbgYwWW6SzhiCj5XXq3RZh0c2QHtrsfHVM6HPXuh60zDsQEfm+a2jBeiLaQReC8q+CvMnHewyOqIsr4W4JWx80Y5Wq9DivJaOPieqkuINlq7mKVqZJy4WKbxhFKTkIeE+W1G5xfx97rWI4OoBBl7xVamwOp7Cp9r0NG6L+x8GHo4JVGKlESU0cF5Gu5GXolf4vOuVSGe24xP9P0S7HsPOo81vPciz+mt9vsdeA/vnfg8k+lIEZbjt3rXI2/HSMQDQwklu656C9K64HMrItxr1kLKK/jr5OZaHVPxN4b0s3YaDe/lyKO0GinHXOtfjOHmPN1Z+O3dWxB/FODTjFwLjIAjD9sJ8IetX0/gT+u6+dULGQBN+OvEbsafYq8mtLijGsmGSnziY/MS8ygyeHpZ3cmo8QIr04A/UJZq9eWg8c3HX1uYbXSvQHN9IT4+cB6SK+MMv2fRNYiVcOgq6ODizUYieeEMKxB/uj7FG36paPFXigyyNOvbWPtbhIzTDMQru+3ZSBuHJPvtJaNBLDJgN6L5U4yMn9U2lr0Q35QZnaOQV3UHMtbWWb8L8AdNopB8bcLnY9xsNIpF3tN6w7nJ+u1w64XGfK3R0u38uMXSNGvTzZ/1+JCPJKiqhbTzCMV00mDvOvnnQnzc98uMrlMMnwx8UmwXa56MP2RzBB1Q2wpUfflq3B8C2fQFHwIZED4E8iVB2AA8HjwYQc3N+jdlEhLY2UiI1gJ1cGQbtJuClH41Ei6j8KcY0/AGRDX+irhk5PJ3yjIaH0NXiITnZhSAHQ/BQojIxKddaLJ31qKAe7flEIP3HFVbfWn4a4wO4BO9RiPhNg6fqT4dKYohyLuTTiiHIevwOda2Ik9RH3xM1AikXHoDN8DObdDjdvxWSCM+ZcVAZID2Paq+iXilfrsZBtNEZzKtjlXIW1RldGhGxsdAfCxZP6QQXOxUkuGYCCyE8gP6KWIa/hqoAvwp6XtRXNMa/PVarl5nuB1AwrcdUugLrGwSWhS8gpTcEryBPth+34NXpNcjA72f9f/H+Ns9RhgOKXjPQ52NbRrigUj8qd8ko4Mbo77IIGo1nB9A3q8CBGttPGrw9+yuRh4PF7fk+CQDf3tInY1pKdqqbrH/4/HxiF2PwsktEort3QlWdot9d7GtvRH/bDF82iNvicV3Vd1iHqVeeG+yecR4ynBssk8fxK9xVnYcMjwijdYJSInORwZHOW1PrjtFW2z19TaaNRHy7IVOkLs+xyPFvQW/Y5CHPP0/RUZOHP5wQTYyJpyBjNG7AfFhD3yS41rEN2lGz8mIN6oIyQhqrM+j0Ty5TLT506Nw/ljapD05eCckTDfa90UTYoVwbtgBcRfaGMRaH3bYOEQbPQcbvi34FDUpKH72XeB1OLIL2vWz92OsjWX4K/tW4T3qFgNNNT5xM2hOrMN7/0Yig3UP4sUtyAirtjGMsTJuJyYNf/Wb45sJwDcQb96GPwG8B41zkbXttliXWD1ViMeGIh2wGZ+kuQjJ7YNIJmRb+YX4cIyDyEhswIcKbbfffmBtLkYycJThtRGCVyHiTOtvE5qjI/En4l+031acSANwAwkJHb+gOt8nMXFQ2AD8kiC8BXw82CsdkNIeCejHkdIciowuty2Yio8bcgc+8vFB1qn4G0MakRCKR4rmJ/jbAEwpBA9bQtsxhDLWR1yLBHoe/q7XtcgzswN/vdlAJJwW4AOe+1l5p8yc8ZVuODThPVxNhl+c9bkG7/1bBQfdlm2q8OJx/NVFLhC5Re/0uBYJ4dFGn3j8ydKF+ATOdYZbBfIi2LZJ2uX4QPcNyIBJRYosx95dh789pA7vEduKlEAJ/tq8/sBEyIqEiCuNli1wcNFRdIm2Ohbi79l0xsAWo9lSJNyLkHLYiz9IgNUzQLgffNXo9zMk+J3CdB7GZ/F3n87G53HrjfdEDgBioeFxo8MG/NWAS62+vYaTOyhQgRRuK+KLcsNjE1LQLo4sGSmUTMRP38OfvJ2Mvxd5t7W7wvDeggzJnUaDvla2Hf6+1sHIa7cRf4KxKzJKXwMOwqFn0Zy4BM2dvvbJtmcQMubSeiLv3x5r4wAyBhyvb7TnMdbvQuvzUKN7KvKAd0VSbwzizQr8qc8k2t5B6zxKLv4qGhlm3aw/Y5DyjbZ2uhKKPXO58YIDVrYffrG02OgdbbRoNnpVW59dSEE/a3c44gnXj2XIgDqA+KMPHHzaxi4af393Ipw/6qi+rAEqISHTno3BL3CaNdZxY62t16zvTm6U45PIRyJeS8FfZ1aFctTFA8OgXQ6SaaU2NrMRz3XFe9iGI/k13/4WotAFtxPyLJpnzpu+CnnyuwI1cMR5UrfgvcKp+HRA+fh7kt2OzGLkNRxjv9Xib7OpQYvfOvskq88tz1obRcjgijavfwNeD6Ay+2ZbGyORcXohPqNDGd54A78YugMtxlcYHZsIhWVEnIf4c4x9WoyOK/DyJ58whOFzQdgAPB6sh4hFyPBqxifBXUYoO367h/GTOx8Juzzgedh5HxIg1cj4mIq27WLQ5F2GDmA8ioRoI5AGEbdbeo8itEK9HH9AYBASeA3WXip+BLPxKVZG4q9MexZ/r+UKZEjdjL8n1x0G2Gp9nYg3Jlcjr0Y1EAsJ86zdH0DLDsPNGW/RSCHNtf/jDO8lR9XvFM1Yw+11YAGUv2vlz7J3C5AgzifkzTz0lpWZZ3gV4a/xisYrgN2wb4e19zTaGkrHH2JwymQBvPFXC6/Kwm/FOU/Se1a2yMon4QOunYdntOGYhb8S6lnD5RFISEYGyzIbo2qkNPOsDifI18D+Z1GajGlWzxEr/7joGDcDGUOpyHMwRWPyRi3ihxvV99DdtY/ib65ptvKTkJJbhsIJ0q0PWcjrsAoZK0OtzECkdNz2c7LhnofPXTgZbyCci7xrB6wPV+NjKtMQX45DRijQYRqev4rwV9v1Q3z6c2QYjDdcfmjtleOTWV+NT7MRDcxABsXz1pdK+1Qgw345UsDb8NuT9fZ7Nv6k7WzEe13t/VjEvwfR3KnVuIViK+vwp7CXWn1DIOJZtKW8Rd/dFWyhGxwO4Lf1JuN5tAuat30R70Za/UX4lCZTkLd5h4W1ZeC3QKsI3Zdd85ThPs7GrdJoVG941xpOLxn+v4Oa9/B3+i638XOhHu/ivWbRVm+L9c3xaB6aG2MM13LEk8koJnIk2mU4F5gIQSWSRbH21xnBufht1ixkJPYDfgDtTkXyoMr6EGPj5hbgrfiDd7Fo/m42GubiU/cstL42Gp1y8LfmZFkMYho+HU8XS5XkDvQMRDKuXndpk2x4ufCPTCQLtyKZXoUM7Fg0Z93293346wk3WN2NSH88rDoPHbDy0+x5tLV1QuGDL/gThi8LwlvAx4MxET6uZhxS1NFo0vdFwulRZMzcjd9iaUDCbQ7+nt3vwRuXwxkuBiwS3nkLuo9EgiAbKeIa/M0DDwKPwBuLpIPKgbNikaLuaji+hIReNv7OytFIyK1FimOqlctB3ot4QnFVDc9BXD/rl9vCy0JG0zagBYJFEHG29W0abbdsL0QKYgxeyabq88bjcEYrijcbgjeWboGqZyHtTPwhl8uR4l+Dtv2WGB7rkWekDhkrUcj4icGffHMC23le9qr9fbOh80h8DrACe6cPMi4G4g22XHu+GwnobcgAmwWHKqFDF8N1FBLu6xAPFOFP8xYi47deaO1bDZ1PM1ydUWEHJtiC7pieB3En47fQeho9JiLDb7PVa9ugpOBj2Vbj48QyDF+39edo96x9L0cK5Sr7uwYZefVIOf1MYxlsgIjHja73IG/sd6z+lUjx5hqds2xc8o+qcyTe212N5+v1aHx7Ix5znqwsxPPjrb2BRqu9wnn/Iujktv+d5yvGaLLHxvQ5+z3ZxnCvjQP4O3uz8NcrPg+sgCOvQrv7DN9ypNwftrodHzs+G4BiBYfib63Iom3S8nuszZH4rcxGxMv3o9CCvvjE2F3wi5jHkQfoLDRXCxEvNFpbq/HGUJM9d95JR8+/IhkwHc9z1WiMz8XfC94bzcd1yOjZbbguwfNSH8SnFWjcL1W5hjshbia88zB0H2XlUqwfsxG/rUM81RV5y6ajMI1s/Lb9q4ZzOjK+Mo222TY+zlsaaeMyy3A8V/XXvAAp11t/nSHehAzKjWj+gA+XuQeFW2ywdhvttx0oFMd5ywdZn6PQnJtqNJxo/Yuz9xYbfg3IgM/De6FduMcAK5+Mv3e9Gh9GAj7vpAsbGIVkz0uG0wF8WqJxRo9VKAn+DWjBkArB7RBxAtS43wJe/QVvAeeHt4C/JAgbgMeDERGafCnIUzUQCcjVSPAV2v/V+PQpLtYpCa3Krsd7ZI7gT4JegQRML/wR/8vwqWRmGw5OsS/Fx+nF4K80y7HvQ5AQybDfmpCwi0XevpuQUHXbXwuRUFts+HcBRsP+x+XcGDEEvzXmVvMbkXE6BCmFKWgluxp5n/KRQeiCw4ei7bkM/FVYVxieB+3/cqQcdiGl7byHZyGFeC7s/zN0OhWf8+9FtHUCPgkw+C3JTUbPEchg3YqEao59/zFSer2sT72QUqqyMXnX6lqFjBN3cKES8UA2Po1JBd4j8zD+JKJT0O1RnJw7HLTHcF+OPzyRhFc8E/E3QdTjby7Yib/Z42p8Pr5yK+OgxMZhGOKLNYhPF+OTcT9qNJqBjxlMxt9Ak42U0gEgEfY9CJ2H4G/lGGe0cR7PDYi3IhGv59gYrkN8cafRIUrpUxKW4hMCD0UG/QS0wFqNP8jgthDboTnxEn6bPAcfG1aGDJTqo8a0ABk8WL397Nk9+AMt2cijeB2az877C1o8rcJvFeeisW1AfDzCyr+IjI4f4U/Kz8Fv8+VZ+TVoTIcBR+Dg3ZBwHz5+sh6f3me+0fRcqzvf3p9n9dZZv53ndDX+5KjznhfbmO62MT5iuLiQCPDxpU3IwPox/sRxHex8HHp0wxsfzdqy7/AU/saX3fjr6GrRvM5F/DYGnbqPxR9mcfOy2cbpOqPXOnxO1FJkmDUgQ2qjfqv5FaT0szHcYHX1RvNmPjqc5OIL1+GN8C7W51Ybi41IJlYajbCyhVbnYqurFM37wWhOFyIeHGr4b8GnD3rJ3umC5pfjsRVIrscdNc5HEL/Px8fyZuLz+bnt98vQ1vDVVmcqPvNAk9UXi08bdlHYAAzDZ4fwFvDxIAdNxoPIK5WNJmMTPig+GZ9nLQFN9Fo0WX9q9WzFX7mWjb+bsx4JvRqkLMqRMVOFvz7tJaSsM5BidFu7EyHYgoSsi8spQQZinOGdrGdVU+y9S6zss/j8d12s7nbAYug0AEbkGI4F6u+RHfg8gPn42MMSe89tgbhYst74mwfc1l+0nlXdgoRWJT5ObxzaIluHFORAo8k04F3oNNLaAX/IZR0+NrEfEqobkYfzIFIs243O25CRU4wEaSs+Ue9VRqf1yKOz1+o6Gwn365CSO4y8vFH4WDPnibvRaPUwUmbmgaQc8c1rhK72Igttax5ABxeS8cZCDOKJkfgbMfKRkdED73l60fAegjxK/Y1uXZFy6oH3DqUjJVJhtF6DQgRusPaq8DfN7Lbx2oqPLVwNnSch5ZdnOABBkbWx2+rLs3bLkTJdiowLrA+2nZdwG1LCvY4av03I2K+3sUzFbwEfxt/EUY/4dRAhDypN+NOhBxAvFiDFnYyPC0XjdmgbBHfjDblbkQH5rrXlvIx90PhONdyHIg+Zi5stQpJzND6WbI39dQa0215NMlyLUYwrFkqx1eh+2P6WIb6vw6eUWmHjtw2fhL4en2j8WXxKkeFWT4uNxzYrv91+L8Kn81lt49MFf2DMDD+OCNceLhZwIKG7gDtcjjyTxWj8pxitSozGg62tjRqjltVH/VZlbT2Gwg+KkHwbb/1ebLQusr6MtvfSVVdKIj7e7zH81YLR+GsFF+BPHifjPed7De8++MNhLlbQGcUZSDbUG14u9tHNyWK8B7eP0euI6itvRfN8L5or7QzXPDxfpONjOTfiT6a7eEXnEMixdjYgI9DVmWk0rEO8U44W4vfi09qcMAhvAf+rwkknGoF/WhiKPGeT0aRrQJPZGV1LgX5wsBISKtFWQg4SrmnImzUQH4S+DsorJXM7RCMBHU/oVhHqkQA9elumWm2QgQSE27YrgwgXTxiJhMRqJKjm299UYCIkP2ttZQF/QkK7EB+XFomUlDMYnHCsA2Kh3QBru6t9nAJ2sXLJKM9VCqHYSHrh4whfRx69WEi7Eh9HU239nI5iIRut3VxkZDrjaAUyMlYhI3YYEoQuHmkU3kAYggRsPTLWi5HgLLZ34vH5wFbgPQh19lsfZEg8i0+/MhwppjGGd6XV+bjRazM+nUUpoXjJUFqTEvx1ZjfitzMn4Y1xt21UYf2usvJuMRFtuLgt8x8TyrG4vxE6XWltOq9VluHiPApHkKEYCftegc698F7kTPz21EDrt6OtM7SSDb89wFaIyMefEC/F36TiDLIMe3/hUbRp0rshfq7EexKvQwaA2zZbir/jdBTyJI00OpXg7ziOxqdPSkXG0mVW/xqkSHNtfJdBh0x8AuEmdJgj0+j6Q3zowT02PnsNh6tg/SYY7Laox1j9Li5yHX4B04SP6+pjdLoRwXy8IZZk9Fho9LfYYVINn1rgaqi5B1J6442ZWqurHn+/bBri0aO90/HI+BuBjGznDVxqdHTGdrH1PQ3xfiw+5YybG+nASti3GzqPMtzbI8PNGZJp+MMTaao76m5kqESj8S7E71Ikw6H7oEMvPLhYvnj8PeEu/jbHxnKr4bkVqt6HtNuQTNrh2wXra631ezh+LvwAyeZMo8V2/C0du4HB0PICRLXH38TiDpmko3GOsvpi9DdrAOKVeKOJCzPpazhMRYucahuPuYgn1h01Bl3wJ8qjjRbj7e8Bw3O30bfC6umL5qvzZIYhDJ8Rwh7A48EDyDjZiKhUgSbbFCSEdgJP2o7Krfh8dbFoYrci70cZ8gzdB1mP2m0LBUhYL7B6uyIF+xISNvFIUGYjQdho/yfjvV4bkSApsjZy4NBSe6/OygyCdm572hkh8/F3jt6Iv9NzNP4e03J7P91+Pwd51rD3xlqbdUgQ5QCb4Z29hu9tSEn+yd59HXlOqvHbMG7Lah3+1pR4JJSX4benAGKUVoIK/JbaKnw8Tx5S3IsNl1QkPJ0x0gUJ6MuQAdELGRqZSKiuQ56YbOQpWGj9TkbGQDUyoh9HPLAWb5y7LeJmqzPSaBMtvBmHjx190X4rwW9jtxg+i63Pd+MVXpX1NR1/r/FeK3+3+thpCmLCdSjk4BUbv1yjTTc0tluhZRZ0jkRKphgpyQLDsxSCGUbfq9WvFrfdFIcPe0hD8anxyGCpNDrcYmM9V22B0d0ZEfnW1uNoK3e9xiNYjTzA9Wie7EZzbJD1vcRo0QtvAOXjr+0aaDQvxye8LkHGWMVR9G7F3/pSjBYri6yOaELbnKFTxsWIFzL0ffAQvHfSjPM99yG+aURzp5u16by4zrNdijxEW9S3PQ/b/0vxp9qXGR4V9rcGuBdSLjccYpGRk4q/RajO+vSg0W88Cq2Ix8f0DrL68vE3pyQZjmuMpr/Ap8/ZaLhn27P7kXyaBp27obkRaWXmELp3eM8tNrYl+ETyzou2yJ5V2Htp6Aalmfh5EoPm9FNw6GbDsRRBqY3zk1bfdGCyLSiX4GOv8/GpbZrt3XJ8loRiK1+NvL7VyCgchua00TnqNPyJ9BirJ0uxh+/sRTwyyp6nIJ6Mts829YFcjcfmldavSnz+wInAz+HtIsNvGj5so85o1Wz02mB9A8mr0fjFQyJ+oXRCoYUvzvvX8iXj/u8N4RjA48EfI7xr3W3xlSPh5U4XFqIJ64y5FMS/8/GKMxsvEN0WQCzwKOxZC12nI0NpIhKeTUgwX4HfOnLxMCvQ1rJbGRcZHl2s/Br8RfG9kRejDq0+dyDDrQTvhVls/WtE24pPwZ4t0NXF9Ryx3zKQQOpibSTgvT1uy62n1dff+uli5MqsjWp7p8Do47bWkpEidMp2rvXFGdwu7m6V/f5z4D+RoWAev+Aq3SxCFFKk96G4piP2PmgVnqc+styeJ+FzhuXgFephpOS22bt1+EvkV9nvfUTrllsgyhlrSciQeB1/BytI+Rcgpb8bebqG4WPbViPeGmb1LsRfN1UMb98JX+mDDz9wRtB8o+Fuo1cXZBi6PrVaP+qRMmsA2kHLoxB1J21Te6RZHwqQoVwKVEJwPUT0tDGIwxsXOcItpNgW2HsViL9LEM+YQd4yS6RoABJuwudzbMJ7a0rxd/a6sXgJLTKOIB6ego9p3GI07oc/7etiEYut7en4eXIpSrbs4uJ2G54TjRYD7a/bXk5C/FmE+Pc+xBPJ+NjHFYgf2qMUJufZ32cIXZ925FHLF7oMaIEjB6DdcGSwuQVXEYpNW433JrsYy2zEzyvV54OPQkI+MuIaCHnLqp6GtOn4cIF0FDsYh3ijAN6cDqefi5+/STb26/XsyAvQ7nIbyyuRXDhg/QSfMqjQ8CpDxkgt8uT+DhmMudavVDRnJiNjdKXVk2z0aITlW+CcjujGGPPM1RTpphL621gl2Weu2lpSAqPPtrFzsrcKzYcmo2e0tZuHdnMut/fHqA8NRRA33up3fdqJZKeFZBycBwkzrP4669MqjXHL9RC1GG/IL0N8nQ4Uw59WwvnTEU9WoznpwiV+Y3QvNPynI74uMq9+T6NdMv5AUZmNtTtE0gAtr0DUbcBPTmQM4DISEuK+oDobSEwsDMcAfkkQNgCPB9dFKEnsXOR+B03cZCR0D6MJXWO/3Y2ExqPIS3MtUoB7kcHRHwl1J3Q3W30rkOAfiWKE4pBiH4EMuEVI0UUjIb7N3q9CRs0dhC4oJxEpyXz8HapL8JeSg4THbrSCvRMJqwardxQSnOOQcTXH2orHe7qcwPoeEogL8cJ8ID6pdYvR7jKkkH9t7V+GVuSR+K2664xOpXjDpcDKr0KerTjg+1+FMX+TMK7Gn6LLRWO1GQnSx5F3qg/aZs1Dwn8ZUrBXix5H5kG725ACeNbK7Mbf6uA8ec5z4mKFdsKRP0O7QfhFQSHik65Gty74q6tczNx0o1ep/TYVKcTXrM1a61MGEvgDbUyfxZ903o4MjCLkiZhsuP0a8UGTjWcTPsl4HVKM6cj7NtXwfgW/MHFe66mItyrQOGcjw3SrxqLhsE6C0oLP2XcR4u9ko1mx0SPV8BiFj0uMsXonWztlSImX4m+VKMd7MB/Ab/ElG367kZHdiBYafWjr7bsM8dEawz3SnucKt/Ld8sZTiU/50Sg6bZ8FvXNsDKoNl8FmLKTjY3a7WB9duEIsOvDTgM+f1xuf9L0AGWM/tf67OMfJaPFzD23yFgbPQ8Qoo80Se3+o6t1zOXSdgTcqahHvvovm+la7EegJtP1bYPitRJ78OLy3caPRazSSXaX4wyRPmLFq3vS374GvFOHn/LNIRj2P5MNuo8tIdDr7UjSv1yGYgebGDsM9Eu/16o9Pw7PG8KhF3rEtQBO88SqcMdN+qzN6OXlWhz8k85R9diOjqhjJM7cYLzI8D6DFmDNk3VhuwM+JfMN9B5q/9Xhv7Wp8eppB+DQ8YxC/lVpbObDkVRh99G6M8673wV/7NxHJuy3I8H7Bxm0Uks9bEQ+4gz+1+MN2e8IGYBg+O4QNwOPBmAgJ8GYk5B5GSi8Zf7q1L1rFz0WTuDdaSa9BQsFtPxbhk3+6bdV0e6cfEij1+LQOWcjTsRQJmj7IA7gK7+UpsO8FaOspFQmRfCQ4BiAh6wTRJVZuL/4KpJ76+04RdI/E30+5AQm7HGQw1OGTSY8yPNOsvRr7bbgC7CPykUHlDN0io2G24Vpp7TuvxL1W1q26E5HgPaA6QylZrkDKYzNSVuugYQPEXYmE883AfyDhW2z0SEEKrxEph6XWVr2NRTckTIvxqTOcQem8nHuQoO2LlMhaZLSPQ8K/hJA39WCz5QdLxqfT2IQMvP72XhT+RoksQtvcNdsgZTryHA02OmQbfo34WL2teM8ySPHMNXxdbJzb2nbbvEl4D+FiG4dUZBDkWVtJyFOcbWPh+LKYUFqOQ5ugw9lG13q8EXQPUubOiI1G28FbkQKvVLvvbIPuU+CdedD9WvwhILc1Nw5/U0y60cdtVz9jz69Dc3EnMrhA8yAd78VziyAXpzcXf09rJZoPf0Sxb+2sf014w8LFQ7Yij1YBfrt5Ln47ugv+wNM4G6tzEY+UIr7/FeLxKWjBlYkMrmetXyPxHrhp1pdheK+3kzfOeOmLFohuOzrN2ncxbjkaq4ZnIe4eQvnpQiEVZUa7XGRo5Fn/FiJ+dnGO4ONOC+3Zens/CvHdK0iG7MXHDU+zv5VoEfuEtd+A5kSU/c1HhvFgxDvxVncZ3tv3iNEtVfTef5+GK+5cfDxsKprjrTZu0UaDd/EHNrbhk8g3oDl5ueFYhw/DKLH/q218e1mfD+BvHyqxftUZjaJtTJ5Ei72F+NQ8UTZ+XdF8vwwZ2YMM9zlWZoq1NwotXiPxIR/PoDRNkUh+OdovJpS2p2o2pJ3QNDB//oINwPPCBuCXBOEYwOPB99AES0eTuhxN6t1IuYGMmzRkkGSjST4MDr2PJuplSKC6OI2RSODlIcGSae85j08T/kqs1cjLkWxtPooMo72GUw3adnrN2ppj9WwjdAE7tcB4qHkLKaQo/CGBAUjoXgjdz7X/L0dC7Fx8cHsVEkab8YmLk/DbYzGEAqUjzkTbzK7Pxcgg63JUfX/CC+oK5PUpQ0rGxXx1NVz64oV7GVoVtyN0GjhuhrUxFikZd3LwMvv+mo1hrrXtvHLVSKnmWjsjkPDta/SLwd+QkmJj1Acf95hm9O8HRzbY83xIONnKZVj786zddsgYGmjvJdr/mcIh2AYp+YjHnDe4l/V7otEpHu+NTcdvBz2BeGKI0WqQ0TAa8Vk5Pu/cAWRcpB01fs5rPNfaHYoPxl8H7+wglB6mw5X4k5Ht8dcXzlDdwS6j/WQb51F4D9U46H6m6NP9TLzCXod4txXvPdtqbS62+ss0Fnuc8V2F+MsZPkeHF6wzGpehOVRnNAWfaDkNbS278ID5wGzY/xxtb5KoQAeYUgidqg/dvZuPDL0yxD+V9s5tyHBLxSeHT7ExGo5PJTXcxgz8PeG70eLLeUFj8LFpSxHPbjZ6Nlv5GPyiMtJo1gBxl9v/i4w+LrQqB8mvZmg5jObaFsTj56D5MUF92vcW4idnwO3F33yyyNrdZu1eh18cPoUWD1vtvWI0B5II3QHtbgziUaOpi8OtxM/RmWge2KKp02kQ19H6vtVocdC+N9mzUvy1k8mGazIcLMIfLmmPT9+Ug+RADOKbvUbLgdbvA/isBM/h7/J23uYmtHjZbeNUbuPjwghcqEO9jWE94vVKQvNv/8P4WPFlRsM9SEf0Q6eeW60e56GMsn6vExnCEIbPA2ED8HgwB024jUiI/BRNXlOMjNBl36G7gTPQJN4MHW7HB1mX2HsuWP8gPmVACv6E6XDkpUtFQrkFBXfPRUYZ+Dsi64BKeGcXEtZpaAt0NxJGa9DquQlYCSlXImHtjMkipPRnG+5J1l47FD+419pNBYbBa0WG+3bYvwt/2jXD8EoSPiQhIXcArWQb8ffxukDtgfi0Eyn4IOsmJOwL8CkcVhhtLkcK6ryveKWJvZ+KPwRxBHkVmpFSc8rAlPeRX+Hzg6WjvIUg46gVn7i2l9XnvHlR9nuc4X+L0a8A2uUjJZlH6Fq34E5rvxIf1N9sfb7V6NwXGazjIeJkvCchHe+R2ogPZi9GnhtnhLyGlA1oFt+EFJnb1nVeujRCN6TQHhl82/FXbC1FfJuKDBnnBWsCboTuMxAvt+LvqI1G/OYWLyOAfLtfOUrlXn8ff/1WF2uzh9XhvD6DhPPbzbCvEoKn8dtjSfg7X3uK1l3dNmM/o1WVXdNVgz9JX4a/xzbGvm8zukbi03lsQrwwH82BSOjkvEIxhG7RYCf+/lsXg7kKf39zF6unJ1qIDEBGxQgbvzvtbxn+Tt0a5DXdYf1twadkisEnf3ZhDqvQAikff53YZsPHxZ424+dbL3xsZR7+qsl4q8fCK6J64vn2cny+w1Vqu/NMfOjAJuuf2/qPBYpgeYnV+U3E1ynWx5/hD0ZdDXuKrH+5SA6std/aw56Z+KsGS/Gx11lWx2v4pNxdgSg4sgnK38LnZKy0MYvHb/kWErojO+FM/PZsLv5U9Ur7lFmbLYZHnY1bH3zOxTy0+zIAn2mhwPDMs/d62W/JwFbYPs/wy8XnGp1vOGbpead0/IKzgFBc7p5XEI80IUP5gNEpGR9+VA3fvJwTDB98wZ8wfFkQNgCPB9F4b4czqMpQXEssUGVp0TKQctuIFLo7iZiMBF0V8gq4+JY85IWZDi2XoqS4i49qMx/vNVlndT6ChMo6pBieBnpB9+FIiZYTigfa91d4+TlkxI2z3wvx1xM1I+WxHXk5n0CCcRyKSVoAR5airataoTXsFUJb2p2cEjqClOB1Vv94pCyw367He2eaaZvHDGTIOA/jQLR9OxEJuPVIERUhxZOEVvffe9vfmVqFBG6l4V1nNHoZCUaHV7HRLw/aPWHvZVr5qchb8aw9/wUy9Fcghf4IfqU9H5+65Ckbx9mG81Abu976PeI2NLN64U9TJ6NFRanRpg7dBT3b3l9jeG3A5xNMgv23ICU00up/Gp9QutLoVGx9mYgUzRSjwQo49Ly1lYm/q9Yd3mlGvJyP+HOe9XeL0WAd/kT5cuShybF3XsIfTpqBFOcg6/cKOGsk4p8ExM/xCqhnjb3ntlRXwVcWQ+d7ICITgvfQnHoSzQsXa7UBv0U/xfo6GKLOs3Heiwyg8fCGO2yTg4zBkcD8c7xhkQp0gUOzkFdwnPp48FnRnHz81uwleO/MQHx+wWKjQ9ej2qlFBkkJHLwdfyBhEOKfOnTy3hnpbuuzwN57BvHWJnysWBLiNWe8DERe1r9aPzLxB8Vc2EAR4r0MtGhsBKrNWP4J2voeYTSMtjoXocVjMf4u4TVW10K0eJiJN7J6ATfDOWfi83OuQLw1FXmySqzPe6Drw4j3H0UyYDkhT27X2zVuZKKFxRzEg7ORLGg1XLZY22Oh3VWQNQd4SbcV0cXGthIfJ9qMP9y1Llu4LUIGXwHyrFXjc5bG4W/r2Is3uN3icaRo+pfr7P2zUfzjMvx96vVGx2FAf+g9xXjAeeXdXKvAe5vj7Z1+Vm8uMBi6JqL5PtnGPg/xxRrYv9ven8E/wV3AJ84AfPXVVzn//PNJT08nIiKCP/7xj6Hfmpub+c///E/69u1LXFwc6enpXH755VRWVn5snXfeeScRERFtPr179/5MeP2rQNgAPB44gbAViIKdr6CJ+QBSOIPtOqJypCh2IKXotgqWIYWZgYTRFiRMZhGa/FEP4HPc1SIhNhsJkwx8TE8syv0Wj+LWFiIDJc3+301oy69zJHzzSrzBMQcplZVqk9eAVmh51fBoRQKyF1KuQ6HdDMPDeYYm4E8VDsJv3zYbDvciQbgRn6S2NzJ8r0OGRBkcehwp23okFPtZva1G8634TPn1SCG8CJyPjLTFRuNeeC/hNELB8axFnssyZFg3469UehEJ/wUohmww7PsVPu6xBHllrkTxekvx3rduyFg+Gymko72WC/Dxhq/ZWJXbeNZb/6dobI40Gs6345MWj7B6RuMPwuTYs83QaRreg7cMGRKr7DMOKYY/oPGdZm0/S+hQRYdn8bGGc+y9FPx2UwyUP25td7E6b0X86ry38+zvhfgE4H3RqdifGP1TrNwCq6vBcEtEvPAuJJyGv1+3TmO1vwgZRKtVPuJOxBO3Gt2W2DuXWh/+y8axyOjt5uUg5KFKgjP6Id5Kwhurty7XXLnbxq4XdJhl+JarjYR+9v9e+0zCb1W6bbk6vcsCq2c+/uqwTMMrDxK6WZ8uQ17lkfhbTtYiOTAYzSnnxe6FknTHGj3T0FZ6X6v7XfzNN24L9lXD6TJ8Dr08NEeX2HiZdyzqUTS+0w2XOehE/Qp8KMl4q3+49b3FypfhY9MqEP8+g3hrATAQ9i1ChntfJA9jkSG0DHnN51hdKUAj7H/eaPCwlWlGMmQcfss6xsZkhPWxDsmBSkJGf8TJRo9mdCvLamTU/cnwWAVcUwYbLYl5pvXDGVxDrV63nT7G6LgOWtYC1bZ4nAc8Al+73PAsQQuteiS7VkDDSvztIOvRYjsbn8qpFfHxFEKxgFUl1t5GfOaFI0bbe9GYOwP4NdG+0wAbw5WEEoz/O0JDQwP9+vXj8ccfP+a3xsZGNm7cyO23387GjRt58cUXKSkpYezYsZ9Y7+mnn87f//730Gf16tWf+M6/Ipx0ohH4p4UMfP6teuhxCxL2fZHwWY8E2RaksEFGyWpkMHRFAnMdPlFtAT4J7gIk5FKszjr8qcMj9lsmEiZ70ZZKKTIoXPzPSGT41CCFm4eE2EL8vb2LkbHYil/tD4aoIUg4RxOK+Tq4FxKqkfETj88Ldg3+irkKaycV7xkZg1b2DdbHEtVHCzIMc0WeDufhA6S3I6EWDaTB9h3QeyvyGC2xdscgZeC2rl866rtTsC/gU/HMQV4N5wlZCjubocfD+Ps2+yGBud7GLBYplxr8lmEdUv4peC/CXBu7MUa3R/Hba9X4mDjrD5VI6K9HRkoDtDtTtOewPc9ACsIZwE1I+O+1PjyIFhOFhvtCo+slyKCtJuRxC92SUIoM3hyjYzwySnpaWVcu1z5dIKsZn0x2rtFuJFQtgrRcfKzco/gtRBeP5LZzsedpSBmOQcpplbXdFb81n2197QqdzkYpMVLtve/hE1O3IuVcgg/q/7W15bbyhxpdcpEx1A2fH83NyzJktPay95fhT27ejXh6ndVRg9+WzcWfFB5i47XOnjVYPRmIV6OAODi0wRJOF9izTLQNPB3xaxMyJBYjz00xir1bgzxXk1AM7hL8CewYo0UF4qmt+IVQKhrzNfhcm1W0vaPbLXDijBZdrP/m+aQW8eRBfMqRTMS3KYRiMEM32/QyetQhGZOr9jpHWjt9jbbOm5aBeHqe0WC96NBphtUx/qj3xuLj47bhQ0uesHKOZhk2Bi5EYxL+XupifFxfHeLJAaJ5xBDr02lGt3gb371IFvQ23G0bPOo25CkswhuM5SjEpcA+OwyXdIhrh3gn2z5J+IV2tuG41MZrjcY3LRYfT3t0WECj9WuNfT+CwoGihF/DJoibhI9JP2HQwhe3ddvyyUWOglGjRjFq1KiP/C0xMZHly5e3efbYY49x5pln8s4779C9e/fj1nvSSSeRlpb2mXD5V4SwB/B48CI+HYEz4A4gBe+2aNejSTkFP2FrkICpQYKzCimKeOSZmYu2P8xACV0JlI+E5WC8slyFhHaOvVOClHAxEkKVSKGcY20uwB/k2IaE7TgkhEbhE+luR4LGVpP0F/4JZ+K3JKKREHWxVKCV7lR0Irfe8J1kNJqIlFyz4VSHlNVOZOSsQ8Zdo/1vXqAjm4BI6H0VMhg24+NxMBpsQkLxLuSFq7N6GqGlCG9wDTJcXiG0PdbjPBvHYuBkq2+ExqzzIKQwXsd7T5wydQrhiP3mDu40299uhutQo8+rtvrfSyhtBVWGS57Rp5K2KXCKCSkaSu0dF8NUhvjtYWv/h4gP4pHy6osUdj3+lPU0G7NRSEnXIWOhABnQSfjbQtINv8VWv+Nx50FKhbSzrWy24e5ikGYgwykT8e08xP+1yBC2RRPrRWea7Xku4s+9VnYI/oTqWYjnHkP86RZKzfjYqTr7PxofL1eBj/97DX8NXSXip9Vo3rxuZUrs93y8h38NoYTPrDL8Fx41NtHIKz0HGQFj0DxtRoo/x/AotVsL8/EyYB1+G7UKf1d1A+x/Cn+bzFYbn3SjTxT+RHQ8/tQphq/b4nzNcCpSPkzm4T1FWVZHz6NwrLF+LrDx6YU8m/0QT7nxd/y4Hn/Ixsm0aOtTEn4L9QjyCPczvJzHai7+QFwj/s7k0aqj5Wn8dmsXJBcW2Ng4/qjA35c7FNgGwQv4rdRcfIL3CvxCNBHJhD5oHmbgk7UvJHSIImSsXYPG/ofo1PoOfD5KJ49LkLG8HfGU8+hV4he9bvwHI0N9nY1bHT5v6zgbnwZruwFt9SaiRdZINL/r8Mn6k4wethUcl4i/9zgMnwoOHDhAREQESUlJH1uutLSU9PR0srOzufTSS3nnnXe+HAS/ZAgbgMeDCfgEuQPxNz/EoAl6NRJGWUjATUTUnIwm+UA0YUcggVuLhKrbgluHF3oxSAj2RQKgCgnwEsNlHhJgbkW9m5DCCR0I6IKE114kqN3WxEtIYCxEq+gYfGLqLMPNGZfnI+PBKbhN+KD0ZfaZg4RVL/yJPifotuCVlvMajEAGYzXesOxtOI1TjrG3/wysgIO78QlXJ6HV/Tj8VVMxRtsthksiRHXBX4FVipRBi41RP2QAxONT0mQggb3UxiwZiIPgWRubMnxuPRffc9DoPA1/t+0Gw2W5jeVWiOuJPz3o8BxrY7obKYsaw6evje0PkDE1AinqlqPG+dfWVpWVc9uyLp4owepwBwYqbGyLRU9uPIp+OUbzjKPw26i+Mwh/4CQWf4tD9VHlypASXoO/jqpZdNy/DZ8DzsXCrUOnb4ejedKK+M3hNw3NsQwUE7nZfq+1tq7FHx6JI3Sak0oUi9YTGaQuXKIY8c4YpLAr0OKjHzI4nAewF6F8gMzBpziK1bM3XNhGjuHjDATnGU7FJ2+ORXzW13BLhqjZRvOd+BRI16G54jzXG4VXpwH4BUQU8A2rzxmWLqxgNj5dSL6NXwviK4f/SIg4z/DLNtyXobnZ2z7OO5xlYxmPtmxjkGxYajR5xPodjTyyLsThHKunEs9DY/BbxG6Mm5Dn0sJKGlaiRUMd4qlsG79xEHU9Ie9caEs11/B418r2RXwTi/gvHiJG4nPnxeLzKg7C39nr4ojj8Ac0eiIe+5G9k4M/gJJL6IpPRkH3C9Hc24LPz4n172a0aAGfJWA1HFmLDzUpRnpiBFoAOe94M/5e5nhkUO7Gwx7VxfeQPN+tPuxZZG0vtDp/gOTNQU4wfPExgAcPHmzzOXz48D+MZVNTE//5n//JlClTPjbFTF5eHk8//TQvv/wyTzzxBLt27WLYsGHU19f/wzj8s0HYADweJCLFlI/iMOqRoJmIT0GRhpTqEj1r+DPy0G1H3ocKJLj7wv5n0WT9PbAA3lyLvHAH8Cu8W/D3w16HvCUr9He+2xaxAPaQobMeCZpNSHhtVp37/ooUXwYSJr3w10StsT4uQYabKdh9tyPhuAUpuJ74fIbjkMBbSOjgxL5fIaG9EAkrtx1WTihR6c6n7P+p9lsB8hbMFR1ogq9MARotjcpWa2sw7JmFDIUMZJiuQV6GZYb/1cjIycFf71RntPsNEuwjDccqG4te+Hs9U41mxRDhZsJ4w3M8EuqXIUWzzPo5HSnnvogXkqx/UchwdNs8XfBJZx9GPHELEuj9DYeBaNx/bPhYvN7BEqtnKPBVo8czVt6FHMShGMt46+MG63cNoZPdDXejeMMHjHarkeIfiHjre0B7OPKc0S9F7bPD6oy3scyGmkbY+pRS1jCS0E0Q+5+1U4wXIl7KxRtK21CsW7T1r87oNdPaWIDPnwcyRNzW3XBkXFyBtkQzEP+Z0ckqZFyPQ3OkGr+N7bbMRhh9LXif5fh7gBfYuLmwgC3C64xpRttKq2sTWoBNQko/y573QAb4OLxRuBDxyTrEC0lobsZbH/sjZZ4Ff/ozvLMJH895OT4P5hDEWwMRD2QiQ/AZo3uc4dBsv6da/9y4uQMwOUabCnzIQDbyssYJt/0v4G936YnPR5dh7a4DjkCwEOVEnGrv1qLx/CHaDl2IZN+LaF4+hIzdXpa6pQS4oZ3mzTlortar3YMPQtUONCcWoHnQZOODPc+192LwcZwlEExHfJYGm1+xd5Otn3vwd1mPMxwbjQ4P4E9wF1i/foLGuR2887TR9xpkeKco7yjFwHQovxvN6VV4GZx/1C0qg2wstiCeuxV/OjwZ8edh/M7DcLxXu9XKuMV7jX7veipaFGYieXYQzU9niJ4w+OINwIyMDBITE0Ofe++99x/CsLm5mUmTJhEEAU888cTHlh01ahQXXXQRX/3qVyksLGTJkiXU1dXx/PPP/0M4/DNCOBH08eCuCK1CY/F3ob6CDgpsQMK5Cgngefb7PHv2JPJuXI2UwBqkaMrxp/NuwifBvQwZbs1oxd4TCbIyfPD4Q0ghbkMr+cuQIIgyfKORkpmJv8u2AhgFwXiIKELCdRtSLgOQkZqJT+DrThvOQ4JqCDJgnrd+5eIvQ09Fq/pN1n4fOHibBfpnIaGWhhRXMRKy44x+t0DLdLtz82kU5+a2mA7gt9V3IwW21+idg4RiExKUY5CwLEdbRxDKz8ej9nszBE+Zx8AZS24sHkGKforR2G3hRxudMo0OD8HLu+Cb51m5OGiYDXGzUFzoTKTgBgOXwPz3YHI6UjAlRtdYtO0Vrf6Hru9zi4l4xFeFhsMWo9dDyHgqRF6sJuQtmWi0yBR9d75lsY6rjN434uPkKpEyftza2Wq4OsN5stHjOcPLbbE+ZePmvNTOaJ5jfUk2nA/iT3g7D9VSo+s9huOt+Bs+niJ0uIpsYB0EJRDRx/o11/p5GTKo4pBx6/p72NpZbHSKVJtHdkC706ytJ/A3N8QbDYrw8aKVhk8WfjvYbS8/heageUCDw+Zdc17t0dByD0S1B/7b+vFr9WNfI3Q+Gxkp19q4XIr4qc7GdAl+Z+AZNE/62dhdYbRpsr5fYWOwDJ9XMA0ZFcX2v4td3IEMSfBhKuU2vvNtzBtQDOl5aC7EyLiLmGW06mftt1qfmvAhBfFQNR3SZqC5Aprf11i9K9FCIBUddHExeHVINg3C37G9A/Fkb2T03oc/Oe9iYnciT6LbyZhiz9fZWA076p18/OGPMiTHbsWf6O6DT7BuMblkoXlZjU/9lGn/u5CDrngd4JwASfh47RQ0L9wiMFp0or+NwTIku4qFw84HoccM2m5fx9n/yUieRuKT2fdFxmI0ksvx+AMsbiG7Ctj45atxnwj6WRISYj/5hU9VZyOJiZdTUVHRxkvXvn172rdv/7HvRkREsHDhQi644II2z53xV1ZWxooVK0hJSfnMeA0ePJiRI0f+w4boPxuEDcDjQEtEhPJkjbEHdfgEwxX4FBVOUbsYm71I+f4OCZpB9gEpy634i+rTkdAcgoTea2jSt8Nnzl+HvCjr1FbDUojLRIcfLjWcqvEr73VIGUzGC+lk/H23bgUchbYlnKGThTdkN+KDwachI2YuEuhuu7CXvVOGhNXt+GDoViRA11mb0ciAKcF7Ad22iov1KcYbBDFW9iDyFJyLhOhGfM6/JqsnyejYjN9eKVGahE6zjBar8fcZ1xhuSfjg+Fn465XW4uM5s5GQH4+MYLeF2mJ4r0Or/zR85v+hSOhXWf8m2/dCJKhH4rd/3DZxH3yqi3SrryfipSpk/MxFHqdixDsFtD252EibeDDGGT49kZcmwdrJguAeiBiPlGEx7P8rdBqLP7QQZ7R2hlKM4TECf6/pbLSNlmd0+InR5hwr45TVCPz2fyT+yq1Me3cDXhnmo0Tql0KHfPyBmj2Iz5sQn30PGYrfMdwi8YeuBkL5TMgaixR+e7SomG80i8ZfIbcRzY1qo5eL8XTG6UBkrE1GBkgUoQMPLEYGZj/D8Rl7lo734DSheZSG+LYOGVMPI14biBY56UaLaYgvcvDbwNfh7xh3nsss6/cjeAM9yerqg+THQfyp2ijDYST+lOmvrc3nEJyLz02XhI8RzcCHjKxH/DABxb/1sHbc3IwxfIYhvptl/VwMwUqImGHj7U4alyGj7na8R7zB8NpoY+q24KsQf7xiYwoyHici3svFz/FsvLHfjD94loU8bavt+Th8wuoUqyOeUKqVUOjOXMOzCfFuIj5Gdag9L0Q8Vo/GvhXxRgPivwYbgwZrc7S1s86er0D82gcfA+wWQKX43JS2IKW31dVsuCw5kQbgb75gA/Dbn+smkI8yAJ3xV1paysqVK0lNTf3MOL3//vt0796dO++8k+nTp3/m9/+ZIbwFfByIOhtNrIVooschz8cT+C2KHmhbqA/++rDXgO8iw6oKf6n8avuegCZtPt4L4YRzb/w2jQsm7mft1QE5EDfFys5DyuR+tbnnXaQQ+uBjt6qQwnjG6oux39cYXo8jxbvSyrfgrzZLQgJqGRLi45FAb4fPI+dWu9OQQl2GDwB39bsYF7eqzsJv8aUeVYd7b6nhVIU3Jpyx1Wq0GYg3cHYbLbD68oBJ0Gk88qpuQEJ8uNGwFh+M7uLCFtv7e5EyXYM3XN3YObmxGL/V1oQEeR/8IZTr8XGGE/CHNNw2aLrReZvqbVkIW+/GeyGcIbgbfzihztoegN+uK8PfsrAb8VG0Pc/DHxA6ertujcYooiP+/tWz7ODCbmSU7cVf85Z8VF/i0fhV4k/nVuNvO0kgdK0dvWyMZh5VRwXeSDkXjV2tlS9DCrQWqIEOwxG/xSCjw8U9lll9kfY8G23DbUFGYpJ+r3B1OtocwS8WGq0vO/GnNJ33bTcyBmKsT4vxhxwKkcEXgz9YUW04L4fgOfVz/1vwZomVy7L+1eFvl1mMDMprCOUDJcloNt3qHGr1RiGD41HDba+1XQSH7sFfqeYWesVovB9BRkc/G4coo9kcfLJo54GciLY/B0LDLsRDLyG5V0TbJMbn4A8u1CJerrR2V+Nvp3kRpbPpZs+jIeJcNA962li6mNVV1vd0/L3VztNscZWhawE3wEEXlw3euMvFxyaW4bf9k6ytH+Fv5+iKxn8LMoILkeHltowLrK8p+MMhbgG9Ay0ge+MXsOCzPazAn/rdjj/YtxHNmXU2jpMNl73W53LkMd6DP8wSgz/clGKfeCSLU+3TajTox78tvP/++2zevJnNmzcDsGvXLjZv3sw777xDc3MzEydOZMOGDTz33HO0tLRQVVVFVVUVR44cCdXxjW98g8ceeyz0/Qc/+AGrVq2ivLycNWvWMH78eKKiopgyZcqHm/+Xh7ABeDzoDTRBzW68MgKf3NQF56ehSf1DtBpOwXtl3PbDMiRws9AkzsbnwhuIlE4dUsB9kcKKsraygEQIfoUXRtX4lBi5et41Ha1SV8G+XUiIV0PgDg5sQULnABL4TUjQjLFPBf7QwGh7JwMJzOftPedhOMee1Vr7FYQMxUNP4a8fm4dPN7IRCdut9t5zyKBoQEZfjv3uYixRv8nFe4swHMYbfvV2S0my1bXFxi3F+tTOyg4xXFwQdzK0vAV0g5qFSME34+9AbUQGxla8ZyLW2nCG5Fzr50a8p24giudzHoLt9k6ejes4o2NfxBfrIaof9M3EJ6zNxXsJ+xqdf4UU+yKjdT+8cZeOh3OQkilBPHQEJdgeb3QdZ/1w3ov26mPctdYXFzvWHvFXETp1XW99m2J0OoKM0RaktBJRPN5g/P21MchLuNHqi0cLAherFm04u7i3FkJjyvWIn1IR/2fhDb5z0Dhvw49xHP4Gintg2HB8Hsmh+Pnmxv8yq9Mp5zij+XA0B4Yartl4xbsBfztLjL27AnkI19muZYyKnR5t/WnAewxH2d9qtDBxHsEqxGMbgRpocd7JTMQDa+yd3+GTUU+EDoPwhirWhz34+33dwqzO8KhG0r7W2pttZXtiFrPdq+280zn42zci8dfhNRh+0dZ2PyvTF3krV1i9a+3jwjkGo8XzhKPwaDS6L0dzLRnJnkobKxeLDCG5kZBuND3L+rnM963lKWRgj0JzciqSVzOQLB6LvI1RhquL37ZF86EXkOF6n9prmI4P25mIttcn4sMm7sZ7n90CbiDeeHscv8htQjzsFk4j8eEItfhT+uWIF9KN1i1W13i8QbsbzeMC/JWbJxS++BjATwsbNmxgwIABDBgwAID/+I//YMCAAfzoRz9iz549LFq0iHfffZf+/ftzyimnhD5r1qwJ1bFz507ee++90Pd3332XKVOmkJOTw6RJk0hJSeH111//XN7Df3Y46UQj8E8LTUA+pIzA3/mYj4wVt+2Vh99+6Y+UezlSIlHICHsCKalX0ArfbRdNRML9CqASDk2zxLRum7AHUhQFwD2S0d2dEVOKz1/WHwkRi6XZ8yvoutBwfsmSpGbjtz2qkPJcgg/Yj0eey0vxgdHFSMj8wOiRir8WLwv2bIGubiu1C1IQ50OHDCTkVtizudDyPkRNwZ/IbELegWaU2uVRZKC5+KB6pKSeRx7WIryBchn+Oq+dMKwfMgq243NpuZibGKtnFcqjuJBQrr0qoOsiSJmDDKt8ZKRfaf+7rZ1lSBFlIM9GE/40q/FFywHpgYgS/AXzzstYh8+Z6LbqzzGaTrS6nPFbhoz6x/H3jiaqnyGF4OKGIhEfnX9UO7XWxnVGyy2wZAeMnmg0dluLrfjTj/WG62homAdxztB1xv9CG/MW+yTjt/bn4O9trTYar7Rxi0SHVHYjvslChvg1wjt4BSKaEB+vtP71wl/DlY03uvKQYl9lZQcinluBj98sRYMag8+J12zvLLA+1Br+/2Hletn3xUZn8PF1GfgtwMOG+w4bs43An5GhmqV2o24BKiF+Az6OLEs3giTcifh7EOLfEny82bv4y1zzIKoMHTq7DvFeGj7xcrzhuxKfaw77uxklyV6C33VwnqJi60e0fZ9q/VkqnMm1OpOtvmuQsTISv0jD6nTbqTH2Nw/tDCRb/YPRfJmE5u1WtbFnE3Sdhvg7A/HLdtHh0C7o8CgyGJsJJZYmGhl6JfjDWOtF/32vQOdMfIqWdRA10/q/w2j7CDIo1wAL7apBIGI47JwBPW5Dc2EVUAcdkhE/X6624vIRD6ZZ3zfAnnnQ9TxkNDZb/5cajW7GJ/KfiPTB7/AeyBgb2+H48XdeXTdvXXzjSnyKo+uQbIjCxwMPNPoON3qfUGjhROUB/PrXv87HRbF9mgi38vLyNt9///vffyYc/pUhHAN4PIiLkBJLg2AHRNyCvxYuD4iDqoelNzpcj4RsDhLaV+ONxsuRgBiEVpbpsGcldL0Tv2V6J1L42fh4qflIMI/A58UbgpS1i81ZhoysaiRMCqxcBTLyeuID5h8y/HeofEuzdjM6d0MC/zHgL8CFcGgLdBiJFEULUgLtrH9d8PeabrZ+NyLB6QzRnyNh/TNEw73IAJ2EhHul6MrlSIi5bawypDDy8fGCVSghci+jUyL+buWVeE9EnPU3CnmGHkHKaA8+p19/wznF2skxem3Dp8pxHsd6JPTXG61dnGEl/nBHP/x1TlVH0XoZPqB+MvBdeOdd6H63tR9ndO13VB9yjSY3oYXCM0h5OW/CGmRsHDFauKD6bcjD1nxUP2Ygflpu/RuIFN1w++5iNZ13dgNU3Q5pg5BBNpdQGpH9G+z6vxrr0w9snHKMFpkopm0U/l5W5ynLRIaPM7aSrP+V+AMozuNbgr8veqX1qRLefhe+cpWVewxv2MxG/OPCHYaiMV5m43Cn/e8Msput3jr8IqrIaD5VdD9YCQlD0OGBe/Gnkhcio3yxlY+0enuhRV4fxPev4a/tuhPtCMSgxcpiG68S/CGbrVbPGHxeUDf/s/FGcSxS8n3t/5VGv1LE2+VoDi+38rWIT0BGfLTRbzEy1IrxCaRT0dyuw+faW4W/R7fK6LgXGXob8fGJLgTiASR7Yg3vvfh0Qe2sP12OwnWC0aarHaa51HAtRONagw9lSMJnXPgZcBFtZc3tVn8mmjduYdYNLey2Ilmx1vqbj8//OMboHWO0ScInz27C38axxvrnxuUpvBHXbDRth79LvBot8pYYjnvQ3KxBsvk6NJ4jjB5J1v9cNK5bEF+kCo+ahyFlpuG5BIX9LADqbdF2HrD4RMYAPkFCQocvqM5DJCZe97liAMPw2SFsAB4PUiN88lKQMGiHJuBlSJBMss8r+CzxsXgP2gE0oUehrcE4JFj64i+QT0WCxG0/VhI6QUgM3rAchk8SXI22BG5CQiMaH3xdhAR4DX57eSISikORwPodEsLl+BxoaUiZ9UUGwHCkzBbgY6Wi0DbKbKQocpCS2mD9fwkfzxVl9a/Ae5xSrN5Kw3MG/oTxfyHlsg4J7K1GvzVIcbrYtGS892MwMpRikIAtNHq4wyDp9v8OFI+2GA6ttBtJJlrbGdau8+RW2phmWN/fNZyXauyCDRBxNj7Fh/PkDjX8ovHXb2UaPmuMXm4c+hgN3ZZ4vJV7CSkFZ9y7bVIXI1mJeGkOodsy9u9QlWflI0UzzPDfYv0bgozFWnseb3StsvpjDPcKOHQndOiIlFc14nkXC9Vi7Tei7d92iM9i4J0i6H6l/R6FP+AwEinowYgvSo3OBYQ8lKEYrjK8d+12o12/o2jRG+/BesKelyN+7Yc3Hnsgj2mGPau28VuEz7HoxqzS6FiJ+GoZPnUH+KTBbsu83PqVCwfvhoRTjT4uTjWe0HWKNOIPehUhHm7EL3hikLExx9pegOak80g672Rfw6sFf7d1IZJDjdb2YRvbamQcluJ5qgF/VWIc8rpVWD/a4RN911u7zfh4Scdr4A9I5dk7B/C39TRb/S42eTz+UJWLC/0RkoEjjJZb8Qd82uFPtDovXBGSG30IGVqHdkOH+4weOfi44JFWPhnxR6zRpAZ/xWUvNIejjVbb0CLxOsQfr6It85uMRoPxHtRoJH8OGp4WHhRK8eM85vVWfib++sQ85PVMQYvXWjyv7LGxnImMyi1GU+dxHYHPY1po5Zutrp0qH2yCiJ5A6Yk0AB/7gg3A74UNwC8JwjGAx4MJ+FOe85HAW4GE7AYUKzIeny4lG03gRUgobUSCZxUSFIPxedySkULohQRFHBJYB5CwcV6hOPwF5S5+aIX9v9J+y8Ar1GIkJCwukInI83EACa5EQl5NluAPfqQipVCGvwIvCn8bQjEyGHYiT4jzbq1GythtW2Tjt5ycUjwLvyUejTwr0chDsgVt1U1AnqFIJNirjK4uvqjXUc/XIa9GltHxCiQghyBhm4gUbBU+QbJbrcdCh1Px98dWWznUzorn8Hnm9trzUYanxdpFuG3UXGs/Hn/Fn4vvcTGgadZvR9cifJ67m5HiSkZKwSneGPw9v5MNh8H4E42OZgV6t1M3OKsbGvcR1v6jSJmPJHQPLN2snWJCuf2IsfbLgLnQwQWTb6St8VGJPxTktv8akIE4FrpPwt993Acpx1ak2IZZ20Pw28oxyOBxhorzitVZ+4n2vvNUuW1uZ1QNtnI3Gh1WG84p+BOiu/Eny6Og4VnEA26L0m2FJx2Fw158DG+l1ZOEV9h97b21dmtOV/yBqVjEK73xhz/c1rjji0oUZlGOT9beZLiOwx+uyMTHB9ageTwU7yV1YQkDra0xSB48jPegu+1S7DdnDHc1mg6037ehcXMxeV3QGFegdEUNVj7J/kaq/3RBC7U0tCBwMXyx+NjAPPyuxLyj6NITeQ77oZQ07j13EO5dK3sQ8ekI9b/D48g4G2FjEWN1Ob5Lxiehd3GnTUardVZ/LFpgPEEohRKLrd6tyNA6C8mbAqPFHMSrJfgxzcHH/KXivbVJSN4Oxh8IKbRxPIyPh95ufajAz8FYo0kKPlm5K/uA0TAaHxKQBxFnQsMOwhCGzwVhA/B44Dx1GWiCbkaT8ho02Z03zcW9OGNxDBIutyIDcaCVW4FXpFuQ8nkCCUJnONXjV9YjkJGYaXUMsve6IkXTgASgCyJehr+fNgefeHQ0EiDOw7jN3m/Cx1y5rdZsYA0ccdvXzgO1EnnRNsA+F+NSYzjHIAXvtvlc0Hs/JMBakHESLfxr3odDs5B3qt7aX44Efy0StCvR6nw7/naMubB/r9E008YgBq/Eqq0fC4UnNyIl1d/GsRQpq7uQB8B5ISAkeEeMtTbdVnQZsBhqnsffYmAGW/l9yPh8CX9353ob32j8FWENeD4ajvjoMfw2crujaJmAzzVWib9eaj0yELLh4Cv4dDxn4W9bScNvrbot6V8DI+GNlUihdsEnQo7Bp7qJNLrEI16ORkbVKHxCaOeF7IeMt2WIV7epbwcXaXzZjY8XdcZgkT03erLX2nUhALGIDx7Dp7yYaO9sMbyb8YejDqqv79xt7020d1ZYn7JsXMvtswDiRhlttuNPWaZanW68ulj7cXjj2BlkN1ud7lDJT2wMXMxkEYp5bEL8/qTh09fadB7VNMSTPRCvJiEex2g70ujTZG1tsLGsMvwyEa/lIF5qQLy6E+0cTMfHlV6N5Eys3XRTZx/nuQJ54Zvw3vV6xM971OeWP+OvChwPf3ka8XKtxoBo6894ez4Qzc3F+BPjeXDkKcTHFYhvYo3+Q9CYJuFP5jojx21Pb7f+TLV2G9C4ugWL25p1vLcDGVt5NjZj8PnyHG0fN/qOwh+8Wm1tHSGUKJtlaO6V4dMSpRseW208MvCyMBUf0+ninFfZd7c7MQh52VsIpZ4J7jS89yL+dP263r5fivhrAj5tUr1wixvACYbPc9Dj4z5h+LIgbAAeDybi7wJ9F4iCwG0j5es79Wgrr8qyxCfDoRlIIU5DK8ciJNhuRML3eiRAeqKt5Gx88ujXkQC6DAmTeiALjryAVn8F9q7zdkTic+NlIAG5CK/Ah0N5Jj79hfNA3YMEWRUSgk7BJ/0/9v44vuvyyvOG3yGbGMgGYtJIJhhJAzRMSp4ghaZQKAOLslgKg7q4VNeW1WHq2nFsbV1bx9aO06mjdeq4ZbSOjspquWVRBofCoAzcaAaakqJ5YFJSMA2N5AmmiYFMMCYTeP74fK7fiXsv9+708daZffJ9vfL6/fL9fb/Xda5zneucc51zrnPUXu5Ggqne4j67gVof0O03jJ/yb31+9hrEqFfDubXqn1VEjGAXFD8AY+9GSt9exIzLiQD3Qr9TigRkC2LEfwkX3ml4c/xsO2Kks5BArURMsgsp1y1Evr21xInKNWhXvhL6XyBiL5sIK1O5YauF4o1+ZgnQA8MtUFHluX+DOKRQD/3r0WbgKx7L08QpwlOet6vRjv60x31GbZ17yDiZbli/g65vGLfVMP4L7i/BWQiMg+H9+jz3IFGq6x4gDz52ucdTCr/8BRLKOZ67BP9BeKueTKWCcz3+Psu4XoJo/6DGMfwz08EioATGp7isNrefLJ0HjddOeKsJzo10t24ic3p7+Ceeg7PIKvRjz918wo2Y1txlGtsll/v+bn/O8Tz0Iav9FH+/zp9LPdY0nk7CajaHSK9T4HF3ICX2JFJMlhneWqKsYaHhqjWsK9Ham+d5PAB/96LhO4OsyxMR/Sc8TSIUhWeJzd0Z+HmK9axEm42tROL2bPd1FClKyTPQ7O/3weAOtZt1J6LDtUh5zXebmz3mqcbJZ4lcoc/qlDpHEf9ZCp+sJzZ2Cfe7oPtJlJexmchR12B4WyD3VtNMIVGjeimKK3zJ8M4i3LTJNX/Qn/cBv0WUx5yJFL4zRKznbLdbRtTnzfNYS91fOaKHPYbvcb9bD2+uRwr7AkTHz6L1mqy7NxIhKM3GWyOihR2ItlrJ5NEcvolw4d6O+PWr8PNn0NrdgtbAWsj6TaQw1iG+0EMcFspB6/QdRLPf8/izUYqwpXzA16gC+C/1Go0BPN91WRbdu6D4C0A+/PwB+MidyD21i3Bp1hEFzt9Bi/wuIot7LRLm0wnmsYNIprzMv+cj4XEULf4ZRPqJXikq+TcQCYfz3H8jYtYpNm8O2oGWIwa5lIjJuYBIsrwMHRRoJyyVKdg8BX0/iYTSHxLMc5X7qURMvoxIFnvAz1d6PK1I2F2jfgbvhNwVRM68DsNYRCiKa5AQX4jicYoQs15HJMYeJmMt/PvD8NFaz0slEqbJmjERMdjEoNd5PnYSqTnOGP/JpfSQYeglk6/vlUZYcNWIOUxu8tluo4TIb5YU6xTT9CWPIykTzejq8fxdofvDd0L2BCJ58cjYzErPzxYksOd7TNuI2qvNxuFepHw879PXl5PZDLAHnj8DV24z3u/zvL6ChGK7233VuNuI3LhPEK7OFA/VS+RznIUUqcnu+yASxF8k4kodatB5D5R+nDhEM4To7DHPc7nb2WN8HSVKnXUhC0incfOi52MvtB2DivlE1YQkuAtGtFtF1K7+SyLfXjXhKp+BaGWQSN693DgpQhu4XWQOU2UqnxyEX26CS+72fNQjC2e28XrA8JagPIjjkZJRRcTQlqDQiBsQX2mB0/fC+FuNw4eNj1nwyz1wyfWG7SF4vd6nWhv8zB2ek0JEo4dHzNcwEa5S5v7uIw6wrCdqLY9U9nKMx0bde/MwXDQVrdVWIvH4WSLXXnKVjvM87ENrwiEAR07C9M+jddBv3C8HvomU0W4ivm6AiGs+ZdjrkUX/99CGpws674fSZX53np+ZgdbUKs3V4O2QO9n3iz2PyaNjpYytxEZzPOIx+cArMPgTyF2EePYY4tAaiL7me/xpU3EAKZDtMHwvZF+F6OsBw/kttKb2mhaWelzV/v06tB6zEX9qIpKDHwWaP8gYwO++xzGAXxmNAXyfrn/1QQPwz/bKgeIlZE7PfWQdss4lZW8KWtxbkYDeS7gBkvt4AhIS0xGTmAxshu5GKL6UCNqegBjNEcLFke3vDcB1kL8WMeNKxNDSTngVYjBDiNmtIXJEHTRM3ycy69cSMVdjiGSsffr8u8PwyVVu7yjQCa+fgSkniDJprxFpYhYazmGiaHsZEhJVHvsWoBdy1yNGWICY7CBxivk2okxZz4jnat3mMIpbTJaBVrX50SeIPHyNiFFXIoViNry1By7McTs5wj8liLFuRLv9Fvcxw88l92gXMBEWTEaK1jY/U2h87vbfNzwXA0Qt43bD2k8IsF7P63W+n0/GPZi90fOQBGwnsnR1oxyTyxEtlLivs4bphOb1RA9MSlYKW66zjyCBmdz9+XDlEBKemz1Ps4mExmeRkrIC0dI24/kKf09W2lb/fvEIPPUTFq1Hjad6z0WKEWx2ppFZhDVvGlJW1vr7PkQv84zD5HKbY/wdRKct3zHsPXqu4irDuRdObIBJi5CiPw/RaTOix2xEd18io8hkDuvch2j7dmSZehodEjiGrFRf8dgakVLVhei0WO1fMgbRwzTP0x6k5LW7zbROGhDdpc1cos2TxmlSSI/D+Mv9fh5hlQYumW18fI93xxwmy+5h42MCmu9ez9uwPwsRr0hW92w/s8f4OYb4w3yPaTpRJcMxoBeVeJxPI5qHKJm2zc+mNd2LLMonyJwyPlAPcx7w+J5C/COFpRSjNXw1QUdPed5ANPDHaKOSjRSoSs1b6Wzj4TNoM1Vt3D0suAaPQ+4a4zrf7Q8Yf/vR2kvwp43pA8blUbWXeyNRGWQ+ooMU73ka0XEKTUmbefPM7Nmek2Q1nEucpE4bsGLkzUjr6yBa20eJMn9TidPhH+g1zAeVBmb0+v/tGnUBn+9KcTJnkcA6gZh2PrKeTCBOsjUhRngFYubJUnQGMaLT8NMHkRl/DhTPQIv2ScRkphMHNZLrYitRoP3HRNm5EqIA/HzEHI4iBrsLMa98IlN/OswwEzH2dAp1j8exkEgdUQ2fvJZIKWNrypQliMlOcl9niUMCxxEDb0RKQIqbSrvT7yCFosj4muZnFxJpD+YiK8T1hFU1DwmmVxGVboPTzyFGPo4oQXWSEOCTfT+dErwMLlzm/kGCaY/faSBclOOIKhUg4TIGCYhTSNEY47lo9e8HycTYsRdZyfKhew9SVOvc3yYkHEZabhv8fDORpqOPOM2aFKzXgEr4618hhWee4SgEHobTIyxROSALyPfc101I0VyLBH2KGSoBfp+IS6o37vYiGr+YiKOsUdu0EjVXrzQcP/a7IKtpqXG+1/NYjixdPcbrgGDIfZCg8TOILi43XNl+b5nhvZFQKJLg206EP/QavpeREmT37KQqv/MloqRhnftcRFRlgBD8E5HVpd1zVo3Wex5RNSVtAAuIpN1pzQ6P6KPFcLZD9/4R99PV7s/PGjfZbqsEKUnHyVRFoY84+b+csKglC3qtcDHljhHjKDRMU4nwjWGi/N0SNM9tI8YHUkhSbOdE43Qv2gye8OeQx/eq4S1AfK+AqEbTRlStaDXMM4nDcK3AIMxZguisAc1zC7JGpzCWpcRJ3PmGtckwLCZS6HijymH3mfhXcqVONDzXCV+5q4kUL8lTcBuRD7IEzm00fusRXz1h3JQTsax7jOd+93ONxzfNc3iCKAN6lkgVNZlMihd2u60riNQ7S4gE2FsRH3gBbeRXehzj0NpPHp3Ra/T6Na5RBfA81+BxtLieRwu0mThhW04E6VeihTyEFngLYiIpftA7wY9dhRZtsg6MtBCkAPFsJPzOIqZZhRZ5D3E68VG/MxkJ6t2IgTYSAcOniWTKE1ElievJnCz7+TNEhYxZiDkmJW0xUQZrL2Lckw3bfI/nbiR4dntMdW5jr9+dQJRQm4Z2ydlI8doD/Bek/A4RgjjFMHXA8N2G/xDawdv6ML4MCdZaop7yRqLmaYmf/ZJx9YrHlE+knrnU455D1AYuJ5TGLsPZSFhnTwpvmWoYs4yLb7nNAfd9AopridJ1EK7+Lv9fqbGcPkWcPDyL3OzPGb8nkDDeD3wPPjMGCZnd7rtKczJ+teFZBRfdg0IPepHQ2Oz3v0Ac0Jns+foaElYptu4IUhhPCs7+w2TyjHGSSNWxnLCMpBCDQ25/Mpr/Q8Tp3qRkDxgPu1AKogoiJc0TyFJzkCizl9ZHD+JQSTlqRkJymeGfhCx1Hy+LcnyNxGagHyl81cTBgWqPeQ1R1ivb89NrPEzze2md16NDFoV+dqJ/e504fJOL6O4aOF2v535ZD8V3GNYmIkH4Jzz2BF+vcfcYkQswxRr2QvfPiDJ/NSg27ThRhWIeUhoqjdtkbS427Gf8Xp3b7PG4p7nNPMS7UozkOLfxGnGaN8X1NRuWTqIMXLvhnULUvV6O1miyOA/4/hEiXMLu5bf3EyePpxMH3zoQHRYRlXySy73PMNxLxAn2eW67EC0dJtbiHxLJrpcguh4m4gfr4O1Xjc+Dzv7VijY3hzxfyQKbR6TBKtBcsxLevhdZir/o8c8yjGc81mKPrdL4OgJvv0F4egqMr6RY9xIK32qPawKi24Oeh5s8Xx/oNRoD+C/1GlUAz3PlfthfNiDGdz9i4pXIolNL5KNb48/5aPEuR4u2CDGDtCMdhzCejRZx2tXvIwLuf4YWfoq/Wet2HiaEWL7hOooY5ko/uwW5KrIRA7maCDyvdh83w0eqiPq5bXB6PToY0kqcehv2e1uJrPyb9f2tNUT801H/tpzMSWFe9HtLCIbdj5TQQ4h5/Sffv5FITFwOtIyo5boS7YyvNexXEy7gdmSNWufndpApAP/65X7+T4gTn5cbzs8Rwr6cyPc1FwmNWuNqGE4cRy7l1Ui47UXxUUlwPoTiKHv9ztfQjn8imQTOw4eJXf3fkMlr1odhvInIR5cE0jc853OREL0H0U+yDq2H4WOInvYiF9wrRMLw/wMpRskyvE198gKa90NIeK1D7tcvAt+BwUbo/4lPzM72+8NIQA0gN9Wl/v5Fw97oPoeQAnSTYT0e88mjHuNioiLKFqT8fBH4z4YvKRPHDFsKqt8MPACDr5Kpyf33D0D3RsN/e4fWwzBaH4OG46j7vhYdZmol4hk/f5EscGMMzynPUyERi7bb/SW3/z7jJY/IIbkFmO+Si81AH4y/CpgMl1yKFLAy5K6sQLS6BvGQPM/dIbdVa9wdJWoJ/zEU30DU0N6H6CNZwZoN5w7jMPGos4Z7F1p3yUV5E3Q/iuhih3GykQidKNR8n36cOGixUG2//TjiW2Vus1M4f6vR89Y/YkwPE6EWycr6NMqikCzOp4ElMPYqxBNaEd2fJZSg64hKL7Vw7nbof9y/lyL+eIVxkOLsznq+qxAPqES0mUIWhtAGuM54vwPIgbHXGtYnIf9WjXPwJHE4ZqZxvsrjaSAs42v9fj7w24SXYI7xV+sxDwtHg41ALoy9njih30rE9tqz8vZdqHLNLsRLa4At8Pob7vdhokzpB3aNKoD/Uq/RQyDnu5ZmwUn4ZRNcsgoogJ9ugI99CO245wNfzYXPDkax8CaifNIcfyar0hvImpVNpNMoQUw47fZ6ieoP44g4vOQOzUMM/zQSJE8TpZ3uI+LH1hCVSNLO+wSR9mMyYvrTETPvRILsOsRobkAMqMDjaifiDO93n9+Te6v4CY97HbIiJpdSIWJ2a2H4dsieQRzOuAkpT18iUlBgPCVF5muGdyohRGchhldnuOrV1pt3w0UPGle479no8EUJYtibkXDoRQpGHxHTmNwyRUihy0HW1P1+pkYnBC+6inDj7vJzsxHT30mUahpCSsdMP/OfkYUoDwmqVbxb2ahAiskYz0lyWd8BbSeh4vPIirQX2AonfgaTpgL/FVlEd7rtW4i6ysk1uRLRxG7jp9Btpbi9djTPVxKuuz1+9kZ0Ov1Gv/csok9bXIbrIftT8PrLMCW56MYRlRXKND98x+/VEVbCFF7Razg/5zHPIHIPJmvVGT//FKK/Xf4rQcJ1hce4Aa2FAyPwm2K/1hA57GZo/Kefg/HrhVNKkWLyBFF3GjS/xw3P60joniGTyPnta11CbB1Rvq4VyIFzOyBrBuGybSHShjyA1uQPPRezUDL0BrTROYysZQsM+8NEzO0eeGsHXPh591WJaK7PY74eKTXJ7VtClEfsR/yrlVA2d3lOV7rPeUjRvA6tlROITvYjGrsebSaeQBvHx4mT/Afdx3Hhh2zi4FI2kaZoL5kqPn93Bj45n7CKFiOFaJL7TYpW4oOveDyH3V4Fykiwx7gqM6yH/P4QEReZY/yOQZuDAmIT+Jp/exHR5AREl18gMiXkEN6GYiLGcSHh5l2D6GWc8bQM8ZZ6pMC9gnjOAc/PEs9HM6LBXZ67zwmmn7/sajgD7mczEdM93+1/+v0X43EI5FuMH//eaKGnTw8wYcI3Rw+BvE/X6CGQ812OT7rkbrQgByXPWUqUG/q7QQmwHyMmk5SfxMCGUZHxH8HY+YhJtqNFW0gk2p1NBLwnC8ksxFiWEMlajwomViAFbrl/uxcJ2j7EoKoQw2twH21EqpbvI2vWRCLZbIo92osEwF7EjP4CCfKkfMzws48BFVCcdvLvEJn430BWolak9GRD9jIiQWwBEk5rkEAvJoKk64id8G7idGSd8bWXqMubXMub4aKH/P405IKp8Hir3P5JInanhAiobyYKr1cgBf1ej/kNQmmeBRfdZtxd6X76DGtyc6c4r3RYpw4JyDz3v4RIcL0XKbid/u2Yfn9rPVw4Bynzueqj4k4kiFL8UR5M+pTheh4prHVuaz26Gg1bHRmrKKWIRtNBhauRkD9jHO0iAtUPQvdJKO5CQuYwEjLLiVPTCyH7hGCfcpX7qiDy+iVr0lEyqWUyFS3q3O6g8bwX+r8N+bWeox8jmlqAFIl9nqMyJFAHDcsAUammDCm/NyAaudP3ehF9PYViH2d4PBNg/G2GrxH6eyD/40i5SS7UIeI0djtR1m0qmdi1sZcatzme1x1kTt1mPYgUaJALsoagncm+fx+xAcw3boqJE+KveC5rR8A0TmRz4Ry0YTiC6Ol33P5mwo2L30txgLVELF+9cVtElLEEKWozkUIzhgwtZiqWJEV/IZHmZHAEfMeJSh2FI8Z6A1oT2wivwk3wyW1+f77brzNO9xHlLm8yDm9C/DJtqNrd188MdwPhnWgncrTWI+W1maibfT9R/jEdwOgjQl0mkUlwPbgfcu9B6+wsWpPTCOVyH+ItM932NuMojbmRKMVY4n6WozUzmairnTZvnYi+r4SPFBOHix5yf5VEAvrbgE/zAV7vpeVu1AL4fl6jLuDzXe1owW1HQqMUsm9GDGQvYiKPEfUw65Dw6EEMOSlcs2DsbxIxTKuRcEuxJXXuK8WBLCQqC5T4+wrkvihFQqQVMYFCIv3HCcRQO4hTuD1up8x9tCNXT4qVayATv9e/EQmJxYZ7LaGALSesBhuRAN1nWKYghpyNlIpbEWMuJRhpt3FZCXTBaz2GpcLwJYX6aaQQ3I6EyHVI0j0NfBf67yVc3O2GKwWGt3g+jnlOWt1+UnrOGL52JGyS8jcXMdFB5AZM1pKdxl+h2ytElrAy4K89x8s9zjzjstz4eQwFyafYzVrNTeePEN2sdZtPEFbLHMl/jqCd/zARoN5IWIgXGu8zkJDpJKyXrcZnOlTRQ8TTzSDStFQjq1ej+s2c7r3U46yG4k8RVprtyA1VPGJMA0hArjBOW4h41KP+vRIpRCWGvc59dpAJtk/l4fI/jeioCp1+XoGUgVK1NfgTMtVPMiEOKfYQNNcH0cai2jC3GEclHncbEZtZbJjOAl+C/FXuKymqTR7X836+1njvJiycOb43H9HXWUKxWeS+qv1/jts4gjYS2YRClkIS9rmNNK4c911ApHGp0rvTJyO6PuK2kmU5B62ZCs9VJ5GmKR8dmPkeUc0lD635K4i0OHVIYd6OaDRtFFoJ93W9+9/l//eQsZJ2HicOcs0lFJajbme5xz1gGCrR2kvW3hR3mUuUdxwHg8nymhTbPWiO84DfRPT7sMf7qPHwrPHyJHHgZjOZQzqZ8ms/MC6/R8RFXkwmL2Huh4jKJMma20jw+jrDvpmwtLaOGNN8RIdNiI4PE1VSetxmqcdeQGRH+IHGzo3EwbxpHsdeZPVOa2D0Gr3+ideoAni+ayJiXDMRUz6EBEgJEl7FSNjX+Z7dcxkrzza0u+9BcU5HiVQZc9Fi30MwiCHEgO8jAvkHiID+JuJU4GPEIY9qv38SuAs6X0XMawYRYJ5vmKqRZaQZMbIZZJLK5k8ldu6Fhm8CsmZsRYynVO8OdhhHaefeghhnExHrVUkkMx0iUq4sh5kf9r02wz5gOIv8fZNhbfcYCoHLIH81Yuz7iJOzbUScUXLPbvPfFiQADhMn7rr8vYpIz9Lt8STFJbl4liLG3Od3lxB57xa6j6Sopb9FSLk+ROSkK1PzpQ8RWf7z3UclEjQnIfdSIvVLNZHAeDKywu4kTsKOR4LiXj8zhQgKX2B87vZnGeE+2zVibqrRnB5yG/mES74C0eNsf36WSLI7hqhsMolQOJJbcTpStmcTbujFI/C5iqh60EkcvOkgwiPOkKne8Ne/gtxaREs1/txhXP4usTaOw7nb3Mck4gBQNRHzmeCvRDTwiv9fYhgfRNa0WX6+Gq3TeQT9vEAoEt2IHncjBeF246+eCOJfZHy0eoxXG+Y+Jw7PQcpEsuYdQXwnKbmHiUMEL3vOZgG/Iqz+m5Ay0IcUhrlEEuTjxtkBw5TCUnKITUsLYf0qJeI+kwV0gDgoVEEcgiv0e1PJVLYoXYL4nOmeeZ6L54gY5qNofU/yWFYjhfkWt5/WabKm73DevUqiTvIEMid7eRnR1DTCBQ2irxRvORF5CCoNU4rJ202c0v5LP1/mNvo8L9cRJ/ZTfOwEIjZ0P3HIphBY6gMeOz2WasLb0YDoppVMInS+RJRgTOFBhwxXL5FtAc/PGaQgF3osH+j1XsT9jfwbvd6va1QBPN+VLGS9iAn8LlqM1USG+cXI6teLBPfVyLrT5TaS2+1RxJRuQzE/N6Dd6hi04KchZncaMakidDIyvb+AOH28C/iP7n8YMc/TficHSv81Eo5DSKhsc5mho8iS1oKEVGJA1e5/HlIm5rjtBxDzfdTPJOFRALllficHxf/0Ei6bo4hxXY9cqkNEvcwUC/lFMq7uzi1E8uhXkfA7iIRUrXFd4zYLkUCtJurEpni2ASIdzHzEpGuJUk99Sv6aOSiR47nKd79NSFhs5t35HI+iyglJCLYTCsskNOe9nqN5iOGvM3yNfnYDoqezKM5oD1LiEuNuJqxAr/m5Ver77TvJHHzIVCn4K0RLS4nNx2ainNX3Cbf3EJGQdjkS4vl+Ns94vx3RzxNo/mcj5XYFUZUkBykFqe2j/j/hPlnMkzt4NxKK5SiP4ZdRTOZJRANHiEoLVxhnR5DF7fuGbRbwOnxmAqKnM4jmJxKhAg1ErrtmyLob0Umlx72bKJH4LOGyTG66YsJq+Zrb2kjkHzzle/uAP0P014to7U7PyQ5/vopo/koH+Rf52YeQ1b+ESAq/WmPMX4asO2sRnZ6Ec88QMaX/xbjvRsrqTmSdm0FYmbKJg0PbYPgZFGZRbrymcIA98OYORLevoPi9YcLFfYCo9rIIOAtvNsHbLxPp2ab6uT4iPGOacZ1iNjvhQGq7g8gckGP8DQDzoPsXiB+2EXV26912l8d2UvOasbY3efxbjcfdbv8IEfu5maDJcsP0VWLtt/heqcdeQaS5qte42ULUW07ek81ojbyMFMKktJUSpSDnIf6zFcau0RxnDhrtQpv3WpSZ4Qyio0sRb05ejTLd6z5G5MzshsFH0Fo9aFxONv6TpfgDu4Z575S/YUav9+8aVQDPd5Ug4b4cCcSnCbfGn/v3ViKhZy8h8Mqg+w3E1GcjC894IkXF/Yix3U4oV4lRTSLy7HUTOf8KEONKSmIdwQD3IwvAfj9/HVKYuvVM1gNu/yvu61rDfBORpX8uEmCvIEZVS7jPWohDKJWI8dR6fGkMee431+M4hqxSSYHpQwL2WcQMK/VO6Qq30WUYutxfvnF0m/uqJYrW70VKQ5fbS4JnCXJ/LjVs7YQFYwhyk7Vsl+fuoHF0C+I75R7DgGHZq/eoQQL8DLJinEKC+AdIkUvWxX3o6iUqtYAUNoiayWs8Tzca5j8lkiWPd58v6NmxGz2W5Lp9Fp0y/AiiySZCkTyJlM1mRLsliP5uIqxEO4l6sFXEqUOQQDuKFKKbUEzhViS0JyIhe43xfYo4WVqELMHb/M6w52AQTjcahulu+2akRFyJlNm0eXkACdLPovWR3Jl/gDZaLUTS8ZWG9cd+/wxxOjW5YKs8pjVoXvcj2kyK717C4j0frfMpwJ9B/xaPaz1SSN9ANP8SUZorKb57CdfcbSjFSy/kPqTfTt/ifq4yzpN3YI/77UZ0tBFtDMshawVa34uQ0pGNlJRCpIwOGR+7EE10IiW7ALgaspcgOu5E9FGMOP1auGgJkbYqhZ906j2+5D4KkCK0UCQ0dj6isU5kjfxjz1kHkaYnm6gZXApz7oLh54SD/uc87ylObprgL/48cSp/l+HpJjZvdWTK7719M1FnuRzxyFrgJLx5j3+bjuhwMXE4Yo6ef6vec/1fDMtkpIylzd4408BjxnsdcAiOPIRoqNnt1blkY73vpTAb98NW00QNUXd6GuF1mYf47CZiE3ucTBaI/oX+vQGKvwp/t4dMTHPuVLRJ7yJOyHciZXX0Gr1+jWv0FPB5rnNZWWR9Hi3SKSi27QuIYXWjxdyAFJ3JaAF/GQmxWsLduxwpGp1IuesldqdXIKUouS47iQoaw0Ti5bVEkHBSwnp4d3mkI4Q1Yy5S8JIboxopfQ2ImYzx/S2I2R1HTOsRj3crDL/qckVrELNqIfKr7TacZYTLdyISIpuAQTj3HGR9Ggl5EJO8AylbG5HCk4Ksm8iUp+o+C8XX+/laj7sYKQR7iFJuKZ6mEzHVyURanScIYb/d7/cR+dvqiXi4XUQS2xLDe8RzUkQkO04u5glI4WgC6mB4h8ut5XvO1yIlrA4pBtuRYjbO748jTm3uQEz8duPxqGHe6X6HkWUnxdiVEYceaojk4OlgwWmkgDwP5171CdQUS5osar0eTyWh4O/33OX59xI/M0TEHiaL6BAMb4DsO4harX+MaOs+pIBPQZawDiIOsRU4Aed+AVkf91zVINpLG6Tk9l/jOTjledrg/luJBMcLCHf/aqQMFyBLfFJ8JiKF5iRR3WKG8XXIYyz2GCzwM3FY1UQC4RSf20Xk6pyFFOD9xuVc95lNWHNL3X8bcSBmFqE4VSJ63OTv13usR/z8TUhpqjee5qL120y44es9d9s9vtsN66Bh6QAWwuADkDuOOEH/Fbf3AhHu8obHlFznBYjWj0LnDiid6/bs6mWIUECvNp7/DPG9RYhHTPZzhe53HhF+cZzYWCdre6/7KCVy35WgDRhIQZuM1vcXgDuhswdKZwC3wWtrYea1RMaDbcQJ5E7jLptM3k6+ZThPQ9uTPnTVabwvQBuuOWhd9hI5G0uJ1FUNSOF+WPBkaGIisB66j0NxCg9pgHNvQNZDiA8OGPe5iPbHjBhvp2GdbNwVICX9e7y7stC3gN9//8V4nAL+MuPHX/AetfkOEyb86egp4PfpGrUAnufKmoGUgzkoxcBetFhzkGCqR4pPL1r86WTb5YSrAcTMNyJl53XCKpNDpHhpJgrQr0aCMR8xgFlEHFU+4WY8hKwTSZk8TMSb7SKE0SHDWO++ExOehoT0ScM5FykXhfo9+2LE5F5GDHk+cTKtGAmJBYZrhsf8Hd9fDllLiFqqKV5vGpkyUJwk0lZch/J13ejDB9M8ni1EOpAn3Hc3UZJqFZFja4hMPGMKGmcfYu55RC3QQo9jJ5mKBHQTQv4looZypdtZrTHRSiS1XiJcZn/aeBpjnJ8hTkduNG7LCXd0oduYZTgKEB0c9Rha/VnueWpEwqHK81hBMP/kpuz32N9BwrkYsi41/FOIQPly4sRvCoif4vGN899RJLheIWLbUkzbPuje4JQ+jUixGSBKra0g4ruSWzuNx5uXrKt8f4FhHURr7HJEj68Rgu+A+12uMb7ZRJRh7CYOuHQgpaPD/aY4u7OIjtoQ3S3xuHs9pkluZxVxah3DesD/L0Z0WGY4vkCk7zlFWP/2E7GmOUTy7alERZVZmtPhLUQOymbjqw9tcJaQKdvHBvfR5TmZSaSo6fK7KUykBikYHUT5PhDdL3X1i9WI3paijcy9aH6f9nxuJ5TNzcbTaWAflJYZphv92UUouLmIxvo9piLPURGZeLhMbHS/+7rPczKVmO/PERubWWhN1qA57vc8LnR/Xejk721QWmV8vwEzvyBc/nIL4hnJfd87AuYyw3aNx98sGCsuRfyxgUhuPw/RxyEi1c6Q4R1PHKjLJ04hD7j9V4HJUGzrK3XqJ2sVov9Cz1GKoSx0W8v9fjNyD9cS9dXXIvpYg/hmOaLV0Wv0+jWuUQXwfNdS6HwDMZZjiFkuRSb+p4nTaGVI0F8KFEL//YSSklydSxFTOIWYfVrMKQg7uaFa/d5OxFAP+d0aFPfSjpjgfvd7mdt6xbCUB+zcQQiLDrf1qPv/HkqpMETssF9FjPglokLENsMyCzGkmwnmfDVirnnAPuhPitEuw3cpUsQayMQiMgMx02SFaSdy9v0QBh8lLCaTiZyGw0gw3uoxlBMnSJf53jLCnVJOpnTUW2eQYpFDlKNLCvZexHDnGVdLjI9lnos+4+4FQrkuQUpKn79f6fmb42d+YNyN8Tg2IWH6DnFqsRDFJCUrQY/77EUCCf+/xN9TLNWrhMJTi6wT5R5Lij/tMJxnCWUkud0OEdab3cbHOI8lG9HxHKQEjDc8LYbB1rTiqzzece43j8hDWOh5W+p3ZiGlZBma4zqkQMwik36FVkQHW5ACdSVyB+9Awnqa+27W0qDVeM31GF4nlJolxkcn8GU4t8FwFiDL1DBx+KMSCc5spLQvItZsm3HyMBEOUWJ4Gvz7FsFEN3F4qIRIelwK/BjevMXPJMvfn0F2ClnYS2yAyt1O14g56yfCK5KVeq/j+KYgvpQsyrP9edr91RvuRkSvBWTiEAc3uZ8L0DpJ7vwhtD5nE3k464g6v73AX8OJFo+lynPTjWhrQJ/nXjUcS4nTw8kKu9d99yFF5zIi52gvopWZxInncf6/gUzCZS427Mnqn0NU1TklOEqAcz2Ifh1ukqnk8yDiAb1EvsWDwvXPE901I/pKMXZdRI7CtG5zDFe/fy/zuPKIjZRDgjJ1hlt977u8OwfhO36uGoWZ9CNrbgWi6zn+/WniEEgfouk5fMDXexX/l/5Gr/frGlUAz3e1QekyfbIPftqDFvzOMrlKDyIBnHaoTwDbnUusmMgVdRZZN9qBbuh/AXgETu9AizcJ2C1IKJxCjGMNEuonUBD5NX7nOBKQs/yb4+W6mxBzaAV2wy/v0SfV7r+UTBWDwUeIE3YXk4l3evNlOPIjt1mMBNOR3HcXSp+HmNdO9/eSxj8O99FLJPL9OplapVT53SHEyHvRbv67SDH6FuRea3xWEu6OJuMlFykFnyNOTp9Biu4KpKSBBM+AcTYLLlyFGKUFQyb1TI7heszflwmPTEKC9Y+Q4laDGO8c4tDFJI9rluaUAaKKyEwi916l4bvVc9XgOdlLuH0uI6PgMAs4C8ObPNYtbnefYVtj3KYYsJs85oNIuB4wnncZD6uEmzf3+9544hDLJzy2IaRAfw/R2y1EXrNeJACLPLcVxv9ivzvGz25DAqsB0ebTSJEb8jsPo/g4jO+VSJA/CKffgXOPEylSnvdvlYQ7dRuwAMZ+lRCSmxFtXIasSUXu6xBSLP7KB0LKPa4fECXMNgnPLIVzmwz3vYQ1/aznohYJ8hqkKMxw37mC4+16JOyvJJPyiQqP8wlgHlx0N1qzzYZxknGaNoddRC1bDEM94i0n0cYvWW+rNC8XrUdKXZX6HL7Bfb+D3Lg1aM2/AK8cRrGMKSb4EOSmjcVaj2eu8Zinv8H7idJsC4iwgQKNY9Iaj2UXmVJqg88J3td+BlkfIko7QqS1qfb/ZWjt70UHftImto3ICvC6cf4rlEi9FlnJnnIbfWjeT8LgYXRorsrvHVfqrawV6uvvfgQ0QPd6ZOXc67GdQZvapWQS9H/kUrQGFhCu6x1EycwU0lOhcZ17FR1qm6pn+l8k4pAbidjGtcZDCYpd3UqU78tHa3ctoumLiVPZfcjqnBT0J4gN6l63OcgHfI0qgP9Sr9EYwPNdNVla8O1IcalFAuRRtGAvJkoavYEYWorRugUJlVLURnIJvwaDP/Mp2rmIkSwGOuCXO+CSuUhYrCVi0srg0O1QczFa7MeRQrFN7Z5+B8ZPIOID1/hzPXL57ETMvc1wb0ZMrMmwTUDM7neQENxIlKTaj5jqBiL31U1I2D9EJLZeSiQj3oysB21w7ibImoASShcQltPjSDlpJhSH65AimOIrU2zWSsTUTyJm/CnEkJs9nhOExakGKVgVRO3ONn+/xXM3DwmvHR7DKiIjf3Jrvuj7szxPBcCrypWY/2kkaHcjS8Jh911nXDxkfD0B9MDbe2DszcZrP2L68/zeS4Z5mIiJSsI5WcgGUGxYC0GDfXD6Hhh/LbLgdCAF7ncRPda5/1mE0pKEx5eISgIQhyWKeXdia5BgGT+ije8RSvzTfiZZhHuR0N9rmG4kkpq/7vn6IuFqn0ykPxlHrLNCVPpuPVKC+pCiWYqU8LnEAYy7yVTkoM9wFhMWm+R+tvv8rQfhwuRqK0JrpcFjO4iU9/s0B29vgLGfQq7Uvf79FqQc5iA62YnmrwhtGuYanv1IobmJSK3yIBLk947A10oif+NRpCxMQyd3k5VsO1pPX0Z0vRj4vkqU5a7RHL3+HEy5gXBzppjYSYYjWY13eqxnEb0lflNFlGI7iOioHSka9yHaswv33A7IupZMzOvbz8DYW5FC04UO8RTB4L2Q+ylkYX8ere8ht/lVw/C3SDE/jvjLTrSWU7zrDsMx7LFdbRiTdX6J56wbXckKN4047NWB+GArsUEd4N1VStJY0+Gfa/zcUrRhsXWQakQP+5AiOotIz7LM7/0H4z3RWRlwGP7PLfBbN+udwRsgt8pzD5EofYdwShVah88RPHGb8PfmYbjowyNwDaK5P/sgYwD/03scA/jnozGA79P1rz5oAP7ZXvcghtCKFthGxHCWAPVw+kcwfh5ipCeRIjUGMbLZiLk8i5hDDpkddu7HiSTENW7vabjk08BeOPEPMCnFmXTpnZoqIideCRI2ucA8GJ8OJrQjxrMNMdAqpOQlJnECKRDtHsvFSODnEwleO5BQ6kNKTDaZqg+0EXWLq31v/ohn+pCVZxFwl9rL+jCyjuwi3LPJZdXpMXYQAeBWeLdvgCuOAd9Eebn+CKWLKCGKws8xfg4Zjhfc1nT/v9F4Ty7XdYSLNLkRFxk3jUgJTm7ljUhZPIwY8gL1lZ/c2l1Ect4u4iRwDlJC0iGCkzC2dwQODxCnVKcSrt0ZmvsMrpYZn4Oeh3uRQvq053gGjL/B7WZ7nqcjQVFL5DFMgfh5hjUpG7ORgpDt/7sRfdgC9tZhuHAGUhZ3E6mAhpAQ3UrEvkFYBy9FdJWPlNsujyvbz6a4vgNE9Yjb/UyKh+tCVtM+f1+JhPEBtJ62I2Vqh8Y4fByykwW1AVk+zyIaqyNCNfrgwnuNwzLDexatpU1ISUhuzHYYm5TeI4hWbjOcFe7rJqRQ3xiw8CIS2M1ICSlDSsuDxkufx5Ni354iDpXke+7+BIVnJNf8NEQjFUh5bgPWQO5Rz9kxmDIZrfezhMLS5DZOGtZdxkWPcV+LLGANGm8mvjTFxr7md2Z4frv0fWAHjE1WpzwY+wDQAm/+Ai76KpkcornL3D9IMUo4+arHOgx8HFhhZbsEhn8E2QvQmrvPeMv1vG8jkllXuN0mIq73AOIvU4lcjbXE5rGKUKpTzOBBw5Fi+lIcXydBt0PAQjjxjPlyPplSgilpeiZVVYfHWDji3S2691sXCJ+D6yH3X6PNxG6kZJZ6zouIBM/XID4023NzI5ADF20xXnMR/96P+PIHeg3z3lnuhv/nj4xe79k1qgCe72omLDNJiPQihWKaDCMZ92S1P/cgZWyT378FCYkUhJ/cSvsRA52OGGIdslzMg0n5iImldhoRk9iHBE0tWvhtep4uJHiPIUFST8T+VRJ5sc4ihlOLGE0lsAX6n3GC5U7EKC9AsWYDSFAMGP5xRLzgdqJOZ98IuGa4/3lEpY8SJET7ifiYA0QOrmmIge0lE+dzRRVi+Deh3XGDYZtnPOf5/YVImTpOHEgoImLjjiNhtsKfyaJ0xnN2DRKY12uu36wXf88vIFPqiwLPkwPFUw4zKvzuEBL0SZkrIcriDRCK2aCfa4XuZ6B4g8dRbzyUEopBrb8XI5pKCtwa4uBMg+egC53w7vKc71c8Zv4MIpcccG4XZFW53WR9nkZsFMrInBi98KDvH/L4k6W7HCkGa4hDORj23cZBIZEeZQpwmdKq9AGlySo73fjYr76H10P2xz3GlYQ7NLlg+zzW2Z6XejKpL7Knem6SothKxLE+bVi70fpoIg5eNbifRQTd5iDFeJjMCePuN6A4bRRm+tlCj3e6cVZkPE1FKXquQxu7RqS8b0eWI8eXDTdBdq/xeoiodVxhvLZ5HHXGQQopmOz+Vnh8NWg9NCOr9FH3l+2+dqN5fd6/JY9Eh+/lu68DKBXRE2i9DQrn3U1QPNd9lOv9sTluZxviQ50ax0WfQkxxr8fa73d6/XwlmdQymXjoWXpm7EPAccieiyyChR73YcO7kEgUnzaPDYbrgOcBIgwgudh70Bq/lMiX6HnlMFIO89H6SzGNkw1b2pAUAV+DomcQXewVbvgsUR+4hqgYM4ysruOR0n+UiA8th/4WyL2f2MTf7fGtNkzTPCfpIM5tHnOy4BZ4blcLvsFdTpI+eo1ev8Y1GgN4vusoYkLlaEFXIldvDmIw1xCpDQr9e3K/tiFmdxgxhwLEiIcQw0mutu/7/0okPPoIV0zeiDZ/SDCaJUggJIvTIT+/FSmMjyJhUI0EbB8RvL4DKS2bDVs25M/17/lEXGByJ6cg5omIIdYhxt5D1Nnt8v03kMK21rjLJqpMJGWtGlktqokqGH2EVeIKxABvRgx/DhLQg0j4lxDF7RuIWLoqIlh+ksfxl4Swf4ewelS4rX6kGPSSUZIuuh7yJ6Dk1vl+t8Nzk01k5N+E5jxZmCBKn9X72WMjxrgKCYRy/V48G1k3qwn3ZTWyXE4hqj6UAe1wbgthmdyClEL8+ziPrwnRRb+TCw8Z1g6gBbIu8DyOIZO77twzxv11nqN2j+trfreeTH4yyozDZwxHD/z8VcdAJTd1UkKThbhSY8m/HErne1zH0Yag3c92Q/YXkJV1EVprhdD5Apx4Ea2lXrdZZxwOICX2ZsN3yvO5iDhhPA9Z38rdT4HHcTtRhzu5uh+DE41EguxsIEeVHIovQHO8B8Wr7vU4dhq2bUghaSUUmxzkvtuCNmVXEmX6pkL2IsM2jrAcPUXQ+p+QSdxME1Jo8wjLVOEIPCRr9AF/73C/tcAX4W9e9jxcSSYOd7gRrddS43Uxoq/kliyEwVdNpxXGWZ6eOTGEQgGKiI3RHuOs2WNvNf6bEd0cQPO9yu2k+LYhP3MGKZ+FiBZxv08TaWFSDPDDRBqWe9AaG+N2+ojk2OmQxEHEBzsIXpssmOMJq2gnUt6OE4c6xrmvbzsc4A3DllzNHUR+01zEe/4MWc4nozW83HNRAMyFC29BSl0ncbDsx2iD3kqErmxHtLMZ8Zt9RFL96xDtnrTy18sHfI3GAP5LvUYtgOe7FiAmkeLIaojYn26kgMxESkqy/sxFCtAkZEWrRha1XsSs1yLmshUxnVq3N4SC58vQoq8iUlIkC0ETYlZnEQNI1sfkcriOcK+2u1+QUtCGBFsnYnB5HstViDmWCJbuG6B4DXKXdnpMa4EPIwb/10hxtGLCs0j5mIgY10SinNE+w72EsKJO8FhXGn+bCME2i0gkm2IjPwtUwPAjkP15on7pbjKVUAZ3QW45Eqhp7G0w+A+QO9ttXUCGeb5+O0xZh3Jn/YFxt9ewFiHFJRsx8DbP4W2eqw2qR5o7AYZ3OQVMipdM8WylSNB1Ewd8XkVKZ6GfbYXBB6F3FVx0qed0tfspc59t2Bwp0HPHGG9n3PYGv5Pit65BSkSKP+o1jhaiuMmkAH8PHeQ4Dln3IJo56LlcThymKTEun0DKyNVI2HW6zT74yGzCojSfsLqUkKnMQa/HlY1CA4bhpZNw2aeR5fIFt5Hjfv1Z+pvw5s88nmZEWwNoA3Exovd84+5pIgVQN1IMthkf00bA5lhAcoikyl3w9j/ApKmet2+TOXw19kOEQD4C596BrOT2X0i4rBcjOt+L5vwJ3y9H628vWkd9RJjFPsF7eg+ML3K/DxjW14gqOOV+Pll+vkRYXncbDy8ghfRP3F+V4VgE//ZipKC1ua3bIfuQf+8lk27qx3fCJ6xMdB6H0q+izWQDUkb6gCth0hLj+EbEx84iq+gUj/NitN7WIFrdhSznT/jZyUA7/PINuOSrROm6uz2OCsR7U2xq2oRdhQ7fJa9MG1IGS42TxZ7X+Yiv9BFhJbOJE9tViNc0GcfDhEt4vmDjGSK3a7VxtB3RRyOim3JiM3AaKZOH/HzFCHggDnltQPz9OULZS5b4frSheQHxoTtRHGwyHkxENL3BcJf5r4mI5/3Arn/kvbMljSqA7+c1agE837ULLcbk+puKFnchsu5kI4aSrHnLEaM9jCwca5G17XFCcD6BFnAS8gsR42lHQqsMMd1HoO0GwkKwg0ih0oWYx21I4Uwu3ceI2qH97reViHnDfZr5HVqPhHQ5coXsguJbkBJ1iKgA8ixi9u2GN1lZtiEldTYK7u+Ht9ahnf8af6Y4oANIOJ9FzPGA7+/xGM6gne7lgo1tHo9dNtmriID+a4Aq6D+pe7lbkMVjHvB7bvM2xyAVImHSZBz+Nky5FjH3HyK3cg9i/FWe5+TubyJSxzQhJl0JufOBUsj+qvHTYVhfICodbDese4nA+xRfdlLP5d4JF30aWVGSm6/M+H/WfecAY1yI/hgZa8Hgg0px0f+I4ZoJ/Gc497LfuZfIrTeIBGKV6eNPYPgBj+vHRPH654zDOz3Pa6H/sHHRgoTgcveXLLb1BE3d5/dnBNysMz6+7vHdDG+fFMlwxv22+b0+08eXPAdXO+3LMAqlqIfhte632DBtRmttHVGTdhoh3P/Av5Uiwb0XKVOrEf09rHGNnYqU4oPEqe4mpHx+i0yam6yPE4l6cz13NX72pPGclN8aRGfLkGX8ILEJ6oIfbhKNjM8xzl922z1I8PchJSLFX55FdOr4vjfvdL+/h5SuNZ7jqz1/RaiySClSTheg9bTC7VQSqYGq4RNpkzDLyh+EFa8YKYuPETznaSLGrxkpW9XIenwKucJ7iVjIZJX+ClABlzysTzajjcFBRKuHiNjpEt8/6nZqgWvg9Zc9xirP5XpEX/lIkT4KdEphZ7rwnamoUu/f70GxpmlTfcjvnjRu9qHNUiPiVzcSvK3Jzy9Ga2yScfElRK99HkPiZW8YJ9eTKaPYfTPKm7rEsCT+mCyxQ3D6uHGWR1RyWkScSJ6MZEIvo9fo9Wtdo6eAz3N1ZmVRehcSvJ2I0exBiy65Ldb58xW0UJcjZncV8F+JEkcFbiMfMYWpiCEscpuz0UK+CzG9QiJm7qwBmkHsFlPcWIF/H6d3Tj8O49f4+ZcQo5jj92rdbjNRN/cdJPDakeBoIcpLJYbegZjUEBFLlSyEe4iKBFuRkFmNhFY2kYz5s0gg9SGmOs39pbiWiUj5KicUqirEOJchK0QNoWD1IcGf3Osr0QnLIo95MlHIvgAJ+pVEjrhZRP66FBf1Axj+lVCXfanHeJLI93Yp4VaqJOoT3+92moj8bwMe0zG33YCE8ZDH1+RxZfu5vUSAegUSGouIGtHTPPYUu9bh37M9rylWsQfohe7DUGyL1uD9kDuRECLDI/rP9XuvuJ8r9Q4ttgbPRZVINhhvhYZ1InFyEcN9GCkHiYamInroR7S+nYhhbPVzcz3ejUgxSVbYs4Yzxzg8QRz6ucHjLEMCPLn+NyMamo/obyGikauRdelez2OKUT3oeUx01EuUM7vYz6a1tYBIeXMYKT+LiY3dYsE7uB5yLzUcKTY1KWrbjOMqpNDeYZi/iOgoxad1Eq7MLlR2cqLhn0xU/9nvsQ4hxelGIq/iGEJx2Or+Ko3nSr+7gjjQ8t0i+FxPVNa5CdHYpehgSy8wG7rvh+J6tD73uP/khXge0c/zyGWcY1wPGYfJUnuN2qKBqDLUj+jqas97qfs8hRSpa4jsABi2HOP1OuMnWa5zCNraQOSHLEBrKIUslBl21Pa5N/Rv8QNud7n7OeDnFxIVk2Yg3nmpcZ5iE/M9lhYipvRGpOj1eNxpA9ON6D2FEF1rXG112+sQXxlGfGih5zXFWT6JeLxDPNj+/ovxOAV8LePH5/7PX/hfanOQCROeGT0F/D5doxbA81yln0eLG7TINiABuoUocJ52bEPE6dA7kLKx28/mIYGUdmzj0IL2gYCMxWSj29uHGMIsxCAm+K+FSO+S4gT3uN2TQC+Mn4yY8my0812KmHWfn5uPGGY2UQbsOGEhTIxssv9aiXxgRwlX3RPo1Fqvx34WOn/h3zqIxLUHETPbR5R7u9ptHUACsNt43Uwm8fRbx4jYuiridO4RtzOAhPgS42mr8VmLduFziKz9O5CAT4cD1hmX2z2ufn+/ErKvh+y7kNUnwVCCmHOJFCtKPNcPw+Aez3EvEhiLPZ8TiWSxA2j+PbZMqp4eorLIKaIiSB6Zeqr0IUH5CqKFcuLAQYlxUEoUo7diVnyB/x8vSyPzkSLX5baTFaHX+K9GQrYfKQBHHAqwEgmyIjKJtTOHFTpGjCEfCcNmt7FC8972AKLNiUixzCeqnCwic+qbWxBNLjSMkz3ebaaBs35vmCjjtcW/JRpdbdiT8N9m/O2F4R60Vsp9rxspqCVud6nxsVxjp8uwViHlpggJ7SGiJGIdWsuL0fochtxFhJs+1+9tIupVtxgfnzb+lhKnSQeJdZaDlM7FvvcC4a4tc9sXozWUNg8HiWTr5YR7cBqZeDGKiU3DFkRXAHU9GvdExDu+a5gPGwcDwBtQXIvc1E3uY59hPY3oaqfh3IFoZbrntMNwTnYfxxHttOveWy8QSdYnIVo8iWgpxdE2++8UEUud+FEDYY1Mm9uNRKxxs/t8x+ObQSSingEsgawbzHKKPAfXoo3jAJEgvNe4mwdvbiRKFDYQB9HSJiOPSAhdh1J3rXK/mxFN96tv1viddqTg1RKVnM54PIc0B5myfV91v2WMWgBHr1/7GlUAz3dVELVQZxP5qGYh5leB3Ijb0EJsGvF3G1rIE5FwuBwJ6f0E0yoggo4P/nef3USR+XTAogsxxTG+V4aEaCkwCKefQ27npJCkk3BlbreFqC4yBsULVSKmNIc4nVlIpEC4HZ0ObCQsIUkQJ4WnEjhgQuo1/AsR4y2A/g73eYRIv/FFIjangkimW6V7F96i++eSVW1IOHv7H5Dgu959FxJxeimu64dErrAUm/ganKtHwvlhJLByibqgKXD9iMfehhj/E+jqAK5wSacZgmdwyJY1x3JlFMwKxLSzR+A1KUu1xsVdSFk5aPguQMK51LBMJIRXEuBpLHOInIkDbm/Q/U73vfRuN2GBbCXKS33Cc9ru8RYgusn3eEuREtXt9gvJlFlrO45oaxaheBxEAje505qAMqhYQVgHu4iEuMm6nX4rJ+pJd7mt16DtDSIus9/4a3V/fcRp93KkGDR5PqaNwEkHZKdwiiTMD3r8K9CGZDcZ1zy1RDzXcUTnZzy3zUQM2iKPxSEBmUTjLUhg/9j9/CVhpbyWCGXodlsDHlspkSwdRIstnrttxGnSWcbTeCIuGWQhrHT7yUU/aPg7iUMiu91Pjd9LlrQpxtccpIBVEod6pgFzdXo5E1c3Adp+QYQ1lBu2a9z3MT+7FQZf9LiyCSv8fI+vRmT35s/cVi+xMUwhHyksI22UksW3w32O9/1kEWxBNFOA5v24x3zCsFeg+tV7yfCNI487neDViE8lRbGCTEonWozbrY7dzTbMCxAtnDKcEwhr7TiPqQHlCE2bt7IRc7cL0R1EbN9spBiuRLyhEM3nDqIkZyvyrlzNB3wNv8d/o9f7dY26gM93XZMlpnyQyMuWjxh7D5HP65D/n4EY5XEUS7KJCAouJNwKlX7+oN8vQQy9DzHJxLx+SOwAL0XM8KjbaifSgjTwrnQLwxsh+xYi51+v+2o2LMnNVoOE2IvESbpHkdKX4HMg+puPwkW3+flTaKcPYm6NiKkl+DCMq5EA2eR+ZyNL3Y2IgZepbXYRh0TuQIrN1xFj70fMb4af+z9Qwupa4vRwUvwKjLvZhuUpZGHZA79sgku2eA6eFp6YSuRX207ko6tEwuWU4ZpKCK5uJIxvRAy+wX0lxfwaMgcdTpyESRNG4AIkFFN7vYY34e6Av3/CczXb81RvOHuR0HQwPg2eh8l+P33mIwE0xTi4yW30AlWqApO7zv8XAMehfxfkXwBvvwNj7yROP96OlOAm3l1PeReypCZXZTXapBzwd29KeAYJqhnqh+uR1WTA46shEjHnoXXwDOFaHiaUiRwibCEJ3y1orWwjqioUeY6nIdo84bb/EvgkYV0tIGo05xAne1ei+U9uww7hffAm5/D8hPvY6vcWe152EJUZ7iRiHAeJMo3Nhqkc0ckjSPlPlvhFHn8J2pjtRjTyp8jiXkccboBYRxvRYZazTtNShHhWMe+2aFYSFuZOj3Olx1JhXD7scafxJwvuKc/ZJiIc4BCZeLWMlT0p8V9D6xjEqx5BNHcfWrfpkEWOx5Tc06vdVydh3a0h4iOvQUmxy42PSjh3G2TNRvzzApQKqR7yJ/qdVf67AfGEFA9bQJTVTBbGRUih+57frUM0VGg4+hB9rSJCQi72s0VEpaMO476SsOIdQxuruz2uxYTyn9z/C9xfUtAPEXXc55NJ7fTT/fCxWhT6cgz47gfpAv53jB+f8z9/4X+pzSEmTPhvoy7g9+katQCe70rCJ7kEqhCD34EW/fcRc1iMj2kSMT6LgAMw+DJiMJ1ol7aGiJfrRQrhMFF4PMWBtZPZXf58F1HvsoJIOnsnYljzifqSzT4tOxVZsZoQ812KGMrnkAs5WT2a3NdWwqXYiZhUcleXwUWrkdD/Q+I0aTdSGI8g5rgWMcWv+X4JEkBFRFb8P0PMsIdIlrqOSK3zVaKCwmvw1ov+rRcJqk8hZeFq47qNKDT/Hb+7y2ObhZjwDXBJlWF6irBalhvmZUQ+xauJlCzlnsuTHkMVun4XCevDhrmVqAH6A/XHepiUhMEsIm6z1XDsg3OPGF9NxuVLRDzXDN9/lTih+EXiAMYYMpbkt18lDmR0EEJ7s+F6zPipAvbbJVxLWC+uh/xvCxdj5yPB10pUF5lPpPeZ7veWEnkA65EC9gphzX0FKTq3q30qEZ2nTchS//51/7bbv23zcwfdTyOwxSXXliAlcxFh6VvqzxNEOpTfQZuapIzc5DnfLAUpU2ZumXGdBH8+UuyHCIUAoN3K311oje8jrHizEG/YgeizBF5v9DNbTQ/7kdI037iqlMLdttDz96Th7Qd+5j4rYfh6ZI0rQimDBpCCnUId8gQbPcbDX8DYC4zfPyUTk3ziRyi9knGQUfwfQPxgD6LdFEt3G1oP+8nkrqOFTDoU2hDdX+/PzyDFsZbIO7qUSCmUj3jJNuNgFlLgy/389+fpc4bxOp3wDiQPQTlS/NOm0LF0w+uB70HWKqIW8ixgDORfi5TOew3PdI/rBOJB3zE+uuDcA8brjehA1VFCGZ2MFLRaxA9fcjtpI5149TZiM5ws8Om5pPhX+vf/4vmwlyRTB3kPWqsJj2s9D9PcVgobmAMfW40Oj0wiYiNHr9Hrn3iNWgDPd302S6djU8DxTDK53gbXQ+4qPzeImPBCxJS3I6FzF2LgE9EiTbvBtBMfQ+SymwLMgP47IX8yYmLDaLHvJMoEpT6eR4yxBikPyeqzlqj8kNw1SREqIk6ofgPFpFxNpHKYhRjV5YjZHiKKrS9wP+8gRrmbyKTf43fHIcHzPSRQVqNd/WIiNqgJKQUn/W5idinWqAwx0+TyTML5pMd3nediFxKmCT83I2tQUtg64fSrMP4O46AJMeU+QnguJUqbdRLKcBe8dgpmLjHOrkMMepfnOh/FR11HHPpIVpZWoh7sABlX5blGyPoCUiD6kQJZQOSGS9ax3cjC0efxtSHhV4JoBSImaicSElVI0W72HP0uEROZrBLDSHlKrqdaolTcbELhqyUOAW1237OREDvkflPca4/xdgsSXEXExiKfOERUSibm85e/gEuudV+liCaSFbyWOHw0x/3N8VgGkHB+yv2d9Ni7kJI107hOCvxvIkV7OeGSTTFWV/i9auPzBLKgDBOpiJJCP4jWWIvm4q0X4cLPI5regCzWxYiO5iKFvYSghWFUzeIOpODYUnzoSahZRFTfOIJosoKwjO0yvtsQreQariOEhT6t7dnG4/1ISUvxdNMR3ZwgTiEvVr+/fAYu+ddIcU1XsqbP8zxeDdwMgy0+3FKLeNuDHlsPWqc1SDldalwly1szkQ5n2POxgTjs8TBRbzdtEnI9TzcT1vqDSFm9nDiUUi9cZtbS1YQFbsDvFnoepiFaXUOERAz5Xg1RDaTVeFiHFMdqt5WHNh470XxXo7V2mEwNYKYjy/yfI14y2WN4kDg0NMafTUTqpUIiF2iKn56A1tRKIm9qq2HHn2/4s9t9/9EHaQFc9R5bALeMWgDfp2vUAni+q4w4ZfoJoqB5K+Sm2KapSPiUETEZHYjBzEPCPg+4AN58BjGrXiJusAvFr4wBGl2RYwApBa2EmyntQAsQM5vldqYStVi7ELN5FhVP7ybSj5xGDLkPKZtfdT/DRDLcHOSaKHX//YgZL/e4G5FgLfMzaxHDv45QBv8QKQvDKA1NCvRe435uR4raaSIB6zThh1XEjnifnyshYmq6YDgd4BgyzG1k6vRmYrOa1ef4JYi5FiNLxTbiFOdOJHwKjYdCv1sDrISZyWq1ElkK2pGQ3Uwoed8xzp5CzHgKEgC1noujbnM+ZK0jKnBUErFOtURKoEIiSe5uIuh8oZ9fQ+Qpy0EWwSLj72KkGPyucVlExM6VozihfkQTye2+kjg5nGJFW9xecstVur8ViA5KDUONYb/RsNUSMZONRAqhNP9lwGdc7nAaEcO6AljvdBfpcEc6ydqOaLkK0U3azNQT83/IY7xASgo/9Dg3GsaNxuUupGAnBXEu9H/bc9dInDhuI2In6whXfSucexEuXETUN17t8T2MaK6VCPrvJGodX4qsapCJEayZiBSmEvc9Eyk45UjBTnGqzyKFqRmtr0NuJwetv4PwVnI9l3os04yTYsQ/2pCCsMDzWavfJkHU2x4gTqgfJDYBG9RX7gS3+w5al/0E/6khyrwddhtvjMBDjedwElERo8PPdxEHrQqJEIk+ovRbpWH5A7RpfcW/dZFZ65nUVSnUZTOQD+f2EIr+PWgTvcv9lSLelTaIu5ByVoD446XEoaBqIqxgGFmuWxBfaiCqLBUTh2TSpu77xJUsjx2EtfI4sYG7TfhmCrJ4v2S45hjmM2gT0eW+F7ndlxi9Rq9f6xpVAM93JZdfNhKqg0jApmDyHDIMMu2qMwHzBUR92DzgGFyUYnrOIAZajxbyrYhBLyJSdUxHDL6GqH8LcWDgGOFWPImYRwmZLP6n/4E4OdmOBMwJgpl90XCvd7vjkEBqQkw7myh6X2qYl/veLqIMU46fn4gU3krenf4juVLPEqX0FiK3xkTE5DqQgpIYejahQB30OPN073UId3W18ZSDhOVMP5/GW0CkiylGrul8j3W652m2+2wiXFhF8PMNiMH+wPDsRALgCsPVCJ3/QJS9muv77Uj49fmdDqQYVCMaSfFVXcQJ06SgdiBhMAGGX0Yrs8ZzPMt4HDL+kgKWLDz1RKoMCOX6FsJNmu85KjPuh4kY0QG/t4KwaE9ENDiEhFqDx9hA0H+Kcax220sNZxMRA9qr9wYfgrd+RCiXKRayGsZ/agROJnp+LyPK6W1xv0s91lc81hJEj4WQezmyoLUjS1i5cfInHsNhz3WBfstfhugyWfhLUexZJQw+idZqBZlqG1mzgYMO69hmfB0wjMvRmoQ47ZyuOkQLc4jDF7fF2DPu+Ur/vUSUJluKFMkmpMgly/cS46MXLqxFykhSrIY8rrTBLDEchUix2AJcCtmXIwvVAsPS53dLkLXySqJSxdAIGFOYynQinUm3YKHf8/eScTkN0V6zf2v0mNuJnIw3ElVjkkXvFiLOGOIgxgJ0Qv964/0K436I2Di0uK3ZzttYhHjfBv/eiFzy3R5LgmkNcYhrDuKrwOmfERvotMZXESfD0/wmpTxZ2HMM141E6cnE27f63jSi3vQIhZtBwuqX2m8yfq4nFOZGz8VCPuDrH9/jv9Hr/bpGFcDzXfsQk+onXKRdRK6waf7+baJU0efQQYVWf+9DDL7N7SQ3wBkkcP4cWa+2olQPyQ2XlJ92xOC64MCLRB6rcWjRDxvWq/x+ieAaPw5e24KE2FrC/THebfQj5nkbYpa3+Lm9yHWWmPpupKDuQ0LvAFG+rZnMoYnBZ3h3OoQjSICkmMgGohScXT0/fgbe3gRtW4yfdr97AClmc4hKIrfo/49cjJjuPs/FaaTYvWh4ZiKB2UsolAWEqxkizc1UMu6nc1uQYBwAyuEjVYi51pEpBE+x8WHXW+lVSJjPJwrEr0WC4VKkeMxQezzmvk8SFrPlSJkvIJPJ/63j6i97LmGRHPR4v07U5j1knKb5P0PkFetAQmi55vLACnh9PRFTlWLXhohTvMeMl+NurxtZbJJSWqq/7heB7fD2Fo+zgziJ/N0x4aL6MbLCHEDz3wi5RXDhh4iyVt8mqoOkgyD7kYA8aty8iGi6n6gJvMxz8ziRz/GocdXpOasm6qa+YlyWG//j0Hq+CdHAGkRrK423bZDbQBy2OAWnm8ik9MktQ8ruE4Y33zAe8rh2+b0zhq2KKPV2ijj8M+BnUkzsdqRQFxMbrhzD/ee8+6R7I7JmJavpKmJjdrHnNLkOjxMxbeOIk8K9hFt5KxGTN8ufz8NbjxOVhrYRfKncc4JhfYfIDnAc0U4tEYbSgZTxo8ZTjvsfVj/0eu7K4b9tINIVDSE6S1axMcB/RIpSl2FoQOtyruciKZEJV82IR+x2m+sQL5qO5j2FOJR7Hqai+Gp7YMY/QLiJm3yiuZHI31pCWK7TeBsInuk4Wx7z2E8gPjEXrZEUh7qSKLN53Hj/Y89H2jy/YViThXwY0Usfo9fo9Wtdowrg+a5d/ixFjKMCHWLoREJ/kDix9xqyELUioTcTMZnPocVdipjTPiR8qhED+Dpy81SjxbyI2LXv9P8PAEtgzlwiA3wFkWy5ElVxmI0Y+ZDuzSxznz1u70UigHsPUa94MxJmLUgYLEHMZTpyu0w03GuRArkZCadCf9ZD7lz3PcvvnyXScCQX2xN+t1PwfmKRD+F+HLsP/19y9R2HE48YPgxjE4rJmYeUqhsNazHhQpqNmPw0Ir9XsmJNQ0rpBMT0OxGTnqx+sjYTedOeIArP7wIOwqSPo/ijAp2UpU5wZiyV6fo9OH03Es5PuL1Ww/2Y+5hJlAhL8VcFaufCa4FPXyLhuYaopDANuS9zkVDpJWLEUmxVLqKZvUQC4H6YcylM+QJR53k9kVqnjoirbBiB06+QKdmVcXcO2/NaCGM/jiYvxQLWAHVnY9NSgzYY9xB555JVr8z9fQ4pPUc9/n7PX9qgvOFx5ROWn39PpP44itbA40ixm+p2XoC/Mj7ZZnxU+51FfrYNKZ4p3OKA5jmTd+5hIq9iA4y/zfAud1qj7ZrD0/WEdXgeot8StLG6hTi8UI2seQeMm2ok3JNVqNvz8QKZPIxsQrTTR6Y+MnnISrvPc3I/omk8l9cixfoIkULoqGErcXtPeK7ziZyDKxFt9CBL6R7gXrjwTuP0cqQUDSO6HON5GOf27iBOp08FXncOyCEyh2MY9vwXEFWETiF6e5RMPOscjI9ctKZfJVI09aBwHHzvKFJ+06a0CtHKzYh+FhgHtxoHydVdgPjuY4heU8xgl/H+OzCcwiGG/dta4BrIvZeIw11MJN5+BYYfNQz9sPs5OHKcSHdUiNZeD6K/bOLAzUngPxCx3weAXdB/m/H8rGEcQOE9JcLbuQeJtDIf6DVqAfyXeo0eAjnf9UiWYupWI650AjGqpUgYf4c4BFCLhHIxUUKsACkRpUAb9N8P+Vehxb4UMblr0GJP7qlZaAffghSMIn9fgoRjB3H4IAUsz0JCtR/O3ePDBnsRg1mGdoxPIYHcgxjpjUiYPAscheF/UOqY1x+CKSkurY9IgTNACPoSpNy1+P8jiNFOQIpfisspIITWbji3H7JWI8XiVbcx3e/NMg4KjI8/cN/Pu48UX1mmPoYfdXm4TqFgeD9kP+R+yxHDTi7T457DeSh9RKHbqyGS0s4mLJclcO5JyLqDsGrmGKc3+v0LkGJagfzS5Wqz7UmoyDHOKgzv19HhmKeJAvD3IwXuCuNrqsc/QASoH0B0Uknk5dtCVKi4FFnuUjxQB1Gt4IjfOeD5r/UcXex39hHWpTRPhb632bi5H9F4IaGgHdTcDXdA9hgYPuuqKd9CcVO3GcZ9yOqyDSkMyVq0CQntlR7XNkTP3cZnIfx8C3wkx3OW4/e6kHLxA+j+CRRf7zF+AlkbJxHxrmvdx36PbwxR2i3H+BjnuZ1leMsItzdEGo9u4y3P89cNr/8Kpnze9w8RMbOHkWA/Spw6LTMuGj3Hx4jcdEOGed+INqYRm68j8PYj0pFKLxf8gychd4mfXYJidmcgxeqvUSjFU4g+7jTeGtxnM1IuWtAGtgApWYOGvddzPc64m+Ex4ns5iC5+DyldA5rPN8/ARZtj/piNNsZz3dcBIin7F9HaXozWY5vfK0W0OY8o1VZF1JYG0csSw/Rpt19PHNQoJGJpL0CbsGkEbS0mYnmTJT4PKdfziKwMhxGdDxFpdg4gXt3kedyv5/sPC72z3dxF1yM+tRDRUitStAcRLe6AIztg+qeRRTMH8bhy46cG8cddRH3thZ7DZiLV1hVEuMR0tCG574M8BHLZe3wI5KXRQyDv0zVqATzfldyWjcjtUI8WZT7wHXjrORxJ7Wc2IiFRQMTJdCEhdxzyLydOxbUTjC6tmwHof4g4tTYGMaAleu71VxGjBDHDm/3baSREWiDrFiQxWpECiPuvQYxsPhIMu1FwcgswBbI/BTwFUyYQsTEpiL0cCc3kgjuAmHA5Ypr7ERPLRgKxCylKrYarQe9nzScTUJ9JnTCMrIpdRF6+PsTQfg+Gf0SUyTqKFJl9kP2vjcte4SB7mce9FDH+KYhZz0fK+gbCLT2PcMVgPLeQUagHn4SsFf4tx3Nxk3H9LBIux9Gcnybykb3jxXQropFepEzNQgJ0FZFIu5VIY9OMLD+tyNo7bJgXICUyWQxuQMLpGj+TBMQKwl3bigRjqauUrDR8K/1OPeG26vI4dvq9Q0h5mIcU2HSaeCohRKcItux1wDrInm842z3uZkQ3X0TK7lLP26vI+pGHrGSL3F+KRSxANLobPjKbOC3/DlJmWhBN/D4UJ7d6cv1iXG4n0jTVeM72EomK1xBpapLrupPYDAwiOluO1sxyIk/d88ZJCUxZZNhy4O8fdB9PIEUi3++c9P9HPd+1aA0NEgdYKo3TpIiUGEePo9jT4zB2kciNAvWfOxcpvMeRlW6iYT6oecnUm64iY73OxOnWEonlZxJxuTWIJoaJShyXEiexK4lURN8kqut4w3bRg4S7uwOtw80o6XGe4VvjNpoNZw9RBWc2sjCmdoeJii+ziGop5W67gUitdJNxedBj7EAHgRqN56SEriOq8uST2fhm4oFb0EbgXoIfzzA+OokYybn+foPeHwAWXwrjPw4XfQHObTC+Bg3TGSLHpF3W0+cjQ8Ew4hHVnpPkLdpJJBpvIGg1WXBLEaPp9e+b/ff/p9fLL7/MZz7zGcrKysjKyuKv/uqv3vX7uXPn+MY3vsFv/MZvMHbsWJYsWcLRo0f/x42NuNavX09FRQV5eXnU1dXxk5/85P+hEXyw16gCeL6rGC26XiKH3mIkjIfsrpuAFmsOWpgpaD1ZEM6gBd5MJHHtQpUpGolSc21AN+TXIgFZh3Z5ybxfBlMmIoWjmUgdku3PPPfZ7OfXIiaeT9QD7iNOF08jiptPRUpFsiTmI2Exx89VeFzXGM4cwh3TiwQ+xIGTLsSQphmu5YiZ1SLBeNbPp/ikNv+/3585fq4Asqe6/0NI0WhElqWlhnsa4Rppdnt90L0RWaOOIevTDD/bjywNS5HiuQvNaw+ZWsUHQEy8FylAe4mTn11ErGA/UUu3EOj0fuANxPxnEGWaWpFCmqydJ/3OkNvvRUrWpUSsYa/xUYRoLN/vDxB5xpKg6/Nz85CVrd1pivJh8DhS9rf5vYNIGNd5PLloDte4rX4ycVxvv2EY9vjZNsPbbPjnGqbtiNZ73f5Ot/UaEq4nEC10E8nIT/kvhzjYMY0oVZfc28nlm1x+SSE+SRwY6TV+K/z/QaTMniDS4zQRFWqG0HqdQ5RfW0Cc3rzRbTQQSaaTNWc2mcMEH52N5nkl4YbORussWd9KiLqwl8HwLve/y+MqJFyTlxKHkUoMD+53L1JGKpDSX0ac7u5HyhPuv9RjShbjFuNtj+Bmkds4TcQjXmN8Jnp7yTDvQ8mbjyP6WmuaGgdvnkS01UNYunajjUkKqVhOlDscQPRWbfws8ecppFCO0edrP/FYmz225Yb7PqQcX+D2x/n+TMSjTyD+Uoji7nKJTALHydT2ffOU8XhU/2eqCZUg3lVK8Opaokxicmu3Aiuh+PNEBoEOyMohwkP2G77dbs8bYdrd3zQixCO5ipcgmuwgDnKlDXCR8bqWSFBeY/g+ywd8DfPeuX+H/0k99/f3U1tby/r16/+Hv99333089NBDPPLIIzQ0NJCfn8/SpUsZGBg4b5vPPvssX/7yl/nmN7/JwYMHqa2tZenSpbz55pv/JNj+JVyjLuDzXVdmSXAcR4xrCWJy89GiPo6YUB5ayHnQ+QyUXosWe2Kkk4hSX50ovmgBsbOcS5Qla0aKyy7/fgYxsm8jIVNFxDZVIWZUTCTTrSXqtX7Fz/0QJWvNQQy+AwnoBYjBdSKm8yxiVEsQUzyLhNs1KO6kBjG7YcTkDnosS+GtR+DCRwn37xnEzF9Flq+9fqcHCb024oBHu8fxQ8N+yu8uNL47ieTXVcBk6H8S8sd5vIV+fili3icNdz1xWnItYRWdaHx3IyX700SC1ptH/L2IBHpSdqqQAGwn8pMdRS7MxcgKlILpryNyPOYgwXuV4c0larViXNQia1cDopVmJBCKkcJxFA79CGpSHOgSRJNf9xg2Gj8vIzf3gOHbbJwOE2l4vowsJ3nGew8S3IWIXopGzNd+VFv5NcNyyPPZ4987keDei+i2C835BR5bCgmoNe6fR7FvyVp8VvPJsMdhS3YmifAgkV6lBN5+BsZeShyyuMfPv4LW2RJCeKYwhBWGqRMpuTuJyiKFwKVw+iYY7/V6qAlqqlCuzF7C4rSXcOXVE1bKRcZBEZEO5wFEj0l5fMo4L0E5A7+hNt563Kd4k2J/xvN2wvhuN74LiPyhSZFPluLHkBJ0lKhU0WkcrELW5bVu62FEA2V+1nFv5zZB1kOev2OIVh93O2lT0AenfwXjLzU8szy3eUQi+7PE6d0ZaC0fMnyriJRVib4r4fS9Ttm0gkyeVR5Ha/oyIg9qsuSXeeytxkOH/1+CFN5Wf9/k+ahBiu4LxMGvaYQH4pD/vxfx2gRnqXG2hcijWuUxvYJ45rV+rxDJhWa0nscbzjXGy15isz9M5COdQ9Rcr0V8fnjEXKfYvl69/zf18G9X+16P8ZViQLd9kC7gxYwf/6/eozb/kQkTdv9aLuCsrCy2bNnCb//2bwOy/pWVlXHbbbfxla98BYBTp04xceJEnnzySf79v//3/8N26urqmDNnDt///vcBOHv2LOXl5fze7/0ed9xxx68/uH+G16gF8HzXa0TVgy/CkWfQrngYLb4UtzEMDMHPn3GszirgL9CutAvtAh90mw1ELcz7kZJT6jZOI2awGwn9cuKU43ykoEHU0y0nXMK7EbNIO9vjSNg+jwRkcsVWw+CdHtcEQllrRy4uu7nY4n53oZifk4bxKST8DiKFZZJwcGFaE32I0e4VTriWiGE8ioTCD33vNJG2odc4vQIx4TUEwywgqqgMCaf5E5Ei147cO1cg4VYBXGf3/OPG7xB03kkI8e8ZhnY0xxuQS2cRUqTSLv564/hLiCHPJE4/X4asEmuRoP6u255FJLR+zXA/gmJHr0bKX7K0fhMpoe3IatiM6CLfbS403k4CfwI1mxAdlCBl8260KWgX/IMtSJmdgwRSr/u6BW0GrkOCfBZRv/coUgKGULxfOYrnm06Ur/ojGH7O37N9f6HfHw/sg8HDKAawUvPDNxB9HCTcx8/6/RTPVIcsXjuRwvQ9OL0O0WwHYZ2sQbSxC8be6r7/I1ojG5AA/6Jx+oeeh73GVRFSip8yTn7HfZcRm6bjMP5uMgpszQa0htuMm3a/cwRe+xFSqscRp7d3IuWl3fDsHTGva/3s/9tjOovc6LnAUrhwnOG8BtEuSKjP0e/cgjYdew1Ljj/v83ie4N3J0mcZlhRjN4DW2QVovdyDaHia8Vauec0a5zk6ZhjuA3qhLYWdTAMmwfg1iD7XItq8AtHLccN20PPaDK/dT0iXMYh/NCAaaCfj2hx/A6FoVyCe043WWDtQpY01jWid9vj+NMJdmyyqJ5Ei1YPmeBmqHlQMb/YQVZYqyRySypzU/ipxYnubYdroOSgjqu3UetzjjJsZxnkbcdhpEWGl2+e+Wj0H5YZ5DqLPzTB8G5rjq/2bN0XddxO0Ogz/dh2Z08W7d/nZK9B8f6DXe2X9S39SLkf+vfPOO/9kqH7xi1/Q2dnJkiVLMvcmTJhAXV0d+/fv/x++Mzg4yE9/+tN3vTNmzBiWLFly3nf+JV+jFsDzXJ1ZWZRORYt6B1qENciFmI2Y7hL/VoCEdw9iKNVEgt9W30879gKkECxDdRyvR7vVm/3sHsTIOxETmuPPtWiHuBApoqUEc0hWip1ESoBKwhqSYgkHEKNM1rhBIq1MEwreHkaC7DiyTqY4qnL/HUdMbz8SmgN+Jh1e6CKsdfuQMlFNJp0N24kDJc3wd/XwyRs83j7DcQuR+b6VcBunoOfN0N0ExauQtWS6fy/0mPYjIb4MMfEmj7kQCc3kqluFFJpHPa9TkMAc7zH2EGlsUszjE0SZpzyksJQSqTX+AllMqwkrwlmkcC9AAv8O/15AuEZLiGTjJzXGzPc0l+VEbdl2JNCTq6kd0U4BEVP3MNA9C751MBIp7yViVFPc1qXGXyuK/WtFAi2PSJsyDikIExG9LvfvoLaL/D1ZNa5GCldycRV5DkqRQrLLuJ9NWG56PcY0L/d57Dnw2n6YucH35vv+Cx5TsvCk+MFpaC0MECfnu6D7ESie6vZPEUmSk6XrWqSc/cBjHiZCEGo8R7s9zlXEaforieoqXSPmc+YIXNf6c8Bz9Q2iEkYemtelSLFPcYZTYfB2x/7lIPoZdhvJ7V+BFJMjRCLvQbQ5aCCSfO8ybuYQ+e6aPI4h4kDKsHHwBaQ8LyGqGT1M5NUsB/6IOKl90G11eY6bCB6WR7hVR1ooc5BF+FHjJA94Eq2pdDhnDsHnHhqBP4gSatchK+whtF6zkRt/G2Fpn4w2WsnDuJOg30J/n4vWQzaq2HIP1HwK8dydiAaWog1fHtpI/j6i3WIkExaiTem3CQvdQsK1nMII0kZlFfAO/LeN8O+WEBvHRCspZhniNPlGNJ9jPHe9QOsHaQGc9x5bAPf9X+5/85vf5O677/6/ffe/twDu27ePT37yk3R0dPAbv/EbmedWr15NVlYWzz777P+ljY6ODiZNmsS+ffuYO3du5v7tt9/O3r17aWho+PUG9s/0em9m7X/Dq/RaxKCmI8b7BJzb6LqT5Whx3gWDp5wb7Gq02FNs3C6080uxfwWElcdxQedOQtZexMCS8jgDCYsDBqTc730TMY8uoo7rTiJdQo9h2EKcgM1BjHMLEppJkdnpPiaRyYF1ej+MP4OEeTaZlBpcT5yILCHKaNUjJt9EFGx/xfAvNDwrkZBMimEOkevQysUna+GXj8Ml3yZiGZOyU0rk5yog4rVOQ/GnEFOfT5Qlex0J3bnaPRc/TFRGaPNzy4id/M1I2RyDBGKKO9sK5x5yBY+jiAEPIYXrCiTsNiKF4ZTxuNz9bHef042vCqIGa6PntRcJ1m5kHa5GQvA24pBMJVJe9vmdmwh3azcSnC8b9maPqQVZlhvcxxrgawfhPvj5WfhIObAYTtwKk+ZDdz0U/2u3vZ5QeFPsXKXdg0sQbVcC/TA4BLkHiBjM5EIv1Tz3f9v1ha/2vM9AG6U8/3/Izw74vQFEw2ljlRSpIv9VwswzyFJYSZT46veclxB1ZrORlSwpXUNu/zAUX+XvdYS1qMXvdIz4m0ec6k0u9FbjpcYwP07EB+8mDiLU+d0USvEqshC9gjYYE93WNrezxfg56L7X+bstRLkpJOAWIi6swu0lV/ABt5li1YqQxbXY8KVxpIMwW5EisZqIpdw2Au6tSEmvAYim+QABAABJREFURtbTEuJEbSW6CpD1byuin8lEIvsOt5mUr7TRmOb7OWgtbEZzvYKIZf26cbgc0fRuf9+J6LrSv5ejzcBs4iBNp5/tRevkJXRSvBvR9U4iDrXSYx8kk0WBCrSu5wBHoOZyf98Gh14VKsknKh99k4gBnmA8bCdSDx3wb3f5vWriRHgjor+zGs+/a/N7N5GxDPInRHL3FrQm0oavFvHeSr/3v9nV3t7+LhfwBRdc8H/z9Oj1616jLuDzXeWIYechBjoXspahhd/l3+9xjcwiosJBigXqI9K15BOVFFoQU90HWXch6wGIYfe5/V7idOlRaKtHgb5FhIWxC6VUmEqUFmtELstkVepAysRCxGhz0I7zK4YpxYaVw/i5iGEm5WEQWYb2+f9XkWDNR0xoADHNCYjJlSGlZzqy+OwjTuJdTqZKCXOQgB0kkyPwkm8TqW8a/L3UY2gm4pAgyk7d6Ptfd9uLfb9NbRQ/Qxx0+DOiduySEfdTTGUlYqppLMdc+SGHcMkmRXo2UiJXIOW2hLDe7SOy9zd7Tg8i4TgN0cVxw9iJ4ocuR6twjPGxCykkVX53HhLsENVezhrul5AQ20MkQH6JsC6sQfM+Gz5yPRlhOWmc5qV4keFqMoyFRI3hAo05awOysBWSoanc5HafBlRA9yaPZxgog/xbCEWn1zDkCWeDtyFLTBVh5ShHNJVHbG6SBXU6obykDU1yea7x3JwkrCYg61raPBQiOpk4Yi7XozUxAymjFxDpUOoMw26/e4Q46XzM/ydXf7VhGiBqCQ8hC14rsS6WGl9FhLs0KauDfv86w56MH0cMw0I4cMr4SZbhfkQLA8btNHS6f6r7ykV0U0VsOucjetqHlMwCvzfVbRb6frbx9iJSNncCj8Lp29Bm8VXEV15Dc1uG1tJJIpykAylTyd18gKjIcRDNf79hTRuy+cSp6XbEB/oNZwo9mEwc1mjwXE1Blskc91lqXHUT+Q+7gLuhv95tnSKqFn2LTBJqmkaMAeIAXxnUJEU8hdVMQnN+hqjqkhTR3bw7Bc+3iFyJt7j/TuPpFWRJnId4Strgp83Qq76Xj2jnhGGtHvH5JT7g6713AY8fP/5df7+OAlhaqhipkydPvuv+yZMnM7/999eHPvQhsrOz/0nv/Eu+RhXA812zCSG+Ge2QW4l0CWnR34SET1K4GuD07Yi53IgWdgH0/wxOP0jEIJUQTMgunV8+7vYe1b3BZ4A2qPg8EmB5aIfZAqdfRULiFBlX81sbkJI2gARdKZH3K51M20akG9jp8bUTJyNnIUZbjJTOs2jruxDF5VyOFAKQknEMMagCwg2c4p0GiFi+GiJP2A4kbMs1bvYh5ahN9/6+3s9tI2oE1xvuG9zmTve7wM+ddh874e9fJdwmlUj4liIhlOv2cjwPe5BSmOLuOoiKHgcIS8YQ4dIqIUrlrUFKbglhHWj12K4ghGQ7oSQs4N31Y1sNeyWyAia3cw/8zc3IyvIqoaBeDzwE5152O1VEubAWYoPwO0QqmLnIYthlXG/z+I76ex9yWzV77P+F2JDcTtQdvt9jWmZ8JUUyHwm+HkLBqfU8z4f+XcJ1boqxPIaE337DPej5mug5KIVn6/3MVsSp+ghX/gSPeafH34vWwxhEKyClZLfGeO454sBClfDHi/6/hbDg7/XncsNS7bZWIIvxNUT832yPcS1xqnM+EfubNi7rjedht7nK7fa53xoiXKSDoPcWYADmrEK0d5DYROxBMY/JPZ+HrFdPGN4S4ZAVSNGCSEeywW3tIOY8xTpWEjSdbdimubrdQbeT3Lxb0Q9NHku/f1uBaCrP4y4h+EviCyf9XNpcHUfK1AliA1OOlNJOoA3efJWo97vQeB8kEoOXus8JhmMNmt8c4AbIn0/EKm4yjvs8zh3QfRf8solY43mInnKR4vaXaAP7x0jJT6E9w0QZv5UjxlFAHGJJ1tsdRJ7Oa5CFsoaoyZx46S3+vtcwXk5ko6hDdD2MNpHNjF7/g+vDH/4wpaWl/O3f/m3m3unTp2loaHiXe3fklZuby8c+9rF3vXP27Fn+9m//9rzv/Eu+Rl3A57tOIqY4x5/zEHNvIuKzBojksbmIudXC8Rehphwt/Gbgs5D/NGL2yS2yh8wu8c1H4KLb4JI+4B040ANz5kGud5Q/fxI+chgJLitN41cbllmG55RAoJBI1pzcLSnWJ7kXC4ng8hnQ+QCUftXj24wExhmkHPYiS+AhJORa4KUfwWVfIMo/4d+/7XFdq3HwLGGFOUS42lLM2FQifU0TGbffR+9ASmEfckMX8e6ErzcRDHYZUY4qD6iAjw4RMWnVSDA9q8MKuamNYrexwn0cJOpwNiAB87rntB0pc0eQgj2TyL3WDf/nHfBbS4ynOuIQTHKRdsC5eudCfBgJlduREoLhv8DjzSdSdwzCv73VuNhHpDTJ03NZ8zz+U+hwy1LP658RJaTmEgd62pHQu3PEnCQL9WIknOqQcMxGtL9V4+l/FfIneEybiSoT/WTi9Djg32Ybf7PJlFfLH6M54CZkPSokTlQ2AkvgrQdcfeLPga/CNTd7THcRp+3vQHS9khDgTYgWDiOLYT9S4Ko8vjsg6z5ktXmKSLhcRcQn9iCFMLmYy4jDTkeJsIX9hHKxy3C0ErkUq4kTp9dpXJmarfVIiN8Hb9+iqR97uWGogOGTkP0lGLzDCZ/rkDIzhNb0ZLSOMW7vQvR1HIUntBrOP/T9hWo3o1jmGr586N8I+atHvHcfmQ0UBXDkZYfWvgDcDdlVaEP7HcNVT+YQWCan5WWer0lwqB5qKoWT1/fDlCVoPV2B6GQydN8ExTcTh98WIno+7Ge64PTVML4KuAkuulF0sftFWFxPuJ3/BK3lZ8jU7277EVSc9TOXmS52AOXQ+SqU3gE8DoMrIPc3NVfFV8PuO+CSTrSGr0H87GbD1Ijo/Hc8r0mZzCE2c8/D240wdiIRA3y3x7aYqCt8hkhfVavxDa+G7BUE7XUjOvNmkDoUX7xA4+AEcD8M3wrZT/MBXsP/80f+H2rrH/7hHzh27Fjm/1/84he89tprFBUVcckll3DrrbfyR3/0R0ybNo0Pf/jD3HXXXZSVlWXiBAH+zb/5N6xatYovfvGLAHz5y1/mc5/7HLNnz+bjH/84Dz74IP39/axdu/Y9GeE/p2tUATzfdZc/q5ESsIXI8r8a7aLvJxJEDwL7YPgY1KxGAulVtMi/7FjB+WjB70cMzvFIF60nykRlO7XePjJuno98iMhlNgvYBW82wkU5iFE9CeyA/GRx6AVyYPgZyK4llL4l/nzKMFcCjVA6DjG6XKT45gEt0P8C5D9DxPlVq+3L7iaqHBwzXq5HTGohUgTPGAczjKsJKIffV5Fg3EIcnhlwWwNIyNh1+tqvYGaecZ7ca32I2eYQloUOMuWYaEaK7Ab/lm8Yltp1meKctnle70exZSmOcitS7I66n2YkCFKsTZ5wPNgIucuB4/BbZUjR2uF3UwxlOZmEr1n3EFU/8t3Wt5ASWIeUkPuQNafMfQ4iZSPFHH3Gz04lXKEvGOerjJvNSHEbp7/utVB8L+EeXWTY7iMqOnQgwd/ieZyH0sUsRO7UQ5B/DZnNT/dZKN7msY4x7MdQOEMTorFSpGjWEgcxWv3ZgxSrhdC/BfLvBB6GC1cjoTYMb3fA2AKkcI8jLGbrDOdeohbzUaQAp7CBHqJmbQ+RFHoK4RouQTR4k5/tQwr8fsJqO9PvrUSB/9chOknWzoWEpWoiHNgBc/Ybr48i+rvF89jvd4uB3/XYAPbBuRch6wRkfxr4JuR+G63ZZz0XSbm8kchF1+32+jx/tyHGsY1Ibj0J0c5SItZwstrLr/IYN7mPn6L1Yxdx0R6P/07kopzpvuYQh3lyPBfNut/5EJTO13zVpLi+r8GUhxF9LQaegLYzUFE6Iqn3LuPgFsLiPEFjGX8zYeHaqrYXX6z+3t4CY59AZdQSjZQCy6HiOPCbHl8Z4jOHgCuhdKLnbSLkriXqvjfDv7sDhu9VhZv+jd70tHj+VhGH6LKh80fmnWnTMAlYBGMLDe9OIgH3SffTTSRI70WbkG59z77Zc7Qd8anZ7u9epHx+XTCcfsYhO8s1tux7+YCvfwTOvUdt/dMUwMbGRhYtWpT5/8tf/jIAn/vc53jyySe5/fbb6e/vZ926dfT29jJ//nz+5m/+hry8vMw7r7/+Or/61a8y/19zzTV0dXXxjW98g87OTmbOnMnf/M3fMHHiRP53u0ZPAZ/vWpsl5tmMhNs4Ing8CftFiMkdRMynBwmWcrT4VyNBtw8x3krkpmlBC78HCbBTyKqRLDDjCLdsCmR/iEzaBi5D1qikBBxGlRgS0+gBtsAv6+GSPf7tWuIQSBJuB4mC6n1IwBUi5WAyUW1ih+G7wnClQPFZCEcp3mw/EuCFhnel4ZsI/S870fUS42YNElJHkbJ2vdtuQSfwfgQ1RcgiUEiU1qtEqVXGEKWfJnuOioyrV4yDRs9Vcq9VEG7lAkKxykbM+HqiEPtRJOTnuI+pKPC/GylnPzD8XX73T5HA/iPj5ikiMfF2z1m/8fkOSqCb5r6OcPG1eIxriXQmQ8iqsxwpeL/r8ReSSWDNLMN+CNFoipmchoTIMOFanmfYL/Cz5by7Luyw+2lBwqvcOM0bcS8J5aR02PqdKS/XRMR5HfS7C5FCt8DjnSacvbIfFqw27HWGdx5x0OlGj7HX7+WieU0HErYg5SK5HHsIV/Z8z8Fh6H8U8lf53Xwyru8T9TDpN5EAn208fxltYnrQCfGZxKnLHqKs2AuIvqpRypFkaSwiXKUlRO67qWgj9KjvVRMxYDWIVvI8T18icg42IKWo1OO9E63PE8bjHs9vcid2Gt+1nrODaO1VIyWykCj/lqzPeO72eJwT3EYrooukdLd7TOM8z2kjdsQ4G0a0X43WSyuZlEyZ/KbJRb4DhXUcMzxH0fpKdL3F8/FdtBaXu80WIj/iAeKU9HYi7vNLRFqtlw3fPKJU4UTPQ4efbyCSU89DVVcqCHf6BMM+CwYvh9zLiXQvGJYUMnLG/SRLfAFRTrKBmPdTiBeW+PmTRIaAI8Rp+5PE4ZIGz1m54dwFbH//xXicAq5l/Pjs96jNYSZMaBotBfc+XaMxgOe7rkYLejoSACVIKFcSDLADMbB2xAgP+nsnEn4tSJD2ICb9AhIOtxFCYhJyC+xCwqoaWQkaDMc2/7bY/59BjGgeUcqpCjG4A4hx5AAT4ZKb3c5ExNRbkKvpdmI3mk1UrGgjAto7/H8+UsLqiNJFKcYqxa3sJONOzriE7iQsdTMteMvcZ4p12eV+FhlvxWRihWpmEDWW+wx7C3AM3tyFFI4h/+0hLB8pkLsZMf61eifjvrlU7WfS9qRAaivNdCGh2k9YG0EMeJzvbUSMvZ6o2PGY4dhK1IBu1f9tvyBKjZ30s70ef5vxtxUp8JVkTq0yHSk7TyAB2oLo4IDb6fFvj4+A76Th7/RY5qL5v45QRrIRzfURaUVajf8UHwWirQNIMUubhOVEMfth912C1ktSJMs93jV+N8/PP4w2ERe7/X1AASz4lOeghkhd1EfEUR7y/zuRktvscXYQ+e9K3GaL2x8kKubkq6/8O5FQLUd0MkP9T1qBlIbXiaoNaSNwMVGmq0bPZ8IK9hMnYFeg+X8HzW0KdyjzeCqJxNYPIzd9OVq7n4DXnyEUsgr3uc9z10kki26Cc0OGvdrjrkabzdUe3zi31Yv4T1KK8hAPqvQzlUSd5UKiYkat25qJlKoyooLQQaIsZAuigSJEh5PQGhtCbv7dcOIRROflxGn6tPHaiehzHlFyMOH4+35+GZJSf264hsm4xDufQXxjFpmTuyxFtF7l9qehdZrWX5mfeZpQqgrcxiqiesnFmpdMpZpLkbI2DpgMuWvcdo/np4k4aNSD1k3yKGw3rssRbW033DV+5xDBt3vR+mpFtP+A265Cc17lv0Ki3GYOH/D16x72ON/f6PV+XaMK4PmuFDezl8j7todIWjpA1CI9iRhnKWFNKkRMbhFRwi1ZI4aQ0EgWvRTTkdw5E1By21VECbN1hIXle4iJPI0UnwrEBD5DKB7lunfkDiRMxiEm931tbMlBSkYSzn0e6xGgSy7OTOmws4Yx7fq3Eu60FmAt/PR+4lRsLZHvapnHU4fcYWfQLrscuWiSFTMFVFcallL3/7tqnxXCx+AdaiKTmHiz26olrE4pZmotskRejJhnsh5t9ruVMLiedwdZJ8tWrvH6GhIk6WBAUpb3I6G13HM0k8jf+Dia/3HA1VCxBtFPodsuNz7nEYXpc4m40umIZl7XfFEBbIG39xs/NxIhAXkw3OLnOpDSNRPlMjxDWBSTpXaa4c/lXWWsmOyxJwUMQrA1IqGaYkhnEcmQlxKJd1PgfInvN43opxnNaRui2WQF30vk2stBCskcIra21+9f7s9aggZbDfcTiG63IstIDbIiDSGr7EZEC45/o9HtX2Z45xmnV7qNeiKf3pXIGthmGM8Qh4AG3G9SrJoRnSeX4wzjKoeovnOW2FR0Ies3MOUB4AE494LHlzwEt6ANYzWa12HIehhZwRuQ0rVtxNjy3f8swdP9HBE6MYs4lFCGeNMMIsb5JiJfZ7LoTkAbgzGG+yUitjNt2tL6qUTrqh8pS+VKN0QVUbEkxcsdNz7yED+rh86fEOl/Uhxyuz+fJ9a2Y3yLIBSgtJneQYR+HAGeVQWZjBWulVDQt6GMCB1+djZxOr3ZY/T6HFxuGL6KciRONc6rEP1OM5xHEE1nI943HfHys4ZzEQy+Q3gkKoy3HYi+VxOpmBzPmEnNdWrE3JUS3qcuRq/R69e6RhXA810tiBGv8P95RHBuEhhXIKayBjGE14iC74khNBGWjRZgIpy7k7Au7UTuteuIPHcn/f0wYho1SChlIwZwO0oBc7v7GUcI9j1IwTkIVMD0jyNXQ7IWfA0+eTdRmWOX2z4Dgz/x++sgt4jIhTaIEp62I4XqG0R2/1rgLHxsDRKIIOXwIPz0ccSIXyCTePjtTXDgEY97P9AAwy8SCnURYVkABVxvREJ+PeR+GCrW+fdkxdiGXD1NQB28VY+UiEbPRXJLNhLu+vn6nntunMb4gse6yTio9tj+GJgNp9cSqS6GySQXHr5f7Z6+C05vGfHuEFFybDZi7p8ico6t8HzMRVa9Fchq+hWk1LQY7go/kwNjZ3u8u9x+mfCXnazH8zyGZ5HyONXwnjSOFhIB+4NIeD5KnKxNG5rk1r3Z765DilED4WobQgrJbESfDWROgw6vQDRVBjwNP38AWQiLRry3GtHzy4iWkjJRjeh4HlFZoZo4uDLd+KpDSt4A8CP3n9zMX4af3o0UqGuQMt2LNk1HiBQv33Pb30dWuXKkcCUFZCGisVuQcrSD4AlTjav7/Ww3EtyvIWHf5ecfRmt7rd+5wHivIVzayRJ3LWR93s8egNONiPZfdZsFaO2ddTtjiHKVTxgXlyIa7hKuipchPlXrOVlrPB83zooRv2hGa/KbmkNO+/6jHlel8fIDz8nzwDQYfhT6H3R73Sjc5HaiqsXVaG6rjbvkRVlIHCzrBSZDaZHwd24dcfgrDylSpXDuScH01+vh3P2Qe5tx32L4r0b0mHAzXbgaeytRPeQm46KOSPE1Gbm4h4zjiWgdtrmtJZD7EOJly4kY8O3G4WzjPVl600Z/ElEQ4AJ/Xw+5E4ybTo2x80milGeu5/uziH6v9TiSZ6LDc5aD3Ngr8Y74g7xGLYD/Uq/RGMDzXH+flUUFkJ9i0IDTG336th/e/hGMvRstwO1k3FJv74Kx64i0FWeRUEgKyGIk0NuJWJAUl1ePmPCfopiv+WjBL0U76DP+fhWKNXqeyETfhQRNMxKsM9zfuhHvdhFpXkqQZeEexOjvRIrNc373e4jZTdd4WUDEgeUjJWM5YTnMRpa9AiIf4uPu8w7330ccfhlCSlo/YXnb7fZvdP8PCkdv3QsXrkIC4wpCYajzZOUS+QuTElqCFJrpiFlWEUpJKxIqLyAryCmknK02zr+BFLlXCKtdUrKmeaxbiRJ3C/V7/37IX4OUr6QEJqGyHAn+HqLI+xK3fdJ9FRv+KqLM214iv9hy399MxESCFI1ZSAmohuEtkH0nkTaoBllyZiGlfgyinz8mXGrZyKLcgRScw0hAzQP+KzIbP2g4v4LmP1l5DxvGHOLEbLJOpfiuIuKEugP8MzGz41BuuAJgLpxbK5Bya/3+JrRGPoUUxhRfV4sUrFsQrfUj2tkMPz8MH/k8Ya2/hnAjVyCLVVKAnicsr0eQe/YIoqvtiB6a0Vq+Hh1yehalyukk4umaPP5JRDmzV1As2gZkiZ5kGDqIBMeLPK/r3c8F7msVWgfJHdzqMU9HysBJz1WN577Pc5CH6Gs5Urafd3tHiIMSzyKFKa3FbUSJs3qC59xmGOchemw3frbD4D2Qu8L4v9t9e2NFOWHlelp4G34Esj8Eb/8Kxl6PrqOIJ843XuciOltovBwlky+UGsN8yPicgTabL3muBhFdrCUstcNo7YJ4YYPHcBmRT7DKeDhEeDGqgGz4+w3w0TLBdm6tT/IvRXxspcd41J+Pex4aPS+txusc9z/gOW0nrKDdiP/M4N2HawoQPRQZv1sQz6kg491J8dUUA099kDGAU97jGMDXR2MA36frX33QAPxzvT56g7/MQ0rDDTB+MhKi/TD2E77/GGIwg0ApjH2IcMu2IiE5h8iD1YUWc1rkIMadrH1r/cwXgQ8h4dmDFLSTfvZlePsnMPYHRAmiCjLJeznsd5J7rhnIhddbYMqAn3sCWS63ISZyN1KAxhA75GNIgExGVpK0m70bKS/TEPMrJE7IOq4qk7W/GjG+V/y9gogDxM90ErvwZuAEdB6H0kZgH1w4FTHNMiKJcjna+dciq2QvoZwm3C4hyk81EK7x65HS1E3kEmwkrBV7kGJxJ2LSA8ZHJZGjrZJI/VCgz/wVhFXjuNtObqBGj/c4sq5U+v8BMqc539wFF93gcT2LaO8MUVqwi3ClJUtZgWF6Ct5sgYsmQvZUQkGv9pzuN27GIGVgnnHQ5XE/jdztBXDuBsia6vloQhbkZvf3MySsk8W3xfAnXAy73wWCiYVEXNjjfrd3xNj7DP9epCDthqxFcHQPfLSWOLzQ5r5eImrlJtdlcsf3kHF9fmR4xBwUE3nrFiKan4oUj4cM22cRLX0VWblOIiWgxP0eRor9BKQ8FqB1vJsI2k/WZojqN32IXi5ANLhcbQ7ugNyFRB7ILUSs8UG30e72dhPl0AqM4z7DN9NzM86fBYgGp7i/ZB1Kys4uwpp6nIiTTDF6Sz1HVWhNbjTOjqKwgkLEC/AhiKXGWTZxcOy4cTtExBVnQ/ZqwT22D+iGcz+CrORy/T1EFyuQNa0LbXTK0MZ2LxF+U0gcNEqhNxAVg7qIeLvJaF4Peh66iDrDbWg+R7aVh2g7G9gHH/20x/QGZH3az/SiDUUbUd2oEnkrPmt4TiLFLimJ5cZhs+ei2DDMRMp82qAfRutxm9tvBz5vfM4yPo6hzU6rYZ/C6DV6/VrXqAv4fFc2EmxJ6CZmN+C/cQQzWoeCv2ehhZuNMLuWUEYGidJN03h36aky39tBHFC4FFkLehET6CFcRj+GsasQs3gaMdrlRAWF2UR93j4yStyUq9COchtiMkNoJzsHMaUy/51Cu+06JHAfJZjqfiIWcavbuB4x6YlEmad8tJv/MVKmcgkGNjTie3JBnzTea/VRmk6s9vheNyHcZhJJVg8S9T8LPf55RB3YbOLAQrvbaSTSNhwnguSTEH/FbR4yvtP8pHinqf6tn0iEm+KsjhLxcSXG5T7jqmvEHH0NufFnuq/TcNGnRrQznajRmiwMzWjOIZPmhR40pwvgonUw+DLhDm4kLAbdxgPGWSVxkvYpIs1NE2R9CtF0mcf6BsqRV0icKC7wvCQL+QHjoR3lY/tDJLQeIFNthmrjcyuRGLvQ81hiXM7X30dTPGSX36nwczOJKhi9HlMJETe62fi4mihNlkMofJ1oAzOJOGVfRVhb7ydqMi/0O+WGNa3/B4i8lZUELRcSBxJWEnV4W5HQbkbrZ4YPERShNXMG0WBSUJYRSd5riCoXyaNQh/jGbnTqvJt385EhpKzWqa9MFZIZxvEbHvdy4+xew30dcXjo68b1F4mYsx8b12eIOLvJRL3yM26nyP8f97vjCCW2DVn2Cl1isMTPD3tOXkXK1TARmpBiU1uJzeNsFJ95m+eozH1eSaTmWorW0TEiZnkW2lBcT5zi7yfjhmYNkau0DK35Do/hDFGac9DwNKH1dNC4L/KzrR5DOXFaH8TnHiI2F5MNbxHBg0+gTccsRB8JznLCI5Nv/NT5+Q/0Gua9c/8OM3q9f9eoAni+Kwcxo+fJ5LlKOeA4hgRN2oUnt00fkUOuCyln8xFNzyF22skluYBM+SxOwNtn0O46MaHH/E4SFn1I4OTDW1uInFJXGIYz/tyGmMYhIlFpIxLeX0TCaYCwMKSTcENI0PcTaSSakcWg1DA1I4XmKGJmOURVh2QVW0YEpdcQwfvHjI/kEskjhG8dYnbZHnMB4VIsN9yHkZBq8zPzYPhJ4oDNNURN0B6Pud3wLvU4Cgi37qXGSR86Xfk4EizTPdbxxklyzR8hXGRDbqOcSEhcatzmG5ZeJCieIATnGI/xMaQIFSDFH+RSTIJ6kEhlkiyZQ8SpwU3+fbLv3wrcArm3kqkgkSm/lYcUhz9037XEic9yP/eOYRnjuXMM4rkn0Ro4bbgeJ5TeY4jGu4nSagvd3myCpg4jGqtCSs1nCNr4lGG4lsjbV4HWz2rjs4awJJ12+w8TKYAmenxT/duXkPDsRgrBfEQHNxEnq8cRbtCl7qsa0dxZP5NcxruJU8CHiaotdUj5OIjW3CHPY9qYTCRSxlQhy9MM46DM451JWNZzEB/pItKPDCFa7CXc/oc8p9M9hw/79y+6/ZXGcReirf9MWFInI9prIk5XtxDxocUa0/Apw3IpWufNhjW57QuRIncv2hxXIBocQ4anDO5C1vQrjZfkim80PI75A49lOeJ1nYgvNiLL6IDwPXzMcO/WuM49BG+tRTwin4ijLjI8aV7GGP6LjePv+LkDaE3uJyqwpA1YL5nDem+ehEO3ecw5nqtNRNWYR9Bm/3OGdzNa2wcRLbQbvnGeg3nuY+2I/4sQH8kzzB3GywLjpdDz1YU2KZP9zkHCAjp6jV7/xGtUATzP9dp6f5lO5MC6FDGLYcQI16DFNxsFPt+MGN6TiJnMJFKN7EKMKrlOVyJGcgYJhDkw9lrfewLeXocY4TjE9DYgQVYLzIMLr0LCKLnm2gxXige7A8W4zUaMYhGy6ux1GyeRolZLBE9vIuJuziCGs8B9T0TxK3MF67kXkNB4DAWO17jt/dB5i2GaQVTvaEVKzb/JVVv7DTOI0e4kFNw8w7EKMcmDHucq5Ka8HQn52yH7QWCpS339Gby5kbBMNLj9JcQJ06S8T0CxRpcjAfya3Z4b3f/9iImDYi4PG2dbPJ9ryMQjnTsObzUhhaOESLPhueI2wgK23O1v0jxnYrfWuo9b3Ockw7UVOu9FrrF5iBZKDXeu+0vWxasI5bnA83jYv+fB4JDfv1FwvL0BCcFpSOnvQEK3ASk3myGriHAZVxiP6a8X0fijhnejcbQQzXk9mv/ksr8TKXAXEBbZw8ga8/ue0/nEydqHDFMRWi+n/EwtUua2E6mE5qO1l4csdJuItCQQrvxFhMWmArntzhjWfhhM8btJybydSOj7h35vnPt5mghzWEucIF3r99OGpZw47FDg5xeh3HZPGT+gNTTT/eF5q0brOZ/IS1jtdscgGqxA62IvUY7wNFpzi43bQuNpseEvJZPmp/9moopJs97LftCwnPLY5xP58eqI9EEDaP7nGucNiF7+ygfJ6tBaSe7NXW5rMZnQlAy9jUProQPR0Sr316WxZm8kXP+LNRUXfso4aEbeis3EKfUhRIt1aC21GNZi4fK1Rwz/9YbxGNqALyIyCiyEi9ZAzS1I8TyO5rzS40hxxTkEb13sfq8g6o2PQRvKzYg3Pey/es9TKaLpPrRRSG73Qrc7jDZvbX6mkAi7mMcHfA2/x3+j1/t1jR4COd+VlxUF1YG2R6BiLlAi5SfrZqT85CHh1ImYbz9iztvQQp8HPAz/9UX4DxNQQPlOpIy9CIOPiuTHfhoJieXIRdUAR4Zg+qcQc9iHdtKvw/YfwRXziSSwn0PMog4FzXvHzCzEMLqQ8FiDGOAD/hwm4ryW+97FKj2XC1Tc7X6vIWrOPg2chLYWn8YdJJP76vRGGF+rNjKpQVYipj+XSPxaZfzUEbv3eo/7KOr8AKFc34cY4hz3l2JgpiPBdQuRuLjd409W1HbE8A/4uWNI0D1GlGobINxfO5Fi+AYSzsvh9C0w/h7j9M/8dwK4GTpPQukXyKSzOfGkkwond1OD+z3ktnv82yC6piNGnnBSixSIP0SCYDdR+7YEKeS/52eaPa5ypBhtJoQGHl8X8DfG4YeQkpRivbqQwrdIc0oZUlCaDes1RHxTDhKqGwl3Z43Hl2LK/jMhoHPhzYfgoipE8+1IoRlGa+IsoXReT+YwUf8zkH89cXr6OaQwpoMJyfrUT8QQ9vn7AqQY34zW1w5EBwWoss9ywlWdLPrVxOGIfUjA34wOm6wm8sVBKBV9aG6TO78I0Xiyuvcji2qyANaRUWgHOyD3KiK2s9wwtfi5pcgymU3EzY43Dio8N4VoPRwZgYOVRKzYEbc7H9HQw0RS4lpEI5uIBORXID5RSNBiO1G9Yj5SBjcj/lLgNs+izc1mInXMIqQ0XjoCB8nb8AbaFNYbT59D8aXTPa8FcG49ZD0Ap2+D8Z9CSuPt7mMRkZIoh6g/nNzxZZ7X7yJeWY6sfWs9r2OImN1m4+GPiRRFuwXnuZ9A1mzP+RL/9pjn6IYRuC4g8nMm1/RMz1kb4jsr0AZ6p/HfRRQLOGG4Vnk+rieshtmCu/9ByP8qEdKR4r7HuJ0a4/QLH+QhkN9g/Pj3xpZ0+vRZJkz4/4weAnmfrlEF8HzXI1la+GlXl+JzpiEGu4Aoop6NBHQuYtSNxEnbQjIBxdyIFu5m1HYTkTS4GwmsGxFTO44W9jYkuM4QQdYpXquSiD0Z6catJTLUNyOGMZtIRtqMdps3EDGJk5F16w4kLFsJS+FkP7OPTAxW90NQ/HmPuRv6n4P82YhJH/c7O5Dw/x4RJ8QI2Gs81m2IqR41PCVIODyFmGYvshh2G44GJOjG+J2ZSOBsIKoo3EtUR9hHCPunkYBaYdjT4YEWFDf1Nf9fhYTMi56vEuPwKOHe3UQoIjlI2B4yfm/xnB2F7hegeA2ikWOE2z+5gp7yu8OEIrDbOCkiSu5dixSNQsLlPEwExyel+2rCDZxnnL+OaLZAKVI+Nt9z0Eu4/waIk9Eg2rrGMNyrdwfrIXeyYbiaoJtKpLAki84Kt1FKnHqsNU7q/P8Ej8MKYyZn3itIgHcQhz56jf8nkKIx23P5JURvVYY3F7mslxgH1YahF85tUvf5tWht5RlfHR57j+dkM6FgnSHSDRUQiX+zDVclUub3+p0dxEnqBw3HRKJiRiXiJfPR2hpEdFpG5OmbZdztQ27dtAmYh9bKVYgeyglXZcJ/IZGsfprxlSz5SbHrIBSXcW5zqecnxcSuIcJBZiE6W2PYvwb8JzL5+FiK6KOKOOFeihS3FAN5xJ+njIcL3P5NaA3uNewbkVKZwkoSnc83nLNgcAPkrkZ0+ipRmWk3Ug4HiIMWr4yYs9RWtedjPpF7dbXnYZ/HVEuEbZQSsZH9RKhCG1Kee1B8ZB7i38NE3edxiJ+meNFy34PIEFCK1tBsz0kfoplHkKLd4bGkjVie4YcItfidUQVw9PqnX//qgwbgn+3Vh5hKNVH54jhiuEvRrn0pEVCfTxSmT0wmxeT1IkbxtNsZh5hk2hlXICUvH1lJZhNlzq5AArUMMZFCJKDa/X0OYoQl/n2P4UkWnbNI0PYQu188phzEaKqJk3Ab3e5i97sGKR1n3QaCuwi0O5+sceavJnakiWH9BRIm0wxDr9vrRtaAA0TamoVIGL4Brz8HUz5nfBcbxjrh961H7fa52PPTj5h2H3FN9GcnEkpDyAqwwXMznbA6lBkXU5FCuQ9ZXLqJmJy7EWNO1rXNiNFfioTafj+Tb5yWEwy/CoqTS66MKOe1kjiFOMe/v4oUhh+iGK69vDtB80a/W4uU6ulEGo9phi25inqJOr9XIwF8FKiAj11A1GnOJSzGhz0PKQbqHqTk9hnWGjtokoKSrJoYl7lIGC8jLGn3EbVppxuGZ9HaWGRYm5HC14uE/CK3myzXZZ6HPciqlMIUaogYqKeJQx8riSo5RcZJDmSt0hTR7HkaRjQ+3f9nG+dFhIW5HdFTK+EC/4SfOUDk/5vp32cZlynesxnRYR2ZUI7Xj8OU6w1z2tglF2IBsYFrQHPb6Hb3Gq5nULzdC0QS751E2b/rDHuf5+gaRAPj9M7fPwcfLSdzYjpzUG0ncpuv1P1zTZA1A9HVILLG5hjucvc93fAlxajT87EQrZPZSHF92u/+mFCcUwxgcpFuc189xAGho4bnOrTeeyE3WSFTuMOLup/hWXn+M/946zm48GaCf/WhZM7VSDk763no5t2xzzOJSkQl/usnYiZbiZPOyZp7aAQ+mtF6zjP86f1p7u91Iu4weW4SreR5LsePGEsJITPGECe8n0ahDB/Y9Y+8d9FkZ9+jdkav/5VrNAbwfFdyhexDjL0FMaV8opzQUcRUWsnEznCp3+8g4l7OIObWRcRvvIoW/gCcSzFU5Uh4TkFCrtTvLEdMtIA4AHE1EROyCSkHFUiod7mtcsRkCoiDHZvVJxM9vgJEBd9BAjvtjPOQctKMXBXjiEoVQNYexATr3dYyojxeNmKmm5UrjDIkjJPAbUAKT4FhvN14PgtU22g1TJR+2o+EXx9cOAMpkZMQc/0LokxSofv5A+Kk9W63sc7/f83j7Te8u/xuORJQyz3eCuO/HynTw0QAeo/Ht5c4LZ6DlMhK/14sPNFEKNePISVpIbJMNCO6KkEWwG5EQ9VuO8VCfYY44JLtd/I8T/2elzJiU5AOLQwYpokeyypEL7cYZ5MM4ziipF+yWg8gGl3qZz4HzIWxX0WCexZxqjhZQZNLLM+wbDYceUjQ32Jcz0GW4WwyB2qGH9Afs4iT30m4NhLxexv8Xp7nZAqR7NpK8NubRuCgA63TpchVXW2Y56C1/DnCStSOaCsHzeNUz8l0wzKMLFM7Eb1Pc3v70dr6D4R7sYzIb1fre8MweBym3IqUuKQUDBm+aWgNVCH6SXGD17nvgyPa3oUEf7J2JZzPA55XKh8mAP+RqMSxSfj56Fy0MdtMuBKTKzlZBBdC1kaPYYCQyzcgJSdtnhYRdcQnEzWAX/f/vf670/N0tdqmxDhIVrE5xKnyNsKKWA4/fQD4bY+v3n8txGboKiKVULLCJav8PrjwLmIDXa373TcZpmlEehYQjQ55PHX+W2Y4h9xnme8tQnSd5MIZw5BjeJagTcuNRHWiXPe1HW2C9yPa3YXW20LjrQsZA46OGEur+jq93m20odyJaUM2eo1e/8RrVAE833UFUrSuQ0xxNnHyqhkt2g3I8tOOFugyv3sCMb2r/X+ZP6vQwm5B7ryFwFL9y/NIiExDTG2L25xrOJKl4F7ElKahE5RNhNs3ufGOu71yGPwFsq4MEYdGqpEFqdUwbHTfyUJ4hcc8xTBfh5SHSR7bAbTjbBb8nHKfB+DcHURKiC9B7m+inXuytOQR1R2WE9abhJsqmHQnYqSbkfBfjZjdVMRs74G2Ww1/NnJvTUOKzqDHPg4lUs31OO7h3Wl6BpB7sdx9NSLLzi6kbFWieLIrUOzcE8ji8i3juoM4oTrH47tZYzmxCwnrQiJ1z02EEv9tJFjPwuCDnptO4Kee2+VwogmtzkrjodVtYJx9xd/neSwHPf7k0spDMUWnyJwqHF5LnK6sQq7n+/TXttbz/LrntxjRxnbgCBx5iLBIlCFLzmqk4KTA+BRo32fYW9A66UT08B8RDfYBv694L+ZrXrI3QvblRIxbPbJyfQkpZW3EwYvtbvsUWhPVfm5IuBt7B5x+x/N9JxKyO4jSbcuA7/igVxtKvL7P+Gw3fLs0rrdfJnJZdqC5TzF/ZUQYRzNSInd7vB0ovqwA0c3vAvk+k9ICB3ahNV5oXHcRrsdGYiOwE63zO4mkwPvQWrrJfW0jrJ97gVLIus3jfn3EnDQBf41oxhuI4WeIMo9pfU70PPWidT8G0fdRosxhu3BNBbQ96j4g4ki/gdbFHsIKPBt4A4bv1ly9VYvWaB5ydbYgOk7udYeDfOwGYrM61d9r0WZ7GqKVQrQexxHpW4rQelqg/ljt91ZB8ccRLWwjLMw1RKL8ZC18ACnOmxEv7kE0vZrIi9hHJL9+w31caRyuMd6OICX8efdRJZycrve45iOFcQOi6waiVF4PsdHcB+PvROEFHW7rOB/w9Y/v8d/o9X5dozGA57u+liVG8AiyVv0lCsxPLo4BovrGbsSMW4Cn4cgxmP5t/78bmK6UCLm3IuZR4HauJmrtNiPmVouUymvIJJfO1E+tBE7BuRbIuhwJhY1IkFzj919AyuULbn8MUjjmo93595FgKvL9K5CAeYRImLrS8B0kYqTy3cZdiKml2MEh999OBGkfQcw07cT7ieohyd2b4o9KkUBbhpjsTMQwkxJTYlgL3NduQqi3IwF1yrjZDfRB/ybIvxUJ0EeJPFw1SLnJU78HHoI51xovEz03tcb1K0jhXUTmsER/I+RPJQ5ZfIko+t6JBEAN4aKvNv6OIsH7H5CwSvPSh5j+NmRZWgYn7oVJ65DlaQs6wHHEbeYaruQSG0DzPwYJvF7Dc7Xfneb2dxOuyVPI2rsM3n4Sxs5HdDPkOXqNSH5+BlliKpFge8m4qyAUlCrDsI6ImTqChN96pABcjBSHgyjVzQ88z7Vupw0ptJsRTTQSp9JnofU3BdFJLdAAbx92NYkyz+0eIqZqtscyFQnwJW6rmzjVm+L16j2GVuKwykK0QZpHhBPUI5q/0ePId3+9aN6fNd6/RxzEuJ04fXzScLYjy06Kba0hYoBrRrTzgsf7IFIKbjMcG4nclx1ISbiAUD4XI6UnGymp2z2OeTB4F+Q+iujxOKLtd5BVdoU/KwzvGTK1a5njudmHeMdjxEGE2w3TWWINpXjMasP1iuFJdDjkMd7l9vP97CG/34WUrVOIHvOAFjh3xofvZiFldpzupwMh53ZA1jqiulArkStzK6KNMs/zzWgNATTAS7vgshWe45Mj2p7l93qJcJgz6o8J0PZc5Oa+6B73M81juBjRxibE37a6v2piM9Hhtud4/NPROuomynxuMAzzEB0e8Dg+R5TO/O4HGQM4nvHjs96jNs8xYcLp0RjA9+kaVQDPd/1+lphnNlp8k5FQG0aLbg4ZixX1RJDwAHLn9RI77W4iVnA5kRi2lqgkkI8YaTVipMmi0IqEa4qHOmSYxiGFLFkG90DbDqhYYViSq2u6+6gnTugV8e60FKVoZ5rvd0qJOpzDSFi8hpTFMuDrufCVwYjvqzJMjcjiUOGxtyLh9AZx2KIf7Y5fQTvoH6u/t1+AsauJgyvLYPAZyF2HrI7VSAE6SuSTO2F8V/j+NEJpfMWVBmYQp5FbiJJdMwhG3EOccu5BjDspvTmEYpFDKCdn/Nx4IhC/y3O3GQkgyBwG+vE98IkyNP9J+Usu6CIkyE8gC84CRCvZyOI11/A/hJSlpe6rCQl8j5evGL8jleP7/Hylx/9j+PFP4BOJpmYhmmrxHLUiBeKkcXmAjFt7sBFybyHyM6aY2G5kGU3Ws1bDtA4pgdUeR79hsSuLQjLKODchxXAOUoj3IUWsgUhV9BA6pLTN8F5vHCQFq8T4TgpUpeFHltbc3yRCEYY8jgbjrZU4dZtvnKxRu23fhooUArGAOMzU6+c3+/tnPFf3EmXf0vp2W2w33tYTh8GqiNx41yDlsc/4u4+g0xsRvaTQjFm+3wbsgMGzkDsbrfPxRAzscTIKC2cRvZ1FtDYXKc+3ISv3UcJV24bofrrHt9b43oeUmbRRmIOUwg7jdS7iZZXG10HgZuheC8XXEgfn6ggFeZDYWBxFoRq7iSo87yDF3SEomVRE04i8n8s9hmt4d0m7Zz3+aZ7Dp93/MiIdzg+QstaIlOEG91uPNnrbDcMgoaQ9Tca1z1K310jUAr4XWQp3EGttqucwhbEkGbMQrf89hnOQsKrmE4dxtrrtRsRHZnkOnhhVAEevf/o16gI+z/X2Q0i4TiQqDUxAQmsaWpBFiIEsIwJ3pxAHAJoRM28nguyfQ8pfOrSQT7jJJiBFKwcJxNlEaalKZAnJ1rPD+wnrRYfeqbje784zXGcRI0y7zVmIOZcgRt7o9rcjRu2DFpnqC0Xuo5tQvuqBJwdFOUkB2IssTgVIGKTd91T3Ndew5Bgnz3rcXUg5bICxNxAn9ez2yE3xWGeNu363uxcJ6wXGXb/naplx1gv9P4Ks5K6r8HirkGC/2n0chhPrkZJaRwT8DyGhVYIYeKVhG/KzIEGSZ9wl5XDQ7fQSmwMz608s81z8f9n7//CuqzPfG31BJjEkDcSkkVzBlDRCoSmcYAZKZbDZsKNsKJUHZXRTOVhHx9bHbsbW0dPWsXXq2DpaW8fRbXV0sG4cth4qg0PhQXPBplIswkSzYVJSMJMYyATTYCCTEJIr4fzxfq/vjc88nP10H49MZ+dzXd8r+X4/n89a97rXWvd9r/vnIUJY3up2ZhGRshXuqx0xwDo/vwetOwhTfwmRnmUW4Sx+FM31VCQ0zDXO9gpf1XicE8nkOqMOCfjlROBFFbxzmMwBKKeI0AgnAbgW5UxMPq+9hilpuMrQ+plHlMlKPqEHPIZBpF3MRxo0C23dL3oM2UigmIGYasJtlecp+ei1I03jvWivbCCTvDfnesOQAixKEQMeQELDIBG41er3XgZeczqkMuO4gVijdYS/5zy01toMb5vxu4gwA28xjFOIsnB4bO3G4WuEULSZ0OIWIc3tBZ4nCOGnRvdz7nM7SfvUQFQbeoswVe5B895NBGwkQSZpKqehPZ+PBL4C43WTcHT0Hz13p9HaGvH3GYi+/SlRX7cSeBOKbycObO3uJ/m2jSeCP+YQB49FaO++hujjWI+50nM05HmcRySNP4j2VhLqKwih6pjxt8zP9JNxl8n47V3huUmC87c9zglntdvqd75F5AEc8W8b0Zrq83ivQnu0y23u85g6Dc8hj3cf4fc94meSFrDQbV2Nrgm+145o33m9Rk3Av63XqAB4jmtcNlFfdjtiorMIf7434eCPiCLxs4gEoEnQqUaMrknmtoyP2iAiTMmxPDk3H0fEagcRATiIGNlOpPWqBQohq05/MxHChYQjfj8iRGV+dyUimFsRwalBgs0MIl3INP+fzK0V/q3V45rg8YxFWoljSNiaCe++4f8XE74+Pcj0UYUI1VG38wUi0s+MnqmI2M3y81ci5nTQuO9DBL6YSAxdhZjabON/OyLMB4B8yJ+PTuJZRNWBZBZdRyZR9aQiJIQmAt2LiP9SxBB+RGjF8pDQuQsR8hykZdtO+Ai+iDQGD3mM65CpqYiIzIWouPEZ47uBSEuSBKQhImVPMkMVo6jHWYZrwGNOY0xCcrfxux+tgVyPaTKM+yIRrFPk51vIMHdmk0kZ8rGLieCTZIafjYSYNLeTfG8h4ff6pPFag9btQcPyEmFCnYvWXA6RKiT52tVB8SeJ4KIixLRvJKJX70RmsB4iejb5LQ4RPnpthKZzwHBXI8ZcY5inEtUX6jxniwlNaBbaA5MRw07ap73ASTjzCtpPHUibtthzVQWn0r1jMPxrQnuUfCdrDFcZcVhMFoZmtAbbkMCwE7kSJJ++KuNiLhEscMSfVWjN5xn+fEQXVqH9VEdEHjchwacOCRnHCY14PtK+tZKpTDRphmG94Kw2Kt1+AZmULUx1G12e9xbDXQhv3eVxpENCEqJm+9kC47oH7YEh43cOYSruIqp6QEQnX+u5akZWiXZEV3uIYKvJZNxGqER7t9ptFbqtGjT/FxM5/BYgn8USz8kkovLIHuN6nmF+xX1dgfZMB9L+1hA5Ue9C+z1ZB8qJ4J897r8Xuh9ANO+4n883nF2MXqPX/9Q1KgCe43p7CBG8LEQQ1yEGkHzpVsD0NUid3wlsgeHngGEY3kGUy/oeMF08jhzi1H8rYmYg4j3Z7bQQTiXWwtCPmMkAYUo7igSNDkSUDxKJegcMVwlxMk+avcQU7yUiP3MRUer1eCuR0AAyj/0YEZwR30++fwuBbLiojvAhqyIi025Bjvzr/f4hw9KKCN9c43MyoeUYJJye7fdy8mfIhy6PqL/cZHy2EeaVXYgw/5BIyJsYE2RSa/Q1I6J9wP3fhISfCo2HduNkABHtbETc9xnma4mAn52IGSWz37Db+T4i4pcY7sT8a/29HDGfk4QpZwBpJ3YRTK/MOHzYOGn380nL3Gg4cpGmogutlW2+t5ZgWjVIcEx+l7MJF4WxxmkDYkxthu/PCZNzJfKDfRDNfT9Kir3WbQ24jaUe35+5XTvzZ6K0k+YqaS/tVzn807N+34gEs3YyOTPfW07kSqzy7w1oXc0ntCfJx3QdkbvvOcOcg4SjzWSqTpy5x89Xoj3wIqGNLjEeJnuek7apiBBSptrV4E2P50F0GOjT/+M+a3wsh6xL1cbJXxM1o+v8t8vPzfX82Ex9qh7Rij8gfDmbiPrFQ57rbW7rAv+/EYabPZ+56ABad9Z7h9E+TAFY6/zcJrSHktVjgnGaDpUbjYMbiZRRV3oeDiFaNEho+S91nzd4/kvkTjBrhsdSi9b7C8hasBtp3O4hknBvA0ag74if3Y72x4vGySQigK4KuUEkq8BnPF8rjedeJBQ2oj1V43meb/iTINfqfvJxZnwisf1KtM5HjNMn/f5cgm+0I7owQqRz6kM+2g1EMuttHuOjhumvYfAZolRnl94rnub5SZr4G5EbxGgQyOj1P3mNCoDnuC55Hm34/Yg4/C0MPoQYSQNimsOIMbcBcyFrNrAZslajE/98Mqe3rPsRcXucIDwPIuKTzEqViNh2EtFlI2TKcjEfCR91ZErGZUwXfwQcgNbTiIBvJ4jdIUKzsA1FEt6PBIW5iChfhgShLsQcVqFgi6TxbEPCafKZG/DfXUQi3D8zfhb4sxURzD7hcfABxGRqeX+i7KTZmYjMeTORs/kKoAnGT0NMe6fhWULkbhvg/Xm2VhvH2YZ7HiFwbAOKIH+B5y8JbJuB0/BOPWJSQ0jYW48YSTU6pRe6jR1I0NwGp35KOGJP97wOGzeFwv3J5eov68uIeSb/s+f9Oex5XUEkId6MKmv0GpeDGuupY563bvcxFQlQB5EA0oC0BB1Ectlktv0aUT94BDGydmR+SwzzfuPtNaJiSdIgzjWMK4hasvOQ+fYJQqM9hDQeyb2h13/rDB/G6Roiv1uDhKNT/0ykrHmCSE9SDhcuRoLVANo3yc+1FwkL693uEo2z+wTaB4fQ/O9GjH/Q+FsCbx9wQFWe25viNnYTh5ACGHwFHbqS9m8zWm9LjN/5fuc4ivh9SOP/Vb/xUEP405XA+E963pvQQaTYY64GbofOHRrX0R0wLg8J90kY2ZandZpM4JuRMJSHBJUu4+gyyCojknJX+rPPzxwlNI89hItBH/KjS9q1YrS+jxuGQb+zlag+s4dw1bia8CNc6d/Gegz1wLU2V69C62Wt+1jmduoQHfxbQhAvFuz59xqO5wXz2wWI3nUSFTdqPYaDSAN3EK2/ASScJYvNHxNa1naUKeAEkV9vCK3RWpSbs5NIGbTDc/UwEfxVgtbgIqISyBMEfcj3c8uIaiiPefxziUNlr0uNbycSaA+hOd5MHNL/wnAk7ed5u0YFwN/WazQI5FxXqZ1akwN9LtromxEhSOaW64iEq5WIoKxFm3wAEdvbEGEqJjQYE4H/gojXACJUs9FGf5r35/07ggS0DsQkRpCw9V0kmDUgBtRBBKNc7v/rUTWBHsT0ZrnNdqIywR5EnJoQ8U0CSj0SFub6/3wimCWLjEN061NQcZPf3UZUg0hCXgf8/Vb43Y8rLU3O7YjZbEBMq8LPbSNMtisIRlyLGH/ydxxr+K8magyfIDRjBUSeui1uf7K/l5HRJmSKrL/qMSet0jfhvWNw4XzEiCYRGfhBzOU5wwcR7JN8k/YR2rUSouSaffcGt+q1nMXufz6ZPIcZ+Ptg+A29kn+V52YVWn+3eHwtSPC50WM/4DE96rZu8pwNGA/LPL6ksevxuHcjIfAmIpF4k3E1cBZM+Z6zxYhJ9vheKdobJ42PUiKxcRVRBmwpUSKx2jiZ4XY3Ii1SIRFEs8l/J/rZYWmA8pd7Dsr87nb3WUmk1JlJrKcvI3NxBWEK3wMsgDN3wZjPElVcdqK0MNsIM/IMIqdgOrR83+11oTV82jiaiNZiwmU64M0n6suChL+Cs2BNeOoxzH1IaMkmXDySf2qP8V/gd9I108/uR2shzeNCIrq4x7gvROtkGVpHL/n/bYRpP5kWa9AeqULzWEIElOz2M+2GeyVaX2uI+sOD/m0pEmKuNZx1aJ2mIJhS47IJTr4M41e67YuN026imkcJoeF/CglzrcShd72fuQvNaY/h6ST8VW/wvSs9N/lEIFQhUcXkuOFIpvuvon1RilJE7UEC3Z+iQ9tMospIFZxcDeMnonVfTGil70E+4bPJBL7xsmHrMW4nIdpfQVQN+iraI1WGuwXo/vDZeASBjP2Ag0BGRoNAPqRrVAN4rusHiFjlIoKxHxGKKiTEdSKCsQcRrRIy5sZTpxHRP46IXXLErkGEu5aIeGtGRLUOEZydyHG/032WI8I/z32g9zr/GRGh1xDx2GhYr+b9UbHfInzm0vu5iBCNIMJbi1ZCMgMm37J7ESE84uenECajZNZ7Cyrq3OdObOtGTCb5BO2E350MzIGci4mUJVMRUSsCvgOnUvTqMkQsk5A6QGjr2uHdnyDB+yrjrdjPJh+1S4znXOOtDwmwWUjTlYU0hRs81nLjYh1igDfDhUXG+2m3nZzkG/yZhxhrPlE5owXIhr6NBMPZ6XlsIJPYNecqyPmy8XQUuQkkQeKg+7sCsnZB/mXGwQoiwOIQodmajRjj1z0vjUhgXw68CKduQgeKrICPdrRuDxuGCuO30X3tJ2reNiLm1uHvKxGTSpqIle5vip8rJqKXZ3oecpBANNnjHDa+k69V0uSeQPuhgnDor3b7M/R//mTjqNTj4aw+e4gAmafddjcS/oqJwK3H/O7LMOajfvdV37/PcwbhE7oDMeIEa7bmhy50QMxCtKISCTmDaF9N9G/J124+2mvb/H+RcdTu3yrRPr3MeJtP1KLtQGtxBN59FtGKXj+XZbztRAfCXHRImOlx7CfM5QOEn1zyNes5Cz9JuJqMDhzpEDqLiJBd4P9fIyK91xheiFJ/jZ63SWj9LvE4F/i9EY/9NFoTyXdzGoy/lvCPS+sgHSwGCIEz+QRO9Dir0N6cT6zlhNfjhO9rpefpSqLKxhT3ke/PE0gIayB8ZcuJusqvEf6Wy4nUP9MIIXKr66OvMlyLCf/LA8h0vM3zV++5GEuY2GcQh4N1wFI4dYfh6/Qzz3J+rzMjcGb4A/qMnOfB/K91jQqA57r2IiaUR6R9OUKcMkuJuqwdiHAdAZ6Bcdcg4aAOEeEsxCgmoA2ejYjFD4nycC8RpssXCKftJYgh7kFEqwOogtJPI/+XWYj4lBmGCcSpfzY6MU70GPoR0bJPScac0YuI8F+6/z4yZcMYRsy01e3mE7V8rxYsmfxmjcj82E44bycfpV7/XYJMFyOIUb1OJk3LuE8jobCbSFzd5b+nyVQluCj5bCXhKgnhI4ixtCINwDZEiPMRg3uVEHAfRoT9Jc0ZTYgJtMOp+z2uSkLg2u/35iHt6n4khB0lBGb7DuVfhYTLvUSi1hK0BpLvYwkReTtAVBP4qt+Zq/a6X/ezhzyu48bzi0QwS4Krze1sj3vjPul5TIeL/0SUyprid6YioXk7mUoJGYaZ63eziNJ9M4jIx7WIca8ngkn+BgX77CQOCslklsxcJchUCtJ0pajkGYRzfav7eBOtga1E2pFcYl9M8LvtaB3MQwLJ1z1XNcZ1ie+tMFyDbicP7aN842MnkW5oAO2jXiKh9Vb/fzcRMHUSCblNRKBTBVof6zyODsP7GaKKUBHhC9tECIVTCI1zCRIK3xZ+CiEOpW/5uRke0360N1qIVDFlhncREkSy/UwnWj95SOA7bVzuQUL7biLR+VGi+kkTcWja4L/NRDWdSrQXXkWC4l7iMPGyx/OU8ZyEuVz41WEiCXeBx7jFz6fD4izjqJVwgUj+mcfg1VvI+Ne9sw9O/ojIaViADo4lwFIY/hERSbwH0e/6s2D6AvB5OHOMsNoM+J07iXWYTaaqUyZdV4/nogAdrpLVYeise9uRFrIN0bx+IjvEfkQb095pdb/9MO5SQtP5baK28Og1ev2G16gAeK5rHtJoJMbe478Lifx4K4DX4OQDRGqFVj+XzFlNSKDrQBt8EXBI5r1MjrMFQAl0NyOiUoCYz23ECb0DEYJkBvgGYUY9DEdfIdIH1KCT5ctIU1FIFBVvg+5n/H8OIh49QBF0zvXv7b633e29TKTt6EOmjgbC56/HOJkG//AG4VSeTNJTgMXWjCU/qAvcxlXGcwFRH3cPYib3GJaXEKP7DlFQvgVpoRoQc78BMbpHjJfHkfb1OGLgC9xmu59rQlqbryDm2kOmfuy4asRUniNK0v0xEi7/BgYfhr7k0zaJqEu6CPp+4nYXoavQ/V5GFJhPqSR2Im1Uqdtv92+tZAIyiu8jTMvlSNi/y/j+ARIypiMBYh1an38KzIG/P0AkwW6Ewddhy2HCf7ULUYCj/qQ5n46Eozr3mXzjvmLYaw1DFRm/wLdfJzR8JZ7XZiItTQtaq22GuZvIyTgN7bd+xPTeMl7/ktBa1iKhN9vjyXMfYwmH+0sM0xAhBJ30bxVIs96OhMJv6blXjxCl/no9/jIyATa/eA4JBduNn1Ue50K0ttYatnzj5jTKbTngeSs2DhsN8wokZLUZxxZ+2OQ57yI0sV3wtw/43jHjp925GKcaxplobc9E2qABj73J4z7k9yo9rh1EsMrVRADBCnQovdnj+zsyeSPZ7XdeRmXXkibsIXSAudJzWepnk9atwv/nor26GAlRuSg44jK0l68Vnj6xEgnRdxFa811E2bNytMZzEF1bAK33ovXzGjAXrrjW426Gj33a2rcLPFcFaO2MFZ6yJqM11YX25n4krA67zYP6jPms52krIcj+JRJwX/KzUw1XcrFI5vtW5F5xK9pDu91XrmHaQtTf3umxFqu9vl3ucwpaV/v9KfTzTUggfp3ze418wJ/R60O7Rn0Az3X92RhFr13l76uICC/QCfZHSMhpRBtyPpy5Q/Tkos+hDXoCCWTdSGjrAXZA33rIn+i2FhP+N5cTNYWP6fbgTcpjNvw8ZC0mqnDchYSiXNTpLhQx+joiGgsRczyKNDTJTFbiNqYTp+xCoByO7oJJVxInz0F0Ej6IGOtUt9dMpCDYipjA1YhxlCAiPMfja0EE9EbDMewkzxMQk6gmfPlOI8Fjrsf1GJno3XePwEUvI+H2NGKwTU6A+zhR0zOXqGhSRuSbuxQxsqTdqSK0mi+RST/CiPHTjyL2JhF1V4s81/mGt8Z4qff/vW6/z3AcIpI4zxXM7/wjfOwa42YeUcliIvIFHUAC3TTETG52uxuJ8mATiRJevcZvDeHo3oAEth+7/37EsJKWZ7rxWkj4hx4n0psc95x/CTHva5HWtB0x/gF0qJh21ri7iGjqrxJR6SVExZsZiJG3aE4pQOvyJiKfXLurOtQhxl6GBORkIksCWjJLV6M1OJFIDWItzHsnFDzyq63wiY8K1r5XIH+xx3SvYTqC3AK+Dq1DUJH8M7cg4Wg9oWn7KlEO7mWP4evG2z1I+N9NJOv9AfC/EybXG5Dm5nJCm3oYrbFq4erkry28PI72eA9RK3gBkbx8CK3VnURewuXCy5k7YMxkQoM5AMxULdnxj3qudxHBCyVIUFlrHN6HDq+zNJedd0PpHb4/gagQstxrZKHhX2dc1brPOcZHmrtJRBR1YvhdbnMh0q4tJ2OqHn4Osu5E6z8LCY41aP0MIPq7yLjc6LG/7uCefqIKx2fICH80EPWEZxiGJj9/h+/PIXxW2xE9Xul5/TvY/88wswxp+BK9fITISpA0v5e7vf1EuqE6tJ+S5abIcCXTcYHm4uRNMH6+4c5BLgZP+plFHsNYYN159AHsgg/KXe/kSZhQwqgP4Id0jQqA57qWjAlfpUQ0aokqB72Ief+QqKqRzESXI+Z5HRIE1vrZTiQQtqMNfzMiYhMJpngjYiJ1aHN3I0aTfGASgf1jRJyGiES7q1CkZwmhVbgBMYzkWG+H+r5nIP/rRA7CafD2K3BJnmFZg4jpZKICya2EH1EStJKGbTchaEwk/FaeQwRvruF7zGOYSvii3UyYMwsQYavxu8khei5xWp7jdwc8xq8iJrDVY60gIu/6iBQmNyDiOZ2oWjHZY2j3fKVx9RCa11zEFL6JmGEPYriz/d4Kz+ug8Xya8P/q8Fg3e257jO8iz105YgLbkTbmG+h6y7gu1dyQ6/GVECldyhCjnEsmqjWjRZqt730LIP8OQouyyu3OIqLPb3K7PcSc9rm/ZAbfaHiXIaF2rt8d9nPV/r6X0EY96LEns9kMIkjjdaIUYDcSgCYTZsR8wzAHaXBakCZlt9ub7LaG/FwvUZ5sLuGP2klEV88jXDCOua9Ot9VLpFNahQ54d/u9Jf69y3CuJGq/Dp01t5cRpvCCs/C1Eh0A8ohcm9s8xlzjvgkJ/WXEvB9CeyPRg1K30UiUI9zrd+qRsJ7Muc0a66lnYdwGItDrBSRE7CIyCCTBf6LbLiD2/NXu4yvGwTbDkYv2Y5XH2GF4d6K1kCMNVv4X3dYC46UL7Y8txss6tJZXob08YrjyCD+8MrT/1nsOG3m/20QPoRUrMZy17isfCZaH0fobIXIapnfmoXUz4t+/QviXPkEEhkwlqtNM8zydJlNx52Q/jC9Dh0Y87rloXW0j0n59zXjrIpNGiHK01u4g8ibmEqmYnkSC81hEc+aiQ0uBcXE+S8GNCoC/tdeoCfhc10rEbGagjdiPiFWKutqJiEMLYkBJE5gPp9YjIcRmPG5GhCMXEfkUDJJOdilC9DpELJI2J50qk4Yly/0UImI7jJhj0qKsJ0rI7SUcnVsQw9sI/BKYC/lm2N3Pk8lHdsmlhPZtKhEdV0QkhU6MMjnp30KkhnkRCS47/P8RtzcFEekH/c4xQohY4XHWEGaU9OxYwz+NCA5IfjQtwjV/ighnFxElPEwICVl+LmlMprvvLo97jucv+S/uRlcv4fReQkSF9hFR4ZVIY5E0cDsQQ5hNxoH+zOtuO2lpm5Dmaab76TS8C42LPMTA2hCTKDCeUhs9xsFG43gFUV5vN8GoAOohfw3hBH+dxz6EBIFh1aimn6jy0m6c9SKGlUVEaM5Ha6CO0LR1EWX+muDUrz22XR5bN5EceCdhFq1DjHTQsF7geRo2HpN2aDvwa7Qu9nou8pBWt8W/VSHmmNbEiPufgATqQ/r0PWwcNSIh5SDhGlDq95f43TK3cyOhRU5mxGRW7UJr5jbjbaNxlbQ6KwitdLpfSAQRgdbsWEKrtY0orziTMP0PIQ1ui98d9FzMRAemqwlh4GUyGvpxE9ABdI8+w780PPP8fPKBTFrQ6cblNLQ3fmHYk2/hPCLpfIlxlDRqHXDmNBKgsiF/NTLLD6N9sRmtgb/zby1IaF2C/IxzEa05aBy+dNbcdKC132dcjTVuJhPBOQc9vxV+p9/tJH/DRrd5l8cxRLiDJDw3Em4wR8kEzr37a8IfuICIpJ5NRns6PmnER9Ba2e1Pl+FuQAfg5MoxkyhHeJCM5pYmRNMmo73/luE5SRx8D7vdVsIqdb6u4Q/4M3p9aNeoAHiuK5swEU4jAiuSn1A/IhT3+f9qxNRyYVzSiPWgTf7t8ZESYx4iNsnBHCJi+AlE5P8PtKl7iFJbtYQT8nWEZiYx12pEyJNzfg3SjM0iggOy3Ufyx9kNxYnxctaYxiLTYQ6wH7ofdxsjiCgNIKbThExZlYjoV/mdXETwrjEekhn8eo/zKFEaLw9pUbepLyYgJtqEGM6In6tCGsgSjyHNw27EMIfddifhcH/c7dcRwSU7EeFeftZ4Roh0GDWGbywSbEY0J3uTL16/5yb55ewhIi9XwXCHcfmo2htzp9+rIzSjW/1M0qC1G18z3F4LoeXrRYwqmR+T6WyYEEgLkSBU6vG0GndlxmeajyQglqB10wQ5n/W9zxBm2VeRENZFxi9xcJfxmZz9k1a1AmmUTgrv48oQw9qD0hRVeqxFSDNeiLQ/oL1wAgkatYQv1VKibF8LkS6o1HjpJ3zdctCBotv43ULsg3zEsC1o5K8myvp1eS7edDuH/H4z2k+1hvl5IhK9mTiI3XgWPnYQ1W1Sf7MJS8G34d3nCD+v5Gt7CxIKNxNasX1EIuJ2j2G2YR6vOWMnEv4LPNa1KEAEIlF4H9KopcNhMzADsq702FchGrDfcN3rNj5pOPrRAXAICdE3e04WEZq8eX7/ZcNcC2NWGsZSj38vWi8NSLj8vOd7BZmIXwaIRPctaE2dNo66jdMOJPSM95ycILTzSRDyoWVwIxL08oyH+YbhOiT8NXi+agxnL5Eou4rIM1lrHLXARemwmI/2+2niIF+D3AeS73IRot1taG1ABHYVeMyTCBP+SqKMY9K0l/n7cSLHYj+htdxLVJxKNGf0Gr1+w2tUADzX9RyhOduJzEGdiIB8FW2+ZAYYQBt+Ndq4i6HzGNr0+4D/flIEtwsxswbkP5iPGNJqtOkPIsLwRaI03F5E7BPT7kcEYI+f2UNEsYE0AdMQgTwG79xumEr991JCIFyCCMtEIqFz8p86BuyG7tNQvBzO7EOM6hlEgP8UEchaQvitI8zgTYTGbyUiigf8N/nvTCGTkqH1gN+dSyYp8jtJm1KJNERrgVY400wI6AsJ4W8yYlRJOElBIhuRhmEz79ditgL/3n38jfE3DQkgx4h6nL1O+VcKZ5IvZSH86lE4uYM4hU+DrAfdRjKLbfb85rn940iISabHSxDx34nW3FzDv8f9AL9a4+8n3EaV/99OmJGSZjjHz9QQkcF7NR+drxgfNxL+SIV+/i8IrVYXogz3AH3w7gjkTNQYTjYb9ka/N4zWTpZhn4bW6t2G+TTy42t2xY0Gok7uNMTcQdqnLfDeA/5tBK2teWg9LDHMrxBXNRKKr0KCSdKYXoeCBEqRafSraD3MRf6sfwfMhv/2CGF6e5GIkk8m6ycIge6v0LqoFR7o1OfMCXTQGesgpx8QGrvtZHIHXjQbCeM3up8qlJ9wIhJ+so3DZC04jmjKHxnuQiTczXM7K5BA0utxlqL9W4gEqEq0hv+SiOJei1KG5KMArn1+ptn9Xk64q3QhX7xazQuHhMsti5GAv9r9Vxj2P/b8jCUi0RvcNkiQ6/PfhYQJ+hCijTd6LDd7bG+hA1+Fx1SChLG/8/wkU38h8nuucD8t0SWlaF3d4O9PE5HDCwzfXreT7/7/OzoADZz1bD/S8s5Hc1/psSR3hlYkBNe7vQ63Z15An5/bqLaO/gQdMiejNZUFJ3+C9sxKIq1U8hvOdf91xu0yj2my71Vxfq+RD/gzen1o16gP4LmuPx4TgQ9fgeGrIGsDil6z2e2/3Sc+O/5hwvQ2BTGOfYgpJRNvK5GGYina0M8Am+HdZ+CibETY/wgRiywklPSjTV+HhIX5yKQxDdgLp94QDRgzAxG82UjDtBgx4CS8HkYEej6R3iNpDwsQ0Wk1fInJ95DJ48fliNE973ZmEibJLR57mf9mERqqIaRJuAkR99fQJq8kNAmzibx9JYj4DyHm8BXELLp97xjS3rUQptHdiFF1+N1ZhiuZ0hchv5tqJISu9Bz3EZGF5YTG96vQt9w558oRY01wvIaYzjzCsb4BMbFtfr7VczHV40saganAd2DLCVhyFRJUXiKqhmxDiWEfJExMm9E6XEYmfQVdyIfuJLBB1TPGLUbCxJ8gRrffeNhCuBIY578agk9cRgQl9KM1/RI6gFQZj+XIvJXWx41oTfUYfy1Iy5eHhIVexJjygJnKQThusZ8d8vvb0do9gvbGVMQ4u913A7zXLLmx9MuEab0OzXHSDCcXh3KimsgMz8s2JJTMRJpskED4feH8vYfhwkvRek0a6zzDsg0x1l1E9ZNqt7HTY55pWL6G1lI/4erRQqTsGSGjRT66FSZdRiQML0BrqgvlgZyF9tdmwm+0yt/XICEn+WpugM43oLQIzfl9xuFh+FU9fGKshPaLrvW4ij2m/1QGP+gIn8JswvdyrsdeQdTD/jHhTzwTBr8OOV80zk8YHwug7xEr8a7xmPcQaW06PSfJJ2/Yv7/uZ1ahPVxG+OHu9PzuI9w5yg1zPlrTc93u02jd9BDZGR4iLAAvENaGCcId/9nvFfv5AkSDOlAlnELCSrME+d8l+pY0wVlESqO1BO0YQtrU+/09CWrpQPcase+uM34a/FwzkZB7vufmJc1H52EovQXtmT8Gfh+tuxK0xtaeRx/A9g/YB7B81Afww7pGBcBzXV8YI4J1Gdpoa9GpsQBt5jWIQDUjgWQjIgJXEtFd6cTejISNZiJx7jYi91wxkfR4F4oofBExkTWIQO5BxGgvIiTZiHCsEWytR6BiLWIUSwjTyc2IOdYhQlTv9zcRQk+vn52HCNJCRCznoQjEmYTT9WHElO5Cgo0DUX6xCz4zGWlbjhoHi9xHNzL/HEWCxheJZL8rkLBUZLwns2iHA1UuRlrNJnihHq77sp+rRoJwNux/A2bWEUld+5CAcba5r4GoYHKb57SZEPLnn4WLfI83+csNIAb2GGIEi4AX4J2fwse+jNbEPsT85/v7Ej+fT/iurUGagiIiansREraSFuc2JHQ1EKb5h1E0aLnbSkJEDSEclxpvlxCl/2pRuboTMO5Ow7gfMZ4NaI3WEjnpOogyfS+iNXAJ8gO73O2nQIksxLzHGo/JTJ1gW4YOMCVIWNiDGGaV+5uDmO7/hgShdZ7TLxgf48kEAJ16zrnP/hIJSYuR6XaAiBxfjdZtAxEVnHzXjhMpeCYjoe81QhM9k6g7fNDw5vqdRqIqQ6FhqkP76ttkAqiogfeegQvriMTBhWgP/xMSkLsM1xG0Tw8ixr8JmKr0QjmricoQOWjPjCChJ59IsbOP0Bb3E+4VZws2tcDX5sFXdkdQ1G4UqTpE5OjM9nwcIiqrLCWCsJJLxXLj/EZEvyYhAQXCXD2PSNuzyXAuRutmgp+t8bPf9P/J3zmNtcn9t6J1OJcIKhtGtGS3+zpIVEXZ6fYrUTDFg8Irx9C+qUAC8Srfm+c+h4jchWs8p11E1G85nLkexlQjfnAc7Z3NhHtAo+EcJjS8ha6SdKfxV4+0mhORADzk/g+gtb0Wree5HkeW4fwWkYD+K2To4/sOwS+NCoCj129+/c75BuBf7TUFbc59iKElc+cutLmPEYSnExHtY0Q6kGzERBrhzA4Y8zghjLQRptxL/OwyosxXLmJUnYRPzAgiiGWIWL/mFBcGt2I+IqorkTAAURWhFQmoeUTZtVxE8Iv821iP4VHe7xtXijQrh93mi0jQGjwLF9fCZw4gAtaDCNcKojJFgdsvRifjKsLBPZmqCgznPsTM+yF/JUqxUKx71030OL6CmJP9YmbOJlM/t/VnUFGNtJ4gDUCtx5g0Qz/ymJLQ20Iks91J+HeVGP6ZiFl9SXhnk+7nQwQOtCGCnm9cPYnmuhAxla2I0B/0/Rf9/AZk3p4F3euh+Kjxk0swxKQRaUHzv8H4zCVjxj1VD+OmeKxna1Kmwbik5SqBd34NH+skqkzsQAJPG8HMNqP1n0OY31v8fdBttxP5MRe6/YsJRp9MzCMeZ4FxOmx8tvqZP3Pb1WitbfIYi8iUAhxX7T53EFGVeYZhPpFe5Md6nj/1b68Srg9Jw52CacYSidZfRYLMQiRcTDSO1xFVdCrR+ljkvlvdZk3cu3C2370b7e+DxsGDRE7QuUhIS3sxacnyIWc5ErIKyQRK7f8JzJyscXS3QfEKpIV/FK2jjYS2f57eG/4ZZF3vMbTvjtrA05Ag+TgRWV5IHAgriLyXzUhQ/KHhm4YOwI3GfxaRVH0FWjtlSLi9mNCa/8lZ456H6GMDWktj/XmLKGOYbXwmLd9tcPI+GN/ndwY8B8NoneJ3SoSjtxvhkqXocFhM+FCWEAfvHxvPWehwc4mfu9nwVfjeccN6CMaMRWttmIjmLgR6oPOXULrAcBwgUxEKoGK5YVmCDsfDyIq0AK2P44brIJqn+W5jwL+vRnQ2Cd+vGbZcIuinh/N7fZCm21ET8Id6jfoAnuu6H2iDU1sR8VhJJJxNzC8R1ROIyBUQfi/9SGNTCGM+icxFDYgZbiFT+qkv+ZT1EIR3t+5lTIDVwEQYfJkorP4ZuHCGn69EAs03icCSFvd3DAmMDyGCchROPoKI8Z/6t3bDsMNwn0SE6kFEEJsRM2lERPoGt9nt8bzpNiYTheVXu+9OxFRnIsK5xu928f7o0IOGO5k42w1Hr3F1C2LQDyAN30KiFmg20lrMhIqVcKYRMe+9xuE+Milk/uEYkcvtsOEaNtxVhPbvk4YhCS8FiFlUkHFWL74UrY2XiNJ9w5orjhE+afuI1A5TETHf7f9vMwwXQ/EC/zaV0IqsJCI/+9x2Lbz7z0QFgW4YV4SEptNEkEYStlZ6HCXwscv0PqXIBSELmAJvJ4f7bM/XIkIrW+pxT0OawO3G10xC2zyADgBNRI3YWrdXrD5YgNbzbqQBOeD/nyTSeTQRmsS1SNNxJVrzaW31GK4VaE0mIbnM7TzmuVhiXK8i0gNNJIJykrm2hAg2mW8Ym6Fvh9trRHtsHtpT7YarlAgwgHCvSAeehWitdXjujhtXWcZlAZH4Nwk9TYY/V/3MvMDv1EHxGo9jChLOmoga4Yku1UJWMu+WeCyH/P24cVGN9mCyInSifd9H0LiJSHBpI9bkRrSujwo/NMDJE4RAUkrkp3zQc/AZ/15oHE90m6ZhmeCZ2wgN/UreF2U8/nZ0AHjb+NxHBNVNM35eEG4uuZvIlpCN1nAHoiXbhR+u9ly/RrhorPUYdxDJ0+uMk9WEy8tOwUSn4VzlqkzLgD3Q2mGYCv3MBD93seE4++BXR+SUbEJrtQbR3OS/e9ww7DdOpqM1meakiagvPXqNXr/hNSoAnutaCsyAcYlx7EEbcyGZSMB3NxJli9oRQUq+LtsRce1GJ9evwC9+6ntzEVFog/yPotNyNmG2vYLI3N+FNAbZkHMPkUblVcPZQlRQSOZiO55zndu8Fvk/tQDTYfydhPn2FcLfr5OI2txM+OeV+bc8//7/ND5ykfCTTGXNZCLz3l4Kv3oKEf9Of3a4rRsJArwLaZhOIwL3Y+Hr5y+6z3WICG4ggjuSP06+2/+vRD6za2DMfUQN260o8GWNPp/6CDLD7Nb8sgVpKXqQ72YRIvK/JMydycfvIc9zv8e8ihBwIKOFzGiF2mD4GcIvsAytlaRpbCAitpOfVqnxMOT+O4lTcTaZyg0XPYyYaRbSXCxFB5EXYHA9UVN5CJmUt6P5X4qYRrragSNwyZVovaT1W08EiDwFbILtj6P5XurxdaM5fJkoe1gC3U8RUY7Jd/MIkX9wOhKWbje+v4X21AkimfFLhLa1nEgmnTQebR7TjUgIykHz/SMi/14REvL3QvdDiJFX+Z3H4d31RK3vfuN+o+dwOeSP1V8eJlKFVBv+ErQf+g3PGuKQ04vW6E4iHc54w1Pi+XjO96cigSEdao65nQvI5EQc/mfen+w9C1HuAj+TBJ7NaA3d73ms8pjaiWoRhYS7yXjjOp+ITG5B6zFH/Q0nE/geopb4Tvd/H4y/Bt5+yjhvJxKIL3Rbb3tOd/vdhUhDWo0E2W+gg0ryKUza488T5vdGzSH9RPm4YWQK34v25eWIVrSifT3Z/aUD3FbjdTuh1d+HhNAyw/K0xjh4j+EtMN7vJPb2AJHHdAOZGtbv3a6AoIp0wEh+lLOIpM0XoD27HGkdR/zbHDSv2R5v0iQv8FwUuL01xu0EJITvOWsM5/Ma4YNLATOqAfxQr1EfwHNd5WN0OutEGzgRj0OI4BYgwvMwkcS5BLgAjt4Fkx5AxCgfEZNOt2FNx6mtMO4BoMu+P5MRkUnXgJ+/Cmn2mtxPs/7u3QdzriSTaubgMzB9DQw/qmohw1shazWwGbqPQ/G1Hsd+RHRbibQf+xGBWuY+HkTEqAydlv9fhulmj7se+p6D/M+ik2oWEp5uQYT2JGKS9TA8BFlFbus2pHWqcZ9zeH+wiYWrd3aJ1k+/TTg9cy+MeYTQ6JUhreBSxOxK3eYGpC2aihjaPe5jE2KyyddpgKjfut5zcpPm4p2H4GPZntdiwneoF5mqkoCc/LmGkabptbPGczORZLkB3n4TLrkT2KVybNnAmOeQGW8ZUX+5SRGCk+4ADsKZn8KY1UTlklK3OQNO3grjLyZq4l5JaBovJRJT5wOTYfAuyJlB5Kfr1hiGfwJZT8DBW2H6nW5vh3Hb4XEmP7qEwx3IjHiI8NkqIzQnBxADayEqN6SDSgfB0Po89yfcXwO07oOKjyKGng20Qt8bkH+9x7cSaXuSuX0C9P3MlT3K4MwzDoha5GdmeExPIeGsnND+bfTYSgznVKL292nPc9r/XURVl6QBrNQh8KLPIS34cre1yWO8HAkLX0P76Ek4ehomfRT5Oh71M3/m79OQXyFoX/0ARQpfY5xPc78zico5XV4X67wO7kHCxffJaKXYT5RmLEMHl31IQPxztM57PO6kBW8mtIhLfX8jiiJuQLRwiEhmXoiEk0Iybh+tW6Hi48Lt0cMw6cuEK0bSgk72/D9B5MEsRzSvwvDs9dzsRr7HL6M1dSMSpK0tzZhme+AffgmfmmL8Fxi/bxOuHinQp4HI+VqCAixeQ+t9CNGsWzy3XWfB3WyYZ6B9P0PjHnzAB/UOQjN6qXFyDNGQYrT/KogMBtOIcnr16IC0CK2HSWjNVqK1VSgcdh+x1WAnMHwefQAPw/iCD6jNXpgwZdQH8MO6RgXAc11/NSZOey8SOfk2EM7XvUTG/IcQEe4girsXok3/EiIyz/H+ygYNSFBqJRILNyDC1owYzgkydUGZoff6XnEw4npEWDYZ5rnIPLuBcBqvIeNfNXw9ZF3j39rgzFNmlrkey2wiD1oZIvzrCZ/CQY9nKVFCapqf2U9USllOCMX1iIi/hBhTJdJINnvc0+G1r8PlH0cE7zAcrIfpO5G5N5k5at3/HiJHViMS1L5OxqH7vTdsGi8iCta3Gv+LDGvSylQjZvQNxECKiTQmv+9xthEMZj4RPZ18GccSKUuWwluXwazPIaGwnhByLkFE/BBRImyl/0/MJzHpZJaudv+9ZMrIZbSGXzB+KxFTeZ2oaPEtQvOx3u/uRYJZLVFNYqph24s0FTOIvGVj3eZVaP6bDPtMonJJLhJgNrmdRUQx+0Lj/Rj0NToh9aDeefcRuOiLRDDGlwzTbsKR//Mef3K3AAk03zOebjHuCtB6fgrtm7fQniojUyLtzE+1TfO/7DENekx1hAN/CWEmLXDfaz3GpIVpJiLV9/r9hcbxS25zIWLuZcbZPqRxxt8nE0EpQ/7d6/Ho6zCpWlq3rMTwc5HrwXgi4CaZN+92f9sJv7Ea4+coUZe5hkz+0NYZUPFlQsNb7eCVa5BAkkyi+/z3K8bXYdh/O8y8zW0ljWAREtxeR+vnCY+txjB1Gr+3qs/X6uHyKw33TsL9oo2o/JJwMgXRv+W+l4PWYBnhN9xEpvLH4I8g56Nw5tcw5uNoj4wlDiLJzeGY4XnVY7nB+NyEhONmwl97nudgN5lcipQblu9pnIO7IGc+ogd5Z7WRh9buCeRScsBjayb8KdNhtBJZIlYRa2yICBRsIbSzr6K9W4IWdhHwwqgAOHr95teoCfhc1zYkSD1P5FhbTzCJdNJPAtZxIi1FCTIhNiBC3ItSNeQjYv06Ok3PI1MloO8VtPGnk/GvyST6Tb4rBcAxyC87a+JagBH4+S8R827xe59BRMJaKJrMVHA7w3YXnEbkSMtGTLYSEavHEZG+EhHdN/3uOhhejwhRKwJmPrz3MhIE0t8yj3EP0ooljWViHoVq7/LHkYA0oHem3+Y2kuCVBO5yJFjNQsy2FhHRLrf7eUdhzjec0xHDnernHkQM5AIiCnISEh4qfW8PIuxfQgLhPMRgbjYutwKboPW44ZqDtF2bgG0uwvEZtB7GIoL+FcP3Y+Mkz3PVjASJ6YRmKh0e7iXyjPUhs0/yrxvxe13ILPUmcbUYxh8TKWSe9jt5wkvf3f6/1vN3ufHcTAhwHUT6jW1E6hsz0394g9C6JA1alp8bIvI9ToT8aeiAcAK4AS6aaPy0EAeirUT92OmIUe9GgspyZJ5O+2CF52aITB5JKo1jmy7Zg/ZuFoy53gJoMVqD5URi7GYkhPSgtZ9LmL6TCRXfrySjlcyYXh/V+6d+7ee+7Pf2IaHtRrTO9rifI37ucaJMog9MkyYIvqzrhbeUxPjkG8bziOYvU70ENMft6LBSRFgbOoyLScQ6OQQVqZ0KMimqLrwKrQFrhUnMvAcFgbysuZp5CyGIdBEC52HjphFpOrOIZO75ZPwgu+vh8k8SUdUDxn+LYS8lalHPJarhdKI1vp8wkxYQNaxtOcmx5nfMBR53GZG4vAUdDrLRenvMfYzXO5kgqHuIROU5/r4frY0apBlOLhblgiMn+S/OR8qC3WiNbCLSJr2IaH6lcXfAsLT7t5eIilPHCfp5iNiTtxpnpcZXP/ItPM75vUY+4M/o9aFdowLgua5yolh7NdqQtxKCyj60YZNpNvlqzUQbs5sooTWTCNQATm5Em7oDCSjXQf5ERIBmE+kFqpFQsBwx8lzE/MrgUx9Fvilmmr+XggCSb90wIli5RDb5ZPpaB+yCcVcRJ/56IthjgBAObtSz1COT6QKNL+sjiHkP+b1VcOEniYS8z3gMiZgmk0gJwRw2E6bS1W6vxvD0uf/EaNp5f93UAb3b/QgyC2UhoajaMJQAf208jPWcJDjaCMEwDwabPY4eolTTNkLwmmr8V3keS+xitNf47DDM+yH/WiL9SA2hAZzqd7ci5lWH1sZpwhdsD1HlooNMAudMWbIKJLDPJxzJ5xt3h9xeAVFWrRMJXdOMxxaNO/8O5Gd6gd/Z4/vJT2tyjJPjxHqeRybf2aeS4Jfr56s81gHCvLYJ7aECompFAxLm5iLT5gwU0NDnZ+ZpDgefRwJfFfJTfdp4rXfbu4m0Itvd7zxCW9Tp/paSERb67jf+Cz3+pEUpR1qh7USptz3GeYP7HCAEv5nuq4iM2XHcZ93Xi2hdl/peofrmWrQuL5aZmhIiEr2JjDaOI0T1lxLga3b7LEUmzCzj71bjI5n1NwiOM1v9fi9aG0uJ/Jztxk832tdHgJPQ/TIyq6cD3uOEgJOH1ucGwpxejTR4+YanC835fo+lxzjb7/bG6//iawn/yRbjNB16mwjhcj8h9OxGguVKtA6Tb+9OogxccgdI2rrLiYolBYRfYgU6BCQYCo2b9cAe6H6WiMLNQkL4Ix7vIURHOtG8HyIqhcxAa2698dVEVJrJMqxFxMF0osfW5Xkc9BhqjfcmMon8Mwf4Io+h3O20EZkApvC/7FVRUcGYMWP+xee22277v3z+2Wef/RfP5ubmfshQ/+u5RgXAc13r0MbrIiqAbCYiHvOQv8hcRHy+gzboOj9TSVQDGELEwALB+Kvg3X7/Pgdt8mmIOT2OzFulRC67AwRhGCRjHviHIzD8HCEczkP5pHIQ8+0+azxJCJiOiE61YT/m8XwdEfUCMsXLO48gRpi0WTmGq5UQxra5/deRtqsQnWyHkZZrHsE0awjNRfK92uXvBR7jW2RyDZ5503CO9d9eQqu1GljqAhvr/f4q4/kpxCj+HAmiIx5jFsGIC5CA1QM505AwPRUxhBWGLd+4uNzjS4JjCYxZjISI2UQkY5bmJZP37zrjt8u/jcg3k37EICcaruTL12wclBr+SUjj2eV2J6K1kczBlW53K/JX3UnUGN6uOs+nGj3+Xs/FZr175no051uRP9VBj70MaSo2kHHAH7RPF03G71GC+b6ADkWtiMFuQULQanjvl2gdzEda0J1Iu3pUY333VmAPvNvssR8mY9rL+SxR/vAgoQG+mzCTdSFB+huICT+JzLalhEa1Bzp/pGfzP06U/0qal73AU3B0l+e33jiZgxjrc4jZX0LkzdzmeVtH+Mn1w8kHPC9zke+YNURUINPoncAz6pr9RG3kLuAt+IefEcEgvWSE05lrBMvgPxLRvy3IdDng+S4HFtiloxvt8W3oQJPlce8kypldgMz7t0LxI0T+vkNkqrSced44qVTbZKPDZQlyvbgYCeGfJQ45nYjGFCFXhIOGrw+tzUNEZPEw2j83uv/k43ir27mJCE75ntp4N9GU3Z7rCo+niSinBnGIbDb8XyIEpjy0j6qIPJ89UDwZzfONnpN1iI4edj/TicjdaqKG+Ouej3KCXh9AQtwM6GsjU/Ejo2Ft8pzsIHwLtxr2m9zPfkI7nTSin3S7lZ7Lfw1as+EP+PMbXHv37uWf/umfMp9XX30VgN///d8/5zvjx49/3zttbW3nfPbf+jXqA3iOa++YMcz5HNqYpYhYbEDEcSeq2JFNOKRXEQl4s5Ag14iYYTuhzdunU/qYKYhoJA3fvcjM0ApnXoExdch5+HL324SIyLWIcSd/rD4yJcPIQgS5kUjy22L4ewh/uOWCI5Pfz75STEUn7XWIuU8lsuBPRwS0Bt5eDZcsJrLv57n/yxFhLnff9cjk9hQMvgI5a4FeOLkGxk9DgsCXjNMcj8Vm1r2NMOcaJATh8UxGZtwKMpGGg49DzjXILAwyfSY/qFL3kWvc7EHCw4DHXEEUWk/Ca71xlASAY+5zqedwE/It6jsLr99DTKuFcNBPGp7HkYD1CFoLDR7rW4hBHiKY0YDxn0yxDYTvadLoTEXrYadhSprDAc9Pucc+E9gPv/oJfGI9Yh5/Bu/9M1z4aWSmrkTrrxElbS4mck8WeD4vRmtkAAkUddD3PORfZRym/VFA5MZrJJOs+WQjjP8sEmQTXpJPHyiycQMK4LgPxnwUmdu3ua0lwGoYnC3QPjbjLJhr4dS9ThJ9K5FGZiYSYqcYF1cjE1sNEhbGGu5+z+NThLtCmrPFhJblOBIUa9EaKUSHjtWGvde4mEf4AO4horjr0N7+IfT9EvKfNz43IKFnCO3J/4RK8iUN0iYYPgBZt5EJEsr4fZag/Zzr/qYiYXUK4ZZS4f4PE+lXXoDBE5BTRpRTm0X4daaDTxKoWtXWmR/BmNsEa99T9qfc6Ge+hQJdkrtEP1GVowodlF8lMgdMReul3/O8kkhjM+Q18R3/noMOvc8YV+kgmOs1sI3Y03vgx21ww+eI/HrVhIZ5JqJVWYhOLSHSq7TBzzvg91YSVWbygQXwd2vg84vd/x4i6Xqlv2cbzquQMDjgeUjm3DLNJXOQFSWfjN/jmY1OE/ZVIqAlz+thK1rTDYT2vQN+9Rx84ss62JRORvvlT86jD+CBD9gHcMb/vA/g7bffzubNmzl06BBjxoz5F/efffZZbr/9dnp6ej4AaH/7r1EB8FzXjWPEOIqI0lxJmzMBnfAmIyJyEG3uVkJtX4aITD8ibCsIc2YygdxEBEqMoGz+LxLannZExHcTVSEG4GAzTE/m1nIUkLBY/x99ACZ9FhG5KiSgliJimo0EvyEkTDQiQp8Y2DT31244R5CAMsP970dCRyEiVguQRqkYEdvXDOMVHutBt3U54ZR/wP1dRQiuyfG/BBHqWYS5Yx+RRmWqPz82PJcSefmS5u1qpG1Z6fHtd99fJUyih/xeEeFsXqX5ONPvpN07CeaM38klNAbpfq7x0UGYiJPp8yhiDA2qXzzmSrSONvv3uUTk92ZE9L9GaH9uQcLgZiRYlCBmnxjslb6ftNTJJJtP1IZ+BM37lwlhstC//RAJJj1ornuA4zKJ53wRmQT70DrDbU5D89Zn3BVpLH2NkP85JBy/RAS4bHafucBE2HIXLLne7/Yi4fiw8TMZMbr7UYqYfrT2StAeGPH9QUKTXAP8Bz8zBwafg5x6tzHV8CVNfLH7yycCb0p5f4WciYb7MqKu8lqPp9MwFxLaw3zCfFeNDgP5hPDUidbCgPuaQiaLwOBqC2JXImFqDVpDSYu53ZGeu9zuaaQxa0R7qQStu7TW06Ew1/Mz4nHWeO5mEKlHkh/fUvfVgvbqHMHGX3j8E93mXKRNnEWkSJmDBLv/ariudTvPoHV3qeHK8fNXoQodnUS6oUIikfUuImq3wu+vRwL0I8i6sdmwVfn51W5nEeEjOxftoxLj+i5Ew1e47ZcMVxVhpu2Dzp9A6U1ob6xHGvxBRDMaCA1fHkHXc41X73M6iJyVk5BGvIYIutqL9ly3cbkUpdWyNpzjyNSfNK9ziYofTUQmgDLCraMW+P6oADg4OEhZWRlf+9rX+OY3v/l/+cyzzz7LzTffzKRJkxgZGaGmpobvfve7fOpTn/ogwP+tu0ZNwOe6ctCGfJTIIl+EhL8WYlMPIMayFhHxbUTN0ORrshj4/kUR1NGAowX8TgkRYVtNEK65iFDUICJYovvTl6O2OgjtovuatBgRiqRFuBzOnB191oiIdjbBNC4jIho7iCjD+Sh45bRxcTkiuqV+vp9Idvua+1timN9GppQVRJ3MSsRQxhLllhoRQZxKnM6LfA/3M4XwNzxk+KoRo20wrgsRwd2OGOJxxJQWei4bEWPa67G87XfykIZkrcY75krEoJKAcJzIm5eE+/1q98wbRBTfIo9ti3/rRMwBoMBlpG4wzi4n8g0Oez4GjfMbiUoEDyCGN9X4aPVYxxrnO/3ZatxsNIzDiNkuJpOzse91z1sya5eiqMMkSF3r8U6AnKv8XCX86jCR4zHP+JhqvM9Ewn435M9HwtmTRK3U/UiobSYTab7kUmJfVLutiTDmc4gZghzv29Uux5FANmKc/Q0ym9V7vGvRGrtVfeTMJvI5JqZZ6Hk76veyjeN6tB6GPf4mtJcnEtrBpFmt9L0BdPBZb7gayZhwaSTqBichxmbATP69NuOuEXIu87NthvWw2yw0vq6D4s+iNTCJ0DqltgqN33y0V3uJIJVDxCFsLBKUstz382hfTEJayPWGo9BztoUw1xYSLiK1RH3gMiKZ9df9fimxT5PrSxuhvXzc83Jc8J96mTi85RoH+5FGdi86BGB428msT652X4uJXJ/b0LrKRXtvjeHrRQJf8r8bIHJRLiSSzrdD6TVEPspbiHraSetcjWjxVJUnzJipp6L5Tz7dS4hI/0Lj/Q5EZ79KrMlhNLdf9Zh7iDrwq/3MHuM0/Z1I5H8cIqw35/Ma+YA/SLg8+3P69On/IRh/+7d/S09PD1/84hfP+cy0adP467/+azZt2sS6desYGRlh3rx5HDly5Jzv/Fu+RgXAc11HESHdTUTu9vhvFSJEy4gAicl+bzJiJtloM6cT5KvvivgcJ5KgtiAmMR+p8dsR0X3N99KJr4tIqNuOiOgc399DCJObEGG41e3ZJDxmPiL81sLwLCKAlaoTyxHECHajFZG0CxD+ieWIkHe4nw3+nutPDiJ8Y8loEt5bg4hTLyK2xYiglhtPjYiwHvXnCOHkXYmIbRNiIvuR+eSg8Y9xc4ff+WuiUkSjYesiEmpvN94m+fcXPYe7jf/pSNi9z++DtJWH/H4WcfqfCcMbYUy2YWx1vzth+A3jrodMap3OfYQZqIDQtt5KaPBmepxJy1CJTFsPIGY0Gwm2FUhQGiDKTpWhNfVdJAz1osCJQ6jay1OQ/0miFu5rRLDPkPF4zHP7ecK8VgWfuOYsHB6Gzq2E/2al8VyH1lWT4UuO9UNEiqA+xNBSYEwekeZmpmEo8Xi2Eel3ZhDJcacSguAmz91x46GR0Ewl5rgJaV/vISNQZXLcVcNwm7+f8NjTWiwh0nC0onW7h9CwtUhjmUkMPZlM5PLbD/nZvcZJMilv8bx0ejxVSOC5GO2X6fDees9Lp//+0HCkwK5cYg0li8BcJHwUEtHX/cZfFzL1J5eCArTuJhjHyZ94BVGpJgtZGV4mUiflI3oGod3cgfZUvsbNfM9zD4oano/oYvLpmu97FZ6fUhi3Bu31DqJ8Wj+wGoafJ5MlgKVozfyR2z54Fqz5yL90D5FbshC5sZxALgs/RRaL9F46IHW7/+N+tt0w7ieCLbbp9+FnPJ7NGvcIiJZVEKmTLuP9yd2b1D97idKglcIvlb4/hYyf4juvEEFsx4lgkR6iZvcQ2l+voblchA4B/8au8vJyJkyYkPl873vf+x++88wzz7B48WLKysrO+cxll13G6tWrmTVrFrW1tbz00kuUlJTw5JNPfpDg/9ZcowLgua6paDOWI2LdRfhunEBMYQPa9EvRJj5OZMQvJfxvepBW4my/mA73cykiXtsQs5mHmEwv0rz1IAazFRGO5FdylNDUbSG0ipsQkSxCJ2QQ4ctCBPuwnxuL6sRegzQahYhoVfi+GQG4rUrCJyppIhJDyjce9vqZQsFz4YOEP94vEAE96N/meVzrECHd6jFMQ4TvtNveS6Q86UACXJqXdkR8VyHt2gKkXesiAioS8W1DJ+1LNV/vJSY5nXB834qCDBLh7kVMcA+cetE4nyd8ZH0U+W0m09829Zk1wfOz2/AOQ+lzxmWTx3fcz3/fODiCBIUJMHwL0AqDCR8LiUTZe/z/MY+tj6jt3K+5GbyeuHYC/5GoY518P+uIWtPdRKqMIuAt6LZw3Hm/cVmG1tLXoXQNtCY3hasI4fh1z2sSRjcbv/nuaz2cucO4zkdCxwFib632/JXB0V8T5d5SIFSzcZRMsDcSPnsrCYF7snGfNEFD1r4mYTsJYHslN9GKNETtHmPytUoCyzFCEMolY2YfcxkSvmYTPmk74ZKPIJNjmrdZHscywsetDWkuk5auBBh2hph0AEjBER1E3dq96GC4ybhuQGupEZn4xyLBv0W+g4P10FdPmP1r0d5OZsRJRN7DnYQ2M9/nv0WG86jhr0V7aZHnaqXhq0V5UIs9lrc87plksiIM3uHxt3rOtqO1ci0RLbzTc1DplFXLPJ49xnXpWbBMIw51Sdv4NjosjT1rDhdD/v3uKx3MnkN78U1CIF6MhOUCtAYKjPMWPZf1WWAK/Kpe48m/1Lj63z2PX3C7X/E4a5C2ukpmfLY6/VIzvHCA0Awmq0QnfCxpIAsN/0KiAskw2ldL/Psi46uLiJg+X9fwB/wB2tvbOXHiRObzjW984/8rCG1tbdTX13PzzTf/RqBnZ2dz6aWXcvjw4d/ovX8r16gP4Lmuu8bI9LASEZ/1wHwRspzLCKKcHLOnIa3bU4hI7UBMbYhwBM4jyhuNED4h85AQuVFagBycOy35kNUhhvAAUVC8FzG6byHC8wtEHIYJR+Uij6XG35NbRNIg/hj4z0iAPIxMOU8RNWsPIq1JDUFshhDBrkOn524Y/kfIugf+231C1bg6QkBMglWlns0kth4k0jd0I6a50Xjq9f83G8bkR5hMpMPG3yZCy7cPEe1bPIYW4AJ49x/hojrEFHqJBLzDnteXPYcrPAdPIMb6V26/gjADniB87R4F/hYxn2okyNYQNWKTFqEBCZXXu63rEaMcNl6KiMCOpUQgRTmRBPoZ5BQ/DTHEt/zsBiTAVqH1ea3xMkG4OPUQjJtvmOcgDeoJ91EHw89C1gK0lheiqgMJl3uR0HT2WA4Dn4Uzt8KYawj3h3UoErfLv3W5z0eQ9gvjbB1RnzibCEpKmp+bEGNrRML1A8TBZ7ZxXoOEj61IAD6GIm73GPfz0T7YidZ1jsdSpiThI0DxcqISQ69hWIYYbRpr0qweIYJGcsmYIvv2Qf5KZGrcifbKdI/hbqShKUTr+l60zpqR0F+I1ucjKJq2igiAKjeeklk37bdyQoOUtF/dhG/hbo9nrsezx30PeSybCQEUJFjUofQ6V8OZq2BMERLOdhGat31o3ZURKYyuM27nuK0uwge4H63pHuGJ5wlN1RY480sYM9Ew3264lqL1mzRfvW6rwv22IoGygKApLyPBKwmpF5w1h991e28Jb30/g/wLCNP1AaI83Qp0GKtBLhFXozUzH+3tCrQ/XiTji/zu63DRE+hAm4/mP/lXFhGHvQpCI5s0f12Em8Cj8Ktj8Ikij6PBfTWiNXfaY97usR0iaN8yj+XH/v7aefQBbIDxH/mA2vxnmFDzm/sA3nvvvTz55JO0t7fzO7/zO/+33xseHuZTn/oUS5Ys4Qc/+MH/DMi/1deoBvBc19k+KVuQMHYIcm4iEpO2+/5cxIiuRMS7HzHP5KM0EQl/xUQeqqlIWKlGxCMP2KuUWfm3I4KUhRhlNiIGVxH52nYSJ/AZyDzS6r5negxbkRCxDwkzzyJmU0DkBdyOhILky1OEGFUyl76AHNArDfOlHm86lV8huYJG+HcTYNwUQghNwTLX+9mpQC0c3IiYTK362f8zwlR8WP+/26Z+3znscZa5jeTzcxQxsxYinUZyir/FMPwRXPRZZGa7DmkKVyLifAwl656BBM0NSHBM/ojJHPQXnqMTRMR1OSLU3yOChBLjavLziXm3IGZ3I+HD9xjycytHDGu352IPYlwViKiXIcbWjhjNATJ+nWxEjKrVMNQa9qPu4zkY9zkkOFVqHs/81G0dQ7kc70VXNYpArTAuj6PyZMeQ4DmEBOPdwCQHyRxDDHA30npcRiSFLkem9JvcdhFRxu8zRNDMWI+rCq33rUgoqHdb21HaCxBzrEPrdK2fqUFM8UWkgRzSuPgi4W6RtPF/DhdeBsXVSCCZgwTJpC1qR+vrK8itoNg4vfSseRxLphJM/uNoDWwzfM8joeVat1+D9u3DSEB9DM1vPtqftUTFnu2e22V+psj9/tj3kvtBH+FucJBMgumTjxvfw8gHs9HPP4HW5HOEZusnxkeHcTdPYxjzaaIaTSnaLzcSSdafIpMFgLGEoNOD0sAkP78Vfve08bkGaWX7hOMxHzV+Ej2YjmiU1yRrkeA2B9HTQj/zNNLUFhlX29F+vhjt6RoiQfMuzRE3AJc7Yv1P3ecX9Hz3y8Z3MdpHI8AfIJ/WMo/hj42nFOw0GciFi8YiAfRl4zyZ0JPbwHEizU3PWbgaQGtrN1oHC+ETCwiXm3ZEq1MAyVL/X0FoBmvh3X1obawnAr/+F75GRkZYu3YtN9xww78Q/lavXv0+7eF3vvMdXnnlFVpaWmhoaGDVqlW0tbX9xprDfyvX/31R+X+16zASDpL/zGcIhtWLCONKRJiGgCx477gEuKwiRKD74L0RuPBLyNE/mcWWIqKyCxGKtWTKkGWtJOp8ziey1Cd/nSOIwGQTBcT7EeO4DTFRa4BYSmhlBok0No0eWzIftSKtSTtiTg7a+Hk9/N5ioubnWKSNqiQqY7TAhbcgE8odiDHvQUQtFwkwI0TJpyMwfQHh0zILZtYhAWseErgK4aIZwsfHeo2jXYTZ8XXj8jicaYMx97jPdqKY/HKPKwcxlz9CzLHeOO0jUkx8HzGDPuOtys+VEQmcdyIhpBIJ02le9hARtPWI2HcIvsGfQc4axPxzPb5mIuJvE5FSoogoWTfR9495LJuQYLfE914ntMbryGin+SGhbZhORM+WCN4xTXC0DSZdioSpdji5A8YXQd8OyD8O3Y1QfJPfS+bfFqQhLHN/x5HA1218DRKCY9J+lPvT5P5nEXnZytE6SweReUiYXkZUe5iGhKoJfrfa81fl8V2H1u4K4ycXCbsFxmEvIaxvQfsUYn2cMGxJ47IfMdrEyCvRXhtxn7lk/GYzVUgajZO97rcYaZF+TLiD1BE1lXuJXIuDSHCdR6ZUI5MJ/8AOj62RyAgwnUgLkoTdiTD+AiJ7wCy0nuqsqV2JDkcFfv73kJZrv+cszVXOWbBlISGsEwlNSZufRaZmMyeIRPjLkab3GFGJJNHODsP9mvAz3AZZNWhN7fbctRI1pZNP3CK055PrylzP33q0jlb4ey5RPeSYfzvkT7X7LnC7w4Z9PxR/0c+Uu+3kC5w0v1s8N+1EwNkAkS6oGx0gkvm/jVg/U4mE6PuI4JDdnr95aD2WEdVlJqP1n2cc9xsvJWjNTvbzr8BF1Yi/JJekLM7vdZbp9gNp6ze86uvreeedd/iDP/iDf3HvnXfeYezY0HO99957/OEf/iGdnZ1ceOGF/O7v/i67d++mqqrq/xeof2uvURPwua6lYyJf2wkkXDUSSVRB2oJ7EEEaIqJKDyCi2EYUl29DBO1SouRRMt0cc5s1aON3EZn4z/I7ygSWgAjn1US9YvuA8QxR7mqASLq6l3DgfhsxmQNExG2l30nmyAIkTF7s/qe7/yGieHoxkeT4K/6tktAA9hNVUtoRc2og0scUEFGoG5EA2YVNGn7n80irssvtliNtRDVRSukEmXQiifkN3g05n3RbE+Gdn8HHHkCMZ4bHWEIkT17kvrvcbj7hGzjX73HWXOwhUsIM+bcJZ8GwkMhx9hjwDTh6C0yaTCSg7XbbxxFT+L7xfKVxuwkx6s9A33prMha7j2SSHiRy8T1mnN9BBNJ0IsbymJ+/GK23uYZzkft5Ba3xfo8jCUL1RJ7G5BM6DzGgJvfRgdwdTiDhJwephW8w/HOIg0fSYnZ7rO3AFDnq5y8gclmmtVWChN9jRGDPFxDjSw73pYLl5FYYv9jP5BFBEV1ob+YTaWHWI6FpEdJ8v2V41iIT7gbC1zW5QEzy++1E5G43EahVaXgf1DjOdDif5wzjNRtp1Je6rSeQQLDQY7zPeO84C/cDhLmyiyi7lgfsgXdOwMfmu48mIup4ORKgy4y3NiQgP480gvd4LNONj2wklCwkgnFeIw6su2H4l5D1MBJ6dhm2uYSAmfyQDxE+tz8+C2+HiLQsOz2GDUhwSgeCne4vWUw6kQBY6nnYQKQxegjV7E4m+5lI4zrF7yatqd1hBtdAzkd87xHi0NJ71jwmzV2p2+z0c/nIknEA7dNV/j/RgzLP13zCTJ3vOekjrBPtbvc02iOn0V6uQ/um1r8/SKTXKvKYNyGBcjHhY96ADh2fP48m4L0fsAl4zmgt4A/rGjUBn+uyeTJDSPagzTYBEZwOJLQUEoR/CE7+BBGgQsToZ6PTcQHauNeRyQOWKbM0jLQ7bUTkYdIG9Li9QSRozUdE6nIkSDUj5rAXCUbXIwLfTGjNEvEdQMxhOmLG5Yal2G3m+/mFRDm1XkRYk79RHpET7wVEkFuIXG/rkRA6y2NIGrIKJDT0+nsi6PPc7mIiInQnMsX0k2F0DCMC+D1kWhzwGLLgTCMRRTpT93LuIOoyF8LHFiBi/GPCjDoLCRN3GMeTEWMph3cedfsdhuPBIuH0FSTYjRBpadJ7OwlH8zSmfuM7y/kZb/S8TUPro4WIPCxE1whietOQP9M99glN5u/k87eXqLqQh7QiDxiGaUTy21a3fSliFl1o/S00bpNZr8Lw7HE/2UTC7OX+voqox1qJGFqdcdHg+ws8r0P+24GEnX3G1SDaB8mdoBHyb/K9JLiVGIe7iUCFWUiYKDC+VhNRnUMw/hrCzzPfOJhEBE4lAfMgUb/5dsRIl/r5KYa10+NuRvNdSdSxLjOsm2HwJ34maQ07/Xesa9IuJ/ZxMn0e83NXuZ1pnqd7jP8N7nMyHN1KxvTOIjL5Ppmrtj9WR5QfK0FrYQeiOdVorYwnNLOL3cew8Vnp8WyD904QGtYaJMANxbNZi93Ofs8haO5vQIeMUrR2J0H3MeOxnCjNV+m5+7Gffc7P70R0dQpRCnMEra2daC3uNn6+5TE0owCnHMORDhPVRFaE5Ie3GqXd+Syq9JG05RuJzAdLDVMDWq+DaH3sMB66iEPIiGHqQgeHJqLue5efOULkgnwTrcsew3WCOMgPE8njb/V4nyPKdz6K9s0u97vCcMz22EuMl/N5DX/An9HrQ7tGNYDnumrHiHhlI0L5NGw54CS2x9CmX+D/T/qZa9EG7ieKjyftVBMS8qqR5qGI0DS1I4KfTD73Emk0cpHD/m2QNZkowdVBRNImP6R8/f+rfvjEWOTzNwx/cyN84VI4+SaMT/5pzXCyHsbfbVi+hxKtDhHasR5gJQyvgKz56uLkLsl9f3glEqL6DOstiGglQSWZpjcRyY6TGfkWdMJdSGg2qoiEsa1Ezq7rEMN5kSgl9wS0DkHFDDh1AMZd77HvdVvXwdsPwyW3E5q8mUSpuSxCE3Q5UUpvF2IW+4zTJKTtRgLpNvffTURglxCVCAoQw2hHjH8mIuBdSLvzRcTEa4nAgyaixFormQjizl9CaR2ql9uMmPRxxBz7PK4uwhw1F848BGO+6HuthnUZihBNWoSkGZxCmEg3APNh+CeuOtFDaLSfzIPafgkgDYgBtxtnLUSATg9auzcjLWPSnue6nd1ETrhKxJirDcOPiWjxXLeVQyQxLkFM/3seL8bXMGLEye9yADH/40Si7kLikJZ8sdqItCObPR/p4NVD+BfejTRKLX52JREpW0KkTlmA1sJ1yGcx18/vR4z/OuO4hjjY1SHtTxORamgZUYbvR2RMlCdvg/GzkXD/TY+92HjL9Xu70b7KIYTizZ6zfLTnpxHpcawZZw6RaPgQEqQG0R6oJjS8fbD/IZh5CxFoknzQStBen0wEiAygdbvUY7oMHT62IIGlEAmux/w3l4ju30cEui30XGajuW9Fa6YMCZiLiaCqXo/1VTLuHUd/CZOy0f5Lc3LMz4xHa3UWEt5qkHvFAuJwP9nzUek520HUtP68cXGB2/gh4S+YR5jW/44otVcFg3dBziOIXrUTuRaTZaDF+GhAwudBtGYH0FrdA8NbIeuTiMZsB7rOowZw9wesAZw3qgH8sK5RAfBc1/QxIojfJSJIGxFBakXE6jgiDt8i45CdYTC1KDK3HAl+6QQ42c8W+/nZRDTbHKIKxOeRuabD/SZTXC0icsOI8T7o/5P5ciEy3SQiOQ2d0L+HiIr9tH6xDz4zBZlROlDlhMsMr/0eu3dB8S3ILFaFNGWJCRcRFS3qkMDzNGIgexGDfZ1gED3AfDh6H0yagAhbo3EwmdBUJbN4PhHpOg0Rz7lkogVPPSBamRFgCwiz2W7j/CgSwHsILc2NhMm+3Pi7DRHiZkKQuZGoQjKNjAP4+xK8NhsvB4F+pUepuN5zeEjPDL8OWbMRo6o1TuxL+POH4ffme2yVfi/5GybNzAl49ym46HFi7Ywgf7h5hNCU3AyWI2F5mvCd8TOboHkefsYHiSpC6Ey43ux21qMrCeBTkEbjBSLxcDdaw6v9KTPeS4mKC0PGzRDSeNQYnst8/7hh3+w21humpUiwm+a5yCXysq0nSt4NoPWWRyTsTlrJuYgZj0XCewGZeswMeNyTjNNkBuwBFsF7t8OF2R5HNyHc9SDtTJnmmyrC120y8AAc7IDp1xqOachMOIQEj6RZ+mu0p5JW+gCZRNksILSqeyzUXwaDr7vk4RwkwN1MlCFc4bGcRIL1XuN0wM81E/nj0uFhmLBkpPXW7Hlo0vRzs+e8BAnWT/l+h/H6uNtY6fYHjcsV6NBR4nYmon24wGOdhtbDceMRolJQh+B4pwM+dj3az9cRFTQe87in+701SKBcRgSwHSUsGifRvriR8PlLWt3FaD2ONfzXEQdPWw4yUeKNhqEUrcN+wm9zwHDnEgn4W5FGbwkSeqcSyoFrUdBVLqFx7dW94fWQ9Wk/X2gcXQCnnoVxH0fBWa8SfqnH3H7DqAA4ev3m12gQyLmuahhuhqzNiHGZybMFCVcliAAMIWZVhgSkZELLRT56rUggy0G+XeuJAI0G95WDNvpawjx2I4oy60HE3Bq4TCqGBEMfYna9iAjtRYSvDp1iy93vxUhT1QQchs9c4+fXERqhdkR4ynSv+G7gb2BwCHIKEPOrQIQzCR5ZiBht9LgrkKDzIiL4hwln6TabQXOJPHbd7m8+YhTJL3GIqPs5gAj2gL8/BePGwrhswlSTBLrdBLPO9RgL3eZYwvTSiASHRUSt3+SzOMv3u42XPqR9WYqY1H4ksC42/iqAUqhI5rmkIRpyQFAxmvdVxot9rH5vNsFMOv1/MuUdI+OfddEXEZNbrX74IeFflExCDcikuIuI9J7ltpLWqs7+4k2EY/xlRFWZGuNvJhEUsRcxzmzD14AEyhYyTvsHG2H6gPE9xfcu9ZwlbVGFYbvS8M4knNeTlmMeWgOJsSbTb7q+jwSd6YTm7TCRkiTti+P+u4KIvj1J1Ozu8pz0+J0UdGVtyoWr/XuPP6WEFig5+NeiqNQ8z10hcBNMvo/I8XYMraERopZzLhJqUpBRCpwZQn5p9xvWesE/5pjg7QWKvaa42WMeQgJGCZEo3vv/zK9dY7bdv3eitfsyEtiqkTZrmMgXmovmuhEJVN9E+2M/YU5+AQlztZ6jOUigazI8+zz2NUTVkyzBMXgf5HyZyABQTgjfO9FBbEj4+Nj1nrevEqmVnkRrtAftmWTWLjPcY93OJe53mX+bRyQXX4Ho8VRESxcgTewhz+V3PM7biMCy+UTKo2WaD7YRtdhnE8njy9Gh4HU0/z8k46PKC4gn3O5xTkfrLI1/yPlFq9A634/2YyGMS4f/VsQvHjZeB9Gh/3xeH6TpdtQE/KFeoz6A57rmQtaViKC+ijb/fLRxpwHrYfAmRDS3I8LxPNroyRw4kUhgXEnUki3m/VGWlW6jGzHKbYjQDRNO4BARanPc1lOEtqnG7yU/oR5EMKYh4n2Dn92GhMEDSKCpR4QxnZI7CPPcWOASlwbrRsEjJcCdiMBNd79Jo4L/HiaST5cYJ1nAk2WRYDoJSn/i8a8lfFo+j4SlDWc9O1+wUIUYzwpEgIuRoFPj71MR4U7m+TKNe/AAUc0gMdIrkMluBeEkn0xSWYhwNxO5G0vOejf5XRUjJrGHmPu5xmMrEi62ICL9HNLEZRPpWQ4RqVwaznp38ll9vIK0LflIkLwVzhzzWJsRQ5uh3zlO5Ip7wrjJJbQUw2iNtCAz48/c5h60zg8hprLaODxuOOag+f5/E/5uvcAJmH43WmtlhP/eEcOeizTVkz2HkwjtXQkhSBb62X4iwXUrcSg5hCLxa4kI8yT49hPa02zDscH3SpCJLvmozkPrOh08JiDhqMlzfIjQJG0jKkSUoTWRh/b122jNPWfYt+j7uHv03pn7kfCZb9xmu53dxsP1brOIMCNuISJa07rzgaM4+fuuc/9tcY96v7Pb42h0rr0u6P6JcZzlz2VoHaSD3uVu64R/m0mkfklC8HG0lpImsJbQIJchIScXCZSXGb/2YeR6vdfdZh+8DqIOcL3nephIsJ+sC0lz+hgREDbHfV5uOBZ67rOge6t//7znZgmhVc1G620K0g7mGoY8wm+yxvO0CAl/WcALMrVmNNVVyOy+l3APakaCZK7HMZbI39fjMW4iApHSgf1xpE0vI1wC2g130rAPkSnJyFfQ3slFe70a0ZBaIkBt9Bq9fsNrVAA815WIwVKCWCS/PDOEnM+hTViDfEySH08bOtG9CO/9DBGfeiJx9EFEmFYQ5phZ/iQN1WYizH+p7yWm30OclicjgWszkbIhCxGIaYioJqYyQPjRna15m42I4FpEqF5CAuk+RNDzUbBEFtCqxM9kI4JagpjmCY8nCRsLDWcrIly7YXhMh8Z2mDDDfh/efdH4nUww5GEiBUKnx3UQCbdLDfNtnpt2P/9tjymZFQeRUF4JOROI1DMvGD9Pw89vRUw823i5CTG03cbZIiSMlrq/bOMq+QMNEIlyE5MeRvPcjoTMUo+jC/kdlRFmohoiGnoSYmg9xv12xHTWoPXYTSYwYswC/3YQaY6WIqa50rjCeLtA5nJa3OYlwAY4tU/3MubzCfDeTwkhLgcxrGWIsb6AhIBHibJWMwk/phX+zENMdoJh6iRyDzYhv8BhIpKzjMjpmO0xt6D1UIEOJUloHtCcZdKFoHnoa0b77Wm/W268JqHwaqIMWjMS2nMN2yv+P194P/hLtM5no9RBdUjwSP50GNbX3ed8j/lWtO8fBmbAmCfQuilzW0mLNdX/H3E7XW77EqQd7vFYl3u+cg3Dtf77MNqn+4kceVX+vsvwrUZrphaKFxt/Je73gMfxOOFbWuL3J3o+lwp/P3+dcCUoF74GnyeTSoVa46rZz61Be3s6kT/vEmARFBfBqZ+RSV2TsWj0EoeWo4R/Y7tgGD6CNKgJfvxcDxIyFwBvQfFKNPcnofufiUjzBsI95rBxttDfW5CwNtXtX+7+T5CpUpP1RbRHStB8LxNcLCVcMDrh758zzLWGbyJBU3KN52yPrQ7RhdlEbtjD/r2MyDbR7meWoH2xFx0ADsPfv2I4CgmT/fm6hj/gz+j1oV2jAuC5rr2IoNcjwr0WYWsNvPcTtPFmIiHgECIIRYhR3oQIVBNceDsSnoaRoJEi/qoIx+VSJAy8jZhcPqHZaUTCShciVAWESa4POAKDzb5XhwSWPBR5th0laa1AQl3SxiTtzxNE6bY6whx1F5AFrVuh+z5EhJIfVAdkXeO2G4g8bhOJKNiXiJQfZR7basi6HzHFmzzmPth7Ai66hqh5mkzrL5MJ+GAAEeUsRLBfJIIrvonMijORv2av528O/MNPZb6mCxHRuYQWqB1+/rhjCu73b8PAVjh5Gt7uNyybECM7ikzFe9xWPxJGe4kKDIWEr+gMzQ2/IMq9NXmOW9xnrucrCa2tSPC8Cwk4I577TcZbDWLmZWj93Oi+DxOVL5L2Jo+Mj+O4O9z3L/zcPBi33O0VGZ5muHAaEcgz0b8fdP9LkBl8CxJMegnzYqHHtY9IBdQMNEDfM8RaHfH8rSPqSR9H2ukS4/a7xmUK7kiak2q09ld5rhrIpILJvx5pmlJQzbDhnmq8tsDgregAd9DwpcNdAXLdKAQmwfTjZPw3udj3N6F9kzSDSQD+kWH4E7SPWpBAMqLnjj6A6Ee752osIcgsQOuqVPC++yjhs/smsii0EiXbXvbzXWitLyGE6n73+a77aUAuA+lgVOh2JhiPS5HwfyOiVzuRWbkRadNe1DwXg/ZdA5nE5Dk7iPQoGzymOQStLCNM4wPEQacSxs1GrgEnPL8r/P9Bw9rkZ/8S6IH3Hoes61EwTgESGjfyfpP3i+iwcpXb64DiyWi/7kJrotHPJP/QzcCVMPxrpPXu0VhO3UOU1Uyawho4ebfn/C5EJ/f5e6tx+AX43ZfRXnzR+D5G5CJcSZiQN7m/T6N9m/broMfda5zuR2shuRR1GrdrAOB3i4Dd8ItniOwB5+sa+YA/o9eHdo0GgZzr+u6YyFHXgzbvNxHjTVqOhWhj9iPi14Q2+3xEaCB8xBy9lYkgXYWIfPJxmoOIQdKcvIm0JUeIWqQvIAJRh4hLPSJqsxDjzUPE66/dx/cQ05rpNpLA1gh8A05dD+M+gjLgT/D9pDW5BDGpb3p8JYQzfhHhYF/tvv4Y+eiUu50GxBi/7TFOd9sXI0K9i8iv9bbxeoPHOBP5e33e+G00TA3QNwL5a5Cf1DzjPNt4agcegL75TitSQZh9exFxLzael/J+h/gmwk9rMyKqqwimlrR22X5nHRJIUwRgFRHRW46EiheQpqjK8K+HU4125p6DNEATkFZwGPoeN9zZxmtymD+BhPBdhN9n8mFs9txWAZ/z/0eQn9EOIj1NIWECLkXrJQUh9AP/lfBP7UBCWDJd3YyYfTYR2PE3RBDCISKP4gl/ny2Nz7jPIub3FcN7H9oL3Uj46zY8ya/uGJkSXpQTpshHiMhYrwVuAP4dYsLHPM9XosPBXxqeB9CaK/T9ZsTIe5E5Lwn0eW7jOXTYaydKODYAFdD3HORf5jlYihz5q2D4TTWddb3mrK/R/P5iovRgGdrTyfzZgw54S4g0SnsNXxZRZWQukcrpBDp41TgK9HOGsRrtzcfRekmCbQVag7keexJ81/q3o8is3oOEmXTQSoLWasMwhPbCD4H/gurfzkV7aa9xO9U4TZqvR33/RsIveT/h2pFrPBw0bFVE1PBStGbTOl1oXA0ROTOvNU7uIZI03wxnroQxnzNMc4gk61tRTe82GHep8bCROARVQ98trvCyzjBVGOZlaN31+f9HkeZ4NTroHdD8nbwHxl9GJJMvIxKf9yBa8giiCemQcS/SXO9TGzSivV6G0mktJ/Iqthueh9BevY7QBH/7PAaBbP+Ag0AWjgaBfFjXaBDIua4hxAhnIyEkmRCHiHQg96ATWTaRQ6wLMdT1qPzQ5WhT9yJmdxhpwL53VnvdRARjISIa17vNBxER+xqRRiL5hVyOiPWms9ofT/iiFSIimhzPy/29HvgTGHeV284DdsGZHTDmUsInsM+w1LqPhcTJdAI6Ve9CDCvhpAMJqLMQ41xk/GQROQ/XI2K21O8VI6FlJxHo8VW/s8DzUQXD+1wmbz4imPP97J3uZwbwohMmdxGm1Sy3kYTtC9CJOx8xvirDaF/F4Tch607js8vtbjQeHjXcDyFz3Nse52vG1TIknK5FjOUqxLReA8pg3JVI6Eim/GGkmZsE+R8nTD9tRL60VUQgUJbnotC4GW/YH0Das81IgH6eSM7bbTxMRGvzYqJ273hCozzDsA4jIawdCZTrCB/F69zWJuNuLhH524TWVjVwI4wrROv7Bo/nYuP6JNoT04jUJGmd/sJjKyASEhchoa3HMDUQKUFuADqg72XIX+42RsgkY85UEulGV7bHu+usftDcMIAEi2HjsJc4/LVC/m2Ev1XSJJdCViGRl60K8gsgf76fWey5ySVM3JVEhPFkRF+GkMBRaDiKUFqXQsL0uQcGj0HOYsjKJvx/k2B/KdIWbieCNyoJzfwht72KOBy2et6morV3HK2hKUho6kZ0px7t/yc8juNusw7t52VonRz1OCqICiYDhOY0WTwmG7aNRPqZdMBq9PeDRF7Qq4DDTu+0jMhMcNh/Z2rcx4DSXsLPeq/hbgL6YNxiP7/LsCc/wa2u7bydqHG8G9H3eqKGb6PxVk8msp75+m38GkQPCtF6Oux+7kca3IWG+zhad28jAf0V97cX7c8dxu1ydCCZj2h0nf9P/tJJodDP+b1G+OBMt6MawA/1GjUBn+tKTO5FdMJ7mTAfNRHleTYgJpFMjw3IBDLVz0/xO0OEpqEeEYBhRKTb/G7yx9qFhMvtfn4f4UDei7Rj9X7nkNueiAhXNxGUkJhmLyLAG9TG0WP+rYDIGdYCYz5C+G1NI6od9CLhp5BIS9NGmJVzEXFscH9vEjn5KhExbib8b0oQozpmPC82DNMIh/pm/z1EJiVCVjViQGsRgV1tWO9CDG/AczaPKNn3lGEaa7y0IjMkSMDKJurF2gdxGMTAhgjt4GWE71IW8EOVymOR5+1yxNT3e2zJib/csM7y+Prd3sVEFZCkCVlBmPjzgavh1GHDNYTWQbNhH/DfT6L1WYk0CbPd1iHjptXtlyOmehli8K+itVUAZ/YhQTwxvslontegdZTmsArN6wVEDeZyIqBghNBG9hMBDnuM65fdVguRH7CCiBTe5ray0Vrd7Ll5Rfc77zBe8g3H/WSS8ObXoXV12DCkNZ6DDjK97qcWMdhGj7UCHQbGu+8pxnOR5+NN47OCqPDQJvhO7SCSCNcI32fudhsdaN29ghh3F1qXc9F6XW98T/W8THZbSaPXj+Z6E9oHuXo2ZwYhvNbqc6YNfnWE8JkdjzR5+42r6WSSkTOE1kktkcy8gaj7m7RpU+HtXW7nuPHRS+S286GRx9EeSMEp0zXHZ5J5uxUogMF9xtt2tH6e9tiqjac+vzserZ9Oz+FE6NtKJuDnkmlozWeh9XExcXjbDKWLibQx2cR6ykN7sMi4W+7frCF/byNawwvRPt3u708RuSu9V8/8DAnQ043j/WojcyDpQms+HWKeJ8zWpWg9pfH9Eq2XBcb1gNfCQcQDOoma1ruJ6wLju5jwGx+9Rq/f8BoVAM91fQ0R3W2ECSULBu8nsuAvJBKlricy6B9D2pTn/PtuREQeRURjJZk0GAePEMlhR5B54DaCkU4iU8uVcv8GIhZ5hi+dvqYSaSH2IyK4GxGniWSY9aQZyKxXYzgKiQS7xxChPGC4SpCD+x8bxsXuJ/mgJQ3fEx5bEqSyECFNWsuJiFFO9jgHkZDS4/bmGs5biEjAGkJzsRMx0mzElJsNw0JErJN55lrPRx4hJO5CGqErjJ8JiHnM89hPICJarvZybjfetiGCvBkx634481MyQSm/N9HwJQ1iKxFs8DYSGhqIAIcmj6WESDadtIGF6LCR5zGN1bPjPkKU/yox3gaMi0a3M4AYxiI/k8zctci0u4hMNYa+hzyWBiSIjYUx693ng4TPXSkhbJ5t9i9Ba7QDmeh3+5MYZRbSajzt9rdB31NE4vO5aN0kwbgFCQJbDXc+WmsDRC5JC/al84myWcmH9iroPkKUzLoY3rsXCcK3IkafzIlNxKGq2vPa5XsYl39C5ObcQkQr9/nZI+qTpTBupX97moyv8JhbPJ9jPa7HCF+45P/5nO8nk2+lx3qd++pCa2iYiDx/jTgMbUcC12xgBMZUwycuBr6EBMdEDxahdZHMkX2eq4T7KcbDzYheNBAC1Vq45HPC9a+2ur8ZHksPEfVa4vfqjWf75o1ZQ9SyrXIE8B1o7d1FVA75quc7Ccz7idrWeZqj/PkEzcRz0ImEohHigFaF6MfTRD3lRUSwxluE+b2JOFAthQuvIcy0+4n6zQVEeiS7iYy5TIna2et+So3PWwmf4Nf9WzogriNzEHvrHwlf0AFEa3fDyY1ojz1N+M12IK34MUSnHjUe3kT7NbnAnM9r1Afwt/Ya9QE811U8Rpt3PlFc/Eto0+8hkg0fRwRwNmKiA8AWePsNuGQsIuozoO9u+64ddfsD6ESciNMmwvflVsSEuxAR3EimegfzEdFeT6TMmE74hySNXRYiMulU+ydEguOtyAz0FiJWSaM20+3MI8rGtWk8LEKE7jVk/rwGEfHko/YY4WeY66oS9xuWx5DW4A6P80tIIzSCBLTNhJ9TO6EV+jYS2rqQALkCJZjF8DQgYaDJcC42jHmIyS5CWsolbnsNMr9vR4L1z5BfUwfSON2GTKObkUBbiIS0byCt6zyiDN58ImCnnIievtyw9iKiPx4xg9sN52TDuAAR9R4idc0AYqr5ZKKpu5/SFH/sTuOixzg8gPzpvgZ0wt/9M3z+s4ipfRUJGFvRuvw/m7D8d/AE5JQhLcRjguHdl+GilZ7XLcbTTs9Tvvqiy/18nUgvswcx9x4keN6J1skeJPQs93wlXJUTyYTvRUJucnp/Cgnpq4iqEMsIH8QUMDDV8/aU5/YkWmerENM8TNR+XuL5WYr2w7eJOtlFfq7V/ZSg/d1v/A0Zh8uNj3aPYSxR/QXjqN/9FPq9AkJztoaICl6P9vUixPCTlrXVcC1A66QDCXGFxu924qA05DbS4e0t9z/Xn32GeT+h1cwnSqkl7XYx2tf9hieZy0HrbBCogL1bYU4dEuDeRkLIfiep/qLxPYjW7gE0r21EBY46tH+nemzX+5mbjdOb0JruI1wcXkFrLvlITnXbRz0XM9xWNZEQ/3bjvMi4KSMyKkwjtLrLkZ9dIQqW+zXaB9loDZUT9ZGTvy1oraxC819vvF1vuHa63WNEqqAUrDPX7/YSOWHz0VpvQlHnF0Sb72yFj13mMVQjreNUInq7BwmgA8CG8+gDuBXG539AbfbBhMWjPoAf1jUqAJ7rWjZGG7UGCQO1aMNfjYjEMjh6L0y6E2nM9hCBFHuQIJBHpjYt+4lgkSvRxq9HAtkWRNwXoFPdGiIH1BN+5ikyNU+5liBGHUjw2o0El1v9exYi5HsIX6pKv9eJGGIHka/qEOFwnosIcj5QB4PXQs4tHv8GRPxayCQXJgsJO0sRzurdxkq3PcfwzTBsDyJG04uEmZmICbxGOJRXG6bbkUB4CBHWTSho5W33kcwwezQndCOmk7RWyWcrmbCmIyJb7jFuRAR+AhLUZri/esMz13hYYTxu8VzUyHQ6ZrnbXUqcxqsI89tuw3kbEsj6jbfL/N2mQxqI5OAj/u1LHucut3UlYohj0ZoqJfLnve3vyd9yv39fYdxtRUJrDmJSlxFlwu4yXhcZj1mE0/tn3PYsMrkt+26E/IvR3E83TpJ2psO47kQHg6TdSqbfEvdR5P/XAj8HvgZvvQKzvgyDPxIqZ30Z7bvn3F6xx9CH1tQhePduuCgJfyeQgH0zCrCqBu6BfxhR9+MOI0H+ANpDW4nawZORa8AgWuPz0OFur2H/rtpiucf5FDJpb0ZrKNfjrHXfq/ze943PDiR4NXhOCtE+TxYCiJyDXYTpPPnsdRi2AsM1h0gtVAy8Bf/wLHwq+VUmYagZ7en7jMevo3V+JaItwNGfwqRLPZcXIw1bGbGeS8mkUjm6yzQvaaXzDPMJw5fo5ULDedD4XYQE5WmE28VfoHq+TW4/HY5We246NC6ucLtz0TrYSdRmTn6JaR++bDxvdHtpjc9H+/qo8Vrqtr5LZFzYqb7OPAVj7jMOa8mkeuFhRF9+CsO1kHUBYRqebRyUeMxLDUOdcTWFCGo5RNTk7kL8pJBIA5Pl/p4zLnoJ3+jkCnIbWgt7oPUVqDgPbHxUAPztv0YFwHNdPxijjXk5OnUvQ4RsJxHhW0HkC5yGNuh65Ix9CSJsVyEC9E2iNFPyVbvB7eyEdzfCRQuIElY97q8ZpUF4jqiZWYI0UxX+vgXoh3eH4KJPEtqxAcMxGxGN1W7vST9TgJhhn8f1LaSRuZQo49SEBJoW4AQMN0LWo4gBphPtNI9xASJQ9xLl0yoQQ8VjrwT+0LBVI4H6NUT8+pEgOR0xk3Rq3kdGs5gxuTYiovoZ42o3kRNrKtI03EREVydTcieRy2ssEhYne+42kqnowIvQuRVKP+tnH0WCeBLCiw3jiPGfj7Qy7R77nZ7fBsJUdQ9iau2IyDd4/D1EwtpDxtEJw3cMhushq47QRs0/q/3XEGOp8thno/Wz379/y/32el6norW03G31EcmUE4PNMizTiaTaSRgZ8ly2wuA/Qk4RWlftSKguITTWxxEj/L7n6Um0nxKOeg3zs56/E4hBJxN1kfG2F839zci83I/2wkrNU8ZnbAe81wEXfo7Q3mW5jwRPh5/NRWu+xr8vJiK9c5FgV01oPQvQ2thHlBdLa+gEEsaKjZtkuuv391rC5JxcOLYT0fBdwDAcfR0mXU/ULk6HvoeI/Is9aO/Wk8nNlxHGiLbo8HhWoTWVZViTH2CVcdwhf70BYNyniVrC8+HUMzCujqj0kQS3WYT/XgqYWUSsp3zjqATRkxK0x44ZlpWI7hSgdQqhiVxDVMNJrh89aE3UGLYGgialdT+foEljkZav7Cx8NxB5UnehNfVVMoc5Vvi3rWjdzSFK8k0lDnL1BL1oRRr+ASIvZT0yaW/xPDagvZZLJC9PWvRvoMPVCWSJWIj2y5P+v8847ScyA3zdY5pG7I8GYPt5FAA3f8AC4NJRAfDDukZ9AM91bSXSVNxARMIlrcGAv7egE1/y2+giUlkkbUerK1EsQgQzaTCyyTi7X7TA7RYiYjSXYJTJvwrCT6bC9xrc7q1w0fVISAExnfEEUxtGTvObiVx1aSylKG1GLiJkBxBRGkFmls2IcF4sus5TBAFeigh5PXJ23mi4Nvr9Q8bjkHGymzBPJ3+nZMJKzPBvPK7piLmUGCeryTiok5z+8whNZgFi+tvRaX0+0nTU+rk8xGAKiFJM2Z6vPUS9X+Or9FrCKf4BolRcmpt8QvN4lf8/gEyqk9zfPOQGkI3MTTvcRq/hP0poiJPWpZFguFMEDgUeN4QZ6SBimh2e38Tcyj1/cz0vCzxPpf+ncbb7/iTEXKaeNd4qourAAeNrqt59dwfwGZf1yiMEnyFCqNrq3/YjX8GTRP64o2hNdKK10uN+JxL5EBd7DMnFoZ8QFloN42GiRJ3n/8KPoj20HwkBHW7zYUKznEsIFV1IMO5HQlaJn+1B67eR0Lq1GGeTkBBT7jauJ/xVk+Z1puaOFWjNb/d8FRHJlbcS2rFOmPTRs/psR2sq+Z8lYSafqAxRTrhqnK3lLwJmw9uHySQO5yW0Zlpg+DmimsV8GHMtjFuNNHKLyGQaGPdRw9Hhvns8rqNIsKx2v4meNBn3e5E2tYNITN+G1mgFcYBuR4JOOXGIGfA4sjUGVrj9KuNjP+Fze9T3+jyPj6P1eLYAOuT2C9DhN/lcJuvKVPf5F8AmONNsnGf7s8l9bvYYk/UgG62DJHhPR4dt0Hq63M8li4r326lH0dpOSoB6IqXTIHGg2OS2Dnl+bXUZPk4E6GX53WTlGL1Gr9/wGhUAz3XlISLdRZQB6yFj5jzzBmJgC4Bm+NWbiMiXoA36FaSdWafmcm5Dm3kTEjiSIDgWEYzkx5JMQB3AdHj7eSRElCCCcp1+pxUJSYvd3wASuvb5/V5EBMcKPvIQYyhCJr9sxFRAQu4+RFS+5HG3Gr5s9z0dGIEL5xN+KJWEpiMJMwVE1NwCosTa9USy5zoi99sL/rT5928gYjmJqCebbThLiaoMBe53NxFxPEBE8Q0gwX2IYLxTkYajzjAOEgxkCBHwWmRmLUFEtw4xtZ2en2NIIMr22G/y30mep/lo3hcTaS0meu4KyQRGZErnHSdqKHcQgTXX+v5SKH3C/ZSh9bEdMcE5aI53IkawH4ZfROvyJvfZSjDUDsT4suGde91+BWIwiflmG8aX0XqtQvM4lUz+xIvWIEbYbJxsI6KbpyOhe9jtbECHo+eMo/lIw5GNGG0uEnqK/H4HWou4/90e4yE/304Eukzw/N1PRCAXEpUWrkJC8FJCA9ZIpFt5HGkVR4jKL43GXfLlHGt4kqapgPDjrTJ+h4S7kw/79wOEL+0FRI3imcbDdiIFzSY0f0n7kzTjydzXTqQCutkwvml8tBjfJYgWNBqPh4E9cMmlhHm6xHOaB1mz3X4e7zfRryVo3ABah6vR/O5Ee6cUraECz8Exr7nd/nwNCbYvocNIM5EvdIlxt09/u19E5vJdHusvCB/r9Ygm7DKukjYwaQeTFvMXZ81vm/GxAE79M1pjB4HJcOp1MlHTqRzd/5GCSYbRYfkLMOYafOIi6Fjy4+wkBLbkvtJO1Asf6zH+hBAe8Tw1Cr5xyf9ymsdVgvZf0sz2+HtaK0uM5yY1lXU7US3oBUQjE308X9fIB/wZvT60a1QAPNdVgojCdELIaEan4zoYMx8JY2VAL3ziy2hDF6HNuwcRvRx00ryWEOIKESE9gYjpVJSK4BlE1DaRUfNfMhE5/24izInJxLOSiA7tI+Nf0/c8kVD2Rd9r87ha9Hv3K4SAcAMKyliKiErSONQQTLoPBXHMQAysFhGsRsQ48hDTfRr6fioccb/xNRNpBBJ++hAxTVqwZMLb6/5rDetziIGZAb19B2LKecZBKyLWNR7LPMJEdRcRnNDhsS1GhHMnkQ+xyngq99hech8zjZOVHstpFDiwF04+hUw8E5AwCpEEeg/87Zueq8Muw/YwDO6CwXro7ECMdbrhnooEZIyDB42DrYgY1iNzb737qjHeO8mY5bnPuOyyeb4dzftExMizPM6kuZwFH/u08beXYPb1hDY1j3ANaPQ81SIh7qhxXOs5rSIiVzvRvBcZd/+ZCFxqRybvdWhd3EWUk9vl+4Xw7j5/n4uEtTfhVD/QDO/90u80IOFyiNB+TiTjVrF/PTKX9RG1nO9FzLLOY/yaYdyr8Q5v9e+n0aEoaUOXI3eQNEcdnustaB1tANpg/Oc8nxPQWpyHtIm1LhW2ncjF2IYOIHMIQbcJabyKjNdidJDsRULTg2jOO9GeOOSxLfX4ejy+AnSQu8WfAqJ82XQiPdE24TRzyO07q810OBxA6zOZOccS0ebVGlvWl2F4B9KEz0UJu4eIEojHPbdJ41ao8RfneV4HDUMF8j+sMZ6eRlaDG42DNuTjPGyYTqO1Xan2+77udm5wpZsi43CPv19MpKrKg/+w2n0lH0Hg1Z+gtdlKHOrSIbSU8AXH420wbIvQnp2itmklkmOXE4JkNxx8wPh5lYjiHzJeNqN9X2e8rUP7fxOcecCwX4B8F5ehNZP8vs/XNfwBf0avD+0a9QE817VoTFSGOI3MAMeJAuLViJD/EM6MuHzqfLSAlyKCsRWd7C8gk0Nv8F7IuZMoF3SUTEDIux1w0WRE4JPJ5Y8QYT5OEKG30Mb/DBJSsuDUcRhXRNS7TSa87ciHcBcShobd5mTEqIc8ph7CzF3g8U1yn5OBtdD9JhTf6zYakKZjMSLMvYj45wmevntF86dPNg7P9r9ZR5iA+5GmpgMRvlcRk+pBBO470H0cij9HJFpuMF4vRdqOp4movElIA9fgSOyPIh+qZ5AAsJGohtHg8fUbX6/B4Buu8Tze7UwnU9mCJYQ/VdLELSQCBZJgshKZyVcZv71IeLwbMYV1iDFVEgEghfD2DrhkufHSSQTEXOBxpkCNa5H2qhsxqN1ubzliKGPJCPoZbZP9uqgFtkL3Piiu8zy3+vmVaJ3htuqRANjvfv8cmXMnEGbn5DPWgoSNIrSuhhHjfsbw1gM/g5NlMP6LvD/1ThPyVXzKeF2ANHCPue1k8luC1uIOOHU/jLseCQ1dhF/sITh1AsYlc3Ktx1dB5FprJRK996nPwV+qizEXe85vR5q2k2h/NhJR5oOEFm6v8XyD5vidEfjYx5Ef2DSkDX0drbt2QiPWgbTdjxm/dwtvr7XB5df7t6XG0QCRZH2n8DH8OGR9FB1C01op8bPbgAI4+QaMX0D48+a53UoJE2MuNp6noHXTifziWpBwOwut+6Ttmuv3JyMN3SJ0WDntuXvd/byF1mu58bvQuEqm9J3osJUOv63+vw+twVLgPwB/ZbxNgF89C5+4B623TyJt5nzfT0L+UuPobrTedqFgpxTcs4w4/JUgV5PXiYN5JeG/OY3wGU0C/TaP61bDv5k4+DSh9fYs7zsMZmoIfwvN/ffR2luBDmkDxu8CIn1T8o3sIUrZHUQa4ArDUen2etD+uvM8+gBu/IB9AJeP+gB+WNeoAHiu6wdjot7pNGCPagBfuBhtxuQPVgTvvQIXzvZzBxDRvBFt8BJEYBsRUzyOCOKriIBcgYhTJdIWlPuzCG305Lw/hJjMnyCCNgURQ5t5Wl+HisVEoEryOxlCAsQxwq+lkUh30oq0Bbvh5FYYv9hwFiOz1kYYfwvSCBxSX8wwfE1EZGcyk8wlTI2D7ncyYiDfMDwvEMmgC4iox4OGbY7bvdh/RxCB/CEy016DiHsZ0hIuRIT+AGL40xABTT6OZUSlh+SnVu77v0RC+L2Gu9v3VhG1Y/8OCXArPH/1hIm32e9UeaxdSPO0kYjyuxox1y2IgBehayGhRTpExidx+HEnvR7wc6uQlumknxkitHuve2xJyF7v+TuGGEMBUbu02LgvJCKyy4mScY2I2SWTf/J5y3bb9rOjntBQNCKmnY00PKWIidag+W1AgnQlUZFjqucyy7Ck9icb1lv9XtdZfe0yXmsIn9gmQmB9wPC+7rk4iJj1VWgfVKL9dJxM1Qjm+t3ke1pkmGZ7XLVn/d7md48aj+VEwEwBofGvQRrx293v1/13teep33NwteF8wv1sNjwbkCBTaNiq0d5pMVxdRI3oFkIQn4nWQ4PbbYfh1yHrXrTHR4hgsAZ47wBcONFzUev21hI+nAkXSYOXzJN/hTSRfYavkTCvTzeeuwit8UQitdNlaH2+jtZMkXF3jPBnbSBcb6qJCNqXjfPJbqsSHfxyjdf9aK8vOQuGGuM1zVMeYW5+Bu3pzxi+TYR5eKXHVuA+rgM2qsTfIHDhtYSP7jzjNx0ih932ZL+327gq9py2o/X9F4SVqc/zNwHN/zHjcyaRrqdCzw+vh6zLDKfh+Pufwu+ezyjgDR+wALhiVAD8sK7fOd8A/Ku9DiFCdRSZqoALn0DM8guIkc3Ucxe2E5v8YsIhuQsRgl8gQrEOEal+RAguR0SpCxGoRIiTP0wKCkj+dpuAETjZCOO7iaTP86AiFzHKrUSy4XmI0RUTSZ1bCDNRpfucADTC+JsIQQ6NcfxKRGQnEwEYlyBCOo0wYx8jKp50EOlq2oyThR7HD5H5IvmIdbuvesOXCHUV0jgtMYwnkGBYSaYqApuJyNOniLxr2xHBnIyEwu2IOc0g6nSOIIHu24SDfy4i2psQ4e/yHN9pnKwlcrX1EjVDc9z+bYg57vF7LxA1aoc9jl6iCsVcgqEXGIYvQ9YmNO93IYZ8yDg8jhjqRjS3PURgRQM6dCQh7StEKb+ztVfJbNRi2IaI0nq3ej5aiCo3dUjzsJkIQqkgEtnmuf2JhD9aFxGIcSWRB66WCIjI9/cFaO6T5mXA+F5JCPK5RBqRJGAmv6hZRF3sZs/NQcN6O/LnTELbBMJ5Ph0E1rjPJrQnP0+UW9yP1soe6PwZlN7iZ7vR3hwifEzz/Pxqv/8UkSZlqmHb4d/K/c5YQgg6SWhpW9zWDcZV8ncrRWsl2222ez6Oe2zJFWMZsAqyfh+tkQNora1D+7AQLlyOaFQnEtAhTKtXkKmwwnwiqKLScA+5vZeJrAZD/ptM8Un71UhEfCf/wWQhSQeSpPVuIcqo9SDh6Cjap/VI+krzN4DWQYvxN8vvlRAJ5nsIE23StM03vv4jEhD3GpdT/d5CojxkEvqn6nv+xZB/2PenERrPzwg/Jx+C8Z8mDpuv+v4VyI0nBX8kN4BZhA/iFLQ+0tgSPu5GNAXgYsi6hozFgCzgT+B3Oxi9/pVep0+fZs+ePbS1tdHf309JSQmXXnopH//4x883aMCoD+C5rwOIwAwgRjEJMcy5ROLOR9FGvQH4b0QB9nykdetCRCcX2Gb/pQGkUVrN++tttiMCcQwRmA7ELEsQEWomk4B4/MVuNxGhViKB7AXIjLOIjF8LqxFhno6EqCFkOh5ATDgLMf+pHvdziDBXI83YIGJozYZ5NuFvl4dMPlXuLzlqJ3h+iBKsDiAGNZMwwVXy/tyEyVzcQZTnyjbMZYjw7SL80gqJ4vO9xt9B91t0Fv5LERPZ4fEdQpqZq4gUPg8hATFpUX+CmEG+S6XdBQffJONjRBsi4EsNQ4dxVIHWyljjqIBIRHsc2Ad9h91Grv+uRoeKFIGdi4TnPqLGcb7xcJhIiJyLhImlRNqKMiIdzgHja7L7HiByCCYt5xw0/3d7fvINdweZKNGMtiObME/v8diuRgLELiKf5GIiurfen2NIqMnzXAy7nTaCoSVz2gqirNsliBFOIAS1NrRuPqN5yaTmGUI+ZAVo/lPwxnQiIW+dcbDGsD9q+Io9n2P9/iqidFuu0wH1EpquMkLT1IPWzOVur4Bg0FPdVnKlSPifhNZjM1GVZr/HOhdaTxCBKWlPbEdXL5FC51qi3u8rSJh6wp9VxL6YjdbSJpT0eIiIQJ4F79yP9uaNSNhb6XEsIgI+8pHp+ygy8yffvXloD+w0/JsNRzcRfTvR8K40zMmyUUjQ2LForacDXhNhRj7m3y5Ge/wFJFjl+fmx7mO25zPhv5z319Et8PO3EHviELFv5vpvjtssRS4z1xO5COcYtuWGbT9w2ll/LvfvZe57hb8vJOoGFxMpo9Kees7PzjPOZxJ0DcKNKCkKbvccbkGH5PN5neGDCwD5N2KP/PnPf861115LYWEhCxcu5Pbbb+e+++5j1apVTJkyhalTp/LQQw/R29v7P27s/4/XqAn4XNcVY7TRkqnjRmAtnHwAxj+AhJj18Fcn4A8nIJ+fOui7TPQl65OIUUxE7TyDGMU0oBFePQBX7CM0PCd873to43+DcPCdCeyGwcWOJr4ZnebLCKfZHCI3VRcibDOJNCCbgSEYboOshxGQawUzAN8bDwUnRfAWEjm/liFCvhURn4sRITzhd18wnLUwfDdkfQRO/jOMr0MM/jb1y4jHWqA0FFnzPd4XiByL3/Gz1xHmm2yCMM/1eNoRg99A5MTqQgyhAZn6ShGj2WnYJyKCPNPjqUDE9mnj5wgSVpe57TK3u5VI5zBoPBcQfkeLCS1jL5w5AmOuR+tlIxKUajwv2Ybnr5EpLJdMWTmG/EwTEkySL1A7kdQ6HQKSz2EOYng2f+39Gcy5DwlkCxHjO+E5e8Xw5yMmO4IEjKRF+ipiQj3ue5X/L/QY29Fh41XD8VfIpHcTvP06XLLAz+bx/lKI84nI0maimop92g7ebz/RGr/zQ6KizhzC/7IeaTW3ECXz/tRttxA1i6vQurnX8P4YTh6B8fd7zN2e4y2EVrOCTMT78I8g60r3PwsJTI+jtf9tIifcXxHVYgaIlEDAYKMSp596CsZdi9ZRPbFWnyLyy5X4//WexyHNJcNuu5DMYXDwmMDIuhat7Xv9br1x1IHWzIj6+fsH4HcnEhkJ5hCm0uNEAuwlaB/ONxzJZWEyEraTFrbLuK4kKr8MEBVGJrrv5H+5nkjYXOLPHLQOf4KE+iWaIzqM4+vRwW8DWmtXwD9shE+NRamqvuf2VqFDRxaiMTvQAaqeEPxme/5mEYJewmuVcb1d8/CrF+ET1WhNv2w83kEmYpidnvcdSKCbTZR4PKG2j74Ik1LAzGLP898at81+ZwMS9I4Zxj3G9SIiQ8CLaG3NJEoYbiZKjSbNZzoEzAD2nEcT8H+F8XkfUJv9MOE//nabgK+66ioaGhr4whe+wOc//3lmz57NuHHjMvdbWlp47bXXWL9+PY2NjTz33HNcccUV5wXWUQHwXNeSMSIstxG1VBuJU2w+4Qu45qx7yW+rzt8r/G4yC/Yi4jOFSC1hP5Z358JFdxCOwzcRKRFuRQSvDjGRSYi4/RhVxnjL/S4yLNmGZQPyG9yOCPkSt3cVEnragUHo/kfJFf/hc4ZzMjplXu5naolEsocQIX/Nv9cg/8UapF3oQsmHczyGEfddQCTHTaf2bP+fnMtfR0S4ixC+usmYq1+7Cy6/mAh66DNM2wTr4KMWkvcgU/Pf+P8bkUapEwkhLxBajlwkIN2OmH0vkAfvPg8XXaM5+odfuprElxExv9V/H/N8JL+sJDjPRYJhifvYRmhLSjwHPR7fFCQMlCPmtJ1gck2Gey+cOaycbeQS5caSj+VMxFi+g4TXPUjoqiK0VB3upwwxs2Q6G+vvaY0B7ITtr8PCR/z+BiQo70Da081ELssB463IeN5GJrKdmZ7PJ4icbHmIsVV4frL83Fi0pksNdy9imgfcdwmRSLcQrfNOQvO+x3h/DUVGHyC0SyBB6AX/34TW3O3Gy2bgByiBeB1R09luBoPPQM71RH3Z5NPbTgjySXva43sDSJgdIlKLdKG9sBQdwG5Baw/D3uOxH/LnEnSo6UPrdSpKKv8l4DEYPgxZz3j+hpAwcMTvdHpOZiGhq5lIppyN1tdTxsFmtF83ofU0nki+PpHwIe70PJS+fwwnb4HxMwjNfh3hn3hIv7/6ClxxDVEx47NIGP9/CJYzj8CYRxBNW0OY+XNR6qBlSHDtQQLw82htHfGYThKCVZXhbkAHw02EG8JKwkd3BRKk+1D08m2e09uQtn4H5C9A67OM0BA/B+++Dhfdjvb+FMN2LVp72wiNYdJ2j/fY5kBfPeRXI6F7FTr4lCAagt+vcxvLPU/PG+cvIZpwPVpzh4AfjgqA/1quJ598kj/4gz8gOzv7f/hsU1MT//RP/8S///f//kOA7F9eowLgua7aMVEH9RZEIGzKpQJoguEDkJVMfWPRZm0gSjpNRoRoKjKZrHB7dUQetHQyLSAcp/vPer8OMdcaRNCKESGbTlSgGITBnzkKeYr7z0VZ5scbtiS0lSCCtAMRq1xCUBwh0n1s8f9NRDm0QkR81ynyecxEIp9ZJeHknhy/k0YumQOziAoEG+G9fc4r2EM4yifGPIQI+kzitJvenYwYUJbHskdpKLIWG9efRcT8euARJETMRILwFYazETHLhXqfLiS8rPf9XGTOyyPMnjVIwzAXCdL7EdHPJerHJlPSNv++GJkiJxN1Y4uIqMN1yPzbR0YT3NcI+fORoPELFPST5/5TgE+v22xyOxWIUWw2Tk6gq5OIhu00/NVE0vHkN1lA1L9dig43OwxDq8c5138L3EcWsZ5y/ftWz9vniXq8SYs73/OXhQ4gx93HW0hIKUHrtx8GH4acO4go3mfOajdpQ9Me6SfWxUIYvhey1iDh8q+N4xYiCrQP7atkSh0g1ucKJNTPcvs9ZDRYfT+D/I8aJ3cT/oM3ov3TiEzSScs2zf3Veqytnst2FBX6pHB28qcw/uOew2VEhOk2pI1LgTtdHnspvLcVLlyA1tsuz8Ea47OX8I3dS2jPGwkN5nLjcCc6KP0nP1vs/ucQFoUGZG7vRnuwg0xS6fea4cKPoINit2DjZhTteoywosxBvmzT3E/VWW2lK2nJcvx9u+f0Eo8zGwluyUduxln3GpFpeilxGLnbvx+D/ffDzGrDUuX2K/y33fjz3j3zAIzZSuT5q/W8bdN8ZUr6tRjnLWjttBi3PUSqnCzPTxUy424laqZXkDH5ZyweyU3imPvJRweaGuPlSWQdSoefQ25j4DwKgH/zAQuAX/jtFgB/m65RH8BzXYVEXdcCxIDKiXQMM32oX4MY2xJCg3MBYmRJ4zURCSIX837H9v2IWJxAwmbydWvxO/WEP009ElRa3P8nEZExU84pI9JMtCMGPkKkP8jx+0nwSMT3OJEpfzUinIf87kxgnqKfaUUELRuohjFrEFG6DRG0BQQh3YMYwX6PNwsR0J3+rUPjv/CTyAw0Wf1kIuBqDd9pQuCoNm7mEbWBk6arHbIe8L1uZMIZMO5m+P/HPDfdiAkkJrnN/XcTWrkuwsctCZqXqN+DJ4jI1jYi+XU2EVSz3/OSTvPlhAm8DGnaOt1uDVoXS/37Usj/ovF2yLC85ecbNQ+tW4kqDFluuxCZx5qMt07EQEsN32Nk1i0VRImwXt9vNSz7kcB0msyh56VX3FcZkftwpvuYgYSpRo9/ApE+aAlRLrCVMGMtR2u+AK2b6UTk5f0ac86DbnM3WgPDgmH4UaKayWz/fxQJNsUo0fFHidQiG5CQe5thsgBFKdor+YTfbpth+IFxk0yZpcAyyP8IkbD9Trc1Aa2F+WjN7kWHihWel3Q4KDLOJiKt1X731RVpcU7tMLzNntccz1sVkfuyXHN34S0eVwlRirHY/WT7M5YQYhuIpOP7kDCWDlt/6HGVo/U7E62VyYajnwg8SZpAa2cvvB4JkIUeTwc6PN5suI8al2vRgbMICfGXuK1Vfq8HCTxDyDcwre8cJPh3onXZTaS7AQlEPURqnREieGs9sihcCzMvQHT5ZrSOO/zcBKJO7wlgs+sA7zFOD6N10e3nyokE1TVn4fOHRFWmu4jo+6QF3Yt4SDL7X06kj+lAWj3cfsLxsbPmqAGZxIvPgr3a47ya0etf6dXe3s6RI0cy39944w1uv/12nnrqqfMIVVyjAuC5rhVogyWCmhhfMuUMQumliLm1ICJnrc9/e4Oo7AAy8QwjolGAGEQukeR4N5Ho+XIkiFUgprgImXvmEua05Pezngg2qENCZg1R3mgyYq7J+RhEsNo8pn2IETUQkajViBheSSYZ9IUXu73vEUEvef7tOCLOX0fah07/No3QVD3tfhYjgvZjomzUVGlA6EQmuhmIab1FmIJT0EcyjSbzZovbWWUcHvVY64i6m03+zEME/YRxu5gQNhsJZjAVMb3JhmUiEqJnAEUwvQ4J1B1Ii/V5IvlrEiySX2WzcZG0wzcYp82Ea8B0z+Umz0sXEqDqiYTU/8XtNQhnFTMI/7DVRMWK44Ix01aFYWsnkvE+bfxtJKJu84jasTcTzHemcH71BcZnYvxXEZrrbUhoGTQec4hI9gdh8DnDuIQIxKhHDPEw0p5VGbZ2z1Mt4U5xyLBVC1dZ93q8TxDR7v0e5yBa039EBHC8ZtzkICZ7i5/diZh6Evar0Jr6IVrnXcZZg/GzwfNXgny0Cj2erxKm8Gnu8yXj4i205+80vPs8pi7jYBFaL4Ua37j7iMNQ0jov1RxQTmiONvu55cjMXe05SVp8iEj/W5E2cyqZdEyDjYTmv48Qyi8ntKu7PVdlRMWSvUS1oyQoLvO8vQ1H3yBM8hvQ/u1HtDRp3QbgzHrCVWIfkXR6yP/vA4bhzAHjfsT9LySsEPOArTCYzL+7jfudhMl1JnEQ/EvjYAOiAzei9dNA1MCuJireDBj3z3ue5ngshwzDdcRB+VpkcchFNKmUiITfjvZMJTINtxLawm1u+wK0X3ej9dprfJx9eEgC+mVIu7oF0bIGIqXT+bqGP+DPv6HrC1/4Ajt27ACgs7OTK664gjfeeIO7776b73znO+cZulEB8NzXZCLacgSZDrcTUZ8HiJqS9SqcnkxS/y6ZHlYhzUMNtK4hEtaWoI17CyIsE91nN3I0fxoxn0pkMtiAiFRiFsnPZgoSINb6twOG+21ELOoQofgbMs7awxvd9jxEmBciYj4XCZQY5i5ElAaIgIMCdArfb7gSkzyMhI1v+XsZEnpzCWKaNBpfQD6LV5HJ7zZ+gvttIHKJFSIh4CokGOahSN3jiBE2q5+9xxDhHDZMA8CdcLSe8Pcb8diS87a1WZ2PEo7gszSPmfJK+UgQyyFKziUH8ELDm4T3B42XvyNyHK4AroW+F4mayB2EkLwcneZvdHsjxluRx7DMffR4XP1EndS7jY/dhnOJ368g8qCBmPvrxmMKbPkBUW5rGhJ6k+/aA4jR7vK8zUYCxI3w3kbCqb8ZMaovud8XCX/SZF7eqz5zFiBmORFppWvg1ItEzrpLjccGImJ6iuHuQmtoAqGd6/FvaV7LiWo1p9E6SM8UoPU2B+Vdq0B7qYOoQzsRMeUXCCFoBeGXeraws834uAMdqLYQms8m+NXjhvtmIq3QM8BceO/XSCCcCUyFo/sM/3LjotPjLDesl7mfDWhtZhm2JYQWbCucqicq8uQSmrAkVLUhgWETmaTaObd7jqdrjvhrQmvdr2e4j6Bvj+jzzhueSwzDNrfTIdgmXet+29zWkPGUBMku9Tmm2vdn+5OsDdlkymD23Q1j6ggh9QbP53cNwxSgDnKmeOxfMhwz3FYNIWQ9QaTTykVrf5PnPge5ehRp3G9v1L3WB4DPAV88q50GJPA/htZ/IaIdLyL+cJvxcTuiifv9TCUSQp+CM296TBPQemz1vYPG6XQkFJYbF+sIa0Qb0i5e5/EdJOoon89rVAA853XgwAE+/elPA/Diiy8yY8YMdu/ezfPPP8+zzz57foFj1Afw3NfSMdrsQ4iw7IV3fgQfW4AEuz5EVHoRYZhE1CmdDKeeh3EPIEL3fUKgykM+HG1kCoQzzX2WIUKffGVmImFwnvvpRgLQjeqDZsScniAiMPchxvak4TxOpOYwA2DAY9qICEk3DP8Ush4xPClybw9i6Fke637f/1vEKJIweB3hWL7NuDiJzDzTDGeRx1sIJ+/RcCal36aRCUwY/EfIuQAxpaNIKGtDwuJS99foNhcj4bfS42lBzNu+YNyOCOd8JBzfjZj2Ps1nJhVL0uJeapjfQv6CL3ic3WgdZCGmkXwPa5G/0cVE+ax6RJTvM0wdxglE5Yxqz0ErmRJuFCMBugAxhEF461lNa8VH0TqaatgnIkHkDz1/I0TpvZ8hhoYKz4+7GAnMR5Bv5E/c1iYkZG4zPOXAevt0TUHMqdowN6H1cLnxnXxXk1/sPKK6ypNAPrx7AC6aTaQqaULrudHzkcyXLxDBKNdr7CcfgvGr/d5WPzMLmA3vPRBZYLKBcbcDPyyD/96hG3uQ8PQ8EUldhgSt/+QxDyHheSXaH0loryDye67zeJ5DGryZbuN+z8EtxuHNnsPLkCCwhagMctDznjRLu5HWJplTW9H6ec54/Jqf/yqRG7SeCHRKB78Jvr+D2F81RMBUPlqbFxCJj/eitbeISMjcRURW16O9Yz/R907AhY+i9fHnnr/vEDkmv4/wvYhMMnqqNX+Z6j7T3d5GQrhbaLy3IGH1BKJb69GeSQekQ4jOZQE74cyLMOYwEsj6YPhNu31UI+vD19GazEF04zvooPk2dP4USm8yXAfJVENhqfFSiNbZXOMmmWpPosNDol+9+tv6CFSsISrMdBPl9eYTfr5zEW1egfbHK2if7iY0sDOJyjrHCf/GbwD/G1pfTxrnYw3LPiQAl6ADRzXw4/PoA/jsB+wD+MV/Oz6AH/nIRzhw4MD/h73/j++yvPL88WfIJIZkAiEYyQQjaYRCI2wwA6VQKIWiLJSRQR1ZR0drdayMHaq1tbaOrdVxtO04trSOlrG1sv4YGSrFUinKwKZSkMJEs9AUJpAGA0wwTQykgZB8At8/Xq/rfeA7w+7Mrl26Lvfjkcc77/t939d1rnOu65xznXOuc6ioqOCyyy7jgx/8IJ/73Od48803GT16NEePHj2j8J1VAE93VWRFndbRiEmnYPZOwtKxBTEJnPbhFiIBcxNiSFWIWdyEhFA72rEWEvFMeYiZrkfM8HrEGPYRdX7bEKPfBT+/Fy4aiZjMar+Pv89BTO4AkRKkFgmwKqQMpRi2FLsGUmRLkcAcjhjMC4blDaIUW7vhSm7dzX5nnvtpQ4ri1UiZvRMxrUlELFIzUVtzC3ITPkZYwgqR8JpqGIejGJg84z5ZtmoQM6xBwvRSP3s+ylnWRJxAbkWHYlIc5nyCQechIYif+yvD1YDi1PLQrn4AYvTJ/VODDjtcQuRtbCUsSFX+3ud3J/rdFt8bZRyPIVKlFCKar0FK/EtE6pRCwkraSFhavoVSEa03fmf42ceNzxTPVUeEAxQgha4MCbJh/n2o2x6HBFmL4SyEo/fCwJH+voA4WfwlIl9dKbFhKSVcdikmsYewZnYRybenojm5Ds2dl4zH+abXcGQdu8nvLkFzqxOts/ORsvEZ5MpNG4M5Hk8DUQLwBv/+kGn2RdOhAM2nJqTQpZjcspPG0mkc5aF5+zyaCzcgpWMK4WJcbrqNPum9FJaQrGKtwN+j5MTTiXmRT6TvaUPWzFVovqZY2kK12/sT6RgD802X5E7dRCafJc8jBeTkOL55xvMGxOtyiANENYSnop44QJTiou9DG5EStO6zkffhCBErfZlpmsINJqM1WIjm1lLitHex4WhGNJ/qttYRyaaHIavofDTgAuJkc6+/NxHxyIVobXa6rxF+vpJIxVTmPoci/r4KKeErCF4/iVBW16P51oSU7GlE0v4W47QA0b/Hv+W63cQbiozrMWqv9SCUPkQkd19LxDpPNWxfBa71xu6zxnEH4hVfPYMK4HffYQXw4+8eBXDSpEnMmDGDj370o1x66aW89tprVFdX89prr3HllVeeEh94Jq6zLuDTXVMIK9gyokpHJ5FdvwIt9EJguzOcZKNUEmvQQr+COEnbjwTWXOKkaZd/byNOZlYjy9+j7msFUma6yBxUuCgftu+GE8+gfGhVBMPcS1ipmhDjGYcOR9yBmHkXYh5JkNV6POneq0igzibcR9MJBnwVcUK3DDGj5w1vgf/2IabZYPhqiTjIPsSMN5HJwcYUJCAK3XbVSf/vIdJqXEIorp1EneNCj/F8InawhSgFdyGZGKc9vziJLp3Gx1i3Ox0pRoVEObAmZFlICuwuxOh7Tvr9SiQgdrvNqUgoNRGpSjo9ruTyaiJcmPVIoDxr/OW4v3oyZeIy8W4HjLt6pPwVkrG+ZeixhYjv20LGvcZ3iPJoU9zuC2je5RI5E2cQSaT7gRWOvU8K5Tbj8BU/n+Ir1xu28UiRKSVyABaaJhM5tXLIRKJ03iy3kUfEtA4nrJYppnGWx1tr+q1GAvMxdDirGW2iHoDer6FNQ6Of3UYkMl+FBG6KM11G5JGbiZS3IjJ5/ihwu51IoA83DH8Orb8kvALHPPZmNEd3EKUGxyLlvgidsL0PreNPELHHh5DyWkVY1XtMi+NEXPERyJ0AA2cYx2V+pwbNX9B6PoDW9h7ER9rd3l5gGPS/7H660LzdfhKuRiJp0YWsf7vcRzVRjq+EjFLSvoaYI0kZSwrvduOklYgnzUZ0HoDWRp/bfAnNpXz/TfKYOt3fFv8OUmzr0JrqQ/NvIloX3cSmPRvReJRxsBXx12biUNurRI7GZL3c6nZz/dutSJlNdJhFpC96gTjsd4w4APYpIrVLheG6CUrfT/ChtR7XMH9fiubllfptYDGRQP0AER5x9vqtu77yla/w7W9/mw9/+MNcffXVVFdXA/Diiy9mXMNn8jqrAJ7uSlaKfsRgnkKMbgxizOWIefUiLFbD0Pf7nUVokTtO58Q6xHDSDjEPKXibEIPf67Y/jZhYlf/KEINIweGTkPBZDYyFcSMhayyRhmO3+1jhZ3YQJxJbCBfFWv8/CQmyhUiwdhPVA5K16REkCEch5SPF65R5/AcQIyonSsJ1EpajSxETLvf7xYTA2I0E4FyklGwjDk5UIQWr3O3tQ8KpDTHn7cidU+gx9yGFpdv/7yIqNywk0n2Uqu0LP0bk3ivBqRSQENhl+Drc1t94vJcTp5s7/M63kRBoQAKtC/bXIyW1HFmkiv3sSo+xzTSaTVgDm5CgWYMUgqeJhK83GJ4xptNmZHWsRLSfiZTbDjRP2oiUHz1uvwDNkRugt8P4vs/jHYMsYLVEVZMDSJnYiObJXn0fMtnP7zX+k5Wt1OOcgSxwc5DATApPo2HtIiznybpWTJQqPHDSM0c8jgfdbikxL9cal1uM/7FIoA4lcrKNU5v9eyG3Gt7uQGs3WbF/4XeeISyFM9G6rEOKwqvQ/n3/Pg8J7mQN6kdrrJaMJbY0zfe9hvFWNBcqPcYm4BVo/h6aLzuRQjYGWTRf8rg3o41AslpeTChnnYRS0mmYFiDLaKVonJlT093eEbT53O/38j3WLuM2G7KXISWmG/GvbONyv8Zz9HUit+lWv38fmXnPq8b/BBg6B/a8aHotIipsdLn9UW7/UbTpKzPMFxIVgh6G7h8Rh6quPonmKRxiKlEKsdb32tEcqTM+JxJxriMQz6tCMbu5hmMqtP4C5S8tJQ4opY1ktWGagDa/g93XpWhdTSCq0RQYvksMWy+a4yWiPR3uc7Lb3Y+UVSuzzT8hk2osE7JT4uf2e2wHiDKTjZzZq/8d/nsXXR/+8If51a9+xa9+9Su++93vZu7ffPPNfPvb3z6DkOk6qwCe7qpDTDYbCepPol3YpYiRtqHYoNuJWKp9SHnIgcNryBT/zjofMZ51aJF3An9LpDMYgRb1QqRcDCLSO7xIpIbYgSxEeUgRm4qY2hbEHKt9bwFyTe1HTGwLYsDJRZesZ3MQw06xfrs9jjK3d8j3DxjmCURM2C7EvEuJUkQvIeabg4RlHpGxv839jSMOWZwcA1lomJLS1o+U5APG40jE6Lr9fTpyTRcRJeCaiLiYpfBP9e53FZGX6zqP75Bx3YNoPYxQJHPc1wzEuO91O2uJtBh3+flRhvUcMsmvS0AK3Gfd11rjYyxxoGE0nLiZON09h0jTcpg43drq9ruhexkSWg1ojvyFafYEsWHB8G8kkjUvIBP3ufVeHzK8iiiRt8l0OIIEzzjorwf+FI6+SKQVKiNc2Cl+swApL0lQdSJLcHKBr0UWo04k5DoMXzq5OAptMjqBfXB0I5naq5mqNkeQ+2uS8f86UizmIOHaaRjbhJu3vkZYrfcpLcyJehjyHjSHmz32Vnh7KVKGNxFWsTFEXdZxMDThr9Dj2kicHk0bq3K01r6O5neZaMwmf38ezTmHPVSUobn7ccL6Px+t9bTGPunflhIpjkYZhzuF++7jSEkeZ3r3EOXlqoDPwoklaN08r0onlCAFrx4pco8hhTDF3pUatmSNz1WbA6cSFrhZaM4na+1Ywmo6R2O/8FHE25KC2GkcDPKYNqJ1N8p4qnV/tWgtzISC+4nTvU1kwmCOPqo23/iO369EvOZGwXp4DbywEZp/ZjpsIVz8u93Of0VrLUdwl17jvorVXu+N8POX4e1fG942xI8PQv/3TpoPrWi+rYDm5cTp77FEScZC4af7J8g63YYUyFEeazPs3KDnKuxm7/4RunJEq6M/guZfa0OTSRa+HcVonskrWaPfib/j/4dh/w1fM2fOpKuriyFDhpxyv7i4mIULF57mrf9z19kYwNNdY7K0aNuQQjKK2HFPBKrgjXth/ASkJK1CjHIspzLY9UQpsxYkYFtR2zWE+3c22k3/BFkNEiPajnbaa4m4s9FEFYMrkCLUSaQ72YqsBW8QpaH+CjHAKn9v9TjXI6a4lciRVU9YpJK7tdDwLIf2lTD0GSSs2okC7gfcZiESnu3+rDBeapCwmYCsLtUaU//LkL0cBdif73H8MVIMqpBwagO+Dz+vcuzjdI+hCQnKjcb7YCTANnr80wn34SzDuAIdCClAgtRWQVa4nzJCYfwqEQ+Xj5h5ckHNML06yaSv6K+H7BS/No84AZzil1IsX7KAPOuxJiW8jBDe2+HwL2DQDNOgAM2NLWRy8a1fqp+mlaHNSRu89SN1WzoY/vkQvHcyMB+a74KKYZ4Lf4qUlVFIOVlpGEcT+Qs3ovnUTFj41iIL5flovj/tcSbLNh7XHGT1vtdjfsa0ySMONLSYJk8aN+PJhBS8sAwuH01USchHFtiRRBqNpLCMP4mOG5HCW+iyjXOIwxMppnCB359CKPbziaD+ZmA3vL1RUzvrY2i9tRGJz1v9zn1oPsw2XXa6jw4i5jNtarqQInOnYb7P407u3TY0N0CW4CK0btrI1Ns9uggGLnY/V54EVwPa8B1EcyQX+Bw6TLAVuSHrZAU9BpSmzU0HcXgsbfZ6TNNC/9+ENi0z0Fp70Hie7bYn+rNAfTAHKdPfJTbSBwi3aikRMtHqtm4mTn13IgU8eStA62EaUhI6iE1bKaLvCsK1XmJ8QPDtZGleCKyGnctgzD0oRnMY2sSmwxY5xFxeR1TlSTGB3uQzH83dAQQPSgfavEl49RBMS16aQuPqCcOx1uNahGJoD2oMh+th0FSiVnshkbu0zrCNcztps/T8GYwB/DYMGvgOtXkUBn/i3RMDOGDAAFpbWznvvPNOuf/WW28xfPhw+vr6TvPm/5nrd85o77/N12K06HYjBnkECZ4yYKVqR44fiRhzL1qI1YjR1BMJPBcSJx7nI4YyDrkCWhADSwztJ8itWo4YfB3hBjuIBHwKqJ6OmOSXkXKTR5SPOo6UrXlErMhfEuXVphMnX68mlNEutMMdTCbdSf/Nrj2a3Ik9MPRjSLlKbun7kCDvJ3NSdM8uuPCjRC6u5YajDQmnq8kwtOwPIUXiSqL81WWIQQ5AgmYdsBEuyiFc2QeJNDjJtbMUKbLfAf4zcejlDv/lE3VTs42rA0TKig0aN41Igckn4jQXmFZr0fwoQIK73X9XQna36TEFXSuQQOwgKnUkK2MtUmpWEArtJDL5FxkFgzqJGM117meen5sCM6vRwaKkFN8D500lU9Gi7xCag01QMZoIaxiNlL6pyCKTFJg2JOD6TNNJxvNLQKdi3EorkAt6HHFKvADNidmGtcdtdyEl8GY0j/catjZk9XnC7yYlfCRwCVz+NFLEXjI+ZiKL0wa0lt4gTtIPI8rqjfB7uTAox2OtJKpRJOtaBXAfnDhkBa9MNOq/V/WsKYEh1e7zZbX/5hG44FaPbzRRuqvINLcLsPcnkPtRpGlNMH2qPO4UmvAUkedypGlRbhwl93u3n1/gcb4OA69Bc66O4DkNhGW8ynQs8d9TRH3eb8KQJ+Gf10FpDhHbWYGU0GFIERxrmte6j+nGdR+ZXJQZpa7KfYzx/WvR9Q9uu9307TYc0xDfyyZS/3SfRNexGtPbX4MhB5BFD8SraolQgbyT2qhFa77UbXcRSuD1wJfkvh64AFnlR9lKX04oeomnbiETa5fJlpC8PXOIFE4TEd+f4f63IP5TiNZpsfA6bTDaRF+M+FQKryl2fym8Zh7yINXCoGHIU/EksfkqJQ4UVqD5VkKUVjyT13HeOcvdu8QC+N//+3/P/N/Q0EBra2vme39/Pz/+8Y8ZPnz4mQDtlOusAni6az1iipVIUOUgofwkMB3eW0nEoKVYqDXAHGh+Wet70EjEMKoQs12BFutlSJm7Bwmukf68DzGXHNhzP1yYYqqOw+HXYVAHkesKt53cs4dQrNqnkTDrIpPbiklEbJUPQdCCrDivElaKZG3oR4KgHLLLkJDZRihClWiHW+Hnp550f5M+L6xCQvJVIvVFHpFSZKhx0YEsF9cihtpHWCGHEXF4Re5/ltvd57F8y/fakKKXNlRPEKWWRiAmfQdSEOf7e4HxM9b9JaVlKaFQ5iIGfbPp22BYcwihlCxMr8DW3TDxQ0Rc5t1IQNYAv0LWs28g4TWRcP8nV+9KwzGSqOnahRSrYiRov4OU068SKSOSQC9zW7bIXPQhIrH2MEJApWD1IiKeKodIN5Msui+jeXITcAuUXoaU4+nIFfuQcdCL1kuycoCEF8SJztVweDMMKiZOm/cZtx1oEzDAdJmAlKBGv1+F1l4xmj8VSAFNG7Mk9Kt8bw9yc1egdTeYKHWYLFuVkDXRv9UIluzVyIJ2Ldq0pAMMz8AFKwnL/nxE/+N+Px8pMfmKN8xsfPaaZlN872a3W+jfxnrspcQJX8f87bzTiccb/UyKNaw8CSeb0NyvI1zRu4kNZzay/Hd4LLXw3qlkSkiC278fzak20Yls43odcdiqxc9PNU6OAe3Q/SsoSG2lMIxHCCt7m9u4HHkA+ok8luXIGposesXC6ZBLTcdSw3MVUeN8L3H4pMJ/2ciyOgidfO9Ca/dVPTfwHKKEYRkMvcL4dPwj6w3/dLSuRphmdb6XrKvzifCCCsN0ORFvmSyYx0RDVms8nIP400jTbS9ao4kP1BDZC3KRN6QfKZZFxlM3cTJ/DOKha1FarrPXb9U1fvx4srKyyMrKYubMmf/q94EDB/LNb37zDEB26nVWATzddQSVW3sdLdbJQB20H4ChV6HFPpoo1fYi2iEeh4r3EZa9csQwPoEE8wakiNyImGI2ESPWRiZdxYWXIeY5TM8Meg9yB19CKB5XIaE4nMzuMVMW6Fl/ziVi6HYi5tNJlCGa5va+iXJsJbdOJVKskkKW4vSmur0U8D3Yn51AD7z2Mnwgn1B+6hDj7EZKRhVSMEYi5tZI5LLLRZbRecRp5lFE2oWVhqXH7+30u+MNcweKyXwWKS8LCYtWUm5nocTda2xNGWy61BI5EKuRhXKD748kBOzJCv8B02gcmgN1MHGu4W00LvNMn0lur4cIiE+u7SLjudz9IBgzZZ/m+d5QJIgmoflmN3DGolmmKjQfTsKuCgmyLjRfNxumZYSVcZdh+BCahwcIy8gAIuFerf/f5jY3IkEE4fofQRzsaEFC6jmPczSwHQZdbFgeIWJHl7mtqe5nieFP97uMn2Y/P9ljTxuCWuPgeTTXJqB1eyeaY6ntNA+70PocQ1QFcRxWZhOQNkKziTxvk/1/v/svItJ09BuWHmJTUIYUgT7iNG07UTXlMJEDroJI/jsRGAVj3oPmbYr9HY3WyRakkK9Dc7cBzde9Hle+aVAlOh3dBwPL3PZYpPQUoHlahhTGdR5HPafWDk4bhU40Dzo9jr1katwW3Gw8VBGpfepQNoQaw12PeF0bov8A339ZY81YVIvI8JJ0QOutNXBeEaEEJitnk2laYpq/gtb8UrSGh7u/SWhu7iAOmGxxH3l+dztaw0vd3nGiDGEbYZnqQ5u4FYQFvIgoC5eL5lhS3i4hkkVPRxbttHGp8HNL0dxqRpvM5J4vQBuRdR7DFrfdZjwn70hSzM/UleL33qm23gXXL3/5S06cOEFlZSU/+9nPKCkpyfyWm5vLeeedR3Z29hmEUNdZBfB0V3KHFqMFVgyMsvtzHWIeW4iEyCkOZwFxgCPF/CQ3cTGRkHmo768mUp1s8jvJ+lBGxNB0ILfjOCLVwUbEwOoQM2hDAqCBqCRR49/vIpIeL0SMahJi/uuRIpmPBEpyOaSYsN0aOzlu+2L//5jvp0MoK+ADU91vG7JwJtdnIVI2XiDiYQqQxeMlfzYgRrvNzzajwPrnCKtND2KY1yFlb5f7m+JxtHhsN/j/FCuTBMA1GtPAFP910DjP8/fFSCn+I7efBNsh/78AKS4rjdcpRJ3ejUQer68hBeMeJCReRkrqEaTQzfX3ZK2djRSfHv/fddLYnkBCahAqVv8dyPosmiclxtcbwE/gw8la+rxplA4vgCzPSQldbzr8FbI2pNi/PuNtE2HlqUTCaxSyjuxC860QzYHtaAOxkkgFc7PpNJVI9TLebZUY9mTdTEHtbYZzOrEOXvH3fcid95hpeQNSaO9Dc28tccq8BylMHWjuLvbYao3DJPyTCzatqYNI+UqK8Cfdzj7ixG9yk8/0vQnGV6tpuhrNiXy/t999z0ZrLymffUReyp3+PgEpPa8gQf8XgmXPOrhwDnH6tpRMPW3K4M3NcMEmdBggucdrycSm9gADe0zjPuBhtAnN8Ziq0IGueYQFbxlxqGgsWlMbTKtaw7kA8cKdpu1hj2kW4olHjNteMnn4TnRAVh7igyVofuV7vHv93Dc8vgF657xbjPPHiCTKab0uNP5XEnkxX3A7n0EbjRnQ/DWouJRQaHPQ3NxmHKSDMzUED73W7dcbD3VkKhAx1jAOJ1LSbACG6TRx6Uj4h1/BqOdg/BI0V/PRpngxkWqmDX6+Cy4qMe660Rr5BJrbbabbdGJTuc2wdKN5+ATwZ5y566wL+F9dI0aMAOD48d/uAZ09BHK6a1GWFncSUs+gzOyFQCkcXgaD7kJCYDhRV3QlYozLEKN9HAmB6/1sH9rh/cLvrEYLP7mJkpIwCTHOCsIN0IsWyH7V3Rz+AGIKG8kw77delG6ZvQAYCf1fg+ykeO6FNze6bvxDyH0KsiYkC8sqdCihgah0spgQziP0/57H4cJbgM3w03r4YDUSAF8jYrNWGfYat3Wnx5tDJDndDi/8Ci6/g7Cm3Efke0sbp2wieSsorqwBMedphus+t1uIFN67gWI4vBwGfRYJ92bj8iBR2WCAcZ8sfAeIxNv1iFlvQQIaIm1Ps9u5n8j1Vo8Urd1IwegyPScTlVd2IQXtbeDDSGnqRPMmxY52wts7YMhnkZBN+HuRUEouNjwp3nM3ilVKVtkkcNJGJrnz1iBrShWi+3bjcCuR1mQeUsrXIyGeDnmsNW6qEH2z0fzYRrj0u4kUFluIVD1/gGh8K1ob+YbzWrX15nK4oBrN/ZFIGakjkpInF3Gex9EEfALeegDOS8H3s91fM1JAyzyWBiTY5xBxuhXIkpNiI3t8bwASuEORMlGE5t0i4rSx1xOr/WyZ20/uwwTHk4Z3JjoNfbvHMZ9IjTMRrcV/RJuA7WjejHI/9xDu60nGL0R95t0e4xbTJ7nwRzlp8CxiM7UcWeZ2m77JYlmMQgsaifjDPiInZKnvHSEOYnT5XoFxOhrNY0QXDhsff+X3nkTKcD5RtnItUcpsnJ75b33w4Tvc1nzkmRiL5v8ONCdK0BwoJKqPjHT76zy2JuPoS/rs/hUUTCaS7ncSczPF7V1pmOebDp9wfy3GVWr/BjTnr0HK8bfQGgKtv2y0xqeIRkfvh4FjPebjiPc3eMwz0cGaFMN6kIg1nIn4Rr/HmIOU4HmmQRGOF/8/L8Yzh0CWvMOHQBb/+w+B3HvvvXz5y18+5d7o0aPZuXPnad/5h3/4B+655x6am5sZNWoUX/nKV5g7d+5pn//fvRobG9mwYQNvvfXWv1IIv/jFL/7G+v33XGcVwNNdU7LEILIRk/s4UTaoBC26MYjJVCKmktyQfYjJjfNzlxDxPS8hS8EBJMjh1Hxd293uU4ghzHT7KY6p1M8nd9EBxLRHIaH6NHDQCuotiEkfREpnGWKWmwlhOQwxouRKGew+r4CjR8y07kAKR7LU1SJmvJKo2NFLxLgki8k446EQMdGZfvdk9+oEj6/e/ez2uCYbNy8b1iQsm9xOE7AJug9BwXUe/znISvAHiFE/Z3hy/f1B95WsL3/s71sJq2yz+1pIKKrPEznqFiMlqxAJiWI/f0zvNr8IFVchRl4pfHf/CAo+a/omK2wlEpiPQfPrUDHV97YTLqgG02MKwfRX6Pf9P4Ph50PzPr+baJdwdpv/X4HmcKP7H43mXzeZMltAJIROVtSZxKnU5zyeZPEZ43Hn+Jlq0zMPWZ/muu2krDzh565Fym+KL2sjcrs1IItIBZqrzxApQpKb7jk0/ycLhyceh6wr4PD3YVAS7KB5t9/07CFzAvfN78MFUwlr8GHTsAYpi6ON5zFo3jaSySWYSVlURsTzFiKFrtVwr0X8IcUU7iWsjdWE8tiDLGaNbrsRejdD7jCUj3Gl20iHOS43zOuRMrwTKV0DkAXxMFrTU932GOIU7KvQu0/dDn8fcD303qXE0dyJ5vtexH+2ElVCCpCSgnDVXw/ZM4yHfCI047hh7kaKU4rBbEFrJeHjfrfbbxq+SuTM6/Zz89zGTUFjtun+0Vth4EeRkrUOzZG9RKm/XYhfJ8X8ScO/CG2wdpLJP9q+wzGAG9H8Tm7HhcTmp4nI7ZlibJP18TjseQAuvNqwz0GbqmSVHUBUGfq8abfXOFlK5GOcTuQmfQKt2xSi04w2BjlE/PQoxIPHeMwFyMJ5HfDIGVQAH3mHFcDb/2MK4IoVK1i3bl3m3u/8zu9w7rnn/pvPb9q0iQ996EM8+OCDzJs3j2effZavfOUr1NXVMXbs2H/znf+d6+/+7u9YtGgR5557LqWlpWRlZWV+y8rKoq6u7n/w9m/+OqsAnu6amCXmkZjwViTAlhKJZovQglxE1ITsI057JQtKErhdbjuPKH/0GGIIXUSOtALEZLMRMzji91PwcDNhvUmu5y7EZNuQEpFcTv1IEIKYa4pZSQHY+UhB2okYzquIea1235OQsEzxUKWE5avdsLYQCk+K9UlxSSe7MkuQkJzkvrtQsP40j7sYKYDFhu8mj/cNImntAcT8lxmOFOuX8LWTELBPIHdUvcd8P/AFxDw3INfnGsLVXu6xzDX81abVfYiRr0BK0HJkfZvo5xoNf59xXWdctCMmX29c7kbM+jgSNDnEHEmWzjF+tg+5Au9HFpt0OKMezYXpyG2Xh+bCrR5LFxJAuf7cgpS7GrQ5KCGSmG8xftvQXLnZ41+OFEgLTBrd9ih/H0u4M9vQPB5OWLOT9fZWJHxTvOgqtIYOEFUOioh0LklBLjcO6vT84Udh0FLDO5o40LCaiKmcZLy+RFhzD7jt5AYvMV560Ny4k7AmtaF52Gjcp3jXfo89WYoKEd0f8+/jiNyOzaZPsuAnus/wuyVkag63Pgql70PK0xq0tp4kKs/kGz+biMoeeX52vseVxtqC1tjdRFgIRF61tUhJ2YYUqxSvXIbm4TQ0V4cabyuQhX0FmSohbDPeL3Zf4wxDtXGyjahx3GS49pApn3f0cRj4MRSbmWM81Pn3O/0OaK0XEelh0vhqCPf0Tt8/gHhFssqVehzDTcdZhu07RPzgCiK+ucL39qBNazqAUWj8H/fYt/idue57G3RvgILb/MxraN22oPX9MlLKjximc5Ci3km4bVegdVOC1tvNiM++FHB3fw0K7jCeZqP59RzBh5NHYinw6v+7CuAPfvAD3njjjX9X+wsXLqS7u5vVq1dn7n3gAx9g/PjxPP744/+rYJ/2GjFiBH/2Z3/G5z73uXe87XfiGnCmAfitvYYRAc1tRKB+PmKwfYiBLkJMZCeZskwUweGDfrYGCarViGEly06n+0kxdOsQY02unt1I6dyLlKsC99PsfjYQLtDRhiMF9Gf79xXI1byUiAkrJFIodCGB0kYUQj9mOGcjhldFxHsVIyVim2EchZheC1GlpA0pF0kA1iIFppKYbWmjVYIEST+hiPQQLtZsjy0J3FbEDFcSweTFxk8dcWJvL3LdpZ1zsuxsd799SCAccj81RL6wMUS6iw2EEKp1/6uRwOhz388RyZCTxSspw3MJa+UHEMNOcZBXI0a/Cs2ZZOk612MtQdalF5Fb/ULCrXvQ41hgGMYSbt4FSACWAeXwT7uRMHyKiH/CuN1seHf6t0oi5ijNo03AcWg9YvoNQIKqA36+g3D7Jld62tzcgCzSI4ybAUQuzDacqJDY4FSgud2JFAK7MGmFQSm+9YDbTPPjeuMk1zhp8TiyiTJvC4kDMCs91maiDFuau01ovbUgt143UZprE7Heq4F2V1MpJqzSqw3L5UgxTO//vfE9DU78DPb8CFgKpVfrHrv9e7Lw9vv9ZYgvHEdK0zPAOjixDc3jZGFvQUrbQjRny4ynfNOg33Q97uemERbN4+6/D633NsQzNhHpjhrQnC1HSuPraN7OQXM7WdEmmQYNaK0k92a9YByYwiLGuu8Dfh/3mUNUjakjE6P80zXEvNxu+k4nXLK1SMFLG9sdfnYUUvJXut1i93WTn3vR77ajtZXPqcrpQSQDktJ+yG0cg94NUHAZmvPJkllsunUQNbDzjKsiNLfqEK/Y7mc3onmZNlZtHqvDLV6FKD+4k8iNmGPYnyQyBpzJq/8d/kPK5cl/x44dO233jY2NlJWVUVlZyTXXXMObb7552mc3b97MrFmzTrk3e/ZsNm/e/B8f97/jevvtt/mjP/qj30jb78R1VgE83TWbsO40o4X2FGHhKiJORe4iTv1aSAxaTOzEr0VKTDFiAjWI0e1FDDnF/1UjYfI0SkpahBjmNqR8dLqdqUipWoF2jkdQDEq33+9ETKYT3l6ChPCdhFDvQYrCMGTZusbvPYOUjQ6kMI5GCuJ4pFTsIqwldYZtLHQfBL4OP7/XeDjiZ1Nc1VIkTCrRDnkb4m51hJusDe2S85B7ZArakb+EGF2Z2202DeaYNn+BlLCrT4L38ygA/AfGxXEUX5VDlOibb9rMAFqg/xk/V48UsFnG0WT3PQwJ6ArTa5P77df7LEECblW+XF99xvVXUaqUCYjRdyOav+C+8txGtnGxnFAknzPe1rmPHDKltjLu0BrTKwmqJuPFnoXfv9dtJ0V+FjolvZawIlcQyldyWT4HbzxAxvJa+oBxdieyApXARe9HoRHTiZCEeWgD8JLx22T6bTSN0hopJBT0ctPu42SSg7PhpPEuMG3WGVdXoHl0KbJUzTMebjIOUqzaQSKO8Bki12UtkaJousc/FU68qE82G44e0/QzZHJtvvQ1wZSb3KEdZBSO/mVo7nQRKYZeNR46lW/wwvcRSsZ0orb2auPpKRQreI3x3YnW2Uhgm6sKdaPNQYdxN4bIGZjGX4TmXb/x1298VaFwjfXusx/eWoLmYpt/uxopLBe6nauJVCSzTvo/Wcif0/gZ7M8vIv6ziTjA00iUp2tF9L8C8bHJhJU5m0gndSV88FHCqni1YV5jmk0HJsLbu5CCdjEKI3mDSM58FaL71433rcQ6bCeS/XfCD/qIPIz9aA6OIdLgbAD2QO5iIkH/NKLMZRvyKlQgXl9MJmTjxGakEHejOXXAsJYZrmq13fwykj0P++xWD2EBvplwEVcj+j+IcsGeyev4O/wHlJeXM3jw4Mzfgw8++G92PWnSJL73ve/x4x//mMcee4xf/vKXTJs2ja6urn/z+dbWVoYNG3bKvWHDhp2Sp++dvP7oj/6Il19++TfS9jtx/c6ZBuC39sojXKs1RBxaD2LUNxNupruIWrJ9hNs4DwnDIiJu5wa02/wqYi5pt14BrHeetGWIufURp+F2Iub412i3vwEJwQ4i7ucx4O+QgKsHjsGQKxDD3EKcNL2LUCY7EAOvIuqPtpFhjL0bILfYvz+EGGmytK0F5kHBJ4STi3YaX88jZr3OeJiGFnaT2v35BrjofOO102P7IXKl7IQTP4GshWRKq9HtPnvIKM7774bhH3WbzSfhf6zp9pLb3ouUxW8h5eUZoo5nG5lqCD1AQZ5x9LRxkQ6zXOZxvO53kjL4vPGSrElPAdce0TN9/n2R35tKnFJ8xmP6POHeGWE8rEECey8SlDehuXIvEbt4pemdFPphaG6VmL4LCYvcUiS0s/39SVSQPinyU0Wbo3fBQAup/pWQPQGq9hHKUh/hattBxFy+SpzOvZ2wzCz0s0vQWoGIH0vxfkOJGtR9SOHf7vE+Y/hS/ODVbmcGUnzvM32n+N2taE6muMb7kaDtR3Oozri7wbS6jzicsUl0yboSuVFzkHs9bU46kVJfB3Pfg+ZWDbzwOFxei5SSZyD7Do+9GFnwqoVPHvM4+wgX/VgiVON14E/Qmv0ikUtuK5qzDyClfwI6VLYGWbamo3m9y3i5kjhhX4QUoo1o/Y1DVq9taDPXjXjaCDgPxAc2GR9riFrON3gsi4m4whSrnGha6TFPMY63u78ctC6rHZN8tWl5E1rH95E5nLT/CAy/2N9fdb8bCIUnjwglaVU7by1V0vMhtwFLoH8pZN/s8f4F4pXLkfL896bNCuPoi2h9jvLn38MfLidCBSDiYRvQ3N7kMT6BNgVriWTR44zv5A3qcRu7hY+s5Wjt5Rj+iwmrfQ5SBndBxUi01qfDB7+OktnvNa32IdfyRI+hyHS8B/Ged9HV0tJyigv4nHPO+TefmzNnTub///Sf/hOTJk1ixIgRLF++nBtvvPE3Duf/7Bo5ciT33HMPr732GuPGjSMnJ+eU3xcvXnyGINN1NgbwdFdulhSlQ2inmHaFNyEmUk9Y37YgIVyBmN9oIrh+H2L22YjZvErkgJuPhMQziOn0EtUyCvzOVUjA5BGMqQkxyC4ikLwTCa05SMlYhtxls5AQ2U7U+U0Wx8N+N5tINNsO+38Bwy81nE2ISaW4vjuEi0yZrTyPPQnbzyPrZYrluwox7lWGfRQSMiOJGsF7DXOJcd1BFFc/jnb0u4kUEx1EGabrkBAcoz5aD0FpEl5/j0qeDTVNKqD3Acj9kOEuJRhyExF79Aki9movYbGZj3bc6QDDHxM1SgcjxvyCaTffbWR7LOuJ+L4q2POQUwQu0PeM9a4YCaTjSOlpNf5mI6HSgkCu4OQAAQAASURBVARuA5p7lR7Lq35u4UnjSeELkwhr2xykFC5Cc63aeKxEh5VqCYtkDhH/tF7440rjoJRQ7Jb5meTi7kNKeZpbYwnX/gTovRNyzyWsdquJTVISqD1kwhpOLIOsSz32TwIroHUDlKaDMzWiPaVoTn/AuJ+NlLUu4nTyXLTOqtxnvcfYSliFO03DRo+nClmrHvW4piHFeR2x/g4qR2gLMP4yt9PoNpca7wUorrMLrYNkfZpmeJuNj+QKPEKshxT/eos/X/T7I5Dy0UxYyAuR0ttmOPLRGkiK1ToixVOyzHYi2mcbT58wbs4n0q50+b3HiMouyZqbNktXIYU1uWRfAqbD0e/BwIuJFDgzPI6nfG+B4S8nymPebpirofdKyH3Y76TQmHykyO027PnG29NEVZKXiM1RI+J7XcQBv360Dhb6/lY/twhtdAYYriuNt4TLEWhuzHIfn4DmyVCxmsiROAy6vw4FOUiBXoeUtc3EKfp+tzPRfQ01zl/3M2/4vWrjpcD4S2FBgw3nzjMYA/gQDMr7nz//72qzBwbf9b9XCm7ixInMmjXr37QaXnDBBXz605/mtttuy9z70pe+xA9+8APq6+v/V8E+7fWe97zntL9lZWXR1NT0jvf5H7nOuoBPd92AFtphoBFObCByT/UjhrICMYTzERNOgf3DCSWrECk8PYixpniPI4gBrSHSIXyVKEnUB4fX+d50v9NK7NzzkMDcggTEFnihg4jHGkYEhtciJltBBBMPMwzbEANsRJauUhg+FjHiFMOTY/iL3GYxDHqIiOeaRAROP+//C4k8Z+VIKB837JuA7yPG1eWx9BA1iItR7OJDSDnZa5zfiRTycUgwXkWcCu4GpgrUjNV2uelQQ0Zxyr2DSMS60zg5ZvwkodXkZ5YR6RgKUZWVSr8zhbAQDCAC/q9FgvVZfd+5zuOZSMSRdsCF74fhv4tov93PDDYtprmfFL9VTFRFqTCuCpBQ6kYKzXyP87B/rzFe70RXmrOFRMmscUjgVCA3f4vp00KksrjVz/2x8ZXc7ZCpd0o7UggayRyeOLGZUEiOo3mSA+yD3FlozvQh4VZjHDQZlnnIwrIXKIesYUhxm4fWRwmUnmu8DyMSROcgIXnuSbB0Gu8j3EY1mpPjCOtxUqYbCKW9E212Fvn+rwjrUDOaY/1obR4XvoaOhvEfIubRtUhZGkBUmxgD//wr4yvRqNlt/R0RB7sFKQhp85f4zAa0LqaeBM8BYn4NRYpDHXHSfANxun8Dmk9FhDWu2rSciPhDcucPQJvTOv+lPu8WjEc3GG8lxm8vYYEuItLqrIGB1cRhrSrCPf8BYjM2grA2b0W8pB74qtBHB1IIW4y7NqSU1xKphxpgz69g/26/n0dUxxlnPA/zZ9qUpNjYXpSyZhxxyOK48fVZ4rDPq8RhwKfJnM6tON/vpba6bAhMynIvEaPY4/HXoHk52jQv82cD8ohUErGeJWgjMdh0WEDwrbMXAL/+9a/Zs2cPv/d7v/dv/j558mT+8R//8ZR7r7zyCpMnT/6NwPPLX/7ytH9nWvmDswrg6a8UO1QKfMKxNyWIIe9CmCsidmVJienye/mIoY6Gw3eRqa2bUdDS6bZNRLLhzyOFxe7jQVOJ+KYcIp/ZAcTw6hFTt2Xv8gmI0X2WUGqGETv4SqAP3vwFYvjJEjGRsJJkE8yvDI4mK9woomrCZj9/HWKkB/09WfQ6PJ5SpMA97XEOQEHyadc9klBk0++FyGr6hv+fR1Td2IbcI1vdx17TKkcwnfgRDJ1BuMvTQa+dRF7CfONoABnL2dvriNrIRXIlMQAx1mok5JYjYV/l52aJtrQaf/MNU67hHa5nx0xFSkAbsnoUIkVgn/FTQMTzbDZMrcZ3l8e8Bll89ritPKLOaglhIWr180eIAxGdpuVQ3yswXp5A1ogVxm0vEQpQ5nYSbd8wHue7z70eSzmau9PcTnJdboGsy0zTpLyPQuumEdH9A8Zjvv+mIuWjjFgjVxFxqs1oHu403m8g8le2ECd/O4jKK3XIPZ3n8T/r/rtNgyaiBm0NUT0lrfF1vjeBTIlD+tAa3WL67TVODqJ5WE5YFNvc3jii8koRvHcBUuL6iLRAs9HcLjGs6fkcOLrMNBrjd+YQJfGK3G8zsfGb6HsvECeSS9CG4WbEQ/oNWzFxMOw54mBWq9+tMt4nGYYif58JA+8icvpt8/0eIsZvJqL7fMOVlOs2tFGZhDadVyIelnCXY9omC3aRQhK42M8mb8RQwupXBt27haMLb4Th1USM7HEili8X8akSopZygXGV1tBcwsrah9blFKTMjnT/yXo7ys9sMN62eCx79dzQK06i10QiiX9SYqcjHjeMKAaw2/eLiBCP+UQFoWvJpB3qfZSINT9T1783tu/f+/cfuD7zmc9QW1tLc3MzmzZtYsGCBWRnZ3P11dqlXnfddXz+85/PPP+pT32KH//4xzz88MPs3LmTe++9l23btvHJT37yf3n4/zdfZ2MAT3e9gQRbGZHkOcV2JQvcAcRUJnEq4ytCAvVBeGsHnHe+f+9EsUn5SLhsdF9pARcjBSvFhSUXbbKSVSBGlkck0m0k0sLUGZavIAb/bcRIxhrmSrV/wWToXQa5k4kSV1WG53vA51CM0c0uoN7ldh9BFqFnjJ9LkAtmNby1TvE4rEPujh7ErFPwdwNieH/iPh5FjHAaEtqNRK7ETUQliSeQwC8jLBdVfmcnEhrLgOsga5JxXEkcSNhPCPiE5w1I8BxQ/0PGGF+bgALIfgYJhHFq5+itMHAEUQGhFaV62A7r98HMFBuUrFHJ1b4WDv/KB4JeISxSo5ASOwEJu+eJ6gCr0Bwb4HHOgreXw5BbjdtjHnueaTSTKP91JVJUByPhfQdK57IfWfBy1MYP+uDCX8K4WcTp43LD/CfCV/s+GJqHFM7ZyOoxCgmfJmSJzUeC+oDmAJPc1iZgLhx+0YXta4lTkknI9RpnbX7/JtM7WWxKCCtYgXH+N2gOdhCHezqIvIRboP0QDO0kE3yf2ZRUeYxLiITfXXDiCGTNMQzz0Wbldr+/Q+3/04vw+7XIGl9OWKvvR3P7MqTQ5/u340hZTxb4J/1/l2lwLUpHVGx8NLi92/z+LuPmOWARDHzSuBsFzIDWeVB6J1I6t0D7Ghh6I5GguZnYUA6A/TfA8AGEy3Ca4Thi/M42PudpfrSug9KxaE4uQ4p4KVqLnab9DKKW8wuCbf8aGD7LbafN7Ta03pf43V1onuaRcZ1yF/QekW6WqbF8wPj8K6L++SNIIbrSc2OLcTgLuA8KbkRrv5UoYTj0pOdWIGUsz7jbSITrjCMs0iVEvN6txEaojcgqcLLbvcR0y/eYuwnF9CXEt1sJD1ILkilfRuu11W0s8u+1RFaJBcShu4/73WzET0ZB7mVovp7Jq58zVgpu3759XH311bS3t1NSUsLUqVN57bXXMqXX3nzzTQYMCDvXlClTePbZZ/mLv/gLvvCFLzBq1Ch+8IMf/EZyAAJ8/OMf/x/+/t3vfvc30u+/9zobA3i6a1KWBMtqpIzMRwxoAWIkfUR5tD4i71QPHL4TBo1ETKcECZJCJOSS6f9JxKQnk3F1ZSoSTPI72/03nAhin0jEhhxBTGOpf5tKxJw1GM6/9GcvYvRFcOIOyLrGzz9MMN0H3FYDkVz6BrQjrUQKZTlSPH6GhEeL8fWicTHH41yGhM1yMilJaCWC9EchBjoXuTouN65vRQrUQaRcrkNMdLRxuZkoC5eYeQ0SmE8jAdNKlBNLbs/niNiZMuG3d6UT4k5HsUgpvuYhZB1LzKgICdZ8IgVFOWL2LYZhiuB+8xhc8FHDtNbj+ksk9NuQ8JyDBP46ZPU5QsT9dBJu8JX+nMepZeYOGK6rDUc9Ejrzjb9Fpl9SUjqNlxV+r9g0akWCvc5juZtIZ5JDpCiaIHzxQ4//QjLJs/t3+/DDakLoXU1UQLnO41hluDuRgjQHbVT+zH3PRzTe7LYvR1a6ZqQkbEFhAysIYZqsjpXGZZ7xUKr2+++E7KuMj3ugfyNkX0HkJhxDnATtIFMSrr0Dhn4UWSnfMD5Wo3U3mDjxf5np0oLmzU6kaA9A66gfudanG8e3orjiKcbvLKRU7UJzrAutyRXAbPj5MrjoSX8fTygfyQPwpPutQbXFm5C1eDRRcWYk4i8rDe8jSJF+kIjz/Bra0OwkrLmXur0OImtBq9tphZ2HYMwdRAzq84g3bdG84Ea0jgYQB+J64MTrkFWMlJYn4Od7Nc1ybyUsv41EjFtSkmuJ2MCpREnDxcZ/J1HpZrzpWuHP48SmsMG47kd89gl/ZqP1MsFwpJCBlYa/hUwVJFrQHD+I+HOB20mHfqoRX72MCNtYBYe3waBk7b6d4EtPwNFjMPBWxG8nm26Vpuc8dFW5v6FEWq5x/vzIGYwBvP8djgG8538vBvC36VqwYMEp3/v6+tixYwednZ3MnDmTF1544QxBpuusAni66/YsLcAuxNTKCRdBDWK8KT5nBmL4KVZsjJ9bR9SU3IoYTZfbqoW3N8OQrxOK1Eyi+sJL8PPdcFExEr5fJWpRViMh8gASsI1IKJQTiY83oZ1yNXALnDgGWecAZfDTX2by0ZKbhMXFSFB0G87krmtDDHSE4VuPBPss42kvMABavw6l1yFuXouY1UuIoY12ewmu5H5pcH+zibyEi5AFchoSvuOM28VE7NVBw5NcV0W+v51MbFv7chj6HqRkPG96NQAd0NrhgyJ5wDw4caNr6+4jSpAlK1s3kST5biTMH/YYU/xPh/E/1WP+AyRInlD7vA4nnoOsu/zedMNUiph3n/G5wfA3IwWo5aTnpyChVUzGvcRIIh/iauN3HPA+FEOZBPoANBfneCy1SIiUIKtlvWm+jbAUZwNrlXcua5jGc+JHkDUSzbOrPC82os3FTRrv9u/DuMHI+tRNJIAu8feX0ZytJfLAPYXiw/qRwB1LWKQXIIUtWd5WoDnyhmEsMLyNaM3lup0tfn4EWn81wDI4utSl0aoJC3K1nz8mOHcugTELiMo3x5EVdRcZKxwbiTyhxWiTNJVQcA/7mQqPdxORaqWacCOnuK4j+u3VJTDtfUiZSha2AXD4azDoXOIEb5/7Go1iGgf43rVutx0ohf2PwvC0diqMk53GaeJHyYLd6vfrjd9FSMnchhSW7xqP2USi6hbD1EPEopUTORYbiFCFUcZJH1Iw17mv6URe1D40R+93v40e21xU0q1Pfb1xDMbfSmRdKCTq+y5Ca3Sn234SbTDqiEpKw9zfOuMBw582UWkj8zTi1YdgyFRiDY5zn6uQAr0SzcV+t5nj53ahOXyQOMR3A5pbx/y9hUiKf6X646BxnTZgjWhOJEW2y7jrR7znTB4CufcdVgDvffcogP/Wdfz4cRYtWsSFF17InXfe+T9/4Td4nY0BPN31NFrUSxCDfAz+bhlxgqydCNLPIyo+rEHCeCVx6rIbMd/dbtuMb8hdbmcEEXidXAD9cNEEZAXbBIe/7r5TkP5K5OJrRTvemxBjrkFMLhspCseBasj6EGJ04+CD+fDeiyH3CsP/gmFqQwL6+x73GCJX3HSCsZ+8q7drtxfipF2P25qNmOTNiFEmK0sSXtnIyrIDCZQJ7mO635/p8Y0k8uiluKAm4gRord8v9xjXwtB7iBJoo/TbP+8WTKUz/HwdildbYFxdTMYKxAFkJTpI1NGtQoLjPjQ/cg1PF3EqsMA0Ti4/uyCzzjeuygkLYisc/pHxuM3jWYSUvz7Ys4FwW2/1+68bv4P9uQOddN5kXNci93oDoRSnObvS78wmc2iGacRGodDPb0Gu3+6T5k09ZN1mHDQY3i7jqMs4KYFx1WguFiNr01PQv8HwdZmOR4jEw1X+v9Z4GeW2Rxkfj6H5+SyR6HiA6YTpC3HasxVZXoeZxoeQ0rhczw4cQCgL09zmNuP1Q3p/zGf9/iTT7w4kfD/pvvd6LFXGZx6i/wo0H/egdXIDsvB2obVUbtzsBnrhaL2frSMzh6f9Llof7UQeTWDQLKRoTSZq4I52m3cRperqicMN/TD8s74/z/fb1c6J9NxYw5ms89uRNXo8mkcH/dxfu80egufNMk4SPGuJkJQ65DF4gbAqrkHzMLmfC1FaqX7fLyPS2GTDiSWIF4xDc7cU8YShQj39SMkbYPwnD8Faw1SFkkTnEQfTGoiav0eQYltE8LJlxkc1WuM5gmHIBPeRYgZHI+tdBfJ+bECbhAIiZjvFDR4nDqI0AH9OnDg+7DEUepwHPMYqP/8d07gAbb47iBKcoLXazdnr/6JrwIABfPrTn+aRRx4506CcVQBPew0FsuGtg/DffgUU+zDuTLTohiLmsR8t6jwiKPscwl0DEnCL4J/XIYFdROQj+w4ShGVEqpTDRKzbs8AvYNAEwp15k2pz0onccW8gixJECbdkIatHSlYRYiJ7ofsIEZM3HSkc2YhpViJF6HbCGgiywq32+Oe6vwYyueuGgxhWv+GchYTOEbSjHo4E8gy3kW0YO5HQ2I2Y4goi3qYDMf3xRJWVtIuuIBJgN/qdRqJ82QPuJ8WTjYD3DiOU7DGEhbeJcDuvJlxq1yIB+QFE70qkmJQCU+CtjYR7LFmCbkVC9lXicMc5yHrYRQSwF6CDPmVoPvUR8XBXCfcX3gZcbUVwFFF2r8D42IFW8Hg0D8rJ1A49us44SopUo2kzHylj49Q/dUQprxlEdZdupOC2EKlwkpW2DAm/VURaj5luL8fjW0LGMpT9PsOWLFSTPOZXibi6Ayh322LRimw0N7/i5+e573SqNVnTqpBlqMZwTRVtMsL9OtP6fWh+Fpk+KR5vkMey0/BVeR7UE1eyBhcSYQKtxBpr8nNFbqvGbSSlqAWt0R4ycZ2UwMBHTY9thFt7lH5juGHY6t+bobUeKThjPDavZ+qNw1KkvCXlNFnvrcBnYhebIasM0XgTUaN3J5nwiExMWxmaxynu7ULjuMq42wi8BN3LjfNDSNlu8xiOIMXlj4n41xQrtxfNg81ojfUh3tII1EHW1WhebCKs4D36P/cWxDMSnaqNt3MQD3kJWfSHoblZQmx8NhqnV5q2M4hDMt1ErHeKIaxEG5cm0/V7hns6cQjpMuN9GFHbfZtx3+k2Ewx9xnEfkVqnDfGPNX5vGJElooeY1+ejefcI9CaXd9p0nqnr33Ow4z/y9//AtWfPHv6//+//O9NgnD0EctprLDAUzquGxnqgCt57CJm6JgE7ofthKDgHMcIrgMvg5zfA0HooHYyY6mLE3PvgvQ8QVor5wJ9C6wEoLUOMtRoxgqFo0b9A5Pibh5jVVqAUshcQ6U1K3W5i5skqkY929InRzAEGQEGyXBajWLdLiBQEiQFOQS6QO4jYpxYix9w0JMCnACME1gd6kBCscHuXIgWjiHBjvI6Y87XECcb0+17jdxhhaZqOrCNFSJCkuMlcxCDvRMppOxIWuUjA7iKy9/chwVDjMW0zXFuRcDgHuWm2IwaUQ1hs65ElZDkS5p1EQlqQsJmJGHOBx1dGnJjdR1RHqSGT8Z8RRA7CLtFjz8Nw4UpkUegkU47qwvOJCgp5xInUpNyuQRuRrcZJEwycimIrr0anwue6n1XG0UtIeTzmcTUhIbMXWXXakdVzHqcI7v0Pw/BitIHp9Hh2IEtdIRJwVXo2kyeunrDY3gR80O+OlxDLHUFmbmbqGOcRB40GEXOhjkjPsZZQQDEuPutxTEDzdY1x0ut2bzVMg4jyfiP8/lCPZTWR/uVqIig/WcySpWwiclUOIJK8P+1+06nObyG696M50OuxPYaUojy0WbmGWDs1pmeOysdllQmfpXOI9b2OyBPagtZbsk6leL2khG1CLvbVHmcrmu/r/P4RMlb3t+6E80YSG79hHt8i97GL4DvnGNctUFBIVCcZQcSsjnI7r7rNDtNtFOI/s4nqNhsFW+sxKE1u6BLj8RzCkp2P+E+D8TULzdtiRNsH/fsVaK0/4u+taG5eb3quIA6RVSNX8dXGS7KKt/n7WKQE70bu5XFIcUyehnJE+3mG4UVE83LDOQqtgW3GX1KCNxKHZor9dzXilY8ia+ou47YYzZdFolluI5ozkzmzVz9n7BDIb/v16U9/+pTvJ06c4F/+5V/40Y9+xPXXX3+GoIrrbAzg6a5PZEnpASkWZUSalE7EGO5GC3oLYg45yPJRiQTJXUChg9FvJOL7iv1cDhK2yUqUCycegKwHCKF5F/AkvPYj+MAViPkPRwzqDxDDvxUx0VcQQ5mFmH2hny0mrEwLiADoMYiBTSWTGqF7HRR8jEgvUoaY7PPu+3EkNFIM3RHD2W4YcPtlRGxejuB5ezcM+RhSknrhrV/AeR9FVoPZbqMTMdxpHlc9sqq0+95gpPBdjZjsjYi5PoCUwVaPPcdj2ECcIM4zvtf6e73ha0CKyVbsWzqJ5uXGTaXvJ3fdDW4nm4zl4MTjkLXB8L+AlOMUw/MycmWXIsEySrD2L4bsGcLpiUUCZ9BV6qd7NxTsMI3q3M8CNOeeRVaTNrXztxvgz+5GQnO8Ya1E8+xSJLhuM74eRwH6C4AnoX+b9P5Bz6B4vEnu7wNIAdhhXA5GQnO4adyIBOFDbnsCmgNLjLtJfn+66bDWdEkuxN1oju4n8tKN88bqAeBR2HPA+5vLkNK10LCsQsJ2KVHb+XUkSP8eWbUPue8taK58yf1sQFbuiUhBm+Sx9pu2NWge9riP5IbP9uc6ZKUfh+ZOHZG6JcVsdiNFo5iwlK4nrEj3ExbCJmAudN8DBfmIr9QYthL/3wOshP2bYfiNaI7/jfG7lzi1PgW545vRHN3hfquNp8VoPU0j4sme9fd+Ir5sr99ZhTZrU9U/ffDdlfDxBWg+HEfrNG2QitGGaB2a71PQ+mkmwkY60FyuRwrxXt8f4Pf+yv1WG29L4OheGHiz28hH63SlcV9i+OqRUvUpdPBqPlrTR4gNWYVo9PY2u3W70JrY7HcnGLYK0zbFrw5FPL4R8ap1bnu2x7yPyN9oBXznBseS9nscDWrr7ZU+1V+D4p0riZQvPcQ86yY2FbnC4dHdMLANKX03me4VwPNnMAbw7nc4BvCBd08M4IwZM075PmDAAEpKSpg5cyYf//jH+Z3fObM2uLMK4OmuqqxIjPoK2oV9EilCnYRFYx5a/MVIuDUSu8rRxMnTMUiATSEsSP2Ikc9zu8Ohux4KbkOCrIOIWZmIGORBJGSWoR1rNrHLThagSsNYR8RaLUQMughYAPunwvAJSJErN9xbCAV3NmLkj6E4nUeQ4Pgi2mHfTeRQ24hcINW6d+JRyLoHHQRYQiiIlcbDtUTR+VYkqKvg6BIYuJhIu3MzUhI2Efm21hvetbDnOFx4MRLgTxLpPW4wjp9Glq/BsPNxGFNtPF1lWFe6veNIMG5HQmQakedxk/GR6/YhYqrW+zPFr41Hwr8HxT8lgfuo20hxXO2Ief+Z6fptj282EYM3HClGl6A50OP3S4m4zSLfX+PxXEm4JyGqRwwjXF4vEbGGG5GyUO225iHrxmK/n9zDGwnLxgEiVm+VcT8XxYgVEW6zEcgaNwFtUhxP9982wofvIILl70DzCtNsPrKm7kJz8y4i1+VswgX9EpEHbh5SdIqJfHJlHvPXEQ3ziOTDZUSoxCeN/6F+P8V03kAc+ionDi+BFIoRRD63csP9Z/BPx+D334MU0C4icXyD8XQNWhOJL6whyhhWIZ6R3Mzbic1Cm8c8F827IsNShOh6APGpLwNfgv6fQPZdhuEpxHc63H6e+7UVt/1RGPohNNcS/2r02KcZX1uRgr3Z9MozfEfQ5nWDYWkm8uSNIg6oXWn8vYSUtWWEpTKFiWxAfGQX4bXo9Lvbgadh/68d1/icx1rn/6cSlXIuBx6Dwx0w6DL3fyNSNNuIeNS/JdJuNbiNy4m8od40t66RrjtwJBEmMxfaL4Ohd6O1WEok3y5FfKDRcJeguddJVCWZQORFbDBdjqO1Wktmg5iZ8zvQXOj353S0LpvQfGg4gwrgXe+wAvjQu0cB/G2/zsYAnu6qUa48voqSA29GzG0dYpTXEyleKgn34jx/L0cCexRiotvRTq0DKQCT0KKfjRjDGOSevZlwBV2FrCvJ4oLfSfElyVXWgphIC3HKcy8SnHuJE5gViCktheF3EXVUrWz+4CeGe6b7uk8w8ecEg2wiisNXIYE/AilxHxCOsmYhRepBJKCLiQSwS9zuY5xahqoFBp5PHCCpQcyywWNIcVaViEmOsvJXiqw7E4nqKqntCqREHXHKijaPpxIJm+SSLPE4UvuNSEFNeByHmPEe4hRgu8bfu5g4NfyU+/62cNH/OmFlTAcu3jB+t3p83YZhkdsYhISnLcIZN9hIIsfb00hhSTFexYQr9A2ikkAXEmYdyIVVhxSVAyj2tMswHER0X4UsaquIyiZJqe5yO5XGS3InHgT+kJjPLcZTJVLuct1Wg/A1DsOUY1iOqI/el+Gne43D7xjGlcR1K6Fsr3Z/VyMlJrlc+9zeXmh93DB8gThpej5SZrqQBa6YKJM3HQnWcmRdaUdzJylf17qdUWhj0olomjZpDk/4/feZhskKPcNwTEHWrjXuqxjNmwloMzUB6IG3vu/xVCMFuAXRPB0O6EK0Nz6pNqx1xtMTgjH7fYg3jCUSB1eehIsBSHEZAUOnonm0Fc3vVmLTUekxH/fBka+aft3CcyY5fbImpsMOxaZJEVEpCDKVQSgmkiDPI9LZpLCLJjSf+olN2gdcovKg21qFeGw5onsR4s35wEinXKkkYv2WEfWUv2wabRIOuB/N1f0EvykGOqF0GAy8lJgjRcL30BlERZ3kLSkxzK8SVTw+YDoUuq+xRKxospTvRVb0AmKTV0AcsJns55uQN+EBoiZzctefvX5rr7a2NjZu3MjGjRtpa2s70+BkrrMxgKe7tkHuNUiYVkH2VOJ02WNoJz8bMZAqZIG6mHApJldkcgssIOpItiKmtgspeF9AzH4NWuxPEFaU5fD2BhjyIbczzvA1ELvkOsQcJhJl0goIC1CP21uPGFUOEe9Vhxj6XvjDfL/b7HemEad10y73L4CP+d1NhOuuBik+SUj2E8HzHb7veCG6EBNs8f0Bfm4MYWWbZXxvIeoc34aY8DBCKe4wblcjxa3J7V9lHBQa7wXG9beIFCHbiUTZtW5zOBIso8icHmY9YuI5RFxbn77nTkbC/SXYf8y1fRuAFZD9USS4bkQKe4q/azJccwxX/km03AT9e+HwL2HI+5Hwek7wnvgFZH3U468mKnSUIpdktfFQgoTXZkT3pBh1oTl3AAnR85Ey8xQS6FuMo07oXeqSbaWGb4Vx9RJRI7ncOOQk+Kf4/nNo/raYTrOBla6MUICUmVXGRRvkzoEPVhEhA2uJXI77kfLUhyyCywnFfC1hYdmCFOUqKE3xkvM8hoNIuWjwWF81nFvRvHsWKQ/rUSjBWr9f4DGOIupTp7jWNUi5qvWz2Wj+jHa7nWiDOMFj2uD+OwnlYJf/n6hnzmsi0qwMBvZB7/c8zy5F62AenNgIWdvRxqvG9G41bhYhS2E9WsvlRAqYOWjdXovm4ItIuTiE1k+j/08bnbWGpQWyzvU42ky3UsQ7ksVyD+IZk3xvOmH1+jba6ExAfHKb21mFXNkvGHdNfudOoj5280k4rjD9KomMCOcYrzke622IJ+dC89ehYqp/KzP+JqF5leJ+X0drotA4KzEeSoka2nl+fx2yGidvS7XbgqgE1Y3mR6LzcjQnZvr5RqKG8hGCJy8hTsqXu/0xaA6NMq6LiMTY5xCZD87kdYJ37vDGu8wf2d3dzZ//+Z+zbNkyjh8XkrKzs7nuuuv45je/SX5+/hmF76wF8HTXtUQ5pia0QHehxZZcsY5Zoh0txmSlWunnJyL3cFIImxAzvQot/hrkWs1DjGe/nylHzHMt0KefT/yEOLU5hTiVWAfNHUig7SKSSTfBPy0hlKtXDNMiZHl5GDHf5J6ej063dbnd+YR7JcUPLnY7y4i4ufmohmkPETM1yngZTbiuWv13tdvZjRjcRqSk1CLhvZ9I8luLhODdiDnOIk5w9iMBXI4YbYVhmYUE2hqiMsUBJGDaiJOWO5CQTDAcIU4SjiBOydYgt/doIj9dihWz9SopdMPf4/uj/VwF4QobQZzEfBAponVEDFSf8PTjvZA9AobciNz2rxunl0DWaHRo4yAS4hcihTJZU7aSEdZsRgoDwnvrco9/FhFvmWs4LjEeLieToy73s342nXacYByO93gWGPYit9di+j5nepQgIV9EVHeBKGyfwiC6kYKcZ1j2E1aWDrfV5d+f9LgmkbHC9H/POF9rHCdr0nTjN7lYX3VfM0yzO5Hyu8LjvAmtleTaTsH5pchitBoJuWRpTxuryzz+I4ZxMPQ/atjrDNs6NN8mEaEM2YSVaZvxk55vQorubuE496Nkyh2mVExZ5yNLY6fb6CFyVW7XuN5eieZglcdfb7gr3M5e4+mYx9EG+3+B1k8+4Yo95DFeKdiav2eYn0PxqbuIkInkYs5BKXCOe6xVxoPn5j8vhxPLkHL4nH8vMRyVyEuw1Di7CfGIxUTsboVxvtufo8mUIeSOwHdFsek+i4jlbDDMG/29x8/XECd0kxs78bly4tDQNxBvPx/Ni1blHc2ctB9uuBv83mVE2NB6MmluKEI8b/RJ8FW4ny3EXEwbqUJ9Hk3W+UcN/y84s1f/O/z3Lro+/elPU1tbyw9/+EM6Ozvp7Oxk1apV1NbWcscdd5xp8M7GAJ72+rusYCzbkbA8HzHhq9Di3owmbA5iXrvQQq5Gi7geMbNDSFgeIGp5fgH4JnJ3zYHuu6Hgd6H112p65sWI6a71e1OI2K5k9dqGGOdY4oTtSMRIElMuJRI8T/FYdiCX2sNIAFxKpJJILq3tyC2y3O0fRErDFDJpKQAxulqkROyHt78PQyYjQbyJqCPcBtTB4V0w6F6CISZFeBlxKnax8N1+Gwy9FynidYZtCrLWfMXtHyQS9uYQ9UtfNf6SZehi4+caIobtIArCnmQclxAluGYYvu1uz+5tRruPTiQEtioJdjGQe7Fxd70taJfC1pdh4gwijmmuYVuoMb19MwxZ4vdWI2UcpPiOQxuEWcbnGHSQ4U7Tq964H6Y+6YTmu6FipN8d5jHVe1xbfX8SEsY1hmmO+m9d5BC+AUiROISUgRakfDWhqhwj0bw8jub3DJRu5XbT6inTO9tjKUUIqjHNJhmeZKFai4RmmWE56HH+nXHSZxzUEafo84gYzVuJAwCjgOVwuM8l+KyMb50BEy8FdsCJA85vuA0pC8miW2DYv0goSzcgIZxiepN79hARB9zl8TUTm4aDRJWcOWgDU0Wk2en3u01ELOENZDZ5J34CWYuRZfcQYfW6HMVbziVcrsnqXWt6D0DWpo3+vQ2t7TykxM8lUr9MIwRvrfAD7q8EraEc4jBHn9sYQ1TtaEZrocS0HmCcTSM2hslNvNDwDkb85yE016qNw5uBW4yL1WitDUMK4AC0Nv+GmI/XEqUXq023ZO1LYRLNBC8/juZro2lSQZTvvNSw9BOblCqPHTS/ytx+B7yxHMbfTVRnaTV9l4qeb3fAkMfcTrbH0QCHvweDvooU7wYkT/6LcbOJKAhwDZHHcgsc7YCB16E1W0TUW34W2HIGYwA/A4POeYfaPAaD//rdEwN47rnnsmLFCj784Q+fcn/Dhg1cddVVZ9wdfFYBPN310ywJiJ1ocecRJ8bWEOW3FiFGluJ1jhDWgbFAB7RvhqG3oMU8FgnfbiT0XkVM81oiF1kfwcxvQEyiATEFK5v9P4Hs9+je278WXxua4qPWIAZRjphmB1FBohwxojWIuU9CDCRZOpsIoZF2sq8ga8OHPIYlRG3SduOiiEjKm4eYWXL3HiZOBFcReb3W+d4otEvejYREilurd5vXop1zDaLJWOHszUfhghSYPVfP9j8K2bcSaW42u73NiGFejQRCrnFTi4TAAuP9PqQUJDdpvnEyDik+RcZxLSGwQYKpDAmwMhSn003EMk1EgmUvYaW7kKgWspGo4ZvipfL8TgoIz/ZfCZoPl3pc+UQC6+QWG2MYr/Ezs5BSdS3hZhuE3HYVxtdSoBfaf+0qKp9wm6s91nKknOchIf2CYX8ZzZMUH1iElM25SJCuJVxj09zOfuJUdxtyRV6DBPtQIldgu+kw3b89TlR0aEbzpAsJ9TWm4SQ0z9Yaf2OI6iCtRJ63UUj4JpdnnnFThay0o5FVObkX08GU3cTmpcx9P6P39z8Hw6eafkn5+AyyGlW5vXGmRTkRQ9aCLD3T/E6yQE5xv2XAt6D/GGRPJsqCXY42QOm5VuPkKjTH/9K4O2icz0UHzv4OzZsHiENkeURqnXOMl28iHlXh5/eiEJg8pCh/Fc2be02rQYj3TfZY+zyWYqS4/iUKXdiHeOlyNJ/3ozV3N7JsbUIeieVonv03aB4LFbOINXEDSqXzX9E6ykPzbRniFbjf7/h+GWHJS1bCl9D8SeEEryNX/XakxE6E/ctM0xFkyrrRhObsMiJcJXlAlhEppaaijcUr0P4rGPox//5jIrl7k+mXT4SYbCY297OEh6MPw8A5iJ8li+lQxHv2n0EF8NPvsAL4N+8eBTA/P59/+qd/4n3ve98p93/+85/z/ve/n+7u7jMEma6zCuDprhuyxKTGEqXQkhtrG1qUNYTgT0G7Pf5/rZ8t8+cRtNgTI+pBruJdiNHfgATcMMSkxhEKXwVimokpXOa2diMGd4AI2G8hEhNXEznjhgLt0PojKL2FcDfsN9wJjjLChdOEGHiKUSv2b8uQgEkM6Gn3X2p8DUJMO4dweW00Dr+ODpVMQ1axv/bYVxjP+USt1T2EZWaV3/2O+00B+y8QjDbFX9YiwZUspjtOGk8xUac2uXNbkTB70jRJu/xpSOk7JNyx2f02ICtck+nYipTZAvd3t+9tND1udp+HjO8qpMR0GfaRSDnpQEpF9UkwNxB1Q3e5vzK3fQ5RXSUHKQ5txmuyUI1ASnQ2ceq1hYgDa0JzNbm/txGbhRGmX5Hbf56wxBQQbr1LCasohBV5BLAaevdB7o0eYyNSWgYjYVeM6N3jMW7w/3cRFWIGIUGNn201PAUe81CP+RzgJ36/1/fH+rkHDeNVJ+G2lYhX6zZcS5Dy0oMUqOeB6dD9HShYTLioupGyMANZYWpNl1FofidL4ef9WxtSCvai0mFLPd6rjN8nEX2nEsmDs9H66EMbiMHEpmQ4UiBuQHOh3OPpJJOonG94bCmmbKPhq0IntK9z2zMIT0AxUtyKkPU5eR6OIyv6BsSjDhBXodvpR5byncSm9hXiBPbziG+2oFCCqpPwUoHW+3E0D/uI3KMJ9hzj9GXkjfkUUvzGoXV1HFgP3YegYCqaAxeiDejLgr//Z5A9y/dmok1DPpGA+wXk8t1B8F+79hnkZ5KlObmjR6C1nNy495h2raiqUh/y+Ow1jvcifvOY+04b38TLk8LfgOb1BDR3HzFdSog430Y0F75xVgH8bbw+8pGPMHToUJYtW0ZenqwFR48e5frrr6ejo4N169adUfjOxgCe7nodMdPE6OrR7i5ZJ5Jb7W60QLuIRMy1iBlMIIReLxJGDYT7zoHxTHDc0ATEiFs4tVxRg+FI7t1yYsdY5O9VhAJXjhjKaOK07jqgFEpvJYKt25HAn4cYXCtiZt1ENYARhnEdURYuMaktyDpwQG0zBzHNfMQABxFB79WG6Tn3vwIxW5DVrdRw73afFyPLzUIkcOYhQTPW77SbDkeQ5ajTNJiHXETrDUuyGE4iduWVbqOKUAJ2ICEzAFm5ioig7u0e4zl+pxwpTauIAxYVnJrYucnPzUOKxFCkrEwiLLEpEH4HovmVSCjXeaxTkFDdbpjPIebApUjJKzYNst1PAZGjrBgJx+v9znGkSOWjOfkIEsqF/m0bQdscNH863H6ylI70M+2m3znIQriEOBQ0gLA890Du+4AyeGsHEq7rPd5Ow5ZjePYipeFatPnqMJ2aPa5ipFyUIIVoNJGyZwsS5pVorQ0yrY4QSZLT+qs0/v/YdGpA670dfrwOzdEm9zsbuNp6Xzfa7LQZB/P8XJ0/69X30b3uYxc6kJDc3uVkUvH01qN5soZIrTOZKIW32+1PIxJr56H5NhspPPOJetjTiY1lg2nWQcQxNhFxh98iyv71I+vdc/DmbuKQ0rfc/qvGdwobaDBNUrxccnlPNcwlRG3sp4yDWsRfFnssSWHe779CIrymH82hw8ZXrr8XI5pfiPhDDWG17RZN9q8UHgru9u9jTKuJaJ4W2WsyASmqyTsxlajGcZA4+NOG5s1+QvHqPAnPqwh3bD9aB2kDNdI0WU6sxW4ipOE2v5dL5E2FqHmd6FxkXDVr3G9vdHv7ic3Ia5zZ63811u90f++i6xvf+AY//elPOf/88/nIRz7CRz7yEcrLy9m0aRPf+MY3zjR4ZxXA0163ERaeZsTQVxPJbAegXfSDiNnN8HtNSEg5iLh7M2KIWwjrxQi3ccDPTYTs+5Ew3I1O2nYhxauBsAr1IeG2wvemI6ZUSdTMnO4+apWAmgYk3DuJQP12gmFPRUJtKRLCmxHje8HjrTbcB9QmDchtVIUY5T7jogdZBtuMo27Ysw4xsHFIsWlAStZ+95t2+lWw/yHff824vw8x94TLbMOxAQnDjYgBz0axNLvd1kqkYIwzParJxPI1H3MfBxGDHkYkz80xjPONix4k8LYhxaTBz3a6jW2EcjgJKWSVbivFdBUi2n8B7fbbyKTl+ef73f8MP/fHhNtqHHHK8b8SJ06Hu73tyKLRQJQkLEFCthQJvjIkdBIt+5HLrQoJfEzPpLyvJVP79M01fm+X8Z9rnC4w/BP9+zG3kVzDx02XScB0+OFyJOwLgUNw3lc9xtvRvEnxTWuREtxIVHpYi5SCI4b1fOPoCFKStvv5y5CilDYdDUhpKiZOKHdB74+Muya59DKxk18kUnisg/88GA7vJlMKkjw9m4dhu4HI69eP5stKIt7vdYOcNmP3EBbRStOiwXW4x/rvAFoPhWh9v4YsZ/WmVwozaEMbyyOGrc7jWIX4SxMRG3ktmVi1DK+oIlI4LTBtDngMbXDBUuIQx0RCOowjkrTnEIpVD+IBQ/3ODciKtxht4I7A0RfRnNlHbFJmo83iSmj9HlqPz3nsO0y3Qe6j3Z/zjL+08RtKbJQb9f/wy5Br93y3tR3xu2GGcxoc/SWZgxv9jxqXLxruHKK2eA9aU23IbX7A+GhB66WMOPy0BPGeccgiOcnvFrnvg0hxPeL2xxJxzW1E9Zy5pu8c9zUYzYNtaC5cD0NGmrbbjMtxRNquM3WdVQBPe40dO5bGxkYefPBBxo8fz/jx43nooYdobGzkoosuOtPgnXUBn/ZamCUBPwBe/ZF54gjEhJrRwisGKuCf75VsLriCcCVYSWt/WTzoopFkjvK3/xKGvggnLoOsqWihH0IM6++QIE4u16tR2pVxSNC2ndRHIRHbVOP3FyCGlIcY+2oiXi+5rXYixejrauPtDhiSQhSSwO4hqhd0ECdt9yPX0hso/ie5Eae4/6mIOa1HjG8OUQ/080i5KiFiW9qQMjMYCanjRBWNVYapVOPfvwSGP0AkwG5yO19BSlZyJeeYTtuIoPg2j6MCCafhyMqRLJN97quNELirjbfBSMGc4T7HEPFq04n6wnVELOQ047sOMerpSLAMIFKrrDP++gn3/giiGkC3x1DpZwsJF9pe5IZeIqvAkOvczg7iwMlaJNDLkQI5AwmisUTC5B2Eu3SOx/0Yim2tMz0uRbF3NxL5BReYbk3IurLb717j318mNhflHtcxtHbuMF1WEqd755g2/cRhnBVo3uQS1tjUVj2ak3M9rk7fewzNh61EiqYaj380EaOYg2LzkoCtRPN0p9tciyxF89GGZAaRbqkICe0tRAL4g6bNI2QU3hPrIOt9iPYp/rcEbaBmGZZJRHhCBVI09piGBcZBOgw2yrQqMN4mEDFvxWhtVfiZa/3uGOIASSmyMj5uPLdoPP2PQvYE42cKmvfliNYDkLu2Gd5+EYbcS1h6VxsflYS3pNvPjzVO9hBK7H1lMO+A5u4EIn1MColoQPOuHngBTuxz2qPh7iPNp2wiXGYhmhtfIVLW1BOHRKrcZgva0DQg5Svf8KfNQinwXcSjChB/G4N43N3us119vPIzuOQKw7QPzbVkja6DE8sha4JpnLwBLf5+EPGHbiIPZxWa/ylWdh2ZLAxb98LEwWijsh640Anzr0CyoQ8dpHnjDLqAP/kOu4C/9e5xAf+2X2ctgKe7Gsicep32EEwcTShRgxCDHANsgvfOgIJrEHN/jqjdeDUMXQwXXQNv7SaTwHjorcBW59UagBiiDzacuAoxlusQs7gXMdQcYAu0r0HMaQ9ysSR3YjdiEl0olqmSiPnZiJhLPWI0XXD0a9Dbp/+HfAi2/gLaf4GE8hJ/biJqCP+tYUixK+vIJE3e+SJiqEmQjUZC+/uIOU+BE8fc3lj/DlISlqsNbjLu6tBuPxsJmeF+fr93+Mv97KNEhY0Ut7SCOBRwkChunyyjX0YWo2Q9SHGbG5EwqHN7q0zDz6NYQpAQ2Ua4gq4g8iimWMeFSKDvgv6HVRouk0C7HSkGmxEjH4Nc1esIS2yt8bf8pGcaCBfTAqR0XO//DwC3wpCxfneu23nBMM2EN3+GFIjFSDloNb5SWMGNRI6xA6ZBKVIMZxGVbhqRorHLeLifOEzShxSWLo9lF1GuK4c4gbrHtDuILLm2klOD1tS3/O6VSKF/hEh0XU4k687297n+bZn77TG8rcSJ2b8gTla3mY63+9nD6KDLYo9jBlEJIllxmvzOeDRvuoynR/z8LmIDkZQTu4+zbkRKxTi0cdqBFNGxxvlcwhMwiUgn9QG3sxat/3rCTTgRzYHtiOYb0FxeDicOGaZFRG7EdsKNONa060frpB+YYOWv2ziy5frnS5GiXUomhnRINfAYvPYyWst3IOU4udRrjOORhvNGtO6WoTX2wwOc+JHHOpyMhfnt29xHDbGp+zxkVfu9PPd3G5E37zpkES4yTdYTqW5qkXJ0tXGxjkjcPdl91Zs2LabdfnQYbrmyD5BLVCIpQopgCfAHcMkc4SgTWlBtXD8iGmW9x7R6Wu/8fI3xuJLM4cC3f6Fxvb0LyYDVaAM7Bs3hcmC0Pd2HiOogdTDwVuNpo+l5mDN7HX+H/95F14MPPsh3v/vdf3X/u9/9Ll/5ylfOAESnXmctgKe7PpEVO+fk/stDTPNpQqnYSNQMTnFA13FqouKvIuUPxEQqiHikg4QVaT8RY/gckfC4ESiDo9+BgbcAXdD/DGRfihZMP9rVLyUYWiuyAqwj0lWUI8a11X02+F5yHxUS5chykNVimD9TbNtUJLD6nariLuOlynCsJNxOY93/zejwRorjqfPzW4kSZcnlUksExx8g4ol63H4B4SY8YrhKkIAe7TGNRa63McbHBN//C+NyLXFgI1ncktWujijrNIk40XolERf4lHExHwmeRYQiXgzcq1xdA881/W5FymqylPT5ndVEXsNipBBsQnOq0LiuI5IJ1xCVUroMU7bbXEuUqSrw7w/CPx2C31/gcRai+fIIUiCPoDle7vdaiDqwJciSNM591ho/KQZvk7/nofmSjax7hWgOP0yUBxxn+BqJuZLt9veZVi8SBw42ojnTaBhz1Mfhn8CgqWhOnhyP2kwc2ihG86DCdLpEbf70O/DBqWgz8xxaG9PRIYs2whJlVzCgDQDIrdeJ1upgtFammx6rPKbhyHX7Kd+rJOLZmgznHKSw9RObpGRdzkG8YyxaQ+OMuwlIgZpPuPQLiXCMYqKSy21+N5soJXkYen8JuZcRcyVtZnd5fGuJAyGb0Bp9HvgHtKlaRSh3SWl/BSlglSiuNFn1MM0Sf8lH86GRSHx8vvvqEX5eeQgueQ8RWtCKlPQdRJqttWjzkCyhKaYX43+o39tEJCUvRjRdiqxku43vpPxCZDtoR3NiI5rjhwj+nUNY6vPQmswmTiN3mh5fQZbHfOLEf9rgtKJ51++2DqH5fxCt4xVIoU4K+hGN8fA1MOh9Hl8hkSvxDuPjJY/xiTNoAfyzd9gC+LfvHgtgRUUFzz77LFOmTDnl/pYtW/gv/+W/8Mtf/vIMQabrrAXwdFchYsA9RDD6yf/noMV+GC3Wg0TdzzZkpVuHmFSKDyxDgqkZCYJkQWn0e11IKWhBQq4QKYVtenbgYCQ0Sl3nswO53HIQk17te6/A0c1IsctHzG0AkRi4HAndAuL05XXESdAcxETLiODyYiJ1xnSNI+t8t9dn+FMc4WG/k2LATnYVDSYOhoxBDHWr+07xS32IeR4xrpqIhNYDiETLedD/C0IobvSYu5Ai2wPNe5HSc6V/g4glKiTSghwz7ElotJh2vUgxmWk4igx3lX8/iITuVuJwwEgYmBS2qUQZsx63PwcJqsFIyJ2MizHGb3LZpbjA6Ya3zf3VG0erde9osqYeI04fXwm/fyvhhlpLlAxrIpKA97ntSuJQUQuyyu0ihFSakx1I4W71GGr8+ycIi9GLyHpVQ1iHkxJTjObLMeg/QMRvrhd8/fXITTobWWxegv0/gUGfRUrGcSJQPp2an+nPFFebBGO7+vvgZOOhhqjg8yoR7jCacEunkImtRPzkOKK293BCqTuAhHmb8dpM5JGbQiQin+rfy+DN5O7vRXM8KTlpDdQY/y95LHchpSkppi2ERXuMcX+ncXbccFaj9V7kii4VxOnvBiIJeZdhb0D8IK2T65EVtlFtZEoNjiTq93b5vVLikEsukd8wwd+D5tp6xM+S6zNbOL9kFnLHT/G4ypHSmzZroNPr24jDQ1uIA1SPEBVqJiLlsZSIr5tAxA5Xqs9Mgv5S06zL7ae4xl4i5roarfU1REx1IVLaWnyvkYhZrEdrZxYR/5kr2N78tWGZgNZQNaL/PCLuucDtf8MZoXrczzqknO5Ac+QV0yHh6Exd/e/w37voam1t5fd+7/f+1f2SkhL+5V/+5QxAdOp1VgE83ZV2l11oEVsJA6LE2e1EHqdeohTbBmJ3nYcW8LVoYdv6tWcjYj7X+be9iCEMQwKyhageUOR27kcuom6kfE4h6r7eRSalyYlfW1ncahg+TwT1JwG+gAi67zwJ3tXECcykSNUQLsGx7rsPWYBKEeNd6bEkWKuQMGkjUofMQ8pEJXESdAlR07IRMcQU21JPpPc4oH56v4+Yaa36yP6s8VdpOEYjQbhF+KiYRQSNJyVxFeEOBAn75JpOymGyGq5E1pA/QspOD3Eg55BxUm/4vuDfb0BxQ1OQ4O93WyX+fRsRGzgYKU6bkAs0KUuVhGvpYcO5Re//83HCmnsd0OSwxVnGaz8SamuQ4J2gNo/+zLDdhOZuH1Is5hGnRCuN7xGEBWo9oTQkC3E5EtZFhKVnNKcqWH1uY41p0296zvSzV6rqCa/CW+vIxJb1gNbT88ZVBQwfScTFpfmYrLSbgBY48Zzx9CRag0cMQxGa7xBKfJp7uUhYJytVDoqN7fTzaTNS69+ykSu7U7Rgumkwi1CE5qJ5NpWo97vN7YyCCwYTaX/6/GwnWnspxnI24icthGLSiebIao/x02gOLNJ7z74Orc8R6aqmG5b9xudUtGZriJQ1Iw3/OGKdtSJ61hEpSVr97ExE0xpiU9hIlJvM9/NlaK7l+G8csQY7EG96CrrvQIpqnmHOJw5PFKI19ATaXA4z/lO8ZYlxcq1plo3W8SqimlEnUthHuf96oiJTkfE70fdeEd5PLDcNWonT/Ts83uSd6EQK+BZ4+2Xktn/W4+5E/KWTsPgPBkbDBUnh+yw030Mo5C2E0lrrv/HeZBcYP5NNvzS+SzSOo/s4s1fyQr0Tf+8yF3B5eTk//elP/9X9n/70p5SVlZ0BiE69fudMA/Bbe+1AQmQqEuZrEZObhYTkSsSYasjEAmaUo3MQs9qCFm4lvPEQjM8nk8PpwocQk1mIssCDdncHUVLZZI3bhpSpp5HFYpC//w1yLSdGtxVe+hnMvR+yCv3MaMTAGwnX61y3uwopCY3u82I48TPH3VQSJbnWI2Y0m0xG/jf2wvibCGXlQcNwC1Im5qJEtzcQLosNyFpZjIRBEZkScT/f5UMyFcRJQxBDyD4Jxy9B7sNIsH8RHWgZTMT0NSBrq13U5JoWPabVtcITg9zGR43rjUg4tyDlaA0SUvOMi0mm60GiVug6wzDVuL4PCasiFCNZThSVX0wk184hLHtvGFer4eeb4aIUY9dn2m1Dyv5zhFLdBe+9yzRpJ2N9zEqCttx0W2m49xnnY2DgV/1ONVJ+uomSUwPc/gY0L4oN+xwyCteJesiaRlTMmOcx34Dcj8kaeNx024AUr063l02U75tuXC4HHoLziqOvgk7TZwpxyjifsLK95vZWG7f3CkfHgewyInF7vvFZAa13aojnPUDkLSxE8/vLaD0cJ0529rnvSrRGBxN1WPMJi2ofmcMWR+th4N+g7ADTiTyBxw17MZF/cwehKA9Ga6LP95a5zdFo7o5C87nTv2/0/QmG9QVgO/zxuabNEfe3xjgai6ys57uvKo9pFdosLULrYrlo8Npu+EALmidVaI4MNe63oHlZY9xO9v1CNJ+OoLXwFY/hSsP4aRSCkayX7UA2FCRr3AZE71eIfKAViA990uPaRFjOvop44D7j7QrFMA9danhuNX2WI75VB70bIfdD7n/3Sbh8w+/MBR70AY5ytLY7kPI8DPHQ7R5fNpnqQ0PuILw3ZabNHo//MqIsXPJoLgaehIqhRC3yUXB0LAzsJ/LIXoV45HzCdfwncPh1GJQ2BFNhYBFnr9/S60//9E+57bbb6OvrY+bMmQD84z/+I3feeefZUnC/1dffZIUgeAqYCz9YDn94BVIADhFuwGoiBqzd7+cRO8haoizcXX5/NWF1ayN2+6MRk3WMVvddeqTgDqIQfRlith2EMM03DMMQU2swfMn6ZiWPUUihWIEEeJHHcD3awS5EysNg//44maoAzbug4neRxWoFYrIFiCHeBG89AOd9lHBlboKdGzSkrPMRI+9ByscEJHg64M0b4YKHyezM+78G2Y/q/f5nIHuscTQaWa7qiaL0BUQC1UZ460U4736i8sAHiHi2vYghHyTKcY0jEi6nuKW1wB8Q+fXyjIu/dFuXIIb/KpEodjgSgsmlNgkx9ib32+Pn2gmrz2X67cQGyLqYiOHr8FiPIcX/TrQ5WImE1vqTcNyGBFU9EpZj/D0bCfUCpNAdIuL3Pk+4tFIMZJfnygxiLp9DBJsXIQHUieZPIVFCqwQJqgqixvBUwpqVR9SrbTLdHzwJX5ebHtciYdxBlBscjYT8HMOV3HG1TjD9ddOl1LC0QutuKL3ZNDfteudB7r3oakSWrLVIkS9HMYd1bmMVcWIzB62jSjJJoakElkH/i5B9G1HiscnvFRP1ZicZtgXGzePGV7/HPA8pM7ejdT8Zuc/TtdMw1RBlIb+H1v9G99uC6Jzj+y1w+JcuhQdRWWaC8ZFiFNPhgske423ExjK52pOb+lU0H29G86pPsOw8CGMeI3JZViNlLo+oq72RiB9udjtVaB5/HSnwk9BcvhZtzG50v8lSV+93Bgj33GTYat3XNI9zGNqIXm7cVSDPyQHkKnbs6M+XwEWjDdf5RCqbPDTnChG9j5lulYj2aRybDVODn59qOJ8zrXYCE3UYLPtSj6+CSEv1LeM/WQwnmr43I0V3N1IU/wzN1cvQJnQcmr+fQPN1vWlbdwZjAG+EQbnvUJu9MPg7754YwBMnTnDXXXexZMkSent7AcjLy+Nzn/scX/ziF88wdGcVwNNfN2VJYNn1SA4c/Z5rMR4gFLWvoYXbgJj/k4jxzXU7+YhJtcCbP4ELLiVcGFuJZLWNaKfXhJjHTCLNSHLUlyLmsAMJjueJcku9iDlsM2zJevYSUQqqGDGqI0TtW9xfF1APb+8WH8t6P2JKpQSTS+6zPe5zF+E+LSNqXvb4ucHut5UoeVVOxLQkAbgRCfWJyP3bjaxoFcZLieEd5f/XE+62PKJ03B3osEkvYb09ggTMi0gYlbjNLSfRdqa/jyBcRkmxT5aPLqLc1GxE12/5/3LEjD/pMa11uzsId3E7sgC8QdQfTRaTfCL32io/PxYJ4CT01ni8VwJ/QiTiLSRTaipD552GpZ4QaOsMw16/24uE7V2ES6+WqGjQSgTTzycj0LiNyJG5lFBwuhHtD8Db9TDkfCSkWxC97yQOAj1tmK5FysYniRi4ZUScV4P7zjYNutG8gjgsUkZUEpmO5vsNhjvNoQFobqbDOmPRBmwdUdrtfJQipxcpsPlEDsKXiFCMqYiuaw1DzUkwrSWq1HQSiZI7iJPFGwxncvNv8Lg+T8Qt7jCtWog0LhXuowkpCS1oQ2CFJlMfucc4Tm79NMeTAlVElKjrI6q+5BMHH7JNnxw/v5bIndmE1vEo46oImh+CimT5usxw5hGppmajeZ1CQnrcX65hSyEFhaZTB7HuiwjX7zA058ZpvId3w6AH3F4dHP6RDyMsRnO1gbBwvw/NlzGmzU5gKDQvg4o5aOOTFNXkFu5Ga2sEmZjVzMGbtabnOI8pKbppU1Nomq2Hoztcwm268fKInx9HHBRK1tKlxtsIxIeeRnMsKZ55ZMJbMmE6I4EVZxXA3+br17/+Nb/4xS8YOHAgo0aN4pxz3qFTM/+b19kYwNNdHcAKWWcoR1nkQUpXiunqRkpcPRJUyeU3hoy7jm2IAVWqmUw8YRsSXCXEbrkOLfzbEYPGbQ8nKo20oQWfmFM2YkivExVB1hOJdbMRo52EmOvLhOBMisohtKMthiGTnXtrjH9vIUrBlRm+8UhR60TK4F7hhyvdZ6vv5yGrY3Kb90D3A4alzG2u8bvJ7dOChNY+oi5mgX8bSaRcmeT/E46nIsZ4HfAZOJFciMWiI08RFSuSUtLp/5NbrAMpQZi+2UQy2UaPvRhZ2nKQ1SFZYNrcR6XhPUTUAh6ArIZtSAjM9tjGeRwdRN3bpPDnEulTdhC5Adcgl2UFcXKzxTip8HNzkTKx331uJSyVNUTFlaQ0pdjIfkIQ5xHVPwoJJe8642Y42vyUQutKpHBYcA35XdOtyHibaxoMIE52H3GfC5EiloMUmm6kqCUFv56It2sglKrppm8pmhuFplUeUvwePmnsR9zWao+xHq21UR5vM1o/65HCn9Zh4tHlRJ7LIuN7AeG2HYsUn1kn0eM4UV2lCh1y2Gz8jzIODhIZBBr9+xbjoNCwNCFlpIiwIidrebKMVxn3W9x3hceYNgh5hELa7nFuMO5mEonWy/xuGVpXu4g8ijMQzWcbvu1uYxtUXErwhuFovpYbxn4iFjnHcCVr80tEjfPRROjDvW5vgD8LjbNcFHZxGTDPqTuXID41EQZ9FLmZx3mMzX7/DfeZXLOryNR9rlhAWLEnEpvANsO2xzAfQq72FAt6tfuZTSjeUxF/WGB4NwEdMPAaZFHtB16B5/eiObZWv7PJY+xE8/Ratz2ATL7F3nWGYy2ZU/GZONXVnNnrnYj9O/nvXXj97u/+LhMnTmTs2LG/NcofnFUAT39NBkbZddkP7IXcqxCTX4AYy3HEEJIbLVkBX0OL9Hy00ywELoSsy4A8aE2B1tsRo0nWu34i9cWViOmmhT4KcbwSZEnKRhavAf79UiJg+yYkWHYhwdzr/5N7cgIRpF9HprTYP/2McFmm3X+3nx8ArIWddxBJVW9GStDdbuNPiRJoizyO3UjA23JWMBYx/uPGSw4StNV+ZiWyfHZ7DJegnX8p9H4NMcykNCeLy11IOGxAVrolkLWAYMhFcPQQEkQzkHI7HAng65FlaqvHeL3hShaAmUjpSkKsGCl4bf69gUgL0oAUnMlIGE9yW2OIBMej/NtlRF3fecRp3Dr32UumVN5PlyEhvAUpdUsJRbXTeKow3pJb6ohxsoc47TjPdNlk2GsN13zCXdvp9o+btpN8byOqcbuesCbnAROg9GFCKNXqEBLHkIX0IDoVnui9HVndkpttNWHxK3Gfm4zHSqTc9RBWuF4/+7TbTVaVJEzL3XcRGdft4Q3ESfVJSBmd6N/3ovXc6nbXEwd1ZqM13ELUX11DWF/mGc4jKJa0AM3jI/7sdps3E2EEY03DyYTLfiYxp0oMYzZR1i5tqro85otNz93oyjE89xr2AWiuFKF5k3MS/BXqq7WDOKV7GVJwZxOnoScRFXfaiLjaA0gZuoFIyp1n+IrIVNnIbKjm+3ty/w8jY11sT/F76dBXEZoTjxmGaxHv2UFUYkqW0xYY+iFoT3G5tcb7ADiRvDQHjLNkJd2FNpaVxOarCSmK+9DaO4LWiPlGhgeOJVOX96fPELkua9FcPCKYmEZUIWo1/Q4gvlYDzISFszy2avcziogvvhIp6UdQ6qgRei73OrdZTISh3ITm1ic5s9fxd/jv//LrlltuYd++f9/JnOeff55nnnnmNwzR6a+zCuBprrfvhO4daHFuQAy+FCkOFf5/GPy3HyFm8CRSEmaiXGC1KB6kCjGAtFOf4/ikWajM13AiXqsBLYC/QMzgdcI9t9HvFyIhcDJTKzXQWxHjX4YsfeVEaadixAwbEaNfkR8Z+F8BJsHvv0+fzAEqoT1ZL1KMzU0wZhhi2PUEg1yChMMNSCBXA/cpJjBzUGQTYrRTjMvBRK3LuUgIV6HA57nIulTg54ZD84uQey5RID3F0hQTh0A6ibyGPcbZILUz8KsobuYQUhaeN2wDyFgnMy6VYsSEa8jU4OQzhNs8WU2/bNpNN92HmubHicoXM1GM2jKPrdJ4muj/H0UpeQ4TLtenjfNvCtYPfoc4QNLndmYZ5oVIKWmD/SmmNO2kkxCrgBNL0Dx+wWM4Ylz1ue07kIIyFc2BYcCH0DzucN8TUWzV1UjRnuKxr0VKdLZomPVVpFSU+71foBiyDWg+bkDzdj0SrFf5/QcNX4HHlt4/gBSJUuTKb/NvBShutRjleas2/FON109ofIPe4/YOos3IaPc7jwjzWIDc1EmR6kLz+kkkaP+acAd+Bq2jlUgZ6zHetxB1lluN51nAZCcXbkL8pN6bGZBy8Sya14uFP9YqvpADxvk6wgU9ze8t8LgnwNt3GU91aD6MN32fR4c8lhl3f+1nFkPpM6bdFiJ9SaPhq/Lp3B1EmEiO7r+1xt+3G94u0+IzgoXrjKeHjZ8t8OYvCd7VGbga+jDhcm4xDpaJZnyZSHR8B2HlWu2/vcBQGPqkcZRCHY5A1hxExyIiddIu0y7RPXlUHGvNLOLg3Ebja5bxPt64WCdYP5gP+zf43mAiBOYA4iefQ27e25FyNt14bTBMi/1uH8GvcN/rkTI6wn83EWEkNWRCct5aSsiMds5ev0VXSUkJF110EXPnzuWxxx5j69at7N+/n/b2dnbv3s2LL77InXfeyQUXXMAjjzzCuHHj/ueN/oauszGAp7tmZ4X7sRsJre+hxQ0SJn1IiG9HQrEFWZAa0C72vyPhfily79QRAq0ZMZzkXkqWhVGIOZ1DCOnkCq1Ciz7FIE5HCscRJIS+iJhjI5GyIjH1PqRkjCJin8b5/Rn67N0HubcQsWstbq/SMPYghaAaMccNHtN83xvrceQh4VNDZNLPMbxLEXOuJhI8VxuWN5CQ3OY+FyAhcjvwbWj9GZTeQiRMXooUjecI9/ftiOk/AIdXwqBZRKm9YUg5a0CMfQtSFu4gTmx/Gpp/CRXvAf7KsFxtehYjBWSGcTSDiI9aBHwLvrEPPjXS4xks2pxYDlk3o2sVUgpHEjFm04m8aTcgZagEWYk2E5UtOjzG/cZzcoV3IuH7OFJAitHhl6dMt3IkJPIIS04eMS960YbhfsO3CQnY/45OqCdrXYpHGmr4Xzb8xZxSWYJsZOG4D82/ocA86H8AskcCbdB8CCo+hoTdYMIa02RaPKT2T6y0NfcGNG9fgd5fQO4DnhvNhJIyE63Vl427UiRQP0Oc4r1HsDbvgoqriHQfRR7jOLTWkgu8HlmYnySU/3VoEziJOF3+tH8fTeZAEvuIE8d9ptM4xC/mobl1MdAA7T+CobeY3veTcR+yCq3V5LZvNrzfRfO/27A2+JntaI6k6wiaX6+6vVa0fkb7/VlufwdaR1OJvIU9yIp8GG2kaol0Ptcji/8Et/+k6VxMbFZB8zHBcTHs+RpceKvxPQApqAvQRniAaLD/fhh+qXFViObcVrSG8wklaST0/wSyN5pOf40Urk6ifnc/kWi+w/SaZvheI3MKn27Tbyh0r4SC9xExtathfx8MX0ykWDpuXM/2eNNmq5Yo0fkpw3oD4ofjgV5482twwWLE71J8Kx5vt2FNFnP0DvXExmgGEcbwGtqofOMMxgBe8w7HAD7zf38M4MGDB3niiSf4+7//exoaGk75rbCwkFmzZnHTTTfxn//zfz5DEOo6qwCe7hqTpYX3B2iRTSSSNZegxbmSOBTQiRhDEVAAO5fCmAVEBZAatNjrUUBy2l2PRgyoCilcQ4lDDWXuN8WgdbmtAiQo2xEzmwCsgvafwNCLkXApAzbD28sU10cNvP0oDJlFWMpKkADa7P5fQIrMXWQOfezfB8NnIMZ7LXIDFiHGeY7hTcx+BXEit48I7h5x0r0iJIymETU5V/p+vmHvcDsQDHKdrA/nPUocWGgnkvAuRifm8oiM+WuIusN5SFF9hMhDtxQJnSQ870fWqlXEye5hRDmqEuRy2Y6UjEqkCC1ze5cAj8HhAzDofCT0ZpuuMzTO9h0iY8U1hnkFmgujOTUQfILpPY04GTtVqOh/HLJvNW4GI2GRxlFhmHYZ3noiXrQfKVd3Gqd1/txl/KfYry6kHD6PlPzb/FxSsh8jXNsp1OCbKPVPcg9jGl3vfhcjZXIWUaqv2Lgc7uf7idQbz/u5pDwVQ/9B5w1MFpW0Lns8rsWm2RakMMz1Z7bxWUok7B6uMRxeCoPK0BqrNs4KiCo8nYQrOrm6Ex62Ge99hnu1cbiBcGuONX6mE3Fzxf4tuSGfMD6zEa3/wO9tQkpfvnF9PporzchLsJo4gLDO7Zcgq1GdcTGLqDrkuN7m5VAxw323Gof1RA7MJj9/CeJVfaZXk/s7jHhBH1H5YorHMAmt5wOEtbXFY52D1mQrWuclxmED4VVoMx3WEjGaq5EinSzrKS42B62XlwzfenSYJr0zkjj5/xPjpAmt4aeQQrYe8YMqxHc/gxTJzUR1ombDlDaR84lKLBWGqcXjLSOqgyQr9l7DfI9/W0mk8sknEt7PMl5Wk0mSzUpiLjUbB1egDAyFREjK+rMK4G/r9fbbb/Pmm29y9OhRzj33XC688EKysrLONFjA2TyAp78moMWWLBgtaCEu8+91RMD1diI9xUw9M+YqJGymIGZVSFifthO77BbEJDqI4O08JJA3EuWuEpOZRqQr6EFCoQu4CYbuNowHycT8DbmGTEb9IR9DDKgWMccGpOQmBbYKMbFZZE6bDZ+FhNxcxHCHG5btxkMLkWh6wEmwOXYlc9I1WWmyEdMeTiQXHocExV5k/boRMcG5hul14eq8UcZFORIiDcZrcmXPdztPImHX5LG1EQH7jyOmmqykiYEe8tgTk08w7yCUv2okwD4kHO1/GIZ/1n1mG6clVijKkaI7wbDa/Tm0EIYmHK0myguuJeoflyFhvAX4oemZLLKzXY66j1By5iGFNLmAk0voCJG8ts1tTyeS2h5y3ymAfZPhGevxjkNzf6/fqyWC+/OR4pDi0Frc3gIiyfFUw9hIpI15GAngKveRTcSb9hAu6UHQ/0vILiZTTSR7nWFrNQw3EpUWQPn3Sg3rJiLdyi6/94ToxgHTeBsM+hhaZ8mi3uIxNRLVWNYa/wvRepmFFJIKt3MJ2gztEOzdR6Cgy/hL4R/JirvDONyKNlzNhrOKKCv4JFJGxhAHo7qIQxHlSAkf5n7Xo0lRRcSSlRFWzUPCV+/XIPf9UHGvYVpHuJm7jbPkZpxpvO0g+FWJ73+DUGo7iPRAO3x/F5FUHTK5UDOxkW2mc1pjbX5msPv/BuKbncbbWCK+s5w4RAcRj5w2x4V+Zz9xgGsscrPPIzIHzPS7dws3XIzmylPG+1DjexRSUi9DoQHJQ9OC+EaL8T/Hv01FdG8m6kQfR3xgNdoQrSBOSfe5n2bjYx1aP+M9numIX40lynVOMd6mEHWSz+SVQk7eqbbeZdeQIUMYMmTImQbj37zOxgCe7lqDBGkKvt6GGHMjUQ5qJmLQ0xGD3IiE0QS08D+OhNqtiCknhrQdxT3VIqY9i6gHPICw7OQhhrgfulPgcbW+c9iwbHPbKR6pAzG5xxBTH6dn3tyN3DlziVN4CxHTakcB+y8ipptiCqsIi0cfUsTaiMoPnYghNRnebI+vG7lohhEnLxvcbxliXgM8jkLf9wGCTIqO8USx+6RY9iMhvtPwJWZchZjnXj3f/RO1cfh1xKCTpakQKYpJIRwnnLx1kEjYW+vfxxLB85MJq1ga03ZXpxiFLGpziPq8A4gTvPXG58kCqhsJnnVEofoaxNC7kBJQhObObNNzFhI4tTDoOkT7QW5/BXCu33/R/aag9EJCMczx8+3GwwDjppo4FVqD5vUGlLh7FadWWVmP3LOXuI3B7ucJQnGuNo32IeVikcd5BPqPc2r93k1ETrzR7icbuBqyP0ooNvOR4jjOOBqGXPcplrDRsM/zO1MJF91VHk8FcQq1yzjdTCh6ICWmzGPoQmt5IlFWrgJtUsoIC30LWls3AtdCwaXGe9o4VaE1shoOrzGuZvpvksdWRNTn7SKT05B6xBs6kWKw2u+s8vflxFoeiuZhmjfJkmdlKXc0si7mmwYTjMNNxIGsTuJg03rC+jjPMD2L1uZa4K+h94Dx0IQ8B7OJgw1jPca/Mi6e8v1ZhulOP3sDcdAtuX5z0DxOITOlxAndJ4gSlikkZ4DfW0KmDGKmJvTLiF9/kkzKGDYSB0V6EO/Y5HGMNH6XnvQ7RExgmi+FaMOTNvlXmX6bjJNJhv9SNBf3onk63fis8P0dxAnyzW5vJJHQ/MaT+hpjuDrR2vNm9Ixe/5EDHv+ev7PX/7HrrAJ4uqsYLbZKJFgWQOvLhOsU4hRnESHgN0F3iuEaRpQ060LKRTFi1E/7vXVIibsHWYx6EJMoJWLugIL3EBaUEvjpjwjLUz/8fCUSco1I+btdsNADzIcLbkEM9KPESdRsIjnpGLTLPWbY+uHnryPhPRMJoxyikkUJYtyz0Y49xUWBlNCRiFEdJNIsbPFYHyBO4F7rMeYiQdnjdl4gLHsrkeV1HWEZW07knitGSlol0AYFE4T7QQ8Y3o0ew2eREluH3JVA70o47xYkWNcTBeuPIyvNFYbjZcSsu9znOI3rxM1IiO4zfC8RVo1RRImy60QHqlC+ubuQcnAtmg8l6MRusd9tOen+lUSS6HrDOdpjG4yEVnK3DSOU6nIU15VPpJG5Gx3kyPH4vwcnvkckw77eNOxCAusWIs3KDYFj9ns8U1Dy4CNI+J2rflv3EdVPypEwnATZ70MK5HTTuImw6qxzvzM8rgJk0a5GoQcb3U8rWldJ2Z6P5kyR8V/q3640XXORMrLedLgfCeA8tDnb63fqhe8Ta9A8LUOKwFeIOtftSBBfiRTSncSGpMbPJ6WhzZ/1vjceBi1DikgbUoJXo/XzCnGqvZCISX0GrZVRHvsCPdN73PiZQkapOboOzZsBaE1dqeT1mZCSOURM7iS3uRHR/IDhtDLy04dN61oicT2GpdP3bofc+9U3fSjnX7PpA1KOO5HVtAD4e3QgqsXtfYE4ALLKsCxzv6OM73K31Y945HI0f9Nm7kIihngEUsJWApvhze8Tc2/kSXDlBx6pQnMnbZo2IB5hJa//Yff1pOFsMp6qCaU5B629tUipLiYs2ylUogPxqyeIKjtJdiTltdmwjfP7I9Cc3UXkdOxEfGkx2uwt49Q4wrPX2es/cJ1VAE933YAW6nAyO+XSWYTb8FW0EHugO7miHDxe8BDalTUiBtoJTIe/PoYW92VkTgRnXC8T0M56xQD1kQR6Ujj/HgmiEbr3wUuRkDoHqICLhiFl7AARV/JtIvHrFiTgbyVSQuxHgfp3GYaDiFEWCs6L7kIMu8ntbCGSIEMosH+MmONVavfoBuK07rWImY8zThdA63FkwXzSbSxDislkIqlyNpEc+zrBeGIXEiT1SDFoImJx6pGgScz4M8BqKTe9m5H1YYJptJCMcpD7sGFf5N/uFg15CTgEby1CFp8V8MZ3CHdpj2iXtcTvnk/kNkyWzglCU9ZHkUBJ/b9iGIuRUppiynKJOq8dxvMO9Z0phVWM5lYTWr0dfu9y42AamdOmP16KXN4p9rIYbSCSG/gI5M6ArLGwPx1C+hPhvn8jisN7HAnIlf5tMxELVmja/TFR93eoxl+6mrDs7iDi+i4x/I+qf+YTFqvpSMA/ihTXpGj3EQL7FfdXY7rbGksZkbewzm02k0nGS67pfgApEvcStacHi5ZJ2chKbtZ5wiOfMxx5Hu+Dvr/ZcCxEiuEaaP0FcaK9CFmkRkH7i4QiVoj4x3ii5u9M4+URNJ+mmB5pLDnABDhxv8afm0IzUsxZBwy80e8B+78OvAF/+IDbT8rDArTOu42vN9xnvWFpBVrczBHC8nmH8Zh4Rz+i9WDicEwx4klf8/9fIDwoJUiJ/mMiWXUX8jw0EO7gpGx2EJbzRUgJmm14vk2kL0oemQq0xnaTqVt8wVg4utJ4/QxSmEqQgtfgNjcg3rCKDJ+mlUxKnuzriBRVeUSe0mQBPo54SQnaPNcR1vcmtHZLDNsB42y5cbqKCAEoIjYmbyC+uhDxzHzXG96r/0/8CHmoriOqC53J6zhnawH/X3qdPQRyuuuvsjQZX0OM+ga0mA+QsTJ0b4SCG4laqgfQQp6BAueLkBAvRczzOGJ8Rwi3AkiZyUM7yNFI2M5DAnARYni7idirG4gSbkeQMLkcMZMdiLkfQMHgFyOFIgVyj0DWwa8iQdRj+EYTRelHIIH0BFHWa7377CB26PMQ85zu9iZ5PKOQUJmETiavJU6xPo9OqE4iqmZAZPpvIdwrnUjx6QJyoHU5lF5DVCpoIkqNNfvZEmf3vxkJ8deMu+nQvhSG3hPtcYzIo3iMSEhbaFwkF/BopAAk61KfcXSypbeHiPe62Z8HiaD29abb3pPgrkJCeAKhTM0n0gnlEaeuS4y/rURt2KEe917TeRqyqtVz6onWbGQBK0IKYB5RmSLPeOuF1u9BabHpuoU4LFQJNMKJ1yFrNNokDCeSGB8zPrb5swOtgXuISiF1hnUoUkJSDFWy5MwkkgEPRgraHCTYl6J1kxS2g6bNPDRHO9zmRLR2DqM1203kvlxnWD6PNjWTiIof6/xZfhJujxNxeC2+3+z7lWhNTiMO66z3vWbkOp9CxA4mhakGCe5uovTbS2j9HCNcs9f5txV+fyaica1hqhHdejdYERxrvL5qeiQ+U2Y83YTWZ5vfbXSb/YYnWZGTxbkEKUgDjP82ohrJ88jS94zfn238v3ZSv+OIwyRVZGIjKXYbrxnOG5A3II8oK1iO5m86oLLAsI8xnitNjyLE/xrRAaRsTs0rWeJPkAJYgvjAG/9/tGkzLpYSWRg60ZztJCzp3UTISQojudtjz3O/M4gwmCKkYO5EKW1uI071liGaVyEap7Xabbhq0NooQ96YZCksJVJBdbqtXThH6xk8BHIlDMp5h9rsg8Er3l2HQH6br7MK4OmuO7O0sIciK1spYWr/tnZhWVfB0eUw8AEkRAoRY89Bxc8PIeaclLJriWTHI4ki62MJRlmMFns7UUatEt56Bs67lTgtmoeUFPx+hX+rRcxlFxllrX+peG4VMGS0YUmCbYDhvIsoaF5C5MM7TpxkTAcnqlD82VpkzUwK7Wi0y56NFJE+olRSIWLYlURuxE0e6yy9v/NxGHMpUf5qI1KubFViJ3J9bDOM+cSJzq3EjnwV8I/IUpNctjXE4RcfyOA4UjKeJxLvVhDpGcadhJ9Vfna44W5H1qwnjZ9sj6WaiLVrQQrBGNizBi58FCkKbUj5SJa8JqS0HENW0ArTocttTHe7EAm8S4BOOPEyZOWjudXiz1eNo3luYxWyUhwQDfpfFynGTzAOp+n+8y/CwsmG/yARx3TAcKUxPu3/uwiX4mikNHT67wOmXR6huBYZp8OINDr9fuZy4oT5Ybd1IVp7g1Ds2ZeJcIoBhJDuIA6VtBMVXUoJJWgC8H23d8z9ViHhXoYUyS40T3OR6z8XKWgdaB3ehOZ6DqHgpEMdvR7DaOL0+RtoDt+PLJoAPXBiM2RddxKsVUQ+xaHGz0Eil2OLcbCHCEcYbdp8y/RL7u6ktPX53dVEbeJk0etEc72LqN6xnwin2EWs6cSHOtFmrsg43OhxdxKHTno89rTGU1spVGUjwSca0Mb3O0Q98+1oLnQbt9VEvsf1fmab8TjMOM71GLqN4zz3MYZIYF6F5m8T4gPJZT2J8G5sNw7riDU2kagHPgHxixrgSWiudwWUaWhuJEUsWfOyEQ9MG99xJ9EyKZ1jEb/MN81WuZ8DxEGh6YQbfB30vuiCBFuMg1HG/VfPoAK44B1WAFe+exTAL33pS3z84x9nxIgRZxqUf/M66wI+3VWKFu/FaDHXonioz+p71nVAnzeZm5Cgb0RKynNox94JbIajuxBj307EeSQG3O/3nybif95Au9Vut7EWzrsCLfTdbqsPKVopALsRMYUCJJRaUfBxP2QPgw++H4ZcRbgz8jm1/FGKcZqImH8bIaRbiBNn05HA6EYKjE9MHv0OsmC1ISZagqyXycpYQ8StOb6udR9hodkNY85BjPR8JEhmI+VrK+xMCvYS06MPCcUjok3vy0g4DEMM/7NIOMwDpsKJB8gI/KN7/f45iOlOJA5h7Dd+O42Xg0TM5wbTcBgZVxsDTOtkJciH/juIWsfDNcYLnyEqhVyCFIYS4zTBvAAJhKT8JbdacvV2Gu580wrIGunv151EqyRk64iSZ8VorlVD9sUw/mL3cYhMmqGFs5AQvN5tzTR+O5Gg7kcKQQ5SpC9Fbv85ZJRLdnnMlXZD1iGhtvAk/Fah4PUC02wNcrNuRy7elUSsXxWRhLqTmE99hmGCYZpheq5Aa7eaSGhdTST5hShPVmaaPI8sr0nQJl69BSkQW4zDJ5ASluL61iLreIOfLyMsfKtMx0K0trJNmzLI+hBh0d1rmFcTeTPXIZ6QNo97CcV0g3G4ligtmCz0tUTliu+SiQOkEykqVyH+NM64LTcMTYi+24kSiK3IMng5mbATupBC3Ib4VS1xuvsgEdYyCdF2H6Jjvdse4GfTBiYXzflhiM/O87MVxmdypa4iEr0nHnsIzbUe/95EpF2ZRMS8JstxHZGfNK3pJiJ9V6nxfBzR+nK0RttMV9zWK3C4Xq9mwlW6icolSTFuIE4t70KKZPptnmmYxrcGzYE+IoH9RuO7wvCuUZu5yUqcPCDZpuHZ67fyWrVqFRdeeCEf+chHePbZZzl27NiZBumU66wCeLrrSaTQeOGxDCkmOYiBTgA64b05SAkahRbvOsQUysjk4xu4BAmr14m6rqsR8/48CgKvQkLpYSTszidTWYEaeOv7iNG9BOtXIqbci5jWJBTjUg7kKZEpl7u/p5GidiVizk0eXwqIrkaHEWYjJjgWMbPZiEl1eRytRM3aZiSkkyujEwY+ScTfDUaC6EHE+EYjV/EHiKoSz0Lp+1G80EHkNk2HSnb7nTYyqRvGnIMUkhzjYRVSFpr1Tu4cv3/Q715t/Bw0ziFzEnngaMORrACdhm8lElxFSGFJwv0KQgncigTfk+77E4T7fgpwD2QPJk41JiXkJY9lPmFVaHZbyQr4mPFRh+g/lUiZ8ZL7GUfUAl6ArFI3IAvuFsO1F44+A9TADzcY7mXG2XO6z+0o1q7E9w4RFUIOIAH0KjEHUmzW9933UGRlSQrDVMN3rfHfCMNvdnsrkHu0y+3W6fPENb7X7LY+iWLEUuwrSJnsM273mk7d0Lvc9HoYCe29xInrPcRhlX1o/n8f3nrU/R3w+Fci5TltoHbBzp8gBXGf+mG0359oGA8axz1EDswrjdNuZPWZRChn5YjLjkXrrMjjGebxdaM5sd5wNSDekvBdLjr1P4B4SB+yxD1EuM5tFT26y7jeRVjeaxEfWGscdRB581I4yWD3U+M+nzXsryPFbZRw0H2X33tdz/a+6P5ajO+vm95b0Drd6b9KtAanwdsb5DWh0jgpNZy3mpbnGy8TkbJ4PbL6X0ucyh1O5G3s9pg2+n6b6TTCtOjwWI4gXjgNzdU6w3ncz09HYSvt/m2Vf/sMmpcFbmubpvvM5LUZgdZDFVIuCzn1EFCO4f6x6foktN+N4rnzTNOb3f9tHuftyL38TcLiXkycBB7rd64kShieyev4O/z3LrreeOMNtm7dykUXXcSnPvUpSktLWbRoEVu3bj3ToAFnXcCnvyZliSkOJfKt9RM70QbETLr8/x1IIdqGFm1i6HZJHN0GAz9LMJJKxGhnImYzDjHfRcQpsX7E1J5EisF+5L7a6n6+h06z3oRcsSMRwyp2H+ciBRbExPOQkjGDTFZ6XkRMd5rfX07GdfPPG+C9kz2eEUhJeB4pATuMi14ib1gVspLUIcX2BbXDRqLk2geQgC5FyjIoHcQqxOjLiZjFFUTs5d8iZreXqEiRlEI8zrFEDsJt/ptEuGYeMJ6rEDP9a/e5wbAV+3MXEaN3n+9PR8rpA+67C1ndUuxQBRHnVUzUdp3lsfUQOdXyUIzbHYjeyfJSZlxdSZTJ+zQRFzjPeGs33Pf7/ngkaJsMfydyn+YbZ0kg9nisbe4vKSIzEe2/T+ZgE3P9zDzjdwtShjqJPGQNos3bu2HICNPqObVzdCwMvA0J3gbkgisiyu2VI/flXxHr5hU/A5FjrtfjX4PWyycMS7vb/alp8JLHMZWwHj5JpFe5haiaU+7xrkFzeC+aO41ovae4xKv9TIpFrEdzdzxhbe1A9C0iU0Xj7WdgyFh/H2pYVqC5UISUscWEJSfFkdU7cfv9hmcd0jaegsOHYNBUwjWY4thudtuTiLizHtGp+XWoWIA8CnvRfClGPGSV+jyxBrLuISxWU9Hca0DpR+YZ7kKklIwg4u2Gofm6kDjItpLIMZmsfWVw4mHIWkOk10pxw3ejeEKHc7y5GS4YRsSSdhIKXCealy0e862IF7ShlFsL3N8jxlEumdPnmTlTI3gy1tcFiKfVocoddW73brcxzngo8PvfR/wybZbyiQOAB/1+DRFjm55Jm6obkFK4wnPgAcQHOoybarfVSeTpPEB4h6qQbKl3O9ejtfWhM+gCnvcOu4BXv3tcwCdffX19/PCHP+TJJ59k7dq1jBkzhhtvvJGPfexjDB48+H/ewG/gOmsBPN2VTca92Po6ITCfI2LQaoh8VcvRDm4E9N+GmPDNcNRm/4GfRQJpqX/bioRDNlHMezFRaH4XYtSNxAnT7yDmOhEx2tT3WuAZ6D6EhOQXEINPcXzTCRf1YCJ/4Q6kMAzy2J7xOz1AFbz3fKTINSOr3E7DkWIBe4nULquRdaba43nJbZcg19NnPN5vDIrYq+v8108oChuM4+mIMSbL6jcQA11vvFcad9sRw+9EDP2AYd3l94cZPzuIWLA3/DfJOJ7u9ja4jyLipPNCQrg+ioRhsX9rJVy1DW7/W9B/s2F6AfgIYS2a436KDGel6ZSNFPDhaPf/HFJOcX/5RNxkJ/Ar03EJOon6Q0TTbCS8LydyWB4kypl93fQZ5b57ybjwmhcZN8k9N9p9PofoP8F4byMShncBC5xgfAER57oEBr7PuMz3GA4ZRym+tM70WYEE8NPQ+xNC0Cfc7nVfbyABnWd4sokDAZVIKWtGSsXziOaz3d8EeHs6si7kwJsPIMV+JVLs5njc46F7m9u73fdyDMdThKu3l3ApJkvyLjInq4dc5t92oU1bE5rvX+bUE6GriHnZAOyEITMQV07zoh2YAoPudpt70PzZhOahPQR82b9PMq2mQUU+MXfuNgyb0RqaJDiybjYsSdFfh5Tlq0zr1cDH0IalnQgPqYc9dxLu4kmIB85CG46dxEGSFofMLEXzpIcIE9lhfBZrnBfcghTvGsRHbkJrpwxtBEBzeDayGP4BSkNUiPhrHZoHC9FGqh+tk3bisEWh7+9B83uL2xtgWj6A5nK1YV3qZx5HvG+z8VCE1lcpcdq83jC2oTk/i0gbM4Co/VtNxB8uc//JMplicAt9P8dwJFjqiZPHLUQy9v8HrwcffJCJEydSWFjIeeedxx/+4R+ya9eu/+E73/ve98jKyjrlLy8v7zcO64kTJ+jr66O3t5cTJ04wZMgQvvWtb1FeXs7zzz//G+//37rOKoCnu+aihdprV+V2ompFOZkcb5lDDBMQI7hZCWwPvwysgoHXIWG9Dgn4FLtxhIjf20TEsuxCzGkEYqzZRPxQCVGB5Blit7kLeML5XocjpllEMKgUI9OPGGYDYuw7URxXPlIiupEQySWTE41vu4+LkTC+kshr1oEYX7PbTszvJcT8foGE+QG3XQg8fjgsQNnuawsRZ7QaCZNGpJB1eRwNwBtOMbPJ/TUhBa7G+HvasBQipW6VYZxInDR82t9TXOZ6QqmZY/ruNC03Ea7cIo99FpGoey9Rs/lS42Kac91VEidocT/bjKNZhreLzIlOdhJVNm5HFocHjZs6MkKXWwzXeCS0r0Q0X20cbiTo3eV77YRL9QF4ewlRds8u2kKMky96HFuQFSrFnh13mwuRcrWCqCtbQSTDLT7pnX7DutrPDvW9FP94yPjN1ThyP4oE40bjttswNiGl4v/H3h/Hd11ed//4M+ROCMkCIWkkC8bESApmZCgDEW4ogx/KjWVSW6c306/W6mydzmptna2rdXrbOq23ltbZWr2tTMtkKMNRKMoPhjIUocHc0AgNxmAkC2aJgSwQk1/C74/X6/ocvFe6bzc3ut68H4/PI5+8P+/3dZ3rXOc651znnOuc/3HMfJUh4figx7LfeJjqTwVRPmuzLZQWwKd9zLhIVqy0xrb6cP4+gp5ziLV0yPNVRWy8Ek6uRzR0PkEzSXkcQHT1qL9PBprg3beg/2FklS/wnJYb96UoTnLAcG9BNNdBJkn1u28QlYRWE4m/DyBL/K1oY3AucgVvIdZejnG5xuNYj+izikxN38ED8He7PPaJnoOHPMfD4YxziA3gM8bFOiJ5+IDHVY34Z6XnKyn2KY52MrJYNxD5+OoM40pELyn2Nrk8dyM+97881uRyThvrpBwVAOPg0EuIhnuRAgfBy9NGATTv08is+f3b3Wex2mEYstonnjwR8f71HtMc35tKZGVIuLiDyBSx138nG4YH0frb599SGFEfkYh9C1FHOsUpTkabkxN5nUAX8KZNm7j++ut59dVXefHFFxkYGOD888+nt7f3F743cuRI/uEf/iHz2bdv3y/X8S9x/eQnP+GGG27gN3/zN7n55ps5++yzeeONN9i0aRNNTU3cc8893Hjjjf9u/f+i66QCeLxrELlXzkWMuRYtxJlEst7HCLfjoO73DgCfhJH5KFYmBykhf0akZ+n085cSikQhspL1EGkIaols+uuQUnTYbQ4jDgXUCoaqcYSSuMnv1CLGOkS4cno8lk8iBnkx2l0X+LdkOShBzL2ESH2SjZjRcMRAU2xTn3GzCgmFLYhR1RNKQAeRjmE1Uuo2GN9NRDWTFKdVYVyVIStlKYyYYjza5XB0CWKcxYgZ1hiG5FZ9hsjrN9ZjHkfUU06bxaUe62z3tc/jTO6/ZuP7CbfZR5QtqnRffUTi1mTpShaANr9bgYTEO0jw1B/TznPH9NNBpKWpMzxWHo6shcH1iAb63X+xx77bbdV53NuJZLVjNKbRM4lDNLOBRVAyCQm69bZaNxKVZ3qRe7aJKE02DyluyfW8nqhz2gPcAm/vIE4I7yNOR9cQOe7SRmoDGdo9MkTEC6Z5HESK8eVo3YwDSmDDj4h4xKTk9hu+RoK+H4ej3zV8NUAZHOky3PuR0K+FkjGI5l4hTsCmjdFsol5tElaDRCqlROdVnpsJGn/vZo/tEHGwCDjlIsgdhZSfer8zRJSQe8ljMu1T4La3CKZTPmU4VxD5Bbd6nm8mNq2bjM8GInVKAVovXTD4jsc3HdGFLffZV8Pvnu45fBwpt/cRHpAyRMtvIvflNOLUfQFh9erTXAw+RNBpoeeqm3DF5xMlKbuJU7BjjbPtRIxj8r6cZ9jHE5vXiWQ275QLznZUS5x1REqrbvdZ73YfdTuryGxmx05CYQoHEJ1N9fOVaP32EO7lG5HVcjLihRcY9yXuaw9SWJsR/fYQsdg1bnM20AuDu4BlMHgFEU96Hrw9hCytS4jk1GP5v/b68Y9/zKc//Wl+67d+i0mTJvGDH/yAt99+m5/85Ce/8L2srCzKysoynzFjxvzC5/+1V11dHeeeey5vvfUWjz/+OK2trdx7772MGzcu88zixYvp6Oj4d+n/X7pOKoDHu/oQE8sh4sKy0ULdQpRcq0ZMxS7MgqtR8Hxyf2YjRvj7QA/sTK6i7cgatZao4bkC7Rq3+502ZAVbA51PI0ZUjJj4ZsRgzkcMf7z7nEO4XdOJxIsQc9mDmORVHlsLsj4sQwJ6AXLLFaLddavG+xdLENNZjATcDMSUHkEMbicRu7WHcLU+g2K2PosE2eVI0ZqOlOtSxIBvRQxuscecTcSvbTccVyHhBGK8VtCyvkTkACwkEkNv971aMrWaD603Pl4iStZdBNTDq+uRYHydOEBwnuclWazWIGa+kAwttLzmMTeSseQeGvJ4FxGWsyKPMyl/Ez2WIjJu5P07kCBZQ5QCe8F4bCGTRmLEfS6Tts5wJkvUJmh5yXCsIVITve858aGEH272e28SCv90OPKKcDHi4xob65EyMo/IRfc4UYT+VM9JIRKajcIz7cBdcNqZnoP1SLGvJOIA+wjlsNT9nKF+R3xc83Z0D6KHaxG9NxnuSrSJyIG5ySrUgazYY9BafJ7YcPwQeAKyzjEcK4SHPKDfbtuMZekaj2E2cRDoAvc5I3BIIVJ+O4jT/YsRbbb4EE6r5qXgAcQT5mue31tGlEwrgLfXk1Gs31umeXz3aXh3icf/t25/A3GQ6Hvo8kbjp181rqeggV2MlJWFnvu5RP64emLtlkP2RW5nwGPZ6f8rjIML/blJc3w0KcG1QBm89xqRu7PU85Xc3OuN74cge6ZhTFbQNc4eUIoU6SKkND+D1s1cYrO9ACllizRe5hqnM+FnLyEXdVIcK4gDNY+o7Y+eDaec7WeuQnys2Ti8lQjDaUNrfrhx14jotYXMYYvepf5tksdRJDzSaZgeRWE430LrfBMRP52N6DjFA7e7z8sRb+nQM9mVqP71l9zPcqAVTnvcc1vo+w386tQC/rA+/4br4MGDABQXF//C5/7pn/6JyspKKioqWLRoET/96U//bR0f57rkkktoaWnhRz/6EZ/4xCfIzs7+Z8985CMfYWjolzR9fkjXyUMgx7tqs+CviRq5pYgBHoAjt8GIs1EAbinaAbb6vfvRwi5AAuIeYKEY3YizEdP5OvBH58ANr0XZt4fVNp9Di+AqJNiTha8LMYtSoEll6co+jYTSViTEbkHJd2uQkvUKYlpTEFMqQ8J8DZlKGVyIdpMlHsM0xMRqiSSwKebrMr/zvp49ullCtfM1ya8RT/vZQaTogRIQjkSMfAApCtMQAytBLy5GDHwxEhaDOo05IbnWNyGBMhe5AXuIQwrXAhvhp8vEh8cuNvxdRLxkDxKef+bfkqu1DAn2Jt/bR5StyvHYm5CAuB4pYwmWlYLzvR854D/feKkmLG54nDVISaxAtDEZjt4GWVuRRSVZfcci4Z0sXuOB+bD7WphwL6GYjvG4vmZ870DWh+8QMWXVhruDiBdNVulywrXZqLltH4Cy5M4CCeJViA6fICOYmI4E1j5/rzFO8lCc2SOEpazJ89RGVEFp9lyPg/4fqRIJZWht1SMLTgW0nA9Vq4HtcOhOB5nfD703QcGnDOMuIuatAymFL7v/FMt5q9rtfwByzyRiyx5Byl0PcrVfQCQBXuX2rzRM89BarEBrJI9IeLzVfS82Th8gDhSlqwcJ672EstqNlIJb0JrehNbWtWSSPu/fB2NvMc4WEId4cohKHpcSBxCmeT63EvGmX4Ejb8GIBYhWWjwmew1odBtpg1MEbz8NpyUr/wQUp7uecE/3ILrOQ2tlBfJkFBIpjkai+Z+G6LWNODjW4HEm70kD4g2LDMNjiK81EofThgjlbv0xuL3QeDnseSxHOUA/ZTwVoDXe4Pbq0LxvJk7hvo82Q81EHG09vL3L4QLX+NnHyFRdocXw3oXm+ouGZ4X/bycs7N8iUx7yZ/8IHy1G6zvHfbUS+QmTNXmDx3wVDD4M2ZMQHQ4SeRlrPaZGoOUEHgKZ9yEfAlkPra2tHzgEMnz4cIYPH/4L3x0aGuLCCy+ku7ubzZs3H/e5V155haamJn77t3+bgwcP8s1vfpOXXnqJn/70p5x66qkfzkB83XXXXXzxi18kPz//A/ePHDnC/fffzx133PGh9vfLXicVwONd38qS5WARYrqHEJNZSJwCriBKGHUQ7rsBpCwcG4t1EZFzayLQpzJjp4xDO7oUO/UAEaSfrB2TkeVlDpH8uRcJnY3EydIJZNwtGQViLrHT7SBKvi2EQyth5CVEPUpb/DIxe0VIAbgJMc1ZiOFNJgRZO/CXSKAuJE6ZJhf6UsIamYRNsrZUGZ4U9wXB+HuR4Lme2PH2IKa5nAgKzwf2wOBK8eQzLkTKUQfake9B7rAX3d83CYG3mRDuKaj6AfdxCZEYu8xwrCUCsm3xZbz7mEKmPjBP6t7RfZD1G8CPkGB5gMgxtwsJ6e8AFfAXL8EfnY0ERhEcudPxo2P8bDkSYhVE1YpkCc4mk44nUz6vFCFkLBLOWwnr6mSkvD1PWEKLYP8LMHYcosUy4+lbSIjmGYa5yEp60O3WEbGWydKEYTnsfvrcf4WfnYFoLZeIkSxEtJDte4+5z0WINhPcLWj+J8Ga5XDBOX6/iIi5vM9wLfbcrDKuK4g8eimWL8XNNSErzEoiQfqlwD3wsy5ZkLgOzXeKTe0kk+eR5MF53ePbjiyk9yMlbxDRZR+yBF2MLO+3E2XGVhNxtOOI5POvE4cKapGi8IDxm43m8FHEV+r8fz0Kd0iu9mxE6496bv7CY06/v4NovhrR9XLixOo+jyWFBSTFo9h/a9x+ntvMJg4FjT0GvknH4Gma/n/zRjhjInFiupmgb5Ai1EO42ycTNdKLof8NJ0YuJZIxP4s21MV+dgWR5ugS5HkZ6/v3INpJ63kKscbuI1NdCHw/KWAvG58ribyUV7nty4kwmMl+t4io836z5+82IifhqYhXHkZ8HkRD3WRKjGZybd7qfrMJ78SKXy8F8P+8vva1r3HnnXf+wnevu+461q5dy+bNm38pRW5gYIAzzzyTxYsXc/fdd/+SEP/iKzs7m3/4h3/glFNO+cD9zs5OTjnlFAYH/40mz3/j9V9OaO+/ytcKxOgOowU4nUhQexgxkMScSxAD6yfKmbUixtmKBEkrsjKABEgvnDIGMZUVSAjtQkpTL2KqWxHTeBwxlW+gmMRyIn9ZHXEC9VXkRitGTKHK7+8xbGchITRK90be5n5b0Q72/yEy779I5BEbT+S5G08EudchK+cSIm4vWSkHPM5NHnMbEmibkGLXC6yHoxsha4DIM5hig5r8d57/7jT8W4l0LYNkypRlnw9nFHhs+4laoklg9ghPh/4JRnYjRl/vdmsN12SirulGFIOUh4TP0x53spIOEpaNFt9bh6y7s4XDrJlIKV9rmMe7jyV6f/AWu3j2SlZnwgxugRE3ehxptz+eiJ2rRoLG8Xu8jGhhlJ9vdD/bjYcZnquDiI4fIA5odApW3oexTyBFoJMoWTdEHGiYYlytIcrx1aP1MJsIMWhHiukW/z7bMKz2HKa4yy6ktDQcM752JPTHe85MqxlFLdHYJLhgOKKz2R73Po/lSqT0rUcK93iPY8BjK0ECucn3xhPJm5MlqwEparVQthlN0EY/M9tjyyMOOpS6jTpi49bmua9B62+Y8TgLDj3ktC7bEe/YScTZVvi5ZG0cyQfCHtiPrLJJ0e9CtNhM5HFMIUaFSNEuJkIRqtEm5TCZqh39P4Lciz3GZsQ3dhoPV7i/g0hRafc4B43Xu5E1eiIR1pGP1g9+b47hAK3LPGAvnHGqx5E2bMOIakdFfrbIvxch2puMeOomyB2HaDG5+8cjRWq38Xam35tBHLRLSul4w3aQsAbWorXV7Hfa0FwvQutwkCjvuQJZPIuJijTZxzyT5/FOIk6U/wDJkWoieXaKh872GPqM1zTXVR7PMMQPk1wpIDagJ/I6yoeXv8967M+zAP6i64YbbmD16tW89NJLv7QVLycnh7PPPpu9e/f+0uD+S9fRo0fJysr6Z/cbGhr+RTf1f8R1MgbweFeKZ2lHzNcB3ZlUAflkgqgZRMrbDiQcSgk3RSFins8jxrKZyNOXjaxmRUS5tAF/+glGM4iY6RASFpW+34Zy/ZUiRnIWUYS+jyjZlKwJ5xvmRf69lShltcn91iNmkwschrfv9rsTkDC4CDH6bUjxqSaYZ5f/7yN2+pOAdXCkAX62nggUXw2MhayriRxxs4l4s/+JGHm18TKXSFtSgYTqHCRg9xGK20H33+7x1SPBXK3PyDHIFTeWqBgwaNhT7FOf5y9tADp8b4zf8Wm9Qz8iTqN2GLfrjdPxSBGZQpzsy/G4x6u/7CQYhkHZxxAtjEEnAnOIihbXGRf1fmaX+6rTGN98S9UJuBQpd8n9V3YM/O2G/aD/JotrCRLg8zyujxBxRd2IVke5rS6iTNYYP7/R73Yj5aCFqExQiui12+85TpH1iHZzCMvOLEK53eWxXYPCGJJwb0bznuLw5hIu6E0e40zjd4rHmWApJmLoUthFGZEepMzzVEVUrCjS95FnEq7Tg2ht1MOh5w1vs/u6wbAN+v3vE4mXO4hwjnkwcrr77iFy0iUXYDIK5BJu4gHD0IborQ650nOIDdEEt+8DLTQYl1P87hbEUxqJTUWb+si9zf2+qN/+1z5iE/U0cAAG24yfnsDtz9Yar7XExneTx9ul95iENrD9GnumdFopstK2uO+k+O51Hyk+dT6RdHkhkcplEvTvRXGdNxNWx0eNvw602aowjNVoA3wFUu6uJ1MFh5fRWmky3rqIQynzPC/bkbt4HeJ5Z7nv5DpO9H4YKYZXGV/mFxR6rO1EDKnxzQbEf3Z7zpo0P0dfI0I6kqX4EbebeOM0fu2ukSNHfuBzPAXw6NGj3HDDDaxcuZINGzZw+umn/9J9DQ4OsnPnTn7zN3/z3wp25ho9ejTFxcVkZWXx0Y9+lOLi4sxn1KhRnHfeeVxyySUfWn//2uukBfB4VzUSPtVoYS4DhsH+zTD2akJBvBEx6ha0UO8BZsN7D8DoSYhplaKFvQlZ+FYiRlZLlKiajHbl89zfRiQQ9iIL4OPQfhjKUnBzoT/fNQw7CUUyCcGVZNwwnd+Fkmbf30kkL12KGPNYpFCdhxTVQj172jXu7xLE6JYZ5k8iwbOVYE4/BP4UKYcpBrJH/YzIho+2GR9L3P4PgAbonAclM5HweITIfTVg3D1OWEL3IctMjucnWUIrESPtRLvm8xBTTy7tbqTs4H6eElw/+5F49xkf8ziSxeZCj+EwEl77/W5yZXbCyOHu78vIGrAEVWhoNDxPEjVAk0KarIfJZduKhGSKGzuAqmGsc39L/U4SLI1Ikd9H5mTwGTei04VPue3rEC18zjC1ELVpm+HoGyKvub+BhNgSJKSTAG9EwnQbou0nkHKRQh3uIZTGFAu32fheiGhjhXG4hwgnyPP3WuKUdHIRj/L/BUiRTBartKGo8PvD/P8k4+T/QVZx3PcWNLh70XobZlj7kGB/EAn/MxH9r0NKeYlyABbcQaQlajXODxqvc9BGbhvwRRi5W/N1ZD2M2IDcvW3ICj/ReEzrssDtJev8DWTyFravhLKzPaZsj2UXou1hRN64hJMCQugvIWo+TyAsh5OJesJ5KP1Tcq1nu508wqq/lbBCtcFnFhtnyXL2OmTvcJ/JyrwfPjoGzX8DUV2khjjFPhmt9eVoPfcghfZzxs2XkaWu3uPK9pxc4XZ2Em7nWkRnF5I5PZ97E1LKHiNilssRfysiUqh83XP3ptv/stvzGqLc765Ha7WMODSTrm6ksC1CPKGROJwzDa2VPMQjb3WbKz1veYZvh3HfgjYwPYjWdnu89cQp8PXO09jqGPIHkFt6jOaDO4TX/ZthbDoUdCKuQeCfG7n+9W39Etf111/PD3/4Q1atWkVhYSHt7e0AjBo1ihEjRgBwxRVXMHbsWL7xjW8Aiss799xzGTduHN3d3dx///3s27ePa6655kMaBDz00EMcPXqUz3zmM/zZn/3ZBxI95+bmUlVVxfTp0z+0/v6110kF8HhXBbAVDi2FkXejBT8Fxj5CZG1fCru7YMKZRFHyG4EHnRw3xY5MRYzlNmK3+Axyt3UT+fT+jHD7TAEG4Oh6yLpN1RbKxqEFcgsRIzUNMYsmJICedXvr4dDT4lHnVkDJ+YZlxjF9JEvaAcR4ygxnsmg9gxj7GCKofh7hJvyh2xuDhO1nga9A7wEouBcpTV8mlN/JRF63QqQkvA8l1yLBkWO4HkVC52aPbRhRlm0jYbFrRIJileEsQDvy2UiAVnkM93vMy9yHd+LvLYePPur/H3bf1yGmPYXIZ/ak37uUuHKIwyR/ghj7oOHr9vcbkcLfCEcGYMQYpBi8jxSfvcbdK24vxUI+6e/7XHR+EMWK3et29yDhVCHcdi6BkoS/GqQU3HgMnksQDT4PPARZd8Hcbnj3HThluse1xOOfiQTQMGSB+69AGRzaDCPvQ1aQ24zbdUh5+DgMDkD2tZ7n7Ui4Jjp6nozi8+7DcMq9bn8lYVX8hp9JoRWXGxdPwXuvwOhbyJyCziTdrUH0dSMRq9kErIUjC2DELUiBPuD+JruNLe4bZAVaprEX3G04ZhIW7HlIadyH6GoYorN89/ltGJFDnHat8Nh7PEetSBm4F9HSO4imD5OJcSyrh1d3WI9NCtx8QhHtQLG3Pmjy9nfhtBVIOUru6rFErO0UtCGYS9TCTVa/iz2vjxjGGo9zkvF0AUpHNB3xlseJXJz9gqVzD5Tc6HuDHnc1otGnEC/cRJzKXWU4y42zYqQA1yMvRSXaGDQRbu5Ww7WUcP1fbJzdY5xcinjQO4T1/JtEipoiv7PZ8BQgJbkJNlwIc29E/OFS9/mk3/2M8bfdz79IHPgrNX63IT5VDlRB54+g5NOE8ttAWHNneSzrDfvnPwo3/0xzUoXW52Ei5rLeOHodxeBu8QHC1Wgzk4dqndvFPPaXN3p9uNcJVAAfeeQRAH73d3/3A/efeOIJPv3pTwPw9ttvM2xYODvfe+89/vAP/5D29nZGjx7N7/zO77BlyxZqa2v/LZB/4LryyisBOP3005kxYwY5OR9SkOSHfJ08BHK866+yxITuQfFxaxATmogYZSUR85SseJuRAGhBjO/zyCKWSwTlFyFG91n/34OYfdoBHkDKVVJwhiGmeA3hcn0FKRbHHix6XM8eeQlGXEak26h2e2sRQ8pBgqTU7ZYht995SBCuMywL0UGW55GwTDvP+4E/JtzVC5FgTAHKyUpViJSOTsPS7HHP8HMziJOzhUjoJHdgFRI2KV6uxvAmixPAJjj0LIxM1sS7iHyJ89XHkddgRDlwBfTfC7nTPV/Jrb4GKRp5RLB7o/uZgYRJGZGwudDt13k8c4yDTUiAgSwtryLamI8UlZuRFa0O0U2P221FAmSy27nP4+wkBM6f+PedRHm6AmSlmAX8L/jZWzI6Zd+OhEYe4dJehBSqiZ7fZH3sINxsdUi4XO1xDSB6SQpPH6KjEjh0G4w8x2N6nsi314YOStmCenQPZN2IBHQekfutgAhJaPEYlyPaSpatDuLkawMRt1VLnOK+3Dh/Awnj8URll/1+v9Rjb4D+zZB7NaKRZzzuxz1mjoEnuduqiLJbA4TCOx3RyXhc2cbzMBmO3gRZDxNu8CYif2APUujmur+PIUW6Bq2PTdDfALlT/E6yCINo4Xd9vwMlY25DCsiFfuYqtAm5S7AwDNEfSOEtML4S7BVoEznZY5uGlN/JaD4XENbIZkSr44lcoQ2IlqYQpQhLCRdwieEdgyyo+WhNrUe0mGTtavf9lJ+/xjh/xW07nGRwB2QXHwNDiu18B/GOUiL8ZgWi+0nI0ngpopEqxFdmECXzLiKqsqRY4FlIMd1rfOehjeVGRMM3up8liF7WCxdv/yOcdqrf/yqa74NoPT9PlHgbQHN+oXHb5b6/h6z/xfD6LXDWTUimzDxmXHVEXHMxcWr4GyfwEMgsGPkhmZIO/f9g1Mv/uUvBHTp0KAP7oUOHfuGzJ3qMJy2Ax7vuQkKnmlDoBtGucBFiaCn2bA5iRDVIuCVmeAgxyxRMXIIYyzXIFfE+4RYpQgy3GDGb54lDCasIV0Sb4XgEMZ8SxNjnohxxbUTS0onHtNno8WwzPClWLMdtfs/jGYOUi1zEmL+AdsEL0U64DTG4DWRi0Gg1Xi4yXBXIGvE54yq5M2uJZMXr3NYYxMCuI/IpVhP57cYjxr7+GBzlA1+GkROQALnUMKWYPh8UGXE+mdQYuZf4t2a/nyyG65GgSNa8IuN6ped0rMfwDBLoKe6mFAnQ94kapGcTcUxdRFm61cTp2RQv1eRn1xFJksd4nvKQorOXyLP3DqKtfYa/Dwn2GfDRbKIiyyy1cfRZyLrF7zYjC2dyDVYT6WDqidQXKUYtuRyneF4b3W8xjBzn9pYSB37GuO9mj2kBZC30nM8lLJMrEU2mWOsX0AbrIsJFusVzllxzQ4avmzjxWI4EY6XvFbnvA4i2FiGaKiaTwD13nGHrIwLnkxvyk2hddBM1iHcQ1tOX0Rqd43aHPG9/ShwC2A1ZdyMlfjZxlSFr9DB/b0Tu7ueJk/o9GlfuPYgeF7qdImTB2oDWbDccOQAj+jxvaQNSYVw6Lo79xtlSIN/1dT9O1Cu+FCk6l3usWw1LWgebDPM8z0cKg9iDeMBnCYviPqIG8WH1lzmEU+720mbjEXh3H5xSjzYQuwmF9CqPvRXRcAFRiaUAsm9Ca2YHUtqfNwwz3A9uC4KuN7jvFkSHDyJam0lUmmknDnxsJsIcyt1+J+JNid4akHJ3ub/PJmM9Pa3WG/Axbn8JceDlEKK96UT1o8fhvQYY/RuG40rjtBDOOtP4SEp1HREW003UF07W5hN5DfHhHQL5sNo5gdfo0aMzJ3+Liop+7iGQdDjk5CngX9WriijAvR4xrBVEWa6rEJPMQ8wXxDhnIkE0ATHFarRgxyDibkQLOpvIHt+DBEo5cVqyHrl6D6PFP4AYVTFhfStFTKGb2FFXIAG6nw9aRsYSiauTIE5umkYiD2Cf4ej1/X4k5JpRzEk5YlZ9Hvv7yOqThxjSZD/bAEffh6w8xCCTla0RMfEUoD8X/v5x+K+1KBYoWRKnGs/9yBpQZLyUIiaajWLFkuViPpFrr9Rw3ON5LCJcbt3Qu0ug5E4k4hf3eO6KiJrJk4g0DPP8qXb7SThM9rhKjcNXPJ9VBD0kgeMYpvbnoWwBcZJ0GxL6w9zuNX73dSRAut3v5W7nEc9fC2FpLSbi5AYg6zLjsh65ILs9b1d4fJ3G39Ax41hHnO5NYQFTEO0uJ9yl6/1ONhKoy5ArNin6VWhDMdX/9yF6ykfW511uIym0h4kg+R60npLrcj9xMnwfosVWQkh3oLX5p2TcezQR5ezSpmYApdyZjSxWVYS1tAk6d0DJOURd2AT3bCT47/GzdW5/JFIAkquyW38G99lwloeUmKQMriKU+wrBPvgaZCcX7ETDVGwYCtF8rSFO3ObAiI8QsXVLj3l3s/tKsWYTkcVqH5w2h6ipfLPhf87tvE9Y1TYjeqvwPHV5bjqJ2NCzjLdiYuOZrNPJ8l+K+FglUtRGkTlIdcokIh9hh+FcjtZWsnwmy2ktEZ/cgOh5GqKzSwzbFo+hj6idPo+ogDPXsDyF1uE0xK86DP8AUXYth+Bvg4TLt8TvrRfeDy2Hkc3Igjfb87Xc8zPFeLuJiDu9A62jlYg/jDL8y4W20TMIl/F57r8KrZe9xOGyLrQuE19pQ1eyKp+8fiWuDRs2ZE74bty48QRD84uvky7g412TsyK1RQ5ijs8jJeQBpLg9iRhWNoqdGYV2mbOJ+J9aQilJQnWQyCnYhBSAkUROtvmIeae4ri7EHFLQcDHhvisl8lP1EPEzSSFLSlcREXyfGF8hYlzXIRflfkJgVSEmPZlQEjYipagIWQIKEPO/0e/sPabtJsJdspco17QCMecbydQ3PnonZN0ErIL+tyD3FiT4ZiEBcJXxPhmdfLzd49rqfj5JJJ+e7z6uRoKiiEiSu444MT2eCLpuInbaG9HpzS8Y5yle6Sa38W0kTOo9h93IkpHifC7xuF/y/dmG/3zP0zAi1U635+yYIPzOd6BkHJkA9reXw2kPIabfSlRGuBUJpicNR1LgJhIphJISkayzPX53o3GQj5S0Q8iHvMowJzqbTZwOH0PkYNtomJO7fxj07oCCjxGnTwcI6/JCZG1eTRyY6kBCLG2Sknuzze9egdbV55FS8mUiF2cjUQ+527A0Gd4XdNr8ozOJTUeh+6hELjmvqXfv0bQPAdkfMZzvE5usekTv3tBQh5TWbGT9LfCc53tc84jDHymW7evI3TvNc5IUga8bHxBhJa97TocRVs0uwsJVZvwvROtsJby5Ec4YjpTfQePpZfV1aIcePyUH+gecCPuHiEdt8hivcruvw+D9kH2h53AKWk97EO2v9bzcjKzhewg3/VYymytmIl7WInhe/RGcW4lCRz5H1EwvNU6vRxuabIJ3DCI6mAC966HgEuhfDrkXEjV2OzxP2cZJF+xeCxNmojW4HfGmKqAfen8EBbcQ+TN7CGtwshY7jOTdx52i6xpio7NBh6eyPgKv/yOcdTZa84XAFnh7I5x2D5FBYBDRS+LJjYQlOCl6+R7HHs9pJ6KjZEFM66MR0W6ecV1O5DG9yni/6wS6gM/9kF3Ar/7ndgH/Z7pOKoDHu2ZlSQCkneYGxHyaELNoQ0rfBv8/RFhbViMG0OH7ixHTOdZ1ko8EaXKJdrj9PGSZwf22+H4tUkqyERNJbuUFhPVvGRJYaxFT20nEnexDcYNJkLUj5jIO6IE1z8IFW4GPIwXoCSJP2nVEKo7koqshGHUVCkq+xGMfMBxDxGnlbUjIzSGqNpQa/jrE2HZ7HCWE++Zm4jQqHkuKZZzkca5EDPQpInZqBpFuJzHlFPuW5msYUna+Q7h/ZiIL1PnGYx1h7SwmgvL3eF52E3E4SekFKaUp/u2ThnsFUWKwEykMy4m4oIlkYjdbtkPVGM3fuzvglNMJpT0XVWXZ4TEkxbYCKWkptnAfUc5qO1KgrjS+yo2PFf6e3I47kdvxHURPn0JKdbKGgRSC+Uhg9wCHoXMllJyKFJFRRNxcv3Fw2LCONXyrUTjE/0LK1GxUPmuR2ty/HMbeaNwsRBuCHUSt6BRLuh2O7tJBKaYQ1UFWuJ+LEc0mZbbNc95IWMmKjNcxhnM1kXbkYf92LVKc/tzjW0mcsC3Q84O3QfYmwlq9h7CuNSPa3ITopMewJUV4PQrw/wS0vAFVZyLLUQpbWIcUqz5o2SzQss5BSsgWPhifXGGYbzG+fuC5fBnR7Gy0vttRQuhBePNaOGMxwReu9tgnur0m4kT942hDMcpjmqU5fPcVOCVtvC4gLH3VyPKVXLNpk1vucc8lQlMKEW+YREbJpR3R8h3H4KKIOFU/m0jCnz4HCQ9JL6HYN8oDUHCJ5/dxlElh0zFwbzsGh8krkmh4OlHDewjRRDaij8vRGkqbr1GIHica/ps9VxchXnm729zp5y8mc2r/p2/Bb33Jc9Fm3KRN6mqkOO9ye5XAphOoAJ7zISuAr/36KIA//vGP+Y3f+A1mzpwJwMMPP8z3v/99amtrefjhhxk9evQJhe+kAni868osmfhfQowhuXD/CllKtvLB9BTpEEMx4TJ8ACkGtyLh97jbmYCCx/MQI5vm55qJpMLNxIGFZsNUjCw+ZWRSJBx9B7Kuh50PQ90tfi4FKCcXZnK3JitaNbFLbvLfMQTDbCSUgBT31kUw768jxt3kE5rXw9GHbcXb7jHUIYb3iN8fQIJuBVKIHyVcLl9ECl8fYnZ7CAW6GCkSq1S0PrsYKZF9iOHOAP479L4CBYvJJOB9dymccjdRiq7XY5hEWHaqkeK32H21+v99aGfdQbhnDxCJXf/Wcz3Gz1QhhvwGkcpnj+fpEBKEI4lSVmlc+5AiOIAUgnp4/QCcdbr7murf3vT7+USpub1Ied6IlKzVyA36Q7e/yP3tRYrAauO22GNoRQI1wXKb30mWnEeQYM1BAusNv1epuWAqkVNus8d4NxFQPx8pdPOJEmurjZsGz8c7HkOT57PGOHsCbUyqdFJ79PlufwApi3WIptcgeu7xfBwiNjoVZE7DMwYpvp9HynNyMV5OpjRfJv5tCbKElxAJz/Pdfo/h3g5r2rxhWkqkPMlGgvxe42+727qc2NRUH9PuJo8jhYTsRMpUWhdJYa2wQlyJlJUNiIe0GQfzgMMudzccWdtqEA12u929wJsw+E+Qfb3noBgohf2Pw9jnDev7SDF/iSh3Vu92LvKc5SNLfLLsJmvUarQ5SHGBTR5HcrPmGA9TgMdhsMGxfVWIf3YRMboFnpPVxtnNxlO753A8Sj59AZEWKMWgdiD+XIeUsLGGI8Xf7kOKUx3iDzOMrxakKP8VERu7E9FKiq98FlmnNyDl9Cr3eRfyEM1xX9lorSQP0VYi8fkkRJclxnUu2nCm+UqeBfN4qtC1CXkgeoW/TP33fcC3TiqAv4pXXV0df/7nf84FF1zAzp07mTJlCrfccgsbN25kwoQJPPHEEycUvmEntPdf5SsJiXYiBclE5P5wPA7jff91xAgWEkLsabSzTVaxrUiA7CNOus4grIQvI8FZg5SN7dD7OFI+DxGxQTcQ1rdbIesK9VeX3LDfQcx9OmLMFxNxUVcgRpYsd4WEZSJZjBxDxmqkeMwjLG6FKID8GSR4umB0OTAfsha7zT6PrRydFh6DGPqZhqsFMb8rPKY9SFi2Iqa7iXBZdyNrXAtQB9nnIGVmPmHV+gJQAAX5yHLaClQ45V8hshpZITqymagwUIKEXY5xfJCwGpyPFLkNaputnptKj2ek28glqgzsMV5mEopsh+F3zFfGyrnfzy/2XLf6/nw460tE4Pg2tzGSOF1ZTxwUeMpzNF/z1P5V5FqaQCRAPoTi15LiX+wxXYsEzUakxNyChE1SxG4gLCsgoV2J6KKAsBTNdv8ptnSb56YJ0X+ylHURrvds42e4YTig+aGGCNT/X8B1MPpqv7tLc9P5PuJaq4i8byVERYwKpBQcQGvqfKL82l6gG44eJnLsDRqPO43XaR5Pp+c1bTb6DO9ijeOC6YQLN8dj6yRKug343vvut9Vj70CKQuIvDxKnwi8llI0GIhfdZBh7G+I/f4zo6CvGQRlSxtt8gngeWscdSPnpc//DgVnaK2RciuXAFmerSl6NpzznLxuPW4iQle/C4D3+Ps04XI+U8kLP20bPQaN/206UcMtD66NL/WWfjtbeIx7nNGR1TDGIxcQp9ja0oYaw3H+PSImU+E6F5pjrEN84iMJVXnb/piNmGidD/uT42ckot2QfkaVhncf3kMecFMnFhEVyBuJpKXZxJhx9yfDuQ3z4RqKW8bVu833iwFc2UbO4zW1dRRQgmIIsjH2IDxX5mT5O7DX4IX9+ja633nork17m2Wef5fd+7/f4+te/zsMPP8zatWtPMHQnD4Ec/xok8rP1ocX2CpEaoQgt1omIoecSua62oAWfFMVyxPwX+529aPfZQgTnFyBmNx4J+n4omIMsSxsRkxkk6k6WErVBdyLm+zCRm2ux4d3pZ/YiZpYC6C9GzK4DMeQkoBoQI84zjPcRykOyFu0wvEm5+wzwN8h9XEGcrqtFCstVSBGqN5xDaAeL4VxC1FjtN2x71G//G5D7PhLEBxADr0Yxa2uQ8NuDLBGbDV8nZM/zfCTFdCeMOB0Jr62Gs9K4rCFqKPcTh0aS26XXeEnxN2cb9vm+n4LIB4m0KrVu7w4yFkwaDfsUGLwRsluJShQ9hqmViA9bgSxhW4378X4uGwnqyYa1CngHylIA+273n4OsH0kg5xHpUSqI9DWTjMPkrm8nc8KYGkTfjtl6rwFGz/Q7DUiYzTBcw4jYyBTn1W7YX/ehh3bPY5Hb7uWD5czu9zw+YxwvJerQXgYlpyO6T/geixTqg57LRUSOtH7hJZNg+hY4ei9kfdr4aTMOb0Q0uY04HZvm76DxPeR7G4mDSAVEOpEqIoFvvXGyUnOdKaOX47HdjzZq1Z6HA26rkYhN3UPkG8wnUiBV+NPgMU5GCkQ3H6whXk9Y4U71GBbBiOT2zyaThy7nDeAPkQW3hKDHZ4hcptM0l9lpI5JoObmHK9Ean0KcvC1H9NxJeDLK3HcZmSTf7z3tjWQV4i1lRPnKGiJ3400oLKMa8bV9hjWPSAqfYn0LifWSNgk9fqbcOJtPVA7q9hiGeTy5/tQSCbPf9/gcr8sMPhirOcO/bwFyIGue5mfDD2Buu+dwsZ9ZbliSBXwJ4nvZSLltQ/SY3MkziSpIzQRPGCL40cnrV+7Kzc3l8OHDAKxfv54rrrgCgOLi4n8xRcx/xHXSAni861y0w1xMlAPqJROv1f8AYkBjkZDOR8z4UaSkFBJWosSQbyEOSkxBDAOkHKwmcj1NQPGCyUK3wf1/HTGIq5HiuY5IPv0CESB+DWKiE4ESaL8fMY7xiOHkod30Src/3L9PIZSEZG0a5rE9ScSgXUQmCfXgDsSkl7n9y4nanpOMi5WIaV9PpIoodNtrkYK4F+2gz/IzRcJ1D0gQPIzcRFcbvkGiHFw+ceil1Di/GikfnchluwcOveX3riVSUzQY9kKixujFnqtJSGhvgpYDxmmO+jryA+AeaL+XOCTQDC8+S+RgzHY/q4lTq/nqN/ta4vRtkfH2MqKNe4iNwUEkGC/y9w7PQxlSJhqA35M7jUeJHICtSEheRgjlHqLWbRdhwSxym8m9nINocxNRwWS28DE6WUGf8t9swkq4jzjUsgwphwVkTmpmv4Boez4S6oMeB8iKdBCthW1EmblrDcsit1+H5jzbOMtHdP9VpMAMN57P97jOROtmADjsWMEC420pUgxWoo1Ol8f7Ivw4bUxO9Ry1EyXGrkLrNK3ZGrTWOo3rw0Ri4+uM1/VojSb3ZKnHsEfvtS8lDtlsNW56PaYLPBdDaM6rUKnEiYi2V/jdaYahAlnenkD86SwinVWL53K65203ZH0JWY6fR7TRC+8d9FgrEe1tQXylGnk8kkUq0dty/59N0P9URA/J0n6dx7cGKe1NGtPoK4zfFsIam0fEtG4yzpcTmRk2o7nOQ3Q4hSh9dw0c+gGi7xnABOhfCdTDe88TpdnWEjyuEdHCt42vLcZfn+EpQ3z+USJsIG2S5yHeNpEI+xnpuXtFU/n6LveReG6y8H7F/d9qvOQZZ+2IrjrR+mtwP7VkDlm99wKRU/VEXkN8eNa/of9g2P+dr5kzZ/KFL3yBu+++m9dee42Pf/zjAPzsZz/7pWsW/3tcJ2MAj3edl6VFWIkW7OVw9HYHXieXzWziRNZdiOHlEDVGbcWiHimSPf79MeL4YTESTiku5zBSdGYjhpFLxKtNQkLxdSJQugkJtNeRAL0DMZTDyBLURuSRMsPlbCJY/CrE6LYgBSwpr3s89nTVuZ2tSHhc7Ge/hNzcQ3zwNOcoj70BCYNRHs9eogxameEeQozzcSLB6WakzC7z324iB1YBH0wYnCwCNUTy4hVIUDYZ/tmEFWKT+5iEXHAFhHt0AO3AOzxXh4mcfcllnU/EtTUQKX2SRbXKfe4nEys3eCNkjzfclUio/39QbFM/UhC63Xe7cTagT2cDlMwhYjFHIQHznO/VQvt3oexGw/wUHD0AWfcZ3z/0vHzWY9hEJHzuNV4uEcg/uRV+pxzFeJUhxagRCcKZxGnWAkRfVW5nPFFu70UkRC9B9Po9FNt2BeEaT662aeiQQnID1vj3cciqk5SaJ/zuDWQOUfSvhdzTjYtiw/mXcPR8yPoYEftahOgoxU5tNXwve/5KNZ6fLoPfukxtZ3JUTiUOATyBAvdrPF/D/FyX216IBHyrn1lI5tR//w8gd7Fh+hrwE+AThm0UWgOXQ/9lkHs9EYM2B21++hHNlB7TT3KXLUT0eSFS+DuQJW0m/Oxh+Og5SOl+EPGT2cRafdFjLD9mXr7j+c3xvWl+70wUA1ft8VYZzh60lrqgfZmGM2KxYXsO8Zg9RLL1iYifbID+lyD3XoLnvOo+xxI5H59EFv/bCGXoi4iuCzyOfDh6I2Sdalx6w8IopLRdTCaXJZsVDnIAqFpAHAaq97y0I3qp1pzwFeKU/WJEZ2sRfaeN1WoiVOhcMrHUf78d/uu1nr99xIEX01zmcFUhEfKxDtHnGo9lGlpftUSt7CcJfvcI0HICYwAnwcjsf/n5/1dtDsKohl+fGMC3336bP/qjP6K1tZUbb7yRq6++GoCbb76ZwcFBlixZckLhO6kAHu+6NCtj+anEsXbLEBMCLeY2JASSJakCMeNqP9NN5Kabhhj2A4gZnEUc+89HzL4fMYVkCUwCfzIRb/JNolTXWCTEcL+PIqY8nIhneQwFjjcRyY+T++wAEnIpdm82GuyjSAH7/yLm14AE8gQkPBciYbIIMdnHkOL5KBJoF/mdpxDDLXOf1yBLTa37WYoEXFL20i77Pvd1CDHAImAB9F7hVCMY3h67iB8m4n3GGKfD/G6+8VmPFIxGxJwnAIVKBXLKRUiZKUGCbRoStsXI6jANWWHb/H6t20xzX4sUi+HIypFclCvQrn0q8A14biN88kwi2XWF2ylCc5+E5FpkgewQHls2QlWyRB07fzP9TDq4stRwjCKK1g96zr5LpJ7p8Fx1EjVlv+XPVuIQUgmwE9r3QlkxUiJKkaBqQ0LpAuO+CSlsCxDNjkeC6RqP6UoytYgzBzHKDfdOIlgeaFlpwbwb+B+Ixv7I715KHCKZiiyGyfU+2Ti8Cm1oagxHM7z+MJw102OuQnS3BwnkeuLKJ8Ib3iEscmmNNiPrYpe/13gsk4zTaxBdLPNvNxFJnesNcwoPWea5qHHfpYQFNQ+5YK9EdDUBCf1Eb+WGf7twcWQHjLjQeNwCva9Bwe1ETrlhHkc7sQkrRrQ46DmdSpx8rUPreqGffcxjmIQU7o8YxkainF8VEV98jee11TBeb3hB63wNYcVPY5l9zG8HjJvLDG8xHH0csj7nd+Yar+uQkva3hm8+8I1hsHBI9PAU4rWXeV5TDHCF8VtzDMzlhHU9hYTsNFz74GiD5cBqaO+Csk8jWq73nNWjPjej9Zss4s8ZrqTMJQtfFxHiUX7MmHce8/9Cw5ZCLsYI7+1Pq4mzJiFF9CsnFcCT1y9/nVQAj3edlyUBu5Nwa+xHC74ZMYhaopxVt0uPjScEczFhUZuJGEQJEog9hBWmH5UAOkTU8kyHF8r9/0EkeLYahgTLMCIO6VI49FUYeQliLt1EvNd0P/MOcCocvQ6yLkIMq5OofbpM4zq6C7KeRwxzvX+biJS+DUggdZPJkQXupwgpIHvg6EbIuhoJmT4ktLcipciuzEOvwMgLESOuNL6SxWW823zSuG4m4qtqCCtJnnFXQJz8uxztyr9DCOqp/n8mEgqNxAnbYUiApdjFZFU9y+11eYzNiCkn5XA6EV/U6LYfRYx6JVIIu/0sxOGcHuRuug0J3zqPZy5STtr8aSFi2PoMS3ItV7vPZFX4M6JAfQmay8+5j8vdfxcRS/aU701CQi5tGBqQUMwm4uxGGYePIiFW6t+q+eDhoRROUAi9V0HB9UgpLEHCOh/RbYoN24xctPVEMuPXkRBOcW45wB5ob4OyjxExgykGcxwSlimGbgpB/0mGbCKzaeAQSufSR8TpdXgc01Gw/71ubxpS4i42XvcGLg49DiNv8TiGiMMb491nj+F4HdHMQbSGSgmLb7LwdyMayCfcqesRHV1FJEPPIXLZ1XtOkiu6gshM0ETkb2xEinozHFkJI64gci72o7XcSayTpBT3ur/ZyH3dBEf2OdlxWq99hNJ4BqFM7nUftUQu1EcIha7ZY70Irfle43A/4lGzDcO1HmszkdA5hdfs9LjrjOdEK4knliO660c8aCaisRxE24kntxFKdR5SZmsRzTQY/yWeu2ZCkW7mg+X2znCbvYhelnqMe4hSmCBLfCuirW73tZ4ouZg2JMkSXYF4QlIotxGZBmo8nsdOoAI48UNWAHf9eimAQ0ND7N27l3fffZehoQ/6uD/2sY8d563/mOvkIZDjXU2ICZWgneJ1iPH3EO7LbsR8tsDRvT5kMJ6oItJB1IF9nCi9ZmZ5ZDmMuMf3diAGlGIOU1zMYcSsmpCgK0XCYI76ZRaxi58JI68g4n0gAt23IiE5HVgOWQuQYOh1G8n6NAnYB1nj0OGOyW6vGQmKR9GOs859bPQYG/zcNsTQd0NWMVGppJJIXFqGhNZhGHkmUAtv3wunfc7PJYViA4pNGokYfBWRnmMQKTLJ6uPYPFqQxQHfT4K5hohfTAKrDwmVJqKGbJHHVo6EYrdxlALBJyN6qHAfuR5zmd/v9bxN0nxk8hYWG4axSCiXA9+An62Ej16L3PLdamv/Mhh7DhIodWQOBTEZKaythvMapORBhBp0ESW0krIxF9HQcKLKwQpCwS5CcYfzjsHdqURC3xQDlYdcoK0e93BEF3M1FuqQQHxZzxac77aaEc2uQ3SdlLEyJPy2eRxTCcUzxdLVewzXQ1kjopsL3M9zaOP0LY/jeqIqw2xE+5v992L3ewlSjDYTsWOb/EwZivsrNaxdvp+P3NjnesyzBePISe6vklBALzZ+hrm9DpR+Jm36JhOndGcQgf54vAuIyjmFxu3TxsvFnos1nqc5xm0OHNoOI4vcTrV/SxukIsM0B0Y0Go5BRHNbEW0dJEIBUpzuJW6/UePp3QcF5UgpW0VUt0kKy3CixnWH5yiFW+Qb/rF+71SiBN8UwuX8FFHSbwwRS7vH9y8lrKbVaO3mIGVqC6LpIbT2thMng/P54MGuHiIW84D7GSRiF7vQZrfO/SQ+0ub+zkJ0kDYpeYTieSGi90JCES9CcX6HER9/Eq3PHMNdhNZNn++dJxiPfBVG5KH4zPFEyM8r7v9JxAdOXr+S16uvvsof/MEfsG/fPv5PW9uvQim4YSe091/lawyRmLgaLdCDiKnl+f5MMic3syqBCdD5PFIEUpDwOsTUF5JReigAXoYRNxHxbq8ihWcXUkZWIGbai5hCKx90Y04lAuy7iWTV3/bzkxEDvx8xuV2GeRYR13gVYmgvIsUgnehc6PdnIcV3OhIS+5FwKkWMepnhX4OYXQrevoQoA9aBFIuFRBqQbYb5bDJB5KctJk7xVhNlyr5JlM8rNgx1ZCoFUKL3M6XUUrtFhGsxuaAfREwz17DlqM+jr3kuz/OY2z1vQ4Z3AAmQdcfgO+3WvwN/913DOJI48FKMFJVJhjOfOA1ejgTWQfjoFcbdEo+xDMbmG5ZDhmUxYQEcSxxiWOV5bHUbjyKBu49IPXOn8TINCc9Wop5xn8fYhYTRHsN3NRKKKWYuWUYOIOWqx/1sJioVTEU0s03jysRsQtRdvdzPZxMVHwoQHR0mUstkI+F9G6KZFMe1iaj33ILo92rhpPMl95OU42a3XYVouh7RyD2as0NLNI6jt/vZ8YjGJ/mzyXO9BilhlxpHZYZvARLo443PauhvQwJ6mGHpIiqiNLmf9f7ejdZVEVGW7GNoXbUZD7cSB7vGE1WBCo3XA4iuBmHkjXAkWZHaED0Vedw7/bytXi0ribq6UxG/eROObkcxfuuQ8lPlz/v6W3A1UuIakQW1WO9lqqZsdV9TkOve8alH7vV430RK4kLj8y5EM8lqfZ/n6mI0z9OIBMzzoX8P4slryJT3a3nNeICocZ2P1uJk3/8msj6Wup/nyNTSPvq4n9tPlHwrRnRShjbvB43T9cI1FQQfLPH4ZxPJ519B8YmHifRAyauxlVDAV/j+do2nfweRVqkeaHc983HG6QB07vL3fLeVTXgnTtQ1+CF/fo2uz33uc0yZMoVdu3bR1dXFe++9l/l0dZ3oiTvpAj7+tTBLCzrFbXQg4ZJM+guQAFxCCOh+MkyPexEDfxm5T7r9m+PJ+l+D3K8i5eCHRKxHG/zscfjoFLTQq5FL6mkUNH+57z2AgsPrkdt4EhIQu4g6kV1IkZrhd2t8bxiyLk1BgmoU4VotQEzsC/lw1WExq0K/l4KyVxO73zLCJZEE/0VIwMz3szXGU3I3T3Y7XUjol0Lv3VDwJSAX3rtHTY0CRp9uWJNSNYCEQLJsPXHMnC1CLswkOFoM4yw31mz4xhLuqyokIMqMjxo4dDuMPN/4eMJjewYJ1BvQrnshsn7OIyqPPGCL3plE+bly/7Ye0dB+l5R6FF6+FmbNMZwDfr7R8K4yHieggzajPN4apCxUIAFT7M8THttk4FF4dwhOuZ2o69xGhCDMRsJ2CpHupIewhtUhmn4euS+LjNsKRM9zj8FtPWFFnIkE7V6k3Bwk0mqUud0OeHclnLIaradnyYRQ0Ibo9fNE/sICZFHZTFR92Irott59PmP8XYyEaVK0DyKFI4VNdBApWZYj5XEHoqcatIZKiBQtTZ6LFPdYY/x2G/eHCIvuDLQOpyOF1nFjfAP2H4axHycsf/nG1/2Go8X4XYPoqsFtTYPB5ZA9Ha37Ac/Tm0S+0WLP2VZEzzcbr2uR4vC+5yC56NOWfyuZVEDvvg+njPI4xxuP6xGfux3R4gFic9ZBJDpOf18iYzk9tBlGrkcbrsmeh/1IMbqVSHg/z3PY6PE16H1qNIZ396oCztG3IOsWIrXN60TarN3oQMhqtPav8bzVEiElqz3uFHsKkQR9C1JGfwg/fQ1+63bPQxVSRi5Ca6uDKPFZS8R9jzMcSbmvQHJgFlKGc5CiV2G8thmfGNbEu+cjiyhE9ahn/M6Q4UyWyCloLX1P89r7NBScADGecQGP/5BdwHt+fVzABQUFNDQ0MG7cuBMNys+9TiqAx7s+nyVGej5i3Nlo4d6JrCKTEDNZhBjdEBJeG4kTaJMQU2h2m/lox73B7VUTNX3nI0UrnSZLCkcOEjL9/r8UMYN00KCbzKGC95bC6AcIYd+O4k2eJBIWv04kT02xgtX67d05cMqFbvcwkZIgCeg+wrpyNhHs/wBSjLYQcVDXIcUjCd1s/z6XSN2wkaiVCVGfs8KfFiIn1zbgZnj3FjjlXsSkb0DCNsE3j0isnNwjZUig3IUUiucJ9+JM4NNISOUbP96NZ04XJpf9NiLtwx7fu5qobVtAVFIoJpK3Pq/njt4NWWcjwXOTcf+0YR4AXoT3/lFVVTInm9sQrVzq59f7/2uIQwj9xu1h43AmmvflbrvZ96oNdy9RT7cS0VEPot1kXT6ANjh/gWj+HiSQJiMLVZPb7AFWwv4DQs8nZnrMlyIlKgW8dxAHDFo0jzxgHM4k3PhJwZyF6L/Kz++B9/bA6MuQQH7Fzx0+po8BYm3UeQ4fQ9VRKtGG7HzPTwsR6L+KSJ6cBPE3EP10IVrpMaxpA1GA6OXPPZflRExkih+b7Oc7EI3O93y+CfyJcdhuXHTAi1fAeStQ/NtW97vKbbUSh03qDGuf4S0k1l0e2jC8jlyUvd7MjEcu4bQ2FxHu1hrDNdO/Hza8u9EBlBVEHdoqOLQMRi4myrotJg5/eYP49gNw2mVEubR8lNj7k3wwznYrSmFVjeh1G7rOM3yr/E6ywuYHftt/AGWbUEiBvS8ZXlSKNg3LEC22EymskjV2FlKg2hBt9ssTkDUdHeBoQHQ2CY4+AFkpnONhj/V5ZEmscv+F8O7dcMqZRIjCZ1y9KOF/F3Kft3mcLWjDsdvz+b7hziFKZfaitTGHSDL+PJGy6XLP486TCuCv4jV37lxuvfVW/tt/+28nGpSfe52MATzeVUjUcMwhknB+FzGa5AJ8gkxZp8EGyF6MFvFyxKxKEZNchwTHOmI3PoAWeD1iOM8SmfdnkUk70vsoFFzh9x9DDGuk4UtxLjkwOsWI7EGWhGfcRrfh70CMcjvhJllBpkRVCURW+WKi3NkQskDtdfsXEdUOcpBwnUXsUstwAj+i9u0aMlbJF3fBecmF0mH8rEIKWKPhSUHXE4hYnUGf2F1muOoJJbiUiI8Z5rFnI+bf7XtFnpunkaVmNdphb0OKRDZitCsNS3IJrkMM/XkiuDwfKbCF7mMICf1S99VKnNpbD1nXErnP5hGxRN2ez20w+hr3cx+ReqYW0ctKQhncRLhQq5ESd7VwyxeQYnwhceo7BaC3+b3Zx8xzPlI22ohTyemdV4nTxs2EEL2VoLvFMLYExq4j3MgtaNMy5PaSZek2JAD/GCmZo9zXZqJGdgFRBSZf7x16HkbPQ9aS2cSBgbSpKDSe+o3bR+Do+5A1hjipf5mf7yOSGA8gOu8jBG0hol+HdrCXzAnt9zbC6OlECby7EP0uIKpDNCDlq4PIBlBExlXL/YSFcA5azx1w3kzjZCeyKq03vK8gBethjyWXSHmUXOm43w7iMMTrwC4YeSqiqUr3PxdZ4xd6jPVI6VtIJicheG5rEf02kDlsMLKLCFfIRgcdphj2Tj1zWjGRpLkU0fe3EE8oMn7b/XcrUnyOjddN3owUVzeDyE85X22WJUU5hzjNvQWtg61Ejesm4tDUbKS0JXqZQBzCOw+y8jx3O1AuxPUwuh6ynkDrLx8lu/8famdwB2Qnq3O7pyJZBjuBasi+GSlouUQ+yArECyp874dI0W065tmJaJNTYRibiWwSc4lE0Q2Ih53I68N02/6auYD/+I//mFtuuYX29nbq6urIycn5wO+//du/fYIg0zXsX37k/9LrKcJttxEpdL1E3csDiGF1I6IdgOwzoXcZEhrzkBJT4WeGkODeTigs45HiVIuEwTLf2+M2dwGVVv5y9J1r/G4/Uj73ExVCOgk3YhdinBv8vQTN9pNOajwLMZBcMlUIsn+DKOHluCbWImvfomP+LjeOyhBj2kjULk74eBApEHsM6wASIrfDeWe7rUJk2boJCYxNSDCk+J+V/n+U2+nxHIzz2AaIAyV9hJsvuagmemw9yCI51e/NQwJuHhIouUQdzlbEXDvd9lO+97z7PYQETYo1K/Xfcvc3nghaL0AKWxFRo3efcbWPKMc1DClZMwn3dhlRC7rY83gWYvirCUHf4HlabXxOJVIDXUOc7Kx1n9WGv814OYzovBDRQ4HhHIPo8SmPJc/zmuL0ctxOsoQnQZVvfKa4pYOGbSWyVhQjwVvqOWggYsdyiDJvbe67wSG3vUhpL/bYBpDVMs17NnIdW0hm3Y3ouBApo1v9PbnwCj0n7/vdi4l6sn3uez6iCbvzRycr0I5j5qUX0W2un78SKRkpLrOEONjUbVzscZt1RNWYFAIylzictAB6G9CGYIzh60S0XIE2Jsla3OUxpPjfGcZTld9de8y8TfTcNRKZBtYTyYfHON4urcUKtzkWKUBNBM9JHoJmP1OMlPxJwO8RlTb2IctYBaKzPkQrBxDNVxJhNk+qvf4HPIaL3X5qa51h3otoPCneA0Ri84VkNsZvL0M8KPGhtPmoOAamLUTYSLv+H309US980OOrIGN9zZ6OeOFW/T/2USL3YFq73Yj3z3FbC4yztYhv7kMWzzHG61j31eExTSA8SXsIz0W/5yZt7E5ev5LXpz71Kd544w0+85nPMHXqVM466yzOPvvszN8TfZ1UAI93TSJqM+YQJ0E/h4LPSxEz/0u00Bfobz5EjdEpRFb8aYgpXex3r0KLehvh+hxG7Mbt1s3kjAIpj63ASuh81vdmE0ldxyKLQZ/f2QydP0AWtiYySkvVx2D3UuhPSs0CxIhKoP8tGLwEMahN7uNcQmiNQkrnYeBFaHnBz/0BEXfVi4T9XuPmgHGx0GO+FAnMKW73kPFdjhTM2YghVwgmLkVZ9Ve4raRQrEfJaUsNwxi3t4soAzbeY6hGbsnNSJg853lpIeqa7kEuqdlICaknTraejSxfs4iThJWGIVl5k2LcJDxzMVLcBqHzn5DisIIQNHsQja33/dJj2pjn/1f5mSoyVoNtB/17siAOGh9fIeIDO4A/hDeTNeKgYZxBHNDpMnwNcOheQvlr929DiKa63W6P8LP7NiSEi5EQK0fKToHH2Og+1hv/kxFt53j+xnuuVhBK5Cwip2ErcpXPU7u5H/c7afOVXLJ56KR6i9t6RCECZPPBkn1fQvTWheirGrlhC5C7L1mzWvw3WYeqPMYWtPaSu3mQcB3O9rg6kSLQTcTnJSvrF4kKLmmtdyAL4hqPoxJtmtqNv0bgPsfFpgMGI42n7W6j0n23Gc5VHnefcL97O5lyaxl6mUakbhkg4nFLCctwD+SmEI+pyM36Mc9BEXGobByR3D25MfuIzcvLsH8z4YEoRUr7lcbtNBjcjPjCVKQ4TiOj0Od+CfGZTW6/G81/2qCM8f3xRJjJte7/PDL5FU87ncjhWOzxJhqaRuS0HEDrHUQntdC+HSiAlh8BK+HVNxDPrkKW9kuMh3r9zg60/vKIeuvzPI5uon73NYinpflJm/ZmIin3PGi/B62Z6/z7IygEqYDIlpBCaE7UNfQhf36NrrfeeuuffZqbmzN/T/R1MgbweNf8rEjAmYMUor9B8UGTkZBYg6wym4iYnHr42xfg9x5HlphGpECBYk6+jpjoc4hxTiTid+ai3f5iIg/YasQgk8Wnl8jLtpmIe6kllNZKxHC2IIVmnftKylAjIQSa0S54v9tYhhhKDZkM+5R4jNWGayOx6+x2f0VI8enyO2WISXYaXz3ut8LPlfr+GreT7XHfidyXKUangojjuc8w7jLeqpEwvMN4SuPqQ4x5ue799QH4/c8RucqakPD4JBIIKVZrFRJEzYb7f/r7CpRiZDkRczWBKGtVRVgB8z0/u5FA2YQExfmap0PLYWSOYU+C18rBe5th9PmGYTdxws+u0v1LYWx6pxcpBS3+uweOroSsjxuvK/TOe9th9JnG2wwk8OpdmWQMUbHiSWLDcbn/fgcJKrv/MpVkknut3c+WwIuXwXlTyCSH3nkZ1N3k8c8wvHsJy/IYJOiXILrtMF5nw5rn4YI5xmWD8THTfd5n2KYS4Qj3oMMKeRrbu0+7CuNXiRJ3jxre+UQowN3w0/XwW8k9ep3n8jtoLa5G1ukDhqWNUN4aPT/TyFSEObILRnwMOAuOLnFmgEVIya5E1sZRKhd43jmIbm/32Mr83BXIsp8UkQXG9w6kRC/0/WwifjZtHlLs7k5E65cT4QP70SZtmOd0BXEitY9IoF2BLPeHCYUquYafIqOIvPswnFJp/N7otuphcB9k36Zn2O/+cz2uR/zcDUC/YuZKgOy7kcv1Xi3tWecYV1cRefquRm7wPkSLc9HaSx6WOs3vkWdhxJ0efwpVSfFpXUQJzT6/+0VEozlEKqRNnpcUMrOFODDWgWihw/dKNBZmINosRIpZClWYRLiqL0Y8u5s43TzG7a5B62wfmcowR1dC1hQi5GGMKqe0Amd8jEx9Z54E6k9gDGAVjPyQTEmHhmBUy69PDOCv+nVSATze9WyWdmhnI+Y7G/qXQO6nkDVlGmEJKCRcE8mVc7nvbQJaof8FyE2B2PuQIjWNqOc6zc/WIkVkERJYSdDNUDuZvFcpjuks4FSV8JrAMakatpBh1ACnnE2Ur7sLMfJpRIB/E1I4e1Hg/BQkUJYZrslEvjJbZliHFLU2JHSmEe7rKsQknzFO6siksnl3F5zyObexXuM48iyMmIMEymI4dD2MnOm2xiOBUIgYeg5SyHYgZrwdXm+AsyqJZK3VRDWL2cSOf7/xn9xKybXZQMTAbUMCpsn4b4f+5ZA7CQmVDuOpBikUNyFhnmL3KpGyM8d9tfu9d4yjWvcxhDYUV3ls2UQqnW7DlJSIISSAr4cjN1rIrXC7yX1+BVJW6pFi+H1EgwNEBZD7PKYxiHZWEcmF6wkFbRJxqhv/1oQsD5M853VIUb0ICfnPo81OIVI0DkDvZii4BFnaXnAbhcbNVkTXDxuG8R77HsPwDNqcTEEbsGHG3WbCupYsd2XGbQPwOvT+ExTchJQ9W8T2b4exKX4tKefwwQoQk4zXdHDmBd97iohLKwRqXJliFKKBFmh/DcqeRnGYsxFtvmMc1SLamoTo9kZEAweQYjQG+LKr3ZwJg29AdqXmmwlow9iINKTlhn0HOuWcXNjLiIMO64j0SEVE4vSJHmuF+3wWrYkywvLbg3hCNtogPWgcXex57kSxf8lVeStxCKkRbeJeMJ4XIBrbhFJU3etn5qE19Fmk+Cbr7gY48r5LyVUjxehy9/WK4U+WsuuOafsitBlMG45SotpMDpGSZx4Zq3r7Lii7jNiIPe/5GQBehs43oGSKcdAD770Co88mwkuKiHRJmA7ShrDZcAwn8rZuJ5OH9acb4beuJxTLVcSmqN1zMI1IrdQA7+2A0WOMi/OEq94DUPAo8IcnFcBf1esv//Iv+e53v8tbb73FK6+8QmVlJQ899BCnn346ixYtOqGwnTwEcrzrHSJ2pRDohNyr0eIv8DNb0II91feHkKC6CC3oLjJZ5nM/RdSinI0EqwPf326A0xagBb9AfVGJGMshxLx3IobTo/YyiWdLgEH4nevd52Yim/wAnJKDhEAnEkjfcds9hn8YkRB6seFbgZhOslCXE4I0wZAsgq1+d65hLff/3R5PP5EPEd07pYJwVU9G+a4qkQJQCGyAkfchZa8YCadk3VtgHG83XvI1zrMOEdVC/hwx4my/97rnaYthGkvUSM7zmCr97PVE5v27yCRCzp1C5N7aSeT8ug8JmHoi/ie5dSCS+u71+53GbYrZS9bSbsOUYvrK/bnccA15HnNgxCTC1buPOE2bR9SbTrTbg+hzl9udj4T1nxlXLxLCqszzVQe8D4dug5GjYPAgZH/M/eUSKU7akQLfhpS0pGhPJJM3sADP8QtIiSpFLrO1xuN2QqHuhEOv+dRqchk2GYdpc9BtnOUja247UZs2uVg/CQWlhCVssuZ97GREt1ONoyqi3vV2j2U2UaljyOPqRmt6BaLzPRpL1hT32aLxlp3tOctGQrrWuB4ic8hh8FbInoPWwwpCcS0FnnGpwz7ITocfNiHFpJKg2yeQcp5vPGcTB3S60DqsJizTfcZjr0+qTiGqFc1Uv5l43nxEI8lt/LTbmErkuuxGfKMP+CvCSr6QSO3SofnMuDProHehK8NMIjapTxn/HcgqWwEjHDbBi0Sd3EHDuhWtkW8bJ6uNp0eNi+TSrvY7OYgIR3pszYiGR7mqTJf6zFTYmO5na6Bkr99fpD5HT0W0vQLRbx1xQDDxqPNQbGkRcvWeYbxsIA6sZMNvPe4xv0zENh803C8SCf4bkCyaB6MnIb6Zj2iuFgqmoLV4Iq9B4MPSP3/NXMCPPPIId9xxBzfddBP33HNPJvFzUVERDz300AlXAE/GAB7vqkfMoJUok9ZE5Pl73X+LkZWiCe32biZct71uoxMx+GKidNpOMm6b01J81NWIIU7w+8l900uUWxsGbIF3k8UqnzhxO02/v3c7YnqL3eZ+4hRzIRx6g4gdKkEWihRruFYwMcHjyjOcTxAuzjoiK36HCt2zCfrvR0ywijh5+n3EuIuImq0DyLJaadwkhbieKAO1lLC27HOfyZp1oZ/9oXG9ibBE1iBFuMNjXEww2MVIkOd7zMnicyESIhOQ0rff788l3OGVxukMJGS6kFJVSqRP6UQMe8i/pUD7UsKiWm94krvtT93XbsTUC4l4OtxO4zG4Wmr43ydOo6b4oVY/Uwt82f+v85zu9/dJSAF7AlmepiIlM7mpkkJ5F4y0ezb7bs9fIXH4KFk1b/b8JngL0OYkxWq9TsQVDhJlueqR0jVgvFTBu6/Z6juRSJKcj+hon8eRh2hkF9oMDBKVWEqJMIdLPA97CatWr58bROvrdY+5mUjm+3nPG2TqCGdKj13gNqqJ1EGLkTKT6GKT28lDoRkQhynmQfY4t5mU7kIiLcpZxl+Zx7YNWfnyfK/Qz2UTB8m6iYoT89Fp5wqkLCaaaxe+33sBTploOLf73S3EifdCRPOVbrsRhS5MIujsSY9pNrLAbSbWWw+RIL7FMDQa5kEoSKEyoA3aLiKlUhfKJTieOLT0PrFR2+VxJH5UjyzHab1UIddyHhFveVieBbqNn+Tunm5cpfWdQkBuNWz9hqsUhZ/k+N3XDUsFvHcgvnMJsdko9Dx2eZ5eJOqIN7iva42fFsPQDcyC3ZuJEJ9tHu9s46KazGafISLhdw+RV/Dk9St3ffvb3+b73/8+t99+O9nZ2Zn7U6ZMYefOnScQMl0nFcDjXcklCpmA6aMvIWawDCkLY/3MXX5uAlrwLce810coAnN9fxFiDmsRA12EmE2b23wKBUrnIQaUTuLNRMKv27v4w0RJs2fIxEuN/jhi4mWISfeQ2dUyGUZ+3HDnICH+vt4jFykOyXXRQpxG60RMLde4qSJTWqkP/Z77G362m6he8JjHNcrvdBOB7kOEEvBtomLDXKRYNBFK4Ca/m5TVPKT05QgvvUvJlMVipvE5y/AmZplO0U1F8VDJ6pcsgTeRiZU7ei1i3Os8R/Wex21AEbSvN34HjvnNVr3BFJM5mwg6X4aE+T7C3bXH77zp77MIq2g1UW7uPI9tM1HW7hCRiLkSfrLWY5xH1BFeA69uRhaLTR5rA6KxYUiBeZk4HXwhUgazkZJ47EGUakR7e/17K7LqVSDlMeG2mHCP96OY18mek1okePOJgw27ySjRp1xBJqcaOxANJaF9B3KRJWE43/NThNbdk4hee8lUeMlYYyvg6FIihGEpoqXZRIhDjfv5c2QdTQrjAFLKmvhgSbwuIh9fsoj3IQt7C1JYFgmWziEixrGbiCHr1ztHd6Bk5FuRgJ9k2JJS/TqhbDaTSZTMZLSmytH6byZSqZQarsfQWhoPo+cQp4OnEXG689Cczva4OpAS/w5xetqKx9GDnodpRIqpDoJ/lCD6Gk+cAj9sPL6EaDCHSBuTwmL6jI9BWLMW8cBaYI1yaJJDhH/k+/saRFcgPpZcufMN03gYkRTg5MUoNO7WG5Y9ntcu/96ocfTuIQ6agA7/jSVTVm/0RYZ9NwpvSGsvbear3cdhFDPdRGw4nyHiSB8gs3mbMM/tz4bef0T09x3ghzB4HaK7SciiX0tUF6njxF5DH/Ln1+h66623fu5p3+HDh9Pb2/tz3viPvU4qgMe7LiXSQHhHn5XiWfoRk/9b3acRMetCIsXFBf6cgZhjm9tb5ecWIQY+l4wV7+hixBgmIQEPYgK1/r4PetuAbNi2nUi/MBtZIQoQE7rA7dQDj8PrbyCB/h0i3utCtNjWwaEGxCRfdz+tKI6nG8XsbEXKSRdQCbvvRMx3DjDcucZu95juND42kSmH1Xu3701DwqsO3nsN7cQvJKwCK43b/+px/QVR+SK5T+YbPzVkTiV2Pg0F1xJJc59we5VI+akxni9HTLnbOF+PduMv+N4yMrnlOghYWeD5q0KKxWoom45oIB8JgD7PRxdkXwG92/3sEJHm5U1CEd0K/Umhm+2+Woi0HelQy8uEJedG/75eiXApRkpBGfzOFOOsy89uAM6Dc++Fo0PASDhyAAmiHxJW49nIatSO4kE7jMcCFB9WhxSxPuOzFtH8LuO62zAsREpCCREXNeDnVmm8mQTWuwxHB7KELkL0aSvHkR8gobzW7aU53WOczka0vd59pvCIVfD2j5CAXW98DKrvrI3warIG5iGl6AXCmteC1k6j4k8pRcrLTLezlkihs5soTVjtOT7sz5eNI7szjzwNJaMQrV7k57ONzxpgEmSdY1wtRPTdjNy+yXp2ge/9Phlr3k8OoFjDNCd1RF64IWTxrUIW5m60FtOGbLVxVu9PoXGS8FWENj43+b1kleyFrEuIcpU9bns+oQyej/hIcvmneMu0aWiDtx/S30zVlloi/nMzXDAT8cxy4Br3eQM6sFHh8U0U7ljruVuj+R9c6TEmvjzbn7mey9XQ+SPjagDFzdYRCuIZwl3BtcABeG8xkbrpbLSuLvff9YZzwGNYR9BoETAPfrYdXTOIGOnnPQdFhmm8ns3kcTxbrvJDrxn3dT5Y04xoMveYNofc7om8Bj/kz6/Rdfrpp/P666//s/s//vGPOfPMM//jAfo/rpOHQI53fTJLjGwyYWHIQdaB5NarR0zvDCQUZqF3Svx8stotRYt0MVrAZ6KE0tV+bgZRRL7J7U8mUi40IIZX4v66iNNlY8goZsxFgqcW+tdD7ucI68xkIk9YB2JWpUTOrDwUzzYNMdMriaTGbUQi0na3scvtJYU1KVRpR74cMdYt/q0AMd2/Nq7uIRIKFyOl4VOIuX0PCZBpxMnUaYhp3moYVrqdXMKqNOh2ypHS9JDgPtQGI8/xPCZX7deRwF7j9iuIlBN9SKlbQpT/2uL5W0G4h25HguAP3OcjxOGcP0MB7iD6aCFOI75OFKlPG4KLjb8fo6S5B5Hgaifq/i5y+1OQovEEITTykVDPFsy9S9VVDpC7Es3vJrc3GaWS6Icjb/jk6kH9T49h+zaixy1E+oz1sH8vjF2BLIGthLVwOXGqvNPj/4rbGkcmxcuRIRgxnUgifoPhXoY2FMmCNQnRTK/HXgY7H4e6xWje6/z8C4TCOwutgzakBPZoLjp3QMktnrM1xvsNwFcJN/pUJOB70JpItDCJSBQ+DdHUZcZ9ssYVGb87kSCfa3hs7W1Z6oI+txGhH1OJ+NFqIpcfxuludEp9m2EpJTYZ7URuuGTtnAz9233Q7HLj+3tobZQQScRfJyyXhwnL4mTDk8IdHvEcPnpM3z3G0ZVEZZzkok3reK5xXI9coY2ez+sM0xS0hiBiJLd4PvOJUpWF/i3Fh05wn1sJPtbluSw2HCleMCldh5DnY4zH8LD7L0ZWOYgT6BWGI633DUC5wltyryY2n6eiub/DY1uH6K7b+P0Kov8bEP30EK7iYrffQebwG+uIk+pTPFedhNK9xmOe7HnZjLJRfIlIRbUU6DyBh0DKPuRDIO2/PodAHnvsMe68804eeOABrr76ah577DHefPNNvvGNb/DYY4/x3//7fz+h8J1UAI93fT1LzLWUOHCxDgnhUUjg5SMLxibErK5AgjEdFLgILeRNSDlJ8UfNwKUweI8rh1zr9tuJPHWVSBC0IUG3GSlXhxEzmI6EUWIWxYjRlKMd5mIiZug7RIB0KRIASZHoRsJ/CpmA5qPXQdYVSJht998NSJjvhDcfhzOu9m9ziNOCmxCjq0O78CLDV0Iw4QbjsNv9rSROO1/kcQ7z37v9d4DIlVXjtlKM30jjtJU4tFKDLCBDhGsmKb5TfG+X5+h+JOySQjLebX6KqFNbiE5hJotQE5HIeBRS4Hch9/9kIklvqT6vPgTnViKh8aBhrXaflyLFq56wduUjxbYSCarzkdCpRTTUgGhtI1IsF/rd5xCtppiwycZJsrzmIzr4hk9aziFOSU52H4XG23jj5wKkGEwmqmWUus9tsP81GPtVQnFcR7hqtxK0sZSwmJ2FQgwa3c7dZIrdM4iEZ5P/b/WcliPab0ZW0WqkCKz29wIid+QkRJsLj8FnNlIkKwjLU1L2kuWhgDgxmlzRVcb3cCLlUytSBnqPaeMZ4qR+NVE9o9r3eghFarz7LyCs1cllvgNZ3TcopjY3nfpMayatx0lo7eQRpRO/SYQPNHkem465V+m5XIh4QOJHHcbRIqQkXYXWbRFRcWIfURJuAqK7xNMOe/7aER1+D216cFvb0Po4FR1iSG1XexyPI3opJxItb0BW2h7jqInYQOfHPPavh9yJRPWkFsRLknV6J6KTRYgG+xCfWY4s8lcSFYyWe17L0ca8kDj8kmNYi4hqHgvQGuwmrPh/4u/Nxke2cTYNzffLRML0pPBPQvRyK3EoZhZBNy+juawkMkLUECEMxcA3TqACWPohK4Advz4KIMDTTz/NnXfeyZtvvglAeXk5f/Znf8bVV199giE76QI+/rUVMZukzLUQAcN7oX8fYgaPo9iQAX/GIMZR7P/biHicQSKOYxRkn4+Yy1eBrxECb5Gfb9E7R76LGMhBon7pVnRiFSIrP4jB3oEUs1Ki6kMrUZ5uHZH6pYBMKpUUyJ01HlkgtvrvGv8+CqiHM27xGMsNfw9SDPrczzeNp2FEvFKr8MbFSAB0I6aXbXi7/b0QKTE1hjPh7GUkOIcjYVLicY3x/CRrzGSk/J1BuF5XEcmKe/ysXaeZ9BBjiFOtgyheaRJSDrf5vduM0wq3td+wPGAYbiQO12xBymuNQKITMes8pFTMOGYODhveSYbzHcOTTSSqrfInWdxWEWXuuoiqGLVuJ7kbi1GQeoKrWWMYkay2PW4zHQqoJk4Qp8Mchcb9DERXRWSqyIz9lPF+g8c8GV0rkNAadL+L0cYgj0hsfhayMI0yXvPddrL67XT/Kw3j+X7vZiSoNyO62kS4rtOmqQ8ph91onp8gSuuVIjqch5SA2Z6DWqJ6SHI1JivbBW6rn0zal4yFqgHRY6PxlhTfZqQY1iL6zSPK3b3k7+PJnNBlERnlj72KqT2S1kKx5yqt+eZj7vUia9c84zjFLS5A9DSJcGnPJqqTDHf/cz2OtX5uzDE4THGbU4j1PcZ9zyIOe21GbuFOvzPcn63G0zi3eZHHWYcslXVo3aQNXSFR9QakEI1HPKIE0UgfGWto7iXur4fYGFb6ex3izaXI47CfyGLQiOZ4tdtKsY0LEM+7Bh0Imog2Z4sM/2Y032kzM5M4ZPLEMe03IWW+h0xGhkxsZhFaL4uNm1y0tg6jNZfc8YV+dxaReutm42XQeDqbOPh08vqVvC677DKampr4p3/6J9rb23nnnXd+JZQ/OKkAHv8aj1xx6/x/LWEJ3A2544Ai2L2HYF7dqErHSsRcdiJht9PtJZfHXLRznO9709GuehliEGnn90lgEEYkRv8mEYNVS2TyTxayA8jVst9tDSIG+Fk/fypiXsOQJaqKKJ+U4sc2us3fIyxGA8Tu90q3W4+YYBES4BcSQqMCMcxP+rlSw1GNBP4Ow7HHOGtG7tSkGOQjK0WjcTscMeMctCPvNE6H3F82Yqb3GpfXGFcVxKGQTUjgNCBhXo6E0jaNp/01pBR83/B8zOM51X+/7XcLiVPJ+/zOGONnrz8prqlT7ecWw9/8E5FiY4zxttpznWLKlhCnt1O8Xqv7vNy4SsrtGQTj30C4jJLC2EqchkxWq/WIjuqIE7873c8a46WUTMxl7z6k8PQax8lFdeox4yhDSk6KqUoHLa4hTszeYPjGE6c2B4hUQT1o7nOJE+c1iBY7kOK3Bllkx6M1mU0cdJpLWIeTpfhCJMyrEa0X+X6576cQjtluc6HhTlZ/iAMqPcRp22Y/00CU9BtESnZyyZUZV/lIsH8Dramd7nMDUqSfQIrHIo8nuejLDP8dMOLTcOgV9zOLSHeymii5d9jtDCHFu4dI7zQXrbNmj+e7RFWeRqT4t7j/UuPjFcNZRBzq2uz+LkCK/l5ENyk2dCJxUCwb0f9eaFniuXseKTFFiEbGoYMV3yDiefcRJ56HE4pTmb/PQXwy8aRkYevxvVfQhqzNz9Ui78p6IgF4PVGxpQveXUvUlN6LNuJr0QbkW4gGrzFeVxEpj3oQj12LeECywKc5r/D3SYjGm9Fm8gKkBCd4+xGv6iCU9c1EebwW/+1DPDRZvNP6HkJ870ReQx/y59fomjt3Lt3d3QDk5+dzyimnALKezp079wRCpuukAni8qxQxniLCMjcJ7bjOQox1Gkz4DcT8QMyyFEqeQIrK+2h3O48ofF5KuIT3uc3k1l2gZvav9G/P6f8jXSjmq47ID/cUGVcy4xEzuZCoPbkQMcNRyDpwgfv4JPB7cGgvccKyEjGrzYZvpsf0DhJ2yUWWh1yPm5AwbPD35OaoQsy8AgntXuDvkXDMR8y3HTHbAY+jEjHo5f5UGF/fREKtyO33e7zJtZMUo+TmexTt8ncgBaHIbe0hUmhs8ruXEPGCQ5rHsvM93iVIMM43rAkfa5HCm1xmTxp/z2lcvbfDu7dpvmg0nquQQJwCn5hj/D9leJ5DLvxvE8KqBtgGg7fpL1OPwXMvYV3YQiS8vQ4Jnz3GcaX64333fa2f3UTGgssliC7ziNObcz3eXUjAAAW3+f8/QUppBcExtvt93NcBMnWBj/zAc3bAsD+IlJ6n4PVniVOmLURM0wQyYQJvv2Ac1freQmA2HFmC6KaK2PAkF2gv4Zbs8m9VhrMBKQ/J7Zs++4hNWZfnbStxwGTIOJtJuIWn+ffvIsHdjdZlqb7vfwXNc6O/16NYulc8pglos7OcqJ6zzc+tQi7Y9YQFtgJGFhtn1WRywx3ZS8TJtcLRR93OOLd1ECnd5Z7XXqTIbEcW1TvRnPcQ5SRTHG0LwVfGEGmL5iG6L+ODqWku8BzMJ6pcOFSjah70v4Ouw8gF3Y0UqjGE4lSL4hMfRHywANFGhZ5vfxbRcrLGzyRy7aWwk0GiKlCz564BhRj0oTXSRIS7zIVTFnucxf6cgeKDZyNrWwdSxmaiQ38LNO6jj/PBEqE70aG1gyj8pRutzxzPZ+JX3/P8bEYbqUsR36kneEO15+YFtPFtJmi8Aq2BEsJDlTYsJ+pKlv4P4/NrpgD+3d/9Hf39/f/sfl9fHy+//PIJgOiD18kYwONd384SE2ol4kv+B1qAK5EwuBQxlRSTdJ2f34oY4ABiIBuQAJmDFu1lhEKxFilVY4kSW3l+bxAJi+1I8Px3w9GPmG4xgvF7aOHsJqyDyTUzF8iBo0/rkezPoR3rU2h3exHamX4WMdOpfjfFNyVlph0J4mIUH/d9PlhSrsEwX0wkA17vcf3QOP0yH0znMBYpazmGeaf76va7lxDF3TcZrwmOZEE6TAi5PUSFE4zPeqTgDDOsuci60HHMHCXX4CgUJzRZ83jocRg5CQm+bj5wIpx71O/+V2DsRGTFGYPc+Zcjq1ghYuiTCffqXo+5m6h9+lWUfmQDkSeyA/Yf9IELkIJbhpS/bMOc4ubsuudi42gbUtjWCqc/fQd+60KiykY3UmCa/G4hsmakGMM0zj63nw5OlCJ39+2IdlqRwN0NHIBDjzqJ8yjP2T4UY/klJGTbgbnwd/fC74732B039uYyOGMiHNoFI8/0GB9GQvBBw7aQyJnXQSRan6JxDz6vn8cmN2kHotexwJ8uhlnLZCE+6N8WoLXch2jzwWPGnT7TjPtGIi7XIQBvPg5nnKOxM8d/8TvP+PutSNlaRKQw2U7klpvi9rqQ4nAVUaO2AdHeG25rmOf/BcRDDhLxflP9TI7x/1XP15VoLS3wb/ei070Pwc42qCtHOJmFFDHHrb79XThtpse8BSmuOfD283DatYS7tpFIpD4BpcQa5nF0EDG6mwznLOTGXuHfk2JUR2QuaEVzfYBYpy1EnOezQC/8/cXwX2+BQw/AyI8RFv5uRJ+XGcc3o4170TH4ThbY+e7jCa+T64lk/U8QufYa0Aa7y7CuM07TJnMMop+LySSaBkR7ycpe4LZXIjq4AFlNv2i8pdjMFcr2UPAl+Pv74b9eRoQYjCNzAn9wnzIOcCdw+gmMARwNI7M+pDaPwqj3/vPHAP7v//2/ATjrrLPYsGEDxcXFmd8GBwf58Y9/zPe+9z1aWlpOEIS6TiqAx7v+IkvCNFmcriIy3PcoJ2DWFUg4ziBOsS1HyZcLEJOrRUynFC389Yi55yAmk3Z9EwmX7nbEGKoRcyl23+ciJa3HMK5FzG0dErBbkTKUYnqS1a4UKTaFx9w7bHhbiTJv5Uhgp9iqCo9hMhH0nnAyCx16SO7nHLc5HzH7ct9PVot2pIHm+9kij7MWWShnEMw/132+jhgfSKhsRe7EXjh0N4y8DFl5XiRKXVW57zLovwdyR5GxyHCF56AHMe+zkJunxPiejwRpOpk33riY4nGkOLO1ZFyZg/dC9kyipuczxs2wgIOdRPzeOv9/OVK8UyB8wuMNiKYaPO67kQJ1wDDPg87FULLEz2x230Nud7JxexuKT+1E878L0VEfYRm+GNHCMqT0zyfiApMrcoCId9pFnMTGbfW6z5XHzFEtWg8NiMaHIcG5B9gChw7DyPPdZo2f6/M7TxkfScFd73ENIAtWN1FFp+eYuU1Wp2ZEN/+DSJC7wXA0oE3bd/y3AikuGxDNlSMlbBYS2OMJZWSp26r2/w8iumkhU4uZYjL1wKlA6+BKtIYuJtbPTKRYHPAYmwgX8DwipOR9wlqdYmJnuY1KYrOZbTgcF8cnNU8vb4ZZDxDpZKrdRhUcuQ1GDCdiG8uIvIpLkXV6oXG5w7BPg/aHoOxTxnsfspiXIyVmK8FvXkTr60009+PRuplCJMxuJCp39BDpruqNnwE/D3FgoozMye0jL8CIyxCvtRWucyOUjPOYriNy5W0mU3UoE5d4sWEoNr7TfA1DtFLm+XiUOOSS6+dnGOYnEc1dg2hnofsuIQ4DdaNNGUhpH+Z+1hi++UQZyAI/X4z4/Q+JpN5tHvtaIl52H65vfAIVwFEfsgJ48D+/Ajhs2DCysoSUn6dijRgxgm9/+9t85jOf+Y8G7QPXfzmhvf8qX33+mxjTfUgY5wKNkNWMBO9VfmY3WtwX6ncmELFOe4hksqOIepbZiMnsBjbA4DuQXY6YTg/hllqAFvo+ojTRJMRc8oiTeZsMT3IZ1hIB6lcRcXPPwavvw7nDkeUvKQ6dSKjUEopiEZHwNI2rDQm13UQZq27EqHoQQ0vWTAjmdxgJggo/12l89LqNw8ZHipdMit0eZPWcjRjfOhg5zu2PQowSP5uU22cg91QifcR4MjWdM7WKtyKFLwmtDUTsU53HkEekheggThpPBF6C7EmIWadYzNuR4jUbWT+2I2tcsggNeu5WoTJaDyK66TWMT/ndVsPYBr07oOB046ECSqYjS87VHv9h4qRojvGyyzDfi9yPlYgGO4/B1V4iaXkRUkx2Irq8HFkl5iuX3YiPI2UhxTRtR/RcRFhJxun5TNWFhYS7NVlsa2Bkp99Jgnu6YTwbzXkfEpL1REqPe5FiUk+c1E7WaoiT2jWepxXHzEs+WgOlxMnmYYh+k7CvRvRd53vVRLqUpKi94/+TVbf1mHHt96cSrdsS4yoJ+Xbi8FAPwQ/GHNPHpR5/ge+3IIXts4guRhLxwWs9X3ONw+cIa9QaoBxmfYnYVHUQMayVMOIiojQanqtktX/U7zyGlKjJaJOwDsqWGC/LiWTSYw3DGI+9E9HPZkTbXUQN6VK07ur83ibjqsjPVKB4wVPd5nbXDk8K2AwylVlGjDsGl1uAQUclTCJK6I3x/Bzw3CQemtzqb/p7HhHH3YrWzp8gOjrbz+4iNvedaANX4PlrQDx2NeFRaEM0cRgp7tvgyFsw4lHCQnupn0su9Uoy6YsyB1hGEbS507DMcNsXIpo/ef1KXW+99RZHjx6lurqa1157jdLS8NPn5uZyyimnfKAyyIm6TsYAHu/6GnGqbACYBy1XIbfAIBLqexCj2U7szgqAbDhyE7y3nAg2fw65WycjxvgEYu7V6MBFrZMPVwIvQ/tK9X1oO2KitxAn/AaJ2rOvE+7eKsORmMVWxLQ6icMSzwmGc2cSSlYxYv4zEMPrdFutSMhsJNyBu4mTi0lB5Ji/pUgRSIpzERIudR5DkcffTZThKjWeUxuNSNgeNo6n+f0DhFJbRdRsfcxw4X4Pua/rCKsdSKhsRwJnC2Ksqz2uTsIyudnjqyMCuNvcXw1S0N9Hc5fcYcm61oCUhcdQaMCphIWvBAmxOsIFOAPRxSSNb/9y99+DmP0eKEjxifluf8Dt7EVWlvmweweZfHmZ1BE1qJbrc0hobiWsKluMkyLDczEKyE+WwrS52AQjPkIcgNpJpKNoNd56kLttrnHyebRpeZJIRbQTCcYmP7PI720k4uuW+94k43wpEYO30zi7HinIo4QbxiNlo4JISXIzYWVrIMIjqvzshWgjV+FxFRhXhZ7bqUR1mGpCQagUrtkPLIRDLxEhF7nw5j8Sp97zUQWXG5GLLs1bFaLBZkSjOWTcrlSqXW73nGQjxfhPjPttiGdMR0r+ZsQHvmNcdJApq5dJTlxDWNNqCaWykbA0VaMNynLDlYfWTrIOr/V8TSMSlKeN3xiPdT+RXirH3zt9fxgRE/e+x9iErsVErsNBpHjdSVQ5AU6ZgxSvIaJk4ZRj5iVZkguh4DKipF4/WpPXIHoc6+Tr5yEaehXR2zr/nUCEp/yB+2hGtNJ1DD76CL7Vg9bQTkSL3W7vFd9LlsRujWXETMM9jQjJqET0WeO+riFCe2oRTY5HvN4KPPi3ISKv4om6/q1xf//n59fgqqyspKqqiqGhIaZMmUJlZWXm85u/+Zu/EsofnLQAHv/6NmKos8mUH9sJVG0jU9P3hufhO9OJxLE7EWP43zCiEUbUEAmSH0c7xmmIYSWBWOhnboSy2UgwXwRlHcAyGLkALf4qoBLeWwaje5GL62L3dxgxg8RoSxCDLSRiRoqJmqStwF8iZrqVCL5dgQRiCWEpaTQ+ShHju52MZYhOxNQf8d/rUYxYPzoEUuTvbR5DPWL2s93mi0hpSTGLOcZ5k8c0h4zFkkvh3cfhlFuQkCvWuPpfcKLWfYRlsRZ+dh18tNxjuxMpS/XwZgOc0UkcyhlDFI5/RzC/ehjO7fJ4diHrb63miAbkkr0UKRNjiPiezXBkM4w4GzHtszyem4DncuGO/lBc/4CI3dtsPHXC2GT5mog2F2d5jIsNw5vG1XjjbyywHyac7jlpRYJrF5Ewt920kCzP5UT85RjDPh1ZmnqNy7ShSIIu330mBTdZU75M5GCcapx2EOWpOpBQTetjsZ9/Ebkqk+B8lMhZ2KZ23jwAZ3zb/W8jEoDXEUnHVyG3eXLTd3ms25Ai+CKRnDuFYbyKNl3t+m3wNcjeajjuIhT2mUS8Vgc6ILDEMHa4bnGn782HM2YjgZ5NnMRuJFJI9SDatzuV+wzvAve3wjDcidbtOCL3ZI7nsgjRSy8RD3o/4e7u89hr0FruQ3SwD9HVK0RS+mzjYBNy66cwkO3EQaoFnptNaKOSLIYXeqyLUPLjrUihrUFKXzUM/giyv4TmfqFxexjRVjdxgCcHKTel7juFwfQQVubb3T7G1WpEr4kuOhEdtyH6uI5MYv2fXQsfPQd4x8nx+9AGaRGi5xRuMd99ryNOmRe77WFu70bi9P4E4gR6nvFUYRyMQ8pcK4o5roTe56FgMQrPqPG8poNHiTbuItz5PYRHZodxcTeZAyysIpKUn8hrEPiQXMD8GgakNTU1sXHjRt59912Ghj54yuWOO+44QVDpOhkDeLzr+1liemWICVSghZ+NFl03URx+H2KoyUWxHC36fWiRb0XKQgoqvg4xvCG9M/gCZOejhdyLBG8TvL4DzroaMbxpaKdfhuKCypCFJLkL2hGD/ipSPpI7thjt7osQw56IGOQa/z6ViIlrJtyoGxAzXIMUoylE4thFwB8jQXuN4boACaSLEXO9xn0+goT8WKRkgJSTfKAeBt+A7BuRQPkKYqrDDOsM4ywFr2frHQ4jITMfCeg2JCASI00K3R4kLG/12PoMM2h3PdXtzfd75e4zue3GAj+Ev/sn+N2HjMdqImg/HeA42/O7HdHIFCK/Yo3n7wl4u0Hdj/64+56ChEY2cfCn9pi5eQEpA3lIKIz3XIz32EYRtaQL4d0H4JT7kOBP1uglZDYxvTugIAdZLXuNh2b330FYwZKLNllor0GCqksWlNyrEf1P8JwXI9pohZefh1kXIRq4Cll8BhF9zkc0O5044bkFxUPleIw1/p6s2EnhGPB7tyP6uAa+dSd8fjWyIvZ6blYiGqxDNJxc8OmAwyIkZPcZf4sQPf3QePqsx7+eKK/2CtqMrCVS15Qazr9EJdk2E/kBv064+LZ5LF8nknqvdZ/96IRwr+e5wvdKkctwDVI4nnWfycq9jygB+TKi1fOMyxRSUuRxryQqZZR7Lh/UXFKM6HA6oqc297/Xc5aD6Ho1EduZTXgAcgzPVEQ/3R7DIFJmq4gUK/n+fqn7SDFw9xIW0HaC1w4Rp+5Xu492tF7LPOaFhiEb0e1Ozd/OC6FuOIpL7CDyTD5kfLYh/pvCSxyXPPgjyJ5ivC1FtPYIUjxfcTtPmGfNIazE8wzDGETv7YgO3kFK5N+gdbgVzXUzikceIFzzi5G8met3HyYsod3ql5sN7/cIi2UFdC6BkhMgxjMxgCM+5BjAI//5YwDT9f3vf5/rrruOj3zkI5SVlWXiAgGysrKor68/gdCdVACPf72ZpUU7HjHMFLicXDXZRGmvYUS+qjFokc5EDOarRNzKNiScb0JCIAnhFLfWhnaOrYgJXuDf1yDhthkxxLlIGZlMZLCfiBjJLMQ4W9zvSsQkFyLmORZZkQ4Qp4hTDN8eJOjGI+ayG+1i65EAKifigwYRs/scwtMiPhiPlayfjxKl1sYRBdE3E1UolhJVIXDbZ/h7pXEzDQmO9YhpHiYqo6Rd8lNuc5rH8yjhCqxy/yl4vchzsMF4mmRcTSSsLhwDe4pp3OCxbiJK2FUYpplIOHcgwZysG9XEycAUBL7SeE6upW4knJYJ/sGnbT15hUi3khTbZWgTcJAov7WCsNIcRrS1k4gPLUZC+6Cf2UkIrjbP2WNI6bnEuGvwPE4hTt02+t6jyFLyj8bZEiT4kiWwGSmAeUgBGIYE8hrkll6AFKavEQrEFI9hACkK3YZhvNu42XjYguigT7jisHHdgGhkDxKeSwXr7qUw4WyixvEW46cDKXwpsD6HSBDeiFzYsxGtzkM09BSRdmkYcSp/E5EKJQX+lxIJq99ElvWZaAN0NqKBqYhWmhH9lxOuxUbDvJ3IHddg+JMCOYs4wX6LcdSOlKakxM0g4u6SgpesyIbzZzvgo59C62MqUjJe8TPJAlpivLYSYSGbPAdpXGkjlTwLs4hDO+Wm61MNUxGiS44Zz0Kk7HSh9fCg2jy6A7LuJg7UdPjZFLeb7T7u9zODxInt55AC3kDEh15JHMrpIGo0N6K1MAfxoHWIBjb492ri8FON52YbUTrzQuNmKxEedC+RhqoGGGY8TCJTxWjwIGQvRetoit9Lm+mxiD9tIEIkHke01YdkziMnUAEc/iErgO//+iiAlZWV/NEf/RF/8id/cqJB+bnXSRfw8a4HibxzFyAmcTO8dxuMzod3XxP/yp2DFnsFEqSXIIZ5AAmyZE1LCmERUgayEUNu9b2JRE6nGUS94fVoN1yAGP0mQkGEOKkGEW/X4fcPEHn7htxvE2IaM4hYvX1+v5TIP/YGil/bgZTJXrdR7v4e9HjeIJLtTiNiVBKzuxwxsS4kZKcRJfXKCQV1MlFdoM19pxinCW4vjzj1luL0DhDpT6a4n8NIAJYhoXWYcIX/ARKgzxOKYLIcDkNCIp3kS5uzHjSHZWhu2wzvKqR8lRO5xJLlbAKKRSxFikM3Md9tyBqxFs0riJlXIwtNjy0MG4kqL4PG3QqPv5E4/HLI/+cTFtAGw1aBFIKniHqiNUjIpvjGpBhdY9x0EO7fckLpKzcso5CFYjdSFrZ77KVEGpVkralHNHEDccBk0O/0uZ9BomLCNCK10h5/dqO11Oy2qolTli8an4uQJWuv79+OLGfrfRZpBnHaOAe557eROVTEeKIU3BL383vIMjjLsPUgep7j8eejNdVp/CSlOrnkNiNFeYmfTwpTkf4/ug+yKhBN9CGaafJ7V3ksZYa7yPhNhyPyjOM03x2+9ypSXOah9bDvmLl7hTglXkiEhBzwIdsat7NF9zIxsT3GVwpXSIpSNVoraT04bplWwqLVSNADkD3dz65HcX273Mck33vB759nvHmNZp3ttm4n4gcfIcIbOuEn++B3VhPWwRWIHicQcbqHkWJZgrwIA57H2QQfLDcO30S8pQV5bbo99hXuow3RXIpT3WT4i5Gldw5S/hxLSwdau0WQvYCojLIWsquJ+MYGMoeUOn/kKotzEA+eRRw2nEwcVDp5/Upe7733Hr//+79/osE47jXsRAPwK3utJmrn5iAhMQ9GD0MLGMhNcUcLiTQLJei0XlLgFhJ1erchJWATEsTJGtWMrHh1SBGoQIypHs3QFCQIr0KCLgXSz0ZMc4PbSHFEu9z/i0Ts3jPILd2CuP0ot5+DGNVOxBBHIab6OhJA7a5EkFwbyXJwluE8ZHxNRcKq4phxbSMCtIvh3QP+v9z43ON3FhMlslLfywjleBNifu1EXsQ6okJJK7LWTCKUZ5CVaYzHuMd//wQJ5WSVqvEz6+AnL0HLK4a922M5iBS973pcBxBtjEfWozR/bUQh+0HE6AuI3GNdKD6yymPe42eLiQMkbZ6H1UQi6/lIUF5JpCQpRda0ib63y+/PQ9IixYQWGm+PI0E2zXCscrtb0Rwn912ypCXL6FREpxWI5va4zURPtUSapEKPYz2R++0qOHrAMFcTyY5vQFbwIUJ49RFpXNqM9ys0h4feQLS+2uNbjug0ueOTMriPsGTfjZTefBj5aeLQQFLQphBKC2SSX7NR3/vXIjr6LFAOLa+htdVCnP49TLjJJxv+tUTZwWGGew9htf668VbscwHJBX+slTfFC5YiZeBUIv/lWjKnwclFlsRuYvOZNhnTDFMPsuweNuzfQ/SXgza2ucB+OPcyz90B93uR+xhFnJyfY3i2GpYi4qR/CnO5Fdo3ojyZQ2jjNcZz1E0oUr2EUvu3xHxOR/RT7zF3qI+/3UGk7GkSzEAm/IEah52mjcRm43YPmvMCf4b7/6eAg/DuHrQm693XfqQQbjOe1vlzF5F3stt4nuL+2txeH6KZ1UjhP4Csmj9ECuJk5BYeQvN9od/5OpFb8Ab3e5bmp+RqqBrmcZYZn08Z3w8QHoYTeQ1+yJ9/xfXwww9TVVVFXl4e06ZN47XXXvuFz//1X/81EyZMIC8vj7q6OtasWfOv6/hfuH7/93+fF1544d+l7Q/jOqkAHu/qJiwgHfDTLhS0fT/QBSXlRMqLNsIK2IeUkRziBPGfImY3F1k2GoEGOHqJ71UiJruVKL80DwnZXUAt9C9DhxLa3M/sY9ovIyonpDi3yYKT5xEj6iVi/sa4nSt9b6/gOPIGsvgNIGHWA1wCIy80zJcjwXAREgYDyD1RgpS2IoLZJmUlxaAd0ONcgBh8MbJCjfF4LnZ7DxCn7urRLr8PMcwm4I+QYHA82P47PS/zPabVwBZ48w3j7mlCsVqMDvdg3J1LnHieD79zH1SdTlhCq9AKmYssnMMN5xX+/SbiwMDLiIEfRjGHXUQpqkc8x9WE9WARUugvQHSWD4O3E6dKx2ksh+4l6hNf4rlNlr/tyNLQCPv3IZfnQo2f54gap+X+bbXGc3S52/8kcZr4GTJ0mXGHQVTJKPBvrUAPvP0OsBZ++jhSog4gIVZgnJUAmyHrY/6tBylXC4GPICF4NhKMUz2+bo+/DY6uR7QwDUaeY1hqiNrEU5HSMA2tiwK/P4xI24Nhr0B0upU4JJANzFbi6Uxd6aTkboPcj3hOv6X2q65HbunlKKxji/GaYkjL1R61iA4e9Rh7jbNef7oRLdwIo5/3ezf63U7jqQatmxzNYftrhBu+l3DLpkMi5Wgz8JTxtxspfff63k7CAvxJtH7n+/1qf08K64tokzRIbID3E+lW5hjvdcZZB8EnvwEchrL7kIW7H/GEz/qduWitViDa3+JxHTSsPcQmo9X4rlWbvzfd40ibliLDksJODkDuOWizOFXzcnSv35mN6GQlovFdaFOVB6d8mvBctMKat4AVcOh5RA8zkTVysnEOWvOriZjw5J1Jm7Buj7Ec8ZAeIp1RJRxaQljdcw3jKP++jThln8JNriNSZZ1l3E4mkuo/w4m9TrAC+Mwzz/CFL3yBr33ta9TX1zNp0iTmz5/Pu++++3Of37JlC4sXL+bqq69mx44dfOITn+ATn/gEu3bt+uU7/xeucePG8dWvfpVPf/rTPPDAAyxZsuQDnxN9nYwBPN717ayoAXqQOM2a3C8rESNdj2JvkoJW4N/6kWJQhxjGHiQsUpxTIWJM24kg4GeQwHrcz29HC6IInnlUG8YRXyITP9f5LJScT1gHt6JdfoqhaiRi2TYhRrIOMZcUX1SCmNYqxGweQ+M+YPiKCRfgfnjzR3DG9URM3BBSeB503xcbB08ha1y78NWyEao+Tlh5Ooy/Xciqt4xIczLP/e8mgq+7EGMfB0fvhiwrVEc3QtY4xHDtXjy6EbJGeZ6SYthExAVV+reFfNCCttBzcDkSSB1+Nrkd6/zeI8bxw4ZrE1Jkl8N7+2D0JYbjWciaQyTDXoUEdRmwahh8fkgW1PWai8EXIDu5zgoRg59HpKRZ7Pn6NFKAW5EimiwAiV6bieoMIMVromHvIVL8DMKRV2DEOX4+j6he8hxSTucjQd1FJJV+BVlXWxHt5SF6rzE97CdStJQhxXUk4V7uQJU79tklmNx184lYqwb3sQvRYDdy32/zfBb592K0HjZ53h5FSvFUIufeo56fw4Y1HZ7IIRL7LtD8kQM/eRp+ZwFaK7fCy/c6p16K39wI5EP/Dsi9nYwFmQLjaThh0asiXIfPoTWx03g8iJSwhch12Ig2FaXI0nc5sqSNQut7H1pXF7vt1Yju84AW6H0BCs4hrN2zjINniLyk+UTd7MWeo11oraV40AqPZxKR2L0Q8Y+VRGzkFqQcJeVkPVE6cT3BOy73PCxFMYRdiEYHPc+3IMVwMbHhON99FhDVl76FKs+0aWPUDZw2Ea2R9QS/qSJKWK41/mZ4nFs05kM7nEg+z/NxMfA+9H8Vcq9133lIIc5FCuMLiGe2Gwff8hykOOVu4qBcL+JhNcZ5E6LJFDrS5bnvQuvhRnSYKI9I87QIaIX9z8LY4WjDvtPzku13mzwvnzmBMYBZH3IM4NFfLgZw2rRpTJ06le985zsADA0NUVFRwR//8R9z2223/bPnL730Unp7e1m9enXm3rnnnstZZ53Fd7/73Q9nIL5OP/304/6WlZVFc3PzcX//j7hOKoDHu0qytFjTzvoGIpj6ZcQg9vmvrYRsRBafdiIeaisSikkB6XH7RciSdJ+/J+Z6GRIMG1BM1leJlBulBLNajRjODe67DzHzdURy1Rzi0MUatNNNQupNtCPfR+aE3tFHIWslUT4t7URXICWjx+O5Edrvh7JHCLfIPKQQ5SBh9RiRUmEC8BwceR9G3EYI3p1EouAKYrdejBjnMqJCRukx4xxDWIN63U8HYs5NZE5XU0pUV2n1O4+hQPlJwMeNyyJkwRyJrAr3wOBhx+kk6+xS47eZjFA/uhSyihGNXKV+B6+G7NWe1wPHvL/Wz6X300632Djs8lgOE5aHsUSt02Yk0D8i+KgyzgeNh2lEypU8JFR3uTzbTOL0dwWRN2zQweg5hu8+47AbWRrwfI9Hgjq5eB92Pw2eyxrofBxKFhDxS9OIE7j7kPJYTsS+rkZ0N58oT5eUmTVEvGGu5qT3Rih4CAnPpEQ1eE6nofluMp7men7XGb4S/X/0Fsh6GilE2e5vuOFL/TUg+p9MHGbK8fcDfu5rw+CLQ6L7McZnjWEo8BgmCv8A3A6dC6BkjnGZNnY5yCV4FVFVZx8wDnpfg4L7DE8XGcv+e8/D6Pvc51jPyXZgFPS+BAWfI+qXP4GUy93G+WS0SWvw+JO3YDzh2tyM1sSg39ns3/cRMXXVHmsfcXgjz99TTGIN2twMkEnt034vlC1FPG0Rild833Rw2HM2iNZnE6LJDrSOixAfvtNtbyGSptcQYTONiEanax5bXoOqxWh9FyA6TM82GffbEZ1/ncip2uR2dyMLZpvnsppIudOG6PdMRM+zDV8R2q0PETF9W42Hee7jfsOfwgmK3NdKRN/FRNqXZLmcYfykUJttRNzg1hOoACLW+aG0ifT11tbWDyiAw4cPZ/jw4f/s+f7+fvLz81mxYgWf+MQnMvevvPJKuru7WbVq1T9757TTTuMLX/gCN910U+be1772Nf7mb/6GhoaGD2kk/zmu/3KiAfiVvSaixd5MpgA7rUjwTCCC+58iXEG1iLFO9buJqe5Di7SNUBiTS+qTRImmcYQL5DASyJWIOZQh5p1HHK543bC2IkH0BFFwvpOwPG075vv7iGGf67GVIYtNFWR9mgj8Xmo4b0NCeA2RnLUCymaiAyAdiNne5T4KibqxrYjhjUQJhScRlSOaDGOu+9lGpGWYiJj4y26nCwmlycA3ieSsxciiupGIfWv330IiVie5mychy2iylE4h3EiLiEMnX4bsFZ7PXqK03R7DsE39ZX3O+M92/32Qfb7Hd9i/TSBOFiYlcBjsfxTGTieTCuUn78DvfISwGNcQyYcfQYLmZd3b+Q7UzVV/7CTykN1LWF32CfaRZ6o/lnscn0SC6C71k/0xw/oAEjBNhrXlmPa7DecYZLXdQaQYege4BUqOPZwyx+Mo9Hwna3MD2ujY4skoQsGf5+d6/dwNxEnSpcbl53NhXX+knhnv95OSOoNwZRYRymW/xpV1GaK5BBN+b7bf24AU72bDk4cs24Vo3VyMhPG0IeHnFSJBewoVaEDCvwKtjTbBWHK1x7LJY+8hkkBvJZP6iTuBXVYAlxMKaKvGNzq9ewaSlpPIHGApSApsemcxUq7aiXXQAC0vQNU5hncPWrcHDe9koixdigFtQkp0LVozSdlNlto+ZLFrAFbD4FrI7iWs7aWCuWwikWmgG63hMqJOdpvnfxWRnqWIUPhqPTdDhALfQxwGG+e+dnss86DqDM/NnyIeO9n/b/ezjWguaxGPq0F0vkEu5KzbDE+p6WAdkWcz8YJ29/+KxzuXOLzViqyH5R5bnufm993nXrQOtrrdqxBNpzCcBs07GNYOoizcasN1Hr92V0VFxQf+/9rXvsadd975z577x3/8RwYHBxkzZsxBxJWyAAEAAElEQVQH7o8ZM4bdu3f/3Lbb29t/7vPt7e3/NqD/E14nFcDjXXloYV6ImPNEMoxm8DXIvh4xozrExK5DjGkTWvQ5RF7AXCLRZ1IsNhPpJtqRYN3u9waIU6tb3ccYPlhKKcVZpR1rM5Gipt1tdDlvW47HkY8UgzqiEgTuZxayjJS4nelucz1iUgNEBv5aj2UVUiYmGUd5iDE/6HYXIMUiBbRvQ8w/KRkDSPmscJvzjbstaLf+ecQ0sz3eHGQdWI+YfIVw1r8Hcj8G9MHgXrsVi5HATvGZKcamkhDMXZ6DZ5EwTXFOtxoXMxHDX4vcmHabUoSY/xUoHqeWUNyriBOio4jKCNP8Xq+eH/tp47UWKIffaSTqELcjy81ewlKzEwnpUqibR1hWOgil5xYkbFIQfYoB7CFSdSSLbQ2xMZltPHyEOD1ejeg2WedyiMo1l3iukxKa7fdziYMZyVX2BJFAPAnrSYbhFY/zfL+/Xr93HoaSNcaXlYiCUmBWvxS4bHhvI4w+HW2E8pCgTO+k8IXEzx1HRgFxyKEHCf7ZhLKRFPcpyPpcS5yMzjMexiGaLodDbTCy1uN50GO/0XPVRRyM2UYcbmlHdDnk9ta4PNiniJJ5eXDKJON8mOf4UqTIbnE7jyPX9kbfvxzxoKeNg8eQEjvZOHidjIJcdQniPymGby9xIrvUz9+KNg0zEU2MJVI1NRrWLZ63LcjzsVX/Z88nFJ0B4CYn9Z7p8eQR8Yo3I565jbC4DiCltse/Dfr+3yIX8Kpj5jYpSGOI0+iJvjoRjWajTVQpor/ZxOZwHUH/C1CS94PAZyHrWIt6DnHSvsH4ucf4KzbOW9xWoq86RNN/gGgvWeya3M5eInNAsjqvJ0JOcgje65hVmtBmZJpx2cYJr57xrwzdO25b8PMtgP9Zri984QvcfffdFBQU8IUvfOEXPvs//+f//A+C6udfJxXA412T0YK/GTHZ+1DS1g7IPkC4FEHMZQoS4K8jZtlEVB+4FwnmtIstQsz5AbTjm4aE52biFGARkaYluQNyiPQMiVkuQTv9ov8DfjPZ3Cvg0FIYudVj6kXM9TIYnObaw9ci60ctilOarXEOvgDZyZ3aRsSUlSDmkwKZd+id3pug4CPEwZZGf29HDHYYkdqhBAb3QPZMJMA6PP5KQhhdhiwiFyGm+z1kUZtNnOQrh9zkIs6D7A6itNd2jffo85B1BWKyqz1PncQp1j4+aH1Mitky3RvcB9nvIwG71DCWE2UBk8VtClL6kpunE9HDVdB/J+SOc7tfRbR0HYorWowsS4PI4no74eLP9u+5SMjtsqv+er/T5/HuMUx3EUpjOVIIvuLvS5Dgucb4no/obbjn5x6i/NUmRC+PIkUtn0h8+yyivxS7eo7nqMXz8Kjb2IoUiAriVH2TcWUXYudNUHKRcPXuS3DKYiipIiqC9HjeRiL6uVVjHT3D+Iew3uXBmzvgjNs87vFoDS1Bls8O3+v3361IAYCwDvUgZcrxpMxHG4AfIVrsJrORG3mJx7nO+CsE/hCd0G52e+n3IsPyOFJQUtxdE4wo8FhnE16GYfCTV+B3KvxuigkrQutkC+IpdyOaPUzEmA6gOtMtSIlLYQaHjYOdwjfPCE+HLta05l7kfrYAc2HneqgbRZQFvA0prqVEvG8KV2hBVsmzjN/k/u4RHs94AClYL7i93/OcFfiZRW7nj4lE5zvRukwWwCJkBZ9JnPbuMh4rjetVQB387B346HWIbm8j3L/dfna8YZ5omLsR3V6J+EoH2pxei/h/PXEIoxvRznXEKfAxsO1WGNwB505CG4E8tP5He3ynoo3480R1kWyPZzeij52IR0xGa3mR4d2CNl69xu0TyIszlai1foKufw8FcOTIkf+vYgA/8pGPkJ2dzYEDBz5w/8CBA5SVlf3cd8rKyn6p53/Za8eOHQwMDGS+H+86Nin0ibpOxgAe77oyS8JwK2IGOcRBgyYiz1ovEfRcBNwC706DU05HVrVSxDA2EHmbat1HE1AMf/8jbRZH3omUjoWI0c338zuR4ncvUjaSC7WGUERbiPQrZUTuvdnADGi/EMoWE7FYEAlhC4G7ofcSKLieqN36OFGz8k3iQEUZYmyXGh+7fa/c8E9GAv9u/00KZGLkFxHBz1cRqVI+R+SNm0okeN5EWEtvI1zvCw2/BVamwHsOEnal7ns2Yu5TYOetArPkU0Tutd1Iqa2Hzl1WSLYAPdB/GHJPRQz9HaIaw2IkiAt0qm/keN9LMUtpjqYRm4EXCCH0SSvmF3nsKwT30dfsij/gZy9FzL4djv6j3ZgpKH+IcDmVIitUnsc8C1k1JxgvW/1OPbS8YStQcrGt9Ziu8JgmEtbIRo9riEgufIlhGkkkYx6EwYcg+z6krPYbX0kZ6HP/l3vuBtx/KfQ+DQU3CZepcs1PX9M0F+RrjO++AadcSFSr6fY4v4wmdLVxnw2DSyH7035uMor1nO/+DnoOU/xWDZEHcDKyIl7n+fgsUiguRsrxYkS341EC6yridG6ycq9GCl4hWrsDiHaWGE/jgH+E9q9C2XLEP15BysYXkbI+QCYVzF9vhN+f5LmsINzoyfJ/sZ9fgDYWXf49HykaOYg3nYosZckT0eR+a5G18HlEc08SKYQKkfIzxmNbgtb0Hrf1HOHq30JUyMlHdLieTAlLrkJrd577XoRo6SYUe5cswwPAI7DzFVu6k0W1yHNRgTYwXyIyCDSSKQPYuRxK7lEbTCUOYMxCytdKYKsPP92H+FVSsjuRgpfCQlbCkbUw4nx0IGoYot0hIh1SPTABehug4BI4tBxGXuH5azaeXjZOB5AcqENr+FrEY8fD4POQPYaoub6I4MNJuc0j0jSNM64HkLfmvhMXA9jBhxsDWMovfwjknHPO4dvf/jagQyCnnXYaN9xww3EPgRw+fJi//du/zdybMWMGv/3bv/2hHwL5Vb9OKoDHuyZkRaxOYjSbEcMvQgu/DQnL98nUB2Y/WqhDSCmrQxT9un/PIVOsvXMJlOSgxXwxUlqe8/MTELP8DhIw7yBXWSkRwDwbMdnJSLCuRLv6fBQX8rLHUmaX2alEpYIBlIbjsP+vQNarIY9tEnHKN7mcFyEGtYqMNZSnoPMVKBmHds8vovitL7nNYURMXgchOJJbsg/heRGZrP+ZJNfp0EWexzsSCYwdxmeymvR4DDd6/D1IoZuOhPtGtwVSKv+AsC4md8tWJNhuEb54yrCPJSyujcZ/PVJAvo4EyGpCEdtj3OX5ufEeTzcSsOuQ4rrJc9ZElO/qAYokDNqBsTMJt3gfUSlmwG0sQ3S31302qt0jh2FEPpHcuFptH13p09OFiGbKiYoa1yBLwnbhbfAHYsajpxNxUR1IUbjEeFjmv8Wem4mINnKRwH/C47vccLcRSvdjhIWnmigPmGLQSpHyOMFt7kQhASuOaavDY8j1fPYgJeNiIg3RekQbt6O1lIdoYxqit0GikspMt1tKuOBSfF6Ku2oTrv/uXvjd5Z7PZo+xAfqXQO756KpDSuN33E6ecf9FpGwcND4eJFKurID3DsPos/3ci5rXo69B1kSlrRl5r+fqWDdzUr6KCetrNVq/o9zX6x73SM9nl8e/kqjtPZkoZVlg3DxinGcjmspGiusLaI2dZ3iS2/aAf59ApJp53vO5GilZlUSVkz4iSfOg3y3x30I48gO7yKcB78DgEsgebpxXEbGjPUTM9C7DU0rkEk2u3LT2hhlvWwn+3eZ+nkQWzVdhw1swt9hzuRopbh2e47Vo47QMbdC/RGYdU4OU8XpEhzcZZx3Gf9rsJutxG+L5S932dcjzVOrxpZCLbiI22ps6Npw4BfAAH64COIZfTgF85plnuPLKK/ne977HOeecw0MPPcTy5cvZvXs3Y8aM4YorrmDs2LF84xvfAJQGZvbs2dx77718/OMf56/+6q/4+te/Tn19PRMnTvyQRvKf4zrpAj7eNQkpfBcRu7kpaLGl2LsOP7eNSMFRRVgFmpEV4BbEDNv9txd40EpTitHZ57460W5zM2JMZ7idxYajGzGCZBFLsVnZiGldihhbNZli99TA6Eq043eMEdMQU1qIlJZX0cm0ZiRsk3WziwiW/iaR2mYbmXQLJTchhrvtmOeSC6/K8E1zW+uQsveMx1djnDzl34uItCYFfj8f7eZfJGLSioyDZsIF9RVC0duCFJIKJFSqkSI4xbBsJHJ3FRPxQasIpXAQKRJ1xvMi/19gWJL14Brj/GLkwso3DAuQgKtGFshpRBqMZYhmCt1PLRLWbZKTY9NcTyNTN5l+P3MtEhLtaC7zPYb5mrMRuZ67GZ6vx4DFkHUJossiNPfTjK8iZPnqQKmMuiD7Mhi9yf1WI/pMylw9UWu40fAfRsJ3B1HObDdSYnI83vuRgnbBMWOe6+9JoUuxkGvQ2ppFHMDZ6XFWIXqrMo63otQ4X/d46okaq5ORRWcFYXWe5vkp99w2elwtHtMVSKkej2hzDVIyaxDddsDvXovWarL29aL8gacTKYYedf97EJ3sIqp2VKP1mg4jjEH0PANGp9i/tOmpg6xS4ACMHAVv3gZnzDF8zxOVXyb7/3lIAU9xfr1EHHE1UeFoNXKNz0DrcoHbyCOUw2FIgSzR38FnITtt9lLsIIb1MJk8kVQSp3qfRhbIg2iNbDYsaz32WiJNT1qPD7vdThiR+O9YoBuyL9TfjDLU4jmtJeLvyohNxHa31ULEbfcjmvwyov8S46MX0coCMqfs59YRJ+BnG9eVhnc+kb9yGdqEPuy+z0drdNDj24H4XLHnZzGijVbjr8p97/dYX0Qa0eeQUt1MGBX6PIZVRCjS/6XXpZdeSkdHB3fccQft7e2cddZZ/PjHP84c9Hj77bcZNmxY5vkZM2bwwx/+kD/90z/lK1/5CjU1NfzN3/zN/3XKH5xUAI9/9RFpR3ah3fN5aGEOQ4vuIiRsqtDCzEcLfSVizjegnXQDEhwX+JkWpBQeRIv6ZSS8qoid5SgkoHYhIZR26sVEOo9epBA0EoH3pUQptmK31YKE8Bj+/+z9cXjX5ZXnjb9CNiEkE4hJI9lgShqhoRnYKANFGCiFQSmWytIyulRWy2ipjj6OHVvH1mnHqePUWl0dt67W0VpZLatLZWGoDMrCoAyUwkSy0AgFMqGBbDBNDMkkhOQKPH+83/f3xOf5sbuddWT29+RzXbm++X6+n899n/vc5z7n3Oec+5ywFoxBClJyCY1Hbq5k+XsOCesav3sCWQyPICFy3O8eGAJLPxHHVE6kL8lzv+WI6R8zLjqQBWEroeS2+ZmphIv4caQUfAkxwxRXlqf56b8DcsdC+0koKTQeixDjP+ExfcV4WoIUk9VkUpjQYZinE2lQ+tx+Upg2E4cq+gmLDWhOx3kMR4hYq72GtRIpia3o5HSyUkxF1qEaJBhuJK7Jhm8CkULimP9PLs9CwsW72e1M8f/dSJF50L/nIEF/kLBY9iEhtQkJt2q0SejzmJKyv9R9j4dz34Ws+/x8PvScgoJqwppyG1ELtYxwRTZ67qYgYTcfKQI5xMGGKsN1ENHVXES7VYalwHPTbJhWu93LUUxZEXFC9Ea0qTmI1tdcomTcQaR8fdbtJSU4xXFuRAL3e8Ad0PUGjJ7o8U303LxiPI03/qoM3xyinnE6UZwsNzWIBhsRTd9geAcR3cxFSu5ZpJA0EAeWxqDrP8GlDxCnUpM1aieRumbQc5Bcnj3EASaInHWlREhIoq+1BN84jhShtEnshezL4advwRX7CGvq64R34ITfnUsm515mI1loOLLRmmwkeOAJpCBdgtbhbPV3bo3jd/M9b17zTCJTyq1/F+TuRzTQT5zKX4DWXAW0PqvhZn8axc59AtFo4qmNZOJS3+mFiwcQb0/rKrnl02YsH20Kktt6JdpoHTCe84zndYjei9CayUM0lCx/u9zedcTGZSJaJyl85STi3UWI3k4S9Y8L4eAmoeNCXf8UMYC/7nX77bdz++23///87W/+5m/+X/d+93d/9591ibYP6hrxP3/k/6PXMWQ5mIaw9GW0aEEukmqCEa0CuqHr75EQWIwW6SByn+xBO8VpSNlpQUJxhNtYhhjNm0hR2I2EyIuIudxgeKYiZeJRwsq0CilGf0bsnvuQcK1HiuUyvzuJCFo+gBhQnZ+fThwmGYcYaQdiWp3ARBj8CVLY1iHh0u12p6rdc294/CnebztiYDcjRj/V8Cw1/kYiIVeAmPUO99mJhPpQS814JDTbjMdpfncq5C4HZkHJWEJpqoGedYhJfx74t4RVaAu8c4yoc3qLcd5KuJaf829/5jH3eTxJwMwmkjUfQ1UQ7vOcrfC7+43r+iG4moc2Aw8gYVeKhMLVSKC1O1figMbAvyJOld9hWAqRlaGIOAncQRzqeJCICbuXyJWWA6yFvz7uNm1VYjZSTO823quBvyRiyErc/jHI+ghRJ7sVCu5DcWG3E2ER0whLRQtSBOYYjtlI2c02vBuIQwDNwDrY9xqRG24TOmm9joi124muGsN6qfEy0e3nI6XkJKLrSYRV6qd+txJ6HkBrMw/R4lK0dkviGVbD6MmIRrMRLZYB/w6t781offX4r1Ew/c0mOPEYosVyz2cKp1iL5r6OSIPTidbkYqSU5Ri2GbDnEKLjBYifTDauHjde1pM59c90REvLifxzE93uLDj3IpHnbz4Rq1eMFKxDiE6TQrfVuFiA4tWWwxWX+7kxHu8yYt3+se+3IXpNG9FsIs1Sj9/vQ4pYE3Ew6KTbsgU86wG0rprJhM5QiRSvUwIt96vIkldD8JylHttzwKNQdq+s2kyF9m+4/waPbT/8fB1S4ubAxfeov64niINuSXHNNm7T+kybrWekGHMJorexHlOL4agEfkHUma4jqrZM9P0i42w5UaEl8d9tyLOygfCA5KuvSWO5oNfZ9/lv+PrgruEYwPNdf5AlgVyHLCfJPdGCmGmy9DUg5piDmEopYq4dRMLRmSj2YxpShl5FDCWXqLlbg5SqQqQcHEY72gm61343lKxCCoQPppx+EUbN9LN5iHHMQox/q+Gdi04b3+s+NiIhsR4JUrt2+Lw/d+ivtQXKPk7UMJ2CGPdSw9tNxAN1wS+/66z88z22auNnqnE4AQmm54nUHTnG07eQwNmDlM+xhut5JNA/ixhlA7ASfrEMPjoeKb/NRLWKXiKmbjYSHMuJE6sjCKvPH6L6vgXG5x7Pa4K5DQmTZYZzGRJQjxEuv5Oe2wJ/n0goh9uNs+d9vwxZlJZ4fN8CfgY9NVAwm6gZ+rj7GW+4VsLWlTD/E57PSxEdveB2Fw0ZQ7Iy9xFJu18k8uS1EQeY5iIhuwFd1UjZug1ZLg8jIXuWTLqSTBH65Npv9pxMRrQ8wffq3fYy4+Et4/AGIu4NpBi0og3M99xXLRJsjcRhgwbjfBqiz05EhzsM90N+7hhSiBvdxkQySb5P/xhGfcFw499GILfdAqQsFyGa2iv4jz4Bl97h+aohhPd2ZFHeQ+SVXGA4GtTuuV/ZcpWs3q1IgRpAc38/sv7kGJ/JClXnMVQhes4nQ4vn3vYBoQp4937HCV5HpHdqJXJNzicsrS2EdWkqUlIOITp9TWUmc0/6/XsM8yCitc2IVnKIWLpvInr/t8bLbhTXdxVQAn97E/z2DYgXziNydL4AJ7bBuGuIsphz4fQdMGqscbPcbdYgRbkUre+H1XamLN04IoZ0MVGTu53wSuwkNjDtnqO3iBCPajh3L2TNNs7XD+nvaeLg0mLEpxr8fy0qSfkfyRwWy8RaroX2t70Z/TJxqK+aSCW0Hn7xE/hoLZkDVJnY6MVo/RS4/2T9T+EiNZ6H3cRBwFbgpQsXA3ic9zcG8BJ+vRjA4esffw0rgOe7bs+SMCtEgv4EmSBk+hCjfAIt6DYiWWcrUra+6vcWIkFRjhSfAcSwXyTDhDK7wVrEoPqQcNqNGEKd+m2vh5IFhuXzSAlYiFZNmfuuQUxnuuFZRsSdNCNBcQP8/A74zZlEYfhsIh3MWTLpFBiPBEgN7y1hdBwF3r8G72yBiz9GZkeecaNVIEFdiITqXI9/BLKiziNSsNQgpjtICK7kNkruxQXoRGeBcVdMVMv4vJ/z7p1i93cAKVd3Gy/LPC+dxnkPmdi9rrdh9ONu45uEheAGt2nFgCokQDcSrr0B4rTtbM9lrsdTR5wQ7fNYViABlub9iOHfSJScK/WcdhOnvSd7zlKYwWr/toqMRZTdRL7DJcZ1IRKCe4may+uIGtalntfRxtfLSEnZjIR3F6KRGcblfchyc9jjrBEu333NpfCKfR849zhkLUBKfDo8tNv9jCMS5s71vDhmq2cHFCwlSssVEodExhgftR73GeAHSAF+Aa3HJqQg4Xe+T6QVaSXcfouN70a3v5bYBCyA/nshd7XnK22IUgztCOPsWiKfYJ7ncROR0mm2cdVBJIHOQ8rlSURjm4yjywzTNzyv2Ui5n2r8TEc0VeF2pqL6vbVEuEgVovdW4Dt+P48offYZMhWGfvkN+HCx328iqnMk13+tcXIzQctP+/1ORMs1HkeT3xmB5v+E5yLxjbT2pqANz0rEXzZ7ntJmqRgpV6V+9gFkHdviebrG/TRC0yGonOfnKxGdlBKJ80F88jkycbKZ/IXlxPoq9VzVei5TOM5stD6vNR4S/d5BVHW5EtFhsdtLsaZpnZai+S9AfC9Z7ZOLP9FNtWE7RsQdzoZ3HoSLJyDaW+6xOSaSHOCZC6cAHuP9VQDHM6wAflDXsAv4fFcDYqLJrXoZYnJ7kCB9WRnvKSaSfBYh5WkTUdGhDVmdepH75jBiCinWqRNZMg6jxd6AmMFuv38S7STnOnXJJYhBvEoUKG8AWnVyM3Pato8IqgcxlBIylspOEFNKDLATMfcOIjbuTfdRNOReKZz+CZFjrMouk1zjIil8tf4/CYY2JJgnIEuK3TGcRO7T9YHrn79N5LjrRIwuGwnVMsLiOpVwDW1GzHmWf1/hd4o9F91IOG5HSm56Lhsx8TIYPdNzUey+kituteF/FTHpZvU1uBeaDvj7m4RCuc143IrcpUc8t8k9Z/dWpoTVRs/FIUKA5RFW3AoiPcTbhqnJY0p4ftzjq0HK8xSPtZ5wYZcRB1EG/NtSpHhVIBp5hajgsB4JpyYiZKARKchtKM5uBRKQewT7RcsJ5WmkxpK11LhebvykGKnRSDFJ1pG1RG7Fcij4gp+HSDNUS7jXmtEmLBspxlcaN4k2Otz+Wv/NH4K7Mj3T/iu0mdmG1uBetCGYRSaXZO4iwzAD0WI1cWhrANFFPVHZpHPIeGsRzac1OaDE5TQa5mMeVwfiB1cjrvxtj3On268iqux0ub1Wj+dHRMqWrYiGmz3eG32vgnD1HkNr/SVgO3x4JlFOrwJZ8qYimqw1fivd7gARCpFia+uQJflN4jDQKcN/FtFmt/utdhuTEV/chOa+iDhx3+2+KgmL7kKPeQSi52xEm8uh8hp/P0vQVhtRkWku4Z1pIxKML0NWzhsRjz2DQhWOE7WkF6iNnnWGo8/9lCF+PtcwPkkmDRF9hNK7gLBIHyY2kCeJOuOgjUrCUTdRMaZU3y++nigJOJ7YuLcRab2Gr+Hr17yGFcDzXXMRQ12MFul+pJTMR0JvogOKCwjlqIkoTt9GpGvJ9zOfJeI/WtCC3oYWcA1iFMXEaboXDUud/44hJp52/4l5XAHsdQWMRuJkcD6hjA0SheB3w29/iKhIUuN2ugmlYyxibLuRIE/KayGMus3PNiKF4Zisk5mTx4WIyVYjpa+QUDITY8sVzIwg3InjgSPwmyNUZYFpxtGPkABPbplqxLSTpbTJbV8HLICfv4asVxUe115kQUgMN9uw/z5SOK7xc0l5+XO3143mdTxi+p8hqqwMQvZkqKz2syWemzmCIWMB7UZxdXchYb7H8/goUrA2I+XqeSJofq/H1ud2ZyNrwxgUw7YFWWNy3HcNcWoxKbBT4J1/MLyn/NtQd9xuRONFfr/L780nhGYVUoILiJPGN7uNfUjgnxTqeo4TuemSu7EICdlSt3OMOATR4D4TbaSg/RT3mQQ6wCwrTbcb9grPU7KmNCMlPbmOZ5GJxeMrhIV5LFoLSQkcdIm2FIc5HSk/01AM4RikbKSYtdWEa3otcuW2IOWkCdH0VOK0fYvnZRuR2qYVcqd53OsMewozSe7cicgyNt/tTfeYXyXCGcaSCddgNPBlaO0lDkkkK9VOtAYPGbfPwS9OEhvDYj9f5HGUGd4dRC3bAULxHoHoc6rfT7FxSXFLyk+/31+E6Gg2SmuS7b7q/W6n8XR2yDy8jtbcHuIkczlxaKTB9y4nlK7L0cam2HB1EkpgH3FgqxjRaOq33bDMcB/NiC7fJPIuboGCx5E1PY9IYfNNxBefJTIXJBNWJ7J+TkC0cRatnTzEi1o9J4kvzyNOi+cRim8hcfCr1O/2GgdpQ2pL+4W6kt79fvwNxwB+sNewAni+qxUtuD6kFKU4jUHEFHKQ2zHFiVQjJjmPSNB6E3IHFyJGdNBtdxBpFiaihb4LMbJWMm6gXw6ghf6y255BuBqT224rYjArCdfhlUS+wB7ErMoMV7Nh/A5iKtcQpzdLDetGP7eETILVjDv3boJBVfmdUijJh3c2eCzJCvWs8TAXMctCIh7qVaRElLrdJHxsDRj9JBI0SZFYhKwMSWlN7p4BJDgLfH8j/GYxoSxebdxVIEvEGeOkCXr+3rh4CLl3KojqGxORgHgUCehr4fTj7iMdLsg23BXAeFU2oZBwn9Uhpr8XCfu5hMBaYdi+QyTbLkXK4EqkKFcYV3f5foHn+Biiv71kXJSZNBadZKw0F/skZcYq3A3cAP0PeO4m+/mvo83JFCLfXRGyXJ4iyncVIrpoQpaTUiScOqHger97q+e8DNHAVxGN7iZc5UXIAtyLNliDgisT+wSwGdpXkzktmfshMvFw795LxCguNNxNxlGb79+CNgnt0L8O0fpct33A33MM7wjilG0RotFD/p6HlPpkrR/pObkf0UAtsan5qnE8BqiDni3AG0j5me22ZiG6rNXcUeC560IWudXoNOlatPZXu70b0IGkdKhjF1q7xWRCKcruch9VSJJOIMrZNbmN2+GjH0dzP9p99LoPu1QpROvhIXj3bsP7PY95h+GeaxiSmzThdjyikXFE0vM05vUeF8TJ8y8Rrtjk7p1FKJhfRhuzmzz2Ve7rJjR39US+y15Ed48NeT8fra+ZiD7L/OzkIf+PR0pvFRE6MYM4zT0d0cwtRMqWFL6xBAY7EA1Ndp+lHltSokuJOM39hncQbT7TIY/d0N6C6GysnulP4SyTjU+7gPvvMZ4mDcHxBbzOvs9/w9cHdw3HAJ7neicri4sfRwKhDS3WO5Dw/r8QgzyCGMV4pBQkK0MFir+ZSFRPWErkvZrsdp9DFqDpZKoitL9oV+8RpPRMAZph/z0w5UFiB56DFLR6pEzNcJuHiB1jHpF6opZw7zYh4ZhvmNYjhWQGEdN1GPpXi2+PnoyYcqlhvh4JjxcQ89mAFMgG46UbMcy0200xWvuNCysuGfdzmcf1oOGtQMLqaeKkdB5hKZyCBNYhZDkbhxSJrcbHQiIFyt1IaG8gypktBl6DrnXO2t9gXJe5vQKi9F0lEQ7wJhLW85BS8HsO9v8Ccht1ut8WpCT0Em6gscbJWoJOjiFlrxgpiPcjl+brSMHaB0yF1heh7DHjbgAJ6LXG33yitvIa4mDMRKRIlLivXCRcjiDFYbtw+vO34DfHIiW6CdHidkLBfxVdm5we4waPZ7Ng4xtEPGaV8MrdhPVpkfE3h6hosB9x+snG9X7Y8+OQ49n3DsFPued1nO/VEzGDf0HUkS1BNJFwM4WoMHPEfc42HSTBegY4AV1PwejbPH+NRKqYtCE57PtXe9wHUVLq591HMzqF2oAsXyWIbjuQ1fJ3Ee+4Gvg3HndyFU5HylWKQyxH66PdfX6ZKAGZXMXNxvVz0L4XSsYjpTsfbTpqkPdgru9d5/vfdzurEF+aBbwAPz0LVzyG6CUHTr8Fox5DfKGMUA57CAvcUsQXZxvOs36umYjzyzYerjPcIw3Tc4ZnBeIpLxCHs6YgOn+JiKHd6faXE/F6VQQNViNF6m4U25qDNk3dnp+kMA6itVnt95vRut3neZjq/5OH4hW3sRIG73f+w1mENXaKPxeS2QhRBkyDv/sh/NYthIcH3c+EenQS3ohZHm/iB88R2QYWEl6ackQ7T/q5NwgF/PULFwN4lHBG/e9e3Yj1DccAfjDXsAJ4vuvGLDGMIsQsGpAgGIcW53Qk0F4mTsrNIvJrHUQLugjIg9NrYNQdxEmuHiIuJ7lRZiBhkFxWB5FEzDNMjYTLbz/aPZ9ADCWHcNW1EakKVkPPJii4nKiHOo2ordmBmN4ZpNgkF91ij+1uwu35CnAd7H8QpiwiTqbdhoRusv4NtSSmWKkG4lRtcsFa6aUDMehNQ55p8+cWIjB+MvAg9Ne7bulYIlFvqftbQFRVeYawqhYjAVzkvkvRoYFKIrFqI7IUJEV5DWE97SHiEf/K/Xb63eRGS5aR30EWz25kJTmKrLIvEMmpD6AdfSOZgyyDP5Nb+dwByJpG5AFrRLSRTcT9NXlOUrD4DKJE1DF0lRF56ko9hmyP8QASzilmrRLRxXakMPXDX/8MPnUHou9WRPPJUrORCFe4zWN6hKhy0IGE7b2ESzLhtJag63zjJddtl3v+6okauylE4U1kzVoHfM7tZyNaTlbbLjTP3UiQ5iMlbD9hUV6A6KDY8OQBY6D/h7Y03o0UnZ1oQwNaC4eMh0XG23wiRU+ysC738wVEGp5X/e4hpDAdQgrhA0gZP4uUyds9pgbjYTFR5/Ut4zFZP59E/Ca5fK1IUys4Dm6ASUvR3JZ7LANIOXVcGxt9r9p4uImoXXw7nHsQsj5EpJD6KWGRn+t5SQdfUsjMPiK1UwpTaIb+B3WQ5twNkJXmrpKI2YM41dtgPPYSp6K/h1LM7EP0V+LP/YbpkMeeYomXeUy3ozWwGSmm9xBpgbYbBzXEJh7jZr3x0mIYJ6G5h0zi9HN/D1mfRryzzbjOR0rbXrdTRKyzFCKRYhM3w3/eC7+73HjaijYSE1HYS4qrbkD0PZZwvydLa73hv4CHQH7B+6sAfpRhBfCDuoZdwP+jK1mCWgmhdAvBDAaQMrgaLdqfIgGfFI/tZGJBRn0CMeETCOuDiPmkZ3oQM7uDKDI+GgmBm4i0FXa5shgpRm+RyX/VtRoJ4hYkjB9UnwU5iEG+ihS1FxAzGu9xFiHlrwZZauYbpnn+3IkE1Gg4/SBM+RhislWI4e1CDLmNcMNVoJ1rslKWEMpMCjDvQyu+DgnBAcTspxiurf6tDDHELUA55M423k75/RV6d/AUkfB3p39r9z3HbfIjj/tVpFRW+dm1yIIxncjbV4qEfY/v7UDM+DJ/TkEKV6uf34TcT8sM22Zk9TjuuVvgedjrNlsQk68SbNkjgGLIutO4mGq8TCEqNEDEfu1F9NfnvjqH4HYnUthzEM3tc5uOs8zESB7z3AwgAV4S/X4qxY4WImWj2HjoMT7vRTTTS5x4Xay23t2L6LjVsKSDD0koFhIJsScaZ8ktm40Un0IiZ+QEpFSmeMezhPUjKQMjfW+L8dJmHKUA/TbjagtSDg95XhxLlXstEVaQaLgRmSQWI+XtXuIUdz1RHu0YnN6A1tbT7vNapKhvQ1awtCmr9v0uaH0KCfIy46nJ+C1H4QctaI2PJBTvXLSuOtC85yhhMkVkLG+TriEqBZ1FFv88OFdv3DYjhaLJc/ITtI6PkKnDnTUW8YOlxtNliPclK+V6ZPk6ZXyd8DhuJ07bvgoMGrfFkFVOlP9L4yn3+wvRJmQhUsZaCWV9HFq7GIcHPYYWP7vc7cxB9NuLFMYWtGG4kkim/zhRLq8P0eFGaH0c8ZWxROqvQ0Q+v1XCIdnAH0DWKsKS24d4YaXxkuZwPLFRSWuoE/GaIvjdmWRSw2QOqXV6HBCn8JPinYfWghVrFiLeM3wNX/+Ia1gBPN9VjwKL0+63gdjh/wURZHwcCZQupLAd9zvTiJi8cUjg5iFLwm5gDfQ8QcQQNvt+L2IaW5BCeBgJlDeJQO0KP3cK2reQSYw7+nJCKCVr4Cw4PQC/TMxsMRE/8hihGOQg5tSmPvt/iJj0DMQAZwHXwKjrkfBLLrZG5L5oQhbGFFTdgoTUSiSwi4nTxK1IuUwKSC3vtSq0I0FywJ8TEUPciRh/OdrJDxpeP5NdS6RFKXT/SZBkI2HTS+zqxwpvGTfSJqIEWa3f6R2Cy2nEYYgU43eGUGKaPU+TiNq+p4gTk9Ue41zC8tlJVLdIbvpewjrYYzh7DHMrtK8xTOWItloIy3KL5/NqJMhPIqGUrLK9RGxZnmEqRq7rPM/PDMQZkkI3g3CfT0CKwgjPTw1hmc5xG9fCRXcBH0KKardxN1RYFiPhtsXzB6K3WWgdFPqZrX4mKWtrjPd6wj3/CqH4TSRcuYUa9+lN7iMprJM1psEHiMNaJ9F6y0F0shWOrnabv+t+xiPlq97zdMIwHtBvo6rdT5nHUURYDTt97wbj4ThwBZRVe+wrPZ5q4kRuAaKVWj0LRFnG/YgXzBYes65B62kSUYZyEVLGkmW3CrJu8hw9iRTDbiJGc7bnJgfRSTkRX5Y2fBWI5scbB/n+fTPhck+KWYdxu9djPkCEbqSwj6VEvXWQsttCKO07iWTU2ciFv8P4q0P8MeFwN1ojuz3+Ws/PEmRBTDB1Ip7VMGS8BVA2AeUG3e7xFRvGqUQpyWS1Tu1X+/fkHp87BK8txHo8jGTEgNvdPgS+OuJwSo/hT1bgs8RBwnLES6YYN/Pd/iwu6PXrHPL4X/kbvj64a1gBPN+1ADEDCOUsxUV9lsi3dg9SCnNRVvtOtFi3ISUn7djOEmWV1gFtUDAZLeJxZHbdbEZWhMWodupqghEvJQrU70SHLyYjoTZD3zNJW68iU+li1E3w4aUonigJ1QUeWwHhXkyKxAzIvRMxoX/rZ2uRUNiCFKWxfq+CyFv1upLV9u9y3xOQNfQYEpo7EcOdihKmvogY3gBi+C/4ubVIwI0nqg0k6+Ay42n7kGeeJ04/DyCGX4GU0puQoEyxmCm4u8jvdGgumIIUg0rjYpLb3sp73ccTh3yuclt4Dia632YkDAqRYJ9LxEOWEvE8Ve4z3/9PH/LsMeg/YHiOofQv1YK3pBopCLOJJOBliL42E+ky+v2Zr3nuf5lIH9TlcZa5jzWEFXgjcBze3WT8NSEarSAsuTuM97VIYV9MlDLc5XndRcSx9RlXKcSg0c8Xu/06IkfhVW5nEnEStg+dzk5roQ8J8Xo4d8b930GmdvHgd42bPhh1g/G1gyjDBWQvch9pHlLYARrrpb8hfJw+RcRfbnVfDX5uN1JolqOYrDzEB0bq2XefRbT1LZTaJQn2fsQ7Jht/TYiezyB6PYLW3GK01qz804o2GQNEPOlXCKt5HVoHqzyn3/K9tHFs8PNV9hhUe06/b/zMNX6vBQrgaFJ6VridMUTM6nQiHCGXTNocNiLluIWMdbl1DVr/K+Cn66DnET83F1m/xnmupwGvQ/8AWmfJBTwGTuxCCnTagBUZ1q2Gfarw0XMGevai5NGlaIPwLLLClhNl+hYS6YJOaLx8wXh7APG7/2Ccb/Y4p7mNVmIjfgly3V6CNn4rER1VGW/r3ec4/wZRIrOF4L/Jcvl9onzo02j9zEA0gXHfQqS32swFvc6+z3/D1wd3DSuA57uSktSPFpgFSyZz/6NE7dT7UJzLrUjhyUPur7R77UZxYd9GzGsiMMM55NoQ4+9DTKmIYAxPIkZThVKQpOD8axBjOeF3ziJhmlbPl5Hi2OpnknIzk0hy+xzwY7ezAyl1dxrejURur8SwNiOhvtJjexIGrzc+esi487I+4jiqbsT06ojTrDM8rh0ab8k0jfXoNqAJurYhYXrAfbbB/qf0G8sNV4qfyvO41qvN/jVE8fpve/zJdZPcecnClWJ6JiElYSahZE5ACtMXCQvaYuOrz2NdYJiSgleOFKuZ/i3fcBxDzDxZfhd7Dh8lyopVIMZ+GZHx/4Twmfs54lTrWBRTthDNfw1SPpIrqBW6FrvNVchiegVhDc2B3LuQ8O8h8gTuQ0KyGdqXG5/bgbuc3HUymTyTmTjVkUTc20hEUzuQxS/FYDUS8aZPIoU4HwnUSs/jNCTwSlEcYXL5tRFJxZOC3wdNy9H6KiJqrY5wnNpzhmMG8AJkv+gxLvHc1CDX518ZN5uJDUkFEqp5wBbo2oUUjFnAYRh1FVIKGonKI8mVfpDMoSJ+xzDnIqXiarjoBji9V/hkgFBCz6L11eG5L3CfEEmIi5BSf4gI+5jv+bwaOAHt3yXqSR8kYnsb0fpu85wcREpFA+IlhTD6LsQr6lDcY6fGy2jhgROOOjgpnLLFOJlHWL1We87S2qowbEe8Jl+FwR1QtogMn7liARR8Ayn06XBLE6LnrcC/h9xVSAGaYrg+C+MWEJvUR+Fv0xovRevWG6F+oGCRcfWCYd5IxDC+SZRQfNXj/0vd6zmE1v0yxHMcivDuXo//G8ZjK+qoAtHD14lTyQ3udz1SQp81fG8KbvqQMtjq+R5ECdJLPYYBIrn/v/GcnDJuJhne9Sj5d6l/H76Gr3/ENXwI5HzXpKzIxdSJGP44tHj3I6VsPmJS16GFXYYYwhNIKUxuuy9B60wom412+zWIcVUggb8I7Zx3EulO7kWLvBIxxmKk7BUgQZd282OI1Bl1yNVc6PbqiFqjs5Bl5ZDHkyxUaxADLyTqVnYarmRdKkaMphPF0nwfun4Fo2f7tw6PazpRouuw7+cRLu4it7FMY/rFOvjoLUPwsB0pLxB1f+cjwfeoxzkBKavT/NtWJHC73L5dnefu9+nc5KYfiRSiS5CbqdMwXUfk09tPnOZ80rgf6/naQCYdDC3wiw3w0c8Z7hokyF41Pm53vxuRsK827G8a/+WILlLs2lSUQuRaRENvGtZ8pPxtQ/FPXyIOJh32/duQwtwDgxsg+04UfH+ncTIF0fBy4rDRWkRnx9zWWiJNSicSYk3I0j3b73/Pz96oNDK5nzbuHvCctcDpR2DURzyfcxDd7UFCa4yeyZQ3yyMssF2eyzqksE1EYRbzjZ8ETx5xIGsKkbLjDHHCczJSBvP8t8ztFngeZqFT1seRAG80HiYb3jOIxnYIp3wJ0QfEacwxbnuH5/M+FEO2AcpuMkyVxCGnZ5DCl95diGi9jshVl+1n+5EyWiRlpOAOItXMRHSgqAWt2V1EiqM9RPnEuYimZsC7s+GiBUgBLTcuU4zyFLRu24jk43hOmtxfCgs5OwSfDxsnc/3sNKKG8CF45wG4+FrigMUItzWXSHsyndgkzfWcPmo4BhB9FhGhEmOM7+mI9lNIw/MEzXQiWv4sUnhziANUENVk+jz+Cve9lvBYJM8NRPjJ62iN5RNhFLPRpq6fKH04hUiWPhJtECqIKkoVhmmKxzPJ94v9WxWi027DWAdffgIerfXYWomk7aVkSiqefgxGXQAxng6B/Dfe30Mg/4rhQyAf1DWsAJ7vujJLMYAprqoICZYViNmmuLB1aBf8GFqk04iyXjWIIexxG2mHnARONlr8lcgisZdQDs4iJrHNbV6NrDUFSFGpIRSIOuTmACmpM5GCN1mwNK2Byk8gxpPitPb6+ZHuawacvg9GLTLsZYiBr0DCJ530WwBH58KlixADSrvYGUQKljrErO/3uPuI031Dd6vdvg+RruYSJNgmIMFTioRytXHT6zG8hLhOBxEH02NcTkA78q8Rikae4ao1PFMMyzHjq4qo1ZoCtUs8F+M9b5uJ5Lj7jZtjhm2b28hDytrNhu0Gt/kSmu8kLJYieur2X/kQWJd5LEX+vt19DSIl7iy8uxouusbjechzM4eI03uCUH4WIEU/h4i3G0Gk3DjkzwKP+wD8bQv89iPEieh64yC5lwv9/BZEn5uNr/1IcbvGz9/puRlJ0G6V8TbRY05KS3LPJSv5c2j+kwLdRrgEDyAlYZA4JXkMCeRJHnM3kY6nEynoVxM1p7uNq1y30wTshaaTUGn3L5V+r8Rw9CJFvW4I3tIm5wiR9qYQKY9rBUP/Bsi9jUi63EBsAsagDcR6RC9FntfHkVK1ivcmj29C9DVgGHYS+RtLiXrURSh+sQodAlnv/zehDcFmRF/dKNzkGWITW+Xfu9Vf/68M/wq0GUibzGQ5bDQszURZvFLiIE6yfk9AtDiLUKZq0Bpc4zE1Efxt/BCcHUProtu4S1b9FD9biVym1UQ2hOSqvhzxxEFCgTyEFOPniNrL+P8BQqtZSyi8KV65DvHHpNCldV1FpINp8dzUIH64lEiancJOWv1cBaL7HiJXaJHbKkZ8ZyEhSyoQ3ZzxvReGFcDh69e/hl3A57sKEfN4Ai3iCsQ4DqPF2E+m8DuvEakhtvuZbKKmboq1GkDKTTFwRmkWMpalbOLwwTGkCOWiRb8ErYxcIvXKOCLVynYyljkqEKObgITkPqhcbliSAjODTLJRCgxXlYPYZyChc4zIql/kvncAD8ClN/neVMTIjyFmPQMx2LQ734gUgaQAdSLmV0VUDDmKmN7r/v0scZr1FGLKg/6eAtZbkZBPp+qS27UI7d53ISvADsOQlO+JhvWQcXFY4+EUsBp+uUmw/93LxunrbveA52GOYS70eB8iBFK5cZV29o1ELGOKUUqKJIZjN+9N49FqmLYSG4vNHnudYZ0oGC+a7LYOIeE+A9HaCLe5nIjNO0SkuhmL4voGDM9q3lsmrkHz89sjkUWm2HDl+/mxSIFIFt1r/bkAnZBfRJzqxPB0kbFqUUKcpNxH1E/dbxi6idO61RorexH99BBWnS5EG7vQWmgjrMDJJZ+P6KmBoOH9/tyJXO2nyMR4nf4JsAoqP0fEhzVB+z8gWktB++lQQw2iiUkw+BNoPeQ5qTV+koJQCrk3EDF9zUQi4ZGI1sa4vcuMo1zCIr/Rfe4irEBPE4ei6olT1hCJxB91n2nz1Wp4xiAam4EUx2V+N1mv2oh8m9XA/ZB7ld//I8KDkE9YEJNHo9rzMYOwZh1GtD6GsKr1EfFvPShjQTNa6+kgRQ1hfWwhlEEIV2nC8RnC21GH6Loe+lMowSkiV2WV2jx6AFmC02ZgLlH+s4dMec93Dhi/JxHtdxL0fwVx8r7bY65H9DENfnnEc3EvmvvJRP7UVz0P1f4/KX5p49njtt8iKiulMIcZHvuTbvcCXsMxgP/nXsMK4PmuMWhB263Bc/DqKeRqW4MW382IUTchQd1HuCjSTnOAqH6Rdujb9FvWp4kcXfuJklaL3P4eJNQG0a57PBJMje7recSE8xET3IMYfTtibFVENY9piMFVI4Xma0hJKEOCcAuihnpCUbgM+h9BQsoxNqdfc3vlSChvhZ5ewi2R4hHv9/g3+N0UdN0N5551v1Og52fAX0LXG8SBgG5k/ZhLKKTPE/FsbchikFxwyQ1Wg5SQHuO9lbAoVRneVz2/A4hZHycTt/bhy/X5W+Pd5xzjc4/n4xXPYRWy1KxEwmOW52yM/08WhHIkmGb5+bNIsU8K8HyiUss84+9GIv9XgmO2x7eQUAryDVc+oociP5fiohrdbrOshZkEvduRgEnKYworGAD2wrnj7nsSkdssxcntJdyFJUQexHmGKVl0U3ziC8bvgMd1PxJWyVJ2GVEX9YzHNhHRxnNEnNZ4ZLlbizY6txLVJRYQ6WQqPJYiP3PIYxnjuVuElMZkQfou2oDkA5UwagFaG8lV+bLgKfkNZL1JykS9D3dcR6ZyT/bnfKK3xc+0ud8tBC02o7WeDsxMNZ6n+pkO4sTnTsKSVU9YqldqHn6+CW1Od/t+MeHGvcXzXIzi+SoId+xa47hSz/ziJ4a1Hq15K708TeSNfNnP/NDzkO2+5qI4w07/JYX9JehaRWz2riWTG/LdB91/DuJJncbNSMLSWeL3thvujYSSezUZy2nXE2iNlXisybtxM5naybkzjdNnPBfZZKzWl96LNvK3ELlVm/33WaTcPQwXlxv+w4b3NcPR4/573N8htzeAePAO+PBdiNeWE7F8Mwj39iREjwuJlDFpEwTiV2ORorjH97YDf+Jn8pF8uYDXWYZLwf2feg0rgOe7auCdk2jh9QI7HUpyKWQvJeLPUkxWsoG3osXegoRQBe9xs3IQBlsQ5rch5jaOcJUuJpKI3oyYSgsK1t6OclutQIzoS/DzDYI1kw6hnFhF7YI7Ux5sB7JaPY+sA1uQkJqBBOQSpMimmLDlzrl3BRnXxaiZRK6qG4GJUFCLGHeD23kVWGdlYh5i3mVk0kNkfYjMab+CZ4F/A6N/AwnzWR5fMWJ82w1zHxx9jEitstG4r3bfSbGbiwRVuefIFpuuVX6+DAa3ee4qBAOL4d3XPMYJMHiMKMpe5jn5HuE+TUHxHSiZ9O8hITkO+hcBi+CddYSS3ogsHCnG7Bm3PRu6fkbEXh0QDt7Z4nnf5vnYiBSiQc9ND1ISr0a02WWcLUWhCNvd5xz1cdE8pCRPQAL+IVxyw2NbbFwXQ9blREm4OcQhhfGIjivhnYSbUsNzk8eQ+s5HQe2VKD6uCjgKp0egAzpJed+NrID/FSnGyWqa4/Yf8TO58HePoNKLg8bJFM1h+23GfRGiix4YvJNImPxHvLfqRj6yShcZtlIyKUm6tiCh2uL2r/UYpnseR6A18iBctFrz3vqi21+ODid1+v2pSNHtQRuPEmi/H625l9C6HPRnvcf+Xb+7iQhbmO45mIfo/Anh7jdTTF+hn11H5Icc6ZP464zD7URVoFJ03QN8GT46E/qTS/hKw3QdEV/ZQngKngMeh54NiF42EjkM9/v5POCYK6t8H9HxJOGza5ct12MRfWzw/J6F9p8hZfIpwn3fiWhnDKKP23xvCTDBFYqKPLdNxuUERB8DhutmpHC+gdbIw4iu1xnWU2hzlhTkPM/9Hs/5HM9hief/RrRp2okOjqRQiEbDvZc4ZFfl+2kT8FnEpyZ5HHchmrGb+Ed3G991iDfNIHjORKSoLvCY/0J4+/lbhKdm+Bq+fs1rOAbwfNeXsySM5iKmWYHM7ck6Mg8x2w7EcGqBAfjbvfDbyxEjzUYCpwZYDe31UJJio/KIHeUKoiB8HzoQsBIxlx2IoeQgxWE6Es4pXnAxUhSSdaEc6IYTR2DcPLd3P2LOmwgmfRAx57lEmohrERPu8zP7CcVzKmKSq1ApuDGGbwrKqXavFeNqxPieQMw4CZCTRCH7WwlrYyFwDHrWQcGHCEvSAuOtDuiFc9sg66uIcX8TMdhWpAQ1EclWcwx/wukxIo7mDqSk3kdYDM4iy9AMj3ML8B+B30fMv93t7oBzLZB1lXH+BIp/anI/pwxvn2lhBxJ06eDDWKIG7XLDn4OUtreRwCk1LD9GB3Z2e47+C6K9GUjxbCBOjJ4ik16D7cbrdmT5uZ+oQlHl9u5FiskEMqdoeQ3RRTmc2wRZIxBdNPu9OiJdyiNIABYiAdYIXffC6G+4nzxE3yk4vtP4yyZOc6dYwB1ISW9AVuhxwtPp12DUXWjtHNFv7W9Ayb3G0xKkRNUgRfg2j3knEaC/2WPqJWJfBxAtJqWp3vN4LaKHuUR1H28GqEV0M4+glUbiBHObx7sW0UWdfytBJ0aXIRqahDYbWzyPs9CaehjRRbLydhPhDDvRid2/8PyuMexFxl8JkYevlsjLmEe4oYuJzdlnNMd8G9FuHRGPOMPjmo0U/ucJ1yZoAzTdMMw1zjqBB6D1OJR9AikrfR5DctH2kQmXaf0HnwZucBtvGqeJj9W575eI0+wHiIT1ZWjNP4To/h7C5Zp49QLDehL6WyD3IWCHD0jN8/hOITooIjbOu9AaXU9sMicYvtnIIroUzfeX0QbypOd2PkGP2/yZrOBpk3oA8a2vI/qoROshW2Nq/zGUfBoperUe7xr/3kkUAjiLNtkV/n+F73/9wsUA7gF+431q8x8QmQ3HAH4w17ACeJ6rJytLlq0qxGT7iHqTzYhKv48YVid0vQyj82F/L0y5BSkqdyGG0UAIwWLEdCr1HouQAG5EzGQ5kUC2BjH8VgP1HFJK+n3/lD8PI0XjZmRRSbvQKrd/F7K6pIMfRf7sQ0zqTiRor0KKQ6vhfglZ/0qR9WcNUSbrTSI2rRQxwxyP4bOI8T1EpIcoJ9x7bR7nTiKg+iRyx40m4s6uJNLwjHY/XYapHSl/yZJyAAmxASSs17vNBsQkG42blUQJtRNIgV9JMOtc4/l+NO8lbmsqUjIGiVO/16CDI1WG8y1kLShGykynxzvS8B9F9DKTyKk4wnOYS8T1XI6Uo+QSbSSE4FbDPNvjf5goCp8rHA2ug+yPedxpg1KOBMtlSFgWE/VIXwZu9SneqwhlooUoLVcz5C/hwfGCnCSURW8wBjcJJaNucntJ0UpWsRxEj8Xu63rjrMdtzTVuZxMnxFcQ4RVFnpM2z+d+j7XHz5UTVtqkNC3wWAaJGMgDiA4qh+A1HZhZhGg9x7hvImNlZLn77iBiF48SB8DKDOsdiC4Gh/TXSmwumgmFqcBtFyBlZKLnKFmENiO6uRXO3QNZS4nT+xDrNCliOUSYROGQ+RtE/OsZ99GGJms3Um4a/PwuxBuOI6VxIqLNY+5vgeE/QtQhriDiG28kc3q7/0XITfGhpf5MClQfGdplAlKcpxMniEsR/Yw13tYbxsXIevcGETdcjPjWRCIsZT2iteR6TSE2Keau2Djv8vNHiJPevZ63Xaj84HG0CbmaiP3djNboXI9nodvYTMRslhFx0ff6t92In+4gUkIlpTwPbd4KiaTcxf5tjedwIVIC9wNbhxXA4evXv4ZdwOe5Cj5CppIAFcBUaDqFFLs5vjcdLfC6yGDeBmKQy4lTohVol1gx5L3EeJ9yP7ORVeAQEhL1brCUKNOVjYTsAWj/IVIGmtz2bWRKGjEbKWiFiMnOc7splmsrnNiCrIAg5gvwKyKty36Nmb9CQuUQ/PI4UQosHVZZarhriITJe4jkpHm+N5WIG2t2H7uJOLOrDMflfvYrxm0j4T7ej1yo+YiZp/ibbnh3B5Eg+gXE8A8iIdVOCP1e912NGOiAn99OVJtYQKSh6fNvi/1/PRLszYZ7NhKepUTMXhIgte67wX+XEkl8e4jcg41I6dxvnKV4sCTA9vl+s+qPUo5caDlIoWv373YbZV/jcW5Dyt4qRCOTCDdwke/lkVF6skHzeIBQ3LsJ5aEaKZwz0NwnwZ3CHnb6XjNkV8Oo25ByssHvnPIYu4lT21UoML7IeEyWo13IUlKI6GohUlgKh8CfYmo3uv8xRFqXI26jjKjbnWIYG4kYyZeI9VZKnDouQjQw6P7zkJI42232ImXgGBF3e4w4ZZ7nNgqQAlJP1MAuQLSZXLMzEK9ocLsnPJ5m4D4Y/LHncY7b3AhZi4gDRdVo3ZzwuFuQYnAGBncZx4uJ2NX1iKZyPAftRAnApFCnNfIWUkqmQk+972e7323+3k8cYJuITj9f6THuACZB7oOez0FiXUHw2IOINrZA+3Hj5k3PWTNRMakO0W2KXz5J8MidBM+rIHhLmttuxJde9hwMarNEm3EzmqCPvUR6qwJEW+uIU/lPuu12j7kdBh/xPD5g2PcD/944KCYOzazz/Cz3vDYT6Xk2GRcjkSu+cgjeOgg5MAnxjKOEm/gCXWff57/h64O7hhXA811XI6vEeCTgzkJlvpO6voSY6GS0QB+Di+4AVsH8q9CCriMKsde5jR7kStiPGEFSBKzEUOh3Rg6591W314aEz0FgGpRMIHaMhxHjyEbWt0J/9gBb4dxjRN6rUug/C+PKUSLWS5Dg2A7vfANZpZoRE73df5VAGXx4MpGgei/aoR5HjGmQSLycDkasIE5PbycUqAG/mw4NHEFMcaz67XoEWVevNf7K3dYK3xvvd1LMVi1c9HGkDDcRlo5JRFB1juFch5huCxKmywjX12yiUkUrnL4fxUom1256/oz65GXP5Ro4+l2kQG5HFsXkzh8grEZ7CIXvCHEKtpewyDULxq5NfqdauGe35iHrAaTQvESkJJmD3EtdhDUoBdSnTchmJDA2+d12z8sUMm7y3LvIpD06vY4Qonnuv8N9rUUCcSUSugVutwTO7SLyRrZ5rpp8b6bhO+R+xyF37gASwBs8p1vQxgPjpMO4vI6wGL7p8a31HDRC10/8zE+JDUiN8X4S0eg+YvOxCfhbFJJwwm2+hBSMmYg2DxMB/OOJE5yt/mtHvKDE81TqOV1EnPSchoR/GTAV+tchpa+GTLUeCuF0PWEpX+f5mg3Zt6FNSQNRMq3c/S92W73EJqSRzIGG7BuI9CUtxhfEafvNSFkrF2zsJbwOi/zsQT1bcJ/vJdf4oN8vc9vHkXWqBc3nGTTPVvL33E8ozx3Gdy9Rz/sQUA8l1xueI567J5BlcrfxtQfxpFvhnduI1EdJQSwlePAu/z/P98eiuX5SeMouN0zbNZcHf4jWeIVxsJs48VvgNpI7tp04kHWrcXVIc0YNsoBer7EzQIZf9TztsZd6DmrQWl+F1vtxFAZyEPGtxe6nFdH/ZOGn65TnsZoLeg2+z3/D1wd3/YsLDcA/26scLcRO6PkhFHwMeAxGFaGFnOL/xhJlgwYRs19AuAxWIMZRwXsz5u/wb/MQA/w3fm4l8GcoeL2fiBd8AgmFHnjzhzDnCSQgihCzL0TK4lik7HUjBjMPsg6QiYuhEXK/SjDrcUgBeAAuno6EYnJd/BEwR0z24g8hQZPndme7733ICuXgc35EuIDbDNt1iEm9TOZ0Hs2IsW0xviuRgKvxmYjb3X4bEiIb/e5GopzXZCRk2pAAricSHh92u0kYjIP2DihJgfx5HusTiIFXuP874OAymLQGRn3LsPcYV5ORUCp2nzejWMEFcOl0pNyv8Fh3ADPhF0/BR/Od1Pd+JDjqjIuVSIjMRcLiN5F76EEYfT28+yJclOIZiz2nO+Dv1sFv3UkUru9Dbv5WpBx+DMUVFiKrwlL/7UaCLyl9LcbTGEL5rQJug1FJKfsesox8DtHVw4Rr8k6l2cj9NHJl9TtG8pDbriVqxBYA/xdym59F8XC3AY1Ks3Fxssrd67kY4TnqIepg1/i3PqTcrxXuUyL10Xd6vr5t3GQjBX6BcbjbNNGNrHsbiQ3BZZ7fU8AfEqevB9x3JWH92j4EniYiFrDZOJzo9juRBe0epLTWoYNVTR7bVH0O3gbZ3TDqC4ju6xEfuQdOb4NRtYi+0zWViCtNSl01kVuxEFn6NhmmHkJx6kab262Ix11N1PtNm4cBz9cRxKumE2Umv0W4ivvQJnI7kS9yMtpUPocOBf0+UoJzYHpSQvs1X5mDdMuQEp3iF4+hk7P7iEMRrZ6j1/3MVo354nVuy7wtk0svWQf3o3COM0QC8UqPLRfRfhOZFEWTdhNu+huJ9CuNhiPfYx1vOB4kc1o92/HX3I1o65TxkTw/U9VOwXzDsU3v8ShxGn+J5yBtRDs1hnd+AhdPJkr+zYPRh9EaS3Q9fA1fv+Y1HAN4vmthlhbkCKJ+5pNo4c9BTKMcBu+G7HzEJK5GO9Fn0c7vTsTcepEQeoZMXjCuQMJxrL+fQgxvHHKHDo09WosWeoOf3YIsTPuQu6UCWcwqkausx+9UEjnTCqD9AJQsIFxlh4gA7ORaq0MMcCdiouWIiS5GwiXFzKxz/3MN1zHEDJN163L/vw0xynmIcW5BMT4tSDjt8LvLkMIxQm33HICCa91nHTRtgsoRhGA+7LE+SqRPSXF8Ka5qBeF2+5hgOfcyZH2MiFf7pvGwn3DdvoVOZCYm3A6Dr0H2x8nk4cvEXXbAuWOQNdn3k+t8PqKRRDsnkEAuRQJ+FRKmMzzHuUSZunK3vRkJ/rFEnGAPsB1OnIFxKc7oUeg5AgVr/O5zSMFvRYrhOsK9PJGMJYqHiBrS05AQvhEJ1wEirUijcNW6Dcr2IgUrWZ3WGn/LjJOxbv8QOh18wGPaDoNvQfZzwItEHr9pnofLPGcNSEGYRSiRLb5vVyTjkQJ4wnA+QeQfTG5p/Hua/yb3Mc/t5CI6Xg+nfwWjJruvckSTRW6jhbAAdiBFeqPn9w5UK7sAKT1n/ewmRMdzifQ1M9xvHaL3cuOqjUjEvAvxjyLD0EDG/XnulN2+zcSpXNxGifuYjHjTTrT2L0c8qNiwNSFr3wykSBUYnulIWWtBytqmIXCYxjPJi5PV9yya3xak7N5KrB2HxfAVYg1kG/cvozlPMa670OnW5M5+hVCWDhN1xvcjmtht3F6N1sJcIob4IFKgXiA2oVsMz3Ki/nS/x7S+Br7ZoLl4g6jylKzuU4gwlA60wS0hEqsn13GrYG56ESqXIrobTcRDJ2vxgHE96O/JOglSUjcbV6uQUWEQhXo8hNboFGSZ3oLW11jE//8QaL1wMYBv8v7GAM5hOAbwg7qGLYDnu+Yjpeos4UaqIQq3W3nKriUS2b5AJJ/di1K2JAZSjhZwimtJAr8eMbbJiMEMEDEsKUZnAXGKsQ0xuXa0C09uujIkGBvQrrECCbR+MvVwS64h6gMvQUwWxGinIgU3DwnzaYQSUkQks04xLLWGqQidLuwisu5PQ/FqT0HWbUjg7He7LUQQefo+2++9QOYEb8FBxAleEO7HQpxKrPdcDCCFphcJ5bGC5/UX4cqZRBxlMxkrUtYNxCndy5DV7pjbHeOddgpoP2w8/hSyv2A8J5dZN1AA/W/5fE8OEoJfMFzdSNj0IaHVimimDgmTJ+Dd43BRLaKXDkRzI9H8F3lcVUQwegmZ2Mdx9chd2WB8Pez5uMf9HkVCqArR3xQixCC5N8cQlUgqPL/ryZxop5hMaMGJbQ41+pbb34EE5SCy6lVpnqhHm58ZaCNUSsb1nT3bYzvhvkb7+YlIeI9DdFTitg8bX2cN+/3ubyLabNyLlNFFhMs5j1BscozTZR7jc0TZr2KPsxdGHSYObixBh7IqPMZ8RDu/R8S6Ffu3bxGxvFeiDd4N/j0Xremn0SZjM5E3cRax9legjcdZw3AKKQblwtm7J+GiByCrj6hk0Uzk0jtCJDEeRPSw3zDlQP8RyJ1nXKaNa75/nzMEb7+H6DMHuA7+9j4tt1G3EJb+5IYu4b2VMMYS8XfZhm2j/4bGb+7yGLcgei8H/jWx8WtA/OAz/j3xwQlISSzws/WIhmZ5/rM1j9zk/+8lcvLd4/k85efTJvqzwF82aIPaQxyau8pz1mGcpTXXQhzkWWOcJJ5sD0Xl5W47B9gD+/4eLkub/0nuZyyR4uda/19rXCVvz2oiN2DCUy/ivSeN+xuJNFMrGb6Gr3/UNRwDeL5rJ1FvsQ0xlpeIdCaJgePfZ+jZrW+j3WYeErAvwN/uItJUHEcCqQ8x7fmISb7sd5a439eJgOWFRJB2iqmqQIxhL5Hg9KT7SfE8Ew1ftttuIJSCJwl30SlCMKZ4kqR4pIMSTYbprMeejZjTXhRzVWk4foCYWQFk1SKht8n9nUC72waPbbX7n4OYWlJU8olSezOEp1ELiATYxUP67yRSXpwFLoErxyJBPM1wDyLlISnQeYTic8w47dH3i+d5zLj9rcbTAeJA0FGPdybkXuM4q6nGy62GOblspxD1n1cKx+2O+bvoE2QCuN9JgmiAUOrv8ucqwlL2MEFv3cTBocuQAp4OxpQYnm1EhZFuJPhqjLdKz0UXkdQ2x3isQFaRPME6boJPFpd57tIcJNo86HeWImGVB/07iLxnS5AinJSyYqImdilSZLYOmZuzRFWMSzzG3ch63IHodI3naSpxyAe0LjsMz2T3sYIoQ1eOLPBTCIFdQmx4ktK8ANFWiivMNcxVHuN+YrPV53a3E1Uytrn9Jo/3JJlDMmwh8sYlpW6b52MCmuMb4KLbDFcLcbgkuV93Ix7T5r4bEI86TqYqUe4YIlfd64Q7cy5xUKWDSA5+Vvj77Y/5EM9atG4LjK/kYagxrlP4RR5RvaWdSJE04Pm4zM/P8ucCwo3ageY/G7rOGBcOXyGXTJJyWtBmYqnhnOJxLEHK1ADiPbsRr02bvAUEP0vK6Zueq4PqN7O5W01UHsG/N6GN2WYizVEXkVWhlTi5OxnRYwdcVovWbcOQflKMZAGirWzCE9OHaKHb741Hm7Ub3e4B46YA0VMzGUX/Ql5n3+e/4euDu4YVwPNdhcgqtRAtxnLdPreXKOfWhhbxITJWuPmXE+6qXUAR/PZMpAAtRMw97ZrdZiZP3RYUx/cMcnF8k3AdHUaMN8UOFSFX2n4kbAbdZq3bSTvwbMR0rxZ87DOsSxEzbUTMaaqfTcyzDTGfUqK28AEyB0boQQwr3/e/jix52Yaln3AvtnvspUS+tCLE4A4RMYelSGFIgeXphGU6UDHP7Q943DlABbz7BPz0gN9LCvYZw3wKCZHthiFZdg66vxuIQP8ribjDGiRcrybqll6JhHiJcfIy4QpuRgKhiEjBkeNxXUfkJyy1G74PuAxOP4timZ4kguOfJ3LajUNutXTg5Uqi9ug6j3my576TqHGa4pMOIeF3mcfwPIrne8jPX+u2FnscG5FbvV4463nZuJuOlNvLEd0VGJ7DKJbvaiJnXB6wD3LHoliupPRPQEreYcNT69863G4nUkqXk3EbZ0oLtnssZ5Fb05uXra8R4QBTPL/XCqfMR7GQHwJ+2+1M9Vh7EA0d9feDRILtPUPmL1nSb0S030AkVS4nYzlmi2Fa4rnd7PebjdMipIikNZNHWL5Kfa8crYkyw3QMrf1y43ofot25xn+Z8VWD6GU6UryqkFV0P1FibRER0zYXrfukBLUTCsVYf37Hn0uIetvlno/lxkWecVxE1BSuR5bWQ8Q6SBuLVwm37gy0llP4iHEyOm3ctqPrciIJ9XbEa56Ddzcha/Up47eaUIYK0PqfiDZM3zDu9vhvoWHeBgd/RbicF6E1cglxSrlmCM6qkHUwhQMk9/MxNLcd/n87YcFeg+iyiIhlvA2thes9R0Wev7T5Kie8B/WEhfcpz8lsYiO0gqhhfIGuwff5b/j64K5hBfB813SisPfLSHFZAllj0CIs0e/tq5GbYSpiogNIqVhFJGn+st6lAQnKpYj5HUaMs4E46TiIFIaNiOnVIiaQg05Jdvv3W4kDJccNcx+Z1CLv7EWM9DAcfZz3Wr7m+PlGxKgWI4YzmSg3VoZcKoNEkuMUz5j6XoaUox+jeKhZ/lvs53+EmGEVYrK9hILaiZSbZAka59++ZtyP9Hv3CzfndqjP/VtciaDUONoBF90CV1yChNFmxCCb4Og3DOM+xISPut8RwH8gKmuUIMHST1g9+5DFLg+67oOubR7LJUhwTtJYT7+NBGgRcar3cSQI1nvcg8Z/m/u/3vg/bN6dLMlbkQK+HAkUCME8n4jXXObx3OtPxyO9+xNEI8vcxwuGeQqa57NIgf0mYZGciGjyOc3L4K8Qvc4UrAXJqpmPLLnb4KfT4OATxtE4z8N+wmW7BylS4/1eUrjnoE1RDhJ4rxpPrcgyWWWYvkdsQPr8zjLjYwFScuzunH8tEvjHjP8+wjV52P0WGKcLPW87EOKfI2OtZCqc2+A5uFffWY7WwoDfWYksVWsQfc5B6zjXcNUJpnS4gwZkxa1yO52GbzSii9uAUzD4hOHvQTTTqLngBbeTYlq/hniNXdbvbEOK/Abj7xpEe32In3zJsHZ7nM+jdZPc6r1E3sG05m+Epr3AH8AvNxDJ5a9BdPpFt3cFmYwApx83jGnc4wzDjUQ8XSOiq26kMO+E/mfhxHFEI2sRTa4w3sYbxi2e1yfc323IMlpL5BycgGhkEClKOwzzQTL5Sbt+QnhRutG6GmGjcXKpJx653b+XQM+P3c8X0DoqQ0piGSoJei1xohtEPyvQBmcq4ml1Lh046LayEY3WEpVOkos5nygNudBj2Y42ZXvcx27o70XrZiJx4n34Gr5+zWv4EMj5ru9kRcWMNei026OE+2oHUrC6CfdTLbAN/m4T/NY9bicPMbGb0CnI6WRirs6tg6xrEMO7EbmZUmxOOtk1Dwkd0IL/JmJGNxAuACujZCPl53tw7seQZUWIryDBUgj9GyD3NiQMxpGpr/vOSbj404hppZiTDnj3AbgoKYnJMnk5kUV/nds4jpS1o0QesT4iEfIUj+FmFLdjiynt6p91SDHO81+LcMRyFGhfipjqeiQEnjTOBg1zi2F8gMzuu+kpl629DwmUJ93mOMSok5DpRMy2k4i5qXe77X7++/CL4/DRqzze24kd+gjjYztSgFuRUlBFpvxcprZtigsr8nNJQO70+HqI1BB9grf/fsi9H1koOpDw2Eckwq4gUo/Yvcxu32tDm482wrpZhRSWJ5GidMC4H48Eaad/fw7R60wi1uwoUXEihxBk5UR6nE7P6SYkIDuIXI7V0H8X5I73s/cbzqelZI8egzZPJYhGq1D+xMke22F07VT8ZW6xf6shyoddZngOeUzbDet2NN8zgUb4m7fhk4uIcmmPoI3Bo25vLrLIzzU+HvC7m4gKQfnuZ4CI190C/a95nW1HPONVIsfmQjj3BGRNQEH+rxBWtmI//ygM9kL2XUQ6oUJkodtCnAT+mPFxqfD47p1w0ceAK+GvHofPfMG470AbnKlIYZiKlKMmf45xH6WEi7gIrbc2YBm0PwElV6E1edxjqnV7t6LN7h3IwrySyCmYh9zPX0EK63jjt8zjWe3nBv3+fWTiiJmCeN0xzTm7ET33ILo/QKYeds82KHiC99J+UszGagzsRXTdTZwqf4moY3wH2tD2u//Rfn+pn63X3PCEx33K8G0nUiUtQorZAuNikee2EsUYbkB0ug7RbuJt+PcjxAGXm6BrB4y+hjiMs4LILVmBeNcff/BiPB0C+a/EmaT/3asH+B2GD4F8UNewAni+62YrgHvIBOP/cgN8eDZadJ3K1TZ6BGIaA0QQczkS7ncgRbGPiPlI7oNkWeskkuIWI+bbixh0J2IkP0JuwBqkSOYgpr3HfSaLXisSnCeIE5w7kMXwTcIy8J8Qw1qDGFtyX9bAiXUw7k7EJK8jXNP5SBFqJaooQOTQWoosFrlICYWwiuURZePayCRhpZ7I23eSqGlcgQTGjcbHHr/TSSg7eUhIlHpMA0TpuGIiPmeGn8/3fHQgpWItccKxCgmIHcpjl1WMBNgNiMkXIkWgxvORFAsQUx9BJN+tQ8Kk2uNL8Xq9vDehdCNx0rTU455mHNcR1o2phj+HSMhcT7iIcgx/g+HqJIRjcrMlZfr/aZ0rIE4e1xElshrhvxyDf32f8VRmXLQQSbDTPI41/Hvh6F64dDwSXC1D8Drg8fYQJ3rnojm/G/qvh9xLiMMfA/48BD9dB1d8DikX1yDhv9542+129iJ6SLTcTVRgeBEJ86sN+1aU3qgKbUQaiHyVhUQ5vYVu65Rxg+erFtFso+fySeSWT+EJa4k0OfuJWN2zxsWzyCpYb3hWGP+7iYM3EIpqCXL3VRGW50Ji7u82nM8SOTdnDGlrs3Fe4/tbkbLxI8M5UW7+gi8QtX8LkGJSiDYEJzzGVsLKV+fPRURsbrmfzydKQG4irGbjEW94bciYlyDl80GkMM3zu41EjewOwjW+lYjlGyAqpaSY1MVEHPQ4VEavG639RkIp7UPK47XE5i3h8TEihrfI8H0F8bcEVwtReeVSz9VdiD+XEIdNnkMK7ZO+fz9SIJuJGMzkJq/297WGcRoKYdhKHNpK/L4I5cpsQ+v54LACOHz9+tewC/h8VwoYP0smgP7DsxFjOKHfRl9FlJwqQswoByiD/rfR4i1AO8+liBnXE7n0SpGQOoEYwhqinFUDYWUrRYJnrN9/Ayl2ZW6/nBA0KSD4DGL0ZUPGVImsK1vR7j0fKSbJ9dID4z6OmPVEonpCtT/z/deksZ57ESkUY5EwXUakruhEgroIWZEmEclM+4mEthWGMZ9QeA/6+Z2EwrmciMlciKyAxwjLQbOfL/bnMiScxhInsx8jStHVG/fLjI9SYIrLa/UQJfJ2+28F7z2M0zgEVogSZbe6/beIur8pFjIJsVbE3JNiUY7md4rbm0lULUhWpgL3ucHwvOp2XkBWlW63MwsJpFLPBURc1yBxOjIdZhhBnDpOVo4KG07GI4HaigT5Rs/hNuNz0HOyS/Nw6WzjqBgpa+P9ew5hcWkhFEQrm7kfcr+LkIIwz3BOgCtGDsHRJkSHkzx/vcSaOoHWRAmRRmeX6m9nKn90I2G5Gc15DZGTMh+t+WuQBSatqRwiTGI2mWTlrCBCI1JMX75qKdNG1JrOIVPPODOOOiIdzwi07gd8r8HtFyML0SvG/2bgD4gT1/l+/ln3V4isR4XE4ZC0EUuHWVoQbXR6bHOBpVBwD9rkTTFuB43n1wjLcbfnNlnvJhqG3bw3UXiL52Oncb7QsIxwn7lI+XpO+R8ZC7wM5zrczxZCWS0gyuRBxEvO9Zx0EgpZcok+Z9huNQynoOcMUTGkkFiXVyG39DTBdu4U4tnJZV5OrL2vG7ZBj6lV/3f9ECl0Gz2WbrQmd/r/Ef4/xTFvRvyzWjg7+jSRN3Ob21ng9s8QB7iqkOKXh/jqCSL+spMLev2vxPX9On/D1wd3/YsLDcA/2+sUYhLJlbkQCeddaFGeIspCwXvKdbEAcm8hDlnkowU8HblPu5FQWIaUjGyC6eUixvQYmbQmnEVC8aT7eB34L8QJwk7EXEcj4V6JGE1yOYIYRzVRfaOIiHsZieIcRyABusvwrnYb+Ujp2YrikGxByrqJKJuULGMLFEuVVY7cp+WEQnUdEkDbETOuItzqXYatzzDUIOH3NSKP3QS/M55QXDvdbzFi5q/C4N9DdhKahUQ8ZzZK7bPbON3pvid6zAl3pcbbLjTHu42H6URsVqHg/eVP4MND4wY3ei46iIoOeUgALyOT963ruzB6lce8Ewm1NsISVE4kL+4k4paanSMxxzgoQrRR4e9jiAD0Q4RFLIUqrHB/Gz0fNURy66Wmgzb46DykACSXd47xk+P++ozT5Aa8lUz82S+fhg//BjpI0I0sLbXAVug5CQUriVPVr6Lwis0e/zG3j7+vIBScQUT7l7ndasNRSZxSP+xnF6qtklpEL4OEK7gVKVITEW3OIfJevoWsQkmRm4JchMkie8a4GkTxoK8Yhj61kVXs93qQ0K+Dnjeg4CpEa6WIDp92e+ndDsM/xW0npTUbWfnWEVb8UmSRm4ws0zlkTv1mqrDsQutoFqKRJrf9KHGKerefmW1cPEnkCCz1mBuNl4NErN1Y4tRxsu4u85wsQgrtrYZ9i+eznjhgNl94ubjT9ydD1lSP+wco5Uur4WxFCtEBt5Ws86m9fBQWc9j9zkJ8Z5thmwkF4zz+iYYxbSBGo7AWK99Zjln9RT18dKnn8Woi9dXliNZ2GobtMDodEGxDNJ42zCeRYplkxmSiRvKVaC3lw6ULiNrn44gk8meGzOt0/77SOD9sfPQRJfgu4PV+Km7DCuAHew1bAM933YdcLzmIyXyLqEuZQxzhT7FA45DLMsVFlaLF3IEY6dfRrq0bMYdmYDOcOICUn31op3kL8LTrbu5ASlcdCvI+jJjIzUSJsyK1d+Je+LvHEaMqREyzDTHCZNHbDIOPIcaZYqbakdD7ssczWXCee5nI7wVKVHs3YrKLiFJ33e5rCux5WePKWmX8zSAsHeWGvcv3pyFmONfwfZ9geD3G4Vkk4GaTyY128FqiTJbjF1mCBM6TwkX2/bw3zUUREgD7kMCbSOT9WocE0m7C3TcJWVb63MeAcfkqUpbvdZ/N8OGXNXZWQ893Dc9+IrYuWd0SHLuAcWZ0rxMu9k6k/JQQFpcdnoPkor0D+Kar0iz2bxVE7rhaJNi+576r9N5Pn0U0nOt5WOA5GENUZlnoMe8jk5x6cI3xXIZoo8D0U+UxnYSuN6B/NRJQb2ocHx5rnLyA6Gs8tD6oMRXc7/tNaINURqRWSkruGiQg3yJONt+MhPGlxm+NxzjJv+cRFV5KPa8jiTQptfDuWcN1I2GJmmJ8HSLqvJYhpW8H4VLd7WebCHd7ge991vD/AVKS0in3bcCVUDAbKUjNvtfocd7s8d0IvH5xHMYoRYrv14h0Nw5Fef0Nj7sBKXO9SAmtRTQ/RZ+DO4z7ZI2bS5xuvpc4QVoHBxcDf2KcvkDEG7Z5LClObQ2RAqubyHeZ7bG1IjpbiNZYPZHSZjZxGjopM22Ip84wrBXG/3jj5XK0SekjMiUcIFJm5aH1cg9xOn8LUU1jDHK5zjb85YLz9CNEmEAZ/G2i+0aNcRdo3gcMc9pgdiOr3RNDfs/3+Kcj12232qQJxS63ISm7w3NU437u83sz/GwHkg8bEA1c4vsLiBPJ+41//OwIw1DC8DV8/aOu4RjA810/zBKjuR8xlZcQ48sDCuD0Jhg1AbgX2ldCyceRMEqKzSGiTFQ3YqKzEINtQQt/AXLfrULxI6WImSV3SpnfXa6++xe57FYK3q5CyksjYp7NiOne5Xe2EwKgG06/4YoHyxAzXI8YyM1+5hBxSi8pZ+X+/zUklDcZ9mb3cQwx7BIkYLKNv3aP4y9hcDZkV7vf+9AJVbsi330bLvo0cUK4BKVuSBaz7UjB6UdK0GjiFPV2Iq5pBVH/c7o/55LJ0dX6BJR9hNh99yKhMhUpOcsIhW8BUdIrxVpORAz3DFE6aqHb2kEkvN2r+fjlOvjwTYjhZwPfg18ckf6SPRsJpTb3OdFzeQtSri9DgrcaWAcvXQ/XPUHE1X2fyD2XNhQpxGAWcYryiOdhDiHQk/KxCd4ZgIsvAX5IVCFIYTfTjKMUntBBJt3Im2/DnHvJ5ML85Tr48AREi1cRqWhmEXWgHycsYMml1YOUni+jDVIFQX8nieTpY5HLfyUSyIvg9GMwqtowjfUc1njOn0YblnqkHM0iUp80eh6/gpSeAY9jCqKjPxfOM6EHsxEfGOs5yUV0fRTR5Vx/b0J0tx/ReIfx96rHXW38ryMszC96LpYTrlYQTe9EysAlnpPk8utBm80/RPSWwgf2uu+JRMzhesOeXMzGXSbH4VvuvwlZtvZ7fJcYd3OQYjTH+F1EJLneiBTsUve9RO30vAYFt7j/RUjh7YRzqyHrq/4+1+OsQ9bWNXDiDRg3j4hfTS7xhJdL0Np707htQ7x5vD+b0Dpb6vdeJTY869GhrW4yB0ZYgg7ljUUbiyYYfAqyZ3qMEw1DK+LrzxPVTLa4/++g9bWWiPPd77HP8FguQxu9KuN0OlKKG4gUXe1o87WMSIiewmdu8P+TiPyADcBW6Pp7sYCLNwKfvnAxgD/h/Y0B/DTDMYAf1DWsAJ7vmpIlRjALCc9CQrnZjRTCJUQMRhNa3EPdIc2IUU5GO9d5RPD8DCQUqxGjPI4Yw2vIPfV1xJSXICb15JD2Ow1HcqOWE/nxnvO9a5Ey9ZLhzfYYlhJKRyNiqGeQQLvMcF+FmGw3UUN2m+FIcWodRKB5CWLQO5CSMgvYDEcPwaWr0Ko+jARmUkhGuL9XPMYq5I757BCcbQOmwdFn4dLfgMF/cCmxk35/r/EOcgM9ABxSPFHWNca/rWAZt9d8wznD/cwlkiC3kEnwzIMeezq0UIsY/Sp/bzD8yRWaLALjiVgmkHKxFLnTczwHi4n8ZZBxj/a/DbnXk0lozQEiCL3Q81RoHDYigbPI/R9AgqbC/7e7nyN+v4Ao8TZXeDh3p8viXWlc9ZPZSAz+BLJvIg4sTfR7z2lumUvkyNuKlJUxfu4w8B89P+vd9njo+hmMfsLj3iOYeo5BQcLpIBJ4O5ACdClxSCoHCb8XeG8C7GLP0VTCjTsGCdsGtBbriQTnB4bgcCpBu4uNowfQmvgKmTJs1CMltQ4pECeJg0VFiHa6CTdgEVJCk2svWYUWIbp7y31UuO1G47OdSEWyHJ00nWa4y/zcq0QOwFrjZ7HmpP/HkLsIKSFz0AnUB4iKPbOIhPalvn/I/X6GSG7fhnjVfLTWp/h+u/9P4QSFaAO72H+Nhm3HkPEVGf4+pMS/TZyyP+q5yyMO1ZQjGitH85+sikuQYp0OUb1KpKzqRGtmAaKtmYhudyLPwHZCocwnks0/4vuLiJP3mxBPuZWwtE41bjp5rxyoQLxjMfC0cnqO+ioRJpCuesK6nfB2yvNX6t/3EKeWTS9N66Byld9Z47aqiHybSRmfAHxrWAEcvn79a9gFfL5rNhGbM4AW6Rb/7UHMpodwgYAEYzNiStvRrjFZYlYQSUIPqr2mQ0TKlBeIskSNRBzcdqIMWw5ijB3IwpEUiiOG6RnD0YAUjq8jIVyBhGIr4dpqQgx9tH/7MnGI5S0/k2f4sokar+v9WxVRUm2s8TLdYz8BzINLP06cRD1jnGUTFsPDhDtpPWKKzxMpO7qEs0unAZdBtgPGM/hdbJg+gxj/KmAmZD1CJJzt9hhvJoraTzCOv4uYfx/iYH1IeB4iLDYQwfkrkaLeYliTgpACtXuMj0qPewUSxAcQ/RQhIbwJKU1Dra29kDtTz/Sv8TMVhnXAczIZCa7VRH3piZ638W47hR6Uuv1rDGuL21uAlI0+/dz0NlIcFxg/ucAkyB7p+U0urvGIXtLmooE4ALAXbRrSnKaDI3VISFcJF6M/ZvzUeD6qhAZy0HqbYnwX+e8WJPwOIqWgjqjE0Eus0WLDVkrUuG31917E5To9vvwhfQyg9XDG+N5NVNFpJBI573H/FUQanW60njsI9+RaYhNUTdTPLfSYH0Cbvn2ENTfbMLUid+dlwhWzEL1dZlib0Vxf6nlp9/2EiwKnnckn3LRrEC1OdP/fQgi/jqgOlNZQJxGrmuLaWhHtNiKFcA4RQ7nBbX6TqFQy1GU/3ThKCl4TsnK96e/lhjttIlPMYaXnrwKdct2P1u9e91fsMV5HHJZJFvSkaJURB0fWIQtfWqMbCM/MDEQbbUjBTAfdxiGeWkPUSV9LxGPXEUpo2iAWw6hVhLeiEdHNIrfZ588y91dhONr83BwiwX+x5qDyY0Ti/1qPcQZhXSxBVtgLfA2+z3/D1wd3DSuA57uaEZPYR+T7exUpKkuQEC3z751IiAyiBbwVMY0OxGjL0aJOzMpWisqPu+0+MjVeGSDSDSTLxUYi7jC5Pb/ne5vVFtlIgN6FFLIGIlB+MlE6rAIxnCOI0XYRDG/hkOfb0A56O1ETs5NQCh1PQ7ufm4Nidh5BSkQuYuavuu9+Im9VEto9xsWb7q/U40g74yXEoYYxRBLpKo/124ZpK7IcJtf5fCQ8Cz3Gyz03W4gi7suIwxzjkIJTYVhXu93kVkvxSU8TLuaxhmupxvrOz5ByWEyUa+on0oPUEildUmzkcmR9a3dbXxZ+c79KVKApIixuHW67lijJ1YOE2lfd50nDneKGjvv/Ce6vlIwLafQ8qJxM5EXcgQTlHug/4/Fu8zjSxuMlInapCtFHjWG/xmOcbjwn5fbLhvtLQ/C5UeP7zFrPwV6P6xRStm5Hbq9KMidsM5bsco8tBylhlUQOvRs9V1ciej/hZ8e53aQszobBl4l8mqXGwXo/X+r7h1Hs6+X+XoRoo99jAa2NXCIFyWGiTGKv4dtquNMmsBXF8K00XpZ5DGnz04s2IimsIln3T8Dp11A86EjCtXuD2zhFnAifznvry1YhxfKEx9BpfFxHlGWb6fHe6P4HjftTwJ8hGtmG+MYXCOvi4yiPafqtkThk0+D3GxGf2Azn7iFOMhcRsXB7BGf7j423KYh+rzG8a4kDIfuGjHeM/+YZd3UeezHyMvw5cep/C5rnddD6NqKrQ9D1smHJJ/h9PdqALCVqkFcajiuNz5d9v4ZIgVOB6OExRMcTEJ3M9d9RtFY3E/JhPqKdk25jErJO96BrBiFX8sjEMFLJBb2GFcD/c69hBfB815fRIj6GGEY1EqBfQ4z9RSJxah6yoCxBi34uYkR/ihSyhWiBdyLloxIpXJf5vduBWdD1IGIGc5B1o9x9VxDWrCK0St5y/0nR2moYXyKYQxPhJjnpvnrJJGA+fR8SLpcgoTDVfR4mSt21EWlSehEztssDkPWt0vePEbnmit3WEiJ3VWKQKdC5CO2Q5yNGv9hjTcrtcsTsigT/6deIuMSFSHCWIuF1AjHl3cBOePVuOPiy77+GlL0eMkKy63okzB7y+HeTKX/GCOH16BYirqjQY9mPFIEZxvcWtXvxNCRgB4yDLuAQHJ0Nv3icUFyqCMvHWqT8FiCBvsVjP0nk9tvkagltSIikgPi/8rg2elxVROWRkcZnCxJetRoP6/zZ7TkuQkpmj9yHmUomZ1XjOFO+7bjfy1M/p5/1HA8Spay+5b6nw7nbCOvJ95CiXuMx3EHEjWJcZCN3ZY3nfYZh30LUUbbVkpuRRSgfrcPlbmeG8J0Oo7Q/jSxO/w5ZgPLcdi6ZJMjZ5YYp3/hYSRwSSmEPnyU2DmuREva48f0scWBhn+9BVKkZgcp3DXgMtyL6u0L9DP49indd67lJbso8tKnaiWjy+4S1/Aeui91guC7XeFmH1mS5x5GDwkb2oDU027ia7H5WEUnqn/McHkXrYbfndTZxSKuVqB1eahzOR2u5FvHLAs9Xpe91+53kxk7xlzsh60NI8axFVjUrYSlEoeQjHsd4v3eX26v198lEXtFSRFN/bHwVkqniQxlSEh8yDhsM50JgAZR9wn2+osffeZH3pntZrX7ffQrad3ieW42/ZuR1qfNz3yIsmPuG4OOvEL8e4WfXGR9F7ruFOGT2Igo/qSLSPiWreQrLmUnEf1+HNhLD1/D1j7iGFcDzXWuQwJlNJNHNRgt1MhnFr/0NIsltBxIONwO74ZffIEr5JBdgNbQ/TtRuvQ3418ArMPou5IpaRCShXkCU2DpMWJ5qEeOYiRjRzf79iOG/GvivSBAtRQy90mOZDdwBox4zXC8iZvcksaMu9XNVaOe/lXCJNSPm2wj71gCD0P8AYoAY5m+7jR4kLJr8/9VIEA0VKjmI+e3zvT9GQuFxw3UYuAtG3UMI16cJq9V1SBh/loz77uqxMOkqwlqXa3xtFW5Hj0HCcyKRluZa42e+frv0G4jx3uZn1sOJUx5jlXGVduTzkMKT7bHYVXTpF+Cjk43LRr+bR9T0XOg+XjN+JiEh9QyyDvTDuOuRFWM3UQt4DlGC8A40/8vRih6HaKTQMBUjRThZFVeSOczyyx8i93O58Z5cXhuBvyTKsL2CNhdzYdSDxnWbvr+7BgngR2HwAci6RHC1LkO0XEHEzG2H1m1IME/x3E5A1Re+6DkfMA6XqX22ErWHR7idZB2t8u8pML9DeCh5HClwyXWc4qtKiRyX16P1stFzso4oB7gfThxCCmwhnHjLY1lInKLdQLgWX/MY/yNa17eaBr6IlIPpRBzoNP2efYf7ShbmH3ieatQ/t2k+ev7B8/yXpoGZRI68X/m3HUQ94+v83K3udxraZLYh5WKQiIk8DL9oAb49QpvbSuAQ7F8O/avIHGpit+aORX4/T32e3oTW1e//bli2O/17G1p7eUhhSuEWRcbjA2TcnUwh4idfQOu1wu0lS+sIv/+sx7MdbXYSrXQRddH3Il4zHtHQcrSxGkumysjgy8ZboWDMHQsX3+bntyOa+h3h+aLHoWQamUMtHCBiPpd4rqYiHtbk3+/xuHORdbID0f1fuo/xHs90v1uDLIZPEGm8Kozf5xB/OoaU2SNEvsHFXNDr7Pv8909xNTU1cdNNN/GRj3yEUaNGcemll/Inf/In9Pf3/w/f++QnP0lWVtZ7/m655ZZ/Iig/+GtYATzflczsyVqzG5n72wjLSA2U3EbUC65HzOfjeufDTxMWhsT46l1OaRxirLVIEej1c8uIcl6XGpY85JY5hhSMXkJx2o0Y1V7ERCYgRjdomHqQMjsJZcVvQlaOO/zMCMRA1qMs9p3ESc1TcO5eonTSTjJWHso1nsvuUHu5E9Bp0rfc/78zTLuJ3GMnDc9442IKsn48avggypUlS2MeYuQniRQfySWyECm3tyAFbITn6CXEPI/5uQNI6XnQ92qRQNjtlA41RDWUFF9Y4LnII049j4Vx9yFBtoNMWgj6ket7DWLSPzL8J/230jhsRRyuj7BcPOf5S0pMofphCbIQJ+tIcsHuJ3KvpXi0t5Hy0oIEzCrea2ne7u8b/f9ez8Ek+PDnkKBaZrgOG84paL7vJFzpVxMbhBxEgy1w0SrjcACyP42U0zIouwpZ6xYhujirdss+hui4HilnRxDtlXleWpGC8gphsW6wxWyTcbANbZBakDJwg2EYRAoCRNqTXrfX4rE0Qc/98O53CdddDhHPuEYwj6slE8c57g7i2u66wePRmrvM8HegGMTFyKpc7vfvQgrANMFw7i7PX1LQa3hvcP944/isxl2QLH45HvsOInbuBDoFOwKtnTnIqtjpMRcjunrV79yP1sCLZOIDP/ohYP7ZoN9SmPJxyP0EERObLPqrPO/ZwEoYdYOf+fF/jo1Dcjcvd18VnqNetM4rPLZliG7LiXCQHjL5RDmM6PB29z/OY3gaxQrfTOZAE1sQbUwiys/Vuf8GxGf60VouAQog+2NEzsw8wsXcQvCEZMVs8DODaJ1MQ0paHVLgX0TKaAHi5/f48z7PZwrfGIk22gPG0WxEGy8QyexzPK4xREm7MrQ5SnjK8bsdRKqu4eu818GDBzl79izf//73+fnPf86jjz7KU089xde//vX/6btf/OIX+e///b9n/h566KEPAOIP5voXFxqAf7ZXYgrJcvFZtNDzESPZTWSMT4phCtTt9P0tROzSEiL3VrJ4lfvvAGJEx4hYn04ie3wLYvp3EAwzuXtSXMoCxGgKiZOODUTerQGkME0FXoCut2H0evdRRcRX5SBlZLray6pFTGeb25iFGFg1YqZ9RKqTFFOXXIwjDN9yJPDK0a7/a0gRLUQKxeeNuzVI+dkK77wFF1cQJb3OenylBPPtMJ4Lh8Cf3EaPu80cwzfNeB1rWKYCX4JL9/t7JZnYMDb6/UeN14XoSmPb4fenGBfz3FcXcQJ3L5HuYiPvTStRiJj9Q2SqCuw5ANM/brzmec7HImvzQ0SZrxbjbrFxe51hGk+40F4lUx/53MuewzFIgXvTp6qvgnNrIGuM5v/04zDqC0Tllmainm+KZ3oSWZUa0Yah2vPX5zluRwrfYT/b4d/e8ljaEC01Gyf3Gt+PEJUrUrxkGbIoTyRjucvuRMpGOXGq/SV/JuWu1P0cMWxrkcDvQ4roQ2q/4HooWEOkD9niuZjr8b1FrP1yw3w1mYNf3cDoQrdbjWi9WUrlRS1oLV/t33f4fVtwsj5BJHR+yWPdj5SHtzyvZz2XC9BGrpjI55esndchfvISUXWkCW06epHynhQpiDx7xcSBmQFi07UU0dBh4gR2AZHWqpTYFNnCzSAMvgHZi8nUOabNv9XBu0fgokbDNeg5XeD/U7jJJrQu+tCausnf1yCa8OYic/ilDCmZxYjOi/3uXqLE2myPK8XvbUI01kKUrrsSuWdnuJ1DxmcrERea3Pp5nuMyw12DeHqy6G5Dlr7DxGZtBxG3dxtRBaqRzMERzhruYs/NHKKyzHFCEaz3GO5AIQFpw1xNJE6/QNdZ3r/YvX8qC+CnPvUpPvWpT2W+V1VVcejQIZ588kkefvjh/+G7+fn5lJWV/RNBdmGvYQvgea53DxAxcVWIeb+MBNJZJFQGeG/lhVLEWHqREpVNnF7bjxjBMcTYrkaLvh4Jx0oyMVb8FAn0j/mdOvfxMlHhYTJizimubic6CLAG7TKLCZfkFA+qGwmHNhg9kjj9upsI6j5MlMtqcN81xkVSNPqIQOk8JGSnIUa72LCd9RinGE+on3NnkQI9QFRV2ed3Jnj8NXDxZMJyuh0JqxZk1dltuJJFBKIUWxIQfUTW/mrPw3TihOR4ovZmBRLsyaqa4/E2EwH4s32/haj33IyUikrD3YbmfSORl7GOKEGVFPIUP7aKTPqY6bNRbFgV4VJO8XF/SqTiGDBuJ/r7656HqWSqcIDfOwRZHyGSlXcDBXDpTYIj61qP4xSMeoJIjp1v2Ne6H1Dg/gGPbw9RCi2FQ6xGyvdRRAvLCAE/Qu+zx7i52fhuQ5uGNsKK/Qyy9LwE7/4KKSGtxuMKjYkXkWDfjQTmEeO20+MuRdbDyxE9jvHnM8Zjqdt5zJ89RH3Ybrt+jyIrXiH88sdE0uYaze3oq+DE3e67h8zJ8IuWu51+/zUjxWUiovftiAdMIE4uTyZyvE12HyNRbF/adJz0GPcifnHQsD/svj9jPB4g6hrXIBpPdNRDuFlB62HKkN9WG96tRBWdZX6mBlk6G5ElvROthxLIfpA46XsVMe9lAj1jhV2GaGuzcfCq568eKZzd7tMW5MwBnTK0/qb62XIiBGCLP3ON08PECf79xkcNijHNJkqxAUcfH9JnNhGqsh0pWGmtdyLlq8D9FxAVUXYiRa8B8Z9ZRE3rakQLCxDdN6KNZZFhmuV5mIgU53Fo/k8iRXkC8vw85H6WIprvNaxnEQ20cUGvfwoXcFdX13v+zpw5877DferUKYqLi/+nz7344ot86EMfYvLkyXzta1+jt7f3fYflQl3DeQDPd83Jih31XwCfyYf/1CtG8ShROH4+cbozKQ8VaNHeQWS/bycUpoVo0c9DDOGbKO4j7V673OYmxDQ3E/kIB4i4jxQz9iZSQtYgxSYFQpejFVVDHG4oQgykHblWniEsPN9AzKoYxTTN8/PPEbVqZyKmm0PkNNxMpMJZDoyF07fCqI+53bfc5r3IXVuGmOElfn8JEpYNhCWnDp3ce4AovTTO8PYYL2dh8Gmnh5mIBEaLx99jnNR5fEkYPmpYp7mfeo9jAXDSCWs3+H4/obzdg2owdyDmPBkJl7QJSG73MuJk30T33U6UOStGinGK5aokXDiHifyCGD/jiPxjxUP6HOs+W4mTyxP87j7isNApz0mncXEIKXnFfq8e0V0bUiLWEqebnyDqG28lLGvJmjtZY+x5DAquIRSPMkSTtyNLdaH63/8yTBkJ+8/AlI8gBSQbWWGWEK7F9cSp2IOEC7fOvz+BBPBBf58N7y6Hiz5uGG9F9I7hnUdUzmlCCvFqtA6LiLrUnUj52E2cAG5FFmqQcrOayIOXYgo3E7GcDYhGthtP44lDLOuITeAIv7NVMLzzGlz8aUTnu4mcmZWuXAGMmo1cuDvQJrFF4393L1y0AM1/sgy1GqePAnug9e+hbDai+/lIQZ6DrIfLiLyFWxDfqkF0uQYp3g3GyU+gqRYqx6MNzA7jL5uoWpE8FluQEn4M8YFOotRhn/tbRmxsyozPLsKy3+t2t6D18Jrf2U8oRN0EPafQjGSSanCbn0du41pkpR6B6O2P0Jq5Fthgq/hdRIxyNuEe7jYOFgMb4ae74IrJZOJOM0msbWXmSr+X7762+P0mwuo6A9FrivNLG9EdaH39MaooMnXInB4e8n4n/OLH8NELIMZTHsCXiOxK/7tXLzJs/z+vP/mTP+G+++57n3qBI0eO8Fu/9Vs8/PDDfPGLXzzvc08//TTjx4+nvLyc//bf/ht/9Ed/xMc//nFeeeWV9w2WC3kNK4Dnu27P0iI7gRhEG2KIHWg3ehi4Gv7qTvjMZKAP9h+BKdWIGaxBTGAnEoQpVqwDMbBDRHxHim0DLfJbkaJzFo7Ww6XXEGlinkbMYIDIH1WOBO5xxBhvd1sppmuP2z+DmFctYkIpyDoHWWLeQsx/K7I2Pk7U+s0mEplOcduHiFxx3UjY7idOIT8F/BuivunNhvU14CXYdxIuW2o4UuxLM3Qdh9H3Ika6HCksC4HtMHgPZM9DQmOe4Zgra8y4WsS4q+Cnq+GK6w3fJsQ4N/r5qYb5kPG0AFlPJ7qvFN/WiIRMD1L6rkDCrhcJtlNI6B5ALs9fIgVqO1H/+WmksNxG5FmrdX/fNGydxm2j4SlClsUzxCnYbqIiwAikKDxi3FUh5WKL7zVD64tQ9jiy1uR7/Mk6tWUILN+2C+8RaL3LcXvzPe4G6FoKo2cT13hEe1OJMncYR3XGza0ew+sonch+dGAixSbuJ2pRJ+twK+F6W2F85vq3/cAdsOcxmH45WgtH/OxcRFMbDU8FvLPBp7IXIrrqJDZP3X6/mChD1oFofoZxndbmtR5ncuvvMC6/hDZL1yBF9BKiTnSyQO8mQ4vJykq54SlA9HMc8Zci18/egJTapPxheIoM+8NEbd81aANR4n4a/fcN933AY5hG1DS2cv3OMbh4geesFSkfSYFNrulG95UUlhbNAWug/UUfiFhieKcb/51oTQ6gNWCcd+3yAbd7kFfjS0iZXsN764LnEifab0ZrpMh42Oz5eA3R7StEHet7gadh8BBkX+/xVyO+XUcm6ThVBG8qQGvyCeJg2VVEMuu5HnOzcbPPMPyB234SradEK7PR5qDH+LgGbRhHGI9NiFaqkRJ/A6K3QTj9IIxaRCRW343iRlcjflKKaCfH+PprlC25WLA27YXKC6gA/oj3VwH8PNDc3PyeRNAjR45k5MiR/6/n77nnHr7zne/8D9t8++23mTRpUub7iRMnmDt3Lp/85Cd55plnfi34tm7dyu/8zu9w5MgRLr300l/r3X+O17ACeL5rZZbcEieI8kUVyDVUhoTNOiQoZyC3VCmcexqyHkTCYDJSwAqQ4LRbjmnAWdj/DVtCWogA6CSoprjPfH/ORMxwAmI8m/w3kxDmM9zHQ0Qg8UmktGxAzGcxYS3KI1xMq4nM+GWIma9DSuuVhq0UMaCTSIC1oR3qHyHmXUrsTssRY0zxPruM19nu93lix7+eqIN6OQz+zCckjxLxk71IOG0m0mosJsqePY3cJc8jhl1HHKIBMXIQQ26A/l9B7r3Q+gCULXB7YxDDLTNeliNFs9btLyET65U5HLIbbVl/V++d2wZZC5CQme82c9HBkKuRMtPmvy+TKVWXKSO2mrDi5iMFfzyy5FS6z8PEIZIyomReorGhVucaJPhSTFOen9mKlIQR7nsyEn6b/dw3NR/vrIKLNxA1sBs95kai1vUAopc+JLj2EsmotxNWx1okcBcTlXNyPKYDaF1sN+zJEvykYZtOJEmvUJ8nVsO4Txv/zUR1lEI/24mU6HK0Huf53iEid1opUed1su9/j6iY0ooUtX1IkN+KlLh1frcDKUZ5aBO2xTB0objhTs/RQY/rz9HcrkdrYZvhaCNKFFZ5jK1IEdpL0N0cZBGqIBJWX+/3NhJlEve43V2+V0vU7F1vnBe4jzxE298yrtImrxi5yVNscwpZASnDg8bXeI8tD9HGKth3G1w2Eq2hy9A83o02tlcbh8VojTQQG8kjSDG8hci60EecuL4Lzt3t2NXFnrfFyMq4B9F7AVr/s4mUTYf8fnKZ5iC6GXR/nf7+vHHRR4R1XEpUHNro/rzZyCSE7ySqDR12n2ctDyYAA656cy1hwU88cy2hfKYQoqRRnXI/49F8bx3y3lbC29P0/18K4P9qJZC2tjba29v/h89UVVWRm6vdZEtLC5/85Ce54oor+OEPf8iIEb9eFFxPTw+/8Ru/wV//9V+zcOHCX+vdf47X8CGQ813JZD+RKKSeBF4vYgzNZNImpMD0zcCnxhKpAk4gRnIMml524l1bBafMJlJGdCDGPJ1I4Fzvd/uQ9WS9+sgE3t9PuHWnuK/NvNcNlue2yok4sGYiKfAz7vseQvBuRkJrJfD7yA3ZQ+RSq/LfNuS2KCGsNzM8pkFkxXqBcL/mEEl9c5EAKHQ/p8icIsz+HBKYE5GAKENCuRIx34nG4QkUML7Oc/Y8spDVIiX1hO/3oYMnjyIBUwW5ecArdovtQcpSco0fQ3O+wW31IqEwSOQ0W0O4YHs9xmMurXYJUpC7kaCsQEppPe/NabedjOuZjUgBqCISdqdYyxwiX9hL7u8u43YlkZz5JST8UpD6UeO00nNz0u+keKMUxlCDlJxdSJk8g2j/cbj4csM1SCboP5NUfC8R41ROHEqqR0rHfYY9233UIbo8YVj2u7+dxMniHUT5volu41EUylBMVN/Ih3HfQLSa53GvQ5aX7e6/iDgAMRfRRidxkv4qwkWYhwTsQ8ZNE6FkH/eGAbTWjyG63IwEebIUJWtbI5FMvZhIxjwS0fFGoqxjclXOJuijF9F8vcfeZ5hGEBuipLjnE0mnF/rdN43fWrRBbDAeJrrNtMFJ1qWJxGGaPuOowW0lq20bcRBjHXHAqhOt83rP0Q3q67JrjcNcw1hhmLPRvE92/0nxS/yvCCk7O4jN5FS/sxA4AFmfIzaPnR57t+elECnqbxIxy5cR+Te/7XFUE4fOygh+X0lYEHe5vV1ErOt8t5XCHI55Hv/cnx8iNmCDkDUTrYuTUFBkeA+TOWTHX8Hpehh1ifHXYzgTr0zeoUPEKfW5aA3loE1kCne4QNcg798hkF+3ndLSUkpLS//nDyLL37x58/it3/otnnvuuV9b+QPYt28fAP/yX/7LX/vdf47Xr4+B/69ch4nDExCxXXX+Px8xmqcJ8/x6+FSyrBUjJaAFCdd5DjVahpjkCSSsFqDFvd/9JctfiuFrRm6dbsXxcBgxmBqkfL2GmNBzSCFrQ0yqlVA8EmNtRjvx2X5uLVEOqgRZAF/1ePuQ4C9CTKmAKHQ+DzHJoRaZIqJ8VJnbGIDTPxuCD5BgqUAWlWRFyvb/ScAUEQdnHkeuxHLiYE0e4T7vBpZCfy9RPq+UyK02DllNJkLXG0hpHvC9CiIFSrlcSEzy+HIQN9oMfB9a70TMdxuRViUpUPV+bonxfZawUpYRSZDHE2WodhFVFOqQAN5lvJQS+dOSu/NBMlaFwV7C/ZkUvCSUEi0eJtykWxDN7Pf4C5FyuBcJx+8TOdSqiFim2YZvHZHsOxfNfep7suekwXjoQ1xlB3FAapFxuZuoTd1NBMGv9djeJFNv980DRCqd5J5s1lyzwPO3lzghulX3+38IrUMD4zuJmNUGpOwfQxbwMj+3A9FtvedwD3GQawuc2wK5y91vDpH6owqt1xGGI7nKzxKHZ+YTVrWTRJm7sYRi2Iqs+Xs81mSlS0ppmftoMY5GIIVpBJmNQv8WNL8nDPtkIsddIdqMPi8892zivSfZ24jTq2VozSTFuQbR7TSiqkcZ4mElnofX/Gwhmu+TaI4hQmcmIDpa7n6nEqmw6ghPS+sQXC1xXwWGYxFaPwOEhfckkR+1AllMcwzbQTTXLxH1uz9LxHFChF2U+/2VaLM4xfeagGucKD3pGSlWdy6h9H9b88BqQqFdQRyEayZKF16N5vYkMBpGTUabrgHCo1FEnHjPd99rCJ7RZ7wcRhbgC3i9Hwc/hv79U1wnTpzgk5/8JB/+8Id5+OGHaWtro7W1ldbW1vc8M2nSJH72s58BcPToUe6//37+7u/+jqamJjZs2MANN9zAJz7xCf7Vv/pX/0SQfrDXsAXwfFcFsAbeOQIXz0M74ToiY/t0QshsIjLeVyKmczNiPCuQ8H0LRo0khEsNsno1IKqf6/sp0HkPip9qIGP9KUtughQnA2IIfYgBPo0sLIVu71NEbdaktCShk8a4E8XTtCHGtIrILJ+C2AuHvFODmFslqqXbprFlKpHsILMTH3zMO9uJhPu2DJ2AnW/cLEMHPfqRoncSTndIFly9nNgBrzAejhKWlW5kpZsBufd7HEWwfxlMWeU27S6kEUbfhphqCVLO1xJ1ec84L1gDEjTd0P4ylDyo72UbkFDeo7G9fgSuTPNbj9zkXcBCOLfIVopmOP1jGLXVeOwlch+Od98bYHAAsj9hvJZ6HJMIoTQexRP1CFfZu43zNhSrebdh3k244Y7BO4fg4glIUahCArcYCZQVhmOB+9yN8jg2IUG1CcVq1REnpyuJlEGdSBmrNBxj3M5Jz3c+keA2uQo7kIDfRSSo3oKSJ3/D/a4EWmDOA0Ts5F7j/moiJtfzQCWKjbOwzn0Iyg4QaZkWonX2OrE2ZyFlJFloIQ4+tBHJmadoHFnXAX+ClPM8tC6KiVq0Kcxj0DhLikc5ovlq99HjcaTYxg7i8NQiIrdjIVHGbAlao8k9Wke4CpPn4ZDrSC9BFv1qIuXLpcIni31gqg0KxhOKXw1SytYa1jnAPDi3DrJy0YZxPprvJcAfGt5sMod7Tq/zOv8Kkdt0BJrb0cblHL/zR4hOZpGprdz1ExidaxxUIdq7gThYlxTdAn+WE3GGXyP4VAuycBcAKxze0Y54zjGPda3nZ/4QHGKcPaex87zn9jYy/CP3FsNcg+hyHVo7NUQoRwq1qEU8IYX77CBzQKjnDch7Qyi5+B7Dvdq4XIj4/wBhNW/3/CfXcC3wCbRpuxrFRV5gC+D/Cdfrr7/OkSNHOHLkCJdccsl7fktRcAMDAxw6dChzyjc3N5ctW7bw2GOP0dPTQ0VFBZ/73Of44z/+4w8c/n+qazgG8HzXZVliAI8jZe5b0NXhgvbziYoYk4ig8t2IQd+MhMta+C874F//BkqMvBoxlJ+6j15oPwkltxC5wDrg9DYz1D9FTOkgUlqWIiaXGMxhIgi+BDGJPODrSMDeQLgxH0dK4mbgUjhXD1mrCOvFIcT8NiAm+kO31+F3yxGDSpa/siHvNiBF7jZ/riUsQ/lIIXgaTvQ6oe5BJFCuRBaNr6GA7/GIUd+KmHoLkeYlCZz/hBTbGUiAtQzps8UwVSLG2IiU6BSQ3oQYdyMKHO82Pv4LShNRSBzyqQW+mA8re2XBTUp1sgqccjsT/H8KCViMGPZWot7vWcJ93idcMBYYgKZdUDmNSC59L7LW9CMteCoSCm95Plcbvm7j5iQRq7iHSFuz1HN1kMgnV+L3XkB0eAMSKoNIgB1Bwtnu93PPaoiX3ul5WYBo4VY/k0uUrMsnAtgnaV7bfwwlSRHvJWot1xHuWiurLELWmdc9l18ynPlIiRhLpARZZvwOqCzdqFvc7rPG11GPY5vfeQopXgvhF3fAR69HyulOePNlmPMgEvoDmr/+xyB3AXIRP0JU2Sk0fqs9x81ICG8nSioeJ06trkLr6ihSasZ7XF8kShhOJWPd3PpdmF/t7yvdfvIOLCYORMxElVPaDesNiCaTdX4vEQNc58/kvfiMcXkYyIOjT8GlHzddLEM0uNifqa39nr8bhKN37oOLv0BsCF4yXKcQHRXruRNvwbhyIs74ceP9aVWMyX4AKf4LET11eIyTkYJzglCeNnsOexH/3elxveTfJ6G8h/cJL6nSDRPhxF4Y97TH8ipSRpuIeM4S4/oFdIBuEVrnL6DNxjNErfCzRN7JH8GJX8G4z3nOvfHoehlG34Loup5IEl5MhGJUu418t9nkvruJ8IeHiUThu4h8j8klvgTxgq8C3RcuBvAHvL8xgL/H/3oM4PD1v3cNK4Dnu/59lj4rkJAqIg4sdBLMOAVXlxOZ568h3LHbUbxWilnpQEKugNg1ZhMWhXlENYqkQNURZv9JRB3VbMQQtvBe19UEImanAVl7mol0C/tRwPPlfr4D7VQ7EJPpJkrNXYsE60Rid7oRBo9A9jWIgf57j7EUWQU3+/0BJMz3I2abYpBeRbvpPehk7SK3s9146UMK8781TGMIS8Uawr2TXCA3IqbfIjyfexyyvoqY5lTjaprb+H0kOHKJlBX7idrDeUiof8a/pRip2YgWNhLKfoo1nEWkDUlxOyeInHSzPO6RSNit8TMJD0uJ093Jdd+BrDQfQ5uA1McpIg9fFVKExvnz3yOX9iUeey2wA861ODbxRuNyIhK8pe7zGeHkly/Ch29CNDyNqGV6ANFlEsCrhNLBO3wiuxx6XoSCbxiv7R5fsuQki9VO08Fuj2/QbSclYTYSulcatv2Ifu9AymkrERrQjGinj8ihlw4mFLv9KkQrXcZlUmT6BFt/C+ROQ8K42+N9iUx94cEXIftOpITdZlje0jye+CGM+ypSTg8a3qRs7UVK1mgk2Hcj1/tGQmGuQ2swvQOKD5zq31cSm7gUFpE2PJ1IWRsamlFufM9Hm6C5SOFqMW4mksnNRxHahM5BdHkUSd1B9w1RG/kIvPu4U+xcRxyCSGtjpvHVhayNXUgx7PHvRUQ8617PZb1xchQpan2EpbvTc5RHWM0XEIeTNiO+soioAVxFlKdbY3ieJlInnUR89DrEj8qNn91kkrw33eb47BkELc0i4m5LDUee+/wR4rkFxlcp4vtrCLrrJw49ZRv2ZH2sQHM/YwgsKS6xFfGfxWhTVGm8nCU2N58BXoGfHocryoETwwrg8PXrX8Mu4PNdG4iC44eRUrAeMege4lTtNv8+jWDOkxFTrkBM6DgSSDlIkBUiodiMdpiXE3Ftx5CAbyTyotUgwVPlv6lolaw0XA1E0PrVKBD5OBIotYTSYmb4i7PwUdBqq0MMeYx+y5RQG0BC7wG0M81xe7M07uzZhKtiE3EidyvhMn4GKax7EJPfYxwlC99U4sRmFQrAvxEpT18lAuMHkOVgFxnF+9wbPm1bg6wFOUig/QCychBjTu7NDiL1yUR/T66+Pj/TavjPqK2/eRs+mawoOcThnzzE3CchxasEKXILkeJziqCDPjI1k/mrIfM4FynpyWpUiRj9SGQZ60DC5U3j7BRi/t1I8LUiAZViBN9w/48iZfIlohTXRsi6BdFaj/H9VeLAwKD72g8fHum2L0GKwj6kaBwjrG+9ZIRc9njDV+aUkI+7rS2Ipg8S8Wgnh8x3A6LTfcJF/xrI/TRRkaWVyLtW6/uHPZ/riCS4I91eO3Fqeqdz3l3jeXjSc5VnnLzs5y6H3MmEsjaAaPsgUhBe9PiSYE4HDVr1/rjlnr8+w7yfsL42Gf6piN5akRK5EyFqldudQcTNHUdK/SykvLyI6GuuYdqBeEyO30kWsGaPa4HnKYU0HXK7t5EpQZk5AFbi/rYaNzuJahrF7jcfraGtcNHnEA2ORwrcAKLRVxA9pjmaTJQ5bCQOuswi4ml/D9HIeMN9glAmm4ksCD1IoaqH07fBqBRLuoioqFGANo0pzOCA2z5lXF1mXCSFe6txPoU43PWHQJnzGg4gnp1NpK7q8RyWE2UQ5xIVfQ4ghfswkfarhdiYX0ck+y8kDnnN9XPFxEn554zDS91OWieHiYMhBWhjMVO4vmI3kcfyAl2DXLhDIMPX/9414kID8M/2qkYL818jJmZBRz1iBNWIkaed5XEklI6hhT+fOOFajRZxH7FVWoCY6B4/v4PI3ZVLnNR7FSkNk5HSsB8xpWyiTmxiaDPcz1vuZzYqPD5eiWapBG6Aj9ai3Wmr4e1BAq7B7Y1HjPY1xPhS3NOlxOm0bUSd2zyk1IFWcCexgy5Hgq0Per6LGOw6/9ZBJCFejRS4NP65Hl+L291iGLKBa6zUzCWCqq8mXPLTiEofh5G14yXPZ6//iggL3FQkFBP+dsMnryVcZc1EDGRSZAfd/gASLg3G4ZuEQl+GhE4/NP2Dv4/0u92en9Ee44eQ8nkbUTKryG2Wej4nCYQT25ClpZPYSEz2GJKg346ESHKrlxl365Ai2im4MikzzhL5I3cjZdIW7P51bjfHcLQRORNz1VblR5DyPsOwbCIScSc3/RYiJKHbMI2H3HyPs4TIw7iZcLnm+flu953cjJXIonPI/fTBwb+HsmqkfMxF67Aafnk/qplciBSc6YguJyBaLkIbnK8hul5KpFqZTpyk7YYTP/H3ViImD95bJqyIyKu30M8mRegsUs6/5/f3og3CgPE8Am3InvPzHUQJyTyiMsczRHLuPCJMYsC4qkJzf9ZwlBGHecYYrhzC63DI712PaLEbbezmeF4fR3RxCNHIdGT5T6EoJ93+n3u+K4jT4w/4+2iijGNa42kjOcvjqTaMzwm3oz5BbKTTpq2AiJGtJhTYQkKh307QeR7BX5sRLSZrXB5y8+ehTX6yHp9EFunlRPLzGWhDnTZ2Sz2ObMQXUuxfoX/L8Zg2GSe3ojW0n8gUsR2FfvQh2lxrnO1DfHS635mCeEaP4TrscedwQa9zvH8HQIbdkR/sNawAnu9KVqlZiNmUkgl65jBirC/72VYULzSGUMzqkKJYg4RwHXGgoZxIgbEAMdotRA3IBsJ9MR/FIZUgK1EtUd1iqLum3rAtINLVHEOM58tw8S3uFxSTshsxsnmo0sls5G5KbuvkcktKZrff7fa4cogExQeJQuaTiBO6OxGzn6h2Cu5EjLmQqNaRB393q8fk3fzP1yGFaKnhmue/yUTy6j6kTCUryDrj9m4kfDvIpLXo+gmcO4MY9lzP01Kk8NQRbq1dsH8vcSJ5GbJ4LDNszxOln9a5/THI7d2DlOY/RYryCCJlShlUfszzebvxU4PcsBhXXwV+4O9fQrRXRBx2SQp6n+OZKv19F5rvTZ6v/YL/YK/n7k+J8IQUj9Tt+d3l3yqR1SqfqJqSFJdjts7lIdq6m0jgvMD4TDhvdzs3+949npPkFp5NpE2pJhTnqcZ/s/u9HZgCpzcQysFrSIlvQ/TeKJiOHjLua4BxMOlej+EgEZtYCh++xPOXwhQGjP8veTzb/VlIlOvLMU63GNYtGse4fPdZQuZQTfsGZM06RShgaePwAFqbecht3YYE/1S0eWxB1qpyRDNVSJmuNkzJ8jgVWX6meb6LiJCP2zyuckSHKxHf2oSuEre9B9HkdYb9WmIdP2+4niAsVTWes3biMMV6Ig7uEoWDUEqU/PtDpARZafrls/691PP8ebRuNpKJu2Sq8TLd4/o9w1BteMejzVXiawcRPYwj4oXzEI0nHjuV9/omO4mUN3sQXc0lSs4lpSxt8l5C87mJCO9YQ5z4bjMOJ+n/X3wXWSDTGYMDxEGrhe6jF9gBJ+41jN0e82HE67drzOdOonWzzGOt9TwsBL4Mv/wJ4d7fxvA1fP2jruEYwPNdK7NklVpFuD8aiF1qLWLKewm31mzE1D6P3JJTEEMpRSfFkvKQg4TudsQIu5HQexIxrX6kRPwUCWofeOjZBAWfQDFS64mcezlEjNjNRL5A/Ns6xEyb3O8ylLagkrBsFsKJl2HcFxATTYrAJo/1JiIgfAUShoOI0U83zGXusxEpjScI5Wk0YuBfQswuH078GMbNdHs7ybicjh6BSz+CrHqdRBqIqxCzayDSITwDLIGeJ6BgAYq1esmw2oXECqLawwsoeXXOkLn5E6QodQNj4T/fqiFVzjQM7YTLKFkQkkXmPufxWuD+0u/JKvAcUUN4NhHLlYR7BxLqySW4i/fmN0s7/xFI+CQfyRmkhCx3f3uR4BpANLDf7VQgoTzeuOskYxXLzF+hcMjDxsN6t7XDfWUjQWMLWOZQwTIiPqnRY0lWxylEIvCniaTmQ0MgroWD98OkG8jEVXbtgtE5KO50E2Hh6DZ+XkCC+G7jsd5wzSEOYd2N6HOmn3nWuJ+CaLFcsJx7CrK2u58ctN7z4PQhGHWP53ua8b6AONyRQ1gHv+H+a/zsXkSvyd3eh5S5h42fFLc3C/GIWcZjNrL45ApvPeuiXHIFMPojRAmz5LrvRJvCmWgDUeP52oWUqt1oo5PnPh4VPvc8BdM/52cXQP+tTnNT7nceQWs5Kf4pNq6MqEjTTVjkU2hCci8/jg7ebEZ0NhcpbX1IIUoHdS5F/K8Dra9qpDR1I151jEghtZwosXez4fwBUhS/Bz0noeBpIn9gslKP9XsriVyBg0Q8ZQ6i1+0enw+wUIn41Vm0/s8SddC9oel/ywdwOgXPL0/Ch7/qdlqh/btQcof7W44OqNQhpTspk3+O1uphxFPuEdwntsC4F5EnYKpxcy0yOmxF6+gaoAB++ghccQErgTwJjHqf2jyNjKTDMYAfzDWsAJ7v+k6WXIS5SHjsJw5s9KIFmwTrlYhhFaBFWYEEwxK0+Kejk7lDAhwG3/IhilY49zPIugcJl2Rpu4mo6tBHKI01SDJsRsy/HimK2UQs0grENJIbbQdyS4IEfi9iRuuJZNGJwRd7zM2IQdYhYTKCSDuxg6hykY2E8s2GqxkJoH74xd/DR58mdtCN6KBCvttYq3H1H4fcryLh0UTk9soj8iHu8LjaEfMbSSbH3om7YNx4xDkGDfczSEDsQzv0LcC1cG4pZF2LmGg3cpE3+/leJMiOEMH4g0jwr0cKRI7vH0BKfb5xVOD/0wGHm5C7cz+0n4KSe5BiOtfjTG7OW5Ew2ktYhucaNwWGoxZ4Ht79GVy0CtFjB2FlSi6gboLemhH9fsXtb0H0OwYJw2uRwlKGlNQyeKceLl5EVAXpI5JE9xMbhuROrPF4U5hAOaKdN93nCv+eLLYpDAJEN8kVXIc2QK8iOnwSuBr6H4Hcm9BGKwXyj/N4yz0n843HqR7fXI+1mKhJW+6+XnDfU9AabUSbio1IMS807vYhi2NyK5YT6Vku1fhOH3dt3ruQwpQOzfyFx7bE7Y8hDrpM9W+vICV1NVLSB5AS14Fo5DK312y4XhA+aCOstH2E8l6K1t4OolpEIVq3ac3e7rmoRzTWbdjzjL+NxCYnD218B5HClDwSU4h4191EloA8RB+fNYxNaM3chdbTVGITugXR7yAqq9ZKuPBnEJVGRhif1YYpWexme17b/H0yooOziB4TzXUSMZcTiRPNKYwgWTaPw8E1MKla4+p53BvJYuLE97cR39qIaO+kx5tNbH7KjI+lRLqWQiJ+fKPxNOh7yUNR6nlbCV33qImLlxNu4imIJpIl/DbEP3b73bn+ff2wAjh8/frX8CGQ811JATmGlLyX0MJP8VgjEGPpRwLvs/7/NcRsktDoIVy2iQFeAtlJufoSZJ0h0kTsRczjGBF3UoQ4QyORE2uAKLfWi5SRPpQwuB4x6mzeKyw6EANKLs4iaP97KEkCvB65G75J1BH9BHG68JSeGzwA2UuRgJo4BF+HkbJ1DbADPnq1x7ufSK+yFgmILwGFcO4I5KZxfJ4IjG4zPpKlIzHUHsK60gyst85cTVgGZyDGmAThIJlEtFnTiFihOUgILyIqjrQBS+H0XTDq04apgXAPFhJlxPpRLF6Bv58k3OxpfvZAyWwkcK427kuNq+sIAX2bcVNB1CLtJRJmXwkXveUxlRJWsTr/P9395hApWrqRECxEG4AGRBdTPR+jDcd39czFj7uN5JoD0VD9kLEX+V4rcTijxr8d8dxMRTTTh+Y6xb0tJBKnFyJan03UYh1huCYB+yH3ErdxGaKBFOaQXL6XIWWxhohNbETrcSWhFPQZ5j8l6sA+h4T1caLixyTDe4QodTjoeSjVHLAHWAKjDvm3HR4LRMhE6rMd0Wg5EuST0XynAxt3+fdN7qMTrcMu46GTCPNoMu53IOW9G/GZZ5HVqIOoIVyI6Oc1ZHlqQErkMaT0JKvyAcLCvgzRUhUM/gSyyw1b8iwk/jCLOOzSCVRD14swehpSdnEfv0LK2D7PU2pnLlHR5SE/u9i42+NxLUQb03HEwZY24zbFoqY10GxYBpAiu9t4Bq3TiR7vRuh/A3JHGv4RZLIkTBpLplJJwUeIqi57DetUIqVLOaLZZ/1OM+L9J8jkxBz8sfljj+GYQSjd9f7eRLjl+9TP6HkwejcRPlKP1ugswltw0ng4iOjseeIU+QW6Uvze+9XW8PXBXcMxgOe7+tDCW4EOVSShfBgxvtGE8PoskWj3E0S2/LTgW4iTakeB76A8XjOAP0O77aTg5BPu5VaiNFNStEr8uQIJDu/Mz93j+8lCUw79qxGjSJaXbre1AjH7SigpJnbZ0wglrA0Jq8c89hSHUgbZ+YbjGGK4VUjRgFBKq4lYqgVIQT2L4m1udv/lTk9yB1EWqxwJ56nQ/jP3c9Zt9BgPNyDLTYXmZNJvIKY+3uNrIw5tzDecvcZtuZ8dbZycQIpXJ6HE1sGou5CAfAXF9CxFSkqKuVvoPl4e8l7nkDEvJXLbJVfgVN+f7vcfR0JondteRwjXbLc/HSkE7Z6fCuLUYJ3ncjFSJrqRW+mAx7rEcGUTJ7tnoE3CBJTKIhtZsHYTJQPb/Jni0JKlbiPhQqskLIQ7EJ13EW7lsYb9DGER+Z6fqSMORLURKZHSHIwnpEqjnxuPBPtsotxZ2ghMRXSyl6jU82PP3T7D3kPUnx5LxMM1IPorI0o7LvL8TDe+aqU8MJbIATrT+HwFKR7r/H0qoq1Bgi+kcIwaj7cMrZdjvr+CqPZwK2HFy/a9WjIn2Hs6DHPRELxme3w3+P2vGb8TEIcvJSy0RUQptEGipmwdUi56IfvTSGGZ6Ps1vLfKRornO6b0P6M/ZPhf471W4EKktGwm0hsl9/k8pGRvQvSXvCPpnSJiHXcgS3aD24LYRCSFe7bnrQIpwtsJ3mmc5y5Aa+UxIrNBLVFp5izifz2IZpIlsBTxgMVETr4cf+/1MxPJlOfLnkaELKTNRjchA+5DtLEKKd5jEQ+aiBT2Cv/9B/eb1l6z7zcRKcI6kQX0Al6D7/Pf8PXBXcMK4PmunShu51G0CJv89++QEJiAFuYAnHgAxfMkhroDLcwTiLkXI3fkRhQE/g3/tSHmswcxr+2ExSqPqBbS6GeXo8X+KOGCsbsg6xNEfd5GvZu7ADGoHyLrx2I/8woRtJ+HFIt5SHCPJ0pRVUDXISJf4G7/dhcSpiAhdsDja4Ce+9xOGVF/+Kz7POz3k2vtLJGXbQpSENYbJw1Q8iEi/rDeYzuErDYvEordEsREzyImmyyngyg26nWk1Fb6+dnIStmq8R/dRdRXvhZZ5jYbN0kxS/31EC7g9cZ/N6H01vudp4yvYiRMpxL1lPeo/V+2GMZ2om5pNVFX+QRi9If8fdDP3eBnixHN7EbKZJXnYYZ/60ExRiMMXzair1uQQF2oNruSVeIUcbpznPvbZphn+f48pAgUeqwTiYNMvUj5WeRnO9Gm6X610fM2Ues1Wdb2E3GoZ4y7FHe6GNHFIaS0FiCFZBay0nUSudlaPEbHde3fghTYKUTd21y0rl8w3G8hK+5xRDPJUtOA3PdpMwPkTjBc+wmr0FziBOmDhvlWtJ5KibjAdIjiQbfXiJSQ/R7vM8ZlI1KGkpJdSJR2tPJQ8DhSHpYSCtJhpPh/Dx042Qj9P0GK7SbCap6HeFgO8mjUI+Xq37mvEUT93c8aR/eidTPgeSollPclUHANUpryYHAdkdv0O8ArsOcIUtzXEflLq4lY6kVErdsaRAMT3M5Bw9kPp693uzXG4Trj6060FtNmZAHiFwWe281o3luIsoqPInpoMF4OE2UNxyK6uRnNf9oc7vWcNMPgDo2daUiJ2+J+eokcheORtbuByMfa5nE/5zHWIWWwAgZf8/dSoj74n6H12ILW3Vhk8Z5h2LahdXqE4Wv4+kddwzGA57v+U5YW9SS0WFcTBw/SonwAuTqaEHM4jpSLuUh53IsW/Y/9TitasO3AMRjcBNkfIbOjPveTIcXD89xXcuEk4b8DCe7nUbzRRqQoFCD3Xo3/T5aSPOSaOYuY2WFi9zxAWBHa0O5yMnAftB6DsvHoROC3kECvR0L5MJH9fwoSEK8gZXcQMdzL4Z1b4eJ7Dd9u9/kHhLXp6343323OQArNj/x9P2z9IczPRwKvg4zSlnFZFbo/C/nTh2DUncSJuyrP20LEqNe47xrP4SBRoePzHsft0DXPVV9SO3+KrBolZA5YNG2Dyms9Ry8Ap+Bvt4jHZ32VyMV2JfRfJVlzEph0uefjHuJgzdX+rEcKdoNhf8RtpDjBWUiwfMNtjEDCMOFjhnG6j8jBdwwJrO/p/aZHoHIeUmSmu69yPzfC7Xb5s4IQglMJ9+JipEi2AvPh9GoYNdNzU2FcHUO0VW68jTeMRwg35gtE7rUFiL7z/T25jhcRuRsvR+lc+vX93I/1U8klxk0S6p9HAnq75zcbCc+5xtFaw3aGSI7djIRqF1HTuRnF661HNDngee2E1lNQNtO4+QFSwls9FylWtJ6IJz2MFIZ1yEVd7jl6lAgzKCbK601D67tyyDyOcHvr/b1UlYNOAWUfg8G3ASD7Gx5zh8d3hKgzvQN6zkLBXcbBnUjhXY94RzdxiOYQUdf3z4nE4Cf8uZyoybzA8LYiWv4akay9B179IVxdi+hgi995CtHlpUT94hykqI9TW5nKSHcgK2MKoRiL6PpRj28TWhsr0UnkY4iH3m/YZiJeDnJLj0D0OAmlvnnKnzuQVTQPKcLPo3UyllC8GgiL5mGPs4moJ1yFaC+X4H0ppq9PY/jr1fCpZLn8NtqoP4WqEh2Ddx+Ei6YZ7zsRf73Mz32D2PwuBq68cDGAj/L+xgB+meEYwA/qGlYAz3eVZYlBLUaL3UHlP31RvCB7PGJIdUQlj21EmbQKIibmBUIxawVyHXA/Ae2wtxB56UoR074bCY+XULxcDmIoC5AA24EYyQzBxTpkGfoeEl59SLiVIwa5grAyFiGmvZmouVlCuIrHuz7nAsP3LaJKwAxk5UnxUdd6/M2I+S5FgqwFzj0CWceRQgdiVrcgJthOWByLyAjVTA3ZekIAFRL5236AdsZdSOF+HQn1I8bRIeGwfwvk7kVCv8z9jTPcyT3WgZhzDuGOuwsJ8nxkrW1AjPvLSDk8Q1RA2OxnfuS5LURCe8BjfRq5/FNwfBtBT4PG+WbPaS+q+vErlBMul8g1eDMSaG3o1OMrRPxRI5w+BaO+MGQMKSaulyiPN9Hf+4kT6MWGoRrR3hXGZ1K6ipBSsBPN+fVur5c4UZ5PnNbs92/J/ZVciG8SSbgPI+vjHlQ3+QbIWmqYv238Tfd7nUR80wZEq3vRGioyDrqMzz9Ga6WDUIYHhR8qjOO9RL3WQaRIrUaKxwHCMp2U2O3IOvY4mu9mpGn1EFV4Nntc4wX3/83e/4d3XZ15/vgDMgmBNBCTRnIFUtJICk1hQQpFGJSBRVkshUVZXapLy+g4dXRZqa1jx7G1dWwdW0fr1LF1dayOlpWh8sFB+KBZWCoFERrNBU1JA5mkkWwwTQxkEmKyge8fz+d537ifYT+f7jgy3W9e1/W+3r9er3Puc59z7vs+98/998Csi9EaSa4Tl8KZx116sdEw/x1wmQ8sUwhT70JED4ajvdvpfucBe+GdZ4SOC8ajPZ32XrHnKvnlLvFc7UNrLgl2+Uh4O04cpJI/5CikFe0iTPIX+f69xtO96FDY4j7eMD56jaMiP3fQsA/49xln4a/dbT1IBOPMJnIlZnv+0gE2CXt/5jk+iNZeo+csCf6T1F7HXVCUDnmjCLN9q/voJATxNuM2Bx3ai9FhbIXmdvBh1wjP99hWGuajaK9uR3R+nGHdiA4pl3ru3/C8zkR+g8llocbjTEEtNcgX90XCZaSJWEPFnov0+yzjMdGnriEBcOj6za8hE/C5rnmIKA2iDf8cUACXjIGsOWhDH0RMImnm2ohSVMkc3IgEgBn+/w+BZXDhh4noyBIiN9xbSAh5lqg/WeznuxBBeNYw1iLinfyv6pDgsQ8JQoXI0TofEarHCaJ5zOM7hAjsAiQkZQOXw+gV7v8/GL4cw/clxEw7PP5mP/MoMmvlekwFMOw6xJi3EBVDKpAmYidREL6U0GxMR8T6ZsR4OwkN3Q6i9NYh35dHCFcNvncS5IwhTsmH3W8yXSUG3uL2+5DQPMrz2YYY1995Hgc0Z1yCiPt4Ivnvm4gZzUBrZjny3bHpKlPQvVUaX6r93xbPVzeZvGaDN5/V30QiX1wfYiiVfmYdEsqmAlfCyGv8Obkb1BAuBHM8f3lktB8ddxJrCuGLcWi+x/m5zYKZCuOmAvk71iKGV0rkJ5xNaJCzPe5kZjzo9hYTpbO6EQPbDsOSb98LMHjafd7oeUvzWox8PvE8LDQMrxov49BaOeTnGz2WE0TQShLiBtzOaN9XgATe5USgStLAzzfux3uc+4zDWWfNxfXGTS6wFWY96d9mo323EngWhn3UuLA/38mxGsPIW/zbavf/BaJWeLvHsQVprjfDBaPggmUe9w5CK7nRuE1zdQKt7RWIbjxtnN1EBJjku595ROLkkx7blWi9F3ssC/zMXrQ/lhqui9E6WmAcHEZreC3aZ03u4xnjOglqSTBfijRmff49Ceithu8a/z+aOEgUeI6SP+EgWv+dwKNQtATtZWuJafL9fbqnY7fxWeN5tdsA69zOWMO+D7IKEf2+ksgh+oD+Y7PbLnb7x+BkK7EGk+YvaaEvRkJeDeHHt5eIOD5ElBRMPpl9SPB896x5HDCe8ol0XefxOv0+v4auD+4aEgDPcf3yx2gT7kYEZhoissnvpxcJK9cCpXDmgH+fjjblHiRwtSAiVIQEnoP+XoCI9y8I7chkFA2aSxSCX4MI2RcIv7qkoalDRDj5CRUSTtpJmzGFSCHRjojKJv+XTpOViCnbF6ftNiK3YB8iiCng5Y8QQ1iImFA6hQ56TIsRoaogtDTJvHYQMakiJEC/hjR0W2DwJ35uDpFuYgaRPDsHMYhiItVKIyLU64kAlwLEoIo9tn7j6zkkrOUT+fy6EIMuMbxriWz8zQQBXomI+FFCC5D8dYoIjWKOYd6IBNxaxLhKgFch6wrC1IfnZprhGLDzfQo0yDKeB4g8fAVEFYPlxlW+5yn5aC00np51Xy8bN6OQlvQQFF1GCDBHEXOuQmt2uPHXjRhcI+91tF9JVLlIB4Z8IsFzFhz9e8Jc3IjW9T63uYzQJs8gU4uVSsi6EzHb1zz25US91128V6P5LpEUvR0JLmmddrnNZcb9cMPyDBEU86bnaZLv3+E2kvDRYJiTb9cg2ouNRKLfWUTanRmEW8GzxNpp91i+RGgVTyviM1ORpsTzlI8EqRz/V6nPPUmQLSdqdu8g0sL0o0PZIKIPScit8DPNMPgWWje3ndVWK7IY7DIumv066X4G3X4FoiEFSNtd4v4mE5kGkltM0oLtRVHTi4yfbiKauB7R1UIi3dA8JHA2EeXtVqI1OUDkCCwW/pjnMSb6MA+VjUswlLmfdrfXhdbHVx2Vn4JgbjPO6om0MfnowLAP0ZdjhquOsLxABDoNeCxfh9FXE8nwyw3XHUgTCNLeVxCR5dnAaDh1wH29qXGc2en/W4nqT8k/d5BIKzPC7Z3Ha/B9fg1dH9w1JACe4/rYI4RmrgXatiHzRzva1PPQxtsHNDmaNQl4oxBjK0PEJPn2JE3MBkRQixFBOIaY6Q73t8FANPpzCeHUX4MI8RakaViNGF2l703Cxy5CK7MZnbTLkIP0LEQ8knkEokZvJZSMRYS9BjHhTYZrltv4DDrpJ01ZEfzdCUQ0n0Kn6md5bzqRQ9DxJGLu/R7TZ5CWrQyyFhA1blsIAWsS0i4m37+JxstriFqsRILbPCJwoc/vFYgBLfD7lcZFEgwuQibPMrQT2j3uBuSfNw9pTMZ63iqRYHofkTJmEPlBzSZ8vnYgzc1KZDZdiA4Kp93PPOGMjUjA6BCeTr6EtL/7ibQRFYgx3eb+xhAVEiBcAxrhl8mf6gBivrvgTDVi/Pv8/ERCi7PF9w0i89eTbnMbWi/TiAoExejwk+8+exGDs0aLNWRquV50JxFF/5+NixuRFm+b+yhEwQ/9RNnEA2g/YNjuNc7nodREr8DJ+423iZ6rJEhcSRyadvi/fTC4Xmk5+CqRd20Bkey8TrhjCVr/uWg9FSOBrhztkzpCwC9HrgiTfP94JGCkgK4s47rCz91u2CYglwrgnZ0e9wtEfrh0LSIE1kcg71PoUDTJB6VEU64yLCVELsAaglaMQuuhErKu8XjneP7moANIFaIjydQOEjA/799qkGa/Ca3zv4WD2zyeUWgOlxLCzB26hzYikGoHYR3o8O9TCHcM0Lw1GcbT/t521hy1EP7Ltxs3t0P/Bv8/FlgNI+/x53LjaC7a541o7ho85mK0ZmvRuptEBJHlC7ae08Z7K1pnjerrzG6ibvBKQgh8wPd0EWU1NyKa0e55Ok0EiuTDL7cBR2HkAsIasRaGXY321kR9JxferidyfuajPfscIQwPXUPXb3gN+QCe67p0mBjFCeAY/OrH8JF0uksm2DsQoepDJ2YI7cPjhDk0+Xlk+bexvmeufaAeRkw8Maf1iEgtR7WIKwin+OS3k07F28gwnzPXwbDbEKHZ7zbXIcJSg4jbGkSM7oOT1TA6OVe/a7h2IT+a+6FoDnLk/gFisI+REQqa7oDyO4n8W/MR4ylFkbd/5c+1SABpM+5GIWEoBXtcgoSaqYgB5Pi/QWQu2UJoOfMRI7kfCUd3IQHrBsIcOYoot9ZOCJKlhKbgkOdtO/ysEz45zXi/Fmla1xqnD/q5Ce67xM8lISqZq1KgQ51wx4Pwq17J6dm4lmkOUYYr+TYlU3cxEiLWIwaSB4c3weRFSADdgdZPl8c0GXZsgoUfNd56iXWTfKKyjbc1RNTyROP2EsQIl6C1nOX5+obHkvwHF8OZZTDsU14fV7i6QTVa36u1Jt58CaafuRCufzsCUhrQ1euxjfP3rUhzbP9FZrudB4gDUzESeluQoLyAKO/XTTD8E0hQnuD/qgjXh2wikXYhYsS3u59Ww3AZUSVkj+G53u3fSJjB00FnORJg6mDwdci6Gu2LWnQgSua4LiIBdjESkhYZB2OJnHq7ibq1C9GhZhtRMvA7aM39hed2AqIzz6C9X29Yb0aauxa0l3o9B01ub4/HU214VhlffyccHq6Hybe4rQme6z0ebws6oM4wfpZDxy1QdD/a99+Enjma2rwPGa/JJ7AJ+BvP0QH3bd9P1qDDYhnSlh4zvjaTqYbCIuOmhbjKiIo7A2i9zUCa7EbjrQpV9yn3M2ONsxLPZToMvoIsNhvdxiK0vjoQ3d6H9tcEw78ZCVxZ/j/5aNudgSxEn7YR2t86ONUJI+/yGLeh/Ynx0YbW6VLf/8cELfxDIisDHl8yKee73zWIPnz5/PkA3k9k3fqnXn3o7D3kA/jBXEMC4LmuW4eJEeahjXe2FuhVxACvR/ckB/JmRHjrEANqBMbAqZdg5MNEYlBrJxjrdn6MGFMykS7kvWbU55EglwIjqv25iozvUUa4vMX/DxBJYxvQKXgEMknWIaIxlzC3JE3LfyRMItlIc7LT9wwSmkX7mJ3Z6YjXOiQUjUVEqRUxmQmEieQAke4k33D2ImZW7t+Spm83Eg6S5gokZF5mfPUTUY6rkG9MO2J0WSjwZYHnpJLQCq5CgsOrRAWKBsSEVnvsjyFt6lyktXrOMC4gcjsmAaPK43uC8Ocs9pwUuO9p7vsAoTVMjulzCc3Njcb7K4RJvhwxl+T3s5bQjgwQJsb5vq8ECXU9igjN+jIZzQULDXuf759h/A43rDv8fCsSPCuN1wrjuRMx3wf97H7DWAs9RyBvFYpg/AVaB18k/LwK/BpEa30+YnQVaA89atyvR+twLVoDe5BQkfZgGnsDcAl0VEPRI8bFBD8/XbD9/EU1PzIdGKrImNhoBppg67twZVqzU9G+6/K4NnqOvoSEsTwkgB2BU9UW7G/0uNZ7npOwvsR9pPnGeL8EaeWSeRuP/zRakwXGzR4iEf18zSeFhj0d/nL93FiilGAKyClGa7fWY2/1PWVoD3UjQewbfu5Jz1Nam1/zeJKvbRZhxRh/Vh+7DV8KfuojU1Iv47LR6rGVE5GwlSjoZ67vGfT8JL/rXxBlLuuJOul9hq/bc9kMr+6ES6eQcX9582WY/nEkcPcTpffGezyNiE7e7fG8gtbYNML0fyVaPKWEr/NUt9Fp/Ewl8r2+hgS2PsNa7/uaEF1La2clOph1enzriIo230PzPxsJimOMp/VIYzrOOH2E8MGd5vHsGRIAh67f/Pqd8w3Av9irHhGYqYhYt/Pe0lNLgAHo+D4ULUIEcjxiAAOIqWYDRTDyakREDxPljXJ5bwb+BpS6Ip0spxBmo1sRIXmFKL9Wp9eZ0zBsmvsuQKf3fSiXVTYiXJ8hzC1jkdnpduSXUomY9o/dx9eRkDfd35MjfDMiiDcSVSBaYdgIJPjVEFq2ZkQE9yOB4ReEZmMCIlydRFLme5EmbxQSVPfBfzsEv5dMid2qjZq3DJ34+43j08phNzqZlLuJSNPZiNjP83/PIw3fISKopJjI0dfr51oJs08jEgJwf28QDL2RYMzFRBTjNsKn8DSRu2y7/HxGrvZ9zyJhtMm4uMpjO+x2BxCDWEU4arzhMSYhNZ8ogXYf0l4+bjxPgawkqOch5ptF+AslX6mjwjdfQubgHr82GTd3eRwnCJPmTYR/Vb7gz1tGppA92YbvT8mkPOKQcMAKt3nE7R9Ga22GxzkBraUkdJe73R4ifVEuGR/aouQSkEVo0tqASvjEAiIwaLzhLVffg/WQNRauLCUCBJ5CedmSK0W7n+0wztK6PwYjrzAOk/kQQvOYS5RYnIX288VEPriZ6LCx0ePZQwRvJM1ZIxKCHyPoQApEuZHICHAICVdJSG3077sEJzVIcDhseKYRZvhij/tlwpTf5NetwAPWDo6AwXchaymiSw8gmngj2isz3c8A4SpQhiwHRUSk+HG0prvRYerraC/no/U1HO2Fg4Q2vY4IeNiOtJ1PEAeveXDpIvd/s3A6/XbiMDkOHcZ63NZEz9Fut9uF1l4Zmu8rkQZyh2HcjvZCpfoYPAJZ63lv5Z3VxksFOtQ2Ibre6rlIAmGZ53MN2q+FaM3lEjW693nuxhrHj6G13k0kwV7uNvMRT+rkvF6nGaoE8tt6DfkAnuuqIDKy70MEphVtyHFoU+6HolVoMyatTRkZ/w7KCJPPKETkyhDzH0SEbC4yaSbtQzKBNRPO8psRsbsRMcv5iAjMg2HLEKFa6H7q9DvjEbPrR0T+JGJ0hYhZZiGieBQJaHMQgT6IhLZCwnQ9y89uIUzLrf7t20goXWIYZvme0R7HjwlB+jhByDuRSXgL0hisQtqR/cAkW0mSxmwU5N2AGGY9YT7stwvROMNSbrj2IMJbQdSQBRH/Ls/TVM/FAaThHPS9SbCb6vF/hggoGe42+hDTGECn8yOexxyP7yQS6NI66BZeRo4hNKQ23/c8Z1xj3E8m6uzOIjSiJ5AQsIP3au0SY+hHwn+Vx5U0sLWGfRJi8hOMx34ieGgNIRQ/iyLR9xin96EDQ3JbKEYC/w4iB+Mc9zvRfe1yv7v8TI9xcT/SkmQRNXDbEYPeiqi//UzfXk+Y+Up5r39WYqjdSPBeT9SNLvYcrifSiqRggzY/N8UBN7PPgiWfEDTq0PwvMo4f85xUEuv2c4bnoPuylq/nJRg8gDRKrcSB4YTb3YME+eTW0EImn1/GjWIGci9pRqb5BsLKkA4d+f4+BtGCrYRp8wgSWkvIVLzp+YVxuIWwMLR7nMXGZdtZ89IPLIPJtoJkJUHmUdXupsWvZ5G/4CYk/DzutpLf2wK0j6oILeEs42aOvz+K1mUW4TdcS+S/3I3WUslZ4673PXWEprTWczqJiK5PbhZpLgbQPigkSu+dJspjPmFYStA+GY3W3yS9slZ4vvefBU+9cMRYtC+60H5K+++ts3B8CAngSZObbZi/jK4yz3FyPchHh+MCQmu8wvgsIFxDzuM1+D6/hq4P7hoSAM919RMO4B2IeExDmy8bbeJcRIwH4LV/QELiPrTR98Z/Gb+vY/r/539PZNx/E5mJdxLRtPOQRuAIIhTJQfs0YjhtiJhOP6vtfEQI+gmNTQdKvNzk70nYWgJMgmN7DcMLiJHPQ4TxG0Si5yeIU2YiSMk/8WZE0JJ5ctD3fM54WkWYSpuMy24kyM70b+kkn4TsMrWZ81FCG7WHiFzuQrnw5sHbr8OFU9xOA2KayT8m23hLvmLJdJqNCHwjYiwLiRxxWzz+fwP9D/q5k4Z7tnFlk1bPM4ZlsnGV62cXIq1HwlUrYhZvek5rkflpKrzZCXkf9/y9Ak1HEON8iowZlwEkoE4jylzVkUmSm/Fv3Gf8POb3K1EC2WLCp2q/33+IhJkxhJDVRVRn6SFS8uR5jNmE+biHTPDQsU2Gq9n317mtJ5HW6V33v8mwP4YYa2orlyjDVuV+1jpH5nIkVOYjIfWw5y0bmlqFv0wi883IpFjr1zxC2KwHjsCx+9Ge2IYOG30EE51sPLYgqpg0uwVIiJwEvKQxZ1ISDRj+GZ63PsgbC1lTiBROoD1b5nsvR0L1VMNdhHz8TqB5H0B76glgFbyzgDBJH0fC/i4iQvx64aPtOeO/FK3XAY/rsNrJewrRhrQu65C/WpdxvAPt8QYk4DwJTd/2+JOWuVXvOckcux6tg07C5aUVHYYr4dQvPO/JPO7DHMOJw1yv5+Rx4zwJ5WVIMJzlZ4thcC9an8We39Wey4c8J7v03nQT2pNjEU2pdds5aM/vRcLqrUok3rQbaVFriaC4BMdr/pzWSgrQudxj6URrZjyiIZXE4fA1w3Cx57bOMGcj15xK46sH7YFBtCeLPebZSJNY7XlJB9j9SGB8nn8RaWCGrt/ea0gAPNf1LiJMk4mIyD2ICDcgQnsv2uwdcMkCZL5cSuT0+1O02Q+gTV6mez/xUUT87kFC3iz7E5UiRlrvPkYRBerbifxQ2xBBeY0gDs1EAuoCNLOTka9KJRL6SpFpJwf4j1aMdfn/Pe5nttuY5TFUISI3EZgKv/qh+xtERPsVRABvJEyXTyMz0YNEFHCp4VvssXQSQuNBOPOix7sUaQbKPMYvGb7Z0PESYqwLgHK48GFCuzeABOzxwOfg6B2GK42rl6hrmsy6hcicmYsE0i4keI2DnFVozluF28Hvey6PAOtckqvOuGhEAtig8V6FmFEdEchQhJjIcTIat+lvICf54Xq+fDUSem5BGsQTntcZhNaxGGldi4kUQVOFn3duQEE7m1GVmDLEfOvg5y8TgtfzRNk0iFQvLUQ94MVIgJ/k+XwDCcP1huMhwTDuFpS8doRe/a8TGpHPIAFkFGLYzxvf5Wg9VSBBdQeqgNADg08S/p8/ILRB3yP8K+ugfBFM/zwhmGYR6WluJlIcvUmmNNu4u4k6tn9O+E/2eYx7jI9a46REX3ue83x+jcijWQE/W++2jiJ60WhczEfr5GbjcjJRKrLLbc0k/DW/A3yYyBfaZTj+AC7Y6LkYQ2jT8o3XfUg7tBNKHvZ4D6K1PsHj6kX04gEiLUst8JdIODni+z6H9t1XjMNvusrNCCTALPG8HjQMSeB7SPdSQqQuAvgGjLyPqNNcQ2hxVxIRuM8T6W3y0FppMgwViG7mATmQ9SSwFU790HO3zXD9OaIbqzQP5RORUJ1j/Cf/3CfQIW+On98Iw8ZC+Rzft8b/d6GDajs0vY4E8r1oX/UI35lI4+SruwWt42wiyGyq8fmoP3f7dzynA27vEDow9xpHbWgtPonWzHzj8BDa0y8TJuN1Hvd5vE6/z6+h64O7hgTAc13XECkMnkEbdDEiTHcQkXTJPDeAfLCStqwDadJGIyzvJEzAlYiAPIWI6Uwi79c1/p40CxB1Off4vnQ6nU8IfWMR0c9GRLsPMYp1BBMtIVPP99Q/wAV3IhPTUkScipC5osx9jEMCy37ElA/DRxJDq/W4Rvv+KwgTdhbSiLSdhc8W4/Bbhi0bEc5LUdLoiYQQlYWYyvVICLtSTRS9aJiq3X4dwfiLEWEsAK6Diz6FBJzpREDMESKopMvz9LzhzDJ+kumvw/cOIJPhTYg65SJtQdLKrSGjhWG67zlMBJ6sNv7GIEZWSKS+eAz5X9a7nR73vZfQBA8SKUVq/NwfI0fwA4Z3n8Z9wRz3VW5YvuM5mgSfmEQw1U5C8NlEBDwk82A5WrufR/N5o9vp9avKcN6IGPbNhus45CxBvpYVxuGVaN80o/X6Rx7TbuAp6LjBOH1G85X1BcPUSaaM2C8PwLEjhNmvDAn6KWimgKiqsAMdTBYihjmbKNvY6XsLkQYwmTr73FcZSrfzVXTw2KN78iYSkZhL0TreDZ/MJpPuaXAA7Z8vECmUnvLczDEeJxMJ0Q9obKwgElUnjXclEvJXoX2ZjVL63Eskrs4lElDnG76pRBBGnvsehaJ9KxDdqvL3TcZN0qpuAV6GUwmHTURy8TK0Bg+77+TzPBbto7uJyOzNiF5VEOb1dn9e4LE8hA4UzxJ7oZHI8TkL7fGkWd6DaNtWwTRymueuCq2phzQfbCRM392GbSrwU+NtBfLVS/6v1YSm9vcJH9VKtN6nQfkqm/QvPmvukkLgR+hQNNztdaP9UYaE3Dwienwi2ptbPN4W42IGEkwfQ2vmId9TDLTB362BwbuQgDoGjt2JXHQuJawqQxrAoet/8xoKAjnXNYg22TG04XMQ01+ANG/z0Um+iwgCSELWcMScniAivYrdbisiDPPRpk8MtQ8JIocQIVmKmNMbiOEfR6fdTYjRJc1XcmC/E/lYVRrOQsIBuxQxlksRgRwOI6sRs9vle3OJ/FWH3M6/R4RsMWIy85CA+HGiSsQzRJRegWHPIszTPb73c75vsvtMPj/J1DabMJ2UI2K+wzgZTZy0h3tu5hLO50s8P2/6+yjCh2oAaY+64Mw/wLBewhQ0HzG0ZwmH8C7P4VyPuYbwJZpHuAVcjhjGtwx3N6GpnUqUsOo1XhqRALIIMbleMsIZtwKfyYFb+9V+sdveg9bBm0hQzCdyTT5H1Mjt9Bw9QvjM9fi3ciSAJK1BHqG9Scy8EQmqDyHhoNjfd6K11YLWQzMcexzGleJ6iET1jZOED9vFiIklzdelcOYZV4YZ8DiWqM2iF4j8eVvV97FOGDeFjE/YOCBvPFoLd3j8eA6XGO7ZhD/XXZ7zdrTeT3gMyb/uWUctj3IflZ7bN9A6bUHCWxsRLT0KrdvlnutctOc26nvWRKQJXIjoQBcSmC5HAn0BkWR7Gjp4fNV4qiTW2nTjPPmkbfSzvTDsCqLc4CTDU0ocIDuI+sqFaB3+tXHSjiL8x2o+eMH/fw7t+dv0PnIZkcbnEbTepnk8N2sNZEy+WUQQSBI+cwka9z2PcTVaz7MIP8qN+nymFobdbJw963a+i2hiN5GrsAxpMfOIyOJd6OBZRfj/zTUetwiHgxsgq9v4nEUkck7m+R738yqiATvcTieiV1WQtQrR4nloLXUj4XExcdA7QiSongbsg5PrYXRyR+k13pNvYjqETkVCZ4HbSZaLCrX3mS7D2a4xjhthvOcTAWe5KLL7PF3vp+/ekA/gB3sNaQDPdR1CzLGK8MnKRZv4ErRxZ6BEt2MRJmsRQb4eEdCpiCBtRcTnXbedTKB/ThSKn4CI235EhJ5H5rs33cb1iPkkB+g7yGjlABGdDUT+qdOGpQERlAEyEbUZ5/70Ww7SXDQQEXNPeVxtiCEUI/PWJML3p8z9zkeM7hkiPUQPETjT6XbHuJbpckS4XiPKlk1Fuz+d3OsQM7wKaTSvM3yLiGAADE+Ov191Fm7qCcEtT+0PK0WEfJJhXkSkWpju79lIGzeNSCg8ExHpMuOr3vOUBPUlhLl8CmEmnHxWGwcM92wisjrP87QD+Hq/3rcgAafYeHgRuRo0ovne7LbXovWWTPCPu83S/6ndKcgENYqo+PIyYlRpPcz3HI0jDiDNxsVxIuCiAsZ9+qx5fQAJHRM9hzmek1+7v81kKucMm+L+KokE5W2IiSbBp1jjHHcZEbSRD3mTkCaxmxAyutEanY/Mx/VEJROIiOBsMua8TCDFIsj7PNKwJc17s+fyGHFwmGbYPk4k/i30XC7yfBw0TBXGX5fbSObqNz0HaxGTn4kOhKVup4SI5rwKCSItSMAvQ0LOAGH6TWuw0fhO/qt9SIDN83iTS8StiLa85bEsMlxXGfcrjMO30BooMd5biYTWXWit5ZNJWs5Boh7wJP/fZtx1IKFvsvG7h6hpuwft4x5g0KUAaw1jFfAj+OWvjase4oDTYNyVGp/ZyJ2gFq2HOb6nC9HnCcAVkLWWqFI06DZ2CX9ndrrPae4jCXFZGv+pWiQo30DUcu9BVxVRj7gG0aul/lwN7IfRn0I0tBRp7fJhcDdR3WS04Z8L1MPP05zn+Z6jvm+l2uivR5HTY9GeG0UEEJ3Ha/B9fg1dH9w1lAfwXFfWMAkSjzuR53PEyXQucQobhU5inUTx8V7EmCGi6kDEtxrefhEu/Dwiwn36jT5E0BIBWYU0BxuRT1iKRJxPCEL3EXmzlhOJYRNR7UVE+gai1NQYxEybiPqSrxJO8R2ICJX4v2bDnpjjXkSAWhABO4YY2p8Qp+ou4eedI3DBEuIkPwsFpUw33MMNcz7SVpYiAWgSYq4rEFNpJ3KAXUr48jUSQTLDjesHCD8kPE8rEcN4CjHi9YZ/pcc3z7AfJKKBx3qsU9CpPx/O3AnDlhi/9UTewY1Eio19fraTSKOTYE9ztIvwbzpEpE5pREyzwWPM8hjm+X2H2vvRS/DZ1YhJj0C+p8kEt5uItkyC/1yPqxJYDB0XQ9FjSIO1hkiJU4QYTzLP1/iZ7UhA34MOIjX+3ojWTtI0niQOHmvRgeAp/58CNfYIl/y+8TiXOOhMJ1KH3IAY8puej2x4+xBceMjz1OXnrjI+pxCBOOOI+tgvE35/U4zr7yOBoQ8JPm+itZAOZk1uay4SrjoMUzHaP8nkVg39L0LOp5CAsYpwS2ghysFN8PfL0aHvIs9bjtvOhaNvqMtxX3Cfq9A6yPG9JUQN3UcM70y0X941zjf79xloP04lU/+ZcUQVia3GdTZaF6eNw6Wem+8hbd/Zwts8/75OOO5ZCXnpYNSIDqwl1qxOQXPehtZKCZHEPt94vhZO3QIjl8Cb22D6BET/dnue6v1aRNT5Tpr2fW5rAnGIrfLnrXDq1zByClpz2Wj/5rqdUsOwg0hI3g37d8OsBUSZxnZiTe32fNxGHMyyDK9pesZvcRfhkpBjvHwOrdkStD6GE4fYOuP5ZmRSvpbwFW46q52Po7Wa7TmZ7P+vR76pdR88G095AO/i/c0DeB9DeQA/qGtIA3iuay6ZVBgjr4GfX4dO2EcQQxz0/xWEkPI8EiiuQxv4WiRsLUREvVPtXXgNItoP+ZkuOPoM0uL0IkKcfHRuQ5v+K0iAmGHYHiRyzFW5vwLEFNb4niX+/ChhHupEBHsXIjgzEdN4FXihIvKStSNi2GF4dvxPsOUhxlkCb9+EBLuvI4KYB6yFC67z87cgIjYTMZ5pxt9Oj6lXOGAQEfXthFA2izAt5xlnhYiJNRD+hBsQY0ialnKiKkAyv65Evm37EeHtIyI+f9/3bFE7v7oZCXaNiLHOtoNymeEdUK5CWhAjmOB2bidMjknLctrjf8L3tHisL0JH8gXbhxjFWI+vSve804mY7E1IgFgIn32MqDwzGmlSqwRvUzNBjdMaaDJ+m4BP2+3qQY2JciTcJL+1UuOm2fAfICqLLHXbuZ7XUjj8rudrP2KEHUTZrK1E+o8X/H+WcfE0Eij2EMFVNYZpLVFmrt6aj5vgwqeQX2Kt5+pSw+r7qPW4mjz+Ts/vcCQMFhC56NoJwfEI8IxyejIVBebMIgTHBiTIJ2Gq3TirhpzHPAf1hNlwkvA3WI3mO2kXjxIpZ46gw5P3dC4w7hpCu/k8kbNxi+fhObf1OeP3ENqLlxm/awnt0XzkrvIGoSneSgRjJF/Yp5DQnVwT/oSo8d2KBJcmtHYXIuFzFuSlAJh0ADW+8q5BGus6onJFi9suQXujANgNIz8NrLDM1a0++rf5nj7P00E4tYHwdR4wLm84C5ezDIP/H7mMSLnyHGF6H26YHvNzc5AAdQ3M+jJaR08Ta3yx8dnqOZhB5Dq9GK0Lm9z7f4jo7HKP9RlgjOs4J01mm+d0MfzySaJUXhPyjd0yXDh+Fvgu9DxJmJUPQP99RKWdFwj/1qWc1+v0+/wauj64a0gDeK7rh8PgETjzhn1vLiWSLe8mUnSU+fMRtJmvQEThBCLEUxCD2IyIVC2ieGsR8U0EZQAxyg3ITLmYcPDfh4jqv/d9XyVKTyXHYwgTyPX+vt/PjkMEK5nBliACtRr674WcFUSakhNI41CHBKgbdB8rERM6hgj6u4jJjNHYTz3iJMcFhCbvdkI4O4w0H0sI/50Bj/U4EnraESMqN/xNxgHGzU2Ej8zXECNMwsgsw538F/cQ2o1XkICaTfj5LQWeg59ugt9NZrGxhFN6DREYM9awXYqIdqHbvR3N4S1ANQzeC1nj3X+Fx1nneRqjZvtfgpyL/X/SVB4gUvW0IAFisWEpgra7oeRqjfXkczB6hfGRNHzViHllET6hA0SesVw0/+mgkIuYfiM6IOxDEeu70EEgMe4cjztptpuJUmRVaE0mc/BWoipBkceTTORZfr4Irck1aE1sQZqPY0RQ00a0R5YSKUuS9qrK924hgiBeNr5KCA32TmRy+xI6VDUZz5MI7dGXgH9v7f7d3geXuZ1pSKvf7jmaiYSGInhnN1zwIbT+bycElTrkb/cnRMqQQSIv4Wy3d4CoSHO/35OmtwuOPQPjbkLrdyH88hH4WHJNWEJUQikw7hOuP4cEzDfQGmj3/VvQfhpjPDaiA0PSYGURJuMUzNDoNlqJlEpvofXxMqrw0kxocJMJ9HYk6Kz3eBuINVjOewPEfol8m7OI6Ptrff+rbrfB45no+xqJgKOxKEr22/5+DNGKx9AamkUk8s42PMnUm0fkNM1CgXdlhu8paHsDSpZpjEdr4aINhC9mi/u+nChl+SZx6CpHB7ZqdGieZbjnEIfYdsOZ6HyFx7vF/9UTWtBiwzjB7UwlynruQrS5Gfju+dMAfoX3VwP4LYY0gB/UNaQBPNd1GqiFYQuIskMVRD6nejj5JNqg4xBjOYoYRzXwLXhnGxJIuoBBmXkyeZv2I+wnc9Z+tPkn+v0EIlwDiOD/CSJA8/x7u/uq8TNj0Ik/mWS6ELEsQUQrOeVf7/8LgCOO2jwM/X9P+DINIAJTTfh3bTKsNqV0VHu8p4HxLmaetKJJ6PgCkUV/OlF5oIGomrAPmSJz4dQRw9jo35uFR55H5tgJiMG3EQm6QcL0C8b1Qf/W6XG3EZrM7R5bHtJk9MPvXqbxZ6omJELfTgSptBCCZIqmnUlEWb4o/LZDpPFo939jjZNaePslyLna7eciBltrvFQihn0Nwaz2CF8lY8lEdY4ea/zlGie7CR+2fL8KPBfJ1+oVz+WDhtnCDKC1PBcxsy4khE1D668UCQmpzUKiAgVuqw4x73bDWOQ2ZhOO6tuJFEalKP1ROiz9GRGN3GJ4byGqgDQiRoo/dyMmeNr9rTEex3rMA36+HdmSknY1PXPacOwA5sHIx4CdkDPNfRSjw1qZx9FC5MJrhwumoYPMT4iSgCVorWz2PCwyrnoJznjAbS9CB4nTcLTT4ylEa7DOwS/HjIeD1oj0nNVOrd/THlgAPzuCDqeXAxPgTKILVxHatxR4UGpYZ5GpUJMRvp8iSj0WeQ7r3V/SdG9BNOIYEYG9A9GVWiIwZQlxJd+5ds9RLwpIqfZvjYTAVuDP+cb5D4k8p7d4XFvRQWam8bYZCaabDVsyly9GB4k1RJ3eB4l1dhM62BYQVo91ULKWjL/uRVej/dJIRDGfICJ1k/tKPtqLLW5rq9tLHLbP+GmxJm8Dos/laG1uIALHytCeTj6P7cTBMB1Gm4h6xXM4r9dp3j//vyEN4Ad7DQmA57pakTA1SGz6vWgjdgFzYfRNvu9HiAAXI0GuAci3Ym6QjCbjonkw2IyIbiN0vI6I3YDbBxGeKxGB3UWk2ZiEiFY92vRL3VeBYagxfNMRYd3m7wOIGK9EhKnFzxYjgaoAKIOc5Oi+QLBS6f8qfF8SfCYBN0LRNUjg2uj3UYi5TUEM7VXoP1vb1EVkyJ+Bdnqr2qJd7Y6coOc4RvjqTPWY3kSMpDrwnykKv4FIYDxA+EimA2QxUXO4ERHqdkR8C5DQ+SWP9bNEHrDZiHmMI4Jb6gzXJCT4VCNif721BklwKUFMarrbXQQXXkakG6n3+2mPdR5abyWEP1ot8CM4ddy4mkvUAN2MmMBExADuJvIWDhCO8/PP6jMJiMON52TOLkECwiTC3DgFCaODSKh+Fs31CqIGaTtaixUo8jbL89tCCE7L0Hpo9fuLxu9JxDhvNA5HIEGiDq2nPUQ+zUmGeQShRZpGVO9YYPiyDG+L56vU31e4zynGVbH7HESHj3x/XolMnSWG7xXCzy4Jc1VExZMJxkU3YsZ3E1q4PuN3t/F/jedlquF6FS4abxzuQVrLHqQRvIpMUuzJVxB58ioJ/9wDxmcBfHIJUSKxD4Z9Hq3/p5G1YIFx+YTx0EukF0rCxZ8QibD7kKtF0vLtJtLFbDf+pnpOWogo6WR2zXe78w37FCLHX49/byci+TvJ7NszjxA5QnsJjftA3MP1SBCaj7TUhURQWBGRfL7PeLI/XX8yz29GNHCc5/FxRH9Pe6wjPCdlwFro2OmxbkF0dYLvqfXzCzzOZP7NQrS32PM4gogezoecL/jzLsLnu4JIH3OYCADrQrQh0cM699mK9sByolb6ebreL+EvvYauD+4aEgDPdVUgwWA+mSCAlNKCCkQM5qGNPBoRs1ZEnC3QjLud8FspVrNZY+Bkr74XjUAMMJmBWwi/vnpE/HYbnl4i71clIjLLEZOtRYQpCSX5SOjcgQhlFiFMbUfMZhEifHX+nvz79hJRrF3EqX6Pfz/ifmY7P9YYwpQ5jqhTvBhyvuw2Fnt8MxBT2YcYQdJalhuPENVPqjzebEQVcpA/5GwiorHW37MRMewlkq0mjU6W+x/r/5LGdcBjbEGaqKeQWW8KUccVQrM2xv2m9m9Dwvu1RFWOVqIE3krCXNWHzOnJLDYKrZduQojoJcpaPei5uRW4BEbe4/u2IB+h29EaSI7vR5DQ8Q2iAkIP8hvdaBiTWTr5Ix0nNK11yLm/F62L69G67yZT67bnuPGctNjJB/Met3On5uTUBsTQCjzeIrRnLkXrbJVxMxUJoWuIcmQnkMCyn6jI0A2/+r6ffQOVLWwihM3PG2fl6CCGx1xHHDhy0SFoJ2Ly08mYA5tOEGUM1xu2QrQW8tzeCrdxh3HYjsy3rWgtTPA410L/P/j+PKLaT5u/J818m+ft/0J+Y5OF40zezWTqW+o2jqK13kCYBEehvboPrdlthLtBOijMN/yTNdZ3TiOfumTaxePdYZguJXJaJrN+KxG9PtMw1Ho8y/37tYZ3pXCQ2cPdHlcS5MsI4W4uom21ZPxJ+ToMW4XWxP1Iq5/88WqIw9oWIAd+eRMS5rYRpdG2Gq5dGmumZu7ldnUpIDSfL2h8b3eq/5/dR2QVOIDWYT0UPeexQdDTSiJgq5cI5CuGwVuQabYSWRrqDMdMP/uMX5cQabJeMR7K0R5YbFgWoj35LvIrfc7zkTSCrxC+okPX0PUbXkM+gOe6vjQsyrglk0MxIv7jETHLgl/dAx9JAQsVRE655JOVfLUeR4zhCAxuEk3IBiYvIyKA1xPOxw/CYC1kfQgxrDJ0MqxABHcT4dNWhghMrfod/DZk3YYIxQOIwBQTBd+3IuK1GBGQIqR1S2bfLCTcdhG+OI8j5j1ARPUVIGY5AUVWjjqrn2okIDUYznrE2K4gondvclvfQsynEDqehKIpRI679YgAToV37oMLVhBCWgtimjMJH7Et/i0FrjxIRBLegIS472ksJ78Po68zTrYhLdaXkTCUTLgtXgMnCHPkIs9DEgZXepy7CaL9KprkGUgrcgdKzNzq52fzXmf8aR5XNWGOutn92gmcP3PbNYSJKx04Wgnt0yLP0yLkr7UKXa3u9xvG99n+ql1EtHaOvydfx5Nw5tsC9ZJRxL5YIVj7fww5q40D/Ps2tBYWeF6uQSa4xzWvv6qFj6w27suQhjcJgWOJA8v30P6YbphqjI92ePUluPQmw91D+G8mwfWrRFRrIRIsej2PuZ7LAs9lg+BitZ/fqfb6D7j0WQqEqUF+ZzcgYfT3EdO+x23fjea8CQlDn0Z7oxEJBJ8nc0Dkc0hYnYfmPbkrZMM7P7G5uYLwMbwZCTyL3V4DoktLhbOOe6HoXqKs21pi/rs95kVIQKtDQtdEzefhtTB5CRG9eysy0872WGYJtW03QMldnq8khB8hhJdBw/YVwr/3MGG6Po20sCXqlybjts7P3owE21cJTe8+43QLEmrrkGa50fcuIYJ1mtGef8RjTrhKa3sAWC+tXtHFaM/3on38HY83Rzj86Wn43WuMv+TXuY1IeN3p30YhGvA4ElzrDfsfI23ut5HAWU1o3q8lNIO4jXIYXAZZE4zXQuh5XWi8YJ7HPYVIMdSEhMjXgC3nzwfwi2go78f1Lq6MOOQD+IFcQwLgua5vD4t6useR1mUnIkh1iCh1oRNkPVz4GPJDqSHStnwJCVuHCBNqMSJSdUR91wlufw4iNC/DzwfgE+MRo16EmMQDSAD4EZx6yQGVhYi5bkEEvwidXPvI1Ltkj2E9hAjVHKR5GEAML5kQ6shEMLKZ0FIVI6ZSjpjo0wRDbEEn7tvgVK99ARcigXIyYr5lRJReMqOWEbnwtp81xioyTuQdq6BoBWJS1oocvQcuWut+dyAGv5QotVYrWDL+gF1n9ZWDNHndnr/jSCg5iJhY8m2qQ4ym2K+nBE8G7nb3W4uYeIfh6XVbf0gUlf8rxIxqiBQX3YS56BrEFMcQdXm3eB4KPC9TERPdhRhHMQqCmes5qSFKxbUTSb9fRWv4YcQsKhCDSlcZ9DwOecNRBPcxojxcAVpHXyfSt0wlcrbNMA4O+HsxYkyNwEPwq9PwkY/7vqVIMJrv799D63CX5y05548gk4ON/UgbVW1cDRJms3lEfsmDSGiaotq15engUoIY9XGiysqXPbabkKDcAfzfxvfzxMHtQSIp+TQ0t8kU3Ozn24k9tpBYZ/uJNEI7kQDc6zauNM760Hq/jqhfvYbwYVsP5EF/LeRcQWjVB/1/PhIsvkNoT7OQQPcN9/1Zja/t+1DyIY/nRkJImkDkE9xLJJNegPbOCCKB+QzNxZmJMOxDbmeGx/t56GmGvKuN82qZWnOWEebmpb53iXHbhvb5AY9nIxkBiLFkyi+yEkk/u427GsLvdhJaDzlEsE4/Er5WEVHuazzW+4nApLTGprm/l5FAmdo5JNxlAsruB/4NYcKfgw6SL8D+d2HWZcZlWtPNaK0nS8eViA98E2n+Goy/HuPmPuN6LHG4Tn0dJkrpJSH+MGFZGnS7/3ZIABy6fvNryAR8rqsXMZpitMmq4eTdeicbEcwZ0FEPF16MCMIKxBx2+bmDRKQwiDHsJCJeN6ENvQ8JOc1oB6yFT3wKMfFJaPN/ChHIBvU1cg7klCLCdQgJEScQc81xX8nvzyYQ1iHCVosI+yg4czsiSsWGexMiTBWImbUa/isRYd1KpvYszWRSyvy3XufeykdaxeQ3s9BjKELCSx7B9PMIIW6jcX6cjE9a0RIkHL4oLRPARZd5TMnH67jh3mJ8H9b9ZCMBaSFRmWCX8bvF35MmcZ/72eXfr/S4nnT7wKlvI0bUjZjCW0i7sM/PFBBRfvsRgysBHoPDD/pzws9CxJRy3edhpD1IJtmFRJBPi/HYigRqNG9MNf6PofWU/NAKiEjBciJB7gG3vZjwB+yGvE+hQ0AXUQZuBsGkv0yY7ws9hgIiR+RMw/Q4EkieBxZZ+BuH1mYyqa9GguuV7mex+8hCWoy0BrI9/sfR/hnhdrcTUadJ+JuE9tx0KP8o4Sax1HCOIdJyZPvepUQ5sz9GB5oKIjJ5ocf8ClHpJwsJXdM8ng1+Js+46iVyTub799ka5y93EtGfF7vvOiKtzAKN6e3nkAVhpfCcc5/miLX2Ha5AAk4uWg8FaK9udbtPIFpV5HZ3QcnL6EAyjkhA3EZUcCmGg61oTc8xnHN8/xbCteE/2jw73bBv8n9dkLco2mIZ5NymeXrnJ+6vDtGdRzxnrR7X7cZBPnF4usj/34u0jMni8DTaM0vRupnrz5OIg9o+JFDnCWdMRcL0Po99HNojrYi2r0G0MvkqTkb0/TBa20vQmt/i/5YTeUHnATOcPmYdoq91Z837NGAFnPwF2m/rDMMYInVXGVpHyQJQQpTPrEXr4HKivnh6/xzvrf+7ifN6/VN9/v7n19D1wV2/c74B+Bd71RB+VvnAcRidTs3Jpy8fii4jSrnVIEZZgIjBbjLmqkxZtv2IkF2DGPxBQpuYhIsWIiquBRG7PkLzVIwY5lRE3F9AxDJpDCcgQnOCiOh8xfcn5+hrgEMwbJHh3wNHT0D+Ccg7Anm3I0LZhJjMJMPaSVQ8KENEaS383gHDPoZMvsNMWpOxSFBIhO5VxIxHIeJ7EO387USB9CIy9ThZBDlPEUlflxCRzPP9bJfxdClyYJ+JiPYxjzv5yvR7jqrULlcT2rMF7qPReJqICHInjExBGu3u9xrEXAqJyOcq3is0ZwMNMPkaz0nSMO5HTPYo0nK1Eyb1FqI6x2l/rydyrjUjJlWMNDF7iDQe8xBDOUZEKOYTyY13e0yHjPs8xPjqiKTgiQIfR4zmOBIeIdY+fm4HEdlr94im41A+w3ORhwSapNkYcJsVRML0Jo/rr5HW/BW3NYvQ3BYQyZf7yPiEDf49ZGWjfVSImGWP2+vwuHA/DWh9HkERvCMMRx3Slh0ktCoPI2FlvuHYTKy3UrdZjATUUo8zFwWO7DUM2e5vAnzsCiQ04rn5Plrrn0Xz5/m8cI7HmUuswcXAA5D1KcP6OT/bjNZjve95HAkM3Z6T7W7rCbQeSw1bPxJkdpNJPzV1DuGLVmocPU1E404gkjEfRFrhRN8qeW8pzGwypuwLkotBhdtMfQ+Q8a9LwS6Uuq2/M55a0ZpsIQT6aURASg8hVC8l6qQ/SgiEdcZ7G5HYugodNFp5bzqhLjT/ZcZj0uiXoLW1n8hpmO32c8mYlKlHfrD1aE3Xqu9sPB/73VYFQbd+QCSxrvY8voXoa4nnrtuwNcA7r8MF9UTuxQnuJ5nXh66h6ze8hjSA57q6EBPZhZKJthMpWLYgQtRMFPxuQcSnHhHIYjhzCJkPlyPBqBHOHEEMtRmZihejk/BSPUMZEaG425+7EYE8iaqRtBFRq98ifM12G+ZGJEC1u59RRJQZiBEkH7wWRIgq4KIr4MJVkLfafdxDlIc6QEQjFvLekmH7iGTYe5GmZ63vS4y5jPD9K0PCYQ386knjcDoijKuRpiU5TA8K3/1vIcK/HBHmDdC/1+Nd6/EfJMpdtSNhohcJILOISgJJ+/Oi8fIVj60VaX+qkdnnm4jpXE+YAPvcXjWc2kmUewMJV8s9lwOIoV0LgxsQ02lCmtFypF08O4hjOhFBONE4qUIailwiqKWQEAw6PD+5hEnvLiR8rfKcJI1Ljp9PPo3liIn2eF7akdDQS+Sv24yY4SggGwZfMoydaF23+vlpHvdcKL/T+FzodvqR4LDEY16FGNuXkTB0rdt7jKj+UOk+D3oM+xDDPE6YfCshayzS4uQiJvw0mv+kUaon4xf4y+PGXdJsJ9N1lWGcibTpecC/8nMn0GHldrQm8o3Dds/bcvXR/zhaAy+itfhtt1OD9qHXQSoHxnPEYaaLqFSTa5wngf+w8dxAlNrbDiyG1w74WYy/PkIQuxbtpRLNCdcQkc3pUNtHJifi4F4idc1p6E/m00lEAuaxSIj6KyJTQLJuJLeJeYalFq2zdrcJERVeTwhDz6L9Xud7K1AQVZN/G0PU9s5Hc5yPDnmgtVGIIphrjFdQ+qk3/VuFx3un+2tCB+XZhK/tYWQ5ySdSXdWhg3Uh4gGLiWC2bEQzqxDNbfJ9SQGQ47aXq9JJRou62O3W+fks42QtogtJQ1iKNI2Jz3QKLwV4DrLICKuDb6F1fB6v0+/za+j64K4hDeC5rgpEHJIJOBH9ZiLzeiPaqDMQc0rmGJthhl1MJLfNBbKdVDqZP+9EhDdpOnB/ixGB2oQ0FR3u8wFCkEmalxSE0IcI5XNIrN/sNo7Cz5+BT1wG9MLRDXDRnUQW+gq3Vw9/+rIsKKMXEek0liCieJQg5vX+fBwRtscQ8f8bIofaLiIy+VXiJDsXEfJOvT5yGeFDNdzjegMJp/MQ882CnEV+tgsR5jshZztRZSX5Kk0zPF2o7FYDUQQ++Qlt9DyuIWrSjkcM8QeekwcM/0QkkD5kmLsED7NhZDq9TyeScg/4tQMxoesha57xthmd7rciTcdDZ/U/3Pi+13P5DJHjbjNhNsqCwWdc4H4GGVM+U4FH4e1muHAMEgKyPPZ6xNwWIOa1kUhQ24IE9FfRuqs1nvOh7X4o2QNMhpM7YfRHNdYzm2DYZYS2e7fxl/xkv4XW93gUJDEWreV7/V8xEowuR6bdUo/zNY/xWiIJ9IOE1q+BqEYyV3BxJ5w6DSOTD+lO4Htw5h9g2CjjvRs+NlN5GC+01ovb/Rru+fu+4f1PwL91cNdEtCH6jJv9SOjLMR4PCgc5yd3jWiTctgN74NhxGPesYSggAol2eR10EClVCj2uq/xbm+fvNFq3+w3vN4AuuGQj0lLOdnvTPK83IkG4nUjIvozYh31njScf+AFkXeH5aQDWQM7DSKhKmuw5hn2j53y/57cU7dcFev7tTdZiLoczz7hs4gDajysQvdiIBOU2IjfgFLTuHvfY5xNuAL1+7SKyAuwno0XbvxZmjUea0QYk7N5FuGBsRWt8BZFapg7RjAGPoRod2pN1pdowQmjXKjSnVLn9JxFNTP6Nj0Hbt6HkEBII1yAN5j1wajaMvJio3PEDw7nP83LIr7vQ+qhDa+FatPcT7/iwYV5Dpqxf1qcIrfx5ut5P0+2QCfiDvYaCQM51LR4mhj8bEaqNiFCvQ8RrFxEFl07gA4hY1hK513YgoSHX912DTuE1hC/VbsQkIcy0PX6mnjAVbkICz1j3m5yXk7koncQP+Z6/RqfeRkRoBojEq7P9vgSdKm/RGH/aC787HglBlUgDUYGIUQWKHP0+OjW/oXbfeVTy1PRDwL81fq41/NUay8/fgk98+ixYZxI5w+ap7bdXwYXrkUZsOBIU1yEmVoMYwyg/twwR2B2EKbqAYG4bEMNLvnfFnpfThj39PhqdoJM/4XeQF/JBJAAuIXKuJd+rGqAbvtMLXxqFmECT/7sVEfg/Iyp11MDJTRasL0bMf5rn+CnDPA7YDseaYdxtBJO62fctR8xsJ1ENZQ0SCHYTATbbCRN30mQ0uI0qzfuxO2Dc/USy4u8iYWyM4ekl6gdXqK+elyHvJs/BLreX9kLyY1pJRLMuRIwfMmXtzuyEYTMJ4a7X+G0Wns5Uw7B7yFTB4LDxtw/ti4TjSiTIf9Ywzkbrbo2fS9qtJIhPNu7S3un12Ovd3p2G81ngKjh2A4y7jFh7LUT+xTrj+nq0Pve430q0J3d6TJuJKOYSz/dktNYe8RyNBe6HkwMw+hbDeDmR9iSfiKotR4ei/YT5fRza86eJco6z3X4vnKz3mluADhvdvvc6pKm/3eNZhKKa1xDCR7thuAPtA+P51MMw8jqPqcf3pACNWe7rAFElIx/t9Z1u+xbCL6/YOMhxWyVo/SRz8Bzj9HbgJbSOvkZozvYh2pHaGlSalOHAsDForW4n/Fr7gOFw8McwdTwSlpP5Nxu4DzreUtNZ09Dhr47IAlBGuLTsNI5uJ8p/3mvcH/J916K9NUCYpdOemIUOiFWelzWe5xPGWQ2R4Bzf00j4uFYRAn4p8Nj5CwL5I97fIJC/YigI5IO6hkzA57rSSW4AEaZvolNkMkkdInKqDSdOp+lUmfz1ZiCGcz0SFFqRcJHlto4jIpYDg6/79/1+pgcJKVchk+Q8xPh6DWM2IuhdevX/EAke9xPRwCWIeDcjQt7gPjoJB+3ELO+F3/00Iug5iODlESkWNiNG8S3EfOao3wumwPTbUPRtPpmUEUDGzPWJBUioWIkYWTXhX7gTqIULlyDC9xoicom4VxmPHYiJdhn/2f68ETmP7zZu7kGM8SrjeDQRjVpI1Ad+FxH3fUTFhFwUsZqEg0rEIOcihjIW3j4OVMCXVrm9RkTAK5BG64duK99jXASj7wGOQs+3kWDzABKcDiHhrhu4BcbN0Vz1vEzUZ12ItCNH0Hp4Hq2vb5BJ/cMotA5Xed62IyFhgv8vQMwlC8Zd4XsPEJUH2tD6mOp5e5MQIudC3mXG9WNEWp+N6FAylghsGoEYayE6dBQiwTLbVXUmGFczCLeAPqAHht2meTjzMhKspqM1sJAISlnuNuqJPJWDaH+9a3w1+tVB5H4sIfz2tgO7oGeb4f0DwqT9vGvyXmncNBonVxl/3yVK3n2b8NtsNT57jJNDSPi7FQmEFUSaqOQ/Vi7c9QLUQcffe37H6HVqG5HfrtCwF3iMZW5vOtqHXyOTQQCE+9F3owNbCxE88KDxlASLFWhd3YjoVi8RiJCEk/n+fhpGftRjn2k4ThOWhL9DNGAL2nOFnqtHjefkl5eNTL8FiI7MQ3tll/scS0RfH0eHoG+haNlxbqMbHYwxzl9Ve1lXOxH2DM+b8TS4Hu2n0zD1TkTXklm5HZny31KC+6wvG6ZeJJD3EpWC8LNLED0pdR9b0J75PlqXB9ChZDIR9NaK1v4sjzH5juYQpt4WQqgD0Z0ytAeaiINsl+FKGu3zeA2+z69/rqu8vJxhw4a953X//ff/L5/p6+vjlltuoaioiA996ENcffXVHD9+/J8Ryg/2GjIBn+sqRYSuh8g0Pxsx7hlEPqgBoipCAWIuU/38PqTxmUmYsBahjbsFEZECMo7DWRcjIlJJnPqLz+rjBBLqbkVEegaR4+wiyFnldkFasF1EPc8piNg/5P8LDWM2UatyGxIkShExakVmsSzDsIcw0fzI8MxBxHIbEu72EWlRVhp/0wnfpmxCS1cLFMHPNznlzQAyrWQTKUd6DU8PwfwGPf5RiDh2IWFomXE83Pg+SMacSTPhiD+f957msxFTbCAiO4sJM80gYkReExdegxhVk+8/jYTYIuPkfrdVQ0b7RSnwGecW7jGO8o2/Rvf3DBJkhkNeE2KeW1GgSg1aA2+hdfgsofVMwutXCIFqGrF2ZiBhMPnQrfL89hO5FGeTyct2qtd1ndM6aPN8zCRq5E5yX2V+LvkPpvXU4mdGGO8laD0cMlyPEoESuf7NWvNh09z+035mC9Is5REHoAKixNwEImDmKBx7yYJ08iGdb5xsJwSnxZB3FdKSX+I2JxuntYRQswgJXhWG4QeEFr3MeBvlvvcj/7R2Iuhkl/Ezlji0tHq8w4EroaRFc1RUa7wUCl8jx6PrZrczk0iYnoSFWZ77RYa/Ca2vYt/X7N8P+L505J9LVGy5FPkHgoTPpB3PIQ5eu4yrbyOBsQ/NbyuRbqjMbVyLBLZ2JKwV+j2LEEYLoakaylcb10f8nvpPJtcuw5B8GMeiA2QNoittnruF/m0qkdv0af9eCVm1xKFsj/HQh9ZZuz7nfB7tr2R+riQCYG4kgmRqiaCNI75nJlEZpNTwv0Ykis5Be28+0iweILTKrxGFAOaTqejCGIJuriGSy9d6DLt8/3kWAH+brm984xv8wR/8QeZ7fn7+//L+devW8dJLL/G3f/u3jBkzhltvvZWrrrqKn/70p//coH4g15AG8FxXHSI+u4j8bC8jArOHyH02GxGI5I/ViE5zE9CpsYKIuixFm3w1Oj0mP72l6HQ+mcgyP8t9P44Iz0y/ihFDTGaCMiS87HJbBw1vDTKRzXK7Kwmn6JlEKoZpiJls8LMF/n2Z27mWSHszGiWyftLPJ41DJZkgl0xS5gokLLyFiGCJ/69HzHozYgxb4RMfNoyj1O/J48bfjZ6LKXqmJ/kONRMapAJC83AI9u9FBHKASOHwJlFeqZQQ0FvcbzaRZPt6JJS1Gyc7CEf6MiSUT0DMfq/nYTGhkZpgfMxDjPNNxByfMR4a3ObXPbYjwBPQ/4jbb0V+nMc9DwXupxTNfQfSvFUh5pZLlOPqRgLPFI8/G449iFKdDBjOQSKV0WxgPhxeT9QWzYeRk3zPdvd32vOXS5QTPE4ExZRD/68JQXG4x3KSKMd39kGjnaj0krS+zWhNrHebT/n+ZH7vQvukF63ZO4hqFMkFYxDIgXHXeWyziajjAx7LAiKp+qtIc1xK5Onc47aziejMmci0V2oc9KLo+x1ov5Z7jBXw3x5321ORNmg+9OwmzHlZbuf+s/CxmMjtNoVIjr3O+OhBe3Gj4Us+oS1ELrwit5G0k6Ue8ygkhHe5nX3WhjUapkPoMPGy/mMcWqOb3Ecb8odbgOjJ/Wgt7XbbE9F+e5NM/XS2EnkHJxERvRVEzedtUD4cCa6bPX/ZaE2V+b40BohKRDuBDXD022hdTDac6QB+jDD1F3vc6UCUzPfJjNpFBF6VAM/Aq9WEdrbP+B1OuAHs9vPFyBe5z8+3+30SoqPfITIybCWC01oNRyeZqIe2v3d7iSa1I3qerDtJe3gaKSDSWBYaZ0lDe56uM7x/ASD/3Ibs/Px8SkpKMq+8vLxz3nvixAmefPJJ/uIv/oKFCxfyyU9+kqeeeoo9e/bw2muv/TND+sFcQz6A57r+apgcoSsJTUpiYvjzXYg5P4+YzSKiQPoTqBLBAJGOoYaI/luFBIseIh3BLoKp9xMOv1vQhk8n/s1IC/g0YSo4QqQemUn4ny1CgkzSlFUiInkr8B8IE9MSpH1YigS3ixFRTCbSdsSMKoE21fnNWYSI8DxEsEs8rmpEgBuIeqOX+N7dBNO3aeXk6zD6C/7eZfykQJsaxDxGESXhnvacLEKMawmZ8m5tL0LJTMR8HzdsSQM1HxHnBsQw9yBimoSDYkT4kzn/cSKdzMXItJyLmGE1/OwN+GQpEVzxOSJ1Q6PHvQIJgE3w9l64sNDwlnt87cgvajdhnp+IUpXs8PwchLZfQ8kVRPLlpFld5OdaPdYkEFYTfma7DEOb5+c0nHkchi1DQtRRImr8oHB/5sciyFnLDG/Siv6bUpjfGsnNrT3hUhTNuMe43uhgkY8iIavQ+N9iXC1Ca3YrIczuMPz5Z81bDpkI0mPrYdxY/9cMZw7AsDmEMFntdXIVkdB6D6H1rEJrPAm41xJmcpAG9W7D8rT6zFSgAK3xuzw/szRG0Ph7dkLeCI8rh6i0MdX9PeRnCtH+nUXUm+5DwsQlRIkv04Kjj8NFyX3iR/D238OFw9H+fcG4LjDOlqG1keX++5DG6AWgEjruF0k4iYJiGDQOkm/ZQ0RASoHn5g7gNug4DUWbUPT2lYSFpA8JfUeJEnQJpg7Pw12IhiR3jnY094v8bHL56EOLLvns1hsfN3peb/N8fFvzdHibtsFUIpf9uNWElaECrfseNPB8gs4tQ5Vc9vvBRZ6PciQQ98CZXzj34Vg/m9ZuueF4ggjO6vZrlO9bTOSIzPecdKC1+QTa83i+kna6F809fuZWw3fC/09zHys94OGIRnUBjefPB/AGIsnAP/XqR0agfw4fwPLycvr6+hgYGOAjH/kIn/3sZ1m3bh2/8zv/uCF0x44d/Ot//a955513KCgoyPw+YcIEbrvtNtatW/e+wnc+riET8LmuP0bE80nCnNIAx47AuHmEdnAfEjae8utLiOkkM1sdoUFcgzQnNYgI7UaEp5gIImlHJo4uREQfJzQg1xuOXKJ020q3Pxo4DGfegmFVbnsqElDvR8LbKKTpmYaEvP8M76yAC8a7/xWIqCeTWh8iSnlIa7kc+CNgNuQUEFnot7rtVYjYniRSlKwiNBU9bvs+ONkJo8cD5XZUP4KYSPKvOW78Jv/KRkQcj3he9vje64yz44KnZIHve9x99SDBoxlpMIoRQ5lnuLIQw0lagD7EeI4hZlaOCHqH8b8deAze/gf45G1IuK70HD3tdhZ5TpKvVx907LWP42zD9CzwY+DfKo1IziYiOOc/GsYrkKBVBSVNRDqUaXDqRRjZhBhSM2IYu5Gwc5Vh2uM5y4Jj9TAuaXt7YNgXkACz0GPM9xhWCY5hNznSuM4wrEaM/L5WrbtiJPQ9S1Rw2YiYZbv7eACts+SLmrh0K1FVw+bOTHWQbsLVYDIS0OYDT/iscwfaJ+NgWC6hVWkhglradT9/SaQOmkpEy9Z5LC+iNVKA1tImwmw8G43zAGLi30T7qBTtjR8IhlRGLu8uP5en8Z6ph2H4t+SzOUu4fectuOD/Qm4LUwzPSuPwCKGpn+CI/WcN10K4sN3wFBNCRiE64D1F1F+eZzx+Fwlmna6qA5T0E9UwGtFh69+j/V9KCDBfMjzFUDSXMPEXoz2/0v0uQAFhSSNaTQiDfWiPtKJDVAFaE03G/Z2INjV4jjbC2z+EC+91P73I7/QmpBGtFX45CJPTYaoPxjWjw1q94Wo17F2GZYzx+iTS5r9IWCrSXn0IBZsc1r3DJiIt8c/8WykcvhkmJ5+/5EbSiPbNLjj5svPFtmqMHb+AomXE4X8copW7iSwOyS9yvn/LMn6+Y3yVw9sHLPh7L7AcCYYr0YHi/7Dr5MmT7/k+YsQIRoz4p4WarF27lhkzZlBYWMiePXv4yle+wn//7/+dv/iLv/hH729rayMnJ+c9wh/A2LFjaWtr+yfB8i/lGhIAz3XlEsSkCjH+WdB+BMZNIQqAzwXu8wa9GBHrXESkvkKkwMj3b8lUdy8iOO2IESbNRjJXtRMlraYhwrEZEcls/34aEZYGRGi/CcOS/9JUImnpN1BKlO8gJthOJjH1BWPcTz5i9K96zLPRse5Zj30/UZ5pHjJT9iHT2HrD/wVCIE2mlmqkgZjuMf+BcDA6330+D/wtcbqeYDiWatz9j0LnT6BkhNscgQjuckQsmw3naCSAzCcirvcgE/R4IA+O/houWu4xbUPMeZfhyIafvwGfSPOQtIF9SAh9CglL+er7wrs8ntnGzULDgud3IhJK+4FKKLrfbSQNST5ifjMg52bjcRAJmtmIySdzUIXbyiez7jJpJZIWs9DPZRuGHvVLF3AQxn0KaRSeMm6fRSlP9hOaqDbCZ3QjEqSXGJf/kUj70kLkQ5thfD9ICE57kLBzWrCf2QnDrvQ8JEGxgPD9a0LMPWm5rySENfuJ0muN5UNEUEQPYpZpb50gcmnOdLu5RNm6mcbNAXRAqCJ8Ky1sHtsJ4yYhoWXA7a02bsYYngpgpaOplxGm75uR4/9CGHad4diHBNEaMvkiL/g4Ki32p4RQv9v4usL46SFy7d1I5MZLpuQetO9vJfwPZxuOCs9v8pPr8nO5ntPRRN7AbnRwScLHu2hvtRJaqWL3ddL37SCqA232fSWENjrXMHzPeM0hSrol7XoxkYrqepQuKE/jvPBhw7cc1TIeROs2BYY8KRzTg2j0fEQfW5Sbc2QVWreHjb8JxvNuItIgaWhL0Pp6jCj7mHwZu4jArtOCbfICwod5PLIKFJBxJxmdSyTQz4Oip1DwyjQkJA8ncjE+YxhWEpHMq/x7rvu8EvhjuHAKkQT+YXQYGeS9+V3P05VQ+n61BVBW9t5Bfe1rX+Oee+75f9x/55138ud//uf/yzZ/8YtfMHnyZL74xS9mfvtX/+pfkZOTwx/+4R/yrW99658sRUVNswAAnNxJREFUXP62XkMC4Dmutl9DST46tR7yj+Nh+ioiAng52tDDfTLPRcS5GxGzWn+/l4jgO4CEmFrEvJYgs0o2Wv0HEFFKZqlBIpVMDmI2swkzRycisslEPUAQ+8T0qokItrl+bj8iOIXIRFeCmG4Bceoe5zaSqW8BItrJf+yg++tAvkxJuGp0mz1+9SIC+fv+fQEh+M4H/h1iAslZvxMJzfMgZyaUJMfrDkQsmz0Huw3LPCS4jpE/2+TL0Ol/tsfXCYyGiz5NMKp6476bjIn0E59CRPeQ2srU2uwmkn2XEUm0cxHDfhkx7WluN2ktd0LHBii6GwmcFZ6Hm8hoDJhknM0wnl42Lss8R3tgcBtkjfHYG9znux7jSc9RMsdZI0Kh5+pOtKbKiOjJXUi4Tb5XZYR5vYPQti4kTHGtiIF9Ffqfg5wHiUTQ2ciklQIKkvBts/Swawz3UeOx3Pfm+b5646bZ8E3x2Mu8LhqJ0ll5Hu8+w9iANCqT/Tlpy3FbncbtQ4ZzCWK6g25nLVpHbXpu3Bz/N8fzO4pwsj/iZ8ep/3FXuP085AO3Hu2fJIQm371H/HyB+yxBB56zfSwn+JVcANqR8DacSDhfIU1y0VyibvAmIsVTp+/d47kcR0T7TvZ8HEeC8hjf24PW8xoi6ft1wIPQ8wuXeVtjOA4Re2MP0qinA1cbEQneBtQ7F2Ml2hu3oTV7gnAtScLi7/t7FspisIeorlNIrJeL0IGuCwle30D7qAvtnZthZCOhEeszfpMfZa8//wStjeSveo3mM+PjvQMJaTny38y7EtE6DOcOJOC1ofW5ixDYZhOpl1qNjzyPocjzNBetl7ROsj2OViKl0h4i6G0uWqMFaC1/n8h7mrS4/4ddLS0t7zEBn0tAu/322/n85z//v2yroqLiH/199uzZ/I//8T9oampi0qRJ/4//S0pK6O/vp6ur6z1awOPHj1NSUvL/PojfgmtIADzHVXIF2qyj0UZ9Gs5s88k+EdzkV9KNiHET4Xi82N+bCBPdIJmqChmn4DvRBi9GgsyLaKM3oxNm8rUqNSy7ECHM9W8DiAAMdztfRr5ok9x3PRG4UkUUTx9EAkspEhAriBqcBxFhso8edyCi9igicJciE3m+f1/q+6cZnhR4ke97ZxueA77nEOFjuBElfnqaqFAwSW2cugdGJrP0SkTE33IbW/z7PEKY7oPJySdyKaEVyQG2Q1M9lJ9AjLbR49tIJJxdAtwIb8+DC5cZ/iLE5HYiYTrHY2s0Xr6DGO144/3zhvM5zXPRp9EpPgkgU4l8cs3ACjj4C5haQJR3KkGMqkLwZ5UTEa/rzprLJ4mUQ3OJAIUU7XmH5/Niz88RQlv4jGHpQkzyUTL+nRmn85nGbYPbXw48DzmjPJdTyKQyYTFivM97Dr+OGGU6jNyBNEK4jzYiefpitK6L/P8DRLWHGsLBvsZzUe/nytAab0H76hbjtMFjSSbmJwhn+r1kDm10of2StI/L3dYGt1WE1kch0gLmE75lf2f8lbv9mzzexWg/DXd/Swm/snnG6XSi6kTvWfjIJeqIX+926pFAthP6N+i2osNojeS5n2cJwbLXv3UTSY+bPQ/L9P3MARg2D62TCuP0cbRO9pEJeMibqXcOG5ZpREm+i4zzKsPYa/inA1dB/52Qc6d/X0GUGkya5ioip2YdoSXs0vdTb8DIbLQuj0PbBihZYVhr0P4vR4JRO5Gncxqa/92Ge7LvX+n5/L77mep5S9r+aiJ59DiU5eBLjtp/yrAuQLS5yHO3xn3mosjjuYI1Uz7xdsO/wGNcTCZlF3sR/R9FFAvIQTTgVaRB33MWvurRoWQWWmfDDcMi33cerxTA8X61BTB69Oj/Tz6AxcXFFBcX/7/e949db775JsOHD+fCCy/8R///5Cc/SXZ2Nv/1v/5Xrr76agDq6+v51a9+xZw5c/63+vyXdg0/3wD8i706EbE+hjbnV2FY0txVIuaTTpovIgL8OaAQTv2ESGGQTndlaBNnIQJzE7AF/uaI2ywlojinIcIMIsZdiDg0+TvIWX0qmdP34I9Rjr4rEbFZ73Y7EdGqREJMl8b1zuuIANYh0+0oREy+5z52IyZ7JRIWGg1zNvJ/GkC7tZkQencRzGwW/Ox1RIQXIuGjhBAYa42jvxQcP/8JvHM7Imq1wGQYeQ0yq61FK7UdfnW3n1uETsBJk9PksS41Tg4jZ/FZSMBZAeWPIe1FPiLW2e7rs0Qk7xfhwqcQ5e8koniXuc+/8/taIvH2Vj97t+fwciJn5GjjazLhuL2FWENHYOqnEQNLa2um8TnWMK70bwPws9XG87OEf+mNRIWJBsL3MM/4nO4xDCeqKtyoPk4d8Bx3IW3iWuN8P2JitWj974P+ZNa8CTHLOuPyGaQxfpxIT7IfCUuzjdtHCHNxPRIICjS2N7ehqNqlhv04chuYLZMsy9GeqHSfbxonFWgNLvezOxGj3mf8dCDmX0KUnPuecM4b/i0fradKtJ/3IUb7rO+9zvft8ziToDPZ7696jrahPTyKSOIMkZakzf2vRWuz03MylajkchCtpaRVLEZr7Dic2QA5Y+ETX3DfEBHIyz1/TWRyLtIJg9WGsQBpFfv0GvYpz8n3PdZFwAo4/Dpahytg8H4iUXMv2kdf8ni3ICGkzWNdQhz4DgJfg5wb0Jp8jEh3lEyxO43fPmjaQESnHzCOL4WRX0D7d7f6Kvk4tN2B1qJpAduJ8mlZxskcz2UpWh/jiUwN9xqGZsO/Du2DATK+tU3XGR+Xel6Kkcn1Zt/3FDr45AG/RoeVhzQPR+8nfGl3EoFqzYTZ+YDfE51MWuoiePs+IohsHvLlHUT08xrjvd/zcS2h/fw65/UafJ9f/xzX3r17efjhh6mtraWxsZHnnnuOdevWcf3113PBBRcAcOzYMSZPnszrr78OoACXG27gi1/8Ijt37uRnP/sZa9asYc6cOVxyySX/TJB+sNdQFPC5rsXDgsktRsSshnDczUcbMGkn6hBR+XNEOHLRan6LMDkl0/AgYoirkX/IBBQB+BqR3iQx1xyiJupxP3OlYdlK+PrdDHyaiJgrV3/9d0POfSgv11WEf84OQgDrQ4yzCE7dBiNHIaI51/dMQox2DzAPBr/v8mb9iHBu8r3d/u20n7nSY2gAvqz0LqMfQEJF0nYOECW+NsGpVhh5BbS9DCVfRsRuNyLyJwgN1lPubwUioPmEJin/rDabCV+wAsLfcQDOPOdyVQfh2FtC8Sc/7v9/iLR5F6F1sMLjfNW4muBxjkUM57jmqGkDlN9ERMMOJ6KVS4mqCGXupw0x8Ha3O8rPtbiNQSTANZAx1bY9DCW3Gwd3+Lk3DfP96OoxXvb5noOGcQcKaKgltNLJBFtJxtmdPCLh9kTfu0kw/HwnfGICWotFRK7MaldZuMb/PUoEtmR7TMnceS1Rr3kqMuetMIwT0Pq+3O8d8PbrcOEGj6eGqFRyAK21TveRGHuz+xskTP3VhJ/bKOOpnqh93EtUj2gn8nQ2+f9t7isdetL+SLguIPwjpxBaqi6k0evx+ArQOrrU8zIK7f830Vy3owPHLsLXuB3t8cNoLZSjPbQR7fmDxnWW219GuJ1UG6eLEM26jfDPnYHWYMLnpSj7wWLP2/OEL+t8tDYfJ2p1N3hsDZ6bGWi/9iNBdR5Ro/xmdDCdhATwUe67gEyWgo4fQ9HHkdBTJnyeedRJxKsI15dDxnM7Wmub3Mdo4zFZJN4FLiMCjya7z6Q9tqsGA56zdSjv5kroeAYAiq4zPnoQPWgwrg+7/WPG3wHor7XmMw9YAD+bB5+8ichB2OM52OI+cwkXgz1ozS1Ha+xZz1u3cVTgOfgaEsgHCavCdz54Np6igK/j/Y0Cfo73Pwq4pqaGP/qjP+Lw4cO8++67fPSjH+U//If/wBe/+MWMebmpqYmPfvSj7Ny5k9/7vd8DlAj69ttvZ/369bz77rssXryYv/qrv/o/xgQ8JACe6/rmsNCgDCJG10iYacYTDtfJn+R5tLlLkeZgke9vJkx1SxARnO12ricy6h9EG3wQEaxZSCjsJSqOdCPBpggRvErEzC9GxGgrkY+qAp1QKxDRzEME5T8hIlKLiPAR95vPexnPPOPiKaIEWK5/m0X4Wr1KJK8uRkxiImJMe4At0PNjyPsQOq3WeiwXGa7x8NOb4XfHGr4BRKhnImL7rNt3YAh3EvkLJxCpQtJYxiLhcztRz7gIMadX/dw05GD+JSJCeQth/mwhAnCS31MjkUC3myjJVObxbPTnawnz+wCRyHqW57gdzflKotqIHco7jkPRWM9ZumeBYd5CHD7KzpqrAeP5q4ipDxDat3q/b0drYjnhEF+B1uhjhIN9iZ+dgYS6Yv9+iEx0L1VovZpp8wjSbKV2piEBpZrw9drn+5PwVuz+k29nJWESLfP9SzzWaUiAnuDP1URut3cR19hFlDnrNezJXH+CyDs5HWk6J3ssED6q3UTgTA1RuacBCaeXu5+NROBME7FXqg1zK5HOI9f3HkfC7nSkHf0cSqz8GbQ+DhIVakB04Wm0RzqI/Jz7fU+R8VGFhOAufy8jqv9MI5K4Z3scy5Fm87Rxm3yNd/vdhyPK/Epa611IS/sjQvvZJ7weOwLjVvj5LjS3+9F6bzirnYUe0/VovWahPf4W8oseIPze1nqc2X4l14+kmR6OguweIaJ8d6I1NYpwcRiP1uLzRM1xH9aY4LFs9HjWekyb3OdiIi3PXr/vdvuzEK1tRXO/Ch0YF3sc1Wh/Jto9Hq31S5HpeCOa83Xo0FKH5jQXrdO5aL+/QNDee4lI9ZvOgrMaOHz+BMBVvL8C4HqGSsF9UNeQCfhcVw2Rxb0TEaNrEGFNfj3Jf6wZMdgsRFC2oQ2cfMtWAJth8EVEmEBEohZpwRKRKSPSM/QR+auSX1a3/+9GBG2TX+k+iOCPQ0gb9DAiWsuJKhffJVKhVCDTcRcSnF5GxBZEiB8kUjssJRzvjxF5r252u8kfbwxizBvI1EzOSyutBjGIdIJ+E6iH3y31mEcjhliICHSrYV+MhLvRSLgb8Fwk/AwQvmHTiKof1xOVV77l+3qI6MH9hE9eke+tRET6esKMPxUR70rjYC6RT+5NInrwOFHtodHtT0FM+RFk/p9BaIPbCKaWC0UfNvxT3VfyScryeMe4vTLExMsRM7iZqGaRmMizhiMbrcE8wkw9xjja5vsvRabwFEma8Nnuz7sIh/9yQqvdSFQzmUv47u02ftZ7XtchhvhfjK9sNL/pYLIJra+5RITqfMN6H2KW1Wiv1RPVW9oMM0RJrnzfu9nt7kKCRhERUd1nGFuMz7kweAAdXAYJTTzEnnvTbfZ7zG3GzxjDvYgQnt8gys/VI0HxciLdx3VEKcakgdyJtFllnrfRaK22e0yjDO9XjLt8IhBnKRHQ00L4Ej6PaNNuwkSb/Pc60X6tJ1K2ZKM13+5xPm84ZyOhrZcIttkFPUecJauHKHE5A63HCiKvYCk6fM11f+nQlQ4cuUSC8CTwfA8G7/KcXuf+ixDXykL7KZ/IKzgWmVDXo4NTNlqPk9Ca3UMmmjcjOL2AhM5BImF6Fdr7i42fA257EQrAKvaYphG+0g97bNvJuDZQZBx3eJ5Po8NHndvqItI2tROa7zaiWsmA53iy+xnw3O0yfjvRvhq6hq7/jWtIADzXtQ1ttvHodDeWyGLfhAjGLETM3iSy0G8hqhVchTZnJbDSZtMBIkK4BBGbP1WXZ7YRJ+PkXNyNTqZVbrPNfVchQpCEgY2E2XgXYkonCS3fBiK6LZluujyG4372CBG4kkzOBYI9U092qeHejgh3inyc4HFV+vsOIpJyHmJscxVBCkQZrUpCq1WOiGsRIowHiLrCiYFsQdGkrf69AdkMZqvNo0eQYFBFBDU0GQfJh6xX7Z15idBsJbN1CyLCydx8nNDgTSOqryzyeDFO1nquxhImxGI4+RZRg3ksITTORsLGIcPTjbRH44zXfuP0VT+73XjP8fNHiUjBcvfXiYTB65GwkeAsReugkPA52od2f4fmhRqkodqMBMckGDQbN1nGQbfvqfY9rYQQXkiU/zro5y8m1tsA8Hs5mt8+pFXpR2t1nsdxmeGqQmu6Gc4cQYeA1X7umHH/CuGvWid8sxJpvbvQGqyAjnrP0yLfN0/zcOoQ0tbWqp1B4OC9ROL3SUhwneaxJL+uhWgdXunPvcZJFhJ42jyGGrSG5xo3IwzXIDowVJFZt5lKODd5jPv8vZFIEL7TONoBg3uRcDPWz+9Ca6cdrZMkMMxFGuSD6GDXS1gVEn3YTRwcksAxz2O+yW2m/dBOXFMg7xrImUBUTukhIpe7DVuyLiT4atAafMt4W0CUiJvp57bouayP+/e9aM11IQFzPlr7i31/Erz+M6LXxYY3l6CNU9F+6kN7pJRIyZL2TBMSrFoQXV9GVAB5FB2QC8n4WffsJqqFlKD1NwetnTo4Vu05q0RC7HG0Pp83PFuQRraWKMW5iaiys9o4WUocHloJP9NFSPg8j9c/5sf3T3kNXR/cNSQAnutagDZ/MRKMkkmxnTgVzkaE8jW0UYuRQ3ASuHYhIvos2ri3IOK1lIzz9NuPICK4x9afFYTmsQ8Rn3sJJ/IyRAiLkfmyAhGGMkT4auGd44g4zDKcSfMxGmndkgkwmTinENoLELE6jZj3KCJ31TOIMN1CnJbvIPywtvj5Zr9fY5gTkfyc6xUfdN+1RLLnNYaj3/B1uo1sxOwPIa3CZcZlFSL4lyBNgE2yFy3zWIdDzyNErq/rPV8HPAebYNiHEUNZYFiTZq+RKOk2lzC5bifqHM/0OE4iwr3B988w3ns1R6NvMDxJ8P+iYT9ufN2MCHgSBhYgIa8VrYWVhnmp8THJOPmm4cpFDCRp+h5Da6UBMZjDiHlPNtz5xvN8w3gIMbQZHncyHze5z62IEU4h0sikcdbB/mSOTfO6i4wGe3ATtP0Q2lKQSCPwuX6NIxftgVvRutyKtDfVan9wPRkh5TTIdLjNsKaxN6u/jvuR9vJqxKC7jbMCzUXRM263EZgHpx4G6mHkAqThtv9XzjKY+mF/X+JnnjA+k4k227CvQxrlBs9Rvl9dgunoy8ZDIeJqo5G2/ytobac9OsV4nk34DyZBIA/tz5VExYp1QDlklRLar2S2TfccM1x3ewydmu/DidZ8yf1e7mdLPd9VRJqdZqLObB7SlG0myuV1+56kBWv0Kw8dyFrRvk8C3VtEWqglREaELLe508+sd3uViO5dBf3fJ5Jcj0L7cB5RNWg20iyvcp/X+r8U7DEVCYqlxEG6HWUf+CYSzAsIi8MPjI9HjMMK42KNX6eNl+sh7+P+v8rjGvAYsjXGcXf62TrPwxLjpISgMeuNhznooLPQ7W1CvKee8NlO1pMyw7cdBfOcx+s075/wd/oDhv3/368hH8BzXX8zTBuzERHLTrRx0wm4nfA5WYg28xa0kS8mEn0mrdARwqcoXXOI3bMdel6GvGROS8T9SiQUfRcxoyzETC+Gnpcg7zbDttj3J7Pft3mvCbmMqOF5EBHJg0TS6huh43Youg8JZgcRQ8olykWNJXKyTUVE81se4xoimfNCRLCsfTp2M4wrRMLNASTYLEXRx7OIXHLJPNeANA/d6OR/A2IABWQScmci/5YjIech6P97pwsbYRjz3ea1RJDBi0AdvNMJFzxApNWZZphnI6ZyQLCzCxHpfOh5EvI+5bkc43aTNrSYCLB43N8PEkl125HGbz5RDeYgEsCmuI8exLQqiCCW1Nc+xFxnE2alyf7vuO99CmnFPmPcP0EkmR1lWEGC1feAv0DrajqRu3C38fQoYqj1RFLrJURkZ69/H4B33oALlnh+H4Gjb8FFH0VC4ldQqpUugsniuZ0l3Lz9CFz4KJF+aBuxnroJ7eH9cHQ1XHSN8fAEEmJa0B4qQSa6VYKDTUhLNsLjqERr5WXP9yS0h0e4nYNE+Tx83zIyeUD7H4SciZ6je5Fwtw+t3Uo/s4vwD00a5eRu0EMkAy5Ca2QzWiOT0F5aTghgyaR7jEyUKs8Yb08YJxM9J39JJBNuJvwWH/N9NxN+qenAdj2RSuRR93szoWGag/ZLjZ8bgdbKLe6zGe2z/R5vFpl8itQTKWO6/N9Sj7eMOFQ9gYT3i9G+WQ2nboaR8whhZ7ZfHWhtFKN12ofoxEFEj6qJOuY73X4xmRRZb6+GC28hNHy7DeMEIp1WrvH2CBK4/hRp26sRra4yvqrQul5F5NZcTaTUSgehGwn3oU1o/c1H2tAeJMiuJ1wmFpIxr7PKuM5GfCAd6p5ANONdtCaeB1rOnw9gsra/H9cAUtgO+QB+MNeQBvBc17NErrFctPHe8OfkqL0OEcw3CZPHJHTaTQJEOnVWIWyXIoLSRtR/bQfWQt4Vfj6fME10IA3OR4gkuT8EFkLePb6nCRGB/X5uCRJKdiJzwwxECPe47YVIczXoZ1qBWii6AhHrNlQS6ZuI8PQQwlnlWTioMbxLiai2So+n0v13wriPI6Jc4ns7kcN0FpHzahw6JW9G1KSe8I98EE4+RwgNyfcy+Q9Vax5ylsBFcxAz/MlZcNYiYS1pNybCBYWGL8/jS2aiu2HwDsKEvI6MIJk3DQlXNxCRj18mKq70eh2UIaE9+RmNRwJYnX+7mExS444BIrF2sfE5z3NeTmgySgxHLWImLe5jA5HOpgF+9g/GyQ69v3I7YnC5KEikETGNuW7jEv3/yx8joSQbaSDXkTGNZqJetxDJgu/1/BW5skUl0vwUwEU3+PsAWptZaI191W3Vemy1gvPCzwvvKT3O0d2Ej+hJ990hXFyUTMqdHtOPkICUXDE2ofX/feNuIxIaCohAkWXoMHcI7YEvoX25EK395DPaAeyGnz8oXOZMNG5XokjZ5MYxYHjqCWYPEsiyPNelaO1Wob00SPjXbQdGwWvvov1R6f/f9NwdILTMyQevhyj7thhVaqk8678pHu9it1FHuJbM97wMR/too++fgNZrExLmdxIpqe4APguv7oYzqzzeLo+3xXB1uI8c4fudnWg9rUGl93IJX8VOIt3NUiKQpRFGjkd7/Ria/x1EcFkn4R+aDtkF6PAzAQlhUxHNsxsGczWvF15G+IT2efytHnOb22sjtJJLicpOqzy+4e7rTwgNZKPxnOAqQftnNRFoU4fW5eWexx8ZlrvRIW+V57KLMLk/SWgJ3wRGwbG74UwztL1keHI9N+fxOv0+v4auD+4aEgDPdU1DxGYUYgx9iCjP5b11Y3chpvYa2uwFvq+HqP+ZmFURYu7JB6qFSFJ7wO00IiJS5n6S71UFkevtu0TZrGTKTSbA/YgQJaZZiRhRA1HabBc6PW5ChGua++jzb8WIKG5DRPCE+xtuGNrdT4c/TyKE4i5EuF/wPXcgTVMBkfKhCQnOZYQGbQTymUyO1w0eczswFkavdv8nURRwEmi/bNhO+P5e4/se37McMZAeQhM6zrB2EGb2eXo/fAKyFrj/UuN4PqHtXeo5Sdqp+YSZaxXSyFyBTH5lhOC8CzGmZ4i0LUR9VqrRukimn+F+ZjeRPqbKbX7D7abggja0JpbAJ6/xmNo19mLcxhFUxzkLaf+OefwAlfCxRWidJEG8jkionHz4OonSdBMMzxzjpIsQ1jrRmpqFtDrJ9JwY+UKiXFaFf0vt/AlcNNbwFRsnM1EE6mP+fpTQ7l6O1tTNiOkf95wkoXAs2heHkTalx/CVEmW01vuV/K6+gda+TYif+KifWY7W5zHoSNqgpOnZ4dckf59B+Pim37oMe5/HWoX2xwLgXbjk04a5g6iRvIOoAd6A/MiSZiuNIQWQfQftg3q/p8CN6b4XIhdngceYTeydcWjdtRDCbZu/twEXw6WFMOyjxlUxonXNHlse0khP0/sFyeetEa2tsUij32Ac1KE904To7EokAUx1u3nIReBaogxeChDpQQexpH0tIaSHGqJMYZX7PkJEBXf5cxmRxqmACOjY5c+fQ4LrYrc91zgdS9RixrjNRmvzAcO5B+2Vp6H/fkTnj6C5TdaNQaIaVJnhbjUMPUTqlxVkaOW4UtUoHoFxN4GI4B66hq7f8BoSAM915aHNt4SI1NuDCF8uUYasHgkPkxER3Y8IcjcRKZp8V76FCMECtKnn+5UErmV6pv9JRGRW+dm/QhG9YxChK3HfaxFxHkCE7l0iOKGWEFqKCSf5dkQ4P44IbhbBtJMmowwRzeQrOAkR9dEeX9J6dBMBCKuRRqqVqP5QRSQH3ubfJhBC0XgiD1ny3bodaYr2eWwTkSBRgIh2K/KzfIYwM/cRJ/dCj52zxmZG/cvk4N9t/M8gUjDsFpyTRyHCXkckQm5HAtug76+Htjc8rmsIzcoeJDy0IwpdhMxTB42nGYZ1A9K8FSOG3gpt2xADSOPu8ivhLwmdg8bFCcI01Oox1hq/iYGPgumrfZ+FDC5D6/KviDQuO4g0RVPcR4f7vZiM1ua1BCO8N/3Fu8b7JURwUjNR1zj5UE0z7icQmo42P3/Ic3k9YvgzkMA1ikyuy2O/IHJStiIh8F5/b0cCzBL3/Z+M6xKPLQtFE/capnTQKUXC4myPoRHtn0lE2pjr0fzvN25PQFHyeWzyfYuIwIQaj2u+729FWrY+434c4YbRSAjyoxGNOEGkLbKfIwXGxxvG4UK/NxLlI/PRuj3seUyHyrFov9Uj4X8Ekedxp59pJLR5lxpPkKlGk6lDexdymZhjmHf5nu0EPcslauyWGo4J7r+f8HFN1o67PYfriTRDA0SUc5/bH+PfZhsXJ4yjHiJA61Fij5caF+2EEFtGRLHP8n3ZRKqnfLRuWwm/021EdHi120p0rBHN/bNErfePE+UZvwk589Aa3+x5PG74ZxMVmB4zHD3GaQ06LBxCe7mYTPAai+CCs/dBE+f1+t/19zvXa+j64K4hAfBcV2IQtcDzcPRJxOy2I03C44jhHyaSkpYSpq/JaINvRht6E9r8owlN324gD169F366G5k8l0LOXsR8k1bpGNKO9RKmz72+fz8iAJsRUepFDvUdcGoATj7uMUwlzMTNRA69aYiZZBumfiLFylhgDJy6HxHhq5HAcQ0iUJuMq53IZ+ZPEQO4Cn71a5Tzb6thm0IkN+1GWp1L1T4FSBhsQcLRFiJast6/9wnfbW8gpjyFKJN0KdIq5BOZ+KcZtvVk0uJ8bL3vHyM8sxER7AJEiOcarlbku5YY4iNIGzCbTN3YkiWei2mGs89z/odEeSqbyPvfOAu+5OzfhPz1HgayoOQ6w5BlHH6WyFG2gjD9OtDm57sRA5jn+xe77SMedyma47Fo7msM5y/QXD9O+DwlX618w15IpEppNJyz4JIPua0qwi/sOTj2iJ+v8HsBYVpLvm55aL1OMG6y3c8bqLrKN5FgsRsx22wignaUxlEMElYnoH33oudygeEsdP9dbrNMMJz6IbANTu5FAnaJcfYs4Wf5tHA8+BMkIH2FKF22HgkbfWivzIOe9Z6fLN93v3FZS8ZkO3gb2vuzkeBUR6znZrQXkxBdZ5zOQGs7+cB9BTico4NCH3KZmEhofSahNfGq8boIaUOb/T2ZE29Bh7TlnospaE1XGrZtbv+0YVmBaN0mInl9r2Efrj7/5iXjcrnbeAFOriEqnSQNewdad6XG9RTPVRLsrvVvNxIlF2caVwUe40HDtsaw7UNC+QZEC5PGrxQF9hx0m3s9x8lP8DRaPxuITAtZhMBce1afeG4Gkdm3RT/99O/RmhhEe/JbSKic7zntNP5PECm+LiYq02z2+FaieT4M5KvOM1VoPV2K1tpqMhaOwUeIlDnXo2CVLOPpPF5DAuBv7zUUBHKua9YwEbYGxKzz5XdRsppw/l6MhMAKRDSy0GauQkTZDKit18/5VNv/OOSsRWbSxUQtYRBBbSSqjuyGw60weQ6RZuNygqBtQcLTcURslvq5fdD0MpTfjZhxPdpd+WTSl/RvspW50DBsAu6Htt1Q4gCOnz0DnyxFTCdpdX6AiG81EdTQhojSq4bpWkQcKxEBuwEGm/X4hRcbXyXGXy4SPIuRdiSZedBzPImEYNxHn/tIpvlSwc29xn8lIuaJmjwxGn50UmN/GhHmq43nZPIcjoj9g57zenjnNFxwm3D+o+fgs/cTKXey4ZUNcPktSEBJws5q9zkBaSMWu+16zQkD8M5uuOA+pMl8HTGMGo25/znI+Thae2VE2pU6oprETq2Fjjeg6Msewyvu12uO2UTuxgOevxbEqNYRkYq7kJCYh9bgHj/7MmK22xFzzDFeNqNDQBNaDxPP+n7I87WXqE+8C63PEiKgZor/32B4k9Zkt2Hu0e9tv4CSpC2sQgwzaUfT2Mo9z7mGLZeIik0a+hIihdIstNbeJdKgdMLJAzD6Tt/3eULjbY0LNUh4SBqvQeOnz/MxBglIjxPRoD4ApLrNgwOQ9aGz5nUNYV4vRfs5BZUcME5noP3QCGyFn/89fOJejyUJA4Vo7bX4+S73PwPtm1s9j7VEWqYdyJ81aT3T4S8XBn/s9CszCC3e825/OWHuneR2TyMB9YhgzESJTyFS5OxFe6PLOFqMaNitaA3dR5RnTP6E1YiOrDDOFxBpcw66/23IjJ605w2IDpjG8TT8/HX4RDb0D0DOTLQfczzHTWj9Pk0ESdWgg1yal160/5YZH28if9cJhqEdHV7SwWa72/k2Ube7nYjIvte4nEYEjVQSkc5biTRZ1cgicjch0N9EaMAHjN+aD56NpyCQpby/QSBbGAoC+aCuIQHwXNe6YSKwOYhRdPv3MkScy4h8V0kTVECclG9BGzMPqfdvRRv768QpcSIiWrluo1EarpJHkNap3e3d6HtKEXFKxDTtlsQguhHhTH5SBYjhvUmkVqgkklZ3EWaMAd93LVHhoxUxpToi6W0xIkzlqOzdfCJ1SBESwHIJ37GlyByXzKTLCb/BpR5POxIiu3xPgmsRInLbiKjkPH/Oh1N/DyOtIf3lT+BjHzd8VxHVCJIQMM3PbkdEfwBpLXKI9Bn5SChZ6r4qCE3GQuO6CTGvWYgpLkUMLTH1MR57er6WSK6cmEyB+3+eKBc3gLQCZe7n20gg30qURFuHBL2kRSo9C5Z++NVK+MijhvEIMBZ6vi/5dNjH/dtaxEgfRoKZ1x0LXXJrI1rT2/Q88/x50Dho8JgGzpr7sUT6jpmey8P+/wBRm/kPkYm8WPDyOc/lKvdR5rmwZrPnLsi7zO3dQFSfaEbM8hYiqXByJehGQtpXPNdd/q3Q89JN+HXWEMm7k2vETiSY9XgtvIIY8ItEnrsktNUiobTWOJ3mdl5GB6F8JFwUE4mnr0DanQcMfzPwHXjl13D5PMNTaLwU+H0l4X7RgtZS0van8RUTiZ47kdB02t+3u43tbme8224kcm9eQ6Tq6QSyYfAQZC1Ce3UUkflgOVoPhWhNJXN3PaIhR4yD+xDtqDJ+JqEydN0weAKyPuX+vkQEox1E62SX7z+O9tEYdMBILgbbCa1bLRJ0e4n1txVZJPZ5nueh/T+eOBR8z++5RL3gVwh3k2IkzOchLV8bouP73M9ewkUh+fVm+fkrkHY7adcHCXedPiKAphP6m50eK83hHt+XLDRnr7dEgwcI7f8gcPX5EwCv5P0VALcyJAB+UNeQAHiu67vDwrdsAG3AXCI4YwpRGq0NbfpSRFy+CvwboqB7F5FeYQGRCiKL8M0rJPw5LkHCYzLf9bnf7yGi14k2/QoiOWofEgSShmYSInyViAj9iZ9ZZ7gXIsFlPNIsPUjUw8SwJ9+pZD5scHvlSMAc8D0DHn82kftsKRJkcgzfShSwcY37L0IC3mIkCNUSDHoEUYViIRIWuokKFceIsnPthM9RMgPOdZvLkabjhPuqJfKPjUYCeDKfVPHe6h0lSKjoNy4a/L2JMB21IOH8e7zXb6mciKBtRIQ6CSdXus2dcOoAjJyABKBqwzsPmSat/aUdBbQkZlKIcgl2E35fFUSEc5P7rzOMwwkfUzxHncbRCOPmsPFcQNQHbiBSFi0mIlkfIhzrs5CwNIjW13ykWZvkMUxDZtJ0UOozjoYbtgbC8b6C0HDkI41uMuVN9/0Xo4NRJVEC8BW3vQoJPF1ovd6IhKxriHrZyU9t0M/sIXxwuwj/LDxfk4zHFJzwCprXbCTEJW1pEkpnIEGl2eN8FWlnX0S+v3meh6sJc3hyM6hA83mAEMznGuc73U8BkZppkmHMRWu4gRDIk0tCCbx9O1z4UbTHNxq3pz2OhPNFgutkJ4xOfnkFaJ90uY8DSAPWiNZM8oErR3ttGloT7Wh9rSYCGgrJHAKOvQ7jRhEC2YSzcN4BZ16GYZd5TDM9nmmIVlWchacKz/OAX8mK0UUEKk31WNb790XEIf059Ue+57eAMDNP0hwPfhuybiDKIm70fQeQFi7feEx+l++iPXUREaWfXCCStv1hZNGYhNZPOZEm6jq3l2UYiwkz+Hi1+8rdcPk1xlcZofH+4yEBcOj6za8hH8BzXbuIiLhutFEXIsFmEBHyAkQQTyOi94b/u87/JQ3KXYi4pGCMw4gZtRI5qTqBra5O0Y6Ib9JqdPr9M0RtV9Bumeo2JhNag3ZEXMvgnUcRAS3xGHKJ1BOlRIqYJUggKkAMdgvSQN2DhMIkgJUhgj8aEbdLkOBSQpjdyghmPgcR+T3Gxz7Ch6zV7zuJFAxthLawz7h5xv1vRebnSvedjxh/r+Gd4XaegJNvIMGsCBHlGiQ4nm1W3UmUzUqagn6kpV2KtD2nCeG/jUhKPR8x1fWI8LcSdUyrNc9v70bzP9u+ZbOI6Me5MPLjRNDFEsO33vN5iEiGfRwJpS8Ad8KZX8ALbxHr5gAR+dhHOOdPMd53GnbQGki4rkdrsZwIdNlofJbAsZfRISPNQ9IWbiIiKJ+FU6uQz2K32+sjIngnqq2MU34y11YiwXI+9Cft3yg/exBpKsuN0+GIWT9JpElJJv5D/m2XcNT/DNJi9xv3SdDcT/h7pQjuYnj1OcR09xmXl7n/xcbPdKTpSdr8xYYp+c7OR3tvnfGRnPnT/3OQ+W6p/0++qdf7fRe88wu3mUeUJJxBVB45W3AuRoeaR4hqLfvQATPbY91p/HTDhSvQgTSLTDnDngNAPgymw88k4Wv0AkJTNYKINp5s3F3pV5/H1k6USiwgcqIu8PjmISGui0wapHEzkXBeigTqcuKgvROGTUKC/Si/56L1VkmkEipFh8kkCE8iNGv5vreZ8MmsRGswy7h7WePNlHu70vPTbfwVA6Mt/FW4vy7PTzoU7SfMuwVo/Y8hkjjnEwJ8B5HK6RBaB4VIsz9IZA84rlfPNo+nGc35IsRbjsHlMz2OpEXMRmvsPF6D7/Nr6PrgriEN4LmusmHaxFcRKVImIwaXhL5FSCB5CxG7At9bTviwjSKTSDhVTyAfmbCuJyL5Boni5LmIaJQiIlBH1LedjrQr1yCGNRUJRm3IpLYJEaJZhuluRCiuRRqJRkSg/tr/9buPJEw96zEPIqEiFwkESbs0n/BZ3ISE0Swi7UYXIqJXIqb4R0TKnDL3ncy5WcLNmcdhWKHx2UREYGeTSWeSCaJ41mObYVwXI03ZfuNikucmCe4T3B9EKaoEx18Dl8OvHoePfJowBb2FzLU3EbnjpnhukivABMTsuw3vKjJm+1MDMHIZURIvaV3LPMZt7mc/YjzVwk//9yHnbmLOp3h8OUg42wdnfg3D5hBm8iTQVqF110IkJU8Hl4NEIvBRvsdm1oz2aRZKJdOpfjLrdRSh8YFI45OE02aiHFkLEWW5CDqWQdE8tJbToaUbCQBfIKJGNxOJyQ8g89nLRPqjPMIMX4PWYxsyoe4z3hehQ8kYtC6TEDbLeG4nUqAkTdJe46Db40xzOwMx+mSGzHY7ScOdjTSh/7efOUiUtqtxv0lrlA4QfUiYXm/4bjdMjyMh6XpEE7I0T7+shY99HmkK7yVcRh4jolwXGt9fRprYY8j8ehiO/hoummi8N/r/OmAPvPM6XDAPCRcricoj+b5vA/zyUfjY1USGgSSAHvfYRhhXm4iD7DSiHncr2j8/Ngwb0XruQ7Rhj3FxE2HC7kNrLR04N7rvdKCoNa42wJleGPY4GX9dyg1P8mlNGtrkx7oJHSo6Ped9RGDHV/zMCURfVnleZrjNbM/lo2idbUGHkaf8f6vxczGR2L0aBTYddLu4341w5kUYNh75LtajvfUIisxvN9wtHtufGxdPoLWYC2e+D8MeIQ43dcCW86cBvJz3VwP4CkMawA/qGhIAz3X9aJiEoC1EFvw1iFimk/Ma6LkT8h4lHJr/GOgpg8IPi3G1EJUmRqFouL1o834MEdWRhPN+EVEhJGlcxhM5p2qQgHKlYDp5q34e/SXgl8A7wAWIsb5IVBN5BzGH1+HXp+DDfwj85a/hD1vEzJZD28NQ8gX3fQzeeclBEJvc9/VEdv7vIULZR9TkTE7vWxAB3ofyI85FhLEPTtZrkxd9ihAMr0XC6DWImJUjgvs9j6McEe6lKIFq8rHLR0LdKCQ4dPie44ZlLtKaXY4K19dKVv531xEVSDbBOwfggjGIGZYjJjvbczQIpza4MsESxCAuN147kFB4NRJCDyHBbAl0rNUtk+24fvJ+GD0eCT/txufNCKDk07mPMDHahJcxBeWjeSomTOHbEZNKJs2xyJ+qGngV3n4LLrxbY+dzQAMM3g9Zd7ufaj9/HRJIrkHCZhVi6L+vdZFxzG9HprMaxCBbPQ93IWY4y3NRACfvdBm8uZ7bOZ7bhdD0KJTfQpj5kwm2TLD+shM+NgcdgIqQgFjpNVFq3C0kTI5J61zm+dmFmO0MtB5PIAZ7k+e0Gpkzt6O9edD4nUZUe+ggql2sRf5styDmvM39Dyfy6NX4+81IOCgznO3+70HDewJFKE9XHdm8eWTSP7Hda2EmWu9jDG+dv7cYtluNkxK0hisN63eREDxomB8h0uKUEnWgq4DD8pstAoqmIeFyMpl0V8d+AuMeNm72eG5z/ezTcOoNGPl54y/b+Kv2/C9F62EhkdJklWF4kdCCL0F7f49hazUuqtEenkRG2P/VTvjIMrQu+9A+HUtUWdmEDrPVRFBKnp6lRvhWGLn7TQeHUuNkgWGpdrs7fW8fUev3KnSYaEBa//m6t+cuyHvgrDF0E2lgZrjNMQT/6IZXtsHli4BC2L8BZt1mWOZ6LK8aR+PQWu41DL1ETetsJCT/V+BjQwLg0PWbX0MC4Lmubw4TEWpCm3knEsSSv9FC4M8Qg1yPNv5iYKAMnq2HESPPB9S/2fXuKbh+Eoxv0ek2aXLyEKEfS2gA9yDtZxEafzLhJgEwEdfku5Q0IZ0Ih30w+DJkrXA7OYiYt/nZGj+/BuH0GTj1iG7LGoWI348IoTwRwCOEmSYxgsNuI8vPlSNhch7vDQQYQMR0j9rp2A1F1xERt8mBvRNO3gejLyaqwgxHQucGJOA0ICYzBjHxg0RATfKhzCNyQ243fnIIDVKN+91N+DJVIoa1l/C7SkJ4gfFXS1Q8SP55b3r8+H0e4YKwCPmoXmS83YWY4W7ko9RGJAd+yLDMJyMQZ5IIT3SbJ4zPQo9pGxIm24kAiyn+fRGh2U3mvf/ifqbBqXth5H1w6i4YOdFzl0/kh0taoVv9TCERbHMlYpzp8JRP1OftQu4KE93/bkdcHyJyMn4Grfka4iDS4fHWEbnrkolxGSHAbkCC21TPbb7Ht4jIDmDTPy1ERO1GImgr+UIOokNelwMEFhl/WcTB62Yircxuwrd2DRLSS4k0Rz3EWm9GVwUSTCb6c8JZrv/fgoSibve7FWjJgVv7ww92Blr3p5Fw/QdoP95CpHoah/xDJ7n9eiQgpv5zPTe1HuNnjauksc73mJcbPxOM60WEFWIZWoMjEB1bSSZf38lOGP15wof1XbRmetAeeoPQpCc/vcNIuC7zWPYS5tZDSPi+mRDAk9D3JlpDLYg+3IECi1ahQ+UMdLDc5bbGIaH1OFFKsJCIel9P7KexhL/iU4b5CPy33fB7DwBfPn8C4L8Gfud9avN/IHl2SAD8YK73a97+z7tOI2aeCOgNiDG8iwhvlz8n5voi2tgrP/zbIfyB4Pzwh2GgRQwk+d2VIWKTRUScdiFcnEBEP2nYmv1+FOGsAJmi2ols93lqL+tOMqlQWEMEcmxBAsYOxLRa1M7Ie9xGHeEAn42YxVwi11cSpLYSEb6thvcoYR7OM+xHDFcXEeTSBkUT/TkfCYUFZITf0Z/3+LI9vsn+/y5EnFOEnv3ljp6Ai9qBw3DmiM223X7tU7u/+jZ85CbPRSsSBJKAsZ0QNkcY3qRxm0VU6+glqms0Eo7taVyJaWwhE0DTdg+UTEFMZhpiNDM9nucRY1oN/TMhZ85ZeEuCT6lhOm1c5qPDz4+IygbjkGCStHApQKPR8IMYX4HhtC/myIsFz8gb9J2lHlMx0qIl7W8y3y1HkcUpQOEq4ZwHoP9dyPk04Q7Qjg5xaEw5CzzPef7tVc/vdCT8pYCIFDSxgsjrVu8xP0LUp61BDH4CIRQUnzVHiz3mcrQGkjtG8gvciQSAAnRwWgQ5W1EWgj7DvxjRnbs9D2V+/kWi5FjymXwQ+c/OJ7IEtKNI3H2E8JLGMty4A2lst6B1lQ53X+8Pi0YbWk9H0Bx/wXOxizgw7EYWDwjhch5R9WK2YapH9GCu7/0Bmvc8It1VM1Hi7hYiNcsg4dJwEAlbrUgTWQajZ3rsOR5zEnaLkfB30i4oH/Y4kvvJtcbneo+zHNGmsYZ1EhLqZhEav3KUzmg4Ekw3ELWt5/q+ZjS3sz1vTxnetUiwTEEljxBlKJN5PIfwh75UOPi9aUTuz6Fr6PoNr+HnG4B/sVfSArQis8ppRCCGo015HAlNk9DmHItO0x87H8D+E64BwidqMdJqJRPlC0gf34OI9Cxk7kw+N/WEaTuZbJKT8myCmRwjNDmlftUiYmhfrZ71cOo4UTavmkhcPei2kx9QrdusRAx0r/u5yDAWEtU4shBRn0imvNKvdhN+S4VI67ZQffT/gkjwW+D+y5DgW+pxJU3Sq+iQcJowFe0CFsFFH3d/8+3vcy3hV7kZaIGPPOB7ahCzmUlEihYaxr/2WGYj5ldF1IHNRgx1DFqrb/revYjh3Os5qvMcjQJm2Mxf5TFgnL9M+EEBNEDOFN2fqVN6wDitQHuiEPk9jUECwWTDtEXPJ382+ghTJZ67OqIUXTJrL9bvhw8hBprl8dQiZjpFc8R0Yk0dJkplNaFDwG7gVtWGJgcJwFca//chQeBpt3UAmd+vNU6rPK7hmiMqkdCYDjvtSPhYjbSoVf6cfDEPofVwwDipQPObgnoOuo35RB63asM54Sz8pQCGWX4lH8okjBf582k0r2P8+6PuO2m65xNBIZsIwaobrY+5bruBSGC/hsgCcAwJUJei9ViG5rwVCd5VSOArNt5We96ajd+JRODPrUR1lDwih+FS97+DCGTL8Xy+4e9ziIjZGvfX5uc7iWogIKH+ICFg5iK/0l6ipOeg8Q8MW0Ekgh/lcSd8niB8jEcQFZ/ajSf8PY1lJ3EobkD0YAzapw3IRWGB4djnsV1DlCLs9yvxlVbD+VnDVoPWw595Lm5Ce+k8XoPv82vo+uCuIQHwXNccwuSVDafuQ0RpMpGK4TQ6ZSeBqY/I+/TbciVfqqsQ81yGBK+7YfB13zMVEbA0zkIUjfewPz+PmHAuInzJVNZMnFwHEQG/GQkoA/6tCejSx5Ez3X6J+1yIcFyCiPqzbu9mRMxvg2MPn/VfjWEZIMpblSPN1IP+vR4+Mg0R+c8a1u1ImOt2FbkBwzHgvpNvYi6hnVsP/KXh6CeKvl+DiPrfIMGjkwyjyRSMTxqIXsLx/uxggZnG42IkbI4jzOndhGayDAkP5UTU+FzkzzceMeIVHsMq/55NCNIn0NwfMz6e9P9vIoFrNlGeC7eRhLpSz9EsoqrFcMOwkmBcmxGz32f8LCUS5958VtujyESZT/6UYWwhU1P7p7sJYalB87i/mkiYfdztTEPalFaiXvEBpJUsPQt3FWojkz6p3f/lovWUrE91hmWacf8usQZuRkJIr/GWBe887vumIKHpMbdz2LhbCe886L63oz13Gh20OomUUzOJqjxJkElmzzIkrLcZvnfRvF/q+xYjrdODhuuo7230fH7R74+6vevRPs4jtF5bkMBW77nJ9hiSeXraWa/kTvEimZQs79yDhLe9hDtGO/IdTT6tW9Ge3U/U4G30nLzgcR3mvWXk2tAa2k9ULXnec7KJsCqMM76WGi9vEAeebR5HWq8VRKm8uWgNPAwcgl+th/31vie5a9hlJFO9JblW5CF6NhwJi1Ue+7VIMJxHpB2aRgSdJc39cbTuZhDa0kRTNqP1UuDf3yXqcx/kvF6n3+fX0PXBXUM+gOe6fjpMjG0f2rBL4eTFMPoWRMimAgfhZxvgk58nohbvuxhuqTlXq//yrutnQNYbYo6bieCDEkRoqhFR7CLKJR1EBPFd9FwTwUCXIsbSR+TJSubdEkQUi5HQcQgJKLMJB/K3/J7MY2WIebfAqTUwMhuZQXa7r0eIFA1zEJGcSqRtKUHM503E6G1ezuQvnIfMaWWE+ftVIhp7FGIu3zEekhYh39+/ixjMDD+XfPBqkGDWgwIw9hNMpB0FAvx3ZM4qRIw7CZ6p7TokSCfTUBkRZZ6FNAspAGEp8pUqQ1GNz3psjYZ/kuHC783Q0wp5U5Ag02j4vkf4ee42LH2ewyegZy/kXed569VzbfdASfICX4YEkI2Ej+ZEt1mJ/PZucV+PaYz9/wA5j6E1sAoxy2wkxHUa9gbP5asoOKXLc7MRMfuEt37ofwlyHgS+ZnwUeXyPECboauJAk43Wdonwwp8SuQcfJnw47yc0hLlISJqL1tIegi7kE4LRLtj/Bsz6lPsqMexz0V4ahdbu1WhfrDdep6B53kHUlO7zf08iv7oTZA6omeTtc4HH4WgvXHQxOkTZz+6Xj8LHVgsmWpEQeaOfm4b2TjNRV7qKTJkydqI5LzTcqN1fboOPPeL7N6NDUS7aoxM8f8PRHvkBIZjVuv9OZOp8nvADTlaJ9egwUY9MzKPRQfVd5Hqxg/C1tHBNA1HXt9VzcwLtm3ke+9fR2h7hvp4k/EWTr+UO5M8Hom87jI9kUdhDlG7sJ+pWp0PFXCL5e1rrnWiv9aG1OAXN+Xfdx1NAEbz9CFw4Ee2FZmh7Bko+jua+AB0YRxm2RxH9/MH58wG8jPfXB/AnDPkAflDXkAB4rmvGsBBMytBGfw0JBvciYpcIcFLB1wPfuBju/O0RAAdmzCC7+I0oK1eKCOkuxLC/hRjwDDK5xbgDEa8HEY4GEJHfi06r8xFheoUol1dPaCcaETFuRUSuG+F5GmIMNb6vHxG9pE06iJgHBFOscvtpjmYg4vgQmqti//Y15BxvIgtEAu1CwvemDPYfgVlXu9+k9TuGtAhXIMa0zv1ORxqWcrezye0mjdE+ZMb9mmGZ5HvWEUIdhLP714m8dTOMi6lEAEHyvUoMKTntf92/JXP0cMR4krvCVo9nHWLOTcgsWkgElPQZ53Vorv8LWhMtRCoRiDQxBYjRJkEqy7hKJrcv896USGvg1O0w8sNIEJoJZ34okLOuNrwnENP8S7THRhu/ickPokoRFyMhYLjxXIbW4k7jZTMytSXfsUpCszcFrauxHkMKRgBpYnLQutzl7/MNV5PhTj5xw9F6Wk4kVk5O+21k/GbfeU6gj/4UOuDcgTRcFby3RFo5Sl6dgidGua2VaN3s9vykyNcCpN2+0c/nE3Vou4hUOHWENrvd7R4itGXziP22Ca2/425vlPFuFwkeQIJdgeHabTwe8RycNOzbiWTHyVpyEB1UyogAj1qiPnYxmtPVxsFuREP+s+foKSRA7Uamz0c8P5e6zafRPK/w/LT4VWIYmtBaWW5YLkUHiCVoT/R4HMWEoHbQeEqCfiOwH479AsZNcl+dxuFClAWiwGMr0b0MwOAGyLoJKIQ374fpTxI+yPt8Xx4R5DXGOOp2O6M87kGietMe95cP/KfzJwD+Lu+vAPhThgTAD+oaMgGf62ogcqQlf8BbEUHtQsLFGLQhE1MoBf7hPMD6T7iyZxMasUl+r0PjfwUxmhuR0DQNEebhiCAvQvmpXoUz9yIC2kAkvZ6FCPJytTN4GuEumUKTwJfMTE2ImC335wO+fzghZHQjBlOLGNyA4ZyBiGkhkRh3JmLEybdqG1EVost9Jl+rS5HmLB9mLSIKy7f52alI6HiLyL+Wgxhe8oOqRcz9S4gpVCIh6wmiWkky7Wz15zFEqcFJMHib+7vI468iIlp7EGNOmqf5hBnpAb+6EFMsRxqr3YipJ/NiGnvyu5pC5HWs831FRALfLsO9mjA3TTCuXyWqijQTJv2DiJEnrXCfcVELIxeRyRMIMGwsZN3mOS13f6vcbjnQD6deJ4TncUhblBh1IxLmSglz3Faiskehx33oLHy3ExqzZP5vg7c3EfWWt3gsvQQDzkVrqxsJcM2elyeIRMjPEGthBpANF1wMo7+M5jIdeE6juexDguZzRCL1GiL44bDveYyofDPqrH5rDdfLaB/lGiethMawkcgvlw4GY9zGJN+71TDku58UFPRZtB9b0P5JAmYnkYYn37hKtPBVt1mH9sj1aL1d6XZqDMd3/H0lkeD7brdV6/YWE0E8hWe9P07QYQzDUiSMJi3gccOcZ5xdgg4xzYTGfyZa70s87huQFjsJfsllopvQ0l8O4xYggTk5rT2ETOvlhJm+AZqe03iyxqN11QzTL0ZruNPz24P20wh00Bx71rwlt5DTRM3nWiIJ/2bOuwl46PrtvYYEwHNd30abrBP6T0BTK9L8NCDic4woizYcCTNVhGr+t+X6XSLR9XZEXNJJPQv5jWX5nqmI0d6JiGgBYv4VMGwJDO5FRHILIs7JZ28+MFtanjN7Eb52IIbUhDRktUQlic1ue4meo4KootDp/6cQTO1PiKz5yQF+PSL+XUSAQhViNngcy4kKJ8eIQvZ3E8JEHpHgej4yZxYa/uS/VoYIdC5iKMkstw2tl7m+r8f3/SkSOPs87uTPA2Rd5mdHIA3W5e67Ewlyw1F1lm4kODSo//5NiAl2I+axgwg0wDisIiJriwj/uPlozscQpsYjiAEtIkyAuzyWYrc33WN+lgioyCIqa1QZ93Pdzg5gvErgUWf8f9XwJU1RJfI/3Iq0IvscGXyCSGHS5/83exzZyP9sPVpPBdB/xO2dQAe3fKJyThnvjeRdKbx1Q0S3p31cizTduUT5vz1IuNvl8ZYQtXHvQoL3SsJHssqfC9AaOIbWSaXnAnS4aPBvv28Y+z0++zzSAmd+jISrxwkXh82EANxJJGnO0bgyAUP5vLeEYg+RFmg2WmcDiKZNdvsvEJpQkHBUiNbmDMPyIhJsLfAy3XN0wGPtI3IrJq3WW2htHzY+G/z/AcOYtISziHrDC4k6ufMIbf8xIj3QIbT+k5vKKiJzQPKT7UB7aaPbTkJsrvsArb1d8PaPiVQ1PeiwNg3Rps+gdd6huWGTvz9PRlNYvgDtr79yH+1EFY/DRG3n2URN+EPGRwVan7XIhWWacV/n+xr8fS3n9Rp8n19D1wd3DQmA57paEZGeBDnXQPkX0KbPQUQ/i0iJcSPanD3Q/f1fQ/+p8wT0b3i9ewqO/1pmsX2IOM5ABGgbkRamDxHdo4hBVSDi+S3/Po7IBLoLEfcKwuH+Ibf7ORh2BfzqYSQAZcGpHwN/i4jji4i4zUME+A4iz2DjWXDnI0ZxkT+XIa3UXMQ0txL+dFcSCWa/59/K3M8mospBN2Ke+xHBXeQ+DxJU6bjH0w0Hf+zPoxHjbUJE/T7jci/SJpS4jTs8jmS27YZ3NrntNrQTv460h8mX7IDaHvyJ8bsQMeelbrOSTELonNWEcNuo3xgk6k23qu+Ol/3cRuOp3e99SBhbTAjczcZRYjzz3G5iyNOMr1ZgJ5xMka4zCBPqbsR4dyDT7R0wciwSMJ5Fgs1p4/9VIt/ZcaL2dovHlbTvy4FqePs4YsqrkGZnBWK2KX3NU4a7AZmkpyLBL48QJp71HJa6mESB5y+Z/6f42Rq0xiqJMmWzPd4uIoL2BpRmZT8RMNBI7JUkPK807G1ov5UTZtFsjY/7EM2ZbzgmwrA7kUA7G2msF6D9V4cEr0a/t/iZVmLdLUdrZx8SrNYRlTaahbdMLd8O4YSriJRXI4yTCs9ZE1Gj+AASFncaT1OgP62FdNCBqGP+mtu61DDnEXkMkwtKj3GyzHC0q79Tu6HtUSJJ/HTjfcBjqIGmQ2j+U/RtOsTdh9baV4nI5TZE75LvabnhGgsXfhze2SZ8nvk+UXpvgKg3Pgv4T36237Amv9dstAea0PqY5/ma4TGXIAvGcONxMkFLT7v9sWiNXEoEwQyHpuSj3sp5vU6/z6+h64O7hnwAz3G9M2wYFyQz2AxEtDqJgIe/RJsvaS3wPd+A3pFljLriwyIMI4iEuBOIclP/AHwIVQEZhU6D3TBQA9kPInPMR1BamSPAv0M+Lxe4z70oDUUy3zURJqN0yh+LCHM6WY5FjOzTiED9w6+htUXMYg4igmW+p484hSe/tzwkyHwDEcJioAd+eQQ+dh0iZq2E/8pSP7ePTFqDgz+BqWvJOPZ3PAxFYwX7YDVkLTKc6xFzmWTY34WfV8MnZhK+Vp3AdyrgvzRKoMkyzMuMq8RwE5NOJ+p2YBO8XQ0XfgEJNVUe3+1EAugWIj1Krdu7AQm4h93udj+3wHOcjRjYDuPjJuQXOc3z3KpxZ6Jhm4loyiNIYLgRmfeW+P8m/5d819oDn2xDTHMf0sy+iNbrRr+PhXfuggvGI+Zy1PAOEFqb5CM26Ofmu/1vIAG6GvpbIecL7rPa9yTNyS7E1PKRX9/DMOw6Iql4ciXoJLS2SVN0E6Epzfb/yZSYtCBlaL1NRYyy8qy+N3tcfUjoKvZ92cgPNRut/2VI6Gt1/+W+HyTs1xO+vDPIMFmeIdKfTER7K7kNlKF570SCxHrPxSOEpuc1pCma7zl+F965Gy643XOWxlpHlGKc/f9r7/3js67OPO93yCbEpIGYTCQTTEkDFJrCRFkoymARizJYKoPy4NDy0Lq6WlfH0bV17HScWp1Wa+vq2rq2PrpUBmV1qDw4VIumOCiFUtxoGoxkgDRphCeYSQykwZhsYP/4fM59pTvDPNt5UZkd7/N65ZXkvr/fc65znXOu6zrXT382znMYRZjlp/vZnURi5/3wo5/BH7SgWsZ3IrqwzvA+iPZmL9qXh4i0OUfRmb4L7bcxZATa/etg4v2ET+oxz6GdqPrTZxxfoXXsfA4qKoGfuc9rySSCZyLaX8NIuNzptfue3y+Gzlug4lxEOxu8bi3w9qNw+pUeb7vx0YEErhRZW432w9eRcLbGuJxlWLeQcR350RvwB4WEkLyESLz+GtJOJnP5Os/xTc9jodegzet+FpEceyvh3lLtdxehy+dN6EJVCCyD47dAzsc9xlHof8oVRWqA76ILTYnXo5mIal/uvXCD/78S2H7qfAA/xsn1AfwZWR/A96plBcATtak5IshJxb8PHbQCOHInjFlAOP/3EAXRJxG36FUocGAPIggP+fk6dKNbgJhpDeGX1ejPbkRC3ljgKRg8CvkXEfm5UsTkZiKdSB5iZMsQIbyKcEZvJyomrCAy8R8g/NRyEZFpJcq+1SPhoBMRu6uAvTB4A+RfibRupYiwJ2GmGBG9TYggViPG9T3EfM5GgmkVUYpsHeEztAwR9oc8t6s8xrcIQr8EOX1XE8mmk2mriai5nBzVZxrnV3guJURt4aTV2e35fxkR2QpCC5ECDQY9VjLdbSUctwsJwp18nWr8MwMJVLMIX8dq9zETmT2bCJeCDiRUFSABvBmZMwv93WFCqE+XkDTGWL9bqe6PPAVj1qO9/Lzfn42Ely4i6vQZMhoU8BwPIw1hM9JyFJPRpA7ug/yrEcOcg9b7YuAj8M61cNooxPSSkFBK5NKbRyTxLUD7dhxa/07ExB8wTmYgpr8Y7ffcEeOtgHdut2/hXMO53nO/mAgUGvZcBoz/xWgPVhj+8cZBBxEkUIP2QoL9LOO/F9jhKOpngCeh+3Eou2zEWnQgweqThDtDCdp/X0RC1VfR/kmm+WEkIMwlauqepXV7/SB8dBLaW+vRXrwCnZEkkFR5LVMQTC6iXXXo3M33Z3cggagWBr/viOl0aWwjtOdJ8B0iArqGiLyfBcACePs6OP1edJaK4ZcvwQevI/ILlhDpbJ5EeyBdItIF+QJ/NgrRnoeNj1mE6brOcM02XP8e7ZMWJNxdjej0C2gflXvtapGbSLqQAxyDd56D0z5mON71mjUbx03onC/ymiTzf5/xvs7PbSDqli/z/ArQ/lprGD6L/HPPRbToq+gMTfYcl6Lz30iUjDtkGHoQTUr07SBxJpJP6xjg06dOAPy3nFwB8L+TFQDfq5YVAE/UvpkjwjMV+L/RQbwFMdG9MLwPcsehHE8g5rGNKCm0GQk93YhAgJjWTsRM8tChryFSjbT62aeRmaeXMCF0IgY8DgmG5YggbUQMqxkJcwWIme0nHJ3HIyH0Cj/XjgjbM0TeutWGb77HO+ix1iLT3S4iinApEbWbaxguR0SthqjCcRAxsTzChDTZ8zpMJASuJghd0nDUEGbfYaQRXYSE8ORr1oCI6puE6XA84fe30rDVEhG5VUSwSJNxuZRwnp/htVtIMMNkzu4gIpIX+/mniZJh7xIJZosQM5tDBG80jOgvretSIphhm3DQ9gxUfw4JpP2IqS/02rzotZiMhIYl6NKxzfM+hhjagOc0Afk03uQxXjOOagjn/cPoMnKQSHT+CuHT9QrhS9iHUtiU+O9vIw0eKPfgeMKnK8/4TnhPPodJeO5GWpov+f88r8kh95U0VZ0jcNVMVBdpR0xzNJF4eCs6O11onZO/1XyPlXyo0l5eYPwPAd+B47+CnKdQ1PZZ7m82EW1c4LmPQ+d3PnFROwcGH7BAVYwEk02INhSgdawbgdOlRLDDXqQhKzd8oz12l98tg3da4LQV/n6+55WPLiUTkfD+HSLP40KvTSnhn5q+6zN86RJZRZSCTNrVIbRny9AF8XJ0eetDe7MSCV6Jxhwwzgv8bCGiHwsIATXB/yiZutE0ISFmFdqraU0a/Vkfkc6o3vOZYtzmoUCVdJF4jahAs4SohlNpmPf4u1oiZUur+8tF9PAe467U3z/s30nTXOf1SmvVgWhzHbq01LrPUuLy2YvO2TgiXU1y85jmec1EdA7iTOchwXW7cbfX832ESCe0C9iUFQCz7TdvWR/AE7UqRJzuQYdwFeEzMh1y69DhOxMd1mRG205E04IYzcfPgy88A0sPKF/T6CVi0mMRM5xKVNBYhb5LjuMH0e14DGLW2xARAmnAihEDKAeugbY3EEOZ7jkkk24JEpTGEOkOFhPBBckX5h5EeLvV91u/8LhJIGtDwmITIl5J+9aFCGuZ8bAWCR/FiDBXAodg+P4Rzydz+LVI61HsfpPJuhjljFuFCOONI+ZfSphRkyayBQknezzefUQJrQJEeAuMrz6vzZ2IsTzpseuI5MibjYdmIrp5IyL4a5GJ6yrjuMDv1BiOM4n8gUlrMn0EnhZBWwtRVq8T7YGDUH0Zocm5BTH7pD1r8ryXoX1xwOPNJfLdNRjOrYjppQtCOWLUYw1zj2HCcKxGAuQQ2oNpzqVEkMMkMlHdFHicpf68CDHgPOA/IZ+lRjJRt/2J6Q8btkNoX27R93zPMA4SEeBTiPxsJUSy3RKPnwSLVq/p5f5/KxIkKj1m0uCWIKFplr+rJqPleutXkDMJCSEVXsMziXx2GF+LjKeV7qfbsPxXyP8M0rBt9TrMI/Zal+Fd5zEfIdwtqolcfHWET+G/I3OpPO0jaJ+C1jvf4xZp7EyOyHVojy4lhKU843sqGVM9i9zXcsKXD7Tv1niufYZvGREMlOhgD5EbbxdRDajAa5RnHN/rflejs9VCCNLJJzYJ6rMJrf5s4pK7jkg3VYXoQBFa73Qu96LI2NlEFZNEYzDMaa80E/trtudwEVGnt9nzWYPOazlRV3q7YV+ItMznoX3/LY/xLLqA13h+7e7vbKI2eKK7ezxeB7qQF7qvJJwmeJOv5jGk4VxFpD4q55S245w8/7+sNuq9bVkB8EStCwlD/UjLUYxMN8khfSwieokJbfP/vYhoJLNFK5BbBL9qhPrr9G4BMuPMQgLeIDIdXue+ytCtcywSjgbR4Z+CiM92f5aLCGAuIjbbofo2RKyryETfZlJ/5ElDwXlEabReIkExRGb8csFxxucQ0d9ApERY7D6GkYZhEiKc9QQRv4kgTIeMm/sh9wPo5t6NiOA5ns9cxJzmIIbUQAQAFCBhd58/b3Qfo+D4Dw3DKMTQlhmnBxERxeMkoW3IsE4A5sNbVyJmdZ7H+yOk2Vnr98Z7DRMcC9xHcobv8u9ZyByVtDyNHqfK8+8wvhPOy6H6bELreJjICZgE5kqiDm2jn/0L4I5C4excv3vUP3sN3x5CMOlFJvYJSFs5CG9/33B0kNlng/cjoaCbqMpQQgT0zCMYcfJRW+Q+/h0hbOcb5j9DzPBqJCTfBEXzNW82EqXvVhnPm3zZeBEx1sVEBZG90P8S2v/7kWB9kCiRV2wY+wjNbotxNhmdp3QROeRxk0angczZOONKovpCusyMF2zkIoGo1v/v8VqVoDN6J6ITHUSQVBLqrxjxd6v7f4zQ1HYgIXit1od64rx1ISHjas/1/9Uzx5d5rdo81++hZMlDKJDochQtvpDQ6j0BPAlt9/u7PH493VK6jNzt+T/gOe4itGR5cv+g3Tho8eefNQ7G+PciIvn3MrQvriGEoQIir2My0R/Us50PoD1UiehqOwwfNZ5267kjaZ/cRwh5bcjnspoI5un1d0s9Tg26wKQLYY/72I7oyFREC4qFK2agPTXZ7xSPwOc9cPxWIpjqAiJRdAFxYZvmfrpQgMgstNZr0Vnb7efriJRJbf5djrTBSaNcbJzUozO4BtGdU9iGT/JPtr13LWsCPlH76xwRvleI4I1iRJiTYFWPfITWEaWaBohUHIkw3IKI6SzgiePwwz+E+zdK0/ASYg4NiEDVIqJ0xldg8R/CN++FP7sTyk+H7c/BY/8envyVUomsehEONUHbMFz0WTg2CCV/Dn/+BMz9DsxdBm8fglv+mONP/oiccUTurGR2noBMWHcT5pU1nsN+dNv8Y6IqSB0SkJYiwvMtREhfgcFbIb8UCXBrRowxnWB2TYhJHSQqGExHmo+rECG+GV6fLR4wfS4S7q6F4RWSW8YsJ4IuyhFBbEaM6GrDuZPQ/M3xPCF89V7y+iS/sQrEcHIJH7IuIjITJNT2ELVtWxBx3+GxJ6M9s83zRXNhPWIqDSP6Lhgx3zw/v5u4YAwYx3vR/krmzJvIRP5mfMH60OWhATHa7Z5z0mgkn64Kwpz9JCEwrSSEwUpCyMVj9wDnKgVJziS/fxAJNo+hfb0MMaofGM7F7vsBYq0vRxraZv+9DJ2nsSNwnS4fjSNwMUSknxkgtPLfRoJTCZmqGxmtdpOfP+xxvke4J7Si/d5MpKsZj7R9z6E9eYvxdRu8tU3oL1uAtEoLjbO7PI9+wfTWG3DGUqJCywDaO3uJijYvej7nGp7bDU8hCjzo9HM7kUBRjMy6tehA9BKCU57nMwtdwurQfksa/kUeo4gwxSZtdBKYO9D+HUB7eSowAbpvgLKZRO7D2Wg/tnvN65AglXw3dwjnPz0E53wInc2Vntvnke/pTo8xFwm1W7y+O4havWu8Djv87jTDdiHa89MIH8tyr9HTXodSIphpJ3LJKEV7/ULjo9B43InoxCX+eyeiZ1vg7R9o2qcl0305kdtxpsdeic7bpf68lCgWsNDvbILXH4ePnm2YK9DemIVoaj+i462IF6xCeyUJmrMQnVuLaMP9hnmj17kIWT8mAc2nzgScPIFORhvGVSqzJuD3pGU1gCdqJegGW44YywWIYH4fEd5jiPg9RPjBJfNBOyIS1YjJT0cE92z3/Q4igreiFWhDDHAnOvzJZPu7Exle8IewdTF8ajGcMw9m3SohYAscbwWmfVapXK78GHzg23DsIbjir6FlO3x2Bux9Hr77V+R84zQRuK8Dlc7Z9zJihPtREtNawsdtHCLwLxMmtQmGb6nh/b8QUb5deMhf7vm0I6b7WUKAeg0JLEXGzSjCV2iRYMr4tD0AH/0kTL+T0CK2Q24pjFmKbtM7kbC1ARHSpA27T+O98KJxDrHLC5FwdKfwx1cJc3O6eqZ5fhUR+TpEiPNQCok93gcPEdqlcn+WTI3P+N1KxGCuIoS4v0DRe5sQIy03fj+HCP0wv57sNmkHKvxsgWFciwTtCcZfq1PqTPPnpWS0ZG8/7nUaQMLpXWhvzkCC6U7CXy9pJA4SSbqnaR1zbiMEhaWGYYnxv5ZI41ML1MPwUqJ+8V7j91LjrQqZBpvIBArteRzeuRNdupYQFVvyPM/XgL9EZ+dLhm2f51aABMI+z388UfruEST83IAEmcXG/6Vekx4iynMKYvYPo7XuhTPuhLKPwWA9Euz2IgZejoSbdq3xGZ9EdCCZ6ZL5tNLPJvx1CT+85HkuRVq7A0iA3oFowFZEI2YQJuzJxlk6p6DzlOt+V/jvOnS2VhI5/zqNh6meXwsR+DXKOOvQ2GXJ7eBmdL7nGZYuwqz9gGFcpnk1HXKcxqXW2BW47zXGZS06S2tQAMeAcbcUuXg8iS4y44ko1xn+aSPjBvHO3eg8rfa8Lkfn5kbjYxPah3lIuLoT0bhmz/tbaL82IXqUTK3oknP6JxUc0r8b0eEJRLDGpRpvz51ep9uMky1es6OIJj2g/z+6gEi0n+dxH0F08VKCbyzwWi3wPLuIy3cFutjeYzjSpe9hJPwl680palkN4P+5LSsAnqgd8e9k5qvXzZAHiaztw4gI7EFEqAEd/DYkDFQR6Skq0I0UJFTdQyRbPkxEsN6BiNovAUaR+9rn4G9eh7nb4KW/gvM/IY3AXMgpBF5thB99DXL3wWN3wbsD8Mu/h22PQPk+ePUOKPkdmPR7QaAKIHcmYv7f9e92RIh2EY7RexGDTcLgZ/15o/GSnPfnEUz9As9jF2JQN6BddsjPdSOBeDERKfsKYpqdyARW4WdGalOHEXGcQDCTXMLnssq4b4S3vgwXXul+8Ng97mcWocFtQBqZauj+IeGjdYHHSFrE7UgjhOEsRMS7mEgIXUJG+ORR43IzwThfMB4eBp6Bt455/DzEyJK/1Gai9NQiwkSWBLReojxWO5H2pBlOe8j9r3cfu4F34fQJXuOtiBHWIk3cOsQs93pN9xA525o99pXGVTK73kBEgCZzewfhy5X28cuQ+xm0Ty4x3p9AZ2OG3+k2nO9qDad+DU670eNuRPthr2FJwsqXjItNRB3qgx47Oc23o8vWLI89CjHJ3V6feWSEFr7suZUSQSxpP7cQyZBLIP9+QsPYifbTBOi/ncgNtxgJyJ9CzPkgIbwn7eu9RCLvaUSZtPFEgvNZaL8n94Ja912OhPc8z6kGCX3r0Flejdo8tDf/DO2jC8hEvnfeSAi62wmTaTIJVxPl3l70HA4Yl+lS1SOcD7aQKaM2fQGctkrzzv0iEew2ZHw+TZhTj6Jzv5MITsr1Gp2JzsY646KKiEZeAqfNR1q2pf5sk3GZLoYVSOOXh/bSauO7Ee2nhUjA/BI6K41kyrbl3IvcUsqg6DoiACldVge1FlMXobMxkxAutxqvUwi3jA4kvFUb1irDfTM6x12InuwnXDeS2bnKeF7o3wn3zUgb2Ot1HU22Zds/q2UFwBO1pMVrQcRhAE6fjw5vMZF3rhcRsgWIUeYTUcBt6OAeQsThEff9u4jBDyAz5AHCv2wRYugfBw63KSyxND33/8HvniGNUrXHGv65YN0O/PUxGOiGgSYRm17gvkMac9cZIrRHEJNZhAhVutkWEP5oe9Ft9Bp/fwzdausJE1Ifup3mQv/XEDMvR0y5zvPsQAT5cURIn0XCbq1xNdf97fG784hozg1IiNxLlHbqIMox9RBpFJKgNk59F0CkGElO1WsR4awktGPjyAhvZSv0/IFkZqv2Oud6XQoIH8gC43EPYr7liDFcaFjaEFNpN7xtRHRzoVBzxirkL1XisY4SEYXT0L475M9r0EnNc7/XIg3KQa/NY35uHZGTslvPv1NvGEYRfm+zibx0eD6zCL+rPMJEmFwf+vxZ8mdsIyq+LPZ4A0RgwVTCh22H/67wGD2e32yiDu5WtMbNxncfHH8RReLOMK673E/y7VqLGOE8QoDJRZqkXMO0g4jW3G74C5FgU2i8dZNJ7cIgSt1U7P7KDFeZ+33SsF9PpsRgUSESWCYjs/i77u95/y5A63+530lCVQ26tDzt/kuJajP9RJLw7cbPY35mKZGWaYbxdS5RM3nIa7Ta49zu51uAe6HidsOy1PMYcB+bR8yz0TCU+72jSBAZ8GfDwCWQf4PHmgNchvbPu5r7gdsNzzASRhcTQTuXEAm0t6G9UWf83+0xVqB20HO90n3VIMHrKq9RH9pvNxMm92HDUoZoTtLUD6E9WGGcJHP4EXQO6/35FONgmed9Obq4ryE0sDuIC+pspHlOsFyNLs3LjbNNRADZ4x63zO8tQ5G9FWifl5BJOM9ktLdeNlzH4LVD6OJygeb62s84pe3YSf7Jtveu/ZtTDcC/2HYvIopNhFn2AmRmuBbdEncRRPs5RMinosN7IRIwehCjyoUDB3XJJ4cwC+1DDD/d9qagg/4qMGFI5opKpH37nePwb0bxzv1w2heBjwJnDIUWaBfQdxwODmVu6BmzyNhRIuiNRDqO2SPmlsw1PYQzcgMi/lciIrmLiP7Mg11PwayboehBwhxVjwh9unHnEbnrzkLC2FIk4FUalhJ/v824qFaE7IEW+P2l/m60n1uChJ+kcaxBTGWh4eqBMROAVuh+CcomIEGzDxHhZPJtVYb/0z+GGM13gFkwPjmKb0TMNc2plzB3Jh+vXM+7ndDgTSdMS1P8948RQ3rK/e4kTH/tXockPG1GVHArYoYFSKM4iShxNRmGj9nvZjuhIUx+lpVkLgAbgE+PEk4Zj/bWQSSEXOU1rUGMZxGR8LqLiFxtN8xz0SVmMmJ+xR6/wrBe7Oc70F7qIoS3rcbJXHQeqoy/C93Xi0h4akBC2hLIqRkxRpVx+yTSZhaiM3fYuG0mIsu70VltJpJJ2zR9fAPkTECBMe2ef5Hn/LLXto4oOTbJ/U5CLbk/NBHVN9LlqQpZCKYQAulK9/MEYe4fRgx8wLhq9LqkPVfltTrmOa/1+m0TXqgikx+QNiSUjAW+4T42eT0uIFxYkqbzj4hAqibDkiJZZyOh8WL45b3wwSHCp64A7d8p7vObRJLyryMXkgLjbR7weRi/G9GTBV7TMuOuiMizmI9owqcN11giefeA8dEEu+6EWV/297Pdx38kkjzvIWjIEbQnF/h3LXTfZp/GC4gL00yP/1W0n7rQxb7U63mDcddjHGwnrD7pzBX6uU7kIrKMTPT82y/B6Z8xrkehc5mE7aRFL0VnqphIBj/TfdwAr38TPrqXqEU9ZHfmYvc5C85KbjKnqJ1Ms23WBPzetqwA+E+1WkTsJiFG8z2kschDxKAOEfpt6GBXIAJVgpx3G9GOXgRMhfHJJ6mLSPtyi/stIvKlfZNIDLsYEatkSumH01YDn8sXAf6p4dyGiMLvENrLGnSb/iISOouRMNOHGPFi5B+zh6gI8SJiclWGtY6IOBtP1MQsglmfQwT0OjhyC4wpQQS2lTAJ7iWqbEBUU0kmyjrkR7UUMagNQAtUF0L1t5EAXEZE1DUgJrjLuC5HWowGQvipBSZAWWIg7V6TFYTAcgmcbuUoD5Mx6/30B3DOze7jkHE2g9DQjCJyObYihrkdEf51xvnlRIT4HMQYCozn6xGDftrfTUZCTT/aZ5sMnxng8XbIuRGO3A9jppCpwJH7eUIbNxM4CoM3Q/5olBJkK3AmfPpjGmf4XldZmeR3RpqZNiDf1hQEUosYUT0SyFcQgSd4zcpgeJ3haDFcewgT133eC0lzmbTD6zz2AaL6xn8jSum1EWXXbJbjGLpY9SFBqAAGH4b8mciVotR4H0Aal8PI9FlCpEpZIhhyyj1OEmaOIu3UDKKiRAnhjV6P9u48tEf2EWbmfKJ6yHQkBCS/M4hKDsmMvdJ95BJpo5Lmq4aILrXFgW1EJZwkyPaji+VD6PxORsIbfg6i2k0XEqrL0T5dhs59nnD81j44Y7nXpQ9p6Ev0/Qcf9vtPIW3dQuP6SUKrV4DO8LeInIwLCK1n8m3bimjgA0Qloe/63VpCEz2TyI5QTiS9nizXZ+5DWtW16AxOHoGvIsJ381UiYv1a4DEH8GyGzjegYpLfPQbd9VDWRdCOycbrbvdVQJjCP+NxL1VfNBnuPrQ/ZiBacI5wffph4+9stObVOjP7X4UPLzJedyC6OhudlW7j8XngTfhooT+v8Fy64PQ6IpL9QbTvsi3b/hktawI+URtF+CmNQYz5QiI1yQXo4E9ADGiOvysjcsclv5RZRfCTOrjDlO4jH4Kn6qC3Sre/b6ADPQox6EeJIIlvE75So/zzKLB4UELdOURVi0R8P0TkiEqai7cN203+7lVE5EYjone1n39AYx2/DhG5pxADTsJcI2LCc4jIzDIYU4iI1ATEpFJLwul2xMy/RGjypvj7lR5jM2IkQ8Aq+LsrEXNbgASDawhN46WIeLYgplTvNTnPONiNHOsPE9F7Rz1Okd+rdb+1iIgWwTnn+rPN7if5M27054cIDUUzDP4QjhzzczcQmoEa42kz4ad0AWJerYah0393IQYxmTDNLQOWQ87ZwGIY84D7PBcJZLWIYS0kY87K/wASlNYTmsly4Xc/Kn1HF79Wxo9OP3s+YlQLsGuB/+5ADBu/d43x2Qa5dxr+m5DwkJzRv4a0i7nGd7X7Gg8H9iFtRjWRuPkPkab6So/RQ2ghn0P77M+JBN9tzrc3zrAnwSoP+q2N2f8KYtDnGo4SxNDTHmlS/8NPwZ6/J+oP57uvKhjcbTiXIeGnDzHdlUTi5AHDPpOod5vcK3YjwasC0Y4Wr90442WR13Q2kU6oGglEzxLm3y4YPEScpXr3/ywSlr5lPD6M9vh2w3ER8AS8/gBRt3m253oMzpjrvlrRhaQG0aseP9ODhJ7DaL9OJoLa9hE+dWVIsJxPpvLNkdvJ5P7M1L2t9Jzudh8vGO5SdL4GDF8zOtPrCTP0GkQz7jG800esxQsEvV6DBMVatOeSEHyL4KwYh6wps4FzoezKEeMuQ5rTz6H9MQed5f3oItRIREWnufX5mTzj8hr3txntiXPRXvkysAdyl8OHH0K04nGPm2c8HPW7w+hivhlZosoMSwPiF68YDxuJM3AK2/BJ/sm2965lNYAnarmIeNciAj4bHbYyMlGUb7XAGZsJLUMtYSJ8Cmk5bgPOmgk/+tvo+8r7xOzqvw+5V4jQbYFfvgkfbEVEIPl9PYZ8aloRcfofRHb4t8nkfzu+DXIWGZbTyTCa43dLTuR/uL/NhB/YXj//HDp5vhXTBzmVRHRkpeeSbtzzCQfuKsM7zn9vQeapZ/1eDRIKjyHB5kz1zx1EtYF5iOiPM1wL9f+HRyPB6QYi+GLY/e0FjsDwIcidx6+bHScbpo1E2oadXqODSJAoRczoJiSUJQ1sHVFlYKb7exExkF1ad5oMywTInwf524gSYZ2eTyLmyU+03/AMecydnm8uoU3pItLtFBKJcnuI9CU3I+awmvA9GiQqJRQYr8VEFY56+PByw52PNByPeD2OEQmy2xGTWUwI8bP8e5xxttWwTPcaTEWC8QV+7mHP+yha/1GIWc0BZsD4jUTanSHEWIcJIXgCEjhaPKeF7vccr1PyRTzP7xzwvLoFU9EDmks16AxMQWu7AQkCPZ7bLKADcqfB1LnGVznhr9kI+Wd7nDZ/twd1nNZ4s2FIpsnxXr8Gw9xkvKQ9W4w0NsVIqNhNJoKbo+67ze9fRSSFboD8sZ73HCKRc6nHmYIEtSSg9HuerXr+ozMMZxmRuH2PYVtsOC8mqre8ivYVSJifC/yp55wCbQbdTzFRbm8vEooOwpib0f7djvbgIa9H8vNsJ3w3D3l8CMtBMxEQVYnoRjlR3u2gcXMXkQB9iceaSaQegnC16UZnZCw6y11EqcgutMfT5ayHCLLoQnR4KlH+s8rjVKJ1f5BI49LteZWivTyEaGyyguxD53AsOk870VlMfr9DhNm50vN4GZ2Pa4mgqFloraeSbdn2z2pZDeCJ2tcRMUq31wFEVCoRsRoLZ9QhBjqFCDZIPlPFSBCaCby2lXdycqAgB67KUZ3hohz4yRVi5HvVzweXIuJfAdR/Ff7qbPV9ieE4+p/h8Q+JKR4E/p/5cOtNAOTMRYRz3ofgR/9ZcBxEuf++kAN7N0qQTL4j8wln7ukEce1EhGsFIjZV/qwdCXflHif5LTUjYvqXyPx8lt+vQFq6LkTMRvv9g4hY9rqvJxCT2EbU730NEbwiROD6EKO8A97+BSJ+JcBKb+DtcOQXiMjuQwR1KyKmOwmhYQpiJJ1EJv+0VsVIUOxAzLjIsB8mcoXNMb7mIMZxjtflWsSEcskwQCYg4eZuIs1ElfG6kajY0UQw8l6kgd0onHXfD/2PG297NV8mEOlheo27kWbIJOS2+dnP+P1StFdHIa3EEsRoxiFhdwJRxvCw+jz+lOebmPBkooTcKLTnz0aXgQL3VYe0yWktm42bTiIyfDGZYBgaPEaT3zuGGOMWIghiFyGw1BCa+VHG7QCR+HcIeBFy7/U6rkZrXEXUYO4izmfyO+wyTvvc/5XonL+stcjUTC4gTOHFen64xXMcIHILHnPf3UhDBxnt+ODDaP8UIqF2tGFr8hpWGuakUa4iLjWFxMXpJuNjIdJ8tSDtVx5xcWhHF4rVhuUl95MsB0NE+cNDXstC46GSENKSmXQmOhv70BlfRgTfJAFnwGv2gvtIJv8aogRa7Yh+97iPaR6/SLge3Odo+alEQvJ+9z9oGDDO04W9AwlGSRB/kqi0c6Fxt4+orNFMZGzo8tiD/rvU39ehS9cowz7k9ehDdCsJqjuIOtUlxudoQqNd4/dmGp601rcQQWIQyeRrDHsuEhgHjL8KQvgbQnvoFLZjJ/kn2967lhUAT9Ra0Y0uHbqD6GDu9HdvIgKRzIc9SChq8N+V8M42GP4hMBlOSz5ulUQi4GWIMLaiG+CtiIj3EYEamwzPTsSMniWIUymZHGCD2xCxT87FB52xv5/w4WsibovPEBG3Z/rzYcKXpRURlhp0KssRYXsCEb0hIgjjcsN0N1EyaQgx8amIGM7y72aPuYCoKFKGmGO1cX2W+74OaVeeNawLHbSxiQzxzfmi+hgzEwkQyTdmHhJOOuF4I1GfdxbKxVeBBKknDNOAYWhCAtgqIpqywXCNg8FHPddnvB5f8zolQXmlcb4VMZoOwZ2J4B3yWm7XGjHec0npKGbCkXe1RGULoGipnytD2oc+zbPTZvKM8J7WrYCIRt/nOdYSQvD1RBLdOUgQrPQYKThjs9Yip877YBn0X+v13AzHd8Dw44KRnYiKdBHm3mZ/1krkqysgY1pNPnLvPIzcKxYjgSS1HnRWdhJ1VYvIXJQy6XCq9N6e7xLC30YkUMwjkisPEma2TcZl0nCWIwY8HjHdIrRPG9F5OITcKHqJC1MRIcxOgdxL3Mc042geQROKkCCZhIUO58vsQ/sqna3pRKR8j9foy4ZjhmHrMA42unLLK+hslxHBJZ9F56cf7fE8w1Du+X6cuHRUEeXjDvqZpHHtQnu0jxBmSpBJ8ghRYq+X0ALP83g3onOdLsczkV/zPrTnNhD+hjWee4Pnk4IoFkL+CjhjLIouLkVauCGPOQMJ6T1ESp8ZnkOD5zUZnYNGw/8CEgxnem5HEb1NfsvPGL6VRBWnCUTy6ISjcuNgKVFubxo6n6XuN1ly3kR7/xGvVRORtqbZ+JqCgtAq0ZlehC6zWzy3ZLa/GNGvPveTrAfjOKXtGCfP/PvbEgD/9m//lpycnH/0Z9euXSd87/zzz/8Hz3/+85//LUH53rdsJZATtb/O4Z3lcNrZRD3eRejgtSMC0oOIXgciYp8h0sNUIOHuT5CZYgWRjmEpIoaTELHoIghxuml3EU70lcCD8Mt2+OAkRLhXEjVIR2pbegiNyDDh//QZlY/K+TyR2+9FpA1K5sLkW1NP+AxhuPao7+59ULYK+QxNNy5Go3Jg3yCEwTzE2N80XGM97iBiUsOE0NyGGP16xID65VuX/yBiFBcSuea6iEjaPML0mEzIcxAjSWaYiw37c0jr2UJo3AYQg5jkfpMQXI60en8Awz9z3eekQaz0s2th1y9g1hcJk28rvHOrhf1RRIqIWqTVPOR1KyQYTA90N0JZJWIiVxkPSSt1NRFtuY+IGL8YMZHXjIPJaJ/djyI9hzxW0mDker7bkbAz33icg5jKJs+tmRAQO4n6uRY86SDM/+OQsPFfYf+vYOJY42i88dvr3wuJKhPbEYPvMfwWzP+uHj6cIky7iOCBPM+1DQlZXyTanUg4OEqmRjezCQ3rdOQHl3wTdxLanmeh82dQcaXGzzDZJz1+NVHO8VyiDNdaYB48+QPFm+SsMh62EZrvo+qfLxBR08m82GSc1bo/XwjbVkD1RUiYyyPKN9YSQmAXunR8H2nzh4mEwOVIY7gLrfNWFFn7PeN9i+ddioT+UiKH4wYUqf4f4K2X4IxKtGfqiAjnUegsn400xE1oH8wHFsM7Nyh5MpOJnJXbPNZ8dDncCP1HoehOr8cWtA/S3sDwTybSsGwmErEXEhr9HyI3koPGSyUSOl/w/M8y/vq8ths850WEAN8FTFTpuYpJaJ/uIvy7++CXa+CDHzNsR+H4bsi5yHAvJ6K/01magrSs26GzHSrShXSY2IejDfdsouLLsL/r8e9etJceRQFN6RLbBf2vQtGH0Bm/HNGLtveejadKINWcPE3SMXTUT3YlkMHBQXp6en7ts9tuu40f//jH7N+/n5ycnH/0vfPPP58Pf/jD3HHHHZnPCgsL/9VUKckKgCdqk3OgGprqYfonEYE6iBjYcsQgkk/SZEQE9qODehMyyUxARHApHHkUxtxKqPSbEXFfhQ7wAJEo+naC0I0D3kACVzViBEuBr8CRl1wZIzG1PERkBpE562V9/varjhyrMew3etxmw/6Uv1tm+DoQIRqLtGX1ns80QltXTBSt3+DvkgD6HLqdH/bn98Dxw5AzxWMddZ8QEWwz3U8yd33Fc60kSsd1+fu5BEPe6vfHec4L/fdGJLhcjphIJVFu7gEkTNQYjm6kiRpGTGK3+5iOtA7LvFbNaB9sMm7OQgLYEFrTQsIP63E/d9D/3+E5VhmHT3ueI6MH0/znAbvg+DOQ8yHPrwJdIto1r8HbIP9ctCfGImaScNLhuSZNxizEGPOj71RtpPP7UDHfn7cROfrq0B6pQE7xxR7r44gpJdNelWGf6WcfMB4OuY9i43Gn1+QGr9kcZLIb8lqUIoHgMKFlSUL8asJXbgCYaJxM8jhdZExjbfUWpK5C2tlx6FzMIRj/buA6GLwa8j+OzmgNEjRXIAF6JmEOPctjbTDeytG+nkWkIFrrOTR47GG09quJUoTt6KKX6zW6FjH6Vz2fpIGG8D1LWs05RP3mMcbtTKLqxQq0rzYaj59CWqoitMeTBeJZzb+zByrmIoFlBdJ+7UV7dfkIvCe/0tFw/BZXg8n3s4VAi+ryjpkk/PMZJCj2I1eP5bD/cZi4iEgCXo722QQib2Cex/xTRD+/ggTYAX+3CJ2pZLKdivZHBVEpJM+4edow93ptWv13CSH81hOVNKYTaZTuQIJkPyGU5RPR2I2I/swxbr7n70ch2rbCuOnTqz96Ef5gJhFhDpFqZjZhKfkC2jtzjffNaJ/cRJiYp/vZyWSq82Q0xZedOgHwg5xcAfCX/PZLwQ0NDTF+/Hj++I//mNtuu+2Ez51//vmcddZZ3H///b81WE5lywqAJ2pTc0QIX0SarF5EbEuQaXI9EQF2iEgLMg8RrSK/MwERm3bEkI8gIrIChfpPQwe/FbrfdELiYr9bjBjTHUhrtpUIdLgWuA2OH7Kf30JEKNLteQMiJrOQmbMIMY2FhEPzsGHuIyKINxFRoDORMFeFBKBdfu8iJERe5Xe2wU/a4fcneZwJhDaknUjX0oIIeR9BlFsQwbzcfxcah6tViqloCuEPcxCOrIExZ6uPd/aJj4z/nN59ZwecthwR/01EObadhLP6Xs9lKpl8gMMtkkPHfwwJF4nBJ9+2fveTNEJpnXe6j4u9ht3G90bjvhUJu1VEHr0hvzOWaEkLvAj5Ed2ACHs9EmDGEHvpPCTYryU0bW2ICQ4Q5tOxnuNdyPyWgiqGgTlw4EYY/wG/PxG4G4YXaXuefjba15M9t/9oHHzJuFnrsZJZsBAxsxmIQe00nINas4wP7WRCyO4iSmm96Pe6oPPvoSL5EPZ6DZO25hqicsmCEfM9iLS8Rcb1RUQ6nVI/k4S8Lq9dMbEvOoyHRehMTiECXM5FGpiVhrPW85qIuF6K9u0jzm0y1+cDW2GwB/LvRBaBerQPGgnT7Dj/nfe/wNRClA1MLgaTkSa2B6WCSXMsR4L4LKLCSAFKhXI5UXf7Oa9XuXF8i2Gp8zwqje8yz282onUF/mzQeEkXts1EJOu7I/rtRvvhFa9Hmb+7h0h58xq6XOUiOpHOTzERcb/Yn49HezntoYOG7xqvXRKCb4X99TDRNCIT5LGKcB0pMc7LDd8Mj9GF/JbXG/9J+Jxq3HcYDzMN52YklL1MWFrK0dl70+/3er6LCTeL/4Q0w1PQnroSCeA3IyHyTJSJ4ZvWNiZfwIV+fhKxD7YimvJH/7oEwI6Ojl8TAEePHs3o0aNP0ijwgx/8gOXLl9Pe3s6ZZ555wufOP/98Xn/9dY4fP05FRQWf+tSnuO222ygsLDzhO/8ntX9zqgH4F9umoAObCEMJIpLViJEm4WYGYgZJOwWhvVuCmPu1iKHsQITtGDro/cRNsAvKzkSMox0R6U8hIr8MEZXkD7YXEfZKyFlKRH4OI6E1EdkeYD0ceQPGrEDEPwVvjNH7HCXyXO1FTDDf855KRPomc+ABpLG4gMhNNgC/X4eIXyERjLDVONtMOKU/BIM/cw632SOeXYo0S73utxKKDhqexOSnwJjPkNGenjYE4+0DSS2c1uP3DxGEOJnZShGhvtB4H4VMU5MhdzaMP+h3dxkfo5G56xnCj6qFiEJsJxNJSqnXu4sw1b0Lwy9B7mX+bBdhfq4hqoVUEtUVdvj3OuMxOaqPIkyBXV6rEiJyuMi4vpMQUAeJsmeJceW5n3EwfhpiSu2oLNyVkLtIAeTMMbz1I8ZKQknSUA4hhno2cT52at5vvQFnzCGc94eRQHKUjLb0+HetEb4XnY9i4DtQ8QGvT8LRMGL+aW2T/9YriGEmc3QNkbh5H9pXwyNwnNZ8ORJqtyCmei8hyG+Fv2uEDw96vKRdWulxprnv0UjATReoXLQ/L0FnDOAQDL8CuZWQP83rUWUYa/27QXhnh9e5lDC3L/SabfRcIDTzm4VnCtE5HUaavQr/3kskgl5F+KIWG/YKIi/jXcbvSiKtyTKPs5pM3kUaDUe3P2sxzNPR5XQT2jPNI9auAO2xRZ5Hn9dgBRIeUzBbuXNKTkP05Swk4GwzjhIdSMJWCZFr8zuGuxvRqHNh4ii0h+u1phkhOY9ITL0aBfptRZeHViJYaSwh6D5JXBZ7CIG/AK37JLSX0rvpInCAKOk30kxfiy70CwxfnnGbtMd9ZNLu5JyNeEXiLYeIQJRer8Ea4+ePOGVtGDhZ4mfyAayqqvq1z7/yla9w++23n6RR4NFHH2XhwoX/pPAH8OlPf5oJEyZQWVnJz3/+c/70T/+UlpYWnn766ZMGy6lsWQHwRC3dqqvI+AzRhw5eMouMQoQtqfLbEHHrRQRlC2KWICbdAG8fhdOXAy3w39+AfzuPqHGaHIjX+t256DbaS2hk5vnzG1GUYfJFnIEIagpIOOr3ZsOYxYhA1xnWPqAbBu8VbRl/puc6BRHWTkTcn0Magkfd9yOIsJ7rcZNGZa7nl27N3USah0We11OIgY6F/INEGo0rCAF2ElGOrpFgENNRwusNiOldjPwEn4H8S4go3nLE9J5EjHUnYq7L3U+t8VjnMXIRA5hMRJVOQQQ/HzHGNsTs/8breCGR5iZptbZoXk31MH0L0jQ2Qu7ZSEMxCPwXP3cM2A8HXoLx53qO97mfXiIdTxKC7kMM95jhfsx4vMAwnE0krW4lnNXTBeA6xJTfRYxzv9fjauAVM94Vxv0xpJXZ4rHKiaTGrUSU9E7iQvTnyFTXj/ZNGZwxwTB3IyG7Fe2H/wZ8EtVcvQEJIPcRWqZkpq3UunVvg7IKtFeSP+AUw79LOOZ3jLOjRDRp0sLmkRFojv8Aco56zf4E7bkVhFCNYC/BAuwKMr6etCPBczyhmUuXmuRGMA75uV3ivlZK+AMy+SePfxlypvHr7hOrPb/D/myn8d4O+3fAxI+gfXrAn48SbjK1etO+SPt6Hwy/Crnt6Fx+mQhkKEDBNtOICjJJmN/u+XYSuff6/Xk7Elg2IK1/cjEo9vhbkGtA0mCmc7iJ8I89RCTLT1aF55CA2uXa5CWe62vG6XlELsRkhZlDCM+L0Pm5kbiYlRuuXZ7vDYb1u16LCX63HQmPVWivTkXn6c88RrokJ0vOBUh4HnI/fX73TUKLOxt+eSt8cBo6w9XG1Qp00Sj2Orwaz9NA1CrO9e/5RMDHHsKXNPlBp72XTPbjOKXttyEA/mMawH+s3XrrrXzjG9/4J/t84403mDp1aub/N998k82bN/PUU0/9/8Jz9dVXZ/6ePn06v/u7v8snPvEJ9u/fz8SJE/+JN//PaFkT8AnacE4OuTcigtWGiNefIyf0Jv9/AyIITyFC142YwxJExDYRKULWEoEPU5BQkwhVMxIqzkWEbzoivruJG/MQ0gZeSxQGr0cE9wp0a20kEkC3+P9ViPh0IS1bK+ETdynyDStTf7+8Fz642s8P+fkU9JBP1BPtRrfnFnQjn4MYbpfnlI98B580jJMJ5/6ZRnAL0jSk+SdT11okND2LhLQrDM8u6P57KLua0OitQUzgEBIQOomaynjcNsRoCoio6es9fjL9tZJh3G9/F04/lyjh9ZqfT35ozwMfQcEWN6K1H4YtL4mvnvEAYRJMflLXE3WIk4bkYSRcv+LnrkQCRD/0vwFFyYye63XY6jkUwdvr4PRb0Z5rMo43j4ILjkUt5mLE9JqQwPAgYtTFFnAeJPKfzUZC6hiPkz7r9PhVRE3my7Xm/S9BUanXcJFx3IX21l8QEaSV7vNSz+Mhz6PK+M/zWGsIRpr83BITryNyIj6LmHWpx1yBBN4kED6INKGVBDPNRYLzLOh+CsoWCQ/dT0HZM0jjOsXjXgH8ARIsUrBDOVE2scJrug3tmWcMc4PhGvI7f4WE7bO8X6YogKAPGPN5QtN7FAlqXTD8IuRe5LHTOZvneQ4YB/2EFWK+33+SuPBdRdRgngaDt0D+l/1/GxFZ3GOcVBuXBZ7jJrQ/S1C08RsKxuq/Doo+RpQqgwhYmOIx9yEatpOo2FPteaTLZCtBP5MQuBAJV0uIetZbCM14JRI+pxCZBJKkMIjoxU4kLN0CPOW8qNPQnuswfno8x27CB/NxtH+3eIyLCTebLkKLXOfndwB/WQi1RyNDQp2fScLvfOOzA478AMbsRq4OlyAf2Qoi60GvcZL2Ta//XkYEYu323Js11vD3IXcKcWmbCdxx6kzAFZxcE3An//s+gF1dXXR3d/+Tz9TU1JCfn5/5/8477+Tb3/42Bw4cIC8v7zeCr7+/nw984AP86Ec/YuHChb/Ru/8SW1YAPFGbmiMB5GV0EC8H7rE/z2WI6QwrShQg95No9yYtWDsSvurhrXY4YxESKsYCt8GuV2HWfEQoi+HImzDmdkQAOxDhr0In62FEBFciIjMdEbsGeOvvXc6pgoj43IVuz8ksmMwzlyMmMB4RvBWG8zHEqD6OGE+Bx9tE5KQqR4yinUiZAmKSZ3qcZAKu8fu7CS1fJeELVye8ZHKyXYVO/a1EEuKt8F9+Bf9hKxIApxgnrxC1dsd5jiVI4DiLCBz5M+RrM0CYjY76u72G9WIUvfxfDFcTmTJn1PqzbuPtIcS9kz9jMhmPJzS0SavyKaSxKfD4eE3nGIZ+5fgrm0QEAlQhQeAGIsjmIWRe60Z761njdpafKSP8Pqc5qCE5h08yvr7sOSUT/pD7XoG0Gi+77/XuJ5nApxH5yxoRk+kxjqqIuq/ViOENGffJZLbLsJZ5jJWEL1uv1280EhqaUMWbVq/NZ437SR7ni0ioryX2pIUm6sgI4Zl9+oxwzGeNsyFiTw4QLg7JT7bY8GxCAsx0IupyNhJM1nu8pN1ehDQ5Y4mo+gLDvRKdz21EYuMG9zUZnZGhEfMtMxzTveadfq8e+tuhaJLnf9BzPExElZcSUaT3IM3ueCLgJvnbLiACC77nd6rR2T02Ah9VhBk6mSyb/e4iOP445Cw3/OXobD9AXNhuBrZC2zeh+hLP626034qNt93Gx1GPX0akR0k+mwNImMuFt26HMx6H1z4DZ01DWu01fu5agi6loKB0GezxZ2+i81hLVC/qIIJimglT97Cf2WScvUKklqpE9DEXXf5uJTIk7AQqYFcLzDrXY2wxTr5K1GD/SyTwrieqrZTA29vg9HuIpNmj/N3Xkcb6UsO0EO2jdO7xOFtOnQB4BidXAHyL314QyPHjx5k4cSKXXnop3/rWt37j93/yk58wd+5cGhsb+b3f+72TDt973bIC4Ina13PgIOx/ECZOQwc5OW93IEJxDtAG3Y9C2ccR0exBkVuFhJ9fDeE3tpC49VWguq1HkYAzCzGSgx5vL+FTUoGYwwwi+WoTInZliBlfTAghdYaxDBG4UiJSeDciWosRM3wWEbqr/f4cv1PlsbYTSV47iESx6xFRBI78DMZcRAi/1kCQb/gbiLQkUzyfuzzmYeGx/3koGosYUAnhB1eEAlnGAzfD8VWOjq02HNMI/6WZhCYq+cS9StzKB5BfWyEySV5meHr9/sWGcTGKIk0C1bNIWDxA+EPV+J3VaG8kZ238u8AwViKiPWB4bCqlkMgv+VO0N0oQg9lFaHKXELWDjxrvtyEN3ySPNYbIQ9hBlLJahPZhnuGZgRh2H9KeXIoCFVqcn67Fz46C4z+DnNuNhxVIOEpasTykDR32vIcR05toHA2hs9Dt7/MJP7gZxlmN8dPvZ+d6bc5EjHCAKH+WtJpJoJyM1nwKYZIe9DgjhZ1eItFxsd9rRudsnn92ez5J0FmJBKjkmpC0f48Q1TrmIW1Qq5+ZZlgbiAo6A8hl4IDXsJG4IH2XSAhcTviA1o54t5e4gPUQ+SjvRpqzEr83pL72rIKp8wkNeI/HWE2kNkmXxFLjeiuRM7HEMI5CnHiA8LOs19+dq6DibrSnk3b0GSJDwLDgHHze+ykpWAqM++3uq4YQjO9GZ3YTOlfrEV0oIoK3KohSe4mmdvn3p5FQ22OcJU3uVMIMfRe6kLeis73Ja7KasMxM8HyOGk8T/OxOwtow3d8lt4Fi4oKx3rifjYTpZsOxgYiY3294XoXBdcbRYvcx4DluISL4k39tk+G8FikEignasgnYkxUA/3faj3/8YxYsWPAPzMIABw4c4BOf+ARr1qzhYx/7GPv37+eJJ57g4osvpqysjJ///OfcdNNNnHnmmWzduvWkw3Yq2slat399bTKQBxOTViYPEashIkGsTb5NIGJ2vd9LQkLS5MxBt9DURwEiGB/3d5cj7VUSIA8hU+hhf/YcImyJCb6MmGUfYjC1SNg5ggjHQkQE84nkwCDi24C0FtcQfmNFZDQOXEGY25J2ZD0SGDchbeEhIuN+D1AFYyYQUZJ1iHFUEH5ZM9BuGyYI2xJ//iww21kXkslxLGLufUjIuZBMsEXOTMTQhhAzOESk2jhEMPpNnlvyEXsSCROjDcPXiLJUE70me4zrxzx+uXF3lvHb5c9bkSCw07B2EWbPYs8tMeI2JFi86PHPIpzJ88gISEd+QEQbJkGllUj90GfYFiHN3jTE5Jb476TpG0bMYQ3SCm01Xhv9d7/7OI9MLdH8S4Tf4UYyjKYVwzLFuM4nItqriT2SBIsyQhhPZsEitA8meY2qicCjiwmhLgky3cblF4iUNLVE7dk+xEALkNBSavyNNY4a0PloJcrXJX+7Cs99BhK8e4j0NAVo/wwhLXwZETH6DOFfOQ/tlX1AJRwYguM97usY4Z5Ri/bsJI91D9oHXcDfC1/djyLh6qBhSxfFYUKgHuW1S0J3LfAAHHkG7YukqWyAqenZo57HI0h73ur16UMXi+1EGb4aoiZ1rvGwEgn7A0S+xmkaq+JsokZ2s79Lz41C+6XVmugO42+Bx9jouc5FdGYOkaS83GP3oQv0CiKo7np0lkcREbCHDPsVqNKKL6bDrxI+0B1oP7V5bmPQWUmX2xaPN4S0ikVe1yHk4nGVcE052jNDRPDLXKLMYKnnNRmdkQ3CR9thvztXsPEs7P+hcdYE+Vd7nrO93o8RuVdnGPZLkBn7cuSC9Axxxjv8zr8AH8CT+fPbbI8++ihz5sz5B8IfKDVMS0sLR48eBSA/P5/6+nouuugipk6dys0338xll13G3/zN3/yWoXzvWjYI5EStCzHofsQk9vrzav+uIuPoe34f4eR8GSKGtUjzlG51c4lAkcIRn5cgjUc7MuuUIcL4U/exx79XIE3OOnTrux8Rr1cQAbgECY1TDNsgInpjDf8gIoq3IEHoLMQIphApSQYQgeslKlQ8SVRjmGt8vIAEyCTMNiAmW4WEwu8hol+BCPSbSHhqJ8qs9SGC/gwZYj5mFCJ8w0jA26LP2YeEajz+fMKUOB8R6W8jDWkhkYn/YoKR7iSqkryANF+1wDzoXgNlSUuzl0iNcwVhSu+AI9+EMUmgaEUCQ/KH6iBS8SwjmO5ypGW4GK3vNBS4kfy6kq/RNBizz7ipcd+v+v9cwrQIGf+iTIWaAXQB+Brak52IESVn8bR+EwzTMq/lbiRYzCSTizD3asRsd8DEjxMaCNzvKCIFTqfXIwnfNYa1xvO7mriI9BPR77ONhynocoE/6yX8ux6D1/bBWXNRgNRzhjX5slWgc5aEz6sIhrkDutudXPtqJLT2ErW6pxh3/UioWkYmCjgT+NWL9lInysvZ5WefhXd+BqddpvUYP0zUn92q1ESn3YKCW5Z57Fy0/l3EhWI0lM11n3uh+3koO5uo073FcyxCAm0N2nebgH0wZr7ncQztq8Vw5Jj3Z9JwTiZMuLuIwKwG5KfZZfwlv7v96JzdhVIHnYfy8Q0YN18iLoQl7qvS728QDJmguTx0hpOmsVbrknFfeZhITVNB1JNuNS7rPW41CtZYiWhRH6HNTBeEFf79iqZUcR6iN0lozyOqlrzmd4sMfzq/jWg/tJLJb0iT308m5SVoP6eAlcWee4vXZgIRYd0L1TMJ39C9emfi54m8oY8h+rQJ0eWjZC7DPOa+7jR885EA3UBYCUZ53c7jlLZjnLwgkN+2HvOJJ5444XfV1dWMNIhWVVX9q9H0nahlTcAnaitzRAxKEMPtRAe0yd8vBjaWwld7oAXeWgdnXImI1jGiMkfyPUpmwHTrfBgxrErCp2ye35mHiNV5hLlkPaFZ+BYyexwhfNCSGcz+eccfh5xJSChpJoSCB9znPiR49hH54r6OtAbzCH+rCsNZ7vfSPO6BwXch/5NImFxPmFlXG1eFSJPTgYjoWES8c91P0pgVAEPw1gY4YynhT5WLTJ8grUsKTJiM1mYSYjzfdF+b0E15r+HeqH5ZghhbPnLI34BMKY/A8R9CzkdG4GOl8VhPmG23wvAOyL2HKK3WjHwMV7uvzfDCvXDhTKRN+CISjJI5/DlCGwMSkJsJ82YyHW1ETLJKOCYPCTF/LHy8vg8+OpcwGw4g5r4QMcungd0w3OMSZasIU1g58if6W8R4HjQM/UTy8rFE/dlc43LIMCetWxIkStEFZBSRv266cTRCUHx9H3x0lQXt+YRrQhIUdqE99IJhnUaUKawhItuLiTJ7A+h87IL//gP4t4+jc9Dv5+ehM7XY+H/E8O5DQsBNSMM+mvD/g8hRV+q1WYbO21HE+K1JO/ImjGlEgt5WItDnFXTZWql1YDS64ID27cvuv0n9cP0IHLcaN4mG5CHrQ7vnsd3P7vOYyw1XuXH4Dfc/gM55NeF79woSUAbQJe3P+fU8me+SMf1ncv49RFwOOozfXHjrGTjjNsN8LwpgqkP7KfnlTfI7CdZp/m46olvVhnEpuoxsI2qBJR/WqWTqXfc/DkXJzeKg1zDfOKgjaOcDhNZ4Glr7ekQDBvzMKuTDt4CoYbweXcyuQcJXLVEdqJ8Q5Ed5vD50Xr+GaNo4z3U2ElivR0JtLxGpXIf2wAzjPNGzFkQ7H0L7bQwSBJM2foX/rjBOK9C+aDI+buWUVgI5Hcg5SX0eB97mt58IOtvUsgLgidp/zdHhrEeM/Ep00LoQIW1Eh/sadEv+AtAAx5+zf9pBpGXKQwS6T9/ThoS3xehGW4382+YB++B4PeRc7c+OIWIxAaiD/lug6GxEIIcQg2xCQmAtwXQTg2xBxD9pZXYh4tHr8RvR7Xyex7iecC63M3/nA1DxSUSYWpBQcgsi5puQwFDtcZa674Modcx1hOnxSfjRMfiDUmR+nIqI8SVEKb1XyFQGOfAgjJ/iOb7i/hcghvWc/7/JeGpHzGyd1+cz/p3rd55Bwt1CxCC2ud/Z/ruNSAuyj8gB1osEgfGoQkGV532l5/QUSnJ7DsEg87RWGa3nq/7/EkT0tyIGnNYgCcvD/nw7EgTqDOMcxGRHmtGLENMcNm7WQVs7VN+MhNcjhOP7hcB61UPOuQEJcB1E8uo8/58Y5fe9rq3G72tEtZtHPP9FiPmuNd6+i8om3mq4H/bnc702g4bzfsTkJhO55Wrcz/VEfrbkB2szXuc2qFhu3Mz2us427DuQkJJ8LxNjfgrt6emEBmceOjd2JchcMp40bov9ThFRiqwSeBQGGyH/BnSWn/bzX0ICdRKQ87zGXyDKDj5pGFr9bh0StPfC8CGn4GnyeCvR+ZpMBAEcJPwEp3rNi2H4UVcXu9XvLyTKS+4wzm/wT3INKUBnpw8JO7uRAPUu8HmkbRoHr38ZPjrNc1rldWgg9ucMpKFMrgYbUcBNh39SwMo8rW33Dii70vgYVi3po0DRRwhf0UVei7mIbtQgwesa49V0IWN1WOy5zEBC72HCnF3quXW5/4uM0yKkFe8l6o7Xuo/1nus2IpXMBKIyyCWC/5374bTPoejh+5EvbilwAXQ+DBU7iFJ4q42n5NYy4PV/DtHQP/M6zyKq8yThsBidiV5CSLxC+KTccHYC34DjV0LOKWDjSQAcy8kVAA+TFQDfq5YVAE/U/iInkn+WoAOaGLvNTEwiCpivRcLVdiQIJa1C8rMZeaNuQIy9i9AKbkYE6HnCNHU9IkzJzNFB+AFO9vu3Gt5+RDwv9DjfRATna4jIP4z88g57rCokFD2OmE8xUVponj/LRQLTbOJWftS4aCU0kvb7YYaf3YfMSUkwTaamBvfxPGJKs4hcWJVI4J2NGHYDcg7f53GavQ6vGefT3Vcygxca3mlIODkTMdohZHK61LichwSMKr+3B9245yENVDJDJTNQl3D+k+fh9y8i/LB2GoYewoy0DYY3QO7tXo9ter9tH1RPIDQO4zy329F+ST5gBw3PBUSOr+lIkBtvnI0xrsYZj+XGQ7Nh+hKRKqXD+Oknkgn3EFGQl6A98jzS2rQh4X+P12Ku12GmYUu4vhsJWJXG7xHCnLyCMBsvBeZD0zGYPhftv3r3swedkTYkQCc/ylwkDJQYN9sI/7IZRKm15zyfXYY9rVmJn1+GtLCLCV/RCjICFHcQbgyFxt0cImHweH/2U+N0CGmOF4yAvYMoaTff8GxGUeWdxlWu+0tm03GEADLs8Sd6LSaj85x8LjegfZw0R+XEnkw0IJ2BVs850YMJhJvKfUREfBL0p6FzNhvt5xYiOGkvUR6tH+2tZiLfYw2iA1u9VnehyHcM42j/fZiMjyntBI1o9Rwf89xrPWYeEYxhlwQwzifC/otg4mfIpA7qvhbK7kU0LpnxqzzmfUTWgOWe8y1on75gOMvQvjrL46S5lhGBIWn/TyUC73rhlz+ED06BAy0wfhISzvr83qeRUNyCzu4V6CL4NNqDy5BVZSccfwlyLjHcbcbZJHRpXWT450rgz11hXHyLqI9eavz9x6wAmG2/eRt1qgH4F9ueQ0JeIzrElegmeKY/P4CYZdK8lfi9xUSEXApi6EPak3J0gC/UgSYPCSmdiDAcIyLNkk9UYlpJ9V+MCN0XCYa7wuO3IKY1CgmkuwzfBsJct4uobTsDmSaSn2MKbphGVFjA8B1B5tUkPFQhAaXWn13jeb9o+AoQEbyLKD01ze+Vw+CvEIGf4PeeR0SxHjH9Xo/fbBjmeL43Ec7jB70Gizxek/sZS5TaSoJU3oh+xnh+/cbFNOO1yjAVEqbAlepnBp53kddmIWGS3GuYByF3AaHNKwV6oPrjiAkMI23FbsJxvBbtqy3weiMyzXUSSXoXoT01yp8tIJNomouMs0NkgpbY5n5f9rsDBIPtQwJKB1FH+U3jq4HwSzyEhMh646zHnxcTGophJAjtISK+57i/ArRXW4DrYXpyDbiR2Auz0J6tJnytkstFEjRr/Nxyfz+d8Gk822N+kxBU8gm/3Q6v7RaimsRYIj/jYsTYx/HrGvIe47DD6/wn7nMInf2kjatEgnojYthFRLqo5wjh7wCh7V1KVAEZ+l/mUo723kXGRyWiDbWed7nHGiAuWqvcT/ITbSBqS9cR6WjmuO9GtP7T/GwvWuvkX1qC9tNcz7OEKIdY7jng+SWa2GD4BpAA003U7h55fifD20n7OBrt/3/nd14jE4F7fJvfGUZ7/RgSNoth4rlkzNA0WqvYSZSvS5rCZrQXhoyPh5EA9yLaY68gk/BmtLadhn+3cZfO9KDn3oLOX8JhOXxwHDAJxi9yX7vRfjqEaHOJcd/h75K/bpfnVqB1y1mK/JdnE3toDKI9Df7d7kTZe9F+LjeMSYO9jlPahk/yT7a9dy0rAJ6o9SLCnvymxiAGuNCfLUG3/tXI3DnodxrRFaYCEcYixORqELN5Tr9zLyMErEpEAMchonUAEYpSxDRm+5l0K+3wew8hhroI3RKT5u0aj70PEdgqxAS2opv4NKTZ2IyE2XMQ8UpawFcJM0iv59qHbuwdiFlYuGEbUXu3wDD2GuabCA1ng59fA6yC/A8RVVJ2+O9z3Md846wKEc271P/bjxrfeUSqkQOIAOYhs2byRVpP5E5MWo5RiEENaD4HXvI484ji8XXu91LC8b9fS8oS47EHMYqZXtcZiHFXISZZjRjSn2iuFCOBfIdhLEXmo4WIuZUCn5WfHMXQ/SpRQ/g7hqMSMZV18M7znkM9EvzGof32DULgaPc6XqGya5T5/VvJMKB3vo+Y1FR4+1av7Sai4kazxziE9nULqoiy3Tja53GTmb+CKEG33WvdTVRx6CVSaCQzZ2Jij3nO2z23p9Han4n28p0oh9phdG5a/P0ydP6Sn2tyut9JaIS60R7oIZKFtyIt4HPGh7VKmaj4ycbBNGK/HfO7ryKmPs5zOMfPPEf4zM4eMdYQmT33zuNepwVoL00lIrb3oX36aY/9ANqbJURS5DoiMXcDEZRWSPhzVhIVI7qIPHZjjevk0zeGiEbfhejDMSLo7T7CFD0d+M9E7kX8XPI9fplfr7Rzhf8/hvbi03D6R8iUSaSeSEQ9HZ2NIci50v1VoLXuNlzD8sOlzGMcGwFvBWGN2ITowQ1Efd5S4Kdw/GEkXM/2WuV5fUtG4G7Y63We12PYn292P9ehM7yEyAQxyt/tIPyl1xueEkQ7r0N7+Dq0d4c8fqXX4qB/H0BuLaOJgLhK/z0Zhh8neEonou/Jr/gUtWMn+Sfb3ruWFQBP1BYQFQBeICIItxIavvuIwuULEZHtRwd+AvAIdK5DzH8GImib0OG/ATGAOn/eReSfKkdEco3Hu5/wTelBzKkKaUeakUluO+TeS5QsmkSUG7oaEaLliOhsRUTkKBIARiMi3YcIWSsSPDpRtOBOZCZ5k6jAUEGUoDpk2NZ6vC+7j7WIUDUChaq8kEm/sEQ4OXItNLUgAn4EpZ9Y5p/1/uyocHv6J70uQ8b7QeOjkAiG2Ej41Wz12lxsXJ5lWM8G5sP4mw3LViIhcqPnd4f7bxLuKm40zh5DGoR7jI8+v58Eh+8Y97O1/tQixrnYcxowPq9Dgu2zaE8Mkim3V3am5538SFs953u03qetMf4hUt8Mj3juchTQs1iw5ZzrtVyB9twyYDucNhP5bnUJxaD13PMcMA+GW7z+ow1LAxLqbyKiq2cjDXMrDD+AhInVRHm0LjK1m1kMR36IGPSAYen1Gg3JnMbl7nOW17bHfaXAndlESo42dC4G/PyXkFl9EtqnzYTpdCuRVqfZa11CaPOqiUTpVxtf/wGdrWIkpMxE5+RMwpx6sfGzBu2FPOQDaG39lueJy8WAHqGDYO4lRO69SUiYfdbzugldANuQSRBCY5UCLJqN73KvT65xk/xc+/xeLzp7e4T/I7/wvIvRBXI1/N0++OUGtE8K0D65lqjnXU5UcTnqOdQQLi6bBF/TFe73b4iLSw0yq7+ALp+VxtEi/z2ayLtYg87IBCJo4nbInUuUghzrtXwS3k5uBc1+9nLPF0SPvw58CXJe9ZhXIVrYQ1Q46oL+Z/x9l/ufBj/a7f4KkMD8IrqkvKZaz0xFbjQ10Hmv12QAnefkO9vq9ao2jANovzRonV6ei9b/AsNTS7i+lPudQT2Te/aI9e8HzlPA16lsx0/yT7a9dy3rA5ht2ZZt2ZZt2ZZtv1EbGBjgQx/6EJ2dnSe134qKCn7xi19QUFBwUvvNtn/YsgJgtmVbtmVbtmVbtv3GbWBggMHBwZPaZ35+flb4e49aVgDMtmzLtmzLtmzLtmx7n7WsD2C2ZVu2ZVu2ZVu2Zdv7rGUFwGzLtmzLtmzLtmzLtvdZywqA2ZZt2ZZt2ZZt2ZZt77OWFQCzLduyLduyLduyLdveZy0rAGZbtmVbtmVbtmVbtr3PWlYAzLZsy7Zsy7Zsy7Zse5+1rACYbdmWbdmWbdmWbdn2PmtZATDbsi3bsi3bsi3bsu191rICYLZlW7ZlW7ZlW7Zl2/usZQXAbMu2bMu2bMu2bMu291nLCoDZlm3Zlm3Zlm3Zlm3vs5YVALMt27It27It27It295n7X8CGojV6af1yAwAAAAASUVORK5CYII=", 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\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "kwargs = {'scale_bar': True, 'cmap': 'hot', }\n", "dset.set_dimension(0, sidpy.Dimension(np.arange(dset.shape[0]),'frame'))\n", "dset.frame.dimension_type = 'temporal'\n", "\n", "view = sidpy.viz.dataset_viz.ImageStackVisualizer(dset, **kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the same way as above, we can plot the dataset as an image. \n", "\n", "Please note, that we did not have to set the `data_type` of the dataset." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(25, 512, 512)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3736dbf200d440c7b68f10eb74d7aad7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Dropdown(description='Image', layout=Layout(height='40px', width='20%'), options=(('z', 1), ('σ…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "acb16b47fbd7492aa0f07c7a77008935", "version_major": 2, "version_minor": 0 }, "image/png": 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4wzicA0wwDvWmU7tpvBq4Exjt5/OAqcAiYL6fneDvNwKnAw8Z30vhh5Xw7gVAo/Fs8/tlnqPVwESPrRk4AMxxez1AtT/v8rMFwHigFThofNuMQxvijVLgE3D4TDhlFlABXO9nDwC3ecwTgVtN5w3ur9XtTxPdKAf+7E1Q+3Px0VDouwZy3gp8H7jGNCzUvB5eA6csMd0HmhbtwOoSGHFA9JsE3Au9pZB7tXH/JHClcV3k+ewBchH/4HHNQjw8C1hjOg3zuBqh8ykoPAPIB77iObsIeNo41gEz4Xu3wofOR7xbClzt9hYCSxH/TAG2A92m9Wbh9aMr4R0LTe+JHusuz79v2utrgZzrEX9vRvxWCzyBeLbO4xztNrZ67vL9/AX+XQ+MAi41PdYDiz2W6zyuwW77Bc9ZFdAMfQ9Dzg2eyy96nFM9zhUD4btHJB8O6nk6Pe7lno82f1cB+y+EoeNMizLP0yy0DmsQ3+xB6yHPdKwznqWaby7T932bIOd8z/Fk4FPAUOAu951kz063d49pcTe8+C+i78grTa+tiL/GIr76GuLfatNuicbcuQ4Kx5ne+41zNfAScL/n6Xb3NQXx3UTEvwAD4dAGGPyo/28BtnjeL/PcjEXyc71pvstjP4TWb4ffnWO6LHY7l3vO7gVmA48DXwD+2GOZ57Y/AlzsPmagNb0f+CegyW11eS4fdJtXu70R/v6TiL9zgX9A8nmEn91uOq9H+uZsJCfW+p0iJMM7TfNPIB6u83gPAk++/plc3d3dvPWtb/1PT/3/plBWVsZPf/rTzAh8HSDLATwRfDVHizwXLfxxSFA+BjyKlN9BtJg/hhZ/LTI4WpFB0oEE9UQ48hQMnIsUeysSwLuRcVGCBNN8JOS3ImEww+/f6T7HIsWzFQnYD7rveuA8JDTzkeICGXH17qcIKaxq4BGkZAo9vheBV5DgGYqEWq7xPA8p2FLjXogE8B7o3ACF85FQL/IzG5ChOxop+QqNn/VIyHUBp7mdqX5vj+l7m8c/2viVIqU2GimdJKyHIsVRjIz0qabNU257uPtq9nsV9BsR3OXPioG5yAAsMN1mmFbNyOhajZT2eM9Rs3E4aJqUG8c2v7se+DYyfnpM+zPdf53HcT+a+zzPUTJMS5BC2O6+1xwzvoEeb4PfmYj4rNj9VBj/pCDeZ9w7kUKaaHovBNYhY2Kz6TDT738SKUg8B4Wem+8iHliDlNpE4E+QwTPuGNrmeyw1iO/bCcM41z8FcOgxGHwx4rGjbq/df5/p/p9HRsh0f7fZuAxFvD0JbTiK0QbsHuD97nsl8NdI+U5A6yRduXstYYyvNk5j/X8v4vd1yBjYjdZKjds66nFNRWtyLNrAbUA88Rnj2uH2Stz+RuAZtFlci3juGdN6pvGrRvPY7vHMoX+j1no7lN3gd/MRv81ERsUUf77Nfc1B6+sAdN4IhY+ijcatSGZs8LguMz06TNe0oUmbxKXI4BmN+GeIx3g6mtMtnpPJiJd6gL3Q+lMoG+cx1nssF5sGW03j2Z63YmKj+CjipV3Een8W8f0MJGsrPLb7jUu67rnG4y9D873YdDlgGqVnD7iNI2gdjUDyfQri9T1u70w0v3WIz9K6H2va1yPjtcnjbzdNVrmtbUjGgHhxjMfydWKDvwxtSOqMV7vxXGJ6TTa9nkOypxLx5WjTux7xTJLRVwDvfP3V+KFDhxgyZAjNzc0MHjz4NWuzoqKCgwcPvmZtZnBiyDyAJ4KvIQXUSXhErqbf08BRJEzOQkKjFi3Gbn+fj5T5ROAADJzlz5uRINmGDIirkICsR8bN15HAakICoR1YgQR9PRIGC5DQuUPvvvxTOBV/NxQJh0ZCGa3yZ+e6/2Jk8E1xP93GtwcJ0IluZygSUh2mQ5vHWAoUQOEMJHjb3E6Xv6tCAv0Rv1fuPiv0/d6lMPwaP7ccKZj7jetZbm+W+1/vNlaa5qUe5x1oh77LYxmKjIZnkUCsRsoOZFgWGo8qpDQ2IgVV7/GWISOnFyn5DUg5VXnMJf58H/IsDCO8LLOAXyDlVi/a9Bu+uaZNUtoliFfWe7xVxnEHUrB47noIpT/af8/0/C1B3rVOz00yQsv83hZ/Xo2Mo6RMkpdkLvIcjHZ/7ca90H+3e8zP+3eRx7geGU517mchUrpV/q7dc1CGNjM/9rt7TYcNMHgKsSEZZ5qMRgbFLrc9GSnnccAmpHwrTNcePzMcbYbOMU6fMO0TXW5CfH+R+ytDxv4opMC7Pb7JhGFfbhw63Hcd4od6tHa2mY7laG3XIqNytOfvbNO41zjXIT77FOIbPBeNfmeF6dvisbWjdZIUfQeUvQttqGr8ffKOLkBrYITH1kkYPVugcCLi19VofTRA3wbIKfH4kuG2HEUzqoxXvvuyZ5exxrHM7xX4szHGvwjx+TAoq3A7Fe53PzJgJhDRianI4BuNDNEG6N0AudWIB/OQgYTHOsBtHjDOc91nrseaNkKHTO9KJMNy3Xex6dXgPpvcVj7ijW7PayeSL/ehNXq68c7355uRQbrT4y/3c2mDPcG0ud7Ppwt+9rrfpe5jANIj2wmeaDd+7X6uEPHZbr+7yvN0hfHtJgzgIkLnnCQYPLiAwYNfqysg//01aieD/wpkh0BOBOORYDiAFlkJEiZ5xOKrQkZIFzJ2upEgmImEUQkSsiBFswIJ0XykoLYjQ7MVLfShSKhVu50Ov3eJ/5+BlM4AtGNtAsbAqWcQAm8cClElb9ZWJLBqkSdjMxJeyVt0lNiJX2TcRxrvK43rZMLwnWQ8z0KKM3n+Sj1mTLN6tXPoB8ggqXR/FTD8D9xugceyzfh+CYXchiHlVuR2mpFQnGucbvTfbR7vMLeTPE3P+912tHO+xGNbSRj0yXCdixTNBP+0oHlO3qxLPI/n+rMXkSK4wHTMNQ23+/uNnpvdpsk3jPtWJORHImMuGblHkLLYjsI6i5C3qBHx2gZkXOWaZvuM30o03z3AI9B3veeu2/Rq888+xCeT/H4+0AVHHkCeijuQh6jbfSZl92FkZFzrMa12u4s1h5xjuiwxjQ+a5jMRL78beZQKEO8VIg/VjGPmOHkuG4lUhxpkGKxExljy0mxBRlaX3+v2/O42PXvdVynyBFe5rwmEwkx82us5ut5jG+txVxmHUabxPmQsXup5weObBnzT4zyK+HeI31nkPpJC/7bnbgsy1iaiee/0ePIJz3qu3y32d8VoQ1iHjKJlHlsu8szNcLtNBH8M8zhS2kojMkKGQc67PEctpluP6dNLzO9Qj7Md8ddE/y5D89njZx+Czj2IB8r8TEo/2YPWRxni67TRKUGb3A73tVy/c2eYhvXGdwHweSQHX3F7N6J5b0P8dMD4vM9jznX7u5BsajWe3yVk+HrT6DrTfL3ns5zgw82ew07E1/WmXwPiRTxH89znTtO63rTZAHzLfZ6D+GeM5yiP2MQOdbsl/rsAydkZno8q+g3Y1qWIZyqRAX2T8ahAPLWaDDL4rSAzAE8ENUjxpNBcIVI0e9DCexYJuCaU51OOlOE+PzOH8Fx0I6GVPG55hCfuE8igyEPGzAAkhF5BnsUaImTb6H4GIyE6DwmdNrSrTGHCaWgneT8KiZYgYTbdz2xFIas97mMAEqJ3IwEzyPjfYlxaPf4ywgtZToTc0vgKkKDNQwJ9Lww+HwnAZYSh9G4kKB9Ewmy8x7QLGSQveUwlxncHUsp1sL8aeZeuQMJ7gXHbZnqmfLMR/q7SY1iBBO+lxhPkQepACmM98gA2uq1uz+eXkbJchJRivuc1ecUq/ewCZDRN8jyWoLBqMsraTNcm5F2Y4vbGuP+j+r71Jb9f4M/GebwT0HzP8Xtp7u3pzVkIvAXx1QjjnMJ6M5GXZxxSYq0w8GJkaCXlfMBzOMTtDjDN93isk4CN0HSQ8BIl46UeetcQ+bBTkNd1vf9OntgliA8qEZ/2uM9kyK4RbtxqunQgI+thxHuXony7bsSnz7qtlDu6QnQ/sgblSG5HSnqm52wL4VW5GK3byZ6bOqRI8xH/rTW9Un77XGQ4dBwzjt3utwYZVXe5vQOmY4vbWUJ48GqQ1/8V/9+DeCIZqPloTdW6/Q5k9NYC58ChVW53MjIcioz/Zzz+fW47eZHG0O995SyPKXnGEp+uMz2bkUE8SykrHDGNnkR8tsRtzROuhcMQX7SijcGTiF8akXF3JfJePQiHv+939xh33N86In/2SeQdXOL3U75cype9F/HbM2juW5ABtsC0LCQ8elehTV+LxzEObd62o3W61O887d/VyKNdC1RB52OEwZg2jpciedWF5EEH2tCl1IAG49di2j9i2jQan260HtoQr5f6dz7aYK6GvtvRui9GsrkSyh709w1Inv+p8WxBaT/bOMnw76/xTwavF2Q5gCeCc3O04B9CAr0cCetVSBmUIgE2HgmHLrSjewkJ1XlECCUX+DPkDViLBOAaZHTdiwyzIsK46PZ3R5ExVQeHHobBVyKD8n4/u4AQIrPQoY1vG8+dSNgWI8E3k0gU34d29g+isEuHv6tEhsYmjbfvUci50m20E4n/HUjZb0SC6QWPP4VX8XP7kBHSbhxLTMcy0/RptOMFGcGzgEfhhxvg3feZFsuRwMxDQroCKaUZhOFwv3EHKcUJxuNbfn678SwgFHgZ4bkpJXbT3R7HMr9X48+vRgrZXs4jt8DAy5DiXo4S2fd5rBOQMroNzW0KG+UiL+wFiK9qPIZ2z9WlptcRFLqdDzTDiz+Akbea3pcQSfAVpn2u6XMBUg5VsP/HMPQ9aN7bkYF9BCmNZW6nkTg0MBQprVLjcjFSvFP87E7P4TpkGH4bKcZtHm8KgzUi5fQptLlpRvNeY5rvRIZWD5FPWeDnLgn6skXfv/wdOPV60+3zxi2F284BPuC/RyCedi5X3/WQMxFthsqQB2m28Wgk1ubDwAOIxxYR3vI1KKz4PmQ0r3HbQ/x9PuKJHYi37vA4m5Dh8Sl/juep1XQv83ydYdq1uc99xjUZhOuQl/Fet9uBjPKRfu5+t5XntgFugsO1cMo0wqvV7P5qEG93uq9qxHs7ES/NRQZGJ+y/HYbegvj6TrROUy7lNMRD693e6abTOuP3MeCNyNi8zWNvRnxdjjziT3o+rnabB4jc29lIxqYQfAtaY+MJ73w9WguPIh58n8dThwy765DnbwSSt1/xcym/cLPbH47k8lzTt8bf5bmdM1H+a7tp3IN4oN3jWot4uMvtDiE8/0mOXoa86EXG/Yifn0xEKZLMWY88vikV526kR9oQb6cNdJ5xPYvw3t598nIADx7c+5rmAA4ZMjzLAXydIDMATwTn5mhRD0CKIRkCa5FSrKA/z6fzTigsR4v1CBF2Rd/3hwo7iR10Dxy6xqfbGpCQ7/LzpcTJ4EPIOEp5R/nATOi7FnKuRoLhLj83Ewm6zyMl8hISLFuQsF2OBEYuEjCl/rvK7z7pdxuJxPApSMG+gnbdpUhhpPDTaCQ8ZyNBtRoJutVIeBZ4HAVIuBaiXKmnCOPzTJR7M8x0K/M45rqvYR7jROK03QxCGJcjT8QbjXee+xpNhHqSMTnB89ANnd+Hwk8S4b+UC5lCTWOR4VHszxcZ1zWmZ5rL9Ug5JQ/SBM9VCov2mq73up9O0/UZf9+I+KsJKfjkuf0Eceq2Anl4uk2Ti02HFn9WipRko+cN93MJ4tnPuF2QYZK8GkVI4cxAofPt7it58T6BDL4lfrYBKbQWIoyXwvHFiPfnIV6Zh+Z7u+eqAPHyEY/hTH9fadrUEp7XPOKQ0Ese0wxkjJ3uz+tEh97ZkHux311rnJ8kwvyNxEGhGn823s+2+7t2ZIikjd0ufzbAdJmAjIDpQD703gq5VxK5dMkIbkBznEKtZyKDqxdYBn27bJjuR/JknvtrNt13+/1iIt1kk38Xarz9J5kvMU1u9M9I47iEOECQ52fu8dha0cYxl+PDrpfK+5TzlHFJXqXkUUsHRI4iL3Q9WouViI/PRsZwr8c+HfHbaI9rCJEbd53n4H7EP3PcV4/xLSR4cBnsvQeGp3F0uI9e0+o84vTwl4n0hE632fEfcM0nNq41Hlsv2gzvB/4e8WtK/5mFNpJXEYebVkFfA+Qkj6QP3vA44qWZwps1aP43IQN1uunfRcjFZmRs/hHyTJYiuTqW4NPFRDrRuZ7bXI+vNOGeGYAZ/OaQHQI5EdShhbyRyC1LCmoMUorrgHIoXIgWdi8SBAuQAG8mTvQVIQHS4mcrYHAlcRouD+3G8/192mmXE7vPMUghbYScpByWIaE0EQngy41DMrZqifBICRI23ciA6fFzncSpvtVI6CwlTvmtRUq3Bu3Oi/1Tioyf6f6dh3bVgzS+fi9HNXGSrsc4F5q2eURo+zNIsaWwVT1xku9Kz8tQIuxRatp1+f1KJDQ73G4bYUxvhyPbYGAK3w6FwouNb1LuyUjfizxDMzyO9cblYiS0pyODcyUyytYRhug2t19GnCSdgAycAuRNucC03Odnm/35N5Hn6E631Yi8cHnHtDvU7y4mjNURwIWmawp59rrPetP9XtMypTbUEXlgk5GB9jRRuqgFKbOVRNI/nrcfAD8k+KAGKfOZyLPS6jG2IH7tIjwgKdx6LsHXTcfQrpooBbPefRag9dDt8SV+qQBWQe4HCC9Rsd85x3TrOQbPdrQ2NhKnfnvc5m60IbnV81VKhKqvRkZVMiCGab91ai/hkVoLDHY5ng+YTlXGpRF5c2shZ7fn6S+Bm5HnMG2CVhGHjsYajx1ECaKnjX81Efpd4u9rTNetnrMitN5bkAE713g0eawHPJYp7qsEcm5Hhtow49DhtieYnvuRtzSFRlP6yflIxqVwbt4x9B7lvgZ4bH9BlJpq82djjVMKD7cTh2jyNAzKj5mzc5CsnGT6biUOQU1HXvHhxncdWseVpmU34ZVMMq/D9EmbjybEs2drzlhs2g01PcdCzkzEN0vdb8oLLHK/pUgPpHVejgy3MtO0muCpNWitVZoG+UiufJ7wNg4w3VIKD/RvRijiJMNrGbrNQsCvJ2Q5gCeCC4hSH7cjwyC537cA2+DwfUgwrCV2zPPpT26mBQmCsWhh1yNhWoaUwHwkEJuQEp/i9vORYJyAFGouEqTtSFith94e99mCDJN9fqYeCcnZSNDUI2FzP2EAjEaKbR8Rzs5HQnwr2skm72fKVzmCvDetyBjba5xrkMBLHpAhSFC2Ii9fCldsJU4pVrjfuW4zGQcp52uIaZjCwsnzl0Ij1cRp1xqPM+WuNRiPbs/dXGSY1MDAC4myH1M9vjzjnocE8D4ip2etx78Nzf8kwoNZYdx2uJ1293e55ywPhXS7Pbb0Ow/67iJOJeP+z0JezmL3uQIZBLnGdwLhpRyt+TqyiUh+3+a/9yLeW0N4MHcYn4uBb7xJHrzk+U2etG5UAy2FptqQ1zcptE7Es41E+D2P8DpXIQ9PPlKsj3t+GoxLA1LMecj4O81tNnhsyVAbSJTbqDB9JhFhzNmEgVqCwq1jCANhovu4zH9PIQzllBd3nvvdjPjqk8ZxEeLdFC5faVpWE16nAuAgnHqh3y0hSoScD6eMIjY6KS+sCJWPSgbteM/XUD+zwO+0E6c6i1GIM31WZzp8GRk3k0y/lId3LfJarj7m/eTFf8HjSKkbO/zeMLfdZLo/4jaSp3oF4VV+xOOdj9bIeOM8Fm0Ihh7Tb9qEVSAZcMC0vJEwZto9l7XI8zXcc7rX+Nzt/vLhlDMQz9ceQ6dcjenIgx7zhcYp8W2KOAwwTnuMXxHaaLZ4/E+jDWUzcUq+2O2V+rsb0HyWGI/ksdyCZMZE413jca7y993GocD/7zB+B4i6sinkPNrjaDeOR5GBeA8R+m5Ha7AAeVHP8NzcQgYZ/FaQGYAngpR3k4R0sf8uRsKqGU65Fi3oEchDMYUI+610G4uQ0dKBwggpV+Ys4pTvUGRclvmziUgwLEfCpx0pqFq06/sq5N6BFMB+JLyTkEkep61EmGM3cRqz0+1/BiXKH/V7o/Te4a1EqYEOjeNIOolXhU7qfqhKOD9NFCsdQQj7KmQIbTY9HgFmw94N7v8BpLRTblQz4TkoQh6LTyFDqwcZyQvd9jSUiF1A5JhVESVvmkyv85A3ZzEStne4/dV+9m6kMGagEE8hEtxHkJIZgoyLDtPqY2iuq5HCG+b5udjzdw7yAIwi6ujtQEZij7/rFp1yrkZ5jF9EhlKLf7fDzueRd2MhUUoizX0x4p07NFcD/wB5W/OIshgVyBiaYJreZHr3eOx/83PRJ3nRuhF/bPd8diMe3Yj4N4/wsKSUgW5kDI0kPDVjgbcTh4VGIONwttv4ITIcNqJQ/el+v92fTzY+5cSGJOG30fh1Grflfr4UFem9l9g0LfPfcxH/PYTW73a3lTxlUzxnVcavFs31KMRDHWitFSNDKBmRK9Ca20yU+Xncv9eZzp9AfLzE/c4z7dehdTQRuRB7iRBkB/Sm5P9XiHzZFBJNXrVCVIdxjvsqJ0L59xK5lecjY7fWtE45eEOIkkA9iGdGIHk13ePah4zP8YShdoHHUY94cBpR3+8IHLmX/kMyrT/2e2nDW0Lwgg9ZcJ//z0XrYLtpvwPxz0wiD/oLCDqMQyvixwtg4NVojTUTh61STmQu4eG7yePpQpvh/Z7PXuThHWGcLz6G5ps9F1ceM5/PmjZHiNPadUSJmAuQHFpLHMSaiXiwBlgPTQ+bLin6sAQZgIWey23QtJX++qBb1hAbpwqkR7abhu0oReKkQi+v3QGQ3tcZ999vyHIATwQVORIKKeF7CBy6DgbfiIyaErQD/LCfbycOA7wXFWnuRALpcrT4VxCKZjfKU7qEOM22Dy3w+5BABhk6O9zXVUjBdSMh2UrsDqcZzzVE3bJhxEnlg0igbCZy3V5EgqsdeatSeKyb8DgUQOs9UHah+9qOjNcSJGCTF+Ee4iaNRiIfqgJ5DerdPkg4jkbCfAMyKm/leGNikWl1tZ9fBi/+GEYuB/4CjvzY9vl7kNBNCrAFeVTqTbsxpvcwjy0p08nEDRzlfmei56kWlecZrjH9/Evwpnehk8cbTMfd7ivt3uuQYpyJFNp4ZDiuJVIJFhOFl1NOXNsxdOxy248Yt9FE4d1PIy/D9R7vIY9lN1KAFyAD7i5k/BQTZVJGuI2ZRI27pzzmB/3+rYgHUsi98xgcq/zzOXT4YxXyKt1JhO7Gue0qZHAsIw4zVBHFrieYFhXI+GkmTsFXu42UQzuBWBfbEO9dRBSfHosMqyHImNhAeA5nmHb5bq/E7a8yzm2IZ3cQ+WITkTKuJWogjhWOL2yC029we6tNy0b/3YYMjMcRv+Wh+Z+Lcruugp/dCG8Z5La/gDYgm9Hc3454I+XOfYvIJU3fzwG6oHcb5L4HGa9Tgdug7ynImYP4uA5tfkAypN00veuY/lbAoR/D4JSLONY07oGfbYO3vIsIL7Yg72Ir4r0y2L/ch0QmInmzD3mjutR2f5mVawiDdzPS7VfRv4FhDDLiF7vtYrXPbUhWpPaLPZ7kVW/391uAvyEO4eUh2VlKGJ1PI168nDiotwf4DuL99yC5Md59NCH++CzwV27jftOjmagNmI94biVRt/JzhOd3NOLzZvedhwzHIsSbd/vz2zyX3URB6DFoM9Xg54rd/xyPb6pxbXQ/nwEGnswcwJ8wePBrE4c+dKiDIUPeluUAvk6QGYAngh/kSHE8inam24iTcl2EgpmIBMZKtJAL/E41cWXTbpTP0eJ3yombCKaghZx4fRQSYLcjwTwMhSUWIkF1NVJ0jcJj7w4YXo4E45NIkM1CO+fniFO5+5CAafL/k+HQDS7TMpSor1UP9ELfY5DzUWQE7USC+2niAEIXkZ9yJpFQnkvcBLLcfaXcrW4kIAuIHfd+4iDJaPdRYxqWIEFbjYRgo2k43WPrQNei/QmRF4Sf6fEc1buvF1D4bBHavaew81a0q0879+3EoZg1bmcYEvbLiJ36WlTN/51IYKeNQi8yKlIfA5HSOR3N+WhkLO1A8/sCcUL4NuBvTZM9puk0NJcp5H258ZpIhGQ/YVqs8JgfNC5nIL5KuWpbkPGRjON17ruAUG4/QAZVPVHrMHlXFyGPY5npdoX/H0gosJR+0EUUIz8m/M1BxOMHTdcNaINwu/HoMu1TjmMtkSLR6O/2I6VcSlwnWETcltOO+PJBz2UlMnZ3Ezd6fA1tcI6gtVlHnHTf7XdWGofhpsdpyDAsNY3GEPUJq5FsGEbkak0lwn3dKCx9gedvCzIcbkXrtYy4Rm0FOtm685i20uapHPFfC3HqupE4fDIXrfU6/1/q948Shb9nEdcmbiY8YQ3EjRjJgCpy+3tMl4nHjPMcxDcr/M4CNK+7kcf3XCJlYTdxveNfEJvrbmTANKM5Takb53n8KxFPP4DkzlqP6SDaQJ5m3FYQJ513mY7zkLcZYjNaTxy4u5jw2vZ6TOuItIp5RHi8kagiMBVaZ0DZHcQp+EVEsfs0T2lOUppDO9oknGVcVxO5rgsRb11JXE9ZQRwq7DS9ewnZtBvJj8uBv8gMwAx+c8gOgZwIUr5YCRK4lyOB1OHve5DAS4oxCZG1SCiV+bvzkBB9GnkfypCgGY8MgWeI8OcBJKjb3cfTSEB0IWPqfUjYNfu5ShhehATiRCTcphFXpvUgpVjrd1IYqVjjGPweJJzyjFcJEnjzICd5XQa7r8eRoZgUys3GrRMp9A3EoYwWpIBWmG5F9J+eYxiR45Vyp4b4/T3HjH+aaZAMw8eRIq729ykJO+VeJq9SA/JsfsZjLzAd3keUXtnod/KQsZLCaB1E/k03MgbbkfFbY5p2E3mdn0JGRA9xChbkVdhh/A4hL+/jplsytJrR/Kck7vVIybV4DvYRp2arkbLo9bMDCM9vGTJmahEPJEO+1TQ9h6jDlwyDSsLAhqhn9wvi3ultpuFmt9NtGs0x7q3ICN9HXO3XQ3iZJiJFdikyBHqQR2Ws22oicmpXovX2A9Nmr8c5jbi1Yzua38eR4flBP7ObuNd6InGf8dPEuroceZ3KkVF2BHnsHL7kfrcz+RhaHUAG3mrEt5OJ2mwVyFC4xnOWixT3fKI002oV2x44jbjGcDdxoOAV/76MuNqxzXPyecTHZ/md04zDdsLT2Yv4osW4TUcyInmHkgcq4dyC1teTxK0huxD/fpe4hSblzXYQBaNLEX+kPNtKxGPbTfdi5PVLXv8pqH5fB1o3VR7bRWiuDyC+TvmijxjfXMI7m7y3PaZNSvNIRmcHmuu5hOdsK1AEfUt8F/OTHs9ZyGC6DsndZUR904vcTwPiv1bk/WslTj0n7z4e7zYoW4rW5xqihuZRPzcG8fcwz+cZaDMyExmVg5EuKfeczEO1UZ9B6TR1fq+dOEm/kzgB/yDi6/mei/XIqD5pkB0C+Z8KWQ7giSAljc9GCy95EJJ3qJk4zbcZKbo8VHPqXGQEjKZfGfQnTk9DQm0bEjx5SJjvIu4RfooQxOciQ2Gy361GQrvC311wDD4VSLHfi4TyFOJE6iQkpMqIOoQpLJOS5lcazx1EAeGHiBsyUrjoeSKh/BW0G0+eiD1I+C0lPJ13Ga8pSPi9HSneEiSkV6Ld+H4k6LZ6DrqIPLA7kaGZdvfVxKnkMuIkYafpXO/2krerzn18bKDwaDD9B/jd5MVpQ4ptFRLsg/35VtPww8ahCQne8URI6DQiV2ij+3yJMIxrPa41HtMXkZDfhHhiIDKq1hv3FGZKRmwZERovJfhiuts9QHieD6C5vcb/VyPjoNVjS4dKmvzuQSLvcyo6tLDKc3C1nx1B3I09Hc33AGQoXqS5ermF8NCUIiW4EhkZ6XDALtOnlTgctYsoN5Trsb3k76qMx0bTowwZNRuJUGKn2vjZS8TtOxX0h3D7jcU6tJHoRsZ5DXGiuAKtnS7iZHYLvNCF1mI5AUfQ+n+B2NCcSxQGz4OBE4nctGrPyVK0fgrQPBe634uh76jxvAutjc2ID1Ku11j3s524qi15XosQ/3UR9zSnEOTDcORqj/19xAnYQ2pv778g4+4C4p7pbcZvvcf016ZrM+KHVW4vGWrtRP7t0+6njYhWpHluNC1PQ5vlJmLTOBfJ2aH+WYDWXBWa7yri1PYY0+FTyKh+xePNdymbKqIM0Eb6D89RizYI1xv/xYg/d3rsEKWoVng8aR2nDeFWxId4XCuRF342kVeZvN5DiWtEr0UG7LG5tO1u59Noc1WA5G6JcUs5saebpnvh8HLCKz+UOKGfQQa/IWQG4IlgY4kU4Xro+z5S2hehhbcTCauxSIHchJTZCOL02wF/9iQhFOYgYXcpEghtSNBuJYqHTibuJU3evhrCK7MJCY1KlB9Sh4ysg25vgp+5gAj5HEIC5Xbk7dhuPKYa56PIOBrvvqZ6vCuJEjVpF38aUeYg5dDdZpweQt630UTl/IeJuzOPIkW4znRK4fThyACbjIRoNVAIjz/qPi1YD92JFEEHOlF4s8cyyzSfh5RwvX8mIeXUAZ2PojDLpUcit7ABGToFyIgpIYw00HxPRWG6x4nbTiqPma88pKhKgEdh7/WEYV6E5nwJ8BT8/AHCc9Nh+vUgpViKitKe7nFUuO0vox3/bo91Gso1WoFy2AYgg7aYuLnlfuL6uk8SB3iKTI95RGmjGUSdtg6/v5jwFg1AimqiadFJFLRN4cL7jGcnnPpJwmN3BlHDrsJ9lxKHLnYR5You8zPj3OZSxGtXex4nEvlX6TBAkcfaiXioCt5yLeKZlKdqL/iRpcfM19PIKDhAXKOVPOcXofDsUvc1H04f5r8Tj3ZA3w88/kLCyC1BPF5AXGlX5OcOmm7V7usBwmM0DtgGOeMQH15hXF4kvEEQ14h9Bq3XWuQ5vQjxxzxk5KxEPHcOWnsdLhuX1nwDEZY9G4Zfi9bqEej7ElGnrs3jmKDvuBoZ/huIzVWz6b+UOHRRjOTCVL+zxvgXIP6eTOQrj0UyaBEyfFOaTAHyyHUDT8HL13mujyDeWgl/nw4PjTEdP2+aLEF5lBOQ970B8ehFxvsm4orL5K1rI661fMSfbSOu2HwOradqxM/no7XV6rla4neKEf+cR+S7rvX4ViJ+XIH0Ry68uM7z+1m0Vm4jCo9fhQqdryW8oqPhlGpCFqUN40mF/+xgx2/6k8HrBZkBeCL4zgEt7HMh51a0CLuR0v0EUh4fR8bMuUg434EUWjL4iohrotqRITEQCZZtSGgMQQu7EymYR5AAKUIu/i8WqN8UOptK5GCdhXDsRcp6CDK4ypHy2EGcWj6ABPcOZMxU+buNxm+2x5gHhy5ESrYcGUjFxF2lP0Yhta8QSniuv7+TyAEajZTcDUgRzkBe0QMex1nIMN2NcvCajdd4fX/4Lrjgo0g5LAeuhMFLiUr/U033FNrqIK4ESyGiGiSIS6FwgfG5AIVaViND7tmSKM8wFSnCCzy2CaZBFYLkiWvQOH7+fc9LI/0HHYY/ijxDSdk+hRR+HrzpjWrz8H0of3ApcTKyBgl8iDt2dxOK6gri1per3PYcJPzbED+s8txcQdxNeh5R9+0502gFcatKqfFN+VTFnpvPAf/kuVrt9zuPafd9aC2MQDx/wLitN92KiZO8DUi5Ja/hDNNtAToAdDWR05j48QEi92kjURB6N+L1T7rfy02znX52AprPccahVzgPXI7mNoX1phqfPWhN7nIfZ/rz243nUmJDtN60eBxy3g7fvR0ZFSmcuYXY2OxGBk0XWpdpnVTAv94DfNQ434LWzOfdxh60DjcYp1rCW1SKPD5rCK/6DmKttiEjpIrgi5XqP/cDiI92EgXXz0PeQpCB8kXLui3GpRjJkYOIb640binMfNRVA2qIGnY1xOa33fiUeA5L0dq81zS7AcnPB5F3rBY6b0e8sA0ZQnl6/9Qz9O6R6xB/fBL+eAexNvOINf8ckj9Jxl5HFARfQmy4pnteLjddrzQ9ypDhXYA05DcGqp/FHttCz1sVER25nyj3NN5jbzMtB7qvTUQZl7/QeEauN96rgdnQ+kvjV+G+iwjv9ZeJYvZFwB3Q+QyR+3zSoPc1/sng9YLsEMiJ4MM5En7vRonu64kE8irCW5RHlGNICcRPEif6etHCn4dCn7uIK9n2IwVaiQTuUOAMeHEsjLzH33cjRTeQCOX1IGOynPAU1iIFuAGogL6lkJPCJnORYTgAKe5BRMgkGQE9HH+lXQ9xEvkCFH4td/uzTYsm4nqszR5jCpdUEacl0w55ov9PO9Y2ZMQlj+Rq95/C3I1I4Yx1vwVEKQQbNH/7A/hIClG2qp0jN8DA841jGlM5ERrtQkoQJPC7ifymo8hQvZUIm2/XfB+6AgbfgYR94oX7PI5C470YKZ8VRK00kHfvE4inij2GCxCv5BI1G7f6nXM9P4+YZjs8T6XIoE4eqnxkQA81PjP8XhcyECchY2MqcdXgfuKmk0b3exEyKpLHJ83fVMJL+RA6pLIMKckZyNhpP+a5p02bNhQe/SxSgiMQn9+FlGeL53QAWhPzCa/tZv9s8jwMIK6AayZOgO5BRt3jwqdzBxReazq0mU73u58RRA3BZmRETSDWF/77UmRI1yD+3oyMMxAvJi/p37mPaSgM2YXmfSpS1nvdbwMyyKe4vVzibtgvmo5DED9cgrz6rYQRUur3PksU2/6ycVqMjLgmwvu/mf46kUxEG5ApRFiyBRnHm6BzCRRWE4fb2vxdCslPRMZOC+HFvQeto3bCA/gJtE7uRXKthX6DhkrTZKnb3u2fcr8zwvQ4SoREyxBPv0R/3mvnJii80XgWEweo2uDI92HgDZ6LB02PGmJ97CFq9z2FeG0x2uh0E165WnjhPjj9c8Zns2lTggyvYrdRgtbjpWjD8BT8/Bp4U8oPzSeiNkXEwb9moqbrJqLUU5vbuwytj8QTtcZtH5JJzxApBeuRftnuvp4+mYdAnn+ND4GckR0CeZ0g8wCeCJqRUh2ElHJKxK1FgjaFCnchgXcjMnIG+bskCI6ixbyQuF7qBSQoT0dGA2iR7wTugZHnI2UDErLDUU28A0iorECKqJi4f3ei+vvXTcBKyBmEhJdzgOhExl/Kzxrkz8YSO/PVbrsMCdqL4NBR49GCDNcZ/v0IUlCjkKDO9f/JQG5EBmE1EfJbTXg7U+gE91eGlFkNCl2VEyd493msIwhPUyNwAXyk0vTd6HnaBgPfSHi+piPF04sUwbeQAbSbuFO0ExnEHciweMp4JWNhBLAOBpcjQ3ym8fwOEtiT3eZKIpG9mKgn14Zu+TjNc1lkOi9Cgj+FePOhN+Ui5iGj8AvEQZBmoj7laGRktCLD4Wq0moeZ1uM8jibEr6OQcbbd/+9zW+8jclWnIiXa5HF1+5kG/32O6NvP33cYT5Ax8yxxYf0c0+U5j3cLUrpVKAeuHBkHKc+2m7iJphqFOB/3mPOJu2HridDcbON3FXApFE4jbreo9fjLiNPMU4i8uF7T8QLiSsJqIlfziN+t9Fyl8HkZWlffQvN9P+HVrjZ9XzCu4z0v6d2thML/AHE1W53x+hvEBzWER/sKtF4fIjywp7vPGuJg0FbE3+OJkjd3eC4bPGaQjNkAvAcKbyCKlneidXQAhUWTMbONuJFnj+dz9THt5SM+/ajbGaef/V1E2PIxwkNXh2TIUGTQrCU2GC2eo+3u80z6N0uFU5DRlKDAc9EFAxeg9Yfp8W73tYaoYVl3zN/J45/yaNvpv87v9I8S1QBqiA3tFPpzBDvvIQ4rPQX8uY2/Lr8zy7gkD2EjIRvne+yziduMiv17EVqLlYi36olC8w2EEZ5HeNOv8BycVOjlZNUB/MEPfsAHP/hBysvLycnJ4Xvf+17/dz09Pfz5n/8548ePp7CwkPLyci6++GJaWlp+bZu33HILOTk5x/2MGfO/M9EyMwBPBNOQYM1FgmY08k7Yy0Qz9F5NHMvvRot7k58digRaB9ppPkjU/8tFhtd6JNxxu7uQIB6NBPgi4nqsfKKcQ8KvhLjj0gbNOycioTYbKYrzkNBoQQp4GeGR2+d+65GBMNTvliHjrQAGz0BGRS4SQjuIE6gzCc9OGXEbwGZCqG4mCsJe4fEdJe7IXYGU1FpkQEwkDo/MIE7JHkFCdJzHnXIwp5le05ByakIG07eQQdPocaYd/VD/vZuod7gKGSi5pu0SwqPYjBT6NuLE4S63u9203UgcbMhFoc2FyGAqIk59H/X4agnPYIfbXCma5t7ovxOvbEd5XsVus9DfnYGUQNpALPbYdhEFhGsRD1X681LTKuUILkFKDOJgSvK+XeZnX0HKsgEp3Y2EFycf8cIa/z2VCMG2E0q4yj8plNmLwpcHjxn7UuPRixT1aGQ4FCE+WkOkIlQjXoK4Z7WdKOrc63cmIi/NKNPboft0TdfLjyKe2+a22jz2UcQhqekoNNhI1DIs9lhecD81/r/rmJ/BpvvTbq+V8Fw3GC+nDTASGV0bTLNO4i7kVmTcdPj9FEIfh+TRg2geR/mzxO/NaM6TdzsZj8kTvMc0O4uIJKT8vXoUFk0evnSwZD3y9tX7/7M8P5tN8yrEg5th6JkorPuSccsjrjEEaPFhhpc8tly0xpcY58+73SPEuiwhTuvvJgyuTuPThuRRHuKfFH3Y62e7kSE3CI78AG0wliK+Hkd4M79ClPnaTGw81gmNwo8ah0TDQs/pGW5rucc8BHgEDu/wvBSZhhOJ8ktdnpMKxEsp5aIIHTgbhPj7KOLJtEG/EoXQcxGf/Z5CZ2cnEyZM4L777nvVd11dXdTV1XHjjTdSV1fHk08+SUNDA3PmzPkVLR0P73jHO/i3f/u3/p8NGzb8LtA/6fCGk43Af1vYQySITyJKnlyKFFQu5KaFWY0W5XLYvw+G7ieurVqDhNh3CQWZjxRzFQrnnOvnlqIwSjtRoiEp83wkaLYYh5Tv9ghS0mMJo289ceKxGwmfzyNz/3pkqPb4nTwkQNYRt0c8hYydJqRgFiEB1eQ+r0UCaKmfX4WMk+Tlq0DKZBkyhjqRITPD9ErC7DxkGDXou5dnw6nJ+O1AQn0YUpojjfM602IfUv6Jdrv99zT9f/gJOGW2n58KFMA/Xwt/+EYk7MebHtVIMa0kiu/eROzGu4mDOJWaYyYhYV/o8W0gvFsvetx7CC9KLjqwMgEpyvUolLobKffziSLgeZqXvZfB8IVEHtBC02kl8rCl+buXuH+3BymeBiKloArxx2oisf+bpulDfmY0Uq5FHl9KMdiB+D6F8R5BfLScKEdTaTxSrlod9D4Duds9Hx3IIHfIue92yEkbq6S4jiIlPRp5M29DJWNGuZ8Jare/riTGsQIZgsm7+7TnKQ8ZZ6OJIsUtyCN2C/03hhwATm0zrS43js8ShsUBz9VYtEH5LOKLArSev+V+UyhvvfudinjzNOO6BxmdLcTGrdl47jMd9/qz1Mdm5Emab3wrPJ4r0JpMEbfZHFdiZ/9jMPQatLZ63O+tBC/neXzbgRHw8jVw6j1onRwwLccinqxF6yQZ+O3IQLzEfTu/kDYiMvAtosh7ynfbR6Q4DPP4gFOeIe7EHYRSZEqNy2i0vpLRegjx4h1I1k4hSipNRXLxgGm2wX2PR57oJpTi8DHkUW+CgQtNl3VE3dDNRM5uhecveTprkZzbiXj6EiLfuMI0ex9Rk3GTnm36FxgxzriNRjy9Cs15IeHRH4A2G3egXOohbrsMbZRSFOk+03C+x97h3ycVkvfutWrrvw6zZs1i1qxZv/K7IUOG8Oyzzx732be+9S3e9a538bOf/Yy3vOUtJ2z3DW94A2VlZb8RLv8TIfMAnghKkSHwHfpPsq19DAmI54g6gEXIIAK4BYZOIU6AbSZOuLWhkMcOtOgnowT4y4n7QHcgRf0UWvwbkbBfjwTwYuIuyQlIYEwGxsKhNUhxPkfcEbkfCbl2JPCfR0KkBgm0dMCh1u/lI0WbkuWTV2I1UbOtizA6Ui5fCjmBcoSu8LPzkGDehJTAKqSk64nbPs6lv87ZqdOM6z3+PCmE3f7sIsLQ3Y+MnkX+vZ7w5GyGU+4iDMgCoBv+8EEkuNchRbWPOIBwPXFKdTlShLd53BuRQtxE/x2kjEEGzBJk9C0gwpLF8I/LkYLc5nnbirxCydPShhLBr3e7w9xfLjAUhl+NDkIcRIZ2g/ua5DbHId7YbHxHonDmGPexj8jR3ItOjg5H830hyjU76rnIcz+TiKuxLvfvqYTH9CrgUehdRdw2kkJUzyFDv0pezL3Pu/8ByDg5T2POmU/cNpLy3FIS+/uhaY7H14M8TGNNl1YUFr7W42v13x9ABzauR+upHvHiNo99C+KRBs9R8jaug5FTTLP95oFh6MBACqklb3TyeHV43CnXdRah0I8ij0y321iA1ucFnqOnkdGSSxRXTgbUUGSYXIC8UqXIUD3qeU4bx250+KUArcHtpu9G+g9+DL1adOYO47ib/g0QpR7HA4gHm+HUOcjovcd9vER4p6b7nRrT4RL31WY8dxKpFs+bbiOJk/gH/Gw7kWMMkonfAb4BvMufp3SvzxH19QqQLColjMFzPE8PIrmzmzg4tAiForeZ1qPQ2qhAtzNNJ4yzfGQcfoy4j7fY89JDFBNPObXXuL9LCGN6Nlovc5ABmzZL3rj1PgEjLka8cKnH9wPNExvQ+liMZNFDyGgtR2tpCXFFZj3isaXI8LvPfS8kjPKTCq9V+Pe1NCR/NRw8eJCcnByKi4t/7XONjY2Ul5dTVVXFhRdeyM9+9rPfKV4nC7JDICeCr+ZIMI1Bu8+UNrAaCexLkcBpRAtxD1JK+USCfT1SYlM4vhRCvf8uRB4DkPBfhoRLK1EuY5LfWUTcOZy8YBVICYxHXsZbkfIbjwTTbqSsVhIFVKuI0OV64gRvSqxvcBvTidPI+5Ag2ooE1HokDMcS98+udlsYz9HIa1Lh8XZ4PK2EYutAxudWt58OaKTQ3WLifs9PAx8iTuKWEjdFLPNn40zvnUiojzc+HYRQH+f+m5Fwz0XKDOPxA5TP6byon3wf3jaOCNFMMH0+6favNw1aEV88Qtxl2mZazfT81BAhtdv8eQpxNxrnMcgD0Gz6/rH7/CukHAeZTlPdd/K0VCLD43OmXaNxvtT4LTHtniJuiFhrXG8C/vSYeRlr3LvQPI8n7ppe6b67iJOSG5EBk+/x5noMXyN4p504HTvBP5NEYx4E1r4F9v5M79SYnkuOoXvKqR2C1tp+ot5fIeKDFuNSi/h/OPLU7Ubry0YSZabvd4xjMzIaLvD31/r3GMS76SBEk8eT5+8bkJf3dOIgVIu/r3A/O4lCwxXETRoDkEJPeWIVaG1tQjxfccxY2xEPm58774XCDUimLPd81Lr/NpQvvNQ0GOHvniRCjGOJnMRx7r+BuBml1uPrQLz2KNpUps1Ayle72O3tIGr0fRFFB0Yjw2Stn98MvesgtxLN+eeJYuqlSEam1IHJfma5P6/wM3h8493/labP/cggqkGGFMR1mTXQdyvkzHJbW4EiOPwonFJC/xV7dCC5eDniia8At0LvPsh9F8orrCIiK7NMo25/vss0fQHJgWri/u4e+iss9J9MH25ab0K8XIRk3Fr6jXPWEBuAW4ncySIkB1IkZD9wx+uvxuMQyAYGD/6D16jNXzJkyBSam5uPOwQyaNAgBg0a9GvfzcnJYfny5XzoQx/6ld93d3fzh3/4h4wZM4bHHnvshO2sWrWKX/7yl1RXV/Nv//ZvfOELX2Dv3r3s2LGDoqKiE773PxEyA/BEcEmOhPsv0IJLi3QpEk5T0a7sHiJ82E1chbUcCc+UAL4A+CLs3wVDS9BirkPesqRs69DCvxgl4V9O5MMlRZHv9q9BAmYlEjbrkSJ6EQmr0whlUo0E8iIiGX43UnpFhIA9QBzMGIB2qXOJEhnrjeNUJMRa9M6LDT64sgMZJGOBFfCzpb7/9DxEyx4krL+AjOofI6G4xd+NIE7MzUBeKodEyYXDD8MpnyRuHZngMTQir8NoIkRTQxgDxxquuUgJt/jvIYSyXo0USptpVO2xH0FhzCIk4NPhlA7kJet2W03EIZCUQ7gcGRbjTNcS4uTjHe57BPJWNBN5lE8T1761em7zCGO61v93E168QiIUnLwVGxFvnY28NHOR8TkeGWYN7v9Z97cKeTtTgn6x284lCovP9HPPEZ7uC5Dyvg15VfIIhVzm9zrcZ4fxGk+kFowx7o2mQSexCRrhdqqJPLW1pmcPsUlZ6Ha3EXyzlbhBYYo/34344VGipMYe47LXz55tPDeZvkcQ/0z1uMqQ0TyZCMMdcB9rkSfsRRR+r/bYxni+0ppcj9YMnpdWxGcp53blMX+PQLx6rmnUbLwGIuNsKMoPm+TxNaG5TakL493fc0R4dQDHb8baiBqLSwjv1yTihG6jn2sxLkWmyWoiglBsOqwznauN7yp/vtl0azXOVxDe+lLiRPki4t7f6aZrtedqADKYVhN5i+f7nWeIgtzlps1kJLOSdxGP7yXiZO0EYpPXbjo2w5EnYOC1RIpEJ1ECqxnJ9muJUkCJb4ejdX0pKvvUSXjrijw/P/TczDB9ytFcL0Oe+m0ee7f/Lkd8Mc1zkNJ4PncyDcB/eo0NwPe+6vObb76ZW2655de+++sMwJ6eHs4//3xeeukl/umf/uk3OmHc3t5OZWUlX//617nsssv+y+/9T4AsB/BEMBYttFfQ4lxJJD2PRQt/PnFHZwtxQngpErzD/Nl9yMvU5uhSDVrMlxCHAPap3UMbYHAVcaVUPRIcK5AQq0CCIeXnrDym3+eR8befMPRWIAXyEJGb97Bxb0YKoAsJzglI6A3z51P8XcIjDxkJM4hDB/thZAkS4unwwnagB94yzmNLXrxtRJkPiMKr891vMnZecH9XI4NxoNo45T3Gazr03gu5802DjYQi/5b7uspz8xIyGNtN92okhBuQsP6wn6vzeEqIkiPdyHDdig5F7DBda4gbHF5wf8ORl+U64rRjN1HI+DR0qGGz6XA7ChluMN6FiIfOcjsj3e885JHE85JP3C89gChdsg0p9oc8ZyNQmHA+UlqzkPJLG4hXiDIZ25HSLT6mj6TIr0PGaPLodKK5T7yTkvaTx6bY9BiCjPyUX1hIXFeYvJvV/i7lx27xOAuIMiMlxvci47naNBvr9g745wgyevL8/2XGLW3AzjB+gxDPlBm3HuNR7rkpc1uPEZ6pIcR6HeXv7kTz2mHcjhDrZAdai2cjpV7p8dug6N0AueWek7nus9G0O0DUWkxe7mLTYRhxhVnKsd13DJ2KTMM0/y1E7mEu2hydg7zJpchoL0Hr9iLiBHKzn51CeD4PIKOjEF3z1uXvjvj58cR9ysWEVz+tvXLj30WUfqpCYeBt9JdaYqzHVYgMtqHGfb/HucCfNfq5tBFs9DvDEc+vcvuFxH3qoxDPnoX4IEV1IK7HyyPuOnZay8ALPaZ2j/dyIkdzKzJ0axA/7kXh7AnECeLrieLauWjO09wkL3iKFkwlIhUdiIfT/I01rucSVRsOIHn8vwx+lQfwt4Wenh7mz5/Pnj17WLt27W9cXqa4uJi3ve1t7Nq167fG4b8rZDmAJ4K0KCuRwC1HuzUIqp2OrhZr9//biRN4u5FguAMt0grgKig8HwmZZvjZjcTp2nlAhzenDyNhvhcpgEuR96AZeRXWE2GYiUixPeZnzyQStlN+X1LS7YRCHY3ywq7xc1OQMEqhtW6kxDqR4E35KSvcTjsShinM/CBSAt8lSmFcgbxF85GArCBy8p4llNmxBmG+8brOeOcTN48Mpb/ode4oZFwdQYcaik3XZKA1EaUtpiCh+ajHesB0GErUI6vxWEYSdR7LCcOgBU1OyuHrBP4KXvgO4oPnjMN9noNqz9lmZMRuJU7Plvi5pX7mUWRUbXM/XUgRFB2D22luv8jzNwMp8xqk7GYTJ0cvJMpc3EtcG9aGPHTFSAl1IyUzy2Nbg3jxk6bXXL8z1P2s9d8Vxit5A69DijZ56xYYvwc0V6xAJU7yPBfJi9eAjJ9ixI/Tiav/dpsOk1DYup7woKU8q8Snaa0+gxR8OXFH7SXI+HuKMO6TsZ08zw3IGBqPePUOxD8TCe8uyIBIxmYvkU+7EW1S0uGbCYgHuuDIPiLEXy4a5H7U89RtmiePczFaZ6cThsIY03ox4plW6PyS33s3khUzidzUQreXPI1Vpt1EZLgsMM4fJFIuqoiDPdvQZjWfKFFSZDrdRVyld71wSSdj+28nGo+Mx93IQBlA8GwbUe+z0WNIxmsKZ+4g7taeieTFdsRDFxGnaSvdzzgivHq36NqXonsP+50uNNc2wHkSGWYjPPZqj6nOY29CfPILj7+LKIUzAq3nzci4PUKc6q5HoeNHPd4HkKxLPD3WeOf62bd77GPc9zx/nwzpfI9hMuKnnX4+5T6+j0htOanQy2tdBmbw4MHH/fy2BmAy/hobG1mzZg1Dhw79jdv45S9/yYsvvsib3/zm3wqH/86QhYBPBNflSCntIWpOPU7cyNFNlHCZQOQWNfmZhwrgH7v07hzoOygZ/pGniHDLFiQwS9Eivpm4p3QeUmwppNOKlPJOtOh3IKG+gqhqv4HIt2rxZ83GMeUKrofOrWpu5AZkIKxBim8sEmbjkMJsJ3JwbkeKaOIxNJmCFEceEkZfJkqTpHDzWKRcFyPB3U3cKvIIcfXW6URh7CeJ/LObiLzEOciwKCau1dpOhN+KiTIRa9zP+R7LTLd/HjJSV5rGo4k7WVPu1QDCiKhBRtMVxP2+xcBXUcL5Gca5APHDZvinJfDe+W77OaTsdyFlswUZg9cRB2eWwA+XwrsXIqH/dsIotme4/9qsYcRd0hDXZlUhg3AACltOJcrSvECcyG2jv7zNoX+RuD31DjSny5HnrNxtr0Nzvs19bzH9rzbtWqFznZotu9K0T+H0bR5fqeevxG3OJm6pOGr63W68W9B8J6/RZLSeuhCf1yB+aTc9SpDyfwXxfcoVHID4MI8oGZK8vx/x88OIE81bkHG0Ahl1xcahBxFoFlLEU4h8vDbTYoffWeTPHzfek9zH59DJ3ia/+1njfJTYSBWjdfUwWnv70HzakHrxeRg5zXOQ0kNqkby4isg/vQn4E8/DWab9RuIWjnOJYu75hKd1lXEYgHh5rMczg/70g0P3wuBrkBFaguZ/IZIbl6K8uUsQ36Z0j6GI/0EyoNd9lxwzNw1ILhzw97XEjTcVaG3PNM3uRhu0WYThutjflbuP+/1Ol+k/BvHGDr+3xLhN9Vyljend/n40sfbXo4NF24i6fKOJaxDPNc32Gfdc4/SI6PncOjhrlsefPH4TER/lu89zkIxuI3Joy4mC+SPc3wHT5lHTfa3fbUHzfdvJDAGvZvDgwteozU6GDJn5Xy4E/ctf/rLfM3fGGWfw9a9/nWnTplFSUsKb3/xm5s2bR11dHStXrmTYsGH975WUlDBw4EAA/uiP/oi5c+dy1VVXAfDZz36WD37wg1RWVtLS0sLNN9/MCy+8QH19PaWlpa9G4n8wZB7AE0EPcY3THrTI/hYJgF3IzZ/CQ5ORIB2GjKxvAp/t0gLeBfy1CjN/ZA1a5F+jvwbYkeuREGtEXoRrUb5II3r/bqIkwDIkEBqMz7nuu4W4am4NcdPEMmI3fcB/z4DCz8HIB5ExU4sEfhcygkb72XKidt0eP7cfCbuU45K8TGWm2Xiidt4IYAf8/E7i9PIm4uaN/UiQNQOXwJFb9TyjiNpeIzye+5HiSeFUkCe0k0j2bkRKtowowltO/7VUe69ByukbyAD4POHVe2iwhGyF296DjLQG2HumP3vQdH0fUlifQN7TT7uPh5GibYL3nkZ/DtHhH7itZuTRHY88Ox1IqbSIlu9OaQa96CDKaCLMW4VCVu8x3euMQwlStvuI0joVns8m4qaPZuNWQnj9RsLghXDqQjTvFyHlNMb/7yHKY5zp9xpMh0pkPEyGwkFQtsi4rjetUj7YAaSoHkGKdzJR5HcDCiUm4/B0ZBy2EJudjf7/BeKqtAEeZwq3Tjc+XdC31TROeXktpn0H2lR8yvS/FvHGzYiv7zS9LkDGUEoPaPcY7kXh3ArEA7vd9mbjvMjjWuM25sKhG1E4/lLgjcjgGGOcc932CiL/bJPHtsr03kv/XeMjnSrw4n2Iz3Z7Xv7G48iHvrtQOkMzEcIf5n6vQwZFOVHGqc5jKzP9L0JGXbFpMcrvXA3M8DWMO1AYOG0Ur0H8mo+MvxrEn6X056Mefgb++Rnou8zjfA7x5KP+/0rP4RXI819sGpQQRao/heBZIn/3cXj5QvdVbtzbjEeKGsxzGw2enzbC0DqNyKGuRzwz3v1u1ed9S6Gv2vNfizx6VyJZeQ9xoO8Mz+kE43eTxnHWUtGOWqIofjFxreBIooJCA/2FpmlEaR9zURrFKiTrUkTiYiLKVMXvNWzdupUzzjiDM844A4A/+7M/44wzzuCmm25i7969PPXUU7z00kucfvrpvPnNb+7/2bhxY38bL774Ir/4xS/6/3/ppZdYsGAB1dXVzJ8/n6FDh/LDH/7wf53xB5kH8MRwU44W1za04NsJw6qNuJUhlxDWm5HQrEM78M3+Ltcn4KYRyn+z/36QONFbQxyu2IaUfhcSjs3EoYzNKC9rLFIqc5GCLUcejWnElUkTiFPFTxHFkycgQ3M7MjaeRMJzJWH8PYcU2AGkjBuR8ZS8FAOQoK8iagl2EAcOkjIqIcq5VHscvUjobiMOElyMBGoZ7L0Fhk9BXr9c4vRwiekz0bRZSxh79US4bIT7uRYp2n2et7nEKbwC999M5ErNQJ6B3R77Y0gZjkDK8EHCAO5BytK5kMyGvgWQMwUp8aOe12RMjETKYjNSADVu6ygymnqBH6HTzskL1+Xf9UjpbECeoalIMd1u+vwFHH4eTrnVzx8g7tetOqaPWvd/qWla6LbH+72UJ5bCePnECetdHs8/IENnOwqldaJ5PcvjWuvvi4myGt0o53GFx5SHFO1E4rqso56TQve/zfQtJk4edyJ+rfB3EEWP2xBv70F8mPJom/zdZKIAeDJOxxP1FyfLqDj1BuOxDs3jcjT3zW57GHGAyoYOk9Ba30Mc9MFjryNy9Gb4nYeIgyejjOdkxHtDPSczkaFyL1Fb8EXTuYY4zb8dre10iCHlV24mrjFLOcM7kVdrl/FNXqaNaP0/5+/mIl4bhvhnK1qLK5GxXuS5mIxSBhZ4XI3EBqQZramjpvk4Qg7UeA7OQBuevf5uNlpjI/x/ols3kSPZ6jaK0JopJozqmURh6CLEB8ljl2+6pNy/WnRF41cQf8xGMih5DoegTTmEIbnF719BnJSeShze2+SxLzfd0obmBhRSTnmx+fRfYchYJL+nGo96+MlWeNu9KKrQTFyQsdF4pjSGjYgHTupVcN9/jT2AH8iugnudIPMAngiWI6FRhARIG1IY+9Gi6yC8D3uQsK1Ai7WO8HR06bPcOURZhmIkFFISdi0yVNqJnfsc4uaBJcAG6FxOlJ+YzvF3mzYSie3PIOHZiwT3QCLfpREJy1Ue59Me1yTCYBvrfqeicNMWIp/qFo/pMj83BRlcR93OAGR07ETK83qiFELKXcmjP4TIAL93tcbe+QBwAIafQdRSSwnSKeRWjBReD+GdrUZC8W6klO5HyuVuogbZDKJER5PnEdMmhWOT8TucSBwfToRf2onE/J2m5yj6L4DPOQMpoTFur9K/DyJ+2Ewo6XYinF6DlMpzREioA3n9jiC+exBtCoqgbyGhzJaJLqc8hQyTFabtUrdfT9z520GUAtqHeKnK/S42DVcSHppS5B3agTxxs4n6bsmI2IM8oylH8gbj+1WP7T1IgW7w3CT6VSKetbe136PcRH/R4hd/SZwKLYF/fYYIe19hHD5N5N0NN12qCENvvN+ByI8dZjxHu78i4JAv2tnpsUwjDsqUEbesPO/2T/e4piM+H03cFJRy4FrRnG8nbtDZgvgjyYsO478CRRbq/N5KwpA5C/Hg6abbeqLAdA3K+3RUgUFECL7Nfc4g1vFCIsVgPPLQTkJ8VkispyEe71K3NxDJje3EFYObiYMTySB7yGMd6nFOIe7zPkjw/ZnI+JtG5LOtRxuQIYhnH0fyIcnVQcSNL7VE3mUKz7ai9VxC3OU80GPPRxM8gygJNMn0P0qE+ZNxm0Lxq5GsGOs+a93PGuL6wDbEF097TmYRYe4axDvNwnfvQj+fi2TpAc9pOf33yr/tWtNrL8cZwC/8mCioXe13UnpFBhn8hpAZgCeCWiRwxiABMhUt5OVIyJ2GFnEeMl6KkTCtR0J6hN/pIEIANSj8m+dnGpFRtYG4p3IG4Y2qRzlr+fqu8GIkPIuQsPsFEgQJqomyACn0sB8Jj5SrN944NSPhOhWVZXmcqA0HEnzFRNmOdj9zHxLYHUSNMudqHTqIhOZ4ZDSksOQAonTEQsLrUotymLpNg+FQeL3/T6Hy84mE9zokjI8gJdSLlFAx8kRtJ3LOJiIFVIuE+nSkmLYSynG852ACceNAmX+uJYzmStPlkx5rnX8+jxTIQSKHbTxSXn8KrXchRTHT4y4lrvlLeXWtnpdRxn8zMm6a/bvDP4VEAfLFkJM2CNB/kwrVxG0wC0Tjn21FG43kST4XKadJyBN0vcdb6/ae85iHIb4+YJwq0cGBQ/78IvdxHVoLG9HmoA1tGja7ry7TfKrbTCHdduP+V8Q1Z6OIGn31wnHk1UTOVDm881bP2Tpk3E9E3uvdfncQWrNtRLmQ3R7Hfe7rFeO0HBkheH6fgKHXa272r0OG9Uq02RmAPDy9/n0OcYq9FPHlDsRzDcKVB4z7nciw6wVWQOedhJGRDnqs8u/n1H+//Ejh5ueI+4p3EBGAFO5N3q3kzZ4KTfeg9TXPbUw2XccRtf8gIhAtRPHlNciYTobaZWiDcDHip6Me39OIh/cSIfvpiEeGeZ7XevN6LsoZ/DDyZN6F+Koa8e8Iz9M/IDnSgmTvUeLwVjLKek3jXOJO872er2Sc7UZrttJtlyPjfoX/3gZHVpmug4iC1ck73IryhW/wnGxDa30N8Bi8uAf6voTkYgviwyo/k2TsDCQn9hvHQhj+SX9fZtxXIwNxtGlW4+9bEU9MQ/xaA6cv9JwO9xj+BvHYSYVfd6jjt/nJ4PWCLAR8IpiZE1ebDSVyfbYRScgpfw4kCGuRYroYCYQ2JGzGIMGzEHmukidwCnHXZCEcWQMDryESg3f6+y4kxKrcx3wU+kuhqU2o/MmlxMnM5X53IZHLBFEqosZjOYAMln1IQG9BCqTLeF1H3Oc7hRB05yGBvxAJvNtQDk8VUiyNyKAZiwT4hUjItRNGUQUSnpei3fMA912HvA7DkDJJu/q00y4VTi8vhVPTIYxnkSE4mShk3EyEjmcRyd5T3f8BpNzu8jy9YJonr0eu/9/j9mqIMNhFqA7cB43natPhBo8xhd8Gmk6TCY/XEuMFsB5+fhTedD8yEg75+1wiN6jNY5qFDN2LgT8y3WYjhZTn36uBMdC5DQqnIYV2O5Frdwsy2D5hWqQcrtVV8PXd4psBplWF5zF50KoIT24P4WHeD/wlWh/jiDp83/T/M5HCv5SoAVdkut4MNA+EvzwiOm5Axsl9yMDMB47A/jth6CjC63Mp4sXT/GzKiazxTwvyAnfAlj0w6TR00KMEeXrOQPzYiwzjYgJuREo3hSvTpuizfr7Mc3krcXp8HDK8z/T8HUBrcozw5yzEX7nIKLgGGVLvcT/bTOf9RCWB1YjPCtG6nOQxzUU82E4UgU5esHLPW4PbnErwdBVK+7iQCF2v8Xja/X4xMkouIk5Nb/RcrEJy7iJofQDKRiF+T7JiIvLozSVOEd9P3J+c55+XjM9+oqTSGoKv9iM5eQuSObPoL7fz83vhTXNRLt5ij3Wf+/oG8Hei/88fgzdNMZ2Xev4cCTj8DJxyIVo7pZ6TDZ67dpRqgftNHvSxiD+L0Xw/5PkoMR0PIn7/OOKNGj+7nDjlXe7P65BO2ejveoHPwN6LYfg4xBdNnr9Hj/mZgOa6mcjBzjf+j5zMEPATr3EI+PwsBPw6QWYAnghqcqIEQzcSuBciJVeGhFYpKg3zVeIE7xGkFOfTb6gw3M+djoTDJCQ07iVOFR8gqt0PQ8KiDe32L6Y/N4RcQvFORcJyDBImDyCBk4+8F2ORgHwOCaixSIBfQAjjC5CieQgJ2XONR8q5SXlSzcgwaCCUcInxKkRG1AakBDqQkm30/+uQAEyekuFEkdwJxMnNKqKi/j94jBMIL9+lSGFeTZwg3I8MvB7kQdqAjNYWt9uLBPZEZChX+/8K02IrUib1yMu4zP+vN60XEDlyTxIbgRRyKkdKIs/9LiOS4PciQV/n/jqNe4PeefkaOPVswit7EzpJnfIum4mr5IqRIbeBCB36AADVSDk/hBT3FL+/Gm0mGv1O8krMMn6LPH9TURhwLOLtNYQn4k50sjR5lp8n7lbdhgzi9dD7Hci91t/NREbQLNOjw3g8gng0GbjFaN7HIiN+s2l3s2nRgfiviMizvAgZruMR/x1Aa3M2Ut5XoE3DzX7nZmS4tRP5ZeWmyWjTqgQZHCmcWWn61BBlkbYSB2iuJe7TLSLuVZ5NpCpA3Box1uMaY7pONt2Xuc9JmsPDt8ApC9CGbAzi+z9H/PUvbmux2z4draGB9N9awz7ioEcb8hRtJu4A3oY2bLn+PIVs1yM+aiaKPqecxBRKPYu4vm6baZNvGmwwzRZ4XjYTnv8u5IEsIcrNpA1dqZ9Z7TbLEJ8W+Pe5xD25mwjZmtJbivwz8pi5GI2uWLwU8VfakJ7m9heZRkl+N6I1udE45Hq+tvnzLyJZ/km0wS09hlZ1fuao/x+HNiPbjUcd/OQleNt8oiD3MtNko2ldRaSV1HnMazW+F/8FRn6AKOy/lKhN2YJ4+RCS4Q9lBmAGvzlkBuCJ4NycuJ5pPSEU0+5/NxIsTUjp1BJ3+Y5GxkgXcdXYuWgn3oUUZBESPo1IWKYDCA1IyT1H7PwnEwZiL1AFffdILw1MOWfVCN/NSJgkZVqGhMQyIh+ny+1+BgnkdBhiivFNobrRyBtyFxJgy4iQVBdSJik8nJLJDyGBvN/jmokMp/chL10uceBlNVIYo4g8GIhcntuR8B1OeEZGqd2fPwNvehdRQ67d9BqCPAyDkAJsIQTyGqRExri9CuKk9hnGbTJxYf1BoBw6H4XCC+kPQ/afgmzy++ehyTjkvqcQRXvXI2NmmfHrIWokrkeK/Agyhp9zW6OQ8VJAJJq3I8NmM/KqfN60+gei8CyID1KeZwr5J6MghcXaiSulak3720zrHciQ6XS7RUi51Xh8v/C4k+dhisf0UeSlqEOK6RL3+7TpfaZpOom4Qm6sx/c88rp0uq1Cj7mR8HS0IB59hrgxJxnW3ciIHYoM6KuIGn/7iJqF69G8lyED/puINw4iY32e230FGR15xC0/+YiHCpDRNdbz2GXcGo3fEJSTuIUwqHqJslHVhCFZ5TlrQQbSF4+hWxFRFmQqUdKnl4CUupBLnHhP3+/z73HG8UyPZTdx6n8zMtIbPK7Rxm20v9tv/NuJWoANyOh/FCjVSdmc+cRVj2VEisS1RGmeDmQstRKnwFPYuYn+HNr+wxBj/VwVMnonu+2NSKYU+PutHvNst7GW8FLjfkEbpDmIt8oRnxcQh0gSL1YgnsgXPQ+v873iuaZHs3GZgni2l6gTOQ95AGcgfhjuPkrQWtrpficQtWV3I9nYimTEdCSLukzPfURqSz7iyzTf69xvOfD4yTQAlzJ4cMFr1GYXQ4bMzwzA1wmyHMATwWjiRG4PEoBVSABs9f/TkdenHgnEFCpqR8I+hV173M770OJdjIyfxUjwTEZevn1o59qMlEwLkRczhbjuqxxyboCBlyFjsZfji/2m/Lhu4rqoEcgo2Yi8YVcjId9ofHuN53ZkKHQhYXgFEtZtpkmL8buMCLE2IKE9x+NeS4SrH4LDP6W/LEPrLo+jiTD4HnYfu5CQzycMu23GeR9SriPUdiHuN+VbjfD3xxqLqzy2POQ1akJCs4e4ueA84/mK6VKAwlqT6T/pXViOjIs9pulTbmM3yp9sQsbLUbdRgq656/B7A5HgLzO9x/jnFffXhZTPPuQp/rj7qfP4S4gbH/KRAnmWuK92EFFbbTJRcyzNxUqkjFsQH9U5J6uXuD0jGf9z0bwfRAZfO2G0tqDNTCva0CRjpgDN/wGCZ2qJW2DWmlZp3eQT5YkOIljvsa5Gxliv20qHey4ibiXp9jgXwP7H/H8X8CT86MdEgeluxPNPei46Uf7ZuYRnaxfhaT/oeXsEKeNdiJ+SoZt03GqiDEc1cVBmgdv/rr+bThwqSN65sURe8DK0AdtN1LvbSdxKUYMMm6/Bc4/58zw051s95qQj9xF5kN0EH+aj9TAQ+LrbS+HjAWidJY9/g3FrIry2xf4ued7GEofjpkJOA3HYoQOtwXzi8Faz230a5ZvuJmTbNuLUdpHHk/Iqe4mbYmo9rmmmT57bHI8M2C/42Y3EzTP1xE0b46H1AFGAuc7PT/BnZcf8biPyvrf4VP1u4uDOaOSJW4k2bhDFnp903zOQLNhJFDTfafqMQ7LxAr87hihf1U5UBVhv2h8icqLLj6HdGNN6AVETNIMMfkPIDMATQStahJORMHsfMoyuQgsxGWv7kBBOHrhqJHwXI+o2IqE5AS3Yaaie4Gy0Yywj7qu8GAnMLUjoVRK1pVqJ/Jh8FBKoRoZTEqAjYO89SIj1IGEyDSnvYuOZT5z0vAMpmbTjXoaEI8gb9QhRNmMhMqIakACrIKrjj0UG8MNIwF3rNi4AeuGUSuTpKnPeUJ7xGernFyBFXeQ2m/zdVKSE2ogyEb1AFxS+C+V0TSMuqC8m7uOdhQR0N5Gwvsfz2UjkBiaP6Zn+vYvwPkzwZ8OQYdZuXFJawCWmT63H2+pnNvj/NpTH9A3kLTiClFYRMrQXIsPjAOIHCCXThAySycZxovFOhxGODV92eewbUbh2I/Rd6/4HEnlUU02b032gaKvmhJnIgGlxX20o3eEgUUh8E+LplNv0NcQL9xM80kj/VX10E9cFJkPxHqQcL0KGZjGat1FEmaEDiI+2u9/hRP5V8ra0e35WwdBx/rtdY3lHKiuTT/8VgkxFh00+4352o7V3l+n8rD97wbSqIkrOlKM1cYv7riE2hkP92RnIKGxxO3hcBdA3F8mIaxGvL/bYChDfJAPnRuL2l8fg5VuRV7ZEbZ21AK2nlFIAsQGrQWtqJnFtWLVpuY2447gIrcM0J1NR7msyYquQXNjscWx3H7l+dwPaONYc885fECfi63n16fcGtzPC7T/jsTYgGfM5f15k+l9L3OG8DvFgjb9fhwzsOs/b44hnQOu2mLgpZLrpugXohLJbPG8l/r7VtOlAPPE04XHejNb+BYhPC/zOTuAvBsS1m6cTecnJyzzUtJvj38l73w30wL8+htb0WuO73zg97nGC+GQ0/fnOhze570riFPta/528zycV/v01/sng9YLMADwRlBGHQPYi462G8CKtRcovCYflSFDeg4TX/0cYSw8ggbPJz30aKYK7iDDWKDh0LRJKp6MFfwGwCjrvQcJiFhIMR1FeSA+xi+8GHvIJszZofQoO3+72DxqPF/Xuz852O/OQgHwYDj9A/9VQvU8g4b6ECF2VIuHWrGe4l/A2bvaz17qN5X6vCBlJtUjYb0fh0E8jQTsOKccCZAj9FQofPmH6ViLlXIWU+VOel2Eo77KdKGexERmhKYdrsfHb6H5XoZBypWl8P8rXWQt99yLjcJHbqkXKYj4S9ld7/DOIk7u73ccEoszHdM9xHtq5F6HQ6LlI0e0i7sxNHrgi5MGbQ4T6FpqO7Z6fO4m6cqtQbcLk3fwM8rgtBNaP7b/yK+c0pNSeJkr3PGI8L/Q8TkVK+2tEqK7W9FmE+D4dIqhCm6B2wpPU7fY2ErUod6NQZpXxmu/3xiNFtRgZHUvR3E/zWM9DxsP3jMcMtCG52Tjdi4yOVj9/t/v+psbbH+ruIe6nTWHCo0SeZTkyOPfAz5PH+WPQd8Dfjzadb/LcrvUzc93ng6blOM9fCv1OcL/Xehy3A8WQ80b4+YNorb/kz0sRT4EMzocIL3AxcAhOHYX46lOE0bmGMLRSDl6TaV1juqYw5SbTpxjJgF+g3NbVpu16/0z3mC7yuwfQuhlHeKyTV/F64E3vCw/bciTbStDhi7mep06iJl4P4v9u4XX4Xj83Cvh74gq2+5G8Sx7IJvjhBo/Jh3kYR+QHbvU4XvEzaVN5gWmZQs2479uIEPdAxBNlfi9tWFb6dzLM69BBnxSufxD47FF5+rajNZKH1t5WNO93EVUdksd0Av03v7zzao+nHfF/DZKvs43/2/1+SosZDaecaRwmo43qdcLzn58wvldxkiEzAP+nQmYAngh60KLbiQTlM/SHNHq3IYG5GgmNncgbWI28WXlol3sdEn6L3N5G4oDAaCQcdtPvhRpcggTcMCScVuv5wtOQ8Fnmtmeh3fJR/0xBgq8MCY13Q9nn4JQv2bDLM47nAeXwlg8QYcKxwFw4ZQJSerMh90t+9k5gO/xkCby4hvA2FBnPy5FgLEfKALe5BwmxDqLgcykSeN8iDkosQgbOAWQA/TlSUl/y2NciBXEAKacX3Mcef9eFjOW1pudc4/0NIhT6ChKSbaZ1IfKegoyPGshJYcBJqIj1SiSUlyPDNAnqQmRk9hAey2uMz1HiYEYK0bwbGcBHiWuuaomcynqkLHORUbYaeXFmIkNuu+fncyi3bQlasdci5bcHheKfBv4PcEV9JNefhwywq5BiSblrg4nDMPlEAv5aFPpu9s+Hjf8q41xN3DF7B3GH61DoewIZCjON3zR/3+N52YvWUA8yKJPHotxtPkXUYVyMeGGN8a8hwvTbTM+FwP9FY73N+L5AhMeLTb9eP/McWgMpzH6v2nrTHLRud0LOAOh9AJ2OrkO8vg+tq9luJxfx4gQ07+d6LCm0/W6iBuMTQAP0/gLeNMQ0+CHaIF5O3PrQ5TGWmzZLPY57PGefMR4dpksFbGkwjQsQn9Yhfuhw22nD8ndEqHqZccjzvJ1LFIwugMMzkCH0jMebT7/RxiHj9yfAp5/VhiLxzhrjsMS/r0d89J6BMtTykZcwVzQ/5Sm07od67o54nkuJwuN7gL+Gd9+LNqkTjPe9aE1WGsdnjP9kJG9OI1JZRoj+/bfCzDSdx0Dng2j9PkR42uqBnyG+34iMtKme2x7E8xcRB8K+5L42w8+mAt+Fb1yGNpUziDzCCuTRXkEcekmpGgUeR4oUDUMGdSVRMSFFcooRb6RoRwX84Xw/20QGGfxWkB0CORH8fU7sKrcgYZOS87+GBNMMJPiKiRs2zkeG2AIi12cjMlSeRUIyH3nj2tDpumL661L1JyZfhYTCw+53LFLcc5AHciMSRE1ImPci4+RK4qaN6UjIlROFiC81bltQGGOIv6tARsBM4tL0ZFR9jTiR2+JntiNlswwJ2suI2x2S4n/SNGpDinOe3/8L5EV6jrhbdwaRbzmFqDNY4fl4AQnNWmQMDyPylJqQETHF7dR7/Ev8Tq/H2EncYDKafgObrxvXWqTQdiHD9gXTfTQy/tpQqGaSnz0NGXNFhDchhUyTl6hLtP75j+FN9s7SAr2bIPcuz+MBz+V2/z0IHaSZ4rkoIQ6DjEdegKWEMm8hlNN2ZDS3ovnvJcpYHPCY1iAF9pjbSuOt9DPPobIuf4wOm1QjHupA812DeDDls51tWqe5yvO8pty5fOIKrAL3l3KwUti3FinJA0R5jR7iXusC0/gRtGamIT7PRbzQSGysniVKC+VxvKfsdLThaiJCdM2Il8/0fE5Hay6NIeXWFaFw7z3uby5ae2s8nnriKr4U+sv133WmYbPfuZ+ovbibKN9yFzIOqz0/+6HvechZSNzN20146oo5HlIaRR5R3Hk5kZOZwuJPIsNrn59tQeujy/iORXJiDf23GbGb2Mx1mtbnIj4p9+fmb243/nsRv6wyveqQvLgYreMZxO00yejcQcjNar+/Gc33OOIQy263OdZ4TiE8n53IWBpr3LaiDe0jplPKl56H5O5mNO8DkKz4ITLomzzOFabfOR5fiiRUIN7sRrohefXzjN9oohB9LZJpTyOveIGfW246FhDXfA70dykVZTXhza3wuF8h6l5+7GQeAvmb1/gQyMeyQyCvE2QewBPBbUhA7CRKLmxFC/AiosbTVCSUH0c1sE5Hi7MKCaIGJNBSKKWXuEqqBQmVQcRpx4cGSLitRAIsGREzkPLZ6rbyiSvKhritKiTQW5ER0IyUwDwi0bsEeSc7kKJvcFs7kQBa4s97/X/yZk1xO5P9bAWh+M4mvH71RGHYZiT41xvPZUSh2heIE4y1pluu281FQrPKzz9HnIjcSnivkrK7w33ej0I2IOV/lt7pu9P9lXn8kwhPznSivmOH5/ZCJODHGJekLHo9D5V+p5S4kWUa8rysMc3mEQphrA8KVpgOFb4ZJp/wwpb7/SMoVFjpdze6rwriIFDKZWw4hlat/v+g6bQZKa3FptNa4hTuBKKM0CbEF98lQn0pgf8c4r7lge5noPvp9XdrkfF/tftZ6XkbSnikkwIuIIznMsT7q4gDP8Vo7TxJGPiPeO62+rPrTLdN7j8ZQWM4vjBzCvdOJG63KUXr9IemXydxD+4LbhPjd9Dv9BKG9jCiEPFNhGcuGZH7/Fwy+NqR53o+UZ9wEjIAZhFlYoaZpt3ur5C4bacdcmYZh1bjVIx4LN9tnGk61CE+tjHdew2RW7yHuLYxz7hsJXJI5xEFvq93m73G6SW/kyIcpxHX6u1EczwQyZyD8JNNpsGZRCH7GuI0cYfbnE5cGdiG+LbN/RcQXu4Wjq/vucm/BxiH246Zg+3EzSlzPZ91aK7vRny2zs+3oY1UO+L1DsKwLjQO202jhcSVge2m2WyiZFeh+0hytpjYUJYbjwoiD7cWzXn9Me+0oTmuIA54bPRzM4kyTR3+PUH0ZicZZPBbQWYAngjy0Q51JRLYxfD3W9GirPUz2wiDMCWqp2TgdiQQk6FUjwygDcRl9l8gipzmIwH2iaPy8ixCu74O5M24FhmEo9zGRUig7UT5PeVIOX4DCd1OJACHoLDEAOL0ZfJIXkrcQDKdSMrPRwJ3NtDoi+bH6u/+wwmlRI5VEsYTPN4i06cWKZZ5SBFOMO7VSGFMJm6vaPMzEEbp/Uhhd3vsuYQhNwYJ3Y2oGn6RadmOFFcKfQE5F6JQ2mVEna/Z/qzZND3XfS4iDNsCj3MrUSYG9z+OuBouDymlu5BCm0AYDh3qZ+gwpHjy3calRMJ6G/KydhN1FuuNRwqjt5pmxYjP/oZImG8wjruRwTOTKDN0ETJMDnpMybtc4H5GIWOzlDDeGxBvTfGYP4XyNu9Tmz+6DxlujYS3aw8ybicj5d4KfQ+avj9GfDoQKdQXiKvu0pzt9PjHo9DlJo/zM2gdzUcbqXWIp5chZX4mCjmmcOMXTaM9HusOZFRP97imuL2V6ut7zxOFi1uN/wse2330X+XYf+93BVpb00y/TsJ73YkMiR7C6GwhytYknppM5KBtIDyUvYgvdxL19mYSh0OaTZ/EF3koFD2QqFvZYTqeBbnnE6VSOojbhfKQByoZXUfQZuExfzfCuDcjfprhcV+ONhSbjEMzcAdsbwBKofdGoAretgT4wLviYNI2PfvynZ6bGuMIWt8PEjKolDgRnk8crEke44GepzbCW4lp/xRxovpx4cYjyAg+F6Wf1CH+KUX8s58oozSR4+smLjGuo4lNeKFxO+KxDEeeuI3G+17PaZdpPc2flxK1+yYh2daK+OAq992AvOMptWeJ5pF7iJqyw9DdxRtQWkgjcbfxSYMsB/B/KmQG4IlgHlHeYipwDvxxSgpPuVtXw8tPoBytlPw+jvAw1SJB/gxSMuOR8JmKhMmT/i7t/q9ACmonUni9SPi8d4AE8kpk+OxAAufDSEBtRALxI0hRNxPXZXUhz+R6+OcDxn8u8mhaibQugZ9chozIciQs1yMDZxzkDDKOu1Hi9jik5Lr8k3LjktfxIFHOpd4/SQGXIAP1atNjNvLWpfDzTqIMxbkoVPkZFNq+GoVGn0YCeKzw535CaaWQXA8y1mcYlwaUl/WkcViJlGcVEQpch4yRcmRUHDAdpng+koH6LY85FynFH/vziz2ez/v/XMJwKEJKZgBSHNuQEbUNzfFNfmeu+yr38zOAedD3feL0ZwsS/pNNw2RoVZrmvShXbiZSGqP8d8phOwA/+w6wF1qfMD7f9u9dbmceESb/tPu5WuN5xyeJvKSxyMD8JHEaeLvGkTPOnz1J3C8N4a3b5Xe/Tdx4cgcR8r4XKcKUI7YGKcDhyNg+SoSlCzxPVxqHCuJE/SrCq7OD/qvo/vEp+NCVaP0UIqPxiMdbipRynfF9GHn8O5ECfkq0/dl1/rwQ8dy3PN7iY/AbShjZy4l83olE8eDhHnuaoztNq92I7/Ygnn0PUa6k2e+2u+12ZBCXGM8N7jeFjZMXqxbl9VUhg/Uq/502eU8jo6MHnr2a8PK9f4B+FyOvvzcT488EzofclUi2FQK/+BdFGlKY9lo49QZ/NxUZ/kWm0aWIX8eZXl1+Z4fxKkAh5Tp/fpHbSJ9davqlvFlHFlp3wf5fEPnE1/n7ZNTjfo8gY6sSbRIrkeF1BZr7DmJzUITkymeIKMZ+QhbNJwrLDyeiH45O7FyCdMBsIn2hGW2ycj1/lX5/t8efvLoFcPhfiDzw8ZojVnCSoZfXzvjrfZ1x//2GLAfwRPC+HAmXcUgIzEW73wYkaHL9Mw4pkJFEXSh0R2TOGUiR7kHGxkGUSN0JnAe9D4rdB74V7SJHoZ35MuRxugAJ82VEuDApsmXw4lZXik/CD+TBSp6g0f57Egp77UOGwEa3tZ24IH2QcSghCgi3I+GTQhzXIqWymMijO0rcXTudOCldiHbh6dThVCQML0KKvAGFWpe4ncuR4hyOTgMfRYLws8btqOnQipTQucCfIUXZixTrBI+vAxkPNUQ5kTwkvFuIwrfTkTC/y/NUaXxTeGWV6THF775iHPcgpViDFGobUvJr3dcBFIpehQT4PNPsUqR8FhO3kSQDrcvPViOhvo241i95CPMRLw7w9+cSeYcjoO9G1YekyW3kmjYdSLGdhXJCU0rCRM9nHlKEbUQB8QGm+QS3X0Xkaxag0Nnlbv8Rj+XzxnGN219BGG/laP4nEbcf/MlbYPrPRLNK5N0Z4Pfb0YGM7wIfI+oV1ruPEUiRrnIfyTDcDYcvhFP6xsK8etEhjwj/T0fezQrjOA8ZVkVIKR9APDKEKHlzIZFTWWHcriVK60xDPLEdrdlEw52e56vQnC/w/KY1ezb9XsC+5ZBzvvEoRAbbCLRROxdtTk5D87/ZP3eaLovcdwdaLxP8f8qXbT+mv81oHX0YGcctaPPxIMovnoR4JNfzlORELnFv9aIS+OGBOABzPvKiPef5uEi07b0Fcq/0e3VoXa8gcvoWwfYeGD8XrY0kjzDdEs03e57uR3wy03PXRUQFPoPkWj2a82K3U0+kIJS6n93ElZJNxPo9ZBqO8DszEG88ivjhfcSVc0k+7iMKTLcQ1ze2og1hCpX/2TE0zYNnvw/vS3JxLfzTOjUx6UtEDuOlwJXQtw9yRqEN2HqPr9btFvvnL09mDuD9DB58ymvU5mGGDLkiywF8nSAzAE8El+RELk+Rf2qQQKgl7nVtI+o8HSVypsYTnp52Qmg1I2UxFu3mc4lTrcmgTOGDCqS4P4WUSLuf3Y+U+ZNIIM0kduYplzDtSJuRAE3hi17iuq8XiFpWaZx7kKKZiQTfeujsgsJbiFIn25Gi2I+MtxS+Tjk8Cc/7kIFRSNTdmo8E11Ii9DtWNO2cD4UlRP3DPUT9wdP9XMqZWkWEZ/YjI20XURerFQnKrxAJ1CnP5mnT+mLjNwkJ/x1urxF5C+4n6s6NQ57LJW77CiJvsJWoL5Y8o82ev/mEZzEP8ciLSHFsQ96C5CFs8hjuIg4DPI8g5YA1IqVxbC5QPfQu0WO5H0XKOIVyJxJ3F9dzfP7kDtOk3TTfTBg9VaZVkcdT4P6m+LkeIil/pvtI0O3vP+8+U25gO3EAoIW4+zeFUNuJq8TaEA+s9BgH+PdupHDvQLz/96hwdpHHNZ6oy9ZOeHgr1NeRe317Tpk/m0Ao1CJkxGwmDkh9BRkPq4m7d1OuWCVxFVypn38e8XUdcailm0h3cLjuJ4/C28qRUVaMeG28aTmFONX6MFqz8xCftCDD4CH3cTHyQHcSV/8NIG7aKDUNq5E3vQMZjBM8N5OQ4XQu4U3bgvg8HZBYD4yFpqdgxK2m7z60SV1nnAag9XoTcXq9xX/nIeO+kshLTtGGK41LJeKzatO7GK3rcrTWLjM+rW5jURV8Z3fUIk2b1ma0hregVIQtpudstLbShrCcuJ1oq/v9rMfdiYzAZvpz8vrug5yLjecIz+2lxGZin+crF/HIcuLav0Wm2UJ/v9LzcDEhk+41Xc4kZP9opBfmEfnS9YhHmjyOXWgD8vGTaQB+6zU2AK/KDMDXCd5wshH4bwsthMetFymzv/BnJUSJgBLicu5SJEi2+X+QF6AMKaItSPBMIgzBlUioVBG3T+xDHoZNaMGfQ+TOfAt5AfKAD6LDFpuNz0q3fx5xAfxTSDgPREZfFeGlOAt5n9qI/L86/7/H+BZD4UVIie4jks0hbkWZQAjMLujdCrmnIcU0GinBEiLpvwwJ/vFIIDvPp3AKkdszGHlYUm5TSsK/iEjy7kZexlrTPO28u9BuuQUJ0ALjv9djeJ/bO2g6P0bcRbwXlbk4HQn55HmtIGoTJsOljfAUtfnnJmTcTjE92gmPQ6fn6TwUZjwbeR6eI4z3Vs9Tq+jz8+vgTZcRp4zzTJse4tTtGZBbTXiBvDHZvg3Gp7DrTuI08AriJoFhsH85DJ1B5BhWeI73EJ7MPUSe0maPs93zuxUpvHFEYfOpyLBIYeAXCK9dMo4r3e5699truv7QNBpPnJzdgQz32cBTcOgXMHgqMoRS2D95Iy8lvHopHeKocBn4RrQhySdSFFIe2dPEgYNK03g9UfLkWn9fS3jF1iE+TYZLAVo36xE/FXsMowke6YC3LSAODVQhpd6NDJJWIncwhQqbify2F4xzNeLhIX43FxkGF3guO93fNI9lE3E6OOUuNni8iZdWm9YdRP3QPXq/ErQpnEWcWE1y62+I6wjr0fxXud9ez0WuaTSCqOG5Dm120gZ2m8dyGtqUpXzYFtOl1Ph9ZXdsTlKYdAlxsnyHcUz4b0JrfJ9/DzUtDhAG3EZi87LeY/GmLuc9xIn6o6bBVuRZLXefryDZ0E0Uk3c6BAf8XjJuS44ZVyVRXLvH9ClyO/OIWoq3+tmkAwpMxxFkkMFvBVkO4IlgHBKS9yEBd7M/O5c4xTiMMKpyETW7iUKs24iThB9FhsEmJCz2IoGWPIv4721+vsHtPEjkwTyIBGsvElBvJxZ/8qjVIyG+CymMTyJhvp0oV/B+pDQWG+9itzHLY53ntq4y/inMNUA47vwX10wr9hg2ozCtPUu5JchAm4iU4YvIaEmKrtb4LUJCMXle6lCY7AkkVFNuW7npvcM4fMvvfg0ZCXchhT8DGcszTJd9HvsYIjRzrdub6zEWotD9Qc3Lyz/weP7B7zcY/2XGb7JpPMzv7ya8GQORYTsKGUT1xBV2EIZkCzoANII4udoMLz+GvB0Nfm4FvOla0wyisO1+wvjsQIpnBlEwOx+ogfFvNH7JsE7K5DOEt3I1DE3K9hCRl5SM0Hvd73PEndhr9B5TkXFYx/HlT1b5vac9n0s81tFo3j1ebgW2wc+fMM6ziTIcS4jN0HriwMk5QC8MHuV3lhiXSmS0T0I81Ujc5jDS7bYRNyk8rbH8633EQZutiC+mmj6jPY+PIoWdjIp7jUsRcYvFUKIsSDJmWogyIN3GqZi4CzZ5f27n+BJC1W53lmn1V8T9wzuMZzJQNyKefJIo4fQc8jynPMgRxmktsTndjgyjPOJ6v7Vozdnj1w8v6rucM4haixOIUkclxCGt/Uie3eZ3D/mdNvf1GaLm3zIi6lFCpCsc8Hc7jvnpcJ+liD9TfmW3fy4lDnIdPQb30R5LE+KlatPpELEJbzc9S5C8GufnitC66vbfk9Aa2u22X/C4BiMZM9b0W+P+zkTzdgBtusYRUYFa4vDPesLAbSQM1wr3meYx5QuOJoztcURpm5MGr1X+X/rJ4PWCzAA8EcxGAm4gEowXEPk/y4hL7GcQCdino+t+NhHhoQo/MwEp7LOJnWw6tLAPCa09fn4PElILkUJaixb+AuNUqn74Mf0n7KhEhsMFSNAmr9dB4oJ3kACuQTvvauTVrECc0E6EPFOe3j3E1VBb1f+YCyF3lcd3KTJ+9hB3n16KPE5/6ndakEFVZ9yeRuGPWuJeT5BgrCfu7Z1KhO+2ICX3F0ih9RKFpXuQwdlrmo4lPH4pp6sGCdWtSGgvAxbB4VkeawkwFE6t9Lu/EA37Noh2Tcmb+efEncjPeX7WIwO11XTcRdzxux0p/Smeh81EDtF85PHpAibBqR9wO1s8rwfhxbuADui91fSqgCOPEYn/ydOyGm1Whhwz5rMQHx8lTqemFICE09XELQSfNo4PokMwxcjzPZW4l3Y90FseBtDZ8OIqOLQNfv4AUQ+uxO3dgAyUZHhVoc1KadDnTYM83/civpzt51Z4rvCzX0IGRhdSxEeNX4Px2i98eBzx4B7TZYLHsAxtambA4VeEwzuv91wtRso0GbTlnsuZhNc85XOOQkZJE8oP7PGc5bvvZJTORusy8eR0ZBSA+D4p+TzEc8MJOVBA1O5MaQ4bkdGTvptkOp6PcvpGeZ7HIP6ZiAzTJvV/aB1aE7uR7LrLONcSB2/GIcM8D63ZNuQxf8L0vxPxVRXy0g32+zXIILrDNDrPdHme2Lh0EqVjKtze11CUIqUA9KLNZhdxJ3KP5ow1aIM1GhlwPUT9x6Ue/2xkEOWhzXYrUWT8apS3uhrxzAGPt84/q4z3UqI4+zCiyPletHb2u72RaH7Xu79RqJDzFL9zD+KhVqLYdClaVweJ60C7kGwfjdZUOcoZvwPlv243rvsIr3AbsdEo5CRDZgD+T4UsB/BE8O0cLcQUjjmIBGIxEgbtSNFfhgR+OdCma9RyP0fkIpUjwdWFhMQ0pIj2oAU/CwnrcWghJy9gKTJuHiDuHU15VDNg+8Uw/ny30U54Di8gDikkj8cIJJDKiTwmh5R+9gN4y0RkjIwnDlTMRrlVxcSuswVdvbXW4xnm/j7uti8yjj1I0W0iCgUfRUqjmwj5bUaK8VlCmKbv02GHUuJavntN+1b/fpIo25AOf6xDCqzH7zZonP1lavYQByxmIGVwOjLktiPD9DHj96Tfn4qUqMNPL65S82MWIUW2DSmMbr+Tr+f678D9EjrtPcg0TLlZucS1W5vR/NcS97OuNO1TSH8S4WX7M6KuZDdS/CncOt3vrXabjYRiGUoUIf+2x7zSc36F+1/rcV/t/++BvffC8Aky9Aa/lTiB3GbcGozfAsJjV0yEx+sQf/yZ8b8TrYcvEnUgm43LFXD4RjgleWG6iRp0X6I/6f7Q9TD4euM+xXRNHrB8+nMQe2+H3A8Qt8Ns998pP6/HfScl3+Y5X0ccWBnt96qQLCjy/93EhqwarYsiot7kRtNjBv1lhnZugjErjcsaov7ibGRc3Ov+JrvvVrQRxe31EHmpKXQ73vN3I+FhPfZAwhjT6HHPyUOIbycD86H1Ct+X2014Cfcgo2Y3dD4PhW9FBcLXIl4aiwz8dJioWvPCGcjwqzYuRcSGZZXxmUGknqQQ6YOEQVqBNoojTMsSz0cv7H0Mhr8L8fJO6PupSJBzF3E94w7gXPjRjfCOq5FMuIO4K3kL2iRA1M4s9/hXe2yXIqNrEJLZID7NQ7w8BKWLrEcpEXebZtcheVPv9iZ5zlb6nYuQh/QT9NdF7LxeS3nwHyB++AxxAPAo/fdX778Rhs5xm6B0iRrgiyczB/Brr3EO4GezHMDXCTID8EQwIkeLcwrsfADGnIYEU/KkJc9EO6EwjiIBNgAJk6XISBpBnIzM97OdSBim06+jCU9cansfkY+03p+XIQF1HRJoVUSezCSkQIb6nY3IMFiBhF45Udw5GV/V7ieFM/F7I5DBey6RxH0bEnRPu4/9SCAmr8w2pJz3uZ0SpJjWErlCDyKjdwNRHLbStNvo/y8AnoPD34FT5hKnLKcQN06UIGUzjKjfN54IB4437s96vDvoDwV1PgWF70Iequ1uZz/yDnYiD0ebv5uFFEQJMlyS0bbfOJ9BhKKGIyPlXqRgHK6kGBk6V3sOlhE3i6SxzUcevAOm3QTP1QTkIZlBnN6uQmHDzyBjbiSRbzTRc5V4agJx6GeiP0snkicQ94/mIeW+nShLdJnH2GQa56I5LnFbmwhjtg6tl0rTcbRxGkXUIIQobHsFsBmOLIeB97nfxDcpvFmMwvMtRMmZLcYjGU1zkVFZigzfu/xOoefydGTUXUrcN3sAGUiTjxnvbONW7OfHoDkeg+b3ScSvuUSh6f2mdS/itSmmbTK8p4iuvXe6Jt9w4haNDf6+1WNtQLyzFR0OG4nkxb1wxB7ygQuI+nmlaGO1wXP0JDLGdqOUgHGEgbWWOCywmtjAVnhu9hrfUo9lAtqwHNEcpbliPTotPsHtTPPny/3ZUON8AXTeCIXXm2YlxCa4F63hBcatybScTaQV5JsezxE3jwB9N0DOtURlgVK0nnb4s1nII/lG93cUrblHTIvJHH/SfSs6ZHNrzBWbiPqf9YRHO23O1iJDLnnEG4lbRJIXcQIyDJO38mz3u8R0P4s47Lfez+9Da/pWIvLxHHEgawOhO2YjA97GddPtMOIkqPEwAL/K4MH5r1Gb3QwZ8ueZAfg6QXYI5EQwGRljLTDmYuKi9NFICVYhgbGU/jshmUTsHrvRYr+U8BINIMopjEFCJA8Jwwbi9oOjyCDoQgKmGwmEJKCTpyMp4hFIUG0mijGvI0qvdBOnYNsJRTvafY1GSulpjzvlwpzr94o8rvFIEA0nbq9IuYrz3OYYpBRLrQQeJu46XYsEX4vLXnQSFfnHGofx9BtTp7zLbbd53Hchz9HnkJBO+TgVyCA4zX8nr04dESIvpD8XsTAZpikkPQF5W0chIX/AtGj3eA+YBvOIGzDGIYPtTCJvaTRSDkeIHKpKZKhfe8zcDnU7C4habYMRr0wGPiQa9XtRFxK5UM7lO3wATnnBc1mPvF0Xuv3nTI/xpk+P+2o+hp6bCUPlCn+213RNnpuhREivlbhK7vOIN/MJz0kzUqJlxvcytEbKiFyz5Yhf0yGKjTCwkjAWU7iuG62T7abjBCJnrIwoVYTnd7jpmeu2k1GVUgHGofUw2biUICW6xnN8tmlXS2y+liDjPiXqj3afY4Am8+9lHntaHyvQepnnz5YCAyB3EMcfFNqLNnBfI/ikHa31anR6daXnrwAGVqO1PMA49RivH3t8az1XS0zzFsJjnW96VnrOZvr3BchY2e+2ZiMjsRXxVJIn44lT9VMQ7z9nWqUNUY37qKHfs1f4VvefwqethJf8euPZYdxTCsJnEB/9SQFc3hW5bx1AIeSUoM+uJIqPH3X70/z3YuM8gig/k6/3KSZu8djofhs8FhuZ/VdtgozH65HHDsR/FX6+i4iwzPb31f4sGfQziQLyXX6uzHN5P5KvExEfFqE5T4fy2oGvEmW9aokIyWbjsV54j5hDBhn8VpDlAJ4ICog7aTtgy/eJ5NyUFFyKBO0IJLyOoAU9CynLXLSoVxAnV7+JvH4pJJeSsQvRzn08EWYqRgJsK1JelyJlfSuauS6k/FJuyVGkPMr8fVLmx8IB5DW5HN0zeheRj1SDBHYu/WUzaEZJ8M1IOKU2BxA7+PchpTHP7+cBZ0LhHCRoIUKx+4CSY4pL1xD18CYToaECpNTweKuJ6+tKiaus1hj3YShPaRTHG9MplHatabXa784kQjSlyMg4HSnFhmPmabZxGmZcGqHvYaLECoRhvQMJ/z3GaSXKJRxO3Ce7GymZ1P9qpLDvRkbMF5FS6XQ7mxEP3O05mCX6nvKw+65CiiR51p40Hik3MM+fD/DftcDf+t1ktOYSYWLnxqXyH1saiI3F2cA3oPNa5B1Ohl8KeU72Z/nI4EwpB8mzO9vf1RF5gCk8+Lifr0cezxQmrEc8fdCfdRJlh8YS6RJrjcM1noNp/vxM4/IcWlsTiTD6PMKAuQcV1/4zZPwfQnw5Bq3Nq1Ce3VT9nTPR/Y0lcneL6T8JzzbTeBJx5247YQh1+rsG/38D4pc8Il8wzzToNJ2SB2sFmvMi4zPymLl4HvHtVxCvpfSEscTtK02egyOiVe+PkQybSBxIW+b5qvbY/hJ4Nxx6GBkeo5F82O1nbkfroB7xypfdXg86APeSaVjkcW9A67rd7yxCBuwa4M4uzWfaJBea1qNQusHzbm8aMrBuI9IOCjyuUuKQTSHh9d2NNifnIV4vIryRlYjXW5Eh+ahpbjnWt5Qwsqs8d6XGJxl5I9zOWiTbN3kO1iCeTxGkif5+ledmL3FvdRUyzOvQep9IHHhaRNQR7eT4IvInDbIcwP+pkHkATwT5SJAeAO6GSQ+jBbsMCayNaGeYi3akW1Hoz3lGO5fCmGuRUq/0c/koR2cLEkDb0AJvREJ8GBLuZX6mnRCAO5Gi+aLxegUZnYvRTjUPKfkb3McLyPC4BQnav0OC/Gai0O02yDkbCZX5HtNoJBS3Esqzlf47bFlBFNFtJm69SOGbFGLaZnpNRcJvP/I63I+E5lXEtU9pV72d40/OViHvzdWmezUK2fx/SCElxbABGT37PeYJxM0S9ciQvwP6noGcq/19NconW0XUBXzBY7rC7433nNQZhw2ic86ZSLHtJuolTiHCqxVur9pjmm98thDe2VKizuFYol5bh+fxWtPtMuP4gN8/SpxUrCLu6C0mCvg2uO/kWRxrHLtdoLza+CYDbZD7vwcp0CvoN+QnLXBfZyEP6wwoHEccKqmE3qWQux4Z4CnEWYzWwiFkaI3xeLYgnkpe8ZQzOA8ZPStQPbYv+v9r/Vwr8pr1+PllptcNiF/3E1diTUa8MRoZqrvRJuVu5EHejdZ1ClGXoYLTFxCbsDFEmZheZFSkXMPpxG0NLyEebPL4ziXqEc5AvLza40ue6COEwVZOFBE+x/g/6XmdYVyvQPP+OHGfNO6zG/i+6ZaHeGA9WsvehLy4D0ZuQ7zWhYycMvrr2eVWE3VBz0VGVQoXlyJe8kYlH9MwGeFFaA20uM8m470Cya4eZIzuRGtjq98bY7rVeKzJaL4CbTQ2EaFakPf+OrTOk2fvWbRJvMbvJvm1ioisLCXCxZV+brH7vxUZpxXufy9aLylsfa7HdRSYDDlTPS573tiA+Dl5LlNObsqt3Ak/exTesow49b0fybazkCxsQDpgOMdfqTjGeD1H1N0cj9ZWs3/jNs4E3slJhNfScMsMwNcTMg/giaASevOQIhrK8QWhSwgjoZK4BP4T9Bt8Y+5AQvUAChVWEqHSi4haaI30n87cfwMSEsnTOBkJ2uSd2en37kYKsBYJ32qkcNcRnqSxfv7jsL3Lzy8j7vPNRQp7qvFYRNxJvBgJ4eHI4CqkP3RLHlGWpRsJxC+77ZEDwvhLynASHNrgsVQhI6PN49lMv7fvxeuJa7OSgZHCww/6dxP8pAH4YySENyOvXfIYXuo52YQMth4isfsCyHnQ+PcSJTSSJ3IVEv7nobBPKzIwnnL/nyJOSqewXxVSPjXIMJiMPIfthOe1ivBYghR6u+eh1nTcRngGypEyuoc4wFNrHFKOYj7iuQMe+3I091f7uzPU/8vfJ+5gzQUmQE5ShqPdbhNSqqVI6SfD7FyCDx9Fp5/LoXcbUdduJ/BZyD0DKdv9SGHdgPiwERlUeL46iVtWRvjvZYh3z/I4Ooh1sw1tZu4nrtTbSBxAeN50yEf8shgZzE2IhyrRRmM8MvT+GinY5Yhv04GAduRFKSfqyKW5ayc88l0oTHoN4o2hhPH+ecR/ze53BpId+9F6b0WbgSGEJ67UeD+nMfQ+SP+Br8NLkDH3gse+ndhsLDD+043vImJ9X4d4bQJaCyUw8kLP5Q7CC5aHTqO3GN9O4gRyrse/0v93mJ6zYeBlaLO6mv511V8GJuUcpmhFm3H6HFExYRrHn2JNHkmMQyPadBUcM4drkZH1vJ8fTkQIrjnm8yKPbyGRnrCQOFT1sMdbhrx6daZhD5KDSY48jfgxH/HKHWjzk/iuGMmKSUherEcysoCoZdiq797S4GfHI/mxGvHFIuLk/lTlwoLHPMA02o7WdCFxC02Z+xhjGl/Pf4O7gDP4nwrZIZATweU5EibnoJ3mWUTxzjpiN1aIBMhOotxIO1qgO/3/uWj3PgEpkjriwvKpREL4euLe2zORcisiQgOTieLHw/3u+UiYNCAFNREpiRRiToZHORI4kz2GAiRUipDiLEJG0GgkLPcg7+H9HF+odzJxL/E8+sun8AIyvKqJYtizkdA94u/m+P06JLBrkHCehxT9TOMFkXC+BRkqmOYNSCEUEdchLSU8Tc8jo64MDj0Fg88mSi8sRgZBSmDfY9qf4Xbykaf0nUixlbvtFPJvQd68XHRa+I+M03iPsQLxyTeQYVqFFEHy4DZ7jEVu81aiTtwoOPQdGHwDUohjkPHg/CeWIWW2BSnOyUQeXB6a879GvLCXMLRSzlsRCv0/7L/new63uo90mKbWbbTA/l0w9GLCW7XH8zPeOFYhPvwW4vH9aN5vQlcezibWyVh0urrKfaRQfgcyqua4j3yi5ND/Bd5L1OD7JkroH4N4uJbwZtcTt3KMd1s1iGe70MEA0L3YvcThhibiyjnnrrLTeJUThsUe4o7YJ027XH82nNjg5SNjKM84VRHrqcdtJd77FjIgqpGxl+95S3PajfjnEmSIVCIDaRJaQy3IAEo5bVMJ4wMiH3Ez2kCUolDtMqKI/eVEqPQAkj0PoXDjWYRBNpSoAXktkk1NRNh3rMed8kd3E/X3NiMeXkAYv2mMdyBDZgPigQbiJo+UN1lFFJJOG/AuFHGZ4v6HojzONX6/Aa3JMUQ5mQLCg9qIZMwVpsc44u7e5K0bTXghD6D1tMX0bycMyInIyE0515ON53riEF33MeNucx+XEkWxl6HN5oXEtXXjEW/vQUbhcOM9DXme84zX24FPnsxDIDe+xodAvpQdAnmdIPMAngiKON7oWYsW4lAUfpxAhAtSsnUFWswLCYOkHBlHG5Fn5Da3+TgyMjYQ124lZX8lUTh5FlJ01WjnmEeUfjmCdpD17n8/UjKzkNCeCL0px28jsUvt9Dj2GJfZwD7o3UcUia0ybmOJoriTPNaNRK7MUo4vYZIS9pcjgTbM76U8lqQYxxL11fKJUhotRB5M8nZtJnKY1hK3BeQhATrNfdQi5VCh9gafiQRkOZGMXos8EcP9/neMexLaLShkVerPJ3oeq5BRnkI99f7+gmNwH4oMibHGr86/z0ZGJ0gxbUUCfLzntVXvDr7Dz49BKzOFKQ95DPcjI6XeeE00zrsQrz7u98uQYkzesa3+/g7jsQUpyoPEgYp6pGB30X9IZegw02ysaTrKdOrw5/tMj28SnplJnq/PEon+W/1ThRRa8iwtR7xyBrG21hDXxN3hcZ+OPFuJD/KR4u42Tj3GfQTsf8rPdSBD5maiZtwOInT+JeLGhoTPRtNwDMHvRcS1fcv9/XQUBv07tE6fNf22eU4h8ufmeR43ms6Vxm0nMv5Kge1w+HnidpJaZEyMIK78OxvN93S0zmchQ+wXRBQhV+395PvQ+X340WOmQbHxqUHGfQVap5OQobebyPNcT5xMfZI4LZ083JcRJZ1mEYbjBaZtvt9d67lLh5AaiXD6OOI2jJvcd6X7rSPyd/e4rxZkjDYQtQU3EpuxCv+/XHN6+Epoutfz1kgY6vuIazc3EAfcphrPFWiTlHishciB/KH/vojIA5+M5Ok6xDMHiChRq2mevIL76M9vfvkpBGcRRvRNnuMOxHtVRNmoAsRnecavEHkpe4lajBlk8FtAlgN4Isgn7v8sR0KgCQmTI/5+LRKMy4jQ4jxUQuEocW/wBiTs1xJFTcuJHKgkNPYhI+8iJBQaiEKoU5ASzUcC4HoUXtuJhFeH2y0jLn/f4cvYNyDhPwQJ/BYkjN9N1BOrhtxGpMAGub1mJBRLkbDdQexi57tdCKOrlTCcrkSCbyVxync5cfNIylXbjxTRBchQLkPCfCTa5Z9DVPHv9XdDkSGUDknkmmb7iYMBFUSNsVKX4jiTUHa9hKeogKgN95D7OMs0KvOcfg0pp2Kibtxsz+lVRKiu1vO0ws9ORfl77yZuOqgVDj9fBW86Hyn6YX7+dqIkSfJyjkZ80Ghafo+4O3YAEb7dh5ToTOM62vR7kjCKxiJeqiIKhu8gjIJ9SNHtNZ1XI09FSitYTPBvOXFCe6/bGEZclzUDeeyK0Vy3E2VykkfnHL+zB/H8xX53A+LtoZ6z2e77JuSp3mLaDkM8jeZw6Fzi0EQJcVL9PPd7runY7nEfIcohlfm7u03PqcSJ2zlEiZ5e47XY8zRMz23ZBZOa3d52j+9pxKuTkRFSiry1BURR5yI45QPElYftyNAs8hwnL+dAIgR5B/L+PUScQm0S7d5WrnbfcQ9xI0g74rP9REQhpZV0ekz7kSxLnvS/9HvFno/niNp1EAdYignv5x7E/2d5zE5vYT1Rf/QxNNdT/LsE8cpQosLCVCIC0kpEApK3t9D9TTPONYjHvwKn1EPlDxBvHvG7n0XGbyFx0KYYefFriBD4ve7vKuKASivahDxqXC/w2Naj+S1GvHEFUbe0nZjrTUgeLBc9Tp2BDLc/I2p4bibqn84lIOX8FhGHd9qJm2MGepwf5iRClgP4PxUyD+CJYC30HkRC6XK0+y5GgiMZZtcBH4edDWghj/C7tUjwbfb/k5FCGI2EzS7ixG0xEnpt/z97bxzfdX3d+z9Jfgkx+QVi0pQsmJJGKDSDG2XQFAblwkW5OCbDMhzVi+XqbJ2Wam2ZndW6Wqdr52pdndbpqA7LDy7KsBSGMrgRBqYwJL9wU9JgGhrJTZoSA1liTG7g98fr9c7Be8fj3u7607stn8fj+/gm38/n836f93mf9znnfc55n4OEzDhkRWojcqw9joRePVG6KMWSJStGPXAFDNwJbz6NGEwuEmwHkZJ3FJUguxcJRIialZ3Af0KKzBuIWU0idryH3V5yPXURLuRc4oTvZveXYrnqCIvgrURy5gb3dRwx5tcN0yriJN1GxPQWEDkUlxAWlGcJpth+3v0U/3QHspDeaAPcMiSw6xCjHTLsNyBl0xatcz9FwjDHeH1Ufbz+BlE7tN9/3+E25vn/lzxXp437VveT5iwXCYOFDqg/jNw+5UjIViNau4Uor3bIbazxs81u+098P8P9jPUzqwhr61MeWzdy6fcgej6LlOw8Iln5Do/rOzDwI8KC0UxUaJmGhP4monLM54zDXiLfYj+i27Mex7Puf4HnuYxwsU4nNiW/ME4akCI7hDYku5Bg7UUWtz1o7m9FgnMfUfovWZ6aPSczkcI0h6jNnfC21M+kgzkr3Wa1+9yH1sBLiIaTpX6rx38LWi/AzEeJCkBdSJFK45yF6PIQUfWh1mM9688c97cP8Z17Pa8fRUrEdkSL44zf15BCkuY6uSmXGP7xbmOl5+kS3mnF325cLPX9+9BhmWmohGSL8bXLczTFMD+F3NcQ6X5q0WbosD+vo81fCtGoRfTzINosHENrNm2Cc/x/i2Fq8zMHEb12GN5DhNU8xYG+gXjnI4iXXAGjLjGOroD2H7nvP0dr61bPXz/aTDXByR957jqRgtdgGB6H7c8hS3NSyF9A9FRgvDQb792e11XEwZJn/ekiUhwlz0YWUizHGVd/hWRFsnrmwc8e9jyOQXxzGqK/8zcVZbzP1xDv3gngofcY9n/d10gM4IWuH4ySAlJCnCj7BpHGYBYS8k+hRV2ETPgvETFPs1FOt8WIga0gavTOgHN3wqi7iNx8hxEzHuKdrtR0arOYCMp+CgWe9yMmcAJrFIhZrUNWpyainuUuw/ZxxIRT3FSBnysG2uDkazC+0HBP93sZSEik+JfExHYiS8GfIOWjhii4niwamYZvieF+2n3XIAbYQFR7SLvajcjFdcL9T0WuxquRUmeFofduyEvKS6dxUUBYBUDMNwsJuANuu9Ptv4EE12LCTdiMlI630TynmKIjfibHOFjgOStEgqyaUOYne9z7kJv5KffxqGH6FJrnGciC1YeE0i3G46OIfg7yzhJuLYS7tRoJjleRFeFOJJAnG+ZveS4uR0JikKhZvAoJ41a3V3Yevno8Plule7dA3hwiEXM/w8KVY4bl+2PgL868syb1lYQbOlla30Yu2TFELdU1nodc46/H87fMMCeLboPhShbyXN8vRHS0DdHxYuPzBbQuJ0H7Bii5jrAKP2Y8NaE5n08cDPoBKmM4Dc1/LVrrl6Pce3uRCzltbob87iOIVvd4bCkmtJMoc7YB8ZJdRCLz2UQsbYvundsDoz6KNhYvwkAbZE+EoeOQOYGwvq5EayHF0u5CoSaTgP+AlPyxnqNXiZQwkzy/B91Oo/ExxfP3HbTWe4CN8PPj8MFSRF9FiH8ki3MnUtIm+u8dKAzmKfdVBFTINXvR5UT6n9PoutzwJOXusNu+lTgshmEfa/x2uY925A0pVB9chnhTptvdAFwFA3dB9v3GdweinUyizNpUj/Vawf3W0/p5fK7xmmgmg6CLt4k64tuQQeDzhuMEUVN9I6LHArSBu9WwN3ruWomUQh2EVyYb8dUCtF5riNCgLEQrjWij9NX3MwZwLWPGjH6X2nybsWO/MRID+B5dIwrgha5rRwVj+yqcOw6jUjB1PbJi9CCGWIMUvzmIAZchZp8W9hgkWDqREjgGCbDdfm4KUX90kAicHyKqM9QgYZUEil1HNCPm0oYUgl4ihUux4TkBrzbCxz9JlGCbgwQfbisPMfXxiBG1G6ZrkTUsxU/VoR1qciWlvqv82YNcU0mJKzNcuejUcTti9inurxQJr+SqS66Y2W4v4S3T7ewkGGCXcVRuuCoN180oaXQVceovCwm7t4nA/Fq3XUYoE5m+/4z7y/a9EuPjoOen1POWBCHETvzbqIZnBlESrp2oc3u1YYQ4eVvrdsuIE8TPGFf9iNbqjLfHkMApJhS4QY9luuFciJTIBQy7nIetRb1ImSogyrnth1MHoGiNx7ufSFsyRGxg9hD1j0vdfrIyznPf1YbjlOdyCNFqimFc4TFlE2XpTrvd2f4/C3oXQ96XkFD/KqLFesJVO5lQ3hoMQ4P7z0HK6aVEBZyjRNUZjI/9fj65BpOLrdxwTHKf9Whzlmd8J2V0tsfX7rnLh4H7IXshEtq7EW9YQVjtczz+6xG9tSK6fhopzQsN40qkCO/2OFJ/KdThKBFXl2LNivz/ZONrC7KYJtdkOrQyGvGJbYYjhbCkMX/Nba3y30lhvB7R1UqPpx0pOzMM91G3VYboLPHCDURoQp3hmen+XicOgmSiNfsJoqLMMT/7J4gWIEom3mEcFXpc+9G6r0MbgW2IZ77qOc0iTmDnG++dHuvD6DDHbUSVpXSAbwFy+U9y38fQZucB4zjPOGhCm/xbjOMfq+/eH0LeRLdd47m8kyiXWYwstJluL8O/N6G1VELUMj9leNOcZQHr3k8F8AvvsgL4pyMK4Ht0jbiAL3QdRozEsV6jNiNG3o8YTVKANqOFOhox4SsRU+xGDOdS4iTdLUQw72TExK8hUpgkheUIkTi5BjHVLsJtfArtsjf43gtAIZx5gKgJvI8Ilr4ePp4sHD0aD/1E2ovkNksWniE/cw1ikIcQ05viMWUgi9NxtXmyCzHKGqI6Ry9RfSLB2omUhCbf34yYeB8SHm1EuawBJNyWEm6VXCKVSGKU+US+wL1u40ri5F09odwNGs9HPL8tROqKbHSdRIJqKeHuK0IK9QwkiJIwbPI73UTAP77fioTGlUQlj+Td6BDMp7o89mL/XukxDCLFHfdxAs1/JRLAm32/AylO4w1zemYmkZi2h6gwMINIGP4SUXO1EJgDRZuQda7NMJS43Zs8tqQEFnu8CUfTkBWv0DCXAA3w6lqUIuhJpDhepn4oMI4y3NcSpPw1Isv1buAQ5E0mkn+XE+70Fn1OPoGUqc2GIVnG2vzMJPd3CnrvJypLpHmrQ/NYRoQ2ZPqTXMhJ+Z9CVOU5TMTdjTVurvIY9kD2pxE97vKYphInv68nDmU0uf0upAw8itbBNWit7xMeh1MoFSA+00FY8q70WGf6/ljjodl4f5XYyAwiWvk6ooE84mTrZkSjp4V7HkFr8ITbbTNuTxGb12r/PQ9tMDYRVw1h+d9hHG7xvWl+byxRKvMJ/5bmotm4O4bm8XHEO7f6vRbCclpCZBQo8Pu9/rsa0X6y2mcSlZHyiVRbrR43fn+O+93osU4gNjIDnqMZSIFLm5wGZAWch/jmcTTPlZB3K+E9SVbFE8bpVMQrHycS2581npYT5R8Po/nOJBKalxOen5Fr5PolrxEF8ELXEqLMVgVRlaIDLcrpaOH1EYrYw4iBVqDFW08kfZ2CmNoHiFxj3YjBHULMucP9rkaL/ihhaRmHGGIJErrJ8neSYWEx5qNue4goVF9JJFtObpI2/54sc2ksY5DAL/W4tyImuISoE3wlElgpBmcijJ9qeK9Fu+4cIo7rJuPgMFF26jCQCefqYOhFpEw8SdSSnWwcTEXKbRthkbwBuXa61PdwBZEUT5b+r0AK8FIkeE4it12373cTTPYkcnmt9zw/g5hzH1KIBtz2k4RydBwJGccNMg1ZWsYiZaDKsNQiwb6GONCRqzEWfYJwVy4krExnCNdUttuHSAPS5naPIIXpZeN2HhEzh79He6xtSBDv9DPJ4rrXzyZFdAraNEyDcwcI4ZMsvOngz1lEh72EYt7uZ0ar/Y/PQjQ1myh7WOK+kwW3kjhYlOG5OIGEYaKjbb7X6/YKgCtg/G+4zwLDMMX4XYToPcVALnB2oRfczyIiF+VcNIcziNOvWf6e4PHWIlq8l1DWDxIbuGb381lkjcxGa+EOj7uCsKYmy+1LHv9OIrVImWE76TZPEuX0uo3Xs0gh2Uqc1k9KTHLvJ7c5RIzxEjTvTxEHGFLsb3qnEFmn6wlX4zGP/Uq11bLWY+pDSuNcRIeLiU1kihnE/aTDIkmZm+q5Oo7mutL4ziYqlNQhJa8L8ccJaBKXInppJCzbyQXfaZi7EO9c7z62oPV3lZ9NvGmH270euZGTBT2FeZz0WL6C1sRzaCP4knF0vuU98aAXiY1lD6LjTyGedrnhTTDuJxKa/zaa53rj6bDb7iJCRK71b2nNZaA1fz/v8/W/E/P3j31GrvfqGlEAL3RlIQExGQmBbYRQ+DZi1hlE3ro5QBccuQsxmcf181v3E0w+Cb0uYuc8G+0aCxGz3oWYxym0sKuRkH+byAu1CDHNhShwOAnXucjKOB0xi1lEfMt+pNxsRAwmDzHW5CLdQJw83YWU2TS+FG+VlNKp7m+J21yAGNd0It3JIuBrGXpuGvCbhm2Q4ZiWUWMhcyXh2r4fKUs1KFYmD506vI6weGxAQdWjkbI9SNRTXYAExJ1ImMw6b962IaHyOHH6tAIpkcm1upIomXYfoUh0Ibd2ifEzmnBh9SBhXe9520UotMeMr43Eyb6diJ4GPUetfj/T7T+F3E8LPa7diDbmEQeDpqH4zy8ji9wiJOj6jbs3EJ1+BdHN/Uhg1hKl75KlZafbTfO+neHUP6OqiHCAVuIAzEIUjzlEHOrpRAp3GXJz3eH3zhinH0Ubp34kzO4iYj+zibJiM93ncuO5j0jRk8dw2MOpJz3mRxDdnSU2JSls4Kxx1AqjriPKLJYSVugjhGLb4vmc4vk867He6Pa2eS7Hee5qUZzddz3ehNscz8N6z6s3UENbPJ4tKO9lCe88JTuZiMc7wjvjfQuNm4ke8ySktJ5Gm6JjxuflyCpUhZTEHM/bH6D1fjNx4juXOGHcadieIzZb1xPKzVVqr3yiYVhEZAmY5n7qjPtb4MzthKXc4/rPP3XfPcQp7Xke+0F0+K0Cxcy2EZuPiciTMgddaQ3UEjyr07hcJ1yzgpjrbci9/oxhvoZIaH+/4dlAlPVcRVRhKUSbw2bEE46jtZzc+IUeX4th/hPDWEXEWOYTVvzvGc8/QLx9seezgDi8s0jwnnkU0c9taA11EgepCpD7+XOes/f1GlEA/7leIwrgha5GIqnrbKLU0WSklOxEgquNEK4r4LJHiROGVXDRfYiRVRKnuI4QykgVEjabEAN8yX0m11QdsiokRawGKQjT/ds6t5tOo25yX32G/w73WYmY+qcQ091IHCppRwrDMiKtw9OIgW8mss9P9G9J6O9GyuAZwoLyKHHqr+ysmGeKsdtpuB9DQu3PPc4UZ3MP/Czh+++JAyifM547CDdqARJm96PEzS95HPuR0tWLhHq78XuD4V4lvAzc6vtbkGCxEvXy9/z+fYipJ+HyFyjv29W8s0xYGUrdktxPfwQ/X6WxMA0p0uVIKd3j9ycjAfS4YbqOyH233LAeMHw3ECdG5yFayECCZ4vh2GS8HPP/c5AlIsVZ/nvjLLn898Bb+4gcjPvRwYF+4lDBXsP1KPTe4meOuJ0bkdJTixTMNqScrEH0+h88/muQ4t+MlI+veezPeB5STFhyKS9AAno/UkqKPc7kjr4F0VEVFE0khG4aQx385C60eVpGhDcka3uO//+PiHYuM/z7iQMbPUgJTqEIrZ6/qzyeVrS21rv/QsOw0TCmONYsRA/JFToFMm9GAv9yz20Goq+/JNyJHYblz9z2GpTCJCkTG3z/BSL2tJ5QlLedN54nPB8T0FpaTiQQ32q8diP6uQkpIKMRzQEDK5ES2IfoqxrRzEPIkzENeg8hOkhW6lbgcRhTilzN9QzHJ//bLYgH7DGOcxEv60L0c4N/e9bwJly/RqShmkiUxUteibRJq4aftBGJvjsJF2sGkb80eQluInIAFntOvu253QZcBm+9gfhTisOrNc4eNz7mu93phuHzbrvV85U218sQbX0Ora3Z/n+a21pNHMYy/xyzwXDgOWtCvPZRdJBkCaKndkaukeufdI0cArnQNX2UhEQbEVc3CS3oY2jBlyLF4xYkEMuJyg+Tz/t7HxFA34IW807ECLYRWeWTsFvtZ/MJhWc5EqDlSFjsR0K1iSh9dBopGN1IgbvDf28lgsbb/c5NiAk9znDwOmsMz1bE3FrRjrcaMewphPt1PxKS+YbnQSTQ+4jkuoWIQSWryO1EGbEuInH1zUjRvJc4KDCWsLDtRUz/RqSYFRv2ye7/MeSuSQJkvWGpRcz7ccPyRbRDn0vkXuwn4i1b3N8846CCcG3X+F6Wx9hpfPUQyWvLiHi2QiQcCoh0IrPOG3suYuQzEW0kYdeGqrtsM767iRqjFUQJsz4ir1w+caDiBOH6r0S024YUrjoiNjKLiEl6HNFzilcrQ8pFOwxsguzLCUvxDES713jMO5Ci1YuswW0efysS5KUe6zWeo2y0Vma7v/l+fxNSFpKysxvR6zw0/81IQdyJ6Ho7Wlt3E2lq+vx7OaKtRYZnyHA+4DZzPP7ZSAGpRmtiDlGt4zRxonifx36J52UJcWhnPaKx5LKrhPYHoOQ39M5bT8NF84nY2274u0fg12fxztCFXM9dtfGWQYQp3EHUfz1OKDP3GocFQCG8eTNc/AnjpZhwuW9Eiv3TaCMyGSki/xGFD4x3/6Uee4/Hn7wb09zGIaSAdRGpUo6h1FL1RPzaDM/zC343tbnPMLX5uUFEb/2+t9JzdMJwTkf8qtBz3I+UwXFobXwf0Uc/UuYTDusRbR80Lg4S1YCWeV6LAm/kGo91hmcfoslKRJd3G+bdfq/e87HUYxgwrHs8R9Pd7jq/t5Y4XNRHHNhJHoBnEE/a7/nK8TO70LptFsxDP4LMx4nT19XGzwag//08BPIZxozJfpfaHGDs2O+OHAJ5j64RC+CFrjsI19hyxBCPooX9NmJ+TyKG+C3EhFqIYuvfIWIIxyElpZtImjwd7ZAPEnEhEHm2UgxfH2IArUQFiieRe+8WxJyWEjErDcTpuVrEuM8iBtdCJKseRBadJqTMHPS7aee8BzHTzyKmdhNRsSFZNy4nLH83EVbH3cRhgWYkfE8QCmKyrs4lcohdhqxW5cSJzVxidz4VCZUWeH0TEij9iGkWEieCpxPu4hTLczMSBO2IaRajue1EjDkDWT4qkMBfhhSWucjy89+/V4qUgZNEWbA33N6njZ92P/uA8PpqIxK0mUhJL0fuoGVECb7C897LQ4pZredlM7L4TUGKw0SPv9X4/66fK2U4V9+ZO4nT4fcQLr8TRHWLPUgxmeTndiDBM0ef7Awi7+Dlvn+DYa01vrKIigylhinFYx1Dlq2Xjb8kREuRkn8WCfubkHV6osdTRJzKbYZzb3h8KV4wnygl1+55Hu1xLPOYDhG5+io97mKiRN5Jj2MXop9mInauFCmRBzwPrcbVQvd1gNgEthObuO9CyQo/UwgX3W2YmhmO3f31mwkFoQDR9nTC4pjiGW8jQkAaDFehvz9O0MkEzcXF81FqnmSBTO77ROsriFq0NxGboOSuTJuJ6xGNNiFa2kbQVolh3YsUn8RnCon0Mrs8h30Mb0AGHiJKui0iyhEmi/88RFtlaM3MJ6oNvYbm+QCRDqkQ0UsjUfGjA837bMSXphnmWzwfk4kUM0Oe3zZEJ4lfbCZKv01CvPsPiLQxhYY7WVLHeW53EnGwWW6r2X83ExuPduTOLkOblW6kCFYb70OGpYR3pqYahMxPGNZWj2uZ20myY+QauX7Ja0QBvNDVixbuWcQ06hGjTkHKHcTO9pi/S4jTtR/3711EreBcpMQ0Q+8rSAiVEXE6HcjKkZSBJNSSW64MMZwJhFKwz7A1EvV8C/05iBhLPmJ2hUjxKEPK4AHEZK4kUpGMdf93IuXyMiLL/2kkAHf6mbeNjxTXs9ifcuBzuRIAbxBW1CTgViKmtd/fC43TY8TJ6360s12MAtKvQika1sKlH0OU20QEeyeLx2HE6JMl8neNp0GkWDcbp1lEJZcy91HOcLZ+aogydG3+bSPB5Md7vooNa3LPTXH/HQwrrFT4FHY+YSlqcn/5HneKr5xtuOcjYZaDLI0ziZxhjUjIDnoelhqHi4y/OWpvzH1EBYEUo3UQ3tpCxJH2I0tkhue1Ggn4AqKE1myPp4CwIG8matdWE/kPB4n6qVlok1OMhPEWtDZWELVWe5DF+3GkYPYR1vOFfv5uGFVIlFdLMWEZaN0UI1p62v0/btiuJJTkFEeag9ZMPnGIazSyzjYjWkguyqRoTHAbWcgCl0MkQE8xvUnZuJ9Irn7Uba4nYksXIBqagly0WYQVbzlR5eJyouzj1WjNpDCSdCCnD7kEkwIwG8ValiFFIc1HOmBRg+b9UiK5eqKxAs/pBuPvgH9vMi5y4K1DhEI50/C2IKXlUve13XO623ieBByE7M8Sh9F6DVut4R8iUltVIDo5X1FPSm8KG5hExEkvI9JfNRnX+H6yZpe4vS97nAWInlcSFX0aPX9r1d+5TYSlsIywZqeN2Eq0yS8lEn6nGNkm4+5a43mIOFV8GbLU1/53zzYYd/tRyqMBRGuthv0qj6MM8Y1Bov77Gt7nayQG8J/rNaIAXug63yJwAuWw24qsQlOQO/MAnLof7bo70KKegZjUZLRDLUSL+26iSHwW5P0GYqRtxE6yGzHBVvcxFgmtRxFz2I0EWj5idG2IwRxCjKiGKLFVh5QniPQfS4j8UllEnVi77t68CwmR1UQ6jX/rsZUiRWW/YetETK3RcJ9Agf0PuN21fYJlrXGx3+O6jUixsRoxtBNEDrNBoq5mMRJIo/U9tA9ZP48SpzHxuG4i6rWecPuPE4H5DxD5F48a5iWe12/42R5k3XrK9wY8zkOI2SdL12xkVUwCKwWNlxN1dpPi/WV/lnhsOw3jIUQXg8iVt5MQAk8arhrj+BNIGE4k6GkKYZHZh5TASuQi/wYRFjDZuBzHcJ3hiz7rNpKV6H74+Vr3tQWohforPbdfJ+pR1yKhVuW+u4k4xoWIXk8a7o3uo8F4qDcuxxv/mwxbL5HTsotIBZKHaDwp4BAnTTsMzxZouQ44AG+9BC1dSCHJRUpcLXFwZ9D9JwvbYiLwfzaRy3IiIcxTKMcijy+184Dfv4/IJ5kE+nQk8K9AgnktovM+v5+FhHkS9gV+77No3rcgJaMOWcRzEa1sNOwrUNhJUkjLjc+kkPYh5SzLY+g5b+yzEW1noI1Il/EEkbA5KfDtROqR2zQvF12NLHUzjI8eRNu7EW/oJ+r3Xkskhr7KbY4zziuMt0NEHdvZxGn1hcRhlGoiV2q/4bscre9yon7wdKRMpXjEWmQ5PYZopgt5akrQxqAbWUL3Gv7vIwtcreZkVK7bPYbmup+gh2TZvAOt9Z3G3X7/3uXfG4gNVDrENeR+0oYhxYJP929TED/pJJKTVxoPlf693u8WCsZXb+F9vkYUwH+u14gCeKEr7cwq0A58A2Lm/4Fwzd4ORXchZjIBLdzTaFe7hTjR140sYdt4Z16xSxCjLmTY+vOzpxED3Uuc7KxC7sJ7kbBYh5jAGuLU37NICUjWwclEqo7H3f6LSIEtAKbDTzYgJvcFoBguvhUxqX1+50EiPnAj2unOQwx2IWLQJe7/HsKtcxgxxMvRbvcWtGPu9v1liPKSW+xZ93sQKbvJEpjp9sdrSk4B/ClSpkqN02Ue534iNvBePd/eQZy0/Jxxco3xsx8JsDmIuSaLyw88d8mltYZI1vwZ//YdeDnhuZGoXYvxeweRLBnjpg0pR/ciIZBcR+OIAxspsLvdg32asOaeclvHEU0u0RyyGM35s0jA3eu5SbGlbYY/ubMnup3vIuuorUAfnOWxXC74pp0rZehR460PxV8uIE7A3u8x9iNa6UNWoyJ0gCEbrZch91ni955BoQeTDUc3optxHvcm4z/Fr92EBO8dSLHtQLQzJHjKPw3Mg4vWQPl9Hvulfn+H8brO+NhMzPkgtL9kXKZ5etH9n3Hfp+DNlcZzPpE2ZTGin5uJ0owZREwt/n+DxzvVbZ8l8nxOMu6zkBJ8p+F6GK2zWuAgnLsZze9MpFhUCC72o3W2Dq3NLsO4j6jk0kmUCkvWq0xE7xlEjfMGRAulnpfkMoY4kZxnPJWjtfnbSHncjJTdLe7rPs0H9xEHtYo91i8Krt673WcTvP4oUnxPobnuRXM7QJTsszudCsQz7ybyPx6FMwcQXfzQc9Vj/JYZ/m94vIPG92tIqf9j4G8Qf3mZqA++xe10wrlXUHnCZnRYx3APb1qy3PcZpLtMJrImzEHrsp6o7pFrPDqc5NxrhJVvMrJUpw1QjvGxHVWUetzttBv3j2vOPv5/86/2euWVV/jN3/xNSktLGTVqFH/913/9jvvnzp3j3nvv5Vd+5Ve46KKLWLhwIU1NTf/Tdh977DHKy8vJycmhurqaH/3oR/8/jeD9vUYOgVzoumOUFvplaHFmIAF7LWIqY9Hi/kMiD9VBpNjdSCQ+LoRT34SidWjB3oYY7dXAfGj5IZSvQkwzA+0cS/09hQiaLkbML7nyqohcZ3mIMTxAxPa8AEOvQOYlfja5DCqIdBw5hBUiuWbTqcU6pLDkIWWjEAn5Fnj9e3BplXFQYdgmSViNWnFe2ykOZi3hJt+FmHohYfUpd1+dhCs9jflVxKD7EQNdQFg2spDQ6TfMm5Gy2Wr8lxI5xh41Pos9Tzke1z499+Z9cPEM46IbKX4NROWUfEQPzUjQ4/GfMCxT3WYzURVgKzoFm48Uk+uQMjSbOKF8leaKfM/XJsK6m2BZTZw4zjKeUqzSdM/VROSS70eCpAzRZT/alGx1eznIUvskccJ5ElJ2vqdHRs0g8hPOM75qJAxHpXCBaUiwzSMO57R6/EXIorWPOJHaCf/5eT0+KlkNk9KyDW1ediE6WYBoNSlZR932KsL1XHEe7NsIJacKWWTOIgV1F1Iu6wjrYTFSuDa7nc8iWmhD1pktnqvphuFZYv4vM04fIuJbDxP1vtNhn7mGZTbaOOQRB7r6EY2fQAr3eKJ0nNfYcDxbreez1P3tN6460JopN86mEaUGjyOa2er31xDl4h5B63I9WjMrGV4Dw/GV9YZlkseWrH6NSPHoRwrTp4hDEyWEUtRJhHwUuO0WROtJ2bsb0eWfELG6Oe77fmTlXEJUwSkmYqgPEJuKSnRoahWip2WIHrqIQ2bdxsdzSCnLNK5q/ewMIjwgeVKme+yTCP7/hsex3Lg47P5vJtznCxCttqM1uxzR2joiMXgVoru17isLrd3H0RxPdRtPoM3jIj+zzX9vMcz2ktRvgmnvgxiPQyDXvcuHQJ77Xz4EsmPHDv7u7/6OX/u1X+Oaa65hy5Yt/NZv/dbw/T/+4z/mwQcf5JlnnuHDH/4w99xzD/X19TQ0NJCTk/OPtrlx40ZWrVrFE088QXV1NY888gj/6T/9JxobG/ngBz/4rozz/5Tr/3q/Afg/9TrzCIyZgYTHHsRgehDDn0PECzUg5pBisYqJRLb5wDQo+jQSGCuRu6EPHQrZAeUHEPM/6N+3uf0S5PrsQsxuPOG27UKM46zb7AcOwM9OwIeaENP7jHgaHUgYrSMqSTQg5lhN5MNKcTf9SFiksef4mRqGGfKlMxADK0OM6jRQB6PSAYICxHS3Acdy4S/7wn0xG+1oFxjO2UhwHETC7Ijfv97PLfI4mwg3Ubqm+tkSv5dj/OYZR9cTbtssJBzqPHfL/e6A5u7i+5GAbCDycbUTKTCOIWWul6g2kIOY+NeQxWsJEjKj/V6KIetEgiO5zerdTrKM2O2dmUkkU16IrFEzkJC8yjB0eOxVxMGjZefBmUWcTC0WnodmQObDhmcyEdOZNjL5wtXF091Or/G32X3lasyjViBFYy/hVv4aslxuQjReY/ysMwyFiJ7Owr+9BNFrilNNsXKlROxmhuZrYANkzzG+KonydIc1JnIMyzyi7GIBkXB5AnFyOilP+4icm/vRZukBZBV+3f2kcb1m2Gaqj4GHIHsGopc2v5fvOahGyliV8Z/W0XYUOrAEKbEFyFJ0DVrTv+N3k7WrwfOehdb7QaRoJdfnPiLWLW2KUizjZN+fQpxMrUfW33Y/d4fnvAtZ7Y54vsd5nmuJJM37/c4SRL9Jqbva3/2eq+T+P8RwyMS5p513scTzdcR/byfWBoiOriJOG2ca5kGkICYL6WRkGU20+wmPf7thqCBSwjQSyd2vRsp7AXGgo8fvPAU/fx4+eL6Fd915ODLNU4d4f7KytiM+Ve82G4jN2Dz4L4/Cr34MxYDPRHSYeHUnWhNzkLL4mOduYcD/s0H4ULmfr0W8OxMpiCWIbx31vOwBdkUa1H+N1+LFi1m8ePE/eu/cuXM88sgjfOUrX2Hp0qUAPPvss4wbN46//uu/5nd+53f+0ff+9E//lN/93d9l9erVADzxxBP88Ic/5C//8i+56667/v8ZyPt0jbiAL3CNuR/tlpNVox0t3irC+lSIrDdziJx1DcB34O9Pw8AbBJO8BTG0FPB9Ggkox4SdeYLh0j7/5Yf+OxsJmy1IqB9EVsBe4kDGOj+TAR9aaDjGEVaJqz2GGX7vW0Tqk0H1B0Sg9VWIuTQQqU9Ou48XkWBYg4TNYf/d77+/gZhcDRK6xcBNfWKWh/3cCiRgkkXhKaQQpMD/HCQwthKVItLOfA9yi9YbD7VI8DxI5A6b5vlaRZxGbSdquh5ETLnTML2NlMZXkXI5YFxvIdyG85BSv9l4aDD8yToxZPh2ENadeqKUWBIw04mcbNd6fiYhmsogNhY1SImxwDrzU/d5gEgjdI3x8zZh7VjvT7/H9BRQB5ljDedEw/is57vU+KpDLuEtSKkfJE5V1xEWjWJkbbkK2Au9TxBW3W5EW/s8dz2eny5/knUubZDKiRJc09FV5u9qyB5t3PUg4ZtpHBQToQdDSJDmoNjNHsNbh2j2RbS+UvxppvG6BymsmxEdvIwE9h8SlX/Gei6+ARyC7Cc9hs8ji+ccIq6x32PajzYsmZ6PHnjrFeLgw3zDelBt8nlEW+sMew6irwluK5uwII9Da22BcddimFOMXx9RAq8bbUQq/VsPw/FiHIKBJxG9FxDlE+uIXIkpRm+Sx5xialv9bBeRmzG52XF/S2HUSuM5bYzHEIrqJKTEdPr/NOdpXd1LnJ49H/a0OUnW/YNo/dQZ1kw//6jb2Ybm+DCy/jUSqaFygGtd3CnxlQbPyX9EdLXFbc9BSnzyVFyKNmL5xlcBUsjahI9fnUzUHs5H6zAp6MVos/YdZGG2wpw8NhTCh240ftZ4HjYSJQivN6zHjIsqPZuZwg7et2voXf7Iunj+5+233/6lofrpT39Ke3s7CxcuHP5t7NixVFdXc+DAgX/0nYGBAf7+7//+He9kZGSwcOHCC77zz/kaUQAvdFUTdVSXEAHAE4ikp20oQfBZolpCJ7AIfu1RyH7R775NnMCdw7BwYB4SItUw5naGc3396n1IkKdTmRMQ88hDjGwuUoCSta3/PDhTTN0upEhsI8o+TUGCpBoJ/h2IsWUiy8ghpDTMQIcQbvK9M4i5P4+4ZiFiUkuILPtziJQYrYhpTUQM+IThzzasJ5GAziLyfc0jhEGB2+j2/0PGcStSCsqQOybb/W0jUiYkt1MxkdamRHMynC8uWQv2GcYlSLB+Gfhrwn26yHB1+v5iNOfbkIWoiCjZllyd0wXnz1JcX3K/dhqP/Uip6PVcfMDwHSKUwcsMd6XGMWYNUAc/azTsC31/EpHeI58QyjcQrqs/8bgA/h1RHeIxouxeNRKSKS6t39+ViFbfNh7a0Tx3ApmQN5pIXTMO0SZEPrk0H6m9Pv9WDQP3Ead1e2DoISTs1iNr3CMexwzCojPX4ztu2JqImtzJGlbqca1334sI1+o4pOgOut18tMZmu/2vEpuDE5qHk4dgYBdae5OMv3yk7GYiRb5Tc87a89ouAaY7EXxSRLbq/qlH/PdoRGfzCLpOysoQUlDTZiPzvLmpJw7h1BI5Sv/IsDXCqTqkTLX5s964zoDs25EyUaYxDtf0vQMpV99CG4w5SKE6idZbjsc1Dq2ZCrTWnjWcuxD/SIeXdqD1dSdaP+OJeuQY1pNoHTV6DAXIpdqN6KrFMGWgeS5yG6fVz989QVgh56vNt54kchvehqyHNWjdrWI4vVfmi8TBjKNIsa8hcmguJ0oN3oFCBdYSlr9GZIU8jFzbM4jY3HK06b+NyNZQjtbvacKS34losNh/L0G8JMtz9IjhyEebku/Dm28g/lFpvDzH+3wN8e4dAJECWFZWxtixY4c/Dz744C8NVXt7OwDjxo17x+/jxo0bvvffX7/4xS8YGhr6pd7553yNuIAvdL3u7zsQM0qxazVEnNUfEcl5xzNcZYIhFL9xxv+fQHFBqxEzOK73e1dA3grEnKcjhpIsiWuQUNlOKF3FaGfaS+QAW4t2kufHhy3zc0PIDTGEmFMH2o1noJ3pTcDnM+D/PQsfQ0HaE5DA/xwSuElpKkcCsgkpK6eMo0YkXAeIE5CdSGE7gRTk3/W9r7mPm5AQm4AYXCVSVG7wvRo49xiMegAJp3mI+d5s/CdLYTXwLfh5B2TdAhdfZ5xNRgx5KlKoDqFd8w4iIXKdcbqQqJYwCSmzxUjpaDDcnzWcjUSKlm3oJHhS8o8iYTkA/A186ASc2wejSpHl+GnEyLNRvN8ayLwHCY8e9/NpZIU6RqRoScrocvjQ94myZyAhO874XYAUwU7PdY7nrgsJmCzgmyi26nLiRGaRx3FY4zh3NYy6Wrg59xKMWmM8VhpPyYU2AylXO5BAXm78tXqsdxknGwhLSS6iy4MfIbvwJ5FM+RBkzvC7Zeh3EH23ECmO3kauwROmgeR2z3E/ufA3T8C/f9TwXossK81Iec80jLuRMH2UOGCxm6C154z7F2D8fWg94vH+gdu71GPc4HcSTZ01bq0kk+MxJBfuTCh6wGPb5TEkS1A/cqcPES79M4gOczyXd3hMR91uJVozTUj5agA2QdFxZDEuI+jrILIoFqG1WMZwPOrAHshOYRjL3dbNRDm25E1IYRQn0Prp8dwccdsr0Jr6qv9/2zi6laj13Q68YvhvRpupycTmrNh9LEB84igRH7zanyPAIfj1xWgd7HEbd8NFy4F/g3hjj+e6l1AsG5Al+5tu/xbjd4L7n8dw8mUWIqW3hjhQNIHI57mPUM63EDSciU7QlyFenbxBExlO5TV0H2TeSByE+qHxOh/xle8gK2+9YfoxkAcXfxQptdOQXFnCv7irtbX1HTGAo0ePfh+h+Zd7jSiAF7r2IEbYC2TDwLPOZXUWMf8XkYK0Eyl0e/zePCJVRHKBVSDGcD4jux3yNiOBXA7tz0PJnWixz0CMoNb9TScOQUxGzKkbCcb682CuQYzoGmTheZhIGLwBMbJ0ACEHWUPaz0YR9LGGsYmI35mHlNEU+ziTqEKwCyltW4gTz4mhp9jCjUQs1eeRErDZ8M9EQmAJYvqHGU4rMqoWCfujRJLaTs/Js0jwrQNOwwfv9tgnGdcpbmyh8XfI49jlZ1Kc2n6iKkEpspAuRoz9kNupQe6aRiIGqRR6D0De1whrwF3G21nj+ohr6XYiQbkaKQ9LgL+AzC8YnuVoA9HtZ15AysGg560CKe17kLIw0/1d6f83G98dyNJSg2hnLDANTh2Aops8lj2qR3tyC3woCY7jpoUXgfmO89stuEatIfItbkYKUicM3A/Zl6B1MA5tBh72WJahAyFbjZuF/rsX0dgi4Ms/kfDqMa6yiID9s4jWrvf43/B71SgP5CWG+yXPRyP85G7pLdnLfD4nWfA+o7G9+gp8/JNESpMK4+tWf+/VO8fWwpRvIIXsJUTvmwmL5inisESWaSJZQFN4wgTPT9qIJAtoMaGsNnnuqolUIUnZrdd4z70Eoy4nDhylTc+X0JqegxSc5KrcBgP7IPtLiHZTTF6uYTyL6LsB0fd44nDTacj+KLFZ3e+2HyfqFKdYuH1EGbNFRHzy73hcn0ZzuxytsTwisXyypk8mLLLPolPjuxEvnUyk8ukmkiwvRorRTrRBSpvldrSBmYTWcjOixUqkxA6ijUAdcaij3/cfRDzmDaQcg/jXU267Bq3r1W43hTGsIuKS891v8oDMJ9J6bfB7FUgRbEZrebm6ylxDJIqeB/y6ny0hQnN60Rra5XmpRJvojX7mabSh+33ex+u/AaPexbZgzJgx/9uVQEpKSgDo6OjgV37lV4Z/7+jo4LLLLvtH3/nABz5AZmYmHR0d7/i9o6NjuL1/SdeIAnihK8WLFABVkF2FFvQctPAnoNxRXcQpzauQNW8OskCBmGUJkdrkGYZdFTyJGGI1lJSiBZ4UrKsQE5lI5P2rN1wFRMmvJYhRdROnP4/5/5VI+H4RMZ7X0Y4xBwmI2Qyf0KTK8KbxpRjB6cja9bLhWuDndhPCqwIx5qv93jgkKMYhpvhlIvlxLbx5Ai7+GHF6cbP7OWSYS5GATHGBpe4jx7/nGhdZiEEnl26x+z2FXFvPen6uRgx0rfG3kShJlw48lBgPXcbrg8QpzgJCIJWpj7xlxAnsBUjA1RMHYD51How1Hluxx/k5Ijdalv9uQwr59URC8bQByEeCeCyRnuN2pHwXIeGYYsjSpmSh/i8qRFZkgD+EzGL40H6PvwgJ701IKG03Pso9zuSaOoqUlT6NL/shJExbkXIx12PYeR4+DxM5Ha/y70kQnkZz/rL7LSZOv3YZ793GbRVxGrfC7yX3eKvw95GkEHXBmMWesz6GYz8/XmT8fB25XQuM04eQ0nwDcAqmPA00wtDTkPkxhtMlDdPuFn8PGW/PIDo7QlSxOIIUisuJ5MsFSAnZ6/lM8YVHiDi+CR5zJtAJo76B1niyfhb7/enECeK0eemG3ftgQVIUOpHVeTuyDq43vJMJC9g8w3Hc7X8b0Wta4zvd9j6P65Tn9AbjfohIaVWPNokpDKIa+P1s+K8DguUSRJvrDXOv5o0pnjcQTYzRHA6f7m4zvub57/sNUxeRQL8N8ZCFhDu3ENH0Co+13WNNru6FHkdyqY9DNDwaWfonEgegqohNTI3n6TSa+4WIXzWgud7uzzVuM8/434ZotYtI/L7TcH+f2AxNQbKh098ziDRiiUd7c3HuJRj1MbQ57WPk+keuD3/4w5SUlPC3f/u3wwrfmTNnqK2t5ZZbbvlH38nOzubXfu3X+Nu//dvh08Rnz57lb//2b7ntttveI8jfu2skBvBCVztRj3Mr4U7MQwx9IQz9EDHATYgh1SPmVkO49grRTryciNHqRws8BXTvI+r0nvFz25AwOETEhiRXRStiBCVEtvpr/PsExKBTQPwdxEnlIqIs3BLE1JOFMlnOkqVynu/v9bg+bvheQAwrtbNO8J3pQkLuMBJWKb4oWRKb/PspuPhLRBxPcicWEBVAThCn68r8bANR9aPgvHnadd57zZ6rMmQhm0fUdZ2ABFaD8Z5JHJaZ53l7xnBuhJ+dJmLzCokUKrmI4fYQdU5TrFsS0iWEIrUICd5ZyLqw33iagCxlJ5CrpxW5pBYgWqtGSl4xEhZtbjcDKUpnieorKVh/D5rzHDTXR1Bc00zjpztwPfS8//8okS6nD9F3CmtYQCQkz/Hz1/v5AmS5meR5WOyxnibiB3OM3+2EOzrR4gQUcJ+N6D/Fk002PkvROhry+7lufzai3QI/d5wo/bbQv/82ke6oCimMfSjerJE4+HCzcduN6OGbQBZk3opOyfYY/haPbyKy/lzl/8vc/kxEQ92er4lo7oeIvHlJkV3lMd+F6GAWEYebc97YivzOGcPfgjaaMz03pYR7fx8s+Ciyzr5G5KebZriuMGz9aE4ric0L5+G6wPAmq29SlmZ5zAuQ8rLLeMlDim8W2hBCxDU/MaB5rUXW1CeJk8MQG89mtLF+3e/VGLfNaK2kzV4OsnadQDSSZTycIk5GT/aYV6PNXrZxvBXx03oi7GCQqBxU5/5rPNY3EK/JRDxvJ1KopxsH+xFtPExUhckwHjM9VylM5ilC0R5HhNTMNB7G+/mNiI6bjdda4jDVGPdfgnjqeudGn00cFHxfr3cr/i99/tevf/iHf+DIkSMcOXIE0MGPI0eO8LOf/YxRo0Zx++238/Wvf50XX3yR+vp6Vq1aRWlp6TtSxfy7f/fv+M53vjP8/xe+8AX+4i/+gmeeeYYf//jH3HLLLfT29g6fCv6XdI1YAC90JavWG0iB6yKSjFYCJa7NmJSdMuKE7xak4LUhhnUEen8IeXcjQbSPOBSST2R670E7yusQQ+knypHdihhmFlHLN9/9dSJGdgeRBmXI8O5AjAnEXL7v528jMt1XGPYJiBnPQEw+H8VJ3e3xL9K4eQHtmOczbNUa04sY4ljkVqsgYhPziRrJS/25DLlEywzjYiI312zfu8MwTkNKUYrzKUVMfoufr0aM0LFy9BBpeqqJ1BaXI6tADxIQm/zd4XauYjg334fKzxvnYb9fhpTE8YQ1roFIz1OIksX+NrIA9np++pAyO4WIJ6tAwr4SudOTK6iNd55+TBaM5J5uMj4qCXfjd/Xb0AHIfAzRwF5keU34PEXE4lVCZhMSOI2GKYuowzvTfXzb+JyOLCZJWE4wLkcjOi1Acz8P0VOrP982vN2Ilvd5/nr8+2eIAw27gT/373/g36qN5wKkCIwhLCkVaEP0LSRcm5FSPITopgcJ807i8M4OJMCTi73L40wuxi94DJOIU+u9fvZFxAuSFfew52AXkVh5yL+XE6dcIdZjmuOJSJmZaNyMIzYkBcbxcuNmLeGqLnbbtyF6ave4bkNKyWTITrDYWstnkSI5j0hJ9C3CPV+NlLPZxn2T4f4UwS/6EO1PNK4KjMcHCaX9BLKevejnDxlvi4h43Qbj4zYihGUs9K6FvDXGa5bnpVD3eJtICr8VKXTTEJ3O9XeFcXzI7WYRG8onofenkLfS7W43vCfRoZCziN/lEZWV6t1OBXAMTj4E45f5vRxgYyF8vivmM1lUy9Gm6zGPZSXK43cTisfMM7zJkjfOn3nEQaYctC6TUWEe4sllHncGUAeDxw3jNCKNzr/C69ChQ8yfP3/4/y984QsA3HDDDXzve99j7dq19Pb2cvPNN9Pd3c2cOXP4m7/5m3fkAHz99df5xS9+Mfz/tddeS2dnJ/feey/t7e1cdtll/M3f/M3/cDDkX8I1kgj6Atdbo0Zx0WKGc4i9vAeueJZg0C2IwSX3Rw+y6OQTQmcm2vE/jBbxCqTEpJ3gQsTYryRyCh5HQnSl/4ZIonwauVMKkLIygBjuXiQMj7qt1xDzuxopSVZY2YuY5li3+4SfP46UuUNE0HoBUiBe9fvNRL3QFF+T3LGzcdJBv5sU01qitFMpEjSFiMkWI0G0CilUi4kcYOmdR4n4pRXIpZuDmOxlSAAOGnd7CUtYamOVYdiJGPItnodewiX2LcNa4XHNQPPbgIT5NYQyuRcJt2QxyzLOUqxXO4pz/JzhzvRz84hScrXooMdepBA1uL1FiOknl5gF8dABH5DoIUIHvrEY1u6Q8vFlhlNinFkGYw54vIcN6wz/vRDY5mTTnyVScKTNQydxwnM5UvqXwLkHYNTtnvdvIgtsoqFDfqcUKYUlKG5sDlII6pCb9wq3P9HvLGH40AYlxluRSv1lJgW/HimIW43fcuLUeb/nuB0J1Uc8j8litxbhphYJzEq0dpJSV0bEGH4D3uyCi5PVtR8piv8Podz/vuen2P2t8/hTeMczxmcVWnu3o3U9EfGG5xH9HSOUlDl+9hIkxA8LtwOvQPYqZLndRiip2xDdzyYUwxQfW4F4zF3ut8H42ez7axg+FPSTJ+EjLyHeccAw1cHrx+HSDyOrbrNwd+45GJVh2O8n4nwrkCLVB+d2wKjN7vs2tE4/aTzeQri3TzB80Gg4kfE+z8PvEfWnvQEb3oBOIE6770JraTOi0UuQgph44k501Rg/jf5tteaZ8W4vy3/3ei76hAPSWBuIjeQ6tGFNbuxaIsVNJpHiZxtai1sQTz3LcBxs78NWQPONtycIy+m1iL4fRTR8s+GpIcrbVQi+c0eda7XJ+Jxu+KuBX3vvxXgkgl7MmDFZ71Kbg4wdu+N/ORH0yPW/d40ogBe6/myUFngvUrKSGX81sSg7iNN7xxDDm4sE8iBiKsllWEokWc4lKldcS6Q/GiSY3QTERArRYs9iOGXM8AGR1W7zT9CJ5H6kdG4gFJuFfq8SMdtOpIjdiJhQUk7OIqHUhxSN5P4oJQTNOhg4AdlTiWDlJsQwb/bYcgxrK/z98/Brn0TCsx8pON3AXxlfyQX1IAwcgOx1xulhxBR3+pmksO0n4t36kZtsBhJYHYgp5xBJo0uREGgxbquMu0Gi2sdWorbsZKRkVhsnt3leujxnS4lSUGUaz8A+B9BXoEMRKX5tAhIG3UhxSFbDHiR49wJvosNCWX4vud9bPLZXUbmqrxmObGQdaDVsv+lxLoTemyHvEUR7yVowhE5jXoZi3UoRbewx3roZzj3JZuO5z+OvN16mE5bnOrSpWO25bCI2FouMuzVEzr0mRBc3IoHc5DZHExaXCUhprEJhBi97jlahtfC4x1KB6CJZlPsN9zg/P8Rw+Tb6CStSn+F5BNF3FqKhbLc91u2dQJbDHCKGtQspSjf4t0fO66cYWYj7iDRG3WjN9BvnTcZNuvKQEp0U1Ub/PdrPb0clH7ehdfuQ2z2J1mcuWtPfQZuoFF5RjRSrZz3mbUgRS3yjz3BXEYrhHxr+fsTXCt3eYuPlXoZPFFOKrKkFHmM2UXnobj/v2EWqCBd0A5H6qhzR5lXIYu0+B/Z4/Zz1OFrQ5rccKXxtbmsJWqsplGSB+8G/9aC18Kz7XUkcMnnasOah+WwmUjx9zWPPM84zDV+xn/sUmsMCIjdmFaLHFw3vtUSS6elok3QHUuIeQLScePMAKi3XjxT614g42PXG9VLktbiVyJ3ZTpx634s8AttRfPd3gc3vpwJ4xbusAL48ogC+R9eIC/hCVxHa/Y1DTLERMZiDiFHVofJnjzpgeypiVvvRQl6JlJzPoMW7we8vIpIsVxEVJa4mXAopFmYpYvpjkbLWjRjjbuh9DfJykUC81+1XEYHGVUihOU0wnn2+14MYYykSBskaltzb3UhJmYoYdR1SRsogexzhzklCOZNIF7GZ4bJOvzaWOBU9DSmtp5Bb6TWklDRovNk3G9YcxBzrkECaqfGyxrivJdyq4/1+P2LEPR7TaaI2aQkSOin+p5yIMTthOHI9b7uNn14iJ971iOk/gFw7Sz2G14Sr7JOIVkqQ4pxcgrXGSx9yHZYTNWf3wsm3YfyXDHPC4yEi5nEyYvbriVisLn8mIeZ/rfHdBXlZRL3gZn+GjKMlSEj3EO7KyUjo1rnvLEQfC4k0FjVIOKZYvBSY/zHPZ6bb7vbvVR7/ZM/VUqRszifi2spRvFMKYaj1+G8jYlNfZLj+M1VoDc1DMYNp7hqQC/IJQkFN4QBHiTQ4tyAL3SZED01E6MVij2EACdR2IsXMPN8bDW/dCBedy4WjfVJCJhrmfkT3ucbNOqJu9GbD9Rn3OQkpG7+HFMpaxCOaiXXf77lI7uo0L8niPInYmExyHzOIMIHjHksBUbJwOrJ+ZSHlu9dj2+/5aCXcjl3uu5/Y/JUSybpTfNppRDeLCQt4OsiwgchbN8X9pZi214n0Wi3AJeYnuK1WIqUOxCGXxciVmkkkkR8kTsUmq3AxotMJxKYv3U9u7CGPu44oqVlpnGUi5aoH0cgXER33EHlEW93P44ivVLvNBrffRBzkG0Kb7ReJsI4st/Wk321DdLLQz/f4c4LYpBX42WKkNJ5GsqUc0XYrI9fI9U+6Mv7nj/wrvc7A0C7EPJLS1IgEU53/PigeM3xazYoPc4g0MRn+vxsxShCTXoUWfjFiQLVIcJ0fO/MDxMSPI4Zci5jQNZD3YSRcTyAGkYKK9yOhXeX+Kwz/caRI5qK4oJWIWSX33VS/fwwpAhlE4HI3ss5UE0mVQQKlBCmvKU7uK/5OMXW1SPAlq8YJpHyCBPi3jIMJSNF4gbCE9hAxf7cjITTF+JxqXExHjDBZiZKCM5bI67bd89WBFMFT/j+52nOM++SG70HMOBPNdbJWFaPYt063W0kc6EiwFnh8iR5WeKxd/q1IYxj/ASQMen0/xZYlC1eK42tAVpZGNHcn/WwNUUUgg0h5k4Tgdvc/33NQ698LjK8TyMLaYlxPZrh04fCzYz13LUioJ1f4NUhRG02kOZnssZQR6U7qCOE52uNs8Tv5uv9mG6KT3Z6XF93G64j2xyMh932iys0hj7+YUNi2IXoudDtTiZjYSiKx9yBSQDuJ0mdvE3G844jKORXAArjoTuD/7QtesAttADI8nsPICp/reaohwi6SkG9FbtqZ7vch4iBHKxHretZ9tBKHApLlbpdhOoHc0hl+vtntXIJoPFlA+xE/6CfSGhUYtl7jocftjDdcW5A1cg9S5tr8bNro5Rp3lYZrKhFX2cxwSqc3HyYU5QE/dxPaDKXYwy441YGU5BQusRRdrxCVeR733FyClMEOz3cesootINZuL2GBrTfc2R7jYeMrxdslJWqQCFnZgugs8fIUX1hIxAAWeq5K/f+TSMHsRvO/ESnw+9BGutTt1BrfySI7wbh4nTj5XOp+XoCfP4fW2ozzxpziX5NMuMqwvK/XEO92IuiR6725RhTAC119kLkMhk4TiziXOBXcC5RA9qf1LDn+/euEwMlCjGAbWqTrkSDbB7RCy2vECdw+xLCSwtOMmO9niVqef4GYxkEiwfPlbr+b2GUOAbVw8kaiTuoKIobnOSIlR1IYkgunADHEZDVsJHbJxcbNbmQZq0LKSQ5imn+AmHVSSCYQedQOIiVrGVK6lgCL4NwbhPJWjxSkAiTMv6lxDLtUk4XAFoW/2YUExmbjKJOwELUhq8/nkcBcCQONyPqSrJIz3c8MdPCmn3BXz3W/L2i8p1LMTab73Oj5Puxx7QeegFMPGC970fz3EK7AFsLKWE0cJMhCFo7DRPm9YsL9VUu48k4TZfZudjsFSFl62c/s8di6jKt+P3saKYS3ImGzjEg8niwROwkFstVzUOnxjTOukkvqKo9tvdvuQa7CRIffN9xH3cZBRNcnGC7zd/FKQijuRO7iSobrXQ88ioT0s+fN8VbD2os2STPRpmAhoqMriBKF24icilnGU4Xn9hZ0jSZK1w0it/s291fndncSh5nK0LpbZ/zNJbIDzEaKSJ/bOmjYniI2PknB2GR8bPKYVyFaPkykO5lDHAzaiZSZTrczaPxXEdajWUQt8aeMa5DbPo2zjlhn+Ui5+Irba0EKSSuis9GGI9HtOrS+B4iDJZVoYzKE+FeFkxXvAx6HM2uJE+rFSHmxslO0C20Cm5GrtsXwnEF8ohjRzxxEb/aYnNljHLUYn+M91msMTz5ay2sN11LEZ5K1bCma5ypEU9vh2KNWSHcRXphJiFbe8P873c88t9OA6Ggccdq+3H0eItLnzCIUN/zbo27rbaRs/57hqxGsH5yF1haI5rYQYSW9vLPW9sg1cv0TrhEX8IWuKmAjZE5AzO+PEAPbhxhMchEXEukmDqNg6V2E1a8FKUuZaPe6EAmP6VD+G4iB3IEEaw1iJg8ioZJcafOJXGkTgGY4twlGPYIY1Q60206xPHXAShifYlWeRMzvKBIAy4ikqX1+pxHe3AEXX40sejuQEG1ETGgIncIs9b1k4duClINr1OdwnGMPElQ/IJSKw8btdMNdDqOWGQd3Gi85SLjkue0Kv5vjdzczbE399wv8/wJkrdqAGO4dhqEJ7fi7NW/ZH0CMugUpXCVIwVuEYuVuI06TdiIF4Aq1V5R27SkAvQEJ+3key1iBV/RR930FoolTHsMi4aT3AdWIvvgRdEo2D9HLNLTBWI2EZYoJXAtvzoGLJxi304DfgoG3IXsDopdkQb4CWUnyiXQkxw3PKaQE3uPnS4kUEoWExWiFx5/peW3yuFPsaRuxIUlxqtegOKQC5G6cJriH02Gk+LQC97XKY84krGO3IKHWSJTzq3X+zVwU5lDreXkFHbj6rP9+HQnD/Wiz1o0sic9orOcegVF3IqUuWbQKkALwBSI+dDWy0iUL1za0zueg+L8VaP0De1fD3N8gwkKS23wyookUjvCwxzuI6LTd47uTqBjykHG/jqg/exStw2K3cyeioVLj9SaPcxJRg/wJ4gDKEj+7XfAyFq3jJ4yDWr87C/GX14iNSRNSlnH/X+Sdrt3kbn7E/S5GvODzyEJfgtbSHwDTYEwK9ViC5j+Fw3yB4RQrQ49C5m94LP3EKd4yxBtvR5UyjqudMfOB/XDkebgsy2PoRnPbAUO3Q+YspIxVEiEWV/q35Mr9nMd5LUxZiNZJChlp9/ytJizX96K5zSWSPJ8yTqd4nnYhutnmv0vQOl9EKOZziYTW1yEFcwVSCjOJmuGbDesgki0Fxt9MQllMG4L37fpv/4e2NXL9z66RQyAXur44KlI2dPm3lYhhJUvaIsQIjhNlmVIsSid6vwQxzmQtyEdMeZCIoelHTOZxIplpNbKqXElk029Du++jiCnu8Hc5YvCPEYdUslGOt+S6LCOsScltBFLQrnGfW1EQ+htEVZD5hvlFpDjWEC6sPsRY9/iZK5GS0EkUll/qfp9FQrYNWcOuQBar8Ugx+Q1Ckd7nZ65FQuqI26pE1pIUfzWbKEU1gHbSye0FYpApvicFdYMY+xLPYZNhHCTyCfYjhWSd+y1W+/XfhGkLkdA8S6S/aHcfLxOKeoq3S/FK3W5viX/fBCc3wfiFyD3WgYTjU4hm5hi31/u3Bee1swDRUIVhvoJISn43UelgDxJAZYax5jwc1xEHGtYhZaEQzfkiJIx2IRqrIA6f5KAN0dtontf4exyikXnEuqmBU69A0SyiCke92xtPHKZ5yrA2ICtQJeGe/HNEi9mI7jbCm0d9anee+24iEvtWI1q7EQnPZD1aD6yGgScg+1bi0EaKuf04Wt/rCOWt3uN+Gm3ccoFD8NbTcNHNRKqQLD9TSCRXb0UCvsP4XoaU0yb/PWj4Bglr0jykTDa67S8LN+eehlEf9hjbjNs97quTiH+djdZNxXnz0EiUiFyDFJrpvl/jE6rj0Hp2/PCpmz1n0xDdpLi5Avc1x+1kId7xUSKWtuO8MTYLX9xqnDyMlNqdaI0sRjRzEG3aMj3XM1HoQSNaY7ca/g7EG1LYijeIZ16EMauQIlZApFOZjpSlUkQX+xEdgRTFaX5+FVJGG5FiDRFDmIv48H2E2/6Y4V2PeGW74exHa7OLYf756sPw8TlIefs942s9UhQ3oPXdjTbpqxFNDBnOWf6uMi5zCU/TnwrmN1/RvjzzfRDjcQhkNmPGvDu2pDNn/htjx+4fOQTyHl0jCuCFrmtGiWl9FCUy7Uc73keJ1CqlSPloRgtzIWIyOWj3t4rYWaeDDP2I6aX4rBT8DZHS4SXiJN1kpAQNEikKThJB+uVE7qtuxFhXEHUrS4i0DVcgQbEbMcS1yNpYRFicSgzPbuQW2oYU03lEYuVT/n6N2H1+EjHoAiQ4Uzvp0Mg+pPzsOA9H+YjZlaG0Ey+5dm7azY8n3MfTzhtfGe84bTysvEHU8+wkLHpJcVnh8VS5nfVEDduTRPqX2Wh+9wkuFgvX5w7AqLuNzxfcxzTCglbo/iqR0JhMpIX4huGeiRTaAqRwbEDK5FTjZpnx14asBJ1E6oqZiPl3ISGU5qPIOE2hAwuM1/1+doGf6yaUyA1IOZ1C5GnbhpTYazxvCa9NRGmqQWSxaDZuatBJySoiRdB+wzYDKeyTCUvtBrROZhAbmxb33+zv55FCn8Z7k8e2BdFUg3HXTZyevIKY537iBHglcTo3xdwucBuTkWLagE5wX2eY6oiToCvRmr/GOEzhFpPc11iiikml8VmFaPYA4gENbquH4fCPdxwAW0XkUWwk4jqvMt4yiZOnuf7trOciKfGXEyfhU0zaVMN/1H0tJTYrk4lqH7We29PGe5Fh2esxnUUK0xTCAljnZ5LHYKnnLoUwpHjnOqKKTwtaAwcM2wki5co4vzeI5rnUnyzEB7rR+j1M8NQWRCOFHvt3iVQso9GcDp2Ht6PG/W4UIzjPMA8Qp53LjY8UGpNFhL70+fk/8HdaUyfQGp3tuUhj3Um4kDcTVs3lfq6B4Fe1aF0kGj5h3NejGMkWwzHo+wfROvuU8fb9EQVw5Prlr5EYwAtdE9FifMX/z0O7xBa0MJsQA0jCboAo7zNEKB/j/Pwy30vWkYVI2Mw77/c8QtBMQEy0k2CopxCD7UPM41okSJcjBryasEDVIWY2wc/cRuSEK0Q70P1E/dwUyN9CnPDd67HcjRjeB5CC8DSyALUQu+tkwWpFClMGkAVvrUWMd5vHmCyWg35vPBJwV8Goq42nZWjn24MExSSiKkdyrR9DzDFZa04RsUBnjd8k9IeQALuLKMtWR9Rz/X2PMRspTBOQ0O8mTlYXw6jHiJJ4DWjrfcJtnSDyKNYggZPr8b5GJPEFCdo2JISWIAWgkkgIu5SIvzqFFPV0UGYSEbw+m0iwnUMI0Fbj/Cq3dblhTs/VnDcP/Yh29yJlJgnLBreV4vcyEc3PQRsZjK/TxEnhxYQSV0XUQk2KTpf7zUJKwG5EQyngPtv9/ABZH3uQwrAd1cCdYDx0G9+7/P8VRBD97W4/xWnVIsHb47EdYNi6077G/X2ROMhTY/yVEzVZ+9EGoQ1tTqajNVxPlLlbihSnjYh2znetr0cbgBTCkdzeSwxzI+FiX0jQu9dk+y4iV2MHorvJyPVdQCRMnm58TzKct/j/Oo+z/jxctxqeBYgOFxmXU4j4zdP+bQ6RjPtBtJaTAvQGvHXcuG4gcoB2EJUyWtEaLHablxM8b5a/1xn3jcjaVkZUkxny/W8QWQ6Sd6Xbz7xObIor3dc4xD+2IgVvEqLLzxtnY9G6OOF7xW57hvGXQxwWSspaE8MJ2c88gjbr3WizloNo9XUiS0GKwUxKMISSn4/WyBJEZzMRLy8nskhUE7G99chi2Gq4LkNrt5f3+Xq3DoCkz8j1Xl0jCuCFrreJGK7kOn2bsPxVIkGyCAmD1TD0ov/OQcxngds6iJSDFsQQdiDM1/j/HqLcGL7X5c8Bt7eBSNNSjwTCfsQI2/1+Ytj9hFXrBiLn4AYiLu4QEu5ZhOWmnEg3cZqIYSlAyu0vEOMuJnK/ZRmWOW6zgLAEzkHJtBPDq3X/lxMnOJNwSW7LQeTKXmUc3mM4G5Bbb6ufm+k2byEsGEnhq0FCto5wKc1EQjq5TbsRMz3i8Wa5vfuQIj7J+G73fOzz3yWGu9y/VyHBvwwx7QpksUpzdsLjvYPIUdjv51712A97jMWem4TzqxCtfc44vRP4Lgz82P0md+o4wj00zzDOMg7GeR7n+f53iLQd4wg3+fXudy5hbVqElNBswsVVQChYlyABm5TdFIze53ZvIGLezvp7IaIHu9bPfc9tzUbC8ynjahkSuMlamzY23ShkwHGr7COUyCHP224kGE/6+eTC3OEx2xqXC3HgZRkRTtCNhO90YCf85Kd+px6Fe3QjhWQNosdpxInY2UigT0M0sgG564aAT/jvsWizlizgR9Ea7fe4IErYHYWS0UQ92GpEszfxztOq3nCRg2i4nIhlzCVOD3cbr3VEHr3RRLWgU0iJnklYiDcQVWceQWuzDfGNT8FFY42HNiImLfHNeuJkcjGimTzEu0YjZbbacLWiNZHpOdmN6DYXuVAzPYakqFUi+spB/Op8F+qlxsEhw5Ms6+mdOkSTZWhNXE2EJEBY40FJwfMJHlCpz5j5SBlcQljoyv152ePJR+tqCqLb1cSp/i5EL/3IS5A2SgXGfTYRvtKJFMrEa5cbtumMHJwduf7J14gCeKHrB4QlbBISKIsR08hADC4dqtiiVzI/iwREii3biBjQIZT8OO0iK/Re7z4kaKaihb8XKVpriYMP44gSdLsQo72FOBxRSrjBCpGyOZkIav8UUgBuQkxkj9tL7pV18O2jiAHfjhjfJRrvm68hxtuP4nBaiLjDLCQYutEONFk96g1HE3JFPYeU4pkeWy1xorPfY9pJ1FBeh5hwGxEbNRUx4/sIBbjG9z7pd3+TsASuhYHnjJ9N7quZSFmxEimD65EwOky4ILcha1MvETtVQpxMbTBctyCm/icfFJ6TBTcdNNhOlH6qMc5ziZQVBUQi4Ex06nGnx1/ldw8SAuEZpDiUQ/aVfvcy4mTobI+pCc1/n7/fQJbiNE9XeRzdbnsxotl+pGDOQfFXe5AAK0D0vt04Wu/5q0QCaS0SjH2EeznRbrPH1kFYZBeh0+xDwGkYdTNxWGQlIeSuIHLX3Wk4G4zfAsPytOcmH819HTpR+gZRVWeJYR2HhOYiRMOfgDEriZySjYhGi4nTxh7DR+b499VupwzxhKWEcH7IY1+O1vi3kJIzAa3Zs8iC9VtIMV7nd7uIUnhDxOnb0273m8gFOAXaHyNiKZ9CV1LGz6KDTJmen3H+vZqofpJhmI4S1vrf9btn0dx3oA1Yv3HyXaKe7240/8f97P3ImnY/oYD3E0nf7/f8LATmwZtrUT7Ngx7bFchaltzSU4hsCSnu+nOe1w3+fyziBXsN33REm4VofT2E+O4ziLdWofXxLBFbudt9bSUyD9zq+XjQ9/ej0JdFaD0M6v6b3zQ8W9D8Jwvni4a10n0tQArfMcM9FWiAoSc8L7m+l/j7c4i+etDGZZfHmY94QbVwyAvEZnGR8fm+X0OMpIH553mNxABe6LpmlGJOMtEO8Xq08GYRueE+gQTSXMTUCpDQnY2UqSHChbSdSKS7BjHy5cSp1jygFIbuhsxHkeWvlXBT2jX31ktw0QTEQB82LIlZL0AMaCtiJPvR7vVqJIjmICZy1M+kAPazRHLYw0Rw/TKGEx7TZLzUoh3sY4h5zkAMLR8pF3sJV0uybr7u31rhrefhonFIiI1GjHSe8TiDiIlsRnV125EyMoAEdS5imBVIyB9Agubr7iMJ571IQUiC73qkqFyNmOsC9XPkVrjsY0QurTwifu4a95tp2Db7mUv99xI9e+4hGFWIrCNthj0Fqc9zfzcSQf+pnwnEKeLZRB6yW5EC8Aw6ddjrOWtDloW/NHxlhHss4bySKH3XTMTCZSFhnILoC5EQKfY7KdZss9tK1qw/QMpuDsPpdn7+Y/jgCiIn2UNIaG4g8k1WIsGaXLb1RALmw8ThgE4kaP/Sc3YUdu+DBYuRpamGSAxc7P5KkUIyWnANPQ+Z842fFOaQLIZPI4X+eiL+tYhIdJziupa6j297Tu4mYhVf9HjSYZejaJ0PInpKMXn1hrWS4RyCtCLazgA6oLcR8j6KsgocRda8DuNsCmFJbTPMZwzjFvd/o+c7E/GRPUQ5xsMeQytxUCtZ+VMIQR6ixxy/l2KLk9J1xnOTxjDk+3mIJqYhHnglkUXgb9FGrB1tNrYj5aQX8YD5hNLZYXgSfSbemjZBtUTy/LTeITZEg4RH5lrj5HLDmjayg352jOfnaRT+sRnR5yVue41/6yFiqZeh9dNF5Imc7ud38c5Sm08TcandhvMo2qwUELywyPf6Ec2VoXWQNjQ5RDx4AxE+8deIdjsRfY6DoTWQ+WHEF3YQMZnb3s8YwCrGjMl8l9ocYuzYupEYwPfoGrEAXuhqQsxqHZErbQAx5uSuSfE2aQfb48/vIKtGFrJITUTM6jakVO5ADOtzhAXC8S6ZnyXyRjUhhjQQfV10O3KRrUSCvQ8JgeRG+bbhO4wUsmb4z4OI2V2DhO0x91+FrB7JTXjCYy5DTDTtmJN7ugIpPV8jUjrM9dhmI8a6kBCOGR5Dpfsab+V1CRKWTzKcguOto8blFD9/FbJo7HQ/M43LhYSVo8rtP4oYZKvnIjH/RUiAJ2W0iojH69XYLlt23piTG7jXc/UB46AOKU51SPlLVtxewT9qE8MnDk/eRRxIuRMpObnuP1n/+pFSnMZShARJGRJYzX5njvt9kbBujlc/dPrdxwnXZQ4S5H1+Lw8JqcOElbXQ8z2NsFh+wWPr9DyneKom99tknPYD91v5K/M8p8D1LxEnzkECtBcpA6XGTy+hpKw0vtuAH/u3SUAlLCiE3h2EZXImcboyE1k9Ghgug5f5UY8/xY0VIPopM+4WIMWvyX9nEFVykmW3Ba2dSuN/DZGL70qPo4WIC9uK1uUGz8lDhiFt8rrcR5bhmA3Mg7xVfv4LaI7zfP/jxmMRWhsNSNnfRJwon47Wy/XIot/hPpuIJN8Qh046kTVsiMgjuRspJil8oRfNbRZSQtrczjRkwRqD+FVS8FOMXBcR4vIRov75MeNwtseZeEcxssQlF2sNoovl/u42fNcSuQoH3O4g4oOFxv9k991iGDuR4tXo7zpkmR6H6H6CcVVO5L5sI0pqDhEhKlmGrZs4rJHiVecRLuW7idrlE4yvlWhT0ubfT6ON2kkkAzqRHEkWzac81hx/dxGZCla7zxwi7rfWdcGL0Oa/EtHBpYxcI9c/6RpRAC90zUAMuA4xrxS7VUhUm3gJMZFGpHj0IIa9CC3cNxAz2IeYThU6TboBMaQUQJ5icZJQrvT9R9Rmyy+IU6n73U4K2i5ATPUQEii/SRxeOAXUwr+tIiyCILdwAWIsXURQegWy9iSLS47yDbKfOHW23u9djVJ0tPozGTHLpATXI2WuGyldmYjRHyDKslUwnNfqouuM31zEaKf7O7kE1yErywGPOwu5TWYTp6L7kRCZhKwJEwll9ghxajIPKc1PIaacFPzTRH6vU8RBkVbP52yi2kSW/76aYfcQ82H8YuOuFtHFIdU5Hj7hWYmE1gLiBGoNUvZqkJAsNy7PIstQhv9ehKxiQ/D6c+4jKTEFHn8JUbs4h2HrypmfejzTjMvJfibF0vUSp43LEI1XoAMS2R5foeey1zjqMcy/IzjeOkCcSp1vnHYQJ197CCVmjp/LNVwHjY8mYAXkfZZQ6rKQornc7T+E8tl1G/ZphnU+cTL4Hrfd6zanud8a30+WqBcI1/siP9OBBPQuv7uLUC7rCQW/C63/fuRKnYkE92LDsp44AJGsh5N1f+inxkuD4RzNcKqecz/yeGaeN+YK4uR0N1EP+3KkBHzLsHZ6XAuQMnSWODG/i0gXU4Do5xBaU1M8pnlIkTmCNlat571fQsTJ9TN8mvnnHcZztp+rRGvyJBEXmvjcYSJp/S63kWKHSwkr5FSi7FuW5+i0n5mN1sRs4+wQUbv8Srd/3PDuItbzZN97Fq2Lg2jOyw3fnxFu5bmE0pzc2onPLPF8TCEU7U4idrqZqFu91mPL8TtzkOL6ArFJzkByIwNtZEs83rfdxmFC2a5C63CJ+9mOwpXe1+vdcv+mz8j1Xl0jCuCFrmloMc9BC64cMfs9hAsmi3dWbVjsv/cT7thuIifYeqADRk0gTg+fQLv0XWjHl3aE89xXF5Rfh1ws1fqfryHmvY9wieYippd2sxWIkSdBv4OI+9pPBPN/lGD6K/3sco+5wsmmpxsfBzyuUiT0y9xmExEUnkMcSFlvmJM1sxMl753OcMWA4cMwiZG2EqchU4B0NXJ9FRBJj3M8zptRLFEzUeu0l4i9bPHcJUZaTJzW6/Pf9R7TJxHzHUTCMwWcj0MCNgnXzcTBnudQnNRCIA9e30EEptsdn30PcbhgIxJAt7j/cUgYFSIF7CRRaWYCEjJFcPAez2GWnr80gwhILzA+D7idlcQBjiqNfUyyLH/U7zyJlI+znttK4nDGcT+bTyTxbkPWoBrDNItI8dOpPi4aTYQs1HlcxUiYVxKHfGa4nUQv+/x7GbKs1Hou9xJ5+rYSLsQfE2mIuhHNpji+BiQclxPJfvuQC7CFSCOTFO/TcCRZS58kyi22e3wpHnO12znkufse4cY/iCypx4jsAKcJN3ByXU83vhdA5sNuv9TPp01Yr0/DT0brtJWoEYznotG/J+5dgxSHlcb5FUSZwXxE/zmehz7DMsFjKfQzxwgrWgGKU5yEPBZz3E8T4j8biDi0JvjgZ9FaOUZYs5P7v8z3tqCTxZ2I3pJ3pJSgywaiTjTG6dtuZ7PbP38jO9btVKDNXvJCzkaxfOuIwzDJPbzMn78kQkZWIJfqC362jji8dDO8tY/Ig5hFVJhJlsJy47qLcO9Wo/nt8PgeQHTVTWR2KDUM+Ya/gzgElOP3ct1nF6LpP0N0VABnniUSvY9cI9c/4RpRAC901aEF3I8W72QiwWipv+8mTgSDhOUSotD4y4gpZRMpKxYSJxw/jRj6f0KuutlIOKaTjSm2JQ8xhwkM18w88jBRPm0CYlATEbMoRNbFpMCsQZaqfML92o2Yyh7EcJMrZLLvJ3cKRNmx5w1TFVIGvoWY32Iil1VSiEcTucw6kStuPbJgLVTOv2GFbgn8/SPG71kkaL6EXMBp9z0XKWgHkZLU43Eld/vL7ncfElA1iAk3ejwzkQAch5S/ufB6B1IsJxHWnxzPTymRNLvEY+93v1P83NOIKR9GtWAPwqUrPVdtgv/N54garBnIDV+OXLczkGLyFeLw0B8hgdTqsXxO8M38BFG26xRyUUO4uZo9fzd43A1IEagkrLpHkGWsnEgZUwNv7gN2QMsmlAuvEtgIQ0sIi8sCJBTnG7dHCYv0IuJkd5GfeRoJ7WWI/vuIBL6DiPZWGtYs3ukGW4M2JSl+aol/3+nnl6EY23YiXnUqsgpO93NzUCzZZsKyfZYIqE+bpNVw2RakTEwjYl1v8hyuI6rYHESCvt7PpNCPbL9bR6ToaEc0l+HflyD6aXdbSajj57+VrbGsJXIRps3PLkQbXyMqRdQgN2BySe4mLJnJ7Xm/cdxOxJAeROv3Jc/rdORqXYj4xnyi3m8+Ebc6xnAlfvMEYW3+UxTqcAIGHvZc/C4RInMQ8ZVyw5KscWmTdBPD5Rq5HtF4I1rvA0ipWorcwy2ER+YVRAeTEX1d5z7nG49jkcKUwl2S+/4QkUR7N1oTD3p8DxM8ehLw7+GipEzP8HzWKu6XxwmFLh8d8ilF6yEpe08iel5IhMJ0eywYliLhcWCH5+9hoghAH+KhE9AG8HOej6kw5hFgoqoCvb/XiAXwn+s1cgjkQtenRolJtyA35ABRvaCCKHL/DBHcnoME70QiHcpoxGSPEzn2OpGAGkSCoxcx2m8iBvBFVBljvvs44Tbv9G8YlpNw7nkYlevfBxGDOY0E0VTDtt7wTEBMewJiTGuRcIGoOzqOKBM1DSl5DX6nADGrdpjkVAABAABJREFU5C5Oh1i+S9RV7URKTjey2ixHzHwQ7ZorkUDdZphrkUt6DgoIx7hKVsmn/G4aX7JATCCqpqxGVrVCQmF5DMUkZnrM7W47KSQ7PS8vIMZvdxZLPd56InVGL4q7bCSsQyWIqdcJv6c6oGiy8bCdqORyBtFFEZECo51wX91nOLr921a/W0TEXzYTloyrPbZ2/19x3uc5tCk57P+nIavJax77HM9XLpFuZqpxlhTCm4j6t7+HlIjHDXcLEr5veFwQOeuS5WuN23oWbRp2+N5KJNhmIWvQXyEaGOt5LPW8LDO+7yaE9l504nmCcVHs+eowHAsI13Od5+4m4ygbxTTORmtrORLkxz3+WUiRneC5e8xKDJB9I1ESrdNzku++M913CjE4jNbTWcQr7nI/bxuetDGYg5TmHYRLP8V/fs3tLzb+UyxnK1r7KWSgx7jp8b1pRAL2ZN2vNN6T4t1sXK7174OIFgqJFD2T0XpcgDZUHcZjPeHhqECbuf3G5VQ4uBpmfhpt0grR+pyE6P1FIu3MJI9/J5HD8gPERvRW4yIpcX2I7z3hufwmoVyn8IxyogJTKcP5Dofuh8xxxlsxYR1Oa3eecV1H0HXapKe1mxS/EsI9PREp4SAevNjPlSKeWoKUxGb3eT9a04kODnje/oBQgFPM4n1oo1CDaH7QOE5rc4ZxtwbRzyBhPX7h/TwEcum7fAjk9ZFDIO/RNWIBvNA1FgnPQcQYvVs/l1ypq4hYu3rE9JKi9BhiZMl1VouEUNqJViElKcvv1CFGcICwFiZFJQmXxxADqCbix2pgVFLacgzPHveXi5hZg8czmQiwTsreFrXBbsPSTLjihpDyt89wJxfTGbeZhRhbP7K0NCPmOxcx1GRVW4+UDBCTKyTqh7bBsV8ghakZWSmSS2Y0spAOEta4acb1dYhRToc33zCeFhMKao+6G/iR8Z9i28oRY1+ILG25RELem5DFZiGyQnzGzzZ5LI8iBSK5Mp/JlnC4WvNRNN/jnIgU+LOIiSdrcK/hSvFoj6Pdf4oNLfZzq42HFt8rITYAH/Xf4zw/Szzms0TS7E7/1oAUrKPIAtOAFLoJKGi8GAmXAkQP13gu05x2e9ydyOKZrJ/j3cbnPZ9FnqMCw3MaCafFiMb6fW+93+v0exsNw7OE2z39/zwS3MnV2YdoPxPR8S6PJx186DZsJ4hyiymgfgbDh6GYjOj9NURHU9EayPXzmwBUfzi7FCkLOxENTUNrPsV7jUb0XUmEiNQSJQeXEqEOc4g4unoiNmwOYUXqB+bDmS4ihc4GNNcL/U6J29xD1GC+1v2n8Ibkdr0JWYD7DedNfhdEF3l+rs9jaCXii5NLeYl/S7TVhNbifr9zSvMwcwZBs1sJq1it8d+PFK7kESg3vLsZjnPmJJrvDOPptH9/220sJcpMJkt9gcdQZ9g+h3jWdMisQ+mGipCiey+in9OG/xtEXsm0QV+JFNPT/r0byIdzjxnHyZ3dZjwnmPcgmu8ybMfd15/CsZuR9fRGIva1EX7yQ+IkcZZhH0vkOb0NyYDdhst4/M+vGGdbiGo9sxi5Rq5/0vV/vd8A/B97HSVKpHUh5nsQRn0DMYwDiAEuIU7dDaGFX412s0l5SxawE0i52Ebs9hMDm00km80g0gVkod1wJ2IsHURt0fXu8woiH+A+QhmqQ8yqynD2EjEuuxGTKUECcDeKi/kuErB3u99+JGQy3HYxYqbrEQN6yfcqkAKVXFf7PO7liEFWIMY7n8gb1wVTPso7Y5SakGJUjJhmG2LE05ASlo+UkB6N/SxImcjyvMxBbqF5kJ0sf8lKUoYEbi1SqE4bd71EbOEGIjlyCtzOM56HkLJaBNwyoO9Fxu9YRBPNRFm+pAwkl9Uit1GDaCYFkSdXZlIK53j+TiFL0nwiX9x6z+FvEmXUMt1uN1L2ivzJNf6WeSzJ/druMV9KHOjIRbSVDqt0EQdiFhqWpcQJ6oNoIzLf+PkCooPxRE6+TuMxbS46jdsdRPWQpNQ0+LdeRD81iB7S2kmu2SKick05URViIaLHbCTwX0AK0qDbzkE0UEusux1oPbZ5fCC6mug+TiLFqcawf4GIGx00viYaD9WIzo8QrtNJhEW51vNwyLD2EIe/co2zEhjzG+67CQn9UiTsu5BFrQ0pn71oQ9Lvcf8FCodoI/JxLkf0sRnRyFTfuwkptgW+nwGvH4VLl3l8TX53PhEb+qrHfYA45NYPZ74JY64mYiub0SbwFHGKPcWE5hC5ChchPlNFbNy2Id7VhdZZJuFNaTBuy4hSkLPR2q9EjOAGImXSHMRvm9z2rxNhGDkEP6w8by5Oez7G+blO4WvUXf79ZWKD2Y+8AvlI6bzd7/+e4XOc65RlaG1uRoraceA78JEZxkHyRqwkUglluI9paH0ka2Ib/NuFKNxihsfRhujwfb2GgHfLAnn2XWpn5PpfuUYUwAtdKa6umDhMUEqcaG31c5uIMl67EPPuRsxnK2IKvUhATCHiiK5HjLwLWbwakTu5gAhYPooY4tNowc9C66PWfc0ngvU7icTCFSg+5jBisCALYgVR4HwJYq6nkKA6geJgUlB4M2IsM4nySrUe9xvGS4IvMdkUR1hIuAYxrKsQw3scWuqgPLncCo2DJuIU63io3wLTbjS+m+Dg8zDzs37mIMMHWormG2fHfK8TWeAuR4KzFllhsolaooOI6V5GVFLY5LE1GIZqwz6XKGNVjCwS843r7SiwvZywBLUR6WTOIkvPo4Yhxek1uP0mv9NP5OzbjgT0NjTn44hYvl3C85nnYUxyY83xfG4gqgxUQMsOKP8EtLwC5XcSB0Ys1LjF/Y8mBMlYpABkoA3KSuNgI3EyvIRwPVYTqTGWI+HVgtZOK3ECtRspee2G99nz8JlJpAs6ZlweQ0J0OqLL5URuxps8jgbCpVth+KqRhXqe2xjrTx1S5k67zf1IaZ2BLEEpfKMfOAxnzsKY6wxDueeigji5XqrfTj7n7CY1HstBj/EmIpatyvhbavylOM7ZHvNhIqvALONyH3HArNX4e9lzk0+kgJlMVN94mbD+5SCaG0Trsv+8ezPO6/eLxmu2lb9St7nXbRYSh6CmEcpxK8OJ3McsM05OIevePmKzc5TgP1VoPSZLbxdxYngDoZyeMJw7Ea2dZjjh/Zs74OLZhvMoceAnk6g53ErUW29H/ClZJ4sJS/YeIqwFz+2g35+F+F6nn51kfGUaFzcT1nUM41Hh68034OJpnp+06U0xpzsRXfSjMIF8RI/9SDEc8NxuRjSc+ELaSA4YlseMt7Q+Shm5Rq5/0jXiAr7QtQgtwDYUjDybKEtUiBZzEqqn0c74WWAO/OxpFLtWglyEN7u9I34vuX5/TBwm2Y0Y8hLkLs41HFsQU74JKR9NiKmNNXyfQkwdJIDXut1xgoVmZOlY6L4+gRhGAcNpStgPDMDJPUjhWYIY2mqk4FxHCOs/c7vHiIDm54mSU6V+fy28+SgSipWIIdYCy6E8uT3qEcNdh5gxDJ++nHanYbfyPPNjiNnVECfvClBKkPv9bBli2gNwZgmRIHm2x/AEUcf1WuN97HnPLDfsW5BVpxMpxckFVey+a9X3mdcQTZQh5Wwx4bJMLsd9SIAtQspdgfF3B3FaOLmXrkdWjOT2qwP+2O3cw3AqlTGXEy7OUo8pWfkssMtXaIzldyELRQESHN3ASWi/EikJbyBafAqGXiFi61Z73nZ7PHlEDdoe/1+ChNU24vR2g/spN3wtiP7HGscNRFhEnX9/0ThZ6ncuRbR2O8O1n998CLnlu4lSfxnuG+P4oNtsNnyDKK7qZuNoKqLbLqTEl3puW4lKEvfDmE977HMR/fcTufka3dZMGH83ZC8jwjimoDWchdZPL7JiVxFKar/gOnW55+R6w3/aY5mE5v5mwoo/z/isIizhTdB+H9o0ZqLT9avc1l+hJMJdwu+558+bs81+ZgmR2ilH4yYfzeG1xssktInN8d8pvOUOtDYWGacZhNUvuXBB8z3Nc7bBY5jvAxQbEf1UoXVY5PeSgrnUeMhANFAMFz+E1kma8xK3u9PzlzZ7B4hNbiNag3OJMpdr/X5SogsJq9+NSJG9xLAcIUINFqD10kN4ZzKIEm8PwMW/YXhOGrcpbrIN8ZcfnNf3BkT7A8hinayvtcbZA0TJwHrC8rrKOFkifHIH7/M19C5/Rq736ho5BHKh67JRYm5LiTi/t5EAyUNMaKJ/uxwtzAcQM0jWs9FEsfcsYBCGdkDmQsTUTyFmMR4x1CX6f+gQZF5HJBRNlo8jMPQjyKxFCkEFsmxlGCYIS8ZYxKwXn3dvp/o48hBcVuV7KQh5N/BlwkL0OFImXkRK4lOEW7MMrdMTxMnkGsSwxwvOYWsT/i0FmK9DTC0LKaZVbusOop7oasLtNtP4TIxzObEzryRSqjQjQTzV477XbfcT7sMtxuVMpJgfRcL8sL/3Em7/XUSZp9WG8TGiqsIqxLD3wsD3IPsB39uElLEa9/Mgcfo7WZOvQ0p6UhgLER1NQArLKSLI/zSiudsMw1bj4G2kXOUTee1SfGUtkah5CZHc9wgMfBOyv+G5S0Hte/zdjGjqbSTAypDQe9ywfM94OYjWxW7f34qE03QiL+RXPT9XeL72o7VQiwRwq5//E0LZzSOsx9Xu5zEkzKvd5xYiri1tEI5qbPyR+3rM43scWSFPomu25ycX0cRn3N8n/c5xwoK32+3vBsbAUB1kPkCkpTmE3K7TgY8hZbXC/W4mXJuNnodFiD9kIVr/jx7TTM/jWY+lHCkElWjt9SJFNSkw6PnX18Cln0Abht1EbFw7EZN5t9/d5H7XGNZk3c4k1nKn/59unDyMeFk7oqnxiOc8hmhhm5/NMj4rGY5jbXkIyncQhx5e9twlS/9fGMa9iK5/3/eWIGWtmtg4TXA/RehkcQZR2nAn2ixMNd7qiQpHqz2fg8bnbCLGMIXBzPEzOYgfJKtgnp+b5jFtNL4O+Tsp86Xn/d9NpFW6mUjyPI2ohFJlmA8Z5kXusx+t4wLEt5M18Bq3M43w1DQRp6v7PaYlwNffz0Mgv8KYMe+OLenMmbOMHftfRw6BvEfXiAJ4oeuyUWJqV6OFfYKocVpBJNDtIJIB16GFWokWebWfqUUMqRftWv8AMbU/QrVBM1AVgv1IoH6NsKgl5WoS2pUeRcysmygkn9w0G5AyVIqUqyHCgpBjOFqQ4vIIEctShxjOao/jkGE6hIR1ErrJYrLJY6tDAmAVEacyHTHRA0hIVKO4tRzCxVSEhPsU43o0Yspve4zjDeto46OBOADwHSLz/ZVISDYYV182TOsQU8TtrjIML/rvOj+f4h/rPPZdiAm3IUZbRZQsK0Hl5sYYxi4kUNcTp1DfQAKzENFFckmXwZtb4OIVxgNI+DyMhEMPsjrsRIIgEzH+MsMwyfPWabi+iVz89cZ7UvLKiJKEczyXKT4wE9HYF5GCMpvIGTkBOA5DD0Pm5UiA1aL4zkuA1+DNQbh4MRETi/s7iQTUbKRwzXKbybKYgRTsSiSkK5BSfBWi2To49xyMWub2jhGnfJPrK8/z8zih7KdNQC6is5XEwaYypEBg/N5knPwH32t23xAWqn6idm4rUszajYdawpWZXIrbkQKZ4hXnef7+EMXRLnCfycrbS2yustzPa0Qpvu8ghWAeUmL2IxqahOi+CSmBr2quhvMHzvNYew3fWaK2eIpPm+1+ZxOegzeMu4WG62nkPu8yvh5FdJBlHDUbB/cQ8cYOFRnYA9mriHyUm5HCV08oNz2GdQuR37MVKambDH874iF5aFOdNlHNaK31Gmf5RCaBRPs98JNn4SOXG855fvYwoo1JKIZznuFKG9RiIv/lBPdZhPjEUY/lcv+WgzZKO9A6uw0p32V+rx/xtMmEy3iOcTqNqAyz3rhqIlzrU9HGYL3HlWINT3p81YgHpY3zQiIp9z7gyIgCOHL98tdIDOCFrjnEwk5xGCmwuI44cPA0YrJtRGmvA2jx7kQ79BsRM29HCtRqxOB7kUDpQAwlWXiSeyGHSBkyDjHGE0gIFCAmMUCUW/oLouRVr59fSaStqUaCq5GwGiSmkqxI+5DQP0GUt2v2u2l3uhIpmyWIma5DilES3EnZPOV3b0LMsQIJ8YVImOWi3fVlRP3OTsR4ryYsWW1I+frPhKuogqjNnEvUmx1nOCcjIZZcQ33EwZou43WIsBLsI1w1Gb63gEhsnEcI8nLE6Peh+S7wvSnus1T/927R4x/shos/SpQdAzH57vPw3Iro6i7jbCWRZ68TCb1+9/cw2jy8jmijBF5/DC5NsX6thHUiWfnGIOWpAgnBcUTR+plANmTOQYpmIxEc3yVcXtxNVJwpJg5NzDcOzxC1dfG7NxOpiTKRgt1JWJ2nwpnnYMwsolxXUhhKPJZyt7dXcLCOYZfg8CGKhYY5n6Dl1WgdZHucGf59OlqDjYapkYhDLTR+trjfOqIWbv95/Q0hK1Qvoq0SNId/5f7ned5uQIrA1UiZzkY0kkEkOB5Cbtyk1HzH7V2KTlrvNV5ziTJrfcjKvAnR6zhgLpy7DkbdTqSPmW6YegxrEaKF59HmKs/jr0Ebwi8RtDzP904ZF6cMX4+/+xmOQcuuQrSx2zBONkwHEb1MdF9biIToC5FylTbKxzyWPvj5AfjgHESXKX6ugrDe7iUO4oBo9hB85BNEnfDTiA8scX/fJ9JtpY3Vax7vEJr7KYTVLxfxl9FEZoQUK3sLUtYPGjddiJYmoXVbS8SVTkJyYIBwp9+AaDPx6QzPVxeil9XAHvj5BvjgBBR2c4zYICdDROKz9/A+X/+Ndy+abOQQyHt5jcQAXuiqZLg4/fBJwkbCGlWHTPS5RLqMHCKOrAIx8pOI6XwVCfcuIqZqPcMlxNiGhFspYjop5qgYCcdtiNGVIaZV7veWEDnAmg1vn+GYTOS324kY9HQiX9Y1fm8OUkR7ibqWyXp12GOsRQJ8P1L+epDQX42EVSeRMuNL8MIGYtebQcS5FBtP58feJUvBJCKPXx/BePMRc76dyB2Ygxjhl3IjBUizn02K1gqiVmoBsiKkXfkk4kAAfqYYCYKS8+awGAn10UQZs0KgDtqTSzgp7p81Dtv0fN4a+OCTvn+ZcXSSOIWa+juGlJ0B47sfCeBFRI6/fmQZvcn3SpGQWwaMg0uvQwIpbR5SjN5hj+8DiGYa3ddr7rseWca6jZeVSCi/TQTA50P7G4h+kuWkjcjreJJIlN3ludqKUlvsN8663dYypIjsFSxNwKkDiE6m+t06ZEGa69+v8xiSkt5lnDV4XvPc9iTikFST759Cbub7/H8voi3HU9JJrMka99WDlLIyP3ed2+4mLFDrgGw49ZJhexmt8a3EIaONRHxdsgyDFJnDHu8yIudmHVq/58fWJV6RjSxOfURy+qmIJvOAr8CoTxJu4E5EI6XEJqKXcJvnGB/55/1fimirFW0uqo2brWjd9CBlrsFwjEVW+PnGTR4R7nDWsN2KwgB6kOKdrJlbYOhpRD9fJcrtzYIPznJ7mcja1YNOOM9HNH0TsTHYQhwUWk2kRdqJNuIYv+MNxxzPw/NEybZJKBYzC21QUxzrXsTbahCN5xKhLGMRjc72mJe4zQcIy2KH4b2N4NmLCBmSafzORfOf5qROMH5wBdrUD7jPBpTrswPRQ7J0JhocuUauX/IaUQAvdBUg5jQWMciFaOFVI0Y0jnBR5Pn3cnSqMAvlMVuJrCMNiMFMRAt5LxJgaXfYiJjJNBi4GzGCcmRBWY4YY4or244E9DEi51k3ctXuNlwtRHB+NbKATEOM91nDexNSyGaiOJTHiJOpjYZtMeEuPobcdtXGSxkSNlnG16tEctVKuGYFUSUjWYaKEAM96P8HkCWl2PhZhRhkKeHuXIcUqO+5vSmGKwVK5/RJ4E1HwvNpj/Vy/z/T8E3w8x9Agjnfz/0hUWu1nyhfNx25Yfs8huNEEucG4GooGYsUoUNuax5i6s1EbOie8+CZ7H63oqSxSQFealivIerrPoroLgn45Obv97xuRQdgmojqH+VIGM5FMXFHgL8z3tYg+puD6GocUiC7kIJSwXAS8df3uZ/U7zwoudG4fAbIhaGjaG3chVxXMzyWlcbv/0O4Xg97XMcMby5ynxoFRXMQzTUi5SOLsGK2I3f3HxJlD1uImK8Gfy4hYm2TW3gywymWfj5IxHQmuj1MbBAuQTQ9Dm002tA6XY7WzGjkPh+LaPNaYDsU3eh3ryCqlxwyTrLQmmxH63erYP5Jnfu8CykMbcbNI4jPbER0fztSYL8LP3/AuF8JPzmA5j7Hv2UZpoWetzme0+nI+9BsmEqR9fhZRGOjEe2fRpJgEqL3yUR5wBXE+rrPY5xr2JKHrkLt1acN4j6kdCfX8TPEgZbkat8NmfehtCm3EbkSDxDu1zlIgSw0XFuJmN5a369G9NyKlKMUI3d+XF4DUtA2M5wjlPv9Tonb+AZa70uJ0oWlSMH9pp+rRWEkndB+C1I+n3K7eZ6TxKtXo0Nn+YheU7jNoyjE5zBaew3G2V6i/vZ+IjVNr/G8yLAUez6+bRxtRDzrfb3+27v8Gbneq2skBvBCV/UoCbwCxAiqELPuRW6z/WjR+2AF84FfIKFUgphTChYvAw7DsUMwZRZi1NPdz2GijuZliCEPEvFZBUiI5LnNViItwU4keDIIy082Yu6LiKSzyTLUSJwe6/D7yUJTgYTWU4jJp9iYpPB0ozyBNYhZlRPMqJ+IpTrBsBvxSAdcNhodyDhBWNNWE6lF5hEnka9FAm+r/06MdQlRCqkTMdgyxOwfdf9/ZDwVGO6VyDWfrG/1hEXSruq/ewR+PZ12fJWIQ1zl35LVYgHhZpkA1MLPN8EHr0MCowe5z26GgYcg+0miPvL9SLBf4rYHkCKcBHcOUpwGkeBK7roUz9aHLFApTmkIWTOOGZ5pGtupl6DoEaJqxveh98eQdzOij+S2P+05m0wcWBnyfI5DCuxRRKPfIcIH8omi9J0EbXUgpagcCc02JLCHiGB1PDcfJ9LzZCL3/deQgH3Dz3UigXaH+zuGYuoeRwJwh99diej6Ls/XIMNW65OvwPipRBqbfKQQlvnddkR3j6L5PerncpH1aA1aBys9Vy0e01FiMzOVyGeYxr0dmA0Dj9gtOp+wNKbDGAVoA7MI0XwmWje7kII1CSlXywiLYzei2ZWINp70+0mhwu32ovU5l9iMpbW6DimDsw3LBo8tKeTlxvO1aG5ziINEP/Y4rDAPHIXsu9FcHgA2w89PwAdnIHqZjeKc7yDq4TagDUkmsSl7Es3pbcZdnvt8ECl+Y9zHQ3530DjoMn7qkFK2yOPYjOhzMXGafRxxiGWvcfJxpPzeAPwb427heXic53Y3Ij6dQZziTVbSO4kE4K+iDepriOe0AqvhrTvhoruQwr3SMN+HeMBpxBMWGCeXoHm/FtFWifs/67Gt8pwtRQp2p9sBbcy/+n7GAI5hzJhR71Kb5xg79sxIDOB7dI0ogBe6/nxUJFEeh3Z+G9FCLkNCIBcxo15gE7Sf0KOjXkKMZgNi3llokffCqeeg6CHE/JML8hbgKAzdA5l3I8vcUve9DzG+HsQU2/yO41NO/hDGXw2Uwk+egI/UIWGy2TAMuq3NaIc5BwnTAiLmLcdj7iQClc8SOQETA69CjH0VEjINyKq0gYgbykHKSq/hmOP+c5GLpB0pdM3G3QTE7PKJEkpHCetXjnGQdtIriTJaGUgx+7LHORFZOPo9X7cTqWGGkODJirnrvRvy7uEddXGHqzfsIPI1nkCKQoPfP2G4xiGhOO08XOUQMaO1RD63EqRUTDf+ThiuBsP2m0hAdXmMPX7/ev/ehwRBl+exFCk1C4yX5C5vQoLmLKKjFHDfRSRt3gftj0LJKiLH4WHj8TBRaeGY+00u80HiFHxy+ecgumgzHjqRQrgPCUmQkpdiq1JcVDNyM75N1IWdhSymnR7j7cbXZ4nydPcgeiqFUyuhaBVhzTzp/lLIxg+JsnTVxn8eUTFlJlIiS4y3EiKpeQ/aTB0579kliNZTuEIDooseglcMus0cj3kiosUXkHDvMrx9RFmydiLWdQaa9yXGRdrc5RMnfYcIl3QfYYVPsWFJUe1EVqjHiPCOYmTtmk7ErzYYt2kzeglyfdYTZc4K0LqeyjsPFtV5bF8hQkWmICVoMeIBM/xcD9oIzDa+KxHNPe62KwznFOMQZHnPMm5PEC7YXM9PN5EL8KDxcorgef1EUvSkMBcbhqmej/XuO7Wxlqjs0Ws4jxqf8wxXFpG6qhGtwSuIE+C5581XJZHr7xiipRQzmdzsOShOcZrxNIew7DYTNeZTPPV+f2+BM8dhzPsgxkcUwH/+18ghkAtdG5FLrhMt4AzENNuIouQvogVcA6yCkkzf/ypavDOJgPZTwH4ouos4UdeKGJGVucyPIQaThRhSH2LCBYixbSPqB/cJxvELGS6LVIRhSUykxX193bCnOJsOJAibz2s/xTv1GNabCKG7y89sRMx8PO9M51JOVL84jhhrvmEpI/LJnSROJJYhBvqs/16OBN5m5NLJc5uTCKtoNZFW42okjOcSB2Vq/WweYuDpYECJYTrk+9VqN+8uItdjvtuYgATNLKI0XoqDTAw9iwh2zybiyHqIsnfNSAkbg4L5X/A4J/nZ2cSBnWK/02E4diGF8pTbqyEqK3R47A8jOjzo91Pc0QSPZwuR2DmLqNSQA4yGkiy3eanxNc342oCU6wl+v5Y4EHDQ41/ovgqIJOFtaI2M9TjvRha2IcJ9DlIMl3gsYwkLWAsRp5bc6Fcbpyle6jlEe7aMF9Ugq9b1xEnSZIFK43nKY08wtqHY1UmEFfYqw96AFNKkGH3f+Ko27N9CtNDtPhuIGNv0fon7qCDovtDPH/Xvk9zWHI+vHdHBUsI6loPWWhdxIvpZwi1bRyRAz0b0OY3gUYUw8BhkTzdeTqC5nU9Y93ORtbiZWJONxOGyHj+b1mKdPxMNxxRkxUuu3hmE4llGHE5L4QRlRJWTAiI1y2G0Njv9Tlpn+QQ/bGO4fORwOpxxxneDYS30ezOJWOW0rid7HtuRtXkCoqcUBDXdY1hNJHkuJBTtqcRJ9060aU9K7VhkVcw3fvd4js76/mS0BuZ47C2GZbb7bCUOr1R4PHv8zFy0ht/2788YJ2lTuRLGtPE+X/8NeHcUwHevosjI9b9yjcQAXujKR0xiHBJkmUQeqK1ECbRkBUjxSylOpZUQEN8mTshuRYK0GTG2ce7rbWTNOeW2+v13H2GhK3Q72xHTKUbCJ0fPF81CTKcaKXYdSLjdgphGFXFCbQJiVssRY1pOKHHl7qMYKSNLiTQwY4CT8PN9iAGuJ8pFdRGnZpOgzkKCq8g4BLlrk5VmBrJwbSay9R/ybz0oR1jaVXcbh6cR8z7h9o66r3yPdwgJleWEq7rIv92ALFePEpbVJiSoixk+YPCTb8bfwwlkhxgO5h54hTiMkmLlJhj2Bvf3CaRI9sOp+5HgqPKclhmfg342H7keMUx9SKisIE6WFvnvVxAtXEkk721G1qqjHksv9P4IubqqkAIyxXCeRta1Nzy+U7D9Mb+/XO8y3m1lej7XIXdfH5F253KPOVlQU5znLuO63H3OIGK8qoi8gEkh36DPwMPuq5lwj04i1kOF+5zm9jfBW8cRDa7w3HSgNTALrbVm4yjFYKbn+oiKIk3E6eXbDetu46kA0cx+4oDSZTDwhJ+Fd9JRcp/uJxJwlyC6bfHz2w1LI7K2XU+EmkxF9JSJ5jjLvy037GnTMhnR0FmgG3qfM35eRpufBsj+v4mwjMlovRf5/xVI0d/t98YSru7JiPYLiCos2R7bSaSEHSVqAucQIQLFxv1mtAlI1ufJRALw8YYrWUsPEdb+Is9ZP6EwViFeeMLz1U/EZBcSqbmuMX57iHyCZX6/HM1xD+HiTbGpRZ6LbURVj+nEYaExRB3yCYifTjc+x7qNZJHbZvghlOp0SGOQKIGX8Hy54UsbwRKiYkuG2j23CW2+03rY4rHXEHGMI9fI9U+4RhTAC11fQYxgHWKWhci1thkt1MkoJq4QCdJJRF6nHxBm/pmI4XwNxbpUEgXRB1F82DgkUPch5WSISAtyH1r46xHjeMrv7pDpn4NEAtocxIB+HQmRqxHDmo0Y52GkEMxye8nVdI3v300IlgYkTFqIE7CfR7v/Mvjg/b5X4rHN8TuLgDZ48zo492OPcRXDVSF6H0IJXU8hReANOPMPhv1aFC+4AgXcFxNB5/sId/V8wi2UhRh9M1KgUyB+kfFXSaSJ+SOPeYzfbfC7xUQSbFs9PlKIBPJ4j3M7oYi3QvaNxtV+pGi0uK9TRJzVaRS/1A1Fa5AiMY1wZ+/ymCcA34a3biRcuPOQteJm4tRuchFe4vm90/2PJvK5DXk+S/XasKXnq0jYb0FC5wSR6mU8XPVp46vYYy70WCvOw32Ka60EnoaBeR5PO6KPXkR/jYgOxvlzLeHm32U8niVSayzUeLNXMFyZYeAlIs9krWGZ7rld77nbCRfdg2jsuNu5w/g77j7maTzt85BCM1PzxzyknP4mUXzgDbRmqgzbdYbvKePhuP8/7DjPLYTwT0prsvgkN+lYZElcgmj7ckSnyVq3h4gRO4Tcs00oHm8bseEZQBuEHiKcI8PwLIO8D/vdDiJJ/AuIf3UTqUomey5+TFhmG3w/xWEO+f/vZ8ShjkJEbz8mDjy1etx3I7qvQF6RdUQ4RLfmiX2IFmoRnV5K0GMhYQlrQkrnFf77JIq9XoYUnlxiM1BBHH5pRHxoJ7GuKj2P/cLbzx41Tl9Fyt2f+f6Vhr8AXr8LKIZTT8Jb+9A62Ys2Sg+jjfo64LeJgxx1hmO38bnW7+1Em6qpbn8zkTB7i98dJCzAfUS5t5uM4zwYdTWRCmeDcXE5w7x2OCXT+3b9zw51/LKfkeu9ukZiAC9wDYwaRfZ8xJg70OnRDmQNG0BxSSBhO5qI+XkAVRa4hwhu3wlD+yDzSsSsjiPmvQFOnYai2xETfZrhFADDdSAXxbPDVqArkbDoIYqrr0AKxkFkARhEzH5Q/VMBtMNbP4WLPkrsmJuRUgeyqq1GTHqq33/RMLUQJeIWG8bTRD3jUiTIKgjX1uNEfsQihgukDxekH3D7zxu/uUg5bvZ4VyLG+0UklGageKZSw1EPf9MB//5O4+g0YoYlHvduP/sV368x3nI93i7EbG9xPynO6h7EjO9DCsM2z0evcbDL47nOz+xEVpxdRDH7ASKPYlK2jiGBX2o8b0XCbp9hy3L7KV6v0nPRYfh3Gd7lxtF1xCnzYo+3CCkdmxBNbEKWqieJ8l0niCTAxw1rHtoYdCAF6EHB1PsPkPckUWd6BooD7Yehf4DMFEe4xH3kICXKIQrM9bgPAPfCz2+HD36WsIo1IkXoauMyxdQ94rHv9LsTPZZvEtaQSuBPDWszEfxfhxTnRzzOLGSFmY82YQeRAnUn4UJN8Z7tSHEcQvSf3InJyuUQi+0Pw1WPo7VWhmj1cT+3i0hQXEi4wScj/rHLbeYa3rf9THI/TkA034OuBs9/PlorINf3Y5oHpsKb34OLd7jPAbS2/4Th3HpMIHIclhDJjQ8Sa26sx9dA1CuuMo7mIfrZRYQwjPW4kuXvZvfxBeRaTqEzSYn9ptu6y/fS5mw64gMfRfRwFl6/By4d7Xm6jKgB3oQU24PE5mY2opMipDiv9bghKhY9h9bedzxXBUS4xC7iEFZSRmd4Xo+jkIFBv9fp+WpFtFKMeNlJ4/VLxkEzYeXvR3RyPaLBfUSy/HnGyTqNmxVIjuznna7yOveZIe9D9m/43Wy/+74mgs54l2MAz47EAL5H14gCeKHrjlHh1vkKYjDzkSB5nThEMR0x/tloh7uOSC1QTeziFyLLk92Zx+6DKYWICa5BTLSNCICvQ0KxmXBXdSLFqNrPFyBr0FXEDngWsXveiwTOIFEyKx0QmOA2sgiXxWXEwYEU95XvsY9DDLYUubaSi3QICZUJhBUmxZwdR4rjbchydMLvNxOJVo/5t2QRyWH40MPP9sCHvkS4n0qJKhMgxlxOuG+Xekxlfr6cCMCeRKR52IAUii7ExD9DnFrdgoR3r2GZ7n4PEXF8/URuwnzj6Yzxl9xKA363BwnRIo/1EBHcfS9SeiqNv93IOvQkspqmuL5CIiapCAnpcUjIf5tIUDtkmFo89sOej/1E3srLDe+gcXIQCe4dHlcaW77mgFqiCsFiP/cD95fipm5Fm5fDhCVrwLC0EeEKZz32pZ6L2YTivcjPbCPi6PYStU9Ba28sWkcDSEm5GeUbzIeh1yCzGVldBwx3B3FooNpj2mD8VSJhXIrovQMp5XcYl41E7eEZhIt5rN+9luE0I733Qd4ctJn4LnE6PLlJu4yrIaL+b7K8TSDCRtK8rTBsJe67A222XiIOlszwO+M9N095XkvRXHcTBy+StewQUnz3EYdU1hEHHxYiWqkh8ogmhSUpyifQGpmB5n0qUshzjbPZ/juf2IzO8lz2GIaxRDz1EHFg6makkG0hLJHJenvMeH2S8MKUEKXZug3vrchiPkQcxrvWfSbFbJAIOekhNg3XoRO99f4t03AfQXysGm2A5hIn+w8zbBkeznTQiJTCyxANPITWywaPpRnRYApxcGwuB4ik3Xe6zS2IZtJclhKnnCe4vY0jCuDI9ctfIy7gC10pMD8d5JhPCPY+xEAGiZJwKWboWkIJ2Kd7555HTDYLLdh2mHKP3+1FQucQUfh8M2IOXYahBrm9NhAWkqVIeViEmNV3kYWkDLmc9hKpIIaIlA7V7vMQYm6vIQY6E7k5yxHzfpyoK7vav9e4726POVk2QFap6f69DVmUWtzHp5FwSgw5fbcixrYWMcAW4lTtbIdldSJGmwKyn0MM8HL3W4pSrSRFt4SoiVtIKI4p1igpZBX6fegVw/eon5lHlAlLzLkCWRbn8s6Et99FgvsHBOPuJw7GpNicOuN0D1FrdL/b6kbCZSNS8oYMbyOarwokEKuJk79H3N96Qgkr93cNUgimI2HehRTc6cbjFve/wn3nELkWd6O5S1a5JX6uxPBu9HyMIdZFlu9XIgFahCw8RTjLM+HGT/Gu9Z6bUkIAP4s2GhiGg0iJqUCKx1mijCBIuVzoPv5KfWSuAp6Dc08iwfw8or+p8JPnCStKpXGUQ7iYU2zsSkLpO+K/J7u9HPd3mnC1WlHJ+5JxVo8UoFaGFZ5TjxAn7Bd43toRfY01jEuIiiOVbn+Tf3sIKWqXEgeJ1vi9642jVv9eRhzOaSYOuRxFNLIY8ZdWw9Tqewf83n4iRKELrcUqz1Op8T/HeNjmOV0uHHMa0cIx99WG+OZiIoygx89OcjsDxOZiLZrndMAlX/M6XAUl222kjXETWhe70XqvMO5Oo7VYh67rEW9LMbv9xIGgLo+/FtH9vURd6iqPrYc4jLOPyB152rANoXlfcR6uKuDcP8B/2UfEgab41oMez6DbO+l5OmB8JOvlDiIecj8yPCRLdRlxQKeA9/c6dxbODb1Ln7Pv82D+dV0jCuCFrk6kWFQh5vAwUX6omFj0jyFBNBMx9hrERPajE1ttMOqziIHUIAaaLGmL/O4y99ngv28mXIFFiAmuQUJgH3HIYZLfO43cXe1EctT/iJjEAcTATqOd+x4kANJptMXEKbpJDDMvhvxMP2LyExBz7UYMaCESQKOJWJv0TiERfD4BWAVv/gIxyw1EktlKt7HOMBz3e/uAqTBljsd/AAngbqJKyha/n3bX7W47WUbnIoVvD4qNKvLY6v3JU762YxjGZMFs8LvJlTja/T5GJAuu8/d4JHzmIatDJeE6qjBsffCzR4gSca8hOviMx43hHY9W41ki9+IrvFM56SAOpKQDQF3IYrUSKeqLiHJZxe5rP1KQSwzDI0SC6wRvPoqRTPF7OUSZsFeJeK8GhLRcz12W4R2H1kwuorV+tCYGDf82f5J16TLf2+zvXESD5cbzEiIpdD4Siq3+TnGyNUhJSrGsM4CXYdREogrGYWCPYzqTO76bqLKRYrI6iTQpOw1HHqKbuYRFPwX+d6L1U0McMGg1XFmeqyx48wAU7TOOU7+ZiD6SNXS78XLWMM53W6sEy1AdUgaOEcpphsfX4P8zjYdJhnshUtjbkNIwQMRo9nuOxyJ6TJupXiIPZSaR57MIKbUpXjNtxho8d79rGKp8P5uoqZxi10qIQzl7jK8OYs32Ge8t/p7gPrsMxxRkRez0HM4n0qqcQGu61/0cdD+JXvsNT5HhXUJs5JsQvT1ieOcQSaSnGaeT/XsV2oz1EZVrhoyrUuMkC63n6TDqHvjViYifJ+UvHymVs43rCWjdjjNeLyXSZn3XY7sWKINz3/OYv+axJO/GdkaukeufdI0ogBe65qGdYR3aoaUA3BLECG4jstsfQkyrEgm9Rt+7CjHZE0hIJ+tSilNbghZ7LRF3cz9i9tuI03T7EeM5jRhILmJyv++28w1zLWKUySXZgFway5GCMBa5b5MwOo3cKVchRpbrZ1Kc0STEkOcjBWi7x3gLYT140d97DFMuYpA7kADLAYrh4gcIl+tuZGGchxhnnp9badwWEsLjmPG+HSnYZYRrvhJZVNLYMxDTHjLuJhvXVyFmeisSiDcAzZB9M/zqB5ByfRzONSJhcMztvOp21yCrVhtS2lLsU4qDbEZ0sM74eMbj3AMMwIc+4LHleH7nI+G8wPA1IHfPvQy7sP/uEGzfgJSCKrf9ODqY040E3jcM327/PZ6IY5rkT4bfr0GKdXKB7zRMLyHaugJZlK8jciXmI2tfJ1EGsdTj6NTfb/3IfZ4mDrgkpXgJsuosM15n+rdXkHUtxaHuJ0qI9Rimp4jY1GSZ7Xc7lUSZtUXG90NoLdxCpOsoPq/dCYQCXEXktEuxbQXGcXJLrnQ/VyPaqTI+yw33bo91suciWaGyjc8hvX/xzZ6v7W57n9vN82+f9v1vESlQDhDJtqvVLO2es3y3v45IcbIYzfNkIr5x0N/j3O8Cj+vPgaNw7j7CZfllZF2qdb/ZxnsrkWi63zDUAG9D+7PEQZDVfma/8Z4LP59IKHv9GtvAG56HxUhZn0Vshofc73Nog5nCWqxs/+RJRF+L3cZRt/E4spLej3jfAKKDSUTd5G5/d3qOHvL9k4iGTiMeXoL4ymH/XQdvPelnZiJFbojIn9qI+MA2RIf3wLn7Ef+YiNbZXxNeoe2es4eIDUQx4i97PL4Whut+17+NlPMmtT9qC+ITd6B15DU4nJvw/brOvsufkes9u0ZiAC90fWqUmNtJxJCyCQabDhucAH6EGEA+YjQz/c5DiFGleLvp6IBIBlq4axCTbkOMfpBh183QCcj8BBISVmBO3QdFn0VC/qBhXIqEU7KkrURC4hrE6J5CDPUxhuvTcppQAHchBrKCSFmwGylqq4EOePUe+PgaJMiPoQMu1/uZM8CX4Wdz4EOfhDPPw5gPo7JdjcAAvPVNuGgZEqT9SEEqxUdUkTJWB3z938DX/18JokyPaYNhSwHaXwZ+CzHNZcYjROqIqUiBWIwstgVEzNU3/Pc0999ExPB9Din0A0hQ1pw3vmQheszfyf00DwV8rzbuCvz9FDqY0Oy5WON+nyWYdg/0Pgt5dyIh9i34Lz/SUH51ltssJiy1v0mUipru33IIV98MJABHa+4HHobsse4vDykWm/1OipOsIHLSHUSCLCmKBR53PlEFoYdIWjva+OhCykqh/99F1HrNZ7io/XA4xQtIyX0OKSZp81JJuCzX+v2taL43Gp6dhNtrP/AponxZExEu8RBRj7UA0cQ+oB0O/ghmJhdvK9AIrw/CpV9C62IhstROJZSrZj+7A1mJtrifhWitrPQY65EC9RlCMPcQcZxXI+UxtbXKcH3X73zOOFlC5ErsIk6Y3kOUuisiEpw3E7GlKwzbWSLk4WH3lwu8BD9rgw99g6jY8wbakPT5mWa30YTWTT9RQi8dbsn3p4uwFH7V9w55bOuRlbcK0cQh4vTuuvPmLp9IsXQC0WY7Wl97iIT4+/3eNMO3EsXilSKaOY1CXwY9N31E5objRLaEFGfaijY9Jz2OMsH51hq4qBAGunzQ4jIi9+Mc4/ZRwoJbRcRIJuWlishXOIth+qMIzfEeRGOvEQe/+tE63kWE2dyANgZ/iA46ZRIVQV5Ap5i7DE/L+xgD2AnvVrjemTMwtpiRGMD36BpRAC90XT8qqj7kEkrPLiLhbCeRr+s0YmrLEaMqJZSdhUgQ1xM7+79AAuZ3EQNYR5xA+yPEJNoRY2wjrCPp2Z1IQCaGMQMJiFuR0NiPLIpPI6UpEzGwNsIFmdwXLcA8ePNOuHgDYj47DOdcj2ECSgHRQrg5rkXCp9Zwz0aKynT3M0hk2E/wnTK8BUSC14S/UuN+OWJwp/1/NWGBOAADuyD7SrRLvwW5RJLFcBpRyeEkYuxJiLf5/nz/lhSQ85l1h2FMfduSM3w6u4I4KZuEb3LFHPfc/BQx7I3wk1/AR24nEt2u8HtLiVhKjLMvIStDLlLgGz32q5DFrBkpVR/LhR/2yVJ2kHBVXY6EUBp/ARLEs4hDDt3++3aPJ8WtJUvZCfeZaPspomzbWeNqFqH8dhEbkFcRTWwxPPe5z4mInia5rxbPy3T3l4WseJWI3pLSkUPkrOwTPrke0dULxAGJ+5ACPMQ7D9nUoHW3HClrc5FikwW9L0LeSo97rHHVadiXIVpr9PtnPM7phIu7kLBKNns8SVmdTMSf5aNNRY7naoOfLSOSTA8SFS6qiZQgR4jYsylEqqE58NZauOiTvneCSGLeYBhakFVqOrKSlRIbphRzt8ntJxdpisFM4QVtHmMDcYBlEeI5p9DaW4CsxoXG1WSk8Mx3nyXG6SxiU9TpccxDyvo0wlNyC3G6N8c4GUQ8twptxKcav5s9VynkYMjj6PZvDUhRbEZ08+B5eG82DC1E7eBJxtUi4uBMF+GpSKEyQ8ZBOZHPbwo6/XyLf6sh3M05xmWL4XsRbSaeMv56/eyG8/DbAkPHIfMDhqPMY5+A6KPcczUBbb6OjSiAI9cvf424gC90TUNMZB4SHGfRgm1ACzC5GPC9fMJtluK07iYC+Y8gplbsNrsQc8tDzGwmEcT9DSJNxyS3kRSS293PaiJ5bTuRA67W/U8DsuBcFQz8FDGipBzkI+VoF3GiEbj4k0iJ2YEEy2WGo5BItjzoZyah3exxgqGPQ8mLZxr2esQ4cxFDTIH9KXbvqOHv8PcpIl1LEiZlSDnYhgTDlZB9F3Fa9RnDdAtRXWUrkcOvGgmWPPc74GcG/X6V8TbaeH/ZeE1xZJlEzeQUB5WE6WLj+0njodR4TtagSvjIhwklKJ8ILj8/vg2i2kMxUv5T0P4uVMllm/G6A/jbPgn5QqIKQ4pf6zWeW5E1dTGitW2IbtN4HiMC7c96zNsIAb7P87HIeFtK1B8tQgJ1ktufbPykeMPJRGmx84Pv85HgKyISZhcSCbyLkUKdRcSeDiILxzMoNUq94ar0uN8gFMkOIi62kThh2oMUxATXGsj7LKK1yz2uOr+zBq2nDqS8XImsjV3+ZBOW8s3+NHqe0mGYPI8nxfj1e076iEMvkwg6Sly4Fs6sICyKNW4zy3ivZHiTeNEat3MDsgRdjWg9KTfJevggYfktQ7xsPRGTWo2U6F7jZgZxWKXBuCggYiBXIwVstXGfvBo3uY0mIo9pn/uaiOj5OJr7axBdJWV1NeI1vUgJuhrR6GE015MRnafNXeI3M4y3sec9U4Asaynmsd/jbicO8SRcHiP4SCZRASmF/OT53qNEYum08asgDpNlI/6xiqCTYrThLvS7zf7s8PeDhIcixTXWGm9Xar4yP2G4PuX5mGCcfNGw7/f9W3l/r6F3+fNLXOXl5YwaNep/+Nx66z+OlO9973v/w7M5OTm/7Ij/xVwjCuCFrgy0UFOs29cR056FmHspYqYrEGPJIAK0mxBjqXcbDxM1KbvdfiuK4Uu7yjnEydgkxLqRAHgDMaWjaCdZCvwxEkJLEOMrIVxMNYiZZMKoLMW6De9WM5Hr43xlY7N/T+7lArdzHVE2KrkjMwg3SjNS7pLgW+D3/oAInt+JmG8+2iWnWMekPJcgQX+J/84kXCB3MVyAfrh83ttIKbhGcBx7wjjYSlhdkkA/Sbgv+42DDuOzmCgv9UduswoJi2NIKRqLGHsJ2rU3EJVe6pESk9xkzcY7yB02iATIvUhQXYFopZWwIrzmuWwxPlNQ/UGGS84NHYXeVwzPYYLh79J9hpDAyHLf4/2Z4+99RCWQO5AQW49cS+3G05D7Ti7Wcf7uI9LhtPn5VpRfLrls+5HAO+W5WYSU8Rr3v5Ao9TbO/U5Glpku4/Gw4X8D0cwJ5NbLNL4nGb4jxkMzsn42Gn/zDddhtCZsJaPNsOQTpesANsO5J4jk2V3+7vfYbkJWwzs99p3GQ6H7e93t5BGHs0phYJN/byWUm81IcKeg/ZWG96ThrfE4yoS7MbOImr63EYeNjhOpP/Yhq+RKP7sV0cs24gBWt3E7BW04D/lTjxTjmWg9tyOlq5ioWT6b4AnpnRRTmumx7DEeBgzT19E66fP3LqQcNnt84zwPy4hDMseh5YRxNQvR6DJkmWxAdJDgbPKYCon8kG8Y9iLjvdnz1IIOU8wlYo4PoXVXgbwfLYR1cof66/0mSu6fh3jFPM3DqUEiZ+AJRAPlBA/tJjYftyKeWGbc98HAFv821/O1wuNLHqFsw7MUKdcNiGdk+7d895HpPjYSeSbT6ep/pdfBgwf5r//1vw5/Xn75ZQB++7d/+4LvjBkz5h3vnDhx4r0C9/+4a0QBvNA1l1CYmpGVYTZS/pLAP0aU7UlunR6/nywB5WhHW0Lk/WpGTOl6pPgks34pUhTaEAPtQBap5AJ6wHB0Ecy7zvCk2MIliKmVOpP9LMP1AnFy+EZ/r0CCcg5KkHqSyDEF2tE+ROTDqzXMVxJ1N29CzH09EhpDiNnVEyd9i4n0L7WE6znLnw1w6pvu9xDa2U4n0rGcRULzLHGwo0b3p1xNpKdo9JysJsrTbSbijEYTym0OUv6KiIz+ef6ei4TqE0hJ64W/qzAsxR73HuQuanbbK4XPU1sIN2aLn1tMnE5eSSTZzTA+FhJKUbHn8DqNK3Mh5I0lLGXJErMLbQjakXLQ6L7OGL+PI3pLrsUNyJLRjZS04/77sO9XGPdPI1qdD+33Ifoaa3wcRhZe0DpocDvbkSXqe8bftwzDeiSokyVlm3GVQyjcb0PLS0iwFhonyfVWRtDTEwyn7hku/XWz8ThIxEh2eRxziYoxZYjmzyB6yINRd6KNWYrfykc0+7bHOpY4nPKY52QxER6RabiS4t4A2Vcjq38lUYe5n/+Pvf+P77o+7/3xO6SJkZxADE3JCaZkkTQ0I0MplMoXm8JQDpTBsBwclWGZzpbZ47RWZ+vaWq2t1Tpbp6MyHeqwTEZlOAoHzWAUBqawaA4sIwtmiZEsNA0GssRIboHPH4/HMxd8N3bWHY5sO3ndbrkleb9fr+frel7P63ld1/P6GS7jPuNuNNrjt6LDRy5KBplNWOj3EW79dPj7IlKQShFfWYUOSfXGS7Hf/VWihV2df0oJRT+PqBk5DSm71USYStrvdyH6qSeSlbYTyuUplCQ0F8WplhP1Sp9GvGM50Ru5wLhtRTxzPJTcgtb8oPGzHe3h2xF9LCFiXo8Rh+I8+Js6z7XJn43wusxCPG0coqsMVKqpk8gm34r4RPLSjICcG43X5Cpv1BijHyHijBuJck2TET0nXrUe7bPXjPPhGjvrEcI1fJ3HzzHuqhFvXkTUPexHNHczWv8UvlKDLOFdRPepiwz/hbxOneefn+MqKCigsLBw8Gfz5s1cdtllVFWdOzNm2LBhZz0zZsyYc977n/0aigE81/X1YRJglUiJSkH8C5ErOB+4HJqvh5LZSJFLB4np6CR3jKjofjk6nU5GDGkXvPFDuOzDSIlqRUyhDzGyHCRwRyAXwHgkwFPsym7ENI7ByQcg61LEyHcgppaCx28Bfs2w3eTxX0dKwASP9WXDu9kw3+R5vE60kev3u2cQDHkvYqSrjZMKJHRASkAp0TQ9WRFAzHIvEtCPIEtLLnJvF/n9tRr7D47Bb32FqKv4sH93eaxk8agjOmfUIKZYgJj8caKdXWLe29DadniO+xDzGYOE1FQi3rIfKXNfJNpw9aOg8xVIoM5HAnkDvPO3cPECIhOzmBCaKR4sF7mckyuvGwmP4UTsZErK+LTh2+H1uwPRy2ZEJ1POwEEukS2+hFBCdhtf7UjRSELlZuA1eOcxuHgccYi51Lg5SpQMSZaGSURbvGo/MwLtjbVAFgw8BhnXw9vPwyXjkSJWanytNnxZRFmZW5FQ6/AalBAJCX1ey11EbcB9iJ6Ste2LaN1nes3mosSJOYimXyTKiQw3Xu73e9YimighimZPQgpEA9HDuY0IAUiHpL3G6RQijKPL991HuBeX+7N2ZE1eguhxDaLVPDh5h631ySI2Gll7JqBD1w6/s9/vfx1ZuuYimmgjOs08hA5r47xGaz3fPsQLkkX8CFLi24kOGCVEPGIBUbWgD/HBBz3Pfj8/Gu2PbEQvJ423hYT1f73XZgbR9eKQcZDcqJd6rNWIV+YSxeIneA12ocL85WifvuTnFxEx1EcRjad4vlbOdgMnHtqA6HI6WuNS4/uY8ZCBFNI/RPys3p9NEp7fqIPLHkL7JcVDX01ULNhrOCoN92iiTmQBQQ/tfvdDI+CPezX3auMoHQLGItosQ8r1VsR/3kIHm6YLGAPYep5jAIv/bTGAJ0+epKioiC984Qt8+ctf/mfveeaZZ7jpppsYO3Ysp06dYvLkyXzzm9/kF3/xF88H+P/hriEF8FzX7cOkhKRA6mYk6B9FDHIKYmQr/fl+xLS/ghhkctk0ISawFTGtzehk/bi/W4qYXDnR2HsywbCaCEZR77GuRcytgihx8Krh+H1C8UxCvxgpQAf9+SSimn8xoaBlICWszT8vcHZCRzeyOOR4nGRFGIVKxzQjBlUr/Lz5I/jgbYiZzWCwJMxgZ45axKzv8XuvJPrdViHLaVKiKghrTgMSFnuJTiqriZjDlGXXgZTJenj1DvjYJwn39wjjo97jzjD+F8Mbk+CyryCG3e7vNxoXyU3cjYK+v0RYQ/Yhy0WKHWxlMBuaSsLiVkAo/G0M1ntjL6FgL/X/SxBNzSesy11E14Z6wsKa4p1yDMsk4fH0Chi2yuuckm2yjZ8ev3saUUOuy5+XGZfTiD6s0/x8jWFJ4QBTCQtXUniTW3o02ispLjTFv2G8JiV6rcdP8YYplvMUEpCbiBIc3Ujo1yHFop6wYC1G9N1IKMjJwtiDrHzb0Z5r8++xiGYeM1wVaF+NQXuyljj4bEUHkfHGzwhij4wgWj12oL1+DO2BRURySxPaL6eImnT1aL13+p6KM3A7w9/PQIplUiIyfc88v3sCUT9ummEuRGtc5rn2GW9PAx8dCStPiC7Gee5riJ7b09A+KSA6tzzodZnrdagx/Ml1nQ4oTYh/rjUOliAl9G6iRMxzZ7yv3t8v8HNTfF8GEYpQR8RVT0brDVFndYL/v8gVCG7z/S8jmrzZ42xAdLfW6zUD7fVjRKWAbHQ4XkRYguf43hr/3k+0IdzqMdIBvtSwjzP+pxMxnRNRNnw7EVfYZzwm/tKF5Mu3ODtber/nvgvtlXzgzv9cCmBra+tZCuBFF13ERRdd9C8+u379ej796U/z5ptvUlRU9M/es3fvXhobG/mlX/oljh8/zne+8x1+/OMf8zd/8zdceuml52cS/4GuIQXwXNfXh4mJJcbyNNEa6ARiVnVE3b5CZGW4yc/MIDpnLELCKxsxvTka48AzULmUUOQ64Y1n4LLniM4i+YhJVHisViRYPo0Y6o1ISKWYE5CbYDYSSGuRVa4bWQwOE51NDnq824m6Zolp9RElPC5C7ooXUfzMOCJg+SakoJxEQicJ73zEpOoYrGPFIXRSPoAYXj8hgNuJciEjEYNMGajTiR6aLcDHkeK4z3hPCk8ZEYs2jsG+rYNlUCYTzL0cKbENRKZvNhJ4NR4/WYW+gyxMKRYnDwnvZGloNn4PojI5f+v3jfK6TPIcthLlWJI15CCRidzj+9YgS+EYr0NyH88jOgm0IAXkKOE2vdprVEZYG1NMYXJZXub3rEG026t7TzwNI+/w2k1EAnMSEZu0Ggmue1Edv4mGIbmxZ/mzpz3GAsLNXE4oZCv8+wmvR5fXY6nxk03UnysgYvMWIhqdjgRgLpGd3kRYvHOJPsBFxsUIIk4xy8+sQYpiDqLvDCKWN4Po55piJNMhro+w8N2AaGO01/oywvWbrJejvAYNhm0KUYS7g0HrHy1IoH/W65NH9ORuIuoPptjkDET7w9G+SLG44zz+MaK7zmR/f9SwdBB1GN8i4lyzCZdvisFb5vnn+bOJREu3o8bpbuOyk7BuTyVc/s3oALOWcNUWIOUvwVZjPE4jWp4t9eftaN+WGG+7EK+7DdHpfERT9UT2egeRcFNFWJbrEH3N97t2I345izh81vpd3V6LWrS2pYa10nNdg/hwp3GejWTDmbjsR3vxuMfKNFwjDcd4Yv0zie4pexGfazPepiPaKyTqoXZ5riuA37qACmDLeVYAx/3Tz7/2ta9x7733/ovPzpkzh6ysLP78z//8X/2+/v5+PvzhD7N06VLuv//+nxPa//jXUAzgua5vIWHdh5hCI9qEVxKK4BzitJ6PLHMpZqkTMZ1+ZDUsJILsW4HjrnO7AJn4x+veyz6KGNnlROZYA2K2yZ04lshuLSPiRvYgRayD6CtciBSpGUAFnGzwPEYSreG2EEHyOwnrUhFSAjIQU78VeL/vTdaJbcRp/JuIovo81niir/ERP9OK3DbJWtWNmOfniRZ7SXGaY7w/RWQMzzzj8xLENJMVbA+RkbeNiL+0FfHkDqK93U6v7zGPW0a0+EpwbiMUxwHCKlVr+K/0/zV+10mvTReihyQUihnsvDKQDgenkBK0kcieLTE8idmn+KWrkIAdQWTJLvW4w5EwLEaC5nIiI7ZIn739Q6QYZDDYYePto6iUy3bNa+Q1nhdI+IwirIJfI2qevYwE8kai88Iiv/sYURw517if63vzkeKWwgyWEQKsm2hN2O15VvneUsLF100UiB5jnK1HtF5meKcjemsnMgrXIqsYaF82EdmvRUQv1nziEFVtuPOMy4Net0xC0dxivKcD2Am0rjciBSspHOVEh5t7EL1M9vuStT1ZT0cRCWEpvnGs4Wj3eMkqvoEo8ZIUjSairNHHjbMtaF9sRntjBjqI1aBEjreIjPS9hHLZSiRrjSf6+5b4+xko7nMSkU2ehXhLvdfnBHEAhqij2k0cuo4T9QIPEZbFY0jxr/SzKe5uqvH0BNpzA8RhYY9/H/IanEC850XDXGhc9qH1LUZ7q4+oI9pChJjsJuo6TkIHhh5in76CaPCIcbmMyMZeQ6zrPsRPsokErsT/0nqmQ/U6pOR2INqcx2BXndP3Ij58jOhL3sB/uqu1tZXjx48P/nzpS1/6F+9vaWmhurqam2666V+87///yszM5IorruDw4cP/J+D+h72GFMBzXbciC8k4dDp8Am3ag2jjTSXS/TOR66jP905Dgngpg27O9gfhzXuQUKgCJrlA8qPIapI29DcQo9+DmEqG6kENtgI7TjD/Pt+TmFQ+EpafRwy+AjGrXqTQNkBWEWJ0mxDjmuL3HvWzExDTbAOeLRLjT0Ht+42HxxCD/JHvG+F5v27c2WrxzmIklK4wniYbH48gpnYAMbxJhAXnoOfzKaAaTvzM8KVkjkpUNPcoUqqy/L7ZXodm+OmPkDLeQbgqMyHrVuM2Wc46kBX104SSPIAsE81IUMz1/F4gBH8+cgGu8VrMRgKn1eu7BAmuxYQSuFFzzrjN445BSstyrfFgi70Cz3e8cXWAqJ24B9HdNsE38F0iCSNZxq4h1qxB77pkhHHQi9yHe+CSexGN5CI6vYHo0HDSa/A7XrMS6EzW4HrjfQRRymWkcfEFoo9qsvB8xmO1IsWohaCfXEKpygPGwd+sdsurL8NfHyW626xH7tQ5hAv4uzCwlcgKzfBY21Dx3AK0P66GgX80rKONl1PImpmPaKobepYiQf0tj59PlEnq8RqWof07ASlUN3jdqoyPRD9Xo4MUiKbvQopQsvisQbSbXIKgta1C4RSPwbe/4nduR0kMmz1WP5G5u8zPpb9rEM30Ee0hq43rbKSANiOlvgq5GN/weP2+f5/X64/97gakGOZ4brv8/LMoMS0lOeV6/p1Ee7JeomNGBuIZK4gSVnOJZLE637cJ7aE+QtnqQ+s+gPbHaz7Q9RuflWjPjfa6zUOJKf2G/XmiS05SmDfB2zcSVkuQEjod8aTJyDtSgg6eM4kEki7Ek44iGZGNeM5R4/AA4gubvIYpfnY6ouHN/q4D0dtww5aN9uMc+OuV/n47ckXvhGGfImpVNiGePZ4Le53i/JWAOaUhR44cedbP/879u2bNGj7wgQ/wyU9+8ucCfWBggAMHDvBf/+t//bme+89yDSmA57qOoU1aSQQRH0JEuhwGvoKEy1so5uU5pAzUIiY9HAnKxUABFC6ADy4leqM2o417u8d9Cimc+5DFpwcJl0WSx4NWxYt8TyZisnOImmj3fVRJH81IqfkfQM1HYQq8/WM/cyPwm1nwF0hxqiaCuR9BFoXXEDOe3KZg8mzCrTjXcDQAv064CJcSsZDrVKz54ucRs11GZHfdgCwy45CrLuG1GFnCEiOYCRTCyE8hZttkWLch4ZovvHLC49Qj5jofPvBdr8Vyj9tGBPOn+Lw+JPye8xiLGewGwDQUS5nigi5Dwi/F/kw3HguIcgytRLmYdUg44HkWIVd5DVF7MRcJ4z1+d4XxWYGE/XG/o8Jr0oqUju0M9hHO+AXjfRnh9nrM9y5ANLzO61qA6O16z63CeF6kvw9crzEHhfE9SEEw7kZfj2h7ptdyMxKQLxj3f0oUG3/ScIxDSsStvjdZsRcT9eYyUJC9hdgvroNhNwvfH5lyxhp0A58kyiTtB16GjJvRHlpE1OLMQjQzFilHJ5WQ8s5P4O3VSDB3EokEo4BGyLlba3Ty74ni4WuIgvAtaG8fJZIenkTCObk3+wzzNR4/xamtRkrFZ9Fenku4UpM772nPq1PdgG6C6LrxK0QJoGRNKzaMu/xZCzqIJat/vtaO25HiP4rozrKQsPBea9gyGeRXg0kVmQz2xv3pI17LkVqfwbqYrxme5Yimksu71LAXevx0KPgtpDyWIiv0JOGdIqIcVJ5hrEZ8qoio/TdKn2Ut9Rgb0cG5FdF2CaLTdiIWN8cwlhmGDOAv3KrvKkQn1f45ifbxASI0ZjU6lE4i+l6v8JyTAv8s4s1PGwf9RKu7m4ikmnrfl+s1m4f2ex7iZTsF70fu9nzSwfMedJBIPCe5k9NB40Jd50v5Sz8/53Xq1CnWrFnDDTfcwPve976zvlu+fPlZ1sP77ruPl19+maamJmpra1m2bBktLS0/t+XwP8s1FAN4ruubw8SUpyOG+SpifIl5ZxBlIpLlKrlG8xATuRadrn+INv0oJJwsaKj3c6VIIE0gSgE0+/dXCUbWgwTrDciqdmbMSwFRzqGZ6HowHSmKRX5XD2JMJUhY9SFmlIKWiwiFazqR6LHTP980PLsR8+9CTHkrUhiSNSqdsvP93g7CDZmLGOvlxgme11I/uwcpGbcjq1KF4UzJJNOIgP+jSEAkd8oAEj7HkFJRSdQ0+zjh3mk1Hn8HZRU2eX7p1H0pUoLLiDJAPchi20tYHN/1O1Pc0MfPgC3LY2Ubx91n4HfqGbjo8lhFftcsIoasSf1IL/4cUZ5iNnLFjiBK2KQYy5uIuMpTntMRr81bRJeKFGe2HfgDaJ4JJXcQcUhNSNCsI6wwRejgsMrPTTU+phGJUQ3+LikXPYatBtHBKLTOOUh5LOTsONE2z+/LnuNDSMCPQLSX4gSLEP1XEXFwIDpJsZ7pUHEf2hsTiRZhHYguVhLW4KXIVbjTsKwikkN2G3ejER0nd+spr2G7x1pHdNroIzKIW4iYtC3GXb2/g3C1J3i2E7GjLxi2DONqBFIYrkEKYx7Rqm0uEb+abXzOMPwX+blkfe5ASlZSqtcgl+Y8w56U/R5E5x9GdLCGiEu+zn9fbrj2oLW/xes41XjeBad/BsOWE0ltVZrbmz+BDy5H+73e908kaHs2UsgmGtf7ibjOOxDNtROZwqVIIU9xpDuIPuxPQPN+keEl4wnemayX6Zm2M+aYh2j3InQY3IR4ejuip53w5nr44GeMx22ed5fncjNhLe7zezYiekqHrPnIEliI6HeL12oJEdeaT7TGu9OwPYxkx6cuYAzgYRiZ+7+//181ZjeMGv/zZQG//PLLzJkzh4aGBj70oQ+d9d0nPvEJSkpKeOaZZwC4/fbbefHFF2lvb+eSSy7hIx/5CN/4xje44oorzs8E/oNdQwrgua4VwyQwxxOZkYsJBWMSYgovwIm9MHI2cAW8/TBcUu0xChATayCC+buRkB2JGGsBYpj1SGBtIlyHm4h6XGuQEjEabfwDRDbm1Uj4biAE3AwkBB/w2A8jJrQcCcQMdMrPYLDeFc8hAVKHhOAKojF8H7I0PIyYcTkRJL2bKNRcbLhyDdspoun5ZM4O7s9DDPxGj7GUcG0cQJYtDPdWjz2OSC7p8v21yApxpcesPgOvuxG330xkSkIUp03upQmIoS8k2s5t9fj1ROmNMqQcrie6dzyIFNgcxLDvI3pHFyNhXoaYeztRfqXN8FUYRyBF4SQSkg3ITfkYSkLZTCgMFUSXhzyin25STPuI8jdP+P2LzsDzDCREWgxzhmF9xfO9gTgsZBins43zbf57umHC8zpOZHC3+r0z0VofN0zjiYzdZmTZ6jKuktVtIaFIPAft+6FwP1rn/cAWOPkWZM0kDjFJKd+CaG4aslZt8jreRbivu5GAfhHRRL+/u9v42WQcNSDlq5LIgP2Y7x1h3M1GSh/osDfdfxcTmbr1xGGtzTiahQR3DuIpe5CSVeSfOo+Rj5Tglcb7dUToxDz/ziO6+xwnwlK6iOz9VrTmDYhetyEaTrHLa9B+OOV7WxG9nPK4Y5HC+ZrnmWX49nlOBYjGEg+4k7AkNrjM0CT/3+Z1+BLw20j5qSeK1ed5HU4SSSuFiK62IVopJJJTMvx3PkpI+0OvYQZR3aAf8dUmZPHf4XXKJ8oJJeVtiu+fjHjijUSJrn5EN9cieij1fA8jvrCD6JjUhA4nV/ldA17bPq9Jo2FLB5kOz+2w4SpB+7cJ0X4lUU2gndgrGcBvXEAF8O/OswL4oaFWcO/VNeQCPtd1FdFTFf/ehATyVuQSWAy0wMgU15cNlyxCp8+NSCE77ueTQpLcqZ/z59OJgsgpXmYDYpJfQgzjPqL90mx/l9wkiz1OE1EqpsbwNiG3xRiiQ0IXYrj5hDKUSXS16CbiVPA9RzzWtUjgJ2sWhuNmFHd4DDGqpz3/pXr36dcQk5yLBEpSEDdpvPb1xnWV8MkapFw/RMSUPUC44NNcnz0Dh3ORctxPVNNv9vc5yDLWbPw2IOZ7yvD2o51Q5HvWEkI1xVOlZIQXgZfhnR1EosoPDe8WJCCTJTcpTm1IMNZ7fp1IWevwmGM8ThHRhqqfSDi4Cq1fveedjxSTXsLqnBIRxvi9eUjxrUfrXeR3jieSONoIS2cXYXlNytlhRK/jkHL9Dd9bSQSoFwtH7/zIcJR7nMOIVkGC9Er/vR8pUeVExm85UqKSG3CnYS7RI4XlSAku8HfXQdZHiczxPOM3HRpyDd8OpEg0EnuiI/D2zt8aphNI6JcRsVwJP3MQvSXrXVqDHM+pANFthce9Du2DPMIyeIDBenwn64iDU+sZeBxtWOr8U+DvNxiPm9FeKyW6rzQjGmn179FofQ8hy2wdkclcqJ+B9Wh/jSRqQfYRhapfRTS4zGPuIVy5GWjv7z5jDt2IJ07zew+hPbWUoK0euPjD/r+HiOM7YFznEJnTBV6DxSiMJZtwsaZYzORKLfV3B4hC059FYQ57z1infSgkYheitRKiZ/PDfsckRHMzED9J3o8iwzYJ8ZVy1IWp0/CO9Rp+mEiAKiCSB2d5ndq9Zplobxd6fpMRb6pDytzVaA83oJjqo4gWOzzWAUJBzSP48dA1dP0briEF8FxXiq8pIepR5RHugnbgo/7/+wiToxEDaECbshoxvccRI91BZEpu9LML0CbPQow3jygavdn3VKLSG3mIUV9KlHXo9XM7gW44ktx/HX5nMWIcLejk/hJSVhqRBeB1IhN2j2EbgQTfUSTwTiHmWMFgYgFPIcbW5nu6gCVw5DWiRMc6vXfYGKQ0H0aK2lWeb6XwWngng3FYg+VckoXlKX9+N9GOqs3P3oDcdC1IUNagmKznvD57idI2DYjRPw1Mhua9XtNc/U8D4QZuFi4HrTvVXt/tiHEXwMWf5GwldAESNp1EPcRMFIg/2+NtRif6ywlX8DgiOaPXY6RYuZsM+y4G+6L2fNffFwjfg+7jDUT9sS4kgO77QOCsx3BONb6S9fldopdusk5PQWs8A1nRsgX73/0jEk47CZfwOGAhXDwX0eZWouZlERKc3QzWoOs86Geu8nshaq4lq0eR1+NRRL/zCat0o9bjpz9BNDTd76iFd35M1F2bT9TRLCM68NQzqJxcfIvHPIosP+s8vxmefzcStL/n5/qMt+SmziESUI4Af4KsVskV2u15fNFw3QhZTyAr2G1+Rw/Rku7jfn+yotYTGdJtxtFaP99MhA+MQoePCkSfWURf3yLDMwDshozkxv2an33YeOr3mkz388lyVopopMbvbjUeLiJ4wksMFmB/52GkYHYar93GU5ZhS+ECpZ7/FR5n4Ax8b0SW6P/BYJb6IM+9FvGvryCaLfV49YjPLUD74lIiI/+zxmEfEU5wvd/3knE7FtHa7xOHZ1ttex5BPGUH4gWH0N5Z6nlO9Vz3CsdkoEPxPYZlqud90rjYSSSATEbKbj3wm4hHZBNZ75mInruJGoyHiZjrFId5Ia8LHAM4dP3bryEX8LmusmHamHVIaZmPMhqTOzad2LsJt+Yhwh11O7IWOUaEOYTFogm5b04gIW/G9/YCuCQfMYUzhdg8xAizkIB40GMuQDCm2LCnkcvtqP/fQ/RMfRwxuGwi3mo/EgAPEcVo70ACYgNRtuYFxJB6kSJyHRJcWw3HJKI8xD7NhQEkUH7gzyuIshLJ3d1tOI4Ad86Fb2+VIMsg+uc+pISSrDuJWKcMJKTLYGCKm6YX+L13ePz9/uwAcuUPR4rsAiQ0xiKh9efIIvVHKL5nJlFg+SBxRNphmCZ43NcRU/8Z0dez2WuX7ffP9XfXIoGZXIWFSOis8DhV/i7Tn6V1TPGDGz1Wtce/x+sxzfAtQ4ryBiJG7RRyGzYSbr7vEF0ervR4rYgeixnMxB18TxkSNL+FBJetlKdvhGFFSPH4LKLNWqQsHkQC9kvIapvr76oIC9JcpBgf0XtP/i1kTURJUNVEeaU1yP12leFMcZU9xtV1qMvNcuO0G+2XOUTSD37/ZNjyCMxL61uH9uo0oqfwNJQwkxI3+v18je9/jnDXlaJ9ss1rtpso41GA9uVJJKw70V5O4RDrva63IMW/DAny8Wjv1niOFf5/BKKBKrSv7kD0dYxwY28nikpf6zkvh5NLIetzhIu2DB2SlhF1TvdD5zoYfSXRtWSPsrGHpWf7vZbJCpoOsqWIRm5De7Ic0cpTiBaqBFPPjyDnZs/tekRrk43vSZ5vEfKYTAZ+g2hJl2IG6zz/ef58s3F0yPB9lWjNN9vv+B3NdeC7kDET0UaHn8tH+6PMcK32XDoMy3oGy2exBu2NPKJw+HCvzyhEr/2e+yik9G0iLHX1RIz1FOBzH4SVb0aR7hKi5uV4ov5sChXB+LzFeMg0fLme0/cuoAv44Hl2AU8ccgG/V9eQAniu64fDokZXrT+bhhhkLXS+BKOXEI3qKxBzqgReMfO8BTGNx/x5D1KiOlHs0x4Y2A8ZMxBTPEzEhrQTMXMpRnA0EkxJGUmCcgnRazglLuxCTGU2YuDb/PluxDQqEBPZiRjsCI97r+e8wTCnk+tbxkENUqLKiDil7yDGN5xoq7ScSLgoIRJNvubf9xoXu4DfhtPXwLCPI+HV6fe+5ASIUUjYlyJBNwcx72qPkZJL8gxjM+HOa0VMuBwllCwlsi9bPWaKz7rWeH3CnzcS7qb9SGGZjhhxsqxk+F3dqEbiz5Ab7TKkrM8yXjYQ7fMakbvnCGERmIaUogc8h2SdnEkkskxDgmEMESf0gNfxJiLWscW4KEbKyWeIUh3zkNKZYsWSmzsH0cQ0GLgRMt4P7/wMLv6455iB6GAa0Z1iinExE1liq/zdBEQjB433as/9CFK2TxBtAXd4veag/TMfKcBH0Jony81mZPV513/3oXWf5nkVEt0ophPu/24iAWMUUpJGEZm83ciSfAQpl31obTcZHyl2cRNSqKuRgK7yWo5B+3Qd0SrsSsOQ4iI3of1QcgbszR4jy3/PIFzuRcbPKs+xnoitO4z21CTjdgfahw8YbyPOWIcutL7FyNJ1DRGzdq9hsueAbMQrqolkqnkoznSG8b7Pzy8kkjDSAbke7ad3kVWvhKhNmvjNAFKis423Ns9zBYoZLCRKMR0jCr2XorXt8nvTQThZBicg62QZOqxmeq1a/a7rkKKdSVRNaEf7bYrXYyc6WK1Be7cTHQwmEwW8GwxXPpG8kcJRJsFP74EPXINo7BpEm+vQXulEvLef6FEMossudPi5E/HXZPV2uANPIt7fAz2vQc6txslMr1c98NSQAjh0/fzX+y40AP9ur34is3c2EhjJjTQGRk9BG3mx/mctkRxRAcPeT8S03IOUwDbE3CuRwndS8oLFflctUhiSEJuCmEYZUsD2IKY9DTGlFBxciGL9bkAn51x0Ql2GGMU2P1NIZBFeRJTLWIYY32QkcHuIuli1RN21AkJAl/v7XYa/ATHYFxCTvQ1ZQfuQm3QaEl6jkcBoMfwrgK/BsBnINQpi7M8DjXDxcsLKmk7WNUSCzXwiZm0MEriZiDFe73mkORcRFrkBtMbHjOsuIvNwPlFkeSeyQB3xOL1EXF2rf+/x/Cf63VcR9dh2E9nSGG8zkKVzLBIeNxOt0Sb6HUWe193ItXgMnfhHENauDUQM6QtIELb5XXOM9yvRASET0VaX1uDEj2Dk9cb/Ko+5SfPOeFBzv7gGCbp7kbDJMwwPIjpJAv5WootLntfvNiIWtIfBfcMB42aR8XAbQQtjPH4SogcIC/NSzu4A0oSUjEqi9WE2ouXkVl1tfA+gPXSQ6EqR4fGn+tkM4/N6IvEl22uQFKRMoh0gRAeZTV7HRcZlPVFIud+wNyFlLd/wH0O0esTrVuIxk1v8McNXTCRGHSPctb1IuTiF+NCZ1uoMIpZyoWFf4XVpQkrFtDPmlYsUpiavSb9hTftsLKK78cRBJimJ0xB93EvspVKkgOZxdqed9WgfZxJtEJcZ/yOIHtudxKEKRLOVxKFvPpEt/ywREnENoqGj/hmJ+NyjRIu6FDuZLI+5hrMK8ZzEPwqIw0WP59FFtIzMJ4wD+4BN8IHrvR75Hid5IXKJOMsOz2G657bY73vC/08mrP0plGch2iPNkDOJaP/XZNg+z4W9ThFlvs7HWEPXe3YNxQCe6zpIFFeuQQxkAG36at/zFoqryyXaiBUSiuMkf74bxQF96Yz79iIGPRe5MJJVaAQR1J1OmBlEfbpeJCxG+z2zkWL0EmKGlcj98iKylq3xmOmEXoQUmf1IeZuFGPZXEbPs8vg3IeZWS7iCT3rMIn9WiBhZjr9fiFyCt3ueGYYzxUK9jpj0ZMQ0u5BwTEH0rxIK2DjPeYPXYAAxwWuILMzFyOJQatwWIAHwkr9PAdIbkEA9hZTQdURW5Hw/fwq5y7caL7ON6wwkpMs836TA9fkddURyTiNnJw5AlNSZhYT3NiKWMNe/dyKl6nOExaoD0V0ZUgS6kTJ3FCl72xHdlBufoxEtwmDXAI4jgTeVcEM16L0jP2lcb/N8ijynV/zeCkSjqxnsKUw1UlaT5SXba9BsvPWjA8pwRKtNBE2MIuhpBFHYvBbtjwHCOpqBDlSnCIVvjuHK8//Jqr2ZiFcrMo4dj0a213S98TKCUEDyjJsRxnGybm1AQnUJUkySe68A7bdO/+A12UlYgRuAT8PAj1FYRQmRPZ5DdAbJ9Vo5/vOdRzyXFt/7NFFCZQBZpFqIYshNiK7ajPt8w5NNtEpLtFuEaO9Sv3ckWtMM31ON9tDLRE2/o4ZtCqKvGqI8yWRER+sQb3kWHQAqDEsL4i2nDN9wxEta0eFmkd8zHSmixUQm+QBR7+8YkWU71e9PCtw6ZEnc7vdNMVzvErG3J71+bUTtzhpCuc1Dinc3ouccwzaX6GSTElHSwbnA9+5Giv4rnstViP/MJvosY9iSvKgxbtsI/tGBaOkFoh0ehNu4kEgWakd7aRSRGV3geW9i6Bq6/k3XkAXwXFcrIaiS9Sm5e/agTb8OMeslKJYuxfeNIfpTXosY4xriBNhHFMD9Nd/TihjACMLdsxMxxFL/3UdkWC7x5x1w8vuQlYniEzuRApZi8boRM36AiGdJ4+QSGbAlSPhdRZTpGE4woUzDX4gUuTwUPzeBEMB5hneV5/AsciOVIgb8JVTM9y3PbweR/DEZfroaPtBAuLy7iNI7pUhwbYefHoQPFCKBsgsprmuJIsYnkNCrRUJuhOGfiQRds+G8imjPlFxUHycC3kcgYTCJKKi8w2Pv83xvIIT5o/B3p+BD/R6/03NdgGjiVqQoZWh+bzbAB5d4fQoIV2vBGTAvIARgufG4k7DAHfO7J3qcFDKQFOlkGav1/F8iasRVCJ9chpTLWcblNOSOSpa4RYQVdarXdTKR2fgSUvwOEKVR5hmHhxH97ECK8rOGu8A4TdbPpGDlIAHbR4QpJMUvxRNOJuK4mojkKVtdXn8OLr+DQXr862r4yDGtDzcggTzV65oEdjmyQHf5+2P+2YtocB/aP98nEkuWIfqrJ5SJLieAJ5d3ivkqIOKDi1DM6U7h6uJsw7AUZZRf4f8vNT5mI/7RYBx/HR3ypiB6afFYE5CSsN/rWIH2zTcQHU5DMWR3EDUBU4hDsg6C6LaQaE94tfHyHNr7y4i2apOM573IUliBaDEpWym8pN7PrSUy4hvRoa/MMLUZ9n1E159G/59NtATciXjRc8ZXrtd2LVIqi4gD6AE/l3jvFs4+yBQiOv4B4knXEiEDG/2+FxANbzNMsxGv/KrXZJPnMNHvxLCkWOgGtEe7iHCSFErR7nUrRHtmn/EwjvBSFHpeKVSlHB3wG4lkrQt5DXD+kjfO1zhD17/qGrIA/ktXATLNH0fCv5toTVWDNukdSPlLp7oCxDDLkNXkWX/eiBjsHLRph/uzu/xML2K430QCroao5P8Q0c5pKmJAexjMDs2aTRRXzkCMqJKwMHUj4VpIxCmVeV6jkWDeAK/+I2JA5Yhx3o6UhuQGmYusVFchpecyxNS+iBTSDiS4WpDg97i0IIa4x2MkpWQ8Err9wGr4wFLkinrc+JhnfE1DDDAfuAk+MBMJqVOo08TLxttCJLS/YRwli2cBiqPpNk5uR0Locc+zlmhFdxQJmDrPrw0pL5mEYjKcaEv2gt//qHD6oaVwegeijRLk/m/xu7cSlty73BkmGwn8MdCz0fANJ+JJu1Ds20aiCHItclWOMVwPeE3H+/7HkLC6xvcfQnS0GikzzyFlfLJhbPR7r0dK4F5E42V+R43xOpazs7R7icPBBKJHaweixV4ilvIe3/dVIht7PKKn4V6rLuP3ANo7KYSilOh2kiyEM9A+ykYKUQlSYnvg8l/wOLl67iMPGO9/SCgSNQxmur+znnD1LjGMNxJJF9sM2wOERa2ZODxkw+lH/M7ZkDXOsOz1exMN1fn5LKTQFSGFfpzv+RZRmzNZ49pRWAeG7a+REjYD0ecxoqduHeE96Df8a/yOpVqbk5OQ0jXLa9uP9vgAcci7icEeyM1PoP0wDllS53kuk4iDR7LGOtzkZLKeJwVowPh7Ae39MqIE0tWIptYjBWwaEctb6XlmIz41g8F9evrHxu1aVILloHE37oz1XYvochai/2cR/1uJ9sIMtPdy/FkFsuoNEDwvhXl8CdFLv+cx19+neNZtRPefrURpr6uIFnXjzpjjgD+foEfevttj5MELT0Dnw2i/lSJeV4v2w1WEB6EfKfJruLDXacIN/H/6M5SR8J5eQxbAc12zkYIwgJjwCrTpkvu3Em3KvcjdsAOdyv8YCdyXEJOcgATRIiKzspGoOt+ETpDZ/m4TClie5s8mEp0KRvuzHsT4r0PM63Wia0MBYpLdSEilgOeNRFD4VMPegawUdwBF8LGJSFD3I4F7t2Ga7Pc9goTDi/A3P4Zf/CQSvE95Ph2GI8XsZTJYZqXnAcjJQ8LmViLOahxiro97fol5j0EWjr2IaR9HymKFcdnvsW9D8UdJwbwKnaxHIKtUg9fqPgbb+LEDWVdSvGUB0cpthNc3Fykii5ECdhditDcjYf0cEpa1xsG1fjYPhg14bgsRDSTlbTyD3Vh+8GP49E7jtQFogpwNXuvVwt87R4XKD9xLWH8O+J5K4+0xRCvzkYJaR9Sn+1uiI8hCpGgl6/JTHqfYa7XB+D3g9U6u/mzjutj3pHi67ZxduHwMSga6y+87RlivC7w2072Od3mMryBlaxWi4+VEO7dE57Vel4c8z2Ik9A74HeWE620/Epr1SPlJikayri1Ah7U9SPA/BDwCF4/XPZ1PwOhNiO7f8PjJ0phDdM+pQkI5hYRkw7A7iOStbKTkzUWKTSminfnG7YuI1vuA34ETP4GRH/c7fp2wMl2KlIJlwsvbS+GS9yO3ZCtRd/EYUoI3GMZ5xtsR4oBTBuRD1se9Xq2+9wCirRFE1vQaBhWVknxEt1vRoW6h5+9QAt4lyqI8rndnLUB8rZmw5E31umT7HZONv9UoGaMZ7eFbiVp9ybMwC1nuliF+cBSGDSeKbdcgXlNnnELU3izyuHMQv+xGh5TvI0Xz6hFQ16sDyTS1C7z4aqLbyHTPNVmej3guvV7Dq7yel6H99ZDn+67n8gJSNHuIep05RIJKn/ByyVIYWAcZK+C6ciK+L1kA+z2vleiwWYF4ZxZRJ3HoGrp+zmsoC/hc1x8ME0PfTCgcvUjZqiQKoX4RlRroRgxqOtGzdgZiEh9HSkoGEjxHkTB4HDGVYsQct6Eio8mVlo9O34WICb+ElLgHkTXiCb/3IcRoVxBWk+MEs9uNBF8HUkYSY53DoBuZJuI0vxoGtrpm2B8jYb+eCPSfTFiNkkWxCjG3bqQUvEvEZH3RuKoxLHOJgsgZhGuoDDG1SsQgc4k+u7kMJjDQ6Pfh+59HjHkyUVC72/OcjhTJQsNV6u93E3Xzijx2A2LiLUT25XVEt5BOvyOHqP/1GLICZBuHt/rv4/78cuB7ntNyxLC3IxpIQfXJMlnoZ2cgRbGVyELsNFwjiGz0lGDSQ8Rh7kRK8TG/81cR7ZV4bjnw5lz44AYkfE8RzewxHpLrcL/ntMTvakX7YDpRfDYD0dhoBuNMew5CzgL/fyuRCLAerfU0gp4LkHDvIjp45BGWmRTvNoPIcj2IFLpHvQatRGmXVwj3aj/hvi4l3I+nkHK1Gu29fn+eQ3Rp6CLiAm8ismX7kSKZhfZ9G5GokVx9+5EScx1ypR9C2c/zEf8oIJIkkpK5kii4XEd039hn3B5BClguUbD8OiJLttHjbjec04j2aVORklJIJDNUEUWk+4hY2qRYndLy0uk5lxKldXL9XRERt9aD+E8x2hP1iJdtQ/TUQcTDlhPelVxEIy3+PJ/wdjShdd1BdAvZZVg/h3hZWpsMoB161kPOTCK5I7mT23xvBlGe5RaiI0iaT5Pfc6Wfr4TOG13xYSnRwegFz3eyx2kSHk83wLDr0X4qIMoLPYb2TS3RBjTx/nGI9zUTfHuuxxyBaGSS16rX8x7l50sRXVzIMjD7YOR/OU9j/iOMmjqUBfxeXUMu4HNd2QQjaiAYbYqXKUBMJcWnpDiudYhpZvqZTsRkBpDlYT1nuacGu2LkI0bd7PGH+36ITT5A9IbMQIz1IBL016JT+n4Gi+4yBpgAf7WDcA2N8nNFhAtxOGLUBxkMDO8BCZ42xGAhElNWICaVTtmLDH83kTl5KRG4PMbw5BFtwpKlp4yon9ZM1O7b7uf3EApcFdHhIwmvl5GCPRkxzMRAM43fMkKYrUCWuw3IipRBWHizz3hmrmE9hdxGM5E79fO+bxuRADBNOGYxEnibiUzIEsP0rv8+hhh6UmqKkKXvLcIavMR/j/F7k7UrKSeOcxvsLjGNaB+2BSkn1UjI7SQCxXcil2IrfPBTxocV9Lef9niNhn0akYTSSlhqcwz/q/6uz/ceR4cJKwY544kitUn5y0I08LjXdL9xOAOt7VVE7+i099r8ji4iuWYNvLPb468kslOLER3PQQKyH9H5ZiI5pwQp3qVEy7P0jkeMgxcJ6142UsKOIQWlDClzu5EFtQLFFCYr6ThgKwzsILLnQXQ7F3kK5qE9UYJobQqyOKaYuBrP5zI/ex1R4PkKop7fb5wxfq3hO4Zi/TrQoXCc1+YFpEAtI+LhuogyTqeMx/lep1xES0k5bPZazfdnIPoc0OftO9BaH/PPBKJ7SUp8mev3LiJqcE5De+4+gt+lmMd+opj7yTNgKEZK7WHEezYYb+i7bIjC15nIWgo6hCYaqUCW/GOIHw5HtDnRP0sJl+RsGH2L372GSMaZ7vlt9nqNgc4GGLbU97QQPchbEGBpP842PmaivTmVaKfZ6jVqJboFdSBlOa13ikU/iuTNxxm6hq5/0zWkAJ7r6kC1me5CikMdYjRvoU1v5kcJ2tQ3cnbM2VJiA9eiTT0DMZg8ZMFLFrVGxJxmI8a9Dilz1/mZI4hZrkAus4NEqZNSBjtqMAMJlOTKmS9Yp0N0RSggFJESxFTuM9zXMBjHNHK8n9mOBNZ0Dfk3dYixZfr7HXDkDiQQuwhBdIoI1n/a787378OExayVSJrpIiyK6bSbTWR1rjcuThEZjLln/J+HrAnTDG8rEuprUA3AOzzeQuMsxeNsJjJNG7wO6QQ/mki46CMUgzoksOYTBarvRAJ+G2GJKYWBNqQ4PGJcl3rOvcCvwMXrCAXoIGFlbDEuupQp2r6DqEk3y3je4vdfafytRwL4OuNqAJUISsKtS3M8sZjBQtyX3Et0J2k0nOkg00UIsrsQzVQhi/IKPXNyIxG8PV3zOP0cUcD3JaLF3SSkzHQh2tiHaLEAKdFlaC8VEIpPMTpEvah5XXyLcdBqHPRrnF0/JMqTjDf8rcblk0gRakfKxGbgGc2fGchKWmv4q1AoxELDkWLdBgwLXt8+ZNEbTvRjLoOMUUjJecj3rET7pYGw1j5MlEcpRXQ/nbBwjkG0m+L60v7NRXRX6/nlM9iC8fRtfiabUPhmCGeDCsZOFHeXlC/8/gNeo3zCytiF9sLHiFaBiYfkMNhxpPDjxlmO1+UNRBuH/Fk5UuSyCOvuVCLRqcCfj0Z7OnkJfsX4mYd4xnbCJdxEHIpKGcyGzrjT65ZnfPZ5vi8YZ/nGbTuip1MeZwM6OIz2WNsJi/dir0Mt0dYNYn+OFr5Hf8rzSQfeW5Hlbx/aN+WInvvcOnECOig3EzHDNwrGgXuIw2JaozzjN4U7tCIZVceFvQbO88/Q9Z5dQwrgua5dSOjUog3/OtpwS1FsVT+Ku+lHAu4xxBCWEJmM8xEDK0Yn8RSs3UVkUCaB4ji/vz6OmHMfnL7bYx4lAuVXISFyu+5hDXJ7DUdMog4pj3mISYyBYbMJZfMJjz8SCZ3vQc/PEMP9lMfoQoz/jwlLUgdQCb94EeFy/jbwVRj7EMqiSy6kS4nElx4kCA4armeQxWMBUA4ntiLG24LaMN1j3K8gYnBmG/Zk4VtEuIcaib658wmlsAYx5+mEZbIVnbrzjZsiz+9+j19F9Nks4+z4uDPjgO4hBOwhwnVT4XuOGcZa4SLjGiTEHiAU1tc89ouIfpKbaAxyVRYQBcgL4OIboXANUUajnbAyd0HnYuBPjdck7C73uDmES/gejTfyM4a3yvDP9Dp0G655MPC3RO3EXiIZYrXxnwn8D8i6h0hw2AQnW2DYFYaxkuhj/SXkhsv0GJcSLciavJabEH0dNRzliPZ2IkWmHCltybLbw2CG/FWjvC7LvV4voQNOSnaY7rGOI4XnsOe2FSk3c/yzCVmK1yA3821E6MAcZD36KqLX0cgy3E9Y8RYi2ilCSsA6RP+OF+QLRGJXF6K/+cilXIOE4Dp/NgUps2VIaTqEEp460WE0E/GmIhh2zHDNQ/T9EIrLbDQsa1DW7DbgPvjpy0Syw7Veh3yiBFap/8brUUgkWtQYN9uRIpNtuGcj3vMcsR/HEG30Nhn/6SBQiHhZruDseQzR5eV+zytEPOOXjOc6w7eZ4LV5/nwd2meJZ3Yg+s4xzA1+fz+yzI8hLP75vrfXa1rq+TWjw+NstI/uQfS6DR18m4zzPMS7ion44OFE3OcxBovDX/xxog3hvYg2C4yv5yDjU0ghnGVY7iXc/sn1m4nkzWEu7HXqPP8MXe/ZNRQDeK6rZJgYeS7ayNv99zfRRj5AVPYvQKfavUQJj0ke55h/7yay7I4hhj4KKSkNiMl3+6fCn9X47wJCKapGzPTbiMGn+MR2xBiq0YlzJVKqNnvMN4jSFTv9/R6kFHUjpngRg7EsXE1k1B1Fgmc/Ekj1RBHjfDj5Q8i6Hwml5K6egeJ0UnxMDWF9TNaHDM9pLaHsTCfi/fqQYDmI4umSK245EjYHEfPOhl33wFUzPeYir9dYjzkcMehmlGk5lSgjsZ5oa5XWcsB4XIaESkpy6EbK4FqkyGxCVo1sZIEr8bObDUeyet5MlADJRorACa9xg/ExguhkkUeUnplCCNIq4+W7xhtEgHuPn0+HlDV694kfwciLkJDNIK6TSMgm68QAUhQ6iMzgbkQzrZzdKeJGRBPPEqV0JnptjMfTp9wDOh8J3wF/N4+z+0f/CbIwTScKDHf5/qlEXN0BZBHKJY6tOf4/E+2BnYZ7AmEpfYJYy9VE9vgepExO9VhFntMRtOcyjJvL0N5u0LyYhGgixW0VeLxDRCxvhn8vQfuhkNi3rUT5kOQibfP8Ojyn6UTB6AzPK48o11Thz0YhJfAQWvMcRA/5iCZ2EMk3tV6/MuNojZ8pRnTahuj8iPH3NX93K1FFYLzxeSVa6+nEnk/WsEZEH6sRbUwhYlU7kLJaTLjNs5ESdQ/RQm4akd1cSiTMJe9CAWFp221c+j0nWyDrw17Tox4jxYcmvtNKhNs8gPbVCrTfuohuLCnmewJROHuE59aBDk0l6OB0KVLO2o3j9WjffgHxin0oXKCRyESu95iPIVqZ5/WaSBTV/g20T2cgmrvf82n3/Hq8bl+4gDGAe85zDOD0oRjA9+oaUgDPdX1pmDZjH1FEdzQREL+UUO62IcaSrGcvoc2fiZhBE2IkHeikPYZI8U9CqwS585IrNNvjdxFxhyne637DkWLQNgB/gOCt87MpZutZokbZAn/fZthmIGVtImJ2G4h6Vinm5SbDfrlx0IQYX5nn3UcUQ84kepfuRszpS4jhJitOJcEAqwl3NUiYNyMhO9UwJFfbUSQ42gnhuxYJmRWIwU5DmaUpxusaZK0dTsRoNSGBMYMozrvB6zGJyLatIOqZdaO1v9LPtxj2uX7mMFGE+iYUkzMNCeY6JERLjJfjRMB+ij9LCn5y7Zd5rApkpWhFgrYbrf+7fvc2j3UMKf8ZSClYabg3EMH0KUYyxdc97/FqiYNACaFQTTceJiArzE2G63Oez3eQwNpsXJZ4DUcxWPKmfT8UjiOyZr/kd21AFtClXpdXkIJ2DAn7GqScJQvsKb97gKDBfL//ENHPt9Fr87DfcxTRS4bnVEsUGi4yHq4y3MntuYnojnGM6O9cTezxWsOz0N+/RtT5aydq0KUwkGK0F055jCbDsM2wp7UZQDT5AlLYOwnLax7aK5MJBbgVHTqLkIXoh2iPP4To9Rqk4PSjQ1NS5C4i6kq+bFxOI+inF47cC2N/ASXU1DPoZh/M1O/y3OuQQvKW55hHlCeZ7O+nEK7ssR5nFqFM47nmoD1xpedVatx1EfFvo5ACVgqnX3NP6gGk8B5Ch4nHUdhJDdqbn0ItGtPh6hXisAXaL31Ea7gC42S9YZiDeEHiV/VoLU8g5Ssb0d2DfmcHosd+/70L8fX5iHYe9DPD0f4aIPoE5yGFcBaRkHaPcbcJ7f1XvVaziQSiX76ACuCu86wAXjWkAL5X15AL+FxXF9pkUxGjyoSf1hGN2nejDXnm5t2GBOFcxCwaEFO6xuMUIyWgzffuRIK+EjHMbsQQG4gT8HTEdK9Drqo1iBk1GM5u5CLoQUwlDzH2er9jAjq5T/JYLfq+5yUkDJKylWKHmokSMXmIWR9HFqMjiFEe9PxBjDQJ70rPpwAJ/TzD0EYkrbQRwrLUY17q7yb5viRMxhBZow8gpbeSiEEaiwTnFiKb8naiFdVbRHzRHmQRKSPqonUbT1lEJnSK00m47CfKZCR3dT5yCTUhQXCF8bcH+C3C9V9FZF5uQxbH7/idRYg2phAZvslClkkozB83LvBYKT6y0LgcyWAP32QN5RGPNxUJqNsJl+RsIu5olWEugDd/Rig4rZ57id9diOi+y//nE3FWi71umwjrB8BMKPyo1xDDneK7yuBkcuWN8vhzjOMUt/ay1ytZid9vfFf5ff0edylnW4qLDF+lx57mOVURCudOPzvZML9BlEpaRli5U+hAvWGs9DMTCOV9JJFQNRutYTlSYlb57+2eh/kIXYjzDic6UXSgA8rriKfsQvv+Ba3PYALEHrTnspB1cj6i/df8DESduaPwTnK3lhJxrNcZRyOJMjIzjKNiYB+MXRp/sw3Rd55xlWK1eokkh4s8bj9SxMf6/mlEKawS4ert17wGyduQDtnF/tkKb2wkaK3fOBhPFEyeDcMeQjQ/B9HzLN+z0nD1ISVtP5HstcVjzUX7MoXNHCX4UiZhoSxCVsnP+O9JnvcViAfloUNEG7J83ud3J2twinFMoRQtxJ4pJizHGUhZLSdCFVq9LvlErdhRROz2UT+XDBFD19D1c15DCuC5rhQ31YWYfiF84EGibEqfPhs01Z9yzFSKkxqPGNNbRCHhZI1JVrAaotjrceQuPYYsLNORQKpBQqGRwQB7MpAV7vOc3aarARXzrSRicuqBOnjjEeTWWQQ8556SLyIm8xRRP6seMZfpgv+vX4YDu5FSUQ8HGpACUQADLyHlZR0SXilouhcJwJuItm51cPJ5OF2NBOxmolRHN1HEGMT09vqeBqK9VCMScmluE4lkGJCSuAr2PYyEfVJwjnndeogemp8jKumf9DjFhFLehSwNeV6PGsL9tNs4fpgohZNc9sVIGVqL6GMi4TrNI1z6IKtqiuO7F9HZeOQyPu7vS87AaTmRJdrKYEeEd14ilMJiRBvdhuMBvyvF421E9Pug4cjWOz74GaS89SFXFX5+DRGHt56IJesi2qKd0v+nn0a0nuJWf5uI74M4hEyGrLlIaE8wftYhpeYYsnJcYfjK0Vq/S7T1g0Glq6cKWXf2IQWywrDt9LoMEHG4+z3+7USs4xSP14SUmFqie45j+npePmOMpDwmV++n/N1of7eY6J3dSHSgyDU+uzzOXqTAJQt6J7JaTSWsfU8RyUBJeRxHxLy2ErX2NiEFaC3hRm6Di+9A657chX2IVrPR4asYHaxOIeXICjpLiE4zA2gf93gdNnqMm1FiUI9x8KLXbKLxuMnv3Yjo2XGAl9yKFO1q42ulcPfO3QwePC9LilCylCWPxji0H9sMaxWi7floj2QaV7VEO7VkhSz2PXuRlyZZJuu8XtcYxvVoL95GWI1bkPv7eb/zOEE7KZwgWey+jGixk9h3KUY03zBOIcIDuhC/bEb1CRPt5iEedDdwCwysR4fIZF3u8vuf4sJeA+f5Z+h6z64hBfBcVw2RCXcfYq5FaNOeEff1zgMMnugzbkUMsQKVHWgl+nRm+rnkwpqs5xmPBMVOJKC6kBL5DT8zEU7/EDG8KUip3IaYWDNSMBsRoz2JTvUHiGSB4RqnEOJ0/n1/fx3hcpuKGNt0xCSf1XgfWQCVG5DCMAEqX0Ju7HbXCUyujTzEYAcQE3+ZOAGfAsoh6w4Yltym2UhR+aqf34gY6ULEhG8xjroIS81+JASuIk7QmSixpMeflcDUcQxathgObzxGVPv/EhJudxF9Pd8lXNr5XoscYAS8+gRSGiuRkrbEcG9GgiO5fMZgJDNYuJdSRAt1hGCs9Oel6DS/zrA+qDVuv9X4vxsp/m1+ps/4XIGE1zoGrckXzyYOC/sRLeQievkhYZmrRy7C1z2H+YRFdh4SKhmEotJYqnCDXmTJuR/RyB8BLdD5faK+4OddDHmi16UMqIWfPkIoHncSlrEuj/kDouTMq353suAtQFbXBwl3aTZRJ/MWyJmNFI+RRK20jf57LLIKXuP1ugYpeVsJxSspbTORsnPQsPcwGOOVcwtSrDhjbjd7PusM/4toXx9FCm2OcMLlRBZwcjtX+70pNKIZ7ZFaorzMu3BgL6K3AcNT4J83CIV3DrAI/vKg16HJz6TDUTowjDAcN3m8DIJGb/b8qhB/aPU4U/2+QqLbxAIi/nQjsox93bisRzzoG0RprEbPrxPx0Klor7xOlHzaBlxlZTW54hcRNQuTt6DDa/dp47YQ2A1vv4xo9CRRU7ObSPRaiCz2kxg8xDy7G8U5L0P7YJPnsIOw+nUDP9JU3+n1PD5lvI4n1vsA0crxNrT+DUS/7D7gMLz9MNpvNxMhQHehfbfcsIxC1snZhutKRG8PQMZnjPsxiFYLEa3cxYW9Tp3nn6HrPbuGYgDPdf3RMJWO+BhwBP7uh/ChNUiQLEQMowb4n4QiMR8JtzGI+XQigfElBtskpWK5lKBYnSeQFXA8UVqkEDGZhUSplCWI2STX71y/6xhi7rXIctNNFBAeg4TneGShSBaVdv+Pn7Ob7fQ1MGyG39WErC1/RJRneQAxquSyThaRFuNjBdFHuA0xwB7EzPbDyR2QdQ1SPB8jshuTi2YhsvxUEy3tmjynvf5uLBJ0Lxmny1Gg9dWIEXcRdbcWe74ptrGRaONVgiyNv4qUnxSH14tcOQeJ9npHkeXhKFICC5DgL0JKdavH34mY+GNEgPpLhAIIEiyfRetcQbQ+S8LyD5AysYbBsiKUIVdgDuHW7EUC+AlEV0XIOjCJ6A6wxWs10/PZaBwmF1WT1+cmwmI4GilityNhdrthT3Fb+R4rxWxNM/yT0D5oIwL+5xAxccVIqX8AeAgO/Rgm/Be0Nxo5OzFgAO2zrzPYCuuNKr2u8ArP7WqixFIforGNiAbzkYXmDURby7xeyVqUYvsykFJb45/k/i7yWlYhOslEdFCJaPIB4+v7aD9WeNw1RPzpIuDX82HxMcGX9nIeilN9Al2FhuUg2tMg+slFe3iS7/kOcBiO1MHYGw3PaETDXyRKw8wmklJq9cxgxmgxOtitRDR1PaLnxZ7vYWQVTOEfLYbhFXToK0C0drPhewIpL/89Hz7veSbX5RfPmNcYxKsmIxqqQPQ8jUiks3WQlR73VkRjz6m14rBH9DdzEb1dhuh1hOG6lXD7voT20CS0Z+civnEjopE1vm+EP+/2upYB62BgN2QkvlmHDpxbvDbJg3GcsH4v4Ox+yhkeq4tIailAfC/F8n0f0dwjiOYPocPCgPG20njp9zrcSNDo11HGeFqfJcBHL2AM4PbzHAM4aygG8L26hhTAc11/MExCYwbhvutBQmoZYcovQQrGMSScy5Dgb0Sbd5TvbyF6Yqag9BzEUKcQfV+TZe86xFjWMZj11/xdKLkSMfmrECNLcUSF8M5quHgJkUXahwTgOH82xnNpJaw6uYRArEAMrJ8INs9DTHAEYnYHELM9hU7c9UgQ5RKWyOSemkI0ZC9EAmkSEc9UR9TYmmH8vORx7kWCoAcpNNcjBngTkYVXSsTmzfB8DhP10hYR9buKkTL4KLJCpGSADKKY6yrE2Jd77HKknGcjRXgqEeM12vg5jhTHbqIG4GSkaJ3y+m33ei5Fwr8ZKSqHkeDZjdb+OiS48hBtXI8E81bDlG24v0sIjGmEK3CyYWsgsjqrjbdFfsdW4+dGQlncbHwm93G1578KWYSXeQ3ziVi7fN/fQHSteN34LEf0XuSfSsOQ4p2SolhFJOQUIsHYgdZ+PZE40U3EqnUgwTudKEVzFFlKOggL5rXGbR1ByzWE666eyArtRQpcsvJko7Veg+grn+h73Y2UgS+idS8jLL+bvIYjiE4z1URsWYXn0e2/6/2eNj+7jHAPfo4o6J6B6CCbcN3ehKx2WUTR4wKiA04xotFiomB3jfGwFCn3ZYg3HCbiR1s9l+Tqr0T7sdTw/ilSgAeMs350SHgK0VOKTaswnCnspQDt7XToyyCyvTs8nxQb+2WU/VqOeNZWw3EpUgKT5fIoEX+6DNFLHXForEIKXQtkrEKH4XZ/3oH2ayln86VNaO9NRrx7rPFWi5TsTJS5/mW0TzPRwf9lw9qADj4FyLpdQGSLb0W0lOIr96M9Mw/eXAIfvJGIQc4VrCdXQtbdfs8xgmff6Tks85x3XUAFsBpG5pynMXtg1OwhBfC9uoZcwOe6ZhOJDItRMHE/Ys7dRBHV7xCnyV59N7Ba1i5GoRinY4Ty+BaRtTkc3tiBlL5tSFiUe9wDyB2xlcFM1JKbDUsuEtogJp8DdMHF9xjGcqLtUj3R63YNkcXa5/d1E0rqMSJ7eYrn/jXEtOoQs16MTtFJMdlAJECgOdGIGFWb78syXJVIsF9OdBUZjhjZGqKkxXTjJMU7Ffv7d5E7vhwx2XLjNN/3d/jve5CFIbnbkpu7lEg2qPX9jxMlWdJ3HUi4fBcJtgOIFpLQyva7DxP9cjuQVTQpDDV+br6/H+/5dvj/jcgt1YtorQIJlSbD8ufAQ3DiYSJztJ/BnqWDtcGuQErbaKKDQqZh7TkDJ+sQzUxEyuRmlCFdh1yjCwzDJsPXiyw9xR4n07is9/umGRfJQlXk3+OQ8nXI802WyuRybUX0u5QoDD6C6DE8B9FLPqLrpZ5nme/r8Ri7kbLWhiwgFQwmGQxakzKI7hq3eexJ6Mo44/1lXstVxmGX124G2iujPf+kZC7Uu7f/GCn3I/xdheffRRR0no/obrJ/t/ldzxk3TUSB6e94bi8RcaWHPdYs47SGqJE4DSlFmUT3lp1EBv0+wiX/op49+X2vXy+ilQ2IPxz03/2G7SBSvhsIZfta3zOcCEfJ9Wd5xlVSOMcQnpEafz8F7f2khLUZt3sRbRT6vVchJb/NY84yTlMcXXLzDkc0WYmU4SYGeSF5xms5ZCSXfHIHd3id96D93414XyuR5Zzp932NCP1Z6nV0fDTdSEF8y+PVEkWdjxCdZ+rRNULw8Bzy/hwyjvvhg1N8/3HP6Vuae9aHCcW6C9HtQ0TB7gLCmjx0DV0/5/W+Cw3Av9urBjGq5MKa48+LkAVqMdq8lYgBJrftlZBxN2TkIya0GzHhDsQYFyE3zCrgT+GyFr9jK2KarURpkC1IqOQiIXobYijbiGzJNR5/GmKeMxGzGEBuhBRflZS70X6+Elnw8pAwqPI89/j/z/izzyJmM5tgqgeQYjuXiGmsRMy/hFCQPo8UqKQM/b7n+udEM/iUlDGLaHl2BRIKk5HS10EExz+G4uOu87hLjZ+j/gFl4i7z34uILMvHiID4Ls8rCaMDiJkvJFy7eI7HkWv2bqIg8BoiAWSbx6jwOiVcd8Pp+TDsF5BgLvPzi84YfyXRo/h2QijMAPp8W4rNa0WCssxw3+3vigz/caJx/GucbY2rMr5LjLfPIkHVh+ilkTjoHCesLF0eb63XKLlzk2VrFGFhS1atk55LUngvJTItF6O1fY5QFsaiuLEbkAB9gUiy2ErQdNo/fwsDj0HGHWjPbUYWpD7iKvfnUwnX7kHC2tPnNTxOxMPdRbSc2432VLPxtJcI1n9COC8FuaJbiXisrf7/kHHdhWhhwLCsRq7wjYQFMyV4LCNKO6WD3hzDfSmi+S6ifds0v2+Ox1pMlPBZDu/Mh4t3EMrxZZCVQiLGELGfKYyhAykpi4gs1nbjciERF9fn+dYjK9bdRKhKihU8MxThKo9TSHSj2em/k7KYeNUIohVdstKl8ImtiA8dR4pkG1FzNfGhUs2TRq9hvmFOh5ARaC+863VoQUruTiIzOJPodT2JUHoPQ88xyNlsnLQa9o1ESEQe0Sr0lOdygOgrXu11Gu7vd3r8GYZ1O1rvEYiPfJuw/OYiq/NUtMcSPWwGfokLd53P2L2hGMD39BqyAJ7rmo2Ydj9ifln+vBoJwt0oVqoIMeJyJOQmEA3dk4Usg2gZlE77eeiUtwEpFBUoHul5xAzzEOMch5hHOWE9nI1Wbh/wh0iwPkJYFguIFmopWWKJ4U+JGe2EUlGL3DrVRCLELP/9LNqUdR5vN2K+C5A7tRgx9iOIeT3k33v9O1nIbkWM8DrPYy/QBgM/YrDNHJM8t4TrTmQly0TMeS7w/l8SLlYZ7w8i90sXEatWgpj+OqKNVlLiDyDhchmcXEG4eF4jguiTxa6ZiE96ASlHLyKmn9w624yPy4lOGgUM1iAbNomwjEzz/FIM3n2IBu4DdkDzA8DvojW3i3X0lURtwn5CKdlvfOxBynoekfV7AOiB5uWG4xqidM4Ij/Gq1+8Rom3eJsFBMWcXRAbFHfUiJe0B4+Ux5Eo+hOiij+jy8QTRGuwgEuQgC/CzRPzhJKRcFvve6X5+kfE8xTjah/bTS/DOY5CRXOk5RBeRHv/cgPbVYs/jBT/fihSEb8G+57xmc4lCzMl9uQoJ8TsJ69gdvuc1ZBntgpIxxmcS4oneuvzcNxEtPoEylXej/V/g55Z6vkl5aSEsWF1o37X475eNv7uM4/RMJmEN/ZY/m6Z13Aait++h/fuCx3oe0dIWIuuygkjYajW+7vd9NYjONhGZmo8Th6e9vq8aKTJf8ljNxAH4s4QFa65xMA3xq2V+X7NxX4B4X6Fh+Yyf3UWU4fptIm50DVEHs4PgybkeI1mPP0+UXOkkeFW97ylCdLTOfKnXsCxBfGs+5NxLdPVo0GfMJg6qfcL9YG/pDYTHqN3z+A7igb/l/3OJMkt1RNH1Zw33IcOWPCHNXq8Bz6WGC3sNnOefoes9u4ZiAM91fWNYbFi7dhlHEOhmZE1Kpv+paFMmQZKLmMouxJQToy8mWpGV+/8KxFB3IEEDEfdUiJjSEx5jDIMV+AdWQsbNRLZvL+Fm2IyY0nYksA4iJrLY3y1B7qVvIKG4grBKpWDuR9zRYRQMHIeM2zyXr3t+rYiZlRjGpR67gmgMX4qsFz88Y74QrcNeRIz5W8ZncrN2IkX4OGLCTxony5BSsN9w9nv+24l6ZqPh7x6DD/0CUsgbPb82JKQmEhl6yR3eioTZSq/Fy0hxykEMFwazmWnzGiZrQYnxf8z43eP7jiIhc7v/349oIM3tUc93BqKfbONvBBIs/USLtjz/rCUyYacTPVqXeR4TgDVwohpG3k0oEo3Ipf0oUpDWep26EZ0na1yHcT4c0fU3keI1zrgqIOJiD3mMl/zsOKIn7Crkaj5u+MpQUsbVhn8Uoo1q4NEPwQ1/F27TBxB9diN3/nrDcxAJ1r1Igf2fwEcMw1jP41b/v9vr8wo6zEwxXK1EMewrkUJ3Kdo745BSe5Nx3eX1GI4U819HdFSCYrc+TbjGX0H024qUhZ3+O7kSc43bWUgpTfsxxYv1GEeriXqHS5AS+nnD1QPMh3fugYvvJRSZ5D6di5S0ZB1sNi6eJvr9FqB9elRw/M0z8ItTkGK5iVCmf9vr1U1ULUhWwWL/bCfKr3QhZWeS4UoHvwHCi5EsyW1EhvFyosh2GeIHeL7z/I4UO7kC0WAKl7nLf1dA+3eh8MNepx8QSU6Z6JD8kOHZ6ffPR3TZjQ4bKUQkR8/23A05dxOltED7bafn+xyiw+953LRn+hB9jIGTz0DWUqTcFnnsFO7xuNcsz2u11utTSdSu3ItocyuhzL6C4iN/YPwsQDSz9gLGAG49zzGAc4diAN+ra0gBPNf1vWFiJknI9iKGXkFY2fYhwZPpe5YiJrcHMZTFyEKVXBwggdKGhGil/y8lBPt0omvBDKIC/nwiMH6Ov29ETHMFEn55iMm8iJS/4UTbozYktGYhQdTucY8jwVHGYDbn6RUw7BYkWDKJsgrpdN2ClIn9SMGt8n33IgY3yeMOEPE2GwkFcyZilDVE8ssAOhW/AT1/Dzlj/N0ixOS7CVfdRP9fjQR+E1E76zrj7TkiFjK5rZIV7inETHu9hj82/vuJYrnZRP26fH82Cykp+z1uK6KDsUjYzkVKfLJc9BHV/VsJl/c846WCs92WcwzbQuTiaUC01mqctxJxfdcZ968RcWs3Ea7Z15EyCNHea4xxtBe57ZKL9hiRcZ3mXmlYKhHtHkBCORPVPzzq97x4BmyZntMcv6fU63A9UqgPET1tX0C0fgTRbD3hok70ktxsN3teO/zOyYjGdhrmScLL6Wfc9/ppv3Oi5zqb6Lbzit+b4tE+RbRFrPDnWz3fFLOXFPinkZLUg+LhjiCFdhTR0aLfMHahtT+MrMnPoX3SRyhuR43/K4nM9W7ENxJtjULKYhWwHd7+PlzyUcN6GNFcB9FGsgPRYrL4TibiXVPCR5H/bvPPw4QylGhgvuF73evZ4GeyjJd2w5jGy/fnfYSX4jbj86jXdjSyOvZ6jpd6DZ5CeyR5LvYRyUWFhPtzEmENv9ZjTUA8oprw2ryEaL3WuJ5MVAco9Zx+F/GVFPtXRcS3HvT7Oon4z15UlP0w0VllHVGkPR3oQLyviMg4X0rU0IToDrIS8ZIM37PPY+/2O3uM73HEweMQosMxRLJeLbDpAiqAm8+zAjh/SAF8r64hF/C5rs2oBEwxEoI9+rvTrsvBzNZkJUwuiGTCz0FMYzliLhuJYs7FiDFs8vONfn4mESMzhYgrnIOYaCPRbmoqoSwO+H2jiQzbFOtSjJhFO9ENYIRhrfSzyQp2CvguDLuSwfiW0zuI0jLlfk8mEr5veIythDU0hyjP0WwYdhuOiSi+b6lxWIJO+G1ES65vQM4MIuD/caKLQkpUyCOUwc1w5EFCKW/2ODOJTOwBolj0MdRTdj4S/jcjt0oBUSon37hfQiRxZBmHp4gs0qv8eZfx0+b3XYmEfnJb7zR8h4mkilrjNc8wtyKB02H8NvizZw3LRUixnU8UjJ5KKADJWtJ3BvyZKM7pKBIuq4g2ZBDJG4v9vm7CelTt9VluOLYSiQxf8T21DBZLJhcpyP1ExuRG47PJ8F6F6KMT0ct8/1T62Qn+bjXRl7jI4+QQ/YT7kPBe4fvRGMOuQftylT8fS+ynDiIhousMOHM9bkreeMTznWC4a+GnS5Dym6xno/2e0cgSU030Tk5xZr1+ZxcS8mkPJZznov1UgOiigejp3IH2bB9RUNqu9EsuRTUPcwz/zjPmkyzZHyMyxtf4uzxC2cwlrhQGstuffx2t8W7CErnXcDchBWQfUZPvVY+T62cKPN/HkGKWrH9JmV1MJIicQlbDRJP7kNJ3zM+mOOdJiE76PM5d/n6Fn+nxXBs8z1JEU1WGO8UQFnlt5hGVAsYRyXIjiOz+DhRLeAjR5jR/NxGFgqzy/V8l2ndu9bok78B0dJibjA5ckxHvSPwuKconCQ9Gq8ftQjQ4nwjR2EDwkGSRTa7ooWvo+jdcQwrgua65RBmHPMQ0imH0CAZ7YJ5+GjHQAiITdpv/n04E2Od4vDrEQCYRxVaXEQH3dUSP2QxCkUyCKhcxhVPI8tKIGMCPkXKSTr0HCXdicnd1I2bYh1yAt/r/lE02SWP8dR3hpl5si8o+InFhIO4d7Hc5iiiAehEhVC5DFDbf72hAjPkJ4zgTuAne3ooYbQrWL0WWlULCfVfi5+uRMNwjWP7uJRj7YSQo7kHJGseIcgvJFf95r+MLSGDsQVapd33/BCQgpngdxiCG3k1YCmuJ7OrRfmczskRkIOY8xb87iO4geV7T+X7mmOefhH0OUkQ3Gu5tZ8CcgdxXrzFIC3/1kmHag2htChLslWi9u4zbbSikoBQJjNGa59tJoRplHI8nsmc7DccugTgw2+8Zg4RUNhKG6/yOw57Lcv9fhRS4Yo/V6PGSwLqUULqajZ8xfseNKFYs0d9OZPXrItzVI4heu08RrtJJhqMcWdtWInpM1qMGj1lCJC9sNA42+dl0cFnu900FVsAHlhs3GxC9J7ftOGTB6kV0lOJrdyNLYzWRUJJc43uQAjPW75po3N9tXCVLbQrwbyJ6yI4QPFzqeyeiNbUld+AlovNOiZ9fjg5OVZxdp3GW53gr0ce5Fh046om2kFnG2xtERYEWIm7uWsOfQhcKieShOv9MQuu4w/A8jw6CmxBfmYv2V43nexvaM1mEpbweKVNlnsNTXp8coo7jcCIxpNp/V2j8wa5FbUSrxWRVW0KU0sk3LvpR/defEGVgaoj6qQWG8SCipV7PswnxxmbD8Q20Dzcinr3e79rj9/UhemghstlvMnyL0UF2id/zFFFuJ5tQFFdwYa9T5/ln6HrPriEF8FzXAbQZVxDlIgqQknEtMB+GjUOCZpLvS7Ewj6JTXRcS+M8iQXmKiOf4LmIUTUhAbEHMqIIQru3+O8PflxNWg9nw5o8RQ+xETHUDUT2+BgmfZClrhbef9pibYWARkelWhBhsBXxktcdP7qH5SCj1IiVlpf/OMbx9iHklxedypCiM8/i/QjCyO5BinM9gZ4oTt8Aly1E81RHCPV6DBFeecbQdBp5AQm+e7/k0fGg5nPxb5Go/TBTa3k+0n+onmtNfZRxOIKwkjSjGcJ3HqSIY7HAkpFYi5v+A8TkKlW0pQZ1IpvsdyeU5gVB+klKZb/zMJNxZI4yLjV6bq4zfTf5ZigTAUsP6Lvz/Mo3zq5AwWWvYR/knDwmQryPFYhxRMDwbLplBuILXGrZ+IgZqkudVCxnJ5d9t/N1q/NX6uXIkmLOJGM+VHu86pITN8bsGDEcS5hVIETlGFNKtRjXwriK6RiSlf7nxVEooFfPRQeNRwzwZ7b0WIsFqtOHO8zw+7XFXEF191qHD2FLiMDCeKCmzASleY9B++yY6yLQbv/ONu3SYutbzXoPooggpNpnIqn2SKBFST5TrGUP0ji6Sy5c1SOm2Qsop4/jThq1K78roQHtlHbL8VhPVAfYxWNOxcz3wPzy3nYi3pH2SrJwPeh7bCBptQfUPJ6I9Nso4Wgd0wd88TLSbnEAcnqrhQDr8zvRadKM9fJ+fbyIKTD+N9swBZO1MYTI7vAYtxsF2z3OqYW8jCrZ3eT2Ga+yMT8Khn3icPYjX7GawLNORvehgfKYyCXxolf+u8jseQ0puDuKtLxJx3xVEhvaA8T4HKX2ZnvcYj/X3xnkWUat1r9doGqo1uM64WI0OZH1o3/zO5IgpXEm0R7xQ18B5/hm63rNrSAE819WLmNSzSEjORJu43N+3oE3/CmIYLyFGCZABf3M/UTLjOiQwLyJKINxBWEUWI4HX7e8S8zxKWEs+j6wGkxhsffXBR5BAmIAy9vYi4ZSHGMoIwvU7Hy6ZiIT2U24rtAd++mPD0e95Nup7Ggkrw2QkyB9BgraUKCw9yu+uRgx5LWJqFX7mO0igbzZ8rxN9Ryth5IPG6a8jAf4qEjp3E0H1dvtmrEKMb65h+J7wlfU8IViaiLIpqxADbvN6/o7xsRt4En66DlnXFhv+TyHlrRwpeg1ENvOXvc7Jstri+aUA8pNIeF4JJ0uJmm3zDEst4XasQsrMbMPS47le5+euRMrIHMMNYSEZ4e9Pem16fV8loVDs0Xqzi+gnupZI1MgV3igyftf5HbOIfsm5aN1XEwktbf79tD9Lbt8DiI6PEiEHfUQv5eQKbYcDT3uNvud1biNah+33+7ciBeANtPbz9I7TDyNauxSt/wykyFyFFK4kgDGOpiAOl9x1aT81ErUbJxrX9wCZ0PO84RpAe/pqj/8H/l1HhGFUEC3AkuI30uNuRwJ6hWF7DClVrV6jCeiAM5oowJxi8FYzyAcuuQfR2liv61zC3VtEdL/JRXtmqvHyKcO3iig1U6yf0Z9D9FGBLFUt/n8eUXtws98xkVj7+1HSTbI6HkRK2WzgV+AXxxEdRpLHoUP4rLzG78lFLtQ+4/J+43Ky8XAMHY63on0yE7lZX0JK236/L1mtMzk7Vvde4zfFbdbGOk14BNEjiEcNR/soB8YmOIqQcl/ltX3Ec93ssVacgd9mZK1bTHQFqfH9l3mMzxsfdf48Cyn0HyLaxO2Ed55HNNqIeM0TaK2T9fHzfvcC4LO1UdrqyBnzH7qGrp/zGkoCOddVNSzcsCn2axxiJjX+nU9YM14ggnLLETPuJmI4DiEm/BAS1nehzX8HUgzLiELT1b63GjG43/dzs5EgWIWUnGoirqSBKIuxhQhEXkS4EecCjfD2j+CS/4KsUfPRibMICc1RRIzcAUKprUFMrIJIcij2Z59CSkES/mWI0a8hhFOKDSzz75uR8lOk95+YCyMfQYJvp7OOP8dgTOXpHTBsucefYLxXG891xn+V8bPTc9rgNaowHIc9v+lETNOA792GhENa6x7DXOG5bjMeb0XCvYoQCG0e6yBSWq4lsjobvFabvL5dRNeBViR0m4mEmFyixhpI6Lf6vauQABkF7/wYLn7E+OsgDiClRMxfLsqU3UkkCIwgSkcMGJcnPIc9hJUpkygcPg94yh0VHvM8x2ld2EQUyT7md7yBrI8DRPJPt3D4Zi98cBVyafchmnnF6zYKKRcvIFrfQViLdsKRHTB2ptd0IVG/sNtzbSFKqFQR7RCfJmI7jxKFrcsIN/L8M9Z9EhG0vxp4EAaW+gBSaLj2EfGboxENzURZu5cTSVwdRCedJPAr0P486c8aEM+Y7ntPEq6wFV6XFFucDpWlXtdOz+2Y4Z3ttdiPDgCLvYYdRKvCKiK+sBzFdN7leb2K9v94oltLv+ebXMDbiCLIdyCLZiVSlh7z2pYbpmxEDzVImYZoNzefoHOI7iu5RLLPPs9nOqGETfT81wyHeae0P8ah/X2j577HY15nWB4xbl4wXBWoXM0uolB/mcefZfw0EhUdcuDkRsj6DKG01yBa6fJPG+IflxMHgmNoHzQg+k6HqFHowJuU1DbiILHc869Fh7MSot0gRAmqxzzvQuDJ916MDyaBbDjPSSCLh5JA3qtrSAE81/X1YRIeNURx4250atxJFNk9ipjlZKIq/uVEuY8OJLSXIstdAbISDBDxJtuQFecUUrpu9zsaEXObRgQqX+6xdiIhNsNj7ERM8jeJFlttyJL1Mz97BeEqSW7tEsRolnq8vcCHGazTx22+r8nzn4ssGbcQtb+SGzq5sqYZplNEAHa+33MTYuTdhjNlSPYSbbK2Gy67wZhF2Kr7ifimxKDnI0Xjer+/BQmkUX4m1+PuRuu0EwnkGuRqWYuUvTEoLnAJEkRlRKeUd5GiNAdZJyYigVNhONvhzbfgg8v9zBi/9zXP8xrjNJ3yW4jCs8VEIkmKwexDwmMSEaieFKlc43268VLke5NlKFnneolyHZmee5/nmNwtNwl29vt3FrIyJKtDshzuPmPMuYYn388ld/mtSChBdKpIz51Ch6CpRLxhF1Ii8PueQa74erTmKWliwO/4FqL/ciSwryJalO3zd82Ivor8XTZa72vRXnnA9/6ucfec1yqP6CSSS5RM6UM0luf5tvmzHM7O7q1ASuknvGZJgF9OZEinsIqVRJZvreefR7g3u4ynG1ENvLEe/3Wi12+Kv/smoqEGr0tyJRYQPYxHEokiS5EVf7nfmeIWjxuvL5yBh6QYXe7fLUTcWg3aA1VEK8hkcU9xxevRmvcTfa0TXV+K1vhzSIk8iBTDxUjpmUNYOduMjx+gPZfikacg+kyegg1oTySFeIvfORPR4luEJXi71ynPczpouEcYF93oYL4b7ZFn/Z50SBpAe+GIP090UuG55xEZ7PcSbUJ7PUYV2me7vTbriANgkeG5CNFvIeJxNUTB7kK/aw2ik/8xpAD+e7zeffddampqaGlpobe3l4KCAq644gp+4Rd+4UKDBgy5gM99tSNhfJyomVaOmMYNaDOfIoRMi+/PQYxkLWJClWiDrjvjmSNEjGEKoj9OFI3uQEwijV+IGMxkxMyOIMHRhywOBz3OGsRgPosY3q1IgWsnys70Ed1BEiMv9ndthvs1xLjnI3dMByH8dxM9VqsN7yuC9e9+jJjjfiR0KhBzHUcospzxeTsSXI2EkN/n3xOJrE8MSx4MPI0Usbln3HemoJ6MBEQtUhCSqykFo28hyivMMgx9RAmKk0R9tmSlK/SankLKXh+RZXjA8FfCB+cSCvlBv2Om37HcOJ1HxDUmRW4zkalaiZRZiD6qyaJagYTeUr+n08+B6GUBUWZkBxGM3+1nRxP9Zru8VltR/OhcJDyvQetXaNgOepx8omPFRt+fa3gmGfd3Em3kFiP6L/N8R6GDTSMSsgcQzf65/7+DaH+W5TmVE9aVGhTztoSwZu4ihPhR/1+IrD4LPZ9JSIHp9n0L/N0rxuFk4zMHHXz64fQtSGlMAf9HET2VEGWZ8gkr1SK0xhchGp9OZKLWE8koc4yvZB1/HSmfc4g43J2Gpc/rcDmyAm4nXKStyKp2HaLJdDjMR2vaaThXIXqbaBjeQApyUtDSnoGIWR6L6KfN782A018hSqQkC2kBEdPcQ7RcTCEtdxLxcBWEkr4B8a8GxGtWEJbCDONlKVGi6D60Pw4Y192IFl5EVj2IRIhpRCmtr6D92USEbbyIaLOWCLkYZ9zMJmoVzif6s6eDSgmi34VIahajA8C1Z8y7DtFgo9exx2uYpGy11yYb8YKXkNxYSBT3Tp6g2X42WUM3e/wM4sD9CnHovZDXac5fAsh/EnPUX/3VX7FkyRLy8vKYNWsWt912G/fffz/Lli1j/PjxlJWV8fDDD9Pd3X1B4RxSAM915RLxWn+AYtkGiP6pixBDfBIJyW8hRmYLwUAvUQy5DW3iQiQYqvzZl9Em7kBMowAxjXFIOUmnxmqiUHCKrUlWhSzEVFNs218QZWWagL/2uHvQ6b4DMZc+otPIi0gY7SSKmRYiZnw9YRX5OFEouM+wXYrKTnTDh25FisEdhNUyKVDZyBX3qOFd63k9QpSAGU60MmtCJ+8Kv/9pYD9kXIOE1AOI6WYhgZBcjY8iC8gM5Mp6kIgbrEDCtgIJ612e412IIUPE1uQRdRazPZ9kpa1Elp99SPBU+Z4KxKivR1aeZPXoQoeA3YYvKUZZRD22w57Xk6WyGlQQAreQcCVV+90QGamnkKLwGKKDtWj9UxxnJhJ8TxKHmd8jYoi6EF0mt26+x00KcdYZeLjb9+03zMn9NhlZ1f7Y9zxEZLcuJvofT0Fu598mSnpguLfrp3Ojx80w7C1IqG/w52VIGE9A1pVfR/RSg+g8339nEPXbCnzPc2gPVSLF91Lk5u1DtJIJw25FilOCocdzqSFqsmUiS2qZPz+IFK09SPE66jVb4LnPRft5mvH9DJz8EVFuJg8puNM8diNSYq5B+7bNMHV7DimudR9SIu9F1rR9RMvB6UTyxyYis/73CZdyv9d3L9rXKXayEO2JTBi21GMe8zpnor13q/8/hfZuqfG8GrlXr/F4yeV8DN45iuik1O/ehg7H7Wi/9yI6aSW6H7US/X2n+P3z4MWtRJeiZEHbhPbJDxGfuxm4ryJ6p3fDn29ESuwhtCe/i2gvgzjkpVjoQv9dhPZOIdobeZ77AY/bASf7kUKYYmkPI7kwHymQXyXcvocRLQ0nPBP3o/2WvCCFRGepVcZtspI2EVcvF/YaOM8//8GvBQsWcN1111FSUsLLL79Md3c3nZ2dvPXWW/T29tLY2Mjv/u7v8hd/8Rd86EMf4pVXXrlgsA65gM91fXGYNvEExCjmICVsFRKch4i4m+TSqSEsgHbr/fQe+MBc5DJ9DTHr/UjhKiFq5UEUBW0nevWm2Kg+dGquRgHrq5GS04SEQTESNr2+b49hSzF7zyJFNsWMbSCq9g/4nu8wmFFII/AcnNgLI68kyi/MRAx3HBIY2xHDewUxsULfdw/wax5vin+v93wriTIp2YhRtxLFrU/BkQYYe4/nPhYx8BmIoV5FFBrOJDJ5+71Gdt/0PAw570euzC5/t0l/n34Zhq1BStNPkMVsl+dQhgRKM1Fjb4vXuxxZ0TYgQfElwzIXKSq1hJUCjzWRsPzmep2nIMG7xfgoQoLiAXS4SMp9D1IuJhExmO1EEkIF0WLsYSTIXzS+f20k/PCELA21fvcyRMNlSPh0I2VlvtewAtFEKaK1ciTUkrKYBFcuYRnu99ocNI62+/8iFH5wCMiBE6th5BVEBmM2kbSzBdFuHdHhAeLg1IMEX50/7/G85yOFqNTw3YQsKr/iZ3d6jcqQ8l2O9kqu8bvP8x+BhPoM4gBVR9DqAKKdRHd5XqeDiI5nEqVmsv1di9fzMVSq6XGvYyURHlJMKFedRGxpB6KZ9D4Iz8K1aK8fIxJnZp+xHi/D6Y0u5j6O6C27Fu2lKwlX61YUBnGAKCi9BK19n+9Lh75vICVmtOdyAIVGVOudg/10+z2HPCKm9mbfvxPR0ETjYZ+/70b7734iPGI1USLrIcQvxxPt7qbB6ZXqVEQRSiwaTpSTaUd0Np9oc3fEML/h+SfeW4T4841n4DzF5S1GvPEgoo/l6JC7EHg/opPEGzL8fLLE93iNF3kOR43fDMSTE385ZjhPGvapiA4PECERbYhvPITofA/aGzOAL19AF/CfwMgR//v7/1Vj9sKoX/uP7QJ+8skn+Y3f+A0yMzP/t/fW19fzD//wD/zyL//yewDZP72GLIDnupIlaAsSqnVIgPQgpSQHbcZj6MTagRjfIaRs1AO74QNLkYDrgJNfIZrbX6fvmYoY4mHEFEYg4QNiFm8QMSHTkbKZTRQjLkPMBcSkxyDGMNHjbSYKUG8mgv0zEJPeZrgbkBWpGikM3UCvEzN6/dxFSFFKymgFUvTuRgzqq4anHWVnHkPM77BxlOs57SEK4Y4zzjYQje7vhbF3IqY9GjHFcsLl1mNYMpFQvAkJ86R87RO+cmYgpn8MKT5rkICZB8M+SQSiP4GEz34ioWMbYq5JmWtFjLuRYLx9/nsEEmyPE71GU6zkTKJ8T3JdziDq3+F1zEWMvd64ajBsLSh2swdZnpoJxQevaZnHPojKcJQimvn2CWUjdiPFIwdZNRYhgXzYz34OCZrf8/8ZRHeEzxJxgGMRLdd4voXIQtNtmPuIhJIiw3nIc3rFnqrpxtFNRMZosi4md1yR5zmZ6Fs8GVnMM4nDQxlSzK4n3LmPoaSktHZf9e9vEe3Jeom41nTQOkYUXd5/xvhdRLjB7cbhVCLBZ7NhSgfAVuh50J/vMXynjId7iM4vRwnFocHvmefxE83mGv4K46UKJdf0IVrehJTtBs/7FINxbsPuRLSXSxTyfgbtoTKk7JxCykyz13O7YXsMKUcjkYX+Kyj+DnSIS/ykjOhIMR7xxacQrbYTB8J6xFda0WEsxVa3ETUq5yLL4NMe/2nX7sv0/G7w+5ehfXCdxh52EPHEAqQAHiVCJvIQT3jB83gd8a8NRJ/fFuP3IKLtFJqR4rxXEslS4w3Da2gPjUe0kuN7Ev8pQDQ+nyiV1IZoeYHflWf8/wpRFmoX4mPHCR4z33OvNE47oOddzyG5nQu4sNf5cv+mn//g12c/+9l/lfIHUFFRccGUP4D3XbA3/3u/itDm6kIMKwMx8Y8hpWwMskTlIWZyHG3Ym4k6bh1IOagEdkHWPYhRbECM4kr/vxwx3EYkBLsJK8VYZD2YYrgmIiaR3KogJtqBGE0dYhRzDdtExHwWoVN6NWKIGYiBVfjdUxAjGuexmojMtnLPoREJ2i947HKkiFWj0/BOohtCCVEXsdBz/bHHmGUcVKENnxJYOpHCd8rPZhOxXsVEp4FDRJD7WKSkFyMBOgDv7IWLV/td5eiYU+exVhnGfiQUZxiWKb53IxJg5Ya5Da31aCQ8xhLlVhYioTaLsM41ENaNFNOVrBg5xi/IilOPFKx6z2WRf5LC8ChhBd5MuH0qPO5UpCh0EX2n+/1ZDZHBXuzxuw1bHlJWpxiPWxG9byNae3V53huIckK9RG/UDL8/FwneZcShJLkFe72mvcDH4LI8w/IVv2sy4Wq9HCl684kkgHbPd7rxOtVj5fu7TqL47zEiGSUXKUxPG+fv+r0lxst8RPsHkdCejKyIybI5g1DMJiFLdiZhHc8kOk6MJiz0e7U2ORmIRqqMy19DSksu2gsp7KLfP8mCV4oOL+P8U4dou59IXhjhd01Hysd8w/AUYY09hZS3LLQ3i/z5A4hm+9BeOobc4xnGaeMZuE1W+QPIIpsUxhSbO94wJrd8UmiziRaA30I0MYaoYzfVP9OIKgudSDnLIDp6PAEZP0Ru5FakiHV7PnVonybXcqbfeZAoyVRoeCcT3W6OE+WbxvmZBmATvP33cMkjHv8wUTS7A/H0PcZ5D5Eg0ubxOwhL4Vj/P8m/J3u8cr97EtojiQ7b0UFxKaLPdqSQF6OD11z/3eZ57oCcz3ltRxDJKEPXv8urtbWVYcOGcemllwLwk5/8hB/84AdUVFRw8803X2DohiyA574Sc8pDjOUHaNNdioTVaCJT8AXkPisilLsZhIB9ljDpryKKFc8nFIUpaKMnpbKAcPVdSliYDiPh2UMoZVsRs/kyYqafJeL4liEFcydSLr6CLBmXEp08ZiBLQw0S/KMRs0pMtwoxywHE2F8x7N1EW6pmoitFikEaT7ge6xADm234O4gCvUXIpVKCBMGXPe9qOHS3Y6VqjbOZRHxTisXqJ4K198DFEw1/UhjnEXXNCgx3sk5mERnRjUTdxWyiq0YV0Y6qxz+t8HdPe5ynkNBJrvFtRCzSMiR48tBuyyEyFHNQ4sFEJDjeRfFISVnd4PW+D1nWTnnNTiCBu+0MOMs9RplxtRXR5TokrCo8l2PGy8eIOmpdxkuucbSOSB5JNI7vfxyFEtxKxBf2e/yRyOKYcNBnvCaLXlL2J0Ln3xPlZGqR8pcEWbM/O0XEd7Z7nq8TxdQ7iJIkc5H77vOILtYhAbnZ61DlufT4HW8ZV2WIDhcgpWQ4g72FeYJIcuk1LDcTQfcHzphXtu/b4M9eR9bYPGRZ7SeU+P2Ifm9G9FyC4hFbkTKwH1mctiPrTzc6vL1A1FV8yjg6arhLkaI1kUiwehHtgzyijWC+n5mELKd7kdI52jAmt3Suv+9H9DLCzxz1eD8kSrYcRArKdOLgt5DoZZ5BKGRf8Jq1GMZ0aMxGNNTjZxqQRfwKxEd+oPFOP+177/B7NnidRhLKWCGi2SKi9dop4zOfKG2VDkefh0tmozCdU35fCiEY53XLJ3qmbycy9ZsIC/aNSOndjvjso8D3Catynp9Jh4zXiV7hA0QHqMSDuomSL+OIEkZlSCHNRkpnMxf2+tfG9v1rf/4TXZ/+9KfZsWMHAO3t7Vx99dX85Cc/4Z577uG+++67wNANKYDnvnYSsX8nkGKWjxg0iKlsQApdIdqI+5AQ/jJiThlECYkutImnIkb3u4hZ7fY7chFTuRkx81L/PRtZmEDKxBVo1Y4hBjYdGAMnXkZKXBORDZmNXB8/RkrIasRUq/2u+4lT+ROEsN3lZ3uQItOKTqP1hAXiMGLiIEFYhpjyamheD3/3iJ97mCikvAoJoaeJWnVrkGJ6gOjzu8R4XAgTPgpZN/p+EIOdZXw+R8TpJfdwA2L6ydXXjYRHh/FcQtSIm4iyUMsJi0gXEdy9ERiAI4s8TiYStvu1Fh96xGuUabx/Fwnl0US9sRGEkE+xQD1IgVphvG9A8VUDHq8LKYN5SOgWESUsRgiW01v9XRL6m5FiPhzFvM3x+B1EX1orij0bkWUlCd6VSPA95PFv8zq1EZniKUwgxXt2ISGWRwSlN/o9i/z+AUSHcxGdTvFzvTB6ucd+CO2TG4jwhmzjeBqRPd5MdBWpIBJd8oj40oeM2y8YvzcRytwUtKfbgK/B2w94rTYjGjiM9lwF2isg1/hLRILBdERzKVQgren3hKMjz6HDxmLPqcnr20hk2icL6jSkCG31WlyB9sditK93oa4bHyvV3jriOYwyfgoQvU0k2i3ej2hpE5x8gqD1fnSoaEXKWjNhTT3A4CHoxGGkEB4zLC+hPd5CKPr1xsEWzZl64/gEWvu5/j2FUNTz0LpPRJYu0zD1hq/VY29Eitc1xCG3AvHehYJr2FakrL9MHEyWEa76CuP9cSJ7PJ+I2zzosZs8r3KkTF/hd/T4uzHooFxJHGCv9/cziBZ0e7w2pX7/AeJw345osRDJklzjvAfJgne9ltcYD3Vem27Pucp4/zUizruPKKxefcZ6XchrSAE853Xw4EE++tGPArB+/XomTpzInj17eP7553nmmWcuLHAMKYDnvg6gDdiI4k3y0WY8gqw9VyKB9H3EtFuREHkSbfBHiWzPTKTolBL10Z5ESs52xFBTgsRvIgYzzc/sh865HmsWUuay/Pc8xDSy3FGjH7lrRjsOqREJjNeRMnAXUfV/AlLKWoj+uJlICBd6bkfhZHJ3jkBM5yBS3Kb4J52ia5GyOQ5KnocPbUUCejcSqL+BFJl1iNG/6zlUISF8m+HIJgrjdiBrJih+aTcRe7Wewcy+ge96fTYQ9dbe8LsqjKevIiF1H9HeKilAv+15JwFdhoTBHv099kqvx7eEW6YQbp91DPbY5XnCzXu1x/+x8bDWeF8LTIWfrkDK9e8ZL9cS8YZJiOYjBTrFPa5lsMf0sE8ajw8Kd28+j4RBCVKEahBdDRDda6YCT0HOvUiJqfDna5AAnIVovQTRW3J7dxCN61d57cqQArLWY3QYF51EH9lTxsVoJKST0M9F+2UKEsqvEkWnS1FSwleN/xmE+3UA0XuNcXIQ+LbwMVgE/S2kZGxGSnU1UkTWeIw/A34Kl9zhsa9AiiK+90GihmAlopnpsGUd2tN9nmOp55uNsmqXwdi7/d4ar9Pthms54hfLvQZLCQVxosc4bBwNeL470X5e1qT9Vuj7qwn6u89z+03gCx+Q4n0MeBqyNhBZy2lNLkdKU79x2+z/T8LJe2BkJqKV7USv4PFenz4/dxLxlMn+fjLaF/sJ9/ZmpOhM9prXEiVrOgxHkT8vANrg9Hp4uw7x1zp0IBiNFL0twtfffcVjP4QU5FK0tyEqJBxCCuIKz2O0P7/PcxhA1u9ksW0wPN/1eNOIklvbiSzf7yL8bvY8fsv3LPdc8zxuir+tJpKaDqD4zx7/vxgdaDN9XwERgznBcG33e3egfbcGBnYjGllGhMwsQgfM/0eve++9l2HDhp31M2HChH/xmT/90z9lwoQJZGdnU1lZyZYtW/6vwdff389FF10EQHV1NQsWLABgwoQJ/MM//MP/tff+a6+hLOBzXX82TEytAwmJY2jjN6IN3o2Y+Bi0eRcSjLqcKAEz4DFK0IbNQUy0jCii3EzEQSUm3EpYb3oIF0k2YrK7CYF6O7LutRJdJ0YjxrAeuSYyEPPJRUx9I9G/dCd0Pgejx/mZyUiZOOFxxnnOyeowHilZlYRrMZ/oL5uSFUqJWoHJ7XIYMbgDiGkWo2PIZo9VghTBhcbNTiT4qohi091+dzNSapMgSfMrQJaiauNiOlG6ZCYR57bTMM1GgiDD65WUnQHCXd1HtHVr9vvHex16kZt1nmF8kCiRk2X4FiHlqRcJgFKiJEuf8XEdEm5fRYp1hu+ZRGSD7/B765HS2uK5PY/oahkS7huMw5tQmZQbgCPw9jNwyZ2Ilkf49ygisWK/12+81qT9h1B4v+HpMUx7PNd2IoB+NtEV4yRBX7uRopWLhFuPcbqBaEE4HVkVP4uU383IcpbCFCYZripEd+O1ticOw8gFfuZnXq+0PrORAtRiXC1DikSX16AUKTm5xn0u0Ah//SP4yIOE9bOLKOib3I+5SHF7ArkNS5DA7vOYDV7z4UTva4iaexAF2MvRPtiM9v5yr2WV373R+OtCNLedKIJcj+grWafbEV1PMCx3oYNVtuc4CfGnzjPuSfhINDDWeH8LWTav9f3zUW2/q7yGx9Fe6jbMKf6u0/O7mrD493ueGwwDwFp4/Wdw+XoUIlNG1N07gNbvXcRfu4l+1R2eYz46wFYRh4tetKeXor1/EtHYGiJust/jZ/r/pDimddrmeZQSpai+gazqLYT7v4DoSLLD4z5pvF5qPBcRCmO58bYR+F/Iulzg8VKyTgpbWeDv8oQn5hCWy0Voz30B0VolOuBveO/F+GAW8B+d5yzg3/jXZwHfe++9bNiwgerq6sHP3ve+9/H+97//n71/z549fPzjH+db3/oW8+fP5wc/+AHf/va3qa2tZeLEiednEmdc06ZNY+bMmXzyk5/kmmuu4dVXX2XSpEm8+uqrLF68mLfeeuu8v/PnuYYsgOe67iOKliZ3AUi4HCMsGSmQeB/R03MC2qwp5qsduSHLEFN4l8jgTe6gq5FyuRkJit1EPEghYoAliGHvQIwuufaeQifidHovIhIY3iBKy5Qb3v3+bBqD5ShGfxe5zcoQc+8hTsoH/dlRxJifI4RRcgU9TxSu3oyY6miiIn6Lx6giivImhp6C9CcTWZIg5p/tMduJpINcIvh7s599nrCSpDVpQAKsgWhb1e9xtyGFq8j4GEACo97vOkBkgk9FynmX8Zrjz9d7LuOIuoGnDPurguWdBwhlP7nIkqvjXqJO5EKvyRSiNEYmUeT4iH9PQQH5lf7d7/eVE5aFo/47xQpNRUKpEi5Z5Pd0eYwRxs3Lvu8lIvGkFQo/h1zyhYj++ojMzhnI6puP6LMI0UOB12A1EdTfT1jkjhNxohXGXwmixbuNk4sQzaTnb0ECF0QTC2HkxxE9voySGfqI/ZoOb9mEFTyXKNUyzr+LiHp48+Ajd/r+P0f7N+GriSji3GkcPG9c7/Q8Sr12KSavEh1mdqK9UA5Mhp7vIj5S7XfXIyGfY/iGG4970J5cQsQZpti2t3zvCHRAXYwU9zJkBStGPKyTsABCFDEe5fHziCLnSRm6DO3/G4gs7ncNXzdSsI6gbOFeortKUgxHGt9NKA5xnNbiyA6k9DYBWXD5OKQQpTi6AeIwuBftp2SBToew8YZ3K1LK3kUHrt1I4Uw8bTvRmrCK6AY0nEhSK/A7e4iD5kGkUKYQnjp45++JuqvjCDdlNVKKbUUdLOCc52f7kSyY4v8vU4tLPocUzoHADesNQ5VwQxbiQ1sJWTOb6MKzgoiJzeL/6et973sfhYWFgz/nUv4Avve97/Hf/tt/48477+TDH/4w999/P5MnT+bxxx//vwLbt7/9bZ588kk+8YlPsHTpUiZN0gnopZdeGnQNX8hrSAE817UYMddkGalF1qReIgO1BgnvmxDjq0En3Bq08Y/6+auJIqlJwZmPhEMToUhOQgJnNxEEXIoUo2Kk5LUQ/VxH+fOk6OQhodOPGFkXYpIJzk1EMdeJiHHXIAaVBO5+JHzyUBD7E35PUkbqibIGsxAjLfV4jYb7EBJSzxIlIcYhhWEPUqIm+f8CopvGesKS0oQY+6UM9gumiCgNMgYJwRTnd5HHu47IxBtu3PYgRr3D89ht2Pd4XZPlaCpirIs8r61EluYBpGhvQNaqej9XRMQU5qAT+wgkPBfCxb9AZLza2jpYY2w5kY2YrJR7kULTYxhWGt8VDFo52uuIFmLZiNYKkJIx3uuU5TWpJZTyY/5sm9+fLF8gGhhANLLf65EU3TXG2wgGXdCDLcCykSB8nShO3KB73m7h7ED5+5HbdDiitR1EXcSkQN2ClKGrPfcq/w+y3LZ7LtuRIjLBMG5FQno/UUi5ici6/j2PdZPxernvqSPcduVovV/2+rUSmeiriKSVViJJpp3gBRDlQpqM75FEySWAByBnAVE3rt74TXumznhvOeM9u5GCkWJZJyMFMoPop5xCH/rQ+kMclgqQ8lDgtalEiv5Rj5Pp7xYjZXIfUbC4hLO7XSwl9n4u2itFnsd+wj1+nKho8KjmMHYRStK5zu+ajg6oeUSM4hoYeOsMHJ4kaqvuIgo6zzHMzUhBbicKcR837Jl+/yl0mC1ENDsZ8d8+tN8Snfd4zHJEo+Wa48XlHid9X+T1ykO8Zo/HrkKxh/WGsYtwuR8CTkBGuf9+gZAH1USXpPSOWsP2TbR3pvgdeWeMm+lnkoX5Ql0XOAawsbGRoqIiSktLuf7663nzzTfPee/evXuZPXv2WZ/NmTOHvXv3/vwv/ldcn/jEJ/jZz37Gz372M/7oj/5o8PObb76ZJ5988v/KO3+e630XGoB/t1c22uhliGFUE6n8zSj26A2iLEELUkoO+9lLiV6w3YgBbUAb+AA62d+OGE0KsJ+EmN4tSBlKgr/L9+9msEDs6f0w7AmkUOQhZnALys5rMuzrGexHevorMOwiorzF04arBDHWjURcW49x8AxyIWE89PiZBEsuYcnIMfzdRGeL3Z7jccHMnfB2L1wyBTHiJwmLwlyi/EkJUZrmOeg5CjnPE8pyJVJiZiKmu87P7Pf3v+nPkwWs2H+PQ0I8BVFvIeKikvCcbJwdQgplA1G/70/8dxNa4z8GftljFyOmPhsJV3xfIfBXSBle7rXahhh+lddpKrKovObPVyIh/AU/k0lkONZA4RL9Hgxcf4roQ30/0c95PaKtIkRnwz1OJbHex5AAuQ4lJp0yDHlEMdtSr+8eP7PSz9UiWugiCh/vR+t+M1ySg5TGAmCVElc6gA9kIwGfgei3HSlOyeKbFMNNeufpf3RR4wJgFnQ+pi30iYPG2XCiQHayIs83DmuREvbf/fztZ8wbotvLIbSP5/v3AOGi/hNkyUrKfRtSZHZ5zLmGo9nr0o2UmZPGSx46NHQbzl0owaPYuKswHv4KuTW7CeUtkyjI3ACn74VhHfD2a3DJ/Z57B1LSkmu+xvjt91pvJpTwGv/9JUQvbUiZSu7T44iuUrxtgfF4HClam4nQgZQolINo+ZhxcRDRyAavSbK8liKrYaufPUFY7dM9N0FGFqFA7vY7RxMHjno/c6vxewORILGfSFY5DkyEN38MH3wMKZzNfk9SzCZ6rZsQf/ui8VDg5w9ATwPkTETK3gbjdCxxOLyIwcPE6bdgWBHiTclFPdtzHo142hyiFmof2sObEQ12GO9ViP5fMY52GtfJjf8y2qeTCUXwQl2nOH/JG/agnDhx4qyPL7roosFYujOvadOm8cwzz1BeXs4//MM/8PWvf52rrrqKgwcPkpub+0/ub29vZ8yYMWd9NmbMGNrb28/TBM6+Zs2axYsvvsgll1xy1uf5+fn86q/+Ktu3bz/Hk+/NNWQBPNfVhQRHYjhpc2YSwqnA31cihnYtMAUOPIZO101AKxz5ChLIxxDGfw8Jge8gC9tmxOxOIaE9gJhbciOkWJJsJGSzbXD4ZaJNWA5iuu2IGaXg4jbgJAz7Corfm4UUu+1I8UjuqYeRIvYI0Wz9a4alhVASJxvONqLn5XQkJH+P6LhRjk6vVUgxnQH8CC75jOH8mt87zzAdRYrIYWSZnI0Y+1ch5xYk8Q8ZD50MNlBvvxkx4XqiR+lXEWNda9ggSljkE703Z0H7RiJBp4KoyfYWURanxPho8fuTm+4rHvt3gCfhwDPAg3Ay4b4PCdZPwaEVSLF4HCk7q4k6d/ch9/Quwh31EhL6p4ji2mWE5WUSEo5rPK8XkJD5FqLTjUS/3m5Eyw2+/1mknBR4jr3oMNNCuI6bEb30MNiy6+0nPOZTiHZriY4zTUhROESUC6omShothmEPwQeOGg+3Eoec30Z0MsHzThalTOByK3+1wDc+BJ2KVf3ENURiyGHPvdvv/6rxcJBQ9OqQa/M5dPjJZ7BW4skHvK7JGp1PHNa2GBcH/b4ZiL6TYM5AiWBbhCOOIvqc6bVb6LWbj5SNJXDoZ8bNKq/FTERjYxAdFvu5ciK7epvWe9gMYAVcMhNOfIUIt/hlpPzUEmWNytEhawtR0/QudKhIh4IJhFu2DLknj6JSP1N87+UoAzbFq7YiPuA4xYEn0CEswzhdbvy3ov03Fe2ptYg2lyF+9zGkxFQQMa0ppjAdyL6JaHI8UTw+uYP3av34Da/bMbQPRngu3wduhQ/OJspYveV31nld6zRee52f/xqhaM3Q+DnXC+d8b7jmPY6o6zidqOn5BAx7jOjagfGb7fvajJM6oizVPOMlGxkVVqI91UUo4d1EYemdXrcj8DcPECV1/pNdxcXFjBo1avDnW9/61j9739y5c/nv//2/80u/9EvMmTOHLVu20NXVxfr1699jiP/56y//8i85efLkP/m8r6+PXbt2XQCIzr6GkkDOdX1xmDbdIcToD6CNOwttwNFEkPRkxLzrkJAd72faiUzUHOT6fBe5VBsQ0+gn4oamI+aWkkMy0WnxIn9W4meGI0a2GLneuhHzuh0J5uT22eq/ixCzWYGEZScRr9eGGFoBUmpLEVNqJmoU5hOutheRwjACuRnXIsF2zLCmWJlCxED7kQCbQCSGrDLOUmzV9UQFfQz3XKQMPOt5pViwDK9Fso5u4Ow+yZmEyyUHreFJwh1fQCTytPr/Vjjx9zDyU0RSw0zEXJOrLlnzuoy7WqK/LOoDmnUjUZNwOhEH9XlEG/u8ZkVIeVyMrF1XEu26ugl301HPvcb3HEYHiSavWwURf7fD713pz9Z47rcQBXS3IRqYg6w8czxGUgRH+LN+j7nCa9XldSkmitzuJsIZfoQUh1L/dBm3z6E4x7cIhXMnopslXrPtnnuyeOGxl3j81xB9zToDxumI7vL9/Ul/luimkUgK6PS4ZZ5rC1GyYxtayxeJ+nE5fn6ZP0su5HrCMlfiz2uMry1IuXw/UZNzNuIRY5BilmLvrDy9vRQu+RSyMqU4xJQo0UskDS1E/CLt191E0lVyXUIkjqXkp0aiPV+ymk8hFKUuw9VMJEcMGOelaL/u8eelfl8HkVzUCgMvQ8ZXUOLSaK/ZaI9ZblzkCe9vPwCXXGMcJ2t2LqHQpMNuDdE+b5o/b0QW6lVETcNer8kiorrCbyLlv4NQ2BOvTtbEHsM1zXBehehgscdLnphOIgQnxebh73NQokYJOrT1Gi9ziC4pyaI5gGhvi/F9D9Hqbzda9xmEu//zBI85RLR8S0pe8mrkwtvr4ZIbvaY1FzAJZBWMvPg8jfkOjFqpAspnJoGcywL4z11Tp05l9uzZ/6zS+MEPfpAvfOEL3HbbbYOffe1rX+PP/uzPqKur+z+GP13/63/9LwAuv/xytm/fTn5++OkHBgb4n//zf/Lkk0/S3Nx83t75b7mGXMDnutoJy9IpZPJPsTab0al4MhIqjURsShFiDB1I2VuMGNArSEiPIFqrpYDiCqK8SRVieil+6wUi23YD4Z6bjJjKdCImbzvhXsjzzzgkzL5MuPuaPI8xRJeACsRI7PagDTHhXL+riUje6PZcmxDTTy7yNJ8SdFrdQ3SN+Jbn1kC4tTuM0yeIdnZZRFzMFsQQB/zeJMhOEMkWGYRy2A1cASfvgqzHEHPfjBTbQ8gKNsI46Cf6b2bByKuI5vTZZ/wkgb8MKTQFhqVZ+HunAS5eAH0vQVZyFVciAZKFlNNnkbViCVrT1cLZ6Xtg2GeIDhsdXr9tqB7aIa/bJKQEJEtXkT/LNmwf8/8ve013E4V5f0i0sLqaUHBmEXTT4fHriIzITuDTyFKX3HQdhKI0k0hAeYhwG6bxkuBbfcaY+NlCJKzTQWMpEo7diIamGCdVZ7z7G8blAWTdLPY6zPYzKW7uGKEUHTE+8wxXv+fZRnRPqfF78hANvYCEfo1xWeb1SIelA8Q+bfV3ZYgOu7UmJ/bCyJSUU0d0oknKbA5c8hUiTjAfrekBpMCvIzpFdPi+25DlMlmCO9Ha70OHhBHG21uIRmcSCsZ0w5oODVuNq+F+X7/vnUKUbao1vg+h/ZPne3cY5jzIuN7wTUM8Llnhi7w+KXZ3P1zySWTpHY4OdplEt6E8IuN/nHF5uXGQhdZ4A8rEriXqjg74/3SYWkjw1VNEHPA6ZM3OJyy9mb63m0jGKzY++4js+pForzQaLxWIrvZ63BQyMo3BTPLBTOc+r0PigVcSsZIpbjib4MmzjGt8/2ii/mryAqTD2WK4ZKmeH/iJbv/PdI0cOfLf1Av4H//xH3njjTf49V//9X/2+yuvvJK/+Iu/OEsBfOWVV7jyyiv/raD+s9fll18+WJZm1qxZ/+T7iy++mN///d8/r+/8t1xDCuC5rsQgkjvzKMo0PEy4CDqQkMhHykoW2vB9SHk8iXbmp4iq/JMRU7oGCcDfQQrDbo81nkguyERMqZdILLkcMfPj8Jd74RMFfmYLUng6DPNEQoGpJfpy7kPK1haPORWdYlMCQj4RUzId3lwPH5xvOPYTHS+m+R0psLoXCbiZSKEbg5jtdcCTcPIgZM02XkYg4ZX2d6PHTu6lIiQYFiOm+Zhxt0TzZoLn04gEc73vzwXyIOv9xvtFhvMIOmXvR0L1MNFfeBkSFl2IuY8hWqlN9u/HEOPNJhJCsvXcxVfo2ZFTkGJypcca5d8HoGevA/9zPL88wTcsn8gEXUa4jHMMe4dxsdxrM4pIwmjwfDM8r02+9x7/lJzx2Qnj+sOeez4Sgl2E67QIxQ++TLgGi4jkhiRw5yOlrZco4JuLhFMJciV3ExnffUSLtRYirnKq37PUc+tAtFNDKCopli5Zh9qQVbIVCcwUw7XzjLXrNY6ajMcMRB/zjY9ZRDeMRNPjDWsJoqk5vmcncnfv9HPzPVaJ39uK9m6m4a4QjCOTJfgxj7uVSA6rJaxDfUjpSgeyXv++w5+tQXRxG9qzKRwl4W48otE2w9OBQgwqifjBEf6sCNHuaHRQzEB7cy5KkGpHVrNOwvK2CSl9XYi33WfcpsChViJjNiluMxC/+yLR5aKAaP1YYJgyDf+niNjm+YZ7q2Gdh2hgO5x8DbKmob1Z4udneG1GEUkqaxAdF6H9mNzqTxPhO2OITPWdiKY6EV9rMn7HIRrONY7SWNMQH0penWrjIil/84huH/OJvtntiKaqkVWvCil3izz3G9BBscVj7zN8e3zvZcbFw0hmjEB8YRtkLODCXgOcvxjAn3OcL37xi/zKr/wK48aNo62tja997WtkZGSwdOlSAJYvX87YsWMHrYG//du/TVVVFY888gif/OQn+ZM/+RP279/P6tWrz9MEdP393/89p0+fprS0lJ/85CcUFBQMfpeVlcUHPvABMjIuvNo+pACe67ocCfoxRNbkcM7ufTmOcHPkIIZUjpjFUaTktaAT6krkJitHTKyFKK1yld8DDMyHjFtRLN5sxHCqEZO6E05uhKxLgTnwiZnEqbcEuSWSwE4n4AJ0cv4uYiodiNH0Iiabgxj/Y3C6wUpJJWJYM+CDM+HkSsj6DOFmLUAC0TGOdCMhcZvfcwgx9cUMJpZklRPlNpKFIVlS7jEcBxDj3u15/RES0GOQorCKSLjYZbz0+51PGK+biebszxNKUi8SkjuQorEOCcDRhn0KUY8tG7lvP+979iMlcqpxWEAEX/ca3lEotvJ7hBWiW+uXMx0x9UOI4e8wflcSVrVnkTDdRdQ9K/CcXkICqtifd3rNft84OARv73eJlzGEBbfTMHV6Di1IoEzz2PX+XeW1q0YK3Enjf43ndpvn3o3W/Cl/n0+4048Q1u2vA//N48z3u/OJ+pLPohizA6h+X7Ks5freW5G1tYiwJCb32k60z3YjRfRziO4XGsfVROu3fTCwFTKeJrLwK9ChJSkhI4g+06sQLR5E9DEDWf4xXtOBKSmlc4l2g2WIrkYhK1wXUrS6/O4vem2bEC3fYzhKCQV3Osp8H4f2V5FhWor2yyjj9mt+dixhBRzudf0hshrXITq+CO3DfrTfk4uyzuPd5/mUM5hd/JdPwCcWwJ/th1/dgzwZ3YQ7uoGw3h/1OgwYF5O95q/791Ne1wrfm4FosM/j9nrsecbXQs9rk5+rAWZD1g2e92Li0FeEeMl1RF/eFM6wieiXXEwksLUZ1mSxG+c1qiOU1ekav/kZKLke0ch4okPNHCKJJPHzZFnegmRHPaLBVYQFvoA4hI8jeNUhxK9P+V0Xedx0fxURUrLKz1UiWr8ZyZsLeSXYz9dYP8f11ltvsXTpUjo7OykoKGDGjBm8+uqrgwrXm2++yfDhwwfvnz59Oj/4wQ/43d/9Xb785S9TVlbGn/3Zn533GoDjxil25NSp84WY/zvXUAzgua7Lh+n3NWjjJ1djI2Jmu33fdUjo56FNWoUYSzZi9kcQYyhFDCjFcnQRGaGtaKNfiZhVigNLLt1kgUhCb6HvnYBcab2IId+DGE+Px96pcQe2QsYkxHSyiYy5KcgilIKi93kOtcRpPp9wc7X6u4OGoRGdhg8bJ98hMpmvQ4z2Kj/XiNxrExBj2+O5Ffs993l+tyKB9xw6IS8DNsDpFhi23LhbgBS+So3zg/3w6XsQg3zX+H8dKXfFxvU9yApQgRjsS0ioX0vE3/QZzrdQ8ewU5zcfCb02xKB3E8Wfk2u4CgmBLOOrnFAyBwgr22qPt9rvv47oorHAeG8lXHTT/Pf9XteXkKDbA2yBnn+EnGsM/7VIyfg8sj5tQ+7BTKIY+cf97i4iu/kGw54OM22G8QfGcRES3mtREkEpEjoT/dlYohTPdUS4wHaUGPTriH6nIytkA1F7cYbfO93zq/V8HyTipEZ4Lfv8d1Lg7oGBY5Bx+oPw6TdF33VwYiuMfAkd1vKRUHkVCea10N4LhXcQdTs7iLZ2yfrShPZSJfD4L8Er/0tzrSVaM37Y69GFBHG9cddmWMuRsL7LMOyCd34EF8/1utR7jXcgZXKP55nPYLWBEy/DyCVEZvJspHikw1e75zUDKQQpjrPB85iA9sACFI+ZLIE1SJmqcjzi57wm47yei1BsbnrfFehAVYF4YpNpolE4OXQcJjzkdc8hrPKFnuNxeGcdXHy6CG5oE12f6cp9AinQl6G9W0D0YN6O9tV4lDhUYNgnosNJJqLJY8i6lmIlX4C3Dyphhjrj/IDxlqu5s8ZjJe9ADRHzWqp3v/O3cPFMz2W88ZGMAHuMq9sJ1/0+4+mLwLPwxstw2Y2Ge7PX4CDRsnKK8MMuf57CZ+6H5qVQcgVwCn5aBx943mv+KKLNKq/BUxcwBvCx8xwDeOu/vhD0f4SrsbGRHTt28NOf/vSfKIRf/epXLxBUuoYUwHNds4aJEZcRXTAy0Qm7z39nIwUoua2a0MZfjKwchX4+HzHfI4hBtSCBu59Qdhb5+WS1uQ0JgxSaUIpOfxWIEX+OEFJZhuUIYkrliAnn+rkthFtoJ2Iwrb5nF5HNPB0Jjp2IGScL1ABh0Tzl+Uwl3Ne1SICtIdqINXuuXYhhJffVKc8pJdaAhMZYZH1q8t9HkEKzBQmB4Z7nDOOqxrDONS4qvRYt0PwIlDzmNSrz+DvPwHsTEvZzifZayRLWZ7z0I0Ze6XetEEzv3AUX32P4iomSFK0oxqnZ+FnlOZchBWCjv5uDFIlCop5eSqrYiRSfY8bNXq/lASRMhhuWaUhJfRRlSXZ5zIVImRlBhAKkAPdqwi31HbTWSTGa4Pl9zWM3eD4VxlNyt3YiS00T4UJuRlap/+Fne4mCyQOItlN8V4XhvBkpTrcSiQ83IWtRpvH4WaTI1nucbI81HykEbcbVcSSU9yPF7AdEKMQLRIHxZGHc7fW5ASlC65ASvxWVbpqAlIVMInmqBdH+fsIdeqnfvdnzrUT0fxsKhahAQrzT81lrHK8h4iGTm3m5cP7OHXBxUnwrjNsswwHaZ13+OUbQ9pWG42p/N4D4ygNnwJ6U9ENoD50i4jU3owPSAQaTogYTQ3Ya33f5virDUY8Uz7RHhvsdc5FCu4DIki/3PLoI2thM1N5MBpp8tC/avDYdRHmaYqIiQjoQjEP0XUUccDbD6Y0ukVVLtLWbRByqctDeWew5v4t4ShtSetcQ3Z2mwhvXw2WfMgxpz8whkpVKEA3PNYzrEc+7y2uQjEsZ/hnn/3cyGD/ZeRuMnoSSttoQ//2h55BiByuJhJNORE+z0X798gVUAB89zwrg7f95FMA//MM/ZOXKlbz//e+nsLCQYcOGDX43bNgwamtrLyB0Qwrgua9PD4tMxGQtykOCvAIJl3sIK0IuYiQrUFD8KCIr9Tl0QkyWwGbEwPYQ1oMWxACT5W09EhoriUSQfMSE0imykHCznALq4WQbZH2SqIV1F7L4lBPlFjC8o5Ewr0UCeAAJxGIi63SuYTuEBE6KJxyBBGCnn92ETq/5RAzbUSKbuQ2d7POIQra2igz2DJ6OGGcVEafV5WfvQIrePEIp7jVcO5HwucbzfhQlRqxACmQuccK+CikM2zzWGo81k7DezPE79vq7ZNFZQHRfaDGeDpxx3xVore8hXIMQ1t8eZOHo9DocQww8EykXE4mg9Aak7B1BSl4NEnLdaE27iDZuSalp8LMQFuh9RO2wbMNyJ1Ke6pDi047W+QZEkzWEC+otj7fI75yMrM6zibIj8xCtjjdOLjeuqo3npAwtNKzJJTeDiDmcegbcuWgPzUB7q8r4PomUqy5E/4le9hu+XsPTjSy4zxGlQ8rQPqrwexK9dRmeZkQjNxvWUj+bLHzpgJb2dAfaP9sIa9Vkoqh4Cg9oJcrJ1HlOk5HysQnxjOFnrE8touF8/6Q5LffcN6C98JTXK9t4OYzoMtFngefV5PmWIPqs9hjJ05BLtM0rNuwVRNbyE8QeHEckZJUQCTTDEd8YhxKHkmJbjGg8hU2sZLC/9mCt0ZFEZYGEvzlo/X2gowfReo/v6yfaHu5A+2kykfVd53unEu3uCg1D8s5UEyVdqoms5cQHys9YkxqCx+9GStsA4SmpQHSwDPGDYo+1xDieSnQZqkd87ft+Pps4YO8kYhnx3K8kqh9MQ2u7laDjMcbnrw8pgP8er3HjxvFbv/Vb/M7v/M6FBuWfvYb/72/5f/TqQsIlDzGDPUQJkg606fKIchg7kAB8EiklhxHTykOMox8JlUa0wa9Agn0pYhJVRBbierTBpxOlGJoRQ0hWoxSDmILdxwOdkHUlweimIYaz2HM66HFzDdsLen6ghbN7jM5EjHs8Yc3rItxluX7vADpdp2dH+P7ElDr9zHAkIHN972TCpXfcuFqElJnZvrfSeOwmehbf7LG2Iia/BynDWYbxfiSIFhrX30OCOMdj3kBYxyYYztuNR1t639hhvPwYKWTPep7Jtd3idxcTlrIr/b6r4MQpr9vNSJAtRYIpl8j2XUZkb6e4vh7D3eR5HUGC/nYUD1pGlJjZAPzah6KTxAHj6wAStq2IBnqQctZGxEXtRFaGjYgW7zGcG73Ge4xfiOLPJ4hEnZ2EQluEhP0Jz3ee37vLY9QwWOPx9E+8Fj1+JlmYjiBa2ITc1l3+vcLfV3k+E71mFX5Hes82pOxlej2LPWat3zPN86jxMyVI6ZpFWLj6iSSfVsOTS7j/K/0zzeswAVkou9AeLjI+aoh4xX4iaWePn/0usnqmOLUOomxRh9evG1nCdyIlqBXRyzHjYC6xv7vQoaoMKVHJ6tqPlJoEUxsqUbPOa3cTUm6WIxd9L9E+MblANyJr5iS/c6VxMMtzyUZW0BeIzPd6Ik5zJ9prjYi+G4kalfVeh4No7zV4rS41rh73nN4i3MSZRHHoj6MD32ZkjdsDnXegfZF42DzCSlpmGDYQca8LEC/a73FqkPUvx7CNRXtxrcdc6TnPRPx0AhHDWS9cn37COE+8+f9j7/+ju66ufH/8EXITQ9JATBpJgykpksKk8EUZmBQGZaBUBkulWAaG6tVSraPVUq3V2jo6to7V0VotraO1WtSLUr1UBgfholyYVApmoGhWmBQmyEAjaWhKDGQSYnID3z+ez/PeYc0wP+7HGZxeXmu91zt5vV+vc/bZZ5+999l7n713Gi/P+v75yKo91++s87h2I5q5EymLSfGfSiS/TgaFZB3O8f87iZPDp+rqf5c/v0XX22+/zR/90R+dajBOep1WAE92XQ60Q+9stMDGIeG6BDH8YWgB9iCGON3v1aMs+/lIgdmLhNIRxKQhBFIzUUx8lZ+7BM3KC0iQzXSfeYgR5yHBf7XfXe++phEute+hWKyR7q+cOKRST9TevVHvZCf3WSEnBkrXEoK0Eykz491nmXFSSyi6OwiLIr6f8JROGV6FGPFT6qerHbm0S5E1YL/b70NMuMrjaADWQO9DSInY4j4vQQxxguErRMImBzHTzYan3jDUGYc9yMVyM1IgRwqH54w37K8hBt1t+JMbvdQw1XhOi/z+G8DjMOQ8pIz2GG8dnsNO4uDADjh0p59J1ra5SNjUuP/9SDgdRLT1KnStI06u/uPfS3DVezx/+Em5ibOJKh19xttCRCMFRK62PON3FZHKY6nnb7z/z0EKTDZaA8X+3oEEYCUSXNlECbhCgjbx+KZC1nTCUttG5C680fNZQZx8Ti7NZrd7GNHkPrSp6IL+F5E1tRBZHDehuc4BfkikJqlHCkUPEZIwDAnxJf70EsrsfDTHwwnL7wvue4X7Swp7nvvLJlOeq3UemusZhvuraK0c9nsPo3WQDnF0uY9yjX3fKrc3hdhA5CFFaydxCGcIsSFbaTwm63E/csu3IL4wh8yBJHag9THROL6ZqFNdTpxwnan33n7A7ReiDUkTYZn+DBHHu8D/74VfP0Ec1KlAND8GjvwUKT4FRNWe7cTp9z3oOt/9vOrnUzs9fmYvir8sRbxsgquhLdLfrITeO91W2sT3ID5TidZbopdDaEPUhiz5zX72iOG7hcixuNG4WGq4iozricBcyBqFFMWZaA0ucPvDibJ9tYiWxmtMR5ZA/xPIE/FNIvH6TMLiV+o5ShveFJqUZMNCTu117F3+/BZdf/RHf8TLL798qsE46XXaBXyy6xtZkcizEDGgy4HPIuH2FmJSc9HR/C8RMSr1iIGv8PvJZTSSEOhb0eJ90Pe7ibi7HKL8UbLivEII97GEReoRxDAnIxdRMVGuaRwSHuMR42mDoy/D4OdRmbHLECPZAMyE3ish90LDX4aE4V8QufiqCPdII2KiV7vP+xCjG0XE0D3uNuyy3rYJJs0ecDJzIxGMDpHb7nki1im5hHZ4fBcj5aMJeh92ypd08GYI2imvIqxUPcAk6FoKBcuQS282Eet4uZ8pJWr19vv9PuKE3RSPs5qM9fGXm+CDnzCe0+nVZqTcHva4CpEgq/Z85fqZK4mSU28ZjvuAvwL+GG02KpAyn5TqaQPGNpHILXgxElRrPScLoP9JyL7Q87MU0cjZRCm8cf7eh4Rmh3Ff5nuPI7pegxThGe7rPuPhfCLdxQbDs5+oWnDE7TZ6jouQVW89WjPJdXkOspbcZ/zfjiwxOX5nElojFW7rJaL2aiNsuxsmJSvXJiJxez6ik0uRBXUvWl/vIEV7AhKw9xEWloNoHeV6HClOd5b/TnFvaUOUA/1XQ/YCpICtN8x1yKL9OJFWpIs49b4WbVwKPZaJhqsc0d5uMgrl8Scg632oTN+rcPwlyLrUY1vtcSdLZzGheB8m3LrLicoWe43bhf59LZF4er//H4OUiy7CwzCCqPt9u2FtN37TZvbjRDqq1xC/yjNsW4hTups89vkov2Oz2068pQHxvHy05kcTCuuLwHnQ9beqEPTzh+F3FyDe1IY2z0WEUl1G1HRejuKrn/fY0oag3e+t8ljGIT42y312E56bA4Qlbq5/KyfS0jxEuKRLic1Tip3sGIDPxzwvk4gUU/2I7yevQwqT2eN+9qG1OMGw1cKR/TDkFIjxjAv4vnfZBXzLb48L+J577uE73/kOn/jEJxg3bhw5OTkn/L5kyZJTBJmu0wrgya4pWVHCqAEt3uQ+fRkt+InAKp80uxkxniQ4H0aL+mIiv5QVF9oQ8xuEBMp6f1pRfdmniViioShlxodR3NYqxNBmENajOrTL3+z/U2xPueF4mlBkkos2xZwsNnwzoeu6AfnqziYOTtyEmOLdbr8fKXvNxs845LZahgTZWMT85iG3Rh0RM3YGUmru9TNpB70OCbC3POYiwtXV5vcfIlLddHs8+xBjTkK/CSk5YwmFHCIlDkQcYt2AsYwgXKW9yIrUhpSPw24nxQ2e4Wd3IsVtjfEyC9gArQ9D2fuNl+fdZiMhSAqJtDd9hFVxOVLKdxGxYwWILub63ZnICjEcCZoSIlC8DwmYFDu5xHM3EwmRS/xOwkcbcmX2uK9vIsFX63aLjO8eRIv9np9+469eTb2yAj5+AxL0l3gcX0bWkN1IaegyHq8kkwibLth3F1ReY9zkeS43EjFv+4yTpOgkF2Wjx3sLovs6Ih/h95DF7BgRr7UVKaWdaB3PRfS4i0jyPNpwzCSqQnQhhWW8Ky/8HpG8eTFSFpN7+dtIUXsTre/9hEU6WW/XIkvVMfc7migLtgPRTBGZTePxF63wpZjStDn5CqKfDs9J4jO7kZJSyokxtLP93n7CPZlPVAZq8TutcPw3kLUAmAqHlkDJ7yHrXjWikRwiJrJ+wPy0+v9ZaOP4F8ZfC5Gs/Xoylq7Wl6HsYbTZSbhKMXVP+Z3hxtFIIgRjlGGtR4rja35uKuFGT67UvYiee9CVNhaVhGdimp9Lyu3FRG7DNsNUSFj98og47dmG9xGiSlSn4Ss2vGWIj6R7d3hehiNa7zSMTUQoQopN/Arh7m8m4rA7iAMli4HfO4UK4L0wJO9darMHht7626MAfuhDHzrpb1lZWezdu/c/EZp/ev23U9r7e/magJjGNxGjHE6Y4ochBr4HGA9nzidSr3wSLegJhBtzHVEIvN/tvYB2zE+hxVxDlI7rQULiEGJMdxMFwEe6rWwkTN8hrFdTUAxiinvpgzf3wzkrDOsgpIQeBnYp9i+7EzGzsVDwaSTQGpCSU4QUnv1E2aVWt33Q49xCBHzvIfJqJaWtELlYIGLTMCwNHvfLSEgU+vehiEFvJBSfpxGjbkQ76kt8P48oodck3Pz9fvjwZURy6OSeLPXYnzFOszWG/tsh+3c4MVHtMrc9g4gbmkHUIW1AgmkjGXdOUubKrvY7rSgusdW4W0HkqkvWAc8TI5HissP4vsx46kQKXwtxiKcb0eCbfn+yxs0GlFj3Lc0v+5HS/AhRnWY0kZC2CClro/13FxHbtAopYXuRIB6JlJA3kYXn+2jTsQg+nmKSDqIcdLs8J6VozvPd5yrCJVcHTITKQYQyOxUpammenkMK3kakdJV6DK1ofV2NhHCvP3bjU4csXM8hpR33n3CX3HwzkGV9hsfcRbhDG5ASsZ+MlfvMdqRIJ2sTRNL2fqICDIZhv+cs1+NIVp0J7qedOGF+O1prm4k4r6mQ1UekPkl4TsrWFuM84XkvUYpxIfApFPc6D9FaMVqrxj1FyDq8iEgxswOyUkzgfii5jdigHvBc9iGrarICTiMyAlQgRfhLRJLkXkTDi5BCf7HeK5tEpFxJim0Hou0/cZ9tiKfsJ8r45SL6mI14x7mIhiEOptWjOOu9iPYvNTzdhjeHEyspgXhEOeLLzb7fivh38tIcND4XGn8riZyd3ye8GOvRnB92OzWcGPOb8LHac7MZreNSj7PObfUjmsuF3hWQu4iok12D1mIZp6/36PUP//APpxqEf/E6HQN4squNqIjRRxy4aEKM4q/RAhxJHAhoRcytETGhVxFDm0YEQ+cggTOGqH96DlFiLcXUNCJBsR0FEY9CjDTFAD6LDirsIHa7I4myTzUawzlJCbvQfY9GQu+jkP17RLLarUSx+8sJJbPV7e02HnKJpNg5RJqbYqKCRy1icM8Ynp1E3GEZOnjQ5v+b3N9Nxk++4c13+98myuw9jpSFcUQ+xhmEQpCjdz88DwnzQs9Zr/u9Awm+fMPUpOeyryNOz1YYn5uQctSAFImJ/gz19wzitG4hYtorCOVuhPss9jPJ4tPufmqJk3znGccNaP7TtW0AbruQUrrdeH4e0UIRofzVEG6qaW5vC5FrMcVGtvu9y4garzUed7JSPGC4y4h8lYPQ6ep8pICfTyScbkKbhk7kit9g3F9BHC642u+OREI+B8WqrXY/m91eDrJAj0Bz220Y2pFAzPWzmwb8X0BssOoN78eN42a0xj5u/H6NOAE/hkiHsscwlqP5zSNch/XG0UwiGflwgke0o7U1xX3VI7rN8dzVo01Mitmch+jqEbTWv+Y2LyRO767y+yuIMoSdfn6F+5loXO70OEHKbR5SogpR/svnPL7dnsc84303mu8fIYV+AqKdbsM9DK37dLAlz3M4HilWU9Fmtsy4fhOOHvYcjwB2wIGXiLKBK/2drGS7CcVxC+I56XAHaL3lqJ1M0vk3iRydQ4lDaMkV200kmk9XoscDSPFbitYSxt1mFAazcwAOO9C8ziDmcr77+yPjdiKZ2OmuPW6rw/h6Ea3zG4zjIvezzP9vJw6G5aMNwAy3eT5hPa7UOHOHITqdZ3zXI6v2ak7tdToG8L/sddoCeLJrJlEaaT5iHnOREFiGBOF3gc8RyuIOIhEuiCk5/o2piMk9SFjGliImNopwfwwMSN9OCNt9wAbHdl2MhFav4Vnud+7JB7rVTwtilsmdVUMkbv0oshRW+34fEkSXE5U4rvI48ogkycmN0UbkoMrxOy8i4bkTOAC9T0LueMSkynWPHsM11M8N9ycJhVbCHXathkIpoXQkhXGany/ixHQxKXZmGGKQyYXTAodehJIKeHup65K2EImxJ3q8tYSFZpnbr0AMuBEp3Omk4ibE5KcZ3n7Px2JkDbnFbbeg2LMjSCjmGL7xxnsnqobRQbiLnkTKW7dhmq/v4zsha4Tf7XTb3zV8IIXxAFEerITMIYxM4uU9RDqhx5GAy3Y/wzzOXZ6T/43iEb8mfLTusXXzbKLE4HJkZexGpzxnIeFd7vG/SQTdH0bK4QiksO1ASgeES7cJbQZuQMKwk1DmR6L1mFylDcShlWlECbGF/i3FXR4jYjubiJKD3X53rd/fhdZZp+Hfb5xUG6frkJCejkIb1hKxWfl+pgJZgpKbcAVStNd6HgsQjb9hPIxEa2ea8brRY01xadlIMW/18xcjBbQH0dL7gV+guZ9Jht75NlGHfARSJl9FPKzf83sFYbnMQUpzu2G4xO2lTVupYa10H6OQkrLabV9LJp/n4CXEwavLYXhy6y5DYQnfN1wpDKEP0W2B30mKTnLT1xOxc8XoAMgDBM3kICX0RuN8tnGaNuUthMVzEwrRuR6Oj4KsCr//JvzyYvjgBUR89GGiFnSpYS9Cp5Af9bj3uu1pUFCM6KHeuN+JNmq3Ip66DtHJEsSz1hin9YSLexvhJZmL1sES4+MJFBbxp0Pg744ofvUttF5O5dXPKSsF916/Pve5z/2Lv//oRz/6T4Lkn79OWwBPdu1EFo1vEGWwvouY/EIip9sPiKzwuUiI3IUYZx5iIInBPk7kU6tBi/tO4tThDMQkp7q/RxAjHulnvgfZn0CMosrw1BHpV+7oFoOciJj9fsRcqghr4WbDvoQ4gdyJFJbdRK3Na4h8d+ciwZdD1Ghd7vF80zAM8/sTgWsh92YkSPYR+cXqDEMBEiBJeTzofhfCkeRaTn1VIMFYSrhEevz8qyjG8WIk2KcSubUGIQF/GfAElFygaT3zaqSkjPS8VCFG/idIuagC/oA43XkfUoB+QZRLe8vtJkW+GwkZ3P9iolTVICJfWDrB2OfvN9B82YLAHWr/hbHw5iKPL7kXmyDr035us+Go9mc0Eha1iAaTFbnYMOYZd58h6G8koq0aw70U0XGKK6xGQuccj+0KKPsQJ1qmy4kk18mlVe3357vtcUgBG6h8VXps+4g0IjMNxwQiTdELSAhnD8DDFI8vWWAXI+v1HH8uAb4P2x4lUuK85PdvNFyjkVIzz3PRrvEc/6lhSO7WVkSr6zxfHW7vGeKwywjixPEW6J08YMybCav2k8iK9xwK+zhGpKUZ6vl43W0W6P+j/v/oCOQe3GT8n01UTVnqsV3kNlN8Y1Km1hLpSL5rnO82TpcRJSL3onW4V3P0d0vRhu5WpEzv8zx1uf92pIh1eL4eNDzJZVkMRzYTB1pAh4j2ovXyAhEbesTzchXiI/sRj52G+MkEOPq3hmMF4mWjCWt7gXH3RcKT0oPigB2mwBVoTS9CfOgRr6eJhnE/fHCs/77T7eC5W+jxJmvsvUQt+GR1vsb4u52IKaxG8a5TEX1XIx5Qh5TgUs/nTKRMpw1iilfsQLHIPW4vhQl97YgMDz2Iv2zh9PUevd5+++0TPr/+9a/ZuHEjL7zwAh0dHacavNOHQE56zcmSMMr1/21IubsKMdU5iFmsIRSVfEJ4/oBIa5CsHGvRIt+KGMdBwh1Wjhh2s9uqQcx5EMrV1YUESB1i8F2I8fwQxQL2IYG2BQmCN5B7q8e/DUJCoJ8o1XTAz0wkap9uQMxyNRL8w4iYmQ1I8A8i0rbke2zjkTC6GjGzDURwdlLGBnFiBYM8okTXbOAGONoNg8cTQng2UiRrjJ8SIlHwaD/TQKSJeB1+WQ8fXEDkEGtBCu8jSGFJMY2FiNE3Edn6c5EgqyBSdKww7iYQAqMDCfNZnpNFA9pNOEnWhcXIYjAI0VEeEiJLiITb5cSp4F1o45GHGP1VxltSOtZ7rq4iFIB64+INtGGZOqC9Zs/bXMM1CgmsGqLSTTrgMgfRdBI4K42j84TbTMzbTsL91WMcXIaUilLjPllJ6pDC8QQS0qWI7l4l8js+TSjC9yLLYw9S+KqIU6Q5hFu4FtF8smZWoTV1nucsxYxe4vnqczvziXrPM4ia0hciJWuQ4duL6P9542Ka+07PpLY3EmW99iNa2mB8Jpf0aKJKT9o0PEXmBPvbD8GZtR7/UjTf9Sh2bQ1aI+WE5e8Y4fKsN+7/jDg5OtG/lfr3FCJQhJSPWURd4oO2jN+FLLZb0WZnq8f5MqLTGsNyFRHj2gZdL0HBE/6ti1jbKTTDoQ+9D0Puhwir5ggiFcvH3Xeb8dTquR9B1F9eF/iiwX8/iNbmCKKMYlLytyLaSxbkHMSzbiW8GRsQLd+J1vVMtK7fIko4ziG8DXmEYp02WvWGN4VTbEZ0uRut/VVE2c1CIkToW8SJ4mWGfY7xtQet4S3GQ7IOZhNVTYrRBuIHQPMpPARy57t8COTO355DIP/cdezYMa699lrOOeccbrnlllMKy2kL4MmuTf68gRjBFvjZy4hpp9iVJj9bTpRTewoF4o9BDGIx2uFXIGY4iQh2LkVCZzxiGu1+rw0x8Rw//20iBqiQsPJ1IldIJ1ImWhGj2IeYxljidNsmpNS1o/jBoUigb0PMpxMx4SsQc0uKaIX7eAcxwbl+ptIwTELM7jIiwWmK4apA7rKkZLQQJeGGEW6dfN+/FgZfYLiT8H4OMe1fIAHTjxheFycmZW739yg1xTwibqkCCYKkuDQj5bcNKRHNhBJwBlLAOpCVqBspKGPVNi1o3tcTaSC+RuT9m4aY9F3uqx0pI68hmiky7koIpbMYCYT70Yr8ivFRS7ih93pMad73I0X1PMLaWIzSyIAOuSQloRsJ7zeIagQLiVxiSdlbhiy6T/mZZsN4AXFQI8fjLjdOkxBqQkpYvuEch+irCNHipaiCSLJ+volopx8pUMnCdzaympQa7+1IWBd5XpICnYcOF/UYF6WGLcXoFRKhC6+53zcQPXYiwTqEKK1Xjui81s/uIOJqi91mk+FI8YuziMMLx4gDEXn+LfGEUqSQvIHoeY1xORrRxCY480rP207jswrN9etojZyHaK/Zz5T7/cVI4XnVOJ9CHPJZRcSJJqvgLqT09hP5KtfCmRM9d5XG4cPGXxVy715vPF1vOFJc4GEoyFcbdBAxzskSOZIMDee+H9FiAeI/sxFdzPF8FCAemWc4ZiOeVG+YziZc8iORF2Kcf9uM+FvafNyHaLkZrdVupFQdICydw4mqRauQktuP1ibq/++3I5p4we3s8285ZGKI2UFkiahGdAuRBmcKmTyPmZPulWjuk4V4AnQd9v/ZRK1uPK5+RNP7jP/JBG0v4vT1X+gaNGgQX/7yl3nwwQdPNSinFcCTXvORsMglw+x+fxRRdzJZ1trRwtzm/5OFaiNatK3I8pEEZQtiUukgxUHEfFoIV3GV+5hN7EArEBOZTShMBURJpfsQk1tMuDe3ImFcihhu2qn2uc9SIolvE2KWxYR7LN/tzUXMrxMxnEI/vxdZE1YgK0kDUhS3+3s/Uf6uTDjMMMQKIs5nCnFicAywGw7c7v/fQVaoV1FaiY2ceBr3kOHe5+fHwVmzCUtJFVFqLsUSQZSyupCI3dqJlIAxSCDV+7d0QCAx8DUO+K4nkms3orlMlo8+QgFpg/5/8G/FyFpxKXGSusPj3ILctF3G/VjjbTmRk203MAcObCDy600nqks8TqQBGkrUVU0xUPlEnF8d9M8jkkYnC+0k4jBKin3s8Gc3kTYkG7mIx8Khf0AWrxJkTcn3c9XIRV+AlM4W4+xcwzeVqOxSiBSefCREC9CBpSJ/d/uZhPvDyFJTatxsInLStRMVLhqR8j6JOLSyFtHVWrc1iFDakruvmCgX14ZotE74/fVLBC9IsWa4nyIiPVMzYcWaQFi/s5GlfInwRz+KwYU4gJDj+4c8vlGG54hh24ToutAwpBjOPOJwUjtROq2RiOV72X3t9O/VaL0mN/to4tTyVI/vabe73vA9QcQs13mcRYjeEuzridRXs5CyNpZwUYMUyzkEL12M3MXJMvYK2hSUuf1aPzvXbZah9TvScExAtHE5kTKn2jBXI15y0HAeMgzlbnM/kbz9GfjwBcZZGZGSqtdzN5yogHSJ8fs6IQeGorW1FtFOmsvJxOGg6WROHReMNgyNhDeogEg/1o0279M9f/s9ziJO7XXsXf78P3C9+eab/J//839ONRinD4Gc9JpNxLfkIgaY3EVJacpGi78TCbSk8N2BFmobEQP3IGIqzcQueRhSxIa5v2NkykkdvxeyrkaWkn2IAbUiIXoAMf8uwzaRiAfZScQqFRLVOS5CikRSyCYQhx42Az9GrpEaIs6v1P1cQOZEX8Z1NEvtbTwIY1+Gsy7y8z2IwT1MxPyUESk4khJUYPwcQspIEZFiogqGzyfSWvR7TmqRBWI1wXDHEOWynkBK6EtEhYsUFD4audZGIoGU72d+4rFeD3yBsNoU+f0dxMGXZLGaBQVDPV8thMVwCrLuzDBOk7C/CLJnImXpceLgQiti9CMQ4x9HWGk7ieorDxD57NC9/sc8pp2IDuYhunod0UeKh/s6URd4nOaMObDtTpFA9gjDOwLFQSbXeC2yjCQr4e2esyYUmlBOKMfLoWSN3+k0bt8h8g42ok3CLM/VfsOy1zhIVraDRFzk1z8Id/xSVtFxavvoTTA4B9HXMffVgBSEKrd3iFCsczwneURJw0rDMNef3ciy1Gacpfne5j72+7uHcCVOgLO2A0vh+HbIGoToL7l06913r+HLJmp896EDCp16/u9/Ax/+rOFIil8PtK4TGodMJCqCbEX0sxApVskl3uV+yuDoCgDjaaTH0YnW9KeNm+VIOUmW0vHGeZ9hX442wJ1kLNhHnoEh04nQiD6PNVlMZ7m9UqAJ/u5O+Mh0ZGVtMZyVfncrUYs4xfEtdlvJQp+Ut2PIml2P+NN2QoHKIUpNtnmMlWiDcDZhyVvv3+b5u4UoMTjL7VQQSc0noM3YWOP6PmKD8BRRbWWnnx1H8L4RxmdSvNcR1r8KxENeRDxpM5FDch+QB/taoLLVY8wTLinzWKYi/jIRaIS3W+DM8YiPfZ1Td/Vz+hDISa4vf/nLJ/x//PhxfvWrX/HSSy9xxRVXnCKo4jqtAJ7segAJvmlI+H0Pjk924HAFWtgzUTqYSxAzuA0Jx28ixlWI3Cf3Ieaxm3DhJTfvhYgh9un3rk1QUKifsnNQ8HgdEqLbUX6thW6nDTH4MiQEf+y+r0IMfRBiHB2IOc0hdqx9SOlZi5ji44b/PqIqQLnHcA3h1ppGCLAbYUZyFSalsgBZA1MAdoonuwwx2i1Egt1rkWulGni8GG5sl5C6Ep1suxgxwsWGdz2RCucgUpxTPOIeP7cLKXIQ1oLdxMnXZYZzOhIsVcbNFqS4N6DddRfamb9BWC6/bty1QOtbUPYJw1eImPBSFL+213OzAAn+pUiRWel57CIOOyz2/dGeg0p48wk453IkIJpRPFaVxzEC+AR88Grj90qktHQTdZrnEMLlfwhefldt/Hw7/G4eTLrXMPyAiCs8RsSkjiUC7XONg43G6XyiJFgSfBP87CAk5Nqg/zzInoyUvFv87Fa0GRqN6LkdKeHXoM3OLKSY3fHLsKRcDByBwQ+hgwwziAoaVUTVl0eNy6T8VAAF8MufwAd3ovU6y3guQPGIVxN5B1uI8n1/S7hB800HRehqsKB+GLJ2CjYOEApXkf8uh4YFMO5OtA5u9fgfRa7gSvjwXD8/Dgn4HGAelKWwkm8S8bVz0TrK95ysJnIeGueDZxMHe6oRXQ5H6zm5JC/SGJiLLNHlyLo4x8/lEZbBlcC1MKQWWYLL/d5SMi7x469D1jjD9RTQAx/J9xxnGxeFxKn3Tv+902NfTSjr5W73IFqHb/r5+8nwjkOPQskoROP3o83F/Z7XgRbRNmCIlfQFxskxolRlO+FSTmttDpGMusjPjkd8ssn4SUrfJUTqmmZi83Mdiu9rJcILKoybpIw+5nvTEM18F9gHlS2GfQqxvoqIpN/tiAfcCmcmeO/j9PUevV5//fUT/h80aBClpaU88MAD/+oJ4f+M67QCeLIrDwmoNqQQbYOsR5CytQ4x/AqU+Pl+ZH3aT+TpqkYMo5hICdBGuAlbiPqcwxAj+QoU2OWbXYOYzx7EEJLCWeT37ybq/VYgBnozkXj5bMRENiEhtxIxj1ZgPPz6TjjrIaLEWB5SdEsRQx5u2G8w/MUe1w6kZCV3zWj3/2WkCMxE1tBm970VKZ3I+tYAAQAASURBVFaNSDmoRlaBWv+eYnEmtUegdiViah2E4jcBKUvrkLLwMmKEINfUCCJO5irjtxZZEpLCvQ0xbpBwH+q29xvuPiLAv9Jzshe5jGqh9xeQewHwl1CWrC//3XNwqebqwGIYfpvxUo6UosUeT7/637YCJlUTruZu5MocCTw4hIKlR+KQRrvxMMNwFSPFcrTne7zfG4WUlBSHWeTxvB8pDf3ARPjdQiIG9ZDn9iBRWuoQmXyKmfjR7xKumULjo404eFKMlP5s97VO/WXP8xgGoTl/lHArLkPK3TykjI9FFtIO4vDAFuPvDELxucjtzTQsNYge5xqGYkSHScnohw9eiRSu3ehQzY1EObj1fi5Ze4ej061/7DFPNR6uB/48H/5HN+yFyruQEO8hgv4XonW2HdFWA4y713M/E22crvTYn0ObhSLP8RrgWej/R8hObuQ50PUOFBQgha+MqMqSg9bZZM/3LLQGniZT0YOVxGnVQUTMYTniD3uJg2oVSAmeDEcfhcEFfu5RtCaeQetnjOdrFpm8n1mLiPKK9YjfbEc0e8jjW4LoNdtzs91zPpYIPynxb3XG1yjPbTnaNL0D5EHJ1ZrXzEGZaj+XjTYFyeL6DvAmZI03Xp70nBWhzcwViKYm+V6h8bvbv0+CI0/DkLvRQbzHCUvhPuNtIuIfS4h8f3PQxmo+oRhWGZ7DiC+PI/JAzkEyYCOipfHGyXV+fhvK05jrcacNdpJFeZza67QF8KTXpk2bTjUI/+J1+hTwya5bsmJnV4qE5AiifmcJ4dpIQclNSHFYSyRUTQw3nXbrQQK/hzg5uBzFtyWhOpYI1r8RKStJeUruhx1ETcl0uqwNKT2LiITDEPWIU6ByBbIMdCDmNwopVGcgRljusc1Au9aRRGD8fMSgCoiaw3OJRKl7iXrFuwnX6AR/vkqUVhtPxLnM87tPue9J8OtVcNZMIk3Iqyj2phgp1NMR811DlJ/rdL83eZ46kQBr870knEs8H68Tyl+yJLUS6R6qkABP+LWFhp2G/3GkuCQX1C6kLCxFjD2HTB3mjOAe7b56kOJ3yDgY5HvrUBzdb4i0H3uIuLK56FDAW0ho4HHdQCTSzUabFAz7RR5XOtl5zPCD5r+TyF/5IrLOPkVY/Fb7/a8QFVjqiTCEDsJd3IZqvL7iNtd6fEkhnkfk90uW8Sa0LoqJE+PdSDAmZWElUgSzjYc6RLcNxKGYqf4dREdFxu8Mz9UORP9TiBrW84iY2tVo/jZ6PHWEy7nIc/B9z9MtRLLhFuNjF7L+pLU8xW0u8r12j7UVbQx2e252EPS6DY7/I2RdbPwVGsb0/nKkcOw2jg6QsQBmKhhNNk7Sekw4GEtsjjqJAyEpLrED0WYzmVKXGbxkIy/BIESPLYh2mgmXfDlaB+1IwVmIrPZVaE1M8ZxmIyUp4TrPn2zjbQ7a0FQaplVErG41Ohh3I6LTWiI+dQ2iqR7fn0yE4OT5/vluYwui7WmEZ6LWMIwGiqDrMS3Z3Of9TJ7xnDwaTyN+kut3k5flFc9ripcsJ06pj/d89BEHYtLcghToh4lSdin+r4vIr7kMKeNDPaa6/3wxnjkFfOu7fAr43t++U8BtbW3s3i1mMXr0aEpLS08xRLoGnWoA3rPX60Sy3RwkhDuJHfhUpIDkox1aBXFScA6R4T0FAo8irGb9SCF83e19BjGKOmIHlJSu/YhhjkPMei9QooTQGWH5BmK2uwjmm2MYVxMZ548hBrTVz/Qg4VGEmNRn1HbGVTMUCakhSOGZ73fKELMrI6o+tBrWPsQ825F1Js+wbPP4Zridq4lTxuVEjq8x/gyCs+5Fis5Ij/tPiIDyQ4bhFo95ElIAk3vn64SQG4aER6Pf7SSqm4wl8ix2+d1GJFjaCGvEVH/K/f50IhHsBchNOcjjfdDPtqF5noMUug6Pd7xx4gMdVPreXCSc5qFTzyP8qTK8fZ6P9cgKsI+IWbyYyEUIEnwpQD4phc8jYVxCbAR6EE1sIBIk1xj2tKlJilS34dnkZ9MmZQRScEYa/8uIGMpGJOQ2EspXFVIujhH56PqM2xGInscbN/UDYCkiDiFUeO4aiFqvlRr6r28yzLPQmtiMNg/ZRHLxpGhfQVjutxEJmPcSZQG3GvZePzODyK1ZTCQdH0bEAjcjGnmKSEuS53FNQrS8Fa2r7xIHjEYAl9lqlWc8lRuedzw3MwhLVAozmeBnBiE6ex3xjDzipC3G+U7fb0BKdZ1/zydzaIkRhm+M+5hBxP59lJj3RuM3xVyWe45WEgplNpHCpYfY9H7OuP/ogDlIY3qDcKWXGX+lxscutL5qkIu8zc+sIaxRaa2P81z8NXGiPvH1Rn9vRPQ/AvGx2cbFeii4wKlrWhAdJx7wHKKJLqToFaOwoQ6/e63nYxpSeoe439lIkdxqPDQj6+omogZ0nfubQxwAHOH+a+D4o56P89zO+Zza6zjv3gGQ3zJzVFdXF5/73Of4wAc+wAUXXMAFF1xAeXk5V155Jd3d3acavNMK4EmvocSO6yDBOA4ScRnZaCHOQMxnPWJQ9yFr2m2IOW4mSkuNQYJqLGJU7/jdOvc3DjGunYj5gVya2wY89yBkvw8J8t1oF70QWaKKEWPqQTvuKuR2AgmJfMSA7vLnfqAA3t6PhH2KZUqKwyrEkG/Sc+QC6+D4E0QAOkA99K9DDG0HkWMsjafN8Dcjd0ayIvUbrif8nQT/ygkSniuhf5U+GZfwTiSoS9BOe5/HVI6E6zA48AsybsCjP/V8tCOGvMh9zEDK3RrjLRvFc2UjoX0FooM8JKB2E2X/Dnvsn0ECrAIxsNlEzOBy47OVsAblIXpI1pl7kLArJIq8TzA8q5FyVOv3OhHdNXvM3URs1yJCICVrThGhiBYiui3xM01EHsRO420SobB3eDxTkIDMJ6pWDEPKeieKSU2CP/Xd4bbqCNd7EaLjycgieLnnch8wCI4/TIQplBhH97nPuWjdTUP0lNyPRYguk0WrXDg567OI/p8yPIfQlfBcigT05Ya5zG30+F4tUed1AprTecZ7eq4ZKRAbjIvdftbK2tuPAu/AvteNu8cQb2hFNDvSnzsJV+kIosZ4NVoLJWht7zQepxEhHzlEfd9DRA3r1Yimv442WiWITnqAT+ZqzXVoLMc3G+ef9r1Eg48aT4d8bxhSdPYiS2oNGWWlaw9Sjre472HGzYXEaf1ewtKYwmL+3Hhc6fFehdblLMQn242TamRVfQVtZqvRmkvzNMV/TzCMbe7rSvdXSSivE9xmAxw/bBw/AL0vGvdVBG/PJxLLJ+U6uf0x7FXG1wbg93KD//UQG9/LEN2PI3hLctnXIONAUuhL/H4hcTK+k0h11A1Zv0fQ8RyiMtKpuvrf5c9v0fXlL3+Z2tpa/vqv/5qOjg46OjpYvXo1tbW13HTTTacavNMK4EmvOqJ002o4tAQxnh3Adji6AFkVJqLFXo4WdwliCoeRUG5GDGQtUjY60IIfQyYHF4uJpNOjEDPbSwS470IBx5f6nakouD8J8VVECpcJSKgkV2WF20o7yzGImW5BwuxitXnmM8ialku4aIsJZSC1PwyYBlmJuff598WQ/TRxMOZ/E2XdDiLLyFT45RPIdbNY8B+/H964GDHrUYjpvgBctUMCYDhkL4DsVUQ1kBrEPPORFabNY1lhuL8Bw+8ik+Q5F8SEpxAu4duNl+eN42RZ2oOE9CPIzfS652M9UlaSwEqbgWeRovLHSFFKc7gXWYBSTFGjcZ4UvTWIvjoQXY0wfjvJ1Dnu/Yn/H0OcCmzwPGUjhWY0sk7/RPPCbwx/EnjJat2F3IYXG54808OrGl/v3YgWK5DCNZo4EZ5ix+YSZfaWEBa4PR5TOZFjcJfb7yWU0aeQIKv0u6+RiT/Mmuf2JxkX1xPVQRI+k3u5j6ha8zRS5irQ/H/bfd1J5KH7CvTfT5yGbSHcuA2e02vRxmQ94T5fgARSHnESPMWM4rb3IzpsBZbB3z8tuM68Um1XJutdstABdEPXdONnjduo8ZhmISVjtuGcadzeadgfQDT0sN+bipTD5NrMhl2/QYcnxqKDJ9e43znAd3ojE8CTkPU8WuN57icdzkmhC02IroqQkjiScFe2AeOhYBgceQnRxDG3czcRQzkH8bJq4zPN3XeA26C3Himaa9x+v8deTVhizQuYTsRC3oLotxFttko1P4eeJ3KiNrq9ZYhGIBOakpU2HPMh93cGtPF1/c750PCPfmcQ4cF4RP1kkja3IV7/496wlPch2fAV4X/XEr8z1PcbjJNBiOaTbDiAaCX114zoNnkXkrI3mpjHpzh9vUevn/zkJzzxxBPMnj2bIUOGMGTIEC666CJ++MMfsnLlylMN3ulDICe9kvLXCcyEks1I+E0HhsDg6Uh5KCVSo/QTu/RCMiWGfrkbPphi3IqQklCKXIe3IiZVhXbPh933BMQQktL3F0hxWYwEzmjEsCYhxvaa79cjITEMCdoDyFWQhxSgfOSOKUWuyhR/1IUYSRL6u5BVrhoJxGT5qCKSD+cY1sc8pqV+rw9ZPzuNg9lkXLEfXICUuLsEQ9adcG4S6ElAZiPLZCkSQBOImqpJEHfDX90Ln7oSCe8GMvm0+GPPSRFwMWQnS+l6JJCScEmB6/PQDj65628kFN56j2Wv8VqNlJYewu06Q/Pd/xhkH0IKYiuinynut0DPZE5h9iDhk9w3m/3efOQuWgW5lyPFb6fnZ4P7m2e8XGb4Bxnn25EgKUcCb5LhXuj31iKauINQZi9RW7k9EuJDbkaK6hrN9Rsb4NzbDP9SROPFHjvqt+tOKKhGtFthGrgIWh+AsiVEMHuP8bKLCGfYhRSZkZ7D8UhxuA/R6m5E6yke7whxWKjLMF2ElILkCm9HSk+ZaWALZH8IbXxKPY+H/VuRcVLv/p+FhvNg3OVu53n3vcD9DCdKmOX4fqPxfwV8OLlP6/0pRfT7GJE0+mEomOz7DX5mp+ftBbSu9xqXSQlNcz+esPCOQHRTjBThm/T7mDlEYvTdaL2ORrT9NFJAtiPr9TZEo62CP5Pf80o/O9XznG+8dfv9Ls/DQmArDLnOeMxBfGoakch4u2HfgtbqF/3eTOCbkHshso71IIt4PZlcqP0PQPZotJ4vRjzrGFoDre7zi0Sd8AVQctDjSkriBmLTW4t46CAUP9pDpOrqJPIZtgElMG62x7DMsO9CccxPec7q0Toab7x1ESfKW1Gc7ssw5qvIQ1NA8I+nyRy4+qtb4VOXEvGreX62iKiEcytS/As87q0ezyxO7ZXct+9WW79FV3d3N8OGDfsn988666z3hAv49CGQk103Zonhr0G7003IpdGNFIc2JGCfQ4Kmmag+0IYY4Ay0yBsR469FzLPZz/wYCf0xfmYfYihNRKWGZsSQViNlYbz7fAe5qzYgJjIOuUj2GL6L3N+rxI55JmJSy5GCtQcptGlHm1wUjj/sXwHZlxPVD2oRoywlGNxIJMg6DesaxGAPIqGz0X2PMVxbiaoZdchKs8XjT66t/ciCcB9x8m00kcJlBnHauoEoyzdZcNPt30cQAfBFRLB3heelGllJ0iGDnf6t17ClAw8j/cw+TnTTHEJuqw1AFxx4CYZP9DMVSKDfhpSUpAzfgKyKP0BK/ReJQyhNfuYhIuFyM2L2FcSBpHbC/bcDKQHXe7xrCItVMSGM89GVXM7JbVjk+zcYH6MQE25FtHCr29iHlKG/QtazRUQeydeJtERJITloeNajSimrkDKTLNFTBjyb6KnPsEwn1sVqw/kd42qG+7kYCdo+4iDWLiQgpyOL8zDCbVuK6GeJ8ZfGneI8k8vuWuTinISE+Srk9n6QOJDQ4Xs/MUydaK0OJWI153guZhLW/keIWMAUjrALrflKIg41xRDvQ+tjNXFI6F5E58n6c9j3x7ufZe5zPVJ+lpNR8jMKZzeRKqcf0f0aokpKGfDtQTDpWBwUqvG4NqC5riMyHeAxJbdki/F4D3FwbZnHdhVRy3qJn30VrZUyROfDEA9o8LPf8L3lHkMJEWuXbfyk+NTk1q0g3PuJ9lMYSZnH0kokSq8keFjFgHca4deH4awLiPjFQqJU4GzEG0aidX4ecQJ/Ghy/DrKuI+qq7yYSOw9C89uCFMoy4sDHAf9daZymMJV2w1tKlIUrAP78P1+MZw6BfBmGnPEutfkODP3Ob88hkI997GOUlJTw9NNPk5eXB8DRo0e54ooraG9vZ8OGDacUvtMu4JNdHYgxJYvZdfDrJ4n6sHXIKrWCKKaehPwYpLjcR2TKn4MY836imH0TErbbkDKT4lgWosW/ixCsc4mqA+cSlQdGIOazElmOyhFz2ELk3CpDTKoUMZRLkIL1gn8/BvwpkV4mT+PPTsy/x8/NI4Luy5FQWIWEoy1GzPS4XkUWp3MJZaQTueieJU7brkaCp9p/93lMtX7mbOM8GymWya3zJhIgnYa3nGDOhR7bOiJOawORNuQgEihDiRqbbxGHO0qQgpPgW0XERpa6nwmGeQKZSiDDFxBu6jzP3RNEPdvJyOqwHlnvVhovTYSCvkS4z8R/vTMA/+kqJoLiJyDaes2wFhHCrRnFPhYTSWonEIpltmFJAmwfYcWbQOT2a0E0MgfRczESxs0D+ur0c7ON4+RKnOtnpiJl42WkcOz3fFyPaPpiz8d4tKYSjY0mkvG2GPZ0gGQk2mCs9Ry0u78iIqXGA8ZziqlN7stnibrLXYRQ7iTidY/B8f3Iglli2CqJE6udHu9IwzQXra1k/c/2OOqQwpXWY+kAfFcRQf7nESlSDnn8FyABX+e5mEykEenx/21IOR/kufg+Ua6y021NNY52I9pvRwrpM37P8ZN0e95/fkwKe7n/z0brpMbvlntM3US1lWSFriTKryWr92UDfu8zLms5sRpRB1Htps+/LSROs78zAJ9dhAX3mGFcRFhntxgv3zeuGo23g4b7VsTbK5BFfBYR7jCZKG05b0BChVxEG4fRxutyIray1P2/g9bFBMGQlebnoPsbjzZZF7uPDs9LD5HG6jXjYwiRTWKs53KCcTPOc5Fwdiqv0zGAJ72++93v8rOf/Yyzzz6bj33sY3zsYx+joqKCLVu28N3vfvdUg3daAfwXr2NEcs82OKuYiEFKAnEWYiI1iCleRaQTmY8sCVVIIZqGCLyJcLV0IA4zmqiLm3avpUSOMrtQGYEYeg2yVOxBTHU6sBH+ZpNhexy6kuVpKGJqm91/F/TvIdLBlCIGcwsS0M2IIV0Ibz6NhGRSbvOI02r1hns4wcwWIwEwBgVqNxG5C0HMtsSwVBDWlFb3uYuoRDHN49uDFM+Dxukbfv/bxvVqIlC7kbDS5RuPBYjh1yHLXz5RPmw0EkBvcqILaLdhrzHcuUQd2wPEQZfvez4+SSjFlf5+DAn5ZKXabXiSoErxUblIYCWrQr3H9oKfuYqwULUPwGVyK482rqo9rqmI5nKRwBhpmF50Ow8YpkbDfAYSZn+JBNMwpOTlI4taoZ+71vhrQ4Iv1/OULK7Fnhf87nT3vw/R1GGk5IxHJ1+XI+VsF1HebRBSpv7EOJmD5r4A0Vi1+/oqUvKmIbpoNL5XEjVzN8PRg4j+BhkPs9DBiGpkdVlMBPUnN/lqw/YmZN1kvDu+jDM0T/0txOGcmUQYyAbCSpmD1v8LHksKPyhB6zBZwss58SRtH1obv3HbjxOc+i2i0skMz8H9fv4a42suUZKwzHOVYuHG+tl+oAqObEAW3Co/32T870Xrus7vNhlXrUiBucTw7FUcbyamtAopikvRvO9GVtJaw9JrXFajddOP+I6tom8/TWaddT3pcSZr8XmI1kahjWUTUa95LLIygizW29C8j0E0MQvxwYcM4/PG5YVE/GMrUcc3bciHw5gL/f50Yt2mzU/6/nMyvJVpRJWStKFNXodmos58UuYOeVznEtkA8hBPqkXzf65xlhTwPI+/wvNyKq/TCuBJr7Fjx9LU1MQ999zDueeey7nnnsu9995LU1MTH/nIR041eKdjAE96nYusYp8BFsLxByBrNlLkNqEF/hRRuLwJ6IbeuyB3g/7OuIWfdTvLiFNmbyEGcB8nnubqQfE3NxMlqPYTpv7NRGWP+cRp0jrgWviDFFc130mlRxPWjZWImV4B2cnlej0SpAXAx9xPqT9Pwzk5yPX8ChJmY4gYx0rDsRf4XDX8aaOEUCVilnmEq7KCcD/lEbnnmgU3zxuWQcLP2w/Bmbf4vTnELnek4Oce4+ImIin0TiLtyN+iFBGHPKbDfr/UbW4kMvnfhHbUI4kA9tFIYOV47voJ9+pwj3E1EsB5xsNYpADUoxjHOcgKOQsp2iBl80HPyTQkAO4krGN7/UmKVrISLjfcg4AZcPxWyPosoVwmpayTOHU4klD2DriPFA6wiyhe/7Dv7yJcsWuRBWKE56gL0fh2+PV+OCsH0WElEuq7VYZs8GREG8ORm3uO323zd43x5TjLrhugYDpRwxZCgUPzcfwlyEqK6GrDOhetw1uIROZD0ZqoRWtnOgx+ANHcIeOnHq27q5BgbUZzX4yseeOInHwpNCPfeM92Gw9C9icIl2gRsWHZ5TF+C9HLee5zFVKwi4zbMk4su1jo8e53u2cQrtqeeO7IyzCkAVnGipHSssnwpvCAycRmZhpaD63u+0vFcG675qwMhqwkcu8lS+XtiIbWE7WzGzWe4zsh6z7ikM/FkLUGKUVlBE1fAxyB3idEzmNaPD9j1P6RPhhyAcHPiuDtFXDmNWRSHRXcgWhuDVpDO4gQlB3EQac6FBaTeOQcz+XDZA5NHJ0Kg5d6zlMM4XzEZw8h3lODrM8zCWvbI4iX1RDxfjPRuihHfCQHGv4Bxq3xvGwieGsDCsdZjVzZGw3/q56n+URIQgdaJ8WG+1Uyp7XTwaPjT7hEaCuZmu69d0Hu6UCu9+yVn5/P5z//+VMNxj97nY4BPNm1MEsMsJJMBQeGocV4GZGzbyRSPHYRZY0uQa7LDWghOw6t9yXn/lyKFJ7ZhGsgByk2byBLzTT3UevnViMr03qkPB0mrD0zkJIyHDGV7UipehApN5XoZNtspGS0+p03/N4ZKJ5pJhIMKdj8HJfzetjw3ePnBxHK6mEitmkLUojGEmXLKggX1j7EDN/we70ozq4NCfAXyMQOHn3RB22SglME+1ZB5WeN56Qk9CHmnCxkO4m6ojlEPNwaIndcPTAJfnk7fPAxJGRKkJAbhpTzLj3DEePsGY9hre/3eC7mIcH3OLLsVQyY0xXGSy5hZUoK8wjiRGopoqWPu98k1KvQ5mGa53YEkQcx22PrM9582pChRIWYHKLSwxHjYByRn2wsUjxe9He2n3sH6IDW7VA21OPt8DNVxtF6w5RCFI4hofwG4fIud5svEKURJxInqMd7vHmG92VEIx1Eypx+j+GQ8ZWU3DnISvonHscmj/0g4YJLlvI9RK7OaYg2NiIL9WSP5Ry0ZpMwTrQDsvLO8Jh6ifx2w5GwXm8cJYWgw33tQTQxlEhz9AJa52k9XEucTk7xnMVEubtr0dqdA7RA/72uK70E+KyfvQ2tsW1EyiIr67yE6LrTuFyrMR7aAyV3G1/rDe+liGdBbAIrCFdtj2FKXo8U9tKIaCAf0Xoh4W7PJ5KGJ+9BDVFWbYlxksIZbiWSZieL/gQ//z+RVewgkaz9KrSxXkC45Rs8N8nid7fHlUdsdLajNb3dbdURB2ymEBuSWsRPniIs0Hcji3mF2y333H2FSPD8IBz6KZRcTGxgc9zuOCJ8pp8IC2gjTp0XIjr5I8O1E9HCPqRE7zKuy3FOwlMYA/iFdzkG8C9/e2IA77nnHoYNG/ZPyr796Ec/oq2tja9+9aunCDJdp13AJ7vKkWulAzH8QiIJ8oNIMKfTdOcRebEWI4GUSyiEFcBnIPdSKJiKmMFt7mcsEljbEQO4jVASxhMxdZ8hEuIeBG6B3lX+7QvEoYvdiBndR8T5rEQMbqu/axAjKkOWvTbEBF8gTiq3ALsg+/cIheRLxMGKuxFTfwRZlwoQc+xxe1Ue3w7DOAxZNOYQ8ZDJbdZIVFfpV/uDL4VfbiKsoxOgMikbTWhXXglHtxM1azcRO+ZkDUiHbiqI+rW2Xn3wZsP7DlKORqhNzjb8tWjue5ES0GBY1w747aD7HodcaTcTiWhbDX+yIu0iKiUUErF3nZ6bL7qPWwnFrohwb+/0328hod3mZ2Z5LqYSylcLsuQ0IKFRZDwmur7R8/hT5Padj5SJScZnm2sdJ6U5B7nHLjdsLcZnh7/Hoo3J55CSn6zPo4k8ifm+n2LD6oTDA3cha006MJAE5nTCYpnc+tMMwzC01s42ft/wc/ke7wiilm2j260yPsahTVpSwA8ixeIYooNRxMZjLVLMy9xuctenEIFtxtuPiDqvhz0fEIdqDhmWKqJO9hXueztSHK/yXI7z+31obTeidVQG2c9obrjPY1zsNkAKxWEix2ANEb7RipTtrwG3QclExJv2GadnEAmhs9HaqSe8A3lEebc9nsMWIia6Cs3zpcYHHssxRLfVhjXFEVch2igm+MB9SJHbTsTQplCYK4A/M96b3f984gRuKVFB5BzEk1rcXhtSNJNiVYKUyv3Eadty4+wyw5fCARxaw/VE3s07EG3ch9b6Qc/VRv/9uNotgihJ2O05bDe+VnhueogDHkVEKMBh5Fae5TanGJ5Cfz/hMdYat6ev9+T1gx/8gDFjxvyT+x/5yEd49NFHTwFEJ16nFcCTXev8mY2YS4rNSbvsbyBm0I8W904/V4YWcwNiYBchYfUD/z4TCc1GtHNNMXigXeZ1hMtsG2LOH0UuwNcMQw9QC7lnEIlOLyOSGc9Fu8XJiJEUG64UA7QGMaNmojJIH9o1b/D95R7/60SesaQYZROpCRoRky9FAmcyYrCliKGmWLxyIm/cYSS8xrndawlGXknm5OsHryTce4MQ056FXNLnAhfA4MuRYJ1IMPE24pRyp/+vISxoBUggj/QczEGCqwIJmIPEQYscwqKWjQTobujfRJx6nEiUypuDFNF2QgA2EhVkQIJqg9/d7t82GraLkGBLbq4CpFgUI+vcTiJGs5UoSdWD3KEdSKiPJE5DNngecomcjs8SMUsPEyeVewiLxEIktBNdlyHX9HLPXanb/A6ZCjWMQopEk/HYTsR8TUOKZyvh0m+H4e9D9JTjd5O7fhnh9htnfE81/lYQSZjXGf8HiXQeBf4eggRlp5/N9f8D6e1y9z/VY06WyHrDfC+RdDhZuWYR1URed387/P8wj6UJCep6tCYnEiXKlnmOJqN1tZNIfbMD0Wsz/N3fEpbIDqTojiLiSNs8H7s9VxOJuNUJyNOwy/OQ67av8bvJIvaq8bDfvzcgersK0e8W4uR5G1EVqcrjmkHEn24mak0nxbAfKZvtRFnFQrRGN6DYzRTTWIpoMilEH3d7WzwX9YbjkHHY4v83E/TjMBJmILoYQrhvD6G1foioGvSm311HpmTmkQ1I6RtPKKs1hjvFs1YNwF+Px5g2suXOoFCB6D2HOACTeNFBf6YimtjneZ2L6KDK41pArMsJav9In+cv3/dO5fVuxv/1/yfD/h98tba28oEPfOCf3C8tLeVXv/rVKYDoxOu0Aniy62J/tyOG3oOYQTp9uQExg0bE7OahhbwbMZzR/p7idurQAl5PZKOvgkPr0CJvJeqJvgyZKiE9iBmOcL9HkPApRMrCFCJg/zBSEivQjn44YkjdRFJiEGNPhwNS8P4DA8aZjdzNHX5+BGJOmxHzm0oIpWmE9SgFuJ+NmNxOP3uu8dhCpMzZbdzVIMV3lu/nIKVmuv8e5Hc7kCUuuaiS2zG52EuIE3EXG38VxEnTcvc/HAm4rX5/idt6wjC9iRTKFDhf6jnbTFhOJ0D2pUhxGOG+e+DQds/HETT/M5EwO0QkMt45YGwVbqMcMf4phIVjveGrNsw9hqmLyPuVgs1rkSV3xwD8PkeU0JrhPsoRLdQiZSBZRq5FynkfoVwWES7YHiI1Sz5yQS1CysZspBTWE6dfe91WoedvEJH3LFnW0nqqRpblakR/l7nffYQCtoBIg7MD0e0kw3gZoRBVuf1kNZyB1lWJ8TUZKVB9brPe/VQjBe4B99FAHOLI9buFRCgIfnc8sgZNI2JIpxMu/2sNbylRvabA/U9GdN9HxOB1DBjfK2q/ArSGJhkvh8nEIVLnNuehDcQ6tD5uMPwdiO7Smj3g+akkYvVq0frdbVwdRBus0cbVHKS07yNyJhb52REe5wuEBRbDV0ZYCPe7nYTrdsJqDMFnWpCCOMF/p/CIZCUcQVjUihA9DzJMM43HbOIwjS2frbcj2kgu4rTJG+1+C/T/8e3G13wY8llgF/QnF/2dSMGrRha4HQPGUkhkfphrmOci+piH5naUxzWM8PZMJhTRrYYlWZrrEa+5jDgRnaz7+2DInbqfSRZ/Kq9jvHvK37H/ZNj/g6+Kigp+9rOf/ZP7P/vZzygvLz8FEJ14nT4EcrJrL9rZvYR2qI1IMPw5YqhbgP1wZCsMqUIM/g0iFipZPTr87iQkpFaQCajuetmumNFEIXRQbMpS/90DfJkI5v4WYkA5iKk2IUa4CWXqX+736pAw+IbH8gZyjfwF2lXfaPjOJ8qfFSBlaR8SFn1IwHcgy8gaInVIP3JL5yCXx/WIOe2AQ09Ayb3oIES98TjJODmDKA/VQ5y4XUUmziljHUo77pWIEXYiQdaNBF6927zL473Cz2+ANx6Cc0f4mXz3346U5rNRrd1Cw7+MsMJeYfzMMxzJWnCXxtZ7L+Qm11QLiou8BbgVSvKNm7kD5hFkmZmGlMyxSDCsMx7uRq7VSuNpCxHHNMlt/yFhZSgxXsqIXG6TPIbtZA4D0WAY7zbs18DRPhj8CSLWaRMShEXAs+VwRYtosZOI16okXFcN8Hc/hY+s8XzVIeE+gkgVshcJ7DsQ3bYaH2U+QLDc89xuOLYiGvs0UijxXJxvuL/gdochpe1hw7bF/z+OaDOFGJQa7m3q4+hOvZ47Ga2/+4j0PZ8k0qYgvG27FCZ9goiB60dxWOcYX42Gp8nwj3AbU1GsXsJBCtu4mrBQlnvOV7i/mURljy7P2UK02VgNfA2GXOi+9yJlbQGi5z4iTGS3711i3HcQsacvERU9kjU8B63lbuHi+F2QtYw4wNWDwhE+idZPOVKmv+525hNJ5w8hLXU+Ck3oQDQ1kyifVk5YZkcaxuRVKBOeW1+GshxEN/NQKEHypmxFfKPc7SYr6hi3NxNZOZOCt4OwBs+BsmlEycNG4+kniBYOoQ1WNWSVEmmwrtVv2ZOB6XDkaRjSS3hyhgzA6UhEJxdD78WQO8/9nUEkoV5vGK/y3KRQmwo/04kUvAZi8/RDRJsvI56VjfjNFMFLPuQmpfL09Z68Pv/5z3PDDTfQ19fHjBkzAPjf//t/c8stt7wnSsGdVgBPdiWX2m1IeK9Di/zP/HsLUAFD5qEFmOJ8yhBz6nUbI5HQr0PumGykpL3gU25r3PZMYAX8ugXOSu7OqYiBXouY9vPuPw8x/ibDttn9bUfKQS+yHu5HylxiLJMRA0sxfDVEYtEpHt8SwZFxYxUjRjvev1d6/IPcz6tIyO1FAqQNSi70KdUFRAqSfYa/hwh2/hoR37XEeCnwvfPc3vlIwXkACYYXiZJOhYZvjdvcQcYCcO6n3fc7iPHmEIJiB/x8Hfzu1W6niUzuw0yg9ja1wxke+2PAHZB7DZkqDMdvgqybPVeNRGqbWYip9xCJpA97Hp9FytM0JEQXofnfgqxIR4gckfmI4S9BQv45wloDcj2+7L6vRZaYBUihGkVUPmkALobBbYa9HAm/8Wj+pwPfaNF7O4mUO0cIS+Jsjesjy5HgGo+UlRzD2YjoZhFSoL+M6sne6XlaBFmLjJfDbvMxJMi2ow1JF5GrrtB/5yJrVx5SBEYavh7gLWj9BZSt8ZiuRxbcYZ6DRhg803NS7P4c30gLorU1/tvW/EkPAYPgwBIYfimhpAxDBwoeQYrTbR7zvX6mUrjctg4mXWP8P06kDxmO1koDERfaTliGkoXzQeN3JNrkNPtzkZ95kYzwZ5DHMxcpjPlo4zgC0X0d4kl5xCnlZPm6yHioFfonrUHlJdcivnG/v+chS+WxAXjc5rlJ1v5Gz2mTx1JInHqHcOslS9ZIFEfYg/hGH5TdjdbBWI9vkn/7OOJZ5xO5K5uQorjZfRcbnn4i116CNx3+WolocavH/XnE3/MRb6lB638RUbZvL9qE3OsTy4nvXOu2rkS0Wma8T4HcDxGemjzj4tvG1fVozl8n+Fa3P3MNfznymGxBnoyr3dYcNKe9iE8UERb5Lk7tdYzTlUBOct18880cOnSIL3zhC/T29gKQl5fHV7/6Vb72ta+dYuhOnwI++XVRlhbZAaLc1yjELcvQwmtAz8xFVoDdiLnWEycDpyKhNwcJ03P9bjERt/QKqp/7CJFYtwwx1zokaNf7/jH3OwYxgzmEK6fasKx1nzOR220RYuaLEdO9m4iHq0CK2D73P87tn0+k+Xg/UgxGIKa2yeMqQkLomNtqRYIgWfDmIabWh5jq7ciS12Y8rTJsnUiAzUAu7FnAF/PhS93C/xikHE4kTqsWI6a6Bvo3Q/YFxvcspLTtJ2oD9/v/CsN8hBAQHUiI7EXCJ7mWqwkLSyNhtXrVY5vv/huQ5fMFz205kVj6kNv/DNrJLyRO/S1AQrDOuKwgagm/ghTyFjLWO7oQrYwlToDOIoLYz0NC6GuEoOwhDqSM9LzlA99DisYe4389Up6v8N/JjdmMhMxtROxVD0GDZcbTAiLH4GjNCePd5mX+noUE9g6i7Fg9otU7/PcwtIZuJJS1HcZFinW7Bc39DYheQDSxz3hdSLhnm4jY3bQxS5uYqcbLU563FEe1Awn4RxBtFyLamUaEBDS6rxpCyE9DCl+NYVyF6CdZtlMc7hq0blPM8GTjehtSENZ77gZ5vAsN2zbPV7LMz3A7s4gDCVcRVrBliP7G+ZlWz89m3+slTjsXEZasTWj9vOV3biQOghxAdJrWzQzPw72I3/QReVNLje8UMlOD6Hctkd5qJnEa/NvGQ6/xUI3oeZpwdnwDZN3qcY0wLtM1k8jltxvN7WbELwqMxwTbEn8vR7xyn8e/l3AzF/v9buIkc7f72ov4x0c9R21EqMQ7iBYmoDVcbxzVImVuuOckxVPOQPO/BvGBY0Sy+o3uK1lPc/3bKONgFnECvBp49T9fjGdOAV8JQ3L/9ef/TW32wtAnfntOAafrH//xH/nFL37B4MGDqaqq4owzzjjVIAGnYwBPfl1GJGmeixKG7iXi4Vai3el+xOR3okUNYgYlcHwVYZWrRQt5G5EuJQcxgB7E7PIRc+lzG8lV2oqYzDjfryaSCL+FmEcpEhZbiNxijyEGstfvvYCUirFEKalxiHE+hphtldsaTZT6WmNYn0aMcShiiE+hXHudiNFe4THcghSkbH+Pc5vlRJ3c9P0CsorNNRyfcXuvdIcbbizaCTcTcU8vkjlVm30eoYS+CsenIYUyjW8MYvI9RCD7MILBtvr3jxLWlx4kQJLFbKPHnWK2VhrX24mUEev9aUGMejFSSpMi3OU2K5CganPfG/1JFpJqxNzzjO99yGIz2u29g1JOtBBlCn6B6GwzosU2ZLXb7Hl8xONaiKwXbciC0eBx5Bk3s4lKGuXus8n9bEZCqwIpGzke151IORiF6GIzUr7P9XzZjX/oAfdT4/HdhOjrHj+zF958ESm3wwjLWD6iiVn+Hqg8jScqc4wjqpFscB/JyrfS48FwTlN/XOIxDkJhEhCW+1y0xia6n2Ljoc33RiPanOl+bzMcqe9it9dvvNQRFvCxxlklovsJ/r0VbfzKUbjGSMOZDf0b0PxP8v1pSClch1ySU91fOXI7VnjuWpBiN8742+J7lWTCNphCVCQZR+QSbPCYetGGp5ZIWrzMuGxE663HY64FtkNXsrZtRafNQYrbWLQm0treRiRPv8p9bnCWg43qI2sykbfyk0SMb/aAT7Vh3enf9hNWtWq08a0jaCZtDnYTefj2EnWU+wlPSB5ah/MQTfV7vC0eV73x30OkARqJFL7kHm9DCu8S99eArOQricMiM4l48d2+n2LPRxjvhz2mSkTrp9oFfLJ4vv/bz2/h9b73vY9JkyYxduzY94zyB6cVwH/5mogY4wQkRJJLajdiCI1E6pNetCj3+JlSyPoQkdesAi3Y8cjq0oF2zstRELwtW0efJBSQ+gH97NPvjHE7KXbuTcIi0WO469zGtUhYXIUE2XaihNfVSMm9CwnWy4hcWecQdXeTi7PIv00zDMlSVuH+DxFutfXA7yBrWS1xqji5YfuR4JlInC5NhyJ6kED6c7eR3C4F0PW38Mtf+P1BZMpH/fp1Iuh7L2SVEznMehHTTodgVnqOjhFW2IuQMjSCzCnAjOvvTb8/xviv8TNjiBjGXUT5uG4i7cPTZNLa8FUkWAvI5CE8/gBi+u1+J81rNVFdYyNKMdJPlHlLf89EwmWUnxuHlJhXiTrBfcbDDMOf3NQTDEe353S/56nR3/OQIn+R3xuLhNA2MgXsMzWZh6K5r0PW4rORcpfc6ZWap5LJiNYu8nvtROqNWcBcOGcUWkfjiFKFEDkI13hub/Mc7iMqlYxGlpe9xsl+z80Mw9hDKJEJP3XGZQrMbzXMLYjGNiHLzfPEYZwUW3qQyBvYieiq3e0eQ7RSY/w9izZ3xSh34W7CrdtPbICyifg+iE1YqfP/9Rm/rYa3nHDRP4TW8AOo0gqI3kYTIQF5vt+JaKUa0cSXkNK+xuN5hHCXFxC5Mef63Vq0piAUlTxgJhzfo7YLriaU3aFubyTaKC53H8nFPZ6I7etGyhFEqbRpRFL0tLHLQUrdWP+dFCN8b7M/Y4iavV1u53JEy6XIgm1LYybsZRWitzbjo5RIr5TiKBvRJq8Mjr6F5q+TTGJrriZCcrZ5TGORwt6BFPFDRK3mYs3RkafRPKcDKC8immsx3oa67zrDUcKpvY69y5//4tc111zDW2+99W969rnnnuOZZ5751x/8D7pOxwCe7HoOCY7dwA9g30+h8mnEtMYiBtRHVPvII2I06tAOegla4EsRkz6EhPkuvzsKKYtPIcFRBYMvQMwhWQ9mGoYSt3srEjwjUcqYkYQAWokYeiURn7MeMYkVRO3KBrRbfZGI99uClIkzCPdMNax9Hi7ahJhkkWG4kqh+8qBhuhIxwO1EAugij2G1cTTPcLX6/yJ/f+n3oPBvZdV6HI5vhaxLkdAd4T4nQcEiKOgh0ncMAmrgrFp4cxOc8wn3Oxox9K3QvxSyZyDmDmLYuz0X5xBuvg1IKbrF938XMf2kcG8i4o/KPKa/QhbLg0gYtnqMyaI2wfPYb5iuQYr4NcD3IeteJNySe24QEag/xvA2EKlb2vz3VCIR7lyk/I1H81+iectUQzgP0eL5iBaKDN9oJBwLES3ne3wlhv1FpPB0DJin25BysZOwRH/a83QbiqvaipSspJzMQa7/GmSVWmXcryOU9HbhgwLCMrcR0VOu4a0hLOFrkRLRjJTsyw1Lid9PSuNIIgfkSE5M+LudUGaa0FrMh+MvQ9ZYpIhgHC0aMI+74PhvIOvTsPYxuKicqDTyI0QrMzxvh4zXF9BhLDwHXzCcU4kky48QJ2tHEom8r/a8Fnken0feiFoyCeb5KFJOmgi39GT3sd5wJ1dkHtrU/sBjX2w46pByNIKwTHUTpfXS+03Gcyc65DTH47sF0cYhb8DGGu85aG3tg1fugo8/RByeOObfvoc2mhPR5uVa4C3IvZVI6L3T97+P3MXZxAY5D9HVRODxIfAnR3TgqhzxtH7CxZ/ctS/6/zR/g5Ale7PHO9pzkoMU9q8b5hGe0xQzeA8wCQYPNX7uIhTkQrSGZiA6bTLcrYgGxxm/SQmu0jiHDDLuJhMW5zpEW98n6k2n9Zw2C6ev98RVWlrKRz7yEX7/93+fT37yk0ycOJHy8nLy8vJ4++23aWxsZPPmzfz4xz+mvLycxx577JTBejoG8GTXwqwo19SMFIhOpOisQwzjMrQwu4nSVaX+rQ4xn3VIQD0GvTsh99NIQM5AJ+2qkJAuJbL5T0LxRAVEDeHFiHkdRMxoOhISNyPrkneP7CXq3A5z+19BFoEKxHQvRAz+CBIGPfDzuyRDBz+NmOLDwEQ4cBsM34Qsgompr0IK4hbE0NqIWLJ3DEs6lFHlMX0GMc9VRKxYcpkfdJ+z0A63FAnnNUSuqyIkaAo8J2uMwxeMm72IIQ/x/73IItMIFMr9WPI+FCNXhZSIve47xRYlpX4JYv6rkBI1wTj/FhKOKwl37VVEXNdwwbrvGajMISqUXC4YeB0p5ZOQ4rzXc1tBKPyboetpKLjS8zsBHfx5y7CMRIJkWT58p1u0dwSYD/01kH2bYRxJuOxSzGWj4b6ccGOlDUBV9M8Ez0cRii3r8txMQ7T2J0jQ3W0cXO05vhre3g1n3uexLQY+4e/tSNDXeo4nINrJN26Se6zLuEgxVzXEYadmz9GnDfMY6F8H2Z+GfT+ByrFIEVnq92bBkYt9UOtV4SizVmcahhRmkdbOJM9lHpF2ZySixxqkiKwmEjcXGd8rULzcLkSDbxjHxb6/GdH/UqQ05aOwix4i1cxstzMCWQeTBb4NeNJtvk6EYEz1HD7v+dqA1lSl8fWGx5BvGPYSFq0U9lDvd+vROlgjvNLFiafxS+DXl8JZN6M1Po/IB9nvdtf7e4px9FPD1Ga8Lvd4B77TadgOoI3CFERTuw1XDrGhSeEQjyH+dJA4xT8Vrfdb/Wk07Hf4mTsRzzwIb2/QT2dORIrii0RC7GXEgbgUw5jiP/PQ+plClAQdj/jOLI/5K0Qi6SJ49VY4/7OG9UqP+Sa0ftYTMaZ3wKGLoaSYyPvX7nFtNXy3IbmSNiV1RIm6x09hDOCl73IM4DP/9WMADx48yOOPP86Pf/xjGhsbT/itsLCQmTNnctVVV/GHf/iHpwhCXacVwJNd47IkoCYSFR3uJ9J3dCJG8Djhsn0MMYRyxNRH+u9VRPHzOsJ1dggxlxYkUD5OuNfw/S1I0KwkqpFUG4YytJOf634OImXym0gIzEGC4gASeh81TKORFeJbiCnnIoafYmGGIeE11u/N8bhTTNEaIo9YsqQNI5LgliJFM8ftliGGOgkxy+SaS+7d7yBBlwR8D5Go+RiRWHix+7YLvfd+K9QtxEGBtaiG81DjPbmWiwihV4UY+HaPo8x4Huu/2xETT/PU6t9+6rE1E279WUjhT4cWkpsoh6igcjUSIBVIqBa6nzdQTE+X568Qze0NSHm73PhZj5j8QmShdUwYfURc4l73dz7hVh7t33D/Kf7xeiLQfQei2Wbju8owrDd+CgmL2Wj304IEWBMSmGnDMgUJzVHu53+43Rbjrc73xxOuyfGIZpN7sNZjmIyUh/F+P1lqyokTtP1EZZxiwp263/3tMt5SyMIBoqTbION4OZGo93r3Odv4rSPiRIuRgn8lEvR7iFPTa/3+JiSQFyFloQspZ7OIU80H0GZoLVpvVcSBlLSJG0JUjUmbUIgKKq8S1s5aRKuVxJrpRHS+gbC6Tydi4JLrdiHa6OR5Lq/3/QOeg7WE0lPh7w4i9UsfUlJXE4eZQJuVaiLt08B5TBviJuP2MiL3ZSuRXHopUpQ2e+4uRCfeh3kenkV8ab/7PYgU3XWEm3W84ax3W5vQelmPaGKt4V+E6CnF8yY4Ei9OG9PkEk5hAlcQh8c2oc0dnrN69/09tHlMsdf1cHwTZP2O5283QcfnE/W4J7j/xP92eTyb0TpL9FTu8X/xtAL4Xr3efvttfvnLX3L06FHe//73c84555CVlXWqwQJOxwCe/EqWkqlEcPnvocWcXHbViIE1oYV8GWK85yJmdB+y9CSh1UMInxGIuabA8iJ0Anas26lAu75xSOkZjZhfHmKoFcAFyKqRFNBmxNCnIHdJD7ALWn/i99qQJXA1sjCsQ4IjjWMBEYNW4+dLUM6sGYgRpbiTpAik+L+HEdPqQxa3VUQ5rA7C1VlNlNqqMsyv6p1M2pUcJDyS4B+JBFkPUsg3C47ciR7XEiLlxRQUB3af3wMJzvWE5W4Nihcaj4ThKDTPI5EimE+UjXvN+Oo0vq80fFOJUmhlxKnheqTM17mPfuKEZTtRQWG1vw/4Xr3beZiIy6slat9ORQpFt/uqd5spXrABuYYGESmLUsxQo/FY4DnqdP+HiPjDOiREdyK3ViOhEB4kTn6WEYce9iM6a0IWlquN03MJJTJZzvuRIJ3pNsrR3C9FtF/g5y4j8gHO4IQULZlEwU3up4yo/JCU6xSL+KrxPwOtm9Eee7JsJQUtxevNQmut1TCl8adNieNNuYbI9bfS9wr8ed5tJvf2RxHNbjCMjZrT3kuN5wXEgai9fr8UzfW3kKK4naCrs93OHvczlLCcj0JrerjxmVzfUxGNrfK9aWj9DB+A49mIDhqNqyFo7RcSyek70brYhnhOntvfgdbM9Z6PhUQcXodxNYGo7jIZ0dMVyDq3wu2n+LluFBIzD83zAeKwx16kuKWN1h7f3+7xd/i34e6zGtF2HpHLtYhI3nyYKLuW4lFv8BhGEIeVRiI670P0i3Feg2RENrHpSa7mEYiXr0fhRD1ovdRAVg6RyquIiHvLQXSxmQj5OIA2SLMM1zyi4kkXmdChU3r1v8uf37LrzDPPZPz48Xz0ox9l1KhR7xnlD04rgCe/LkYC+C3E0Byw3f8WYlj1wH8nSsOl3WGy0tWhHWo68HHAz5QjRrYdMZT9fqebqPzxCpFTsJ+MUtb7pNu4Di3+nxL560a6nU+6/XwybrWyG4jYuwnw60cR055IWJ/2I1dMEVKiUmzcenQQ4hBiglMQE55F5Ns6iHbobxKpE0DMtYRg3slSlU2kQqhGu/N+opLEc0R90O1IgI4h3FuFRNxaGVFTt4iITTsXMegphvMif1oRU1/t+3cSbqluf55H1tjzPL4qQohc534GWqO+apjmEHnHJiJhdBmhvDxHpsRf106iFN9KwiWbLG5tRKxiPRIKSYlv9NgmIGWjAbm2QVaSPKLcVh4c3eTfcgzfXkTXh4l8hDnEydUxRHm/ZMls871tiHYa0PpYZxjfIZS8ocZFUk4HbhTWGd8PEsrNtSh+DOIgU7LAFbifxYa1GFmsiz13kzzOYYiWO4jKGXPQ2sFwFxBxu8VkYg57WwzH40AZ9O5Bc16B1sR5RCqXZqSs5BBW5/FEZoBLiEM5jxDW5uQS/yjkvp/IDTkHKc43enyXeQwr1favW+D4E+7nUeSybPbzK4hyki8TilSZ5+5OFHtWiOh9hH5vPYjW6uNE3PCVnqMNaNPQQsSjDiXSz3R7Dpv93ghOLNPXgNZFin3EMDQTh16akXKXLIgdxs3X3MYYojRhkdtKcY2tRC3qPOSuX0kod9ejkAgQnXYQSvIIwurajnj8PEQb+R5PLhHaMdp9pHjZfiKMooOwutcZb18hsgRU+J0m4oBSOaL7pEzuNpyjPWf3eVzJ8p88JAM3fdcSdZ3nIpo80cP4n3/9Ww52/Hs+/47rnnvuYdKkSRQWFnLWWWfxqU99it27d/+L7zz55JNkZWWd8MnLy/v3dfxbcp1WAE92bSUsPJ3IBP8VyH6YSHxchHahX0GMfCpR/qqcyH5fgeKmxiGm1KZPf3JTjATOho31iJlch3axeUSs1HjIvQ8x4xL45aPupwwxkhXwRmLM3UTlgWQl6kEM7DCcNRWOvq6/KUVMqRdVvVhPnOpLMUqdRGD+CONiDRx9GlnQupAl4Uv+vwJKriPSLWQj4daAGO/1iPH6oMnP9hsnKX5xJByfR8RHnocCv9chZt2v9vp/6vc2ErnnZgD3fTJS3ST3+kbgEHQ9Y5jKhTOKUOxRIxLoczyGC4ki7/ORi6fE+BgGv3wAMePRSNncjmhmOidWCMgmchFWIEE7DQouQLRzMRKMI5BC34aUideQUFpJlLmqJ+jxVcKddxXhkmtAVowUH/Q9GDx9wLgu87tnIzp+zWPLhdaHiNPfXUAO9N9p+C/0nN2ogxKsIDYZ+5GS9rBwTI/m8cjVhvcMIvl1s5/pJzYZh4zbbkSTFUgJme7fjxGHi+YToRg3G//bkZJRgGg9WYzWezwb0KnyaUSN7M3IBbgAci9HG4tyzUduG5FQeDRSRvo9linGfyG0Xo7orAMpnDM8lmOeq2ThGwdH7kcK0XnA/0RX8hykGNv1HvfjiC7Pg7MWQNYNmgtqUOaAqcSBjnLEE2YKdhYRJfZWuf/9aM1tFqxls4lT54NQDFwboSyleUyblzKPKcUnps3JzUSt7mbDdCuKVZuC+EE+istcjDa2x9DmaJ3nrRat+f2E2xqPZTeRkmW3P92I/9ziPhehihnJgmf+wQFED8lSNpGowTuVCOVIfDJZpJ81rncZLzs9N4mXJSvcEsSLGhHffAaF3tS6rT6i3OBHPba1wCU6mJapsjMPyYVy4xvCKt0JXfcTG9AG46wCbVB7iE3I/6NXbW0t1113Ha+99hqvvPIKfX19XHjhhXR1df2L7w0ZMoRf/epXmc/+/fv/kyB+b12nYwBPdv1llhj/LYS750UUR9OPGE5ys4wmSok1ImZ2KRIUya0xCgVwLyBKGx0mEkcPQkxkE+FSqyYy+M9Ei/8CxGSnEW6p5I5pQMxiL2J4W/z3Q0hBOghcB6/MgY/fQKTPeMLtjfI4zkHxaY8h5aSBiIWbZfja0E79PKTINBExZknpKSAE/GHj7hNIuS01jBvQTnY5IdAS3vIQg2xH1rO0I04xM1M8L1WIoR5yu8kSd5CMa+m1evjo2YYtxWbO9HvPG9dPofl+jFCUcpFCVklYykrcbnLvQqR9KPb7k4zDlgHvNhH5EduJ2sslSPj3ELnJpiCBtcvPf9FzlOIr5xEu7RRnmdyWeYZtu3GeQhmSpfpVwuJZ7PeTS+oxZF3a4f52IAF4CbIa7SFo7jo4shiGjECHFNqQkHvHc9Fn3MxGdHg9mucUA9iL6H20x5RwkOe52eG/U5zfs0TKjELj+3ZEFwVE7sQNxvNQRHfvh6PXwuAHkKJ4pe9PRQrcM2RKKB7Z7vEM8/hnIUXnUTTXNxBrbJ7xNczzlazxq8mkfPq7J+AjNxBlIWvR+mpDCtEkRIsLPS8PIAV8NFq/H0dKy3Ljr8E4m02cIr3QOL+HyJU3AtHVSrdThNbvXyDlCI9jGlK27jBMbyA+d7XfnU8ogZ2IBsYYnpGGNcUg7kIb3WaNPZMvs9XzUwFvr4AzLzX+piDaXIfopg4pczmIJz5NnLitRhvVrxDhLvsJ/nkAnbROCl2e5yFtZvMGjLvPeH/NbaS1WEbU6s037huhdRWUXemxriBihfP9bNow9RGHbIoQ/fYSh8v2Id6XwkfWGG9taLNTjujyDbTOiomk98WIdj5DZgOSOTj2HLDmFMYAzochOe9Sm30wdOX/fQxgW1sbZ511FrW1tVxwwQX/7DNPPvkkN9xwAx0dHf8fof2vf51WAE92fSZLjCjFdGwj8uTNIgKE24iFWu37L6IF3U+UCmpADOEO4tTZt9Au/U0imPl8JAjykNAbYFHJBLdfjpjhRbLQZE9EbtQHEEOfaDgeRcxzPWIa13oszxHMbjdhwbsKpfJY4PsFSGjtRgx9i8c+DyXrTYyzxPcbjI92j/Uqj63FcFyGrAvriWTEZYiJfxopbo1ENZOLPN6NSClpdVvJBbMC3twD50wlymMVGk8p6DwfMftGJAjaiMMpfR7jKOSuK0MCax6hkKSdebKEQihp7QPaTbGT7Ya9iAjiThbASYjxNyGhMNP9JmthElojkZAdRBS37/F7PURVg1KiTnM9kUKjjUgS3WW4QApfv9uYhmhvuufvz5BlbafxvINwt05HykIHIdja3X8Kd6jy81VEPdMqIvfcxUhpOObx1RDutJWIZkv92YosO895bKOQ0pOs6vvRBqoOWV/fIA7FjICjD8HgTyMlYySi7aVE3O4stK42EVUs6tA8rzFsQz32ZKFKsYNpnd+HlN50QCefoJstiKZfHDBHc9x/Nprjq5Eb/jWkCK13u3+CFO5mIin4qx7zTLexE/rrnQD9Is9HE7KybUa0ccC/tSNrb5l/O4To42uGa43a40JCGeki6pJXIHo+130nS3tyoVd5XkcZ1j1EecA2ZAW8j0gts53YQD/nuSklqixd5d8fgyOrfIK7UPPKGiJhdQcRN5jCHQoQ3ewg8mimMJNvew6GAL1w6C4X0Xk/cfjqHMPdTsTQFvr/h1Cox2HjcDGRRWCYcbfCeP+S528QcaL+XkSLic5qiKoo/cbVFuM9Wc5rEJ+dRBxwqvL4CgjDwz7gvt8uBbC5ufkEBfCMM874NyVQ3rNnD1VVVTQ0NDB27Nh/9pknn3ySq666iuHDh3Ps2DEmTJjAt771LT7ykY+8O4P4L3SddgGf7KpBDH0t4R5pR4t3EGEd2YuY2HjCFfUaYR3cjxZ2Kdq9rSFOytYRdXtTHGByk6ZDDT1IEFUg1/AI/35QYGZPRswrxU50EglQOxDTK3U/DUhxnIUY0xz3m+v/dxCn91YTMVTVSLjWuN2Vfj5ZlHKQItPv+zf5cwERQznGv1/o9+qMr1akdC4mYgunezzfR4IRJIivIQL9Vwmmc6YanylOqxAJsO2IMTYb583+/wUiRxfICvMiUfpslttOAqwJCeSknKwnThJ3IKW9AykR65GwnY8U1mqP/TnEsL+P6KnJz25xn/1IuCeGfj4SiJ1ornuIkIM8ouTXDCJ2sAvR4AS3vw/RaLIwjTNs/cZ5K5rbaf6e43HvMwzJ8roLKWptSMi1EOUChxFxWiOQ8OpAQrocOKQyfQxzW8mat884aYD+p42vaiImcizwIBx5nbDilCMlbovnbQ8hqKsNUwdwDAZ/1u3sJfJNjiUskq8QaVlWEwmBtyHFr5Mod1doeHs89hF+ZrHbyvPf1yNF8M/d9jFkpUthHE1ofY9Aytgct1NG5P1bgujzYrRBmkZUJxk2YA4GQXZSKO6D4+sIt2aXnz3XuHHISKbE5AzisFNSHIcR6ZxSSEkBUT6v0rgY77HOQkrjq0jhGeHxNhLlFGv8/g605qehdXg5cRBrKuJr04hKM19AFsFiT1cj4i/J4rfB87DB7U9Dc5+s47cS1ThSGEirx7CTTIWXkulQcLPHMsS/lXucyVKYFMjLPdY5RHjBFOIwTtr8JEV7PZG4f7dhgigY0Orxrx4wvgnoxPASw1Jn2LvJ1B5nivupJKzjEDlOT9XV/y5/gIqKCoYOHZr53HPPPf8qGMeOHeOGG27g93//90+q/AGMHj2aH/3oR6xevZrly5dz7NgxpkyZ8m9O3vzvuf7sz/7sPe1ePq0AnuxajhbieKQMzEAKWBtxGrbKn92IQfX42euRUJuErGkFbmsf4fpoJKwKZxMl2JKVqWzA5zyiXm2n27kKMY5yxAxWIyaI4SlFAigHKRdjBsCbhxhRPWIwwxBD/T5xwrcC7Wj3ICveq0RS0nWIwV6LhG+Zny8j6m9WIcV2KBJGHUiAtyDlIg8pPJs8lhLDXoMYfblxmEPkvitASsowP5uE5kV+ZxkSWJ1I+Sw1/Os9vnoiXm4TmYocdKH5G+P+RxunNcgaOd9j60HpT3YjhlyIhEMD9G43jPVIciWlIt8wNnseZhCnrHuIwzv3IVd8H3JFj/TYR7mtpLwUIoY/nag0UIosOtcSVpFpaCMywn2vJ6qypBjREt8rQKcytyBhOhEp7sn6Uo/mvM7vpQ3NO0jIVns+j3h+m8mk88i+2GPuQ9at8cbnueo/+2oiz9p2or72t2DIZ4kT7hPQuikhkix3OUaxyM9c5fF2eg7nIsU+WaYTrpJikzY/SbAmJfUMQvg3uM9jnusS99eIYvWS8v0DtF7GoBi4iX72QqJWdy3iA9MRrSarVR2yPqVDL31EecdJZKx+meTaNe6rUrhrwvPT7f4PexzJ+9BqeKvc7xDPUZHnIYVsZGsO+7caf9cBwKHn3WeyUl/tMdzo+9eijfEQosIMxl0/Ucd2M+Kr1ch1fwwpWymkZJTGw+Uojvh9iE8VGD/9RHWSHKJM2yD3OUrv9t/lsXYSKWsuQ7Q3yHPTPwDPyUtR6vde5sTk6n3GZR0RS72UKCeZLJrv+Huo8ZAsl9dqXL3PIxfwMjJWv94NnttiwrLeYNzsRAr3WGIDmkInRrmNFP7zW3Y1Nzdz+PDhzOdrX/vav/rOddddx86dO/nxj3/8Lz43efJkLr/8cs4991ymTZvGCy+8QGlpKT/4wQ/eLfAz1+rVqznnnHP42Mc+xrPPPss777zzrvfx/+X6b6cagPfs9S0keIuQ4L6SzOEDRiKFJsWvfBfe/gc4cygSJjmImf4RWsijUGzZNr/7fSTsX3dfW4kg87QbTi7ag4hpdBNZ/L9NnFibjhhdckXu871qv7MPKYfXIoa0y30tR8J+LFKeZhu+FYhZzvV7SbEbR7gtbyBqV5bD0cdg8FYkEA8jRWAFEsjTkIWtmNjRJovXSo+t3Ti6nWCcedD7KOReTaRFqfQ4lw14bq3byCbcv43G5VeIGJpFKI5uNpFSo9Rw3ub2diPmuxgdyPj0ADx/ybhvh97HIHcl9LdD9hSNJReP/ZjxOATRQYoj7EdK2Ejh9++ug4+sQArtHCLpbtp0PI2EazsR8N2GLBbJQnEPil/sGdBGqXBHpfHSRKSkmW68z0KKySy/swbR5//ys88QcVZXEC61x5FSuBrRx3hklX0UKbBX+7dkhfoiUhIaEa28YdwnYZwUrHzDN4soz1eH4v1aibQ1bUTt5FlqO3sVoaT1IlpNa7AHWab/ByFYh7r9R5FiugYpZo1IuV8+4P5EIglvk/Ewy3OZ4zbzibjSw4heNhr/ZWjjMYew7uYgJePbxMnUAlT28GG3nY/WWj2qPPK3kHWr251IuCU3aDwfHk8ouLXuYwFRNrACKSxJuU7Wb/zcUiLtUgNkj3Dbjp8r+T2iYkoesmj1G897/dyfIyU4x2P9DbJk5yElrAO6dkPBMSLOborbyXb/M9TeoYlQsgrR/eQBY1pkmNf42SmIF8zy358XrrPv5cRUUkuN0/mIHuagXKHbiLyj1Z7ffcZjPhGm8wNEiy1+brTGw2bYtQfG7CbW5yJ/N3vs29EGvghyb0Z8YbThO998I81HCeF+T/cWozW+k7BKT0G01URUEjmV1zHevRJubmfIkCH/rhjA66+/njVr1vDTn/6Us88++9/VZU5ODueddx579uz5d733b7neeOMNXn/9dZYtW8aXvvQlrrvuOv74j/+Yz33uc0yaNOld7+/fe52OATzZVZUlpWMLEqSDkJXlBiS8liPm/SVCAbwQLdQaQuHpQ0pWCRLAPrGb2dkmV+xqpLw0IQH8OJEkNFnSkvWumVBCnzdsU/z8o8CXERPq9nNTERO83rAsI5SqfYjBJtdvNWJADUQ+s0MeewMSctvcdzdSKGrQwm01vAuR8nQZUuxehOObIWsTUgoqiDii3UTM32LCupquRiI/29eQ4EzWqj6PrZtIlpp208VISNyIlJR84lDN+cbZBsNR5z52uc9uZBEZ7/8fQZaSfR73XKQIDQc+h4LPpyKamIXKyH3Sc5aEWzknFnd/EClMw43P0Uior0I0kOLj5rntX/jvVrR5uB+5UUF0+LRx/5foYMIhwqJcQMalffwZyLrTz+5B7sYK4FMoR+Rawu3cjgTwOCTol6M5fRAp0oeNu2nIatZoHA8nknavd/spyP1FIt1NH2HRHEfQeFLCRhjvrcLBayPho79D0Gqux1VLpAG6Gh3amENYrCcS4RiPGGdTPNZxiFaayCjQP3odPrcIKdrtaI1uRHTTYLi2Go7kenwEufDKPea0eTqGNhsr4fg/QNZjRDzpFJRKqsZjWe/2FhB54Nagzdsaj7EMjt4Jg22dA8J7sIawkh1Dh2b+1DA/jdb+LMKa/ByR3P2jfnez+9viub3X36sIN/2Nnt8XjJ9ytAEo8pguRBuov0AWyXlorU1Dyt9mpAANIWIka9C8t3u+iogDFSNRjOptSLF8v9vYbdgXEVWTjhHJ9Y94vN1os3S95/QworuRiGazES0sEX4zG38M51voUF9ytTYjuklKXg5S+FuJ0+qpr+1kQhOYTqbS0a4nYUwxsrIeRrxnEvTfDdkXECea9w6Yr0VI+b4Mfn03nHWn+3oeWHsKYwDnvMsxgGv+7YdAjh8/zhe/+EVWrVrF3/zN31BVVfXv7rO/v5+PfOQjXHTRRXznO9/5vwH733T19fXx13/91yxbtoz169czZswYrrzySj772c8ydOjQ/7B+/6XrtAJ4suuVLMXszUIMLu1YVyKlZgxigF9DCtVoxDyeQ8L5GcQk1yIl6R3EEF4n3K/fcHuz/b0Txc61EQKti3AVPUDkbUuM4QBRs3UqYhA1A2Aej5hUKRLebYjZHDPMG/xuD1HerNX/H0TC5Q3kZs1HiuRM6P0J5E4mFNh1iBknC9Rz/v4OURYuWU72+++rEBMvJna29f5+CjHnS5ASkuPfDiNhO9PtLkEKSTYRYzadULx2EIpJH7ARfv1TOGuy57YaCbfkHtpvvCxHgmMGss6scNvnCj+990LubDKl5qgmKo0UIAV4N7TeBmXzkOBbiZTHSiIOr81t5vj/hYZ5N1IcKpAALjQ80wnL7+1+L7nVDgM/RsJyHEFv17nPpHzf5fmZghSRHUR6mQnG/RSivNnlA3C8nTidXIKE0lqiTvIMw1SABOllyOI9nCgB2EIc0CkgDhQ0EnWLv0DkgMslTtG+ityBKVZvKhG/usbtTkMKz8VonSTFJdH/08bDIc9HM1rD84kNRbtxNMH4vxitlRoip2YnkQh6I3LX3oUC9ycgRabPbVcb5p2IdtOGoM/PbUJroBpd+US5t6Qs3Ue4sZuICiJ9iPZrPcZ9RBqnBZ6bMmSlSyEbi5FSVEfG8sZUtCa2EaEuzYT7vWcAvjf7mWRlhKDPFsIyWI1opcD4monSpUxD6+0t4lR+i/GZT8RGtnuMIwhvwlC0ebkSKfvV7iutx72I/icQlTXaiXCISuMnxfnWeF6GIoX3Ybd/BPHAxEP3Gd5893cdYVmG2GjhPurRRiCF42Acvgxv1MO5txHJ2Fcj2qlxn3cTB0reRDTXT1QDKSWshO3At//fVAC/8IUv8Oyzz7J69WpGjx6duT906FAGDx4MwOWXX87w4cMzcYTf/OY3M0mZOzo6uP/++/mrv/orfv7zn1NdXf3P9vNuXL29vaxatYof/ehHbNy4kSlTptDS0sLBgwf54Q9/yMKFC//D+j7ZdToG8GTXNqTcpWDfPsRovoeEWTMSTEWIWaXA4C/5+X6ipFA9Yh4QAb/zkCLTRGSmn4OY3lK/sxwxy+3+lLudp4lTg0XEadFk7RuPmOVNSIGcTShhW9zWLiTQGpHQuAIJ+xRMPgUJxFGIMdlituuw4M29289ke5wvEGlQRiHF7DaPIbmeKohdfzuRJiHFPa73/V4if+EWtMN9hzgJ+gBhVVvvuRmGlIsiIvdfEmDJErRP4z1rEFFWq4oT6z3vGzCf85Bwft7wJXdpIeTmIIFSpP4PJDdNJaGM7YayBUTZuAIkANYSCkA3oolaQonZ4ueOERaQQuN5D1E9YiNSWEuQ0lTuz0Dl42G3/5ThOoBcWjuRlXmv257hPnuIuKJiREvdhGJf4/7L3G5SklqJ+DUQfbV7LHlECpQOv9fvdscSIQxNSLht87tz0Fo7RNTn3Y5iG/f6nVykwG/3nM1BNDETze1lSKme5LYLjM+haBM3wu1ewQnlzo7fDr/c6bH0o7iwmcjyd4/7+ZrbaESKcA2y6FWj9bKdiCWcY5wlq2eL/08uznK3P9Hz2ocU705Ex1s8HyuJFDvdRMqVpHynzVubPy8TibIXEzzjOaJu72VIUSzyWGsQnZUN+Ew1HCuQ4vUcWtfFRE3bff77bsIy1orocyqiiR5ic5s8CIVEDGA14TnpMS4akDL7CbfXTKTdKkN8YQJxEC/FKq5F9LHZsPURcb6jEC3kEFVO3vG7OxB91BHKbbJkL0bK5WjjaI3f34nothyt/VeBsdC1H9H8QY93v9o+d6zHNZxIVL0dbRbqkKfph+77LSL+to0oNtCG1nAPp/Y69i5//h3XI488wuHDh/mDP/gDPvCBD2Q+zz33XOaZX/7yl/zqV7/K/P/222/z+c9/nt/5nd/hoosu4siRI2zZsuU/TPn7+c9/zvXXX88HPvABbrzxRs477zx+8YtfUFtbS1NTE3fffTdLliz5D+n7X7tOWwBPdv1FlhjRLkKo5RO1MavRQmxCSs0gIsP8KCSURhCpAi5FjCgpTFMQc9tKnLgsBMbbxfNZxBRS0H4/YkxT/H9y1S1wH4eBxXDgUhiegvdTLODVhm8cEh7bkfA6YNhq3Hal+9tFxAp1q10eR/gYTdQdLSBO/n5/AB6yicz105GAbPb90f6/hMg7+DrhPl5sXHcSp4Qh3LclSLntJ1Op4PjTkHU7YsJNnoc5ZHK7UYziAZ9z/9n+JObf5bEka+RoxIhzjIdsj/cgURu0CW0G/oBI8zPNf7e5343AfDh+v2O4tri9jcgymlzQG9C8bkMMcIlh7USKxdeRkN5DnPTc6H7Od5ujiYoZORrLm3fCOeuQkNqILI3DkMCcjGhvFJr7v0RW3LM9zqSUj/B3UihSHNtCP1+IhOUmJIiK/X6Kh2oWXEe2wpBRxAn0ZPXbhQTr15FF2Nathrtg3KcRzTYhIfpRwko7EikjMz3+I2hdbkLxjK8S1twdHkNaO82I5p9DrrqN7je924+sqLM8n46NY677f51INDzSzxcQtb6LkDLWq3mggwgjaXM7X0cCfgVav8V+bw4Rf1nktlcQSse30Uat0n8vJg7ZrECKymyi5nYfmuNsRJ97iZrO9Yh/7EEKUgoXqCVOh/f53VeIJM7NxOYpzXWpYW1AdDbRfXS5/TlEjexav/vXnpNxiK8+gjYsLxLK1Ezj8BFE69M8xmuBz3wQ/vSXwmue4V8+AMcQ6/wQcaCmlrCujiD4wXhEC53ExrGGqLk+3OOZSYQtdCHe2+vv+cZ9i3H0hp+rQpv5GrffQrjk1xDZFBLPKfWcbDdez0Ubnz4ir2HC7TzgnlNoAbzoXbYArv3tqQU8btw4du3axYUXXsjnP/95PvnJT5KdnX3CM7/5zW8466yzOHbs3Qqk/Ldfpy2AJ7tq0ALeixbbfOSy/R5x8rcJMZVe4DHovQ4xtR4k5FqI02gtaPcJUmZWIqGeduhdiBlthsHnEbF4sxBDe95w+GBH1+uGaYjbvRBYC8MvRO6qsUgYvUgoZckCV+U2dxJB8OWIyc9Gbldbg36+yu3MRC6PbGQBaSGTr4sVSBBcQ1TL2IKCn2uMq71AM7z2KGEh3UMkty5FDHY4YoBrkNAoR0yxw/DPRMJ+JBISTZC1wH0Wuj8Ii1Jy2ybBswwJvkbE4GuFc6YbT00DcJ9L1M6tRAKifMD89iDL0U8Iy+9GpHC0IlflBMja7HkbhwTkZ1CcZjuig6QUJyvuNjKpHrYtIuo6e0xvPIFiDC8jrDD73O92MkrrORcT1SAKkDDajGhiPyG0JiBlbgSZQyqZ0mF5RALcQs9rO7JSHyA2NjMN5wykXCarUxVwBwy5EgncMqSQjPd41iChP8PvrNI4RmKYVxvnH0cxbds91leNtzqPaziivZlIQcgmkmzf4e8UojDHczrGuDmIaPRCw/M4UbYsHykzI9Gm6yCxuZlPHKrJQaecP05sHHONy+8Yb4WIjgchoW2L/JF64eXXqwg6bkJrZr37PsOw3mEc3EekA7rZ/Z9NJCtP7v1LEY1v8RyDlKS9fu5upMBvRcpVjufxBc/5ZjLhIUc265sZbqeDqKCRrFDZaDO4n+B7+cQBtZGIP3XAoX8gUjcNRcpoHaKLhWjNvOX/xyN6muh3aoGFv9QcrPecPYdofxKa5+TyvRzRxwoiifz9ns+HifrfdcRhuyvIHOg4cANSfpPl9hgwDg48jdbPTf7tPGRpP+Q57oQ3/9YwbHPf1yLePAop8omOBnmcV7j/9R7DBCLGswop/JWI7qahDUsep/bqf5c/v0XXggUL2LdvHy+99BKf+tSn/onyB/D+97//lCh/cNoCePLriix6n/bJrTLEdB5ATGoV4VIAMZDEdPoQ47sQMelZA+63ICZWQJygnIeYzny3WYwUiS3EbvFaIi6syG3vQMrXD4kSad1IcMwjal7uQ4Ijl8hgvwEJ6XeIU2/bEeO6HwnBPse5XUMcykgWszlIYHQR6SMgSjbNNnxzkYLyPGKmazzuuUh5GENYxJqJ2KEFSMheRwjsUuOkgog3a0XK3TbjbSuRJmMeEYeUXO4fRIpDB2H1yfaY+pDAaIG3b4Uzb/J4p7qNZLUYT7gy1/n3FLd3L4qzSxbW5PJahegjn3A3dfudKqBQLuR+4IOzCbdrDhE0P8XjLTT+7kYKUBVSXvciZXIkspRsQQJvrmEcBP3TIDsf0dNa43Wn/79Zz3AV4Qoe43YHeQyj3V4TEkJHUHjAQx5TIxJG9UjpWU4kEt5hHGT7vVuMi3s9rr3G/xVISR+LBG0dosvz4dAqKLkPXS8TFRWSNarWY+lHtJCPrHqjkNI7mxNP8/YQ8XB1Ht9O0UBGYI9BtFWDlN4K47iWOHhQ6vsVRMm2J/xep+8PI074txBVdb6JLL59nJiKphXRGERlidWEhagWKRAt8OYDcM6Vnrthxt9Iwr2fFMtJSLGYQFQmGkUc7PoGUrInQe+dkPuI8dFo/K5BG8c5aO03Eom2Gwx3ct+WEpVu5iAaSe/+gFDwFsHxiyHrYTKHn379Mpw1gqiwsReFk8xCPLUEbVbPJWJrbyHCZJoRj0shGmV+5nK3ucH4vSr6zMQMrxJM7EUK3QOIF81FdNNjXLS6v0uRklqM6L7c7cxC9L8dxQPnGc9J0YNI35LiQg+5jTLCmvxDxOdz/PwO/z3J/7/q53acQgvgzHfZArjht8cC+M1vfpOvfOUr5Ofnn3D/6NGj3H///dxxxx2nCDJdpxXAk13fy9JuvxkpJInxzEeWk4eRa7UPCbghSODsRwLgbCTA8ojYuw5kFbqdiB/pdbt1SPCXEikoCv1+O2IMKXamGi38HP82GjHUTiT0O4hs+X1uI7luVhGxgkWEu2EFUlhGIiY5DSkzKdarA7ngkuv3SiQQm5AguZQo7r4RCeJ1iNnejwTMQbfbipSq5GqdggRIHWKuEwn34WOejzGeh83GxTTjrg3t7o8YjmFuZ47hfMT9THFfNcZHI/Tvhuy7kAWp3bDv9Rj9THJdHX0RBt/psWUjZeIRj22Tn70WWUc7kSCoJNK2POFxdfr9C9HJ3n1+bzm8/YxTCS1EAmUdoXw8jawkEKe5k8t7v3E1Gyn6ydLV6r7GGkf7EE3mIGViHGH1exop3C8SVSWagG8Oga8cEV7zjMMCoupHm8fa6nnpIZS79Nse4/RPiDjKs5FAnIZouRnR2tVIuXmLcJ/tI+pqT0BKzCzj4mkUL7XSzxZ6LlOc3DGkRMwzPMl9W+E+F3pc9xkveUTS6LShGUlYofKQYliJ6KzTz28xbFeg9V1NJDBuN6y9RAL5TYiXVBj28YRCsM/zNx7NbbdhOAL8Bo7cDkNuNgzJst2ILPfnGd/pexiik1pgAby2GD56OZGepcp/FxHxqXORUrqASAB9mEhivYPYzPQYRze5jaQwlhKb5UFw5EkY8gRSeK92v33oYEraYD8Ib78FZ95pXHTpHofdzsPEBuVx43+x++t2uw2eh/2I1lo9niNovfUjxewrxnUOmbx7R1YYxiYyfLd1qcvAbSFOnzd7Hi9B66HVOJnjMS1ElsDkvr2WCNFIRoPkzWnyeJ77MMz9e/GXbiIsZyGRtqoMrbsKgq+3+v1TWQnktAJ40is7O5tf/epXnHXWWSfcP3ToEGeddRb9/afW5PnfTmnv7+VrBVq0IKUjD46ugsHl/v8mIpB/HFq0XYhJ7EMLvwwxjvH+/iQRr9KBmGg6sboLqIWjB2HwICIepAAxtPsR41mJBH0vEVicYoBWus05iLEUE+7bbvfzMEoVU0TU+NxCnBQ+l6jbez6RsqLIfc5ETP4hIm1Hgdv6uPvcjxhjavMpQqEajZiW3bcsMlxXol3+SCSYHnBftxGl376OGHcbEmzLgRI4/ihkjUeC+KCf3+4+FnkOc+BoOwxO4+mE7EuR8jHJOBiEFLPdajdT2usYDJ7KiXkg9yBh320cpBOFi4hcgH2e11Ki7u10z+tviBq+NcLFmd0eZ4PbuolMNYHjLZA12785ri5TanAksm5sRbSW4scKDcsDRN6+ZiJR8zTPySbD8mLgig1+7tEjcQq9nnBFTUFKZHIlVvt+sqKVIzpcZlhmGe9bjJsu/73E71yJhPlu4OZiuKJdSn8DEYox0+0VEwL5UqRgfRS5SVPs1VQ/c5CI+xqH6GaUx/6AxzhCn96XIfdeRH/dhncUmTyOx++FrBEoDKSRUAReJeo7v+D+/xrB32G8NBIHKSZ4nIXECfWxiKZSGMNbaJ2e7fdfQetyBwyZiNZaUiry3H8jUiByiENijcTBk3Xw0fuIGLftA8aZNgKViCaTVflCtGndZfw9a1yDlKpK46AAKWkd/gxzv6OBIhiSTVRt6fT89xB5+PqBO+DM5X6/Ca3BTyJldo3baiBO2s8zPqsRvW0mKmRUErkAR6ONwi7DOo8ot5ncpy1iW6OudBnBCuG+LB0ES8p8sm6u8HtNaC3sQPT5acMxn6gotNzjTIdlihGfbSYs3y/9vfA+iVDI06agHm3gJhMpmg4bh21EvO6puo7z7uUB/C0zRx0/fpysrKx/cr++vp7i4uJTANGJ12kF8GRXEpKHyNSgHTzwsMZexFxnEZaP0UT8WzmRsT5Z0UBKWqF/O4iEb4rErIbBlyGmsQcx2G7EWCpQbE2yuhx2P1cRMU+1hOLYgoT4OMPXgoRMN1LcUtxfK1q8ySV4MXKvjSCEURrTYSKpb7JGtaGd/gZksdmBFNIDiBGOR4rkGYh59hGMrouMCzSTyb8QMdhXkJAECYTDnpMuJEiL3W4TZF3pdscbthyiKsoQMnV0B/8OYq7Ffm7ngPbnI4WmCAmVuUjovIXmdRQSKungxgH3uR4pahDxogeQkG8jqrfciOb7gNscQpT2eg4JtDVICeo0/N1kqstkvc8wVxivE91eEtqbCDcUeidzAGIxYSlYM2CsM5GgT27HVz3+TjTvfZ6DGiItSLJirSXS8CSaLiNSqKTQgHQoosh47kdWjW1oTvsRbR3yfHSiBtrQupiD1kKdv6sRHRQgOknjLTIuRqOYzCIibquJUMZ6yKQc6V8H2dNQzroKyL3NeGtCikYF4UKthqybiBRMLUTqogrisMpWfydesJmw2mA8bx4AaxFxmvsFpNgtQJuLREdpjpsR7fWjnI2LiQMnU9CafJ3IY3c1OpVc4nbORhuPUoJ3tRGl+2oJ9/9UpDDtJSqFNKE5PkAc6BqNlKFV7rPG81nle6VEKbl5hCV1BZF0fLPxNgjRdLk/OwlLVx5yUX/d7+W4nVtQ+EILkQy/2s/sIEoPDiJy9w03vq9GabOq9dy4Ur9X5rb7EE8bhugy17iuM5wptONxxC8PEgdaOt132hDnofVWgjwHafOdgyz6jxBW071E+EWbcTzZ/5cRiZ+z3V6irdPXe+Y688wzycrKIisriw9/+MMnKIH9/f384z/+I9dcc80phFDXaQXwZNcYIu5sFuHu3YyUkNuQ4Nvn3x4kDlpMRGV+nhSvHjwECb0GlFm+hSixNsPvVRGVO0qJOLomxCT+kMjttwQxpofdVgUSiDOJAxQTfH8jEXPYQRxyeBqOv2yLBojhlwhu3kDux+mIkSXLTA5R7H4MoXRtNezJ7dyPmFVSjA4jha4bCfRvEyWj9vmZtxDDH4QE+OUec4PhOmC41/l7GLAaenc6TjMbMdzkipxPBM+PRIJ7M2LYSUlObqwUqzWPSBXSQpxc3O756SJSgpxPnETe6bnKRspREvz1iHE/jywpbe7/4yhB7yzE3Pvc5zDP3VLj6nYkOC5G7qY2P5tc8slN+Cr8zUuSPSXneW56kCJQg4Rsm+G8CwmkGcJfUqIpNwxTiGD4fiRsjhn/bchN3EnkDRzhed5OlGjrcV+7/cz4AfgZ7XaSVSy5Mr9GVPi4BgnCne67H9H2ZLRukuK0g1D05hCxhMkyVk8cClpvOLKR9bpU+6szVxuWhINbkbUdt5FNKGNdiMbbCEtTUpq3GYaZhr/I+BxBnBgdZryOMr7WI+tahce4Cx0OeoxQytsJ6/4I4rTyBLd72GNsNN6nAE9Cfzlk+yAC5yLFHaQ4XkKcKp1BxJr2E7V6+zwnK9z3TL+3w7+9YtgOE1WRXkTrLo8ok9aB5nUxstbOMl6ykYXzBrTZmkjEJm9G626ucb4BfvkEfNB0d+R5W0GbEa/oRTRU47E0EhkY9ru9dve9FtHFPOQSXkIkeO5Ea64IxabOR+t0h37rfxGyK5BHYxLaUCcXfzZSCHcjl+9qYn3MJsJWGjymsYjuRhL8qNT4bDfOq4i4681E9ohViJbboWsDFFzKqb36gX9q5Pq/b+u34HrooYc4fvw4n/vc5/jGN75xQqLn3NxcKisrmTx58imEUNdpBfBk14VI2ehDi/wXSLjNhKPzYPAo5XgqmId2hrMIF9sm4GLILUEM4EG0ex1CuKJ6gB8hRreFSGA6kYyrh07EBD6LGMVXkNSqQkxrhdvZY5grgGeLYVt7nOp7h1BUWhBzqwQKIWs04dLcAYeWQkkbcVijGymB9yHrQiM6wZoLR9fB4OmImY1HTD65dAHqoH8JZC8lk5aEcuD/5zF2u+8+4EvlMKNFik4ZUmybkKttA1IEbkRMdRISKjuFh9wuxDhrkOCsQgLhEbe/0ni/EAmUEYTbfgSR2Pgwci+WEtbaZejU5TLjeTYRQ/hnSHikcTUhobrX84mfrzMcnzH+2siUrOJPgK/C378MH56JhOxCj6PLuD8HMf4J7uduJLzWEDWby+EPtnsO3vL416GNSZnfy0PKMIYvufiWI6FSAEc3w+BkhehAilQ/EqIvIiE+0n//JRKm63xvHWFlSQdv2hHdN/vZ5HKejixR5e67BdHnXOOsGylWkEm0vO8Bn0u5gRNq1/Y/BNkJz4VEbsg9hOJfiaxre8mUJ+xd4WIPTxinh9GaqUAbghzPxwTkKh7vNjsNcyFxGjwHDt0FJWMIy88yRIu3IlodRiSDvwvxgdcH9NcCXWdAwTBEG2MJK2izx1Hk96YS1uODhgGirN51kD2VSGpdg9bRQaScZKNN2EVoDbQRJRSXed6mImtbjectxbLO9lzfSNTHTtbhCSjG73xE7xVIWWkjFL3rEH/MQXxxPHEgZzns2gRjrjRODpCx9H7wXjLWwCF3wtt3wpm3ITopI7wt2Z6fNo/jTaLqTzGR2icdLnqTsA4XI1q4HimijYhublRbR4Az1wIvIVr674Pgfx6TEr2eSL9zyPPXIiNA7g3EwbxL3G6KTV2G+HgF/P2t8OFPG560ialE/HKp4UpW2hw9U5DiwU/ldVoB/CfXFVdcAcCHPvQhpkyZQk5Ozr/yxqm5Bv3rj/w/etUjhnsbEgSgRfsgDF4KLIaCG9BifxUxjiYiN9lKxGQSo65DCz8x6TbEYA8hxlCKmNNw5KrZjZhwITpRWUWmpuvRmYgxVxPxcR2I6fx5uxjcaOA5+HULsjqW+53NiBkBv96NrH0AU6BkGYq3eRQpm4sMxxtIsHUjxWMoDL6AcON1IsFfQRS0fxSyb0KK2CD3X4SUpivc527EYM9tkRWpgCjSXoiE8/PqL3NiEyKTfweyDuYa/yP9XiVSMpJ15moi5qbU965DArgUKTTNRJB3GxxZajiTwj6VcPMVo9N5LdC627B9Dc3rKDIxZTQjBXIyYRltMmzJbToJPjzbz0wi4qSS62yU4GE9J7roiogYuPuNmy8RYQPTkPKxHm0mhhGWwX3G6yHPzbP6fXA+UjafNu5vdbspRq0B0c8B4IPFkXfuIFJaknUMj7cXCblt7jsp9ynE4DvG64VEKpwNZE6iMsxj7oXK22DMdX5ml5478gRkX0dUyxiJFJAipFjcRLieQQpIr3CbezlklRPJtg+5jVuIpNJjyJwE/7vnPe4Swo083XieBCWzkTBvIOL6FiGlshSO32I8TENxbR/3+3VkLH0F1xAHaPYYTy2IFxUQMY07/N3gcW6DN28lTtEfIGrsHkBr7E0itUgHUmoudNtrfC+FfIxAa+ouj2Gh530PWit9aDO2g4g3vMjzNQNtND6PrGkNiB6PaX72TUPKZ6nnpABtDnYBZTDmdrTOXnef/YYt8ZE84eHMoYazUn0cXefnXpii7y5Eyzd6zl4j8nWuIQ7spfjGfr/zjP+u8ziStftqOPNmIh/rg8CfHlNYzlpOjHEuMl5rIfc8+PVDaBNQ5jk8B8UKnoP4egNQDR++l/ACNXr+NhJpyJqMr7Th6UCyYi6nr/fQdeTIkczf5513HkePHuXIkSP/7OdUX6ctgCe7JiFBlNxZxUQwfB2RFLUFMapypDiNJawdtUhBqoDehx1gPlBIFhFMtBwJvzz3OQsx3DwkOKrJpCkZfB5RFaEbuV+qkVBYgwTLemARnNWFGEobUkiSRaEDzkquzy0Ec1yMlNAdRAD+cMSMbkEMcJXH+k2PoxgJ301IgHQh5WAbUXS9jFBu8N/TkYLzBJF8OAXpryVyZqWYmh5kwXnB75f68xwRD5OsJn2Gt42oLrDA/09HAqYNMflsIkauXLgf0kTkEixw/w2GZZ3hbLZRrR0J3iIivyBEGTFbWDNJgWe6zW8bzm1+ZjtR6q+byEc3jtgojCIO9vT4veWG7aOeg9mEq7sPCYgOt/G88HH0SRi8xHjMRVauCW53A6KpNsIidAmRpugw8MN2KdVtRGLrgTGK2UgpX0PUNG0iDiDc7vnII2Jo85CgL/IctRmWKj9/leaG/cCbPgmb4iQT/XYTcWOdbqMJ0XG/4atxPzXIur0LmAFdT0PBVKIObxeyZrXDRx4hYs/aPa7/gRSAa4lUT+XI+nUB4hEtwA+cCDzFkVV6jvf5mXa3fT9BL/uRq7reY2lCithqog7yUM3r2nvhojVuJ63p5JKcgPjI+WgddxN5+zo1L/0vQfY77ncnUUWjBdHjdKTY1HsuRhFK1tOID24hNmApFraPONU/VP1WfoIo+bjX408u2UJiw1dO1CmGiN/sNA1MMh5vB2bC4FxEA29u0XpZaxynNV1MJMcuNtxdZFIMZfIGPo3oL1ncL0J87sce05eI6iiNxtdsIoxnDZE2p1pzdtY3EU86G/HlZAxaaViuMJ6/6fHNR7KjGI0ltT3If9cQciN5Pk7ldYx37xDIu9XOKbzOPPPMzMnfoqKif/YQSDoccvoU8Hv1+gFiVCAB0YYEXjFiPCPQYm1FjKaKSFrcjhSaSj8/VBYHRiGBOsHvV/j59G7aRU4gYt+mIkX0K0hpaCYSChchIVJNnORMsVCL3OYtbnea4U3CZo7H9qLh/GvECFNM3CG0GNvgwGZXF2lCAqQRxTlVEnFo242jBiScOpE1IMVLlRHlobr9aXMflyOFcRASNsndmIcY7FTPxXZkPZmJ3GllSGgUIsZ4hts437jYSlh/XidiqPYaDwcQA91LKIm9hvtaIuh6LRJuh5HyV46E1iI4M4+wnKSTnceIWLhm4jR2odtOsUGlfmeK229Dytw0QrntN74nEjkYmwhFod3/7yaC1zuJcntjifxoyYXeB4M/5GeOQNdmKJiO6KmVSCS8FAnb9YRFM7mS2wzbWrdZ68984z6HqF07nNhoJHrvJA7YlA1oL5tQYPKIUnFjiVQYyTr/lu/XEoc1yj2GGkRT+cgNvcW4WerfKuH4w04iPkY4LrjSOPo4kTy7DdHQKmRJ3uR2Zxl/c43/YcSBmw5i4zGKTFJgKojDNx1EHeB+z/lEpBQkS/V6t7HXc51CF/I9p2cIDxddTMS7DUMxal9Ca+kgGXchPcTJ0WbDMwiy34eUrjSe/Ya5mEyy7UOPQsmVxAnUVsM91H+vRvxxhHDJLUSIxV60Qfgsoqs+z+NFhm0vWnuD/P9cj7URejdD7hlw5B0YspsIMbkc+D3jJ4/wMLzqcfQg+ug27h4j0rD0oHW0jVgvOYiWJiIldNyAdj6OeEm973USSmWDcTkFbYSGImt+LnFCPm1uKolUOlsHzE9a63VEOc0UU/mi52AWccq62Lh+FfHBQk5f76Fr48aNmRO+mzZtOsXQ/MvXaQXwZFcPYg4tiBlMREwpue7SacokaF8g3I/NiMnnEocBkoUsMaAKt92CmIIVCnrc5gNIkfg+cpvuIw5JTEOK5F4/ewAxrbsQA9rhNu9FTO03RPqB1UjBHI4E1zTDe4XHlENYHUo17uELPfbkElmEmGkNYqKdRFLjHMTINhB5C+e5j9HQtRgKxhqePj9TT5wIvtrPTkdMr8Lt1+r9jHKc73n6keenCFkCEw43E26mKhRf2Og+k2WgzM+8gRhru59JltdhiAaKkEJ3DprzQsJ1twnNdavhfMHvX+r5m+DnphC59JYTB3fmEBUrbGHselhgF9zpOUnKxji3M8V9VbuPg0hoViCFbD9Smi9AQnCR7+d47C1IgBxyP+f5mWWIXva733JC2Z1DnADeiZSTbiIY/utojTQY1nmIHpP18pj7rEdhBinov999jTKuphBlDpsJxecYEacGUaElKb6v+92ZSNhXIyVyh+EeuNYqNIdZIwiX+jS3243WWLvv73JbG4kDKMmi+2fGWx5wGPp3QvZT7ucm919EJpVPJoEvHkuh4SlwG2uQAnkAuX/7icoe9aiqSOI3VxmmY4ZlA1HnNynhiS73EYdKWhA9JR7WgZSzlcRG8gxiQ7QXaIOSeWgN3kLkOk34vch9zBrwf1pfi93OWmjdBGWlhrsCnb7+LKKFGXqGHjIxcQyD3CuBBuj+WxhyNSfWxK0gUqlM8Fi6B+B9mJ97irAYNiOaXoJoaS2RtaHJv+823s92H+8MgLnT87fG81Hgvm4Evmr8FXjOdhCn4x/2HOQS1vg+woDwA+LA0hoir+RsIs5yESFrLibSLyWP0qm6TscAnnBNmzbtn/37vXidjgE82XUOUiKKEDNdg5jwFGRhWoQWbZ+fPx8xwKl+ZzNS3lrQAp6PlKU238tGTOAQYkS70W75edi3yP10+Xsr2gmWIga3CTGHGUhw7Xf7ZUhI9RA5qJLLYC9i8vXIirXRvydXIsgF1UhYrn6EmN5TiPkfJHJUjUEMsx2d5ExWhXXEqelphPutAHgdCkYRJ95ykNJxwP13Gn/XIQtcN7y91LBvJHbc6XQuSNjMhePriBxjQ4iyTe0e81IU39NHpMxJSv4EwkUzxzh4w/h80PO2GLks893HbYh5LzAcG9zenyLFp9Jj6UB0UGtcNgL/iygl2Izckc+RUVIK7oOCx4g0HdMJ9+lXgc8JN4CsABaWbENuwsUe9zWfFPxd7v8ypDBMNawH3f8iz1Vy3yWrWrJszwF2QNeTHm8Oou/kMr4CKQZVHveN6HTmeR5bPVHFZCIRe3k5ovkLEX2MA+bDtp+iWMZipCx3IcGwzzi5dsCYxxOux0eQC28fcg1uRPRyO9r47HIfnQPwN8F/Lydc6g7b4DmkcHSRsZxSj5S2CYQSWqh5zn4fkVZlq/ve7b4HDZjLGUQy9IO+X4RoqA5t5to8h2OIiic7kHK00XjbY3iSYnIjWvcr9f/b04E10P8TpOg0QO8TukedcZFLxDu+bpgPE9VnZhiOTrTW2txWNqKP9Ugp7jZcrYjeOzxnIxA9VkDZ80jRmoHo8E7gT+HAy8bRVYhG7yX4rAwpysd3pXHUZ/hnIL73M+Ik7RzgU+77e8bdfM9th+Ev8zMdZNJQHXnRMM0l1t0exDeOIB4625+1nq+9aKNXiNZ1AaKJauN4HxEPusD9nUGEC8xE9FsJ3K9DWFQYl8M1borQmm4yLN8mEl3nu+9tnNrr2Lv8+S26/tf/+l9s3rw58//DDz/Mueeey2c+8xnefvvtUwiZrtOVQE52vZGlxVaJGOpitGh7/fsexBQ2IobRQbhs8LOzEANbiiwipUhBKkJKT3JH9iHGlixEE4i4tGNEXrs6JEDHIYawmnCDjvep22sQM69B8K9CO+06It/YXGQpPOjnliHG+yBilJcT5dE2+5la4gRgCkKuMIz7kQJwK1KMqv17PhLIw4ii70uRtaebKIP3BlHuaBJijM8ZF+cahys9dhBzLEVM+gBRQaQSCZ+riZQ4V2osve2QexdSmCoR05xFlKCrIk51lnsuVxn2SsPS63ipJUQJqTyP83rDXG585CC3/RnIFVpFuDpHem7ONa66iPgr3N4xJMTGep6KjZtH/PsEQqGfSZQj60ACPAmlLqJaTLI634osEivRAaFkPRmElO9ZiB5TjF4KSC/333Vong96zIsNUykSiMlFfx6Z09oZy3aX5+U+v7eCSLRb4vHORbRY7z73us12w9aFlJVy4oBMqceYC/1LIftqROPZwBZ45afw8duRYjaWoJlOtI7SmjhsPMxCClpa01VoDVQDrXD8Rci6ndiMdSGaGI4sr/vJ5BvkEaScNXgc2w1vC6LVx42v0e5zKKKVRwg6Xo0Unib31+jnK5GyUYiUn6Sw7vc430HXMLcDERZRifjZ+URIyUbfb0VrM8VOrnO/o5EC9HG3s9v9XU3EwW5BStcTHudUw/IMUvpud5sl+v6bh+APJhrGhYhXPOe+N3gu9vr7OaJGc67hHui1SJu5wxrirsdgTL7fnWZYW4BqePs6OPN98Oo/wvnnkanIQ4Vx8P9n7//Du6yuvF/8BXkSY3ICMTQlTzCFRtLQDJkoA6XwYHngUBmYtBws1aH60DJaW0eH+qM6tlZrtU6t1tahdbRWh8poeWSwDA4DRRkYCgdKYaKZMClpME0ayRWaEgOZxJh8E75/vN87C86U55zO8C09/ea+rs/1Se7Pfe+99tp7r7X2+vkakYs0CWsNhrvK89SJBPbL/FsBUXVnv8eS/P6a0D64EtGtNJ+fRfuhwLidjfZ/F3HAaEb0cyGRr7TNeBgENv/m2fhwJZD3wZhzZEs8+f+BsT/+3akEUllZyVe/+lUWL15MXV0d06dP5/bbb2fnzp1MmTKFNWvWnFf4RkzAZ7saCdPGckRgcogT5AVos670888iorwSEXhrBShHGx9EIKqIk+gbhC/XSkScjiBm0+Y2ThiG6wzTNrd/o+E5wXBuuIwct9VI1MlN2r0S/z/X318gItzKETFLJtkM930P0go+RUSeNiKGXYgIbhLGnkZMwI7lqYD8cNqMLsO0CDGsOUReu1cJYXK8378VCWfHfO96lHtssZ97wm20weBPICMJbslnLYOo2zkPspI/WBJGlhmOMo9/ChKEO4zvLxnm2YiZvhdFNt9HmHeuJ/zzdiOivQsxoTzjqgdpJJMbAGjd/AVaC2s8zgwkPCW/xWRGakBCVDIJVRDau2yigsMhfyfG8bpxsQcJjt1EYEQTYhqrPI6k9T1guBee1u6rREqg456nMsSs9hC+UBsQQ65AQkcfUbu63X3vR8J/AdpTA+6vFB0ykmb8KSLfZhKsj3lcbYb9OOHDtcrvZAmejA0e4060Novgg9cZ7nxCG5TMgQuM+2TyXGDYWlAU94D7K2U459yodyPGP5nQsM5A62edx5GB9tVsQjj6idudRLgqfBz51BYSidd7CL/LTjT3ew1jFVpbM4kcfIeIEmYdBN2ZQOTNTGNrNd5fRmv9SqJ0WhZaz39M+NImP7Y69/eG+/iexnZyH4wpI0pZlqADRoXx1W7YqpBQmEnkhpzgM/NEJFhxGpybjbNniQCoOW5jIZEnsQbRlef8+yTf64Ep6bCWXD32oYPnIFx0F9ANl+cR0dsQiacnoLVcaPwf8xx8Brk8zDZe2g3r5cbLPrRWutFeT+Pq9TiLCYFwAO2do8gC8UHDcY3fX2t8Xm64k7CbT2gTmzm/14gJ+KzXz372MyoqpBV68cUX+dCHPsRf/MVfUFNTw+LFi88zdCMm4LNfu4hM8qCNXE/kFpuNCPBdSGi7F+V16yb84rqBPXDyEKF9ARHefiQ0JM1VE2IY4/1c0g6OJ5K+JtPAPKKSyHFE4G6B/l50yt6ECEW/n9+DTtIbPK5thIklCQZ7kMan1c9uRpQ5aTKSQHUz0jSMI8xA1UiozDNO+v1bB/xrA/SvRgSr07AmATOZjTNRGpUJfi4HCWTJxLbd95JZ5BCRNifPeeAWEoR1BtLeFSDNQ5/fT4JFEs6KiEjhb5yG60mIIUwi8qRNQAS6DmlDMhHR7kMEeYioD/ol43qb4S0kfO+SxrHY4zpBBH3sI7SKuN3EVNOnC376Xfc9CXgbTj1ACN1JI9BpOJs8l7d7fNcQiWu7jaNCNNc3uu+/RC4H9Ugj9BJifPlo/Vtw6V9L1OR9g9BWlBq+AiSIpkNIwsEQYprbEMNuJfwK65AAkjSef0r4lyZzV7Hb6DMOMJ5Oon2KxzHT72xzewcJh/8vEa4RyRTaq7yCPIOEuFfQIajR7xxGayGth0VElZJyzQ2Xob21iKgHjceY3CF6jM8BQpPcT9TD3k8kJK91u0XwiyOEy8cDRA3arf77aaJkWgWK1E8avFrjfqdhftU4WYYEoz6Nn+l6tv+XHn8+YYUYRGtoHhLQ/kr4HHOD57FbcA6XJ8z0++vV5uBNHner52IC8G1450S0HjFukxWiHvm6LURm4T1EwFar8fAK2rNLPJbk4zjdvyehtYqoVZ5tnD2NaFfy06sn/HuLPaaVSPDqJMru7SCi9acb94XwwoPE4Wo6UZ89uS2sdLvfJ/bEgPv5uOflFc/LL42LFZ7PTLSOWzyvqwlhsYiR67f0ysrKordX/ibbt2/niiuuAKCgoGAkDcxv9bUc+AoiHhloAyYT2wChScgh8lHlIMKUhZj7RmAmjLnYfw+4jTvd9jTEADoQgz5GpK7YBhTB4POQ8QlE2AeQ5ijbv09n2JzB5yAraRwSA8lBhP5BRISaEcGoRQLcZYQTcSsibBVEypsKJKROQERwo8d6C+Gv1O62qpFPZCKis4HX4PcWIKK3wWPfZtweQcJrKzI/P40IYTo5P4GI9nXG548Qc7/b89PtvicgoabJ+CsnoguHCKa02Hj7GpGPaxlREm48NL8Mk75ORLAu8ZiT6a/DbRz3+K5E+c6qENHfgQSLT8Pgv0HGfrdRiXyO3lY/VHp+jhGJitd4flYRgvpniFxo4xnWwrxnjef1MuAAjNqKGEIbWiP1hAnrXo9vh/v/ifFXSES2TnefSUs10+O8gtBS9BFayB36zvoj99nhOej1u5vQ4SIfCbjHYLAFMm5B62Y+Yo7JBD+NiJrNR4LP9R73Huh5A3I7oP95yBprmJajtb7D40nMNhdpXPcbngYYPAEZL6E13EQcLAaIJOY5GvMgkHGUMNm3+fdriYTB25BmZqfhv5NI5J7ruUpr0ab8/p2QVYX2Ux8SJv+EiPIegsGdOpGPKkZ7phQlHT6ott/5qOEZjw56pUAlnHrUAS2VHtNetCdL/Wz5afO3CwkjrYgOJBeCa5F/6SLhIusKz0EX2s/Jx26Pn80nNIPJZSMdcJNGeC7a0/3AKsioNY6WEmldkn8ebm8vETyS1BMDhA/zNDT3vf7/yygYpxSt6Ws9X1vdTwc6SNzhPq/1eB5CWs5b0MG3kogYzicCKzYTabYSbStBe2atYf9L9Xn1UrQmD7n/Avj5G/Cu11Fk8CK38y3CejOIhNQWIjCkGLkRdKPDVz86YNf7+QJEH9ehef4w5/dK7irnqq3foWvOnDncdttt/Lf/9t/48Y9/zAsvvADAT3/6Uy6++OLzDN2IBvDsVxsiTsWIAMxHhCkxje2I6a5CBKMQnRIPIYbQ53enEelQkvn3M4gALEOMehFi6IeR4NGKCMVoZ/R/m0gV8hwSFPYiJjsVMdwcZCK5nAgwSD5Zk4CPIoJ8OVE6q5s43eYa1k8hAtfsseQSCVhTBGUyFyUh6IuIKHUynBD41FVElGsxkRw1H2mLUvTj+43vDCTclhECSBORG+5FRMiTP+1+4//7HuPj/vttP99quKcbPxVEROdo97vP41kJ3OqSmu/3PF1rOB5BQuhXjM9PESl0WhHzm0H4id2mech43vjpR2vmATT3SQOSjVLvgAj/WLRmbvJ8XQ3HHzSur2VY61F3BNgOr69G/lTV/m0eoRWaj5j8I0j7chgxkg1IY/R9JMSMRszyHiSQ7/OYC5Cg8iIRHDIFrf8awUY+4dN4EOWsK0Fa1l6iXu/jwHdtOk/+e9/3uI4QEd7L/H4noYEZAtZC7ruBWS6BOpMwr34KBdSUeY4PoP3xsMf7rODP+IhxdNBzcMDzknxKVyHmuhmy7kN7JNO4XOzxVggGCpFQ9ajfedT4KfPzXWhdZhtGz3/WROOyEgkwy4iaw4PAnZBxBYx6DK3ntP7r3d9lhA/eBLcxWfM4Ks19G5Fep9nzWUmYKsvcb43xvAAFSnQgoaQIHcQO+Z2PTtN+S5rkLHRgAK2lW+Dkq8Zzh/E4gOhP0jj3edw3IUEl7e10iP645phuIony94hSkS+idVOBhOH5hNb1RrTfFvr9/Uioe9RzOwXRzBeQkDeTEJBLPJelaIx/Dq+vQ4emKWg9DbrtY+jgmLTseWiddRvHWwxfHQoeLEY0ZQa8aw5aTxsJC1KGx1eIFvXLaB9eqXbeetL4ety4+6BhecHv/xFRArCQ838NnePP79D1rW99i//yX/4LGzZs4IknnmDCBJmxtm7dyh/+4R+eZ+hGgkDOfn1plDb1FrTRrwc2wWtPag+OuYtw+j6JmEsyMSShI9f3xiOmftTPV6MNX0iUXUum1uSfU+t781CHyTxYTSQDbkBCUzIdFRL+aTcjop40V5WE+bGUKExegwSEY4Z1nse72fB81n8fRYRxG8HIk//NcsQwNqET/w1IsEz+SDVuKwu4DvqXmyGONlwz3cZ9RETjC4QvVjKrVCLBZBo6ESeCuA34qvG8wbhaRkTvdhhX45HWJh9pFMchAfpOxFS6PC/H4J8Own8f63mr8HNzkbBiTcfg4xZsKojkzFvd3wIk2BX5t2uRYFBP+Cr+peHMJRzGMz2Pm4yXViLQp5EoTVhs+HuM30/5vfv9WwURGLEBDnwXZlxzGjz49wLDts3zORMJMg+6z6sR0ztKJFhe4LHcigSGpPFN2iX8dyNRgaWC0JokLXeF57WIYV/Un98N7/o0Wgf3EwJT2mv1RPT6RuQ68H3P77WGcx1aa9cgAaPWcD6FtJrT1O6/XgW/97zb2+DxdxH1skFzPsk4nof2ZAMSZrYjJv2YcZT22h6itvC1yIw9n4givhhprr6NGHmH52IL0ky1GCdHPf4at5k0eUnj2Oe53Om5eNbzs5BwT7lT/fT8MDwDJj2KDm2F6MDzEc3nyR/CmGLConG75+sC4/p/Av8dGAOn2mBU0hJ+kagTjL9LCLPwIJHProhIRL3CAFUSaaX+1H1fx7AGF5Dm//PG4VTikDuATPYvo/2dzOplxs1M42qm8XQxUYptNKIF2YaxwfjaiTIgfBsJowvd92cJ/9EWtGeqCZp/yH32M5wv8Bcvwjt3IZo0aBxsRXseJNjv5ExN63rj/R6ipGKZn0kH2SZ08Mx3mx89j0EgVTAm4//++f9HbQ7C2NrfnSCQ3/ZrRAA82zVzlDbxx5B57jJENAqJHFTfQ+amuUiDcwSdvHPRZi1BTGOnf38VaRGSBmgfwxnyyUXE/3F0Wq0nIi4nIaHjMkQIhhguAUU1QXimImK6CRHhSdCz1Xn3MomUEg1ESo5EeJuQ5iRpK5Mf3mHE3Ovd9wFEmIs11sEf27TXhRj0biSYJD/AWkTok9P5IiIQYSY6jed5/Ic9zilEstYvEiln7iRM4ccNwyWEL087Enw3EjWTu4hglP1IoMl0O7cgoeBKouRUg9teSAhKfUi70eExNSHBbS8SQJ9Ags5kz+GDRHqgeiSQ3IC0k8knqQgJm9VElO00z0chEnAyDFcZErhGI4E2Mb/FyGco12P/ONJMNBACcx5i6DWEv1sypTWh9TAXCQ+Vfm8Cw2be4Tq/H0HamMQo8xBTzkQM6mki5VAGOnwUenwt/nssoVHe6OdugbduhAtvIA4iC9E+2clwoA+r0Zpa5r4LiSjQPMJsV+L2pyCBagIScvPRmhggAiau8v+9RJLzQqLEWnKvmIuY8SqkCZrvZ3chgeLjxvFjxJ7KQ/s0RYouQ+ugH63Zi4lo8V7ExBNdSPSjmcizeD+xplYhWtLvtm2+ppvhyFrmoLWd5zZGI7pwhDC9pv03jvAV3O4+mpEGudbvf57IL7jfz18pGN68Ey6a7rFMI6oIfZ0wxQMn18GYdxN+zCfQIa4S7eVuItBoseE5iNZ2HaJDxWiN1BPBLbVEWq1Jp+FptPu/neEKONzuuSw6Db/1nCmwNrvNPPeVgm4qiDQ0h5F7Rx9aJ9e6jSfg+EaXBrzL85TjcVQbf11oT/UiTd8et5mEwTzj8VlEVy5Aa73HfewgcpiC1t6nz6MAOPUcC4CHfrcEwKGhIY4cOcIvfvELhobOVHF+4AMfOE9Q6RrxATzLNfhj++Pcioh6PlEvdDeRK+pSFCE6jvAR3EmkhnkJbdx6xEiSb95CwsT8OGLyvUS5ozeIXGV9iFl1+rfEmNsRk0matyEkAAxA+0+gKBtyL0bEaRNiJNVEhv58RHyTL08SOMp9/wAiepsMRybBzAs0voypDPs6DjubVxFJZ0uJXGeNxmW5YVmHmPEthJDWiRhbjvu5GmlbViMGmZjddCLBMoTAuQ0RyErPx73IJJTMun1EWpQTRKqeLv9W7rncjYSwV/3u24bxZaKcWZvfyyfyOS5A/lnZhjXXMDYw7Bc5nBvyeuNlIWJEW9A8bzaeVwF/gITHUt/fahyvJFJObCGuZMKdjwSRBF+u5qH9VSiqQot7BcNJevk4EbzUTiQ7biKq3/R7vhb4mR1+/gThPD/ovr/p33p8L5nvm/z7YsS8W+DCe4yHLCQYdPnZZUhoG/TfdWj9HiSCJFYQe2a74SrSWMnwPNYgtwv87KDH90uiIksrCgJZDRnlSJP5AhGMdRORfqf8tLbHEWbc8e4jaW2KiYowz6LDZKPHt54IdmkhcgtuIDSHB9E+2er+BtBaKCSE+gUe9xCiARuN382GNwlPc4zzOrQvKvxsshgkbdJVgqf/IGQln7cdRALsduO9E2kos6zMKmC4chDVhFCd7zZaYMxSj/k+IiI/0zCNRmurws8f9vdotIbmExVi+pF2dyuhfW7ymGuQb2WPYZ2G0mBlEsneFxCVZ+p877hx/Um0Vqo8zgb45/vgDxKey9Eey0T7tpYIFqkHgHEfQPv/0x5j0uh3ofXdZri2IWG02GM4RiTg/xo68J5AAmCvx92I9vVCw91DBNCMXL91149+9CM+9rGP0dLSwv9V1/bbUApuxAfwLFfGAzDqMkSoaxCxP4btv2hjJt+sI4hINvnlqUSUah06ZXdB+4OExqUCEax9iEiMRUJCGSI0cxBDXYiIS4Z/KyWIzSFk/spETO9Zw3QDFL3PMOQhwrkEEbQNbms/kaakDRHY9UTC22ZkVuomfKB2IWKVtCPFyPxyOxEdl01EsV7p7yZE4G5ChCxpVlrhRw2Ef99Rw5H86ZqJHIm5iGA2Eb5oG5FA9SP/3o+YWx8SHPr8zDKkSWxCzLYHEfEtRKLnRrc/HzGzJo+j1Di7AwkdrZyZQqTY7+cj5lnjfo/7mQ2IWTcQOQCnG6ddnp+daI2AtInj0HroQYJXLZrjFYYpD2nY9nJm+cHVxl0pkVsw/Z4NfBiK7kL+Yt2EeSwbCTtZxveA7yezZynSgpciBjgbraFJhnk04euVj5j7bW73GNor+wkH+xq0vnPRGs2VawUQprmkkd0uHL61GmlX96M5TsJ+I1o7sxmOHG1+3G21EZUxMgnfv0lu/1mPu9X4OgoZ45H2cZPx8ByxNtZ7LNuR8FGN5nKrx1rh5xLOD/v5DHSQ+AqwBfofQ5rwQs/bLWhP7fVYjrmdRWj9QhyQspBmLwmhx4iEy88QZQLLEJ2C0ILn+P1xRMR2IfziJ4Rp85i+s+5DeyVpFouRFvIbxhVAk/xUs/4Ire2paI6XGS/P+/4uJIztIvJ/tjEcpd1+zPiaAz3bCY1pLlExZxdx6ClC1opMNL9TidRCU5B2tRUdztvRXirzGHcgwfp+99OC6MxstC6/TgQ+1QrGP7gd7ecCoAKOPoro+hbCvWe/cVVlHDYKt+x13yXoULrVML7g56/2b+sMczrAZamdU7cQddCXITryVXTwG+8528X5vQbP8ed36Pr0pz/N9OnTOXToEJ2dnbz55pvDn87OzvMN3ogJ+KzXZ0eJEfwQMcGnfP8hRNTuATLhX38Iv3cFEcHYQSR97kUEYKvfvYLIAn8ICSl/jU579Qzn9xu8SXzqwqWIYC1DxGIJYmLfAL6LmMpfEjVhew3rDCJX3kJE0BYx7Fj98/vgXcl8V0c4ZSeNwnyixm4NIkCVSAh+1eNrIDSHg0jY+6hhuJEIathAVG64jjjtLiDSgJwgzDZ7kaCY5f4a3Pc7iPqZsxEzzkSEtoAQEgsR88l1W33w1ktw4R2IyFYjIaMHEerriJyFOxGhL0SC9FqUxuQEcBTq/g0qR0PPEOSOhZMnYMwiohbxco/zC8ZHE1pDf4yig5PgfYvn/zYisetCz/9MxNzrPe4UrFFEaF9HE9HClUSt0RKouwoqVyOtZ4nnvN/9Lnbf96A1c5CoI1oJx2+BcUnbfZhwL5jnZ+cQptJ0aClCa22r522d8Xqv++xCB52biDKC+z1/cxHz3YV8+faorcH1kPEMWuudhL/bDZxZA3k22gtlhqHAY7nS73QZ55chpvvfS+FHTRpbvvH/TcOXfBuTVvdttDcvd39daL29QeS0XGDYuxmuXMOfF8C4Tglkn/X8DXgsGe5nJdq3x9DhqZ7IFZe0Usn5fyLwPwgh9zkY3AoZC9BeSgeHEs/N5WjfVLmNWs9dgXH3f6L1VkAEMVxD5DfNR3vpw2jvt3kM6bDbjtbhgMfxHJHu6DH313saTjH+SlDQU5FhK9Kzbz0FFz5KuDn0oIMnhCa/x31uIQJp6tGhZLbbK2F4vw/TsE1+r5xI39Pk558CfooEwcOEkDbntL7mEylrchjWZDKI6EsVIVh2GOZs47Yd0co5aO6nIUVB0nRegQT0i30/mZ8LCH/y0f6/zfiqQ+u4hoheP4TW3p+fRxNw+Tk2ATf87piAc3Nzqa2tZfLkyecblF95jQiAZ7v+apSIzCrErPoQEShBgsE8ZMLbgojKLpRG4mXIWI1MvwVIeBtEBHEKEij2E9GV2USG/mR+S75NNrk0/wNMSr5z09xGMj/vIEyulYalGAkKuxCBvNj3h9x3FWLUdyMGOs7jrHP7+xGzamX41EsZIkiPIcH1EBIq85DJJJleh5BfWikSOJLmq8dt1Ph7FSGE1cZYh2uwZiLCnUw8WxGRfM59HPZzUwzXfLe9Ab73Bnxsq3G+DhH7NYhQ5iKtSh46hS/2OGs8v6XIMX4vYfrc7PtVRALXxEBr3O5BJBg8AkeLYcI8jy+L8CHqhFMHYVTSEE9EPoubjIOlhq2SqKJS7n4aiXyBEH6Tx/37BKIecQWRtiIJO0k7XeZ7p/sUNSKtwj2nvdeITHXdRIDMROPybrQn7jYOt6K1kHy3ViPBLk/44FGiysVctGeWofV5BGkyagh/wXIiyCO5ECQ3iPsMd1r/e5FQmek5mI3W4XIkiBxGQlGO4SohKutsRIz4DYKBl6D1m0yZZe6jA627a5HQcJ3fu4DQbP2df2snkv6C9splhBDyVc9HI6INzxP5MzsZTl5OIVoDg4R/4ky0lsvRoSHD481F+/4FjzdpZL/vORntvusJn9qk9RsybvuMQ/uZnfohjLrF8E82PjYSgsqA72017EME3VqF6MoThvEkOki1G84SJGhWeRxlHmcroiVZfm4HEugz0R7pdzuHifX/N57bDiRsXY7Mp9vRuqpFWsJkRckk6lMnM3E7QWcme5wpAKqYoKutHneirT54keOxb4DX/w0uWUFYCYqQULsD0aMuwz7WfdQbn61orx40/N/y348gbfVsP/cyspZUo4PK94D6EQHwt/GaP38+d955529FxO+vukYEwLNdPx4lJldAnPD2IEb3Btq84xAxyUPEph4R6XzEwGYhwtHgz3Si4PefIZ+g76ET3nL3sYxw5O5EzvXdSPDqIE6gtYhgzEEEvQcJElOIGqrV7uNPieTL7YjoHkJEOmmhq9xm8ifsRILgvYhZJKEnCR6lyMm5gMjn1uh2JhBBBUn7mGkclCPGfZwzK53MQUx1JvJHy0RENuF0msbYvAomLUdai2koJULSCuz1+JoQE6hCzGQr0pjejxj4LM9lDVGNpI8Q8gYQMc8lgjWWe14bCJ/BTMN74rT/e9FamGx8Jn/KJjTP5X5/DdIKtnuMA8b5NUQVhWSazUPaoAOI2SwH/o8x8IWTupfMwplo/usI09h6Qku4F2nRqjz+TuPgABJ6rzsNFwk/ywhzfCFiaGsQg7yZ8FmDSHO0GOWUyyLMgpcTa3m04RuH1uguIsdjrceQDgPH/fud7mMtkbg37ZEkjKfDw3gUHb8d7bmniPx5xzzGi423cX6mi+F8exQQwRiZaJ+8gJhtEnRKDXMhOiQ8i7SJSTDIR3viBbRGuv3sCcK8fYxIwt3sfl8i0oisIUpKnnT/H0BrbBvSWCczf1oDg5yZt7QbCaLZxs8ewr/1CDp0NBLav3QIrSF8bLsMX5bHVI0ORSuJVFOXoTX6l2j+E0wNRIm9vUTu0/keQ/Id7kVzvdjwPmGYyoiqMzmcmX9wwH1MQkJ7hcdYRPiFTiV8T8cTPnm5nrP5aP5XE3V5s5GAecjf/4QE6T1IE/sRIkp+Gdq/begaQHvcriun9sColwzzDj9X6z6SNSQdtKcQbjRz0FoaZ9yPQ7RqPBGo9hniMPeF8ygATj7HAuCR3x0BcOPGjXzhC1/gjjvuoLKykszMzDN+//3f//3zBJmukSCQs11PEcLdEKod+SRctIhIXdGAGD1ow3Yh4vgc2rSDRDDENER89vheEeHM26T22UPUqT2INnsGUbA+wTSTSE2STuM97vthxPDHIQazAzGCVn9nI0KShLmJ7uMgIvzVhicPCUvJ5NNJmFK2ImadR/jKrETC7BuIUS1EhPUNRPibkRDwBlHqaZfbu9rvtLvNo4SpI2kyfBKfVIw0oDsJBthKVIrA+J6LGEwVItq7kOB3EjEgCLOzhUsyCC1GMqMXIg1fYjoXENrQx4mIW/zcy4SP4CNoTje7j9fc9yNuqwkJUMktIAcx5UK0Pk5qzIxxmzPROugGvndSAlWKRKxBzDyP0Ba97Hv2ZaKJ8Clbj7RfGchpvot/7/PVSPgXZbiddWheryc0vXPlCzauxP8PIsaWfL02elyDRDR5E2LCu9F6X4Y0hTcgxpxLBNJcSQgfOYRvZjPaAwdh8CBkPGxcf5BIbp5hHG1mOB1I/1qNJesW42Utkccy4amMqIDzDffZhfbWeOOvl/BZnET46O1B+2qDYUhjnUW4WlSidVaBtH1fRsLIIFHG7YDn5kdoz1URAVxXEUmJJ6HDzTzDlIS/RiIZdyESaK4ihMdsImn4JrQn6wxjEqJGe76bjfPj6GA4hzCzJ1Nol/tpIA4Ml7u/fCKyvsw4XEas2wbD0+dnxhiPSaONx5T2bIXfKSECVLYQpThrjKtk9ZiF1sEuwk+4mtjXbUR2gJvR+k0avm6/M4howk6/M4TW9iCiYeuIesEDgrFtD0zI83N7jbcn4bU5cOk1xlPiCZcT0cZtRDLyRBc7iCjlLM9ZNqLTI9dv5fWRj3wEgD/5kz8Zvjdq1ChOnTo1EgTyW30tISIAdwPr4aLE0LLRZu8jBKoMREAzUU62XX4mOfBPRxu6FRHRfESw1xDmuXykOXmY8GfKQYxgG1GXdi0iasmcs0nwsZUIAihRe4O3u61qgpA0IAGxE/kg1XqMpYTPUqdhSz5nff7tmOHpIkoUvY5O+Z9CRPckEsCmENGIk4jydBlIOMn3/WbkN/cp46iBOFU3GGcbPcbVSFhsIhjlTKL+7lLEbL9BJF4dS0RovkE4VXcTEXSZbmMmEm6SuQeGk96ylahtC5E2phOl9tiMGOx45P9zv59Jmospp8GZjfKMTXdfPfD6S0ig2YIEzW4kzCTzeLbHstVjW+L7BwgheQsR0XwcMcIut7ECraFeQlhe7u/xSFu1Cwl29Whd3Kc2B+8hyk61ue25hqkOxs0jtDvFaB12G69XIaE+D83ZRELAaEWCSQ2RHLiSuAaNk13+vN/PNCMmOFdjylhO7M3JiNnuJwJvrkBCQIWqXGRNR+t7veGd6X6SCTb5XzXB8eeJurSFKHp4vp/vQXM9HmkcCz0nM4Fc+NdfIkEhD621qzX2N9e7jSXGc5Fxlkyd+5GA1I/W+3y30YQEwnUe/1q0d6Yh4SMfCRSriUPWa8axhbaTnR7zDcZNnfG81X0d8tibiRyk+P+rPcZtfu/jhPZ7k8cxHtGp5BZRROTim4XWfC+RbPmY2y/2fHYizdZSj7vEvy1HNDMdPGej9XYPsnLs9aeRSEKd9nDyhax0+63owLaZ0IR+k9AqjjeudxiWfWgvLvI8TTA+evx8DdJS70H+rj3AFpjwPrQuRhP1z7Ph0gKPax8KPJmI5ryJoHd5iJY2Gb8Xe3y9hKA+iOjT+byGzvHnd+j62c9+9u8+TU1Nw9/n+xoxAZ/tWjlKhOtqJOD8EG26u4k0FSlQ4D5EaNcj4nYX2tBXI63YDMKnrgs5vO9GBLwGEe50yuxHBDjT76VT/ElEDLsMX/Lb2YgYWRli/uV+roGIfgQR7Ar3AdDs/F03EAlUCwh/qBxgEby2DC69B+h1yak7kPCWB8yFwQchoxiZRHYRaWCWGDc1iLDWI41VGWIEDxMpEXKRduD7SEjCMHzafWUjIrcKEdLthBA1zu3tNk7mEXm28txfh/G00m3vRowgCVW9iIG2IgaVBNBFRPmvZBZNc9WANFUz3GcloZ39PBFVuJ3Qvi70+zuRQ/xXCQ1bO2K2yeRTSSTBvYnwN1yPGF6ux/eEx72CcFM4Dm+9CBd+BDG7x/xeKYqGnmQ8zkTCyz2IEQ24zWaPfzvyr+pBjOcSJBx+jmD2STB60H+D1m8ySc8nzLwv+Jk70Tpd7+dP17KUE4LFOsSAM4Ey6F8PWQ+jtZD87z6IGGcOkU+uDe2FK93n55AAmgI4Mg37bmL+84gAlTwiqfM6JOh92c8/hzQ1HUQQyzHjcx1ymagnEgP3GQc9aG/s9dg2IQ3yJW77CVTVxD5zbz0FF2Yad0v9fNKWdxMHgTLiYLDfY95seK4l0pw0EtrKXMPdi9bpAT+7jgjW+BhnBk80qp9TLwuFY4CMtB93EYEZ8wg3iTJCo34ACVzrGU5nc+AmmHG7+8n1vB733/WIfh1HdHKBYQaty2LCRSPHY3kdXvkufHCF+3gdafMeRO4NDxufG+DNf9P0XL7c/T6CDt+fM+yH3HcpOhCuJaLrXyLS1WQSOVrToecZIqjoy8bfx4yb5JM4g/CNvhaZlR9A62WNf/uO29tC1J7+JFqnQyhQ5yUYfBQyzgMbHzYBT4Ix50iVdHIIxjb/7piAf9uvEQHwbNd8C4BTEVFeiTb+Uob9zAZfhozr9PewuSHb77f6nXX+vxgR4kJgNwy+Chl/hAjw00S5sCcIJ/6NiIh0EjnVShDByUCC5j3oZLmVKOtWi4TDXESckym6BTGmZEaoJdKRdBEJSNf6uVKCKXT5+WqEl3sQ0YPIQZauDMQobyeiCD9GRByPJlIwrEDOzjOMg3bDdycihMn8U0tEVn4bEe3L/fwSRCRn+5ntiCFkE6bnPqQJaUGMO5nAQUJTvuemDDGUJqJM2Y3GwZ8RpvekOewifIr6jZsCxIyLiOCCmUQZu6nup9Hj6kJMLgfNVbefKySqRFQQNYk7EXP8hOHuJyqqNCIG8aLbXYGYxlyPKQMdWFYjYfM4kcevx/g9avg2eAzJn2s0WmdFRMWMKiRYlcP374MrP02k7llCmJKLEMNaT/hC7kFrqMnjWIqE4ybjezzh79eAmG2P8XPIOMojaqh2nwZv0v4u99x8z+OcSkSxthP7IflmXY80bJVo77Wi/dDhe/2GNWmIxxF+rs95rvrQPmo5bUzjGU7g/osPwzvvNg72EsLmESKyvRwx/X7DNuTfUmDKNzyGLqIyxnLfS24fR4ggkGK0D28l/DhnuP0nkFDaThxwOtzmd9FazieE0EHkx3vYuF3gZ08K72/+A1x0nXHbgNbttcbNdrSWkqZ4J1oXfegwcjsS1DKQwLQNen4CuR8wjN2EL+xCpPm7nMjN9w3h/VQnjJqD9slraF10+5MOJDkem+dluJxbidfBH6PD18OES8KNnptGw9xC1FheidZYt+9NJCwhewkL0S50aMgkaEcO4Qoy2u/9kecox/A/gfbuHUhQ3aaxn3oQRo0IgL+119/8zd/w5JNP8rOf/Yx9+/YxceJEHnvsMd797nezZMmS8wrbiAn4bFcjImhDaLOVIA3UDrSB94jvMEg4d7chotCMiFs7IvxHEfHsQUmjS22yer/b/Rw6uR9BGoG3CYZYiAjPdYarC+rWu901hPYkaW+6Cb+jo4Rw14JOxC3uM4vIW9ZE+BmudZtZiMkmreMMJCwecl9zEAH+ICL0x4hkvzmIGSVTawnDzIFFiJCXegwHgb/yWK9wG9MNw0wi8/0s/70FEcNHEEFtIbQafUgYaUHE9QqP6zhR3ir5NTYRvphf8v1ixFySnxko7U8NUY7uJiKo4gBhumsnyp4N+ffNRHH3vURJtDWIESbTZ4J9kKg0kuHfr0frbR9R8aUbaStKPQ/5xu09bj+ZkfoQs/gYYXItgva5xksSagqISNLn3FeT4U8uKoNojS8jqpNUEgEwwJULEEPPIFwOkj9dK1obPZ7LJEAnQX6B+zvMv9egtXlOBtzGIaTxmoMYch5hnj7gMVQQSdkPwE+/i0zDQ8QB61YUgXuESOuSGHEX0kAfMSzTiGCKSsNUT9TJ3u/xtBh/a4lgiVZET/YKtne+F62JTe5rP1rbVWhPrADynTf0JYbT4zCIDks1hOCbtE6DRD7IS4AHoWedYWh0H7lo/3V5To4S0a71hu+Q+2yAkzvRAXAPOnQ1Ezk0k4tGh+fosP8+ARc9SrjPlCAheaPnupkI2ikzzmejNXCx567UY6kCMiC3AAmxSbDabzxvB+ZC/1Pw1nUe+yTgmzDqJs/XIKJlaa/v4kxz/pCf6yB8jvORIFnivr5LVHZq8jjyjZtkiq5G+3+8ny0mEsAnt5pNws9w2qJ0WO/wuDb6/iGktU+H4uRX6fbe+jf/n6HfRj3E+b0Gz/Hnd+h64oknuO2221i8eDFdXV3DPn/5+fk89thj5xc4RgTAs19JuGkm/ExGI8K7CrgR3rkIbfoCdAovR4Qhw+88gYhkCWIgBUhQWoCi+ZqRJmYrIiDlhAYhGxGGR93G44hYDTi7xON+bilRQaHFbV7rdzcjZjUBMbFJRLmsZkRQZvqZ5FM4HhGvV5Cw1YwYx2Z0Oh5LaM9a/E4uUct0CSJgtYjATTEuX3V/16C8h4eMkysM007EaE76vR1E4uUyxIif82c8IuYvIIa23/dakcZjNmHOLjfOG/xOqfFRiRhTn+eqFRHoPPfbZ9wc9Hz+veety3hIpvkSwlRb43uNnrNmIjoz27A2Ecm0C4h6vosI/8dK9zPLfS0zjq71Mzs8l3cjAeBawdvea7hfRoyuwX0lZ/c24CgUJVNU8i/dgjQOxxEjP0xUSJlN5EL7MlGVos9zvBkdTgr9/nHPRRJmtxkfC3Xv1JOe31kMB5CwHzHTPOBS6G9ABw8QAywlcu/NRlrD2X7vgN7hBuJQkmmYP+45LIb3vM9tJm3mUcO2imF/PfKRgPU2URVn0P3fblxvdXJeM9/hg0GN56HJzyUt0Y2n4a+PKI83aDxko32dAgcOMawpLkoHwImnzV8pkSJmgPDNHUMEHPUAVZB7BxGMled+G4i6ubvc9k1o/X0drecFGu+YT6A10o8Oh3PR/i5x/1X+vQGty7lutxPRhbc97snGRxLG5qA1U+D5OOi5POE5mEn4bpYhS0cPIQTfIPzwmuDJug8uXOV7PUTuvDyi6sl2gl5ORMLm44a/059SIlfgRiKqf7Vxt8gw1CJNdj2iGdv8924ktM11n0sJv+JSxE8u9hjyiej58UQ5wBok6C7yOznGWT/DxQYuXOF3CpEwOpaR67f0+uY3v8l3vvMd7r77bjIyMobvT58+nbq6uvMIma4RAfBs1zRElMoJzV4G0hrkIyK50Pd7EeHeAeyFv9+KCMqVRHH2bnTS3ouIdPIj6UCE4LnT+slDBOgEIvaHEdGwhm/Me+HNAffbiYjGFMH3ZjL9lRBRfrPcT5nH9goiHjv87DsI5+dkar6VSG7bg4jmbDj8InFSTtrOxACr/Gw/Yn7P+e88RASPwptDSCCbjBjHRsT8R6NT/gn1w+eQGTud+uuQiWUSMpXnEOXB5hv/iRGNJiJN643nHiQ4TEMMqBD5kO1FjLPN74wjUuK0+7kfEf5MPzGMh6D/ZcNxnIiIbEbm61zY8Us4nkytrZofCgiH9A6k7bkcMeMaj3k0wezrPeapiMEkc9wCpGna4HYmQdEVhNa1GDHZGYhBJY1B0txNMQwvI+Hhbt+/BDGwQSQo7yHM6dmI6XYROdauIhz6qzyHFYZzLJxqIdL5TINR16C1NIAY7CSkLS5i2P80q9x9dMDg3WgddfveDX6u0mN8yt+XGvdNyJduPhHNfgwxUbfJDsOc4Xenur11wIcQ8+0ggoHS+hgAboVRn0braYzbTy4Z3cicOZ2or7sHKIDXk8k6kzAPVxmHEFHNH3afQ4ZrEWGyLna7DYQfbDGh7UyuKA1ojYzhzAjWGuRr1oPoRobxcgJpvDagw8CAYZpNBGpNR2smlwis2o4EoxeIPIt74AcP+rmPEBaSFrTu57q/XiL/ZyFaI3VKAj7sR7zZY+r1MzVoH7Ug+vIpImq6nsiVudd4nGa8rHF71xlXyWLjgxPj0UG0iNBM30Ackro9H9sRLa8g3CRyPScfJwT6Ws/NfZ7TbWg9fRmtlWOEm8Jct1NOREJf5r8nGrflnpPFwJNuoxvRkip0uD6f19A5/vwOXT/72c+47LLL/t39Cy64gJ6envMA0ZnXiAB4tqsLbX4QwSxHxCL9lo3McGkjTkNEpRo+9AnEgNKJPxdpKraiTVyEGP9axPwKEKH4CjQ/hQSvHDj+FCIcDyACMB4xhA/CRR8mfGlmISa5Fy56ADHfpMEaS+RKG+3nk4ZjFSLuybQ3HvmALUbE92EkLF2KGHwTTKlCBHixx7AQEeWrjJteJDh8B+jK0Vg7GfaPu+geP5fMRknLcggFT/T4/z9z/0kwm+12rkLE8wQivDci7exVhDmp0ILDxcBL8OZqxOA3IiFql/FwzHg4jE7rXYjgrveYEiG/0/jf5nks0Niz3m28FyFCfqf7+bRgmH8HjPuExzrD+C9HJm8L1MN+dJ1oLWwnhLDkR5iJGHERURUkDzGKQSSoXoAYUzVi1jmGdzpRt/h6/53uFRrmQiI6fKJhScLyAiJKNM/zXovcCZZ5DvYgITYJfhb2aIJRa/zOBs0FQ0jovMPzmoEY9KOIaadAgj8EsKvEbMOeNNUF6BCzEq3hPr93gijHd8DPpCCcbMJvcK/v9xj2euPwLoIilqG9vR8dhtYSOfWGjM+/R5rrOsSY9xj/eZ7XtUjYnAmXvJc4PNa4z7RXqtzebOAn8ItHkF/wM4QfctI8NbntJFxu8jPH3UYhWi/taP7zCE1aj3+vFEzMRfthkPARvtLwYDy/QNTQbXc/d7jtUiQUlqH5nQksgD/MRIL5HUS98hs8Xx1ofpepzaM3oP0yT/jJGG84k2A9k4gkrjHMGYhWPU0cgFd6bM95zl4VjG/e4Dkr9vw+jOa+G9GHCkQPxxG5+Ub7948HnGw23vcYXy3wdy8RJvFWIr9nFxElPZeo6X6vfx8kTP6Ffne1nxlH1HTPQoeZDsMzHvGQ8Wh/pQP7Ls7vNWICPuv17ne/m9dee+3f3f/BD37Ae9/73t88QP+XayQI5GzXtaMk/JQgTUs34Rs2FTGb3XDyuzDmKkRIkg/NPX5nGkqMugQximvg1J0wailxitwO7W9A0QJETJYRlQXsJHxqrd7p3whZDyKCdwQx/SmCg68iYrseCYRTDPv3ieCVDLc9EzGqLYhwlyHCdwQJm41+t544wdd4zElwm0QQwy8hotmEmEtiRslvqgQRrg2ET9cyQuuxgIh87kXMezFininAJRsR0wNIO7jBfU9DBHqQYT+sU8/DqIcJ03oHUbt4LWHW+xuiVnMu4auzzPPd5Dkv99gfRgJHvmFNvmpwZvmovYQrwHFUfusPOTN4J5m9foiIfy3SijYZtwVEUvHdhjv5PNYTEc1J2NqCNBothDN/YvS73HcVYsrpMJP8nfIJB/lkuixjuBLF8VoY95Db2gZvPQQX3o4Y3V4k/GxFwsIH3e4qxDTHo4CUHCJ4oNkwlbnvo0hbtdw4OYr2RzLhb4Ljr8K4OwhT/jVovUIkKs8kyr8tR3uhEM1/BZx8EMa8w21nuZ9jyJ+0FLgf3tzpXJ+tSGg5hISRKkIA6CMEopmnzesCpPV5DO3NNYavCQlWl3m+jqCDU9JoJY1jvuGfjYSUBWjdP2U8ZqJ1mEzcyUyfhIBb/d1KaG2PE5H/O5DfcRdhom9EtGGMcbrdMOYhgeUeP9uG1tyfobltQQLYzUTC7kloPyXtVj6RSzH5BNegfVTs4I6XjavVRF3jXuDHqBRfi2HZReQyvAXthx5ktk5BdNvgn34J/30ROpzuQsJUjf9v8Vx0IbpS67HvR4L4j9Beqmc4jdbwnBQGnk9tdTaEjYhW1KK1XIWCUv6W8HkdRGs2B1mACgxv72nz24HWeTJTz/WYW93/LuM43+2sQHtrKxF8t+E8BoEUneMgkPbfnSCQp59+mvvuu49HH32U6667jqeffprXX3+dr3zlKzz99NP88R//8XmFb0QAPNt1/aioqDAbEfspaINuB+bAqftg1KOI8RUi4vpF5FtyCBG/9X5nFpEQtgARgD6ivFsOImqXESkV5hKmp1biJNyFhIgH/H0MpSQZIHzicpFZ70H3cRgJCNlI+ChFRCdpFaqJsmK9hA9Nl/ExHQm1mxFj6DJ8HchBfBIhtBSjXGkbkEamjdDyNfm9TCIfWDtR/aQMCXmbObMEXS0SrvYjAriBMHEnn6sMxOQm+J0hRKAPGx/fJnLHNRPF2E+6/+2ESbnVzxwhEuz2IYH2GKHdLTbcL0H/HshaRZR1ajI+rzXcH0FCwceNv/1ud6xxU0FELKa28z1fCVfFRLqVPOOu0v0lk3EmErQykfB4CZrbaW6jGTGlQo9tkseeolL3IOEiGzHP64hDSQXwFaTRrEJrrpGo/Vtn3CQTfCXQAT2rIfcyIs/dKs/T9zxHhYaj3uNbZtjno32RTJ27CbNkB5G4+QCRjDj1X03UjN7m8U1Ba3IdUeJrP5E+phAd2LKJXJNr0LxX+t6Hkdbqc0QetmPEOiwlTPofRIJKDRJWBpHg0ITW6o2ei/sM69OGJYvIb7mNCDZJfl95noMKv9/l8W4iqngsR0EudxNlGpPp9mok/HQTycgH0bx3EEmxbzEMB4gE10VIQKpxGx3GcQW6dhm//9Pf3cQeL3C7DWjt/RkSjuf4t1xEQwcIv7ykIe9Ce/8QkVC80WMuN0xTiPKHRQynrxkeXxdBT4vQWnjc/TQYtmzCD3GD2yglBLZ02HgK7Yk5aI4r0BrZftr/4zjTN7jRY5yLBO5DhLb2ZiK6uAztk2YiZUwbogNWDNDp/1/l/FYCKTzHAmDH744ACPD8889z33338frrrwNQXFzMl770Ja677rrzDNmICfjs181IoJhE5MICbdYcYMD+TG1oQ4PMYjMRw+hBBHc6EsqSliD5AC1FTG6y7yUNwhBRdSEJTseR1nGmn5+IzGjlhMkiCXJvE1GMa/zuUkQcWwx7MRI0d7j96YjQJMI42u/lEKflFA1ZjZhIMiPuQgyzGxG505zYmUcwhxN+7wq3OdrPdxARkk3GbwZicAWIUJYY5hy3eRvhR3cr4Uu42c8nn53xiDl0GpczCL/CIeNjk98tIU7XxzkzuWoLkRg2CfNd/i2ZBMdb+Evm0gF93jpIlKhbS/jS7WE4YvvUk26/0W3m+tOL1sg8Yi3s8vt3E2WwGhGzmWu4xvm5bUi4SWuskYiwXuqxbCWSRU9EDPRaxPAuRnP/KhHdPRWtgcmIMc/z3GxB67GTCNhp9djHQu5Y95G0K+MM8xK/s87wNiPB5ftIiG1H+6LQOKg03p4lavZ+22Oa6GezDO/TvteOBJskRKwxXDvRnsrw78kdIwks24hgnnxCK3wTEv52IWZ/wjg4TKSCakXrrdptT0DatPuN2wpUri4dgD5i2D6FhOliQgAYPA2vDcZBpXH2Dfdr7SwVRC3cCs/TFo/xCuIAVEtELi8yHB3GzeNI4PuK4ThqfBZLE8txIhHx08Ax+OkjxucbnlMI/+Z0SCj32Hrd904k/B0wvhqIjATJN7Ddn4nGw063eRORO3MachEY8jMVaF/0GIbRhFCc9tYEQjBMgus8w5FobhdQBm8eQeu1lwgYSRaJuR7Xh5DVoo3IINBk3A0aXzPd1z2Ivj1D+MJO8fhL0dzmnTaOtJ5e9zxmED7nm1FmiZHrt/a65ppraGxs5N/+7d9ob2/njTfe+K0Q/mBEADz7dT/DvnuHHycIbAnamH3+Tmr6xIyr0ak/l8ib1UVs4lIikjSdmusQE+tATLaPSMibIsgaELFtQZv/+4jwPIoY0+cQ8elBBKScSBS7AzGqo+jknxy2GxBh3IROs3sIYTeN807/n0ckeN2DNB61hOk1+eY0G8Y6P5f81dqJ0kbb/UwrYlAVBOFuRERtPmJ4n/F3p9+biYjtHCSMNPu9AY9rHzpZgxhhYiSvERrGKiTcFLq/TrdTgphIJ2Jik1D05y1656d7kDk9Oa83+dkDiDnkIGbU5/4nw4U3IKHrc0iTPAOZwDYwnNNu1FIkuIC0dVWIWZd4XvYiwanVc/mon3WwxMmXCCG6D83zPLfzBTTH+wxzIxGwkyIN0xpc7/f6EGVYT+SSTMyzw/PRjbRgG5AAPYTWJ+hw8XWivmyT8f4hZGqsNhyZbjMJaNlIQHkQpa55zeOe6HbqCO3op9D8Pmv45xCBOhPd3gARef8sodVJh7EdRMLfzUSFiSR4HEdrZj5xTSTy8EHUP7aZmTwkpFT6uRrj6gVCW7uTYV/V4RrU+UigG08E8SwnklRnEwJ3mf+vRmv0IFHS7AmPuRLt/6Qtzkfr9nb//zpaL3+G1lMSMif5/Vc97kbC77ICxt2EBPPkxvIFYBG8pwAJmrvQevgqketzKpFnsAdlQMjzGMcaFxi+JIBPIQ4bQ/59CtK+DqJ18RxRPWadYa0myrr1EX7Bc4io6aNI69/j9poI60WzYdts2DfAReXI33M14TaRzNKT0ZqsJvy0pxNa/p1ENZMdaD1MQj7RR4FX4OePEJYKBw3xyTGRg7SQOKgc9vjSXs84DX/n6xo6x5/foWv+/Pl0dXUBkJOTwzvf+U5A2tP58+f/L978zVwjAuDZruMMmxmmLEAn2z4UqPAC9D9KpAooRZswExGCqwin7V1EeaubEWNKvizbETGdQTjXJ5PhdKIm7ADhOJyDfIw+jhhCNiLQ05Gg0omCU95GQs5MpCX7O+TDsx3efIDQblYjIe9qxFz+GhGshchf7AnE8OsIwSGZZpKZ0z6Epx40DjKJiLZdRM3flciv6m6/kwTBSwlfqv1E5B1IsISok5rrce1BzPEJw1RuHCdfn7GIiCehpwIJ8Bvd9gkin1kJEYFdCoOPIyFmtdv/oebiPe+DngbgKfjrx5Aw8KDxcLf7XeP5Wu4+d6D5f9Y42I8Er3zDv8jj20/UZq5AGt7N/lyOGO5WpDFpPe23TBjzvHFbQziHdyKmMhYxoYmeq2fQemsiopK3ez5K/HcTWp9lyET7mNtfjgSDmwiN2wCRz3Ch7+0k6mHv9rjrDHc1EoabkBBViITTp4mk6dWem3Fufx8SWBqQVqoQMfYCz2+L8ZUOQ8lvrBQJR2NP+9tayp/vJITuPCKwAiQc5CKBo9s4ud7PjDOOb/XznURGgAmIQZej/d9MuG4s9PMLiEPgare5nUgSf4hIq1SLhKBDxKFxPaExS5qm7xKHlvl+732EabsECdPrkMCe/EiTtmo7DN6H1mrS2DWjtTyXyB6QzJuvET676WCy0J/kNrPGbVxjnHW7rxsIF4Dm0/C3l0hTcz1R1zrfOK1BKXpedNsvQ88J4zkFyVxPmJ+r3Wc9YVGoQut4Cpq/TuO61ThpQQewPHRo2I3250Ln3ltqHNURrjsDhv1bwMfGRE3oJ5BL0FS095OF45vG4xgk3JXCu5Lv60qkId0KfPUkb25FKbPSoewBotZ2mfsvJup7n68raV7Pxed3TAD8p3/6J/r7+//d/b6+Pnbv3n0eIDrzGvEBPNtVNkob/hCx4fLQ5t1FmImWoM2ejU6UfYgIzCVMqJWE/0xyDB5Cm7kAMYxuomRTk99PAQa5DEco/vxVE4xMRNC2o1NsrZ/9OhJSnyLqAi8kmGs79ByC3P/NcJYhInkj0gTOJAI19hD1dgvc5+UeU6+f7UcEtRUx/aQ9uAIR0eTvk0zN3b5/ExIwkzY1CbJJA+Dghbd+7LxX5b731Gk47kLEcR3808vw35d6TioQ4U7muTmIaC/2XFV7LDUe473u/znChywPMYMPIAb2LSKQ5FN+botxl3wT5yOCvdQ4OO75aDUsjR4DSMj/vN/pQGunDdhbABM69Uy156/W4ypCGofFSANc5HnoQhrkQs7UgrUgge+Lvv8cEvR7kCbzJrS+xxO+U9sQY0k+iC3G305461G48DEiaW6h/z5AaBpLjb/9aG98n6jQMd73K4jqC9Ph59XwrllEpOrlBEOoNc6GkAD6Fc/dElTh5FNu7w6kkdxo/O9BAnK28bPS95K/ba7vLyD2eNJIjve8HoKerZD7DhQostGw5BMphmYTGtNCdCBI5vltwKUweA9kPICEh0oYXA0Zi4g8kDnueydaK9cynM6n/ynI2keU0StAh7TVXgsYlq1EhZm9aF0UocNdMToUdhpXC9BaK0DCUYPbOoLWzUG0ttPBtxQ4Cj3/ALkPMnzwYJ+fWQCnbodRU9GaWotozjQk5HVr3GSj6jXXEVG+ewlt+w4iyG6zcbsZCVlprabgmHK0f75OlLv8tP4/fh2MG0skfa9x//MNT6FxtgCtmwRLERHROxutxbVEkvLvElVt1iBXkE6PM/kkVxFa53pEn2ajfXTcOEvCaCf89B54z1Voj30DZWFI1oWkMfwB0tTONb7ej9ZbPVp7HcCu8+gDeBGMGXWO2jwFY9/89X0AH3/8cR555BHa29upqqrim9/8Ju973/vO+vzf/u3fcs8999Dc3ExZWRlf/epXWbx48bkYAgD/8i//AsCll17Kjh07KCgoGP5tcHCQH/zgB3z729+mubn5nPX5H7lGBMCzXV8aJWJxAgkuyUG8iqgzO4CEwWJC0DtMlHU7iDbqMUSIknN3OyIcNuENm6T6EUF7mPBX2gM9A5D7BJFkdjYidtmI+M0mkiEnp+MmwqdrKmIISRO2GRG3LxFVKpL5t5oITtmEGOJ8RIy63eatyI+lmHC8n0RoTVuQ1mEWUTe43G2NJapH5CHG+3Hgf0ea0xOG8VuIebURJunDSAOWHNk3EHVCtxN+kmmOkl9Rlb9vMtxfIqpOLPH4L0cmmSuNyw1IiE2O5vvc70LjthYR/t0eVzESMpJDfj3StLUa14lYfwoxrkqPc6bhmEaU/iswDDXG9X2ERvQWtL42ur0lhFP4C36mGq29JsRY1hGuBJlI+JtLJO1d59+SVq8RaSggnN/rkAB5qeHtRcxtnO9PN04/a9xMR4LScUL7s4tgsnlEBPp+/55Ml+M9xhrDu9hzk4UEwMVIC7wTrZ/ZRBTtAUIAzHD/+J0t8NY/wIXTjbv70PrsIYSaqwkhqoSI/t3pOQJpwCrQWi1F2suZfqcSabm2EgETGw1ngds8htZSr8dYTtQ+nuP+MtzHccOzk0i2Xkm4VeR7/HVEycYiv/cU0op1GJa1HkPS0te7zbmGKWn5qz3ufYQJ/CRae+kAuNw47iIipo+hfViBNOgZRORqhuE5ivbLA342x/hZhPZOB0oh9TUip95DSHDMIALdknk93/dziH0/z7DNRGtzCVpjDYarDc1ztnFdb1xO86fCz65Ba7HDsCZf7luJ2uA5BB3ej/Z3DaIH64AF8OZDcNFHiNKAjURqpEG0p+Yht6MGw9BNBA5me27zPJ7vECVDM/13/XkUAMeeYwHwxK8nAL7wwgusWLGCJ598kpkzZ/LYY4/xt3/7tzQ0NAybXE+/9u7dywc+8AG+8pWvUF1dzfe+9z2++tWvUlNTw9SpU8/JOEaPHs2oUULKrxKxLrzwQr75zW/yJ3/yJ+ekv//oNWICPtuViQj0UUSEspFAuBExjOTPN4tw3B3t7yqksUj+GvmEZqaYEO4yOLO4ewciKnkM+9ywEnKnIvPSCcQENxi+PKKMWA6RMiDT/eS53eSH1ESkgEh+TluR8NaJiEwvYQ68GRHq8URd1asNYxVhbppEJOtdhk74VxmPPURQyibj8dv+LWmE1iBi3Wt4dxuGGUQN4kTsuxHT3ub/+9xuM1EW7ZjbLyZ83CyA9B8horbLjOdsJHy3IuHsKJGOotZ4X4CYT62/p3ts7yfqz/ZFP/QirUuX/x7n/uqIiMc5RNm29YbrW4hRdxKa1Qok3C4zvh4nksduInwEuwltQx8SApJj/UHDNp9gQkNorUw0/sYTlU8aiXQtSduV/Czzof+gcZzpsZxAgkSx20l+fYnxQpTfqkWCXLafG4uYawVRh7nP+GhA6/0Z4/JG95fMmAsJLVGfx99LmDHziaCbLPtk9njMbe47w7hcRqTCaXcb9gNjrnGyS+2csWeTkDxErONBRAvmIaGj1WMvRGlP2tAeWuB7yfe3mcgzONPzsQ3tiR2EabOQKHm42c9s9nt1SGDMd3tJ85SPNH3dxCG2zXh9xXiZRJTWS5rwvX5/CaIBi9D6PIoOVclUu9C/93neEt3cbviKkbCT1m0OALx1iKiY04XWRnIhyTQOrkRC5q3Gdzth3p2H9uBOwi+3mEgE3iF8Daa9m8zPrxBBNVPQWm1Ch4h2Ikq3kvC7TMEyif5nE9WFLiUOLb1on1XCRQ2e0xLjZIb7rfdzF6D1uZhI95NneJLQ2+5+bySqxrQQibn///j6+te/zic/+UlWrlxJRUUFTz75JDk5Ofz1X//1r3z+L//yL/nDP/xD7rjjDt773vfywAMPMG3aNL71rW+dM5h+9rOf8frrr3Pq1Cl+/OMf87Of/Wz4c/ToUU6ePHnehT8YEQDPfiUH3A8hApMYZgciNkW+PwMxZxMzMtCmfAERogYiovBpwsdtGiIWTyOmfAkSXNJJtpeofrAMEfcuFBSRTNCzDcM0QnNw1O3NRULHIcSwaogounSq3ICI4DX+rc997CLygR1EAkkv4XCctCWNRK3hcuh5zG1eYlwdQsw6mWwnImaS/B2fd7vJfJY0T/sRw3+WiNIbMOxzkHaglqjJe4Ph2kmYdrMNf9KWzFM7WcsRI5uIGNkQceKeJsHm9ScJolrq8c7xGNoZjjw8+SRRLSSZsB/2mKr9mU8wrAXuJ2l8bzOs6UAw358kvGQaD9Pd92uem1sQo1jpZzqICga9SABoMU7yDEeyQKz23wcQ40lz8QW0RgcR85zpMT9ArJcrjc9CJ8EeMOz70R6Y4L4Tk/wyIcwNEdrUYoKpjkOMsYQIGqjz37OJiMxCQrDudN8lRFLgQaKc4tPGtWHlGNIYZp4GZ77beZ7Yj8fcViXa3/VoH2cR67iKCDppIwTvGiTEzzQ+tyHz4kG0LvuIKN7k/tFBmMuTlrTBc9OINEiDhKtGDlGjvJmI7s/z+/M8x8m3cwEhuPf5t0l+b47xUI32+hDyG84THP2PoHWXBJXDaO+ORvRtBxH9PwOZvl8nBKPktvBtdGiuNQyXEQnDhzSfFxYTws58ImF3pufhoNsd5/snDMMMZCWYZHjy/KlB67qVSJydDxmZxCGu0O2M9rggIsUf87tHiLx9Vb6XNH5lRIWbR/3+WDRfB42LZEXZSwR1JX++v9ccHN+KNPKT0Rq9F63h09dvo//ehvbEDuNmEJmNL+H8Xv8RX7//1efXuPr7+/nnf/5nFixYMHxv9OjRLFiwgH379v3Kd/bt23fG8wALFy486/P/kWvixIlMmjSJoaEhpk+fzsSJE4c///W//tczysKdz2tEADzLdfxlIuryGHHCbUOCySZkhn0O+cnsQcRmPGEWTibSDMK5ejr0P08kyU3mmVmIQBzx+8v9Tqb7mwrUwNFfEjnoXnP7mYiYNSDzwMWEUPoKkXplyH3mESffI36+GxHuPW67hKjVOx4x5f3In+wu46ETEUabO3PXIML8isdQTuTiugcxmmSCWoAE26QR3e/+/974zkWEs9h9jEaC6J8iwaeMKHH3qHG4HRHEBrc/w7hLDLkPBYnUIEFjBhLCdjGsmcq6Bi55n8e0HzH0FwnBfTsy1WyHMR8gNG3PEdrb5BM0E62TDCLApYmIGC4zbmd7jOP97H7E1JrQtR3ogbdedJuPej6TJjAbrYVcouB8v8eQg9bdSuPmOcP8dZSr8TBRTeNOpGEZ8r1upBFtjnGevM64e7/72nwaTB3+LZkU13kcMwQ/k0+b60F0WBmLBKwmw5p8t5I2dy4KGlmJ9t9KgkGuQtrjMuOow7B/Cu3dpNFeQeTU24CYcGKoPURA1GRCszgTJbjOIEzey93333vOnifMc3MNwwzP2Y2Er+lCw9pKJODeCe0PIWFhCdLIHfUzSVs4EQkHNcT6+SASkG4khIOZ6BB0H5FcvYWIVk+a3qNorX9eYxxcTWjl70bCzjVAN2TdQmhuO5CQsdDwTkR7t8tzV4XWTZM/bUjIu1q/t3f6+Vv9fqJZm9QXj3oMOZ4PiCjuQqKmbxs6YH3N+K83DBv8Tg+xBu8gUrXsR/Sjiqhj3ECY06uISOu/d1sNRM7CRAvnIxeHcsMxk+Ecsf23oPnP9P1DaH0lIbSCqFF8P4py3wPj7kP7/nm0Z1rRmnvQcOw2fJn+/rTH2+nxl/qZ83n9/0AAPHny5Bmft99++1d2/ctf/pLBwUHGjx9/xv3x48fT3t7+K99pb2//tZ7/z16NjY089dRTfPnLX+b+++8/43O+r/9yvgH4bb3G7SHKdD2FToWfR8ypBhGDHuIUOBsRjyWIeeUhAtwLp7bDqMeQMHQfZH0YEZXpiFknYt2BstHfi4hENyKCZYiIzIYJy4GD8NpLcOkiRMz2A8fh+CEYl7RgtUgImI+Yy31u41WixNd2RCDte9hTALnXEFq5RiTULEDmjTxE1NYZSYWIqSwhmGc5UYd4Eww+DxkbPfbNhAasAW32bOOxjKhe0uvxZxv3SSDNRwJAL2Kix5B/UBK+70QCTgUS7MYgJpzuPY80avXwwk64+jXfz0fam7Geu68jpv+ScM1lHtd8wufxkOHt8vjnelztHk8y7eUh4SwJd0kA3ouY4j6iAH05Ym6LkXZyHdLWPAdUwYXX+LlH1MfgMshYgDR5HUQaijHGbRERXfx5w77E3/cZ5jXC5c9/CO/6ABJyetDB53LExJ9GwncejHnotH6SD1o9UW0in8jbdtVpcNX5nn3HBtdJkXXRRN/vMF6akMasxHO8D62BSsTospFQP4RSmsxFa/JO9/MnSJu5379VoXX5FBIwZhvGcr83l9CQVfqzxf3/D2iuhUnvRfWyswn/1u1ERPnFSLNaAicvhjE3oD31xBh48mSYBOcat9nAAigaRPssuUm0G+7lhqMB1Z4+Tmjikrbqz4yrKiSEtaK1W00IxLs15lMvwqhPqI/mx2HSdD2bUUxEjXcgwfgIoW3b43m+Gu27FwzfWiR4ZBiHyUw7D621WYjm7FD/RQ8ZZ19G9GcApYnZQkRapwMlxsWz/m22+/qkf/sLj2sNop/3+7vP4+5zHymYI5PIm1eF1tpGw1pt3B9EgXS9aG3cTFhc8t3uV4yr5GuZQwjrlZC11PA9iHjERCLhfRbwEgxaU5hxD+FWsBkJhkNobjsQ7XiSEKa/jOjRPrQWlhiGo8ZRM79zV0lJyRn/f/GLX+S+++47P8D8J67vfOc73HjjjbzjHe+gqKho2C8QYNSoUdx7773nEboRAfDsV9IodCDitxmdSlPAwAuIgCTG1I4YT/KFuw5pFb4XxHc4n1k32sh9iFB9AxHOpUhwuwOZZ3qRRsEmRzYhQrYCLh0g8lU5uGPchxGRq0XCTBKMbvffpYjrriXMcsn/6GnIfQAxugHjYJfHsBEJJXsN73Z/H0fCRjMMXgMZH0Dal/F+phYy5iGGfhIxmqOcmQqlmRCEdvheMrd1eow7iJq/XUTFi5lus+205/KJxKrlgvnUL2FUvWHbKBxd3Upk0k++RpyGp9OFx0xCKDiGNJzLEMPNQUJoNzAIp47AqD1EGqBkquz0Pfz3x43PyZ7nfvfzVd/b4DE0weCrkFFNBAesA4ohI2ndJhIl5EqMm93IDFVKHCK63ccu473c7xTCu+5ETHstYnK47SokfCxCa30HEpo7iNyNE4lo23qkRUom+2QeryAq4VwAGdPhonykndvv9vYapmSSH3T/zUTVjxrCn7JebQ3vjRJkpn4CraEaIpjiKrTGit12I5HUfbRhfMLzWs9wGpxJVxO+dS1EOqZvIS1ogceaoTbGrPbcXQ38y0m9twbRjkyi5vPm0/DWZriST+gJIvfmVxDD30BoIbcDRdD8Q5i0nDiAljEsiHIYHVimwahJhvMNC39L3H4yky7w2NYR+QkbkLapxc88jNbFLhgcgowrPB/JZw2ijN1DhI/lXe7rOWSVaPF834NoVJHfTy4bpYQWfRwSrpv9zHwigOIwEtxyiHyjTxg/J4iAmFIiu0AxEaE84PudiGasNv7+GvgivPkSXJSE2x8Qmv5pRP5Gu7k0d8KkZWjt/gUSKpOC6Zif3QEZ7yNy3d1IBO5Von1xGN58GS6ajdZXOjBuIRJpb0UC5h3IOtPt8Z7PawgY9X/71P+zy/ESra2tZwSBXHDBBb/y8Xe84x1kZGRw7NixM+4fO3aMoqKiX/lOUVHRr/X8f+b68pe/zIMPPsif//mfn/O2z8U1YgI+21WHCO0AElqSyaLJ/ycT0kK0ScsRc0jCXS2Rt24MkVttMsJ6NTLBPez+5vrdOkREL0ZErYPIAZZB+PKc9N/J/FRDVAcoR8T5fyIG24s0Psn/6GokXNR7TI2IOfb7+xjhV9TufuoYTk3BAoKZ+7mMVW7zBUTs85FZahFi0pcQTturOFPr0nQa7pLPWAci2POREPw2Ivotvn+JcZyHBI5rjdOkZSk2nibBqCqCkO5FhP4GQjtV4c+1xvl6ItdaBRIapxCmuSXG6dOnvT9NbY4q9vtriCjXDPdXjRhSHVoDeYiJHSBq8RZ4nl5lWLuSMR1pZq4l6oTmMqxhZr+fT9qlPo8j+colbWsew9UNhnPf1fnvlxAjnYvWdJ/n8ARaQ8mXsclwJQH8MBL0thmfM4ho8eToP54wDw4Y9pnGwTrD3+r+IPIYJo1dMZGnr9vjnOXntxLm3jK0bx5FwlTygUzapAVofdca5mpCQK0nqss0oX16I5GovNdw7NGc8DHiAJB8A6uJRNlP+dOEhMoaz0MZw1pXTvj/Bo9rNRGw1I3WagPSciXNcjpsdsOkPyIqj+T72U7jqtffW4gUU7ejA0/y9exw/1vRnp3pPnKJCNw2v5/HcH7LjI/4vSz3+SCRJaEOacyTKXqz23qESLh+l+c5adyKibQ0wFsbDUsXkbrnRreZ4/uXEzkKBwhTeA2hoVuCaN0yP1vvuXsF0fZq3xuNDkL5aM0uhItW6Xs46Ga62201LiYhOvsATLrJeNtO1B/uddtJ6B132lyBeMTrRJ3fC4ApcNEKomRdWgebCC3vApSb80FEpycb779j15gxY874nE0AzMrK4g/+4A/4x3/8x+F7Q0ND/OM//iOzZs36le/MmjXrjOcBXnnllbM+/5+53nzzTT760Y+e83bP1TUiAJ7tuhwR6WWIwKRTafLx6CL83aYQARfNyE+nmygldZhI23ACEatj6MQ6kxB+uog8cW8QSVyHEBE5igjbfkR4Z7jddYjRNBOM/QgiHG8T2oUyROSSH2AyPV2CGM8aeGut+3kDMbWFfi+Z9ToIDWMzyvOWiP9EIrJwADHTbsM62jhdgph3K5F7rZgQBisIoWkKYuilRARlNRJW2uGtJ5EAvd5wbCeYZpE/E/3eOMQEbnB/Rf4/aRqbja8uIpJwq8fag4S0QjTfrciEVIuEvCrjow0J2jl+9ohywA07lG8nKjgcJDRj85FQnGn8FSKt84eNv2LEVLMJDdlhf8Z7bBON89uISgiJeVURDvb5fmYKUdGgBgn/yQTZQaSYgAg0qTF+rkQMqwGty6sQQ2wgIuS7PKZc43IlSklysfEz0X0kof1OJGjOQEyugjhwLPHv4wjfyQ7jfoFxm4T5SmTm7zSOWwxbDcPRoMP7N2kMjxm3093vYr//RY9pB3EQGkDMPdN/txFVb2qNv6VoPbQTqUYWuL+nPeZWJBwUEVVQ8tC6qieqACVXgvcbN1uN03x0sNziNl4gDiy4r4V+N5lYNxM+mxXu4yQSVNNBYBAJJ9PRvptqePs9piH/v9xzMdf9VRvXewmf5+s9Zx0wONX3Jxh3ycTf4bGMZjh45cLRRJWiYuLQNZHIerDFY0p+xKV+JtewP4dcL75M0Nd8zgwgaiP8lIuRMNWO6PbrxsMQEVSVhehAjeBkHZFNAD832b8l0/w6tFeme9xjiTyWvYS7zcvGXY77rDDOBxgW/k69itZHPqFd3+gxnM9r8Bx/fs3rtttu4zvf+Q7PPvssP/nJT7jxxhvp6elh5cqVAKxYsYLPfe5zw89/5jOf4Qc/+AGPPvoohw8f5r777uPgwYPcfPPN/6Hh/6+uj370o7z88svnvN1zdY2YgM921SEn+3ri5J+BCNZWP9NKZPhPviQz/XwzkTqlxb+XIqI0BhGmZILZjzZ1KTqxPoiYxgVExYqZSEDZR+Ta2u0ks+MRQUnmkOlECpN+RCxuIkrIXY8YYBIMxvrv43BhByKOFwvu3Svg8uke5xykvXgF5dKrN7wQgkQlkSC4HTGmxYarzv83weEhmNKhFBAXfgIxikzDbt+j/gcg672IaWxFzGasP/vc7xj3fcL9lhK1hm0qZRYiok8gBnCT359IRK4mH6QlwnP/QcgqQMJWAVEaL2mnWuAXJ+Cd9cRJfTkSrHYZ/1dBRjeh0e1DBLsNuQhsJ6pavOix3+TvFo9hofDFUx7n5b5/ujah2/M403OyFDHQvZ6PpInbDfwtkaOsGmnLRqP1mLQ+ecZ1E9K81DBspvz5L+Fd1yDh7lt+5xChDRswrMuRYJ5M0HvcT5HhLTNO7zDOt2lOTtZC5oNw4UNEZHeX+hlcCxmrjZsMJBAVAT92OztgcCdk3ITW8zbj51q0DzYa113G4z50mNiruRpO9tzjdwrQni4wHpvhzTa4CI/pFbR3FyPB4DARPNNFVNr4oPFbQQR8JM3mxYR2qgaZT5cTORl3Grd9/j29N9H9fMRzvQkO/xCmzCPS2jQTyby7Pd/5hMay3rCfIFxemjxnz3nu9yH/32QanoBo1k603h4mql8kTW2+8beGYb/ojGuAB+FkoSvXrPD8fxoJNd1wag+MmoY0lZd7/tYSfqzb0bpJdDnRuAHj634i7+a9/q0MrfW1aF9UI8HuemQ5qEBCRzpYFxHJ4KcZjuMM07Q3fwYXFRD0Jgm8+9G+NT059aRLPN7u31YTyaW3+DuH0GLfjoTzJsLHeQaiNT2az1FTPaZkLWojArjO5zXIOTcB/zrX1VdfTUdHB/feey/t7e1ceuml/OAHPxgO9Pj5z3/O6NGh65o9ezbf+973+MIXvsDnP/95ysrK+Lu/+7tzlgPw9Gvy5Mncc889/OhHP6KyspLMzMwzfl+1atU57/PXuUYSQZ/tunxUVAxoRExgClK/Z6GouVuRc3IuYjCJcWYRfhtLEJEYQkS5AAl42YiA7vF7SxCDTL5OmxEhfoMw/9yIiEXSFC1AhKMMEbd7/H8TIsYLiVJWOUR0aC7atNP8bAcifNchBrgHMbNKRKC+TZzoGwmTQzLHpj4eJwI8kklkgDDVJHP2gOGdjphaForu/WskpK0wPH/qMWNYvkVUIJhMmBJbjNdB460ECaYHkHbnAiSktCMm/2ce+xS3dxhpQed4fI+6337P2wLjdoX73Az/vBH+4CGiAkvCXTcSchd6Ths9Z+OJUlj7EdM8TBD8VYjhZRNVY0qNy1vRweEp4/BetKamIe1XYgCl/j6AhIibENPJJvw3bzI8+4nAk5uR1vft0+YtA2l/5hLCQSEy6T/v7xoi4Gg7EsSajYsbDE9ar4uNm1cILU4HERxRaHyUESXjtiKh9Ur/ngTZQiSYLTfOStEaKNXc8BIS+m8mtM0DRFWHduh/EbIy/f94pIG5gUjX80M0/ximu4hI2SNII5s0umm9TyMqzsxDAtBGtEduJZJ516JDwu3G4QA6UN2DBMKbiTVQY/iTWbTKv6U5uwtpdiuMv6HT4J5EVGcpIZLNb0T7+jrfm2NYZhr+NG8r0Vqa5LG9QAj23YbrdrQfd8FPa+E9S9Fau1k4OfmQg2Imo33eaNjm+f/tBB0YQoe7biK/4QcN8wSklXuJ4eTVrz0Fl64gkojvQPt9GloXKz2fu5AGeTGcmgqjrkAC5PVIwOo3Xg8SKZXmIt/s6/37GiKo423jYiWRPSHXz1+H1usONO/TUADWEBKKjyKhsptIbp78xp8kSh3meVwDxMF1BWHy7kLr4wS8dQ9ceB7Y+HAi6FHnOBH0qV+/Eshv6/Xud7/7rL+NGjWKpqam3yA0vwKGEQHwLNfVo4KxjEWEpR9t/kHCNLUbEXcIv78GwvyTiMESfyeH5Aok1KwlhKKJiAhnEjVZM/z7zYi5znafB4m0Lcv9fiuc2gqj7ibU6Y8hxlZm+BOzTkRrFpGXa5L7/jO/k7RyPUQZqa3Qvx2yVrjPLUhYavcYWxExP4IIXD6hPVtgeE837V2AGGuTYbHPWn8DZD3udlsN//XQPh6KFhknX0L+XWVEIEjyV5rpcRYgwXEu0ha2I8L8AGImPUT5s+SjNs34mIgE0e8TtYgr/VsXUQf6DaKObz2RL3DAnwLDUowErhN+79vA3yBTZh+ceh5G3YAY8hPoSibi48TBodrzsdftn0SmsDa0LioQwz2EhPKNp8EwBXgvEjbyiUoSk5EwMc/zXowY00rP1WPG4UqPfbzb7UD1TZNf23rob3OewHyPYab7uAN6jkFuMWL6yXe1DGlvlqA1mtwv2pEWJJm+V6ND0gBRMzSHqODQIZhPrYdRc4hqNN8gEkZnonVwE9JiTSU06h1oj70frYsuwz7Jz6SAnS4ioXUJUQ1j22j45yFpxQ4QidIrCI3qaLexi8jPeIQIgNjlcc1CfoavIWGsC9GCZhRsVmfYbkO0oc59rCcqsHjPsA25E6x2P0mwuNq/zTNsy5EGMpsIbrrDY5yE6GA28jcd7TnKRfurFF7/idAy5jJEP8YgM3rSypfA4IuQsZzwV52DBMgDaK7rfK+XcGWoIpKst6N9W04cnHcQmvc8PzsRCeJNnrubPP5daC2MRsJlOpTuIIIuCgjt2gaP/wkk7G5EWQLuIEqE9hDWoQoiPc9GRBvzjHeIms9JQ93qeUvWpA2Ihk0ifK0LCb5Rh4ThWWg9p4P1C+dRACQKB/2n28TFon5HBMDf9mvEBHy2q4qI+isiiHQSCo4jIpLMqW+gE94nETG4FGksvo8iPrf7/eTTU4g2+3giirMIEalixDz2wu5/g8tHIwKZjwhYI2F2fIAo0zYbRmUTqQoSMa1132VIgFmFmFTyi3kIEc1BIoCkFzHZyUgQyGPY8TkrmXkOG1ezESOvdzvJxDUPCSRJM5KBNKg3e/y7kTD0ECKw6XQ9B7KmesxvECX5MqHoCuNrqnFR6raziJJrCwlhodu4OoEEm+Qb+Ljx0uV+WwnfxSSoTUWEt4/ws0lMZsD4OIiIevKBOoaY0Vy3X+x2ZxE53W4wXOOQ4GR/xFFDRC3WfHSw2OC5Sn5XA56n3UTC6eNEup5VHls+kfS2FAmDCxDTGCTqFw8iQf1ixCQb3M8tKOfZV4zPiz3up5Fw8HEierQVMeYjcKoNsuYQlT3uI/JpzofcdiL6OJnAOpBAOJ7IIdfved1EJKK+z/M6FzHPTt+rRcJwLVAFo7a5jzq3U0TU1+4yzAfRehki6k93e/7eizSAQ8bhBrQn1hD53A55rmYYh8eA7wxpjdeh9T2ADjd7PF9TED2YgvZEEvq8bndvh8s/4t+no8PNTrTHNyFha57b2keUhewwHC2Gs5s4kOX5uVc9D7lEBYsaoipGJ9pD09EYthufGYhmHCBy76XAiWR6rdZzl6z0O4tRFZy/QgeTPcCdcGqZMwVkEoJLq+fqMv/faLwvRbR0kucqHV4XeM7zCUvEGsOZZ7jSnluN1vRliCYdIHz23ksIULXuZzza37OJYJ0GRNdf8DOfI/L9lRGHvk5/2tAaG4dobw9aDyfQeu8g8gsmn9Kr0OFrO2H6ziRqhI82XqahtTzLY80gasuPXCPXf+AaEQDPdhUQhb/HI8IxCzGRDkQMvoY28WcQg3wKMctGJDw8hwhuIprpFFsCNMDRF2HCR4jIzc1EIfEm4P2iB7yOiMoMQsuVmGdyGs4kcoT1IWbwacJf8QFEWI74/04kiDS5/YWI8O0lBIwbEeNoh9eOwKWtiDnN1L1hAeU5vwvDp+iTG2HMWP09rOHsQafuMuPjcUKw2k9Ep5Yb35uNxyQYJF+gGiTA9HseWt3GJPe1gMgT+Ip/azJu5zMcRck8JEQdJqLsbvSYlvpeLzpxV56G32zDV0ykiOkgAhu2+dNFHCSStjAxrdFoPeUiLWjS2BYgzcF1QCMMPgUZS5GQN9MwfQOtocQMjqD5zHBfA0QJrM1I09Pj+4kR5bi9MsP858DfGcd7kLnpONL4pXGWuJ19RGWHEuN2HDAGRvWd1vZdaC6T28sgUUmmiEjEfD0RZFFNBPTkEZGijUgQuNnzdCkSKjuIGttJeJ+NGHeav0G03ioIn75mIm/cpUTJs7koOn8QMe4uJBA/TgjUEFrmDUTZxQo4dQWMWoXWZhPyPxsg8tPVE1G2KTBjBbAUyh83zIfRGi9Ea6qWqDGd5/fmELkTO9H6vBat98eJSjN3IyGyn9hf6bC04LSxdCPhp4GIdu0ghMUK93PMuPmw312L3Cq+gtZhJZr/Kf49mbFbYFQyA3cR5uRsJADtNG7K/f5aRPwWEEngk5a7CQmZ/wMdHpMlJsGdidbMNMLHNB2AS9E8HzZ+M9AeGue5uJIIJOuD/kPQMR0mLPUcNhm+QUSXWtAaKyHKKa43rNVECb16QhNYg9b+RqIiTJnfr/Pvcw1jJ6IhPR7bMsKyUU4EwpzHK5Guc9XW/9uv2267jQceeIDc3Fxuu+22/+WzX//6139DUP3qa0QAPNv1KBIWMlEy1o8gQrwPEfwCdHp+FBGzaUTJrwqiEkYXUdGijkgm2w4T7iKiLROjS4z+baRF6SJO2RYK6EYMdBoinOXuuwYRsi2ET0s1URt3iIjcrUBE1b4xTAfWwfGDMG4qUY90KnAULk0nziRIzdbz7CXycyXhrBbGPID0+aefyIfg8FMwJRsRvVs5M+fc1USd0xRIAWL6zwrmA6tgxjpEGMuRwFLt58b43UmGZZzhOUoUj682To94zE2IWQ0iRt1hvO4zzIuQeXKH/2/0c8eNw8T8k09UHyLIXzS+9iJTXtIuDHjOkvb4mOckaShyoOcg5OYL7oxnkMDX5LHeSEQCJuEymzhoJBO3tXK7X4LLFxj2hWitbUVrt9btjvVYroDXOuHSDyOmNQ9pZluNh0OEL9tu95njMa9DAs4Ov5Pmt9nvZ3nOuomqD0VEzdadxnse0voWeU4+6Pm6AepugsrVno/LEdPdCT9dD+95NyrbuBEJBZ90ewNIC/SYcXA34fPa5znpIKJBNxE1bf8ArfWklW8lEl7vRWsy+fhWAC/AqDWepyqP+e8dOPAOojJIMgk/60TTR/TbO9cTpr5eIlXQVHRIOyHcHX0UJlQTWqA8tKdb/ZkGJ9fCmMlETfKFSDC53f33IM3ylZqTf94Df7AQ0Zh1SPBPB90niHWeDnSbPYZrkTtBsfGyweO887T3qpBA9YhhKPTf+3z/AcJq0QZv1cKFn/a6WIAOEmmvHfS8XYv2WS9aS6Vov7/gMeUZlgrCzzIbrcFFhrHNc/c22jePIQ33JCRgb4aslTChFx3S2j3WlWivdbjPArSHbiUOP02EoN/icc4S7D9/Fd5VRAh+B4DvGl8tbv8wOvDnILq40+49NxqW/YbxCuJQcp6uEQHwzOvVV19lYGBg+O+zXacnhT5f14gP4FmunlGjyL2FqLHa7O+r0Wb+BiJi1UjTlscZlRW4ATGjJCh8B23wZ1D02T5EHGYRqQl2Eb49yafE5uKfroX3HETamQN+ZxAxwrGImI9Dwk42IoxrERG8nCBKOYZjHGFWrjassxBBWo+YzxSP62KPuRUR5GcQwR1EjLKB8GVKBHQeEiw3IO3DpURd49/7fVj8L2IMA4gQLzUMu5HAtwUR7o8bpmeRk/TdSHBLztqJybUjBrLO47ybqImaTE2Jwd9JCFQTicCDXYb5WcS8k5ZVezn80mZ7LFcjQn25x3nYz4xDAl+n2y72nGxEQvEERLhBzG+pcXnC8OYhon4VEWAzkxBQk4nzcuPuAGKISQitRwz+UiRwNBOasB+g9ToaMfOtnsvtnjMfTsiD4z+EcZ8gzHWFBAMvRFVr7Gf286vgXY8RgShfQ2tjHJFs/H4UbPMBw1ME/KXnYTYR/ViNBO0VHtM9DAueb90kNBV9wHM0Fn76IrznYaKW7Rwi+KKRWAfHCM3VHyOhqMpje4OonbubSJ/S4fmahxh6BxIaqpDG/3ZCCFuKItwXOBp3moW1p4hyZ01+/3YUePK857DG83gpYvBtRHqXJLhnIG37XyDtXiEh5Oa7jeuM50IiijRpvMv93FNobQ2h9XI3oW1/w/jb5N+m+L16wnc3mYRTUFitYW1FNPCA4ewlKuSADi8dsPtBuDzTMEwjEmUPIPq3Cu3F/afhLGl5W/37OsN8nfGS/ASTEHWj268hcjumw+Uq399AuDlku61XCbeSce6/w/OQRyR1b0bCf3LFuQwdFA4her/UsFyNaHILkSngFrTG7/I7x5T78MLriKj5Gr+7yfgcA/21kLWLSDe2BtGrPs6rD2AH59YHsJARH8Df1DUiAJ7tunmUGGYhkcC1iwgMKUdEagkRgVqENA/z0SYejYjBXqKe8CtEsEWR300msiqUDuQ4IrLZbredIHJzCWKSNG7tiHHkIEZepb5O3QWjriN8oMYjYj0RCWul6DTcS2gFX0WE8ynECPYQgsg+vTv4DGRMRMRwgWE84nZfQBqvWYTZNt9w5iMBcAfhSN/n/lcigo7fG0uUlEuO0J2E78wuj6uG0AS2E3Vn62Hwl5CxiDCdbCaY/mYiovIaxNzXGQ9/5baOIWEgHxHa7xHm1UrCaX8pYXK7kjMTwCaGmcw5y0+boxI/m3zKutDaOeyxVRie93uMtSgo4CBRwiybKA/VTaQn6SOuEo+lmGHNxrDJcDeRp+9bxGEEIp1JJ1rLLUSN0v1oX9j/a7gUYPKRO4bWQS5i8sc9tunIF7IYzft4P3OUqFn8INozn0WHjw6/U2CcH0Pr46DHs93fLR5rLRIaHzcex6I1VOP/kwA7D/n6HSaCHJI7hX1wafCzo933WiLq/hMMm0hPHoQx4xHTHkton+vQmnjdeOhEh5sZhvW45+NWQpvearzvc1tvu5zko6f9lg4ofYapnNAsPozW6yzjKPVnQb5nNeRO9TtzPQ+biMj5DKJqxu2EpqrVY+pEwvkgEQg032O5BK2/VYgm9BAa0UIih1054Rt8j/tZSGj+0759Cq2PyW4vD62hIffXaniWeY4OEr6Q/cZJLSFQj/b3OuNuKZF0fKthmm5Ykw/heoLeJ9eLLiJReY1hK/e4P0Lk2Nzu+5Weh48TFo7JhI/mekKLmu+2k6vHtcbxISQ0JteKQ+iwMAH4/vkTAI9xbgXA8YwIgL+pa0QAPNv1j6N0UrsWEbEGRKz6EDGcRCReXolOyjW+t5EQNArQKb+R8H0bj5jdNkQMFqMNX402ddLM9SCC2o+IwRcMWx9iUl2IWLchAgFREzT5+hUQGqKkHVqJzNo9RCm09YbvKBImj542pj4iUW0DkTA2nfCLkSDxBkEU+4i0E3sIRrwEEemksUsMLd9tZiGmvx0x+gX+LZlxSxGzakMasC6G/aTefAYu+iPDfdiwnTB89Z6Xqwz3dL1DO2IwE31/AVHmq9nwlaL5bkSMbrdhK3U/Sehr97ycIDRhaZ72oflt9rMDSAO0FTHIyUSOr0wiavAYYYZeSeQ/LDFOEn52Er6PPcb5TKIsVxlhHr4EMamNxuWNiIE+RJQo7CYCJGqIhL1JiHuU4XXQvg+KrnJ/0z2el4garZMIP9DdhmUcYaZbRuTHHCR83FrQempE+2IuYtBjPVcD/ns7IbRmMlxRgk7CH6yFqK+cTJk9SMiZ5rlJazyZ1F7wmErQHvoMETgzFc1zF7H/5np8Cw3fVDS/dWj9N3qsyf/vMBHxOYDWTIFxVkusvaSNvJbIp9lNRKxu9ztJe5RoQAYRvLGdKN13xLDVE9aCuWivdqI9nmhUITqwVRqP/WiPHkVrbp5/24OEkl4iGTVEYus2tzuVyDVZimhg0jI+Z5jHE0JWHpr7bMKPtsLtpQNcpvHfCVwAv/gHeOeHiYj4y+CtR+DCYrTW32HYa5DZ90HPcabxkKwdg0SZymYiaXyp8buVSALdQVhzMN563HYVWitVhE9nL+FXm0e4EXQR9DOZvDM0LnoNxxS3UWBYJwMbRgTAkevXv0afbwB+a6/kJzUaOKHUEpQiApFLpGCp9LOvIGZ6EBGOvUQx70zO3OQT0Ka+1u2UI2b7HNrgQ4RJsAMR3MWE4NDpvg8iQjWeiKa8m0i7MFZDefNniMks9vvHCF+59xNpOka7r+NIOGgnksfWG76lhFDQRTirV/u9IsS069Apd6O/d3nMG4hySo2EUJG0P8l8NwA9tYihvoAYeL77TkR4gX8/ApTARVcR2rYSJITsNl7akLl1tseyhzAjp/kpMOxvEBqVBUT93elELeNdRHoc/Ow4JDxPRsx5tWFP+B70/WJ/P0EIe0+gdbURaUQmIQF3gNBsTSMqxyRz8z5kCsoxXmuRFhciMCU9f9Bwd6FDyU7DttPjeIEIpmgjfEtnwOAbhMP6Do9hhbopmmXctCKG1ITWQrfh2YKY12b/PokwCz9q+JPQXInW7Qa0FpKmfKr7fB2tuabTcFBOpFipQOukCM11hj9XEb5+yfezxs+XEPWGcb9j0f5MZv83kGat1f+/6HHhOZnq+/nur5TIs5j2RJrD2UhYeD+iBYVEBZrZwjeZhIYt3/0kvCeT7ha0Zqfq5399Bq3TfCQwJdN2BtIw5hKa6wzCpDvPeC1D9K2K8PUbJITjDyPho52o4VtB+COnPdkDx+9BQv1eYq570FwXoPXQjWjsIsMzCU4dRGsQtJ8mETlPbzK8TxPl3+yuQCGiTePgncllZaVxsw8uzCECe94gtKUHPY5Cwi/0MFq329D6nEgIo+mAexBpmJuJoJAaoqRd2reLEd1Jfoclxu8qJJx/n6hAlIvqlRcjgf1utN4mEvsyH9H7hPdyonrUeboGz/Fn5PrNXSMC4NmuDQw7mdMOo+7x/ysRsfu2v5uJyMk2/385UvWXIiKWjzZyBpFyI4eo6dqGCNDbDEefUQvUwVurkekxAzGuV/w9GxHAXKKWbQUirDMR86oXbBddjBjydkTsLJA134OY0CeI8mjJ8ftJIhnr0x7rZwlNQ9Jw9bufBsRkLvM7bURFha8jYjYbnaivQsxsE2HOHY2Yw15EKFdB7qeJ0k5JaEyap2zNC1MQY/ia25pOaOQOQ/8PCRPneEJgHe++ighfr26k9Z3j+ek2Pucg/6ATiDlnENqipZ7zp4nUHUnztNVjXgWD242jDMT0y/13veA59TOiiPxCz+0uxCy2oPX1oMfYpzaHS4aVEuXEkp/gADpILPRzJYgRXUkIZT8ighi+4Tme7HFXIvPbJGAeZBQYpisIU1zSvqQDQLnnMPkmLSdSvtQRaWrGG+5upNnsM8y7DNN+why5gUjIXkX45B4l/LveZpgJNj/k/rKh/y7jdClxOCt0m3mep7uQYJME9MloDbR4vIuJg0zSEP018CS89UMi3UuCa8hj3kbkWtx72jO3IZ+z/WjNjPVvC4FZcHQWoi1lxsFVhIYz3zBcjgSbm5Gwf0I4LDQISWP95uPGeR4RWToe0ZNWP7vQffW6nx3uI/lQ9kH/I/CLtURO1EzP3fVoLW/y3Ayh9d8L45Jm+iVkXbiSKE13kEjrsg/t43rBPeoq4WFYGkiHtBw0z0UeT3OMe/iZDiRIFRkH9R7/XKK84HQ/00BUy1mC1sKzaE0PAZPhn/7NsDUZ5wmP/UhofgbRs2m+v5lIyXPA+E0+jPlIOL8f7Z8cZNGpIPbQYbQuegmf2+R61EpETu9D8z/RcJdwXq+hc/wZuX5z14gJ+GzXlFERXn+UMIc9jswGnQTBOY78nV4hiqfPRqe+1YhQ9yH/wBJEWO8lTuDJL+8gkfKiBDGCZLK5jqivW4GIWzUivN0o2/zthIlosdtMviUHiZPic8RJtJhhv6pfPAjvfEp/8w5EuA55nFMRs7mLYASvElqObvd7r+F/GXglC77XL4aT/JguI4TZ5Bt1kEjnkfy7Sjzuv0fCSDKhV0P73VB0N+FX2GNcl3n87YhILiV8EDOJUnPrEJGehhhAGxJsSjSvPSsgdyJilp9FhH8TkYV/i/v+LhLy5qvt166CSzcjIbXYeFrqOd6JNCLjiIoi45GQle3nu/zODUSpp2qUmLvCfSdtbwFi0isQw9iI1ukyJLB83GM/jOwzizyO1Z6bNdBzN+TO8vzNN16S+XQZWicTkBY1OcN/FdgMPbdAbhUSnjbDv74Kv/c+9/tUzOepgzCqHDH1bMIUm+nnjLvhsmtLiFrUex0R/SBRReMpdKi4gCixeITw7Wr0OK9GjP8rwM3wrx+G31tB+E71e2wvAIPw5i/hokzj+wG/V+E+tnh+8onatw8SbhX5aH9UIkHjFiIdiH34WIT22UEkaIxD+ezu8XtTiSoZl6O91IvoTVojA4TP6Vzg9wmXgSkouOdxJHwkoWae5xvjuBit2eNIgFpKpKF6GAkYiwzf19A6bUKC3BXwt4fgo/8bErInMlyVg2f83Ugk1S4i8oNW6Lf2Q1C03PO3C2m2niH87Ar9bgfhspJMogNoTV+BDpv3E0FJ2foe3K6pvfDTaM1MIUpzNvm5BWgNjEb05hrjuJCoLLMf7YHPAu9BQXw5fm8Q0YqHEQ1dhNZrOqT2GLc9DCe2Z5XHlHx6H0HroJ5IabMECfz3A/8nWoPJXDyZ8LfdxnAA1HDWgfedPxPwG5xbE/DFjJiAf1PXiAB4tmvlKH3nopPr5wkTyVhEIDL82/2ICC+A9geg6CUiD5iZ08knYcxaIkruYSSEPO12bkREGaSh+IKfSz5xTYiYVBG1N48jAriAKMt1DBGoSYj4DxGJo3sQkUvEvZpwgE4m4WmI4Y1D2hiIslAthn8iETWa7T42+ZlERAeQMDaPMFXORMS10eMoI+oX97nNSo1r8EnIeJzwR0pm5JvdfhnhAzUbCZLbiYCR5My9Hfm2VRN5xsoIzdKXUMqWmcZFHxFQsdn3KxCRf5CoXGGt1FuPwoWXeS5Bgk7y30om5CcRAR+H/AsXEI7/nUSOsLmESfM5IglxhfE2AVHIttPmNQ8ogl9sh3dmIm3lbMRsRrvPHsO3mzhidxCCZ/Lvq0draAAJo91EtOhcJJS0ELWTVxAR2EVozU8xjgaJhNm1hHvBXZ7vNYazCu2lDyMhOdvvLvB9iDyD5URt6WcJs2M2Uf+20fN0A2LWxUQVkQrCn66IWMf5aJ53+PcKovpMjZ8fJOrh7nLVlkUe1xtI+Kolom7z3e+VbmsDErZaCPPzCrSWF/vdo/BX/wB/ehexxp5D6/FpNP8HiHXVhuhTs3Gx0TiHyGmZ/Mq+gmjDTD8/32N6lci9WYxowDq0ZlIwRjmiM31ur9w4Tub2LUA1HH8Sxr2bqP2N8dJwGg6T6TIf0YwSz9Va4+JGz0OT8fUTtD6TQL3U/X0IrdV2OPk8jJmIhKptRF7Sq9z+BuPmanQgmmAcZBL+vy2eg5v892v+rZA4bCeLAWg/dGrOqEQ84GrjfbrxvNPP9nk87Yar2ThZ5Pv1xnsJ4V+dbfzMRLRzNDpwZBA0KBvRgzLgk+dPAGzh3AqAExkRAH9T13853wD81l6XIkKTgZhN8u3YizZpPtqw30cEugdogKI5iLlnI+Y0C3gexnwYEbatSIC8FJ3cepCGYDJihk2IaWQajtcRAalDhPKIYXoCnUqPIELUggSwK5B/UjJ3LkMEZhoy801F6TROwIWJoDe7r8Puv9/jrUcC7nzCkftiP9eFGEqX8VKPCFY3IprHiECPHYR5LRsRtaT5mGkcJBPjNIGScRfDuf+SsNXzNuQ2ExVRcoybjYa/CmlPOhjOKTbs45Q0E0kQnOrnnnKfJYR/4A4kcCT/zC2ET9oe9/9h4Dm4cLrbSRqlPcbZDre9C62XzyEmXo7WR57ncZDQBG8w7kqJ2q3JxJ9MQyWEE/ztSDPTaDexucb5cYajx089CaOeQnN8EGkn93gOkwl6FhIoLkBCWjpc5BIBId9z/3n+jEdC5bcQA/q+57PY8zmf0GoMIsb6PBLEZhIm02LPzz6PfSahua713CQNVXJPSD6FpX7uw0Si5EeJIKha4+sNjy+ZmSuMpwKPpd99LkECRJNhO4gYfKd/n8RwkuNRyxn2vSMDCZu9p41/EO3J7Whf3OBx7Cc00zsR58xHNKFVstNwgEMm0hb1IoHnFY95msc24DHkIsGk3e10EuXcniICSW5G9CvT45mC6NBeP78Z7Z/L3PZeQpM30zAnrdu1RELiq4XHcQvQuuhBnPxSz0uL52Y6EZW71rjfbDwmreEaz9l8JPxdhmjJfuRGAqEdLNDfY+5zv03G1WPEfmn12PLROp6B5ngOUYN6NbH2HkXr8UPALvj5enjXdCRUTiJKYiZteYvxvxTtn8lErsK0zjCu+hEd6mCYznEpsa8KifyaaZ8ni8Fev5tobuJBizyekWvk+g9cIz6AZ7uSz8witPHTafIA2sDJ9LmeSOVSDEyG/sT46xCDvRsRjv0EsZuKCHMvYrYvEVqjDr83hcjzdyNiNBP92yokRB1HhL4fMZ19iNkVICL0uOFKws80YCFcONV9v+CxlHksyR+l8zRczPL7dxNRkSWIeNcgAruCqMPZi07Q0w1TcjgvIbSDyUfQvj/Dmf53IaZRi5jqEMNO5rlziMoV3YQPZPKVa0LCQR0ScqoRc5riccxFZvUu95XPmek5Cj1HlWieG4nTts3kFBMpI7KB0XC0hYgC3uH2h9DaOeG+nyPS3NxMVNV42v3kwJtbkSBVgxjGdKQl3E4EfyQNyufc9mXAVMi6AuU3vBetmSUay6ipxk0bYf6+B5p/aPws8PPJHNWOGBlEibQjBNPa4bYbkFBYSFTQSEJ3JRI4l7uNIjS/Yw3LMaQJzHF/24kD1rc8p7MRIx1Aa6qZiPhOWvklHlOCu4vwhZ1u2FZ4LB1+PvmqDfj+QY/vceNmOZFku99zlfzIkokvBV4k94s2olpKlWFPjDq5LiQ8lfh7ItpXFxuOhfDWVpi0nKh8sRXto22Gv5eIRq8n/DzLkWBWiuayFAlE3cZLP5HLbhzaDwv9/wFkiu4hajznGgcH0SHjpPuc5f+Tpvwewkd3DzKlzkD7PcvjKDeuFvjZu5HQVONP8pFrN/5rEE0rNr5biMj0QiRM5xPJ8fM9jpcZ3ke0oEP1C8ZBwkcf4Xd8zJ9cQlOd7eeOecy74V2TCUFxLOG/mtbuPX63wn0mn+G0vso8V/OM90p0GMgjrCyz0eE9abPb4FRyj6hB9C0frdMytIY6CDeKg5zXa4hzFwAy4gP4m71GNIBnu+aijdvr/9cQzGEAEeXJaGNb89WzD3KnQ9YKgmg1IOKwDN68BS6qJxLSbkOrfgqqTZnPmSXjkiYmw/BcR6Rd6UYn1zW+N9v/5yFmMB34OPRcB7nZRHb/Y0i4KkREbBpiKgXA/0TEOGkfF/jZcdD/DGQVEWbULCLX3NXIpHwEEaekeWxC2rnPEZqkDvd7A1ESaSZiSD3+nouI91VEuoZixMj2G/arEMNK2sP9/tyJCHQV8p0bh4h5DmIQhZ6/N/z+JR5DHmK4LUT5tL2E2a3UuGj2PNUZn4dgwgf8XBchXOQgYakKraEWonJBOxIaNhLVNDrgovciop9MWHuMp9fQ2luLBL4V7n/otDm4HgmPJcZDk9/JQ+a65D84G7gWJnUY9/OJ6NzkE/oMmvsqonqTpCsVAAEAAElEQVRHwttnkPa5nAiGqfI4tvh7puevymNOZrNbCNNql++dIKJS89D83+L3OpDW6/2E4LMJrdM2tM7TwaIQrb86tCaKPJZvABvGwJdPau0WEYE0c/z7hwm/3RbDuILQxO3y3weN21bCZ7PI98cTEbitxs9sJNweJYTZbUQSbmtvmQQctzb5MuMknzDx5qM1M0/3Tr3oiiMz0JieIvLDtRgfj2ue+ndCVolxPQYJMXlIkCtBa+x2pHnK0jvMNz6K0drZSqTTmUQchjb7uScI7VuX52AikTMVv9uLhM2taC/P8XymA9hc4/0atNeSBroNHaZ2EXsr+fP1uP1CtDeTabzCeC5Ba3sCUUKuFM37FcgVpwQdxB5AND35LS4m9n4dWrcb0bzWocPZM8bBIuNyI1q7uz2u2z13vYhOHeVMTTBozW8w7DcKtlEFaD3NIZQEo93WjZ6bbf6/g/N6DXHuBLcRAfA3e41oAM92bQC+lqO/pxJmhI8jrC1DhKwaCRnzIfc6tGEXIqZVigS824FmuGg6Yg7pRJ42fTsieOuI6ODnEPP5LCKCd7mtpPHIhJNzEYO5DTH/HyPC9xLDJo/cVcBx+FEbEkgOqzwY05H5ocD9P4qqI8xERHkcYhblQA1kLSWiZEsQMR7vcXwLaQ/TKbYCCW55/n+N25yITEeHjLeFiOB/CzGR9R7bV4govhuQD18BIqifA66HXzzvdrrdTgHSimb7s83tZSPGNwlOPY6YSjci9DOQtiv5beUIz6+nE/W1hD/SGkSwF3r+khb0PkIYvhKtjTrjf7b7qiEY2Vh46yF4K8E+F2mmZhB+iwNENYQVSLC5HzG7ZN6fgdbHTMJfaIH/BiU43o80wq1I0NiKmPuzt6qNcYZpHtLMDBAajELPSYnvd3v+rvMYVxJCYh4SUud6Xpvg6Btuf71xe5CIoE++dGUe08cN8xG0dpuQ8P0gEXyV5ueHfraWqFFbxnCg0PEhxJiriIjMq09qPzUYJ91I4LwG+BvEuMehZzrQvO8HtsGb5UTgR4JjpfGWgYSg76O9PN3PdRNR4ss81la3vcpzMNNjvZ6I8pxjfM3RWJqT390eImdiBow6ZPj2EqXUXgK+SVTJ2QjcCVmfMCxXo/W7nch3+V5gKgyu9RwvQ4eGJPDPRftwmd/tRmvyJcNzRHAOZwUY9NxVGg8z0H7pJirwPOi/53reHvG4tiOhaQ1yF1ji5681zjaiPbWE8EG8y2NbgtbB9YgmVBI58g4SydIr0L7vQUJUocbPWEKQq0WavHzC7zqT8LteLryf/BmiT4vQukym//4c7a1q4+gWJEgmjWcv0vrORftxB6IX1yCh9C60NorRnmxEh91kxdiOhNIUpDLD+Bm5Rq7/wDUSBHK269ZRIgLJ3+p70P9jyPoIYmAFiFh1IoLeReQSW4yIRylRZSCZ0Nr87Ewin149YkDFRM3IQ8RJtwVpKWb43SdQxNvLkJE2/1IivcEGIu/ZMiREzCNqAo92W8k/pRgJn1MMyzWIwExze6PRCXoQEbBGRDiHPL58f/oQ492HCG61Yc80LC1ENCWImRQjhrcX1X3dS1Qn2UCUcivzOyuQueew+ysifLPykSYyaTq3I+3fI0irMh4xr4kew36/34YYw6Ue13VuK927gEjNMMdzc5hI5pr84rKQsLqWqFCSmGkpUSt4O2IsiVnPR0zgpdP6zESE/gEURNLOcKkxFhL5yZLvUJ7fuRRpgi9z3xs8lqQ17vX7rxFBJfuMm1a0drMNe4nb3YUY7jyUAuUatL4aCK1LWhe5hnUICcpDhmuh26lB62I+0oa8TJT2qicCQBoRI+5He+VV91XosQwRCYox/opR8NSXEYPdj4SnpOnOJVwvut3fvR5Pcqr/ERI6tvn3R+Dny+BdJ9B+H3S7G9zOxz2m0YTj/yKiVvUxVO6uxPj9kp+v8lw9QdRGXmfc3GecLSI06n1EtZ9thvchtB/6iLrJjURewmeINXKECEJIZvc8QiPaRVQ+grA65Hm8N6NAuGQVSHRxvMdyB1Elo8zzsZGIXq0mtN7JJWGzxzkNre1jaP/Xo0PzesMyQATZ9CE6kuf/VxDZCsYbrmw0742EpeFzxjV+d4hIX7SBSK6/1H2nw1iv273O71qze/I+GLMKCX3ZKNVVo/9+mkjOPY0oyzgW0Ybk5lPj+UhuJS1+fpp/f814SoeJBX5mNBKSf4AOy4Wc11JwPyVij/6zVzcKuh4JAvnNXCMm4LNd2xFzqUOb92bIykCn/ScQQzuGCCxoE7YjIWAyYroXI6LYT5RDS6ajBnQCn0A4+/cTJdIqiFqcVYjAHSESvA5AxnREnJK5uJ0IGEk+Y22IUex3nyWIceUhAWOS2homeO2E31cpoQnIRua4cr/X7feKEBHe6N8OIkL3AWRmzSOCO9oQkb0MBQQUu7+koegk6tJ2E8LAGoJRzECM4wQSMme73zuQ1rXC7y9AhPIzSHjYhRhqws1OVPJtG2Gabvb793ps7URaiIWG86DnZQISYHoRI1ri/q4wrH+BTvT7EZPJIeoCX46El2S+2et5XuR+B5FgBpr3i/2pJ7SvyVcqn8gH1sdwNQSO+JklRCLjfuOqH837AuPvfyfywu1Fh5FmJJgl/7PZxv+3iGCKY4b1ZSRwTCIiiAeIMncYtuT/lYPWwqtoLa12/9P820Giakc6FLUTSbmbPE870F5rQuunz/AnH7xpROWTGsJpfxty8k8BANmeu0ygCPpXQdZ7GQ50eFe5n0uC0kw9x8WcGVm7Ac1xPnHo2UVoO5P2udfzlEzYBUiz1OaxTPTf21DaHYj8oOmAcDFwg/wGL7wO/vYZ+OjVxl8tUVWmjjDPpr1xhWFuJHxcixCdeQEJks2nzVMFYW4s9Ni2ob19mNCGTkFrawM6uKTx56M11UUcGkCHqTlIE5bW6mZET8YZxmTaTYfZZ9x+teGo9KeIKEGYgdZ50oQOAO9zW08Se6UOpeZKfojT3H85Ufv5+0hYnHPa/EyBMe9gOKCORnToaPBzGDcT3Var56vb/e4nFABJa9yEBMUtnrseosoH/vuY5+ICJJC3euxp745cI9eveY2YgM92Jc1NPmK0c4jC8Ml3YzkiQjVEofACRNBSgMMWIuVGBzJ/lLvtxxEBrUJCUdLibPCzPUiYmun7OUiwWgj9hxBB7fX9vYiwJD+nJiSM7kRErcDw7Xa7yT8mDxGROYhYziGy/Td4bKuJ4uw7jJtOIhptwN8HdW/wSXTSbUdmtUyPO5knsxBxrPDYn0On5iZEdGs95ha3U0T4Zm10O8kkXIJ8oK4moljzkMDTiwh4qz9DhMCVzHLFSNioQkzle4i5XUz4vzV6HpPmqIAI8BiEo8eI1B9z0bpoJKqJNHjcowkzWiXDtVnpNeyNfj7DbS0kohNnEOkz9qO1cy0R8dltfNQjk3daL6WezwOGdxvhP1VHMOMipM2aQjixb/T9I4SGNQkEixFzyiTSv2zxc3MQY5tOBGGMJqK31yHhp8BjbCXyaQ4Swljyn2wjar4mc2yNcZj+b/ZzLxDRl6VI+DuOhPUCwmfwgPGw1fdaGNZkZ72PqBSRR0QLbyKitjs4M3iggkiBk43WYdKkXkKkLNoAdU8hQWwiEmq70R690XB3ogPEYuNlPxJIDnisdWieJ8KF5XrnowuIijd5RBWYSsL/Nh/RmXFETeRBz9+d7mscnFzt99cRbgFdyB1lv9srIJKVlxBuA6Weu2Si/AES7A4YN5edhq9MdMBZjoT2Vo+riSiTNtGwdqJ9PpowA+cS1W+6iPq8GzxXEAnjc9F+e46gkYsNR6fxtN1j7kZa8a/5/0Lj7Fb/XUfkB20izN4ZSItffxrOa/3bOCKdUtLupvc7Ga6EwhSPsYNIyl6E1m8/0iY/YTwn7W495/UaPMefkes3d41oAM9yvdkCF61EBK4JMcMGRMBmI8LQhvy+koBQBMd3wrjkuJuIYnJEHkDan3xCozWAiFI1cYp+DWn2ZgEPwesPwCWTEbNsU7tZC9BpeAciINMRgUlavmyiZumHkKkgB/gTIuDhWhQokRzud6GT5Wai0kgxIrh1yGwHw0LAqfuc5DePyE9VBBmLiKi9Hxq+WrX3yn3wwSS8Jd+adB1HAm+3x5GJCPwAoelMmrMkdG5GTGQBUaUkg0iQ+rDHNI/wucpDQvAOJCydQKa/zYgp/Iio2TzRMJYbl32+96Lv9cCESiISMocIqHjptHmeTwiCdXDqQRj1OFoPxcZVFsMJc8kzHtYQCcmbPfZmxKx2oTXRQfjEVRFm6aQ57nG7BcbvTn/WE3nuliLGmZjNJCRUNyFzbwbDPpJMND57iWCT1wg/pSOEVuVljyfDMHYT+e8GGfYxJdP9NaK1t9F4WeBxzjX8ZUSN1l1ExGkGoZ3bjtbNTnTAqjf+9xmmQeBuOLUKRs1C++UyQuAoNE4f8tjmeqz5xue1/vtutMeLEdOegw4cO4hgl3H+bSM6VFRAZZqvdHDrRutuvduajgSY2e4nmfg7PKY5HvNrSJBIdGoPOoTVoT2XfJcriDrBzf49aernIrpRx/ABZkwSUtoNR4XhGnQb45BJ9nm3kWhbu2GrQAeptDYzUImzh/1uLdLM7/IYGjxnY5FpsxTtxQHDdTfhrjEPHRbbkFvMV9xmldvoQcnjv+1xJXN5cjVJB5AaZB7ei/ZnP/Rvh6w5RM3sBYR7x98junCCENxGE9kiWo2fLsM15HbqkMDW6v7fIMoEfs9wHgIaof+IAwgziAPMCcM8DpgAv/glvPOzaI/uQmvoRc7rNcRIEMj/W68RH8CzXZWjojbpCsREDqG0IVOI6h23Mlx2jGNEhFcrIiD1RMqTZE5NBLOfSFeS/OV63E8rwz4pR++ECXf4/a1u80Yit1rSIr5BaAv3M+wIziAiggfcX6fbuswwDyCCUo/G3AAsg188BO+8ATGoVkRs/goRnqTRfNZjKiLqpE4iIgjnEZqSCiJ/Yh4SOOqQ39ZXDGMSUOYiZlVNnIK7EUFvRAR0gb8/a7i/7PmaDXwSTh6DMVVAAQzuhIw5hPb2dDNSBWIK8xEDvxUx/+T4ngSYo0TU9HEiwjETCQgTCT+oJxBjTsE8lW7nKT9Tj7Sb+xDTmGZcPIcOFf2EryGEoDjgezORILIHMaLniXxutxJpidI7e9CaGI2YSRMSosaidTINCTtP+bcCwteukIhqvh4dGpJrQrb72kMkc84jIqBXIgbVgwTxP4KTAzBmLGKI9aiKDmg9JRPXRsTQq5D2c7/bTabLYmINzkd7rMNwLvfc1Hu864y/5O91jefz00gA3Y+0nzORkFJMVJ/4PvLvugPtpc2+fz2RYw9CaH7BY56AEozfaHw2G48ZaH1MRmukAbkc1BmHDxD+bUm4SCbYCWjui4hSlccNex3wxLtgyc/56UvwnsluJ62bpFrJNX73oQNmoeE7gXzfHjbev4P285fRnm0xbPuN42fgF23wzgeJlDfdhjXRwpXGwXwi2fiPjNsG47vceLjT8/mQ56fXzxQQCe6nEnn89hN5OZeiuZ6L1s1yIsiukPApLUL0s4Dwv07uNpcaPweIpNszCU3mIMMl9jhOCOS70IFnJVobVYRrDcbtTH9qib27ncjGkOD/mOdxPxK8v4QsNrca9q+g9bQfrfcaRAN2Ac+ePx/Af+Xc+gD+HiM+gL+pa0QDeLarBGm+jgP/DW3absSce4jAgI8QmqE2wkdmKhIIksailyh9NB0R4EsQ4WkmfFXGuv8+xLAeggm3IKZ9wnBMJxLorkJM5ZeIAJUSjP4yf3+bYCbjkBC7Cp3CrzfcZf4sRUSyE975afdZjkzPvUib+E3CcXoJwZCTebAfBSkkU3cTUV1jjfveSyRw3uwxJb/CpcL/W4/ChfnEKXwDEjKS836O8MMTiPB+EBHY1UB7MWP+sm040CAjmdIzkNahBTGVOiLvYC2RDzEJilP9TInHNtZjuBwxvUNonhf6/gDSmE1GwvAipG1JWtekUaxCEYLJbHvAfdxKrJW7BDt/T5iEDiKhbJ3HW4cY4jHg79B6bELMfQOa80men2sQE97qvzsRI+xCTHID4cc4030nM1OvYf0EEjjmED5TdYZvHOFPVw39T0LWCeOsEAlTL8OYLcblG8LbyVUw5jI/M91j6UUa7ja0zl4n8ktmISY/yTh7AjnpT/UY6omI3jHG12uei2IkLPcB90H/Msj6AJx8EMZcxXDKJnYhjV3C8Qo9zwJCUJ4Mgw9AxsVIyLmBSMXUY/w8SGiE5qJ1d4DIFnCQCMgYJPwCOzy+5WifJH+4HWjNzfZ4GvzuCeAzP4daeM87/F4yudYS1SY60NrrQoLLG8bfj4m0SX3IZ24nolGvIEHjfr9bL1jeWYzW/AJEJ5MGu5hIgJ6H6MNctE6/hASslYblPjjQCTPaEE04itZ2HtqLeZ7jrZ6Pr6FrudtJpttCItdkwsmAn21Ea70NCaCX+70sRKvGEQEcjcjMmtxwJiLrTpX7+BPCNSO5dywHcmBwI2RUoP3R5L7XEP6AbWj+P0iUazyMhNhCz0MfWgffROtnLFHqsdJjLDC+02HoPF/n0nQ7YgL+zV4jGsCzXZ8aFXnpHkWbvA6doEGEIwsxnscQ82lFBL0AMZE9SEjbiRhbJyKe+Ygo1BEOy93o1Hez3zuKCEWP25uJTuDlRHBGi58rQcRuhWEZS2jcDiITyhfdbzIvgBjEJESwpxE+cQP+JCf3rxERhisIZ+fthBm3liBWGYiYzyFMUZcb7o967NnG5XKCGaX7lxnnBcZx8rfqIXLIFaJT8XOGvRwJbos8tmQ220tEMM9GJ+pLCbP7C/7/R0i4hfAnK3GfSQvWTvhRliBmdof7n4kCJHoN74fRnHYTTLwCCa1v+LvVsIEYe7nxtIHQfk7zGE4goW49kaOyGAkiVxnGFwhz8QIUhPIh93MUMfNBw56JhJObDONWj+9uNL/T/GzSxm6Dnq1Oxn0T8kGchgSzLPc52fPTiszfH0YHnT6/g/vNQEx+LlqDnWaeqzyWjYbls8ZFOmDkuq1GtJa2IYG5xb/3IcZaSviKLiMS5j6A9lgrIYy1o3X2BtrLNR5Psdu5389cS5QZnERoCb8s+GlG62E7Ys7TGK6m074Piq4h0s/0E3kytxrOqwi/uxRIcKnhaUT7cSFhVWgntJzXIneFFrQG2hiuWEIJEraeNsxPAD8H/g+PpY2ogtJBpE1p9Ri6iLrEKUjpWSL6vt9jyvT8JL9XC8jU+tnjHs9ow9l8Wv8NaK1PdN+7PP5kMSgnapLPIyLNa2Hwcch4jAhMq/azmf5/ptt5+rQ229B+m0Qk9872eA8briHfew1pHQ8TZS+nEzk/a4zXRkTDphFBWXOM7xvQoXwI0ZNi/z+A1l6G2070coA4KH7P38mKs8HwlBJl5w4CXzt/GsB/4dxqAH+fEQ3gb+oaCQI527WbyIk1ngiKGEvk9OpGm3oqEigKEWGfS5jmkhlvJyI62W6/GRG6qW63mPCZKSB8Aeegk9+liKBmEImPq5BgMEQISzWIUDSi02IaSxKixiKiOkj4CS73b1e5nWY/n4sI0scIh+QNiBm8QZhzNyImMY3wNbsR+XQtQ0JOtp/5EhIqS1B6lgbCsbwcRUavZTjSmQuI03YeYTqag5hc8kvcSxDFAaJ0UoZhKEMC2gChkWgl8pclwSUFaswmmGw/tK9GRL+dMP0kf8Acoj5u8gfbgzQLx5AwW4YYfi1iCvVElYtBv3fQcF+B5j8bmSkvFQzDvpmtiJHMRkLWEbQWFvr9dqL83zbPR6XxN914TibEY4Qwcrq26TnDsIvhyhe5BW57n9u+GDHcQUKjt8n4yyFMcPf6O5dIIZI0Q9mCI+M697Pe/c/2+68QWqVM9zXjtDl62PhqI4KS1nqcE42HTiKIYy4SogoJX9llHvsJz21yMaj3/a+i62oiEfJ+dDA6RmhDG5GAkq6FGlPRLUQk8AK3kbRjMw1nFRIGW5HWsgSt/U1+pgrRoWeN2wwkVCUN/KOIziSXlSTsj0drbClab7loHRX73a+73+3+7kKHhWoiFdA4ZB4+hmjDtUQqk2wUsFKI1uhBP/MaohM7iejofqKK0kzfS9aSZCWpJNKeZKJ53Y/mG7SeN/j/Msh4HyGwdRjemYZhrHGyyXOR1EsFnodB46DUz9e7//Vo3V2M9kAekb+02Hh4Awl2xYgOXoLWXA2iGX3GcyfanzuJg0HSpoNo42wiM0ON4W1B62vA93f6/lK03hO8GYQbwnm6hs7xZ+T6zV0jAuDZrs8hRrcNMY0qxMjG+O8BpDXagZjmBEQMkqr/bqTNq0AEYD8ivnORv0uJnz+ChKUUiJGJiNU8RHQeRQT+BfcxFpnGOoiKF/MQofpLRISyEeNqBw7A8RuJzP0HCQ3DfPfViwhV02mw7iVSpiShqgyZjC8mTq72DTx5hKgW0EYIH1uQFuVZJFSU+7dxgm3YV22vx/hD3/uu5yAJxTmn4WfIc3PQ9wqJEnizEbO6HhH3PuRjeNw4ajIsuxET2U/UVx1vmJNz+vWIOR2CohzCzH+t8fKgcbIGEeNnEAN9xP0cRVqTpAHpREL6g0T9JDymOcg0/qzH8gAR4T0azX0FEsSb/f7pwu5zSNMz5Hk56LZXMVxeimY/f5/hvg4xl0eRn94hw7QLrdUnoKcT+CT0PILWdCkSSqs8njL4wUuGcxdax/uBfHjzJ8bfw4apmghYySC0i3mEZn2q+mWTAmWGffEe928DaA01ec6qDcfF6pNriQCkvcLd8dX+v8HzU6n2me72k0m41Ti8Hq3vbPdX498PoHXRRfiArnLbE5DWsw/RiI3IHPx+JJAWe3ybjasVnpNuNM/r4K2DSLjdi/bHQc/TDLebgeZ3ktu6BAlVPUT1k0V+N89wriMCPBItK0D7/Bv+TCeigis8P7sR/diP1k0ewxaBk2ltFhAuL2uJQ+k4ZDFYZvysMK4qidJsrxIlFofQWsC4GXC7U4Cj8NZjRB7MAc/LOrc1zuNKbgataG0kl5nNiEY3or23DR3O6o2XvYhWriR8L8ud6HkI0dZajZvlaA3udV97iECTk2gtFiPBvZQ4oFUgejFgeGYi8+80z9thJNQ+77ldSuSNPYbo9ArEMzqNu3HGdTuisefxSqTsXHxGBMDf7DViAj7bVT1KBCYPEbYXEYOfjohHFiKIOxEB+BxQCDtugfk3IeJ7LxJ+tiEisAQRjWQGm4OY0iAiLM8j34+nERF4zrCsyYH5vYLlEcRQCxCDKCFSIMxHDOCPkQD1oGAiGzG1zxPRpVWG/WLDnpyka4jTaC1yPP44ItbdhnMTw3kFdwzB/KnIX+xxRID3Is1CDeHjth8RQ9DJeS4SuB5AgscWRCBL3f4QElCS1uIwEjBmIyK5BTHFIeM2na63Iea0hMhH1+/f7kcEtNrj3+SxtXpOkg9QLfS/ClmLEJO41r8lXDQhQl+Hojczfa8VMb12hvOY/XQWvOdi4B+QgPA64feZnNaPI4a8wPO6DUVNZiJh6yqkcehA6+IbyNS/E95aCxfO8/wmBrzBbX4IMaYnEEOe6Dlu9lxW+vn73fbp/oJthnWh5+QetGavJSp7JO31DnTYGUCCwwzE2BqN90pkEvyIxzmaKK92PeFjOMd/tyPhaYphGEe4PjyH1mISIuvQelpCCJoXeCxJE/gyOnR9DQZfhYybiGCn3e77u8b7SuPrENob2xHjv5rQdE8y/K3GXx0SAG9CgslNSFh+CDH8ScBrMPgiZKwzDua538MEs88znuxjN1xRpBnt82lofaVggBbB8OajcNGHPfal7vN6j/FZJET0o4NFct0YRPsq+S2WobX2NhJ0NxpnDZ6PVn9yfT9pXKuJSPVv+7d6tF4nIm3adWgNfJHwn6thWNjiDUIL3uB7+cZrnmGrQethG5GSa8DfXQRtHTQOGoiylW1uYz86QJ1AWrRs4yKfoHlbCYGt1bhvILSSe4kgoOT/m+G2L0NrNdGBXWh9ZBACervHsthwjUZ7aRPa/0eQMLvc/bYQUemjDcdOtH46jY8coOn8mYBrgP/tHLX5b2iZj5iAfzPXiAB4tuvLo8LXKxG+LrSRMxCRSOalQcKc04SISRsiVAfRxp7vdx8nKiCMRoSgnajo0IUI1FxEfJYhAluCCMI9huVbiHDVEsT8KkScNsJbL8OFdyGGOYMw+RYRTKsUEbyFhAD4feRXVYoYzVyPscXjqUNCWC3/X/b+OLrrMrv3xV+QJgbSQCY0khuNSSOcMCmcaArDQGEyUJTiMHJxOHpQikN1nLFaq3W0Oo4zVmu1OsxYO5YZr5YZinLwohTKQFEONCPFycCN5kBT0mBOYiQLJk0mkJMQk5Vw/3i/n2y890d/d86x0nNWPmtlJfl+P5/ns5+997P3fvbez95RqiIpzeTNOYQU7wbDu4vov/oWMmzuQMIuHynC48bzLOPjOkLZ9xKnDJuQkppsOkAUa76d6AX6Tc+nj6hTlxKm85DivQd5vx4n2qal3MEqZETVIqO7FdihJu1jrjY9txCdBlqN1zIiJ6yLMMqSFyeFfHsMa53fOx3xzp+ivJ+0wXgWGV0pN62MOFU6l+ho8g3jf73xmIcU4RNEB4xDhmkDOoDSjDYcmUhB34gU+lTiIEwDUf5jF/Ku9iBFmDYyi+DsShizzrhfjxRyynvNNg2TF7kJ8fJklPP4x0SIbSLirTZkTJz0fX2ITz9F5NhWmsa/4znmwpm3YFyR6ZLokwzNPybyapvRmupAm4nPE4czjhD1EYuJPM5JxsXDRC5so3GZUiZ2+/cSonf0NcbBRqKEyFzgV9AGpgWGnoaMk8gA/Ani8ZQKUIuMiTmmayMRzl5qWi2Cnz0AF99L5NHd7jkl71ev6fkDxE9vI5kx5O/r0fqvRweKFqD1cdj9h+8zfH+C5M9stE4Sj6aIwfsohDqXSDPZYViXeT4ZyLhPIfT1xvc1aEOx2jjoQGt1lr/v8DzqoPMfYdIW0+s7iNaLEZ9t9rsSDSagzdA0ZLTVw9knYcwURlr/vbcLLltgmFM+Yq7p/hpR5B+0matAm412tK5vZiRiQC+S438JA//N5V1WGp9tHn8p4oXnCP2RZE+SGyk1ph0Zq4Voc9FmGqT0mj+7cAbgQT5aA3AWowbgx3X90oUG4N/sdRESYC1EyYtMpOh/QtRcm0Ts0JLxtwMJn6nEwoVIZP4yEt77kBC5Fim4350Av39aO+lMJCDqkOIuQ4qw0c99AXkllxuWDYTHchjGpRyXB4kQQS1RuHcp8jj2IEO3EBlx1yPh1G8Y2z2/JISykIBOBs8g8C048yMYV00UZd1vHO4jjIhZhNJPxt88oqfuKd9XiYyvNsOR5//3GO4Zvq/O4zxEeAXbiZ7NW5GSuREZUynJupdoqZVwV294riUE/5VImR8QTpkDYzabVj2Egh00foeMl2bDnU7pJe8CnlO+f95Cxm450fHkUaKUxmSC95KBftQwzfU8Zxgn+5GBMQPReKznn2X8TvDzxX7PN5CneTfiyZQnV4+UShnwh8gQH+9nyokSR8XnPNcHYxYQSnGSP99v2NMBjQyixlofUYh4DfAK9O6DnDmeQxkyDr7vuQ4S/YBnE+2+itBp9sNAI4xb7Xv2IMO9xjClsHWzvz/huV/n+XYYjykU2YPW8E60/vqJHsa5hm2J/9+OvFzNhusg0TWkAfFkNTJKFqP1dRTxYwtwCjKWmx6z0XpcZJheIVqa5Rg3C4n80Y2MGDoXJ4/1MrSpecnwJG9cF+Knp4yLSv+9EK2l5KG/Hxni6TDGfhgz3c/fShSg7yNyizNNsxS2/rJh60Qe7y8Th95OaM7MJIrPrzEMU1Ae8C4ixFlAeC6vZ6SzyaRi/70HrfcMJBdvJ7rSjDfOkjftKkT3Lhhzr8feAeTDZS96vj803kzD04/BhMeQAdlO5NnebdzOJ6oIdHuMZkbqfmZNJw6RLUeb0ylIdg0hQzsDGb3p+VyiluSw37vG9z+P+GQS4p+0ER69Rq9f8Bo1AM93NRMFjAvQ4l+IFGsy+LYiT8SVaCEuITxulUhQ7CFCGD1IqGxCi/4WohTKRGDZaSm0f0RCatjPVhKn64qQYXAM5dccJ5KOWw3HHI85l/AOliGlnfL92pERcpDolTrk+Q4TZUe6CI/PfKKn8Otwth3GGLZx05GA6yR69y49Z6yU8F1lWFKYshUJ/CTMX0TG4EkkwM/JM2Shf9oM5y0eJ5s42dmGjKitxsMS4+Yq4LOmTRWRw1aDPC0g5fb7KNyX8pBAnogXDEvyCOciXsjw/BItl/r7iUjhFSBDbYvvLyW8Hpv9nmuQYZXlZ/qNr1ZE37eQIdFo+D+JlDueY7dxsIww2HuIXqKX+p5kSGwhDPxG02WR8d9NtA9LhvMhIoet0LCl8GibYBvYB1nXIkPjFmQUXen51SJjLRfxcSXi4ToiZH0CcjL9rh7UQeIPkGdurHFxBCnBpGgfRAZimWGejPjkFPxsP1zcbJi7DVMydofOwed30brCY8w0vQ4yYtyOGNV1xvNSxDfHiZPdHcT6TnmLKaQ5mzh9XezPGzzXWuI06G69Z+B1yMojSjO1e/4dhq8JGYkLPW6Gny8jiqEP+76/Rh7LTj97Cm2AjhGnnd80Xk/5/5Sn2eUxMolaguXISC5mpLPK6bdhQinaWM4nSqDM9TxzDdMcj/uWvz9NhNbnER06phvnyUjMQh7PLOJU+WzDsp7Id5xsONcTUY5hfzbF9FpHnGY+bDiWEt1YJhvu9cbrlTDhM0j+FSFZ+nmih/s+Rg77MBWt6VbEbylveBHRSq/P8xj0Z8WGtdjP9RCb4A7k2VxFpIzsQYZno+GZg9baBbyGGS0E/T/rNfZCA/Bv9qohEqPbCMOpA+36lqPF14l2q5OQkMjw7zykHO5HQuzPgI3/XsKmH352G1rw2UjAfuD3pMTlRiRUDvtdzUh4pl1r8uS8iATNGiQkav1zDCmYl4jSEWk+U4k8o0XELrMICb/JhJGZjwTsNMHXu9LPToExyZt4PdGdIwMJ2JnGy33GQ7PnWY8E8UEkCK83vE1EjcNsZEAtIGq3XcGHC+T2GkczjK/nkfH9FjJykuJ+wfM+iAw8iHInQ8hbMEwUgV5FFPneTvTcXYyEeguRx5QrGFte9VwzUDhsOlF3bRB+9gj8037js9nP/SFRiLqbOEW5yLQdQor/iD//PCPdGQ7eDmf3I4WQgQyopGQmGsZuz3Ue4ZnKI0pZzPb3KZTb4/mt9rsb/d0u0+eIn7kI8QZEK66VrizUh/hkP9qcvI0MrFzDWkYouWOEcTjPuLgfGY1lSJGeQBLqAPJODfm9uZ7HeiKXMRfxyCno3QQXX4s2AoeJwugZpmUR4rekfBeKjuQhA2YD8ih9BoaOGKYsok5hyuMqJTY4NYZlr8dOBtlX0Pq9hCi10k4YfTMQD65GJ+RL7THK8PNNfm8VWguv+9nkmU0bxAxkSM0kStHUGEe7ET9UoPVWZJy/aTyWIZnW6fFKiZaUdabNNZ5DNTKmVgBdcPwZmPA5zyV5aQcM0+lzaLbRYx3283OQzNyJ8mMLibqOGNZtaD2lDcQUokd2DZEfZ0OUfORpnI1o2oU8rQf8zCyi13gK2VehtX4CrZ1aJANXEgeM1iC61nq8gx6vB9XqfNO06US0Xo7WzTKiC0uKCFXAiXa0CaxG9Tj7iY3fm6bVE0S7zZ3EBvkAknX7CEO7jAt6DX3EP6PXx3eN5gCe77p1TBQOrkeKbw8S1sVIWdjDN7QLMp5CSrMJLcwvIwG2BOWazUE7N3s4hl6BjLVIMHwNcf6WsfD7w1FjMOUgzkKC73UkEMqRsOhDgryVEOY96AAGSCmmMCdIqG9ERs5Goqdr8nTNRDvk2eiQwXOEFwbPLQ8ZGPcio2ATMABDP4WMpPTmIoG5htgNdxOFkFNO1neRInvY907xvN43bnONr+OoM8FaZHwkg26C4fgmUh79SHA3IAWUktvTzny+8ZQMknrj/00i1FtItM3ajQyrE8bp1wzvG8QJwRrjKXkAio2fK5DxUogUdUof2Ob5ZxPdNyqMrycF99A+yPgV46jKOM5GtP4uMkC7PP7byMj/HeKUeA8yprYQdQPHErXNBhBf74GhVsiYbtzdizYgxab1XmQITTTuczzn9YiH2n3fEFKWV3juswjvZjsybg6dg8/veK6vEsbnEBGifhedcK1BPJmP+PIbSOH+uccvNq1SXuRW+PkR+MSnkMd3B/JUXmdcfAPVv1uO1k4DykXdhdbAIDJsklHcQZSFAfF1H9pUrDQOV6K1lMLaeWh9InhGCie3G+/XmB5f9Htf9zsa/O6UjtGLeHMjnPkejJtMGO0njOOp0Hs35HwKedKe99wOEBu8ZDxWIdonb/k6Pc87vqfetPk02kA9cQ5cpabFRJRHWeNnkld4nX9nog3AG4Zns+cyA85sgnEzYegQZExGh4auZKQN2kgLwJQ/+V1kIOYRXug2optSMqKTd3inn1/l+bcT3XUK4czjMG7OObR5H3n+q9C6xriZT6RrNCC5nVJ79uid390Od95teK5B67OXyPOu9bP558ypHMmjh0y730DruN/wzoafvQ4Xz0F0fwFt7l8xDjOR8bnBuE0b8x5///ULlwN4gI82B3AuozmAH9c1agCe77pxjIyVRiInrJ8IZ7b577uR4F3mz5JhMxspgBIkbPrR7rvVnyXBtxMJkUrCU9aNFFYeWviTiJyRbqQA/pLoGlBB5CoeRIqkwc/nI8U0ERkLKam4n5EyHmyGgSOQ9QhSuI1IuDYQnpISpAj2G+YVyCB5GwnhWUjBHxS+3ngbrrqb6EdbT+TCNSEhmY1OAaek+KO+bx5xWvVtz7EI+NYSeHCXcNCFDKsJyECcRXhgDqPQ4HgkXFv9dw78/Dn4xB165892ySYqXKkxhn7sumJJqE9BimQZ4RXrQdddxsVhouD2u2iDsB7xwdA53y0mugiksPY2oge0PWm0G0cpl3CO8Xoj0dpsBeK9R43DPhhohKwvErlreMwuP5cOdAwal5WEhzN5EDKQ97Ke6BiRR+SeLvX71op+p9thwmTPqfuc57cThluO6fi+8VltfLwG/9d/hV+/GynWFrTR6ESh3T8k8mSLjYdjyDioNbyVxtchtCYK0UYpzz9pDSTvdAmxaZpBlFHaini0G945BFfc63tOoI1Yk+mxxPfmQ++LkPMZonbcvciY7PWcU07bbefA24OMp3v8+17jZSbimWQM3ONnMxgpqTPSiaKbqC9XjDamHZ57FeFhriNObG80LjsQX1cavuTdnWu6JYMibRL7iTp8xUh+nfSY30AGzWbE683CC11IZs3S+wYecN/yFuN0BzL+kiG6gjDA5hKbgWzCQzzeuCo2TPcRp23T5nUIbUD6CcOyyvCXISOyFvFKt+GsJA6ZlSH+vsPzu8rvBfiDCbDqtN55EZEW1G88bUE8/Jzft4PIDV+J1nkzovP7iEe3I9m3Ax2q+W0+nDrQQBzaayMOjOxEPLXJOC4wXX71whmAb/LRGoDzGTUAP65r1AA833XVGAm0EiIJtwMZUoVImCxHRkAV0bw7GYpJeE8l2v0sQsbFQbSINxDlWKYgRbKf8KL0Eh0NJvo9x4hCwIV+bwZRVX+IyPcbT5zC7CIKpHYgI+EEEdbuRop3KzJkjhkPaZwCpNh2+n3NyPgFCbNswzSDKPoMSsQ/jPL6qom8r1K/s4TI8SlDxkTKUcpFHpFJSHiWwsC1kDXHuCxDRu+NwF8hoTjX89pB5GwWEUWUc4n6a8n4mW0a9BL9P3OQoK5CBtgp5P3qQTQtJzpx5CJDbwOiayOhmBr8kz5/E3nVkocpk+j4UOt3JIMmeW/6/P8Q8vK1GL6DiAcgTjrnIfpNQIeVZhjefMRbSZk0EMWLlxHFdt8g+Kkf8Ws/EdYsMG1WoPqKl6AwVspTXI0UYhvyJo1H62Q7Ms4X+//knUzh3CVEaaMawmv1GlKaC9A6PIEMjpTHOBmlTwwRxuopIs/U6QpMM40yCQOjhzhotMuwlvqzRK8K4pR5+i6fEcN75CDDI8ggyjS+NxqPD55D29uJ3LF2ZExMIkqBNCPj+n3CSBkiuuk0IO1Yg/hvqt+fvOX1jJyG5oDhuxvxwmtEjt9ORPMGv6fY9y80fo+ZRhuIgyGDiNfmoLVUQKRuVBNF6FOe21r/3oP4dDFx+r0K3nsILvtV5J09SJywXk7kIBYgw/uwYaxGRuQWvy/X92w5h8YNRJmYAmQI7iW6LRX770b/neH3pvzTlcRhuZS7OWwaFxlfiRdOeP7DSD63Ewb4/cbTY8jb3YE2qnORd+8v/PxTaD2mA2F5RH/nbo99kXF/2HD8hceqN4wdwKOjBuDo9YtfozmA57umICHxATIMpqAFOh8t0uQFHEucGBskuiS0oQXbjQT4vWj3fBoJ32ai6GqLn91LCJDNSFCUod06HvdKJExTmG6uv1+KvFI2lEa6PGQavjdQeZEk+PZ6THv3el8CvoQUaI/HSiGpeqQcPokMrxKivEYPYSwsM54WIWFZhIT7EeTVmEkUZz7quTb7/0KkvF5AhsFmj4/fd0xzyXoIKQIIj2wPUjQlKBzcjJT5u0h57zDuNiNF30OU9IHwXhQTNRUrkGHWgwzMlFeY4XnVIaMm5Z/leIwNfneDx+4kTp+WI9qdMOwNSAFsQfS/GtFzLBH+6jDdMvzzGqJd8sgWeNwUruswrnqRRy3RuU/4G6knl4z/YSLBvUTvOP0W4pM24289EVI7iZTPn52D5z5kVJR5/im1oIjojVtG1FNcZ3wVEJuLImSgDvr+lJifhwxMiP65J/z3oPHfi9Zap2mSadim+L77iVOaXX5mENG5lSgonWVaVRMHVg4hw2I7UT5moeFNuWeLED90E3X/Cog2botMl41oDUw3Pgo93j7P6TbTpxMp9iV+bjrihwpE/7S+NppOXShlox2dYO02/m9Hcueo8d5N1M8bRDKsyjiwB/3sjzyvViRrso23lK+cjL8UHbickVI2yZP8Xpdh2Y8MqOt8fw8jXq7L5qB1VYN4IaVInELyoo6IvFQTqSP7iKLcbxAdW15HG8AhxKPZhAFXYhylvMqTRDH8TP+dYDhhPPd5vOTxXO77C5ARtpsPh/zTmAWIP94yzX7ZKjalE5T5mUPG2yxk8D9ErOVlvq+PKCx+yPAUo81ekr2DRFTiAl3DH/HP6PXxXaMG4PmuPUjxXEp0BPkBWnygBdqAvCCLkCD9FnEatAaFEeaiHWXKx8HP/UekUPqIsHIBUqQppPN5oBvO3kIU092EBF8SNPOI05y/MyEMwyEkPBqRAp+GFFO+x5mKBFQvcBJybkPeG1COyTzCYMhGRsouIhzV6PEKkQAa8Dy3E4ZpO1GX7mUkrDcjYVuHhPuXkaL+CeLGbqTwQIrrj3zfgN9/pcebBPwzCvX+ABk6KTRWzsiBAC7x+/YTPVJ7PIebFcob6aVchYzeY8Rp5kXG7+0o1JLy+RqI4s9vmS4FiC9mIUPloOmW4Xmv888+xB8NSDmONR2mIc/J5X7/oMe/AnkYigijrQcZCEuJE6IlfmcucZpylvH4Z0QLwU6/v8bwptD5Dv0/4Sbh55/+0fNNHoyTwBYY+hFS0snIaSX6E1eYduWGZSphnPYQhm+Pv89hpKwHMOJROr2aKAScHAHriINOh1G4bq/oevYfkQH3JJGnerlpmmE8LTAdnkDr+SVkdF1juJOhfsLv2IFy5DJhqN0wdhJtuJp8zw7jBo+zA62Zco+3l0jvqCVyRuehfMbJRIHfTYi32/XeEaNgwHieT3iVZxqWZBAXIH7vQUb6af+9z3ieh+h9M1pzuZ7T9YjX9rvG5VTkOUwhWJAcfMGwNaO1dRXRkjFFGRbBZXejPM8thOGzEMmLFBlYivj+IaIrTyVwAE5vMM3SpnoTkscLPMcJiN9mAX8DA13GYSVR5mo32kju9NgnkEF4wPAng3YQ5do5IvCzu9Cae9fz2ovk5aCf3Y/W2WtE7dIO4jRu2gzXwZmHgW8PyyhfTbSD/KphrDMNc4k6ickjP5OQEUeRfFiBNvu1aGOVRxw2vIDX0Ef8M3p9fNcvXWgA/s1eVURSfxbizF4iPPYGkcT7qH63/Dco3Y4E1mMorPkCWrjFSGE3A+vgtf8G15V67HzCC/VT4Pc8bi7wEIzJIXab/UgAtSKh9qbhLAKuOS1Bs4hI5q9FAqILCd8ewvvwqMdagwyPDs+zGgnPiURJm21+fytSOv3GR8pdu4+o61ZPKMCFhJAECeg3iabsf2D4Ux7ZSSKMVYgEbr2fLSby8JKBlocMw+8hL0C2YazzvSV+bi5SIG8RBugg5NxEnDDE816DDM1jRL26g0hxj0VCuZ4ooJ1JnDZdQBQGX0Mo9KnIwBtL9CvdaDxsIUKWl3pepYi+c/3+zcbPXKKe2hCi70zjtNTvvYSoSVjs71Z5Pkv92V8hI66T8MQ+iZSO80//3Wq0DnYh3t2p32O/R3QVuQsp+x8gb1MmqkH3j4iP70HKbQHyUl1K5LL9LdpkvQQDC8RO2cC422DC1UTJoAbDv0O4OfsqjNmB+ODLwLdgzGdMo+Q9bSIOiNSZni8hg7wZ8dnVxvHX0Bo9DFTDwPcgayZaU/eIRhnl5oX7jbcc5JVZarhqPMcsou5mtumWPEopZFjl759Dyn6N/04pJHv99wvIQC1ERtxqwlvejAyDXMJb+w3TNnmUkzE023TeYniPEhu7FuG0cytMWuB5jUXroBYZeO8jOdBsWH9ffDDC5wWmz3dQxGQ3wYvLja+0qfuPl8Hfvaex603bG5GM6QbydECOO4iyS48ZvxuIzj5XMBLKzTqFNlLJaOqA3tchp8j07gP+Bhn+pR6zAa3NF4Gvj4e/6INMuPgxjzOE5OgexP8XEYdlUspD2ry3m7bT4e+ehs9+CpjqQvwphzzl9J1kJD/1zKsw7m5/3kmUF1vvdy+C3ucg5yHTfQ+xMZ+JwvNdSI58idFr9PqFr9EcwPNd94+RUHmBOMWVT5QfWIo8fkkRV6Ccpyb42Qa4+BGkeArRAj9A9PndjwRpGfJgpTDMZCQAJhEnUvsJJXISCdIMtDud5N8ppPunSMhPRsopExkDU9DudCwSGAfRIZJcFIKcDXxzLEfHDDPtcSScFyGBmw4FNCElOQkJux6kEFYbrrFIuNYTeZKPIuNw2DhI3oBKw5xLHHrpIurULUXKZNDP/gQpoes97hQklPv8zGQinDSEjJK1hPLPJryvjcgIyfC8dxm36cDPWKRs85Ah3Ok5J7j/gGg11Yw8g9lIOOcSiepTkGGQZTrkEyd38zyvbqRINyFj434k1L+NeCsTGXx1hrWUEc/00ArI+FXkzdlMdAmZ7XvKkXKvQXx8gPDKLiV6I7cItt7bIedK5A1Lnroyw72ekbIfIyebUxh9GTIwE63eMZ5vRuF4fO8s4/AuIu2hEPHvsx7nDaK13jAyyMaj9VFP5A5OQvw+CxkD/wnx91HEu5OQ9yvHzxYjD81tRLh2NTK6Coh82kHipPIQ8jS9bHxdZ5zUEO0he6HzpwJ3whf93vXG/e2mwxzPfxdxIKsK8czLyJC52jBu8D23+f56xA9TUcQAJDfmA1+EzkaYNNN4XYW8UnehtbLZcHxAeKh2IR6YSeQa5xI5fz1oLZUhw2ktkTM8GxnD+9B6fMufTUf8vN9w/iVRKSEZcHX+2Y82ASk3eotp9g2P9xnDvgXJjTuQ8ZdJFL5Om/Aiz2WjYawynKfQ+r6G8NB3+97nkQxJqQXzkFe5z/NO6/wgWhtXGv5NSEbUofXzCDqcNp7YuNcgWb4D8XEbWq9PIv6e7XkkA/NWw76EyMX9se8p8biVSD4/hvjqcrS+Co3joygKUge8duFyAP8z0cr7f/TqBX6T0RzAj+saNQDPd905RsogjzC6+ogcvI1ogfciIdtCeKj2EjW/ypACeQoZTivRYr4BhT9u8jPL/a430WqqREIlE52a24aESg8SAPlIgVmAnHlEnpMR4ysdZjhXcXYTTdbfREo9eQsqiZ6cycBMu/sMog1bIVIuKQet2s9fjwTRJqJFXabx8qDnWEfsqlcbjymZPxcp/b3G0S5/XmVczyW8jc8R+UEn/J4bfN9C4nDFPKJI8joijHmvcTtIhFAXeax9xt2zntt4oliyw5NUIl4YREq2gOh7ehgGXoWsu/3dDuKkZzJCs4kuC4uQMtzq7+cQOXzFSBHs5sPdMyYib1Y1UZw3DxkQc3zPKd+XhRTioN/ZSBzCGYuU8mQY+BFklRBJ5XWo9M5eZPjNRYptBjImH0T8lcuH+0e/dc4Y2Wi9rDd8fcZTh8dJeZkFfraDMD5LiZ7LucbHJciTUkycIK1FRmDyjrwC77TCFXMQ7W/2s6Wm6bm5e6WeS69p0IV4Yx+RdvA+kfbRhtYOxnmrx0t/96B12u35tRFrKNM4ymCk8PHAi67597RpV0bkRO4y/d5Ha2o7MkI6CKM8HTSZjozVfOM55en+HuKRWmJj8BhaAx1EDme9cTCMDKGUUtKGjOwmw1SD+KDTdLjBz+0h+LgG8cMkxGdDptchw/cYWr9F6KT3H/vzXs+hiThEtcA43Yp4JZPIGU68m+bbiDZdaYM6m+ju8wGR81qBZNJu5D1uIzZtdRp34EeQVel7TyOZ0opkZoHnPo3g807DkfixGW3m1hEGa3IGlCD+uosITTcg43q/xxtARv73iTSbvcblYtNvtuHpwCHkUQNw9PrFr9EcwPNdGWhBV6PF3o4EclJSN6OFl/JphpDHYhES5Cmk0Y4EdT9avDkoHyuFK1YhA63J901CQmkvkTfzMFKeFf4ZQoorHbiogHE3IWPyJBKGKTyaQg8VRN/ITciT8CJRGPp1FObIRvkv/cRJxxq028xH3opVRChlD5HXtYUwMm4jhH/KDVtp+Er83L2GIRsZLB2GZQPa4e7zWIWmw2KkDJp972SigGyucb4TcXU/Ujr7iZO7v4MM8y7j+bBxlO6fZlifMOxthOE3w/Ro5sNGcS0S9N9DuT5lkPWs5/S235MO6SRFUMTIaUhykKL6CRLo1ahHbDI2kyFe57+7ifJDx/1/8hSPJQ4ilSHjbzVRgHo2ke84kThpm+EuHpNNmyV+Z6efbUJKcyFxGGUbUl4lKMS3GynaFAJvQdfbfnce8l40I55t8TOthr8eGT1FaM20eL4VxAnOFmSEvosMy8NIQWd7/A80nysWIb6dSxjStUTh9h7jfch4q0DGzEqi+HQDMrJLjMsc5K0bNM5TuL/Y89hmepSjwsZLiL7U2cZzg2G2gZI1Ha2pRz2PsUT+72TE79XGyx7EX03EoZFCz7vU80x8shGlZFyODMgMosPKfsTX6eDPbDizD/HKYsKLX4mMvzYiZJ0M9aXGSYdxN0zU6ctD3vh5SH72+PMphuNuIgd1EBk53UgutCHPWC/yBiajugrJpkEinSQLreXJRJmrtPnONL6K/MxRwts50ThNHr20mUiRgrch6zNIPpcZHzNNp9lEv+F3iCoIbcRJ+RZG2mNSQrQMTPhqRjxSYjyUe7wNiEenELL0OsPVgmg9y/hdSlRcqCbKPl2g63y5fP+9P6PXx3eNGoDnu5aghV9HhEBTSG8tUvoZwMWf0sK9BCmI7xMn9yqJXLV+IszShkID+4nQcD1RjuV2FE5uAIbgzH7DcdLvXAXcCmf+0eN+19+lvKgav+M1JLQ2EGGYDqTo9vg9EwiPwST/vtnzzUHCppLwgNYghTeDOLBRgwyzaSj0sgApuquQEi4mCiDPRIphFhLI1xI9Q9M7av1Z8p70+medYZmJvFplfk+C4Qgj/ZZ7XyS8sni+lR6v0HhIYaJWj/MdIkduISo6PEm4phIptYWe60Vop56MpYuQYbmD8GCkXMVmw9CMlGUr8v72I0/OYSI/aANS3AuJ0FUG4q3lRJmdLGTUnvT7d5muKYRUZBheQEqigGj91XsOfBuJPqUFxt8epJS2M9Ji8MwzvqeXD4fRjhGh6uR5aSdKlODP8ozvRPd7jNNrkVGX6XcVn4PnfGT0bCTqxvUZj4NISW43ThYQh0tOEx6pk4zU7hsxVJYiQ2AV4cHejPjlRdOwgjh0k/JVxxIbu3riME+XYTgEP3/VNM1E6+d+4mDA+8iY+TbirZSiUYD4bA9RG3QL8ii3GCefIXKST3ueCxCtk3dxs/8uRBuq8cigKDQtqgkDMKVPNMO4sYQXrsXv2Y82JK8RJ047PF6HcVnu+a8h2r41IHnURZzAz0EGdifikSrDMh+trVrEHwVITjzoeb5jOCYahnWEvEzh2Bmex0niJPVW02AHnHnIdCz1HDL8vlKPu814O4hyV8uQl7IMGfRdpvN0xC/vE+Hw9Ybj08ThmBsQ/05Ca2Ms4p9somblvabBbMNdTJwM307keq9Gxt1y4z15flOUKMm9lCN9ga7/GQzAlpYWbrnlFn71V3+VcePGcfnll/PNb36TgYGBf/G5z372s4wZM+ZDP1/5ylf+laD8+K9futAA/Ju9Hibqp3WgMM0daDfZjwReEfDgT8MDVoqUdxf8vBo+cS1a4A8gpf8MEl6v+fkmoj3VENH384DGYA7wmLsA5CJFeQUSqoMwLtOwrtBn//A0/Np0tCteC52tMOlqIrcl5aOccKJ7Cr+2ICNkMVI+U5Fw7kAKuA0J2r2E1+cAI8WVRwyIlUiJrEVGwhXGV7fxMxsJ3KlI2GVrnmd+Chk/haznDPtdRPmYbo9bStTfS6HchcLDSLHfRcigeAly5hGGZx7w9yh/r8J4HO9uLOVI0DYRNc/GIwX5kPH+n4nm7ymPcj8S9k8KnpcbXTnmMeN0G1IOKU9tHTKA0pWMaJCCTh7FTUR4u8G4L0FGw3fhr5+F//1SfzfVMO8gukScRIptN9FB4PeIcPhCz28rnN4HE5Jie15zPL0PJmR6vCn67EwXjLuWaFc4AxlCL3rc8nPm8puIt1uNsxqU45UM0DKkxDrQQZF3fP8exMfJ2KjxM8tQN4wfwbjHkPJu8zxT2sTvE6Vtkhc5PT8X8XcPMvweMb5akecN4yWtvdVICXegDdJB37vdz+0lysVUAC/Bme0w7ivAEHxiCjJu1poOc5BHucd4PgRDj0HGI0iBv244K4nCy1OJPDf8zgzPI+HRa4dsP5tNdLfJRetwrz/7AlpLKd1jg+ez03A9jTYye4h+3SfQOkzG2WumRSGSDRhHe4yfWo/T4HffYhx81fi9yfOdiwyqTMN10ve8afimG+eNRI/d/cShqimEXBgicndbiHqexYiXlsK4DDjzNIx7mDiodRCt3RPQe0ioy7gSrb0e03k7Mpr7iXp7+Shvs4LwTq4wLCkGWoB0xzJiw32fcZbh79ci2u72HGuNbzxePzJitxnvtYalFumgZiTDKhAvVDB6/f+5jh49yvDwMN///veZMmUKR44c4Utf+hK9vb1861vf+hef/dKXvsSjjz468v/48eP/tcH92K7RHMDzXXeOkYB8mhBwh5EQzyX6Sd7p7y5CHrBeYvc7CMcPwSWVSOiuR8JqKhE2WEAImO1o13mnxxkkenJO4MMJ9PuJE8Epz+stJCCXoh36JqSgNyNjbAB6fwQ525G3a7xhTuHOezzXLR6nhvAG1vv9jSi5fxlRm2uW4T+Idr0r9S4GjYtGJDinEsV4c4gTjzM8Vj1hNG1G3oFcP/8CMsR+iJRIvcfuJYToPI9VgxTKXOIgSIdhzSHapRV6/Hx/XuJndxEK+xGkVN5A4czr/a4ZhMd2HRLQdyIBv4fol3sEKdAphqWW6MvcT4R56z3mJqQMHvTvF4jk+x4U/sxgxDvMQqJmWI/hayLqEBYb78moSvckb9mw31ONFE6p53QD8mYPI4PgJ4RXcjHil/tMy2qiveCnfe8RpOQWo3XzDcRzzYg/Gw3zT0zHxf59NfKSHPa75/m77yDPzFt6zz80wq9d73fvNI4yCeWb6HgT4s1NwtPAD8Smv3EbWnv70JprRgbGKx5nlfE4FU7cAYXzEJ/0o9JEr6F18udIWad80MSTKQd2EuKdhYjXBv2eGuP4AOFJW4c8grnIEDqE8uVOGr5HkVGaUgWyTOMtxnWSKxNNx0bPfYdhX24apVw7G9AH74BZlYRXcg/8/Hvwic8Y7s3IgJmHNgopLaFKeBoqgYwvEH3T65HceA2t5UzCoK0kvHlLiBZ2LUSNvusN53VovVf52WO+J21uxvrzD/yOA4a3wvekdf2CYZ7hsXqNq0Z/3kF05MjwZydQWsc8w3CN5zUW8e8G0z7P37ci4+1lz6nA49yC+Haf6WKj/cwrMO5qxLffRMWdv4tk1iBan3XGc7efW0bUKszy+GWGffeFywH8ER9tDuDn+HhyAJ9++mnWrVtHc3Pzee/57Gc/yxVXXMEzzzzzrwrLhbpGDcDzXf9ljBRILhJOWUjA5KOFeBgZdDOI3rfXEIZbNpHQewoJjQNIac/134uBJ+F0PUy4Fin9jcQp3P1ImPUSiftbkLGxCwnBeUQF+neQENqHDJAqw9GIBFcfUVz4AaTsGjxGg7/bg/KrOpAQ229485CSKkFGRa/hmY0E+JDh2YEE3T1EZ44W47TZz1yEPKVNSIBVI4VQghT8eKSA1xOJ09lAOZxdCWMWEeHCTqJjQ9qxv4KM0Gyk7bMYKXI8Uj+t2f9DeMYO+LOUhH0QKbCkQPuJshjJY5sOSSwmTlseYaRbR+fzMKkI7dz7/P6Uu9hlWBqIU+CFhimlDTShU67bfE8bUgCHEB92E6dLSxCvHTddtvieV5Ahu4HoBTyN8HS+5vmWEJudSxEvrRXeR9ID6pER8h3Ek13Eqck642eW711HJLdPRqGvNZ7XEJHv1kMcDpnGiOHFpabdFOQpS8o1Jf3v8XfDSOFPRQbkJv9fQJQvKSc8xe1EV5VKw12K+LaNaJc3A/hdxMPZRN/iGzy3lYZ1kufU43dNJtZ3SjcoM63wZ99FvLbGMNyLeLnU765CvJRtGsw1bMuRJzsP8X+fxz2KSvEc83hbiHaJ3UQO4imPP5mQDc2Ih5LRnnIgf+QxdhHrvcbjp7n2Em3PrkSb2GbD+bw/m0EYNSuMj8PI2LsfGbvPErUAk9zNPQevxYgvU55rvj+7Bhl4hYjvv474IoWaU3i0m+i0kXLzdiK+X+C5jyfqtnYT5XZakDw6jPjqdmIDvgzJgGzDudCfH0Y0fx5tnLMJ73EPkaKxwPD2EHmdmZ5jjt+dPMHJ2/u84axA/L8BeOF/LQOwra3tQwbgRRddxEUXXfQRvUXX17/+df72b/+WQ4cOnfeez372s/zDP/wDZ8+epbCwkM9//vM8/PDD/8t4AUdzAM93NSEBk4eMki1ICLQSuVnFSBClndpptCgPICEzhIRj8jD0IAGcgRZ4GTATJiwgdtrJc7MbKfHk0XkRKZ18JKAqiVBP+jvlh61AwrqL6GxxyN8t8HtvQcZmSkzuRgLofmScTSUMyLTjryRKiVT4822GtwwZFuORUD2JjIEWQhFWoRDbJM/1qGE8SpwCLEah12RcLyK8bU+5UO1sj1PmcSuRIh02bis8n/GGH+O32HifhBRUBVK6rcQJ13zjL5NowL6E6JzQTNCzA+UcpcMFLchYLGLEUJu02vd0I0OpjDiRO0Dw2aVIip44B/4OZMB8CRkBTyDFM4Ho31pHtApbbHjn+r5S02oeUeriuHE00fOeSHgutvq984iDFQmH15oWsxFvNnjsBuT1m050JWgyzm8g8vWqiK40yVBPHuwWQsm1ef5twKsev9bf34AMll1E3bTnkQE7hHh0OzLsvooUdS5aQxlEsn8RUVx7qud02O8qIELILxiXlYatmeDzKj9zDPHOQd93GBkd04yLXI+ZRxwgSoZ3s3Gy3zioRrxU6fltRYZJgruD8AinfOCJhCc5GVZ7jY98RONStAlYRJxYP4k8ijuRTFiO6J6vcDtdSGZsIcrr7DZsKTKRvH0dREH0Yt+zEdG5jzAyJ6OTv1vQWl1inB9AvL/R7zjhz9qNt+QRHTJeZyDv4CAynpLRdAky9CcTB+UmmnaTkQwYImrzTT3n+0qiO0iR53TUtFro+R9BvPm2cP/u657/fN/TRJS66iL6DO/zZ0meXuq5zSa6C3UjuVRGFP9eZPoNE514sj3/yYZtvd9zAa9/jRzA4uJiJk6cOPLzxBNPfKQwHzt2jD//8z/ny1/+8r9434033sjGjRvZt28fDz74IH/1V3/FqlWrPlJYLuQ1agCe7+pCyhIi1yR5AHuQQO5HO9BsJOCOEAr395Cg6SXacC1Cwu01tIgfR4J7KRLG9cj4GevPl/tdy5DBVuKxViKhMhMZlCcIz2M7ElbVSBgfIQrq5iKB8gJSMNcjwZcOt+SiE8rbCG/Fk0gIHzQ8sw1fEvzzkdJKXs9lRGHiLsLYqUFC/IB/ktduOVJGXURJhGc91y8QRtUQUuiLiVyp5f47hZ4mIuPgWmTUVBD9Yn9I5LV1GK89SHjegQzIXKQM2/zdNZ57E3Hgo4o46b3Xv98neqrmIt7I8N9r/f9CImcxeVB2EN1YUu7nXKTEmzz35AnugN4P/P6vEKUr8om8sGaivloy3Hs8twNIkdxv+vQY9j2m5RVELmgmUrSZqBtNne9v9Jw3eX41hFe11vitRAr5OdPwS8i4fJLIMa31vCchfkx4aUdrI9fPPkB4eHuQsddAtKcbCwPH/L5VxIGbBuJATinig9uRAfk7xInzbKJe4CLP521E/3ZkZPT53hwiT2+S4fl9pMxLCeOi3D9DaI3OMP5e8zwOGd95RH/tDt/XijZ6L/jvBncBKkf81eN31/u9xUQHmGbPq9H4PUW0TEveqg1EIfJVfn8KJe9C6/N+GPfLRMTiiPG0Gq2rIaL/eBG8dpIw4vYSXtIcj1FEeL0bDUcesRHa7fF3EqeaU75diX6ffRHxwhq/+yDi0xwU9Tjo53tRWkKfv9tr+K9G9J+MePAG422u7z1k/BWa7u3G93zD24lk+kNos/MFjXX5EqKl5WHD10kcdmtFod15/ruV8CwXAXvgzPWIPwpQulG/aVFA6JsWorj9Ij58mKyQSDe5QNe/hgHY1tbGqVOnRn4efPDB/5/vfuCBB/5fhzT+nz9Hjx790DPHjx/nt37rt/gP/+E/8KUvfelfnNttt93G4sWLmTFjBjfddBMbNmxg69atvPvuu78wnv4tXqMh4PNdS8fI+7IFKaRvIMXXjhZcGxJWd8I/XA2/di0SXPuBajjzAIx7xmNtRrv2Ex7jBmRoPIeE1CIkkI547FwkLAeBIXjvfbjsc8RpvlN+br7H30aEmJqRcFlB5MrMQIJpHzJkUvjzMUIQpjzY95HQwb93IQGWa3j6PEaf4d5LtHq6Cyn6a1GI8M+RUq1ASuktol1aj58dj0JXq31fMTLk+gz/nciAOGkcXorCmbmEUk55MMn7eB/RV7eW8Ki1I2XdpP/f2w6XXeu5JiFdgIyHTCTQ61ENx5Rb+Rai5zx450dwxS1+bggplwrDtok4wHKf35Hn/w8AN8B718NljxHlaIbOga+fyN0bIsL8TfCzPrh4P1FyZzIqWfIs8kjtRIb4o6jjx3rDddQ020UcbNqKDIxHPN8Ueh5LGE87PfdNjOSSjngr+5G37TtECHkQ+P6nYO5PZWjUGp+rkYG1k+h60IbWTUqLSDQoQx7vFDKsQwr4GLE+DhMt0PqMtzxkcAwiPtzqsZ+GzokwabKfuYbw1jzr991G9PRN6Rzrfc8f+X3JO5xyUMuITeHzKG91G+LLfYgPjxCFs+uAKji7z6kM+UgWTNX9Q49DxueM9zx/32ga/Rit4/nGwWF/t8j03kLUvWxjpC4iZaZ9lnBy/H645CV0rUcboO/Aez+Gyy7yWCuIE+L9aOPQR5SQOYBk4t0oV26T57AUefmOIYOyDa3h9Yhfvu4xt6C1/kO/qwQdCso37bxGOYRkXiNRKuUUWusvmE6HkSE7jGTHMWQUdiEZN4h4Lnl4Z/m9awzXUuO7Ea21m33/VahO4SRCljUgx0CVn53tZ6sQH2V6vO3GzX6iO0uFcZpypjuRjLvCcyhE/JJSbw4hedGF1mM7EYrPJkLVK4DfvXAh4K18tCHg5fx/zwHs6Oigs7PzX7ynrKyMrKwsANrb2/nsZz/Lpz/9aX7wgx8wduwv5gPr7e3ll3/5l/nbv/1bFi9e/As9+2/xGjUAz3d9e4yE7h4kyOcgI+QdJCCyier0uXD2ARhzGxI8GUggFSGBf4DoulB9zrPlcPYuGHMtkeOzB3mNniPqiK1DwqoBCecWorxLFeEdKPN37zPSquno/TDtSWQ8XIuEeDvKQfqGn91O5JIl+DKR8qgwPnYSJTwWI8HVQ9Roa0ACNHnSlhBJ5UnJz/dz45Fye8jfPUecDk7KYBcjPVSPvyo0X55CeW8Swn6W4c02rpf6XQ97/C1EjtkEpBRvgqF2yNiCcq8KGGlDxV3Ab+fD/V3C5ya948xaGPek330JEuqHiXqLQxproBqyDnk+zxqnVcTJZ5DgXuxnvmI6J4OqCPHZXUR+4Z1EHmLC0xAyRtcgoziFwpJH8Fk4+xaMucXvS3lt9xCGfT8Rls01LTKIWn0pd60OGTRvEyVkthrXJcTp0WyiyHi253Cf6bEUGUjFRC7TJmSYTPM7bzEuv2Mc3+FnsokC2B8YL8uJ3LJm46WcyJuqQbmTzyN+P+2xZyD+K0S8fZ9x2G5YriKMiHIivWMj6rRzwDS8gajXWUzU+3sZeRlTCPYVZMwUEQn+JcLvz5fCJ+aZDvciQ6PI86lDBtQptLY6Pf66sXDrcFQimIta2bUQeZyViPdWGh9NhFF8wDgoR7KgGPikaPgP34NfmwKnj8GETzKyAWUZI32geZ4I4fYT+aCHjbvkBS8gNpvVjJwyH8l7vcKwbiO8+jV+bjHirxNoc7GTOIy2mfASpg3uYsTj09FGqIwPR15aCEO5j+D/1xnp6MLbhDH+lvE1HW1IdqLahm8gnqlG/Nnhv39imB5HhuIOol7ht4kSWBOREb4Fye4NRBWITCJf9YTp2ujvEq6Pw/d/Cl/+FFFovBjxzKMXzgB8lY/WAPwC/zqHQI4fP86CBQv49V//dTZu3EhGRsYvPMbf//3fM2/ePOrr6/n3//7ff6TwXYhr1AA83/XgGC3KqcQJ4Goily+FZ19ECqQFLdQ3kJD5P9AONQ9efh5u/CQS0NVIkO1BgutdosXXcaL46w7iJGEuUqBdRG23acgrNRUJqQ4/fxXwBzAwDFnPEI3Wi5HgfZuobzZA5I88j4RMORJyZUiRrNe9p7fDhMm+v4kI0/R5/Olw9mkNMW46UpDvEInUf44E28P+fQwZfoVEMemkbPYSoe7dRFxgLlFXLN/j7CKUawMfLo0y1XRLO+ouZJSMN75qidBdn/G3H4Vtjpq2Uzy/Q6Z5GdoQVBqnQ6ZRAzLc+5GRNQUJ6T9AYdS/8efDRNmHw0TOVOKraUQpjdm+dzlxSrjdeJlu2tQSocQipES7fV+559vgeZ1ESfn4sxlETuk0OP44XHKL8TQZDn8PZjQgD887RJu4cr3n5y/CJxYhvnqekVzUs9thzE2mSfJ87SHy1HqR4ptpGu0xTBlI2eYSHuyvIuMrHQoqMg3aGEkvGDoCGVOMl0Oe20PI8/Y3aE2MJ9o2dnmcFD5LXsXdcHaXjeZ2QrkmmFOYPaVzFBjuPcQhpMf993rPN3n4j6BN0VxkMOWYroOMbHRoRZuy9UTbs2QItBFdZ/b7u4XEBvFutMFZes7zZcZnFjKApzHifR4pxH0DWiurkWGSwrBXey45aNOU4THTYYh6v3vAc1hIhH9T6kUP0c0oGZ0pZzgXGbfJY37C8+833ruJ7i/HkeGfjwzOdX7nYeThO+R3VRPtBHcT1RhuRcZXLWE8dhEG+11EIetGv2On4cgkOjwtQJvrXMRj44lT8/meYz2SHe8TJbYyUeg4l4hztpo+mabPRCJlphMZg9ei6MMHfsdtSJ5UIQO6ASiCobWQcQHU+P9MBuDx48f57Gc/S0lJCT/84Q8/ZPwVFhaO3PObv/mbbNiwgU996lO8++67vPzyy1xzzTVMmjSJ//Jf/gv33HMPl156KTU1NR8ZbBfy+qULDcC/2Ssp0C4ktOYTRskWJBDbkWu+Fwm6FWjRryROoR2GGz+HFvztHvsNJDz+lChf0YkEYhsSyIuRwEjXFpSTVIFWW8qzOYIEWgpxXA7Mg6xCvZsszyGFLHI8r8T/M5FhADIqOohTjXkef4GMv96TkFNFlLSYg5RfGfAKjCmCcdXEycMGJJRTSDSfMJxAwjLDONyLhF4y/kpQmGYQCdkyJKRBQvtWz+sVpEgf9L1/Y9ib/d6TgvHnx+ATN5lOxUSuTg9SilVI0GYjAb7R91zqz4oNWzvR2qvD77nIuL0XeV4KPN+UPzXRn633XI4SBzTWGO4OpIyHPN5bnt9thrHA9OxCRn05MqCnIwMjGUSDRJ7QLOTlaEb5nvsMVxWRO9qPeKIaLvmO8TwEXAczTiKl2XQODb6JwngL4RNFRI7cD1AaQak92h1ETuchIh+sBdG8Eq2JDMPzNWS0TUWGUjNx6rTEMBUQ3T2WMVL0OuOY559Cfjs8z0HEJ8ljCTL4uokesdX+O0vvGPNF/z/sdw6iDdcM4gT1Ms+tm1grMFL6KaWBcD1hkN1F5M9dg9bOftN2MfJsdZi2A4i+h9G6T160ArS2qpFMOoqMvuc991rjdRGSB90eu9nwzSby54qIEjEliB+eNr4Tn9xPeLhTGP5Nj9Xh8TuR0Vp/zlz2Gn8g/kwyook4SNVMtBPc43k1EafuL/J7s5ERWGt8fhdt+pYbp2+hdX70nOcHiTJbySM5Cxn63YQXvYFo33YDIQvKfP944/MDw9Lo79KhlkrT5DXEw4OI1gXIaD6CZHa38Q7B38OGKxPx0CBaC8kIPEF0MupDsmUPMuJzDBvCe8avcEGvtFQ+qrH+Na433niDY8eOcezYMS699NIPfZd8YIODgzQ2NtLX1wdAVlYWe/bs4ZlnnqG3t5fi4mK+8IUv8PWvf/1fCcqP/xo1AM93jUVKYzIMvQ8ZucirswcJCghjoAkJ+hokGIeQkMlGQgCUG7fO4+4gOk70o0V/lf5+93m4/ElCie9BwuQej9+OBEg7MgYfRgK3yf9PJ/JfypGwLEZKLHn7Zvq5ucjLlcJn05Ai+CQKC3fBPxyCX+sGCiEn3+9ZhgR+8hYOI8GYdrQVHud+v/96w5u8buXIiMwzLm7yc13Erns/YQB3I0W4AimgO42/FqSoLjIeOwjDYB4Skg6df6LbY9ai0Es70ed0JjIADiJBvsd0/SOkXHOJAzQdRPmMAuL07q2e818jI/AKj9tl2BYS6QCZSMhfQ4ThyghP3ZWEdzEZUOP9/uRV24IM6kuQkVtPhHlr/a5B4qTzOqRoUkg+GZZZRJ3FYqIWY5X/Tzl5y/3/n/rdtyBDrNY/L1wGO9+TpzDHtEpG2GJGyviMHIxqJDYQM4yrYb/jGMFLfchQ2UoYvgc85wHffyUyir6GeLmaqFlXZpzkoW4azcZnFwoxr0derTyPsx9+9gM1+KGOOEU72fOaShwwmUN0pskiyqhUGI9bTJ/DhNdoDeExrPW7tyFDrtrznUaUV7GXhzlEBYJkPBxBxttO06fAzzWYfrmea7Vp/QLRtaTa911h2vyQMM7rjOdM/9+EDK3b/P4af77S47QLb2QS5U1uQJ7OJCNnwMAtkHUpWnf15/ykgykpneQHxt07fk8vWuPt/v9+Qg4fQLIzz3C2CYYzr8O4TxKVCTqJnu6HkfzY5TEuR3yaQrfLzhl7u3Gc47E6TIfVvj85ggqIuoMdplE9kfqwzLjb7WfmIRlfS9RshKhL2kTkOc8gNtQ1aKN0APFQCfJMXsBrmI/OcPvXMgC/+MUv8sUvfvFfvKe0tJRzA6LFxcX/y3j6zneNhoDPd60Zo8WXQkxridp4HUjYrUXJ89uRkJmKFmoBUdG/139/GxU4mo0ESQUSqk1od5dCLF/zPcmbN0CcIi1HijAZEBlEuHY9cfJzLhIwG5CCmk3UwxqPlNVYoi0UwHI4+5gT02ca5sPw87fhE5cigV5HhC8WoJ0xjIS/ji+BS871mKXv8omTsXUorPddIhG/3zD2IKE3GyWS348UYlKYNRpr4CXIetH3v+a5/QlSOIUec4OfLTBOU27VSSSUu9EuO9t/lyIDsDYLzg7o9Gqh4amFv90Kv/Uw4olBJMhvQZ7hbb73KaJQbC5STF8jDMBPGifNSHknBdBsOKuQd3M+H6qH9rPnbZC0+p4h5P1oR4pgKrGRyCB4JgMpxl4i/3KRabkbGR9vAvvg9PMw4SmiRFE7cdI5bXLKCIVfYNinEl7TZaZBj/FdzEiv4RHDIRcpsLeM/+TdzCZaGC5CHthBw9hNhPj2GteDHvu7iF+aCCN+KpGflYm8xGv9zk4i3NqCSs3cR4RJL4fjD8Ml6z3+8+fgdBHin+eJg2DXmQa9MPRTyEiHCnYRHq91/nuFn1no+a6H04dgwrOI/0DFt+cTaQWHoLPe5YQSLQ+hzWiF6Pzej+Cyh4z7dCghGdwdplcR0cUl5X5WIBnQjngqeZLNdyNlo141bLMRX+Yh46wWrb2/Q8ZlD7Abzhxx15Z3kEafTYTeK5EM60EG+BbPvQCto+sN41eMr8nABvjbt+C3Pgl82Th4AuUFppSHHLQx7kI8sMo0H0Cbs7SBnGRanEAG7OuGr8Tz3YA267We8zfROv4Jksf9yIif7GdXi44U6+fM1TCunKi7Wunfp9FmYADxUAqntyF5W2q4+4jDYM8S4fGppsUu3z9IFNTvB564cCHgzYRD8n/06kPi6eMoBD16jRqA57++MUZKchISFLlIkZQgodeMBM9dSCBuQwu6EgkFkGerGRmIq4kitCl01ub7TyLh1IkEZjeR29VpGHYgAb0GCdsyIicpeVSKkABNCqXDsLchg/B1Qvh3Ezk4IEHaSeQuDqPQ5R5kqBUQJ+COIAHYgwyW9UTx6DykoJYQ/Tl3+/c9RO2425GRshhOL4EJ45GgngnHV8MlXzBc1URIJ5vIFyxGgruFyKHZQXg97iR22gv83H7jsAsGNkDWF5GR2YUMtRT27yO8azWe2zTj6vtI6Xcggd2PDIvZnvtPkIejgdj1J6/pNmT87PP3LYhnNhAKIYXHFhClIPYR4bzdyBPzimlQjAyi6UhRLyDKplzt++5AtF9gHGQYn9NNg8uJMkKVyPB5AfFkMqrrCQ/Rq0gRvUF4cyqNsxTefd7wpvB3DfIIfgOFO6cROZApx6qAKII8gPhsLFHvbSmwEAauhqzPId540Xi7yHhqAp6Bd5fC5esRb3wNrdPZnk8eI3mkQ4/YaBsk+tDm+9018N5zcNkngf/T9E7e1wzi5O10IkS82/gu9XvrkJdtKXEKtQNpzHa0Li4h2tGNN16aEZ99y/gfIrxkhR5nIzKCvmm47kFXvXH8nwiPdT3wMrT8M5TeRnivsz2HZIS/ZTovMy4riTI4/SjE/lvEoaYsxIfzTMM643cJ2hBl+76pxukQUVGgG8mnXZ5zt3G107Qq85xfJDxmWwzHLKJcS4PH2YbkY45pdQjIhPdeF1oue95j1KH1UIzWSKthf8Pvb4OzW2HMfYSX9hUif+8udCK733j7M7SpXW74aj3vFLYdND6P+ru5yMDdgNI40kYl5UAOGvcFKKLwNaKsVjEf3tR9fQK8ehq+cOEMwJf5aA3AGxk1AD+ua9QAPN81a4wE0HjgiHfqu4gSGpPR91uQITUPLdBcotBtKzKqjsF7+71L3wtkws9/DJ/4FNEfdQhx/14kyHuRIlyMlHGWnmMHUSZjBxIG7yKFk4m2T+2Gu40o2zKVKD47hIRVK1F+INfv24kU0vcZKWA7tA8yriZOxaYk8uTduRIJ5GKUON6AFEC3cDdiAJQQJxEXG6bdRD9NezrP3OE2SYs9j38munAcRoq+ABm7hwzHa6ZNhedzr2EpRsZBjnG6negE0ooUXCZSACv9vhReGkRGUwrTJq/Zw4RRuBh1iyj3XBcTJw97iL6uY4nE86nGxxBRYqcWKb0sz/UAMkSu8PeJpruAr8CZFTDueiJPrRoZBEs9/72IT7P83bvECd1pRKiplWhnNdfzfIdIT0gh4JTwXuP/iw1Tk5+vQAZFF1FzsBMpyx5/nrxRyw33Ef+/lDg9n0ecyp6OTnVWI2/rTURx6V6iRWEX4qE6ZNB/yTheSfDgPKJe3TBRbLraf2+Dd7fD5ZORgV1E9Hs9QRRynmG4ajzWzeiqMz6nedxkUH0XmA8/3wSfuM9zzEA8MhN5qQqRIbLW8NcStSrLkMesBHrfgpzPGZa/QHUIhz3OFsQrS4ic0u8SXqjFaI3kwdmXYMwnPfbNiFfShvbzfLg3+CvEqf1VRH/w6xDPp/zIFX7fPtNqGHkpBwnjt9X/p03kc8B/NAx5ogEl/u5p4mTwfjiz1fxeQtTqqzSdHzfeDngOi5DRWGTaNKMNeCPRoSkT8WAy5P8YbVgXoM3jVtNzq+d9kIjMJN6+02Mlo3QhMrx/4OeuNG0fQYd0tiDrJpM4OV0NZ38AY75CeC0N48AeyHoSyY4NhIyZ4nkVoA3Vs8B3Rg3A0esXv8ZeaAD+zV6DSElOAsphwq8Qi7AECZUtKDwAEg6XEnkmBQi7OcAiuGwR0cA9Cz5xEfJ4zUFCKwMJ5HlImA6iXKgH/PlkZOhUIcHXIqN0JGG9kCjs+wYSbs2G4RKiSn2v35VyTvqQQOlFiqAVCagUpuqAjJuQkEze0KQcUojpRaJAcArp5hIHQHKRMmsldvV5yBiZSux2s4SfcSm8dYgoslri3/uRoBwkTkXXe5wUep+OlMFy4YlXkABfgTxSHaIJy9GuPJ9oFbeLOOE8lyiuW0GcdMw3rDsM04NIyVX6vTXGa4XHykbGU5U/e4fgo0bDf9Jz22yaTTW+Zhrmi875rg/Gfcp4PkQUG04HOpLxMctwnfb7T6Cw/U6i7EsP4pcVfLgjyymiG8hK4uDLXKQE843nfMSbc4gT3W8hnq44B3eLEA+NRQbO+4g/6hDvcA4+GwzHK+e89yHCODqOeGgWWi/XEmVXtsDAKdN4j8dvQoq12jSYRxgBg4ykR1yeSZy8/kfT8l5GwrVkM+L5PvMSnP2vpknylL1F1OabibTizcLFJ641DcqJbhUvESd7G4m2dIX+WWT6zBa9ch73GHWmWTVhjHcg+TOeyM1chjZ/yYO4EOiHMSnFYypaHy8S8uA0WgefJtJD7vIYdYZxoXGzy/hsIeoALjYsKeWiwN/VIP77GuKvKiS31hi2ASJcOkQUOc/QXMZdaXjeJoqNZxu+W/x8A7ExWWJYyoyHrcZjsejBDMIT/EMij3Cq4X0T8UWSjeXEwbjdRNed3UTB9XmIp2oRfd9Cxmk50h3JyG4y3JcDl8CYX0Zr/T4kG4qAiyBrLFGEvYLo/HJKz/E+MqLruKDX0Ef8M3p9fNeoAXi+a7p/p7y0MrTQUnhtBlKGuUS9sFKkxDJREd5GIjF/pj9fgYTVKrSgL0ECqoM4tZryjjqIkOBbyPN0gBElPGElYagUEQWMZxGlLSYRBmYLEporkdItIRLku/y7h6j/lsLDKRRZ7nGSYjrlMar8/GHPrQX4IhL2lcbJdGSMDBmedUQYqAsJ6ALgNHQeI/La+ozHGiRYe5AxN8/PNhI9Q+edA/Miv+OAv89D3r9i4ygpmAoin7IBeRVTPh1IafwEGROvIaO/5Jzv6pEiKABOQOcRInk+jwjz1Rj3Y4nWdllE2Y1sw5NJtMAqIbx/yUNbhkJr0wgP9USC7pORAqv1892I525DfDLR86tHCjh5d5K3eRmqw7AFer8Hh7cSbQT3EW3XUsi3yrjY4netNk5n+P5yf/+icXAJ0Q7uBOKJ/b6/gjgl3uHxxxrWNtMg0bLMY9Qgg6IiPssqJ4z6lJZQgPhhGK3TXsT7hw1jEfL+3I/o+q7H3GS8LCIKdG+QAT7mV9A6TiHsXrSpyzRcdYj30/rMJ+rONRpfrcjYaCcOSPUjI70VGRgpTF5A1LpcT7TEex55/z7w55uQR7EEeakmE63dVp2DqyLPbQmSCT3G9Qmi/3OVaTaHCBcf5sNddS4huvs0EH1vJ6A1t5voOLPANE2y7Z99z/vG7zqUHvI1P/OOYb1O+DuzBxmBlxIF671xHMk7nerPexnJZxw4QrQ2nEGUb2ozvrPR2k9yZZr//rTxVkBstJJH2wbdiVeJlJBBvyfDOMszXdsMWzNRS/BKz+ED47sUreFe4/QeInRegmTFBONvPOLJ5AW9gNfwR/wzen1816gBeL5rMVqwc4Ay+IefIiG5Ey30ISSgJxN5dr06fUYmsevuJRT6ZLSQ5yNB8btoJ38ACdRuvYt3iWKqc5AxedrjzkaG4Cn/D3EC9iBRvHo9yt1LYeoepDjqUejoTqLR+RHicEGZ4UjGRzrJ2oME//VIuU31+3KQUdHmZ2bA0NtIwL3vZ5cigdqPDNMvI49MmT9rQ4L1u8AGy/UW5NlJnsR+QuClebfC0LPIC9NIFIV+AuWpVcLZfzbu84zLHN+XgYztt4znChRi3Gq45xnuHYgXUiJ28tCk0GGmx9gOrIFJKwmPWZG//4bxuBkZQvWILyrQydQK5OH7APFcDTowkkWc/G4GXoHO7xmu3UQy+XZ0YrkXSdAG422Y8EhtMX2vINpnNRDdXRIN9goHZwdly89YYjql8FiW53Y3qg84A/FkO1HWZYHx9Q1kjLxOdKiYbDr2E/muqz3vE4ahjiiWPomoPTid6ERRg7zjII9gg3/6PN4gUUJjwM8W+XdaS3caJ4eRwh0GfiM/eq82o/DaJsLgnoX4fBby/JYavq1EGHOqabwLfl7v+7sMSzZab6uQ8TaIjJt+ogDykGGbbfy2IH5LHvEyxKN5fiYZ0xOJvrQnCQPyJJIPy1F4vIHIObzXsOQQfbVPEOHxoyjHMHn2M+DEj4mcwH18uA900Tm0O4DkWw/aWPwR4t1mwou5EXkB7fU+PYxkV7IEPiAOEGX5ZG/KpcsgPNh9fiZtMpcBxdDrnOOsyag95360idlj/E5H3r85cPYZ0zoZh3XwTw8jD2PtOXQYQsbqFr2z8HqipuU3jPP9jGwKR0pZ5Rner5iuKbrxNPLkJc/0MFonHYzUMT3+NAw8Cz/ZQ9SnzCbKH41eo9d/xzWaA3i+68ExWujppFtKZM5EwuUFImyThwRYKzqNug0tynd8bwNa3P9M9CvtI7wPKSy6AQmQTCKMAzIQnkfehToksMcSOYD5SIE9hQTYk0hAPYiMx4P/Dmb9k4RjNVJqLX5vClGmn/1+7xrkRXjOn00xjIeAZ5AwfYYP5xVORad33/cc7iC8NxUefxNSOqeInqsLkFLsJw54JKV/wDixEdO7C3I+g3LBxiNvwGvIGKkxDhfCmU0Ok95AdKtYVwT/rV1G2KNE2DYdcFgsOHo3Qc4C4zUPeXCeIGp/pcMs16KT3VcZV88S3VB2nEOn5H1Iivo6FO7JRHwxiSih0e55pJy+LtMyeTcfJ05jDqMNye1Ed4MG3/cgMvIK/XwTMgTygJfHQuGwcpOO+P4lHnsXMqwG/fcQkbu5BymueUQ9ulrCwzFsumUixTrJ72uB4y9KL/7O9cQp3TpUIHwz4WFPB4z2+7smFAqejnhtBeKPl5Exe9R4foAoxtxIFFAvNG6eI4r1XorW6h4+XKonwZU8bfkeYx/aPBxCxmvyVG7w/eXIME4HwQYYMdhHah8uRPw1GX6+Aj6RvI4zkPEPsRmZbLyt8DvS4Z0eOPM8jLsPhY9T+LQQzt4NYx7mwyWqkvd1KjIsF/iZefBuPVz+rHH6ApJTIP4sRPz7A+OhBq2Ru4C90PuqS0KVI6Pua0R4vxxtUG+Fd+6FK6aj9fIacXI75R9vNv4Kibp8DyHZssq4bzZc/YQHuAIZhfvRWtgGZz+AMU8a/iSX85G86UOyqp4wuuuJWoaT0KbheSRHvmk8XofkzxJkbD6F1lo3URZqJirO/PtEItwOIhQ9m+hEtMHvn2s4b0T8W4MMxD9Bm/8jSJYsJaIUTcgYPog29pMN00G0GfmdC5cD+Jd8tDmAv8NoDuDHdY0agOe7Fo6JcGs+EszDaEE3E4KkGRk6i5HgqkYGxhCRA9dMFFxOuYUHUGmY/4AE/TkhBWYSAmQvI6Godz+Ayy9FArWWSLDeiARkoX9XIePzcuI0ZQoddSPFM83PD/i7DsPxLBIw3f5/K1JQjWin2oGEV63xkef5txrWeUhhFiMh/DXBdPZtPXrxryIPYB0yGqYal8XIiO1E3oVcIvyVPFtvoJONh4iWZCnEfojYbRcTJ/RSSKrW368hvLPZpslYwvjsQR61642HLefAV0WUPkletsVIwddD5yswaS1RzDuFZzKRV+MhgmeW+575aCOQFG0WUYan0nTYReRzlRqOiUi5NXuezUiJFZhGGejqIvKOSg3zKlTMeRLBx7OQB3Yq0ZruXT+TcNXhny6iB++zyLhpMtx1iCf6jMNHkZFTYPx2I6M8haqPISV2A9EjdoFpsAIZDieJU/TTPK/Dhn8KWpPPGO/f83xeQ+umGfHmHcb3SeNx2HNKHujpiPZXIHoWGQdHz8EbyMi7zjjZi/hikv+vR2vzDeKgwEm01v8I8c4SZGAUIz48YHzejui6zTjqRzxwj+fzBLHeiw3XVCSfUk5ZDZI9rwhXZx5zOZZOolDzWqLl3lKiBNNWYq0sNl6TJzaF8kuJkGm7aZKBNPZPidPsKUe4FsnAbt9Xj9bCbtPmdiK9oRF5cgv9903GXR2RnwvhuWxF/LnK+Gs33QqIPObxxIGnxP/9RG3FIrTxeEl06G2FnJuI3OI0lxsRv9Yb30cN+39EPNKNvP4dpuE6lHLRhfh5LtGBpcPjvGscFaH1nXi7E4WdfwU5DPai9eNNwdAdkPEA0W60wHAcHTUAR69f/BoNAZ/vSsIjeYHykILbRpzwLISf7UOC+TniMEby0rQS+WTHPGYDEoD491IknCYT7aquIDo19CCBdz9cvhoZDK0eM5/oPpBCCu2MeAXA904hirZWI2NikKhIX+D3ZqFd/iLP9wWiP+xctDpnIGNrH1I4E/3e7xuWZ5CCyvfnecLFmM/AxdejU4bjiULIGCc9hKHtE5Y/S4ZkKRLoN/q+JMy7kNJZ53GqDHuZ72vz2HVICVyDlFI7MgbsfTn6DFKabyEvw0PG2zbk5TvkZ4uRIpyDBPeQfzdonpMqDUcxUtyFRLu6WcZfgqPD9zUQnSS+ihTLY34+w3MoQx1OcolaeXj+y4y3JZ7/SaJQcJvhHSROmnejTUcWUmQfINq+g4zSXN93GvHmAUTXZNykPMMm4yfP9yZF1keU+uk2zRJvQnQOGfTPHNMs4eEWw38bCoMXoFyptAE7gZQqaBNQh4zri5BhtwV5RRMtcpAR8wqRR9eF1sRniBP11xDhwwLkRU9hwnIi/AqRApFwnTYPK9HpzmSAePPy8+TtLicS+vs8/y8TIfRa43GVx33JsExH3v/HjdMrifDnbsO23rCtNI5Owri70QGHXUTf4Od932y/7xAKSU71Z51oLR9Bhu0pJDNyPFaHny8nDtJ8FfFN4ukZiG9Wo7U+i9iYzUTG8EJE8zbkyeswDD2m3VqinMohxA9rjLcGzbHzR8grt584/ZsMxm6inmWv4XuHyM1s9ff7EM3bPa88z63bsKc82+T1PYrWWQ+RH17tv/cZlqUeZw8y8vYTbfjGwtAmFDUoQhGMuWizXoly/E74N8bTZMQPP4SMh42X3WiTcxTR/AJeQx/xz+j18V2jBuD5rqeJ0hftRO2+uUQoNBMuvgsZIT8kmq7XI6E3HQnwciTompEgmUucpGtAnq9dxK5uPeF5WIy8LMXIGMnUd2f3EAbidCRAn0ZCpAgpubFIqV9OnJgrRjvs13xPBTJUFhrGY4Swno0E4AF/945hSeHIOzynDj8/jMJrbcZFObHDn4QMiOTdPIoMl17idPQBJHgn67MOkLDeYjwuRYppKvKIpJyyYeh9jlBAHcQhlgri5GUl/M3bputsv68QppUbrjJCmE8kuir8ACnGJqQQHY6jEG1X642LB1BYq83j3UN4mVoQz6w1HuuR4XqQSCqfaLxsN22TsVKF+OlyxA+DpuH1ntfNSDm+4vG2EbmKNR6zEfFvKSohko2MwOStfBspnUWe3zGUR1lGKPQa5JH4MtqkXG6YMU3ONSCbPMfDfqabMLbSgY68c+iV7XuOGVfX+/n5RA7oAWAYOlsJY+46ZAQXEPUMH0RrqNG4Pm58dPv75G3+Z+LATsrNazF+v0fksR5DRkUFcTigzePNQzw4iDzx1xMdWFr184kUmm0wbQ7Bu4cQTx3WvYc3oQ1XrceZSBjKW4muQD1EODMdTOkgCkB3ECdpd/me5J06TXixNkHnLiQvso2jxHPJEDxheI8TNTV7GckFpM739yAZmA64DJouu4zj3Wg99PqzK4nyR0XIUPwMkWvcS+S19XguW1FoORfxYy9MGk/UM5zqv2chHltF5EbbMznwAyQTk1wbDwNrEV/dBTklSJbWE23ZLjetHyEK7+9DcvBmv6cIrcOZxncuUXA+bUrHI3lXBhnLTbcMIs1gCPFC2lAPmAZjibJSWX7XcuIgzz5iY3KBrrN8dAdARsORH+81GgI+3/WXY5TbsdytoS5CCmEuEsTXEgnazyH3/gtwthXGbEGelbFICaXQVxVSMncjgbEWGTdtfr6IUNxJiO9F3ol8P3scCZ1rkKJoQwJmLgotPW84b0NC8bfRyca1SCBt8HvuQYJ4LVLEdUgwFhCV6xsMS4PfV4PCc83I45RNFDodQuGnKcgYHA+8DgP1kDUTGS6dhu+vkdCdiZRrCp9VIaUzyfh9FAnSJ42nSURx6hPICLkNGSxPeL7FHu8gMnQGkWIaQsqkzs/c6/elBP1comdpJaHgk8Hfiwy4Qb/7Vs9hnueQjU5zJgOtgzhE8wphTC70+PZ4DXwPsq5FCiF5GTcSrbwOG9/ziIMEJ4iTiiWmxQZkBK71c3MRb+7wu/sYaZP3D6/Arz1OnH5+1/idT3i8E65WeZwGxB+bUGi2g5FiytQTh5hSSL0e8VkTcSq42/NqMDxjiYLn8/1/PlFO5gXByxX+3YQKMtf4u9uQgQZS3A1Eb9fZxkm28HX2FRhzB5FrmQyMFDosNJ5vQUZ+J1qTycBKRkGv5/0Oqh23jShFlDy50xFP5hE1Dds892SU/BFay/uQx/c7cOIxKLzSdMo2LjZ6TtPQ5iLXsG/2GBONg7GIJ58i2qI1+f6lyIDKIWpCDhsXh5CxcZ1hLyPWQRGRBnPCv9carpQveRwZSflEIeyUozeDkTqqlPi7LOPubzxmOiyxzWPmIdlTiw7CpDG3IDk32/R517SrgrMvwphbTK8a42wxURGgBRmG3yeiDvOMqy7khV6KPK4rUGHnt4n8vYuIlJuqc/CyjChjdQQZZs3Eaf924zHPOG2GoXrI+CJRZzCP8MYuIjySMzzGJCJMX0TkhGYj3TId6Z+9Fy4E/Dww7iMa8wxa1qMh4I/nGvUAnu+qR4vwGrh4A2F09SCsnUSL8QMkNMqB22HMM2hBpp3wbCAXhr6HFEgukYScco5uRIKl75x3HEZCPpsouLsbhQ4OoEME9f77EXT4ot1wLkPCvg7+/h89xnbCw/gnhvd7RGmGaiSQizz/U8jTkzyYvURHjRTW3IqUSD2R69ZDnJgtgKwrkXCdiYTzIiTMC1HINXkvMpHguw0ppM8hz0Wt8dmNjMsXiRpeTZ5/j/HXDO9tIlpBNaGd+gDhxUxKZwlSRIeIRu73Ei2hCpEC2YUUxSnCk1hGdMp41u95g+h6cAApvkbTpsPjlZ5Dy27hbaTWV8rHakKHXoqJxPc1fn+RP7sVKbgrDXs2UdS2mihGnWccXuPTlabvr00k8iubDOefeOyUp7rEuOj0Z1XIALhOdGUFcZI4n+g/PFPfnziC+Ot3iJDnLOBLRbp3AVJuy5DiPOH3P4NyaesQ/02Dd/Yj/qtEa6LX83wAebJ7DV8h0X3jN/6DcGDvyZjbkLI8RLRFrEXrbIVxOxmdWK7zPVs9bspVfQcZDMfhxE+JtndNHjvTOMozfNP9s4Q46FDh9/0x8IUlou1m4C0o/BzyXu5Am5+HTaMKdHp3EZFPfKfx9Yo/m+XnOohDOTcjT2cKOxcZnhyivd8cwwPiydeQUfmUx0ppFI2Izwb83peJPL4tpmG/5zrd899CdOlI4fKLEK93Gk9p81dItH475PftJYp+32AYn0d82eN3V8CYff7+CSIacjVxmrccOm9Cm96DyDucizZjucjzvd/4qfG8CgzLYSSjryD6nS9FcnqHYe8j6N1ElK9q81gJrgWQsQnpjGXo4MhKJAcb4OdPm7bz0Rq8nChRk+QSHnshUT/y21zQazQE/D/vNeoBPN/152MkHJKhlk2cuhpECzcf7RST5y7tlpehHe084lTjU0horCG6HuzUWENvQ0YSzplIwT9C9Fdd5fd/y/CUwJmf+jRggeHNRoL/j/zuZGg0IiHU6ftm6HleRwplB5zZD+N+GfhLf7cFCd6FRM5ciee5iegZOosPtxXbjvKJjnhus5Bw+pbhS8qyi+ji0eb5/iES0P0e51q0G36HqDV3Jzqo0OV5XEmUQtiK6HW75z/LcF9pGu1FCqbJc5tK9KgtJxLpZxGhyzzTbgJSKpWIzkV+z68DfXB2GMb8qj/bj3hjj545ux3GZCIvbIPxeAAp9oWGdYbpmEKj1xOFpFP4u4xQBskIn0scSmpHBnIPMjQ7/XwtUq7JICxDNF8L476ClHIK0c5FvJaM6tuRkZM8X8kj0mE8VBNh5JnGX/q+HvF/HuKZEyi/9G3CWD3od6Rneogiul3GVSvij6Oe5zTP4WU4+1MYc5HnNxOtrUeIkGchUSvySuPtiOH+DJGrBzLkE37T5iF5LYv87EKiv2+lcT5gWjURJZEGjZ8G4Eem4RHPb4A4mJCBwpfHDN/nNS8+8NyH/I564jToLrRJ6iNC+zOJQut558DWTfBxFeL/dJjk3PSW5NU8QXTFmW5cXII8aDcgg6WHCK9uQRuCq4kezykcupJoczYW0b3OY65AG6aTyON6jDi93O+f5KUtJtIHUmpHnZ9tQ5udl4mSNseQYVaC+KWQ8ERP8vNvGr79iP8P+P9CoqTMVtPmVs9zKh8+jLLQ42ShOqGFHif7nO/TQbAMRPcMv/9ZZEQWmQZTkPcxRYq67Cl8yONtNE5nE97U543vqcgL+5UL5wFcx0frAbydUQ/gx3X90oUG4N/s1YgE3dtIOK4jiuZORwv+BcLNn7xGc5ESK0ZCKiWQ3488hpVETtU1QD1kVDHiOeFVJDiuRcrjKDIep3qcmUAVjOvxu/uIIqgVaHfbQhgYpUjA7DdsSZF3MVIqY9wXkdJpIwq0ZhEnOhcZH6fg7DEYk/J+rkNC6yee/3IkUPfA6Z/ChDwUlk7hinKUMH6YCJlVovzJbyBvRjkScG/5uc8bv53G681IgO4mWpYVwJm3YNx047UWKa6kcI4i4bsWKc9jRIHlGmSsLkYK4lmUk9RGdBaZhMJSh4neqPtFBwpgzLDxOo04CNSt78fkE2VxkhFXioyjTchgSOHWfiLElLw4qzz3dcj7e9g80EeUZ6nwWDOREdBG1HWb6vHH+/PXBP+4BX7fPTDwPGTdYfjaiS4r6wz3duMry+9fZNq3E3lnxxjxtjBI5FQm4yx5kbaZ1j1IceUR9dDmIs/guRuDg4wcahhRdt0ad8wUOH4MLrmVyE894bH7kbcp3+/ONNylflc/MrCqkHG3THxw+p/dl7rwnGcPI2/kUcN7AhkKazyffKJt2+OM9JP++T/DJ54lCqWfYGTNj/QAbib6v67z/HIMW4c/+zZa03XIeNqHDMbhc+BLvLGY6Ks7jWhp+C1k6Kf0jDLE+wNI4/6e8Zvr71I+3V7E6+WGvYUwplJqRB9Rn7DF97b6PZ2I/5OHN+UGX+WxU07uABFWBvFApeFpQWvixDl4TJvCo8jIBG0wbkNezT7igMb3CS9oWm+TiLJO+cTmK1ufHxyEWaVEhKPS9DpMHO7LQzyWUgVSiL8dbWJAPD9AlEyqMW6XeV5b0Dqu9P21GjdjMuKBd42fHNMhE8n4Eo+VywWvnpzy9z6qsUavj+8aDQGf72okDKKjSPCWEb0qk2dlDlqUPrgASOC0IGHwLAxs8nidHrPFzyRBPYgU+CEkxAaRMHqKkcMm7EeKdDYSWMvQDvBW4pDJEf+9xnDs9buGiQbjR5ACqSbaeE3w3JqRQqlCQqsbGXU1yIg8DWOWI2E1FxlhY5HgnmgYv6nnJsxDuX7Je5pCoy8bruRtWUEUZG4hjOh5SKlXEochvoN23b1ECYe5wCQY96uEoXEzUpbjTbssOLqfSLDuIPJ4qpEAbzV+phC5fm2Gpwd5TJPwTh6uDj5cTuQpz2EnUYKmjxFP69BaIpSePBt9fr6CyGHajgR+h+ebgQyu40iZbvWz+LN+opj1EqLF1ybjeuic+RUAl8O7+zyv9ZD1y8SJ2nVEEeTE/6uRYdFmuuxHhs86wzuTMAwOEKHkdX5/CiNmIC/NQcNd7++2IL6uRl7ecuPkgN+/F/Fanj+fgzw2M+CSa42HSzz+LD/XRxx6WWq8NBCeOoiOMClvtAIm3IdoftDvriAMtZTnmIPofsS4PkaUc1nl8YxeulEod7Y/b0MpIa8al1MRn6aTnbXEYbIyoq3kckT7AsPagIzQEo+RvElDRP3QmUTNxmzi5PoRtMm8xH/vRbKoFtFxhXHwLDIi70IyYzIRDu0n8ia3+r4VxIGc/efMOZvwQlYjL/4O5e6xA238LjG9Tnrso0TFhFaickIyUJNH9BDhDaw2zd4nDhWVE6WSMs75aUOGbyni9y0e+01gOswq9307PPevovVxAvFfSvN4G62dtNkpQznLs0WblpQfewmRw7jG46ZNUI5xv42QizlItuUgGXglsbGrOmesIUb47UJdoyHg/3mvUQ/gea6BPZBVgYRoUtDp4MZVhGeiGi3usUgpbSMEwVTgGcgqQjk9n0dCZSragV5DKNmkBJJnqhUt8HVEncACJDi+7c9bPW4psdPEsBQjwbeG6ILwQyQs85HiTs98QLQhK0KCeBJSRD3EQYcJSCg1+h29yFC9x58tRDlY9cbbD4nTl7We+1eRsO0lWlzdhUJTSfEWEeVwXkM5ks3ICMnx95d4jB1IkM9FOTOT/P4CZMSd1JjTxhoXR0zLY0iwdhq+TmScJIPkHqIuWjHy9qad/mLjfirRci15nWYib9E6vwONMXAIslaajrsI3tnr57+KeGcFCk9uQMYMhLF3ChmPHYSxfxUyBIo87jvw8x/DJyo9VqVxNJ44mToDLl9JdFi4mWgTWEn0H05zTDwzligH1O/PlgI/9rs2Ii9htvF/C8rZTKHbTKSwO4iiwTOFh9NPw4QaojbaGmJdLDYuJpoWTxkft2oubPM8KhH9FxClaAr83i2IZ2YTXvxdyFBJYcQKP1dAGPZ5iM+GTLupHm8VyuN70N9PRGG5tYgX5sJlDYZpqcdejIylfsi4HhnoKb8rbfT6/HyKHsxDa+gF//0m4p1WP/tpVDMuiziE8hfGbxMyUOYQp7xrTY8TiMdbiL6889Ca2UxUBcgnNhI9RA29lKu42GO9a1hyDffjxEnle1GtunlEWLbfPYmXGn8d/j3FNE25g+tNswbgMRgogKzPEcX2O/xMlWm327SpZ6QbCHlIRjxv+G4j6pYOGv/zBMvfvQKfne3/ZxjHv+Xf3yaMycWIF/qIGqQF6ADJG4wU0C+dgwqa30ekdZQTZbwK/ews4fu95903fiJqp5k29P2MdCE5uwnGNBjGVuPt84xeo9cvfI3mAJ7v+vIYCZml6BThjxRy4lYk1HYz4tkZKWfRSdQ3S16hLmQIFRB5Z+8iI+FNInx7Ehljf+zvb0ILfxAJwSX+G8M0CcGHxxzrsWYShZFBQqOV8H4cRcLitN+ZQle3e05NyCA7Sex4i5ESqPY8dhoPPegE4/8G/CkyRlcy0gd0aA9k3KtSC1lXE6cQX0BC+rDnMhUZPJlIYbYaF+s8t1WEt+aA554M1mMopDgWKYPvIOWV578fIU5QriCS30Hh9in+uwop5z3oIEIuMhqWIGH+58Sp1ArkpZuJjO+UHnAD0Qe1Ehlsv2Ja/AXwq0igL0E0vZXoIb0FnQ7fblg6kbKZYRi3G855yCNxLfKapvBj8kzmE6H1ZEgUEKHRpPC2I69SF1KI3X5/PlJGTxnONtPoBs/xeaJ22wJkgNb4+SEi5DyE+Ogu4vDFkGlVgHhtt2mVQtlPECkVtxrmrHPwmYX4+B1ktKbn1hHFqq8QfO/th8u+QHSYWYY8WG3G23K0vl4gCpcPwHuvwmXlRBeGHf69H4ZOQcZLfiYX8e8y06UZeAlO58KETxIFol8jEv0rDP90YEs+Z8Z0Me4WopNPDRGqvA1tph40XhYhXp6J1kQ7Kvv0Z35/FzLubiPqgqYN43Eit7UO/q4LPltOlPBpNn3LiLaRTxpHyYP4acN+Pyoi3ujvrgJuXAk3bBppAfezTXDx+nNwn2+YS9Ca+D6SBWXws1fg4j0Ef9R7HksZ2VgdfAxmXek5ZCKj9aRxX4L46EHPYyNa5y0eK8fzSOkzu9DaSpurw4T8LUa1J1PYvg1teMo9ToPHfRvRfyKSM7uBYfinRvh31/u7RaYfxu1Txtd8z2EjMv6KEX+XIx54x/cfhIEfiZQ515s2tWg9PUyEjPMQP/ZfuBzA7/DR5gDew2gO4Md1jRqA57tuHhN5XdORwCtgZAfPWlT7rRsJmGxkjIxFiz/l53QgYfaHKIy3Chlh2UiZzkX5OYs97i6iqflEf57t91YQJ3WXolOWU5CgPYGEQSZRmqOSEGzTDV+dYZxvuOuQQdSHFOKzSMj1EXUMZyCFMx0piCuQx2SF51hDnHDsQQbNy0jZ7+HDjexTaGW84R4kjLAapCCqPIctRDL4IuPuM8ir1EiUQxlGArILGdVH0AnR5LHZ6XF6DHsdkYy9hGiDlxL/dyAPxinjag9SrMNIWA8Todecc+bThQT4JKLlUwUyWhMdWw3Lt5ASmutn+pAiWWw8zObD/XxnGkcTfF8yAA8QuYWPoXDaZKKjQjbyqhYgJXiT5nj2XhhzE9HFAmT43u0x1xgXj/rvesQXbYh36tFhlSbEVwXECcxkdN6BjPAsItSVjMMU+u5k5MQ60xFfv+jfm9GaGUuUnLkDebRuMGw3oGT9KmSU7vH416BNxIumwSPG/UTEVwvRWknfDxiP05BC7SBOqeZ6vnmGJW0k3ke8lYOU+0zTZK8/O014nvIQj/QQJ+VXGTfzUA7eXOJA1Qd+V0oryUF8kbxH/cj4yTPuhhAfrvHYj3iO+X6+BxmjpciI2EScKG9mpCYm3Wi9voE2ik3GTzEy3L7hdz5hGnUQfF2A5MREFMq/Gx0Q6UNr6i600VtqeE4Tfcg7EC885s8OIkOrAhllLyL+bEQb6lNoTTYT3ZW+iw7BlSA+aEL0LPffrZ5HStc57PHz0NrYafyMReviHc/n/zTu3ge+gGobvmHaXYrWQ53H3EJsmiuR/NlMlGHqJcohlcKZW2BcSiOq8M9zwuHQTyHjccNd7Xt2eM4HPX6x575+1AAcvX7xazQH8HzXTKR8k+DoQAKzl0jAnYkW8vtI8HcSwqwICZrlRLHnyf77LcJgS17FbkLpL0FKrprIc1lB5DYVI0VeiYR+JRKu1/qeLUgBliKF0oYEb7fHv8Lv/RJSLF1IYNYgAyp5k6YhgZu8mq1ELmMBUlJTkRIu9nvrkYF7BdG5Ypvvz0Werz3GbS9ROuMkUROwHAnnKiQ0q4k+wuOJkjiDxKGJFqKf63KkzHIZOTH47g/8/ZWmQ6d/NxIhwTvRipjssTuN+xnAU3D2WqJlVQqHnZsrWoY8uyuQIsqAsw+hqwEptrGmQwfiozbD3IsUdhfRPmsaUpLlxmMrUqjj0UYheXy+QeQ+NRn3BUQXi1zP4Sbja7FyOTtfMlwzDfvzRG3AlLPZZdja/Nkkw30RovcHyBNYgpRlypurRcbfBMPUThT+3WE4rvWYyXAcRjywgDhMstc0bDGd1iDe3otyEw8TfJVLtFibSfRiTblvew3zIJGQf5qRNd5yPzJekgIfS/D9DKL+3UniIECB6XWYCNuW+v2FxBqq58O9u3+f6GxylDgMk2fYTvvd/UTZoAKPW06ccs3077Te9xE8+wbRNSYdbDmBNq/ZRC3RtKnMJ1r+zScMDHsOuREZlruIIsTDROi8geCTOuLU6ja0lsp9715ic5zt+afN2T7DczMytgqIzXeKAlyBNmjZfl+m/74NbTz3GNcp1/NZxNNpc5xnfFzj71v9/j8iDO4yRNdVRPvDAbRGtiHP5/3G807DNZYo0XQb0cXpOqLUTgdaQ5VAM4y7ntgoTkLy3WHwjGeJfuGdaIOTNq95RIi7hwt6DX/EP6PXx3eNGoDnu2qJKvcHCc9MBlqkOUR4qBsZDSmJP53My0ACLdPf9xCJvj9GQn4r8ji0EvWiUqgrjfUOkUzfD+RB7/tIiLUQuSiNSDhmIUEznThEsRgJtEF/1o+8E01EUnSrx8kiEq17kNI87XuGPMYNSHDj++YSYfBGZEimnKRqf9ZEJNyfQLv4YmTM9RD163YSuVeHjecUei4kyorsRMou37C3eIxG33PY+GuAyz/pMRuQoj6JhHcyCucTSfcVSHEVI4G7TZ+NKSJCkYuInsATDVe935sUWq3KlJz+EZHcn0OUXDlEHN4pNK5yCe9T8qal0Hk30ZGgCBlAFf58PuKHfuRxaDUttsHA2/670Hj8A2AAJv2ycZS81XuIAwMvEnlK7xunVxGG3gq/O4X3GoyrZOjNQR7OnyADJYMwfMsQPzcSp5ZnI8/QHmQEvkWUFJkEP/keWofliC8madyf7TPeu4jQbI//TqkRtUS/WAxX8tJnMcLXpXcjQ7IKGVp5xnsZUYewSveykWgx1mv8TyaM5x6iKPC9iMceJPLKnkJ832WaTPE4Y43Xt4kTs4Meu8DPJQ//LMRDGR7rXbQuOvzsbEKGndSJaZahzWIxIwdS2IqMx0I+fPjmgOmY5p1yZn9C1M4cbximGPYS+Nn9yBs31rBOI3o4ZxK1DU8Zz9M8zrumcSlRAxViU5Hn7ycTKSp70Lp/0zibZdhmo3WZ1mfy+tUhfkib+j1EL/BOv2ep4Swleoj3IEMuG3m+O9A6P9dI70Obw6lEO8Me47DFYyaj822PMcPvnQpDj5uOc8+hRaN/kgxc7ndWE233Mrig19BH/DN6fXzXqAF4vmsqEha9SAg8AUe3E4WRu9FCfQUpxRQOKYfXfoAESxdxom82Ujaz/ewa4E4YegVxfS9SqjcQddhSWHYVcfowE2iDnHQ67QO/Y0ifcykSDm8atnYk8K83nIeR4fRjP3OASJQegLMnPf81SIEt9/fDROJ5BmHQ7EfKOt17uXHXjQT8DKRk3kUCdBqhyI4aBoc8aPD9VxC9RnuQ8N6NFOsyJBxnG67kUZrqZ+egWoQL/L4y4zXlY77u+wdRSCl5Mg4Av4uM7jtNj3Yip3I/Cj/mE8V0F6L8rgziJN7Vxk2DYZkGE54hQmRNSKCn3MciwpAoI045JwW1htg89HveIE9D2oi0IwX8AjJEepCH7nU9k/VFwiNaajxNQHUf7/RzZXB8q/EwHc5uRYZvFaJ5JjKOUrrB48Rp4APEKct5xEGORuNhquc2C3m3Ez/vRnx5BdEibgkyBl4znMXC8adLkNLvRrxoD/XF6wmv4SBSwEuMo+kes9KwVwtfZ+427sYjnp2KjJhyZAzNIDZDTf45SXRsSEZ5yh1MeFuL1tdBzz2t9XRYoh2twWH4p3Yiny1tNkGGaRcj9eD+/nbD9QHirRlok5E2g2kjUItofwLxaDkRqp8sOl7yFcOwlyiBkozd2xDPlCN5UYCMoT2e21G/4zoUzTiG0iwyCAP/A9Hg4vGIv7KJjiMY5h963Iv8XD2R4jCMeGEb0X86ecyfBf53j1MPfAvOPEeEgVN+4BBRkB7EX6VEB6QC4yOfMEZbkTd6Gdps5/LhvNl1iN92EIfTjiP61/u7K9CarEB8NRHxyRaCp3YQ9SG7ES98EnlUG4WGkU1oA+LZZuLEcwvhdZ5qPNUiOTZ6jV7/HdeoAXi+qxaOHkKC7hlgKUybiRbgYmCXEphZjhZ3PVr0N8J1HUh5DBC12ZqJxuKrkKDZ4b6Q85HwuBwJqT9BxmUJ8Mtl0A4nfoSEVAYSLCkn6ygjXi5K/V4nUZ99xXM5YBhqBe/ZV1Eu3Q6k3A4jwzHbJ/O2IAOtBAmvFPKdp3FHPHhV/u5aFA45hIRr8gwe9fhLkYHZjZRiKTIES5HBZaXItUQl/Uv8XAYSyCvg568StQkvJ/qNrvL8xxIG3nrCWzLbNAMZSouQAvo9JEgPE6eYZxgm/MyfECVUtniOW5BiLCG8dW8S4f0/9Wc3EqGkp/13LhLmySs8D9F8i995DUq4v8kwptys25GSOexnNiLFn+/x2xg5ScgNxk2+nz1GnG6+D9Ftiuc0INyyCi65m5EctTGPmT71yHjPJRRnF/BXqEfyQmQM1XtOhUSx5LZzcJC8wa8gPmg3Hq4gwvkVKAGoyDBm+L79iN8OE11ymo2jDsIo/iPjbpf/79C8uF7w/9MDGmfcAyg8nUKWTYb9WeNuKeHZWeh3jUWe0Cq05m8jOo0sR+s1HeqpNF73AL/psRqMwxLN8999hpHTwrT7vauRZyhtyG6B39hvnF6PDmYM6h1/k9Ilpho349EaLfBYM+D0A4jX2z3HQ8DTXkdfIMqITENG5euEN/7/IIzdWUAN/OxxokvGUbTOINIFDiPje7XnX4R4Jx8ZlT1oPbWYpilV4xtEnuFYGHoR8e8GwvNVbXrcwEh7wHHJG9aIwt2VaP01MpJjN1La6nailE+e71nv7zqRnDuEQsiXGw9VxsP3/HOTafjHBP/2E0XejxveQ2iDvY5Im2jzPKp8/zzP6fMo77EfrnicKIyej04d30O0D2ww7IdFD7qI9pwX8Brmo/P+jYaAP95r9BDI+a75Y2S81SHP3UXw3o/gspRAvgMloc9DAiPlKQ0iRdDre5JX6nkkGN9HO+NL/Z5aJBTKiFyhvcSOtgB5Mp7y34VowV+OjMi30cpZgE61XknU4Wr23wf9rmlE3+BtyLBZQ+RonSBOD+5Gwu5edOqsDAm8bKJbRznhYZiLFOhYpIgf97zf9/PP+d7xyLDaT7SMOoZOM97psQ/5818hDlmc0Dx/dj9cnEK27wh/LR9A6cPI43gpUiRHDP94JDRrkOAs9fez/f8kInl8s3GxjCjHswZ5yCpRtf5bTLOU8H2n722Dv38SfuMRJJhTyGmtnjnzGIx7nKgvuBHlCw75vgZCUT5kXOciJVJiuFMOVbPx1GxcFiDl8yLigxRCLCLyu46ZHo1+9/c8j+3IILvecHzV8633nAvRxqaGqE1Y5J+FSPF/xfe9SLQiGyJqLhYa13+F1sRS46UA8UvVOffvhqM/hWkpDzCFoSehdZFhmtYjw6GJUI6ZaK0+Z7zlEMWJs9A6uBmtkR1E3+IGIid1KjIy1qG1sp/wpt6E+PkziNe/SnQoOWka/LVxnMKwR4zbdUjRTzIuiwlPXINxtNzj3mVYZmicM/fCuEXIaLsK8V0m4uE6jzdMhC5TKL4ebarmo/WegzxO25Fh8Qbik0sRz29Ea6gD8U3lOTi9zXNYhrx4VegqNI4PGoYiz+tOw/AmsM890hfAwD4NOeZX0Jr/PSQrevzeU8RBm0NE/mOiZR3Rt7vQ71tAHJR4GM42wpgNRPSlDxme7xi/OcjQetTzW21cLkc810NsPp9AfJ548Shxwj+X6IveRZz+/QOiYkOr6d+A+OU7ftc2ojTSbMHNX6O1tp4Icacwezfi3U605o8h/bQDrbeDF+4QyJNEtP5/9OpHjuXRQyAfzzVqAJ7venCMBHkvUb6hgTitm40E0i5kBKZ8jhbipGcKC/YQDcnTOGsIQV5OdFLIQ0LlqN+xEAmVHxKHM0A75C1EM/JKJIBykLItJNotLSNCHruQcTLb96ccupSjNYOROmFnX4IxjxCttHYR7auuQAZTESGQy5ChknbPlefAmDwHdUSLvL1E3cEkCGf4mWyk2NZ6Tk8R3rLnidPE+USYsp4oj9GBBHEpEpQbPO9k4CbFMh8ZMZmCaaAeslKYstNwP0qUZOlExnc+kbuWRxj/s+DsTTBmpXG2nziNezUyIEBGRTrMUIh4JNPzO4V4bAcy2rL9jmT8pBB8CjOlvKNkNKa8r1YUSs0xvu73PZ8iCpt3Ie/HF5CRkwzMfKTUL/c8UsL+Hs1xxLhu8Hwq/XnCwwt+xwee6wd+33HPrwIpwduQQdJg+mYzknM5ErYuMr3KTINvGq9/S4TKksFegQyClHbRZlxNQ16pQbRZSfmYKV+zDa3JS4y/dYx0yuGA5/IcMgwS/yRjvo7I7SpDfDUerZGxxBobJDzadYShsdfwXkfUk6szbVL+YfKo3o74v4xYt/1+rh4ZmN0eYy8Rltxr3HYZzslEncqVnuM237PMv3d6XidMs4mIzhuIA1jFyMhLOcGTPGavYduDvM3vu7bqEsMyH9F+L+EFLUGyYAuSef3+ecvwTjJeMQ6bTK8BP9cLZ34A424jTlzXoI3CDOM0rbcOJBf2E8W4G4j6qekQ0S7TKNfzOYrW8xDRvzvP+Dvlz9cQLR6HiE4p/UQbPafyUOzx0mbzh8bxbMMzyfNM/LOd2CC0+rPDwOYLZwD+CR+tAfg1Rg3Aj+saDQGf72og8v96kLJIOTYdRD/UE2iBL/dz6YRcym8ZQgosEyn0wwjrm9CirkRCshcJJ/z5KiR0+n3vBCTs5hIFXQuI/MEcj9VCJPvnoV1xCRIuKZfnOiScDiGFcAORWD7Wz0xz3cO9huN9v2c8yn3ZbVjbkZEwH0mBpISrPPYywkiYYrxtMkzFnkcFMlCSgTuEduvJONype3/2jGlRZFx0ey4Znsthohh3D0q67/Y7KomCyruJfJ5O4ykXOAFZ85BCOI2E74tIQD+DdvpXEdcsIk+xGinLZhizyOPt99wrkOewg/DqXoLonjwF7YjWjcTBieRZSOFDEI2SZzebUHQr/Wyd37MNGUIFROu/B5Dyvs7vajDeFxDKq58Ik+PPXkf8dYoI6yVvyWIizNWEeCOFuw4h5ZxtfH6T6IvaTBhRZcgb3O3/h0yj14gk/Fak/A74nXcS7fIe9xzLkLH6uOlR6DlkebypRBu/Dn920r9PGd5apIQ7iHWcDPQVhLEI2gDNQAp5ASMeaSqIdmqHEO2LEe1PIK/SAeJAwn4inNzin9n+XYNoW46M2AMe4yBx8GaL3wnih0E/d/s5YybDNdG8y/eORxr3OWKTloHofANa88nbdeqc+Z3wu48jmdSCeGQ6UfA713Sw4ZY12Xgp8nu3onUAkkvbic4sdYShdrXvSTLWmzU+IDqR5ALXua1lJSM1CVmEDOZuj5Xr+S8yvIuJ/s25aONSjeROt3GYPN4DiN5FaB0WEnmeS42rKrQGkneykTi1nY28rIeNx2Tk5xmfKRVhmDggtZfYMNciQ7iX6MxScg5eLtD1UYV/08/o9fFdowbg+a5MtMh6kHAoRsJtKlFo+E5kIDQgIZOSrzuIKv5VSJEXI8VRStR2W04IyeUebzrRCit5C7r9jqR8KpDQGut3ZiFh8jZxEm0ACSWQkPgSEjZVfuZuj9VMVMRPeVd7kIIpJk41jkUhyyoUMklCsdjvGyJqXy1FBlY3IRyHkBFV6XkeQsLzQaAfTvwACbxuIqfrISRo3xYuL/6K59BF1Ps74Hf1EyeEG5ASf9a4LyHCdJlI+aRk+6Q8uggD+lKivEsJI6enh+4iQo4nkJLdYBhBddIqPc/HhLt3jhGlhOYTHoydSOkVEknj5UjZZyGD6ZDmnfqD0omMuPEoDFng911jOl1PeKVu9ZznEm3NBv3OlQQ/3mfYdiLavonCVM8a75uzhMPphrsMGQI1yGjYSORSVRuvrxAlcvrRGtlFpE5kQ8vDRAitjvBGNqA8vvko5DXWzzYgQ2A+kWN3AK2FZKRnE4r3yDmw/oQonZOJFOhcosvN3HPev5zosjIDGXbLPP5htHZKED/PMD73m0ZfJ4pNv00cYNiHjMXdomPnSeNlDXHqtNH02Wc4axAPVXkODZ5Dn2mbgzaJRUQpp2TsTyEM837PbytRhijTnyW+uR2leswlSjdtQZuI9z2n5A09ifjhIf//VbSGX0SbmD2G6SHTo9z3DSEPdEpbSOHqN5GX/3XTpcvwT0V8sZsPe/dzjaslaCMyk/Bab0ab0SYkB4qFg+P70abggOeY5zHweMdQGkgBknGDhMf7CcRTO4y/AsH43jDi/QeMr7fg7NtEsegKvycH6YYcRvIcf/Y9omZiDZH33E+UsOlCvFNG1Etd5jk8iuTFdtP1WUav0eu/6xo1AM9z7X0VKcpcJJhOI8Fch4RWGVp4W1EO1Gq0+FcD/TDwDFIM+9FCXYYMpckomftpf5cNHIH3bmek5Mm7j/nv64nckqv97DVIMLxFlLWYbljHI4G5He2O9xmmh5DyLSQ8BQ+gwwpPE+HFKqRg3iXqEa4mPBKbiMbsyfuTDmCknXHyPF4EFMOZFw1fKcqhSkIshay7gRVQuN54BSnQryLBmU0ksg8ajuTtuQUJ9BSiGUS5bd/2XFuQIrnf+KlCSufLSOkdQAp/NTJeSpF3rIco+tsE1MPhlZDxFlKK3YbzauMvF9H0FFEzbbc+u+IuolzPa8hreIdhWYl4Ih3SKEV0vxop2uuRQriLKFKbjK63iJ7FB0yD/4iMuaQ8bibSCnqRsTIfGYT5xukqZMzmGtcTTMs8w1ExoPE/8FgbiHy9eabjQhSqbkYelG7kLbvLzw0hhdqCjJ5WKC33HL+DFHUH4c0oIw7IrDFsLcb5FuBzptEjKMyfeGQOkQaxG/ht/7+M6Naz2Pf3ILp3mV4pj/JRom7gC0j5ftOwZHi++9G6ryfKcTht4vRLxGn7LWjtzSOS+7th0vNEQegmxAt3Ie/lQ6bJIiKVZD2SPXmmWZ3f+yjiuWsNb6Fx2kiUoOn386XGX4b/ryHKS6XNWj/i/wGiN/OViL5FiMduQXLlKrS2bjQc0/1OfE/yVO9CvJZy1m4jCkNPNqxFen5oO9Gveivi/QLjYbnp0UR4U1OeYfLWliEZsgUZtOUC55KvwNE9SA4UEyH3NM4UVJdxkXGykzAS8/zee9Fm4LBguqwIBt4yDgr1/ZibiIM9+abJF855zxxgLVz8GOLLI3BiF6J/IdpELEI8lo/WajP03oHkTqZp+nUkxw8zcqjoQl7DH/HP6PXxXaM5gOe7vjEmFHM/Uja3EIK2EgnTbEIBFxCHPoaJ5P7JRGu2sSiUk3J4kgfqeqRsWpBwzkJCeBHavd6PFHXyVKV8nVYknJ43rM8h795i3/ckMjiGkLFQ5GfKkfL4MyT0h4hemrlEPleZ55I8j3lEIvQxz3m8xzxgXJX5+QqiFdV1yAAqQ16FSrRjf4g4sJK8AxVIkI5HyioJxcFzYJlMnG6cYVy1IkXUinb0yaO6yTQrNzzVfu45ZKg0I2OnlSjrsNrfJ0/NXqJI83LoXQ45Kw3bQeNhg8dL4beU+5mBhPViYCO0vA2lN6kQ86TPEeH7DNP5tw1vPrz3EFz2RWT4/67HyCNSD6Yiz+BJOL0HJiwgwuuHiBylaj8DkbN2wDjsJPgq0X0X8sy8bTqcMEyHjJsSotXcZqJVFabPg8Shnwy/62bTpZuoKbgJGfCFyPP6PjqJmTxAt/o9JR7nm6b1NOBb4+Hbfbr3TaSwF6M1109snqYT/ZMne5ytwH8y7Mngn0IYVsuQoTMWrbNDojt5iHe6CO/WHsQHtcjYywT+Efgbwkt3yPCNR50lLvcciggP9T6iHErKbbXROHLKe8j/F3kOU9G6eIA4pX7U780xvjPQZqMLber2eQ41iE9WE5uWGcRBs43o8MRav+dK4Dn4vw7Br99BGO49RK5jSod4BMnBKs95heefDpJMMZ1q/b6pRJ/h8Ya1mcjFW2l4lqITtmUET25Ea2+Px6j3e2cgPjjIh/OriwxP2kDnoTD4Us9hEbFZ/A7Bv6uIPNvnkeG9xTi40/O7iCiDdQhtRl/22FsNayniw68SbSS7iBPZQ0S+bcoTXGwaHTVdFyF++D6Sw8cvXA7gH/HR5gB+k9EcwI/rGjUAz3fdM0YCvg8t+AakQBagRXkIcf1k/56EjIAFaFFXEQVCO9AiLiXyB/ci5ZlPhGrzkQcmH+1Cr/M4T/j3ZMPQ5J9hJITykACpR8bWzUS7oLmE0TqPCLMm5dvjz1YjYbwCKY1JxOm+ao8/DxmmbxjeIx4nCyngLxuuJCzXIOUx3Z9lEAcYLvJcVxtfKfQ21p+XIwH8CvK+LvTPU0Rdt3NDmesNS/JAdvj9EAr+W8igaEQ07TTMNcRJ0TlI6M8lkrNnGpY+j3MaGUZ3IGVaQoQze4h0gX1+LuW9pauRKNFSixTSNZ7DasRPNsjfexIu+wpxyKjJMFUhvtnkdyxFnrFqIjz/OrAAzj4DY+5CdO8lWgbm+O9Ez2K/4wTRonCYKMOR8l9biQ1L8gZvMD3aDXsp4vGpiKZ9evbsczBmusc/blzMRYptKlG4/EXBzizj6EqiW02Fxyvy+zL92SBx4vJxxIPpIMByIokfdKL7AcJYnE9sJmo81xsQ385A/D2M+DYPrbHNhOabjTYyU4yXzX7fXYazFG2AhgmjqYooKp1SRZr9/EzEE+lAzDa0Bgf9nt/zvUv9bK3h/D4Rys4hDMhuzzF5YzM9zg7DvsLvKDU+Wgx/l7/PJDZ3KceyyP+fNA4mI55fTtQJTRuWpUiuFKE1XWD4k4w9TRSHrkbybxito21IFs4E2uD0/TBhNdpUzSW6KjUSp5czkMGUgzbIh5G3cgdR0HoHcfXAwOvuWT4Nre2vEYXzv0OcuJ9N5GZfY1y1E4d6thEyP+UAFhk3s9E6SV7gtDlp97NHCIOw3HB3oFzJYiLkvMnfT0Abpy9eOAPwG3y0BuCjjBqAH9c1GgI+37UL7eIz0GKbgRRW8uxV+Wch4T0Z7+/Srr4dCYBqohVaNlL+s5BAm+3Pcoh8u+nAX/i5bR77UcLgKvD9JX62EimeTiTAG5BwXYjKPTSi3Wc3UcQ6GWOlSPEkb+Y64kRqLXHooYM4dDKIhH4VUei1yuOuRMok2/8fQLvkYSTAFnmOHyBl+pRx+zZRb7DJOEjeQ/zO+/x5E3R2+b4WJCRXAFfAez82rCsJb0kX0cZryGN0Gke1xEm9i3z/Jf6/AhmRhcgYSKGZK5DhfYQ41X2AOGhQZzz2+rsh06LU42YQPXELkSLeTPT73cRIl5DL8hGt+4yDaURLvz1Ekd1GwkM723icKLjGXIuUbzq80AS93zONcn1/CiMlfPX6/0xEs8MErUvQZuM1ZLS+ZTqkkHTyPHYQJWkM15gUMp6Hcv1Svuwy46UVGQjFyLORDh3MJcKSKdcwKdx5REuw54ji0ydMkwf8nsmItof8dytR9+5Nw95ChGx3+/+fIP7K9fMdyJA8TBhBe/19H3GSd5W/24No+gFxeCIf8VOe31eO+CmHKK/UwYe7QFyPPNXrUZpHBuE5zyFK2/QTJXiakHF+DIUy64gwcgvRBtD5iSNrPEU+Umh3k99RjIyjyf6/0TSYg67ZhKdqsWF4hyin8pLpOVbvGNjusVJovhjJuhlIpjUiPjlq+m6ysZFk3RBxSGaBn0u5ehmEp7oBGZUveC7H0Cb3LkbyPzPw+1t8Hx7jFKJ7KXFYKL2jAW0cTxD5t5d4jsOI9xYTm/084zof8VSx33HnObha5s+zPXYeonvKh8Xv+QDR9y0u6DX0Ef+MXh/fNWoAnu/KRQKjBy3gWiQAepHAziW6FyxipE4deWjhbyIOeKxHAqMbWAdDzxCnKKcSuTrdSABs9/Mgw20TcYIx2z9NhulZtCN8m/BCjUe7wnpkBMz1+E95vFmGpxkpoZREvZMIj1QQhaabPUarYWhDBtB0RroMjIR8+5FATuHpVUS+WIXhPYyEew/hRc1EYbEuj3fS+F5FKO8SRsq3TDqEBGXKuckGNsNlDxP1DH+MhPWlRKHn+7L0zI1ImPcYz0kBJXr0oHylcs/zi4Z9D1HN/zPGw2LfP4w8fSl3LcPvHCIM0DLffz9SxBORAl3pz7uIfKtJ/myl53/inOfrNN+RkNZ3/I6JhIfpZaQ85xIHfGbru5w7DGs3MkIv8dgpKX0DkWO1lTiQdIPf1+U53grHn0UemjbEM81IySVDpgVtZJ7yO5oIJfjnxuku46fWn88z3U54nu2It6r8ngRnOoAxSJTLWIsUbyky5E4SOXV4HolnhtEavYQwagqRQQ/i62zDbSPizOvEoZNaz/EKoutHkd87dA7e5nmMTLQpq0aypAl5oa/zc+uIdnZbCA9fMije93urjY8tyGAe8PMPEEZ8sfFcQay/buCgyqUwjPizgfD8NRCn/lcQNTi9vthsGGuRYZyMxSNI9mEc3knk8rV6zscRL/cY32sg6yEibzXPMCzXvAa+53uvJozhpZB1G1obb/rZ14l8yZS3PB2l7NQQG73dRM7cgH+OMbI57ccwFxEbrsme42xik1FGrKcUJanyO/uMg2R8ricM+EpGSvGceYmoRLDEY5wkchQr/TvlBWK6HDWu5xPF55cweo1e/13XaAj4fNeXx8SJzZNI2TejBV5OFLhtR8bMSaLMwLeQQJ+NlEM+Un63ISHSgxRLGRJ6G5GwnQish6M/gmkpdHQUCYl6pLTsKTr+I7hkrd/ZTbQ7ewDl31yCjJxlnoMVZO/rkLOf6B86HR2cuMVw1RnGQqKg9A+Q960JCaM96KRrM/Cli4Ffg6v2CdZSf16NQotXIUG2BMiBoecg422Ue/g+EvbpFG0FEmgbiSLRVUiI9yODrQXt2rOI03b7kAfiOaJt2R6PsdjzuQcZSSmsu8S/yzzH5O2oMk3mGo6U73UXEtITiFPRRcijMdZ47oDO1TDpdePxMJF7uIUwBmuQYL8BKebNiB82Eaeph4iTguVolz/W+HyDKB6+GymwsYgHUoh2ifB79mEYc59p0gVH98G0K1Fu1y4ixDjb+HwfKb1uVD/tIeI06zTT6nFkIPaLphQZ9pWeZxXi9RcIA/tOz3s6Cu8OIIOvh2hblgzPlUSNyBuJHtApZJiBjNsqlBB/n+9PYdQniRP2fWh95pke1YY1z/g97HcXEIoaouXf+6ZlCnlXIN7qJw7rHCb4qhrxSZtptYAwBjaJBiMnV5PhegLx/VyiPtxsP5eHTrT3wNDbkHEb8n5VAQfgzI9g3BcQze8wvL9iGLM9n0wivWLQf680fZ70e3Z5ng/53Y8gI6SBCMt3EIfB5hGh6odRke9tRN7dNsO4B8mWe4mNcx4RTi4VHEP7IGMmWq+PAb/hZwYZqb/6d/Pgs3cTXWN2eI69lmt3Gd9NKMQ6y3jP93vaieLoG9EGZhDxVbfpfkjjjWwu+8/B416/dxXwm8vh5a3CGdC71u05TxJpEMkLnjYvhURTgEqiJegD/v8GomNLvn9Aa6wK+B3fX4bW4THi8NXGCxcCfpCPNgT8BKMh4I/rGjUAz3fdP0ZeteVETaqJhGdvP1I2pUio/w3wh0Rnga8SzcrHEzXF7iKMwPVIYCYj8hSRH5h2wnuQsM4hisw+hYTFQf9uQTvio/7dQuSJdRE73ieRV+VGwqu2FciHf/ox/LsSJNAtuHv3Qc4zhu15w3AQCenlSGls1px674ecJZ5bNpFvOICU8GzDNNlzn+p7U5h7FhFCLwb+AHp/CjlriXDpIBLoR4iag5lIAecSzdvv1ZxGTqzeijw6kzzv5UTo7ARxAvgPDPNN6BBDo2lRjwQ0SBHUE0XBn/DvRUAeDF0PGc8j5VRKeIVfI3LC9iHeWOT5fKCyKOOBi79g3GAcPk6csN5NKJIilGD+257TNZ5zI1HbMO+cv5PRuxp5p+7y/DtNx91I6Y0lPIHViCc7idI0l5tONii5HnlG+4m8xhqiQPZOxE9TCX5IvHoF4tUm4iDSCeOpnOjgUkeUKFmHlGAPWhdHjKtcz/G4x81EYe9GxA8L4ewSGJOJvK+rEW33w8BLkDUHra1bkSH9hMe8xn+ntIhitA4aPN9sw/sZJBvShmQAbRauNe4a/He78ZCvd3O1cdxM9IhdYjxuNa4Pow3dbOOx33ha7Wc/Pxb+dDgO3VQRHVk6CeM5ExnibXrXwCuQtYmRw2IHN8CsB4z7/aZvD+LRMs8tpRpsQvIwz2Pfi9b5C6Lv0LOQ8Sm0eWxHOYuPEa3bZhqutYb5gGmM//666W4P49lXYcy9nns7I23jRtZmhufXgDYubcbxHsKjOkikT5zyPFJOafK4ziZSR64wHlNqymxk4B30HPah9dlhuFpN4/eJvMnV6HT+jUgGTIOhx90CdDKSeweAF4rgP7erXNck03el4ftjv3MW8vrVIpkzZFp/EWgbNQBHr1/8+qULDcC/2SspwQNIGJQigXwzI8V8f/IqfLoH7YCvQgJrB1KIB5HASYpmkKjE34AUV6N3rgv8vpWEwEoeryokWGcgQVOPdqBthEFYTHhaFqFjVD3EIZAWf3et378U7YAzkJC8xMbfQ0jAeRebs4DIc+knaoFtIw5f5OldOY8TRkQJCktNRkriEJFjVo5CNvVE4d8O5OUoRt4sG3M5ECUv9iClkTwukzzmcSQMNxM9XPv8U+n7DhFdHy5BBnQhofD3MhIePfwWzFh/Dq1SyL3KdO1FnrBcInzahYzNOZCxjjAMThAJ9q8hXthjGk0x/nKB9VD6K0hJdBruWtOwz58VMXIymEFkHD6BjNlnUOrAINoQbDaMk5FxtZAI760xHdcRXsYC47aXCIGDUhFKPPeUkzaD8HilEOfTKGHeoTQe9LvKPGbC9WtEl4liv/e0cXTA716FPLU3oAMNjf4sed0LTM9M4W3EIF5ChGD3IZ4oJTZoB5x/mOjWyEhuYtZtRBmRRuMvC3ld30c8lAzb5C0daxplIkOhlDBCxyOvVx9aK/1EDcZZRHHm6YbtEDrk0kLkGbej9ZpDeKc2IeP1UmQI1OMCwcOixbWGKR1cqSZqLPYhHnqNkSLiWVcTqSHFMOtawgNVgrxLNX534schtFbz0FrqNT23+h3FwlHGJrSxWE/kbv6jcZA2t/2ebw/yZlV73nORMTbRdCiDMV/0d58xfrMMU7XxU0HUaaxFayuDqOV50u9sM06Weh7z0JpsR/K3znNK9OwkejPvMy5TWP4pRN8/ITbmp9C6S3nBide7EW8NQ0ZK6Zjl+XcDf98u/M1H6+AniP+KPZ8O5GBYRLSQy0Oe2jIu6DXMR5e7N/wRjTN6/X+7RnMAz3e9gnbwKdTbhpTnWLT4TsKn5xG5etVIkM1Ggi4Jw2T8lRAtra5ARsFDkDOdCDd9Eyn/sb7/dj4cjmwlFGoPUaC2lQgJfJNIWC5AwiST6GgwFyn/lO+2jGgNtwMJrRoiDHmQ6NM5FQmg65BgTx6tIqKOXbPH7/bPYr+jy7B2IeVY7bnOJUKXw0RP1xNIqE8l6qmlsPpXiTISjYb308ZD8lY0Ge/vGI5MPzuTaCmXgTw1yVPQAzOmEyGWLqSw5viexciYupboNNGDFHK1cfasabaSqCe3DRk0pUS5ndlIub/tz2cIzvdeIrqsvIVCWkuJkkINhEeu3DhbQpQZgdi8dCBludn02Yp44iji25S2kHBRRHSSKEYGyT4+3GqwGBlEc/37IFG/r8V4O4C8b+3GWavpeA3inxbDmYmU4EKiuPk6w1WElOw8wntT6ucWEHl21cZXgnMuonHyJpcb5hrkrRxLGHnTiRaIe33Pa4ju/f68xXCtIHK/+oz/m9FaO+5515sWjUQ7wwqk1PuIgxUg+ZLyKW8yHMWe4wGiOHAyOG1YjRi504iC5K8hvqjxOw74/U3EBqWb8Gj2GQ9zPcYuImQ5zXjJJdZk8nA9i3hoEdrMpJzGmcBGOPss0W5tj+FOxmxKVVnquW70e23gjZSpSRuucsJTOoGoAZoM8mR85xmHLcZ7hu/NJHKrs5CMK0P8lofWUHpXEVoXaaM1CfFzPuL7VqIIdbnHSet5LpE6sJQoBVZCHPBb7GdmeswOv6PZ+MlAaQz1RBeXJLeXeg5F/iwPHVarR3xfbJpdwGvoI/4ZvT6+a9QAPN+1Cu3KM/1/t3/XEacPq5BAKEShrmpgMvQ+i4TCCaJ91laiOXkdUXw3Hy3gY0igTES7+qQohpDgv42oJ7bZp+dqGckzHHgFCa0eZByVnPPuG/y+Yr8v5UK94/+H4ewrhBFzFBVm/THRLWQQCehkKCxFhk8fMmJmEKu3msj12YcMoL3EoZekELqQAdbh544jxV3tn/nEScmThNejhQ+HGRuJHKUCIpSYfc5PhubJFqLsSz+8t9Q4S167SqQwEi2uI7xkHb6vzzg8bjx3EKVUhhGde4z3GUR9w1pE6ybkLehC/DWdEa/bZZ8TPnufQd6xbt/bZFzlEu2z3iLqNt7h3zuQQdVBtJpL3ucbUO7l7cZdJwr1NRGHH+r5cC7XPj/f7s//kKgn2IT4+gGPc5XnUgz8AAYeN94TPkr8N+fQaSzhgfnAeM9DKQcnTYv9RDj0Qc/jGrQJOYY2BF3GbbbHSEbFJCKX7RW/uwR6X0K8PZMo9dHtMVI4dhHyuG1FRlUR4p8W0+Y4MgxX+b0PoYNHa6Hzp8hD22hY1hBtw3pNrx0obzVtjJaaJikVIx0cmyVYTtQbn2l9LPYzZUQO8LDnnOaVSbSWzESGVwvioX7jPgPxTqHpUkwY3o947vuREXW/cfiKYavXsz9/34WQ2xEvtXlObYhHC4me5yuBVjj+CCqin4zjQcO0CK2vOkTjlwlvai5av8eJA3bb/Y7VSL5c7zm1IH6bbLoeIryFKf80bfBL/H86KHKI6N5zl2GfLBjOLDUOyonDPa2I/79P1OpLcz9EpDHMRRuYGrSOJhqWHP/fT+Q/L0W80UGsk7WeS56/X0qUNhq9Rq9f8Bo1AM937UA7z+vQ4hxCQmgrUbD0XmLnPQsYqw4iOavRIm5AC7wGKYBhFCrci5TbVqImVy8S1M8Rhxc2E22ZdhAhjjLIups4FZwPWZ+Cs/cio3QZEmopdPodpCR7/O4CJByLPcZBHxRIu/cb9HfGOpQzVGR4itCufYvHusbvGIsEbR8SYpkotArRgmzQeNzieRd5Pts1H64hEvP3e6wWpHzy/dyTKMeonSjAfS0MdCEDOYWl9xBxiX7jths232F4bmSk5MdlzyGFsAgpyVuJrghZft9WZITNRsL8Wb9rKyNt4phqGq3w/4Wm3xaf+t5BeFLajOO7/PywP9vreY2FnEWIp271vXORUVaPDPwCP3ur8HX2fqJl2wnDON40SOHw00ih/Z5x2kZ4lw8YrzOINl1vECHEAuDP4ex/9b25RPmYDP8ej4yPbOAOhxj3eJ7FxCGK5CWsM66me+75iC9mIt7YZhpO9ud7kIfwt/39fsLL8xnhbe8raKNQavr8DQpPp7D2MFAOOekQQ7nhm4rWzhd831rRjiPooE8f4os84tBIMzrw5bzLn9yOjIcZMOk5GDrlsfbo87M/Mk2Hie4lC4yLTtNlsd8xAeXMHUY8uB8KbyNCgF8i1mEf2ly0+357D3s3EAZZ8sBlIH6ag4yWQXizHRncuX62zLj7MvLWz0d5dVeZdhsIb90SoAY+8QXilOtBJIPe8JjzEZ/lwMDDpuutcMmnCGN1NrEJW4c2r2XQchLJzlvQGt9N5GQ3o7XQbHrfYdh/j+j+kubchPhih9//LBGa7tC9773qOR4j8iyfRTmWtYyUfBm3yOPVEd7BPOLQ1zDi2ZVEXmoKSyc5/xhaLycR/7f5nhbEWy/8P2j6Cjo8tZQoFJ1Cwym95gJdwx/xz+j18V2jh0DOd1WMkQGQQoIriJ6sDYT37n2knMYSNbJmE4u7gtgRLkU73h60S00J3XlICGYjg68MGRovIAE1RHQlKTBM1yFh/BkkLFOu4jTiIEYTEjjHgInw3n+Fy8YSraee9d8tHmspUlYPEd7GOs91OVGgNw8ZCOVIQW6DgVbIegQJ6Lme/xbPZeo5sB0g+pMeRMqrgNj9X0vUS1uIhHaC5RDa5T+BBHXC+z4iSbtRz3bug0lziDIKw8btaqLd3QeGb47p04LyEwtQcn6Pn+vW3yc2WAmnMPUyw3zUf69HRuRO4vBEA9E3OtG2jjgF+pDnXoAEfcoX6/L3NyGFu5TgtzeQoZa8o/s9x0aituI0QiEfIOo0Jm9kMmCS9/Mp4D2inl8TUUuuyPctQQq70/PdRvR/vY04CZtr2pb63nuIXLIORpQ7dxvfO5BxmuN3VpkmbyN+WuCxdgIr4GePwcX3eQ7b0Xr4snH9RUTjCuLwRvKWbUU8ktZvNmHIJu9p8hgPojW5iSgh8w5K3+g0fkqJepcJX2uQZ3Y5cUhpKVoL/Yj2yxBv1xr3Y4lT7fsN6w4kTy4l6u1digzITsOXwqAVyEi9CRlP1/j7lSg/OeUePmScJ+9ryhtOOaLbkId1C7G+C4iOKNOQx2sH4okUIk6ftRG5dLOJSEY1kUqyFRlt2cbXzTB0N2RcBPylYSgjUkpKiHI2KcVhuv9ehuTD60Q0YjHhRatFPJQ24leYFs1o07MVeWxPGN5bTLun/N7/m733j++6vO7+n5A7ISYLRNJIvsFIFslCM7jRDKRQbAZN5YtjMpRbv1SH5avFetvb6ajOzmldva1W67RWq2NSKlOp3lRuLIWhDG5KCk2hYAZLyYIZNJI7mBIDWWJMFvj+8XpdOfjY2K/byrpv3o/H55FP3p/3+7rOda5znXOuc851zja0zqZ47MeQ4jYa8a8UkpFJZArY7D72GN8P+/sEZMGfhDaGFxmnucA+eHMnXLiYSOmyESmRrxl/o4h0XV1uowpZZF8F/vuHL8bTIZDfR2zng7jeQ2fbfhGHQEpLSzl8+PD77j344IPcddddZ3ynt7eXZcuW8Z3vfIf33nuPOXPm8M1vfpMxY8Z8oLCdrWtIATzTVTos4j2OIkb4CmKQyRo3h1DithG5xTqJfFHNRFqAKsQwdiMBsZ6op/pTPzOZiGErRDvz2UiRyyKsU8nakgK+u4l6vvvczh7E2B4l3MDrCFddCmrfgWLojhCB7LnECdvLDU85EjCLiFO28wkFaCORJLgcWVMWIeZVihhsUjpzEUNN7vQuxDzzDXuF4XjUYy31GBMzPULE9GwgXHe9hm0PUsZKkQvpU34nCeUCfx+BmGgDUq5GEZal9EnxSskllxSxLcZ9sqP7ZGHfPZCVXDuzkAI+2vO71zgf43G+igRMPaE87UH0cdJ4OUJsFDKRReXZ08Y/zm0fJ9ytiS4fI5JZNxEn2JcQCuGNKMXE/UgAdiOlZysRMF+IlKAmRFP9HnOn+x6LFIeLkWLwMY/rYWIdgWjpSaLqwyuoJvU24lDKHcDPET3UITqpN5ztfn82UeGji4ivTGur0XgrQApHLYPhDoNjKCCq/OwyLiuI2LrdSPkoI8qO1fj5XCTQs06DexVSwlYgBXYNKvuWh9KanPT9BHub35+LeMwBRMdJ+RhNKGApHq2IwQ0JJw1bjuEZ4fcfRvO73HOZlPzjHl8OUizG+X692yx3eyuQgrfY7aZ4vi2IRmcarvROv/s4PTRghvu/m6gUM4nIbVeFaKDb+P6Uv7cQG6WxSLFrRkpQo/tKYRhprXe47zqU0uoBtBFPvLbRY3qMwbQyA0ch4wnj8A3ioN10pBi2uf8ThGt8i2H9U7QOfxMpcpP920m0VjqJik8V/v0t91NJVNcZ574mE7W5k7cpuftbEK/uJZLIX2vclhveV4Hnzp4C+Ht8sArg1/nFKYA33HADn/3sZwfv5eXlkZube8Z3br75Zr7//e/z7W9/m1GjRvH5z3+e4cOH88Mf/vADhe1sXUOngM90TcOp4ZFVqhkpFOOQ4peDFmAHov55RFB9DmIgXUiBqEVMvsLtdSDFagpiMkkpOUEcxshGzGAqIWzfJFIgZBAWgIfd/uzT2l9LlERLilyP4bwZMeYUu9JgOBJ8+ernwFMw4VGiZNxMJAALkWKQYquqkLDLJ3JWVSOmW0Ew7fXIcrkGMcIyxOhaCPfGDiKxasJDn3F8PlI0GzwnjYSbsw65qZoRoy1FjLsNKR3ZRF7GLLdVRlh51hMumzIi39xheLMHLrzH+O03vFmEcnCIiHnshqzzPX4M4yMMptuhyDjqJQTNFX7/Kb9TSNT5LCDKnCUBsQlZFL+MhEmy1F1MnBbGuElu/nxCUbgaWU6OE8l5pxJ1q/sRLTe7rX4G860NJiSv9v91RELtMkRjpYiWHyBobCFR8aSXqK+ajZTuFiLGrhj4JFIMK5AwvxspBlMMww7PRwr6T5aYbiK9TbJ65hFJtjs9X8kl2oNoepRx8yxSsHe7vas9jmvc9ibPzxPGQbb/T9b540hg13reVhkXUwnFvJmo5Xwhse72oHWaXOzFHtdMYmOUgTZxtxvWl4jDMbcR4RF7kBW7gdjQ5PpvO5G7dKbhGDBsxX5vrNsoMKyHjM9rjNudvpcs97WE1XQ2orsxSBnMdLsXu99ET/XIalaP1ka3YZrkcVzjvx1EWc20Gb7bc5DmcoLncTmxCU1yfZTnYQc6uDMaMtrd7mrj4Dr3sco46/a7adPeYbinok1LLZrvNHcliGZeMc5GEcnwexFdZBPr8U/RpiuTKC1aQ6zldYZ1EVFlJB/RxV4ifcy402AYuv7ZKy8vj6Kion/+QaSErlixghdffJHZsyVcV65cyUc/+lF+9KMf8bGPfewXCeqHcg3/5x/5/+lVTQQPdxCpRJYjxngULb60U+yNV089gxSVTsRgliGlawUSqNlu7zhiFDuRcE4CowoxmB2IaY4g3GO9iClM8ScLMddmpAS+hBhWBdqVZiGB1kjsTJfDrtVImHWinFtLECMa8DMNHlK9+jzVaJh7iDrHiQFmuv1pxtlw42gRYuYphuxCIpXMMT87xUgr9ud2BvOUMQ4x5ioiBclzSOlY7d8hkv1uIFyPYwjrRw5SsjIQU52OtpozjPvjSDHFYyzw70V6/8IVxmFSMDKQMp6sIP1IeKa5vZ8QcvnG8w5EM9sM5x4itUqG372BcAumE35bCItql+fHcXvvdCDl4nykPCc6bCfKRiWFaTKRX7ADWehK4NDfIkV0FqH0tRMVYvb5nUwiKfVqj2cnUXZundtISmGh560GCdiNRKD/YaRU5SPra637nWRclCML1Rrj4QVE29MMSxEcSRbYFFe1nXCLbyOsJXVIsX4S0dw+oqpPNqFMZridOw3bUmJDUUjEbiYY7yOUhOmI/jORFe8a3x+J1sBRz9lkfw55rqr8XAmh8JWhtZiP1kA22pxsQnFgrxAny3M9R7f6/k5Ej3WID/S7nxkeRwnQBm9/G/Gn5KZtcZ+TCXd/WrML0PopJjaqKeyhCM3tncRhpmvQXG8z/vA4Dvv5J3xvNOJ93Witpli2PLd9zGM8onykPG6Y9qFNz0qNhTmekx1APby5kVgvxxD934g2heWGpYiwOM5FtNCF8oAeICq77IQf3o1oaRyRAuuo2zhuHI83LMfg3Z96XHP8fCVx+K4GWAGn7ve8NKG1eBORezFZ8ycZ5z5kw1pkiS1GXpgp/p5L5Cg9S9cvUwzgQw89REFBARdffDGPPPIIf//3f3/GZ3/yk5/Q399PTU3N4L0JEyZwwQUXsHPnzl8wpB/ONaQAnulai5S9bYhBLkCM7260aIcjwbLJz+9kMPXAsMsQI06Wngq300+4fRcgU/5JxDzaiULl6xFzeB4xvuNEMt06f54nSlY9hBj4WsJKsh8xod8jEtiOQYy4yzxjOGI43UioZyJ3ZS5QAxelOLZrYNjjSBAc8XNrcrQLbTOsdxFKSoqbSafWqhEjfAUx8QGkmLYSCWfvJOKw/hi5n8YihS4dMkjuy+mIqecS9Um/gKwDZUSMYQGRW+xzxCGUBuP5vwKvQ99q2PUEUX+zHSndm4iUKe1EualCt9lApPZZi+hkjeel23ioQ1cTEvyTPVd3+28WoouViN6WC98UIqXzz+HtnSgZ7DG3eUjwnPsJgmtWIiUouZ4KEJ1N8HytMm57PU9WJEqv1Vyz22OpNJ5q/f/LyAKXYrYeQEKzBdHD08hCe9L99CNF8I4LpJhuJnLS9Rqnc9FvmUR821LiZDxI6evyb896Dq7z2I7A2On+vwdtrm5HykGz21pEKOYz/GyR208CdzZSxJOimDZKKZ6xFlnXqw3rHI9nE0Hn/X62Xvjve8b3liEekBSefUip20jEsvUiet1oPF6NaOoQUqYuJayt5Wj9XojWR63ncAtymS9xexs8tq8gHnDIbSxjMBXVeVcY1y1oLeUh/rUT0XGpx7jfcJUi2tnifquMwwOG/TZEs72Gv9o43402XLOJGNVFiMaKkAJ6B1ozabN2Naoq0um5eRlyi4kQh61oEziXwXRHbz6KFKwU0zjZ83QQbeQmub9bDcssj6UErbOPGpa0wShDm7EfwMcvQTxvPZHwvcH93ULEd5cD34NzbiOszX9CJISfjTY1k52PsgHeWUWk9VnoPvcY1gHE22vc/q3IMrufOLk8A9HAWb4GPuAPyL18+ue99977P4bz1ltv5Tvf+Q5bt27lpptu4itf+Qp33nnnGZ9va2sjKyuL/Pz8990fM2YMbW1t//hLv2TXUAzgma6vDou4pbVImHYiwb+GwcDxn70AF9yPmEglEpDJZTGOSJ1yBRHA3wUn9sLIjW7zKLJQLEBKRjZSiP4rEnApRqgFMYr/jgRuFZGeJMXBzUPC4zCxqzyGmOce4lBGLxIC30QM6EtIqUrxOsWISf4eSkragwT+W0TqiTuIHXQnEsS/7+8XIivZBMM9HwmwnUhhKDL8OShTfgES0q8jofE1Ij5qh/ubQASbL0TCq4twE9XBgVqYMNdjTC7LQ26rxbiYobF3fxtykwDcbJxPAa4aCT88IeG/EQmULsTYm5BSWolcu6sYPIlNJlJ4b/ecrHH/04iDOYVEXeAvE1a5Ax73GOPmOcLyfCdRWusA4cIf7j7fQ5aSn0PubyFFowm5vlugbwVk1RhfeEzTjPttyNpxBNFINRLSh5AyWkwoOdfCuzfDORcjergNxR8VGtc3IUW2wp+tnrfJfv85IqXOHiQURxMVZkqRNaWcqGrRhQT5i8BvI8W81TDlICtbCvivANbCloMw+1FEN5cigV3pd42rwbiu5xFdHTDelsI7NXDuA4TFa7/nZT+iu+1+/xiRJy9ZRn+K6H+lx5WsvFfI8jNsuZ/7IpJ2TxOlF18GPgkDP3ZZtDFEnF8ZseEo9hzmua9JiE4qiM1fcuWnzcAVeudv7nLS91loTYA2b6fzoK3EQa71RF7IDYgWO5GivcZzkjaB2Yap0HjrQrT1I+M9SffJiP/kEdbG9YheH0aK0EkiFjLRSwbhFr/S8zifqDHdgfhFnt+70nCez/s26OzwPDYZnnoiafxLHns3ou3JhKW3xLheieRBs/vNJiovrTut7cNE8v1OIq66mUiRM9x/H0F0MZWI9W1G6/GA5ycHrbtM42a24e0Cnj57MYD/lQ82BvCb/8j9L33pS9x3333/4P5dd93FV7/61X+yzZ/+9KdMmDDhH9z/1re+xU033cTf/d3fMWLEPxzBiy++yJIlS/6B8nnJJZcwa9asf7bfX4ZrKAbwTNcWtLiSVWsiWvj5xAnCXLhgAVF8/WHCWrULMZQSJMB6kJK4HTgKIx9HzL8YMeJFiJk9T7gWb0dCOIuoqtFCpEvpQ8xhj9sZ5fs9RELRSf49y+2N8fgafP9pxIRSUHUdsZPOch/vEQlc13kM2WgHfJCIY+lFikWKF6wPXLbdBkU1xOGO4wymkOCrRFH1qYS1tIRQLF9FAmiu8XwAMdykZDQBR2DCtcZFPhEPtclzctTvHwfaIfcOw7oRWVVOIEHBCSnOh5EA6EdCoIJQPjcSJzX7iaTRNyLFYLTxXUjEdOUAr8hNdM4VRHLn5CZKFrX5yPqZgvdnISHb4Xu1RFxZit16Vj/lpvCWSiTMV0PWb502TxORVfllj2+q53UvEcy+yfi7hXB51Qimc2YZzp2e3+FEfd0mRAOHEJ0dc3+bkEBf5DFAuOazkHUsucm2og3XEoIWq/xbieF6mtjk7CEUzv36O/srRAm5duNyk98pJ9J2PEbEan7Rc9sJ5y42rgY8t6OIZNx7iNJhBUR6mRZkBXsWbSZK/f54NPdZMGwp2jAsJk6IV7rtVrR5rISMw0Qez2Qx3+G5OoTWwsVoXeby/hx2Cz1fyWr9Rc9hGzANfu1xYgOzDSk79/j/Ds9pFxEPOtr422A4E//Zfdq8/IjYHKXQl8P+PeGr1XiZi3hS2hwlvrIGzfdS4iBdil+cgzYAD3qMcxBfOows+3N5/4Y3z7j5GlFCcwPaYDYRtYLnI81lqd/PJ0697zYupyCe3EPwo++i9Z+P6KTOeByPlLJOvd/XCFkTiYNPpafhtBBZO/d4PvuJUnVj0Hq9EynYKbY2xbQWoo34Os/XL9pv+s9cp1vuPoi2AFpaWt53COQfU9AAli1bxmc+85l/ss2ysrJ/9P60adP4+7//ew4dOkRFRcU/+L2oqIi+vj46OzvfZwU8evTovziO8N/7NaQAnuk6iXaQbYjBrCWC40cjJtGBdqEDRKLo4YjhNRCnOkGM8xBiGvPRwk8uoLGIGT+KFn8vUW5rJlGeqQoJrB7kEljufj7ldlp5f23LEURFhHFu9yhSZlYgZa3J7070+Ip9v8z/1yImnSwmm9zOCcNdghhR42ljKkVM8hPIAtABRXVEvr16ogZtrfFX5L6PEelLRiOFZDJi+OXGw2TE2F8iEqh+F6UAwbDvNc4bkcDINlxr/PtFiHlXGScDHkst4XZebPxkEAd/2o3LFM/Wb3i+RigxxUhBaicOC232O1VwTqvbnUAo7r3GR6FhHo0EZQ9SeCa4jV6iPnUzUTZrKhRN8f2dRCqNTCKEoBMJo92IrpLL/TmkuKZ5eBYJxVJkyc0wTrYRNZezjPuRSPAuRfTXhARtsoA9htZRcm3mE5bQZBXqch/Zfn+RxzqayK1W5ra+iOY7bUzKED0nHn+YSFzc5blNrr564rR3gXBGo3G40s93GlfZRF7J1H/maXjNJyzfyUJ0BbKm5SMaSdb8RcYfSEmA2Ci97P8nIcv+DEL5uwHNfxLyzR73FMPWhNZhB6HAfQ3Fd05DSkSd52U/cYhiqr+PNYwZfne03+kjshCcJEIqOj0vU/18stBOcH/N7u8WpOieRLR/1H9PovXyLJrD25Ei+wKipybigMpIz0mKOVxHuJkzCF6VvCMd0V/fA5D1EHAMujc6L+tYz+PO0+ZtO5EgvhfRQq7/vwWtpTpEv3sQzWwnsiwkC2kVsq6n9ZmhKc2q8Nyl0JFRRM7CQ4SVdB5x0juPSP3yHHHgr5gwKnQT1tZ24Bv8h7tGjhz5LzoFXFhYSGFh4b+pjzfeeIPhw4dz3nnn/aO//8Zv/AaZmZn85V/+JVdddRUAjY2N/OxnP2P69On/6Du/bNdQDOCZrovRwswn4r92IwECYjqzkPKViZhlFyFoBpBFqBst/C3ECcoHCDddOXJ7jkaMtwIxs58SLsByoB8GVrm9KgZ3qqd+TsTbTQE2wbsP+PdSolxSF1Kucvz/I4jRFiOB1kScJhvtv8lKkdzeGxDjeQoJlA3uZzYR37gZMchpfuZPCevgLr+/QfB03+ff2oyrZsSgU7xaORJmI/1bi2G+CAmk5AbMJWKzjiKhmY0OY5QhZWCN37+dqPO5y3NxNVJaJhg3GUhoZxBKwGjPxzjitHOy6q1DAiabyBe5CwWe5xsfFUQOR8cqnkoxm/VoTouN9zZ/WpFi9TyhAGciq8dCItD8eo+9FCW5voGonpJCEcYj5TspuI3Gcz9Slp8wfGXI4rob0WyD8XAhIXC2EYL5DSI2cx1xKvQmIuXFHvdXhOhsuPGRNk6tKJapAqXxaHf/R/33GEHPO4znLKT8bEdCsYs4uJPpOU1W1XzjdRKiycs8rmTp24HoJ1mwmomDMFPd5pcRXTV7XFcgmt1mWLrQvC9GtFPqsT3r8R1Ecz8WWed+NSfiNWsRvc8g0qOktTfgeSlmMKfnwArP4X6/W0dUuNjr5y4lYm8rEK/qQXT8IKLlPWgjsAttPLv8N4soQbeFQWXrZ4/DwE6P+zLEE/OMk9XEoZxSNN+H3PdHiVx7Jwnleh1SmPP8242n9X9IY+j7O8OVj3hyNrHJTGswKbbFGl/WZxjcVOSm8I0qz1WK38v1GA8Zx3n+fjNyGZ8kNiiH0HWEoPvhaG2mcJi30Hqa7fmafhr+U/hKu3G1z/g/6n4TfwRtCEYJxnfuN5z5iDYSL0oKdQaiz1Wc1esUH9wBkF+UI3vnzp08/vjj1NfX09zczAsvvMDtt9/Oddddx7nnngvAkSNHmDBhAj/+8Y8BGDVqFDfccAO///u/z9atW/nJT37CkiVLmD59+n+IE8AwpACe+SpFwnQiWmR9iHHchQTv+fCzJShp0ZWE2+pKIqFrJVHzsQct3FYkiLe4j41IoTmMGMZWJLQ+gbL5r0VM4SHIuBtYDodWo6DuKhh2idtODH4RnHM+EmogS+G33Eayig1HzPA999eBSnw9jxjNJsIFeyPh2st3m48TJfBakUs2xcCVo135cx7HlUTi4VwUa/jnQDHk3od2/7PhwF4kjGYhIdCHFKMThPsp1+N4w30uJBLh1hr25GLeZ1hHIaVvkX+vRgLhMb+XDs/8JhLC2w1HHVIad/j5VUS6hhuIWqW7kdKwxDA8ZrwVI/oZIBTSHCIn3qdh2K1+bw4STM2INiYj2hiNhPyF0HcnsvxVIcU2HTrZhATsMqRsHEGnL69Ac9wlGAfuh75lrsQwjTh52YmU4DsJod1EnFJOgmme7y1EyuQkpDQdN96S5Xkyskw3IbpKFuVl6ORsOhHcA5TDu48TSdBf9jtpk1VsXF1KpDzKJiwuM5Dymyx79xBJlXcgoZtcdyX+7VG0ociBgZmGdwyhQL1BxJnt9Hi/RbgxpyGecBe0vYCUrTa0NlYh+qojDld9HujzOn7a4yoE/rIn4hbvJepaN/t7AWH1v0y4oge4xfGB2UTe0Puh7263PxnR/krD9WVEK3ke5x0eS7JirkX8baefqUdrZoPntdnvDcAFT0NGihGsg9f3w8B9+r37ZcKK+xtEpZMsRCclyI2blLVeePMpz+lqtO52oMMrZfDuq5qTrKuAPyMst8mjkgd/vdxz3In41za1yyTikMjFbvcWz00xouNs42EhUf98M9qMX4TW0itEHeukiKf41K2o4kgnsY6q3GeNcfs62hBMIVIxFcHbr3oMDQzGI1No/H0frf0WOHcK4od1SFnOJg4ibfPc/DswRP1TBzr+LZ9fxDVixAi+853vUF1dza//+q/zwAMPcPvtt7N8+fLBZ/r7+2lsbKSnp2fw3mOPPca8efO46qqr+MQnPkFRURGvvPLKLwjKD/8aOgRypmv2MC3oecAW6LvfzOgkcSI1xRStJE5/ppielwg3ara/1xHuog6i9mwmsNXuiiuIIOZ8pMCtQkrHXsSYQYxrD3Lp7UGw7nA/JfD2LXDew4hBPogEVzFSuGqIJLopgDmdbmsicvsdRtaBxxHTaUcK3XRkBexFgqSLcMM+TLgwk+tpKSEUv4BcXVkeR6dxsJ9ICP0GEn7TiPQpIEFU5WdSXN3/QgrMFPdxnfGxGikqRUTAdonfO0woBPn+P81LMXFg5GbC8paPrGSdRMxSIRETOM+/X0EEh7ei+W8mTqcWuq8ewuVV6PmwZZB9KC5yA7IcJdfoTPe9yfjdaNhyDOuDKJZxNYPF49+4Fi66yuMFCZdpSOBOJeI9C1Gckk8octL930gIrzGE5WEDnHgGRg5H1uQGIq6v3/DeSFiJBoyzGf5/i+cyuVjx//VEtY4mIrSi67RnkiJ4GClHdrkN5murI9yIIzzmXiKNRgmRML0HKTzJxXmcKMeVAfzBSLjyhN4ZQHRe6b43o7nvc78LfC+tnxLgAPzNt+HXrkBKyU7DkEGkEOpCc5viPFsYTEjd923IWoaUu0KP415iPeHvSclF+Dl2NxRcZXi+4DHt8/fliPZnozn/KnEgZg2RMzPf/XWidfNN+MkN8BuZRDzpSLTe5hjmpxENXE64yyuIk8U9bvsxpJQVEApqs/HQ6PnLIHjABOAVGPguZHyC4KmLkJKVgzZcfW4zHdQoQTS1CK27ZNEeQDxnpsfS4DGsNg4XEhu+FPtYj+IdL/czN6KN7hNok9lpHFb7/wYirGQ6Ukz/kDiE10qUPtzpMfQiXvwQoo15p+FmtHGReGq2+58PPHb2DoHcQLDz/9OrD0Un/SISQQ9d//AaUgDPdN07TEpOigMrIo7qzyRya12GFLRxiCEtQwt6DlrMh4k4vAK3fYgQiJX+JLfX3Yh5pR3qFnQStxMxrh+h1TYTMfQJhmOU2+5AjLbA749GjCTF9R1AQcfFHtsLhnc0UVKu3L8/5ncqUfzNWoJBrfRvo4kYrja/t9ewTeL9O+Re4hRoUj5SXNWTRF3QPOO7jnDNJMUjBVMnpW4GUhg/jZSak2jXXERUK+ki3C8pDuerbqvYczTDc5KE1SvI8nTMeHnD4xpFJOXt8e9HEW10Gi95Ht80t3cXcYghxaqtRwrBbqLcXzGa95PGX6thX4iEV79hLkGK7hbkUuwjrFMtvF/wJJf1BMPSi4TUJMPSiE5rJ7fSdCTQTxLK10yiXFgDoegcPG383Ya1BqiEdxfAOU94Dtf7nWlok3A+4crLRYrEes/RjYS1cTX8zQvwa1ej9fQYoZwk2K5BNL2KUMSf9FhqjdsUBtBHKNxTUXhCUjj7fW80ovMKYt10EtUmitD8znL/u9GmZ4pxtBLFZB3x2NNmYpafTXHBh5DSnkEka68lTo/eTKSPGiAOdzQTORnHI9rsRPOdrIejiA3nbLcxTrWIe4FzPmq81HiMKzx/i1HoyTr3l+LbphAbp5lEybcFhnMW4osFaNOQYqVn+flXiTRX45DC0sRg4nSmeuzdaB1+CW0qWhCPzDVsq4mN6oX+vYywVE9EytENhu8JtE62I6ve9NNwlMZSaFhOEgprC6K3IsQHTqL1PuDf9hMelw5EV3Zr/+whHwxcgrI43IQsyBOIE/H9RCqgCqSMP+C5Go6U6u8Rpd8uRbzhOUQv+URqpq3G2/NnTwH8DB+sAvhthhTAD+sacgGf6WpAFo9CxLBSbFEeWsBl/jyNmEAWYkTjEBM7jFxa+9AC7kfM8RXEsLOQsEtCJrmqdvvvQqJc1hSi0sEcwoqR3IqjCStZnZ+Z6/cm+/seFCD+KGJiyV2TFNE6JNS7CPdCGRHw/RhheWknipLvJkqGpbFMIaw6lcZfJXLX5RGxLG3G8RjjrBAJ3x7iVPCNiHk3MZiWo69ReD2x1e2dRAK1i0gX0YQE2ysMnu5kBKGEHTMeJxi2y4jKDl8i0sg0uq0LCQULwzvXsC4lhG5SiAeIwzHn+50KJEjzCZdSNRJESUF8WmMbrFdb6P/L3Wc5UU2gy58iIs6zn4hZHY1iKAuQsrPZnwy3Xef2s5BgHY8EceK7ndD3KgpKf9Tv1qLDNo2et6NuYzeysvQA++Gch91PAeG+P4iE8ATCopJiZseiec4DfgfR/WH4teTiGkNUJRlHuNk3I7pIG519xkW/206u7h2EEpuHXHwFhqWESCD9HKLffUjIZxqXw4n6u68Y3nr3fR1SqAc8R2kd9SMaKkF03W48j0ZWpKOGtYKIvxtAStU49z2XsGAWotCQcWjd1xBreweR17MfuUvLCevgNTCsAs6Z6N9Ljc8JqGRjPlLQjhFVJ2a63VYiJKHDsBch3nAPcXr7ecLCdjFaN48Tia7HE9VMstG6KTLcKd462/OUNm3Vfn43UeP4IOHu3eN+xhH1rTeia5LnaAZxarzdfVYQ66kc8Y86pPDmGfdt7ms84h8HibjjjtPm81L3Vw4XPEFkgSgheEfa1LYaz6P8eR4plNPQ3KcDaX/sNpOhIYeIhUxy4SgRrjJ0DV3/hmtIATzTleI0mhHzOwJshnca0TZlCxHH9yZinL2EcBtO1O5s8KccKXYXuY370QJ/majTuos4RTya2LWOQ67TNYQgznZfiWH1I+WvjXBR7Ue7SxCjmod204WIcSSLxqWGOY/IeA9RHaQQMa/txAm0k/47imB2exGjmu72ZiHFa6rbuNKwDmew4gj3IQbf53aOIYXjImR5a3PfR4DfhqxbhaORtxCJUNsNTw0S4tmERWEiEiQnkTCZ4LEXGNZit9+GhEYuUb6p1LjfbthG+l6K66xCAue7RP3h7yAlYhzhIp7sdi8ngtJ3+/tKZHnJ8Ge94UmHN6qQAvF1wjLUhBS3H3n8VUggXOMxV7q/JuMw03g55s9z7rvFfSSFM8XXFQP3Q9Ziz18ng7kvKSXcp0mhmoloczsRt5diA/cjgTnNOH/FuL3f/R8kamInhSUpYBWeuyzoS+7VRLflHv9JoBDefoBwwdZ6fIcNw2JEExf5U+T+k9ttArHZazf8E9A6SFbDSuMmy3O2EW3qNhCnXacjfpCLFOJm4uR8GRFGUO05WYOUgFbP4/VE6pzDaENwwDh8EfgBcVhpt9+fSyicuwl+lazehX7+ZuP2NSIp/LMay8BGpNSWos3QCKJySRviSzXEydUS47rAeO33HL/lsez1GPNQLFwDWluXIuWwE/GlVuMxbULXIbrvMSxJ+U5K9UmPbR6ysqXNXCMRo9qL1svnDPMe33uCcEnPJDI7VCK+NMNw5iOFNnk59npsNZ7XL7r/DhTq8ySRVDvx08Me7xjP62jD3Xwa/lKMbBea30YiFddh/z+bSLQ+g0gLtA/RxkboO8hZvU5+wJ+h68O7hhTAM11XAH+JFuAfIRdjO5z7q0gAnAQmQfdTiFGVE+lWapGAyydyeKWAfBDT2Ip2wJVqh/nA/wS+AH0PEScd/5jBQwP8nmAAovZsLlGNAsRYLyVOf+5DDLLL9/pRvMk+wzTRzz/r/2sRg0yWkW5gM7z7EBK2u91PJnFqtRDYAm++hqyMexBDTMHmdYZvDFIW1xKusIOGcy46UV1ifFS4jwzgOvifewkL6/cMRwtSeDo9vkrBOuiKm0QEmecgau82LLUMVpU48RBSwncjS0ByBaXYoIPo4MohpJhe6/aakXCuIFKo9Pj7a8ThjxTjNxwJ8QMEreSdhsPlRBLa+UQKk13IipgsFY2ex0w/P9MwzPNY5xj+fW67FAXS/xFSxG/2/UVIKfsispYWIgXwKY/lB+7jSs9XEYo/y3ZfbX5/A6Ktecjl9btECo9CIk/fejh1m/F2BXFQ4BBRMzW5TbchAX8Ng5HhHfhe2qykOFm7y89bjCxO8xg8/XrkPsN4PVIyqpEwvgGtyxnQ/V20mTvmTqr9fY1xPooImZiHXPojDNedKE73iOGvhF3f9/j3opCOacRp/ELP3YPEqfujiJ7eQ+sxeQEKEQ3lEyfom/w9H9F8np95xri52rj9CqLju4myaC8Rlqx6v7sH/vzHkFFjOEcZr+uh+1ZbgPvh1A88R4947Mc8phQSkSxaS/37FqJ6TCVyWTZ43P2EAp/4RKfHOxspot1ufwqigYWEZfxStGn8L/9ZyvNXNY+MNG5a0Pp5Gf0+333mERV8Tt9MPEuc/G5Ea6gTbebuRRbSLqKSzjq/U4xi/Z4BvgAnUmx42rBWeiwTiRJ0+e6zgShZV2ncp01CueaFm9Hm7EaCZxW63woGT4xnpbjFs3QNfMCfoevDu4ZiAM90fX2YFmkhYs71aAHvQgJiCmJwXWgH24l2y8/7/nVogT5LWD+aiKzzC932o4hhF8Cpq2HY3Wjxp4DvImTBmUAwwGrEYIajRKHHUDxOM1I8MTz5SMCudlslSKh1IUaTXMt2RZ5YACNHI8VkCfyvW+E3P0dUO5jrdh5FMVdPGaZPw7v3uEJEDZGgucl99CMB/CRSyp4kXL6OS+u71Ydskuv4kN+7lYilfAxZ0LpPw0czUU2jEFkvnvK4ZhKxgEVAGwzcDRkjYOA9yMghDmYc1e/cQpxEXQsDuyHjBiTQrjEsxUQ8VqfH3HEaDCAlbze8ux/Oeco4P0S4XZOVYJr/34UsUxuIkmcDSCBuNC73EaXddhBxSgeIGrrJxZcssu1+72NuL9s4b0a0CeFGHkBz3eexHiLyLuYbn72egy97zBcjhWgKUjSvRoJyvse33X9rYOAhyLiFQTf6wKvG7RLgs8Smow3R4PXIytHkz3Ei9KLN/SVL6RxCkew2vKuNyxuAh32gYhzaiHwe0XFyayYr8D6k1M0gYhdXAcuhL880mvCblMNepCwPN95SCMBmYBa8uwTOqSBOrfcS8ZavCe6fPS5dIWMnor1NxkslEfuaTdTCnUYcOCg2Tj5HHBzrJ/jNMs/hKt/bRiSILvG400GEI4jmEs9KvGKM5+f33cY8P78YrfU2w5FMOAtQKEWH5yC5dIuJfHcHjYsBP3MIKc5FwLrz4JG3NUftxEG0Ds/3VER7rxH1s8sIZbuDKNm52fA+6v6qEA2Ocb+1ROhGgr8QrbFit/8/DdsS4+hzSPG7B/G0brRxyUEKeYNhucJwrPb4/hRlNniNSNzfi5TaJ/3uIUTrlUSO1jq02e/0pwjN//c9j18+ezGA1/LBxgC+wFAM4Id1DVkAz3RVIkbTROSN24YWey/a3e9HLpDJfqYAnbKbhwRBC2I2VYi5NBM1ap8A7oO+jQwysWEPIUtPihvKJNJxjEbMYqbhW4wkxgn//ihahXsQ8+k17L9jOLqJigv3oVNmhYiRrgdqYeQiQnHcDb+51OPa7n43IWWkG05VQ9/LiEF9Gc5ZRNQrbiDip04SylGp791KxHBtAnZC1iWI6SVXVlIGjgoWBvx+BXGqtAMpcO2ImSZX5jykrBYQ9We3aQwZdwF/CRmPomD9jYixjkZzvhIx1E2CNSPFhpUbL0lR2IAEcrX7HHda/+kgzGI4ZzphBV5P5KKbA/yQiPO70210IivEAFJmfse460KK18VEHGg3ssom61InEsY7iJi3HYZxlPtPrvyH3fds47UbWfrmuT9bfgdTjexDQq8RCfZiIu5xifvvQNZd/NwxpAiWCLaMF5AgWyZ8Z9xAJBCeh4RnJmEVTdbV2UhQLyUS7V7j58oI62kPovk1hJLUbbjKIesy38vV/0xxe1PQ3M8lFLtk1Uwbos9Jx6HAfeYgpf0oEYMKops3PF/eNJ5zB7KcpZCHLjh1A1qDVm4vuAEy5hr2AY/pNqRAfJbIs5isye1ENoJmRCu3Gt8VnrNqw7oTSdUOImVRDhEHWH/as6Wn4bXJc1jncX7J9+cQhx+ShS/Pc7eFOCxRbXw+aZyksJjs0+atHd59ASkzJciKNwD8xdsa03wikXsJsop1Ghf38P4ybQuQotVqHDURynYecjmn2OUuRFdPEPkvy9FmIfGXccbzNI/9AFpDs9Dm5D63NwutnVFEWEQOooW0UZ7sZ57zGKcQ+SEL4dREBis4nfoBcXjsDaIM6A7ixPiA4W00TGfxGnIB//Je/+lsA/Dv9tqAhNosxLhKERM6hIT4PiSIhhNpT5qJQw4pDiXFwUAket2FmNtD3jkdQoxsKXJblPn5VsIKmOd+kyt3PWJwNyPXUQtSXpoIN8EOouZvDyHINxEF7AsRM3nFY1yAmFIlUQavBFli+hlMaDzsi5CVFLsZxk2t291BnOKtQ8L7mHGULCrbjb/5bruSwcTZ7+yEcz+KrBSPIpfS5URqhq2nzUkXcXK4322nXfhsuehzfwsJ9xfcR7lhecBwbnO7M4j4vPeIwwBpN97tfgoQM+9GAmQyYtKdRMqMYkJxT7v364mTi+uInIaX+l6uYUju52wkrKcbX82IntqRO7cF0VIzUpiziY1KKRFft9+4mWK81fL+AyuLEa1e7fG2ecw3Eq65tNFJyulx/6033AXGUR+i212+v8nPJqtnNlLoZxhPDyIr9i7icMJcpKCONLwpqH+63z+B1swWz0MnmvNZaM3OIeqkNhn/yXJb4jHegxT2tIFo9m/jjL+HkDV4pfE1Hy6YaHiyiY1AcqUWIRqYTZRYOx+t06QYtTNIp8MuI6y8hYa7HimAS5F19ma0oTuA6D8deKkgEjdnE4cndrit9UTt4NF+9llE92/4+WbDusl43Y0sTZOJOr8TkILT63uNHucG4YNriHyc2WhDmpRu/P+raN3VIxrZYzyvRPGCd8E5+R7LcuOqB+UrLCSyBDQipfM5IBcGvu/Nmd34rPN8X0pYytKcTPccbjb+y9E6qjcOsonDK3sQXTyKaCHFA+cSCeFLCEW30WN9AvHnqz2v6Z0qwnrej2j5s0Ss8yRgJwwb57FmwrCPEjx+I1oTKbwluafLES/tRTxi6Bq6/g3XkAXwTFc/EYe3GgngDUT6gT1oAb9MZGcfgRSpzf69BjGfRr+7AzHz+Ygpv4QYbnL5tSOGkIECp/OJ3eRRogRbN1Igk0u6kEg8m48E4BIUv/IHiJF3+/n5hFLXh9wZE/wpMgyLEOMfjpIM3+k2h8Nfv4YYeRJQ49G2rYKoTVmMGPwixKSSdaHbz7SiWLF+JGgWELnYSuDcWUhx+bLH8SlCgB/zJx8pNJmerxJC4HUxWJA9dwERj5ljXCWXeKnHWU0kq64kYnGa3O54xPBL3PZwosrFTIKZV6A5h3AB7kZCIAXCZxvGKr9b4PZeQsL5dFdyBRKiyZo1hVAyv278VXt+moj4tgVuY53HXEkohesJ196nGDwh3b0RWWFeRULydJdlikX8I6Qslnp8lZ6nESguMgnvDERLy5ASNw3N3XoiXjIDKaY3I5ouQmuhCx2Iec/wbDP+UyjBFCIVxs3G9ziP+bDHmzY57Wj91iErzEHj8iLi4FNSTJLbbioRYH+ZYDq0nyjl2IXWcQcR65pp/IxBfCJtQAr9TiVRszgP0Uo1EuJTEU9p9jiSZaccKeUFxmGyLE/z+DcSrtnkXuz0uLP8/EUMujJP/a3H/g33mUuc+E+uy35Er/Vo7eYgHvEYoreJ6Crw3yq0SSgjEoansJjJp+Fipz+9/rxMlLl8wvjOJNLELCU2OvV+9yWPdT8wHjLGIOsonosiIqbwCGE1bEfzv9vfsxHNjCFO0HYR5TM3I3fzbKIkYHLDQiTQ7kIK8DK0RtNaWcOgl4Q842GO5yNtBiajtZ7CXdLmNRO6Vxi/A8LTiZNIqexBa/s+IodgOhiTwk7O0jXwAX+Grg/vGlIAz3TVIaWtGS36qUg5G4EWaw1ipt9FO9wBOPEInPopYl7PIyvDE37nTuKUZbIEXomYehFiYq8iBliJlJYWpKB8EzGsxHxykfBrQoxhH2JuuYhRjUfCPxMxjeQK2WIYMtBhhLFoZzzFY77N/Y0i0rp8hth13mieVYOEy0top/wksryVIeaWXCn90PZtxKw2IUb1c2SpOeAxlcCfPGTYW4m0E2VICH3dY8kw/M1IcCchs40I3K5Ve8wjBGKVcbDZbTQQ8U5XEzv5lYanESk8x5DAvNTjmYSU3gzPzxEiMXBSqPsNR4bHcSORUHuJ56UMCY4/8rtJyCQr1zTD24gE7zp0+rvXsLch+tmNBNhB4ySfqIiSS1QceRzR8TSPKQnJTiSge4HjkFuD6C9Z6jIQ/R72/G5ASWzvQpuCXjhxH5H/72HiEEcT2tQ0GqezCKViEVIANhm+B5AiOg4pPIf8fp/xVY3WSYZxlSxUNcbDZ+DInYTLrxxZloYT66GMqAzRgIL670Q0vpko6daKYrRuRnP/ArAMSu8z7i4n0uzMRZuYHOP+as/jW8bvG4bvG/5/GlrjFT50sh8pmM3AEfjJWo9zAnK1LzauFnoM8zynLYK9b6vhrUWWswrC5ZqU/VkM5jgc9hlo20zEa87xnFQgRSqFDhzzmFZ6XtOmJYUK7DJuJyHeNdzvJdgmI560hyjf10mstW2IZpJlvBCdxr4VMZeD6CpBdLjEMH/TMFyNeOPtRPnNcf4+yXi74z9HyEyDcfodj/kW97vacF4s/NNhHJ8kDpysJw6yXeP+IeL+koejz3B8A21c2nx/CvC78M5laH3Xu88tnrs6Ir/rFPWde4nxnQs8AyMvQ/SVYi2TlXe45pXxRAqws3Sd5INT/oZcwB/uNXQI5EzXp4aJ8W4mqknsQEzieRRE3oAEcSWy9B1EwuVlItFpPWJQDUiA7CDyx21CDLMELepGJACvR4rjLsRoJiNG/hKQASdehZGXENUEJiBmW2F4avz+NPV16m4ngL3VY+uFvuWQ9RE/83vEKeVk+WpATGcbkSy3h8jjtpyID0sWs9eIJLeTEMP9U/c5VrD+bBVc8JTf3UrsvnsN9wDw/5wH33o7LHDFyBqYS8QQbkIMuRUpSnnubyJioA8j4bPEuPkaUlo/gSxMyUp5r+FN1qkiz9tmpAi3et5GE7GA7UR6nBs9vvXG03T/vxHoh3db4ZylSHA9SlQCGctg7Way/W5yGS92G0VEupd2xPQLkWLQ7j6KDMsG6H4LcschAf4MEnJdRLWWHjjWCAXXIlrLZPAE4k+Wwm9MMb6rkeX480RpvTbDVI6UuyfRyeFxfr4JbZKSFboJ0XGXv6cwiTVuJ5vB2rt9q5xuppyoWXyF57MROGGX32eIpNw9RBxsckPu8rvz0GamFAnno4ZrDlofTUQd5WOE4rsAKQLjfe8IorkSpMx9GZ2qXYpobhWigRSyUWR81sHbW+G8pLg1GL7dwjUvorVyA1KURhIWqULP3SLN94klMDIpaZ9CtPqY52YHYSnsFC6Z5rm7XrC1PQRFk4mwkDLPyVwi+fJsosJECsXIQtamP0RK6uWCh5cRfbV6Pk+iNZYs5MW8/3R/CXLz56OT6M8jvjUeKVIv+G8hvPtdOGc8WlPDkTL1ClKM3zB+G9Fa6iXcr4WG8RbEfzuRwthlvGYT+QGHE7HBk4mDPyc9nnLCElnpcT9HpHtaQcQrNjO4Ce9eC7kPeI7nGrY2xDO+4L9H0GGsW1AZuUrj/T3jI1lRSxDt1qOUQhf63TLjbgHawA6gNTwV+O2zdwgkGT8/iCs5hYYOgXw415ACeKZr4TAxnmNoYW6FUxth2P1IKG5Gi7KawTxkbECC41FCYUnK3RSi2sedSPB3ICZU4e9biJ31ABJs/QzWAKUDMax1DLpkmeH7sxAz2k2kVjiKBOIGxPTa3c5RZFm7iEi3MNXtdBOHXYYjBekypHQly1Ry/05ADPZxotrBUbdXgaxHuJ3j7n8PnDgMIxcTsU9vEOXZxiCBsgwxxRSPOB4xymPE4Y9HkTBqJ+Lw8hADv9vvPUaU5CtBzBvPTTWyAIz1c5sJRcEuGQp5f9LX5Ppc5r7n+J1Sz81ExMHa3e+FRF3eeiREriVc1g3GQRLC1f5/NO+PT2xGjD8Dzf/VhuNh4y7RUI3nrsGwryQOqnS6n+Suz/Q7pWhzMYNQ6uuJ0mXtxvduoAuOPQEFKV6snXB3lhCph+Z4HkqR+7sQWVHWGe4ZaBPR5rlqOA3uNgbddwPPSB/59QXGTzbhqi/0nIEUrOQ/2uMTxjX6PuiiXGpc9XtOWv18J5HwuQYpPRCuxRwiMfg2FLOYXNmb/GyV+1jh5ychhW4bUat7M1F9o9Xv9yCayXJbc42jixANHCYOehT7L2ijsd/9thv353u+phuWWsIytsvP34XmsZFIhj4NHVo6gtbQDe7jJNo4/TnarH0dzVspsgLXEhVBlhOnxYvR+mon5mwystqtJk7PdxPWrmQ1/hLiRTn++0dw5O9g7H1E8vg6oo7vThRCUGmYjyJe24xoqZLwyKQ41Ra3MYZQ3t/z907C6plH8Lw6z81xpJSV+9mTBG0cNk6TVb8RBjZDxiq/v8vwTSNc0UkhrkJ8OpNwKe9DtHCzYaxF6x7jdgVRbei/nT0F8Eo+WAXwFYYUwA/rGjoEcqarGu38JiOmkQ3DrkA7zJ3IsnA9Um5uJawyhUSqgdmEm6+FCA7vRbv1LYiJbCPyoFWjPGqlbr8JMYLkempHAjPf73chV14esoh81/dTH5sIF+YBpDBMJpI4H0LMMrnNShBTaUUM6jF0GGMbYpibCCWl2X2NJ5Lv3oyUjtFurx4xs2TBmgYjK5GQaDNM9ciicQgJxNVIgUjM9IT7P46E+D3GSZmfv5XBcleDp6d7gY8Phx0noxxXq+HIML4XIyY6j1B+U7xWJhL030OCKwXZp1jBakIYzULXPrcxjTjZ2oME72bCupdLKApzCNd8P6H47UJCNW0cion4qXFISOwlrMq7iWTTrxieQ0gZOB8p2Z1uZ5+faybi0fA8bSKU9dVEsfrl8PZGOG8BFEwxDg57/I/77+uIzsqRYCtC6yS5wbcY3peIAPkDiG62EIcrkrWxBzJmwa8Xev5mG7+TjavkeuzynHWjTUkvZCxgkN4G6xs/7z7moHWQ6fFBxBU2Gx/b0Pobj+IHM4WXtr+Fou3GW7ufz0UKRDGxWckgrESTPMZkRTtIpGDKJkp6pZi8GUhReIM4pJBNpIaZ7t/7ifyfo4Qv2okNXIfnotmf6UTy5uS+ner5G+d7b3muFrmP6w37drdViJTnfqLKxnoioXadnxvj+8WI5psJl+8IZPXsJiyy84hck8mbMQr4NIxtIRKOFxKJ2J9H66CSqF7Tj9bbtaeNvZc4HLPBfdV4fjYRp++PEAdZ2jynHR5jo3Ha5nduR3O8z/2nMJQSz9mL6jPjCT9XbTjSZr6e2Lge9JyVEYfJNni8F3nO+j1fu4nYvzzj7CzHAA5dv7zXUAzgma6DaEEmppQY2R1IgO1HjPEHaCE+hxj8eMSg2oni7k8ADXBiL2JCzxMxe01E8PoYt9VLWH3WocWfBHeK6dtjuPYRsTnNRKD5BL9bROTe60e77L3Kb8c45G6Zhixh1URS60OGfSdi3MM93ov8TheRGb8eKWHJigViinsJ13YKpN+MBEhSwuYgwZsUgU63sY+oHzwSuU6SMjSPsDIMR5bAXKLOcInh++bJKMfXRKShKEeKySSkUNb6/8lIuc5AgvF5OPa3hvkuj+Flt5124jORIrOPOA2aYTw94DF8ibBYTUIW1TwYOIisP9chWrnX/Sa38ETPR6KjZFmFSO69xvOzgDgVfBES8h3Gy8tI8CRrUy+hMGUb/+VE3NYhwzHH8E8FxsB5I+Tq4hBxmjMP0e5C/z9fuDzxfeKwyQykJP8xEWvXaVgbDeenPY79cOoRv7fptPbLkeUlWWbyiQMHSxHt9yOr2uUe50k0572Grcp4SZY/kPv0pGGpJ+JP30IKwV1E3scSKLqbUED2GK55ws9ghY98wn2/0HiocTtJSWz0c/OJVDMLiZOytmx3P0Sc+K9yP6vQJqiFKFF50ri4GSltCbb1RHL4SW7/W8b1fKKMWmrjYuPxIY8/x/ju9DiqED/LdP+bEF11EQnCJxo/96O5zTbOPu3+7iDCTErQGupANN152v3kni0xrvKJur9j0AZtFKL3HX5vFVrDHUQql6Rc5xGpUzKEX1rRhqeOyCE5hYirq0U0tNi/1SBet8rjaEAb3ny0Bo8Y91lormcY1m6PZR6h0JUh3rbEfdURruce32sjFNla/z+A5Mx8j385Z/X6l8b3/Us/Q9eHdw0pgGe6riJ2l1sQU1uFFu5JtMj3+bMQ+DFidAfRQp1KKDjL4CcvwMjPISZahhhCDooPKSRKrzUhQXm3nuv7ue9nEkmNG5Ei0IgYayGRoy8fKRTZiOl3oxicxMQbBEPGFYiZJhfEzcAfXqDd+XAUbzQNMbF2ZBGYSQiIAbRLPeYxTkMWi8RsU0D4NsS8PoGY3SjE0HcQbrdXkau02p/jiHF+BTG8EcbZNuRaz/PzKf6qzHg5aByMg+67/ftoIj3PCx7zVYbhD/2ZaxyUIiVvrsd7FxQUE7WZl3lMHURsZwGR/uM6wr03BqiEtrv9frIIHULxadMg41qknDW7zfWEJaAD0ViKrVpC1OxdRpQUS67qpOz1E7FPf0aUCixDlp57CHf7TI+nk4g/KnZ7zxuerUjYvKw5ye3ys3MIpaQZxXpe52eLHJ7V4efakXDL93zcSeSCHG74thv2Fhh2CaLHtioJ3tv9/1uGb41xM1UwsdVzUUQcNthpPBQgOnKQ/WBy3YUo9vUhtLYnIOH9hOE+3/hOa2AxcWp+PQr4/yoRr9mJ1myTx9GG6Ljcc3oUKQxPeMw1hCJ90jhKLuCx/l4KuUuJU8EpTmwKcZp3DHE6/suE0lyP+MLk02BsdRvXELlC8ffDRI3imcbn3W6jBVnok+Ww0DifQWwCLjVuryZqjNf5mTrkSbiUcJuuRYrL+YZhJlGftwZtqB8mqpiMJyyIZYiWjnku3iDKpF2NeHKyiE9GG64SpChOYjBm+e273N52IkbyaaL2+JNovfQSVZRu8PdutGErcL/tRDnB7WhT0mbcHkW8Kde42mo8PIto4xVknc0naiUvcruZHvtwpBQeJg7A9CIaGFIAh65/4zUUA3im65vDIo4GeGctnPsUkUi0Di3UL/leIRJQ3yLyAFYSgfHZaPE2IMZfSsSYfB4J0S8jYfXfiKS3+YgBlyCGUkmcIFuEmMLz/j6OiJMrRsxxNlFE/HVC0RlAgu8WxHxnEBY4K4lc43eyDeM6xFjfQMzwGvexGDG1yYbzCBLSM5BCVYgYXDuRTzHTY643vt9Egm8edN8Fubf6nef9XJbhzSPifRqJ4upJ+JQj4XEdUrRq/HnZv/UiIduJmPc+9dH2Uyi6ijhwcQIprGXG/zjjMbkHWxk81TmYc6wVKWf7kBDZrnk69RoMW4MsBZVIGvp/ZQAAdupJREFUSU2Wo2PG9zbjpcjwXY6E0Vx0YOBNz+VapMj1I0tCi3FZz2CORlo9p9n++xTabGxFNNRGnGhchATyS36+kijN1YrcWo9oHMx0OysIN/kMoBtOfBtGXkHU1f0eEpAlxInyckLR6ff4mwz3U8iC8ipSagr8WzcSoPeizUMdorPX/HcD9P0Ysi5GtPYUUdIwWWTb/P12jzNZCzt9cOY4Esb1hIVpFpE78yV/nwFMh7eXwHlPGZY8wlJ7B1JsUyxpv+ex17h9hchb2I0s5Mnl3UCsyzs9lgZkRZ6O1toC4LfgVA8M+xXicFIHkcg8A9Hqbw2He09qLpo9vhR+sB2t506kKI9Ga6aEcHcuRGu4zPMwgzhxuoVQ5h/Ogmf6hPc86NsJWRPdVh1RVWafxzQNbQp2Gf460Uffbsi6xfioMb4e8jwMwMDNkHE/kRaoC637mz1nTyE6/n+RspU2GPs8d0uJEnqbCRd8lz8dREaGLo/xdv+tJPhvj+69+QO4MIUD3Yl44zxEL+vRBvfbiL+ONh6PE5a7tCYz0Rz9NwYP7XHUf/8QrYMapACOFY55iaiONNrP/OnZiwGcxwcbA7ieoRjAD+saUgDPdD0zTAKzgbC4FCMmPcP3mxAT/SoS7ieI3WOzn19NFCgvctvdfqbS99vRIt7BYP46rvTfDBRPMgEJyn1IMBcgRbKaKJhegJhKNSHQJyAm0UKkK9mHGOcmwqqy3s+PQoymDTGzpMiNM+zJbZIsJmVI6UwB1wOIwT+JBP+niJgou7V4y9/bGLRgHrgPJtxA7MSXIKbd7eeuQ8JrHRKEuz2uqcZhp/HXQ1j9Cvy9lzh52EPEHnX5vWuQALwJKVqVHvdRIgXHYWQ9KCRyryUlaCwSZOcjq9KLiHYWI2GTT+QKTHPQRrhek5XjYTT/Uz3OZGHs8vy2GM8zPKaxiEbS3C4kXLOtfv9Zz0Ehka9upXHYBAMvOKFutcd8DAX9p5CCLI9jExKqu9Ep048hIbURKd5pnCkMIVmBFyNFZ4Ta+h+3wX95ynAnBW0HUYqux3OyhyixlUIS8okYznJEx4XEKc5ZHt9hj+dF4lR3g9u6Hili5X52ACkM2Yb7ax7zrchyPgFdI4zHZO38KpGap9K4ThalpOhOZLDySN8VkPU5Isdbv+HZ6ecWGI4DaP7T2q/32CcjOt3g+/1IeV9tuNuRh+J2w5pBxB1XG6cL0QZiNeF6ngZbFsPsRYannsGa1QPTdWvkfW6/y/1XGa/DidPdWQSN5SMls9YwXkcoxblEkvMU6rAMbSoWuJ9MpCw2o81mJeIHYz0/M4yTQs9Ng9tNc7gfra92pGjNJUpUriZKB77k969DytkuxHu+6O89xh8eU7fx6rjCd7fCOTVEVZM6ItZvDdowpXCHaxBNtyHL6g+QcpxkyvMex/9Ait8AooMy4/wiBqsTsRQpx9Pcx1rgubOnAF7OB6sAbmBIAfywriEX8JmuV9HiG4cW9hIkZKoRc9tGBHlvQAzjOFoJbYj5JiHQTJSKykHM83LCWtOqNgYOErmwRrjvPGQVrCZ2+cmNXAhtKxDjyEPCtsS/FSGG8aeI+Ta5n27EWFJs4EQi32ByOyTXSaPbS5a2LMQAL0WKUr7v/8jvnY8Y1xY/8wxiyGsRE4RQeichQb0BeA4mXEZU4jiJGNwO95esp2WEizUHMf3fIyooFLiNDKSo9CGm22M4SuHt+4iTxdXGWzdxKvctQvlb4H7XIiXjCFIeNrrNSUiRrjNstchq9x4hJJPCWYYUg6OCgwz33YWsEUuIqgt9RGWQTqSUrUT0dKXhOo5W70QkUIYjZS/ztPnb7XFNRpuTXjTX9/m9o5BRLDjeeQJZmqYhAbSXKDnWfxp+tiCBWUEk/X6Z2FgMR5uJPETzO/33JPAk/Jca4tT0fuM20WwBEWd1M1E9IYMIyJ/gMY5AiucMFJOa6CrD42gx/toIZanS4xnreXkQrc0r3FYHsozdSaSKOULQwWi0iZhHHOBK1tJkAa9BGwTc11bgv0HWFUS5vgwG6/Seqvd4UkxaigHMJvJGVhrmLqR4Tkb00mD8dyKanWh46z3mDsRnipA1e7fnLYfgPS1W/jp9L1nvntMBnJGTCSvYIcPRThxoyER8Ya4/h9AauB74E7QOVvF+F2m54agiTvIWGwfLic1AN5Gg+R7DUY5otJpYE2OBffDuKqRI5bndeURd9tFEnGKJ+8gl+PAhxOcKDf9uz0OKYdzC+5Oxt8E5cxGPnq7/OWS8TCQSwje73SNo/kvRQb03CD4zxfO1VHgfDGdI8asFSFntNMwbiXyYD/LBaV9D1//vriEF8ExXL1psE4is65lE4HUevPMo2rHtR0pVu9/NRsywCllKulDM2gaiyPiTRAB+KfzNz22JeYmYlUuRRW6d23jPsDQwmPqhaC6Ky2tGjPZrRD3f5LbC/SeBugkJ3pX+bR8Sdkm5bELMbxFSKkchBn0FYqzDPbYSvzMBfricCK5OruT3kGD9ivs9TlTCeI44TNJBlGFqM567katzoscwGilCB4h4vxbj/VoivcY+pBDdT5x0ftPwNMN557v9HKSoTDZOM4hToyXESegvEhan5WhjUEe49dcaxuFI4OxAm4E8v7OQOLE6wzgdg4RSPuG+SocRKhE9FRs3qxhMJzTwfTT3Gz2XzUgxSfNYapzmIQGarMMT/bfVcK5C8ZBJcT/m8IbkpupAAuorSNFKSuBUoiTWOuPgABHj10McsniacEXjMSeLxnuejxbCsrmPKLPX6fE3Goej0PyWEydnH0WK7J8iel1s/O0jTgVPQ5aTFqKc2B/6/yvc3knj5j4iNc4AsviXIFd5IxF7uBZZzyYRKWkq3P8c4MHhmocDRIzjzR5TOmTQ5nu5LvuVhayHyWrbihSQVxF9liFr5joUJrIZ0VOT5yMfWa9v8v8Zhn8NosWJHtcYIm54hGHvQetnHBG2ko/W8GjPXQoXuBQpO3lE7OmV7qPBfR5FdPck2hBWnYafTEQnxR5Dsti1n4aXauM4rclKtJ7L0Zwn2kru9yoGww7O+S1kpT2C1lenf+siYnAhNjKNHsdThq3Gz96IaKTf8NQa19uItEl5aP0lD8MStLbxuKqIddiJ5jjhs4Kou5w2J7uIJOJH3V/aeDYhC2JSCr1poxvRbzZn9RqKAfzlvYYUwDNdiwlLWymRpmULg4zn3M1IAHUi5rXMz3QgodyBhGweEjjTEBNrQYpBFVrEA/BrDzEYQE8+con8LmIyI5GSl1zOjWrrzZeRIPljtJM9gpj0XqJE0FyiOsBhoh5wn+FqRwz+ABIYb7nPpMwWESee16BYlQy30+J7c+HjdyNhn+/+SpCQegYpnG2I2jYTlo43EGOvQgK7lXC14DEcRIxzv2G5hjgVnVzbi/xuH2Lk9xACYg4SCiD3z63uowrebjS+8hFTnuNxJeZdQShSR913mXHViRTIVuP+Ij9zN6KDbqTk5iFlodEwbScOLTQaFy8ienmOSGfzPLI2djOYoDvjVkRHSVDPRJuKo4Ql4yVkCdzvMeeg+ZzvcY1ErtJ9yHKSbdjXGL9jkCKbLE/VxvFEooxfK1EmcR6yYExE9LsHKcpViE4eRXSwGtHAK0jYrUWWta8g+phECLeTaI4PEUl+c5BS0WvYvoJofw6RPiXFLLYYNy8hYX8E+KTvvWD87tT8vrvcz8wiDtAcRZbAfo+5Eynq5cT6ecm42WC81SM6+t7JSK48E9FMstxO89h2oEMRo9E66XRbKS4tm6g4dJvbud243uR5fdT9/ch9dxJVOVJ4SY1xfNK/rzX8swjaO4b4W48/yYVf7fuTET3tQrzkztNgzjC8N6J1nZSxWcbhCkQLm4jDSNVur8rj30Qkdd6CaKgYWcnSmrSS+9cpQCzDY0ub72q09i9Cm4UJwG9B9zNErrx7ifCSLuKk8xgGreE8T1g1r0dr/3XjcA7auCTe+XnEd/L9eZqomPQgosdORDPj3PcuIh/oKv//mH+fiui0gTgQOAkpp/t8fx5RsrODyLuYz1m9Tn7An6Hrw7uGYgDPdM0fJkEzBzHbEciKloMsMBVoAWai3d+jSMD3A3NKYPhH4O+AtxFTu4AoMVaCFI+/Qgu7BiVZvRgxu79AjH8NWvBH/fdXiFQft6GFv4mowtFNBMqf4+fHEyd7S5CFpxMxy3E/h90tYqibPI4Mj/8lxHDWIob+GhIqxUhIbCPcsAPGUytRdu4oEaDeQqRr6CNi6p5HQmYB/GwZXDDTY8sgkh6PQUrgHqJ6Q6XbrECKaQ4SKB2EgrwOMdAcxKy3u686wzdOOP2bG+DXFiDmm9z8KX5oOVLUxyHrxp8hQZdicT5PKMvdno9Mw3gcKbjVSNkYRwj6jf4+j8Gk3m3PQFENEvbtfu4YsBtOrYBhdxOVAzKRNbGJsCIXIuvWYiI+dLX7KCFiJkcjZbEB+K/G21T46yfg15f6/QaiJvEWJBR7Pc8lRP63FOC/2vOUhN1PkVI7yeOpJ0IiepDgnOZ5On2zcT6ikURbGcLr68/Apx5CisP1ftaxWQObIWMlcaDoMDo4sdVz0qJ2dr0KU2ehtTYNCeADSAlOhwq6iJjGy9H6s5uUyUj5ydR49z0Ek1I8VgmDCcB/+G34eHKJH2HQMnrqKRh2ldutIE4Gn28Y1nvMo42Dhz3fbX7+OBxbDQWLEZ0+gPjFe0SloSmEq74I0e9TaEPUgSx9pQTPSGOuImgOREdf8ftJ+Z9EpJxq8tztgi+9BX+8HK2Pce7nXkIx/F2PYQKirTrhj3ykUC73XGw2PrYb57cbtpWI7vLQAbs7EG0/ThxwOYBofgtaB/UMVpkZTH6+Eq2nSmh7DYqmIDro9SdtmI4Yd7cj/jTTcGV7rjbyfg/EA2ht9aDNX8LZERSe8gZRiaTZY2lGKXFGEYfJBvzcIsMyAm3kKxkMN/ibvfBro4CHYNfNMPUTiF/dD/zm2YsB/BQfbAzg6wzFAH5Y15ACeKZryTBZSyoQVe5CTGYEYTH6ARJKf4YYTSfQVAJ/1gj/6ZwPH+Z/7dX3LmyvgJaWyNw/HDGzJiTkv4QEUg5RWWQ8YrKz0YGAg0g45iDBUES4CKcixpuFFMnniVxuzUj4rEXMM8WUpUDtK5EQSHAdRIrhY0TuvYuQIjIWMesUV9flvuYi5XU6Eo77URWDjUhY7EbC8DLETBv97OeQgpeJGPTrSAA9iea/zHCPdxvFxIGQTuL0ZD8SnsliVOG+q4gA7n1IeXoB+EN49xE45yNoThYi5Wo3IazXEzFb5UgwTfD9KiRY6glX1JMo3dBzSIleipSBpESWeUx/SCQvT5a4KiRwUyxdK5Gkux8pRnWe308QVUG+hJTA543TNxEtTHafhSh2cKHhLkMCeQ8RQziHKJN3sb73PQNZU9xOF3FS/joU5/hdpBBM8nyksI2kKM/2XOb6/R1IqUybFQxrF5GUuJGg71YkkMv8bKHbTtawfe77OZSnLlmUnzS+qjzO1e5zAG0Gn0Y0X4vopJJYZynOspY4BT7Z725zmyn8oIA4tX45nJoIwy5G8XhJ4T2ElKN0wOSo3x1NKFVXeV7neWytfnYzkX90KlLYZhAViJIS2YKs7clN+rT7XEnULD4A3ATdV0DuLUjxXuExzUKbi6eh71XIKjY+riTqdmd4nuYZtlZEPzd5PClmcAGy9hUTm9kpxKGiQo91NFGJKW0+m9xHM4PVafg8qs9dRcRM43mZ53nZj2jma/6tCs3/KOQVGYNofP9pY+h0PwsR72xHm8cUdpNruP8bvLsXzrkFWaArgfVnTwH8JB9cRYm/B/6SIQXww7o+qHn7j3e9gITkWrSb+zRahE1IMXwLCd5sJFAuR8LoVz7yy6H8AWSdA4c+An/bItgPobijasSgapHAaEEMNA8pal0IJ8tRjON+pPDNJEqOJXdLPxIQa5E1oh0Jhnai5nElsrbcSMRqHXI7yaraCQP7ISMF82ciwZkC7vOJk5OZhPXkZZS7qwMJhwa/k2K3BpAVJbmb+4BHoe84ZKW4qTZk1djs9/qRpWmK/z/useYgC8k4IpH4s0SFlzzDOsW/5/vebAbLDdLlk4WH3McBJNiS224iEm7LiLxua91uttvZTlgae91+im+sJFxyaW7SHNxOKLXHifxr84y/NuOwmMgH1+C+p7m9PX5+JBLk1yGX2Fjjeo9xdBIpeJuRAD1EHGYqNCzb3eethmU5ZI32vNWgeLgS47GOOBU8n0jf87zHWG+4mpElfxdSDj5vfFxNWB2noE1GLZw6CsMWuZ2klF2OlIs17mc2kZpokuaQmzy284mKDTMRr9iGBPxSJNT3EcmS1xLl+NoRnRQZpjy0Po/5nSqkJHUjOrsQzXMlUjxaYdjDRGqop4zz6UjBGzC+5hG1fSGqYBxGil2Vfy9B9FROpETqJQ6A7PX/m4hDJl3G6zcQD8HvJ0/CLsi9zONvJfKjtht/iyFrHrISZiDav9Owb/R4dxnGKsQz2v05SSSrv5g4xX8LUau9G9HTOKKk5zGCVluJcIZkBT9BbGJTSEQ6kJM2Hwv8PW3yDiOeWWBYktI8hYiFzfBc5BFJ1cuI0IRmwXRiL4z8LT8zlThgN3QNXf/Ka0gBPNO1AMWOPYaE148QIwExjfORkBxFpCcpRVuYX6ZrBGL8tUhxupo43dzpZ0rQDr4EMaQ+xJx2IBwkC0RiYtmIwS1AzDm5ZbYhATQXMczk1n2dcC0lV2oPkfewQX1nzHQbvW7/CmSpy0LupXz/vgUpk/OJ/GMDSEkoIKxB7/lvsmxlIiaeDVlziHxxIKY8CikATxsPaXde7HZqkPBaRSglI4230YTV6C2Ps484VDTK49/vZ2cQLu9CZDVIbuTksm9mMJ0LMzw2iGCaaUjAHCaqmyQL56UoHu9Sz0cbEjIjkBsqA9HEYaKkYA+RQDgJ+Eth4LuQ0WBc1CFlcQoSpE8TZd/Kico2xwxPHxKYAwy6OnnI7W9DFt51hu92RHMzkfCcQ5RSvAwJ/Q73lSw3F3l8LxEl7hL8xYhmZxIHR3YjpapXczQsueRzkEKyCymN1cZNsfvc5D5v9VwVInqqItL7jEbWtkSLB4nyXt1IuR/j9m5AB1PGI4voGM9nC7I0rTZc65BCWooUjE7i5OitiBZTvN8iBq1M23fDpdOJakeZhrmNcNdnIhroIty/x4lY5B3Gwwy00Rrwux3EiedDxDqqRHSRiSykpWiD0IuuZPFe43YPorVSZtjThmMPcQil1O/W+/c7UTjODMLanI82qmP8bp7xMoeIm+wn3POtaG2UEEm9k9t9hnHSa2v0w2itDRiOG4l4xo2IL+YbL+PdXp2fmUworNuAP/KYVhIb5aR4Fxqvq3VrZFKQ08bjLF4DwLAPsK2h68O7hp9tAP7dXr2ICWT7/08R9TU70OKuQ0IJIov72A8Vyv/zqx1ZNzYRuacGEKNNweVXIHzsQC4HEJNNgc6VyBrTihh1OiDyJaSgNSAmXoWEXIrVSm6/qUTalGTlKkQpLzqJWprJglPsNlYSBd4zDV+L4XgCMchJDJ5a7Nvr70kofMT95BPpGubq2cFdeDuKh2pxf2m3/yqKw5xo2DKRoNhMHAqaSeTeuxyohb+5GSVJLkdCaDKisfeQErAKKXV5SOi2Gc/HiAS5xzz+RsOUiwTOAXjnLuMUJLhvYzDZLg8jAfyA57TQfWxCSnQnEoo7ieo0k5AF73mkOOL5SicVGyBjGVof2chC3O/xFRnWWUSOyEnGaZXH+0XD0OI520fUo71B43rnVb//BHJRribyq+UaL3Xu/1q0brcajvP922yPp4/BwyXdy5CymYGsNEWEYvFFePfvDGuK1ZpFHFjI9fwuRta0HUSqlpnIynQIWcmTtesOolTinZ6LLkRz1xkHn0Y0MZPIh1iMrGvric3VIt8/SFjBxyKOPgmFpeyEd+9Dm9c1xmmBxnzpTLezmqgZXOM2J0PfbcbbFCKUIcXy3oVotMJz0o74wibPazWRhqgMKXWHPc59SIHK8P9fNm6OIrfqCrQeC9DaeoOo+9vqcSYtIZOwvBUb3m1orvOIFC71xEbwsD/TgFvhyPcRvfW6r93I8ryTcAtfScx5ot/5kPVRImXNjUTap0zixG6NcTgTrfH9xkmF564Lrb0uHEIkXJ/a7TnLRDxhI7Io1sCEBuLAWibhgj5L17/mgMe/5DN0fXjXkAJ4put/ELmcxiFLRh8KqL6YcHMm5pJNHDD4ZbpS/FIhYoz7oO8RpHg8ghhbD2LI1QyehjtUi4RCDhJMC4mUBK3+/yLk5v0ycQp4DRKKMxFjT4L/DSQcOxDzXIisNo3Qlywvj8PbtYQ7shQpJRuQIJuNBPI4wnLWhuZxh3grBxCTP4GE//PAIXj3VSIeKrl1jiFm/3HovtntVCCL2BJVJ2APoRhvRsy92bhqQ0KsAdpm6dlfm+vfmuCdw0SR+2IUm7QeCfvkgkpurPv9N484/XcrEh7pcEYDnHsFsgQ1IeXvfsOw27i6xX1AxDdOQEJvi5/LRhadR4iTuNcbX1Phb+6Ddzcil2MT8LXhiquch06Jn+/5AK2jPfA3VyN66UMW3+QO3wQD3/aYa9DJ3rlw6hbkIq2Cc1caD7cQitYowg03x/cqhMM3v48E/ESkbMzx/0vRifkvaKy5VyBX+hjPW4b7b9Z759yl/gcTPRf7/xaPoxDR8iKkAOchhbwIbZAK/MwGZKFc4jF2IAtYPoOpkE7danzsQ3zkakIRHu13q42HuWhNZhhvK4hTtRciun0SeMVxYn9E5MSrQ2urRv3yDaSIX4aU92agHbIuIaxdTYhOdyE6u5/IkfcCEU5Q47YOGZ8TDd9njLsH/M54w/C44Z6DFNAMZIHegfjry2gdvog2HYm/ZqI4zXTw4hrjcYrbW0S42Xcier8O0WUnkZC/FsauJ3If5iN+MMcwFsKpFzyeLrS5OOa5qCVc4R0e28NEHN9azVffAmJjMA3xvRR2Mg8pldP8t9tj2u5SijONv6Ue0+VoDdYSCbu7GNKahq5/8zV0CORM18JhYV1IFqE2xIDqkBLwOcR09hCpQ9bPAH54NiD+t12PVsE7e8VMHGs3yGxziEMcq9H4P4UU4MNIYchFDDFZzA4h60uj25rvezOIE3vpoEAFUQe3299nIAG3E1kUctz2SWirhaIbENOrJFKC7EdzUEZYA6cgF1o2kYT5QWRxmk4kL95OlGA7hpTKYqSUJjdwocdyI7Iu1aDd+4BhvNKwHCNO/eYgJt1NMP+97u86xMRnue0uBk9Znnocht1GnA6sRIK9wjjtMP6fIhKVZ+ldKtzPbs1P32qhYthEP1tv2GcTNJtJxFxmIgV4CYohqyRiGccT8aFl6ATxbOGleyfkTkaKaTHaLH0HKYO3I6W/0+92GMY1hjfFQD5KuDirjPcdRM3iXrfRYdwkBe8zSIF9CingyWU2BW0GZnqO7kUWuKT84HdW+7MZ0V4bUihWeg5n+P0OwhpYaLxcR1ij1iGL9bNuY6phuYKwjKU4rnaiEkmfn0uKR7bxsgYpCdf6vf1EsveHiBPdh4n11eA5aza+mlB2gfWEG/vPEJ0+bthWwKm1PqGcNq9bjZsXiLrPR9zuF9zWSvc7FdFNtfsZ5TbGIiWsAq33mR5jcr2/ihTvIsLif9jvFiE+sdn46CKSJbcaD0uImNxMj+1TiG+NQ7ST67k4SuRCXIHo93b3cRQpv7s9l8kal2k8JUtkLXEafguwFI49AgUXI3pK4QqjkXV0M3HKuxnN/zbj9mbDl0JmStEGahKixTUe161+bprh6iVSYD1NrJdq4LNn7xDIx/lgD4H8kKFDIB/WNWQBPNO1HwnV9cjqlxbxe2jBtiCmloWY1EG0OLe+exaA/T+4JiJGeJw4RNCJmGw6UJCBhACI6f8cMah0WrGdSFBcT5S5qkZu4BRP2It2sek0bC1i6Ovc960Ixy8jyhxNVBqYDkVXEG7jbreZbZiykZA4gFwme4lC9s1IabrfcK73c0lYFSGmvxvNeYlhme5PioF6TG0dW4iEAIZtBJGeptdtJ+UqlyihdgQpO8niinG3EVlXWmDYCD+bgxSqLW5zG1LUtnmupvi31wiFOBsJVscPZX0Chj1EuPYrjbOXkBBqItzmHciSkARcKZHo+wii7zz3scfjmAy0Qe6vEKUC9zCYQ+3UQf0+uDk6iRSfOiTECtxOCxLCZZ7X9fDuE0Si5QZ/8olSYu1EcvQStEZTvFoTUsIXIzord3+ViD6bEY0MIKWjHwYLmvYi+hpHpDDKNlzjEK1MQJuLXR5vk8fxomGs9L20CRjrd4qRAjrX7Y1GStZRIt8hHs817u9xw5jicj/jMfWehtd0AGc2gyEPB3YSJ4KTgjQarbWH0Lp+WbAMm+W5KUAu6BT7NwKtkRNIySlAStEbhCKc1uE4xA8vdLuHfD95CdJGaLHHOx7Rz0S324EspZ1EqbzZaJM0EwZeQ3xjuN8pJqyBhYiPbEPr9FHiMM7lhvU64pDFOCJO8ghKW9SM5jltOJs8d11EGchM/1YqeAuSVbjH8O8iYoNb0To6jui91u+OJ2i3hyg3txVtUCoRvaaYvyRzNiNrc1o7o4lE6csZuoauf9M1pACe6ZqOBFk6OTkdMZd9aBHXEkL8z5FAyCPySf2yXO1IuSsjalq2EeWp8hEzPIQYUTZShDqJ3HKtSMFIylYhwlk3skwUE7Vh25Elrt3vbiOy9Pf7/S4iGWqy2BQSherbCYV7PRKI0xGT7CFKquUjZn8zUpQ6CdfSWGTFSNa95NLaiixXrcgFV+r+p/rZXii4DAmETiLmsMP9/cBjGiCU4x7jNsV35aA4qmTVqSaSuc4mLJZ5RHqRZNUZjlyou2GwCGcekdak3G3t8LiTZbTdbSxCNPwsmtOE+8lut9pjTu7VPnSQpYuI9ep0H4fhyN8ipSjFHZYiAb3SisVBRD8TkELRRpy27ToN7yuJgxHdcM4lxKnjTOO5kUievAvN/Z1ojqcR67GQSDK82+NI/Uwj0vj0IlfpOiIBcy5ynWNcz/U7i/zOTMMJUbKtBNFcPVKu97iPDL87ijg80+Fx5Al/fM74POl3iv29Re+8nSyaW5CykuJA85ESW4PowIozrwAzYMJkZOFrgL5V7qvOOKlGvC0f0eA4RBut/v1PDOdURE8jDfNMImVPKaKHJjT/G/1/GUrFk/hJJprnvcjq1mj89HqeZiN6qCAOisxz/zMN4yTIuJjIoZfo8U7jcZfxVoZousmfUYYpi6innHgRRKm51F8zWv85RMqeUf7eG3+765GCXonoegey+uF5S1bs2R7j5z2Ww+77o8iCmU3Ui15H1LdOoRmFiFc1+95Ev7cOKdEzCf5xFq+BD/gzdH1415ACeKZrEmJKbf77MGIOGWhxpsMM+xAjTjE5/+PnMPBLYgX8+3eh7+di+nNR0P1YpGhNInbxLyPlrxrFTN2LmFUShruQ2+IltKvOQcwxKQaNQBe8kZhZKVGXcwESVAOIaZ9AjPx14nBEPbKUpLiX5OpKFpqjiNFPQ7FTrwProXs5UuKGI6a5yuMc5zaS4puC3yf6twrkntrg9pO1pps4LLEaKXpTkGtrG7JC7TEOljNYS3lgMdowfMR4PYpihsYgumknBOVCwkLRT8T3JeX4ciSgkwtsj9/fhQRMOxJIe9xeN5HuogYpnjNQgulvu40jRCLnXYjWG5Ci3+Df8/xej+dvuO6Nrff9UkQbxZ6XMsPQiQTvxYTls4s4uJBHJCYvRgrRVDTvhX43D7nZOxGdbQP2wr7ven7yieoh5RobqxGNpFhDkJWxAdH1AXjzIDoFfbtx00BYl76AUrm8AKeWIQX1KJAF3bWeixpEFynutRSOHEehAoW+3+nxbjKMXzfOdxPW7UVEbd8M4ztbcJ13q+EZQMrGpzy2YkLpHo1iznKQ9akJKe7TgOsgK63tEvfTRFird7nfER77TCI/4pdGa92c7/lc5982I3pagBSdbP9Ncb5TkKKY4l1riM1LIVK4Po1oqRbxkPWIr6z0s+cTJ3vL/OxjfiYHrbU1xutbRIL7DOTmvdm4bSIOpKwgvBvthIVvPeJB/e7rZeMCpOh9jVAG6yD3ISK7wSuIj3hzyMv+W4FocwDJiEYGKxANupLLEI/sJFJB5Rpv4zXe7lr3BVr7f4iU9hy0DmqJVExn6Tr5AX+Grg/vGooBPNP1zDAJyxK0SCvRzi1ZewbQYr7Fzy/y32bYfriES+/9iBjzq2jh9iGmcwD4G+SSWIyY48eBfnj3L+GcCUQFkleRu6XAz/2q3z0HMeFFaGeddpz/CTGQnagKyXT39/8gJnsEMcX/5M97P4ctLWJ884lkrdPdRh4R71cLA2tdr7gJCaC7PebjcOI2GHk3EnA3o116G3HC9hiDyVUHvg0Zd/teIxJKVUjx2I6C1rcjZfTrSIjsR4K6hcH6m933Q+4yw7DFMB32GBLjT7FJ2RoD0wkXfhtSLEYhV1umYZ0mXJzqcRqQA+7jUyjZ8VgkDNMpx2sR51prGEAM/LtI6M1x2z2C/W+Wwq/l+N0BJBA3IotGUmhbToM/CYEkhKuREnA5gxuRQ69CaQ1RUmwHEgw9hBvwm4iexhontxn/tUhZq0TCKlkFbclLsX6D7s20AcL97ETr4DGUbiMpJ5mG4WkGD74w230cJeLVnkGhAlX+XAt8GykZe5CA34aC4e9BwvV/IAXgCFLscxDtz9Oz39sJv73SOGpDSnoXouki4zKdrCwzfG8RLut6ImF0judmFVrDeUS+tnzPSY9xNNPPjHXbRUTccC+izz4iVjTFsz3nd8agtV4P7z7uMuNXGOfFaCPU5bGXeFxH3Vc1EYqw320VEC7HNrSpKSXSv1Qbx6ORgrkPKeA/Ou292YhHPIvWVon7W0hUNzqKriVoPa0hEnUnGBYgWnsebRquRXS42/hsIE6K5xu+N4zDGcbRVOO4BLgM+voha5bbn3IavpOH5gbjDI95jPvsQDRbb3wcQ+t3DlE7ei0RbzqAFPvhxKn6fhRTnIdihqd5jK8az9cQG9uNfvcaj/97yBMymlgTa9DaW+y5+O/IethCxI9eLfhPfRuGXWH4HwWuOnsxgJfwwcYA/pihGMAP6xpSAM90fXGYFuX1aMF3IcGzCVkNNqDFmQRID2KwKW5jAC3OyUixKUfMwafDGI0YwKXEAZMi3p9BvwsxuvVu50H/X4OY1nOIubYiy0nafbYQaR3S7nAbYp6jiJO2lW5jEpxaBcNqfG8iYsqdfn8asBHeaYVzrybiqnagnfcJZLGZjBStPiJfWL7HsR0J0d1IkPcT1TrmIKvMNCRkS9zvEt8vQgrPROQiHIcY5HCi9NTvIuHU6neSgO4nTg3vRgptFuGmTVa3LZ7nLxCnEMvRbrzDv19I1NE9jhj2V5CSNEWWomHLCXdtvdvY7jn4tHEymjhoM9n9DRj+dUTR+nIk2OqIOqs5iNaeRoLwOBJqvYSlsNzzWql5G3RR1rn/JEB3E1a2Per71HuuHDEPOAmnHoBh93heR3sutiIBVo3m/XvGzRhE49nGcbdxUYdopp1wpW8j4q2aPb79yBLVQcRh/TZR3eVHnp8HiSoMA0Ts32EkyK9BtJHi+4rRZmmP8bqROK09Gq2hE4imujzXC/x7Wu9pXPsQbZa7rQyknGcTSu35Hl+z+/9j5GoeML4uc199RIWXIkSjtWgdHCJo92qkiI5Ga7qRqPm7kUi9Mtx4n+I5ehIp1ysNc4WfrSUObsxwu5cRh2LS2tvr58oN66XGRz8R47gfhb/sJHKEHnd7qxCtveXx5BJ5NAvd7jWez2J/bzKeWo3fk0RlnWL3nY3WSZXxnaxhFxG1mStOe3cLESZRTqSyWuj52+f7A3r+1Ldh2FPG/ff8XhNsfwsuzfR8HIVjm6GgxmP+ip87Ypw0GVdTkWV8BxHvm0/wnjykAGYSG9IUc9vtMUz2OKoMa4fbnok22t3Ag2dPAfwNPlgF8CcMKYAf1jXkAj7T1cP7A4MrEHP8PJEgOcVidSCGlHbSW4jTaisJN0m22ylGDCIxnXoifqiUSFbbjJhVen4qYjL17uuP3cYs/3YnEuwFaBe9zTDMRExmOhHHt4soLXbI47kBKSv73U4XEibtQDWcewuRpb/eY/yEfmM8UtDOJ5Seco8hh7DOVSDhVO9+8/1uuT+LiTJNDUiY9BO5rvr8WxKWq4Hz3H6n8VWLhEoKDN/msZT5t3YkgJKCOhopzb1I2V5gHGYY/tlIII5BjLwHzXclcg/ZKjTsbqJsVieRFPsL/ptpHJ8kqlwkZTOboKXjSKDuIKojrEP0cZhQLHYSrtsOw7XF4y3w2K4m3JFzUdqPpICdQArTNrd3KQy7GlmnJ6ntYeONk150+OfLnoNcw5aDXNL3IcXlINrkfNnzZzf4oJu0HdFhUpCzCKtnDpGTrkrwMA7R6reQwL6DyMXX4P6mIgXvMrR5mkwkwS7x/G42/jcSNYLTwQQI998oz0szcap8HhEflzYujZ7fQ4j+d/j5rw8X7ksN1wrjq8HzO8b4ft7/1/u9EYb1fMNfSZz+3k3Elj3g51Ks5q1ERYp8w9NCxJY1etxp09hgnH6ByJ25FdF8o3H7J56D0cSp3QyiGsohwzgPDqUNbzGy1tvKz0Nu71H38R3f34ho5yJEjwcIJT2HyD9ahNZnJu8/RNRNWFMHPObXiFP51yPeOAmFXIzxPKT5bEAb0LQ5foMIQ8gEsmHYRwmre+KDxXDpZOQVct8Fn0C8tQNZR99AoR9PoHWaa3hXo7U6ElkdZxL1vJMCuQ+t3zpkQdyO+E7iTT2I/vLQmu7yuLoJZXnoGrr+ldeQAnimK+2+WhAT2IgYajNiaIVoFz8ZMfRWpDh1IObTrO+ndgIZl8KyV2HBEXjsFOTO1644Bbe/QaQi+RFiZrsRo91LxJDMRAzjC0Q+sl2IMdyFGOA4Ir1Iq9taTmS4TwpHJWKIU4CpMGwZkcz6hMeUSeSpavKYipCrqtvjHOv3dhvWDuPvGr+XrDy/jRh7j2E55DGPIcqEJeWn67QxJOaY6fmYTxRwn8j7D1C0IQZa6Wc3EaXhdhjmRqKU1q0e/9NEypVMj/0l933Yc3I3suQmS+8C47oHWRXvIyx/xR5HEuw7kFKVDgNkGt4UzI/h3GP8v0nUnM5HSlM2Em5FaB7v9HvJopZnvOYjhW20n1uJaOZJjysJ/GreX0N2FFI+Mjy+BiQERxP50V4/ra8vEqej2932JMRRyoicjw1IyPcS9J7WS4pJm0GcME7P1/mZVf5+EVIs08ap0Lgv4f0u3AbCfdjusaxC855c9EV+73rjYDqRAmYzkermVSKfYy1hrashNjftxAna14FvnoyT1Z1oTexFYRFT3EY6wNRL1Hwt97yNRUp8D2HNm+k52mxcfBmtpSaPo8Pz10PwhbS5y0eKdIn7SXSzizhB/QZhRU0WpZeJesvHiLRNXUiR7AEO+/B3rX+7iah2Mgtt5so81loi518FWu8zEL3VIN75MBFikDbMvYb7JdTZe4hn3OtndyCLWQpHmWu497nNBqLsZDehLJ80DJf794TzXqICT7Jkz0U0M8647HdfiReljWK78bkM8YRFRDaFicZN2iCORUpes9uqRHSf47+5aDM7h6hYk41CVR437g6h+Z3CWb1O8cHF/w25Iz/ca0gBPNOVXL67kBLQghSapPz1o4Dc0cTib0MCox8xwCUw7A7gd3Ph3HqodcDgfGStSNatajixnzgg0WkYShEjqUSMpRYxtj1IoO1HTHUSkaz0SsQ8PoIYw48QE0ouiBSEn48CmA8iAXOMUO5AysBc4sTiOI+tEMVrlSPmugkx+RlIaelxP53IOnYNUpY6kSKagxhjARJoXSjWpQQx+TbCxZRBlIKbjRj2y4QlsRkx/0VIsC43LEsZPIzQ/QDQDT97DTHQ5UiIFhn/z582D18krC/fRsLhkH+fA/xP463RsJ9EQi4Jxz7kei1i8JAEk4zPtbz/ut047HZbKWZxtj/5hnMuEZtVTORq23TaOJN1eDQRW1WNaKzQY1iIFN49no9MNNcniXRGyTI4h1BMpxo3X0VKWBWR0BnPRY/v7SMqqnyaUE4r0Txf4zGuIE6IP8ngiVfq0JzXI9q9mEg3dMhjvdzwXYzo4feyNOef9P9FSKHf6bYykPCd5d/WI1qr8t8UyzXutLkZiRTjGv+237i9wuPdiuh0n/F2BCn45Yi+Woy/ElfUGOd+jxv3oz1H/R5PFYMW/La1RLzfXPe9yvOV3PfJc5AUHdx/jeGej9Z7CsfINMy3G7fXoPXSh9ZjI1Gqr5CoB305Qd/NxlWF56pF7Q97iKgB3Y6U8ErjP9Nztcg4aHW7pWjOtnqsvShe9jq00U7u0WrC4vh5w5UUrjWEdXO/+9+IYkRz3W4DkdXgEJGLdDWyxBURNZhneO4OGhdpM5VD1K2e5jFWGNfrPPZlhHUxG9HIWONoj3/bhOj7ZcJ9fpHnswfxirFEourhhuvLhreE4OHPo3W60fee5qxeAx/wZ+j68K4hBfBMVxOxC30FLfrk7ridyM6/HjHU6xCj+1202FsRsykB3vkLuPMeWP0/1fZrhGJlq9rIXyXi31qB674EX9gL918H9/4tPNsJf7wafudXogD6tVvh4ifgtsdgXwdc3gbn3ghX58AnvwVPnYCDTbDg/xZTmY0Y31K00sYiBjpAVCwoJA6urCUqUvTCgbsQ83mECNzORC6PTcRp1P3ANBj4qcf3MGKUL/i5TyIr12T/PsF95RkvrYj5vYQEyeVEGo/khn0ZMdixQA/89W4/N9P3M4BjruTXARfMRcLyVWRBvZpBS8lAPVJKEmNdZHjGCKaf3QAcgoGJhGUlBW7vIeoTT9Vcsp2om7sWMepez/sP/D3b/SUXc6PbWW2aKjd+kkXxTiL/XRJEdRoju42TjUiZHIsE1HcJBbpQz735mueihFCqThLlysYYF6NMD81IERlwX8lylZTtLkIQdiEFI9vznI0UHc/R4MnTKciCk5TkKwnFfSZy9z7g8Yzw8z8hLI7Pe/6LgEl9UgZPeswDcGqj53YcUoILUHxbq/tpQUpBnue6FQntg3DgNaLOd4qPLCZO2p7vfuaj9V/p7yMQzT2OlIVbNNasKcbNYnT1o3ixi/zcNuPp40AfFH3C87wOHapZRFi/Z/izBU7djzaEHYbzAY9nlmmgXbC9cz+irWJEqw/6+QHEs7o83xOJ1ExFbjtZjZ9CCssfua3kgkzSOuGymsgj2onorgOthweQ0rYTWUpHu78U2nGt+5gO7LlkkF4H82A2uY8xiLZWEwm2ixFNzET8p9M4m09kaFhofH8bWAZ9qd8DxBpY4jHtQC7dFO6RNimdiCdV+f69aE00Ixo7TJTH7PXYb0aKbtqIXUvEOnYi3thBWD2L0Jx3+XMDsm62IstqIxGP2m4cXM7QNXT9m64hBfBMVzFx4rcXTrxKxImtRMKqHTGD54n8ZJ9Hi7bO94uJfG7dbrsDCX2INBF/ghj3E4ihngcUXQgHfweenQd/Mg8+Wg0X3RUJdAuBa6+HrJ/DA5fAT78B5U/Du/8DduyAH1bBttfgxj+HnnPENHPR3/nEbnMVUdZoAzAFuq9GgiqfwfxyEy4hlI+LCffbJL0zeKLOsUMZKxBD/TRi3D8yHpLFoYFQLDYjBjrcuNuC4ojGI6be676+RZR7m+S+xsCvP4AsNOlUawkwAzJuRYy/3zh7xW0Xus3PQ0YxYtIzEHOt95xZabjgNo0r4w6iAkMncVpxHpGoN20KOolqFZchYXHEY52CYtnSe11IUJQigZRP5FZsgrcfQspzPqLBP0V0tQvF5d1KxIAVIsF1H1Li/sTt2PV14UwkkNe5v1rClfYqoq1ZHvsfEYctuhisoTrwjMd+ObKaTvOnjnC3X4uuWqQ0LTS8pYZxIxEucROi+fVEYvB8tOl6z/e/5v6mEImrewkhf4Xffc9WqeFIqNeiuW9DClAPUjpS7FWLf68HxsOETzB4Cpx+wnX8hOF+DdH/G4gOPoIUm83u8xhRt7YY0f104+46ItZti/ExxuPt9Ts9xsmlSPC/5PmZZtx2A3Ng2Cd4vxI72uO7x/B9QWM6d4FgOrXRuPi6YX0GKSsv+LNNcAw85T7SoaFJ6m8woXEXUkQqiMNIeL5SXG+nYc8QTinwuzegtTvf94YbF/lonRxHrtqbfyz8VKHN4e3uq8vzlejudmIDPsG4yiZqhQ8nwgxaEc/6smDJSuEOb3mOtyMa7SIOR5UT6Zm2eNyjES0cQvlC6w1Dl3G1mMhPmsJaypBlcRKRk3KanytE66MB8Z6XDHMpUpTvQPQ3Cq2p3/L4HjK8M4hDZmfpGrIA/vJeQwrgma46IiZpJIy8CjHJYqR8vODvmYgpNBEncrMZVEwGy5x1EDUoz0GMuJ+IQ2p3X1MQc/krgOHwymdg819DfS3s/XMY9kn9PsVtHquHtQ/A2IPw2IPQ1ws9P4e/fhYOHIR1X4Zf+Qic+5+1Q16HFII9SNkYQIzusL8XADsgdwpR7WIqEpqTiGLnb7mNWiK9RTlRV/O3EBNcT7ip2okSXS1IyFxkPNxJxLTciaxGjxqfBcQJ6W1EmalSJECSC7WVsGrMJGII65FgzkTKRgayIOUjpW28n2v0veWGt8V/k6vwIuO8/7S53IFivEoI5etKj20MEiY3GoYC4+AF47OQOOG4DSleHYYzEwmDdjhvPRIcqw1fEVGbOcdjG+M2rzJ+txgf1xqmLW7jCpTOptXt7zJsSYlrR8pZDVI05xE5BUuADZAxS3/ZgGhomp8tQ8K9ECmmnUQC4lcI91W+x3vIMH3V700iqm3MIKxwhW6nEtHTUX/fQ2weUsxime9fRxwGGGHcfAcpxsWel92G7QARG/Ypj7+JSPR8F1JoBtC6Xu+5yDZOtrqdk8ShiRQ2kkEkRt7l+f2M+0oWshlos1VK5NCb6HlpQArMLiIlVR5RNq0RWYESPf3cOGxClqsx6nvYryJayUY0vwyt5TGel/nAjZCRwge2EeEiOX6mHW1mWoh6v+0e71EioXax4TpMlK97Bm0wmtz/IfVHtse5GSkyX/O8VKL10Ou2psM7axHfedPtdBHl5ioMTxbhsq8kvDXVRL4+3Pcuwp37Bfd/L1rf+X6u2/i/kpAHeYZ5CZF6ZwySpj2I3qpO67OBSBe2FtHsW56zT6G1nDwtBWj++5Ex4RqidN5aJC8uMo6eRHR0nKFr6Po3XUMK4JmuIsRo8hBj6UAMeBNi2FOI6gPXECkAjiCmVE4IujoiaSyIgZ5EVoQFSAEZjZjFEpRLDaD9EBT8nfqYDgz73zD2PO0m96Oz9y1/JSaVDUw8CZ3H4Gf7xDSnAllH1dZ550Ws4mWIgYIUgynuP7n1riMsdMdQ+1sJJWc1vP0EEpydhGWqEwnPl5AwG4WExw4knMcixteP4qxOImGVYhwLkMDcRuTB60A75AOI4R8yTFWIepO1b6zxPBwx47XEiepdyGpQ4jFPQdaTVxCTTrFAJ4hA96QUHCYOhHR7zpo9H2UM5vYbjCF7D9FAE5FS40G/OxFZIccZx/sIZbYJzWmKw2pBSuByt9HlNo6gnX+n4d7huanxuPORMC3xGCvdTiVRBSG564uMo0X+LdvvbPNvNyHFbr/vz0WWqQqiBmsGcZilnLCKVxBJkvf691KiUkOnx273Op2IXsaeNg/t7uO4cb7e/c8k3IyXE5bCEYQC2IXm+YjHshDR+n7jZ5P/VhIbl31EHFcRYTlOcY7Zmpe3dyLF8mmPaTKxXvb7t12IvkuRcJ9NnIa+kTjFWUUkxd6D1vZ4RMu7iZOkV6I1fRtSPkoQn8j03LxGxMLt9fvziFyQlxOJ099A67KJOPCQ534Po81RNbJK9xIHZfYgyxOIfitQbeFe436a8bzTz7Yg2lyD1mMJUhTzPf6XCHdpOXFyfQkRSlCI1uLP4dwxnqc5xKG2yYh+BpAVsMzzWOfxtSCPzS7ER4oR/2532/lEeqCdRB7QBYjOlhBW9260JhK/aPQcFSHFM4V95KM5LTEcLYS7e7LhTPwiWX53egxl/ttLKKI9xCGSfUQs8JXGQzVn9Tr5AX+Grg/vGlIAz3QtQrnNLkdCogVh6ygR7H4fkToBfy/j/WkMcon4kgw/l6yGVYSSsZ4Qxm8ippbZz6n7kLL0BNB8CoYNF/P+keHL6VcfL6D7Wafg7/rFvD5zWp9Thit2pxgx47GIkU4lTuem4PEyt1VL1Do+5HFO9v8QwdGXIQa1h6i+sBgpe9M8vl7EQJf6t4uJdBzbkJI0hXC5pHizdiJtzACh4FRrbn62EzHTHCKGMMXTjUDM9nqkXDQTTLmEwcMVbckaWIWUtib3nXnaOA6e9l4Gg9bNgd3I8lGPBOsG4fJn3zZONyLL02EkHIYjhv878MYLSLiVIKXhJcKCUmUcT0aCcSYSXPcj2jlgeAqIk9dFhjPFcl1JpFsZS8R3Ffv5QqQIvUSkE/oKUsra3M6Vxu00wtLdSKTtyUdzfj2DlScG45Oa3Nf5aN7L0fqpQha1yUix2O+x2A1JBnEyuAHN70KkGBYTJf4mIcG8Cs3vW0SVizUed77h3go8eF4E9W8mcgF2IjqfiuhujfH2IlEK8hBaD9Vw3mUELaxFdLIP0edK4nAChGL9MFIYpxg3ICWjnNgUjPM4ZiMaSVasBrRerkV00ESc1p+PaHW753LAc7eOSPlzzM+PQfGDf+t5WuB3JrqP+4j62+mQQx3CWRdxmCK5Sh80LFmE+5/T5msusZE6Qhwi6yRKz7UjpbaCSJdVgvhKuWGu8rhqPHcPoHm9DPHLcsQjVwu+7qPG4U4iR2YPsY7SGI8iHlRrOMcY3ynGrpaoGJRFbGBa3GeL52uUx3yDx9hIxPftMuwtiD+u8Lh3E6eJvwe7OhCP6DSceZorKhBPLkIKfdpcNLjNQs/bWbx+GVzA/+t//S+GDRv2j3527dp1xvd+8zd/8x88/7nPfe4XBOWHfw0pgGe6kkAcj5h6MRKGS4gYvLmIoW9AAqUKMb58xChmI8Y7C50YrnDbucA86HuNwXQxdCD32LV+/3xguNOzdCLGkYOE42QiFUsGihWZ6e/vIcXwKGKkKdawAQmrw4hp308I437Cank1YjJNiGkeQ0pMF2Jy+/T8eVMIt+Mqt3+9YW1FzO5aomzbTDi2F1UlSTvnMqJMVFK6UvzRPCSIE3zpqvM41wK/Cxd8hjh52ep5mGz8dSHhdoXH9CUi71g2g0ruKPSXV5HSkKwlZb5/J7KGpAMqab6HQ8ZwoBqOrEDK1zT1f8EtRKm3+xCdzDb+C4Eb4aIHiIL0m5CS9ACRVqMSKanbfG8dcBecupXBGEfyPKZtwNPww3sIy8FzRJmpOsM8hrBaH0L0XEOcXB3v39cTVRMyPF9HNY7urYhOdsPAE3BiCqGA72HQtTvwY0QvH0Oxaf8FxW7VRVvMh59sFA7JJRTkdrQ2hhOnk7M8BweIROO3IyXgIj/bjiqe3M1gDeV370TXM28Lnkpk5ZrlOe4k4vBudJv3I4XwCeAPiBjZHuH9nUb//wZR0u12tP6TlacXKQUzNccDJ42HYoKGdhkvq+HtO5GgL0dr7TBSPmr97F7jqNd9f8ywpxCKxX6/i6jZXWVctSMFZjcMG0ds+mag9X2r+8wnYlwzEQ0c9ffrEN1sMj5Hut3XPd60aWrSfJ54HCn3ryF+tQYpqjXEie0OggemjXYOUjZTyMOrRKqcEWhD1YGskSmGNXk0LoXctYgWkyWwjShr+LTnqhe5ZScRB2BuRnw6Wd3HAWvhnWWexwHPwwLP4VaPudTP1nm+Zvj7GI91AhHKUIHo7gSRp/VSmDrC7S9ACt8rfu8JRE/j0AZnB6KBasI6eQ9D1z9zzZgxg//9v//3+z433ngjv/qrv8qUKVP+yXc/+9nPvu+9hx9++EOC+hd/DSmAZ7r2IebSihbtUeK0bIobKkMLdJLvr0RBxjMQgyhHp8n250L1ZLhksto+9aswejJZN5Woj2Ii1UU/EjwnkTLXhZhqOljQRaRleAsx2AlEPq8BlE691202ezy5RGxUO9q970G71Gx9f+ceeOdmJAzHApdB917CbdWPmN5CxHiaCaVpEiE0kiVuKxFInQ8FTxv2euPuDo9hKnKtzEexOMnyUO3nLkaK1eWIaU4kkldvhtePE5nz5yNBU40Y+dMolvDLhjvFknUyWLf4nG2Ee2YxssDeblyNc3vJHVlIpOEoZLAm79jhhFt24Wn4X6c+BnPIFSOFIMXkXebn70eCuhYJ7eTCT4rZ1Uh5utOphS5339s1noHvq++PP+AxJMvVIuMyhRisRIpXK5G37y2i3Ndy4rRsA6KRbrdThiogYDgHIOOjMHKNx7maKLu3DjJqjMc/ddsVxm2++12hPn5jGVHi7RvGbY77qyOSTm9HwjyXiDVtNi6KCbf6NnSIpQPIgHNGeW4uJMqorTW+dyPlcZpxs9pjedZ9FXo8yTJ0Qvg4dzyRT/BypLyXuP+E39F+ZzVwK2Qs82+N7rtB95kPXAPnrUHKwQBaB3OMuzwGvQxvr/b96f7MRJulB4yLtBFaSFj37yMOMpQZzynGdL3hrCOSXV9HlIdMyt/n/X+KizxMHOLYjdZHhufmZuBpGHmZn0kxg/cajnaNFxD/eZVIlZOLaH0yEfeYlMUlhuf30dq61mPOQPR5N5r7O+DdRwnX9qVECcl+wZbqkw/mr6w2DhYRbtlCoArOvQqO3Okxz0a0U0bkSd3iOQbxmRxEU++hU+EpprIX0X7ywFQSJ7S/QhxaSjDnEDWF65CivQPR9Qjjp5g4YX6Wrl8GC2BWVhZFRUWDn4KCAtatW8eSJUsYNmzYP/luTk7O+979j1ShZEgBPNOVixhtiv0p8ScHKSRWdN79KRIYU5DiV4KwmoF2+UuAj02Bz74BZW+o7c88Bpe9AXd8OZSlDnRisx3Fm30KMavE/JOlYgApNMnq8HNCWOYDw5D1K9P3U6xJFpH643LDl2L/DgGT4dy5cO4dfrZdMOVWuJ1WfxYgIbcUCbEDSPFMbvDkziyCgVWIcSW3VTNRNaEDCYpRhuF5xNCedTs3ESdku9zmFgbz+zHfn8nwqfvQbnuS52oqEq7ViGFXEglmFxPlyRr87FFCyO8n0uO8jJTruUj5yPZnnMbHfuP8j5Egr+L91ScaiED+PsTID/u9MsKylax83yPmv9O4KPN8TiWqKLR7zv7AfTb7YEar4T5snE3w9+lovv8ACZkyRFc5xsNHiWof9YYlw3OQ2v2cnymEC2YZlntRcP9RIgn6NOh7FSkF49EGKdN9fgwp3S2EQjLO/SZheJAIMbgXHZbaZlhvR5auSt5fXi65JnsQTfb5+WQ9vxMpSfsQjUx330kxSTGRyW2ZaxgmIG03ufuy/VuVP03G7w7iVPZUjxUiZcdCpOgMEPGFNxvup4gDOQXIKrXHY/4RUgQKGdxUnHcDYU3aTByiWEikRZlmeCYSrscqv1Pme+nwQzURB1ls/NxBJMGfi2h5E5HntMzzlizlidf0Eodh1hMJuucapl3EIbnDhuuLhHs9WcGuI+Kr16O1tQ4pTl1u/+uItrLR+moilO9vwDlXE67qCqRg5aH1ehdRUjG5xtuNz2a310rU5S2HsY8Tm4F8jeVUj8dU4Hd3ElbMY+hA1g3E5uMtwq3dRRwUTDGQ09z3aMLKOwnRVA+SBxch6+AkIp56M0PXv/J69dVXOXbsGEuWLPlnn33hhRf4yEc+wsSJE/niF79IT0/PhwDhh3MN1QI+0/XdYRJGrUjJmYqEyDq0oHMR46pBC7+CyDw/QMSOzEGC8CmiVudMIl3BPYjRLEU73xVo8S9CgmWn27oCKSRliFnNRcLuEGIGZUjhzEVCuxYJ4cnIqrPYcK9HStENKCddO2JYeUiBvdh9HnObb/n7QqL2aBGwBwYaIeMKt5sCq+cY3l6icggeyzikLEw07COMhwKCIXYSVsN1iOEVehybkFC9Agm9dSgnYSva1S/0mCv8e41hynebnUhgveTx9iABdcL3FyEhs88wlRApPJLrsM5jnWkcPkIcPikwrIuRkr7LOCzz3KU4pzbjMVnleoyvQ4gWZnmekuvILrXBU6XZSAicNIzH/FyKaSp2nzuJmLnXieD7GqI8Xj5hFWolci32e7ztxuVoz+dk9zscWThqkNWjiji9nOZwDZr/bjj1Uxh2lZ/b7HG3Gve1hBUv0d1hwmKziUid1Osxv07kEGwh6g4na0+nYU0Wluf9/kTDtBtZ1+YT1pxLkRXwCBHb2Ow+Cwxbso7mEnWdk0X8INrQJGV2LYpv20+cRC8lgv3LiYMKBw1b2hgcR8pMLaLLNUQVllbC7ZmJaD/FZPajdbbc7zW5zcnIFVuHeM4KRGcZyHp1M/IKLEAhHfOIzcx+99nE++tKg/jFSqS0dRreTuO4Gq21XM9PLrEZa0WKeg9xIKPf+JtGpB0acJuTEd3MdX8FDHoWuMXP/j6yJqYQgzx/70fWwlrDNZ6oB91LHMrrResgbfSPu79bEB9Iyms/sTFNsah/gOgrbV5yiENnyeq5HvGLdcR6PYasoc2IlhJdpY3R5cTmeR+SH8lM1m781QBf+/DFeKoFXEaEmv+fXgPYkN3S8j5L24gRIxgxYsQH1AtcfvnlAGzYsOGffG758uWMGzeO4uJi/uqv/oo/+IM/4JJLLuGVV175wGA5m9eQBfBMV3I5dSJGdozY8RcgJtJPxMcVIsZ1l//fiZhpL+H+SxaqDMQQk1Xgc0j4v4CYQBlhCbsYCeTLCYaTArZfJwKRDxHJgW9FAiPPv19LnKYsI8pHnXB7xcTpzycMU5XHBmJQXYQQ3qW2M2wZ3PeD035r8vcpHmcxYvTz3e8owoV+Ujh59wFk7ZjpZ7YgK0SyFtURB2+ygc8M19gKPObHEGPcaFyd7gpLOHjesOxDQiPb8zOGKM6eSbjuS5DwqjeObvX4ShFNbEYCdBRhQdhnfN1GHO7oJQL1GwzHRqTwTyBKy/X7/yVos9BIpB3KIArev4QUl1r3tdR/q4AKlx48SORoTFaGAqQQ/AlSdNqJpNJJaa8k8ucVG3cpoD7feCww3krdbqdhmEsoCElxrTae58Cwe/xuLlEqDY/pMBLwc93PSOP9JFEPeZ7nZzhR6/hjaO4fQGunmUjRMUAkAE4COAnkOn+S27uX928M3kJCeh1SNvL8fi4Stp3EAZV8/12F+EWy6O9Caze5WEcjOhhv/F1DKN+z0VosJ8pIXuxxjuH9NYg7jKstRJWSMs9Htu/v0b2B+/xbAVHm8Wrjp5AIVXkcTt1AlI7MIHIErgCehRNPaAw/W47odTTiiV2Ir6wBfkqEI2wj4kfzjdM24kBJJxFr14uU305EC48TaYJyibi4mf49A96udSxqNlqLK9EaKCXSqtQiPjvK/VehNdtM1BtvRjSSYliTgj3SMLxsfB53u2lz9QZxivhFxJ+b3V8hWgOjiXRBKRTnOeOkCtHCUsOywO8NJyq37Ea8ZDMhd1JoTAZRVnMaZ/U6yQfn/j3pNktKShg1atTg58EHH/xH+77rrrvOeLgjfQ4cOPC+d9566y02bdrEDTfc8M+ObenSpcyZM4dJkyZx7bXXsmrVKtauXcubb775r8LRv9dryAJ4puvTw2RpakaMZDgSQrXQ9wJkPYWYXrKItCPmsI44UZpDJCvejxjaYcTspgLb4IfL4eN3ETv1FBB9O2IcmUjAbUeM8SnDMQ0pE5eqz1NbYdjTRBLTLcjtMBMxjaTk1Xt8cxDjSULkMaQgtBvmuW6/HgmrUcjS802/00zE/8xCQuwPPPaZyNow2X3kIQa63Ti7GQmaHYT1cg9h9dzsdu9HikI9ElzZ/n8SyoG1ECnaF/v5XiSgC9CBjyXG94uCa+AtuUpPbYVhcxGzvh4J2Br3fT0Sunv93nHEZBsNQ4b7eZaosdppHKwhmPsG4fLIj2Hsrxo/7cg6uAJ+uBE+/hS8fQucdzESGN2Go8QwjUVKPkiwFSOBmJTaicbjduTmXA3vvgDnfJRQJh42HjOJOsRlSAjXIVpL41qF3M3JFT7TY6n1/Nzieb0MbRSK0LrIR0ptNRGvdYQIn6giLOP9iG6qjMc9nse9HmcvWncPuN1jns+voANEd3n8O5GArkaWq5uJgzT1hJVpHXKt/zrwF2idtXtOXzYsuW4rwVRHuHnTYYCkgILWYCWRH68FWUNnonm/3r/t95zWE3kp97uNNs/PeDTP7YimWoi8h/f7/iwU3zbTc5FczpMM1wC8uwjOueW0Z55GSbt3Qsb9aBN2qXF0CNFbH6LvWYSSWQiUwiu3wZUPITrZhei/G20gknv/MaQojSKs7JuJE//Fntu3kLU/hWXsNf5AbtK9iNcsJvKmHiQqxUw1PvuN+xOIP76KLHPHkUW8B22M9xvuI4h2En/I9zgXECd1u4h4v0y3Mc1wjkBeElsvDz0BpZ8wHI+7/z2ey0WIJ550fy8gGrsT8eLX1N/AVsi4B9FbJZFfM1mra4gT0qPg9Zsd4rLNcKS47kZ4Zz+cOxnxud87exbAUj44S9JJfND6X2gBbG9v59ixY/9km2VlZWRlZQ3+f//99/ONb3yDI0eOkJmZ+U+8+Q+v7u5ufuVXfoW/+Iu/YM6cOf+qd/89Xv/pbAPw7/YaQLu3YiRUMhisx5s1GS3cJUjRmIMW8xaiqsMmtMPehRjUET8zjkhGOgAfH0dk1S9CgqQRCYNSxOwOIKFylEiVkkHE90yCYbPRDjzFzY10P4mh9iAGvRYx8QfdTrmfuw4J+ClEjFY2YpzJQjTD700i3HAnoW0RFN3g5+rh7YVw3njEGIejOMUTxlcuUhbyrIhVEWkU7jAsNSh+6zUkaLrdXwNRoSX5HY7AidUw8jN+rgEx8D8zrPOQpagGMl4BVsCwlR4XiJlu8phmeD6nMnjaeXB3XUi4kNr9NynaC5DQOYkskbuQEjEVxk4kgsobUMzcxVL+OAznpQDudUhJzEOCNcGylHCP9SDh/01EH6sJpWSs+j9nhdtq9HwsRrWWf9tz8RDwkeFwr/faKYYs32Pu9TjvdpspBKLQ/c0D7oNTu2HYAiS4MuHtRjhvMuHie1046euBrH6iRF+2cZOUni6kTPZ7rCsN/yIkoKs8131I4H4UbajyiIMlye13IxLMA8DTF8B//xlcBFvGw+xxnqtmQtjPcn8fQ7S/kYgXywb+K+Fy/wTaCA03Tp5Hwr0BWbrm+O9cj6fT7x2DDX8Lly9Dik6Dx9yE1t4zSJHv8JwVojUymoghrDOe01xkR9usA7bBOUsJK+6tmlraIOMStPZ2E2EF+437AuN3MXAttL0ARYt1/8plbiO59bP9XBNSxlYQFrdKovLGIqIqUr3xm2IPLyLSIY1HtPYUca0hDt31EwfK8N9biPRZTxG89CRSjDr97DEipKWI4JfJ0r4NxZeu9fuPEQc0somqMYXEqfkGKL2COPCXXPr73HaD/7Yj691k9/9rw2HTyUHLYcYAWkOzjZvJnoet7hci7rEOPvWwcZ1n/HYgHnMznPskUjbHc1avk//8I//qtkaOHPkvOmxRWFhIYWHhP/tcuk6dOsXKlStZvHjxv1r5A3jjjTcA+L/+r//rX/3uv8dryAJ4puv3hkVAfjlRGHw1Ub9yIWFdK0IC5jhRgLwHKTeJ2bb4/nyiAsRuIl3IXLe/h0H34bEfQMHjhiMXMce1RHmrZiJ33q16Z9CNkUEonfcR7r1CoupHJ7Ig3U6U2Grze5uRayNZAG5xmzuQAE/Wm3Xu/17/BnFyugsxyoNEapZ5/v+I2+lCLuCjSOHa5/6uI9I5XA9v3wDnLfB4yhEjr/EcDKCA8ibjspvY9W9BDHe0f9viOZxGuKkGkOL3PBICZUTy7vXQ/QzkXubx9vv3G5B7vZFwl15BuLU6kMDIRELrs0jBvsYwTCNOc4MsDm8Z9lL3vQ0J1T2EkEqu9gR3ij8dbfwfRgrJQs9tLZrzpLguQcKnz//vQ9biMUSamm2EpXCAwWoMHCUE4wpEJ9VESosyPzuZiJm7FNHQHnj7LTjvfiJBeCMSlL2n4SMPTt0Hw3YSudSmEW7XLkS7o31/s/+uQfT6hmHMEu6PrYKC0UR1lqPIUncdcRK3Ba29LMLqOB4pTHXw5mq48Gr3meF3t3u+BjzX7rfvB5C1mCgrWIaUwjEMWmZ/dgNc8ABxyjabsCgeJWLLxnl8GYg+9iKe0uvvM43jAc/3KsRHEjyNaEO6zzhaRfCvRvf5DWSJy/BcYDjSYYVtHku++ztIHHJaS5x6ziMSjm8lMgXcQuSGbHQ/mUQ94BbPW5ufK9f4Tt1iS30OUh7XId454PFs9ZhHn9Z3JnItl/jTj6yDVxIlIGfBsZuh4Fq05pMHoJaIhc33/CYe2UZU5ZhpPNYbp/W+347W/8/dZjeinwZkZS9BvCoZC3D73Wgt3EuEo5Qi3tRg/CQ3/3CPqf+09m4APnn2LIAX8MFaAH8GHD9+/Bdy2vYv//Ivqamp4ac//SkTJkx4329Hjhzhk5/8JKtWreKSSy7hzTff5MUXX+Tyyy+noKCAv/qrv+L222/n/PPPZ9u2bR84bGfj+qDm7T/eVYx2V6WImR4gysPd6Gc2I+a4GzGnEiSsL0YMpc3vXomYxnDE1HcQ8SUpPqkTuQ3SrrxSzxbUoNiku93HJiJDfj5Rk/XziFnkEsHeSTisRcz8uNudiAR9ilf5Y+K0YSWygCQFaJz/vxsJpj2IGTcYjgGi7NGTwKNw4iHChTMNCas+xGxL9AwLEaPf5fFP8PPr3f5TSFDVIyFSb+VvNKEY1hHJtpO7ro6owFFi+Ca77S5CeTxpOBIMvX6nnKhUkWLNSiH3c0jxegwJsXYkiLMI91em28lFltweQtAN+P0JiC6KkPKQguBHECdaK4icYVPc3liPsRwJj8OGs4E4DVpBCNcWFOxfRiTCbfGYEpxJmR9JVLs5RFQtaEc0MhVZ5y5EQqqYsE4VG7dzifrYE4kSVUWEBXc2nHcrSgXTgtZKleckbcbH6c+wcYZtACloBYav1N8rEF2UEgpNF3JPnyRisNZBQbLiNHleWuDdn/v9fLQ+NhqWBe7nmMddB3TChfe4n2b9P5icdyayJN6F1sBYyLqPKB+4CdFMCzrZP0lzdcHdiIaPGV+ZhOv3fMOVaLIC0VouUdowzVkz4k3JdZiJlIMmIuayzu3mIwUqQ3CS7zndhtZ4pds6QpyqT3GPJxGdFXosyQ3d5HFkEpvak4a5w/ebEa9rN463ExazfKRY7SMSgrcKR8Ou9ViyiOTNeUTC6BQu8RhSBjsRL04bkc2IZyYPBoi+esxXbzVsZW4nrYtj7m82Uj5TKE+b53Ojv08j+GkvUnRbEZ10EUnx5yLe2UIcXLmJCDnoRXS8F8WDf5cI2biOUMoPnDbOErTuliCeeRavfy6u71/7+UVeK1asYMaMGf9A+QPo7++nsbFx8JRvVlYWmzdv5rLLLmPChAksW7aMq666iu9973u/YCg/vGvIBXymqxgJrjzEPIsRs5hAxLk0I6vCfiJFxGpExUuIU5OdyGV3FDGLyUhBAjHa/f7bghb9t1Ggfz5SUn4HCbbZhMWqAblJE7PfgpSqare7DwmATUiJOIgYW7Lm3Ow21vn5LsNXjJSFrYhhv+VxzCZSPHyEqP9aSSQPHgDmwMgtiDkli0w7g/nyBitRvIKUmduNmyuAHBh4BjLmIQtNntssNoyjPJYUr5TuJwbeT9RgTRaH9YRlcAKKY5xKBJ5XIUF9JXJBj4Z3j8I5zYSlIVlm+v3ucP9/L1E5ZIN/W4vmtwy53loI19DFxutwIpauk0gbUmC45yNBtg3R1AYk7JLVNBcJr3uIlDTH/O40ogxaIwoL6HUfx9zfHuP+deO4kChfV67xvvkIXLgUKUTPEkrk+e6/yO9O9vtpE9ThvouICjINRDqju5D18bDbm25cHDK8C3l/MH6ln1uCBGCD5yxZkv/Q8ziBcAcnxWqPYO1rlQ4xmIC3CM653/Cm+L4upFg1Gv6kqPcSJ6mvM0w1hGVmIWGVbDQO30QhG63GVfIUbHPbnUQ1END6aiYUsOT2bHL/z58HX3hb9yd4jnKJOtjXuf9at52sTDke40y3/zyi4VqkVMwirL21xEni0YiOLycsz7uRUrLZfZz0HH7F81ntttcQh4WG+35SIAs8Lz3QvRtya4yjZr87xmPuQzQ/j1jPvYatASmQLWij+C3iUNNaROczEd+aRxxuWwJ8Ngf+e4+eW0ic/H8SbQKbkBL3qvFfzPtzeCaX9z6Cv7QaHws99nzCMtjmcY8iao3vIk4cJ4VwB1JSRxlf3Xr2yMswtoKox/28+7vVsG0mNmxn8RoAPij74wfpTv7HrhdffPGMv5WWlnK6Q7SkpOQ/jKXvTNeQBfAM17uLkdumHjGMfHjncbSQ9yFGkIJ+e4mTapvg7RVI0CXhshkxoEVIuHcgBl2DJNPViNneiITZcKIc0HIkALMQA5kNzIIDrYQLJxsxoCr44USiJumLuscqZJ25Bil5HUjg9xHujTakhG1ksEj9qa3IvfA8iqfbTaSC6SQy0T+FmPNMYue8nEi6m2scrkcCcBFSnFqJGsG9QCFkXI0Y/BcMZ55xPRcx1SRI24jchw8axymmp8STWIoERL9x9Ybv5bjv+YZvkts8H6iGc25D8U6lfm+C+2lFimtS8uo93rFEIu9Owu3dbtz3GofJ+laHlOZywnW0VPh757tutx0J6QNIsUxK2leR8n6X7/UwWGN6YB7h2i5wP0uw9uPxJMVtFBKclyNFoIs4FHIQLszxWE63sE4h4mIbPJZXibyEOwlFeR1SeGuJAyxVwEez1OfdxEnzew1zDpy4jagM02uYHgD+b0Q/2xAtrCGC8QsJTvaGcVCLLG4rIGspmuNGRMN9aBNxL1IinkdK7p8KhsGwhOTqT+NdbvxsQmv9ZZR65GXgBnh7K9pslANfg7YHPI6nEU1Wqp/tjxhvDYanFfGJbLRmi5H1tNr35r2t329BysphotburW5nGwpHKEG09yxal48TVqjkur6awWoo7y5FnodnjO9NRG3ubj87EvGlcsTzmgi3d6bbHuM+k7V6MVF7+genzanXSu5tfnePYZ/nORpL1NbNctt5iGfMI6q+XGr4ZqC1NI5IXP0MkUS8B63dYqT8VRCu57R5a0L08hfEYY4yxGNajeekmOYAjXDqBUTzrxiefre5H22QhyPPSgOi8Vbj9lK0jroRnc5Ba/Qa9zNNn+6rYexDxvH/i7xAJYhu/hBtZHfAqaWCZ+gauv4t11AM4Jmu6mFiHEkINxOZ/0uIKgnJdZAsCCuQ1a4NMYtPE/U8GxBzrkRC8Um/X0UoNHsRY9hDxF2BGEgbYuAHiaLhOUSgdT9h0dmElKiJSEBl+J1tiFmeJOrtptiqaagk1VE4bxZS6MqJLPcDSCkohRMvw8ikOKQYyMvQycVHkdA7iBSN+UQs1C0o3qgI7fIfQXi+w+Oe7XeT1arM+GlBitdMJCjWIKZ8ExEjk2KSVhFu2hQr8wnPzelxOZ0M1qvt+zFk3eP+DyBhP9W4KTb8eYS7vgox5d9GVqUCv3el8VRpfExBCttsxLhL3PY4tJH4OqKfVUj4HCDKUt1GKPlfMcyH3X4rETt2s+e7wnjdjITf+UjZ2kckkE6WjAZEJxcLnkM7oXSF+89F9LIHCbJsJIiyja9HkMV5pfGfgzYzTyOhvR4pHo8Znrm+12RYVxuX+z2WGYj2NhDWnArCpXcM0dBmpFgsQ5uVdo+vFSlxFs6D8XNTkZXzJs/RUSS0Sz22NMYaw7MV0cc0PzMd1dO+9DQ8HEUC+0WkGCUlpgQp0jv9PVlq5wBfMzwLPZZaoopJq+8tRWvgcuOpyjhbhGhnvOfsp0jJzUXr5hN6792/hXOKjZcCpIDP9bt3IT5QgCxQjxBKQ6ef+RpSWPC7ic5yPIcvwb4fw6QaIhdjr/soRjRdZhw9QZyivhYpQCmWsRjxhSYizKHDv1f6ezlStpLSNIPIrnAzsTGs83ztR3R9LVFhJXlaQDxgpdtZ4O8Zvj8R0VgOOtSz3/dA6+//a++MY7M+jzv+Ac+Og2dwzRwsExfqxoJaiRJcmAODWTAoSmqFkrBEJBktTZo2JWuTskZNu6XRaLUsUxuWLkrLmtFkabNEXiiZSxSCElEYqYdnBUEZCOrYdYMgHq7BMzi27OyP7/fxOZr4IxMLk3hOevXa7/v+nt89d89zd7+7e+660YPCc0RFgALz7BTy9q5FPH2NKBGzjPD6pfD8sGm2kGgrWMZ7Yp8j34eCcuOzxNd+Ha2fWb6uEsmNz3r+w8BTFy4H8DLObw7g2/zf5QBmeC9kA/Ac8O8TJvDx24iSG7Vo83UT/Wlbier+C/3ehwRdChMtQEL9EST8ZhBerD9DT5DVRF267zBWAoDpRNJvLVLkjf77EUKhprDtc/5+FhKyg0hxbkMCvRgZJFVISP+IOPn4KlKo1yDhto8o7tpL5Lv1E8WwV3uuJUjwHUIKsnzcfZchb0nKwZpNhHcGTd9aJGxXjhurw3NPeU23GL9E41F/fwKdDN2MhOk882gYGTe9nks1MAnOboRL55pvB5BB0YYUWZ9p3oeUeRvRIu4GIp8xHUDoQGvhFZRL1e057DSeT3n+/Uip7ByHyzTgy74mGTxVyBBMBnryRDSYJ62+vgQd6vmq57ySqCe4gqh7WIi8yv2IvylPcdT4zyRae73g+00lOi+UEDUER5DhlvKZOpGC2kWcJH7INGsjci6Xe94NaI9825/XoPVQbByPIGVZ7b/vQmt4EpFkX0a0DOsiergWm0dlSDnuJdbNAbQXk9JOXrBv+bqj/j49mNV4/l8jkvBTKPRryACb4vFXoPXbgQyAX6D9WGZepYeYZmQkVJiGvWj9dxAHNvagdZfwLUFGX4/nNILWeOLfPuLwSgr71/r/bsTvBabTW+ZVu++5BuVCXoLWTR+Rm1xCFLIfNh/6fd9HiA4do8atDj1ovGYejBAHjib7u6PImE4pNI8Se+Kw751Sbpaih8gUIj3pefQh2XYrWntb0d4agZE1ULDJOOwl6nhOIzoTOY3h9I9h8nyi/3UJkS95OXEK+4jnvMW8eto4Lkewi+j5W+15v472Yj/aXyeQUbqRKBiePIrP+LoVREeeOvPtGuPS5nu1mGf7/H8hvPtzmFDFWNpNNgAz/G8gh4DPAR9fj4RCEWFElCGBvg9RbgAJiQYk/HqRQF2EhEgFkUvXgAy0mUTB4+OEUdWDBHw/UTV/Mdrc3f7sKl+7Hwmw5H04gQTLg74+PW0eI9qRDRnnl5CnZqa/GzAerZ7fNuK0aTNSWM+hMM5JpPxW+vp3PK8ajzPdf99NHHR4wngfRAK8h8hxqyBaqv09UjaFSIhvRQK1GBlXXePwhCjKvdfzXICU+y4Yepro2btyHM264dLfIwqpNiIF3og8DaWoZtxe4zdAGPQpr6+a6NaQ8oBWIWMz5SeWEL2A15lmVUj5NSGD5wQS3jOQIkx5b8kT0k50idiK1tUxpMjnm4+3Ey3rmj2nZJzXoPImx5GyKTPtkqHeMo6/A57D9f47KaOJRB3FbuA6+PVGwuM70ThUIAP9P0yTtcgwXm/aLzXeB4iaZ7N8z88TBb5HiK4Q+/y7SqJeWhnR4aEY+HOPl2jbZfqlh53kYTyMjIYOj/11Ir9wjuk8zzj/JwwNo32Z5tWH1uxu0/NJ47MNKezH0P6qNz5d/vwwCsM+SBRyXoQMk1a/bzFtWzy3WuOcjMwTxIGTdIAgrZmU81nhzw+Zpg3IE33G300nZMEafz+NSF9pNL51pn8Kp65HMi3hAfx2u+cxhNbhgOdY6t/WEH2mO8fR+RTRcajG16729eW8t0XcQtPgZsLjN4pkTg9atx3Iq3scClaanlOIHLyTaC+2IS/uGfFn8mq0rp4mPNoFRG7rZiLtoQStXYznEtOtmeBzKZGnOsnXJkOtB5VdGiBK/2zz7xeg9JoZRM3O5FzoI+pwViAZcorYHwvcVWehX6VcUBg5z68MHxxkA/BccAUSlL8i6ulV+b0PGYf3EXkzKedtFtqUK9BGn+sxklfoIHoiPkz0fgQJgBYkPJqQEj2DBO8KJHz6kUB7BgmK+wjvUErUbkXCox0piXZkWPUS/W+T8dFBFDZuQkqmz3gmo6SfEHSnkCJL4eN0SGEXUpjJiEmh15RknfJ7nkbhkWrk+Uj5tZ2+7z8brwbjssq038pYgejTT+uduZ7LGcLYPan3opvQyh5BBmgyiq5Byni+aTofKZUB47QdhceKzdOU49mHDIwtiL87Ce/OlHHz7ff8byBOaB9E6yDlAlYjJYA/f4zoEPIQWhfPE31XK33dVqLUS/KotZr+/abnDmTEVRKt9h4n1l05yiGqJDxMjWjdvEZ0rknhy6vNrzLGOmt8+HHjXuH7vWycp/h++4lyG9cQNdrmeN4VRC26UyhMXkYksk9BCq8J8fQtpITrkLJMyrkO7ZcGtM4OIn7uJnrhPggsgoHdvm6YONH5abQP632vw4wZjEWbUB5XMqDqiDZdlabL7egBqAvt1XSo4ABaIx9FcmClcUrfzSDygZPB1wf8o+9xCO2lhNu9HuObvl+16VdgWvejHNj9yDD5rP8uRR7JScQBoOuJnNsyf9dHGDEpN3gjkR5SjgyTEeAe+NDHiPD9KeO2Bj00dRIpLT3EQZ+JRG7hNmKvfJsoCp2MwEeJVoIH/P2VRPrKN5GxXmue7SaMyKvNy79AMErkh3Yivq8hHlIH/RrxZw8gedCM9l8/0eLyRuKUe7JSDqJ15XSMN4769+kwSznidx9xcKTec5yP+D/J3yWDPhnqx4jajQeNfwqZtxN1EVd5jAsIo5z/TiAZPhjIBuC5YB8SxlORMLjNnw/AwOtIYR9GgqiJCCn0IGE3AwnIQaK/ahUSkN9ASuA7SCCNIMH7JlJazxP5gs/5+03QuQlOr0dehE3GJ3mmCt1SrZDoK1lKFA/dgATg95GSPY4EUzkSdNPHvW9BSnSl8a4jehe3Io9QCVIUfXD23+DsYyjk2W+6DAPtcPYh+OUconZcAVICh5EAfAkJulc959eQ0J9PeCJLYz6TLydKdzSYNm2m90wi1LUZGZSzidDYo+brw0QR7RT2uR+F+CDawdX5HhuRQVDqv3cZ7yq/TyXqmdUTocxyJNGWIGMgJZE3ep4FhMJJxsUSj9FlGp4mmthPQYZwCm+2I8WQwrRJOez3dQdRmLAWGRzLRNdfPkyEVJv991v+XT1Sqn8FJ+9HivI+xP85aG1XoPVzD/JQ3wydG8zPmZ5LIVFY+hE4Wef5tZhXPUjpPYZC6LOIQ0qjyGA+ggyhv/RY6SDNQtN2kDhM8Swylp4yTbp1X8qhZAPQMUl75SVfP8e8SLmvx2Hk24R3/DfIqL4C7Z815t9e4sT7W8hLP810+x5RcuU6tC7riZOaQ75/i+lTjfbLKFpTK82nYjj7rOe7w68y4mBDNzI07ja/Kz1WSn04Zjp8GsmPFzwH0Jr7DNr7x3x98rRXIY/sLGR8NZv2hURBbNDDyO3I4E8PE6D9YoP5t+vRQ1orUTPvJcTvk0S5mClE+8hZ6CBGtX+TIg5X+T71SM4uRnvgu0RpoB2eRwF6kKs3Lp2+/ha0d/agvdeBQtQP+LuZRG3OCs/5Bx7veiSPPuV7NBCdYxYxVpz6mnuJ1pJXmU51yHjcoHsPPQwnDxgHUGQFIj+ykYgwVPv65MlPD0lLkWG92/ikfM4MGd4n5BzAc8H0CRI+s4nWXvcjI6IECasaJJhSyPVXxMGRPUi4r0Ib9SdAIwz8CEruQGHAdpSbUs2Yh4VJSLA94u9H4ew+uPSTSBFvIZ4UrzCuo0hZNCElMoqEdycSULuQETHquUwhwtoVSNC+jpTPXM+hEwnv1+HXh+HDdwDH4dc/gw/fgATTIHF6tJuo+fYA8KfI+zTse29H4dUvG8/PIeE6jQjrLfeYu4gyO6XEadmXTJ9len93I0xYihR68nBVmz9X+prHkSAuNC1m+H4pz+yxcbzoMX1eNQ3uMh1vRAblDCKRuxTYAb9qg49+DBlDKTxdbDwHCWO4Dx3k6DaNt/jzEeMyEymWJ/z5On/2OWSwnkKt9tIBhTRGrXnVhh4qBtB6O2z8j5hXHebver9aiVpq6XDJDZ5vsXnZCWyeCPeMRkmUFMatQLzfDvwBWk/rkJF1wvPYi4yiLb73V41jD2H0zCMK3yZ+96C99kXzpgyt2Tb04JPyWEfRHijytVsR3+vQvn2OyKvtQnt2AeL1fUSJkDLCYO9FxuEOz/cUcUK3hyhpk7zEd6BTtF9CnjjMv7keaxCtqZfFq7efh8smmqfbPdZOZBzOJjpd7CNyi1MKyTDhfe5ARvEtpkWb57cf7SuQkfZFlGtc7tdN9fBP7dGN6I8ug3veFv5fQPukxGPOMg5bTKtVaF28YHqtRYb3oHkwjThk1Gc8niIejJegdfc9z2+QCMGm8Sd5bjvMx1rTPK3TI0Tx7ZSDudVzS+HuuWgPLCTqiG5AfP8KsAz2boJ5hSgPOnnrkitrs3EZQDJnCVE0/Rlf8zw6zPYFJB92EWHef0D64pSv+zw68Hen8WknyjAtR7riGqILU5fv/bJwpR7xP4WXm0yvE2itDgOfunA5gFOACedpzHcR2XIO4AcD2QA8F/zrBG329OR8ECmTqWiTLkQbuQ1t3BQGTEK71b+FqFdWizwzM4ER+MkGuPUmJGDKiO4LKTQ6kaieX4uU12mkhBcgBTuMBMNKpAwmIQG5Cz25voCEx9d8jyuJAqRrkQFUTRQw7QW2wemfw+TrPMe1SEj3I2F1N9FTtcR4rECGZAchfOeZVjuIen51pssez6nVuB8xvoeAF+G3++BD61Ee0CZkmGzw7wuRsvwh0ft1FXqqHkIeliXoafoQEuzJ29NFlPo4jYRoMVIkfYShsIRoHbWY8OZs8xyXw9BDUPT7SEhPQd6quf5tylsb8BhPEKU0GkyDrxDdLKrN13tNP5Dh0mha7iAOhxwh2mX1EeGwlNN4xLSZatpWE6WLdpifKeZSQISUjvp9D1or5Wg9TiVKjcz33Hb6ntcR3suniLy9XvRAdJXpeC1aL3uQYZLCteV+7/E11WidNAB/gpRmoeeZDJNkCMwmFGMvWpd/h9b2AqJDSQux5kf82y+ZT00orAiRz3oCGTbJIEy5ahC5pU8jw+Muos93ShV5CxldzciQ3Uzk8LYQp91bjGPyHB8iTrj2mTflyLh6AxlB2/0+YFx/Q5zCrfLva03PwXG8KkRr5yGiQPge0/tJ4jDUHCLXLq2vxUgrt/u6M8b1FrTGXyCM4qNE+HSa57wayZ8FSEakiMh0ogbmPqIDzpWEd7IFPTgNe9w9pnOjv7ufWJslaB+mPdJE1Ca8kyju3WccXjF93jFPVgGfJOTUDP1uaD0UPYzWxEnzdtB8K0O8Zxwf6gkvZkpleRbJ81No7f/Y95iF1uSjpmclYdA2ENUH0kPtAdOzn6jrWA98KxuAGd4//M6FRuD/LXwVbcLZREHodqKNWzdjVf3HFGDKT+olWpXVI6GVDMYKZIAchVvv8PVbiXpuPUQB5F60wVd73FIkZA4gQ2OPcUlG1atIMLQTScV1yLv3oMfchwyltUhQlhEniKv8fSVMTgcyUti2wuPeTHjLUsinh8jLm40USS0SeGVE6Hsn8sJ1I+Fc4HsMECfvZgufD1X5PrN87QIkGY6ZlikxvgmFjA8TSqkbGcANpsN+f1+JlE4KvzWjnL92j5cU5lKizMogUl6rkNI56fufgaLbTO8fIkNriq+Zb95dYl63ELUHF/m7Xs9pElHz7CRSIMmzWkwknS9Fa6kEKYs6ZAwngzt5ZAeI+nH9/nsPMmqKkAFwBIVOD6E1NR2tvfXIUB0xn3tNO4znDUS5mmHzuBHtlVq/ZhM5Xc1EKLQUGUb9ptN3jG+5f3/Y489G3uMqpLQLPMfjwE+JvdJP5CbuQTl36YGkCz1wfR4ZoZ3I4Bv232dMw2PICDiIwqWlnvc65FlMDwuT0Hq6hcgNTblg7ab1Ao+3wzTZ7892eswNps91pn2xebWNseLbQOQEzvQYd5q+NchQuQN5n5LH6xIiD24VEcqfSIQJk6c5GZuDyKt3EhmQlxMnTouJtIsWtP9OEd7t5LHuIUL8BzyvWsKo22/a9Pqey4k2j6VEG8b5xjMZVIW+/jXTYY15ttuf1/nebR6zi6iZ2Yn4v4CQOTvG0anWc+4nSigdMK1OEn2AFxNVAoCidebTIaLcVMrzSw/9/cbnduOxDqWLgNZ2t+dTgWT1J5Bs2qIxf/FfcG3y/pWj9ZjSaY4SYemJyKv7155jkfG4gDDC+TUAM3xwkHMAzwVdhPJsR5vvWuIARhcSYiVEV4BiIhw8goRa8i6t8jVFSEjWIQFQ6vvMIIRLJTIUHiCMrz3IOOvz75OBUIOUBUR9wGuR4H0UhTCKidBCJRG+Sh6RdCgh5S6tIoykM8Ztin9zCgncJFwHicMQEKGZDqTohoietQuRkdjkeQ8ROYylnl+n5zeIPIuPI4H4NygP7zBSqjONZw/ywoCe7puRQfhdouVeC5FAfTnRAm8aMnj2EF7JicZjF1H+JCmEg55/C1JA82DgRRg6gfjZhQykJ4nae8eQQr0TGRAjSOhvQQK8ynP+BVoX+4i+qikc32MabkEGzdUoRaDH319N9DAtJQpiDxDhwB6i1VwX0YZtJlHkucfXLQJeh1ce8vgt42g5wnvL0Rz29a3m1w9QjbIjnm9ae/uJbgetiMeF8O6PTNc7TNtOouj2AHHKOnl4y/ybFHLc7OtqCGk2BGffMQ4nUK3GFiJHbgQ9/FSi/VHgebR6/sfQXjpEhIdvRLzd7WsuJ3qEVxHG+gPIkH3ZNCslUhfKPJ8av19FGHzdaN2MoL3TT7Ram0J0Vdluumwh8kiTId2O9muv51GDPHplyJj5hufQgQyrVcgQud60W4jWfCHRcWU34nU3esBsg4GX0ANJl+9fZbq2iPYc9zwW+X26cR/ynI8SDzopBzh5gWd5nJQmUMRYIfaRF32fRfCuDSd2mlZPeI4p/Lzf/J1mXH/jeTxOPIBdb17N87y3IcOrmjhQN4D27qB/X22+HoW3t/vzSUSoPqX+PIaMzqXGsdf4Xk+cxk/RpRq49hGiVusBX9+N1uWg53E1km3DyKPYjR5gyrmgMHqeXxk+OMgG4LngSqRkq5FBdZAIb1UQBgtoA+5HwvwKpASPofDENCSYtyKv216Pu404NdlDPHmvQcbAG8izdDdR5HkpY4crWE7kzYA4WY2StpOSuYcouNyLBPtJ5K3qQUL4NSLksYtQPoXGpYwoUzODyIFKCmozkRj/HGP1Ckee93ybidO2lejkXzcKgZWYdqeIE7SPImG+Dgn/taZls97fPoYMyFeM79JxfNiLDMVnUT7WKiRAi3zNVKSMmjy/1UTOU7VpmnLqutAa2IeUymp44xPA38LIzxhrf1dS5U4TS5EhutHjTDWf6kzvuz3eCd9zDtGxZNS/6zTN1/DewxSNxIGBx41bCnGm9y7j8CQRTq1AYdQi5IFaibxJd3vcjeghptvjPiPeMU24LLtJdD97FBlA9egQ0Raib+7N/v1dKLfrAc+lG4VWW417WpM3mgc79P+EPzRN7jUt+pExWufPZyD+g4yVNM5itF6+QXhfX0TKfxFc2kzkCpYShlHyxoAMvEaPcYnmygqi7twthLE1QPR/nWXcbve1y4gQ6xT/frLxSg9DI0TOYfKqNqI90YiU/23IyJrme7xD5PdNI9ITVqO1lNIAXjXOtUT/26NEa7t/8f2a/Jv1xvUdtDZqiPIyS/3b5C0b8OsQOlT1JffFnmn6NnvcekI+/VA8oMn0+Yrv91GUM4dpdytxej95AkuQTErertUof7oYCn6XsQoFExYb3wbT7iq0zn6AZFKR6Zz4fjnR6rDTeA8hY2sxkT+dPKsVRP3EPyb6YteKj8efhcs+Y7otYax7En1ENYXdKHpwhcduQGthEMneF9Ca/uJkrYGfImNyufFpQoYlpstUtI/v91zaiMM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\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(dset.shape)\n", "kwargs = {'scale_bar': True, 'cmap': 'hot'}\n", "view = sidpy.viz.dataset_viz.ImageVisualizer(dset, image_number=5, **kwargs)" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## 4-Dimensional Dataset\n", "A 4-dimensional dataset can be visualized as easily.\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4611b9c716ea49f88bcc5f1ff7b8a773", "version_major": 2, "version_minor": 0 }, "image/png": 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\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = np.random.random([5,5,10,10])\n", "for i in range(5):\n", " for j in range(5):\n", " data[i,j]+=(i+j)\n", "#kwargs=(scan_x=0, scan_y=1)\n", "dataset = sidpy.Dataset.from_array(data)\n", "dataset.data_type='Image_4d'\n", "dataset.plot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have the following parameters to influence the plot:\n", "- scan_x, scan_y\n", "- image_4d_x, image_4d_y\n", "Those parameters are supposed to be integers givin the dimension of the dataset.\n", "\n", "In the plot above where none of these parameters are specified we assume slowest to fastest change of dimensions.\n", "\n", "But below, we switch scanned and image dimensions of th 4d dataset.\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { 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", 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To customize spatial units and quantities for specific coordinates, simply extend the ``sidpy.Dataset.point_cloud`` dictionary with the relevant values.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2D" ] }, { "cell_type": "code", "execution_count": 270, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d0fd5692625e4ef591aa9df300d8f9d7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Dropdown(description='Image', layout=Layout(height='50px', width='30%'), options=(('Pixel Wise'…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "070f6c458e6049c991c85cd1ea090780", "version_major": 2, "version_minor": 0 }, "image/png": 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e2NcHNhpNdiuPc62SihoUlLX9utV6A05ma2G033we0WECSERE1IKCUp2ga8Je0Yp7Mkh+WbVdZ0df62J+28rCGIwmnMjS2GWyipgxASQiImpBrlbYFji9yCeDCNlCWVljQFZJ68vCnM0tRVm1+LvUbY0JIBERUTMqdLWiSBjEOhmkrFoPTaWws2jTCyta1aKXUViBPAG6qsWICSAREVEzhBjb1hixTgaxxbq/bVVrMCHtavMTQgrLdS3u40qYABIRETUjX6DZv40R22QQg0CTPxpzRVOFcl3jCXKFrhanc7SCjuMUGyaARERETdBUims9XrFNBsnVVsFgEEc8dWVhyhps1xuMOJGtQa1I4hQLJoBERERNEKr2X1P0tUYUlIlnMojYWiSLy2tw9ZrXx2Qy4XSOFpU68STxYsEEkIiIqBEmkwn5peJJturlaFo/49WWtFV6UUyOud6F/DJzK2na1XIUlQtXv1HM5EIHQEREJEaF5TWC1bZrTkmFHhW6WniqhP0Iz25D6RV7qqwxILukCkq5FBmF4oxRDJgAEhERNUJMkz+ul6OpQvcQb8GurzcYUSDC1tF6lwrLOeGjBewCJiIiuk6twWgxlkxsrmiqBJ0MkqethkFEk1GuV2swiTo+MWACSEREdJ2r5TpRJxC1BhPy27EGrrWItSg1tR4TQCIiouuIpbZdc4SagVtSUYOKJurtkeNw2ARw2bJliI6OhpubG5KSknDo0KFm9//mm2/Qs2dPuLm5oV+/fti2bZudIiUiIkeiqzWgpEL8M0c1lfomCx/bkhhW/qCOc8gEcN26dZg7dy7+/ve/49ixY4iPj8fYsWNRUFDQ6P779u3D1KlT8eijj+L48eOYMmUKpkyZgtOnT9s5ciIiErt8rc5hJhDYuxWwptaIAgG7nsl6JCaTo/yY/yUpKQmDBg3CJ598AgAwGo2IiIjA008/jXnz5jXY/7777kNFRQW2bNli3jZkyBAkJCRg+fLldoubiMjRlJaWQq1WQ6vVwsfHR+hw7OJQejFKq/RCh9EqcpkEQ7sEQCWX2eV6GYUVuFjA9XRtKTrQE12DvVrcr6O/mw5XBqampgZHjx7F/PnzzdukUilGjx6N/fv3N3rM/v37MXfuXIttY8eOxaZNm5q8jk6ng0731wwwo9GI4uJiBAQEQCKRdOxJEJHVmEwmlJWVITw8HFKpQ3ZqOA2D0YRao9FuyYgtVNbUOkzyB9RNBvn9YiGCvd0Q4e8BtbvCptdj96/zcLgEsLCwEAaDASEhIRbbQ0JCcO7cuUaPycvLa3T/vLy8Jq+zePFiLFq0qOMBE5FdZGVloXPnzkKH4dJqjUYcTi9BfIQa3m62TURsxREmf1zPaKwry5KnrYbaQ4EIPw+E+Kis3lhRVK4T1brI1DEOlwDay/z58y1aDbVaLSIjIzEcEyCHY/5hswknbw29+thgq5znt5f+a5XzUEOl5UZE9c+At7dwRXHpL9V6A45klKBPJx8Ee7sJHU6b5TtgAngtbaUe2kotLhRI0dnPA5183aGUW6dlnKVfnIvDJYCBgYGQyWTIz8+32J6fn4/Q0NBGjwkNDW3T/gCgUqmgUqkabJdDAbmECaCZkyeAMqV1PsB8vNk1aWscmiEeBqMJJ7O06BJsQEygp9DhtJq2Uo9KJ2nh0umNSCsoR3phOUJ86rqHfTrQKlutN6CwXLyFsantHO5TSalUYsCAAUhOTjZvMxqNSE5OxtChQxs9ZujQoRb7A8COHTua3J+IiDouraAcp3O0gq5Y0RZ5Il76rb2MRiBXU41Dl4pxJKMY+aXVaM/czyuaKoeZGU2t43AtgAAwd+5czJgxAwMHDsTgwYOxdOlSVFRU4OGHHwYATJ8+HZ06dcLixYsBAM8++yxuvvlmfPDBB7jtttuwdu1aHDlyBJ9//rmQT4OIyOnlaatRpTcgrrNa1JNDTCaTUyaA19JU6qGp1EKlaFv3sMlk4uQPJ+SQCeB9992Hq1evYsGCBcjLy0NCQgK2b99unuiRmZlpMRtw2LBhWLNmDV577TW8+uqr6NatGzZt2oS+ffsK9RRIhApm2a5FOPGtpxpsO/7apza7HpGYaCv1op8cUlRRA32tUegw7OL67uFIf49m35er5Tro9K7x2rgSh+sCrjdnzhxcvnwZOp0OBw8eRFJSkvmx3bt3Y8WKFRb733PPPUhNTYVOp8Pp06cxYcIEO0dMRGR9e/fuxcSJExEeHg6JRNKgvJXJZMKCBQsQFhYGd3d3jB49GhcuXLB7nPWTQwpE2MqWX1qN1LwyocOwu/ru4YN/dg8XNNE9zMkfzslhE0AiIgIqKioQHx+PZcuWNfr4kiVL8PHHH2P58uU4ePAgPD09MXbsWFRX2z8RMxhNOJmtRXphhd2v3RhNZQ0OZxTjVLbW5cubaCr1OJmtxe8Xi5BRWAG9oa7Fr6rGgOJy8S+LR23nkF3ARGQHK7WQfFIC0xw/YIZa6GioCePHj8f48eMbfcxkMmHp0qV47bXXMHnyZADAqlWrEBISgk2bNuH++++3Z6hmaQXlqNDVoneYD6RS+8/ertDV4mJBOa6WcVbr9ar1BlwsKEd6YQVCfNxg5MwPp8UWQCJqlOSTEkiyayH5pEToUKid0tPTkZeXh9GjR5u3qdVqJCUlNblykr3kaatx5HIJqvX2a3nT1RpwNrcUBy4VMflrgcFowhVNFfIcvC4iNY0tgETUKNMcP6C+BZAcUv1qR21ZCen6ZTBLS0ttFl9plR6HM4oRH+HboRp1LTEYTbhcVIHLxZUwGNiiRQSwBZCImjJDDdPhaHb/upjFixdDrVabbxERETa9nk5vxFEbTQ6pL1+yL60Ql65WMPkjugYTQCIiJ1W/2lFbVkKaP38+tFqt+ZaVlWXzOOsnh1y6Wm61c14t0+HApWKcvVLKEiZEjWACSETkpGJiYhAaGmqxElJpaSkOHjzY5EpIKpUKPj4+Fjd7uXS1AqeytTB0YOWQ0mo9jl4uwYksDSp0tVaMjsi5cAwgkUCuLw7NwtDUHuXl5bh48aL5fnp6OlJSUuDv74/IyEg899xzeOutt9CtWzfExMTg9ddfR3h4OKZMmSJc0M3IL/1r5RA3RetXDqmfvcpJC0StwwSQiMiBHTlyBCNHjjTfnzt3LgBgxowZWLFiBV5++WVUVFTgiSeegEajwfDhw7F9+3a4ubkJFXKL6ieHxHX2hdq9+ckheoMRGYUVyCqphJE9vUStxgSQiMiBjRgxotHVG+pJJBK88cYbeOONN+wYVcfp9EYcu1yC3uE+CPFpmKwajSZkl1ThUmE5ajm5g6jNmAASEZEoGYwmnMrWojyoFl2CvMzb87TVSLta7vKrdxB1BBNAckkFsxofAE9E4pN+tQKVOgPCfd2QdrUCpVV6oUMicnhMAImISPTyS6uRb4NagUSuimVgiIiIiFwME0AiIiIiF8MEkIiIiMjFcAwgkUhcXxgaYHHoxozvdoPF/VpTDYBLwgRDROSg2AJIRERE5GKYABIRERG5GCaARERERC6GCSARERGRi+EkEHIJjrryh6tNDLl+ggcREdkGWwCJiIiIXAxbAJ2c2lQED5SjEl7QSgKEDoeIiIhEgAmgE+tmOolYnDffv2TqjguSOAEjIiIiIjFgAuik1KYixOI8bgKQCOA4AOA8CkydRNkSeLspDfebzmGtpCe2SLoIHQ7ZAMf3ERGJB8cAOikPlAOoS/4Uf/577Xaxud90DiGoxP2mc0KHQkRE5PSYADqpSngBqGv506O+BfCv7WKzVtIT+fDAWklPoUMhIiJyeuwCdlJaSQAumboDOI+9f267hB6i7P4FgC2SLuz6JSIishMmgE7sgiQOBaZOnAVMREREFpgAOjmtJABaMPFzJtcXhxZjYWhO+CAiEjeOASQiIiJyMUwAiYiIiFwME0AiIiIiF8MEkIiIiMjFcBIIOZ2CWUOFDsGpcYIHEZHjYwsgERERkYthAkhERETkYpgAEjXhntP78OOqN3HP6X1Ch0JERGRVHANI1IRHjiUjvKwEjxxLxjd9hwkdTpMGLJxls3MHrz5ps3MTEZFw2AJI1IQv+4/CFW8/fNl/lNChEBERWRVbAIma8E3fYaJu+SMiImovtgASERERuRgmgEREREQuhl3A5PCqJg2yuO+dU9uu85R1coxfB2mtyW7XKpgW12AbJ4YQETk+tgASERERuRjRJYB79+7FxIkTER4eDolEgk2bNpkf0+v1eOWVV9CvXz94enoiPDwc06dPx5UrV5o958KFCyGRSCxuPXv2tPEzISIiIhIn0SWAFRUViI+Px7Jlyxo8VllZiWPHjuH111/HsWPHsGHDBqSmpmLSpEktnrdPnz7Izc0133777TdbhE9EREQkeqIb9DR+/HiMHz++0cfUajV27Nhhse2TTz7B4MGDkZmZicjIyCbPK5fLERoaatVYqXHy8DChQ2gXZx87SEREVE90LYBtpdVqIZFI4Ovr2+x+Fy5cQHh4OGJjYzFt2jRkZmbaJ0AiIiIikXHoBLC6uhqvvPIKpk6dCh8fnyb3S0pKwooVK7B9+3Z89tlnSE9Px4033oiysrImj9HpdCgtLbW4ERERUcdpq/T46vd0nM9v+nOYbMthE0C9Xo97770XJpMJn332WbP7jh8/Hvfccw/i4uIwduxYbNu2DRqNBuvXr2/ymMWLF0OtVptvERER1n4KRERELmnNwUz8nlaENYfYGycUh0wA65O/y5cvY8eOHc22/jXG19cX3bt3x8WLF5vcZ/78+dBqteZbVlZWR8MmEZucvh/f/PQOJqfvFzoUl3WbPhWrKr7FbfpUoUMhshqD0YTtp/NwgS1dZn9c0eJoZgkAILukClc0VQJH5JocbvR6ffJ34cIF7Nq1CwEBAW0+R3l5OdLS0vDQQw81uY9KpYJKpepIqE5J3ilc6BAa8D6SbXG/bGDnNp/jwfO7EFalwYPnd+H7mKFtu74NJ4/Ys+hza9mqOPR9NacQYqrAfTWnsFXRo8PnIxKDPeev4ttj2fDzUOC9u+IglUiEDklQtQYjvj5c16Aik0hgMJlwKL0YUxI7CRyZ6xFdC2B5eTlSUlKQkpICAEhPT0dKSgoyMzOh1+tx991348iRI1i9ejUMBgPy8vKQl5eHmpoa8zlGjRqFTz75xHz/xRdfxJ49e5CRkYF9+/bhjjvugEwmw9SpU+399Eik/td9JHLdffG/7iOFDsVlrVP2Q77EE+uU/YQOhcgqamqN2HoqFwBQUqlHRlGFwBEJL/lcAfK01fB2k+P+QXVDqw5lFMNkEt+XXWcnuhbAI0eOYOTIvz6E586dCwCYMWMGFi5ciM2bNwMAEhISLI7btWsXRowYAQBIS0tDYWGh+bHs7GxMnToVRUVFCAoKwvDhw3HgwAEEBQXZ9smQw/g+ZmibW/7IurYqerDlj5zKnvNXoa3Sm++nZGoQG+glYETC0lTWYPOJuoUb7urfGQOj/PDN0WwUlOlwuagS0YGeAkfoWkSXAI4YMaLZbwKt+ZaQkZFhcX/t2rUdDYuIiKjVdHoDtp2ua/3rHeaDM7mlOJ6lwZ392z5ExVl8czQbulojYgM9MaxLAKQSCeIj1DicUYKDGcVMAO1MdF3AREREjm73+asoq65FoJcSj98YA5lUglxtNfK01UKHJojz+WU4mF4MCYAHkiLNYyEHR/sDAI5kFMPIbmC7El0LIImHGCd8OJPWTB6pCJHZIRIisqZqvQE/ns4DAEyMC4e3mwI9Q73xx5VSHM8qwXi1Y66W1F4GowmrD9aVe7mpexCiA/5q6evbSQ13hQwllXpcLChH9xBvocJ0OUwAiYg6SKfT4eDBg7h8+TIqKysRFBSExMRExMTECB0aCeCXcwUo19UixFuFIbF1lSoSI3zrEsBMDcb3da0EcHdqAXI0VfBUynBHguVsX4VMiv6Rvvg9rQgH04uZANoRE0Aionb6/fff8c9//hM//PAD9Ho91Go13N3dUVxcDJ1Oh9jYWDzxxBN48skn4e3NDzZXUFVjwPY//mz9iw+HTFrX1ZkQ4Yv/HczEpcIKaCpr4OuhFDJMuymt0mNTSt3EjzsSO8HLrWHaMTjGH7+nFeHo5RJMHRwBuZSj0+yBrzIRUTtMmjQJ9913H6Kjo/Hzzz+jrKwMRUVFyM7ORmVlJS5cuIDXXnsNycnJ6N69O3bs2CF0yGQHO8/lo7LGgFC1m3l8GwD4eigR++ckh5QsjUDR2d+G4zmo0hsQ6e+Bm7o1XnmjZ6gPvN3kKNfV4lwuC2bbC1sACYBzjfe7vjA00L7i0NR61xeHtkZhaLG77bbb8N1330GhUDT6eGxsLGJjYzFjxgycOXMGubm5do6wzsKFC7Fo0SKLbT169MC5c+cEiceZVdbU4uc/8gEAk+LCIZVaFn1OjPTFpcIKHM/UYESPYCFCtKtLV8vx28W6kmwPDI5s8HrUk0klGBjlh12pV3EwvRh9O6ntGabLancCWFpa2uZj2rpkGxGRWP3tb39r9b69e/dG7969bRhN8/r06YOdO3ea78vl/O5vCzvO5KNKb0C4rxsGRvs1eDwx0g/fHcvBubwyVNbUwkPpvO+D0WjC6j/X+R3WJQBdg5uvfzg42h+7Uq/ieFYJ9IYoKGTsoLS1dv/0+fr6QtKGJW0kEgnOnz+P2NjY9l6SiIjaQS6XIzQ0VOgwnFq5rhY7zta1/k2O79Tokm+hPm4IU7shV1uNUzlaJMW0fSlTR/HbxUJcLqqEu0KGu1pR+7BLsBf8PZQorqzByWwtBkQ1TKDJujr09ePbb7+Fv79/i/uZTCZMmDChI5ciInI4M2bMQFZWFn755RdB47hw4QLCw8Ph5uaGoUOHYvHixYiMjGx0X51OB51OZ77fnt4eV/TzmTxU642I8HNHYqRvk/slRvoi91QejmdqnDYBLNfVYsPxHADApPhwqN0bHyZxLalEgkExfvjpj3wcyihmAmgH7U4Ao6KicNNNNyEgoHU/wLGxsU2OlSG63oTyU7i37AjWew/ENi+uDUuOqVOnTpAKPKMxKSkJK1asQI8ePZCbm4tFixbhxhtvxOnTpxudmbx48eIGYwapeWXVeiSfLQAATE5ovPWvXmKEH7adysOpHC30BqNTdnV+n5KDcl0twn3dMLJn65dcHRztj5/+yMfJbA2q9Qa4KVgH1ZbanQCmp6e3af/Tp0+391JkA2Kf9HFv2RGEGMpwb9kRl0kAnano8/WTQgDXmBhyvXfeeUfoEDB+/Hjz/+Pi4pCUlISoqCisX78ejz76aIP958+fb16DHahrAYyIiLBLrI5q+x950NUaERXggfjOzU9giArwgJ+HAiWVepzNLUVcZ1/7BGknmUWV2H3+KoC6iR9tKekS6e+BEB8V8kt1OJ6lwdBY52whFQubf/WorKy09SXICa33Hoh8mTfWew8UOhQip+Lr64vu3bvj4sWLjT6uUqng4+NjcaOmaav02HWuLuGZHB/e4th4qUSChAhfAMDxTI2No7Mvo8mE1Ycuw2Sqa83rGdq2nx2JRGIunXMovdgWIdI1rDIFadSoUVi1ahU6dbKs8H3o0CE8+OCDOH/+vDUuQy5km1c/l2n5I8f3yCOPNPv4l19+aadIWlZeXo60tDQ89NBDQofiFLafzkONwYjYQE/0a2X5ksSIupInKdkaPGQ0NVkexdEcuFSEtKsVUMmluHtA+0pvDY7xxw8nc3HmSinKq2sbLRzt7Cp0LS8Tag1WaQF0c3NDXFwc1q1bBwAwGo1YuHAhhg8fzskfROT0SkpKLG4FBQX45ZdfsGHDBmg0GkFje/HFF7Fnzx5kZGRg3759uOOOOyCTyTB16lRB43IGmsoa7D5fP/av5da/et1DveChlKGsuhZpV8ttGaLdVNbU4tujdTVYb48Lg79n+1Y6CVO7I8LPHQaTCUczS6wZokM4l1eKB/97ED//uZqMLVkltd66dSuWLVuGRx55BN9//z0yMjJw+fJlbNmyBWPGjLHGJYjIhu4+sw8PH/8FXyXegm97DxM6HIezcePGBtuMRiNmzZqFLl26CBDRX7KzszF16lQUFRUhKCgIw4cPx4EDBxAU1PrB+dS4bafzoDeY0DXIC73DWt/dKZdKEddZjQOXinE8S4NuTrD+7Q8nclFaXYsQHxVG9wrp0LkGx/gjqyQHh9KLcXN31/k5zS+txme701BRY8CWk7kY08e2pZus1rY6e/ZsZGdn47333oNcLsfu3bsxbBg/SEgcrl8dxN4rg3j+crbFfSqm9rVDJI17+PgvCC8vwcPHf2ECaCVSqRRz587FiBEj8PLLLwsWx9q1awW7tjMrrqjB3j8nO7Sl9a9eYoRfXQKYqcE9Azq3+XgxydFUIflcXQ3EqYMiOzyzeXC0P747loPz+WUus25yua4WH/9yARU1BvQM9caSuxtOpLM2q3QBl5SU4K677sJnn32Gf//737j33nsxZswYfPrpp9Y4PRHZ2FeJt+CKlx++SrxF6FCcSlpaGmpr7TOeh+xr66lc1BpN6BHijV5taP2r1zfcBwqZBFfLdcjRVNkgQvswmUz4+lAmjCYgMcLXKsu4BXip0CXIEyYAhzOcvxu41mjE8j1pyC/Vwd9DiTcm97VLCRyrtAD27dsXMTExOH78OGJiYvD4449j3bp1eOqpp7B161Zs3brVGpchIhv5tvcwtvx1wLVlU4C6D8Xc3Fxs3boVM2bMECgqspXCcp15jdvJCe0rqaVSyNA7zAcnsrU4nqVBZz8Pa4ZoN0cul+BcXhkUMgnuHWi9ckFJMQFIu1qBQxnFuLV3x7qUxcxkMmH1gUycyyuDSi7F06O6tnv8ZFtZpQXwySefxN69exETE2Pedt999+HEiROoqamxxiWIiETr+PHjFreTJ+tqHn7wwQdYunSpsMGR1W09mQuD0YReYd7o3oHxe4kRdatdOGo5mGq9AeuPZAEAxvcNQ5C3ymrnHhDlB4kESC+swNUyXcsHOKgdZ/Px68VCSCTAEzfFIsKOXwSs0gL4+uuvN7q9c+fO2LFjhzUuIRo1o/vDqHBr0zHK7YdtFE3riL3osyNpzVi+9gr+umGx9AIBxwVamzMXh961a5fQIZCdFJRV4/e0P1v/4ju1sHfz4iPUkBwAMosrUVSuQ4CX9RIoe9h2KhcllXoEeikxzsoTFtTuCvQM9cbZ3DIcyijGbf3CrHp+MUjJ0uCbP8en3zsgAvF2LgputUkgJSUl+OKLL3D2bN0HZK9evfDII4+0aq1gIiIiR7DlZC6MproxfF2DvTp0Lm83BboFe+F8fjmOZ2k6PHvWnvJKq/HTmbqJH/cNjIBSbv11JZKiA+oSwHTnSwCziivxn18vwQTg5u5BGN0r2O4xWOUdq+/+/fjjj811sP71r38hJiYGe/futcYliIgczquvvtpikWhyHPml1dh/qQgAMKmdY/+u54jdwCaTCWsPZcJgNKFvuI95ZRNr6x/lC5lUghxNFXJKHHeizPU0lTX41y8Xoas1oleoN6YOjhBkFrhVEsDZs2fj3nvvRXp6OjZs2IANGzbg0qVLuP/++zF79mxrXIKIyOHk5OQgIyND6DDISn44eQUmExDfWY3YwI61/tWrT57OF5ShvNoxZoyfyNbi9JVSyKQS3D840mbJi4dSjn7hdbOKD2U4x9JwuloDPtl1EcWVNQj1ccOTN3dp03rJ1mSVq168eBEvvPACZLK/pi3LZDLMnTu3yfUmiYic3cqVK/HLL78IHQZZwRVNFQ5eqktCOjr271pB3ipE+LnDZAJOZGusdl5bqak1Yu3hTADAmN4hCPVp25j4thoc89fawCaTyabXsjWjyYQvf89ARlElPJUyPH1LV3iqhFvqzipX7t+/P86ePYsePXpYbD979izi4+OtcQmHVjNukM3OnXFXw209nztjcd+o0bZ4Hqlvx2s3ORJbTuYgIufzw8krMAFIjPRFZIB1Z2omRvohq6QKx7M0uKFroFXPbW0//ZGHwvIa+HkocLsdxuXFd1ZDKZfiarkOGUWViAn0tPk1bWVzyhUcvVwCmVSCp0Z0RYiNk+eWWCUBfOaZZ/Dss8/i4sWLGDJkCADgwIEDWLZsGd59911zSQQAiIuzfXVrIiJ7q6iowJ49e5CZmdmg/NUzzzwjUFRkDdkllTjyZ0HiSfHWr6qQGOGLzSeu4I8rWuj0BqjsUAS4Pa6W6bDtdC4A4N6BEXaJU6WQIaGzLw5lFONQerHDJoD7LxVhy6m61+6hIVHoESr88n9WSQDrFxVvbLmjqVOnQiKRwGQyQSKRwGAwWOOSRESicfz4cUyYMAGVlZWoqKiAv78/CgsL4eHhgeDgYCaADu6HE7kwARgY5WeTOm2d/dwR6KVEYXkN/sgtRf9IP6tfwxrWH8mC3mBCz1BvDIyyX4yDY/xxKKMYhzOKcc+AzpBKHWvZvAsFZVi5LwMAMK5PKIaLpJXXKglgenq6NU5DROSQnn/+eUycOBHLly+HWq3GgQMHoFAo8OCDD+LZZ58VOjzqgMziShzNLIEEwEQbtP4BgEQiQWKEH3aczcfxTI0oE8DTOXUrlkglwFQbTvxoTJ9wH3goZdBU6XGhoFwUrWetdbVMh2W70lBrNCEx0hd39rfe+NGOskoCGBUVZY3TUDtEf9dw27mlvS3uXz8msDGtGSdob642LrExrlYc2lELQ6ekpODf//43pFIpZDIZdDodYmNjsWTJEsyYMQN33nmn0CFSO20+cQUAMCjaH5183W12ncRIX+w4m48T2RrUGo2CzQxtjN5gxNeH6iZ+jOoZYtPXoTEKmRT9I/3w28VCHMoodpgEsLKmFv/65QLKdbWI9PfAYzfEQCpAuZemtPsnbPPmzdDr9a3ef9u2baiqcp46PkRE9RQKBaR/fmAHBwcjM7Puw1KtViMrK0vI0KgDMgorkJKlgURim7F/1+oa5AUvlRyVNQZcyC+36bXaaufZfOSX6eDjJsfEeGEKMg+OrpsNfPRyCWqNRkFiaAuD0YR/772EK9pq+Lor8PQtXUU3trPdCeAdd9wBjUbT6v3vv/9+5ObmtvdyRESilZiYiMOH65Z8vPnmm7FgwQKsXr0azz33HPr2dZ4WW1fz/Z+tf0NiAhCqtu2MTalUYq4JKKai0MUVNdhysu6z++4BneGhFKZsSc9Qb/i4yVGuq8XZ3DJBYmiLtYcz8ceVUijlUjx9S1f4eSiFDqmBdr+TJpMJM2fOhErVurULq6ur23spIiJRe+edd1BWVveh9Pbbb2P69OmYNWsWunXrhi+//FLg6Kg90q6W41SOFlIJcHucfVq9EiJ88dvFQhzPKhFsdYjrfXs0G7paI7oEeWJIbIBgcUilEgyM8scvqQU4lF6Mfp3EO0Tol3MF2JV6FQDw2PAYRAWIc+ZyuxPAGTNmtGn/adOmwcfHp72XIyISrYEDB5r/HxwcjO3btwsYDVnD5pS61r9hXQLtVq+td5gPVHIpSir1uFxciWiBE4dzeaU4lFEMCYBpg6MEH782OKYuATyWWYKHaqNssv5wR53O0eLrPwtl39W/kygn9NRrdwL41VdfWTMOsqHrJ4U0pjUTRezNqhNTRDSgui3yHurXruOktY5dMZ9ISBfyy/BHbilkEglus0Ox43pKuRR9w9U4mlmC45kaQRNAvcGI1QfrEpmbuwdZvfh1e3QJ8kSApxJFFTU4laPFADuWommNnJIqLN+bBpMJGNYlAOP6hAodUrMc81ORiEhg48aNw4EDB1rcr6ysDO+99x6WLVtmh6jIGurH/t3QNQBB3q0b5mQtiZG+AIDjWSV2ve71tv+Rh1xtNbzd5JiSKI7SJRKJBIOi/1oaTkxKq/T4164LqNYb0T3EC9OHRImiC785wi1CR0TkwO655x7cddddUKvVmDhxIgYOHIjw8HC4ubmhpKQEZ86cwW+//YZt27bhtttuw/vvvy90yNQK5/JKcS6vDHKpfVv/6vXrpIZMIsEVTTXyS6sFWS4sr7QaW/+c+HH/oAh4Cbhe7fUGx/hj+x95OJGtQVWNAe5K4WfW6g1GLNt9EYXlNQjyVmHWzV0gl4m/fU087yoRkQN59NFH8eCDD+Kbb77BunXr8Pnnn0OrrRu2IJFI0Lt3b4wdOxaHDx9Gr169BI6WWsNkMuH7P8f+3dgtEAFe9m39AwBPlRzdQ71wNrcMxzM1GNfXvt2IJpMJ/ztwGbVGE/qE+ZjLr4hFhJ87QtVuyNNWIyVLg6FdhJuYAtS9Xiv2ZSDtagU8lDI8c0tXeLspBI2ptZgAEhG1k0qlwoMPPogHH3wQAKDValFVVYWAgAAoFI7xIUB/OZtbhgsF5ZBLJZggQOtfvf4RfnUJYFaJ3RPAfZeKcC6vDEqZFA+KsBtTIpEgKdof35+4goMZRYIngFtP5eJgejGkEmDWzV0QprZvkeyOsHoCWF1dDTc3+zdZU8c0NlFEjBNDqHWM8oZ/tB1hYsj1K4MAjrU6iFqthlot3vIU1DSTyYTvT+QAAEb0CBK0bltCpC9WH8rEpasV0FbpoXa3z5eJsmo9vjmSDQCYGB9m9/GPrTUopi4BPHulDGXVesFa3A5nFGPTny3G05Ki0CvMsSqdWKWT2mg04s0330SnTp3g5eWFS5cuAQBef/11fPHFF9a4BBERkc38caUUaVcroJRJMb6vcK1/AODnoURMoCdMAFKyNHa77voj2SjX1aKznztu7R1it+u2VaiPGyL9PWAwmXBMoKLZl66W48vf0wEAt/YKwc3dgwSJoyOskgC+9dZbWLFiBZYsWQKl8q9vTX379sV///tfa1yCiIjIJkwmEzal/NX6Z68Wt+Yk1q8KYqfZwGdzS7H/UhEkAKYPiRLVWsSNSYqpG5t4ML3I7tcuKtfhk10XoTeYENdJjXsGdLZ7DNZglXd41apV+PzzzzFt2jTIZH/NyImPj8e5c+escQkiUbqt5hxWla3HbTX8OSdyVMcyNcgoqoRSLhVN7bb6ZeHO5ZahqsZg02vV1Brx/w5cBlCXAMcGedn0etYw8M8agBfyy1FSWWO36+Zpq/HBjvMora5FJ193PHFTLKRScY2TbC2rjAHMyclB165dG2w3Go3Q6/XWuAS1QfR3lvcz7mrfeRy1gLQ93ac7iRBTBe7TncRWZc8Onau9RZ9bq7Fxgdez5ThBnX/7/khmPR3f7OMGXTXw4dftOjdRVkklvtpX15U3qmcwfETQ+gcA4b7uCPVxQ15pNU7laDE4xnazcbedykVBmQ5qdwXuEEnNv5YEeKnQLdgLFwrKcTijGGN62z5xP5dXik93p6GyxoAATyWeuaUr3BTCl6FpL6u0APbu3Ru//vprg+3ffvstEhMTrXEJIlFap4pDvsQT61QNJy+Q65gxYwb27t0rdBjURsUVNfg4+a/ivZPiw4UOyUKCHbqBr2iq8OMfeQCABwZHwkPpOMVBBtuxKPTeC1fx0Y4LqKwxoEuQJ/5vQi9BygRZk1Xe6QULFmDGjBnIycmB0WjEhg0bkJqailWrVmHLli3WuASRKG1V9uxwyx85Pq1Wi9GjRyMqKgoPP/wwZsyYgU6dHKMlxVVV1tTin8kXUFKpR7jaDbNHdIVCZMV7+0f6YvsfeTiVo4XeYLR6fEaTCav2X4bBaEJ8ZzX6/7kKiaMYEOWHrw9nIqOo0mZFs41GE747lo2fzuQDqEs6H74hWnQ/K+1hlWcwefJk/PDDD9i5cyc8PT2xYMECnD17Fj/88ANuvfVWa1yCiEi0Nm3ahJycHMyaNQvr1q1DdHQ0xo8fj2+//ZbDYESo1mDEZ3vSkKOpgtpdgWdHdYOniFa7qBcd6Am1uwLVeiNS88qsfv5fLxTi4tVyqORSPDA4UnQ1/1ri465Ar9C60iuHM6zfCqjTG/DpnjRz8jcpPhyP3xjjFMkfYMW1gG+88Ubs2LEDBQUFqKysxG+//YYxY8ZY6/Tk5G7Tp2JVxbe4TZ8qdChE7RIUFIS5c+fixIkTOHjwILp27YqHHnoI4eHheP7553HhwgWhQyTUzfhduf8yzuaWQSWX4plbuoq2K08qkZi7gY9lWrcbWFulx7dH62r+TUnoJNrXoCX1YyMPWTkBLKmswXs/pSIlSwO5VILHb4zBpPhwh0uSm2OVBPDw4cM4ePBgg+0HDx7EkSNH2nSuxYsXY9CgQfD29kZwcDCmTJmC1FTLpGDEiBGQSCQWtyeffLLZ85pMJixYsABhYWFwd3fH6NGj+QfZCs4t7d3g1h731Zyqm0xRc8rKEf7JaLS8iUDeQ/0sbmJglEssbjp/692sxSfdaHHzviyO97Nebm4uduzYgR07dkAmk2HChAk4deoUevfujY8++kjo8Fze5hNXsP9SEaQS4MmbuyAqwFPokJpV3y2bkqWB0WS9SVrrDmehSm9AVIAHRvUMttp57S0x0hdyad3aydkllVY5Z0ZRBd7eehaZxZXwdpPjxTE9kBQj7IojtmCVBHD27NnIyspqsD0nJwezZ89u07n27NmD2bNn48CBA9ixYwf0ej3GjBmDiooKi/0ef/xx5Obmmm9Llixp9rxLlizBxx9/jOXLl+PgwYPw9PTE2LFjUV1d3ab4yDbWKfvVTaZQiiMRImoLvV6P7777DrfffjuioqLwzTff4LnnnsOVK1ewcuVK7Ny5E+vXr8cbb7whdKgu7dcLV/HDyVwAwINDotCvk/hXbekR4g13hQyl1bW4dLWi5QNa4XSOFocyiiGR1NX8c9QyJgDgoZSb30drTAY5llmCJT+lQlOlR7ivG/5vQi90DRZ/WZz2sMqghzNnzqB///4NticmJuLMmbaVCdm+fbvF/RUrViA4OBhHjx7FTTfdZN7u4eGB0NDWTfs2mUxYunQpXnvtNUyePBlAXe3CkJAQbNq0Cffff3+bYiTr26roga2KHkKHQdQuYWFhMBqNmDp1Kg4dOoSEhIQG+4wcORK+vr52j43qnM7Rmmvd3d4vDDd1c4yVG+QyKfp1UuNQRjGOZ5V0OBnR6Q3438G612F0zxDRt4C2xuAYfxzP0uBQRjHuSOzUrm5ak8mE7X/kYcOxHJgA9An3wd9uinWoWdFtZZUWQJVKhfz8/Abbc3NzIZd37MXTarUAAH9/yxpIq1evRmBgIPr27Yv58+ejsrLppt/09HTk5eVh9OjR5m1qtRpJSUnYv39/h+IjIvroo49w5coVLFu2rNHkDwB8fX2Rnp5u38AIAJBZVInP9qTBaAKGxgZgcoK4yr20pL4b+HimBqYOdgNvPnkFheU18PdUOtzr0JS4zmqo5FIUltcgvbDtraS1BiNW7r+M7/5M/kb2CMIzt3Rz6uQPsFIL4JgxYzB//nx8//335oXQNRoNXn311Q7NAjYajXjuuedwww03oG/fvubtDzzwAKKiohAeHo6TJ0/ilVdeQWpqKjZs2NDoefLy6mochYRYrm0YEhJifux6Op0OOp3OfL+0tLTdz0No1xeGBtpfHLo1WEDacekday1z0di1axemTJkCNzfLMhQVFRV4+umn8eWXXwoUGRWV6/DPXy5AV2tEr1BvzBga5XAD+ft2UkMulaCgTIcr2mp08nVv13kyiyux488ZrdOSIh26iPG1VHIZEiJ8cTC9GIcyitu0kkm5rhaf7U5Dan4ZJBLg/oERGNVLvOsgW5NVWgD/8Y9/ICsrC1FRURg5ciRGjhyJmJgY5OXl4YMPPmj3eWfPno3Tp09j7dq1FtufeOIJjB07Fv369cO0adOwatUqbNy4EWlpaR19KmaLFy+GWq023yIiIqx2biJyLitXrkRVVVWD7VVVVVi1apUAEREAVOjqav1pq/To5OuOWSO6QO6AJTzcFDL0Dqv7dna8nbOBjUYTVu3PgNFUVz8vvrOvFSMUXv1s4MMZJTAaW9dKml9ajcXbziI1vwxuCimeHtnVZZI/wEoJYKdOnXDy5EksWbIEvXv3xoABA/DPf/4Tp06danfiNGfOHGzZsgW7du1C587NL7SclJQEALh48WKjj9ePFby+mzo/P7/JcYTz58+HVqs13xqb5EJErq20tBRarRYmkwllZWUoLS0130pKSrBt2zYEBzvuDEtHpjcY8enuNFzRVsPPo67WnyN36SXUdwNnadp1/O7zV5FRVAl3hQxTBzlfg0afMB94KGXQVulxvqDlmonn8krx9razyC/Twd9TiXnjeiLOyZLilljtt8HT0xNPPPFEh89jMpnw9NNPY+PGjdi9ezdiYmJaPCYlJQVA3UDsxsTExCA0NBTJycnm8TmlpaU4ePAgZs2a1egxKpUKKpVj1kUiIvvw9fU1l6Lq3r17g8clEgkWLVokQGSuzWgyYcW+DHPLzjOjusHfUyl0WB0S39kXElzG5aJKFFfUtOn5lFTWYMPxupp/dyZ2gq+HY78WjZHLpBgQ6YdfLxbiUHoxeoY2PZ7lt4uF+H8H6lZAiQ30xOyRXaEWyRrQ9mS1BPDChQvYtWsXCgoKYLyuztqCBQtafZ7Zs2djzZo1+P777+Ht7W0eo6dWq+Hu7o60tDSsWbMGEyZMQEBAAE6ePInnn38eN910E+Li/lqPtWfPnli8eDHuuOMOSCQSPPfcc3jrrbfQrVs3xMTE4PXXX0d4eDimTJliledPZG33nN6HR44l48v+o/BN32FCh0ON2LVrF0wmE2655RZ89913FpPVlEqleawy2dem4zk4mF4MmUSCWTd3QYSfh9AhdZjaXYGuwV64UFCOlCwNbmlD7b41hzJRrTciNtATN/dwjNnP7TE4xh+/XizE0csleGBwZIPufqPJhA3HcrD9z7WPB0X74eFhMVDKHW9YgDVYJQH8z3/+g1mzZiEwMBChoaEWA2wlEkmbEsDPPvsMQF2x52t99dVXmDlzJpRKJXbu3ImlS5eioqICERERuOuuu/Daa69Z7J+ammqeQQwAL7/8MioqKvDEE09Ao9Fg+PDh2L59e4NB22QfjU0UceaJIe0p9PzIsWSEl5XgkWPJLp0A+qSLq9DztW6++WYAdZUGIiMdbyktZ7Tn/FVsO133AT99aBT6hIu/1l9rJUT44kJBOY5llrQ6ATyeWYLjmRrIJBJMHxoFqRP/jPYI8YbaXQFtlR5nckstunR1egP++3s6jmdqAAC3x4VhUny4U78eLbFKAvjWW2/h7bffxiuvvNLhc7U0xT0iIgJ79uxp83kkEgneeOMNFmIlh/Fl/1HmFkASn5MnT6Jv376QSqXQarU4darpVWyu7Z0g2zmRrTHXuJscH44bugYKHJF1JUb64puj2TifX4ZyXS28Wli/uFpvwJpDmQCAMX1C0NkJWkKbI5VKMDDKD8nnCnAwvdicAGoqa/CvXRdxuagScqkEM4dFY0is863s0VZWSQBLSkpwzz33WONURPSnb/oOc+mWP7FLSEhAXl4egoODkZCQAIlE0ugXWIlEAoPBIECEriWjsAL/3nsJJhMwvGsgbo9rfEy4Iwv2dkMnX3fkaKpwMluDYV2aT3A3peSgpFKPIC8VJsa5xlCEwTH+SD5XgJQsDXS1BuRrdfjXrgsoqdTDSyXH7JFd0C3YW+gwRcEqCeA999yDn3/+ucX1eImInEV6ejqCgoLM/yfhXC2rq/VXU2tEn3AfPDjEebvjEyN9kaOpwvGs5hPAjMIKJJ8rAAA8OCTSZca5xQZ6ItBLicLyGqw7nIUD6cWoqTUiTO2GZ27phiBvTu6sZ5UEsGvXrnj99ddx4MAB9OvXDwqF5WyaZ555xhqXIQc0bfcBPPXjLnw6fiRWjxgidDhEVhMVFdXo/8m+yqvrav2VVdciws8ds27uArnUeZOd/hF+2HIyF3/klEJXa4BK3rCYs8Fowsr9GTCZgKQYf6caB9kSiUSCQdH++PF0HvZeKAQA9A7zwZM3O/eybu1hlVfj888/h5eXF/bs2dNgfJ5EInGqBFBWY4DM+Fd3jkHlHJXUbeWpH3ehc5EGT/24q8UEkCuI2BdX/bCelStXIjAwELfddhuAuklnn3/+OXr37o2vv/6aCaKN6A1GLNt9EXml1fD3UOLZUd2cZnWLpkT4uyPAU4miihqcuVKKxEi/BvvsPJuPrJIqeChluG+g89X8a8ngmLoEEABu7h6EqYMjnPpLQXtZ5RVJT09v8nbp0iVrXIIc1KfjRyI7wBefjh8pdChENvPOO+/A3b1uea79+/fjk08+wZIlSxAYGIjnn39e4Oick9Fkwhe/peNCQTncFTI8O7qbU9a3u55EIkFiM0WhC8t1+P7EFQDAPQM6w8cF69tF+HlgxtAoPD48Bg8mRTL5awLbQ8mmVo8Ywq5fcnpZWVno2rUrAGDTpk24++678cQTT+CGG25oUNKKrOPbo9k4crkEMqkEs0d2aff6uI4oIcIXO88W4GS2FgajCTJp3XhHk8mE1QczUVNrRPcQLwx3slnQbXFjN+etd2gtVksAs7OzsXnzZmRmZqKmpsbisQ8//NBalyEiEh0vLy8UFRUhMjISP//8M+bOnQsAcHNza3SNYOqY5LP5+PlM3dKeDw+LbnbVB2fULdgbXio5ynW1uFBQZn7+Ry+X4FSOFnKpBA8NiXLaiTBkHVZJAJOTkzFp0iTExsbi3Llz6Nu3LzIyMmAymdC/f39rXEK0ZDrL8g6OMiYw+jvL+xl3CRNHW7WmgPTF/3Sz2vW6/i2tXce1p/Azibvoc3NuvfVWPPbYY0hMTMT58+cxYcIEAMAff/yB6OhoYYNzMsczS7D2cN3a7HcmdnLJem4yqQRxndXYl1aElCwNeob6oLKmFl//+bqM7xuKMLXrtIhS+1ilY3z+/Pl48cUXcerUKbi5ueG7775DVlYWbr75ZtYHJCKnt2zZMgwdOhRXr17Fd999h4CAuqTk6NGjmDp1qsDROY+0q+X4/NdLMAG4qVsgxvcNFTokwSRG+AIAjmVqYDKZ8N2xHGir9AjxUWFCP+ergUjWZ5UWwLNnz+Lrr7+uO6FcjqqqKnh5eeGNN97A5MmTMWvWLGtchohIlHx9ffHJJ5802L5o0SIBomncsmXL8P777yMvLw/x8fH417/+hcGDBwsdVqvll1bjX79chN5gQlwnNaYluXYXZ+9wHyhlUhRX1GBX6lXsOX8VAPDQkCgoZJz0QC2zSgLo6elpHvcXFhaGtLQ09OnTBwBQWFhojUsQEYmaRqPBoUOHUFBQAKPxr65siUSChx56SMDIgHXr1mHu3LlYvnw5kpKSsHTpUowdOxapqakIDm7dmrJCqaypxY4zdWP+dLVGRAV44ImbYs0TH1yVSi5Dn04+OJ6pMS/3dkOXAJcbD0ntZ5UEcMiQIfjtt9/Qq1cvTJgwAS+88AJOnTqFDRs2YMgQzgAlIuf2ww8/YNq0aSgvL4ePj49Fy5QYEsAPP/wQjz/+OB5++GEAwPLly7F161Z8+eWXmDdvnqCxNUWnN+CX1AL8eDoPlTV1Y62jAzzw9C3OX+uvtRIjfHE8UwMA8FLJcc8A16v5R+1nlQTwww8/RHl5OYC6Lo/y8nKsW7cO3bp1c7kZwNdPCgEcZ2KIo7LmpI8G5/53l3Yd5/WblQOxAjEWfnbUSR/Xe+GFF/DII4/gnXfegYeHh9DhWKipqcHRo0cxf/588zapVIrRo0dj//79AkbWOL3BiL3nr2LrqVyUVtcCAMLUbpiS0An9I31dutv3enGdfSGTSGAwmXDfoAh4ubGyG7WeVX5aYmNjzf/39PTE8uXLrXFaIiKHkJOTg2eeeUZ0yR9QNwzHYDAgJCTEYntISAjOnTvXYH+dTgedTme+X1paavMYgbrly/alFeKHk7korqgbUhTopcSk+HAMiQmA1MW7fBvjpZLjsRtjoK3SY0iMv9DhkIOxWgJ4+PBh88y3ehqNBv379+dqIETk1MaOHYsjR45YfBl2VIsXL7br5BWjyYTDGcXYnHIF+WV1iaevuwK3x4VheNdAyDmhoVmDopn4UftYJQHMyMiAwdCw61On0yEnJ8calyAiEq3bbrsNL730Es6cOYN+/fpBobBcfmvSpEkCRQYEBgZCJpMhPz/fYnt+fj5CQxuWUZk/f765kDVQ1wIYEWH9sWUmkwknsrXYeDwHOZq6YtleKjkm9AvFiO7BUMqZ+BHZUocSwM2bN5v//9NPP0GtVpvvGwwGJCcnswgqkRO579g+PLY/Gf8dOgrr+g8TOhzRePzxxwEAb7zxRoPHJBJJo1+Q7UWpVGLAgAFITk7GlClTAABGoxHJycmYM2dOg/1VKhVUKpXN4jGZTDibW4aNKTlIL6wAALgrZBjbJwSje4VwggeRnXQoAaz/YyKRSDBjxgyLxxQKBaKjo/HBBx905BJOwVFXCyG63mP7k9GptASP7U9mAniNa8u+iNHcuXMxY8YMDBw4EIMHD8bSpUtRUVFhnhVsLxcLyrHxeA5S88sAAEq5FKN7BmNMn1B4qTiBgcieOvQbV/9HLyYmBocPH0ZgoOsuPE3kCv47dJS5BZAaV11dDTc3N6HDsHDffffh6tWrWLBgAfLy8pCQkIDt27c3mBhiK5lFldiYkoNTOVoAgFwqwc3dgzChXxjU7ooWjiYiW7DKV6709PQG2zQaDXx9fa1xeiISiXX9h7HlrxEGgwHvvPMOli9fjvz8fJw/fx6xsbF4/fXXER0djUcffVToEDFnzpxGu3xt6YqmCt+fuIKjl0sAAFIJcEOXQNweF4YAL9t1MxNRy6wyyva9997DunXrzPfvuece+Pv7o1OnTjhx4oQ1LuFyJmUewLpdizEp84DQoRBRC95++22sWLECS5YsgVKpNG/v27cv/vvf/woYmTCulunwxW/p+PsPf+Do5RJIACTF+OPNyX0xY1g0kz8iEbBKC+Dy5cuxevVqAMCOHTuwc+dObN++HevXr8dLL72En3/+2RqXcRqtKRY9LW0XQqs1mJa2C5sjbb+aSvR3Dbdl3GXzy7aZ3FMvdAitUj680uK+12/2rQ/Hos/2tWrVKnz++ecYNWoUnnzySfP2+Pj4RmvtOav80mr878Bl/HqxEAajCUDdahWTE8LR2U98NRKJXJlVEsC8vDxzmYAtW7bg3nvvxZgxYxAdHY2kpCRrXMLlrO4yEtPSdmF1l5FCh0JELcjJyUHXrl0bbDcajdDrHeNLS0ftu1iIh1cchq62LtHvHeaDKYnhiA30EjgyImqMVRJAPz8/ZGVlISIiAtu3b8dbb70FoG66v5DlDxzZ5sghdmn5I6KO6927N3799VdERUVZbP/222+RmJgoUFT2lRDpC283OSLclbgjsRN6hHoLHRIRNcMqCeCdd96JBx54AN26dUNRURHGjx8PADh+/Hij34qJiJzJggULMGPGDOTk5MBoNGLDhg1ITU3FqlWrsGXLFqHDswsPpRzfPjkUF/LLuV4vkQOwyiSQjz76CHPmzEHv3r2xY8cOeHnVNfnn5ubiqaeessYlnNrEnIOc8EHkwCZPnowffvgBO3fuhKenJxYsWICzZ8/ihx9+wK233ip0eHYT5uvO5I/IQVilBVChUODFF19ssP3555+3xumd3tSMPQjVaTDt4i5sDRlk3u7qBaMdZcJHa1w/KQSw/8QQsq0bb7wRO3bsEDoMIqJWaXcCuHnzZowfPx4KhcJiSbjGCLkOpiP4OvpmTM3Yg6+jbxY6FCJqh9jYWBw+fBgBAQEW2zUaDfr3749Lly4JFBkRUePanQBOmTIFeXl5CA4ONi8J1xih18F0BD90SsIPnThbmshRZWRkNPp3TqfTIScnR4CIiIia1+4E8Nq1L8W+DibRtabuOIQnN+/F8kk34etbBwsdDv3pjov7MP3MLqzqPRIbuzrGaiPX9n789NNPUKvV5vsGgwHJycmIjo4WIDIiouZx9W0Ru75gtLOPCbTXmL8nN+9F50INnty8V9AE0FrFop2l6PP0M7sQVlmC6Wd2OUwCWN/7IZFIMGPGDIvHFAoFoqOj8cEHHwgQGRFR8zo8C9hoNOLLL7/E7bffjr59+6Jfv36YNGkSVq1aBZPJZI0Yiaxq+aSbkB3oi+WTbhI6FLrGqt4jkevhh1W9Haf4udFohNFoRGRkJAoKCsz3jUYjdDodUlNTcfvttwsdJhFRAx1qATSZTJg0aRK2bduG+Ph49OvXDyaTCWfPnsXMmTOxYcMGbNq0yUqhElnH17cOZtevCG3sOsxhWv6ul56eLnQIRERt0qEEcMWKFdi7dy+Sk5MxcqTlt/ZffvkFU6ZMwapVqzB9+vQOBUlEJHbJyclITk42twRe68svvxQoKiKixnWoC/jrr7/Gq6++2iD5A4BbbrkF8+bNw+rVqztyCSIi0Vu0aBHGjBmD5ORkFBYWoqSkxOJGRCQ2HWoBPHnyJJYsWdLk4+PHj8fHH3/ckUvQNa6fFALYdmJI9HcNt2XcZbPLkQNrz6QPZ7J8+XKsWLECDz30kNChEBG1SodaAIuLixESEtLk4yEhIfz2S0ROr6amBsOGOeb4RSJyTR1KAA0GA+TyphsRZTIZamtrO3IJIiLRe+yxx7BmzRqhwyAiarUOzwKeOXMmVCpVo4/rdLqOnJ6IyCFUV1fj888/x86dOxEXFweFQmHx+IcffihQZEREjetQAnh94dPGcAYwETm7kydPIiEhAQBw+vRpi8ckEokAERERNa9DCeBXX31lrTionew9McRa7LXqhyO5fmUQoPHVQcS48oer27Vrl9AhEBG1SYdXAiEiIiIix8K1gImI2unOO+9s1X4bNmywcSRERG3DBJCIqJ3UarXQIRA5JJlUggh/d2QUNhz6QvbBBLCNlPnlkMv+Gr9WE+otYDSNu35coDXHBF5fHLq1haE55q99GhsXqDrZcFygPbl60edrcRw0Udsp5FIkdPaFl5scmcWVMPJPiiA4BpCIiIjswk0hw8AoP6g9FJBJJfBxU7R8ENkEE0AiIiKyOU+VHAOj/eCp+qvz0ddDKWBErk10CeBnn32GuLg4+Pj4wMfHB0OHDsWPP/4IAMjIyIBEImn09s033zR5zpkzZzbYf9y4cfZ6SkRERC7N10OBgdF+cFNYDkny92QCKBTRjQHs3Lkz3n33XXTr1g0mkwkrV67E5MmTcfz4cfTs2RO5ubkW+3/++ed4//33MX78+GbPO27cOIvxOk2tXkJERETWE+itQr9OasikDYuiq90VkErBcYACEF0COHHiRIv7b7/9Nj777DMcOHAAffr0QWhoqMXjGzduxL333gsvL69mz6tSqRocaw3KvLI2H2PviSP2LhbNCR9ERAQAYb5u6B3m0+SKOPXjADWV/NywN9F1AV/LYDBg7dq1qKiowNChQxs8fvToUaSkpODRRx9t8Vy7d+9GcHAwevTogVmzZqGoqKjZ/XU6HUpLSy1uRERE1DrRgR7oE65ucTlEjgMUhuhaAAHg1KlTGDp0KKqrq+Hl5YWNGzeid+/eDfb74osv0KtXLwwbNqzZ840bNw533nknYmJikJaWhldffRXjx4/H/v37IZM13hK2ePFiLFq0yCrPh4iIyJV0D/FGZEDrSlb5eSiQYdtwqBGiTAB79OiBlJQUaLVafPvtt5gxYwb27NljkQRWVVVhzZo1eP3111s83/3332/+f79+/RAXF4cuXbpg9+7dGDVqVKPHzJ8/H3PnzjXfLy0tRURERAeeFRERkXOTSoHeYWqEqt1afYyvh5LjAAUgyi5gpVKJrl27YsCAAVi8eDHi4+Pxz3/+02Kfb7/9FpWVlZg+fXqbzx8bG4vAwEBcvHixyX1UKpV5JnL9jYiIiBonk0kQ39m3TckfANYDFIgoWwCvZzQaodPpLLZ98cUXmDRpEoKCgtp8vuzsbBQVFSEsLMxaIbZJeyaOANadPGLL1UKofRTK2lbtZxzY8nhU6RHrfGHhqh9E1BoKuRQJEb5Qu7cvkfP1UHIiiJ2JrgVw/vz52Lt3LzIyMnDq1CnMnz8fu3fvxrRp08z7XLx4EXv37sVjjz3W6Dl69uyJjRs3AgDKy8vx0ksv4cCBA8jIyEBycjImT56Mrl27YuzYsXZ5TkRERM7KTSHDoGi/did/QN04QLIv0bUAFhQUYPr06cjNzYVarUZcXBx++ukn3HrrreZ9vvzyS3Tu3Bljxoxp9BypqanQarUAAJlMhpMnT2LlypXQaDQIDw/HmDFj8Oabb7IWIBERUQd4ucmREOHboMBzW3EcoP2JLgH84osvWtznnXfewTvvvNPk4yaTyfx/d3d3/PTTT1aJjYiIiOr4eigQH+ELhazjnYkyqQTebgpo2Q1sN6JLAKlprR07eO6V9oz/at/XruarO1G91o7vs5bGxgm2Zlwgx/wRWV9skCcuXa0QOgyrCvpzdQ9pI6t7tJefh5IJoB2JbgwgERGRs4gJ8kRskBc8lM4z0S7c1x1xna2b/AEcB2hvTACJiIhsINzXHV2C6pYp9fN0jtUuogM90Tu86aXdOsLXQwkbnJaawASQiIjIygK8lOgV9lfpLn8nSAB7hHqja7CXzc4vk0rg04GZxNQ2TACJiIisyNtNjrjOvhatZH4OvN6tVAr066xGhH/rlnbrCHYD2w8ngTi49k34IGuy9wSP9mpNAWmk2+7bPZErcFfKkBDpC9l14+OUcim83OQor3aMvxf1ZDIJ4jqpEeBln7Jpvh5KAJV2uZarYwsgERGRFSjkUiRG+kIlb3zChyN2A/cO87Fb8gcAvu4KjgO0EyaAREREHSSTSpDQ2RceyqY71nwdrHtTKgUC7Zj8AYBcJuU4QDthAkhERNQBEgnQp5MP1C0keH4ONstV7a5o0JVtDxwHaB8cA+hAHGW8n6GmYfeHTGkQIJKOc5Txfdaiuafc4r7vNxwTSNSS7iHeCPZ2a3E/hUwKbzcFSqsco9hxgKcwy6VyHKB9sAWQiIionaIDPdo0O9bf03Fat/y9hBmzyHGA9sEEkIiIqB1C1W7oGuzd8o7XcJRyMEq5FD5uwiSr8j9bSsm2mAASERG1kZ+nEr3D2j4sx9dDCakDfPIKPWOZ4wBtzwF+DB3HhOJjWHlhGSYUHxM6FCIishEvNzni27kWrkwqEaxlrS0CBOr+recsS+eJGSeBWNF9RfsRoi/FfUX7sc2/f4fP5yiTPlrj+okhQk8KcbXJHe11/aQQgBNDHE10dDQuX75ssW3x4sWYN2+eQBE5NjeFDAkRvpDL2t9+4uephKZS3BNBhG4BrB8HaDIJGoZTYwJoResChuK+ov1YFzBU6FCIiMzeeOMNPP744+b73t5tG7dGdeQyCRIifeGmaLzQc2v5eyiRjgorRWV9Xm7yJotZ24vcwWZMOyImgFa0zb+/VVr+iIisydvbG6GhoUKH4dCkUiC+sy+8VB3/2Kyvr2cwirN5K1Dg7t96fh5MAG2JYwCJiJzcu+++i4CAACQmJuL9999HbS2HQLRVn3C11calSaWSFotGC8lfoPp/1/N1kBnTjootgERETuyZZ55B//794e/vj3379mH+/PnIzc3Fhx9+2Oj+Op0OOp3OfL+0tNReoYpWtxAvhPi0XOi5Lfw9lCgur7HqOa1BJpXAVyRLsfl6cBygLTEBFAlnmvAhNE7wsC1ODBHevHnz8N577zW7z9mzZ9GzZ0/MnTvXvC0uLg5KpRJ/+9vfsHjxYqhUDVt6Fi9ejEWLFlk9ZkcVGeCBqABPq59XrLNc/TyV7ZrdbAsKmRReKjnKqvk33RaYABIROZgXXngBM2fObHaf2NjYRrcnJSWhtrYWGRkZ6NGjR4PH58+fb5E0lpaWIiIiokPxOqpgHxW6Bdvmy42PmxxymQS1BnE1bwWILDH191QyAbQRJoBERA4mKCgIQUFB7To2JSUFUqkUwcHBjT6uUqkabRl0Nb4eCvQNV0NiozXJJBIJfD2UKCzTtbyzHQld/+96vh5KXC7iusC2wASQiMhJ7d+/HwcPHsTIkSPh7e2N/fv34/nnn8eDDz4IPz8/ocMTLQ+VDPERvjbvCvUXWQLorpTBQymutMAVxwEqO1Bjsi3E9U67CI73a1gYGmhdcWiO7xOn68cFckygOKhUKqxduxYLFy6ETqdDTEwMnn/+eYsuXrKkUkjRP9IPCjt8CPt5imOyRT2hiz83xtXGAcplEoT7WnfCUZPXsstViIjI7vr3748DBw4IHYbDkMkkSIjoeKHn1vJ2U0Ahl0Jfa7TL9Voitu7fen4uNA4wKsCzQ6vMtAXrABIRkcuTSoG4Tmp423mdXn+R1LqTSMQTy/V8RVwz0ZoUciki/T3sdj0mgERE5PK6BXsjwMv+k1/E0g2sdlfYreWprfw8lLDRXBxRiQnwhMyOJXjE+W4TERHZiZebHJ393AW5tljG3YkljsbUjwN0ZiqFFJ3s/DPo3K+oSHDSR+tcPzHEzUs8s+OobVgsmhxJ9xBvm5V7aYmHUg6VQgqdXthxgEK0fraFs48DjLZz6x/AFkAiInJhwT4qwVu//AQee6eQS+HjJu72IGceB+imkKGTr/1boJkAEhGRS5JK68b+CU3oBNTfQylYC2hrOfM4wJggT0GW32MCKFLTfjmI3+a+h2m/HBQ6FCIipxTp7wl3pX1KvjRH6ARQrOVfrqWQSeHphOMA3ZUyhKvtU/fvekwARWrWlt3oXKTBrC27hQ6FiMjpqBRSxAR6Ch0GgLouQA8BE1GhE9DWcpQ42yIm0FOw1lfnS6cFZq0JH5/dPgKztuzGZ7ePsMr5iITGiSEkJl2Dvew+6L45vh5KVNZU2f26niq53Qpfd5SvhwKZRUJHYT0eKhnCBGr9A5gAitbqW5Kw+pYkocMgInI6ag8FwtTClH1pir+nElc09k8AHaH7t57Qk2WsLTbQS9Cxl+wCJiIil9JdBBM/ridUQegAB+pWVcik8BL5bOXW8lTJESpg6x/ABJBIEPf9dBjJT36E+346LHQoRC4lVO0GtQhLiqjkMrtPcpBJJQ7XquZo8TalS5Dw40+dI5UWEIs8W48rFX5+YuNv6HRViyc2/oZ1YwcJHY5grh8XyDGBZEsymQRdg8X7M+bvqUSFzn7FjtUeCkHKj3SEn6cCWcVCR9Ex3m5yBPsI2/oHsAWQSBCf3zEcOUFqfH7HcKFDIXIZ0QGeop7wYO9u4EBPca/+0Rhfd8dvAYwNEseXELYAEglg3dhBLt3yR2Rv7koZovw9hA6jWfXFjk0m+1zP34EmgNRTyuvGAZY76LJwag8FgrzFkXizBZCIiJxet2Av0Xd3KmRSeNlpHKBKYb9rWZsjjwOMFUntSYAJIBEROTk/T6Uoxly1hr2KHQc4YPdvPT8RTuJpDT9PBQK8xPO6O2b6L6Dzz3tD6u4Yf0iIiFydRAJ0DxHHmKvW8PNU4nJRpc2v40j1/67n66AtgLGB4vo5ZAsgERE5rU5+7vB2c5wWIz8PJaQ2/mSWSBx7WTWl3PHWBfb3UsJPZK85E0AiInJKcplEdK0uLZFJJfCxccLq466AQubYH/+OlsB2EeHPoWP/BBARETWhS5AXlHLH+5izdUuRoyVPjXGkcYCB3ipRFh8X3W/GwoULIZFILG49e/Y0P15dXY3Zs2cjICAAXl5euOuuu5Cfn9/sOU0mExYsWICwsDC4u7tj9OjRuHDhgq2fChG1keae8gY3ovbwVMnR2U9c6/22lr+Nx7g50vJvTXGkcYCxIlj1ozGiSwABoE+fPsjNzTXffvvtN/Njzz//PH744Qd888032LNnD65cuYI777yz2fMtWbIEH3/8MZYvX46DBw/C09MTY8eORXV1ta2fChERCaB7iBckEnGXfWmK2l0BmY1K1shlEqjdxdca1VaOMg4wyFtl8y799hLlqyeXyxEaGtpgu1arxRdffIE1a9bglltuAQB89dVX6NWrFw4cOIAhQ4Y0OMZkMmHp0qV47bXXMHnyZADAqlWrEBISgk2bNuH++++37ZMhIiK7CvRWiarcRltJpRL4uCtQUlFj9XP7eyodNjG+np+nwq5L57WVRAJ0EfHSg6JsAbxw4QLCw8MRGxuLadOmITMzEwBw9OhR6PV6jB492rxvz549ERkZif379zd6rvT0dOTl5Vkco1arkZSU1OQxRETkmKRSxyr70hRbjdNzhvF/9cReEDrEx03UxbZFF1lSUhJWrFiBHj16IDc3F4sWLcKNN96I06dPIy8vD0qlEr6+vhbHhISEIC8vr9Hz1W8PCQlp9TEAoNPpoNPpzPe1Wi0AwFila+oQ6iCDlK8tNVSrb/7PlEFfN5TDZK/1s0jUIvw84KEU3Udbm/l7KJFmg/MGOnDL6PXEnABKJECMiFb9aIzofkvGjx9v/n9cXBySkpIQFRWF9evXw93dfgN6Fy9ejEWLFjXYnvP8u3aLgYhar6ioCGq1WugwSEBKuVT0H7qt5eMuh0wmgcFgvS82HioZ3BQyq51PaPXjAMXYDRzi4yb6MYrijg6Ar68vunfvjosXL+LWW29FTU0NNBqNRStgfn5+o2MGAZi35+fnIywszOKYhISEJq87f/58zJ0713xfo9EgKioKmZmZTv0hU1paioiICGRlZcHHx0focGzCFZ4j4DrPU6vVIjIyEv7+/kKHQgLrEuwFuYPXt6snkUjg56FEYZn1ekYcefm3pohxHKBEUleCSOxEnwCWl5cjLS0NDz30EAYMGACFQoHk5GTcddddAIDU1FRkZmZi6NChjR4fExOD0NBQJCcnmxO+0tJSHDx4ELNmzWryuiqVCipVw18WtVrt1B+m9Xx8fJz+ebrCcwRc53lKbb18Aomaj7sCnXwds+xLU/ytnAA60/i/en4eSmQXVwkdhoUwtTvcleJvaRXdX8wXX3wRe/bsQUZGBvbt24c77rgDMpkMU6dOhVqtxqOPPoq5c+di165dOHr0KB5++GEMHTrUYgZwz549sXHjRgB136Kee+45vPXWW9i8eTNOnTqF6dOnIzw8HFOmTBHoWRIRkTU5w8SP6/l5Wq98iFTqnAmgr8gKLEul4q37dz3RtQBmZ2dj6tSpKCoqQlBQEIYPH44DBw4gKCgIAPDRRx9BKpXirrvugk6nw9ixY/Hpp59anCM1NdU8aQMAXn75ZVRUVOCJJ56ARqPB8OHDsX37dri5udn1uRERkfWFqt0cqjBwa3m7KaCQS6GvNXb4XGp3pc1qCwpJJZfBQyVDpc4gdCgAgHBfd4cZZym6BHDt2rXNPu7m5oZly5Zh2bJlTe5z/WxAiUSCN954A2+88Ua741KpVPj73//eaLewM3GF5+kKzxHg8yTXIJNK0FXEtdY6ys9DgYLSjncDO8PqH03x81CiUid8N7BMKkF0gGO0/gGAxMTaCURE1ITS0lKo1WpotdoWx5Lqag349XyhnSKrExvkiVgHGHDfXlnFlUjNK+vweZJi/eEt0hUpOiq/tBqnsrUt72hjkQEe6B7ibbfrteV3szGiGwNIRETUGm4KGaIcqMWlPawxbk8plzpt8geIYxygo7X+AUwAiYjIQXUL8XLKcW3X8lTJoVJ07KPaGSd/XKt+HKCQIvzdoZQ7VkrlWNESERGhboZsiI9rTOTr6IoXzrT6R1OEXBVEJpM4ZEs0E0AiInIoEgnsOtZKaH4dbMFz9hZAQNgEMNLfAwoHLEDueBHb0LJlyxAdHQ03NzckJSXh0KFDze7/zTffoGfPnnBzc0O/fv2wbds2O0XaOnv37sXEiRMRHh4OiUSCTZs2mR/T6/V45ZVX0K9fP3h6eiI8PBzTp0/HlStXmj3nwoULIZFILG49e/a08TNp2uLFizFo0CB4e3sjODgYU6ZMQWpqqsU+I0aMaBDzk08+2ex5TSYTFixYgLCwMLi7u2P06NG4cOGCLZ9Ksz777DPExcWZizoPHToUP/74IwAgIyOjwfOrv33zzTdNnnPmzJkN9h83bpy9nlIDLf1sVVdXY/bs2QgICICXlxfuuusu5OfnN3tOsb2PZB1hanenHtN2Pf8OJDfebnKH65psD2vWTGwLuUyCSH8PQa7dUc7/U9FK69atw9y5c/H3v/8dx44dQ3x8PMaOHYuCgoJG99+3bx+mTp2KRx99FMePH8eUKVMwZcoUnD592s6RN62iogLx8fGNlsyprKzEsWPH8Prrr+PYsWPYsGEDUlNTMWnSpBbP26dPH+Tm5ppvv/32my3Cb5U9e/Zg9uzZOHDgAHbs2AG9Xo8xY8agoqLCYr/HH3/cIuYlS5Y0e94lS5bg448/xvLly3Hw4EF4enpi7NixqK6utuXTaVLnzp3x7rvv4ujRozhy5AhuueUWTJ48GX/88QciIiIsnltubi4WLVoELy8vi7W1GzNu3DiL477++ms7PaPGNfez9fzzz+OHH37AN998gz179uDKlSu48847mz2f2N5H6ji5TIIuwY7X3dYR7kpZu1eWCHCB7l/gz3GAAqy+ERXg6ZCtfwDLwJglJSVh0KBB+OSTTwAARqMRERERePrppzFv3rwG+993332oqKjAli1bzNuGDBmChIQELF++3G5xt5ZEIsHGjRubXf3k8OHDGDx4MC5fvozIyMhG91m4cCE2bdqElJQU2wTaQVevXkVwcDD27NmDm266CUBdC2BCQgKWLl3aqnOYTCaEh4fjhRdewIsvvgigbr3ZkJAQrFixAvfff7+twm8Tf39/vP/++3j00UcbPJaYmIj+/fvjiy++aPL4mTNnQqPRWLQMC6m5ny2tVougoCCsWbMGd999NwDg3Llz6NWrF/bv32+xElA9R3kfxU5sZWC6hXg55HirjjpzpRRXNG2vdTcgyq/DXciO4mxuKXJK7FcPUCGXYnjXQMEmIrEMjBXU1NTg6NGjGD16tHmbVCrF6NGjsX///kaP2b9/v8X+ADB27Ngm93cEWq0WEokEvr6+ze534cIFhIeHIzY2FtOmTUNmZqZ9AmyF+hVg/P39LbavXr0agYGB6Nu3L+bPn4/Kysomz5Geno68vDyL91etViMpKUkU76/BYMDatWtRUVHR6BrYR48eRUpKSqOJ4fV2796N4OBg9OjRA7NmzUJRUZEtQm61pn62jh49Cr1eb/Ge9OzZE5GRkU2+J2J/H6ntPJQyRPg5ZndbR7VnHJ9MJoHa3XW6yu09DjA6wMOhZ6GLbiUQIRQWFsJgMCAkJMRie0hICM6dO9foMXl5eY3un5eXZ7M4bam6uhqvvPIKpk6d2uw3iaSkJKxYsQI9evQwdzXeeOONOH36NLy9hR2UbTQa8dxzz+GGG25A3759zdsfeOABREVFITw8HCdPnsQrr7yC1NRUbNiwodHz1L+HYnt/T506haFDh6K6uhpeXl7YuHEjevfu3WC/L774Ar169cKwYcOaPd+4ceNw5513IiYmBmlpaXj11Vcxfvx47N+/HzKZ/btSmvvZysvLg1KpbPDlpLn3RKzvI7VftxBvSB34A7cj2lPrzs9D6VKvlz3rAaoUUnR28C8jTAAJer0e9957L0wmEz777LNm9712TFlcXBySkpIQFRWF9evXt6rFyZZmz56N06dPNxiT+MQTT5j/369fP4SFhWHUqFFIS0tDly5d7B1mu/Xo0QMpKSnQarX49ttvMWPGDOzZs8ciCayqqsKaNWvw+uuvt3i+a7tA+/Xrh7i4OHTp0gW7d+/GqFGjbPIcmtPcz5a7u7vd4yFx8fNUIMjbNcazNcZN0fY1b515+bfGuCnqxgFW1th+XeDoAE+Hbv0D2AUMAAgMDIRMJmswozA/Px+hoaGNHhMaGtqm/cWqPvm7fPkyduzY0eZxBL6+vujevTsuXrxoowhbZ86cOdiyZQt27dqFzp07N7tvUlISADQZc/17KLb3V6lUomvXrhgwYAAWL16M+Ph4/POf/7TY59tvv0VlZSWmT5/e5vPHxsYiMDBQ8Pey3rU/W6GhoaipqYFGo7HYp6Xf0fp9WnsMiVfXINcp+9KUtnYDB3i5VgIIAL427gYO8lZhULQ/Ihx05u+1mACi7oN1wIABSE5ONm8zGo1ITk5udIwVAAwdOtRifwDYsWNHk/uLUX3yd+HCBezcuRMBAQFtPkd5eTnS0tIQFhZmgwhbZjKZMGfOHGzcuBG//PILYmJiWjymfpJBUzHHxMQgNDTU4v0tLS3FwYMHRfX+Go1G6HSWi8R/8cUXmDRpEoKCgtp8vuzsbBQVFQn2Xl7v2p+tAQMGQKFQWLwnqampyMzMbPI9cZT3kVoW5K2CWgTLfQmtLeVg3JUyeChdr5PPFjUPpVIg3NcdQ7sEID7C12l+FpkA/mnu3Ln4z3/+g5UrV+Ls2bOYNWsWKioq8PDDDwMApk+fjvnz55v3f/bZZ7F9+3Z88MEHOHfuHBYuXIgjR45gzpw5Qj2FBsrLy5GSkmJOeNLT05GSkoLMzEzo9XrcfffdOHLkCFavXg2DwYC8vDzk5eWhpqbGfI5Ro0aZZ0YDwIsvvog9e/YgIyMD+/btwx133AGZTIapU6fa++kBqOv2/d///oc1a9bA29vb/ByqqupmgqWlpeHNN9/E0aNHkZGRgc2bN2P69Om46aabEBcXZz5Pz549sXHjRgB1M6afe+45vPXWW9i8eTNOnTqF6dOnIzw8vNlZ1LY0f/587N27FxkZGTh16hTmz5+P3bt3Y9q0aeZ9Ll68iL179+Kxxx5r9BzXPsfy8nK89NJLOHDgADIyMpCcnIzJkyeja9euGDt2rF2e0/Wa+9lSq9V49NFHMXfuXOzatQtHjx7Fww8/jKFDh1rMABb7+0htJ5EAXYO9hA5DFNoym9cVW/8A644DrFvhwwPDugSid7gPPFXOlVA717PpgPvuuw9Xr17FggULkJeXh4SEBGzfvt08gDwzMxNS6V/58rBhw7BmzRq89tprePXVV9GtWzds2rTJYvKB0I4cOYKRI0ea78+dOxcAMGPGDCxcuBCbN28GACQkJFgct2vXLowYMQJAXQJVWPhXWYfs7GxMnToVRUVFCAoKwvDhw3HgwIF2tThZQ/2Yxfp463311VeYOXMmlEoldu7ciaVLl6KiogIRERG466678Nprr1nsn5qaap5BDAAvv/wyKioq8MQTT0Cj0WD48OHYvn073NyEWXqqoKAA06dPR25uLtRqNeLi4vDTTz/h1ltvNe/z5ZdfonPnzhgzZkyj57j2OcpkMpw8eRIrV66ERqNBeHg4xowZgzfffBMqlTDjrFr62froo48glUpx1113QafTYezYsfj0008tziH295HaLkzt7nQfvO2lkEnh5SZHeXVti/u6wuofjbHGOEClXIoIfw909nN32Bp/rcE6gERE1CQh6wBKpcCwLoFwU9h/VrpYnc8vQ2ZR02WsgLpW05u7B0HuxMlLc9pbM9FDKUNkgAfC1e4OMXu6o3UA+bWKiIhEKcLPg8nfdfw8lC0mgGp3hcsmf0DdjPG2JIDebnJEB3oi2FsFiUT8iZ+1MAEkIiLRkcskiA50vRU/WuLnoYBEAjTXd+cqy781pbUFof29lIjy93DZ14sJIBERiY4jr7FqS3KZFD7uCmgr9U3u46rj/+o1Nw5QIgGCvd0QFegBHzfnmM3bXkwAiYhIVFQKKSKdoM6arfh5KJtMABVyKXzc+NHu66FEZc1f3cBSad2EoqgAD5csj9MYfr0iInJQb7/9NoYNGwYPD48m1/DOzMzEbbfdBg8PDwQHB+Oll15CbW3Ls0iFFBPo+Kss2FJzLXwBnkqXGsfWFD/Puta9+qEEN3QNRK8wHyZ/1+ArQUTkoGpqanDPPfdg6NCh+OKLLxo8bjAYcNtttyE0NBT79u1Dbm4upk+fDoVCgXfeeUeAiFvmoZShky+X/muOr7sCUilgNDZ8zNW7f+v5eSjRLcQLnXzdXXpCTHP4qhAROahFixbh+eefR79+/Rp9/Oeff8aZM2fwv//9DwkJCRg/fjzefPNNLFu2zKLgu5h0CfZiC1YLpFIJ1O6Nj19jAljHTSFDVIAnk79m8JUhu5JIJNi0aZPQYVjdzJkzIZFIbPL8oqOjzee+fi1coubs378f/fr1Mxe0B4CxY8eitLQUf/zxR6PH6HQ6lJaWWtzsxdtNjhAfFulujcZmunq5yVk2h1qNCSB12LXJj0KhQEhICG699VZ8+eWXMF7XR5Gbm4vx48e36ryOliyOGzeuTc+vtQ4fPozvvvvOquck15CXl2eR/AEw38/Ly2v0mMWLF0OtVptvERERNo+zHpd8a73GWvoC2PpHbcAEkKyiPvnJyMjAjz/+iJEjR+LZZ5/F7bffbjHgPDQ0VLClxmxNpVLZ5PkFBQXB39/fquck8Zo3b575C1VTt3Pnztns+vPnz4dWqzXfsrKybHata/l5Kl22Hlt7+LgpIJNZdpWz+5faggkgWUV98tOpUyf0798fr776Kr7//nv8+OOPWLFihXm/a1v1ampqMGfOHISFhcHNzQ1RUVFYvHgxgLpuTwC44447IJFIzPfT0tIwefJkhISEwMvLC4MGDcLOnTstYomOjsY777yDRx55BN7e3oiMjMTnn39usU/9urP+/v7w9PTEwIEDcfDgQfPj33//Pfr37w83NzfExsZi0aJFbZ45mZGRAYlEgrVr12LYsGFwc3ND3759sWfPHvM+b7zxBsLDw1FUVGTedtttt2HkyJENWk/JNbzwwgs4e/Zss7fY2NhWnSs0NBT5+fkW2+rvh4aGNnqMSqWCj4+Pxc0e2PrXNlKpBL7XjAOUSSWtLoBMBDABJBu65ZZbEB8fjw0bNjT6+Mcff4zNmzdj/fr1SE1NxerVq82J3uHDhwEAX331FXJzc833y8vLMWHCBCQnJ+P48eMYN24cJk6ciMzMTItzf/DBBxg4cCCOHz+Op556CrNmzUJqaqr5HDfffDNycnKwefNmnDhxAi+//LI54fr1118xffp0PPvsszhz5gz+/e9/Y8WKFXj77bfb9Tq89NJLeOGFF3D8+HEMHToUEydONCd8//d//4fo6Gg89thjAIBly5Zh3759WLlyJaRS/nq6oqCgIPTs2bPZm1LZug/6oUOH4tSpUygoKDBv27FjB3x8fNC7d29bPYU2C/ZRNTmpgZp2bYufr4fCIdavJfFgGRiyqZ49e+LkyZONPpaZmYlu3bph+PDhkEgkiIqKMj8WFBQEAPD19bVoqYiPj0d8fLz5/ptvvomNGzdi8+bNmDNnjnn7hAkT8NRTTwEAXnnlFXz00UfYtWsXevTogTVr1uDq1as4fPiwuWu1a9eu5mMXLVqEefPmYcaMGQCA2NhYvPnmm3j55Zfx97//vc2vwZw5c3DXXXcBAD777DNs374dX3zxBV5++WXIZDLzDM158+bh448/xn//+19ERka2+TrkejIzM1FcXIzMzEwYDAakpKQAqPt59vLywpgxY9C7d2889NBDWLJkCfLy8vDaa69h9uzZohmKIZGw9a+9/K5JAAM8xfF+kuNgAkg2ZTKZmizpMHPmTNx6663o0aMHxo0bh9tvvx1jxoxp9nzl5eVYuHAhtm7ditzcXNTW1qKqqqpBC2BcXJz5/xKJBKGhoeZWkJSUFCQmJjY5ru7EiRP4/fffLVr8DAYDqqurUVlZCQ+Ptq1QMHToUPP/5XI5Bg4ciLNnz5q3xcbG4h//+Af+9re/4b777sMDDzzQpvOT61qwYAFWrlxpvp+YmAgA2LVrF0aMGAGZTIYtW7Zg1qxZGDp0KDw9PTFjxgy88cYbQoXcQLivO4vztpO3Sg65TIJagwkBXuz+pbbhbx3Z1NmzZxETE9PoY/3790d6ejp+/PFH7Ny5E/feey9Gjx6Nb7/9tsnzvfjii9ixYwf+8Y9/oGvXrnB3d8fdd9/doKaZQmHZnSSRSMxdvO7uzReZLS8vx6JFi3DnnXc2eMzNzTYlKvbu3QuZTIaMjAzU1tZCLuevJrVsxYoVFmNsGxMVFYVt27bZJ6A2kkkliAn0FDoMhyWR1I37K6uuhaeKfzOobTjIiGzml19+walTp8zdn43x8fHBfffdh//85z9Yt24dvvvuOxQXFwOoS+IMBsvFvH///XfMnDkTd9xxB/r164fQ0FBkZGS0Ka64uDikpKSYr3O9/v37IzU1FV27dm1wa8+4vAMHDpj/X1tbi6NHj6JXr17mbevWrcOGDRuwe/duZGZm4s0332zzNYgcUYS/O+vWdZC/p5Kzf6ld+JWBrEKn0yEvLw8GgwH5+fnYvn07Fi9ejNtvvx3Tp09v9JgPP/wQYWFhSExMhFQqxTfffIPQ0FDzmqbR0dFITk7GDTfcAJVKBT8/P3Tr1g0bNmzAxIkTIZFI8Prrr7d5tuzUqVPxzjvvYMqUKVi8eDHCwsJw/PhxhIeHY+jQoViwYAFuv/12REZG4u6774ZUKsWJEydw+vRpvPXWW21+bZYtW4Zu3bqhV69e+Oijj1BSUoJHHnkEQN1s5FmzZuG9997D8OHD8dVXX+H222/H+PHjMWTIkDZfi8hRyGUSRAWw9a+j/DyVUMnZlkNtx58asort27cjLCwM0dHRGDduHHbt2oWPP/4Y33//PWSyxr/he3t7Y8mSJRg4cCAGDRqEjIwMbNu2zdzK9sEHH2DHjh2IiIgwj2368MMP4efnh2HDhmHixIkYO3Ys+vfv36ZYlUolfv75ZwQHB2PChAno168f3n33XXOcY8eOxZYtW/Dzzz9j0KBBGDJkCD766COLSSpt8e677+Ldd99FfHw8fvvtN2zevBmBgYEwmUyYOXMmBg8ebJ7AMnbsWMyaNQsPPvggysvL23U9IkcQHeAJBZfp6jAvlZwtgNQuEpPJZBI6CCJHN3PmTGg0GouVSzIyMhATE4Pjx48jISGhQ+ffvXs3Ro4ciZKSEnMLKZE9lJaWQq1WQ6vVtlgTUFdrwK/nC1s8p0ohxbAugZCxbAlRu7Xld7Mx/PpFZCVbtmyBl5cXtmzZYtXz9unTx+rLyxEJKTbIi8kfkcA4BpDICpYsWYLXXnsNABAWFmbVc2/btg16vR4A7LYqA5GteKhkCFfbZjY9EbUeE0AiKwgODkZwcLDFtujoaFhjhEV7xx4SiVHXIK8ma4MSkf2wC5iIiOzCx12BYB+2/hGJARNAIiKyCy75RiQeTACJiMjm/L1YsJhITJgAEhGRzbH1j0hcmAASEZFNhard4OOmaHlHIrIbJoBERGQzUikQG8Ql34jEhgkgERHZTLivOzyUrDhGJDZMAImIyCZkUgliAtn6RyRGTACJiMgmIvw9oJLLhA6DiBrBBJCIiKxOIZciKsBD6DCIqAlMAImIyOqiAzygkPEjhkis+NtJRERW5aaQIcKPrX9EYsYEkIiIrCo2yBNSqUToMIioGUwAiYjIajxVcoSp3YQOg4hawASQiIispkuwJyQStv4RiR0TQCIisgqlTIpgb7b+ETkCJoBERGQVbPkjchxMAImIiIhcDBNAIiIiIhfDBJCIiIjIxTABJCIiInIxTACJiIiIXAwTQCIiIiIXwwSQiIiIyMUwASQiIiJyMUwAiYiIiFwME0AiIiIiF8MEkIiIiMjFMAEkIiIicjFMAImIiIhcDBNAIiIiIhfDBJCIiIjIxciFDoCIiMTLZDIBAEpLSwWOhIiuVf87Wf872lZMAImIqEllZWUAgIiICIEjIaLGlJWVQa1Wt/k4iam9qSMRETk9o9GIK1euwNvbGxKJpNl9S0tLERERgaysLPj4+Ngpwo5z1LgBxi4EscRtMplQVlaG8PBwSKVtH9HHFkAiImqSVCpF586d23SMj4+PQ32g13PUuAHGLgQxxN2elr96nARCRERE5GKYABIRERG5GCaARERkFSqVCn//+9+hUqmEDqVNHDVugLELwVHjvh4ngRARERG5GLYAEhEREbkYJoBERERELoYJIBEREZGLYQJIREQdtmzZMkRHR8PNzQ1JSUk4dOiQ0CG1aPHixRg0aBC8vb0RHByMKVOmIDU1Veiw2uzdd9+FRCLBc889J3QorZKTk4MHH3wQAQEBcHd3R79+/XDkyBGhw2qRwWDA66+/jpiYGLi7u6NLly548803270Um9CYABIRUYesW7cOc+fOxd///nccO3YM8fHxGDt2LAoKCoQOrVl79uzB7NmzceDAAezYsQN6vR5jxoxBRUWF0KG12uHDh/Hvf/8bcXFxQofSKiUlJbjhhhugUCjw448/4syZM/jggw/g5+cndGgteu+99/DZZ5/hk08+wdmzZ/Hee+9hyZIl+Ne//iV0aO3CWcBERNQhSUlJGDRoED755BMAdcvHRURE4Omnn8a8efMEjq71rl69iuDgYOzZswc33XST0OG0qLy8HP3798enn36Kt956CwkJCVi6dKnQYTVr3rx5+P333/Hrr78KHUqb3X777QgJCcEXX3xh3nbXXXfB3d0d//vf/wSMrH3YAkhERO1WU1ODo0ePYvTo0eZtUqkUo0ePxv79+wWMrO20Wi0AwN/fX+BIWmf27Nm47bbbLF57sdu8eTMGDhyIe+65B8HBwUhMTMR//vMfocNqlWHDhiE5ORnnz58HAJw4cQK//fYbxo8fL3Bk7cO1gImIqN0KCwthMBgQEhJisT0kJATnzp0TKKq2MxqNeO6553DDDTegb9++QofTorVr1+LYsWM4fPiw0KG0yaVLl/DZZ59h7ty5ePXVV3H48GE888wzUCqVmDFjhtDhNWvevHkoLS1Fz549IZPJYDAY8Pbbb2PatGlCh9YuTACJiMjlzZ49G6dPn8Zvv/0mdCgtysrKwrPPPosdO3bAzc1N6HDaxGg0YuDAgXjnnXcAAImJiTh9+jSWL18u+gRw/fr1WL16NdasWYM+ffogJSUFzz33HMLDw0Ufe2OYABIRUbsFBgZCJpMhPz/fYnt+fj5CQ0MFiqpt5syZgy1btmDv3r3o3Lmz0OG06OjRoygoKED//v3N2wwGA/bu3YtPPvkEOp0OMplMwAibFhYWht69e1ts69WrF7777juBImq9l156CfPmzcP9998PAOjXrx8uX76MxYsXO2QCyDGARETUbkqlEgMGDEBycrJ5m9FoRHJyMoYOHSpgZC0zmUyYM2cONm7ciF9++QUxMTFCh9Qqo0aNwqlTp5CSkmK+DRw4ENOmTUNKSopokz8AuOGGGxqU2jl//jyioqIEiqj1KisrIZVapk0ymQxGo1GgiDqGLYBERNQhc+fOxYwZMzBw4EAMHjwYS5cuRUVFBR5++GGhQ2vW7NmzsWbNGnz//ffw9vZGXl4eAECtVsPd3V3g6Jrm7e3dYJyip6cnAgICRD9+8fnnn8ewYcPwzjvv4N5778WhQ4fw+eef4/PPPxc6tBZNnDgRb7/9NiIjI9GnTx8cP34cH374IR555BGhQ2sXloEhIqIO++STT/D+++8jLy8PCQkJ+Pjjj5GUlCR0WM2SSCSNbv/qq68wc+ZM+wbTQSNGjHCIMjAAsGXLFsyfPx8XLlxATEwM5s6di8cff1zosFpUVlaG119/HRs3bkRBQQHCw8MxdepULFiwAEqlUujw2owJIBEREZGL4RhAIiIiIhfDBJCIiIjIxTABJCIiInIxTACJiIiIXAwTQCIiIiIXwwSQiIiIyMUwASQiIiJyMUwAiYiIiFwME0AiIiIiF8MEkIiIyAmkpqYiNDQUZWVldr3umTNn0LlzZ1RUVNj1utQxTACJiIhEwGAwYNiwYbjzzjsttmu1WkREROD//u//mj1+/vz5ePrpp+Ht7W3LMBvo3bs3hgwZgg8//NCu16WO4VrAREREInH+/HkkJCTgP//5D6ZNmwYAmD59Ok6cOIHDhw9DqVQ2elxmZia6du2K9PR0dOrUyZ4hAwC2bt2Kxx9/HJmZmZDL5Xa/PrUdWwCJiIhEonv37nj33Xfx9NNPIzc3F99//z3Wrl2LVatWNZn8AcD69esRHx9vkfwVFRVh6tSp6NSpEzw8PNCvXz98/fXXzV5/4cKFSEhIsNi2dOlSREdHN3vcrbfeiuLiYuzZs6fF50jiwASQiIhIRJ5++mnEx8fjoYcewhNPPIEFCxYgPj6+2WN+/fVXDBw40GJbdXU1BgwYgK1bt+L06dN44okn8NBDD+HQoUNWj1mpVCIhIQG//vqr1c9NtsF2WiIiIhGRSCT47LPP0KtXL/Tr1w/z5s1r8ZjLly83SAA7deqEF1980Xz/6aefxk8//YT169dj8ODBVo87PDwcly9ftvp5yTaYABIREYnMl19+CQ8PD6SnpyM7O7vFLtiqqiq4ublZbDMYDHjnnXewfv165OTkoKamBjqdDh4eHjaJ2d3dHZWVlTY5N1kfu4CJiIhEZN++ffjoo4+wZcsWDB48GI8++ihamq8ZGBiIkpISi23vv/8+/vnPf+KVV17Brl27kJKSgrFjx6KmpqbJ80il0gbX0uv1rYq7uLgYQUFBrdqXhMcEkIiISCQqKysxc+ZMzJo1CyNHjsQXX3yBQ4cOYfny5c0el5iYiDNnzlhs+/333zF58mQ8+OCDiI+PR2xsLM6fP9/seYKCgpCXl2eRBKakpLQq9tOnTyMxMbFV+5LwmAASERGJxPz582EymfDuu+8CAKKjo/GPf/wDL7/8MjIyMpo8buzYsdi/fz8MBoN5W7du3bBjxw7s27cPZ8+exd/+9jfk5+dbHPfJJ59g1KhR5vsjRozA1atXsWTJEqSlpWHZsmX48ccfLY7ZuHEjevbsabEtIyMDOTk5GD16dHufOtkZE0AiIiIR2LNnD5YtW4avvvrKYpze3/72NwwbNqzZruDx48dDLpdj586d5m2vvfYa+vfvj7Fjx2LEiBEIDQ3FlClTLI4rLCxEWlqa+X6vXr3w6aefYtmyZYiPj8ehQ4csJpIAdYWpU1NTLbZ9/fXXGDNmDKKiotr79MnOWAiaiIjICSxbtgybN2/GTz/9ZNfr1tTUoFu3blizZg1uuOEGu16b2o+zgImIiJzA3/72N2g0GpSVldl1ObjMzEy8+uqrTP4cDFsAiYiIiFwMxwASERERuRgmgEREREQuhgkgERERkYthAkhERETkYpgAEhEREbkYJoBERERELoYJIBEREZGLYQJIRERE5GKYABIRERG5GCaARERERC6GCSARERGRi2ECSERERORimAASERERuRgmgEREREQu5v8DnME8bTTwZkwAAAAASUVORK5CYII=", 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\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = np.random.normal(3, 2.5, size=(20,10))\n", "data_var = np.random.normal(10, 2.5, size=(20,10))\n", "coordinates = np.random.rand(20,2)+10\n", "\n", "dset = sidpy.Dataset.from_array(data, coordinates = coordinates)\n", "dset.data_type = 'point_cloud'\n", "\n", "dset.variance = data_var\n", "dset.point_cloud['spacial_units'] = 'um'\n", "dset.point_cloud['quantity'] = 'Distance'\n", "\n", "dset.set_dimension(0, sidpy.Dimension(np.arange(data.shape[0]),\n", " name='point number',\n", " quantity='Point number',\n", " dimension_type='point_cloud'))\n", "\n", "dset.set_dimension(1, sidpy.Dimension(np.arange(data.shape[1]),\n", " name='X',\n", " units='a.u.',\n", " quantity='X',\n", " dimension_type='spectral'))\n", "dset.units = 'a.u.'\n", "dset.quantity = 'Intensity'\n", "\n", "view = dset.plot();" ] }, { "cell_type": "code", "execution_count": 265, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([4.6805256 , 3.65467992, 2.67673733, 1.64671048, 5.68927792,\n", " 2.19507524, 3.83120101, 3.21568454, 3.63582981, 6.77946736])" ] }, "execution_count": 265, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dset.view.axes[1].lines[0].get_ydata()" ] }, { "cell_type": "code", "execution_count": 269, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([4.6805256 , 3.65467992, 2.67673733, 1.64671048, 5.68927792,\n", " 2.19507524, 3.83120101, 3.21568454, 3.63582981, 6.77946736])" ] }, "execution_count": 269, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dset[8].compute()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3D" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a7f67fe1dc334df68364a4a243d07798", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Dropdown(description='Image', layout=Layout(height='50px', width='30%'), options=(('Pixel Wise'…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1783b4fb43324a159151e1eeefb60c9c", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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In this case, you can call the function as follows: ``dset.plot(base_name=image_dataset)``, where image_dataset should be an instance of ``sidpy.Dataset`` with a data_type set to 'IMAGE'.\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "#image_dataset initializtion. image_dataset represents field of view\n", "t = np.linspace(0, 6*np.pi, 512)\n", "im_data = 4*abs(np.cos(t)[:, np.newaxis])+ 8*abs(np.cos(t)[np.newaxis,:]) + np.random.normal(0, 0.2, size=(512, 512))\n", "\n", "image_dataset = sidpy.Dataset.from_array(im_data, datatype='image', units='counts', quantity='intensity')\n", "image_dataset.title = 'random'\n", "image_dataset.set_dimension(0, sidpy.Dimension(np.linspace(7, 8, image_dataset.shape[0]), 'x'))\n", "image_dataset.x.dimension_type = 'spatial'\n", "image_dataset.x.units = 'um'\n", "image_dataset.x.quantity = 'distance'\n", "image_dataset.set_dimension(1, sidpy.Dimension(np.linspace(7, 8, image_dataset.shape[1]), 'y'))\n", "image_dataset.y.dimension_type = 'spatial'\n", "image_dataset.y.units = 'um'\n", "image_dataset.y.quantity = 'distance'" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cf34ee08b0e04934a4ea8f5c6ec4fbe2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Dropdown(description='Image', layout=Layout(height='50px', width='30%'), options=(('Pixel Wise'…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e38483c705584ea4a1c7535050bceb0f", "version_major": 2, "version_minor": 0 }, "image/png": 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"toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 4 } sidpy-0.12.3/notebooks/01_parallel_computing/000077500000000000000000000000001455261647000211235ustar00rootroot00000000000000sidpy-0.12.3/notebooks/01_parallel_computing/README.rst000066400000000000000000000000121455261647000226030ustar00rootroot00000000000000index.rst sidpy-0.12.3/notebooks/01_parallel_computing/Sidpy_Fitter_Complex.ipynb000066400000000000000000034262111455261647000262730ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "source": [ "# Sidpy Complex Fitter\n", "\n", "Here we are testing the sidpy Fitter class for SHO Fits, these are using complex data and also fancier priors." ], "metadata": { "id": "dngD_B5OdQyB" }, "id": "dngD_B5OdQyB" }, { "cell_type": "code", "execution_count": null, "id": "17d178cb-0ab0-4d87-8d2c-5c6f4336f4fb", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "17d178cb-0ab0-4d87-8d2c-5c6f4336f4fb", "outputId": "f17b9ac1-8c9e-4c52-b0d3-4dd8f3adf2c9" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Collecting git+https://github.com/pycroscopy/sidpy.git@rama_dev\n", " Cloning https://github.com/pycroscopy/sidpy.git (to revision rama_dev) to /tmp/pip-req-build-r1f16rav\n", " Running command git clone --filter=blob:none --quiet https://github.com/pycroscopy/sidpy.git /tmp/pip-req-build-r1f16rav\n", " Running command git checkout -b rama_dev --track origin/rama_dev\n", " Switched to a new branch 'rama_dev'\n", " Branch 'rama_dev' set up to track remote branch 'rama_dev' from 'origin'.\n", " Resolved https://github.com/pycroscopy/sidpy.git to commit 0da33043426bd42e0df51336aea55a37b0fb63f9\n", " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "Requirement already satisfied: numpy>=1.10 in 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jsonschema>=2.6 in /usr/local/lib/python3.10/dist-packages (from nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=5.2.2->sidpy>=0.11.2->SciFiReaders) (4.3.3)\n", "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=5.2.2->sidpy>=0.11.2->SciFiReaders) (23.1.0)\n", "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=5.2.2->sidpy>=0.11.2->SciFiReaders) (0.19.3)\n", "Requirement already satisfied: cffi>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=5.2.2->sidpy>=0.11.2->SciFiReaders) (1.15.1)\n", "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=5.2.2->sidpy>=0.11.2->SciFiReaders) (2.4.1)\n", "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=5.2.2->sidpy>=0.11.2->SciFiReaders) (0.5.1)\n", "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=5.2.2->sidpy>=0.11.2->SciFiReaders) (2.21)\n", "\u001b[33mWARNING: The candidate selected for download or install is a yanked version: 'ipython' candidate (version 7.1.0 at https://files.pythonhosted.org/packages/ea/56/d75d25b30936bff3f6b4d7b80cfd399b703e2162f31afee926f66ff77b13/ipython-7.1.0-py3-none-any.whl (from https://pypi.org/simple/ipython/) (requires-python:>=3.5))\n", "Reason for being yanked: \u001b[0m\u001b[33m\n", "\u001b[0mBuilding wheels for collected packages: wget\n", " Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9657 sha256=3b7836a4d0cc5e12c6148c376ea4c0596421020651b4c97f6558553447fe2d4c\n", " Stored in directory: /root/.cache/pip/wheels/8b/f1/7f/5c94f0a7a505ca1c81cd1d9208ae2064675d97582078e6c769\n", "Successfully built wget\n", "Installing collected packages: wget, prompt-toolkit, ipython, SciFiReaders, pyUSID, pyNSID\n", " Attempting uninstall: prompt-toolkit\n", " Found existing installation: prompt-toolkit 3.0.38\n", " Uninstalling prompt-toolkit-3.0.38:\n", " Successfully uninstalled prompt-toolkit-3.0.38\n", " Attempting uninstall: ipython\n", " Found existing installation: ipython 7.34.0\n", " Uninstalling ipython-7.34.0:\n", " Successfully uninstalled ipython-7.34.0\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "google-colab 1.0.0 requires ipython~=7.34.0, but you have ipython 7.1.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed SciFiReaders-0.0.9 ipython-7.1.0 prompt-toolkit-2.0.10 pyNSID-0.0.7 pyUSID-0.0.10.post2 wget-3.2\n" ] }, { "output_type": "display_data", "data": { "application/vnd.colab-display-data+json": { "pip_warning": { "packages": [ "IPython", "prompt_toolkit" ] } } }, "metadata": {} } ], "source": [ "!pip install SciFiReaders pyNSID pyUSID wget sidpy" ] }, { "cell_type": "code", "execution_count": null, "id": "4f2fc877", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 374 }, "id": "4f2fc877", "outputId": "5385809b-877a-4b84-87a4-81550a350ef3" }, "outputs": [ { "output_type": "error", "ename": "ModuleNotFoundError", "evalue": "ignored", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mh5py\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpyNSID\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumba\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pyNSID'", "", "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n" ], "errorDetails": { "actions": [ { "action": "open_url", "actionText": "Open Examples", "url": "/notebooks/snippets/importing_libraries.ipynb" } ] } } ], "source": [ "import os\n", "import sys\n", "\n", "import numpy as np\n", "import time\n", "import h5py\n", "\n", "import pyNSID\n", "import matplotlib.pyplot as plt\n", "import numba\n", "\n", "import sidpy\n", "#Let's open up a sample dataset and see...\n", "\n", "import SciFiReaders as sr\n", "from scipy.optimize import curve_fit" ] }, { "cell_type": "code", "execution_count": null, "id": "7db15e18-29f4-46ee-83f9-e9767e85f7a7", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "id": "7db15e18-29f4-46ee-83f9-e9767e85f7a7", "outputId": "f8076397-b00a-48f7-a1b4-a7e8960cfd7e" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'beps_file.h5'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 3 } ], "source": [ "import wget\n", "wget.download(url=r'https://www.dropbox.com/s/6jwi0bzdepjidf5/PTO_60x60_3rdarea_0003.h5?dl=1', out='beps_file.h5')" ] }, { "cell_type": "markdown", "id": "105a8642-2d9a-4e7b-9c27-96b2b82df883", "metadata": { "id": "105a8642-2d9a-4e7b-9c27-96b2b82df883" }, "source": [ "# Download the data" ] }, { "cell_type": "code", "execution_count": null, "id": "405f174c-a933-4cc6-9434-44fd955650ba", "metadata": { "id": "405f174c-a933-4cc6-9434-44fd955650ba" }, "outputs": [], "source": [ "beps_path = r'beps_file.h5'\n", "reader = sr.Usid_reader(beps_path)\n", "data_beps = reader.read() #read the data\n", "beps_raw = data_beps[0] #take the main sidpy dataset\n", "freq_axis = beps_raw.labels.index('Frequency (Hz)') #grab the frequency axis\n", "freq_vec = beps_raw._axes[freq_axis].values" ] }, { "cell_type": "markdown", "id": "7b8257ce-b71b-4003-a7d5-f3ad46191591", "metadata": { "id": "7b8257ce-b71b-4003-a7d5-f3ad46191591" }, "source": [ "# Define Functions\n", "\n", "Note that the guess function is critical here! It needs to be tuned carefully for the equation you are fitting." ] }, { "cell_type": "code", "execution_count": null, "id": "cee7fcbd-0be4-4a53-93fa-b17cd47c676c", "metadata": { "id": "cee7fcbd-0be4-4a53-93fa-b17cd47c676c" }, "outputs": [], "source": [ "def SHO_fit_flattened(wvec,*p):\n", " Amp, w_0, Q, phi=p[0],p[1],p[2],p[3]\n", " func = Amp * np.exp(1.j * phi) * w_0 ** 2 / (wvec ** 2 - 1j * wvec * w_0 / Q - w_0 ** 2)\n", " return np.hstack([np.real(func),np.imag(func)])\n", "\n", "def my_guess_fn(freq_vec,ydata):\n", " ydata = np.array(ydata)\n", " amp_guess = np.abs(ydata)[np.argmax(np.abs(ydata))]\n", " Q_guess = 50\n", " max_min_ratio = np.max(abs(ydata)) / np.min(abs(ydata))\n", " phi_guess = np.angle(ydata)[np.argmax(np.abs(ydata))]\n", " w_guess = freq_vec[np.argmax(np.abs(ydata))]\n", "\n", " #Let's just run some Q values to find the closest one\n", " Q_values = [5,10,20,50,100,200,500]\n", " err_vals = []\n", " for q_val in Q_values:\n", " p_test = [amp_guess/q_val, w_guess, q_val, phi_guess]\n", " func_out = SHO_fit_flattened(freq_vec,*p_test)\n", " complex_output = func_out[:len(func_out)//2] + 1j*func_out[(len(func_out)//2):]\n", " amp_output = np.abs(complex_output)\n", " err = np.mean((amp_output - np.abs(ydata))**2)\n", " err_vals.append(err)\n", " Q_guess = Q_values[np.argmin(err_vals)]\n", " p0 = [amp_guess/Q_guess, w_guess, Q_guess, phi_guess]\n", " return p0\n" ] }, { "cell_type": "markdown", "id": "678bd1d7-ecf9-4c4c-add1-65a63d76d75e", "metadata": { "id": "678bd1d7-ecf9-4c4c-add1-65a63d76d75e" }, "source": [ "## Load the data, take a small slice of it, and plot it\n", "\n", "Here we will only consider a subset of the data. Then take a single curve fromit and then see how well our guess function performs\n", "The guess function returns the fitting parameters that the optimization starts from, given the data.\n", "So it is important to make sure it is somewhat close to modeling the data.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "3b3048f3-5976-4750-84c0-8a8113f2c903", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 504 }, "id": "3b3048f3-5976-4750-84c0-8a8113f2c903", "outputId": "43e960f3-a226-48f9-e445-a058595097cd" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "using generic parameters for dimension 3\n", "[1.8177233869209886e-05, 376059.875, 200, -2.5212026]\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.0, 'Amplitude and Phase, with iniital guess plotted')" ] }, "metadata": {}, "execution_count": 6 }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ], "source": [ "beps_small = beps_raw[:5,:5,:,5:15,:]\n", "\n", "ydata = np.array(beps_small[2,2,:,3,-1])\n", "p0 = my_guess_fn(freq_vec, ydata)\n", "print(p0)\n", "ydata_guess = SHO_fit_flattened(freq_vec, *p0)\n", "complex_output = ydata_guess[:len(ydata_guess)//2] + 1j*ydata_guess[(len(ydata_guess)//2):]\n", "amp_output = np.abs(complex_output)\n", "phase_output = np.angle(complex_output)\n", "\n", "plt.figure()\n", "plt.plot(freq_vec, np.abs(ydata), 'bo')\n", "plt.plot(freq_vec, amp_output, 'b-')\n", "ax2 = plt.twinx()\n", "ax2.plot(freq_vec,np.angle(ydata), 'yo')\n", "ax2.plot(freq_vec, phase_output, 'y-')\n", "plt.title('Amplitude and Phase, with iniital guess plotted')\n" ] }, { "cell_type": "markdown", "id": "2da0a5cc-2b9b-4ebf-a4d8-77f75c950ed2", "metadata": { "id": "2da0a5cc-2b9b-4ebf-a4d8-77f75c950ed2" }, "source": [ "## Based on the initial guess above, perform optimization and plot" ] }, { "cell_type": "code", "execution_count": null, "id": "22d58a6a-20a4-4f96-b815-37554ca99465", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 469 }, "id": "22d58a6a-20a4-4f96-b815-37554ca99465", "outputId": "8c020d52-dc16-4ff1-a211-88562e82a7cc" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.0, 'Amplitude and Phase, with optimized fit plotted')" ] }, "metadata": {}, "execution_count": 7 }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ], "source": [ "ydata = np.array(ydata)\n", "pg = my_guess_fn(freq_vec, ydata)\n", "ydata_ri = np.hstack([np.real(ydata),np.imag(ydata)])\n", "popt, pcov = curve_fit(SHO_fit_flattened,freq_vec, ydata_ri,p0=pg)\n", "\n", "ydata_fit = SHO_fit_flattened(freq_vec, *popt)\n", "complex_output = ydata_fit[:len(ydata_guess)//2] + 1j*ydata_fit[(len(ydata_guess)//2):]\n", "amp_output = np.abs(complex_output)\n", "phase_output = np.angle(complex_output)\n", "\n", "plt.figure()\n", "plt.plot(freq_vec, np.abs(ydata), 'bo')\n", "plt.plot(freq_vec, amp_output, 'b-')\n", "ax2 = plt.twinx()\n", "ax2.plot(freq_vec,np.angle(ydata), 'yo')\n", "ax2.plot(freq_vec, phase_output, 'y-')\n", "plt.title('Amplitude and Phase, with optimized fit plotted')" ] }, { "cell_type": "markdown", "id": "abcf2873-567c-438c-a63c-eca5de67a066", "metadata": { "id": "abcf2873-567c-438c-a63c-eca5de67a066" }, "source": [ "# Now try the Sidpy Fitter\n", "\n", "This can be used to fit all spectra at once. There is an option for km_guess, which determines whether the kmeans priors will be used. This means that the spectra will be clustered by KMeans into a given number of clusters, and then the guess function will be used to extract the prior for each cluster mean, and then this optimized fit will be used as priors for each cluster member.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "e9121601-bd86-4c75-992b-dd2cedc231af", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e9121601-bd86-4c75-992b-dd2cedc231af", "outputId": "92c8df15-649a-4898-ade9-aa334eb78a6d" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "INFO:distributed.http.proxy:To route to workers diagnostics web server please install jupyter-server-proxy: python -m pip install jupyter-server-proxy\n", "INFO:distributed.scheduler:State start\n", "INFO:distributed.scheduler: Scheduler at: tcp://127.0.0.1:38133\n", "INFO:distributed.scheduler: dashboard at: 127.0.0.1:8787\n", "INFO:distributed.nanny: Start Nanny at: 'tcp://127.0.0.1:45173'\n", "INFO:distributed.nanny: Start Nanny at: 'tcp://127.0.0.1:44227'\n", "INFO:distributed.nanny: Start Nanny at: 'tcp://127.0.0.1:37661'\n", "INFO:distributed.nanny: Start Nanny at: 'tcp://127.0.0.1:35209'\n", "INFO:distributed.scheduler:Register worker \n", "INFO:distributed.scheduler:Starting worker compute stream, tcp://127.0.0.1:44419\n", "INFO:distributed.core:Starting established connection to tcp://127.0.0.1:33098\n", "INFO:distributed.scheduler:Register worker \n", "INFO:distributed.scheduler:Starting worker compute stream, tcp://127.0.0.1:36079\n", "INFO:distributed.core:Starting established connection to tcp://127.0.0.1:33090\n", "INFO:distributed.scheduler:Register worker \n", "INFO:distributed.scheduler:Starting worker compute stream, tcp://127.0.0.1:41503\n", "INFO:distributed.core:Starting established connection to tcp://127.0.0.1:33108\n", "INFO:distributed.scheduler:Register worker \n", "INFO:distributed.scheduler:Starting worker compute stream, tcp://127.0.0.1:43353\n", "INFO:distributed.core:Starting established connection to tcp://127.0.0.1:33104\n", "INFO:distributed.scheduler:Receive client connection: Client-5eccbb79-eeb5-11ed-81e2-0242ac1c000c\n", "INFO:distributed.core:Starting established connection to tcp://127.0.0.1:33112\n" ] } ], "source": [ "#Let's try sidpy fitter\n", "#Instantiate the SidFitter class\n", "fitter = sidpy.proc.fitter.SidFitter(beps_small, SHO_fit_flattened,num_workers=4,guess_fn = my_guess_fn,ind_dims=[0,1,3,4],\n", " threads=1, return_cov=False, return_fit=False, return_std=False,\n", " km_guess=True,num_fit_parms = 4, n_clus = 5)" ] }, { "cell_type": "code", "execution_count": null, "id": "61e781ab-d0a1-425c-992f-de988e77bc61", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "61e781ab-d0a1-425c-992f-de988e77bc61", "outputId": "ca675876-da4f-4c38-b362-ecb1b854e393" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "sidpy.Dataset of type UNKNOWN with:\n", " dask.array\n", " data contains: quantity (a.u.)\n", " and Dimensions: \n", "a: generic (generic) of size (750,)\n", "b: generic (generic) of size (44,)\n", " with metadata: ['fold_attr'] complex64\n", "Warning: complex dataset detected. For Kmeans priors, we will treat real part only\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " warnings.warn(\n", "INFO:distributed.nanny:Closing Nanny at 'tcp://127.0.0.1:45173'. Reason: nanny-close\n", "INFO:distributed.nanny:Nanny asking worker to close. Reason: nanny-close\n", "INFO:distributed.nanny:Closing Nanny at 'tcp://127.0.0.1:44227'. Reason: nanny-close\n", "INFO:distributed.nanny:Nanny asking worker to close. Reason: nanny-close\n", "INFO:distributed.nanny:Closing Nanny at 'tcp://127.0.0.1:37661'. Reason: nanny-close\n", "INFO:distributed.nanny:Nanny asking worker to close. Reason: nanny-close\n", "INFO:distributed.nanny:Closing Nanny at 'tcp://127.0.0.1:35209'. Reason: nanny-close\n", "INFO:distributed.nanny:Nanny asking worker to close. Reason: nanny-close\n", "INFO:distributed.core:Received 'close-stream' from tcp://127.0.0.1:33104; closing.\n", "INFO:distributed.core:Received 'close-stream' from tcp://127.0.0.1:33098; closing.\n", "INFO:distributed.scheduler:Remove worker \n", "INFO:distributed.core:Removing comms to tcp://127.0.0.1:43353\n", "INFO:distributed.scheduler:Remove worker \n", "INFO:distributed.core:Removing comms to tcp://127.0.0.1:44419\n", "INFO:distributed.core:Received 'close-stream' from tcp://127.0.0.1:33108; closing.\n", "INFO:distributed.scheduler:Remove worker \n", "INFO:distributed.core:Removing comms to tcp://127.0.0.1:41503\n", "INFO:distributed.core:Received 'close-stream' from tcp://127.0.0.1:33090; closing.\n", "INFO:distributed.scheduler:Remove worker \n", "INFO:distributed.core:Removing comms to tcp://127.0.0.1:36079\n", "INFO:distributed.scheduler:Lost all workers\n", "INFO:distributed.scheduler:Scheduler closing...\n", "INFO:distributed.scheduler:Scheduler closing all comms\n" ] } ], "source": [ "#Note that without bounds, the fitting is not very good\n", "\n", "lb = [0, 0, 0, -2*np.pi]\n", "ub = [1000, np.inf, 10000, 2*np.pi]\n", "\n", "fit_parameters = fitter.do_fit(bounds = (lb,ub))" ] }, { "cell_type": "code", "execution_count": null, "id": "63b111be-134e-4c48-b404-ee408de2289a", "metadata": { "id": "63b111be-134e-4c48-b404-ee408de2289a" }, "outputs": [], "source": [ "fit_parameters_mat = np.array(fit_parameters[0])" ] }, { "cell_type": "markdown", "id": "5091a774-b780-4b1f-9e52-7c2e09484a2a", "metadata": { "id": "5091a774-b780-4b1f-9e52-7c2e09484a2a" }, "source": [ "# Plot the results of 25 random spectra" ] }, { "cell_type": "code", "execution_count": null, "id": "858fa4b3-49ae-4880-a5dd-ca724f6668e4", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "858fa4b3-49ae-4880-a5dd-ca724f6668e4", "outputId": "1a2c1f16-9aa0-4c97-eaa6-546da4cbb24e" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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rTl+fykck4wwk0uv//fffR6PRBEUoGY1Gbr/9drKzszlw4ECd73UyRPo9h2PVqlWYzWZGjx4dcr/T6cRqtZ5EDyVNTbTtV33o1KkTQJ3/r6F+Q71eL0uXLuWaa65h0KBBuN3uGuk/aqOpxxnptfLmm2+yc+dO5s+fH3K/zz9q6DyvJflPtc1p1Wp1DXsP4Ha765UGRNJ8OJW2KjExkbi4uMYdQAhO1tfq379/jfSebdq04YILLohIZ2sqH8Rnq+uirKyM1q1bYzAY/Nu0Wi1JSUlBtmr37t3897//5f7776dNmzbY7XZcLlfE/Wkpvtbx48fJycnhmmuuCbJfvXv3pkePHhGnRjzVnFFC+o4dO3C73QwYMCBou16vp0+fPmzbtq3W4337qx/fv39/1Gp1nccXFBQA1BBK6ovVaqW4uJj//e9/PPvss/z3v//lD3/4Q63HfPLJJ5SUlPDnP/855P7HHnuM2NhYjEYjAwcO5LPPPouoL9u2baNfv3410qIMGjQIm81WZ17zSBFCUFhYGPKz+/HHH1m5cqW/UEooLrzwQtRqNVOnTuWHH37g4MGDfPLJJ8yfP58xY8bUmfv7VI0zkLKyMoqLi9m9ezcPPfQQO3furPN7DsW2bdvIzc0NcqxB+XEK5ViaTCYAtmzZ0rCOS06KaNipkpISioqK2LFjB3/9618pKytr0P9aIE1hp6ZNm8Yll1xSI2VCXTQHO1Wd+vweFBQUYDKZ/NdmOLZt24bZbKZHjx5B2wcNGuTf39hUVFRQXFxMXl4eK1eu5PXXX2fIkCH1Tl/mcrl49913GTp0aJAj6hMOq59P2qnoI23V6elTBVLXOBt6/W/bto2uXbsGiQJQZasaYzl5dRryPYeiqKiI9evXM2bMmJApxr744gtMJhOxsbF06tSJpUuXNkb3JY1MtOeEteF2uykuLubw4cN89tlnzJo1i7i4OP/1EYpwv6E5OTkcPnyYXr16MWnSJMxmM2azmV69evHll1/W2ZemHGek14rFYuHBBx/koYceIi0tLWSbs846i4yMDJ5++mn+85//cPDgQX788UcmT55MZmZmnalZWpL/5AvWu/3229m+fTsHDhzgnXfe4cUXX+See+6pYZf27t2L2WwmLi6OtLQ0Zs+eXS8xThJdmrOtqg9N4WtVp6CgIKJ5VTR8kEAuvvhidu3axezZs/ntt9/43//+x7x589i8eTMPPPCAv52vRlZqaip/+MMfiImJISYmhssvvzyi3PUtxdcKN9cDZb53+PBh/7y5OXHa5kgPxZEjR4Cqu9OBpKen+yPfajteo9GQkpIStF2v19OmTRsOHz5c6/FPPvkkGo2G66+/vp49D+bee+/lpZdeApQI4muvvZbnn3++1mPeeustDAZDjfdWq9VcdtllXHPNNbRr147ff/+dZ555hssvv5x///vfjBo1qtbzHjlyhAsvvLDGdt9nfPjwYc4999z6DC9s/w8dOsTcuXODtgshuPvuu7nxxhsZMmRIWKOSlZXFyy+/zH333ReUp3T8+PH885//rPP9T9U4A7nhhhv8+bX0ej133HEHs2fPrvd53nrrLYAaP0LdunXj888/Z//+/UF5A33XQfVCRZJTQzTs1HnnnceePXsAiI2NZdasWREXMQlHY9opgLVr1/LZZ5/x888/17sv0bZToVi0aBHx8fFcfvnltbb77bff+PDDDxk7diwajabWtkeOHPEXuAkkcJyNzdKlS5k5c6b/9R/+8Adef/31ep/n008/5dixYyHtFMD3339PZmamf7u0U9FH2qrTz6cK1S6cTYaGX/9HjhwJ+38DTWOrGvI9h+Kdd97B7XaHnNj36tWLYcOG0a1bN44dO8aKFSuYNm0ahw8f5sknnzzpMUgaj2jPCWtj8+bNQXOVbt268e9//5vExMSwx4T7Df31118BePbZZ0lMTPRfA0888QQjR47kp59+olevXmHP21TjrM+1MnfuXGJiYmrNh6vT6fjggw/405/+xNVXX+3f3r9/fzZu3EirVq1q7U9L8p9GjhzJvHnzeOKJJ/j3v//t3/7www/z+OOPB7U966yzuOSSSzj33HOxWq28//77PP744+zdu1eu6GshNGdbVR8ae15YnW+//Zbs7GxmzZpVZ9to+CCBzJ49m3379jF//nz/NWsymfjggw+CVrr57PekSZMYOHAg77zzDvn5+Tz22GMMHz6cX375pdYgq5bia6WmptKqVSu+//77oO3Hjh0jJycHUOZ74W6kRoszSkivqKgACFpG4cNoNPr313Z8qOVSkRy/atUqXn31VR544IGTLkw5bdo0rr/+eg4fPsy7776Lx+OpdRlYWVkZa9eu5YorrqjhSHTo0KFGMYRbbrmFrKws7r333jonfRUVFWE/T9/+k2X37t3cddddDBkyhPHjxwftW7FiBTt27OD999+v8zzt2rVj0KBBXHHFFXTs2JFvv/2WZcuWkZSUxFNPPVXrsadinNVZuHAh9957LwcOHGDlypU4nU7cbne9zuH1enn77bfp27dvjSiLv/71ryxfvpwbbriBZ599ltTUVN59911/kZmmGJOkbqJhp15//XXKysr4/fffef3116moqMDj8dRagLcuGtNOOZ1Opk+fzuTJk8nKyqp3X6Jtp6rzxBNP8Pnnn/OPf/yj1smdzWZj7NixxMTEsHDhwjr7EA07ddNNNzFgwACKior4+OOPKSwsbND7rFq1Cp1Oxw033BC03Wev77vvPkwmE/3792fTpk08/PDDaLVaaaeiiLRVp59PFUht4/TR0Os/Graqvt9zOFatWkVycjJ//OMfa+wLFLUAJkyYwOWXX84zzzzD3XffHVF6HMmpIZpzwrrIyspi/fr1WK1WNm7cyOeff15nWo5wv6G+4ywWC9u2baN9+/YAXHrppZx99tksWrSIf/3rX2HP21TjjPRa2bt3L0uXLuX//u//Qn5XgbRu3Zo+ffowduxYzjvvPH777TcWLFjA2LFjWb9+fdgipdDy/KdOnTpx4YUXct1119GmTRvWrl3LE088QVpaGlOmTPG3e/XVV4OOu+WWW5g0aRKvvPIK06dP57zzzmvUMUkan+Zsq+pDY/pa1Tl69Ch/+tOfyMzMDIroDkc0rvdADAYDXbt25frrr+faa6/F4/Hw8ssvc/PNN7N+/Xr/demz32lpaaxdu9bv62ZkZHDTTTexatWqGsXQA2kpvpZareaOO+7gySefZObMmfzlL3+hrKyMBx54wH9sc5zvnVFCum+5QKgck3a7vc6lVDExMWH/EWo7/ttvv+X2229nxIgRYXO71QdfZW1Q8mNddtllXHXVVWzatClkapMPPvgAu90e8bKYxMREJkyYwMKFCzl48GCtjn9MTEzYz9O3/2QoKChg1KhRJCQk+PM8+SgrK2PmzJncf//9fscwHN9//z1XXnklP/zwg39p05gxY4iPj+exxx7jL3/5S60CXWOP0+l0cvz48aBtycnJQePr06eP/++bb76Zfv36+SsvR8rXX3/NoUOHQkZx9OrVi1WrVjF58mR/Nei0tDSWLFnCnXfeWSP3mOTUEA07FRj5NG7cOP9Nl7puMNVGY9qpZ599luLiYh577LEG9SWadqo677zzjj+K9s477wzbzuPxMG7cOHJycvjvf/9L27Zt6+xHU4yz+lK6hISEoPN07NjRv6LlpptuYtKkSQwfPpw9e/ZE/H7l5eV89NFHjBgxgjZt2gTtMxqNrF27lhtuuIHrrrsOUBzQRYsWMX/+fGmnooi0VaeXT1WdSMbZ0Ou/scfp8XgoKioK2paYmBgkHtT3ew7F77//TnZ2NlOmTImoTodKpWL69Ol8+umnfPXVV2Hr+EhOPdGaE0ZCfHw8w4cPB2D06NGsWrWK0aNHs3XrVnr37l2jfW2/ob5+nH/++UFzpQ4dOjBs2DA2btxYa1+acpyBhLtWpk6dytChQ/2//+EoLS3lggsu4P777+fee+/1bx8wYAAXX3wxr7/+eq0+V0vyn95++20mTZrE3r17/b8n1157LV6vlwcffJCbbrqpxv9BIPfeey+vvPIKn3/+uRTSWwDN2VbVh6bytaxWK1deeSUWi4XvvvsuonlBU/tadTFlyhR++OEHtm7d6hfHb7jhBnr27MnUqVPZtGlTUD9uuOGGoICRsWPHcsstt7Bx48ZahfSW5GvNnTuX4uJiFi1a5A8eu+yyy7j99ttZvnx5s5zvnVE50n3LGHxLZAI5cuRInUJFeno6Ho+Ho0ePBm13Op0cO3Ys5PE///wzV199Neeccw7vv/9+vQrkRcr111/PTz/9FDZ35ltvvUVCQgJXXnllxOf0OVvVxd7qpKenh/08gYjEn3CUlpZy+eWXU1JSwrp162qc66mnnsLpdHLjjTeSl5dHXl4eBw8eBODEiRPk5eX5fzheeuklUlNTa+QHu/rqqxFC1OlINvY4N27cSHp6etCjtoIPer2eq6++mg8//LBed+Teeust1Go1N910U8j9vjuGP/74I9nZ2ezfv5/OnTsD0LVr13qNSdI4RMNOBdK6dWsuvfRSf0qgxqKhdqq0tJTHH3+ciRMnUlZW5r/Wy8vLEUKQl5dXY6zViaadCmT9+vXceuutjBo1iuXLl9d63okTJ/Lxxx+zYsWKoILAtZGenk5BQQFCiKDtJzPO6naqrqXA119/PQcOHOCbb76J+D3WrFmDzWYL6yz37NmTnTt3snPnTr799lsOHz7MxIkTKS4ulnYqikhbdXr5VNVpyDgjvf4be5wHDhyoYavq8uvq+p5DsWrVKqBmqrzaiPS7l5xaom2/6oOv+HC4gmu1/Yb6+lG9MCUoBZtPnDhR63ufynFWv1a++OIL1q1bx9SpU/2+X15eHm63m4qKCvLy8vyFNz/44AMKCwuD0roAXHTRRcTHx9dIGVCdluQ//eMf/6Bv3741bspeffXV2Gy2OnNeS5vUsmhJtqo+NIav5XQ6ufbaa/nll1/46KOPOOeccyJ676b0terC6XTy6quvMmrUqCBxXKfTcfnll7N582a/fhXOfms0Gtq0aROR/W4pvpZer+ef//wnhw8f5ptvvmHPnj18+umnlJaWolarOfvss+vV11PBGSWkn3POOWi1WjZv3hy03el0sn379qAI4FD49lc/fvPmzXi93hrH/+9//2PkyJGkpKTwySefNNmdFJ+wWlpaWmPfkSNH+PLLL7nuuuvqXBIXyO+//w4oUdK10adPH7Zu3YrX6w3avmnTJkwmU4NFDrvdzlVXXcXevXv5+OOPQ0aL5+fnc+LECXr27ElmZiaZmZlccMEFgJI6ITMz059XqbCwEI/HU+McvmIrdaVMaexx9u7dm/Xr1wc96sr7VFFRgRDCX5m+LnyVri+++OJaDaVer2fgwIGcd9556PV6f2ELXzSM5NRyqu1UKCoqKkLak5OhoXbqxIkTlJeXs2jRIv91npmZyQcffIDNZiMzMzOoGnkoommnAt/rmmuuYcCAAbz77ru13lS9//77ef3113n22WfD3gQLRZ8+fbDZbDUq1vsiGyL57qtT3U6NGDGi1va1fc/heOutt4iNja0xAQ5EpVLRs2dPhg0bRmJiIl9++SVer1faqSgibdXp5VMF0tBxRnr99+nTh7179/pFMB8NtVVpaWk1bFWoyN2G9DWQVatWcdZZZ9UrijPS715yamkO9itSHA4HXq837P9qbb+h5557LjqdLmQ9kcOHD0dkk+DUjLP6tZKfnw8oNxIC/b9Dhw7xxRdfkJmZyWuvvQYo8zygxlxPCIHH44lontdS/KeTndNKm9SyaEm2qj6crK/l9Xq59dZb2bBhA6tWreKiiy6K+L0b2wepD8eOHcPtdoe9hr1er39f//79gZr1oJxOJ8XFxRHZ75bma6WmpnLBBRfQtWtXPB4PX331FYMHD26WEemI05Dx48eLjh07htw3cuRIkZ6eLsrKyvzb/vnPfwpA/Pe///Vvs1qtIjc3VxQVFfm32Ww2kZiYKK688sqgc958883CZDKJY8eO+bcdOXJEdO7cWbRt21bs27evUcZVWFhYY5vT6RT9+vUTMTExwmKx1Nj/zDPPCEBs2LAh5DmPHj1aY9vBgwdF69atRa9evYK2Hz58WOTm5gqn0+nf9vbbbwtAvPfee/5tRUVFolWrVuLGG2+MeGyBuN1ucfXVVwutVivWrl0btt2WLVvE6tWrgx4vvfSSAMRtt90mVq9eLUpKSoQQQkyZMkUA4ssvvww6x7Rp0wQgfvjhh1M+znCE+p5PnDgh2rdvL9q3bx+0ff/+/SI3NzfkeT788EMBiFdffTXi9967d6+Ii4ur8T/u46KLLhI9e/aM+HyS8DQHOxXqf23fvn0iLi5OXHDBBQ0aV2PbKavVWuM6X716tbjkkkuE0WgUq1evjsr1G6mdEkKInJwc0aZNG9GzZ09x/PjxWtsuWrRIAOKhhx6qtV1JSYnIzc312zghhDhw4IDQ6XTirrvu8m/zer3iggsuEO3atRNutzuCkUVGqN8OIYS46qqrhEqlEr/++qt/W1FRkcjNzRVWqzXkebRarbjlllsifm+bzSb69etX4xrxMWfOHAEEXReShiNtVRWns08VSEPGKUTk1/8PP/wgALF48WL/NrvdLs4++2wxePDgiPoYKfX5nkN9Jz62bt0qADF79uyQ73Ps2LEaNtbpdIrzzz9f6PV6ceTIkRrHjB8/XpjN5voOSVIPmoP9ipQTJ06E/N976qmnwvrzkfyGjh49Wmg0mqD5Qk5OjtBoNOJvf/ubf9upGmek18r+/ftD+n/JycliwIABYvXq1eK3334TQgjx/vvvC0DMmTMn6Lxr1qwRgFi4cKF/W0v3n6688kqh1+vFnj17go4fM2aMUKvV4tChQ0IIIUpLS4Xdbg9q4/V6xY033igAsWXLlhp9kP5T9Ghutmrx4sUCOGkdqyl8LSGE+Nvf/iYA8dJLL9X6/tHwQYqKikLaIyEUn6xVq1aia9euwuFw+LdbLBaRkZEhunfvHtSnlJQU0blzZ1FRUeHf7tO73n333aiOM5DG8rUCWbhwoQDE+++/X2Pfvn37aoztVHPGCelbtmwRBoNB9O3bV7z44ovi4YcfFkajUVx22WVB7b788suQF8ALL7wgAHH99deLV155Rdx6660CEPPnzw9q17t3bwGIBx54QLz55ptBj88++6xGfyMxVGPGjBGXXnqpePTRR8Urr7wi5s2bJ7p37y4A8fTTT4c8pn///qJt27bC4/GE3H/bbbeJCy64QDz66KPi5ZdfFg899JBo06aN0Ov1NUTnUP10u93ivPPOE7GxseKxxx4TL7zwgujZs6eIi4sTu3fvbtA4p06dKgBx1VVX1fjs3nzzzVqPDXdR7d69W5jNZhEbGytmzpwpli9fLm666SYBiD/+8Y9RGWc4+vXrJ66++moxf/588corr4jZs2eLjIwMoVargybXQijCdrj7Ydddd50wGAxBjmJ1evToIR555BHxz3/+Uzz88MMiMTFRdOzYURw8eDBkeymkNx7NwU6lpKSIm266STz55JPi5ZdfFvfff79ITEwURqNRfP/99zX6Gy07FYpwAkRzs1NlZWWiffv2Qq1Wi4ULF9Zot3HjRn9b382vLl26hDxnQUGBv+3rr78uAPH6668H9ev+++8XgJg0aZJ45ZVXxKhRowQg3nrrraB24Y6PlKlTp4oBAwaIWbNmiZdfflksXLhQDBw4UADi7rvvDmrrm5hV/00RQojnnntOAGLdunVh32vs2LFi6tSp4qWXXhKLFy8WPXr0EAaDQXz++ech28uJYOMibVUVZ4pPVdc4G+P6Hzt2rNBqteL+++8XL730khg6dKjQarXi66+/juj4SKnP91zbZ3rvvfcKoMb34OP1118XZ511lnjwwQfF8uXLxRNPPCHOOeccAYgnnngi5DFSSG96moP9Cnd8dVavXi3at28vpk+fLv7xj3+IJUuWiOuuu06oVCoxYMCAINHFRyS/obt27RKxsbEiPT1dLFiwQCxYsECkp6eL5OTkIH//VI2zIddKIB07dhSjRo0K2uZwOETPnj2FSqUSt912m1i+fLm47777hNFoFOnp6UH+QEv3n77++muh0WhESkqKmDt3rnjhhRfE5ZdfLgDx17/+1d/uyy+/FGlpaWL69OnihRdeEE899ZQ4//zz/WMMhfSfokdzsFUlJSVi3rx5Yt68eWLkyJECEPfee6+YN2+eeO6552r0N1q+1rPPPisAMWTIkJB+TXl5ub/tqfRB3njjDTFv3jwxc+ZMAYhLLrnE/3nm5eX52z3++OMCEH379hXPPvuseOqpp0SPHj0EIP71r38FnXPlypUCEAMHDhTLli0T9913n9DpdOKCCy4IusHX0n2tN998U4wZM0Y888wz4uWXXxY33HBDDZsWiBTSm4jaDJEQQnz77bdi6NChwmg0iuTkZHHXXXfViGqrzRl4+eWXRbdu3YRerxdnnXWWePbZZ4XX6w1qA4R9XHTRRUFtr7vuOhETEyNOnDhR67j+7//+TwwfPlykpqYKrVYrWrduLYYPHy4++uijkO13794tADFjxoyw51y1apW48MILRXJystBqtSIpKUlcc801Ie9ShzOYx48fF7fffrto06aNMJlM4qKLLhI//fRTjeMjHadPHA73qI3aLqrdu3eL66+/XrRv317odDrRsWNHcd9999WIkjxV4wzH888/L4YNGyaSkpKEVqsVycnJ4qqrrhLffPNNjbbhhPTS0lJhNBrFtddeW+t7jRs3TrRv317o9XrRtm1bMXny5JB3FAPfTwrpjUNzsFNz5swRAwYMEK1btxZarVa0bdtWjBs3Tvzyyy81zhdNOxWK+gjpQkTPTvlsUrjH+PHj/W19Tky4R6BzE24i5/F4xBNPPCE6duwo9Hq96NmzZw2nTIjIJt+18dlnn4krr7xStG3bVuh0OhEXFyfOP/988frrr4f8PwvnnJ133nkiJSWl1mivJ598UnTv3l0YjUbRunVrcfXVV4tt27aFbS8ngo2LtFUKZ4pPFck4G+P6r6ioEPfdd59IS0sTBoNBDBw4MKQ9uvfee4VKpQq7+q4u6vM9h/tOPB6PaNeunejXr1/Y99m8ebO46qqrRLt27YRerxexsbFi2LBhQRFjod5PCulNS3OwX//5z38EIJYvX15rX3/77Tdx6623is6dO4uYmBhhNBpFz549xZw5c4KEoUAi+Q0VQhHihg8fLsxms4iLixOjR48We/fujco4G3KtBBJKSBdCsZ3Tp08XXbt2FQaDQSQlJYlx48aJ33//Pajd6eA/bdq0SVx++eUiLS1N6HQ60bVrVzF//nzhcrn8bX7//XcxduxY0alTJ2E0GoXJZBL9+/cXy5cvr/E+1d9P+k+nnuZgq2qbs1TvWzR9Ld9vdbhH4G/4qfRBavO3qr//W2+9JQYNGiRatWolYmJixODBg0NGXguhfIa9e/cWBoNBpKamiilTptT47lu6r7Vp0yZx4YUXitatWwuj0Sh69+5dq62SQnoTMX78eNG+fXtRVFTUYDHzVJKSkiLuu+++aHejyZHjbLmUlZWJoqIiMXToUCmkNxLSTjVPzpRxjh07VgwcODDa3WhUKioqRFFRkT+qTE4EGwdpq5onZ8o4Bw4cKK6//vpod6NRKS8vF0VFRWLcuHFSSG9imoP9uv/++0VGRkaNNBunG2fKOKX/JGkKmoOtqg/SBzm9aCnj9Hq9oqioyJ9uL5pCevhqZy2cAwcOkJycTM+ePdm5c2e0uxOWXbt2UVFRwYMPPhjtrjQpcpwtm1tuuYWPPvoIgJ49e0a5N6cP0k41L86UcQoh+Oqrr/jXv/4V7a40KsuXL2f69OnR7sZpibRVzYszZZxlZWX8/PPPrFy5MtpdaVQefvhhli5dCoDZbI5yb05/om2/vvzyS2bPnl2vwr0tkTNhnNJ/kjQl0bZVkSJ9kNOLljTO0tLSZlMoWSWEENHuRGOTk5PD4cOHAYiNjeW8886Lco8kkpbNL7/8wtGjRwF5TTUW0k5JJI3LgQMH2LNnj//1RRddhE6ni2KPTg+krZJIGpe9e/eSn58PgFar5eKLL45uh05jpP2SSOpG+k/RR9oqiaRu3G43X331lf91165d6dChQ1T6cloK6RKJRCKRSCQSiUQikUgkEolEIpE0Fupod0AikUgkEolEIpFIJBKJRCKRSCSS5owU0iUSiUQikUgkEolEIpFIJBKJRCKphdO22GhDcLvdbNu2jdTUVNRqeY9Bcvri9XopLCykb9++aLXSDLQ0pK2SnClIW9VykXZKcqYg7VTLRtoqyZmCtFUtG2mrJGcKLcFWNc9eRYlt27YxaNCgaHdDIjll/PjjjwwcODDa3ZDUE2mrJGca0la1PKSdkpxpSDvVMpG2SnKmIW1Vy0TaKsmZRnO2VVJIDyA1NRVQvrD09PQo90YiaTqOHDnCoEGD/P/zZyovvPACixcvpqCggN69e/Pcc8/V6qC89957zJ49m7y8PLp06cKTTz7JFVdc4d8vhGDOnDm88sorlJSUcP755/Piiy/SpUuXoPOsXbuWuXPn8ssvv2A0GrnoootYs2ZNxP2WtkpypiBtVctF2inJmYK0Uy0baaskZwrSVrVspK2SnCm0BFslhfQAfEtk0tPTycjIiHJvJJKm50xeFvbOO+8wY8YMli9fzuDBg1myZAkjRoxgz549pKSk1Gi/ceNGbrrpJhYsWMCVV17JqlWrGDNmDFu3buWcc84BYNGiRSxbtoyVK1eSmZnJ7NmzGTFiBDk5ORiNRgA++OADJk6cyBNPPMGll16K2+1m586d9eq7tFWSM40z2Va1VKSdkpxpSDvVMpG2SnKmIW1Vy0TaKsmZRnO2Vc23ZxKJpNnw4osv0qtXL+Lj44mPj2fIkCH897//jXa3TopnnnmGiRMnMmHCBLKysli+fDkmk4nXXnstZPulS5cycuRI7r//fnr06MG8efPo168fzz//PKBEoy9ZsoRZs2YxevRoevXqxRtvvMHhw4f90eZut5upU6eyePFiJk+eTNeuXcnKyuKGG26ota8Oh4OysjL/w2KxNOpnIZFIJBKJRCKRSCQSiUQiqR0ppEskkjrJyMhg4cKFbNmyhc2bN3PppZcyevRodu3aFe2uNQin08mWLVsYPny4f5tarWb48OFkZ2eHPCY7OzuoPcCIESP87fft20dBQUFQm4SEBAYPHuxvs3XrVg4dOoRaraZv376kp6dz+eWX1xmRvmDBAhISEvyPrKysBo1bIpFIJBKJRCKRSCQSiUTSMKSQLpFI6uSqq67iiiuuoEuXLnTt2pX58+cTGxvLDz/8EO2uNYji4mI8Hk+NvFupqakUFBSEPKagoKDW9r7n2tr8/vvvADz66KPMmjWLjz/+mNatW3PxxRdz/PjxsP2dOXMmpaWl/kdOTk49RiuRSCQSiUQikUgkEolEIjlZpJAukZzBWCyWoJQhDoejzmM8Hg9vv/02VquVIUOGnIJenj54vV4AHn74Ya677jr69+/P66+/jkql4r333gt7nMFg8KfViY+PJy4u7lR1WSKRSCQSSSNyOqbLk0gkEolEIjlTkEK6RHIGk5WVFZQyZMGCBWHb7tixg9jYWAwGA5MnT2b16tUtNsVIUlISGo2GwsLCoO2FhYWkpaWFPCYtLa3W9r7n2tr4KqwHfm4Gg4HOnTuTn59/EiOSSCRSnJJIJC2B0y1dnkQikUgkEsmZhBTSJZIzmJycnKCUITNnzgzbtlu3bmzfvp1NmzZx5513Mn78+BabYkSv19O/f382bNjg3+b1etmwYUPYKPshQ4YEtQdYv369v31mZiZpaWlBbcrKyti0aZO/Tf/+/TEYDOzZs8ffxuVykZeXR8eOHRttfJJTg/AKKvIqKN9RTkVeBcIrot2lMxopTkkkkmgS6Sq/0y1dnkTSWEi/SiKRSCQtAW20OyCR1IXwCuz5djwWD5o4DcYORlRqVbS7dVoQFxdHfHx8RG31ej1nn302oAjCP/30E0uXLuWll15qyi42GTNmzGD8+PEMGDCAQYMGsWTJEqxWKxMmTADg1ltvpV27dv4o/alTp3LRRRfx9NNPM2rUKN5++202b97Myy+/DIBKpWLatGk8/vjjdOnShczMTGbPnk3btm0ZM2YMAPHx8UyePJk5c+bQvn17OnbsyOLFiwEYO3bsqf8QJA3GmmuleHUxtt02PHYPGqMGU3cTSdckYe5hjnb3Tjt8ApUPg8GAwWAIanPVVVcFvZ4/fz4vvvgiP/zwAz179jwl/ZRIWgLSr2oaqq/SmzNnDo8++mitx3g8Ht577z2ZLk9yxiP9KolEEg2kTyRpCDIiXdIseOUV6NgRcnODt1tzreQvzGfnNTvZNnQbv971K/kL87HmWqPTUYkfr9cbUU715sqNN97IU089xSOPPEKfPn3Yvn0769at8xcLzc/P58iRI/72Q4cOZdWqVbz88sv07t2b999/nzVr1nDOOef42zzwwAPcfffdTJo0iYEDB1JeXs66deswGo3+NosXL2bcuHHccsstDBw4kP379/PFF1/QunXrUzd4ScRMmQK9ekF5edU2a66Vg8sOYtlmoezHMiw/WFDHq7Fss3Bw2UFpn5qA+qShAlnLQXJmY7XCuefCXXeF2JdrZd+sfWzutZldY3eR90ie9Ksaifqs8muJ6fJeeOEFOnXqhNFoZPDgwfz444+1tn/vvffo3r07RqORc889l08++SRovxCCRx55hPT0dGJiYhg+fDi//vprUJv58+czdOhQTCYTrVq1Cvk++fn5jBo1CpPJREpKCvfffz9ut/ukxio5tfj8qn3vHmf/v4o4fAC0SVrpV0kkkibh008hIwM+e0XRmvb8ZQ9bh2xl7x17pU8kiQgppEuaBR9+CPn58OWXVdsCxSrnYSeecg8lX5VQ8l2JdKpOMTNnzuSbb74hLy+PHTt2MHPmTL766iv+/Oc/R7trJ8WUKVPYv38/DoeDTZs2MXjwYP++r776ihUrVgS1Hzt2LHv27MHhcLBz506uuOKKoP0qlYq5c+dSUFCA3W7n888/p2vXrkFtdDodTz31FIWFhZSVlbF+/XoZLduMefdd2LEDfvpJeS28guLVxbiKXcT0iKFiTwWOAw7KfyzHlGXCVeyieE2xXI7cyEQqULVEcUoiaWx27YKdO+HVV8Hjqdru86tOfH0Cj8VDxZ4KnMecUqxqJHyr/HyP6qtmAmlp6fLeeecdZsyYwZw5c9i6dSu9e/dmxIgRHD16NGT7jRs3ctNNN3H77bezbds2xowZw5gxY9i5c6e/zaJFi1i2bBnLly9n06ZNmM1mRowYgd1u97dxOp2MHTuWO++8M+T7eDweRo0ahdPpZOPGjaxcuZIVK1bwyCOPNO4HIGkyAv2q4mKBUXix51jRxmulXyWRSJqEjz4CzaEAranIidfqxZprlT5RCyQadbKkkC5pFlgr7ZTFojwHOlWmLJO/ndfmxfKTBcdhh3SqTiFHjx7l1ltvpVu3bvzhD3/gp59+4tNPP+WPf/xjtLsmkTQpPtu0f7/ybM+3Y9ttw9DegEpULfuz/GTBccCBIcOALdeGPd8e4myShhKpQNXSxCmJpCmw2ZRnhwPy8pS/A/0qQ7uq66f0i1L07fRSrDrF+NLl9e/fnwULFtC7d2+WLl0a7W6F5ZlnnmHixIlMmDCBrKwsli9fjslk4rXXXgvZfunSpYwcOZL777+fHj16MG/ePPr168fzzz8PKNHoS5YsYdasWYwePZpevXrxxhtvcPjwYdasWeM/z2OPPcb06dM599xzQ77PZ599Rk5ODv/617/o06cPl19+OfPmzeOFF17A6XSGPMbhcATlsrf4Jh+SqBDoV7kqF7oabcp3p1KppF8lkUganYIjgmEUo7YoWpNKq8zpnAec6NJ00idqYUSjTpYU0iXNgupCepBYpVJVGTE1uIvdlG8px7rTKp2qU8Srr75KXl4eDoeDo0eP8vnnn0sRXXLa4/VWCVI+Id1j8Si5O82aGs7Vsf8cQ21Q47V78Vg8SE49LU2ckkiagoqKqr99KfMC/Sq8Vfu9di/HPjqGvp1eilVRpDmny3M6nWzZsoXhw4f7t6nVaoYPH052dnbIY7Kzs4PaA4wYMcLfft++fRQUFAS1SUhIYPDgwWHPGe59zj33XH9aPt/7lJWVhZ1AL1iwIChVmFy1FF0C/SqfkK53e/CUK36UxqyRflWUiEaUp0RyKrDvt9MBGyd0itZE4Oq97VZ5A6+Z0JyLuEshXdIsqC6kBzpVygblKfHyRFQGFc5DTkq+KcFV5Dr1nZVIJGcEgWKUT0jXxGnQGDV4rJ4gp0ttUuMudlPyZQlqoxpNnObUdlYSkuYsTkkkTYXvBiBUCemhbgJq22hR6VQ48hzYdtqkWHWKaGnp8oqLi/F4PEFiNUBqaioFBQUhjykoKKi1ve+5Puesz/sEvkd1Zs6cGZQqTK5aii6BfpXLURWg4DyiRKV7rB7pV0WJaER5SiSnAstRD3o8WD2KXQkMjrJut6I2ysCo5kB9a2TBqauTpW2yM0sk9aC6kB7oVGnjtX7jpk/Xk3xjMkf/dRTnQSe7rt3F2cvOJmlMknI3USKRSBoJa0BqPJ+QbuxgxNTdhGWbBUOnqvQIiaMSKX6vGMtPFuIGx2HsYERyapk5cyaXX345HTp0wGKxsGrVKr766is+/fTTaHdNIjmlhIpID3UTUJeoI35IPMc/Pk7JlyUkjkyUYtUpwJcu78iRIyQkJNCrVy+ZLu8UYjAYgtKDlZWVRbE3Zxher1IUy2KBuDjo0KHKr9pqwe2sErMcRxwYzzbiOOggrp/0qxoTX5Snj+rXhI+rrroq6PX8+fN58cUX+eGHH2R9J0mLRQg4UKzBiQZdhQfQBq3U85R7sO6yomulkz5RlMnJyaFdu3b+17XVntmxYwdDhgzBbrcTGxvb5HWymiwiPRqV3X04HA769OmDSqVi+/btjTUkSRNSXUj3OVWOAw6EEP5Jn0qtwtjJSPyQeDQJGhwHHey6dhe/jPgFa44sCCGRSBqPwKhOX55hlVpF0jVJ6JJ0VORWqlUqMLQzoG+nBwEnPj2BszB0blZJ0yFrOUgkChXWqhlhzjYHeL3BfpWnKl1ebL9YYrrFgBdOfH6C4/89rvhdkiajpaXLS0pKQqPRUFhYGLS9sLCQtLS0kMekpaXV2t73XJ9z1ud9At9D0kzIzYWFC+GRR2DePOV54UJUe3aTdE0SnlgdcVStNHbkO7Dl2NAn6ZWAKbUMmGosmnOUp0TS1JSXQ77DSD4mzDZFa/L5RWqzIo9aNlsw9TDJG3hRpjkXcW8SIT1ald19PPDAA7Rt27YphiZpIqoL6YFilS3H5jduHpsHW46N2HNj6b2hNx0e7oBKr+LE+hP81PMntg7byqEXD+E6JlO+SCSSkyMwIv3AASWQCsDcw0zGPRmYs8zKBhW4j7lJvTUVbSstFXsr2Nx3Mye+OnHqO30G09LEKYmkScjNxfafDVUvd3kQC6rEKl2SDsfhynRHKvCUeYjpGoM+TY9wCn7926/8MuIXmRdU4kev19O/f382bKj6v/J6vWzYsCGsoDZkyJCg9gDr16/3t8/MzCQtLS2oTVlZGZs2baqXSDdkyBB27NgRNMdcv3498fHxMvd5cyI3F5Ytg23bICkJunVTnrdtg2XLMJOPZ3QG5ej8hzgLnMT1i6PdPe0w9zBHsfOnHzk5OUHpjWbOnBm27Y4dO4iNjcVgMDB58uQmj/KUSJqaggIQqPiOJE54Fa3J61AmeaYsEwCuAhfxQ+LlDbwWxKmuk9UkQnq0KrsD/Pe//+Wzzz7jqaeeqrOfsmp788DtBmdl8GbgV+ATq+L6xlUJ6WUeYvvEknRdEmq9mvS/pjNw50CSrkkCFZR9X8avf/uVjWkb+Xnkz+xfsJ+Sb0vw2GV+K4lEUj8ChXSXC44cqXpt7mEmfVI6AGqDmk5zO9H58c70+6Ef5nPMuApd/PyHn9m/cL+s+C6RSE4NlWJVxf4qUbHMZeJIdp5frMq4JwNDWyWix2v14j7mJmFIAr3W9+KsZ89CbVQrwQnn/MS+R/dJQV0CwIwZM3jllVdYuXIlubm53HnnnVitViZMmADArbfeGiTGTZ06lXXr1vH000+ze/duHn30UTZv3syUKVMAUKlUTJs2jccff5x///vf7Nixg1tvvZW2bdsyZswY/3ny8/PZvn07+fn5eDwetm/fzvbt2ykvLwfgsssuIysri1tuuYWff/6ZTz/9lFmzZnHXXXfVGrkmOYV4vbB6NRQXQ1YWxMeDRqM8Z2Up29esoUAXQz6mqsOsXtJvT5ciehPQnKM8JZKmxlc+Ix8zq9WVWlNlfQa1Qa2k7hRQ+l1pFHspOVmauk5Wo+dI91V2D3SmIqnsPmPGjKBtI0aM8IvkdVV2HzduHKAs5Zs4cSJr1qzBZDJRFwsWLOCxxx6r7xAljUygWFX9Xoa5hxlTNxP75+9HOAVJ1yXhOOCg8I1CpWiWUYOpu4nM+Zl0ea4LR98+SuFbhZRvK+fEpyc48akSEaoyqIjtE0vsubGYepown6Oc19DWgEoj7zRKJJKaWKtli9q/HwLStPlTTqkNamI6xQBg6mai36Z+7P3bXgpXFrJv5j5OfHaCjrM60uqSVrKWg0QiaRoCxCpb63ZBu3JbD6Vt8VpYswbzgw/S6qJWHF97nNjesXSa2wljByMqtYrYc2Jpc0Ubdt+2m7LsMvY/tp/9c/fT+o+tSb89ncQrEtHGKnVr7Pl2PBYPmjiN/3jJ6cuNN95IUVERjzzyCAUFBfTp04d169b5C3vm5+ejVlfFZw0dOpRVq1Yxa9YsHnroIbp06cKaNWs455xz/G0eeOABrFYrkyZNoqSkhGHDhrFu3TqMxqql9I888ggrV670v+7bty8AX375JRdffDEajYaPP/6YO++8kyFDhmA2mxk/fjxz585t6o9EEin5+bB7N7RvD9V9IJUKMjIgN5dDHEdLcOBB+fZyEi9LPIWdlVTHF+UJ0L9/f3766SeWLl3KSy+9FOWeSSQNI7AO9W6bmfYPmij+dzHOAicpN6agidWwZ8IeCl4toONDHaV/0wKIRp2sRhfSa6vsvnv37pDHNEZldyEEt912G5MnT2bAgAHk+RLa1sLMmTODBPxDhw7JpUpRoDYhHZQ0L76I9BOfncDr8GJob8BgNuCxerBss2A/YCfjngza39ue9ve2x5pr5cRnJyj5toTSb0txHXVh2WTBsin4DVQ6FcaORoydjBjaG9C31WNIV571qXp0STp0STq0rbTSiEokZxhWi5fAhVt5v3sZOrTqtXArdkmlDbYNGpOG7q93p9UFrdh7115Kviyh5MsSYvvEknFvBik3pqDWqaUYJZFIGo8AsarikC5oV+6xFP7QTRGryM9HeBQ7o0vREdMpBuEVVORV+G1R7y97c3j5YY6+dRTLTxZOfHaCE5+dQKVVYeppQpugRaVToTar0SXoMHU3kXRNkowcPc2ZMmWKP6K8Ol999VWNbWPHjmXs2LFhz6dSqZg7d26toveKFStYsWJFrf3q2LFjjdpakgYSohgo6pNcwG6xgN0O5jD2wWyGQ4c4lO9BU01It2yxSCG9mdHUUZ4SSVMTKKS73eB0qfyBlYZ2BhJHJPLbtN+w59k59vExkq5OilJPJZESjSLujS6kR4vnnnsOi8VSa46v6siq7c2DQLHKUuoBr6qG0yZcimPlLnET2z/WH9WpjdeiydJgy7FRvKYYUzcTKrUKcw+zkhpmagZCCCp+raB8eznWnVasu6xYd1qx/25HuAQVv1VQ8VtF7Z3UgK61IqhrW2uV5wQtmniN0od4DZo4DRqzBk1s5bNZg9qkRmPSoI5R+x+aGOW1SqeS0akSSXMlNxfrqt3ANf5N+9/8GvqnQY8eAHhdSj49la7mdaxSqUi/PZ1Wl7Ti4LMHOfLaEcq3l7P7lt38ds9vxA+NR2NWKsELIfyra6QYJZFIGkSAWFXhVoR0FQKBityiJOiniFVYLAhXnLJfq8Kaa6V4dTG23TY8dg/CIfBUeNDEaDB0Uooou0vc2H+z4zjowPpz8FIdtUmNJlbD0XeOkjQ6ifih8cRkxmDoaEBj1Jzyj0EikTSQ3FxlVcvu3YotMRqhe3e45hq/39Mg4uKUc1mtSjqX6litYDRy6ISJzih+1SGtiXZuG+Vbyxv+vpKTJhpRnhJJU1OtPjUWS5XWpNKp0Jg0pP8lnYPPHmT3hN30/7E/MWfFRKGnkkh59dVXT/l7NrqQ3tSV3dPT04Pa9OnTB4AvvviC7OzsGjm+BgwYwJ///OegZYGSZkRuLtYXvgf+CoCl1AsLFwc5bcIrqPSr0Gfoa4jPKpUKQ4YBW64Ne77dn2IhcL+pqwlTVxPcULVdeASOQw7seXbs+5QJouOIA+dhJ84jTpxHnbiKXXjKPOABV7ELV3EjFjFVgdqoRm1QozaqUelVyt+Gyr/1Ac86lfK3rvLvykfQa23Vs1qnVgS5LCnISST1pjLPsO1/5wRt3v+bSymWdc890KNHkNMFhIwwj+kcQ5fnutDp0U4cXn6YQ88fwlng5Pja48pJVaBP06NL1mHNtWL52UKnWZ2IPTf2lA5ZIpG0cALEKptLEdK7JxWTW5xMbnGyX6wiLs5vuzzlHg4uO4ir2IWhvQF1hZrS70txH3ejTdSSMDQBtUmN44CDmG4xCKfAusOKcAkc+Q7cJ9x4bV68Ni+uoy6svwSL7No2WgxtDejT9cojuXKlX7IOXRudEpjgC05opUVj1shVORJJNPAVAy0uVlKwmM2Kzdi2Tam2Xun3NIgOHRRBfts2JSd64DxOCDh4EPr149A3sXStjEjfq46lHTYsW2T9smgSjShPiaSpCYxIBygrqxLS1TolmDPz8UxKvy3FstnCjit30De7L7pWuuqnkpzBNLqQHljZ3VcsxlfZPdxyQF9l92nTpvm3havs7hPOfZXd77zzTgCWLVvG448/7j/+8OHDjBgxgnfeeYfBgwc39jAljUGl02bd3ca/yenV4dyyA32A0+YzbADauND/shqzBuchJx5L5EVFVRoVxg5GjB2McGH4dl6HVxHRT7hwn3DjLnErz2VuPGUePBYP7lI3HqsHT3nVw2vz4rEFPFd48VZ4q04sqLmtEYnpFiOFdImkvgTkGbYmdQratV+V6S+KRbduQUJ69ajO6hHmujY6Oj7ckfYPtOfXKb9S8nUJ7hNuXEddyo27I0rF5fLN5RR/UIypuwlTNxOm7iZiOivRncaOSgoqGeUpkUhqECBWVbgUX6lf+hFyi5PJKUryi1V06IBw5QFgz7Ojb6vHlKXUFSr/uRzhFhg6G3AXu6n4tYKEYQlosjRYfrLgPOKk1cWt0CYo5/c6FQHdedSJ46ADd5EbtVmt+GPlHtzH3LiPubHusIbscg1UKKv64jVo47SozUq0u3+ln2+Fn6lqdZ/aqK56NlQ9qwxKIELI4AR9mGAEuUpQciZSvRio7zrwFQPNyfH7PQ1K86JWKwFSBw4o58rIqBLqDx6EpCQYM4ZDKx3+HOk5bhOXAPZ9dlw/7EJ3Xs9GG64kcqIR5SmRNDXVhXSLpWa6To1Jwzn/PoctA7dg220j58Yczl17LmrtSaa6kpw2NElqlxkzZjB+/HgGDBjAoEGDWLJkSY3K7u3atWPBggWAUtn9oosu4umnn2bUqFG8/fbbbN68mZdffhkIruzepUsXMjMzmT17dlBl9w4dOgT1ITZWieY766yzyMjIaIphSk6GQLGq3ZCgXZbOvWmTt6VKrHJXCekeu8efDiEQj9WD2qhGE9f4ApPaoMbQzoChXfiK5pEihMDrUMRzr8OLcAi8dq/ycFZuc4qqZ2fAa5fA61KehVMg3AGvXcrrwEdMZ7kESSKpNwF5hq2/6AFoH1/KgbIE9pe28hfFIj8f4W6lHOMlKKrTV7+hbGsZ5bvKSbkhhdjesRg7GHEccuCt8CpF++K1uEvdOA46cB5y4jisPAu3wLbLhm2XLWQXtW206NMq6zlURrPr2uiqajr4Ijx9z/FaWVRZIjndCRCrbMftAPRNOcRb9KLQGseJuA60HjMG1Gq/X+UucRM3OA6VSoW7xI2r2IU2QYtarUYbr8VV7MJd6kbbSouujQ7bLluQT6bWqzFkGDBkGIjtFUvF3go6zu6I+Rwz7uNuxaZV3ih0HHEoQQlFLv8KP3dJVXCCcAkQ4LEoAQpOnKf8Iwxc2ed/rnyYupvo/WnvU94niaTJibAYKPn50KlTw96jRw8lQMqXOubQIWWFTL9+UDmXP7TfjbZyCfIxYcSYYMVeaqZ8/ru0XjTu5NLLSCQSSSWhhHR9QLrOwBXGXV/qSs4NOZz47AS/TfuNrs93jUKPJc2RJhHSo1XZXdKCCBSrDumDdllcRtoEOG3eVu38+5yHnegSdUFRQ0IIHAcdxPWLU6LLmzEqlQqNUSMjSiWS5kpAnmGrS7FNWclFHChLIK+kFcJkRuXPM5wAgMfmwVXswpRl8tsm4RC4j7up+F8F1l+sxPaJxdzDjKmnCY/dg8Gs3JjTJij1Fsw9ldUjXpcX63Yrba5sg9fhxbbbhmO/A/t+O/b9drw2rz/KM5zQHgq1WRHGNHGaqgjP6vUcfM8xmqooT1+kpzEg4tOXjspQmY4qRKSnSiOjOxuTF154gcWLF1NQUEDv3r157rnnGDRoUNj27733HrNnzyYvL48uXbrw5JNPcsUVV/j3CyGYM2cOr7zyCiUlJZx//vm8+OKLdOnSJeg8a9euZe7cufzyyy8YjUYuuugi1qxZ01TDlJwslWJVxeeKfUm1/EY703EO2RLJHX43Q3t0AqrqOwiv8AcneB1ehFv4U1Wp9CqERbmRD/ij0N0lbnRtai5vDgxoUKlUys29Njo4t+5uCyHwVniVFX5lbv+z1+pVVvlVrvjzVnjxVASs9PMFIlRUPjuUZ+GoDFoICE7wOqsCEbxOrz9lYFA/KgMRQuEbv0Ry2hFhMVAsJ5lmpUcPJaq9ejFTwD5vMcfsD/oj0h1CS2xbG/ZSM5b9BlqfTES8RCKRBOAT0jUa8HgUc5RYucrYcchB/sL8oBXGSdckcXTVUQ6/cBh3iZsuy7qgS5RpXs50mswrjEZl90A6deqEEKGdYUkzIIRY5d/l0EObgKJY5qrvUZesw5Zjw5BhQGPW4LF6cBx0oE/SkzQmSebWlEgkJ0dAnmGrsyrP8Kf/O5sKt47iYkiuzDPsPVQpRrkEhvYGv3DsKnJRtqkMj82DLlGHcAvUejWWbRbKd5UrxfysHrTxNX+CvTYvujY6Ei9PrFHvQQhFnHccqYzyLFAevujOoCjPE8rDa1f66LV6cVqdcKQpP7wAVNRe1yEg4rP3Z71DCnMShXfeeYcZM2awfPlyBg8ezJIlSxgxYgR79uwhJSWlRvuNGzdy0003sWDBAq688kpWrVrFmDFj2Lp1qz9AYdGiRSxbtoyVK1f6V/mNGDGCnJwcf4DCBx98wMSJE3niiSe49NJLcbvd7Ny585SOXdIAevSgIk3Ab2AadzVZH8Zw6DvIrejE0Mom/lygerXfFqkNalRaFcIlUBlUCKdQaq4YKoUrLWgTtbiOuTB2NjZqQINKpRT30pg06FP1dR/QCAivsprPL7C7qq308wSv9lPrpYAnOU2JsBgocXEn/15qdc2o9rw8Dm8/CuAX0l2oiEm1Qm4y5Y4OkPvZyUXES04bohFYMH/+fNauXcv27dvR6/WUlJTUeJ9QwSP/93//x7hx4/yvv/rqK2bMmMGuXbto3749s2bN4rbbbmvYByFpEF4vHFXMDZ06wf/+p8hSrSv9oqPvH0WlVgWtMHYccBA/NJ6y7DKOvnWUkg0ldH2pK0lXJ0VvIJKoI8MrJNEhhFjlw+I0BBfF8kUHqaH91Pb+PMTOQ07URjVx/eJIGqPkIZZIJJKTIiDPsLWyYF9rYwXpsRaOlMex/zcXycN7KHmGdx73H+aL6hRCYN1tVUT0ZB0IcB9zo9KrMGWZsO6y4rF7cOQ70PTU1EuMCoryPKfG7pB4Hd6gCE9PmSdsPQePVfnbH/VZUZWGyh/5aQ9IS+WL+KwUoYIQSlS+xxF53QpJaJ555hkmTpzoT4+3fPly1q5dy2uvvcbf//73Gu2XLl3KyJEjuf/++wGYN28e69ev5/nnn2f58uUIIViyZAmzZs1i9OjRALzxxhukpqayZs0axo0bh9vtZurUqSxevJjbb7/df+6srKxTMGLJyWKzKXYlpntHevSD9d8pi/x8+PwqXYoOxwEHmiwNmgQNuiQdziNOtEla3GVuDOkGtAlahBA4Dyn50b0V3tMioEGlVvlX1UgkZzQRFgOlWhrVRsNi4VCJMoczqt3gBQ9qdCkVyu7i1krw1clGxEtaPNEKLHA6nYwdO5YhQ4bUmjf+9ddfZ+TIkf7XrVq18v+9b98+Ro0axeTJk3nrrbfYsGEDf/3rX0lPT2fEiBGN9AlJ6uLECXC5lL/PPlsR0gOLjXrKPMQPjffPz7TxWjRZyhwvfVI6pV+VYtttY+fonaT8OYVOczph6mKKylgk0UUK6ZLoEChWVRfS7TooCyiKdVDJk6nSqZTUCN1M/rxVmjgNxg7GFjNxk0gkzZzAPMM/KqlTzFo7HWOPKUK69iwGjLlAyTPsKzaqUfmjOj2lHn+eYZVKhdfh9Ud1qlQqjO2N2H+3ozaqT4kYpTao0SfrIblRThcWIapSJvijPJ3Voj19dR58tRwqoz018TLVVTicTidbtmxh5syZ/m1qtZrhw4eTnZ0d8pjs7GxmzJgRtG3EiBH+lCz79u2joKCA4cOH+/cnJCQwePBgsrOzGTduHFu3buXQoUOo1Wr69u3rT9O3ePHioLR7gTgcDhwOh/+1RYoeUaNC0Z+IialKKxwkpFfarthzY/FUePy2yNTFhKvIheN3B9pELTFdYnCXuf22qe2ktgAyoEEiOZ2IsBhok6VViYvjkFtJ/2pUe8ALblSo2yi/JxXHzbjVcWgbIyJe0qKJRmABwGOPPQbAihUrau1fq1atSEtLC7lv+fLlZGZm8vTTTwPQo0cPvvvuO5599lkppJ9CfGldEhOVByj36LxOZQWvPl1fY3WBSqXCkGHAfcxNz9U9KXitgANPH+DoW0c5+tZREkcm0u7udiSOTJSa1BmEFNIl0SFQrMo9EbTL8nsR9Kly2vxiVWUVZZVaFZTyQHgFFXkVUliXSCSNQ2WeYeu3ih0xnzhIJ2MrfqATeb1HQw9lwuezTZpYjT+qMzDPsBAiKKoTlMh1tUFN8thkpaDoaSJGqVQyurMpKC4uxuPx+GvM+EhNTWX37t0hjykoKAjZvqBy9uB7rq3N77//DsCjjz7KM888Q6dOnXj66ae5+OKL2bt3L4m+2UcACxYs8E82JdHFVlk+wWSqXUg3tDOQeEWiXxj32r3EZMbgSVfygrqPu0PapsCABrVZjQrlZmJFXoX0wSSSlkhdxUCbstBnhw4ciusGgF6lrGJzo8KjFxgS7DhKjZTH9aNVU0XES1oE0QosqA933XUXf/3rX+ncuTOTJ09mwoQJflE2Ozs76H18fZk2bVrY88kAhcbHJ6SnpVVlsrJYgud0odCYNTgPOREuwVmLziL5umTy5uVx/JPjHF+nPIydjLQZ3YY2V7Sh1UWt5JzoNEcK6ZLo4ROrcqoJ6e26wT1X+J02X1Esta6mMbLmWv0TQF9BiJiuMcSfF48+VS+FdYlE0jB69MDaTsAuMF87ko4H42E57LdXiY8+p0ufpkeXpNRv0MRpUKlVeCwevA4vGpMGU3cTVJogX0G+2N6xJF2VJFfXSJolXq/yu/vwww9z3XXXAcqS5YyMDN577z3uuOOOGsfMnDkzaMJ66NAhmQomSgRGpPtSCuflKQK7yVTlV4Vb6WfIMOA46Ahrm3wBDdZcK0VvFwX5YKbuJpKuqRLdhVdIOyeRtATCFQNt6gKfajWHUvoCoBduQI0bFY6SCuLMh3GUduaEfhCtZKHRM5poBRZEyty5c7n00ksxmUx89tln/O1vf6O8vJx77rmn1r6UlZVRUVFBTExMjXPKAIXGJ1BI9y1ysViqUt75CqwHIoTAcdiBp8KDu9SN8AriB8fT6+NeVPyvgkP/OMSRV49gz7NzaOkhDi09hNqspvUlrYk/L564wXHEDYhD10rWgzqdkEK6JLr06IG1l4BvqjZZLrgCelQ5Sz7DptIFT7ysuVYOLjuIq9jlLwhhz7dz9O2jFLxRQEznGPQp+hqTOolEIokEa2WeYXP39nSsDMDdv79qv882aRO0ZNyTQfHqYqy5VgDcx90YzzJi7mFWcqVTMwd69dU1Ekl1kpKS0Gg0FBYWBm0vLCwMu3w4LS2t1va+58LCQtLT04Pa9OnTB8C/PVAINxgMdO7cmfz8/JDvazAYMBgM/tdlZWWRDFHSBPiEdJMJkpOhTRs4dgz27IG+fQP8qjAr/YA6bVMoH8xj9WDZZsF+wE7GPRkANYIdpE8mkTRjQhUDPQUcqmgDgLbyJpsHNfZjVpIGCYrXwP6XrMSPOkabK9qc8r5JJJEwe/Zs/999+/bFarWyePFiv5DeEGSAQuPjc4/DCenOAieGDIN/JYGryIU110rF/yrQxms5/NJhSr8p9fsxMWfFcPbTZ5M5N5Pjnx7n2CfHOP7JcZxHnBz7+BjHPj7mf++YLjGYskyYs8yYepgwdTNhzDSiS9KFLFYrad5IIV0SdXxilQ9LeXDEgT+1S4CQLryC4tXFuIpdmLJMqFQqXEUubLtsCK8AodxR1LbRBk3q5MRNIpFEilXRxDGZoGNH5e9AIT1cVGf5z+UUvVeE1+5FpVch3AKP1YP9gB2NQYMpS2knIzMldaHX6+nfvz8bNmxgzJgxgBItvmHDBqZMmRLymCFDhrBhw4ag5cLr169nyJAhAGRmZpKWlsaGDRv8wnlZWRmbNm3izjvvBKB///4YDAb27NnDsGHDAHC5XOTl5dHRdzFImi2+1C4xMUrdwB49BN99pyL303z6tlbqF0DNAIVICeWDQVVRLluOjcMvH8Zb4cV1LLzQLn0yiUQCSiYZAI1eB063EpH+17tIH5VK6V/2UvhmIbvG7qLPF32IHxwf3c5KokK0AgsayuDBg5k3bx4OhwODwRC2L/Hx8SGj0UEGKDQFvoj01NQqIb2sLKAIe+UKY0OGAW+Fl9KNpbiPu9EmakkYmoDapA7px2jMGpKvTSb52mSEV1C+vZySr0uw/GihbFMZ9n12Kn6toOLXCo59dCyoT2qzGmMnI8YORvRt9RjaGTC0NSgrnlN06JJ16FOUTAtScG8+SCFdEnV8YpVGAx5PzaLs1XOkA9jz7dh22zC0V+4YCiGw7rbisXnQJesQDoH7uBsEmLJM2HJsFK8pxtTNJIUriUQSET7bZDYrtbagWkR6tZt8vqjOmE4xmLqaggryeR1ePHYPuKDwrUKKPyg+pZGZTZ1eQaZvaDpmzJjB+PHjGTBgAIMGDWLJkiVYrVZ/sa1bb72Vdu3asWDBAgCmTp3KRRddxNNPP82oUaN4++232bx5My+//DKg5LOfNm0ajz/+OF26dCEzM5PZs2fTtm1bv1gfHx/P5MmTmTNnDu3bt6djx44sXrwYgLFjx576D0ESMS6X4kuBIqSTm0sPu53v6EvuGz9BzkeIPdcArSMW0qtf33gJ8sECUalU6NvpKfmqBH26nriBcSGFdumTSSQSHz4hXe1V/Co3Khxt2qLSQrdXu+E86uTEpyf4ZdQv9Pu+H6Zupij2VhINohVY0FC2b99O69at/UL4kCFD+OSTT4LaBPZFcmoIldqlvEwJwgRoN7kdJV+WYM21Ur69HE+Zh5iuMUErjOvyY1RqFXH94ojrV1Ug2VnkxPqLFWuOFVuuDesuKxW/VeA87MRr9Sp1s3bZau27SqdCm6hF10aHLlGHtpUWbSstmgQN2gSt4mPFadDEaZS/YzVoYjWozWrlb7MGjUmDOkYtfa9GQArpkqjjE6tSU+Hw4RBCeojULh6LB4/dg8Gs/Dh5Sj24il1oE7TKhE0PwiKUqHSVFkOGAVuuDXu+XaZSkEgkEeGL6jSbqyLSS0qgtBQSEkKvlvERKkJdpVNh6GBAY9ac0sjMULUkGlPEb+rzn+nceOONFBUV8cgjj1BQUECfPn1Yt26dP9dmfn4+6oDcsUOHDmXVqlXMmjWLhx56iC5durBmzRrOOeccf5sHHngAq9XKpEmTKCkpYdiwYaxbtw6j0ehvs3jxYrRaLbfccgsVFRUMHjyYL774gtatW5+6wUvqjS1gHmbK3w3Ll9HDORjoS67oDknfI0osQGvUR48A6WHOpBDq+ta20uI86sTQ0RD6ILeS3sp8jjmk0C59MolE4kMIZf4HoPIIBOBGjd2ubFPr1PR8vyc/X/Izls0Wfh7xM+esOYe4PnFhzyk5PYlGYAEoftbx48fJz8/H4/Gwfft2AM4++2xiY2P5z3/+Q2FhIeeddx5Go5H169fzxBNPcN999/nPMXnyZJ5//nkeeOAB/vKXv/DFF1/w7rvvsnbt2lPz4UmAYCHd5/Layqryopt7mokfHE/pxlLyn8xHn6LHkGHw17qChvkx+mQ9+j/oaf2HYB/a6/Biz7dj32fHccCB47ADxyEHzkNOnIVOXEUunEedeG1ehEvgKnThKnSd9OegNqpRx1Q9NDEaZVvlQ2VQoTaoqx5GNSp91TaVXoVaH/CsU/kfal3la33A3zoVKm3Vs1qnxtDegDa+5crRLbfnktOGOoX0SrEqsNioJk6DxqiIUdp4LV6HF+EWfkFLOIVykVZWS/ZVWvZYPE0/IIlEcloQGJFuNlflGd6/H3r1qplnuDoqtQpjByNHVx3F6/Bi6hk6BUKkkZkNifqOJI/xyYjdTX1+icKUKVPCRlx99dVXNbaNHTu21shxlUrF3LlzmTt3btg2Op2Op556iqeeeqre/ZVED19+dJVKoP/4QygupntPDfwCe48nQXw8IkYRoFTbNoO3T9higuGub9tuGxW/V6BP1WPMNNY4zl3qBkDbKvQ0Q/pkEonER3ExOJ3K38KtCFpuVDgcVW20sVrOXXsu287fRsVvFWzpu4XEKxLpMLMDrYa1OvWdlkSFaAUWPPLII6xcudL/um9fpTjul19+ycUXX4xOp+OFF15g+vTpCCE4++yzeeaZZ5g4caL/mMzMTNauXcv06dNZunQpGRkZ/POf/2TEiBFN9nlJahIopPtW79nKhH+/SqdCpVahTdCijlGjb6sPEtF9NJYfozaoMXUxYepS+yobj9WD67gL93E3ruMuXMdcuEvcuEvceEo9yt8WtxJsWubBbXHjtXrxlHv8D6+96oaB1+5VXp84qe6fFFnvZJFyQ0r0OnCSSCFdEnV8YpUvvVl1Id2fhzhArDJ2MGLqbsKyzYImS6PcGdOqFNFdD+4yN4Z0A9oE5V/cY/WgNqqVJckSiUQSAYFCOihR6UFCeoibfNWpnoYqkPpENDQk6juSPMYnk16hqc8vkUjqjy8i3RQjUO3ZDe3b08aihHaWOpQIcq9XsVmqIwcgPz9kccHaru/YAbE48h1YtlgwdDQEXd9CCFzHXGgTtWFvMtbmk8k0URLJmYUvrUtykoBi5W83Kn9Eug99ip7eX/Tm9wd+5+i7Rzn+yXGOf3Kc+PPjSb42mdZ/aI35XLO0F6c50QgsWLFiBStWrAi7f+TIkYwcOTLsfh8XX3wx27Ztq7OdpOkILDbqSzlvswQL6VAzaLM6p1pb0piV1Cy0b/g5hFfgrfDisXnwWBVh3VsR8LB78Tq8VdsdVQ/hEHidlX87RdWzK+C1SyBcVduEO+C1q/J15TbhFsp4WjBSSJdEnbqE9FDpE1RqFUnXJGE/YMeWY0PfTo8uUYfjoAO0oDVrMXU3gUqZ1DkOOojrF4exQ83IKYlEIqmO210VIeUT0jt1gq1bq/KkBxYbDUf1NFTVqS2iwScoBRYvNXSIPOq7sUT8cDT1+SUSSf3xRaTHGLxgt4PZjNmuGDOrUw+A8FbWdXDbazpdldR2favVamL7x2LJtmDZbMHUzeRPWeU46FCCHbqacBx0oIkPLo5Vm08m00RJJGcePiG9Q9tgIT0wIt2Hsb2RrP/LotO8ThxYfICCFQWUfV9G2feKIqZL0tHq4laYe5kxZ5kx9TQRc1ZMrQEPEonkzMDthqIi5e/UVCWtFFQT0jWKv1I9aDNSP6Y5o1KrqgT55Gj3puUjhXRJ1KlTSA+RIx2UHMQZ92T4J11qgxqVSoVKo8KcZUbbWou71I3joAN9kp6kMUlRiVKQ0VUSScvDZ5cATJWr7Tp2EICKvJ+KIM+KCLFapjoNjWjwCUqBxW6MZxnRp+pRxasiivo+GRE/Epr6/BKJpP74hfQYlASgVitmvZJP0+ZSCmX5hXSDrqraVjXqur6N7Y24Cl3EdInBfcyN85ATtVFNXL84ksYo1ZkPLjuILceGIcMQJLSH8slkmiiJ5MzEJ6S3T/fCL8rfHtQhhXQfprNNdHupG53mdOLo/x3lxIYTlHxTgqvYRdH7RRS9X1TVWAOGtgYMHQwY2xsxZBjQpejQJevQp+jRJVUW7WutVdI56KXoLpGcjhQVKeK5Wg1JSVVzPXt5VapOn2BePWgzEj9GcmYhhXRJ1GlIRLqPwIJ+HosHZ6GTsk1lVOypoGJvRdCkzjcBO5XC9ukSXbVgwQI+/PBDdu/eTUxMDEOHDuXJJ5+kW7du0e7aSfHCCy+wePFiCgoK6N27N8899xyDBg0K2/69995j9uzZ5OXl0aVLF5588kmuuOIK/34hBHPmzOGVV16hpKSE888/nxdffJEuXbr423Tq1In9vpDmShYsWMDf//73xh+gpMH47JJaDQYDkJtLx18PAn9k/xe/gXgRkX8ZkFFrRHpDIhoCBSVNvAZUoG2jxVngxFPmIX5wfFXl+DgNpRtLKd1YSsLQhCBb1tTLEpvbskeJRBKQ2iVOA927w7ZtmDsq+WMr3Dq8QoXwVE4UO2VAhw4hzxPJ9a1P0ZMxNQOVWhXSpwoMdvAJ7bF9YokfHI9wCyryKvy2T6aJkkjOTHxCeru0qqjQUKldQmFoa6D9ve1pf297vE4vZT+WUbaxDGuOFdsuG9ZcK16rVynid8BBGWV1nlMdo/gtmjgN2jgtmlgNapMajbny2aQJLtRXWYjPX4TPULMIX9hifL4CfNUfmqpn1NRYFSSRSOqPLz96SgpoNBAfr7x22UOvMK4etFk9YKAlaTmSxkcK6ZKoU11ILy8P3u8X0msp6OdPG3AutL60dVih/FQK26dTdNXXX3/NXXfdxcCBA3G73Tz00ENcdtll5OTkYDa3jDFU55133mHGjBksX76cwYMHs2TJEkaMGMGePXtISalZ+GLjxo3cdNNNLFiwgCuvvJJVq1YxZswYtm7d6i9as2jRIpYtW8bKlSv9ld9HjBhBTk5OUNGauXPnBhWgiQsTESiJHoH50VW7c2HZMjoezwT+yH5ve0hKQmw6CmSgKgtfqaW+EQ3V8xK7jroQHoG2lRZNnAZXkQvbbhsmYcK6x4rrqFJsJv/JfBKGJATZsqZelng6LnuUSFo6VRHpKrjmGjhwANP/dvj324ptiAol1Yv6ovPDFhqN9PqO6RQTnCPdqwjkPh+s/QPtcRx0BAU7FL5RGOSDxQ2Kk2miJJIzFL+QnhIspNcWkR4KtV5Nq2GtgoqPCq/AecSJ/YAdR74ipjsOOXAVuXAWOXEddeEqduEudeMpU1bP+fIFu466TnZojYeaKoFdowINVX+rK/9WK9vTbk0jc25mtHsskTQ7AguNQtWCPC2RB23K7AISH1JIl0QVr7cqeqqu1C6R5rcLEtYDOJXC9ulWhG/dunVBr1esWEFKSgpbtmzhwgsvjFKvTg5fNfUJEyYAsHz5ctauXctrr70WMjp86dKljBw5kvvvvx+AefPmsX79ep5//nmWL1+OEIIlS5Ywa9YsRo8eDcAbb7xBamoqa9asYdy4cf5zxcXFkeb7h5c0S3x2yWwWsHo1FBfT6dxekA37S1tBfDyitZJgTr3vV/AOCCtIRRrRILxCiS7PLkWfokeFKqiQstqgRhuvxXHQgbPIiXBXbmulRZ+ir2HLmnpZolz2KJE0P3y2KyYG6NED7rmHmA9Wwxplu/WoFaFXfCTV2Z3Cnqch13dtwQoqrYqiD4pC+mCW7RY8ZR4MHWWaKInkTMMnpKdXCuleFSDqL6SHQqVWYWhnwNDOAOfV3tbr9uIp8yiiusWDx+LBbXHjKffgtSkF+vzPFQEF+ex1FORzKdt8hfiESyAcLrwVLuVvoUIINYha5rlelOJ9iPBtKnGfcNfzU5JIzgwCC42CsuJYpwNNfYI2JZJKpJAuiSq+yCkIFtKFAF9QUiQF/WrDFx11aPkh7PvtxA2I80/8mkrYbilF+CwWC2VlVcscDQYDBkPoiWwgpaWlACQmJjZZ35oSp9PJli1bmDlzpn+bWq1m+PDhZGdnhzwmOzubGTNmBG0bMWIEa9asAWDfvn0UFBQwfPhw//6EhAQGDx5MdnZ2kJC+cOFC5s2bR4cOHfjTn/7E9OnT0WrDm2OHw4EjYEZhCVMcTtJ4+CPSDW7YvRvat6ejTvm/P2qNpcKlrcozfLwI8vOVaqRhqCuiwSdAlWaXUr69XBHH8/TEdItBl6TDecSppHPRKZMkdawafYYed7EbQ7oBQ4YBPfoatqyplyXKZY8SSfPC51f5ajvQowfqh7phesKLrUKN7Z6/4/3pABxz1FrfAep3fdcWrFCRX4HGqAkbXGD5yYKz0Imn3IM2QaaJkkjOJPxCerIy3/Oq1eAhotQujYlaq0adqEaXqGvaN8pVVjlSXAzt2ytLH61WRP4BRGIy4m9TEGd3RbiFEkzmAeERysMtFFHd99pT9dr3rEtp4v5LJC0UX0R6amrVtrg40B4PH5EukYRDCumSqBJY0M+XTcNT6TzFVOrLdaV2qfX8leKUZbOFsi1laEwahENg6m7y5xhuCmG7pRThy8rKCno9Z84cHn300VqP8Xq9TJs2jfPPP9+f0qSlUVxcjMfjITXwlxRITU1l9+7dIY8pKCgI2b6g8lfZ91xbG4B77rmHfv36kZiYyMaNG5k5cyZHjhzhmWeeCdvfBQsW8Nhjj0U+QMlJEySk2+1gNtNKbcesc2J16TlkicfrVaKHVF5nzaU0IYhktYwuRYe2tRaVVoXjiAN3qZuYLjF4Sj24ipRlxl6nF22MFnexG41JifhEBSqCbZmxgxF7vh3hFiSPS0aFCo+18ZclhrpJYMgw4DjooHxH+SlbBnm61nKQSOpDULFRH2o15liwVYC1dQbClQ9ENmmMZFlzXavwLD9ZcB5x0uriViGDC2K6xuA84sS2x0bcwLjTPk2UtFUSSRU+IT21jaAEEBoVeGiUiPRmh9frX+VIVlZV1Fh8PKqeWahycuC/H8GDD4Zd5SiRSBpG9dQuECykR5r9QCIBKaRLooxPrIqJqSr4AIomVUNIr+ddwkBxSh2rFInRxGn84lRQwb5GFrZbShG+nJwc2rVr538dSTT6XXfdxc6dO/nuu++asmunLYFR7b169UKv13PHHXewYMGCsJ//zJkzg447dOhQjZsgksbFZ5tMZhUYjWC1ooqPJ8Fox+rSY3HoifEV7NOpqxLt1ZPqAhSAPc+O84gTbZIilruOuogfFI91txVrrhUhBKjAkGYIuikIVbas/Odyjq46GjLFQlOsggm8SWDNtXJg0YFTXmT5dKzlIJHUF3+xUVPwdrMZiooU21Zfv6quZc11rcLTtdFh22Xzp+qrjjZWiz5VjzZWe9Jpok5lQfmGIm2VRKJQUQHHjyt/pyQGCOmcpkJ6fr5/lSPVC4iqVJCRoUSs17HKMSRer3KcxaL4pB06SDFeIgkglJAeHw9uTi77geTMRArpkqgSWNBPrfavbsNiqYpQ90286mPcqotTnlKPUhldrUKXrPMX7EtISgBV7cJ2QyZlLaUIX1xcHPGBdzDqYMqUKXz88cd88803ZGRkNGHPmpakpCQ0Gg2FvmRplRQWFobNXZ6WllZre99zYWEh6enpQW369OkTti+DBw/G7XaTl5cXNhqtesqdwHQ8kqbBb5sSDdC9O2zbBllZxOmVIn0WpwGj4nehSktWJiwNIJQAZe5uxlPqwV3sRm1Q4zzqxNDJgDZRS8KgBNxlbmI6x2DIMEA1U+SxevA6vBS9V4TX4T3lhY6jWWT5dKzlIJHUl5AR6VQJ61Yr6OtZe6Yu6lqF50vX4i5xo2tTM+2Ax+pBn6In9dZULD9aGpwm6lQWlA9FpOnypK2SSBQOH1aejUaIMyl2SWgUu3SqU7ucEiwW/yrHkJjNSoh+fVM45uYqke67dyvnNxoV3/Waa5RaGRKJJGxEuqWWYqOhaAk37CVNjxTSJVElUEgHxZj5hHQfvsip+kz4qotTmgRNUJ5hbbzWX6Vdk6AJK2w3dFJ2uhXhE0Jw9913s3r1ar766isyM1t2NXi9Xk///v3ZsGEDY8aMAZSUNRs2bGDKlCkhjxkyZAgbNmxg2rRp/m3r169nyJAhAGRmZpKWlsaGDRv8wnlZWRmbNm3izjvvDNuX7du3o1arSfHdOZI0C6psk0qZiBw4ADk5xGmVcM/yEy6STriAdFR9ejY46ieUAKVL1hE/WIlAdx114S5RotIThibQ5uo2HPvoGJZtFvQoBUl9eL1erLuteEu9eJ1eJUVCI9WDiMRpbMoiyw2p59DSazlIJA0hqNhoAD4/y2oF3UmkzAtFXavw0II2UYvrmAtjZ2PY4ILWl7am9aWtGzRBjeZNPB8NSZcH0lZJzlx8aV3atQPhroxOOJ0j0uPi/KscCRXIZLVW3lWoxyrHMDnX2bZN8V3vuUeK6RIJVcVGq+dItxG5T3QqbthLob5lIIV0SVQJJaQXFAQL6f5io/WY8FUXp1QqlT/K01XkQhOnwev04jrmwnEotLAdblJWtrWM8l3lpNyQQmzv2LDGLdIiXS3BWN51112sWrWKjz76iLi4OH/O74SEBGKqz9ZbCDNmzGD8+PEMGDCAQYMGsWTJEqxWKxMmTADg1ltvpV27dixYsACAqVOnctFFF/H0008zatQo3n77bTZv3szLL78MKP9j06ZN4/HHH6dLly5kZmYye/Zs2rZt6xfrs7Oz2bRpE5dccglxcXFkZ2czffp0br75Zlq3bh2Vz0ESGp8YZTajTEDuuQdWryb223IALMddiIQ0OADqjPTwJ6qDcAKULllHQlICjgMOXEUuOjzYgYShCajUysqa6jfp7AfslG8p90ekaxO0CGfj1IMI5TTGdI0h/rx49Kl6v91qyiLL9RWoTodaDhJJQ6hRbLQSn59ls0H8SRZxr05dq/Cch5T86N4Kb0TBBfW1D015E68+NCRdnrRVkjOZICHdWZn6qXK+d1pGpHfoELTKMSi9ixBw8CD06xf5Ksdacq6TlQU5ObBmDXTrJtO8SM54wkWkH4swIv1U3LCP9so6SeRIIV0SVUIJ6VAtIr0BqV1CiVOBUZ7Ow068Ni8eq4f4gfE1lg2Hm5QJh8B93E3F/yqw/mIltk8s5h7msMatriJdLcVYvvjiiwBcfPHFQdtff/11brvttlPfoUbgxhtvpKioiEceeYSCggL69OnDunXr/MVC8/PzUQc4nUOHDmXVqlXMmjWLhx56iC5durBmzZqgie8DDzyA1Wpl0qRJlJSUMGzYMNatW4fRqKx0MBgMvP322zz66KM4HA4yMzOZPn16UP5zSfOgum2iRw/o1o24L+xQCJYrbsSbfQR2HjspMao2AQqUm4IJQxP8IjrUvElny7Vh/92OSq/CfK6Zit8qGq0eRCin0Z5v5+jbRyl4o4CYzjHoU/SYupsw9TQ1WZHl+gpUspaD5EwlXGqXwIj0hvhVtRHJKry2k9oC1Blc0BCa8iZefahvujyQtkpyZuMTttLTq1Yg+4T00zIiXa0OWuVIRkZVBPnBg5CUBGPGRC56N2XOdYnkNMJuh5IS5e8axUapO/vBydywjzRosjmsrJNEjhTSJVElIiG9AcVGw4lTumQd8W3iKd9cjqmLiYypGRg71TRmoSZlriIXZZvK8Ng86BJ1CLdArVfXMG6hjGWoiVtLMpZChC4Q1tKZMmVK2FQuX331VY1tY8eOZezYsWHPp1KpmDt3LnPnzg25v1+/fvzwww8N6qvk1OIvNhoY1alWE5uibCg3JiE8SnLPk0mP0NA0UL6bdBV5FRxcehDUEDcgDk+ZB/t+e4PqQVQnlNPoKnIpRQO9AgRK9HsbLZZtFsp3lSMcIqIiy/VdiVMfgep0qeUgkTSE2oqNAljLBVTey2qs1C4Q+Sq82oILIiGU7agrR3tjF5RvLKStkpzp+HytuLiqFchoFTHLYRdU5DXvFbsNImCVI7t3K2H5RqMSiT5mTP3SsDRVznWJ5DTDl9ZFr4dWraq2x8WBJoJiow29YR9p0GRzWVnXUlmwYAEffvghu3fvJiYmhqFDh/Lkk0+GrT/XGEghXRJVqk/4ahXS6zHhq0uciukYQ9vJbYnpHDoyqfqkTAiBdbdVEdGTdSDAfcyNSq/ClGXyGzfhFRz76Jg0lhJJC6dGRHolgTaqITf5QhGpAFUdX5oXzwkPpu6KrYi0HoQhw0BFXkWtE9TqTmN1O+hboYMAU5YJ6y4rHrsHR74DTc/wRZY9Vg/5C/MbfSXO6VbLQSJpCHUVG7WVVd0Yb6yIdB91rcIDxW41NCo83IQ0blBcrTna63MD8VQgbZVEohBorwLnex2w0je3mLxHmveK3QZTucqR/HzFoYyLU9K51Df9SlPkXJdITkN8QnpaWvDijfh40ESQI70hN+zrEzTZXFbWtVS+/vpr7rrrLgYOHIjb7eahhx7isssuIycnB3O4G40niRTSJVGlqSLSoeHiFNRMDeMp9eAqdqFN0KJSqfA6vKi0KtQGtd+4lf1QhnWXFa/DK42lRNLCqUtILy9vPCEdIhOgQtGQehDmc80cWHSgViFbeAW23TYcBQ40CRoQ1LCD6EFYhBKVrtJibG/E/rsdtVEdNrrefK6ZQ88fapKVOKdjLQeJpL7UVWzUZqkS0utTxD1STkYoD4UvAr3853KK3ivCa/di6BBsOyryK9AmanEccITM0R6uoHzg+X1215BhwHHQ0aRRsNJWSSQKgQFVPp9KLQTXcpCUUhfapOa9YvekUKtPPt1KY+dcl0hOU3xppAILjUJwapfa5nN1FVWvvuq2Iq+CQ8sPYd9vJ25AnN+PCBc02VJX1jU1FouFsrIy/2uDwRAyvee6deuCXq9YsYKUlBS2bNnChRde2CR9k0K6JKrUJ0d6QyZ8DRWnqqeG8Tq8CLdApVOiMt1lbgzpBrQJyiWkNqmp+L0Cfbqe+CHxdUaYS2MpkTRvwgnpsbHKs8USkGe4kdIjNESAqm89CPO5Zo6tPVZrEWWNWUPZpjLKt5Rj22PDccCBoa1BiUKvtIOgFAbz3VAExW6pDWqSxyZj22WrcQOzzdVtOPbRsSZbiXM61nKQSOpLXcVG7dami0hvbHwR6NZcK+Xby/GUeTCeZUSfqkcVrwqyHZoMDbo2unqlyKoe4S4cAk+FB02MBpVB1WRRsNJWSSQKoSLS1XY3Cbg4ojehjZcrdv14vaEj2Bsz57pEcpoSqtAoRC6k11VUvfqqW8tmC2VbytCYNAiHwNTd5K9XFSposj5CfXXqmy6zJZGVlRX0es6cOTz66KN1HldaWgpAYmJiU3QLkEK6JMpEIqT7cuY1dMJXX3HKZ4xMPU1Yc6zYdtnQxGv8ArjX4UVjUiZXVHbJecSJx+rB2N4YUYT5yRhLiUTS9PiipCJJ7dIUUZ2RUp96EIYOBg4sOlBrEWXLjxZQgVqvJrZfLDGdY3AccmA/bMd51AnuynHrqXFD0We3YnvHknRVUg2nrqlX4pyutRwkkvpQV7HRCovXv60xc6Q3NoFLojXxGlCBto0WZ4ETT5nHX0TZZzvcx9yk3pqK5UdLRKsQqy+5VleoKf2+FPdxN9pELQlDE1CbatbBaQxaqq164YUXWLx4MQUFBfTu3ZvnnnuOQYMGhW3/3nvvMXv2bPLy8ujSpQtPPvkkV1xxhX+/EII5c+bwyiuvUFJSwvnnn8+LL75Ily5d/G2OHz/O3XffzX/+8x/UajXXXXcdS5cuJdZ3Vxv49NNPmTNnDrt27cJoNHLhhRfy9NNP00kWV2z2BEak++d7Li9HMRDvadoVuy1KfMrNrcqpbrcr6Vq6d1dE9MbMuS6RnKb4UrvUFpFe23wukppWgatu1bFqNGYNmjgNjiMO3KVuv98CNYMmIxXqq6+sizQHe0slJyeHdu3a+V+HikavjtfrZdq0aZx//vmcc845TdY3KaRLokq9UrucgglfuOgkr1Nx7tzH3RjPMmLuYfYbQp9x05g16NP1Ic/bWMayLlqUUyiRNGPqikgvLz/5m3yNQX3qQVTkVdRaRFnbWourwIXaqMar8WLLsRHTJUZZQWPz4K5wo/KqcBW7QAtas9Z/Q7G63Qp1A1OuxJFImp66io3ayytFXDXN1j+oXkfGddSF8Ai0rbRo4jQ1iij7bIc+VU+Hv3eo0w+qfn6A8p/LEW6BobMBd7Gbil8rSBiWIKNgK3nnnXeYMWMGy5cvZ/DgwSxZsoQRI0awZ88eUlJSarTfuHEjN910EwsWLODKK69k1apVjBkzhq1bt/ontosWLWLZsmWsXLmSzMxMZs+ezYgRI8jJycFoVPzfP//5zxw5coT169fjcrmYMGECkyZNYtWqVQDs27eP0aNHM2PGDN566y1KS0uZPn061157LVu3bj11H5CkQYTMkQ7Y0WBy12x/Un5CQES3tdBA8SY9tj0VzV98ys2FZcuguBjat6+KON+2TYlEv+eexsu5LpGcppSUKM+tWwdvj7TYKNSeNrj6qltPqQeVTqllpUvW1fBbqgdNRiLUV19ZV58c7IG0JL0oLi6O+FD1H2rhrrvuYufOnXz33XdN1CsFKaRLokp9Urs0tVgVzhjZ8+2oDWra3tGWsh/K8Nq9qPQqhFtUGbdkPZoYjSJGRRBhHs5YusvdVOytQBurJW5gzcIwtRm+0/2OpERyKvHZpupiVFMUGz1ZIq0HUVcRZW+FF2+FF12KThHVi1y4jrqIHxSPdY+SJsZd6kZtUKMxaDBnmdG21uIuddeaPsGHXIkjkTQ9dRUb9aV2ibbdqo3qq1fUBjUqrQrhEqgN6qAiytpW2iDbEckqxOrnd5e4/fUf1Gp1jSLNmjgNpRtLKd1YSsLQhGY74WxKnnnmGSZOnMiECRMAWL58OWvXruW1117j73//e432S5cuZeTIkdx///0AzJs3j/Xr1/P888+zfPlyhBAsWbKEWbNmMXr0aADeeOMNUlNTWbNmDePGjSM3N5d169bx008/MWDAAACee+45rrjiCp566inatm3Lli1b8Hg8PP7446grRcP77ruP0aNH43K50Ol0p+LjkTSQoBzpzsqbfFoVRjy43Y3oJwREdFuPxnDw9z649MkY+rfH0C25+eZg93qVfhcXB+dAj49XXufkwJo1iojeGDnXJZLTlMpMHyQkBG+PtNioj3Bpg6v7FZoEDbokHc4jTnTJuiC/Qh2vxrbHhqmLCbyKvqNSq+pV3696QECk6TJPd71oypQpfPzxx3zzzTdkZGQ06XtJIV0SVZqy2Gh9qM0YmXuaseXY8Fg8dJjZgWMfHQt7F7I+EebVjaUt14az0KnsTIXCNwqx/GjxG7baDB/QoDuSEokkNJFEpDd2jvSTIZJ6EHUWUbYrERlqo1JE2ef0qc5VkTAsAfcxJUoz6bokXEUuKvZUULG3IuIizk21EkcikVRRV7FRh7X52C0IHSBQ/aZf9QmpSq/yFzpuiO2ofv7AOjiA//yOAgeuHcoNRXeJm/wn80kYknDaTDgjxel0smXLFmbOnOnfplarGT58ONnZ2SGPyc7OZsaMGUHbRowYwZo1awAlkrygoIDhw4f79yckJDB48GCys7MZN24c2dnZtGrVyi+iAwwfPhy1Ws2mTZu45ppr6N+/P2q12p9fvry8nDfffJPhw4eHFdEdDgcOh8P/2hI46ZCcUgJv/HlLK32QOC0pRQ6OuTX4c2hyEn5CQES3yGhP8YEsXMRg8vyOalcBxA5Gm5zUPFef5Ocr6Vratw8uJArK64wMZXz5+VJEl0hqIZyQHmmO9ECq37AXXoFttw1HgQNNggaEkorK3N2szLWKXGjiNHidXip+r8D+u125ceiFvEfzgoTsSOv7NSRdZkMj2FsCQgjuvvtuVq9ezVdffUVmZmaTv6cU0iVRpbmkdonUGKXclBJ22bBKrarXchyoEr9OfHGCIy8fATWYupnQxGqCDFubUW1CFgi0bLNQkV+BxqhpsgJ+EsmZSDghvTlGpPuoKxKzriLKHpsHdYzabycCxSqtSotKp0KfqifxskR/9EWkywJD1Z4wtI/MTkokksipq9io0xb92g4+wgUIxA2KC7rpV31CqjaoQQ1ep5KCqr62o/pNxcCId5VBhXAKhFtgy7Eh3JVR8K206FP0p8WEs74UFxfj8XhIrZZcNjU1ld27d4c8pqCgIGT7gsqKb77nutpUTxuj1WpJTEz0t8nMzOSzzz7jhhtu4I477sDj8TBkyBA++eSTsONZsGABjz32WF3DlpwCgiLSixXbpE03UPq7jnSnDXfpSfoJ1SK67aUx2I7HYmjjQqVPhqIiRahOOr/Rc7A3ChaLkhO9ujPqw2xWcqLLm0ESSa00ppAeiM+PsWy2YNtjw3HAgaGtwV9cNH5wPNbdVat6rTusaMwa4ofEY+gQWsiOZGVdfdNlNjSCvaVw1113sWrVKj766CPi4uL8PkJCQgIx1SNLGonoe9GSM5r6FBttykmfzxhpzKGXCmrMGrx2Lx6Lx2/cYs+NJaZTjN/Y+CLM4/rGKZGbeytwH3MT1y+Odve0q3XCZfnRghCCuIFxSnSoRokGNWWZcBY5OfjcQVxFiuHTxgfvd+Q7KPm6BENG3XckJRJJZNRHSG8OglQk+FJK6ZJ02HJseB1efxFlV5ESmW7sZMRd5kYIgXAKVFolrYIvEszUwxSUA726HQyFNddK/sJ88h7Jo/CtQjwWD+4yN/bf7fWykxKJpG7qjEi3Rb+2A1RFRlm2WdAmaTF1M6FN0mLZZuHo+0fRJmpxHHD4C3P6JqT6ND2u4y5ASQVRX9shvALhFWhaa7Dttil/V0a8u0vdeL1eXKUuvG7lRqM2SYvX4UWfoseQYVBythe7KF5TjPC2zKKhpxMFBQVMnDiR8ePH89NPP/H111+j1+u5/vrrwxZ1nTlzJqWlpf5HTk7OKe61xEeoHOnaNjo+JINfqf98qgbVIro9Dg0etxqNrjLHusGg5Bk/cACECJrvNQvi4pTCoT6ntDpWq7I/rmY60MZgwYIFDBw4kLi4OFJSUhgzZgx79uxpkveSSJqS2oT0qtQu9ZvPBfox+o56YjrHIDwC+2E7ZZvKcBW5FN/l/HgMnQzEdIpRos+vTcKYaQzSc+rrVwQGBISiehqs+kSw14bwCiryKijfUU5FXkWz8YNefPFFSktLufjii0lPT/c/3nnnnSZ7TxmRLokqzSVH+snk7q2+LLn9A+1xHHREHKlZl2HTxmsp31pO60tah9yva6PDtsvm/5xqjE0W8JNI6o1PjKo1tYu5eaVIiITAlFLWXMUABxZRBvzOn9flxZihLJ9uSNQn1F17IuWGFGJ7xzbrQjfNgRdeeIHFixdTUFBA7969ee655xg0aFDY9u+99x6zZ88mLy+PLl268OSTT3LFFVf49wshmDNnDq+88golJSWcf/75vPjii3Tp0sXfplOnTuzfvz/ovAsWLAiZD1nSfAgXke577bBFfyVNJJFRmgyN4t8ErPJT6VVoE7W0Sm9F8g3J9bYdgRHwjqMOHL87cOQrqSJMXUy4ilw4fnegjlWj0ig3Ed3FbjQmjb+wsopmGLXaxCQlJaHRaCgsLAzaXlhYSFpaWshj0tLSam3vey4sLCQ9PT2oTZ8+ffxtjh49GnQOt9vN8ePH/ce/8MILJCQksGjRIn+bf/3rX7Rv355NmzZx3nnn1eibwWDAYKiK4isrK6t1/JKmIygi3SekG9XkY+ZfHhP/mHuSBfGqRXRrDB40Wi+eUgfasgJlImq3w3ffQ/v2eNp1RW2Mbz61Wjp0gO7dlcKigTnSAYSAgwehXz+lXRPw9ddfc9dddzFw4EDcbjcPPfQQl112GTk5OZjDRclLJM2Q0lIBqEg49jvkqf3FeAOFdA+R25dQfow5y6wEZ9o8uEpdWHOsxPaOxXHIgT5Rj0fvwXhWTTvWkNUw9U2XWd8I9lA05/zq4W6cNyUtI4xOctrSXHKk+4xRYPST//2rRWIGEhhpmTcvj7xH8jiw6ADeCm9EkZpQdzS8b7kxIXYLIUClRO07DjkghA2RBfwkkvohRGQR6b7VMtGO7Kwv5h5mOvy9A5nzMsl8PJOECxLQJSp5h7WttZh6Ksv61Cq1IiQdb1gkWHUnM3A1jbmnGeFUUidIEb123nnnHWbMmMGcOXPYunUrvXv3ZsSIETUEJh8bN27kpptu4vbbb2fbtm2MGTOGMWPGsHPnTn+bRYsWsWzZMpYvX86mTZswm82MGDECuz04EmXu3LkcOXLE/7j77rubdKySk8PtBpcSrB02It1VEf0bgJFERrmPuUm+PrnGKr/4/vF0nNWR5NHJEflYvuipoo+K2D9/P5atSgR8/MB44oYoBr30h1LseXZiMmMw9TRhSDfgrVAi0g3pBuIHx6NLrsq33eyiVpsYvV5P//792bBhg3+b1+tlw4YNDBkyJOQxQ4YMCWoPsH79en/7zMxM0tLSgtqUlZWxadMmf5shQ4ZQUlLCli1b/G2++OILvF4vgwcPBsBms/mLjPrQaDT+PkpOEq8X8vJgxw7luZE/06Ac6ZU+ldaoXNNurwpdRmQr38JSLaLbmGDHFFOEI9+OsJQrBTqNRjCbEYeP4PhuD6Y25c2nVotaDddcA0lJSmHR0lLF0JeWKq+TkmDMGKVdPbBYLJSVlfkfgTUDAlm3bh233XYbPXv2pHfv3qxYsYL8/Pyga1Iiafbk5lJ6SLEBCf/3IjzyCCxcCLm5mM2gQ7E97noI6aH8GP/KuXQ9aq0a++927Pl24vrFkXxDMiqDKqLsB5FQfZWxu9SNcAvcpe6QwU/1jWCvTm2rCA8uO+gPzjqTkEK6JKo0lxzp9TVG0HgGpS7D5i+AVW23q8hF6XellP1YhtfmpWxTGSXfluAqclUdW8tNAIlEEhqHo2quWD2q0xeR7nY3vxzp9cGXmiV5dDIdH+5IXL8qsUolVKTclEKXf3ThrMVn0WluJzo82KHe0QaNtYzwTOeZZ55h4sSJTJgwgaysLJYvX47JZOK1114L2X7p0qWMHDmS+++/nx49ejBv3jz69evH888/Dyi/C0uWLGHWrFmMHj2aXr168cYbb3D48GF/IUAfcXFxpKWl+R8yAq154xOloBYh3R59uxVpOj19qp4Of+9Ap7md6Di7Y71tkS/YYd/sfeybtY/Sb0txHXchHAKVRkVMZgxtrm1DzNkxmLqZOPvZs+m1theZ8zKJ7RNL/KB4EoYlBInocGYGKMyYMYNXXnmFlStXkpuby5133onVamXChAkA3HrrrUHFSKdOncq6det4+umn2b17N48++iibN29mypQpgPIbMG3aNB5//HH+/e9/s2PHDm699Vbatm3LmDFjAOjRowcjR45k4sSJ/Pjjj3z//fdMmTKFcePG0bZtWwBGjRrFTz/9xNy5c/n111/ZunUrEyZMoGPHjvTt2/fUfkinG7m5itj0yCMwb16Q+NRYhIxIN1TZpiB9tyGivi+iuzJ1iwpBEt+hoxQbnXA7dQhTLG59a2yqTuhVpSTxPapQkUnRokcPuOce6NsXjh2DvXuV5379lO09etT7lFlZWSQkJPgfCxYsiOi40sr8GImJifV+z8bihRdeoFOnThiNRgYPHsyPP/5Ya/v33nuP7t27YzQaOffcc2vUTxBC8Mgjj5Cenk5MTAzDhw/n119/DWozf/58hg4dislkolWrVjXe4+eff+amm26iffv2xMTE0KNHD5YuXRrU5quvvkKlUtV4+HI5S5qIymLDpRXK73hC1zTlBtS2bbBsGeo9ucTolevd5Y3cLwrnx+iSdSQMS6D1H1pj6m6i7aS2dHiwA7G9Y09KyA5FfdIKNzRoFGoPjDqT093J1C6SqFJdSA9Mm+D1KjfYmzq1iy81i3ALkq9LpmxTGRV7KnAecqI2qonrF0fSmOAlKydTsKF6KhhDhqHWpTnuMjcxXWJwl7rRZ+hRqVS4ilyUbSrDY/OAB2K6xuC1e6nYW4Gr2EXC0ATUMWpZwE8iaQCBqSjDpXYB8DqjL0g1BpFWiK8vjbGM8EzH6XSyZcuWIIFKrVYzfPhwsrOzQx6TnZ3NjBkzgraNGDHCL5Lv27ePgoIChg8f7t+fkJDA4MGDyc7OZty4cf7tCxcuZN68eXTo0IE//elPTJ8+Ha02tOvocDiCotossvjZKSdQSDdWmwtVj0iPZm2H+qTTi6ToVigC00pp4jWgAm0bLc4CJ54yjz/KXK1WY+pmwn3MDWpQa9UkDE0gYUiCkvcUPSqC/bLqS6bPBG688UaKiop45JFHKCgooE+fPqxbt85fLDQ/Pz8oMnzo0KGsWrWKWbNm8dBDD9GlSxfWrFnDOeec42/zwAMPYLVamTRpEiUlJQwbNox169ZhDPjnfeutt5gyZQp/+MMfUKvVXHfddSxbtsy//9JLL2XVqlUsWrSIRYsWYTKZGDJkCOvWrWuyAmNnBJXiE8XFSn5xs1lxjrZtU0TpBgq41QmOSPeldgkW0s3myv6sXq3kO7fbFQPXvbsSrV1bP3wR3QcOKBHccXGYrTlkpJVQfKwHNpGBU5uK2q4jLr2cpIyjmI/tV3Krd+p00uNrNHr0gG7dlH5ZLErkWWVqioaQk5NDu3bt/K8DUx2Fw+v1Mm3aNM4///yg6/hU4luht3z5cgYPHsySJUsYMWIEe/bsqVGYGKpW6C1YsIArr7ySVatWMWbMGLZu3eofg2+F3sqVK8nMzGT27NmMGDGCnJwcvy1yOp2MHTuWIUOG8Oqrr9Z4ny1btpCSkuJPK7Vx40YmTZqERqPx3zz0sWfPHuLj4/2vQ/Vb0khUFht2HT1BhUf5H08wuyEmXkmVlJMDa9YQo7senOCsh5Bemx+jUqlQ6VToU/WYuitaUH1TsURKpHM4X9Co/YA9KGVeJIWc6xMYdSaku/MhhXRJVAkXke7bFxfXtAX9QuV6iukaQ+qtqehT9WGNUUMNSrjcUuZe5rCGzZBsIPG2RI6tPaZEx7fTY82x4i51gxa0CcoSZd/5K/5XQen3pcT2iQ15E0AikdSOzy7p9VBdM9RolMgpmy3gJl8LypEejoaKVbVxMrUnJArFxcV4PB6/WOUjNTWV3bt3hzymoKAgZHtf1JPvubY2APfccw/9+vUjMTGRjRs3MnPmTI4cOcIzzzwT8n0XLFjAY489Vr8BShoVX3Sn0VhTX/H5Wc3BbjXVhNJ/jmrBDq6jLoRHoG2lRROnwVXkwrbbRkJSAqhq3tQ7mQnn6cyUKVNqiEI+vvrqqxrbxo4dy9ixY8OeT6VSMXfuXObOnRu2TWJiIqtWraq1X+PGjQu6ASg5SSrFJ4qLg/NyxweLT3Tr1mAh1/c2vmxiJhOUOZUIc41BjUYDHk9lRPrJivq+iO7VqyE7G0pKMLcC0zlu7O10eGIFGoMHY4IdlUfAXnvw0ujmglrdaOJ+XFxckJgbCXfddRc7d+7ku+++a5Q+NITAFXoAy5cvZ+3atbz22msh67cErtADmDdvHuvXr+f5559n+fLlNVboAbzxxhukpqayZs0av13x+TYrVqwI2a+//OUvQa87d+5MdnY2H374YQ2bmZKSEjKqPRQyQOEkqSw2XJpSVf8n3lD5eapUkJEBubnEaBTb4/RE/pteXz+mKf2KSOdwgXWybLtttQaNBiIDo0IjhXRJ1AiVh9hkUvwEr7fqhrs/D3EjT/rCFcEr/7kcxyEHGfdkhDVKDTEo4d7Pss2C/YCdNqPaYP3FGtawxZwVQ/HqYiybLdh/t6M2qTG0VaLZfcuOE5ISMHYy4ipy0faOtiQMTTjjJnoSyckSLj+6j9jYYCE9mpGdzZmmFsskTUtgVHuvXr3Q6/XccccdLFiwIGT02syZM4OOOXToEFlZWaekrxKFcIVGA7dpiH5th8acUFZf5Wf8f/b+PUyq8k73xj9rrTpXV3XTdNNHmoNyVAICEUFn1IQZMDqRzIQdneytMW599R0SDXtrNC+iohniKUHUXxiTcas7snUcI0l0wpbBuLNnQCInRzl5QGga6KYPdHV1nWvV+v2xalVXVdexu/oEz+e6+iqoWlW1qrvqqee5v/dzf5ts/cwOslVO9JuRrTImt4lIR4SoJ4qpwpSxqDfQBadAMOaJi09MnJja3BJSxKfBuraTW3LY7eBJisuzWvV5VtBfIlHfcHTv2AGPPQYTJiA1NmKXJCBpG6LPp1cik51dAlatWsVbb73FH//4RxobG0fkHEZ6h16xeDyejBE48+bNIxQKcfHFF/PQQw9x+eWXZ30MYVAYJPFmwx7XOACc5jAmOR4JpWl6U5m2NmySHosbUgtfzw1kHjMa5hUD2YWcyxilaRqhUyHUgKpHI8e080Z7EkK6YMQIh3W3AfQJVpKki1Q9PX1mgKHIIR5MNAsU77Qs5Pl8H/mYeO9EQi2hjAObMfB1vdNF9KdRHDMcmCvNJPfFkCQJa72VWG8MU7npvBnIBIJSkk9Id7ngzBkNhjh2aqwjXJ2Dp6qqCkVRaGtrS7m+ra2N2trajPepra3Nebxx2dbWRl1dXcox8+bNy3ouixYtIhqNcuzYMWbMmNHvdqvVmiKw9/T05H5xgpKTHJOQjtWq60ym2OgYt0qxoMy2y89xkSPF7KCUK5irzIRPhzFX642VNa9GLBTLWdQbqtgrgWBUExefsk6CnE44eXLQrm1jBw3oY5aWQUgPHTtdOlFflmHJEli8WHezp6Np0NKiZ483NQ3qtZ0raJrG9773Pd58803ee+89pkyZMmLnMpI79Iplx44dvPbaa7z99tuJ6+rq6ti0aRMLFy4kFArxy1/+kquuuopdu3Yxf/78jI8jDAqDJN5s2NOtjy0JN3p7uz6unDoFfj+2oD6WhZpPwTEKjk0ayDxmNMwr8jnYC40hjrRHEmkIJreJU/9wCs8fPVR94/wwGwghXTBiZMshdrnShPQhEKsGm/VUrNOy0OcLtYRyDmySLOGY6cBaa9V/Hxl+JSIuQSAYHMbiLpcjXU5qRHUuRLsMFaPBfTGWsVgsLFiwgO3btyca8MViMbZv3541YmHx4sVs376du+++O3Hdtm3bWLx4MQBTpkyhtraW7du3J4Tznp4edu3axZ133pn1XPbv348syyLPcxRjjF2ZhHRJ0sc0xTs6hHQY3IIy1y6/3gO9aCEtYXaQJAnnTCeqRyXSHkG2yiBDLBzL2lDeYChirwSCUU1cfMLn053f6ZTItW0U/sxmPUYvWUg3YvKDXf7SivrpmemNjX1RMS0tehPCFSsGFVmTIBYrWab5SPF3f/d3bN68md/85je4XK6EuFxeXi56EGTh448/5vrrr+fBBx/kL//yLxPXz5gxI8WEsGTJEj7//HN+9rOf8T//5//M+FjCoDBI4s2GPf9bHx/KbUFdRN+1S58wRaNQU4O1VRfYg/sOwF2PwaJF+fsvxBnIPGY0zysKjSGOBWJ4dniIdkUxVZr0/nwOOZG00Pj9xoxrvEy7CMeqOUEI6YIRwxDSzWb9x8CYlw2lI32wWU/FOi0H+nyZBhsRlyAQDC3G2JQpHgH0McqULKSPAkFqNDMa3BdjmdWrV3PzzTezcOFCLr30UjZs2IDP50tkhN500000NDSwfv16AO666y6uvPJKnnrqKa699lpeffVVdu/ezfPPPw/oxdu7776bRx99lGnTpiWaa9XX1yfE+p07d7Jr1y6uvvpqXC4XO3fu5Ac/+AH/+T//Z8aNGzcivwdBfnJFu4CuF5m8I5+RnsxAFpT5dvn5DvhQgyqh5hDKRfo8yVxtxr3IneKe0sKaKOoJBOnExSf27UuNU4GSurbTxysjylO2yBjaYcjkLL2on5yZfviwLsTbbPprWrGiJE1UB9wcdZTx85//HICrrroq5fr/8T/+B9/5zneG9VxG0w69bBw8eJCvfvWr3H777axZsybv8ZdeeumIZs6f88QLZ54//h8Ayk0+vYDm8ejVO4sF/H6ssQhgJihZ9BipvXuLaqo8moXxYig0hth3yEfv/l7UHhX7dDvOWc5EzHCuZIdsIv1YdbALIV0wYmQTq7IK6SVc9JWiCV4xTsuBPF+uwUbEJQgEQ0chGelCSC+Oc2WSORJ861vfor29nbVr19La2sq8efPYunVrYityc3MzcpLLbcmSJWzevJk1a9bwox/9iGnTprFlyxYuvvjixDH33nsvPp+P22+/ne7ubq644gq2bt2KLW4DtFqtvPrqqzz00EOEQiGmTJnCD37wg35Zo4LRRS5HOsSFdMZ+b4d8u/xsE216LxmbnDJPkiwSpkoTFXUVVP+nasrmlominkCQzjC5ttPHq/RoF4DQuNqhEfWNzPShcIwPtjlqgQyHs1PTtPwHDROjaYdeJg4cOMBXvvIVbr75Zn784x8XdJ/9+/enCPiCIWDWLDxXlsP/hvLYWTh6VBef6ur0QainB4tJgygETA7o7YWGBj32pQRNlQthNLi0i4kh7nm/h+bHmrFMsGBttPaPGc6Q7JBPpM/mYB/NCCFdMGJkE6v6CelD0NCvVK7uQp2WxT5fIYONiEsQCIaGQjLSk4X0sSxICcYGq1atyrpQfO+99/pdt3LlSlauXJn18SRJYt26daxbty7j7fPnz+f9998f0LkKRo58jnSHY3Q0Gx0shezyk60y1Sur8R/wp8yT3AvcYp4kEORjGFzb6eNVxmiXcJ+orx04SNA9DdXkQol6sfV8ilQ9CFFflgfVLDVjdAuUpjlqHs41Z2ehjMQOPdANC11dXTQ3N6OqKvv37wfgwgsvpKysjI8//pivfOUrLFu2jNWrVycicBRFobq6GoANGzYwZcoULrroIoLBIL/85S959913eeedd4bpt3f+0lNWD0D5jFqwzej7/L33HpSXY5b0RIAAVj3uJRwuWVPlfAzHZ7kQob6YGGJTuQnZLmOpt2SMGU5PWhhsb8LRypAJ6c899xxPPPEEra2tzJ07l2eeeYZLL7006/Gvv/46DzzwAMeOHWPatGk89thjfO1rX0vcrmkaDz74IL/4xS/o7u7m8ssv5+c//znTpk1LHPP1r3+d/fv3c+bMGcaNG8fSpUt57LHHqK+vH6qXKRgEhQrpxla/Ui76StkErxCnZTHPV+hg0/TDJpruaxrxCqZAcK5RiJCuJAnpCB1dIBCMAnI1G4V4RjqjK9plIBS6y69sbhlVf1Ul5kkCwUAYStc22R3psjkp2iWkn4fv2v+Xjo178e8No0YkFHMVjmkzqPrOfJwjEZWSLbrl0ktL1xw1C+eis7NQRmKHHsDatWt56aWXEv+/5JJLAPjDH/7AVVddxT//8z/T3t7Or371K371q18ljps0aRLHjh0DIBwO89/+23/j5MmTOBwOvvSlL/Gv//qvXH311UPyuxL04fHol+X1ZWCp1TOFAwFdNDebscQNBgE13rDBai1ZU+VcDMdnuVChvpgY4mKSFrSYhmeHB89OD5YJFiSyi/TZehOOVoZk+f/aa6+xevVqHnzwQfbu3cvcuXNZtmwZZ86cyXj8jh07uPHGG7n11lvZt28fK1asYMWKFXz88ceJYx5//HE2btzIpk2b2LVrF06nk2XLlhEMBhPHXH311fzTP/0TR44c4Y033uDzzz/nm9/85lC8REEJyNbQzxDSe3v1y6GIdoG+aBbXJS6inVECnwSIdkZxzXfR8P2Gkk9CCn2+YhqhGiJ+2Zwy7JPtYnEoEJSAQqJdkl2d6Z9TgUAgGAmKiXYZy450Y5df6ESoX/SAscvPMcuREM3FPEkgGCCGa3vOHP2yWBE9FoNjx+Cjj/TLWCxxU7aM9GRHeigUF5vetuB1L8B09Xwc11yM6er5eF0LaHnbgu+QbzCvsHiM6JZ9+/SYmxkz9Mt9++D55+HMmdzNUYPBAYtz6WYrk9uEpEiY3CYcsx1EOiJ0bOlAi42eSJZSs2rVKo4fP04oFGLXrl0sWrQocdt7773Hiy++mHL8ypUrOXLkCKFQiI8//jjFqAl9O/RaW1sJBoP867/+K9OnT0855sUXX0TTtH4/Rnb8Qw89lPF2Q0QHXbD/7LPPCAQCdHZ28oc//EGI6MNEQkhvKNMLXidO6PnoJhNEIpjjazp/VNE/y+XlJWuqnI3h+CwbQr13nxdTlQnHDAemKhPefV5aNrakjJ3J4ngmksXxQudgqk+l+SfNND/WTO/+Xnr+1IPn3zxE2iMp91GcCrFgLGtvwtHKkDjSf/rTn3Lbbbclttls2rSJt99+mxdeeIH77ruv3/FPP/00y5cv55577gHgkUceYdu2bTz77LNs2rQJTdPYsGEDa9as4frrrwfg5Zdfpqamhi1btnDDDTcA8IMf/CDxmJMmTeK+++5jxYoVRCIRzMndLOOEQiFCoVDi/94hrDgJ+lNwtMsQNBs1GO4meIU832AboQoEgsGRrchnkBztMpbFKIFAcG5RULPRc2DsKuWuQoHgvCJTHMlQ5f/mabjZz5Ee7p+RHvQniU0XOZAkfWJmApRGbfgjAWKx3NEtH3wAbW26G6y8vP/9BynOFWO2GkvOToFgKEkI6RVSX/+Hkyd1Z1RXFyZN11T8kl0fo6BkTZWzUcxn2dZkK1qrKjZOpZgY4kLmYM45Tk4+e5JIRwTzBDOmcSYkk0TodIioJ4p7kTvRoLSQ3oSjkZIL6eFwmD179nD//fcnrpNlmaVLl7Jz586M99m5c2e/BlbLli1jy5YtAHzxxRe0traydOnSxO3l5eUsWrSInTt3JoT0ZLq6unjllVdYsmRJRhEdYP369Tz88MPFvkRBiSg2I32oFn3D3QQv3/OVohGqQCAYONkaIRvojvSxL0YJBKOJ0dBsaaxTiCM9eg40G4XiGr4LBALyCtslf648DTcDAf05M2WkG0K6enqUCcfNzbmjW6ZPh9On4cgR+PKXS9ccNY4wWwkExZMQ0stJ7f+waxe0tmKKhgHotZbrTvWDB0vWVDkbhX6Wez/s5czmM0VnqBdbdCvWoJBrDjb+6+Pp/E1nQsQHCB4LEj4dxlRlItoRxX/YT3lVORqF9yYcbZRcSO/o6EBV1URWlUFNTQ2HDx/OeJ/W1taMxxuNGozLXMcY/PCHP+TZZ5/F7/dz2WWX8dZbb2U91/vvvz9FwD958iSzZ8/O8woFpaJYR/pYX/QVSqkaoQoEgoFRTLPRsZwzLBCMFs7XxmmlppBmo75zaOwa7l2FAsGYpQBhu2Riej7Xdrzhpt89A5AThT8j2kU2y4lol4incOF4WIqxXq9ehMiV/VdTo18ePKhnohu/65aWQYtzwmwlEBRPipAOqf0fPvwQ5bsW6ILesAk6O0vaVDkbhXyWY6EY7a+3EwvFis5QH0jRrViDQrY5WCYR3znTiepRiXZEka0y4TNhgi1B1B51zO4iHLJmoyPFPffcw6233srx48d5+OGHuemmm3jrrbcyZtharVas1r43V09PT9bHFU6p0lN0s9FzYNFXCKNxy/If//hHnnjiCfbs2cPp06d58803U7qZCwTnEsUI6edLgU8gGCrO58ZppaaQZqMhSt/AfSQZ7l2FAsGYo0BhmxkzSuO+zOfajjfcDDR1A5U5HelBuTDhONwWpvN3nUNfjHW5dCe/z6f//tLx+WDCBLjpJvjTn/Tfw8mT+n1KIM4Js5VAUDz9hHTo6/8weTJK9W60rl567eNh3bqhjbyKk++zHDwRRA2qSGYpHmuVO5olnYEW3Yo1KKTPwbSYhv+wn1BrCKVcAQ2QwFxtxr3Ije+wj8iZCNHuKJEzEcqXlI/ZXYQlF9KrqqpQFIW2traU69va2qitrc14n9ra2pzHG5dtbW3U1dWlHDNv3rx+z19VVcX06dOZNWsWEydO5P3332fx4sUDfk3CKTU0ZBXSnTFAxnuqB+2LTogXys6VRV8hjLYtyz6fj7lz5/Ld736Xv/7rvx7W5xYIhptim40KBIKBUWyGoyA3hUS79IhYKoHg/KJAYZvmZl1YGiz5XNtOJ5w8id+jxykkMtINId3SJ6R77fmFY2ujlfZ/bifSOQzF2KYmPQ5n377UooR+Qn3RLV/5iv5T4jz60Wi2EghGOxmF9CRkTUMFeiNmmFyT+aASk++zrFgViIC1aWCxVoMpug3UoGBopt7dXvxH/IROhLDWW3HMdGCuNmOuNlNeVU7oRIhIe4SmHzZRvqR8zI5XJS+1WCwWFixYwPbt2xPXxWIxtm/fnlXMXrx4ccrxANu2bUscP2XKFGpra1OO6enpYdeuXTkF8li8M3hyQ9FiKabbraA4MopVhw7h2r4FAO+hFrQHHkrcdL4t+pyznDTd18TkdZOZ9MAkJq+bTNMPm0akeHPNNdfw6KOP8o1vfGPYn1sgGG4KcaQr51A8gkAwUhST4SjITyHNRsXYJRCcZxQibAeDfVuBB0uyazsT8YabgZgu4KQ70pOjXYJhXWwyV5nxH/QT9UTRohpRTxT/QT/m8WbQINKpF2NNbhOSImFym3DMdhDpiNCxpQMtppXmtcmynilfVaU7+T0eiEb1y/RcZcPxOmeOflkih6thtnJd4iLaGSXwSYBoZxTXfBcN328QJj+BII1ChHQAb2B450W5PsvV/6kaySqhODPHNClOhVgwlrUfgiHUZxs7S110S9ZMLZMs2Kfa0VSN4KkgPbt6iLRHEseqXpXyJeVjWkSHIRDSAVavXs0vfvELXnrpJQ4dOsSdd96Jz+fjlltuAeCmm25KaUZ61113sXXrVp566ikOHz7MQw89xO7du1m1ahWgL6buvvtuHn30UX7729/y0UcfcdNNN1FfX5+Il9i1axfPPvss+/fv5/jx47z77rvceOONXHDBBQN2o6c7pYb8yznPuQSOBej9qJfAscCwPOdQ00+siuf3uVoOAeBVxqGNm5A4Xvr8k2E+w5HHqAiWzSlLNIIoJV6vl56ensTPYIpOY5HnnnuOyZMnY7PZWLRoEX/6059yHv/6668zc+ZMbDYbc+bM4V/+5V9Sbtc0jbVr11JXV4fdbmfp0qV8+umnGR8rFAoxb948JEli//79pXpJghJguDpzNRs1CVenQDBojAzHgS4UBKnkc6Q7HGLsEgjOOwoUthPZmoPFcG2fOKG7tJMxXNuzZuE366pWekZ6crRLKJRHbPpmNdGu6PAWY41mhZdcoucpf/JJX65yKbPmczCazFYCwWgnn5AuxXW1QEQiHB6mk4qT7bNcNrcsEc2SiUL6IRRadBuszpiumZrLzThnO3XtVJKIeCL4DvqIdg+NiD9SDElG+re+9S3a29tZu3Ytra2tzJs3j61btyaahTY3NyMnVWWXLFnC5s2bWbNmDT/60Y+YNm0aW7Zs4eKLL04cc++99+Lz+bj99tvp7u7miiuuYOvWrdhsRjXbwa9//WsefPBBfD4fdXV1LF++nDVr1qTkoBdDsd1uB0OuDPZzNVomRUhPyu9zXbgA/g28YSuasy9/TvqX38H8WUOeWXU+kd5c98EHH+Shhx4amZMZZl577TVWr17Npk2bWLRoERs2bGDZsmUcOXKECRMm9Dt+x44d3Hjjjaxfv57rrruOzZs3s2LFCvbu3ZsYqx5//HE2btzISy+9xJQpU3jggQdYtmwZBw8eTIxVBvfeey/19fV8+OGHw/J6BYVTVLNRIUYJBANGNE4rLcU40kV/h5FF9J4RDBuFxpE0NZXm+QzX9okTORtuBp7WzyNXRrrh78mW2+s74Cu6oV5JSG5WWMLolmTy9WcT/SEEgvxEo33ruqxCuqqPPSoyXi+MHz9MJ2c8f4bPcqn6IeTLPC+FzphJM03ORA+fChM8GsQ0zoT7y+4xm4mezpA1G121alXCUZ7Oe++91++6lStXsnLlyqyPJ0kS69atY926dRlvnzNnDu++++6AzjUbA+l2OxByvYGBc7YJV4pYlZTf5wroWz+8YQtarG/QkD8pYX6fAICDBw/S0NCQ+P9Ai05jkZ/+9KfcdtttiZ0ymzZt4u233+aFF17gvvvu63f8008/zfLly7nnnnsAeOSRR9i2bRvPPvssmzZtQtM0NmzYwJo1a7j++usBePnll6mpqWHLli3ccMMNicf6/e9/zzvvvMMbb7zB73//+7znGgqFUnYLeEu1/VaQkcIy0oUYNRoR4tTYQjROKy2FZKQrogg4KhC9ZwTDRoHCdkmNSoZr+803szbcTB+vtHDf2JSIdkkykmcSm0ayGKshEaQGlSoUFGxIlGpUPVdNdIKRI19h5lylp6fv39mEdC2+GyaKVBohPRYbdJEtV4Z68EQQxargmK0L5Ol/y0x/60xFNyOOJZ/OmO+9k00zNTLRo51RAp8GqL+9nsq/rDxn3ndDJqSfCwzHl3OuN3CgOYBiU87ZJlwpYlVSfp9L1QVDb8hKLGYMOhpSOFC6/D4BAC6XC3emrvPnOOFwmD179qRETMmyzNKlS9m5c2fG++zcuZPVq1enXLds2TK2bNkCwBdffEFraytLly5N3F5eXs6iRYvYuXNnQkhva2vjtttuY8uWLTiy2QbTWL9+PQ8//HAxL1EwCIpxpKOMvbH3XEaIU2ML0TittBiO9FxCuslolCwy0keUa665hmuuuWakT0NwvlCAsD0kz5nDtZ2+g8aIdpHNcj9HejZGqhg7lEJ3oeKWQFAo53Nhxoh1sdvBbM58jLEbRo0L6YPi0KG+cTYY1MfZ6dPhssugpqYoYd2IZjH+duGTYWKhGGpQhQi0vdJGxxsdKX/LQv/W6XEs2XRGLabR+ZvOnI+XSzOVJAnJLGGpseCYOTY1y2wIIT0HQ/3lnO8N7P3AS/h0mIqrKvpFy4D+pvXs8ODZ4ekX1j8Wqo4pYlVSfp/Lqmf0BaJmohG9SCHJWmnz+wTnNR0dHaiqmoibMqipqeHw4cMZ79Pa2prx+NbW1sTtxnXZjtE0je985zvccccdLFy4kGPHjhV0vvfff3+KiH/y5Ml+sTyC0lFYs1F9wRcbZePq+Y4Qp8YemRYKsk3GNd91zmz/HC4KiXYRsVRDh9F3xsBqtZ5XO/0EA6AEzsWCGYY4kn4YDTcz0M+RHsntSM/ESBRjByp0F7I2L1TcymSiGwtrf8Hwc74XZvLlowNoUX3siQ5WSI/3+6OjAyZO1Cddzc3w6qvw8sswdSpMmKBHbX3jGwUVMJOjWXo/7KX99XY9/qqpb6wz/pbjrx1P59udBf2tC4mw7nm/B98BH7FQLOfjna+7S4WQnoPBbKkohHxvYPN4M/4D/sSH2yDSHsF32EfkTIRod5Tmx5opX1xedCVqpEkRq5Ly+1zTKxLH9IYsAEiSqg82pcrvExRFb28vn332WeL/X3zxBfv376eyspIm8TcpmGeeeQav15vihC+E9MV48kJdUHryCekWC1gVDVQhpA8XQqA6t8mU4WhttBJqCdH7Ua8QBQpENBsdWc7nvjOCAZDJuViEwDIgcgjbw0164S9XRnouhrMYO1Chu9C1+UD7s42Vtb9geBlMYeZcoSAhPdInpA94iZ3U7y/Ri6K9HQ4c0G/TNH1AGz9e71dx4kTBzYklWcLWZOPM5jPEQjEcF/X/W/oO+Gh5pgWTy5Tx9vS/db4Ia9khEzgawFJnwb3YnffxzsfdpUJIz8NAtlQUSr43sKlc//NEu6OYx+t7USLtEXp29aD6VWSrjKnChGWCZUCVqJEmRaxKyu+zfvIRZjlKJGait1PPS5cUrfT5fYKC2b17N1dffXXi/4Y7+uabb+bFF18cobMaOFVVVSiKQltbW8r1bW1t1NbWZrxPbW1tzuONy7a2Nurq6lKOmTdvHgDvvvsuO3fu7CcALly4kG9/+9u89NJLg3pdgsETi/U5oHIl75TZNPBBrGSJmIJcCIHq3Cc5/9Z3yMeJx08IUaBIimk2KqJdSs/53HdGUCSZnIs+X9ECS7qjXWucSLAlXLAreSRdzNkc6bKl8GgXg3wN9UrFQITuYhzBA+nPdr47jgXZGWhh5lyiGCHdaDY6IJL6/SFJunB++LA+0FVX64NZV5d+/ezZer+KLVv0XUIF6Fv5/pYmt4nevb2Mu3pcQX/rfBHW4dNhVJ+KbaKtoMc7H3eXCiG9AIrZUlHMl1W+NzAmMFWaiHRGsE3Vt0L4DvtQ/SqmKhPRjijWOivWRisWLEVXokaafq7PpPw+12+DdIXL8HWEAZAc1qFzZwjyctVVV6FpWv4DxwgWi4UFCxawffv2RCPCWCzG9u3bszZJXrx4Mdu3b+fuu+9OXLdt2zYWL14MwJQpU6itrWX79u0J4bynp4ddu3Zx5513ArBx40YeffTRxP1PnTrFsmXLeO2111i0aFHpX6igaIyFHWR3pAM4rbqQHs0QuyUoPUKgOn8QosDAKabZqGiUXHrO174zglTyitOZnIsAbndxAkuao90XqqEjsBC/fTqq1ZW3ADnSLuZsGenFRLskk6kZaakpVujO5QiWZ8n07u7l1KZTNN7ViG2yrej+bMJxLMjFQAoz5xqFCOmxtGajAyKp31/iiTs69CeWJH07s9erC+qSpDd9PnRIF+AL2CWU728pmSS9IJCldWP637qQOBbFqWCpsxT0eDB8Bc3RghDSC6SQLRXFflnlegPHYjECnwRwXuyEGPgP+FHcCpEzEWSrTLQjiuLQJzxIIFF8JWrEiLsnfD2NgAmnPQbEJ4rx/D7X/y9G10nw/dXfwnvNyLYs3SEEggGyevVqbr75ZhYuXMill17Khg0b8Pl83HLLLQDcdNNNNDQ0sH79egDuuusurrzySp566imuvfZaXn31VXbv3s3zzz8P6J+xu+++m0cffZRp06YxZcoUHnjgAerr6xNifXoMTllZGQAXXHABjY2Nw/TKBbkwCnyQXYwCcNr63AuCoUcIVOcHQhQYHMU0G0U40gWCklOQOJ3uXEymUIElzdHuC1TT8u+1RLoiWCs/xLpkPqpjXNYC5GgoWCYX/jRNj8uD4qNdhpNihe5sLlIjpjV8Koz/gJ/giSDuhW7GXz++qKxh4TgW5KLY9+u5SD4hPXnsGVSz0aR+f7jd+uAVjfZ1OA2HwWQiMbg5nXrT5wKfMN/fUotqemRflppI+t86bxxLtQXFrugG3iLeO8NR0MzEH//4R5544gn27NnD6dOnefPNNxP6y1AhFIAiKObLqhCMN7C5yoz/oJ+oJ4oW1Qh8EaDz150EPwsSaY+g9qpEe6IEvwgS7daPsdZZcS9yY67uE5gLqUTFgrGRrToeOgQ/+QmsXYvPqy/mHC/9XL/eQJZxjdM/sD5rFSCyPAWl51vf+hZPPvkka9euZd68eezfv5+tW7cmmoU2Nzdz+vTpxPFLlixh8+bNPP/888ydO5d//ud/ZsuWLVx88cWJY+69916+973vcfvtt/PlL3+Z3t5etm7dis12bjXXOJcxhHSHI7cRzGHty9MTCASlodTzrGS0mEbgWIDej3oJHAugxc6dXVagexQM0amQaJeY2E0zovT29rJ//372798P9PWeaW5uHtkTEwwYQ5z27vNiqjLhmOHAVGXCu89Ly8YWfIfiE4x052I6Tqd+ezaBJc3RrrncdHxSQyTqxDFVwRTtQfr0CCaX3ssr0hGhY0tHYsxLL1ia3CYkRTdkZTp+qEh2pBvRCjCCQnosBseOwUcf6ZexWL9DDBNc6ESo305dQ+h2zHIkhG7DRao4+xbnRkxr+HQYxa0gO2UUp4J3n5eTz57E+SVnP20g6oniP+jvlzWc6fGTGRVrf8GIUez79Vwkr5Ce1ItwUBnpRr+/Eyf0+BarVRfOIxH9/z09UFXVdyI+ny68u1wFPXy+v2W0J4p9ml0fMwr8WxtxLK5LXEQ7owQ+CRDtjOKa76LpR024F7nHzHvH5/Mxd+5cnnvuuWF7TuFIL4Kh2B6TnifkP+QneDSIZJFwL3ZjbdIdAsHmIFpEw36hHftUO9ZGK8n6jaZpRM9GQYOoR498Sdd3clUdhyUjL8k9oTY0EYrpW0WcR/bCxo9T8gDjRl38Xg03QkgXDA2rVq3KGuXy3nvv9btu5cqVrFy5MuvjSZLEunXrWLduXUHPP3ny5GGPzBnJPMyxQL5GowYOi77AisTE7240IRojj22GahvySEcYDAeGKAWFNRsNq2LsGknOtd4z5xVpueQ0NaEhFb6bJt25mE4+gSXN0R7stuHvcGAtD+nzObdbF9k9HqSKin6u5NHiYk52pMfCfaK1bJYHFO2S6e9ScG+tAhu/FttUL91FqmlaIqbVXG1GC2nIZhnzeDNKuf4+8X3ko2FVA52/6cybNSwcx4JcnK9NIJPJK6RHUoX0rq4BPlFSvz8OHoSGBqishJYWXVB3OvUxxchPb2mB+fP1caoA8v0trdVWKr9TSefbnUX9rXPFsUiyNGbeO9dccw3XXHPNsD6nENKLYKi+rIw3cOBYgJanW0AG10JX4o1pcptwXuTEd8CHZJGIeqJYGi1IcaU80h7Bd8iH/zM/RMG700ukPYJzljPhWM+0HcxgWBaYae4JXzipGnbxFPj0w5Q8wESxzhMX0sUWZIFg0JwPYtJgKVRIt1v0iVdEE2PTaEKIU2OboZhnjYYIg+GgECHdbAazpIEGIVEEHFHOtd4z5w1ZBNfgpV/Hf1gpTJw2nIv79qVmpENhAkuao10NKahRGas5XmBMZPHqfabSC5AlLVgOQrwuqSO9QCE86303bkRr7yBYPh3V7kKJerHt3YeUofFrMU310mNcVY9KpCOCqVz/fov26OY3U7kJJBLvkwk3TqDpvqa8xpdCco4zrf0F5w+jtQnkcBm7ihHSVWQ6OgbxZEn9/jh8WHelSxIoij7Wjxunn1BLi+5OX7Gi8GIfhf0t7RfYi/5bZ4tjGQ3vHa/XS0/SNgGr1Tpq+mQJIb0IhvLLyqj6qGdVHDP7Z39KkoRtoo3g0SCyTU5UhmKBGJ4dHqJdUcyVZhyzHPgP+Ql8EiDSEaF8STmyXc5aORq2BWaae8IX1gV+CQ27OdovD3DcuPj5efTBTTjSBYLBcb6ISYMlOdolF3ZzXEgXYtSoQohTY5tSz7POp8x1w91psehrtmxYZD0PNKyKdEeBoCjScslxOvVJw759qPt7UHuuxzqpJuNdU8TpdOdiY2PfY6UJLBnFpjRHu2JVUUwx1IiCyaomZfHqO3/TC5AlK1hmEq+nT4fLLoOampzCeizW5za32wcmpCd+Nx9+ivJPL2ELNSM1pf5dyCCE9zuRN9/Ed1SlI7Ic/6dO1KiMYorhGD+VKu8unBkavxbaVC/dRSrbZLSwhmbViLan9juD1PdJIVnDwnEsKITR1gRyOI1d+YR0o9Eo6BnpnZ2DfMJ4v79EgbGtDXbtgiNH4JNP9HFy/nx9jM9X5MtAvr9lqf/WI/3emT17dsr/H3zwQR566KFhee58CCG9CIb6y6oQh4BslaleWY3/gB/fIR+9+3tRe1Ts0+0JB7qlyoLvkI/A5wE8/+6hbF5ZxspRsZ3EB/WBSXNP+CL65M5hjuhGjLSGCxUV+t0MIV02iwWfQDAQjFzgk5tOEjwe7Lfb5VwTkwaLIUblc6Tb4kJ6WAjpAkHJKPU8a7REGAwH+RqNGpjjQnooKsYugaBg0nbWJlzkbjfMno3ywQmUthOoveMxlZv73b2fOJ3uXDx5sp/AklVsur4KZ5Kj3VYexFHlx3u6DKXKh9TTA3V1UF5OLBbDf8SPY5oDYvqcsCQFy0xFheZmePVVePllmDoVJkzI6gpPjmxxOECLr/dQ4uaxPNEuid/NIR/q/k9QeqbhuGA2VTWdON1+cLvRZs0muPsE6qZ3UO6ahG2yvf93R3Mzvl2naTm9mEjUibU8hNWsokYUvK0ugqbFNL6/F2eGxq+FNtVLdnV6d3uJ+XXhzlpvxTHTkdLvbCC7rkaDa1Qw+hmpJpDpDLexq9CMdE2CmCYV5kjPtxNHlvvGizlz4CtfGXjsVAby/S1L/bceyffOwYMHaWhoSPx/tLjRQQjpRTOUX1aFOgTK5pZR9VdVeHZ4aH6sGcsES0pmurnaTHlVObbJNiLtEer/n3rKl5T3mzwU20l8UFXCNPeEP6JPGpwWfdtheh5gwpHeo082hCNdICgeY6Hh3e2lZ08PikNBC2kpE/dzTUwaLIVGu9hMRs6wKPIJBKWklPOsocpcH40kxyTkwiLr8yohpAsERZC2szYFScI2vRzH6Wa8RyajLByvi9mhMFgtaG53ZnE63bmYJLDkFZuu/SuccUe71NhI1bRWgu11+I+qWCvLUabNIHgsSO+eXrSwBjE49tCxhOtzUAXLTEWF9nY4cEC/TdN0K/n48Vld4YZpAfTiX7hDH5dkiz6nyuVIT/nduENYaUEd79KF7x4bjYtOAtBxuAr/qQtQD6goJw5gn1+N+zI3lhpLwlWJp4eOow1Eog4cE/yJP63JqqJU+/GfcdBxtAGHp2dQreXTY1wDnwZSjC0wuN3tI+0aFQgKYSR2CRYa7SKZJIiQX0gfSIxUsrAuKAqXy4U7Uy+RUYAQ0gfAUH1ZFeMQkGQJU7kJ2S5jqbf0aywqSRLWeiux3himclPGc8u0wDQ6iat+FcWtgESik/igqoRpeYBGtIvTHMmYB2gI6QFv0uAmEAgKJnmhIZfJKE4FxaUQOh0i6oniXuROiOnnkpg0WAoV0q2muBglGvYJBCWnVPOs86kRW3LjvlyYJH1eFRRCukBQOGk7a1PQNKRohCrHXoK+C/G/0Yw11opCEBUbIaUWy8UTqVoxpf8YlkFgKUhs+siNY9X3kH6zBQ4fxhk8SeOU03TULcRvuwj/MSvBo14ki4R7sRtrU3/X54ALlulFBU3T/+/3Q3W1rn53denXz56tx9ekxaMYhT8jiiohZsWNU9kc6f1+N2d8EItgKpNQXH787Q5O7akjFpWJ+M1Y3QGsUifBaIQzr56h9eVW7FPtWCZYcMx04Goy4feNx1rRiySlfg9IElhtXvye8QS9dgZrM5FkCcdUBw13NNCysQX/odLubh8tjmOBIBsjsUvQiNfOK6SbdSE9Z7RLjnivvDFSgnMOYaUbIMaXVdmcMuyZtooN8DGrvlGFucqM/6CfqCeKFtWIeqL4D/pzdgLPRL4FYvr90zuJS5KU6CTumO0g0hGhY0sHWmwA+bNGHmBVFRw8iK87AoBTCegTrLSGC0a0i9+bOrESCAT5SV9omCvNSGa9D4O52ozqV/Ef9kP8o3wuiUmDpVhHuhCjBMPBc889x+TJk7HZbCxatIg//elPOY9//fXXmTlzJjabjTlz5vAv//IvKbdrmsbatWupq6vDbrezdOlSPv3004yPFQqFmDdvHpIksX///lK9pETsVO9HvQSOBfrNLUoxzzIMCqEToX7Z+YZBwTHLcU40YivUkW4I6YGIGLsEgoJJ3lmbTHs7/Nu/wfbtOL94j8bPHsfV8e9EwzYC0kSiUjku6VMaeAMnzXnHPShCbHJOgfvug3Xr4IEHcD7932h6+z8z6Wdzsc+wY7vQRtVfV2GbYkNSJExuU8p6zjHDQdN9TUxeN5lJD0xi8rrJNP2wKb9hKr2o4PHowlJ5ua4+WywQjeqCuiSl9sGKk174SxfSkx3pyUN3v9+N1aLnwUf0qFCLK0T3sQqCHhuOaj8mKUBUdeA/pn/noEEsFMM03oR3n5dTv4kRVqpQAl2pTwSgaSiBs8Sc41Bdtbl/J+T/TjMwdl25LnER7YwS+CRAtDOKa76Lhu83iCgWwZikkPe/YeJUnFm0KadCLBgrqbGrYEd6PEa4p0dvM9GP9J04brdeBYzHe9HRoRcMY7EMdxYMNb29vezfvz+xTvniiy/Yv38/zUnfO6VGCOklotAvz3wU8+U62AVi+v0zdRI3V5l1R3talXBAGHmAl1yCr0vfq+eU/LoTPa2CZzjSg71CSBcIiiV9oaGUK5irzEQ9UUB3NUU6Inqx7hwTkwZLoUK6RdbHppAQowRDzGuvvcbq1at58MEH2bt3L3PnzmXZsmWcOXMm4/E7duzgxhtv5NZbb2Xfvn2sWLGCFStW8PHHHyeOefzxx9m4cSObNm1i165dOJ1Oli1bRjBDIO29995LfX19SV+T75CP5p80c2ztMY49coxja4/R/JNmfId8+e9cBMUaFMYyhTrSzYYjXYxdAkHhGDtrT5zoE1zb22HXLrRTrQT8bnrlC5DNMSZWvMNkx2tM+tJ+Jv/lSZq+EcSpfoHv+f9N8/rjece9osQmw9E+Zw5MnoxkUpBkCfWsimOmQ48q6e6GtjPQ3Y0EKeu5ARUs04sKoZAunJvjWd+JZqdxNdzp1IX3eB8s6F/4M8QsoyeWcVdN0x866++mvFw3Y3k8+sGaRDRgwmwPI6GheXrwRetRowrmajPm8WaiXVHQwDHbgepTCZuqUM0u/e8ZDPZ1Qm1vR7W4kac2oJTn3sRf7Heac5ZzYEUMwYgYC3784x+zZMkSHA4HFYbbL43m5mauvfZaHA4HEyZM4J577iGa/OYF3nvvPebPn4/VauXCCy/kxRdfLPr1j0YKff8P1gQ6EArNSJfNUiLaqasrw4F54r0yFQwFw8fu3bu55JJLuOSSSwBYvXo1l1xyCWvXrh2y5xTRLiWg1J2HB9oJvNjtYYPpJD5g4nmAPmcnvAfO6Q3wwx/2a7iQENJ9ItpFICiW9NgmSZJwznTqxbL2CIpLIRaOEemMEDpZ3FZSLaad0xmMxrowb86wIhzpguHhpz/9Kbfddhu33HILAJs2beLtt9/mhRde4L777ut3/NNPP83y5cu55557AHjkkUfYtm0bzz77LJs2bULTNDZs2MCaNWu4/vrrAXj55Zepqalhy5Yt3HDDDYnH+v3vf88777zDG2+8we9///uc5xkKhQglBdp6k0STZIaq0VS2sel8acRWaLNRBcORLrw0AkHBGDtr47nkNDToO2zb7XT4r8AfqUNVFRSbjEPqoiq6g7L2P8GMK0CS8Dlm0vK2lciFZ7DOHJdz3BtsJFViDuj3wodHdKdkNKqL21VVKNNmEA46B76eS4vrxGpNuMKxWHRLZ7zZKdCvDxb0H69ikdSeWLYkX0cw2KfR9/vdSJJ+Lh4PtLcTjenOcZPkh/ZOVFM5kdj4hCkMC2heTXelSybs0+2ET4fxu76Ey/Q5UmeHLvibTGi1dYTMF+K6rD6n0WSg32kiiqV4DGPBpk2bWLRoERs2bGDZsmUcOXKECRMm9DveMBasX7+e6667js2bN7NixQr27t3LxRdfDPQZC1566SWmTJnCAw88wLJlyzh48CC2+BsxHA6zcuVKFi9ezD/+4z/2ex5VVbn22mupra1lx44dnD59mptuugmz2czf//3fA7pD9tprr+WOO+7glVdeYfv27fzX//pfqaurY9myZUP4Wxtainn/54oxztQYebDrS1Xtq99li9k2xh7ZLFFZqUe7dHRAbfomlFzxXqBff/JkSsFQMHxcddVV/YzFQ42YRQ8SY/Dw7vNiqjLhmOHAVKVvF2vZ2JKoxKU71mPRWEm2NA92e1jy/WO+GDG/7nCw1llTMpQhdeI2KAe+LONzVOvPP96esWuxUewN9hqDm3irCgSFkqnib642417kxlJnQe1R9c+6Ty1qK+lwuUhHEsPVmc+RbpaFGCUYesLhMHv27GHp0qWJ62RZZunSpezcuTPjfXbu3JlyPMCyZcsSx3/xxRe0tramHFNeXs6iRYtSHrOtrY3bbruN//k//yeOfJUlYP369ZSXlyd+Zs+e3e+Y9Ngpk9uUMXKg2F19+cam88H9V3C0i6bPq/xhUQQUFMdIOEG7urr49re/jdvtpqKigltvvZXe3t5+j/Pkk08yffp0rFYrDQ0N/PjHPy7Ni04maWctzc34jgRo6fkLvNJ0TDU2HNYzmCxBvL11tPT+Jb4WBTweNA06micRCZhxNMXyjnuD3XGsuBSUkBd1x144fVofFMaP1y9Pn0bdsQ851DNw12daXCcAlZW6+nTmjP48M2f25ae3tOi/u3gfLOibayUc6eHM0S6Q2nA04++mugoWLUKrrSPilTBJvUjhANTVEbtoHprJknhcLawhmSRkqz53M5WZsNRYMNWW46+8hOjCP0e7/M+ILvxz/JWXYJk6PqfRZKi+0wSZSTYWzJ49m02bNuFwOHjhhRcyHp9sLJg1axaPPPII8+fP59lnnwXoZyz40pe+xMsvv8ypU6fYsmVL4nEefvhhfvCDHzBnzpyMz/POO+9w8OBBfvWrXzFv3jyuueYaHnnkEZ577jnC8ZyQTZs2MWXKFJ566ilmzZrFqlWr+OY3v8nPfvazrK83FArR09OT+MlmUBgpinn/G2YHx0UOZKuM/0DfLsHAFwE6f91J8LMg/iN+jj1UmvVl8q+rkIz0qir9uowNR7PFexlkKBgKzm2EI30QFNp5WItpdP6mM+FY10IaakBFsStIVmnYHOz57l9oJ3HVp9L8k+ZBOfDzxScYjvSQX0S7CATFkq3ib6424x7vpnd3L45pDhrvasQ2ubCxYqhcpKONYqJdNCAgxCjBENLR0YGqqtTU1KRcX1NTw+HDhzPep7W1NePxra2tiduN67Ido2ka3/nOd7jjjjtYuHAhx44dy3uu999/P6tXr078/+TJk/3E9KFoNFXo2HSuu/8KjXaRDUd6SIxdgsIZKSfot7/9bU6fPs22bduIRCLccsst3H777WzevDnxXHfddRfvvPMOTz75JHPmzKGrq4uujHvzS0B8Z6229R06/mMnEccUHPVRpLAM3RImyY/iCOP3VdPRPRtHMEQQG/5WC9ayHiSbNeXhMo17g91xbGu04Ah8grfLhDK1uu84qxWtqprQURVX3SfYGi8d3O/h+9/XM4MPH9aVb0nSM4Nnz9YXch6PLqKn9cGC/o709Ix0WdZN7kbUeuL3le13Y6kgVHkJtquCOOQIoc5JKAvHI/fEkA6d1R/foseWWuusiRhT1adimWCh5qYavH/y4j+sEPbG9F1LCxwpu5Yy7XoaieaJ5yuGseD+++9PXFeIsSB5XgK6scAQyfMZC5J36OVi586dzJkzJ2VetWzZMu68804OHDjAJZdcktXkcPfdd2d93PXr1/Pwww8XdA4jQaHv/7Pvno1/vlK1MO2ovjskeDSYszHyQNeXRqyL1ZpanEsmeewZP16/LmPD0fSdOMmv1ygYzp+fUjAUnNsIIX0QFDJ49Lzfg++Aj1gohnWiFTkg4/l3D9GuKKZKE+VLypEd8qAHi8EuEAvtJO6c4+TksycHLablE6sMR3osIoR0gaBY8i3C7JPs1N9Rj31qYWNGoUVDxwzHmI95KVRIN0kxIghXp+Dc5JlnnsHr9aYsWPNhtVqxJq1Uenp6+h2THjuVTrERcufT2JSPQh3pStzFKcYuQTGMRMTUoUOH2Lp1Kx988AELFy4E9LHpa1/7Gk8++ST19fUcOnSIn//853z88cfMmDEDgClTpuR8LYXGUGVFlgm6L8AfO4G1IowkK7pK43BAby+SQ8Fq6sIfqicY7kZFRvWGsU4rz2iLzDTuDSaSSmo5QZV9N8HK+fg7nFjdIRSLihpWCPVYsVT6qLLtRmq5Us9XHyjxogLNzbr1s60Ndu2CI0fgk090d+b8+bqIntQHC/o70vviFfrEdpsNenv1NIVksv5uFrioWqH/7fW1bABLgwVzpZlQSwhMYHKaErGlySaxcV8Zx7ivjMtqSssW4+q4yFHS7zRBdkbKWFAI2Z4n+TmyHdPT00MgEMCeoQpeiEEhG8MRxZkyp9M0XbkOhfUmwOXlKE4F/yE/p58/jaZpKdpRsFkXz81uM8ikmDhLNYfLl48OSUK6KY8jPT3eq7FRXyz6fFkLhoJzGyGkD4J8C0LZIRM4GsBSZ8G9WA9m6v2wFy2qYZ1qJdoRJfBpgPIrykfNgi/XxG3818fT+ZvOkixY84lVxoBnQmSkCwQDoZS5wOeT46ZgIR1NF9KFq1MwhFRVVaEoCm1tbSnXt7W1UdsvwFGntrY25/HGZVtbG3V1dSnHzJs3D4B3332XnTt3pgjjAAsXLuTb3/42L7300oBez2Czf9MZqrFpLPaCKNiRHo8Y8ImxS1AgI+UE3blzJxUVFQkRHWDp0qXIssyuXbv4xje+we9+9zumTp3KW2+9xfLly9E0jaVLl/L4449TWVmZ8dxK4fJUXbWoznFYA1+Aq0p3J1ZV6fZpvx8lqhE2T0EN96K0HkZx/Blq03RM6U3qyD7uDXjHsdeL09pG4+WtdHxSg7/DQdhrRTbFcNX1UjWtFWdXW2myfI1mp6A3PP3KV/qEdZdLd2dmEJayOtItfa/NatWF9GRHukG+303y/Fe2ykiShKRIOGc7MY0zEfVEM7r7M31P5Nr11HugV3fXZvhO0zSN0KkQakDVIyxKkPksOL8oxKCQiVL378tGYk7X3I6p5ZN+/Rii9dMItym6UP5lV4p25LzIifcDL4HTASququjfC7AE68uChPREs1E5tyMd+u/EOXkyZ8FQcG4jhPRBkG9BGD4dRvWp2CbakCSJaHeUSEcEU7kJWZYxuU1EOiJEPVFMFaZRI0Zlm5yUcsGaT6xSFH3QUzzCkS4QDJTBxj4ZlNpFOpop3JHe5+qMxYQBQTA0WCwWFixYwPbt21mxYgWgN2Tavn07q1atynifxYsXs3379pTtwtu2bWPx4sWA7tasra1l+/btCeG8p6eHXbt2ceeddwKwceNGHn300cT9T506xbJly3jttddYtGjRgF9PrkZTye7AXI3dkhmKsamQBehoFNoLbTYqaYaQLgYtQWGMlBO0tbW1X2yMyWSisrIycczRo0c5fvw4r7/+Oi+//DKqqvKDH/yAb37zm7z77rsZz20wLk8DpdyEMrUB9YsOTO3teic7ux2qq/Uccs2CbIqh+DuwXT4Nx/SL8Z60o2haUePegHYcx7N8nfZ2HFeECHpsqCEFxapiKw8i9XiGLss3WVjPQcKRbtfg2HG0T3XlKnm9Z+iH6Y50g1y/m/T5b7gtTM+uHgJHAgQ+CRRsLMm368l3wIcaVAk1h1Au6vtOi7RH8B3yEfg8gMlt4tQ/nMLzR0/JhczziZEyFhRCbW1tv54RxvMmP1emc3G73Rnd6MVizEt6P+yl/fV2YsEY1qahjeK0NdlwVPbi/e0RFGsrUkW53hk4EkE7dZrAJzKUNeGYUZFROzKPN+M/4E+I2enkmsMVMg8rREhPbnSc05FukL4TJ0fBUHBuI4T0QVDIglBxKljqLADEQjG0qJaYJEgWKdE5HEaXGJVpclLKBWshYlVFRZ+QLpqNCgQDoxS5wKV0kY5GASoZY2wqNB4hioTfD2VlQ3xigvOW1atXc/PNN7Nw4UIuvfRSNmzYgM/nS0Qs3HTTTTQ0NLB+/XpAzwu+8soreeqpp7j22mt59dVX2b17N88//zygL17uvvtuHn30UaZNm5bIJq6vr0+I9U1pGY9l8Tf4BRdcQGNj44Bfy2Czf9MptcO9kLx1YFicXsWSHpWQDVmNC+mB0TPuCgQDJRaLEQqFePnll5k+fToA//iP/8iCBQs4cuRIIu4lmYG6PJOxNdlwLKrHGwQl8hlSZ4cuqphMaBddTEhtxHWJE9td1yFNnkTVkQDBjS0lGffykpTlK82ejb0iSYkeJVm+icLf0QOw9nG0z+qB5citJ+GQA2bNIh6Tn9GRXggp89855IxuyUY+E5ltoo3g0SCyTU78bWOBGJ4dpY9xPd8ZKWNBISxevJgf//jHnDlzJlH827ZtG263O1GkW7x4cb+Gy8nnMhgMA4DvkI/e/b2oPSq2C2xYaixIbmnI4u4kNKr4N4KSGb80GasWQkFF1RyEpHGY6Aa1A8VZn/H+Rq+CaHcU83hzv9uzzeEKddwXFe1SqJAOBRcMBec2QkgfBHkXhNUWFLuC6tcXeLJVRjJJaBENySr16xxe7IJvuCnlgtXYTZhLfBo3DkzH41VCEe0iEIwYpXKRDtdWwwETi+HvjgBWnL1tEKvO6jCQ40K6ikxvrxDSBUPHt771Ldrb21m7di2tra3MmzePrVu3Jpyczc3NyEnv0yVLlrB582bWrFnDj370I6ZNm8aWLVsSDf4A7r33Xnw+H7fffjvd3d1cccUVbN26NdHgbygpZexUKR3uheStn3r+FLFAjEhnqtDes7eH3gO9TPhPEyibWzYiBcJCHOmapiHFo116g2JeJSiMkXKC1tbWcubMmZTHiEajdHV1Je5fV1eHyWRKiOgAs+Lb65ubmzMK6aUgZQ3YfgnWKSEUJYKqmgl5rFiqLVTd0YA01YkW05DtMpXLK/H8m4dwa3hQ415exkCWr//4GWACjp5WqKoi1qsXKSV/D2zcCN//Plar/nccqJCezkCMJYWYyGSrTPXKavwH/ClCpn26HecsJ+ZqXSAcrJA5EkaUP/7xjzzxxBPs2bOH06dP8+abbyZE7JFgJIwFoI8lXV1dNDc3o6oq+/fvB+DCCy+krKyMv/zLv2T27Nn8l//yX3j88cdpbW1lzZo1/N3f/V2iaHfHHXfw7LPPcu+99/Ld736Xd999l3/6p3/i7bffHtTvJNkAoLgVkMA03kS4NYzao+Je5MZcbR6aKM7mZpxd+2i8YjIdLY5+MVKuWe207ZNQT3djmpghassEpkoTkc4Itqm2guZwhTaYh+KF9LzRLgJBEkJIHyTZFoRl88pwfdlF59ud+A/7cS10oZQrmKvMhE+HMVWZUjqHD2RL83BTygWrMUAZA1Ymxo1LykgX0S4CwYhRChdpMROfEeHQIXjzTXqabwVqKPtfv4CT8cVohsw7Lb4VMIqE1wtZtASBoCSsWrUqq+Pqvffe63fdypUrWblyZdbHkySJdevWsW7duoKef/LkyWha5q23A6FUsVOldLjncx5aGix0v9eNpc6SkvWphTSiXVECnwfw/YePsnllOGc5h71AWEiz0eTt00JIFxTKSDlBFy9eTHd3N3v27GHBggWA3r8hFoslIqYuv/xyotEon3/+ORdccAEAn3zyCQCTJk0q6e8hndQ1oELYG4s3vXQkxPF0A4FslbHUWii/onxoi27DlOU7IHE3FiPw4afABOzVZeB2o2m6CUtyOXQ76JYt2GwzASlrtMtwUKiJrGxuGVV/VYVnh4fmx5qxTLBgbbRC0q+iaCEzFkvER/jarHTssuA/EhhWI4rP52Pu3Ll897vf5a//+q+H7HkKZaSMBWvXrk3pDXPJJZcA8Ic//IGrrroKRVF46623uPPOO1m8eDFOp5Obb745ZY41ZcoU3n77bX7wgx/w9NNP09jYyC9/+UuWLVs24N9HugEgciaCpmqYKkwoLoVIewT/YT/lVeUgpaYHlKQw4/VCMIhzhoZjcnO/GCmiUbyfdOI9EURp7B9rFT4ZpuKqCmKBWEFzuKIazKPhOeYBxlGueCHmzFg8NOZFeZuNCgRpCCG9BGTLYTvzqzOEzoQIHQ0RatZFZsc0B5H2CKGjIUyVJuzT7ER7Mjc8GU0Yg63jIge+gz78B/TF5kAXrIUI6RUVoAghXSAYUYzPvhbVqP6b6kTGZDFuqqImPiMx/h06pDugOjpoD+u2hQl1Cuzbqzu6vv/9fotOY+KlxoV0gUBQHKWInYLchgb3IjdaVCNwLJB3kZjPeUgUol1RnBc7U3Jwe3b1oPpVzJVmtKiGbBmZLfyFNBtNFtK9ItpFUAQj4QSdNWsWy5cv57bbbmPTpk1EIhFWrVrFDTfcQH29HhWwdOlS5s+fz3e/+102bNhALBbj7/7u7/iLv/iLFJf6UJGrKJjNQBA8FkTtVXFMH+I5zxBn+Q54l2FzM/42feLkMEcA0NR47Kmi6Q76Q4ewEgJsJXOkD4RiTGSSLOl90OwylnpLiohuUHAMatzcweHD+M7YaTk6j4ilGuuCiVhnVA/aiOL1elMijdLjjgyuueYarrnmmqIee6gZCWPBiy++yIsvvpjzvCZNmtQvuiWdq666in379uU8phjSDQDJ6QeytX8/PqPwE24L0/m7zsHvEI73Y8DnQ3K7U2OkAPw+qqZ6CLqsWYXy+tv1sbyQXYoF9+t79wD2P/0Wz798CbiO8o/+HX6yN6M5ynCky2ZZCOmCohBCeokwFoS+Qz7a32jvmzRNshKsCdK7pxfP+x7sU+3Yp9hR6/RBK9oVHbqtfUkMpuqYPlHSQhpqQEU7qg/SAzl/Y4AyBqxMCEe6QDCyZFok2afbqbmpBkuNJeNYkmmsKWWj4pITi+mLlY4OgtO/hDesO1CqJ0jQNFvfFr1li74YTVp8GhOvKBK9vcN7ygKBIJVshoa2l9sKXiTmcx5GPVEATBX6bZqm4Tvs00X0ajNoEO2MIlkkHLMdw14gLCjaJZIkpPvFvEpQOCPlBH3llVdYtWoVX/3qV5Flmb/5m79h48aNidtlWeZ3v/sd3/ve9/jzP/9znE4n11xzDU899dQw/FZ0MhUFCzUQ2KfZCbWEhi6uY4iyfAe1y9DrJRDWHeh2sz6uxmL6e0dWYnoMzcmTWBX9tpEU0ovd9VSSGNQkc4fWOJGOE7OJYMehHkU60AplizBVVw3KiJLeXPfBBx/koYceKvj+gtFBugEgOf3AXG1O6cdnFH6sjVba/7m9X0RdsYUZLaYRjE1ArZiPcvg/sC10pb4H4/0YnJfNp/HrM+n4TWdOobyQXYoF9es71IH6/G9A+xyPciUA5RUS7NuX0RyV3GxURLsIikEI6SUk26TJPsWOdZKV3t29OKY5aLyrEWuTdWgnTkkMJpc4q5uiOYhslQecB1potEuYvu02AoFg+Mj22e/9sJfQyRCN32/st3DMNtY4LnKUrFFxyWlu1rc9T5xIu18fD02ySoUtCJKUcEbR3JyyGE0W0oUjXSAYebIaGgpcJOZzHkY6I5gqTYn5iOpRiXREMJWbkCSJWCiW6HszEgXCQpqNJgvpPX7RxF1QHCPhBK2srGTz5s05z6u+vp433ngj5zGDIilio1BXd4qBAKC7G0JhsFqQysuxNlrpeb+Ho/cfJdIWGZ19Y7Iw6F2GLhd+9PDifo50WdOz3G02bA5dbB7JaBcorq/HYGJQtZhG8FgA9ef/G+V4CNvC2QR77Pi7yrCOjyBZqqG9XZ+zVl0+qO+ZgwcP0tDQkPh/Jje6YPSTXriRJAnnTKc+P2mP6H34ZIiF9egU83i96B/pHNwO4ZT13plFKEfLcTS3UrXAj3Oi1q8fg3NWGY5ZzpxCeSG7FPMWqnqjyG0nUOQz8OXZeA67ASh3azA7szkqU7NRjwciETD3738qyMcAvi/HKkJILyG5XJeyLOOY4SDaGQUZZJM8LAurwTgGck2UnBc58R/04z/op+qviouj8fv7nFP5ol066dtuIxAI8lOKzLuBLJJyjTW9B3r1nSwlaFRccuL5fjidnGnTx8Jqh5/EEB53RqWr5X3RLrJwpAsEw0AhY9tgBJ58zkNbkw3HdAehlhCKW9EdXlENySyhaVpK3xsY/gJhQY70+LgVA3qFI10gyE9SxAbBINhsaNNnErzs66g1k/M7J/1e+PCIvhU3GgWTSW+uWTUN38egBlScFztHX9+YHAx6l2FTEwGHPmDZTXEhPWZEu8R0AW7+fKzdutg8ko50g0L7egy0b0dCmNx9BnVPOYrzahyhCI5qH2pUxmpWdXOH262/lzweqKgY8PeMy+XC7XYP+vciGFkyFW7M1Wbci9z4DvkIfB7A5DahhTVc8124vuyi7eW2Qe0Q7rfem1SDWiPj3eMiuLOdxrb9OCcE0ObNJ7jor1CjE1Hi8XqD1b7yFqo+6cbFcWwzKkCS8IT0AlG5LZTVHJUQ0k0S48bph2kadHVBfMOVoFAyfF8yc2bWfmNjHSGkl5CCtpsM46JqsI6BoYpjMNzoJpM+H8jGuHHgoW+7jUAgyM1gdp8kU+xnP99Y4zvgQw2qhJpDKBelTnxisRj+I34c0xwQ08etYc1JT8r3MxzpE5y+vtvjzihcrpS7GRMvkZEuEAw9hY5tg5235HMeArRsbMF/0I/iUpBkCdWrEgvFUBz6ORm5uMNdICyk2aixhVlFSjjYBQJBFpIiNpg4EZxOfM0SHa9K+F/+AHWqF2VCRcaxSHEpKCEv6o4PMUV7oLxctzdGIminTuM7YEOTJ+CYVZkwGIyavjF5GPR6V5bxV04EwNF+DDwetIjujpZ7ziZcrNZ9+msfDUI6FN7XI+v3yCVlVF0axhk9Csf6nJopwqRTxersRHVV4z1dRu8ZB1pURo0omKwqWCy6sSMUBkbYiCIYcbIVbiSLhKnSREVdBdX/qTqRHmCsxzJ9djVNQ4tohNvC+A/7M0Z3Bo4FOLnpJMHjQVxJUS6mKRNQJlXh391Jx7T5aNeOo/MDC/6XA6jBYyXbbZO3UOVSqYp9hlQ2AQBPUC/GlVvj21oymKMSzUbNEoqi609dXfqwL4T0IsjwfYnPlzVS51xACOklpCS5aCUk14IS9PP17PDg2eGhfEn5wHKoBlAYSI51yXBaCSoqkjLSRbSLQJCTQeVVplHsZz+feGWbaCN4NIhsk1MmPsETev8ILaxBDI49dGz4tzU3NenV8n37OBO5AoBqQ0iP5/sxf75+XBKGICUy0gWCoaWYsa0U85ZMzkNrY18cn9F02X9YV6KjXVFsF9hwznLqWenk38I/FBTUbDQpksrv14e4XPMwgeC8Jal/CrNngyTha3fQcqCBSMyMVTuJNfQZ6vhFGcciW6MFR+ATvF0mlKnVfWssq5Woq47gcSu26m7M5RNTnrZYo1IpdiEWSynWuwGlDAD7BQ3QuYvYmS4ApAmVCcHFiMof6WiXgdDve6TtGLb3NyO9nOrU1K5fQcdvnH1GFE8EzAomOYhSreE740CNyoQ8VpRqP1I4rDvRrJYR+Z4RjD6yFW7cC9z9ooeyfXYj7RF8h32ET4WJ+WOcev4UvXt7E+sxw8zg3e2lZ08PikNBC2k4ZjoS8x5JlrHOGEfP0SC+l6PEQuFBr0eLeb2u+S6qvmzB+XJAF3Dd7lRHOmQ0RyU3GwW9jmcI6YICyfB9CeiO2SyROucCQkgvIYPJRRsKsi0ojcEyciZCtDtK82PNlC8uz+ymGILCQCGNRkE0Gx1tPPfcczzxxBO0trYyd+5cnnnmGS699NKRPi0BJcirTKPYz34h4pVslaleWY3/gB//Yb++SDwaRLJIuBe7sTaN0LZmWda3nJ04QfsO3aEwwd6rb5tNyvdL/+IXGekCwdCTa2yTZ8n07u7l1KZTNN7ViG2yrWTzlmTnoe+QjxOPn+jXdLn25lrUb6q0v95OLBjTm3pFtbxb+IeKYpqNqkhomn6fXA52geC8Jal/CpL+eek4XEXEb8ZR7UcK2aGrA5PmQ5ld3m+eJbWcoMq+m2DlfPwdTqzuEIpFRQ0r+DscyGYZp7UZepp055CBpqFEvITbAqiHFWi6MKvwUKpdiMVSivVuoqfDDV+HefPQftIGBwJIC+fBrBkAGLHdI+VIH2yRIvE9cugQvPFcRqdm8EAn/t5vYr2gSv89lpfr887Tp5Gqq7GVhwielZBNMfztDqyRbpTGKlSchA76h/x7pre3l88++yzx/y+++IL9+/dTWVlJU5rBRDByFBo9lOmzG2mP0LOrB9WvokU1bFNt2CbZ6NnbQ++BXsoXl9Pzfg+xYAy5TEZxKiguhdDpEFFPFPcid0JMlx0ygaMBLHUW3Ivdg16PFv160eBPujmK2bNTHelZzFHJzUYB0XB0IKR9X6aQo9/YWEcI6SVkoLloQ0WmBWXyYClbZUwVJiwTLJndFENUGCik0SjoQroihPRRwWuvvcbq1avZtGkTixYtYsOGDSxbtowjR44wYcKEkT69855SxjBpMQ0tpqGMU/Af9qds3YPMn/1CxauyuWVU/VUVgWMBWp5uAZnUrYEjta151iz4/vc5c6AbgAnhFn2gmj9fF9EzbEXry0gXQrpAMFRkG9uS3VP+A36CJ4K4F7oZf/34ks5bCmm6POn/m1RQE7qhpqBmo0njFuhajhDSBYIMJPVPAQh6bPg7HFjLQ7pOkBSxkXGe5fXitLbReHkrHZ/U4O9wEPZakU0xymp9KCYVmXAiogOA9g44fBj1lAfZb0J5/j9gb2PGfNlS7kIsllKsdxOFP6cMkyejVcaAZiRLX9HAENJHwpFesiJFHqemurMV9fRJrHPiaylJ0ndJejzQ3o5SVo6sWKiefAz/UQ2/VEvYeiFylzos3zO7d+/m6quvTvx/9erVANx88828+OKLQ/a8guIpJHoo/bNrabDgO+gj6omCCUzlJpyznWhhjWhXFP9nfrq3dyNbZGwX2rDarUhmCUnWs9gj7RH8h/2UV5WDBOHTYVSfim2iraSxwIW/XilhjuLgQTxBCwDlkQ7dFZ3BHJXcbBT6jJ7CkV4Ead+X/cjSb2ysI4T0ElNMZ++hJl0IB/Ad9qH6VUxVJqIdemMsa6MVC5b+boohKgwU6kivqOgT0kWz0ZHlpz/9Kbfddhu33HILAJs2beLtt9/mhRde4L777hvhsxMMJs4g2XETbgvTs6uHwJEAoTMhQkdDhJp14clwjGf67BdTdDPGFvWsimNmhoZ/JZ5oFcysWbRfoMH/herlC2D1RTk7jSc70kW0i0AwNGQa25INAYpbAUkf4wwBafy140sybyl0p0/TD5touq9p2OMV0inOka6Paz4fVFcP9ZkJBGOQpP4puN2oIaWv6SNAUsQGZJhnxe/vtLfjuCJE0GNDDSkoVhWrO8iJ7RPwtpSjWMx6Wau9A3btQvP5CakNuC4IYJtky5gvm3NsmiXrOcWbunHcNQFp8qSittIX6sLOtt4tm1eGe5EbLarnKWe7f3rhLxbWXaHJ6z0j2mW4HeklLVLkcWoqjeNRPjuLerob08RK/frqKli0KKmocpYyUzNVN04kuOjLOZvclpqrrroKTdOG9DkEw0vyZ9e726tHbzpkrPVWvc8LJOZYpjIT4VNhTONNhFvDRD1RFLtC1BPFXG3G5DYR6Yjo15crejN2p4KlzpLxuYelX2DcHBV74028/xyPdvGdymqOSmSkm4QjfcCkfV/2I0u/sbGOENKHgEK31ww16UK44lKInIkgW2WiHdGUxlgSmcWroSgMFONIN8WbjWqjsNnOuYDX66Wnpyfxf6vVitWaKsaGw2H27NnD/fffn7hOlmWWLl3Kzp07h+1cBdkZaJxBsuPGEM4li4RrgQv3l90Ea/QMc8/7Huxn7FgmWDJ+9ostuo22xswGZ9r185vwpVqYnPvYZEHqHCuwCwSjhvSxTdO0hCHAXG1GC2nIZhnzeDNKuS5u+z7y0bCqgc7fdBY1b0kXkIhR1E6fYSv6ZcCIaYECm43Kkv76RMNRgSAzSf1TmD0bxaqimGJ600dLFHp6oK5Oj+Igwzwr6f7S7NnYK5Js1ZpGVdl+grVfwX/SglWKoBw8jOqJEjJPxuKOUDWrC6ncDe7++bJZdyG2dyAdPoz1lAf/ARPBE/+BfWFmR3sminVhp693DTNG28ttee+fXvhLd4XCyES7lDoqMZ9T01Yn43B24j0RRGnU+v6e1VVo45cQ2t2Ja5qG7a5lSJMnYT+H8oUFI4fx2e16p4voT6M4ZjgwV5rR0PD8mycxx1J7VbSYhmzVx7ZIewTZLKM49H8rLoVYOEakM0LoZAhLtQXFrugi/Ej2C5w1C+/fzUB7QP+8lD96D0ybmLGoKBzpgyQW03/GjdOLhgsXpv6ec/QbG+sIIX2IKLSz91CTLIR7dnqIdkcxVZiw1llTGkRAdvGq1IWBQoX0ZEd6SBVC+lAwe/bslP8/+OCDPPTQQynXdXR0oKoqNWmtq2tqajh8+PBQn6KgAAYSw5TsuLE0WtBOaGhoaKqG74APpUzBPsWOdZKV3t29OKY5EjnExbiTMolXo60xs0F7u35ZiENTNBsVCIae9LFN9ahEOiKYyvVxI9qj76wzlZtAIiFuT7hxQlEu8UwCkqnCRPhMGOukoS/4DTaLNzn6IKcjPe680uJCus830DMWCM5xkvqncPAgtoZGHJVVeFvsKKZ2JKdDF8olKfM8K+3+NDb2ZWO3tOCcWkXjtRfR8R8u/LvPED4aQXaOx1XXS9XMDpzV8SpXWr6s1jRJNz+0hlDKFdAAiYSjHb8fxV1BWCpHLavO6GjPxEBd2MZ613fIR/sb7QXfP705ci4hfTijXUoZlQjkdWpKfh9VUz0EXdbMRpRJFVTd0YA0dfh2tAvODyRZwjHTgbVWj2tBArW7b44lSfo8QVIk0PT3v8ltIhaI4ZzrJHQylGhOqvpU3F92M/7r4+n8TefQ9AuMxfQdHl6v/rnKsWsYwOPVb7NYwDZjUtbjMjUbBSGkF8ShQ3p01eHDcOYMHD2q/40M0Tz+fZet39hYRwjp5wGGEO7Z4aH5sWYsEyxYG62QtkbLJV6VsjBQaLSLzQYWWdNdUyEhpA8FBw8epKGhIfH/dDf6uU6xDVRff/11HnjgAY4dO8a0adN47LHH+NrXvpa4XdM0HnzwQX7xi1/Q3d3N5Zdfzs9//nOmTZuWOObrX/86+/fv58yZM4wbN46lS5fy2GOPUV9fP+DXUawjPN1xo3pUIl0RzOPNSBYpJfNOlmUcMxxEO6Mg00/cSReAJt47kVBLqOhmN8m/w0InWoMVn9I5c0a/LCT2X2SkCwRDT/rYJttktLCGZtWItqfurINUcbvQeUs2Acl/2K83zaqxYJvSfywqVcGvFFm8hrsTCot2MXb5CSF9ZBFN3Ec58YgA3nwT6fBhqqwfEJTm4VcuwDq7EWXceN1Fni02Kun+HD6sZ8TabImIAeesWTi+phF85yzqz/6EMn0itspIvwQQI1/W9+FZOjZLeHd78R/xEzoR0uMYZtgxHzmsq9PV1aghE7JZQ6m0Q3l/R3s6g3VhD+T+6TtoMgnpIxHtUvIdk2k7G1L+uHGnpvOy+TR+fSYdRe6iEggGS/p6LBaKoUU1JLNeIFSDKqZKk369pulN1b0ailPBfbk7o9FKkqXS9wtMFmyDQX1wmDkz524bj0e/jG8ayopoNjpADh2CjRv7mihPmgQ1NbBnD7z/vr6onjAhZ7+xsY4Q0s8TJFmifEk55YvL8e7zYsGCRAmrhEVQqCMdwGrSICyE9KHC5XLhzpRllURVVRWKotDW1pZyfVtbG7W1tUN5ekNKsQ1Ud+zYwY033sj69eu57rrr2Lx5MytWrGDv3r1cfPHFADz++ONs3LiRl156iSlTpvDAAw+wbNkyDh48iC2+Irj66qv50Y9+RF1dHSdPnuS///f/zje/+U127NgxqNdTjCM83XGTPHEyHAdG5p2pwpR14ZBLACqbU5b1XEvRf6FkjaCSKMaRnpyRLoT00YUQp84t0vM8Y3590WPkeSbvrCtW3M4lAJUtLCPUHMK7x4t1kjVv0+X0x81V5DNu7/2wl/bX24kFY1ibBp7Fa7g7TSYwm7MfJ4T00YNo4j5GmDVLF6Cbm3F6vTS2WenYZcF/JED4k0B+wTPp/pmclJIsYZ9ZATV+MHeClDlf1heqoeV1lUjIi2WSBftZO6GTIYKngkTbfbhDXsyV5WhIhHqsuOp6sZUHMzra08emwbqwi71/LNbnMjcKf4aYJWdoNjqcQnrJd0zm2ZlgODWds8pwzHKOeCys4PwiUwywJEuoXpVYKIbJacI+z07g04Ae62KVQdZ7GgQOBbBPslN/Rz32qX3jQsljgdMFW+Pzk2e3TaFCeqKIZxLRLgWTrYnylCm6oL57N0ybBnfdBZMnn3NOdAMhpJ9HDFXz0GIp1JEOYFX0wa03KCYSI4XFYmHBggVs376dFStWABCLxdi+fTurVq0a2ZMbBMU2UH366adZvnw599xzDwCPPPII27Zt49lnn2XTpk1omsaGDRtYs2YN119/PQAvv/wyNTU1bNmyhRtuuAGAH/zgB4nHnDRpEvfddx8rVqwgEolgzqWAFEChMUzpjhvZKiOZJLSIhmSVEo6DWCiep5th4TDYZkwDnWhpMY2z757l9POnifbquX7WskE0gorj9/eJSgU50pObjXYG4Vhr3m2GgqFHiFPnJsbYFjgWoOXpFgKfBnAtdBUlbmcilwAkyzJlC8rw7vTi3e3FMcNR0JwpX5HPuN13yEfv/l7UHhXbBTYsNRYktzSgLF5j7MqVjw5JQrqij1MiI33kEE3cxxCyrIsBgHMOOL5S5G64pPtnJI9rWTvRQkfwOiJmG46L9IKfc7ZTn8v5VSKeKD6/m7IKlVC7HYsjQtXMjr6HSXO0p49NjoscWV3YmqahRTTCbWH8h/3YGi1ILSdSigLFuriTo1oKcaQPZ7RLqXZMppBnZ4IhAo6WWFjB+UXyesx3SJ9MRLui2C6w4ZzlxFxtxlxpxnfIR+DzgN6vJqzlXK+VLBY4m2Drduv/z7HbxmgBl1dIj6aOPcKRXgC5mijLsv736OzU/30Or4uFkH6eMRTNQ6G4iIViHOkW2RDSz90P4Vhg9erV3HzzzSxcuJBLL72UDRs24PP5EgvAscZAGqju3LmT1atXp1y3bNkytmzZAsAXX3xBa2srS5cuTdxeXl7OokWL2LlzZ0JIT6arq4tXXnmFJUuW5BTRQ6EQoSRLjjeHBbqQiXi640YpVzBXmQmfDusN/MIakklCtsoZFw6lasZU7ETLyOA0cjhN40xoIS3hSh1QI6g4hhvdYimsqXhftIuM91QPrF2bd5uhYOAU0hgZhDh1LiPJEo6pDhruaKBlYwv+Q4M3BOQTgGwTbUTaItin2Yl2RvPOmfIVGMdfO57OtzuJdERQ3ApIYBpvItwaRu1RcS9yY642F53Fe6Y1BshMqAjBsdNZi3rGuIUSd6QfOQHHVFEELBGFjlOiifvYpuSCZx7XctDahD8yHWtTX8HPXG3GvciN77CP8PEIQX85pk4v7klpGevQz9GePjb1HuhFC2n9XNiR9oj++PEc5FNPHaH3p59SZd+N09qWiFdQLv16US7u5ALeaGs2OmSmszw7EwSCkSR5PZa8U06ySPqOZYuEqdJERV0F1f+pmrK5ZXmF8ZKMk7kE27TdNunFyqId6aLZaOHkaaJsFG/P9S3bQkg/Dyl189BiIxYMIb0QR7pF0R2xwpE+snzrW9+ivb2dtWvX0trayrx589i6dWu/BqRjhYE0UG1tbc14fGtra+J247psxxj88Ic/5Nlnn8Xv93PZZZfx1ltv5Tzf9evX8/DDD+d/YQWSyXHjnOnUs9LbI8QiMWyNumjuP+jvt3AoZhuvsW0421iTPtHSYhqBY4F+xxsCVfB4kFgohqXegiRLhE6HiHqiCQGq6EZQcZLz0fvlk6ahfXyQeB9kVCR6NYc+oBXY1EtQPIU0Rhbi1PlBKQ0BhWzjt0yw0HhXY2K7c7Y5U64CozxLxvuBl+a/b0YpV3B92UWkPYKmanp8lktJ6U2BVEQW76FDtG46BPw1Nb4vYO3fZy3qGfEJRHSLp+/X78An/0cUAUtEIeMUiCbuggzkcC2rs69FfUXG6kyNEzFXmymvKic600HgnXbqa3dTeXnqTp1sjnboMz/4DvhQgyqh5hDKRfqcMNIeoWdXD6pfRYtq2GpUbG0f4+0yEaycT+PlrTjt7bBvH7bmEzgqv4v3hL0gF7eRj26xgBJ/SekN/2Bkmo3C0JnO8u5MEAhGEGM9Zp9sxzHd0e/9717gHv7M/kEItsUK6enNRru7IRrVI/MEaeRpoozPp99eiDNtDCPeGucppXJTFBvvEA73jXWFONLNkj64ef1CSB9pVq1aNaajXEYT99xzD7feeivHjx/n4Ycf5qabbuKtt97qJ0ob3H///Slu+JMnT/ZbsBdDJseNaZwJx0UOevf0IksyslUm2hXNuHAodBtv74e9nNl8puAiW7ai3Pjr9S7wkY4I1iYrweNBPY5GljBXm1MEqKIbQcUpOB89FiP2xm+AxYAe7eIPW9FcbqQ82wwFA6eQxshCnDp/KJUhoNBt/PbJ9ryPna3AaLg6g8eCRFojWOosaGFNLwbGI7Vkq9yvN0VBWbzx7NC2wwsBqK1Wcxb1tC9aAJBiEQB84xpFEbCEnO8N3AWDJItrWWkOobxxLGPBT5IkJIuMZXYtjjIJ6VBhjvbk+9sm2ggeDSLbZN080WDBd9BH1BMFE5jKTTjlLzBFe5CnVNN72sWpvSYaL5OwzXIhHTpIVeO/Exx/TUEubsORntwYOb3hH4xMs1GDUpvOCiIWE451wagg4/vfiHX66OjwvT8HIdgaBql8elP62DNuXN9tXV3xuM/R/tkc7vMroIky8+frx53DCCFdMGAGEu9guNFlGSoq8j+HSQjpgiFgIA1Ua2trcx5vXLa1tVFXV5dyzLx58/o9f1VVFdOnT2fWrFlMnDiR999/n8WLF2d87vTt4clbxwdKNsfNhBsn4F7kxlJjybpwKMTFGQvF9K2BoVhBRbZcRbneA73EemPYLrBBjNQ897TmqEgU1wgqTrIjPSfNzWiHPyVZSI/GFEKqCZspmnOboWDgFNIYWXB+UQpDQCm38WcqMCa7OmWHDApINn0nTaQ7gmJXiHqiepxLUm+KgrJ4k7JDW8suBKCmzJc9OzQWQ3v/A2A6kkWGEPii1oKyRgWFUeg4da42cReUgAyu5YIKfpfVY/v6rfCbLQU72g0Up4JslaleWY3/gB/vbq8urDtkvalzQxTzh61ELNX4WioIey34250Ee2y4671UNcZwdu6l8aav0fEnV14Xt+FIT+7poIWHINplkOLSsGaWHzrUtxshGEzE5oidQoKRIuX9f+gQPD4C789BCLYnTuiXEyfmfopERnq82ajJpIvpZ8/q8S4TOkf5Z3Mkxo4Cmyif63PJIXt1zz33HJMnT8Zms7Fo0SL+9Kc/5Tz+9ddfZ+bMmdhsNubMmcO//Mu/pNyuaRpr166lrq4Ou93O0qVL+fTTTxO3Hzt2jFtvvZUpU6Zgt9u54IILePDBBwmHw0Py+gTFxTsYGHlTlZWFfbZM8fwEj08I6YLSkdxA1cBooJpNzF68eHHK8QDbtm1LHD9lyhRqa2tTjunp6WHXrl1ZH9N4XiAlA324cM5y0nRfE5PXTWbSA5P0y/snUbm0krI5ZVldmMaiLnQihKZpKbdpmkbwRBA1qBILxnDMdmBym5AUXfB2zHYQ6YjQsaUDLRZvfJdWlOt3fHuEwNEAikNJ5LlHPdHEcxsZfmpQF78csxzFNYKiz5GeV0j3etECkcR/VfTfjzdkif9SnfpE5hzPhRuNCHFKMBCMoqLrEhfRziiBTwJEO/XdOA3fbyh4G3NygRH0sdB32IfqVzFXm5FNMpIiIZtlzNVmYgF97FcceqyL6lVBhlg4ljFSqx9J2aFtvjIAast69dvSs0Pjx2stesyYFO8/44+Ysx8vGDIGMgcRnL8YBT9zlRn/Qb8+/4lqRD3R1LHiotlw332wbh088IB++cMfosydljI2pWPsfimbW0bTfU3U3V6HfYadcUvHUX5FOWanSsRnoqetknCvGcUaRTbHUMxRvKfLaPloOr4zdpw1oX5zyqYfNvUbQzM50nNlpA8o2uXQIfjJT/T+NY88ol/+5Cf69aON+M4i9u3TBagZM/p2Cm3cODrPWXD+MJLvT0OwrapCO3CQQHOY3lNWAs1htAMHcwq2BQvpGcaeRE76nmOj+7M5kn8bI47skkt0p+wnn+iX8+efN7sbh8SR/tprr7F69Wo2bdrEokWL2LBhA8uWLePIkSNMyKBS7NixgxtvvJH169dz3XXXsXnzZlasWMHevXu5+OKLAXj88cfZuHEjL730ElOmTOGBBx5g2bJlHDx4EJvNxuHDh4nFYvzDP/wDF154IR9//DG33XYbPp+PJ598cihe5nlPsV3aobhGowBKXCjz9AohXVBa8jVQvemmm2hoaGD9+vUA3HXXXVx55ZU89dRTXHvttbz66qvs3r2b559/HtCLR3fffTePPvoo06ZNS4xT9fX1rFixAoBdu3bxwQcfcMUVVzBu3Dg+//xzHnjgAS644IIRWzwPxHGTz8WpWBWIkHUbcXqOeSFFucBnAcKnw1gnWlPy3E1uE1pMQ4tphJpD2CfZB9QIynCk5412cbnQzH2/rwpbgM6gk9beMqqd/vMmF240kixOGZ85Q5wSsVSCXJRiG3+6a1T1qHpT5HJ9qq0GVUyVJmKhGAoKJreJWCCGc66TUEuIwOcBfTwLa4Vl8SZlh7amC+nQPzvU60UL6/MxWdHnVr6IJfvxgiHlXGviLhhaCs7tHqijPb77RZIlHDMdWGuturAkgWYx4+sdjxqRMJdF0VQJWdEwO1QUawj/KTMd2oU4nGUFzSkzOdKNeIXkjPQBR7sY4lJHh66iGU7J0RhhlbSzKMVxK3YKCUYDo+H9OWsWvmv/Xzo27sW/N4wakVDMVTimzaDqO/NxZvksG56AgQjp48fDp59C59bdEB6ln81R8rcZTU2Un3vuOZ544glaW1uZO3cuzzzzDJdeeumQPd+QCOk//elPue222xKTwU2bNvH222/zwgsvcN999/U7/umnn2b58uXcc889ADzyyCNs27aNZ599lk2bNqFpGhs2bGDNmjVcf/31ALz88svU1NSwZcsWbrjhBpYvX87y5csTjzl16lSOHDnCz3/+cyGkDxGFxDukRywYjvRCGo0CyJo+sfL4xORBUFryNVBtbm5GTvoiWLJkCZs3b2bNmjX86Ec/Ytq0aWzZsiVR7AO499578fl83H777XR3d3PFFVewdetWbPHVgMPh4Ne//jUPPvggPp+Puro6li9fzpo1a8ZclmquRZ1jtoO2V9pQcmwjTi6y5SvKWeosKE6F4IkglkYL5moz7kVufId9RNojRLv0aITyK8qp/kb1gBrhFOxIb2pCmzodAEmO0VDupTPo5KTXzZwJbedNLtxoRYhTgoEy2G386QVG2SajhTU0q0a0PYrJacI+z07g0wCR9giKSyEWjoEGpkoTFXUVVP+nasrmlhUm4idlh7b26kJ6TZmv7/b0op7LRUzRhXMl3sjdZzjSMx0vGFLOtSbugtKgxbTMucReL06XC8e9Ewm2hIsq+BUbYdWvKEgZEcoxaT2AjWjIjLUsjMkaBTSstONnEkFqKGQENYT0jI50yyCjXUaDuFQMSTuL+nW6T98pJOICBcPNKHh/+g75aHnbQsS9AOvVIaxKBFU14/VYCb5tpvECX791n6YV70hPLuIlHOlHe2DxKP1sjoK/DTBqmigXa+QuBSUX0sPhMHv27OH+++9PXCfLMkuXLmXnzp0Z77Nz586URnoAy5YtY8uWLQB88cUXtLa2snTp0sTt5eXlLFq0iJ07d3LDDTdkfFyPx0NlZWXWcw2FQilxCl7hwimKYhwOBsU60uW4I73bKxzpgtKTq4Hqe++91++6lStXsnLlyqyPJ0kS69atY926dRlvnzNnDu++++6AznU0ks3FGWwO0vFGR8FFtnxFuZg/hn2qHcWlpDRHdc5xEvgkgHOmk7rb6xj3lXEDbgRVsCNdltH+Yjk83oEkqTTYu/gPajnZZoJQ7m2GgqFHiFOCkSS5wOjd7SXm1wVra70Vx0wH5moz5kozvsM+wqfCxPwxVJ+K+8vu/A70dJKyQ9t69fslHOmZskObmtCq9f4dsiGkhy3ZjxcMOaKJuyCZfg3XQ14cgU+osu/GaW0Dmw1p5kzs3/gGzCnOUV2wo50sRUGnCy0WIHpWQ7GHcYzvhVAQenpQXE7CEyai+mIFnYsR7ZKSkV6qaJfRIi4VStLOooyInUKCkWQE359aTCNwLMDJTScJHg/iWuhCkvXzMAFKo5axHx+AxwO98elQPiE90WzUlOpIB+j0WUfvZ/M8GTu8Xm9Kb7j0vnEGxRq5S0HJhfSOjg5UVe23aK2pqeHw4cMZ79Pa2prx+NbW1sTtxnXZjknns88+45lnnsnpRl+/fj0PP/xw7hckyMpAmnQZQnrBjvR4hvJZIaQLBKOSTC7OYotsuY6PxWL4j/hxTHMw/trx9HzQQ+BIILEIrPiziuIFeqQV2gABAABJREFUqAwU7EgHYpOnAh1IJolGUxswm5YzFvjKfF1EHy1bhs9ThDglGEmMAmPgWICWp1sIfBqILwD1cc1cbcY93k3v7l4c0xw03tWIbXJxMTJAIjtUaz5BqzfuSLd59BVkpmZPsox20ZfgrV6UsL7C9IVM2Y8XCATDRr+G634v6o4P8XaZCFbOp/HyVpz29kHFkxQTYdWvKBg1Q9kErK4uHKZTmAM+vStfXR1qw3Rk3AU3ec/lSB90tMtYE5eSdhaRqVGx2CkkGElG6P1pFBW9u7307OlBcShoIS1hSIDMUaEGhht9/PjUgl0mEs1GM2Wkq+NG72cz199G0+DUKX2w9Xj0nTpjdG43e/bslP8/+OCDPPTQQynXDcTIXQrG5m80DydPnmT58uWsXLmS2267Letx999/Px6PJ/Fz8ODBYTzLc4Nim3QZ0S6FOtJRhZAuEIw1Cm6MFV+8ZTs+8EWAzl93EvwsiP+In7ZftYEGNTfV5GxkNRAKdqST5Jxy2mi4bh4AJ2f/Bfzwh0JEFwwJw93AHeDrX/86TU1N2Gw26urq+C//5b9w6tSpkr+2cxFJlnBMddBwRwO2STb8h1LHwcChAPZJdurvqMc+NXNT54KYNYuzN99NRNN9MTWn9uVs9qRV6pVCk1tfcPp7ouddcyiBYLTRr+G6S0H69AimaA+OqQqRqJOOT2rQXPF4ko4OPZ4kVpgDPBnD/JCrobyB0ZB+6pNTGb9iPLaZ5bj/dg7maxbDn/85XH0V2uVLCAWcRTV5z+RIT7hCMzjSixLSk8WlTIy0+JWOsbPoxAld/ErG2Ck0a5bYKSQYGUbg/WkUFb37vMhlMopTQXErhE6H6NnVQ6Q9kjhWcSrEgrGUfnxQeKwL5Gk2am0cvZ/NbH+b9nb4v/8Xtm+HY8fgH/5h9DZaLoCDBw+m6LXJYrlBLiN3NtN1KSi5kF5VVYWiKLS1taVc39bWRm1tbcb71NbW5jzeuCzkMU+dOsXVV1/NkiVLEk0As2G1WnG73Ykf12j5Uh1jGBOtfF3aoXhHuiGkd3mkfmOYQCAYvRRbZEs/3rvbi3en7hhyL3bj+rILU5WJ3g97aX+jHckk5V0EFoqm9QnphTjSkyddDReNA+Bkj2vMVvsFoxsj9+/BBx9k7969zJ07l2XLlnHGeNOmYTRwv/XWW9m3bx8rVqxgxYoVfPzxx4ljjAbumzZtYteuXTidTpYtW0YwaQ/91VdfzT/90z9x5MgR3njjDT7//HO++c1vDvnrPZcodhwcCG0VMwCocKvYHroP1q3LWtQznFfmGZMA6K6ckvN4gUAw9PRruO7x6GJ5eTmSLGF1h/B3OAh6bP3jSYaYfkXBwwGiUhna+GqiUhn+Q4GMO5BzkTMjPUnMsll0cT0UAu2LY4UVDkop/MViuhD10Uf65QAKF3mJ7yyiqkrPb/d4IBrVLw+KuMBMjISxoKuri29/+9u43W4qKiq49dZb6TWyQ4CHHnoISZL6/TiTdka8+OKL/W43emiVhKF4vw7z+zO9qGiuNCOZJSRZwlxtRvWr+A/7If7RztSPDwYvpCeiXcqnjN7PZqa/TWurLqJ/8gmUl8Pll+sOsX379AbMY1BMd7lcKXrtaOopV/JoF4vFwoIFC9i+fTsrVqwA9K3527dvz7rVevHixWzfvp277747cd22bdtYvHgxAFOmTKG2tpbt27czb948AHp6eti1axd33nln4j4nT57k6quvZsGCBfyP//E/UhoFCoaWQpt0FeNI1zQN4oNbICITDKZOugQCweimmG3EyccbkQjIpEQimNwmlNlK1ky8geLz9WVwFuRIN7YBmiQaG/XrWloGfRoCQUZGooE7wA9+8IPEY06aNIn77ruPFStWEIlEMJvN/Z5X9J3JTLHjYLEYZpuaOgXmzMl5rLFgdLj1+fGpLvvoyAkWCM5j+jVcD4V1wSY+zioWlbDXihqKi0UjEE9STMZ6PjJmpIfTxKxDh7D+r38B/hsA4TXrsF50oS4c5Sr6GeLSiRO6uNTYqP++fL7iIqwOHdKblh4+rE8QbTZdoM/3/ANh1ix9R5DxfCdP6s83X8QFplNsQ0HDWLB+/Xquu+46Nm/ezIoVK9i7dy8XX3wx0GcseOmll5gyZQoPPPAAy5Yt4+DBgwmh+9vf/janT59m27ZtRCIRbrnlFm6//XY2b94MwH//7/+dO+64I+W5v/rVr/LlL3855Tq3282RI0cS/5fSc/wHylC+X4fx/ZleVFTKFcxVZsKnw5irzZjcJiIdEaKeKEq5krEfHwxMSM/YbDToGt2fzeS/zaFDsH8/9PTA9On6bcaidiQbLcdietHX69V3AjU1lfz5B2LkLgUlF9IBVq9ezc0338zChQu59NJL2bBhAz6fL7EIvOmmm2hoaGD9+vUA3HXXXVx55ZU89dRTXHvttbz66qvs3r074SiXJIm7776bRx99lGnTpiUGufr6+oRYf/LkSa666iomTZrEk08+SbsReAtD+gsUFEcxzUY1tc9JoCJx9qwQ0gWCsUahRbbk4yVZQj2r4pjZXyjPlYk3UAxjr92ePVYzmeRJV0ODft3Jk4M+DYGgH6OlgXtXVxevvPIKS5YsySiig+g7k4tix0EtphUsvBvrhkKmukZ8QlmF/lgdHX1rboFAMDL0a7hutej545EIWK2oYQXZFEOxxuMLRiiepFRFwUyOdGNsks2yLght3Ii1zZO4PVhRi7XQfPjBCn/x56ejQ1fiDCF+EPn0eZk1Sxe4hlhwGuuMhLHg0KFDbN26lQ8++ICFCxcC8Mwzz/C1r32NJ598kvr6esrKyigrK0s874cffsjBgwfZtGlTyvlIklSULlWQQWE43q/D9P5MLypKkoRzphPVoxJpj6C4FGLhGJHOCKGTmfvxQZFCepI5yiDhSO9k9H82jfPbsQMee0zfWt3YmNpseaQaLQ9TQXIgRu5SMCRC+re+9S3a29tZu3Ytra2tzJs3j61btyZya5qbm1Pc4kuWLGHz5s2sWbOGH/3oR0ybNo0tW7YkKoUA9957Lz6fj9tvv53u7m6uuOIKtm7dmqgUbtu2jc8++4zPPvuMRsMiGEcTmSCjBsORXki0iyFWAUTjQnp9/RCdmEAgGDX0c2eloTgVwifD/TLxBopRd62uTp13ZCM5y9P4uuns1BeHotgnKCUj3cD9hz/8Ic8++yx+v5/LLruMt956K+u53n///SkC/smTJ/s1CRLkx2iy5T/sRw2qKDYF+3Q77svcWGos/cQr409WyNrcmFfZyyRsNn1dc+oUTJ06VK9GIBDko1/D9fJyfaF0+jRaVTWhHiuuul5s5cG+eJL580ckm7fYomAmMjrSjXgFRdOFl44OrBfPhjf120OOccW5KgcqfsViiedn9uy+SaHbPfSuTlkWO4RyMFLGgp07d1JRUZEQ0QGWLl2KLMvs2rWLb3zjG/2e95e//CXTp0/nz/7sz1Ku7+3tZdKkScRiMebPn8/f//3fc9FFF2V9zXkNCsP5fh2G92e/oiLxBu2L3PgO+wifChPzx1B9Ku4vu7PuhilGSM/UnyHhSI/rVsP+2SzWxS3LepSL3a6LZZkWs8O9k2mYC5L5jNxDwZAI6QCrVq3KWgF47733+l23cuVKVq5cmfXxJEli3bp1rFu3LuPt3/nOd/jOd74zkFMVDAHZ3FRFOdKjqUJ6d/fQnKtAIBhdZJpIJZMtE2+gFJOPDqkd3isq9HlLIKALUhdcUJJTEghGBffccw+33norx48f5+GHH+amm27irbfeyrgd2Wq1pmQX9vT0DOepnhMYTbYiHRGsE61YnVaCzUHOvHqG1pdbsU+1Y5lgwTHTQdU39AVkItqlJvdjQ9LYZZFoaIDPP9fXVUJIFwiyU8wOkYFgNFwPngjiP+jH2mhFmTYDtd1P6KiKpdJH1bRWpB5PcfEko5ScGekdrbp7ceJEJFnCokQJqyZCUaV4V+VAxK/m5sTz9xOjRsrVKQBGzljQ2traLzbGZDJRWVmZsZFhMBjklVde6eeQnzFjBi+88AJf+tKX8Hg8PPnkkyxZsoQDBw70M4Ea5DUojOX3awaxuF9RMf6azNVm3OPd9O7uxTHNQeNdjdgmZx+HS9Vs9OxZPWXLNGSKaQYKcXFnEtqTGy273f0fdzh3Mo1AQTKfkXsoGM63heA8IZObyjHTwbi/qqK7W68aDtSRLhAIzn2yTaRA32GULRNvoBiO9IKF9EjfNkBJgoYG+OwzXZASQrqglAx1A/e6urqUY4w+NMnPX1VVxfTp05k1axYTJ07k/fffT/SwEZSO9CZbkiQRaY/gP+BHi2mgQSwUwzTehHefl+CJII3fb6StTZ9XFeNIl0ypQrpAIMhMtjWNUcgqFf0yyINO5ClfwlV3hCrbHpxdbaMrm3cQ5HKky6F405p4zp5VUQmrJoLRuGQx1K5Krzfl+fsxAvn0grHFm2++idfr5eabb065fvHixSlzpyVLljBr1iz+4R/+gUceeSTjY+U1KIzV92smsXj6dKTLLqPqovEED6r4D4B1ohXFqRurQi0h7JPs1N9Rj31q9l0xxqYdGLiQXlnZd/vZs4X1zyoJhbi4IbPQfv31+uW+faniNQz/TqYRKvDkMnIPBUJIF5SUTG6qaG+U7n/rpnOXl/nUs49xjBuX38mRLKTHhJAuEJw3ZHRnJU2ksmXiDRTDkV7oRCl90tXYqAvpouGooNSMZAP3dGIxfftrcl6noHSkN9nSNA3fYR+qX8VcbUYLaUS7oqCBY7Yj0XS59bQDkApzpCf1dxCNkgWC3GRa06g+NaWQVWoxvV8GeeOlSC1Xjc5s3gGSKyNdqihLcVXaTFG8YSshNS5ZDLWrcjS5OgUpjJSxoLa2ljPGQiFONBqlq6sr4/P+8pe/5LrrrsvrhDWbzVxyySV89tlnOY/LyVh8v2YSi5ub4dVX4eWXcU6dSqN1Gh2BhfiPTidsdRfV2Njo/WIYnfKRbDAwMJmgogK6u/XHy7s+LEVDzUJc3M8/rw+gnZ2ZhfZrrx18o+VSMFYLPEUytr+JBaOKdDeVyW0i2hXF9x8+Qi0hvO/38P/wObdajxP61Jf/8eJbkFVJAhHtIhCcVxjuLNclLqKdUQKfBIh2RnHNd9Hw/YaSLl4H6kg3OryLhqOCoWT16tX84he/4KWXXuLQoUPceeed/Rq4J2eG3nXXXWzdupWnnnqKw4cP89BDD7F79+6E8J7cwP23v/0tH330ETfddFNKA/ddu3bx7LPPsn//fo4fP867777LjTfeyAUXXCDc6EOE0RtCceqRVapHJdIRwVRuQpIkJIuEFtWIhWIpTZfDJ4JAgY70pFgqMW4JBNnJtKaRFAmT24RjtoNIR4SOLR36bpESYmSQl80pwz7ZjmRSdMfenDn65RgX0aG/I13TNIi3vJEmN+quyhMnQNOwmqIAerSL4aqcNWvoXJVNTSnPn8JwPL8gK8nGAgPDWJBtXmIYC5LJZiwwMIwFxjGLFy+mu7ubPXv2JI559913icViLFq0KOWxv/jiC/7whz9w66235n09qqry0UcfpQj4RTPW3q/pYrHbDV1dcOCAfpumQSiEc6qJJvdbTC77ZyZ9W2Xyusk0/bCpoLVfc7N+WVMDFkv+UzLmRcaazqBfTno2Dh2Cn/wE1q6FRx7RL3/yE/36Ysjn4m5ogPfe048zfneK0ie0d3TARx/BqlVwySW62P7JJ/rl/PlD0yQ5G8kFnkyMxgLPABCOdEHJSHdTRdoj9OzqQfWrmMpNaONlzGdUviR7aNkYyuvkMNwJmiyBinCkCwTnGRndWSXOJoUBONLTOrwLZ6dgKBmJBu4Oh4Nf//rXPPjgg/h8Purq6li+fDlr1qxJ2WYsKB3pvSFioRhaVEvsfNHCGpJJQrbqf2uj6XLvGV2BKkRIT7g+TUJIFwhykb6mSSa5kBVsDg66+eb5RrojPXkHsmRV9CzguKvSKoUBCHpCcPbg0LsqZTnl+UfU1SnoR76GgjfddBMNDQ2sX78e0I0FV155JU899RTXXnstr776Krt37+b5558HUo0F06ZNY8qUKTzwwAMpxoJZs2axfPlybrvtNjZt2kQkEmHVqlXccMMN1NfXp5zfCy+8QF1dHddcc02/c1+3bh2XXXYZF154Id3d3TzxxBMcP36c//pf/+vAfyFj7f2aLhZrmv5/v19fhIVCurCuaUgXzcZ+8CAcfBv+6odQ4NrPyEcvtHaQqdko6P38PvuMRH+/jJSyoWY+F3c0qv9uLr44d1zKjTfCffcN3iE/GIwCz2iImRlChJBeBJ/f9zmnf3laz8XN8COb5b7/m/t+ZLPc929L/N+W+L8t+qJItshI1vi/rTKyLenSHr+M/1txKMiOvkvZJmds/DXcGG4qq9Pab0uyJElE0YAYvWXWhJPDMcORVRRLTKwUXUgXjnSB4PzDcGcVwkAbghXrSE+fdAlBSjDUDHcD9zlz5vDuu+8O6FwFAyO9N4Rs1eeUWkQDC0R7oljrrJjK9am76lORrDItXbqDvZhoF8ksiQKgQJCD5DVNJoxClupVh/nMxj79HOlJQrpslnXh6fvfhzffxLZNjxILdfngK8OUD5/0/Bw+rE/uzpF8+rHOSBgLAF555RVWrVrFV7/6VWRZ5m/+5m/YuHFjyrnFYjFefPFFvvOd76AoSr9zP3v2LLfddhutra2MGzeOBQsWsGPHjtTmoQNhLL1f08Vij0cXocvLdbHVYtGPCYUGnKVdTKNRyJyRDgU40gfTUHMgzUI9Hv2yoiLz+STHpQyk0XKhFBJjM9YKPANECOlFoPaoRDujI30a/ZH1CZ3iVHRHk0vB5Dbpl+UmTBVJP5UmzFVmzOPN+uUEc2Lb8GBJdlMRI2VLMkA0qKEiYXEqWBuVvE4OY2DTFP3+wpEuEAiykakhmH26Hfdlbiw1lpzC+mAz0oWQLhCMPQZaeBsq0ntDWBosmCvNhFpCYAKT04RjpgOkvqbL8gwXp2P6Yr+QQmDy2CXGLYEgO+k7RNJRfSqyTUZx9RfMBLlJd6THwrHEbQkxa9YsmDED6/+KgAeCf/tduLl6+ISX+POPqKszH6XIZR6DDLexAKCyspLNmzfnPC9ZljlhqLgZ+NnPfsbPfvaznI8xYMbC+xX6i8WhkO60Npv128NhPaDc2Pk4gCztUgnphjkh6xxpoA01MzVaLaRZaGen3gXVlEW+HY64lGzn/o1v9C/YjKUCzwARQnoRTFo7iYZVDWhRre8noqGpaf+PasQiMf3f8Z9YOJZyqYXj/06+DMX6foIxtJCGGlCJBWN9P/4YsUAM1a+iheMV/FjcOeFVobX41yXbZCy1Fiy1FqwTrdgm2bBNtmGdZMUx3YFtqg3ZlH8gTnZTmcabUrckaxqx3igerMhuE4pTy+vkSM7yJCCEdIFAkJlMDcGCzUHOvHqG1pdbsU+1Y5lgySqsF52RHk2ddAlnp0AwtshUeHPMdFD1jfyNrIYSozeEcW6yVd9xKCkSztlOTONMRD3RRNNl35er0JCoqupbh+YiOQvUENJPndL1mNG23haMPp577jmeeOIJWltbmTt3Ls888wyXXnpp1uNff/11HnjgAY4dO8a0adN47LHH+NrXvpa4XdM0HnzwQX7xi1/Q3d3N5Zdfzs9//nOmTZuWOKarq4vvfe97/O53v0s4QZ9++mnKysr6Pd9nn33GJZdcgqIodA9yG2v6DpFkw5FRyHLNd2FrsuV4FEEmcjnSU8QsWcbm1gW1UEXN8Hd2G0pX52ApRtASnB+M5verQXrkh9WqC8ORiO5G7+mBujrdoQ4DEoeLEdK1mAbx4Se52SiA8TX06adZ7jyQhpr5omByNQttaoLp0/V/u93DH5cykBibsVLgGSBCSC8Ca60Va+3oyQaNRXVhXfWpqL1JP16VaE9Ud9D3RIl2x3/ORol2RYl0Roh06D+qVxfqg8eCBI8F4f3+zyNZJOzT7DhnO3EtcOFe4sa10IViT3VhJLupgseDECPRFCvaEyUsKzTjYLajMCdHXxdl/cMmol0EAkE66Q3BjP4M/gP+xAQpFoqhSVo/Yd0x08H4FVWcOaNPgop2pJtSHemnT4Oq6r1fBALB6CRT4U31qXj3eQmeCObt3zLUpPeGCLeF6dnVQ+BIgMAnAWSbjGu+i6oVVexo0c+zkFgXSB27amv1tUw0qu/KKSRjXXD+8tprr7F69Wo2bdrEokWL2LBhA8uWLePIkSNMyFCF3rFjBzfeeCPr16/nuuuuY/PmzaxYsYK9e/cmYhUef/xxNm7cyEsvvZTIJl62bBkHDx5MxCp8+9vf5vTp02zbto1IJMItt9zC7bff3s8dGolEuPHGG/mzP/szduzYMejXm75DxNpoRXHqDnWjkFW1oqr4XSznqYs4mWwZ6ZJJ6rdD2jCmhkLDdXZjgFLmMgsEw0l65EdDg+6ybmnRBXWnUxfajfz0AYjDRQnp6bFSSUyfrl9+8kmWO+eLYkkvAhQSBWM0C/3NbzK7uEH/7A93XMpgYmzGQoFngAghfQwjm2Rkt5xxy2GhqH59kRY+rf8Em3URPHQ8RPBYEP8RP7FADP8BP/4Dftpf162bkkmi7JIyKq+ppHplNc6LnEiSlHBTtb/Rrj/mqTCmcSasdVZaNQeeNjMOe2FODiOHWI67rM6eDsCxtvNy0ikQCDKT3hAsvT+DFtIInwoT6YykCOum8Sa8+7x4Pw9SG26kGWfRQrox6aqt1cVzVYW2NkjrPSQQCDIQ6Y6w99K9fb1llL6eMyjk70NjSutFY0nqRWPp60kjW/v60Ugmic7fdRI4FsB2gQ21VzcTSCYJy0QLwc+DtP9zO/Yf2ZGVkZtnpPSGmAPjvjIuYwxN2x79kEJF8OT+DmazLsCfPq2v1YSQLsjFT3/6U2677bZEU79Nmzbx9ttv88ILL3Dffff1O/7pp59m+fLl3HPPPQA88sgjbNu2jWeffZZNmzahaRobNmxgzZo1XH/99QC8/PLL1NTUsGXLFm644QYOHTrE1q1b+eCDD1i4cCEAzzzzDF/72td48sknUxr9rVmzhpkzZ/LVr361JEI69N8hEj4ZTilkFV1wEy5ioL8jPVuzP+gT0oPB4TizMcBgBC2BYDSQHvlhtervY0XR38Pjxul54AMUh4sR0o2xB/qPPzNm6JeffKJr+v1SkIttqFloFEy+ZqGliEsptqA70BibcxwhpJ/nKA4F+xQ79ilZcsrjGaL+Q358H/no2dVDz84ewqfDeD/w4v3Ay/F1x7HPsFP9zWrq76jX3VQ/cuC+zM3p508T7Y3imO6gu1nBQZRyTwjLjPxOjsQW5GAvAGdP+GDt2vNy0ikQCDKT3hBM9agp/Rk0s0b0bBS5TMY60YoW0oh2RUED+yw7Z/5PL9dxiq22Ruw2G5DfXZa+6FMUXYQ6eVL/EUK6QJAfLawR+DQwYs/f8397Ml7f9dsujj10rK+xu1NBKUv6ccf70LiT+tCMM2EeZ9b70FSbsVRbMI0zlSxzPVvT5dZ4nF/BjvQMsVSnT+vrvQULSnKqgnOQcDjMnj17uP/++xPXybLM0qVL2blzZ8b77Ny5k9WrV6dct2zZMrZs2QLAF198QWtrK0uXLk3cXl5ezqJFi9i5cyc33HADO3fupKKiIiGiAyxduhRZltm1axff+MY3AHj33Xd5/fXX2b9/P7/+9a/zvp5QKEQoyeLszZG/m75DZMD9FISLOEFWR3oGId3o9ygc6XGEoCU4F0iP/Ghrg1274MgRXbkeYJa2qvZlmhfrSE8ffy64QP9IeTzQ3p4h/rPYhprFRMHkcnEPNi5lIAXdgcTYnAcIIV2QE2PxZp9sZ/w144F4NmBziO73umn/dTtd/7uLwJEAzT9upuWpFhq+10DTfU1ULq3E2mBNODmcZ8OUI6PMdNHw/fxODu2z4wAoUX3G1a2W6YPSeTjpFAiK4eiao4RPhxOuTdksJ5ybKY5Nc5Jz07i06peyNf5vq4xsy/JjHnm3S3pDsFgoltKfIdYbIxaJYSm36FuGLaB5NUKtISIfRQgdD/Nn+JkiBWn+ibugjOSEGJWUp9fQ0Cekf/nLQ/d6BYJzBVOFiXl/nJfoM4Oqf7YSfWfUpD40kTx9aDL1pTF6z4T1njOxUIzI2Qih4yEkk5Ta3yb+2EZWJjEScXkRIgN7gQpYqi1Y6i1Y661YGizYJtqwTbVhv8CO/QI7psqBNXtPmBw+VqlBobamsCJgpliqDz4QDUcFueno6EBVVWrSKjY1NTUcPnw4431aW1szHt8ar/4Yl/mOSY+NMZlMVFZWJo7p7OzkO9/5Dr/61a9wZ9pen4H169fz8MMPF3QsZC9kFcx57CJOb+psabQRDOqvPz0jPZcjXQjpcYSgJRgqhjt2KlksnjMHvvKVQT9/a6supptMhe2ySxHS0zLSbTaYNAmOHdO1/Yx9tIppqFlsFEwuBhqXMtCCbinP/RxCCOmCopEkCdskG7U311J7cy3Rniidb3VyatMpPP/Xw4knTnDq+VM03dtE4+pGmu5rItgc5M3/q3KwU2HZrTacs/Is+GIxtP/zb8BFmBwK+MAbthEtq8A023VOTzoFgsHS8WYH/oP+IX8eySQh22XdtZns3nTEnZuu+E9Z3MFZ3ufkTLg3K82YxplQXMqABKX0hmBGfIMW0cACUU8UySyhlOnB5VpYF838B/1oUY2oWSEASA6l4IzkTIu+xkb4059Ew1GBoFBki0zFn1UM63MGjgU4tvYYpipTxli8SFeEyJkIjasbsYy3oPrV1D40PfE+NN54H5p4/5nI2UiiD024PYzqUUGFcGuYcGuY3r29Gc/HNN5E2ZfKKJun/7gWunDMcuQcC5MbpTb+H5VbUJh90IHvUAFFwLSxy+jvIIR0wVjltttu42//9m/58z//84Lvc//996e45U+ePMns2bOH4vR0zlMXcaamzuYLHDRRRTPOfo70TOYMEe2ShhC0BEPBaIidKkGWthHrUl9fWL8qwxiFQsZ51/TpfUL6FVdkeZBCHeLFRsGUklhMfyGbNsHx47BwYd/5FVLQHclzH8UIIV0waExuEzV/W8OEGyfQ9fsujt5/FN9/+Pji//uC7j90c/FvLsY+2c5/eKETqMpU0UunuRnt+EngIhRTX36VJ2hlvCNwzk46BYJSMHH1RMJnwikuzmTHpuHWTPw7ors1tbD+/1ioz8EZC8WIBfsutVBf9V6Lanq0incQrs04kkXCXGVOxCJY6uJOzgYr1nor1klW7FPtelxC0hd4ekMwS4MFc6WZUEsITKA4FWSzrE+WZIh4ImhRDVmRMVebiZ7QUJHBZcYxW8F/0E/Hlg4cMxxZt25nEtKFICUQjH7SC2/JY4mmaYRPh3HNd+Fe6B5ULEssHCPSESHcFiZ0MkT4ZJjQqRDB40GCR4MEPg8QPhUm2hml+w/ddP+hO3FfS72FcUvHMW7pOCqXVWKZYEnclt4o9aRsxYNKZaeXlo2FFwENwaqxUb9eFAAFuaiqqkJRFNra2lKub2trozaL7a+2tjbn8cZlW1sbdXV1KcfMmzcvccyZM2dSHiMajdLV1ZW4/7vvvstvf/tbnnzySUD/HMdiMUwmE88//zzf/e53+52b1WrFaii0QE9P5pinknEeuoizNXXu2ePlrwnyaxqx2/XfR66MdBHtkoYQtASlwnCgf/ghvP66PkY1NQ1d7NQwON6LyUeH3EU80IX0d97J0XDUoJAiQLFRMKXCKJLs3g179uhbgUIhfRwxmoPlK+iO1LmPcoSQLigZkiQx/mvjqVxeSdvmNj6981PO/utZPr7+Y2a/eTFnz+qlwfHjC3gwrxctFAVAUWKUWUL0hq2cDdp1If0cnHSeK/z4xz/m7bffZv/+/VgsFrq7u0f6lM476m6ty3/QANFicYE9oP+oAZWYP4bqV4n5Yv0dnHGhPdoTd3B6ovrP2biTsyuCFtLQwnpT0PCpMD58WZ9fcSvYp9pxzHLgvMiJ82L9p+F7DXRu6cR/2K+70iW9eWHZvDJCLaGEsC6bZLSYhqlc//qL9UbxYMVUYUKSwNpoxX/IT7A5mHUrd6J/Q9LESwhSAsHoJ73wZm20ojj1aKhQSwhLVf7+LYUgW2S9AFhvxXVJZmeg6lfxH/HTu79X/9nXi/cDL+FTYdpebqPt5TYkk0T1N6tpWNWA6zIXHW92EOmI4Jitu9Z7/eDHhG2GQqSjgCJgVDjSBcVjsVhYsGAB27dvZ8WKFQDEYjG2b9/OqlWrMt5n8eLFbN++nbvvvjtx3bZt21i8eDEAU6ZMoba2lu3btyeE856eHnbt2sWdd96ZeIzu7m727NnDgniI/7vvvkssFmPRokWAnsWuqmriOX7zm9/w2GOPsWPHDhqMN/hIc565iLWY1m+sAt14JU9VKMfPlXIHsuQAJLSwiHYpGCFoCQoll3BtiKuHDsH+/dDTo4eC19ToY1SpY6eGyfFerJCeq4gHupAOBQjphVJMFEwpSI5yKSvTxwqXS2+O4/HAokV9Yno+bW24z30MIIR0QcmRZIna/1yLbbKNj675iLP/epYPr/sYc+xiQiiFCekuFzGTLf54GhW2oC6kB+LWhHNs0nkuEQ6HWblyJYsXL+Yf//EfR/p0BCVGkiUUu4JiL2DPXIGoAb1BaORMhHB7WL88rTs4w6fChFpCBI8FCbeGUXvUhPCUjLnajHuxG+dFTsb95TjM1Wa8u70EjgRShHXbZBuBzwJoqka0PUpIUmjGwZy4UUxxKoRPhlG9aoYz1UnPGQYhSAkEYwXnLCeN329MRA6ET4aRbTKu+S6qVuSPRykVikPBdYkrRWhXgyo9/97D2X89S9f/7qJ3Xy9nXj3DmVfP4JjtwFRpomxhWUKY6o0Pg06XhLU6fxEwsWg0CSFdUByrV6/m5ptvZuHChVx66aVs2LABn8/HLbfcAsBNN91EQ0MD69evB+Cuu+7iyiuv5KmnnuLaa6/l1VdfZffu3Tz//POAbr65++67efTRR5k2bRpTpkzhgQceoL6+PiHWz5o1i+XLl3PbbbexadMmIpEIq1at4oYbbqA+3tV7Vtrifffu3ciyzMUXXzxMv5kCOM9cxMHmIP7DfqwTrf3iEqJRiTNYmaL0jVW5MtLLyvRLj2fIT3vsMAKC1nPPPccTTzxBa2src+fO5ZlnnuHSSy8t+fMISkQu4Rr6xFW3Wx+Pxo/XA8Z7evrE1Xwu5UId5sPYaLm5Wb8s1pE+bEI6DL5ZaKGk9+bweMBs1p+nulrvoHr4sF58Azh1Su8E7fHo9810PsN17mMEIaQLhoyKKyqY8/s5fHTNR/T+n7P8mI9YXzYHi6UAAa6pCa1Wt3hKssY4W5CWnnK6AvZzctI5Uni93pQtrenbXQeC0cDpxRdfHNTjCM4fFLuCMlHBNtGW8zjVrxI8psci+A748B/w4zvgw3fQR6Q9QudvO+n8bScAljoL1X9Tzfi/Go99up1Ie4SeXT307ukl5tfFJGu9hVMBCx7MOOUAaDZUn4psk1Fc2cepTA4GIUgJBGMH5ywnjhmOlCZ4tibboJ3og0WxKYz76jjGfXUcU9dPxbvfy6nnTtH2yv+fvTMPb6rK3/h7kzRJk+60pRuFlq0tW1lLKypotVVQcEFxnEFQYX7MoCCKgLIoqIgiIuBYURGYkUFx6agoiixu7EtZ2lIoULrvdG+TNsnvj29vtmZtk67n8zz3SZvc3Nw2ybnnvOc977dIW/ei6WYTet3XC3ARoK65FIabnJ5r8yRgc9vFVtIwbOXRRx9FSUkJVq5cicLCQkRHR2Pfvn3aYqHZ2dkQ6A2m4+LisGvXLixfvhwvvfQSBg4ciOTkZAOB+8UXX0RtbS3mzp2LiooKTJgwAfv27YNUqusLfPbZZ5g/fz7uvPNOCAQCPPTQQ9i0aVP7/eGOoIe5iFXVKqgaVJDIW44nGpuABgghFejaKr5PZSpegRfEeIGM0Uw7Clqff/45Fi1ahKSkJMTExGDjxo1ISEhARkZGi2LAjE6AJeE6O5tEdV5cLS6m6pxeXvQZ0hdXOc68S9lWh3k7F1rmHem2ykPaVXoiy0J6Zib9m2zJXbcJB+TBW8W4NoenJ72vBQUkpHt40Pty/Tpdh65epfs+/BD47TfD97K9C9F2EZiQznAqvJieknABo+sqMFOUDSDM+hMFAmhGjQG+qAJXX40+sjJcQG9kFUgARVq363R2FMbFlVatWoVXXnmlY06GwbCCUCaEPEoOeZQcvvf5au9XK9SoPlONyj8qUflnJSoOV0BZoETeljzkbcmDJESCPkv6oM+iPlAUKJD7Xi7qz5bCXX0RZSWDAMggy0qD5vd6KFwGwH1iEKSh5kV9c8VGAeqLaDQt64kxGIzOBSfgzDq3Owvu0e4Y/NFghK8LR9aaLOS9l4e61DooC5WQ3+sHDcTgQJGXqmrrk4Dmol1qasiIZip1gsHgmT9/vtkol8OHD7e4b/r06Zg+fbrZ43Ech9WrV2P16tVm9/Hx8cGuXbtsPsdZs2Zh1qxZNu/fbvSgZfFCdyGEUorMMi7q3NgISKGCxkXXVmn7VOKWHae+femWCekmaA8xDsCGDRswZ84c7eqTpKQk7N27F9u2bcPSpUud/voMO7AmXJ88SULqxIn0mEQCiET0xZRIdOJqZSWJ66YSAOxxmLdzoeXWZqSbc6SHhgJiMUVL5eR0sbJ8xrU5OI4mOyoracLE3Z32OXYMqKsDfHyAuDjqUOq/l0DHF6LtpDAhneF0vCZ4of4fg+C6Ph2TavKhVvaFQGxdANf4BQKoAuflhkFcNn5AFC4XuAPx3a/T2VGkpaUZZEi21Y3OYHQEAokAnrGe8Iz1BBaTsH7zl5so/qIYpcmlUOQqkPlMJnLW5yDs1TAE3dGAvF/SUFcJKJukEEADdzGHussKiD1T4TvMzaIz1ZSDgf8a1dXp+p8MBoPhCFx8XDDgnQFQ16lR9O8iNJU1oXJXAfqjF4pc5dBo1KjLqINsoAxQU0axqTbMOJZKLieTUmUl6XpMSGcwnEgPWRZvqahzk1IDfyhQInfXGhYsiVm8s5QJ6Y7F1hXJSqUSp0+fxrJly7T3CQQCxMfH4+jRo+1yrgw7sCZc9+oFpKYCTVSHroVLWSymtkmhMJ0AYI/DHKBzKSyk1zHlMnJwzTtHFxsVCoEBA+jPuny5iwnppmpz+PlRdA8/mVteTu/B4MF0feLz0vn3cutWinspK3N6LE9XpHtduRmdlqLBfiiBGO5NjSj5qsSm52gbt4iBGPzXsQCAy4G3A0uW9OgvrSNxd3eHh4eHdjMnpC9dupQypi1sly5dauezZzBMI5AI0GtyL0TuiMQtxbdg4AcDIQ4UQ3FDgUuzLiFtbi56eV2C6wAh1AoX9EE9PMUauA/mEBxyAvIL31Nn0QymBn2uroC3N/3MYhIYDIaj4QQcQhaGwC9BDBc3JaACbkEZBjcWo2xrBhrSKlGXUYesV7KQ/WY2atNbFm02NWhk8S4MRjvCu4iHDaPbbiaiA7qizi6+LqhLq0NTZRM0TRo0VTZBda0OlRAj019X1NmSmMXrd+XlupoQWtRqICsLuHCBbi302xiGREVFwdPTU7vxtQ2MKS0thUql0kY48fTu3RuFhYXtcaoMezB2IRvj6Um3FRV0y7uUZTJyKVdXU5ukVJKQapwAYKvD/OBB4M03SYjNyAB++QX44w96DX0cWPNOqQSKiuhnRxUbBZyUk94e8LU5cnJoEoPHzw+45RYgIAAIDAQSEoBbb9WJ6AC9l8HBwOHD9J5HRZEYLxTqJk1KS2nSpAe3u8yRzmgXSisEOIQgzEYW8v6VB49YD6u5pLrGTYBBsVSh9HKuDEBzx6kbuzk6G88//7zVpbLh4eHtczIMhh0IJAIE/18wAmYGIO/9PGS/kYW6Yhkyqu+CJDEf2xCGAHEdlt71HaReDeCq5FaXGZob9IWEADdv0iR/Z6pzxmAwugdyZCNU8jVcQ72ReW0sRA0iRDQ1QFPfAA+vYkj6DYFKRk7QhpwGhDwbYlA81Vx9h9RUVt+hs/L6669j7969SElJgVgsRgUvgDAYnRhzRZ0Voe74+ogvwnwst0s8Hh60wq+iQqfnALA9o5lhErYiuZtiyoWsj0hEER5lZUB4OAmmvEs5PV2Xk61Umo6dsibUy5vHUFu3knjbt69uYJSfT8vf+GKmDq55l5ujgb+mAd5iFdxqhND0sl73xniVnim6rJBurTaHvz99VoKDTeeRNjXRDObQoe0Sy9MVYUI6o10oKwP2IhBPcDdQ9UcVrjxzBQJXAYRSIWQRMvg+4Gsw2AMMszz5RuzaNQ0aX38LLlfSWMepHfHz84Of/kwlg9HFEMqECF0cioCYm7gw7RKqb/aG4ru+CIACHgE1cPVuoB1tWGZonDPMExxMxigmSHUNmEDF6FI0L6mWq65D9pArvt8LRJzh4AINXAPVkApLgCsZEE24BcIoGerS6lCaXArZYJnO+Wmi7WKFkjs3SqUS06dPR2xsLD755JOOPh1GR9OFir6ZKup84pAU2bs5DJHp9rMlp7iiArhxo1lItyejmWESfkWyNXx9fSEUClHEW32bKSoqQkBAgLNOj9FaeBfy2bOG0SsACdd5eZSPXl9vKK6KxSSwBwYCjzwCjBhhum2xJtTX1JAtXCAAxo6l14+KovaKz75MS6Pj5+U5rOZdbXotst8txWzUwcdFhaxV5vUlfcyN5/TpskI6YLk2x9ixwM6d5t/Lykq6NZdV6uBYnq5I57zyMrodBQWAHE1QeNGMd8ONBsgGyyDyFaH6bDVyN+W2WIas37EKCgJkrmo0NXG4/mc+NbyDB9Pt2bPUoUpPb/e/i9GS7OxspKSkIDs7GyqVCikpKUhJSUFNizWZHc/777+Pfv36QSqVIiYmBidOnLC4/549exAREQGpVIphw4bhhx9+MHhco9Fg5cqVCAwMhKurK+Lj43HlyhXt41lZWXjqqacQFhYGV1dX9O/fH6tWrYJSqXTK38doiTjUEyMSfoJXnxIImgRYh/O4S5yv28GGZYZa95SRg6FFRAJbdtyp4QWqefPmdfSpMBjW0VtSzQk45KtluAoaIFZc94Fa5qUtEsZxHCQhEtSl16Ehu0F7CFPuKxbt0rl59dVX8dxzz2HYsGEdfSqMjiY9neISVq4E1qyh2zff7NTjH76os9swN7j2c0VhMbU9Pj66fTRKy2KWQcFR44xmFjfgVMRiMUaPHo0DBw5o71Or1Thw4ABiY2M78MwYJuFdyL6+JFhXVpKzmBewfX2BuXOBBQuAkSPJ6Xj5Mt2OHg0sXw5MnWo+dspcXAhAv/Nq8+DBOhGfd7wHBpIj/to1+jKPGuWQSa/a9FrkbspFXUo1KiFCva9lfcnglK1M4gFdXEgH6P+7dCmwejWwYgXdLlkC3HGH5feyrIwaapEZ37UDY3m6KsyRzmgX0lM1mIBSCAIlwM0G1F+qh+ZeDUQeIgijhKadU3oDPgHUGOhZgnP1vXHZOwaDPJrFSVPFLTqpM6OnsHLlSuzYsUP7+8iRIwEAhw4dwsSJEzvorFry+eefY9GiRUhKSkJMTAw2btyIhIQEZGRkwN/fv8X+R44cwWOPPYa1a9diypQp2LVrF6ZNm4YzZ85gaHOOx1tvvYVNmzZhx44dCAsLw4oVK5CQkIC0tDRIpVJcunQJarUaH374IQYMGICLFy9izpw5qK2txfr169v7X9AzCQ2FaNgADFN8hU/LF2BArQITrjagOt8N7oHVNi0zNNfxMnB2smXHDsXWwlj28OqrrwIAtm/f3qbjMBjtgtGS6soaVxRAhHCXarg0ClGR3xs+3pcBBU3MCuVCKPOUUFWrtIcw1XYxR7rjcEY7xWAA6DYu7KtX6bZ/f919vDnBXME/vjt24wZsz2juwXEDjmbRokV44oknMGbMGIwbNw4bN25EbW0tZs+e3dGnxjCiIbsBjQ0hENz7Dwh+/RmCrCsQNJZCIHOBIHoUuAen6dqJ1hQ+thYX4uYG9O5Nt/r4+ZGIX1YGXLlCYv7dd1t+PRtW32jUGpR+U4rG0kbkCmWoA4defrCoLxk830qxUUAnpGdlUQ3WLnlJ52tzGGPpvQwNpT8+N5f0NuPVDQ6M5emqMCGd4XTUaqAktQF3oA6eo+VQldShsaQRNedq4BHj0cI55drPFYDRcpvsbAySFOEceuNyuS8AncuXdZw6F9u3b+8SwtSGDRswZ84cbUcwKSkJe/fuxbZt27B06dIW+7/33ntITEzE4sWLAQBr1qzB/v37sWXLFiQlJUGj0WDjxo1Yvnw5pk6dCgDYuXMnevfujeTkZMyYMQOJiYlITEzUHjM8PBwZGRn44IMPLArpCoUCCoVC+3t1D15G1WaaO4HCnBysVYfjSRTjFk0Zrv8SgmGjvkKDJBSqqMkQZivM1m+wJqTnXqrpFgPezkSUNhiVWLVqFV555ZWOORkGoyMwWlKdX+8GDzShyVcJlwIJqgs84ObmDrFEDABQ1aogkAogdBdqD8H3q/QHjdp2iznS2wxrpxhOwdiFzQsaXdBMZEpIt+YKNXCk25LR3MPjBhzNo48+ipKSEqxcuRKFhYWIjo7Gvn37WhQgZXQ8OetzkLeZnxUf3bzp4F4thkBaSvG6MiEErgIIZAII5U0QyqogkAsgdBMabCJ3EYQeQhKnPYQQeQZDdP8/IPrjR4hupEJga1wIxwEuLiS0R0RYbqtsMSOp1Wg4chV1R0sg8Zcgv4DahKAg/uVM60v62FJs1N+f/pSqKmq/jC7zXRtL0S/TptE+mzaZFtodFMvTlWFCOsPpZGcDqFdByqngGyJB3Vh3lP9QjupT1XAf5w6O4yw6pwQuAqC6GoNluQBicLmsV8sXYR0nhh0olUqcPn0ay5Yt094nEAgQHx+Po0ePmnzO0aNHsWjRIoP7EhISkJycDAC4fv06CgsLER8fr33c09MTMTExOHr0KGbMmGHyuJWVlfDRX+NqgrVr12rdswwHEBmJ6qcW4lpSb3wAd9yCEpRfD8CV3g9C5d8Pqs8EEH6VZb1+g5lol7zLtYB/1x/wdiZYYSxGj8co+/RygydkqIfEpQkyv0bUlbihvKQfAjw9odFooMhVwH2UO6ShUgCARqUBmlfvmop2YY70tmNrO7V06VKsW7fO4rHS09MRERHh0PNjdFG6kQvbopAuNp+RDjSPJ61lNLO4Aacwf/58zJ8/v6NPg2EFoZsQ4iAx1Ao11A1qaBQa7ZgFoO+aqlEFVbUKjWhs46uNAzAOAlfAxUcIl1RXuBxygUvxfRDX5UEc6g6xuxJiNyUkngpI3eshtMXFbMvqGwD45huojhZBlTIMYs8a5OdMASBEkO4SbFJf0sfceE4fjiNj9qlTFO/SrYR0gMR0S6sTLAntPdwUxoR0htNJTQXqIISrpxCaBhXkw+W4+ctNNJU2QZGlgDRMatI5ZTBL6O6OQT4lAGBaSNfvOHWhQjyMjqG0tBQqlaqFm6J37964dOmSyecUFhaa3L+wsFD7OH+fuX2MyczMxObNm63GuixbtsxAxM/Ly2vhfGPYR5pqMABA7S9Gr0gJyn5tROmNvvCL9YVELoSqVoXqs9VoyGlAyLMhBmK6uaWAwYFqAALklUtND/C62IC3M2FrYSwmUDG6LUZLqgtq5KiBHM9IaiBR1KMOblBUSKEsbURjcSPEvmL4TvPVrqrh+1SA6WiX4mJAeeYixD5urN/USmxtp55//nnMmjXL4j7h4eEOOitGl6ebuLAVCmq+ANNCuk3RLtaKKTozboCNLxmdnPA3whH+huG1Q91Egrq6QQ1VvQrqerV2U9WpoK6jW1Vt88819LOqhgR3VbUKTVVNUFXRbVMFbaoqEqfV9YAiTwVFHl8LzY+2K2iBSBwL6UUZXM+kw3WAK1wHuEIWIYN8mBwiD5Ftq2+2bqViqWVlEPoPgtBbjpJGOZRqIcRcE/xQAcAXgOmVefrYkpEOGArp3RJz0S+AdaG9B8OEdIbTuXgRKIYUmlAZFDnVkEXJIIuUofZcLRpuNEDST9LCOQUYFcUKDcWgYVLgTyDDWEjX7zjV1lLhHZZLzOjk5OXlITExEdOnT8ecOXMs7mucs6qfwcpoHampdDtkmBDSYf7Ar3lQFiihrlND5CGymK9ncilgejpCvvwRwCKUKj1Re+wC5AFZ1P74+en26yID3q4KE6gY3ZrmZbiKPd/i5pdy3ATQp/8ZNKmjUPkboG4AGq42wOt2L/hO8zU5AQgYtl2+JekQCwdCqRIhf8UH6NermvWbnIyfnx/89K8LDIYluokLm6+5LpdTugOPtXgFPtolLw9oUgsgspTr66y4AVb3htFFEYgEgIjc2S5wcdhxNSoNmiqb0FjeiKayJjSWNaKxtBGNJY1QphZAeTYLyqJGKGqlUNS7Q9UoRpNSjJpLTai5VNLieNIwKeQDOHiUSOA5cATcVWoIRXpFgzmOZv4PH6bCpWPHQgoOsqx6XLsUAkCDQOlNcBkZgN8tUGs0qMuog2ygDFBTnrpxXKc9QjrQjYV0a1gS2nswTEhnOJ3UVEADDqLbfeHS2IC6tDoI5TQz2FjciLq0uhbOKcAoI10gwMCZsUASkF/tgZqSerh5uxh2nIYNA7ZsYbnEDKv4+vpCKBSiqKjI4P6ioiIEBASYfE5AQIDF/fnboqIiBAYGGuwTHR1t8Lz8/HxMmjQJcXFx2Lp1a1v/HEYruHiRbseGNqCprAmug11Rn1GPyt8q4fcICRw21W8AtMsQfUpLEehagYJ6L5xtHIoJBceAykqqVs+LJl1kwNtVYQIVo9sTGYkbjwwGVgFSiRqB788DF9oHN+++gIpDFfC8xROhS0JbDhibTAjp6engNm9CsPQVXK/tjTy/aPTzSWf9pk5EdnY2ysvLkZ2dDZVKhZSUFADAgAED4GZc0I3RPelIF7YD0Y91MfgTrIhZAQEUrdzYCOTnA6HWcn0d3WZ1k0KvDIYj4YQcXHxc4OLjAgwwfrQPoB5j4GJu8gqCIq8R9dfrUZ/ZvF2pR21qLZR5SjRcb0DDdaAMo4EUgBOq4RFSBZ/+5fAfUgxXnwagqQkoLweGDgU4DhwA34hSFF4YhH6oQx+3WmiKy9BwsRQ1GYBGqQHUQNYrpuM6za6GMVp9MmhAKACB84R0ttqlS8LeIYbT4Z2f4ZPkCHk2BO4j3bUDvMayRriPckfws8Etc4iNOlY+sYPh690EALhyQ0zTgmVl1HGaPx84f163FMjDAxAKdUuBSkspl1itBoMhFosxevRoHDhwQHufWq3GgQMHEBsba/I5sbGxBvsDwP79+7X7h4WFISAgwGCfqqoqHD9+3OCYeXl5mDhxIkaPHo1PP/0UAnah7BB4IT2yrwqqBhU8J3oCAOrS66AsVmr3E8qFtBzSRP0GTsS1WIY4tg/F+JysHEQTfHV1NNDTaHQD3sjITj/g7QlkZ2cjJSXFQKBKSUlBTU2N9Se3M++//z769esHqVSKmJgYnDhxwuL+e/bsQUREBKRSKYYNG4YffvjB4HGNRoOVK1ciMDAQrq6uiI+Px5UrunW4WVlZeOqppxAWFgZXV1f0798fq1atglKpNH4pRgdw9hxdN4aPEEAQ3g+cSAiP8eRUVeQoLBZJBmgArN92Bfs0AADyar1Yv6mTsXLlSowcORKrVq1CTU0NRo4ciZEjR+LUqVMdfWqM9oKPdfL1JRd2ZSUJSpWV9HsXKfpmKh8dsC6kCwSkXwPNOekA9aOWLgVWrwZWrKDbJUscL2gbR02w8SWjp6BW0zKSCxd0y0nsgXcxDxsG9OsHkZcY8iFy+E7xRZ+FfTBoyyCM+GkE4nLjcEvpLRhxaAT6r/CGX9/rcJE1QKMSoPKGF64fDMfxzeNxautoZB8JhULlCXh5aV9G7leHZC4IV+COQGkDqku8UX2K+jQesR5wH+sOka8I1WerkbspF7XptdrnmsxIT0+ndIOVK4E1a4CVKzHoyHYATnKkm3g9vPkm3c9wCq+//jri4uIgk8ngpfdZspfOfcVldHnUal07MGQIII+UI3RpKEIWUXUrTsghdEloCxEdMBKrmhkcRYsoLic+Y9hxksttL8TDYABYtGgRPvroI+zYsQPp6emYN28eamtrMXv2bADAzJkzDYqRLliwAPv27cM777yDS5cu4ZVXXsGpU6e0xXc4jsPChQvx2muv4dtvv8WFCxcwc+ZMBAUFYVpz5WteRA8NDcX69etRUlKCwsJCsxnqDAej1ym8eI4m5QYMF0IoFUIoE0IWJQMAVP5WqX2KqXw9g0GfURGwscH5AICTjdE0wJJIKHw4N7dLDXh7Al1FoPr888+xaNEirFq1CmfOnMGIESOQkJCA4uJik/sfOXIEjz32GJ566imcPXsW06ZNw7Rp03CRnz0C8NZbb2HTpk1ISkrC8ePHIZfLkZCQgIYGGnxcunQJarUaH374IVJTU/Huu+8iKSkJL730Urv8zQzLnDlDt6NG6e5ziyZ3cs050xNB+n0qjjNsu4I9KGoqt6o5NoL1mzoN27dvh0ajabFNnDixo0+N0Z7wLuyRI8lEpG8m6iKO6KtXAQ4aDPWtR82FGtRn1UOj1ljNSAeMctJ5jIQ6p/Sr7Cn0ymgX2ttYAADl5eV4/PHH4eHhAS8vLzz11FMGpousrCxwHNdiO3bsmF3n0mloZ3HXpZcLvCd6o88rwzBkbgHi7vkE4/55DIMmZ8A7vBzgNKgpcMe1s+NwPH89Mo+MgLKWYmqUKiH2l4XivwhF2NhUuHrVQBomhu+DvpCGScEJOYg8RJBFydBY2ojS5FJo1NTmtIiV4lefnD1L47XBgwFfXwzMOwwAKCqi+UuHYeb1cPYs3c/EdKegVCoxffp0zJs3r03HYdEuDKdy/TrVg5BIdA4ETsDBcwK5P5WFSmgaNeAkJtxTTS07VoMGAX/+CVy+6Q8M89ft3E0K8TDaj0cffRQlJSVYuXIlCgsLER0djX379mmLhWZnZxu4xePi4rBr1y4sX74cL730EgYOHIjk5GQMHTpUu8+LL76I2tpazJ07FxUVFZgwYQL27dsHqZSy//fv34/MzExkZmYiJCTE4Hw0Gg0YTkQv37KsUoSC4m0AgCHBN3AzQobqs9XwmOCBurQ61KXWofHORoi8RBbrNwhcBC3anrFBeQCAU+qRlOFXXAxUVNBtXByrct6J2L59O7Zv397Rp2GVDRs2YM6cOdpJvqSkJOzduxfbtm3D0qVLW+z/3nvvITExEYsXLwYArFmzBvv378eWLVuQlJQEjUaDjRs3Yvny5Zg6dSoAYOfOnejduzeSk5MxY8YMJCYmIjExUXvM8PBwZGRk4IMPPrBaHJnhfHghffRo3X1uI0hIr71QC41KQ65zPVpEUum1XSEeVHcjr0ovcor1mxiMzkUXL/p2M6UWj6EUo8/XIWuNCkKpELIIGRSFCgCWc4r5nPR216vZ+LJTwRsLkpKSEBMTg40bNyIhIQEZGRnw9/dvsT9vLFi7di2mTJmCXbt2Ydq0aThz5ox2/MYbC3bs2IGwsDCsWLECCQkJSEtL047fHn/8cRQUFGD//v1obGzE7NmzMXfuXOzatcvg9X755RcMGTJE+3uvXr3sOpdOQUdGGTWvvuFyciArOg3ZwBAERedAWdKIkpOuKMwbhuriXsg9MwD5F8IQPC4PFf0VUKhE8JbWoV9dGrJcEyEb6dNiZZ6puE4DY5SFQqceI9wR8NNNFDZ440qGGmPGOaDNtaWwanIytfldpI13BtXV1Qa14YzrxrWGV199FQDaPAbsue8Ko13gY10iI2klHI+LrwsEcgGgARpuNJh8rqniM2aLPegX4jEFyyVmmGD+/Pm4ceMGFAoFjh8/jpiYGO1jhw8fbtHATp8+HRkZGVAoFLh48SLuvfdeg8c5jsPq1atRWFiIhoYG/PLLLxjEf2gBzJo1y6SzjInoTsZoxj/VMw4A0E9WDI9P34Pv8Cq4+LqgqbwJ4kAxAKD+cr31+g0irkXbMyaIHOlXKnujYkw8MHYsEB1NK2ecseyY0a1RKpU4ffo04uPjtfcJBALEx8fj6NGjJp9z9OhRg/0BICEhQbv/9evXUVhYaLCPp6cnYmJizB4TACorK+Hj42P2cYVCgaqqKu1WzYQFp6DRmHakuw5whUAmgLpejbordS2ep+1T8av89NquYPdmIb1ar5Ah6zcxGJ2P9nBhO4Ha9Fr0P5eLgaiGW5AIssEybdxCzRly9loS0nlHersL6Wx82anQNxZERUUhKSkJMpkM27ZtM7m/vrEgMjISa9aswahRo7BlyxYAaGEsGD58OHbu3In8/HwkJycDANLT07Fv3z58/PHHiImJwYQJE7B582bs3r0b+fn5Bq/Xq1cvBAQEaDcXFxebz6VT0BmijEysvhHXFyB4ugSjDkVi2IdecPcrg7pRiJw/Q1G2pz/6oQZjPK5A5e4PVe8+ELqZ9gobx3VaWmFsAMdhUK8yAED6H2WO+TvZahebiIqKgqenp3Zbu3ZtR5+Slq5x9WV0WXghfUjfGoOMLY7j4BpGhfvqr9WbfK6pzDxek8zIMNqZL8STk0OjTIMDsVxiBqPHYqJTeLGMisEODb4JlJZCfuF7hMwPhvtId22EiyJHYbZ+g8Ekn1Hb00tWj3DvcgDAqfwgcinFxdHWRQa8jM5DaWkpVCqVdqUMT+/evc1GQhUWFlrcn7+155iZmZnYvHkz/v73v5s917Vr1xp0dqOioiz/cYxWceMG1dpycaHIPB5OyMFteHO8S0rLeJcWfSq9tquf100AQEZZs3uO9ZsYDIaD0Kg1KPm6FKhsRBZk8O4jMohbUNWTqGWLkG4Q7dIesPFlp6GjjAVHjx6Fl5cXxowZo90nPj4eAoEAx48fNzj2/fffD39/f0yYMAHffvutXediinY3KHQWcddMDQQuKgq95kZj1OFIDP1bOqTuVRDXC7AFZ3GPfxWE98dD6KKBKv9my+8rWsZ1WlphbMzo4CIAwB9HHTSWs2W1S0NDj1/tkpaWhsrKSu2mH7vb0bBRPcOpXPyTgqSG3PihRcaWNJyWSzVcN+1IN5WRru9I17aPfKXjIUMoQyY1tcsW4mEwGA7GRKfwYjEt/xziX6LtFMrlpQhdGorefyVxUdxbbL1+gwtnsgjY2MBcAMDJFBFrexhdnry8PCQmJmL69OmYM2eO2f2WLVtm0NlNS0trx7Ps5ujVdzizj7Lxhw6lLo8+8hHUXtWea+mebCGk67VdY2sOAwAuFPmjtrSe9ZsYDIbDaMhuQOnZOhRqJBBwHDw9dY9xHAehlEQtVY3KzBE6MNqlmxR67Q50lLGgsLCwRWyMSCSCj4+Pdh83Nze888472LNnD/bu3YsJEyZg2rRpBmK6tXMxRbsbFDqTuGth9Q0XFQXf7X/H6F+H4IqrO+RQITq1CaUbT8P1+m9Q/HIemt//BEpKtc/RaDRQ5Cogi5Rp4zotrTA2ZlIA5ZUfOuVh8nG7YatdbMLd3R0eHh7azVysy9KlS03WKdDfLl265NBzYxnpDOeRno7UY24APDEktJoynvQytqSelPdqVkg3zvME5axzHPVfSkoA/zJd7jEaGgCFgkLZr12jEaZUSmufWS4xg9EzMdEpTC3xAwAM9S82yLfkBBy8bvcCANRfrW+Rr8fTom3ilyE2t0VjRSn4HMNxUj0GeHYUa3sYrcbX1xdCoRBFRUUG9xcVFSEgIMDkcwICAizuz98WFRUhMDDQYJ/o6GiD5+Xn52PSpEmIi4vD1q1bLZ6rcW6hfqYhow2kG/ZzzqQ/DmAqRoVXAPAy2FVbcNSUI91E3Rm+7Qr55hsEHy5HXp0PTl/xwG0TWb+JwWA4BlW1CjWlKjRAAi8vE5pz8+98AUBT6DvSNZqWZlmnYtTHQ16e6fElb+zqgvn1jLbh6+uLRYsWaX8fO3Ys8vPz8fbbb+P+++9v9XGXLVtmcNy8vDzniun64q6HCcG4M4m7AgEaB/fHMwoV5iMTUzQFuHo6BkGjc+AiaELdZQUkpWcgjBsJlas3FLmKFnGdJlcYnz1rmFkOABoNbuN+h4B7EleyXJCbSz6sNmHl9ZCbS20MW+1iE88//zxmzZplcZ/w8HCHviYT0hnOQa2G6qtkXLr5PIDmaBf9jK20NLg2pAIIsxrtoj/oc3UlV0JWFnD55yz4/2miGEZ2NonojzwCjBjBOjIMRk/GqFOo0egc6UP9i1t0CuVDSXBX3FCgqaoJIo+Wl0lTq2X0i4CNOaQGngROVg0CIttztMfobojFYowePRoHDhzAtGnTAABqtRoHDhzA/PnzTT4nNjYWBw4cwMKFC7X37d+/H7GxsQCAsLAwBAQE4MCBA1rhvKqqCsePHzeoYJ+Xl4dJkyZh9OjR+PTTTw2KLzPaCRNFv86cpKV5o4p+BNKjDcRuvuBozTkL0S4iozapue0a/3s9vtoHHBs5D7ct8WL9JgaD4RCE7kJUK4WQQgVvbxN9KiW1TUK5sMVjPLyWVFND9du9vZ1xphawVujVaMITUimJZA88wCYkHURHGQsCAgJQXFxscIympiaUl5ebfV0AiImJwf79+20+F1O0u0Ghi4m7KWfUUKiF+Ezih2dv/R3XfhmA/DMhGDL9IupKZKi7qoHyz2wIot3gPsodvtN8DVYam1xhnJNDq01CQnTaUm4uPAN9MXqoAicvuOLQIeBvf2vjyVt5vW672sVJE45+fn7w8/NzwAnaTjd7ZxidhuxsXD11Ewq1GK6iRoR5V+gea87YktZkAjDvSG9RGKsZbbzL1xdNF8MYMgRQKqlRYiI6g9GzMcq3LKxxQ3m9DAJOjYheJS3yLV18XCAOooKjtamml9uZmuSjO2gZ4qiHw8FxQG4uBwsrNhkMm1i0aBE++ugj7NixA+np6Zg3bx5qa2sxezat6po5c6ZBZuCCBQuwb98+vPPOO7h06RJeeeUVnDp1Siu8cxyHhQsX4rXXXsO3336LCxcuYObMmQgKCtKK9Xl5eZg4cSJCQ0Oxfv16lJSUoLCw0OISZIaDMVHfQSMQ4nRJHwDAaJfzLYp+yYfJAQ5QFiihLFYaHs5EAXctAgHG30mDy2OXfVi/icFgOAxpqBSFYhn8oYCPt6HrXKPRQFVLkS5if7HZY7i6ArxG0u456TzmoiaMCtpj8GC6PXuW7k9P76AT7l7oGwt4eGMBbxQwhjcW6GPOWMDDGwv4fWJjY1FRUYHTp09r9zl48CDUajViYmLMnm9KSoqBOG/tXJyOXkQcX7OuBV0syujkfqrvMjY4H6G35MI3sgTQcMj+sy/6TMhBvzuz0bff7+j3dxeTcZ0tIu9MFDpFWRlNHjz7LO64l+r7HTzooD/AyusZrHax9t51BdLTKeJ55coWkc/tSXZ2NlJSUpCdnQ2VSoWUlBSkpKSgpqalCcUSzJHOcA7V1dr4hEi/Egg4o+V6cjlcxSUAgIZrtke7ACSk//wzkHEZwN02FMPo16/Nfw6DweiiGM34X1TdAQAY6FUC6eXzJjuF8qFyKPOVqL1YC89YzxaHtChIgSbYIyOpz3nyJHDffY7/sxg9h0cffRQlJSVYuXIlCgsLER0djX379mmzNrOzsw3c4nFxcdi1axeWL1+Ol156CQMHDkRycjKGDh2q3efFF19EbW0t5s6di4qKCkyYMAH79u2DVEq5kfv370dmZiYyMzMRYrR+VWOigBPDCZio71BQ447iWjcIOTWGD1G16OeI3ERwHeCK+iv1qDlXA5+7fLSHM9en4hk/nm6PHeuA6AQGg9Ft4QQcTrn6QoYGjFDWoalSAqFcCFWtCopchdYwJZBYFuf69qVYz+xswCiFrOMwnvDkG069FdhITiZxvZOIj12ZRYsW4YknnsCYMWMwbtw4bNy4sYWxIDg4GGvXrgVAxoLbb78d77zzDiZPnozdu3fj1KlT2qg6fWPBwIEDERYWhhUrVhgYCyIjI5GYmIg5c+YgKSkJjY2NmD9/PmbMmIGgoCAAwI4dOyAWizFy5EgAwNdff41t27bh448/1p67tXNxKvasmLA1yqgTcPI0fafG9iGn/8DEK7h51RvVeR4oOB2E4JHZQE0B4NkAmIjrNMhI57Gw+mTSJGDdOhLSHdZP6imrXUyssNSPfDaYOHAyK1euxI4dO7S/89/bQ4cOYeLEiTYfhwnpDOfg7o7U2jAAzfEJxtTWQurTCABoqmhC481GuHi7GOzSYpawGa0j/aa/5WIYzbnHLLOOwejh6HUKL35NmX9D3W+Y7RTKh8lx8+ebqL1oxpFuRZACgLFjmZDOcBzz5883G+Vy+PDhFvdNnz4d06dPN3s8juOwevVqrF692uTjs2bNspo1yHAyJuo7nM4nd1ukXwlcPcVAUcuiX27RbiSkpxgJ6eaiXZoZNQoQiYCCAiA3W40+GtZvYjAYjuF0iRwlCMHdw0vRVFYHZZ4SAqkA7qPcocxXQpGtsNinAqgZOnWqAwqOWsLEhKcWZuxyOB1hLACAzz77DPPnz8edd94JgUCAhx56CJs2bTI4tzVr1uDGjRsQiUSIiIjA559/jocfftiuc3EKrREwrYm7nYSTaRRnN9bnKgBA4qFE2B3XkblvIK4dCIdv8DVILGS6W1thbMyECdRPys4Grl8HHBa5beb1OpP43CY62YTj9u3bsX379jYfhwnpDOcQGopUDYlQQ3yNhPTmjC3hqFFwOeSCxqJGNFxvsF9Irw2yXgyjqAj47ruuP4vHYDDaRnOn8OK5WuA0MPSBQcCSMSYv2HxOeu0Fy9Eu5gQpgIT0HTtISGcwGAy7MVH060wBCemjAgvMFv1yG+GGkj0lLQqOmutT8chkwPDhwJkzwLGlyejjksz6TQwGwyFcvQpUQI7gxTL082yAqloFobsQ0lApbv5M8Qy2COlAB0a7mMLEhKcB+sYuhkNob2MBAPj4+GDXrl1mH3/iiSfwxBNPmD9pG8/F4bRFwDQn7nYSKiqAy9dJOxrTdAzQ0GRW8Ng8FJ3vjep8D2T+2B9D/uFpNtPd2gpjY+RyICYG+PNPcqU7uHal0cl1LvG5TXTTCcdO/l9ndBmMs5sAXKzvDwAY0nDabMaWaxhlTZnKSTcnVg0eTLeZNQFQZeeRMG/wxOZiGL16AV9+yTLrGAwGIRDg6AUSnYbf5mW246EV0k040jVqDdAcTWfNkQ6QkM6SMBgMht0Y1XcAgDOFzUJ6QEGL+g48btGmC46adV7pMX5wOQDg2GkX1m9iMBgOobycRC8A6D+Ag2s/V7gNc4NrP1dwAk4rZgnE1qNdgE7mSNef8DSFmQlPBqNdsEfA7GKcOkW3/YKV8A2WaDPdOXUTBk08A0CNktyBKOt1j9nxnjWDgSnuoIRQHDrUlrO3ge703tky4djQcoVlZ4cJ6Yy2Y6JwQOPrbyHjOlWZHhLrYbaAgjSMlk3VX69vcVg+PsF40NenDyCRAEqVCNmSgaaLYfTqRQPPsrKWxUijomh2z6hIF4PB6OJYKcZy7Ro1V0KhriNkCnkUFexrLGmEssiwYB/fLgGWBakRIwAXF2qCmucWu0+xGAaD4XxMFP060xztMqrhiNmiX7yQXnepDqoGlfZ+q5FUajXGN/4BADhWP4L1mxgMhkO4SqkLCAgwraPYKmZ1Ske6iQlPLbyxy8SEJ4PRLnRTAROgwAEAuO1OcYuCne7Cawi5pRAAkJdsfqzWFiGdz0l3Gt3pveumE44s2oXRNsxkN2X+XoDGJg5yaRNC/34PILiHviRGGVvScBLSTRUcNbfcRiikPsu5c8DJ0f+HsIadLYthjB0L7NzZ7ZaQMBgMM9hQjOX772nXW28FvLzMH0ooE8K1vyvqM+tRe7EW4t5i7WN8pwuwHO0ikVBMwunT5EoPa+gmxWIYDEb7oVffofhsHnKrPcFBjeiJXsCjfzHZdoiDxBD1EqGprAl1qXVwH00DE22fyly7lZ2N8Y2/A7gfpwuCoFQJIRY2C/Gs38RgMFoJL6T372/6cVvFrE7pSDcqaI+QEF2OcW6u2QlPBqNdMBERZ0AXFTDVagodAICHH4bJTPeACh/kjjyDil8roFaqTa54MVls1Arjx9MYr7AQyMigoZxT6Mj3ztH1BfkJx7NnDWNqAN2E46hRXW7C0Wmt+vvvv49+/fpBKpUiJiYGJ06csLj/nj17EBERAalUimHDhuGHH34weFyj0WDlypUIDAyEq6sr4uPjceXKFYN9Xn/9dcTFxUEmk8HLkkrCcAzG2U28e0mhQGq+NwAgikuH4NVVwO7dVJ2hXz+DL2Jrol0A4M476fan1BBg6VJg9WpgxQq6XbIE6N27+8ziMRgMy/ATelZinHghfcoU64c0F+/Ci1GA9UGfNt5lX5lN58dgMBgtiIwEli7F2SkrAACDwlVwX/mc2Qk4juO0rvTS70tRn1UPjVpjXayqrsYAlxvwca2DQiXCucLeho+zfhODwWgFvJA+YIDpx22JnQJ0GktBAaBQOOrsHAA/4anniDVegc1gdAjddMXE0aNAfj5JT3ff3Xwnn+k+bBjQrx/kw93h4u8CdZ0aVceqTB7H1rZHH6kUuOUW+vngwTb8EdboqPfORNIE3nyzbWNVEyssTUU+d7UJR6ec7eeff45FixZh1apVOHPmDEaMGIGEhAQUFxeb3P/IkSN47LHH8NRTT+Hs2bOYNm0apk2bhosXL2r3eeutt7Bp0yYkJSXh+PHjkMvlSEhIQEODToBVKpWYPn065s2b54w/i2GMqeymkhLg+HFcKPIHAAyRXAXEYrOCEe9Ir79mPtrF1KAvMZFu9+0DNJxhwwmBoGstIXFk3AOLjmD0NMxN6BnFEVRXqsHXILJJSB9GQnrNBdM5w4DtQvqpwzVWz8+m7yr7fjMYPROBAKfzAgAAo2JcLA42atNroW6gtqHoP0XIWpmF7Dez0ZBN/WWz7Za7OzhXKcYHZAEAjuWGGB24E/WbGAxGl8GaI93Wgn++voAr+a+Qm+uos3MQzROeLYxdTERndCTdVMD84gu6nTqV3OGm4AQcvO8kY+fNX26a3MfeYqM8kybRrVOF9I5472w0prWKbjjh6JRolw0bNmDOnDmYPXs2ACApKQl79+7Ftm3bsHTp0hb7v/fee0hMTMTixYsBAGvWrMH+/fuxZcsWJCUlQaPRYOPGjVi+fDmmTp0KANi5cyd69+6N5ORkzJgxAwDw6quvAgC2b99u03kqFAoo9Ka0q5nLxj6Ms5s0GhLW6+qwTzERABDrmkJCupnqwnxGekNWAzRqDTiBriGz5J669VbqTOXnAxcvkoZuQFdZQmJDHEWHHIvB6CrYWIxl/39L0djojwEDgEGDrB/WnCNdm5EuJOenJbRCeo4/lPf1g7gtMVPs+81g9BxMLKs9c4b6TaNGmX9abXotcjflQq2kwaFGoYHIV4Tqs9VoLG4EYGEJc3O/afzp8/gBUTiWF4Jn0LyatDP1mxgMRpfCUdEuHEfNT0YGNY/mjtdh8I5YBqMzoRcR1yIKd9q0LjeG0I91mT7d8r7e8d4o/m8xbv5yE2Grw1o83pqMdIBy0lesAA4fpvNx2jxEe753xsY0fszKG79M6Hh2YyKCp82xMR2Iw4V0pVKJ06dPY9myZdr7BAIB4uPjcfToUZPPOXr0KBYtWmRwX0JCApKTkwEA169fR2FhIeLj47WPe3p6IiYmBkePHtUK6faydu1arfjOaAXG2U2VlUBpKfJd++NEdRQA4D6v3wHJMLOCkSREAk7EQaPUQJGvgDREqj28peU2UinNBv7wA7nSWwjpnSmzzlzOlJl8eZw9S+dtz+ycI4/FYHQlbCnGkpeH73+iy92UKS31dpNPG6YT0vUn+SxFThkzZAgQ5N+I/GJX/FQ4Avd5Z5o9P4txCez7zWD0HMxMmp05/gIAsVkhXaPWoPSbUjSWNsIt2g01J2vQWN4IkYcIwighyjLLAFhou5r7TeOP/AykAceyg8j9xLJ+GQxGG7AqpCttF7N4Ib1TFRxlMDo73UjAPHLERKyLGbzjyZFedaIKTZVNEHkaSp+tyUgHyCgll5Oh+vx5IDrarqfbR3u9dzYa09pcJ6cbTTg6/NtTWloKlUqF3r0NsxV79+6NwsJCk88pLCy0uD9/a88xbWHZsmWorKzUbmlpaa0+Vo/EOLtJoQCamvBd1e0AgBjpOQQGAvD0pP1N5GsKRAJIQmlNjnFOurXCWPrxLibpDEtIzOVMpabaFEdhc9yDo47FYHQ1bIhxUktcsfd3aofuu8+2w7oOcAUn5qCuVaPhhq5tsidPTyAAHplSBwDYfd5Me2MtLoF9vxmMnoOZZbW5f97A9VwqejxypOmnNmQ3oO5SHSR9JBB50GBRXaemiUCO096nH0/VgshIjFuZCI7T4FqlL0ouFHT5pbcMBqPjqK8nrwBgPdrFln5VWLOp9NIlR5wdg9GDMMoQ74oiOgDs2UO3lmJdeKShUrgOdAVUQMWvFS0eb60j3cUFmDjR8HycSnu8d7YY01idHAOcEu3SVZBIJJDofQOrqkwXImCYwdj17e4OCAT4X9F4AMBUn99JaOdntcwIRtIwKRquNZCQfqvufksZ6YBOSP/9d6CmBnBzM7FTR87AWnKRpqbSSffv3/ZZv/aaQWQwOiOWYpzUaiAjAyfd41FcJoSHhwYTJtjWWRK4CCCLkKH2fC1qL9ZqCyNba5eMeWyOOzZuA/53ZQjqlD9CJm7SPWhLXAL7fjMYPQMLy2q/aCTb1a39suHtGQJTPhhVtQqqBhUkcgnANxUaQF2vhlAu1LVZFnR0APAcNxiRkRqkpQHH71pBNSXa0m8ytyqPwWB0e65do1sPD6BXL9P72CNmTZgAbN0K/PKLo86QwWB0FeyJdeHxjvdG/ZV63PzlJnzv9zV4rDXFRnlmzQL27gW2bQNeeYXE9S6NcdKEMaxOTgscLqT7+vpCKBSiqKjI4P6ioiIEBASYfE5AQIDF/fnboqIiBAYGGuwT7dS1FAyr6Gc3paejWiXDgWoKBp56203Az4/2syAYuYa7ouJARYuCo9Y6VgMGAOHh1Ek7dMiC07QjlpBYy5k6epTKzrfIpGnGlrgHHhujLdpjBjErKwtr1qzBwYMHUVhYiKCgIPz1r3/Fyy+/DLFY7PTXZ/RAzMU45eQAp08DSiW+V/0VAJAQegniq7DZVSkfJich/UItfO+jzpe1lTLGjI0RICxEieu5Unx/UIZHYnPsi5my5/vNBCsGo2uiVtN65aNHAX//Fg/vTqW+wgz/Q0D27Sb7NEJ3IYRSIVS1Kog8RBDIBFDXqaGqVUEoF0Jd3+z6dLXeJsTEcEhLA44V9sOUli9lO6y2A6MbUHm0EjnrcyjiTUhF7CCgW06o+xkCgBPq3Sc0vI8TctR3EEL7s/79JjcXvVsXDgKxQHcr5gx+FkgE2o0Tc1bruLQHV67QrSnfEI92vCe2fr533UW3Z84AJSW6YSaDwej+2BPrwuMd7438D/JNFhxtbbFRALj/fuquFRYC339P3ZouTVepL9iJcLiQLhaLMXr0aBw4cADTpk0DAKjVahw4cADz5883+ZzY2FgcOHAACxcu1N63f/9+xMbGAgDCwsIQEBCAAwcOaIXzqqoqHD9+HPPmzXP0n9A+WBM8OloQsef19VzfP31QDGW6BAPkBYjsXQ40WReMtAVHjaJdrGURcxy50v/1L4p3sTWywW5a817Y4iLNzCQxvU+fls+3Z9avE80gXrp0CWq1Gh9++CEGDBiAixcvYs6cOaitrcX69eud/vqMHopxMZb0dJphE4uB2Fh8/xOVV5/i+guwKc3miAL5EBKvK/+oRH1WPaShUruXAXIcMONvYqxdC+yunoxHyl63r1iMrd/voiLgu++YYMVgdDV4sfnoUSAlBfDyArKy6Pvr54er5d44mR8MAafGw71/B6pNh6RLQ6WQRchQfbYawighied1aqhqVND4adBUQathRF7Wu/5xccCnnwI//gi89lob/i5W26Fb8f777+Ptt99GYWEhRowYgc2bN2PcuHFm99+zZw9WrFiBrKwsDBw4EOvWrcO9996rfVyj0WDVqlX46KOPUFFRgVtuuQUffPABBg4cqN2nvLwczzzzDL777jsIBAI89NBDeO+99+DWvAz18OHDePfdd3HixAlUVVVh4MCBWLx4MR5//HGH/d2KXAVKvy512PHaC4FUAIGrQHsrlAkhkDXfygUQuYsgdBPSJJyHECIvkXZz8XGBi68LXPxc4OLjQpMCrYB3jo8ZY34fe1yhAQHAiBHAuXN07Mcea9VpMRiMLog9sS48XpO8AA6oS6+DIk8BSbDuia2NdgFoiDl7NrBuHfDRR91ASO9M9QW7CE6Jdlm0aBGeeOIJjBkzBuPGjcPGjRtRW1uL2bNnAwBmzpyJ4OBgrF27FgCwYMEC3H777XjnnXcwefJk7N69G6dOncLWrVsBABzHYeHChXjttdcwcOBAhIWFYcWKFQgKCtKK9QCQnZ2N8vJyZGdnQ6VSISUlBQAwYMAAbYerU2DNodPRDp7WvH6z6/t/+f0AAFNH3gBXXgbkWxeMXMMpMqHhml4OsVqjXX5sqXFLSCAh/ccfabLM4eaL1r4X1lykgYE612xISNtm/dowg1hdXW0QaWQcd2QviYmJSOQzdwCEh4cjIyMDH3zwARPSGbbR2klEfkIvKwt47z16zpgxyK3xQkpRIDhocM9tdcCNUpuqjtem16IunfLNK49UImtlFmQRMkj708SfPZ2uxx4D1q4Ffrg6CJX/XQNPrsr2v82W73dICK11LCtjghWD0ZXQF5v9/QFvb0Akokn2ykogJgafX6LMuzv7XIG/l9LspDgn4OD7gC8achpQl1YHTkJtRWNJI9T1aq0TXSC23p5Omwb84x/k+rxwwfziObNYW5WXlmZTO8zoPHz++edYtGgRkpKSEBMTg40bNyIhIQEZGRnwN7GK4siRI3jsscewdu1aTJkyBbt27cK0adNw5swZDB06FADw1ltvYdOmTdixY4d2bJeQkIC0tDRIpXStffzxx1FQUID9+/ejsbERs2fPxty5c7Fr1y7t6wwfPhxLlixB79698f3332PmzJnw9PTElClTHPK3u49yx8B/DQTUzeMTNaBRaXQ/qzWAim41Kr3HjX9WNf/cpNHep2nSbVBB91gj3aduVNN9jbSplWr6Wdn8mLL5PqWmRf0DdYMa6gYH1E/hABd/F0hCJNrNNdwVsggZ9Yv6Sk0K7RoN8O239PP995s/vL2u0LvvJiH9p5+YkM5g9BT0Y10eecT257l4u8B9jDuqT1bj5oGbCJipS8hobbFRnqefJiF93z4qgNy3b6sO03kwNqbZY/zqgThFSH/00UdRUlKClStXorCwENHR0di3b5+2WGh2djYEeh3nuLg47Nq1C8uXL8dLL72EgQMHIjk5WdvRAoAXX3wRtbW1mDt3LioqKjBhwgTs27dP29ECgJUrV2LHjh3a30c2V2Q6dOgQJvIVAToaaw6dyZMpcKmjHDxtcBA1NtKpA8DU18YBfQNsEsN4R3r9dV20i35n0JJDYdIkyqS6fp0M3nomlrbTFjeVNRdpXR3l0ri7t33Wrw0ziFFRUQa/r1q1Cq+88or117SDyspK+Pj4OPSYjG6KLRNXloR2gYC2mzfpeQIB9l6mRmF8SC783OptyhSvTa9F7qZcKEuVACh7WOgtRPXZalSdpIkne/L0hg7ltSMOyWf74okn7PifWPt+9+pFo9WyMiZYMRhdCWOxGaCJwIICum6XlgKXLmH3xSEAgBl+B6gdtDDBLo+UI+TZEJR+U4rqkxTn1lTaBK/bvCDyEqHmdI1NA0ZfX2DKFDq9HTsAu+fBWW2HbseGDRswZ84crSkqKSkJe/fuxbZt27B06dIW+7/33ntITEzE4sWLAQBr1qzB/v37sWXLFiQlJUGj0WDjxo1Yvnw5pk6dCgDYuXMnevfujeTkZMyYMQPp6enYt28fTp48iTHNlubNmzfj3nvvxfr16xEUFISXXnrJ4HUXLFiAn3/+GV9//bXDhHTX/q4InhfskGM5E42aRHV1gxoahQbqBjVU9SoS1OvUUNeroapTaVeqqGpUaKpuovoK1So0VTTRdrMJjeWNaCxpRNPNJkADNBY1orGoETWna1q8Lifh4D7SHZ63ecLrdi943uIJkacI589T18XVFbjzTjPnrKEJBMB2IT0hAXj7beDnn51komIwGJ2OQ4d0sS58xJOteMd7o/pkNUqTS+E20g1Cd2GrVhkbM2AAtW0HDgCffAKsXt2qw3QuOrK+YBfDacVG58+fbzbK5fDhwy3umz59OqZbqBrAcRxWr16N1RY+odu3b8f27dvtPdX2w5pDJzUV2LyZPrBDhrS/INJGB9Gff5J+5esLxE0QAMJ+Nr2stB8J6co8JWozaiEbKNO6EwDLjZu7OxWeOXSInAkOE9Lb6qayxUU6fjxZNP73v7bP+rVyBjEtLQ3BwbrBQVvc6KbIzMzE5s2bmRudYR1bJq4A60K70WqQ3ak0ITtl0GV63ErNAI1ag9JvStFY2gi3MW6oOFBBTq8mDWRRMlT+WgnAPvcCxwEzZgArVwK7d8M+IR2w/P0eOxbYuZMJVgxGV8OU2BwRQU700lJAIkFqtjsuFAfAhWvCA8OvAtOettr/k0fKIRssQ93lOhTtKIL7OHeELgnF1RevArB9wDhrFjU5//kP8OabZJS3mU5Uu4XRdpRKJU6fPo1ly5Zp7xMIBIiPj8fRo0dNPufo0aNYtGiRwX0JCQlITk4GAFy/fh2FhYWIj4/XPu7p6YmYmBgcPXoUM2bMwNGjR+Hl5aUV0QEgPj4eAoEAx48fxwNm1tJXVlYi0kIfWqFQQKFQaH+v7iafQ07AQSilWgk2ozUn1JkUTNRNajSVNUFRoIAit3nLVqA+sx51l+pQd7kOGoUGVceqUHWsCjlv5QACEq5+9wsBBx/cdRcHV1fTL69vnLK1bZowgcT5ggLg4sVWrJhhMBhdjtdfp9vHH7c91oXHdSA1QOX7yqERaiByFUEWIYOqnmbxWlNslGfuXJ2QvnKlnX2lzkpH1BfsgnSHt7rrYM2h4+FB62gnTeoYQaSNDqL//Y9up0wBhDb24WrTa1HydQk4EQdNkwbXXrwGjxgPeN3hpXtpK4JVYiIJ6fv2AWbmbuzDStEvm94LW13ikZG02TrrZ8mN24oZRHd3d3iYcswbsXTpUqxbt87iPunp6YiIiND+npeXh8TEREyfPh1z5syx+hqMHowtE1dbtwL19dbjS/RWg5ysjsDhrDCIBCr8bfg5OqaVmgEN2Q2ou1QHSR8JBAIBRF4iNBY3QlWpgthPDFEvumxqNBqTzzcHL6Tv309/pq+v9ecYYO77nZrKBCsGoytiSmz28wNiYqgvVlyM3UVU3yFx8HV4L37a5gl2TsBBNlAGAFAr1OAEnN3Oq3vuodMpKiKjwuTJdvxtnah2C6PtlJaWQqVSaVcW8/Tu3RuXLl0y+ZzCwkKT+xcWFmof5++ztI9xbIxIJIKPj492H2O++OILnDx5Eh9++KHZv2ft2rV49dVXzT7eY7BhFaBAJIC4txji3mK4R7f8vmpUGtRfr0fVkSpU/FqByt8qUZ9Zj5s/38RQ3MROuEIiC4aqNhBCecvBoa0rkPWRSICJEynW8+efmZDOYHR3fv+dtB4XF8DEAiiL1KbXovJIJSAA1PVqiHuJIZAJUH22GqpKEtJb60gHKK/d15fc8j/+6MSafYxOB/PotyfWHDoiEeWjmFOh5XJ6vrMEEVscRGZeX6PRCenNKzStwscn1KTUQOjV/DcLgeqz1chLytPuZ61x4yO5Dx2i02sT6elkvVq3jop+nTgB/PEHlYbXx5b3gneRjhxJ4t/ly3Q7apRhLAw/6zdsGN2aE775c1u5Elizhm7ffJPu57H1WHby/PPPIz093eIWHh6u3T8/Px+TJk1CXFycttYBg2EWa5N4wcHA4cO0X1QUCTNCoU5oL23OPVerdatBcnLw1p+3AAD+MuwC+nhW6VaDWIhHUFWroGpQaQd8Qg+h9n5AN9DjBPZ1ugYOBEaPBlQq4Kuvmu9UqynK4cIFulUbZZkaPw60/H7rC1amYIIVg9E5Mffd9fMDJkyAZsxY7FbRSs0ZL/e3O9bPxd8FAEUyAHpZoDYOGF1cgL/+lX62e7GnXjsM40lHG9phBqM1HDp0CLNnz8ZHH32EIUOGmN1v2bJlqKys1G5paWnteJadBH4V4NmzpAINHky3Z8/S/fpjCwtwQg6yATIEzAxAxCcRiLkSg5jMGHj/PQQ1ECIE9fDbnYkzcWegLFa2eL5aadsKZGMSEuj2p59sfgqDweii8IEUs2fb123gVxk3VTRpExAashog8hBBFiWj2hVAmxRRiYRW8AHk+WL0HJgjvT2x5tBpaqKRi0pl+vmOFERMOZvb4CC6eJFyyqVS23Kr9OMTZFEy1F6oRVNpEzQKDeRj5Kg+3SxQCynWxxLDhpHOlpdHRSj4gZ/d2FD0C35+tK+t74WjcqbaktfuAPz8/ODH/+1WyMvLw6RJkzB69Gh8+umnBvUQGAyTWJvEa2oCysspbNyW1TIPPIDMiw34Kp2+E4vH/UbfYRvqDwjdaVm0qlYFkYcIIne6TDZVNQGAdhkgJ7bfvTBjBnD6NLBrF/D32xxUdLoNxYZ7MllZWVizZg0OHjyIwsJCBAUF4a9//StefvlliMXijj49Rk/A0ncXwJkcP2TWBcHVVYP7p9l/HRX70+eYF6+0jnQ7YqmeeAJ4910qGFhWRiUZbKINtVtaXXC6G9JZ2ilfX18IhUIUFRUZ3F9UVISAgACTzwkICLC4P39bVFSEwMBAg32io6O1+xQXFxsco6mpCeXl5S1e99dff8V9992Hd999FzNnzrT490gkEoMow6qqKov7dzucXAzYtb8rTowegGfRD3P6FeHR+izUnq9Fyu0pGPHLCEiCdf/71kS7AFRwFAB++43KTslkdp8mg8HoAhw5AvzyC0kyeuliNqG/yrixrBEN1xqgLKA+kb6+1FjS2KZznDOHasn88EM3KTrKsIme2TPtKKw5dKqqyLZYWelcB48pZ/Mbb5Bj2tubhBtjZ6SV1//sM7qNjzevhemj37BxHAeRh06s4jgO4t40QDBVBd4YjgPmzaOf33675b/OJow7lSEhJJo3NNCAr66O/i8ajf3vRVtc4mo1cO0akJRELXNkpGU3bgeTl5eHiRMnIjQ0FOvXr0dJSQkKCwvNLsFlMABYd1VXUi45vLxMP268QiQyEu+on4MGAkwOTsHQij9MrwYxgTRUClmEDIocBTQaDYTuOke6RqPRdraEbnZkkDbz6KP01f3tN+DYy9+Zd4J9/73tTjFesPL1pcFvZSVNPFRW0u/2FC7uQVy6dAlqtRoffvghUlNT8e677yIpKalF4ToGw2lY+e7uLqXs6ClTOLi52X94l97kSFcWGQrp9mSBjhgBREcDSiWw+/0y86tnTGHrqjx9bFl552isrQzqQDpLOyUWizF69GgcOHBAe59arcaBAwcQGxtr8jmxsbEG+wPA/v37tfuHhYUhICDAYJ+qqiocP35cu09sbCwqKipw+vRp7T4HDx6EWq1GTEyM9r7Dhw9j8uTJWLduHebOndv2P7i7Y0+UZyv59lugASL0fjoYI38fCUkfCeou1eHsbWfRcEO3fFh/gs+acUqfiAg6fYWCYh8YDEb3hHejz5plf7Kx/ipjF2/qEzVVNul2aL7cqxVtu+4PGkRFR9VqYMWKNh2K0YVgjvT2xJpDx8+PWom9e+138NiKKWdzdjZVwNu5k5zYxcV0H+9itPL65eXA++/Tz08/bdtp8A2bRE6uBG18QlVzfIK0OT7BRufUvHnA2rXA+fOUQcw7FWzGhqJfKC6m/0NVVfuIU7wj9dQpsrHKZNRjjIjQOeM7WTHB/fv3IzMzE5mZmQgJCTF4zN5MaUYPwpIzU62mNlMmo7bIx6flwM9ohUhREfBpsjcA4MV3egNRK2x2NnICDr4P+KIhpwF1aXXaNqipvAl1aXUQyemy2ZrCNH36AE/M1GDbpxxe/uMeHJhX65ii060sNmwX3cwlmpiYiEQ+FwxAeHg4MjIy8MEHH7DiyIz2w8x3t3HEGOz+YyIAWsnSGnhHemNxIzQajd3RLjyz7inEwpQA7NhUgX9eXGN+dYwp7FmV1xEr72xd+WMj1dXVBu5mY+ezvXSmdmrRokV44oknMGbMGIwbNw4bN25EbW0tZs+eDQCYOXMmgoODsXbtWgDAggULcPvtt+Odd97B5MmTsXv3bpw6dUob98dxHBYuXIjXXnsNAwcORFhYGFasWIGgoCBMmzYNABAZGYnExETMmTMHSUlJaGxsxPz58zFjxgwEBQUBoDiXKVOmYMGCBXjooYe0xg2xWAwfH592/R91GZxcDLiujhykAOUFywbKEP1bNM7deQ4N1xpw9taziD4UDdf+rlA3koBlb7vEcTTW++QTinfho14YDEb34fhx+n4Lhfa70QHDVcZ8jHBThU5I16ipXyTyarskunYtMG4c8O9/A//8JwUZMLo3XXcU3FWx5tCZMsV+B4+tGLuuPTxIBU9Npcc0GoqW4d0lx46RiGvq9fUcPO+uqkBNDTmX7r/ftlPRb9gAQORpFJ9QY1/xBx8fnYj/9tu2nYMBlop+BQaSS6yigsR0R7wX1tDPLnRzo/Py8KCYmePHDTPbnZ2dbwezZs2iAbuJrTPy/vvvo1+/fpBKpYiJicGJEycs7r9nzx5ERERAKpVi2LBh+OGHHwwe12g0WLlyJQIDA+Hq6or4+HhcuXLFYJ/XX38dcXFxkMlk8DLnsO7OmHL/mXNmXr8OfP01DehUKiqL/vvvus+/RgPcvEltWEAATSqBdGiFgr6+tz4SaPdqEHmkHCHPhsB9pDvQ/NFtqmyC+yh3eN9FAn1rC9OsfDIXYkEjDpYMw4Hr4YYP8kWnr1wBPD3tc4pFRlIFntWryQ6xejWwZIlj2qmOcIkawQtU/KZQKBz+GpWVlUx4YbQ/Jr67H3svRm6hC/z9qehna+BX9qnr1VDVqnSClR3RLkhPx1/y34GIa8LJsv5I9bnV/hxlW1blmeqfOnvlnYMyovWJioqCp6enduNFZUfSUe3Uo48+ivXr12PlypWIjo5GSkoK9u3bpy0Wmp2djYKCAu3+cXFx2LVrF7Zu3YoRI0bgyy+/RHJyMoYOHard58UXX8QzzzyDuXPnYuzYsaipqcG+ffsglUq1+3z22WeIiIjAnXfeiXvvvRcTJkwwqL2zY8cO1NXVYe3atQgMDNRuDz74YDv8V7ooTq6t8ssvNCzp21dXCNS1nytG/jYSroNdochRIPP5TACwuwiyPrx4/vPPrTpNhoPp8eMbhsPha0LPnAmEh1ve1xT6q4z1TZsatQZqtVrrSOfz09vC2LEUhwcACxe2MiGB0aVgjvSOwJpDx1G52sYYu641Gvq9ro5EY4WChPURI4AHHyQRfeBAYMECw8GPnoPnZpUQm34gO/rKJ3PBcSHmX18PvmGrPlsNYZRQ17hVUnwCn18lkNj+Nz/3HLBlC3Xgzpwhvdsk9uTD+/nRwConh8S7JUuAuDjnOjGNB5SVlTTBIRDQ+ZSU0Pvm60vvIysm2Co+//xzLFq0CElJSYiJicHGjRuRkJCAjIwM+Pv7t9j/yJEjeOyxx7B27VpMmTIFu3btwrRp03DmzBntwPCtt97Cpk2bsGPHDq27KiEhAWlpadqBoVKpxPTp0xEbG4tPPvmkXf/mDsea+0/fmZmeTrFGYjF952Qy4M8/aXKxtJT2z82lTSQCXF2Bt95Czd0P4v33IwDQ19WOlcIGyCPlkA2WQT5CjopDFQAHhC4JReFOcrvZJUbp0dezAn8Pv4jNmffg5YN34o6wjw3P0Zai07xTzFRb5uhVKR1cn4EnKirK4PdVq1bhlVdecdjxMzMzsXnzZuZGZ3QMvNgM+nqtfo3uXrGCmrbWIJQLIZAJoK5To7Go0X7Bqrkv4lebhcmDruB/GZHYenYs3runjL7zp05R7JxxH7E12BM14Yg2zkkZ0WlpaQgODtb+3hY3uik6up2aP38+5s+fb/Kxw4cPt7hv+vTpmD59utnjcRyH1atXYzW/dt8EPj4+2LVrl9nHt2/fju12V8Pt4Ti5tsq339Lt/fcbHloSLEHUriicHn0aFQcqoG5Ut0lIv/NO+nqmptIph9g2BGU4iR49vmE4nIMHgR9/pOHQyy+37hj6q4wVOQqyEKsBRZ7CwJkuEDtG13njDarXd+wY1cN6/HGHHJbRSWGO9I7C2KEDGLo0gdbnapvD2HXNx5bwzkexmFygCgW93uDB5PYUCAxFdD0Hz3vlf0NVowzDPG9gWtobtld5b27YXHxdUJdWBzT3n5qqmlCbWqvNTLdHrOrblzKIASr4YBJzzsraWvP59QD97+LinC+iAy0HlJ6eJJrzOdEeHvS+8Vn6jsrO72Fs2LABc+bMwezZsxEVFYWkpCTIZDJs27bN5P7vvfceEhMTsXjxYkRGRmLNmjUYNWoUtmzZAoDc6Bs3bsTy5csxdepUDB8+HDt37kR+fj6Sk5O1x3n11Vfx3HPPYRhv07EBhUJh4Mat7sjVB63Nk7XF/cc7M195hR4fMIAm9cLCgN69gVtvpSC60lLKcMrJoZHTXXcB/fsDZ8/iw2cvoqKCdrN1hYw5OAEHj7E0sdZU1gRNk6ZNgz4AgLs7Xor+ATKRAsfzQvDd5cGGj9tadLqoyPku8Y5wiZohLS0NlZWV2m2ZmTWeS5cuBcdxFrdLly4ZPCcvLw+JiYmYPn065syZ4/S/hdENcWDO9saNQGEhua/aGvesX3DU7rZLry8yb8wpAMAHp8YiMxM0qXn9On3/n3+eZi3/97/W/+22RE04cuWdkzKi3d3d4eHhod3MCemsnWJ0KE6sraJWU5kXgGJdjHGLdoOLrwtUNSpUn6huVe0GHh8fcoECOvGe0XG0ZnzDYJiispLSjgHqB/Xv3/pj8auMPUZ5QCgnk5IyVwm3EbriM60e0xkRFATwJUyWLDG/6IfRPWBCemegvZbNGy/lUyh0og1A1aREIsoDB1oOXIxElUqxHzaeiAMArLjrGARlJXaJKvrxCRpFs3itAuSD5fCbThng9jZsixfT7Rdf6OYjtFgS8rZsAYYPb12n0tGFqowHlBxHIr9MRm50jYbeq7IyVkywlSiVSpw+fRrx8fHa+wQCAeLj43H06FGTzzl69KjB/gCQkJCg3f/69esoLCw02MfT0xMxMTFmj2kra9euNVgubuzObTda21bZI8jyE3c3b9LnXv9z7ecHTJhAcUsSCdmR4uMp1sXDA1lBcXjl5GQAwIsvqM2auu3BxddF2w4pC1shRhkTGoqAkYF4tj/FAi0/eAfUmuZj2Vp0ulcvsjw4MJLAJO1QkMxWbBWonn/+eaSnp1vcwvXWh+bn52PSpEmIi4sziAvobLAYqnbGnuu6A/twZWXAW2/Rz6+9Rv6GtqBfcJTPSLdZsNLri9zd/yru7p+JRrUQi/feTjFzHh7UZ7x+nTpdixaRO701f7uToyZa0N7CvRHdtZ1idCFaUwzYBv74g+b53d2B229v+Tgn4OB1hxcA4OaBm63OSOfha0isX0+L+Ri20R5xeQxGa3n2WfJK9e+v6xO1BXmkHKFLQyEfRtd87wRvhDyrW8LiKCEdoK5Qv360eNgR587ovDDlraNxQkajWfilfLzrWiLRxQjwAo6vLzmggZYDFyNRZdPxGFQqpIjyK8ZDUZdaJarwDVvY62Fw8aUBn+8DvpCGUAyGvQ6F6GjS1VQq4N139R6wRci7cAGYP9++TmVrBtDWBuimBpT6ee1VVRTHU1vbPnnt3ZDS0lKoVCpttidP7969tYWqjCksLLS4P39rzzFtZdmyZQZu3LS0tDYdr1W0pa2yV5C1JHJUVdFEEv89aT6eRgM8/d1U1DS54lb/S5h9p2PEXU7AQRxEapYiT6Er2NfKaBfeCbY49g94utTiQnFvfH4+Ujdp5+cHPPMM3Zqa1OvVi/7YsjLnu8Q7WGxqDX5+foiIiLC4iZvVyby8PEycOBGjR4/Gp59+CkEnnYzkY6hWrVqFM2fOYMSIEUhISEBxcbHJ/fkYqqeeegpnz57FtGnTMG3aNFy8eFG7Dx9DlZSUhOPHj0MulyMhIQENDQ3affhl2vPmzXP639ipsOe67uA+3BtvUBM3cqRuhV1bMCg42mhn26XXF+E44N2790HIqZBcMQkHRXfTd7+igvoigYGk+peWUraevX+7cf9UH2esvGtv4d6I7thOMbogDq6totEAq1bRz488Yn4i0PtOqjVz85eb0CjbZk6YOxfw96f5vP/8p1WH6JG0Rz0HZ9ERxoLy8nI8/vjj8PDwgJeXF5566inU1NRoHz98+DCmTp2KwMBAyOVyREdH47PPPjM4xvbt21usPNKvB8Egvv4a2LmThks7dlCpOEfACTjIBssAAOo6NaC38Lc1K2LMIZXq6vW99RaVvWJ0T1hvrCNp72Xzxkv5AFoXV1pKRTRlMhrI8PnpxgMXPVGlSiHBu8eoKOmK236DgNO0WlThBBxc+7lCGtacIZ2nbF1RrGZefJFuP/6YZgMB2C7kyeW2dypbM4C2ZYBubkDp5wfccgvFXEybRvYLRxUTZHRqJBKJgRvX3dLg3tErJPhjtqWtsleQtSRyKBS0SSS61TMAtp4ejQPXw+EqUmLb6PchqHWcuCsJotdR5CnatAxZS2QkfF58GotvPwkAWPbzRFQU1NtWdPrhh6mWRXu4xDtYbHImvDgVGhqK9evXo6SkBIWFhW2e9HIGXSmGqstj7rp+5gxZxPXjSyy1i5GRwI0blCF+7VrLttFEO52dTYvjAGDtWscsMuMLjiqLlPY7P436IlHiTMzz+QIA8Ny1+VDlF9HfGxhI7YCnJ1BTAwQH299/dWLUhMlroj3CvTOuqTbSldopRhfFlmLANvLzz8DhwySgr1xpfj/veBLSq45WaXOKWyuky2S61civvcZc6bbizLg8Z9JRxoLHH38cqamp2L9/P77//nv89ttvmKuXvXbkyBEMHz4cX331Fc6fP4/Zs2dj5syZ+J7POWrGw8MDBQUF2u3GjRsO/g91bQoLdZF2L75IsocjkfYlrUlxQ2eMAuBwRfShh4A77qCh7YMPsoiX7gorNtqRtHdxJaBlQT+JhF5LKKSBoLc3DVxyc1sOXPRElZVHJuNmgysifEswPSqVHm+jqCLpI0H1yWo05DRAJqcZw9Z0rOLjgfHjqdDD008DP/wAcJaEPI2Gel5FRfQ/saVgX2sKVdlatI8fUObk0HFCQnT75uZSGPz//V/rylczAAC+vr4QCoUoKioyuL+oqAgBAQEmnxMQEGBxf/62qKgIgYGBBvtER0c78OwtYK2YZ2tpa1tlrpgvj3HbYakQllhMQrqfn3b1zI0KT7yw/24AwNpb9mKAb6VDxV1JMAnpBpN8bV0GGBmJBV8NxidDG3E9xx9zq9/B5y+6ghNaKTqdmmp9UoIvRtpWnFyQrCPZv38/MjMzkZmZiRCjCmUaU3UyOgg+hkp/kGtLDNWiRYsM7ktISNCK5NZiqGbwa+XtRKFQGCwP79BaDq3B3HWdL8R+9Spw/jwtfYuMBMaNM90u8gXB8/Pp+5qTQ9+T8eOp1kNREXD8OJCRYdBOrzz5DyiVXpg0Cbj7bsf8SS7+tNKvsbhRt5rG1rbLuC8ileIVn034rOJenK8dgI81f8HfBxzQ9XHEYmp3lMrW9V+N+6d5efS/GTWK+qKtuYZZuiZa6mfx/d+MDOdcU22kq7RTDIZaDfCXqX/+03K3wDWczFMN1xtw88BNAG0r9jdvHjk/r10DPvtMl63MMA8fl2eN559/HrOs/EPD23E8qm8sAICkpCTs3bsX27Ztw9KlS1vsr28sAIA1a9Zg//792LJlC5KSkloYCwBg586d6N27N5KTkzFjxgykp6dj3759OHnyJMaMGQMA2Lx5M+69916sX78eQUFBeIkPxm5mwYIF+Pnnn/H1119jypQp2vs5jjM7zuzpaDTAnDnkGxoxAnj1Vce/hqQvjecabjQYjOc447FtG+E4WiEzahRw8SL9XZ991nIIzWh/srKysGbNGhw8eBCFhYUICgrCX//6V7z88svalYC2whzprcWaQ8UWB0tHLZvXX8r39tvAv/4FPPYYPWYpzqRZVNl7wg/vHR8PAHjn7p8hFGgcsvRWEtLs+sxVtCmHmOOAbdtojmDfPmDrVph3VpaUUKDfgQM0UNq61bZ8T3ujKux19Dopu5BBiMVijB49GgcOHNDep1arceDAAcTGxpp8TmxsrMH+AA1y+f3DwsIQEBBgsE9VVRWOHz9u9pgOxZkxUfa0VW11/wGW3Yl5eZSJ3rzWjyJd7keNUoIJfW7gGd//Orz4rjjYRLSLA/L03DwE2P2lC0QiYM8Pcnz0idEl2ZRTrD1d4s50iXYws2bNgkajMbl1JrpSDFWnqeXQWkxd10tKSPQuLKQVfAAJxmfPUn+huNiwXeT35zPE5XL6zuzeDfzjH6T4/OMfwH//S6/R3E5/+Z0EO5K9AFAXxFGDLa0jXb/YqD0r/fT7IrW16KXIxyu9yDa/vGE5KuVBun316+y0tv/qyKgJa9dEwHI/C2i/6EUzdJV2isH44gv6ari764rtWYKPdynfVw6gbX0qudzQld7U1OpDMYywJ4bK2XRUfaujR4/Cy8tLK6IDQHx8PAQCAY4fP272fCsrK+HD9xuaqampQd++fdGnTx9MnToVqampFv9mhUJhkGXf5QwKdrB0KRUqFouBf/+77TViTME70htuNLS95pUVAgOpXRSJqMu3ebNTXoZhJ5cuXYJarcaHH36I1NRUvPvuu0hKSmoxGWYLzJHeGqy5Pm11hdrr0nQkvEADkEhzxx0tnY/G4ohAgIIJ0zFrDc2kLoj+FfeGpQOVtaYd7HYi6dMspOe0TUgH6N/8xhvA88/TdtedoQg3dlbyA966Oup1hYeT29vYIW6MWk3vbWEhOWI1mpajXmNXaGscveYcqV1QtOqMLFq0CE888QTGjBmDcePGYePGjaitrdW6HGbOnIng4GBtbuCCBQtw++2345133sHkyZOxe/dunDp1Slv4i+M4LFy4EK+99hoGDhyIsLAwrFixAkFBQZg2bZr2dbOzs1FeXo7s7GyoVCqkpKQAAAYMGAC31gbBWVohERkJnDpFEQMLFrRu6a6tbVVREfDdd613/+mflzl34ujRZDfauxdIS8Pmksfwy7X+kAqV2Ba1HgK/Xg4Xd3lHuiJPAVdXVwBtyEg3Ytw4inFYvJjenrg4YOhQC09ob5e4M1yijG7JsmXLDJzweXl5XUtMN54w1GjoM19XRytg+NoEYjF9906epDavpkbXF9DfX6Gggi36UTD5+TSJrlKRW93NDZcFEXjyzBwAwOJbj2HcmHFwlM+Fd6Qri9pQKJnvi2RlAe+9h3kZx/DBiUJcuhmA5zL/iU8i14NDc52dwED6X1RVtb7/qt8/bS22rhpcsoRG78b9LIBmNOxZdchg9FCUSmD5cvp58WLq0lnDO94bBR8XoC6tDkDbxax//INc6VevArt2ATNntulwjFbglPGNHpaMBebiZRxhLCgsLIS/v7/B4yKRCD4+PmbNB1988QVOnjyJDz/8UHvf4MGDsW3bNgwfPhyVlZVYv3494uLikJqa2mLFEc/atWvxqjOs2Z2Mt9/WFeb84AOSppyBtJ+ekN7G+gy2cOutlMS7cCHpUaNGARMmOO3lGDaQmJiIxMRE7e/h4eHIyMjABx98gPXr19t1LCak24u1eI7Jk0ngsRbfAXSuZfPWBi5qNdRZ2Zi5JAClDe6IDizEuqgdwOUah4kq0j7NuVX6QnobxKqFCynO9LffgFlPCnDoXw9AyAt5wcE6h6VIRAO/qCi69fAwP0jiJ0lOnaIlvzk5QFAQvY9+frr9jCdBbHH0mopjcMSAkmGSRx99FCUlJVi5ciUKCwsRHR2Nffv2aTtT2dnZBkW94uLisGvXLixfvhwvvfQSBg4ciOTkZAzVUz1ffPFF1NbWYu7cuaioqMCECROwb98+g2IyK1euxI4dO7S/jxw5EgBw6NAhTJw4sXV/jLmJGlMRA2PG2LcsnReAvL3pWGPGGH4n+LYqJAT48ksSmsy1ffYKspYmk/r3x1dr0rDw12kAgLUj92Dg7UFOEXe10S75Sq2bwdEV3g8coBU0jz5K+pxMZmZna9FP5iY01erWT8qxSb0OoyvFUEkkEkj06hZUVVW1+lgdAu8ez8rSFfUtLaV+AceRMM47rjkOGDSInOcZGcDYsdSf4PcHdCs4hELqH1RV0bH79aN+RkkJalOz8FD6SlQrpbgtOBNvhCYB2QEOu+7rFxvlhNRmtaq+g0BAZoP/+z+4bNqE9+uScNfvK/BpyRSMkqVjvuxTXZ0djYb+JwMH6q4f7d1W2Gte0P9/q9XAkSPA0aNUxdAYZ0UvMhhdlE8+IQHb3x947jnbnuN1h5fB720t9ieXAy+8QPNir70G/OUv1Fwz2g+njG+6IIcOHcLs2bPx0UcfYciQIdr7Y2NjDVYox8XFITIyEh9++CHWrFlj8lhd3qBgA59+qqtvt24d8OSTznstSYgEEAAahQaKPIoidGShUVM8+yzFDe/eDUyfDvz+OzBggFNfsltRXV1tMJ4wHms4AlOrR2yBXWLswZrDJTWV1m24uwNDhtjmCm2NINLeNIvH678Kxy9nZkAmVOC/07+B5L6/UN6ng0QVA0d6U9sL+gkEwPbtwPDh1Ght/CkSz/NC3qlTFKYnk7UUws0NkvQnUfr2BW7eJDEwP58GzDExOtea8SSIrY5euZwG2kysahfmz5+P+fPnm3zs8OHDLe6bPn06pk+fbvZ4HMdh9erVWL16tdl9tm/fju3bt9t7qpYxNVGjv+LCw4M+13K59RUX+uivrikupu9Mdrbus823VbzoVFbWOvefpc+4mcmkw0WR+MtXEdCAw9yHy7DgrVuAvo855fui70h3SLFRI/jK9NHR9K/6+9/pd7N/ir0ucVtWSVkT2tmkXoegH0PFr2zhY6jMtV18DNXChQu195mLoeKFcz6Gat68ec78c9oXeyaP0tOBr74Crl+na7y3N+DqSm0c7zbXd1wDFC/VuzfdNmeIQ6kkob2khFQctVonxAsE5ETnOIDjoHH3wP+dm4eLFb3RW16D3Q99CVFBnUPj/PSLjfKiepsmAZvbnju++QbrynZicdpsLLzxHKKGZOOOMY3kzv/1V/o/qNXAK69YzxRvyySfOVprXuDbyqNHgZQUwMuL+mTGZglH1qJgMLowVVWUwARQGpOtxmOxnxjyEXLUnqOYOkeYE/75T3K2XrkCvPceOUAZ7YdTxjd6dJSxICAgoEUx06amJpSXl7d43V9//RX33Xcf3n33Xcy0sizCxcUFI0eORGZmptl9urxBwQrJyVTPDqDVLLyg7iwELgJIgiRQ5CpQn1kPwHErjM3BccDHH5NMeOECcPvtZJ6KiHDqy3YbjCeOVq1ahVdeecVhx8/MzMTmzZvtdqMDTEi3D2sOFw8P4MwZYNIk+1yhnXnZfLN4fDjNHy+nkIC46Y5kROQfBL46T+fuIHFFm5Gep4Ba6ZiCfmFhwIYNVAH6pZeA4cMjcdfSwVRafsMGcln6+FiPZjE1iRIVRY/X1ekyg0eMIGFRIqHHs7NpUGjL6oOQEArRMipA1u4DUEbXw3iixlTEgIsLCd6enrYtSzdefdO3L4lGp0/T1HpxMdmPRo0iN+bOna1z/7WCc+eAqVMBpZLDAw8A/9rdC5ywV5uOaQlxkC4jXVucxsEdL39/Kkxz1110K5PR8kaLYrotLnFbihwDLYX2QYN0xREd0bYYt1UhIdTusbbLKt0qhkofe69f9grjthaJ1P+ODB9OlaGqq0khqqqitpPjdI5rvo2rraUv7syZwIkTNEFfRzEFCAqitvfCBXo+f/5CobZORFLZdPynYgqEnAqfP7wHgYIih8f58dEuTeVNELoLATig7Wpue55/LBvnZufgP7/2wfQrb+CkzzMILzxK0TexsbrJVkuTt46Y5DP1eGuiE/U/B/7+NJkiEtGqA32zhLnnMxg9DJUKePxxSroMD6exlj143+ntUCHdzY3c6PPmkW9j7FjgttvafFhGJ6GjjAWxsbGoqKjA6dOnMXr0aADAwYMHoVarERMToz3u4cOHMWXKFKxbtw5zbfgyqFQqXLhwAffee6+9/4puwX/+Azz1FF3CZ88mN3p7IOnbLKRfbRbSnRjtwiOXA/v3A/Hx1MWcOBH45RcrUZ4MAEBaWhqCg4O1v5tzoy9duhTrrHyI0tPTEaE3g5GXl4fExERMnz4dc+bMsfvcmJBuD9YcLiIR0NhIAyUeW12hbV0235qBhrXjN4vHh9P8Mfnoy2hSC/HokIt4MjYdgOMzIsVBYlpu06hbbuMIserpp6mx+uILmpv46ScBJkREUMFCfoBsjPEgydQkip8fDaz4SZJr1+i9Fwrpc/DZZ+RwsyUjWiikwVpurvVIIB57hAJG98Z4osY4YkDfSWnLsnRzq2/CwkhQP3WKlu3zq2tSU1vn/msFV68CiYn0J912G2Vh6je5zoB3pKvr1GgqoypWzuh43XEHzUfMnEl1DIVC4P33LRQetCGSy2pO8NatQH29YSRPdjatQdy5k0bH/v72i1v6GLdVCgW9pqsrTTqytssi3SqGisfe61drhXFr11NT3xE3N90qnJs36do8ahQ9hxdS9Vee3XEHbc0Z4rhyhcwSVVV0Lo2NJC43NNDEvUKBT/LvwT+vUgbC2lu+x+19s4A0x8f5ufRyobh1NaAsUAJwUNslEIAL64etPwKXbtPg1Cl3TE17A0dGPQP38UN0bYGpTHGA2o5z54A9e+j/Ehpq+ySfLTWJpk41b15Qq1tGz/Cvw38OAHo/CwpodWhpKb0GH/7cntGLDEYn5aWXqDigVEo+IHuLA3rHeyN3Qy4Ax/Wp/v53WoW8axfwyCPkbwsKsv48RtegI4wFkZGRSExMxJw5c5CUlITGxkbMnz8fM2bMQFDzh+vQoUOYMmUKFixYgIceekibnS4Wi7WREatXr8b48eMxYMAAVFRU4O2338aNGzfwNG/J7iGo1cCqVTTpBVDcydatjiuybg1pXymq/qxC/ZX2E9IB8iUdOkSGqZQU8t3+8gt5MBnmcXd3h4cpQ4QRzz//PGbNmmVxn/DwcO3P+fn5mDRpEuLi4rTtgb0wId0erDlcmppImFWp6Hd7XaGtdWk6qvipMdnZOHSYw+QjL6O+SYzEAVewfVpyc0Pn+IxIgUgAcaAYyjwlGq41AHBM48ZxVP25qooyiCdPBg7sD8UYe/LpzU2i+PnRwKqsjI4lkZA4ZG5QaGr1QXQ0Tbjk5dle1MoeoYDR/TGOiTKOGDB2UloTti2tvhEI6LNYVkY/CwTtVjj5jz/ozywtpUI0//sfHdbZCGVCiLxEaKpoQkOW49omUzz+OHUyn3hC50jfvLmVHUxL7yNAguGPP9Kk4m230YuVlNDEiFpNbaFCQdcse8QtfYzbqvp64M8/gfJyEhXj4ujzydoui3SbGCrA/utXW4VxwPz11Nwkua8vTUheu0aucomEVKKmJnrtnJyWK8+aM8SxaROdc3AwfcZzc8loIZcD0dHY9OcoLMhZDAD4v6D/4YVRh4C0PKfE+XECDi5+LmgsanRKYS1XVyA5mcOYkU24WBKExIvr8d3IL+DjWq93Enr9xYMHyb2fnk4jyaoqoH9/GmF6eFif5KutJWUsNZVc78eOmRfiJ09uaV7IyaFVVcbRM+PGtfwcREToJqUlEppYyc2lc+4s0YsMRgexc6euOOC2bfQVshfPWz3BuXDQNGocFpfHcdR0XLhA28MPA4cP2y/yMzonHWUs+OyzzzB//nzceeedEAgEeOihh7Bp0ybt4zt27EBdXR3Wrl2rFfEB4Pbbb9f20W7evIk5c+agsLAQ3t7eGD16NI4cOdLtMs8tUV8PzJpF5kaAVo688Ub7Xkr5gqPaaJd2EtIB6jocOAAkJJAnbeJEcuZPntxup9Bt8fPzg59+BJ8F8vLyMGnSJIwePRqffvqpQZthD0xItwdr8RxVVeRwqaykTntbXaG24Mjip0YcOqDG5IOLUK8S454BV/D1o59DKmrS7eCEjEhpHykJ6dcdK1aJxWQOv/deiu9MuEeAXz+dgaG25tNbEgo5jgbJdXX03hrn41vLiOYHc7bGYtgrFDB6BvoxUcYRA9aK4eqjVpOgUFioywa2Fn3UDoWT//1vWl2iVAKjRwPffUfxte2FOFjcLkI6APztbzQf++ST5EhXKoEtW1oxEDQ3AcjHjeXm0nVAoSBxe/BgcmrqT/6Wl9N7aE3cskXUBMiF2tREomNpKbl3J0xgbVdPwdL1y1QtGaDtwjiPqeupue8Ix1EDM3w4fQEHDaLPfF4efS8aGkyvPDOO6+MLkwqFQFQU3sj5G17OuQsA8LzXJ3h76B5wN/2dGucn9hejsahR+7ujC2sFBwPfvpeFu58IwJGCMNz66Wz89Nf/IMRDL8tVLqf/+9at1J7wKzR79aJrTVWVLjqF4+ighw9Tn3nsWN17ybdJmZk0GhWLqWqXKSH+wgVg/nyacb10iV7/2jXT0TO8qN+3r+6c9VcdFhcDFRV0GxfXOaIXGYwO4uhRgF8F//LLwGOPte44IjcRPMZ7oPL3SnBix/Wp5HLg669pYdDRo1TQfcsWhx2e0cF0hLHAx8cHu3btMvu4LcaDd999F++++67Ffbozly+TWejUKfKVfvghRbq0N9K+zUI6H+3i5Ix0Y3x8yIk+eTINvaZModU9r77KCiS3B3l5eZg4cSL69u2L9evXo6SkRPuYuVoL5mBvlz1YKw7q50fTbHv3OsYVagm1mpadJiUBN25Qb8F4Ka2l4qdWRIvdu4En5/dDvUqAe/ql4+tHvzIU0QGnZETyOen116hxc+SATyYj8S0+ngxRk54Kx571SzEx7zPr+fTWhMLLl+nnwYPtz4i+cMG+WAx7hQJGz4GPiTKOGND/flsStvnVK6dOkaCak2ObEO/EwslqNbByJfD66/T7gw+SqC6T2X2oNiEJlqAutQ6KfMfFTrVALy5l1kR3qLeG4um5Anz0ETUTe/bQv9ZmTE0A6seNcRz1Zj08KMKguJiuWXzdCLGY2h2FwrK4ZS6+4cgRGsX6+9Pv+pPLAgE9r7SU7vfyYm1XT8Dc9ctcLRlTTmEee4RxHuPrqS2rafz9SdgXCHRxJC4u5leeGcf1FRWh8chJLNk9Eu+mk4j+yh2/YeXSvuAC3nZ6jQBxbzFqL9Rqf3dG2zU2VoQ/El9HwqElSCvxR9wnT+Gnv/4bkX6ltENNDVBURH/j2LHU1qhU9L13d9e9/76+9L42NZFgPnSo7n3Xb7vc3OizYkqIB+iYR44At95K1cuys+maKBCY7i+fPEnnV1OjM78AutUJOTn0+kuWkJDOJvoYPZSzZ6k7p1TSrQXt0Sa87/JG5e+V2hoOjmLAAHJ63ncfGRJ8fSlOor3iIxgMBqFWk+dz2TJdwt3XX1PRzY6AF9LVdRTr5mhzgS14etICvRdeIKnujTdouPTf/5IvgOE89u/fj8zMTGRmZiLEaFCtaa5hZCtMSLcXW4qD9u/fdleoJfTFrtOnSVFSKAyPb6n4Kf+4CdGioYFm7j/4AAAEuHdQJr4asgZS4UAAjneZGiPp01xwNNs5YpW7OyUZ3HUX/Wvin+qLt99aioWvZoOrsZD1a00odHOjls9cATZLkyb2xmLYKxQwehYCQcuIAVuEbf3VLX37UjZwXh6JFfqF1sx9951QOPn6dSpEc+gQ/b5sGWXqdYSGweekozlO1+EdLxMRXE9GRCDgX3/F48tCcewYOfF376Ym3SaMJwABXdyYry+9t66utHl5UftWU6MTvpVKskfwhV1MiVs8puIbjh4lp6eXF03u+PnpItAAQ6EeYG1XT8DU9ctcLZkzZ8iuU1FhenWMRkOu8KIi+ly3psikratp+ImdXbvo82qLOaH5OdfdhuGx5XfieDrt//aycrzw2oR2a8j4gqM8TllNExqKIePdcUS1DHcfX42MMj9M+PRJ7H7oS9wVfrWl2UAi0dUVkkhaTqpVVtL+/LIj46jEmhoanUskhkK8RkOTwLyDfN06cp+PG0fXtIiIlv93jqMVBwUF9Fz9SUKe6moS0JmIzujBfPMN8Ne/0tcwOppMDW39OoQ8GwJNowa9/+Z49WjKFBKoeLdndja5YF1crD+XwWC0nWvXaHXtr7/S73fdBXzyCXkjOgpJX8Nile0Z7aKPWEzD7ltuoRXXhw7RIsh336VVPmzSzznMmjXLapa6rTAhvTWYKg4aEkIDrgsX6Hd9B4y9rlBL6Itdbm402HR3pwGAvtgFmC5+qo+RaHH1KhV8OHuWHn75ZeCVRxsh+penw12m5uCFdE2T47M8eXx8qBDN3/9OboVFzwtw4mQ/fPyxeW0agGWhcOxYCgxsTUa0vbEY7ZRHzeji2CNsm4pbiIqitqGujtqWtDSqiJJnIcvXEYWTm08nKYma0dpamiv8178oN7yjEAcZ5qo4tG2yENF1r28OTn/+PB5aMgApKbSiZsUKmlQwU7hch/EEoLs7CUwSic4Z7utLbk4/P/r95k0SqTw8DOPIgJbiljHG8Q3+/oC3N12LeMd7U5NOPDMW6lnb1f0xvn6ZqyXDcTRpk5FB+5aXGxoS9B3sdXX0mTt1igRTb296zLjfZarIpD2rabKy7F4Ntns39TWqqjh4eQEffww89JBPO/2zCXFvJ7ZdPM3/x9CcTfhDuARTTq7E8cJ+uPs/M7FgwF6sHZ4J195qndmAb3sKCuj91J9U02goRsfHR7fOWX81C8fReycU6iZXPDzo/SopoTZGIqF2yt/ffHSLPvpmiHbq7zIYXQWNhuakli2j3+++G/j8c/PeIXsQeYoQtjqs7Qcyw7Jl1JT84x/Ap5/SJWPPHtbNYDCcSWUl8OabwMaNOu/EO+8Ac+d2vEAsDTUsrtVRQjrPo4/S8Prhh2lB5uOPU1v1r39Rd5XReWFCemvRLw6ank4VV0wVXrPXFWoJY7GrspIGnAKB4cBSf2msfvFTY5pFi0apO/71HsUn8DWU/vMfKoQAON5lagleSOdxVuMmk5HuPW4cOfB376ZxVlKSlaVG5oRCgByYrcmItjcWox3yqBndBFsm/UJDzRfc4/Nh8/PJVuDtTZNG+t99vTgS7fHaEMuRmgo884zOhX7bbVTIqn//Nv0n2ozWkd6Mw1bL2FDzIPz0Hvz5+xLM+6cAO3eSs2r3bmqvJk60cnz9CZWjR8ml6eVFAnlEBO1z/DhdP9zcSLgqK9P1fPk4MlPilj4aDV0fsrJ0xUs5jn4vKKB2jBe5Kit1Aj4v1LO2q2dgfP0yVUvGw4Ouhby4rlJRLj+/OmbgQDIo1NXp8vbd3elLsXMniafFxdQu8Z8nc0UmTeWam+vn2LEaLDeXJgL/+1966JZbKE7dnI7rTFo40p2VB9r8f/T95hscdHsNL5yYjg+uJuC9zMnY33g7Phv6JqL5CRSO0xXzLCkh4VsgoPcnLY3es0GDqE3w8CCBne/TajS69eG88O7iQpOAbm50jSstpbaFX7ZrLrqFh4/wmTmT+nLt0N9lMLoClZXUJ/v3v+n3f/6TxLGulOX7979TMt2jjwI//UTjvP/+V5dCx2AwHENjI636ePVVugwDwB13kIkgzHnzZXYhlAmpCHsJ1Y7paCEdoO7Q6dPA22/TyutffgGGDaN+5PPPm+62MDqeLnQZ7KRYK/b57LOOE6KNxS5jR4/+0lhPz5bFT00Iroc8p+GZB/oiNZXunjCBOhcGkUEOcpnaAp+RzuPMAhAcR53D6GjgkUfoXztxIrle337bMIXHAP1JFH3akhFtj3vYiXnUjG6ILZN+Q4aYFoj4fNiyMhKu5s4lKxL/2TIRR2IgTtlCsxCfc6UBr3wcjO1fukGt5iCTkQPqH//oHB/lFkK6ozpeNtY8kJVmY/v2frjnHmDhQjLWTppE7dW6dWYy9fhJjqYmYMYMauDffpsEI/1rgv6EiYsLOT2biyPC25uuIbm5LcUt/ezi9HSde5jjKJIjIkInlpWW0uejqYnO69o1EsIGDqRrFWu7egbG1y/jWjKurrQfL6JrNCR+SiT0c0UFTfwIhaTkeHrSxE1qKn2ueFE1NpZGJceO0WetuLhlkckzZ+h5jzxCdqAXX6TPobl+jg2rwepF7lj/aQje/FBXhuDllymbt6OEp3ZxpPM09xdl2dn4V3U1ppwqwpPL/JF2ww3jcl/Fs5f34uVpqfCWKXSTtenptCTSw4M+C3y/B6D+Nb+aRiDQudblcuq8XblCnxuAnuvqSm2NcU0iS9Et+pN4d9xBWzv0dxmMzoxGA3zxBfU3CgupyX3vPRLSuyJTplCJl8mTaYg+YgS1yy+8wKJeGIy2UldHLuoNG6jLBdAleN06qlPQ0S50Y6T9pDohvZ2LjZpDIgGWL6fh2j//Cfz8M7BmDRVKXryYNCtHrAJiOA4mpLcFG5yESE6m4kRLl7a9Y27shjJ29Li700CirIyEWOPip3qC68VUDq9eXYovr40GQPWa1q6lHCuTSTDmxGMHI+3T/sttbr2V/j3LltHq8B07gG+/pcbr6adtiE/gaWtGtD0TFk7Io2Z0cyxN+qWmkjhhSiDiC1L27m2YL2vLJKK1z2F6Ogp3/owN3w7A5kt3oUFNgs+Dd1Xh7SQPhIc74f/QSpwmpNvhcuU46mAlJlLmZ1IStVd79gDz51NHy9e3+XmmJjkGDSJrf16e4Wv4+dFF4NQpErYnTyYHZ0YGZRvrty2ATtwKCSGn8JEjFL0hl9O+fPFSPm6MF+qLi+lc+venhlUqpeextqtnoX/9Mq4lExQEnD+vi/DgRVM+Viori5zFgYG07+DB9DnVj4YpL6f9H3yQPseVlfSZGztW137x+129Sq8XHU3n9cADZAMyhYXVYMomAf77R3+szPwbsivIOjRhAjk3R4922n/SJsT+hkK60wtr6fUX7x0GXJhCjtBvvhHinSv3Y9vGO7Aydj/+EZcCsVhME2qBgboJDf1+D/85SU+n38vL6b2MjKT328eHHktP18W8BAS0rElkb3QLK3bM6MFcvUpCzk8/0e+DBtH4qKOKAzqKsWPpkvP3vwP79lE/6osvyC3b0e00g9EVKSmhCJLNm0l+Asir8+qrpKF01pUr0r5SVJ+kaOOOKDZqiQEDqH36+muK8kxPp7ZqwwZyp8+dS10fRsfTST/eXQQbnYTavMy2dsxNuaGM4xfq6uhx/fgFvvjppUs4dl6GNy7/Dd/ljgJAY4Z586jqemf4UooDxOBEnDYjvb0aN29vEqVmzaI0nnPnSJhau5bmQJ5+mv71Vmmre9+eCYt2XCnA6OJYmvSLjNSJTUqlodgEmI7csHUSkS+6Z4Lzydfw7tKb2HXlH1CqyQ50W/BVrBv0Ccb3vwkongXQeURVcbCTxKhW1Dzw8qKO68yZwIIFlETw1lvA+++TY+G5yZfh/5mJSY5z53Ruc1NiUt++1ABGRlIYu7m2RV/c4rOHBw2itdPnz7eMG5swgbacHLpvyRJg/HjL7l9G94a/fmVlGdaS4eN/+AgPPv4nLIw2f38qZjt2LH3mjLOz9bO2BQJ6bkYGvR7/+dIvburjQ68nFlufBDSxGqzGxRsfHR+ODScnILeeZrFCQ+n7+MgjncOF1S7FRi3g5wd89RWJci8804DUTDc899sD2Hw6Fi8O/REz7w2C6/Qppv/n+v2cc+do1rChgd4v/n3z8aGcvqoqivkxXoEJsOgWBsMGMjLIQfrvf+u+Xi+9ROMgm01FnZzQUOCHHyjCdOFC6sKMHUvt9fLlVEudwWCYR6Wi6JFPPqGhXiMZuxEeTkLvrFm0KKwzo19wtDNEuxjDccBDD1HXZPduSiPMzCTT5+rVlKP+zDNUnJTRcTAhvS3Y4SRsM3xhLFNFtIzdhAsWkBjb/HhDWCS+Dh2MD/+nxG8nSA3mOA0eeojDihWd60vICTmIg8RQZCvo93Zu3MaPp3/j1q1U6T0vjxqqN96gf+vs2TQWs0g7uffb/bUYXRdzk376BfsqK2mklJOjE83NufXsnURspr4e+N//gI8/1uDAgXAAZDmPDcnBy7f+hnsHXgEHFyCt1KoQ396I/cWAEEBzyQmHLQVsQ82D8eMpueKHH6jGxZkzVNxnw9vheCT4McyPv4Rx7vl0SP1JjpAQek8zMsyLSZbaFl7cOnKERt18VAxAnyVzcWPV1UBcHG2s7WIIBDTy0q8lYxzhYRzR4epKfSuZTOdY54V3oGUBW+Oi68bFTfnsf7HYtknAZjd9+oe/4ZOf+2Dbldtws5HW2gb6NWLhCy6YP79zDSJbRLt0wDJmjqOVNPHpUmzfpsaK5WpcKwnA/x2fjeVXNfiHisM//2mmf8W3Ff360eSJ8Uq80aOB+++ni8vZsy2fz6JbGAyzaDQ07nn7beDLL+l3gFL8Nm+mr1x3g+OAv/2NaoEtXEiRpp9/TttDD1Ec18iRHX2WDEbnQaMhn8yePTTRlp2te2zcOBLQH3yw8zrQjZH21bkjO6OQziMUkmj+6KPArl20yvHsWVpF8/HHNJyaOZMmAr29O/psex5d5OPeSWmFk7BV6C/RLy6m8Cn9IlrGbsLwcGg0QMpZyqv6z3+AmzcFAKQQiajzsGQJ12mLrEj6SHRCegcM+EQiymV+6in6/61dS//upUtpic2DD9KywIkTO4fbjMGwiqlJP31XJp93PWAAtS/HjlFb4+9v2q1nxySiukmNo8lF2Pm5GJ//5I3KagEADgJOjYcHnsNzt57G+JBcvSebF+I7Ek7AQRIogSLXwZN8bax5wHGUxHLvvRRJ9cYqBU6ck+A/2bfhP9tuw+jAfDw58iweGZIKX1kdHb+sjGYIecGyNWKSQEDiuKsrRWzwjaGluDGWgc4whX7Ui7kID0DnUNev/SKR6MRysdiwgC3Qsui6sYNdodAJ7xYmAQGqZfnVV8Ann0Ti2DFdezgorBGLlwrxtydcOqVrs6Md6fqIRMDTcwWY8RcBPvmEBoVZWRxWr6ZJwKlTyc12991mBuSWVuIJBLa3o53kusJgdBSFhVQAeft24OJF3f3330+ux/HjO+zU2g1/fxKnli6lAn9ffklt/FdfkTj41FMUp2dqiM9gdHfUajLofPMNCehXruge8/YG/vpX+o6MGNFx59haDIT0TpKRbgmRiATzv/2NSlBt3kzt1JEjtD37LI0FH3uMTAttlR4ZtsGE9LbQBiehzRjnEPftSzmPfBEtPbFLM3UaTtVE4ssl9OW6etXwVJ98kho8g0KinRD9gqMdOeCTSGhe4sknqbP5wQeUgMG7Fvr1A6ZPp1nA0aOZqM7oxBhP+hm7MhUKEpvCwykr2MzqFrPHM6Kxqh6Hy0fjm5e9kHy4FgXVgdrHQr0q8cTdhXiyZhP6jfIxXZTBkat5HIg4WOx4IR1wSM0DjiMRamr4ZZx8bhfeL5mO3anDcbogCKcLgrBgXyLu7n8Vj0elYDK3F561teazoG2ltXFjDIYx1iI8eEHUuPZLcDBFe+Tm0khDLtc52E0J7/oOdv3oGF54N2p7SkrIoP7VV8CBA/RUgJqtKVOofzB5sovp2jKdBKFUCKGHEKoqFSAEuE7QWXFzo8vLP/9JOaDvvEOJK3v20BYQAPzlL+QOHT/e6BJkbjULqx3DYFgkLw/47jtavLF/v25+USql8cyLL/bMaJPhwykrPTUVeP11aoNOnKDtueeoHXrwQZrg60yrjRgMR1NWRn2dH34AfvyRZCYeiYRMO488QpdUmyJvOyldxZFuDMfp0jLz82ki8N//ptUC33xDm0RC6ZzTppG4Hhho9bCMVsKE9LbQRiehVczlEIeFkaB+6hTKQkbgl6EL8XOKP366mzOoISeVqDHltmo8/Xg94v/iD2EnK6ZgDn0hXVWjgkatASfouEZOLKZIl9mzac7kww9JWM/KoqWQb79Nb8m0aTQLeNttXfviwuiGGE/66bsyAUMxieNI0Cor07n8rB2P43D9phd+vtofP1/tjwNX+6KyUedWdxc3YNrgS5g9+Ahu1xyGQOQCiGqAWolzV/M4GEmwBNUggc3hHS9H1Txwd8fYoDxsH/4frE8IwL/PDcdnF0hQ/+HKIPxwZRBE3IOYWNKI+x4B7ruP2q9WYW4y2UrcGINhEmsRHvqCqF7tF62bXCikz6G3N7VxpoR3K9ExTdX1OF41DD9/3Ac/NwsparXuFIcMAZ54glxBAQEd8D9qJWJ/Meqr6jud80okokH5I49QVvH27dS/KiykwlobNpB3ZOpU2m6/3fxCKACsdgyDoUd9PXmuDh0iUezUKcPHY2JobPPoo1R7paczZIguPuHf/6bohEuX6Od//5sW4CUkUL/pzjtpKM5gdGVKSsjhfOgQcPgwCbL6uLnRBNLDD5N5oJMNy1qNfkZ6Zys2aitBQcALL9B2/jwlUHz9NRlp9+6lDaDJ0bvvpm3CBCt9KIZdMCG9rTjTAWMih7i4Vo4/skPx+41Q/H59Ds78GAwNdA2AXA5MvrUKD3kdwL34AW6qSuCAFMiLING/kztyatNrUXepTvv7zQM3kf1mNnwf8IU8suO/+SNHUlHSDRuoU/rFF8D33wPXrwPvvkubqyswaRJ1sm6/nZY8dZXMMEdw//33IyUlBcXFxfD29kZ8fDzWrVuHoKCgjj61novxpJ9USpEbEgn1ooxziK04wjWcAJkjH8Hv+0Pw+44++L1iKK5W+hns4+dajakBx/HA+ELcGX4dElGz9UkTSbafhgZq44YMcc5qHicgCdZbLeMMQcoRueF64rZvlDueiz2G52KPIaO0F3ZdGIbPzw5ERnUwfvlThF/+JI17wABqr+68k9ouX187ztfSZLJe3BiDYRfWBFHjx4uKKKoqIwO4fNm88G4UHaMYNAwn60fg999D8Ud2KP7ICkFVkwz4SXcqo0frHImdNRLPGi7+LqjPrO/UA8boaBKw3n6b3HBffEEDwaIiqluzdSsZG+LiyG115530FovFRgdi9RcYPZSiIpr8O34c+OMPEtEVCt3jHEcrPO6/n5rGiIgOO9VOjb8/ZT4vWgQcPUoO9W++AW7coBVKycm0X//+VHLh9tvp/xoezlYnMzovDQ3AhQu0uv7oUdr00wt4hgwB7rmH3Oe33GLiGtsNcPFygdBTCFWlqks50s0xfDgVuV+3Tlfq53//o8nTixdp27CB/CajRgG33kpbbCyZFRitowfJe21ErbZ9QOcgB0x1YS1ScvrhZEEsThaG4GReMK7e9Gmx39ABDbj7fikSEoBb/S7Bdet7uigYeQCJGmfPktjx7LOdVkyvTa9F7qZcNJY3au8TuglRfbYaDTkNCHk2pFOI6QDpjg89RFttLbBvHwnrP/5IS21++IE2gD4Ot9xCjVVMDCUc+LR8G7sNkyZNwksvvYTAwEDk5eXhhRdewMMPP4wjR4509Kn1bPQn/U6dosgNgKa0IyJ0OcRAC0d4SQmlSZ04QR2wEyeA4uIBAAZonyLkVIj1y8Tdw4tw9/1SjDn+PoT+vVo6zjmO2qZr1+g1nLGax0noC+mdVpAyI24PdrmGV/1/w6uP++Ly/S/gu7T++O47GmxnZtL24Yd0iMhIEqtuuYVuBw608FawOAWGs7AmiOo/PmyY5SKSkZFo6j8YGYcLcMqnCCf3leJkShhSfguDUmXYFfbxbMJdiSLcfTdw113UXHV1+IKjXWHA6OKic6ArleSS++Yb6l/duEG/Hz4MLF9OTc24cbq2asyYrrVSgMFoDWo1GXjOn6ckrPPnqY+mXwCQJzBQZ+6ZPJmJJvbAcbr66Bs20P86ORn4+WfqB1+9SttHH9H+vr7UHo0dSyaq6Gi6RDFxndHelJSQaH7hAn1uz5whDxMfT6dPZCTVfZs0iSaFTBb97oZI+0pRe762S/SLbIXjaCJkyBAqmsxH9fz8M8V5ZWfTOP7kSWrTAPI8jRtH26hR1Hb16tWxf0dXgQnptqBf7LOhgXruEUYO7zY4YBQKKuCQnk6NHN8punZtCIDVLfYf6l+EW0OzcatvOm6Tn0bw+ufotdVq4M2vW0bBeHjQ7/wU1eDBnU6g0qg1KP2mFI2ljZBFyVB9lJywQrkQsigZ6tLqUJpcCtlgWYfGvJhCLteJ6hoNXbR++gn49VcSqSorSWjft0/3nP79yd0eHU0N1ogRpHe1d2eruroaVVVV2t8lEgkkbayW9txzz2l/7tu3L5YuXYpp06ahsbERLi4uFp7JcDr8pF9WFvDee9TwjBmjbQ8amkS4XOqD1LNeuOA6HSnz+uLceZocMkYspsHCrRM0uHVwMSZElMIjUA6ETqCG7Pd6y8VIJRIK5UxN7TICrDhQZ8tQlig7PHbKLFbE7UGR/fH8PeS4qqykturAAdpSU+lalJ4OfPIJHc7Dg546ZoyukzVokN5KGxanwOgMNPfDNBqaR0rfT59jXmxKTRVAoQgGEGzwNH9JBSb0voJbh1Xi1ln9Ef1AWKfOPG8NLn7N114OqM+qhzRU2jnbLiPEYt2SZI2GRKv9+2n77TcaJP72G208wcG0imDUKHJpDRtGTlHWHDG6EhoNiWG8WJuZSZfzS5do4U1DQ8vncBwN98aNI4f0xIk0Ed6ZhVyNWoOG7AaoqlUQugs7bdvEcTRmi44GXnmFujq//w4cPEjF/k6fpuG3vpEKoP7T0KHUTYqIoNtBg2jYzoZEDEfz8svUdy8qMv14r150fYyNpW3cOErD64lIQ0lIb6po6lL9Invo1UsXnweQGeGPP6jt+v136iPfuEHbnj265/XpQ23d0KE6YT4igkUXG8OEdGsYF/vkXZN2OrwVCvqQXr9O2lVmJt1evkzGTP0MTn2CPaow1u0Sxg6uwpjgAowJyoePaz31sNLSDOMPTETBaOE4UmrT02m/TrbstSG7AXWX6iDpIzFsxARUGEsSIkFdeh0ashvg2s+1407UChxHA7fhw4HFi6mQz4UL1FgdP04OhitXdB3jL7/UPdfdnT5KUVG6jtbAgSS6O6vhioqKMvh91apVeOWVVxx2/PLycnz22WeIi4tjInonQQ0B8sXhyBy1CJdT/sCVz91xuSkM6RWBuHrTB2qNabVh0CCd02bsWJoIos8lB6B389aMlWKkWsf7iBEUNtkFBNja9FpU/Fqh/b3gowLUpdV1mtipFtgobnt60jLv+++n30tKaLknXwn+5EmK0OddoDxiMbVVw4bxA0QBIiP7oX8EGxwynI9GQzna16/r+lP87aVL1MSYQi7XTQqNHa3G2OB89Pe5Cc7DDwgd3SnbnrZSm16L+iv1AAB1nRpZK7Mgi5B13rbLDBxHMVQDBgDz5tFn4PJlGhT++Sf1r9LTad4wLw/49lvdc2Uyaq8iIgzFrPBwVjyQ0THU1uo+q3l5tBjvxg26ZPPCRk2N+edLJCRuDB+uM+SMGdO1Moxr02tR+k0p6i7VQdWgglAq7DJtk7s7RV/cey/9rlDQhO2xYyQRpKSQMaGqStef0kcoJCdo//660mf9+tFtnz60koD1pRj20tioE9HDw6mPPmyYbnLZlETUE6lNr0VjGSUgVJ+u7rL9Invp25e2xx+n36uqdCvOT5ygduvaNZI4c3KoODUP7xkePFi3DRhAbVifPj0rxpinB/7JdmCu2KeRw1s1YDCKSwXIz6fOUHY2ffiys2nLyjLt6NTHw0MnovKdomHDAN+SPGDTp3QOviGAixyoNBN/UF1NFgVLLlALuccdiapaBVWDChK5BBCANjW0orpQLoQyTwlVtapDz9NehEKdg+GZZ+i+8nJqtM6d023p6fS28A2ZPnwSRliY4cZ3toKDW59flpaWhuBgnTuvrW50niVLlmDLli2oq6vD+PHj8f333zvkuAzbKCykdocfjN24Qb9fu0bCE2Vm9m3eDPGS1iMqUoOhY2XaFRPDhtk5ODNXhBJomYHeBfJs+diphjydBUzUS9QpY6cMaMX/1s/PUFhvaqJL3enTlAh05gxl7dXUUIcrJcXw+SIRtU8DBugmAvXbLVbkhmELvMiUn6/rT/G3169Te2bKkckjEtHnLzKS2i++XxUWpq+VCwCENG/dE21k3k0aMHISDiLfLtB22QBfF3vwYOCpp+g+vl06dUq3ujMtjZLMTp1qWWwRoD4UPxjka93yglZQUM8cHDLsp6mJVkiUldGQrawMKC423AoLaSsosG0oxnug+venjZ8Iioigz2hX/mxq26bSRkj6SCCRS6CqVXXZtkki0cUj8CiVNLGblkbjvEuX6DYzkwrBXrtGmyk4jmJ4goOpHQoIIHE9IIDu9/enWz8/MkQwcZQBAHPm0Ar5IUOoUCijJXzbI/AQwDXSFV4TvSCQCrps29MWPDwo0mfSJN19lZXUd+InA/nt5k1dm/Xjj4bHEYnomhQWZtiP6tNHp1V1x4nBLnwJbgeMHN4/XhmAIzl9UFjjhsJaNxRWuqLgBzcUruCgskHflcsNBQbeWRMZSRdGkxdBXzvyZ211gXZCu4LQXQihVAhVrQoiDxFEHiI0VTQBzcurVbUqCKQCCN27/nprHx/KXb3rLt19SiV1rNLSdB2uK1doq6rSTcr8+mvL43EcfX6Cgw23adNIw7SEu7s7PEx9VoxYunQp1q1bZ3Gf9PR0RDRXLlq8eDGeeuop3LhxA6+++ipmzpyJ77//Hhzr6bUL48aR6GQOkYhEgkGDgEEDNRjkW45BAZUYMsIFAaODwQnb6Mq0VoSyk2agm0I/dsptpBsqfqoAAGqnwkSdOnbKEYhEulU2s2fTfWo1Tc6cP0+iOj9A5J3AfNtl3NECaNAXGqrb+vYFZs3quUtLezIVFbQEuaBAtxUWknheWWn9+QKBTmQaOFDXr4qIoPu6Y6fdHvTbLtcBrqhNqQUn5CDyEEEYJeyWbZebGzBhAm08KpUuPlFfzLpyhT5nvCPYVP9KICDxqk8fWuLMZyEzehapqZSMWVFBYkJFBW3l5fR7eTn11e1FLqc2jO+39+2ruy6GhpIQ0R2X0hvHefJjg+7WNonFuv6TPhoNXe8yM2l1claWofklL4+cxfzEy+nTll9HJKJuNb898ABJB4yex8CBHX0GnRv9tscjxgPceF370p3anrbg6akrRsrDrwLNyKCVgBkZtF29SsI6r2NlZpo+Zlu0qs4ME9ItYeTw/jZjMJJOjzW5q0CgmznmZ19CQ3VO4n796OLWKh3R1vxZe1ygnQxpqBSyCBmqz1ZDGCWEyJuEdIFEAI1GA0WuAu6j3CEN7YY9SuhiEowbEz4jMTOTXHj8lpWlc+gpFDoRQt9t1b+/4xqn559/HrNmzbK4T3h4uPZnX19f+Pr6YtCgQYiMjESfPn1w7NgxxMbGOuaEGBbp359u+SVc/Na/Py31M1yCxQHo1bw5kG5ShFI/dkooEULoLoSqRgWBq6BLxU45EoFA5zCfOlV3v1pNIqh+zMbVq9RmXbtGolVJia54Lc9f/tL+fwOj41EogBdeMP+4XK7rU/ETL336GDpdWrsaqyeg33apasntIZBRv7EntV1Coc7F+8ADuvs1GhJA+cEf37fKyqKfc3JIzOKF9vr6jvoLGB1NaioVt7UGx9GksK8vZdP6+9Pm50e3AQE6Z3Hv3uR56on+EoM4T6N/QE9omziOXOZBQcBtt7V8XK2mfhIf+cNPMvNjPX6FQ0kJyQJNTTrRHWgp3DMYDKKntz2thePouhUYSLU39FGrqa3SnxTkt5wcasOUSudrVR0BE9ItYeTwnhSWBQGnQYBbDW2CYgQ05iBo5dPoPaaPc5fY2bJEvwu7QDkBB98HfNGQ04C6tDq4x7pD0lcCF18X1KXVQewrhu803x43O8hxuo54XFzLx3mhPTvbMGsxL8+xDZOfnx/8/Pxa9Vx1cwEABeWJMNqBgwc7yeCsGxShNIidAuD/uD9UtSoI5bQ6pqvGTjkD3iEcEmK4TJDn5k1dBiy/5eaSyMDoefj6Uk6j/pJ1vqMeHGx6YR3DdvTbLpGHCL7TfSH218089PS2i+NI7OzVC4iJafm4Wk1iFR8p1JVjNBhtY9Agig/y8iKh3MuLNh8f2ry9afPxQbcrVOwMjPtVxvT0tok35/XuTd4TS9TX6+KE+I030zAYDENY2+N4BAKdidgUfF8qN9e5WlVHwLqFljByeD8yJBWPDEmlx/hin+NHAeOCKWqzM9CFXaDySDlCng3RFp4R+4mhqlTBfZQ7fKd17+IPrUVfaB8zpqPPBjh+/DhOnjyJCRMmwNvbG1evXsWKFSvQv39/5kZvRzqFiM7TBTLQLWEcOyXubWiB7U6xU86GFxuiozv6TIj7778fKSkpKC4uhre3N+Lj47Fu3ToEBQV19Kn1CIRC4D//6eiz6L4Yt13yKMM+FGu7LCMQ6BzEY00vRm0XWDvV8URHAx9/3NFn0X0wbpuMYW2T7bi66gwMDAbDMqztaX/0+1KdQatyJJ1F/u2c8A5vX18SzSsraf1UZSX93lkd3pGRwNKlwOrVwIoVdLtkSacW0XnkkXKELg1Fv9X90HdFX/Rb3Q+hS0KZiN5FkMlk+Prrr3HnnXdi8ODBeOqppzB8+HD8+uuvDitkymC0J3zslCJHAY1GY/AYHzsli5R129ip7sykSZPwxRdfICMjA1999RWuXr2Khx9+uKNPi8FwCKzt6h50pnbq/fffR79+/SCVShETE4MTJ05Y3H/Pnj2IiIiAVCrFsGHD8MMPPxg8rtFosHLlSgQGBsLV1RXx8fG4cuWKwT7l5eV4/PHH4eHhAS8vLzz11FOoqakx2Of8+fO49dZbIZVK0adPH7z11luO+YMZToG1TQwGoyNgbQ/DkTBHujW6qsO7C7tAOQHHcqm6KMOGDcPBgwc7+jQYDIdhHDslCZFAKCc3gyJX0WNjp9qb6upqVOlVc5NIJG2enHvuuee0P/ft2xdLly7FtGnT0NjYCJeeXqmS0eVhbVf7053bqc8//xyLFi1CUlISYmJisHHjRiQkJCAjIwP+/v4t9j9y5Agee+wxrF27FlOmTMGuXbswbdo0nDlzBkOHDgUAvPXWW9i0aRN27NiBsLAwrFixAgkJCUhLS4O0ucrm448/joKCAuzfvx+NjY2YPXs25s6di127dgEAqqqqcPfddyM+Ph5JSUm4cOECnnzySXh5eWHu3Lnt9v9h2A5rmxgMRkfA2h6GI3GalbqzuhZaRRd2eDMYDAajbfCxU+4j3dFU1oT6y/VoKmuC+yh3BD8bzFbMtANRUVHw9PTUbmvXrnXo8cvLy/HZZ58hLi6uU4ro3apPxWg3WNvVvnTndmrDhg2YM2cOZs+ejaioKCQlJUEmk2Hbtm0m93/vvfeQmJiIxYsXIzIyEmvWrMGoUaOwZcsWANQGbdy4EcuXL8fUqVMxfPhw7Ny5E/n5+UhOTgYApKenY9++ffj4448RExODCRMmYPPmzdi9ezfy8/MBAJ999hmUSiW2bduGIUOGYMaMGXj22WexYcMGs3+LQqFAVVWVdquurnbsP4thFdY2MRiMjoC1PQyHoXECu3fv1ojFYs22bds0qampmjlz5mi8vLw0RUVFJvf/888/NUKhUPPWW29p0tLSNMuXL9e4uLhoLly4oN3nzTff1Hh6emqSk5M1586d09x///2asLAwTX19vXafxMREzYgRIzTHjh3T/P7775oBAwZoHnvsMZvPOycnRwNAk5OT0/o/nsHoArDPOrFlyxZN3759NRKJRDNu3DjN8ePHLe7/xRdfaAYPHqyRSCSaoUOHavbu3WvwuFqt1qxYsUITEPD/7J15eFNl9se/N/vSdN8phYIsLftOEcUFBUUFR1EZFWT8gTgiKCouw6KgAyoqoijqjOvAgBvouCAI6qjUyj5AyyaUAqU7bdLsy/v74zZp9iZN0nQ5n+fJk+be99689yY5Pe95v+856Uwmk7Grr76aHT9+3KVNTU0N+/Of/8xUKhWLi4tjf/nLX5hGowmq3/T5RQeb1cZ0p3VM8z8N053WMZvVFu0udXjs3/WioiJWX1/veBgMhrCcf+HChUyhUDAAbPTo0ay6ujos5w0n5FMRoUK2K7J0dDtlNBqZUChkmzdvdtk+ffp0dtNNN3k9pmvXruyVV15x2bZkyRI2cOBAxhhjf/zxBwPA9u/f79Lm8ssvZ/PmzWOMMfbPf/6TxcfHu+w3m81MKBSyzz//nDHG2N13380mT57s0mbnzp0MAKutrfXat6VLlzIAHg+yVa0P2abWpSP8X26rY7eDBw+ysWPHMqlUyrKystjzzz8fdF+aoyN8fm0Fsj1tm/bwXY9IIH3kyJHsgQcecLy2Wq0sMzOTrVixwmv72267jU2aNMll26hRo9h9993HGOMNXHp6OnvxxRcd++vq6phUKmX//ve/GWOMFRUVMQBs9+7djjbffvst4ziOnT9/PqB+t4cPjCDCAX3X229wijH6/IjOQ7Df9ccff9xrgMT5UVxc7GhfVVXFjh07xrZt28YuvfRSdv311zObrW050+RTEUTbpqPbqfPnzzMAbNeuXS7bH3vsMTZy5Eivx4jFYrZhwwaXbWvXrmWpqamMMd6nAsDKyspc2kydOpXddtttjDHGnnvuOda7d2+Pc6ekpLA33niDMcbYNddcw2bPnu2y/8iRI46JDW8YDAaXCQ+7vSNbRXR02vv/5bY6dquvr2dpaWnszjvvZIcPH2b//ve/mVwuZ2+99VZQfWmO9v75EUSgtIfvetgD6W1ZteAOOVJEZ6U9GKdI016DU4zR50d0HoL9rldWVrLi4mK/D6PR6Pe93INF0YR8KoJo+3R0O9XRAunukE9FdBba+3e9rY7d3njjDZaQkOBitx9//HHWp0+fgPsSCO398yOIQGkP3/Ww50ivrq6G1WpFWlqay/a0tDSUl5d7Paa8vNxve/tzc23ci92IRCIkJib6fN8VK1a45DLMy8sL8CoJgmjPmEwm7N27F+PHj3dsEwgEGD9+PAoKCrweU1BQ4NIeACZMmOBof/r0aZSXl7u0iYuLw6hRoxxtCgoKEB8fj+HDhzvajB8/HgKBAIWFhT77S/k8CSIwUlJS0LdvX78PiUTi9VibzQaA/721FcinIoiOR3uzU8nJyRAKhaioqHDZXlFRgfT0dK/HpKen+21vf26uTWVlpct+i8WC2tpalzbezuH8HgRBtH/a8titoKAAl19+uYvdthdjvnjxYkB98QaN/wii7SKKdgeiyZNPPokFCxY4Xp89exb9+/fHhQsXotgrgog89u+4fUDW2fAXnDp69KjXY6IVnAL4ANUzzzzjsZ1sFdHRiZStKiwsxO7duzF27FgkJCTgjz/+wOLFi9GzZ0/k5+eH9b06C+RTEZ2Vjm6nJBIJhg0bhh07dmDKlCkA+GvdsWMH5s6d6/WY/Px87NixAw899JBj2/bt2x39zsnJQXp6Onbs2IHBgwcDANRqNQoLC3H//fc7zlFXV4e9e/di2LBhAICdO3fCZrNh1KhRjjZ/+9vfYDabHQVYt2/fjj59+iAhISGg67N/bmSriI5Oex7/teWxW3l5OXJycjzOYd+XkJDQbF+8QeM/orPSHmxV2APpkVYtZGRkuLSxO1+BqBbckUqlkEqljtc6nQ4AMHLkyOYukyA6BBUVFcjOzo52N4hmcA9Q7d27F1dddRXZKqLTEG5bpVAo8Pnnn2Pp0qXQarXIyMjAxIkTsWjRIhe/INqQT0UQ7YeObKcWLFiAGTNmYPjw4Rg5ciRWr14NrVaLmTNnAgCmT5+OLl26YMWKFQCA+fPnY9y4cXjppZcwadIkbNy4EXv27MHbb78NAOA4Dg899BCeffZZ9OrVCzk5OVi8eDEyMzMdwfrc3FxMnDgRs2bNwrp162A2mzF37lzccccdyMzMBAD8+c9/xjPPPIN7770Xjz/+OA4fPoxXX30Vr7zySsDXZreXZKuIzgKN/9oHNP4jOjtt2VaFPZDellULzTFkyBD8/vvvSEtLg0AQ9qw3AaPRaJCXl4eioiKoVKqo9aM9Q/fQPzabDRUVFRgyZEi0uxIV2lNwCvAMUF122WVkqzoIdA/9EylbNWDAAOzcuTOs54wE5FOFB/qdhQ7dQ990Bjt1++23o6qqCkuWLEF5eTkGDx6MrVu3OhSWpaWlLr/zMWPGYMOGDVi0aBGeeuop9OrVC1u2bEH//v0dbRYuXAitVovZs2ejrq4OY8eOxdatWyGTyRxt1q9fj7lz5+Lqq6+GQCDALbfcgjVr1jj2x8XFYdu2bXjggQcwbNgwJCcnY8mSJZg9e3bA10a2quNA988/7Xn815bHboGkmGquL96g8V/Hhe6hf9qFrYpE4vWNGzcyqVTK3n//fVZUVMRmz57N4uPjWXl5OWOMsbvvvps98cQTjva//vorE4lEbNWqVay4uJgtXbrUa0Xl+Ph49sUXX7D//e9/bPLkyV4rKg8ZMoQVFhayX375hfXq1culonJ7ob6+ngFg9fX10e5Ku4XuIdEcI0eOZHPnznW8tlqtrEuXLn4L1txwww0u2/Lz8z0K1qxatcqxv76+3mvBmj179jjafPfdd0EXG20r0O8sdOgeEs1BPlXo0O8sdOgeEkTkod9ZaND969i01bGbvdioyWRytHnyySc9io3660t7gn5noUP3sP0TkUA6Y4y99tprLDs7m0kkEjZy5Ej222+/OfaNGzeOzZgxw6X9xx9/zHr37s0kEgnr168f+/rrr13222w2tnjxYpaWlsakUim7+uqr2bFjx1za1NTUsGnTprGYmBgWGxvLZs6cyTQaTaQuMWLQDyt06B4SzUHBqdCh31no0D0kAoF8qtCg31no0D0kiMhDv7PQoPvXsWmrY7e6ujqWlpbG7r77bnb48GG2ceNGplAo2FtvvRVUX9oL9DsLHbqH7Z+IBdKJlkM/rNChe0gEAgWnQoN+Z6FD95AgIg/9zkKH7iFBRB76nYUG3b+OT1sdux08eJCNHTuWSaVS1qVLF7Zy5UqPvjfXl/YC/c5Ch+5h+4djjLHIJ5AhgsFoNGLFihV48skn21TRs/YE3UOCiDz0OwsduocEEXnodxY6dA8JIvLQ7yw06P4RROSh31no0D1s/1AgnSAIgiAIgiAIgiAIgiAIgiD8EN3S5ARBEARBEARBEARBEARBEATRxqFAOkEQBEEQBEEQBEEQBEEQBEH4gQLpBEEQBEEQBEEQBEEQBEEQBOEHCqQTBEEQBEEQBEEQBEEQBEEQhB8okE4QBEEQBEEQBEEQBEEQBEEQfqBAeivx5ptvYuDAgYiNjUVsbCzy8/Px7bffurQpKCjAVVddBaVSidjYWFx++eXQ6/WO/bW1tbjzzjsRGxuL+Ph43HvvvWhoaGjtS4kazd3D8vJy3H333UhPT4dSqcTQoUPx2WefuZyjs99DgmgOslWhQ7aKICIP2arQIVtFEJGF7FTokJ0iiMhDtip0yFZ1MhjRKnz55Zfs66+/ZsePH2fHjh1jTz31FBOLxezw4cOMMcZ27drFYmNj2YoVK9jhw4fZ0aNH2aZNm5jBYHCcY+LEiWzQoEHst99+Yz///DO75JJL2LRp06J1Sa1Oc/fwmmuuYSNGjGCFhYXsjz/+YMuXL2cCgYDt27fPcY7Ofg8JojnIVoUO2SqCiDxkq0KHbBVBRBayU6FDdoogIg/ZqtAhW9W5oEB6FElISGD/+Mc/GGOMjRo1ii1atMhn26KiIgaA7d6927Ht22+/ZRzHsfPnz0e8r20V53uoVCrZhx9+6LI/MTGRvfPOO4wxuocE0VLIVoUO2SqCiDxkq0KHbBVBRBayU6FDdoogIg/ZqtAhW9VxodQuUcBqtWLjxo3QarXIz89HZWUlCgsLkZqaijFjxiAtLQ3jxo3DL7/84jimoKAA8fHxGD58uGPb+PHjIRAIUFhYGI3LiCru9xAAxowZg02bNqG2thY2mw0bN26EwWDAFVdcAYDuIUEEC9mq0CFbRRCRh2xV6JCtIojIQnYqdMhOEUTkIVsVOmSrOj6iaHegM3Ho0CHk5+fDYDAgJiYGmzdvRl5eHn777TcAwNNPP41Vq1Zh8ODB+PDDD3H11Vfj8OHD6NWrF8rLy5GamupyPpFIhMTERJSXl0fjcqKCr3sIAB9//DFuv/12JCUlQSQSQaFQYPPmzbjkkksAgO4hQQQI2arQIVtFEJGHbFXokK0iiMhCdip0yE4RROQhWxU6ZKs6DxRIb0X69OmDAwcOoL6+Hp9++ilmzJiBn376CTabDQBw3333YebMmQCAIUOGYMeOHXj33XexYsWKaHa7TeHrHubl5WHx4sWoq6vD999/j+TkZGzZsgW33XYbfv75ZwwYMCDaXSeIdgPZqtAhW0UQkYdsVeiQrSKIyEJ2KnTIThFE5CFbFTpkqzoPFEhvRSQSiWPGadiwYdi9ezdeffVVPPHEEwDgmK2yk5ubi9LSUgBAeno6KisrXfZbLBbU1tYiPT29FXrfNvB1DxcuXIjXX38dhw8fRr9+/QAAgwYNws8//4y1a9di3bp1dA8JIkDIVoUO2SqCiDxkq0KHbBVBRBayU6FDdoogIg/ZqtAhW9V5oBzpUcRms8FoNKJ79+7IzMzEsWPHXPYfP34c3bp1AwDk5+ejrq4Oe/fudezfuXMnbDYbRo0a1ar9bkvY76FOpwMACASuX2mhUOiYRaV7SBAtg2xV6JCtIojIQ7YqdMhWEURkITsVOmSnCCLykK0KHbJVHZhoVzvtLDzxxBPsp59+YqdPn2b/+9//2BNPPME4jmPbtm1jjDH2yiuvsNjYWPbJJ5+wEydOsEWLFjGZTMZOnjzpOMfEiRPZkCFDWGFhIfvll19Yr1692LRp06J1Sa2Ov3toMpnYJZdcwi677DJWWFjITp48yVatWsU4jmNff/214xyd/R4SRHOQrQodslUEEXnIVoUO2SqCiCxkp0KH7BRBRB6yVaFDtqpzQYH0VuIvf/kL69atG5NIJCwlJYVdffXVDsNkZ8WKFSwrK4spFAqWn5/Pfv75Z5f9NTU1bNq0aSwmJobFxsaymTNnMo1G05qXEVWau4fHjx9nf/rTn1hqaipTKBRs4MCB7MMPP3Q5R2e/hwTRHGSrQodsFUFEHrJVoUO2iiAiC9mp0CE7RRCRh2xV6JCt6lxwjDEWbVU8QRAEQRAEQRAEQRAEQRAEQbRVKEc6QRAEQRAEQRAEQRAEQRAEQfiBAukEQRAEQRAEQRAEQRAEQRAE4QcKpBMEQRAEQRAEQRAEQRAEQRCEHyiQThAEQRAEQRAEQRAEQRAEQRB+oEA6QRAEQRAEQRAEQRAEQRAEQfiBAukEQRAEQRAEQRAEQRAEQRAE4QcKpBMEQRAEQRAEQRAEQRAEQRCEHyiQThAEQRAEQRAEQRAEQRAEQRB+oEA6QRAEQRAEQRAEQRAEQRAEQfiBAukEQRAEQRAEQRAEQRAEQRAE4QcKpBMEQRAEQRAEQRAEQRAEQRCEHyiQThAEQRAEQRAEQRAEQRAEQRB+oEA6QRAEQRAEQRAEQRAEQRAEQfiBAukEQRAEQRAEQRAEQRAEQRAE4QcKpBMEQRAEQRAEQRAEQRAEQRCEHyiQThAEQRAEQRAEQRAEQRAEQRB+oEA6QRAEQRAEQRAEQRAEQRAEQfihQwbS77nnHnAcB47j0L9//2h3hyDaPQ899JDjNxUTExPt7nQIyE4RRHhZvXq14zfFcRyqq6uj3aUOAdkqgggv5FNFBrJVBBFepkyZQr+pKEI2jejMtPVxXYcMpANAcnIyPvroI6xcudJj365duzB27FgoFAqkp6dj3rx5aGhoCPjc//znP5GbmwuZTIZevXrhtdde82jz+eef4/bbb0ePHj2gUCjQp08fPPLII6irq2vxNR05cgRTp051nDM5ORmXX345/vOf/3i0df7SuT+uueYan++xfv36oB37uro6zJ49GykpKVAqlbjyyiuxb9++Fl0jAOzevRtz585Fv379oFQqkZ2djdtuuw3Hjx/3aPv777/jr3/9K4YNGwaxWAyO43yet6KiAjNnzkRqairkcjmGDh2KTz75JOB+GY1GPP7448jMzIRcLseoUaOwffv2Fl2jP4L5nL1xxRVX+PzsxWKxz+P++OMPyGQycByHPXv2uOy7++678dFHH+Gyyy4L6doIV6JtpzZv3owJEyYgMzMTUqkUWVlZuPXWW3H48OEWX1Mk7FRJSYnPdhs3bgyoX+3FTtXX12PhwoXo1asX5HI5unXrhnvvvRelpaUB9auj2amamhq8+OKLuPzyy5GSkoL4+HiMHj0amzZt8jjnxIkT8dFHH+Hmm28O23USPNG2VeRTRd5W2Ww2vP/++7jpppvQtWtXKJVK9O/fH88++ywMBoPHeX3dD2/fEW+0F1tl5/vvv8dVV12FuLg4qFQqDBs2zMMOGQwGrFixAnl5eVAoFOjSpQumTp2KI0eOuLQjnypykK3qHH5Ve7bJvvjvf//rsL8ymQzp6emYOHEifv3116DOs2nTJuTn50OpVCI+Ph5jxozBzp07XdoE6ms+/PDD+Oijj9C3b9+Qr49oGdG2acHw8MMPY+jQoUhMTIRCoUBubi6efvppjz45TxB4e5w/fx4AoNPpsHbtWlx77bXIyMiASqXCkCFD8Oabb8JqtQbcry+//BJDhw6FTCZDdnY2li5dCovF0qJrDKZPZWVluOuuu9CnTx+oVCrEx8dj5MiR+OCDD8AYc2kbjjF5cXExJk6ciJiYGCQmJuLuu+9GVVVVi67TH++88w7GjRuHtLQ0SKVS5OTkYObMmSgpKQn6XHV1dUhNTQXHcfj0009d9rX1cZ0o2h2IFEqlEnfddZfH9gMHDuDqq69Gbm4uXn75ZZw7dw6rVq3CiRMn8O233zZ73rfeegtz5szBLbfcggULFuDnn3/GvHnzoNPp8PjjjzvazZ49G5mZmbjrrruQnZ2NQ4cO4fXXX8c333yDffv2QS6XB31NZ86cgUajwYwZM5CZmQmdTofPPvsMN910E9566y3Mnj3b0fajjz7yOH7Pnj149dVXce2113o9f0NDAxYuXAilUhlwn2w2GyZNmoSDBw/iscceQ3JyMt544w1cccUV2Lt3L3r16hX0dT7//PP49ddfMXXqVAwcOBDl5eV4/fXXMXToUPz2228uM7LffPMN/vGPf2DgwIHo0aOHV2cLANRqNcaOHYuKigrMnz8f6enp+Pjjj3Hbbbdh/fr1+POf/9xsv+655x58+umneOihh9CrVy+8//77uP766/HDDz9g7NixQV+nL4L5nL3xt7/9Df/3f//nsk2r1WLOnDk+P3uA/+cnEolgNBo99g0bNgzDhg3D999/HxHnsbMSbTt16NAhJCQkYP78+UhOTkZ5eTneffddjBw5EgUFBRg0aFDQ1xRJOzVt2jRcf/31Ltvy8/Ob7VN7sVM2mw3XXHMNioqK8Ne//hW9e/fGyZMn8cYbb+C7775DcXExVCqV3351NDtVUFCAv/3tb7j++uuxaNEiiEQifPbZZ7jjjjtQVFSEZ555xtG2b9++6Nu3L06ePInNmzeH7VqJ6Nsq8qkib6t0Oh1mzpyJ0aNHY86cOUhNTUVBQQGWLl2KHTt2YOfOnR6TgNdccw2mT5/usm3IkCEB9au92CoAeO+993Dvvffimmuuwd///ncIhUIcO3YMZ8+edWl355134ssvv8SsWbMwdOhQlJWVYe3atcjPz8ehQ4fQrVs3AORTRRKyVZ3Dr2rPNtkXx48fh0AgwJw5c5Ceno6LFy/iX//6Fy6//HJ8/fXXmDhxYrPnePrpp7Fs2TLceuutuOeee2A2m3H48GFHYNJ+TYH6muPGjQMA/OMf/2hzatDOQrRtWjDs3r0bl112GWbOnAmZTIb9+/dj5cqV+P777/Hf//4XAgGv4b3vvvswfvx4l2MZY5gzZw66d++OLl26AABOnTqFBx98EFdffTUWLFiA2NhYfPfdd/jrX/+K3377DR988EGzffr2228xZcoUXHHFFXjttddw6NAhPPvss6isrMSbb74Z9DUG06fq6mqcO3cOt956K7Kzs2E2m7F9+3bcc889OHbsGP7+97872oY6Jj937hwuv/xyxMXF4e9//zsaGhqwatUqHDp0CL///jskEknQ1+qL/fv3IycnBzfddBMSEhJw+vRpvPPOO/jqq69w8OBBZGZmBnyuJUuWQKfTed3X5sd1rAMyY8YM1q1bN6/7rrvuOpaRkcHq6+sd29555x0GgH333Xd+z6vT6VhSUhKbNGmSy/Y777yTKZVKVltb69j2ww8/eBz/wQcfMADsnXfeCfximsFisbBBgwaxPn36NNv23nvvZRzHsbNnz3rd//jjj7M+ffo4ricQNm3axACwTz75xLGtsrKSxcfHs2nTpgV2EW78+uuvzGg0umw7fvw4k0ql7M4773TZXl5eznQ6HWOMsQceeID5+kq/8MILDADbsWOHY5vVamUjRoxg6enpHu/nTmFhIQPAXnzxRcc2vV7PevbsyfLz84O6vpYQzOfsjY8++ogBYOvXr/e6f+vWrUwikbBFixYxAGz37t1e282YMSPg7wbhn7Zgp7xRXl7ORCIRu++++wK7kAAI1U6dPn3a4/cXDO3FTv36668MAHv99dddtr/77rsMAPv888/99qkj2qlTp06xkpISl3Y2m41dddVVTCqVsoaGBo/zLF26lAFgVVVVLeoH4UpbsFXkU0XeVhmNRvbrr796HP/MM88wAGz79u0u2wGwBx54oEV9ak+26vTp00wul7N58+b5bXfu3DkGgD366KMu23fu3MkAsJdfftnjGPKpwgvZKu90RL/KG+3FJgeDVqtlaWlpbMKECc22LSgoYBzHebU1zrTE1xw3bhzr169fcJ0nQqYt2LRQWbVqFQPACgoK/Lb7+eefGQD23HPPObZVVVWxw4cPe7SdOXMmA8BOnDjR7Pvn5eWxQYMGMbPZ7Nj2t7/9jXEcx4qLi4O4kvD16YYbbmBKpZJZLBa/7YIZk99///1MLpezM2fOOLZt376dAWBvvfVWs8eHyp49exgAtmLFioCPOXToEBOJRGzZsmUedtaZtjqu67CpXbyhVquxfft23HXXXYiNjXVsnz59OmJiYvDxxx/7Pf6HH35ATU0N/vrXv7psf+CBB6DVavH11187tl1xxRUex9uXJRQXF4dwFa4IhUJ07dq12SWDRqMRn332GcaNG4esrCyP/SdOnMArr7yCl19+GSJR4AsVPv30U6SlpeFPf/qTY1tKSgpuu+02fPHFF17Vzc0xZswYj1mzXr16oV+/fh73Li0tLSB1x88//4yUlBRcddVVjm0CgQC33XYbysvL8dNPP/k9/tNPP4VQKHRRfchkMtx7770oKCjwUCWFm0A/Z19s2LABSqUSkydP9thnNpsxf/58zJ8/Hz179gyxp0SotKad8kZqaioUCkVIy5DdCZedAnjVsslkCur924udUqvVjvbOZGRkAECz5+iIdionJ8eh4rTDcRymTJkCo9GIU6dOhdJlIgTIp+pYPpVEIsGYMWM8jm/uPuv1eq+pX/zRnmzVunXrYLVasWzZMgC8qpW5LckGAI1GA6Dl9puIHGSrOp5f5U57ssnBoFAokJKSEpCtWr16NdLT0zF//nwwxnym+AjV1ySiT7THisHQvXt3AGj2O7xhwwZwHOeSJSA5ORn9+vXzaBuoTS0qKkJRURFmz57t8rv/61//CsaYRyqRQAi1TwB/T3Q6XbN2N5gx+WeffYYbbrgB2dnZjm3jx49H7969m/0+hINAP2dn5s+fj5tvvrndprnrVIH0Q4cOwWKxYPjw4S7bJRIJBg8ejP379/s93r7f/fhhw4ZBIBA0e3x5eTkA/gcYClqtFtXV1fjjjz/wyiuv4Ntvv8XVV1/t95hvvvkGdXV1uPPOO73uf+ihh3DllVd6LO9rjv3792Po0KGOpTp2Ro4cCZ1O5zOFQbAwxlBRUdHie2c0Gr06BgqFAgCwd+9ev8fv378fvXv3dvlnBfDXCfDLq8JNSz5nb1RVVWH79u2YMmWK1+WMq1evxsWLF7Fo0aJwdJsIkWjYqbq6OlRVVeHQoUP4v//7P6jV6hZ915yJhJ165plnEBMTA5lMhhEjRmDbtm0B9aW92Knhw4dDqVRi8eLF2LlzJ86fP4+ffvoJCxcuxIgRIzyWQbrTke2UO+H6f0q0HPKpOodP5e8+v//++1AqlZDL5cjLy8OGDRsCev/2ZKu+//579O3bF9988w2ysrKgUqmQlJSExYsXw2azOdr17NkTWVlZeOmll/Cf//wH586dw++//445c+YgJycHd9xxR9iviQgMslUd369q7zbZGbVajerqahw9ehRPPfUUDh8+HJCt2rFjB0aMGIE1a9YgJSUFKpUKGRkZeP31113aheprEtEn2jbNHxaLBdXV1SgrK8O2bduwaNEiqFQqx/93b5jNZnz88ccYM2aMIyDrj0Btqq/rzMzMRFZWVkjXGUyf9Ho9qqurUVJSgg8++ADvvfce8vPzvcamWjImP3/+PCorKz2uE+BtVTiv05mamhpUVlZiz549mDlzJgAEPAb85JNPsGvXLrzwwgsR6Vtr0GFzpHvjwoULAJpmXJ3JyMjAzz//3OzxQqEQqampLtslEgmSkpJQVlbm9/jnn38eQqEQt956a5A9d+WRRx7BW2+9BYBXVf/pT3/y+Cfpzvr16yGVSr2+99dff41t27bh4MGDQfflwoULuPzyyz222+9xWVkZBgwYEPR53Vm/fj3Onz/vUAQFS58+ffD999/jzJkzLupG+2funDvOGxcuXPD5vQHQ7GffElryOXtj06ZNsFgsXp3L8vJyLF++HKtWrfIY0BLRIRp2avTo0Th27BgAICYmBosWLcK9997b0ksAEF47JRAIcO211+Lmm29Gly5dcOrUKbz88su47rrr8OWXX2LSpEl+z9te7FRycjI2bdqEWbNmuTgiEyZMwKefftqsiqqj2il3amtr8Y9//AOXXXaZ1+slWgfyqTqHT/XCCy8gNjYW1113ncv2MWPG4LbbbkNOTo4jF/idd96J+vp63H///X7P2Z5s1YkTJyAUCjFz5kwsXLgQgwYNwueff45nn30WFosFK1asAACIxWJ89tln+POf/4ybbrrJcfywYcOwa9cuxMfHh/2aiMAgW9Xx/ar2bpOdue222/Ddd98B4L9j9913HxYvXuz3mIsXL6K6uhq//vordu7ciaVLlyI7OxvvvfceHnzwQYjFYtx3330AQvc1iegTbZvmjz179rjUWejTpw++/PJLJCYm+jzmu+++Q01NTUBjAJPJhNWrVyMnJwcjRozw27a5+xQuX6O5Pr366qt48sknHa+vvvpqvPfee17P1ZIxeXPXWVtbC6PRCKlUGvA1BUKXLl0cq3KSkpKwZs0av8We7ej1ejz66KN4+OGH0b179xYVKW0LdCpLqdfrAcDrl0gmkzn2+zveV6L+5o7fsGED/vnPfzoqZIfCQw89hFtvvRVlZWX4+OOPYbVa/S4NUavV+Prrr3H99dd7OPImkwkPP/ww5syZg7y8vKD7otfrfd5P+/5QOXr0KB544AHk5+djxowZLTrH//3f/2HdunW47bbb8MorryAtLQ0ff/yxo3BBIJ99pK/TnWA/Z19s2LABKSkpXg3b448/jh49engU/SOiRzTs1HvvvQe1Wo1Tp07hvffeg16vh9Vq9VDgBEM47VR2drZjUGHn7rvvRl5eHh555JFmB3ztxU4B/JLhIUOGYO7cuejXrx8OHDiAF154ATNnzsQnn3zi99iOaqecsdlsuPPOO1FXV4fXXnutpd0mwgD5VB3fp/r73/+O77//Hm+88YbHtf76668ur//yl79g2LBheOqpp3DPPff4TQ/QnmxVQ0MDbDYbVq5c6SjAdsstt6C2thavvvoqnnrqKUdhvoSEBAwePBhTp07F6NGjcfLkSaxYsQJTp07F9u3bHddHtC5kqzq2X9XebbI7K1euxCOPPIKzZ8/igw8+gMlkgsVi8XuMPY1LTU0NNm7ciNtvvx0AcOutt2LAgAF49tlnHYF0IDRfk4g+0bRpzZGXl4ft27dDq9Vi165d+P77732mGbKzYcMGiMVi3Hbbbc2ef+7cuSgqKsLXX3/d7KRPc/fJnuYoVJrr07Rp0zB8+HBUVVXhq6++QkVFhc973JIxeXPXaW8T7kD6t99+C4PBgOLiYvzrX/+CVqsN6LiVK1fCbDbjqaeeCmt/WptOFUi3O/Xe8pkZDIZmc4LJ5XKfDou/43/++Wfce++9mDBhAp577rkge+2JvYItwOfCuvbaa3HjjTeisLAQHMd5tP/ss89gMBi8zvK98sorqK6uxjPPPNOivsjlcp/3074/FMrLyzFp0iTExcU5cmq2hIEDB2LDhg2YM2cOLr30UgBAeno6Vq9ejfvvvx8xMTF+jw/3dVqtVlRVVblsS0xMdPmnFuzn7I1Tp06hoKAAc+fO9TDsv/32Gz766CPs2LEjpIApEV6iYaeclQN33HEHcnNzAQCrVq0KuN/uhNNOeSMxMREzZ87EypUrce7cOZ+5P4H2Y6dOnTqFK6+8Eh9++CFuueUWAMDkyZPRvXt33HPPPfj22289VKHOdEQ75c6DDz6IrVu34sMPP2y2gj0RWcin6tg+1aZNmxxKqOYU5gCvZJs7dy7mzJmDvXv3YuzYsT7btidbJZfLodVqMW3aNJft06ZNw9atW7F//35cfvnlqK+vx2WXXYbHHnsMjzzyiKPd8OHDccUVV+C9994L6D4S4YdsVcf2q9qTTTaZTKitrXXZlpKS4nJ9gwcPdvx91113YejQobjnnnv85nO290MsFruo8gUCAW6//XYsXboUpaWlyM7ODtnXJKJPtGxaIMTGxjrSA02ePBkbNmzA5MmTsW/fPq9+e0NDA7744gtMmDABSUlJfs/94osv4p133sHy5csDStMU6n0KhED61K1bN0dGhGnTpmH27NkYP348jh075tGHlozJm7tO5zaB0tDQ4DIBIhQKkZKS4tLmyiuvBABcd911mDx5Mvr374+YmBjMnTvX53lLSkrw4osvYu3atc3G39o6nSp6Zl/uYF/+4MyFCxeQmZnZ7PFWqxWVlZUu200mE2pqarwef/DgQdx0003o379/xJZL3Xrrrdi9e7fPPG3r169HXFwcbrjhBpft9fX1ePbZZzFr1iyo1WqUlJSgpKTEUUippKTE41rdycjI8Hk/ATR7T/1RX1+P6667DnV1ddi6dWtI5wLgUHH8/vvvKCgowJkzZ9CjRw8AQO/evf0eG+7rPHv2LDIyMlweu3btarb//j5nb9jzlXpzLhcuXIjLLrsMOTk5js++uroaAH9dpaWlQVwRES6iYaecSUhIwFVXXYX169cH2XP/tNRO+aNr164A4DEocae92Kn3338fBoPB4x7Y0wS4K0Dd6Yh2yplnnnkGb7zxBlauXIm777474PMTkYF8qo7rU23fvh3Tp0/HpEmTsG7duoDfI1o2OZK2yt4X98J89iXxFy9eBMAH8yoqKlzSugDAuHHjEBsb26z9JiIH2aqO7Ve1J5u8a9cuD1vlr7iyRCLBTTfdhM8//9yvSjgxMREymQxJSUkekw7utipUX5OIPtEeKwaDvVDvxo0bve7fsmULdDpds2OA999/H48//jjmzJkTcF23UO9Tc7SkTwBvu8+ePYv//ve/ftsFOiZv7joTExODVqOvWrXKxU41l0anZ8+eGDJkSLN9XbJkCbp06YIrrrjCYZPt+eWrqqpQUlLiUn+mLdOpFOn9+/eHSCTCnj17XJaOmEwmHDhwoNnlJPYZ4j179rjMOO3Zswc2m81lBhkA/vjjD0ycOBGpqan45ptvIjbrYv/HWl9f77HvwoUL+OGHH3DPPfd4/IAuXryIhoYGvPDCC14T/efk5GDy5MnYsmWLz/cePHgwfv75Z9hsNhdVc2FhIRQKRbMBal8YDAbceOONOH78OL7//vsWLcfzhkQicTEE33//PQA0W1hl8ODB+OGHH6BWq11yiRcWFjr2B0N6ejq2b9/usq05daW/z9kXGzZsQM+ePTF69GiPfaWlpThz5gxycnI89t10002Ii4sLqvIyER5a2055Q6/XB/U9C4SW2il/nDp1CgA8ZsjdaS92qqKiAowxWK1Wl+1msxkAml3a2xHtlJ21a9fi6aefxkMPPeRIr0BEF/KpOqZPVVhYiJtvvhnDhw/Hxx9/HFQAMBib3F5s1bBhw3DixAmcP3/eIb4AmvK426+1oqICADzst92mN2e/ichBtqrj+lXtzSYPGjTIw1alp6f7PUav14MxBo1G41NVKhAIMHjwYOzevRsmk8llNY43WxWKr0lEn7YwVgwUo9EIm83m83/t+vXrERMT4zEJ7cwXX3yB//u//8Of/vQnrF27NuD3dr5O52KnZWVlOHfuHGbPnh3wucLVJyC4sVIgY/IuXbogJSUFe/bs8dj3+++/t+jznD59usvKwkAU7Xq93qsq3pnS0lKcPHnSxZ+y89e//hUAb7vbRV0Z1gGZMWMG69atm9d9EydOZBkZGUytVju2/eMf/2AA2LfffuvYptVqWXFxMauqqnJs0+l0LDExkd1www0u57zrrruYQqFgNTU1jm0XLlxgPXr0YJmZmez06dNhua6KigqPbSaTiQ0dOpTJ5XKm0Wg89r/88ssMANuxY4fHPq1WyzZv3uzxuPLKK5lMJmObN29mv/32m6N9WVkZKy4uZiaTybFt48aNDAD75JNPHNuqqqpYfHw8u/3221t0nRaLhd10001MJBKxr7/+OuDjHnjgARbMV/r48eNMpVJ5fJ5VVVWsuLiYabVax7bffvuNAWAvvviiY5vBYGCXXHIJGzVqVMDvGQjBfM7ePhM7+/btYwDY4sWLvb7Pd9995/HZP/jggwwAW7VqFfvqq688jpkxYwZTKpUhXB1hpy3YKW/ftdOnTzOVSsUuu+yyFl1XuO0UY4xVVlZ6bDt37hxLSEhgAwcOdNnenu3UqlWrGAD23nvvuWxfvXo1A8A2btzo0v/OYKcY4z8/gUDA7rzzTmaz2Zrt29KlSxkAl98F0XLagq0in6p1bFVRURFLSkpi/fr1Y7W1tT7bebPJarWa9ezZkyUnJzOj0ejS//ZsqzZv3swAsKeeesqxzWq1srFjx7LExERmMBgYY4x9+umnDABbunSpy3tt2bKFAWArV6706Af5VOGFbFUTncWvao822RfePueLFy+yrl27sq5du7psP3PmDCsuLnbZ9sorrzAA7O2333Zs0+v1rEePHiwvL8+xLRhf0864ceNYv379WnJZRAi0BZsWKBcvXvTq59u/b//85z899lVWVjKRSMTuvvtun+f96aefmEwmY1deeaXj/603TCYTKy4uZmVlZS7b+/btywYNGsQsFotj26JFixjHcayoqCiQS2txn7zZWcYYu/HGGxnHcezEiROObcGMyU+ePMlOnjzpsm3OnDlMLpez0tJSx7bvv/+eAWBvvvlmQNcVCGaz2at/WFhYyIRCocdnWVxczM6cOeN4/fPPP3vY5OXLlzMAbOHChWzz5s0e36O2Oq7rdIH0vXv3MqlUyoYMGcLefPNN9re//Y3JZDJ27bXXurT74YcfvDrEa9euZQDYrbfeyt555x02ffp0BoA999xzLu0GDRrk+EJ89NFHLo9t27Z59BdAsw7XlClT2FVXXcWefvpp9s4777Dly5ezvn37MgDspZde8nrMsGHDWGZmJrNarX7P7d4fb469t35aLBY2evRoFhMTw5555hm2du1a1q9fP6ZSqdjRo0dbdJ3z589nANiNN97oce8++ugjl7YlJSVs+fLlbPny5WzUqFEMgOP1hx9+6NI2NzeXLVmyhP3jH/9gf/vb31hiYiLr1q0bO3funEs7+4/1hx9+cNk+depUJhKJ2GOPPcbeeustNmbMGCYSidhPP/0U0PGBEszn7O+ePvLIIwyAx+fgj/fee48BYLt37/a6nwZ94aMt2KnU1FQ2bdo09vzzz7O3336bPfbYYywxMZHJZDL266+/evQ3WnbqnnvuYZdddhl7+umn2dtvv82eeuoplpSUxCQSicfvrD3bqerqapaens4kEgmbN28ee+utt9h9993HhEIh69evn0twqrPYqcLCQiaRSFhKSgp79913Pe7zH3/84XFMW3W42ittwVaRTxV5W6VWq1nXrl2ZQCBgK1eu9Gi3a9cuR9ulS5eyQYMGsUWLFrG3336bPfPMM6xbt26M4zj2r3/9y+X927utstls7Oqrr2Ycx7HZs2eztWvXsmuuuYYBYG+99ZajndFoZP369WMcx7F77rmHrVu3jj366KNMJpOxjIwMr/aIfKrwQraqiY7uVwV6nd6Itk32xdChQ9lNN93EnnvuOfbOO++wxYsXs6ysLCYQCFwC+YzxgW13UYZOp2P9+vVjYrGYPfroo2zNmjVsxIgRTCgUsm+++cbRLhhf0/n9KJDe+rQFm+breHc2b97Munbtyh5++GH2xhtvsNWrV7NbbrmFcRzHhg8f7vV79dprrzEAbOvWrV7PWVJSwuLi4phcLmdr1671sAkHDx50tD19+jQDwGbMmOFyjv/85z+M4zh21VVXsbfffpvNmzePCQQCNmvWLJd2vo4PpU/z589nw4cPd/hKK1euZCNGjGAA2IMPPuhy3mDG5N26dfP4XpSWlrKkpCTWs2dPtmbNGvb3v/+dJSQksAEDBngE+70dHygXL15kSqWS/eUvf2EvvfQSW7duHXvggQeYQqFgiYmJ7Pjx4y7tAbBx48b5Paf9O+Zu5+y01XFdpwukM8bPhIwZM4bJZDKWkpLCHnjgAZfZPMb8G423336b9enTh0kkEtazZ0/2yiuveKjkAPh8uH+ZbrnlFiaXy9nFixf9Xte///1vNn78eJaWlsZEIhFLSEhg48ePZ1988YXX9kePHmUA2IIFC/ye151gHAzGGKutrWX33nsvS0pKYgqFgo0bN85rIDbQ67Q7B74eztg/p0Du8x133MG6du3KJBIJy8zMZHPmzPE6++dr0KbX69mjjz7K0tPTmVQqZSNGjPBq+B955BHGcZyHUiBQgvmcfX0mVquVdenShQ0dOjSo96ZAeuvRFuzU0qVL2fDhw1lCQgITiUQsMzOT3XHHHex///ufx/miaac2bNjALr/8cpaSksJEIhFLTk5mN998M9u7d69H2/Zup86dO8f+8pe/sJycHCaRSFhGRgabNWuWh/PQWeyU3Sb5ergrqhhruw5Xe6Ut2CryqSJvq+yDSF8P58Hltm3b2DXXXMPS09OZWCxm8fHx7Nprr/WqCu0Itkqj0bD58+c7gk8DBgzwmDBgjP/sHn74Yda7d28mlUpZcnIyu+OOO9ipU6e89o18qvBCtoqnM/hVgV6nN6Jtk33x+uuvs7Fjx7Lk5GQmEolYSkoKu/HGG9l///tfj7beAumM8arWGTNmsMTERCaVStmoUaO82tVAfU3n96NAeuvTFmzaf/7zHwaArVu3zm9fT548yaZPn8569OjB5HI5k8lkrF+/fmzp0qWsoaHB6zGjR49mqampLmpxb3339XC+Jn+B8M2bN7PBgwczqVTKsrKy2KJFizxUz4cOHWIA2BNPPOH3OoPp07Zt29gNN9zAMjMzmVgsZiqVil166aXsvffeC2lM7isQfvjwYXbttdcyhULB4uPj2Z133snKy8s92iUnJ7PRo0f7vU5fGI1GNn/+fDZw4EAWGxvLxGIx69atG7v33nu9TiJSIL2dMWPGDNa1a1dWVVXV4n9mrUlqaip79NFHo92NiNNZrnPEiBHs1ltvjXY3wkpDQwOrqqpid9xxBw36wgTZqbZJZ7nOjmin9Ho9q6qqYo899libdLjaK2Sr2iad5To7oq0inyoykK1qm9B1tl/UajWrqqpiY8aMoUB6FGgLNu2xxx5jWVlZflOYdATWrl3LlEql18BzR+LIkSMMgNcUvm2Ntj6ua6qa0cE4e/YsUlJSXJLkt0WOHDkCvV7f4YundZbrVKvVOHjwIJYtWxbtroSVv/3tb0hJSfFZcZtoGWSn2had5To7qp1at24dUlJS8OKLL0a7Kx0OslVti85ynR3VVpFPFTnIVrUt6DrbN3fffTdSUlKwa9euaHel0xJtm/bDDz9g8eLFQRUtbo/88MMPmDdvHtLS0qLdlYjyww8/ID8/H5MmTYp2V5qlrY/rOMYYi3Ynwk1RUZGjQnVMTAxGjx4d5R4RRPvm+PHjKC0tBQCIRCJcccUV0e1QB4DsFEGEl7Nnz+LYsWOO1+PGjYNYLI5ijzoGZKsIIryQTxUZyFYRRHj53//+h8rKSgD0m4oGZNOIzkxbH9d1yEA6QRAEQRAEQRAEQRAEQRAEQYSLDpvahSAIgiAIgiAIgiAIgiAIgiDCAQXSCYIgCIIgCIIgCIIgCIIgCMIPomh3oC1hsViwf/9+pKWlQSCgOQai42Kz2VBRUYEhQ4ZAJCIz0N4gW0V0FshWtV/IThGdBbJT7RuyVURngWxV+4ZsFdFZaA+2qm32Kkrs378fI0eOjHY3CKLV+P333zFixIhod4MIErJVRGeDbFX7g+wU0dkgO9U+IVtFdDbIVrVPyFYRnY22bKsokO5EWloaAP4Dy8jIiHJvCCJyXLhwASNHjnR854n2BdkqorNAtqr9QnaK6CyQnWrfkK0iOgtkq9o3ZKuIzkJ7sFUUSHfCvkQmIyMDWVlZUe4NQUQeWhbWPiFbRXQ2yFa1P8hOEZ0NslPtE7JVRGeDbFX7hGwV0dloy7aq7faMIAiCIAiCIAiCaBesXbsW3bt3h0wmw6hRo/D777/7bf/JJ5+gb9++kMlkGDBgAL755huX/YwxLFmyBBkZGZDL5Rg/fjxOnDjh9VxGoxGDBw8Gx3E4cOBAuC6JIAiCIAjCBQqkEwRBEARBtGGiEZzq3r07OI5zeaxcuTLs10YQRMdg06ZNWLBgAZYuXYp9+/Zh0KBBmDBhAiorK72237VrF6ZNm4Z7770X+/fvx5QpUzBlyhQcPnzY0eaFF17AmjVrsG7dOhQWFkKpVGLChAkwGAwe51u4cCEyMzMjdn0EQRAEQRAABdIJgr52YLsAAQAASURBVCAIgiDaLNEMTi1btgwXLlxwPB588MGIXitBEO2Xl19+GbNmzcLMmTORl5eHdevWQaFQ4N133/Xa/tVXX8XEiRPx2GOPITc3F8uXL8fQoUPx+uuvA+An/FavXo1FixZh8uTJGDhwID788EOUlZVhy5YtLuf69ttvsW3bNqxatarZfhqNRqjVasdDo9GEfO0EQRAEQXQeIhZIJ/UU0VKYjUFfokfDoQboS/RgNhbtLhEEQRBtnDfffBMDBw5EbGwsYmNjkZ+fj2+//Tba3QqZaAanVCoV0tPTHQ+lUhnpyyUIoh1iMpmwd+9ejB8/3rFNIBBg/PjxKCgo8HpMQUGBS3sAmDBhgqP96dOnUV5e7tImLi4Oo0aNcjlnRUUFZs2ahY8++ggKhaLZvq5YsQJxcXGOR15eXlDXShAEQRARw2YDSkqAQ4f4Z5st2j0ivBCRQDqpp4iWoi3WonRlKUqWlKBkeQlKlpSgdGUptMXaaHetU9NRA1QEQXQcsrKysHLlSuzduxd79uzBVVddhcmTJ+PIkSPR7lqLiWZwCgBWrlyJpKQkDBkyBC+++CIsFovPvpLKs+1CAgUi0lRXV8NqtSItLc1le1paGsrLy70eU15e7re9/dlfG8YY7rnnHsyZMwfDhw8PqK9PPvkk6uvrHY+ioqKAjiOIzgaN/wiilSkuBlauBJYsAZYv559XruS3E20KUSRO6qyeAoB169bh66+/xrvvvosnnnjCo72zegoAli9fju3bt+P111/HunXrPNRTAPDhhx8iLS0NW7ZswR133OE4l109RbQ/tMVanFtzDuZqM6RdpZAqpbBqrdDs18Bw1oCseVlQ5pIaLhrYA1S9evUCYwwffPABJk+ejP3796Nfv37R7h5BEB0cjUYDtVrteC2VSiGVSl3a3HjjjS6vn3vuObz55pv47bff2q2d8hecOnr0qNdjwhGcAoB58+Zh6NChSExMxK5du/Dkk0/iwoULePnll72+74oVK/DMM88Ed4FExNEWa1G9uRq6ozpYDVYIZUIo+iqQfHMy+VREu+e1116DRqPBk08+GfAx7v8/nP+3EATRBI3/CKIVKS4G1qwBqquBrl0BpRLQaoH9+4GzZ4F584Dc3Gj3kmgk7Ip0Uk8RLYHZGKo3V8NcbYYiTwFRrAickIMoVgRFngLmajOqt1STiirM2INT9ofRaPTa7sYbb8T111+PXr16oXfv3njuuecQExOD3377rZV7TBBEZyQvL89lKf6KFSv8trdardi4cSO0Wi3y8/NbqZcdiwULFuCKK67AwIEDMWfOHLz00kt47bXXfP6fIJVn28MuUNDs10CULIKijwKiZBE0+zU4t+YcrfYjwkZycjKEQiEqKipctldUVPgUOKWnp/ttb3/212bnzp0oKCiAVCqFSCTCJZdcAgAYPnw4ZsyYEfqFEUQnhsZ/BNFK2GzA5s18ED0vD4iNBYRC/jkvj9++ZQuleWlDhD2QHq2lfQCvntq4cSN++OEH3Hffffj73/+OhQsX+uwr5ciLHuvXA9nZwN69/GtDqQG6ozpIu0rBcRyYjcGi5idBOI6DNEsKXbEOhlKDn7MSwRJscAqgABXRuZk5ExgxAjCZPPcxxvC/6/6H/Zfvh6nSSwOixRQVFbkEaX2pDw8dOoSYmBhIpVLMmTMHmzdvbtf/26MVnPLGqFGjYLFYUFJS4nW/VCp1LP+OjY2FSqXye21E+DCZgOHDgXvuadrmLFCQ9ZSh8l+VuPjdRQhVQhIoRJmOmC5BIpFg2LBh2LFjh2ObzWbDjh07fPqK+fn5Lu0BYPv27Y72OTk5SE9Pd2mjVqtRWFjoaLNmzRocPHgQBw4cwIEDBxw1tjZt2oTnnnsurNdIhJ+9e/nx4L/+5X1/7fZaFHQrQPm/vMcQiJYRqJDKmfY0/otGrb7nnnsOY8aMgUKhQHx8vNf3KS0txaRJk6BQKJCamorHHnvMr+iTaDucf/M8fk3/FScfPhmZeFRpKXD0KK9E5zjXfRwHZGXxivXS0vC/N9EiIlZsNBqQeqr9sGULv0Jl+3b+tVVj5ZccK4UAgNqva3H+lfPQ7OFXCQiVQtgMNlg11ij1uGMSaHAK6HgBKoIIFsb4wd6ePcDJk577rRorarfWov7nehy46gAF08OISqVyCdK6p3Wx06dPHxw4cACFhYW4//77MWPGjHb9vz1awSlvHDhwAAKBAKmpqaFcEhEBTp7kA1L/+hdgbXSTnAUK5gtmmKvM0OzWQHdYRwKFKNMR6zkA/DjsnXfewQcffIDi4mLcf//90Gq1jlSf06dPd/Ez58+fj61bt+Kll17C0aNH8fTTT2PPnj2YO3cuAF5I89BDD+HZZ5/Fl19+iUOHDmH69OnIzMzElClTAADZ2dno37+/49G7d28AQM+ePZGVldW6N4AImh07+PGgW51rB7Xf1sJYasSxvxxD3X/rWrNrHZpghFTtbfwXrVp9JpMJU6dOxf333+/1faxWKyZNmgSTyYRdu3bhgw8+wPvvv48lS5aE9wYQIeOtrkzN1zUwV5hxbvU5FPYsRPH0YjT8ryF8b6rRAAYDn87FG0olv58yaLQZwp4jPdLqqYyMDJc2gwcP9tkXZ/VUnz59PPZTjrzoYb/VdlsgVAkhlAlh1VohihXBXG0GANR+UwthrBCSNAkEMgGEKmGUetwxsQenAsEeoKqvr8enn36KGTNm4KeffmrTzhRBhBONBrALR7ReMiIwc5OyU3dEhwNXHcDgnYMhSZW0Ug8JiUTiWNo/bNgw7N69G6+++ireeuutKPes5SxYsAAzZszA8OHDMXLkSKxevdojONWlSxfHQHj+/PkYN24cXnrpJUyaNAkbN27Enj178PbbbwNwDU716tULOTk5WLx4sUtwqqCgAIWFhbjyyiuhUqlQUFCAhx9+GHfddRcSEhKich8I39jtkdUKVFUB6elNAgWpUgpzldnRtvabWki7SyFUCmE6byKBQhToiPUcAOD2229HVVUVlixZgvLycgwePBhbt251rCguLS2FQNCk4RozZgw2bNiARYsW4amnnkKvXr2wZcsW9O/f39Fm4cKF0Gq1mD17Nurq6jB27Fhs3boVMpms1a+PCD86Hf988aL3/Xa/ipkZjtxyBEN/Hwp5jryVetdxKSoqQpcuXRyvfYkTgPY3/otWrT57jZj333/fa7+2bduGoqIifP/990hLS8PgwYOxfPlyPP7443j66achkXiOFYxGo4sglNIQRx5fdWUsdfwAUJolhfGcERUfVaDiowrI+8iRNCkJSTckIW5sHATiFuqUVSpAJuMdOm+xGa2W30+rPb3y5ptv4s0333Ssmu3Xrx+WLFmC6667LmLvGXZFOqmniECw/x+wP8uyZVD0VcB41gjGWNNSYwZUf1oN7SEtFLkKyLLJcY4W9gDVsGHDsGLFCgwaNAivvvpqtLtFEK1GdXXT314D6ZamQLokU+IIppMyPXrYbLaAliy3ZW6//XasWrUKS5YsweDBg3HgwAGP4NSFCxcc7e3BqbfffhuDBg3Cp59+6jU49eCDD2L27NkYMWIEGhoaXIJTUqkUGzduxLhx49CvXz8899xzePjhhx3BeKJt4WyPzp/nn50FCnBKqWkz2FD7VS0sDRYSKISZjp4uIRDmzp2LM2fOwGg0orCwEKNGjXLs+/HHHz2CTFOnTsWxY8dgNBpx+PBhXH/99S77OY7DsmXLUF5eDoPBgO+//96hOvdG9+7dwRjzK7Qi2g56Pf/sLZDObAzmi02TgOZqMw7fdBgWDaXCCJVAV/kB7Wv8F+1aff4oKCjAgAEDXFIVT5gwAWq12udqJEpD3Lr4qytjKOFXH/R4oQeG7h6KlKkp4EQc9Mf0OPfyORy86iB+SfgF+8ftx8lHTqLi3xXQHdPBZg4wp3l2NtC3L79Eh7ml3GMMOHeOLzSanR3mq+4YRGOlX9gV6QCpp4jmcVekcwIOyTcnw3DWAF2RDszEGxCBQgCbzgZ1gRrZT2SDE3A+zki0Nh0hQEUQwVBT0/R3g5fVfHZniRNzGPzjYBy44gB0R3TYN3ofer7YE8l/SgbnnveOCBtPPvkkrrvuOmRnZ0Oj0WDDhg348ccf8d1330W7ayEzd+5cR7oDd3788UePbVOnTsXUqVN9ns8enFq2bJnX/UOHDqViYu0I50B6WRkwbFiTQEGzXwM0xsqFMUJY9Vboj+shjBMi5eYUEiiEEfcgx9KlS/H00097bXvo0CHk5+fDYDAgJiamzadLIIhIYFek19W5brerQtWF/IBR3lsOU5kJ2sNaFN9ZjP6b+4MTkj8VDdry+M9frb6jR496PSZctfqaw9f7OL+HO08++SQWLFjgeH3+/Hn6PxEhnOvKKPIUjvGaKFYEYZ4QdT/WAQA4IYfY4bHo93E/WOotqN1ei5qvalD7TS3MVWbU/7ce9f+td5yXE3GQ9ZBB0VsB+SVySLOlkHaVQpYtg7SLFOJUMa9iFwiAm2/mA+lFRXxOdKWSd/DOnQOSk4EpU/h2nQi7QMGOe0YRO9FY6ReRQHo0lvbZ1VNPP/00jEYjcnJy8PDDD7sYH6Lt4K5IBwBlrhJZ87JQvbkadTvqAACq4SrojutgLjfj5LyT6P9Ffyh6Kfyem9kYDKUGWDVWCFVCyLJlFIAPkY4coCKIQHEOpPtL7cKJOCh6KTD4x8E4ePVBGE4bcOTWI4gdE4ueq3oiLj+ulXrcuaisrMT06dNx4cIFxMXFYeDAgfjuu+9wzTXXRLtrBBFR7MEogA+kA64ChYYD/MyfKEEE5UAl1LvU0B7UoueLPck/CiMdOV0CQUQCb4p0uyrUXG0GJ+btkzhNDEkXCer/W4+a/9TgwBUH0Ov1XogZFBOFXnceaPwXXSgNcevhXFfGXfTEcRw4Eb/NXNe0SkYUJ0LqralIvTUVzMagK9ZBs0cDzR4N1LvV0B7SwqazQX9cD/1xvc/3FiWKIEmTQJwqhlgyHeKLZyAqroRY0ACRQg5RTi6EQ0ZCdDETwkMNEKqEEKlEEMYIwUm4Di3SCkagYMdqteKTTz6J+Eq/iATSAVJPEf7xFkgH+GC6oo8C5e+Xw3LRgow5GYi/NB77xuyDrliH3f13I/uJbGQ/kQ2h3HM5sq+8Vsk3J0OZ66N4A9EsFKAiiABSu9gD6Y0DP0UvBUYcGYGzq87i7KqzUO9SY/+Y/UicmIi0u9KQNDkJopiI/RvudPzzn/+MdhcIIiq4K9Lt2AUKJU+XQPObBjaDDbIcGQxnDDCdN+HEX0+g91u9ETcmjgLqYSCYujMdsZ4DQQSLfRKwvh6w2QAOrqpQ3VG+gVAuhGq0Cja9DQ17G1D/Sz32DN2DLg90Qfdl3SGOF0fxKjou7W3815Zq9Xl7n99//93jfZzfg4geznVlvMGB95Gc62G57BdwUPZTQtlPifQZ/OfJbAzGMiP0x/TQHddB/4cexrNGGM8aYSg1wFRuAqyApdYCS60FKLafLb3x0UghgI11APZ7vrEQECqFECqFECgEECoan+VCCOQCCGRuD6kAnJSDQCpwPDgJB4HE9ZkTN/4tbvxb3Pi3iHNs40SN20WNEw1COP7mRBw4YeNzCIH+YAQKrb3Sj0bwRKvDmGdqF2ecB3OyrjLIsmUY8vMQHL//OC5+dxFnlp1BxfoKXLL6EiRdn+Ro76xgkHaVQqqUwqq18nmtzhqQNS+LgukthAJUBBFAahdTY2oXAQd9iR6ybBlEKhFynslB5n2ZOL3kNMrfLUft1lrUbq2FQC5A0o1JSJ6cjLjL4iDrSikWCIIIHm2DDfayR+ePagCb0rH8V5mrRNL1Saj6uAryXnKk35MOUZIIF9ZdgK5Ih4NXH0TKrSnotqgb+UhRpC2nSyCISGFXpNvHhtI6N1WoPb2wkBfVxY6OhTRTCpvehtpva3H+tfOo/Hcl0memI/XPqYgZFNOh1ZmtTXsb/znX6rOn/7XX6vMl8LTX6nvooYcc23zV6rMHzu21+u6///6A+5afn4/nnnsOlZWVjvp927dvR2xsLK1EagM415URxXqGSO11sIKpK8MJOMiyZJBlyZBwtWeqaWZjMNeYYaowwVxhhqnKBEuNBeZaM8w1ZlguWmCpt8Bab4WlzgKL2sIH/DVW2AyNxtEKWNVWWNVtuHA8B0dgHUI+PQ4n5ND3vb5Inpzs99BgBAqtvdKPAulEq2MwANbG37qvFUr22T575WN5jhwDvx2Iqs+qcPKhkzD8YcDhGw9D1kOG9BnpSLsrzW9eK12RDtVbqqHooyDVFUEQLcJfahdtsRZlb/FSUKvBipIlJS6rYaSZUvT9R19kL8xGxfoKVP67EvoTelR9XIWqj6sAANJuUsRfFg/VCBWUA5RQDlBCkixprcsjCKI9UlwM7VcXAFwFACj75RSw8ms+12ZuLgCAWZtWy1R9VgVztRmpd6Xi4ncXYTpvQuWGSjQcbEDuv3KhGqyK1pV0GihdAkHwOKelungRSGlwVYUyW6Ptahy7CZVCcEIOPZ7vgawFWTj54Enojupw9sWzOPviWShyFUi9IxXxV/K+lFBGxZQ7G9Go1QfwqYtra2tRWloKq9WKAwcOAAAuueQSxMTE4Nprr0VeXh7uvvtuvPDCCygvL8eiRYvwwAMP+FXZEq2Dc10ZYZ7QZUKOMeYQS0m7hO+z4gQcJCkSSFIkQP/m2ztjs9hg09pg1Vr5R4MVNr0NVp0VNl3js8HW9NDbYDPawIwMNmPjNlPja1PjPhMDM/OvmYnBZraBmZ22Wfi/XZ6d/vYJg+M8Lpv9HdMCWnulHwXSiVbHOXjuTZEOuBbts8NxHFJvTUXihEScWXYGZW+VwXDKgJKlJShZWgJxqhiynjIIY4SQZkkdRWg4joM0SwpdsQ6GUgPk3eURuzaCIDouzqldnBXp9tUwuuP8iFAgFjRVeXdbDaPorUDOMzno/nR3NOxrQOWmSlzceREN+xtgPGNExZkKVPyraYmpOE0MRR8FZDkyyHvIIcuR8StuukghzZRCqKRBIkF0WoqLgTVroC29yrGpzJwM7N/PF6yaNw/IzXUMXsyVZphTmgQH6TPTUbezDupdauiO6HDg8gPourArMudk0iReBGlv6RIIIlLondIG19UB6QluqtBG4ZV9TGfVWiGQCSBUCZE4IBHD/zccNf+pQeW/K1H9n2roinUoWVoCLOXHkKphKsTmx0LZXwlFngLKXCVEcRT+6MhEo1YfACxZsgQffPCB4/WQIUMAAD/88AOuuOIKCIVCfPXVV7j//vuRn58PpVKJGTNm+ExbTLQuznVldEU6SLP4MZZVa4XhrAFojPmaa8xgNhZ1YaZAJIAgTtBm7BljDLDxwg1HcN3Kml5b+L9hhWN7OCclvBHplX5t484TnQrn4LmvQLp7rmFnRCoRer7YE92f6Y7qzdW48N4F1O2s4weIlWZoCjTgRBwkWRJI0viHOFnMz9Zp2vCyF4Ig2jQ11QxozJGnvaAGbDFg4ByrYWTdZKhHPTgh1+xqGI7jB3iqYbz606KxQF2gRv0v9Wj4XwO0h7QwnDLAXGFGfYVrBXhnhHFCSFL5AjWSFAnEKWKIk8QQJYggShRBnCCGME4IUZwIojgRhLF8gRqBQkDLnwmiPWOzAZs3A9XV0MVnOjaf18YDeXlAURGwZQvQp49D9WPVWl0KaXFCDgnXJECWI0P1lmpYNVaULC5B6XOlSLsrDWl3pyE2P9axOpAID+0tXQJBRAp3RbpskKsq1K5Ih4AP1BjPGaEaqoIsmw9gCsQCpPwpBSl/SoGl3oKqzVWo+aoG6l/VMJWboP5NDfVvrsufJRkSyLrLIOsmg6y7DNJsKSTpkqZHqoR8pHZOa9fqA4D3338f77//vt9+devWDd98843fNkT0sNeVsdfbM503wWa0wWqwOlb2lb9XDsNJA9Xfc4PjmtK2IAo6jGis9KNAOtHquAfSGQPcfRX7oM9eIdkbQoUQaXemIe3ONNT9Woc/HvkDlosWGM8bYdPaYCwxwljiOgulLlBD3lsOec9GZWcXKaRZvLpTkiGBKF5EjhNBEJ4UF6NmnwxADgBA+/M+YOUuGEbeBN1RIaRdpbBctPBtnUTiQpUQ9bvqUb+r3qOgH7MxGEoNsGqsEKqESBifgMRrEx37LQ0W6Ir54jSGUwboT/HPxvNGh52z1luhr9dDf8J3NXivcIAwRsg/lEIIlIKmYjVyAQRyp0I17sVqpG4FaySufzsK1EicCtRI3IrViJ2K0ZDNJYjgKS0Fjh4FunaFtqRp1FKtU8JoFUGalcUr1ktLYTM3GiUGr6tY5JfI0eXBLqjbWQerxgpdsQ4X/nEBF/5xAcI4IRKvTUTi9YmIGxsHeU85/WYJgggLzor0ixc9VaHMxI8HbQYbdEU6SJIlSJ6S7FUNKooTIeOeDGTckwHGGAynDaj/pR6aPRpoi7TQFelgumByPNQFPvKLAhDIBBAliSBOEkOcKIYoXsSLEuJFvFBCJYRQxQsThDGNPlSjPyVUNPpOjQX/OAn5OQTRXlDmKqHoo4Ch1ICGgw2o+qSKH7M02hxRovcVx0R0icZKPwqkE62Oc2oXiwUwGgGZW409f4p0b8TlxyH5pmRo9muQfHsyLNUWmMpMMJWbYKowwVRmAjMxh/NU/5N3dScn5iBOEfMKz2Qxr+hM4p9F8SKHqlMU1+RECVVCR0BKICUFA0F0OBrTJ1RXP+7Y1CCMBfbvh/WAGlb1ZEi7pcFcYwbADwTNVWZoj2phrjTDUmdB6fOlvJ1qVDBoi7UOxYPVYIVQJnTJqQ4AohgRYkfEInaEZ5EVxhisGiuMZUaYK80wVZpgrjLzj9rGAjW1FpgvmvkiNerGgjVqK788kcFRsCbajCkfA0kapZEgiKDQaPiiM0oltGaxy67yhhh0izEC588DGg2YOQ4A7+P4KqRlM9ig6KNAt2e6wXTehAtvXUDNtzWw1FhQ9UkVqj7hazmI4kWIGRYD1XAVlHn8gFPeRw5xvNjjnARBEP5wVqTX1fHPzqpQ+2o8m86G+MvjkTwlMBUox3GQ95BD3kOO9Onpju3mOjP0x/UwnDHAcMYA4xkjjOeM/Hix3ATjBSOfM9hgg+m8CabzptAvkoOHGIGTcrwoofHBSRoFCNIm8YFdmGB/CMQCXnzgLEZwFiU0PpT9lIjLjwu93wTRSeEEHGTZMlRuqITNaIOinwK1W2sB8JkRxGliqr/XxojGSj8KpBOtjns6F43GdyA90OXEzgoGfbEe0iwplP2VkOXIYDxnhDhJjLS708AJORj+MPAKz1ID7zydN8Fw1gBrvRXMzPgAfFkLHScBmlSdCkGTKsFZ4enkTDkcKZmgyYGSChxqTg9Vp7tDJXZ1qJydLFGcCAIpLccmiJBwSp9Qw5oqrmuZEsjLg3D3WQgrzsLakOTI5clsDOpCNaw6KwRSAUTxIkhSJQ4FQ9KkJNR8XQNztZnPd66Uwqq1eigc3BXrsmyZw1njOD59jChWBPQN/HKYjcGmt8Giaaz83sAXqbHpmgrW2PQ2x8NXsRqXgjXuxWrMjC9S46VYDbzUlQl0wpQgCCdUKt550mqhNblORJVpVOjGlfL7VSqHTyVKEMF41ui1kJY9ZYK8mxyKHAXix8aDWRnUu9Wo/aYWtdtq0XCgAZY6C+p21KFuR53Le4pTxI40CbJsvpaDJMMpXUIarfojCMIVd0W6HbsqtOrTKpgrzUi9MxWZ/5cZUsCK2RgsdRYI5AKoRqiQckuKx/nsIgVzjRnmGjMsNRZenFBvgbXeyosT1I2+k8bK+1IN1qaif04F/2CznxQOn6o1yHwgkwLpBBEihlIDdEd1Tenw7D9fIdXfI3gokE60Ou6BdLUaSElx3eat2KgdX8Elb3mtBDIBVENVLgqGuNHenQurwcqrOe3qzhozr+is5R0pSx2v6LQ/7GpOq4YPNPEdbzsqz7xNeUi9LTXa3SCI9o1T+oQancKxucEkATgOst5xUFwoheZYd3BxvI1hJgarzgpRsgiWagukGXwKKQkk0B7R4txr5yBSiaDop3AEldxzqjMbQ80XNX4V6y2BE3COyT6kN98+3DCrU2C9sRiNKJ5cEYIImuxsoG9fYP9+aE2uavDzahWgPgcMHQpkZ4NZSgEA8p5yiBJFHoW0jOeMXlMmcEIOcaPjEDc6DjnLcmAz2aA9ooVmjwYN+xqgO6aD7pgOprKmFTGa3T6K3wCAEBAniptW/CXwKRNECU01HIQqYVPqBKXQNQWVk0CBFGAE0f5xz5HuDCfgHEVGZV1kIf3mA1kFCLiKFOQ5LQ+OMcb7N77ECA4hgtHmKjxoFCPYTK7P9ofNbGsq5Odc0M/S9IgZENPifhOdCJuNH+NoNPzEfHY2ICABnh2rxgqrwQqpki+Iaa/XYLdDQqUQpvOmNhHzIaIDjV6JVkftlpLOPbDOGGuq0u4WSPfmCMl7yxE7OhaSNAmEKiG6LuwK4zmjVxWnP4QyIYRdhZB1lTXb1h2bxdakRtA1KhN0TaoEm87VibLqrS5OlM3Q5EA5qzsdTpTRVdVpMzk5UmYnh8rpQSpPgggDjekTdJJ46C1NwSp7KgUuRonktBMwxFyKhjMGAHyNB4FUAEu1BUIFP1gDB3DgB2gN+xqQcGWChzLTrnBQ/6aG9ogWNqPNr2K9PcIJOQiFQiB4M0sQhDMCAXDzzcDZs9DV82mlhJwVViZE2QktkJ8MTJkCCAQORbokVYLMv2Y2Kzjw+ZYSAVRDVFANUblst2gs0J/Uw1hq5Ff7NT6bKkyOlAnWeitghSPgHvLly5zqN7jXcZA5pUxwX+lnT6PgXLvBKYWCr/QJongR4sfGh9xvgiCacFak21O7OONPWBUo2mItzq051+wqwHDCcZxjZTFBtDmKi/nVtkeP8iniZDJ+Yv7mm4Hc3Gj3rk0gVAkhlAmb0uHZY1ONk3tWrRUCmQBClWfdGaJzQIF0otXxltrFGXuhUcC12Kg3R8hQakDlxkqUf1gOeQ85JKkSh8KgNWfkBSIBBHECiOLaxk+KMS/5EwiCCJ7G9Ak1Na6bHakUtFooU/XImp6EM/+0QFOgcaiDpBlSKPoqIE5pCsBzIo4PavnwuwQKAfSn9JBkSBCbH+uiWBfkCtCwpwFl68qQNT8Lsu6hKbQIgmjn5OYC8+ZB+2U8ACBHWYmTDRkoi8sF5uU7BsSOYJSIcymkFazgwBcilchrgN0Zm9HmSJdgruaf7akSLHX8qj+rxgqrujFdgtrqSJVgf2bGJt/GLk5oLRR5Cow8MrLV3o8gOjpmM18ry467Ih0IvmaWx/E2hurN1TBXm6HI870KkPIcE52GxrpPqK4GunYFlEpAqwX27wfOngXmzaNgOgBZtgyKvgpo9msgzBM6FOkQuKbDk2WTMqiz0jaifkSnQl3PADQ5K5p6G4CmGXu70wTwjhOzMehL9Di/7jwMZwxQDVc5ivnpjuh4w8b4QZooiSopA6AcpAQRLhrTJ9T84DrCazBJAMaAc3z6BOVVlyCxvBKVGyshVAkROzIW0iyps6kDgKbVIj5WApoumGDVWiHrKnP5HduLl5rKTNAd0cFw1oDY4bEhp3ohCKKdk5sLbSwDyoBeg2Nw8hegLHs0kOuUA93iGoziBFxQOT391WsIFIFUAGmmFNJMaVDHufTDyhcBtOoa6zk4p0vQN63wsxnCnz5B1oMGywQRTpzV6IB3RbojkC5q2bjGI8+xE5TnmOh0ONV9Ql4eYP9NxMbyr4uKgC1bgD59On2aF+f6e9ojWkeOdGuDFcaz3tPhEZ0LCqQTrUtxMTQ7NQCaVD2af30B9OzrmP10DqTr/9Cj9utaaPZooN6rhlAhBDMyyPvIoTumg1VnhThFDGZksNRaAMarhkhhQBBEWGhMn1BdsA1AU+oErUnEO5zJTekT7E6WOFEMi9oCCSTgnCLpjDFY1BbIe8lhqbdAkiXxWvBPqBRCktFUPNBcZXYULxXGCgGOz81Hk4YEQQCAVsvbkV5DVfj2F+B8mVsBvRBUnYHmFo4U3oL4QqUQSGn+WIIg2i7O+dEBH4p0S2iKdPc8x+5QnmOiU+FU9wnuojuOA7KyeMV6aSnQvXtUutiWsNffq/q0CjWf8UuTrfXWgNPhER0bCqQTrUfjUiJN2a0umzUny4E12xxLiexLkAHg/JvnYamxQBAj4ItOqYQwXjDCVGkCMzGIEkV8IEoCMA2vMhJxIlIYEAQRPnJzUXOlCvgKyFLU4ow2hVekDx3KB9HdJgElXSQQJ4u9FvSTpkiReE8iar6u8V7wL0UCoVzIFyuNFYExBu1RrcukoUAsgDhJDGGc67JkAGFN10AQRPtAq+Wfe/Xin8vKXPe3VNUZjdzC7u8fzSA+QRCRw12R7i+1i0DcMnWsR55jNyjPMdGpaKz7BKWP/59KJXD+vGfe3U6MMlcJ6QIpSpaUAACyF2Ujpl8Mja8IdO41G0Tr4bSUSK1Ic9mlTszhlxht2QLYbE050jnAUmOBIk8BcaKYL/ok4CBOEcOq5fNq2geFzMTAifgiUgCvMLAZbKQwIAgiLFRLswAA3frxOYANVgmsjz7ukkfQPgkojhcja14WVENUsNRYoD+uh6XGgpjBMUi+JRmybjKk3JKCmMExLvtVQ1XIfiobsaNiYTxrBGOMzyFcbXbUX7CoLRAniyGKE7ksS7648yJKV5aiZEkJSpaXoGRJCUpXlkJbrG3lO0UQRGtjV3Y2G0gPQtXpnltYFCsCJ+QLJivyFDBXm1G9pbopb2iYsQfxNfs1ECWLoOijgCiZT993bs05sm0E0c5xV6TX1Vj58aITzsVG7ak+Gw41QF+i97A93vbb8xzbfSqX9o2rABW5CspzTHQOGus+OWbf3dFq+f0q3/VOOgoFBcDo0fxzsziFkxSXULYDgocU6UTr4LSUSHOAX14nE5lhsIihMUldlhIxYTp/DAdHTjthnBDiZDFMF0wQp/BBJMtFC6wNfKoDi9oCaYbUEWwihQFBEOHEXmy0Wx8Z8Dv/t1YvQGxTHVGXJcjuBf1MFSaoC9Wo+LDCoayU95YjbXoaJGkSFwU5J+BgOGuArkgHgUwAZmJgUgZLlQVCBa/ItGeMESqF0BXrcOHtC2CMRUU1ShBE9LBaeYEZ0BRIV6uBhgYgprHmut02BaPqjGZuYSoQSBAdH33RaQA5EHA22JgAF2ttYCteBPenmz1W+hnOGFDzVY3P1Sn+Vq/Y8xx7XQVIeY6JzkRj3Sfs3++aIx1wqfuE7Ozo9bGV+Oc/gcJC4OOPgfx8/20dIk+0PM0U0fEgRTrROjgtJdIY+UB6popfNqQxSfmlRAYDoNE05UhvzAMM8IM2ZV8lhAohzFVmQMwvUbbUWGCqNLkEl5pTGDSnaCAIgnDHHkjv0qXJ73QXdLirPu0F/TgRh6rPqtBwoMFFWdlwsAFVn1WBEzW2axzI2XPyqYaoYNPaYNPxq2ukGVLEjoqFOKUpem9psMBUYYKlwRIV1ShBENHFWdWZnt4UPHdWpTurOr3hzS+y5xa2+2HuRHLlXzBBfIIg2iHFxdCv/xwAkKbgx4Mmmxj6PUeANWt4cRWaAlgVGyp8rk6p/qra7+oVAF5XCaqGqtBlXhcSGhCdh8a6T0hO5us81dcDFgv/7F73qYNTVMQ/+xLnO+Ncv48TUiCd4CFFOtE6OC0lUjcG0ruoNDh1MREao8RlKREzNgajhJxLTjtxihixo2KhPaqFqczEDwiFgEAogDJPCVGCCJZ6i1+FQTjybXorfEVKBoLo2FRX88/JyXygSqPxE0h3ykPcUmWlXdGuL9Hj3KvnoD+hh2q4yqWNzWZDw4EG2Aw2SNOlLoVNgcirRgmCiD7Odkgu5yf7jh3jA+m9e/Pb/eVI9+UXqUaqopZbmAoEEkQHpjHdp66Gn+BLidGjUqeClQlQlzMEirOFfLrPPn0ctstSb4FqhMrDh9Ie0eLca+cgUomg6Ofbx8p+PBvZT2TT+I0gcnP5unSbN/PZAs6f52MwbnWfOjKMNQXSGxqab+8QI4g4j8l9ovPS8aebiLaBfSnR2bPQmCQAnBXpEn4pUW4ukJ3topxyz2knThEj9tJYyHJkSJ2aij5v9UHqtFQAaFZhEI58m9piLeUh7kCsXbsW3bt3h0wmw6hRo/D777/7bf/JJ5+gb9++kMlkGDBgAL755huX/YwxLFmyBBkZGZDL5Rg/fjxOnDjh0qZ79+7gOM7lsXLlyrBfGxFe7Ir0pKSmGj3uzpe3PMShKCs5AQdFDwW6zOkCWTcZdMU6WOotYBYG/Wk9aj6vgfG0EZaLFqh3q1H/Sz2/YscJqhdBEB0buyJdoeBXy2Rm8q+dFenOaaec8eUXqfepUfZuGRhj0B3VeeYi9rPyLxyr/pwLBHqD0vcRRDumMd2nPoE3VkqxGfEy3ge6aJC7pvts9KukWd59KFGsCPoTekfdGPf9zj6WfZVgzIAYl1WABNHpyM0FnngCWLYMWLyYf3788U4RRAeA8nJehA8EqEj34UMRnRtSpBOtg30p0dmzUOv4r12XmDoAgKbG5LKUyG6shAo+L7q3nHbybnJkzsmEMleJhPEJzSoMwpFv0z7gNFebKQ9xB2DTpk1YsGAB1q1bh1GjRmH16tWYMGECjh07htTUVI/2u3btwrRp07BixQrccMMN2LBhA6ZMmYJ9+/ahf//+AIAXXngBa9aswQcffICcnBwsXrwYEyZMQFFREWSypmDDsmXLMGvWLMdrVSco6tLesQfS7Yp0wIsi3Use4nAoK+2pXuyqUV2xDoZTBnASDqrhKuhO6MCJOBgvGGGpt7ikf6GAE0F0bOx2yD7BZw+knz/f1MbbJJ8vv4gZGSy1Fuj/0EMgEQAcYCw1QjVUBWm21G9u4XCs+gPgKBCo2a+BME/oEiCzB/FVQ1VUIJAg2iON6T51Qt73lYvNSJDrUaNX8IH0RCVw/jyYWt00JvSRYooTcbx98+Hi0OoVgvCBQAB07x7tXkQFuxodCEyR3pKC7UTHhxTpROvRuJRIY1UAADL1pwAAamkqv8TIrbCMQC4IKKddIAqDUPNtug84KQ9x++fll1/GrFmzMHPmTOTl5WHdunVQKBR49913vbZ/9dVXMXHiRDz22GPIzc3F8uXLMXToULz++usA+MH96tWrsWjRIkyePBkDBw7Ehx9+iLKyMmzZssXlXCqVCunp6Y6HUuk/wGA0GqFWqx0PjUYTlntABI49tYs/Rbq3PMThUlYqc5XIfiIb3Z7uBnkfOWSXyJD8p2Qo+isgThHDZrBBlCyCVWeF7qgOYM3XiyAIov3jHkjv0oV/dlGke0nt4s0vMleZoS5Uw1RugjhRDIFMgJgB/Mxh/W/10OzR+Fz5F45Vf3Y4AYfkm5MdYgr7ShxLvQW6Ih0VCCSI9kxjuk+9lveZFE6K9DqDzJHukymaRCY2o83rqZiF8T6Xjzg5iQkIgnDHOZAeTI50b+nxiM4LBdKJVsXYIxdmK+/MZP75CgCAJqGry1Ii51k/e/Co+7Lu6La4G7ov647sx7ODVn6HWjSLCl91LEwmE/bu3Yvx48c7tgkEAowfPx4FBQVejykoKHBpDwATJkxwtD99+jTKy8td2sTFxWHUqFEe51y5ciWSkpIwZMgQvPjii7BYLH77u2LFCsTFxTkeeXl5QV0vETreUrsEkiPdrqx0T1MFBB/o5gQcOAEH60UrFH351TPOhZgt1RYIpAKYKk0wnDNQwIkgOgG+FOneio16Wy1j94sYY9Ae1cKqs0KcIuYDTzZAnCpG0p+SIL9EDkUfBbo/7emHRUJs4Fx0mQoEEkQHojHdp66KVyPIRRYk2FO76KWOdJ8sI8txiLHMuw9lUVsg7yXnJ9tC9LEIgugcNNYyBkCKdCA8Kfk6I5TahWhV1OqmvzNHZwMANBq3NCxm1/QIdsV5KDirQltSNIsKX3UsqqurYbVakZaW5rI9LS0NR48e9XpMeXm51/bl5eWO/fZtvtoAwLx58zB06FAkJiZi165dePLJJ3HhwgW8/PLLPvv75JNPYsGCBY7X58+fp2B6K2Iy8SuRgWZSu3hxtOzKSsNZg9c0Vc6B7kAKGXuzRc6FmM2VZljqLDBXmhE3Jg7JU4JLqUAQRPsioNQuXvJ7uvtF1norzNVmR65hm9EGTsRBIBVAIBBA0UcBS40FEMDDLgUjNgjGn7MXXaYCgQTRgWhM96n/ko9mKZgWkPKTfXUldcBoPt0nszb9ziUpEq8+lDRFisR7ElHzdY3HfsNZA4RSIRR5vA0h20EQBNACRbqX1J0dhXCl5OuMUCCdaFXswSiFAoiPd91mx1t6hFAJNd9mqIF4grDjHBAfOHAgJBIJ7rvvPqxYsQJSqfeJGqlU6rJP7TwjRUQcuxpdIODtls9ioz6K0bjnODedN0EgE0A1VOUIdAfqyPiyReIUMeKS42A8a4S5yozsx7MRNyaOBo0E0cFxLjYK+Cg26mWSz90vshltjjQJdqWnNEMKURxvZ/wJBkIRGzQ3gRgOMQVBEG2M3FzohiUDhYDcqoHUUg9gAC4m9QLmjeEV6bVNxdOz5mWh5j81Lj5UzOAYxI6KhSRNgpRbUqAuVEN/TA/TeRNsRhusBitgBirWV6D6s2oKDhEEASD4QHokYlNtAar/FxoUSCdaFXv8LzaWT5EHeAbSHcGoMOWhsg/SFP0U0BZpoTvCq6Z8qUK9Ec7CV4GoTonIkpycDKFQiIqKCpftFRUVSE9P93pMenq63/b254qKCmRkZLi0GTx4sM++jBo1ChaLBSUlJejTp09LLoeIMPZAekICH0xvTpHuTbHgT1kZjCPjzxYBfEArbkwcBdEJopPgL0c6YwDHeU875b5aRqgS8qmjNFbYjDYIFfxkHhoPcRYMuPsxQmXLxAYtUUKRD0UQHQN9TAoAQHHZcIiZETgFXOx/GZDbWPjY3JReQNlfCWU/peO3b6owQV2oRsWHFQ7bIe8tR9r0NFi1VlR9UgVOzEGa3TTei3ZwiGwXQUSf6mqgqqrpdVCpXTpQjnRfBedFsSII84TQFelQvaUaij4KslM+oEA60arYg+YqVVMgXacDrFZA2Di+CmceKvdBGjMyWPVWsFOMX67spgr1RTDpGYLpDy2fiQ4SiQTDhg3Djh07MGXKFACAzWbDjh07MHfuXK/H5OfnY8eOHXjooYcc27Zv3478/HwAQE5ODtLT07Fjxw5H4FytVqOwsBD333+/z74cOHAAAoEAqampYbk2IvzYA+nJyfxzs8VGfTha3pSVwToy4U4VEymi8d4rVqzA559/jqNHj0Iul2PMmDF4/vnnaYKK6NC4B9Lt87hGI3DxIpCYGNhqGXsxUEutBbKeMihzlRCniAG4CgasWitKV5a6+DHy3nKIEkUwnjUGLDZoiRKKfCiC6DjYV9PI0+McAoW6+ibb4awC5TgO4AB5dzm0xVpUfVblYTsaDjbAcM4AoYxfYaPo13aCQ2S7CKJtYM+PHhPDj+P0etc4FADAZgNKS/nAlUoFZooF0LEU6ZFKyeeB271EdjavSusAUCCdaFXsgXRnRbp9uz3VS7gC6b4GaYZSAwRSAVJvS0XMoBiPAI97AEiaJYXxnBHMwjyWDgYaiPfXn2grJAKhIwaoFixYgBkzZmD48OEYOXIkVq9eDa1Wi5kzZwIApk+fji5dumDFihUAgPnz52PcuHF46aWXMGnSJGzcuBF79uzB22+/DYD/p/PQQw/h2WefRa9evZCTk4PFixcjMzPTEawvKChAYWEhrrzySqhUKhQUFODhhx/GXXfdhYSEhKjcB6J5qqv556Qk/tlnsVEfwSp/tMSRCWeqmEgQrff+6aef8MADD2DEiBGwWCx46qmncO2116KoqAhKZdu0rQQRKu6BdKmUt1U1NXye9MTEwFfLNBxsQNUnVbAZbOAkHJiFuUzSKQcocf71814DWBACnJALSGzQEiVUe/ahCILwRK/nnxUKfsUfwE/+2fGmAm3Odmh2a2C6YEL8FfFBB4ciJQAg20UQbQd7Wpdhw4CffuL/1umc4lLFxcDmzcDRo4DBAMhkYMJRAPp1qEB6q9T/83Iv0bcvcPPNQG5uy8/bRqBAOtGq2FO7qFT8YE8kAiwW74H0UAo6+HO0lP2U0BXpoCvSIflGVxW5LwW7UC4EJ+Vclg5K0iQBO1rtfflMRwxQ3X777aiqqsKSJUtQXl6OwYMHY+vWrY5ioaWlpRA4zZiOGTMGGzZswKJFi/DUU0+hV69e2LJlC/r37+9os3DhQmi1WsyePRt1dXUYO3Ystm7dCpmMV+FJpVJs3LgRTz/9NIxGI3JycvDwww+75E0n2h52Rbo9kB5MsdHmaKkjE65UMb5o6YAymgPGrVu3urx+//33kZqair179+Lyyy+PyHsSRLRxD6QDfHqXmho+vcuAAYGvlpF3l0PRW+F1ki7ppiTUfFHj14+RZEkgThY3KzYIdgKxvftQBEF44lCky30E0r2IE5qzHeIkMXRHdI5j3fHlU0VKANDebVdHFFIRnRvnQPp//8unwNNqGwPpxcXAmjW8gqprV96x0mrBfj8NoB8EVmM0ux5WIl7/z8e9xP79wNmzwLx57T6YToF0olVxTu3CcbwyvbbWNU96OAo6tETl6R4AEugFqP+1HpZaC0SJIsSNiYNAIUDDwQYYzxuRNS/L61IXbwGoVls+EyQajcalcKV7UUs7HTVANXfuXJ+pXH788UePbVOnTsXUqVN9no/jOCxbtgzLli3zun/o0KH47bffWtRXInoEmtqlJTn0QnFkwpEqxhstzVusL9Hj/LrzMJwxQDVc5Ti/KFYEQa4ADXsaULauDFnzsyDrHpzSK1Bb5Ux9fT0AIDExMeD3IYj2hnuxUYAvOPq//zUVHA1mks/XJF0gfoylxoIuD3Zx5Fr3NQkX7ARiW/WhCIJoOc6KdLuYqq6uab83u9Wc7bAXR7bUWSBOEnvs9+ZT+RIAqPep0XCkwecK5kBoTdsVCUV9RxRSEZ0be2qXvDx+PNfQ0Dies9l49XR1Nb/T/nuNjYWtC6+k4uov8u06QGqScNb/88DPvUReHj+bsWUL0KdPu76XFEgnWhXn1C4AH1B3D6SHo9hosIM09+ATADQcbACzMEh7SGGptkB/Qo+4sXF+g1G+AlCKforIL59pAXl5eS6vly5diqeffrrZ4yhARXQm3FO7hFORHm5HJtRBW6CKcucBm73oV8PeBqj3qiFUCMGMDIq+CohTxDBXmaE9qoWpzATdER0MZw1QDVUhdnRswCt7grVVNpsNDz30EC699FKXVSME0dHwpkjPzGAAOJTtKweuNASddsrbJF2gfpVNa0PMgBi/5w92ArFVliD7gAoEEkRkaFaR7mWFcnO2AyJAlCiCucYMWQ9Zsz6VL/EBMzJYai3Q/6GH9n9axAyOgTJXGbRCvbVsV6QU9R1VSEV0XuyKdOdAulYLPo/30aO8etpt/MQYb4M4o5Zv171763Y6AoSr/p9X/NxLcByQlcXPaLTze0mBdKJVcU7t4vzsEkgPQ470YAdp7sEnS50F5mozRHEiCAQCiGJFMFebYam3QBQvcglG2ZVazrlFpdmuAaiGIw18mphm+iNQCqAv0bfagK2oqAhdunRxvG5O4QlQgIrofLindvGpSLcEn5Yq3I5MKIM2f2p2Z0V50qQkqHfztSKMlUYYTxnBSTjIesggVAohVAlhvGCEpd4CeS859Cf0sOqsEMYKAY6/T5UbK1H+YTnkPeSQpEqaHfAFa6seeOABHD58GL/88ktA940g2isegfTiYmSergEwFue/PQjUrwe7eBcASav6Vf4IdgIxnO8dTGCcCgQSROSwK9Ll8iZFutfULk7CquZsh+k8nx/dprcF5FN5Ex+Yq8xQF6ph1VkhThSDWRgEEkGLUtSFYrsCtVUtSanXklV+AAmpiPZNfT1fOwbgs4rExAAVFY1+lEDD5/H2stKCWfnfHQeLa9CqnRNIza0WofF9L/k3VvIfRDu/lxRIJ1oV59Quzs/hDqQHO0hzDz7ZjDYwC3P0gZNwYBoGm5FPO2MPRjUcbEDlhkpoi7VoONAAq9oKWU8ZJGkScLGcI52C9ogWVoMVxlIjhP2890eaJUXlvyuhP6Z3DNjkveVBqTaDRaVSIda+PCBAKEBFdDZ8pXZxV6S3NC1VOB2ZUAZtvtTszoryhgMNqP6yGkKlEDFDY8CMDAwMzMpgOG0ArPzkgDhFDFOlCerf1RBIBRCniMGMDLAChhIDmI0BjLe1oiRRswPUYGzV3Llz8dVXX+G///0vsrKyAr53BNEecQmkN+ak7FI/BMBYlAmzgeRkMCM/ccaVlgCDB7TofcK5eibYCcRwvXcwgfFI1nugvMME4ZqWyq5Ib2jga2eJRN59qkBsR+bsTAAIyKdyH/8xxqA9quWD6CligAGWGgs4CQdFniLonOYttV3ebJW3MaH9OoNN59eSFckkpCLaO0eP8s8ZGfzknYswKlPFF8PUaptSJzTCbI3xICGcqpJ2DPzV3GoxKt/3EgC/XSYL672Mhl9FgXSiVbFPfjundnHeDoSn2GiwgzT34JNAKgAn4sDMDJyUAzMxcCIOAinfJ6vWCpvRxivQjTaH0lKUJIKp3ASr2orYUbEQp4jBcRxkXWUwnDJAIBN49Mdw1gBmYNAV6wABoOijgDRGCkOpoVnVprtaQZolhfGcMWKKdgpQEZ2RoFO7tCAtVbgcmVACTt7U7M7KLIFKAFbDABFgs9rQsL8BzMQgThKDk3AwVZrALAyWegvEKWIIZUIYyg2Qdeffy1xv5pVdwqbAuqXWAjC0aIDqDmMMDz74IDZv3owff/wROTk5QZ+DINobjkC6vCknZWbvGGA/UNYQC8TGggl4d1/w4/fADf1alJMy3KtngplADMd7BxMYj3SBQMo7TBDeFekAnyc9Odm3sCpQ2xGIT+U+/rPWWx0rkjmOg81ocxn/CVVC1O+qR/2uesSOjm12zNUS2+XNVvkaE6pGqlqUzq8lK5JJSEW0d5zTugBu47nsbKBvX74YpnNebwCscREvF6vk23UwvKXzCwk/9xKMAefOAUOHhvVeRsOvokA60aoEpEgPQ450ILhBmnvwSRgnhDhZDNMFE0TJIljUFkgzpBDFicAYg+GsAVaDFZyYg6KfAuZKM5iVQRQvglAlhLnKDN1RHeKS4wCOV7ALpAKkTE2B7ojO0R+b0QaL3gLTWROsDVaIEkRgRgZxqhj6E3q/qk0ALmoFZmSw6q0QyoXgpFxYlyBTgIrozASc2iXE1TThcGRCCTi5DyjdlVlWtRXMzCDqIoIwVgjjWSOsDVaIU/kJQ3GcGJaLFnAiDuYqMyDk7bnNbIOtygaBSABmY44BKiRwrPQRcaKQi2498MAD2LBhA7744guoVCqUl5cDAOLi4iCXUwFComPiUHXqqh05KTM0vHG60MCPEm3Wxvyep0+GlJPSm1/FSTnIcmSIuzQOAjn/Gw8mmB7oBGIoK3eCDYy3tNZEZy/gThDB4KxIF4n4oFZDA5/exSWQ7mU8GIjtCMSnch//Oa9IZow5xn/MxFD/Sz3MlWZY6iw4veg0OAkX0JgrGNvlzVaZq8zQHdF5HRNqDmhgVVsh7RZcOr9gVySTkIroCLgH0l3GcwIBcPPNwNmzfMOsLL6BVgt2HgByIchKb9fFMVsNP/cS587xBn7KlIDuZVv2qyiQTrQq7sVG7c/OgfSWpkfwRqCDNG/BJ0UvBcxVZhhPGSFKFEHeSw6L2gLjOSOEUiFgBqTZ/CDLWcEukHrmVLenU4gZFIPkG5Ndc6qbbAAHSDIl/ACujB/ACWQCSLIkXlWbZW+Xwaa3wVzDqxUEegHqf62HpdYCUaIIcWPiIFC0LJ+fNyhARXRmAlakB1nQL1K0NODkPqB0VmYBgKXeAk7MQRjDK91FcSJYLlr4ScA4ETgJB07EL382V5lhKDEANoAZGGTdZBCniNFwqMFxf9xX+oRadOvNN98EAFxxxRUu29977z3cc889LTpnW2Ht2rV48cUXUV5ejkGDBuG1117DyJEjfbb/5JNPsHjxYpSUlKBXr154/vnncf311zv2M8awdOlSvPPOO6irq8Oll16KN998E7169fI4l9FoxKhRo3Dw4EHs378fgwcPjsQlEi3EoUiH1pGTMs5sBACojY3pCuzLks36kHNSOvtVDQcbUP9LPUwXTKhYX4Hqz6qDnsAPZgKxpSt3gg2Mt7TWBBVwJ4jAcVakA3x6l4YGXpEONO9ThSo+sK/qVfRTQFukhe6IDsJYITgBB6uGX3ksVAghThVD/XvjyjypAAKZgF993GANeMwViO1iNsar3QvqIUmVgAPnIWhwHxNqdmtgqjA5/DB3gqkf4fUekZCK6EAUF/PPubn8s0eqztxcYN48fnXf0aN8Hm+ZDLYuA4F9AJcYXDrccBFy0XObjRdRaDS8ijU7O/ITAj7uJRs8FIZRN8Jq6Qphib7Za2nLflXEAuk06CO80VrFRp0J1NFyDz7ZDDbIc+SwZvC56Sy1FkcwSpGnQMX6CgiVvGPirGAXp4hdcqq7p1PgBBxk2TJUbqiEzWiDtBu/ZE8gFYAT8HnVdWU6iAQicPBUbUq6SFD3Yx0kGRKoRvA3sOFgA5iFQdpDCku1BfoTesSNjQvLEmSgYweoCMIfVmvToM49R7ovRXooaanCRUsCTu4TigKZAMzEwKQMlioLv7JGLODVWkIOghgBBGIBLPUWCGOFjsC4NF0KeW85OAkHaZoUwjghVCNUsKqt4Ir5CUdI4LLSBwjPgK8jsmnTJixYsADr1q3DqFGjsHr1akyYMAHHjh1DamqqR/tdu3Zh2rRpWLFiBW644QZs2LABU6ZMwb59+xx5TV944QWsWbMGH3zwAXJycrB48WJMmDABRUVFkMlc0/4sXLgQmZmZOHjwYKtcLxEcjkB6ksyRk1Il4QPpDSYJGHMqlCUThyUnJSfgYNPbULu11pF+wL7yJVwT+P7eO9jgWbCB8ZbWmqAC7gQROM6KdIAPpJ8921RwNJI+lXsOcvuqXpuJF3NZai2Q9eTFBbpjOlh1VoiSRTBXmflVN2Iu6DGXP9tl7099QT0aDjRAFC+CpEQCSabEJdWM+5hQ3lsO0wUTdMd0UI1QhVS7whskpCI6Er5Su7iM53JzgT59XALP7HMB8J9TIWdLaAkhFz0vLm4KZhsMvJ/Yty+vGLfPKEQKt3uprZCiulAC3Yd6WA0lAV1LW/arIjLatw/6li5din379mHQoEGYMGECKisrvba3D/ruvfde7N+/H1OmTMGUKVNw+PBhRxv7oG/dunUoLCyEUqnEhAkTYDAYPM5nH/QRbY/WKjbaUpS5SmQ/kY3uy7qj2+JuuOTVSzDw64G45NVL0G1xN3Rf1h3Zj2cjZlCMY5AF8IomZV8lhAo+rYtVYwUEgM3EV433VyVeKBM61OwA+GJ9Qj4vn81o81BtwsI7eOIkPp2Cs2pUIHBVw7srrVoKY8zrg4LoREfn4kU+nRsA2Ce1nRUMzrFbx2qaKDha3rAP2mIGxEDeXR7QRJp9QlE1RAWb1gabzgarxgpphhRxl8VBkiWBpd7CB63NgChBBGEMb/dMNSaIEvmgk75YD0V3BbKfyoasuwy6Yh0YGMSJYpirzTBVmiBU8A4UuKYBnyJX0eIBX0fl5ZdfxqxZszBz5kzk5eVh3bp1UCgUePfdd722f/XVVzFx4kQ89thjyM3NxfLlyzF06FC8/vrrAPh7vXr1aixatAiTJ0/GwIED8eGHH6KsrAxbtmxxOde3336Lbdu2YdWqVc3202g0Qq1WOx6aEJXPRGA4Auk90vjB0dmzjkC6jQmgNYkBNAbS+14SlpyU7ukHRLEicEJeCKDIU8BcbUb1lmo+FUEbwDkw7g33wLh9dY7xrNFjgs6frbKnS7A/gsk7vHHjxhZeHUG0T9wV6fY86XbxQjhXKDtjz0Gu2a+BKFkERR8FZD1lEMYKIVQJkXlfJuIui4M4UQybwQZzpRkCqQCWagsEIgEEIkFYx1zO/RGniiFKEIETczBeMEKzTwOb1uZzJZ8oRgRJmgSiGBF0RTreP2usVeNt/Bksb775Jurr63HFFVcgIyPD8di0aVOLzkcQ0UKnA0pK+L/dU7u4rzCGQMCnwBswAOjevSlHegttEbMx6Ev0aDjUAH2JPmDfyJutEiXzaZ3OrTkHbbF7x91oLECP/ft5NVifPvzz/v38drtEP5I03kutqAfOfcagOdAQ1LW0Zb8qIoH09jLoI1ofX8VGvQXSW0vV6W7cALgEnwQigUcwytsgS5wiRuyoWEjSJTDXmvlzmxhUQ1XoMq+L1yrxQmVTPnZ7cIoT8ekRmIXBZrHBorZAnCx2Sa8AAKJ4/rVzPj8AvBrewqsVAF5pZTPYWpwugSA6M/a0LrGxgFjM/21XMNhs/OS+nbaS2iVU7BOKPVb1QNKUJMhyZIi9NBaSVInLhKGpxgRJpgQxg2PACTgIOAE/2Ky1OOxe8g3JjsC8tZZfFs1xHARCAZR5SogSRGEb8HVETCYT9u7di/Hjxzu2CQQCjB8/HgUFBV6PKSgocGkPABMmTHC0P336NMrLy13axMXFYdSoUS7nrKiowKxZs/DRRx9BYZcM+mHFihWIi4tzPNyXYxKRwRFIVzXmpExOhvLEAXDgfQB1pcnRVnDT9WFZzhtMqpS2QLCBcfvqHHGyOCLBKTv2vMM//PAD5R0mOhVmM2DhhzMuinTASZEepppZzvibBFT2U4KZGKwaK7KfzIZqqArmKj4nOrMwSDOkUPZTAiKEbczl3h9plhTiFD6AL0oWwWa0wdpg5VcHNuZsdx4TWrVWSFIlyJidAdUQFSw1FuiP62GpsXgdfwZ9v0hIRXQQjh3jxU/JyUBKCr/NV6pOd0IReWqLtShdWYqSJSUoWV6CkiUlKF1Z2mwQPGTBgq2pAD3y8viBrFDIP+fl8du3bOHbRZjWFF+0pl8V9khlexr0kXqq9fGlSHeqIRARx8kXLTVuvgZZnISDKFGE+LHxyHk2BznLc5D9eLaHE+OsjnJXszPGIJDw6RMsdRYP1aa5xgxRoshxf5zzswOeaoVQ0yUQRGfGXmjUntYFaFIwAK7OVzRX04QbTsBB0UOBLnO6QNaNV5Rb6i0QJYig6KdwCZxz4JA6LRW93uiFni/2dKzcsds955U+PV/siV5v9ELqND4lSTgHfB2R6upqWK1WpKWluWxPS0tzLLF2p7y83G97+7O/NvaB8pw5czB8+PCA+vrkk0+ivr7e8Siyr6ElIopLeoTGnJTc0CGIEfOqdE2F0dGW69c3LO/pLAbwRlubwG9JYNx5dU4kglNz587F5s2bsXPnTso7THQ67Gp0wFOR7p7aJZw+VaCTgEKlENlPZPOrkAfHIHZkLOLGxkGSLvE55mKMwVhmhFVv5W1MAEEh9/44jwkt1RYIY3gbayo3+V3Jl3BVgsuKanc/rLOxdu1adO/eHTKZDKNGjcLvv//ut/0nn3yCvn37QiaTYcCAAfjmm29c9jPGsGTJEmRkZEAul2P8+PE4ceKES5va2lrceeediI2NRXx8PO699140uOWA/O677zB69GioVCqkpKTglltuQYldJk34x2bjJeWHDvHPQQaA7S6pczYTX6k63fFX+NgfoSjKQxYslJY6CtDD7XhwHF8AtLiYbxdhWkN8EQ2/Kuw50v0N+o4ePer1mEgM+gIxSitWrMAzzzwT0HUR4SGQ1C6RWsrnjt242fN7SpXSoPJ7+iroFzss1m9BP8CzqJ9dza49quWD6WY+mC6UCV1Um8ZzRv7Y3gq+6Gms0CU/uyhZ5JJ3OBz58QiiM2MPpNsLjQL8hL5MxqvRtdqmIHtLHa22jC87lzotlV+BkyYJOP+6IzfoACDhqoTQCucQEeW1116DRqPBk08+GfAxUqnUZcml2nmGnIgIZjP/AJwm+BpzUqrW2KCpABruegD4+g8A4fOrgskhHnKRrDDhy5bFDI5B7KhYMAu/OtG5fy0tbtoclHeY6OzYJwAB3p8CmhTpjmKjEQikB1MvgRNwiBsTh7j8OGj2ayCBxOeYi5kY6n+uh/4PPUSxIpS9VYa6H+sQO9q/n+StPy5jwspGA8/gsZLPeM7oMQkYSvHVjkK06srceeeduHDhArZv3w6z2YyZM2di9uzZ2LBhAwBeGDp58mQsWLAA69evR319PR5++GH86U9/wr59+1rvBrVHwpDn+w/eDULv3k3bAlakW4LPluCuwrYHkEWxooBq2LW06LkDjcZRgN6jbwwwmJNgrdBDeLQOsmwWUb8s5GsJgGj4VRErNtratGTQ9+STT2LBggWO1+fPn6elyBHEZAKMjcIoe2oX+3Nr50gP1bjZaekgy72onzRLClGCCMoBSuiP66Hsq0TChASYyk3QH9NDf1zvKHSaPIWP2p1bc85xrKKXAuYqM4ynjBAliiDvJYdF7d3JIggicOypXZwD6QDvlxgMriqGtqxIDyWYFYlgUksKBnZGkpOTIRQKUVFR4bK9oqIC6enpXo9JT0/3297+XFFRgYyMDJc29uLsO3fuREFBgUcuwuHDh+POO+/EBx98ENJ1EeHBefDnMlYSCKCKFwAVgEaeBqAxkB6mST53MYCvAndWrRWlK0tbXiQrzLjbMlOFCepCNSo+rPDZv0jYKirgTnR2nPOj282HR2qXCKT6DLaQsLfxmvuYS5QsQt0vdbDUWiBKFCFuTByseisqN1ai/MNyyHvIIUmVeLV9vvojThEjLjkOxrNGmKvMSL09FYYzBq9jws6qOveFc4phAFi3bh2+/vprvPvuu3jiiSc82junGAaA5cuXY/v27Xj99dexbt06jxTDAPDhhx8iLS0NW7ZswR133IHi4mJs3boVu3fvdqzie+2113D99ddj1apVyMzMxN69e2G1WvHss89C0Jhi7dFHH8XkyZNhNpshtuePdMJoNMJobFpV1qmyJ9hsvFr64EHgk0/4QVd2Nu/saLV8nu+zZ4F58wIKptfX88/2eldA4Ir0log8g1Fhe/MxWlr03IFK5ShA7wi4AdBWKVB9NBm6MhGsui4Qvm2GYl9pRP2ykK8lAKLhV4U9kN6eBn2knmpdnG1/tIuNhmrcXNo3M8jyFcDypY6Kvyze4Rj5C345H2sz2CDPkcOawQ8ELbUWcrIIIgx4S+0C8M5XTY1bapcWKBZag5ArvoMC39FCIpFg2LBh2LFjB6ZMmQKAr0a/Y8cOzJ071+sx+fn52LFjBx566CHHtu3btyM/Px8AkJOTg/T0dOzYscPhQ6nVahQWFuL+++8HAKxZswbPPvus4/iysjJMmDABmzZtwqhRo8J/oUSLsNsfoRCQSFz32f0rbR2DEgAECNuEurfgklDJD5LsE/jKAUqcf/18i1f9RQq7LdMWa1H1WVVU+ueep50gOhsuKaka8UjtEoFUn4FOAjqv4nUfrzmPuQRSAXRFOljVVsh7yx02Q3dEx6d2YXwtK1GSyKtt8dcfgFdyxo2JQ9qf+VX5bWF1T1vGnmLYWVgZSIphZ2ElwKcYttfhay7F8B133IGCggLEx8e7pMIbP348BAIBCgsLcfPNN2PYsGEQCASOwF5DQwM++ugjjB8/3msQHWjn2RPsgXCNhndIsrMDr9FiV6AXFwMHDvA5gHv2BNLS+KCwPc93URGf57tPn2bP7Z4VAfBTbNSNlsSmQlVht8RWuZCdzav29+/n7xXHQVulwLnCLjDrxJBazkPaIxnWbnER93tCvpYAiIZfFfZAOg36CF/YDZhMBogav3nRKjbaGktMgOYDWM0pPf0Fr7wdK82SwnjOSE4WQYQJb6ldgKblgM4qhtZKSxUMvlJYqfep0XCkAam3pSJmUAzZijbMggULMGPGDAwfPhwjR47E6tWrodVqHWqr6dOno0uXLlixYgUAYP78+Rg3bhxeeuklTJo0CRs3bsSePXvw9ttvA+Anix966CE8++yz6NWrl2OZcmZmpsNvy87OdulDTOMXvmfPnlQUsQ1hH/wpFJ4pMB2B9Ho+kB5uu+RLDKAaqkLSTUmo+aIm5FV/kSJcqxIJgmgZzop0O47ULhf0wKGTsJWHX1gVyCSgt1W8vsZc6t/UKH2+FJJUCaRZUjAw1P9SD6vOCnGKGMzIYKm1AAxQ5Ck8bEuw/SFBg3+ilWK4vLzcI22MSCRCYmKio01OTg62bduG2267Dffddx+sVivy8/M98rE7026zJ4SSiqW4GFizhl8SHBvLOzdJSUB5OR9QHzWKrxbqnue7e3e/p/UWSPc2lvNGS1J3hqrCbqmtciBoLEB/9ixQVATWJQvVRVkw1wMKUQm4OAWQ1xeiODGEsaKI+j0hX0sbJSKpXWjQR3jDLvh3Wl3iPZDeCsVGW2OJSaA52INRenpTqLsfS04WQYQPf6ldAB/FRttIjnRfwSL7wE7/hx7a/2kRMzgGylxl1NItEP65/fbbUVVVhSVLlqC8vByDBw/G1q1bHYO60tJSxzJhABgzZgw2bNiARYsW4amnnkKvXr2wZcsWR65PAFi4cCG0Wi1mz56Nuro6jB07Flu3bnXk+iTaB3ZVp5cUmE2BdE3kxAm+xADhXPUXLIGksYpm/wiC8KFI15wF0BUXiy8Ay5eDnRoGYBQ4XXjTWfibBPS3itfbeE0UJ4JALoAkUwJwgLXOCnO1GaI4EW9bJADTMF6Vzom82paW9odoX5SXl2PWrFmYMWMGpk2bBo1GgyVLluDWW2/F9u3bPf4XAe00e4JzILxr1+BSsdhsfAC+uppXUVdWAlYrv1xFpQKqqvjgfHIyH0hXKoHz510DST4ISZFucZ3UC8TPCIcKO2Tb0FiAHps3w7DnHHSnLJAqLoLLzOAnNlL45dat4fd0RDsXkUA6DfoIb3gzYPa/1Wq+8AHHtY6qM9JLTCKhdgpHegaCIILDV2oXbwVq3B2taOMtWGSuMkNdqObVUoliMAtf2Dja6RYI/8ydO9fnqr4ff/zRY9vUqVMxdepUn+fjOA7Lli3DsmXLAnr/7t27UzqKNojd/vgNpKsj61N5Cy75W/XHGAMzM5gqTNAd1YV1NUygflJrrUrsjKxduxYvvvgiysvLMWjQILz22msYOXKkz/affPIJFi9ejJKSEvTq1QvPP/88rr/+esd+xhiWLl2Kd955B3V1dbj00kvx5ptvolevXo42N910Ew4cOIDKykokJCRg/PjxeP7555GZmRnRayVajocivbgYCdu+BvAoLlpigT59wC7wRow7UQwUxwVcVDAQwlX7xV2YZTPawCysKeBmYuBEHARSPu7hy7ZEqrBxZyNaKYbT09NRWVnpcg6LxYLa2lrH8WvXrkVcXBxeeOEFR5t//etf6Nq1KwoLCzF69OgWXHEbwz0Qbo+vBJqKpbSUD5R37cofK5XyaQzMZv7v2Fj+3PX1fHBdq+XV7s7BJR/4U6QHmtpFIBYE7GeES4Udsm1oLEBv3XYS1perIO0jBxLjPZYxOtumSBWJ72h2LmK5M+bOnYszZ87AaDSisLDQJb3Kjz/+iPfff9+l/dSpU3Hs2DEYjUYcPnzYxYkCmgZ95eXlMBgM+P7779HbueyuG/ZBn93AEdHHnyLdZmtyqlojR7rduImTxdAV6WCpt4BZGCz1FuiKdCEvMQlG7RQIdnW7Zr8GomQRFH0UECXz+fbOrTkHbXEz/wEIgmgRvlK7eCtQ09aKjdqDRUIlv7KGMQbtUa1jybFQJQRsACfhIM+Vw3DGgLJ1ZdCf0vO5PQmCaNMEEkjXq1t/pYxzcMkZc5UZ9b/U4+KOi9Ad1aHs7TKUriwNiw8TjJ/kq392wrEqsTOyadMmLFiwAEuXLsW+ffswaNAgTJgwwSPAZGfXrl2YNm0a7r33Xuzfvx9TpkzBlClTcPjwYUebF154AWvWrMG6detQWFgIpVKJCRMmwGBo8p+vvPJKfPzxxzh27Bg+++wz/PHHH7j11lsjfr1Ey3FRpDcG3xJ05wEAdSYFIBSCSXgxE2fS88E3my2sfbBPAsYMiIG8u7xFYz67MMt41gjGGARSATgRB2ZmYIzBorZAnCyGKI7XLvqzLeHoT2fHOcWwHXuKYXvKYHfsKYad8ZVi2I49xbC9TX5+Purq6rB3715Hm507d8JmszliYDqdzkVICgBCodDRxw6BeyDcGfdULN7QaPhUMHanJi6OVzLV1/OKS4kEsFgAo5F/fe4cHyh2y0zh69SAd0V6oMVGzbXmoOIxdhW2aogKlhoL9Mf1sNRYoBqqQpd5XYKuU9Vi2yAQQNi3K4TpibCKVZ6fDZpsk6nChNKVpShZUoKS5SUoWVISNj8tLNfShmhbFdGIDo2/mUDn/a0VjAqXcfOGewDLHaFSCJvBFpDayV3dLooVgRNyEMWKoMhTwFxtRvWWagp8EUS4sdlQfcEEAEgyl7sM4tyXA9pVlkDbKTbqHiyy1rsuObYrpaxaK9S/qmE4bUD1lmqcfPRkWJ0mgiAiQyCBdF1D60/wuQeXgKbVMKYLJjALg6yHDLJusrAIAoL1k7z1z3GuxlWJilxFSIWvOiMvv/wyZs2ahZkzZyIvLw/r1q2DQqHAu+++67X9q6++iokTJ+Kxxx5Dbm4uli9fjqFDh+L1118HwH8Wq1evxqJFizB58mQMHDgQH374IcrKyhyFAAHg4YcfxujRo9GtWzeMGTMGTzzxBH777TeYzWav72s0GqFWqx0PTQBpAYjw4qJIbwy+xXePBwDUGWRgDGBW3mYJVHL/wbco4i7MYmAQJ4phrjbDVGmCUMGrVcGRbWktFixYgHfeeQcffPABiouLcf/993ukGHYuRjp//nxs3boVL730Eo4ePYqnn34ae/bscawCdE4x/OWXX+LQoUOYPn26S4rh3NxcTJw4EbNmzcLvv/+OX3/9FXPnzsUdd9zhWBkzadIk7N69G8uWLcOJEyewb98+zJw5E926dcOQIUNa9yZFCvdAuDOM8cryigo+2O5t8kCl4hXmdueG4/gUJAoFn9ZFo+GV7CYTr25PTgamTAmoiKk3QWfAivTGFce6Yh3vZ+TKIbI1gKuugsjWAEWu3Gc8RpmrRPYT2ei+rDu6Le6G7su6I/vx7FZf/RuI3yNKEqHq06qghJvMxqAv0aPhUAP0JZ1LiBWR1C4E4Q1vgXSBgDdiDQ38/rS01s0zHKklJuHMwU65PAkiCjQWyqk991cAEiT+ey1wRu4olONeoIZZmxyHtpIj3T2FlfOSY7tSStRYYMaqs0IYKwQ4fqKPUr0QRNvHudioO3Zfy6Bp/UC6+5JmSRcJtEVaWOotgIjPK6zMU0IUJ4IwNvTinsH6SR218FU0MZlM2Lt3r0uASiAQYPz48SgoKPB6TEFBgUshPQCYMGGCI0h++vRplJeXY/z48Y79cXFxGDVqFAoKCnDHHXd4nLO2thbr16/HmDFjIBaLvb7vihUr8MwzzwR7iUQYcVGkNwbfEjL535uVCdBgksBm44NjnFTIB+fa6ISHe+5fgVQAjuPACTneziWIYKm3kG1pJaKVYnj9+vWYO3curr76aggEAtxyyy1Ys2aNY/9VV12FDRs24P/Z+/fwuMr73Bv/rDXnGc3obB0sySd8kMBgbIKx4wZo3NoJaTBNaaBpCYQNbd5NCXF/nFLbJCYtCSGpw+GNQ3dIw278ktIEZxdSGtfsNE1wTMCGgCXb+IQs2TpbM6M5z6z1+2NpjWZGc5RG0kh+Ptela6yZNTNryZpHz3M/9/f+Pv744zz++OPY7XbWrVvHq6++is02R9bviUJ4omKtZ5ufO6d9+J99Fg4dGt98tKVFE84PHx6Lhqmt1RqMdnTAyZPa64bDsHq1JqLnGfmUy5GuRwynQ9emIgMRXLVRpF8f1iJmolEwGpFqarDMX4a/w5BWj0mNwNPF56mKN8kUzZJt3mOqNoEKkcH8Y4kv9thhIaQLpo10O4GgDWi6kA5ju37T5eospNlnvhQzg11keQoE00xCoxxPVFOoKuosSY1yHA5t4hZ3pEcThPQSiXZJnTQZnAYkWSLmjaGEFGSbNsbqUS9qSEU2yZiqTRjKJy9uCQSCqSWfZqOBkemPdoFkccn7ppfgqSCyXcbSaMG+wo6pVhM5i2EImMg8aS42vppJBgYGiMVicbFKp66ujqNHj6Z9Tk9PT9rje3p64o/r92U6RufBBx/k6aefxu/3c8011/Dyyy9nPNeHH344ScDv7u6mra0txxUKikmSI31UfLOFhjEbooRjRi4EbaiKNmZJsXDeOcgzRaoxK9wbxnPQQ+BYgMDxwJSMLVOVYzwXmIm+MlVVVezZsyfred1yyy1pNwCnBEXRqji8Xu2z09KSl3N7UqQTwvv74eBBbcISjcLixbBgQfrmo7Ksietnz2qO86YmbYJjNkNVFTQ0wJ/+KVxxhfZYVxe8+25e15dNSFcULS0mU+tFXUhXfAEM774DQZ8WO2MyaS778+cxXPAQnrc6px5TDPE522c/1+tnmvc4P+Sk9/nevA0JepxeZCCCpdmCxWEh5otdVEYsIaQLpo10A5j+/fnzY49PR7PRqaaYbqdiutsFAkEOEhrlRJZfRiBqBsBVbYKmsUY5ZY4VgDQmpEdKT0iHZLFIL8mLDkWxLrFimW9h5Hcj8ezOqCeKpcGifS8hql0EghInn2iXkE+bU81E5JQuLg39fIjot6LYl9sxVZkgZYicrCFgovOkudb46mLm/vvv58477+SDDz7gK1/5Crfddhsvv/zyOEEAwGKxYLGMbbp4dKePYNrQhXS7nbj4Jh0+TIU1SJ+vjAsBK47RaBfJ58k7B3kmSTJmrYTK36+csrHlYneCCnIwWlXL0aNaNYfVqgncqQ7wYpMqhM+fr9263VrT0PJyTWAvL9eclemaj7a2auK6fv7d3dr5r1kz5kDv6IDHH8/7+qLRsTEnnZAOmqkzl5Aue4eIhcMYG2vH7OsWC9TWEjvnRlbPYnBckfHHUwzxOdtnH8jr9dPNe3xHfGOGBFXV/s9CYbCYobx8bJ7mjqKeOs3A7j4iH0jYr6pGGv2/y+Zen4sIIV0wbWQT0mHMsV5qDfsmSrHcTsV0twsEghwkNMrxhMc+Uy5LKKlRjqNuGKgci3aJlF60i07ipGnknRH6X+xHCWrimhpWUS0q0f5oUpYniGoXgaDUyUdID/pmdk4lyRL2FXYs9RbtHNKcxmQNAZOZJ01FVeLFSE1NDQaDgd7e3qT7e3t7qa+vT/uc+vr6rMfrt729vTQ0NCQds2rVqnHvX1NTw7Jly2htbaW5uZnf/OY3GRsMCmYWvZrGZiNJfKuU3fRRxrDPhD2oZdxLZRbY8rGpd9MWmakaW4QTVJCVhKpampu1CYLPl94BPhUkCuFvvgmnTmk7Zo2NmthdW6sdl9p8dOHC5NdYvjy9o34C15fYTDRRhzIaNR08FNJeoqYm/SXpVccWaZCQNA8DkaSpjIpEiFqcfICVXmDR+NdI6eWST3RKKtk++4HOAAarIe/XTx2b4oaEzn6MXceTomuoqSE2fxlySMHwz88SPHkS/1uXY7FHkULlo/+vNaP/rRdP7PDs+oskmNVki3aB6W82Oh0Uo8FEaiObqDuKGlWJuqNa9qjI2xMIikdCoxx3SBNd7KYwJsNoUxyHA4JBHLJmbUjrSC8xIR3GFnS1N9ay4G8X4FztRPEpKH6t6bGlwYJrrSsetwCi2kUgKHXyc6TP/Jxqqpt7innSzGM2m1mzZg379++P36coCvv3788oZq9bty7peIB9+/bFj1+0aBH19fVJx3g8Hg4ePJhVIFdGm9iFQqEJX49gaklypENcfKuo0L69cOoCql/7/5PXXT21wt8sotDGyoKLjISqWtraNNHFYNBu29q0+/fuTd/os5i0tsJDD8Hdd2uC+MaNsGHDmIgOuZuPyrImrq9cqd3K8oSvT9eYTCZNOE8kn4ajelqCq/w8JqeKv99ONGhAVSAaNODvt2N2KdTUnUDyjaR9jUJ6uaQj12c/1Bli+L+GsTRN7PWtLVbsVSOE/vsY6rnz2uBcXQ12O+q584T2vY294z+wvLsfv9JEyFCLarZpsRIHD0L/QPy1DA4DSlCZ80Ys4UgXTBuZHOm6sJ6akV6KYtREKIYjQWR5CgTTREKjHE9oPjDqRtfx+cBqpaxKi3yJO9ITxq10peSlhO5QD5wJ0PXtLgLvB3Be5UwSmUS1i0BQ+uTTbDQcmPk51XQ098w0TypbVYZrrQs1qjX3EtEtU8fWrVv57Gc/y1VXXcXVV1/Nrl278Pl83HHHHQDcdtttzJ8/n8ceewyAL3zhC1x77bV885vf5IYbbuCFF17gzTff5NlnnwW0xf99993HV7/6VZYuXcqiRYvYvn07jY2NbNmyBYCDBw/y29/+lg0bNlBZWcnJkyfZvn07S5YsEW70EibJka7T2kplmwpn4cLmW1GP9MDxEaSGeTNyjqVIoY2VBRcZCVW14zpnZnOAMwWZ+7KsOZXr6zUFO/F88m0+WqTry6RBgWZEGBxMdq2nopulrBUhqhcdZ6BrIf4BO2GvBdmo4GwYoabpNA41kLGXw2R73uX67JuqTfiP+JN6dhXy+hIqNfyKoGTCLy3EooYwECPmh9CAA7PnFA73bznrWYU3VoE/aCXkqcfirMQe6sZ09CjUfBgk6aIxYgkhXTBtFOpIn4k8z1JGZHkKBNNAQqMct/0yAMp1IV1VtcY2q1fjaK4CxoSs2dbbQZIl7IvtzP+r+XQ92YW/Y2rELYFgoohGarnJp9loXEif4bFpqgwBqb8nzQ80E+oKJTX8632+V+QITwOf/vSn6e/vZ8eOHfT09LBq1SpeffXVeLPQzs5O5IR4jvXr17Nnzx62bdvGl770JZYuXcrevXu57LLL4sc88MAD+Hw+7r77boaHh9mwYQOvvvoq1tEwW7vdzk9+8hMeeeQRfD4fDQ0NbN68mW3btiXloAtKi3GO9FEqq7RxarisCcXqBUZmfOwqJSYrxgnmOAlVtWlxOLTMcV10GWXKMvcn23y0SNeXSYPSnwLZHelxs1RzAw7/fuwflgh6bMRCBgyWGFZXAKnjKKxenbGXw2R73uX67Mf7XQ1HMVWbxj2eU9zu7MQxdJimDQsZ6LJrGwUDIF/w4uQDHKYOBqNriQTrMEvnsWEiFKkj6LUQNTTh6jqPye1GLS+/aIxYQkgXTBu5MtLnUrPRqUJkeQoEU0xCVqfnd30AuMxBrelKV5cWoLdlC2UnNTEgNdpltlXSiGoXQSkiGqnlRz7RLpFA6ZgTim0IyPZ7Ihkl+n/cX3COsNjAmRz33HMP99xzT9rHfvGLX4y77+abb+bmm2/O+HqSJLFz50527tyZ9vGVK1fy2muvTehcBTNHWkc6jEW7XJhbUZ/FYrJinGCOk1BVm1Y1Hq2qTRRjpipzX/tbGiJ26ScwHBnEeqQdqWkCzUcneX2Q3ZGuR7vk40iXNlwDv3sHqaMdW1MTVI/ms3eMrQ8z9XLI1stFURT8x/zYl9pB0X52qfOOXJ99jGCsMhIZjGBdbC28p97oJoVjuYp9YSfBYQux3/wOA2exuMKcPXoFEcWJ3daHZDbh8JwjFrUTo5xI2IxvyEXZQJBQt+miMWIJIV0wbeQrpIuJk0AgmFFGszrdjxwHoDw2pNX9rV4d7xjv6NEOHRftMgvHLVHtIiglRCO1/MlHSI+GSsucUCxDQDGbbiW+ptjAEUwWsRmTm4yO9ErtNklIn2UGhalkMo2VBRcB6RzgOglVtbpruhgNMNOR/LdUxhD6E+yB49S890scE2k+OsHr08kV7QI5HOl6WsLiFrhutJHq0aOa+91qTVofZiJTxF3wbJCRt0ZQwyoocObLZ9LOO3J99sPdYSquq0AJKBOL0EvYpJBcLmxSLwTbodZOIFiNPzYfi2EQyWADScLkiOAKnMRnW0HYZyXoL8c4IOO69uIxYgkhXTD1KAp0duIZaAAsuMoUEvvc6oOaXnYjhHSBQDDjtLbiuXY5vAiulQtg586xjvGMn3jN9nFLVLsISoGpWtTNVfIR0g3MPTEq1++J97dewue1RWW+OcJiA0dQDMRmTH5kcqSXl2u3Hs/sNihMFdPRb0Iwi0moqqW9XROmHaOu6a7xrumpyNxP/7fUgrfTSTAwn6b5fhxraqCqarwQnth8NGHNNdHr05msIz2elmCUNLF8+XJN7Pd6welEbWom2BUm9u5I1s3T1Cpgf4ef4KkgklnCtc6FpSXzvCOfz37j3Y0AE6syTt2kCIW06B2TiVjYQQwzFikEhtEfmMGASfZSPq+P6HCMgNxC471LqNpcfdGMPzNf5ymY23R0wNe+Bjt24O3T7AfOF7+n3T/KOEf6HGs2KihdnnnmGRYuXIjVamXt2rW88cYbWY9/8cUXWbFiBVarlZUrV/Kzn/0s6XFVVdmxYwcNDQ3YbDY2btzI+++/n/a1QqEQq1atQpIk3n777WJdkqCIuD3an8jylvKxjvGjpE68hHNKIJg8hSzqBNmbjVosWuW0kbknRuXTdCs6FM3adEsJKvEc4VRh3ugyIhkkjC4j9jY7kYEIA3sHUJX0rycQwJiA5D3sxVhjxL7cjrHGiPewl64nu/B1ZLE8XmRkcqTraQ0ej+iZlQldjHNe6SQ6GCVwPEB0MIpztZP5984XGzYXO6NVtVx5pVZNe/z4WFVtSv64nrttcKSPAkr9W5mIqmgNvEfeHSFwJoCqqNn/ll5qJ6K6GAisRjWmaT76q1/B/v2aiP7ss5p+lKAXTeT6dCbtSE/d1JNlbV24ciW+QC2dj3dxZscZzjx6hjM7ztD5tc6M472j1UHLQy0s+PICbMttWC+xUvPHNVgXWXPOO/L57Ouvv3DnQhZsX8DCnQtpebAl97igb1LU1GibFKGQdp/Xi8Hdo8XK2KrGsu2jUVBVJI8byWHF3FaPvc1x0YjoIBzpgqmkowOefBIGBqC5GU9M+wA7T7wNTx6KD3b6pEk0GxVMJz/60Y/YunUru3fvZu3atezatYtNmzZx7Ngx5s2bN+74119/nVtvvZXHHnuMT3ziE+zZs4ctW7Zw6NCheGOsxx9/nCeffJIf/OAHLFq0iO3bt7Np0yba29vjjbF0HnjgARobG3nnnXem5XoFhVNIcxrdrSDGLYFg4ohGaoWRrdmoJGmLRuOFuSekF7vp1lS48gQXF6KapjAyOdITzVWKsbRiqUoJEcknyEoa13Q6h/dEM/czVd44r3Zm/1u6rAL/+QUEj3Vg+5Bz4s1H87w+nXwc6flEu6SORZkq2TyHPIwcGWHen86j7IqycZ9NSZaQZInYhRj2FeP/JmSbd+Tz2Z9wlbG+SfHSS2ObGENDWBdXYq+x4+1vxBA7hRTwa7uhdjtqcwsh0yU4r2lMGyk1l6POhJAumBoURfsQDgxAWxtR1UAwqi1mXJc2w5m34s0knE5t0BPNRgXTybe+9S3uuusu7rjjDgB2797NK6+8wnPPPcdDDz007vhvf/vbbN68mfvvvx+ARx99lH379vH000+ze/duVFVl165dbNu2jRtvvBGA559/nrq6Ovbu3cstt9wSf61///d/5+c//zk//vGP+fd///dpuFrBRHC7tVu91DgRfeKlV76JEmSBYPKIRmqFkS3aBbRxKi6kz6FqmWI33RIbOILJIjZjCiMvR7pr7o1dxURE8gmyorumszCRzP1s4vHwr4eJDkdxljtBBVI+uoYyI+G6ZmJl8zTX8/xJNB/N4/p08nGk59VsNGEsyrR5qoZUokNRAicD+H7no2xVGY5Wx7h4r2zzDlVVUSMq4d4w/qP+4gnl+ZC4SfHOO/DiixAI4lQ9jHhq8fqXY7edxVAlEWtbTUiah7k2faTUXI86E9Y5wdTQ2amV5jQ3gyThDZnjDzmtkaRmEqLZqGC6CYfDvPXWW2zcuDF+nyzLbNy4kQMHDqR9zoEDB5KOB9i0aVP8+NOnT9PT05N0THl5OWvXrk16zd7eXu666y7+9//+39jT1eOnIRQK4fF44l9e/cMimFLycaSDJmaJcUsgmDz6oi50NoSqJsdo6Is6e6tdNFIbJZeQ7nSCzNyr8sv1e6I33bK2WPG3+4m6tZiXqDuKv90/Lkc4UZhPh9jAEeRiMhEJFyOZHOnpol3EvEogmBr03G1TjSmvv5WZolt08Xjk7RH8R/wM/ecQ7l+5ifRHkt4v5oshz6vAcPefa9EsnZ1w6pQmojc2wtq1mZuPToJCKozTkc4slW7zNNIfwXPQQ7gnjKlKM5DKZjltvFemeUekP4L7V24u7L+A/6ifc8+eyxoVMyXomxQ33ojvlgfp9P4RvW/PIzIUIuw2MhxuxVu9jqijAeea9JFSF0PU2dyZVQtKC68XgsH46OQJabttZkMUsyGm3R8MgtebWUgXDoSS4Ze//CV/9Ed/RGNjI5IksXfv3pk+pUkxMDBALBajrq4u6f66ujp6enrSPqenpyfr8fpttmNUVeX222/nr/7qr7jqqqvyPt/HHnuM8vLy+FdbW1vezxVMnGyOdItlzByRJKSLcWtGmWtj1cVGoYu6i518hHQjc6/KL5/fk8a7G2n6Qn45wmIDRzBZpmszJl0u8WwklyPd6xVCukAwHRSSuZ9TPK41IVtl1IBK8FwQz0FPXExP+lv6+5fCQw/B3Xdr7ueNG2HDhjERPX5yY3rRZJhss9F0scOpm6eqquI76iPmj2GqNWljvQKSWUqbeZ5u3hH/WZ4Po0ZVrIutWBdYZ0x89nX46HrFjNe1BuP1q3F9cjnlNy3EfEUzhsZy6m6rS5u/frH0nRHRLnOUGc8jcjrBatVWeS4X3rAmpLssIe1xn0973OnEOfoZ0ncLRURC6eHz+bjiiiv43Oc+xx//8R/P9OnMWp566im8Xi8PP/xwQc97+OGH2bp1a/z77u5uIaZPA9kcDJKkTb48Hm3yZRHjVkkgxqrZj76o08tBw91hZKuMc7WTmi1zoxy0GKhq9majoAvpc3Nsyvf3JJ8cYV2YD54N4m/3Y2myYHBoomioKyQ2cAQ5mUhEQqHMpTL5XBnpotmoQDB95Ju5nxpHkioeo4IyoiBbZFRVJeKO4Gv3UXZFGaHu1L+lEqxYAfX1YEppPqqToBdNhsk2G00XO5waMRdzx4gMRDCWG5EkCSWkIBklZIucNt4rdd5hnm/G1+4j6o5q8XTlRhxtDozlRgyu6e+zkSSGX2pHkrQflAkwNqv42/14f+ul8vcrxz33Yok6E0L6DDGVQndJTLRaWrTB8fBhaGuLR7s4zWFt9dfVpXVXbmnB+YH2FK8X1JjK6JpPTJxKiI997GN87GMfm+nTKBo1NTUYDAZ6e3uT7u/t7aW+vj7tc+rr67Mer9/29vbS0NCQdMyqVasAeO211zhw4AAWS3Ie2lVXXcVnPvMZfvCDH6R9b4vFkvQcj67wCqaUbI500CZfHo82+aoSC74pxev1Jv3ep34mdObaWHWxIhqp5SYU0qZTkN2RbmB2VcsUMj8uZtMtsYEjmAxTvRmTKZfYe9hL8GyQpnubZtXvaC5H+sgIKNHZNXYJBLOZfP5W5iMeGxwGHFc4CHWHCJ8LEzwVxFhpxPUh1/i/pSl6UZKYnqIXTYZiOdITx6LUzVMlpKBGVSSThKqqRD1RLA2WePPzdL1WEucd3je9BE8Fke0ylkYL9hV2bXOCyYvPE9EdJyOGXyx9Z4SQnifFFL6nUugumYmWLMNNN2ndltvb8UQ1YdFl9GuNI2pqYMsWkOX4pCkYhEhAib/EXHNPlSL5ilNzDbPZzJo1a9i/fz9btmwBQFEU9u/fzz333JP2OevWrWP//v3cd9998fv27dvHunXrAFi0aBH19fXs378/Lpx7PB4OHjzI5z//eQCefPJJvvrVr8aff+7cOTZt2sSPfvQj1q5dW/wLFUyKbI50SJ58iSbJU0tqBcYjjzzCl7/85Zk5GcG0IBqpZSfRPZU92mX2ONInMj8u5u+J2MARTIap2ozJ1NTO6DJiaJt+p+JkiUS0Ju2QOSMdIBYS86qZ5pe//CXf+MY3eOuttzh//jwvvfRSfN0kKG2mwrSZr3hsW2TDusiqRcW8H6Dx7kaq/rBq/Pun6EU0NWkTGp9PE9ET9KLJMFlHerq0hNTNU4PTgCRLxLwxbUPBrs1f9IarmeK99HnH0M+HiH4rin25XctXT23UOkHxeaK640TFcFXRYvaUgEL4XBhLk2XctcyVvjNCSM+DYgrfUyl0l9xEq7UV7r0XXnoJ739o+VhOeUTbWdyyRXuc5EHNMzSWlSQmTlPPxSxObd26lc9+9rNcddVVXH311ezatQufz8cdd9wBwG233cb8+fN57LHHAPjCF77Atddeyze/+U1uuOEGXnjhBd58802effZZQNudve+++/jqV7/K0qVLWbRoEdu3b6exsTE+6WxJ2VEvG1VilyxZQlNT0zRduSBf8nGkg8hInw7a29uZP39+/PuLYcNPIMiGvugzm7U+XelwOiE6S5qNlooRRGzgCCbDVGzGzLUyed2NDuOFdItFS3mIRCAWmj2bgHMVEZc3O5kq02Yh4rGEhGSSMNeZsa/Ioj0l6EUcPQrd3VqcS4peNBnycaRnFdIz9GtI3DzV88ujQ1GsS6w4Wh1xR3mueC9JlrCvsGOpt2jvkeZHNRHxeTLzqtTqg3zOR/+983X4CJ4J4vudr+CfxUSZiU0/IaTnoJgT+6kWuktyotXaCsuX4zEOwS/B2dYMDz6YtLNoNmtf4TB43QlCuhCkppyLWZz69Kc/TX9/Pzt27KCnp4dVq1bx6quvxpuFdnZ2Iif8nq5fv549e/awbds2vvSlL7F06VL27t3LZZddFj/mgQcewOfzcffddzM8PMyGDRt49dVXsVpFg7LZhqrmdqQnCekiI31KcTqduDL9RwgEFyG5Go2Ctmj06s1GS3hOVXJGEIGgANI5P4u5zpprZfJ6PjpoelkikqSNW0NDEBMGhSlDxOXNXaZ6UzqzeGzB0aRgUi7AsBnV5UoSTLM65Ef1Ijo7NdXb6dTiXCbpRNfJx5GeKdolMXY43ViUuHk68s4I/S/2owQVJLOEGlW1eK+zIczWEDVtEaRONe21FbvPxmTnVYWeT+rvXfmGcty/dhM4HiAyEKF8fTmyTZ6yvjMzseknhPQsFHtiP9VCd8lOtGQZr6UGAFedHdKMiU4nDA7CyAUhpE8nF7s4dc8992SMcvnFL34x7r6bb76Zm2++OePrSZLEzp072blzZ17vv3DhwninbkFpEQiMlR5ncqQnRruoMSGkCwSlzIw3YS8yuRqNgja38s+CaJeSNIJMMSIyYW4wHX2pJuIMLGV0R7rNlr6/oMulCelKuPTHrtnKxVyRPJcppnaVbc40Tjz+x5Mop08hne9BJUgMKyFDPebLmqnZsgj/MX/ucVKWYeHCKfm56HtGE4l20Y1SkLmyT69ksy20YV9mT473CnlwBo9RE3kLxw974cdWLRf+ppuS3PaT6bOR7v9qsvOqQs4n7e+dCyp+rwJfh4/AyQDuX7spW1WG88oyaq4O44iegjPF2zCZiU0/IaRnoZBfQP0XtpBOx6lMVugu5YlWtp1A/f7BQfAOj7kPUn/mAoEgN3NNLJop9EmXJGV2fCY50g3COSUQlCol0YS9yOiuzlyO9MFZIKSXrBFkChGRCbOf6YojKrZTcabRx65Mm4C6v0eNqEiUfizVbORirkiey0xKPFWUuCPc12th4KAZ/7FAxjlTXDwOnMFe9hMGpEr8UhNhapEJ45Tep4Y34KSdrlfMMxbbpqpjOlQ672CuZqN6DyzIbx6VFO/1zvsY/uVfsZo6kVqawbFcWzQePqzlwt97b5KYPpE+G5nmt/ZL7ZOeV+V7Ppl+70y1JspryrEutBLpj9C4KUL5mT1Izx/VmiNa028qJFLK/fyEkJ6FfCf2I++M0LenL+cCbaqF7lKeaGXbCUy8f2RYwUxpL/guRkZGRjhx4kT8+9OnT/P2229TVVU1LvdbMHPMhFg0V4V7PR/d5cq8UZ7UbNSuTbTEgm9mEWOVIJVSyd4uNvlGu8yGZqOlbASZKkRkwuxmOuOIJuNULEUSHenp0NeEakRBorTHrskwk/Pni70iea4y4U3pjo54Rrmvz0bXqVVEzLVY1jRjWV6bec6kKPDSSzhip7H/sY2gp5tYyIDBEsPqCkL7aTqfOkTEuQb7pdnHSWBKPg+BgHaaMEFHeqTw/n2SLGFrscCelyHcCZe2jZXfuFzQ1qY1V927V4u0SVhoFtJnI9v8duTICGpInfS8Kp/zyfZ7J0kSlkYLyrlhjP/np0jqSWhuHmsqm2FTQaeUq2eEkJ6FfCb2SkjRspBCSs4F2lQL3aU80crlSNf/lvs8qhDSS5A333yT66+/Pv791q1bAfjsZz/LP/3TP83QWQkSmQmxaC66PHVy5aNDiiPdXPpi1cWAGKsEiczl7O18hXQDpV8tU8pGkEIoZeeUoLgUu2o5FxNxKpYqeTvSo6U/dk2UuTx/FswcWbUrVSV2/gJyIITBfR6UJZqA29EBTz4JAwOoTc0MnG0jgg177BTSkR4oW4uxtib9nKmzU2sQ2tysiccVwYQ3lAi4luI/FMZyfQhJSv69ThwnL7x2Ae8b3in5POgaFKTMl0Yd+GVn/UAboZAW6ZnavD1JSC9kLEr42YzLsJIkaGrSfvadneMibfJpep5rfus74iMWjBHqDGG4dHLzqlznk1MzHYki957FIPfBh/LfVIDSrp4RQnoWck3sg2eDxIIxJJOUc5dNkqVpEbpLdaJ14YJ2W1WV/nFdYPe5VSqZm5Om2cx1110nsrxLmJkQi+aqy1NHd6RnykeHFCG9TAjppYAYqwSJzOXs7UKF9FKulillI0ghlLJzSlBcil21nA+FOBVLmVyO9LiBYY72npnr82fBzJFRu+ofQO04SuhkFKerF+t32+GXK+DGG+GnP4WBAWhrI+i24R8qw1IdQTLXQn+/JgbXfDj9nMnr1SI6MkxEYkYnsYiExRBJ+7jBYcDf4ef8s+dRVXVKPg+6kF5WlqDRJjjwHb4Y8EMAfIeOUX718qTnxzPSDRQWO5zjZ4PDAd3dyUp/AeSa31qbrQRPBZGt8pTPq3KaIY4P4+QDrMsrCt5UKOXqGSGkZyHXxN5gMUAELC35L9CmQ+guxYnW0JB2W1mZ5kFFocLkB8oY6RwG5t6kSSCYSqZbLJrLLk+dfBzpSc1Gq+auc0ogmK3M5eztfJuNGtFqmkt9XlWqRpBCKGXnlKC4FLtqOV/ycSqWOvk60qU5KKTPtvmziMubXaTVrgIXiL1+mOCgAYPLgr3VRtDcjPXQYaQjR7RFzJIlIEnEQgZiURmLKaYJnC6XJrK73VBRMX7O5HRqOdc+X9oFkyHqxWCqIRYzpRUdoyNRwr1hkMH5IeeUfB7GpSIkOPBpbsbS4kCWFBRVxvf09yl/+LNJESO6I71gM0KOnw0+n/Z4priGHOQzv5UtMrU31+I/4p/SeVVOM4QzRo1yAqlsXvoXmOSmwkwhhPQcZJvY29vs9P6wF4MjfbZQpgXadAjdpTbRyuhIH90RrDy2Fvgo3tcOAU5kafYtagWCmWK6xaJswj1oC0z3627cr7spX19eEouBQinYkR6dews+gWC2M5ezt/NtNmqcBdEuOqVoBCmEUnZOCSZOuizrYlctX0zkl5GuIsdKv5qmUGZblZSIy5t9JGlXHT7Cb3eiDJqI2cpBUek90sCAsQ579WJqgv+Jw3sKVq4EwGCJYTAqxCIGjJYYmM2auBkKA2nmTC0tWrPIw4e1iI7E32lVxep5H/vS5XjdFgxN6rhxMnBcGwzsy+1T9nlIEtJHM911Bz6ShASUmcN4QlZG+vzjIkb0ZqMFz6Fy/Gzo6oLVq7XjJkC+MT5l1WZqHlhMsCs8pfOqrGaID5lxPB+Ysk0FmJlNPyGk50GmiX2wM8jAjwcmtEArNaF7qknrSE/YEayq+BAAXiqAGJLPoz2eoYOvQCAYY7rFokzCfaQ/gu+oj0hfhOhwlM6vd1K+rnxWZj4W7EifqGNBIBBMGXMlezsdBWekz5JNvottfiwobbJlWRe7avliIR9HeuJMajZsAubLbKuSEnF5s5O4dvX6CUa2vU1/ZDmSHMNSHsJgihGLGPD2OAkq19E00onj/HlobsZaHsRe48d7vgxDrR8pHNZCwy3m9HMmWYabbtKaRba3axEdehPJri6k2hpqbl9N8BVT2nHSWGaEOjCUFWZKLQR9Ped0kjG33GGK4AlZ8VW3QMfvkiJGJmyUyvGzoaYGtmwZlwmeL4XE+Ei/XIHtpptg5dTqahnNEKjwxtRtKsDMbPoJIT1P0k3s5/ICrdiMc6Sn7AhWebTdPp9iB7xIajRj0wGBQJDMdI9F6YT7SH8Ez0EPMX8M2SJjrDBinmeetZmPhTrS446FWSJWCQQXA3MlezsdhQrp6iy8xrmMiEwofTJlWXsOeRg5MsK8P51H7adq8Rz0EDgWKErV8sVAPhnphtFIKphb86q5XCUlKC0kWcLqDNA3WIYimbHX+uP6pdESw1Drx99bwUD0auydR5CampAkiZoVAwTdFvz9diyRYQxNNcRwEDziw2AxYG+zx5soS7KkmR7vvTeeOU53t+YuXr0atmzB0dpK0xJfWqey80NOep/vndLPg+5Id7nImFteZtYc9yOGcu3xhIgR3Sg1oXEox89mMobRbDE+oSED5moDNetVJFuNJmCfPaudyxSbVNObIaQp3VSAmdn0E0L6JJjLC7RiEovB8LD277gjPWVHsNKqzapGghbAi2SWMzYdEAgEyUxmLEpXspx4XD4lzQC+oz5i/hjGGiPRgSiWBguWJgtmzLOyhDkfR3pStEtk9sQnCAQXE3Mhezsd+QrperRLMCpMCaWEiEwobTJlWashlehQlMDJAL7f+ShbVYZ9uZ262+ow15mLUrU818nHka6PWzC3hHRhwhNMJ0GvDb+vGkvFCJKUPNZIElhsXvy+pQQdAWyjAqejMkrTpV4G3rLjl+oJWy5BOR0iFoxBBHp/2MvAjweSmya3tmrmx85OTYR2OjV38agwmsmpDOB9wzuln4ekaJcMueWOUSHd51XAmRwxMikhHXL+bCZDuhgf2aPiXC5Rs6IfR20IcGku8Pb2mTWpTuGmwkwhhPRJMlcXaMXE7daqNiBBSE/ZEayyaUK6P2QGRsWolB1BgUCQmYmMRdlKlh2tjrxLmg1OA5G+CLJFJjoQxWDXjkMCidlZwpyPIz0p2kVkpAsEJctsz95ORz7NRk0mMEkqqBCMzN5rnYuIyITSJl2WdWLlnanKhBpVkc0yI++MEOoO0XRvU3yOIwTTzOSTkW5IFNLnkEFBmPAE00nMWU/MUYklcBqcNeMiNQyBC4QrFhH77Oeh45W4wOmwWrHf2kpw7YcY8dXR/2I/kknC0jL2+zqu4liWs5ofM8W2TfXnIUlIz5BbHnek94zA1a1JESNFMUrl+NlMhsQYn9jX/xvDvDKsTaak/2okSXOBz7RJdQo3FWYCIaQXgXwWaBNxfc6VP6J6rIvdDhY9Ei5lR3BMSDcBIEvRSTcdEAguNtKNRZYmC6GuECPvjiSNLZlKlvWJUfUN1Qy+Mpjx8aZ7m+LCvfuAm+hwFGOFEUuDBfsKO6ZaU/y8ZmMJs+5Iz7vZ6GQdCwKBYEqZa9nb+TQbBTAbFIhCMCrGJoEgX1KzrFVVjVfemWpNoEJ0MIpklrC32cdV3gnBNDMFO9LnkJAOwoQnmCIUZZxAaSg3Ylg8n9jpAYz9/dqHy2yGcBg8HmLmcuRF8zFcuQxuXJn0fKmlBSsSfV/rRAkpRW+arGtfalTNGJFVjM9DkpCeIbfcIWmDks9WA1uuThJ2daNUKffAkmQJW3kQbOehcTmk+69wODQX+EybVKdwU2G6EUJ6kci2QJuM63Mu/DHVG43G89Fh3I5gpS0IgD886kiPBLVdK5ETKRAUROJY5Ovwcfbxs+PGluobqxn86eC4kmV9YuQ74qPrqS6MTmPWiVPLgy20PNSC+3U3nV/vxDzPjKXJMu4P+GwsYdYd6YU2G51rCz5BafDMM8/wjW98g56eHq644gqeeuoprr766ozHv/jii2zfvp0zZ86wdOlSvv71r/Pxj388/riqqjzyyCP84z/+I8PDw3z4wx/mO9/5DkuXLo0f88lPfpK3336bvr4+Kisr2bhxI1//+tdpbGyc0msV5Ec+0S4AZlkbmwLCkS4Q5E1qlnXMHSMyEMFYbkSSJJSQgmSUkC1yxuah+Qimc9lIlYl8MtJ1IV0ySuOatc4F5mKVlGAG6egYi8wIBjUz4ooVWG/cgn1tI94gGCInkAYHNCHVaEStbyBkugTnNY1aZYwsjRM4g2cC4ypzdCbTNDmd9mVbZhsXkVWMz0OSkA5pI0Yc6oh2Xhs2QWt90vNnTQ+sDLE1cXw+YVItMlO2tfLMM8+wcOFCrFYra9eu5Y033sh6/IsvvsiKFSuwWq2sXLmSn/3sZ0mPq6rKjh07aGhowGazsXHjRt5///2kYz75yU/S0tKC1WqloaGBv/iLv+DcuXNFv7ZC0F2f3sNejDVG7MvtGGuMeA976Xqyi4GXB7I+7uvwzej5FwPdkR6PdYGxHcGaGmhvpyrSC0AwrO3tSBbDpJsOCAQXM5nGHs8hD6e+dIqhfUMYXUYkxk+MjC4jgfcD8QVj6uOJEydJlihfX075unKinigqyaXqegmzvdU+q0qYC3akzwLHgmB28qMf/YitW7fyyCOPcOjQIa644go2bdpEX19f2uNff/11br31Vu68804OHz7Mli1b2LJlC++99178mMcff5wnn3yS3bt3c/DgQRwOB5s2bSIYDMaPuf766/mXf/kXjh07xo9//GNOnjzJn/zJn0z59c4GVEUlcCbAyLsjBM4EUJXpj+goWEgPlfgiUCAoIfRoltDZEKqqooQU1KiKZJJQVZWoJ4qpxoSxXFu3GBwGlKAyrvLO0eqg5aEWFu5cyILtC1i4cyEtD7bEjVSdX+vkzI4znHn0DGd2nKHza51zYu2XjXwc6Xqz0ZIXryaBbnwpW1mGbaFNiOiCidHRAU8+qZkTa2q06IwarcGk9PRT1FzuwbS4Cn/VlUSv+gjqh3+P6FUfwV91JebF1VkrY/TKnGxNk9ONe9nItD4deWeE/h/3Ixmlon4exgnpoInpDz0EO3fC9u2UXaXlc4+U1Y97/qwxSukm1bNnxzKVdVRVa+opTKpFZUpW/GLRp5HaqMboMiIZNJHK3mYn3B+m66kuIv3pH48MRBjYOzAjC7RiktaRDmM7gldeSaWvCxj7hZRammZl0wGBoBTINPboTbI8b3jwHvLiPujG/Ss3kf7I2HNVFSWgoPgUYv4YpBl+UidOegmzqcaEv91P1B1FjapE3VH87f5ZWcKcjyM9UUifNY4FwazjW9/6FnfddRd33HEHbW1t7N69G7vdznPPPZf2+G9/+9ts3ryZ+++/n9bWVh599FFWr17N008/DWif8V27drFt2zZuvPFGLr/8cp5//nnOnTvH3r1746/zxS9+kWuuuYYFCxawfv16HnroIX7zm98QiUTSvu/FQqmIX/kK6UZJG8T9YTE2CQT5kjqvUUIKkiwR88aI9EeSesFA9sq7dIJpLqPVXBbTC8lIL3nxSiCYSRRFc1YPDGiZ3y4XGAzabVsbDAzgePdlmu6Zj3O1k2jETmBYu3WucTL/3vlZ0w8SK3PSUWjFcS5tbCq0L90YNc6IrUeMrFyJo04rMfalGXZnTQ+sFJMqbjdEo9pte7t2vzCpFpUp+UnOlkVfKBTC4/HEv7xFzgxK16hGp1DX52wmrSNdZ3RH0Pb327FalLFSvorZH2kjEMwU2ZpkhXvCmCpN2v0qhM6H8Bz0EOmPEOmP4P6VG88bHmIjMby/9Y4T2iH9xEkvYXZe6SQ6GCVwPEB0MIpzde6JWimSjyO9zK6J56oK0UFt9lXyEy3BrCIcDvPWW2+xcePG+H2yLLNx40YOHDiQ9jkHDhxIOh5g06ZN8eNPnz5NT09P0jHl5eWsXbs242sODQ3xwx/+kPXr12MymdIeU+icqhRc3YVSSuJXPs1GIVFIF4sngaAQEuc1uisxOhTFXG/GtdYV7wVTaOXdTIhJpYQupOeTkS7mVIKJMBPJCENDQ3zmM5/B5XJRUVHBnXfeycjIyLjXeeKJJ1i2bBkWi4X58+fzd3/3dxO/0M5OLZ6kuTm5kSgkNZh0OAYyVsZkI7UyJ/VaCq04zqWNTYX2pU9F843qTGVW9cBKMKkyOAjHj2u3q1dr9wuTalEpeka6vuh7+OGH4/fls+jbunVr0n2bNm2Ki+S5Fn233HLLuNfMZ9H32GOP8ZWvfKXQS8yb1EY1qUhGSftwZtjEm40N+tKR0ZGuM7ojWFUNxnMiHkEgmCy5mmSpikp0IIoSUDA3mYkORPG+5UWNqpoLXQJTnQkkCJ4LEnVH44tGfeLkXO0cN3GaS5mPOR3pHR3Yf7wX0P7WRX53DGhCGugFmqb+BAUXBQMDA8RiMerq6pLur6ur4+jRo2mf09PTk/b4np6e+OP6fZmO0XnwwQd5+umn8fv9XHPNNbz88ssZz7WQOdVU9IaZ6qzhVPGrmE23JkK+zUaNoxEJvuDsG4cFgpkmcV4z8s4I/S/2owQVJLOkzZkm0Dy0EDGp2A2SSyGTXR+7sjnS481GhSNdUCB6MsLu3btZu3Ytu3btYtOmTRw7dox58+aNO15PRnjsscf4xCc+wZ49e9iyZQuHDh3isssuA8aSEX7wgx+waNEitm/fzqZNm2hvb8dq1dZCn/nMZzh//jz79u0jEolwxx13cPfdd7Nnz574e33hC1/g5z//OU888QQrV65kaGiIIV0omQher5aJnmkikNBgciIN14vdNDmXNjYV2lfaaJcUEiuMU9GF9FmjTbW2avE+KY1nhRO9+BT9J5pt0Ze6QNMp9qLP4XBQXV1NZ2cnP/3pTzOe68MPP4zb7Y5/tbe353eReZKrHEbP2iPNw6qqEjoXIhaIaTEJs9iVoDvSMwrpo1RWXhyZeALBVJM69qQ2ySICxkojhjID0YEoklkieCZIZCCCqqqYKky41rrix0fcEXztPqLDuaNa5kLmo6KMTbzSOtJH8wgN7xyi3KJZq8KyZq2S/us17XGBYA5w//33c/jwYX7+859jMBi47bbbxrmSdPKdU02Fq3s64lZmwkmVjXyjXfSIBCGkCwQTQ5/X1N5Yy4K/XaBFJEyi8q6YucO5KnsSHx/6zyE+eOyDGY+lyuVINxrBYdGuQzUI8UdQGDORjNDR0cGrr77K//pf/4u1a9eyYcMGnnrqKV544YV4v76Ojg6+853v8NOf/pRPfvKTLFq0iDVr1vAHf/AHGa8lZ6VfYoPJdBShwWQxK46LHRWTD/kI6dkc6fHoztm0qZcQW8PChUJEnyKK7kifae6//37uvPNOPvjgA77yla9w22238fLLL6ft+G2xWLBYxnbEPHotfx7ks6Ovl8N4D3sxtBmSzkFvVGNbaiPqjmJuMidFMPg6fAROBjC6jJz77jncv3RPyqk1k+gbrWmjXRKoqhKZeAJBMUgde9I1ybI0WbAvt+M75iPUFSLqiWK2mbE2as811ZowVZnwHfURPhcmeCqIsdKI60MuarbMzrEoXxLnqeMc6Sl5hLWv+XGHbIRVzZEi+9ywd6/mBhATF8EkqampwWAw0Nvbm3R/b28v9fXjmyIB1NfXZz1ev+3t7aWhoSHpmFWrVo17/5qaGpYtW0ZrayvNzc385je/Yd26dePeN5851WRc3ZnmXbowHxmIYGm2YHFYiPlieA97CZ4N0nRvU1HGq5lwUmUjbyF9dONjRAjpAsGkKUblXaKYZHSNX4rnKyblquxJfDzUFyJ0KoRklnCucWJfbp+ScTInioJ/OAJYsHl6QalNO1dyOVQIgZpm/S4QZGKmkhEOHDhARUUFV111VfyYjRs3IssyBw8e5KabbuLf/u3fWLx4MS+//DKbN29GVVU2btzI448/TlUGt2HOSj+9weThw1omeuLnRW8wuXr1pBtMFqviOJc2lqnieTLEhXSHAmfSu7SzOtJnS0a6YNopupA+mxZ9EyXfkuRc5TCWWgtVt1cx+Mpg/HEloOB+3U10KIqxykj5+nJkuzz9E50ikq8jvapKZOIJBMUgdewxOA3xJllKSIk3yTLVmiivLWfknRFinhjlHynXyv5GP36mWhPlNeWaA+H9AI13N1L1h1VFj0uY6TLjVHT9z2zWjBxJpOQR1tj9nBiqJhw1YAKkmkroeEc7buHCaT5zwVzDbDazZs0a9u/fz5YtWwBQFIX9+/dzzz33pH3OunXr2L9/P/fdd1/8vn379sXnQYsWLaK+vp79+/fH51Aej4eDBw/y+c9/PuO5KIrmygmFQhO+nolGGmSad1XfWM3gTwfTCvNyq8zImyOc232Opi80YV04ubGlWOJXMVCUMVenENIFgullIhEJOqqioioqhkoD/qN+nFc5k8alfMWkXBuI1TdUM/iKNjaam8yoZ1VUVNSYiu+ID0OZAVOtaXpjqTo64KWXCHTeBjRhf+E56FG1Bnkp2b0uuwJDoMzCqkbBzDFTcXg9PT3jYmOMRiNVVVXxY06dOsUHH3zAiy++yPPPP08sFuOLX/wif/Inf8Jrr72W9twefvjhJJG/u7ubtra2sQP0BpNnz2oNJZuatEmBz6eJ6EVsMDmZcS/xNYoZFZMPcSH9xe/B8H9rUThWq7YBMTr25BPtIrQpQSpFF9Ln2qIvlUKdT3o5jL4ADHeHka0yztXOuKvTtsTGwEsD+Dp8jLytCVq2ZTYcrY54I5vJTnRSxSpLk4VQV2haxKt8HemVlRAUQrpAUBQSxx69bDc6FMW6xJo0tgCoYRVzvRlTlSkuoutIkoRkkjDXmbGvKO4iaypykotB1nz0lDzCGrsW9hmNjArpNpP2eJGbVwsuXrZu3cpnP/tZrrrqKq6++mp27dqFz+fjjjvuAOC2225j/vz5PPbYY4CWwXnttdfyzW9+kxtuuIEXXniBN998k2effRbQPtP33XcfX/3qV1m6dGk877OxsTE+bzt48CC//e1v2bBhA5WVlZw8eZLt27ezZMmSSRkTJuLqzjbvGjkygjKiYF1iTRLmI/2ReDWN/4if4NkgrqtckxpbZsJJlQk9YxhyNxuVR6MevH4xrxIIZpJ0DvFQpzZuWFoseYtJuSp7fEd8dD3VhdFpxH6pXYv3G4pgqjYhmSUi/RH8R/2U12jZdQanAffrbtyvuylfXz4168HRSDwGBvCrfwmArdoOh3+liYApjfCcdm3cEkK6YK6gKAqhUIjnn3+eZcuWAfC9732PNWvWcOzYMZYvXz7uOXmlJ+gNJl96STP6dHdrQvHq1ZqIXmINJvPRxoqJ16MAMs4Tb8OKmrGNhsOH42NPWZn2M8rabLSU0xIURWSizwBTEu0ylxZ9iUy0JDlXOYz+uPt1N51f78Q8z4ylyZIkaE2m+UyqWKWGVGKBGAabAckiTbl4VYgjvZdZ1tBBIChh8m2SZW2xYl9mJ9QVwuCaWoFI39RLPB9Ly9TFMUwEfZ6aNh89MY/Q5YoL6ZGYARsgRYJQNrk8QoEgkU9/+tP09/ezY8cOenp6WLVqFa+++mrcHdXZ2YmcMGFev349e/bsYdu2bXzpS19i6dKl7N27N940C+CBBx7A5/Nx9913Mzw8zIYNG3j11VfjTbPsdjs/+clPeOSRR/D5fDQ0NLB582a2bduWtKgrlEJd3bnmXZ4DHsLnwzhWjo0Vkf4InoMeYv4YBpcBJE2gn+zYMhNOqkwkuqZyCenSqCPdGxDzKoFgphi3IbjAQrAuyMhbI7h/48bWZ8M8z0zZqjJca12oUS3bPJ3xKVdlj9FlZOTQCJXXVyJJUlK8n/54ZCBC4HSAUHeISF+E6HCUzq93Ur6uvPjrwZRIvMC/a39D7BVmmNemOWlTIvHKrNq4FUt1dwgEWZipZIT6+nr6+vqSXiMajTI0NBR/fkNDA0ajMS6iA7SOitydnZ1phfS8mWUNJosVFZOLaFghENR+Bs5LW8AxWsrncmlROKNjj+PK5YA8O6NdRit9OHo0rdteMHVMiZA+lxZ9iUymy3quchhJljCWG5FtMuZG8zhXqKqqqBGVcG8Y/1F/3oNN6sRNDsi4fz290TGFZKQPMgsbOggEJYw+9tgW2rAvs2d0AAB0Pdk1pQKRvqmXWH1jXWLFXGdGckl55SRPB1kd6Sl5hDU2TUiPRWVARRrqg/Wtk84jFAgSueeeezJW9f3iF78Yd9/NN9/MzTffnPH1JEli586d7Ny5M+3jK1euzFhqPBkKdXXnM+8KnAgQPh/G0mxBVVV8R33E/DFMtSbUkIpskjFVmzCU5z+2ZIqcmm4nVSYGB7Xbysrc62Qpps2rPD4xrxIIZoJMG4K2RTYsCyyMvDmCfamd6huq8fzWQ+/zvVmNT/ZL7VkreySjpLkoR1OmZIscv0+ySEhmidhAjJG3RlBVFdkiY6wwYp5nLng9mFc8X0oknj+iVUTajBEt07mpSRODEiLxymxCSBcUzkwlI6xbt47h4WHeeust1qxZA8Brr72GoiisXbsWgA9/+MNEo1FOnjzJkiVLADh+/DgACxYsmPzF6w0mZwnFiIrJhbejC9DWY05LOOUExsaesmW9QENWR3pJmjwTKn1obk7rthdi+tQxZc1G58qiL5GpbjSVyamVWKKs+BXOPXuOkUMjOR0DqRM3gJF3RlCjKpbFFqIDWu5x+YbyKRWv8nWkV1YmNBst1V0/gWAWk8sBMJUCUeKmnu4SNVYbCfeEiXliuNa6MNWaJlV9UyyyOtJT8ghrZK2xkBKVABWpwlm0PEKBYK5RqKs717zL3GDG4DAQPBvE3GTWIgwGIhjLtTlU1BPF0mDRvpfIa2zJFTk1XU6qbOjGt9ra7Mepqoqk6eh4RLSLQDAjZNsQlGVZG09OBel5vgclpOQ0Po0cGdFE9gyVPbr7nNHlqKHcgKnGRPh8GFOtCSWkEBuJgQSmBhPRAW2ctDRZMGPOez2YdzxfSiReIKqds90U0R53OLQ4ioRIPN2RHpXEXEpQGDORjNDa2srmzZu566672L17N5FIhHvuuYdbbrmFxsZGQGs+unr1aj73uc+xa9cuFEXhf/7P/8kf/MEfJLnUBRrF6KHl7dXMTmZDFIsxjT43OvY4VE1BT+dIVyKjJs9S06ZSKn3ijWZT3PaJlT5Jz50l1QulzJQJ6XORqW40lc6plViirEZVrIutWBdY83IMpE7cosPR+AJTluV4aV/UHcVYYZwS8SoUGsvyzMeRLpqNCgRTSzYHQD4C0UQmNqmbepG+CGpMxVhhxOA0JOd1Stk3JaejOWlWRzok5RHWdGnloMroqcqfulHs/gsEWSjE1Z1r3qX4FWyLbRicmhlAtsqoYRXVohLtj8YbK+umxlyGh3z74EyHkyob/f3abU4hfbQkGcAtHOkCwYyQa0NQtssETgUwN5hxrdMmHtmMT74jPmLBGKHOEIZLx1f2RD1RbEttRN1RzE1mJEnCscKhbTT2R4j5tfHPUGYgOpA8TkrkZ2YoqGdYQiRexFFBVNHWyTZTdPTFfNrjCZF4DqsmXkVVMW4JCmMmkhEAfvjDH3LPPffw0Y9+FFmW+dSnPsWTTz4Zf1yWZf7t3/6Nv/7rv+YjH/kIDoeDj33sY3zzm9+chp/K7KJYPbS8aGOK0xTM8Eba2OOo0ca5omWkT4dQnVLpk0SGSh9ARMEUESGkF8BUN5pKdWqZ55vxtfuIuqNgBGO5EUebA2O5EYMrt4M8deKWmJEHaDnJXhUlpE1WJuuoT4fuRpekDO7OBISQLhDMPNkEoolObFI39RLLjGXL+E29TJuS09WcNKsjXWc0j7Cmph9+A6rBDNEg0iULi3YeAsFcJd2mnZ4FPPLuSHyTLJ95l+saF9WfrGbwp4N43/Si+LU5jaXRgn2FPamxcjbDw0T74MwEupA+b1724/QFIIB7RMyrBIKZINeGYPh8mJgvhrXZmpfxydpsJXgqiGyV01b2WGotVN1exeArg/HHjZVa49GRt0aQVAkVVRPqG8aPk7nWgwWPlQmReIElNfHXsZsioKrQ1aU1RkyIxLObtbEroohxS1A4052MAFBVVcWePXuynldjYyM//vGPsx5zsVPQJl0OvGVapr1T9mtjTaLgnDD2lC3Tqgb86Q4rNCN9uoTqlEqfcaSp9BFRMMVFCOkFMB2NphKdWt43vdpEyS6PWxCmxh/ozWcSXZqpE7fUjDw1rCIZNVFLVVVC50LEAjGi7iiqohZloajno1dU5N6IS4x2KckcKoHgImYyE5vUTb3UMuPETb1Mm5LFnFjlIqcjXUeWqb2sbvQitRuxCSgQ5Efipp2vw8fZx8+m3STLZ96lR64EzgTo+nYXgfcDOK9yJlfT5DA8TKYPznSTd7RLgpA+PCLmVQLBTJDPhqDBYcDcYAbyMz7JFpnam2vxH/EnVfbozUrNdWZqP1WL56CHwLFA/PF5t87DusBK34/6MM8zY2myjOvLlavCuuCxMiESz3/ktHYcCpaRQejugpqacZF4DsuokC4c6QLBRUOxDQ1e32ijUWtYizppahoTj7vGxh6HUztOVSEQSG7iHnek57O+m06hOqHSJ+2CNbXSZzJRMIK0CCG9QKaj0ZTu1Br6+RDRb0WxL7djqjIlTXQSm48O/XyISH+EwLFA0gK0+sbqpIlbonhlrDHGs0PVsIr7v90ETgYwuoyc++453L90F8XlmW8+un6MUTQbFQhKjslObFI39VLLjGWLDDIoYUWrxknZlJxup2hejvRRakbNVVJsAqV/AoEgr02yfOZdkixhX2xn/l/N1xond2Q3PKTGREXd0YzxCxNt+D5V5OtI17M9AdxeaZzTSiAQTD05jVi1Zgw2AzF/buMTjAndZVeUUfNHNfFxLNwbxnNwrFmpwWrAtsxG3W11mOvMcaMVQKgzhPewFzNmJPLfcIQJ9gwbjcQLfO81eBlshjDS0KDmRN+yZZy4ZBt1pIeFI10gyIvpiL6caoptaNDN2M4FVXDllZpLvLtbE5gTxh57wlA1MpJBSM+1vptuoTqh0ifp/SB9pc9Eo2AEGRFC+gSYjkZTkixhX2HHUm/RdsASXjqx+WjUHcV/3I/BYcC5xol9uT1pAVp9Q3XSxM2+1E6kP0LoVAhjlRFjjZHhXw2Pa2bjOeRh5MgI8/50HmVXlE34+nRHeq58dNCEdN2RHhMrPYGgZJjsxCadG8tUa8K11oWvwxffxFPDatpNyel2iubtSCdBSFdFLJVAUCj5bpK1PNhCy0Mtec278jE8pIuJMtWZ0jbwm2jD96mkv08FJGoj5+BMOGP+pl6SrADhmEQ4DJb02pdAIJhCso1L8WiqHMYnY7lxnNCtV/b4Onz0/7h/3IbkyDsjhLpDNN3blDQ/mkyFdbaomqwVzq2t+D+7HL4JNqcRdu7MOHbFhfSYcEYKBLmYSPRlKQrv2TbpJmJoiAvp8+zw0EMZc8sNBrDZNDd6asNRfR6VMy1huoXqhEqfbG77+Pg6kSgYQVaEkD5BpqPRVK7mo0pEQbbKSAYJJabgO+LDUGbAVGuKL0B97/qYf898Bn86iP+oHyWoYFtkI9YQQ7Zo2XoxTwzbMhuOVgemWhOR/gjRoSiBkwF8v/NRtqoMR6tjQovGQhzp5eVjGenBqBCjBIJSYULuowQyubEks4SxykhFQwW1f1qbcdNusu9fKIU40vXYKqMiYqlKhWeeeYZvfOMb9PT0cMUVV/DUU09x9dVXz/RpCdJQ6CZZvvOubIaHTA74wOkAoZ4QsWAM1zWuSTd8nzI6Ouh7yw4soPYXL8K5tzLmb+pOquioG8PrFUK6QDBTZBuXJFnKanyyLbUR9UTTCt0TqdrLt8I6ndiWKaom0h9JMkekq3AOhLQ5kt1pzCom2Uza2BUS60GBICsTib6crp5ThZJpk26ihgZ9PedyoS3Wsow5ZWWakJ7acFSv7MtplJoJoXq00ieeyZ7GbR+n0CgYQU7Eir+E0cUnU40Jf7ufyHAk3nxUVVUMNgOyScZUY8I8z0zMH8N/1A9q8gLU4DDQ8lALC3cuZMH2BVzy7Uu4/JXLaXmgBetCK5UbK6n4vYq4iO456CHcE9biZADZLOM97KXryS58Hb4cZ51MIY50WR6bOAUiYuJUajzzzDMsXLgQq9XK2rVreeONN2b6lCZNodf04osvsmLFCqxWKytXruRnP/tZ0uOqqrJjxw4aGhqw2Wxs3LiR999/P+mYT37yk7S0tGC1WmloaOAv/uIvOHfuXNGvrZgkTmzSkStPE8YWbc4rnUQHowSOB4gORnGtcbFg2wJqb6zFttCWMxpmou9fCIU40mUZqqtFLFWp8KMf/YitW7fyyCOPcOjQIa644go2bdpEnx4qLSgp9E0ygyP9Z9fgMKAElQltkumGh7KVZfGxJVVwMrqMSAYJo8uI41KH1ux4OIb/yPg5V2LDd3ubnchAhIG9A6iKmvtkisVo/mb/oDZ9n3eJS3MdHT6s5XJ2dCQdrgvpsQQhXSAQzBzpxiUYP0eKDkWxLbJhv9SObZGN6FCU6GAU52on8++dP+GqvUQcrY6k9eHCnQtpebAl/tq+Dh+dX+vkzI4znHn0DGd2nKHza534j/mT1qdRd5RwT5jh/x4mcDyAsdxI+YfLMdWaxq0f/X7tvW059kStRm1OFYqJOZVAkIlsc5pM8xRdePce9mKsMWJfbsdYY5yw1lNM9E260NkQ6milb1ybOh9OMjR4Dnk489Uz9P+0n8CZQNq5WNyRnoc2rOvf4xzp+WakJwrV6Zgqobq1VXPb79wJ27drtw8+OD6LXY+COXtWi35JRI+CaW1NavosyI4Q0kucxIlVuDNM8FQQyShhbbTiuNQBRu2DLUlSUld3SF6Apk7cZKOsdYO3yZgbzSBpIqDvqI+YP6a52p0GULQmNxNdNBbiSIexLu3+kPjVLCXmojhV6DW9/vrr3Hrrrdx5550cPnyYLVu2sGXLFt577734MY8//jhPPvkku3fv5uDBgzgcDjZt2kQwOLZ4uf766/mXf/kXjh07xo9//GNOnjzJn/zJn0z59U6GdBMbHb3M2N5qz5inqZNr0Vbs91cVlcCZACPvjmScZKWjEEc6aDqWHkslol1mlm9961vcdddd3HHHHbS1tbF7927sdjvPPffcTJ+aIA3TvUmWS3BytDkw15uxLrKOm3O51royNnyfFhLyN/ujmjuh1hkcy98cGNDyN5WxXPR4tIskhHSBoNRJnSPpxqdLvn1J1jnTZDYkMwn7ucQ2IL4+jQxEcP/aTcytVThX/F4F5npzWjEvENDeN5eQbjGKCmWBIBeFbqJNRHifTnKZSHVDgxpWiQ5Fcf/Kzeltpzm9/TSdX+sctwkwESE91ZGed0b6TArVutt+5UrtNl0Gux4FU1OjRcG43RCNarft7WmbPguyI35SswB9YtVwdwO25TYqN1ZSvqEcc7053owGRru6R8e6uudagKYuYGPuGJGBCMZyrRlgYnObiS4aC3GkA1hN2rn7Q2LiNB14vV48Hk/8KxQKpT1uLopThV7Tt7/9bTZv3sz9999Pa2srjz76KKtXr+bpp58GNEF3165dbNu2jRtvvJHLL7+c559/nnPnzrF3797463zxi1/kmmuuYcGCBaxfv56HHnqI3/zmN0QikYznGgqFkv6fvNOshqRObKLuKGpU1Xo0pGkOmuu10i3adNKJ3xN5/0xOqqxOC0WBM2dw92ufA1eZkvnYBISQPvXkM1aFw2HeeustNm7cGL9PlmU2btzIgQMHpvN0BXlSrE26fMlHcJItMnV/XjduzqWL6InHTtQtPyFG8zdj81sY8GsrvnmO0fEsNX9zFH1+qEjadF8I6QJBaZPO+JRtzgTF35DMV2yzL7fT8lALjX/ZOK7COX49KetH3ZGe2MwvHVZdSBcVygJBRgrdRJto9cp0ks1E6lqrlQrnm55QiJBeVqbdZspIz7m+mw1CtR4Fc+WVMDgIx49rt6tXa/enutgFWRFC+iwhXfNRvRmNvkuXKHznswBNXcAqIQU1qiKZJFRVJeqJYqoxYSzXMqomsmjUhfR8Hen6xMknhPRpoa2tjfLy8vjXY489Nu6YuShOTeSaDhw4kHQ8wKZNm+LHnz59mp6enqRjysvLWbt2bcbXHBoa4oc//CHr16/HZDKlPQbgscceS/p/amtry/tai0WmaJZ0ZcYTJZv4Xcj7T6hssaMDvvY12LEDT49mmSr/8ffGRSWko7Z2rL+DENKnhnzGqoGBAWKxGHV1dUn319XV0dPTM12nKiiAYm7S5UO+gpOx3Jix4XvqscVyy+dkNH9zSK5BHT2hapt/7HGHQ8vnTFDL9WxPRRaOdMH0MN2ReWfOnOHOO+9k0aJF2Gw2lixZwiOPPEI4HJ6S6ytFir0hWYjYJsnSuArnVBLXj/k60s2G0ajPsDTO3CmYfuZivOdcoNBNtKmM0ysmmUykxhpjQekJxYx2yasHVr5C9ahxi3ff1W6V/IxbRSHfKJhZyHSPU6LZ6CwiXXMXxwqH5iTvj6BEFKxN2iQpnwVoagNAg9OAJEvEvDGUkILBrjWe0CdFE1k06tEu+TrS9VK+kYAQo6aD9vZ25s+fH//ekqYLWTZx6ujRo1N+jlPBRK6pp6cnq0Cn3+Yj4j344IM8/fTT+P1+rrnmGl5++eWs5/vwww+zdevW+Pfd3d0zJqZnapQ1WfJtlpPr/SfSdEvPHWZgAJqbcce02ZTrxGF48lDOXfraKjW+Ky0y0qeGfMYqwewk36Z3xSBTkzwYE5ycq51xwSnfY9M15EsdlyY9bo7mb+r56JXWACZDwuIrTf6mvgBUDRJEhZAumFr0yLzdu3ezdu1adu3axaZNmzh27Bjz5s0bd7wemffYY4/xiU98gj179rBlyxYOHTrEZZddBoxF5v3gBz9g0aJFbN++nU2bNtHe3o7VauXo0aMoisJ3v/tdLrnkEt577z3uuusufD4fTzzxxHT/CGaETA3dY75Y2uak2VAVFf9RP6GeEIZyA6iME8dTG7xnahCok7h+1EWqXI50szyaj4xMIJD7eMHUUejnWjB9FDqnKeSzOtOkM5HGhpPTE5SQkjE9wbbQNiFHesZmo/mu71pbYflyrTrQ69XevKVlzIne0THWHDQY1OZtGRrGTxk5Gq/ORmZinBJC+iwi3UTJWGnEfqmdkbdGkCUZ2SITHYrmvQBNXMDqLs3oUBTrEiuOVke8PC/dYJwPhTrS9YnTSFCIUdOB0+nElU9HRUFRuf/++7nzzjv54IMP+MpXvsJtt93Gyy+/PM75o2OxWJKEQ48e4j0D6GXHxaRQ8Tvb+xfipLIttCXlDtPWRlgxEoxq4175Zc1w+pCWO7x8ecZyvJqqMbtUXo4FQcHkM1bV1NRgMBjo7e1Nur+3t5f6+vqpPD3BJJnKTbpEChWc8jnWf8wf3wSIBWMYrJoJoeYmbQ7m6/BlfTxvRvM3+/5Dc6HHY11gLH9z9eqk/E29JBmDcKQLpp7EyDyA3bt388orr/Dcc8/x0EMPjTs+MTIP4NFHH2Xfvn08/fTT7N69e1xkHsDzzz9PXV0de/fu5ZZbbmHz5s1s3rw5/pqLFy/m2LFjfOc737lohHQozoakPlZ53/TiP+YndDaEpdGCfYU9Ka4lVWwrRMwbGNDur67Ofi56A/cYEh6PENKLjR6Xp5O6zkik0M+1YPoodE5TqPA+06Seb7r0BEuDJSk9IXGTryiO9HyjXRLJJFSnGLdwOLQ3PHxYy1YX8SrjyHesmolxSgjps4xME6V5t87DtdaFuc5c8AI0cQE78s4I/S/2owSVeOb6RBwNOoU2G9VL+bx+IaSXCnNRnJrINdXX12c9Xr/t7e2loaEh6ZhVq1aNe/+amhqWLVtGa2srzc3N/OY3v2HdunWTvbRZScHidxb0skWLI/2CIHWSpecO09wMkoQnNPY8pzWSnDucYfe+tnLMFSqiXWYOs9nMmjVr2L9/P1u2bAFAURT279/PPffcM7MnJ8jJVGzSpaMQwSnXsUDaShrPIQ8jR0YoX1eO5zcelKCCpSVzpU1ejOZv9v/iVwDUWjxa/qbPp4noafI3kxzpgPfYOTgTTnZHCQRFQI/Me/jhh+P35ROZl1htB1pknt5XJldk3i233JL2dd1uN1VZFh6hUCipz8Z0952ZKiazIZlYFWheYMZ2wUaoO0TwXJCoOxpvtpxObCtEzOvr094vpXhzHGpMG7t0IX2WLjdKltSq1kceeYQvf/nL446byOdaML0UMqcpZvXKdDDZ9ARdfy3EkZ765yDei3Cy67sU4xb6eldvGN/entO4dTGSz1g1U+OUENJnIVPh3NIXsLaFNuzL7EUrsS602ahp1JHu8YsBpFSYi+LURK5p3bp17N+/n/vuuy9+3759++Li96JFi6ivr2f//v1x4dzj8XDw4EE+//nPZzwXZTQXLVOj14uBgsXvLBRctjiaO6xbEXQh3WEKY5QV7f7u7qxWzuqKMUe6ENJnlq1bt/LZz36Wq666iquvvppdu3bh8/niDgWBAAqbR2U6FqDza53jKmnUkEp0KIr/hJ/h/cPIZhnrJVbMdWYkl5Q9ZioXra30X1MJ+6DWMKTlb1qtmhN9y5ZxTqb4AjCq/X3xvvJLOP+z6S8jFqTlmWee4Rvf+AY9PT1cccUVPPXUU1x99dUzfVoTYqYj83ROnDjBU089ldWN/thjj/GVr3wl+wXNUiayIZmuKtDR5tDmZv4YEXcEX7uPsivKCHWnF9vyFfN0IT1Xpb0+dkWRRCXNFJBvXN5cjPecixQ6p5muOL1iMJn0BH3syKf4vrZWu9XHKJ2iCekpxq0kUhvGz7HYlclQyjHEQkifpUylcyvdYGxpshDqCjHy7kjewr2qFu5IN42W8nl8QowqJeaiOJXrmm677Tbmz58fb2r4hS98gWuvvZZvfvOb3HDDDbzwwgu8+eabPPvss4DmnL7vvvv46le/ytKlS+NZno2NjXGx/uDBg/z2t79lw4YNVFZWcvLkSbZv386SJUsuWjc6FDezr+CyxdHcYXw+cLlwB7U/0OXW0Y71aXKHU6kuTxDSRUb6jPLpT3+a/v5+duzYQU9PD6tWreLVV18dN7kSCAqZR6U7NnAmMK6SJtIfwXPQQ8wfw1hmJHwujLHaSLgnTMwTi7s6C620SaRP1qyZ835vOTy0fXz+ZgLqqQ+081ciAHhd8zXnuigjnnFE7nDx6e7uZvPmzdx8883cddddGY8rlb4zpUK6qkBTrQnXWhe+oz7C58IETwUxVhpxfciVUWzLR8zTCzsLEdJnMM1wziKiPecehcxp8hLeFSU557upSat+S5f7PcVMND2hkGgXvZj8/Pnk++NC+mTXdynGrXHkYdy6GCnlsUoI6YK0JA7Gvg4fZx8/W3C+p9cLsVEDab6OdKOkDVbDQkgvKeaiOJXrmjo7O5ETJgjr169nz549bNu2jS996UssXbqUvXv3xhtiATzwwAP4fD7uvvtuhoeH2bBhA6+++ipW62jjOrudn/zkJzzyyCP4fD4aGhrYvHkz27Ztm7PNE/NpsjfRzL5Mr11Q2eJo7jCHD0NbW9yR7rKEMuYOp1JVrhIAYpAx514wfdxzzz2ztlpGMHMU2hA0tZJGVVV8R33E/DFMtSZiIzFURUW2aJuAkf4I/qN+ymvKUVFRIyrh3rAmYI2aFfJ57/5+7bZ2sRNWrsx8QYqC8t8HgFYkswwh8Easooy4RJhrucMzHZl37tw5rr/+etavXx83OGSilPrOlAKZqgJNtSbKa8qJDkYJvB+g8e5GKjdWZjVW5RLz8o52iepCuiyE9BlkLsZ7CjSyflZTG2KGQhAIgM0GFsuMNMgsND1BVVRs7iCLiGFzG1CV7HM6/dc5tdhJH4sm3QMrxbg1jjyMW4L0zNQ4JYR0QVYSM/MS8z/zyffU3egWizbu5oNBHRXSvUKMKjXmojiV7Zp+8YtfjLvv5ptv5uabb874epIksXPnTnbu3Jn28ZUrV/Laa69N6FxnI/k22csmfgfPBjFYDNjbNCeCvmjL9dp5ly2O5g5z9iy0t+MONQNQbvRpQlOa3OFUql0qXWgLPlUdX7EnEAiKT6HCdzYm0hA0tZIm5o4RGYhgLDdqG2oKSAYJVO1vg9FlJDIQIXA6QKg7RPhcGMWv0PlEJ2e/dRaDzYBkkXK+d77RCHR2onaeA1qRR2PzvGGz9pgoI54S8m2KNRdzh2cyMq+7u5vrr7+eNWvW8P3vfz/JBCHITbaqQEmSkEySFk1llCZkrEqkUEd6TDjSZ5S5GO8pyEFqQ8xAAH79ay2vt6oK1q/Xuv/OYGVbLke9r8NH/0sD/InXj4kYwe8Y6DyYfazK5EhXIlpawqSjXVKMW0mLxTyNW4L0zNQ4JYR0QUbSZeYBeed7Juaj5yssyaqKArhHhBIlEMxmCt2ESyd+KyGFWDAGEej9YS8DPx7AvsKO43IHg68M5nztvHtJtLZqE8GXXsLzsygALsmbMXc4lQqnQhfags/vz1y1JxAIisNEhO9srzURw0BqJY0SUlCjKpJJQlVVYsEYxiqjdr+qIpklYgMxRt4aQVVV1KiKqc5EpC9CdCiKscpI+fpyZLuc9b3jjvTaHBfm9aKGtPFMNmgLQW9CM2VRRlx88m3gN1dzh2ciMq+7u5vrrruOBQsW8MQTT9Cvf0BAOGbzJJ+qQEuThf5/7ScyWLixSicW07Q5yO1I18WrmMhIn3HmYrynIAOpDTEB3nlHa2y+eLF2//vvw4YNM17ZlslRr8/pgj0RhrEQxILNPoz3UCzrWKX/uTh/niRTVNGiXVKMWzQ1afOwLA3jBfkzE+OUENKniGI6pWaKdJl5iRicBtyvu3G/7qZ8ffm46ys0Hx3GhPQhj4yiiLFEIJiNTHQTLl0GnmSSsLSMOdQ9hzz0/7QfY4UR1zWunK+dd/ZwayssX447egF+DeWrFsODD+Y1CNnMY1meAwNCSBcIppLJVMqlMhnDQGoljcFpQJIlYt4YSkjB6DBiW2Uj8H6ASH9EE9JHYprYbpMwuLTj1aiKZbGF6IAWn1C+oTzre+ctpDudqEZNODfoQrruSAdRRjwF5NvAb64yE5F5+/bt48SJE5w4cYKmpqak81FVFUFuckXimapNoEJkcGLGKp2hIU2nA00zyobISC8d5mK8pyADqQ0xh4c18by8XFsPuVza9243VFSUXGVbfE53agjZ9wF+1gJgffu/oKYGv/cSBvZa045VuiM9GASPR7tkGIt2mbQjHZKMWxw9qpkZsjSMF+TPTIxTQkifAorplJpJMmXmRfoj+I76NBfVcJTOr3dSvq583PUlOtLzRYppM6yIqjkQ9EFMIBDMHrJtwuVqsifJEtYWK317+lBCCvZLkxdtlvkWvL/1IhtlJArf4MuKLOMxVQPganBAnht5Y1mempC+YEH+bykQCPJnspVyqUxmrILkShpfhw+A6FAU6xIrjlYHploTpioTvg4f/mN+lKCCodKAtdGKudHMyO9GMJYbkWU5Hv0SdUcxVhgzvnfe0S4tLah1mqhrMKY40kUZ8ZSQb1OsuZw7PN2Rebfffju33377RE5VkEC2SDznh5z0Pt874XFSR/91r64Gkyn7+ejzKhHtUhrMxXhPQRpSG2KGQpobXf/Ams3aMaGQ9n0pVbYpCsHXT+LfdxpLzzE8YQMAZjmG5LBDz3ksRj/+30Cwc17yWKUo2Ps6cZU14xkxcL5bobxcWwTGHenFENIhbtxKauQ6jY1b5zLTPU4JIb3IFNMpNdOky8yL9EfwHPQQ88eQLTLGCiPmeea01zcRRzoJgtSFC0JIFwhmI5k24XQMDgPh7jAxbyzt49nELTWsNe+LeqJxwQny3+DLhdut3RYy9iRmeeplywKBoPhMVvhOZbJjFaSvpFGCCpJZc5tLZgljlRHnKieR4QjO1U5MVSbCfeF4FAygHe9VUUJKxveOxWBwUPt3Tke6LKNcsRp+7MUY1ha57qBZG+REGfGMInKHBaVIpkg83xHfpMdJKGATkGRHeilodALBRUFqQ0yLBYxGiES0f4fD2vd6tVWpVLaNNkeNvd5D7NBCLGonobIlAFiMUe18a2sx9A4QPtVNzH3ZuOdy9Cj1fA0PjfR8aw8r/mYNtLbGx6JJNxtNRJZLwsEvmBxCSC8ixXZKzTSpmXkAvqM+Yv4Yxhoj0YEolgYLliYLZszjrm8ijvTEidPQkBhjBILZSLbGVQAxXwzZKmNwGtI+P5u4JVtkZIuMElLiglMhG3y50J1PeZgK4yQK6QnxrAKBoMgUQ/hOOn6SY5WOHiNlW2jDvsw+ztXpWuOKuzolkwSSNpZJRgk1oiJZJNSwimSUkC1yxvceGtLM5KC5OnOh1tYDXqyVFhiEbrcTdWAQSZQRzzgid1hQiqSLxCvWODkRIT2GLBzpAkGeuA+48Xf4kcwSsknWbs1pbi1SfD0lW8e+pOZmpMSGmOXl2qb7+fParcejZaCUl5dOZVtCc1SDvQGDHCVmqiDs03rEWKSIdpwkEbNVIrsvYPD2AM5xjVUbqkIcH4Hz7w5o999771iz0clmpAvmHEJILyLFdkrNNOnyPyN9Ec0NOhDFYNcia5BAYvz1TcSRnihI6UK8QCCYXeTTuMq52om1xZr2+dkWbYZyA0aXkVBXSHNwqmreG3xAzt4VE3KkxytpZAaOD8G73aJUTyCYAool6OhMdqxKRyZXJ4D3DW/8vQzlBkw1JsLnw9rY5dHGLmO5MeN760JUVVXuaAQYm1PZL18CJ8AXszL8//sqlSubxNg0w4jcYcFsoVjjpB7tks+vuC5eiYx0gSB/en/Yy7lnzk38BSSQLeuR1TXIr0QwWECWV2EIDCOf8mEwqxgiLgw/UTGEhjG4rsW46BoMz/VgdBkxlhsxVhgxVhkxVZkwVhiRDFMoQKc0R7X29mE3ncerXELYrBkuzGoQVBUViVDQidNxGqszML6xqiRR79Qi+noqVsDAr2HvXtToddqPpljRLoI5gxDSi0ixnVKlQGJmnvuAm+jwaHZngwX7Cjum2rGVXOr1TciRHh1zIOhCvEAgmF3kalxlrjFTs6UmY2VOtkUbgFwmY643x8ebfDb4Lrx2Ae8b3py9KybiSE9c8A289N9w9IdaqeOKFVqHduH6FAiKQrGF78mOVdleN51hIvW97EvtRPojhE6FMFYZsS21EfVEM7533o1GR9GFdKNNoqZGWy92yS1UCg29JBC5w4LZQLHGyYIc6SIjXSAoGMelDqpuqEKNqKhhFSWsjLtVQgpqSI1X9qrhhKbMKihBULAAFgjoD4yKOSEgNWrprRHgeMZzMlYaMc0zYZ5nxjTPhKXBgnm+GUuTZnqyLrBqxqaJCO4pzVElq4WayiMEh+uIBeZhJ4pVCRL1xAiFXJhNPmoWdSOVu8Y3VgUayrSLOz/ihDatkaoa3ACUmJCuKCJjvQQQQnoRKbZTqlTQ3VXu1910fr0T8zxt8Evt85d6fRNxpCcKUsKRLhDMXrI1rqrZkj23PNeizb7YTtUNVfh+58trg8/f4ef8s+dRVTVn74oJOdJPfgCMZqRLtVoTGZ9PK408e1br0C7EdIFg0kyF8D2ZsapQUt9LCSrYFtmINWibe9GhaNb31oX0fIQoSGiSZZRoatKE9LNnYeXKol2SQCC4CCjGOFmIIz0x6jMkMtIFgryY//n5zP/8/IKeo6qa6B4LxFCCCkpg9MsXJXbmPMqQj5hkRbFWEuseJHYhQCxqJmYsIzaiEPVEiXliRN1a76rocJToUDRurIxeiBK9ECVwLJDxHCSThHWRFdslNuytdpxXOilbVYZtuQ3ZmEUgTm2OWl6OoylGU/Tn/C72OcqJUqdGifoMOJu91BgP4rhmkSY8HzmS/FygwTkCwPmRsngjVTWsxM+xJEjIdCcYFMatGUQI6UVkKkqESwVJlihfX075unK8h72YMSOR/foKdaSrqgqjZn292ahAIJi9ZIo4yEfkymfRVv3x6pwbfNGRKOHeMMjg/JAzZ++Kgh3pioL6y9eBNk1Ij1aAwaC9QFsbtLfD3r2auC7cAgLBpJkK4XsyY1Ux3svSZCHUFcr53rqjM29HenSsSVZzM7z9thZpKhAIBIUykXFSVdT48cEzBiSszJuXe1xNFNKFI10gmDokSYpnpo/jQ6muosa8X1eJKESHokQGI4T7wkT6IoR7w4R7woS6QvGv4JkgalglcDxA4HiAoZ+NOSllq4xzrZOqP6iicmMlzqucyc71hOaoqtNF0G0jVns1hr43OeXu5fss5NP2k9y26jBW5TxSbUKT9dTGqkB9mSak94yUxRupqooEqKWRkZ6S6Y7DIYxbM4gQ0ovIVJUIlwqFXl+hjnR90gSjGenH+uGMT5SrCASzmEwRB/mQa9GWzwZf4LjmgLAvt+fVu6KnR3ssX6GKzk7Uzm6gTYt28dsT3wCatNJAOjtF92SBoEhMhfA9mbGqGO+V+L2qqATOBMZdW6HRLvEmWSbNkQ7aWksgEAgmQiHjpK/DF9/wjAVjXPm2ARU79aEaIPuGZ2LUpxDSBYISJEe8iGySMdeZMdeZcbRl/ryrMZVQd4jAiQCB9wOMvDvCyNsj+N7xERuJ4f4vN+7/cnN622mMFUZqbqqh8f9pxHWVS3vPFSvw/dcZBiKX4h90EIvKGKLLsUfqsKBQZj2HzXIB2lKarI8+N95YVZLGol28ZfFGqupL2rxS7j0HkeDMRamkyXQHhHFrBhFCepGZzhLhmaCQ6yvYkZ4gpEeRGPqv38GOH4hyFYHgIibXoi3XBp+xzAh1YChLH6mV2NshEBgrPV6wIM8T9HpRQ6Pli8jJQjrESwPxitpkgaCYTKfwPZ2kik+J/Rz6+7U5VsHRLiaJ5nrtPuFIFwgEU42vw0fXk11EBiLxSL3+V2MsxUvFa0F8G5uyron1sUtkpAsEJUgR40Ukg4S1RctJr/z9MdFIVVT8x/0M/99hLuy7wIXXLhAdjtLz/R56vt+D8yonjf9PI2UrPsG5H75BxK1iqR3BUikT8ylYQmX8MV0s+f2r4NE/HC9+y7J2vmfPaiJ0UxP1Ni3fs8dtgxrNva58WVsYSk9+CwyDMxelkibTPY4wbs0IQkifAqazRHgmyPf6CnakHzka/3cMmQuWem0QE+UqAoEgC9k2+JwfctL7fG9evSs6O7X7nM4CmiQ7nSgGrcF0DIn+VCF9tDQQp3MSVygQCC4G0olPif0cIqeaAEfB0S6SUaK5WbtPONIFAsFUoioqAy8NEBmIYG8bqwYcDBiJYMASTI7US0dizyyfD2IxLTVPIBDMMNMULyLJEo4VDhwrHMz//HyUqILndQ/nnj1H/4v9eN/0cuxzxzDVmHBc0oqr7hTS4ABciGI0GjllrKA8FKHJUo3a0pJ+rGlt1c53dFOgwa3t2g2GXYT/6l7MgBpRABmpugLqamYuSiU1Dz4VYdyadoSQPkXMVaeUTq7ri0aJOwjyEtIVBeWnrwBXa89HYihoF+UqAsEEOL39NKHzISSjhGySkYwSkmnsSzbJY/82j/7bPPrvxFuLHP+SLBKydfR76+iXTc7eBGYaybTBB+B9w5tX74oP/lO7f8GC8Zv9GWlpQW3QGvtEkRj021FUCVlSQVXjpYG0tBTzcgUCwRwjk/iU2M9h3vEBJOx5ZQxDsiNdRLsIBILpINgZxH/Uj6XZEh/HwmGIRAEknEuSI/XSkZiRDjAyUlgTeIFAMAXMYLyIbJSp+EgFFR+pIPwPYXq+38PZb54l0hdheAi47jJc16pIkQhYzLiPl+EhStmF7GMNra3a+XZ2UuX2Yvp3lUhEoqd8Oc3//DVQ1wMglZeBITJz2lSaTPckhHFr2hFCegEETgYI94bTilHx7xNEKMkojcvkvVgYHh77d0VFHk/o7EQ9dhK4GlVSUVWJocDogCfKVQSCguj/ST/+dv/0vJkBDDaDJqrbZQx2g3brMIx9lY1+uQwYnAaMLiPGciPGioSvKiOmahOyeeKTkUwbfPn2djhzRjs+71gXAFlGveIqeNFDDImYKuMeMVCpDGoiek1CYxuBQCDIQDrxSUfv51D2cz/zCFJbm59RQxej9GajoA1LqlrAZqFAIBAUQMwbIxaMYXFY4vf5fNqtyQjWcgOBPi1SLxPxRslGCUbNWUJIFwhmmBKJFzHXmml5oAXXehftt7YT7goz/JqbwAkLNX9cg2IzEgiCjAG7IftYA2hrtIULkYG6Om2e1PN2D00dx4FRId0wFkE8I9pUmkz3OMK4NSMIIb0AunZ10f10d0HPkUyaqzPR3al3Ro67OhPdnVZZE6Xs2vcGuyZEyQ55TJByardGpxGDy4Cx3Ihsk0tKtNfz0V0uMObzW+b1ogYjAEiSCipcCCQsFEW5ikCQN01fbCLSF0GNqtpXREWJKKgRNf4V/z48+u/w6P0hBSWkJP87NPrv4Oj34YTJRAxiIzFiIzkmKXliKDNgrDZinmfGXK81qTHXm7E0WbSog2YL1hYrxvL8/3xlin4pW1WGa60LNao19vvgjBWQChPSAbW2HvCArIICA+19VNYOaxOaxMY2AoFAkIF04lMcVcUQ8aIETNiJUVutlRrnIt5s1CgxXyucwe/Xovfyjd0TCC52gl1BPAc8SIZRk5R+a5TAQPzf+le8EjCxGtCYUgU4h81WBqcBg9WQFKmnC+kOB8T8Y5F6mdA3AS0OCdxi+ScQlAQlFi9iabJQcV0F4b4w7l+4CXWG6Pv/+jBtaQAkKswxLM7sY00qDQ2aJn2+M4waCMfvl2Ul+cDp1qbSZLrHY3WEcWtGEEJ6ARirjViXWNOLUaNfqagRlVikOAJTNiSjhKHcgKnSpDk7q0wYq42YakyY55kxzdNuzQ2aIGWeZ0YyTN0EbnBApY4glzhiBM7kkRHvdKKaNOFcMmhi1FCikC7KVQSCvGn8H41T+vqqMiqsB0bF9YBCLBBD8SvE/DEUn3YbG4kR88XiQnvMEyPmjRH1RIm6o0SHE74uREEZE+VDH4SynoOpxoRtmQ37Mju2ZTbKVpXhXOPEPM+c9vjU6JdwbxjPQY+Wnz7a0K/iHRurcdFqMec3buk/j9Gx32g1gB8GPvMFlm4wzExXd4FAMCtJJz4B0D8AR48S7Xbjjfw+fgzM+9FTYP7DnJt08Yx0k4TVCrW10N+vrbmEkC4Q5IfnNx7a/7S96K+bLlYvyXyVGqeXYLpKqgC0p1T/6aYrl1YBaHAZtNedJuHe2mLFvsKeFKnnG9Eec9iTI/Uyoc+rrGUyuBENRwWCUqDE4kWsLVYcrQ6UoEL93fX0PtdLpDdC6L+HgQqarSHsrdnHmlTqRxuz94yUoZrHtChJTtH5ZkKbSsl0p7tbOwdh3JoRhJBeAIu+vIhFX16U8XFVTXB/hjXXpu7eVMIpzk7d3Zn65R8VpAKj4pRPE6IUvzImSHlHxShvVCtVUbTFUnQwSnQwmt/FGMDSYMG60IrtEhvWJVZsS2w42hzYW+2TilfwdfjwfneAO/BT549xZocB+wo7NTfVZO7Q3tKCunAJAPLoQOWLmAnHDJjlqChXEQhKCEmWMNgMGGzF6/ykKipRd5TIQET76osQ7g0T7tG+QmdDBM8GCZ0NER0aO87zevLqyjzfjPMqJ5UfraRqUxW2pbb44lGPfvF1+Oj/cX9SQ79gZ5Dm4318gR7q9ts4M2DOPW7p5x7VhXRZE9LLl8DCov1oBAKeeeYZvvGNb9DT08MVV1zBU089xdVXX53x+BdffJHt27dz5swZli5dyte//nU+/vGPxx9XVZVHHnmEf/zHf2R4eJgPf/jDfOc732Hp0qUAnDlzhkcffZTXXnuNnp4eGhsb+fM//3P+9m//FrM5/WaVYHKkE5/oH4CDB1F9frzhBXyAgz6sVB97HZ48mrPRVWJGOmgGpv5+zdB0+eXTclkCwazHVG2i/CPlqDEVYoxV+8USbiPq2P3Rse+ViKLNEdJ4quKmLJ8y/sEiI5kljJVGzXBVqUXpmWpNmtGqdrQCcP5o9d98y6Tmd5IsjYvU83kM2InRoiZH6mVCr6axObVjhJAuEJQAJRYvkjjWRHoiVPx+BUMvDyG1e7gCCSocOceaVBoatNvzoWqUJcvH3isx2mUmo1QSMt3xejUhXxi3ZoQpE9IvxkWfJGlle5jAYJ+e1uKqqhIbiRF1R4m5Y0QuRDSRaShCdDBKuD+sCVJ9YW2H7nyI8PkwxCDUFSLUFcL9K3fydZgk7K12yq4ow7XeReX1ldiW2fJyMvg6fHQ92UX0aAQ3FmwVFow1MbyHvQTPBmm6tym9KCXLKNf/AewaRCaChIKKzIXeMHUXjopyFYFgjiPJEqZKE6ZKEyzNfmx0JErgRIDA8QD+43787X68h7wEjgcId4cZ7B5k8KeDAFgXWqn6WBX1n6vHdZUrbUO/SH8E/xE/0YiKBFiMCsZqY+5xaxR9wWe0amPkwEBRfiQCAQA/+tGP2Lp1K7t372bt2rXs2rWLTZs2cezYMebNmzfu+Ndff51bb72Vxx57jE984hPs2bOHLVu2cOjQIS677DIAHn/8cZ588kl+8IMfsGjRIrZv386mTZtob2/HarVy9OhRFEXhu9/9Lpdccgnvvfced911Fz6fjyeeeGK6fwQXBePEp/lmDO1HibmjhEwLidgM/JoaKm0BjJetyKvRVaqQ3tysrX9Fw1GBIH8qr6+k8vrKSb2GqowK6+EssXrhUcNVOCFKL6QmGa6SzFb+lErAhArAqEczW8U8moKvhlUivREivZG8ztdUa8J2iS3+ZW+1ay7yxda81oOpkXpKZ5hyZPxNTubfm79BwV4mhHRB4Uy3DgUwNDTEX//1X/Nv//ZvyLLMpz71Kb797W9TVlY27v1OnDjBlVdeicFgYDixsVypU4LxIqljjWWhhdCZEEvwceCqJTnHmlR0Ib2nV0L9s4/D3w8ACpLHPePXGmc0010ws0yJkC4WfdOHJEkYnUaMTiM05fccJaoQ6Y0QPBskeDpI4GSA4Mkg/vf9+N71EfPE8P3Oh+93Pnr/dy8A5gYzFb9fQe0f11L9ieq0jvVEgWqkyo4fCasdraywzYC/3c/A3gHsy+1pdwbV5oXAIJJZpkL1cyFcxlBvhLqrV8HatRCNwpkzYtdNILjIMZYZca5y4lyVXE4X9UYZeWcEz+sehv5jCPev3ATPBDn3nXOc+845nFc7qf3TWnxHfPGGfqqq4jvqI+aLMRAzYULFHIyCCvY2e85xC8bEKrNNCOmC4vOtb32Lu+66izvuuAOA3bt388orr/Dcc8/x0EMPjTv+29/+Nps3b+b+++8H4NFHH2Xfvn08/fTT7N69G1VV2bVrF9u2bePGG28E4Pnnn6euro69e/dyyy23sHnzZjZv3hx/zcWLF3Ps2DG+853vzOk51UyTtCB8s4/wqQiyoxpnwwgDTgud7Q5WOPrzbnQVj3YxjgnpoK0BBQLB9CHJYzEu04mqjBquRiP04oarwQiR/lGjVV9Eq/zr1gxWil8h0q897jmQrGAbXAbKriyj/MPlVG2uwnWNC9mU/poSI/V++KUY3z9i4HMbrTha84/M0x3pIiNdkC8zoUMBfOYzn+H8+fPs27ePSCTCHXfcwd13382ePXuS3i8SiXDrrbfye7/3e7z++utT/wMpNiUYL5I41oTPh/nVxiO4/GFWdZ4H0kTQZEGPdjl/HtSFlwADSAZgcLAkrlVQOkyJkD5bFn2hUIhQaCyH13uR/JWWjTKW+VrpXvk1yS3QVVUl+EEQ3zs+vIe8uH/pxv26m/D5MH0/7KPvh30Yq43UfaaO+jvqk4SsYGdQ2wlsthA4rE18bKPRUpIkYWmy4O/wE+wMYltoI5W4c6rSRSUWLnTChd//FIz8HJ5/XmtuYbVqJUU33SQGL4FAkITRaaRiQwUVGypoeaCFmC/G8C+G6f3/eul/sR/vG168b3iRLBJVH6+i7PIyrZJnIELUagQkFMCA5g4zSsac4xaMiVVmuzbu9fdP0wUL5jzhcJi33nqLhx9+OH6fLMts3LiRAwcOpH3OgQMH2Lp1a9J9mzZtYu/evQCcPn2anp4eNm7cGH+8vLyctWvXcuDAAW655Za0r+t2u6nKEqxd6JxKCSn0/nNvUiM+yTTaqM+U8H1CfnBSA/eEPOG51LgvviD8+QVi//AGhmXNWKsiHGy/FIBau3/0wNyNrvR5lS50NY0aLoQjXSC4OJBkCaPLqPVdyCOBQFW1mL3g6aBW+XcyQOD9AL7f+Rh5d4SYJ4b7v9y4/8tN5993YnAZqPxoJfNumUfNH9cgG5NFdT1S74QCvcC8uvzOWx+77C7hSBcUxkzoUB0dHbz66qv89re/5aqrrgLgqaee4uMf/zhPPPEEjY1jvau2bdvGihUr+OhHPzo7hXQoyXgRfayxLbTxr8va+Iu336buzfMMvlJD9Q3Veb9OPNrlfIIZwWqEnTtL5loFpUHRhfTZtOh77LHH+MpXvpLvpV0USNLYIFRzYw0AsUAMzwEPQ68O0fvPvYTPh+l+spvuJ7up+ngVS59ainWhFf9RP6GeEIZyAyOj6zq7fey1DQ4D4e6wluuehsSmWFWVJk51wtB/vAGOtzUblV5Oo9cl58gGFQgEFzcGh4HqG6qpvqGa8LfCnP/eebqf7iZ8LszgS4METgRwXuVEjaqER93mdouKbNTEOv01so1bMLbgs9iFI11QXAYGBojFYtTVJasPdXV1HD16NO1zenp60h7f09MTf1y/L9MxqZw4cYKnnnoqqxu90DlV1Bvl2P84lvfxWZEY35QvtUGfXcZgG23O5xi7jTfmKzNgdBrHGvSVa7fGSmNGx+VUIckSthUVUOcH0yBILvp92oRqnsOnHZRHoys9diox2gWEI10gEKRHkiRMFSZMV5pwXpk8tigRBX+HH+9bXi785wWG/mOI6GCUgZcGGHhpAMsCC01faKLhzobkhslAr1bgTF0eQrqqqDAaR+wo18ZeIaQL8mGmdKgDBw5QUVERF9EBNm7ciCzLHDx4kJtuugmA1157jRdffJG3336bn/zkJzmvp6RNnyUcL/Lf3grsNHEzXXQ91VWQkB5vNtqTYkYo0WsVzBxFF9Jn06Lv4YcfTho4u7u7aWtry3j8xYrBZqDy9yup/P1KFv39Ii78/AI9/9TDwN4Bhn42xBv736Di9yqQ7TL+Y35CZ0PYey2UY6eqyhR/nZgvhmyVMTjT58cnDlZ181RAorvXCJ9IaGbhcmnNLfLIBhUIBAId8zwzCx5eQNPWJt792LsM/2IY/7t+QqdDGGuNhCRt/HEZophqLBjLtT+PucYtGBu7rGXaWCSEdMFcoru7m82bN3PzzTdz1113ZTyu0DmVZJCouqEqqTFfvPleRIn/e1wD99E8YRL6PqESzxCeCgxlBoyVRoxVRkw1WnM+vVGfpdGCudGMpcGCeb4ZU7WpOO74lKZe/X4t57PW7su70VW6ZqMgHOkCgaBwZJNM2eVllF1eRsMdDagxFe8hLwM/HeD8s+cJfRDi5NaTnPnyGRZsX0Dz3zTHx8K+Pu010iRrjEMftwBcsheoYuiMGxSnWPMJsjJTOlRPT8+42Bij0UhVVVX8mMHBQW6//Xb++Z//GZcrv7gRYfosHEXRpkevUs/NdOH+lRslouRtiIhnpPeAEk42IwgEiUxZs9GZJN9Fn8ViwWKxxL/3iO3unMhGmeqPV1P98Wp8R30cvf0o3oOaM8FYacS6xErUHcXsCdJKlCqDCzChqiqhrpDWqKbFmva1E51TS+s9QDnvS8tAOp98YJ7ZoAKBQJCKwWJg6TNLOfW3p7iw70K8MRdlChUYkO1G7CvsIJHXuAVjY5etTDjSBcWlpqYGg8FAr27nG6W3t5d63TaTQn19fdbj9dve3l4a9BXD6PerVq1Ket65c+e4/vrrWb9+Pc8++2zWcy10TmWqNHH5y5dnPSYTqqqixtSxxnwhTURXQ2r830pwtCFfns35Yt4YUW+UmGe0UZ97dGyA+DGhs6EcZ6aJ7taFVqyLrNiW2LC32XFc6sBxqSO+QZcXCU291CPtjPRuZBEjNMeGUI+0I9XmbnSVKSP97FlNi58jaTgCgWAGkAwSrg+5cH3IxYK/XUDvP/fS9a0u/Ef9nLr/FCNvjbD8e8sx2A1xR/q8WpXAmSAxbwyD04C1xTqu/4w+pwJoevdl4DbO7HsfvvZzEe0pmLXcdddd/Nmf/Rkf+chH8n6OMH0WTn8/hEJwBgfGKiPRoSjet7zj4owzoe+XRCIwPJg8hxIIEim6kD6bFn2CyWFfZqf6j6pRVRX/ET/RC1H8R/yYF1uJxFQcRLD2+ogOlxHqDmGuMVOzpSZnwz7JKLG0fgQo57inIe2x+WSDCgQCQTocrQ4W/91i+i7vo/cHvQTPBDH7IkgYiM53YKw0EnVHCXXlHrdgTKzSm2IJIV1QLMxmM2vWrGH//v1s2bIFAEVR2L9/P/fcc0/a56xbt479+/dz3333xe/bt28f69atA2DRokXU19ezf//++BzK4/Fw8OBBPv/5z8ef093dzfXXX8+aNWv4/ve/j1xCTkBJ0nLRMWrxS1OFElW0PgoXIkQvRIkORQn3h+ON+MJ9YcLnw4TPhQmdCxHpixAbieF7z4fvPd+417O0WChfX47rwy7KN5RTtrIMyZBlgdbaiu+G/4eBJw9xSSf8BWdoPRuhc94nqLl9NY4cglKqI33+fO3+YBCGhqA6/2pngUAgyIjBZqDxrkYa7mzg3HfPceLeE/S90If/fT+t/3oZg4NWWvAhvzDAmS4/sWAMg9WAfYWdmptqcLQ64q+lvjcW+bWoKQhvwclAo4j2FORkpnSo+vp6+vSyi1Gi0ShDQ0Px57/22mv8n//zf+JpCaqqoigKRqORZ599ls997nPjzk2YPguns1O7bZgvUXF1BQMvDTD8i2FcV7sIdmbfxAOwWKCqSpsj9fckz6EEgkSKLqSLRd/FQ7AzSOBYgPIPl1NxXQW9P+gl0hsh1BVmGDtVUoTYaQ9BZxjXR+qouak2aaKUSuKCb1mrtjA+PlCZ/uA8skEFM8Pf/d3f8corr/D2229jNpsZHh6e6VMSCMbhaHWwcMdC5t06jyM3HcHf4ccElDlUAscDyFYZ52onNVtqso5bkNAUSwjps4rZMlZt3bqVz372s1x11VVcffXV7Nq1C5/PF2+kddtttzF//nwee+wxAL7whS9w7bXX8s1vfpMbbriBF154gTfffDNuLpAkifvuu4+vfvWrLF26lEWLFrF9+3YaGxvj87bu7m6uu+46FixYwBNPPEF/QgfdTIvRuYhslJGrZUzVptwHozVQDX4Q1Br1nR5t0ndEE9XD3WFCnSH6Ovvoe0FbcBsrjdRsqaH2T2up/GjluNJjX4ePrlfMRFxruOAyctZv5uqVzXidCwm+YqJpiS+veZX+uhaLFq3Q16fpUUJIFwgExUSSJeZ/fj6ONgfvfeo9Rt4a4dDVb/ERlrKGCyjHIpgXWLA4LMR8MbyHvQTPBmm6t0kbyxQF9d9eAa4GYFGTVgV0yl2D2tqG1CGiPQWZmSkdat26dQwPD/PWW2+xZs0aQBPOFUVh7dq1gJbFHouN9Vv66U9/yte//nVef/115uu73IJJowvpLS1QcZ0mpA++PAgK+I9m38TTaWjQhPTBHhUjQkgXpGdKol3Eou/iIOaNEQvGsDgsSAaJeX82j57/1UPMHWUeIU4ZjVyhfEBjpIMqxYnETUBmB0Fis9FlG7ScsVPD1URjEkZDQhhqntmggpkhHA5z8803s27dOr73ve/N9OkIBBmRZAnHcgeX/fQyfrn8LRrVIFJTNQu2NWR1K6Sii1WOcu3YCxe0kkBTftqbYIaYLWPVpz/9afr7+9mxYwc9PT2sWrWKV199NZ7X2dnZmWQcWL9+PXv27GHbtm186UtfYunSpezdu5fLLrssfswDDzyAz+fj7rvvZnh4mA0bNvDqq69itWoRRvv27ePEiROcOHGCJj1YexRVVRGkR7bI2JfZsS+zj3ssMhxh5NAI7l+7cf/ajeeAh+iFKD3f76Hn+z0YK43U/mktCx5egHWBFVVRGXhpgMhABPuldob+S0IB7C0u7AtV/O1+BvYOYF9uz1zpFx1fltzcrAnpXW/1ssrQpxkSWlqEKCUQCIpGxbUVrPntGt678T187/rYynE6zOU4LnPEc9ONLiOGNkPyWNbZiXrsJHA1SCoLK4aRUPFFzPQHypgnoj0FOZgJHaq1tZXNmzdz1113sXv3biKRCPfccw+33HILjY2N8WMSefPNN5FlOWluJpg8upDe3AwV11cA4D3oxVRnwrrAmnkTL4H6ejhyBIb6VeYhol0E6ZkSIV0s+i4ODE4DBquBmC+G0WXE6DIy72Mmul+M0qiGsEphTLUm7AtlpHcOQ3f2crxE51RTi4zVohAMGfngzX6WrDBpcS4+nyai1+TOBhXkxuv1JpWJpZaQTQS9Kco//dM/Tep1BILpwrLYzj9Iy3hY7UD9126i/7OWspVleT8/7kh3SdhsEAjAmTOwdOkUnfBFyMU+Vt1zzz0Z3VS/+MUvxt138803c/PNN2d8PUmS2LlzJzt37kz7+O23387tt98+kVMVZMBUYYo3bgdQYyruX7npe7GP/n/tJ9Ib4fx3z9Pz/R4a/6qRur+ow3/Uj6XZgiRJ+EaTYuwO7f/P0mTB3+En2BnEttCW9j0Te8/oNJV7eQsnZ5/9d/iPn2nVfStWiOxhgUBQVGyLbFz5yyv47+aDlI9EqTBESJWjxo1lXi9qIAKAbFCwGGM0uTyc9ZRz6kIl8+rdItpTkJWZ0KEAfvjDH3LPPffw0Y9+FFmW+dSnPsWTTz45fRcuAMaaqbe0gL3VjmyXUfwKxnJNq4IMm3gJhgQ9wUcX0vNtVCq4uJiy34p77rmHDz74gFAoxMGDB+NlLaAt+lIXrjfffDPHjh0jFArx3nvv8fGPfzzpcX3R19PTQzAY5D//8z9ZtmxZ/PHbb79daz6V5kswNVhbrNhX2AmdDWk/Z1XFPPA+H9hVFKAqrBKLGLE2maCtTcs72LtXa6echsQFnyzDJUu1X8/j8zbA4CAcP67drl4t8vGKRFtbG+Xl5fEvfXdeILiYOH8efq7U8R9SPSjQ/mftRD3RvJ+vuz4NZpkVK7T72tun4kwvXsRYJZhrSAaJimsrWPb0MtZ3r+eK/7yCiusrUMMq3U928/ZH3sZ7yItsl1EVbYMOwDFqdjc4DChBhZg3lvE9UjPS6eigue9NAM6qTVo8Qk2Nlj385JOa01MgEAiKQUcHxv/3CbzlWpTVoqAH5b9eh/7k/LuksczpRDVrG4OSrI1fS6qGADg5VCmiPQV5Md06FEBVVRV79uzB6/Xidrt57rnnKCvLbMq5/fbbSzZScDaTGO0S6gphqtHKg0MfJDeLT93ES0QX0i8MjDcjCGY/f/d3f8f69eux2+1UVFRM+HXE9opgwkiyRM1NNZhqTPjb/US7LqD2DdKDkzNoK73AoB1JAiQJEsvx0pDYbBRA//v0/uo/hZ07Yft27fbBB4WIXiTa29txu93xr4cffnimT2laeeaZZ1i4cCFWq5W1a9fyxhtvZD3+xRdfZMWKFVitVlauXMnPfvazpMdVVWXHjh00NDRgs9nYuHEj77//fvzxM2fOcOedd7Jo0SJsNhtLlizhkUceIRwOT8n1CfLjgw+0270tl2BdbCXcHWbgp/kHnSduAra1afcJPaq4XOxjlWBuIxkkKj9ayRX7r+Dyn1+O8yonSkDBf8TPwE8GuDCkogJGA9hHhfSYL4ZslTE4MzdbTYzMQ1HgpZdoohuArlAtGAzgcuVldhAIBIK86ejQNucOH+Zsg4kAMhYVRjoUOHgwSUxPGstaWlAWXgKMCemLKy4AcOpCpVaV3Noqoj1LkGKJUwLBZEgU0mPeGMYqzYUe/CA47thMhgQ9Fdo9kGezUUXRSpHffVe7FfOokkaP9kzstTkRhJAumBSOVgdN9zbhvNJJtC9EYLiMSNDMf1MDkop/wMFI32julMMBwWDGcrxU55Qei3D8fVnLwVu5UrsVcS5Fw+l04nK54l+ZohIeeughJEnK+nX06NFpPvvJ8aMf/YitW7fyyCOPcOjQIa644go2bdo0ruu6zuuvv86tt97KnXfeyeHDh9myZQtbtmzhvffeix/z+OOP8+STT7J7924OHjyIw+Fg06ZNBIPaH++jR4+iKArf/e53OXLkCP/wD//A7t27+dKXvjQt1yxIz5kz2m39IiN1f6aVfg6+Mpj38xM3AfU9PuFILy4X81gluHiQJImqP6hi9RurWfbsMpAh0BFgeG8/BlRqa0GStU3bUFcIe6sda4s14+slGRQ6O+HoUZqbtcfOelyJb5zT7CAQCAR5Mbppx8AAtLVx1lzJb6kCwD00H8UbhKNHYbRyPGksk2XU6z4KgKRGwO1mcbk2Hzt1WhLRniVMscQpgWAyJArpBqcBS5O2Xgh1hlBjyUkVmQwJuiN9eGh8n5lxdHTA174GO3bAo49qt1/7mnBUFQk92lP/CoVCuZ+Ug6985St88YtfZOXKlZN6nSnJSBdcXDhaHdiX2wm+Hsb7d8f4f7s+Qx9W/vqSt/G8X0XvO3WU/cGpnOV4Sc4pEhzp76c9XDCN/M3f/E3OvNzFixdPz8kUiW9961vcdddd8eYzu3fv5pVXXuG5557joYceGnf8t7/9bTZv3sz9998PwKOPPsq+fft4+umn2b17N6qqsmvXLrZt28aNN94IwPPPP09dXR179+7llltuYfPmzWzevDn+mosXL+bYsWN85zvf4YknnpiGqxakQ3ekL1gAVR+v4oOvfsCF/7iAElWQjbkXa4ljl+5Ib29HW0x2dmqbh6Kh37QwF8cqwcWHJEk03tWIElY48YUTGLoD/CG99FbWEnVrwpO5xkzNlpqsDZETe8/g9UIwSHOttrHblSikg2Z2ENnDAoFgsoxu2tHcDJJEr7+MX7CQ9YZ+jFEDw55FVPadJdZ1gZDHMm4sU5sXAUNIFgMMDrI42A78AaekxfCpT0E0qjkgxJxqwlzsfWcEc5NQCHp7tX83N4O1yoprrYsLP7+AGlYJnQthbdbMB/omnnO1c5whQXeke4dyONL1ypuBAe0N9X5+hw9rYe0iinjStOkL61EeeeQRvvzlL8/MyaQghHRBUZBkCdv6JRxbspxebNTYfTRfeY4j71fR+24dDWu6UY76MaxahbWpeVyzGUhZ8DEmpB8/Pk0XIchIbW0ttbW1M30aRSMcDvPWW28lxUPIsszGjRs5cOBA2uccOHCArVu3Jt23adMm9u7dC8Dp06fp6elh48aN8cfLy8tZu3YtBw4c4JZbbkn7um63m6qqqqznGwqFknZgvULoKCqJQrrrahfGaiPRwSieAx4qfq8i5/MTq2n0+dLRdgXl77+GfPyoVokjGvpNC3NtrBJc3DT9zyYMdgPt/+M4dUqIqs5eIgOVOFc7qdlSg6PVkfX5Sc1GnU6wWmky9gCakK6qmhkdENnDAoGgOIxu2uHQxqc+n4MzlDG8PEhNu42RvkrMkhtjXwjn+ppxY5luTpCrXLBzJ4t/HYWDcGqoEp5/XsypikApi1MCwUTp6tJubTaortZMCbWfqqXn+z0ETwUJHA1gabAQ88WyGhJ0R7p3OFmbSiKl8iY+mdLj8trbtbi85cvFht8kaG9vZ/78+fHvJ7vhV0zE/6qgeMgyxxdrbtul1i6q553GYI4S9lp4/0fzOXPyw5w5to7Ox7vwdfjGPT1pwcdYtMsHH2hzJsHsoLOzk7fffpvOzk5isRhvv/02b7/9NiMjIzN9anEGBgaIxWLxDu46dXV19PT0pH1OT09P1uP120Je88SJEzz11FP85V/+Zdbzfeyxx5IaLaZOgAWTQ492WbhQyyqu2qxtbOQb75K4CbhkCZiMKr6AzNnXz2plyKKhX0kyG8YqgaDhjgZ2LViFHxmTL4JrnYuWB1tyiuiQUC1jlDT35ooVzB8+AkAwamIwMBq4rqoie1ggEBSH0U07fNpar8+njVXWyy/gqBtBVWRMjggLH6xNO5aNRVJp0Z5LFmnrw25PGcGKejGnKgKi74xgLpIY66Lr2o5WB/NunQdA8HSQwPEA0cEoztVO5t87P+1cSnekhwOj2lS6aJeUypskRFxe0SjlaE8hpAuKyvuBJgCWLQgR6AxjsWhlYyG1BvsfrMC4pAbvYS9dT44X01Objc6bp23qqSqcOjWNFyGYFDt27ODKK6/kkUceYWRkhCuvvJIrr7ySN998c6ZPraTo7u5m8+bN3Hzzzdx1111Zj3344YeTJrztIoC7qCQ60gGqb6gGYOhnQ3k9P74JaJQwGRSWVmlNtDoq12uDmGjoV5KIsUowGwiH4dWz5fwLWrj5+WfPk7asLw1JvWdkGW66CfO8CuoswwCcHXKA2605p0T2cEkiGvgJZh2jm3acPQuqSu9IGQDzynw0r9NEJX+4Edv6JWmjqZKMVYpC9f/9V5xGPyoyZ5QWMacqAqUsTgkEEyVRSE9k3i2akB4djtL8UDMLdy7MakgoL9f2Ag1kiXZJqbwZR47egAUhmpnm5G/+5m/o6OjI+lXsaE8R7SIoKnoMy9ItlzLgvRPTOS+8GSPgdqBWV2M0yRjaDPjb/QzsHcC+3D6WiZeSkS5Jmiv9rbe01xUm3NnBP/3TP5V8Pl5NTQ0Gg4FePUhtlN7eXur1begU6uvrsx6v3/b29tKg14SNfr9q1aqk5507d47rr7+e9evX8+yzz+Y839TswsRcQ8HkUNXxQnrVH1aBDL53fQQ7g1mb+UHK2NXZSZu1j3ZqaR+Yx+alJ8cOTHUoLFw4BVckyJfZMFYJBO+/r0UC/7ysiTvpwveOj8F/G8RxuYOYN4bBacDaYk0rSKU2cae1Fe69l+Z/89PbXcHZoyNcuXgQVq/WRHQRkVBy6A381q1bx/e+972ZPh2BIDejm3acPYt6pJ0+n1b5Uqecp2zkHNCG3+3KuGmXNG51diIdO8riygu802/n1IVKVtRoZgUxp5p6RN8ZwWwik5DuaHNgqjERGYigBlRsC21ZX0eStHgX4+ksQnpi5Y3LNf7xYsXldXRoETJHRVRoNmYi2lPYTgRFRW8MurwqjL/XgmNDHcYKI2pYJXA0AGh5VZYmC/4OP8HOscyWcQs+RMNRwdRgNptZs2YN+/fvj9+nKAr79+9n3bp1aZ+zbt26pOMB9u3bFz9+0aJF1NfXJx3j8Xg4ePBg0mt2d3dz3XXXsWbNGr7//e8jC/ffjNLXp81LJEmrzgMwVZtwXaNNiob+PbcrPWns8nppK9OU+Y7+mvEHF9OhIBAI5jzvvafdLlxpovF/NgLw/j3vc3r7ac48eoYzO87Q+bXOtJF58azhxHzP1laaP6Rt9nZd++ewcyc8+KBYkBUBvYGf/pXY22SifOUrX+GLX/wiK1euLMIZCgTTxOimnaftGkIxEwC1gU5sGxYBEHWrhAfCaZ+aFEk16vpcXOUG4NSFyuSDxZxqSqmtrWXFihVZv8xm80yfpkAAaEUwMLae05FkiYrrKgAY/sVwXq9VXw/GbI70lMqbJIoVl6c3Mz18uHSjQmehW75Y0Z7CkS4oKrrgvaguRiwYw1JmwXG5A/cv3Yz8bgTHSq38xeAwEO4OE/PG4s9NbTYKouGoYOrYunUrn/3sZ7nqqqu4+uqr2bVrFz6fjzvuuAOA2267jfnz5/PYY48B8IUvfIFrr72Wb37zm9xwww288MILvPnmm3FHuSRJ3HfffXz1q19l6dKlLFq0iO3bt9PY2MiWLVuAMRF9wYIFPPHEE/T398fPJ5MTXjC16G70xkZIXAtU31CN53UPg68M0viXjVlfIymWyumktaoPgPaBNDvjoqGfQCAoAF1Iv+wybVw6+8RZQmdDxPwxHG0OYr4Y3sNegmeDNN3bFC9VVlV1XGSeTlOz9v1ZZT4snLZLmfOIBn4CQQKtrfR9Zjk8CmX2GPa/3wYtLVieO0ioM0TgWABzzXgRNsmcMOr6XFzWC7RxcihFSBdzqpKhs7OToaGhJHEK4JJLLqGsrGxmT05wUZDJkQ7g/JCT/n/tx9c+3nSQjoaGhGiXdBnpCZU3tLdr1TEOhzYmdXUVFpenKNrJe73aWKZfQKk3M52lbvkdO3bwgx/8IP79lVdeCcD//b//l+uuuy7v1xFCuqBoXLigfdYBFrYZ6LMaiPlicSE9eDJIbCSGoUy7X7bKGJyG+PNTm43CWMNRIaQLis2nP/1p+vv72bFjBz09PaxatYpXX3013iy0s7MzyS2+fv169uzZw7Zt2/jSl77E0qVL2bt3L5dddln8mAceeACfz8fdd9/N8PAwGzZs4NVXX8Vq1aJB9u3bx4kTJzhx4gRNTU1J56Om7mYLph5F4YPfDgDzWFAfBMUcn4xUfbyK0397mgv7LxALxjBYDRlfJinapaWFtivN8Cto769FVRN60OgOhdWrRUO//z975x3fVN398U+SNkmTTjoppbRA6WBvW1FAiqA4cIDgYMgjLn6i+Cjow3ADTlRU1EfFhaiI40FFAVERyi6zZY+WQgtt6UyTjtzfH6c3q5lN0nner9d9pb355ubem3vPPed8z2AYxikMjvQUAaVbSqGMV0J7QouKPRXw7+0Pn0AfqyXzhDrjM8UymkqM1jp3rqmOon2QlZWFTp06Gf63VXeYYdoLFwtJp4rsKDOUXlElqqDL0UFzVIOgK4MafMYssKo+6rProRMARuJUiYkjnXWqFoWnnFMM01jsOdIVMfQ81uU5lymWmAichpWsPlPqM28MzuS8PHImu1Iuz5YzesgQ55uZNkdZKzFavrCQ9lGcRMjMpMmFRx5psc50T5X2ZEc64zHEaPToaKBDshIVSSqUZ5ZDlaKCvJMc1XnV0GRp4D/YH7pzOgQMCDCrPWwtcopLuzDeZNasWZg1a5bV9/78888G6yZMmIAJEybY3J5EIsFzzz2H5557zur706ZNc1hrkGki6hWXM991BTAJXYoygSWbDbPo/n39IY+Wo/p8NfJX5qPD2A4O6xBLfaWAVIoe914F6XI9SrR+KLigR1SEvnERCgzDtHsMjvQILTTrNQgeEYz8U/nQndUZejhYlszzi/MzyCWgoSNdnMcVjU7GM4gN/Bwxb948LF261O6Y7OxsJCUleWrXGKZZyM+n14gI4zpVogqXN1yG5qjG6mfMghPqoz67bdsAZAKnCgOpaQTrVC0O7jvDNCeC4MCR3okc6dV51ktKWTJwIJCLhkGeDUhOJq+7ZUS5MzLJnjN63z6grMzYwMsStZoc981R1kqvb/nR8k0AO9IZjyE6uxMSqBZV2C1h0OZqocnSQNFJgeq8aujydJCqpZCHyRE2PszMKWXZbFTcFgBcuGCUTQzDMG5horicrbsaABAXbj6LrkGswZGe+1ouyraVQZWkQtgtYQ26vFtm0yj7JaFr52qcyJEj67QfokoyXY9QYBim3aPRACfr+xV3j65DmbYOqi4q+PfzR8XeCpT+XQrl3RSQYFkyT9SpgIZpyaJ/dv9+sofasJ3TIuEGfkx7QZwIFO05APBLpEZ/thzpBp1KlFvJyej6sC/wM3CqJBTC0WOQ+LFOxTCMkcuXyQcNGIMFTJF3ojJSujwdBEGAxDLK24KBA4F19RHpegdjIZW6HhXuyBm9axdQUABUVABBDTN3mrWsVU5Oy46WbyLYkc54DLH8ihhFrk5WI+aRGBR+X2iY/asprEHEpAiEjW/ojLLWbDQ4GAgPBy5dAk6cAOqzxBiGYRqHheJy9gDVMe8SUWWYRa/84Decq7rOIItqi2ohC5VZrUMMWM+mSe4rx4kcIPuqmbjmlvOuRSgwDMOA7BBBID0oPE6GyvqSeYFXBqJibwW0J7XQV+shlUsblMyzF5Heuzfg5weUlpLuxoHPTUt4eDjCw6300GCYNkZmJr0OGGBcp0pUAQCqjlZZ/Yw1e7DLqO6QSARo6hQoeOhZRMX7sU7FMIwBsdFoeDjpN5aIEen6Kj1qS2rhG+Jrd3tdugBqpQBogeIyB470xuDIGd2jB0WSHj0KDB5sPqa5y1rVN4GGWm39/eaMlm9C+OnDeAzTiHQRdbIasfNiEf1/1KxPqpAidm5sAyc6YL3ZKMANRxmG8SAWikv2JXJmdA25DEgkEDrFoPDPWtTklCLo6iBACtRergXqAFWKCjWFNSj8oRCC3uikspZNI/acyyoIJa9VXBwbfAzDuIRpo1FlrBKqJBV0uTr4hPhAqiZ5UnOpBoIgQHdOB1WyylAyz8yRLjM30nx8KNoKAHbs8P5xMI0nJycH+/btM2vgt2/fPlRUVDT3rjGMQ/bupVfTQCiDI/1kFfS1+gafseZIl8uBzvVNkk8pklmnYhjGDHtlXQBA5ieDTwjFEDtTJ10iATpFkiwqKPKCI92RM9rfH4iMpNesLIp8qK2l16ys5i1rVd8E2pACYEk7aQLNTyCmcej1wJkzwMGD9KrXGxzdpo50gMq8BKVSSoo9wdUgla8edqQzDOMxTBSXggo1Tl7uAAkEDOmUBwDQ1oZCU+wPRageMqUMvmEUsVBbXNugDrGINaNPdKRnZzfRcTEM03ao17EObb4EAOjVUzCUzPMN84UmS2MwCLU5VELPsmSe6QSftRTmoUPptVGOdCs6IOMdFi5ciP79+2PRokWoqKhA//790b9/f+zevbu5d41h7FJYaIwS7dfPuF4Ro4DUTwqhRoD2tLbB50TZZRlYJVY7OnXKyR1gOcUw7YYjR+g1Pt72GEOd9PPO1UnvGE6yKP+SF1ymzjijIyKAmTNpJrKoiJxhRUUUid6czTzrm0AjN5ei400Ro+WTk9t8E2gu7cK4jpXuwkJiEo4fnQtAZnB8m6KIUUDiI4FQLUB3XgdljLLBGGvOKMDomOeGowzDuI2J4rI1rxcAoFfERQQpaZKvrrQadVBAEUy19HyCfFBzsQa1pbUAGtYhBqxn04i6TVaW14+IYZi2hImOdWjDYwDC0Sv3VyA7HurkZEPJvMqDZHxVn6tGyN0hDUrm2QpOEGm0I92KDoikJEOjZsazcAM/prUilnXp3p3K/opIpBKoElWo2FcBzVENVAkqs89ZK5cHAN26AX/+6aQjneUUw7Qrtm6lV1G3sYa8kxyVhyqdikgHgIhQkkXnC7wQkS46ozMzzWukA+alW665hpbGNDP1FvVNoJGbS4ZuTIyxUWo7agLdto+O8Txik77MTLpJEhOBsDBc2n4SZRUySCQCrPVHkvpIoYilWUDtqYbRB4D18ggAR6QzDONBTGbRt+Z0BgBc2bk+H1AQICs6B1kHNep8/AEAskCqN1xXRo5zyzrEgHWHlVhzuKAAKC726hExDNNWsNCxDlWSQtWrdCutz842lMyLvCcSACCPlFstmWcrOEFENDYPHACqrJcqdrh/og6IzEzD/jEMwwDW66OLGBqOHmnYcNSygbuI0xHpLKcYpl0hCMC2bfT3lVfaHidGpDvrSA/vQLLo/EUJamrc2sWGiM7osDDHpVvEZqYtqVRocjJFxbe0aPkmhCPSGeex0134WFhPAECX4FIo5YGwNkfj19UP2lNaSuO7uuHmnY5I1+tb1qwcwzCtB5NZ9G1/hAEA0jqdJcXl3DkoY8Og6hGP8nPVkAX6wCeIHpO1pbWGOsQBAwIMdYgB65OAAQFUhj03l2w2e4odwzCMpY5VovPDuXIqi9dziBo4dQz44QcgMRESqRTBVwcDAKpOVRnKuZjiyJHeuTMQFQXk51MdY4cyyo4OKDZqFvePdTKGYazVRxex13DUluwSHeknT9r5UpZTDNPuOHECuHQJUCisT9yJGEq75DlX2iVAKaASgK5OgsOHzUtUeQTRGS1mz+TlUfbMgAHkRG/pzujkZJKl7dQvx450xnnsdBc+XtwBAJAgOw1sqwTS0hrcRMp4cjxVnbLfpd2yJl737vRaXAwUZRxD6OY1nKrHMEzjSU5G1czZ2PNhNwDAlTV/AkVlwIABkIwfjzDEQvvWOWiyNAZDrra41modYsC20ZeSYsx6Y0c6wzA20espnCojg2piAjh8kRohdw4sRZBfNaXOZmeTLhYXB3VPikDXndWhtqIWPv7mKr1hgs9GaReJhKLSf/yRyrs4lFF2dEBIJA32j2GY9o29iHTRka452jAi3ZbscioineUUw7Q7xLIugwaRM90W8mgq2+lsRLooi+ogwZ49XnCkA63fGS1Gy7dD2JHOOI+t7sKXLiFzLwmaJG0msPR7IDW1gXNb2ZUc6dYaywC263mqVMbIzqMv/4g0n0xaIdZiysykN9tJGgnDMO6zuyIJNXVAVHgt4pfcDwQaFRc1YKhDXPJXCQCgtqQWAQMCGtQhFvQCUN+/ylJ2JScDv/3GWcQMw9hBrOWbkQHs2wcEB1Oj0bp/AQB6RlykcWo1RSuVlwMAfDv4Qh4lR3V+NTRZGgQOCTTbrK3gBFNER/r27RZvWMv8s6UDiljsH8MwbYBGZgGXlRlLctqLSLfqSHcQkX7+PJWj8lOwnGIYxrmyLoDrpV1ER3otJNi7F5gxo9G7aJ927IxuzbAjnXEe0+7CYteYS5eAHTuw8eIcAMCIkAMUTWXFue0XT/XwHEWkW0tD7tNbQG6uBBmnIpA23sVUPS4FwzCMBWL0wpVX+0DSp3eD99XJaqgSVQi8KhCXf78MvU6Pzk92hlRmLjtEJQuwHpEOcMNRhmFsINbyLSwk3SkkBPDxAS5cwKFCkjW9wusd6ZWVpIMFBBg+ruqpQnV+NSoPVzZwpNuqM2yK1Yajtpr0DRnSUAc0xcr+MQzTirEmC3r0AK64AoiMtGtT7d9PrzExQHh4w0379SCbsOZiDWpKauAb7Gt4z9YkYGgoiZ6yMuDMppNIPvA1yymGYYw2nacd6TViRLoUe/Y0evfaF+3I79Y2j4pxHb0eOHMGOHiQXvX6hmNMmvRBEGg5cgR5pf7Iru4GCfQYGXeatKaUFDIMf/jBsC1HEem2mo0CwKgBlwEAG0qHOk7VMyU7G1iyBFi4EHj+eXpdsoRDRBmmneOM0iWRShA0NAiQAEK1gNqi2gZjRCULaGj0iY50FjcMwzTAspav6HHSausbjcYBAHpFFJC+de4cBSbExho2IZZ3qTxc2WDz9nQqkUErAeyWAAEAAElEQVSDSIXKyQHyd+VSePqLL1JxY8smfWvWAB06GHVAsy+zvn9M++Odd95BXFwclEolhg4dip07d9od/+233yIpKQlKpRK9e/fGL7/8Yva+IAhYuHAhOnbsCD8/P6Snp+O4oWkS8eKLLyItLQ0qlQrBwcGePqT2ibWGnRIJsHo18NBDwBNP2LWpxLIu1qLRAcAnwMdQZsGyTrot2SWRmNRJf3e99WaiTSmnnLGdm5KWtj8M0wRcvmwMWEpNtT9WdKTXXKwxBBvYQxxTCwn276deoIwd2pnfjSPSGdvRR5Z1x02a9CEri2aZLl7EpurxAIBBqmyE9I4xOrot6tCJNdKrz1ejrqoOMj+Z2W7Yi0gf3b8QQAf8ndcVuloZFD515gOspeqZRnpxKRiGYepxtrs7AEjlUiqfcKEaulwd5BFys/dNFTFL2dWzp9FJde4ciUSGYRgA1mv5JiUBpaUQLhXikK4HAKCXcIh0rrAwaj5lEtkjOtI1h+2UR7BRIx2oT+rrpsXhE0rseGINbi76mMI9u3WjiNPAQPPMv5gYCgsV/xb1qnPnrO4f0774+uuvMWfOHKxYsQJDhw7FsmXLMGbMGBw9ehQR9bX/Tdm2bRsmT56MxYsX44YbbsCqVaswfvx47N27F7169QIAvPzyy3jrrbfw6aefIj4+HgsWLMCYMWOQlZUFpbLerqiuxoQJE5CamoqPPvqoSY+5TWKtYeelS8Dhw/SeIAA6HckCGzaV2GjUXuM/VaIK1eeroTmqQeBQY/S4rVKfANA1XsC+fRKcPK8CbraRodwUcspZ27mpaGn7wzBNREYGvfboYT37xRTfcF9IfCUQagRU51dD2Vlpd7w4qeerlECrpdusd8MkZgZol3431nbbO9YiDsRZ/bfeajiDJHYX7t+flKqSEmwsGwwASE86Zy7B1Gp6mIv1PEN9IQsg57n2bMOodHv1PHv2lyPKrwRVtXJsy+1s8UHBWDCvtJSUPEslMDAQkMmMipZFtDzDMO2Ho0epebGfn+1oKVMUnSmCQZtjW24BDY2+4GBj6YT16xu9uwzDtEWs1fINDweGDsXBwCtRWBcCJbRIqdlP3igrRojdiHQ7wQkGsrMx1IfylXdc7kFOqdBQID+f6r1cukTjxMy/oiLg9ttJcBYVURHkoiKb+8e0L15//XXcd999mD59OlJSUrBixQqoVCp8/PHHVse/+eabGDt2LJ544gkkJyfj+eefx4ABA7B8+XIAFI2+bNkyzJ8/HzfffDP69OmDzz77DOfPn8cPP/xg2M6zzz6Lxx57DL2d9HDodDqUlZUZlnKul22O5SRffQYyNBqSUaGhpEQJgk2byl6jURFbddLtya7esaUAgO3avrYzlL0tp1y1nb1NS9sfhmlCxAzjtDTHYyVSCeQdnW84KsqiTl1I1nB5Fxu0U78bO9LbM4296JOTgXnzgLlzIfTth426qwEAo3tdMB9nUYdOIpEYotKtlXexF4Eg6RKL9MRcAMDGU12Nb1y6BGzZAmzaRGls779PKSR//OF813aGYdoVotI1eDDg62t/LAAoY0lu6XIbKl2GGulSUtAsue46erXIVmcYpr1j2nfGlPBw/E89CQCQHnEAfk8/Bsyda9X5o0ohR5QuV4faMvOcY4fNRut1wKGqQwCAHUXdgLo6wN+fnGUaDelRYnkEMTgiMpJ0wOeeAxYsoFcb+8e0H6qrq7Fnzx6kp6cb1kmlUqSnpyNDDBm0ICMjw2w8AIwZM8Yw/vTp08jPzzcbExQUhKFDh9rcpjMsXrwYQUFBhiVFrMPGEJaTfKWlZBMGBZENJZdTjQOdzqpNpdVS8DpgP1jBL5HqpLviSB/VrwgAsCm3B/SClUlCb8upluYwamn7wzBNjLMZxiJieZfqvGqHY0VZ1KUbO9LtYi3DUqQN+93Ykd6eceeil0qBtDRkd78RF6qC4edTjdTO54zv26hDZ3Ckn7IS2WmvnqdUivSJoQCADYejSanLzycn+rFjpNxdeSUZf5mZwAcfABcv2u/abhItbwbXmGOYtkn9vb11XTEA4Mo0wcEHCGci0m1FfV5/Pb1u3AhUO9bZGIZpjTRGb7DsO2PC/45RWZcb04oozMpGGQLfEF9DdFVllrlD3qBT2SrtUq8DDk0hPWjXpTjUyeRATQ3pgIGB5IQppQhQs+AIqRSIi6Mc57g4LufCoLCwEHV1dYiMjDRbHxkZifz8fKufyc/PtztefHVlm87w1FNPobS01LBktaWO4J6wYSwn+XQ6cpyLkQfV1dQUWUG6kaVNdegQzcmFhdkvaSdGpNuqkW5tEnBomgwqmQ6XNP44dLFhuSCvyim9nrx2GRnWG5k2h8OonTqwGAYgdUVslu6qI92ViPT47nRviSWrGAusZViaYs/v1orhGuntGWcuesu646ZIpdigugkAcFVoFpSVRQ7r0Pl1peiDqtNVDTbnyCGVPiUaeBrYXdINl/M0CMneRrU8e/Qgh71YViYlBdi1CygoACoqyMluia2u7VxjjmHaJib39taNSwAAV+Z9A2T3cXhvK2LrlS5rEekOoj4HDAAiImheb+tWYORIdw6CYZgWR2P1Bsu+M/W1fAsuSrAzrxMAYNzs7g6dP+qealRfqIbmsAZBVxj1HUOWn63SLvU6YM+Ecqh9q1FercQRv/7oWbqd9Cm5nMbodMbgiAEDuJko0+pRKBRQiE5gAGVlZc24Nx7EUzaMOMmXmUk2lUJBjvOaGpILZWVAx45G+8rCphKdTf37N/TtmmIo7XJcA6FOgERGg+31d5B3j8Xwbqfw67Hu2HQqHn0iC4xv6vVUuy8hwVjm01OTfOK5zcgA9u2j2n1nztB5sixras929jTu2vIM04rZv58q+4aEUEUjZ5B3cqG0S/2kXtcEkiP79tEkoUxm50OtEb2eJtvKy0mOx8a6JjtNJ1+tTTLa8ru1cjiEpD1jK61YxImLfuPBKABAev8ip+rQ2Y1Id+BI79SJNicIEvzR51GKLkhPB666ylyJkUjIuQ6QQuVs13auMccwbROTe7tQ3QXHKqIBAKml6526t8VmNLoc26VdbEV9SqXA2LH096+/CJztwjBtCXf1BtO+M/U61C+HYiFAigEpVeg0IsHhLqh6kjPKsk66wxrp9TqgT1U5BkWfBwDsUF8DqFRUNq+8nARYdbXNZqcMIxIWFgaZTIaCggKz9QUFBYiKirL6maioKLvjxVdXttlu8aQNI07yhYXRvQ8AHTpQhsrFiyQjkpKM9dNNbSq9Hpl/08TEgG6ldvUcZRclJAoJBJ1glvFndxJQKsWom8ku3XgggjJmamuB06eBtWuBEyfI9nvmGSr16QnbzfTcRkSQ187XF7hwwbyXBND0DiMP2PIM01oxrY/urGqiiHY+Il2URTFdJFCrjRXv2hTZ2SQrFy4Enn+eXl2VnXYyLG363doArA23Z9y86GtqgD//pL/TXxjpVB06MSLdWo10exEIIqNH0+vGAxHUKTA62nq4g78/1cfz9yclUFS0SkutG4RcY45h2iYW9/a2EqqFmhJ+ER36xTp1b4sR6dpcO70d7DT0M9RJ/6zQPUWFYZiWg6f0BrHvTL0O9b/AOwEAN07wc2o3bDUctVsuDzDTAYd2otJ8O0qTqENyVBQ1EwTIkc7NRBkHyOVyDBw4EJs2bTKs0+v12LRpE1JTU61+JjU11Ww8AGzYsMEwPj4+HlFRUWZjysrKsGPHDpvbbJd4w4YxneQrLqaodImEtpuSQs5kS5vq6FFgyRLsXU+O5f6Hv7Cr50hkEqgSaCKweH0xqs5UQdALDmVX+l1U6uev4l6ovlgC7N5NkeIAkJpKDXA8FQhleW5jYih4S6ul7zDtJdEcDqN26sBiGMDoSHe2rAvQuBrpMqXE0O9h+3aXdrFl46kJWMvJV0d+tzYCl3Zpz9hIK7ZXmsWUnTupckpoKNC3vxSQxjn8SjEivepUFQRBgMTECS46pGw2xgIFoL/1FrBhZyBwtYMUkogIYMoU2tEjRyi1Takkg3D8eHOD0JUac3GOj5NhmBaCxb29NaczAODKzrlO39tijfTq89XQ1+jNZJTDqE8A13Y5CqkkAYcvhiNH3h2xXWpIRmVmkvxlBxXDtD48qTfU1/LVaoHf/6FVN97o3G7YdKQ7Ck4w0QGvPL8VwDD8eqI79FcpIO3QgUo3TJwI9O3repov0y6ZM2cOpk6dikGDBmHIkCFYtmwZKisrMX36dADAlClT0KlTJyxevBgAMHv2bAwfPhyvvfYaxo0bh9WrV2P37t344IMPAAASiQSPPvooXnjhBSQkJCA+Ph4LFixAdHQ0xo8fb/jenJwcFBcXIycnB3V1ddi3bx8AoHv37vD392/Sc9AseMuGSU4mx4qY8l9QQBHYR49SBrKpTQUAb72F2ovFOFDyBID6iHQ7ek5ldiWEeufv+ffPoyyjDKokFWqLqXGyLdnVuzeZqIWFcuy8+QUM2/oyyadBg4xySpxEyMqiSYTExMbJMGvnNinJ2IBVoaAo/XPnqORNUzuM3LTlGaa1IgjmEenO0pjSLhIfCdLTgX/+Ab77Dpgxw+XdbXlYThKK8q2xslOcfBXLi9nzu7UR2JHe3nHjot+4kV5HjXL++ayMI0d6XVkdai/XwreDr+E9h9FTAEaMoGCIk2d9cTpiKOLP/ml+8wPmtTyvuYYWR3WfuMYcw7RNLO7t3052BwBc2bm+8ZIT97Y8Qg6JXAKhWkD1+WoouygN7zl0Vun16LD5O1zR4VZsK0rCr/n9cX/MHs8ZeQzDNA9e0Bv+/JP8Hx07whD95AhVCkV0VudVo6akBr7BpFc56t8AwKADXvvNTwjcpUFuWTC2ZIdi+IjwNmv4uF0LlLHJHXfcgUuXLmHhwoXIz89Hv379sH79ekOz0JycHEhNznVaWhpWrVqF+fPn4+mnn0ZCQgJ++OEH9OrVyzDmySefRGVlJWbOnImSkhIMGzYM69evh1JpfA4vXLgQn376qeH//vU3z+bNmzFixAgvH3ULwJs2jNiwEyAPtjWbCqDI88JCHIkYAW2dLwLkOnSLrQFgXc+pzK7EubfOQair16FkEviE+aA8sxxVJ6iPli17UCol2/Prr4GN29QYdvkyObct72NPBEJZO7fh4ZS5c+QIOdFLSug1La155GY7dGAxTE4OcP48tW8YPNj5zxmajZ7XNQjqtMQ0WOqOO6hi1IYN5HsOC3Nn71sA3piAtZx8beM6FjvSmUZf9KIjXSy34gwylQzyKDmq86tRdarK3JHuRGRnQABwxRU0A7kx8FbcF3bIuRl4RwKgnTZJ8DRnzpzB888/jz/++AP5+fmIjo7G3Xffjf/85z+Qy+XNvXtMW8MZh4jJvb1P0wP7C6Igl9XixsRj9L4T97ZEKoEiRgHtKS20OVpzR7qjCcB6ReW6xFPYti0Jv55IwP2D9pjv37ZttLhS5I9xC5ZVjNt4QW9Yt45eb7jBeVHgG+wLeSc5qvOqocnWICiVGgA6U3YKAJCcDOWCREzIqsRH3wBfdngEw+eq26Ys4obyXmfWrFmYNWuW1ff+FOtBmjBhwgRMmDDB5vYkEgmee+45PPfcczbHrFy5EitXrnR1V9sOTWnDmDrWRc6cMThkfjtEwQqDos9DKhEANHTICHoBhd8XoqawBn7d/VB1pAp1FXXwCfSBLEWG0r9LAcDQfNQaoiN909++eKa7FwOhbJ3b8HCyNXNzqUb63LnNq8O1MwcWw2zYQK8DBlDbBmcRHen6Sj3qyurgE2TbHWoakJCUBPTrRw1H164FZs5s5I63FLw1AWvtGdFGYenKEOJF37s3vTp48JaXG2tEpae79lXKrvUNRy3qpDvjSAdM6qQf7tigSReKikjK3XYb1WZytqEf15jzCEeOHIFer8f777+Pw4cP44033sCKFSvw9NNPN/euMW0NZ5ujmNzbK/f1BQDcnHgUHfyqXLq3lbH1DUdzzVMBHUZ91isq1yefAQBsPNUVuloZGV7//APs2kVa2dKlXDO9CWFZxUCvd6/5rzt6g5XvFgTgf/+jt50t6yJirbyLo0bIZkiluOsBcrJ9uz4Aupo2aB5wQ3mmrdLcNoyJQ+arQ70BABNSDhvfV6vp/XqHjDZHC80RDRSdFZD5ywAAdRV1AGjiRCInmVVbWmvzK0Xbc/tBFSpkQd5rtmnv3AJ0TGlpLSMQwkVbnmFaMx9/TK+33eba52QqGXyCyXnuqLyLISChXo+aPJnWr17t2ne2SLhRsduwhGUaxZ9/kp+6WzfXJ53EOunaU0ZHuiAITjUbBYzK06ZNgD7RvEkXpkyhNz/7zLWGfi21SYK7hn4TM3bsWHzyySe49tpr0bVrV9x0003497//jbVr1zb3rjFtCVccIvX3dnVIJL7M7AkAmNZ7j8v3tlgnXZtjPgHoMOqzXlHpF3ASkeoKVNbI8c/BIKozeuEC5SQGB1NPB3boNBksq9o5zk7E2aOxeoON7z744ynk5JDdMmqUa4ejSqZwrJK/SowN+5wMThAZPpwCR0tKgF9+ce37WzzcUJ5pyzS3DVOv5xw/54c9F6Ihk+hxe0qW8X0Lh0xdeR3qtHWQqWWQBZg70gFAApJZogyzRnw8LbW1EvwtT/feJEJzn1umUbzzzjuIi4uDUqnE0KFDsXPnTrvjv/32WyQlJUGpVKJ37974xeIhKAgCFi5ciI4dO8LPzw/p6ek4fvy42Zji4mLcddddCAwMRHBwMGbMmIGKigrD+3/++SduvvlmdOzYEWq1Gv369cOXX37puYNuR2RnU39hmczo+nEFZ+ukW2YdT5xI6//8k0y4Vk1zT8A2E2fOnMGMGTMQHx8PPz8/dOvWDYsWLUJ1tePms5Z4Teo3hwB78cUXkZaWBpVKheDgYE8fEmPCZ5/R69ixrn/Wr6sfAKDqdJVhnVgjD3BQzxPAkCGkixUVkS/KMAPv40MdIPbtcy3aSHRW19bStGa/fuYR7gMGNE8zQE8Y+g4oLy9HWVmZYdHpHDfecJXS0lJ06NDB49tl2imNcYgkJ+Pn3vNQWB2Ijn6XcW3tLy7f24rY+pp6NiLSbU4A1isq0nM5uK47PbN+yewIaDQkn3Q6cqLHxLBDxw7tXVaxTuVBPBmZLNamtcyMsyVb7Hz3/146CICCBVxJU67MroTuLN0PlzdcxpmFZ5CzJMdgIDrrSJdKjdFWX3xh8WYrm9RvgCu1QBmmNeKqLPIk9XrOVzu7AQBGdzuJcLWG3rPikJEFyCBTylBXWWcWkS42HhUDFEQnuy3EwKqNknTvOrqb6tw6krOtXQ43EV9//TXmzJmDRYsWYe/evejbty/GjBmDixcvWh2/bds2TJ48GTNmzEBmZibGjx+P8ePH49ChQ4YxL7/8Mt566y2sWLECO3bsgFqtxpgxY6DVGoNr7rrrLhw+fBgbNmzAunXr8Pfff2OmSf2Pbdu2oU+fPvjuu+9w4MABTJ8+HVOmTME6saYb4zQffUSvN9wAREW5/nlDnXRHjnSLgIS4OCA1lcTaN9+4/r0tinY6SejJjGSv1EgXBdiKFSswdOhQLFu2DGPGjMHRo0cRERHRYLwowBYvXowbbrgBq1atwvjx47F3715DwxlRgH366aeGru1jxoxBVlaWoeFMdXU1JkyYgNTUVHwk3mGMx8nJIT8WANx/v+uftxqRXmt0pDsy+nx96b7/7DPgvfdIoDW687C1epk9etD0ZmRk89WYE43twkIy/MT675mZNHPoIcUtJSXF7P9FixbhmWeecXu7IidOnMDbb7+NV1991WPbZNo5jWyO8smGGADAPVOk8Hn4Py7f28rO9aVdciwc6Y5qpIuKSm4ursvdjJXoj3UFg/BqBwUkhYXkMUtKMh6Lu42x2ijtWVaxTuVBGqsr2MPZ2rQOvvt/f9Az/Ybr9XA2zkVs2FdTWkNfodEbGvZpz5CO5Sg4wZS77wZeeYVqtV++DISEwP264o56WTRF809uKM+4Q2tpUNtcdbKlUgjjb8FXrwQDACZ33UkOGRt9q5SxSqiSVCjPLIdfIgVXQQ/oq/SQ+kmh15GDWBGtsPu16enAhx8CmzJDgdVebrbp7XPrSM5yfwenef3113Hfffdh+vTpAIAVK1bg559/xscff4x58+Y1GP/mm29i7NixeOKJJwAAzz//PDZs2IDly5djxYoVEAQBy5Ytw/z583HzzTcDAD777DNERkbihx9+wKRJk5CdnY3169dj165dGDRoEADg7bffxvXXX49XX30V0dHRDRx1s2fPxu+//461a9fihhtu8OYpaVNUVxsDOu+9t3HbEB3p1Xn2o5CtZfZNmkTR8KtXA7NnN+77WwztsFHx2LFjMdYkErhr1644evQo3nvvPZdtQK840ptDgAHAs88+CwBON5zR6XRmUW3lrEA7xXvvAXV1wMiRVIbNVQyOdJMa6abpe85ET82aRUJ09Wrg5ZeBKG0jnGu2nNX795MgeeSR5nFkecPQt0FWVhY6depk+F+hsK60zps3D0uXLrW7rezsbCQlJRn+z8vLw9ixYzFhwgTcd999bu0nwxhohEMkP99YqmD6o0FAkuuCS4xI1+Y2ordDvaIy5qtfoNqtxbHqeGwqHYT0rqfIEAoPt7v/TPuWVaxTeZBGTsQ5xJnmSna+e29+NHYU94BUoseNA/IAdHb4laYN+/wH+KN0cynqyqlcgipFhcpD9XUv7Qd1mtGnD+l1Bw9Sgt+/rnRzUr+lOIe4oTzTWJrbgemqE7+ZGr3tr07GkRJA6VOD8SF/AcdKbDpkJFIJwm4JgzZXi6qjVZAqpdBr9ajOr4a+Sg+JlOSjVGHfxhk5kl4PHAAuhiYjYp6XJxG8dW4dBU+NGwf8/LPXg6vcoaU0cK+ursaePXvw1FNPGdZJpVKkp6cjIyPD6mcyMjIwZ84cs3VjxozBDz/8AAA4ffo08vPzkW7SFC4oKAhDhw5FRkYGJk2ahIyMDAQHBxuc6ACQnp4OqVSKHTt24JZbbrH63aWlpUi289u1Cr2qiVm3jtpMRUUB11/fuG3Io10s7WKSdTxhAvDoo9Qr8PRpKjHVqmnhjYrFjGQRhUJh0wZsLI3NSPa4I725BFhjWLx4scFQZJyjqopm/wHg//6vcdtQxtU70s9ooTmpgV+8n8uO9MGDKRI9IwP44ANg4S0uOtc86az2dKSKtwx9KwQEBCDQmlFpweOPP45p06bZHdO1a1fD3+fPn8fIkSORlpaGDz74wK19ZNyktURSOYtabWwkHBoKBAWZ3yemDpH6Y/9yuQJ1dR1xxRUCkpKcK3NgiVgjvUFEupO9HZCcjKBnEjEjKx9vfxeN12oeQfqwNQ3vcXboWKW9yirWqTxMc0Um6/X0XM/PJ5klCGb3/stbrwQATOq8FdGqYDjjSDdr2KeWURC7nkok+AT5GEom6LWupf/fdRe1nvniCwH/uuiGntSSnENiLdDMTPNjAYylJwYMaHO1QBk3sXUN790LHD5MBXP79vWeXmUra/aKK5o3a9aUej3rq7fUAMIx7iYZApc87VDnVCerEfNIDAq/L8TlDZcBLVB9oRoh14SgYm8FaotrHepV4eFUjXPfPuD334G7726eSQQDjdG3Hdmjhw8Db79N2+vZ06vBVe5gWi6he/fuOHToEO677z5UVlY2aaZfYWEh6urqEBkZabY+MjISR44csfqZ/Px8q+Pz8/MN74vr7I2xzBD08fFBhw4dDGMs+eabb7Br1y68//77No+nVehVTYzYZHTqVKrq2xhcLe1imtnXsSMwYgSweTOVd5k7t3H70KJopglYZ2jJGcked6Q3lwBrDE899ZSZsZmXl9fgx2ozeMiZ9tVXVBauSxfgxhtd343K7Epc+u4SIKVZvlNPnkLAwAAEXRVkGCOROefoeuQRcqS/9x4wb1IA5K5EG3nKWe2NSJUWmIIcHh6OcNOoWTvk5eVh5MiRGDhwID755BNIW7PTtrXT3JFUniY7m8IkT58moyMkhCwpMarb1CFSWQksWQIh+wg++ekFAMC0yPVAdlyjjl0ZSxOAtZdrUVtRCx9/enyK0QpOlU+QSvHokii8s1aP9Xm9ceji3+gVecn4Pjt03KatySrWqTxMc0Qmi3J4927g6FFyEkdHG+TWyeIQfJtF5+nJ3uuBAOeyIsSGfQq1AhKJBLIAGepK61BXRo50gy7lYhndO+8kR/pff0mQE3oJsV0aoSe1NOeQSYktZGXRvotOUSulJxjG5jWs0wHFxcDJkxQK3a8f6RSe1qusOfFzcigV97PPgK5dqb9Kc+p09bJNn30UX333GgBgsuJ7oCrFqZRldbIaqkQVin4pQtnWMoSOC0XMIzG48BF18XMmsOqmm8iR/v77VJqq2WjspIcjezQwkCZuRo70enCVO3iyXEJ7YPPmzZg+fTo+/PBD9OzZ0+a4VqFXNSF5ecCvv9LfjS3rApiUdjlvu7SLIAg2y3dOmkSO9NWr24gjvQXTkjOSvVLapbVgmRpgmjbgkNYU5ekhZ5ogkN0DAA8/7PosoKGWZ2ENZIEy1JXUATKgPLMcldmUgizxkUBiqSjY4LbbyBY9fx5YszMWd7oSbeQJZ7W36pi34hTkvLw8jBgxAl26dMGrr76KS5eMTsKoxnQDYRpPE9XZbzJMj6dPH+DQIbo/c3KomG+vXpQyExZGBtzy5UBhIfb4puJwWSyUshrcIawG3lI16th9An1IbpXVQZerg08yCUCxKZazDf26dpfiltFl+O73QLy+Phkfjz/BDp1mgGWV53FLp3IGT+hdTR2ZbCq3unQhWZWXR4pLaSkwdChe3TEOekGK66L2ou+V/k5/t2nDPp9AH/gE+qCutA615bVQQGGoMyz1c+0cde5M0VZ//gmsOj4I81KsT9jY1ZNaonOoHdYCZdzA2jV86RKwYwc1C+/QgbLj5HLP61XWnPiXLtEElF5Pskqno6y85tLpTGTbNunVyK0KQ4Bci+u1a4G3Njq9PxKpBMo4Jcq2lgF19L/D3jMmPPAAsHgx8M8/wK5dlLHc5Lgz6eHIHvXxAWpqAJmNGl2NDK5qyeUS3CEsLAwymQwFBQVm6wsKCmzqdlFRUXbHi68FBQXo2LGj2Zh+/foZxlg2M62trUVxcXGD7/3rr79w44034o033sCUKVPsHo/X9apWxqefkgi86iqap2oszkSkm/Xvs8iOufVW8oXt20e3P6sP3qMlZyR73FL3tgBzdpteJTsbWLIEWLgQeP55el2yhNa3NMSHe2YmOWgSE+k1M5PWu7DP//xDAsPPD5gxw7XdMK3lqUpRwbeDL63XCVClqFBTRI2ynHVGAdR09MEH6e+335E633lYr6f1VVVkzApCw407clZbKrmBgaTkiNFUhYUUTdWYjuqioZ+b23DfREM/Odm6sd3MHd03bNiAEydOYNOmTYiJiUHHjh0NS0vknXfeQVxcHJRKJYYOHYqdO3faHf/tt98iKSkJSqUSvXv3xi9i4e16BEHAwoUL0bFjR/j5+SE9PR3Hjx83G/Piiy8iLS0NKpUKwcHBnj4kwpvXZ3NgeTzx8RTl07kzCaSLF+ma79+fGigcOGAY+8HR4QCAW5KPILhfnFvHLkal63KNipdTNdIt+PezpBB8mXs1LuTWAseOUarPgAGtb4KjldJaZFW70KmcwVN6l9QFXcGUxjxbLeVWUJBRHkskQGkp8jMv4JN9/QAA84ZsdmkSTWzYp8vVQRAEyALI0VJXVgdBEFBXVgcA8A3zdWp7ptxzD72+deJ6VJbUWB9kT0/yhHNIq/V85l1yMoXbP/ccsGABvc6dyzKXaYjlNSwI5FjXaCgDTiwfJ5d7Xq+ydOJbfndoKEXFC4Lj73Ykuzwg2746NRQAcGvyEfj17u7yuZBHUb3i6nyKDnW6ZB6ozIJYjeyNN5z6Os9iTd8uLrY96WFpe5sGT1mjtpYM3ro66+83MrgqJSUFQUFBhmXx4sUufd4RYrmE+++/36PbdYRcLsfAgQOxadMmwzq9Xo9NmzYhNTXV6mdSU1PNxgOkI4rj4+PjERUVZTamrKwMO3bsMIxJTU1FSUkJ9uzZYxjzxx9/QK/XY+jQoYZ1f/75J8aNG4elS5di5syZ7h9wO0KvN5Z1cScaHQDkneplTkE19LXW5ZSZI93CxgsLA8QEjCVL3NsXxjOEh4cjKSnJ7iL2axCDqdzNSPa4I725BFiT4UHHtBnecIB60pmm1+PtJfSQv/vmcnQIdm3/TGt5SiQS+IRQNGdtSS0kEgnkEXRhO6M0mTJzJumw27cDO8vro4369yenlDXnlGiMv/8+neeNG4EtWyjSQ8SRsxpwrTSMqzTW0G8BEzzTpk2jVCgrS0vj66+/xpw5c7Bo0SLs3bsXffv2xZgxYxpEFIhs27YNkydPxowZM5CZmYnx48dj/PjxOHTokGHMyy+/jLfeegsrVqzAjh07oFarMWbMGGi1WsOY6upqTJgwAQ+Ks0DewJ3rs5knY6xi7XjCw4Fhw4BrrqGoxvh4sqbUasPYI0Xh+DizPwDggUG73b43xTrp2hzj7+mKwSdyxRVAWhpQXeeD5ZHPO+/QsfxtxFrxLem3aiW0FlnV5nUqZ/C03pXshK5g+f2NebbakltDh5L3x8cHbx2+Bro6X1zROQ9XLb7eJYeu2LDPN8wXmiwNJHL6jprCGmiyNJDKSUcQX13hrruA+HgBF6pC8ObmPq5P6ttzDgkCReYLAuk1jQlmcAexFmjv3vTagrN/zpw5gxkzZiA+Ph5+fn7o1q0bFi1ahOpq2+nojIewvIZLS8luEvuyVFfThJBC4Xm9ytKJb/ndcjk9/3U6+9/tSHZ5QLbVCjJ8m0WlKSb3OtgoPcumI93JAIXHHqPXb78lsdSkuDvp4Sh4qqwMSEiwLiv1eioXFhJCf7ugA2ZlZaG0tNSwmPZhMWXevHmQSCR2F8syc83dwH3OnDn48MMP8emnnyI7OxsPPvggKisrDQ3bp0yZYna8s2fPxvr16/Haa6/hyJEjeOaZZ7B7927MmjULACCRSPDoo4/ihRdewE8//YSDBw9iypQpiI6Oxvjx4wEAycnJGDt2LO677z7s3LkTW7duxaxZszBp0iRER0cDoHIu48aNwyOPPILbbrsN+fn5yM/PR3FxcdOeoFbKr79SRa2AAGr46Q7yCDnZbXqj3LHEUf++hQvp9fPPSbQzrQPRiR4bG2vISBbvRVfxSmmXOXPmYOrUqRg0aBCGDBmCZcuWNRBgnTp1Msx+zp49G8OHD8drr72GcePGYfXq1di9e7chzN5UgCUkJCA+Ph4LFiwwE2AAkJOTg+LiYuTk5KCurg779u0DAHTv3h3+/v7uH5gnG1Sa4q06xh6sA35u5Uas/fUhAMD/VSwGlvi7tH+mtTwBGB3pl2sBmHRmd9GeiYgg39lnn1HZmc8/t9N52DL1btgwYOtWMqILC8mz5efnXHkFb9cxdzUFua2V8WgCXn/9ddx3330GubRixQr8/PPP+PjjjzFv3rwG4998802MHTsWTzzxBADg+eefx4YNG7B8+XKsWLECgiBg2bJlmD9/Pm6++WYAwGeffYbIyEj88MMPhgZ+YtOYlStXOr2vLndtb+z12VJrqts6HokECA4G/P3pPhYN3vqx/153LeoEKW5KPIKru5yl99y4N0VHevnuclSlV0EZq3QpBdmUf/+bUgPf+yoYT78cbPOnMmD52+h0lFXj50eGfEv5rRiP02Z1Kmfwlt6VbEdXMMWdZ6stuRUeDoSFofR8Jd5ZSb/hvDc7QpLSycpG7GPasE97iib4aotqEXBbAFALaI5oXJZNAImUF16Q4K67gKXHb8HMzIcR1i3I+TJUtkroXLpE5/TECXIEZmTQuuRkOi8A94owoaU08GuXWF7DOp0xOlh0cHbsSM5twLN6lWWJR9PvBsyd+La+21Gj1NRUikLSaulYGynbvj2cgksaNcJVlRjV9bT9c2EDW450p3rPgOZExXJUy5d7MELUmXJizkx6lJdbn/QQJ/Js9W/IzaXfeMQI+q0OHzb+lrm5wJ49dC3o9cAzz7ikA7bkcgnucscdd+DSpUtYuHAh8vPz0a9fP6xfv97QFyYnJ8csAjUtLQ2rVq3C/Pnz8fTTTyMhIQE//PADevXqZRjz5JNPorKyEjNnzkRJSQmGDRuG9evXQ6lUGsZ8+eWXmDVrFkaNGgWpVIrbbrsNb731luH9Tz/9FBqNBosXLzbLABg+fDj+/PNPL56R1k91NSCWip8507Z56ywSqQTyjnLocnWozquGMkbZYIypI92aLBo8GLj9dmDNGuDpp4H//c+9fWKaBjEj+cSJE4iJiTF7z9VgKq840ptLgC1cuBCffvqp4f/+/SkScfPmzRgxYoT7B+Ypx7Qp3nSAerAO+FO/TEadIMPw2NPonaAFMo+7tH8NankGmzvS6yopZc1ZpcmURx4hR/rXXwMvvAB06SJ1rvFVYCAV2crOpinOrVupaZCls9qaIuXJOua2FDVnDX1vORraMNXV1dizZ49ZRIJUKkV6ejoyMjKsfiYjI8Os4QsAjBkzBj/88AMA4PTp08jPz0d6errh/aCgIAwdOhQZGRkGR3pjcLlre2Ouz5Y8GePq8SiV2JDVCT8f7wEfaR1eTt9ge6yTVGZXGhxVxb8VQ6/RQ5WkQm1J/WSgi7LrppuAbt1I9KxcSbX2GiDKhv37KdRKNHirqkheFRdTnda0NEClahm/FeNx2qxO5Qze0LtEpFZ0BVPcfbbak1sSCd4/MhxltWokd6/GjTfLXdt3E8SGfVK1FBV7K+Ab5YvYubHInkJRpa5m+olMmgS8+iqQmemHF4sewBvBy52vK27NOVRVBWzbZpRbYpZgY4IZmpJm7InEDfyaEctrOCCA1olOUZWKHJeiXGisXmVNz7d04isUxnJIcrm5E18QqExlVRU5ccWoZHuNUk+cADZtom11707NMAMDXZZtulItnv5jFADgkaE74CPV2z4XdmjgSG9EgMJjj5Ej/YMPKMmvUY4203u9oIDq4R89an8CxBOTHtaCp3Q6+t6aGuDvv43BE6dO0d+nTtHvl5pK14uX9PXW2sB91qxZhohyS6w5rSdMmIAJdsKcJRIJnnvuOTz33HM2x3To0AGrVq2y+f7KlStdCqJijLz1FqkKERF0f3sCRScFdLk6m3XSTUu72Ar0fOEFum3XraMSyMOGeWbfGO8xbdo0h5ODzuK1ZqPNIcC8LqA8HYXsbQeoO85eMQ1xxQps2NMBX+RcDQkEvHztxkbtn1jLszyzHLIUmVlpF0EQDMqTVOn6cQ4cSJUdNm+m8sg//dTQ3rZpjNdHhiEujqKi7r+fjDnxeGxFktx8s2caljmKVHFk6Ns7NqBFdXRvSRQWFqKurs7giBKJjIxskKIokp+fb3W8mAokvtob01hc7truakO9ljwZI6arhoTQdT5okPk+WDmeuh7JePzV6wAADw3ahcSwIptjncHQKLnUWCvYJ8wH5ZnlhnrprkZ9ymRk+M2aBSxdCkydSoH1BkTZkJ1NzSnKysjzHhFB2mRtLTWwKiwEjh8n7a25fyvGa7RJncoZvJ39ZQ93n6125PDFChVe2Un9G558ysftW1UilSCgH+lytUW11LCvEf0bTJFKSTZdey3wzq7BeGTJS4gPvtzQmWwvGEB0DpnKsR49jBHoYWGOgxm8jT1HuYvRxG21gV+7xfIaBsgR3a2beRaFWGIjIcG8xIYjveqDD+gesOasNXXid+pEk0/nzpFTVq2mcYWFxvsnMJDKVv79NzBkiP1Gqf7+5HwPDQXy8+m+HDqUjscF2bb86044UxKC6IAyzEmtD0JphJ5l6Ug3NHF3YRLwhhtoTuDECWpI+NBDTn+UML3XL140OqoHDiR9ypaj2pVJD8C27W0aPCUGT/j6mmcL5OTQNgMDSUaZ6sPNrK9zA3fGW1y4AIixZEuWGG8ld5FHk9wp21UG/wH+UMYqIZEaZY5BDvlSCSNrJCZSvfYPP6T2K1u2WPFBMW0WrznS2ySejEIGvO8AddWZJiIqE7t3o2rXITxYQNGcs3r/hSGd8hq1f2ItT22uFposjaH5VV1FHSoPVMInkC7Fxhp877wD9O1LM4LffUepNmbYM8YlEiA6GqioIOlsakDZiyQZN856Kp6jaCpbUaaNjQBuTkcD0yS43LXdXqqotevTFVkUG9t00XnWDJucHKPcsnE8H+un4WBpR4T4VmDRgP8BtRLn7k0rmDVKTlahYlcF6soos0aWIoMmW0OnSea67Jo+nSI+z5wBnnoKePtNK7JBTA8WDd5LlyjCqUMHOobAQJJRpaVU5qalTJw5iuJsxihPphXhab3LFdx9ttqQw0JFJR749joUVgeib6IWd93TMKW4MYjNs3R51HzUXUc6AIweDaSnAxs3SrDwgxh8/rl5GqxVR3OPHtQIIjKSfpcnn6SyBEuX0kRgTIx5zXh7wQzexp6jHHA5S8tygnvRokV45plnPLa7YgM/jkZvQqw5OLVaY51yWyU2rDmzRSQSimj/+Wfy/iYlWb++TKOUxVrsMhnZdHV15hkepplp4qRVly70fZZ1uysqaF8VCrpHL12i98PC6DuckG3FI2/DC892BgA8f8XPUEmqgNLG6VmiI72msAb6Gn2jZJdUCsyeDfzf/wHLlgEPPODE11uzxzp3pvMP0Dk+fJgmHsLDrTuqLeW8rUkPsX66vUkGqZTWr1pFUec9e5pPwPTsCezaRZ7FESMaHmAzBk95slwCw5jy1FMksoYMoaAjT1CZXYnqCzRxV/h9IarzqqFKUiHsljCok0nnc1YOLVpEddK3biWRfsMNntlHpuXDjnRXaKxj2hbedoC66kwDzJ3H/v54vurfOFnTBZ188vGCbBFwqZcxAsPF/TOt5SnW7RRqBCi7KBEwMACF3xc22uBLTiZB+9xzpESlp5NPyYCrxrgzEboHD1I46Y8/OlfHHLAdZepsWqW3y8y0E8LCwiCTyVBQUGC2vqCgwGbkRFRUlN3x4mtBQQE6duxoNqZfv34e3HsncaXOvrOyaP9+UvCbooa65URWly50n+zZQ06ZixfJKWNxPOXlwPx36PwvHJ2BDpW5QJHWuXIEVjBtlCzUkVJVV1YHQRAgkUgg85fROm2dy4eoUlEUw+jRVNfzDt0XGFa1wVw2+PiQMRccTPdwbi5plBERtBHT+puAuVxuLme1oyjOllqLn2l52NO7rEWBevL6tvdstVZOwdp3W5HDX10Yie/PD4WPj4BPv1YaKgC4iyKaJlv1Gj1qS2uN5REaWdpFZMkSCnz88kvg8ccpaByA9WCDnBxg9Wqqt9e1K8mppCRyAPn5UdCCNaeitWCGxuKs3LMXLJGTQ7+9i1laWVlZ6NTJWOveVjT6vHnzsHTpUruHkZ2djaSkJMP/zd3Ar10jZofGxdFEkXg/Z2fbLrFh6cw2RRDoGquqos+I8sXy+po7l8IcLcuNHDlCXhvLDA+APr9rF40V7ynLut16PTnkBcFY6tJ0Qt4Ju+HFNYkoqQZ6R17E1Ij1wLGqRutZvh18IfGRQKitz1CuD+h31SacNo3KPhw/Tn7xO+6wM9iWPebnR5MToaH0u1pOMlhzVFvKectJj5AQOrfOTDI4CmwJDSXnfm2t9c83U/CUJ8slMIzI9u2UYQJQLzxPqHhilrFotwk1giHLWJurRcwjMVAnq42OdAc6VKdONIm3dCn5oq67jm59pu3DjnRXaIxj2h5N4QB1xZlm4Tw+eFKFV4qmAwCW93gbgToLZaIR+yfW8tTmaFG+qxyaIxoEjwyGT0B9RLobBt9TT1Gd9KNHSe9cscLkTVcnQZyN0J082VzJtRd5aS/K1Jm0Sm+XmWlHyOVyDBw4EJs2bTI019Pr9di0aZPN8gmpqanYtGkTHn30UcO6DRs2IDU1FQAQHx+PqKgobNq0yeA4Lysrw44dO/Dggw9683Bs42ydfWdkkU5H169O5/0a6rYmsuLjySjdvZucZ7NnG5s11bNgAfnYExKAh74bBeQnuOVINmuUXB9UI9QK0Gv0kKllxkj0RgbcpKcDM24rwUffBWPGV+nYd+s2+FlGoNfWUpqwQkGy4/Jlo4FsWn/T1Lm3fz/VuXJU39MWjXXCO5PJ8/PPLbMWP9PysKV3udlozSkcNcy0LKdg67tN5PD5k1WYdVsPAMDChRL07euZXQUAmUoGn2Af1JbUojqv2uWGfbYYOJBUna++Av71L/LfKXytyOhLl8jBo9eTLNLpSI5lZtJ6nc77E/7OTtI5CpYwjfp0IWO0LTfwY+oR7+czZ4A337RdYsPSmS0iCCS/zp6l57blZIu160u8xnr3Bq65hiLRrWV4iJ/v0YOu36NHqSOeZaNUrZaipnU6+t90Qt4Ju+H0aZr8B4CXPw6DLOVZt/QsiVQC30hfVOdVQ3fOWK/YVdnl709q4bPPkioxahSZrA0w1VMCA83tsYICOg+inWY5yWDLUW2pb5vWWD92zPlJBkeBLeK1VFJC+2wJB08xbQS9noIjAcrgHTLE/W2aZhn79/dHZWYlqguqjVnGWRoU/lAIVaLKpV4Nc+eSKnjoEPDyy+STYto+7Eh3FVcc047wZIS7PaeHs840E+exHlLc/9dk1MIX4/03YnzYP4DORJkICmq0g1YilcAvzg+qnipojmigO6uDLJGm7txJQVYqSYiNGEGvd99t0vTB1UkQV7IF7NUxtxXxYBllKkY8hIbSGI0GKCqicy1uxxtlZtoxc+bMwdSpUzFo0CAMGTIEy5YtQ2VlJaZPp8mjKVOmoFOnTobO6rNnz8bw4cPx2muvYdy4cVi9ejV2795tMGolEgkeffRRvPDCC0hISEB8fDwWLFiA6Ohog7MeoMaAxcXFyMnJQV1dHfbt2wcA6N69O/zNimR7CGca6jmqQZ6bS/eDr2/DVFNP12TU68lIzMgwRl1bHk9iIt0fYlptPd98Q3YtALzxBiBXOjh2J7BslCwLkKGuvA61xbWQqWXQ6yh0SurXyOPW6/Fq8kf4VTkdxyqi8cyBW7C0botRNly8SAZwaSlNtPn70+9QWkrvi/U3q6upON/Jk2QUP/ecc/U9rdHYiHFHzqnDhymkJCDA+9cR03aw1LvsRYF6cjLGmYaZthr9WtHJhC5xuP//gMuldFvOm+f+Lloi7yRHbUktdHk6s/qe7vLyy8Dvv9PcxZw5wDtPWAQbWJaOEJsaCoLx3tdq6ZyY3vuA5yb8XWmY3cxRn621gR9Tj6h7XL5Mz0ZrJTYsndniZNORI3Q9XrhAz76DB80jygH715dUSjaYrQwPgPSEyEh6tdYoVa2m1JLjx2mfFAp6v7qaxjuwG55+moampwNjrpMCkrjGnkkD8ig5OdJzjY70xgRXPfUUlfg8dIiShlevthhgqadcvGhuj+Xl0eRHdTXpPpZZf/Yc1ab6tjjp4WpAgqPAFh8fevYUFVHWDwdPMW2U+fMpbiowEKg3xd3GNMtY6icFJIC+Uo/a8lr4BPhAEaOAJlsDbY7WpWCEkBDgtdeAGTNov6+4gvr3MW0bdqQ3Bmcd047wVIS7M04PZ5pWmjiPF24eiYxzsQjwrcLb8a+TohUQQIpFUREpGm46aP26+gEAqk5Vwa8b/e1u5NTw4STEPvoImDmTAkIMvnBXJkE8kS1gL+LBMso0MJB+840badtVVfT+F1+Q0vTjj54vM9POueOOO3Dp0iUsXLgQ+fn56NevH9avX29oFpqTk2NmsKalpWHVqlWYP38+nn76aSQkJOCHH35Ar169DGOefPJJVFZWYubMmSgpKcGwYcOwfv16KJVKw5iFCxfiUzFPDUD//v0BAJs3b8aIESO8fNQWOFuDXKGgazU21v1+DpYOppgY+g7TCJ7t22niKTiYor6SkhwamYcOUcMXgEryjhvngfODho2SfcN8UVdeh5rCGshj5KgtJUeLb2gj6zPk5CD47H6suHYtbvrpX3h1/2hM6NYHg2rOmEeg+/gYDd6gIMobPHWKDKqwMGoXX1xM2pyfH0UrOVPf0xJbzqi9e2lbEydSQwoHk7FWr5PAQNrOyJHcGJlxLevB2ShQT0/GONMwEzD/br3e+Cw20ck+xTSsWxcNuZxSlT1V0sUURScFNIc10J3XuRRN5YiYGKoBev31wLvvAld1kWKSabCBZekIU+eTREIy4dQpOheenvDX6+m6WLGConwtr4vkZLLGV6wwZjG1kqhPbuDXgnF0DVk6s1Uq0tXLy0mf8vOj57dlVirg+PpyxkaJiACmTAF27rTdKLVDB/PsmupqcrAPHUo2yJkzDeTyzz+Tc1oiAV55xXON9cQ66docrWFdY2SXQgGsXEmH8PXX1DPLrG+WpZ5i2hxUoaD7vaKCbN3o6IZZf644qp2xvS1xFGSXl0cRY1VVHDzFtFm++MLoPH/3XRKlnsA0y1gik8A31Bc1hTWoya+BT4APZGoZqvOqUVde53Iwwr33kin2ySfApElk7phUemPaIOxIbyyNeThaw90Id1cicBxRr5j9d3svvLjlagDA8nHrERMdDxzRUckAjYa2P3iw2w5aZTw5F7WntB6PnFq3jk7NnXcCa9ea1KpydhLE3WwBRxEPllGmtbXkJKupIeW3uprWnz4NvPQSKXXdurlfZoYxY9asWTZLufz5558N1k2YMAETJkywuT2JRILnnnsOzz33nM0xK1euxMqVK13dVc/jSg3ylBQqkOtuPwfLST+djowBPz/6W4wyTUwkh7CPD0VtlZbaNTJLSmjesLKSIqRefNFzp8myUbIskISJLk8HiVwCqYLuL6m8kfdZvTF+Y5+zmHzqIL461Bt35r2CbZKJCOtYn3Lt42OUI6LBGx9P50yhIGNKdO516gQcOOBafU8RWxHlYnTpyZO07X79SJZaRqg7ciyIxqqt4oHcGLn90JisB2eiQL0xGSPqDY7KKcTEkOwUy5iY6GR//67FA/+Q/HruOQrK9gaKTlQmwrS0i7s10kWuuw74z39Ivv7r2Rj0S49HkujIMy0dAZg7nwA6DwoFMGECnR9PTfiL19Hu3fTsUqloX8TJV1H+nT9P35ubS472IUNaRdQnN/BrwbjizN6+ncKkCwtJt+ncmeypsjJ6NhcWGp/TgOPry1kb5ZprjFHR1hqlyuV0nXfsSJPkajUFM3z2mVW5fPQo2VUAxe54sv2P6Eg3Le3SWNklZvy8+CLw0EMUZGWIxbDUU4KC6LxfuECDFAqa/BD1p5oaku2AU9H6buNMkN3MmTTWE9nxDNPC2L6dysgBlGFy112e27ZllrFvR3KkV+dXwy/BD3WVdZAqpZR9XEk11F2RQ8uXkypy4AD1aNi82TtBE0zLgB3pLYHGRrg70xDTlcis2Fj8JrsOD2yiqfuFV/+JKX33Awgnp4ydusSNQYxI157WNqpDuy06dCDn+TXXUIng2bOpmoBBz3RmEsSdbAFrpSksIx5Mo0wvXjRGjwYGkmItpl2GhdF2LlygNEFrOFtmhmFEXKlBHhtLWk1VFTkjLB1IgOsZGp070/a2bjWPogboPjhzhhwiThiZej1wzz3AiRO0q199RbeVJzFtlKzLISOvpqAG4beFQyKRQHNY03jZZWKMv3Xdr9ia2xnHS2Nww/n3sUk6DWo/Pd3XarW5wdu3r9FxZ+rcEyfpfH3N63uWlND/Ytmoy5fp+02fOdYiyi9dIsNao6HvF41vywj1mBia8LB1nQgCfacg0LiOHRt3HTGtH3cCALzdpN0WzpRTUKloMrBjRyo5Uz9mn6YHbtw+Dbo6X9yYdByPP9YNgHecMKIjXZen86heJfLss6TebN4sxe3b/40d0seg7hNgruPI5caSU2Jkt3hv9+0L3Hijc31lXOk74+9Pv31AgHHyNSGBSldoNMasQLXa2Ey0Qwe63lpw1Cc38GvBuOLM7tqVnpm9epE9FRRE8m/HDnpVKOjZfe4cXbsKBW0zJ8e6PeiqjWKtUarofB04kMYCduVy2b2PYvyURJSVUdnMV1/17Ok0ONLF0i5SCmRoLAsWUGLQoUPAww9T6T8ADSdAJBL6HUtLjVl/fn50ro4eNUatFxc3naPa2SA7T2THM0wLIjeXLnGdjlrAvfCCZ7dvmWUsj5RDc1CD6vxqCIIA3TkdAgYEQBmrNMgiV3QolYrmTAcOJBN33jwq+cK0TdiR3lJojAPU2YaYtiIPLR6++w5Icfs3E1EnyDCly594pv+PQK2JYtalC/DAA6QQegBlV4pIrzpVBaHas5FTaWkUQDthAvDOO+QffPxxFzfSmGwBMToqI8O8NEVionnEg2mU6dmz9FsolfS7dOxoXsoiJoa8hBcu0G9tCTufGFexJztMa5CfOkU5vNnZdB0fOGCeFgw0LkMDICdIbS3Jk/PnyYCJiyOD5dIlcgSrVA2NTNG5Pn48BIkUj8+hDBSFAli7Ro+wihzggueNCrFRsqKLAkfuPgKJjwSxc2Nx9F9HAbghu0yM8bCUAKy/6wtc+fEM7KjsjUmFy/G96i74BKkpwlM0eE1lj6Vzz3LSTi6nc7h9OzmcqqrIsfTII+RIUiiMUWc9e5o7KS3rHgsCXReVleYR6vHxxug1a9eJ2JzxxAn6zTMyaJ2r1xHT+nE3AMCdsmuulJKxNtbRd1+4QO+ZyNWTxSEY+8XdKNMpcVWnk/i6/xL4nF/gtQlveXS9M8rEke5uyTxTZDJg1Sqgf3/gcH447tg7F2ski6GMjSB5cu4cyR+1mmSKWD/d9N52pq+MrUwFW31n/PzomSGVkky5eJFKWigUxprtvr5GJ6bofAwNbeiIzM2lz40YYYzQPXqUoz4Zc1xxZldW0n0RF2fMyAoPp0w7sbReSQk9U8VSel9+SR4ZW5k6jbFRbAVuAcCSJTblsv5wNqZMleDIEUp6W7OGVAtPYulId3cCUKGgElpDhtB829tv1zcutDYBIv4WpmVuVCrK9B06lLI1m9pR7UyQHQdPMW2I4mLgppuoymefPlTexdO3m80s4/M6aLI0kIfJETY+DBKppNHBCN27U3mpW28FXn+dZOacOZ49DqZlwI705sIVg87W+MZGZlkxVI6GpmHcqhmo0PjimqGV+PD67ZCcKALOe89wUHZRUpMHjR66855RnEy57TaKmHj8ceDf/6ZTZqcqh3WsKTJiTeeDB81/O9Mou4iIhqUpEhLMIx7EKFN/fzLmhg4laSvWGBXp2JHG5eZaj/Jk5xPjKs7Ijuxs4IMP6Brr3JlCkLZuBY4do2s8LY2cF85E51k67ktKjPV0pVIyWC5dIjknRlFXVVH0Yl6e0ci8eJG+d/x41PVIxkMP0C4CwPuLzmPghpWuN8h0AYlUgpARIQAAbS5l0rhdh9jCGE+OicG6Oz7DqC+mY11RKh4I/ggfPl8GST8bdcktnXuWacqlpRQJLvZoKC8nZ/bp0/SeabNEsSSFuC3Lusdi+YasLHrt0IHKTh07Rq8dOpBhmp1tvE5E55fYnFH8vzHXEdP6cScAALAfBarXk8MzIcHYRFm8llwpJWNr7M03O45AVavpmQ3gQrk/Rn8+BQWV/ugbmY//TVoFv5xyr5YuMotIF2WThwIURKKiyDE1ejTwc05vjFc8j++VL8FPoaBzIpPR+QkJIRni7L3tTFP1n3+23nemtJTkiFguT6mk9eI1ZBohL15nRUXGGtKiI1Kno99cdGQqlRSZOmVK8zjTmJaNs85sW5Nw4eF0b+TmUuBCcDDZDbGxzmXqNCaj2Zrz9cwZu3L5hbzp+DG7BxRyAWvXSjxWr9gUS0e6JyYABwygiNannqLTFxgITJ1qYwLEssyNrV4wTQk7ypl2Qk4OMHYsqQHh4VRRwN/fO99lmmVcsa8CAFBXUgd1ihoRkyKgTibb2J3yeLfcAixcSKX8Hn+c4pH+8x/P9ZRgWgbsSG8OXK0Nam18jx70cLWXRn/+PL1fWmo0KK0YKn8fjcT4d+7G5Rpf9OyuxXfr1ZAHPun1dDGpXApFjAK6XB2qjlXROg9GTgHAY4+Rfvj221RjS6cD7r7b1R01UWSys6kIuzUD27QhKEBffOGCsTTFxYsUGnHkiHljn/79yZHVubP1KDeNhqJ2AwK4sQzjGRxFVlZUUEiAVEr9EETn9lVXGSN2tm6l8kOOJtn0errm8/PJiSEIDevpis1YtVq6F8RGdWo1OfBzc8nRPncukJaGmjoppk2hyEiJBPjvc+cxNed5z/SKcIA8Wk6188rrUHWyyjNRnxbGeJp2E76+SoNb/pqNj06ORPA/wMs32bjFrTkWxTTlixdpkUrJQCwtpXOvVtOkXWEhlT4YNow+e/gw/QY5ORSdbvo7iSVZamvJURYeTr9tXh5pu1270vYKC2l7R45QBPpff9HvadqcMSzM9euIaRu4W5rFVhRobi4VpqyupuvymWeMehXgfCkZZ5y5tiJQw8PJmavR4Eh1V9z41WScLglBt5BirL/7CwTVFXs9e8y0RrosiCKtPBmgIDJsGPDLL8ANNwC/He+GG8KX46fXT0Jdnm+M4D52zPlADEeZCocPkyIXEECyybLvjGkW06VLJKPEhu6XLtF6MUIeMF5nkZHG3jJiqRhfX3NH5v79NPaRR9ipxTTEmYCbmBjbk3AATfSI2WQ9e7qWqeMJZ6sNuSwIwCvbrsSi7aMBAO/Nz8OQITHWtuA2oiO9uqAagOfk1ty5pM4uW0bNAP39gdtuszEBYi3rj2EYr3LwIPVgycsj0+TXX6kIgjcRs4y1OVqU/F2CmoIahIwKMTjRARiCERpr3z3zDIn0hQup1FRlJbW9Y2d624Ed6U2Nq7VBrY3PyaFyCzod3Y220uhFZ+377wN//93Q2SuRYNXB3pj+482orvPB0A7H8NPEXxEc+H9NNguu7Eo1qDRHNQA8b/BJJMAbb9ApWb2aaimfOkUCzWVBZu+3O3y4YUNQ0ZllWpoiLs56neOXX7Yf5XbFFZTv9OOP5kpfv34UyV5bS4775o6eYFo+YqRmSAhdS4MGmV8zgkAOEICMNtPrUXSCxsXRTXX//RRRbOuaM20Ed/QoybjoaFpMy49IJOSAqqqi77dsVFdeTt+TlgZdjRSTJpFN6eMDfPGZHnecXum5XhEOkEgkVF9vVzk0RzSea5RsYYzfFBCA99YD9z9I9fVycihNWSwlb8CaYzEkhIzxjAw6xx06kJEcEkK/fYcO9DmxfnppKTmkOncmAalU0rYCAmhceTk9b3x86PNiVGdFBW1fzCwQt9e7N026hIbSPqSmmjuxXL2OmLaDO6VZRCyjQLOzjY2KU1PpOSg+m8Wyac7IB70e+Phj0p2Sk2kfxElEcezBg9Rlz/JZPGCA4Rm9/lcBk3bNQKnOD12CSvD7PZ8jSl0OZHk/e0zeyeiMUshJfnrDkQ4AI0cC69cD118P/LHND9c/2Qvr1vVCgNjc0JVADEeZCoGBVGN65EjrJawss5jOnKHfU6sli9y0XB5gfp1JpbSPq1aRnHPVkckwzgTc9OljexJOLOcSG9u4TB13sSKX9YIET/w+Gq9vTwMAPNXrJ0y/p4/nv7se0ZGO+h66nsqkkUiovEJ5OfDRR1St5aefgLFjG9mfzB1czUZnmDbOX3+Re6q0lB6169dbr2LrDSRSCfzi/BA4OBBF64pQsb8CwVcHG953176TSMjfpFZTVPqSJWQ2LVtmrPDFtG7Yke4snnj4uVob1Nr4S5fIaaunmxvBwaSo2Uqjt0zbr3f26iHF4r+HYf7mUQCAW5Oz8MWolfDLzfeeomYFv65+KP2rFJpj9Y50D6cgAySsvvySbKmlS4FFi8jm/uADF2r8OfrtrDUEtVb/0KQ0hdmEiTN1FpOTaRGvw4ICivz67DOvlbJg2him2S0XL9KNkJNjdO6I15y/P0XqWcurk0jIEV5RYXSg2vouceKpSxcqL5KXR5kyJSXGNPywMLqe4+JoQujSJTIoY+qjnrKyDPfA8ZPkRN+7l+zONWuAG3rlAL+6USqiEZg60j1aPsFiAnPmA4DCD7jvPgqUzMkh/12DtGpb6eVjxtBEa2IiPQe0WmDLFmMmgBj5r6tv7qVW04mdMIGeF9nZtL64mCYJIyLIkWgaoe7ra7xOTLcnkZDjXiJpWK4KcP46YtoWjS3NYok48XTmDPDmmzTOdFJQfDbv2kXP5hEj7MuHP/4AfvuNahL7+NDzNSzM6IA1HTt5sjGK2UQnFCRSvPlFKB7fGgG9IMWw6FNYO3E1wnGJnOhNkD0mj5BD4iOBUCug+rxnIzutcdVVdNquu47iNYYMAdaskaJnzzjXNuQoU0F0movWp2UJK9MspiuvpP8jI2nc4MENJ4stS+K5W3KIYQDnMloOHGg4CZeSQoZKUzdRFrGQyzV6Ge796WZ8caAvAOCV3p/h35PzgNgbvPP9MHGk1+NJuSWRUExZeTk1Hb31VgpMmDChCUunuJqNzjBtmNpamm985hl6tA8bRvZNhw5Nvy/+/f3JkV5f5kXEUw3b58whE+zBB4Hly+kR8MUXTTdhwHgPdqQ7g6cefq4o6rGxwLZt5KCNiKAxlo3fdDqKwOnXj4wCW2n0gJmz92zsVbj381vxx+muAIDHU7fh5dEbIK2TAwVar9bvtERsOFpTUEOnwUsGn1RKM4FduwIPPUQK1KlTwOefO5k+5MxvZ60hqGn9Q5PSFA0MaWfrLIqOtuxsMviboJQF00awNPC6dCFHw5491Ijy4kWSNQMGkOPhs88aHzVqbeIpJYVki0ZDacxiD4FTp0hz6teP5NmePcaIw+Jiwz2wKjMZ999PftfQUHIsjxwJ4KCbpSIagSpJBQDQZGs8pmjZYupUuuVvvZXmzYYMoWiqvn0tBlpLLxdLXPj70+9YUmIexWka+W9aDiw0FHjySXquiOUOxLI7phHqajU50sVyL5aZBGJZmLo66wfHjZLbH66UZunRg7KxbNWmlkppuXyZdDJrDmq5nMpKFRcb62qbYtoPQrw/IiLomhX7mwwdSs9yU1liMelVVESP3VWrogAA0wfsx3tJb0KRV9GkDSolUgnkHeXQ5XqvRrolaWnApk10eEeO0OPjvfdIdjmNo0wFS1liWsLKtO9MdTX9nnFxxprq2dmOS+K5W3KIYZwJljp40PhsNQ3Myskhnd6dTB13MJHLF/eew5SD/8ZvZ5Mhk9Th40HvYsrALGD8I16dBPTx94FULYW+0kNZfhbIZMDnn+qhKdRi3R8qTJwIPPmEgBdfksDH294QV7PRGaYNc/w4tR3Zvp3+v+MO4JNPrGTcNhH+/SgYqCLTwpHuQR3qgQfI1J0xg4IO+vWjBMibb3Z700wzwo50Rzj78LOMWBfr45n+b1kn2JpBl5dHjotVq8jxvW8fRZ2fOUPRe6aN30wjcOyl0QOARAKhUww+yhyAOe8/jPIaP/j51GDZ2PWYOXAPjWkGp4ZfvLnU9GbkFADMnGlsOrplCwWQv/46CTa7pV4cGVn2GoKKn68vTeEwws5R5oOrmQ0MY+uaiY8nh/ru3RQFOnu20Tm0c6f9ckP2ShRYm3gyzdA4f54iPhMS6N5RKsnRpVRStOfQoQbnWXlILGY/JsUnn9Bmrr6aArfEgHWPlIpwEVVyvSP9iAY+IfQY9absGj6cFM5x40gBHTyYMmuefNIYXA6gYUkuvd48+tc0ijMszNiAr7qaBKJlObBbbiEtr0cPun4sI9STkiiC2HJ74jOurMzYZJkbJTMizpRmEUvYffYZzYBHRFgPYLD1bL50ibZ97hxdf3//TX9blvgw7QeRnEx/19WRY1YslXfkCF3fNmTJmjXAww8b2xG8/DIw59HekOQubJYUfnm03NCwD/B87xlrDBpEYubuu4HffwemTaO4jrfecrJhmKNMhQsXqMvp+fNURFUqNT5TTEsZVlebT1p06+Y4QAFolucI08ZwNljq3LmGUdD2rv+melYmJ+OnXk/jvs874GKlGn4yHdYMfxvXp9eQE70JHL3yKDm0J7UAvCC3srMh//57fN/xGJ7qcStePXYTXn5Fgr3/VOCrn/wRFubZrzPANhvDAKD58BUrKKZQoyFTYfly6mHXnHXD/fuTklJ5uBL6aj2kcroPPR0oNXEiifFJkyhuZPx4Cu586SU6F0zrgx3p9nD24afXG+tlarXGSHE/PzLGxP/F2sNinWBrNRt1Oor+0+nIcBSjNi9cICe8Tme820yj/xyk0WdfCsOcv+/E+sIeAIC0mBysHP8DEkKLaUAzOTXEiHSRpjD4xo4lXXXaNOpzd999FAjy4YcmzjlLHBlZnmoI6kxtek5BZlzF3jUjlZICX1RkjPAEnCs3ZOt6tuXcEjM0iorII/zYY0B6esPoLKkUej05zJ98kkSfVEq15hYssKgt1wwGqCEi/YgG/gNJAfN21GdCAjnTp0+niPT584G1aym7plcvGx+yFv2bkEDOQTETICwM+Ocf6+XATCeLxUk+0wh1haLh9hISyIEuNl+cNo2iQrlRMmOKvdIspiXsxObEoaHWo/esPZsvXaL0DY2G9LDAQGPGhWmEuWU/iMBA83IhYm3uwkKKVj9/3kyWnD9Pu/Ldd7SJlBSKMBo6FACasGSABYpOCpTDGDnt7QAFkfBwakD60kuUUPDJJ8CGDVQP9NZbHRjKzmQqRESQwzw31/g7yOUN+86YTlo4G6DQEhyZTOvGnawGW9d/Ez0ry8pIHfv4Y8qo7Z1Yjc9fyEHfQbc37SSgiSPdozqVSVCcT+fOeGXiLgzaW4J7f5uIjRn+GNinBu/91xfXX++5rzTANhvTzhEEYN06cqCL8TjXXAOsXNkyypso45SQBclQV1oHTbYG/n3JrhMd6Z70TXXvTgUnnn6a+l+9+y6ZVC+9RPYd105vXbD1ag9nHn7btwOLF5PyHRZGxt7p06QInT5N48T/8/MpyrKujiywHTvI4ANIyuTmkhKm1ZIiHxNDlolWS9vW6Sh6qrraGO0XFmZ0rFtJoz9fHoCZ/7sRvd57COtP9YBCWo1X+q/C30OfQILPafpMaalZHeKmdGr4dW3aiHSR7t0pWuq118gXtH492VCLFtnI3BWNrNxcOvemmDYEfeopoH9/chQeO0avAwZ4Nm3PGWVd27QlepgWhF5PzqmDB40N1xpzzYhRo425nk2dW5ZIJCSnIiPpnvLxIeOhd296lUqxdy/Vy5syhcRm9+5UOuCZZ6woGaIBGhZGcqy01Otyza+bHyAD6irqoMuhyM+mmATs0IHmbj//nOZN9+6ln+Ppp+lwrWL5OxYXUyZCz550vsVz1qMHZTZFRRkniwsLjZPF4iTfzTcD//kPfbHl9uLj6X/T6+SGGxp/HTFtG2ulWSxL2IWG0jUlCA2vSaDhs9n082FhpC/FxdHfEolRLpSU0KtpPwixXIhKRbqZVkvyqaqKrL96WXK5VIqnniK5JJZUnz+f7kdyojcvik4Ks/+9PclnikxGk50bNtBpP3cOuP12CmA4ftzBhy1l1e7dlGUJUKbCyJH0CpDuvXs3jRs4kH6Am282PEPMEGWXyTOmAc3wHGHaGPb0HsBxVoM7OlcjqaujyfiePWkSUCIBnngC2LVfjr63J9i+X7yEaZ10j9mDlkFxgYGATIY7Bp/Cjvs+Qnf/C8i54Itx44DbbqNHiUdhm41px2RkUIuam24iNSo0lKLQN2xoGU50AJBIJMbyLiZ10t1tNmoLuRx49VXK3ktMJHXzvvso2/jPPz36VYyX4Yh0ezh6+KlUFIXXsaNRud+/n5Tvrl3pzti9mzy1XbvSQ1wqpYe4RmNU0Pv2pSgFa13bxRqQhYVk6FVUkGfJz4/2SyzhYpFGnx+chLd2XoFl269AVS3l/o+P3okld2QiccYw4MczjlNdmwDfCF9IVVLoNfXCqokNvjlzgOuvB+69l4T9c89R2tHChVQGxlA2wdloEcuGoN5I6eYUZMYWtvo5DBnSuGvG2Wg+SxoZ3bdvH83Kr1lDw9Rq8o889pix7LZVnO0x4CGkcin8uvuh6mgVtKfro6eaaBJQIqHyCaNGAfffD/zvfzSX+8EH5N9+6CEr58ra7yhOBC9dSpGelqVX7EVK2dqelcwCm+ObMMqNacFY6lmivmNZwk5sYGt5TVo+mwMCqMaKQkHbUanI0QoYy0qdOkUzUYMHN+wHYVqCqrCQnOi1tUC/fqiYeC+W/5iEpUvJDw+Q6vfuu1TvsqXQwJHeRLLJlGuuoaSCJUtIxPz+O2XO/OtfFJVmM7DbURNZW+XIPCFLmvg5wrQxPJHV0ETPSkGgAKK5cynmAqDb6NNPqXxec+EVR7qdoLhekZeQOWUZnvlzOJYdvQ5r10rw228UVPXww/T4cBu22Zh2Rl0dFWp4/XXK/AfoEn/0UZI5wcHNuXfWCegfgNK/SlGeWY6oqdTrxtt9ZkaPJvn7zjsUKJaZSfECV15J/eyvv57NpJYOO9Lt4ejhd+ECvSc+nEtKjAagVEqfzc83KvmBgWSUiY5zS4POWtd2U6Pu4kVaJwjkBU5Joc+WlhrS6A+O+D+8sdwXX34/GNV68gKndTyNlxM/wpVJRcB9jzSNs9dJJBIJlPFKaA5r6P9mMPiSkkjQf/cdRXYePw7MmkV1TmfPpvrpQUFwvSGot+AUZMYa9vo55ORQOHNuruvXTGOuZxfTlDMygBdfpAogIpMnA6+8QuVwnaKJnbWqJBWqjlYZ/m9q2dWxIymqP/5IiTBHjtDE4JtvkqI6daqFEWjtdwwKoknZ6GjrdRccpaJbbs/edeJtuci0Tiz1LJ3OmF0HNGxga+2aNH02Z2SQLhYcTDeJaQk907JSM2cC115L6y37QYglqEpKgOxsnIkfieW1c/HfdKkh86NXL5JZN97YvLU9rSHvJDf7vzn0KoDkz3PPUWbRI48Av/5Kkw4ffkgVn556ivziDXDURNZWOTJPwJN+TGPxVHkWLz4r6+qoxMJrr1FbFIDUgKefBv7v/5qv2Z+ImSPdU84rB0Fx/iG+eLXnSkx9rjsefKMHtm6lkoKvvgr8+9/Agw862efBFmyzMe2EggJq8bd8Obm3AFLl7rmHHMUtJQLdGtYajnq6Rro1fH1pguGuu+gc/fe/5JO68UbKFHr8capaZyuml2leWDO0hzPlPNRqMtaAhgagVEpai/jQlMvpfbWa6haMGkXbnzmTPB99+1pPCwwPp/Fi9NSLL5KXCQCOHUNlfjk+l0zBNZmvos//DccnR9NQrfdFWvgx/JD2Mv4ZtQhXXqs2Tw10JtW1iTAt79JcBp9EQunHhw+ToRcZSXbU44+T4H/0UeDECdD5mzePrMMFC+h17tymjVTiFGTGEhupq4YSHUVFdJGHhnr/mhFLy9TWUp5sv35W05TLY5Lx/vsULJqWRk50qZRE24EDpIw57UQXaUK5JtZJF2nKbBrDd0roZzt4kJSv6Gjg7FmKSu/cmYzj8+ftbMDdVHSGcRdLPUuhIMd5TY31Enb2smfmzaPncb9+lIUzbJh5HxrLslKiE9bK87SupBy/7w3D7SeXoNt/5+G1N8iJnpBApZX27aNU5ZbmRAcaRqQ3Rdkpe3TvTrXT//yTItVrasiZnpBAtdN//91YqcdAc5ZDaEH6MdPKaIbyLM5QVkaT7D16kM6wZQuZpHPmUNuBJ59sfic64KWIdCf1nN4D5fj7b+rtEBdHsWtPPkkJMM8950CXsgfbbEwbRqulGt833kg225w5xpZJ//kP2SQffdSynegAoO5LukbF3gpoTmsg6IUmcaSLhIdTZPqZMyR3AgLIJ3XvveRmvP9+ivmwdEcyzQtHpNvDUXRBeDhpHhoNOaxMDUCFgiwDmcx41Vs2B7U06OzNWgNkMKSlAXfeiZoa4O81F/HFt3Ks2RiMikqpYZdvuw2Y86geV0TLgfLrgICJLTqixrThaHMbfL6+FH0wfTolB7z+Ov30b75Jy5VXAlOnSjFxYhyC4ppxRzkFmTHFmX4ORUUUGrhzp2evGb3eGL1XUEC9H44eNZaW6dGDvjcyEnWqAPx1OhZfvS7FV18Z7RqFgsqVzJtHTpfWQANHejNNAgL0WJkxgyYhPvyQZNXp01Ty5dVXScGdOhW47jqTclUAR0oxzY+lntWpE1lg587RhW1Zws5R9kxaGtVbycxs+L6tz9c/T4W13+Pg9kp8fmwIvsy9CheqQgxDRo+mCfWxY1usKmVAEd18NdLtMXw49brYupUcU7//TirM998D3bpRTMmdd9Y3fedyCExrpYVkNdTUUB3izz+nzLWq+gS6kBByyjz8cP291oIwdaR7zB50Qc+RSilb5q67yAZ86SVKYFq0iGTWDTeQnBozxsWmgGyzMW2IkhKaHP/hB8o0qzAGcWPIEPKh3HNP64mirsyuRNH/igAp9b46OeckAgcHovpiNYCm1aE6dqRyeE89Bbz/PpXtPHWKXj/4gETZbbcZ49RaYjBHe4Id6Y6w9/C76SbSTsSHc1AQzSxfuECvWi0ZhDodOZvKyugOCQqybtA5cNxrgjrid/+78f10Kf73P+Dy5SjDbnbtSo6SqVNp9pySDeKa4YS5jl9880ekW6JUkmPq3nvJ2HvzTeC338gA3LqVLolx4+jnuv56UkybnBairDMtAGei9/LyaOJu3jzPXTOmNdkvXqSnvVxOYeaJiUBlJWoyD2HrThW+UU/Hd3+EGCpUAcaEnClTKFi+NaFONj/XzT0JCFAphdmzqTTVTz/RROA//wBr19ISHk6OqokTqTey1FOp6AzjDpZ6lhhsYK2EnaNr0sVrWq+nVgE//JCMH39MwrFjRh0kNFTApEkSPPAAlXJpLbSU0i62uPJK0qcOHyZD8dNPKSp27lxarroKmHRHLG6PGYCI41t5ko9pfTRTKTOtFti8mXqnrFlDrbpERDHbkh1cXolIb4Se4+tLDvV77gG++YYylf/5x1hOLzqaHFkTJtDcrVNOdXu9ZQ4eZBuOabHU1QF79gAbN9Lk3D//UFKFSEwM3StTppBd15qozK7EubfOoaawBr5hvqi5WAOhVkB5Zjm0Z6gHVnPYd8HBpA898QTw118U1f/dd6Qiv/giLfHx1GN9zBjqbeGRng6MS7Aj3RnsOSylUvOHc0ICaS5iXkufPuRsEv9PSCCHui2D0MSg1GcfReYhOX4vTMWGsiHYmhuL6hrjWPHjU6eSYdJaZ6VMI9JbSuSUiERCAmrMGErr++ILMvqyskigffcdBc2NGEGO9dGjrScTeA2uO8wArkXveeqaMa3JHhNDchAA6uqQvy8f68+l45f8Afj9ZFeU6oyTZR06UDr/PfeQw6S1yi2/RPM86JbkrJLJyG685RYqk/PppxRdVVBgzK6JiiIFbPz4ZAyfORt+v67lSCmm+bDUs0yzW44dc+2adBD9dz4oGRs/I4Pw999hMrkngVxOUYdTpgDXXUf/tzZ8/H0gC5ShrqwOQMuSTab07EmPkMWLga++op6vW7aIixSzJLNxRcSNGJe9E+P6nkPfuFJINDzJxzAigkCTUJs3U2To77+bVzAJD6dMtbvvpp69LV3f8kqNdKDREeEyGZ2/yZPJ7vvvf0mfOn8eePttWkRdauxYKl1lTQU3YKp/Z2dTM64jR4wZnElJpLix3sU0I1VV1M9bDB7csgWG/jAiKSl064wfT7FTrfFRLOgFFH5fiJrCGqhSVNCe0qLmYg20Z7QIuzUMlYdImDanb0oqpQakI0dS6Zd168j3tH49ZR4vW0aLQkE29ahR9DpokLG1EOM92JHuLLacT5YPZ62Wpog6dqSHoiCY/19cbPPhLQquf/5Jxj//JGHrPwJKy80lU1ycUXBdeSU5cVs7LaFGujNER1PdqieeoCSEtWsprenwYZql3bjROC49nYTesGGUstzSlVdPctNNN2Hfvn24ePEiQkJCkJ6ejqVLlyI6Orq5d63t0tQlOixqsl84L+DvU4Pxp/b/8FfFAGRr4syGhyorcGOnvbhjQSJG3RlpXl6kleIb7At5lBzV+U2f+ucKffpQY7GlSykKdNUqUsTy8yka9P33AaUyCVcNewqjB13G6AFF6DPQF9K4th8ZxbKqhWGqZ/XuTV6JxmbP1DvmhbM5OHtUi3+yQ/HPkTBsuV2CrCzzoUFBNBE+fjxNmtt1hLQSFJ0U0JTVN3FvobJJRK0G/vUvWnJzqd7qV18Bu3dLkFHQDRkF3TD/MBCtLMbIjtm4pn8JRt6aiPjkVlIHjGE8hF5PpmZGBjm3/vjDGMMgEh1N5dxuvplskdakb8kjvBCRLuJmFm9KCmX5LV5Mk7DffkvR6aa6lI8PRainp5Mza+hQG7XnTQNROnc2RshnZtIP2oz19Jn2hU5Hfow9e4zL/v1UGsqU4GBSydLTKWiwtZThtIc2RwvNEQ0UnRWQSCQIGBxANdIPaVB9RTVkako1qauqa+Y9JYKCqOzUXXeRuPjtN5pA/e03EhumviilksrspKYaWyxaq/7KuEcbcMO2AOyla9n4vzI0Foezpch8H9i1ixzohw5R+gwhASBBQAA5ZEePpqVHj7Z3EyjjTSLSW7AjXUQiIZ/kgAHACy9Q/bwff6RIkC1bKFLhs89oAaiaxrBhpFANGkSfE/umtUVGjhyJp59+Gh07dkReXh7+/e9/4/bbb8e2bduae9faLk1YoqOyEti/vgA7v++C7WXXYfuGeJwtDTYbI4EeA/yyMa53Lq7rfQ6DI85CduIoMGAB4Bvp9j60FFRJKqMjvYXLLh8fchaOG0ftOjZvprmQdesoOGvDRgk2bOwAoAOCg8kYvOoqkl0DBrTNlEGWVS2cRmTPFBaSIbh7N7BrlxS7dsU1aBInkdCzePRoMgqvvBKtMvLcHvJoOTTZGkAGaM9qoYxVQiJt2TIKIENvzhxazp2jOqzr/idg0yYB56s64MvTV+LL0wDW0tjUVOPSrx9HYDFtB0GgZObMTFp276YkHcvIUF9fKtU2ahRl0wwY0HrtRKlcCp9QH9QW1XpHp/JARqZCQef5hhtIl9q4keTUb78BJ04Af/9NC0C/zaBB5s6srnF6SEwCUQw/VmAg/Z+VRVFaiYltPpiBaTpqauj6PHKELrGDB2k5etTU92QkKop0I3EZONDFvgCtgLryOtRp66BQk+Igj5JD3U+Nyn2VuPzbZcg71iuGls3QWwBqNWV333orPSuOHiU/1F9/kS/q0iVzWQQAERHUB7tvXwqy6tuX/IptTf91Bk8FUrEj3VNYezjHxUGno6zkrO+A7Ow4HD5MqfbHj1vvvBsVZXReDBtGF3pbiDq3h1QppbpUhTWoLauFoBdahcEnkpAA/PvftGi1lAa1cSPVENu5kzLUxTIwIj16kNEnCrM+fZpnprC8vBxlZWWG/xUKBRRuWqKPPfaY4e8uXbpg3rx5GD9+PGpqauDbmkJjWhsebmYkCOSXP3SIln37yJg7ehQQhI4A7jKMlUCPPspjGBm8DyNCD+KqgH3oUHYGGHA1zSSVVrTJxnCqJBVK/iwBAFRfrG41sksuN5asEgQKjhLLXPz1l7GR0C+/0HiZjMowDB5MRmHfvhQw7O/fdPvMsooxpaqKZJFoEO7fT4ul0xwgHWrgQNKprrySml526ND0+9xUVGZXora4voCpAJxZeAaqJBXCbglr0NuhJRMTQz00Zs6UQKuVYNs2mgD84w/SrXJzafnmGxrv60tySQx06NePatu3sccO08YQBJJbR45QZOjhw0a9y+SRZ0ClomdxWhoFWl15Zdua6JZHyVFbVAt9pR5VZ6pa9CSgXE59sq6/nv4/dYoc6qIz6/x5yh7IyDB+JiRIwAC/m9EnOg19akrQJ7IAyWGX4OdbS0ZgTAwpZTk5XLqTcZncXJIhJ07QcvIk+ZxOnjSva25KSAg9MwcNIl1p0CC69FrrhJyzyAJkkCllqKusg08gOduCRwZDc1gDXa4OtaV0wqSqlj2hJZFQUnpSErkBBIF8j1u2kK60axfpyRcvknz67TfjZ318qHJCSgq5CXr0IL9W9+5UGqytXgOeCqRq4y7apqGykuoUnT5ND9Hjx43L2bOUimeNiAhySIjOicGDgU6d2u5Fa43K7EoUfl9IAfgAin8phtRH2uoMPhGlkqJCRo2i/7Vaio775x8xQo6uiWPHaBENQICMvaQkEmSmwqxbN+8pySkpKWb/L1q0CM8884zHtl9cXIwvv/wSaWlp7JhqChqRulpZaVS0jh41LllZtAlrdIyoxUDfg0iNO48ruhViUMc8BO7aRI2WQ8MpV9DHh0J32mhjuMrsSmhztYb/897OQ/mO8lYnuyQSUqBSUqhRaW0tTZr88w8pYdu2UerygQO0fPSR8XPdutEkYM+edOmlpJDcsprK7CYsq9oftbVkFJrqVMePk4/hzBnrwQgAPTdFvUo0DNuSo8keYuOsOk19fXQfCXzCfKhxVq4WMY/EtCr5JKJUUlr5NdcAzz8PVFSQgSg6qbZvB4qKgL17aTGlSxdyqPfsSTpWYiK9tuXJFKZlUVNDsQ2nThmXkyfJDjh+3LyuuSlyOU0O9e9PKtQVV9D/bTXAqjK7EnotGc2VWZWtbhKwa1fgwQdpEQTyC5g6s/bvBy6XyrCptCc25QOol1USCIgLLkFSWCGSQwvQoyYL3Tfo0f1a8qu3tUhgxnvMn2/MiLfE39/ocO3d27i0N9+TiDJWCVWSCuWZ5ZClyCCRSOAT6IPA1ECU/l1q6DPjG9a6bAKJhPScxEQqmQdQ8IkYcLJ/v9GmKy832v3ff2++ncBAqk5tunTpQqZ8bCxNwDTFddOSA6na6KPYO2zdSg/CnBzjcvasaaMq6wQFGZ0MycnGKOTItlPhoFGYdkr2S/RDbWktlN2Urd7gM0WpNKZFiVy6RM51UYjt30+RKOXldH3t2tVwO506kYJmTZjFxDQ+LScrKwudOnUy/O+uYBKZO3culi9fDo1GgyuuuALr1q3zyHYZJ7DIjqmqIj92Tg45n86cIbklGnL5+bY35eNDD+JevYyRfv37A1ERUmDJrxSiHpdinA4vLaULvKaGLkyAPPJtrDGcKLvEaAUAbcJZBdBvLjogH33UGC0nyqa9e0lmXbhgjHhZu9b4eYmEsmvEicCEBJJXovxqbHQoy6q2R20tyaKzZ831KjEwISfHesqxSGgo6VS9ehn1qt69228EsmnjLGVXJaqOVkEiJcNQliKDJkuDwh8KoUpUtdgIT2fx9zc61gGSU2fO0CNp716jjnX+vPEa+/ln822EhlLUlbh07UqPzrg4qjPdVp2VjGfR6UiPunCBnOXnztFrbq5Rpp0/bzuoCiBHadeuZCeKkz69epFa1V7mdUW9SqihGVKfEJ9WrVdJJPSbdu0KTJ1K66qrgYO/ncf+xb/ggLYHDlzujP0FkSiuUuF0SQhOl4Tg1xMJAIYBM+kzcjnZe3FxpEPFxZGOJS7u2IBM20O017p1M3++JSXRc609OsxtIZFKEHZLGLS5WmiyNFDEKCBTy6DqpULZzjIIWpJFUt/Wb7v6+dFE7BVXGNeJcW7Z2cblxAma3M3NpYwo0fFuDZWK/FOmS8eOVF1DfI2MJD+oO9ddSw6kYjXRBT79FPjwQ+vvBQcbHZyi80BcoqJYcFli2SlZ3VON4JHBkEglEAShTRl8loSHU3f3sWON66qrSXiJguzIEWP03eXLpJTn5VFkgyUSCQmqmBijIIuJoUZDFrKnAQEBAQh0orPavHnzsHTpUrtjsrOzkZSUBAB44oknMGPGDJw9exbPPvsspkyZgnXr1kHCN0KTMGUKpXHl5lKUniNCQoyKlhitl5RE8su6gm6lJntICFl/e/bQRalQUHPlRpaWaamYyi7/gf4o+4dmyX2CfKjBXxuTXRKJUa6MH29cf+kSKVcHD5LMysqi5fJlo/NAbHpjSmgoTQCKE4FdugDTp9PlYw+WVW2PnBzHDasUCnJEmE7MJCbSsy08vGn2s7Vg2jir+jz1bkB9JKNEIoEiRgFNtgbaHC384ryQNtKMSCRGHfzWW43ri4upRIYop44eJf3q3Dl6NhYVUd1pS3x8SOZ17kxyqnNnuvZmzGi6Y2JaDocPUy+kggIKnhJfL1yga8wZ5HLjpLK49OhBS3x8+3GYW8NUr5JHy6HL0UEia3uTgHI5MHBcFAYevAhk/gakpECABJc0ahwpDEP2pTAcOSrBcVkSTqAbTp2SoLraaA/aIjycnKSdOtHrqFHApElNd1wi3MC9+XniCVoY51AnqxHzSAwKvy+E5ogG1XnVkCqlCL0+FIVrCwG0/B5YjUUMfOrcGbj2WvP3qqoo4O7MGWNwy+nTxgniixcBjcaxbAJI7kVE0BIWRvIqPNwYY9ezp/3Pt+RAKnaku0BaGgVciikN4hIfT450xnksOyUDMChHbd3gs4ZcbiyvYElRETnZTQWZGK2Xk0PlY/Lzadm92/g5MbrFEzz++OOYNm2a3TFdu3Y1/B0WFoawsDD06NEDycnJ6Ny5M7Zv347U1FTP7BBjFzHTQUSlIlklRtuJS7dutDhyYlrFVk32yZOps25kpFOlZVobprJLFiCDT4gP9Fo9ZH6ydiW7wsOpUWN6unGdIJCDXVSsjh2jrIdTp0hmiY6roiKKHBW5807P7RfLqtaD2BM5JsY8XbRLF6OjKSqqTYkPr2LaOMsnmNR7qcJ48mRqGarzqlFXbifMv43RoQNw9dW0mFJRQbJJrCEr6lhixlZNjTGSXSQpiR3p7ZVDh4D//Mf2+3I5ReCZBrN06mQ+aRwRwbLMFqZ6ldRPisp9lVDGKwG0QZtQah6IIomJQYS6FhHB53F1xTkgPQx4pBuQLEFdHQ0Ts0lFp5bYH+LcObIBL10yBjcANAHdHI50buDOtEbUyWqoElXQ5mhRV14HWYAMik4K7Bm0B5UHKg36VHvCz48c3Lac3FqtMfvKdBGzs8TX8nIKFj13jhZLEhIcO9JbciBV+7sy3GDaNFoY97HslGxJezT4bBEaSsvQoQ3fEwRq/J6T01CYORJMrhAeHo7wRob/6evzWXU6ned2iLHL4sWURizONAcHeykrphE12Vs7prJLIpGg4/0dIdQJkPjQCW7PsksiMUYdmJazEiktNS/lcfYsKVYREZ7bB5ZVrQe5nMQGB/97BtPGWb6RvggZEwLfCGOYa11lHaRKKWQBXHDX35/KAfXt2/C9ujoyAHNyjA6rnBzSw5j2SXIyZU5FRFCMQGQk/d2xIy1NVSu2rWI2CRjog5gnY8ycGW1Or7IViGKRwSmTGQNfrCHagBcuUPkgcRk0qKkOxBxu4M60ViRSSYNJul4/9MLFVRcRMcmDRkobQak0lg2yR1UVTfKJWVyXLpHMEif/6n3aHqE5AqnYkc40C9Y6JZvCBp9zSCTGFJmBA5t7b4AdO3Zg165dGDZsGEJCQnDy5EksWLAA3bp14wjPJuS665rwyyxqsrd1LGWXacQnwLLLHkFBVMe6T5/m3hOWVS0Fdj55DtPGWaoUFQKvMEbwCIIA3TkdAgYEQBmrbMa9bPnIZBRRLLb5YJg+fYCPP27uvWi7WOpVlhGBbVKv8kAgiqkN6Kpe5Y0GfqZwA3emteMX74cu/+nS3LvRqvHzM2abepvmCKRqu2GDTItGNPh0uToIgmD2nmjwqZJVbPC1MlQqFdauXYtRo0YhMTERM2bMQJ8+ffDXX395VEFjmOaCZVfbgGUV09YQG2f5hvlCk6VBbWkthFoBtaW10GRpIA+TI2x8WKuuMcwwTNuj3epVYiBK79702oTZnCkpKQgKCjIsixcv9sh2586dC7VajdDQUOTk5ODHH3/0yHYZhmE8wY4dO7B8+XLs27cPZ8+exR9//IHJkyc3KpCKHelMs8AGX9ukd+/e+OOPP1BUVAStVovTp0/jvffeM2sSwTCtGZZdbQOWVUxbRGycFdA/ALVFtag6VoXaoloEDAhAp0c6QZ2sbu5dZBiGMYP1qqYnKysLpaWlhuWpp56yOm7evHmQSCR2lyNHjhjGP/HEE8jMzMTvv/8OmUyGKVOmNJgcaQreeecdxMXFQalUYujQodi5c6fd8d9++y2SkpKgVCrRu3dv/PLLL2bvC4KAhQsXomPHjvDz80N6ejqOW3RZLC4uxl133YXAwEAEBwdjxowZqKioMBtz4MABXHXVVVAqlejcuTNefvllzxwwwzBO4clAKnakM80GG3wMw7RGWHYxDNNSUSerETsvFnHPxaHLgi6Iey4OsXNjWS61Um666SbExsZCqVSiY8eOuOeee3D+/Pnm3i2btFQHFtOyYb2qaREb+ImLLQfS448/juzsbLuLZd3hHj16YPTo0Vi9ejV++eUXbN++vakOCwDw9ddfY86cOVi0aBH27t2Lvn37YsyYMbh48aLV8du2bcPkyZMxY8YMZGZmYvz48Rg/fjwOHTpkGPPyyy/jrbfewooVK7Bjxw6o1WqMGTMGWq3WMOauu+7C4cOHsWHDBqxbtw5///03Zs6caXi/rKwM1157Lbp06YI9e/bglVdewTPPPIMPPvjAeyeDYRgzPBpIJXiJ5cuXC126dBEUCoUwZMgQYceOHXbHf/PNN0JiYqKgUCiEXr16CT///LPZ+3q9XliwYIEQFRUlKJVKYdSoUcKxY8fMxhQVFQl33nmnEBAQIAQFBQn33nuvUF5e7vQ+5+bmCgCE3Nxc5w+UcRt9nV7QnNYI5QfKBc1pjaCv0zf3LrV5+FonWqOcEgT+/VoKLLu8D1/rRGuUVfzbMe0Fb17rr7/+upCRkSGcOXNG2Lp1q5CamiqkpqZ6/Hs8werVqwW5XC58/PHHwuHDh4X77rtPCA4OFgoKCqyO37p1qyCTyYSXX35ZyMrKEubPny/4+voKBw8eNIxZsmSJEBQUJPzwww/C/v37hZtuukmIj48XqqqqDGPGjh0r9O3bV9i+fbuwZcsWoXv37sLkyZOd3m+WVS0H1qu8S1Ne62fPnhUACJs3b/b6d5kyZMgQ4eGHHzb8X1dXJ0RHRwuLFy+2On7ixInCuHHjzNYNHTpUuP/++wVBIH0pKipKeOWVVwzvl5SUCAqFQvjqq68EQRCErKwsAYCwa9cuw5hff/1VkEgkQl5eniAIgvDuu+8KISEhgk6nM4yZO3eukJiY6PSxsaxi2gut4Vr3iiOdFSmGadnwtd565ZQg8O/HtB/4Wm+9sop/O6a9IF7rWVlZQmlpqWHRarUe/64ff/xRkEgkQnV1tce37S4t1YFliVarNfudxG2wrGLaOt56Lm/fvl14++23hczMTOHMmTPCpk2bhLS0NKFbt25ekYO20Ol0gkwmE77//nuz9VOmTBFuuukmq5/p3Lmz8MYbb5itW7hwodCnTx9BEATh5MmTAgAhMzPTbMzVV18tPPLII4IgCMJHH30kBAcHm71fU1MjyGQyYe3atYIgCMI999wj3HzzzWZj/vjjDwGAUFxcbHXfWFYx7ZXWYEN4xZHOihTDtGxag3DyNq1FTlmDfz+mvcDXeuuVVfzbMe0F8Vq3XBYtWuTR7ykqKhImTpwoXHnllR7dridoyQ4sSxYtWmT192JZxbR1vPVcPnDggDBy5EihQ4cOgkKhEOLi4oQHHnhAOHfunEe/xxF5eXkCAGHbtm1m65944glhyJAhVj/j6+srrFq1ymzdO++8I0RERAiCQMEJAITz58+bjZkwYYIwceJEQRAE4cUXXxR69OjRYNvh4eHCu+++KwiCIIwePVqYOXOm2fuHDx82TMJag2UV015pDTaEx2ukV1dXY8+ePUhPTzesk0qlSE9PR0ZGhtXPZGRkmI0HgDFjxhjGnz59Gvn5+WZjgoKCMHToUMOYjIwMBAcHY9CgQYYx6enpkEql2LFjh9XvXbx4sVnH6pSUlMYdNMMwrYrWJKcAQKfToayszLCUl5e7ftAMw7Q6WpOsYjnFtHecbeDnKnPnzoVarUZoaChycnLw448/emS7nqSwsBB1dXWIjIw0Wx8ZGYn8/Hyrn8nPz7c7Xnx1NCYiIsLsfR8fH3To0MHm9z711FNmv1NWVpaTR8kwjDW4gbt3YFnFMC0XH09v0J4iZdrV2ZTmVKTmzJlj+D83Nxe9evXChQsXHB0mw7RqxGtcr9c38540D61JTgE06ffss882WM+yimnrsKxqPbKK5RTTXhGvcbVajcDAQIfj582bh6VLl9odk52djaSkJADAE088gRkzZuDs2bN49tlnMWXKFKxbtw4SicT9nW+HKBQKs+aKJSUlAFhWMW2ftq5ThYWFQSaToaCgwGx9QUEBoqKirH4mKirK7njxtaCgAB07djQb069fP8MYy2amtbW1KC4uNtuOte8x/Q5LWFYx7ZXWIKs87khvTVgKJ41GAwAYMmRIc+0SwzQpBQUFiI2Nbe7dYBxgOem3Z88eXHPNNSyrmHYDy6qWD8sppr3jrJx6/PHHMW3aNLtjunbtavg7LCwMYWFh6NGjB5KTk9G5c2ds374dqamp7u6yx2jJDixHiPvAsoppL7RVnUoul2PgwIHYtGkTxo8fD4AccZs2bcKsWbOsfiY1NRWbNm3Co48+ali3YcMGg3yNj49HVFQUNm3aZJA7ZWVl2LFjBx588EHDNkpKSrBnzx4MHDgQAPDHH39Ar9dj6NChhjH/+c9/UFNTA19fX8P3JCYmIiQkxKnjY1nFtDdasqzyuCO9NStS/fv3x86dOxEZGQmp1ONVb5ymvLwcKSkpyMrKQkBAQLPtR2uGz6F99Ho9CgoK0L9//+belWahtckpy0m/q666imVVG4HPoX1YVrUeWdVS5RTA95kn4HNoG1flVHh4OMLDwxv9XQCVUmpJtGQHliNaiv0H8H3mLnz+7NMedKo5c+Zg6tSpGDRoEIYMGYJly5ahsrIS06dPBwBMmTIFnTp1wuLFiwEAs2fPxvDhw/Haa69h3LhxWL16NXbv3o0PPvgAACCRSPDoo4/ihRdeQEJCAuLj47FgwQJER0cbZF1ycjLGjh2L++67DytWrEBNTQ1mzZqFSZMmITo6GgBw55134tlnn8WMGTMwd+5cHDp0CG+++SbeeOMNp4+tpcgqvs/ch8+hfVqFrPJG4fUhQ4YIs2bNMvxfV1cndOrUyW5jrBtuuMFsXWpqaoPGWK+++qrh/dLSUquNsXbv3m0Y89tvv7ncxK8lUFpaKgAQSktLm3tXWi18DhlHsJxyH77P3IfPIeMIllXuw/eZ+/A5bHq2b98uvP3220JmZqZw5swZYdOmTUJaWprQrVs3QavVNvfuNWD16tWCQqEQVq5cKWRlZQkzZ84UgoODhfz8fEEQBOGee+4R5s2bZxi/detWwcfHR3j11VeF7OxsYdGiRYKvr69w8OBBw5glS5YIwcHBwo8//igcOHBAuPnmm4X4+HihqqrKMGbs2LFC//79hR07dgj//POPkJCQIEyePLnpDtyD8H3mHnz+GEEQhLfffluIjY0V5HK5MGTIEGH79u2G94YPHy5MnTrVbPw333wj9OjRQ5DL5ULPnj2Fn3/+2ex9vV4vLFiwQIiMjBQUCoUwatQo4ejRo2ZjioqKhMmTJwv+/v5CYGCgMH36dKG8vNxszP79+4Vhw4YJCoVC6NSpk7BkyRLPHngTwfeZ+/A5bP14xZHOipR78I3lPnwOGUewnHIfvs/ch88h4wiWVe7D95n78Dlseg4cOCCMHDlS6NChg6BQKIS4uDjhgQceEM6dO9fcu2aTlurAai3wfeYefP4YxvvwfeY+fA5bP15xpAsCK1LuwDeW+/A5ZJyB5ZR78H3mPnwOGWdgWeUefJ+5D59DhvE+fJ+5B58/hvE+fJ+5D5/D1o9EEATB6/VjGJfQ6XRYvHgxnnrqKbN6o4zz8DlkGO/D95n78DlkGO/D95n78DlkGO/D95l78PljGO/D95n78Dls/bAjnWEYhmEYhmEYhmEYhmEYhmHs0LytyRmGYRiGYRiGYRiGYRiGYRimhcOOdIZhGIZhGIZhGIZhGIZhGIaxAzvSGYZhGIZhGIZhGIZhGIZhGMYO7EhnGIZhGIZhGIZhGIZhGIZhGDuwI51hGIZhGIZhGIZhGIZhGIZh7MCO9CbivffeQ58+fRAYGIjAwECkpqbi119/NRuTkZGBa665Bmq1GoGBgbj66qtRVVVleL+4uBh33XUXAgMDERwcjBkzZqCioqKpD6XZcHQO8/Pzcc899yAqKgpqtRoDBgzAd999Z7aN9n4OGcYRLKvch2UVw3gfllXuw7KKYbwLyyn3YTnFMN6HZZX7sKxqZwhMk/DTTz8JP//8s3Ds2DHh6NGjwtNPPy34+voKhw4dEgRBELZt2yYEBgYKixcvFg4dOiQcOXJE+PrrrwWtVmvYxtixY4W+ffsK27dvF7Zs2SJ0795dmDx5cnMdUpPj6ByOHj1aGDx4sLBjxw7h5MmTwvPPPy9IpVJh7969hm2093PIMI5gWeU+LKsYxvuwrHIfllUM411YTrkPyymG8T4sq9yHZVX7gh3pzUhISIjw3//+VxAEQRg6dKgwf/58m2OzsrIEAMKuXbsM63799VdBIpEIeXl5Xt/XlorpOVSr1cJnn31m9n6HDh2EDz/8UBAEPocM01hYVrkPyyqG8T4sq9yHZRXDeBeWU+7DcophvA/LKvdhWdV24dIuzUBdXR1Wr16NyspKpKam4uLFi9ixYwciIiKQlpaGyMhIDB8+HP/884/hMxkZGQgODsagQYMM69LT0yGVSrFjx47mOIxmxfIcAkBaWhq+/vprFBcXQ6/XY/Xq1dBqtRgxYgQAPocM4yosq9yHZRXDeB+WVe7DsophvAvLKfdhOcUw3odllfuwrGr7+DT3DrQnDh48iNTUVGi1Wvj7++P7779HSkoKtm/fDgB45pln8Oqrr6Jfv3747LPPMGrUKBw6dAgJCQnIz89HRESE2fZ8fHzQoUMH5OfnN8fhNAu2ziEAfPPNN7jjjjsQGhoKHx8fqFQqfP/99+jevTsA8DlkGCdhWeU+LKsYxvuwrHIfllUM411YTrkPyymG8T4sq9yHZVX7gR3pTUhiYiL27duH0tJSrFmzBlOnTsVff/0FvV4PALj//vsxffp0AED//v2xadMmfPzxx1i8eHFz7naLwtY5TElJwYIFC1BSUoKNGzciLCwMP/zwAyZOnIgtW7agd+/ezb3rDNNqYFnlPiyrGMb7sKxyH5ZVDONdWE65D8sphvE+LKvch2VV+4Ed6U2IXC43zDgNHDgQu3btwptvvol58+YBgGG2SiQ5ORk5OTkAgKioKFy8eNHs/draWhQXFyMqKqoJ9r5lYOscPvnkk1i+fDkOHTqEnj17AgD69u2LLVu24J133sGKFSv4HDKMk7Csch+WVQzjfVhWuQ/LKobxLiyn3IflFMN4H5ZV7sOyqv3ANdKbEb1eD51Oh7i4OERHR+Po0aNm7x87dgxdunQBAKSmpqKkpAR79uwxvP/HH39Ar9dj6NChTbrfLQnxHGo0GgCAVGp+SctkMsMsKp9DhmkcLKvch2UVw3gfllXuw7KKYbwLyyn3YTnFMN6HZZX7sKxqwzR3t9P2wrx584S//vpLOH36tHDgwAFh3rx5gkQiEX7//XdBEAThjTfeEAIDA4Vvv/1WOH78uDB//nxBqVQKJ06cMGxj7NixQv/+/YUdO3YI//zzj5CQkCBMnjy5uQ6pybF3Dqurq4Xu3bsLV111lbBjxw7hxIkTwquvvipIJBLh559/NmyjvZ9DhnEEyyr3YVnFMN6HZZX7sKxiGO/Ccsp9WE4xjPdhWeU+LKvaF+xIbyLuvfdeoUuXLoJcLhfCw8OFUaNGGQSTyOLFi4WYmBhBpVIJqampwpYtW8zeLyoqEiZPniz4+/sLgYGBwvTp04Xy8vKmPIxmxdE5PHbsmHDrrbcKERERgkqlEvr06SN89tlnZtto7+eQYRzBssp9WFYxjPdhWeU+LKsYxruwnHIfllMM431YVrkPy6r2hUQQBKG5o+IZhmEYhmEYhmEYhmEYhmEYpqXCNdIZhmEYhmEYhmEYhmEYhmEYxg7sSGcYhmEYhmEYhmEYhmEYhmEYO7AjnWEYhmEYhmEYhmEYhmEYhmHswI50hmEYhmEYhmEYhmEYhmEYhrEDO9IZhmEYhmEYhmEYhmEYhmEYxg7sSGcYhmEYhmEYhmEYhmEYhmEYO7AjnWEYhmEYhmEYhmEYhmEYhmHswI50hmEYhmEYhmEYhmEYhmEYhrEDO9IZhmEYhmEYhmEYhmEYhmEYxg7sSGcYhmEYhmEYhmEYhmEYhmEYO7AjnWEYhmEYhmEYhmEYhmEYhmHswI50hmEYhmEYhmEYhmEYhmEYhrEDO9IZhmEYhmEYhmEYhmEYhmEYxg7sSGcYhmEYhmEYhmEYhmEYhmEYO7AjnWEYhmEYhmEYhmEYhmEYhmHswI50hmEYhmEYhmEYhmEYhmEYhrEDO9IZhmEYhmEYhmEYhmEYhmEYxg5t0pE+bdo0SCQSSCQS9OrVq7l3h2FaPY8++qjhnvL392/u3WkTsJxiGM8yfvx4vqe8AMsqhvEsLKu8A8sqhvEsbP95B5ZVDONZmkOvapOOdAAICwvD559/jiVLljR4b9u2bRg2bBhUKhWioqLwyCOPoKKiwultf/TRR0hOToZSqURCQgLefvvtBmOOHj2Kxx57DGlpaVAqlZBIJDhz5ow7h4TDhw9jwoQJ6Nq1K1QqFcLCwnD11Vfjf//7X4Ox4oVkbRk9enSD8SdPnsSdd96JiIgI+Pn5ISEhAf/5z3+c2q+SkhLMnDkT4eHhUKvVGDlyJPbu3ev149Tr9Vi5ciVuuukmdO7cGWq1Gr169cILL7wArVbbYLu2zoe1a8QaOp0Oc+fORXR0NPz8/DB06FBs2LCh0cdpC1d+Z2e47777IJFIcMMNN9gdd/LkScO1unv3brP37rnnHnz++ee46qqrGrUPjHWaW06tXbsWd9xxh+FaS0xMxOOPP46SkpJGH1N7kVO7du3CrFmz0LNnT6jVasTGxmLixIk4duyY1fHffPMNrrjiCgQHByM0NBTDhw/Hzz//3GDciRMncPvttyMkJAQqlQrDhg3D5s2bnd4vTx+nLf7++2+D7FUqlYiKisLYsWOxdetWpz4fFxdn87dPSEgwG1tQUIDp06cbfvcBAwbg22+/bbDNxx57DJ9//jmSkpI8coyMkeaWVd9//z3GjBmD6OhoKBQKxMTE4Pbbb8ehQ4cafUzelFUiX375pcsOiOaWVdnZ2Rg7diz8/f3RoUMH3HPPPbh06VKDcRcuXMDMmTMRHx8PPz8/dOvWDXPmzEFRUVGzHKct3JVVALBx40aMHDkSYWFhCA4OxpAhQ/D55583GFdaWoonn3wSCQkJ8PPzQ5cuXTBjxgzk5OSYjWNZ5T2aW1ax/ee5e/jFF1+06RR56aWXcMUVVyA8PNzwezz66KMNZNX58+dx9913IzExEQEBAYb799NPP4UgCE7tR1PZf5bYO35b5OXlYeLEiQgODkZgYCBuvvlmnDp1qsG49957DxMmTEBsbCwkEgmmTZtmdXts/3kPllXOyaozZ87YHLd69Wqn9qs59SrTSRPTxdrzn23AhjYgQHbg/fffj06dOkGpVCIuLg4zZswwG9McepVPk31TE6NWq3H33Xc3WL9v3z6MGjUKycnJeP3113Hu3Dm8+uqrOH78OH799VeH233//ffxwAMP4LbbbsOcOXOwZcsWPPLII9BoNJg7d65hXEZGBt566y2kpKQgOTkZ+/btc/uYzp49i/LyckydOhXR0dHQaDT47rvvcNNNN+H999/HzJkzDWOtKfi7d+/Gm2++iWuvvdZs/b59+zBixAh06tQJjz/+OEJDQ5GTk4Pc3FyH+6TX6zFu3Djs378fTzzxBMLCwvDuu+9ixIgR2LNnj9WbwVPHqdFoMH36dFxxxRV44IEHEBERgYyMDCxatAibNm3CH3/8AYlEYrbt0aNHY8qUKWbr+vfv79R+TZs2DWvWrMGjjz6KhIQErFy5Etdffz02b96MYcOGuXyctnDld3bE7t27sXLlSiiVSodjH3vsMfj4+ECn0zV4b+DAgRg4cCA2btzoFYHcXmluOTVz5kxER0fj7rvvRmxsLA4ePIjly5fjl19+wd69e+Hn5+fyMbUXObV06VJs3boVEyZMQJ8+fZCfn4/ly5djwIAB2L59u5nh8/bbb+ORRx7BuHHjsGTJEmi1WqxcuRI33HADvvvuO9x6660AgNzcXKSmpkImk+GJJ56AWq3GJ598gmuvvRabNm3C1Vdf3eTHaYtjx45BKpXigQceQFRUFC5fvowvvvgCV199NX7++WeMHTvW7ueXLVvWwCg4e/Ys5s+fb/bbl5WVYdiwYSgoKMDs2bMRFRWFb775BhMnTsSXX36JO++80zB2+PDhAID//ve/KCws9NixMs0vqw4ePIiQkBDMnj0bYWFhyM/Px8cff4whQ4YgIyMDffv2dfmYvCWrRCoqKvDkk09CrVY7vU/NLavOnTuHq6++GkFBQXjppZdQUVGBV199FQcPHsTOnTshl8sNx5aamorKyko89NBD6Ny5M/bv34/ly5dj8+bN2LNnD6RS27E6rUlW/fTTTxg/fjxSU1PxzDPPQCKR4JtvvsGUKVNQWFiIxx57zHBMo0ePRlZWFh566CH06NEDJ06cwLvvvovffvsN2dnZCAgIAMCyyps0t6xi+88z9/C5c+fw0ksv2ZSfe/bsQb9+/TBp0iQEBAQgOzsbH374IX7++Wfs27fP8LnCwkKcO3cOt99+O2JjY1FTU4MNGzZg2rRpOHr0KF566SWH+9JU9p8pjo7fGhUVFRg5ciRKS0vx9NNPw9fXF2+88QaGDx+Offv2ITQ01DB26dKlKC8vx5AhQ3DhwgWb22T7z3uwrHJNr5o8eTKuv/56s3WpqakO96m59SoAUCgU+O9//2u2LigoyOx/tgEb2oAAnZcrr7wSAPDAAw+gU6dOOH/+PHbu3Gk2rln0KqENMnXqVKFLly5W37vuuuuEjh07CqWlpYZ1H374oQBA+O233+xuV6PRCKGhocK4cePM1t91112CWq0WiouLDeuKioqEsrIyQRAE4ZVXXhEACKdPn27cAdmhtrZW6Nu3r5CYmOhw7IwZMwSJRCLk5uYa1tXV1Qm9evUShg4dKmg0Gpe//+uvvxYACN9++61h3cWLF4Xg4GBh8uTJLm/PFtaOU6fTCVu3bm0w9tlnnxUACBs2bDBbD0B4+OGHG/X9O3bsEAAIr7zyimFdVVWV0K1bNyE1NbVR23QFV35nEb1eL6Smpgr33nuv0KVLlwbXrSnr168X5HK5MH/+fAGAsGvXLqvjpk6dKqjVapf3n2lIS5BTmzdvbvD5Tz/9VAAgfPjhh84fjAPaopzaunWroNPpzNYdO3ZMUCgUwl133WW2PiEhQRg8eLCg1+sN60pLSwV/f3/hpptuMqx76KGHBB8fH+HIkSOGdZWVlULnzp2FAQMGONynppLHtqisrBQiIyOFMWPGNOrzzz//vADATK6//PLLAgBh06ZNhnV1dXXC4MGDhaioqAa/gSAIwvDhw4WePXs2ah+YhrQEWWWN/Px8wcfHR7j//vudOxAncFdWmTJ37lwhMTHRcDzO0Nyy6sEHHxT8/PyEs2fPGtZt2LBBACC8//77hnVffvmlAEBYt26d2ecXLlwoABD27t1rd59ak6waPXq0EB0dLWi1WsO6mpoaoVu3bkKfPn0M67Zu3SoAEJYvX272+Y8//lgAIKxdu7bBtllWeZaWIKvY/vPMPXzHHXcI11xzjUv3yJo1awQAwldffeVw7A033CCo1WqhtrbW7rjmsv8ac/xLly4VAAg7d+40rMvOzhZkMpnw1FNPmY09c+aMQSdVq9XC1KlT7W6b7T/PwrLKOtZk1enTpxvcg67Q3HqVs/cO24ANbUBBoPshPj5eKCwsdGo7TalXtdnSLtYoKyvDhg0bcPfddyMwMNCwfsqUKfD398c333xj9/ObN29GUVERHnroIbP1Dz/8MCorK83S9Dt06GCIPPEmMpkMnTt3dliKQafT4bvvvsPw4cMRExNjWP/777/j0KFDWLRoEfz8/KDRaFBXV+f0969ZswaRkZGGiEoACA8Px8SJE/Hjjz9ajW5uDNaOUy6XIy0trcHYW265BQClJ1ujqqrKaukXe6xZswYymcxsJlWpVGLGjBnIyMhwKnrDHZz9nU35/PPPcejQIbz44ot2x9XU1GD27NmYPXs2unXr5uaeMu7SlHJqxIgRDT7v6P5pDG1RTqWlpRmiM0USEhLQs2fPBueurKwMERERZhkygYGB8Pf3N4v637JlC/r374/ExETDOpVKhZtuugl79+7F8ePHm/w4XUGlUiE8PLzRpYFWrVqF+Ph4M7m+ZcsWhIeH45prrjGsk0qlmDhxIvLz8/HXX3+5u9tMI2lKWWWNiIgIqFQqt0pRWeKurBI5fvw43njjDbz++uvw8XE++bO5ZdV3332HG264AbGxsYZ16enp6NGjh9nvWVZWBgCIjIw0+3zHjh0BwGE2U2uSVWVlZQgJCYFCoTCs8/HxQVhYmNlxuntOGO/B9l/r0KtE/v77b6xZswbLli1z6XNxcXEA4NR9HRcXB41Gg+rqarvjmsP+a+zxr1mzBoMHD8bgwYMN65KSkjBq1KgG13iXLl0aZG0zzQ/LKtt6FQBUVlY6vGctaW69SqSurs6gJ1iDbcCGNuCRI0fw66+/4oknnkBoaCi0Wi1qamo8tMfu064c6QcPHkRtbS0GDRpktl4ul6Nfv37IzMy0+3nxfcvPDxw4EFKp1OHnPUVlZSUKCwtx8uRJvPHGG/j1118xatQou5/55ZdfUFJSgrvuusts/caNGwFQysmgQYOgVquhUqkwadIkFBcXO9yXzMxMDBgwoEEK75AhQ6DRaGzW4HSGxhwnAOTn5wOg2mOWrFy5Emq1Gn5+fkhJScGqVauc2pfMzEz06NHD7KEG0HEC8Eg6lCWNPX4AKC8vx9y5c/H0008jKirK7thly5bh8uXLmD9/vid2m3GT5pZT9u4fV2gvcsoUQRBQUFDQ4NyNGDEC69evx9tvv40zZ87gyJEjePjhh1FaWorZs2cbxul0OqvOFpVKBYBSme3RVMdpSllZGQoLC3HkyBE8/fTTOHTokNNyypTMzExkZ2eblWoB3D8njPdoDllVUlKCS5cu4eDBg/jXv/6FsrKyRl1vpnhSVok8+uijGDlyZIM0ZEc0p6zKy8vDxYsXG/we4veb/h5XX301pFIpZs+eje3bt+PcuXP45Zdf8OKLL2L8+PEOa1S2Jlk1YsQIHD58GAsWLMCJEydw8uRJPP/889i9ezeefPJJwzjx2bRgwQL88ccfyMvLw19//YUnn3wSgwcPRnp6usePiXGO5tarPEV70Kvq6urwf//3f/jXv/6F3r172x0rCAIKCwuRn59vKF8hk8msBolUVVWhsLAQZ86cwaeffopPPvkEqampDie4mtr+c+X4TdHr9Thw4IBN+X3y5EmUl5d7clcZL8Cy6v/ZO/P4qKq7/7/v7DPZWLJCCIRFNpGdCNpHbanBpYqtqPzagtRi9REFaUWxLArauAsoNWLr9lQKxYW2olTEWm2JUBBcCMGNEEhISFiyzL6c3x83M5lJZrKRZZKc9+t1X5O599w7505mvnPO53yXyOOqBx98kNjYWEwmE5MnT+a9995rVl86ew4Iajri+Ph4EhIS6NOnD3fccUeD1CZyDthwDuj/nUpJSeEHP/gBZrMZs9nMFVdccc75/NuCbpsjPRz+HGB+75Bg0tLS+Pjjj5s8X6vVkpycHLLfYDDQt29fSkpK2q6zjfDrX/+a559/HlA983784x/z7LPPNnrOa6+9htFo5Prrrw/Z71/duuGGG5gxYwZLly7ls88+Iycnh2PHjvHvf/+70RXrEydOhM3Z5H+PS0pKWjQQCKY19wnw2GOPER8fzxVXXBGyf9q0adxwww1kZmZSUlLC+vXr+elPf0plZSW33357o9c8ceJExM8N0C7/+9beP8CqVaswm82B3J2RKC0tZfXq1TzxxBMNBomSzqGz7dSjjz6KVqttYCtaSk+xU/X7X1xczKpVq0L2r1u3joqKCu666y7uuusuQF2o2LlzZ0h+v+HDh/Pxxx9TXV0d4iXy73//G1CFrsboqPsM5oYbbuAf//gHoH7GfvWrX7F8+fIWX+e1114DaDCAHj58OO+//z5Hjx5l4MCBgf3+70FT74mk/egMW3XhhRdy+PBhAGJjY1m2bFmDgkMtpS1tFcC2bdt47733+Oyzz1rcl860VU39P0+fPo3T6cRoNDJq1Cg2bNjAb37zmxAbNnfu3AZ5QMPRlWzV8uXLOXLkCA8//DAPPfQQoE5s33jjDa699tpAu8TERDZv3sz8+fNDJpLZ2dm8/vrrLYpMkLQtnT2uait6wrgqNzeXo0ePBgSUxigrKwv5n6anp7Nx48awC3lr165l6dKlgec/+MEPeOmll5p8jY6e/7Xk/oPx2+em+hrs7SqJPqStamirNBoNl19+Oddddx39+/fnu+++46mnnuKKK67gb3/7G1dddVWj1+3sOWBaWhpLlixhwoQJ+Hw+tm/fzu9//3s+++wzPvzww8DYQM4BG84B/b9Tt956K5MnT2bz5s0UFRXx4IMPMn36dD7//PPAQkNn0KNGdXa7HSAkPNOPyWQKHG/s/PphHC05v61YtGgR119/PSUlJfzlL3/B6/U2GuZSVVXFtm3buPLKK+nVq1fIMf9q2OTJk/nTn/4EwE9+8hMsFgtLly5l586djXrR2O32iO+n/3hrael9glrF/f333+f3v/99g3utX0n4F7/4BRMnTuT+++/n5ptvbtQroT3vMxKtuX9QC0CsXbuWP//5z2H7HMy9997L4MGD+eUvf9lW3ZacI51ppzZu3Mgf//hHlixZcs5FSXqKnfLj9zKfOnUqc+fODTlmsVgYPnw46enpXH311VRXV/P000/z4x//mI8//pihQ4cCcPvtt/P3v/+dG2+8kYcffpiYmBh+//vfs3fv3mb1szPs1COPPMKvf/1rjh07xiuvvILL5cLj8bToGj6fj02bNjF+/HhGjhwZcuyXv/wlubm53HDDDTz99NOkpKTwl7/8hbfeegton3uSNI/OsFUvvfQSVVVVfPfdd7z00kvY7Xa8Xm+jhS2boi1tlcvl4u677+a2225j1KhRLe5LZ9qqpv6f9fvXv39/pkyZwpVXXsnAgQP5+OOPWbduHYmJiTzxxBON9qEr2Sqj0ch5553H9ddfz49//GO8Xi8bNmzgZz/7GTt27ODCCy8MtE1KSmL8+PEsWLCA0aNHc+DAAR577DHmzZvHli1b2vyeJM1Dzv+6xrjq1KlTrFixguXLl5OUlNRk+z59+rBjxw4cDgf79+/nzTffbODl6Wf27NlMmjSJ8vJy3n77bcrKyprVx460VS29/2Caa78l0Y20VQ1tVUZGRkCs9fPzn/+cUaNG8etf/7pJIb2z54A5OTkhz2+66SbOO+88fvvb3/L6669z0003AXIOGG4O6LfnqampbNu2LTDWT09PZ/bs2WzcuLFTNaweldrFL5SGyxHkcDiaDO8ym80RjUBzzm8rRowYwfTp05kzZw5vv/02NTU1/OhHP0IIEbb9G2+8gcPhCBsq4+/z7NmzQ/b7Qyt27drVaF/MZnPE9zP4+q2hpfe5efPmgHdaUx7moK6cLViwgLNnzzYZLtPW9+n1eiktLQ3Z6n+2Wnr/fhYuXMi0adP4yU9+0mi7Tz75hP/7v//j6aefPicRQtK2dJad+vjjj7nlllvIzs5uMq9+c+gpdgrUyI6rrrqKhISEQD7NYGbNmkVRUREvv/wy119/PfPmzePDDz/E5XLx29/+NtDuiiuu4JlnnuGjjz5iwoQJDB8+nG3btgX+H7GxsR16ny6Xq4Gdqp9Dddy4cfzwhz/kF7/4BTt27GDPnj3cfPPNLXqdf/3rXxQXF4f9319wwQVs3LiRb7/9losuuoihQ4eybt26QO7Qpt4TSfvRGbZq6tSpZGdnc/vtt/OPf/yDP/3pTyFehq2hLW3V008/TUVFBQ8++GCr+tKZtqqp/2dwm//85z9cffXVPPzwwyxcuJCZM2fy5JNPsmzZMp566iny8/Mb7UdXslULFizg73//O5s2beKmm27ipz/9Ke+//z5paWkhqbm+++47LrvsMn7xi19w//33c+2117Jy5Up+//vf8/rrr/Puu++26J4kbYec/3WNcdWyZcvo06cPd955Z7PaGwwGpk+fztVXX83y5ctZv349t9xyC2+//XaDtgMHDmT69OnMnj2b1157jcGDBzN9+vQmxaWOnP+19P7r9xOaZ78l0Yu0VeHTutSnT58+zJs3j8OHD3P8+PFG23b2HDAcd999NxqNJiTyRM4BI/9O3XDDDSFa1axZs9DpdE3+TrU3PUo984c2+MNmgjlx4gT9+vVr8nyv18vJkydD9rtcLk6dOtXk+e3F9ddfz3//+9+IuY9ee+01EhISuPrqqxsc8/e5fnEkf0jQmTNnGn3ttLS0iO9n8PXbgsbuc8eOHcyZM4errrqK3NzcZl9zwIABAE3mA2zr+zx27BhpaWkhW1PGoKn/M8AHH3zA9u3bWbhwIYWFhYHN4/Fgt9spLCwMFLpYsmQJ3/ve98jMzAy0q6ioCNxXUVFRi+5J0jZ0hp367LPPuOaaazj//PPbLQS9u9qpyspKrrjiCs6ePcv27dsbXOu7775j+/btXHPNNSH7+/Tpw8UXX9wgUmbBggWUlZWxa9cu9u7dS0FBAQkJCQCcd955jfalre9z165dDexUY4W1DAYD11xzDW+++WaLPB9ee+01NBpNgwm9H79Xy549e8jLy+Po0aMMHjwYaPo9kbQfnT2m6t27N9///vcDIaFtRWttVWVlJQ899BDz58+nqqoq8LtaU1ODEILCwsIG91qfzrRVTf0/+/TpE/B2ev7550lJSWmQh/Waa65BCNHkeKar2CqXy8Uf//hHrrrqqpBJnF6v54orrmDv3r0B0eLll1/G4XA0+Fz4bX99Wy/pODrbVrUX3Wlc9fXXX7NhwwbuuusuSkpKAvbTX1yusLCwybnatGnTSEtLa9ZvwvXXX8+xY8f46KOPGm3XUfO/c71/v33uqDm5pH2QtqqhrYpEZ2k4wTQ1roqE2Wymb9++Dfou54ChRPqd0mq19O3bt8nfqfamRwnp559/PjqdLhAi4cflcnHgwAHGjRvX6Pn+4/XP37t3Lz6fr8nz2wv/h7WysrLBsRMnTvDPf/6Tn/zkJ2HDPSZOnAg0zLvkz6HVVGjZuHHj+PTTT/H5fCH7d+/ejcViaVORI9J97t69m+uuu45Jkybxl7/8pUUi4HfffQc07z6/+uqrBtWWd+/eHTjeElJTU9mxY0fINnbs2EbPaez/7Mcvfv/4xz8mMzMzsBUXF/PBBx+QmZnJiy++GGj70UcfhbS75557AHXid8EFF7ToniRtQ0fbqW+//ZYZM2aQnJzMO++8024evt3RTjkcDn70ox/x1Vdf8fbbb4dN5VBWVgbQYBUfwO12hw2Bi4mJYerUqUycOBGtVsv777+P2WzmoosuarQ/bX2fY8eObWCnmipebLfbEUI0u6iV0+nkjTfe4NJLL210kGcwGJg8eTIXXnghBoMh4MUhC/h1HtEwprLb7Y3+JraG1tqqM2fOUFNTw2OPPRbyu/rGG29gs9nIzMzk1ltvbfS1O9NW9e/fn6SkpAb/D4A9e/aE/D/Kysoi2jSgydDermKrTp06hcfjiXivPp8vcKysrAwhRIO2zX1PJO1HNNiq9qA7jauKi4vx+XzcddddIfZz9+7dfPXVV2RmZjbIPRwOh8PRrN+E5sypoOPmf+d6/xqNhjFjxoS137t372bw4MEheZcl0Ym0VY2npA2mJRpOZ42rIlFdXU1FRUXYvss5YB2RfqdcLlfE969DEd2QuXPnioEDB4Y9NmPGDJGWliaqqqoC+/7whz8IQLz77ruBfVarVRw6dEiUl5cH9tlsNtGnTx9x9dVXh1zzZz/7mbBYLOLUqVNhX/Pxxx8XgDhy5Ejrb0oIUVZW1mCfy+USEyZMEGazWVRXVzc4/tRTTwlA7Ny5M+w1T5w4IYxGo7j44ouF1+sN7F+6dKkAxJ49ewL7SkpKxKFDh4TL5Qrs27RpkwDEli1bAvvKy8tFr169xI033tju95mfny/69u0rRo8eLU6fPh3xmidPnmywr6qqSgwZMkQkJiYKp9MZ0v9Dhw4Jq9Ua2PfJJ58IQDz++OOBfQ6HQwwdOlRkZWW1+B4boyX3X/9/cvToUfHWW2812JKSksSkSZPEW2+9Jb755hshhBD/+Mc/GrS78847BSCeeOIJ8fbbbzfox9y5c0VMTEyb3m9PJRrs1IkTJ8TgwYNFv379ztk++ekpdsrj8YhrrrlG6HQ6sW3btojtTp48KTQajbj00kuFz+cL7D927JiIjY0VM2bMaPR1/vOf/witVisWLFgQsr+j7jMS4f7PZ86cEQMGDBADBgwI2X/06FFx6NChsNd58803BSD++Mc/Nvu1v/rqKxEXF9fgM+7nkksuEaNHj2729SSNEw22Ktzn7ciRIyIuLk5873vfa9V9tbWtslqtYX9/L7vsMmEymcRbb70lPvnkk0D7aLNVQghx2223CbPZLIqKigL73n//fQGI5557LrBvwYIFAhD//Oc/Q85ftGiRADrlPiNxLrbK4/GIXr16ifPOOy9knFhdXS3S09PFiBEjAvueeOIJAYiXXnop5Jpr1qwRgNi0aVODfkhb1bZEg60KRs7/mk95eXlY+zl69GiRkZEh3nrrLfH5558LIYSoqakJmaP5ef311wUgli9fHtgXbv4nhBA/+tGPhKIo4uuvvw7pQ2fN/1py/0KEH1c98sgjAhD//e9/A/sKCgqEVqsV9957b8TXjomJEXPnzm20f3L+17ZIW1VHU7Yq3Hf4+PHjonfv3uKCCy4I2R9t4yq73R7yf/Rzzz33CEC8+eabjb5OT58DOhwOkZycLAYPHizsdntg//PPPy8A8Ze//KXBOR05rupxQvq+ffuE0WgU48ePF88995z47W9/K0wmk7j88stD2v3zn/8UgFi5cmXI/vXr1wtAXH/99eKFF14Qc+bMEYB4+OGHQ9qdPXtWrF69WqxevVrMmDFDAOLXv/61WL16tXjmmWca9Lc5xmvmzJni+9//vnjggQfECy+8IFavXi1GjBghAPHkk0+GPWfixImiX79+IYOk+qxatUoA4oc//KFYv369uPXWW4WiKGL27NlN9tPj8YgLL7xQxMbGigcffFCsX79ejB49WsTFxYmCgoJ2vc+qqioxYMAAodFoxCOPPCL+7//+L2TbtWtXoO3KlSvF2LFjxbJly8SGDRvEgw8+KAYOHCgURRF/+tOfQl5/5cqVYSeIs2bNEjqdTtxzzz3i+eefF9OmTRM6nU7861//atb5zaUl/+fmvqcDBw4UV111VZOv/dJLLzUYhNV/PTmQahuiwU6NHTtWAGLJkiUNvj/vvfdeg/5KO1XHwoULBSB+9KMfNXjv/u///i+k7S9/+UsBiMsuu0w888wz4ne/+51IT08XWq02xH4UFhaKKVOmiIceekj84Q9/EHfffbcwm81i/PjxDQZiHXWfkZgwYYK45pprxMMPPyxeeOEFsXz5cpGeni40Gk3IIE4IdVATad3+Jz/5iTAajeLs2bMRX2vkyJFixYoV4g9/+IP47W9/K/r06SMGDhwojh8/Hra9FKfalmiwVcnJyWL27Nni0UcfFRs2bBD33HOP6NOnjzCZTOI///lPg/52pq2qT6TfzWi0VUVFRaJv375iyJAhYt26deJ3v/ud6N27txgzZoxwOByBdgUFBSImJkbExsaKpUuXitzcXDF79uyAje6M+4zEudqqhx56SABi/Pjx4umnnxZPPPGEGDlypABCxo8VFRUiNTVVGAwGcdddd4nnn39e/OpXvxJarVaMHj06RIgPfj1pq9qOaLBVcv7Xtt/hcN+R/fv3i759+4r//d//FevWrRPPPvusuPnmm4VOpxODBg0SFRUVgbYLFy4UkyZNCsz/HnnkETF58mQBiDvvvDPkup09/2vu/fv317dVfgex5ORk8dhjj4mnn35aDBgwQPTr16+BGPm3v/0t8Dk1GAxi/PjxgeefffZZg9eT87+2RdqqOpqyVTfffLP43ve+Jx544AGxYcMGcf/994u+ffsKg8HQ4LsWbeOqI0eOiF69eonbb79drF27Vqxdu1ZceeWVAhAzZswIuWc5BwzPK6+8IgAxefJksW7dOvGb3/xG6PV68b3vfU94PJ4G7aWQfo40ZpyEEOLjjz8W06ZNEyaTSSQlJYk77rijwQc0knESQogNGzaI4cOHC4PBIIYMGSKefvrpEE9DIdQvDhB2q9+3n/zkJ8JsNoszZ840el9//vOfxfTp00VKSorQ6XSid+/eYvr06eKvf/1r2PYFBQUCEIsXL270uj6fTzzzzDPivPPOE3q9XgwYMEAsW7YsZJVLiMhfutOnT4tbbrlF9O3bV1gsFnHJJZeEFWLb+j4be4+BkNX19957T/zwhz8UqampQq/Xi169eonLL7887OpnpIGQ3W4Xv/nNb0RqaqowGo1i8uTJYvv27Q3O//Wvfy0URYm4+tYULfk/SyG96xINdqqx788ll1wS0lbaqVD8A4NIWzBut1s888wzYty4cSI2NlbExsaKyy67THzwwQcN+njttdcGxJjMzExx7733hvVm6Kj7jMSzzz4rLr74YpGYmCh0Op1ISkoSP/rRj8RHH33UoG2kQVRlZaUwmUzixz/+caOvddNNN4kBAwYIg8Eg+vXrJ2677baw3hDBryfFqbYjGmzVypUrxaRJk0Tv3r2FTqcT/fr1EzfddFOIh56fzrZV9WmJkC5E59oqIYT48ssvxeWXXy4sFovo1auX+OlPfypKS0sbtCsoKBDXX3+9GDBggNDr9WLgwIHiN7/5TQNP0e5gq1577TUxZcoU0atXL2E2m0VWVpZ4/fXXG7Q7fvy4+MUvfiEyMzOFwWAQaWlpYv78+SEeg/VfT9qqtiMabJWc/7Xtdzjcd6S8vFzceuutYsSIESImJkYYDAYxbNgwsWjRogbftffee09cffXVol+/fkKv14u4uDhx0UUXiZdeeins70xnzv/C0RIhXQg12vH6668X8fHxIjY2Vlx99dUhXvd+/P/TcFv9qBp/ezn/azukrVJpjq3auHGj+J//+R+RlJQkdDqdSExMFNddd53Yt29fg7bRNq46c+aM+NnPfiaGDh0qLBaLMBqNYvTo0eJ3v/tdAzsr54CR+fOf/yzGjh0rjEajSElJEQsWLAj7vvhfr6PGVYoQEcrndmFuvvlmPvjgAz799FN0Oh29evXq7C41SkpKCnPmzOHxxx/v7K60Kz3lPqdMmcLAgQPZsmVLZ3elzbBardjtdu68807+/ve/U1NT09ld6vJIOxWdyPvsulRXV+N0Orn22muprKzkyy+/7OwudQukrYpO5H12XaStah+krYpOesp9yvmfpLlIWxWdyPvsunTGuKrbFhs9duwYSUlJXHzxxZ3dlUY5ePAgdrude++9t7O70q70lPusqqris88+a1ZBnK7Eb3/7W5KSkti0aVNnd6VbIe1UdCHvs2vz85//nKSkJHbt2tXZXel2SFsVXcj77NpIW9V+SFsVXfSU+5TzP0lLkbYqupD32bXpjHFVt/RIz8/PD1Qdj42N5cILL+zkHkkkXZuvvvqKoqIiAHQ6HZdeemnndqgbIO2URNK2fP7555w8eRKQ36m2RNoqiaRtkbaqfZC2SiJpW+T8r32QtkoiaVs6Y1zVLYV0iUQikUgkEolEIpFIJBKJRCKRSNqKbpvaRSKRSCQSiUQikUgkEolEIpFIJJK2QArpEolEIpFIJBKJRCKRSCSSbs/69esZNGgQJpOJrKws9uzZ02j7LVu2MGLECEwmE2PGjOGdd94JOS6EYMWKFaSlpWE2m5k+fTpff/112Gs5nU7GjRuHoigcOHCgrW5JIpF0ILrO7kA04fF42L9/PykpKWg0co1B0n3x+XyUlZUxfvx4dDppBroa0lZJegrSVnVdpJ2S9BSkneraSFsl6SlIW6WyefNmFi9eTG5uLllZWaxZs4bs7GwOHz5McnJyg/a7du1i9uzZ5OTkcPXVV7Nx40ZmzpzJp59+yvnnnw/AY489xrp163jllVfIzMxk+fLlZGdnk5+fj8lkCrnekiVL6NevH5999lmL+i1tlaSn0CVslZAE2LNnjwDkJrces+3Zs6ezv3aSViBtldx62iZtVddD2im59bRN2qmuibRVcutpW0+3VVOmTBF33HFH4LnX6xX9+vUTOTk5YdvfcMMN4qqrrgrZl5WVJX71q18JIYTw+XwiNTVVPP7444HjZ8+eFUajUfz5z38OOe+dd94RI0aMEAcPHhSA2L9/f8R+OhwOUVlZGdg++OCDTv/syE1uHblFs62KUnm/c0hJSQFgz549pKWldXJvJJL248SJE0yZMiXwmZd0LaStkvQUpK1SWb9+PY8//jilpaWMHTuWZ555hilTpkRsv2XLFpYvX05hYSHDhg3j0Ucf5corrwwcF0KwcuVKXnjhBc6ePctFF13Ec889x7Bhw0Kus23bNlatWsXnn3+OyWTikksuYevWrc3qs7RTkp6CtFNdG2mrJD0FaavA5XKxb98+li5dGtin0WiYPn06eXl5Yc/Jy8tj8eLFIfuys7MD46EjR45QWlrK9OnTA8cTEhLIysoiLy+Pm266CYCysjLmz5/P1q1bsVgsTfY1JyeHBx98sMF+aask3Z2uYKukkB6EP0QmLS2N9PT0Tu6NRNL+yLCwrom0VZKeRk+2VZ0VgvzGG28wf/58fve73/H9738fj8fDl19+2ex+Szsl6Wn0ZDvVlZG2StLT6Mm2qqKiAq/X20CgS0lJoaCgIOw5paWlYduXlpYGjvv3RWojhODmm2/mtttuY9KkSRQWFjbZ16VLl4YI+MXFxYwaNUraKkmPIZptVfT2TCKRSCQSiaSH89RTTzF//nzmzZvHqFGjyM3NxWKx8OKLL4Ztv3btWmbMmME999zDyJEjWb16NRMmTODZZ58F1MncmjVrWLZsGddeey0XXHABr776KiUlJQHvKo/Hw8KFC3n88ce57bbbOO+88xg1ahQ33HBDR922RCKRSCQSSbfgmWeeobq6OsQTvimMRiPx8fGBLS4urh17KJFIWoIU0iUSiUQikUiiEH8IcnC4cHNCkIPbgxqC7G/fVAgywKeffkpxcTEajYbx48eTlpbGFVdc0ahHutPppKqqKrBVV1e3+r4lEolEIpFI2prExES0Wi1lZWUh+8vKykhNTQ17TmpqaqPt/Y+Ntfnggw/Iy8vDaDSi0+kYOnQoAJMmTWLu3LnnfmMSiaRDkUK6RCKRSCQSSRTSWAiyP1y4Pm0Rgvzdd98B8MADD7Bs2TLefvttevfuzaWXXsrp06fDvm5OTg4JCQmBbdSoUS28W4lEIpFIJJL2w2AwMHHiRHbu3BnY5/P52LlzJ1OnTg17ztSpU0PaA+zYsSPQPjMzk9TU1JA2VVVV7N69O9Bm3bp1fPbZZxw4cIADBw7wzjvvAGr6vocffrhN71EikbQ/Mke6RCKRSCQSiSSAz+cD4Le//S0/+clPAHjppZdIT09ny5Yt/OpXv2pwTqRcnhKJRCKRNAfhEziKHHirvWjjtJgyTCgapbO7JelmLF68mLlz5zJp0iSmTJnCmjVrsFqtzJs3D4A5c+bQv39/cnJyAFi4cCGXXHIJTz75JFdddRWbNm1i7969bNiwAQBFUVi0aBEPPfQQw4YNC9Se6devHzNnzgQgIyMjpA+xsbEADBkyROY7l0i6IK3ySF+/fj2DBg3CZDKRlZXFnj17Gm2/ZcsWRowYgclkYsyYMYEVOD9CCFasWEFaWhpms5np06fz9ddfB44XFhZyyy23kJmZidlsZsiQIaxcuRKXyxXSRlGUBtsnn3zSmluUSCQSiUQi6VQ6KwQ5LS0NIEQINxqNDB48mKKiorCvK3N5Rg/CJ7AX2qn5ogZ7oR3hE53dJYlEImkU6yErRY8UUbiikMLVhRSuKKTokSKsh6yd3TVJN+PGG2/kiSeeYMWKFYwbN44DBw6wffv2QKReUVERJ06cCLSfNm0aGzduZMOGDYwdO5bXX3+drVu3Bgq4AyxZsoQ777yTW2+9lcmTJ1NTU8P27dsDBdwlEkn3osVC+ubNm1m8eDErV67k008/ZezYsWRnZ3Py5Mmw7Xft2sXs2bO55ZZb2L9/PzNnzmTmzJkheTYfe+wx1q1bR25uLrt37yYmJobs7GwcDgcABQUF+Hw+nn/+eQ4ePMjTTz9Nbm4u999/f4PXe//99zlx4kRgmzhxYktvUSKRSCQSiaTT6awQ5IkTJ2I0Gjl8+HCgjdvtprCwkIEDB7bZ/UnaHilGRT/PPfccF1xwQWDRaerUqbz77rud3S2JpNOwHrJyfN1xqvdXo0vUYRluQZeoo3p/NcfXHZf2S9LmLFiwgKNHj+J0Otm9ezdZWVmBYx9++CEvv/xySPtZs2Zx+PBhnE4nX375JVdeeWXIcUVRWLVqFaWlpTgcDt5//33OO++8iK8/aNAghBCMGzeuLW9LIpF0EC0W0p966inmz5/PvHnzGDVqFLm5uVgsFl588cWw7deuXcuMGTO45557GDlyJKtXr2bChAk8++yzgOqNvmbNGpYtW8a1117LBRdcwKuvvkpJSQlbt24FYMaMGbz00ktcfvnlDB48mGuuuYbf/OY3vPnmmw1er2/fvqSmpgY2vV7f0luUSCQSiUQiiQoWL17MCy+8wCuvvMKhQ4e4/fbbG4QgL126NNB+4cKFbN++nSeffJKCggIeeOAB9u7dy4IFC4DQEOS//e1vfPHFF8yZMyckBDk+Pp7bbruNlStX8t5773H48GFuv/12QJ1MSqITKUZ1DdLT03nkkUfYt28fe/fu5fvf/z7XXnstBw8e7OyuSSQdjvAJKt6qwF3hxjLKgi5eh6JV0MXrsIyy4K5wU7G1QkbWSCQSiSRqaJGQ7nK52LdvH9OnT6+7gEbD9OnTycvLC3tOXl5eSHuA7OzsQPsjR45QWloa0iYhIYGsrKyI1wSorKykT58+DfZfc801JCcnc/HFF/O3v/2t0ftxOp1UVVUFturq6kbbSzqOp5+GzEwoLAx//Nsl3/LJ0E+oeLuiQ/slkUgkwcyaBZdcAj6fujD85U++5POrPkd45YSvo+muXp6dFYL8+OOPc9NNN/Hzn/+cyZMnc/ToUT744AN69+7dcTcvaRZOJ0yZJMj9qSpGmc8zU765nNPvnpZiVBTyox/9iCuvvJJhw4Zx3nnn8fDDDxMbGyvTUUp6JI4iB7YCG8YBRoqOKqx9UnC4QD2mKArGdCO2QzYcRY7O7WgPpLuOqySSlrJhAwwcCIcOqc+L1xezZ9QebN/YOrdjkk6jRUJ6RUUFXq83MHnzk5KSQmlpadhzSktLG23vf2zJNb/55hueeeaZkGJXsbGxPPnkk2zZsoVt27Zx8cUXM3PmzEbF9JycHBISEgKbLIoVPbz+uiqi/+c/4Y9XvFWB41sHX/7oS75a8BVeu7dD+9fTkAMpiaQhHo9qqz76CEpKwFPpoeLNCk6/c5oTr5yQglUH0529PDsjBFmv1/PEE09QVlZGVVUVO3bsYPTo0e12j5LWc/AgFO1zcOYzVYxyl7txlbio2VeDq9QlxagOorq6OsRBx+l0NnmO1+tl06ZNWK3WiOmaJJLujLfai9fhRRujpfBjG9fUFFGxq865TRujxefw4a2Wc72OpjuPqySSlvDmm1BUBB9+qD4/+ZeT2A7ZOP708U7tl6TzaFWx0c6kuLiYGTNmMGvWLObPnx/Yn5iYyOLFi8nKymLy5Mk88sgj/OxnP+Pxxx+PeK2lS5dSWVkZ2PLz8zviFiTNoKZGfXREmO/53L7A3yXrS9g3eR81X9R0QM96JnIgJZE0xBqUJaHqCyvHnjgWeH505VGZl7gNaY5AJb08JT2V4mKw4EXr86IxayFIb6raXQVIMaojGDVqVIiDTk5OTsS2X3zxBbGxsRiNRm677Tbeeust6dAj6ZFo47RoTVq8Vi++ky40QNxZe+C41+pFY9KgjdN2Xid7KHJcJZGo+LUpW60DunCrzlJlr5VJh84eSouE9MTERLRaLWVlZSH7y8rKSE1NDXtOampqo+39j825ZklJCZdddhnTpk1jw4YNTfY3KyuLb775JuJxo9EY8LCNj48nLi6uyWtKOga/sbLbwx8XHtV4ZeZkok/RYztoY++4vXx+xeecfP0kPqcv/ImSViEHUhJJQ/xCegZWzr54nJrP6hbznMVOzu46K/MStxEtEahAenlKehYlJWBDiwstzrPekGgY6xdWvFavFKM6gPz8/BAHneDaBfUZPnw4Bw4cYPfu3dx+++3MnTtXOvRIeiSmDBOWERYcR53YalTbZba7ADVlnvO4E8tIC6YMU2OXkbQAGT0jkbQMvzbln/v5tShvpZeKt2Sq4c6mM7IntEhINxgMTJw4kZ07dwb2+Xw+du7cGdGgTp06NaQ9wI4dOwLtMzMzSU1NDWlTVVXF7t27Q65ZXFzMpZdeysSJE3nppZfQaJru+oEDB0hLS2vJLUqihCaF9NpVwL5X9WXyF5NJvC4RfHB6+2nyZ+Wzq/8uvrr9K05uPomzpOnBQU9FDqQkktZjtYKC4GIqcJ9yYxoaNMkT4LP6ZF7iNqK5ApX08pT0RIqL4SQmirBgO+oMrdHgher/VksxqgOIi4sLcdAxGo0R2xoMBoYOHcrEiRPJyclh7NixrF27tgN7K5FEB4pGIfG6RGx6PfG4ATB4vLjKXNjybRgSDSTOTETRKJ3c0+6DjJ6RSFqGv5RiQEh3142zTvzxRJgzJB1JZ2RPaHFql8WLF/PCCy/wyiuvcOjQIW6//XasVivz5s0DYM6cOSET3IULF7J9+3aefPJJCgoKeOCBB9i7dy8LFiwA1DydixYt4qGHHuJvf/sbX3zxBXPmzKFfv37MnDkTqBPRMzIyeOKJJygvL6e0tDQkh/orr7zCn//8ZwoKCigoKOB3v/sdL774Infeeee5vD+STqKp1C5+46XoFQxJBs5/83ymfD2FjN9mYOhvwHPKQ0luCfk35ZPXP49PhnxC/k/zOfrwUcrfLMdaYMXnkl7rciAlkbQeqxWScZCBDW9vI9QzKTX7azD0M8i8xG1AcwUq6eUp6YmUlIBA4d8kIuL1OI6E2puqT6rQ99ZLMSqK8fl8zXJmaC/Wr1/PoEGDMJlMZGVlsWfPnkbbb9myhREjRmAymRgzZgzvvPNOyHEhBCtWrCAtLQ2z2cz06dP5+uuvQ9o8/PDDTJs2DYvFQq9evcK+jqIoDbZNmzaFtPnwww+ZMGECRqORoUOHNqgZIYl+YkbGUDQpnUr0gX2Obx3ETYij/139iRkZ04m9637I6BmJpGUEp3YRPoHXVpfO5ewHZ7F/F8H7U3JONNfpszOyJ+haesKNN95IeXk5K1asoLS0lHHjxrF9+/ZAsdCioqIQb/Fp06axceNGli1bxv3338+wYcPYunUr559/fqDNkiVLsFqt3HrrrZw9e5aLL76Y7du3YzKpXjM7duzgm2++4ZtvviE9PT2kP0LUrQatXr2ao0ePotPpGDFiBJs3b+b6669v6S1KOhmvty7/VFMe6YqubkJoGWph8EODyXwwk9Pvneb09tNUflxJzWc1OL5z4PiunpClgCHNgGmgCWOGEWM/I4YUA/oUPYZkA/okPfo+enR9dOgSdN1y8pmfn0///v0DzxvznvIPpCorK3n99deZO3cu//rXv6SYLumxWK1qXmIDXtxaI/g8gGqXFJ2Ct9KL+6QbvMi8xB2E38sTYOLEifz3v/9l7dq1PP/8853cM4mk/SguVh+LiEFzfTrmrUVU/bsKbYIW4Rb4bD5MmSYpRkUJS5cu5YorriAjI4Pq6mo2btzIhx9+yD/+8Y9O6c/mzZtZvHgxubm5ZGVlsWbNGrKzszl8+DDJyckN2u/atYvZs2eTk5PD1VdfzcaNG5k5cyaffvppYH732GOPsW7dOl555RUyMzNZvnw52dnZ5OfnB+Z3LpeLWbNmMXXqVP74xz9G7N9LL73EjBkzAs+DRfcjR45w1VVXcdttt/Haa6+xc+dOfvnLX5KWlkZ2dnYbvUOSjmBfRQwOYhmDWtchfmo8GfdmdMv5V2fjd05oDnJcJZHUeaTriq0UPVKBq1RNP6UYFIRLcOyJY5z3+/M6sYfdk/o608qVK3nggQcaPcfr9bJly5Z2z57QYiEdYMGCBQGP8vp86C9lG8SsWbOYNWtWxOspisKqVatYtWpV2OM333wzN998c6N9mjt3LnPnzm20jaRrYK324Q+WcJysBF8c1Evl489LpdE3DKpQtAp9r+hL3yv6AuCp9FCZV0nNgRps+TZsh2xYD1nxWX24Sly4SlyQ10SnNKCL16GN1wYetXFatLFBW4wWjVmD1qJFY9GgNWvRmDRozBr10ahBMSpojJrAphiUukeDBkVf+6jtmEGjHEhJJK3Haq3LS+yp8SKS6iJlYi6IoXp3NdX/rSZhWoLMS9xJdLaXp0TSEZSU1P3tSIkhY2YiZf9XhmmQiT7ZfTj22DHK/1JOxn0ZKIoUpTqbkydPMmfOHE6cOEFCQgIXXHAB//jHP/jhD3/YKf156qmnmD9/fiC6ODc3l23btvHiiy9y3333NWi/du1aZsyYwT333AOojkw7duzg2WefJTc3FyEEa9asYdmyZVx77bUAvPrqq6SkpLB161ZuuukmAB588EGAJj3Ie/XqFbEWV25uLpmZmTz55JMAjBw5kn//+988/fTTUkjvYhw4AOdT5yDnLHZKET0KkeMqSU/D61WdOzOwMvTz41Rr3VBrmiyjLVj3Wyl9tZR+/9uP2PNjO7ez3YyWOH1+8cUXTJ06FYfDQWxsbLtnT2iVkC6RtBuHDlHzyg7gLgDsH++FR3bDddfByJGBZj63mkNB0Tc9wNIl6Og7oy99Z/QN7BM+gbvcjaPIgbPIieOoA9cJF66TLlxlLtxlbtyn3LhPu/FZfeADz1kPnrMenHTA4EFR703Rq56tGr0m8Leiq9uvaIP21f6NVl1MGLRiEL0u6dVuXZQDKUlPx2qty0ssyqsRA9SfVEWrEDshlurd1Ti+c5A0K0nmJe4Aos3LUyLpKPwe6aCGH/tzpOsSdGQsyaD4mWJqDtRQ+nIpvS7rhSnDJAWqTqQx7+uOxuVysW/fvpDUDhqNhunTp5OXF97LJC8vj8WLF4fsy87OZuvWrYDqJV5aWsr06dMDxxMSEsjKyiIvLy8gpDeXO+64g1/+8pcMHjyY2267jXnz5gUWhPLy8kJex9+XRYsWRbye0+kMGb9W+10NJZ3KgQMwNkhIt34uC7V3NnJcJZGE1sTSWd1YRlk4894ZAGLHxWI/ZMdn9XHsyWOM+OMIOb5qQ1ri9NnR2ROkkC6JHg4dgnXrqDlSlx/ProuD/fvh2DG46y4YOVJN51ObJaE5Qno4FI2CIcWAIcUAkxtv63P6cJ9x46304qny4Kn04K304q3x4rXWPlZ78dq9+Gw+vDYvPqsPn6Nu89q9CKfA5/Spm8OHcAl8LvXR72EfQKDud9VWhKblaSHc/+tu8TmRkAMpiaQhVmtdXuKfmBzYv6nNSaWAxqhB11eH55QHn90nB1UdQLR5eUokHYHTCadO1T2v+e4kIq7O2cB10kXM+TFU/7ea7+77jt4/7E3MqBgSr0uUqV4kVFRU4PV6Ayk6/aSkpFBQUBD2nNLS0rDt/bWr/I+NtWkuq1at4vvf/z4Wi4X33nuP//3f/6Wmpoa77rqr0b5UVVVht9sxm80NrpmTkxPwhpdEB6WlUFYG2mAh/Usrwis6LEpX0hA5rpJI1LQu/ppYp3VGdSG3ti6WxqBRo5D3VHP2n2dxFDkwD2r4uyNpfzo6e4IU0iXRgc8Hb70FFRXUZPwgsNuhWGDUKMjPh61bYfhwRJCmHJwjvb3QGDUYU40QPqq0TRA+oeYxdflCHoVbFdn9jz63r+65t1aA99Z66HvVlDf+/XFT4tqsf3IgJZE0xF94pogYSi5MZ3T1cSo/qET4BJ5THnpd1ouK1ysof72cjHszMKZFDkeTnDvR5OUpkXQUJR99AwwNPLf+6S1EmgYYhs/p4/i642j7aEEL7pNuqvdV43V4cRxzkH5XuhTTJVHN8uXLA3+PHz8eq9XK448/HhDSW8PSpUtDPOqLi4tlvZ9O5sAB9VEXVLXdZ/dh/9aO5TxL53RKIsdVEgnqfM9fE8vmU+dywle76KeB2PGxVO+pVrMcHJFCerTQ3tkTpJAuiQ6KiqCgAAYMoPp0ndhk9+hAUSA9XfVYLypCJA8IHG+tR3q0oWiUQP70aEQOpCSShlhr6uo5nPa6SL4piRPPn0CfqGfQqkHok/Ts3bsXR6GDA5cdYNw/x0kxXSKRtB2HDlGy/m3gnsCuGlNfROHnwDDcxVbcqQbis+LR99ZT/pdy7AV2tDFqzYaKrRVYhltkxEwPJjExEa1WS1lZWcj+srKyiHnJU1NTG23vfywrKyMtLS2kzbhx486pv1lZWaxevRqn04nRaIzYl/j4+LDe6KDmWA3Os1pVVXVOfZKcO34hvU+8gKB/R83nNVJIl0gknUpNTV1NLIPTC+gCKfQUrYI+WY8+RY+7zM03i75h/Mfj0cVLmbUj6YzsCdGp2kl6HtXV4HBATAw1LkNgt8NTa4RiYtTj1dUId13YX7hioxKJRNLuHDqEdfu/A09t7/0H/m8jANpYLeZBZnQxOsbuHIsxw4j9sJ0Dlx3AeULWFZBIJG1AbSRfcWloIWOrJh5filqYyXfaijHdgKIoWIZbSLwuEYCafTU4jzix5ltxFDk6vOuS6MFgMDBx4kR27twZ2Ofz+di5cydTp04Ne87UqVND2gPs2LEj0D4zM5PU1NSQNlVVVezevTviNZvLgQMH6N27d0AIb6ovkq6BX0hP7KXO8dy1lfxknnSJRNLZVFfX1cSKdTjVNMP+4BkNCCGIGRuDxqLB+rmVL675Aq+95Wl5Ja3Hnz1h+PDh/OAHP+C///1vu2dPkEslkuggLg5MJrBaQ4R0u7s2X7rVqh6PiwsUGoWOSe0ikUgkIdTWc7Aeyw7ssukTEF99B5yP4nEF9psHmxn34TgOXHpAFdMvrfVM7yc90yUSyTlQG8lXYvpxyO4alwGhU50MhNuD1lMD9AYg5vwYhFtw6m+nqPm0Bk+lB9cJlwxD7uEsXryYuXPnMmnSJKZMmcKaNWuwWq3MmzcPgDlz5tC/f39ycnIAWLhwIZdccglPPvkkV111FZs2bWLv3r1s2LABAEVRWLRoEQ899BDDhg0jMzOT5cuX069fP2bOnBl43aKiIk6fPk1RURFer5cDtWrq0KFDiY2N5e9//ztlZWVceOGFmEwmduzYwe9+9zt+85vfBK5x22238eyzz7JkyRJ+8Ytf8MEHH/CXv/yFbdu2dcybJ2kT/EJ673hVSC9UYhgmaqj5vKbzOiWRSCTUFnGvrYk1yOfAlm8LeKT7rD5sJTYsgy30m9+Pw7ccpvJflRycdZDz3zpfOn12EJ2RPUH+ZyXRQUYGjBgBx46FCukeHQgBx4/DyJGQkVFXmFNBFqCRSCQdS1A9B2tCXci6TYlBpA8CQFNzVm1XizlTFdONA43Yv7Kzd8JeitcX43P5kEgkklZRG8lX4uobstvqNiB86thIgxfvWVfI8djxsfSeoQrrjm8dfHnNl5S+UlqX71PS47jxxht54oknWLFiBePGjePAgQNs3749UMSzqKiIEydOBNpPmzaNjRs3smHDBsaOHcvrr7/O1q1bOf/88wNtlixZwp133smtt97K5MmTqampYfv27ZhMpkCbFStWMH78eFauXElNTQ3jx49n/Pjx7N27FwC9Xs/69euZOnUq48aN4/nnn+epp55i5cqVgWtkZmaybds2duzYwdixY3nyySf5wx/+QHZ23UK3JLqxWuGrr9S/e8WqduiwUOs8SY90iUTS2QTXxHpLSSdufFzAI91T6SFuQhz97+pP8vXJjHl7DBqThtPbTnPo54fwWqVnendFeqRLogONBq67Do4dozrvdGC3w6mohUYTE2HmTNBoAqldukt+dIlE0oUIqudg/aZu0c/m1uMT6tq04rKq7QYNChz3i+mfZ3+O/Ss7Xy/4mmNPHGPQA4NI+VmKXBSUSCQtozaSr/iMmj841uCkxmVUPdJrhXSd0Y3zlAbtYIGi1NmYuClxeGu82A7ZcFe4Kbi5gJLnSxj04CB6/6C3zJneA1mwYAELFiwIe+zDDz9ssG/WrFnMmjUr4vUURWHVqlWsWrUqYpuXX36Zl19+OeLxGTNmMGPGjIjH/Vx66aXs37+/yXaS6OSLL1SfqdRUMOp8uIGviAXAccSBp8oj8w1LJJJOo7q67u/DjhjSf2PmyG+PAJBxfwZxY+MC46Ze3+vF6DdH8+U1X1K+uZzKf1cy+HeD1blehLGV8AkcRQ681V60cVpMGSY5DusCSI90SfQwciTcdRc1SYMDu+wuLUyYAHfdpR4HKaRLJFGE8AnshXZqvqjBXmjv/l6NQfUcrK5QIV14VZukCG/oqKsW8yAzk7+YzLDfD8OQZsBR6KDg5gLyMvL4asFXnPngDD6P9FKXSCTNoDaSr+SUmibqvL6nALAG2SJTfwV9RgK2fBueSg/CI/BUerDl24g9P5axO8cy+JHBaGI0VOVV8fnln/PJoE/4btl32L62ddqtSSSSnoM/rcu4caDUpks4iwF9bQo865fSK10ikXQeNUEZpnw+cNnr5rqWIaozQ/BcuE92H8a8PQbTIBOuYhcFcwv4NOtTzvzzjJpfPQjrIStFjxRRuKKQwtWFFK4opOiRIqyHpN2LduTyriS6GDmSmgkj4H31qSOmL9x7r+qxXos/tYvMjy6RdC7WQ1Yq3qrAVmDD6/CiNWmxjFAL2sWMjOns7rUPQfUcrP4aDtQK6bVeoIqutl0YNAYN/W/vT+rcVIrXF1P0SBGuEhcl60soWV+Crq+O3t/vTfyF8cRfGE/s+Fi0Zm3Ya0kkkh5MbSRf8WN9ABgef4JPT/Sjxqrgc1cAgzGMTqf/wvSAnXYVu9CYNMRNiCNxpmqn48bGkfLzFI4+fJSTG0/iPOak6OEiih4uwjLaQu/v96b3D3qTcEkC+l76xvskkUgkLSRYSGenOsfzoGAYGYO7xEnNZzUkTEvorO5JJJIeTn3fqJrKOjHc9o2NM9vPhJ0LTz40meK1xRx9+CjVe6v57PufYRpsIvmmZFL+Xwpo4Pi647gr3BgHGDHGGPFavVTvr8ZxzEH6Xenddz7dDZBCuiTqqLHWCeR2l65B3ITfI10Wb5BIOg/rIWvP/PH313PYvx+rq56QXpsGT4mzqO0aQWvRknFPBul3pXNm5xnK3yynYmsFnlMeyreUU76lXL2WTsE83IxlhAXLcAuWERZMmSZMA00Y0gxodNIOSiQ9FTFiJCW1tRbOMxYBE7HaFMSAfnAIlOQ+xIyMwTLcEhI2bEw34jzupOaLmkAY8Xnrz2PIk0M49bdTlL5cyul/nMZ20IbtoI3iZ4pBActwC7HjYokdH0vs2FgsIywYBxhlCLJEImk1wUK6+EedkK47LxZ2npZ50iUSSadSU6/msa2qTkgvyS3Bc8YTMheu+rSKmoM1JN+QTNKNSaTMSeHoqqOU/l8pju8cFP2uiKLfFaFP0qON1RIzNkbNua4BXbwO7SgttnwbFVsrsAy3yDFWlCKFdEnUEWys7PaGx31uddIoU7tIJJ2D8Akq3qrAXeHGMsoSyL3bI378g+o5WKvqCsjYnQrieCkwCk3/ZLWeQzNy3mmMGvpe2Ze+V/bFl+ujalcVlf+ppGp3FVWfVOEucwfErAZowdjPiKGfAUOKAUOq+qhP1KPvq0fXV6c+9tKhS1A3jVEK7xJJd6GqCqx29Tt93i8uhkVQkzYMMdkMHx4PjJMUjYJ5kBlQF0GPPXYsYiRR8g3JJN+QjPuUm7MfnuXMB2c4+8FZbAW2wHZy08lAHxSjgnmIGfNQM6YMkzqZrN0Mqapd0sV23nRD5h6VSKIXr1fNkQ6qkF5V6yzlRQNDVGeMms9rIpwtkUgk7U99Id0aJKS7T7uJGR0TmAsLp8Bz2oP9WzvWz63EjoslZmQM/e/qz5AnhnDq7VOUbSzj9LuncZe7cZe7cRxxAKCJ0WBINqgCe5yWsx+eJeHSBBKyEuS4JQqRQrok6ggOn3E41AI0QTWy6nKky9QuknNk/fr1PP7445SWljJ27FieeeYZpkyZErH9li1bWL58OYWFhQwbNoxHH32UK6+8MnBcCMHKlSt54YUXOHv2LBdddBHPPfccw4YNC7QZNGgQR48eDbluTk4O9913X9vfYDvhKHJgK7CpnohK6PdQURSM6UZsh2w4ihwB8aZbUVvPwfpGr8Aum0OLL30Q7AOlV3zYtDfm88zEXxiPIcUQVtDR6DT0+p9e9Pof9bpCCJzHnNgO2UJELMdRB85jToRHPe485mx21xWjgi5OhzZOW7fF1G2aGA1asxaNRYPGXPu3WYPGVLsZNShGJfC3xqhBMShoDHV/K3r1uWJQ0Og1KHpFDgAlknagpER97NULkkYlAWD1mBAedX99h4PmRhIJn8BT7cF8npnYibEMe2YY7nI3NQdqqN5fTc2BGqyfWbF/a0c4BbZ8G7b8yDnVNRZNyCKfPlGPro8OXW+dutDn3+J1aOO1qo2K16KNVe1Sa+1Hj0w/JpF0Ib75Bmw2sFhg6FDYW+ss5UXBN0gtOGr9worwCTmOkEgknUL91C726lohXUGdy9XOhd3lbqp2V+G1edH30SM8Ao1BEzLGSr4xmeQbkzn7n7N8u+Rb8IHzmBPXCRc+qw/HEUdAWAc4894ZFIOCaaAJY4YRY5rqQGXsZ0SfoseQVDu2SlLHV9JhquOQQrok6ghe9RMCXC4wGuv2+WrDmBFqYQfpXSRpDZs3b2bx4sXk5uaSlZXFmjVryM7O5vDhwyQnJzdov2vXLmbPnk1OTg5XX301GzduZObMmXz66aecf/75ADz22GOsW7eOV155hczMTJYvX052djb5+fmYTKbAtVatWsX8+fMDz+Mi5NOOVrzVXrwOL8YYY9jj2hgtrmIX3mpv2OPdgpEjsZqDcuTFJiN+8EP467d47d4GYpWjyMHJTScpfbUU82AzhmRDk4KOoiiYMkyYMkz0ye4Tckx4Ba4yF44iB65SF65SF+4yt/p4yh3YPKc9eM56Av8L4RS4nW7cFe72e2/CoVFFvYCwrlMCjxP3TcSQaGj6GhKJJITiYvWxXz+IVTUnamrCOxw0N5JI+ASn/noqrPjcJ7tPiC0SXtXb2/61Hfs3dpzHnDiOOQILfK4yFz6bD5+t4eSwJWgsGnWhz6JBa6ld9DNrAltgwS9o0c9T5aH6v9X4HD50fXVoLVq81V5sX9s4+9FZkn6chHmIObAQqOiVwBawU8HPDYqsVyGRtDH+tC4XXABabZ3t8qDgTlW/n95qL46jDsyZ3dAxQyKRRD011QKoG0/ZKv25PNU5L6jOT9YCqyqiJ+lBgOeUB8WgYBllaRCtbexvxDLEgi5Rhy5bh8/tw33SjfukG9dJF64SF57THrx2L8Il1HHW12FSNdRDY9aojgm96zkoxNc6UcUGbUHjqgYOVMFOVCaNOm9TpN4WjBTSJVFH/fAZh6NOSLceslL6cikAnkoPhSsKpXeRpFU89dRTzJ8/n3nz5gGQm5vLtm3bePHFF8N6h69du5YZM2Zwzz33ALB69Wp27NjBs88+S25uLkII1qxZw7Jly7j22msBePXVV0lJSWHr1q3cdNNNgWvFxcWRmpra7L46nU6czjqv4+r6S+MdjDZOi9akxWv1ootv+DPitXrRmDRo47q36GANqudgc+kCXqCuUhfaOG1ArHKXq+lZhE+AAJ9TFXbOJZ+8olUw9jNi7Bd+MaM+wqt6mPpFdW+1F29N7aO1bvNZfXjtXnx2Hz67D6/Ni3AKfA4fPod6TDgFPqf63Of0IdwCn8un7nf5EC7RsAM+VcT3OhsursiFUImkdfg90vv3h5haE2K11hVlD64l05xIoqpPqrAetOJz+ppV+0LRKpgzzarAdXn4PnpqPIGFvpBFvlMePJUePGdUu+Q568FT7cFb5cVTpT5Sa0r8YnxbcuYfZ1rU3jLSwpT8yBFrEomk5RQVqY9Dh6qPftvlQcHp0RAzKkaNgPncKoV0iUTS8Rw6RM2XJiAzsMu2+W2MJKNolcBc2FvpxV3hRpegQ1EUfE4fik5Ro3XDRGubMkxYRlio3l+NdpQWjV6Dsb8RY38jQqiRfnET4hjw6wG4TriwH7HjPO7EVeLCWaI+uk66Aulh3Kfc4AOf3YfL7sJ1wtW274OGukhko3pfGkPt38FRyIZGHBJ0dc8VnULqnFRix8a2bT87ECmkS6KO+kK63Q4JCXUhybbDaviwxqRBl3huYpSkZ+Jyudi3bx9Lly4N7NNoNEyfPp28vLyw5+Tl5bF48eKQfdnZ2WzduhWAI0eOUFpayvTp0wPHExISyMrKIi8vL0RIf+SRR1i9ejUZGRn8v//3/7j77rvR6SKb45ycHB588MHW3Gq7UP/HP1iUEULgPO4kbkIcpgxTI1fp+liD6l/ZbHWeVN5qb0Csqu+h4M+dhyCsh0J7oWgV9L306Hvpm258jgghEF6BcAuES+Bzq2J7YPME7fMItPHde8FFImkvInmkh6sl01Qkkcaiwf6dHUOagfip8W1W+0IXq0M3VAdDW3ZvQqgLeN4ab2DzL+75bD514c+/4Odf/Ktd4HOfdFP570r1/jW14pxXXVAUXnUhULhEwGssYKdcqo0Ktll+MV+mE5RI2h5/LSz/QmCwR7rDAckXqEL66R2nSbw2sZN6KZFIeiSHDsG6dVSfuTtkt+PbslohXU2xqR2lVccVHoGiV+d+nioPxjQjugR1fl8/WlvRKCRel4jjmANbvg1juhFtjOqk5jzuxJBoIHFmIhqDBtNAE6aBjc+phU99Tc+ZoK1KdVjwVnnVR6s3dExlbTimCnam8ttjICDS++xt59QQPzVeCukSSVsSziM9OCTZOKB2EqjtIcUNJW1ORUUFXq+XlJSUkP0pKSkUFBSEPae0tDRs+9LS0sBx/75IbQDuuusuJkyYQJ8+fdi1axdLly7lxIkTPPXUUxH7u3Tp0hARv7i4mFGjRjXjTtuH5v74d+fvoter2iY/Nhv4agccQohAqF99DwUMIKpVIUen6LplPnlFUT0N0AHd45YkkqgknEe6zQa+2qiQYCG9qUgi1wkXXqsX0wBTVNS+UBQ1lYrGqMFrVSee+mQ9cRlxTf621HxRg6fGo44JtQ3bCo/A/pWdgcsHEjum8Umc8NUuAPrCRNpIJJJzwi+km2tNiggqNupwQNL1SZS9WkbJ70tIui6J3j/o3Uk9lUgkPQqfD956CyoqqNEmAKBVfHiFBnufASQAGo0bfV89tnwb2ji1nou32ovP6UNrUdPi+TPChIvWjhkZQ/pd6YFaLq5iFxqThrgJcSTObFm2BUUT5DCV2XT75iB8QRHI/ihkf1RyrUOCPxI5EKHsDtrvEeEdqWr3W0ZY2qajnYQU0iVRR4OCDvbQkGRXqRqq4p9I9YjihpJuQ7AgfsEFF2AwGPjVr35FTk4ORmN4T0Gj0RhyrKqqqt372RRt+ePfFbGFqavnttelU/CLVcEeCqB6PvpD/aCH5JOXSCTtQjiPdCHA42iYI72xSCKfz4f9azuKVkFj1qhe2PX0586wVa0tFtqW6ccUjYJi7L6LwhJJZ1JfSPcFFRt1OCDxxkTS5qdx4oUTHPrZISZ9NglDsqypIpFI2pmiIigogAEDqHGpc/DkGCsnauJwetToXg0u0q9XqNgTh/WQGqbsOe3BNMREzMgYNeqNxqO1Y0bGYBluwVHkwFvtRRunjZr6f4pGdWiQ9WHCI4V0SdQRLrWL1xMUkuyv7xDkZSTFKElLSExMRKvVUlZWFrK/rKwsYu7y1NTURtv7H8vKykhLSwtpM27cuIh9ycrKwuPxUFhYyPDhw1tzO51GNP/4tzfBaV38uGrD3fR99YFQP41RzQkn3AIMNAj1a8t88sIneuT/QiLpqQR7pJuDfAg8zoYe6ZEiiRzHHNTsq1HDep0+zuw8g7GfEcsIS2ASCK23Va21S/50fsFFmxvL1x6MTD8mkXQNInmk+1O7AAxdM5TK/1Riy7dRMLeAMdvGyLGNRCJpX6qr1dDjmBiqXeriXUpsDSdq4nA41TmcBi8xKU4s9w3DUeSg5rMayreU43P4UAwKwiPwWr04jjnQGtXaWY4iR4NxkKJRpCNoF0TTdBOJpOPweusGVQlqFA0OR6h3USC8tvbTK4TAWeLEa1fzP8nwW0lTGAwGJk6cyM6dOwP7fD4fO3fuZOrUqWHPmTp1akh7gB07dgTaZ2ZmkpqaGtKmqqqK3bt3R7wmwIEDB9BoNCQnJ5/LLXUa/h//2DGxmAeZe8zkxi+kx8SAvlZrcttU22M+z4w+UQ31Ewj0ffS4K9Qq7MGhfn5BxzLSginDhPAJ7IV2ar6owV5ob5Etsx6yUvRIEYUrCilcXUjhikKKHikKeEhIJJLuR7BHukZTl94lODomGH8kUdz4ODynPFTvraY6Tw0DTPheApbhFoRX4ChxULW7Cne5G2hoq5pLa+1ScDo/yygLungdilZBF6/DMsqCu8JNxdaKiDbSv2jgt8OeSg/CI/BUerDl23pE+jGJpCvQQEj3NBTStRYtozaPQmPScHr7aY4/fbwTeiqRSHoUcXFgMoHVSo1fSI9Rxy5OtyqkK1oBcXGBuXDStUkM/O1A4iaoYyz7V3Yc36mOBN4aL2Wvlcn5WTdCeqRLoopgL8/ERKisVAdZwd5FwlvraaVRcJe7sR6yYv/Wji5eR8nzJVR+VNlk2K9EsnjxYubOncukSZOYMmUKa9aswWq1Mm/ePADmzJlD//79ycnJAWDhwoVccsklPPnkk1x11VVs2rSJvXv3smHDBkBNMbRo0SIeeughhg0bRmZmJsuXL6dfv37MnDkTUAuW7t69m8suu4y4uDjy8vK4++67+dnPfkbv3jLvY1ciWEjX6VRb5a5Np2BIMZB6c2ogJYG/YruiVYgZFYOutw5PpSckn7ztsK1VKQzg3Dw3JRJJ18TngxMn1L/791cfY2JU2+Rx1hYbDVMg0x9JZC+0c3ztcdBA3CR1IqiL06kTPpsXd6Uba76V2LGxOItDa180x8v8XOxScDq/1uZr7+npxySSrkBjHulOZ1272PNjGbpmKF/d9hXf3fcdxnQjSTckNbAPEolE0iZkZMCIEbj2fYHLq0qmKbFq2gRVSBcoJr3aLojgaG2/h7qiVzBm1NUTk/Oz7oH0SJdEFf60Llot+HVFhyPUu8h5TB1ZCY/g7MdnsX9lR5egI+GiBPRJeqr3V3N83XG50idplBtvvJEnnniCFStWMG7cOA4cOMD27dsDxUKLioo44VcpgGnTprFx40Y2bNjA2LFjef3119m6dSvnn39+oM2SJUu48847ufXWW5k8eTI1NTVs374dk0n14DMajWzatIlLLrmE0aNH8/DDD3P33XcHxHhJ1yFYSLfU1krxC+mKXiFmZAwZ92UwaNUghjw+hGG/H0bybDXqwP6VHc8pD3ET4uh/l6qAHV93nOr91egSdViGW9Al6pply87Vc1PSNVi/fj2DBg3CZDKRlZXFnj17Gm2/ZcsWRowYgclkYsyYMbzzzjshx4UQrFixgrS0NMxmM9OnT+frr78OaTNo0CB1AShoe+SRR9r83iSt4+RJNYpPowF/jWt/nnSPo2Fql2AUjaIWxTrjxTKirki7PklPfFY8hjQDGp0Gx3cOHEWOgK2KGRnTLC/zc7VL3mo1nZ+/aHN9tDFafA5fk+n8gu3wwOUDGbRqEBn3ZsiJq0QSJQQL6cIr1PoM1BUbDSbt1jSSZiUhPIL8m/L5/PLP5VxPIpG0DxoNXHcdNfH9AruSzWoEn/O0ariUPvFqu3ooGgVThgnbQRs+pw/LaDk/645Ij3RJVOEvNBobW+ed4B9k+b2Ljtx/hJq9NXjOekBR0ygEF3TQjtJiy7dRsbUCy3CLDN2VRGTBggUsWLAg7LEPP/ywwb5Zs2Yxa9asiNdTFIVVq1axatWqsMcnTJjAJ5980qq+SqKLYCHdP4aqX+AvJOfdGOj9/d4NvDgBih4pCghOfu8qXbyuWbasLTw3JdHN5s2bWbx4Mbm5uWRlZbFmzRqys7M5fPhw2JRQu3btYvbs2eTk5HD11VezceNGZs6cyaeffhpY+HvsscdYt24dr7zySiB6Jjs7m/z8/MDCH8CqVauYP39+4HlcXFz737CkWfjzo6ekqFExUJfaxesSaIgspEOdWG2MCS1yrU/Sk5CYoIYlf22n36396D29N87jTsr/Wh7I/2nMiOxlfq52qa2LhUrbJ5FEJzabWtnYXF6E7xtPYL83KLWLH0VRGPHqCCyjLBQ9UsSZ98+w94K99F/Yn/539MecKb/nEomkDRk5kpqf3QYvg0HjpleNmlbKbekLWFFiI6e6a2wcBOo4p3JXJZW7KkmYliD1qi6I9EiXRBV+j3R/WiqoE9JBFdN7/1B1VdfEaOg9vTe9vtcrpCBW/UmaRCKRtDXhPNL9Bf7q5yX2Ey6ffEsEp3C0leemJHp56qmnmD9/PvPmzWPUqFHk5uZisVh48cUXw7Zfu3YtM2bM4J577mHkyJGsXr2aCRMm8OyzzwKqN/qaNWtYtmwZ1157LRdccAGvvvoqJSUlbN26NeRacXFxpKamBraYGOnJGy34hfR+dc5SAY90n6txj3QIFavroygKil7BkGJA0Skce+wYR5Yf4ciyI1R+XIn7tBvhFChaBW2cFkM/A/Zv7Zx46QQ+j++c7ZI/nZ/zmBMhQr21WpuvXSKRRBmHDmH/Ri30YP7bZsQDDwUOecII6QBak5bMBzKZkj+Fvtf0RXgEx588zu7Bu9k3eR9FjxZh+8bWwG5IJBJJa6hOHQZAXIKGmCsuAcCVNggInz7PT6RxkLvcTeW/K6n+bzU1B2ooerRI5kzvokghXRJV+IX0YI/0+gOpQI50nYKhnwHC2DApHkkkkvYkvJBem5e4EfGqPucqODUmhkHLPDcl0YfL5WLfvn1Mnz49sE+j0TB9+nTy8vLCnpOXlxfSHiA7OzvQ/siRI5SWloa0SUhIICsrq8E1H3nkEfr27cv48eN5/PHH8Xg8RMLpdFJVVRXYqv0hZpJ2wV9o1J8fHUI90qHxSV5zxGpdXx3lr5dTvb8axaCAArq+OlylLqp2V2E7bKPy35Wc/fAsjkIH5a+X893S73CVuVptl/z51y2jLWiMGmwHZbFQiaTbcegQrFuHvUr9TTEPTEb0TgocjiSk+zEPNjPmr2MYs20MvS7rBRqo3lvNd/d9x55he9iVtosvrvmCwocKOfXuKexH7IH5o0QikTSXgDYVr8UyVPVccNfapkiOUxB+fuYud1O1uwrXCReKTkHXS4ch2SDTEndRZGoXSVQRLKSH80iHukI0Gr2mTcJ+JRKJpKUEC+l+DcrrbNoLNBjhU4Uhn92Hq8SFMd0YsjAohMBZ4sRr96pCkk80EI6CCzFrR2lDvNr9YljchDjpudlFqaiowOv1Bmo3+ElJSaGgoCDsOaWlpWHbl5aWBo7790VqA3DXXXcxYcIE+vTpw65du1i6dCknTpzgqaeeCvu6OTk5PPjggy27QUmracwjXbjURb3GJnn+2jOOYw5s+TaM6XWFsJzHnej76kGA+5Sadsp90o3wCnS9dGjjtDiPO3F95EKboEWXoO5zn3RTfaAaR7EDjVaDrcAWKGTqpzG7ZD1kDSm6LJwCr92L+E6gMWpksVCJpDvg88Fbb0FFBXadarTMRh8iJj7QxIuCw66mfWmMvlf2pe+VfXGddFHxVgUnt5yk8l+VuMvcnPr7KU79/VSgrWJQMA82Yx5qxphuxNDfgLG/EWM/I/okfWDTmuTcUSKRqARrU35nBbe9acep+vMzAGuBFa/Niy5Rh6fCgzHNqNoiDDItcRdECumSqCJcjvQGHum1Qrqulw7nMacUjyQSSYcTLKT71PFUnZDeiBdo4Pxawch6yIqj0IH1cyumIaZAvQd3uRvrISv2b+3o4nWUPF9C5UeVJF4XKiA1JYZJz01Ja1m8eHHg7wsuuACDwcCvfvUrcnJyMBqNDdovXbo05Jzi4mJGjRrVIX3tiTTmke7zNG9Rz197xi9eu4pdAbE6bnIcZa+WBdJOaYwaFJ2CcAsUg4JwCbw1XgzpBjRGDT6HD0VR8Fl9VH1VhcagAQWcRep4zJhhbNQuWQ9ZOb7uOO4KN8YBdfnXHUUONEYNyTckEzs2FlOGSdoziaQrU1QEBQUwYAB2t5qa06xzI7zq91qgCuiOU9VA8+pyGJIN9PtVP/r9qh9eu5eaAzVU76mmak8VNftrsH9rR7gEtgIbtgJbo9fSxGjQ9dKh761H10sXWDzUxmvRxenQxmrRxGjQxmgDm8asQWPWoLXU/l278KcxaVCMqv3UGDXSdkkkXQy/NhUXFxSB3ERBd2g4P/M7G2iMGjwVHrQWLZYRFlBAQda06opIIV0SVYRL7dLAI712gmgaYkKfqJfikUQi6XCChXRvbdSetxl5iaGhYJRwcQKV/6nE/pVdLTo60oLtkA3PaQ+6PjoSpiWgsWgaFPTz05gYJj03uzaJiYlotVrKyspC9peVlZGamhr2nNTU1Ebb+x/LyspIS0sLaTNu3LiIfcnKysLj8VBYWMjw4cMbHDcajSECe1VVVeM3JzknGvVIdzc/OiZmZAyW4ZYGhZCtB60hxUi1CVr0iXpcJ1xo47T4nD7QAl7VgcFV7kI4BGhB30eP8Ags51nU9C+fVGI+acaQbAhrl4RPUPFWRdiiyzGjY7Dl27Dl20j80bmP6/ypY4LvVY4VJZIOpLpa9ZKKicHuUaUIs96Dz6dG0AgN4ANnhNRQTaE1a0mYmkDC1ITAPuEVOI45sH9tx/GdA2exM7C5Trhwl7txl7sRHoHP6sNldeEqdp3zrdZH0SnqZlBUkV2voDGojyF/65TAo0avqTuvdkMbdC1t0KZTSLg4gaSfJDXdGYlE0iRhPdKb6TgVPD+rzKvEc9aDrpcOY5oRywhLSI0/bYwWV7FLpiXuQkghXRJVNFVsFMDnVt0/DUkG+t/ZX4pHEomkwwkW0t1u9W+fu/FioxBBMIqHXt/rhfWQFds3Nir/VYnGoMF8njngoQ6gHaWNGPoXSQyTAlHXxmAwMHHiRHbu3MnMmTMB8Pl87Ny5kwULFoQ9Z+rUqezcuZNFixYF9u3YsYOpU6cCkJmZSWpqKjt37gwI51VVVezevZvbb789Yl8OHDiARqMhOTm5Te5Ncm405pGOp/nRMVBXCDmY4PyeungdiqIQMyIGb6VXFZxcAvSqQOU6qYroGpNGtVcCPKc86JP19B3dl5q9NViGWUhfmI5pUKhdEj5B5a5KKvMqMSQbUOqlcqhfdPlcPLXqp47RmlSPsPqRPsG0h/Cek5PDm2++SUFBAWazmWnTpvHoo4+GXaCSSLod/kme1Rrqke6r9UhXVPvl8OkjXqKlKFrVxjVmP4RQ0+15Tnlwn3HjOevBc8aDp9Kj1rOp8uKp8uC1evFZfXitXvVvmw+vXX302X34HKFbyGt4hOoQ5gBvVfsIZj63TwrpEkkbESyk+z3SW5LK0z8/q9xVSdGjRRiSDQ1SeYJMS9wVkUK6JKpoVrHRIE8rKR51DHLSJ5GEEiyku2qdlvx5iRsTrxxFDmwFtkC6BD/6JD0JiQno++qpyqsibmocMSNiGgy0tHFaVXTaVUnCtIQQWxdODJN0fRYvXszcuXOZNGkSU6ZMYc2aNVitVubNmwfAnDlz6N+/Pzk5OQAsXLiQSy65hCeffJKrrrqKTZs2sXfvXjZs2ACowuSiRYt46KGHGDZsGJmZmSxfvpx+/foFxPq8vDx2797NZZddRlxcHHl5edx999387Gc/o3fv3p3yPkhCadQj3duyeg3hCFd/QZ+kJz4rnupPq3GVudBoNQivQN9bj8enRtAoioLP6VM9KY0aNBoNluEWPKc8oKFBOhe/p1bNgRq18FahoV08tSKljokU6RPcv5YI783hX//6F3fccQeTJ0/G4/Fw//33c/nll5Ofn09MjHQCkXRzMjJgxAjEp/tDPNKFw28b1LGUAzP2QnuHze8URUHfS4++lx4zbTOWEkJQ83mNanvK3RhSDWgMGjxWD65iF7p4HUnXJ2Hsb0S4BT6XTxXb3SL00RP03Ftvn1fd8Kq2P/7C+KY7JpFImkVwapdAQXdn045TwSgahYRpapRM9f5qDIQ6Dci0xF0TKaRLoopmFRut52klxaP2R076JJJQgoV0p1P9uzl5ib3V3pB0CcEoioKutw4U0CXoQkR0d7kba4EV90nVS6ro0SISpiacs6AjiX5uvPFGysvLWbFiBaWlpYwbN47t27cHioUWFRWh0dQN5qdNm8bGjRtZtmwZ999/P8OGDWPr1q2cf/75gTZLlizBarVy6623cvbsWS6++GK2b9+OqfaH12g0smnTJh544AGcTieZmZncfffdITnQJZ2H0wkVFerf4TzSFU/LJnnhiFR/QTEo6PvpsdgtaCwa4ibHIZyCsx+fRdErqldnlVpES5egTjPCCeHBwrY+WY+utw5Fp+A84cRT6SE+Kz4gpp+rp1ZjqWMiRfq0RnhvLtu3bw95/vLLL5OcnMy+ffv4n//5n1ZdUyLpMmg0cN11uAtL8AnVRpmxIyprw/s0ggyvlYn5Jyhc7sLr9LXZIlaHI+D0ttN4q7zEjo8N2B59oh5Thglbvg3HEQfJNyRLJzCJJAoJ55HuaUFNLD+yppVKd0qvJ4V0SVTRkmKj5+JpJVGprq4OyWNbP8etHznpk0hCCRbS/TaqObapfrqE+giPUM8Pcrx0l7up2l2F1+ZFY1SLYBmSDW0i6Ei6BgsWLIiYyuXDDz9ssG/WrFnMmjUr4vUURWHVqlWsWrUq7PEJEybwySeftKqvkvbHL6LrdBAcIOD3SMdbGx1zjuOkSPUXEiYlEDMvhlPbTuEqUXOmKxoFb7UXn9MXUkQLGgrh9YVtAEehA9cJF7pEHZ4KD7YCGwmJCQjO3VMrUiQQhE8d0xrhHZo/pqpPZWUlAH369GnV/UkkXY6RI7H/8k54QX1qLjyEu0K1BYoQ/JjjJJWeQffddxjHDMVr6d0lxzwttT0SiSS6CE47HCjo7mrdGKu1Na26i/jcXlF+nYUU0iVRRbCxilhstIk8xN3F2HQEo0aNCnm+cuVKHnjggSbPk5M+SU8nWEj32yjRDI/0cOkS/Pg9Oc3DzHgqPRjSDeprFVjx2rwBgcmYZsSYbsSAIaKg0xORKagkPQW/VpuQAMHajH+Sp/G13FsqEo2l0DMPMVPxVgXWQ6pB9Jz2YBpiCqntEC5kOZy45M+/7qnwoDFqcJ104TjuwFvlbZGnVrgxYGORQBDqMX8uOdtbM6by+XwsWrSIiy66KCRqRCLp7tgz1N9mRREYf34DztwPa59rScBNidaE7sxx2HsaXVYW2lF9u9yYpyW2RyKRRB/BTp5+j3TF13qnzpamJY6W2i7nSntG+XUWrYr5XL9+PYMGDcJkMpGVlcWePXsabb9lyxZGjBiByWRizJgxvPPOOyHHhRCsWLGCtLQ0zGYz06dP5+uvvw4cLyws5JZbbiEzMxOz2cyQIUNYuXIlLldoNe3PP/+c733ve5hMJgYMGMBjjz3WmtuTdCKtSe0SjPWQlaJHiihcUUjh6kIKVxRS9EhRYJInCSU/P5/KysrAtnTp0ibPkZM+iSRUSPcPrJpT4M8f2qdP1GPLt+Gp9CA8aoErW74NY5KR9DvT0Sepx53HnLhPutEYNXgqPCGenvUFnZ6OPwXVJ598wo4dO3C73Vx++eVYrdL+S7oXfiE9Li50v98j/VwmeeHwp9CLHROLeZA5MCGLGRlDxn0ZZK7OJPOhTBK+l4C+jx7FoITYtfpCuF9c0sbUpWrx5183pBnUc896cJ90Ezchjv539W/WBCvSGNBV5gpEAoXD7zHvKnNR9EgRRY8WUXOghqo9VVT+uxJ3uTukvTZGi8/hayB+tWZMdccdd/Dll1+yadOmJtu2Jx09twN4+OGHmTZtGhaLhV69ejV4jc8++4zZs2czYMAAzGYzI0eOZO3atSFtPvzwQxRFabCVlpa27o2QdBj++Z3JBEr+QYTT/31SOIkRj6KDpCSw2aCgAAW63JgnOAoxHLLAoEQS3QRrU35nBR3nNsaKNKaqj198rt5fjS5Rh2W4BV2ijur91RxfdzysvhWNWlj9KD9dvA5Fq6CL12EZZcFd4aZiawWiduzaVWixkL5582YWL17MypUr+fTTTxk7dizZ2dmcPHkybPtdu3Yxe/ZsbrnlFvbv38/MmTOZOXMmX375ZaDNY489xrp168jNzWX37t3ExMSQnZ2NozZevqCgAJ/Px/PPP8/Bgwd5+umnyc3N5f777w9co6qqissvv5yBAweyb98+Hn/8cR544IFAcS1J16A5xUZ97vDhNK0xNj2duLg44uPjA1tzQpCjZdInkXQmjQnpTeUl9of2xY2Pw3PKg/0rO55TnoBglHh1YuC4u1zNiS48AmOaMSRvMEQWdLob/pQJ/s3pT0wfxPbt27n55psZPXo0Y8eO5eWXX6aoqIh9+/Z1Qo8lkvbD7yEVX6+mXAOP9A5IgeefECZdm8TA3w4kbkJ4uxYshEcSl/RJehIuTiBuchyx42LJuDeDjHszmi2iRxoDnnz9JLo+OpzHnAgROlHze8zr+uoof72c6v3VdTnb9WrO9qrdVSFieiTxq6VjqgULFvD222/zz3/+k/T09Cbvsb3ojLkdgMvlYtasWdx+++1hX2ffvn0kJyfzpz/9iYMHD/Lb3/6WpUuX8uyzzzZoe/jwYU6cOBHYkpOTz/FdkbQ3fiHdbPRBQQEiUa37oSBwoMXr06ghN/Hxaj6rysouN+bxRyE2ZnssIy2ywKBEEqUEZ0swGECrBS1tF/UXidaIz9GqhbUkxVVrycnJYfLkycTFxZGcnMzMmTM5fPjwuXa9UVqc2uWpp55i/vz5zJs3D4Dc3Fy2bdvGiy++yH333deg/dq1a5kxYwb33HMPAKtXr2bHjh08++yz5ObmIoRgzZo1LFu2jGuvvRaAV199lZSUFLZu3cpNN93EjBkzmDFjRuCagwcP5vDhwzz33HM88cQTALz22mu4XC5efPFFDAYDo0eP5sCBAzz11FPceuutLX9nJJ1CsJDuU/XyiKldgieIrc1nKWkZ/knfRx991KmTPomkswkrpLcgL3FToX3+45W7Kil6tAhDsgFjupF6WQZ6jDdTa1ImyBRUku5K1VkfoCFOa4XCcsjIAI0m4JGuFedebLQ1NDdkubEUV6B6rCdMSyBhWkKz07k0NQbUpmvR99U3KPLlOOZAY9DgrnDjPuUmblIcKO2bs10IwZ133slbb73Fhx9+SGZmZquu01Z0xtwO4MEHHwTUujvh+MUvfhHyfPDgweTl5fHmm282qBmRnJwc1qs9HE6nM2Qxttq/MiXpUAJCusEHDgdC78+bACa8eHy19stggOpqhMOJs8qJ1+5Vo/l8IurndLLAoETStQlO7aIo6pxPW93+Y6yOqu3SEXREiit/VPLkyZPxeDzcf//9XH755eTn5xMT0z4pY1r033e5XOzbt4/p06fXXUCjYfr06eTl5YU9Jy8vL6Q9QHZ2dqD9kSNHKC0tDWmTkJBAVlZWxGuCOkEOnhzn5eXxP//zPxgMhpDXOXz4MGfOnAl7DafTGeLhJgdSnU9ri412xEpXT0YIwYIFC3jrrbf44IMPOn3SJ5F0NuGEdMXbMg+FpkL7FI2iiklTE/BUeRC0nzeT8AnshXZqvqjBXmiPuvC6lqZMkCmoJN2WQ4eo3rQNgPgTh2HFCnjkETh0qNYjXXSIt1QkmhOy3FSKq5aKS80ZA3pOeUi6PikkEsjxnSr4u8vdnP3oLI4jDqr+U4WnwkPMiBi0Fm2DnO2t6V997rjjDv70pz+xceNG4uLiKC0tpbS0FHt9z5EOIJrmds2h/vzPz7hx40hLS+OHP/wh//nPfxq9Rk5ODgkJCYGt/kKtpGMICOkWwGRC2NTFDY3eRzJOPN7a75fLhdsTQ+XngjM7z+AodFDyfEmnpytoLk1FIXa1vMASSU8i2MkT1HmfjrYp6N4Y4VLgBVM/OieatbCOSHHVGVHJLfJIr6iowOv1kpKSErI/JSWFgoKCsOeUlpaGbe/PXed/bKxNfb755hueeeaZgDe6/zr1xT3/NUtLS+ndu3eD6+Tk5AS8ISTRQXD4jM2m/t2cHOnnstLV1gUZorHAw7lyxx13sHHjRv76178GJn2gTozMZlllXtLzCCukt0M6hY7wZuoKVdT9KROaiz8F1b///e927JVE0sEcOgTr1lH17SQA4vroITER9u+HY8eIvebXaBgcaN4RqV1ai19c8tseV7ELjUlD3IQ4Eme2zPY0dwxoSDGQcV8GjiIHNZ/VUL6lHEWvoLFocBY70cZpcZ5w4qn0EJ8VT3xWPNYCK+6T7kDO9oRpCS3uX32ee+45AC699NKQ/S+99BI333xzq6/bGqJlbtccdu3axebNm9m2bVtgX1paGrm5uUyaNAmn08kf/vAHLr30Unbv3s2ECRPCXmfp0qUsXrw48Ly4uFiK6Z1AQEiP08GIEfjePg2A1uyh0qqnn8+Jx67Bd9JNpT0Tj1Wg66MjYVoCGoumSxWpa2mBQYlEEh34nTz9NWkslo5J7RIsPuviG0q29cXnaC5s3FgUYriC9MH4U3v6MRqNzUpF3BFRyR0b89kGFBcXM2PGDGbNmsX8+fPP6VpLly4N8XDLz89vo15KWku4HOmRUrsEh9O0dqWrrQsyRGOBh7bgueeeo7KykksvvZS0tLTAtnnz5s7umkTSKQQL6X5b1V55iSN5M8WOiyXxJ4kIj2i1F3m05tM7F6Il77BE0qb4fPDWW1BRQVXvQQDEG11q/uBRWPqMZgABAABJREFUo6CigpiP3g0UwYLoFNKDo180Zg0Dlgxg0KpBDFw+kEGrBjU7J3owLRkDKhoFU4YJ20EbPqcPy2iLWiBVr6BoFPRJerw2L7YCG/rE1udsb/Q9ECLs1tEielfiyy+/5Nprr2XlypVcfvnlgf3Dhw/nV7/6FRMnTmTatGm8+OKLTJs2jaeffjritYxGY0gu+7j6VXslHUJASDcrcN11iNgEAHQ6J2+SzmERh/uEjcrKdLyaOMznmen1vV4YUg1dskhdcwsMRhOdkXe4o+iMAsvXXHMNGRkZmEwm0tLS+PnPf05JSUmb35uk7Qjvkd7+dWhaWl8hmgsbn0sU4qhRo0IiyHJycpp8vY6KSm6RR3piYiJarZaysrKQ/WVlZaSmpoY9JzU1tdH2/seysjLS0tJC2owbNy7kvJKSEi677DKmTZvWoIhopNcJfo361F/RCF7tkHQOwcbK41H/bk6x0dasdPkFJHeFG+MAI8YYI16rt9UeDm1xvWj1Zq9vwCWSnowQdREzwR7pfiG9PXLm1fdmcpW5qNpdRdmrZa32Im/LfHottV3tYeuiLe+wRNKmFBVBQQEMGED1CXXsGm+szfOsKJCeTuyRL0KF9E5I7dIYjUW/xI6JbfV1WzoGrB8CrU3Qok/U4zrhQp+kRxevw13hxlPpQZugbXHO9q5EZ8/tmkN+fj4/+MEPuPXWW1m2bFmT7adMmSKjkboAdUI6MHIk4vIrYEcVWp+TImJ4jUGsudyL59sUDIP7NKgTEy5PsKRt6Yy8wx2Bv8Bybm4uWVlZrFmzJpASOFyhYn+B5ZycHK6++mo2btzIzJkz+fTTTwNCnb/A8iuvvEJmZibLly8nOzub/Px8TCb1t+eyyy7j/vvvJy0tjeLiYn7zm99w/fXXs2vXrg69f0nzqS+kWywdI6S3NCL5XLy+O4LWRiHm5+fTv3//wPPmeKN3VFRyi2b7BoOBiRMnsnPnzsA+n8/Hzp07mTp1athzpk6dGtIeYMeOHYH2mZmZpKamhrSpqqpi9+7dIdcsLi7m0ksvZeLEibz00ktoNKFdnzp1Kh999BFutzvkdYYPHx42rYsk+vB66wZVsbFQ+5vTrNQuLV3pak0l5MZoi+t1V292iaS7YberYjrUE9JF7SJfO4lXfm8mRadQ/kY5NQdqzsmLvK3y6bXUdrWXrYumvMMSSZtTXa16FsTEUOVUJxJxRlfd8ZgYYryVUeuR3p7RLy0dA9bPPaooSiAnurvcjRACn8uH+5S7TXKiRzOdObdrDgcPHuSyyy5j7ty5PPzww80658CBAyECviQ6CRHSAZHcDwDNsEHqcxS8t8xBkxSPoZ8BEHD2LJSdVB+FaJAnWNK2dEbe4Y4guMDyqFGjyM3NxWKx8OKLL4ZtH1xgeeTIkaxevZoJEybw7LPPAjQosHzBBRfw6quvUlJSwtatWwPXufvuu7nwwgsZOHAg06ZN47777uOTTz4J0a+CkfX8OhchGqZ2iYmpS+3S3gXdm1tfwe+cZBltQWPUYDt47rVn2ut+Mu7LaFEUoj+1p39rSkjvyKjkFnmkAyxevJi5c+cyadIkpkyZwpo1a7BarYFK73PmzKF///4Bt/uFCxdyySWX8OSTT3LVVVexadMm9u7dG/AoVxSFRYsW8dBDDzFs2LDACl6/fv2YOXMmUCeiDxw4kCeeeILy8vJAf/xeD//v//0/HnzwQW655RbuvfdevvzyS9auXdtoaJ8kuvCv+IEqpPtTJzSn2Ci0bKWrpZWQQ14/jCfluVwPWu/NHq0e7BJJd8YapPdYLMFCevt7KLSlF3lj+fSEEAi3wFXmwlZga2Bb/LbHn2fY5/BhzGjadrV1JFAw0ZR3WCJpc+LiVA8Dq5VqlwEI8kgHsFrRmo1YDF6o1dcVbXSMB9rSbkWiJWPAcLlH9Un6QE50V4kLn82H1+olfnL8OedEj3Y6Y24HUFRUxOnTpykqKsLr9XLgwAEAhg4dSmxsLF9++SXf//73yc7OZvHixYH86lqtlqSkJADWrFlDZmYmo0ePxuFw8Ic//IEPPviA9957r4PePUlraSCk187vdAl18ySvSad+V4vK0R3/Cioq1JBlnQ4SE/H2Pw+NKb5T0hV0ZaI573B74y+wHFy0vjkFloPrKoBaYNkvkjdVYPmmm25qcM3Tp0/z2muvMW3aNPR6fdjXlfX8OheHQ82qB6Ee6R1Z0L2p+gr1I/2EU+C1exHfCTRGTatrz7QXfqewtqYzopJbLKTfeOONlJeXs2LFCkpLSxk3bhzbt28PFJQpKioK8RafNm0aGzduZNmyZdx///0MGzaMrVu3huSrWbJkCVarlVtvvZWzZ89y8cUXs3379kAYzI4dO/jmm2/45ptvGqws+FNOJCQk8N5773HHHXcwceJEEhMTWbFiBbfeemvL3xVJp+AX0nU6MBrrPNIdDnVF0K9PRxLSofnFXFpbkCFSWLJltOWcip22ZoLZFQoESiTdEb+QbjKBVlsnpGs7INTvXBftgolUyMZd7g4Rk0o2lFDzaU3Atvhtj/WQlZoDNXirvJiGmDCkGFDilYi2q73FNJmCStKtyciAESNg/36qHLUe6YZaIV0IOH4cJkwgzoIqpOuUBjais2hLu9UYzR0DRgqB1ifpie8bT83eGizDLKQvTMc0qPs7KHTG3A5gxYoVvPLKK4Hn48ePB+Cf//wnl156Ka+//jrl5eX86U9/4k9/+lOg3cCBAyksLARUUezXv/41xcXFWCwWLrjgAt5//30uu+yydnmvJG1HfSHdn7pTY9Cg14PbDd5EE5Y+NVT/7TBaYylKrwT8B0XJCZxHBHHXjuy0dAVdlfrFdVeuXMkDDzzQ6DkdlXe4vensAsv33nsvzz77LDabjQsvvJC33347Yl9lYeTOJdjJ05/JSM2R3jDNcHsSSXyO5JzkKHKgMWpIviGZ2LGxPcLR8o477mDjxo389a9/DUQlg6oRm83tk/arxUI6qC7zCxYsCHvsww8/bLBv1qxZzJo1K+L1FEVh1apVrFq1Kuzxm2++uVneZBdccAEff/xxk+0k0UlwDipFqRtYATiddcJ6uGKjwTRnpaullZChcU/KmoM16gpgC67nx1HooGpvlZr3qtKLLkEXyAEYaYLZnl6dEomkcYILjUKdkK7rAA+FliwCNhWxEk5Mcpe7qdpdhdfmRXgEpsEmTANNAdvS96q+nNp2CneFG228FhTQ9dXhKnXhrfISnxWPPkkf1nZ1lJgmkXRLNBq47jo4doyqM2oRmXi9DSorVRE9MRFmziT+WQFngSjxRofWOy+Eoym71pwxYFO5R80DzfS7rR/mwT3HDnX03A7g5Zdf5uWXX454/IEHHmhS3FuyZAlLlixptI0kOmngke6pc0YwmVQh3ekQpPJvHIoemzIIo3CixYtXWHAqvTEoZSTyHxQmEpJAXdIo0Zx3uLtzzz33cMstt3D06FEefPBB5syZw9tvvx124VvW8+tc/GldLBbVccr/d0fkSG8Mf9H24txiHEcdxE2KC4yDdPE6YkbHYMu3Ycu3kfij7pmWrj6dEZXcKiFdImkP6hdzCBbS7fYgIT1MjvSW0tKCDE15UloPWvE6vDiLnGhHN7/Ag/WQlZLnSqjZV4MSo6DVqwWvLCMs6JPUMK/6wlhjhrOtQqQlEklk6gvpflvVER7pzV0EdJW5OPX3U41GrNQXkwz9DVjzrXgqPaADXYKOmFEx6BJ0aONVO3f8mePo4nRYRltwn3QjvAJdLx3aODW3sK3ARkJiAoKGqWHaUkyTSHokI0fCXXdR/bdeAMSVH4HYUzBhAsycCSNHEmtWKyGLKCo02hrnhXCEi8Qzn2cm/sJ4DCmGFqW4a23hK4lE0jZESu2i6BWMxtqyEEdOEHN6P+kXD6LiuAVbhQVXtRGNzkdcWg2J6SeJOXVULcY8aFDn3EgEojn9pj/vcHPx5x3+6KOP2j3vcHvT2QWWExMTSUxM5LzzzmPkyJEMGDCATz75pMX1IyTtT31tCkJzpHdGQXf/OKh6bzVV+6rQWrQIpwjRjs7VOSmabVckOiMqWQrpkqihvrHS6VQHLJ9PHWz5a8b6Q//ORaxqaSXkxjwpQRWxPRUehFlgO6i2a+x6UOdV7jjqQBOjTiAVjYLzhBNPpSfg2VlfGGtPwymRSJqmvpCu1YLZINDU5iVuz+IzzVkENKYbKX+9HPeppiNWgsWk6r3VOL5zoLFoMPYzNrAtungdNZ/W0Puy3iiKgsaoQdEpCLeah08Xr8Nd4cZ+xI6z2NkgNUzclLg2EdMkkh7NyJFUxagThvhf3gCXaNW0L7WpN+Is6jERRZOeljovhCNcJJ6jyMHJTScpfbUU82AzhmRDi1LcNTcVjEQiaXsiCuk6pS695xkbOBzEDBdYBhXhqDThdWrRGr2YEhwoXgFfOepcR6OE7pJ+szPyDrc3wQWW/TUb/AWWI0Xl+AssL1q0KLAvUoFlv3DuL7B8++23R+yLrzYBt9PpjNhG0nn4tSl/oVGoJ6R3sEd68DhIE6tBG6NFG6dtoB1B652Tuovt6gikkC6JGvxjIL+Q7k/vYrWGFhxtLEd6S2iJN1IkT0p/LmH3STeesx7MQ8woBqXJAg/BHu6xk2LxOX24TrjQJ+nRJ+kDnp3xfeMbCGPtZTglEknzqC+kA8RZfHUF/tpxYNXUIqC+rx4EuE81nofcPMyM87gzIB4NWDKAM++fwfOUB8twC/o++gZR0n7RnFqdW5ugRtD4bZdiUPBWeKnZV6MWK62XGsZeZEfXR4fzmLPVYppEIoHqavW7Ez9+CAwKPRZrqhXSte23oNdSWuq8UJ9wUYHucje2gzaET4AAn9OHrq+uxSnu2qvwlUQiaZzGPNIDQro2NlBkWYmPx9zLEXoRq1U9Hqx0dTLdKf1mZ+Qd7gg6o8Dy7t27+e9//8vFF19M7969+fbbb1m+fDlDhgyR3uhRSn1tCkJTu7Sn41R96o+DvJVeFL2ColFCtKOExARQQp2Tmuth3p1sV0cghXRJ1BBu1a927BQYbEHbpHbx01xvpHBhycG5hDVGDbpeOsxDzLgr3U0WeAj2cNdoNMSMiMFb6cVd7lYFr1gtzhIn1XurVVEpSBhrieGUSCRtTzgh3S9eQfuH+jW2CBg3OY6yV8sazUNe9UkV3y39DneZO8TbIG5KHMZUo7oQEOYWhEeox7x11wu2XYpBwVuj5lZXzEqD1DC2fBvadC36vvpWiWkSiUTFnyY1nHYUa1I93HxRUmjUz7mkUqkfFSiEwFpgxWvzok/SI5wCz2kPCLCMssgUdxJJFyBSjnSNXlMnpCekBIosM2qU6mXlJ6jIMhkZHdjzyLR3UfWOpjPyDncEnVFg2WKx8Oabb7Jy5UqsVitpaWnMmDGDZcuWNStHvaTjiZzapWOLjULDcVB9ZyZ/VLCn0oM2QRtwTvJavRQ9UtSkh3l3s10dgRTSJVFDOGPlH1y1h0e6n+Z4I9UPSwYCkzhdoprWxZhmxJhuxJBuaLLAQ30Pd32SnviseNW7vcKNcAt8Nh+WYRb6/qhviDDWXMMpvTolkvYhrJBuDhLSO2BgFWkR0F+vIVIecp/dh/VLK167l5jzY0K8DZryGPdUeTAPM+Op9GBIN6AoSp3tOmTFdtiGz+FD21uLqZ8pbNopzykPKXNSqN5TLfMSSyStwOsFm5oGnXApbi1G1RZFm5AOrU+lUn/M5K304q5wo0vQqXbKAKJaqF7pik6muJNIugD1hfTg1J1+Id3priuyTH4+pKergy+rNaTIMproiMDpbkXVOyPvcEfR0QWWx4wZwwcffNCqvko6B79HerDTgsUCnk7IkV5/HFTfmUkbp8Xn8uE+5cZZrDonxYyJofjZ4mZ5mHc329URSCFdEjU0JqSHeKS7Ozacxh8OYxltwZpvxXbQhjZei/uk6nnuqfCgtaireyig0LSxCefhrk/Sk5CYoBrEU258Vh/9F/bHZ/W12HBKr06JpP1oUkjvoIFVuEXASEX9hBB4znqo+qQKn8OHeYQ5cDzY26Axj3FjkpE+N/fh1LZTIccVg4Kuj464cXG4z7qJmxAXNjWMP+2UIcVAxn0ZMi+xRNIKglMBh/NIj6mNjvFGoZAOrUulUt+u+Zy+uggZQLgEik6t2wAyxZ1E0hVoVmoXB4Eiy7z1FhQUQHExmEyIcRNwZP0Ir2cA2kJ7VIwjZFF1iaT7EMkj3drOOdLDpWKJpB35HTH9dam8Vi/xk+Ppe01fTv31VLM9zKXtajlSSJdEDeHyUPkHUsFCelsUG20u9QsuCKfAa1eFbs9ZD7peOoxpoUX5oGljE6nwlt/j3FnsJG5yHOZBZhxFjhYZTunVKZG0L42ldhFaJWxB4o4inG3x13JwFjtxFjnRxmixfWlDGam0ymPcPMTcID1D/MT4QFqZcKlhhBA4S5x47V48lR4A6dEgkbQCf1oXoxEMhobHLYboFtJbQ327FlzoGAN4qtSoQF2COkaSKe4kkuinWcVG/RHJI0fC8OFQVATV1VjLjFTsNmB71Y7XURg1BfEiOTP4kbZJIuk6hBPSLRZwtKOQHqnYZ99r+4bVjvRJeuL7xlOztwbLMAvpC9MxDTK12MO8rWxXc/OxdwekkC6JGpqd2qUNc6Q3RqSCC44iB8ItMA81Yx5sxphubCAaNWVsWlJ4K5LoHslwdldj1R6sX7+exx9/nNLSUsaOHcszzzzDlClTIrbfsmULy5cvp7CwkGHDhvHoo49y5ZVXBo4LIVi5ciUvvPACZ8+e5aKLLuK5555j2LBhDa7ldDrJysris88+Y//+/YEq75LoJ5yQHlObToFO/v7Vty0aiwbbFzY81R58Th8agwZDqgFXqQtvlTdsoeKmPMYjpWcAqN5T3cBWucvdWA9ZsX9rRxevo+T5Eio/quz0Ca9E0hXxOx2ES+sCQUJ6uEIHXZT6ds3Q34C+jx7ncSfoQBejC0QFysLFEknXoDGPdH/K6OD5HxoNDBqkzs/eOI67oibqCuJFmrOBtE0SSVcjXGqXmBiobKfULk0V++x7Vd+I2pF5oJl+t/XDPFg1qC31MG+t7QoWzl1lLqp2V2E/bG80H3t3IToSikkkRC42CuFTuzR3FVD4BPZCOzVf1GAvtCN8Ted7q19wQRevQ9Eq6OJ1xIyOUYt9GhQ8lR4EodfzGxvLSEujAyV/4a248XF4Tnmwf2XHc8pD3IQ4+t/VP2Bw/BNIfaKabsFT6UF4BJ5KD/ZD9hDDKUX05rN582YWL17MypUr+fTTTxk7dizZ2dmcPHkybPtdu3Yxe/ZsbrnlFvbv38/MmTOZOXMmX375ZaDNY489xrp168jNzWX37t3ExMSQnZ2NI2QmoLJkyRL69evXbvcnaT/CCum1Bf6EtvO/g37bEjs2FuvnVlzlLjRmjVrDIcWANlaLPkmP1+bFVmDDb8KCFwD96Rdix8RiHtTQtoQ7Hs5WuUpdnP34LPav7OgSdCRclIA+SU/1/mqOrzuO9ZC1E94hiaTr0lihUQBzrZDu6WZD/OAxk/e0WuRdURQ0Wo1a1Li3Dk+lRxXaZYo7iSTqaVax0XrD58bmZ5ZRFtwVbiq2VjRrrtceNDZna2vb1Jr5rUQiaT6RPNJ1tH2a4ebYNusXVvov6N+kdgSh0THhqO/02RrbZT1kpeiRIgpXFPLNPd/w9f9+zck/nwQFLMMt6BJ13Xq+Jz3SJVFDexQbjRQe09TKWFPhMKYBJhzfOdCYNE16lDdGcwtv+SeQ9dMpyAJ9reepp55i/vz5zJs3D4Dc3Fy2bdvGiy++yH333deg/dq1a5kxYwb33HMPAKtXr2bHjh08++yz5ObmIoRgzZo1LFu2jGuvvRaAV199lZSUFLZu3cpNN90UuNa7777Le++9xxtvvMG7777bAXcraUvCCemBAn9RItzEjIxB8/80VH9aTcz5Mej76tHGa6n8T2W7FioOtlXWQ1ZqDtTgrfJiPs9MzMiYOu93WQFeImkVTXukq4t6HtH9vlP1x0zB3k/2r+xyXCSRdCGaU2y0vpDeFQridcScrbXzW4lE0nwi5UjX0fZphptr25JnJzerzlRjHuY+nw/bYRuWYRbwqSK+olFaZLuCvecN6QbEMYFAILwC60FrwGmrO8/3pJAuiRrCGauwHunNTO3SVHhMY6F/zQmH0Rg1JM1KwnbQdk4DpeYW3mqu6C5pGpfLxb59+1i6dGlgn0ajYfr06eTl5YU9Jy8vj8WLF4fsy87OZuvWrQAcOXKE0tJSpk+fHjiekJBAVlYWeXl5ASG9rKyM+fPns3XrViwWS7P663Q6cTqdgefVwdXmJB1OWI/0Wi9QEUXfR6/Vq+YaHWRCqfWU74hCxX5bVbmrkqJHizAkGxqkwIqWCa9E0tVoyiPdpFdtkdsXPbaoLQkZM42B3t/vLcdFEkkXpFnFRk+cgS+OqwYvI6PR+ZkQAuEWuMpc2ApsnWoL2nPOdi7zW4lE0nzCpXaxWEDbDjnSW5KKpTnaUaQ0wo5jDmr21SBcAnxQ+EBhyCJcc2xXfe95b6UX92k3+r56FINam8tWYCMhMaFbz/ekkC6JGsIVG/UPrvyDLf8gCRoPp6n/BW+qUnF9mltwIXZsLIk/SuywSVxzRXdJ41RUVOD1eklJSQnZn5KSQkFBQdhzSktLw7YvLS0NHPfvi9RGCMHNN9/MbbfdxqRJkygsLGxWf3NycnjwwQeb1VbS/oQT0s3G6Cvw19IK723pxaloFHQJOjRmDYZ+hgZ1JEBWgJdIWoNfSI/kkR4Q0ruhR3o45LhIIumaNFps1H4a6IPzvQ/h6J9Vz6oRI9BOuSbs/MxfVN0/rinZUELNpzWNemi3d1G89rBN5zq/lUgkzSeSR7q2HXKkt0eh4voe5rZDNhzfOVAMCvFT4zFmhF+Ea8p21fee9zl9CI9QUx8rSkjEs66XrtvO96SQLokampPaRXjr8r81tgp4rqF/LSm4ICdxkubyzDPPUF1dHeIJ3xyWLl0a4g1fXFzMqFGj2rp7kmYSNrVLrUe6T4mevMSdXai4PQaFEklPJ5yHVDAmnWqLXN3UI10ikXQPIgrpp05iOnga+CEOQwIMH64OvPbvx1R0DEufX1B9zBwY17jL3VTtrsJr8yI8AtNgE6aBpkY9tLtqapSukNpGIukuhKvfp6Z2afso5PYqVOz3MLcX2jm+9jhoIG5SXGDO15pFuPre8xqjBkWnINwCxajWERTVAp9TTYHTXed70TPjl/R4mlNs1D/IgsaFdP8XXBsT/gurjdHic/girox1ZLGYtkQWnmkeiYmJaLVaysrKQvaXlZWRmpoa9pzU1NRG2/sfG2vzwQcfkJeXh9FoRKfTMXToUAAmTZrE3LlzI/bXaDQSHx8f2OIiKSiSDiGckG7S1Q4WosgjvbMLFfsHhc5jToRoXVFmiUQSSlMe6UZtrZDulUN8iUQSvUQsNvrlAYxOdcXQobGAVqsavFGjUE5VkKj8B31fdVzjPuvGmm9VxzdCoEvQqcWHEyIXH/WnRqneX40uURc1RfGaM4c71/mtRCJpPuGyJQQXG3W24TirPbUnRaOgaBS8Z7xYRjQUyusvwjVF/UKm2gQt+kR9wA4Ll0DRKWiMmm4935OjbEnU0CyPdE+QkN5IOE1LKxUH4x/ICI8g6SdJxI6LbbIycjQQXDm5cHUhhSsKKXqkqFtWST5XDAYDEydOZOfOnYF9Pp+PnTt3MnXq1LDnTJ06NaQ9wI4dOwLtMzMzSU1NDWlTVVXF7t27A23WrVvHZ599xoEDBzhw4ADvvPMOAJs3b+bhhx9u03uUtB9hU7vUeqR7w+Uw6UT8YX3NqfDe1nTVBUmJJJppqtioUasu6rm88nslkUiiE68XXC717wbFRk+WYOqt7nR4gqLZFAXS04k59Snp1yvEjY/DVeRSUxXoFEz9TMRnxQeKmocTh+qnRtHF61C0aiqCSMJ7JNrSeam5c7hzmd9KJJKWEUmb8qd2cbjbdpzVnnO2tlyEq+8opSgKMSNi0Fq0uMvduE650PVRbXd3nu/J1C6SqKE5xUab65He2vCYcKF+5vPMpMxJwZBiiNpCVrLwTMtZvHgxc+fOZdKkSUyZMoU1a9ZgtVqZN28eAHPmzKF///7k5OQAsHDhQi655BKefPJJrrrqKjZt2sTevXvZsGEDoA7YFy1axEMPPcSwYcPIzMxk+fLl9OvXj5kzZwKQkZER0ofY2g/7kCFDSE9P76A7l5wr4T3SVdvkiTIhHTq3UHFLKsBLJJKmaarYqEEncAJOKaRLJJIoxRHk9NggtYvHhSletV8Obz2pIiYGiouJSXFiuW8Yp987jecpD5bhFvR99A3qsdTPzdtWqVHaMjVMS+Zw7ZX+QSKRNCRctgRFAR3qop+9jYV0aL85W1um2wxXyFTXW4dltIWafTVoFA0aowbPaQ+x42KJz4pHeNSFx2jU0VqL9EhvJU2tQssUGy2nWcVGg4V0beQvYWs8ISOF+tV8VkP5G+UoOjUXerR9+dvSu6InceONN/LEE0+wYsUKxo0bx4EDB9i+fXugWGhRUREnTpwItJ82bRobN25kw4YNjB07ltdff52tW7dy/vnnB9osWbKEO++8k1tvvZXJkydTU1PD9u3bMZnkgLY7EVZI10evkA51Ra9ix8R2uB2LGRlDxn0ZDFo1iIHLBzJo1SAy7s2QInoLWL9+PYMGDcJkMpGVlcWePXsabb9lyxZGjBiByWRizJgxgegXP0IIVqxYQVpaGmazmenTp/P111+HvZbT6WTcuHEoisKBAwfa6pYkraRJj3RNbcixJzptkUQikfjndRBGSDdqMXltQD2PdFAHYCYTxKk5fi0jLBhTjapzVRiTV18caguvzLZMDdPSOZyM9JNIOo5w2hTUpXZpa490P+0xZ2vrdJvhvOcVoZA8O5lhvx/GkMeHkDJH1VTKXi3rltkSpEd6K2hqFbqrFjDpTDyeOu+E5qR2UXRKA0+C+rTEE7IrV0GXhWdaz4IFC1iwYEHYYx9++GGDfbNmzWLWrFkRr6coCqtWrWLVqlXNev1BgwY1+DGTRD9hhfTavMQeEV32IVqQRZlbz+bNm1m8eDG5ublkZWWxZs0asrOzOXz4MMnJyQ3a79q1i9mzZ5OTk8PVV1/Nxo0bmTlzJp9++mlg4e+xxx5j3bp1vPLKK4HomezsbPLz8xss/C1ZsoR+/frx2Wefdcj9ShqnKY90fa2Q7hIKLhcYDB3UMYlEImkmfiFdr1dToEOQkD4gDdPhk0A9IV0IOH4cJkyA2gjPlnpon6tXZlvPF1szh5ORfhJJ++Pz1c33grUp4RMBT2Sbq+vM+cJ5kWtjVFvoPO5s1SJcY97z1kNWyt8o79bZEqSQ3kKaCr/qe1VfTm071a0/NO2BNWhhqrFio4H8eY2kdQmmueExXVmMrl85uT71wxolEknrESK8kG70e6QLGeglaVueeuop5s+fH0g7lZuby7Zt23jxxRe57777GrRfu3YtM2bM4J577gFg9erV7Nixg2effZbc3FyEEKxZs4Zly5Zx7bXXAvDqq6+SkpLC1q1buemmmwLXevfdd3nvvfd44403ePfddxvtp9PpxOl0Bp5X+115JG1KU8VG9UpdvQarVQrpEokk+qhfaBSCio1Om4Kp7AAATodQva2sVlVET0yEmTNBo461WioOnWtqlLaeL7Z2DteZKfskkp5AJG0quF6fvQsJ6dA+i3DhHKW6soNqS5BCegto6kNhPWjl+DPH0cWpOYK664emPfDnoNLpQid9DTzS/d4KzRTSoXmekNEqRgufaHKQ1JY5ryQSSeO4XGqRLKgnpPsL/EmPdEkb4nK52LdvH0uXLg3s02g0TJ8+nby8vLDn5OXlsXjx4pB92dnZbN26FYAjR45QWlrK9OnTA8cTEhLIysoiLy8vIKSXlZUxf/58tm7disViabKvOTk5PPjggy29RUkL8a9PRPJI1wh/mikNNTXQu3cHdUwikUiaSTghPeAsNTgD09VJ8EHt/O+rr1TPqgkTVBF95MiQa7VEHGpMeHccc6A1arGMUgXqcHOutp4vnsscrv781p9WVgrrktbQHM2hJ+HXphSl3oJfUJphq7PrOU91xCJcV3ZQbQlSSG8BTX0odPE6aj6tofdlvbv1h6Y9CC40GvzWRSo22hIhvTlEoxjd3BRBHVF45qOPPuLxxx9n3759nDhxgrfeeitQQFMi6UkEeygEC+kGjcAHuH3dc9ApB9idQ0VFBV6vN1C7wU9KSgoFBQVhzyktLQ3bvrS0NHDcvy9SGyEEN998M7fddhuTJk2isLCwyb4uXbo0RMAvLi5m1KhRTZ4naRlNeaT7xSi/R7pEIpFEG2E90t116TuNQ9IBcKQNhuXL1ZXDjIyAJ3p9WiIOhRPefU4fXocX3FD2WhkVb1SEnXO19XyxreZwMq2s5FyQn5+GRNKm/GMsAKuza86D2nsRLlodVNsaKaS3gKY+FIpOUQcBEX47u8uHpj2IVMyhsRzprSWcIBRtVdBbUsG9PXJeNeiP1crYsWP5xS9+wY9//OO2uk2JpMvhF6b0enXzY9AJHIDb2zUHVY0hB9g9j2eeeYbq6uoQT/imMBqNGI1146Mqv+IraVOaKjbqF6M8KIGJoEQikUQTjQrpegX/bMshjDBmTLOu2VQEcv3534AlA3Aed1LzWQ3lW8pR9ArGjLo5VLg5V1vPF9tiDteSOaNEUh/5+QlPpOi/4NQuNkfXn/O1dI4nsyXUIYX0FtDUh0J4hOopHUEn7y4fmvYgeNUvmEge6Rp960JpGjMW7SVGt9STszV5pVqb86q6ujpE7KgvhPi54ooruOKKK1p87xJJdyNcfnRQ8xI7AGc380iXA+zOJTExEa1WS1lZWcj+srIyUlNTw56TmpraaHv/Y1lZGWlpaSFtxo0bB8AHH3xAXl5eg9+DSZMm8dOf/pRXXnnlnO5L0nqaKjbqHydJj/ToRkb6dV3aM0Krp0R/NZYjXdErmGqnyn5HqnMl0vyv77V9sR204XP6mpWWtb0K9rU2b3FPyUUsaR/k5ycykbSpujEWWO1d+z1p6RwvmrIlRANSSG8BTX0oPFUezMPMeCo9GNIN3fZD0x74jVX9iaF/gNXaYqPBNMdYtHUBhnAGx3yemfgL4zGkGMIOklubV6o1Oa/qh92vXLmSBx54oMX3KZH0FCIJ6QaNOrByebtevrxIyAF252MwGJg4cSI7d+4MiGw+n4+dO3eyYMGCsOdMnTqVnTt3smjRosC+HTt2MHXqVAAyMzNJTU1l586dAeG8qqqK3bt3c/vttwOwbt06HnroocD5JSUlZGdns3nzZrKystr+RiXNwu2uE5YieqR7pEd6V0BG+nVN2jpCK1g4d5W5qNpdhf2wvdtHfzXmka7RazDVDinaQkhvbP5Xc7AGX40P0xBTs+dc7VGwr7V5i3tKLmJJ+yA/P5FpWkhXsNk6uFNtSEvneNGWLSEakEJ6C2jqQ2FMMtLn5j6c2naqW39o2oNIxqqtUrs011hk3JtBxn0ZbeINEs7gOIocnNx0ktJXSzEPNmNINjQYJJ9LXqnmFFYNJj8/n/79+weeh/NGl0gkdUT2SK8tNupT8HpB2w0Cj+QAOzpYvHgxc+fOZdKkSUyZMoU1a9ZgtVqZN28eAHPmzKF///7k5OQAsHDhQi655BKefPJJrrrqKjZt2sTevXvZsGEDoP7vFi1axEMPPcSwYcPIzMxk+fLl9OvXLyDWZ2RkhPQhtvbHeciQIaSnp3fQnUvq4w81hqY90v3FRiXRSbRF+q1fv57HH3+c0tJSxo4dyzPPPMOUKVMitt+yZQvLly+nsLCQYcOG8eijj3LllVcGjgshWLlyJS+88AJnz57loosu4rnnnmPYsGGBNg8//DDbtm3jwIEDGAwGzp492+B1ioqKuP322/nnP/9JbGwsc+fOJScnB52ubgr74YcfsnjxYg4ePMiAAQNYtmwZN998c5u8L8G0dYRWsCj//9n79/go6zvvH39e1zWnzGQyIQeSEAgHjRIURaVEKHurXXah1bvibln1u1vR7eK293KrZetxAS3YYj0tom5T21r1d5dbb+9Wdlt7s6VY292FYkGw1gQUJQYCCTmQyWTOM9f1++PKTGYmc87kyOf5eOQxycw111wTyGc+n9fn9X69/Wf9+D/xI5kk7FfZsV5sndLVX2mbjRolLIP/vCMV0jOt//r39xM4E8C2MPnvNtWaazQa9uW6hoPzJ4tYMDqI/z+pSRntEjPHmsxVf7ms8Sx1ljFLS5hMCCE9R7L5T1F0QdGU/k8zGuQa7ZKrIz1XQWikolCyiVuwK4jnAw+aqoEGql/FUG4YNkkey1wpu91OSSpbm0AgGEYqId0gDzkUfL7hj09GxAR7YnDzzTfT1dXF5s2b6ejoYNGiRezevTvaLLStrQ05pgHbsmXL2LlzJxs3buShhx6ivr6eXbt2cemll0aPue+++3C73dx555309fWxfPlydu/ejcUydhVz50uEQSGJxLoUFYEhxQxeNBsdP7KNy5tovPbaa2zYsIGmpiYaGxvZvn07K1eu5NixY0yfPn3Y8fv27ePWW29l27Zt3HDDDezcuZPVq1fz7rvvRseZxx9/nB07dvDyyy9HN+tWrlxJc3NzdJwJBAKsWbOGpUuX8sMf/nDY64TDYa6//nqqq6vZt28fZ86c4bbbbsNoNPLtb38bgBMnTnD99dfz1a9+lR//+Mfs3buXv/u7v6OmpoaVK1cW7HdU6AqtWFHeNNOEdlJDQ0MLa7g/cKMUKxgrjVO2+itTs9HIR5HfP7LXyWb95z3uJXAmgHnW8L/VdGuufITvQnO+ZBELRgfx/ycFqspAay9QQbHiAdUSbXQcMXVOdkd6Lmu8sUxLmEwIIT0PMv2nmOr/aUaDrJuN5imkj7UglDjgaJqG+6ibsCeMsdKI5tcI9YZAA+sCa9wk+XzJlRIIJiORTb9hQjpDcQoez9QQ0sUEe+Kwfv36lFEub7/99rD71qxZw5o1a1KeT5IktmzZwpYtW7J6/Tlz5qBpWuYDs0Q0sM2PTI1GIX6RJxzpY8tkjct7+umnWbduXbTKpampiTfffJMXX3yRBx54YNjxzzzzDKtWreLee+8FYOvWrezZs4fnnnuOpqYmNE1j+/btbNy4kRtvvBGAV155haqqKnbt2sUtt9wCwDe/+U0AXnrppaTX9ctf/pLm5mZ+9atfUVVVxaJFi9i6dSv3338/jzzyCCaTiaamJubOnctTTz0FQENDA//5n//JP//zPxdUSC9khVaiKB92hgn2BjGWG5FMg8abox4cFY4pW/2VqdloZP9ppI70TOs/U40JxabgO+mblLGsYs0oGAni/08SWlrgjTcYeKMO+Bvsn7wHj/0abroJGhqiZoXQJDcr5LLGG8u0hMnE1AlzHWU0VcPb6mXg/QG8rfqnf9GcIooXFlM0p2iYSB75T5Pq8bEm8fo1tXAL4kIw2s1GYweLZBRaEIoMOIpNP1/YGSbYHcTgMCBJEpJJQgtpqH512CQ5EiFkrDDiafYQcobQQhohZwhPs0dEBAkE44Wq4v60GwCb7AVVHXosFC+kTwUiE2z/Sf8wETUywbY2WM+vCbZgxEScmK7DLgwVBqwXWzFU6NVZp3acwt0yiVcmo0ymRqMQW3Y8uRd5k5Hm5macTmf068EHHxzvS8pIIBDg0KFDrFixInqfLMusWLGC/fv3J33O/v37444HWLlyZfT4EydO0NHREXeMw+GgsbEx5TlTvc7ChQuj1TeR1+nv7+eDDz7I6lqS4ff76e/vj365YjOTUpA4r09EsSmoPjUrQ06iKK/6VbSQhmSUkCQJQ4mBYHeQkDOU87knCxmbjQ5OK3w+GMkebqb1n+pRo1GbY73mKsTaXKwZBSNB/P9JoKUFduyAw4dxmcoAKC6W4PBh/f6Wlrg51mRe7+WyxhtrHW2yIBzpWTAZnVOTrXlNpmajgQCEw/lnpI/1jmviLl/sJBlAC2hIBgnZrG8IJO7kTbRcqYGBAY4fPx79+cSJExw5coSysrJhWbqC85spG9cw6FBw/2stsBZb6x/hsT1Rh8JUKfWLZTI2i/ntb3/LE088waFDhzhz5gxvvPFGNPdbMP6IBrYjIytHeswiTzjSx5bJGJfX3d1NOByOE6sBqqqqOHr0aNLndHR0JD2+o6Mj+njkvlTHZEOq14l9jVTH9Pf34/V6KSoa7oTbtm1b1A2fLYWs0Ep098lmGckgoQU1JPOg2calm21yPfdkIWOz0ZjlWCAA+SQkaaqGpmoo0xQ8Rz3YF9vjPlci67+Sq0so/2I5Pf/aM2Zrrmy0hWzn0xNtzSiYXIj/P4OoKrzxBnR3w4IFDJzRP8uLbRosWADNzbBrF9rn/gcw+ePzclnjicqF5AghPQOFbiwzFkzG5jWZmo2CnpMX24gmF8ZaEEoccGInyZgg1B/CXGPG4ND/BJNNkidSRNDBgwe57rrroj9v2LABgLVr16YsyRWcf0zGTcesiDgUurtxmxYCg9Ethw/DyZNw111oQX2VF0KeMkI6TL4Jttvt5vLLL+dv//Zv+Yu/+IvxvhxBAqKB7cjIxZE+2Rd5AsFo8eCDD0bnsQDt7e3DYnkSKaSQkCjKKw4FY4WRwJmAHv8YY7aZqiJFxmajMW/V58tdSE+2Fva36b9Hc5152PrP1mDD1mAbkzVXNtoCkNN8eiKtGQWTD/H/B2hrg6NHYdYskCRcfhMAdnMAJAlmztQd6RedAabGHCvbNd5kNFaNBUJIT8NkdE6NV/OakbpQU2Wkx06kvN78M9JhbAWhxAHHVGvCWGbEf8oPBjDYDFjnW0FKPwGfKLlS1157bUHzcQVTj8m46ZgVCQ4F91ldwbJZiXMoqAE9j3oqOdIjTIQJdrZN/D7/+c/z+c9/fsyuS5AbooHtyIj8CWSXkS4LR/oEZqJU+lVUVKAoCp2dnXH3d3Z2Ul1dnfQ51dXVaY+P3HZ2dlJTUxN3zKJFi7K+turqat55551hrxP7GqmupaSkJKkbHYZ/fsR+tqSikEJCMlHeNt+mx0B2BVGDKpaZ+nogU7zCZK0CTBTSNU2DwWFfMkgYTUPH+nzgcGR/7mHz0dlmfFU+Bg4N4Pydk6KzepRLMrFotNdc2WgLp184jepVCfbkNp+eKGtGweTkvP//43Lpg81gk6uBoD4I2YwB/XGbDdrb0fr1Rd5UMU5ls8bTVA25SKZsVRnO/3QS6AhMOGPVeFQkCyE9DZPNOTVezWsK4UJN2cDPoH+FQvrYpuQZ7RJhLAWhROFeNst6ProiYVtgwzDNQMgZOq938gRTg8m46Zg1CQ4Fd2RiZQrGOxQUJzA1hXQY/wn2ZG3iJ4hHNLAdGRHTQTpH+lRphDXVmSiVfiaTiauuuoq9e/dGF52qqrJ3796UDY6XLl3K3r17ueeee6L37dmzh6VLlwIwd+5cqqur2bt3b1Q47+/v58CBA3zta1/L+tqWLl3Kt771Lc6ePcv06dOjr1NSUhL9TFi6dCm/+MUv4p4Xey2FpFCGnGSivGGaAeslVgYODSBLMrJZJtQbSnvuyVwFOExIDw6ZdfSseN1M5fPl1nA01Xy0aG4R5tlmBg4OYK23MvPumVjmJKz/VFWf87lc+iBbVwdyYdvJZdIWTLUm+t7uw1Rjwv4Ze9x8Wm6QGTg4wOmm08mvXyAQ5I/drg86bjeUlNDn0zczSy2DA5DbDRYLqrEI6J9Sc6x0a7zEzxnZLGOqNuFY7qD48uIJs3k7HhXJQkhPQy7OqZE6AgrhKMileY2h1FAQ51ehXKgRM0gyx4HFogvtXi/YRuBIjzCWglCicB+bV+/90DuhdvIEgnyZbJuOOZHgUHAHjEASh4Ks/zzZm89MVJqbm6mtrY3+nMyNLpj4iJzFkZGVI11kpE8KJlKl34YNG1i7di2LFy9myZIlbN++HbfbzR133AHAbbfdRm1tLdu2bQPg7rvv5pprruGpp57i+uuv59VXX+XgwYO88MILgP65f8899/Doo49SX1/P3Llz2bRpEzNmzIhziLW1tdHb20tbWxvhcJgjR44AcOGFF1JcXMyf//mfs2DBAr785S/z+OOP09HRwcaNG/mHf/iH6GfAV7/6VZ577jnuu+8+/vZv/5a33nqL//N//g9vvvnmqPyuCmXISSXKT791OiWNJZiqTGnPXaj113g52ocJ6aF4IR2GhHS/P/vzppuPyrKM9WIroZ4QyMS/z8E+OBw9qr+oxYJ20Xx8V3+RcNWcgv1uMmkLhCDUG8J2qS3u+oNdQdxH3QROB/B84MF30kfJ4pJJsWkiEEwK6upg/nw9tnPBAs559cFpWtFgx+NTp+DKK9HKKoHOKWuciiXV54yv1Ud4IIz1otE3yE3kimQhpKchW+dUoDNAz8968nYEFMpRMNbNawrpQu3r029LS4c/VlQ0JKRbYxrRTBbihPuFMO1z0yZlGaZAkIopHdeQ4FCIc6RD1KGgBY2AXwjpo8RkbOInGI7IWRwZuTQbnQr5nYKx4eabb6arq4vNmzfT0dHBokWL2L17d7SJZ1tbG3KMM3fZsmXs3LmTjRs38tBDD1FfX8+uXbu49NJLo8fcd999uN1u7rzzTvr6+li+fDm7d+/GEpPZuHnzZl5++eXoz1dccQUAv/71r7n22mtRFIWf//znfO1rX2Pp0qXYbDbWrl3Lli1bos+ZO3cub775Jl//+td55plnmDlzJj/4wQ9YuXLlqP2+CmXIyVeUL9T6azwd7ekc6ZE1XkQrycWRntd8NKYPDrNmgc2Gu02i+1UJzyu/JzzPhTK9lKKLiii5OvMmRzoyaQshZwgAQ+nQY8GuIP0H+gl7wiglCkj6+5j00YmCSc1kjZVKiSzDTTfpva+amznn1o1T08LdeoxnRQWsXo02mMgWRsLdF4DW06NSvTLeTJRq84lckZyXkP7888/zxBNP0NHRweWXX86zzz7LkiVLUh7/+uuvs2nTJlpbW6mvr+c73/kOX/jCF6KPa5rGww8/zPe//336+vr47Gc/y3e/+13q6+ujx3zrW9/izTff5MiRI5hMJvoiymsMiTvPAP/7f/9vbrnllnzeZlbOKfNMM13/tyvnHLMI+ToKkg1eY928ppAuVKeeipDUkR6ZZPl8+TcbnUiMd0SCQJCOfCZGUzquIcGh4A7GONJjHQrHbMAAIeToAlEgEAxnsjWwnUhk1Ww0NORIDwhHuiBL1q9fnzLK5e233x5235o1a1izZk3K80mSxJYtW+JE70ReeumljDE2s2fPHhbdksi1117L4cOH0x4zUclnTVCI9VfW689RijtJFNIj6zsYiu+M7LnkIqTnPB9N6IODJOHusnLqg1qCqhGz1o7ZfxyfdCVnXz1LxysdFM3TM9bz2XTIpC0Ee4IYygzR34GmabiPugl7wvpa3q8hG2WM5UYUxySPThRMWiZzrFRaGhrgrrvgjTc49wt9AJrm74DlV8Lq1dDQgLZH/6wJIeHp8cLmzfo68aab9OdPESZKtflErkjOWUh/7bXX2LBhA01NTTQ2NrJ9+3ZWrlzJsWPHovl1sezbt49bb72Vbdu2ccMNN7Bz505Wr17Nu+++G3UuPP744+zYsYOXX345Wv63cuVKmpubo86FQCDAmjVrWLp0KT/84Q9TXt+PfvQjVq1aFf25NJnFOUsyOaeM5UbQINiTeacGGCZOAXnt9KQavMpvLC9Y85psKJgLVVXp69UAhVJ3O6g1cZO0yETK6x1aIOabkS4QCFKT78RoSsc1JDgU3B79vdlCzniHwrf1w8+HUj+BYKRMhAa2k5HcHOky7l4ftHZMSbeUQHC+MtL1V9ZOQ/VTpH/dFRd3UijBKKUjXQJJyV9Iz3k+mtAHR9Og+2gFQY8Ra6UHyV9E8LQLT48TTVVAA9WvYig35OUIz6QtWOosWC+y4j/lRylR9DV8dxCDQ5dsQv0hzDVm/WeJyR2dKJiUFCpWasLS0AAXX0zft/QfS+9dB/9thj6HamlB+9n/A5bpjnS1SF8HHj6srxPvumvKiOkTpdp8Ilck5yykP/3006xbty6am9fU1MSbb77Jiy++yAMPPDDs+GeeeYZVq1Zx7733ArB161b27NnDc889R1NTE5qmsX37djZu3MiNN94IwCuvvEJVVRW7du2Kusm/+c1vAmR0LpSWlqbsMp+I3+/HHxO85oqsUGJI55yyf8ZO5yudGXdqzr11Dtc7rmHilH2JPeednkyDV/n15Vk1ryleVExJYwlaSMPb6h2V8rSsXKgtLWg/fQNn330AOF54Ag5Nj5ukxTrStQJkpAsEguGMZGI05eMaYhwK7t/qYpTN3wNXDjkU1OAHgBDSx5uBgQGOHz8e/fnEiRMcOXKEsrIy6urqxvHKBImI6qzcycaRrrp0hSqEhOv0wJR1SwkE5ysjXX9l5TT83Wl8f/y/FAXaonEnuN0FE4xSZaTHru/yEdJzno8m9MHxOS14uq2YHX4kCTSjCfc5I+HiEMZZFjS/Rqg3BBpYF1jzcoRnqsoCOLXjFJ5mD7JFRgtoaGaNUFcIxaprCAy+1KSOThRMOiZK3MdoEwzLuAfXctMWzgSZaPWK1q9n5oWQ8QRNaPYSpAULdHPVrl1w8cVTwrgwpavNC0ROQnogEODQoUM8+OCD0ftkWWbFihXs378/6XP2798f7UQfYeXKlezatQvQF7kdHR2sWLEi+rjD4aCxsZH9+/fnHMvyD//wD/zd3/0d8+bN46tf/Sp33HFH0sgXgG3btkUF+nSkck65P3Bn3KnxtHg488IZNE0bJk65jrgI94cxz85upyebwcv9vpva9bX0/GtPyuY1kYaXna90jqgcZ8Qu1MFMOm9HP0FV/69YWlM0bJIW50gXQrpAUHAKMTGa8nENgw4F984gnAXb7X8Fayujk6XYBn9CSB8/Dh48yHXXXRf9OTL/WLt2bcaNeIFgopOx2WhLC9rZHsBBGIlzYfuUdUsJBOcrI11/ZXQaWmUCn7QTrhmApXrcCaAPPAUSjFI50kcqpEOO89GEPjhhv0I4JGM26mvv8IBGMGjF4DDov2cT0X5jBsmQtyM8U1VW5PpdB12oHj32xjzDjHW+FWOlMXoeIWYJxpKJEvcx2pw7N/R9NNxisHpFLdV7eYQGd7O8ISNWYxBmztS1rbY2mDNnTK93NJjS1eYFIichvbu7m3A4HG0+E6GqqoqjR48mfU5HR0fS4zs6OqKPR+5LdUy2bNmyhc997nNYrVZ++ctf8j/+x/9gYGCAu+66K+nxDz74YJzI397ePizQPkIy51TGhiEDIQKdAZDB/hn7MHHK9XsXgc4A4YFwtGQrgqZp+E/7CXvDhJyhaG5xNoPX9FunU/dAXdIPZ3eLm66fdBWkHGdELtSYTDrnXD1fX5ZUisvNUB4/SSsq0idpItpFIBgdCjUxmvJxDbKMO6gvPG3zqnSHwiCxDf6EkD5+XHvttWialvlAwaQjU/+GKdf4Kglpo10ibqmgLpSHkBgImPFZy7AssE85t5RAcL4y0irAjE7Djj5k9zmUWRVDInr0xaWCCEaphPRIo1HIX0iHHOajCX1wFHMYxaASDioYTCFUpwfNWINUrM/9YvuNwcgc4RIaRXQCLsAO1BGxmkeu39vq5dQzp/B+5MW+2B7/mSfELMEYM1HiPkabiJBeUgJKZI9qsHpFM+h/a+HBv1V3YFBIt9mgvX1oojbJmWzV5uNRkZxXs9GJyqZNm6LfX3HFFbjdbp544omUQrrZbI4LrO+PWH2yJNNOjfdDfZZgvdiaVJwquqiIwJkAnmOeOKE92BXE3eLG+7EXQ4mB0987jfO3TqyXWLMevJIJ/6NRjpO3CzUmk67Pr19nidk/OF+Ln6RZLHOA+GiX2ImWQCAYGYWcGCWOPZqqx0dNFXHLrVf0RaqAo8Q2+BNCukBQWDL1b5iyja8SSBvtMjiv0uTL9Z8lDTTocluZ5eifcm4pgeB8Jt/1l6ZqaKqGMk3Bc9STXJw96cNu68FSk2KtVQDBKFWz0VijVGSJHpPCmhNZxYcl9MGx1M7EWlaB61QRiqEL2eZAMtrRQoCsxWeUMwJHeEuLbihLkz8vyRLWeVZqv1qrR720THwxSzC1OV/iPiJC+rRpMXcOVq9oPUEAIpYdT3CwQsTt1v+O02XvTTImU7X5eFQk5ySkV1RUoCgKnZ2dcfd3dnamzCWvrq5Oe3zktrOzk5qamrhjFi1alMvlDaOxsZGtW7fi9/tHpcNrpp0aQ7EBqkApHj6YaJoGIVCsugDv+UB3g6peFec+J6HeEIYyA45lDmSrTP+7/TgPOAn3hwnYA5hnmqP5aBEKkok3CuVpSYnJpHP26zt7pZYYy0HMJC0yyfJ6YyZaItpFIEhKPq7M0ZoYTUVxK6WQHtPgTwjpAkHhyKY3TM+bPVO38VUMaR3pEbeUpo/TxRY/eKHbMyikTzG3lEBwvpPr+it2TuY/68f/iR9/m+5oNteZh8TZ6RYqitqRPMbkg00BBKPRjHbJmZg+ONLRo1SYf49PWoRHuQDTolqMp8z4T/nBAAabIZpRnrcjfDDalO7urPLnx1PM+u1vf8sTTzzBoUOHOHPmDG+88QarV68etdcTTGzSmUhVVcVzzIO13gqqvh6drBs8SYX0weoV7Q9O/WdFgzC4gybQNDh1Su+dNcX6MU2WavPxqEjOSUg3mUxcddVV7N27NzqIqqrK3r17Wb9+fdLnLF26lL1793LPPfdE79uzZw9Lly4FYO7cuVRXV7N3796ocN7f38+BAwf42te+lvs7iuHIkSNMmzZtVET0CNk0I00Up4JdQdxH3QROB1A9KlKRhBpUUT9W8Z3wEe4PU3RREbYGG8ZKI8GuIKHeEJ7jHgiA72Mflgst0cehQJl4IylPy7VpWEwmXZ9Pv16HOcZyEDNJi2s2mqQZjUAg0MlXuB6NHLSp2NVdVYccodHMvMhjg5t8wpEuEBSGSDVLe1M7vk99cc5JQ4kBuUHG9XsXbd9uQ3EoemWfPPJKu4lMWkd6xC01OIUrGRTSuzyD4+wUdEsJBOc72a6/hs3JZpvxVfkYODSA83dOis4WYZpu0sXZL87B9q810biTuHiXAglGo9VsNG8G++DQ1obN5WJmp5nuAyY8x7zI5gCSJCEpErYFNgzTDIScofwc4THRpnG/2wz58+MlZrndbi6//HL+9m//lr/4i78Y1dcSTHxSmUh9J/WxRAtooELrI62T2jiVVEgfrF7Rfv7v+s+SPmZ5zvnhbLPej2b16ikZnZezzneekHO0y4YNG1i7di2LFy9myZIlbN++HbfbzR133AHAbbfdRm1tLdu2bQPg7rvv5pprruGpp57i+uuv59VXX+XgwYO88MILgO6Evueee3j00Uepr69n7ty5bNq0iRkzZsTteLa1tdHb20tbWxvhcJgjR44AcOGFF1JcXMzPfvYzOjs7ufrqq7FYLOzZs4dvf/vbfOMb3xjhrygzqT7cAFzvuOLEqWBXkP4D/YQ9YbSQhmWeheKFxfhO+tCCGsYKIyVLSqKO89jjTeUmwgNhJEXC+6GXYHdQd6wXyYXJxBvLcpyYTDqndjUQ40hPmKQlbTYqMtIFgjhGIlwXOgdtqnZ1dzr1NRAkTK4YWgSKjHSBYORENgVdB130H+pHsSpofi3aaC1iSPC1+gh2BDHVmNACWlwjtqnU+Ar0eINAQP8+qSN9cF6lDo5FJUU+OKdHu0xlt5RAIEhPqjlZ0dwizLPNDBwcwFpvZebdM7HMGRRn5aG4E2bOHHJNnzo1YsFI00bJka6qenSVy6VvGNbV5XaNshyNvbItBOvnhio8A50B+g/04z3mxfuhVzfNXVFMxZIAttAn0Jrl68VEm+aaPz8eYtbnP/95Pv/5z4/pawomNokmUk+LB98nPiSTRMnSkmh1y2Q2TiUV0gEaGtCWumH/ALKkuxbcPV649kp9TBTN3M8rchbSb775Zrq6uti8eTMdHR0sWrSI3bt3R5uFtrW1Icd8iCxbtoydO3eyceNGHnroIerr69m1axeXXnpp9Jj77rsPt9vNnXfeSV9fH8uXL2f37t1YLEPux82bN/Pyyy9Hf77iCr1j7q9//WuuvfZajEYjzz//PF//+tfRNI0LL7yQp59+mnXr1uX+W8mDVB9useKUqdaEu9lNyBnSy8McBn1n22HAVmKjf3+/Lo5/1hEtGXMfdRP2hPWFoaY3ObEutBI8G8T7sRfnfzkpXlScVXnXhOq+G5NJ1/eOPlo5TF5dqUqYpMU50pNMtASC8510wrXcIDNwcIDTTafjF0kJFLJ0dDS7uo9nQ8HeXv3Wah1a4EWvKygy0gWCQhC7KSgXyyg2BcWu4D/jJ+QMUVRfhPcjL2FPGNkqgwKSRYo+XtJYEhXTR6vx1XiMQ7GJLMXFSQ6IuKW+eRKAUqP+hO5eWRfDprBbSiAQpCbdnEyWZawXWwn1hEBmaByLiTvh6FE9Fspi0TfjRigYBQK6mA4FbDaaReZ4rsSt7RfCtM9NGxr3O1ux/G4n0is5vl5MtGlScongGsHGgcvliusNl9g3TiBIR2IzXGSGVQ1OZuNUSiEdUKdVAANIxRY4B55b/w5uqxRzq/OQvJqNrl+/PmWUy9tvvz3svjVr1rBmzZqU55MkiS1btrBly5aUx7z00ktpg+JXrVrFqlWrUj4+XsSKU66DLnyf+JCtMuYZ5qTOKe9xL4EzAcyzzISdYYLdQQwOA5IkofpVJIOEudqM9WIrljkWgl1BZvz9DBzLHBkHqFxcp4mLRPNMPR+uoIvGwUma8+tnACgNnIWenmGTtGSOdNFsVCAYItUiKTZGyvOBB99JHyWLS1KW2hWqdHS0YqTGO3M9IqSXlw9/LFZIjzitBAJBbiRuCoadYSSjhCRLGCuNBM4G6H+nH9ksY6w0Eu7Xq/Rko4xSohDsCuI56sFRoRsSRqPSbrzGoYjmYbOBkurtNDSgyXpfolJFXwl29ciwUrilBILzlbznZDFxJ3m7vJMQO0dK12w0ayE9x8zxfIkK6y0t8JPn83u9mGjTEeXPj3DjYMGCBXE/P/zwwzzyyCMZnycQRJBkfW4WPhfGOj+5UK7YFZz7nDj3ObPSqiYK6YT0qBZl0WVUd3EVCFnqvCQvIV2QGxFxqveXvYSeDmG92IqxzDisWaipxhTNmTLNNKH6VbSQhmSU0LSETuESmGeYUQdUXWjPcmBK5TotXlRMSWMJWkij91e90fK1sC+M5tcIe8MoRQqSWSrsorGhgb4r5sO/g+Ozl8CWLcMmabHNRqMZeiLaRSCIkmyRFBsLpZQoIOmLpUyldoUoHR2NGKmJkLkeEdLLyoY/NtRsVMIvHOkCQV4kbgoqDgVjhZHAmQDGSiOKRcHX4cMyR1dYwr4whjIDql9FQcFQYiDYHSTkDKE4lJwq7bJxmY/nOJS20Wjs+whLgMa0ay+HT6Br4efg/v8u3FICwTgxnpV0MMI5WUzcSaGICOmSBCaT/n3e0S55Zo7nTbrXa2iAgwehqQnuvlv/vSW+Zky0ad758wXYOGhubqa2tjb6s3CjC/Ih1SZdxMgVPBsk1Bei7TttOJY6Jk1mejZCusEixR0rOP8QQvoYIckS1vlWzNVmfZKQZP6kelSK5hWh2PVSGMWu6Dt9rrC+SLQq0U7hkL/TKtF1Gsl963ylM9rFXTJJ2K+yYyw34vwvJ6HeEIYyg57JbpULumh09utvqPSCCphTMezx2GiXqGNBRLsIBFESF0mJsVCaX0M2yhjLjSiO0S+1yyVGKpsF5kTJXE8rpEcz0mUR7SIQ5EniokySJGzzbXqFXlcQFP1vTQ2qqF0qBpuBokV61EuwK4hiV1ADKsGeIP727Ps7ZOMyH+9xKG2j0UE0TYsu8qZdWApAt98u3FICwThR6AqWfET5CRXtSXw+euRSkjUbjWi7fn+ak40gczwvUr1eVxday1F8n/oIHz6IcvSbWP7kYqS/THCIx0Sb5pU/X6CNA7vdTkmmXVmBIAPJNulijVyyWcZQasA03TSpMtPTCumDY5XFqv/tdXeP1VUJJhpiaj2GRCYy/pN+tEg43CCRiUzJ1SXUPViH/Qr7UFRAbwhTtSku9zNyvLXBmtfEJ+I6lQwSXT/pYuDIAEq53sxLQ0MNqwz8cYCBwwNoIQ3zPDNaSMP7kReD3YB1gZVgd5DuXd1oqpb5BdPQ16ffOhzJH0/abFQI6YIC8PzzzzNnzhwsFguNjY288847aY9//fXXmT9/PhaLhYULF/KLX/wi7nFN09i8eTM1NTUUFRWxYsUKPvroo7hjvvjFL1JXV4fFYqGmpoYvf/nLnD59ekTvI3FsiY2FAgj1hzBWGKMxUbEZ5aNBJEbKWGHE0+wh5AyhhTRCzpDeL2JQ3PIc89D2WButm1tp3dpK6+ZW2h5rw93ijjtfLpnro0k6IT2yyRdCwtMfhNbWoc6kAoEgK2IXZRGMlUZKGksw1ZhQPSqooPk0zDVmShpLsF5sjT4e7g+jelTC7jD2K+3U3lWbccEWcZm7DrswVBiwXmzFUGHAddjFqR2nouPReI9D2TjStfDQfKy8Sr/Grq5RuRyBQJCBbMeWXM6XzZwpkWznZGPlkk9sNAojcKRnkznu82WXOZ4NyV6vqwv32ydo++NCWp1/Qav7L2j9aDltr0q4v/kjXciPJZI/f8UVeqTphx8ORZtmcpPnsnEgEIwyievPWCOXoUKvFjRNN2GeaS6odjTaZONIN9t0GbWnZ6yuSjDREI70MSTbjHJbgw1bgw1fm4+B9wboer0L1acimSS0kJY00zwfkmWRBnuDGMuNSCYJf7sfdUDFMseCLMtxJdOGUsOIGgbG4nTqt6WlyR+PazaKENIFheG1115jw4YNNDU10djYyPbt21m5ciXHjh1j+vTpw47ft28ft956K9u2beOGG25g586drF69mnfffTfaPPnxxx9nx44dvPzyy8ydO5dNmzaxcuVKmpubo82Tr7vuOh566CFqampob2/nG9/4Bl/60pfYt29f3u8lcWyRLTJaQEMza4S6QsOqWUbSgC9bN1Sm5qVA0oiE/nf7GfhggOl/NZ3iy4ux1FlGLXM9VyKTpaSOdE8AGBTSz/lh8+YRN7oSCM43UjknjZVGSspLkEwS5iozikPB/pmhxlaRxwcODmCtt6ZtrBxLLi7z8R6HsnKkB4cWpxXThZAuEIwXha5gGWmsVNYN5UfQwDJb0grpuWakFypzPFsSX0/TcB/q5tTpqwkayjEbezHLbsIOBy7/Bfh+f5aZL/w7tqcSHOL55s8XsllpFgwMDHD8+PHozydOnODIkSOUlZVRly5+RnBekLj+VOwKwbNBZLNMqDt+/SkhpdWOxjsCK5a0zUYHjVOWYuFIP98RQvoYk+1EJuIYL5pThPUia+aJTx4kuqtiM9klSUKxKoS6QtFdQ8kkobk0VL8+gGS7aMw0MObkSDeIjHRBYXj66adZt24dd9xxBwBNTU28+eabvPjiizzwwAPDjn/mmWdYtWoV9957LwBbt25lz549PPfcczQ1NaFpGtu3b2fjxo3ceOONALzyyitUVVWxa9cubrnlFgC+/vWvR885e/ZsHnjgAVavXk0wGMRoNCa9Vr/fjz+mttWVZIKc2NhY9eh/p4mNjSH/WKhcS5RTNS8FaHusbdgCU/NrhHpDeD/24v6Dm+JFxdgabNiX2AueuZ4PKZuNtrSgOQcAK2EkPKpFL88tcKMrgWCqk8lwYJ1jpez6Mnre7MHTMvzxotlFzPjqDIrmFaGpGt5Wb9qm6bm4zEej90MuRIT0tI700HBHuljkCQRjTy5jSyYzUqFE+YwN5ZM0sNQumo/v6i8SrppTMHErmZAeEadk45CQnJWQXojM8VxIeD2tz0l360yC8jSsRWeRvB4oLsZQoqDgwXN6Gt1vn8ba+inSvLnx58onf36MNw4OHjzIddddF/15w4YNAKxdu5aXXnqpIK8hmNzErj+d+52E+gYNlzXx689I9FygM4DnqCduLBmvJu6pyCbapWhQSBeO9PMXIaSPAxknMnkcn88uXqK7SjbLSAYJLaghmSVkiz6ZUX16PrsW0JAMErJZvz+bRWM2A2O2jnSvFzTLYKdko0glEuRPIBDg0KFDPPjgg9H7ZFlmxYoV7N+/P+lz9u/fH51ARli5ciW7du0CdJdGR0cHK1asiD7ucDhobGxk//79USE9lt7eXn784x+zbNmylCI6wLZt2/jmN7+Z8X1Fxgpvq5dTz5zC+5EX+2J7/FiRZx5mvm6oZM1Lva3eYQvM2Ew9Y5kRLaQhm/R+DN42L4YyA/6T/nHN90wa7TKYV6mFFwG6Iz0QNhAqLsWwwF74RlcCwRQnG8NB0QVFaR9PnHukappuvcSatcvcdoltXHOGI/un2TrSK2uGFnnhMCiju88oEAhiKGQFSyFF+ZQN5ZM0sHS3SXS/KuF55feE57lQppcWRNwqaLTLSDPHcyXh9XyhCjze2ZiLzukiutGov6YkIQHmshCeDju+D50UzSvA64/xxsG11147LI5WIEgksv507nPS9p22aJxLpBI60nw0cDqA6lE5/cJpBt4doOKm1BXK45mpnk20i9UuzArnO0JIHydSTmTyOD7fXbxEd5XiUDBWGAmcCei7hxLIRTKqV0VVVUL9Icw1ZgwOQ1aLxmyFt0yO9Lhmo4poNioYOd3d3YTDYaqqquLur6qq4ujRo0mf09HRkfT4jo6O6OOR+1IdE+H+++/nueeew+PxcPXVV/Pzn/887fU++OCDcSJ+e3s7CxYsSHqsJEtY51mp/Wotp3acSurazDUWqtAlyokLzMTmqGgQ6gkhmSSsC6x6ueBMBWO5MW0s1miXACYV0gfzKjUW6+9tsPWIy29iWpGv8I2uBILzgEwGgnSPJ849ZK+csmn6wAcDusiehcs823i+0RqHsnKkxwjp5YPRLpqmj12VlaNyWQLBlCcfs1IhK1hGPVYqSQNLd5eVUx/UElSNmLV2zP7jhMsbCyJuJRXSkzQbzUpIh6HM8Yibvr1df/KVV+oieqErAmNeL7z3BOGghNnggpJiXUSPiV1RNB8BTISxFua1x3rjQCDIEkmWcCxz4FjqwHXYhQkTElKcUUoLaVjmWbDMtkSNUopFGbcm7skIBmFgQP8+rZBeIoT08x0xyk5yRtLIJrFBhCRJ2ObbUKwKwa4gwd4g5jozcpGM/xM/kkGiqL6IUH/m5jSJwpuhxICkSBhKhjcqzeRIj4t2CYloF8Hk59577+Xw4cP88pe/RFEUbrvttrSOD7PZTElJSfTLnkXJZsTVab/CTqgnhPdDL6GeUNYN+GIpdJO9xIaCsc1RJUmKq36JnD/UE6LyS5UFeT/5klRIH8yrVFX949RsDALQ7RlcNBW60ZVAcJ4QMRAULyzWm6MnzDWSPZ4491DsCp4PPSmbpqt+lbAvjL8tdRP42KbuhRxXcyWbZqOReATJIGEySdF5lchJFwjyI98Gn4lrrFiSjS3pSNaEOZYRx0olNLDUNOg+WkHQY8Ra6cFQXoTU241BcxekYWBBHekRGhrggQdgyxbYtEm/vf/+0YvVG3w95eF7UWbXEC6ZrrvAY7PLNY3wOS9yWTHKRTML+9r5NisVRHn++eeZM2cOFouFxsZG3nnnnbTHv/7668yfPx+LxcLChQv5xS9+Efe4pmls3ryZmpoaioqKWLFiBR999FH08dbWVr7yla8wd+5cioqKuOCCC3j44YcJBAKj8v4SicTdDbw/gLfVOyoNPxMbGwf7grib3XqDY03D4DBgW2DD4NDnYP42P32/6cM8c3yauCcjYvCE5NpUZKwqLhXNRs93hCN9EjNSl2gyd5VhmgHrJVYGDg0gSzLGMiOyWSZcozvdQ72hrDLasxXePCd8uFz6TCobR3qyiZZAkCsVFRUoikJnZ2fc/Z2dnVRXVyd9TnV1ddrjI7ednZ3U1NTEHbNo0aJhr19RUcFFF11EQ0MDs2bN4ne/+x1Lly4d6VuLI9cYqVQU2g2V2FAwtj+Dpmlx1S+x5zdVmah7oG7cmtEkbTY6mFepqfo1lBT5IOig22Olvry38I2uBAJBShLnHqG+UHSTLlnTdMssC75PfMgWOWuXeaHG1ZxQVfrb3YAde+gcqI6kjsNEV2dlpb4oFI4pgSB38o20izjYrZdYcTe78Xygj0mKTSE0oG/AGYoN2D+T3bwgVRNmGB4rlVfDvoQGlj6nBU+3FbPDryeHmEz6Mf5AzlEyyci22ah5cMoZ0yIoPflkjo8EWcayvB7rSieufwPlbAeSo0T/fQUCaM5+/OFq7NfOxZLH7ykt+TYrFQDw2muvsWHDBpqammhsbGT79u2sXLmSY8eOMX369GHH79u3j1tvvZVt27Zxww03sHPnTlavXs27777LpZdeCsDjjz/Ojh07ePnll5k7dy6bNm1i5cqVNDc3Y7FYOHr0KKqq8r3vfY8LL7yQP/7xj6xbtw63282TTz45qu93LDPIE3t2+T7xIVvlYT27JEnSK40/8MT1d4lltJu4JyMS62K3gyGJUhq5VtugI/3cOQiFkh8rmNqI0XYSUwiXaDJ3laRJTL91OvX/Us8FT1zAhc9cyGVvXsaFz1zI7E2zmbNlDnX316UdeCPCm2JL7o5QbAqqT8XZMTQwZtVsVAjpggJgMpm46qqr2Lt3b/Q+VVXZu3dvSjF76dKlcccD7NmzJ3r83Llzqa6ujjumv7+fAwcOpBXIVVV3EPqzXinkRqJrE8jZkVBoN1SiY0H1q0iyRNgVJtgVjOvynnj+TC7V0SSpI30wr1IL69dRWqSPt10e21BeZUND4RpdCc5Lxto5BfDFL36Ruro6LBYLNTU1fPnLX+b06dMFf2+FJHHuEbtJB4NN00N60/RI4yvVp+L4bw6KFxVn7TIf03GopQUee4z+A80AlPzmZ/DYY/r9CSTOkSJxLsKRLhDkRi6VtbHEOtg7f9xJ2BUm1B/C94kP10EXzt84CZwJEOoP0flKZ1bu9sQ5U8gZQgtphJzxFcKeY5683PNxDSyBsF8hHJJRjINzvkBAV4nMJmBoDZevuFXQZqPjjCRLVPz9AoxLLsajzNP/bbp69X8b5QJMSy6m4s4Fo/MZEdk4WLhQvxUietY8/fTTrFu3jjvuuIMFCxbQ1NSE1WrlxRdfTHr8M888w6pVq7j33ntpaGhg69atXHnllTz33HOAPqfavn07Gzdu5MYbb+Syyy7jlVde4fTp09FeWqtWreJHP/oRf/7nf868efP44he/yDe+8Q1++tOfprxOv99Pf39/9MuVR3VrvukFiQ52NaRmvX60Ndioe6COmjtrKLq4iGkrpuFY7oiK6BEihqlQXyjpeUa7iXsy0uWjw9BYVewY+puOrA8F5xdi72QSUyiXaLbuqlxcB9lmA7pVfWC0WIacB4nENRsNiWaj48Xzzz/PE088QUdHB5dffjnPPvssS5YsGe/LypsNGzawdu1aFi9ezJIlS9i+fTtut5s77rgDgNtuu43a2lq2bdsGwN13380111zDU089xfXXX8+rr77KwYMHeeGFFwB98+qee+7h0Ucfpb6+PupGmDFjBqtXrwbgwIED/P73v2f58uVMmzaNjz/+mE2bNnHBBRcU3I2ejHwdCbm4obIl1rEQmcSFekNYLrBga7DFdXkfq2ai6VDVoYlSeXnMA7KMduNq+KezAJSa9Eludw96fqXIqxSMkPFwTgFcd911PPTQQ9TU1NDe3s43vvENvvSlL7Fv375ReZ95OSkTSJx7JDZRj8RGhd1hPP/piTa+6t3di/1KO1W3VWGqMo15tUtKYhoAuqSbALBPM+iN5k6eHFbGnyikV+h9vISQLhDkSD4NPlM52H1tPlSfLr6YakxYL7aiFCs5NdTL1IQZRtCwL6GBpWIOoxhUwkEFgymkN2ioqYk6nkYqbhU8Iz1bVHVUHNy2BhszH76U7p9W4TnURcAdQLaZsC+upOKmyjFvlChITyAQ4NChQzz44IPR+2RZZsWKFezfvz/pc/bv3x/Xqwpg5cqVUZH8xIkTdHR0sGLFiujjDoeDxsZG9u/fzy233JL0vE6nk7I4d04827Zt45vf/Ga2b20Y+aYXZNuwPd36UZIlrPOtmKvN+t91sumUAQxlBoI9QSzzLGPexD0ZkWiXVEJ6ZJ6lWCSmTdOF954eSDIdF0xxhJA+iSlkI5tcm59mIlvhrdemD4yp8tEhfiKVrPRPMPrkKuRMBm6++Wa6urrYvHkzHR0dLFq0iN27d0ebhba1tSHHTLCXLVvGzp072bhxIw899BD19fXs2rUrKkwB3Hfffbjdbu688076+vpYvnw5u3fvjgpTVquVn/70pzz88MO43W5qampYtWoVGzduxJxqJ6lAjEaJ8kib7MVu4g28N0DX612oPjXqGh3rZqLpcLn0NRgMn1xp9fMBXUgvk3UrQ1e3DNeNUqMrwXlFrHMKoKmpiTfffJMXX3yRBx54YNjxsc4pgK1bt7Jnzx6ee+45mpqahjmnAF555RWqqqrYtWtXdMH39a9/PXrO2bNn88ADD7B69WqCwSBGo3HY646EQpUdJ849YpuoGyoMhPpDGEoMeJo9wxpfDbw3gL/dz8y7ZmKps4xbhFSUhAaA/Qf030NJqQzzF+gbdbt26eX9g59ViX1kIo50Ee0iEORGrmaldIKVdYGVnp/q2XAVfzE0l8m1oV4q4xNA22Nt+TfsS2hgaamdibWsAtepIhRDF5LNqgvtklQQcWtUMtIz0dIy1IzU59NPPn++/r4LMEezNdiwPmjF11Y1vp8bgox0d3cTDoej670IVVVVHD16NOlzOjo6kh7f0dERfTxyX6pjEjl+/DjPPvts2liXBx98ME7Ab29vZ8GCBSmPj0VTNZz7nDj3OzFN1xt/JqLYFf2YfU4cyxw5N2zPtEmXSQ8KtAcovbYU1auOSxP3ZEQc6am0qdh5Vnm5fryYY52fCCF9EjMaLtFCkSx/PdnAeKJTv+ZUsS4Q70iPNtIS0S4FweVy0d/fH/3ZbDYnFXRzFXImC+vXr2f9+vVJH3v77beH3bdmzRrWrFmT8nySJLFlyxa2bNmS9PGFCxfy1ltv5XWtI6HQjgTtEw3ZLGfVLyETkU28ojlFWC+ypnRbjbejJ+JGLyqKX/zB0AIQYNo1l8Ar0L14FdxvF050wYiYKM6p3t5efvzjH7Ns2bKUIrrf74+LqMq2BDnfTb5kJJt7WOutBLuC+D/xo0xT0FQ9EgEDcY2vlBJ9LDz9wmmMFUa8x7yjniWaloQGgC6//tlcYvaDJMHMmbo41NYWzQWOjEWRqj0R7SIQ5EeuZqV0Dna1X0UL63+b4f4whtKh8+WaOZ7M+ORt9ebsnh/GYANL7Sdv4Hv3FNbAh7hDF+ExXYB5wUyUaeWEnaGCiFtjLqTHVPYwa5aeBe92p6zsyZdCm9IEU5f29nZWrVrFmjVrWLduXcrjEtflsWv2dETWb879TgaODGAoNWBqNUUzyoNdQdxH3QTPBgn1hWj7ThuOpQ7Kbyyn5197outFgIH3BqIN20PdIbwfeXEsd+Tdjy9RD5px5wyAYeu/4kXFlDSW6E3iW71jtjGVKdoldqyqqIDjx4WQfr4ihPRJTLZi9XjthmcqQ7Q12HB+qB+bzpEemWiFQiIjvdAk7mo//PDDPPLII3H35SPkCCYWhS5Rls0y0/9qOsWXFxd0YjMuTfyyJGk++iCRDT6A8vpSALoCDtGFRDBixts5df/99/Pcc8/h8Xi4+uqr+fnPf57yWvMpQR5p0/RkJM49VJ9K0dwiwjVhUMH7oTdl4yvZKtP7Zi+WC3WjwkhE/RGT0ACwf1BIt5sDg2/UBu3t+nGDJJoNRLSLQJAfuZqV0jnYVf/gHEGK+T6GkTbUK1TUp5s6uqX/Dw9dhG0BtAsMhGUzmlNB/tBbMHNDts1GRyykqyq0tkJTE3z6KSxePGRuKCmBBckrewRTm4qKChRFobOzM+7+zs5Oqqurkz6nuro67fGR287OTmpqauKOWbRoUdzzTp8+zXXXXceyZcui8aCFJHb9ZpxuxDDNgGSQ8J/xE3KGKKovwvuRl7AnjGyWdZF9ugnXYRcDHwygDqhYLrBk1bA9m026VHpQrFCu2BVm3TcL/yk/YVeYQGeA/gP9dL7SOeaGhmyFdNkoR6M+e3pG9ZIEExQhpE9yshGrR4Nss0wzCWNOp35cOke6JcZQr4pol4LS3NxMbW1t9OdkbvR8hBzBxCLdIivScC/QGcBz1BNdFKYSt2yX2PA0e/A0e6j474XfqJuojp7IJCmZkB7rSC+frv8+hDtBMBW49957+cpXvsKnn37KN7/5TW677TZ+/vOfD9uQg/xKkPPZ5MuGZHMP80wz5351jpNPn8R6sRVjmTEus1PTNPxtfsLeMOY6c9SFOhJRf0TENgAsKcEV0Bv9lZgHXf9ut/643T70HlI0GxXjkUCQG7maldI52GXzoECrxXwfQ7oozmzWW4WI+owzT8wpxTz4XkfDPJFts9HIkiQvIT0S5XLwIBw6BFYr+P16nEtkYExR2SOY2phMJq666ir27t0b7WGlqip79+5NWaW8dOlS9u7dyz333BO9b8+ePdH+VnPnzqW6upq9e/dGhfP+/n4OHDjA1772tehz2tvbue6667jqqqv40Y9+FBchWggSzQkAvlZfNN4u2BWk/51+XUCvMBDqDmGuMWOeacaEif79/QTOBLAt1PWjpA3bXVp0QzDffnzphHLJINH1k66CVCnmQ7bNRiOOdBBzrPMVsfU6BYh0Rp6zZQ6zN81mzpY51N1fN2qDTGxH+my6wkeEseKFxRTNKYqbgEUaOmSTkQ6gBoQjvZDY7XZKSkqiX6Od0y0YH2IXWbEEu4I4/9PJub3n8BzVIw3aHmvj3Fvnsha3xpPEjvLpOsiPlKSNRiPXMZiXhwyVVfrvSzhABYVgtJ1Tmc5ZUVHBRRddxJ/92Z/x6quv8otf/ILf/e53SV/XbDbHfZ7YYwTeVEQ2+RRbcoFHsSmoPjUvp2bi3EM2yGkbX4WdYQIdAZRifbyMO9d4jHuRBoAnT6Kp2pAj3eQHTYNTp/Q4grq66FNSZaSL8Whi8vzzzzNnzhwsFguNjY288847k+ZaXn/9debPn4/FYmHhwoX84he/iHtc0zQ2b95MTU0NRUVFrFixgo8++ijumN7eXv76r/+akpISSktL+cpXvsLAwED08UceeQRJkoZ92WxD65uXXnpp2OOW2IXDCIiYlexX2An1hPB+6CXUE8J+pZ3au2rj1lkRB7v/pB9Ni5+LyCUykiIhKRJKSfzYEnG3Wxusw6I4s11vpXvtdOePHpMgvhlKDEiKFDVPaAENT7OnYBWCuTYbDYUgnMtHQCTK5fBhKC7Wq3dKSuDMGThwIH5AtNl0pT7LKDLB1GDDhg18//vf5+WXX6alpYWvfe1ruN3uaITpbbfdFleJfffdd7N7926eeuopjh49yiOPPMLBgwejwrskSdxzzz08+uij/Nu//Rvvv/8+t912GzNmzIiK9e3t7Vx77bXU1dXx5JNP0tXVRUdHR8oM9XxINCdIkoRtvg3FqhDqDuku854QSBDqDqFYdQEbaWieE3aHCZzRK99iG7YD0YbtkQ3BfPrxRYTygSMDGCoMWC+2Yqgw4Drs4uQzJzn9vdNJx6KihiJ8n/o43XQa7yejt+bL6EiPmWcJIf38RjjSpwhj5eLMNss0W8d6No50RQGjEYLBIUd6rGNBMLrkI+QIJhbJSpSDXUH6D/QPa7jnOuzCdcRFuD+MefbIyoRHk2yaE2Y7DmVDumiXxLw8EJMqQWEYT+dUIupgt93YHPSRUsim6dmQLq4h7AsTdoUpqi/C4Bh+LWM+7sU0APS+f5yQqv8OSvxdcPJjPbdl9eq4OIJUjvSuLg1aP9XFIrtdF99FjMG4MpGauOd6Lfv27ePWW29l27Zt3HDDDezcuZPVq1fz7rvvRhuwP/744+zYsYOXX36ZuXPnsmnTJlauXElzc3NU6P7rv/5rzpw5w549ewgGg9xxxx3ceeed7Ny5E4BvfOMbfPWrX4177T/90z/lM5/5TNx9JSUlHDt2LPpzsoqZfMk2ci6Tg912qT4v8bRkF8UZXW91BTE7/JgtQcJhI653w8NcmSOK+lRVfPs+xrO/C/N0MxLW+Pc1gsqgVOSakQ66mdwaf2nJSWjSjNOpLyJlWR8Qu7r03hMVFbojPUllz6RFVXVnvRjnM3LzzTfT1dXF5s2b6ejoYNGiRezevTtafd3W1hbnFl+2bBk7d+5k48aNPPTQQ9TX17Nr167oeAdw33334Xa7ufPOO+nr62P58uXs3r07Ot7t2bOH48ePc/z4cWbOnBl3PYkbYPmSrALZWGmkpLEE91E3/jY/ql9FDagU1RXFxdsBmGpMKDYF30kfppmmpA3bzTVmDA5DXv34MsX5uX7vInBGb0AaO45HMt0DpwN4PvDgO+mjZHHJqES95JKRLqJdzm+EkC7ImmyzTDVVo+dfe9IKXBGycaSDPtkKBpM7FgSjSz5CjmBikbjIMtWacDe7Uzbcc/3eRaAzQHggnFRQKrS4lSvZbOgBGYX2XMhGSJeNsnCACgrOhg0bWLt2LYsXL2bJkiVs3759mHOqtraWbdu2Abpz6pprruGpp57i+uuv59VXX+XgwYPRLM5Y51R9fX1U5Ip1Th04cIDf//73LF++nGnTpvHxxx+zadMmLrjggqggXwjGuml6WsGpzY9i1UWyRLe6pmn4T+uxLyFnCE3VxibeZbAB4OkfvA2AVfFR3H8arrxSF9ETGuQlNhuNbux1htE2bUby+3TRaP58XaQvQIM9QX5MpCbuuV7LM888w6pVq7j33nsB2Lp1K3v27OG5556jqakJTdPYvn07Gzdu5MYbbwTglVdeoaqqil27dnHLLbfQ0tLC7t27+f3vf8/ixYsBePbZZ/nCF77Ak08+yYwZMyguLqa4uDj6uu+99x7Nzc00NTXFXY8kSTkZO3JtjCyhUUQn4ALsQB3DBgkyx23C8IZ6yaI4o+utT3qxBo8jfdQNoRAGgwGlvAKP60K6d1niYqbyivocjD8J7+8kfGQh5tIBaC0fjD+piB6WbhMxH8NCPkK6z5elkJ7QpBmHQx8Iz5zRhfSSEl1kdzr1x06d0sfTmMqeSUkkyuboUf2XJcb5jKxfvz7lOvbtt98edt+aNWtYs2ZNyvNJksSWLVvYsmVL0sdvv/12br/99nwuNSsijdRVr0rgdADzTHN0mDJWGnFUOPA0e1C9KvYr7djm24YNY6pHpWheEYpdSdqw3VBmoKi+iFB/fo2HM8X5GcuNeD7wDFX6QpzxSylRQNLHpNGKesm12ShAtzArnJcIIV2QNdlkmfb/rh/3B25Uv5pVrlU2jnTQJ1v9/aBFcqlERvqYkknIEUx8YhdZroMufJ/4UjbcK7qoiMCZAJ5jHuyfsY+6uJUL2WzonX7hNKpXJdhTuHy9tEJ6kjI/l0t3UIm0JMFIGQ/nlNVq5ac//SkPP/wwbrebmpoaVq1axcaNGwsaATYeTdNTCU6OzzqwXmTF367HI0TGlmBXEHeLG+/HXgwlBk5/7zTO3zrHpOkVAA0NnLr+Ynh6UBvauiXlIk1NmCNV9hwF5uMPGRhw1GKfZtAdmIcPw8mTcNddQmQpIC6Xi/7+/ujPZrM56d/LRGrins+17N+/P64fAsDKlSvZtWsXACdOnKCjo4MVK1ZEH3c4HDQ2NrJ//35uueUW9u/fT2lpaVREB1ixYgWyLHPgwAFuuummYa/7gx/8gIsuuog/+ZM/ibt/YGCA2bNno6oqV155Jd/+9re55JJLUr7nnBojZyFQJorJsU3zEsXlbNztvjYfngOnMZ95DynUry+SBktzpY4zmA0ePL8DX9v0OId4qt4Q/lN+Bt4fiH+9SPxJdzfK9ItQptkIGyQMZ87oi7PGxqiYnso8kU1lYDKybTZqMOhVyeFwDjnpCU2akST938vp1B0OdjsEArqFtL09aWXPpCPm35JZs/T3Lsb584rI36K7xY2v1Yf7D24sF1iwNdjiHOeapmG9xAph0NCQGL6+K7m6hPIvlkdNkbEN2xWLQqg3lHc/vkyNkSPmrVBfCGO5EU3TcB91E/aEMVYa0fwaslHGWG5EcYxO75p8ol163m+HzZvFJtZ5hhDSBVmTafCTrTLeT7yYakyULC1J6ViPHeyydaRHXQnCkT4uZBJyBJODyCKr95e9hJ4OJW24B2AoNmCqMmEoNoyZuJUtmTb0TLUm+t7uw1RjitsEGGnDwHRCemzjmdLSoYVfdzfE9PIVCPJmrJ1TCxcu5K233srrWnNlPJqmp4pr8BzzcGrHqei4p3pVnPuchHpDGMoMOJY5kK3ymDW9inDqtC7yzLzAkrYhXlzVnqpi+/efUqT8I96wmS6lGrtyTndkLlgAzc2waxdcfPHkFpEmEInNdR9++GEeeeSRYcdNpCbu+VxLR0dH0uMjWb+R20zHJMbGGAwGysrKkmYG+3w+fvzjHw9zyF988cW8+OKLXHbZZTidTp588kmWLVvGBx98MCw+IULWjZGzECjd1KUUk4sXFg87ZTZRnGFniPAn7ZhDLpheqYvBoO/MV1aidHYT+KSdsPPSYc+NPb+7xc3Jx08Ov7Yby7H961D8iQUJa6sX1xk7SoWM1B2JP/ksGiQ1T2Qb9ZmMbJuNwlC/5ayF9IQmzYDuRG9s1N/T6dPg8eiPf+YzSSt7UjIRo1MSo2wi/1fEOH/ekPi36FjuwPlfTrwfegl2B/V5S5GM/5Qfc6WZstvL6HmzJ+36ztZgw9ZgS7opN5K4zExxfhjAUGYg2BPEMs9C2Bkm2B0cEthjomWQKHjsFGTvSJeNMuUDrcAcuntlfVNObGKdVwghXZA1mQa/wJkAYXcYyyxLxgaFkcEuF0c6IIT0cSSdkCOYPEiylLbhHujuI9N0E1W3VeF6xzVm4lY2pNvQ0zSNUG+IQGcAy1xLnNMCRpb1Gcm/S9psNKbMT5b1Y86eFUK6QJAt2eYQF5JkglasqO9ucTNwZIBwf5iii4rinF0j2ZTLh5Mn9dtZs9IfFxePMBhxUGH1cNJlpttjZd60wRWiJMHMmbpQ2NaWVpwXZE9zczO1MYO+aOBeON544w1cLhdr166Nu3/p0qVxcVPLli2joaGB733ve2zdujXpuRIrBWKrCKJkIVC6X/h3Tnk/X9DqNwDF1YHiPke4tAxDYt67JBEumobsPIfi6kCPmhlOWqG7uYuZrjPYLtDjTySgYn43PqcZT7cNszmMcraH8Klz+PvN8eYJVUVr/ZTuprMEP5WwLi5HGhRoszUsZNtsFPIQ0iNNmg8fjv93q6zUJ2cHD0J9Pdx9tz7uZSsuT9TolMQom1jEOD/lSVqlWwKlf1IaraRz/peT4kXFceu3oguKMpoXks2RRipWZ4rzC7Tr+eiqV8XT7EG2yGgBDc2sEeqKb44Ko9O7JpOQHjVPKRoVh34J3El3qHRo405sYp03CCFdkDXZZJkqNgVTjSnp8yODXcgZwtvqJewKI3UqSFgoLU2/CLVY9IxCBiOzRLSLQJA/2eYST/vcNKZ9blpBxa2RNgBNtaEXaUTj+8RHqC+Eu9lN2Bke1kgn30lXVs1GI3EKlbqQLnLSBYLsGaum6ZmIiPrOfU7avtOGabopLmsURqcBXzpOndJvUxhso8QJ6YMRB5U2Dydd0+hyJwQM22x6tEGGfGhB9tjtdkoii+k0TKQm7vlcS3V1ddrjI7ednZ3U1NTEHRNpcFxdXc3Zs2fjzhEKhejt7U36uj/4wQ+44YYbMlZCGo1GrrjiCo4fP572uIxkECi12pl0vx0iWOPE+pmKglW/AVjsXqy2HlzeuSh2b9zLaxr4fXbsthNY7N6kz88YgbffSfeZWqwL3dFhzVbpYWZjO91HK/CctRLoA/msH/uyiiFxbVBM9h08hefQZZitISS/Iy5TPZuxMduMdBiKx8taSI9p0kxzsz5oRlyip07B7Nnw1a/CvHlZnpCJHZ2SGGWTiBjnpzSpqnQjmeiWORaCXUFm/P0MHMsccT0Vxtq8ANnF+c24cwZANIpU9ejCdWIU6Wj0rgmH9ShhyCIjvbuTivb3AOjzFRFSZQyyOvhGxSbW+YDYHhFkTWTwM1YY8TR79EErpDe28DR7MFWaKJpXRNiTXKAKu8OofpXO/9VJ6+ZWWre2cl1rK7fSRsk5d+oXVlXKbV4MERWd4aV/AoEgezL+Lce4jyLiVvHCYormFI1okuJucdP2WFv07791cyttj7Xhbknz959AZBPAf9If7XIfaUQTOBNA0zR9sVik4D/jp/9AP8GuYPT5+TZKzSojfXABGG0+053TSwgEggmCJEsYHAbkIhnTDFPSyh3FpqD6VMKuMJqq4W31MvD+AN5WL5qqDX/CCMhZSDdI0YiDSrMuoHR5EoQWt1t3KdiTO1oFo0dsE/cIkSbuhWzmO1rXsnTp0rjjAfbs2RM9fu7cuVRXV8cd09/fz4EDB6LHLF26lL6+Pg4dOhQ95q233kJVVRobG+POfeLECX7961/zla98JeP7CYfDvP/++3ECfl5kECh9oXI8vcWYy9WMVbi5IjlKqJjXjtHowdNlJeRT0FQI+RQ8XVZMRg8V89qRHMk3bTL2tJplweMux3dGjXvMVumhbnkbcz7zR2Yvep8591dSd3/dkIi+Ywfau4fxaLPwK5VopiK9ieeBA9A1NOGJHRuTkW1GOgxFe8b0hs3MYJNmrrhCLyf88EP99sorcxe9EysTSkr0/L6I67S7W3edqmrGU40KsVE2yRDj/JQmUqWr2IavaSRJwjzDjFKkYHAYhq3fCrm+y4VI5Z/9CjuhnhDeD72EekLYr7RTe1dtNFam7oE65j05j/LV5VjmWij5bElURA92BXH+h5Nze8/ha/Vx+nunc15PJiMSOQxZZKT73ZRpermyhsQ5b0LfMJtN/wwRm1hTFuFIz4FQfwjVpyIpEiggKbrIFP0+8vMUJl2WaaQxRSqXq7vZTbgvjGSUMNfpZYY9PwtTjwvjz3y4FycpgRx0P1SfvgaFoYm19PGHcGWSPEOBQJAVY51LPJI8zVgS3QymWhPuZjchZwgMYCw3IhtlQv0hDBUGQt0hPEc9OCocaOTfKDUbR3pkg6+yUr9fONLHh+eff54nnniCjo4OLr/8cp599lmWLFky3pclmGRkirOLbMoFOgP0/Kwn54Z7uZB1tEsoZiwajDio+M1ZYH68I13TdHX+yiv14wRjzkRq4p7pWm677TZqa2vZtm0bAHfffTfXXHMNTz31FNdffz2vvvoqBw8e5IUXXgB0Aeeee+7h0Ucfpb6+nrlz57Jp0yZmzJjB6tWrAWhoaGDVqlWsW7eOpqYmgsEg69ev55ZbbmHGjBlx1/fiiy9SU1PD5z//+WHXvmXLFq6++mouvPBC+vr6eOKJJ/j000/5u7/7u5H9UpJlbccQdgYIY8Zcmr4KN6/Igbo6bI01zPTtpzvYiKfHRsBlRjao2GtcVBgOYLt6bsq/3Uw9rZTqUgK2aYRPHoSZ1XGOewmNItdHsOxKWHYBDMa58MYbuD8J0x1chetMCZ4eK/5+C2b7NKz+doyDmepIUkbDQi6O9IiQnrUjPUJDgx6pMNJM84kenZIqygbEOH8ekO08JVfz0GiTjSNekiWs86zUfrVW713TMvq9ayKxLjab3t85GdGxylGMwWqi1Oyhz2+l22Ol0uYZOlBsYk15hJCeA5889Amnnz+d/iCJeKHdIA3dJn5vTPK9UUI2yvrPRgnZNPi9afB7k4RslvXvzYPfm2VkS8JXkf6lWBUUm4JslVFsCkqxglwkD3Mo5EK6wU+SpaTlOr6TPsJ9YZRSBeslg2WGGpwLGOhGweBOUgIZU0pXXbYU5cSQw0tqeg6+/j9FAweBYASMVWlfxjLjHEugYzcBXAdd+D7xIVvlaNkfQP+BfkLdIWSzTOBsAN8pH+H+cF6NUjUt+2ajIBzp48lrr73Ghg0baGpqorGxke3bt7Ny5UqOHTs2rLGdQJCObCKwzDPNdP3froJnJCeSrSM9biwajDiofFWft3afUyAUGoo4qKjQG+2J7M5xYSI1cc90LW1tbcgx/0+WLVvGzp072bhxIw899BD19fXs2rWLSy8dan5533334Xa7ufPOO+nr62P58uXs3r0bi2VoE/vHP/4x69ev50//9E+RZZm//Mu/ZMeOHXHXpqoqL730ErfffjuKMlwMOnfuHOvWraOjo4Np06Zx1VVXsW/fvuTNQ3Mhg0Cp9JxCKbuUsKE46WJ6RALW4N+u7eQOrF278c2pJ2ywo4RcWPo/QqpM/7ebUVzzqMjzalGKi5PHnySODW1tuA+c4dSZpQRDNkwOH0VeA36XGZ/LTEiZScmpMxidTjSHI6NhIV1GerJmo5CHkA769Y9U3J7o0SmZomzEOD+lyTaqM1fz0FiQbZxfvr1rgJzXt5ny0SHGPDW7VjcrGJ30+a30eIVZYbwZayOVENJzIZuqLW1wMhACjcKW9hYMeXCSVayX+hgcBgylg7flBozlRowV+pepyoSpxqSXBpUMDdCpBr9ULteieUUQBMsFQ41IQyEIqwAS1jkJeXoJpXRVfcG4aBept0s0cBAICsBY5BJnLDPOI2s4sgnQ+8teQk+HsF5sxVhmjEYwlDSW4D7qJng2SKgvRPCs3rk+H7e9y6WPV5Ah2iUmIx2EI72QuFyuuIZwic3iIjz99NOsW7cu6qRsamrizTff5MUXX+SBBx4Ys+sVTH4yZXkay42gQbAn8wYh5L6gi+DzDY0lOWWkAzQ0UPm5cngfuvqMesSBxaIv7lavFmaEcWYiNXFPdy1vv/32sPvWrFnDmjVrUp5PkiS2bNnCli1bUh5TVlbGzp07016XLMucjJRkJOGf//mf+ed//ue058iLDAKlpa4C60VzcZ0KoJQY0gtYqpq7M3ownkR64w2Kjh6FgcEGl1dl/tvNSly7egaWL34F/nWX7rhub085NmjOfro/qSUYsmKd7kGS9BiYcEAhHJQJBky4e0so7vbhO2VAMStYF+gmjWRjXTIhPdGQEH0vIxHSC0GGyoQJ4TqNRNlEmqGm+bcUTC2yyRzP1Tw0Ecm1d825t87heseVc6VgVkJ6ZM1nUuCmmyh/xsfxAeju0mCGMCuMF+NhpBJCeg5c9C8XUf98PaighTU9AzM8+H3kK5RwXyj+fjWo6o+H9PvUoBr9XgsOfalBVf8+MPh9YPB+v4oaUNH8g99HvnwxX96hr7AnjOpR9Xxy7+BOgAphZ5iwM0ygPZD1+5etMpbZFizzLBTNK8Iyz4LtUhvFi4oxVQyVNiZzuYacIT791qdxGV6+wbw7CbCUKvi6YkogE0rpqosHhoR0SUOaJRo4CASThYxlxnmWQEuyhHW+FXO1WV98xUyoIo12/Cf9BLuC1N1fF9doJxcibnSLBazW4Y8nilfCkV54Et2FDz/8MI888kjcfYFAgEOHDvHggw9G75NlmRUrVrB///6xuEzBFCNdBJb9M3Y6X+nMuEGY74IuQnu7fmu1pl/cwfBNPYDKS/QFRNfsxbBpU/4RBwLB+UYagVJavZoK6vDtOJVewDp2dOj5vkExfP58XaTPJHDmGU+StbjWMBsa5mc8v89VhMddjrl0AEnS13FGW5CSWhfubisBp4LP44BWkGxhCELnjzvp/kn3sLEuHIbgYNuaUY12KRSTJTqlUFE2gknHWEd1jhfZ9q7xtHg488IZNE3LuVIwk5CuaVp8X6yGBioaXHB2cM0nzArjxngYqYSQniOSNBTbMtnQVI2wO0x4IKwLW64wof4Qob4QIWeI0LkQod4QwZ4gwW79K9AR0DsiO3VB3tPiwdPiGXZuU62J4kXFlF5bSvnny7EusMY5S72t3mFlhpFJkdkMqiehBDKhlC5WSJcVdfxL6QQCQdaMZoZfOucV6CK+Y5kjbxEd0se6wPAFoHCkF57m5mZqa2ujPydzo3d3dxMOh4dFI1RVVXH06NFRv0bB1CRVBJb7A3fGDcKRLOgixMa6ZErlSyZGRTf2PFZYuDC7Ny0QCHTSCJQ2SC9g0RaNqGTWrCFH++HDutM9m8aXecaTZC2uZXH+sL2asG0aZu8JsFdEByKjLYijqI+Q3M9AaA5KiRXZqmCuGxLuE8e6iBsdsms2GvmoHzchfTJFpxQiykYwKRmrqM7xJtN6MjQQItAZABnsn7HnHCWaUUgPxaQjRMxTc+zwG+i55ib46yViE6vAZFORPF5GKiGkn0dIsoTBbsBgN0COzezDnjD+0358rT58n/jwfuLF+5EX9x/ceI97CbQH6G3vpffNXj659xPMs8yUfb6MGX8/I1ramCh2+QcnRRZzkgyvhFK6qmI3yqCQLskanD6t1wY6nXrJpBisBIIJy2hm+I1FWWPWQrpBONJHC7vdTkmysmqBYAxIFoE12gu6CJFUi0yxLjC88TGIjT2BYMSkEShTClho8NhQRGV0F6ykRP+5uXnUIyoLJa4pDgPKvFrCJ7oxdHXp78FkgkAAqb8frA7CoTIUGOqDRfKxzusdeu1J4UgHEZ0imBSMRVTneJNpPen9UN+ps15szStKNKOQHhwupJeX6z93h8tgYYqFoiBvsqlIHi8jlRDSBVmhWBWsF1qxXjg81yDkCuH+g5v+d/rp/fde+t7uw3/Sz5kXznDmhTOUrSqj7qG6YWKXz61gJcwckohdCaV01cUDKIMh9ZIahL179Ync974Hv/1tdiWSAoFgXBhtsXu0yxozCukJTbIiwpUQ0seWiooKFEWhs7Mz7v7Ozk6qq6vH6aoEU5XRXtBFiDjSZ83KfE3JcoaFkC4QjC5JBazWT+MiKuOfIOk7Y2MQUVkIcc1SZ8HaOAOXD5TgcaSebt2dbzCgVdfgDV0ALmNWY51X1q/FZIrfP8jUbNTvH9FbGDkiOkUgGHcyrScNxQaoAqU4eYVzpijRnBzpCeapnp783pMgPdlUJI8XQkgXjBiD3YDjsw4cn3Uw6+uzCHvC9P2mj87/1cnZV8/Su7uX3t29OP7EQd2DdQwcHsBz1EOoNYADma4yO7V3JYhdCaV05TNmYUYf9DQ1BA4HLFumh4bmUiIpEAjGhdEWu0ezrDGTkJ4oXsU60jUtcxyDoDCYTCauuuoq9u7dy+rVqwFQVZW9e/dOmIZ+gqlDugWd76QPSZKQrbK+8NIYlueZbW+I2GiXTCTLSI+MR/39EAjoApZAIBhlEiIqhzGJIirjxrquKzDP9aMoQcJhI36nGYMsgTWUlXjlHRx/ihK0/QnbbDQWEZ0iEIw72fSuyTdKtK9Pv83GkR7Z9Is60oV5alTIpiJ5vIxUQkgXFBzFqlD++XLKP1/O3C1zaXu8jY6XOnD+h5OjHx7l8j2XM/3/m86BH4X50e8UllxqwdaQRGmKKaVTWlqoUM7pDVsVBf7kT4ZsViMtkVRV4TAQCMaAfMRuTdWyPn60yhojLoPIZGnYNaZoNhoK6elTpaUFvyRBCjZs2MDatWtZvHgxS5YsYfv27bjd7mjzGYGgkCRb0Kl+lbAvDCr4T/kJ9YYwzzBjnW/FWGmMPjfb3hD5RLvEilHTpoGi6E3+urthxozc36dAIMiRhIjKYbjd+uN2+9hfWx7Ej3UKAZeqi1dXWePFK7uiT3z8ATCbwOGIG+u8ffr5EoX0CR3tUijEelMgKAip1pMArndceUeJZnKkRzb8kIb6JYo4z/FnvIxUQkgXjCpFFxRx8fcuZvbG2bz/39/H/Z6b9z73Hpf/6nK6iovpBEpTDFbAUCndvn1Mf8MNbgibTEMiOoysRLKlZSjzzufTZ2zz54uoGIFglMhF7Ha3uKMCVdgXRrEoWOdbqbhpbLvQZxvtEnGBWixQXAwDA3qcghDSx46bb76Zrq4uNm/eTEdHB4sWLWL37t3DcvMEgkIRu6AbeG+Arte7kIwSplkmtJCGv92P77SPkDNESWMJxkojqqriOebBWm8FVd8wTLVBmEu0SzIxSpb1TcCzZ/XxSAjpAsEYkBBRGVeapmn6H/aVV+rHTRIyile/OT0U/RIK6dEv5RX4jRdiv3YGljoL3jP6uVIK6YbkQrrHM6pvbfQR602BoKCkWk+OJEo0IqSnWrelq/oT0S7jy3gYqfLaBn3++eeZM2cOFouFxsZG3nnnnbTHv/7668yfPx+LxcLChQv5xS9+Efe4pmls3ryZmpoaioqKWLFiBR999FHcMd/61rdYtmwZVquV0hT/u9va2rj++uuxWq1Mnz6de++9l1AolM9bFBQYyywLi95ahH2xnWB3kCPXHUFt0TvwOhwZnizL4HBQaXYDEE6sjwa9RNLny61EsqUFduzQJ7kVFbpgX1Gh/7xjh/64QCAYF9wtbk7tOIXrsAtDhQHrxVYMFQZch12c2nEKd4s753Nqqoa31cvA+wN4W71oqpb5SeTQbDRJLrFwKIw969ev59NPP8Xv93PgwAEaGxvH+5IEUxxJlrDUWfB84EH1q1gvsWJ0GLEtsGEoMSBJEkFnEHezG+8nXnp+2oPvuA/PMQ+tj7TS9lhbyjEtp2iXJM1GQTimBIJRRVWhtRXef1+/VdWhiMqKCr1q1ukcKlNrbtbvX7160jmSI+JV8cJiiuYUIcmSHv1yWT/GUx/g+dBPSClBm1ZOSCnB86Ef06kPqFjYjyRLePXWEVk70iNzqbNnR/udjSJivSkQjDqRNZ4W0qj8y0qKFxUT6gnh/dBLqCeE/Uo7tXfVpjViZdtsNHacEtEuE4Obb76ZJ598ks2bN7No0SKOHDky6kaqnB3pr732Ghs2bKCpqYnGxka2b9/OypUrOXbsGNOnTx92/L59+7j11lvZtm0bN9xwAzt37mT16tW8++67XHrppQA8/vjj7Nixg5dffpm5c+eyadMmVq5cSXNzM5bBrehAIMCaNWtYunQpP/zhD4e9Tjgc5vrrr6e6upp9+/Zx5swZbrvtNoxGI9/+9rdzfZuCUcBYZuTyX13OHz7/B/r393Plj99jNldSWpqFs9Rup8Kiq1lBLcmkM9cSSVXVnQHd3fFOkZKSkUfFCASCEaGpGt1vdBPsDmJdMNS8ylBiQFmg4Gn20L2rW29slWUG+kjc7dkK6bHiVUUFnDghGvwJBOcLvjYfnqMezLPM0THLWGmkpLEE91E3gdMBPMc8+Fp9KDaFkqUlmOvMhN1hXIdd+E76mHnXzLjxyO8fEpDyzUgH0XBUIBg1MjmNByMqOXpUz0S3WHQn+urVU8eJrKrY/vAzZs5spbuqEU+PjcA5GdmgYr9ogIrQ29j2vA8L7sbrngPIw4X0FM1GI+NeZENx0iHWmwLBqJNsjVd0URFVt1VhqjJl3TcrHyE9YlQ4d06P0FPSp/UJRpH169ePaU+snIX0p59+mnXr1kVt8k1NTbz55pu8+OKLPPDAA8OOf+aZZ1i1ahX33nsvAFu3bmXPnj0899xzNDU1oWka27dvZ+PGjdx4440AvPLKK1RVVbFr1y5uueUWAL75zW8C8NJLLyW9rl/+8pc0Nzfzq1/9iqqqKhYtWsTWrVu5//77eeSRRzAl6a7k9/vxx7QBd02Chi+THYPDwGX/fhl/WPUH+vf18yVO4XBcnPmJdXWUl5+C06HhQno+JZJtbfqkdtas4Z0ARxIVIxAIRkwyQSqCJEmYZ5rxtHjwtfmyiomJuNuD3UHMs8yYbenFq0SybjZqEI50geB8JewKE/aFMdvMcfcbK404KhwEu4Oc++U5TFUmSj9XGl3QpdsgbG/Xz1FUlHr8iSVVwz4hpAsEo0DEadzdra8nbDbd2HP4sN7c4K67hiIqp3I29uCaynZJBVb7SXxOC2G/gjJwFkv7u0hdp2GXB06exGv5G+Avsm42OumFdLHeFAhGlVRrvIH3BvC3+5l518ysI0WzFdJjN/wiczNN058fEdYFU5+cPsUDgQCHDh1ixYoVQyeQZVasWMH+/fuTPmf//v1xxwOsXLkyevyJEyfo6OiIO8bhcNDY2JjynKleZ+HChXH2/ZUrV9Lf388HH3yQ9Dnbtm3D4XBEvxYsWJD16wnyx2A3MPfRuQBcQxelNjXzk2SZsvl6191AiJGXSLpcunPElkI8yycqRiAQFISIIKXYkm/rKzYF1acSdoUznivR3W4oMSApEoYSA0UNRfg+9XG66TTeT5JEvQyWa/ee0btclU9LPlZFXaBJHApCuBIIzg8Uu4Ji0bM4E5EkCdWrooW1pJU0iRuEEWJjXRI1mGSkikcQ0S4CQYFJdBqXlOhWxIjTuLtbdxpHYl7mzIGFC/XbqSSiQ9yaSpKgqNRHsdxKUcuvkTrO6L8Tmw1sNrzH9d3BInUg7hSpxq5YIV1LTONLFqkz0RDrTYFg1Ei3xrMusBLsDtK9qztzlKeqon7SitOpHzfNkWG9F2OcMhqHYorFHOv8IidHend3N+FweFjWTFVVFUePHk36nI6OjqTHd3R0RB+P3JfqmGxI9Tqxr5HIgw8+yIYNG6I/t7e3CzF9LFBVSuv6cBqNOIJB1I+7geGxQImU1ZUCp/DLJr2jw0hKJO12/blutz7BSyTXqBiBQFAwYgUpQ8nwj6mwO4xskVHsmevnUrnbg13BobiFDzz4TvooWVwyFPUSU67dc2oHYKFs14sw47PDxhqRkS4QCCx1FqzzrbgOu1AWKHHjjaZp+E/5UWwKpprhFZKgbxAG2gNxG4QnT+q32cS6QOqGfcKRLhAUGOE0HiJxTaVp+u/G49EHH79fV5vKy3FV6EYqW187qPXRTYVUY1dtrX7rduveqWibtLFo3qmqI68kEOtNgWDUGEkFs6ZqeuPk9z5C+c89eD/9CE1rAmDaD56ANV8cNpakqpypqNDHJ7HmO7/IOdplKmE2mzGbh0pw+/v7x/FqzhMGJz7S0aMclO7hTwli/8kh+MqcjBOfaSX6JMsrm2HLlpFNbOrq9AnX4cPxmXWQX1SMQCAoGNkIUvYr7VjqLBnPlSxuIdgVpP9AP2FPGKVEAUkXsaJRL9cHsL35L9DdjTZzFr3BYgDKPjkIOw4NlWtHrilNZp4QrgSC8wNJlqi4qQLfSR+eZg/mmWYUm74h6D/lx1RpQilSCHuy3yCMONJnzcruGpJVx4AQ0gWCgpON07i9/fxwGieuqSKKUsSm2d8PNTXgcNDWXwrAzHAbtJlhzhw0TUvpSLda9eiE3l59PCwtJftInZFQKKFerDcFglEjVaRehGQGBYjJVD9wmvAfP0IJ2fBVNFKHmy6DEfP7B+FMa1brPdDXfB9/rPs8BecPOamPFRUVKIpCZ2dn3P2dnZ1UV1cnfU51dXXa4yO3uZwzl9eJfQ3BOJPQtfxto/7vIreZCD35Lxm7ljuK9cHLFyhAiaQs65Ohigo9GmakUTFjxWQoYxQIRkhEkDJWGPE0ewg5Q2ghjZAzhKfZg6nCRMXqiqwajSbGLWiahvuom7AnjLHSiCRJyEYZY7lRj3pp9XH623/A2+pHa1iAu6iCoKqLXmWXzYwv1x4kWamfcKQLBOcftgYbM++aif0KO6GeEN4PvYR6QtivtFP3UB0ljSX4T/rREjIKIhuE1garvkE4+Fl/6j19AJlZm6EsOXKeJPmdIIR0gaDgxDqNk3E+OY0T11Q9PRAI6J33urp0NXz+fJAk2py6uD7b3DG0yRCzlEkcuyAhJz2XSJ18SVivcvHF+u3hw/r9GdarcUzW9aZAMAlIF6kHyQ0KkUx117suDN0nsJo6MdRYGThXzl9wiktM51KOJakaupeX67dizXd+kdOobTKZuOqqq9i7d2/0PlVV2bt3L0uXLk36nKVLl8YdD7Bnz57o8XPnzqW6ujrumP7+fg4cOJDynKle5/333+fs2bNxr1NSUiLiWiYCSSY+fwhO41OsoCp0/8GRceLjGMxS94UlfL6Uh2VPQ4O+03jFFfqk78MP9dsrryyMm6HQtLTAY4/B5s2wdat++9hjuU3oBIJJQjpBqvau2rTNQWOJuNsj4lXYGSbYHcTg0MXxUH8IY4URLaDR/1/9+D5y0f1eCcc/WkHbf82mo70UAJMSwmoKxZdrD5JMvBKOdIHg/MTWYKPugTrmbJnD7E2zmbNlDnX311F8SXF2G4THjkY/60/+5gQAsz7YndVnfaZmowl+E4FAkC8Rp/HJk8PDuyNO44aG88dpHLumcrv1WBeXS3eiNzZGB6FPB4X0OoczuskQGbdg+NgFCUJ6LpE6+TAaQv1kW28KBJOExDVeLMMMCiRkqtcGMAx0IpWWYLCoBIs1HARZTg8ayceSTH1ohCP9/CLnaJcNGzawdu1aFi9ezJIlS9i+fTtut5s77rgDgNtuu43a2lq2bdsGwN13380111zDU089xfXXX8+rr77KwYMHeeGFFwA9v+iee+7h0Ucfpb6+nrlz57Jp0yZmzJjB6tWro6/b1tZGb28vbW1thMNhjhw5AsCFF15IcXExf/7nf86CBQv48pe/zOOPP05HRwcbN27kH/7hH+LiWwTjRMLEJ6xK9AeK2Mt0/pZWzpxeRPG+/0DZ9zGWZRcmdZoWGfXBK4xEZyfMnl2A62po0J0GI83AG23GoozxPOT555/niSeeoKOjg8svv5xnn32WJUuWpDz+9ddfZ9OmTbS2tlJfX893vvMdvvCFL0Qf1zSNhx9+mO9///v09fXx2c9+lu9+97vU19cD0NraytatW3nrrbfo6OhgxowZ/M3f/A3/9E//hMmUPDf3fMbWYMN6sVXPsHOFUewKljpLVk70CIlxC7JFRgtoaGaNUFcIxapgnG6k/53BqJciFeQgSpGE60wx/WemUYebQJGqr9mSlGsnE6+EI10gOH+RZGlYHicMbRB2v9GN56iHQHsA2SJjv9JOxeoKbLTFfdaf0mYAMLP7COz4t4yf9ancUvPm6beffKKbIQ3ndbCjQDBCItnZl1yiu4o/+CB+bn7q1PnpNI6sqVpb4Zln4KOPYPHiuN/Bp32DjvRL7dFNhog4BcPHLkgQ0kc7Ume0su/zWW8WIqO9QOS6XhIIxoKMkXoxFcyaquHc58S534lpugnJP6BPiIxGALwhA2cxU08fPqeFouLQsLEkVdWfaOh+fpLzVPrmm2+mq6uLzZs309HRwaJFi9i9e3e0sWdbWxtyzCC/bNkydu7cycaNG3nooYeor69n165dXHrppdFj7rvvPtxuN3feeSd9fX0sX76c3bt3Y7EM5d9u3ryZl19+OfrzFVdcAcCvf/1rrr32WhRF4ec//zlf+9rXWLp0KTabjbVr17Jly5bcfyuCwpMw8XEF9M2NFnQ3gvN0OZ9oizB+pwvrUtNQw78YtPCQkN7RUSAhHfRJyURuBJTojohM7CLuiOZm3R1x8cXn14R9hLz22mts2LCBpqYmGhsb2b59OytXruTYsWNMnz68+e2+ffu49dZb2bZtGzfccAM7d+5k9erVvPvuu9Hx7PHHH2fHjh28/PLL0U3BlStX0tzcjMVi4ejRo6iqyve+9z0uvPBC/vjHP7Ju3TrcbjdPPvnkWP8KJgWpBKlciBWvXAddqB5d+DbPMFN0cRGeY55o1IvWH0Y2aBgNHpQyhbOtlXyWbt6PfB4lKddO1iRLONIFAkEyUm4QosFj8Z/1p/p10WnmpaXQ/W7Gz/pUbqnZs6GoCLxeXUy/6KLRfIcCwRQmMTvb7x/6wzKb9fnBlVfqIvr5aHCRZX3n7qtfHYpBmTkTbDaC/V5Ou/S50+xblg5rNApZONJHu3nnaAr1uaw3x6KZapbkul4SCMaSjAaFBls0E92538nAkQEMpQZMdg1ryIYxGEQzmQl4DVgIU0KQkE8BafhYkqrqT0S7nJ/k5UlZv34969evT/rY22+/Pey+NWvWsGbNmpTnkySJLVu2pBW9X3rpJV566aW01zV79mx+8YtfpD1GME4kTHz6fBbqcHON1IXBHCTkM6JhxDDdPNTw766ZcWJ6ZKIVGnSknzeMljviPOfpp59m3bp10WqapqYm3nzzTV588UUeeOCBYcc/88wzrFq1invvvReArVu3smfPHp577jmamprQNI3t27ezceNGbrzxRgBeeeUVqqqq2LVrF7fccgurVq1i1apV0XPOmzePY8eO8d3vfjetkO73+/H7/dGfXedD86oCExGvvK1eTj1zCu9HXuyL7YT7E6Je/ArmMgWDvweoxGeSmI2HAVMwZWOoZA3+Io50l0tfZ4vCKIFAECHpBmHrp3Gf9f6QQqdbb3Q8y9EP1syf9amEdFke6nfX0iKEdIEgL1JVh7a16R/yf/VXcPnlE7OydayJxJlExOD2dk4FZ6IhYzKqTF8+NAhF5lCQhSN9tJt3jrZQnw0TrAo51/WSQDDWpKtgjmSiB7uDGKcbMUwzIBkk/H0yoYFZFHnOEDCUYOoycgV9OIJBOv8wHdn+EbZrG5Kv9wwi2kWQY0a6QJA3CVmCfV4zy+mmXPZhn9EPgNtdiWHmNKwLrAS7g3Tv6kZThyZXQ0K6TEfHuLyL8SEbd4TPl38Z43lIIBDg0KFDrFixInqfLMusWLGC/fv3J33O/v37444HWLlyZfT4EydO0NHREXeMw+GgsbEx5TkBnE4nZWVlaa9327ZtOByO6Jfo+5AfkixhnWel9qu1WGZb8LR4CPYE9aiXsEawK4hiVbA21ujNsbq68AQ1TKhUyq6UjaGSiVcOhx6rCcKhIBAIsiDms17ToK29nLkMUKe4mGbxZvVZn6rsGHTNCfRhTCAQ5Ei67OxLLtGbazY35y6iDzYW5v339duRNMmcaDQ0wAMPwJYtsGkTbTfrRpS62XLcryi2qk9KNAyRIKSPdvPO8c6+H4tmquiGnP7+/uhXrFknlnzWSwLBeBAxKBQvLKZoTlE0ziWaib7AinmmGWOlEdWnYqgwEJQdOHtrCfRoBFSJMBAwaPjagpw6tQT3whsyrvdAONLPV4SQLhgdEieGEDfx6TspU4eHAYOETfkU0Aj0mwmeCyFJEuaZZjwtHnxtQ11FI7uAkWiX84ZYd0QyxsIdkQPf+ta3WLZsGVarldLS0vG+nKR0d3cTDoejkVQRqqqq6Ejxn6ujoyPt8ZHbXM55/Phxnn32Wf7+7/8+7fU++OCDOJ3O6FezUEJGRGwjU9WtonpUwq4w5hozJY0lGC+arjfHqqkh4JYIIGOV+1M2hkomXsmyyMwTCAQ5MPhZ726TaPvPOtp/O4sv08rfKx9z8r/qcJ+UMn7Wp3JLwdCw1dLC1BbvBILRYDSaXLa0RBsLs3WrfvvYY+kbC0+2v91InMnChXwaqAGGR3OmikuIMGuWfnvq1OAdo9m8c7SF+kyMdjPVQRYsWBBn0In0tkskn/WSQDBR8LX58Bz1YJ5lRpL0jTrbfBuKVSHYFUQNKYQlG5LVjBSS8GHAb/VhvchMcOYldL9fktTUmarZqFjvnV+IdkOC5IykwUm6XLfBMr+BN1RMhAlLIZRZlZg9Cv52Fd8JH8YyI4pNIdAeIOwKD13S4ETrvBPSR7uMscAEAgHWrFnD0qVL+eEPfzjelzNhaW9vZ9WqVaxZs4Z169alPdZsNsc1Te7v7x/ty5vypIp6iTYyraxAK18GJ/r5tNvKjD+7HO7/k6TjYOpSP43OTomudz4BhyzKvQUCQVI0VcOnTmdAXUrXb2XU4mIGJAMnsVJvPofrTDG+1ipmfrEcW5rP+nSCVNSR/q4XHvvnCZG9KxBMGgqdnZ1PfMcEys3OFk3VonELHUcUJCzU1cWPT8n6zMRSW6vfOp1Dy9K8mndmS5JYmjHLvh/tZqqDNDc3Uxv5xULcGkMgmCqEXWHCvjBm29D/b2OlkZLGElyHXQTPBFGRUEsq6PGYaAkWs2JxEVLjRZj7w1FTZySKT0S7CGIRQrpgOCOZqGUzMXzgAZzuMwQO9lM6zQbLP4uxrxd/+wDhAV04D7vDyBYZxa5ET50yI30CdTUfFSLuiJMndTfEYNMe3G5dRB9td0SOfPOb3wTI2NNgPKmoqEBRFDoTwvY7Ozuprq5O+pzq6uq0x0duOzs7qampiTtm0aJFcc87ffo01113HcuWLeOFF14Y6dsR5Els1MupHafwtAzv+O4y2fgvKvjqBbaUNVxJxauWFio9VmA23T/8N/iPdyf8glcgGEtCrhDvf+F9JJOEbJKRjNLQ9wm3sllGMkvIZv172RJza5GRi4ZuFaui/2yVUYoUZNvg85NEBkwEIk2w3C1uBt65lHCvE4v3HG5DKSoSFrMPq9aKR6ummwasSKR6J6ncUjA07Bw9JqG+ewS5bua4Z+8KBJOGQmZnJ8Z3RMamSHxHc/PwxsITLDc7GyJjm+eoh7AvTMVhhVux0hA2wfvd0TVbunEL9MMcDl1Ib2/Xp1JAbs07c2U0hfp0jFFGu91upyTZ+RPIZ70kEEwUFLuCYtHXdIaSIdnTWGnEdpmNwJkAGMCx3MG7r1pwA+WzjSCR1NSZKj4vEu3S2wvh8FC0p2BqI4R0QTwjmahlOzG8/356p8+gjRAXKC40TCg2fcQJD4TRNA3/KT/2K+1Y6izR00d2AUOxjvRJ6M7Ii1FyR0Qy8iIkOp+nKiaTiauuuoq9e/eyevVqAFRVZe/evSkbKS9dupS9e/dyzz33RO/bs2cPS5cuBWDu3LlUV1ezd+/eqHDe39/PgQMH+NrXvhZ9Tnt7O9dddx1XXXUVP/rRj5AnyAbI+Uy6ju+/662g7ZiNdDH2wxaBg+NoZfDLwGy6Si+EirYJveAVTGyef/55nnjiCTo6Orj88st59tlnWbJkScrjX3/9dTZt2kRrayv19fV85zvf4Qtf+EL0cU3TePjhh/n+979PX18fn/3sZ/nud79LfX09AK2trWzdupW33nqLjo4OZsyYwd/8zd/wT//0T5hMpoK8J9Wr4vxPZ0HOlRFZXxQpxYNfdv3LYDeglCgYSg0YHINfZQaMZcborbHSiLHCiGwq/Fgd2wRLKVHAYsYwy0GgR8Z4zoCDICXSANKMGsy1F+HpKY5zRyWSTpC6YK6KUdFwhyycrL2a2SWDn/3pxDuBQKBTyOrQXOI75sxJv75qaICDB6GpCe6+W3/9U6fG3VwUO7aZZ5kx28x0/YePelxc/O/HcXf+P2zTvTB/PtqCG/S3nkJIB/1X4nTqU6iokD7ajKZQn4oJVoWcz3pJIJgoWOosWOdbcR12oSxQ4gwVsllGUzUs0y1oFRbcXv3+iCiezNSZquov8hxNg3PnhhzqgqmNENIFQ+TjkIglh4mh0zWH/6SCa4p9eJo9MDhGhfpCeJo9mCpMVKyuGIpZYGiBGI12mYTujBExCu6IxKaVDz/8MI888sgIL3RysGHDBtauXcvixYtZsmQJ27dvx+12R7vS33bbbdTW1kZzA++++26uueYannrqKa6//npeffVVDh48GHWUS5LEPffcw6OPPkp9fT1z585l06ZNzJgxIzr5bG9v59prr2X27Nk8+eSTdHV1Ra9HODvGl1Qd3z/+f/oYlJWQbpDixtGKagOcgm5vsRCrBHnz2muvsWHDBpqammhsbGT79u2sXLmSY8eOMX369GHH79u3j1tvvZVt27Zxww03sHPnTlavXs27777LpZdeCsDjjz/Ojh07ePnll6Nj1cqVK2lubsZisXD06FFUVeV73/seF154IX/84x9Zt24dbrebJ598siDvSylRuOT/XoIaUNGCmn4b0OK+V/2q/r1/8PvIl0+/L+wNR29Vrxr9CnvDqB41+reJOljiG+MsyhVDqQHjdCPmGWZMM0yYZ5gxzzRjmWPBMs+CZa4FQ3H20+rEJljBs0G0sIah0oYy3YZ20E8dHkoumQnL61HCEPjQm/Y9pMtIN55po77YQLNzJi0905k9LSYiLJl4JxAIhihkdWiu8R2p1lddXfr9p0/DBx/oX7IMRUVgNo+buShxbJMkCbq6OXuuiB6sXGOQ6A58Bmt5M9Lhw2jvDgB/nrRJcoSZM/W3F81Jn6qk+3928qT+77pggf5/Yow2STKtlwSCiYokS1TcVIHvpK43xVUdt/sxV5uRi2W6uzVAosQOJhOpTZ0pzApG41DVTE+PENLPF4SQLhgiV4dEIjlMDPv6oA0bZ5bOxH5hN73/3gtAuD+M/Uo7FasrsDXEn2co2kWms1Mbmeg/WSmwOyLbjLwHHniA73znO2nP1dLSwvwxs4mMnJtvvpmuri42b95MR0cHixYtYvfu3dGGOm1tbXFu8WXLlrFz5042btzIQw89RH19Pbt27YoKUwD33XcfbrebO++8k76+PpYvX87u3buxWPQP4T179nD8+HGOHz/OzJkz465H0zQE40uk43ssvfrQlF5ID8VMrGLG0cqzHgC6PINjmRCrBHnw9NNPs27duuiitampiTfffJMXX3yRBx54YNjxzzzzDKtWreLee+8FYOvWrezZs4fnnnuOpqYmNE1j+/btbNy4kRtvvBGAV155haqqKnbt2sUtt9zCqlWrWLVqVfSc8+bN49ixY3z3u99NKaT7/X78fn/0Z1eGDFfFolD5l5W5/TJyRA2pqG6VsDusfw0Mfg2K6iFXiLAzTMgZ0r/6QoTOhQj2Bgn1hgj2BAl2B0HVN/pDfSG8H3pTvp5xuhHbAhvWBVZsl9iwLbRhv8qOYh1e55vYBEs2y0gGCS2oIZtlXJhwEMRht4EkEXaHhrmjEklVdgyAy8UC+wDNzpk0d1Wy6sLj8Y8XKHtXMDkZ66oXgN7eXv7n//yf/OxnP0OWZf7yL/+SZ555huLiYkCvjJk7d+6w196/fz9XX3111tdSMApVHZprfEey9VVXFxw4AB6Pfo5AQJ9XeDz6hGXZMrBax8VclDi2oWnQchSnfykgYa/w69U12nSKFthR/0MPFs7kSIcCC+kTNRo02f8zv1//PxAMwo9/DD/5yZhtkmRaLwkEE5lUVcclV5Vgu91Gz5s9nDrowYqZynKFkFOP9kxq6kxjVigv14X07m5dfhJMfYSQLhhipA1OcpgYOgeruZV5NuoesGK5wILzt04Uq0Ld/XVxg1aE2GajbrfEwPsnKM5X9BcA2Wfk/eM//iO333572mPmzZtXoKsaO9avX5+yNPHtt98edt+aNWtYs2ZNyvNJksSWLVvYsmVL0sdvv/32jL9HwcQiEiMVKdtLRpx4FTOOVlh1Ib3bYx06WIhVghwIBAIcOnSIBx98MHqfLMusWLGC/fv3J33O/v372bBhQ9x9K1euZNeuXQCcOHGCjo4OVqxYEX3c4XDQ2NjI/v37ueWWW5Ke1+l0UpZmR2nbtm3RHhkTBdkgIztkDI78p7uaqhE6FyJwNkCgM0DgTAB/u5/A6QD+k368J7z4Tvh04f1skL6zffS93Rd9vmSQKF5UTMmyEhx/4qBsZRkGu2FYEyzFoWCsMBI4E8BYacQbkLCgYTOrKd1RiaRrNordzoKy43AKWrqS2KUKlL0rmHyMR9ULwF//9V9z5swZ9uzZQzAY5I477uDOO+9k586dca/3q1/9iksuuST6c3nMB3I211JQsqkOzSTQ5hrfkbi+0jRdYPV4oLJSn3O43fpx8+bpSs5HH8Hy5eNiLhrW4M/pxN3RTwgFCXAUewmcKyLsV0CS0Cp0QVbSQinPWXAhfaJHg8b+P3vvPXj9dd32Wlc3LhXY6dZLAsFEJ1XVsSRLFF1QxNt/340DD3OUAKEeOaOpM9kcq6oKPvnkPKiaEUQRQrpgiJE2OMlhYhgZZKZP112gxZfr7pNgbzCpiA4Ju4Ah6HBauPDC0e1qLtCprKyksnJ0nYOCwnH0K0fxfepDUiT9yxBza5BAIfq9bJSj30vG4bfRJoAx38c1AzQnaQZoSWgCOIEb/aXj7Fm9RE+S4KKLUh8XJ17FjKOVtogjPUZIF2KVIAe6u7sJh8PDnF9VVVUcPXo06XM6OjqSHt8xuCsUuU13TCLHjx/n2WefTRvr8uCDD8YJ+O3t7cPiwyYjkixhLDdiLDcOW1TFEnKG8B734v7AHf0aeHeAwJkAroMuXAddtO9oRzJLTFsxDcefOJCQok2wJEnCNt9G2BnG3xlEDsmEAZtZTRl5l0japn11dTQ0HIE/QHNXwuf5OGTvCiYO41H10tLSwu7du/n973/P4sWLAXj22Wf5whe+wJNPPsmMGTOir1deXp4y/i7TtYwK6apDsxFoc42JSVxfRWyPDof+eI/u6Ka8XH9OSYn+uNMJpaVjbi4a1uDPH8Dp00X1YpMfQjKyQUUx6zFVmkmfI0lK6srMggrpkyUaVJb1f/udO3VH+iWXnD8V2IK86P63bvoP9A+t62LWdNH1nnHoSzbGNHo3Dm/0nnSNZ5bTzkMmKsmqjkEX2f/fNCu/w8fWvwoz584hkT2RdFV/CxbA/v16BJXg/EAI6YIhRtrgJIeJYUuL/pTIPMVUpTcvC/frLgbFMrx0OTJ4FdklOAcdahUXuntHtau5IHfa2tro7e2lra2NcDjMkSNHALjwwguj5bqC0aX/d/1674EJhGyRka26sK5YFWSrrDf9sylDTQBLBpv/2RW96V9pzFeZAWOFEcM0A7JhbBYL77+nUYWP+TPDSGcVtFQTq9hNvphxtKLIDcQ40oVYJZiEtLe3s2rVKtasWcO6detSHpfYrDq2kfX5gMFhwH6VHftVQ/MOTdPwt/lx7nfS/1/99P57L96PvPS+2Uvvm72g6M2wym4oizY2LWksoX2fGwdeggYDJknDlsIdlUi6smNkmQW3XAavQUtnGVqfE6k4z4xnwZjxrW99izfffJMjR45gMpno6+sr6PnHq+pl//79lJaWRkV0gBUrViDLMgcOHOCmm26K3v/FL34Rn8/HRRddxH333ccXv/jFrK8lGbnGUGVNLgJtLjExiesri0WPcjGb9YiXyLgbaQRtMulGosh7HGNz0bAGf2YTzrA+LjpMPvz9Zuw1A1gcPgA0j36dUpqGzgUT0kfaD2ysGWnsquC8ovffezn9L6dH/XUkk6Sv68xDpinFqujmKav+vWKLWetFmrwXD67zHAqGEsPQWm+a/r2kjKFAH1M5dPSP8+mkiLmfg6I5qZ+Sbo4VKYB6//1RuFbBhEQI6YIhCtFIJ4uJ4cAAfPqpfnjErGYoNSCZJLSARvBsEKUutZBuHRTSO8sa4OSrE6KruWCIzZs38/LLL0d/vuKKKwD49a9/zbXXXjtOV3V+ccGTFxDqC6GFNLSwpt+GNAgzdF8w/jEtqN+qQb1BX+Rr2M+RJoCxzQAHv+IaAvpUUIeuSfUN3lcADNMMmKpMmKpNmGr0W3OdGctsi/4114JxmnFEr+FucXP2mW7uwMPccJjWzQrW+VYqbspQ6hczjlYe12dTXW6r7gwTYpUgRyoqKlAUhc7Ozrj7Ozs7Uzo0q6ur0x4fue3s7KSmpibumEWLFsU97/Tp01x33XUsW7Ys2lhZkD2SJEXHpapbqtA0DU+zh643uuj+aTcDhwfwnfBx+rnTFF1cROk1pchFMr1BA3+glNCSSr60tTilOyqRtI504KJV85BljXPBYjrbQ1RLH+aX8SwYMwKBAGvWrGHp0qX88Ic/LPj5x6vqpaOjY1hsjMFgoKysLHpMcXExTz31FJ/97GeRZZmf/OQnrF69ml27dkXF9EzXkoxRiaHKR6DNJiYmQuz66uBBPdYFYMYM/esPf9Dzs81mXWQ3GIYE9jE2Fw1r8Fdrw2msxEqIuZIbkzVIxfxu/VekaWhn9WY0ss2c8pwFE9InmzA90thVwXlF6XWlSEYpbl0XvQ2lWNdFfg4kWeMFhtZ3sWgBjXAgTJj8G7gPQxps6l6umwqMlUZM0wfXerUmzLVmzLV6g3fDNMPIKp1jKoeCniAfn/j/AXCx/BFQn/Jp6eLzFi7Ub//4x/wvSzC5EEK6IJ5sHRLp8v8yTAyPHdMPq6wcyh2WJAnTdBP+U34CnYGkGaCRXUCbQz9PxwWfhe5f5S/6C0aFl156iZdeemm8L+O8pvzzaQK9xwhNGxTmfSqqV/8Ke8OoHpWwZ/A20vzPPdT8L+wKE+rXG/+FnWFCfSGC5/TGf6Fzen5m6Jz+vedoate9sdKoN/xrsGG9xErJ1SUUX16cvAlfAu4WN6d2nCLwQRAnZozzzBgqwrgOu/Cd9DHzrplxYvow8WpwHK18eQ/8SnekB872YRJilSBHTCYTV111FXv37mX16tUAqKrK3r17U+aVLl26lL1793LPPfdE79uzZw9Lly4FYO7cuVRXV7N3796ocN7f38+BAwf42te+Fn1Oe3s71113HVdddRU/+tGP4povC/JDkiS9CeklNuZsnEPff/TxyUOf0P+f/XiPevEe82JfYueYVsGPqeB/3mRL646KRdO09M1G0adz8+ZJHD8OzTf9E9ULuyZWkz3BMCKC7/k4r6qoqIhzm3/mM5/h9OnTPPHEE3Gu9FwZlRiqbAXa1lb9by12fZStYBtZX7W2wjPP6Dnoixfr5z99Gs6c0dc//f1QU6NHv4yTuSixwZ8anIaDEEFTP7WXHMU2TQOnvmZTiy8HQEozP4sI6b29+h6C1Zry0PRMNmF6pLGrgvOK6V+azvQvDe9rMVIi84uIUSp6G7PGi1vnuWPWee4kTd7745u8q24VtKH1nfd46qbuoPeUKZpXRNEFRVgbrNgus1F8WTFFFxRldrUnVA594ptNSDNgU3zU/p9/hpr/mXKdls6sEHGkf/zxCMcowaRBCOmC4WRySGSb/5diYtjcPPQysRirjFEhPRmRwctWog9eHVRnXxYpEAjGFEkayt+jQPN7NaQSOhci2BXUm/516F/+dj/+Nj++T334PvUR7AwS7Ari/I0T52+c0efLRTL2JXYcyx1U3lRJ8ZXFwxwNmqrR/UY3we4gHwWseJCYXg2GEgPKAgVPs4fuXd1YL7ZGHaLRiVVsqV9DAzXfuhjHv6g4XQpH/+ZRLvuzKn1B+/77QrwSZM2GDRtYu3YtixcvZsmSJWzfvh232x3NM77tttuora1l27ZtANx9991cc801PPXUU1x//fW8+uqrHDx4MOoolySJe+65h0cffZT6+vpoI8AZM2ZExfr29nauvfZaZs+ezZNPPklXV1f0elI54QW5U/onpVz5H1fS/04/H9/3Mc7fOHEdcOGzFXOaIj7zmRxOFlPwkzTaZZCGBjh+HFrOVfO5heLfspC4XK64SKPEuKOJyHhVvVRXV3P27Nm4c4RCIXp7e9OOMY2NjezZsyfra0nGqMRQZSPQtrToAvi5c/k3uJRlvaHoV7+qi0EtLbrKXF+vx7x88gmUlek/9/ePq7kotsHfU38b5udtCpuWtmHTPoUPfdE1m2ZZBf/uTDtuORxDfqn2dv3t5cVkE6ZHGrsqEBQASZKiuemFWtPFogYG13e9QYI9QYJn9XVc4OxQg3d/u59Au77uCzvDDBweYODwQNx55CKZksYSSq8tpfTaUuyN9vi44CSVQ0dP6xsPF0/vRe7pShvtlKnZaGWlPgw3N+t7nIKpjRDSzxcydZBPJJUQXoAGLZF89ETzRyQnPdgZTP4WBstpikv1wauzk9zKIgUCwaRGNsiYKk2YKk3YFqRp+jcQwnvMi7vFjafZw8B7A/Tv7yd0LhQV19u+1YblAgvT/2o602+dTvFCPb/f1+bDc9SDaaaZs136WFM5aO6QJAnzTDOeFg++Nl+0aU2kWibRBSopMouuhN/8Bg4fkbjs8OPpNyAFgiTcfPPNdHV1sXnzZjo6Oli0aBG7d++Oxhm0tbXFucWXLVvGzp072bhxIw899BD19fXs2rWLSyN2GeC+++7D7XZz55130tfXx/Lly9m9ezcWi14NtmfPHo4fP87x48eZGbECDqJpqRvCCfKjZEkJi369iLZtbZzYeILPuc9QiYfLZl0CmLI6R2SOBKmjXUCfe/3sZ0OmBkHhSHQ1P/zwwzzyyCPjczFZMl5VL0uXLqWvr49Dhw5x1VVXAfDWW2+hqiqNjY0pr/fIkSNx4nymaxkzMgm0J0/qIrcs65/9I21wmVhB7PPB3Lm6E91i0a3bE8BcFGnw924fdAKz1/0ZXHZx3JpNe7ULcKYdtyKm/mPHdO04byF9sgnThYhdFQgmOLJJ1iM7qzLPd8KeML5WH96PvUMN3v/gxv1HN6pXpe/tPvre7gNAMkuU31BO9e3VlK0sQ24fXjl0tLsCgIvLezJGO6XtQ4PuSv/1r/V4FyGkT32EkH4+kI2DPBsK1KAlsdFohMjgmcmRXjJt0JEeiT9M434XCATnH4biJE3/VA3Phx769/XT+/966XmzB9/HPtq2tdG2rY3KNZXMe2yeXoLoC+MNmgkEQZGhvGzo3IpNIdAeIOwaygVMl5l3xRW6kH7kf7ew9uLDeW9ACs5v1q9fn1LUevvtt4fdt2bNGtasWZPyfJIksWXLFrZs2ZL08dtvv53bb789n0sV5IkkScx+aDYf+IpRtzazECdHP3eIxYcXZ9XzITJHgvRCemSoiczFBIWjubmZ2tra6M+p3OgPPPAA3/nOd9Keq6Wlhfnz5xf0+lIxHlUvDQ0NrFq1inXr1tHU1EQwGGT9+vXccsstzJgxA4CXX34Zk8kU7bXz05/+lBdffJEf/OAH0WvPdC1jRjqBVlXh0CG9CejixUNrpJE2uExmJpo5UxdYJ5i5qK1Nv509d/iaLSpOpRm3IF5Iz5vJKEzn0phWIJjiKFYF2wLbMEOVFtbXec7fOqNieqAjQPdPuun+STfGKiNVKxVm9ipYZg8991iPLqTPr+jOGO2UKT5v4UJdSH//Dxq0fjrhxmFBYRFC+kQhV8d4thTAQR6lQA1aUkW7ZBTSByda9kQhXSAQCDIgyRK2+TZs823U/G0NoYEQPT/v4eyrZ+n5tx66Xu+i+1+7qV5bjSRLdJ0MAwYqKkCOqQoMu8PIFhnFPnRnulK/RZepgMzhM9VwU/4bkAKB4Pzgd5TzElfxfPEf4FMfnzz4CRc3XZzxedkK6RHTtHCkFx673U5JMjdyAv/4j/+YcaNq3rx5BbqqzIxH1QvAj3/8Y9avX8+f/umfIssyf/mXf8mOHTvirm3r1q18+umnGAwG5s+fz2uvvcaXvvSlnK5lTEgn0B47pjcAXbp0+Of8SBtcJjMTTTBzkdsNPT3698mM3pnEqQgFazg6GYVpUYEtEKRFUiRsDTZsDTZm/P0MNE3D/Qc3Ha900Pm/Ogl2Bjn1SpAzhv+POQPHqP1vPciyFnWkz6/ozhjtlM44BUM56X/82QnofkRUIE9xhJCeLaMldEPhHOPJrrkADvIoBWjQ4vfrTRhgeLSLcbruuMrkSHeUxUS7CAQCQR4Yig1U3VJF1S1VDLw3wMff+JhzvzrHme+fQbbJeKtLgBKqqoYmS5qm4T/lx36lPa4hcrpSvyuqzwC1HHHNQ0Mi7oiRLqAFAsGU45134CRWev9uPvbtRzjzvTNU/XUV5llmwq4wil3BUmeJ9miIEBmHgLTNtiIm585OPf2hrCzloYJRorKyksrKyvG+jDjGuuoFoKysjJ07d6Z8fO3ataxduzb1RWd5LWNGKoG2vl5fk6WKC5loDS4LzKef6rcOh/6VSFScSpORDgUU0mFyCtOiAlsgyBpJkii+vJgLn7qQeY/No3d3L23b2ujf38/Hv7mUzmMu6q//cCjapawrY7RTpmiXhfYTwFz+eKpUr24RFchTGiGkZ0Myofuii+Dqq/XOAiP58C2kYzyRdA5y0K973z79a9myzNdfgAYtH30E4bB+yGDlZpRoRvrZ5BnpESG9tEK/zo4OPc4u2VsTCASCbCm+vJjLfnkZvbt7+XjDx3iOerC19jMfiZoSK1pIIewO4z/lx1RhomJ1RZyIlc6R3lDVi0mejtNfRGtfKXOn9cUfMMUX0AKBIHs0DX7/e/37S/66lOL+ajpe7OCDL33AtBXTUIMqikXBOt9KxU0V2BqGjA2xTY8TmyjHYrfr08KTJ/Up6Gc/y+iaRQQjoq2tjd7eXtra2giHwxw5cgSACy+8kOLi4vG9OMFwkgm0qgqPPDJ5GlwWmGisy+zkj6ebQ8Uya5Z+WxAhHUZfmBbjqkAwIZCNMhX/vYLy68s58+i7fPKtHgY67Bz+4ZUs5xw/x0z92f+C6vTRTmnHKlVlQfNPgG9w2ldGr7GKMsUrKpCnMEJIz0QyobutDV59FV55Re+cPn36uGaOpySVg7yrSxfYz56Fvj74znf0csNM159vg5aYiUTLb6uA6cOeDtlHu0yr0J8YCOiXP21aht+DQCAQZECSJMo/X07J0hKO/LcjuN9304ALKwreDyVki4z9SjsVq+PFK0hflmwss3Np6Sne7Z3LkY7q4UL6FF9ACwSC7PnkE90lbjLBZZeB9+9qOLvzLMGzQXyf+ii9rpSwO4zrsAvfSR8z75oZHY8ylRzHsmBBjJBeNkpVkYKCsHnzZl5++eXoz5G88F//+tdce+2143RVgrQkCrSqOrkaXBaYiCM91dvLJSMdCiikjyajVW0uEAjyRpIlZmy+iorPfcDx9c2cfa+Su/mQsMWPdcmlGaOd0grpbW2UtP6B2SW9fNpfxh/PTue/zR4c/EQF8pREbIekI1HoLinRVzgffKA/pml6Vkl5uT452rEjt+5NuWSO50OsgzxCVxccOABnzoDBAKWl+kZANtcfyf+rqNBFfqcTQiH99oMP9JXfggX69ar6go6WFnjsMdi8GbZupeX7/wFAQ03fsNMbq9JHu0QWiSarFC0NFDnpAoGgkBhLjSz4+WWcwYKDENazHmrvqWXOljnU3V83TESHDGXJdXUsmusE0HPSY4ksoBsapuwCeiLzrW99i2XLlmG1WiktLR3vyxEIom70RYvAaNDo+3Uf1susAPT/rp+QM4ShxIB1gZVgd5DuXd1o6v+fvTOPi6ru/vhnFmbYQWQHEVREcBcVMU1LTVJTW9yyXOrR6vdYmi1quaWZmVZulbaqPZplpmWWaWplSe47iKIiO4LIDgPM3N8fxzsbsy/MgN/363VfA3e+c+feO/eee875noUMO1OjOgGVnZjy923S/c6cId0uJoZeLdFpGXZh06ZN4DiuwcKc6E0IQ/ZTSopmg0uFAsjIAC5coFfenmrC8I50ayPSm4wjnQ/CY3KVwXBKJP06IvbUYyjr7gsRgBfrs1H16ItGJ7kM9nO4G8DaOegWAOBCQaDm+x4eNKnGMpCbDcyRbghtRzfH0f9VVUBAADnQi4tpfVwcOdx37yalxxRFyJSa49bccHwEeVYW7aP6/vv70yRAYCBpJtr7rw++/l/37tQ55soVCqEqLwcqKoCtW8lp/u67wM8/N1AkUqqpeVJs3qEGigQfkV5/u17pmFJHXdEKC6N1N25YdmqswlwltxkqxQxGcyajXIpX0QUlAhfUXa1CxpsZkIZLG9QkBqhuOuT0t04jUChE9yRyoJ9N9zRsQDMaldraWowZMwYvvPCCo3eFwQBA9dEBoHdvoCazBlWXq+Dd1xuubVwBOVC8txgAZdBIw6WoSq1CTWYNAOO1O9VRNhw9WqIZLCISqbIiTdEJGQyGaeiyn27fpkh0voynVvCR0p5q4o5XU0u7mNps9NYtMmGdEl1BeEyuMhhOh0Akwt/3d8ZFeMO1Xo4LIy+h7o7u8sI8BvWsuwGsnXxzAAAXb2k50lkGstNgq0AqVtrFENqO7tJSegD6+JBjXSKhMTKZZgT5oUNkDRlL57JBzXGDaHeQ9/Ii7UMqpeNwd6f94qPhTU05Ua//d+4csGMH4OJCjnu+xvvp08CPP1LEe58+yu9ILQ0BAMSJ0oDdaRpla1xaugAiAHKgrrAO0lCpxteqO9J79KBDOn4cGDbMstNjEeam6pk7ntXTYzAczsWLQC7c8XXnLph14yxK/ihB8S/F8B/p32CsRoM/PdFU3ZKCgWXAmar2ZDjzDch69DCaRsggysvLUVZWpvxfKpVCKpUa+IRx3nrrLQAU8clgOAN8RHqvXoC8XA55jRxSTyn8hvsh96Nc1FyvQd2dOri0cIHIQ4TanFrIy2kmz5yI9B496PVoRijqHm4NF2NZkSwNmcGwHkMNLu3ZM8vBqJd24RQcajJrNBonm9ps1M+PVKeaGiA3F4iKsveeW4A52ebacpXZgI3KRx99hJUrVyI/Px9du3bFunXr0Lt3b73jd+zYgQULFiAjIwPR0dFYsWIFhqk5ITiOw6JFi/DZZ5+hpKQE9913Hz755BNER0crxyxbtgx79+7F2bNnIZFIUFJSYs9DZBghJV2Ez9EJ3/qdAq5W49KYS+jyaxe9k3oGS+jdDWDtnHYRwCBcLFRzpN8DJbyaEnwgVWJiIr744guLt8Mc6YbQdnTLZBRJ6EIlSFBbS+VReGPew4Mejp9+SjeMMUXI0prj5qDeQT45mYqK+/oCISH03QEBqrHmNL0TCmm/tm2j89Kxo2aN97AwsgjFqktMrhAg7W5n5Nj2ciD1ioYiIRAKIAmQoDa/FrUFtQ0d6fWqiIU+fYD//Y+q1DQa5iq5loxn9fQYDIdz4QK9tkzwQsiDIchenY1b22/pdqTXGXekd+lCr9ml3iia9Tb8XUqZkWQmcXwI7V0WLVqExYsXO2ZnGAw7UF8PnDpFf/fuDYhcRRC5UqNjFz8XSEOlkGXLILspg0sLF8gr5RC6CiHyEgEwPaoToKDYgBb1KLzjhqN3YjGgRVbDQawRMoNhe3Q1uLR3zywHw0ekt+IqkfluEaouV0FeI1c2Tq7No5KexiYBeT90ejqZyE7pSDcl21yXXGU2YKPy7bffYvbs2diwYQMSEhKwevVqDB06FGlpaQgMDGww/ujRo5gwYQKWL1+OESNGYNu2bRg9ejROnz6NTp06AQDee+89rF27Fps3b0ZUVBQWLFiAoUOHIiUlBa6urgBs58BzCM1woictDbgDCVxWdIZw1mmUHCxB7se5CJ8ZrnO8wYCFuwGsnc7uBI4DF/IDwdXVQ1BVSQKLZSBbhDMHUrFf0hDapVGkUnIM19XR/2VldFPwBbsrKoCCAno1JZ3Lmpp59fWmlwuJjQXmzgXmzKHCm717A/36aTrRAfMj4A3NutfW0vkqK6PjAXCjpAVkcjGkonpEhsh0lq1xCdRfJ10pvMQCJCTQumPH6KewO+am6pk7ntXTYzCchosX6bVTJyBwAinURT8WQV4pbzBWIyJdTzSVtzfQrh39fbY4AujcmQxppkyZTEpKCkpLS5XLvHnzHL1LDIZNSUkBqqtJXrRvD7hGuMK9gztkWTJwHAdpJBkONTdrwHEcZNkyuMe6wzWCDHRzmo0KhcDQAVQSZt/lSN2DWBoyg9E42LtnlgOprye/cQQq4fZLNsrPlEPsL4Z7jDvE/mKUnylH2XFykpgiu5y+Trqu/mTq6JKrzAZsdD744ANMmzYNU6dORVxcHDZs2AB3d3d8+eWXOsevWbMGSUlJeO211xAbG4ulS5eiR48eWL9+PQCKRl+9ejXmz5+PUaNGoUuXLtiyZQtyc3Oxe/du5XbeeustvPzyy+jcubNJ+ymTyVBWVqZcyh01sd0My07JZFSdGABihnuizTIqP3xr+y29nzFaQi82Fh3efBxioRyltW7IOVfUsIQXwyzi4uLg4+OjXJYvX+7oXVLCrHhDaDu6AcorKyqiEinqpVE4jurdAfQANFURsqRm3syZVM9k5kzThZlQCPTtCyQmknNbG0ua3hmadZdKaZHJlIXsUgspmrODfxFE1RU6DTS+TnrdrYY1qtSNxC5d6ON37gBXr5q2u1ZhrpJrznhWT4/BcCp4R3rnzoBXLy+4tnGFokqB2z/fbjBWPSLdUCRot270evasDXf0HsLLywve3t7KRV80wty5cyEQCAwuly9fbuS9ZzCMw5d16dmTVDaBUAD/R/3h4u+CqpQquPhToEHN9RpUpVRB4i+B/2h/Ze8Gc2qkA0DSY9TEdN/VNg0jElgjZAaj8bBnzyx792kysv2cHEAh5zBAWASXqjq4x7lD7C2GQCRQNk7mgxRMkV28I50vF+N0aAfhqaNLrjIbsNGpra3FqVOnMHjwYOU6oVCIwYMHIzk5WednkpOTNcYDwNChQ5Xjb9y4gfz8fI0xPj4+SEhI0LtNU1i+fLmGE1E7O7NRaKYTPenpdFt5ewPBwUDA2ABAQI3da7JrdH7GlBJ6kq6xaN+B7MELw+YAS5YAr70GuLmxfnkW4MyBVKy0izHUS6NcvkzOYYGAHnJxcUCLFhRxnZ0NeHoCQUH0qgt96Vzm1Myrrgb++YeanPr5kXPc3d20GnraNdPDw1XlRixJOTFU493Hh9ZlZ1MteQAphRQBH+tfqLdsDe9I1xmRXq8SXhIJffzoUeDffyl6y64YUnI5jrIUCgroGomIMC+1z5p6egwGw6ZUVqoiFDp1oqZ+geMDkflOJgq+KUDgOM2UT2VjZAEgEOlXrLp3B77/nkS16sNmpkk2w7RKW/PKK69gypQpBse0adOmcXaGwTADvtFor16qdR6xHgh/KRxFu4pQcaECEADyMjlco1wRMjUEHrEqHcOcGukA8FCSEAIBh7MlUcg7lYuQaE+VTpiVRfpuXBzJHCZrGAz7Ya+eWfYuF2LC9jMzgUDUoIN7FaQRUgi07ByBQACRO5Wnklc1zPrThg/k/fdf63ffLphrazMbsNEpKiqCXC5HUFCQxvqgoCC9gRb5+fk6x+fn5yvf59fpG2MJ8+bNw+zZs5X/5+TkNK4zvRmXnUpLo1c+JlYaIoV3ojfKjpahaFcRwl9sWN7F1BJ6nToJkJICXKyMwsPVqcB77zm2bJMx+9GJ7Us+kMoYc+fOxYoVKwyOSU1NRYcOHWy1a8yRbhLaju6CAqopkpZGEeR807hevYAtWyxThEypmQdQc8/6eqBNG1p/9SqVaTFVmGlPDPBN77p1AxISVCVjTLmBDNV4B2hCITiYvkMoRGphS9oFLkWv094lyLTSLgD1MOUd6ZMmGd5Vq9Gn5BYW0nnMzQWqqqg+/unTVD7HVKXY0np6DAbD5qSk0NxYUJCq+hXvSC/+tRh1JXVw8XVRjteWS/poEJFu78bF9ygBAQEI0C5bxmA0AdQbjarjEesB9xh31GRSJHrl+Up4xHloONEB8x3pAQFAz54CnDgB7Hcdicm3d5CuIbtbeq+uDti6Fdi5k8kaBsOe2KNnlr2bl5q4/ZQUwB1y+LrLIfLQnUnGZ9XAuB8dAwbQ65EjZCY7ia9HE322tq4G88wGZBhAux50ma6qAvakGU/08IH0MTGqdQGPB5Aj/QfdjnRTS+h17gx89x1w8Z8S4LqDm0gbsx+biX3piEAq5kg3FXVHd+fOwIMPNpy5ASikyFaKkLbwKimhG9HHh/bH25v+Ly2lBqKmCjN9EwNbtph3AxmbdW/TBhg+HDh/Hrh8GanZNIEQ19VFr+BQlnYpaFjaRdtI7NOH1jdKw1FdSm5hIX15VZVqcqN1axqTmUkZA1lZxq+FzEzLI1GceAaRwWiKqNdH5/Hs7An3ju6oulSFvM/y4JfkB5GXCK4RrhqZMobo3p1eL18Gqk5fhvtndmxczDCJzMxMFBcXIzMzE3K5HGfvznK0a9cOnvoyyxgMO5CXp5pk43UbdQRCAdwi3eD3sB8qz1ei9K9ShEwJ0RhjbmkXAEhKIgf+r6IRmLykMwVr7NgBuLiQPsFkDYNhf2ydMWzvKFIztn/okBBVEMEvmBoni70buh4UteScEnoY35cePejUFBeTvsY3c3c6DGWbq2OvbASGXvz9/SESiVBQUKCxvqCgAMHBwTo/ExwcbHA8/1pQUICQkBCNMd34SJqmSDOe6Dl8mF579FCt83/MH9deuYaSv0pQW1gLSYBE4zOm6lm8DXnheA1wnx3ksKn+H2P24/DhwN69zcK+dEQgFfO4WQrvWFdvGmdO81BT0BZeMhltz+VuNKREQv/frUFuVg09fv/FYoo2OnvWsrpXxmq8jxgBzJ0L7q0lSJXRLFDsvNF6b0qDpV200mn4hqPnzpEv265o/7YlJarfmONociMuTvV6+zYJzJYtjV8L5tbT42mGjT8YDEdz4QK9qjvSAaDFoBYAgOwPs5GxNAMZCzOQ+W4mqi6T8DHmSA8OBgIDSfe5+Fmy/RoXM0xm4cKF6N69OxYtWoSKigp0794d3bt3x8mTJx29a4x7jB076HGfmAiEhekf5zvQFwBQ8kdJg/dMTTlWJymJXvcfEEAeFgFcukQ6ZceOTNYwGI2JKT2zTMXezUtN3L4iIxOHDgG34IrQPqrGyepwHAd5BYWiazutdOHiAtx3H/3955+W7X6joctXoI2lNiDDYiQSCeLj43Hw4EHlOoVCgYMHDyIxMVHnZxITEzXGA8CBAweU46OiohAcHKwxpqysDMeOHdO7zSaBJc1zmwDl5cBff9Hfw4er1rtFusGzhyegAIp+LGrwOVMz//gSVCkFfqRb2VIOm+r/MWY/FhYC69bR6z1mX2ZmZuLs2bMagVRnz55FRUWFWdthEem2xpx0LmNoz1JLpeT4rqujv2tr6X8+5UddmJkyU2WriAVjs+5CIXJcIlFeSfdndIz+bZlaIx0g/S0khCK5Tp0C+vc3fkqtQv23PXmSCim7uwOhoaQE8bNgvHC8fZtqzhw/bvhasCQShUWoMhh2Qb3RKE9laiVqC0km1ebXQhpOMrf8TDnKz9HEpTHnlUBA9vFvv9Ft2ruviWmSzTit0tFs2rQJmzZtcvRuMBjYvp1ex483PM7nPh9ABNTcqEFNZg1cI1yV75macqxO796U0HjnDnDi5wL0YbKGwXAcpkYxG8Pcvk623D6gjFK9cLoORUWAp6cAXf/rj/xPqDyVNFwKkQdFqMuyZUr9SSAxTXYNGADs3w/88Qfw4ovm7brTYetsBIZJzJ49G5MnT0bPnj3Ru3dvrF69GpWVlZg6dSoAYNKkSQgLC8Py5csBADNnzsSAAQPw/vvvY/jw4di+fTtOnjyJTz/9FADV+p81axbefvttREdHIyoqCgsWLEBoaChGjx6t/N4mlwlpj7JTTsCBAyQGo6NpUSfg8QBUnK5A0c4ihP4nVOM9Ux3pUVGAu6sCVTUSXJVFoAPuNBxkSTS/Of4fY/ajtzeVI37gAfvqfE5YPWHhwoXYvHmz8v/ud9PGDx8+jIEDB5q8HeZItwe6FKHwcBI2Fy5o/m/ootIWXj4+9EDNy6PXsjLyJPv4aAqzykqamTJW68iWDhpdNd7VSEmh13btlL1HdeISqL9GutJIvJtOIxBQCvSuXVRhxe6OdED12+7fD3zwAf3t59fw/PHCMSgImDvXuAAxZwKmGTf+YDAcSXWlAslHAUCIboG5gCIYHAQo2lUEro6DJFSC2txaVF+uhldvL4jiRCg7SvUKTSmn0K0bOdLPFoYBHtd1D9JWrJpxWiWDwaC2NMnJ9LgeM8bwWLGXGF7xXig/Xo6SP0sQ/LQqDd3cGukAxWIMGUIR8fv2C9GHyRoGw7EYsadMwty+TubWwjWxHMnvZ6g31v33Az5dPCC+2zi56nIVanNqIXQVwquHF+RlclSnV5tclor3c/z1F5m/2iZYk8OWQXgMkxg3bhwKCwuxcOFC5Ofno1u3bti3b5+yWWhmZiaEajZ03759sW3bNsyfPx9vvPEGoqOjsXv3bnRSS199/fXXUVlZienTp6OkpAT9+vXDvn374OqqmvC2lQOv0WimEz1799KrejQ6j/9j/rjx5g3cOXinYV8sE0u7CIVAfOdaHDnhit/TWqFDkA5HurnR/Ob6f4zZj3xwrkik+31b6HxOWn/dVoFUzJFuL9QVoVStbr0yGVBdDbi5UTS5votKl/CKjiZF6Pp1cuBGR5NDPSuLtuXlBSxfTt9jrL5lIzpo+GwTY/eMskZ6YR04OQeBiIQEx3HKJjTqRmJCAt2fjdq9XSik3ys4mHIMdWlw6sLRVKXY1EgUFqHKYNie1FT8tuwSKiqfQLhbEbp/Nw+4EIOa3iNRdVkEaSspPDp5oDa3FpWXKuHV2wsCgQAuAXcVLBN0SL5O+pmyNkDlPtPqYbL6mQxG80MtQue7rWEA/DCwTzVCitIBmeGIHd+BvuRI/0PLkW5BjXQAePhhcqT/muyLxV2YrGEwmjzm9nUyN5PVxCjVg39RSbxBg+gt9cbJ8nK5stdM2TEKSDC1LFXPnmRCFxWRedyxo8lnxnmxVTYCw2RmzJiBGTNm6Hzvjz/+aLBuzJgxGGNgtlsgEGDJkiVYsmSJ3jFNMhOyqU706ImEViiAX36hIboc6R4dPOAe546qlCrkfpKLwAmBcI1whUAoMKuE3uhxEhw5Aey6GI0Z/c/ZvneiOtr+n4gIKidcXU0Tp+HhDT/Dl4uW6+nybK3Odw9UT2COdHujfRFVVwP//ENdUvz8gL59qTyIvotKW3jV1FC+SEgIXdzFxeSYr6mhUi8bN5JjvW1biob29tY/U9WIDprTp+k1Ls7wOKVTSgHUFdcp6+XxBiKg6Uhv1Iaj6hhSIhUKIC2NJjkUCvPaypvidGcRqgyGbbkrp78/MgEA8ETnKxAGtATOnIH8bBnkZaMgbR0E947uuLP/DmSZMsgr5BB5iiCU3E1JFhp3XvENbc7ciUTp9dvw6eplXLFqpmmVDMY9i1aEzvb9ywH4YXzlF8DSv4xG7PgO9EXWe1m4c/AOKi5UqBofWxCRDgBDh9LrifMSFA3vDv8rR5msYTCaMtqBWGFhqn5NYrFmXydvb/MzWU2IUq0dNhp/LSMZwjvSAVXjZHVMbdrOI5GQ+XzwINVJbxaOdMA22QgMhj1oahM9BiKhz1TFIj8f8PSkbBltKlMrIQmSoCqlCjkf5aAqtQruHdzh/6i/WSX0Hn1MiFdeBf4sikPRmSz4t/VRyUk+ADYuTuX4NnYuTfX/nDsHbNtG5yAjAzh/nvyCsbGqMsQcR/7C6Gh6Lmg72q3V+e6R6gkW7flHH32EyMhIuLq6IiEhAcePHzc4fseOHejQoQNcXV3RuXNn/MJPA92F4zgsXLgQISEhcHNzw+DBg3H16lWNMcXFxZg4cSK8vb3h6+uLZ599VqMgfEZGBgQCQYPl30YNVdZC+yLy8qLmMXwkQH09cPUqrTdU1D82lsqDLFkCLFgArFlDOSlr1gATJ9Lnvbzo5uAbXObnk3e5sJC2oaupQSM1OKmrA37+mf4eMsTwWKGLEOKWNL+jXt6FNxABzWir+Hi6/7KzabEJCgUJngsX6FVXkwV9jWVv3AB++AFITydn+uLFtm8AaqjxB8fRzGN1Ne1TM2wQwWDYlLtyuqagFD/l9wYAjOmUqnzYiypuQVSQBXlFPcTeYoh8KAWuvqQeACCvpJl8U5Sqdu1IpNbKxfihdJBpTalt3cSawWA4Dj644swZwN8faaI4nCltCzHq8FjttxRgYaThu4u/CyAAZDdluDbvmrLxcU1mDQDzmo0C1OalSxeA4wQ40GIskzUMRnNAvXlpZiZlMovFdMMnJDTs62Ru0zsjzVGPl8eispLEhnrPGV1YMgk4YAC96ggcZjAY9sCU5rnOgJaehZgYDb3q56/INzZkSMNyw5Wplchemw3crXZSV1AHoY8Q5WfKkb02G5zMdFkVFUXiUc6JsMflMZWcvH6dnOIVFcDWrfqbhWpjSuNXmYxSDM+cIRnfrx9NmF65Ahw5Qv5BXqcLCKAmEwEB5ul8pvjJ7N3w2kkwOyL922+/xezZs7FhwwYkJCRg9erVGDp0KNLS0hAYGNhg/NGjRzFhwgQsX74cI0aMwLZt2zB69GicPn1aWVfqvffew9q1a7F582Zlc4ahQ4ciJSVFWVdq4sSJyMvLw4EDB1BXV4epU6di+vTp2LZtm8b3/f777+ioNjXdsmVLcw/RdmhfRCUl5Cz38aGL0tub/i8tpW5Phkpy6JqljoigGSeZjKbjb92i9AxfX7rZ+Fp4/v70/dqRyo1U9+qvvyhw3t+f7mdjSIIkqL9dj7qCOuBu6TF1R7q6kejpSfL83DmaNwgPt2pXzavlpJ0tkJpKwlEiARIT6fexRwqLvgjVwkLah2vX6NrauJFOvoPrUDEYTotCARw9CiQnY3/NEJTXuiLMqwx9wu/OygkEcG3vA/e8TJSnRULUyx8iTxHkpXLIK+TgOA61t2jCT+huXE4KBDT3OX8+sE32GKZ2zzYtTbKpplUyGAwV2sEVALbvjgQAPNTiJFriNgVX9OunN2KnMrUSeV/mQewrRv2degjFQoj9xSg/U47afJJF5kakA0BSEgUt7T0bhglv2ljWGGs05YSNqBiMZoG5fZ3MzWQ1EKX6+2IaMmiQ8dvZEkc6X076zz+bSZ10BoNhPSZEQu/9tw4AMGKE5kc5BYeiXUWoK6qDV6IXKk5VoP4O+aP4Ui8KmWa/PmM8+ii5a36QDcfU9zuRw2rHDiqpYqwEszbGMpSzssh35eJCfkG+oWj//ir/0D//UMMudZ2ubVvTdT5T/WT3SPUEsx3pH3zwAaZNm6bsarxhwwbs3bsXX375JebOndtg/Jo1a5CUlITXXnsNALB06VIcOHAA69evx4YNG8BxHFavXo358+dj1KhRAIAtW7YgKCgIu3fvxvjx45Gamop9+/bhxIkT6NmzJwBg3bp1GDZsGFatWoXQUFVH3ZYtWyI4OLjBfjgE7YtIJlPVIwLI4VpeTusBzYvKFMNC21EvlaoaB0ilDR31ukq1NIKDZudOeh09mnbPGHw6jXpEOp9KAzRUtPr0UTnSH3/cih21pJYTr0RmZFCGgFBIxfv438oeKSy6JkCqq8khaGrJIAbjXodXBpKTgbNnsaN8OgDgiaiTEArUMmA8PeAfdBU1nvehKqUKQindv3wfB7EXCTVTo0AnTCBH+qFjnsjbMRchdSY6kJpaWiWDwdBES2fj7pRgey7lFY8POmQ0uELdyHPr4Iby5HLIMmXw7OYJUZwI1VerAUDZW8YcRo+mVj47dwKrV8fCf66NZI0uo6t9e1LcgoKAggJS3tLSnKoRFYPRbDC3r5Ml29dRjuTgQXpVL+uiD6Uj3Yz+Dr170y7fukXio0MHkz/KYDCaK4YioQEUiMNwIpv8hsOSFFAvzlGTWYOqy1WQtpJCKBTCNdIVFXcqUHerDoI4AaThUnBy8yb9HnuMAs73HxCgvEUEvC6pBcCqO/ljY4GTJ4ENG4CZM3VH/BsLgJVKyQcYEaF57AEBFM0aGUlBl889R34ifvu67MvwcNrmhQsqHTAtzXQ/2T3S38ssrbi2thanTp3C4MGDVRsQCjF48GAkJyfr/ExycrLGeAAYOnSocvyNGzeQn5+vMcbHxwcJCQnKMcnJyfD19VU60QFg8ODBEAqFOKZVHHvkyJEIDAxEv3798NNPPxk8HplMhrKyMuVSbutZEe0UDHVHN0A1zcViWg+oLqqCAkrxWLgQWLpUf8qHtqPex4dulNJSmpmSSMhxL5MZLtWiXTpmyRJgzhybGDH8xCBAwsQU+IajGqVd+Brpwoa1iBMS6NWqKj7aM5je3tTFmHeE6yu7A5AgEgqBO3dIk9MWfPZIYVFPqSwqohnG0lIyUPv3J4XZlH23MRkZGXj22WcRFRUFNzc3tG3bFosWLUJtba3xDzsAR5SpWrZsGfr27Qt3d3f4+vra+pAYpqCe9hcYCJlPIH4qHwgAGCPbqiqJBQCVlfAIrEb49Jbw6u6ldFLVFdXBq4cXWg6nrCdTlao2bShhRaEAtn9nZppkU0mrZDAYDdHS2c7n+uOyrA2kAhlGtfxHU2cDaFxNjTJiR93Ic42kbM2aDCrnIhAIIPKiXGRFrfnP+j59qFReTQ0lsxmVNaak9upKrxYIgO3bgf/7P+CFF+j1m29ovVb6tU1L4jEY9zKNVMqTp6JCZZOZ4kjng6XMKUsllar6ZP35p7l7yGAwjGLKc96en7cEfZHQhYXA33/j1yOeAIB4n6sI3qTpW5OXyyGvkUPkQbqUyJte5RVUwlPoLgTuHoKpsioujlwztbXAL18X6XbyFxaSH+fGDfLXvPqq/lIvhkpqjR1LglFXFLhAQGW93NxUlTHUUdf5qqspskLdD/nOO6Qcmuona+RnjqMwKyK9qKgIcrkcQUFBGuuDgoJw+fJlnZ/Jz8/XOT4/P1/5Pr/O0BjtsjFisRh+fn7KMZ6ennj//fdx3333QSgUYufOnRg9ejR2796NkSNH6ty35cuX46233jLl0C1DOwWDd3Tn5dFrWRk1DfXxUV1U4eHA99/TTWHubI9AQN9XWko3pVRKN0ZtrfH6lnZqcPLvv1SOydvbNGUKAFwCKWJfV410XdEKvCJ18iTNUfAB/2ZhTi0nXefJESks/Azi0aPAihVAYGDDZhGm7LsNuXz5MhQKBTZu3Ih27drh4sWLmDZtGiorK7Fq1Sq7fre5OKpMVW1tLcaMGYPExER88cUXjXrMDOgsr7D/ZAjKFF4Ik9xCouBf4HIwyUtA2WzF48F2cH9QgNqCWuRczYFHZw9EzIlA0U9FAMyLpHrySQqE37YNePllmx+hY2DlGRgMw2jpbNszSHkZ3uIovMVVQI2e4Iq7ETu8kSf1kEIaTmPq79RDUaeA0EWokkFcg282ikBAsuipp4CPPgJee61h7VAlpqT26kqvLiwELl1SNWHPzSVDTC6n9Z6eFDnVjBpRMRhOQSOV8uQ5coTmBCMjKXjAGOY2G+UZMIBqpP/5JwVZMhgMG2FOqVtbfd4WdoSuSOjCQsp8q6rC3gpqrjA8KrWBb03kJYLIVQR5pZx6YnnedaSXkyOdd6gDptt8AgEFkr77LvDDzxKM89byFantm9KX5+FhWjUE7Qjyf/8lJ3hubkN/EGBaFLi+6gxHj1L/v8RE0/xkjfzMcRRNe+/V8Pf3x+zZs5GQkIBevXrh3XffxVNPPYWVK1fq/cy8efNQWlqqXFJSUmy7U9pN4vjuuGKxqulLdDStT0mhJqEcR050S2d7AgIoRDs4mMp8AORIv9v8pbFTZfmyLo88YsAo04KPSK8rqFOuM1Q/LyaG5iKqq62ISjfFEa4WGdYAUxpA2COFRSikg3dzo5lGXSmbxvbdhiQlJeGrr77CQw89hDZt2mDkyJF49dVX8cMPP9j9u81FvUxVXFwcNmzYAHd3d3z55Zc6x6uXqYqNjcXSpUvRo0cPrF+/HgAalKnq0qULtmzZgtzcXOzevVu5nbfeegsvv/wyOhvrvsSwD9qTZgIBdtQ8AgB43OM3CF0llCucnd1gAlIgFMA91h0AKVQCocCi2p5jx5JoP3mSggmaPKmppmVRMRj3Mmo6m6xOiP9d6QUAGO/2E+l1ZWUkb9SDK9QidtSNPKGbUClz5GVk3Clq7kZ1mtCvQRdjxlBsR14e8N13Wm/ykWU//ggsWwacPq2ziZfynteWsxxH/1dVkZ7q5UVZfJ6eFARQVUXv88WOm0kjKgbDaTDSHNSW9qE5ZV0Ay2qkA6o66X/80TDokcFgWIiRZp1GdXtLPm+KHaEd4V5f3/B/hQJo0YL0CYVCQ/eo8wvC/vK7AQzx+Q18a64RrnDv4A5Zlgwcxymz/PieWLIsmXJXzJFVfEWGX/72Ro2Ll8pXpK0XCQQUDdqypWnVELQjyDdupPPw++80m6meXW1KFLih6gwREbSfmZm6ha0uX1MjPnMchVkR6f7+/hCJRCgoKNBYX1BQoLcueXBwsMHx/GtBQQFCQkI0xnTr1k055tatWxrbqK+vR3FxscF66AkJCThw4IDe96VSKaR85A+AsrIyvWMtRrsGeU0NtfENCSHHanGxqiZ5r17Ali2mR0Xrm+2RSKhOdkgIeWy6dnVIdCDHAbz/1NSyLoDh0i66UmmEQtr+V18Bn39OlU3MxtpaTsYaQNyNarVLCosV+15eXq5x3WvfE7agtLQUfn5+Nt2mtfBlqubNm6dcZ0qZqtmzZ2usGzp0qNJJbqxM1fjx4y3eX5lMBplM9QC3eRkqe+Jskcpak2ayehF+vNkVAPBE1Cmgtp4aQ9+6RTXktHpFSILvyqe7jf0sMQADA4GHHgJ+/ZUatisTo+x5rszdtqnjLektwWDci6jpbJ//EorsMh+EupfgEd8jwPV80tv44AodETu8kVd+phzuce4QeYtQf7se8nI5xH5i1JfUAwDEvma3PwJAquN//0s9HD78kBojCwRQRZalpgJnz9L+tW1LNc69vXX3gtEOTigtJRnh40MbFQopEv3uZGaD+vDNpBEVg+FUNFKvld9/p1d7O9ITEkhu5eVRsGR0tFkfZzAY2pjQrNNgtpglnzfFjgA0fWkyGTmQ3dwoi0/9f5mMAlYzM6muyq1bgFSKv7Nao0zhhUC3MvQMy2vgWxNERsL/UX/UZNWgKqVKKY/qS+tRlVIFlxaqkgfmyKqePfmS40L8LhqKEVk76Fyo60WAZqUKUysKaJ+7fv2oTMyVK7Sub186J6ZEgRuqzsD7kfLzVXqaOvp8Tc28v5dZ2rZEIkF8fDwOHjyI0aNHAwAUCgUOHjyIGTNm6PxMYmIiDh48iFmzZinXHThwAImJiQCAqKgoBAcH4+DBg0rHeVlZGY4dO4YXXnhBuY2SkhKcOnUK8fHxAIBDhw5BoVAggS+QrYOzZ89qOOcdhqEi/uoX1aVL5pcH0dcsND7eZs1CLeXMGZoYc3MDkpJM/5xL0N3SLrdMK+0CANOnkyP9u++A1atpMlIvuhxEhhzhCgU1WIiOVqUkm9sAwpDwstZ5ZoUTP+5uaQueRYsWYfHixaZ/txHS09Oxbt06pyvr4qgyVZZi9zJU9sLa1EDAts5lhYIUALXUtwPX26JM5ooQz3LcN9wXyO5Fs/hz5mg2Y7lLA0d6vWHZpI8nnyRH+rZtwOLFgOCyHdMgzf0dTB1vrcLNYNxrxMaiatpMvP2/cADA/A474BoeBtS0bBhcoaXDCYQCDSNP5E6O9Nr8Wsgr5RC5UvSUUGL5vfbcc8Dbb1PA+d9/A/391Yw0Pu24ZUsypsrKyJPFR1KpG33aE/wyGUWL8bX3FAqKdOIjmyQSkmP8hHEzaUTFaB7U3KxByV8lEIgE1CdFCNXfItXfyv/Fd//XfnW5+ze/uKhe+fJMljQLNgs7lfLkKSwEzp2jvx980LTPWNJsFCD7MiGBgi/372eOdAbDaqwtdWvu502xIz79lOw2vuxxdTU5i4uLKQAhNpbqi/P/9+1LE/2nTtEikwEBAdhcOwEAMCzmGoSCu7qHlm/NI9YD4S+Fo2hXEcpP0zpFlQKe3TzR4sEWyP+S7HlzHOkCAZlP69YBP1Q8hBFBh+m4XF2paoRUSoLT3Z1sLf4cGAso0HXuvL0pojQ1Fbh2jc5Tt246dcoGGKrO4OND1S7S02mMOsYCRu38zHEkZoetzJ49G5MnT0bPnj3Ru3dvrF69GpWVlZg6dSoAYNKkSQgLC8Py5csBADNnzsSAAQPw/vvvY/jw4di+fTtOnjyJTz/9FAA1SJo1axbefvttREdHK+sKh4aGKp31sbGxSEpKwrRp07BhwwbU1dVhxowZGD9+PEJDqfPu5s2bIZFI0L17dwDADz/8gC+//BKff/651SfJJui6iLT/tzSy2Elne/ho9IcfJtlgKi4BZGjJsmWozqiGa4SrshGNPsGVkAB06QKcPw98/bVq8hKAprOpoIBqUaWlNXQQ6XKEZ2WREK6tpe0sXqzfAaVvUsOQ8LKFo9EKJ35KSgrCwsKU/+uLRp87dy5WrFhhcDdSU1PRoUMH5f85OTlISkrCmDFjMG3aNNOOhaGTefPmaUTD5+TkNJgEUaLtXNU1adcYssEWkcq2uD+0t5WaSjN8588Dbdviu5xhAIDHY1NIsSovJyVMhxMd0HSkcxynNADNaZIF0C3p7k46yYnvbqD3HzrO1enTNMGqK7PI1HNj7u9gznhzFOaICKd7RjEYjuDjQx2QXwFEhtfh2c8SAb8hJstpdSOv4nQFAKDuVh18B/hC5CFCxdkKs6M61fH3B55+GvjsM2D1hxz691Qz0m7doihyX1/ax8JCuv/9/VV1PXmjr2NHzQl+qZTKGdbVkdO8poYMXpmMjLBatfrw9s7ic7YsKYbTU3aiDJcn6Q6ysDlCqBzrEgGEEh2vUgGEUiGErlqLmxAiNxGEbkII3YUQuYsg8hBB6CGEyFMEsZeYSkR5iSD2EUPsK6YyUbrKQlrI99/Ta+fO5MsyBUuajfI89hg50j/+mHoX2/BQGIx7D2t7vpn7eWN2RFgY1W4KCaHKDQDN1NXXUwOGwkKqkymV0v9FRcDVqxSZ3bo18NdfQEEBMjok4X87KWP8hZ4nVd+hw7fmEesB9xh3VF+vRtGuIkABhEwN0ZjoM3fC87HHyJH+45++qP/jJYj37KL9rqqiAaGhpDMFBBjcNw30nbuAANLLIiPp/Dz3nF6bVgNDfkiBgHSlnBz6Xjc3/b6me0jHMtuRPm7cOBQWFmLhwoXIz89Ht27dsG/fPmUUZmZmJoRqJ6tv377Ytm0b5s+fjzfeeAPR0dHYvXu3sjkfALz++uuorKzE9OnTUVJSgn79+mHfvn3K5nwAsHXrVsyYMQODBg2CUCjE448/jrVr12rs29KlS3Hz5k2IxWJ06NAB3377LZ544gmzT4rDsKY8iBPO9vCO9McfN/0zlamVKNpJzfvqCutwY8ENeMR6wLUdXQv6DESBgKLSZ8ygElEvvqiVknz5MhmB16+TERcfT5MP2g4idUd4aqpqfGIinXdjjkBzJjVsWRLBEic+AC8vL3jrmrTR4pVXXsGUKVMMjmmj1lEoNzcXDzzwAPr27aucNHMmHFWmylJMLkOl7VzVTn2zxhFtDraIVLbl/aEn9S09pRbfFPQAADwZlWy8KTNUjnROxqG+tN7oJJ8+PD2BUaOAb74Btq0tRO9QrXMlk1F0xbVr5PTv1o2O99FH6X1Tzo25v4O5401VmM+do9B7W0yI3KN89NFHWLlyJfLz89G1a1esW7cOvXv31jt+x44dWLBgATIyMhAdHY0VK1Zg2LBhyvc5jsOiRYvw2WefoaSkBPfddx8++eQTRKuF9C1btgx79+7F2bNnIZFIUFJSYs9DbN7cNSzK8yvx7jsdAIiwaKkLJD1UurCpOhxv5MlyZMj9OFfZ+Dj95XQA5ssibWbOJEf67h+BG1wRoqLuGmnqznCptGE5FnWjT3uCPyyMHOfZ2bQNDw+SaVevkqFXV0eTCYBJcthibDk528xwhIwpLi7Giy++iD179ihtuzVr1sDT0xMA8Mcff+DDDz/E8ePHUVZWhujoaLz22muYOHGichubNm1SBnPxSKVS1GhHzVmBJFCCFg+1ACfnADnAyTn6W6H6W/s9rl7ttZ4m3bX/h47St1CQfiGXyXW8aXsELgKIfcUQ+4nh4u8CSYAELv4ucAlygTRMqlpaS+HS0sWg010uB/gEVHPiZyxtNgoAzzxD5ZRTUigqfehQszfBYDB4rC11a+7njdkR9fVkC3XqRHpISYmqHIpQSNvKz1eVPNbWS7p1A/78E+8dHwA5J8LgNtfQOyyHtm3AtyYQCuDezh2SQAlq82tRm1+rLD0sEAvMnnzs14/824WFwI6LsZgwN4aCutasIT2oZ09NfceUgAJD504gIOd8RYXqXBnDmB+yqgoYPpz0s7Q03b6me0zHsqiQ4owZM/SWcvnjjz8arBszZgzGjBmjd3sCgQBLlizBkiVL9I7x8/PDtm3b9L4/efJkTJ48Wf9ONwWcucOtmbNLqam0uLjQPWcKlamVyF6braqNrqDmWuVnylF2ghyHhpSsp54CXnuNTt3Ro8B9fmrOs/BwOq8AaXqXLpEnKyBA00E0Zw4wd65KuAmFmsLNFEegKZMa9iiJYMfMhICAAASoz5IaICcnBw888ADi4+Px1VdfaUysOQuOKlNlV7Sdxdqpb337Ugh0Y9SutjY10Jb3h4HUtze/fwL1cEGS599IlJwC4oynvolcRRD7Uj3i2rxai2t7AlTe5ZtvgC0n47Dw2Rj48cep3sXdz48USYmEfrvMTFJMTDk35v4O5o43RWGWyYAdO+iV1VC3iG+//RazZ8/Ghg0bkJCQgNWrV2Po0KFIS0tDYGBgg/FHjx7FhAkTsHz5cowYMQLbtm3D6NGjcfr0aWUQw3vvvYe1a9di8+bNykzAoUOHIiUlRRnEUFtbizFjxiAxMRFffPFFox5zs0LNsFhzehhu3+mI9v638VT8LQCWXfsCoQAeHcl4kpdpNT42szyCNh07AkOGAAcOCPD+hYewvtM/9IaPD+mheXmkO6mXY9Fl9GlP8EulJEdEIpJVERG0jVOnVI764mLTUpAtgfVz0IujZMzEiRORl5eHAwcOoK6uDlOnTsX06dOV9t7Ro0fRpUsXzJkzB0FBQfj5558xadIk+Pj4YMSIEcr98fb2RlpamvJ/W0ZYA4Dv/b7wvd/XptsEAE5x17leR4uiTqH8W/l/LQdFrQKcjF4Vsrt/y+hvRY3aUk2LvFoORZUC8ko55JVyKCoVkFfIUV9OPRXkZXLUl9WT076OQ11hHeoK61CdVm1wf8W+Yri1c4NbOzd4dPKAV08veMZ7QuJPDqYffqD4Iz8/cnCbfB6s0KO8vem71qyh3g7Mke442OR7M8Danm/mfF5Huc0GtkdpKb3yNbm1y8Sp91sBGpaJ8/REnm8svjz/EADgze6/0udN9K1JQu860vNqIfYjt6klckosJhVjwQIqbjBmjBDiNm2A559XNWA11+9n7aSHNqb4IadP1+9rugd1LMs6EjHsh4WRxXbFgtmlrVvpdcgQVQ8FQ3AKDkW7ilBXVAePzh4o/qUYnIyjGcE4d5T+SYLUkIHo4wOMH0+10j/dyOG+DmrOs9JSMtBatiQhq52SrKuR6507dJzaAszUBhCGsNbRqA8HZybk5ORg4MCBaN26NVatWoVCtY7RhhoDOwJHlKkCKGunuLgYmZmZkMvlOHv2LACgXbt2yigss9F2FgOaqW/qqW6NUbva2tRAU++PjAxVZLS+iSM92zpe2w3f3XoAAiiwose3pqe+gaLS60uoNrGlNdIB6h3RqV0NLqZ74q2Tw7Fm2G8Nu7hzHNUFlEjotztxghxZAwcalx3m/g7mjjemMGdl0fZcXMg7x2qoW8QHH3yAadOmKWXThg0bsHfvXnz55ZeYO3dug/Fr1qxBUlISXnvtNQCUrXfgwAGsX78eGzZsAMdxWL16NebPn49Ro0YBALZs2YKgoCDs3r1b2RiZ78uwadOmRjjKZoqaYXEnoD1WpdP5fqvtFog/vmKVYSENoywlWTYZjJaWmdLF668DBw4An1x/CM9cv4Qe0eV0/3boQPpUYSE5voVCKsuiL4pce4JfvbzelSukT06YQPX5goLslwbM+jkYxBEyJjU1Ffv27cOJEyfQs2dPAMC6deswbNgwrFq1CqGhoXjjjTc0vnfmzJnYv38/fvjhBw1HukAgMEvHdJYG7gKhAAKhAHAxPtbWcBxHzvWSetTfqUddcR3qiuqUTvXa/FrIcmWozamFLEeG2rxa1JfUo/xkOcpPap4v10hX+Az0xZ4j/pCgBWbMEOlVI3TuixWOdIDE6Nq1wG+/0a2sr/Ihw76wyfdmgLVBnaZ+Pi1NZ7lNxMaqypvw9o+fH3migYaZcdr9VtTLxAFAZSXev/UUZAoJ+kZkYYDbceBKjcm+NUkITRLKcmVwi3EDYHmwwsyZ1MfvyhXgf/8DpkyBdX4/ayc9dGHq/mj7mu5RHYs50m2FLesBOVPNcwtml27dosgA4K6QMIGazBpUXa6CtJUUAoEAIg8R6mX1kFfI4eLvAnFL0y5VVdNRDqtHZaIF7zxTn8HkI1LVU3+sdSiZg0JBwik/n7z/HNfQIWbN9h3IgQMHkJ6ejvT0dITzadp34fiHnJPgqDJVCxcuxObNm5X/830dDh8+jIEDB1p2MNrOYu3UN+3r3ZqJIFNknbWz5Kbcf6mpJGju3DE8wadjWxwHvH5gCABgUpez6BJUYHrqG8iRXnW5ihzpVhiAYjHw4aISDHk6GB+d7I3ne51CrEu66rfjZRevFPKN/i5dInmm79zwssPc38Hc8cYUZqmUlN2ICN2FS728KH3o6FGTJzHuNWpra3Hq1CnMmzdPuU4oFGLw4MFITk7W+Znk5GSNngoAMHToUOzevRsAcOPGDeTn52Pw4MHK9318fJCQkIDk5GSlI91cnMU55TRoGRarDg1CqcwNnQILMPahUiC1yCrDQhqu6Ui3tMyULgYPBsaN5fDtd0JM++VRHJvxNcQijgzchARVIytvbzJcDRl96hP8nTtTB8LG1m8NTc4ChmVRM6/36SgZk5ycDF9fX6UTHQAGDx4MoVCIY8eO4VG+jJkWpaWliNW6zioqKtC6dWsoFAr06NED77zzDjp27Kj3mJtsA3cbIhAIIPYSQ+wlBloZHy+vlqPmeg2q06tRdaUKFWcrUH6yHNVXqlGTUYOaTfl4BvmYACGCTvuh+EAo/Ib4mbQv1mbTtGlD4mfXLlILN260aDMMK2GT700Mfc82a4M6jX0e0FluE1eu0Lq+fakkaXY27VP79vS3t7dmZpy/v2a/FYWCmqCHhCj9K7evlWBDOj2H3vwoDIJOS8x6lktDSc+yNgMZoK+cM4cCFZYsoaxkiQSW+/3sVcnCkv2xV4Cok8Mc6bbAHvWAnKHmuYWzS0uXUkmmnj1Nr48uL5dDXiOH1IMElshThPpicqQDgFBC2zeWrqlqOirE11cS8FL76/SG9gymduqPtQ4lU+GvlZMnaTY2K8uyBhNOypQpU4zWUncmHFGmatOmTbZXMrWdxdqpb9rXu6UTNabKOmtnyY3df1lZlD8sFNL3GJrg07GtX65G48+bkZCK6rGk5x6gzrx7TaPhqJVRoIOfDMTIZVfw0+X2mP3bUPw65JLqt+M4TaUQUL2WlJBTXRt12WHu72DJ72ZIYY6Lo/Qk7QkRPiPo1i06jhUrqA9FM62hZw1FRUWQy+XKCT6eoKAgXL58Wedn8vPzdY7Pz89Xvs+v0zfGEphzSgs1w+JUXihWHr0PALD0gcOkMllpWPCO9NqCWioBYUV2jC5WrxHgt31ynC5pg3U/tcbLD56je1kiIcM1JER3I2RjOEK/1Tc5a0wW3QP1Ph0lY/Lz8xuUjRGLxfDz89Mrh7777jucOHECG9U8pTExMfjyyy/RpUsXlJaWYtWqVejbty8uXbrUIKCDx6wG7gwAgMhNBI+OHsqSUjz1pfUoO16GL/9zG60yixAEGcp+LsL5n4vgl+SHtqvaNviMNtY0G+V5+WW6VbdsAZYtIx8SQz/l5eUa/Za0ezExmjnGnm3WBnXq+zwAvPuuznKbygn6f/6h2ubajnfeWRwdTc9uvo5Uly6qvnZ+fvR+WRmQnY01uVNQWStB9+7Aw8OFgCDSrNOkHpFurSMdAP77X+D994EbNyjw87nn7r6hrRcpFBSpb+zc26uShbl6mj0DUJ0Y5ki3luZcD8iC2aVr14ANG2jIe++ZLm9FXiKIXEWQV8oh9qZO8rJMGervUNSlvIoc6gKpYeGl0XT0xhC8WPEBBD7eDWt7qqf+2MqhZAz1a6V1a4qkzcmhumClpTQLwJdysGT7jHsXbWex9sSRjlQ3sydqzJF11s6SG7r/FAqqqSuRGO9fwI9v0YJkWc+ekEOEuQcpOuGl3v8iouyi2feauiNd5C4CYIXzSijEqg+E+HVEPfZdi8avbdrjYeHfqokPd3c6F/w5EItJUbx9m8KwDMkmc38HS383fQpzZiawc6fmhIh6/XeplDIkAgObxzPzHqfRnVPOHCmslnlW7haI8TufQJ1ChMdiUzAq5q5z0krDwiXABQIXqo1ui2gpbYKDgfdWiTB9OjA/9Uk82vY8IsV3y7HEx6uMNEf/DpZmSemTRadPU9ZPYiLw779kHEZEOES/z8jIwNKlS3Ho0CHk5+cjNDQUTz31FN58801IJBK7frezcfjwYUydOhWfffaZRrR5YmKislcNQNmDsbGx2LhxI5YuXapzWyY3cGcYRewjRlagH17L9INQ0A6XdlVAdDAfuRtyUbyvGMX7ixHynxBELYtS1lLXxppmozz9+pFYOnWKItLffNPiTd0TaD+bFy1ahMWLFztmZxiNi6n2nLWT3ro+n5Gh27cUEEA2RmQkPZu1y22qO4traoCoKJrMd3Ul20f9/+JiwNUVZXF9sG5/EgDgjTd0J6MZQ1dEujUTfu7uJJteegl4+21g8mTaZQ3MncB3hkoW5gSgOlpntCHMkW4Nzb0ekAWzS/PnUzBlUhLwwAOmf5VrhCvcO7ij/Ew5RHEiZUOHuuI6cBw1wgEAkYfI6Laeegp4/XUOKaXh+OZoazyZVNywtmddHTmJAN21PW2dLqPrWomLo3NXVUX7lZJCkV05OY5tLMtoemg7nrVT37RS3cyeqLFE1lkzS27o/ktLo4mBxETD/QsOHQKOH1dFG16/DmRmYrP3LFy8FYQW0krMC/jconuNj1CozauFNIKULGsMwOiH2+GlSbfx/qaWmH1sLAa32gKX4mLd9QJzcqg+enV1w3OTlUUOobg4UlIiIsz/HfSN79aNJvvq60kR1lZ8dEVTaE1iQCBQ1X/396frKSREUxY35WemHfD394dIJEJBQYHG+oKCAr01gYODgw2O518LCgoQEhKiMYZvlGwJjeqcauxIYXMUf63MsxePvYD0spZo5VmMzx7ZozLmrMw8EwgFkIRKILspgyzbNtFS2jz7LPD118CRIxL83523sXfNNQi8tZpLaf8O7dsDffrYt+Y5j6VZUoBuWcQ3PE1PBw4epAnbdu3oWLy9G12/v3z5MhQKBTZu3Ih27drh4sWLmDZtGiorK7Fq1SqbfIejZExwcDBu3bqlsY36+noUFxc3+N4///wTjzzyCD788ENMmjTJ4PG4uLige/fuSE9PNziOYTtWrqTXMWMF6DDKCxjlhbCXwnB9znUU/VCEvE/zUJZchh7JPRrYcRzHARQrZZXsEggoKv2pp4CPPgJee+1u2QSGTlJSUhAWFqb8X180+ty5c7FixQqD20pNTUWHDh1sun8MO2FL35UlDlFDviWBgLL0KypU2bfqUdmvv072K/9/eLjB/99a2xolZQJ06AA89pi5J4qQhN6193Kt64mlzrRpFGyanQ18+imZXEosDdB1dCULUwNQKyspI6GZZPkxR7o1NPd6QGaWNzl1Cti+nQ793XfN+yqBUAD/R/1Rk1WDqpQqCF1JENcV1qEqpQpiD9M7Jfv4APPmCbBgATDz/DN4KGgm/Nv6kEOnY0faUYFAZUKG3l4AAQAASURBVDCZ61CyJF1G17XC1xq9fJmi0q9fp33s1ctxjWUZTRNdjmft1De1VDeDzmNdipGlss6aWXJ99190NO2jvkkAvn76p5/Sw7tVK8oACQpC6tE7mHVsPADgzU4/okWfGIvuNfWIdN6pbq3zasHqltiyl8PlwjB8lLAFs2QrSMmQSBp2mJ8+nT6kfm5kMhpfV0flVHbu1FROzPkd9DUI3LLFNMVH3bmlNomB9u3pf6mUlETtaPum/sy0AxKJBPHx8Th48KCyabFCocDBgwf1lqVKTEzEwYMHMWvWLOW6AwcOKKM2o6KiEBwcjIMHDyqdWmVlZTh27BheeOEFex6ObWjsTEBznPZamWfbLvfA5sxHIYQcW0Nfh1+FFHCzXeaZNFxKjvQcmU2bjfIIhRTd2a0b8Osf7th2oTMmTrz7pq7fITOTFMEtWyhjJjDQfCPJVOPcmiwpL6+GsigwkCZfq6oAT0/Sy1q2pH42ZWWqrEEDzzxbl0tISkpCUlKS8v82bdogLS0Nn3zyic0c6Y6SMYmJiSgpKcGpU6cQHx8PADh06BAUCgUSEhKU2/3jjz8wYsQIrFixAtP5Z58B5HI5Lly4gGHDhpl7KhgWkJZGtzxAfi4e93bu6LSzE0qOlCBlbAoqL1Ti8jOXEbc9TqNMJy+3AOsdVGPG0D7k5gKffELN/Ri68fLygrcu216LV155xWjJzjZt2thorxh2x1a+K0uDGUz1LRUUAHv26N5+586q8dr7ePf/AweADz6kVStXWj7frV7axVZ9aFxdgQULKOj+nXeASZMoIc7gJEdsLAVnbNhAgi0y0nkCjnidrWNH0q8uXdLUyXjbtXNnYP36ZlXFgznSraG51wMyo7wJx1EDBYCiAbp2Nf/rPGI9EP5SOIp2FaH071IAQH1xPbx6eEEgFuDO73dMNhBffx349lvg4kUvzM56GVt8V6occRMmkEFkSrSUrdJl9F0rfCrT7dvA1avkIHvoIecRjoymg7bjWTv17W6qW4OJIHWnBe8wTUvTVFw6drRc1lkzS67r/lMogMWL9SthFRV0HEIhTUrdlVslIbEYVfwflCs8cX9oOl7a2geIHm/RvaarRrq1ipWPD7B0qQDPPw/M29YZfTctRe9r3xiexOPPzblzwI4dVFddvQQBX6LAmjrGqanklDdV8dF2bt2dxMCpU7TIZCT3QkIa9oZo6s9MOzF79mxMnjwZPXv2RO/evbF69WpUVlZi6tSpAIBJkyYhLCwMy5cvBwDMnDkTAwYMwPvvv4/hw4dj+/btOHnyJD799FMA1Mth1qxZePvttxEdHY2oqCgsWLAAoaGhSkcaQA2Xi4uLkZmZCblcjrNnzwIA2rVrB09Pz0Y9B0rMiaYCrH92m+Os1dq36yV+eD6dHH8L/Degv+JPICXcppln6g1HbV0jnSc2ltKiFy+mSKroaKB3Tx2/Q2EhyRuFgnREmYwc0eYYSaYa59ZmSSUnU010X1+SRTEx9NyrqiKZVFFB3yGV0rXD11L396fv0iOrGqNcQmlpKfz8/Gy6TUfImNjYWCQlJWHatGnYsGED6urqMGPGDIwfPx6hoaEAqJzLiBEjMHPmTDz++OPK2ukSiUR5DpYsWYI+ffqgXbt2KCkpwcqVK3Hz5k385z//sek5YjSkthaYOBGQy4Fhw0hF0ca3vy86ft8RZx84i8LvCpHVIwsRc1SThxqOdCv1KImEMqP/7/+AefMoO5p/FDAsIyAgAAHqehqjaWML35WlwQy6MlXV9R/etxQeDnz/PflGLHC4FhVRyRSAZMGIESacFz0oS7sU1IKT2S7rb+pUcvCnpwPPPENmlkDfJAevf+Tmko6VlUXnzhmiuLV1NpmMMqavXyf9ibddR44Efvyx2VXxYI50a7BXQ0pnwYzyJnt/VmXBGuizaBSPWA+4x7ij4kIFin8thqJagbAZYSj6oQiA6QaiRAJ88QVlF399tjMmznoPQ3sUWmZM2yJdxtC1IhCQAywoiAzGJiRAGE6GLsezduqb+vWvK3JYIqFCkzExKsXl0iV6ODpC1ukqGWJogu/KFfo7Jkb5nlwhwISdj+NqsT8ivIqxo++HcJG+ZvG9puFIt6Hz6j//oQCMvXuBkTOjcPzfuYiAAUegUEjrtm2j36djR9X5kMlo8uTaNeD8eQopjY1VKV6mRH2a66zSNz4qihzqf/1Fkxy9eumOhmnqz0w7MW7cOBQWFmLhwoXIz89Ht27dsG/fPmUjv8zMTAjVfru+ffti27ZtmD9/Pt544w1ER0dj9+7d6NSpk3LM66+/jsrKSkyfPh0lJSXo168f9u3bB1e1Yo0LFy7E5s2blf93794dADm3Bg4caOej1oOp0VTqpZ0sTR8112l/9Cg5aAMDcbvKDaO3j0d5nRv6haRjfsxuIF9s88wzaZjKkW6raCldvPkmzbH++ivwyCPAsZ25iFT/HThOVSolIEAlfzjOdCPJHOPc2iypo0epsWhgII0tLaXv9fGhzysUgEhE+883QysqonG+vnpllanlEiwlPT0d69ats1k0Oo+jZMzWrVsxY8YMDBo0CEKhEI8//jjWrl2rfH/z5s2oqqrC8uXLlU58ABgwYICyIfydO3cwbdo05Ofno0WLFoiPj8fRo0dZ89BGYMECmh/386MkQH343OeD6HXRuPL8FVyfdx0eXT3QMomapfM6FGCbbJrnniOxfeAA8PTT1LfQxcXqzTJMwCkn3xmaWOu7srQ0jL5MVT4rj/cttWxJz93bty1yuHIclaTLy6PHPV92ylJcAl0AAQA5RaUDtrH3XFyAb74B7ruPTsuaNcCsQTomOdR7uXh7qybynSGKW5/OlplJTnT1IK5mWsWDOdKtwR4NKZ0NE8qbpKdTWgoAvPii9de/QCiAV1cviP3EqC+uR82NGosMxN69Kftl9WrguUXBuHgxGA57jt8L1wrDOdA18aPrplR/AIaH0wMZoNCiS5cotT0ggGTAiRPkQKitJQeQrgiCxrp+jU3weXrSpJTazf7moUHYlx4NN3Eddo/9BoFlhVZFPfOO9LqiOiiqSTbZwgAUiUix6tePfN+PjBLi778j4RVp4EO6lBN1xcvPj0rDSCQqxWv4cPoCY3WNzVV8DI0XCsmZ/+efVCqhVSvN95kcNMiMGTP0llngHUrqjBkzBmPGjNG7PYFAgCVLlmCJgZnvTZs2YdOmTebuquWYMrljSjSVdmknS9NHDV3PAO3j0aM0kXXzJjWmPHsWJV6t8FD2HFwoD0KwZzm2jtsDsXdfu2SeKSPSc+xTI51HLKYsv/79KQFm+BR//NNJCN/Wd38HbUe0RKJqmGyKkWSucW5NVJ1QSE3MEhPpmgBoP+vrybrlONq2nx+t5zjN4zEgq0wtl2BJ3eGcnBwkJSVhzJgxmDZtmtHvMBdHyBg/Pz9s27ZN7/umyKAPP/wQH374ocExDNvz++9U5xegwCW1+SOdhD4XivLT5cj7NA+pE1LR40QPuLdzV9p3gG1kl1AIfPUV0KkTqa7vvAMsWmT1Zhkm4JST7wxNDPkjFArKzOJLaCoUDfUUSxyihjJV//2XHOuBgfRM7dWLSsNZ6HDduBH46Sd6ZH/zDVVtswahWAhJkAS1+bWQZd51pNtIx+rZE/jgA2DGDOrp0Oc7f/RRn+TQFaDg4kKTDT4+jRvFra2fh4fr19n4Ei8pKRR5Ya3O5sQwR7o12LohpbNioLxJWRkwahRw5w5VS3n7bdt9rVu0G8qPlaM6vdriju5Ll9J9fvMmRU44TNe29FppRp2NGU6EttOitJSiB1u2JO2DTyPjOFKqcnNpjFRK17B2BIE1ss7ca1yhANzcKGf377/JKas+wccrYXcVka/OdMOKf/oBAL4atRvdvdKBWuuinl38XQARKEIhx7aKlZcXRaX37k2+7gkTKBtOpK/PsrZyoq148ZEdEgn91v/+S4KxVStVGRh9dY3NLeljTFFSn+Rozs9MhvmY2rTSWDSVntJOZpd+UShoX/LzVU2a1Seq+KiqwkK6Ub28gJgYlHmHIyljI05XtUeAuBgHR3yECB85APtknqmXdsFdf5Q9HOkAHeLPP5Oul3LNFU9UvoZfWm+CpIWHpiMaoElXsZieGYBxI8kc4zwigp5H1dX0bAoPNz+7RVfNdN7Yk8lof7t1o4mPwkI6DqGQjktXg3ozMbfucG5uLh544AH07dtXWT6FwXAURUWqAKrnnqNbwRSi10aj8kIlypLLcOONG+j4XUdVaRchBVLZgrAw4OOPgSefJHXn4YdJp2LYl0affGeYjz5/RFYWObZra1UlNHVl8RnS8zmO+jQVFNDznJ9oNpSpevIkOe75ut+XLlnscD1zBpg9m/5+913LygzrQhJCjvSazBoAtu1D83//R/FFO3YA42YF4/TT3dEy7ajKNucDFADq1RISogpYaKwobl36eVAQZYC3bWvahEczreLBHOnWYsuGlM6MjihXhYLqoaekUJPlXbvo0G2FW7u7jvSr1RC6k9Ay10D09KS+DA8/TJHp8fG0zw7B3GvF0kYeDIYxtJ0W6k4QPo09O5scCPX1qnSydu0oHU87gkBfzXVjjnFzr3Ht8VIpEBxMIdx8+hgAHD8O7vQZrCyZhjkHHwIAzL3vCMZ1vAikWB/1LBAKKEIhtxayLNs60gHatZ9+AgYMoDIvL7xARqFY1xNbWznRjgyVyTQdWhUV5Bzs04fGG6prbG5JH1MUpcBAssD5shvN9ZnJMB1zmlaOGmV2aSclppZ+AUjOnDxJE4lZWaTk8NHBfLaHRKLarlyOymv5GJ71CY5VdYafuBS/t3oGcbfKgfY0kWePjAt1R7ok8G7jYxvXSFcnPJxkUr9+HA7md8TY78fgm8m/wU0qJTlTV0fnRd3gA4wbSaZGK507RxkAqalARgZNYrRtS3KDr+NranaLuk6Wmkrrios1t+fnR+9du0YyrbbWJrLKnLrDOTk5eOCBBxAfH4+vvvpKo8QKg9HYKBRUhi4vj0TiBx+Y/lmhVIg277XB2f5nUfp3KTiOU2XS2Fhu8UEI335LJV5On9YvXhiMewptf0Rqqqq0Z2KiKlBKVxafPj1fvY53VRVlBZ4+TTNYhjJVY2Io2EcopMVCh+u5c8DgwTS/PnSobRsNS0IlwBmg5iY50m1p7wkEwOef06lOTxfg6b+m4ccO6XBJSaHjrK0l+62wkMLrO3RQncfGiOLWV77l3DnSi0JCdP9O2vvWTCszMEe6LbBVQ8omxsKFFDkplVKAV0iIbbfv1s4NAFCdXg33WMrNsUTRSkoCZs0iR/qUKfTzjBplu/00C1OvFUsbeTAYpqDttFB3gkil5FC/c4dmolq1UqWTtWlDUXraEQS6aq7rc4zzjna+QWZNjWkNMvXdExkZ5Bxu3165H/KRj2LWN4lYf/EBAMDLvf/Bsp67gJQsm0U9S4LJkV6TdVexsrER2Ls3+RDHjQM++4x0jO3bdegr2sqJdokCdYdWaSn9L5WScmasrjEfGcJ3Yzem+JiqKD34IC332DOToYVCQffvhg2UNsY3njLWtHL4cLNKO2lgqPTL6dNUqqW+nvajfXuSgzk5ZByWlNB9VVVFciQ3l7YZHIwchOHxcwtxrKYrfETlOBD+DLr43ARu1dN+lZXZJeOCr5Fem1MLsQ+p9LaMltJFt27Ajh0CPDpagR9ze2PQRh/seWIzWvr50bGKxXROeYPPFCPJFONZJqNnhkxGv1u/flQA+coVei707UvZSuZkt6jrZOrPJIlEVRLLz49kqCVNm60kJycHAwcOROvWrbFq1SoUFhYq3wsODm6UfWAweBQKikD/8Ue6NbZtM790glcPL0AE1ObV2r0k1ccfA0eOkIh45BGyWZkznXHPYCiwiX/2ZWRQgW6hULP5p76yarr0fPVykvX1ZCu2bk1jzp4l/ad1a937aAOH67lzwKBBZL707k2TZ7Z8RPMNR5WlXWxs73l7U2/VhATg1yOeGCddjm/6fwLp2bvnFFAFc6hPwNs7ittQyb3YWAo0uXCBdG5jWYHNtIoHc6TbCls0pGxCrF8PLFtGf3/+OWVQ2xp1Rzr/t6UG4vvvkz28eTPZQr/8QkLXIRi7Vixt5MFgmIJC0TAt3seHHmJ5efSQrqggp7q+dDLtCALAtMkfQBX9xytXbdvSQ9jbW3+DzFGjTOv2HR2N6vQcPDkzFLsvxkIg4PB+j214ufWPQLFto575OunyUjkA+xiBY8ZQSZennqImf/36UWkFDV+UsRIF6hEMMhktUiktxuoat2pFUSqurqYpPuYqSvfQM5OhBT/pdvIkpRO7u9N1FxNDyrmhppUXLlBRyR9/bJjVoFXaqQH6Sr/IZCTTzpyh74qIoHXBwXRPVFXR+zU1JAuLipRZHn9Xx+OJ1KUoqPNDC1Epfh3yIXrUlpATvaSEsnf69rVLxoUkRAIIqGFfbW4tAPuVdlHn4YeBA78LMXKEHMnFMei7/UXs65qDKEEOCa24OGqqWlpqmpFkzHjOyqJz7+KimtTz9qai7XzE+D//0DPDXDnP62SRkTR5op01GB/vsGyZAwcOID09Henp6QgPD9d4j+M4PZ9iMGyPXE5N/DZvplvm88+BuyWwzULkLoJnZ09UnK1A+bFyeHQiz7Y9JgD9/IAffgCGDAEOHyZn+s8/W183mcFwekwtlycUkoNEV8k5XSU6tPX8sDB6LS2lSXQfH3qG+/jQM/rECdK5KipUNqU6VjpceSf67dvkRN+/X/fXWIMkhOw9vrSLPXSsrl3Jmf7EE8Cu370xWvQ6dr6fAfdPV1OJOfVJDqBxorgNldzz9aXfJjub9NwWLYzvWzOs4sEc6QyzUCiA118nxzQAzJljv1Ip7tGk6ZhdI13HDKxQKMTnn5Pfbtcu8sv9/js9T5yOZtrZmOEE8IqVrrT4Dh1IESosJIVHLCanhSnpZKZM/nz6KTnvb99WlYpp2ZLKjJSVUYT71au6G2ReukT7ZKgW27//4t/Jn2Dqz4/jcpkPpMJafD1mD8b8JwgIWmDzqGfekc5jryjQxx6j+nkjR5L/MCEB2LmT/HJKTClRAND5lMnofx8fcvAZq2sslZJH/9Il0xSfZqgoMWyM+qSbpyddZ15eNJF36xZdh35+hptWTpgAzJ3bMNoKoJIt5pR+4aOpSkpIPggEdB/k5ZFMjI6m/bp+nWSVry8QFgYuJBSfHO6AmdfnoJ4To7P7NewK/S/aRkYBgf3IECwsJEWpb1+7THwLXYSUHZNXi7qiOgCN40gHyIf9T7IIDz/M4UpmCBJTPsfO+WdxX+V+mgy5csX0e9+Y8SyV0uRuRITmbxoQQIZ1ZCSd6+eeI8UuO5sEprly38kyTKdMmWK0ljqDYW/q66ki2zff0DzZ//4HjB9v+fa8ErxQcbYCZcfL4BZDgVL2klsJCcBvv1G5h8OHgREjmDOd0UwxlPFrq15IgKaef/Ik6Ubu7g2jpgUCct7n5ZFOoB68AFjtcP3rL7KRbt+mTf/2m+2d6IAqIl1RSY1o7CWreNk0ahSw7zcBhtVEYc+q/4PXF6tJ723sKG5DJfcEAqBzZ7LhU1PpOjJl35xMx7IW5khnmEx1NdWZ27mT/l+2DJg3z37fx0ehy7JlqC+rB2BCOo2B0hLi2Fh88w1FJBw4ADz0EHV2f/xx+x2DRTTTzsYMB6MdMa4rLb5jR4oOFYnIuVpeblo6mbHJn7Aw4I8/KJKzVy9ySsnl5JDy8qL/jx8nZ4muBpnJyaSIde6s89Cqy+ux8M8R+KD0GSggQpBHOXYM24T+8r+Anf6kkNl40omPUFAeph3rEvfqRX6+ESPIN9SvHx3S22+rVbAwVqKgspLkRnCw6kOm1jXu2pUEp6mKTzNTlBg2RFejYxcXujYCAsiRWlFBRh5guGmlvuwuQw5Z7dIv6uWNfH3JmQ6QDAwIIOfsrVvAffeRHPv9dyAuDvlRiZi572F8l9sJADAu4CC+aL0UHrV3AOndWurl5SRX7eRE55GGSVGbV6v8356ySBsSzwIMGwacO+eC+1/phblzemLR/JuQyMy89w0Zz3FxwNat+g260FC6bm7fBt57z7reMvdYhimDYYjycmDyZLotXVzIF/fYY9Zt0zvBG3kb81B2rAyB40nW23MCMDGxoTP9hx9I5DMYzQL1QCntjF+ZzHa9kHh4PX//fmqUEBOjCoBQR13nMqekhwE7QqEAVqwA5s+nQ+rVi3bDXvdzY9p7gweTrBo2jIKoHnwhBt+98wqiTnzX+MFJxkruubkBnTrRZElBgen71ox0LOZIZ5hEVhbV6k1OJn/LV19RN3R7IvYTQ+wrRn1JPaouU40og4qWCaUlpLGx2LWLfEKHD1MKzSuvAMuXq4IyHU4z7WzMcCC6Isb1pcVPmEBayd69pqeTGZv8qa+nCOlOnei7tWuyu7rSrHZkZMMGmXwUano6OdNbtdLYlf3pbTBz11SkVVP9vae6nMOapH3wc6sGOPuVQtKOSLd3FGhEBP1EM2ZQQMmaNXRYGzeScQjAtBIFU6bQb8unZJpa11hb8eHrW+tzlDu5opSRkYGlS5fi0KFDyM/PR2hoKJ566im8+eabkEgkxjfAsAztSTftslI+PpRmXFFBMkp9cofjqBxVdTU54BUK3fe0IYesdukX9fJG6qUyxGKVnCwqov3w8wMXGIQvr96PV399DiU1bhAKFHg3dB1e9doIQUkdySqA7q9GqvkoDZei/KRqYr2xItJ5QkOpDjEvm95ZLsC+3yLxv/8BsZFmbkyX8Xw340ijHJmuepzaNdRZbxkGwyqOHydb79o1sv127iQntLV49ybbpvxkORQ1d6M87TwBmJgI7NtHfbMOH6ayNN9846SZyQyGOaj7P7QzfvlgBVv1QlJHKCSbITiYvkP7uQzQMzgwkFJa+AbvVjhci4poU7/+Sv8/9RTwySf62+LYAklo42Qg8/TrBxw6RLbdyZNAtyfaYcPHczHhyUYOTjKlXn2fPsBrr9HfTSRwypb2H3OkMwyiUFCju9deo/ujRQty3tx/v/2/WyAQwK2dG8pPlht3pJtRV9zDQ4jffgPeeANYtYrK1Bw/Ts0pbN0w1SKaaWdjhgPRFzGuKy2ej55s1YoUM1PSyYxN/pSW0isfLqDtPBMKKUKdd+BqR0aHhND3Z2UpnSh/3WyN+YcexJFMcqCHuBRh42O/4ZEOV1Xfa8dSSI3tSAfoNG/eTIbtc89Rf8akJPopFi+mwHElhqLC27ZVORn5yQpz6hqb0lTWybl8+TIUCgU2btyIdu3a4eLFi5g2bRoqKyuxatUqR+9e80V70k0g0Cwr5elJTmy+Hjk/uVNUpJr08/amGaS//tJ/zem7/gHN0i/qzXkB1bXOK9N8aZmaGly67oYZt7/DH9nRAIDuAdn4/JEf0aP+NHBKCMjvThIWFzdqKSNpuFTjf3sbebrgZdMjj5BsOn2aTsG8eRSsYFaDP3XjOTWVIsx1lSPjs6T01VAHWG8ZBsMC5HK67RYuJPHYqhU1Fu3Xzzbbd+/gDpGXCPJyOSrOVQBoHB2qb19yoo8dS9Uo+ven7OpXX2VigdFE0fZ/aGf85uRQYAIfqGRtLyRtTPVZPPggLRZmqnIcHeZLL6n88OvXA888o9t/b0ukIZo6VmPIqp49KUF84kTg6FHgyaeE2D8lEuvW2WjSwFAjWh5T69WLxU4dOKWNLe0/5khn6OXqVWDaNEotAWjSafNmCnRsLHhHevW1agAGDEQz64q7uAArV1KEwpQpFE3VsSOVSnjuOfIpOYxm2tmY4UCM1Tnj0+J9fDQ7upta69qYInX7NkU+i8Wq71R3ngH0vTIZ7ad2TfaqKqBNGyg8vXHosAgrb47F/kwqnyAV1uIFz/9h4WMX0aK1Die+nUohOcKRzjN0KHDxIrBggSoyffduSrdetAjo0uXuQH1R4dpOxoICqh1jSl1jU5rK2sF5WF5ejrKyMuX/UqkUUqnUwCcMk5SUhKSkJOX/bdq0QVpaGj755BPmSLcnuibdAgKokO3lyxRx7OJCD2F+ckcuJ0uiuJjkSN++JCOMXXOmlH7Rbs4bEEByh4/sUiiQXhOOxXvGYlvuQHAQws1VgaUPHMJMv/9BXF5FxzNhAh2DdhOvRkDbkd7YEenqPPEE/TzPPEPpyYsWUXuMd96hyDGzTokp5cjc3AzXUAdYbxkGwwxOnQJmzQL+/pv+HzsW2LBBs5ectQhEAnj19ELJ4RKU/UPP9caaAIyPp4m+556jAKo5c6hi10cfUTsMBqNJoe3/0M74dXcnO0tBmR826YWkjrk+Cwuev5cuATNnAgcP0v/t21PymdLWsTMuQS6AAMDdpMXG0rEiI8kHt3Qp+ac2baIqqe+9R7qWxRMI5gRDNcO+V7a0/5gjndGA/Hy6ST/5ROXTeucdStttbAezWzTVSYecXvSm/llYV/yxx6jaxLhxVFLsv/8lo2/t2saJutdLMxRcDAdiabkgU2tdG1OkIiJI88nOVqUd8s6z1FQq2+LqSt/Rrl2DaMM71+9gU/2L2HC0P65kkPInFtTjP9F/Yv6DyQjLPQG0aKv72O1UCqmBI70R6xIDFJHw4Yc02bl0KRmEP/xAy9ChZCQ+8ohq7qIB6k7Gzp1NixQxI/PH1k7EuLg4jf8XLVqExYsX2/Q7SktL4efnZ9NtMrTQN+kWEECpyCdPkjdj+HDgxAl6/v3zD2WptG+vKRssveaMNecFgMuXcS1bihXZE/Fl1TjI76rLjz8OrFwpRFTrB4HMdk6RyioJc6ws0iY0lNKud+yg5vQ3b1KN5bVrybE+fLgJp8qccmTGaqgDrLcMg2GEjAzgzTcp8hygW2b9erp37RHx6Z3gjZLDJSj9hzIWG3MC0MeHyroMGQK8+CL1zYqLIxtw4UKar2UwmgTa/g/tjF9XV1pfU0MTz7bqhaSOnXwWublUC/2jjyieQiolnWLOHDOz3KxEKBbCJdAFdQV3G7o3oo4lFgNvvQUMGkTBCBkZNLnZrx+Vpu/Vy8wNWhIM5eC+V7YOpNKFpfYfc6QzlBQUqBzo1RQAjsGDybEcFeWYfeIbjvLUV9aDU3AQCLWEmBV1xdu3J3t940aK8Dx3DhgwgPyCc+aQr88hsIZ9DFthTbkgU2tdG1OkAHp4qzvaJRKyWPr3p/SQf/9VNsisqhbgl0ut8d259tiT1xM1cnIWeXlxeHpUOV59MhdRsW2B8AEkuBq5FJK2I90R5RQAOuRvvqGmO0uXAt99R5Ggv/1GuvKzz1KTaKOZRKb8zmZm/tiSlJQUhIWFKf+3tRKVnp6OdevWsWh0e2Ns0q11a+D550meDB5MkegrVlCNTe3a2NZcc3qa83IuEhws7IK1N17AzzfiwIHu64eTOLy9TIAePZQH4jSRzc4Uka7cBwEZeyNHUtbMsmUU6TpyJGX/vf46BfHr7U1jbjmyzEwq4Mx6yzAYZnHjBt2jn3xCwaoAlRNYtozEsb3w6k33Ys2NGgCNL7cEAtKP+vUDZs8GfvmFzsOWLTSh8Nxz9q27zGDYBG3/h3bGr0SicqDX1ZnXC8kcbOizuHqVqgZs3qySSY8+SqV4HeWPkoZKVY50B+hY999Pqu7KlWTy/v030Ls3BYK+/jrUdFMDWBMM5cC+V/YOpLLG/mMeuXscjiM7dcoUuj8++ICc6H36kCNm/37HCS0ADaZ6Sg+XIvPdTFSmVmq+wTsKs7I0G4YBqgdFbKxeZ5pYTJEIV66QDS8QkKzp04ec6j//rMqKalR4wdW5M70yJzrDEnjnlb8/PShLS6nwZWmpbZvixcYCc+cCS5bQrNSSJTQbFRurcrR3706lXq5codf4ePICz5iBwhcWYqvwaUzYPRaB78/BmL1TsCO7L2rkEnTuTOnFOTkCfPS1N6Ie7kD3hFjcOMemhdhTDJGnKkXH0c6rjh2B7dtJAZ0zh/xNeXmUDhgTQymQS5eSIqYtIk3GlMyfmhq7RH16eXnB29tbuehzpM+dOxcCgcDgcvnyZY3P5OTkICkpCWPGjMG0adNsvu8MLfTJgh49NKNhhEKKmnJzozBnXWGR6tcc3wD3wgV61X5oa78PkAwZNQr50xfig6rn0XnTKxiybSr23OgEDkIk9avA338Dv/wqMM1QcQDO6EjncXUleXT1Khl7Xl6Upj15MtCmDT0isrJ0fNCUcmRubqpyZIZ0QIWCyla1aEF/O0SZYzCcB46jkgGPPUZJgGvWkMPqwQdpwut//7OvEx2giHR1HJVJExND/dd/+40ylO/coZrp4eEks3TKJwbDWdD17OMzfoOD6YL28iKHunYvJFvbSFb4LORy8jk98QQdzmefkUy67z7KGPnhB8f6o9QbjjoqcMrDg/phXblCDVcBykaOjyfZ/csvRuw7c4KhnIiUlBSUlpYql3nz5ukc5wj7j0Wk36Pk5gLff0+C6uJF1frevSmFZOhQ+zdvMEZlaiWK9xdrrBN6CVF+phw1WTUIfykcHrF3jSwb1RX396eojBdfpFm/rVupn9lff1HW96RJtDhJIBqDYTqNVS7I0Ky1VsRChdAb/+a0wp/bhNi3Dzh1qj04ThU63TqsHmMniDB2nADx8QZkkoNKIUmCJahOp/QdR5dT4GnbFnj3XXJQ7doFfPUV1RW8cIGWhQtJGR06lJYHHlBleBrFisyfxuKVV17BlClTDI5p06aN8u/c3Fw88MAD6Nu3Lz799FM77x1DianRS6ZecwUFwJ49+ms+6qgJWRbZBXs8J+DrQ2E4cKA9FAqSPZ7uckx5ohIvzvNE+w7OH5IoDdNypDuJLFInKIgSC+bNownR1atJNVu0iAzDpCSKDh02jPzjZssafTpgVhZ5BmtryYG+eHGTa4zMYNiKjAzKYNu6lSa0eB56iKKyH3qo8Ww/aagU0nApZNkyAI6fAHzoISrx+dVXFPHJR8V+8AGJi8mTSWfSm0XDYDgCfc8+PuM3JITSwzw8TO+F1Ihcv06R55s2afpvhw+nuCxbNTi2FvWGo47WscLD6Zy9/DKwahUFUR0+TEu7dhQc+/TTOmJHLSyD7Gj4QCpjOML+Y470e4jMTJrR27mTSkvys1ZubsD48cD06TSB6WgHOgBwCg5Fu4ogr5BDIBGAq6WdFbmL4B7njqqUKhTtLoJ7jLuqzIsNnWlxcaRMvf02RWps2EAlORctomXAAEpLHjECUKs2wGA4Nw4sF8RxdA+dPCnE8eOR+Ptvavgkl2uO69aNjJVHHwV69xabLo8ccGwajnQnigIFSIceN46W4mLgxx9p8vTAAUrl3rCBFpGIAoP79aPlvvsoiEUn1pQIaiQCAgIQwNfQNkJOTg4eeOABxMfH46uvvoKQZfw0LqakippyzYWH08V9+7bumo/Dh1PIYVERclt0xE9FPfDjiWgc/F806jiVGpyYSMbHk0+K4ONjXGl3FkRuIoj9xKgvrgfguGgpU/D1JeN41izSRT//nJpn/forLZ6eVKZ1zOMRSGrbCW4XT5gua7R1wNRUstIlEvpxIyIapTEyg+FMXL1K4m/HDspA5nFzo8Cgl16iW8wRePX2UjrSnUFuiUTAf/5DzZJ/+YWc6IcP0+Pl++8p2GrcOLKZExMbv28Yg6ETff6P+HhN/4cpvZDsDMfRo5nv6XTmjOo9X18qK/XccxTU7kyoR6Q7i73XrRtlD73zDvmqPv2UWo7Nn09J4Q8+CDz5JOlUAQFoEsFQ1uAI+4850psx5eVkoBw4QOkyaWma7ycm0g321FMkvJyJmswaVF2ugmuEK1xauqA2j4pkCYSUmiENl6IqtQo1mTVwi1Sro25jZ1pYGEUmLFxIAn/zZlKq/vyTFoCeUyNHkvMvPt5Acz8GwxlohDpnVVUUGHHhAnD+PL2eOgWUlDQc27o1lUgfNIjuoZAQK764kWu4qddJdxbFShd+fsDUqbTwz4X9+ymV+epV6u948iRFiQLkl4yPVy2dO98tT22jzB9nICcnBwMHDkTr1q2xatUqFBYWKt8L1juTwGh0jF1zLVuSZXb7ts6aj5Xn0nFkUTIOlD+MA6W9ceGW5m8b45WD8b2u46lP7kO79s5/3epDGi5VOtKdWRbxuLqSwTxxIsmgL76gJodZWRQx+803Qri7vYpBgRfx8LVjeLhbHiJDa43LGl4HzMggy1IoBHr2VI1rhMbIDIYjKSuj+rn795Mz+OpV1XsCgcq58thjjrf9vBO8UfRDEe2bE8ktoZACpUaMoBYamzaRXCoooMaHH31Ej56HH6YxDz1E1TIYDIdhiv/DQXWuS0vJd8L7o9LTNXdp0CCavBo9WtUb1dmQhDivvRcRQfXjFy+mAIXNm8nOO3iQFqGQWsmMHhWBYX73oUPm7xB0dM5gqMbAlvYfc/k1EzgOuHkTSE6maPOjR+nhr14KUiikaMMnniAFKjzccftrDHm5HPIaOaQeUoj9xEpHOu7O/os8RKjNqYW8XN7ww3Z4UHh6qsq6ZGaSQvXjj9Qb8dQpWhYtoudW//5ULqFfP5otdNaHAoNhDXV1JHPS0ynS/MoVCoRIS6N7RFedNqmU7omePemh3r8/BZA2VdQVK2eIpjIFLy+KTnjkEfo/K4ueGX//Tcv586RLZWeTjOPx9aX6oR07xqJDi0WIqfoDMRkn0Fp8FSI3qVOkiJrDgQMHkJ6ejvT0dIRrPQw5i4vIM+yCoWyzXr2oO9zdmo+3q9zwT1YE/s6k5WRuCOoUmqpun/AsjI65jFEd0tDB5Ro54SWtAEQ65PBsgTRcisrz1DvG2Yw8Y0RHUzmqd94Bjh+nyNnvvwcyM4XYc7ML9tzsApyhSY+BwWkY2KMMAyZ0REhstO4NCoW03LlD2QzajnI7N0ZmMBoLjqNn+IkT9Bz/6y+K8FS3/VxcSNd65BGq8BAa6rj91YZvOAo4vlyCPrp2BT78kMq8HDxIEaA//0yPjf/9jxaBgHTbgQMpY7lfP3K0MxiNigMbQqpz6xbJI962OHlSM/tYIgGGDCFflDJa2smRhqqVdnFSHcvLi8q6TJlCsQRbt5LafOoUb+MJ8SqeQ6j7OAw6cQ6DOuRgQHQuWotzIMhpWsFQ1mBL+4850psgtbXkrLp0ieq5nTpFJRKKixuObdOGZsqHDKEoBEdHH5iKyEsEkasI8ko5xC1UlylfxkVeKYfQVQiRV+Pn1UVEUPOsOXMoOmHvXirN+scfFHH7yy+0AKTAdutGJXPi40khi4sjhyKD4axwHEUQ8A7V7GzyN9y8qVqyshqWZVHH358aXHbuTEuPHuSIbU71JV2CVAcjK5DBU+GpKjXVRGjVitKUx4+n/8vL6bly8qTq2XLlCsk23tkOBAMYD2A8JC4KRLWSo02hGG1zBYiKoiyD1q1JVgYEOEe5MG2mTJlitJYew4nQEW1V4h2B8z/ewIlzg3GivjtO5IXh+h2/Bh+NEGVjSLsbGNIpH4Pa3IC/e5XqzXrnrAlpLup10p3VIWUMoZAavPfpQ3U/z58nXerXXzkcPQqklYchrTwMG68C+JZqgSYmqj7TubPa86WJ1gJlMPShUFDQwvnztJw5QxNPBQUNx7ZtS07dYcOAwYN1Z/E7A149vQAhAIXzOqd4xGJVb5n6egpY+/lnWlJT6fc4c4ac7gD9Br170xIfT/qwyf1oGIwmwp07dN2fOqWyG65dazguOlrlj3rgAeeVSfpQt/fk5XJwCs6p7b3ISODNN2nJzAR++omWI0eA3CpffJ05AF9nAtgPhLjdQd82Beg7vAV6FQWhW3mTre5iEra0/5gj3UnhOKCwkKI9r1yhJS2NHtZXr9JDXBsXF3pQ33cfLX37OnfUuSFcI1zh3sEd5WfKIfZTu0yFNFsky5bBq4cXXCMcG+4dFETpSM88Q07F8+cpfemPPyhavbCQIkVOnFB9Riwmn0DHjuRUj42lpW1bFr3OsB+8c7yoiK7LW7doKSig1/x8akKcl0dLdbXxbbq5AW3acGgbUo3o0Ep0iBWgQ18/xMQK4e/vnA5UW1GZWomKUxXK//M25KHybCX8H/VXNUFugvBZNf37q9bJZBQIfPEiTeCmpdFy9SpQWytE2nUh0q7r3p6rK5XICg+n17AwqsEeEqJa2rRpXhMsDNtSU0M6UEqKEJcuReL8ecq4u3kTANreXVR08C9E/4hM9IvIxH2if9Hm1A4I7ntAd/pLE68JySMJU2XH1GTXwK21m1MbecYQCCjwoGtXYN48AUpKKNr2jz9oOXuW9OP0dODrr+kzUinpwN27A90jQtGtvCM63qmBl7+OgItm8rszmh8VFeSISk8nm+/yZVXJ/6qqhuPFYppESkgA7r+flqbSu0nsKYZHnAcqL1ZCUaVAdUY1XCNcnV52icWqc/3ee6Qz//knyaY//6Tf69o1Wr75RvW5yEiSaZ06kd3XoQMt+ub7GAxnoaRElXV86RKV7LxwgeajddGpk8of1b+/UwTKW0xlaqWyBBUAlBwuQea7mU3G3ouIAGbMoKWmhiYBfz/A4eCvtTh9SYK86hbYeakFdl4C8B7pX+3bUwBcly70W3bq5JCS+k4Pc6Q7iLo6clxlZ1NkZ2amKuLz+nVqBldZqf/zPj7kiO3cmWa6+WjP5hLpLBAK4P+oP2qyqFY6j0KmQFVKFST+EviP9ncqZYtv2te9OzB7NjkuMzKoSfbx42T4nT1Ls7f8A0gdgYDs/HbtyKkeFUUPnshIiu4MCmKNbRim8dprZITdvq256JqAM4SfHzlAw8Pp2uQjjVu3puszpCQVgt13yy1U1wAproCiAxDwKBDQNEp8WEJlaiWy12aj9latcp24pRjlZ8pRk1WD8JfCm4RyZSpSqcqppY5cTs+v69fJYLx+nRb+eZaXR0obb1DqIzOzaZf4YVhPXR1dS7wDiQ8guHKFrin1UgXqtGrFId7zCnoJT6FXtzr0DMtDC7caepPjgEvXgPbRNIsYHt4sa0JWplai8txdhVEA3Fx0E+4d3JuMkWcKvr7Ui2bkSPr/zh3Srf79V7WUlqoHLrQEsAgAEOl7B50CbyHOvxAd/IsQ07IIHW7fhF+f2Cb9uzOaJmVlKtuPz/DLyKDl2jXdEeY8rq5k63XpQs/jXr0o69XNTf9nnJnK1EoIpCSTK9MqkbEwo0nKrpAQzcy+4mKKzj1+nOTUuXP0e/O/s3rZPIAmPnjbr107TfsvKKh5B6UwnAOFgmTPzZvkg+J1sWvXKGjGkFyKjKSSnT17qnorNZeeAbrsPaGXsMnae66uVKHiwQcFwHIpqqtJVh09Ssvp0/R84gOm1CcCPT0pMTQmhiYAY2JIZrVt23QqXtga5ki3IXI5OavUoz35SE9+ycujqM9bt3TXEFaHL+MYE0MzQ+3b09+dOtFDt7k/WD1iPRD+Ujjyt+Sj9I9SAICiUgHf/r7wH+38SpZAQMpQVJRKueLt9nPnqNdVaqpqKStTOaAOHWq4PbFYFdkZHk51DkNDVVGdQUG0+Pk5dsZw5MiROHv2LG7duoUWLVpg8ODBWLFiBUKdqTBjM+fXXyliQBeenlRuIyCArpfAQFq0o4RDQgB3dwNfkpoKrFtLIe6tWqkaAJ45QxbDSy81mXrZ5sApOBTtKkJdUR3c49xR9k8ZAEDkLYI0QoqqlCoU7S6Ce4y7U0302QORSGXsPfhgw/dlMnre5eSoSgSpZz3wz8WgoMbec4YzkJ0NTJ5MjnJjpaJ8fSl4IDaWAgi6diVnUosWAiBVAaw9QrKoNhxwUWtGGhBABSP37m3yDXJ1oTTyCu82ZBcJIPZvvpN6PC1aAElJtADkBLh+nYzAM2fo9cKZOuQVuiCjpAUySlrg5ysxalv4D1r+VY+2u4Vo146cV23aqORZWBhrHM8wnfp6Ej/qmX4FBapnHW/7mVpNqGVLuib5iGV+iY5uPgE1vOwSSEhPcotyazayy8+Pylg89JBqXXGxqiyPuv1XWEjXRU4ORbNr4+qqsvv02X/BwU13MoVhf+Rykku8/s3r5FlZqtKdmZmksxsiLIz8ULwe1rkz+aSaWpkWU1G39zw6eeDOb3egqFRA5C6Ce5x7s7D33NwaZiHfukU61OnTlIl88SLFy1VUqPoCatOiBTnU1QPuWrdWyayAgCarZhuEqYlmsG8fzSwXF2sut2+TAnXnjnnbE4vpQRgRobm0aUNLRETziTC3FI9YD0S9HYWctTlQVCkQODEQof8JbbICi486b9WKOr3zcBxdQ3yqcnq6ZpRKVhYp6nz0iiHEYhJY69dTI4/G5oEHHsAbb7yBkJAQ5OTk4NVXX8UTTzyBo0ePNv7O3KPMmUN+Ij8/Msj4JSDARuWDFArqYFJURPWJ+Fk9b2/6PyUF2L2bNK5m9uSsyaQsGWkrKaAmhgQiAQQCAaThUlSlVqEmswZukfe2ZSOVqiYTGQxtvLw0J41dXelaadu2YQBBcLCB4AFDzUj5Brht2xp+vwmiYeR18UDZ32UQuAgg9hZDFCdqFkaeqQiFUDrEx47l17rgdvIVXPryGC6cVyD1dhDSKsORVt0KWaU+uF0ixu3jFDWqDR+40KoV6eKtWpET89lnG/OoGM7CpUuk0vA23+3b9HdhoWX2n68vORdat1ZlnUZGqqL7mnstbXXZ5TvYF94J3hB5iyAQCJqt7PLzo7r1Awdqrr99WxX5y9t/vO2Xk0NZffx6Q3h6qgKqHn+cMqMZ9x7/+x9w4AAFqRQU0Gthof6sPnWEQnruqcsi/rnavn3zdZjrQ93eEwgEEHuJUVtZC4Gwedt7gYGagQoAZY1evaqKVE9Lo4xRPovqzh2KbD95Uvc2XVwaTgCGhgKjRlGQTFOFOdLN4KefgE8+MT6Od1gFBtIrP1scHEwPOL5OrL9/s/Mx2QWhSAiPOA+UnyyHe3TzUarUEQhUUcKJiQ3fr6+nWWT1GWT1KJe8PJUg48eaUnO4vLwcZWVlyv+lUimkVs7evPzyy8q/W7dujblz52L06NGoq6uDCyuE3Cg8/bSdvyAzkxxSrVo19G7xqTSpqTSuKRfG04G8XA55jRxSD3KkC8QCcPWcslGWyEOE2pxayMsNhNcyGAz4+ABbt6oCCIKDrdCJdDQj1SjoaOz9Joi6kSfyEsFngI+yOXtzNvLMoWVie9yf0A73K393TyDCC5XVmqnr6emUzp6RQcEKtbUNAxc6dGCO9HuVixeB+fMNjxEIyK5Tz/TjnQW844DvFXKv18RWl11CoRBCH5UcvtdkFx/okpDQ8L26OlU2H79kZalsP97+k8koWpSvr9+rV+MfB8M5+PdfYMuWhuuFQpJNvExq1UpVtjM8nEy18HDWr0gdDXsPgMhXBORDWY7qXrL3XFwoTi4uruF7lZWq0p683sQvOTk0mVNXpzsYtG1b5ki/Z7j/fprR8/NTLS1akOLELy1asHRQexC9Php3Dt+BT/9mHqahB7FYFcmuy9HOU1tLM88FBeSYMEaclkRctGgRFi9ebN3OqlFcXIytW7eib9++TulE/+ijj7By5Urk5+eja9euWLduHXr37q13/I4dO7BgwQJkZGQgOjoaK1aswLBhw5TvcxyHRYsW4bPPPkNJSQnuu+8+fPLJJ4iOjlaOKS4uxosvvog9e/ZAKBTi8ccfx5o1a+Dp6WnXY7Up5eUUJqPPGvTwMD2HuYkh8hJB5CqCvFIOsbcY/o/5Q1GjgMiN8q3llXIIXYUQeTWT/GsGw448+aQNNyYUGp64M/Z+E0PdyBMIBPAd6Kvx/r1k5BlEx+/u4UGlgbp0aThcoSDnVGamqodRVhbp/Ix7kw4dgGeeIYcnn+nn56cKgPH3p/+bS9kVe6PtoNKGyS7CxcV4Vh/HUWlQvpxQQQHLAryXGTWKfAV88Cb/GhjI5JO5aNt7vg/6QhIigXsHqnvK7D3Cw0NV6kcXdXWqYFDtcme6HPNNCebyNQP1RiKMxsU7wRveCfdYTpEFSCSqjAdTSElJQZjaYGuj0XnmzJmD9evXo6qqCn369MHPP/9sk+3akm+//RazZ8/Ghg0bkJCQgNWrV2Po0KFIS0tDYGBgg/FHjx7FhAkTsHz5cowYMQLbtm3D6NGjcfr0aXTq1AkA8N5772Ht2rXYvHkzoqKisGDBAgwdOhQpKSlwvVtTZeLEicjLy8OBAwdQV1eHqVOnYvr06di2bVujHr9VeHlRaYTKSt25fpWV9L6XV+Pvm51xjXCFewd3lJ8phyhOBPdYVSF5juMgy5bBq4cXXCNsUUOHwWAwdKNt5GnDjDzL4NPbw8IMBy4w7h26dgW++MLRe9F8YLLLdggElN3l40PlNxj3NkOG0MKwHm17TxIggSRAAoDZe+bg4qIqX93csCin9aOPPkJkZCRcXV2RkJCA47oKDKqxY8cOdOjQAa6urujcuTN++eUXjfc5jsPChQsREhICNzc3DB48GFevXtUYU1xcjIkTJ8Lb2xu+vr549tlnUVFRoTHm/Pnz6N+/P1xdXdGqVSu89957lhweg3HP4OXlBW9vb+Wiz5E+d+5cCAQCg8vly5eV41977TWcOXMG+/fvh0gkwqRJk8AZ667byHzwwQeYNm0apk6diri4OGzYsAHu7u748ssvdY5fs2YNkpKS8NprryE2NhZLly5Fjx49sH79egAkx1avXo358+dj1KhR6NKlC7Zs2YLc3Fzs3r0bAJCamop9+/bh888/R0JCAvr164d169Zh+/btyM3NbaxDt56ICArRyspq2DWZ76gbG9ssn5oCoQD+j/rDxd8FVSlVqC+tB1fPob60HlUpVZD4S+A/2r9ZlqBiMBjOA2/kybJkDZ6vvJHnHuvOjDyGSTRl287YvjCcCya7GAyGs8PsPYYxzHak81GcixYtwunTp9G1a1cMHToUt27d0jmej+J89tlncebMGYwePRqjR4/GxYsXlWP4KM4NGzbg2LFj8PDwwNChQ1FTU6McM3HiRFy6dAkHDhzAzz//jL/++gvTp09Xvl9WVoaHHnoIrVu3xqlTp7By5UosXrwYn376qbmHyGAwtHjllVeQmppqcGmjVkvG398f7du3x5AhQ7B9+3b88ssv+Pfffx14BJrU1tbi1KlTGDx4sHKdUCjE4MGDkZycrPMzycnJGuMBYOjQocrxN27cQH5+vsYYHx8fJCQkKMckJyfD19cXPXv2VI4ZPHgwhEIhjh07pnd/ZTIZysrKlEu5o0umCIXAo49SPnNKClBaSsX5S0vpf39/auLXhOsPG8Ij1gPhL4XDq7sX6m/Xo/pKNepv18OrhxfCXgqDR+w9XgCVwWDYHWbkMWxFU7btTNkXhnPBZBeDwWgKMHuPYRDOTHr37s3997//Vf4vl8u50NBQbvny5TrHjx07lhs+fLjGuoSEBO65557jOI7jFAoFFxwczK1cuVL5fklJCSeVSrlvvvmG4ziOS0lJ4QBwJ06cUI759ddfOYFAwOXk5HAcx3Eff/wx16JFC04mkynHzJkzh4uJiTH52LKysjgAXFZWlsmfYTCaIo15rd+8eZMDwB0+fNju32UqOTk5HADu6NGjGutfe+01rnfv3jo/4+Liwm3btk1j3UcffcQFBgZyHMdx//zzDweAy83N1RgzZswYbuzYsRzHcdyyZcu49u3bN9h2QEAA9/HHH+vd30WLFnEAGiwOl1UpKRy3bBnHPf00x40ZQ6/vvEPr7wEUcgVXdaOKKz9fzlXdqOIUcoWjd6nZwZ7LTRf22zUOFSkVXMayDC7l6RTu4piLXMrTKVzGOxlcRUqFo3ftnqGpX+tN2bYzti+m0NR/v6YKk12ND7vWVaxfv55r3bo1J5VKud69e3PHjh0zOP67777jYmJiOKlUynXq1Inbu3evxvsKhYJbsGABFxwczLm6unKDBg3irly5ojHm9u3b3JNPPsl5eXlxPj4+3DPPPMOVl5ebvM/s93MMzN5rfJrCtW5WuKAzR3EmJyfj/vvvh0Qi0fietLQ03LlzR+e+OV2UJ4PRxDl27BjWr1+Ps2fP4ubNmzh06BAmTJiAtm3bIpEVG7WYefPmobS0VLmkpKQ4epeI2Fhg7lxgyRJgwQJ6nTOH1t8DCIQCuEW6wbOzJ9wi3Vj0FMNuOGvZBYbj8Yj1QMTcCEQuiUTrBa0RuSQSEXMiWKQUwySaum1nbF90wew/54DJLoajcNYsHIZzwuw9hi7McqQXFRVBLpcjKChIY31QUBDy8/N1fiY/P9/geP7V2Bjt5n9isRh+fn4aY3RtQ/07tFm+fDl8fHyUS1xTbx3LYDgYd3d3/PDDDxg0aBBiYmLw7LPPokuXLvjzzz9t1sjUFvj7+0MkEqGgoEBjfUFBAYKDg3V+Jjg42OB4/tXYGG0lrb6+HsXFxXq/F6AmsOq17L2cqYmnUAhERlK77sjIZlvOhcFwFMzgYxiDGXkMS2nqtp2xfdEFs/+cBya7GI6gqfTJYpN+DIbzck97PJw2ypPBaKJ07twZhw4dwu3bt1FTU4MbN27gk08+QVhYmKN3TQOJRIL4+HgcPHhQuU6hUODgwYN6I+cTExM1xgPAgQMHlOOjoqIQHBysMaasrAzHjh1TjklMTERJSQlOnTqlHHPo0CEoFAokJCTY7PgYDEbzoakYfAwGg9EUYPYfg3Hv4sxZONqwST8Gw3kxy5HuzFGc+r5H/Tu0ceooTwaDYVdmz56Nzz77DJs3b0ZqaipeeOEFVFZWYurUqQCASZMmYd68ecrxM2fOxL59+/D+++/j8uXLWLx4MU6ePIkZM2YAAAQCAWbNmoW3334bP/30Ey5cuIBJkyYhNDQUo0ePBgDExsYiKSkJ06ZNw/Hjx/HPP/9gxowZGD9+PEJDQxv9HDAYDOemKRl8LHKKwWh6NHXbzti+6ILZfwzGvYszZ+Fowyb9GAznRWzOYPUoTt4xxEdx8s4kbfgozlmzZinX6Yvi7NatGwBVFOcLL7yg3AYfxRkfHw+gYRRnYmIi3nzzTdTV1cHFxUX5PTExMWjRooVJx6dQKAAAeXl5pp8UBqMJwl/j/DV/LzJu3DgUFhZi4cKFyM/PR7du3bBv3z6lEpSZmQmhWpmSvn37Ytu2bZg/fz7eeOMNREdHY/fu3ejUqZNyzOuvv47KykpMnz4dJSUl6NevH/bt2wdXV1flmK1bt2LGjBkYNGgQhEIhHn/8caxdu9asfWeyinGvcK/LKkMG3+XLl3V+xlEG3/Lly/HWW281WM/kFKO505TlVFO37YztiykwnYpxr9CUZdW9iFQq1SiNWlJSAoDJKkbzp0nIKnO7k27fvp2TSqXcpk2buJSUFG769Omcr68vl5+fz3Ecxz399NPc3LlzleP/+ecfTiwWc6tWreJSU1O5RYsWcS4uLtyFCxeUY959913O19eX+/HHH7nz589zo0aN4qKiorjq6mrlmKSkJK579+7csWPHuL///puLjo7mJkyYoHy/pKSECwoK4p5++mnu4sWL3Pbt2zl3d3du48aNJh/b8ePHOQBsYcs9sxw/ftxcEcBwApisYsu9ttyrsionJ4cDwB09elRj/Wuvvcb17t1b52dcXFy4bdu2aaz76KOPuMDAQI7jSC8DwOXm5mqMGTNmDDd27FiO4zhu2bJlXPv27RtsOyAggPv44491fm9NTQ1XWlqqXA4dOuTw64YtbGnMpanKqaZs25myL8ZgOhVb7rWlqcoqWyCTyTiRSMTt2rVLY/2kSZO4kSNH6vxMq1atuA8//FBj3cKFC7kuXbpwHMdx165d4wBwZ86c0Rhz//33cy+99BLHcRz3xRdfcL6+vhrv19XVcSKRiPvhhx9M2ncmq9hyry3OLKvMikgHnDeK08fHB/v378d///tfxMfHw9/fHwsXLjSrMVb37t1x/PhxBAUFaRxDY1NeXo64uDikpKSwdEMLYefQMAqFAgUFBejevbujd4VhAUxWNR/YOTTMvS6r7F12ISQkRGMMHz1qSWNk7cip/v37O4WcAth9ZgvYOdRPU5dTTdm2M2VfjOEsOhXA7jNrYefPME1dVtkCZ87CMYazyCp2n1kPO4eGaQqySsBxHOfonWBoUlZWBh8fH5SWlsLb29vRu9MkYeeQwbA/7D6zHnYOGcZISEhA7969sW7dOgCkXEZERGDGjBmYO3dug/Hjxo1DVVUV9uzZo1zXt29fdOnSBRs2bADHcQgNDcWrr76KV155BQBdh4GBgdi0aRPGjx+P1NRUxMXF4eTJk0qDb//+/UhKSkJ2dnaT6+nA7jPrYeeQwbA/7D6zDnb+GKbw7bffYvLkydi4cSN69+6N1atX47vvvsPly5cRFBSESZMmISwsDMuXLwcAHD16FAMGDMC7776L4cOHY/v27XjnnXdw+vRp5aTdihUr8O6772Lz5s2IiorCggULcP78eaSkpCgnEB9++GEUFBRgw4YNqKurw9SpU9GzZ09s27bNYefCEth9Zj3sHDZ9zI5IZzAYDAaDwWA0DrNnz8bkyZPRs2dPpcGn3RhZ3eCbOXMmBgwYgPfff19p8J08eRKffvopAM3GyNHR0UqDT19jZN7gY42RGQwGg8FgNHWcNQuHwWA0HVhEuhPCZqish51DBsP+sPvMetg5ZJjC+vXrsXLlSqXBt3btWmUq8MCBAxEZGYlNmzYpx+/YsQPz589HRkYGoqOj8d5772HYsGHK9zmOw6JFi/Dpp58qDb6PP/4Y7du3V44pLi7GjBkzsGfPHg2Dz9PTs9GO21aw+8x62DlkMOwPu8+sg50/BsP+sPvMetg5bPqwiHQnRCqVYtGiRRq1Rhnmwc4hg2F/2H1mPewcMkxhxowZemt3/vHHHw3WjRkzBmPGjNG7PYFAgCVLlmDJkiV6x/j5+TW5dGN9sPvMetg5ZDDsD7vPrIOdPwbD/rD7zHrYOWz6sIh0BoPBYDAYDAaDwWAwGAwGg8FgMAzg2NbkDAaDwWAwGAwGg8FgMBgMBoPBYDg5zJHOYDAYDAaDwWAwGAwGg8FgMBgMhgGYI53BYDAYDAaDwWAwGAwGg8FgMBgMAzBHOoPBYDAYDAaDwWAwGAwGg8FgMBgGYI50BoPBYDAYDAaDwWAwGAwGg8FgMAzAHOmNxCeffIIuXbrA29sb3t7eSExMxK+//qoxJjk5GQ8++CA8PDzg7e2N+++/H9XV1cr3i4uLMXHiRHh7e8PX1xfPPvssKioqGvtQHIaxc5ifn4+nn34awcHB8PDwQI8ePbBz506Nbdzr55DBMAaTVdbDZBWDYX+YrLIeJqsYDPvC5JT1MDnFYNgfJqush8mqewyO0Sj89NNP3N69e7krV65waWlp3BtvvMG5uLhwFy9e5DiO444ePcp5e3tzy5cv5y5evMhdvnyZ+/bbb7mamhrlNpKSkriuXbty//77L3fkyBGuXbt23IQJExx1SI2OsXM4ZMgQrlevXtyxY8e4a9eucUuXLuWEQiF3+vRp5Tbu9XPIYBiDySrrYbKKwbA/TFZZD5NVDIZ9YXLKepicYjDsD5NV1sNk1b0Fc6Q7kBYtWnCff/45x3Ecl5CQwM2fP1/v2JSUFA4Ad+LECeW6X3/9lRMIBFxOTo7d99VZUT+HHh4e3JYtWzTe9/Pz4z777DOO49g5ZDAshckq62GyisGwP0xWWQ+TVQyGfWFyynqYnGIw7A+TVdbDZFXzhZV2cQByuRzbt29HZWUlEhMTcevWLRw7dgyBgYHo27cvgoKCMGDAAPz999/KzyQnJ8PX1xc9e/ZUrhs8eDCEQiGOHTvmiMNwKNrnEAD69u2Lb7/9FsXFxVAoFNi+fTtqamowcOBAAOwcMhjmwmSV9TBZxWDYHyarrIfJKgbDvjA5ZT1MTjEY9ofJKuthsqr5I3b0DtxLXLhwAYmJiaipqYGnpyd27dqFuLg4/PvvvwCAxYsXY9WqVejWrRu2bNmCQYMG4eLFi4iOjkZ+fj4CAwM1ticWi+Hn54f8/HxHHI5D0HcOAeC7777DuHHj0LJlS4jFYri7u2PXrl1o164dALBzyGCYCJNV1sNkFYNhf5issh4mqxgM+8LklPUwOcVg2B8mq6yHyap7B+ZIb0RiYmJw9uxZlJaW4vvvv8fkyZPx559/QqFQAACee+45TJ06FQDQvXt3HDx4EF9++SWWL1/uyN12KvSdw7i4OCxYsAAlJSX4/fff4e/vj927d2Ps2LE4cuQIOnfu7OhdZzCaDExWWQ+TVQyG/WGyynqYrGIw7AuTU9bD5BSDYX+YrLIeJqvuHZgjvRGRSCTKGaf4+HicOHECa9aswdy5cwFAOVvFExsbi8zMTABAcHAwbt26pfF+fX09iouLERwc3Ah77xzoO4evv/461q9fj4sXL6Jjx44AgK5du+LIkSP46KOPsGHDBnYOGQwTYbLKepisYjDsD5NV1sNkFYNhX5icsh4mpxgM+8NklfUwWXXvwGqkOxCFQgGZTIbIyEiEhoYiLS1N4/0rV66gdevWAIDExESUlJTg1KlTyvcPHToEhUKBhISERt1vZ4I/h1VVVQAAoVDzkhaJRMpZVHYOGQzLYLLKepisYjDsD5NV1sNkFYNhX5icsh4mpxgM+8NklfUwWdWMcXS303uFuXPncn/++Sd348YN7vz589zcuXM5gUDA7d+/n+M4jvvwww85b29vbseOHdzVq1e5+fPnc66urlx6erpyG0lJSVz37t25Y8eOcX///TcXHR3NTZgwwVGH1OgYOoe1tbVcu3btuP79+3PHjh3j0tPTuVWrVnECgYDbu3evchv3+jlkMIzBZJX1MFnFYNgfJqush8kqBsO+MDllPUxOMRj2h8kq62Gy6t6COdIbiWeeeYZr3bo1J5FIuICAAG7QoEFKwcSzfPlyLjw8nHN3d+cSExO5I0eOaLx/+/ZtbsKECZynpyfn7e3NTZ06lSsvL2/Mw3Aoxs7hlStXuMcee4wLDAzk3N3duS5dunBbtmzR2Ma9fg4ZDGMwWWU9TFYxGPaHySrrYbKKwbAvTE5ZD5NTDIb9YbLKepisurcQcBzHOToqnsFgMBgMBoPBYDAYDAaDwWAwGAxnhdVIZzAYDAaDwWAwGAwGg8FgMBgMBsMAzJHOYDAYDAaDwWAwGAwGg8FgMBgMhgGYI53BYDAYDAaDwWAwGAwGg8FgMBgMAzBHOoPBYDAYDAaDwWAwGAwGg8FgMBgGYI50BoPBYDAYDAaDwWAwGAwGg8FgMAzAHOkMBoPBYDAYDAaDwWAwGAwGg8FgGIA50hkMBoPBYDAYDAaDwWAwGAwGg8EwAHOkMxgMBoPBYDAYDAaDwWAwGAwGg2EA5khnMBgMBoPBYDAYDAaDwWAwGAwGwwDMkc5gMBgMBoPBYDAYDAaDwWAwGAyGAZgjncFgMBgMBoPBYDAYDAaDwWAwGAwDMEc6g8FgMBgMBoPBYDAYDAaDwWAwGAZgjnQGg8FgMBgMBoPBYDAYDAaDwWAwDMAc6QwGg8FgMBgMBoPBYDAYDAaDwWAYgDnSGQwGg8FgMBgMBoPBYDAYDAaDwTAAc6QzGAwGg8FgMBgMBoPBYDAYDAaDYQDmSGcwGAwGg8FgMBgMBoPBYDAYDAbDAM3SkT5lyhQIBAIIBAJ06tTJ0bvDYDR5Ro8eze4pG8PkFINhW1avXq28pwQCAYqKihy9S80CJqsYDNvCZJV9YLKKwbAts2bNUt5Tnp6ejt6dZgOTVQyGbXGErGqWjnQA8Pf3x9dff4133323wXtHjx5Fv3794O7ujuDgYLz00kuoqKgwedtffPEFYmNj4erqiujoaKxbt67BmB9++AHjxo1DmzZt4O7ujpiYGLzyyisoKSmx+JguXbqEMWPGKLfp7++P+++/H3v27GkwVl1B116GDBmi9zu2bt1q9gVYUlKC6dOnIyAgAB4eHnjggQdw+vRpi44RAE6cOIEZM2agY8eO8PDwQEREBMaOHYsrV64Y/FxdXR3i4uIgEAiwatUqnWOuXbuGJ598EoGBgXBzc0N0dDTefPNNk/bL1sepj7/++gsjR45Eq1at4OrqiuDgYCQlJeGff/4x6fORkZF6f/vo6GjluE2bNhm8TrZu3aoc+/LLL+Prr79Ghw4dbH689zKOllNpaWl4+eWX0bdvX7i6ukIgECAjI8OaQ7KLnMrIyNA7bvv27Sbtl6Pl1Pr16xEbGwupVIqwsDDMnj0blZWVDcYtW7YMI0eORFBQEAQCARYvXmzWfslkMsyZMwehoaFwc3NDQkICDhw4YMkhGsSc31kXAwcO1Puburi4KMfdvn0bK1euxP3334+AgAD4+vqiT58++PbbbxtsMykpCV9//TUeffRRmx0ng2CyyjRZdfnyZbz++uvo1q0bvLy8EBISguHDh+PkyZMm75c97mFz5Mr27dvRo0cPuLq6IiAgAM8++2wDR291dTWeffZZdOrUCT4+PvD09ETXrl2xZs0a1NXVmbRPCoUC7733HqKiouDq6oouXbrgm2++seYwdZKbm4unnnoKMTEx8PLygq+vL3r37o3NmzeD4ziTt3P69GmMHDkSfn5+cHd3R6dOnbB27VqNMfrkWlJSksY4Jqvsh6NlVVOz/5zJLjJHr2rqMlkXjaVXAUBNTQ2WL1+OuLg4uLu7IywsDGPGjMGlS5c0xj399NP4+uuv0b9/f5sdJ4NwtKxqKnqVM9qAph6nQqHApk2blH4dDw8PdOrUCW+//TZqampMPie6rhFdMFnVuLJK3Gjf1Mh4eHjgqaeearD+7NmzGDRoEGJjY/HBBx8gOzsbq1atwtWrV/Hrr78a3e7GjRvx/PPP4/HHH8fs2bNx5MgRvPTSS6iqqsKcOXOU46ZPn47Q0FA89dRTiIiIwIULF7B+/Xr88ssvOH36NNzc3Mw+pps3b6K8vByTJ09GaGgoqqqqsHPnTowcORIbN27E9OnTlWO//vrrBp8/efIk1qxZg4ceekjn9isqKvD666/Dw8PD5H1SKBQYPnw4zp07h9deew3+/v74+OOPMXDgQJw6dUrDcWsqK1aswD///IMxY8agS5cuyM/Px/r169GjRw/8+++/emdu161bh8zMTL3bPXv2LAYOHIiwsDC88soraNmyJTIzM5GVleWQ49THlStXIBQK8fzzzyM4OBh37tzB//73P9x///3Yu3dvA4NMm9WrVzd42N68eRPz58/X+O3vv/9+ndfJhx9+iHPnzmHQoEHKdQMGDAAAfP755yxyyoY4Wk4lJydj7dq1iIuLQ2xsLM6ePWv1MdlTTk2YMAHDhg3TWJeYmGh0nxwtp+bMmYP33nsPTzzxBGbOnImUlBSsW7cOly5dwm+//aax3fnz5yM4OBjdu3dv8J4pTJkyBd9//z1mzZqF6OhobNq0CcOGDcPhw4fRr18/s7enD3N+Z128+eab+M9//qOxrrKyEs8//7zGb5+cnIw333wTw4YNw/z58yEWi7Fz506MHz8eKSkpeOutt5RjO3TogA4dOiA9PR27du2y2bEymKwyVVZ9/vnn+OKLL/D444/j//7v/1BaWoqNGzeiT58+2LdvHwYPHmx0v+xxD5sqVz755BP83//9HwYNGqT8PdesWYOTJ0/i2LFjcHV1BUCO9EuXLmHYsGGIjIyEUCjE0aNH8fLLL+PYsWPYtm2b0X1688038e6772LatGno1asXfvzxRzz55JMQCAQYP368Rcepi6KiImRnZ+OJJ55AREQE6urqcODAAUyZMgVpaWl45513jG5j//79eOSRR9C9e3csWLAAnp6euHbtGrKzsxuMDQ8Px/LlyzXWhYaGavzPZJX9cLSsakr2n7PZReboVU1dJuuisfQqAJg4cSJ++uknTJs2DT169EBubi4++ugjJCYm4sKFC2jdujUAID4+HvHx8fj999/tEjx2L+NoWdVU9CoeZ7IBTT3OqqoqTJ06FX369MHzzz+PwMBAJCcnY9GiRTh48CAOHToEgUCgse0hQ4Zg0qRJGuu6d+9u0n4xWdXIsoprhkyePJlr3bq1zvcefvhhLiQkhCstLVWu++yzzzgA3G+//WZwu1VVVVzLli254cOHa6yfOHEi5+HhwRUXFyvXHT58uMHnN2/ezAHgPvvsM9MPxgj19fVc165duZiYGKNjn332WU4gEHBZWVk6358zZw4XExOjPB5T+PbbbzkA3I4dO5Trbt26xfn6+nITJkww7SC0+OeffziZTKax7sqVK5xUKuUmTpyo8zMFBQWcj48Pt2TJEg4At3LlSo335XI516lTJy4hIYGrqqoye5/scZzmUFlZyQUFBXFDhw616PNLly7lAHD//POPwXFVVVWcl5cXN2TIEJ3vDxgwgOvYsaNF+8DQxBnk1O3bt7mysjKO4zhu5cqVHADuxo0blh2QAayVUzdu3NB5X5uKI+VUbm4uJxaLuaefflpj7Lp16zgA3E8//aSxnj//hYWFHABu0aJFJu/TsWPHGpyn6upqrm3btlxiYqLJ27EUc35nXXz99dccAG7r1q3KddevX+cyMjI0xikUCu7BBx/kpFIpV1FR0WA7ixYt4gBwhYWFFu0HQxMmq3SjS1adPHmSKy8v1xhXVFTEBQQEcPfdd5/RbdrrHjZFrshkMs7X15e7//77OYVCoVy/Z88eDgC3du1ao98zY8YMDgCXl5dncFx2djbn4uLC/fe//1WuUygUXP/+/bnw8HCuvr7etAOzghEjRnAeHh5Gv6u0tJQLCgriHn30UU4ulxsca66OxGSVbXEGWdVU7D9ntIsssf/UaUoy2VTsoVdlZ2dzALhXX31VY+yhQ4c4ANwHH3zQYDuTJ0822TfAMI4zyKqmolc5ow2oC13HKZPJdPpe3nrrLQ4Ad+DAAY31ADT0InNgsopoTFnVbEu76KKsrAwHDhzAU089BW9vb+X6SZMmwdPTE999953Bzx8+fBi3b9/G//3f/2ms/+9//4vKykrs3btXuW7gwIENPs+ncKamplpxFJqIRCK0atXKaMqgTCbDzp07MWDAAISHhzd4/+rVq/jwww/xwQcfQCw2PVHh+++/R1BQEB577DHluoCAAIwdOxY//vgjZDKZydvi6du3LyQSica66OhodOzYUe+5mzt3LmJiYnTO7AIUTXTx4kUsWrQIbm5uqKqqglwuN3mf7HGc5uDu7o6AgACLU0O3bduGqKgo9O3b1+C4PXv2oLy8HBMnTrToexjW05hyys/PD15eXrY9AB3YSk4BNGNdW1tr1vc7Uk4lJyejvr6+QYQl/792WmJkZKTZ+8Lz/fffQyQSaUQCuLq64tlnn0VycrJJUWbWYOrvrI9t27bBw8MDo0aNUq6LiopSRhvwCAQCjB49GjKZDNevX7dmlxlWwGRVQ1kVHx/foDRey5Yt0b9/f5N0P3vdw6bIlYsXL6KkpATjxo3TiJAaMWIEPD09TUqh5r/H2Pn78ccfUVdXp/HbCwQCvPDCC8jOzkZycrLR77KWyMhIVFVVGX2ebNu2DQUFBVi2bBmEQiEqKyuhUCgMfqa+vt6sNHyGfWH2X0NZ5Yx2kSX2H09Tk8mmYg+9qry8HAAQFBSkMTYkJAQALMqaYNgGplc1DRtQF7qOUyKR6PS9GHsmVFdX6yz9Yggmqxqfe8qRfuHCBdTX16Nnz54a6yUSCbp164YzZ84Y/Dz/vvbn4+PjIRQKjX4+Pz8fANXEsobKykoUFRXh2rVr+PDDD/Hrr79qlOHQxS+//IKSkhK9DtJZs2bhgQceaJAyY4wzZ86gR48eEAo1L6XevXujqqrKaF1zU+E4DgUFBTrP3fHjx7F582Zl8yZd/P777wAAqVSKnj17wsPDA+7u7hg/fjyKi4uNfn9jHac6ZWVlKCoqwuXLl/HGG2/g4sWLRn9nXZw5cwapqal48sknjY7dunUr3NzcNB42jMbF0XLKVthDTr311lvw9PSEq6srevXqhf3795u0L46UU7yCpv2wd3d3BwCcOnXKJt8N0HG2b99eQ/kG6DgB2CRtUxtLfmddFBYW4sCBAxg9erRJ5cVs9TxlWA6TVfpllTb5+fkmXauOuId59Mkqft2ZM2caOJBra2tRVFSErKws7Nq1C6tWrULr1q3Rrl07g9915swZeHh4IDY2VmM9f5z2+O2rq6tRVFSEjIwMbN68GV999RUSExONGmK///47vL29kZOTg5iYGHh6esLb2xsvvPCCTkP3ypUr8PDwgJeXF4KDg7FgwQKT68Yz7IOjZZUz2n9NxS4yZP+p05xksr31qrZt2yI8PBzvv/8+9uzZg+zsbBw/fhzPP/88oqKibFpai2EejpZVtuJesQEtvVcNPRM2bdoEDw8PuLm5IS4uzqRSeQCTVY7gnnKk5+XlAVDNYqgTEhKC3Nxco58XiUQIDAzUWC+RSNCyZUujn1+xYgVEIhGeeOIJM/dck1deeQUBAQFo164dXn31VTz66KNYv369wc9s3boVUqlU53fv3bsX+/fvxwcffGD2vuTl5ek9nwCMnhNT2bp1K3JycjBu3DiN9RzH4cUXX8S4ceMM1sm6evUqAGDs2LHo0KEDvv/+e8yZMwc7d+7EI488YrThVGMdpzpjx45FQEAAYmNj8f777+O5557DggULzN4O3zTUmHJZXFyMffv24ZFHHmmUGWqGbhwtp2yFLeWUUCjEQw89hJUrV+Knn37Chx9+iFu3buHhhx/WiK7QhyPlVExMDAA0aBZ85MgRAEBOTo5NvhtwjJyy5HfWxbfffov6+nqTjODi4mJ8/vnn6N+/v87jZTQOTFbp1qm0OXLkCJKTkxvoL7pwxD3MEx0dDYFA0EBWpaWlobCwENXV1bhz547Gez/88AMCAgIQERGBxx57DOHh4dizZ4/RzMa8vDxl41N17Hmca9asQUBAAKKiojBlyhT06dPHpCj7q1evor6+HqNGjcLQoUOxc+dOPPPMM9iwYQOmTp2qMbZt27Z488038c0332DLli1ISEjA22+/rTdbktE4OFpWOaP911TsIn32n65xzUUm21uvcnFxwc6dO+Hh4aFsgpiQkICKigocPXoUvr6+NjoShrk4WlbZinvFBrT0Xn3vvffg7e2Nhx9+WGN93759sWzZMuzevRuffPL/7L15fFT1vf//PLMvmYRAQsIWQGQXQUAR3HtRaN2wlartrWi9UHu1LrQuWEAFvVhXQKloW716K1erVvz+tOVKsVZbEQXBjYCgxrAlJIFk9v3z++NkJjOTmcwkmUkmyef5eMwjyTlnzpyTTN7z+bw/r/fr/SRarZYf//jHPPnkk2nPKWNV19Nrm40mw+PxAOrqeyImkym6v63nJ5acZfr8DRs28Ic//IHbb7+9040pb7nlFi6//HIOHz7Mn/70J0KhUJtlLna7nTfffJPvfe97rd5wfr+fW2+9leuvv54JEya0+1o8Hk/K32dkf2fZs2cPN9xwAzNnzmTBggVx+/77v/+bzz77jFdeeaXNc0RKbE899VT++Mc/AvCDH/wAi8XCkiVL2LJlS5sNZ7riPhN54IEH+OUvf8mBAwd47rnn8Pv9BIPBdp0jHA7z4osvcsopp7RSfiXyyiuv4Pf7pa1LN9OdcSqbZDNOVVRUtGqQ95Of/IQJEybwy1/+kgsvvLDNa+nOODV16lRmzJjBb37zG4YMGcJ5551HZWUlP//5z9Hr9Vn9e3RHnGrv3zkVGzZsoLS0lPPPP7/N48LhMD/+8Y9pbGzk8ccf7+hlS7KAjFWtY1UiR48e5Uc/+hEjR47k9ttvT3st3fE/HKGkpIQf/vCHPPfcc4wfP57LLruMQ4cO8Ytf/AK9Xk8gEGj1+ueddx6bN2+msbGRLVu28Mknn+ByudK+Vnfc51VXXcX06dOpq6vjjTfeoLa2NqPXcTqduN1urr/+etauXQvA97//ffx+P0899RQrVqyIjuv/8Ic/xD33Jz/5CYsWLeJ3v/sdt956K6effnrW70uSHjn/ax2resK8qK35Xyy9LSZ3xbiquLiYKVOmMH/+fE4//XT279/PqlWrmD9/Pps3b47en6RrkeOqnjUH7Mj/6n/913/xt7/9jd/+9ret7jVRyPDTn/6UadOmcdddd3HNNde0WUEnY1XX06cU6ZE3XzIvJK/Xm7a802w2p3xztPX89957j+uuu445c+Zw//33t/OqWzNu3Dhmz57N1VdfzRtvvIHT6WxTPfDqq6/i9XqTJkgfe+wx6uvruffeezt0LWazOeXvM7K/M9TU1HDhhRdSVFQU9X6KYLfbWbJkCbfddhvDhg1Le52gTqRiididvP/++2mfn8379Pv91NTUxD0SvQmnTJnC+eefz09/+lM2b97Mhx9+yDXXXNOu1/nHP/7BoUOHMkqOv/DCC/Tv37/V6qika+muOJVtshmnktG/f3+uvfZa9u7dy8GDB9s8tjvjFKj3NnnyZH76058ycuRILr74Yn74wx9yyimntPLu7AzZvs9QKNQqTiW+t9r7d07G119/HVWHpVOy/uIXv2DTpk38/ve/Z/Lkye26H0l2kbGq7Vjlcrm46KKLcDgcvP766xn9r+c6VqXjqaee4nvf+x6/+tWvGDVqFGeffTaTJk3i4osvBmh1D2VlZcyePZvLL7+cJ598kosuuojzzz8/WracilzcZ2KsSpw0Dh8+nNmzZ3PVVVfxwgsvcMIJJzB79uy0k8t048d0fu6//OUvgRYrDUnXI+d/rWNVvs2LEkk3roqlJ8XkfBhXNTU1cdZZZzFz5kxWrVrFpZdeyi9/+UteffVV/vnPf/Lss8+2654k2UOOq3rWHLC99/nSSy+xdOlSrrvuOn7+85+nPb/BYODGG2+ksbExrRWojFVdT59KpEdKGyJlM7EcOXKEwYMHp31+KBTi6NGjcdv9fj8NDQ1Jn//JJ59wySWXcNJJJ/HKK6+0q5Fnplx++eV89NFHKT2eXnjhBYqKirjooovitjc1NXHfffexcOFC7HY7VVVVVFVV4XQ6EUJQVVXV6l4TGTRoUMrfJ5D2d9oWTU1NfPe736WxsZFNmza1OtfDDz+M3+/niiuuiF57JJgeP36cqqqq6D985LmJzQoipU+J5cqJZPs+33//fQYNGhT3aKsJhMFg4JJLLuHPf/5zu1YUX3jhBTQaTauBciLV1dW89957zJ8/H71en/H5JdmnO+JUV9DRONUWkQW0dH6e3RmnAIYMGcI///lPvvzyS959910OHjzIgw8+yIEDBxgzZkyHXzuRbN/ngQMHWsWpdJPrdH/nZET8/9INoO+9915++9vf8sADD/CTn/wk4/NLcoOMValjld/v5/vf/z6ffvopr7/+OieddFJGr53LWJUJRUVFvP7663z77bf84x//oKqqiv/5n//hyJEjlJaWplV7Xn755TidTl5//fU2jxs0aBA1NTWtJluduc/EWPXSSy+lvdYDBw7w7rvvtnlcZ8ePmX5OSXKHnP+1jlX5Ni+KJZNxVSw9KSbnw7jq1Vdfpba2lksuuSRu+znnnENhYWErVayk65Djqp41B0ykrfvcvHkzV199NRdeeCHr16/P+JzddZ8yVqWnTyXSTzrpJHQ6Hdu3b4/b7vf72bVrF1OmTGnz+ZH9ic/fvn074XC41fO/+uor5s6dy8CBA/nLX/6SVeVhLJHEalNTU6t9R44c4e9//zs/+MEPWpV7HD9+HKfTyYMPPsjIkSOjj1dffRW3283IkSPjOv8mY8qUKXz88cetGlBt27YNi8XS4SSR1+vl4osv5ssvv+SNN95IajtTXV3N8ePHmThxYvTazzrrLEAtmxk5ciS7d+8G1CYb0NqPOOIXVVpa2qX3OXnyZDZv3hz3KC8vb/M5Ho8HIUS0g3E6It2vzz333LTB83//938RQkhblzygq+NUV9HRONUWX3/9NdD1/78RMolTsYwePZqzzjqL8vJydu/ezZEjR9osnW4vU6ZM4csvv8Rut8dt37ZtW3R/eygvL28Vp9KpwNv6O6diw4YNjBo1qk3bg3Xr1nHPPfdwyy23cMcdd2R8bknukLEqeawKh8NcffXVbNmyhQ0bNnDOOedk/NrZ/h/uKBUVFZx99tkMHz48qoTKJFZl+v8/ZcoU3G43lZWVcds7c5+JsWrOnDlZudbOjh8z/ZyS5A45/2sdq/JtXhShveOqnhaT82FcVVtbC9CqEloIQSgUareNqCR7yHFVz5kDJiPVfW7bto3LLruM6dOn86c//aldC6vtuU8Zq7oY0QtZsGCBGD58eNJ9c+fOFYMGDRJ2uz267fe//70AxF//+tfoNpfLJSorK0VdXV10m9vtFv379xcXXXRR3Dn//d//XVgsFtHQ0BDdduTIEXHCCSeIwYMHi2+++SYr91VbW9tqm9/vF1OnThVms1k4HI5W+x999FEBiC1btrTa53K5xGuvvdbqcd555wmTySRee+018cEHH0SPP3z4sKisrBR+vz+67cUXXxSAePnll6Pb6urqRL9+/cQVV1zRofsMBoPikksuETqdTrz55pspj9uxY0era3/qqacEIK655hrx2muvicbGRiGE+vcwGo3izDPPFKFQKHqOJUuWCEB8+OGHXX6fqUj2dz5+/LgYNmyYGDZsWNz2b7/9VlRWViY9z5///GcBiD/84Q9pX/Pkk08WFRUVIhwOt3ncOeecIyZOnJj2fJL05EOciuWhhx4SQKfjVbbjlBBCHD16tNW2gwcPiuLiYnHyySfHbc+3OJWMUCgkLrzwQmGxWMS3336b9Ji6ujoBiLvvvjvl/srKSuFyuaLbPvjgAwGIhx56KLrN6/WKE088UcyYMaNd15iO9vydk/1NInz88ccCEMuWLUv5Wi+++KLQaDTixz/+cdoYJYQQd999twDi/i8kHUfGqhbSxSohhPjP//xPAYinnnqqzdfvjv/hdHElGddff73QaDRx46S6urqk/4s33nhjq99PY2OjqKysjI7HhBDiwIEDQq/XixtuuCG6LRwOi7POOksMGTJEBIPBdt5ZapJ9fgghxMUXXywURRH79u2Lbkv2N4nEqB/96Edxz7/qqquETqcThw4dEkII0dTUJLxeb9wx4XBYXHHFFQIQO3bsaHUNMlZll3yIVT1h/he5znybF3VkXNXTY3IsXTWueuWVV5J+DmzcuFEA4oEHHmj1nAULFgir1drOO5KkIh9iVSz5PK7Kxzlge+5z9+7dYsCAAWLixIni2LFjKc+Z7D7tdrsYNWqUKCkpET6fL+76Zazq/ljV5xLpO3bsEEajUZxyyiniySefFL/+9a+FyWQSF1xwQdxxf//735P+4datWycAcfnll4vf/e534uqrrxaAuP/+++OOmzx5sgDE7bffLv7nf/4n7vHWW2+1ut5Mgte8efPEd77zHXHPPfeI3/3ud2LlypVi3LhxAhCPPPJI0udMmzZNDB48OG6QlI5Ub8Bk1xkMBsXpp58uCgoKxL333ivWrVsnJk6cKGw2m9izZ0+H7vPmm28WgLj44otb/e7+53/+p83nfvPNN62CSIQVK1YIQJx//vli3bp1YtGiRUJRFHHVVVd1y32mYurUqeKSSy4R999/v/jd734nli1bJoYOHSo0Gk3ch4AQamI71XrYD37wA2E0GuMmr8n47LPPBCDuvPPOtNcmE+nZIx/iVGNjo1i5cqVYuXKlmDt3rgDEL3/5S7Fy5Urx+OOPt7re7opT11xzjTjrrLPEPffcI55++mlx1113iQEDBgiDwSD+/ve/p73O7o5TN910k1i0aJH47W9/K9asWSNmzJghFEURzz//fKvzPv/882LlypXRyex5550X/RtVVVVFj4skYBLvf/78+UKn04nbbrtNPPXUU2LWrFlCp9OJf/zjH3HHpXp+prTn79zW7/SXv/ylAFr9HSJs27ZNGAwGUVpaKp555plWv+evvvqq1XNkciq7yFjVQrpY9dhjjwlAzJw5M2lccDqd0WO78n8407iyatUq8eMf/1isXbtW/Pa3vxUXXHCBAMR9993X6j7Hjh0r7rjjDvHUU0+Jhx9+WJx//vnRmBjLs88+KwDx7LPPxm2/7bbbBCAWLVokfve734kLL7xQAOKFF17I6PmZcvPNN4vp06eLpUuXiqefflo88MAD4tRTTxWA+MUvfhF3bKrf6U9/+lMBiB/+8Idi3bp1Yv78+QIQS5YsiR7z97//XZSXl4tbb71VrFu3Tjz88MPijDPOiN5jMmSsyi75EKt60vwv3+ZFHZn/9dSYnIyuGlf5fD4xceJEoSiKuOaaa8T69evFr371K2EymcSgQYOSxiOZSM8u+RCresq4Kh/ngJnep91uF8OGDRMajUY88MADreLP+++/Hz327rvvFpMnT46OVe69914xfPhwoSiK+OMf/xj3+jJW5Ues6nOJdCGEeO+998SsWbOEyWQSpaWl4oYbbohb9RMidXASQoinn35ajB07VhgMBjFq1Cjx2GOPtVLmACkf55xzTtyxP/jBD4TZbBbHjx9v877+93//V8yePVuUlZUJnU4niouLxezZs8Xrr7+e9Pg9e/YIQCxevLjN8ybSnkS6EEIcO3ZMXHfddWLAgAHCYrGIc845R3z00Uetnp/pfUaSw6kebdFWIj0cDovHH39cjBkzRuj1ejFs2DCxdOnSVitkXXWfqXjiiSfEmWeeKUpKSoROpxOlpaXi4osvFu+++26rY1Ml0puamoTJZBLf//73077enXfeKQDx6aefpj1WJtKzRz7Eqcj/S7JH4rV1Z5zasGGDOPvss0VpaanQ6XSipKREXHbZZUkVfvkYp5599lkxefJkYbVahc1mE//2b/8m3n777XafN3bAk2oQ5PF4xK9+9StRXl4ujEajOPXUU8WmTZtavc4vf/lLoShKyoqWdLTn75zqbxIKhcSQIUPE1KlTU75OJImW6pEsuSaTU9lFxiqVTGJV5L2e6hH7P9CV/8OZxpU33nhDnHbaacJmswmLxSJOP/108ac//anV+T766CMxf/58UVFRIYxGo7BarWLq1Kni0UcfFYFAIO7YVInwUCgk/uu//ksMHz5cGAwGMXHixFaTRSGEePzxxwWQ9HeQCW+99Za46KKLxODBg4Verxc2m02cccYZ4tlnn231Pkv1N/H7/eKee+4Rw4cPF3q9Xpx44oniscceizvm66+/FvPnzxcjRowQJpNJWCwWMW3aNLF+/fqUlTQyVmWXfIhVPWn+l2/zovbO/3pyTE5GV42rhFD/drfeeqsYM2aMMBqNoqSkRFx55ZXi66+/Tnq8TKRnl3yIVT1lXJWPc8BM77Ot3zEgFixYED32rbfeEueff74oLy8Xer1e9OvXT1xwwQVJlfoyVuVHrOq1ifRhw4aJurq6Diczu5KBAweKX/3qV919GTlH3mfPxW63i7q6OjFr1iyZSM8SMk7lJ33lPk899VRx+eWXd/dlZBWPxyPq6uqiSleZnMoOMlblJ73xfzgZ8+fPF6eeemp3X0ZWkbEqN8hYlZ/0lfvsjTHZ6XSKuro6ceWVV8pEehaRsSo/6Sv3KWNVdsh+C/E84cCBA5SWljJx4kQ+//zz7r6clHzxxRd4PJ5e3zxN3mfP5ic/+Qmvv/46ABMnTuzmq+k9yDiVX/SV+7Tb7XzyySc899xz3X0pWWX9+vXceuut3X0ZvRIZq/KL3vo/nIgQgnfeeYc//vGP3X0pWUXGqtwhY1V+0Vfus7fG5F//+tesWbMGAKvV2s1X07uQsSq/6Cv3KWNV9lCEEKJLXqkL2b17d7TreEFBQdIusBKJJHM+/fRTjh49Csj/qWwh45REkl0OHDjA3r17oz+fc8456PX6bryi3oGMVRJJdpGxKjfIWCWRZJcvv/yS6upqAHQ6Heeee273XlAvQcYqiSS7dEes6pWJdIlEIpFIJBKJRCKRSCQSiUQikUiyhaa7L0AikUgkEolEIpFIJBKJRCKRSCSSfEYm0iUSiUQikUgkEolEIpFIJBKJRCJpg17bbLQjBINBdu7cSVlZGRqNXGOQ9F7C4TC1tbWccsop6HQyDPQ0ZKyS9BVkrOq5yDgl6SvIONWzkbFK0leQsapnI2OVpK/QE2JVfl5VN7Fz505OO+207r4MiaTL+PDDDzn11FO7+zIk7UTGKklfQ8aqnoeMU5K+hoxTPRMZqyR9DRmreiYyVkn6Gvkcq2QiPYaysjJA/YMNGjSom69GIskdR44c4bTTTou+5yU9CxmrJH0FGat6LjJOSfoKMk71bGSskvQVZKzq2chYJekr9IRYJRPpMURKZAYNGsTQoUO7+Wokktwjy8J6JjJWSfoaMlb1PGSckvQ1ZJzqmchYJelryFjVM5GxStLXyDRWPfnkkzz55JNUVVUBMHHiRJYvX853v/vd3F1bzs4skUgkEolEIpFIJBKJRCKRSCQSSZYZOnQoDzzwADt27GD79u185zvf4dJLL+WLL77I2WtKRbpEIpFIJBKJRCKRSCQSiUQikUh6DBdffHHcz/fffz9PPvkkH3zwARMnTszJa0pFukQiScuTTz7JySefTGFhIYWFhcycOZO//vWv3X1ZEolEIpFIJBKJRCLJMnL+J5FIuhOHw4Hdbo8+fD5f2ueEQiFefPFFXC4XM2fOzNm1yUS6RCJJS3eUy0gk+Y4ICzxVHpyfOfFUeRBh0d2XJOmBrFu3jhEjRmAymZgxYwYffvhhm8e//PLLjBs3DpPJxKRJk/jLX/4St18IwfLlyxk0aBBms5nZs2ezb9++6P6qqiquu+46Ro4cidlsZtSoUdx99934/f64YxRFafX44IMPsnvzEolEIpFI8hI5/5NIJN3JhAkTKCoqij5WrVqV8tjPPvuMgoICjEYj119/Pa+99hoTJkzI2bVJaxeJpA8TWeWLYDQaMRqNrY7rjnIZiSSfcVW6qH+tHvceNyFvCK1Ji2WchZLLSrCOt3b35Ul6CC+99BKLFy9m/fr1zJgxg9WrVzNnzhz27t3LwIEDWx3//vvvc9VVV7Fq1SouuugiNmzYwLx58/j444856aSTAHjwwQdZu3Ytzz33HCNHjmTZsmXMmTOH3bt3YzKZ2LNnD+FwmKeeeooTTzyRzz//nIULF+JyuXj44YfjXu9vf/tbXIwfMGBAbn8hEolEIpGgihW81V5CjhBamxZThQlFo3T3ZfUK5PxPIpH0BHbv3s2QIUOiPyeLUxHGjh3Lrl27aGpq4pVXXmHBggX84x//yFkyXSrSJZI+THtW+SJ0VbmMRJKvuCpdHFx7EMdOB7oSHZaxFnQlOhw7HRxcexBXpau7L1HSQ3j00UdZuHAh1157LRMmTGD9+vVYLBaeeeaZpMevWbOGuXPncttttzF+/HhWrlzJ1KlTeeKJJwBVjb569WqWLl3KpZdeysknn8zzzz/P4cOH2bhxIwBz587l2Wef5YILLuCEE07gkksu4Ve/+hV//vOfW73egAEDKC8vjz70en3OfheSjiOrYyQSSW/CVemi+oFqqpZXUbWyiqrlVVQ/UC3HV1lCzv8kEklPwGazRa2lCgsL20ykGwwGTjzxRKZNm8aqVauYPHkya9asydm1SUW6RNKHac8q32effcbMmTPxer0UFBTkvFxGIslHRFhQ/1o9gfoAlgkWFEVVR+kKdWgnaHHvdlO/sR7LWItUTknaxO/3s2PHDpYsWRLdptFomD17Nlu3bk36nK1bt7J48eK4bXPmzIkmyb/55htqamqYPXt2dH9RUREzZsxg69atXHnllUnP29TURP/+/Vttv+SSS/B6vYwZM4bbb7+dSy65JOX9+Hy+OO9Ch8OR8lhJ9pDVMRKJpDcRESsE6gMYhxkxWo2EXCEcOx14D3gZetNQGds6iZz/SSTZQ1bP5CfhcDgjT/WOIhXpkm7hhRdg2DDYsSP5/oNrD7Jt9DbsH9mTHyDJCu1Z5YuUy2zbto2f//znLFiwgN27d3fh1Uok3c8vfujllUfd6Acbo0n0CIqiYBxqxF3pxlvt7aYrlPQU6uvrCYVClJWVxW0vKyujpqYm6XNqamraPD7ytT3n3L9/P48//jg/+9nPotsKCgp45JFHePnll3nzzTc588wzmTdvHv/v//2/lPezatWqOIWbnGhnDyHgu9+FuXPV7yNEEk72HXaObT6G61MX2n5aWR2T58gGfhKJyvbt6nzwuefUnxPFCoH6AIceP4TvoC/6c/3Gell100nk/E8iaR9PPw3Dh0PiWz9SPfPFZV+wc9ZO9t24T1bPdANLlizh3Xffpaqqis8++4wlS5bwzjvv8OMf/zhnrykT6ZJu4fXX4eBBeOut5PvrXq3Ds9/DF/O/IHAs0LUXJ0lKV5fLSCT5yNtvhLA3hGj0aZPu11q1hL1hQo5QF1+ZRCan2s+hQ4eYO3cu8+fPZ+HChdHtJSUlLF68mBkzZnDqqafywAMP8O///u889NBDKc+1ZMkSmpqaog850c4ex47Bpk3wf/8Hx4+r22ITToZyA4GaAJ69HprebcI83iwTTnmMbOAnkai8/bY6H3z6afVnb7UX9x43xmGqWMGzz0OoKUTD6w2EmkJSrNANyPmfRAIbN0J1NWze3LIt1urTd9hHyBmi8Z1GGv/VKMUMXczRo0e5+uqrGTt2LP/2b//GRx99xP/93/9x/vnn5+w1ZSJd0i243erXhobW+0RYEHKqSSjftz4qf1IpJ4J5SK7LZSSSfEMIOObT4kdLwJ48UR5yhdCYNGhtyRPtktzR05JTJSUlaLVaamtr47bX1tZSXl6e9Dnl5eVtHh/5msk5Dx8+zHnnncesWbN4OpLFaIMZM2awf//+lPuNRmOcws1ms6U9pyQzIslzgEh/uNiEE7Eq9U9cOLY6ZMIpj7n44ov53ve+x+jRoxkzZgz3338/BQUFfPDBB919aRJJlxKZD27fDl4vhBwh1aLK2jyGah5qCb+gfmM9GotGihW6GTn/k/RFvM1DqcjwOrF6JkLYFca5w4nviE+KGbqQP/zhD1RVVeHz+Th69Ch/+9vfcppEB5lIl3QTkYHTsWPx2yPlMd5vWyZ+x/5yjK9+9VUXXp0kke4ol5FI8g2vF45iohoLgUM+hIgfHAkh1PLj8RZMFaZuusq+S09LThkMBqZNm8aWLVui28LhMFu2bEnZyGvmzJlxxwNs3rw5evzIkSMpLy+PO8Zut7Nt27a4cx46dIhzzz2XadOm8eyzz6LRpB8O7tq1i0GDBrXrHiXZIVkiPS7hlJBTavxbI76DPplw6mIcDgd2uz36yCTZJBv4SfoyHo/61e9X7T61Ni1ak5aQS41bsUko37c+7P+0S7FCFyLnfxKJSiRWRRLpidUzItQcqxQI1AZw7XLh+sIlxQy9GNlsVNItJFOkxzaXiTRoMI024d3n5eDqg1inWBl0tZzEdweRcpkjR45QVFTEySefnPNyGYkk33C7QaDwT0pYYPHi3u3GONSI1qpO+nwHfRhKDJTMK5FNZrJMJEEVwWg0tunpGQqFePnll/M+ObV48WIWLFjA9OnTOe2001i9ejUul4trr70WgKuvvpohQ4awatUqAG6++WbOOeccHnnkES688EJefPFFtm/fHlWUK4rCLbfcwn333cfo0aMZOXIky5YtY/DgwcybNw9oSaIPHz6chx9+mLq6uuj1RFTrzz33HAaDgVNOOQWAP//5zzzzzDP8/ve/76pfjSSGZIn02IRTJNmkLdJiHm3Gud1Jw+sNFF9QLBNOXUhiX4C7776be+65J+mxsoGfRNIyHwT45z9h1m0mLOMsOHY60E7QQljdpyvREawP0vTPJrUKJ6wm2eVYK7fI+Z9EopKYSI+IGYzW5rlIc6wqnlOsihm+9WHX2gk2Brv+YiVdgkykS7qecBhXYxAwcOywF8IGBEpceUzTP5oAKJxZiMaswf2pm/037sc63krhqYXde/19kD/84Q/dfQkSSbcTGURVY6Vp9lDG+upx73HjP+RHY9Jgm2qjZF4J1vHW7r3QXkimCaqelpy64oorqKurY/ny5dTU1DBlyhQ2bdoUbRZaXV0dpxafNWsWGzZsYOnSpdx1112MHj2ajRs3ctJJJ0WPuf3223G5XCxatIjGxkbOPPNMNm3ahMmkVkls3ryZ/fv3s3//foYOHRp3PbFVFitXruTbb79Fp9Mxbtw4XnrpJS6//PJc/jokKWhsbPk+kkg3VcQknJqT5YpGof/c/gQbgni/8dK4pZHjm49j+g9Tq+bIkuyze/duhgwZEv05kwZ+TU1NvPLKKyxYsIB//OMfeR2vJJJsExlXAfzrX6DcoVByWQneA6pYIeRVlenGCiNhb5iwM0zD/2sAAdaJVkouk2OuXCLnfxKJSqK1S6yYQVeoiwoajEONlPyghLqX6vB+7WX3Fbs58bET6f/d/nIc1suQiXRJ11JZCa+9hvvQdUAZDfsa4IHn8J52Ce492pbymOZgpGgUBlw4AP8hP8GGIB/P/JiK2ysYvnw4WpNUWUkkkq4jVjnVVGSl4qcWvNVeQo4QWpsWU4VJqqNyRKYJqp6YnLrxxhu58cYbk+575513Wm2bP38+8+fPT3k+RVFYsWIFK1asSLr/mmuu4ZprrmnzmhYsWMCCBQvaPEbSdSRTpCuamIRTZXNw0kDIGcIyyULIHSJQG+DLRV9S96c6xjw9BvNIc9dffB/CZrNRWJiZ2CPSwA9g2rRpfPTRR6xZs4annnoql5cokeQVseOq999Xe9FYx1sZetNQ6l+rx7ndCYC/2o9xsBFvlTrmany3kcDxAN4DXobeNFQm0yUSSU5JVKTHiRkmtFjsKVoF81gzBacW4P7cjedLD59d+Bn9zu3HyP8aSeHphTKh3kuQHumSrqOyEtauhZ07cYfVyVxDoBB27iT09B8JHW1saS7TXB6jaBQ0Bg1lPylTS/lCUL2qmh2n7KDp/aZuuhFJT2HdunWMGDECk8nEjBkz+PDDD9s8/uWXX2bcuHGYTCYmTZrEX/7yl7j9QgiWL1/OoEGDMJvNzJ49m3379sUdc8kll1BRUYHJZGLQoEH85Cc/4fDhw3HHfPrpp5x11lmYTCaGDRvGgw8+mJ0bluSUWOWUy6XGJ/MIMwWTCjCPMKNoFPbdvI89/7GHkFv6EmeTSIIq8kiVSI8kp6ZNm8aqVauYPHkya9as6eKrlUiyS7JEOrQknMwnqGMqERQEG4IUnV7E5L9NZtTDo9CYNBz/23E+mvQRVSuq8NXIJnH5iGzgJ+mLxI6rGhpgb2UYqqqwBr+m4sowlvFqbNMN1FF6RSklPyhB0Sv4D/pp3NKI4yMHda/VyYZ+Eokkp0QU6UePqgt+ETGDvkSPe7c76pEecodw73ZTOK2QyW9PZthtw1CMCo3vNLJz1k62jdrG/sX7aXyvscVXXdIjkYl0SdcQDsNrr0F9PUyYgDtkAOCY14IYPwGt8yja2gOEnKqPVDSwxIjO+53bj9HrR6Mv0+Pe42bnGTv5eObHHPnDEYIO6T8lieell15i8eLF3H333Xz88cdMnjyZOXPmcPTo0aTHv//++1x11VVcd9117Ny5k3nz5jFv3jw+//zz6DEPPvgga9euZf369Wzbtg2r1cqcOXPwelsaiZx33nn86U9/Yu/evbz66qt89dVXcXYIdrudCy64gOHDh7Njxw4eeugh7rnnnqjHsSR/iVVOOZ2t9wedQQ6tPUTNH2r47KLPos2yJN2HTE5JegPJrF0iWMdbKflBCQDGQUZGrBhBxR0VFJxUwLBfDmP6p9MpOruIsCtM1d1VfDDsA7744Rccf/t4q4bJkq5BNvCTSFRiE+kA//z1X2H5cli5EuWeuxH7vgbU2KZoFCxjLAy6fhDGYUaEX+Dc4eTwbw/T8JeGJGeXSCSS7BCJVT5fyzgsImawnWJrSaQ3hbBNtTHkpiEUnV7EqAdHMePLGZRfU47GrMH7jZeDjx1k19m7+OeAf7Jr9i6+XvI1da/W4fnKQzgY7qY7lLQXae0i6Rqqq2HPHhg2DIGCy68m0oNhLY6ACduYIixHqnHsHYH21JI4RXo4HMa9141ltIX+5/en9AelfH3H19Q+X4v9Azv2D+zsu3kfpT8opf8F/el3Xj+Mg1P7Ukr6Bo8++igLFy6MNu1bv349b775Js888wx33nlnq+PXrFnD3Llzue222wDVH3jz5s088cQTrF+/HiEEq1evZunSpVx66aUAPP/885SVlbFx40auvPJKAG699dboOYcPH86dd97JvHnzCAQC6PV6XnjhBfx+P8888wwGg4GJEyeya9cuHn30URYtWpTrX4ukE8RO+JIl0oW/JSnV+PdGPv3ep0x6cxK6AvlR2xUsWbKE7373u1RUVOBwONiwYQPvvPMO//d//9fdlyaRdIpUivQozWMmbaEW84h4+xbLaAtT/j6Foy8e5dC6Q9jft1P3ch11L9dhKDdQfEEx/ef0p/j8YgylhtzdhCSKbOAnkahEBApjRvj4ssrIv3Za+I/vl4DVCi4X4UY3UIwm6AaKAND311N2TRn2rXYa/96I/5Cfzy/+nIKpBQz6j0GU/agMXZEcd0kkkuwROwesrYUiNRxhHW/FMtbCt/d/i/ALhv5yKP3O7Bdn9WmqMDHu2XGMXjeaY/93jPrX6ml4o4Hg8SCNWxpp3NIYPVbRK5hGmjCfaMY00oRxqBHjEPVhGGRAX6pH318vrUTzAPkpI+kaHA61JsZqJRDWEhItxRDHPGYKC6yUlO3DW3BGXHmM74iPpn82qQmqMFTdU4VlnIVhvxrGyPtHUvt8LUd+fwTPPg+1z9dS+7xqXGUea6bf2f0omFyAdbKVgkkFclDVh/D7/ezYsYMlS5ZEt2k0GmbPns3WrVuTPmfr1q0sXrw4btucOXPYuHEjAN988w01NTXMnj07ur+oqIgZM2awdevWaCI9lmPHjvHCCy8wa9Ys9Hp99HXOPvtsDIaWhMWcOXP4zW9+w/HjxykuLm51Hp/PF6eqdTgcGfwWJNkmVpHucrXeLwItiXRtoZamd5v4dO6nnPzXk9HZZPzJNTI5JemtpEukR2KPok8+sVI0CmU/KqPsR2U4P3Vy+KnD1P5PLf4af8vYSQHLWAsF0wqwTbdhm2bDOtGKvr8+F7fUp5EN/CQSlUhy6vyyz/iyajr/ckyCwn+oGwsLwaJ6n4tDtSDKodlbWNEoFJ1RhGGQgab3mvAf9uP82Mm+/9zH/lv3UzSriH7n9aPfef0oPK0QjUEW4Uskko4RDKqPCLW1MGZMy8+KRkEE1XGYeaQ5ZZJba9FSelkppZeVEg6GcX3uwrHdgeMj9eGudBP2hvF86cHzpSfpOQDQgH6AHv0APbpiHbp+OvVroQ5toRatTat+X6BFa9WiLdCisWrQWrRozC1fNWYNGpMGjVEjE/MdQM7sJV2DzQYmE7hcuA3xDWEa3GZGKC6sAz0MvXoA9R8aaXhdLdFzfeJCW6ClcGYhxgojIVcIx05HtLlMxe0VDP3lUOperePYX4/h3OHE9bkLz14Pnr3xAcg41IhplAnzKDPmUWZMw00Yhhiiq3xai2xe2luor68nFApRVlYWt72srIw9e/YkfU5NTU3S42tqaqL7I9tSHRPhjjvu4IknnsDtdnP66afzxhtvxL3OyJEjW50jsi9ZIn3VqlXce++9Ke9X0jWkU6SHA6osVNErTN48mU8u+AT7v+zsPGMnJ6w6gf7fkx3bc4lMTkl6K5km0jX69MmigpMLGLNuDCc+eiJN/2ri2P8d49j/HcP1iQv3HjfuPW6OvtBigaYv0WMea8Yy1oJppAlThQljhRFThQnDIANasxw7SSSSjuF2C0DhO8G3WMd09h0r4ajLykCrqlZQNGpsCx4PIBobUWLGyOFwmEB9gJJLSii7uoymd5s48vsjuHe7afx7I41/bwRAY9JgmWCh4OQCrJOsWE+yYjpBjWUywS6RSNIR4+AKtDQcjSWdoCERjU6DbYoN2xQb/EfzOcIC3yEfnn0ePPs8eL/14jvkUx8HfQRqAwQbgxCGQF2AQF2gM7cVh2JQ0Bibk+qJ3xvUr4peQaNP+F6voOjUn6Pfxz60Sb7XKqCFAd8bgGWMJWv30NXIRLqka6iogHHj1EajwwbH7TrmNsHxgzB1KtbvnIjlOwrf3vctIiAwjjDS7+yW8hhdoQ7tBC3u3W7qN9YjwoKG1xtw73ET8oYomFLAgEsHYCgz4D/ix/mJE9enLnwH1ADkO+ij6R/Jm5RqbVr0pXoMA5vLZkrUVT59/5bVPm2hFl1R84qfTV3x0xZo0Zg0MkEmiXLbbbdx3XXX8e2333Lvvfdy9dVX88Ybb3T4PbJkyZI4tfyhQ4eYMGFCti5XkiFprV1iBlGFpxUy+W+T+XTOp7g+c/HZRZ9hm25j+N3DGXDhABkvJBJJxsR6pDclGcJEF/F0mccVjVFD8XeKKf5OMaN+Mwr/UT+OHQ5VHbXDgfNjJ74DPgL1AQL1Aez/SuYpA9oiLcZBRgzlLWOnyCNWKaUv1qtjqObxk1Q/SSR9nMpKPEfKgWIGV3/AScZ9fO4bzb8+L+KyGWoiXYTVOKHVenFXejBOtKG1avEe8OLc4YxWLB9acwjLOAsTXp6AoijRRHrjO40E6gM4P3bi/Dhh4KYB4zAjphEmjIONGAYbWseyUlX1qTHLeZ5E0ldJl0gXYQHNRcntGYclomgUTMNMmIaZKP5Oa2EdqOO9QL2aRA8cCxA8HiTYGFS/2oOE7CFCjpD6vStE2BUm5Ayp33vChNwhwm71KzF27MIvCPnV53YVxqFGmUiXSNKi0cBll8GBA7h2fxu3q2H/MTilBObNA40GBaLlMZbRllaTLUVRMA41Yv/AjusLF2FfGOMwI0arqlh373ETqA8w9KahnHD/CQAEjgVwf+nG+5UXz9cePF958FX7oqt8YVeYkEMNHt6vE6JlRvcHWqsWjUUtl9FaY0pmzBq0Zm1L+UykhMbYstoXu/oXXfnTt3yNPKIrf7E/65KvAGosGjS6vqm0KCkpQavVUpvwSVdbW0t5eXnS55SXl7d5fORrbW0tgwYNijtmypQprV6/pKSEMWPGMH78eIYNG8YHH3zAzJkzU75O7GskYjQaMRpbfP/tSU1yJbkmnbVL2NeczNIqeKo82KbaOK3yNA48coBDTxzCsd3B5xd/jnmMmZJLShhw0QAKzyjss/+nEokkMzpr7ZIJhoEGBnx3AAO+OyC6LegM4vnSg3uvG8+XHrzVXnzVvujXsDdMqCmEu0lVsrcHbUGLGCH6iIyjIl/NScZSpuafI+MoU+txlMbQvK9ZRRVVUOkUmQyTSPKBykpYuxa3exUA5iIDZ4Q/URPpO81cdkIdlJZGE+klww8Smnwy7tog7ko33q+9KAYlZcXykP8cwpD/HIIICzxfeXB95sL1mQvnp071+d94CXvD+L714fs2fUNyxaC02Cf006Gz6VoWBgtirBMKWuaAWktCHDPFWylojOr3ikHGJYkkn0lsitwqkR5j7ZlJZWBn0Og1GAcZMQ7qfD/AcDBM2Bsm7Gn+6gsjfIKwr/n7gCDsDyP8IvpzdFvk+0AYEVS/b/U11Px9KObn5m2EVO/4noxMpEu6jvHj4aabcP/2XxDT++1Y2QS4aa66v5lIIl1bkLxkWGPR4Pnag2GQgcKZhdEBSKJi3TJWTcTr++spOr2IotOLWp1LCEHIHsJf6ydQF8B/VP0aaFBX+aKrfU3qKl+wqfl7p7rKB0CYaCI+QPbKbDrDhJcmMPCHA7v7MroFg8HAtGnT2LJlC/PmzQPUEtAtW7Zw4403Jn3OzJkz2bJlC7fcckt02+bNm5k5cyYAI0eOpLy8nC1btkQT53a7nW3btvHzn/885bWEw+p7JOJxPnPmTH79619Hm49GXmfs2LFJbV0k+UNbinRXpYvD6w8DakK9arnaz6HkshJG/WYUw341jAMPH+DQukN4vvRw4OEDHHj4ALp+OorOKcI2zRZ9GMpkwz+JRNJC2kR6sPOJ9GToCnTYptqwTbW1fk0hCDYF8df48R/x46/xR9XrkUesUipwPEDIHopOOEPOECFn1ymfIig6Jb4sObEkOXZ7zMN0golxfxjX5dcrkfQ6wmF47TWor8eDmkixlFg4w/0RT3E5/7JPgj3/DSUlhENqTDOPL6b/qkl4qn0cXHMQNGCbbourWNaM1+Dc7uTw+sMMvXkophEmFI2CZbQFy2gLpd8vjV6CEAJ/jR/vN16833rxH/HjO+zDf9ivzgcjis/6gJoA8gsCRwMEjuZmjqfoldaiqmQWCm0JqZq/L/5OMWU/Lkv/ohKJJCPSKdIjVYGQ/XFYLtHoNGgKNFDQ3VfSM5GJdEnXMn487ivHwhMtmxqmnQ/jW1bvRKilPCbkDSVtEuo/4ifkCmEaZmq1ih9RrLsr3XirvZhHmNu8JEVRVLuWIh2MafPQVoiwIORSE+jRUhlXS/lM5BHyhAh7m1f5vOrPsSt+YV/zap8/5mvzwK3Vql8gZrUv2LKdUMtkGjpXWtQbWLx4MQsWLGD69OmcdtpprF69GpfLxbXXXgvA1VdfzZAhQ1i1SlXD3HzzzZxzzjk88sgjXHjhhbz44ots376dp59+GlDfJ7fccgv33Xcfo0ePZuTIkSxbtozBgwdHk/Xbtm3jo48+4swzz6S4uJivvvqKZcuWMWrUqGhC/kc/+hH33nsv1113HXfccQeff/45a9as4bHHHuv6X5KkXaRSpLsqXRxcexDPfjXTrjFo0JXo4tRR1vFWRv1mFMN/PZxj/3eMhv+vgYa/NBBsCNLwekO0LwSAboAOy2gL5hPNmE80Y6wwxnVt1xZqpXpJIukjCBFv7ZIrRXp7URQFfT89+n56rOOs6Z/QTNgXJmhvESREH46YMZS7uRzZEz+WCnvD0TFUdEyVOI7yxaunEomMnQBCZJ7It9Znfo8SiaQNqqthzx4YNgxPQBWUmMcM4wzn53AYdngn4qlpwnzwIMI9CQDlrJkoOtUSKnQ8hGVcfMVyoC6Aa48L/2E/7i/ceA94sU21UXh6IYYyA1qbFlOFKfocRVGiqs6iWa1FVhGEEIScoXj7hKZgi3WCPSaGxc4HIxYKrpiYFYljzTEq7nUCglAgOwuLWotWJtIlkiySqEg/ejT+59ixRk9KpEs6h0ykS7octze+5OXY8fifY1f1/Ef8GAYa4pJGQgh8B31orVoMg5IrN7VWLf5D/pz7PCkaBZ1NLfHLB4RQvQJFUEAf7/91xRVXUFdXx/Lly6mpqWHKlCls2rQp2tizuroajablvTdr1iw2bNjA0qVLueuuuxg9ejQbN27kpJNOih5z++2343K5WLRoEY2NjZx55pls2rQJk6lZUWOx8Oc//5m7774bl8vFoEGDmDt3LkuXLo1asxQVFfHWW29xww03MG3aNEpKSli+fDmLFi3qwt+OpCMkU6SLsKD+tXoC9QGMw5vL7LSpq2N0hToGzh/IwPkDESGBfZsd+za76km8w4l7r5tgQxB7gx37B8ktfBSjgqG02cOzVN/SxyFSclzUXHLcXHoc7doe6dxu0eS89FAikWQHh0MVcEbobLPR7kZj1GAoNWAozX3ljRDNYgNfgiDB346S5OZHqgpJiUTSThwO8HoJmm0Ewur/laW8kCFnDWXQl/Uc8ZfwUd0Izj56FGFU/XOVUcOB5upfbwijtcXWIFAXwL7NTsgdQluoBUWdBx198Sg1z9dgPsGMYaAB8xhzysR6KhQlZp5Xkb1fgQg3C6Uii4Ex38fZKbQlpoqNYTGPZBVEEomk46S1dpFCxj5JfmT/JH0Kd4KNZkND/M+xwchQYsC9241xqBGtVUvIFcJ7wIvWrEVfosd/xI9xqBESYlbIFUJj0qC19a2Jj6KoXZAVrQziADfeeGNKK5d33nmn1bb58+czf/78lOdTFIUVK1awYsWKpPsnTZrE22+/nfa6Tj75ZN577720x0nyi9jYFUmke6u9uPe4MQ4zEmpSF+7iFE9tVMcoWoWiWUVxaqigM4hnvyfu4Tug9nLwH/ITbAwifCLaPLnDaFXVUtR/2NTiP9yqf0Os13Ci53CSkmNFl6TsOKZrO1riOrjbptnQGPI/ASiRdAexti6QPJHekWajfQFFUaJxSyKR5Ak2G5hMeJr80U1mXQBlYClnjKrllcoS/jXgYs6+YxTip3o46omqPLU2LVqTOh/UFeoQQuDa4yLkDqEv1SN8qveut8obbQAY9oURSuvEesR+zzq+66tNFI2C1qSFnm0RLJH0CdI2G40o0rXIiuE+hEykS7qcxCZ9x47F/xxbHjPkpiEce+MY7j1u/If8hH1hQt4QGqOGQH0A79deTKNMWMdb0Zeq5YERxbptqq3HNzGQSCT5Q6wiIRLHYtVRweNBdWNMzqa91TG6Ah22KTZsU5IrikLuUEsfh8jjeLMXceThaO7nECk7drWUHUedDEItfR26m1lHZ3WJOlUi6YlEEuk6HQSD6iJeKATaGJ1Ad1i7SCQSSYeoqIBx43Bv/Tq6yaRTx08nl9XySuVEviqYDLNORgQ+BFoWCU0VJizjLDh2OtBO0BJqChGoD0RtQANNAURQoNFqool1/2E/gYZAXGJdN6C1/V6+IcICb7WXkCOUsYJeIpFkH48rDGiwWUM4XFpqawWxKs6eVBUoyR4ykS7pctIq0mMS6QWTCig4qQBvtRfnJ07qXq5D0SuqZ/AQI03/asLzpYdAfYCiWUVozBp8B30YSgyUzCuRAw6JRJI1EhXpQsSro0SoOZkVUxGS7eoYrUWLeYQ5be+HZAjRXEIc6d3Q3Nch4jsc27U96jnsi+/YnrJre8z30e7swfj+Dcm6tougkMk/iaQNIv7oQ4dCVZX6vdMJRTG2vrlqNiqRSCRZR6OByy7D88UfATDr/CihILhcFByrBsBTNhI0mmhsiySoFI1CyWUleA94ce92ozFpVKsmoyBYF0Sj0yDCAl2RDkVREHpB8HgQTYEG4zAjwicIHguCAMsESyv7vXzBVemi/rV63HvchLwhtCZttyroJZI+S2Ul3g1fAJczXHuIz6nA5VJw7diDdZragFyOwfomMpEu6XIiyaiyMrU0JpUiXdEpanmMoioQjm44StgXxjLRom4vhH5n9cNV6cLzlYemfzVRMKUA21QbJfPkQEMikWQXj7tFgRAMgt8bjlNHKYZmS5fmyVhidUx3q4sURS0l1pq0UNxlLyuRSDpBRJFeXg6HD4Pfr9q7xCXSpSJdIpH0JMaPx33lT2EDmDV++PJLMJmwjCyDT8GlUwNc1LYqJrZZx1sZetNQ6l+rx7HdQditHmMcbERfqsf5mTN6fNgZJhwIYyhq7rdlAOFQhQE6Rdem/V53EWlgH6gPYBxmxGg1EnKF8l5BL5H0OiorYe1aPPvGAlBaHMTs9uMJGqh99AVOWPojGD9e2uv1UWQiXdLlRBLpw4apifRUHumxwSjWhzjWe0pfqqeopAjTCBOBugCDfzaYollFeaUqkEgkvYDKStyf6YDR0U2u+1dj/PF3KbmsAu8BL85Pm43TNRBsCsZVx7j3uqW6SCKRtJtIIr24GAoLob6+tU96e8uKu3tRTyKRSDyDRwFgGWCCZcvAZsP6bgW83jJXTLVIaB1vxTLWgqfKw8E1B/Hs82CbbiNkD6FUKurzDOpYTNEr0WbBwi/UPi5GNVa2134v18Q2sLdMsETnvKka2EskkhwRDsNrr0F9Pd7yCwAwG4KUFbioajRQeyTMCRs3wtixreKUHGP1DWQiXdLlRLyFhw2D7dvVSWI4rFb6QXL1QbIu7REURcE42EjYGVZL+WSgkkgk2SSiSDj2n3GbnTv30b9hH9abbmLoTRV8e9+3ON53EPaFCTYEo9UxgFQXSSSSDhGxdmkrkZ6ohmprEictAyQSST4QSZabC3QwaRIAlo/j98VWKSeiaBQsJ1gYcv0QDq49iLvSjWGIAX1/vdqMXacmyjX6ZosYDQTtQYyDjFFP9Wzb73WWVMIxSN/AXiKRZJHqatizB4YNw7NX7eNk0gUps7qoaiym1noCVP4DqqsRgQGAmruSY6y+g3TEl3Q5kcHR0KHq13AYmppa9idTH8T6ECcj3wZCEomklxCjSHAb+sXtcg6fqGa1Nm7EOtbMgIvUgZT5BDMjVoyg4o4KLGMtceoiXaEORaugK9RhmWAhUB+gfmM9IiwQYYGnyoPzMyeeKo/aGEsikfRpIor0fv3URDokUaTH+HO6Kl1UP1BN1fIqqlZWUbW8iuoHqnFVuqKWAY6dDnQlOixjLehK1KZ7B9cexFWZ0A1eImkn69atY8SIEZhMJmbMmMGHH37Y5vEvv/wy48aNw2QyMWnSJP7yl7/E7RdCsHz5cgYNGoTZbGb27Nns27cv7pgRI0agKErc44EHHsj6vUmyS6SBu8XSsi3yfUR0lYn3cMTqxXaKjdCxEBqjBkVR0Gg1FEwpwDDYQKA+gP+oH61FTWqhtNjvWcZbMFWYcnGL7SYiHNNak89ntVYtYW84bxT0EkmvxeEArxesVjwBdeHNrAsy0KoGp9pQibrf4YjGKUCOsfoQUpEu6XIiifTiYrBa1cFSQ4P6MyS3dkns0h67Sp9vPsTtpaddr0TSp4hVJLynj9vlChjUFcHKSlWREFInYrp+uqhSyFPlyUhddPzt4zg+dEgFg0QiiSPR2gXixQfQIkAINgZTVr94qj1oTVppGSDJGS+99BKLFy9m/fr1zJgxg9WrVzNnzhz27t3LwIEDWx3//vvvc9VVV7Fq1SouuugiNmzYwLx58/j444856aSTAHjwwQdZu3Ytzz33HCNHjmTZsmXMmTOH3bt3YzK1JD9XrFjBwoULoz/bbLbc37CkU0QS6eYYYbW1ebiTqEhPZ1sVsXqJzKf8tX7s2+x49nqiiXVFq2CdYEVXrGtlv5cvMS9WOKYrbJ2mkcIxiaSLsNnAZAKXC28wkkgPYNYFADjaqIcSE9hsCLsap8LesBxj9SFkIl3S5UQGRxYLDBigJtJjG44mU6Qndmk3DjWitaoDjVz7EOcy0S3LfySSPCdGkeAOxCfSnX6DOus7dKhZkaBaT8UuArZlSwWqushd6ebI00cQQkjrF4lEEkeitQuk9kh373Oj66dLOolzfOTAf8RPv3P7ScsASU549NFHWbhwIddeey0A69ev58033+SZZ57hzjvvbHX8mjVrmDt3LrfddhsAK1euZPPmzTzxxBOsX78eIQSrV69m6dKlXHrppQA8//zzlJWVsXHjRq688srouWw2G+Xl5V1wl5JsETsfjBD53u1WhVKZKNIjKBqlJXZNguLvFCdNrHu+9KAxaaL2e/k0vmqPcEwikeSQigoYNw527owq0k26IEUmHwC1RzVw8XioqCBcpaobRFBkZMtkqjBJEWUvQCbSJV1OpFzPaoX+/VXBZ2zD0VTqg9gu7e49bvyH/HEDIci+D3EuE92yK7tE0gOIUSR4mhUJWiVMSGjURLrLpe632dLaUiVTFwWdQfy1ftCA7VSbVDBIJJI4kinSUyXSgw1BrJOsSSdx+gF63F+440qQY8m3pnuSnoXf72fHjh0sWbIkuk2j0TB79my2bt2a9Dlbt25l8eLFcdvmzJnDxo0bAfjmm2+oqalh9uzZ0f1FRUXMmDGDrVu3xiXSH3jgAVauXElFRQU/+tGPuPXWW9Hpkk9zfT4fPp8v+rPD4Wj3/Uo6T1uKdJeLuFiVzCM9HW0l1vM1eZWpcCzfrlsiyTbdXrGv0cBll8GBA3g/URPlZq2fMm09ALWiFObNA40mOgYD2rRl8h/y4/zEydENR6WIshcgE+mSLidRkQ7xivRkzUYjJJbuRQIrQPUD1Vktp8llolt2ZZdIeghJFAklFje1rgJcfj0cPAhTp0JFBSJwBIiPXenURZ4v1ZmkZaxFqkQlEkkrMvFIj4ybwqFwyklcpLlesDGIfoC+1X5pGSDpDPX19YRCIcrKyuK2l5WVsWfPnqTPqampSXp8TU1NdH9kW6pjAG666SamTp1K//79ef/991myZAlHjhzh0UcfTfq6q1at4t57723fDUqyTrTZaMzQJk6RHptIz0CRno64xHoek044JpNtkt5O3lTsjx8PN92EZ6carExNtZTp1c+e2uLxMF4NWNHKGa3Spi1T2Bem7uU6wr6wFFH2AmQiXdLlxCbS+/dXv49TpCfxSI8l2UAoUx/iTJNRuU50y67sEkkPIUaR4ParH5mlFie1rgKc39bDqSWtFAmx1TTp1EW6Ah2UgbagbQWDVIlKJH2TWEV6UZH6fapmo1pD6uoXdKDrryPQEMB0gklaBkh6DbGq9pNPPhmDwcDPfvYzVq1ahdHY2lZtyZIlcc85dOgQEyZM6JJrlbTQVrNRnw+C3uwm0hPprOI1l4rZVMIxKa6S9HbyrmJ//Hg84wVsA/P5Z1I2U8B7UOtoCVyR+Z/WqsV3wNdKOBUOh3HtcRFuChP2h9UKZI0UUfZ02u7cIZHkgHSK9GT2COnIdpfz9iS6O4Lsyi6R9CDGj0f84iY8IXVCXopa1ucsHw033aQqFkhdTRNRF9lOsRFsCOL50kOwIYhtqo1BiwZhGGgg5Er+vy5VohJJ36Y9HumGwQZ8B3wIEW/fIoTAf0j1RzdVmHDvdhNsCiKCgmBTEPdud5xlgAgLPFUenJ858VR5EOGE86XZL+l7lJSUoNVqqa2tjdteW1ub0ru8vLy8zeMjX9tzToAZM2YQDAapqqpKut9oNFJYWBh9yMak3UMyRbo1Jj/mbmqJK+majbYXV6WL6geqqVpeRdXKKqqWV/Htf33Lsb8dyyiuJXt+9QPVuCpdWbvGiHCsYFIB5hFmmVxrB+vWrWPEiBGYTCZmzJjBhx9+2ObxL7/8MuPGjcNkMjFp0iT+8pe/xO0XQrB8+XIGDRqE2Wxm9uzZ7Nu3L7q/qqqK6667jpEjR2I2mxk1ahR33303fr8/7jyffvopZ511FiaTiWHDhvHggw9m76Z7AYlCRl2hDkWroCtUe78E6gPUb6zv8jGH16f+75lGlDNw8iAAYj+WImMwfakefYk+bozl+cZDw58b8Ozx4Kny4K3yYv+XnUBdIPr8zuaW5JhMrTQ79dRTsdlsDBw4kHnz5rF3796cvmbOEuldHcBi8fl8TJkyBUVR2LVrV7ZuSZIlEj3SIblHensS6bE+xMlobzIq14nujl6vDJQSSffgHTk++v3AaUMBcJ3+nWgSHdqOXdbxVirurGDEihEMXzacEStGUHFHBcXfKcYyzpIy+eU76MMy3iJVohJJHyUTa5dI7LFNt7WaxAWbgri+cKExaOh3bj9KLy+lYEpBq0W9ITcNwTremjZB1BUJJEnPw2AwMG3aNLZs2RLdFg6H2bJlCzNnzkz6nJkzZ8YdD7B58+bo8SNHjqS8vDzuGLvdzrZt21KeE2DXrl1oNBoGDhzYmVuS5JhkinRTzFDH7Qi3/JDFjEVE8erY6UBXosMy1oJQBEdfPMqX//klX932VZtxLdnzdSU6HDsdHFx7UMbCbuall15i8eLF3H333Xz88cdMnjyZOXPmcPTo0aTHv//++1x11VVcd9117Ny5k3nz5jFv3jw+//zz6DEPPvgga9euZf369Wzbtg2r1cqcOXPwetWk5549ewiHwzz11FN88cUXPPbYY6xfv5677roreg673c4FF1zA8OHD2bFjBw899BD33HMPTz/9dG5/IT2IXAsZO0psP4eI01hTEzT/+aNCKl2RLk445djuwLFV7cFhnWRF10+HtlCL74gP+7b4ZHpHc0tyTKbyj3/8gxtuuIEPPviAzZs3EwgEuOCCC3C5cvd7yIm1SySArV+/nhkzZrB69WrmzJnD3r17kw5qIgFs1apVXHTRRWzYsIF58+bx8ccfc9JJJwEtAey5555j5MiRLFu2jDlz5rB7925MpvgEw+23387gwYP55JNPcnF7kk6SVpGextolGdnucp6uQWBnVaIdud7u9AtbtWoVf/7zn9mzZw9ms5lZs2bxm9/8hrFjx+b0dSWSfCEyiAIoGdUPAKcrfmaXmEhPVvabzKpJNpaSSCTJ8HhUiwPITJFuHGak9PLSOG/dsC9MyBuCANS+UIvWpMU8xkzZ1WUYygxxlgHpSqoHXDiAhjcb8qfkOgO6vWFZH2Lx4sUsWLCA6dOnc9ppp7F69WpcLhfXXnstAFdffTVDhgxh1apVANx8882cc845PPLII1x44YW8+OKLbN++PZpYUhSFW265hfvuu4/Ro0dH53+DBw9m3rx5gNqwdNu2bZx33nnYbDa2bt3Krbfeyr//+79TXFzcLb8HSWYkU6Qrijo/dLvBZW8ZUyUm1TpKMuvOQF1AbcQcFiAg7AujG6BLGtdkj6v859FHH2XhwoXRuLN+/XrefPNNnnnmGe68885Wx69Zs4a5c+dy2223AbBy5Uo2b97ME088wfr16xFCsHr1apYuXcqll14KwPPPP09ZWRkbN27kyiuvZO7cucydOzd6zhNOOIG9e/fy5JNP8vDDDwPwwgsv4Pf7eeaZZzAYDEycOJFdu3bx6KOPsmjRoqT30tcaI0eEjEZra0su6D67y0jC3GxWx2J6PQQCcPSo2kormrvSK1FbJk+Vh4NrDoJGFTmE7CG833pRNAr6Ur0ad/a4KSopAqVjuaW8s8HJAQ6HA3vMoNdoNCa1bNu0aVPcz//93//NwIED2bFjB2effXZOri0nivTYADZhwgTWr1+PxWLhmWeeSXp8bAAbP348K1euZOrUqTzxxBMArQLYySefzPPPP8/hw4ejnd0j/PWvf+Wtt96KBi1J/pHWIz2Jz3A6Ij7EyZRYiSXLmRBJdOdKJdre602lfrB/bKfqvirqXq/LqUK9O1b5JJJ8IhK39HpVGQot1TURYmNXexQCbVm/RFSiEomk7xGxddFowGZL32xUo9fEVb8M/PFAtDYtOpsO0yhTdOzg/MRJ3at1KDolahmQrqTaX+fn4OMHCdTlV8l1W0ilVtdyxRVX8PDDD7N8+XKmTJnCrl272LRpU7RZaHV1NUeOHIkeP2vWLDZs2MDTTz/N5MmTeeWVV9i4cWNURAWqOOoXv/gFixYt4tRTT8XpdLJp06aoiMpoNPLiiy9yzjnnMHHiRO6//35uvfVWqfLsASRTpEOLvYvH0f4K5XQkKl6FELj2uAi5Q6otwwA9wWNBEGAeb8b7rZfD6w/j+doTXZTLR8VsrugOu4TO4Pf72bFjB7Nnz45u02g0zJ49m61btyZ9ztatW+OOB5gzZ070+G+++Yaampq4Y4qKipgxY0bKcwI0NTXRP5LoaH6ds88+G4PBEPc6e/fu5Xik9CyBVatWUVRUFH309l4O2XYYyBaRWGUyqYt9EV1wpMghKqRqFoEqGgVFoxA6HsIyTl1U0xZp0ZfoCTYFAXXxLVAfUHNAHcgt5asNTraZMGFC3P9AZCE+HU1NTQBx/4PZJuuK9EgAW7JkSXRbJgEstukLqIElkiRPF8CuvPJKQPXMW7hwIRs3bsSS+KmchL62ypcvpFOkp/IZTkemXc4T1UnGoUZ8B32t1Eq5Volmcr0RK5dD6w/h/daLbXpLcwrhEwSPBfF85cH1qYuCKQVYx1tzolDvjlU+iSSfiC3rKyhQv3c644+JKBKC9mC7FQJ9rbGUVIlKJOmJtXXRaNpQpAfjE06KRsFUYeLohqOEfWEsE9MrJ9MliHSFOpwfOyk+r7hHNEnvC0qtfOTGG2/kxhtvTLrvnXfeabVt/vz5zJ8/P+X5FEVhxYoVrFixIun+qVOn8sEHH3ToWiXdS+y4KpbIFN7jbH+FcjoSFa+hphCB+gC6Ip0a1wwgHAJfjY/AZwH8h/24v3DjPeDFNtWGvkyPr8aHtkgLAki4tN7WID4ipDr11FMJBoPcddddXHDBBezevRurNf/iZ319PaFQKLp4F6GsrIw9e/YkfU5NTU3S42tqaqL7I9tSHZPI/v37efzxx+OEnTU1NYwcObLVOSL7klXQ9LXGyNl2GMgWsYp0UO1dDh1q8UlPZu2ZGGsURcE6zqrGnLoAWpuWsD9MoCGA71D7c0vtWdTLhzFZR9m9ezdDhgyJ/pxMjZ5IOBzmlltu4YwzzohbmM82WU+kd1cAE0JwzTXXcP311zN9+vSUDWZiWbVqFffee29G9yXJDkLkxiM9QrpkVKI9ivAJQp4QWrMWxai0skvJJDHfGdq63si1OrY7sO+wo7VoET6BZZw6wrRvs6sKiv56RFCgMWjaPUHMtFwmka5Y5ZNI8onYEuTI3CExkR5ZBPR+40Vfpk9b9gtkZP3S2+hOmyqJpCcRm0iH9NYuseOm9kyyTBUm3HvcbSaIFJ2ivk4KIVg+JZCk/YJEkv8ks3aBmES6Q6CQXUV6onVn2BdGBEWLJZ9fIIIC9243IijQFmpBURcrj754lKAriPAJfAd8GAcbsYyzoC/VR8/fUxrE57NdQk/n0KFDzJ07l/nz57Nw4cJOnSvx72JP/PDvZXSFkLEjxCrSocUnPTGRHuumkMwmWF+qp3BGIa49LvyH/YTdYUKuEIWnFrYrtyTCIu2YLVdjsq4WQtlsNgojg98MueGGG/j888/55z//maOrUsmJR3p38Pjjj+NwOOKU8Onoa6t8+UAgAKHm/+dseqTHEulyHj1fpEHnJ07qXq4j7A1jrDCi8Who+lcTwWNBdP11FM0qQmNpnYzOtUo08XohXkmlKdCgtWrR2tTmFIHGABq9JlqGiIBgQxDFoGCZYGnXBDHx/X733Xdzzz33tPmcrlrlk0jyidgS5IgiPZW1S/B4kILpBW0mr46/fRzHh44+l0yWKlGJJHMiifSIUC0yl2hey46SiRoqkcgky/mJk6MbjuLY7sC91500QSSEIHhctTsINgUxDjK2mrTlUwKpryi1JJKeTDprF69TYKZ9Vp/pSFS8aoyalkVCAwSaAqo4SatBX6pH+ASEwFvlRYQFilZBMSmIoMB72EuwKUjhjEL12G5UzLaXjsz/IP+FVCUlJWi1WmojGc5mamtrKS8vT/qc8vLyNo+PfK2trWXQoEFxx0yZMiXueYcPH+a8885j1qxZreylUr1O7GtIMncY6EoSq2ci1i7RRHqw9RgslbpeX6qncEAhzu1OLKMtDL15KKYRmeeWYoWWqcZskJsxWU8QQt1444288cYbvPvuuwwdOjSnr5V1j/RcB7BUx7z99tts3boVo9GITqfjxBNPBGD69OksWLAg6esajUYKCwujD5vN1s67lbSXiPoA4j3SGxshqFpGdUqRnkjEH/ObZd/wzdJvaHqvicCxAGFvGPeXqtrAeIIRERR49nnQ2Vr7SkUS3QWTCqJeom0RTdx/5uyQb3mikkrfX6822mluThFsCuKt8qpeWIqC8AsUnaIOBtvpz7d7926ampqij0wWoiKrfC+++GK77ksi6cnEKqdSWrs0x65wOIzWmnzgorVq8R/1c+TpI616Hjh2Oji49mDUv7ezsSTfyLWfX0/z8pRI0hHxSE9MpDscEA63HJfozwmZeY2GfWHqXq7DsdOBYbgB8wlmREhNENm32QnUBQjUBWh6r4mmbU2IoMCx1UHje40E6gItr5+F3jHZJLKI0FYcDnvD3aael7FKIkmvSPe6Omb12RaJPaoEAn1/PYH6AP6jfjQ6DRqdBl2RqjUMNAUIB1XVur5Uj6HEgEavQWNW51yBpgCu3S6CjR3rydVddGT+1xOEVAaDgWnTprFly5botnA4zJYtW5g5c2bS58ycOTPueIDNmzdHjx85ciTl5eVxx9jtdrZt2xZ3zkOHDnHuuecybdo0nn32WTSa+DTbzJkzeffddwkEWj47N2/ezNixY2Vj5ARie70MXzacEStGUHFHRbclayPWLqkU6VFb4pgxWFv98DyVHszDzQy+fjDmE9LnliLE9sxLNWaD1mOybMwnU/XrS5y7dhdCCG688UZee+013n777VY2Srkg64r02AAW6ageCWCpPPMiAeyWW26JbksVwCIrf5EA9vOf/xyAtWvXct9990Wff/jwYebMmcNLL73EjBkzsn2bkg4SGTRptWrTvtgF7ePHobS0Y81GkxGrfIyU5ukG6PDX+AnUBRB+ga6/Do1GE9fwQddP12G1UjZW6hKVVJHmFP4jfvSlerQWLcG6ICIsVJWYXVWHRQZ97SnlaW+5TFeu8kkk+USsciqVtUs0dhk0caV8sQSdQfy1frWL+6m2lJYDIixoeL0hr1f920uuVaI9zctTIklHKkV6xCYvov+IbTYaIZ3XqPeAl5A3hKJXoh7q1glWNQntDhFoCmD/yE7YGyZ4LIi+vx7LeAvuSjeeLz0E6gNqJZ9Z060l18lIVlIdS3er52WskkhSK9KjifQceKRDa8VrRIikaBVMI0x49nsQIUGwLohGp0GERZyHuqJTK4ADdaqHuvdrL7piXbvtGbqTfLZL6CyLFy9mwYIFTJ8+ndNOO43Vq1fjcrm49tprAbj66qsZMmRItGnhzTffzDnnnMMjjzzChRdeyIsvvsj27dujinJFUbjlllu47777GD16NCNHjmTZsmUMHjw4muuKJNGHDx/Oww8/TF1dXfR6IqLPH/3oR9x7771cd9113HHHHXz++eesWbOGxx57rAt/Oz2HZBX73UWiIj2VtUviol9H1fXJ7FOAVpZ1iWM2124XBZML4jzX3Xvdnc5N9QS7vBtuuIENGzbw+uuvY7PZovbfRUVFmBNXa7NETqxduiOAVVRUxF1DQbNkcNSoUTLhl0fE+qMrCuh06sTQblftXUpLO2/tAq3/4QNHA4iQQNdPp1qkHPARcobQD1RLYBSDgnAIwj51MtoRX6lsWBYk87xKbE6hGBQEgqAjiOJU0FrUgBgps87FBFEIwS9+8Qtee+013nnnnS5Z5ZNI8olkivREa5dIMstQZsB3wJc0eeX5Uh2NWcZaUiaT7R/YcX3hIuwLtyuW5HsDz0ytJpLF3Uz8PKWXp6S3keiRbjarQoRQSB03RRLpycqK03mNao1aCICxwhhfctzs3+k75MPzpQeNVYNlrAXreGtUkemqdOH5ykPTv5oomFKQ1ZLrbMSx7mpYJn2HJZLMSaVIj6wl+dzZq1BOJNG601/rx77NjnOHk7BbHcsZBxvRl+pxfuaM81BXdArGciOWsRaCDUE8+zwMXjSY/hf079IxV1eO+XqSkOqKK66grq6O5cuXU1NTw5QpU9i0aVO01151dXWcWnzWrFls2LCBpUuXctdddzF69Gg2btwYp7q//fbbcblcLFq0iMbGRs4880w2bdqEqVmivHnzZvbv38/+/ftb/X6EUN/HRUVFvPXWW9xwww1MmzaNkpISli9fzqJFi3L9K5F0kmTNRiF9Ih3S9+9LJJUo03aarZUYKdFzPXFRD0iam7J/bMf5hZOBPxxIweSCtLGjJ9jlPfnkkwCce+65cdufffZZrrnmmpy8Zk4S6d0RwCQ9g8igKVZ9MGCAOiGMNByNlsd0YuCU+A8f64GnMaole8HjQULOELoiXZw9CrQ/GZ2Nlbp0nlexk1tFUQjZQ3GTW8jdBLE7VvkkknwimUd6KkV6wZQCgk3BpMkrXYEOykBbkDy2aCwaPF97MAwyUDizMONY0hN86zqjEu2In2e+e3lKJOlItHZRFFV8cPy4Om4aMkTd3hE1lGWChdoXalvZn+hL9RSVFOH9xsvxvx2ncEYhBZMKoov1kf2mESYCdQEG/2wwRbOKUo5tEpM9xqFGfAd9GTWE72gc666GZb3Vd1giyQXpFOm5TKRDguJ1EhR/pxhPlYeDaw7i2efBNt1GyB5CqWzxUI+rAlbUazOUGbCM61olZleN+XqqkOrGG29M6YTwzjvvtNo2f/585s+fn/J8iqKwYsUKVqxYkXT/Nddck1Gy7uSTT+a9995Le5wkv0jbbDTYtptCpur6tkSZjl0OQvYQxuHxi/ORMVnsol7x7GK81V4OrjmI91svtum2aHwSPkHwWBDPVx5cn7oomFKAdby1zdjRGSFUVxFZsOpKctZstKsDWCIjRozoll+opG2SJdL794dvvmlpOJoNj/TEf/hEexRNgQaNXkOwKYjGpokbGLU3GS3Cgqb3m2ja2oRhoAGF9q/UxQZOw3AD5uNmfId8rRrZRJpTFJ5aCGEI+8KqQj0ocjpB7I5VPokkn4hVTqWzdjEONVK+oDxp8sp2qo3a52tTJpP9R/yEXCFMw0wZr/r3lAaenVGJ7t69myGRrCEkVXnG0hO8PCWSdCRau0B8Ij1CR9RQ3mov9a/WJ41FiqKAUCeFxiGtG4sqioJxsJGwM4zWpk2ptEpM9gifIOQJoTVrUYxKXPIHkiunOhrHuqNhWXvjFMhYJem7pPNI97myY/WZKYpGwXKChSHXD+Hg2oO4K90YhhjQ99fjO+gDHeisumgVcLbFS5kqzFON+dqrMs0EKaSS9HWCQbUKEFor0o8eVb8m61PTXtKJMh0fOfDX+qMi0FgURYku6ik6hQMPHsCx3YF9hx2tRYvwCTVuAfZtdkLuEPr+erWxskGTdpyV73Z53UXOEukSSTJSKdKhRZGejUR64j98oj2KxqhBW6RF0Sr4vvah66/DPNpM0B5Mm4yOHehESgHtH9hx7nKi66fDUGVo1Tm5rZW6ZIGzLc8r83AzQ25SJ2pdNUGUi1KSvk4yRbrLpXoVR/LBsbZUqZJXAI4PHW0mk7VWLYZBhqTXkRhLcuVbl4uS4c6oRNvr59lTvDwB1q1bx0MPPURNTQ2TJ0/m8ccf57TTTkt5/Msvv8yyZcuoqqpi9OjR/OY3v+F73/tedL8Qgrvvvpvf/e53NDY2csYZZ/Dkk08yevRoAKqqqli5ciVvv/02NTU1DB48mH//93/n17/+NQZDy/vu008/5YYbbuCjjz6itLSUX/ziF9x+++25+0VIWpEqkQ4pEukpJnHJ1FDpFrYCDQF0/XUpzxlpVlr7x1oCtYFWqkiIT4xrPBqa/tVE8FgQXX+d6q9uUSdwnmoPWpM263GsvSXVnaU3+w5LJNkmlSI9Ilbwu1s38OsK2vJQt06woivWEWxqPV/szLgpU4V5qjFfR1SmmSCFVJK+TiROQYsifeBA9WtDg5poz4WbQiyKomAeY8Z/xI97rzuuxxa0zB+NQ43UvVJHoCGApkCD1qpVLY2P+Ag0BtDoNWoSvVQPAoINQRSD2vOhrXGWcagRfZke5ydOLOMt6PvpowKLXNrl5TsykS7pUiKewskS6VFFehY80pNNEKP2KM3enrpCHaaRJsK+MFqTluCxIBqThoIpBRTOKEQE1Q7HqdRVvqM+fF/71AA01oKuWJ1w+o744lTk0PZKXbLAmc7zKjIo6soJYk8kHxNUVVVVSUsjt27dyumnn57l34AkW8Q2molM8oJB8PshIjpMbJScqpSvzWRyqQGtWUvIndmqfy586zKZ0LV3whg5XgQFpT8oxb7NjmevJyeLgD3Jy/Oll15i8eLFrF+/nhkzZrB69WrmzJnD3r17GRgZqcfw/vvvc9VVV7Fq1SouuugiNmzYwLx58/j444+jatYHH3yQtWvX8txzz0V7ysyZM4fdu3djMpnYs2cP4XCYp556ihNPPJHPP/+chQsX4nK5ePjhhwG1ofsFF1zA7NmzWb9+PZ999hk//elP6devn/Tz7EISPdIBiorUr3GJ9DRlxclIt7BlqjBhGWNRF/cKWyfaXbtdhBrVZqXGingFeWJiHMD5iRMRFBhPMBKsV0uQi84salFaHfHT79x+WfffzKeGZYn0pFglkWSbxAZ+ESJzxIA3t9YubZHKQ92z16P2jkgYt3TGaqU9VYXJxnyBukCHVKaZIIVUkr5OskR6SQloNBAOQ11dbtwUEtEV6DCUGdAV6JKO2fQD1OR4oEEdd4Wa1PGZolFzYL5DPsLOMKYRasVz2BeO2hq3Nc6KxDbPlx68X3nx7PVgHGrEOsmal83muxKZSJd0KRFFujXm8zxiCZmoSO9MKV+qCaJiUND119FvUD9Kf1hKweSCOL/OyECp9vnaNtVVhqEGxAGBQCBCasJda9EStAfRlegI1gdx73FTVFKEoO2VulSBM5nnVWIjm8QJogir1yIT6/mboIrwt7/9jYkTJ0Z/HhBZUZLkJbHVNLHxy+lsSaRnqkhoy3JgwCUDaHi9IWP7k2z71mUyoQPaNWFMNsE0jzFTdnUZhjJD1mJVT/TyfPTRR1m4cGG0Gfv69et58803eeaZZ7jzzjtbHb9mzRrmzp3LbbfdBsDKlSvZvHkzTzzxBOvXr0cIwerVq1m6dCmXXnopAM8//zxlZWVs3LiRK6+8krlz5zJ37tzoOU844QT27t3Lk08+GY1TL7zwAn6/n2eeeQaDwcDEiRPZtWsXjz76qEykdyGJHumQXJHeUTVUOvsTUMc9iZM27wEvocYQ2n5aLBNTlCDHJMaDjUEC9QF0RTo0Gg26Qh2B+gDBpiC6fjr0A/S4v3BHFwQSyQf/zWzSE2OVRJJNQiFViACpE+n+HHukpyOZh3oyAUFn7PXaW1WYOOYTQuDa4+qQylQikaQn0mjUaFST56A2fS8pUa1damvBkqThe3vJxD7FMNBA2dVlOD50pLQOjSyyJdoaay1agnVBRFgghIjv90DycVZsbDONMmEYbMD1qQvvQS++Gh8FJxVQeHphzuzy8h2ZSJd0KW1Zu0QU6dkoj4HUE8TCaa3/4c0jzLgqXdS9Wpd0IJSorgo1hQgcC6AfoEcxKKpdjF6jBqn6IBqjBv9RP96DXkL2UJulf1pr6sAZ63mVrpFNT2g22JXka4IqwoABAygvL8/V7UuyTKxySq9XB1Q+n1plE4lh7VEktGU5oGiUjO1Psulbl8mE7vDThwl7wgQaMpswpppgOj9x4jvkY+hNQ7OmFu1pXp5+v58dO3awZMmS6DaNRsPs2bPZunVr0uds3bqVxYsXx22bM2cOGzduBOCbb76hpqaG2bNnR/cXFRUxY8YMtm7dypVXXpn0vE1NTXGNDrdu3crZZ58dZ/UyZ84cfvOb33D8+HGKYzO7zfh8Pnw+X/Rnh8PRxt1LMiFq7VIUhqpqcDgo1I4ECjL2SE9HOvuTZOMo8wlmCIBpVPJeDomJ8bAvjAiK6PUpBgXhEIR96ngvMpELNgZVVVUC7Y1j+V6p19NilUSSbWJVnqmsXbpTkZ6MZNUtbY2bNOM1OLc7Obz+MENvHqoqQRNiUXurChPHfKGmUHSRsj0qU4lEkhmJjUYjlJW1JNJHZMEjPdM+UsXfKU66qOf6whW3yJZoa6wYFASCoCOI4lTQWrTRfg/QepyVNLYVgn62nmBjEHelG/NYM8NuG4ZG1zV9LPINmUiXdCmpmo1CEo/0LHjiZeqPmVGDhxh1VeykUFEUdIU6wp4w1slWfId8BI4GVAXW0QBFs4raLP0zjzGj66/Dd8DX7gZ8EXpKs8GuIp8TVBEuueQSvF4vY8aM4fbbb+eSSy5JeT8yQdX9JMYuq1VNpMc2HG1vMiuV5UCmTfJEWCDCAm2xFvced1xXdmi/b126CZ1hiIHGdxoxDDLE+fOlUk7lyr89FT3Ny7O+vp5QKERZpGtRM2VlZezZsyfpc2pqapIeH0nERb62dUwi+/fv5/HHH49b7KupqWmlko2cs6amJmkifdWqVdx7771JX0PSMaLWLi89BfVbweulcM/1wHewf1UHlAKdLytuy/4k2Tgq2BTk2/u/RWtNnthOTIxrjBoUnYIICBSjgvCLaLJHfQLo+usINAQwnWDq0DgIeo6goKfFKokk20TGVNA6QRW1dvFkbz6YK1KNmwJ1gag9p/sLN94DXgqnF7aKRe2tKkxMtsXORzNVmUokksyJKNIT17hL1KI96utheA7dFFIJqRLHbMmEVbFWwb5DPhRFIWQPYRlrwTreGrUgTjbOShXbFEVBX6zHOtFKoCaA76Cvzy7SyUS6pEtpl0d6lhQImfhjpksgJaqrEieFEXWV1qql6MwifAd8BOoCVNxRQdGsojZL/5yfOEELilZpdwM+yF2zwZ5MPieoCgoKeOSRRzjjjDPQaDS8+uqrzJs3j40bN6ZMpssEVfeT6OVZUKDGrGSJ9M4MpCKkWwRM1q/BV60OgowVxozjRyzpJnQEIXgsiPUka0bKqVz4t7eF9PJsP4cOHWLu3LnMnz+fhQsXdupcS5YsiVuMPHToEBMmTOjsJfZZgsGW+FK8/yM4sQSsVgq/Vv+X7O/ugsqhiHHjsipASEbiOMpT5WmzEiYxMR5bYqwr0cUle4QQ+A+pQoWwJ9yhcRD0LEGBjFWSvk6sylOTMGSKzBGDvuyNqXJFsnFTrGe5tlALiprQtn9sx/mFk4E/HEjB5AJMFaZ2VxUmJtu0Nm3U8iXsC6dVmUokkvaRSpFeUKB+dbly76aQSR+pVIp2famewgGFOLc7KTy1EMJqlaBiUBBBkXKclW3r0N6ITKRLupT2eKR3ZSlf2gYPCeqqRN+pRHVVyBGiaFZRNImeSbLbMNSAvkTf7gZ8XZ2skmRGqgRVSUlJXLLp1FNP5fDhwzz00EMpE+kyQdX9RGJXbCIdWhYHIXsDqQipFgFbJYyGG/GWeXHucNL0QRPmo2YMAw3tbuCZbkIXbAoCoOuXfOiQOKiSg7C2KSkpQavVUltbG7e9trY2pe1TeXl5m8dHvtbW1jJo0KC4Y6ZMmRL3vMOHD3Peeecxa9Ysnn766YxeJ/Y1EjEajRiNLX9re6z3iKTdNB4LA+qYot/k4aBVx0aFNvVrk12BjRvhV7dHn9NVCad0JcjJEuOW0RYCdQF8X/vQ9ddhHm0maA9GJ3CDFw0G6NAEUgoKJJKeReKYKpZoQ3dfdsdUuSBx3JToWS58Qo3LiipE8HzlwfWpi4IpBVjHWxlw6YCM7Bxiq3Fik22uSnUQGjwWxDTKlFZlKpFI2keqpsix88CoCLQL3RQSSadoNw83M+SmIUD6cZYIC4JNQcKeMP7DfoxDjdHFuQhykU4m0iVdTCYe6dlUdWZKugRSsrLjWN+pcCCMaag6SHHvdrda1csk2R1sCDLkF0OiyoJMA6dMVrUmnxNUyZgxYwabN29OuV8mqLqfyEAq1toFOmft0hFSJYzMI80YhxtxbndiGW1J6cfZFm0lx8LhMN4DXjQWDWFXGPoTN6gSQuA77CPkCamDr2BYDsLSYDAYmDZtGlu2bGHevHmA+nvesmULN954Y9LnzJw5ky1btnDLLbdEt23evJmZM2cCMHLkSMrLy9myZUs0LtntdrZt28bPf/7z6HMOHTrEeeedx7Rp03j22WfRJEgCZ86cya9//WsCgQB6vT76OmPHjk1q6yLJPse/OAwMxWbwotO2KJgLjarNl13fHyorCX/9bXRfVyWcMilBTkyMh71hzCPNhAaplivBY0E0Jg0FUwoonFGICAq0Ni3Dbh8WbQCf6ThICgokkp5F4pgqlsi2UJ55pCcjcdwU61kOELQH0RXqcO92q8n1/npEUKAxaKLVMgMuHJCxnUOE2GSb8xMndS/XEfamV5lKJJL2EbF2SVSkR+aBLlf253+ZuCkkI1NFeyYVz65KF94qL65PXXKRLgUykS7pUjLySM/iql6mdERdpSvWYZlowbnDiUbRoDFqCB4LJl3Vc+9x46vxoS3SgqBVQimS7A67whRMKmjXtWez2WBvIZ8TVMnYtWtXXHJekn+kUqTHJdK7IHa1lTDSaDRYxloINgRBQ7snTqmSY94Dqto97AuDAse3HI8bVAXqArgqXXi+8qAr1FH9YDVhbxiNSSMHYWlYvHgxCxYsYPr06Zx22mmsXr0al8sVbZJ89dVXM2TIEFatWgXAzTffzDnnnMMjjzzChRdeyIsvvsj27dujC3aKonDLLbdw3333MXr0aEaOHMmyZcsYPHhwNBYeOnSIc889l+HDh/Pwww9TV1cXvZ7IguGPfvQj7r33Xq677jruuOMOPv/8c9asWcNjjz3Whb+dvs3xI+rsrZ/JG7c9mkgPWcHrRRxvCUJdmXDq6ITNONQYTZT7a/3Yt9mpfb62lad5e8ZCUlAgkfQsMlGkh/z575GeOG7SmDQIv0AYBcG6IBpzc6Vys0IdAcGGIIpBwTLBgnu3G9dnLobcOISG1xvaVY0TSbaZR5ixjLF0qJpHIpG0TTpFutOZeyFVe5qoZ6Joz7TiuejMIpr+1YTnSw+BerXvn8askYt0zchEuqRLacsj3ekEvz/79giZ0BF1VWSgMvCqgRTOKMRQZki5qufY7sC9143vgA/jYCOWcZZoQgk6l+zOtMtzX0tW5WuC6rnnnsNgMHDKKacA8Oc//5lnnnmG3//+9131q5F0gET1VDJrl65QpOc6YZSYHHNXuvF+7UUxKOoAyqKJG1RZxltwV7oJHgui669r/fME9Wc5CEvOFVdcQV1dHcuXL6empoYpU6awadOmaC+G6urquMW4WbNmsWHDBpYuXcpdd93F6NGj2bhxIyeddFL0mNtvvx2Xy8WiRYtobGzkzDPPZNOmTZia5TSbN29m//797N+/n6FDh8ZdT8S7uaioiLfeeosbbriBadOmUVJSwvLly1m0aFGufyWSZhqDapApNrjjtkcT6R49mEwIkxVQS/q6OuHU0QmbeYQZV6WLulfrsuJp3hlBQXsmqBKJJDukSk5BjCLdn/+KdIgfNzm2Owi71XmscbARw2ADzk+d6Ip0KIqiehM3W4HGVssMvGogFXdWdDgWddQOQiKRtE2qZqPJFOm5cFPoSBP1jijak1Y8F0K/s/pFxVJN/2qiYEqBXKRrRibSJV1KMo/0oiJQFBBCtXfpDo90yE45TCyxq3qG4QbMx834DvnwHvYSbApSOKNQ9c/rZLK7vV2e+wr5mqACWLlyJd9++y06nY5x48bx0ksvcfnll+fy1yHpJInqqbasXXJlS9VVnnWRCZmnysPBNQdBA7bptmgMiQyq3PvdNP2jCY1Bg3mMGcs4C+69alNm4wlGgvVBgvVB+p3ZD9ceOQhLxY033piyUuadd95ptW3+/PnMnz8/5fkURWHFihWsWLEi6f5rrrmGa665Ju11nXzyybz33ntpj5PkhuP6gQAUc1wdIDUvkkcT6S4tjB+PKBsCHAC6R7mZtQkbHfc076igINkE1TzGTOHpycUREokkO2Ri7RL253+z0QiJ4ybPPg+26TYCdQFEUKDoFYQQcY2WIV780FE7hwidfb5EImlNqmajcYn0HFUkd1UTdREWNL3fRNPWJgwDDSgxk0t9qZ6ikiJMI0wE6gIM/tngaA/Avo5MpEu6lGTWLlotFBerSfSGhphEejdMCDtTDhNLskmidYJVVZO6QwSaArh2uyiYXIDvUOeT3Z3p8tybyccE1YIFC1iwYEGbx0jyj0wU6bmspumIZ11nlJaKRlH7NRwPYRkXn8yKDKr0A/TYt9qxzbRhHWcl2BSMeoNqNBp0hToC9QGUSQpFZ8lBmETSHo43qcmj4oIA7N4NQ4eC1UphQPXBs4cKYN48RFj9P1J0Siu7p3wl257mHREUJJugequ9HH3xKDXP12A+QW3anE75JZFI2k8m1i5hf/43G41F0ShYTrAw5PohHFx7EHelG61NG+19FfaF0VpUNWkkT9UX7Tclkp5EKkV6rLVLZ+d/yeZrQJc0UY/ML5u2NuHc5UTXT4ehyhDnnqAoCsbBRsLOsFpd08brJd5LrJ1fbxMnyES6pEtJlkgH1Sf92DH1URTs3lK+bKzoJ5sk6kv1FM4oxLXHhf+wH+/XXnTFOgpPLcxKsluW9UkkuSMjj/QcVdN0xLPOvdfd7lLARNqykVEUBV2xDhRUZZUCYV84qrwC1KZXDkHYF0an6DIehEkkEmhsVL/2mzICTjkF9uyBQ4co9J0AgF3TD8YPIFylrvL1lGQTtM+iKtMFwfYICpKJHQJ1AdxfuBFhAUKNZ7oBuqwrvyQSSWaKdI3If4/0ZMTGIlelqrYIHgvKXjESSQ8kI0V6J+Z/qaxbbKfZct5EPXZ+qR+oR1esQ9Ep+I744twTILNFv8R7ET5ByBNCa9aiGJUOzUXzGZlIl3QpyTzSQfVJ379fVaQX5tgeoStINUmMKDmDDUE8+zwMXjSY/hf0z1pSSZb1SSS5IXHS15a1SzYTWh3xrAPSlgJmsuiWznc4mjRvtmPXGDUoOgUREChGBeEXUS9QkMoriaQ9HD+ufi0e2Q/uvBOqq8HhoNDVD/6fau0iRPfZ4XWGTD3N/bV+Gv6/howXBDMVFCSKHYQQuPa4og0BhU8QPBYEQbQhYDaUXxKJRKUtRXpknKWl58W2CLGxyPmJk7qX6wh7w6rAICj6vP2mRNJTSNXPIXYe2NFxWFvWLY5dDkL2EMbhuemJlTi/BPBWefEf8aMr0RGsD+Le46aopAhB+kW/xHvReNSeWpGeWZE+W71JnCAT6ZIuJZlHOqiKdFAV6cO7odlotmlrkqgoCopewVBmaGWZIJFI8pNUivRkzUazuQiYygIhlWcdQPUD1W2WAh5++jD6Ej2evZ42k1PpfIeD9iDm0WaCTUEMQw1oi7ToS/Qtg7AYL1CpvJJI2kc0kV4MaDQwYgQARc2Ld6GQOsHrTju8jpKJp7lxqJG6V+oINLTPGzQTQUGi2CHUFIraUimKAgbiq2myoPySSCQttKVINxhU209dqOcm0qElFplHmLGMsUj7TYmkBxKxdklUpMfOA0W4/eOwdL1iHB858Nf6CTlD0Z4KsXRWnJRsfmkdZyXUFCJYH0Rj1OA/6sd70EvIHmpz0S9ZUt75iTOuZ5Znn4eiM4uyakvT3chEeg+hM163+UQqa5ciNf+D3Z67hg1dSUcbX0kkkvxDiMwU6bnwSE9nr5Jol+Kp8rRZCqixaDj25jFMJ6oxqq3kVDrfYWOpkf7X9KfhzYbofstoC4G6AL6vfej669REuz0olVcSSTuJJNL79YvfbrW2NGi326Ggm+3wOkK62KIfoAcBgYbceIMmih0SbakSq2k6q/ySSCTxtKVIVxR1rKV19PwK5QjSflOS7/SWXFO2SadId7lA6Nofq9L1ijGPMeM/4se9143tVFvWc0nJ5pexNsSBowGCjUECR1UL0bYW/RLvJdiYvGdWsCmItkiL1qZVm5u+39Sje2bJRHoPIJV3Uk/0F0qVSC8sVL/a7T2zTDmRjjS+kkgk+YnP1/J9mx7pOVgEzNQCIaJIaCvxLoTAV+0j5AlhrDBGz9dWcioT32HzKHN0f9gbxjzSTGiQ+lkVPBaUyiuJpANEPNKLi+O3K4o6ZmpqUsdM1jyzw8uGp7ntVBu1z9fmxBtUhAUiLNAWa3HvcWObbouzpcJAXDUNSFsqiSTbpEpORbBYQOdoFif0YGFVLNJ+U5Kv9KZcU7ZJ2mw0HKbAXgMMxtkUJFzYfiFVul4xugIdhjIDugJdTnJJqeaXkYpn3wEfgboAFXdUpE12J95Lqp5Zvhofgc8C0SR99W+qKZpZ1GPfZzKRnue05Z3UE/2F+koiHdrX+EoikeQvkbgFqa1dhBBRr/Bsxq72Vre0lXgPNYXw1/jRFqjHJJJKIZBOSZVsf2/u0i6RdAVx1i4JRBLpTU1QFsofO7z2TsZTxRbXF66Mm5F29Pp8R334vvbhq/ZRcEoB+v56fAd9oAOdVYdlnAUUWUUokeSCtqxdQFV79mSPdImkp9Dbck3ZplWz0cpKeO01rNvswAO4GrwI33HA1K5YlYlQyjDQQNnVZTg+dGQ9l9TW/BLU5HjRrKKMFOOJ95KsZ5YICty73YigQGPUoOunwzDQ0KPfZzKRnsek807qaf5CQqRuNpo0kd4LFAiylE8i6flEBlE6HejV5uWtrF0icQuyO+lrb3VLWwOjkDdEyBHCPNoc57cXqAvElfElUwikU1Il2y+VVxJJx0mXSIfmMZMhP5JNHZ2MJ4sd7a3E6dD1DTfiLfPi3OHEvs2OYaBB7WGjVbBOsKIr1hFskrZUEkkuaMvaBZoV6TKRLpHklN6Wa8oFcYr0ykpYuxbq67GWTgLAFTIhvAHAhHLgW5h5ckbnzVQoVfydYoq/U5z1XFI23RMS7yWxZ1agKaAm0LWaaCNT4yAjxqFGDBh67PssP+pAJUlJ550UW9raEwgE1OZY0LrZqM2mfnU4WuwR8qVMubNEJokFkwowjzD3qAAhkUiST/gSrV1iE+nZjl2R6hbbKTaCDUE8X3oINgSxTbUx5KYhccmpyMBIX6LHvdtNsCmICAo1IVTtQ2tRB2A0h6FAXQD7Njv+I34UnRKnEDi49iCuSleKq5JIJLkkYu2S6JEO+Sc+SJyM6wp1KFoFXaEOywQLgfoA9Rvrow250hGZlPkO+NRqn9jXap5gWsZbMlaIp7o+80gzA74/APOJZmzTbIx+YjQDrxoI0GaclUgknSOdIl0m0iWS3NPbck25IKpIN4Thtdegvh4mTKBggFox5w/pCGsMACjv/R3C4YzO29Z8zb3bHZfIzlUuqT3zy/bcS8gewjLagqJT8H3tAwGKVu07E6wPorVoo1V/Pfl9JhXpeUw676Se1vwo1h6hLUV6Lhr2SSQSSUdJNuFLtHaJxC3ITexqT3VLKlupojOKsIyx4DvUkpxy7XERcod6lUJAIunphMOpPdIhIZFu7f5kU3sm45lUqrRHKZWJJ3tb16fRaLCMtRBsCGIeY6Z4dvaVXxKJJJ50ivQ4a5deUKGcDNncUdLd9LZcUy6I9nNwN8CePTBsGCgKVr0/ekw4pAqoNN9+BdXVMGJERufOBxvgbLknJN5LbM8s4Rd49nvQGDUYBxmxjLOgL9VHn9tT32cykZ7H5KK0tTuJDJq02hZ7BADCYQq99cBA7Ee9vcYjXSKR9A6STfi6ytollvY0qko1MHLvdXNw7UHcu91obVoCRwPJFQJ0rqGfRCLpOA5Hi6gpbSK9pPur+HIxGc9kgpmpJ3t7rk82BJRIck8mivRIIr23VCjHIps7SvKB3pZrygVRaxfc6g/NE0CDNoRWCRMSGkS4ualmwKMO4NpBPtgAZ2vck6pnlv0DO9W/qcYw0IBxqDFaFR2hp77Pet8nUy8i26Wt3U2sP3pUEFRZCQ88QOGf/xsA+1dHEcftQO9VIEgkkp5FW4p0p1Pt/xBNpGtppXjsLmJLAU0VJrzVXkRQUPqDUgqmFBCoUz3RRVBgHGSkcEZhK4VA2BvucQoBiaSnE1GjG40xDa5iyLcqvtjJeDI6OkmyjrdScWcFI1aMYPiy4YxYMYKKOyqiSfSDaw/i2OlAV6LDMtaCrkSX1JYqV9cnac26desYMWIEJpOJGTNm8OGHH7Z5/Msvv8y4ceMwmUxMmjSJv/zlL3H7hRAsX76cQYMGYTabmT17Nvv27Ut6Lp/Px5QpU1AUhV27dmXrliQ5IDOP9O6PbbmgPbFLIsklbeWawuEw7r1udMU6CJOxNVtvI2rtUmRSB2TNCS1FgQKDqkoXoeZEusnQ4lfcDnqTDXDivWh0GrVp6cwigvYggp6f04wgE+l5THu8k3oCkUFT1B890rBh504KS1WVkCNkRfjViY5yoKrrL1IikUgSaMsjPRQCv78lkZ6PyilXpYvqB6qpWl5F1coqap+vBQEDrxhIwZQCCk8rpOjMorgkOsjkkkTSXbTVaBRSeKR3Y7Ipl8KPZBPM9nqy9zZhSr7y0ksvsXjxYu6++24+/vhjJk+ezJw5czh69GjS499//32uuuoqrrvuOnbu3Mm8efOYN28en3/+efSYBx98kLVr17J+/Xq2bduG1Wplzpw5eL2tvVRvv/12Bg8enLP7k2SPdIp0qzXGI70XCauy3U9CIukMqXJNnm88NPy5Ae9+L+69bqruqaL6geo+ucgTVaRXlMK4cXDggKqgAqyRRHpEkT7mBKio6JbrzGd6W04zQv7N+CVxZKsJQD4QSUZZLKg1yzENG2zFajmRPWBGKGoyR/PO3zJu2CCRSHomIizwVHlwfubEU+XJy8lDsglfbMNkp7OlSXK+TfhSKZ+cnzhp/Gcj5lHmXqcQkEh6Om35o0P+NRvt6klSexuk9dZJXL7x6KOPsnDhQq699lomTJjA+vXrsVgsPPPMM0mPX7NmDXPnzuW2225j/PjxrFy5kqlTp/LEE08A6ufQ6tWrWbp0KZdeeiknn3wyzz//PIcPH2bjxo1x5/rrX//KW2+9xcMPP5z2On0+H3a7PfpwtLMUvyvpCWOkjpCJIl3bC5uNyuaOknwjMdfk2O7AsVWNiYUzC7GdauvTFRNRRbpFA5ddBiUlsHs3NDVRoPepOyOJ9AvngKZvpVcz/YzKdU7z3Xff5eKLL2bw4MEoitJqjJALpEd6DyAfvJOyQVwivbo6rmFDoVENRHafkbBWDUDKN/vb1bBBIpH0LHqKR2SyCZ9Op9ou+HxqIt2UB/YKiSQqnyKTNl2hDu0EreqTPlSLfoA+bUM/iUTSdUQU6f36Jd8fl0gP5keyqSubZnXEkz0fmnr1Zvx+Pzt27GDJkiXRbRqNhtmzZ7N169akz9m6dSuLFy+O2zZnzpzoBPibb76hpqaG2bNnR/cXFRUxY8YMtm7dypVXXglAbW0tCxcuZOPGjVhSSZxjWLVqFffee297b7HL6SljpI4QbeDXxxLpneknIZuTSnJFJNfkqfJwcM1B0IBtui36/oqdN9RvrMcy1pL2vZfu/dpT3s9xsWr8eLjpJlUMumcP1rADDSVETL81E8d223V2B+39jMplTtPlcjF58mR++tOf8v3vf7/T58sEmUjvIfSG5kexHuk4HHENGyKJ9EBYi1A63rBBIpH0DCJK6UB9AOMwI0arkZArhGOnA+8BL0NvGpo3E8VUJcgFBWoi3eWCAaH8m/BlonwKNgQpu7oMx4cOmVySSPKEjli75IOtVFcJPzraIK23CFPykfr6ekKhEGVlZXHby8rK2LNnT9Ln1NTUJD2+pqYmuj+yLdUxQgiuueYarr/+eqZPn05VVVXaa12yZElcAv/QoUNMmDAh7fO6kp40RuoI7bF2yYfYli06Grt686KKJD9QNAqKRiF0PIRlXOtEeWLFRFt5qXTv1570fo5Yu0T71YwfD2PHQnU11s9L0e5sUWDnW1VyLunoZ1R7c5oOhwO73R792Wg0YjS2Xoj87ne/y3e/+92O3UwH6T2fTJK8J84j3WaLa9gQadYAMT5TJn2HGjZIsk93lMtIei89zSMyVQlyxN7F6cwPn+JEIsonrTW5x3mkmaihzJCyoZ9EIul6ItYuqRTpRUXq13xpNhpLVzTN6ozneW9q6iWBxx9/HIfDEaeET4fRaKSwsDD6sOXZXKOnjZE6Qruajfai5FRHYld3NieV87++RabzhmQVExHSvV/r36jvUc12k1bPaDQwYgQFZVa0tNgQ58s4LNd05WfUhAkTKCoqij5WrVqVhTvIDjKRLuky4qxdKiriGjZoNYICgw8FAaI5kT7uRNmwIU+IlMusW7euuy9F0gvoaR6RbSnSQV0PzCdVaIRY5VMyYpVPMrkkkXQj4TBUVcFnn0FVFcePqfGkp3ikdzXS8zz/KCkpQavVUltbG7e9traW8vLypM8pLy9v8/jI17aOefvtt9m6dStGoxGdTseJJ54IwPTp01mwYEHnb6wb6GljpI6QTpHeW61d2hu7untRRc7/ei4d6a/QnnlDqtds6/3qr/Nz8PGDBOra/37ujn4RQsQ0G02y6BdbOQO9K1a1RVd+Ru3evZumpqbooz2L5rlGWrtIuoy4RLqmuWHDgQNqw4ahQ7EZfHj9+ujxysXf63MNG7qafC6XkfQMOuJx1xmPyO4glXIqkkh3OiGszy9VKLQonxw7HWgnaOMGOxHlk22qTTYTlUi6k8rKqN8mXi+YTDTuuwmYnjaR3tSUn9UwXYH0PM8vDAYD06ZNY8uWLcybNw+AcDjMli1buPHGG5M+Z+bMmWzZsoVbbrklum3z5s3MnDkTgJEjR1JeXs6WLVuYMmUKAHa7nW3btvHzn/8cgLVr13LfffdFn3/48GHmzJnDSy+9xIwZM7J/o11ATxsjtZdQSLXFg9SKdKsVVVxF74tt7Yld7UlY5cICVs7/eiaZWKckm791dt6Q7v2qK9Th/NhJ8XnF7Xo/d5cVTCCg6hwgxtolhlaJ9D4iaOjKzyibzUZhZNCbZ8hEuqTLiPNIh1YNGwoVB8dpCYaak8Z1/UX2MRI9Ie+++27uueee7rkYSY+jowObjnpEdheplFNx1i4F+TfhiyifvAe8faaZ6LvvvstDDz3Ejh07OHLkCK+99lo0qSOR5B2VlbB2LdTXq83XrVZwuTj+D3X1rp+vFihr9bTCgjCgwX48iDh6DMiv2NNV9GTP894YqxYvXsyCBQuYPn06p512GqtXr8blcnHttdcCcPXVVzNkyJBoafbNN9/MOeecwyOPPMKFF17Iiy++yPbt23n66acBNbFyyy23cN999zF69GhGjhzJsmXLGDx4cPR3VZFQuVrQvMI9atQohg4d2kV3nl162hipvXhjRIptKdL9vTSRDpnHrlwlrDIVUkl6Hpl4VwMp52+ZzhuSJeLTvV8VnaIu/qcIXcnez93ZLyI2ViVb9CsoaKmcQUurxYHeSm//jMoUmUiXdBlxHukRYho2FO4sRvt53/OZ6k52797NkCFDoj/LQZQkUzozsOlpSumk/ngkt3bJt7jV11Sb3dG1XSLpEOGwKiSor4cJEyASBwsLOW5QbSuKv9oO4e/GV+dVVlL4P38DfkFTE4Q3/Q2YhsZlb/USfYH2Nq7KF3pjrLriiiuoq6tj+fLl1NTUMGXKFDZt2hRtFlpdXY0m5r08a9YsNmzYwNKlS7nrrrsYPXo0Gzdu5KSTTooec/vtt+NyuVi0aBGNjY2ceeaZbNq0CVMyeWAvoaeNkdpLZD4IbSvSw/Ru26pMYleuElZSSNU7SbRWicQOXaEO7QQt7t1uDj99mLAnTKAh9fwt3bwhlZDKdpqtzferCAp1npRi3Sfx/ZzJ/dRvrMcytnVz1GwQmf8BJEuR9NamyOno7Z9RmSIT6ZIuI87aJZbmhg2FZaD7vKXpaG8dOOUT+VwuI8lfOjuwyZVSuiM2M5nQrmajeRi3erJqM4K0oZL0OqqrVTuXYcNakujNNPrUyUe/hq/U40aMUHc0K9j7HXECEBQ6/Aa186iy+zOoLFQFCpKs0Z7PlfaoPHtrrLrxxhtTWrm88847rbbNnz+f+fPnpzyfoiisWLGCFStWZPT6I0aMaNXEsafR26vJIskpgyG1g6fFAu4+mKBKJFcJKymk6p2ks1YxDDHQ+E4jhkEGbKfaUs7fKu6ooOLOiqSffW0JqTzVHnT9dfgO+Fq9X8PhML4jPozlRnyHfRiGGOJiWLL3c3dbG0VilcnUapgGRBTp+WftmWvy8TPK6XSyf//+6M/ffPMNu3bton///q0q17KFTKRLuoyUifRmCgtjymM09NgBokTS28nGwCbbSulc+uelazbqdKoqC8jfCV9PVW1GkOopSa/D4VDrhq2t49Nxj/q/Wsxx9TiIU7AXnDwB3f8XIhjW4kYNRIrfDRs3qlV+sr9MVmjv54qMU5Js0ZurydLNByP7dH0wQZVIrhJWUkjVO0lnrUIQgseCWE+yZjR/S5w3ZCKk0g7Voh+gj3u/eg94ce5wIvwC/UA93q+8+A6oSXNjhTHl+7m7+0VErF1SFUDFKtL7WpzKt8+o7du3c95550V/Xrx4MQALFizgv//7v3PymjKRLukyWnmkJ1BY2HeDkUSSK3Kh0s7WwCZbSulc++elbDZqFYCCq+oo4f51gIxduUKqpyS9DptNnZ25XC3dQ5tp9KqztuKCgHocxCnYFY3CALOHWlcBHr86lFcKLVC5PV7BLukwHflckXGqb5Cr6rdEekM1WTJS2eXFYrW2iKv6+rgq3xJWkvxEhAXBpiBhTxj/YT/GoUZI+NcJNgUB0PVLngJMN3/LREgVbAhSdnUZjg8duPe41cT8114Ug0LhzEKMFUa81WpivemDJsxHzRgGGpK+n9tjbZSLuJwuVsXFqTysSM41+fQZde6553Z5NZpMpEu6jKQe6THYbH3TZ6on0B3lMpLO0xGVdiYDkWx6NnZWKd0V/nlJFemVlVg/OQacgfMfOxB7twHnovg9Sc4g6SxSPSXpdVRUwLhxsHNnvEc6cLw5kd5vwmD1OGilYO/fnEj3+vUogGLUqfsjCnZJh+no54qMU72fXFa/QfIxWE+uJktGqiq/WFRFet9NUCXSXQmrnjr/W7duHQ899BA1NTVMnjyZxx9/nNNOOy3l8S+//DLLli2jqqqK0aNH85vf/Ibvfe970f1CCO6++25+97vf0djYyBlnnMGTTz7J6NGjo8fcf//9vPnmm+zatQuDwUBjY2Or10nWjPJ///d/ufLKKzt1v5G45Kp04a3y4vrUhWmUCet4K/pSffQeAg0BdP11Kf+n0s3f2hJSCSEQAYG/1o8ICobdPgxvtZeDaw6CBmzTbdH3q3mkGeNwI87tTiyjLQy9eSimEa3fz5laG4VcIaofqM56XI4o0lMl0gsKpAi0p1c8dwaZSJd0Ge2xdpGDpvyiO8plJJ2jI2q6TCeI+dRkpCv881op0pt9igvqzwTOwGkpQ1jV5Ily+FuoLJY+xRKJpG00GrjsMjhwAHbvhqFDwWrF2+TDG1QnvsWX/1uLTUuCgr2/Wc1GeQN6zIAm5FP3RxTskg7T3b6skvwkG9VvbYkVcp2kzxdSVfnFYrHIOWEi3ZGw6onzv5deeonFixezfv16ZsyYwerVq5kzZw579+5l4MCBrY5///33ueqqq1i1ahUXXXQRGzZsYN68eXz88cfRxscPPvgga9eu5bnnnmPkyJEsW7aMOXPmsHv37mjjY7/fz/z585k5cyZ/+MMfUl7fs88+y9y5c6M/9+vXr1P3mxiXis4soulfTXi+9BCoD1A0qwiNWYPvoE+dv42x4DvoQ1vY/vlbKiFVoC6Aa48L/2E/YXeYw08fxvmxE9tpNkLHQ1jGtRYzaTQaLGMtBBuC0DzM8VR5WsXGdNZG1klWDj1xKCdVybEe6cnoq81GJSo5+4uvW7eOESNGYDKZmDFjBh9++GGbx7/88suMGzcOk8nEpEmT+Mtf/hK3XwjB8uXLGTRoEGazmdmzZ7Nv3764Yy655BIqKiowmUwMGjSIn/zkJxw+fDjr9ybpGJkl0qUfXj4SKZdJfOTrICpCV8ehqqoqrrvuOkaOHInZbGbUqFHcfffd+P3+uPN8+umnnHXWWZhMJoYNG8aDDz6YvZumtZpOV6hD0SroCnVYJlgI1Aeo31iPCLeUQEUGYo6dDnQlOixjLehKdDh2Oji49iCuSlf02MjARl+ieuAFm4KIoFpS6N7tbtOzUYQFnioPzs+ceKo8cdfQESLqCK01uXpCa9US9oY75Z8Xp56K9Ske2g8AV9CIMKiBTRP0qj7F4XCHX08ikXSezsSabMeplIwfDzfdBKecAg0N8OWXNNaoEihFEdiMfqiqUuNJRMF+4AAIEU2k+yLWLs5G9Xx5rBLsKXTF54qkZ9HWuMo83oz3Wy+H1x/G83XqeOGqdFH9QDVVy6uoWllF1fIqqh+oxlXpatcYrKeTiSI9NkEVCMsEVXfRE+d/jz76KAsXLuTaa69lwoQJrF+/HovFwjPPPJP0+DVr1jB37lxuu+02xo8fz8qVK5k6dSpPPPEEoM79Vq9ezdKlS7n00ks5+eSTef755zl8+DAbN26Mnufee+/l1ltvZdKkSW1eX79+/SgvL48+TKmytBmQLC4Zygz0O6sf5jFmgk1Bmv7VRKA+gG2qjaE3D2XwzwZ3aP4GLUIq3wFf1EYjUBfAvs2O/4iqRDedYMI03IRjp4PDTx/Gf9Sf9rPU+YkzZWyMWBvZTrERbAji+dJDsCGIbaqNwTcOxvWpq13z3faQTpEuLaj6NjlRpHfXSuB5553HXXfdxaBBgzh06BC/+tWvuPzyy3n//fdzcZuSdiI90iVdSXfEoT179hAOh3nqqac48cQT+fzzz1m4cCEul4uHH34YALvdzgUXXMDs2bNZv349n332GT/96U/p168fixYtysq9t1dN154y9sj5RVBQ+oNS7NvsePZ6MvJszIXaKps2M6mIU0/F+BRbvwoA4PQbCDdP9BSLUVWsS5/ibqGnliFLsktnYk2Xq0LHj1cbhFZXg8PB8Q8d8Ar007vQ3L9SlUKNG6eq12MU7P21TQD4vWq8VgrMMO982Wg0C3TF54qMVT2LVOOqWCWm+ws33gNeCqcXtooXbanZPdUetCZtTi3q8olMFOkmk4jOCT0BBWmYJMkEv9/Pjh07WLJkSXSbRqNh9uzZbN26Nelztm7dGlXaR5gzZ040Sf7NN99QU1PD7Nmzo/uLioqYMWMGW7dubbctyw033MB//Md/cMIJJ3D99ddz7bXXJrV8AfD5fPh8vujPjgTrtlRxSV+qp6ikCNMIE4G6AIN/NpiiWUXR+NFRz/1EhbhhiAHXbpfqva4DXZEO6wQruiId2kItjo8c+Gv9hJwhdEXJP0vDvjB1L9cR9oXbVJQnszbKdfVYOkV6QUGMCFRWzvQ5cpJIj10JBFi/fj1vvvkmzzzzDHfeeWer42NXAgFWrlzJ5s2beeKJJ1i/fn2rlUCA559/nrKyMjZu3BgNYLfeemv0nMOHD+fOO+9k3rx5BAIB9Hp9q9dNF5wk2SUTRbr0w5Nki+6IQ3Pnzo0r1zvhhBPYu3cvTz75ZDSR/sILL+D3+3nmmWcwGAxMnDiRXbt28eijj6ZMpLc3VrW3GWimA5Hjbx+PNo+JJJjMY8yUXV2GoczQpmdjrhqCdoXNTJx6KsanuMCgVho4/QZEqDmZZdBIn+JupCeWIUuyS6pYY//YjvMLJwN/OJCCyQVJY1WuGxenRKNRF94qK2l845/ALPqZfGqC3eVSfdQPHFDV6zfdBK+9xoAvGgDwN380KGfNkpZSWaIrPldkrOpZJBtXRZSYIXcIbaEWFHV8lRgv0okVHB858B/x0+/cfn3CSigTRbpOaVGQegJyTijJjPr6ekKhEGVlZXHby8rK2LNnT9Ln1NTUJD2+pqYmuj+yLdUxmbJixQq+853vYLFYeOutt/jP//xPnE4nN910U9LjV61axb333pvyfG3N9xRFwTjYSNgZRlekixvvdMZzP7b5rWO7A+/XXjQWDcbBRizjLFFPdkVRMI8x4z/ix73Xje1UW6vPUu8BLyFvCEWvYJmYfhExMf61d77bXjJpNipFoH2XrMtWIiuBsat2mawExh4P6kpg5Ph0K4HJOHbsGC+88AKzZs1KmkQHNTgVFRVFHxMmTEh5X11W5tuLyaTZqCyPkWSDfIlDAE1NTfTv3z/udc4++2wMBkPc6+zdu5fjx48nPUd7YhXEq+mSkaimy6SM3X/Uz5Gnj7QqO3Z+4qTu1ToUnTrASWXn0l6rmUzpjM1MpsSpp2J8iiOJdFdAjwg3J9LDAelT3I30xDJkSfZIFWuETxA8FqTpn018s/Qbvln2TbRsOEI4GObIM0fwfOXBMNiAzpbdEuG0NNtGHa8LAlBs9YFWq6oMJkyA+nrVNmrsWLjzTvp/73QAAkVqhZUytCzVmSXtpCs+V2Ss6lkkjquEELj2uAi5Q+hL9SiKgkavQT9A3ypepBMr6AfoCR5T32NJX7uXWQllokiP/V14/HJOKOkdLFu2jDPOOINTTjmFO+64g9tvv52HHnoo5fFLliyhqakp+ti9e3fc/vbO92KJJKYLJhWknL+lwjreSsWdFQxaNAjzWDPFs4spOrMomkSPoCtQrWZ0Bbqkn6Vao3r9xor0ivJkdOb+M0E2G5W0RdYT6W2tBKZatcvmSuAdd9yB1WplwIABVFdX8/rrr6e81nTBKUJbnnaSzGmPIl02bJB0hu6OQxH279/P448/zs9+9rO0rxP7GolkGqsiJPOwixBR01nGW6JqunQDkaAziL/WT9AZ7FAivD2ldx2hLf+8ITcN6bSCNE49FeNTbNXHKNIjiXSvU/oUSyTdRLJYE/XvrPGj769O8jQGTZz3sKvSxddLvqbu1Tq8VV6a3mmi6Z9NBOpU+6ZsxKm0NNtGHbepsaPYFPM6iqI2I43YRmk09B9VDEAwqBaXynFTdsn154qkZ5E4rgo1hQjUB6J2BUF7EH2JXlV+JsSLdGKF6Dkag0n3Z8NKKJ9Ip/IEEIGW8aRbJtIlGVJSUoJWq6W2tjZue21tLeXl5UmfU15e3ubxka/tOWemzJgxg4MHD8ZVHcdiNBopLCyMPmwJIp32zveyiaJRsIyzYCw3qknkJP+mIVcIw0ADgxYNSvpZWvrDUhSj0uF+JLm+/0yajbaIQOUYrK+RE2uX7uS2227juuuu49tvv+Xee+/l6quv5o033kjqPWU0GjEaW0pB7HZ7q2OyVebbVpf2TPa393z5SCYe6bLZqKS3cOjQIebOncv8+fNZuHBhp86VSayKJZMu57FqurbK2P9/9t49Pur6zvd/fueemVwhIQmXkCAREkVuFhqkVVtOYWvPSs8uq+5uUdejv7J1vbDrdbm0oEVt6yLqKdVTq54tR9fjlq7VZaWovYEgAookIAghJCEhF3Kb++X7++OT72RmMjOZSWaSkHyej0ceQ2a+M/OdIfOZ9+f1eX1e70AgQM+RHgKugCiWiC+ER267UwMqjuMO3E1u9Dl6UOlXbMXbehc51pmnmnHXu/uNfUPZphgPVY1wT+l0wZzizJNim2iP24TqEKK6LsMEK1fKnGKJZASI3OYb6RpFBV+bD8WkYK204qh20PhCIwFnAOdpJ4pBwTjJCH5wn3fj6/SRvTgbY4FxyFuEB6Q3NqqDHAByLRGCvc0GDQ3B2Chto5PPLeumdJGu7xXJpUdkXaWz6FA9KqpZxdfiQ28VvRS0+iZ0vBgocx8DGCYY8LZ5scywpCVKaDQxkLEKIoV0WU9JEsNkMrFw4UL27NnDypUrATGP2bNnD3fffXfU+1RVVbFnzx7uu+++4HW7d++mqqoKgLKyMoqKitizZw/z5s0DxDxs//79rFmzZkjne+TIEfLy8sLmeMmQ7Hwv1SQag5b3tTzyvpYXNeO89c3WQfcjSffrT6TZqGYCVWVdMO5IuZCe7pXA4uLisGO0AS30+fPz87n88supqKhg2rRpfPjhh8HBMBmSacAX7wM6UOOqZBtbDXsjrBQQKkbFF9JlRrpk6Iz0ONTY2Mj111/PkiVLeOGFFxJ6ntDnSAWhGXYDNZOJVYi4zrno+bgHf7doBtP1URems6awDDyILYRrY1X3wW4cJxy4z7n7ZehB7EIpcqxT3Sp+px99hl44GCLGvmj5eUMl1CQSHLsqKuCee8h88XewB+xuPYEeUW0p866UOcUSyQgRKViFukYVRSHgDqAYFHRmHYqiYJpiouODDkzFJqwVVjzNHvCDzqzDWGDE2+LFcdxBTn5O+l2hvbFRFxvE4+dZnOG32+1hsVGakB5wy7opnaTje0VyaRKZDRxwiEWsgeqagcQmT4PIRw84AyMihg03iTjSA95A3/Fp2gQkGZusXbuWW2+9lauvvppFixaxdetW7HZ7sGfW6tWrmTJlClu2bAHg3nvv5dprr+UnP/kJN9xwA6+99hoHDx4Mzt8UReG+++7jscceo7y8nLKyMtavX8/kyZODYj1AXV0d7e3t1NXV4ff7OXLkCAAzZ84kMzOTt956i+bmZr785S9jsVjYvXs3P/zhD/mnf/qnIb3eZOZ7qSZZITvyuzQV/UjS+foHcqRbrX0m0ECMhrGSsUvKhfTRtBIYCIg/7FjbZQYiFZ2AB3K0T7xhIm1vtyXseB+xRlhDxOsFf6/GFisjPazZqHRWSYbASI5DDQ0NXH/99SxcuJBf/OIX6CKcyVVVVfzzP/9zWBPk3bt3M2vWLPLy8lL0DgiScdNFFiKOGgeu0y4Uk0LW1Vk4TjpQDEo/lyZEF8JDxyrTdBMZFzNwN7hxNbrC7h+rUIoc63ROHZ1/6sTX7sMwwUDOkhx0Vl3axz5tARAiJn0VFdjunwX/Aj0BK+rS6+DTTpRJEyIfQiKRDBORk7KAO4DqU1GMCqqq4uvyYS42B6MU8IGv3YftShuGXAPGfCOe855g5rEh24C31Yu3w4un0ZNeV2hvbFTHhyLeIcyRrqpQXw8LFgRjoyZOFDf5veOrbroUd2RKxg5aXeWsdVL/TD3Ok06yrs4K32UcUdckIjZNvmsywIiIYcNNIs1GtYx0Lwp2h/x8SxLnpptuoqWlhQ0bNtDU1MS8efPYtWtXMEazrq4ubG62ZMkSduzYwbp163j00UcpLy9n586dXHnllcFjHnzwQex2O3fddRcdHR0sXbqUXbt2YQlRWDds2MArr7wS/H3+/PkAvP/++1x33XUYjUaef/557r//flRVZebMmTz99NND3rUMI7t7aihCdqoc5el6/QMt+un1YDWq4IWArEPGHWmJdhmJlcD9+/fz0UcfsXTpUvLy8vjiiy9Yv349l1122aDc6DD0TsADOdrtx+zUP1uPIcuQUKfiVDnkR4JQMSpW4ZSV1Seky8FIMlRGYhxqaGjguuuuY/r06fz4xz+mpaUleD6a2/yv//qv+cEPfsAdd9zBQw89xGeffcYzzzzDv/zLv6TlfUjGTRc5QUQHWVdngYIQks57MOQb8LX6gi5Nlf5CeLSxylZpE2Oqw4+304u92k7m3EzcDf0Lpcj7A/R80oPqUzHPMONr9eE86SRnaU7axz6tiNLrIbJvdWa2KMT9fgWvORvoHDdiliR1PP/88/zoRz+iqamJuXPn8uyzz7Jo0aKYx7/xxhusX7+e2tpaysvLefLJJ/nmN78ZvF1VVTZu3MiLL75IR0cH11xzDT/96U8pLy8PHvP444/z9ttvc+TIEUwmEx0dHf2eJ1ok3v/9v/+Xm2++eWgvOI1ETsr0WXoUnRLcVRMZv+DrFKK1IVc41m2zbcLF3uIV24wN4Hf6cdQ4sF5mTa8rtDc26uL/aQcgT+kAn0840evrIT8/LDYq6EgfR0L6pbgjUzL2UHQK1hlWpnx3CvXb6nHUDCwAJSo2jYcooYSajfaOaz6UsDmkRJIId999d0zj1AcffNDvulWrVrFq1aqYj6coCps2bWLTpk0xj3n55ZfjNotesWIFK1asiHn7UBnJ3VNDEbJT5ShPx+vXol1iOdIBMsxCSPdLR/q4Iy1C+kisBFqtVv793/+djRs3YrfbKS4uZsWKFaxbt27QuVMDZdrF2+arBlQ693bSua8T0yRT1FxhQ7aBnkM95F2fl5DjPRmHvJY7NVoKMS0fPZoYpWGxgEmnQgD80TpWSCRJMBLj0O7duzl16hSnTp1i6tSpYeejNUHJycnh3Xff5Xvf+x4LFy4kPz+fDRs2cNddd6X7LUkIRacI4emiH+vsPmFaE5h8rT50Zh2eCx5c9S78Xf5+E8ZoY5WxwEj24mzsx+14Gj24Trsw5BnI/lJ2v0JiRSt2AAEAAElEQVQp8v6+Dl8wnkGn0wVdor5OH4ZcQ0K7g2BwTsZ4zqnQ3TUeh2yULEme119/nbVr17J9+3YWL17M1q1bWb58OSdOnGDSpEn9jt+7dy+33HILW7Zs4Vvf+hY7duxg5cqVHDp0KDhWPfXUU2zbto1XXnkluOC3fPlyqqurg2OVx+Nh1apVVFVV8fOf/zzm+f3iF78Im/jl5uam7LWny1kcOinTmsL72n1YLrNgq7AFd9Koqoq3zYthgiEYixI6TnlbvQScwtGeOS+T4tuL0y/WVlRwcWoXHIc8Xwt8/rkojhYsECJ6SGyUJqTrAuNj7LlUd2RKxi7JCkCJiE3jIUooIUd6r5Dul0K6RHJJMJSxa7T2I0kkhspmVqFHalfjkbQ1Gx3ulcA5c+bw3nvvDepcYzHY3CbNMdO5r5OeIz0Ycg2YavvnCisGRRQKMeI2Ix3viTrkez7p4cKOC6PKsROajx5rwU5RIDNDBTv4lbE9IZQMD8M9Dt12223cdtttA57XVVddxR/+8IcBjxspoo01YQLTBa8Qty94yVmS02/CGGusMhYYycnPEV3bTzqZfNdkJnxjQr9CKfL+ofEMAIpJQe1WCfQ22UukCeBgnYzxnFMGgxjTHA5w22XDP0nyaNt6tZ0y27dv5+233+all17i4Ycf7nf8M888w4oVK3jggQcA2Lx5M7t37+a5555j+/btqKrK1q1bWbduHTfeeCMAr776KoWFhezcuTPoJv/BD34AENc9BUI4T2XvBo1UOIvjCfGhk7KeT3poeaOFgCsgxg6fGnSNWkosWC+34q53o88WdV5wnOrw4ahxkDkvkxk/nIHOMDx1SYc/G4Dcv7kBrl8otuuVlPRrYJyVJcwJBv/YH3su5R2ZkrFNsgLQeBDKByIhR7qvT0jXzFgSiWTsMhrHxoGajUJvtAvgR2pX4420CeljgXi5Ta5zLvRmPdZKa9ABruiUMMeMcZIRQ54hZq5wUBiKof1EOt4TccgH3AExYXQHRpVjRyuaYuWja2RaAkJIl6t6EsmgSIXLM9ZYowlM7nNuvC1eSh4qIWdJTr/HjzdWKYqCYlQwFZrCHO/x7q8z64ILj4pZQfWowYaBMHBX96E4GQdyTuXlaUK6bPgnSQ6Px8PHH3/MI488ErxOp9OxbNky9u3bF/U++/btY+3atWHXLV++nJ07dwJw5swZmpqaWLZsWfD2nJwcFi9ezL59+5KOZfne977H//yf/5MZM2bw3e9+l9tvvz1q5AuIfjShPWm6u7ujHpcKZ3EiQrw2KcsozcB6uTWmaxQQ8QwRdZ6n0YP1MivFtxcPm4gOcPGiuMy7vADmFMQ8TlGEK93QMvbHnlT0LJJI0sVoFIBGM4m4PPuiXXTSkS6RSEaEgZqNAmSYhJnBJ7WrcYcU0gcg2ra9gDuA3+UHLzT/spnWN1uxzrYy8caJtP26LSzX11Xripkr7OvykVGega/Th2mqaUDH+0AOedc5F36XH8WoJJS5PpyEOtLjkWnpy8STSCTJkar82HhjDQjHeM6SnKgi+kD3T6QLe+T99Tn6YBNAQ74hrGHgQI83VCfjQM6p3FxoaAC3U0XH2HaFSlJLa2srfr8/GDelUVhYyPHjx6Pep6mpKerxTU1Nwdu162IdkyibNm3ia1/7GlarlXfffZe///u/p6enh3vuuSfq8Vu2bAk63WORCmfxYIT4gVyjqcjnTBVaXH0iKToTJ4K+ZexnpA+1Z5FEIhk9JDInDHj7xCkppEskkpEgEUe6xdTbGFkduzWYJDpSSE+AaFuEFaOCuaTPudR1qIvO/Z34u/xkzMgQmehK/Fxhc4GZCbdNoO3ttoQ6FQ/U2Vhv1oMXzCWjz7GjbcsbSEi3mnsHo4AcjCSSZEhlfuxQuqhrjnjrFVbs1XYcx4SLcKj3t5Zb8bZ4cZ92Y5hgIKM8A2+nF+fnTgyZBrK+lBX1tQzJyRgI4KxtAQqxGtwQMPaLV8jLE5ceh4qFsS1mScYX69evD/57/vz52O12fvSjH8UU0h955JEwt3xDQwOVlZVhxwzVWTwUIT6ea3Q05XMGHel5Ax87YQLoGftC+lB6FkkkktFFco50Ge0ikUhGhkQc6RaD1K7GK1JITxBFp2ApsXBhxwUC7kCY41t1q/jafdhr7AQcATwtHsy15mAm+kC5whmXZSTVqCaWc8paaaX5l83obdEnEiPp2EnUkR4U0uWqnkSSMEMRl2JFwQymi3qkI151q/idftTTKjqzbsj3zyjLwF8snPauWheeZo+4YyE0v9pM94Hufu77QTsZa2rgV7/C8V9ZwD+Q0VwLT7wJ3/52WMO/oJDuFEL6WG/4J0kd+fn56PV6mpubw65vbm6OmUteVFQU93jtsrm5meLi4rBj5s2bN6TzXbx4MZs3b8btdkdt4m42m8Ou7+rq6ndMvM+jqqqoXhVPswfHcUdUITudER+jIZ4hEADtbUtUSDcw9puNDnWXk0QiGT0k1GzUJ5uNSiSjiXQ1iB/NJLLol2GUQvp4RQrpSRBtAudt8dK1vwu/w48xz4jH6QGVfpno8XKFk3VCxTreVeei9c3W0efYCQRwnG0DCrDpnRAw93N1ali17TGBsTshlEhSzWDFpYGiYJIZm2I54l11LnRmHZP+ahKZczOHfH/zVDMdH3Rw/oXzoAPrLCv6TH1M9/2gnIw1NbBtG7S24sxYCYA1Q4XDh+HcObjnnqCYroldPufYb/gnSS0mk4mFCxeyZ88eVq5cCUAgEGDPnj0xmyRXVVWxZ88e7rvvvuB1u3fvpqqqCoCysjKKiorYs2dPUDjv6upi//79rFmzZkjne+TIEfLy8qKK6IkS6/PobfFiP27H0+gh4AjQ+EIjPYd6UrcwdonQ2QmqKIMSinYRQnrv2DOGM9KHsktKIpGMLhJqNuqVzUYlktFCqqJDR5LBLARo0S7xHOlmvRirPFK7GndIIT0JIidwqqpiP24XInqBETWg4mv1EXAGME01hWWia/ePlSucrBMq2vGj0rGjuTp/Mwn4n1jrTsAT7/RzdWpYexs2ePxyMiSRJMpgxKVEo2ASGZviOeJtV9hwVDtwVDvI/++x41wSvT9A94FuVFUl60tZA7rvkx4XAwH41a+gtRUqK3F8nA1AhgWorITqati5E2bNAp0uKHb5XGM/XkGSetauXcutt97K1VdfzaJFi9i6dSt2u53bb78dgNWrVzNlyhS2bNkCwL333su1117LT37yE2644QZee+01Dh48yAsvvACIhbP77ruPxx57jPLycsrKyli/fj2TJ08OivUAdXV1tLe3U1dXh9/v58iRIwDMnDmTzMxM3nrrLZqbm/nyl7+MxWJh9+7d/PCHP+Sf/umfhvR6o30eQw0Jqk/FMsOCZbql31ikBlR8naLG8jR6ME81E9lO5VKP+NBiXaxWMJkGPn68RLtA/B2ZI5FlL5EkSiqdnGPBFZqQI102G5VIRgWpjA5NJcmMhYNdCEjEkW42aEL6pTUOS4aOFNKTINJJ5e/04231YsgxoCgKqlfFkGdAMStRM9HT7ZiJ59hxnXOhN+uxVgp36bAUXiGuTrv5FqC3aIri6tTQGja4pZAukSRMsq7rVDT8C2WocQvJ3B9I6rmSdjLW1cHx4zBtGigKTp8RAKvRC4oCU6eKsa2uDkpL+xzp7vEhZklSy0033URLSwsbNmygqamJefPmsWvXrmCz0Lq6OnQhO7iWLFnCjh07WLduHY8++ijl5eXs3LmTK6+8MnjMgw8+iN1u56677qKjo4OlS5eya9cuLCGWmg0bNvDKK68Ef58/fz4A77//Ptdddx1Go5Hnn3+e+++/H1VVmTlzJk8//TR33nnnkF5v5OfRNMWEvdqOr9MHBjDkGLBV2jDkGNBn941FakCl7ddt2GvsuGpd2D+1Y7nMgq3ChrFAfEbHQsRHMo1GobfZ6DgR0mF0ZdlLJImQSidnOlyhIyHMJ+JI15qNymgXiWTkSPV8MVUkMxYOZSEgkWajZr0Yq9w+WYeMN6SQngSRTqqAO4DqU1GMCqqq4uvyYZ5qxjrLiv1E7Ez0dBLNsRNwB/C7/OCF5l820/pma/q340S6Ov8kGgFarUR1dWpk9K7qycFIIkmcZFzXakClc28nnfs6MU0yicbIIQwmZ3iocQvJ3j/Z50rKydjdLSonm7jO4RUiXYbB2/tgNmhoEMfRF+3i94z9nGJJerj77rtjRrl88MEH/a5btWoVq1ativl4iqKwadMmNm3aFPOYl19+mZdffjnm7StWrGDFihUxbx8KoZ/H7oPduE670Fl1mCf39ZaBvrGo68Mu7MfsBNwBzNPM5CzNofNPnTg/d+JtFfWVLkM3JiI+kmk0CuEZ6eNBSIfRkWUvGV8MVmxOpZMzHa7QkYprSKjZqE82G5VIRpp09qUZLMmMhUNdCEik2ahJ16tdOb1w9ChkZUFJScwYY8nYQQrpSRDppNJn6VF0Cv5uPwF3AL1VH5wE5hTEzkSH9DoAQh07PZ/00PJGC4pRwVzS58RM+3acCFenJkbFcnVqaNtjXFJIl0gSJlHXteOEg9ZftdK5r5OeIz0Ycg2Yak1h4hUknzM8qBzyIdx/MM+VsJMxK0tUTHY7ZGfj9IrnsBp7hXS7XdyeJRYHNcEr4Bn7OcUSSarQPo/t77bje9qHdZYV4wRjv6gWnVWH87QTU7GJ7KpsMQnKhtyv5GKvseP8wknnnzrJnJc5JiI+NEf6YIR0uYgnkaSewYrNqXRyxnssXYWOnoM9NG5vZOq9U7GUDr/InwyBQJ/LM5FoF+lIl0hGjtHWlybZcXWoCwGJLPqZ3GKlz32+FTZvE3PE2bNjxhhLxg5SSE+SUCeVvUZ8cHztvn5bjCF2JvpwOAC0bOALOy4QcAewXjHM23FiuDptRo+4PcLVqWGWjnSJZFAM5LoGgpMm4ySjiKEyKP0aI0PyOcND7c+Q7P0H+1wJORlLSkQBdPiw2E2jOdKNPtEFsL4eFiwQx9EXwRDwji9XqEQyVBSdgnW2FXORWXxuonx0POc9+O1+LNMsYZ91rYm7pdSCt8XL5P9vctT+M5camiM90WiXCROAcdBsVCIZCQYrNqd6518sMSi0SbPjmAPXORfZV2cPq8ifLJqIDok1G/VJIV0iGTGGapRKNckK40NdCBiw2WhNDcbz9UAmLsUskhbs9rgxxpKxgxTSB0E0x3fAFUAxKag+NXb2LokXZalwrI/odpwIV6fdK7pmZRh94vYIV6eG1vnY6ZXOKokkWWK5rgHqnqgLTpoAXLUuPOc9GPINYY2RVZLPGU46h3yI9x/Kcw2ITidcBOfOQXU1Tsd/AyDD3yMiqfLzYeXK4JY9zTmqSiFdIkmaRBbR9DY9puL+nTcVRcE82UygJ4AhR5SzzlrnJZ2dPZhol85xFu0ikQwHgxWbNbNUojv/fJ2+AcetaGJQaJNmfbYeFPGYCeX+juD8MFQUT0RI98toF4lkxBiqUSrVJCuMD2UhQFUHcKT3xhgbvbOATFyYQK+H7Oy4McaSsYMU0geJ5mzMKM3Aerk1oezdRIsyranWUB3r8QYbVVVRvSqeZg+O447UTzgjXJ1tTjECTchwRnV1amgNGxweOSGUSAZDNNe1s9bZb9Jkm23D3+lPWWPkpHLIh3j/oT7XgFRUCBfBr36F45CYzFk9HWLMWrkyzF0QFLx8UsySSJJlwEW0AhP6DD1+R/xJkKfZQ9tbQ6+bRppkm41OmKDS0/tvOfZIJKljMGJzqFkqkZ1/AXeA5n9txtvsjTtuRYpBqqpiP27H7/BjLDCiulV0Rh3GiUb0OfEd5WpAxXHcgbvJjT5HDyr9dgOlM65BE6aMRjDEUSH6MtJ10pEukaSAwZg0h2qUSjXJCuNDWQjwePr+HVVI740xNlquBMDpDzmfODHGkrGDFNJTQKLZu4kUZZFNtYaSWRdrsAndChhwBGh8oZGeQz2pnXBGuDpbOoWYP0lpierq1DDqVAKAUwrpEknKiLaoZiwwkr04G/vx1DVGTjiHPAX3H+pzDUhFBcyahXOvE05Dxn9fBg/l9RuzNCFd8cucYolkMMRbGJv45xNp+3Vb3EmQeaqZlv/Xgrdt+LJ+09XnJllHel6WSl3vv90+hf6+fYlEMhiSdT5GmqUg/s4/e7Udf4c/2MMq3rgVKQb5O/14W73BnTi+Lh/mYrP4XSGmo1xzy3cf7MZxwoH7nLtfk2dIb1yD0x4AdFgtfqg9F7MpX8ArjFUy2kUiGTpDiRVOu3kpCZIVxoeyEKAt+kGMaJfeGGODXoxfzkDEeBkjxliSPp5//nl+9KMf0dTUxNy5c3n22WdZtGhR2p5PCukpIpHs3YGKsqhNtRh8Zl20wSZ0K6DqU7HMsGCZbknPhDPE1Xlht3hvCryNUV2dGkadihuwu6WQLpGkiliLalrOcLzGyMmSUA55iu4/1OcaEJ0OhyLGQ+u0iRBFI9eco3otp1i6QiWSpIm3MKbolJiTIONEI6jgbRu+rN909rlJttloVoYa/HenXUdWnGMlkuEg2YnsG2+8wfr166mtraW8vJwnn3ySb37zm8HbVVVl48aNvPjii3R0dHDNNdfw05/+lPLy8uAxf/7nf86RI0e4cOECeXl5LFu2jCeffJLJkycP+nUk63yMZpaKtfPP1+nD3+FHn6tPqIdVpBiks+hQPSqqWcXX4kNvFWOQ5iyP5igPdcubppvIuJiBu8GNq9EV5pZPa1xDTQ2O5/8E/E8yvF2wYUPMpnyh0S5Op0hRkOkIEknypKKxcNrNSwmSjDCuGR5Un0rBXxTQtb8L5wlnwgsBWj66ooApmkuhN8ZY7/fhJ8KRDjFjjCXp4fXXX2ft2rVs376dxYsXs3XrVpYvX86JEyeYNGlSWp5TfiUNI6FFWTRiNdWC/tsII1EDKs5aJz1He3DWOlEDanCwMeYbcVQ78HZ4sVfb8XX6UFUVQ44BW6UNQ44Ba6UVb6s3GC2TMioq4OGHaTEUAzBp7d/CQw/FbLxgVMRzO6SQLpGkDG1RzX3Ojar2/3zHaowsGbhju9UqtijrZU6xRDIktIWxzDmZwQUyZ60zOAnKnJeJr82H83MnvjYfWQuyKPjLAnztvoTiF1KBNiHtPtyNId+AdZYVQ76B7sPd1G+rDzahHyzJNhtVfYHgv9s75dgjGVm0iezGjRs5dOgQc+fOZfny5Vy4cCHq8Xv37uWWW27hjjvu4PDhw6xcuZKVK1fy2WefBY956qmn2LZtG9u3b2f//v3YbDaWL1+OK6Rr5fXXX8+//du/ceLECd58802++OIL/vIv/3JIryVe3aSJzdYKa1Bs1sxSelufK1Hb+WcqNqH61ODOv4wZGZiKTNiusCU8bmmu0Kz5WQTsAQKOAP5uP+Zic1hkDPQX+SPd8sYco5j/ZRuEyaqzd37Y4cNR7UhPXENNDWzbhrP6DABWiyp2Jx8+DNu2idtD3+MQIR3C3aESiSQxIj/7hmwDil7BkJ289hNZo43UfDF0LIysCafcMwVbhQ17jZ26J+qo3VBL7eZaml9tBhUKVxcyff10SjeVUvJQSdwFBG3MsViEmN6P3hhjvVsc6PD1jcHBGOOKin4xxpLk6O7upqurK/jjdrujHvf0009z5513cvvtt1NZWcn27duxWq289NJLaTs36UgfRobSVAtiZ9YN5I7StuN0H+zGddqFzqrrt5Uvnc1lAuho7Z0cFsybEnf5xtArRrn9OtxuMEc370tGgOHeLiNJHaMt4+5SQiukrNbotyuKcI8aLkghXSJJFdHqmozLMyhcXYip0BR0Q9mP2ZOKXwgl2XiWwTYfTIZko120HGGA9i459khGltCJLMD27dt5++23eemll3j44Yf7Hf/MM8+wYsUKHnjgAQA2b97M7t27ee6559i+fTuqqrJ161bWrVvHjTfeCMCrr75KYWEhO3fu5Oabbwbg/vvvDz7m9OnTefjhh1m5ciVerxej0djveRMh2bopmZ1/+iw9Zx8/Gya6hxJr3NJcoc5aJ/XP1OM86STr6qyw8SaaozyaWz403s/T6MF12oUhz0DWwiyyF2ej+oRBKyWu096mfLS24piyGIAMoy9uU76+jHTx3A6HSEqQSCSJM5KNhdNJPId8LAd+zyc9uBvcTL1nakKvdSAjlRZjrH/uGAB2vwF8PuFEr6+PGWMsSY7Kysqw3zdu3Mj3v//9sOs8Hg8ff/wxjzzySPA6nU7HsmXL2LdvX9rOTf7PDiORDnFfp084FDp7HQAFJjJmZOB3RHesR8usS8QdZauwUfJwCcV3FZMxK4O8ZXnkLM0Jcy+AKNwCrkDKm8u0t4saCsSYEg+D0udAkJFSo4dkXUaS0UciK/iS/mjZnDELKXqFdM2RbpBi1kjz/PPPU1paisViYfHixRw4cGCkT0mSBLHqmp5Pemh5swXFoATdUAPt9IuV9RvpVqrdUEvdE3UxHeVqQKVzbyed+zqFo4v0uN+TbTba59qEix1y7JGMHNpEdtmyZcHrBprI7tu3L+x4gOXLlwePP3PmDE1NTWHH5OTksHjx4piP2d7ezi9/+UuWLFkSU0R3u91hDrfuGBOOZOqmZHb+GXIMgxq3QMwlrTOsTPnuFCzTLThqoswnI0T+aG556BX5l+aQ9/U8rLOtTFgxAYDmV5sTGhcTprcpH9Om4fQLs1iG0dv7giKa8vWijW1qb/6wfYinIBkasq66NIn12ddIl/YzHERzyKfMgR8I4DrdCIDF6OsTsiKpqEA/RUSIuVUD3uNfQFubiDG+556YCQySxKmurqazszP4EyqWa7S2tuL3+yksLAy7vrCwkKamprSdmxTSh5l4RVnJoyVkL85OeBthMoOFolOwzrZiLjILx2SU+Va6msu0tIjL3NwYGVOhr9PX11ymqyulpyEZAiOxXUaSerRFtdJNpQlvbRvvDORIByGka9EustnoyCIX/S5tkp0EJRu/AMnHs2iie92TdfQc6aHrQBedf+zE2+INOy4VE9KkHekh8Qft7YN+WskIMZbEqcFMZJuamuIer10m8pgPPfQQNpuNiRMnUldXx69//euY57plyxZycnKCP5GOt1ASrZsGNEuFiNuDGbeinVeiIn+8BUdFUVCMCjqLjs7fd9JzpCf1sVW9Tfmw2XB4xeKG1Rgyftps4vaQBQ2t2ahmTpANR0cOWVddugzWbHCpkowDP1osMiAW9Z54AudzPwcgw9EGTzzRL35KQ9frtPKjYL9/HWzaFDfGWJIcWVlZZGdnB3/MoyiuQs74R4BYRVnmFZkJF2GQ3GABg5twpgLtezaRnH9tUiiF9OEhkdypwbiMJKOX0ZJxd6mQiCM9NxcMstnoqEAu+l3aJFvXJCNeQfJCfajobpxkxJBnQDEquM+76drfFSamD3VCqqrJNxvtq5l0tJ9qh6NHobY2tntKMmqQ4lRqeeCBBzh8+DDvvvsuer2e1atXR3WGAzzyyCNhDrfq6uq4j51o3ZSouJ3suBWLaPPJaQ9MQ5ehCxOHBpr/uc658Lv8BFyBIecoR6W3KR92O06viL3JMPj6bo/SlE8b23S9NZV0pKeWRHOHQdZVlzIjpf2MFIk68Hs+6Ym+K/E3R0XPhsOHcWWKGIUMUyBmLwfxpH3alb30CigtlXEuw0x+fj56vZ7m5uaw65ubmykqKkrb88qM9BFCK8oiCc00dxx3xO0srA0WiWaDjlROsuZILygY+NhQd5WMdkk/ieROxXMZHT9+PN2nKJGMKJrLM17UQqgjXQrp6UGb9GmYzeZ+roSRysiTpI5k6xpIrm5KRqi3lFjCRHcAV60Lz3kPhnwDvlYfjuMOcvJzCKgBHCccWMutEICAL4C73h0zfz1aPrvTpeDxiNsTjXbRXJt+FNre2gtfvCoEqdmz4dvflo6oUUyyeeKjncFMZIuKiuIer102NzdTXFwcdsy8efP6PX9+fj6XX345FRUVTJs2jQ8//JCqqqp+zxv5/dGVQudOvOzeyOMSHbfiETqftNfYOffUuag9s+LN//RmPXjBXJKmHOXepnwcPkynS7zvWeZe4VZryrdgQVhTPi0jXauppCM9tSQy/wNZV13qjLceWbF6VWj47X4C7gAtb7QQcAfCMtS7D3Xj2vkZU/P82L5cifNkDgAWcyBmLwcIN4HKBb+RwWQysXDhQvbs2cPKlSsBCAQC7Nmzh7vvvjttzyuF9FFIokVYIoNFpDsqVYVbMmjmmoSEdJ90pA8n1dXVTJkyJfj7aNouI5GMNE5nnwsq3o6asIx0KaSnBbnoNz4YTF0DiddNyQj10UR322wb/k4/vlYfOrMOzwUPPZ/14DzhRPWoEIBT95/C7/Sjz9CjmJV+DeBjNYgPLMkHbOj1kJmZ2Pulfn4aEDVTuzJRTPDsduGeOndOZnQOM4ks+MHYFKcGM5Gtqqpiz5493HfffcHrdu/eHRS/y8rKKCoqYs+ePUHhvKuri/3797NmzZqY5xLo3ZERz2WbTmKZpSJJdNxKhFgN9roPd+M652LqPVNjzv+slVaaf9mcdPPThOltyse5czQdFbuJiqxd0NkZsylfpCNdCumpJdH5n6yrLn1GQvsZKTQHfvfhbvSV+rCFwdDdN4pRwXpFRNP4Kd04PnLRapqHlQu4fCG7ZyJ7OZSW9j1u0ASqo6en98pAQBzX3S122pSUSJd6mlm7di233norV199NYsWLWLr1q3Y7fagWSEdSCF9lJJIETbQYBHZtV0jlYVbImiO9GSiXfzopJA+DGi5U/EYqe0yEslIo41dRiPE+5jk5aoYZUZ6WpGLfuODwdY1kFjdlIxQH010NxYYyV6cjf24He8FL94WL/4uUUdlV2Wjs+ro/FMnvnYfhgkGcpbkoLPqgmLWxBsm0vZ2W1Sxq+cTFyVMxZ5rQ0mkHAsEUN99D5grhHR/Duj1YrCK456SpI9EXZ5jVZwaaCK7evVqpkyZwpYtWwC49957ufbaa/nJT37CDTfcwGuvvcbBgwd54YUXAOGGvu+++3jssccoLy+nrKyM9evXM3ny5KBYv3//fj766COWLl1KXl4eX3zxBevXr+eyyy6L6kYfbSQquscjMrIqTByq1OOodtC6s5WSh0ooebik3/zPVeei9c3WpBcwk6KiAu65h6YDYttNkau2rynfypX9Fvy0+aDeLJuNpoNE5n+SscNwaz8jxUAO/Li7bzxezOYuHF2TcHV2BWOoLFoMlc0GDQ1ERiZoJlC/5kivqYFf/Uo0WHa55C7BYeKmm26ipaWFDRs20NTUxLx589i1a1e/OiuVSCH9EmYo23WSLdyibUNOdPBNJtpF26YsHemjh5HaLiORjDShY1c8YSs3uy93UDrS04Nc9BsfpHsbcjJCvavOFVV0NxYYycnPwVXnomtfF+YiMzlfzQEFOv/YiepTMc8w42v14TzpJGdpDvpKPfZjduqfrceQZejvhKrU0/Y7B9fQysFcK1E7wkdSV4d6+iwwVzQbdYbUdHHcU5L0Md4X/AaayNbV1aELWdRZsmQJO3bsYN26dTz66KOUl5ezc+dOrrzyyuAxDz74IHa7nbvuuouOjg6WLl3Krl27sFjEYprVauXf//3f2bhxI3a7neLiYlasWMG6devGzfufTGRVRmlGv/nfUBYwk6KiguZCUS8V/eVX4NaqmE5NbT6oN0lH+kgi66qxQyoW7S4F4jnwY+2+UVUVv9uAXzXj7VbwufQ4faIxcobWGDlKLwcI1656quvgyDZobYVp04T4LncJDht33333sGpTUki/xBmO7TqxtiFr25QHIqlmoyGrelJIHz2MxHYZiWSoDGUBEBIfuyZIIX1UIBf9xgbprGuSEerjiUsA3mYv+gw9mfMzUXQKvg4f3lYvhhwDOp0OQ7YBb6sXX6cPQ64BQ7aBnkM95F2fF1XscueYmY6DVqsLSGCy292N6hJOqX5COsR0T0nSR6Iuz7EsTsWbyH7wwQf9rlu1ahWrVq2K+XiKorBp0yY2bdoU9fY5c+bw3nvvDepcxwqD6S0RynDmKDc1i8counoqlMY+TpsPSiF9ZJF1leRSJJYDP9ruG2+LV+wybPETaJ+O6vLQ/OkkVL0Q0i0GX8xeDhCRkf67g+BtFbsCtTpP7hIcs8j/xTFAtK7tJQ+VJDXZVAMqzlpnWJd36Mvc6z7cjSHfgHWWFUO+ge7D3dRvq8deM/Beu8E0G/XJZqOjiptuuokf//jHbNiwgXnz5nHkyJG0b5dJBc8//zylpaVYLBYWL17MgQMH4h7/xhtvMHv2bCwWC3PmzOGdd94Ju11VVTZs2EBxcTEZGRksW7aMkydPhh3z+OOPs2TJEqxWK7kxOsYpitLv57XXXhvSa5WEY6+xR+/InsCYpZHo2JWbFSKkG6SQPpKsXbuWF198kVdeeYWamhrWrFkjF/0uQVJR18R77Kn3TCVrfha+Nh/Oz5342nxkLchiyj1Tgs+hiUvGfCOOage+Th+qT8XX6cNR7cCQacBUaEKfKZxNAXcA1acGF9MUk4LqU8X1qkrAGSBgD+B3+EHtf15OVY+JAAW2BHOIs7JQjcIh6kNHW6SQHsM9JRl5QsUpDU2cuhTiSCSji9DIqkhUVcXd6Mbv9IsxLBBl8CH2uJg5L5P8v8hH9alh88PB0tQkLgeaPgSjXSxiPJXRLiOHrKsklyKaAz9zTiYZpRlhBgn3OTeqquJt8dK1vwvPeQ86qw5ybJiz3LjqvOSe1FGCnYyAQ4jgUXo5QISQfrbXiR65jTlyl6BkTCAd6WOEoWzXieU4n3jjRNp+3RY1c09XoaPnYA+N2xuZeu9ULKWxnZ5JOdK9stnoaGW4t8sMlddff521a9eyfft2Fi9ezNatW1m+fDknTpxgUpQ/xr1793LLLbewZcsWvvWtb7Fjxw5WrlzJoUOHgtuMn3rqKbZt28Yrr7wSzOpcvnw51dXVwW3GHo+HVatWUVVVxc9//vOY5/eLX/yCFStWBH+PJbpLkieRplvRBLlIB3tLswVQBhTS87KkI320MBIZeZL0kM5tyInmhcZzx2d9KYvmV5uDziadWYdiUFC9KopZQfWoKAYFv92P448OXLUu/D1+uj/qxnvBi3W2FWOBMfhcni4/HnRkTEwwh7ikhMCU6UBvs9FQIT2Oe0oyOpA7/SSpItbuGW+LF3uNHecXTgzZBhp/1kjn7ztj7iiOHBc9zR669neJcW4QO5IjCQT65oQDbbzQ5oMGs3SkjzSyrpKMFUJ339iP2fGe9+K3+9Fn6/F3+TFMsJL55ekYmj5H97HKNbSS4euO2csBwtMUepx6sRswGnKX4JhDCunjnHiCU8+xHgI9ASyXWcK2IWtbYDyNHhzHHLjOuci+OjtmYTUYR7qMdpEMlaeffpo777wzOCndvn07b7/9Ni+99BIPP/xwv+OfeeYZVqxYwQMPPADA5s2b2b17N8899xzbt29HVVW2bt3KunXruPHGGwF49dVXKSwsZOfOndx8880A/OAHPwDg5Zdfjnt+ubm5l/wW7tFIok23rLOsYaJZtAXFzForJeRTUBB/wphtU9HKIkUvhfSR5lJb9JOMDIkK9bFEd4DuA91B8Uqfo8eYb8Rz3oMh34Cvy4ch24Cj2iFc6AoYC42ggKvRha/TR/bibIwFRlRVhRY3Z8kia3KCOcQ6Heria+AXHfhRcHhNoq+Vu1OI6DHcU5LRgRSnJKkiWjRLwBmgc2/sxsexDAXauGivsdPyZkvShoR4tLeDr7dv30DmKk2cMvQ60nt6knoqSYqRdZVkrKAZJM6/dJ6eQz3CAOFSMReb+wwO5QXYW51MP+pAmTcHHro+Zi3Vp13psCu9mejRIt7kLsExh6yuxzGRgpMh24CiVzBkG7BWWvG2eHGedqK39rmjQrfA6LP16Gw69DZ9zKgXv1/0W4BkM9J1UkiXDBqPx8PHH3/MsmXLgtfpdDqWLVvGvn37ot5n3759YccDLF++PHj8mTNnaGpqCjsmJyeHxYsXx3zMeHzve98jPz+fRYsW8dJLLwkhJQZut5uurq7gT7dczY5JMk23tEirll+3cPbxs3QfCo+wMtd18z+oZzrx9xTn2ESjGS9Kv+eUSCSXPtG2CEdGv/i7/FjLrSgGBfdpN+hFneXr9KGqKsZcI9mLszHkGFAUBW+nF3u1HV+HiIrp0Zv4E/nkTUh8DFGLpwLCkQ5w8VgjtLUJ95RsajXqufvuuzl79ixut5v9+/ezePHikT4lySVKaDSLt9VL55868Xf6ybg8g9yv5GIqMvXN71q9tO5sjRnTMuD8MOT+saJBo6HFukyYACZT/NejiVO2bCFVaKYsiUQiGSq2ChuFf1uItcJK7nW55F6fS87SnL5dgoqCy5yBiQCZ2da4hoTQZqP2nCmiqWjknF7bJVhRIXcJjiGkI30ck4jg5DzlxHPeg3maGVVVsR+343f4hYPKraIz6jBONKLPie70bG/vG0smThz4nEIHIymkSwZLa2srfr+/n7OrsLCQ48ePR71PU1NT1OObeit/7TLeMYmyadMmvva1r2G1Wnn33Xf5+7//e3p6erjnnnuiHr9ly5ag010Sn0SbbvV80sOFHRew19jpOdKDv8uP5TILpkITSraYMDYa9eTgYNrZVtSANWZ8Vba1L5LK6YSM9KRRSCSSUUZk9EvAFSCjLAN/sR8C4Pzcic6qwzy5z+lknGAM7upznXZhyDOQ/aVsPvHlU3fURjIpX5rYpBgU8EH73/0TxfNNYqImnegSybhC2z3TubeTuifrME0yYZ5qhpDSJdJQEG1XTqKGhIvvXaT7QHe/aNBYO5S1UjmRzZjafDBnonj+xsYE3wSJRCJJAEOOAdNE0edGazwahkvE7Vmz4sfthcYS95RUQka+yFSfOlXEudjtcpfgGEUK6eOYgQQnU7EJvU2P65wL01QT/k4/3lYvhhzxZ+Pr8mEuNovfFaIWZpqDIC8PjMaoTxOGbDYqGQ+sX78++O/58+djt9v50Y9+FFNIf+SRR1i7dm3w94aGBiorK9N+npcioU23ohVGfrufgDtAyxstBNwB9Nl6UMAw0YCnyYO/yx+MW7A7FC5iJrsj9oQTwGrui6Tq6JBCukQynogW/WKeaubiby9y7ulzWGdZMU4wBsUsY4GRnPwc0dDvpJPJd01mwjcmUPs/xAF5eYk/t7aLT2fUgQ/aJl4OpSl+gRKJ5JJB0SkYcgzoMnSYJpvCRHQNzVDg747e2Dje/FBVVVSvivMLJ/X/Uo8uQ4e5JLHol+ZmcZmIkK7NB3NDhfRAQDTq6+4W8QhywVAikQySWL0lQIxz5i4RtzehKH7cXmhGut0yUewG/NWv4PhxkYluscTNWJdcukghfRwzkOAUcATImJGBPku4zXUWHapHRTWr+Fp86K3CeaAVadEKs2QajUL4YCQd6ZLBkp+fj16vp1mr2ntpbm6OmUteVFQU93jtsrm5meLi4rBj5s2bN6TzXbx4MZs3b8btdmM295+4mM3msOu75IcjJgMVRq5zLvwuP4pRwXqFFe8FL6pfxZBrQJ+lx9vixXHcQU5+Dg4HuNBjVmJPOAEUv7YAqOPiRQj585BIJOOAaHnr1tlWzEVm0YA4QsxSFAXFqGAqNGGdLXa7XLwobktGSNdcm4pRAafYBSiRSMY3iRgKdBYd+hhOy1j313pkuRvceJu86Gw6rLOsYTv54vWiScaRrs0H8/QdQBGNZz2w5Udw4gSiGYQFZs+Gb39bilMSiSRpovWW0NvEuOeud9OBiNv72sT4cXthjvQexHg0a5Zc9BsHyP/RcYwmOLnPufvlM6uqirveTfaXsyl5pISs+VkE7AECjgD+bj/mYnPQtakRrTBLptEohA9GUiuUDBaTycTChQvZs2dP8LpAIMCePXuoqqqKep+qqqqw4wF2794dPL6srIyioqKwY7q6uti/f3/Mx0yUI0eOkJeXF1VElyRHZG6xr9OH6hM5xY5qB3qzmCCaS8SWZZ1ZJxrNeFUURUwEva1efJ0+0RcGP5bs2BNOCI+k0sQwiUQyvkmkxrJWWIONSzs6xG2DiXbRm/vi9CQSyfgm2bEnkfuH9sgKuALoLDpMk0x4mjx07e/C2+IF+kfHhKIJ6Yn001U7RW+aCb9/E4C2LhOuX74JiiJEqvx8OHwYtm2DmpqE3xuJRCLRCO0t4Wvz4fzcia/NR9aCLP7LNoU6bHHNUapfhd4h1o8Ou9ZSS6eD0lKYM0dcShF9TCId6eOYgVbiTPkm8leKnDtbhQ1nrZP6Z+pxnnSSdXVWmMtAK8yyFmSFFWZJO9JDOh/3SCFdMgTWrl3LrbfeytVXX82iRYvYunUrdrud22+/HYDVq1czZcoUtmzZAsC9997Ltddey09+8hNuuOEGXnvtNQ4ePMgLL7wAiMnBfffdx2OPPUZ5eTllZWWsX7+eyZMns3LlyuDz1tXV0d7eTl1dHX6/nyNHjgAwc+ZMMjMzeeutt2hububLX/4yFouF3bt388Mf/pB/+qd/Gtb3ZywTmVvsafCgs+jIWpCFtdJK8y+b0duEMK7P0WPMN+I578FYYEQxKajdKl57AI9XZTJucuZmxZxwQui4JYV0iUQiSLTG0mqpwTjStbHHIIV0iUTSS7Jjz0D3N00xicbInT4wiB3IqkEVzvXs8J18KLGjYxJ2pNfUoNafB/LJmmjArHhwqybOuydQduwYZGYKh1Zlpcgi3rlTiOtSrJJIJEkSLZ7PUmLhk21ifIwnpGtGKuhtNmqPfaxk7CGF9HFOPMFJE9FBFFXWGVamfHcK9dvqcdSEF2aucy70Zj3WSjEQWUosKDolaUe6NiD5ezPSVVWYDySSZLnppptoaWlhw4YNNDU1MW/ePHbt2hVsFlpXV4cupOhesmQJO3bsYN26dTz66KOUl5ezc+dOrrzyyuAxDz74IHa7nbvuuouOjg6WLl3Krl27sFj6RNYNGzbwyiuvBH+fP38+AO+//z7XXXcdRqOR559/nvvvvx9VVZk5cyZPP/00d955Z7rfknFFrMLIVeei9c3W4JZlRVGwzbaJHhAtXnRmHejA0RmgFAfdOhNTb4o94YS+Lci+3ox0iUQigcRrLFWFtjZxnwkTEn98KaRLJJJoJDr2JHL/7oPduE67go2TTZNN9Hzag+pV0Zl1YTv59Dl63I1u/E6/2BEYUIP1U0IZ6YEA/OpXBDwzAdDpAkw2XuCMZyqNWbMoc7wvsofz88UEcepU4UivqxPOT4lEIkmSyHg+v7/PDBrXke7r2/Hj16JdJOMGKaRLYgpO0YSjaIVZwB3A7/KDF5p/2Uzrm63Bru0tLaJQS8SRrqoq9JoXfCioqmh0nJmZylcrGU/cfffd3H333VFv++CDD/pdt2rVKlatWhXz8RRFYdOmTWzatCnmMS+//DIvv/xyzNtXrFjBihUrYt4uSR3RcoujZagbC4xkL87GXmPH+YUTQ7YBZ4/K52RxcmI+mZXxJ5yhjvRO6UiXSCQhJFJjtbSAw9GnCyWKNokzZohFYSmkSyQSjWTmd/Hu3/5uO76nfcHGySoq7kZ3v5187iY33k+9wTqq8WeNdP6+k/xvC+E+IUd6XR0cP45quAIAxe9msv4CZ5hKozcfsrOhtRU6O0UOls0mGvp1dw/tzZJIJJJeLlwQa3o6XXwNS5v/gXSkj0fkHigJ0Cc4Zc7JJKM0I26RZauwUfJwCaWbSpn0N5PQZ+kxZBmwXGbBOsuKId9A9+Fu6rfV4z0lRpREHOmhq3pq7/PLnHSJRJJKYmWoKyYFwwQDuUtzKXusDMdNZbxGCd7J8UV0CO3toJPRLhKJpB8D1Vinv1ApxMniST0EzjtRA2qMRwpH28VnzBCPp7na+x8YgNpaOHpUXAYCMQ6USCRjiWTmd7HuH9k4WdvJp7eKWBd/t5+AJ4D9iB3n504MOQZyrsnBWGAMzgftNfbEMtK7u8HlQlVF/J5iNjDFJLY3N7rzwWQCnw/cbnG83S4aj2ZlJfvWSMYwzz//PKWlpVgsFhYvXsyBAwfiHv/GG28we/ZsLBYLc+bM4Z133gm7XVVVNmzYQHFxMRkZGSxbtoyTJ0+GHfP444+zZMkSrFYruTGandTV1XHDDTdgtVqZNGkSDzzwAD6fb0ivVZJ6GhtETbYwrwfPudg1WaiQ7pdC+rgjbUL6cA9gtbW13HHHHZSVlZGRkcFll13Gxo0b8Xg8aXl94x1Fp2ApseA45iDgDmC9wipiEvSiWZ+10oq31cukz1tRUBNzpIcMRtZsKaRLJJL0EKu5TPbCbKavm07BjQU0KxmoKAmNXbLZqEQiGSz2GjsXttZxO7X8ja+W2g211D1Rh71m4BmZVjeZrHGiXWpq4IknYMMG2LxZXD7xhGzQJ5FIEiJa81FtJ5+pyISnTeShBxwBMi7PIPcruZiKTGHzwQv/3kpbi7hvXEd6VhZYLKi92qKSbWVytnCbN7jzweMBgwHMZpGJVV8PFRVQUpLOt0ByCfH666+zdu1aNm7cyKFDh5g7dy7Lly/ngpbVEcHevXu55ZZbuOOOOzh8+DArV65k5cqVfPbZZ8FjnnrqKbZt28b27dvZv38/NpuN5cuX43L1NdT1eDysWrWKNWvWRH0ev9/PDTfcgMfjYe/evbzyyiu8/PLLbNiwIbVvgGRI2GvstD4rarK/HqAmC2pXBgVktMu4Iy1C+kgMYMePHycQCPCzn/2MY8eO8S//8i9s376dRx99NB0vUQK46lw4jjswTzOjRASZa13bM9sdTMKVtCPdJoV0iUSSRkJ31kxfP53STaWUPFQSzA1Npr9DaLSLzEiXSCSJYq+xU7+tHs+xbjox4J8cvqtvIDFdG3vMthhCek0NbNsGhw+LTOFZs8Tl4cPieimmSySSARhoJ1/2/GysFVby/lseuV/JxVhg7Ltv73yw/YiDAlzodGIIiklJCcyejeoRBgVFD5OnCnd6Y1em2HajNZKorhYPtnKlbDQqCaL1nbr99tuprKxk+/btWK1WXnrppajHP/PMM6xYsYIHHniAiooKNm/ezIIFC3juuecAYebcunUr69at48Ybb+Sqq67i1VdfpbGxkZ07dwYf5wc/+AH3338/c+bMifo87777LtXV1fzrv/4r8+bN48/+7M/YvHkzzz//fEzjp9vtpqurK/jTLSOM0kqwJvtM1GSO/Pg1WVC70osaTDrSxxdpyUgPHcAAtm/fzttvv81LL73Eww8/3O/40AEMYPPmzezevZvnnnuO7du39xvAAF599VUKCwvZuXMnN998c7/c4RkzZnDixAl++tOf8uMf/zgdL3Pc4+/243f5MdvMUW/X2/QEXB6s+JMSowCs2aIgkt8XEokkXUTLUNcYjJDuQ+FigwOOfiFcVSUlcnInkUiiogZUWn/VirfVS6PRigOFnAlgyDagr9TjqHbQurMV6yxrzDgGbeyxRDrStSiX7dvh7Fm4+uq+sSg7GyorhQi1c6cQ1+U4JZFI4hCreWn2wmyslVaaf9mMabIJIoYqVVVRvSqOeg/TcKArsKDXx4mX0eng299G/eEZ8auji8mThD290T9JNJIwm8Vgt2CBENErKtL0qiWXGh6Ph48//phHHnkkeJ1Op2PZsmXs27cv6n327dvH2rVrw65bvnx5UCQ/c+YMTU1NLFu2LHh7Tk4OixcvZt++fdx8880Jndu+ffuYM2cOhSHZRsuXL2fNmjUcO3aM+fPn97vPli1b+MEPfpDQ44PoVXdxz0UUvQJ6UPQKik5BMYT8blD6XxpCjjEo6Iy6vuuNvZem3uuNSvBHZ9KJ67VLsw6dSYfOokMxK+gMl05tEVqTtWeJmiwzO35Npu1I1hkVcAshXVXFMCUZ+6RcSB9NA1hnZycTtFXrKLjdbtxaxhrIVb4k0Wfp0Vv0+O1+DNn9/5R83X66PToc6JOKR0CBTLMbsNB58gJcny8neRJJErjOuYRbSCuS9DEKKIOSdF7meEHbQJVMLJUPHRc/qRPxCRYLzJ4N3/62nORJJFHwO/ycvOekmHSZeyddvf8OTsJ6/63L6L206NBn6NFZxXX6DD06mw69TS/ucwnNXkJ39V08KM47L0/cprk4HTUOXHWumAt+mhsqI0vUSBcuQOBYDbpf/woOHoSPPwarVeQJz57dtzKodTWtqRHN/UpL0/paJRLJpU+s5qWuOhetb7b2mw96W7zYj9vxNHrwtgX47zTSpfRgr8kP7v6LSkUFAUsL2AMo3ReZ0n0MWEVjxmXwv/6XCFmXZgVJFFpbW/H7/WFiNUBhYSHHjx+Pep+mpqaoxzf1hvprl/GOSYRYzxP6HJE88sgjYRpZQ0MDlZWVMZ+jc28nTb9I/JzSjo6YNZze2ntp06PPDPnJ0gvxOltcGnINGCYYME4wYphgQJ+hT8uphtZkPbWiJsvMFLfFqsm0+Z9iFMerKjidouySjH1SLqSPlgHs1KlTPPvss3Hd6Mmu8o0n1IA6YJd3LTOv+3A3+kp92ARWVVW6T7s5SxYXsDBxYgLPqQ1GugCFFz4DrqbxpV3QXi/FKIkkCT79s09xHHMkdrBCmBsh6Dzo/XdU90E0F4IpRPgy9xVMwZ+MEOFLK6B6BTC9TRRO+iz9qHEvJONID9TWAyLa5WIgWzg87XYRn3DuHNxzjxy/JJII/HY/TT9P4YRPQYwtmaIBujYhC07Gcgzoc/QYcg0Y88SEzJBnwJhvxJhvxFRgQmcevvEndFefFgkV2p9Mb9PjaRDZw7HQDAjZeQp6Pbhc0PjEq0x1nRIzQJtNCE7nz0NnJyxe3Deo2WzQ0CC3/kkkkoSJtpMv2nzQ2+Kla38Xfocf1afinmihucXCnEA39dtcTL1nalwxXVV1QADl3u8x2anC76DBNRFCTHUSyVjHbDZjNvft/O8aIPM2/8Z8zNPM4AfVr4ommdq/fWrYJX6xGB92vU/sINH+HfAG+n73ip/gdZ7ef7t7Lz0qAU8AQkuWAAQcAQKO1DU411l1mApNGCcZMU0yYZ5qxjzNjKXEgrnETEZ5BqZCU9LGitCaTCuLQnsYR6vJNO1KZ+p7LrtdCunjhbREu4w0DQ0NrFixglWrVnHnnXfGPC7ZVT5XvQvfRV+Y2BTc+mIMEaGMvY7PS8gZFYq9xh7cuud3+dFb9FhnW8n/driDQMvMc51z4ah2YJ5qRm8TDnV3vRuv1cSfyGfCRAVDAn9p6vFT4nHxU1bQA+egNjANDr8lxSiJJAk0sVorjIitw4BKsDgaDegsOuFGyDWE/WhiV1D0KjJhmmzCPNmMPkuf8vE2YSE9EEA9cAiYiQ+FDo8N9HoZnyCRDIAuQ0fZ42UEPL0TMXeAgLt3MuYS/w5eOnv/7Qzgd/rF786AEGncvWOXCgF7gIA9gLfZO6hz0mfpxdhSLH7Mk82YS8xYSi1YpluwlFow5hkHfqAEn0tv0ePt9tPZKYokzZEOYqFBGw9joY3beotCWZnKqVMKp+pMTL2uUgjnRqMYdwoKxKB2/LjIFFYUMduzWMJnihKJRJIkkfNB0xQT9mo7vk4fGMCQY6DTaMOOAVeRHm9r4rFVyvSpFBcJVaqnR6G7Ww5Zktjk5+ej1+tpbm4Ou765uZmiGF1ui4qK4h6vXTY3N1NcXBx2zLx58xI+t6KiIg4cONDveUKfY6hM+MYEJnwjdhrDcBDwhdR0obWcs6+W8ztEc2K/3Y/f4cff4ydgD+Dr9glBu8uPr9Mnfjp8+Np9eC96wS+EedcZF64zrpjnoM8R2pV1tpXsL2WTfU02mXMyxQ7tWPcJSVro6RE1meZIh+g1mbYrUDEoZGQIN3pPT2ImLMmlT8qF9JEewBobG7n++utZsmQJL7zwQtxzTXaV79yT52h4riHuMaEophj5Udo2ZlO4e1MxK+HbXjJCXJzWPhen3tbr5OzdAmPIMvS5OY1DE2q0JgveVi/maWbMNjN+u5/uw924zvV3EMTKzMtakEVLcT51/2ZjdiKDSSCA+p/vAgtQDFA6Sbhpax2FUoySSJJk4UcL+12n+tW+n2gOBF8UN0Kk+0D73RPou/SofeJXrxAWcIsiyu/09xVPrj7hK+DovbT3FlF2P6pHFCMBlzjW25K4EKaz6bCUWsiYkYGlzELGZRnY5tjInJuJccLgRK+Eo13q6lAbm4CZwpHusvTdJuMTJAnw/PPP86Mf/Yimpibmzp3Ls88+y6JFi2Ie/8Ybb7B+/Xpqa2spLy/nySef5Jvf/GbwdlVV2bhxIy+++CIdHR1cc801/PSnP6W8vDx4zOOPP87bb7/NkSNHMJlMdETpkltXV8eaNWt4//33yczM5NZbb2XLli0YElkZTwBDpoHpj04f8uOofhX/4Wr8b76Dv6aWgCOAT5eJv6gM/9wq/NlF+LrEhMzf6cfX4cPb7sV3UVx6W8UPfuFIcnY7cZ50xnw+4yRjcIJmq7SRdXUWmfMz0VuT226suTib/9hNQNVj0Ctk9gpEqqrirneTtSALS4kl5mMExSaDwswpTk6dsnLKVMl1Sg3k5AjR/Px5MavLzobWViGw5+RAfb3IGC4pSeq8JRKJJJLQ+WD3wW5cp13orDrMk81YZ1vp+FjUYplZycVWKUaFrCwhnnd3Q2OjmApKJNEwmUwsXLiQPXv2sHLlSgACgQB79uzh7rvvjnqfqqoq9uzZw3333Re8bvfu3VRVVQFQVlZGUVERe/bsCepOXV1d7N+/nzVr1iR8blVVVTz++ONcuHCBSb2Ti927d5OdnR3XyHmpoTPowCAc3KlEVVX8XX68rV48zR48Fzx4mjx4Gjy46ly4z7lxnXXhqnXh7/TTvb+b7v3dNL8iNEZ9pp7sL2eTvzKfgpsKMOWbwh4/dGdNd5ceGLgmCzrSjTpsNiGky4aj44eUC+kjOYA1NDRw/fXXs3DhQn7xi1+gS7HgqrPpME4y9heafNGdnKqnd9uLPXXbWQY8xwxduJMzz4BxolH85BvFNpgiU9iPJr6HNlmwVlqDDs+BGl9Fy8wzTzXz0XY3ZfRwebYeNdA/GiaMujrUU7XAAnR6ldLcDgDOdORKMUoiSQHBrPRRSsATwN/tF24EzYnQ0ftz0Ye3rVfwavPiveDFc96D+7wbf6cQ5B3HHFHjbMxTzWTOyyT3a7nk/bc8bFfYEnKvJ+xI7+5GdQvLvw+FHo8Zr1+HUd877sv4BEkcXn/9ddauXcv27dtZvHgxW7duZfny5Zw4cSI40Qpl79693HLLLWzZsoVvfetb7Nixg5UrV3Lo0CGuvPJKAJ566im2bdvGK6+8QllZGevXr2f58uVUV1djsYgJgMfjYdWqVVRVVfHzn/+83/P4/X5uuOEGioqK2Lt3L+fPn2f16tUYjUZ++MMfpvdNSRLl8+MYfv4chtZWmDFNfObsdjj3Pnx+NKHdbGpAxdfpE2NLkxhbPOc9uBvcuOvcuGpduM668F4Q40/nhU46f9/Z9wB6sF1pI3txNhOWTyDvv+VhyIpfYmsuzi/2uSjFgTfTDH49vt5dfaZ8E/kr8+PWTqFi08zJDsDKSftkoEbUTrNnC+G8pUUoUR4PtLWJMSk/XzTqS7c5IRAQtZtmJZW5xpJLjIt7LvLFP30RbMQX1mcmollfzIg87XqtYV8ss1Vvs76guSoyKq83a1hv1YvHG0W7n7X5YPu77fie9mGdZRVmBkW4NEEMz4nEVoUKVACTJ8OJE1JIlwzM2rVrufXWW7n66qtZtGgRW7duxW63c/vttwOwevVqpkyZwpYtWwC49957ufbaa/nJT37CDTfcwGuvvcbBgweDhkxFUbjvvvt47LHHKC8vD9ZVkydPDmpdIMwH7e3t1NXV4ff7OXLkCAAzZ84kMzOTb3zjG1RWVvKd73yHp556iqamJtatW8f3vve9MGOnJDqKomDIETF9GZdFX4AD8Lv8OE85cRx3YP/MTte+Lro+7MLf5efiby9y8bcXOXXfKSb82QQKv1NI/rfz0Rl0fTtr6lxM7HHgxUymRY+vM3ZNpsXrKUaFzEzhVRj3Qvo4qvnSEu0yEgNYQ0MD1113HdOnT+fHP/4xLZoKQuq2y1z2xGVc9sRl/a5X1V5npzdOfpT2b3egbxuzp2/LS9gWGG3bS8T25TAXZ49wcfp7/Pi6fMGtzQFnAI/Tg+e8J7EXpYBpsgnLdAvGiUZctS5MU014mjwYJxrRmcQf/kCNr0Iz8+w1ds49dQ79TgffwU9xk566J/pHw4TR3Y3q8vY+Vp+QXtuRK26XYpREMqbRmXToJuowTkzOQe63+3E3uHGdceE87cR12oXjcwf2o3ZcZ1y469246920/aYNAFOxiQnLJ1D8/xWT8+WcqI+pbc2DBIT0rCxUvSiAfYjxssNlocDWK+rL+ARJHJ5++mnuvPPOYH20fft23n77bV566SUefvjhfsc/88wzrFixggceeACAzZs3s3v3bp577jm2b9+Oqqps3bqVdevWceONNwLw6quvUlhYyM6dO4PN2bX+MC+//HLU83r33Xeprq7mt7/9LYWFhcybN4/Nmzfz0EMP8f3vfx+TyRT1fsNOIAC/+pWYvVRWCvEYko5WUnQKxjwjxjwj1lmxwy19PT6cJ3onaDV27EftdH/Ujee8B/snduyf2Dn/wnkUo0LOV3LI//N8Cr9TGHNnjK3CRv2iqZz8TStftjpwft63qy9/5QAN+QgXm8rLxWs/1RIyrhUUiFz048eFAuVwiDHpS18SInq64/JqasT/z/HjIsBdNmGWXIJ427z0HOkZ6dPoj57wHcuZET/ZIU37cnrNVXnGPpNVgTBZaXO9VKDoFKyzrZiLzKIBX++QrNVUmZkDx1apqhq2SAgwZUqfkC6RxOOmm26ipaWFDRs20NTUxLx589i1a1ew115dXV2Y2XLJkiXs2LGDdevW8eijj1JeXs7OnTuD5gSABx98ELvdzl133UVHRwdLly5l165dQXMCwIYNG3jllVeCv8+fPx+A999/n+uuuw69Xs9vfvMb1qxZQ1VVFTabjVtvvZVNmzal+y0ZV+gtejKvzCTzykz4S3Gd6lexV9u5uPsizb9spudQD21vtdH2VhuZCzKZ/dJsMudmYquwYVs9lRM/a6UEB/omDz5r7JostNmorbcs7hmFXxXDxjir+dIipI/EALZ7925OnTrFqVOnmDp1atj5qGp6s38VRbgOMACxF8jSSsAbEC7OrhAXp5Yp1ebtc3P2uq20H9Wn4mkQ22I07Ef7ltIMEwyiicMUM8YiI36nP66DIDQapltn5hxmJufGjoYJkpVFwCj+L0OF9HanlS63mWxXpxSjJBJJP/Q2PdbLrVgv7y9++Tp99BztoXt/N+272+n8fSee8x6aXm6i6eUmshZnMfW+qRT8RUFYLJa2Dms0igSEuJSUEMgXi7WKLgABuOjKEEK6qsr4BElMPB4PH3/8MY888kjwOp1Ox7Jly9i3b1/U++zbty+stwvA8uXL2blzJwBnzpyhqamJZSEN2XJycli8eDH79u0LCukDsW/fPubMmRPW5H358uWsWbOGY8eOBSeIobjdbtxud/D37uFY+K6rEwX7tGl9IrpGGnazGTINZC3MImtheC3ibnDT9VEXHR900P5OO86TTjre66DjvQ5OP3Kawu8Uivrniv71z+duG/8XK8VfczH9odgN3qMR6oaa+SURsH6qJVuMPdr7UVAAEyfCwYNQXg733ivei3Q7hGpqYNs2scgxLWSngGzCLLnEyPlqDlftuipmPB5+8VnsF5EXsns5bkSeV+0zVXmiGKy0y16zFdq0tjeOyt/tx8vg+kIA6LNFbwjzZDOmKSbMU0TjPssMEZVnKbUkJbZHaz4aFNJtA8dWhe70VgxiHJs8WfzekHjCqmQcc/fdd8dMQvjggw/6Xbdq1SpWrVoV8/EURWHTpk1xRe+XX345pjlBY/r06bzzzjtxj5GkHkWvkDknk8w5mUxbOw17tZ3mXzbT+NNGeg718PHVH1PycAnT103nYpaoyWbluvjnjfFrstCM9LxscV1r63C+slHEOKz50tZsdLgHsNtuu43bbrttMKc6JtAZk3dzqgEVzwUP7rNuXHUuug910/r/WvE7/fgu+kTOaLsQ4+2fCnFdMSsoOoXCvy0kb1ke+gx92OOFRsN0nVEIAKZcA9Y40TAAlJSgTisTz6FXyTJ7mJjhoM1p5ezFbOa0HZJilEQiSQpDjoHcpbnkLs1l2j9Ow+/y0/WnLpp/2UzzL5vp3t9NzS01nC45TeWOSnKuEap5aKzLgLumdTrU2VfCrh70ig+Ajh4DGDqFiD5c8QmSS47W1lb8fn+YWA1QWFjI8ePHo96nqakp6vFNTU3B27XrYh2TCLGeJ/Q5ItmyZUvQ6T5sdHcL14sthnN7mHazmaeYKZhSQMHKAtgKjpMO2t9pp+nlJnqO9HD+hfOcf+E8ed/I4/L/dXnYtuQzZ0BFoXBuBplzknveUDfUzMvFGHPKXox6rBpl2tS+iUx9PUyfDt/9LsyYkaqXHZsU7RSQSEYD5iIz5qLREb2gqmKHs98ZslPZ0ddvxt/T+9MrsPs6e+PyOkSzPt/F8Lg8AuDv8uPscuL8PEZvCAXRe2au6D2TOS+T7MXZmCZF35kU2XzUPNWMo1uPFT+WJjemy+LHVoUJ6cZwIV060iUSyVCxVdqY8fgMpvzDFE7efZLWN1s5+9hZWnbU0/Hd6aiUYJg6cE0WWoNNnw5//COcPTsMLyDdJBvPMk5rvrQJ6ZLRj6JTgsVh9uJsCv6iAEOWge7D3VgrrQQcATyNvRmh9SInVHWrtPxbCy3/1oLOqqPo1iJKHinBMs2Cq86F47gD8zQziqLg6E02sNkGjoZBp0O95lr42UUUrws6OynNuUib08qZoz3MuUqKURKJZGjoLXryvp5H3tfzmLFlBo0/a6ThfzXgrnNz5PojXL79cor/rjjxfPRe1Lx8oAeDCXDCxS/aobRNLP4NR3yCRDIKeOSRR8Lc8g0NDelvoJWVJXar2e2iYI9khKKVrOVWrPdamXLPFDr/0En9M/W07mzl4rsX+WjuR5RvK6fo9iIUReH0aXGfsrLknye02WhpKej14PCZOT/zK0xuPCgWESyW4R+LhnmngEQyXlAUReSnm3WQO7THUgMqvos+PC0evM1eMd9rdIvmfbUunF84cX7hJOAI4DzlxHnKSeubfXZL21U28pblkfff8si9NjfMXBXWfPSYgwKPBw86chdnMeWv48dWaeMaSCFdIpGkD3ORmSs362kxHefzX5XgOG1F3XQMHVMpznYAmXHvHxqvp5UytbVpPeX0M5h4lnFa80khXRIkmoPAUmbBOEnk6OV9PY/sqmx6DvXQurMV9zk3jT9t5Pz/Pk/xHcVM/PZE/C4/ZptwbWjNFjSj2EDNZdQpJcBFFKsZ2tooM9TxMVOonbAA7vlzKUZJJJKUYSo0UbqhlGn/OI2aW2tofbOVE3ecwH7UTsvcGYCOKL0eo6IVUsY8qxDSv/Ud+KvAmG6wIhk6+fn56PV6mpubw65vbm6O2dulqKgo7vHaZXNzM8XFxWHHaM3aE6GoqIgDBw70e57Q54jEbDaHNczq6upK+PkGTUmJKPAPHw53wcCoiFZSFIXcr+aS+9VcnF84Of53x+n8fScn7jhB21ttXP7i5Zw5I1ydgzGKh+YIm0zCdH76NJxacTeTS0ew2dMo2SkgkUhio+gUkZU+0Qizox+jqiqeZg/2z0QPiJ4jPXQf7sZxzIH9Uzv2T+3UP12PYYKB4r8rZvJ3Jwd33GjNR0/vc/F/dvjxGvQ8tsGMp8FNz9GemJEJoUK6Frs3ZYr4XQrpEokkZfTGkRR4W8n623I++j/XY+7J4K+ox9TaCDU5cfWn0BpsTAjpg41nucRqvscff5y3336bI0eOYDKZ6OjoGNTjyBm+JAzNQZA1Pwtfmw/n5058bT6yFogs4eLbiinfVs6Xz36Zue/NJfe6XFSvSuP2Rj771me4Trvw24VQrgnp1t7o4gGby2irepMLYNMmSr8+E4DamcukiC6RSNKC3qbnin+7gtLvlwJQv7We7CeOYiSQsCNdyyk2Zoiv1ItZJcOTQSy5pDGZTCxcuJA9e/YErwsEAuzZs4eqqqqo96mqqgo7HkSPGO34srIyioqKwo7p6upi//79MR8z1vMcPXqUCxcuhD1PdnZ2+l3myaDTCZdMfr7YOtrZCT6fuKyuHlXRShmXZTDvvXnMeHIGilGhdWcrH111EJpdwNAc6ZrYNFOUTZw6rRNj0Jw5IzMWhe4UiIZswiyRXBIoiti9PGHZBKb94zQq/k8Fiz5bxJILS6j4vxUU3VGEaYoJX7uPcz8+x/6Z+/n0zz6lc1+nuL9Ooc2YwRkymZIfoP5H56jdUEvt5lpqN9RS90Qd9prwcUKrqYCgUiEd6RKJJKVExJFYppiYueILAG7nDDM9F0QcSSAQ+yG0PjWGMSCkR8azZGeLbY5aPEtra+z34xKr+TweD6tWrWLNmjVDepyRn1lIRh22ChslD5dQuqmU6eunU7qplJKHSsK24SmKQt71ecx7fx7zPphH7vVCUO/5uIf2t9sJ+P047GKCZwt0owYCuOvdWCusMZvL9DXNEhPA0i8JFetM7cANtyQSiWSwKDqF0o2lVL5Ric6qI+fERb5FY+LRLr2OBGOGGKsGubAtGYesXbuWF198kVdeeYWamhrWrFmD3W7n9ttvB2D16tVhzUjvvfdedu3axU9+8hOOHz/O97//fQ4ePBjsSaMoCvfddx+PPfYY//Ef/8HRo0dZvXo1kydPZuXKlcHHqaur48iRI9TV1eH3+zly5AhHjhyhp7cj3De+8Q0qKyv5zne+wyeffMJ//dd/sW7dOr73ve+Fuc5HBRUVwiUzfz60tcHnn4vLBQtGXXMjRa9Q8mAJC/YvIOPyDLznPWzgGBOzA+TlJf94oc1GQfQSBTh5MlVnPEi0nQLnzomdAaFoOwUqKsRxgYCYeR49Ki7jTFolEsnowFRgovDmQmb/79lUna3iyreuZMKKCQC072rn8NLD1D5Wi+pXaWqCEuzc6K+n+3A3hnwD1llWDPkiTrR+W32YmB7q8lSU/tEukUOKRCKRJE2UOJKiuU2czTRhQmVBm5XAsePiuBiEZqSHCukjPkYNpq5KJp4lkmRqvlHAD37wA+6//37mzEmyMVEEMtpFEhVFp/TPMY9B7rW5zN0zl3M/PsfpB0/j/NxJ87NfoLjK0GHA/OE+HIcnYrpyGvkrywZsLqN1aL/kV/bGKanaLiORDDeT/nIS3lYvJ9ec5BbqOJVXDETfQROKVkiZrGLsungxnWcpSRWjYay66aabaGlpYcOGDTQ1NTFv3jx27doVbOxZV1eHLsRNvGTJEnbs2MG6det49NFHKS8vZ+fOnVx55ZXBYx588EHsdjt33XUXHR0dLF26lF27dmGx9C1ib9iwgVdeeSX4+/z58wF4//33ue6669Dr9fzmN79hzZo1VFVVYbPZuPXWW2M2fB9xKipEE6NkmiONIFlzbVz1vyfw4fLzVDq7+QfzF0B50o8TOomDEEf6qVSd6SDRdgqcOyd2BkyNaHyq7RQ4cSL5LE6JRDKqUPQK+d/KJ/9b+Ti/cHJm4xku/PICtetr6Xi/g9bls1lKKxP0XqyV1qA4bsg2oK/U46h20LqzFessK4pO6TeuAWhJZW43tLfDxInD/jIlEslYIkociaLA69mF/H1PI5ndUP/pLErixJGEjlWa/ux0QksLCceDppzBZJzD0OJZEq35BlGTd3d3h0VFRsZIjiRSSJekBEVRKHmgBJO/leOPduDpNHMdrVSThcFgJVM5ST4HsJENRP8QRxZO2jZnKaRfWmjbZaqqqvj5z38+0qcjkSRF8e3FHLr/LAUuD+qJJpy1E/B3+2NmeULf2GWWQvolxWgZq+6+++6gozySDz74oN91q1atYtWqVTEfT1EUNm3aFFf0fvnll3n55Zfjntf06dN555134h4zqtDpLo0mRr2TnIzjxzlb9E1KzxRxbUsDLc/0UHDv/KQeKrTZKKRQSA8Ehr4ooe0U0CZ0kY1PYXBZnBKJZNSScVkGlf9ayYRvTODzv/+cjvc6mLbvIHPJIjCxT0TXUBQF81QzjhoHrjoXGaUZUYV0s1mI521twpUuhXSJRDIkYjSr/9yZx/NYeYgTnDm2iPwWC9YYD6GZQHVGHWaz2DnT0CC0q7hCeipqrGgMNuMcYr4fQQaKZxmo5htkPRcZJ7lx40a+//3vD+qxUo0U0iWpIxCgKLAL45J2ju7/JgU+D4pepewbDSgq+I934vzFf2L54eUohv4uz8jCafp0cX1HB3R8Wkeu0jnqXWYSsV0GGFCkkUhGIzqzjvcKS7jx7Cnyf32aM0onAW8AvUWPdbaV/G/nh8VcQd/YZcnszUiXQnrKSYcjQY5VkmEnYpLz6aRpfHimiJs5x/EHW7DNOop1ReJbTUMjECA82kVV++/OTfgcU+USj7VTAOCJJ/qyOLUT1bI4q6tFFuesWeO+3hsNO2eS4fnnn+dHP/oRTU1NzJ07l2effZZFixbFPP6NN95g/fr11NbWUl5ezpNPPsk3v/nN4O2qqrJx40ZefPFFOjo6uOaaa/jpT39Kee8fe21tLZs3b+a9996jqamJyZMn87d/+7f88z//MyaTKe2vVxKdotVFZC/O5thNx7B/YmcuHZzPjO501Nv0eBo8+LtFj63IHcoaU6b0CelD3JEvSQOX2lglGedEaVavqnC+J5MvyGPDxD/gbpvEhd8bKP1a9IeINDOUlvYJ6TG/9lJZY4USmXGebF0V5f3oe6G98SwLFsSPZ0nD7tDq6mqmaN2mIebc7+GHH+bJJ5+M+1g1NTXMnh2js/YgGN/VqSS19GYrTVwUIPDVFgCq/Bc5/3Extb8ro7b2q9T+vyzqHjnar7EM9B+MbDYomOADoPbRF2DzZtiwQUy+amqG6UWNbTRxSvtxu90jfUoSyYhz2JeDCx06hx/PBU/cLE/oyym2ZMqM9HRRWVlJTk5O8GfLli0jfUoSSXJEaeR0umMi/5sy7Hl+/B4T1X9XR8DtS/ghI5uNan1F7XZobh7EOWpC/+HDYivurFni8vBhcb1WeyWTv6ntFAhtfDqULM5xRqqaYg0Hr7/+OmvXrmXjxo0cOnSIuXPnsnz58rCmxaHs3buXW265hTvuuIPDhw+zcuVKVq5cyWeffRY85qmnnmLbtm1s376d/fv3Y7PZWL58OS6XaNJ7/PhxAoEAP/vZzzh27Bj/8i//wvbt23n00UeH5TVLYmOdZWXBvgV02iyYUSk8F91l4Lf70Vl06LOEySpyXNOQDUdHN5fSWCWRRGtW3+3Q4/CaAIVJM5sA6Dkao4Em/fvUDBhLnI4aS2OodVWU9wOfT1xWVycezxKt5hsCWVlZZGdnB39iCen/+I//SE1NTdyfGTNmDOlcIpGOdEnqCMlWasnV0YWNcuy01uQz4fJ2zFle/BfsdH9ix7Wtnqn3TA1zdoZujwGgpoZSfQYtlFKrm8G8WYrc9ptiRvN2GYlkJFADKmUt7dSTwUzsdB/sJvvL2TGzPCHUkT7C0S7p2io4CkjUkSCRjFqiTHLOdOTiR4fu2gsY/nMiPedtdLx2kgm3JlbbRE7izGbxsa+tFfEuRUVJnF+ibqZAAH7966G5qYaSxTmKGe87Z55++mnuvPPOYLPk7du38/bbb/PSSy/x8MMP9zv+mWeeYcWKFTzwwAMAbN68md27d/Pcc8+xfft2VFVl69atrFu3jhtvvBGAV199lcLCQnbu3MnNN9/MihUrWLFiRfAxZ8yYwYkTJ/jpT3/Kj3/846jn6Xa7w4wj3ZfY39mlhD5Dz5ull/N3xz7F0mjHVZ+JZWpfvw5VVXHXu8lakIWlRFwfOa5paEJ6Q8PwnPtYZryPVZI0cCnOQSLiSM63iLEny+xmwl1f5dz+DuxxhPTINIW4Qnq6a6xU1FVpimcZDgoKCigoKBjW55RCuiR1hGQrXbDbaCCTcuy4OjJQdCqKz4MhQ0VfkYGj0dtPjAornHoHmzLzdXxEKbWeyaCvk9t+U8xo3i4jkYwEnSddFHkcfEI2l1ud+Dv82D+1kzk/M2qWJ/QVUtaRFNLTtVVwlKA5EgZCjlWSUUvEJEdV4czFPADKprShu8JH46FptPxHBxNuTewho2UJl5eLSdzJk7B0aRLnl4ib6cMP4dgx0fFvKLnmQ83iHAppnOyPZ3OCx+Ph448/5pFHHglep9PpWLZsGfv27Yt6n3379rF27dqw65YvX87OnTsBOHPmDE1NTSxbtix4e05ODosXL2bfvn3cfPPNUR+3s7OTCRMmxDzXLVu2BEU/SfrZ65rALCZyDW20vtlK4epCDFkG/HY/7no3pnwT+Svz+5kTYgnp0pE+dMbzWCVJkGS+K0fDHGSw3+0hcSTn3wvALiiebsL2rUpgL85TTvx2P3pblFhiXxJCerprrFTVVWmIZxlt1NXV0d7eTl1dHX6/nyNHjgAwc+ZMMjMzE34cKaRLUkdItlJ3uxUFFZcBLD4d3fVZ5NqaoLgYJTcXs84fU4xSjEpwsCkt+jLUQ21Hbt/zRG5PuRSai41SEhWn/vEf/5Hbbrst7jGp3i4jkYwErXV+TPhxKWayl2TT8dsOOv/QiW2uDUWn9MvyhBAhPXuEhPShNJcZY8ixSjJqiZjktDsz6PaIxevS3A6cpZ1CSH/fS7kvgM4w8KQlMhIPRMPR3bsH0XB0IDeT1QqnT0NxMVRVDS3XPBVZnIMhzZP98bxzprW1Fb/fT2FhYdj1hYWFHD9+POp9mpqaoh7f1NQUvF27LtYxkZw6dYpnn302phsd4JFHHgkT8BsaGvoJi5LU0dQEP2YWSzP34+/w0723G0uZBZ1FR9aCLPJX5kfdoRwtIx2kkJ4KxvNYJUmAZL4rR8McZKjf7b1xJOeFJEVxsYJpkgljoRFvsxf7MTvZi/rrJdEy0iGGkJ7uGiuVdZUWzzJG2bBhA6+88krw9/nz5wPw/vvvc9111yX8OFJIl6QOLVvp3Dnq/2CggAD2PD+WFj1d9VlkX5WJbvZsUGKIUaGFU+9gU1rQA4jtz2Fcott+L1VGYruMRJJ2orgXOtx6POjJy/CT9aUsuv7Yhe+iD895D+Yp5n5ZntA3dtlyRGHT2SkeelgW74faXGaMIccqyaglYpJzuteNXpzZjUXvxUQNBusifBeNdP6uk7yv5w34kJFuKBBCOgxCSB/IzXT+vLgtkfzNgSZgIfUi1dXivtrku74+8SzOZBiGyb7cOTOyNDQ0sGLFClatWsWdd94Z87jIGIvQiAtJaunpER8zMDHth5dRd8/n2GvslG0pI2NmBpYSS9CJriEd6elHjlWSmCTzXTka5iAp/G4/f15camNN5lWZXNx9kZ5Pe+IK6aF9akAI6f0avqe7xhqJuuoS5eWXX05JBJV8JyWppTdb6VPvDDzoyTE0YTQ7UQMGus1zoSAf6N9YBiIKp97BpswiHCe1kUJ6Orf9SoZEXV0dR44cCdsuc+TIEXp6eob9XJ5//nlKS0uxWCwsXryYAwcOxD3+jTfeYPbs2VgsFubMmcM777wTdruqqmzYsIHi4mIyMjJYtmwZJ0+eDDvm8ccfZ8mSJVitVnJzc6M+T11dHTfccANWq5VJkybxwAMP4PMl3mBOkgJqakTj4g0bwhoZt9ZfoA4rkw1uFKOCcZIRAN9FXzDL01phDWZ5Ql8sVWaugl4viietGEs7smnfoBlNY5VkHBDRyOlMoxDyyrJaoboa3aSJFHxTTK4uvHEBZ62TnqM9OGudqAE16kNGa8o3aCFdE/rPnRODWNgT9bqZbDbhloqGzSbcVokaHLQszvnzoa0NPv9cXC5Y0H+inmzTrUiiNHpFr++b7Le29mWTDgMj0RQr3eTn56PX62mO6HLb3NxMUYyw/qKiorjHa5eJPGZjYyPXX389S5Ys4YUXXhjSa5GkDu2/zmqF0r8vJrsqm4A9QMsbLWSUZvQT0UE2Gx1NjMWxShKHZL8rR3oOkuLvdm3uppU5tquEe9z+afSc9Mh+Dtrb4HRCS0vEwcNRYyVaV0lSgnSkS1KOq6yCDy8GmI6X3JkzMGfoaHvXSdenfrK+FkAxKv0ay0BE4dQ72JT+7hgghPTgyl46t/1KhkyqtssMlddff521a9eyfft2Fi9ezNatW1m+fDknTpxg0qRJ/Y7fu3cvt9xyC1u2bOFb3/oWO3bsYOXKlRw6dIgrr7wSgKeeeopt27bxyiuvUFZWxvr161m+fDnV1dVYLOJvWetaX1VVxc9//vN+z+P3+7nhhhsoKipi7969nD9/ntWrV2M0GvnhD3+Y3jdFIojjXmhpL+aP/BULslw4qh3BTDzPeQ8o9MvyhL6xy5ihcNllom6pqenbhpxWxmjTvuFgtIxVknGAtvvF54O/+AvYv58zb4rvjBmWxmAjp4K6Qs7/v09p/tdm/D1+Ap4Aeose62wr+d8Ojz+A6E35ysvF5cmTURxR8RjIzVRQABkZ4HCkLtd8oCzOVEWxJDPZH4btzGNx54zJZGLhwoXs2bOHlStXAhAIBNizZw9333131PtUVVWxZ88e7rvvvuB1u3fvpqqqCoCysjKKiorYs2cP8+bNA4R7fP/+/axZsyZ4n4aGBq6//noWLlzIL37xC3TScTdq0BJ4iopAp1eY/s/TOfqto3T+vjPmfQZqNtrUBH6/0Msk6WUsjlWSOCT7XTnSc5AUf7dHCumZV4m87J5PoxtsIncFms1inGpoEOv+YXLDcNVY4yDjfLQg31FJyqmuBr+q47OcfLIqJ0CmFX2OnoAzQPf+bhzVjqhiVLBwMijBwWZ6iRigutwWLvYYRWZCdbXcnjKKefnll1FVtd/PcAtTTz/9NHfeeSe33347lZWVbN++HavVyksvvRT1+GeeeYYVK1bwwAMPUFFRwebNm1mwYAHPPfccINzoW7duZd26ddx4441cddVVvPrqqzQ2NgYbY4HoWn///fczZ86cqM/z7rvvUl1dzb/+678yb948/uzP/ozNmzfz/PPP4/F4Uv4+SCIYwL3Q0qqjDhun504ha34WikmMUZ4LHrIWZDHlnin9xKzQ3TRazGp1NalxUg5E6FbBaMjdOzEZLWOVZIwTufvl1VdBVTkz+RoAyr51BTz0EFRUYCw2opgVAvYAAU8A6ywrhnwD3Ye7qd9Wj70m/HMeLSO9rEzMH7u7oziiBiKem+nRR2Hx4vhuqoqK5A0OWhbnnDniMlRE37ZNbM/OzxcTw/x88fu2beL2RElksp+Mm34YuZR2zqxdu5YXX3yRV155hZqaGtasWYPdbuf2228HYPXq1WHNSO+991527drFT37yE44fP873v/99Dh48GBTeFUXhvvvu47HHHuM//uM/OHr0KKtXr2by5MlBsb6hoYHrrruOkpISfvzjH9PS0kJTU1PMDHXJ8KK5x7WY+6yrRS3iOOHAb/dHvU+sjPTCQjE8+P1w4UJ6zlcyeC6lsUoSg2S/K5OZg6RjTpTi7/aYjvSjdtTIuofoNVjcnPThqrFi1VWjgeGYGw8T0pEuSTmffiouJy6wMfXeqbT+qhXXSRf2o3bcdW7yluX1aywDUbI+KyrIWLuGwtd6aO7JpPbTLiYUtwWdW3J7iiQWHo+Hjz/+OGzCptPpWLZsGfv27Yt6n3379oU1nwJYvnx5UCQ/c+YMTU1NLFu2LHh7Tk4OixcvZt++fdx8880Jndu+ffuYM2dOWPOs5cuXs2bNGo4dOxZ0xYbidrtxu93B37tH4WT/kmEA98IF01QAjIUBSh4uQZeh44u1X2AqMFHyUMmA25ArK8Uuwuo/XYSen6a/g/1INe2TSCQDE2v3yyefcPoTkctcNj8XdKAGVNp/046pyIT7rBv3WTe2ShuGbAP6Sj2OagetO1uxzrIGx6FoGekWi3iqujoR7xJlA1Z84rmZdLrhyd9MJncVBnZeDZRNOooXHC+lnTM33XQTLS0tbNiwgaamJubNm8euXbuC9U5dXV2YW3zJkiXs2LGDdevW8eijj1JeXs7OnTuDuwABHnzwQex2O3fddRcdHR0sXbqUXbt2BXcB7t69m1OnTnHq1CmmTp0adj7RhA/J8PLZZ+JSi9A2FZowFZvwnPfQ80kPOUty+t0nVka6Xi+c7Y2N4idWAoJkZLiUxipJDJL9rkx0DmK3C0NBqudEKf5u1xb+gkJ6hQ304Gv34WkUvbJCiTZWlZbCn/4UQ0iH0VFjjRRpbvg+3EghXZJyNCH9qqvEAGSdZUWXqeOLe7/AkGsYUIwKK5wqKii7UqX5Q6j95t+z4Nuq3J4iGZDW1lb8fn+YWA1QWFjI8ePHo96nqakp6vGaq0m7jHdMIsR6ntDniGTLli384Ac/SPg5JHEYwL3Q4s0FoMDmQNFlk71YFGbuRnfUcQvCtyFrdUD1H1ohcDj9HexlcxmJZPShOW62b4ezZ+Hqq/s+g71i8Bf/OQGAsukBQIerzoXjuAPbPBvus24cNQ7yVuShKAqKomCeasZR48BV5yKjNAOILTiVl4s52smTsGTJIM5fczNFormptIlQQ4OYCKXa4JDodu333oMDBwaelF3CC46paoo1XNx9990xo1w++OCDftetWrWKVatWxXw8RVHYtGkTmzZtinr7bbfdxm233TaYU5UMA4cOicsFC/quy1yQSfvb7fQcTk5IBxGboAnpCxem5ZQlg+RSG6skEQQC4icvT3ynhtYtEP27MpE5yJw58NxzqW/0PZjzjbx/hJh9/ry4vyak68w6rLOsOKod9HzaE1NID+3nENeRrhFZY2k1Y0j8HydOpK/GGgmGoeH7cCOFdEnKCRXSARSdQu7SXABcZ10xxahYhVNpqcKHH0KtOh1K03HGEsno5pFHHglzyzc0NFCpZYhIkmMA90JLtyiSJk0WX4+WGcL15j7nJuAJoDP1F6RDt/ZVzhaiWE1b4fB1sB8ucUsikQyM5rg5eBA+/lh02XO7hZDbmzXb6rRx2i6aJc7JqwdK8Hf78bv82CpsXPzPi/i7/bjr3VimiTFIb9PjafDg7xZxCKqqxmzKN3Mm7NkziIajiTAc+ZuJbNeuqYEXXhCT5YEmZXLBUSIZEaIJ6Vnzs2h/u53uw9F3V0aLS9CQDUclkjQQ6hS+cAFOnxbf8ZoIHe+7MtYcZN48+NKX4O23YxoKBj0nGsr5Rt6/dxHeedmVdHY+CITvdsm8KhNHtQP7p3Ym/tnEsIeJFkOVkJA+wLlw+eWwerXIs0pHjRVlESGt9U8yuwwvoTpMCumSlKKq8Mkn4t+akA6QcblwT3lbvHjbvRgnGPvfN0YmnjYgnTmT8tOVjFHy8/PR6/U0NzeHXd/c3ExRUVHU+xQVFcU9Xrtsbm6mOOQbtrm5OdgEKxGKioo4cOBAv+cJfY5IzGYzZnPfKnhXV1fCzyeJIJ4zMRDgQpv4WizI80EggKnQhC5DR8AZwFXnwjrT2u8hQxcBZ9vOoTCNVk82LQ4bBTZH34HpbGonm8tIJCNPqOMmM1OItVlZInizs1PkXxYUsPfcNAAqs88x0SCa7umz9OgtegJukY1uP2rHUe0ICul+ux+dRYc+S3TYU/19sRWRBoSZM8VlWoR0iO1YT4REJnADbdfu6YHmZnG/L30psUmZXHCUSIaVpiYheCsKzJ3bd33m/N4GfofiN/CLXCCEPmNp2sY2iWS8EekUnj5dCLgffwwffiiE6kmT4n9XRs5BmpuFq3r79piGgkHPiYZ6vjGc0ec/PAuAxRwgJ6dv7LFdZYPXojccjdYYOSkhPU78Hw0NomZJdQP0kYhXGWUN31OFFNIlKaW5WYwFOh2EGmYNmQbMU8246904TjjIqYq9lS+ycEp6ZU8y7jGZTCxcuJA9e/YEG1IFAgH27NkTc8txVVUVe/bs4b777gtet3v3bqqqqgAoKyujqKiIPXv2BIXzrq4u9u/fz5o1axI+t6qqKh5//HEuXLjApN7w2t27d5OdnS1d5sNBLGfiuXPw8cec7RF/H8W/3g7tJpRvfxvLDAuOYw5cZ2II6SE5xVZfF6W2Fs7YC6luKeBa29nwg9PZwX4o4pZEMt5IpSMnWpRLVxcYjeIxCwpE58/jxyE/nz/WCTVoaeFJyJoBgKXEgnW2le7D3WRUZASF9Lxv5AHgrneTtSALS4kQ1rWaCfobEMrLxeXJk4N7OWkjkQlcItu1P/9c/HvWrOQmZXLBUSIZNg4fFpezZ4dvLslcIIR0+2f2qDv9oolTGpqz/aOPUn++Esm4I5ZTuKxMCNQHD4qC4t57B25aqc1BamrgzTcHNBSgquD1CvHo+PHEvouHer5xnNHni0SPjWJrJ4qaA4q4b+ZVvePVp/0bqsbKSAdREqpq/xIlkXNJm0t7pOJVEtllmK65cRqRQrokpWixLuXlYvExFOtsqxDSj0cX0mMVTmVl4lIK6ZJkWLt2LbfeeitXX301ixYtYuvWrdjtdm6//XYAVq9ezZQpU9iyZQsA9957L9deey0/+clPuOGGG3jttdc4ePAgL7zwAiByOu+77z4ee+wxysvLKSsrY/369UyePDko1oNoptXe3h7WtR5g5syZZGZm8o1vfIPKykq+853v8NRTT9HU1MS6dev43ve+F+Y6l6SRSGdiTQ2cPk2TUkyTfxI6JcCVM11w+BicO0dGwXdwAK7TrqgPF7YImJVFRe75PiG9NEJIH8VN7SSScUMqHTmxolxmzRLbis+fF5PG7Gwxeens5I91wpG+dJ49aLFUdAr5387Hdc6Fp9kDevB3+XGfc+Pv9mPKN5G/Mr9fo1HoXzdpjf2OHROnMiq+WhKZwEFi27UzM4UDLTMz+nPFm5TJBUeJZFj4+GNxGRrrAmCZbsGQZ8B30Yf9mJ2s+eH1ULyM9EWLxOXBg+D3iwakEolkkMRzCut0oo5pa+trhBlKNDMChIvDnZ3RDQWqKjLAGxvB4RAxbQcPwpe/HD/OZCjnO8D9z9vF7rdiQwvUdQbrBNtVQvx1HHf0W/iLNlZpD+10ipcbs+H7cLu0hyLcD9V4cgk3fI+HFNIlKSUyHz2UjFkZXPztRRwnHP1vJHYmXmi0S9yVPYkkhJtuuomWlhY2bNhAU1MT8+bNY9euXcHGnnV1dehCvgSWLFnCjh07WLduHY8++ijl5eXs3LmTK6+8MnjMgw8+iN1u56677qKjo4OlS5eya9cuLBZL8JiButbr9Xp+85vfsGbNGqqqqrDZbNx6660xG2lJ0oTmTKythWeeAZ2Ow7m3wOcwa2IbtokWmCAKC4v7LFCM87Qz6kOFLQKWlFBZ1sQ7DVDTmh9+4ChvaieRjAsSEXQTdS0PFOVSXi4uW1rE9R4PzuYuDjaIoN+ld1aEPa6twsbUe6bS+qtWOvZ0BIX0vK/lkb8yH1tFn5snzJEeIThdfnnfnPWjj2Dp0hS/h8mSyATuhRfEzLOtbeDt2l/6Erz66piblEkkY4lgPnppOxxtCI6lik5H5vxMOt7roOdwT2whPUpG+uzZYqjt6RHDb0iJLpFIkmWwTuFYZoRFi8LF4Zyc/oaC+npRnPh84mfGDDE2vPaa+F6fMUN810czNwzV2Rzn/ue7xcJ8sakduvtuN081o8/R4+/04zjuCDrUIXossdksejk0NIgpZkwhPZUu7USE7sEK96kwnlzCDd/jIYV0SUqJJ6RbZwuLuuN4DCHdF92BoH2m7HYxv8qfMMwNEiSXLHfffXfMKJcPPvig33WrVq1i1apVMR9PURQ2bdoUV/ROpGv99OnTeeedd+IeIxkGNMfCxYswezaHjghxa0HxeXF7b2GRUSuE9IEc6YpRAZ2OyhUl8EeorrUKEU02tZNIRgeJCrr5+cItFW/SEPlY0ZxXFy6IiWWI8+qjhsl4VQPFBV5Kl83sd4q2ChvWWVYu/vYiHe93kHd9HiUPlfRr1B4mpOvDb1MUuO46eOMNeP/9BIT0dDeeGmgCN2UKfPCB6PAVmnkea7s2wIEDY25SJpGMJQ7t9wAmFhzYDp8fCRtLNSG9+1A3xX9XHHa/WPNBEA70q68Ww8WBA1JIl0iGxGCcwvHMCEeOiFi76dPFsYoiPvOaoSAzE9rbISND3C8nB4qKxPa5QEB8f7vdMHFi9LiRoTqb49z/fI+4T3FmN2T19SxTFIXMqzLp/EMnPZ/2hAvpcWKJNSFd20WTzLkk9Fo0EhW6ByPcpyoKZow2fL+0zlYy6okrpM8SQrrzRHRXZ6ytfBZLX5f22vfPwBNPwIYNsHmzuHziCfFBl0gkox8tS/joUXEZCIzs+YQUFoeaxGQuKKQD2GxYzG0AMR3pkWNXxdfFgFXtLBOrf59/Li4XLEhf/pxEMtZIx1gRT9AFkRXw7/8Ou3eLiZwWz3LoEDz2GPz6133nEvlYmvOqUzQPDUa5mExwzTVCFF65kj8uWgvA0uuMMXfYKToF25VisuPr9PUT0SF8J4wS5YGuv15cRlkzDqemJv111UATOJ9PTK4nToy9Xfvixb7FT21Slp8vJmWdneIxOjvF75fopEwiGSu07fucs40mAObN7OkbSw8fhm3byCrsAqDncP8GfrHEKQ1NmJI56RLJENGcwufOCRE7FG1RuqKib1E60kCQnS1WtzQzgtYIvCfkc11QIHLRi4vFXMjhEHXR5Mniw3zhgriuoEDUAO3t4rkrK8Xz7NzZV/8le75JvN6TbRPEIWX6fvfX4l0ic9JjxRIn1N9vqK8F+oTuw4fF+BoxzobVcaHCfTQihfuB/q8j/28GQotVnT9/zMyNpSNdkjK8XjF/gfiOdOcpJwFvoF+BFK9wKi8XZq5Pnv8jVxccHt4GCRKJJDWMRKfwgQgpLA6fF0L6/KIQId1uJ2OCH0gwI52+l3K+O4uOf3qMXKVT7p6RSJIh0aaUybqoYwm6LS3iOaurhZvKZBITBi1svL0dvvhCuAXmzRPncMUV4Y8V6bzqjXKhrU24fKZPh+9+lz/+o5isDeQSt8wQkWHOLwZYwIsSfwDCkQ6wd2+cnPThajw1kPNKW3zIzY1+/2hOqcheFw0N4jkWLBAiuqwHJZLhp3cB9PBTu4HLuSyvjdxJQlAP3fmTafk9MI+eT3pQ/WrYrpp4zUahT0g/cCCNr0MiGQ8k6xQeaHfZ5ZeLGJcTJ8J3l2kiuccjHmv5cvF7Z6eoP3JyxLEmk/ied7ujx40M1dkc5/4f1oro10W3XNbv/poLvefT8IW/WCbQhIT0ob6WZDPPk41XSUeG+xhr+C6FdEnKOHFCiOlZWX07ekIxTzGjs+oIOAK4zriwXh7ejTRazpTG0mtUfvc7hd/XTuOOa1Pc2TjdW5olEsnIdQofiN7C4uL+zznTkQfA/OImcVtvYWFZeDW8Bb4OH96LXox5xrCHCE76eseunByRVNDQADX2EqqqYjy3HHskkv4k25QymUW5aIJuSwvs3w8dHWKSl5Ehth+fPw9NTaLeCARgwgThejaZxLlonTxDH0tzXh0/3tdEy24XE8qVK/FfXsHeveLQgYT0jMsygDgLeHHiD0C8HZMmCbPXgQPwla9EHDCUxlPJMtAErq1NvL+GGNOSWFucx9ikTCK5pAlpvHzog6sBWGA8KsbYggJxTK8AY205jC5jPgF7AMdJB7bZ/fs/xFok1IT0Tz8VbRUyMtL3kiSSMU8yi9ID7S4LbQQeTRwuKRHf9SaTGAvcblFXGXvnVR6PqAO0lf90LKJHuX+9v5h6Zz46ncrVf9FfxIrlSI+lXSUkpCfzWqLNF5MVupMV7lOZ4R7KGGr4LoV0ScoIjXWJtl1Z0SlYZ1npOdyD47ijn5Aez4Fw7exmHqeI3128CpQPIh54CJ2NR6NDViIZawxXp/DBCNO9hcXhD/8LgBk5reQaeqCzr7DQ/9WfY9p+EU+TB9cZVz8hPZojobJS1BfV1UQX0uXYI5H0ZzBNKZNZlIsUdEF8Bh0OsQJ24YKY5GVnC4H3+HFxjOZMb2sTE8DKSiGku1xizLniiv7Oq8hcb52OY58KA1ZmZvSde6FoQrrzCyeqqvaLb4nlhNLQctL/7d9EvEuYkB4ICKv6vn3RO2ENpa6KxkATuJIS4WSrrxfvfTKZ52NoUiaRXLJENF4+FJgHwAL1kFioXLy4T0y32VAaGsi8XE/XJz56DveEC+kDLBJOnSq0uuZmEckc06wgkUhiEzlnevBB8V0bbw6VSK73pEmwerVYwY8Uh//8z0VEnlaDmc1COPd6RW3V1SUiYHJy+h4vHYvoEfff/8dC+He46iolqm5sqxRXes576DrURda8LBSdMjRHeqKvJdZ8MXJXZL+THuIiRKoy3McwUkiXpIx4+egaQSH9RP+Go/EmhVWXt6FXCjjbNYGzHTlMz+0MP2Awq2Kj1SErkYw1klk111bZu7vFLGn//oGb/sHQhOmKCg7PngBvwfyc0yK3LaKwsJQdwtPkwXnaSdaCvqJBDajQGw8XOnZVVIiYZS3uqt+5jrWxR7rrJalgsE0pE3VRRwq6WVlCPDeb+5qFaluM3e6+7EePR1xqTilFEed4+rQYK6KJw71RLsyYEXz6P/5RXFZVxTZfa1jKRLSLv8uPt82LKd8UdvtAOcIQLqSvX997pTZW7tsnVKjcXDHbmz27T+iCwbuNYo0FA03gQIyLY6gRlUQyLojSePmQ6woAFhQ2iIXK48fF51hRggJM5pwMuj7ppudQD4W3FAYfLpFFwkWL4K23hFYnhXSJJEnizZnmzIl9v0TjQb72NfETrRbQ6fpqsClThHmhvl4URTabeHxFSf8iesj99/8fcdWXv9z/MHuNndZftaLP1OPv8XPm4TPkXpdL/rfz4zYbBVFaqWp0g2mscwkj3nwx2q7IsBMf4iJEslEw4xAppEtSRkJCem9OuuN4HCE9yla+zEIbV088w/7Wmfz+7HS+k/tpyB1VsYXa6RQT4UBg4MnWcG5plkjGO4luD/vkE9ixQxR2Fy4IkcpkgoULxWcxlticqDAdR+w91CAmcQv+qhxWr+93u2WGha59Xf1iFjTnFPR3pGunFsZYHHuku16SKhJtSnnllYPPbAwVdPftE5Euubni856fLxxRqiqeK/R5HY5wp5TNJkT1VavEhCaBLcZ/+pO4vOaagd8KfYYe02QTnkYPrtOufkL6QDnCECUn/XTIWDlpEuTlicnr+fOifgp1jQ7GbTTQWDDQBE5mnksklx4RC6Bdlkmc9Ih4hPmZJ0HNFmNOZ6cYP3sFmKz8IvjXbroPhy/WJbJIGCqkSySSJBiKmSfZeJBodVjkorpmTtDrxTwoL0+MFaleRI8zB/zwQ3HI4sXhd7HX2KnfVo+31YtxkhF/jx/Vr9J9uBvXORf+HtFDK7IO07wgTqdItoq28S+h8403X4y1KxJSswgx1Az3cYAU0iUpIxEhPWOW2Koc1ZEebytfSQlfrdjP/j/0Culze59MaxD2xRdiYPnZz+D3vx9YwElHAwWJZDQyGpzCiWwPc7vhjTfE5dSp4osbwO8XxUJmphB4IsVmSEyYDgTEdsIYAs+hQ+JuC76eB3Py+p1ixozemIXT4Y3/tAkfhE/6NCE9zJE+3HEKw8FYdNdLRo50NKWMhibo7t0LTz4pPo9Tp4q/4/37RW2hZXT6/X0CkOaUgj6hee5c+O//PaFxVnOkD5SPrpFxWQaeRg/OL5xkLwp/PwbKEQZxuloEwv59Ab66N2SsBGGXOn9eTIhaW/tco5C82yjRsSDeBE5mnksklx4RC6BHmkXj9mnGJgo6T/VvvNwrwGQ6xZjWc6gnLL4qsu9MNGTDUYlkEKTCzJOKRt+R3/WhO5Cj7AoeMnEW+b0zKzh4UBwW6khXAyqtv2rF2+rFWmnF9YUwUqmqirXSiqPaga9DGC4ixyqzGSZPFm/NF18MUkgfSKsaaFdkKoRu2dQ9LlJIl6QErTYCYRSLRSKO9KgOBJ2Oa/96Kj/6A/zuZLGY1DqdYhLc3i62BS1ZAlZrYgJOuhooSCSjidHiFB5oe9i5c+L8jEaxqt7ZKT7XEycKR3pLS/i24FCxGQZeFPvww74tcFEEHvv/vJcTJ0QG8vz50V+CZYaIWYh0pGsTPugf7QJw9iz09EDmuTTHKYwEY9FdLxlZ0tWUMho6nagbqqrE80F4s9ALF0RupzZhqajo+6xGun0ScPfU1Ykfvb6/6ykWlhkWOv/QGbXh6EA5wtCXk/766/DBf3Ty1daIsXL2bDHetraKmd+FC+J1dXUlNwlL5Vgw1O3ao2HxWCIZT0QsgB46L4T0BVObxS6eKI2XqajA5g6gGBV8F32469xYpos6K5Gx7WrRy5RTp/qmgRKJZABSZSRMxaJ36Hf9nDmxo2CGygCL/EeXPYjTWUZurmjVouGqc+E47sA8zYyiKOis4lwCjgCKomCeaibgir0zcMECMZ374x8HGT+ViFaV5K7IQSENDjGRQrokJRw9Ki5LS6ObyDSs5UJI97X58LR6wrYqD7RN+Zqbp6H8vcrJnsmcr/NS/PmfxGTv8svDJ7iJTNpkAwXJWEUTET75RDi8XS7xhTdSTmHtfK64Qnwujx0LL2Tq60Uh4PWK84zs4q4o4jOqbQvOze0vNscrNKxWsWJfXCwqmSgCzycvHkBVZzN5snBvRiMRR3ro2JWfL4aklhY4/p9nuPq9NMYpjBRyZ48k1aSzKWWiz5eXJyZ1n38u8jsNBvGZNJnEuDRIt48W6zJ/vthgkwihDUcjGShHWCMopP/RyIbSiLEycuGgo0NcLlmS3CRstIwFqVg8lkK8RJIcEQugmpC+cHqbyLGK0ngZQGfWYbvCRs+RHlp+1UL+ynwsJZaExrYJE8RDnjwJH30Ey5en/VVKJJc+qTQSprrRdzoahyewyP/hq58DZSxeHP5V7+/243f5MdvE7kS9TS+ud/iDv6v+2GPV178u4qf27IEH/nEQdUWiWlUSuyIHjWzqHhUppEtSwpEj4jJerAuIQcdcYsZd58Z5whkmpAcdCDG28uXmwrx5CocPw++vupubPGf7tmOHTtwSmbTJBgqSsYgmItTUiA9lVxdcdplQh7Ozh98pHClquN1iJ8np00I811bNKyvhl7/sK+xCu7ibzULA6u4W94f+YnO8QuP8eXFbHIHnUK/AtWBB7JeiOdLdZ92ofhVFLx4rKKTrCW5L1qishN/9Dmr+7ShXk0CcgqqKbY3l5aL4S6Tfw0gRCIjzbmoSkRfRuulcSu56yehhuJtSxnq+r3yl7/lSsK012VgXSExIj5cjDH056fs+teIqz8ISOVYWFIj37dw5sfL30ENCSE/mPRwNu/wGGzMVKpwn0+BaIpEIIhYkDzV8F4AFOV+Iz2WUxssg8ofpHWYaX2ik51AP1tlWvG1eYOBFwkWLpJAukSTFeDMSJrDI/+F/iLolstGoPkuP3qLHb/djyDaEOdIB/HZ/8NhoddjXviYu//D7AJ7HnsJ0qjq5uiIZrUoK3SOCFNIlKWHXLnG5ZMnAx1pnWXHXuXGccJBzTU7w+kQcCNdeK8aT31UXcFNGhgigitYKOd6kLRGHrGygILnUCBURNKfmxIlC6Ozq6nM9J+sOHKw7L5aoUVcnxPG/+iuxil5SIq57882+wi4nR3wGz58X5+zxCGHdbI6+0DVQoWGzCUd6NGw2DrdMBWLHugCYJ5tRTAqqR8VdH7IFOU5OsSakV9co8I0B4hQ++0yINx6PeM+///2RFXDi/b9rCyQHD4pzPndOjMWRMTVjrSCXDB/D3ZRyoOcb4rZWVYX33hP/TqTRqEasSClIrNkoiFMXOek6Dliv46vn/qP/WAnitS1ZkryIDiM/OR9stEzoYm8yDa4lEkk4vQuSjtffoubN3ubthk9jjstaE7+giQoFQ76B7sPdOGpE/Ge8jHSAL10d4Je/1HHgt13wt+1y94hEMhDjzUiYwCL/hxfEAl+kkG4psWCdbaX7cDf6Sj16q3CkBxwBVFXMBel9+6KNVVdeCQUTfLS0Gzjwvp2l8/PT29hVMuxIIV0iGMJW1o4OsW0FxOd9IKyzrVzcfbFfTnoiQvpXvwpbt8Lvj2TB4kFM2hJ1yA4lV0puC5YMN5EiwoULokFebq74G4zMGE/UHTjYbfLxRA1tAau6WmxF0+miF3aa2NzSIpzpU4XYTXV1/+IhXqFRUAAZGSKfM8ZYcajzMiC+I13RKVjKLDhPOHGedgaFdE3MiuZG0N6i6otF8eMUWlrEYkdWloifKSkZWQEn3v879C2QTJ8OFy+Kv6XGxvCYmkDg0nHXS0Ynw92UMt7zRd4WCIidJQk+96FD4uNkscCyZYmfkuZIdze48bv86C364G0D7eLTCMtJt6zgq/l7Uz8pG+nJ+WCiZUIXe5NpcC3HMYkkOhUVHFk2i8APdBTleyn+8T9GHRtDm/hZZlqwf2rH7xCuT32lnq69XQDBnX9Rqalh0fEDwK0c+DCAun4DSsUA9amcn0nGO+NNnB1gkb+tVUQGQ18DYw1Fp5D/7Xxc51w4qh3BWsvf48dR7cCUbwqOUdG0K0UN8LUpn/N6eyV71K+xNPt34obhbuwqSRtSSJcMOVPyN78RsaFXXBHepCEW1lm9DUdPxBDS40wKv/IVcXnscxOtfzmf/M/3Jj5pS8YhO9gvkNHS3FEyvogUESKjUSIzxhNxBw52m3y08wklmqgRK6f4iivg44/7XlN7e/TiIV6h8ed/Dr/+dUyBx322ic86hUgfT0gHyCjLwHnCKdyh1/c+RJymWFqSS033tP5FnBanUFcnGpAWFYmVQm3sGamGnfH+3+vqxPsaukBSWSkmpQ6H+Puqrhb3O3Ro9LjrJWOTkdrKOojv+ZdfFpcrV4ohOFGM+Ub0mXr8PX5cZ1zYKvoW5BLNSAe4/nohpO/5tIAN29MwKRvpyXk815mqiu/C5mbxmrW6MHSxN5kG13L7tEQSk//4jfiML73OGPOzEtrEz9fmA/piEhRFQTGLMc3X7Yv+JL11yrwLXRh0f0uzO5dz5pmUHDokFsGizefk/EwiEYwncXaARf79n4maYdYslQkT+tdStgobU++ZSuuvWun+SJjP/A4/WQuyyF+ZT9OrTUCMOqyujq/bPuR1KnmvtoyN/K7vtuFu7CpJC1JIH+8MRSzr5d//XVz+j/+R2FNmXC4cVj2f9OCsdWIpsaDolKAgFS/vMz9faGvHjsEfilbx7fbPE5u0JeuQHQwpeC8lkkERKSJERqOEZown4hQe7Db5WOcTSTRHfKzC7pZbhMu5sDB+8RCv0NDpYgo8x3Tz8QX0TJggPrbx0GIWQhuOxhOzrrxSXJ7qKaTlVCcF87P6Lyw0NwvH/Pz5/V/XUAWcZN1XA/2/f/SR+Ju67rq+20Ld9Y2N4m+rtla8x6PBXS8Zv6TDfTiI73m3G3bsEP++7bbknk5RFCyXWbB/Ysd1evBCupYf/Ic/QG1GBaUPp2FSNpKT81iuM00Qb2wUi30vvCAW+RYtCl/sTbbBtUQi6YeqigU7EFp2LEKb+AVcYldfwB4I3h7sNxNNRw+pUzLmVHLVh80cOj+Z/ecmU+L9AL74Aj79FObNE2NO5E46OT8bFzz//PP86Ec/oqmpiblz5/Lss8+yKNJyHMIbb7zB+vXrqa2tpby8nCeffJJvfvObwdtVVWXjxo28+OKLdHR0cM011/DTn/6U8vLy4DHt7e38wz/8A2+99RY6nY6/+Iu/4JlnniGzt7t4bW0tZWVl/Z573759fDkyVyTdjBdxdoBF/g9dtwGweHHsOspWYcM6y4q92k7rzlbww5R/mIIh0xC/Duvu5mt5hwHYd24ado8Rm8kb8sAj2NhVkhLS9ml5/vnnKS0txWKxsHjxYg4cOBD3+DfeeIPZs2djsViYM2cO77zzTtjtqqqyYcMGiouLycjIYNmyZZw8eTLsmMcff5wlS5ZgtVrJTcbyM16JFE2ys0Gv7xNNWluFWBYIxHwIu70vHz0RId1eY+fiby8C4K5zc2bdGeqeqMNeY0847/Paa8Xl776YKoqf+fOhrQ0+/1xcLljQvyhKxiE7GFLwXkokgyZURIC+aBSrVQgJ3d3iS/jCBbHydeqUEDy//3144gnxtx/KUD8vkecTSSxHfEUFPPwwbNoE69eLy0ceEVkIc+b0uddjoRUakcdqAk+UseLQ3NsBMWxEa7cQiqVMCOk9h8QioBpQ4xZRhYViLqeqCm91XyuKuM5OIdho7u3MTHFgb6HdD5tNLEokK+DU1Ij/2w0bYPNmcRnt/zqUgf7fJ04Urk1fxOy2oEB0UPza18T5TpkivhDKyuQ4KBkZBvP3PxCD/J7/zW/Ex2by5ORiXTRiNRxNtNkoiOHw618XQtdLLxF7rBwq0cbwhx5Kv0iluc7OnRMvEsR33/79YvHP5xONDqdPF+LZCy+I78NoDa5BLD77fLEbXEskkn4cONC3jn7DDbGPC23ip8/szR12BlD94rMbHNsyo4xLEXXK0mmiDv2Pg5NFT6AJE8RxJpP4rD/zDPzsZ3J+No54/fXXWbt2LRs3buTQoUPMnTuX5cuXc+HChajH7927l1tuuYU77riDw4cPs3LlSlauXMlnn30WPOapp55i27ZtbN++nf3792Oz2Vi+fDkuV1//kr/5m7/h2LFj7N69m9/85jf8/ve/56677ur3fL/97W85f/588GfhwoWpeeFa5NzRo+JyoL/pdNUBo404c8APzUJUGmgdQ9Ep2K6w9e2W6d1JEzdiLyuLGRM7KclqxxvQ86dzEdF2sq4YEWpra7njjjsoKysjIyODyy67jI0bN+LxeJJ+rLQ40rUBbPv27SxevJitW7eyfPlyTpw4waRJk/odrw1gW7Zs4Vvf+hY7duxg5cqVHDp0iCt7LX3aAPbKK69QVlbG+vXrWb58OdXV1VgsQtzweDz8/+ydd3hU1dPHv1uym04ICQm9E0KH0IJUQUFRQAEFQVB5wYYNpSkIigoKiggqWEBR+IENLChKsRMivSV0AqEkpJBCerLn/WO427K9J5nP8+yT7O7du+fcvXfuzJwpY8aMQXx8PD799FN3TK164UhNSSN+/ZVKjDdrRll0lpAay5RmlELmJyNFSQnkH8xHcWoxRIlt0VX9+wMffEBN/PCuDSuqGg3NMy2NInWFqDxfe1YFTUW5ueBYMozDmEpdkyKFk5MpQkelIuVKpbIeKexIRLm18UhYq5frrlV3M9EXB6aRrLBW1qUguQA3Dt4AAOT9l4eUl1MQ2CZQG6Vuzpl1zz3AoUPAZtyDR7pcqhyp2b07sG6da5v0OZodY+13r3WzOXRODjnV9ZHJ6GZQUWE6U4HlIOMstkaYuys7zMH7vFTWZeJE8t/YS0Dzm470c4aOdG3wgZUa6RJTp1I/mzVraF1B6a6cVG9EThlHnTVooFu4VCpJdrVtS39DQym7Jj0duHGDXrO3wTXDMISeXN70cWMAtTB8OMVxmEO/iV9AbAA17BNUMkERrEBFEZV5UUerK3/YSE8Z1/4o3vuvF767PgAfxjZGsLyQnGQqlflMOgnWS6ol77zzDqZMmYKHH6ZAmVWrVmHr1q1Ys2YNZs+eXWn75cuXY+jQoZgxYwYAYOHChdi+fTtWrlyJVatWQQiBd999F3PnzsWIESMAAOvWrUNUVBS2bNmCsWPHIjk5Gdu2bcPevXvRrVs3AMCKFStw5513YunSpahfv772++rUqYPo6Gib5lJSUoISaUEXQL45u6smlC5yJsvQhA2oadgY/71Jn7clIUAmk8Evwg+ll0tRllkGdUM1cHPd3qQN2LgxZLFtMOifQ1ibfyt2nmuG21ucpfdYr/AaJ06cgEajwerVq9GyZUscO3YMU6ZMQUFBAZYuXWrXvtyiRntDgAHAK6+8AgD4TLJarGCzcKquOOssg2FZF0vRnPqNZYLaBSF/dz5Kr5ZCFAsEtg1EYVIhNCW2GYX9+tHfw4fJnxMWZsFok24s+/ZRBG5qKoWFtWlDxpKErc4qczeqdu2cPpYM4zDmUtdUKorOiY4mD05WFtCtm/U63Faas1i8XiRFRyqZdPy4oTPLm81sTDh4EhLob5cu5j+mXQS8RqvVmmINlBFK5B/MR95/eQDMLwDecw8wfz6wPSEEN76fjeBsIyUQoDAuVzXpc6Ysj7XfXamk8ykri6I7TY01KAioV8/02HxIDqakpGDhwoXYtWsX0tLSUL9+fUyYMAEvvfQSVCqVt4dXM7DHKLLVSHS2LJUlHNCZ0tOBX36h/ydNsu/rJPxb0GJd8dlig9ct9WcwxYgRJHovX6Yx3X23HYOoCk369EvL7NtHTeQDAyvrfDIZNfS5epX0wu7d7W9wzTCMgVzWFJXgq58/BADc3zsVgPlaefpN/IqSiyAPkENTqEFpWilEqdA28JOrTVxvRnpKz+DjaKm6gDOlTbAlqy8m1PpRtwgmZdIdP145k07Ch/QSxnlKS0uxf/9+zJkzR/uaXC7H4MGDkSAp/EYkJCRg+vTpBq8NGTIEW7ZsAQCcP38eaWlpGKyXUlarVi307NkTCQkJGDt2LBISEhAWFqZ1ogPA4MGDIZfLkZiYiHukEkMAhg8fjuLiYrRu3RozZ87E8OHDzc5n0aJFWv+WWWpCaVlXLBQY2YAnk+mWHxBAQfm2oO9Il4IZADN62E3b/Nade7A2Bdh1tjEwsNz7tnANZ+jQoRg6dKj2efPmzXHy5El8+OGHdjvSXf7LSQJMX9jYIsAGG+W7DhkyRLu9NQHmKIsWLUKtWrW0j7ZSZ7iagqPlF25SWkopy4D1si76jWVkMhmUdWgNpyy7DDKZDOqGam1KnzWjMDqaZKcQwA8/WNhQurEcPEjpvM2bU7TklSuU7puRQdtJDqDYWMvOKv39RUSQIR4RQc+/+opSgB08lgzjNOZS1+LigEce0TkKrEUKA6bT5CUsXS/65RTWryfDJC+PHBqWSi95ieRkihZXKs2XXNBfBAyOo/IrmkIN5Go5AtsGouw6lQIwJ7fatwdatCDxsO03E2mU0iJIRITp0i/2KlrOlOWx9rtfvkxRXY0bmx5rZCTJ2cLCyvsGfEoO6kckHD9+HMuWLcOqVavw4osvenws3iiFl52djfHjxyM0NBRhYWGYPHkybty4oX0/JSWFmr4ZPfbs2eOaSdtTesXSvfe99ww/484ybg7oTOvXk9rRsyddWo5grbSLrY50tVrnzP/4YzsG4I4yOe5CKi0zdSqdJ4MHU9kp/cAJwLCkliTLpAbXcnnlBtc+cs9iGJ/BSC7/G3gbLheGo5ZfAYYeecuqfJCa+IV0CdE6zMvSyxDSNQR+EX4AzMg2Iz1FVlpCznMAX6TfRjpnRIQug04/k84UPqSXMM6TmZmJiooKREVFGbweFRWFtLQ0k59JS0uzuL3019o2xlUXlEolwsPDtdsEBwfj7bffxtdff42tW7eiT58+GDlyJH6w4MyYM2cOcnNztY+kpCTDDWpCaVl7dEA7kNTZ7t1tz9BTRVKgTWlGqVYHAyzoYbGxuPXlPgCAA9ca4vqxyz5nC/s6+fn5yMvL0z70g6BdRW5uLsKlsmB24HJHurcEmCNYFU7VCeO6WeXl9Frt2mR0GgtYG5zLv/9Otkd0tPWUGKmxjCKI8pqVoUrt6wAgD5QDN4dgS73PBx+kv6tXm9nA+MYipfWGhpKBJDl+cnJsc1ZZu1GVlNAK6cWL9jkeqxmurDvFOIC5+rRRUdYjKfXrcFty7h4/rkuZvXhRJztMKTotWtA1EhwMjB/vuXq5NrJ+Pf294w4asin0FwEV/grIA0hGlOeWU5pfHTL4YEZHlclItAAkQkxioX6f3YqWLVGz5mqu2+LUnzoVeOYZ02N98UXyGNq7AGPTtFyrSA0dOhRr167F7bffjubNm2P48OF44YUX8J2UZuUhamQtT3NG0YEDwGuvAd9/r6vvaa+R6Mz5bw07FxiFANaupbftbTKqj1Tapfh8MYRG9732OtIBYMoU+rt1K62LWcVNBqxTWKsDK5fT7xQdrWseakxBAVC3LskzfVkmBDW4/uADYMkSr9+zWKdifBITcnljUkcAwMi2p6C+nmaT8y4oNgiNZzdGYFuqAxN+Zzgaz2qszUw2maFsrKeUlGBC2FYAwI7rcbjq14iuf+m618+kM5bbGg1lpdSurbvfOIO9NaqZGkVERASmT5+Onj17onv37li8eDEmTJiAJUuWmP2MWq1GaGio9hFivODj7h5w3saNCwVSMGjv3rZ/RlrkK8ss02YFAparKdTv3wpt2ghohBx/DlzgGr2iBsmatm3bGgQ+L1q0yKX7P3PmDFasWIFHH33U7s+6q0JilUCtVkOt1tVfy8vL89yXezJN1jgdpqSEatkGBND/587RWKTyATamnEj+hnvuAeTQACnm56PfWEYZqtQ2l6m4UWHwF7Ct3ucjj1C5hN27qTl7x45GG5i6sUg1o0+coKj0c+dIeereneZpSaBZu1E1akT78/c32RW6pqTvuLLuFOMgpurTOlKqRT9NXqrrLS0YlZWRF/rbb8lgGTGCnF+myilIJV6SkqiWgI9cAxqNzpE+YYL57aRFQHUQ3SsUIQpoijS0CFhXb+HPwrTuuQd4+21yXpWW0jpEJczUcLf7eDlTlkcah/Hv7u9PXVN79iTHekgIMHMmyTbjscrllUsMuUAOGmeMzZ8/HwsWLLB7P5ZwNCLBGapTLU+bMFd6paSEon/PnqWbeufOdC726GFfXXJL578QdO8vKqLFIY3GvnPRXAktM+f3wYPAsWMU2Hz//Y4eMEDdWA0oqKRU6dVSqBuQLLKn2ahETAyVx/vrL3Lyz51rYWN3lslxFFvTu23t03HrrfTw0bI1rFMxPomRTVSukeObZLpH39/uOFDX9rrjMrkM/k39kfdvHqCh51YXCfX1lORktFBfQrxqPxJK4/C/4KmYHnmUttPPpCsqMpTbqanA/v2klGk0wIIFztWUrgk1qi3gK+XyIiIioFAokJ6ebvB6enq6WV0mOjra4vbS3/T0dNTTK12Ynp6Ozp07a7cxDoAoLy9Hdna2RR2qZ8+e2L59u22TM4U9JeeqQok2Y9zUgy49XVfV4IEHbB+OgSO9zDZHOgAMGiTDiRPArrNNMNL2YZqmhsmapKQkNGjQQPtc33erz+zZs/Hmm29a3FdycjLa6KWHXr58GUOHDsWYMWMwRYo0sQOXO9K9JcCqFJ68AIzrZhUVAf/+SwZreDgtw0VFkTKxZw9w7RpF6XTtatG5XFGmwZZvBQAF7mlyAFj0C63qm5mPfmMZRVsFFCE3Hen5FRBCoCRVF11oS3RVdDTt/uuvgVWrKHjIAHM3lshIMnazsoDTpyka6fbbrd9IbLlRqdXAmDEUsWvcUNCao95RnLwpSlGeEsaLS/biyrpTjAtxtPmnvnP38GG64Pz8aDv9GnzHj1PTthYtqkwzp927aRE/JMRyreBKi4AhCpRdK9Nm00hNsUzW8rxJfDyJ2fR04I8/SOSYxBVN+pxp9Cph7NRPT6dyWOvWVZbxxoUFbXHEp6TYLatsVaQcRYpI8KScqm61PG3qO2PKKMrIoPOrsJD0kvJyWm06eJBqL+XlUXk2UxjXtzV3/mdk6Bovh4ZSOttff9mvd5k7v03c56V2PSNG0Jq9o8j95PBv4o/ic8UoOlukdaTb22xUYsoUmvonn1ASidnL0FYDNiWFduJu49yeOrB2LnpYlLt26FmsUzE1AiOb6M+UJrhWEIzwgEIMbn4OEPbVHVfVJUdr2TUqlWdTto2Rfvrg60eQsDcOX57uiekDDxpe61K2lSS3k5Mp+EmlIgVNCiJztKZ0TahRbQVfWfRTqVSIi4vDzp07MfJmOqhGo8HOnTsxbdo0k5+Jj4/Hzp078eyzz2pf2759O+Lj4wEAzZo1Q3R0NHbu3Kn1O+Xl5SExMRGPP/64dh85OTnYv3+/NnNv165d0Gg06Nmzp9nxHjp0yMC3ZTe2Bs+kpwM//lj1nK8u6Odnii++IFWzZ0/b66MDgF/kTUd6hs6RLlNS6UNL3Hor8P771PDdKWqgrAkJCUGoqXPbiOeffx4PWUn/bN68ufb/K1euYODAgejduzc++ugjh8bmcke6twRYlcHcBXDgADmj7rsP6NTJNYaIcTQRQM6w8nKqY5uZSc7kPn3ISN23D2jVitL2pfq9Zuawe9k+XMt6EGHKfAz4ZAKgllMt5pgYkxe0fmOZwqRCbfRmeW45CpMK4VfbT7t7W9OUH3+c/HpffAG8+aZRgKWlG4tMRs7AqCjTNaNNYeuNqlMn8sp5YsXXBQsy1TXKkzHCXqeC8WcbNwY2bKCo0XbtKkcmJiRQ4zZz2ogPNnP68kv6O3o0JeeYw+wiYB4tApZlkOEnZdmYQi4nZ9pHH9Ela9aR7grM/dY3blDpguBgysIxdg41bFg5wrxpU5Iz335rn9JmiyO+dWuqCRYVZZOctFWR8kZEgqNYKoV34sQJk5/xdC3PW265BXK5HN9++y1GjhyJLVu2mHWm29QUy9goEoLuYYWFtNAtBC10S+Wj9u6l8+fGDV2tW32MMyxMnf9FRbRyph9AEBjouOFhQ/bIlSvAp5/S/zeTDZwioHkAOdLPFSGsXxgA+5uNSowaRVO+cAHYvh0YMsTMhrYYsMnJwPLlwPXr7jXOHYmOt2PRwyx26lmsUzE1AiObaOOx9gCAUbHJ8FNogFz76o6rom7WHb7Z0N3mbBsp+KBpU9xX9xSe6VuBgxkNcXxvIdpFZVW+1mNiaOFv+XL6bLduOnnhaKaNL2bueAFfWvSbPn06Jk2ahG7duqFHjx549913UVBQoM38mzhxIho0aKAtEfHMM8+gf//+ePvttzFs2DBs3LgR+/bt0zrXZDIZnn32Wbz22mto1aoVmjVrhnnz5qF+/fpaX1dsbCyGDh2KKVOmYNWqVSgrK8O0adMwduxYbZbf559/DpVKhS5dugAAvvvuO6xZswaffPKJ45O1JXimYUPgm29It6pqzldns2xNIAQFEgDA//2ffcPRj0jXBjPYoIMNGEA/TVISqSJ6cUG2w7LGIpGRkYg07odjhsuXL2PgwIGIi4vD2rVrIXfweLmltIs3BBgAXLx4EdnZ2bh48SIqKipw6NAhAEDLli0RHBzsjqnah70pzc4YIhoNGY4JCRRhDlAqc2YmGaNyOV14mZn0elgYXXhZWbr0fFPcXAhY/jN1rBoZ8Q/8ZOVAhYIWAoKDyRg2cUFLjWUyN2ciby9F7FTkVyC4SzBqD6yNtDVkwNtqFA4YQL6YU6eA//1PF3AAwDVRmfrYsz9rUaWuSK1y0YJMdYzyZMzgjFPBlsjEM2fImd6oUeXPu6uZk4PXUkkJ9QcGLJd1AVBpEVCuov2XZZWhMKlQ2/fBmty65x5ypH//PUUluFXHMf6tk5PJGQmQ43rFCuCtt2gFQa02LPelVuucRZZK9lhT2iQ5aMoRf/EisHEjOdabN6d7lIucb96ISKiOSLU8Jbp3744rV65gyZIlZh3pc+bMMfjM5cuXKzdxNzaK9PUSmYzORaWSzkOZjG7yV69Sxlv37rbdy43S/rVR7a1b03uSou2M4WHlPv/yy7Q20Lu3BUe1Hfi38Ad2AMVni7WvOVIjHaDL/MEHSYV4/30L47NmwKamUlSnVJPcnca5o+ndzpTMciDyi3Uqap68ZMkSpKWloVOnTlixYgV69Ohhdvuvv/4a8+bNQ0pKClq1aoU333wTd955p/Z9IQTmz5+Pjz/+GDk5Objlllvw4YcfolWrVtptXn/9dWzduhWHDh2CSqVCjrnGkoxr0LOJSmM64LsTdB2MbX/MIRvLr+5N51T6zYh0aZHQjmybOvGtcecwge9/AL5sNBuL5hZWvtYl+/b6ddOBVI5kULqp9IS7cXX2jCm8teh3//33IyMjAy+//DLS0tLQuXNnbNu2TRtgcPHiRQPHWe/evbFhwwbMnTsXL774Ilq1aoUtW7agffv22m1mzpyJgoICTJ06FTk5OejTpw+2bdsGf39/7Tbr16/HtGnTMGjQIMjlcowaNQrvvfeewdgWLlyICxcuQKlUok2bNti0aRNGjx7t+GStBUrVqaMLUKiKzldX+3NAhRlOnqTDZG/ZPVM10m2RU+HhwC23AP/8Q7agtZgTkzgja6piWR83cfnyZQwYMABNmjTB0qVLkZGRoX3P3lKWbnGke0uAvfzyy/j888+1z6UVv99//x0DBgxwx1Ttw96UZkcNESmCJiGBDMiwMFqBj4yk/fvdjP5WqeiCklKxrdXRAoDNm3H4TBC+vdwLMmjwQujHQHgd2ldGBs0vIsLsBR0UG4TAmEAUnCxA1pYsoAJo8HgDiFK9OlMK2xQnmQx47DFg+nQq7zJlys3DKo1dqs18/LihEeRIrV5nInpN/TbOpFa5cEGmOkZ5MhZw1KlgLTKxXj1dzcmGDV2i6JhFur4PHyaNJC2Nzn07rqVffiE7qkEDoH9/61+pvwgolaEqv16OkK4hgAzI2Zlj1Zk1cCAd7qtXSeTfTLhyH9JvvWsXaW1yOT0vLjYs7xUbC5w/bzpa19mSPaZkVUYG7VejoXOjpIQUfRc537wRkeAo1a2Wp019Z4yNopISnV4iBDm869XTRZ8HB9PiT3Cwffde6fzfvZtS1urWrSyb3OTkOHZM12R0yRLTvS7tRWo4mn8gH0UpRfBv7O+wIx2gjL6VKynT+88/zchBSwasRkMlAVUq10R1WsOZ9G5HSmY5GPlV03UqqXnyqlWr0LNnT7z77rsYMmQITp48WSkLBtA1T160aBHuuusubNiwASNHjsSBAwe0NqDUPPnzzz/XBlINGTIESUlJWhuwtLQUY8aMQXx8PD6VUkHcSU13SujZRF/9FobsokBEBeWjf9hhICnVbhvLOCLdnkhPfSY8KMP3PwDrf6uL1z8y8/WuLBWh0ZA9l5ZG9ywhKgt8H8zIBNyfPePtRb9p06aZrYTwxx9/VHptzJgxGDNmjNn9yWQyvPrqq3j11VfNbhMeHo4NGzaYfX/SpEmYNGmS+UE7iqVAqe7dKWilii30aHGV/0UPKRp97Fj747tMlnaxUU498wyZrR98AMyebTkb2iSOyi53l5SuYvfD7du348yZMzhz5gwaNmxo8J4wbkhtBbc1G/WGAPvss8/wmVSU0tcwdbMFLKc0O2KI6EfQ1K1LhTmVSvLeXLtGBmtZGUV7lZbqIr8A63W0bjb9eiXlBQDAfS0OoJ3yJOBXh4SxcYS7mQtaJpchODYYyjAlynPKUZpWCmUonYq21JnSZ9IkYM4csvP27gV6hJhprHrunC7S0tGa5c6mCbuqrpWnFmT04CjPaoQjTgVrkYmFhRRZHBLi3ma7kjKQmEjeqvJy+q4OHUyXajBzc5fKujzwADV/twVpEdAv0g/5iflQ1lai8azGSFtLmTTWUpDVamDYMArE3rLFRY50W5SX//6je0v37vT8yBFdea+MDCrppVZXLvflipI9xrLKuIyHtPgnhMcjY1wZkeAoNa6WJ1DZKAoJ0dXXLimh67hNG929raCAdJmJE+lctufeK5eTvhUQANSvb9qj7QYnx8yZdGmOGkXrUs5SkFyAG4duAADy9uQh5eUUBLYJRGk6OZ3saTYq0aYNBSJ88AEZd/v3m5CFlgzYkydJh4yPd01UpzXckN5tETdHmVZXncobzZMBaEtKecQGrO6N3qzpFdL75eUoHT4aL2/qCgB4ptmPUFzPdMjGkiLSJZnm6CLhXXeRyE9NpRgCvVYhOlzVkFo6D/btI3mYmkr3mTZtdFlPgPsyMp3Elxv4MQ5gLlDq+HG31Bj3KK4o03aT3FxdRvLkyfYPxVSzUVvl1MiRpC6kpFBZYoNKCrbgiB7k7prqttwPfczR/tBDD1nVv2zFbY50Rg9zN9v69S2nNNurqJuqiZ6SQo6QiAhympSXkxSJiDCM/LKljtahQzh0KQKbz3SEDAIvx28Hjih1jnnjCHcryoOqnooc6VdLIQ+gC8pepSk8nNJy1q0DVi3OQY8oE8Li4kUanyvqzzsa0Wspuik2ls6NVauAp56ifRUUmN+3vTVmXeCcqkpRnvp4I8U4OzsbTz31FH788Udtat/y5cu15aVSUlLQrFmzSt+dkJCAXr16uXD2DmLqhmdLal2vXsDw4VQKxB3NdiVlICODriOVis7369fp+unZ0/B812h0Y9G7uecMGoUff4wBYL2sizEyuQzBXeh3LMsqg0wusyty6p57yJH+zTfAG2/Y7sQ3iS3Ki7EzKCfHsLyXvz8t7ko9MYwXQ50t2WMsq4zLeOjfMzwcGePKiARnqFG1PCWMS68AtKDSooVh6RX9bJZbb6WHvfdeDztgd+6kjBelErj5kzlFQXIBLr13CaUZNyM1izVQRiiRfzAfhScKAdjfbFTi1VepLN7hw1TP3aRRZ86AbdWKZKy5LCNXG+duSO+2iJsanElUVZ3KEt5qnuwINjVGNkV1b/RmTa8wev/jC8Nw/vogRIeX4JlPOwJ1eztkY+k3GxVCGDTxswd/f9Lr3n8fmDsXGDRQA1mqjfqsPQ2p9c+DJk1ID718mZzwubmkj0r2mKtlk4vw5QZ+jIOYCpTy9CK0u3CmTJse//sfrZO1bUtmq71oHelZZdCUkP1nazCDUkmBC889ByxbRvXZ7Rq+vXqQu2uq23I/BKr1wjM70t2NpZutVJJAcmQbpzTbq6ibiqBp00bnvPD3J0e6RkMR2uHhZAzl5dlWR2vvXiw4ThFtY9sfQ9vmxcCVCHKyREYaRrjboDyo6qtQmFyIkislUDeilXBHDMLHHiNH+safgvDm0FJEdjEau1TiJSmJGoE6a4Q4EtFrLrpJKodz5QqV4dmxg5xLUVHm6wbbW2PWg84pX4jylPBWivH48eNx9epVbN++HWVlZXj44YcxderUSul+O3bsQLt27bTP69Sp48ajYSOWjChbUutiY+nh6pVnfWWgQQOKmg4Lo3NcrdZdR3360Nj27KEojJKSSjf3b35rjNLSGHToAHTsaP9Q1A1IVpWmlUJU2Gfw3XknJQmdOwesX09Btg5hqzFv7AzSL6MB0O9SUaGTScaLoc6W7DGWVcbfb5wV5cHIGFdGJDhDjarlqY++UXT4MHUOLy6mc7C83Hw2i733MA86YDUa4GZgLR57jNQrZxAagczNmSjLLENwXDByduRAU6iBXCVHYNtA3DhAUeqOavJ16gALFpBh99JLFGsQFmZiQ1MGrEZDH/aUce6G9G6L+IjzwZd0Kmt4q3myI9jUGNmY6tLozVx0oDW9YtgwYOtW7fsFfmFYuPUeAMDL7b5DYEhnh20MKSJdlAmU55Q73EgZIAf6Z59R4uLXE77HfX6breuz9jSkNnUetG1Lx7OwkGyypCQK3Lp82fWyycNUx0W/GoWnF6HdiSP+FyP0m4w6UnZPcqRDQ+VdAPt8V488AsyfTybrtm1kF9qMvXqQOzPrbLkffvQRydaq2OTWRljCuRPjk6xWLfobGkonXEEB1Z/NzydHkKmUZnsUdVMRNJGRtDJerx4Zp8XFQHQ0OZebNSOlIetmZ/PRo+m5mQvuQNit+D5vIOSowMv9/qBt2rShcWdk0H6kpiJJSVaVB3X9mw6pK6VOKU29egFxHUpQVOaH2eemWhcW9qLRUGT/0aP0V6Oxfx+mfhupHMvVq+RYKiqi7W7coDI8MhkJm/fe00XtAbqbYmqqrsawcY3ZiAjDBZniYo84p6Qoz507d6Jhw4aoV6+e9uFp9FOM27Zti1WrViEwMBBr1qwxub1+inFsbCwWLlyIrl27YuXKlQBQKcW4Y8eOWLduHa5cuaKNnkpOTsa2bdvwySefoGfPnujTpw9WrFiBjRs34sqVKwbfV6dOHURHR2sffpJz0QQlJSXIy8vTPmyOnrIHyYg6eJDOn5gY+iudgwDd9Lp0oWv91Cmd7NC/GUqKTocOukhnZ9FXBkpLDZ2xxmWlAgPJU33tmk7eKhRAaCgq2rTD+4dvAQBMGO/AdYybkVNyABVUz9Oe1L7gYGDWLPp/wQKaikVMyR7j+4re/NC2Lb0uReTrO4MAclgrb2YRSftXKHSlxowd21LJnrp1Sabn5uqymmyQ8ZVklf73m5JVVSUyxsVMmzYNFy5cQElJCRITEw3Kq/zxxx+VyhWMGTMGJ0+eRElJCY4dO2aQNQPoSuGlpaWhuLgYO3bsQOvWrQ22kWp55ufnIzc3F2vWrDFoyj5p0iQkJSWhoKAAubm5SExMdJ0TXUKSFSNGkCe3a1fLssXR77jnHjrPHDmH7WD9ehKXoaHUbNRZii8Wo/BEIdSN1FD4KyAPojGWZZdBJpNpM/kqCioc/o7HH6fDm5kJLFxoYUNjud60qeG1rY9knMfGutY4l6Ljrd2DXIGx7NLHXfMzgS/pVNWJOXPmIDc3V/tISkqy/iFLTgmA7lu7d9PDETvBEyQnA4sXk4BauJD+Ll5MgQfm9IrYWNI/3niD/sbGAqGhWL7vFqQXhqJF7Sz8X+T3Or3DART+CihCKUWv7JquZIIjZauio4FZj9CC0+wfe6MkLMqyPpuZSX1jcnOpIXXfvrQTae4XLlDG8LlzukUI4/NA39ZWKmnbixfdI5t8FGnRr3HjxtpFv7S0NKcWvBgn0e8Zp1bTde5GHcjXOXiQytj5+VHDdUeQ+8mhqEWyqvQqGXH2+K5CQ6mnHwC8844DA7BHD7Ils85R/5A1J32DBsAff9B21mzVKgxHpDuKLfV+LN1spRTZnBxy+MTEmE9ptlVRNxdBExlJgjI1lZy3s2aR9/nSJdvraAmBBQlDAADj6v+FNul/AqpGFF7Zrh1JJpmMBHV2tk3lHFT1KZWv5EqJU02zZDJg+QuX0GdSC6w53hOP9DiGWxqnGm7kaKSjq2ohGv82+uVYIiKo2Z9GQ0qYvz/9TpcuUYvn5GTDKBdHasx6yDnlK1Ge3koxTkhIQFhYGLp166bdZvDgwZDL5UhMTMQ999yjfX348OEoLi5G69atMXPmTAwfPtzsfByKnrIHWyOtZs2iDimernWmrwxoNDpnrOTw1Y+kzsujc97EzX3t4a44lNMMYaoCPHJbFgD7nSAyhQyqaBVKr5TSIqCdsmvaNODdd+mS//RTcmSZxJTsad2aHFgJCeTcrjQ4o0VD40iUWrVI3kjlvoqLafGzpISOq6lyX86U7DGWVQ0a0PddukS/YVCQTlZVtcgYxrW4KG3X7L5dVF/THBcvAlLZ+tmzDUvkOkpFfgUqiiugDiI551fHDyUFJSjPKqdABOnQOO5Hh58fpRgPHUr+palT6WewiqcjxCXceZ7o4635GeErOpUteKt5siPY1BjZGHNOCSkj7to1sunefJN6B/hK6rp+g3Yp86dxY8PoQHONxaW5paRQFnW9ekBpKbKaxuHNfyko4dWBf8CvcT2nM19VUSoU5RWhNN254CpoNJge/hlW+U/G+YIovH9yMKbHJ5jXZ801pNbPGD5+nGRBt25k85o6DyRbOyuLsianTgVuv910fXkfqRXsSnylXB5zE2MbwpU946ookhktxVY4il+EHypyK1BylbJ37ZVTTz1FduDOnSSWO3WycwC26kHuyqyzpclyeTn5A9u3r5pNbm2EHemOYKtz1ZzSpX+zPXiQcmkDAqynNFvDUvqONJ7evelhTx2tjAzsSZThx5QOFI3e5DNyuOgL43HjaIEgKspm5cAgIt2J6AMAuKWfApNb/oFPzwzAY1vvwoGpq+Gn0FvlskdY2KJ02pOSIkWR1q5N50y3bnT8pHIskvMvNJTGqB9hm5dnWtg4UmO2BjmnvJVinJaWVqlsjFKpRHh4uHab4OBgvP3227jlllsgl8vx7bffYuTIkdiyZYtZZ/qcOXMMnPyXL19GW6kPgiuwN/3L0zc9fdmk7wyOjKTxSZHUKhXVtwwKIoNPj9xiNV7cOQgAsKDtV4jw62bqm2xC3UCN0iulKLlcojX4bJVdQUEUfPvUUxQQ9tBDJjq3m0qxvniRCqxLCwaRkWTcGje20l80NOUMatWKDESpvFfHjvR9psp9uaJkj7ETUyo5pVDQfap2bYqM8aBzivFRXJC2axY3OmBLS6ksSnY23d6N1mMdRhGigMJfgYqCCihDleRIv1iCsizKKBElJHsUwc40WwCGDKGqdz/+SJFSO3fqEn4s4oEFCpO48zzRx1vzq6J4q3myxzBlI0mZpYWFdG8LCyOHrK+krkv2anIylY/MyyM7ISqK5iA5l001FtefW2Ag3bP9/YGrV/Hm8fbIK/FHx6g0jG1/DKhwviybX10/FJ0uQsllXe16h/o/XLyIoHNH8Vq/X/HIb+Ow8K9+eKjzIYQHFJnWZ001pNafu5RFHhSkW3QoKTHtnJLJSHhGRZFupn9/qeZNaqvSol+1x1yZJmd7xlXhhaAffqB4IKXS+YxBvwg/FJ8tdigiHaBKz6NHA5s2USCDQz2ybdGD3FHWx9Ymy7m59NdkvUBUjSa3NsCOdHuxtTatRkMnUVERrWYb15eVbrYtWlCh3P/+c15RdzaCxtQFl5GBvN3HMOH4FwCAidG/oXXPcCC1wOkGnvoR6dqGfQ42zULjxnjz/s3YsqQrjl2LwvLEXnih9256zx5hYavSaWstRH3F6do1Xbpfs2ZkfavVdC7J5aR8m6pVXKeOaWHjaI1ZxqtEREQYOMW7d++OK1euYMmSJWYd6Q5FT9mDmxurOY2xbJJ6P2RkkDKXm6u7TiIjySiSDKCbLPyrPzIKg9CmdjqeaPcXEDLQ4eGYlF12KFJTpgBLl1LG8PvvAy+8oPemqeyAjAwy3jQanYxQKsnw1W9sBVReNDR2BhUXk/yRsl+EMHyenW36HuSM88rYiZmeTkbqyZOUlsjOKcYTuMkBO2MGnc61a9NtWE9UO4V/Y38EtglE/sF8KNoqoKxDKntZNjXkk0q6+EXa4vW2zLJlwO+/A3//DcycSc9twlMR4t6ius/PxXijeTJAvSSys7Nx8eJFVFRU4NChQwCAli1bGpSscgpjPQQwzCzNzKT7qBSN6+2a6fr2quQMrlOHogjz8nR6g+Rc1m8srp81GxlJ2ysUgJ8fTvvFYsWJ+wAAi27dAblMuCTzVRVFelXpFV3NO4ci0m/qsxO7JePdw2k4kh6N1/7qh3eG/ErvG+uzljKGIyPJDvPzo2NXq5Yue1sqmWGLc6q6N6llfAdLGcbO9IyrwgtBBQUUvAQAzz9Ph8EZVJE3ZZXkSHfAdzV9OjnSN2wAXnyREo5djqsz6+xpsiyVe1aacTVXk1Ke7Ei3B1vLH2g0tOyVnEwRg0eOWI4UvvVWeugr6g0b0vtHj5pW3M2tCjoTQWMiDV8cT8LjZ57H2bLGaKy6infu3EmKRGio0w081fVuRqRfdTKN7+bY64wfirf+3YLJf0zEgj/64/42h9FIccV2YWGv0mktJcVYcWrShBzy+/cDx47pGvpFR5OSplLpPqtfq9iSsJEcA02bkhTmyCkA3ksxjo6OxrVr1wz2UV5ejuzsbIuNwXr27Int27fbNjl34CON1cxiShno1o3ko1QmJCICiIvTlSHRWxA8mVkHyxOp7vS77T6GX7vWTmVoaBuOXi6F0Ngvu9RqajbzyCPAokWUAaw97MbZAcZGXXExeeBv3CCjPTOT3pfyFE0ZcaacQdI9xtxzVzuL9J2YHTpUvuexc4qpgnz9ta7k7rp1rvXTy+QyRNwTgeLUYhQmFULuT9dH2bUyen4zC0aucv66adGCxn/vvZRy3L078MADNn7YUxHi3qK6z8+FeKt58ssvv4zPP/9c+7xLly4AgN9//x0DBgxwfmL6tYaTksiZGhpKATJSQIxxWUVvpq4b26vXrlFj8bAwut9KZUsiImi8xo3Fc3N1WbOAtgxcUREw+vSrKBb+uDU4EXdE7gNELZdkvqrq3gxQ0I9Id8QmvKnPKopuYOltv+H2Lydi5X89MDVuP9pEZFbWZ40XSIznrl/yTiYj3ezcOdqHLc4pe5rUAqwXMc7hjgaTVXwhaOFCmm6TJsC8ec7vT2o4KjnSHamm0KMHZQP++iswaRIFMZjzOTuFrX5Ba9kG9jZZbtyY/FKXLul8ahLVqFoCO9LtwRbhtGePLu2rUSOgTx9qYnLqFJ18vXtTtKSpm60k0JKTgbfeMr/qZ21V0JkIGv0Lbt8+fH60KzbcGA6FrAIbhm1A7UbBhvN1QkmUojodqTNsbuwPrQTWDEvFvxca4Znv+uG7W9+3zZlsr9JpLULXnOLUrBlJ8r17SejUqkUOwd27daUqAJ3iFhpKx9gWYcORU1q8lWIcHx+PnJwc7N+/H3FxcQCAXbt2QaPRGDQQNObQoUPebR5WFbq6m4qsbt6c+gjccothZoxcbuB0f/7XsSjXKHBXvX0Y0v4yMPJpp64LqSxVyZUS+NUhhcpe2fXgg1SW8+RJajizYMHNN4yzA/SNOqkPRXAwLbxlZtLza9foN5Kad5paNDTlDLL23J2wc4qp4pw6BUyeTP/PmgXcdZfrvyMoNggNn26IzM2ZyNtDmUjl2eUI7hKMsmtlKE4pdk5v0uOee6js1OuvA//3f3QrcKIMNVNDmTZtmlk9648//qj02pgxYzBmzBiz+5OaJ7/66qtmt/nss88qNWV2GeZqDWdlUU30sDDS1/VT24WgPi7p6fQ5T+vixvaqfqNvtdqwQXtYmK6xeEgI6U3+/rqs2YwM0kc6d8a07WNxpKAl6iqz8UXUDMiymwBXLrsk89UvinSpkks6R7pD5T719Nnb2obgzlan8PPp1rh30/1InPwRQoz1WeNADeO5Gy+QBAXRe2PGkL1vLXjJVsfmrl267PQqFvHL+BCuzjC2ZyHIB/0Nx44Bb79N/7/3nvnDYg+SI93RGukSH39MJcT37AGWLAH02rq5Fmv+IVuyDaz1fbxyhRYYa9emSAwpY+y997zaZ8bdsCPdHqwJp8BAOonq1aNGM1Kt67596SQ9e5ac6p07m3fuWlv1GzYM2LrV+qqgC9LwT675F09+3x0A8MqAP3BLRyOh62S5B1U9cqRrijUou0Y1Px0u7XITebtYfPi9Bl3iBDZf7onlzVrimVm1rV+o9iqd1iJ0LSlOcjkJqHPn6PMnTpCAuX6dBBFArzdoQOeDPcKGnVNavJFiHBsbi6FDh2LKlClYtWoVysrKMG3aNIwdOxb169cHAHz++edQqVTaiKnvvvsOa9aswSeffOLhI6SHM+lfnqyZZ+tikZ7T/ZedKmw90wZ+8nK8PW4/8H/OR05oS7tcLoEylG6j9hp8SiVFSdx3H7B4MSX2xMWhcnZASQmVapKKFpeW0m8jrfpLDc6uXaOF2hqYgcIwniYlhaKJ8vOBfv2A115z33cFxQYhMCYQhacLkfVTFkSZQL2H6yH3D6pB6SpHOkDNuPbvB7Zto1vCvn2UmMcwNRJLtYbLyoCWLckBba5JZWEh8NFHdCH16mVXHymnMLZXjXvL6JeP1GhoRb9VK7Ix9+4lIVBYSJ+9Wf/2s8u3YU3WCMigwYZ6z6N+2QWgIELnNHFWrzIRkQ5HDpGRPvvpgC8Rd/UFJGdG4uEvB+Hruy5BZqzPGgWRGc/doPavZP916kSKmzV91BbHZnIynSdCWC8by8FSjCVcnWHsjgh3DyEE8MQTZEKNGEEJy65AKqfnaI10iUaN6Pby0EOUpXznnQ40HrUVc/4hW7MNbOn7aKrJcjXvM8OOdHuwJJyEoPCk69d1tfMkpJOsaVNSsB59VNfwUx9rq37HjwMrVtA49OuyuWFVsLhUjrHLeqBQo8bARqcxu88/lTdystyDwl8BZbgS5dnlKL5YDMA1BmGHTnIsWkR1Pp9bWAdNu5IAtYg9SqctEbq2KE7GEQ1169K+AfofqFbCxtN4K8V4/fr1mDZtGgYNGgS5XI5Ro0bhPSn3/yYLFy7EhQsXoFQq0aZNG2zatAmjR4928xGxgiNlobxRM8/WxaLYWKSoY/DQO3RNPTO5AK2XTHGJ0aEt7XKlFAEtqVOoI4uAo0frmvyNGkW2ax3j7AD9RT2VSpet0qwZPVJT6b4ya5bp+wrDMC7l/HlgwACyG1u2pB7AbknJ1UMmlyEoJgj+Tf1RfL4YRWeKdJl8TgYg6KNQUM3Obt1orf+uu4BffjHfL4phqi3Wag0fP0735NxcXV10/SaV5eW6KO+NG6l2UvPmpN+7W08ytldlMsPeMmo16QrXrlFNgdJSmu/165SO/9hjFLR1+jTQrRuOZkTjia3DAACvDvgdgwLSgVYjgWeeIX3MBXqHX92bUZ6XdVGeMmPHna3o6bPRJ07g2x5vot8fr+Dby73wVlBzzIqta/ozMTG0Srp8uXbuBnMztv9s0UetOTZv3KDMBbmcFiWslY3liHXGEq7OMPb1HloWWLaMxFtgoK4EnyuQItJFqfM62MSJdJv5/ntdy0RX9dmxij3ZBpbkmKUmy9W8WgI70u3BnHDKyCCHUnIyKU/Hj9NJqb+KLZPRyvaNG+SkNXUCWVr1k/Zx/DiVMTD1notWBYuL6b58KEmNiMBCfNlxCRSyegBcX+5BXV9NjvQL5Eh3KI3PBC+8QH1zPvqI6nz+9dfNaE9z2Kp0lpaSYLEWJW7rirBxRIN0oyoosK82PmMSb6QYh4eHY8OGDWbfnzRpEiZNmmR+0N7Enhuej9fMy80F7houx7UsSgKa/04tx6KbTKDfbNSZslQyGdnW3bpRwtL48cDWrXIojHpVIDxcVws+KEiXZiwE/U69e7MTnWE8wNmzwMCBdHm2bk3Z+J6syhXQKoAc6aeKtL1lXKU3SdSuTbZT//6UcnzrrVTHUz8ok2GqPdYiMY1rZTdoQH9zc+leXasW9UCSGoULQcE4deq4X08yZa9KafhShrRKRX1mVCrKom7cmHS4w4fJMTZsGFBaimsHL2PUP9NQVO6HoU2S8WKd1UBkE3K2N2/usiEbNxt1OrBKT5/tlZ+PFT/m4LGXIvDiu3XRdShw220mPiOX05wee4z02+Rk50sSWHNsnjpF/8fE2F421sf0bcaHcHWDSV/voWWGr74iXxBAvahcWZlUcqRLOCOrZDLyVf37L7VUfOUV4I03nB2hjdiTbeDMAk01rpbAVrc9SMIpIkKnLKWl0XLXqVOkNEVFkVJy9SpFJWRk6D5vTdiYW/XLyAD++Qc4dIhSJw4coOf6+zaux6fRODRFyYm+bRuVcv/mwwzUb6TQzbe8XNdQwAX1jbQOqYvO1ZkyRiYD3n+fUq8LCymq6uJFCx+QBERqqi4qXFI6o6OB7Gx6rbSUBIU1pcXU/iQkgRMbaxjR0KEDKXDNm9P/xlEeyclUA+Lll6kmxMsv0/PkZAeOEMOYQf98NBdpZLyKHRpKoYzSKnZmpi6CxguUlwP33092R/36FPEdHOy6/Us10suzylFxowKA47IrLAz47juSt7/+Crz6KnTRVF26kOxRq0moKRR0fGvXdqkcZhjGOqdOUSR6air5PP74g3xnniSwdSAAoPBUITRlJF9dWdpFokMHml/dumQ3DRigqzzHMDUCezJLu3QhI+PcOXKi169P3eSuXdM1Cq9Th+7nQhjqSeXlFAV99Cj9dYXeZMpeLS8n+zQ8nAKyevWilJp776XsNmMd7uhRXLnvWfRPfBOnc+qiUUAmvuixAvK4Lm5x3EqlXVzSM0tCT5+dOicCkyfT4R07lvRDs+jrYFlZJPyzsmyz/0yNwdRvkZtLg5DJKGS2vLyyvQjoysZeu+aT+jbjg7jy/LXHn+Ej/Pkn9aESApg2DXjqKdfuXyrtIuFsMEPdusDq1fT/4sXAF184tTvbseUeV1xM21mSYzXYFuWIdHvRL3+QnEzO7bw8Ck1q04bqzF29SidUZqauQSVgPYLb1Kqffpqgnx8pbZKjPjeXHL1A5Xp8Bw7Yne5VXEzXwK+/0n1761ag/4AmQHf31TeS6qRLEemuTFFWKmlFsk8f0k/vuAP47Tczhq+5FVxJ6axXj4oZ6zc0lDAXJe6KFWFp34cPA19/TT9S48YcjcB4Fx+umScEZRtLcuzHH3VZ165CWVsJub8cmmKNSxYBO3Yksf3gg+RI79YNuPtuo+yA9HS6F5w8SUpxNaszxzC+zJYtwKRJpO7FxlIkenS058cR0JpKSRWdLnKtw8kEHTtSNt+gQaTG9OsHbN9OPjeGqfbYm1n622/UOTwmhuwG40bh+iUi9SON58yh+7ury3WYK9cXF0clRNatq5yGD2jHdnHfNdy6ujnOZvihUf1y7FydhYj2M92WCSs1G9UOw4X2IEDTWrmS7MH//iN59ssvtN5hEleWJDD1W5SU0G+u0ZBdmJ1tuib71at0rvmgvs34MK46f10d4e5mjh2jcr6lpTTsd981XeTBGVwZkS5x770kIqSa6SoVBYS5FXuzDRwpAVvNYUe6I0jCafdu4M03aSlJajQjk+mUJ7WaVpAvXSLry5qwMU6bAOhELSykz2ZkUESDEDpH/f79tCKkX4+vSRO7Haw3blC9Xsn59PPPlNZrMF83lBSRIjtdHZEuERpKCwK9epH879mT5taxo4mNLSmdjtaIdkbgSPvWX7Bp0YKyHkJDq0zHbKYa4sqaeS4sVyQEJWt88AGJ4vXr6XJzNTKZDKr6KhSfK3ZZWaoJE8hPvnIlBbht2gSMGKGXDtehA9VY4NJODOMxysuBl14C3nqLnvfuTRkkN1tteBz9iHSZnPQldznSAVIr/v6bnOlnz5I8/ewzG/rOMExVx55Udrmcto2OpqAnmcx0o3ClUlcAt6iIvD5FRUD79jrn1IEDFKlsLnjHHszZb8ePW9ThzpY2wqBtE3Ch0A/NmgG7dinRtGmMY2OwEWUtJWQqmbbusKtLVgFkgv3yCzX1S0wkufb996RamcSVJQn0fwspOMrPjxzk5eWkM1+5oguSi4wk/fj0aYpADwig887YK+jDNaoZL+Oq87eKOFDPnqWgydxcSrpZv54uHVdTyZHuokW/ZcvInffJJ1TqU6Uid5LbcKRcSzWveW4v7Eh3FLmcogwCAmgFWTr5pHIgJ06QEz0nh/727m1d2Biv+oWE0GfVanKaBwVRsd/Tp+m5SkVpgP7+NI5atehCqFWLnKw2OliPHSPHzYkTJpzo+mNzw0q3VNqlPKccgHsMwkaNqBLOsGHkk+7Th/SXIUNMbOyOGtGOCBz9fUs12+vUoVJCeXk6JYujERhv4KqaeS5sVlpaSs3CP/+cnr/1Folcd6FuoEbxuWKDxljO8vbbJDq+/56iEz7+GHjkEb0NqnGdOYbxNa5cIWNGaqfx3HMUO+HnZ/FjbkUbkX6mCP5NqMm1qyM3jWnWjJzp995LkZwjR9KxWLyY1FCGqZbYG4lpa6PwWrXISXH0KDlQY2N1elRJCUUmnz1LBXM7d6b39XUi4+CDhg1pPOZsDFN6gwUd7s+UJhj3zb24WlgLrZuVYedffi7P6jOFTCaDqq4KJZfcE1glER4O7NhBP93OneRUp8AFGz7sbOCHXE6f2bCBfut27ciOa9uW9llYqCuV0KgRLaoUFNC2O3dWjlgXgm5URUX0OY2mxjq0GDfj4w7UXbvIl5WdTZfIDz+Qa8wdKMOUgAIAVfZ0maySy6nES0kJlXe5/34ykYcNc8nuTX+hI9kGbItqYUe6M5hTRCIj6eRLTaUo8lmzbG8Ep7/ql5BAjviwMFK+pJtneDg5nqRId8mZb9zc1AYH67p11FOlqIhKnnz9NfWc8RRSRLqEuwzCZs2okcO995JRPGwYsGIFzb1Syo8tAsKeTsf2ChzjfV+7BlRU0HkQEkLnlFQySCbjaATG87iiK7wLm5VmZ9O1/eefFH3w3nvAE084OUcrSIuAuFmW0hWKlEoFfPMN8OijwJo1wOTJdLnPnOn61ESGYUxTUUFZLS+9RLfV4GC6Hi30ofYY/o39KWqzRLi8SbslGjQgZ/rs2RQ1tWwZ6VRffgm0auX2r2cY72BPJKaxU8JSo/Dr1+n1hg1JtwcMS3mGh+tqmuvrRIBh8EFJCRlwAQHkuLc1GMGEDlehkWHhX/2x8K9+0Ag52kddw/Z/IhBd310HtzJ+df3c7kgHSKZv3QqMG0eH8957qe3USy/RT2USRwI/TDneTZVG1A/Cu3KFSvilpNA507cv/f7GEevSmM6eJdtz9WqqxeWKskAMYwofdaB+8AGJx4oKqlq1ZQuJUHchk8ngF+GHsvQyeu5CWSWXk75ZWqpb4HvzTWD6dDfZgVUk28BXYUe6rZi6GVpyJgG0be/etjvRJSyVjgF0jvrDh8mR3q8fCTY70r1ycoDnn6eLFQBuv50MIv2ybJ5A64y6iTsNwtq1qXTN//0frfQ98QRF369eTesQduHOGtHG+9aPalGrSWHKzCRlKizMZztmM9UYZ2vm2bsQZYHjx2kop0/TJfDVV8DQoS6drUkqLQK6SJFSKimtr25divicPZv6TL3zjvlKOgzDuIb9+2kha/9+et6zJ5UyadPGq8PSIlPIENAyAIVJhRAl7q2RboxKRXKof3+q4fnff1SRYuZMKvMcGOiRYTCMZ7EnEtPYKWGuUXhyMt3sO3Sg94XQlfKMjKTnWVl00Uk60UcfkdM8K4vsg6IiWs3KziavUe/edBHaUhrGSIe7HNYO43+biD8vUgOEh2N2Y8X6cATVr+uBA6xDajgKuD/TRq0mffHxx0nnWrCAekCsX08VUg1wJPDDnOO9XTvTZXUk2z4zk+rtR0VRzRm5nM45/Yj1vXtpH8a/PffNYmoQxcWUHbdqFT1/4AG6lrWR6C4sHWqMviPd1b4rpZL8VH5+5Jt74QUKZPjsM926q0vx8WwDX4Yd6bZgaRXaXQ0Y5HK6McbH043RFKWlVI8vPNz0MpUJB6tGQxfnzJkU6CyTAa+8Arz4onvqSFnDXc4oc6hUVPqhfXtg3jzgp5/o//feoxRum1f7XFkj2tq+a9Wic+nqVVK09BsW2Rr9yzCuxplVbBcsRJWWAosWAa+/TmtMTZrormdPoG7gvmwamYzmVrcuRSF89BGlLa5b59mMIYapKZw7R/0V1q0jPalWLVrImjrV92yJwNaBKEwq1D73lCNdYsQIatsydSr5e157jYy95cup5yJnzzDVDnsiMY2dEqYahXfuTN4eafXJuDFpSYmunrpMRtHtf/xB2cndu9NnDh/W9cbKzKRogpgY66Vh9MZZ/sTT+PDFVLy87hbklAYhSFmMVff8hgmvtAJiPb96qN9w1BNyTamkEnoDBpBD/d9/ae3hww+BsWNvyjJHAj8sOd6PH6ff11RpRJmMFkgqKgz3px+xfvkynUdBQbRNbKwuCo77ZjE1hJ07qarAmTM6m8kge9eFpUNNoYpUoRCkh7lj0c/Pj/TR3r2BZ5+lkp9du9LiX7duLv86n8028HXYkW4NW1ah3ZUSYS3qs3FjoHVr+l+qoy1hwsF66BDw5JMU6A7QPXbVKlIgvIUq2jAi3ROKk0xGwvbOOymqav9+4MEHgf/9j+oqt2tnw05cVSPaln3LZCT8c3Mp9VOtpnOjtJTOCx/rmM1UMZxZsXd0FdvJhag9eyiz5Phxen7XXRSF4MkGgJ7IpnnuOVoYePhhUhb79KFKYQsWcG1ihnEFKSnkCP7sM/JdAJTu/847FKfgiwS0Miz86WlHOkALl9u2ker77LN0HEeMoCoE8+dTICU71Jkai75TwrhReFAQ6V0rVpDd2K2bYWNSIQzrqQP0XnY2KQQyGaUVS453uZxshUuXyEYoLzdfGkbPJt21C3jmmVgcO0avdY0twv9WZqH1gLu8Zk/oR6R7omSVxPjxFKQwfjzpl1Jk67JlQMdQOwM/rDnepUavFy/qaqRLSLZ7UBD9/vpIEevnz1OR9549dRkNlsbDMNWIjAyK0F63jp7Xq0fX6p136m3kwtKh5tBvOOouHUwmowW+7t2ptOD583TZP/EEBcG6s3wNYxvsebOE8c0wNJTCtqWbYWambtV39mzg1VcpzPnVV8nb4Yq0Kinqs0sXSuc7dYr+du0KPPMM5SBHRJBDNTeXFCepUclNB+v+g3Lcey/tYvdukidvvknBCt50ogOAXCU3FEZuTuXTp317KkO/cCHprj//DHTsSE6rCxesfFgq65OaSoqPPpIiFBvrWJS4qX1L0QjR0aRMA+RI79qVU/gYx0lOprDLl1+mC+Hll+l5crLt+5AMxg4d6K8tBpj+YpEpzCxEnT4NTJxIK/THj9NlsXEjNZXxpBMdMBGR7iZF6rbbqCfZhAl0S1q0iG4/GzfSc4Zh7EMIuvePH0/1vT/9lJzoQ4dS4OiGDb7rRAd0DUclPKk3GXyvjGoLJyeTCqxSUfrx4MHkUP/tt8rqEcPUSCQ9Samkm/err1KE+pkzwHffUdS6XE6O9owMilSX6qkDZNcBurx+fcc7QH+vXwdu3CDFKCSEFASpNIxkr2o02LuXYm8GDQKOHSNnzAcfAIlHAtD61oZeDcpRRemVdvHwAmHz5lRifMECilfatYvs5kdnhOBajspy4EdxsS7ww1rGZaNGpN/6+5u23SMjaTCFhZW/SyoD5OdHWQrG+xeCUjTT02kMrCQy1YS8PLJ/2rQhJ7pMRsGhyclGTnRbfXdOXhuecKRLdOtGFbvuu4+GvXIlxdF+9JEuAITxDuxIt4Q95QcccSbZSmyseUe9GUe76NIVf/WehTumx6JbN5IpMhlFWp04QRHZvhLRqB/Z6WnFyc8PmDuXHFWjRpGA+uwzElBPPUU6rkmkbAErixgOl/UxtW+VijTePn0ohG7hQtct2DA1D2nF/uBBOtdiYujvwYP0uj3OdHuxcyHqzBnKHomNpdJUQpBDPTmZupp7I/LROCLdnbKrdm2a97ff0oLB2bMky+PiKCqUnVUMY52CArq/d+tGi3EbNtCt9bbbKMjgl1+AHj28PUrrBLY2LEbuychNUwQFkYF77hzpTWo1lUgYMoRU4g8+ICOYYWo0xjpX9+66Wm1Hj5JzPDubVvF69tSV69BoSFcKDCQhJoRh7ySAHOhlZbrSMKWlBqVhRIOG2LULuK1fCXr0oDIBcjkwbRoFKDz+uIUmmx7Er653Aqu03+9HGTUnTuicVh99UwfNf1mJZ7fehgs5tSp/yDjww5aMS7WaQkxNBcm9+CL9/ub046wssgWNf7CMDOCff6jmxYkT5GWzNzCGYXyM3FxyeTRtSpdGdjYFPSYkkEO5lvElaY/vzgn8Ij0rq2rXpuajO3bQekBWFsXSdupE9qF0K2A8iw/cNn0Yd9bBthdLtYv0yitkpRbii+3R+Hh9bSQl0YWtUJDTZc4cuvh8DXV9NQqOUGSqtwzCmBjgm2+oedbs2cDvv5OAfv99Khvx7LPAwIFGMtmdnY7N7TsujrsoM87jwmafDmFDs1LN8JHYsUOOVaso4lxadb/rLjJ03FIjzg483d8BoOjP228H3n0XWLKEynXdcQeJhaeeokUFf3+3D4Nhqgzl5WR4fPkliTwpyE+tpvT9adPoll2VqBSR7oXSLqZo0ID8hHPmUJm8jz6izKEnn6Q1/wkTaAG0Vy8u+8LUMMzpXM2aUZ2kffuAOnVou5ISCpwpLycdaf9+ek0mIydpixYUiCD1ToqIIG+Tnx8QHGxQGiZXXRf/29cRH+/vggNpDQCQTTh+PNk6vmZKeDMiXZ+mTclp9dRTwPTpAnv3+mP5wX5YeegWjOtwDM/HJ6BzdJrpPlW2lv7s1ImaSpgqjSiX21fWNSOD0qkKC3V185s04eajTJXl6FFg9WpyEksL8TExFPw4dqyFhT8P+e48GZGuz6BBZPt9+CElkR8/TnrV3LnA888DkyebnzrjetiRbgl31sF2IUVFwLZtcnz1VVNs3kz6FkDBCxMmUPR5ixZeHaJFDCLSvZSiLNGjB+mpu3ZRjdSffwZ+/JEeMTFUS338eL01DXd2OuYuyoy7cEGzT6cxs1h0odkA/K98DD4eXhfnzuk2v/NOSrmV+mx5G0WgAsowJcpzygF4bhEwOJgUpsceo2CjlSvJzn7oIWDGDGDKFOCRR3xb5jOMOykqIuf5Dz/Q49o13XstWtA1Mnky+Z+qIqooFRQhClTk0+qirzjSJerVo9rCCxZQCvYHH5CIX7WKHs2aUXDHuHGVSwQzTLXEks4ll5Oun5VFtSX/+4+2TU6mNA+VilJoAgMp1ePUKXLIx8aSA/XcOVIMwsKAGzdQXlyOvzV98fnlh/HVX3EoKieHj7+iFJMfKMYLr4b6bOlsg4h0H5BrffoAiYky7FhzEW/OL8TOy23w5ZFO+PJIJ8TVTcUjDbZhXOcbqK2fgSxlXB48aLhoAlR2vJsLkrMWqAXQqmVSEq1gStnLSiWF6LZtS39DQ7n5KFNlyM6mU/6TT6hXgURsLBVkuO8+Wgi0iId8d/qOdE8Hgfr5kXh48EFyqC9fTreYZ54h+/D+++lWEh/P+pW7YUe6Jey5GXqY69eB7duptN5PPxmWGu7cGZg6laKtKqW8+CD6kZ2+oDjJZLTiN2gQlTBcsYLSwU+eJAE1dy7Qrx8JqrvvBho1cmOnY+6izLgDX8m2ublYlLL7Cr7ZrMDXO8Px3xc6eVCrFjBpEskzm5oAexhVfZXWke5p2RURASxdSpGen3xCzqpLl4A33qBHjx7kqLr//so9qximOiEE+ZZ27KC63Nu3kzNdIjKSroMJE+i6qOqGhUwmQ0DrANzYf4Oe+4DeZIpatSiic9o04M8/qRb9li3UMEuSUy1akB51991UV93Pz+puGabqYavOFRVFoeIpKeQdkcsp/U5ygPbtSw72s2fJQdqsGVCvHsr8AvHH8Uh8c6Y/NpfciYyy2tpdt428hin1t2LCXbmIePVpny7q6q1mo5aQyYDbJjfGbb2TsX/lGizZ2hbfXeyG/dcaYf+1KZh+XIMRBXKMGkUZgiEh1jMubSr9aS2YSnK079tHiymBgUD9+uS3kMoCcfNRxsfJyKBSU998Q4GM5WRSQamky+TRR6lXs81rQB7y3RmUdvGSDla7NpW7ee454PPPgbffplKon3xCj5gY8gWOHFm5LzHjGtiRbgkbyg84XAfbTsrKqNHA9u1UEzchwbBPQpMmwOjR5Djp2rVqXSzerJFujZgYivh84w1atPjiCyr78tdf9HjySSpxN3y4rsSCL9QZZBiLeDnbpqiIrp9ffwV+/VWOpKSG2vdkMrIVH3qInF+Bgeb3423U9dUoTKJaEd6SXZGRVEphxgxSRlevJmX0v//oMX06ldu88056dOnCQUlM1UZynP/zj64kbGqq4TaNG9N9efhwaqpe3Ry0ga0DdY50L2fyWUMmo99gwACqPPDTT1Sf/pdfyB/47rv0CA2lbaRABmMbmGGqLPboXFJpj+vXySGkf8OOjCTbs2lTXLgAbIt5Eb+eaY4dO4D8G7rtwv0LcG+bZEyO+Qc9y/+FLDICGP+0z9/8PV132C5iYxG3IgYbZ1xE5sVTWL8zCp9uDsfRo3J89RXw1VdUMmzIEGD48FgMuW86GiZ87VzpT1vKuv72G6VQx8RQ7XRjoenJMrQMY4WyMoo2J/uPMmr1WwF07EjO34ceonVFu/GQ786gtIuXZVVAAGUpT51KDd/XrgW+/poCQOfPp0fz5jTtO+8EbrmFy4C6Cnb5WcOddbAtkJdHwuXff8nhtHu3YdS5NLRhw6hfSffuVdfgUNXzXUe6RGgoCfWHHiI5vHEjRVbt3k2LngcPAq+8YmgI9usHtG/PjnXGB/Fwtk1ODl0rkuPrv/90JagA0mf69iVZdu+9VSeCWtXAd8pSKZXUMHnUKCA9nZSo//2PjvuePfR4+WVSTAcMAPr3p0dsbNW9dzjL8OHDcejQIVy7dg21a9fG4MGD8eabb6J+/freHhqjR1YWBd3t2wfs3Ut6UWam4TYqFRkHgweTodCpU/U+r/XrpPtK5KYtBAZSevZ991F/xO3bqfzO1q0UmSaV4wGAunXpN5UeXbvS78wwVQ57dS6jCHYhgHPXa+Pvi03w14Um+OtCY5y9XgfYpdtNZCRw78DrGB30C/qXbodfaQGg8gc6uddedSVyPzmU4UqUZ5f7pj1407Ed0RR4ph/w9AIKcvv6a2oGf+aMvgxrhbZt52BIfB5u7XEdt/SVo3aHhq5dzJDL6byKjqbVYlM3PR8pQ8vUTPLzyeb7+2+y//bsqezP6tKFgkFHj6by/1o0GsfK23rAd+etGumWkMt1tt2KFRQA+t13tNZ27hytt73zDh2KW24hX1X//nRY2LHuGOziswU316rOywOOHAEOHybneWIiZWEZN+uuXZtO+KFD6dGkiUu+3uuoonWWUUV+BYRGQCb3DaFkioYNgRdeoEdGBhmAP/5IddVzcgwNweBgigbt3ZsWO+LiyElYnQ18pgrgxhX7rCxqErN/Pz327QNOn668XYMGFLkzZAg5v8LDnZ+Wp/G1slQSUVFUTmHaNNIff/mF+j1s305O9k2b6AHQT92zJ8mnHj3ob1WtHW0vAwcOxIsvvoh69erh8uXLeOGFFzB69Gjs3r3b20OrkVRUUNkPSR+S/p4/X3lbtZrO1z59SC/q29e3s1dcTWBr3WR9SfbYQ3Aw3YbuuYd++4MHKbtg504yuK9dIzt482baXqWiBZJu3ejRtSup52q15e9hGK9jp86VVhyGg1k98N+O9tib1Rz/XW6AjELDsjAKWQXi48owZIQ/hg6l60Eurw1oxgIXe1fZ3kqqKJXvOtKNkMnIrouLAxYtAo4dI4f6tm206JuUJENSUi0sQy3IZBRc1acP1S7u3p2chk7/ND5chpapWeTkkP138KAu+OHEicr+rDp1gNtuI/vv9tupIlElkpN1jvDiYvL0tmlDctQWR7ibfXe+6EjXJySESqNOmkS3md9+o6zl7duBK1d0uhZAulVcHPmqJN2qZcsqdduwG1cFUsmEMD69ay6XLl1Co0aNkJqaioYNG1r/gJ3cuEHpyElJusfRozBoqKdP48bk4Ojfn6Kb27Wrfid1QXIB0j5LQ+pblJcd3CUYkaMjEXFPBIJiq1bbYX1DcNcuWnWVOk3rExVFAqtjR6pZ1b493Rs8GWnl7nOdcS8u+/1MKSqxsVZX7IUgJ8fJk/TRkyfJgDh6FLh61fRnWrUiA0J6tGpV9ReULr9/Gaen0SpB92PdEdTOt2VWaSmVBfvzT3okJBjWkpZo0IDkk/SQ9FFvOCo9Kat++OEHjBw5EiUlJfCrbrVAvIC13y4jg0oRSfrQiROGmSr6tGxJjofu3Ukviour2Q7U3MRcHOx1EADQ42wPBDQN8OkABHspKSFH1O7dlIGwe3flLASAMnHatNHpU7Gx9Gje3LPZgKxTVW089vsZ6Vw3FLVwIrw3khoNwbHs+jh8mBYP09Mrf9RPXoEeDS6jX+MU9MXf6D0oALXmP1vtDMODAw4i989chA8LR6uVreDf2L9KyrbsbLIFf/uN9K1TpypvExpK97KuXWmRUNK37LYHk5Op+WhmpulFmqef1ur0LKuqNr7w+2VlkQiT7L/jx8n+My6xJ9GoEQU7SPafVX+W/vncqJHufE5NrXQ+ewuhEfg75G9oCjVotrgZGs9oXCXklBD0m+3YQfLp33/JnjcmOJh6LnbsSL9X+/b0t04dz43Vnef6smXLEB8fbxBIBcDuQCqOSHchQtA1f/48Pc6do8epUxSRac7BBNB9r1MnSm+RogKjoz03dm9QkFyAS+9dQum1Uu1r8hA58g/mozi1GA2fblilnOkKhS5SatYscqwnJZEBuHs3pf8lJZGC/PPP9JBQKsnwa9OGHFYxMeRobNGCItirmZ7M+AoWVuwLC4ELF3SP8+epnq30MLVIJNGsGd2ApeshLs6zN19P4cv9HUyhUunS/gByrB84QA6rvXsp/fLkSYpilyLZ9WnShGRUy5a6R4sW9Lq7nez5+fnI0zvp1Go11C70pGZnZ2P9+vXo3bs3O9E9REUFMG+e4WvSWl6nTrpH586UkccQBckFyP4lGzKVDDI/GS4suIDANoFVMgDBHGq1zugGSL8+f14X5bZvHwUu5OTQIu6xY4afV6lIPrVuTbpU69b0vFkz0rcVCo9PyWG4BFXVpqiIeoeeOwecPh2LU6ltcPpcMU6dU+DiVdMeU5kMaNWkBD38DqFH0DF0j8lH56Y58C/J1TlHx/p+zXN7KUguQHk2dRssOFaAlJdTqqxsCw/XlasAyFn1zz9U4uK//0h+5eVR363ff9d9zs+PZFZsLAWYx8bq5Jip8voAvFaGlqmeFBeTWZiSYmj/nTtHf69fN//ZRo0MM8fi4uz0Z2k0dB5nZhpmWISG0vOkJKqtGxPjNflXkFyAzM2ZwM2hZf2QBVSgSsgpmYzsuDZtKGtZCPpdd++m4KoDB2gx98YNXTlWfSIiSB7FxNDfFi3If9WsGenp7giQc4f999xzz2n/b9KkCWbPno2RI0eirKzMLhuQHel2kJdHguXSJbpHXbpEj4sXdY/CQsv7iIwkOaD/6NSpejqZLCE0ApmbM1GWWYag9kG4/ut1aAo0UAQqENg2EIVJhcjckonAmMAqscJnCoWCIqQ6dKCu0wCdH0eOkKA6epQex44Bubm04GIqYsHfXyekmjYlp1XTpnSzatiQHO2+ZBSy0efblJZSJGhaGi3uXb0qx5UrTXHliqFcy8qyvB+ZjM5DaeGnbVtdhkVNKcXoq6VdbEWlAnr1oodEfj7JJamsxtGjZJdlZekWVX79tfK+6tbVyaeGDXXySZJR9eo5F0Hctm1bg+fz58/HggULHN/hTWbNmoWVK1eisLAQvXr1wk8//eT0PhnbiIqi5kjNm+v0oaZNfet+5mtIAQhlmWWIejgKyhAlRIWosgEItiKT0XnSvDnVVwfIALx8WSerjh+nQLbkZHJeSpkOxvj50XqxJK+kh77M8qUyQVyCyncRgu6Nku6kbw9euEAOispBVDIAuh4HUVE6+dexI9mE7dsDQUFqIDkU2JxON+FzxdXaOSrJtorCCgCAsrYSyghltZFtdetSD6B776Xn5eUks/buhTYT4cgRsgcl2fXtt5X30aqVziaUHk2aAA1axsJvtvtKWTBVn/Jy8k/r7D96XLmis/1SU8lGtIZUUUh6SPZfWJiTg7x4keRdo0aVvbIyGd2gk5NpO3ONeN2Ivg6mCFaQ76qWosrKKZmMnOEtWgAPPkivlZfTT3DwIPmojh+nvxcu0PmTmUmOd2Nq1SJZ1Lix7m+DBvRo2JBK+DiiW7nL/pNwJpCKHel28NRTwLp11rerX193k2veXBcN06qVCwRMNaH4YjEKTxRC3UgNmUwGZYgSpQWlkMllkMlkUDdUozC5EMUXixHQNMD6DqsIgYGVHVeSMahfJuPECVr1vXCBVobNGYQAOR3q1SNBVb++7jFiBKXheBo2+rzPxx9TJEFmJilE0t/0dMuRBMaEhuqcDE2b6m62LVqQfAuoPpemQxg0G62CjnRThIRQnbzevQ1fz8wk3fXkSZJNZ87Q4+xZstmuXaPHf/+Z33edOhSZEh1NzoOoKPp/6lTr98akpCQ0aNBA+9xcNMLs2bPx5ptvWtxXcnIy2rRpAwCYMWMGJk+ejAsXLuCVV17BxIkT8dNPP0FW1esOVQFkMirtwtiGfgBCYNtAg3NU0VZRLQIQ7EGyqRs2pAazElJ/spMnKRtUygo9e5bui2Vluswqc9SuTTpVvXo6nap5c+D//s/t06qEqyKnGMc5fpzqy+o7nyQHlLlyVPqEhOjsQf1HbKyVQCo31/n1FfRlW0CrABSfLYYyRAllqLLayjalUpd1JSEE/dTJyWTzSQuDp0/rdKxr16gcgzEyGVC/vhyNGzfF6NHA9OmemwvjO2zbRqVlMzIMH+nptOhna0HnoCCd7Wds/zVv7sbFZqNGyyYHdvkybedhjHWwktQSlGWWwb+JP/zq+lUbOaVU0qJI+/aGr9+4odOppIdUfSMtjRYBjxyhhzlCQ3XBVdHRFBV/yy2Wx2Or/WcvrgikYke6HTRsSAqPtLIirbJIqy6NG9Pr3PnWOhX5FagoroA6iC4GRZgCSANkahI8iiAFSi+XoiK/wpvD9Aj6xuCgQYbvlZWRUiUZgBcu0N+UFF1mREWFbiVZnxYtvONIZ6PP+7zzDi3GmEOhoMgW6WYmOQuk81CKKOaFP8uoolSQqWUQpQKKoOodRhsRQTUO+/Y1fF0IKq8gyaULFwwjWy5fJodDaSkp8VlZ5JTQ56GHrH9/SEgIQs3mNet4/vnn8ZCVHTZv3lxvXhGIiIhA69atERsbi0aNGmHPnj2Ij4+3PiiG8SDGAQj6VOcABHuRy3XG/5Ahhu9VVJDz89w5w9Jl+nLrxg1acL5+3bBkTGysdUc6l6Cqnhw9Crz0kvn369Yle1CyBaWHFFQVHu5Eyrt0Qldj9GWbIlABZW0l/JuSMV2TZJtMpgteGTrU8L28PApeOH1aVz5WKiWbmko6llSWr2dP74yfM5K9z/ffA6tWmX9fLid9XrL7JBtQPyOrYUMnZZYzhISQI62gwHQto4ICet8Lqc/GOljtIbURNjAMcjUtbFZ3ORUcTCWou3Sp/F5BAdmA+llZFy/qsrUuX6aqDHl59Dh5kj43Zoz177XV/vNGIBU70u3gtdeA11/39iiqB4oQBRT+ClQUVEAZqkTYrWFQ1VMhsA0tcVYUVEDuL4cipHo7p6zh56dbATZFRQWtAl66RMah/sMoE8YkbPRVT+6/nxyWERFUTioykv6XooBr1652AU1eQe4nR9v/tUV5Tjn8atfM81smo/Opdm3TyhVAzvbsbJJLaWkUGSM90tJcW9osMjISkZGRDn1Wo9EAAEpsCTFkGA9jHIBgTE0KQHAUhYIcBo0amd8mL48cU1LEsfQID7e+fy5BVT1p144WfPWDD6RMUGfLljGGsk2mkCGwtWG4K8s28il27UoPYzQaijqWnFfeWnfhjGTv078/6dyS3SfZgJL9FxHh4+XzpJoxBw8a1kgHaGKXLtFF0Lixx4dmrIPJZDJtAChQs+VUUBDdJ80FcApBEetSWSHpryl55ijeCKRiR7odcKa36/Bv7I/ANoHIP5gPRVsFVJEqqCKpTIIQAiWXShDSNQT+jTm83xIKhS4zwhHY6KueuLB0GGOFyHscc9rWJGQycpbXqUN1FH2BxMRE7N27F3369EHt2rVx9uxZzJs3Dy1atOBodMYnMQ5AMIYDEFxDaKhlg9ASXIKqetKhA7B2rbdHUX1h2eYccrnOUdq9u/fGwRnJ3mfsWHpUWeRy4J57aDU7KYnC44OCKORZarQ8cqRXosFYTjmOTEZZ7mFhtE7iDrwRSMWOdMYryOQyRNwTgeLUYhQmFULdUA1FEAmnkkslUEWoEDEyokrXmKoKsNHHMExNJDAwEN999x3mz5+PgoIC1KtXD0OHDsXcuXNdmpXDMK7COABB/37KAQi+AZegYhj7YdnmeTgjmfFZYmOBp58GNm+mOqWXL/tEo2WWU9UDVwZScXI/4zWCYoPQ8OmGCOkSgvKschSdKkJ5VjlCuoagwdMNqlTX46qKZPRJD3NK1PPPP4/k5GSLD2Ojr3Xr1rjtttuwceNG/Pzzz9izZ4+npqXl/fffR9OmTeHv74+ePXviP0udEAF8/fXXaNOmDfz9/dGhQwf8/PPPBu8LIfDyyy+jXr16CAgIwODBg3H69GmDbbKzszF+/HiEhoYiLCwMkydPxo0bNwy2OXLkCPr27Qt/f380atQIb731lmsmzDCMTXTo0AG7du1CVlYWiouLcf78eXz44YcGC4sM40tIAQh+EdTUqjy3HKJcoDy3HIVJhRyAUIWIjIxEmzZtLD5UKpXJz3IJKqa6wbLN87Rt2xa1atXSPhYtWuSS/c6aNQtBQUGoU6cOLl68iO+//94l+7UXtv+qOLGxwOzZwKuvAvPm0d9Zs7zmRAdYTlUXpECqQYMGISYmBpMnT0bHjh3x559/2r2Y6DZHuq8KMMa3CIoNQuPZjdH01aZoMq8Jmr7aFI1nNWYnuo9RFY2+TZs2Yfr06Zg/fz4OHDiATp06YciQIbh27ZrJ7Xfv3o1x48Zh8uTJOHjwIEaOHImRI0fimF63sbfeegvvvfceVq1ahcTERAQFBWHIkCEoLi7WbjN+/HgcP34c27dvx08//YS//voLU6dO1b6fl5eH22+/HU2aNMH+/fuxZMkSLFiwAB999JH7DgbDMAxT5eEAhJpFYmIiVq5ciUOHDuHChQvYtWsXxo0bxyWomGoHyzbPkpSUhNzcXO1jzpw5JrebPXs21YK28Dhx4oR2+xkzZuDgwYP47bffoFAoMHHiRAghPDUtAGz/VRukRssdOtBfH2juxXKq6uPSQCrhBjZu3ChUKpVYs2aNOH78uJgyZYoICwsT6enpJrf/999/hUKhEG+99ZZISkoSc+fOFX5+fuLo0aPabRYvXixq1aoltmzZIg4fPiyGDx8umjVrJoqKirTbDB06VHTq1Ens2bNH/P3336Jly5Zi3LhxNo87NTVVABCpqamOT55hqgDuOtf37NkjVqxYIQ4ePChSUlLEzp07Re/evUWLFi1EcXGxS7/LGj169BBPPvmk9nlFRYWoX7++WLRokcnt77vvPjFs2DCD13r27CkeffRRIYQQGo1GREdHiyVLlmjfz8nJEWq1Wvzvf/8TQgiRlJQkAIi9e/dqt/nll1+ETCYTly9fFkII8cEHH4jatWuLkpIS7TazZs0SMTExZudSXFwscnNztQ/pe1hWMdWdmnBfXrlypWjSpIlQq9WiR48eIjEx0eL2X331lYiJiRFqtVq0b99ebN261eB9jUYj5s2bJ6Kjo4W/v78YNGiQOHXqlME2WVlZ4oEHHhAhISGiVq1a4pFHHhH5+fkG2xw+fFj06dNHqNVq0bBhQ/Hmm2/aNa+a8Nt5C02FRhSeLxT5R/JF4flCoanQeHtINRp3netHjhwRAwcOFOHh4UKtVoumTZuKxx57TFy6dMml3+NKfFWeWYJlle/Ass292HuuX7t2TSQnJ1t86Nszpr5r9+7drpyCVaqT/WcMyyrfgOWU+6kK57pbHOm+KsCsURV+MIZxBdXd6CspKREKhUJs3rzZ4PWJEyeK4cOHm/xMo0aNxLJlywxee/nll0XHjh2FEEKcPXtWABAHDx402KZfv37i6aefFkII8emnn4qwsDCD98vKyoRCoRDfffedEEKIBx98UIwYMcJgm127dgkAIjs72+TY5s+fLwBUerCsYqo71f2+7KuBB7m5uSIqKkqMHz9eHDt2TPzvf/8TAQEBYvXq1TbPrbr/dgwjwec64avyzBr8+zE1BU+e6xcuXBAAxO+//+7275KobvYfB1IxNZWqcF92eY5EaWkp9u/fj8GDB2tfk8vlGDx4MBISEkx+JiEhwWB7ABgyZIh2+/PnzyMtLc1gm1q1aqFnz57abRISEhAWFoZu3bpptxk8eDDkcjkSExNNfm9JSQny8vK0j/z8fMcmzTAMAN+pO5yZmYmKigpERUUZvB4VFYW0tDSTn0lLS7O4vfTX2jZ169Y1eF+pVCI8PNxgG1P70P8OY+bMmWOQgpmUlGR64gzDVCneeecdTJkyBQ8//DDatm2LVatWITAwEGvWrDG5/fLlyzF06FDMmDEDsbGxWLhwIbp27YqVK1cCoDJ47777LubOnYsRI0agY8eOWLduHa5cuYItW7YAoMbQ27ZtwyeffIKePXuiT58+WLFiBTZu3IgrV64AANavX4/S0lKsWbMG7dq1w9ixY/H000/jnXfeMTsX1qkYpmbjq/LMGJZVDONafKUMVXWz/xYtWmRQy75t27amJ84wjMdxuSPdlwWYMSycGIapCqjVaoOmsCEhId4eEsMwTuLLgQcJCQno16+fQe+LIUOG4OTJk7h+/brJsbFOxTA1F1+WZ8awrGIY1+LKBn6MDg6kYhjfRentAXiTOXPmYPr06drnqampaN++Pa5everFUTGM+5HOcakRaHUjIiICCoUC6enpBq+np6cjOjra5Geio6Mtbi/9TU9PR7169Qy26dy5s3Yb42Y25eXlyM7ONtiPqe/R/w5rSL8byyqmulOdZZWlwAP95l36eDJyqlmzZpX2Ib1Xu3btSmNjnYqpqVRnOWUrvizPjGFZxdRU3CWrpIxkb1Pd7D+1Wm2wEJGTkwOA7T+m+lMV9CqXO9J9WYAZYyycCgsLAQA9evSwNk2GqRakp6ejcePG3h6Gy1GpVIiLi8POnTsxcuRIACSId+7ciWnTppn8THx8PHbu3Ilnn31W+9r27du1KYnNmjVDdHQ0du7cqZU7eXl5SExMxOOPP67dR05ODvbv34+4uDgAwK5du6DRaNCzZ0/tNi+99BLKysrg5+en/Z6YmBiTzilTSPKSZRVTU6iusqo6wToVU9NhOVU1YFnF1HSqq6xi+49hqhe+LKtc7kj3ZQFmjS5duuC///5DVFQU5HKXV72xmfz8fLRt2xZJSUlcwsFB+BhaRqPRID09HV26dPH2UNzG9OnTMWnSJHTr1g09evTAu+++i4KCAjz88MMAgIkTJ6JBgwZYtGgRAOCZZ55B//798fbbb2PYsGHYuHEj9u3bh48++ggAIJPJ8Oyzz+K1115Dq1at0KxZM8ybNw/169fXyrrY2FgMHToUU6ZMwapVq1BWVoZp06Zh7NixqF+/PgDggQcewCuvvILJkydj1qxZOHbsGJYvX45ly5bZPDeWVdUHPoaWqc6yypcDD1yROeMrcgrg68wV8DE0T3WWU7biy/LMGiyrqg98/CxTE2QV23/uh68z5+FjaJkqIavc0cF048aNQq1Wi88++0wkJSWJqVOnirCwMJGWliaEoK7Fs2fP1m7/77//CqVSKZYuXSqSk5PF/PnzTXZtDwsLE99//704cuSIGDFihMmu7V26dBGJiYnin3/+Ea1atbKra7uvkJubKwCI3Nxcbw+lysLHkBFCiBUrVojGjRsLlUolevToIfbs2aN9r3///mLSpEkG23/11VeidevWQqVSiXbt2omtW7cavK/RaMS8efNEVFSUUKvVYtCgQeLkyZMG22RlZYlx48aJ4OBgERoaKh5++GGRn59vsM3hw4dFnz59hFqtFg0aNBCLFy927cQ9BF9nzsPHsGbTo0cPMW3aNO3ziooK0aBBA7Fo0SKT2993333irrvuMngtPj5ePProo0IIklHR0dFi6dKl2vdzc3OFWq0W//vf/4QQQiQlJQkAYt++fdptfv31VyGTycTly5eFEEJ88MEHonbt2qK0tFS7zZw5c0RMTIyTM/YOfJ05Dx9Dxhq+Ks+qEnydOQcfP0YItv/cDV9nzsPHsOrjFke6EL4rwKoCfGE5Dx9DhnE/fJ05Dx/Dmo2vBh7k5OSIqKgo8eCDD4pjx46JjRs3isDAQLF69WoPHBXXw9eZ8/AxZKzhq/KsKsHXmXPw8WMY98PXmfPwMaz6uM2RzjgOX1jOw8eQYdwPX2fOw8eQ8dXAg+oSOSUEX2eugI8hYwu+Ks+qCnydOQcfP4ZxP3ydOQ8fw6qPTAgh3F9AhrGHkpISLFq0CHPmzDFohsPYDh9DhnE/fJ05Dx9DhnE/fJ05Dx9DhnE/fJ05Bx8/hnE/fJ05Dx/Dqg870hmGYRiGYRiGYRiGYRiGYRjGAt5tTc4wDMMwDMMwDMMwDMMwDMMwPg470hmGYRiGYRiGYRiGYRiGYRjGAuxIZxiGYRiGYRiGYRiGYRiGYRgLsCOdYRiGYRiGYRiGYRiGYRiGYSzAjnSGYRiGYRiGYRiGYRiGYRiGsQA70j3Ehx9+iI4dOyI0NBShoaGIj4/HL7/8YrBNQkICbr31VgQFBSE0NBT9+vVDUVGR9v3s7GyMHz8eoaGhCAsLw+TJk3Hjxg1PT8VrWDuGaWlpePDBBxEdHY2goCB07doV3377rcE+avoxZBhrsKxyHpZVDON+WFY5D8sqhnEvLKech+UUw7gfllXOw7KqhiEYj/DDDz+IrVu3ilOnTomTJ0+KF198Ufj5+Yljx44JIYTYvXu3CA0NFYsWLRLHjh0TJ06cEJs2bRLFxcXafQwdOlR06tRJ7NmzR/z999+iZcuWYty4cd6aksexdgxvu+020b17d5GYmCjOnj0rFi5cKORyuThw4IB2HzX9GDKMNVhWOQ/LKoZxPyyrnIdlFcO4F5ZTzsNyimHcD8sq52FZVbNgR7oXqV27tvjkk0+EEEL07NlTzJ071+y2SUlJAoDYu3ev9rVffvlFyGQycfnyZbeP1VfRP4ZBQUFi3bp1Bu+Hh4eLjz/+WAjBx5BhHIVllfOwrGIY98OyynlYVjGMe2E55TwspxjG/bCsch6WVdUXLu3iBSoqKrBx40YUFBQgPj4e165dQ2JiIurWrYvevXsjKioK/fv3xz///KP9TEJCAsLCwtCtWzfta4MHD4ZcLkdiYqI3puFVjI8hAPTu3RubNm1CdnY2NBoNNm7ciOLiYgwYMAAAH0OGsReWVc7Dsoph3A/LKudhWcUw7oXllPOwnGIY98OyynlYVlV/lN4eQE3i6NGjiI+PR3FxMYKDg7F582a0bdsWe/bsAQAsWLAAS5cuRefOnbFu3ToMGjQIx44dQ6tWrZCWloa6desa7E+pVCI8PBxpaWnemI5XMHcMAeCrr77C/fffjzp16kCpVCIwMBCbN29Gy5YtAYCPIcPYCMsq52FZxTDuh2WV87CsYhj3wnLKeVhOMYz7YVnlPCyrag7sSPcgMTExOHToEHJzc/HNN99g0qRJ+PPPP6HRaAAAjz76KB5++GEAQJcuXbBz506sWbMGixYt8uawfQpzx7Bt27aYN28ecnJysGPHDkRERGDLli2477778Pfff6NDhw7eHjrDVBlYVjkPyyqGcT8sq5yHZRXDuBeWU87Dcoph3A/LKudhWVVzYEe6B1GpVNoVp7i4OOzduxfLly/H7NmzAUC7WiURGxuLixcvAgCio6Nx7do1g/fLy8uRnZ2N6OhoD4zeNzB3DGfOnImVK1fi2LFjaNeuHQCgU6dO+Pvvv/H+++9j1apVfAwZxkZYVjkPyyqGcT8sq5yHZRXDuBeWU87Dcoph3A/LKudhWVVz4BrpXkSj0aCkpARNmzZF/fr1cfLkSYP3T506hSZNmgAA4uPjkZOTg/3792vf37VrFzQaDXr27OnRcfsS0jEsLCwEAMjlhqe0QqHQrqLyMWQYx2BZ5TwsqxjG/bCsch6WVQzjXlhOOQ/LKYZxPyyrnIdlVTXG291OawqzZ88Wf/75pzh//rw4cuSImD17tpDJZOK3334TQgixbNkyERoaKr7++mtx+vRpMXfuXOHv7y/OnDmj3cfQ9cb0BQABAABJREFUoUNFly5dRGJiovjnn39Eq1atxLhx47w1JY9j6RiWlpaKli1bir59+4rExERx5swZsXTpUiGTycTWrVu1+6jpx5BhrMGyynlYVjGM+2FZ5TwsqxjGvbCcch6WUwzjflhWOQ/LqpoFO9I9xCOPPCKaNGkiVCqViIyMFIMGDdIKJolFixaJhg0bisDAQBEfHy/+/vtvg/ezsrLEuHHjRHBwsAgNDRUPP/ywyM/P9+Q0vIq1Y3jq1Clx7733irp164rAwEDRsWNHsW7dOoN91PRjyDDWYFnlPCyrGMb9sKxyHpZVDONeWE45D8sphnE/LKuch2VVzUImhBDejopnGIZhGIZhGIZhGIZhGIZhGF+Fa6QzDMMwDMMwDMMwDMMwDMMwjAXYkc4wDMMwDMMwDMMwDMMwDMMwFmBHOsMwDMMwDMMwDMMwDMMwDMNYgB3pDMMwDMMwDMMwDMMwDMMwDGMBdqQzDMMwDMMwDMMwDMMwDMMwjAXYkc4wDMMwDMMwDMMwDMMwDMMwFmBHOsMwDMMwDMMwDMMwDMMwDMNYgB3pDMMwDMMwDMMwDMMwDMMwDGMBdqQzDMMwDMMwDMMwDMMwDMMwjAXYkc4wDMMwDMMwDMMwDMMwDMMwFmBHOsMwDMMwDMMwDMMwDMMwDMNYgB3pDMMwDMMwDMMwDMMwDMMwDGMBdqQzDMMwDMMwDMMwDMMwDMMwjAXYkc4wDMMwDMMwDMMwDMMwDMMwFmBHOsMwDMMwDMMwDMMwDMMwDMNYgB3pDMMwDMMwDMMwDMMwDMMwDGMBdqQzDMMwDMMwDMMwDMMwDMMwjAWqpSP9oYcegkwmg0wmQ/v27b09HIap8jz77LPaayo4ONjbw6kWsJxiGNfCcsp5WC4xjHd49913tdeeTCZDZmamt4dULWCZxjDWycnJMZA/S5cu9faQqjUslxjGtXhDh6qWjnQAiIiIwBdffIHFixdXem/37t3o06cPAgMDER0djaeffho3btywed+ffvopYmNj4e/vj1atWmHFihWVttm8eTOGDBmC+vXrQ61Wo2HDhhg9ejSOHTvm8JyOHz+OMWPGoHnz5ggMDERERAT69euHH3/8sdK2+ieS8eO2224z+x3r16+32wmRk5ODqVOnIjIyEkFBQRg4cCAOHDjg0BwBYO/evZg2bRratWuHoKAgNG7cGPfddx9OnTpl8XNlZWVo27atWQXgzJkzGD16NGrXro3AwED06dMHv//+u83jcvU8zfHXX39h+PDhaNSoEfz9/REdHY2hQ4fi33//tenzTZs2Nfvbt2rVSrtdUVERJk+ejPbt26NWrVoIDg5Gp06dsHz5cpSVlRns88EHH8QXX3yBvn37unSuNR2WU7bJqRMnTmDmzJno3LkzQkJCUK9ePQwbNgz79u2zeVwlJSWYNWsW6tevj4CAAPTs2RPbt293eJ4A8Prrr2P48OGIioqCTCbDggULTG5n6zVpzD///GO3QuCOeZrCnt/ZFAMGDDB7TPz8/Ay2LS4uxqJFi9C2bVsEBgaiQYMGGDNmDI4fP26wHcsp1+BtuWQPzz33HLp27Yrw8HAEBgYiNjYWCxYsqDQmfaPV1OPy5csG25eWluKNN95AmzZt4O/vj6ioKAwbNgyXLl3yyjw3bdqECRMmoFWrVpDJZBgwYIDZbe2RAbb+nu7YpzlcfexswRFZe/r0aYwdOxYNGzZEYGAg2rRpg1dffRWFhYUG25mTdUOHDjXYbujQofjiiy9wzz33uGxeDOFtmXby5Ek899xz6N27N/z9/SGTyZCSkuLMlNyia6WkpJjdbuPGjTaNy9W2kqO6hiWb8MqVK5gwYQJiYmIQEhKCsLAw9OjRA59//jmEEDaNq6roWhI7duzArbfeilq1aiEkJARxcXHYtGmT9v2goCB88cUXWLZsmaunwJjB23KpqtiAviiX7PFV/ffff3jiiScQFxcHPz8/yGQys/vNzc3FzJkz0apVKwQEBKBJkyaYPHkyLl68aNO4qopcstUGzMrKwpIlS9CvXz9ERkYiLCwMvXr1MpBdEt7QoZQe+yYPExQUhAkTJlR6/dChQxg0aBBiY2Pxzjvv4NKlS1i6dClOnz6NX375xep+V69ejcceewyjRo3C9OnT8ffff+Ppp59GYWEhZs2apd3u6NGjqF27Np555hlEREQgLS0Na9asQY8ePZCQkIBOnTrZPacLFy4gPz8fkyZNQv369VFYWIhvv/0Ww4cPx+rVqzF16lTttl988UWlz+/btw/Lly/H7bffbnL/N27cwMyZMxEUFGTzmDQaDYYNG4bDhw9jxowZiIiIwAcffIABAwZg//79Fp1E5njzzTfx77//YsyYMejYsSPS0tKwcuVKdO3aFXv27DG7crtixQqzgiY1NRXx8fFQKBSYMWMGgoKCsHbtWtx+++3YuXMn+vXr5/F5muPUqVOQy+V47LHHEB0djevXr+PLL79Ev379sHXr1krGlzHvvvtupZvthQsXMHfuXIPfvqioCMePH8edd96Jpk2bQi6XY/fu3XjuueeQmJiIDRs2aLeNi4tDXFwcduzY4ZbFg5oKyynb5NQnn3yCTz/9FKNGjcITTzyB3NxcrF69Gr169cK2bdswePBgq+N66KGH8M033+DZZ59Fq1at8Nlnn+HOO+/E77//jj59+tg9TwCYO3cuoqOj0aVLF/z6669mt7P1mtRHo9HgqaeeQlBQEAoKCmwekzvmaQp7fmdTvPTSS/i///s/g9cKCgrw2GOPVTom48ePxw8//IApU6aga9euuHLlCt5//33Ex8fj6NGjaNKkCQCWU67C23LJHvbu3Yu+ffvi4Ycfhr+/Pw4ePIjFixdjx44d+OuvvyCXU7zIo48+WklOCCHw2GOPoWnTpmjQoIH29bKyMgwbNgy7d+/GlClT0LFjR1y/fh2JiYnIzc1Fw4YNPT7PDz/8EPv370f37t2RlZVlcVtbZYA9v6c79mkKdxw7azgia1NTU9GjRw/UqlUL06ZNQ3h4OBISEjB//nzs378f33//vcH2DRs2xKJFiwxeq1+/vsHzNm3aoE2bNjhz5gw2b97s3KQYA7wt0xISEvDee++hbdu2iI2NxaFDh5yekzttwnHjxuHOO+80eC0+Pt7qmNxhKzmqa1iyCTMzM3Hp0iWMHj0ajRs3RllZGbZv346HHnoIJ0+exBtvvGF1XFVF1wKAtWvXYvLkybjtttvwxhtvQKFQ4OTJk0hNTdVu4+fnhwkTJiAlJQXPPfecy8bPmMfbcqmq2IASviSX7PFV/fzzz/jkk0/QsWNHNG/e3GxgqEajwW233YakpCQ88cQTaN26Nc6cOYMPPvgAv/76K5KTkxESEmJxXFVFLtlqAyYkJOCll17CnXfeiblz50KpVOLbb7/F2LFjkZSUhFdeeUW7rVd0KFENmTRpkmjSpInJ9+644w5Rr149kZubq33t448/FgDEr7/+anG/hYWFok6dOmLYsGEGr48fP14EBQWJ7Oxsi59PS0sTSqVSPProo7ZNxAbKy8tFp06dRExMjNVtJ0+eLGQymUhNTTX5/qxZs0RMTIx2PrawadMmAUB8/fXX2teuXbsmwsLCxLhx42ybhBH//vuvKCkpMXjt1KlTQq1Wi/Hjx5v8THp6uqhVq5Z49dVXBQCxZMkSg/efeOIJoVQqxYkTJ7SvFRQUiEaNGomuXbtaHZM75mkPBQUFIioqSgwZMsShzy9cuFAAEP/++6/VbadNmyYAiKtXr1Z6b9KkSTafG4xlWE6ZxpSc2rdvn8jPzzfYLjMzU0RGRopbbrnF6j4TExMryYWioiLRokULER8fb8dMDDl//rwQQoiMjAwBQMyfP9/mz1q7Jj/88ENRp04d8cwzzwgAIiMjw+o+3TVPW7HndzbFF198IQCI9evXa1+7dOmSACBeeOEFg2137dolAIh33nmn0n5YTjmOr8ole1i6dKkAIBISEixu9/fffwsA4vXXXzd4/c033xR+fn4iMTHR7u921zwvXrwoKioqhBBCtGvXTvTv39/kdvbIAFt/T3fs0xSePEf0cUTWvv766wKAOHbsmMHrEydOFAAMxtq/f3/Rrl07m8czf/58m8fBWMcXZFpWVpbIy8sTQgixZMkSAUCrP7gSZ3Wt8+fPm7ShbMVTtpK1eVqzCc1x1113iaCgIFFeXm5xu6qka50/f14EBASIp59+2qZ9O3sOMLbhC3LJFL5oA/qiXLLHV5WWliYKCwuFEEI8+eSTwpz79d9//xUAxMqVKw1eX7NmjQAgvvvuO4tjqkpyyRSmbMBz586JlJQUg+00Go249dZbhVqtFjdu3Ki0H0/qUNW2tIsp8vLysH37dkyYMAGhoaHa1ydOnIjg4GB89dVXFj//+++/IysrC0888YTB608++SQKCgqwdetWi5+vW7cuAgMDkZOT4/AcjFEoFGjUqJHVfZaUlODbb79F//79TUZTnT59GsuWLcM777wDpdL2RIVvvvkGUVFRuPfee7WvRUZG4r777sP333+PkpISm/cl0bt3b6hUKoPXWrVqhXbt2iE5OdnkZ2bPno2YmBiTK7sA8Pfff6NLly6IiYnRvhYYGIjhw4fjwIEDOH36tMUxuWOe9hAYGIjIyEiHz50NGzagWbNm6N27t9VtmzZtCgAuPU8Z22E5VVlOxcXFVSo3VadOHfTt29esTNDnm2++gUKhMFgh9/f3x+TJk5GQkGAQlWMP0rXiCJauyezsbMydOxevvvoqwsLCbN6nu+ZpK7b+zubYsGEDgoKCMGLECO1r+fn5AICoqCiDbevVqwcACAgIcGywjF14Wy7Zg633sA0bNkAmk+GBBx7QvqbRaLB8+XLcc8896NGjB8rLyyuV6rCEu+bZqFEjbXS9JWyVAfb8nu7Ypyk8eY5IOCpr8/LyAJiWS3K5vJIOCwDl5eV2peYz7sWT52t4eLjVSEJX4CqbEKDowNLSUru+31O2krV5WrMJzdG0aVMUFhZanXdV0rVWrVqFiooKvPrqqwAo+1zYWL6G8Tzevo/6og2oj6/IJXt8VVFRUTbZKpb0CsC6vVOV5JIpTNmAzZo102YdS8hkMowcORIlJSU4d+6cM0N2mhrlSD969CjKy8vRrVs3g9dVKhU6d+6MgwcPWvy89L7x5+Pi4iCXy01+PicnBxkZGTh69Cj+7//+D3l5eRg0aJBT8ygoKEBmZibOnj2LZcuW4ZdffrG6z59//hk5OTkYP368yfefffZZDBw4sFLKjDUOHjyIrl27VjLwevTogcLCQqt1zW1FCIH09HRERERUeu+///7D559/rm0yYIqSkhKTAigwMBAAsH//fovf76l56pOXl4fMzEycOHECL774Io4dO+bQuXPw4EEkJycbOAv0KS0tRWZmJlJTU7F582YsXboUTZo0QcuWLZ2dAuMALKfMyylj0tLSTMoEYw4ePIjWrVsbKKUAXb8AXJJmbQ/Wrsl58+YhOjoajz76qN379fQ8HfmdTZGRkYHt27dj5MiRBuXFWrRogYYNG+Ltt9/Gjz/+iEuXLuG///7DY489hmbNmmHs2LGunA5jBm/IJVspLy9HZmYmrly5gt9++w1z585FSEiI9rw3RVlZGb766iv07t3bYEEsKSkJV65cQceOHTF16lQEBQUhKCgIHTt2tKmnijvnaQu2ygB7fk937NPc2AHPHjtHZa1Uo37y5Mk4dOgQUlNTsWnTJnz44Yd4+umnK5VIPHXqFIKCghASEoLo6GjMmzevUi8axrNUxfPVFO7QtV555RUEBwfD398f3bt3x2+//WbTWNxpK9k6T1tsQomioiJkZmYiJSUFn3/+OdauXYv4+HirDquqpGvt2LEDbdq0wc8//4yGDRsiJCQEderUwbx586DRaFw+TsY52AasWnJJH0u+Klvo1q0bgoKCMG/ePOzatQuXL1/Gn3/+iZkzZ6J79+5Wy5hWJblkjDkb0BxpaWkA4PCxdhXVtka6Ka5evQpAt7KjT7169fD3339b/bxCoUDdunUNXlepVKhTpw6uXLlS6TO9evXCyZMnAQDBwcGYO3cuJk+e7OgUAADPP/88Vq9eDQCQy+W49957sXLlSoufWb9+PdRqNUaPHl3pva1bt+K3337D4cOH7R7L1atXTdYXl47xlStX0KFDB7v3a8z69etx+fJl7Yq6hBACTz31FO6//37Ex8ebbd4TExODv//+G/n5+QZRIf/88w8AVGo0Zoyn5qnPfffdp627rFKp8Oijj2LevHl272f9+vUAYPbG9N1332HcuHHa5926dcOaNWvsykxgXAfLKdNyypi///4bCQkJmDt3rtVtr169avZ4AjB5TNyJpWvyyJEjWL16NX7++WcoFAq79uuNeTryO5ti06ZNKC8vr3RM/Pz88O233+KBBx7A8OHDta/HxcVh9+7ddkWRMo7jDblkK/v27TOokxkTE4MffvgB4eHhZj/z66+/Iisrq9L5JmWnLVu2DOHh4dpz+4033sDQoUOxd+9edOzY0ex+3TlPW7BVBtjze7pjn+bG7slj54ysHTp0KBYuXIg33ngDP/zwg/b1l156Ca+99prBti1atMDAgQPRoUMHFBQU4JtvvsFrr72GU6dOmWyYxXiGqna+msOVupZcLsftt9+Oe+65Bw0aNMC5c+fwzjvv4I477sAPP/yAYcOGWdyvO20lW+Zpq00osXz5csyZM0f7fNCgQVi7dq3VsVQlXev06dNQKBR4+OGHMXPmTHTq1AnfffcdXnvtNZSXl1fq3cB4F7YBq5ZcMh6/KV+VrURERGDTpk2YMmWKgTN6yJAh+Oabb6z6ZaqSXDLGnA1oiuzsbHzyySfo27evyfl6khrlKSsqKgIAqNXqSu/5+/tr37f0eVPpmpY+v3btWuTl5eHcuXNYu3YtioqKUFFRYVOKrjmeffZZjB49GleuXMFXX32FiooKi2kueXl52Lp1K+68885KTofS0lI899xzeOyxx9C2bVu7x1JUVGT2eErvO8uJEyfw5JNPIj4+HpMmTTJ477PPPsPRo0fxzTffWNzH448/jh9//BH3338/Xn/9dQQFBeGDDz7Avn37bBqnJ+ZpzOLFi/H8888jNTUVn3/+OUpLS1FeXm7XPjQaDTZu3IguXbogNjbW5DYDBw7E9u3bkZOTg507d+Lw4cN2NTdkXAvLqcpyyphr167hgQceQLNmzTBz5kyrY/HG9WsOa9fk008/jTvuuMNsE1JLeGOe9v7O5tiwYQMiIyNx2223VXqvdu3a6Ny5M8aMGYNevXrhzJkzWLRoEcaMGYPt27dr58e4D2/IJVtp27Yttm/fjoKCAuzevRs7duywWkJjw4YN8PPzw3333WfwuvS5/Px8HDx4EI0aNQIA3HrrrWjZsiXeeustfPnll2b368552oKtMsCe39Md+zQ3dk8eO2dkLUBlIPr164dRo0ahTp062Lp1K9544w1ER0dj2rRp2u0+/fRTg889+OCDmDp1Kj7++GM899xz6NWrl1PzYByjqp2v5nClrtW4ceNKjdMffPBBtG3bFs8//7xVh5U7dRBb5mmrTSgxbtw4dOvWDRkZGfjpp5+Qnp5u0xirkq5148YNaDQaLF68WNtoctSoUcjOzsby5cvx4osveqTsEGMbbANWLbkkYclXZQ+RkZHo0qULpk2bhnbt2uHQoUN466238PDDD+Prr7+2+NmqJJeMsWQD6qPRaDB+/Hjk5ORgxYoVjg7bZdQoR7qUqmWqFlJxcbHVVK6AgACzJ4e5z+tHSY0dO1brOFm6dKnN4zZG6koLUM2s22+/HXfffTcSExNNprF9++23KC4uNrnKs2zZMmRmZhp0vbWHgIAAs8dTet8Z0tLSMGzYMNSqVUtb+0kiLy8Pc+bMwYwZM7TGrjnuuOMOrFixArNnz0bXrl0BAC1btsTrr7+OmTNnVqq/bIyr51laWors7GyD1yIjIw3m17lzZ+3/EyZMQNeuXbXdmG3lzz//xOXLly12YI+KitLW4xo9ejTeeOMN3HbbbTh9+jSio6Nt/i7GNbCcsrwaXVBQgLvuugv5+fn4559/rF67gPvllD1YuiY3bdqE3bt349ixYw7t29XzrKioQEZGhsFr4eHhBkq6vb+zKc6dO4eEhARMmzatUsRFbm4u+vbtixkzZuD555/Xvt6tWzcMGDAAa9euxeOPP27XvBj78YZcspXQ0FBtyuuIESOwYcMGjBgxAgcOHECnTp0qbX/jxg18//33GDJkCOrUqVNpnABwyy23GOgVjRs3Rp8+fbB7926LY3HnPG3BVhlgz+/pjn2aG7urj11GRgYqKiq0z4ODgxEcHOy0rN24cSOmTp2KU6dOaWu53nvvvdBoNJg1axbGjRtX6dzS5/nnn8fHH3+MHTt2sCPdS/ji+eoI7tK1JMLDw/Hwww9j8eLFuHTpktnaxYB7dS1r87THJpRo0qSJtv7uuHHjMHXqVAwePBgnT560ONaqpGsFBASgoKDAIPMYoPlu27YNBw8eNBmty3gHtgGrllwCLPuq7OHcuXMYOHAg1q1bh1GjRgEgnbZp06Z46KGH8Msvv+COO+4w+/mqJJf0sWQDGvPUU09h27ZtWLdunUn93tPUqBrpUvi/lDajz9WrV1G/fn2rn6+oqMC1a9cMXi8tLUVWVpbVz9euXRu33nqrNq3fVYwePRp79+41W+Np/fr1qFWrFu666y6D13Nzc/Haa69hypQpyMvLQ0pKClJSUrSNSFJSUirN1Zh69eqZPZ4ArB4TS+Tm5uKOO+5ATk4Otm3bVmlfS5cuRWlpKe6//37t2C9dugQAuH79OlJSUgxuJtOmTUN6ejp2796Nffv24cSJE6hVqxYAoHXr1h6d5+7du1GvXj2Dh6UmECqVCsOHD8d3331n14ri+vXrIZfLKylQlhg9erTW0cB4HpZTd5l8H6A53HvvvThy5Ai+//57tG/f3qbvdqecshdL1+SMGTMwZswYqFQqrUyTmrakpqZaTctz9TxTU1MrySlrjkRrv7MpNmzYAMB0qZtvv/0W6enpBmVdAKB///4IDQ3Fv//+a/P3MI7jbblkD1JDqY0bN5p8f8uWLSgsLDR5vknjMG72BFATruvXr1v8bk/O09z32yID7Pk93bFPc2N39bHr3r27gfySHAPOytoPPvgAXbp0qWS8Dx8+HIWFhVbr2EqOPuOACsZz+OL56gpcoWsZY+v56kldy3ie9tqE5vaZmpqKv/76y+J2VUnXMndPk0p/WLunMZ7F23LJl21AY3xBLlnzVdnDZ599huLi4krHQLJ/rNk7VUku6WPJBtTnlVdewQcffIDFixfjwQcftHn/7qRGOdLbt28PpVKpLechUVpaikOHDhlEAJtCet/48/v27YNGo7H6eYDSKnJzc+0Ztk37BGByv1evXsXvv/+OUaNGVUr3uH79Om7cuIG33noLzZo10z6+/fZbFBYWolmzZgadf03RuXNnHDhwoFLDksTERAQGBlp1UJujuLgYd999N06dOoWffvrJZNmZixcv4vr162jXrp127H379gVA9UybNWuGpKQkg88EBQUhPj4ecXFxUCgU2LFjBwICAnDLLbd4dJ6dOnXC9u3bDR7Wor+LiooghEB+fr5N3yF1vx4wYIBdwtPS+cS4H5ZTldPSAErnmjhxInbu3IkNGzagf//+Nn93586dcerUKW1HdInExETt+57A2jWZmpqKDRs2GMjj5cuXAwC6du1qtRm0q+cZHR1dSU5ZiwBwRH5s2LABLVq0MBmZmZ6eDgAGUaUA1UKtqKiwu9wV4xi+IJdspaSkBBqNxuw5uH79egQHB1danAGADh06wM/Pz2TflCtXriAyMtLid3tynua+3xYZYM/v6Y59mhs74Npjt379egP5NXHiRADOy9r09PRKMgmAtoGoNbl07tw5ALB6PjHuwxfPV1fgrK5lClvPV3fZhKYwnqcjNqG1fZqjKulacXFxACr3ApMWC1kG+Ra+IJd80QY0hbflki2+KntIT0/X2jb62KpXVCW5pI8lG1Di/fffx4IFC/Dss89qS1T5BKIaMmnSJNGkSROT7w0dOlTUq1dP5OXlaV/75JNPBADxyy+/aF8rKCgQycnJIiMjQ/taYWGhCA8PF3fddZfBPidMmCACAwNFVlaW9rX09PRK333+/HkREhIi+vbt69C8TO2ztLRUdO3aVQQEBIj8/PxK77/zzjsCgNi5c2el9woKCsTmzZsrPQYOHCj8/f3F5s2bxZ49e7TbX7lyRSQnJ4vS0lLtaxs3bhQAxNdff619LSMjQ4SFhYn777/foXmWl5eL4cOHC6VSKbZu3Wp2u/3791ca++rVqwUA8dBDD4nNmzeLnJwcs5//999/hUKhENOmTTN43VPzNIep3/n69euiUaNGolGjRgavX7hwQSQnJ5vcz3fffScAiE8//dTk+xkZGUKj0VR6fdq0aWbPmUmTJomgoCBbpsFYgeWUDktySuKJJ54QAMTq1astfn9GRoZITk4WBQUF2tf27NkjAIglS5ZoXysuLhYtW7YUPXv2tGVaVr8TgJg/f77F7axdk6bk8f333y8AiHXr1oldu3YZfKen56mPPb+zKZkqceDAAQFAzJs3z+T3fPPNNyaP7ZYtWwQAsXjx4kqfYTnlOL4gl2zl+vXrJs+ppUuXmr3Orl27JpRKpXjwwQfN7nfEiBFCoVAY3FuTkpKEQqEQTzzxhPY1T83TmHbt2on+/fubfM8eGWDr7+mOfXrr2Ek4K2vvuusuoVKpxMmTJw32O3LkSCGXy8Xly5eFEELk5uaK4uJig200Go32u/bv319pbPPnzxcADI4N4zi+JtOWLFkiAIjz5887PinhHl3r2rVrlV67dOmSqF27tujYsaPB656ylWydpz02oal5CiHE3XffLWQymTh9+rTB+KuyrrV582YBQLz44ova1yoqKkSfPn1EeHh4Jfl0/vz5SnNjXI8vyKWqYgP6olyy1VdlzJNPPinMuV8l3XXt2rUGr7/77rsCgNi4caPB+KuyXJKwZgMKQb+fXC4X48ePN+m3MsaTOlSNc6Tv379fqNVq0aVLF/Hhhx+Kl156Sfj7+4vbb7/dYLvff//dpPH+/vvvCwBi9OjR4uOPPxYTJ04UAMTrr79usF3dunXFuHHjxJtvvik++ugjMWPGDBEeHi78/f3Fv//+W2m8tihVI0eOFLfeeqtYsGCB+Pjjj8XChQtFmzZtBADx9ttvm/xMXFycqF+/vqioqLC4b+PxmHJCmBpneXm56NWrlwgODhavvPKKeP/990W7du1ESEiIOHHihEPzfOaZZwQAcffdd4svvvii0sMS5hSAlJQU0aNHD/Haa6+JTz75RDz33HMiICBAdOnSxeBG5cl5mqNr165i+PDh4vXXXxcff/yxmDdvnmjYsKGQy+UGNwEhhOjfv79ZgTxq1CihVqvNLiYsW7ZMxMTEiFmzZonVq1eLpUuXittuu0177E3BDirXwXJKhzU5tWzZMgFAxMfHm5QJN27c0G4r3UB///13g32MGTNGKJVKMWPGDLF69WrRu3dvoVQqxZ9//mmwnbnPm2LdunVi4cKFYs6cOQKAGDhwoFi4cKFYuHChSElJqbS9tWvSFOYUAk/O0xT2/M6Wzp3nn39eAKgkRyVKSkpEu3bthEwmEw899JBYtWqVeOGFF4S/v7+oV6+eSUWJ5ZTj+IJcMvd5YzZv3iwaNWoknnvuOfHBBx+Id999V4waNUrIZDLRrVs3UVJSUukzK1asEADEtm3bzO73+PHjIjg4WNSrV08sWrRILFq0SNSrV09ERkaKS5cueXyeQgjx559/amVL3bp1RdOmTbXPja9tW2WArb+nO/bpyWNnK/bI2j///FMoFApRt25d8eqrr4r3339f3HHHHQKA+L//+z+DcUZHR4vnnntOvP/++2Lp0qXilltuEQDE1KlT7RoH4xi+INNycnK01+vQoUMFAPH888+LhQsXihUrVlQar7d0rYceekj07dtXLFiwQHz00UfixRdfFHXq1BEqlaqSruApW8mReUqYswmfeeYZ0a1bNzF37lzx0UcficWLF4vu3bsLAOKpp54y2Laq61oajUYMGjRIyGQyMXXqVPH+++9rbT1TgSnsSPcMviCXqooN6ItyyR5fVUpKilb+9+zZUwDQPl+3bp12u8zMTBEdHS1UKpV4+umnxerVq8Wjjz4qFAqFaNeunYFOW9XlkoQ1GzAxMVGoVCoRGRkp1qxZU+k4nz17ttJn2JHuJJaEkxBC/P3336J3797C399fREZGiieffLKSM9WSkv7RRx+JmJgYoVKpRIsWLcSyZcsqrZDMnz9fdOvWTdSuXVsolUpRv359MXbsWHHkyJFK+xs1apQICAgQ169ftziv//3vf2Lw4MEiKipKKJVKUbt2bTF48GDx/fffm9z+xIkTAoCYPn26xf0aY48jXQghsrOzxeTJk0WdOnVEYGCg6N+/v9i7d2+lz9s6T8k5bO5hCXMKQHZ2thgxYoRWQDVr1kzMmjWr0u/uyXmaY+XKlaJPnz4iIiJCKJVKERkZKe6++27x119/VdrWnCM9NzdX+Pv7i3vvvdfs9+zdu1eMGTNGNG7cWKjVahEUFCS6du0q3nnnHVFWVmbyM+ygch0spwhb5JR0TZp76F+r5pSDoqIi8cILL4jo6GihVqtF9+7dTTrTnn/+eSGTycxmeuhjSVYZf78t16Qp7HWku2OeprDndzYnUysqKkSDBg1E165dLX5Xdna2eO6550Tr1q2FWq0WERERYuzYseLcuXMmt2c55Ti+IJd+/PFHAUCsWrXK4ljPnDkjJk6cKJo3by4CAgKEv7+/aNeunZg/f77B4po+vXr1EnXr1hXl5eUW971//34xePBgERQUJEJCQsSIESPEqVOnvDJPIXTXu6mH8ffbKgOEsO33dMc+PXnsbMVeWZuYmCjuuOMOER0dLfz8/ETr1q3F66+/bqA/nTt3TowZM0Y0bdpU+Pv7i8DAQBEXFydWrVplNrKKHemuxRdkmmSbmHoYj82butaGDRtEv379RGRkpFAqlSIiIkLcc889JjMnPGUr2TtPfczZhL/99pu46667RP369YWfn58ICQkRt9xyi1i7dq1JPbmq61r5+fnimWee0drAHTp0EF9++aXJ72NHumfwBblUVWxAX5RL9viqpN/J1MM4u/DSpUvikUceEc2aNRMqlUrUq1dPTJkypcbagGvXrrV4nI2j94VgR7rTTJo0STRq1EhkZGQ47Mz0JHXr1hUvvPCCt4fhdnieVZcbN26IjIwMMXbsWHZQuQiWU75J9+7dxejRo709DLdTHefJcsp5fEEuzZgxQzRs2LBSynl1o6bM0x1Ux2NXVFQkMjIyxIwZM9iR7kJ8QabZQ03RtWrKPKuKrqXRaERGRoa21AI70t0LyyXfpKbMs6rIJXvwhg6lRDUlNTUVkZGRaNeuHY4dO+bt4Zjl+PHjKCoq8q3C+W6A51m1eemll7SNuIKCgrw8muoDyynfIi8vD4cPH8bnn3/u7aG4leo6T5ZTrsHbcun333/HvHnz7Go6VRWpKfN0B9Xx2K1atQrPPfect4dRLfG2TLOVmqJr1ZR5ViVdKzc3lxuPehiWS75FTZlnVZJL9uANHUomhBAe/UYPkJSUpO1GHRwcbLELLMMw1jl16hQuXrwIAFAqlRgwYIB3B1QNYDnFMK6F5ZTzsFxiGO+QmpqKkydPap/3798ffn5+XhxR9YBlGsNYp7y8HH/88Yf2eevWrdG4cWPvDaiaw3KJYVyLN3SoaulIZxiGYRiGYRiGYRiGYRiGYRhXIff2ABiGYRiGYRiGYRiGYRiGYRjGl2FHOsMwDMMwDMMwDMMwDMMwDMNYoNo2G3WE8vJyHDx4EFFRUZDLeY2Bqb5oNBqkp6ejS5cuUCpZDFQ1WFYxNQWWVVUXllNMTYHlVNWGZRVTU2BZVbVhWcXUFKqCrPLNUXmJgwcPokePHt4eBsN4jP/++w/du3f39jAYO2FZxdQ0WFZVPVhOMTUNllNVE5ZVTE2DZVXVhGUVU9PwZVnFjnQ9oqKiANAPVq9ePS+PhmHcx9WrV9GjRw/tOc9ULVhWMTUFllVVF5ZTTE2B5VTVhmUVU1NgWVW1YVnF1BSqgqxiR7oeUopMvXr10LBhQy+PhmHcD6eFVU1YVjE1DZZVVQ+WU0xNg+VU1YRlFVPTYFlVNWFZxdQ0fFlW+e7IGIZhGIZhGIZhGIZhGIZhGMYHYEc6wzAMwzAMwzAMY5b3338fTZs2hb+/P3r27In//vvP4vZff/012rRpA39/f3To0AE///yzwftCCLz88suoV68eAgICMHjwYJw+fVr7fkpKCiZPnoxmzZohICAALVq0wPz581FaWmqwnyNHjqBv377w9/dHo0aN8NZbb7lu0gzDMAzDMEawI51hGIZhGIZhGIYxyaZNmzB9+nTMnz8fBw4cQKdOnTBkyBBcu3bN5Pa7d+/GuHHjMHnyZBw8eBAjR47EyJEjcezYMe02b731Ft577z2sWrUKiYmJCAoKwpAhQ1BcXAwAOHHiBDQaDVavXo3jx49j2bJlWLVqFV588UXtPvLy8nD77bejSZMm2L9/P5YsWYIFCxbgo48+cu8BYRiGYRimxsKOdMbnEBqBopQi3Dh6A0UpRRAa4e0hMdUUT0dXAUDTpk0hk8kMHosXL3b53BiGYRjPIMorUPTPadz49iCK/jkNUV7h7SExjEt55513MGXKFDz88MNo27YtVq1ahcDAQKxZs8bk9suXL8fQoUMxY8YMxMbGYuHChejatStWrlwJgPSld999F3PnzsWIESPQsWNHrFu3DleuXMGWLVsAAEOHDsXatWtx++23o3nz5hg+fDheeOEFfPfdd9rvWb9+PUpLS7FmzRq0a9cOY8eOxdNPP4133nnH7ceEYRiGYRjv8+GHH6Jjx44IDQ1FaGgo4uPj8csvv7j1O9mRzvgUBckFuLj4IlJeTkHKwhSkvJyCi4svoiC5wNtDY6oZ3oiuknj11Vdx9epV7eOpp55y61wZx+BFPYZhrFHw01FcvPNLpEz8AynP7kfKxD9w8c4vUfDTUW8PjWFcQmlpKfbv34/BgwdrX5PL5Rg8eDASEhJMfiYhIcFgewAYMmSIdvvz588jLS3NYJtatWqhZ8+eZvcJALm5uQgPDzf4nn79+kGlUhl8z8mTJ3H9+nWT+ygpKUFeXp72kZ+fb2H2DMMwDMP4Mg0bNsTixYuxf/9+7Nu3D7feeitGjBiB48ePu+072ZHO+AwFyQW49N4l5B/MhzJCicCYQCgjlMg/mI9L711iZzrjUrwRXSUREhKC6Oho7SMoKMjiWNno8zy8qMcwjDUKfjqKS7MSkZ9UBmWYHIFN5FCGyZGfVIZLsxLZmc5UCzIzM1FRUYGoqCiD16OiopCWlmbyM2lpaRa3l/7as88zZ85gxYoVePTRR61+j/53GLNo0SLUqlVL+2jbtq3J7RimpuONKE+GYRiJ/Px8Ax9ISUmJye3uvvtu3HnnnWjVqhVat26N119/HcHBwdizZ4/bxsaOdMYnEBqBzM2ZKMssQ2DbQChDlZApZFCGKhHYNhBlmWXI3JLJEaGMS/B2dNXixYtRp04ddOnSBUuWLEF5ebnF8bLR51l4Ua9qwgYf40lEeQUy3zuAslwgsLkCylAFZEo5lKEKBDZXoCwXyFxxwCfKvHB2DVPVuXz5MoYOHYoxY8ZgypQpTu1rzpw5yM3N1T6SkpJcNEqGqV54I8qTYRhGom3btgY+kEWLFln9TEVFBTZu3IiCggLEx8e7bWzsSGe8wvr1QKNGwL599Lz4YjEKTxRC3UgNmUxmsK1MJoO6oRqFyYUovlhsYm+Mu6luDipvRlc9/fTT2LhxI37//Xc8+uijeOONNzBz5kyL42Wjz3289hrQogUg/UTGi3p5u/Nw9ZOrgAy8qOfjsMHHeJLiPedQeKYU6royyORGeotcBnUkUHi6FMV7znlphISvZ9ewk9/3iYiIgEKhQHp6usHr6enpiI6ONvmZ6Ohoi9tLf23Z55UrVzBw4ED07t27UhNRc9+j/x3GqNVqrT4bGhqKkJAQk9sx7mf/fqBxY7ILTZH9WzYSGiXgykdXPDuwao4vR3kyjC/y0UdAkyZAcrLp9y+8fgGJrRKRm5Dr2YFVc5KSkgx8IHPmzDG77dGjRxEcHAy1Wo3HHnsMmzdvdmvwITvSGa/www/ApUuA1C+oIr8CFcUVUAQpTG6vCFJAU6xBRb73I7tqIuygch3Tp0/HgAED0LFjRzz22GN4++23sWLFCrNKLMBGnzvZtAk4dw745x96bryoV3i8EGVpZcj+KRsAeFHPS9hi9LHBx3iSivQbqCiTQREoM/m+IkgOTZkMFek3PDwyHb6eXePrTn6GUKlUiIuLw86dO7WvaTQa7Ny502y0V3x8vMH2ALB9+3bt9s2aNUN0dLTBNnl5eUhMTDTY5+XLlzFgwADExcVh7dq1kMsNTdf4+Hj89ddfKCsrM/iemJgY1K5d2/FJMx5h504gNRUwqoCoJXtbNkouleDUo6dw5WN2prsKX47yZBhf5OuvgYsXgd9/N/1+xrcZKDpThCNDjiB3DzvTXUVISIiBD0StVpvdNiYmBocOHUJiYiIef/xxTJo0ya3Bh+xIZ7xCYSH9PXmS/ipCFFD4K1BRQI5yUSFQnqsrd1FRUAG5vxyKENOOdsa9VDcHlbejq/Tp2bMnysvLkZKSYu80GBeQe1PXycykv8aLeqKCoiOLThWh4HABL+p5CXuNPjb4GHejiAqGwk+gotB0BHVFgQZyPwFFVLCHR0boZ9cEtA5A9k/ZyN+b7/WSeVIEesb3Gbjw+gXkH/BNJz9jyPTp0/Hxxx/j888/R3JyMh5//HEUFBTg4YcfBgBMnDjRIFLsmWeewbZt2/D222/jxIkTWLBgAfbt24dp06YBoGzTZ599Fq+99hp++OEHHD16FBMnTkT9+vUxcuRIADoneuPGjbF06VJkZGQgLS3NIMvvgQcegEqlwuTJk3H8+HFs2rQJy5cvx/Tp0z13cBiHKb4Zk5Brxu8kynTy6dSjp3D1s6seGFX1x5ejPBnGF8nKor8FZlQTSVZV5FfgyO3sTPcGKpUKLVu2RFxcHBYtWoROnTph+fLlbvs+pdv2zDAWkBzpJ07QX//G/ghsE4j8g/lQtFUg++ds3DhwAxGjIhDYLhAll0oQ0jUE/o39vTfoaogU5SmhVqstrvQB5KD6+uuvq7SDSj+6SjLYpOgqycgzRoquevbZZ7WvmYuu6ty5MwBddNXjjz9udiyHDh2CXC5H3bp1XTI3xj4k4y0jg/7qL+opQ5UGTqbsbdnwi/DjRT0vkJSUhAYNGmifm5NTR48eRXx8PIqLixEcHMwGH+M2/Hs1R2DL3chPKoMiWBiUdxEagZIMIKSdCv69mntlfPrZNaWXS1F0ughF54oQ1CkIigCFQXZNQNMAj4ypILkAmZszUZBcgBuHbqAirwL+LfyhilJBFkp9cRRtFShMKkTmlkwExgRWKpvDeIf7778fGRkZePnll5GWlobOnTtj27Zt2nJ2Fy9eNIgW7927NzZs2IC5c+fixRdfRKtWrbBlyxa0b99eu83MmTNRUFCAqVOnIicnB3369MG2bdvg70+6/vbt23HmzBmcOXMGDRs2NBiPEHRvrlWrFn777Tc8+eSTiIuLQ0REBF5++WVMnTrV3YeEcQGWHOlCI1CWTZkGfhF+KMssw8lHTkLuJ0fU+KjKH2BsRorytAUpyjM3NxfffPMNJk2ahD///JN1K6ZGIQVcmXWkl9M9Sd1QjZJLJThy+xF0/K0javWq5aERMsZoNBqLGf/Owo50xitIjvTTp4HyckCplCHinggUpxajMKkQpemlAICsn7JQUVyBgEYBiBgZwQaVizFWgubPn48FCxaY3La6OaimT5+OSZMmoVu3bujRowfefffdStFVDRo00Ea+PvPMM+jfvz/efvttDBs2DBs3bsS+ffu09Tr1o6tatWqFZs2aYd68eQbRVQkJCUhMTMTAgQMREhKChIQEPPfcc5gwYQKnIHuBigpAWkeSFCTjRT1o6HVFmAIVORXI+ikLDZ5uwIt6HsZWo48NPsZTyJQKRDzdFcWzElF4rgLqSCrnUlGg+X/23j28rfJM9/6tpaWzJTmJHceJ4yRASBwOCSFNmsC0tJOZQOkMoS0tfN0bSjvQdpoCzTdQwiYcAtNwKDQE2GRoNwPMNB9sdod0WlpKGoZNW9JAIKFA7HCKcezEx8Q6n5bW+v5YlizJki3Zki0n7++6fMmWlqUlWXr9vvd7P/dDtAcsHqj5/lIkZWI23ZLVNVanlXjPQOxFAkLvhnB9yoXJaSLWERu36ppkzEy8N47JbQIJlGkKsc4YCV8C9wo35lrzkL44xYr8uqYTaYuQ8CcwuUzYGm1i7lgi1q1bl9ds8Morrwy57rLLLuOyyy7Le3+SJLFp0yY2bdqU8/ZvfOMbfOMb3xjxvM4++2z+8Ic/jHicoPIIh43LNE8PMLjp5nvduMHaYMU2z4b/DT/NVzbjf8vP7H+ajbV+ePOPYOwkXZ4A5557Lm+88QYPPfQQ//Iv/zLBZyYQjB9JR3ogT1qfFjcWjAueWEDbj9rof6Wfv/zNX5izcQ6zrpuFySYMWOVkw4YNXHTRRTQ2NuL3+9m+fTuvvPIKv/vd78r2mEJIF0wISSE9HodDh2D+fHA2OWm4roHe53vxvmJYE/SoTuTDCKfedyrOJucEnvGJSaEuTzjxBKqJcFdZrVaeeeYZ7rjjDqLRKPPmzeMHP/iBKEGeIPz+we+TQrokZ27qJaNdqj9XTd8v+4h3xUFDCDMViljwCcYT5xfPogHo3foWoQ9jxHqNOBfXGRZqvr8U5xfPKsnjjEYczojM0wavD+wP4PqUa1wj87KbOMe74+gJHaVaweQyEe+JE2oJ4anxgMSoRf6k+BZqCRkRXTYTjoUOai6tEXNIgaACyeVIT990k8zGOCe7ZMzTzdj9dsItYdofbKfj0Q5mXjuT2TfNxtYgzA3jRbldngJBpREOD2pXI0W7KB6Fs359Fu9c8g79u/r5+Icf0/FoB6f86BSmXzFdrB/LRHd3N1deeSVHjx7F4/Fw9tln87vf/Y6/+Zu/KdtjCiFdMP5oGiFfAjAD0HJAY/58Q7B0NjlxLHDQ/Uw38V7DQRU+GMa/20/VoonJGT2RKaa070QUqMbbXbV06dJJmyt/IpK+cEsK6TC4qdfzHz30/h/jBskkMXXNVI799hiH7z2M61wX0y6aNs5nLCgWseATlBvnF8/CceEiIn/+mERXAFNdFbZPn1IyJ/poxeH06hrSTiV2JEa0K4raq+aPzNM0o6uW3w8uFzQ2gjz6tkrZTZxlq4ykSOhxHdkqo7gV4r1xVK+KUq2MSuRPF9+ss61YnVYSwQT+fX4ihyM0XNcgxHSBoMLIFtKzN91CLYZ6ZXKYcJ5hfH7dn3YTfj+M7zUfHQ93cOSxI3g+42HqRVOZ9oVpOJocSJIQq0rBRLg8BYJKI+lGh5GFdMksYXKaWPzSYrr+vYtD/+MQ0bYozf+tmbb72pjxjRlM/9p0rDNFNU0p+V//63+N+2OKZqOC8aW5Ge65h1DXoBX04Lb/Mq4fQJIlGJj/TPlbI+7iwx98SOSTyLieqmB4hEAlmOz09w9+ny6kgyGmN97UmPq58ZZGzvzlmVT/dTWJQIJ3Ln6HT+75JJXTKph4NmzYwKuvvkprayvvvPMOGzZs4JVXXuHrX//6RJ+a4ARHUkzYz59P1ZfPwX7+/JKK6O1b2/HvK74hZ7K6xlxjJvpJ5v9q78teLDWW3JF5A/M0brsN7rrLuLznnox5WrFkN3E2eUyYa8yoXhVd15EsErqqo0U1dF0n2h7F0eQoOEIrW3xT3AqSSZrwxqrD8eqrr/J3f/d3zJw5E0mS2LFjx7DHv/LKK0iSNOQrvfGmQDDZSArpoZAR9Zm96ZasppFk4/1um22DBCz894Us3rUYz2c96KpO/8v9fHzjx7xxxhv8ufHPvHvpu7Te2UrPjh7CH4fRVC3/SQjyknR5LliwgL/+67/mjTfeKLvLUyCoNNKF9FzRLrqmo8WMMSbWHUPXjL45M66cwfL3lzPvR/MwuUwE/xLko/UfsbthN/s/t5+ORzsIvBuoqLmJoHCEI10wfjQ3w9at0NtLSBtcHLW0YFx/3XXQ1AQMNmxovKmRRDCB708+Wr7ZwuKXFiOZhMtgvBGOBMGJSD5HepJkrAuA41QHslnm7BfO5oPrPuDo40c5tOEQgbcCLHhiAUqV+Hc60UxEWZ/gxKQScrazxeGkw7KYhpzJ6prWO1vx/9lv2Gc0w5U+8x9nDnVop83TmD0bnE7DfrVvHxw+nDFPK4bsJs6SJOFc6CThTRDviSNbZZBBi2mEDoTyi/x5GCK+pTFS5vpE/a2DwSCLFy/mm9/8Jl/60pcK/r2DBw9mVBKKRuWCyUwyIx2MnHRLWm8HYFBgGrD+JWOftIDGlM9PYcrnpxD6IMSx3x7j2G+Pcfy/jhNtjxJtj9K7Y3BiJykS1kYr9lPs2ObasMy0YKm3YK23YplhwVxjRpmmoHgU4WZPYyJcngJBpTGcIz1ZNZiMojvyP48QeDOQqho02U3M2TCHmdfOpPuZbrr+vy58f/LR/0o//a/0A6BMVfCc58G90o3zLCfOM53Y5tjEWFThiJW/YHzQNHj+eWNxtmgRoectqZtaEqcZ1+/YAQsWgCwPlsdYJRY+uZC9i/fS/3I/+z+/n6Z/axKN/sYZIVAJTkTSHek9PUNvT45DYCzCAGSrzIJ/WYBrqYsPvv8BPc/1EPhLgHmb5lH75Vqx0TeBiAWfoBRUSs72WMThdJxNTqZdNI2eZ3twnesi/HEYtU8l2hal6qy0yLyseRrJx3S7jZ8PHMiYpxVDdhNnSZIw15pxr3ATbA4S/iiM4lbQYzqupS5q1hb3WieyxLds8mWul/pv7ff78aV1TbRarXl7z1x00UVcdNFFRT/G9OnTqa6uLvr3BIJKJJJWbOz1woysTTcGPrLJuVWu2CfHfAeO+Q4armsgEUrge91H8O0ggf0BAvsDBA8E0WM6kY8jRD4eobrZBOYpZqN/g8eE4jHEdVOVydgQdJmM7x0mZIeMyWlCtsuY7CZkm4xsl43L5JdVRrIacVayRUaySEIcEwgmGelmq3RHenqkHANLRmWakjNSzjzNzKzvzWLW92YR+SRC97PdHHvpGL7dPtRjKn2/6qPvV4OKvcllzEdsp9iMDcB5NmxzjE1A6ywrSrXY9JtohJAuGB/a2gzr+ezZqLqJWGLwrdfSWwsNDYYTqq0N5s4dFNIVCcdpDpr+rYmWq1rwvurljbPf4PTHTqfuirqJejYnHUKgEpyIpDvSk41kHI7B65KVMUCq4VWSmd+eifNMJ+995T3CB8Mc+NoBHE0O5mycw/SvTheCukAwApXg+s6mknK2RysO5yJZXWOebsZznof2Le0c/dejTLs4rc9D2jyN7MWZJA2ZpxVDdhNna4MVk9OEZJFQpipU11dT+9VaqhZXjep9kO14zyaX+FaOv3V28/Xbb7+dO+64o6j7GIklS5YQjUY588wzueOOOzjvvPNKev8CwXiSLqT7fDD3rMxNt3RHejL2KW9vB4ws9SkXTGHKBVNS1+maTvRI1BDSD0WIfBIhdjRG9EiU2NEYsa4Y8b44WlCDBMR746k+XeVAMktIZskQ1s0SkkVCNsup6yWzhKQMfslmOeNnTMb6WDJJg5emweurL6im7nKxRhYISkVfj0ayLCZ4PAaago6UUTXY+7yhtiseBets67BVg7Y5NhpvaqTxpka0uEbgrQD9f+gnsC9A8J2gsbnvT+B/w4//DX/26QAg22UsdRbMtWbM081Yai0o0xTMU80oUwYuqxVM7rQNQZfJmHuJNWpJEEK6YHzw+43ZktNJOJ75tusLO+iVaqmJdBjHAVrcyJmSzcagVfslY4F14OsH8O/x0/z/NNP3n33MuXVOqvmMQCAQFEO6kA6G46BxMBY9pyM9Hc95Hj7V/Ck6HuqgfUs7oeYQzf9PM4duOUTtV2upvawW17ku4RgQCLIoxAk83kJ7KaJUSsloxOF8JMcy2Swz4+oZtG9pp+8/++j/Qz/W2VbjtU2bp+XE6YSOwXlasSRjZpJ/91hHDNkm4z7XXbQDPZtcjvckucS3cv2tDxw4wKxZs1I/53Ojj4b6+nq2bdvGsmXLiEaj/OxnP+OCCy5gz549LF26tGSPIxCMJ+nRLl7v0E03PWaMXVp0dLFPYNynrcGGrcEGn8l/XCKSQO1TiR8zGh8nvAnUfhXVp5IIJIzNTX+CRDBBIpRAC2okggm0iIYW1kiEE2hhDS2qoUU09KhuXKqZ+cd6XEeP62ih8uS2S4okhHSBoFQ0N9O3oxv4LADB9mNwzxNElv89oRbTkH4OqRgqlwnva168r3nxrPJkjFnZ81vXp1y4VwxGtmlxjfD7YUIfhIh8HCH8cZjIxxGih6NEj0RRj6loYY1Ia4RIa/E9BGWHbFTWOI0v2WlU15gcRoVN8itVaZOstkmvsBn4kizS4OVA1Y1sHqi+Mef4XknbMJzk62MhpAvGB5cLbDYIBgnJgwPFbLeXwz4PBzuqqLHZjOPI7HycxH6qnXP+cA6f3P0Jn9z9Cd3PdNP9TDeez3iY9Y+zqLm0Btki+ucKBILCSI92gWGEdBN5/9mbq83MvX0uDTc00P5wO+0PthNpjXD4vsMcvu8wtnk2pv39NKo/U43nrzxYai0570cgOFkoxAkMjHu8SqmiVEpFseLwcCSFnOTixVJvIXY0xsFrDuJY4KBqaRXTz7PiHJinkZbBnSIYNOZxA/O00eBscuJY4Cj5Bkk+x3simCDaHh0ivpXrb+1yuTLyy0vJggULWLBgQernVatW8dFHH/GTn/yEf/u3fyvLYwoE5SY72gUyN928rxpXakGN6r+qHvOm23CYbCZMs0xYZ5VuAwyMiiAtpqHHjIbKWkwzxPSYPvh9XEeLD3yvDnwNXK8nsq5LZF2X0CFB6nrXstGP0QKBII2BvjG9rV9MXRVIOGDfPhL7fSR8l2CdU4eu66loF/W4SmB/gHh3HLVfpe3eNjwrPan5ayFGEtks4zzDmdcsmogkiB0ZqKbpiRPrNi7jfXHU4yrqcWNDMOFNoHoHNgO9idRcUAtpaCGNOOWrvCmERc8uYvpXJ2+fFyGkC8aHxkZYuBD27SM8czYADnOMptoeDvs8tHxk5rwvN6VUrPRFXzqyWWbenfOYdvE02u5to/eXxiTL+6oXZarClL+ewpS/Mb7GY6ErEAgmL95+HRgcY3q7B0v3YGhlTDbZjoI5t8xh9vrZ9P2mj57neuj7dR+RQxE6Huqg46EOABwLHbhWuKhaXEXV2VU4z3YKcV1w0lCIE/jI40fQwhrxvvGNVylllEopKFYcHo7kWJYIJWjf2o55upnY0Rjhg2HCB8P0/Wcfhx0yVvsVmPV+TNVWTJYEsqIhmXRkk4bk7UOqW470sIqkfJSKFEA28osleSBaQB64TpKM4TX9+4GvYX9m8HLY69N+TmKbayPeG8f/hh89riOZJayzrNjm2QjsCxDYZ4SbRtoihN4PYfFbBl+/tPvSEzqxzhg9z/WkNipMHhPTLpxGJbF8+XL++Mc/TvRpCASjJjvaJUly063n//QQ744z/evTmfkPMyc8/ms0SCYJk90EYlkqEEwe0vrG9NkbUlcHVSssWoTpjcOYug6TCEzD5ByUVANvBdBiGrJVRqlWsEy3pOav0y6eRt8LfSNGyo1UkWmymbCfYsd+SuGDiq4bG3kJf2KwwiZofGmhgeqasGZU24QNoV2LDH4lwgmjyiaqpb70qLEZmPo+rg1uEMYyNwjJUYCTq9p7MiGEdMH4IMtw6aVw+DChljYAHOY4C11HeInTaFFPg7UNqQZW6RnpuXAvd3PmL84k2hHlyE+PcPTxo8SOGouenueMroHWRiuupS6qllRRtaQK55lOrHOsyIpwrQsEJz3NzXj/bwQ4J3VVzxO/gjmnQ1MTkH9DD4aPppj+lelM/8p0EsEEx148xvGXj+N91UvwXSP3LtQSoouu1H0p0xTsp9lTX7a5NqwNVmyzbVhnWzE5Ro5uEAgmAyM5gS2zLPS/0o+l3oLrU64MoV1ukgnsDXBk2xEarm/ANre0US+ljFIpFfniUIptyJmcU8WOxJDtMlMumgISxDpjqMdUY+ET0giH7ISxw7Fc9zITPgJeay/Z8xsvev53jm7SBeD/82CMjeMMR8UJ6fv376e+vn6iT0MgGDXZ0S7pJDflAGwNE99DQyAQnESk9Y3p2z/YQCsQs6AjYTvdg+NoG/6Dc7Etnpq6PRFJYJ5uRu1VsdZbsTZYsWAh+F6Q9ofbUVwKjjPyR8rpmk7fL/tKXpEpSZJRdWMzQe3oX5bRomt6quImKa6P53y6HAghXTB+NDXBddcReng3AA5CLLR8DHyGlimfhqbBUrRc0S65sM6yMu+Oecy5dQ7+1/0c//1xju88ju/PPqJtUaJtUXp3DLZalhQJ21xbhlhlmWV0P7bUW7BMt2CeZhZNGASCE5mBUr3+zv+ecXXv+8dg61a47jpoaso7DhXapM7kNFH75Vpqv2zMWGI9MXp/2UtgX8DIu/swQvijMGqfir/Pj39P7uxhk9uEZYYFS50FywwL5hqz8TXNuFSmKKkv8xQzJreRaTfZs+cEJx4jub5RQT2m4jzTmfH+jffECbYEiR2JEXovRORwBPcyd0mjXkoZpVJKShGHkhzLVL9qbM5ZTdRcUmPcputoYY1oe5R4T5za8xOY3nuTRFuP4TCSrWhT69BPa0KfMi21CFKPq2hRDUmWMGkh9GgczGawO9F1QGOw3Dnf98kvsq4j63qGXp/3dhj6c45jI63G6yk7h5ortKCGqcqEdY4VacCqbptb2r97IBDgww8/TP186NAh9u/fz9SpU2lsbGTDhg10dHTw9NNPA7BlyxbmzZvHGWecQSQS4Wc/+xkvv/wyL730UknPSyAYT3JFu6QznKFBIBAIykZa35je0KCQriMRURXsVU5q6j4gUnUeoQOh1O2yVUbtVTE5DAEcCSQkFLdC4K0AUz43JW+knO/PPoLvBdGi2oQ3vC81kiwhWSWwgonJLaAnEUK6YHxpaiL01QXwGDjqXCy84UL4f6ClPTPPbaRIhWxkRcazyoNnlYe5t81F9asE3grg3+cnsN8o5w0dDKFHdcIfhgl/GM5/ZxIpoSrV9XiKglI90PF4oPuxyWUyvqpMKC7FaNTgMBkNHJxCyBIIKpK0Uj2vxchlc1mi+GNWet2nQO9vYMcOWLBgSGWMrumEW8N0bOsg8kkE1zJXSswaqUldtoPdUmuh+q+qmbJmCrIip8al8IdhIm0Rou1RooejhvDoSxD2hQm/P8y4lYVkloyxyq2kxilT1cCY5UhrLpMcswYazJgcpsGmMra0L6txmavJjBjnBIUykutb9aoAKNWDt8V74vj2+EiEEpjcJpCMmJVSLyxKGaWSzVgbp0qyNKa4ulSzO8147TLuW5IwOYwyYVTwfOk0qjaeY7ix/H4jE72xMVUxmBzL1D4V/bgPueswNtqoqfsA5/QwnL7QqEAcqOypVDI2RHP8rWddN6usC9a9e/fyuc99LvXz+vXrAbjqqqt48sknOXr0KG1tbanbY7EY/+//+//S0dGBw+Hg7LPP5ve//33GfQgEk4180S5JRqpQFggEgrKQ1t+vL5w5/wrGLdjjfTinh2m4chpd/6Vw7D+NUj49oWOtt+JY6MBca079jqRIxniWR0OWHTLhj8NY6i24V7rHtSJTMDqEkC4Yd0IRYzHmmGJj4QUzAPj4Y4hGwWodcAwNRJBKZmlUC1DFpVD92WqqP1uduk7XdKJHoimxKtoWNcSqDuMy1hVD7VNBNxbu8Z6xN2BICVL2PMKUNa0DsiWr47FFNjocD1wmv2Rz5s+SktYFOe3LeZYTy3SRvSwQZJBWqufdbTgMT5t6jH2d9YbjoKHBcKy3taGrRhm/ZJZS4pF/rx/fmz5MDhN6VB8yUUrv0u7+tJtoe5TA2wF6nutBi2hYG3M7DJKu9WxUr0qsM2Z8dRmX8d648dVnXKr9aqq5jOo1xjA9rqP2qcaYVmYks5SzW3uhY9fpPz0dc7V55AcSTHpGcn3H++IoU5XBzStdJ9gSJBFKYK41o0d1ZLOMeZoZkyf/xtVoKVWUSjqFNJYqN+nVNQVF18gyzJ075JgM8dkRxNr9FolADL9UT6RnBg117+Pctw8OH05V9lQq5fhbF8MFF1ww6JDPwZNPPpnx80033cRNN91U1nMSCMab4aJdoPAKZYFAICgpaf39+kKZQnogaqbmSDssXYrz86cxc2GMth8ZG9+eFR6sDdYhPVx01ejbQp42O7GjMRLBBLbZtnGvyBSMDiGkC8ad0ED1i90OM2YYG35+P3z0ESxalOacAsIfhzn6s6MlWYBKsoStwYatwcaUC6bkPEZTNUOg6h7sfBw/Fkc9ZghUqldNdUBONWoIJFD9qtGoIWQ0Ykjd30CDBo4X/zqNlUX/exHTL5u8nZAFgrKQVqrXHzGE9FOTQnrYAU4ndHSA348eH8y8S4pHcpVRcWJymYgejaJ6Vdwr3AAEW4KpLu2Hbj1kiMk2mcihCAlfAtupNix1FiS3NKKDPYniMSphHAscQ27Lha7pRvMYX1qn9rSxKuFPGI1kgtpgk5kczWUS4YQxfoXTmsgMjGfpYzQYC109rqMFc3SSKYDTt50+qt8TTD5Gcn3bGm04TncQbY9icptIeBPEe+MoHmO6qvqMzEnFo4AE1gYroeYQkbZIyRqMlyJKJUmhMVDlJlnlZ64xEz0cHVV0TUaj2CY70p/2QSSIMrMWE3FCPQ562+fiOE9Caj6QquxJOtkrkVL+rQUCQXGoqvGVZLhol0IrlAUCgaAkDPT3i7d24I0a80uTlCChmwg2t8GpNbB2rXHcwPJHUiRUn4oFSyoWDow5lupTsc+3o3pVLA2WnHMwk9OEpX7QBDleFZmC0SGEdMG4kxTSHQ6QJGOz7403DJPookWD7gOAIz89QsKXGLcFqKzIWGdYsc7Ik99aAJo6IEaFB8So8IAYldb5WAtrmV2Oo1qqw7EWHbhM73w8IFSldz/W1YGv5G2JtOtUHWWK+HgLBENIK9XzRo3P+WlTjHK8nqATgkHjdpcLzWvMjLSIscHmWOQg4U0YbmpZwlxrJt4Tx/+mH13VSYQSqQiUWGeMRCCBXGVMsJRpinGdL4F7hRtzrTmViZcUAm2NtjELOpIsobgUFJeCddbox7Hh0LWBcWlg7NLjaV3bk13aY1rGZWpMi2eNW6qOqerEyMoTFMZITmAwNq5CB0LINtl4T1l11J7MzEkwFhSxjhgJfx6LTxGMNX4l1/2lhOdF+RtLlcpNP+y5DMyrHAsdSGZpVNE1GY1ifT7o7QWPByRjuWh1Rwn1Ooj47NjTKntyOdsriezYnGSElxDWBYLyEo1m/iyiXQQCQUXR1MSx/349/BQkNGbajnM4XEPgtCXw/eWpqrvUOGWRMNeYc86xrLVWpn5jKn0v9OWeg9VaMNlNJEJG1WAxFZmAMARMAEJpE4w76UI6ZArpkCmkq8dVnGc5J3QBWiyyIiO7ZXBP9JkIBIIhpJXqDTrSjZKR3pAd2o1SPRob0Q8Z9ihd1Q3xSJIweUyYa8zEjsYw15oxuUxEWiOYXCbM9YawrmtG+Z71FCuxdkNQt9RZMLlMxHvihFpCeGo8KWdBrCNG4O0A3du7JzT+oVAk2ej8zvj2XBScQIzkBE4K7f69frSQsaFlnTk0czIjjmQMlCN+JUN4ztNYqtRu+nwkXZ2WGRbqvl43qjiTjEaxvTHDSmpOi7WyJIj5rSSiJpg2WNkzmaiEGB6B4GQhnNX2JZcjPVlNI6JdBALBRNA71aianVKt455WBR9B8Ev/HZoGq2RSvf0sck6jSNWSKtwr3FjqLNR+uRbfHh/hg+GMOdi0v59G3y/7UtGHhVZkHn/5OP7X/WLeMgEIIV0w7uQS0mFQSE8ORgDWxolfgAoEghOIgVK9WOsRIqohAp3m6QGg12eFmsFSvfQIk2SDPkmScC50GhOcnjiSRSIRTmCaYkLtVZEVGV3TUTwKsiyjeBTU40a8iuJRUNyKkWvuVVGqFSNaJaoZGeonYJd2gSAfwzXQTArt4dYw7Q+1E/4gnNHcFwqLIymEcsWvZAjPOSilm34k0l2do40zyWgUa7WAokA8bjS3ARIxE7KiYbImMip7JguVEsMjEJwspDcaBZGRLhAIKo++PuNyWq0J5xQTfASBUGbUVHK9KJmHzrFiXTF8e3x0Pd2VErrtp9upu7IuZbJKzsEkWUpFHxZSkRlqDnH08aPoui7mLROACBwTjDsjCenp4pXiyr3XY3KajBzfcViACgSCE4ymJrxXXZf68ZTjewHojbvRv3/d0FI9k9GgL4m51mw4C+otJLwJ4zgdrPVWnGc4QRlc9MlVMrJZRvWq6LqOZJHQ1YFIFF0ncjhCImLkkTsWOVDcCpLJyFB3LHIQ743Tu6MXXcvflE4gOBGRZAnHKQ5mfWcWtjk2Qs0h43Ok6qheldCB0IhxJCORHb9Sys9fuvCci1K56QshOZYlc4aTmxhVZ1Vhn2sv6PVLNoqNHo6iu93GpqPXC7qOrkPUZ8VRE8LmDhuVPU1NRgVQhZGMbgm8EyDcGkbX9LK+DwQCQW6yhfSc0S6qENIFAsHEkRTSa2qMNlpgeAXSyd7wS86xJEWi5xc9BPYHUGqMfldKjULg7QA9v+hBUqSMOVgy+tB1jgstaEQFJ/wJrPXWVCxoEjWgEuuKoQZUMW+ZIIQjXTDu5BPSDx4EXU+LdpGNhabiHvo2Hc8FqEAgOPHon26U6lU5Neru/B68AAlNxjuzieqBY5JjkclpGtKgz1xrxj3NjZ7QkW0ynpUerLOtqF4VqVlCj+tIVgnioExRkKwS8Z44slUGGbSYRuhACJPVBHFRfSMQ5GOkTPWxuG3KGb+SFJ6TZbrFNvcsJaWIR8hoFNscxjrrdEzHfSSOeIlSi8UdpabhEFJzS0ZlTyWRL7rFtdyV/33AQP77az4ir8Wwrzq14p6XQDAZKcqRLjLSBQLBBNDba1xOm2b09oNhhPS0cWq0fXIKqcjUNI3A/gBaRMM6w5rR2BTE+nG8EDNBwbiTLaSfOrAm8fmgszPTBRo9HEXXM3fSNE0jdDBkNNPUEDttAoGgaJILtuopMtZzz0wlEPT0DB6TdEKZa82p5jHpjthwcxjnQidT10xF9avo6KkMddWrommakWnXYKX6/GosMyzEj8WN+47puJa6qP1qLZJVSkXHZCOqbwQCY2HReHMjczfNZc7GOczdNJfZN85GtssZzuJiScavlOPzlxSec40dI7npc7mmx0Kp4hHS3VIqbsLTl6JWzcRVdYRZtX/EqX+CvmQp4S9/j4A6uyTnXiqS0S3+ff4MZ5h/n58jjx8h1h0b+j7o6YU//gnT639A23+AxL2PwD33GI1UBQLBmEhmpCsDfimfzzBUpZNdTSMQlIpHH32UuXPnYrPZWLFiBa+//vqwxz/33HMsXLgQm83GWWedxW9+85uM23Vd57bbbqO+vh673c7q1av54IMPMo6ZO3cukiRlfN1zzz0lf26C0pGKdpk26EgPBDKPyTXHKsaokc1wFZnhQ2H6/qOP6KEo6nEV3xs+vH/0Eu+JZ9yHWD+Wn7L9VxKDkyAf2UK61axxSqPx4W95pRM9anzgZZs8ZAGaHDwiH0YIHQzRekcrbfe0EWwO5noogUAgyElSSPd4jMuaGuMy6TyAQRen4lEGxaM+lfD7YdQ+FddSFw3XNzDz2zNTY1XCl8Ax34GkSEQ/jhple/PtSFYJZapC9fnVzLt7HvPumsfsG2djnmZGC2vEjsQgh940XtU3pRbuKu3xBJOf9DgSLaxx+L7DtN7WSutdrbTeNvJcINd7rtzxKxnCc9bYMeu6WTnd9MHmIG33tBX13EYiFY9QAldnxqbG/Wcx9xdfpPEXX8F5/zqCV95KG1fQ+rSpZOdeCkaKbkkEjBzTRCDtfdDTC3v2wNGjJMwu5ClOTNOrYN8+2LpViOkCwRhJOtLr6ozLRGJwjQjG2l9EuwjKwbPPPsv69eu5/fbbeeutt1i8eDFr1qyhu7s75/GvvfYaV1xxBd/61rfYt28fa9euZe3atbz77rupY+677z62bt3Ktm3b2LNnD06nkzVr1hDJKr3YtGkTR48eTX19//vfL+tzFYyN9GiXqirj+yGOdHXohl8pjBrZc0j/Xj/+3UYTd9cyF+bpZiSzRPRoFN8eX4aYLtIbyk9Zol2Sg9O2bdtYsWIFW7ZsYc2aNRw8eJDp06cPOT45OG3evJkvfvGLbN++nbVr1/LWW29x5plnAoOD01NPPcW8efPYuHEja9as4cCBA9hsg2WxmzZt4pprrkn97JpEjY5OFjKE9OZmeP555scu5kMW8/HWX7N8uR84B9ma2fk41Bwi8nEEySLhXunG2igaKggEgtHR329cpgvphw5lCunFNOhLH6u0iIZ9np1EvREdoB5TkW0y7nPdqSiKYHOQw/cdJtgcJNIaIfiXILZTbTibnKkMvPGKf8gXd1Cuju/j/XiCE4vRNIXM956bdsm0ssevFNPcs1wNL0vdsG9oo9h5xrn/op14b6Diml6N5Ayzn24ndjRG6GAI16dcRpF0SwuEQug1tUR7nbjqA9gazMAiOHAAduyABQtEzItAMEqS+uK0aXD0KGiaYXJIuj71xOAGuxDSBaXkwQcf5JprruHqq68GYNu2bbzwwgs88cQT3HzzzUOOf+ihh7jwwgu58cYbAbjrrrvYuXMnjzzyCNu2bUPXdbZs2cKtt97KJZdcAsDTTz9NXV0dO3bs4PLLL0/dl8vlYsaMGQWdZzQaJRqNpn72+/2jfs6C0ZEe7aKqxvfZjvRc8XkZDdrHEFOcHfWCbIjoSBDvjxM7GkOpUVB7VUItITw1HnTGNz7wZKUss7/0wWnRokVs27YNh8PBE088kfP49MGpqamJu+66i6VLl/LII48ADBmczj77bJ5++mmOHDnCjh07Mu4rOTglv5xOsSivNFJCuq/TcPXs20e1y9iN81tr0A68D4AkJVLOpzl3zMG+wI7tNBs1X6rBNs8mGioIBIJRk4p2qTYucznS8zWPydWgLzt64rSHTuPsF87mtIdOS0VRNP6wMSWiJyMGzLVmPOd7MHlMhN8P0/+HfmKdsZI1UxyJ4eIO2re2l9xJOt6PJzixGE1TyOHecx2PdOA82zmq+JViKKS5ZzkbXpY7HqHSm3WO5AxTqhQsdRaUKsV4H7QfR+/uQ7VNI9TrxOKIU7Ow18hHlSRoaDCMIG1t4/tEBIITiGS0i90ObrfxfXpOeqpnFoahQVSyCUpBLBbjzTffZPXq1anrZFlm9erV7N69O+fv7N69O+N4gDVr1qSOP3ToEJ2dnRnHeDweVqxYMeQ+77nnHqZNm8Y555zD/fffj5pUZ3OwefNmPB5P6mvRokVFP1/B2MgV7VJIRnpGg/aszKqkUcPR5ChI6JZkCUmWSBxP4FhoZKpLkoRzoROTw4TaqyJbZWLdMSLtkXFZPwrKIKRPpsEpGo3i8/lSX2KXb3xITpwcB94wVKtFi3A6jAEmILvR55wKgBQLg6blHDzSGSlnSiAQCLLJdqTX1hqXGUJ6kSXF2WKZrMhDxLNcgpOlzkL1X1VjP92O6lXx/slLvDc+bPxDKRhv8avSxTZB5VNs5mQh77ngO0FmrZtVVPxKOQSdseRpjkQpmo0ORznPvRQUEuFjmW6h/tp6433QHSXcX4WqOnDVB5i1ogNnbVrmhNNp2GnFukEgGDVJR7rNNjgX8/kGb08X0sMfhUseeSU4Oent7SWRSFCXzBQaoK6ujs7Ozpy/09nZOezxycuR7vO6667jmWee4b/+67/49re/zY9+9CNuuummvOe6YcMGvF5v6uvAgQOFP1FBSSgo2iVH1V8xfXIKmVPmMgSYa824V7ix1FuM++5XiXeXf/0oMCh5tMtwg1NLS0vO3ynl4LR06VKmTp3Ka6+9xoYNGzh69CgPPvhgzsfdvHkzd955Z3FPUDBmUo707k9g2WyQJKosMQCCMTO6buzvSImo4faZOzc1eFid1iH3p+s6elwn1hUj1BLKWzJdKLqmF1SCLRAIJi+FZKSXw8WZT3Ay15rx1HiwzbUR74kz89sz8azylHXsKUb8KkXH9/F+vMnCo48+yv33309nZyeLFy/m4YcfZvny5XmPf+6559i4cSOtra3Mnz+fe++9ly984Qup23Vd5/bbb+enP/0p/f39nHfeeTz22GPMnz8/dcyxY8f4/ve/z69+9StkWebLX/4yDz30EFXJVQLwu9/9jttvv5333nsPm83GZz7zGR544AHmzp1bltehEIabC4CRORnriKUyJwt9z02/YjqNNzcWHL9SjmiiYp9bMZQyIz0X5Tz3UpB0ho0U4TPl81OY8vkpRF6Lkbj3HUzTq7A1mJGyX7Zg0FD/RHykQDBqkkK6nRAeqwTY8R7XSPr80oX0jsc6UI+pFRcbJRAUw/r161Pfn3322VgsFr797W+zefNmrNah/z+tVmvG9b70nSbBuJAe7ZK32Wge41Uy4zw5Z4x1xJBtMq6lroyoz0LmlPmiYpLrx+jhKPGeOI0/bCz7+lFgcEIF+61fv54LLriAs88+m+985zs88MADPPzwwxnZUumIXb6JISWka4HUiOS0GM0RgnELemIgRkFKpNw++dxE8Z443j96Ob7rOKGWEEcePzImh0I5mnwJBILKI1+0S0/P4DEpF2cJxafhIgYkScI604rJbkLxKEMmQaV2wZaiEU4lP95kYKIaXn3961/nvffeY+fOnfz617/m1Vdf5dprr03dfujQIS655BI+//nPs3//fn73u9/R29vLl770pbK9FoW8v4ttDlrMe66Q+JVyRhOVs/FpsRnpxY415W7aOlaKcYZJsoR91alUrazD7nsfKbsLtK5Dezs0NUFj44Q8H4HgRCD88VEAbB8fwH2sFQDvUztSjXyT4hRAvE9UsglKQ01NDSaTia6urozru7q68maXz5gxY9jjk5fF3CfAihUrUFWV1tbWYp+GYJxIj3YZyZGey3iVHf2ZL+pzpDnlcFExYMx3Pas8QkQfR0oupE+mwclqteJ2u1NfojHp+JAU0u12UiOR02w40gMxC7pmfPhlk55y++QaPOI9cXx7fMSOxtBVHdspNmxzbEUvaJMLxp5f9vDJP3+C/y2R3SsQnOjkajYKw2ekl4JiBKd0MevY74/xyeZPSrrJN97iV6WLbRPBRPSUaW5u5sUXX+RnP/sZK1as4Pzzz+fhhx/mmWee4ciRIwC8+eabJBIJ7r77bk499VSWLl3KP/3TP7F//37i8XjJX4dCN7GLzZws5Xuu3NFEpczTHHLuRYxlozEUlPPcS0XSGVZQhI8sw6WXGv8YDhwwdl5V1bg8cMC4fu1a0WhUIBgtzc1Edv4BAJtDxlNtfJZ8H3QZ/bOam1NmBmRjjKnE2CjB5MNisXDuueeya9eu1HWaprFr1y5WrlyZ83dWrlyZcTzAzp07U8fPmzePGTNmZBzj8/nYs2dP3vsE2L9/P7IsM3369LE8JUGZSCTg+HHj+5qa/I70kYxXuYwaxc4pSx0VUy4m6rE3b97Mpz71KVwuF9OnT2ft2rUcPHiwrI9Z8hmgGJwEI5FypJ9aD4cPg64PRrvEzWhJR7rdmnL7ZA8e8f44wQNBYxDRdRSPgnORE8VT3II2uWA8tPEQh249hPcPXuLH4uhRvSIdD6LRjkBQGgpqNlpkRnohFCo4JYKJlJj14Y0f8sE/fkD3/9cNEiXb5Btv8WsyiG3jyUT1lNm9ezfV1dUsW7Ysdczq1auRZZk9e/YAcO655yLLMv/6r/9KIpHA6/Xyb//2b6xevRqz2Zzz3Ebbd6YYR04xCwko7Xuu3DngxT63Yig0pmq0jvtynnspGc4ZNoSmJrjuOjjnHMOS9v77xuXSpcb1TU3j/wQEghMBTYPnnyfiM9Z+druEx25Uj3unzjMmYjt2oEcHNkAlRCWboKSsX7+en/70pzz11FM0Nzfz3e9+l2AwyNVXXw3AlVdeyYYNG1LHX3/99bz44os88MADtLS0cMcdd7B3717WrVsHGHOAG264gbvvvpv//M//5J133uHKK69k5syZrF27FjDmXlu2bOHtt9/m448/5uc//zk/+MEP+G//7b8xZcqUcX8NBCPT328MVwBTpw7TbHQU68XRzCkLMQRMZLrCRD72//2//5fvfe97/PnPf2bnzp3E43H+9m//lmD2H6uElDwjHYzB6aqrrmLZsmUsX76cLVu2DBmcZs2axebNmwFjcPrsZz/LAw88wMUXX8wzzzzD3r17efzxx4HMwWn+/PnMmzePjRs3Dhmc9uzZw+c+9zlcLhe7d+8Wg1OFkhLS1/wVvPRnOHAAZ2IhAMGQhN7aDixGqvFkuH3Sc6b8e/1EPo4gO2SsM604Fjow1xqL+0KzdpMLxnhvHJPbBBIo0xRinTESvgTuFW7MteaKye4tVy6rQHAyUkxGeimjXZKCU+Sw0VXd2mDF5DQcs9H2KJYaC86znHQ80kG8N46lwYJ+WEdHR0/oBN8LYqoyYa41Y1pkInQgRO+OXhwLhjZiLsW5lFL8Gu/Hq3QmqqdMZ2fnEIOBoihMnTo1dcy8efN46aWX+OpXv8q3v/1tEokEK1eu5De/+U3e5zOavjPZjpzkYkJxK3nf34VkTiYp5XtuPHLAi3luxVDIIm80f4vxOPexkqvvTcHzuKYmWLDA6Nfj9xtVko2NwokuEIyFtjZoaSFcdS4ANkXFalIB8EZt0NAAzc3obUaFlCRLQ3KBk5yMlWxJRE+v0fO1r32Nnp4ebrvtNjo7O1myZAkvvvhiau7U1taGnDbOr1q1iu3bt3Prrbdyyy23MH/+fHbs2MGZZ56ZOuamm24iGAxy7bXX0t/fz/nnn8+LL76IzWZs1FutVp555hnuuOMOotEo8+bN4wc/+EFGbrqgskjGurhcYLEU12x0JEY7p3Q2OXEscOT87KdrW2PpJzGasaVUjz1aXnzxxYyfn3zySaZPn86bb77JZz7zmbI8ZlmEdDE4CYYjJaSfeQosvA6efx7ni8cACIRk9LmnwWsguRxDfjc5eBx76RjqgyqOBQ7MU82Q9dkeaUGbvWCMd8fREzpKtYLJZSLeEyfUEsJT40k5ISayUdZED04CwYlGdrRLba1xWe5oFxhecJr299Po+2VfamxKeBPEj8UxTzMjWaSMsakUm3zjLX5VqtgmyKSzs5NrrrmGq666iiuuuAK/389tt93GV77yFXbu3DnEPQNG35n0OVdHRweLFi0a9nFG24B2uIVENqV6z+Vr9JSkVIJOMc8tH9mLoEL6PZSiGXApzr2UlMSAIMswgQ12BYITDr8fIhEisjGO2BQVi8lYX/miVsP22dGB7jXyEySzRPRwdNhGwSdLJVuS8TJXbd68mf/4j/+gpaUFu93OqlWruPfee1mwYEHJHmOiWLduXcpRns0rr7wy5LrLLruMyy67LO/9SZLEpk2b2LRpU87bly5dyp///OdRnatgYkgK6UmzVd5mo6MwXo1lTpmMisk4hzGaIZIUO7Yk0xI6tnUQ+SSCa5krdf+KW0FukgnsDXBk2xEarm/ANre4OaHf789ospvdgDcf3gHH3NSpUwt+rGIpi5AOYnAS5CclpDuAWYbbp2pGD/wBgnWnoP/dVNjenFe8kmQJx0IH1hlW45gchw03+Oiajvc1L97dXizTLUhIyFYZSZHQ4zqyVUZxK8R746heFaVamVDHQ6kGRoFAMEi+aJfjxyEeB7N50MU5UhzCaMgnOGWLWVpUQ1d1JLOEJElDxqZSuWBLLX4N52Yol9g22RZ85e4pU19fn3HMkiVLUsdkNzNVVZVjx46lfv/RRx/F4/Fw3333pY7593//d2bPns2ePXv49Kc/PeTcsie36RPffIzF5Z1rIZGPUrznkjEx/n1+TE0yks8H0RhYLehud0kFnWKeWza5FkHxLiPXfrhNwVI57sdy7qVEGBAEggrF5QKbjUjY+NGmqLgsA9EuUZth97TZ0C1OwIvJYUrFRp3slWwwvmNbMi7hU5/6FKqqcsstt/C3f/u3HDhwAKdTjJ+CE5ukuWraNOOylI70jDllCTYJS2GGKGRsSZ9Lx7pi+Pb4CLwZwPemD5PDhB7VU0kR8Z44wZYgsSMxQu+FiByO4Frqwv1pN5Y6S0Fz8WxDzu23384dd9wx7GuhaRo33HAD5513XoYxu9SUTUgXCHIRjxv9mmBASAeQZZynGNUKwbgVfWCNNpx4Vczgky7oJD/wvj/7COwPoFQrWFot2BfYMdeYiR2NGXEuFgndrxsiVpkdDyOVz5RiYBQIBJlkR7tMmQKSBLoOx45BXV1a85gSO9KT5BKcssWs9E0+ySpljE1QOhdsKcWvQtwM5RDbJtuCL72nTDKmLtlTJp8RIdlT5oYbbkhdl6+nTFI4T/aU+e53v5u6j/7+ft58803OPdcorX/55ZfRNI0VK1YAEAqFMioHAUwmU+ocS8V4ubxh7O+5VEzMXzoJ/UcL1kQnJiIksBE1zcBy5mxq1s6bUEEn3yIoETYmVtGOaN7fHc+/RbkRBgSBoIJpbISFCwn/0RiP7Eoct3VASI9Yob0dli5Fq6kDjiDbZVHJNkCpxrZCXZ4TEZcgEFQKSUd6UkjP60gfhfGq1HGXYzVDDDe2JF3ln/zoEyx1FmJdMWI9MaIfR5EsErZTbJicJkwuE9GjUVSvin2+nfAHYRKhRCpCWVd1up/ppvPpTuyn2LFMt4xYSXPgwAFmzZqV+rkQN/r3vvc93n33Xf74xz+OeOxYEEK6YFxJutEhTUgnc2AqpAS50MEndDCUmnhFu6OpD7xjgQNlioKkSBkf+IQ3QbwnjmyVQQYtpuXshFwqJ2UhgtNwA6Ou6+hxnVhXjFBLqGwl1JPN6SkQDIeuD0a7JB3pJpPRSKavz3Ag1NWVJyN9JLLFLJPHlLHJp8d0JMWoohlu03CiIhXK5ZQqZNE3GRd8E9FTpqmpiQsvvJBrrrmGbdu2EY/HWbduHZdffjkzZ84E4OKLL+YnP/kJmzZtSkW73HLLLcyZM4dzzjmnZM+/1I6ccuOkjQZ+QS9TCEkNxKhFJoZL+oAaXseJG5iYJpTDLYIkk/G9949epn91+pBxQdd0dE3HNMVEqCWUUZoL4/u3KMU4JgwIAkEFI8tw6aVEnjIcDTY1gMdsCOm+Y6pRIrh2LfqA4UEySxUXGzVRlGpsG43LE8YnLkEgqBTyRbvEYoYxVBlQUkdrvCpl3OVYzRD5xpakqzxyKEK8x+graJtjQ1cH+3dFDkUgYehz5lozse4Yvtd9yFbZWLtGdUhApDWCrumggxbVUKYpI64PXS4Xbre74Ndh3bp1/PrXv+bVV1+loaGh4N8bDUJIF4wrSSFdlo2mDUnSS2UKLY/JN/hULanCvcJN6P0QPc/1oEU0LLMzG/aFW8OYHCZUn4pSo6D2qsS747iXuwm2BAl/FEZxK+gxPWMwK2UmXaGCU76BMb1cRgtpHHn8CIG3AmVpPjrZnJ4CwXCEQpAY2JBPOtLBmCglhXQoX0b6cOQSFp0LnalNPi2uYWswhKz0Tb70TcPk2GQ/3V5U+dxYKacLdDSLvsmw4JuInjIAP//5z1m3bh1//dd/jSzLfPnLX2br1q2p2z//+c+zfft27rvvPu677z4cDgcrV67kxRdfxG4vnfA4qRrQaprR0yVxCMeX7ER8HSSiJkzWBDZ3BKn5EOzYYTSnnIBmlMMJLAwUEURaI0MElvR5TdJwEG0zRHNro3Vc/xalmmOVsjFsuTYoX331Ve6//37efPNNjh49yvPPP5/a7MrHK6+8wvr163nvvfeYPXs2t956K9/4xjfGfC4CwbjT1ETkFB+8D7aoF0+f0VjUa6mB666Dpib0P/QDg2aGkaqKKsFMUG5KNbaNxuU5XnEJAkGlkC/aBQzNKrmGHIvxqlSbhGM1puQaW+I9cXx7fKhB1XiOEihTFWLdMdTjKtYGK0q18bOu6qheFXOtGZPNRKQzgm2u8Vhxbxxd1ZFNg8K6ekwFHRyLHCWpEtR1ne9///s8//zzvPLKK8ybN29U91MMQkgXjCvp+ejp67ykFluMkA5DB59kdEvnU50E9gdI+BLYTrUh2+UhDftks2yI6b0qslUm1h3DOteKMlWhur6a2q/WUrW4quSdkKE4wSnXwJgc2BKhBLqqYzvFhm2OrehzEaV9gpORpBvdZBoce8AQ0g8eTBPS1fEX0nMJi8oUBccZDgJvBpAlGdkqox5TU5t8wJCxKdIWKbp8bqyU0wVa7KJvMi34xrunDBibC9u3bx/2vC6//HIuv/zyYY8pBZOmAW1bG7S0wOzZhqBTHUm7UYKGBmhuNo6bgOaUw1avJYyxTFf1DIFlyLxmjpVIXYTAmwG8f/Zi7zbGjvH4W5RyjlWqmJpyNvQLBoMsXryYb37zm3zpS18a8fhDhw5x8cUX853vfIef//zn7Nq1i3/4h3+gvr6eNWvWjOlcBIKJIGIxXIb2L12Eu84HfwKvox6ajMqoYtaD49V8c6Ip1dhWrMsTxi8uQSCoFLKjXSwWY+2YSBgpCkOE9FGuF0sRdzlWY0r22KLrOsGWIImQ8XOkJ4JskVGcCppVI95t9OsyV5sxe8yox1UkxdCoMBnzTS2uofVoyIqMrukoHsVYH1pIxZQqklKSKsHvfe97bN++nV/+8pe4XC46OzsB8Hg8JTUApSOEdMG4ktFoNI2kmBWNQiJa3GCUHHyCzUF6ftFDvDeeymJSpinEOmPEumLo0cEPsOJW0MIazsVOoh1RYzDoN1zpnlWejAWjrumEPg6VtBtxsYJT+sBomWUheCCI6lVBAcWj4FzkRPEomNzFuT5FaZ/gZCQ9Hz3941dba1z29BiXExHtAvmFxelXTMe9ItNhDtB2T1vGply8J07ovVDR5XNjpZQu0GyKXfSJBd/kYlKU7fv9EIlk7r6l43RCR4dx3AQwnMCiawP5nWkCS74Nffs8O9Y5VgJ7AzjmOwqe14yFUlezlCIyqNwN/S666CIuuuiigo/ftm0b8+bN44EHHgCMiKY//vGP/OQnPxFCumBSEk42G22owXOmYQrw+dI+q/HCcodPpsbCExWHNp5xCQJBpZDtSJckw5Xu9WY2HJ0I41UuxmJMyR5bEt4E8d44ikcxBPGYhjJVMeKPAdkiowU0tKiGZJGQFAnHIgfxnjiR1ghooEd0bHNsmGvNBN4JpF6f9JhSGNv6MMljjz0GwAUXXJBx/b/+67+WrXJPCOmCcSWfkJ5eKhMJFi9eZS/C4t1x9ISOUq0YjQ86omgBzfjg2gYb9pmcJjzne4gejhLvidP4w0Y8qzyphVrS4eDf6y+4G7F7mXtEB0SxglP6wOjf6yfycQTZIWOdaU2dCxTv+jzZS/seffRR7r//fjo7O1m8eDEPP/wwy5cvz3v8c889x8aNG2ltbWX+/Pnce++9fOELX0jdrus6t99+Oz/96U/p7+/nvPPO47HHHmP+/PlD7isajbJixQrefvtt9u3bl2oMKCg/2Y1GkyQz8JITp2TmXTHNY0pFocJiuDWcsSmX7iAoV/lcPiqlWaFY8E1OytGAtqS4XGCzGaunXJs6waBxu8s1/ufG8AJLMtrFfro9JbAMt6EvyzKOBQ7UPhVkyr6hUepqlrE6s0Yr7Bda5Tcadu/ezerVqzOuW7NmTUbjYYFgMhEZKOqx2QbnY8n5GRQmTlVSY+HxiJYZ7zi0iYhLEAgqheyMdDA8E15vZsPRQjf9xoPRGlOyxxbZJqPHdHSrjtqvIpklFI8CEkhWCVOVCfWYihbXkJGRFAnrDCv20+1IFglrnRWTx4TrUy4SvgRSs2S8ThZQfSrWeqtxf5Rmfajr+qh/d7RM/F9bcFKRT0hPlsoAxELFi1fZizDZanyg9biOJEmYpxlCc7wvbjTozNoJS/gTeFZ5hojo7Vvb8e/zI1fJRjdit9GN2LfHR+hgCN8eH7GjMUxuE7LTOMb3lo/Wu1vp+WUP4dZwygmWTrrglItcA4qzyUnjzY3UX1uPfYGdKaun4DnfkxLRU/ftNKFFtIJ29ZIuz+RXMZ2Qn3nmmRGPrWSeffZZ1q9fz+23385bb73F4sWLWbNmDd3d3TmPf+2117jiiiv41re+xb59+1i7di1r167l3XffTR1z3333sXXrVrZt28aePXtwOp2sWbOGSCQy5P5uuummVGM/wfiS3Wg0SbaQPtEOg6SwWHVWFfa59pwNAkMtIaKdUWNyopPhIJCkgU1D1SifyxakSk1SyIsejg6Z0CSdUo4mR9maFeq6zrp163j++ed5+eWXxYJPUFoaG2HhQjh82OhYnI6uQ3s7NDUZx00AyUWQucZM6EAI1aumMiuTY9m0i6alxpHkhr7JmXvhUsxcYqyU41ySBgTXOS7UPpXw+2HUPiMSa9Z1s4Y1OxQj7KezaNEiPB5P6ivZJLgUdHZ2pnonJKmrq8Pn8xFOWnsFgklEcmpstw/uTYbDEI8b36ca+A1jrBrtZ7UQdM3oqRV4J5B3PZck2Byk7Z42Wm9rpfWuVlpva6XtnjaCzcG8vzNaxjK2Fcv3vvc9/v3f/53t27en4hI6OzvFmCM4KciOdoHMOOIkhYxV48lI68d8pI8tWlBDCxnzLlujDcd8B3pcR9cNbc3kMSGZJRLeBLG+GMpUQxQPN4dxzHXQeEsjtrk2Qs0hdHTMU83Ee+PEumOYHEb0FtL4rA/LhXCkC8aV5P/dbCFdkoyByeeDaLB48Srb4W3ymDDXmIkdjWGuNSNbZUxVJmSLPGzDvuRAk+1wSHgTSGZp2G7EslkGCdRjKuGPwgT/EqRqSRXOJucQh/poS/MkWcKx0IF1htV4fXK8ROV0fZ5ITs8HH3yQa665hquvvhowyqZfeOEFnnjiCW6++eYhxz/00ENceOGF3HjjjQDcdddd7Ny5k0ceeYRt27ah6zpbtmzh1ltv5ZJLLgHg6aefpq6ujh07dmRkDf/2t7/lpZde4he/+AW//e1vRzzXaDRKNBpN/eyfoOiAE4VCHekT0Wy0UNKrZUIHQ0QPR7HOtBrjkaqXtXwuHxPdOHIi8vEEJxGyDJdeagjpBw4YmehOp7Gaam83BpC1ayek0WiSXGW9klWCAf3HuWhwHlIpFSTlPJfROrNGG1M1mio/geBkJRXtYsss8vH5DOGqkDlYuSLlislcn4homfGKQ5uIuASBoFLIjnaBwRSFDEd6hUS7lILk2BJuDdP+UDvhD8K4lhmbdr49PuI9cUwuE1pUw9poNcT1mD6kf5ezyYn9VHtqHJWtsmHwMklGJPEUBdWrjlsz+3IghHTBuJJ0pOfSNKqqjMlTLFz8YJS9CJMkCedCp+HO7IkjW2Vku4z9dDvhg+GcDfvSJznZDodsYT67G7HqU1HcCqEDISNSYaohZskWOedEaiyC00Tk451opX2xWIw333yTDRs2pK6TZZnVq1eze/funL+ze/du1q9fn3HdmjVr2LFjB2A0Auvs7MwovfZ4PKxYsYLdu3enhPSuri6uueYaduzYgSN7RykPmzdv5s477yzmKQqGoWghvUIcBknSF22WORbsx+1EO6JEjkSIdcdApazlc8ORL5+vakkV7hVudNVweIkFn2BS0tQE110Hzz9vNB7t6DBUoKVLDRG9qWmiz3CIwCLbZbr/3ai0Sp9XTVTWbi7KeS6jiQwarbA/mgZ+hTJjxgy6uroyruvq6sLtdouNQsGkJD3axWw2TFahkDFHmzatMHGqHJtwxQjjExktMx5xaBMRlyAQVAK6nj/aBbIy0ivYeDUaJFnCcYqDWd+ZRfvWdkLNhlblWuYi+E6QSHsEWZGxLbDhWu4a0r8rOdZlz0djXTF8e3yED4YJvx8uOL+9UhFCumBcyRftAoMD02iE9FyLMHOtGfcKN8HmIOGPwsakxmHK2bAve3KT7XDIFuaHdCO2D0TEDOQSo4PapyJZpLy5xKNtCDERrs8TzenZ29tLIpHIWSbd0tKS83fylVUnX4vk5XDH6LrON77xDb7zne+wbNkyWltbCzrfDRs2ZIj4HR0dQxrFCgpnpGiXIc1GK2hilGvR5lzkNMasUAI1rCJpEvHeuNGM2KkMKZ8rRJAaS9ZnvolT19NdIzq7xoJY8AnGhaYmWLAA2tqMxqIulxHnMoFO9GzSBZZEZNCJmT6WTXQFSfb5Vsq5QGVtMiRZuXIlv/nNbzKu27lzJytXrhy3cxAISkm6kA6GKz0ppENhZoZSf1aLEcYBvK958e72YpluQcoqEx5NfweBQFAZ+P2gqsb3uRzpOYX0CjNejZVsrUqLaNhOseE+z43nPA9Vi6tGXB9mbPidBVM+P6XslTTjhRDSBeNKIUJ6PKyjUNxglG8RJlkklKkK1fXV1H61tqAPPOR2OKSE+ZbgkG7ElpkWAn8JpHKJtaiWilMYbiI12tK8sXRlHg3C6VkaHn74Yfx+f4YTvhCyG5alNzMTFE8+R3ptrXGZnZFeCc1jkuTKA00fm2JHYqhe1YizsppGVT5XTElzPpITp2BzkJ5f9IxrybNAUHZkGebOnbCHL2ajK7nAg6HzqvGeSwxHJZ3LeAj7gUCADz/8MPXzoUOH2L9/P1OnTqWxsZENGzbQ0dHB008/DcB3vvMdHnnkEW666Sa++c1v8vLLL/O///f/5oUXXhjz8xUIJoJktEvSj+PxQGenUZ0MhTXwG8tnNdc4OlzmOhjrQ+9rXrq2dxH5JILvzz4C+wMo1QqWVguOhY6M3lXljNMTCATlI7kWtNkydaukXpWr2WglGa/SKaU5aqzi93hU0owXQkgXjCvDCenJHb54REOhePEq3yLMfa676EVYPoeDudaMe5p7SDfieE88lUus6/qQOIXhJlKjHVByDWzWBivR9iiBdwIl3eU70ZyeNTU1mEymnGXSM2bMyPk7+cqqk8cnL7u6uqivr884ZsmSJQC8/PLL7N69e0hu6rJly/j617/OU089NabnJSiMpCN9pGiXSmseA/nzQM21Zjw1HqPx1Adhar5cQ7wnXnT5XCmzPiey5FkgOFEpdqMrQ0hPLvI0LeWod7pcOG6aTaQ9NuEOofHK/S30XMop7O/du5fPfe5zqZ+TVWdXXXUVTz75JEePHqWtrS11+7x583jhhRf4wQ9+wEMPPURDQwM/+9nPWLNmzZjOQyCYKLId6ck5mff9LpjajdZZmDg1ms9qvnHUcYYj5xwr3hMn2BIk3h03vv9LEJPLhGOBA2WKgqRIRI9GUb0q7hXulJg+XLTMSOLWWMQvgUAwNnLFukCeaJcKNF4lKaU5SpCJENIF40ohjnR1FNEuqfso0SJsJIeDY66DqRdPpe+FPkLNIUwuE5IskfAn0KJaRjdiKF8ucfrAFmwOcvi+w2MaKE8WLBYL5557Lrt27WLt2rUAaJrGrl27WLduXc7fWblyJbt27eKGG25IXZdeVj1v3jxmzJjBrl27UsK5z+djz549fPe73wVg69at3H333anfP3LkCGvWrOHZZ59lxYoVpX+igpwkHen5ol1CIeOrEh0Gw+WBSpKEZJaw1FmY+rdTU+6qQsfCUgvfwzm7RMmzYDJRrKBRLgFkNBtdGUK6SYLm5sGM90gEbDakhQuxX3opnDXxGe+VtGArp7B/wQUXDGtSePLJJ3P+zr59+8b82AJBJTAk2sUUAKrwPvU87HwZvfUcYCVS2D/ifRXzWR1uHA28F0CP6hlzrHhPHN8eH4lQAskycH8SaAmNcGsYk8Nk9MqqUVB7VUItITw1HnTyR8uMJG6VQvwSCASjJymkp8e6QO5moynjVQWtFyH/WOd7y0fgvQDTvzq94LQGwVCEkC4YVwoS0qNjE69KtQgrxOGQ7EYcbDa2JdVjKrZTbTibnCk3wnjkaU5Ex/jJzvr167nqqqtYtmwZy5cvZ8uWLQSDQa6++moArrzySmbNmsXmzZsBuP766/nsZz/LAw88wMUXX8wzzzzD3r17efzxxwFDGLzhhhu4++67mT9/PvPmzWPjxo3MnDkzJdY3NjZmnEPVwH/jU089lYaGhnF65oJ80S4ul9HwKh43JlCV2IW9mDzQ9LGwEGGv0JJm72tePKs8I0668rnnU/cnSp4Fk4BiBY1yCSCj3ehKH8eklhbYutUou5k925h4BYOwbx8cPmw0Uq2AhqmVRCUJ+wLBiUI8DomBf/12O9DcjKcrDCzFZ6mFBQvQu10ASB++D83VI45NhXxWRxpHg+8FSUQSRNuimM4wzE/BliCJUAKlRiF2JAaAZYYFySoR74kjm2VDTO81YvVi3TEi7RESvkTOaJmR1mzTLp5G3wt9Yk0nEEwgyerkbCF92GajFVTBnG+s06M66jGV8Edhgn8JUrWkCmeTU2zSjYLKqz8QnNAUEu2SEtIrYDByNjlpvLmRuZvmMmfjHOZumkvjDxtTA03y9nl3zWPe3fPw/JUH81QzkkVCV3VUr0roQKisjbKyB0rFrSCZJBS3gmORg3hvnN4dvejaiRXPMla+9rWv8eMf/5jbbruNJUuWsH//fl588cVUs9C2tjaOHj2aOn7VqlVs376dxx9/nMWLF/N//s//YceOHZx55pmpY2666Sa+//3vc+211/KpT32KQCDAiy++iM02fg3JBCOTr9moJGXGu1SiIz1ZLWOuMRM6EEL1qiOONcHmIG33tNF6Wyutd7XSelsrbfe0pTYAkySFb5Mzs3Im3hPH+0cv/jf8BPYHaLu3jU9+9AnHfn+MwDsBwq3hnONLuns+F+Wq1BEISkVS8PDv86PUKEYZf42Cf5+f9q3tQz5DxR5fDMVUeKSTEVH1/PPG4LZokdHZz2QyLhctMq7fscOIfREIcvDoo48yd+5cbDYbK1as4PXXXx/2+Oeee46FCxdis9k466yzhjRL1XWd2267jfr6eux2O6tXr+aDDz7IOOaf//mfWbVqFQ6Hg+rsf9oDSJI05OuZZ54Z03MVlJdI2jBls2jw/PN4tH4AvFI1mEzoZkMUl+Khko1NI42jttk2TDYTsk0mdCBE9HCUeHcc2SqnhHJTldGDS5KMtZYW1nAscmCptxjzsX6VeHcc11IXs66blSFOjbRmi/XEaH+4nXiPWNMJBBNJvmiXnM1GK9B4lWusS1bXxDpjmKcahk/ZIpdkjnoyIoR0wbhSiCM9MUZHeqlJOhyqzqrCPtc+RAxP3l57SS1z/sccXEtdRk7x+2HUPjXnRKpQdE0n3BoeVqwa7eJaAOvWreOTTz4hGo2yZ8+ejHiVV155ZUhp9WWXXcbBgweJRqO8++67fOELX8i4XZIkNm3aRGdnJ5FIhN///vecfvrpeR9/7ty56LqeioIRjA/5HOkw6DzIENIrYFMvnWS1jOuckceaYoS9XMJ3atJ1NIakSCjVCrJFpvuZbt7/x/f56MaP8grzSfd89HB0SIRB0j3vaHKUrVJHIBgLxW5Sl3tTO99GVxKT04QW0YZUeAyOY7oR5zJ7trFrmI4kQUODEfuSlsstECR59tlnWb9+PbfffjtvvfUWixcvZs2aNXR3d+c8/rXXXuOKK67gW9/6Fvv27WPt2rWsXbuWd999N3XMfffdx9atW9m2bRt79uzB6XSyZs0aImkqaywW47LLLktF5OXjX//1Xzl69GjqK1kJKKhM0oV0a1cbtLTgnmKMbd6IUcWmJ4xxSnbZRzU25VpDFTKOylaZ2stqcZ1j9MBS+w3DgrXeimupC9kpD46rA8Ypk9OE53wPrk+5qFpSReMPGzOMV6nnPULlnyRJhN4LgQkkxJpOIJgo8kW7TJZmo9ljna7rqeoac63ZMDFpxhhmb7IT+STCkW1HCH+cW28SDEVEuwjGlYKE9JjhOKjEhg0jUcoGoIWWh4v4BIGgOIYT0qdMGTzGHa/csaiQPNBioyCyY2Mgs6RZ7VVR3AqRTyLGJEsHLaqhTFNylhyP1GuinJU6AsFYKTbjv9w9AYbrjwD5KzxSCzwThnrlzLOp73RCRwf4R84jFpx8PPjgg1xzzTWp+Ltt27bxwgsv8MQTT3DzzTcPOf6hhx7iwgsv5MYbbwTgrrvuYufOnTzyyCNs27YNXdfZsmULt956K5dccgkATz/9NHV1dezYsYPLL78cgDvvvBPInRmfTnV1dd5m8YLKIxw2Lq1WkIN+iETwVBnrFF90QEjXjHFUspiMsauIsSnfGsq13FXQOFq1uIqav6vB+5qXtnvbsEy3YG2wGrnnR6LEjsYw15rRYzqSIiFbjXliwp/As8qTM/5O13RCLSGinVFMHhPopHppJZuZRtuixPvi+N/yo/aqOBY6UlGhINZ0AsG4oGn0HgoAbqaZ+kFzg2x8xoeLdqmk9WL2nDHhTRDvjaN4FCRJQotqSIpEIpgg9H6I2JEYofdCRA5HcC9zi6iXAqicv7bgpKCQaBetAnf1iiHdwa6FNQ7fd3jESIUkSfdEzy97+OSfP8H/1uhcpOmI+ASBIJN80S7p1x0/XpmleumMVC1TbLVKdmzMkJJm+8BCccDNYJ5mRj2mgk5ex20x7nmBYExoGrS2wjvvGJdjjAEo1gE+Wsd4oYy2wiNjgWezZa7+0gkGjdtdrlGdXz4KqaybyPsbQonfRycCsViMN998k9WrV6euk2WZ1atXs3v37py/s3v37ozjAdasWZM6/tChQ3R2dmYc4/F4WLFiRd77HI7vfe971NTUsHz5cp544olhG7lGo1F8Pl/qyy82j8adjEajLhfYbHhkHwDeqDGGaZox55C0eFFjU75KPN9bPo48cQRdNwTt7LEjexyVZMkQxVd6UH0qOjqSJOFc6MTkMBHviRPri6FMNQT54aI8kxF7Rx8/SuhgiOO/P473j17iPfHMyj+zIcrLFpno0Si+PT7iPfHU/Yg1nUBQZpqb4Z57OPaHAwBM3f0C3HOPcT0jNButoArm7DmjFtXQVR3JLKHrOqrPWNeFDoSIHY1hcpuQnTImp0lEvRSIcKQLxpVCHOlarLLFq0IptgFo0j0RbA4S2B8g4UtgO9WGpc6C5JYKdpEO13xQIDjZSSQGTU25HOlJIb2/v3KjXQpluGoVXdfR4zqxrhihllBq0ZjeZNm724var6JUK1jrrVhmWgj8JZByM2AB3W9MzhRJyeu4LcQ9LxCMieZmI/+7pcVQaGw2WLgQLr101M0zi3WAj9YxXiijrfBIbQhaTcZrsm+fkYmevrmm69DeDkuXQlZT7LFQUGWdphmRDX6/IZQ1NqacX6O6v7FQhvfRiUBvby+JRCLVQyZJXV0dLS0tOX+ns7Mz5/GdnZ2p25PX5TumUDZt2sTnP/95HA4HL730Ev/4j/9IIBDguuuuy3n85s2bU053wcSQIaQ3NsLChbgPGH2JvNHMaBcp6DM+fwWMTYU02JMtMkgQbTPWR9ZGa95xNNe4q0xRcJzhIPBmAFmSDbPBMcMgULN26FiUvh60zLFgP24n2hElciRCvN9oVpqs/Iv3xFGmKaCTqgQMtYTw1HgMN7xY0wkE5aO5OdWQ3SddCkD1NDmjIbvTacwFKj0jPXvsMrlMSLJEwp9Ai2pDzFF6VEc2y5inmTF58jewFwwihHTBuFKII32yi1dQfKRC+iTL5DaBBMo0hVhnjIQvgXuFG3OtOWd5uIhPEAgKJ914Nly0S4aQXkETo2LIJ+wlS4hjR2JoIY0jjx8h8FYgJUYlhe/skuZYdyzlZgCGlDQPV3KcdM8LBCUnbeHD7NnGrnwwmLHwGY0ImrFJ3SQj+XwQjYHVgu52DxE0xmNTO32jK9QSItYRQ7bJeQUcSHNKmWVDED58GA4cMDLRk69Ve7vRUWvt2rwidrEUZCagrWDhulhzQtGU6X0kKD8bN25MfX/OOecQDAa5//778wrpGzZsYP369amfOzo6WLRoUdnPUzBIUki32zHGnEsvxfPKHwDwBRVQVfRwDADJaYW1awoam4ZrsJcIJTBPNaOrOo7THYQOhvD+2Yu9245luiXvOJpv3J1+xXTcK9xY6ix5DQK51oPORU7D6BBKEO+Lo0d0LPUWIz7PqWBfYif8QTjV3DTWHSPSHiHhS4g1nUBQLjQtoyG7d48xDrg9MjQtMuZNO3ZQtXQBIOeMdqm09WL62JV0l6vHVGyn2rDOsqbMUQCqT8VabzV+lhhzHOHJgBDSBeNKIY70Sh2MimGkZjImlwnva168r3lxf9qdMcmKd8fREzpKtYLJZZQOJt0ISLnFqtEsrgWCk5FkrIvNZmRzZjOZol1GIpewl76g1FUd2yk2bHNsQ8So9JJm/z4/FizIVhlJkYwx2pI16UKUHAsmgKyFT8pl7XYbPw8sfFiwoGiBOLVJ/ZdOQv/RgjXRiYkICWxETTOwnDmbmrXzcjsX3wth9UQxmeIkEmaiXiuW2tIIIMVWeGTMqZqaDEE4KV53dBiD4dKlhoheIqG4IDPB4wdwhJ9A6htZuC7WnFA0ZXwfnQjU1NRgMpno6urKuL6rqytvLvmMGTOGPT552dXVRX19fcYxY23AvmLFCu666y6i0SjWHP/orVZrxvU+n29MjyconmRGui25r9jUhOdrTtgJ3pAC77+PHjLeF9KKZQWPTdmVeNkN9tBB7VMxTzcz7YxpBPYGcMx30HB9A7a5+cfR0VbW5VoPmmvNuFe4CbYEiXwcIe6LY6o2YZtlS2Wim6eaCbYEiXcbzU7j3XE8qzxiTScQlIu2toyG7MleDR5bNKMhu/P0LqA+d7PRCjSBpo9dgbcD9DzXgxYxDBZ6TEe36qg9KiaHUeGX7Nsg+jGMjBDSBeNKQUK6WrkN/golX6RC0gmanBi13duGY6GD8PthbKfakCQpQ6ySrTKKWyHeG0f1GhEL+cQqEZ8gEIzMcI1G0TSqtX5gKv0dgYpsHlMM2dUqllkWggeCqF4VFFA8Cs5FThSPgsk9VIzK9fvmqWai7VHj951KatIlYqQEY6aIiI8UWQufDNIWPrS1wdy5RZ+SkzYa+AW9TCEkNRCjFpkYLukDangdJ25gUOBxNjlpuDhG79a3CL0VIxaXkM06rvlmar5xbskEkGIqPIaYE5qaDEG42Ne6CEbszzDLQuiVA0Tqo9g/NbJwXe5GruV+H012LBYL5557Lrt27WLt2rUAaJrGrl27WLduXc7fWblyJbt27eKGG25IXbdz505WrlwJwLx585gxYwa7du1KCec+n489e/bw3e9+d0znu3//fqZMmZJTRBdUBhnRLgO4zzSiW7yOmbBxI/q9MTjoR66vLfh+C22wJ1tlZFnGscCB2qemusaFW8N511CjqazLtx4015rx1HiwzrJy/PfHcS5yUnVWVUrESt4ePRwl3hOn8YeNORuYCgSCEuH3ZzRk90aMwcljHRisBhqyO3VDQZ8MjvQkybHLPteO43QHvc/34t/rRwsZmpt1pnVIY2NhjhoZIaQLxpWkA8GeYx6SjHZhkrtAIXekQroTVLbKKNUKlukWAm8HCH8UxlJvATeYPCbMNeZUR3jJIqVyiEcSq0R8gkAwPHkbjQ5k4055uQa4lv69H6GFooCpIh0GhZJereLf6yfycQTZIQ+ZNOUTo7KrXWSrjCRJSCajPFmZoqB6VREjJRgbo82mzlr4DGFg4cNomgkOuJSdiUM4vmQn4usgETVhsiawuSNIzYeGupSbm3G+8D9xuHuJfO50EooLk+rH5n0f6YU9cOr4x4MkK2syNgRluayC8HD9GQBMaoDYsQCJswoTrke8v7E6p8r5PjpBWL9+PVdddRXLli1j+fLlbNmyhWAwyNVXXw3AlVdeyaxZs9i8eTMA119/PZ/97Gd54IEHuPjii3nmmWfYu3cvjz/+OGD8z7nhhhu4++67mT9/PvPmzWPjxo3MnDkzJdYDtLW1cezYMdra2kgkEuzfvx+A0047jaqqKn71q1/R1dXFpz/9aWw2Gzt37uRHP/oR//RP/zSur4+gOHIJ6UmDgy+kwFlnodlaAH9R68HsSrxcDfbSK+mSY0fg7QDd27tL3n9huN4ZkiShTFWw1FnQIprRzJTM55rwJ4zqQCGiCwTlZaDpMcEguN0pR7rbGjVuH2jIXlVrrI8yHOm55lkVStJ4GW4N0/5QO+EPwriWuTLGF2GOKgwhpAvGlUIc6SQqtzymULInckCqtDDZPMZab8XaYEWukgkdDBF8J2g0Fh3oCJ/wJoj3xI38YRm0mDZsR3iBQDAyOR3padm41dMuAuC47kFXewGQWj+CU88c5zMtHclJ07GXjqE+qOJY4MA81UzWei2vGJVd7RLriuHb4yN8MEz4/bCIkRKMjbFkU6ctfHSXm4jXNih2eyJIAwsfXK4hv6pr+vAVXGkuZUmWsFdH0n47h0s5LR5EOmMRdkkCIoAZGiYuHmQiSo5HbLzqjSITw+SxAPGhd5AlXJe7kWv2AnoIw7yPTha+9rWv0dPTw2233UZnZydLlizhxRdfTDULbWtrQ057X69atYrt27dz6623cssttzB//nx27NjBmWcO/i+96aabCAaDXHvttfT393P++efz4osvYktTV2+77Taeeuqp1M/nnHMOAP/1X//FBRdcgNls5tFHH+UHP/gBuq5z2mmn8eCDD3LNNdeU+yURjIFcxqqUkO4z+h+PJl5vpAZ72fEFiaBxfc9zPWhRreT9F0bqnRHriFF9QTVaWBuxz9WI/7MEAsHoGWh6zL59aE1nZEa7pDVkd86fCRialq4be/+DvWgmx+dRkiUcpziY9Z1ZtG9tJ9QseuyNBiGkC8aVQoR0KTH5Hem5JnLxbkMUV3szc6iUagVbg41oe5R4fxzzFPNgfl5zkPBHYRS3gh7ThVglEIwFTcP7YR9Qi8cSBm3A3ZiWjVv9iSEE9Med6LrxvfT7F+FziyZ1Nq4kSzgWOrDOsBpja47hdTgxKqPa5SyY8vkpYkEnGDtjzaYeWPgE/28rvfEzCPU5SagyJkXDMS1IjfkIzguajOPSCDYHU1UWed2HxbqUKzQeZCIWeCM2Xu0z4Zrqx6b0AcML17qmo2s6pikmQi2h8jin0hbQGe9D4wFSC+js99HJxrp16/JGubzyyitDrrvsssu47LLL8t6fJEls2rSJTZs25T3mySef5Mknn8x7+4UXXsiFF16Y93ZBZZIz2mVgKNA0w+050iZgPmF5uAZ7ziZnqhJP13UihyMkIgkks4TjjNL3X8heD+YSq2Zeawhzw/W5Kuh/lkAgGD3yYEP24F8+Qh9YKLkj3fDJoVRD9iq3MRfVdWND0OGo7Iz04RA99saGENIF44auDy+kJ6NdJG3yC+mQOTh5d3tR+42Mc2v90EgF51lOYp0xQs0hnGc4MTlNSBaj5K+6vprar9ZStbhKiFUCwWgZiI7o3zEb+O9UH/4L3LMLli/PEL+m2AyblDdsBX2gieBHB0+IbNwRxa0ixCgRIyUoCWMVn2WZ4Nl/R/vP9xD36lhrA1inyCSCGv73IeJZTsNZK3CmifDB5iDtW9uJ98aHdx8W61Ku0HiQicjuHFE8avRQc7qC1H4Y3PmF62Cwht572gi1hIh2R4l+HCXaZoxT1kbr6JxT+bL4BxbQHDhgvO+SlRHt7akF9GTeTBUIKolcQrrDASYTJBIDrvRhxq6RhOV8DfYki4Su6qmxw2Q1QRysjWXqv0DhYlW+PlcF/88SCARjY6Ahu/fJ3wOgSCp2b2dGQ3aHNnh4IDAgpE/iWGLRY2/0CCFdMG7EYsb6BUZwpGuTJ2dqJJKDk/c1L233tmGZbsHaYB3iBpXtMs4zndhPtxPviqcmWe5z3WJHUCAYK2nREV7zEgA8Lt1wH+7fb6zY5swBoNpmrO784cHVnRQLnxDZuIU4o0QZn2BcGaP4rGs6vX9xE284A0fdh0h9vXBcRVEUTKfXEDKfRu87bhxf0FOl8b3P9xLvjeNYNIL7sFiXcoXGg5RigTeaSIERxSMuhK3v5xWug2d9kfZHOgbFozlWInURAm8G8P7Zi73bjmW6haolVbhXuNFVnXBrePhzGymL/7rrBm/v6DBuT1tACwSC0pAr2kWSjHiXY8eMGL58Dd8LFZZzNdjLHoscixx0/bwLkzN3LNSY+y8MUIhYlcugUNT/LDF3EwjGTlMTvv++AO4zpnLSXZsyGrLLsjFuhcODDUcrvdnoSAhz1OgQQrpg3Ei60WF4IV1hcuVMjYQkS0ajmJUe/Pv8WLBkNJNJOkHdn3Yz+8bZRNujYkdQICgVWdER3g4jhLPalTDEsTfegK4uw1bg8aSEdFUbXFTJdssJk40ryvgEFcUYxedIW4RQSwjrGTVIrjpDfYnGwGpB8niw+hIZbsLU8bMLdB8W41Ku0HiQsZYcjyVSYHjxKL9wrf/9JfT+0km8158hHtnn2bHOsRLYG8Ax38G0i6fhe8NH19NdI59boVn8CxbkdqwLBCcAlZKzncuRDsa/gaSQXpVjE3C0wnK+sSjSFqH3F71l6b+Q67UuVqwq+n+WoGAeffRR7r//fjo7O1m8eDEPP/wwy5cvz3v8c889x8aNG2ltbWX+/Pnce++9fOELX0jdrus6t99+Oz/96U/p7+/nvPPO47HHHmP+/PlD7isajbJixQrefvtt9u3bx5IlS8rxFAWjxOs3/ud7pio5qyGrqgwhPdlwdLJGuwjGRtlmho8++ihz587FZrOxYsUKXn/99WGPf+6551i4cCE2m42zzjqL3/zmNxm367rObbfdRn19PXa7ndWrV/PBBx/kvK9oNMqSJUuQJCnV3V0w8SSFdEUBs3no7Ukh3cSJNxglnaDmGjOhAyFUr4qu6qheNaOBqKzI2OfaqTqrCvtcuxDRBYKxkhUd0R8xVm0eW9QQuk4/3Tju4EHQdVzWGLKkYWKwdk9adPoJlY3rbHLSeHMjczfNZc7GOczdNJfGHzaOSUTXNcMNGngnQLg1jD5QWSQQDEtSfD582BCb00mKz01DM86TJPwJQ0B1mozPc3U11E03LiUJk9OEFtFSbsKM43OQfXzKpXzOOdDXB++/b1wuXTq0CWoyHqSmxhDevV5QVePywIEJiwfJ5+oshKTz07/Pj1Kj4FjgQKlR8O/z0761PZU/PBxJp1POeU1TE9x8M2zaBBs3Gpc//CER57y84pEsSTjqVSJvd9P5Pz8ksC8w8rllZ/G73UaGRDKLv7fXyOLXNOPvM3cunHWWcSlEdMEJQrA5SNs9bbTe1krrXa203tZK2z1tBX2OS00+IT294Wiqv0PaerAYYTmbXGNRMvIuejiKnvU/KGl0cjQ5iu6/UKrXuuj/WYKCePbZZ1m/fj233347b731FosXL2bNmjV0d3fnPP61117jiiuu4Fvf+hb79u1j7dq1rF27lnfffTd1zH333cfWrVvZtm0be/bswel0smbNGiKRoe/Fm266iZkzZ5bt+QnGhs9nXObyd8CgZpV0pCfHqhMhTUFQOGX5a4vBSZCL4fLRwVjT2GygMLnLY/KRdIK6znGh9qmE3w+j9qm4lrqYdd0s4QQVCMpBVnSENzogpFsH/ndUVUFdnXF54ACyrx+PNZIahwCkL/39CSemDCtuFUklLc4Fk4wxis8mlwmTzYgnykW2m7DY44G8Ym/OqI9ihPc0yrkRNdpmo9nOT8WtIJkkFLeCY5GDeG+c3h29Yz/XHMJ1XvGopxf++CfkPX8gvO8osT3v4zi2DyXaP/y5FZPFLxBkkf351FRt0m0cl2JTrJTkinaBQSE9PdolfewqtbBcqNGpmDlSKV/rUf3PEozIgw8+yDXXXMPVV1/NokWL2LZtGw6HgyeeeCLn8Q899BAXXnghN954I01NTdx1110sXbqURx55BDA2XbZs2cKtt97KJZdcwtlnn83TTz/NkSNH2LFjR8Z9/fa3v+Wll17ixz/+cbmfpmCUeL3GZXI8yibZ1y/lSJ/EGemC0VOWaJf0wQlg27ZtvPDCCzzxxBPcfPPNQ45PH5wA7rrrLnbu3MkjjzzCtm3bhgxOAE8//TR1dXXs2LGDyy+/PHVfycHpF7/4Bb/97W/L8fQEo2QkIR0MrUuJnLiDkWjoIBCMM1nRERmOdDCunz4drrwSXn8dWlqoNvnxUW3cLoN0xqKJOfdJgGiCJRgzY8imHq6BrqZphA6GcMx3gGaIYaNuuJsUewt9PkXEg4wlOqUQRrvAy3B+AvT3Z8bmlDFSIF08SsUt9PTCnj0QChGT6kjIDmxTA0id3eDzwooVUFuTO+6gQhvBCiqf7M+nHtVJhBOY7CYkq1Tyz2s5qMSc7eGiXSC/kJ5zbEhjNMJyKSPvSv1al7JJvMAgFovx5ptvsmHDhtR1siyzevVqdu/enfN3du/ezfr16zOuW7NmTUokP3ToEJ2dnaxevTp1u8fjYcWKFezevTulVXV1dXHNNdewY8cOHMMJIgNEo1Gi0WjqZ7/4HzUuFONI13UdBva5TkTtSpCfkgvpYnAS5KMQIb2qCkx9J06z0VyIhg4CwTiSlVt8fEBIn2oPZ+YWf/7zxldbG1P2ugk2n7gbeqWiEhfngknKKLOp8zXQjRw2mlLqMR00aL2jNSV2jUvD3RGE92R2buDtAD3P9aBFNKyNo9+IGi73eLTZnUnnpzXkh7cPGvEnqmrk89XUYJq/gFjEObLzU9OK/rsOEY/A2GQJhdBraom2V2Eya1imAnIt9PQYt9ecl4r0yWgQWKGNYAWVTfZGsRyW8f7Ji3pMRZmq4FnlQXbIFb9xXIk524VEu6Q2AdPGrnIJy6UyOpX6tRZN4ktPb28viUSCurq6jOvr6upoaWnJ+TudnZ05j+/s7Ezdnrwu3zG6rvONb3yD73znOyxbtozW1tYRz3Xz5s3ceeedBT0vQekYyZGeIaTH0yqYT6BY4snGq6++yv3338+bb77J0aNHef7551m7dm1ZH7PkQroYnHJTKc1dJpKCHekMzcQTCASCUZGMjhhoGNgXMBZX0xLduaMj5s6luh46m4264xN1Q68UVOLiXDCJKcb1nUa2mzDUHCLycQTJIuFe6cbaOFScnsiGu0mHa7A5SGB/gIQvge1UG5Y6C5JbKnojaiRHey5XZyGYXCZMUT+J195GUX3GitJshngcjh4l0RNCnnf28M7P5ubBSoNIxFDNFi40xuRhKg2GiEfuKKbuPhK2aUR7nViccUxmnYRqQrEmDHG8t9dY/VZXD3WlVmgjWEHlkr1RDBB4O4Cu6lhPsaL2qoQ/COM531PxG8epTTGnNeftQzaexoFktEs+IT3dkZ4+DyunsFwKo1M5XutCHfNCa6hsHn74Yfx+f4bZdCQ2bNiQYTbt6Ohg0SJRJVtuRnKkp0e7JDf8QJivJpJgMMjixYv55je/yZe+9KVxecyyRLtMBJU8OJW7bHeyUKiQbjpBM9IFAsEEkRYdceyXA470yBH4dO7oiOpqUs1GxYZefipxcS44OUm6CcOtYdofagcZXMtcKREhW5xu/GEjjTc3jrvokO5wNblNIIEyTSHWGSPhS+Be4cZcay54I6qQaKXRNhu1NVhwhN/Hf0zBdErt4GtjtRqu8I8TuOrfx9awPPcdNDfD1q2GwD17tjHBCwYNMfvw4WEz4yFLPNrtI9ZfhTzFgas+wLQFvfQdrMF/tApTbQjJYjEc79FYbldq1oYqDQ2D59PePmGNYAWVS/ZGsdqvEu+No3gUZFlGcSvEe+OoXhWlWqnojeNyxKGMlaQjPTsjPT3aJV9/h1JGsZSacr3WIznmhdZQODU1NZhMJrq6ujKu7+rqYsaMGTl/Z8aMGcMen7zs6uqivr4+45glS5YA8PLLL7N7926s1sw587Jly/j617/OU089NeRxrVZrxvG+pMIrKCujdaQL81Xp8fv9Ge/77M9EkosuuoiLLrpoPE+t9M1Gyz045TsmfXBSFIXTTjsNMAanq666KufjWq1W3G536stVhpLOSmvuMpEUGu1yojYbFQgEE0hTE+o/3Yw3bsx+pt2+Lm/DwClTxIZeIYgmWIJKQpIlJFkicTyBY+FQV2i2OF3KhruFkO1wlS0yekLHVGXCXGsmEUoQagmR7HM8UtO8QpuBJsWo5r9EefPZD6C11YhbGQGp/TA19r2Yp5oI9TpRIyZ0DdSI8bNlqoka216k9sNDf1nTDCd6b6/hAHe7jY7ybrfxc28v7Ngx4nk4m5w03tzI3B/WMmfJO8z91Ls0nt9G1fQQNQt7MTvihHocqH4NXTajxpT8DQJH2Qi2nDz66KPMnTsXm83GihUreP311/Me++STTyJJUsaXLdvOKygZ2U0ttaiGruqpOYFkkdBVHS1qvIeLbXI5niTjUKKHo0aebxrJjSdHk2Ncc7aLinbJMQ9LjQ2b5jJn4xzmbppL4w8bJ1w0Ludrne9/Vjm1hldffZW/+7u/Y+bMmUiSNKRx5mTEYrFw7rnnsmvXrtR1mqaxa9cuVq5cmfN3Vq5cmXE8wM6dO1PHz5s3jxkzZmQc4/P52LNnT+qYrVu38vbbb7N//37279/Pb37zGwCeffZZ/vmf/7mkz1EwNopxpCfnWCDWjOVg0aJFeDye1NfmzZsn+pRSlNyRnj44JXNpkoPTunXrcv5OcnC64YYbUtflG5ySu3rJwem73/0uYAxOd999d+r3jxw5wpo1a3j22WdZsWJFqZ9mQYj82EySQnq2+yAdp0NP7e6IwUggEJSS497BvePqsxvzbiVXV4sNvUKoxCZYE5GRJ6gcKrlKItvhKltlJEVCj+vIFgnFmiB+2Id6OIEye8qIG1GFRiuZEsaK8OXX7bz2XZm9X7ytoHgV/H6c1i4azuuk9/06Qr0OYn4rsqLhqg9QM78T57Gu3A0629qMOJfZszNjVIyTMxzhzc3GcSPE+UiyhH3VqbCyznCzswiQcNaGaFjRQW/zNEIfqcTcDcgxO66lzvyu1FFm8ZeDZ599lvXr17Nt2zZWrFjBli1bWLNmDQcPHmT69Ok5f8ftdnPw4MHUz9l/d0HpyHYWp39eJauEHtORFONzDJW9cVyJOdv5hPSkp83vH7m/QyX2nBrv17rcWsNExCWMB+vXr+eqq65i2bJlLF++nC1bthAMBrn66qsBuPLKK5k1a1ZKtLv++uv57Gc/ywMPPMDFF1/MM888w969e3n88ccBYyy+4YYbuPvuu5k/fz7z5s1j48aNzJw5MzUHbcyKDqsaUGNPPfVUGhoaxumZCwphVI50iZNCzxtvDhw4wKxZs1I/53KjTxRliXYRg5OByI/NJJmHN5wj3WUX5TECgaA89PUZl9XVRr+8fGQI6SLaJS+VuDg/URd9gsKoxAiD1GNnifwmjwlzjZlYawBz4hhSKIwesaD98T30hiqi5tNwXTAz70ZUQZsGzb1Irc3AKahI9GlTjRiTQuJVBhp0Ou09OM6PEvHaSERNmKwJbJ4Iks+bv0Gn328oZc487lCnEzo6covwucgTzeK0HMUx9Q0i9Y0kvroK0+J5I0f0jDKLv9Q8+OCDXHPNNam10bZt23jhhRd44oknuPnmm3P+jiRJeat7BaUle6M49Xk9GkOpUVB9KtZ6K4pHmbCN42KotDiU5Jow21yVU0ifZIaG8XytR6s1VHJcwnjwta99jZ6eHm677TY6OztZsmQJL774YqofX1tbG3LaBuuqVavYvn07t956K7fccgvz589nx44dnHnmmaljbrrpJoLBINdeey39/f2cf/75vPjii6JyaBIykiM9Q0gfpnJGMHZcLhfufH+ICaYsQroYnAwq2Rk1ERQS7eJ2pJXHCAGrYhAuT8GJwLFjxuXUqcMfN2WKcKQXSqUtzk/URZ8AIwZkBCdxJVZJJMkW+SVJwjk9RuIv3cSjINtsYDOhme2E3o9i8bxHzVlVeUXhETcNAipy12GIGtZPFYloQhmMVzlwwIhXWbAgtyM7rUGntGgR9urI4G0jNegcEOEJBnOvRIPB/CJ8vr9zWq8LWloMId5mQzp3KfYcvS4mgkLFqVgsxptvvpnR10mWZVavXs3u3bvz3n8gEGDOnDlomsbSpUv50Y9+xBlnnFHaJyEAcm8UO+Y7iPfEiX4cRZmqYJ9vR/WpE7ZxXCwj5WyPJ/kc6cnhwufL3Wx0sjBer/VotYbsnnC33347d9xxR0nPrdJZt25d3rSEV155Zch1l112GZdddlne+5MkiU2bNrFp06aCHn/u3LlD4n8ElcFIjvSMZqOTdMNPMHbK1mxUDE6V7YyaCArKSLeJzseViHB5Ck4Eko70adOGP85oNjp5F3DjzXgsGAsVqAQnKM3NgwJqJGKoLzniSSqxSiLJEJEfMHe/j9t9nGCigfBxK4o1gW6y4VoQpEbZg/OdHvjCGTmF7hE3Dd7vx8UnxKqNAU9FJqIOzEULiVcZrkHn4cNgtRqCfFvb0E2NNBGeRYsy412GE+FH+juXI5qlgA2aQilUnOrt7SWRSKQMRknq6upoaWnJed8LFizgiSee4Oyzz8br9fLjH/+YVatW8d5774lYgDKRvVGsRTTs8+wk6o1mjuoxtWKaXBZKdhyKrumEW8PjLqwXFO0yyZ2e4xE9M1qtoZLjEgSjQ9f0itgkOxEoypE+QgSV4MSlbEK6oLKdUROBENInL8LlKTgRKNSRbgjpRnWMGIcKo9wLxhPZPfXoo49y//3309nZyeLFi3n44YdZvnx53uOfe+45Nm7cSGtrK/Pnz+fee+/lC1/4Qup2Xde5/fbb+elPf0p/fz/nnXcejz32GPPnz08dc+zYMb7//e/zq1/9ClmW+fKXv8xDDz2UisVL3s8DDzzA448/zieffEJNTQ3/+I//yP/4H/+jPC9EPpqbYetWo0Hl7NmDYm6eeJJKq5JIMkTkd0cxdfcheVwoYZVql5faM3qomhEciE5xDit0j7hp4EpQo31IR089AAmkQSEdCotXyeUCj0YNFSweh5//HH7xi6GbGsOJ8O3tRrzM2rWZgnWhf+dSRrMUuEFTKOUUp1auXJnRCG/VqlU0NTXxL//yL9x1110lexxBJrk2iq0NVqLt0UkvWAWbg6lxMhExNgccCx3UXFr+cTJftEu6Iz3ZxE8IVPkZrdZQyXEJguKZiM/yiSzcF+NIT45Twng1sQQCAT788MPUz4cOHWL//v1MnTp1SAR4qRBCehmpZGfURFCIkO4cyEjXJNFEaTwQLk/ByUShjvSMaBexgKsITlT3VLENB1977TWuuOIKNm/ezBe/+EW2b9/O2rVreeutt1JxePfddx9bt27lqaeeSvWUWbNmDQcOHEjF4X3961/n6NGj7Ny5k3g8ztVXX821117L9u3bU491/fXX89JLL/HjH/+Ys846i2PHjnEsuRs1XmiaIXT29mY6m0eIJ8lZJdFgQWo/DO98PGFNJjNE/t0+Yv1VyFMcuGcGqFnYi7M2lHbwyEL3sJsGn7LgfDqM1mGU9CeFdF0feBmHi1dJJ90F/vbb8NxzYDYbr99wmxp5olhYutQQ0dOF6lH+ncdEkRs0hVCoOFVTU4PJZKKrqyvj+q6uroIz0M1mM+ecc07GwlFQHnJtFBezcVxOwSn7vgsV+YPNQdq3thPvjWOdbcXqtJIIJvDv8xM5HKHhuoayiukFOdK1ye1IHw+E1iCYiM/yRG7CjQciI33ysXfvXj73uc+lfl6/fj0AV111FU8++WRZHlMI6WWmUp1RE0FBQro1KaSLwWg8OJFdngJBNsU50sXEqJI4Ud1TxTYcfOihh7jwwgu58cYbAbjrrrvYuXMnjzzyCNu2bUPXdbZs2cKtt97KJZdcAsDTTz9NXV0dO3bs4PLLL6e5uZkXX3yRN954g2XLlgHw8MMP84UvfIEf//jHzJw5k+bmZh577DHeffddFixYAMC8efPG4yXJpK3NEGFnz86MB4ER40kyxK/mZrivdM7jseBscuKYbyPyfBsJ358w1bmwLZwyVOgoUOjOG62EDq8vRH/VsH6qSOhIqJqMWU4Mn3GejSwbx23fbjjSzzijMLG70CiWMfydR8VECPdpWCwWzj33XHbt2pXqN6NpGrt27cobi5lNIpHgnXfeyahGEVQe5RScsu9bj+okwglMdhOSVcr7WLqm0/t8L/HeOI5FjpRxSXErmBaZCB0I0bujF8cCR9kE2IKEdIuYhxWC0BpOXibiszzRm3DlJh4f1KzyOdJzRruIcWpCueCCC8Y91lsI6eNAJTV3mUgKE9KN8hhNEuUx48GJ6vIUCHJRTEa6aDYqKDejaTi4e/fulMsiyZo1a9ixYwdglDJ2dnayevXq1O0ej4cVK1awe/duLr/8cnbv3k11dXVKRAdYvXo1siyzZ88eLr30Un71q19xyimn8Otf/5oLL7wQXddZvXo19913H1Pz7ERFo1Gi0WjqZ/9wkSGF4vcbioszz6KskHiSMjiPx0RzM9Lzz2NvboZj+6HVB72nGudQW2scM1IzzyxyRytJcOml6P/6OmAI6QCRYyHM3R/ljlcZjtGK3YVEsZTi71wM4y3c52D9+vVcddVVLFu2jOXLl7NlyxaCwWBqU+3KK69k1qxZbN68GYBNmzbx6U9/mtNOO43+/n7uv/9+PvnkE/7hH/6hLOcnGDvlFJyy71sOy3j/5EU9pqJMVfCs8iA7ZHxv+Qi8F2D6V6dTtbgKW6ONSFuEUEsI62zrkOpfSZKwNlgJNYeItEXKFtk2UrRLIpHm9BSVgSNSLq1hIuISBIUz3p/lStiEKzfp04x8/p2czUbFOHXSIYT0UVJsmd54NBypdAoR0h0DjvSEcKSPCyeqy1MgyEWhjvT0aBdRHTO5mEyLvtE0HOzs7Mx5fGdnZ+r25HXDHZMdG6MoClOnTk0d8/HHH/PJJ5/w3HPP8fTTT5NIJPjBD37AV77yFV5++eWc57Z582buvPPOQp564bhchmUxGMy9ohnJtT3BzuMhZIv6558Pf/oTvP++cd2qVYaylC9HvFiamtBP7YcPoySSQnqPH1eueJWRKKfYPda/c7GMt3Cfg6997Wv09PRw22230dnZyZIlS3jxxRdTn922tjbktL/98ePHueaaa+js7GTKlCmce+65vPbaa0MqCwWVQTkFp+z7Bgi8HUBXdaynWFF7VcIfhHEscKAeUwl/FCb4lyBVS6oMwfUMB4lIAqtzqHlG13X0uE6sK0aoJVQW45eu53ekD34k9ZRAJbKHC6McWsNExCUICifhT+T9LAOYnCZiHTES/kRJHm844R6M5rfe17x4X/PiWeWZlGJ6Mh/dbjdS7HIhol0EIIT0UXGi50KVi4KE9IEyvqRzSiAQCEpFoY50mw2sJg0SkEAs4CYTYtFXGjRNIxqN8vTTT3P66acD8L/+1//i3HPP5eDBg6m4l3Q2bNiQ4Zbv6OgYu8jX2GhEsOzblymEQ2Gu7QpwHqfIJeq73fBXf2Wcw0cfGaL6kiW5c8RHiW53AdHUWBb9wc2wYlbxAn05xe6x/p2LZbyF+zysW7cub5TLK6+8kvHzT37yE37yk5+U9XwEpWO0TtFCjFrZ9632q8R74ygeBVmWUdwK0fYosZ4YuqpjnmpGV3Vki4x/n5/AewEjBiaYQHEPSgHxnjjBliCxIzG0kMaRx4/g3+vH/Wk3ljpLyVzO8bjxsYahQrosGx+7oH+wRF8IVBPHRMQlCArH5DJhspmGfJaTJIIJZJuMyWUqyePlE+6TY0e8O47ar9J2bxuelZ5JqY2NlI8OuR3pYsPv5EMI6UVyoudClRMhpE9eJpPLUyDIR1JIH8mRLkngcujgF2PRZGMyLfpG03BwxowZwx6fvOzq6qK+vj7jmCVLlqSO6e7uzrgPVVU5duxY6vfr6+tRFCUlogM0DYi6bW1tOYX07GbV6Y2sR40sGznmhw8b7vGGhsFolkJc2xXgPE6RT9SvrTWex9y50NMD3/624UwvkUNeixuRealol9rZDNkf1LSRc8zLKXaP9e9cLOMt3AtOaHKJ3/kEJ13XSXgTJEIJ4n1xVK+auq1Qo1b2fWtRDV3VBwVnM6jHVeQqGetsK+ig9qlIFgnHIgfB94IkIgmibVFMZ5iQJIl4TxzfHh+JUAJd1bGdYsPkMtH9TDedT3diP8WOZbqlJMaxZKwLDBXSwRiCokJIFwhGxNZow7HQgX+fH9MiU8amna7rRNujuJa6sDXm+KCNglzCffrYIVtllGoFy3TLpNXGko70fPnokOlIT86xxDh18iGE9CKo5FyocnaELxWFCOk2s04USOiVde4nO8LlKTgRSEa7jORIB3APCOlxMRYJysRoGg6uXLmSXbt2ccMNN6Su27lzJytXrgSMhqAzZsxg165dKeHc5/OxZ88evvvd76buo7+/nzfffJNzzz0XgJdffhlN01ixYgUA5513Hqqq8tFHH3HqqacC8P777wMwZ86ckr4OI9LUZOSYPz/QLLSjw1BfCnFtV4jzGBhe1JckmDnTsDd5PCWNmUm6pVJCeiTrgObmwdd2uEas5Ra7x/J3LpbxFu4FJyz5xG/XcldOwSnYEiTeG0cLG+J31793IVuM91mhRq1sMUu2ykiKhB7XkawSWkBDi2tYPBYkSUKLakiKZBwnSdhm24h8HEG2yYQOhLDMshA8EDREfQUUj4JlhoXQeyF0TQfdEOuVaUpJxLH0MShXayaXC46TJqSL7GGBICeSLFFzaQ2RwxFCB0JYG6yYnMbYEG2PYqmxULO2JqcmNJJ2lOv2bOEeINgSJBFKoNQoqL0q1nor1gYrFiyTMjO9EEd6choXj0M8JDLST1aEkF4EldCcJReTJWqmECHdbtaIAnFdLF4qicnk8hQI8lGoIx2gym683+OamBgJykexDQevv/56PvvZz/LAAw9w8cUX88wzz7B3714ef/xxwJiL3HDDDdx9993Mnz+fefPmsXHjRmbOnJkS65uamrjwwgu55ppr2LZtG/F4nHXr1nH55Zczc+ZMwGg+unTpUr75zW+yZcsWNE3je9/7Hn/zN3+T4VIfN5qajBzzkVzT2VSS87iUon4hDvIBkvmdOYX0YhuxllvsHu3febSPNV7CveCEZLgq5XBbGGWqQvRwFNMiE2qvmnJtmtwmtJiGtdZK5FCEww8dxmQzFWzUyhazTB4T5hozsaMxQ8zyqkhmCVOVCV3XUX2GuKV4jGW/yWlCtsrUXlZL6L0Q/r1+Q1h3yFhnWrEvsBM6GCIRSmCuNaNHddRjKujgWOQYsziWno+eqw2N2z3YpwaE01MgGA5nk5OG6xpSWlCsI4Zsk3EtdVGzNrcWNJJ2NNzt6cK9yWUi3h1HtsqovSomh3EcEkhMnDY2FgpxpCejXQAiQZGRfrIihPQiGO+GDoUwmaJmkkJ6dof2dGzKgHglXKACgaCERKOGPgSFOdKdNmMsiiXEWCQoH8U2HFy1ahXbt2/n1ltv5ZZbbmH+/Pns2LGDM888M3XMTTfdRDAY5Nprr6W/v5/zzz+fF198EVtaDf3Pf/5z1q1bx1//9V8jyzJf/vKX2bp1a+p2WZb51a9+xfe//30+85nP4HQ6ueiii3jggQfG4VXJgywXn2NeSc7jUon6hTrIk3c94EhPNhuNRgduGG0j1nKL3aP5O4+W8RTuBScUhVQpmxpMmKeZCb4XJH40TiJoiOgJXwLFqVC1pAqlRsH/hp/Y0RjVF1QXZNTK5UJ1zHcQ74kT/TiKXGXEKyQCCbSoliFuwWBuctXiKmr+roZjLx1DfVDFscCBeaoZ1TuYuS5JElhA9+uGK11SxiyOJaNdcsW6gPExzBDSTWIeJhAMh7PJiWOBI8NBbm2wEm2PEngnkOE4H0k7mnbxNPpe6BtWW0oK997dXtR+FaVawVpvxbHQgbl2sEPnRGhjYyXpSB9OSLdYQFFAVSEcEEL6yYoQ0otgvBs6jEQlR83kotBoFzCE9EQCTOPzUgoEghOcZKyLLA8/OUpSNSCkRzUhqAjKSzENBwEuu+wyLrvssrz3J0kSmzZtYtOmTXmPmTp1Ktu3bx/2vGbOnMkvfvGLYY+ZFFSK83gson7Sgf722/Dcc4aA3tg4soOcdCHduN+UI30sjVjHU+wuNyfScxGMG4VUKat9KnVX1nHsd8cIvBUw4lci+hDByTzNbMSoqEMrP3VdR4/rxLpixuMNiGO6qlP75Vp8e3yED4bRIhr2eXYS9UZOceRQBPWYiu1UG84mZ+qxsnOTJVnCsdCBdYbVEIKkoZnrekxPRcPA2MWx5BiUz1jldoOJQXEq+/UVCARDkWQptbEVbA5y+L7DQxzl0y6ZRt8v+/JqR8H3grQ/3I7iUnCckV9bavxhI403N+J9zUvbvW1YpluwNljJbis13tpYKUg60oeLdgHDld7fD5GAiHY5WRFCehEU09BhPDLLKzVqJh+FCOlW06BzKl/1s0BQKh599FHuv/9+Ojs7Wbx4MQ8//DDLly/Pe/xzzz3Hxo0baW1tZf78+dx777184QtfSN2u6zq33347P/3pT+nv7+e8887jscceY/78+alj/v7v/579+/fT3d3NlClTWL16Nffee28qUqHSmQz9GHKRFNKnTCnMbOiwGs1jYmrlPzeBQDACleI8Ho2on3SgNzfD/v2GXerUU6GuzpgkjeAgH9JsNCmkV1IjVoFgklFolbKlzkLdf6sjeCCIdZYVk8NkRKykTS2SkStqv4p52qCbM5mpHjsSQwtptP24jcMPHsZkNyFZJUw2E/bT7dRdWYelzpLpQn07QM9zPWgRDckioat63tzk7PVteuY6FoZEw4xVHEuPdsmFywUmBhr4CXFKICiKfI5z31s+vHu8JHwJ7KfYkRiqHSluhcBbAaZ8bkpB2pJnlQfPSg/+fX4sWDLusxzNTseDQhzpYEyR+vshOpCRLpuF8epkQwjpRVBoQ4fQwdC4ZJZXYtRMPnR9sJRvOCFdkQazPIWQLignzz77LOvXr2fbtm2sWLGCLVu2sGbNGg4ePMj06dOHHP/aa69xxRVXsHnzZr74xS+yfft21q5dy1tvvZWKVbjvvvvYunUrTz31VCqbeM2aNRw4cCAVq/C5z32OW265hfr6ejo6Ovinf/onvvKVr/Daa6+N6/MfDZOlH0MuislHB3BYjLEoIoR0geDEoFKcx8WI+ukZ5m634RSfNg06O43V3ooVUFs7rIM8OyM9Fe1SSY1YBYJJRrFVypZpFkxVppzHooAyVSHeF8d2ig1Jkoj3xFOZ6rqqY64zE++Oox5TUaYqeFZ5kB0ygbcDRDuiNFzXkDJN2efasc+14zjdUVBucvb61jLLgnmqmWh71Dg3p5KKhhmzOKZpRFq7gRnYTDHQlCFjX3q0i4hLEAgKJ19aQbLPQbA5iBbSiPXEsLZmVsbouo4W1tCCGolQAnSGOMyztaWxNDutVAp1pCc9CJHAwKafGKtOOoSQXiQjNXSAwrquZ7s6kw6CYlyelRY1Mxzpza2GE9J1NemcklN5xgJBOXjwwQe55pprUk39tm3bxgsvvMATTzzBzTffPOT4hx56iAsvvJAbb7wRgLvuuoudO3fyyCOPsG3bNnRdZ8uWLdx6661ccsklADz99NPU1dWxY8cOLr/8cgB+8IMfpO5zzpw53Hzzzaxdu5Z4PI7ZbB7yuJXCZOrHkIukI72QfHQYFNLDcTExEggEJaYQUT87w7y7GxIJqK42lKaeHsPVXlNjCOl5HOTZGemp+ViJMtsna5WSQDAWiqlSBvIeq2ka4ffDOM90ggah90JYGiwEDwRRvSooYHKbkGTDVW49xYraqxL+IIznfM+wUZ65cpPzfT6z17eyVUaSJCSThHORE2WK0cR0TOLYQHVNeJcF+Cfsxzrgnv9vSH+H9GajQpwSCAonV1pB+qaceYqZWDgGOkSPRlG9Ku4VhmIcbAkSaY2QCCTwv+En3h0fknmeS1saTbPTSqYgR7qmUWWJA1ZiXT4siLHqZEQI6aMg38QEoO2ethEzy3VNp++XfSlXpx7VSYQTGaV6hbg8i53ETSTJWBcYvtlounMqECjzSQlOWmKxGG+++SYbNmxIXSfLMqtXr2b37t05f2f37t2sX78+47o1a9awY8cOAA4dOkRnZyerV69O3e7xeFixYgW7d+9OCenpHDt2jJ///OesWrVqWBE9Go0STdkIwT/OpfaTrR9DLpKO9EKF9GS/hogQ0gUCwUSQnWFutRrdreJx43u32xDZvV5DXM/jIE8K6UOiXUrQiHUyVykJBGOhWCdmrmMjhyME3gygx3TQQLbKJMIJ1PdUIh9HkB0y1plWLDMtBP4SQPEoyLKM4laI98ZRvQNN/oaJ8kzPTR6J7PVtrCuWymAPvx8emziWVl0TcRqRiDYrOfs7GNEuIndYICiW7LQCXdcJtgQNEb3WjK7pqL0qWljD0mBB7VXxv+k3op9CCZDAXGcGCSJHIimh3VxrHlZbKmbTrtIZ0ZE+sCFY1f0lYCHRP72FhZlIIRGDd7IhhPRRkmtiEm4Nj5hZ7vuzj+B7QbSohnW2FTks4/2Td0ip3kguz6QDyHGGg+CBIKH3jMet1HKapJCe7HKcj3TnlHCkC8pFb28viUSCurq6jOvr6upoaWnJ+TudnZ05j+/s7Ezdnrwu3zFJfvjDH/LII48QCoX49Kc/za9//ethz3fz5s3ceeedIz+xMjHZ+jHkIulILzTaJSmkh2ITP34KBIKTkOwMc4/HELePHjXiXCwW45hodFgHed5mozCmRqyTvUpJIBgr+ZyYVUuqcK9wo6s64dYwtkbbkGNDzSEiH0eQLBLulW6sjcbnJ9IWSYlcrqUuzFPNxLpjGc0/JYuE7tfRokYVbymjPDPWt2fBlM9PGbs4llVdE3nX2Oyz2fSc/R3SHekid1ggKJzstIKEN0G8N47iUZAko++BMkVBskqovSqSRSLSGkG2yUh2CXO1Gft8O+EPwiRCCeLeOMEDQaoWVxHtGF5bKmbTrpIZ1pGetiE4xfV30A0h2Y0LkD46CM3V49fAXjDhCCG9hIyUWS47ZMIfh7HUW3CvNLa5Am8Hii7Vy3YAJR3t+sc6slWuyHKaQhqNghDSBScHN954I9/61rf45JNPuPPOO7nyyiv59a9/PUSkTrJhw4YMN3xHRweLFi0ar9MddmzTdR09rhPrihFqCVWsA6FoR7oyIKRHxSJOIBBMANkZ5pJkRLF4vUasi9VquMVjMUOIyuMgz5uRnmQUjVgnVZWSpk18k1nBCUs+F3fX0105qzQcCxyEW8O0P9QOMriWuVKfEcWt4DzDif8NP1pIM9zYEhnNPyWrhB7TkRQJ2Wq8j8sZ5VkScSyruiasGhWYNkXN2d8ho9moiEsQCAomO61Ai2qpTThd143GwQ1WHAscBA8GibZHUX0qFrsF20xbKsrFPNWcanQc+TiCMkXB/Sl3RWlL5SKvIz1rQ7D2oxgAIc0Qt+RYOGfDd8GJixDSS8hImeWxozESwQS22UYjGbVfTe0SFlqql88BFGmLIFtlpn91OlWLqypOzCpWSBfRLoJyUlNTg8lkoqurK+P6rq4uZsyYkfN3ZsyYMezxycuuri7q6+szjlmyZMmQx6+pqeH000+nqamJ2bNn8+c//5mVK1fmfGyr1YrVOihi+5Lb5eNEvrEt3hNPTbS0kMaRx48QeCtQkWX9xTrSrYqGCgSjlTOOCgQnFSe7AJorw7y21mgw2twMH31krPRisWEd5FrcEKSGZKSnU2Qj1klTpTRQgk1Li/HEbTbjNc3KZBYIxkJSbA42B+n5Rc+IVRqSLJE4nsCxcOhGkyRJ2E+3EzsaI3QwhOtTLkweE+YaM7GjMZQaxRDD6q0oHqXiojxzklVdE1GNeaRdUY3bs/o7iGajAsHoyI6cMrmM/goJfwItqmFymFJiuafWQ+DtAAlfAs9nPMb/6oGPm7nWjKfGg9pnmDxnXjuTqX87taTaUqX2V8nrSM/aEKxxGOJWOGYBVCSXHZp3D2n4LjhxEUJ6CSkks9zkNGGptwBk7BLCyKV6wzmAnGc4CR0IEToQoubvKiPOJYWmEfqoC6jHYYmDZsq7GE4u+ESzUUE5sVgsnHvuuezatYu1a9cCRsOnXbt2sW7dupy/s3LlSnbt2sUNN9yQum7nzp0p8XvevHnMmDGDXbt2pYRzn8/Hnj17+O53v5v3XDTNeM9Hh9gEK4dcY1t68xpd1bGdYsM2x1axZf3FOtItsm4I6ZEKGksFgpMFIYDmzzC3WIwdwfp6+OpXYfHiYTcZ8makj4GRKjBLGTUxatJKsJk9ezD/PUcms0AwVoqp0hjp86NUKVjqLChVSipT3THfQbwnTvTjKMpUBft8O6pvjM0/S0BBYlhWdU1SSLclhfSs/g5ut8hIFwhGS3qMVLDZEFPUYyq2U42IqfTmoXpMxzLDgnmqOSWiJ5EkCcksYamz5Nz0GwuV3F8lryM9a0MwKaRHYgqgIllMxu3j3MdMMHEIIb2EjNh4ptaCyW4iETJcncWW6k0aB1A6A4vh0Msm4Ic4vEfhnn/PuxhOliCLaBdBuVm/fj1XXXUVy5YtY/ny5WzZsoVgMMjVV18NwJVXXsmsWbPYvHkzANdffz2f/exneeCBB7j44ot55pln2Lt3L48//jhgfAZvuOEG7r77bubPn8+8efPYuHEjM2fOTIn1e/bs4Y033uD8889nypQpfPTRR2zcuJFTTz01rxu9Esge2yyzLAQPBFG9KiigeBSci5woHgWTu8LK+gco1pFukXVCGBnpqjp8bweBQFBChAA6SL4M83PPHTHDPEk5hPSRKjDLGTVREFkl2CTnzG53zkxmgWCsFLNGK+TzY5luoe7KOvyv+wm1hNAiGvZ5dhL1huikHlMnPMozlxhmP92O+9NuLHWWQWE9q7omHE8T0nP0dxCOdIFgbKRHTgXeDtDzXM//z96Zx0dV3vv/fWbPTDaykATCbiBBEQSFQmnVSgtVW/G2VK23LteLrffHrZZWBS+Cgl7Uaut6G22vVW/lam0tXbTcImoXQRAEFxJAkBgIJGQh20wy6/n98cyZzExmJjOTmWw879drXpPMnDlzziTnmef5PJ/n88XX7ROmTY8a0KYs4y1Yp1qF2TM7sgk0VatdtEm34OMxjx9a9VVUNYYjPWxCsMeRbgS6UXyuiAXfJSMXKQ2kmGiFZ7JmZ5H/9Xyaf98ccHUmulRvWDiAggkaDDtsSwCwWnwxB8PBGeky2kWSTq666ioaGxtZu3Yt9fX1zJo1iy1btgSKhdbW1qILGmAvWLCATZs2sWbNGu666y7KysrYvHkz55xzTmCbO+64A7vdzs0330xraysLFy5ky5YtWCziGrZarbz66qusW7cOu91OSUkJS5YsYc2aNSHRLf0h3B1kLjXjPO6M+nu8S+mC27aO3R10f9qNzqrDPMYcWCYIQ3dSL1FHulHXIz61t8cvwEskkn4gBdDeJJFhHkyPQUFsn4rFT/GswBzUqImwJdghRMhklkj6SyJjNNvZtriun1FfGtWr2GeyfbhUEylqtLu2m1MvnaL+hXoyJmdgGm3qcZkGra7ptn8egAyfPWJ9B1lsVCLpP1rkVMbEDKxTrRG1qYKlBQAcf/x4ZBNoila7aJNu9mo7nftEnIxligVTkQklWxky9VW6u8HtFj/3cqSHTQhqQrrLPzGo2NtFfy2s4Ltk5CKF9DQQXngmuKOj6JQQx3oiS/WGvAMomLDBsONjMa1ntfhiDoaDnVP2TxugpuvMy0WVDBgrVqyIGuXy9ttv93ps2bJlLFu2LOr+FEVh/fr1rF+/PuLzM2bM4M0330zqWOMhWiFifYZerHoJ+z3RpXRa29bylxY8P/FgnWaNuBxwyE3qkbgjXfH2iE+nT0shXSIZEKQAGpkEM8yDCTYoQGoc6X2uwBzEqAmg1xLsXoRlMksk/SWRMVqi10+4IWGwDQqRYmzcjW4c+x2oPhVUEV9qyDcEuUzHY/OvrumuEpEuFndHxPoOotiodKRLJKkiljYFRDWBpmK1S/Ckmz5bDwoY8g246l14271kz8vGWGgcEkYszY2uKJCZGfZkWNxegT4XAJdL6G6KzQJLl0jN6gxCCulpIlqV83DHeiJL9Ya8AyiYsMGwwy3cqhlRKrRrqCdOAX4h/c13ofO3Z14uqkSSBOHuIF2XjrZ32vC0eDDkGbBWWOk62hX4PWdBDjqrLuGldIpOwVpuxVxsFgOcCGOcITWp5ydRR3pwgb7W1vQck0QiCUMKoClFVdWAIz2V0S4QewXmYEVNBAhbgt2LsExmiaS/JDpGG9LXTx+Ex9ioqor9gB2vw4ux0IjqVPG0eEAF63Rrj8v0znKUVavoOtAJVWD50gK4s7fwJIR00QdTh0g8oEQy3ImmTUHfQjskVxw0fNLNfcqN6lUx5BrQZ+nFBNwBBzkFOaDENmINRHFSLR89KyuKHh4Ut1fwXh0ALn/NB93nzpda1RmGFNIHgUiNVTxL9Ya8AyiYsMGwJqRbjf71MpEGw9XVqDt2AWfjQUd3ZrFY7ncm5qJKJAkQ3lEB6PygE9WjYp5sxt3opmN3BzqzDvNkM54mUYU9Z2FOUkvphtWkHtDV1SMexessDxafWt//FPJ1cnWMRJJupACaUrR2DHqE9FTWtY5n8D0ohC3BDlndECGTWSLpL8mM0Ybs9dMH4TE23jYv7iY3hhyD6A+aQO1QhStdMfRymXabRNueUZILEbpUmZk90S7e8JVJEokkLcQS2pMtDho+6RZcH1Bn1mHINuBucuNp82DINUQ1Yg1UcdKo+ejB+OP2Cj4+BptBVUUbpZQUpuw4JMMDKaQPEpEaq3iWsCTrYBiIWbwQwgbDvYT08MGwPwrGZ/d3ylCwe8xndi6qRBIn4R0VT6snMKjR6XToLXq667uxTLSg0/XuuCS6lG5YTerR40Y3GOLX39TTYpLPg8LpX/4O/v6BXB0jkaQbKYAKfL7EMtGjbB9JSE+VI10j1uB70Ahbgk1paU/R2uPHe2UySySpIJkx2pC8fvogPMbG5/ShetRADIvqUlEMQjSD3i5TrQ2yRPFa6PVgM6ngAl8iQnqi7aZEIumTSPUQ4i0OGj7pFlwf0FhoFMVP/ZNu0YxY/Xn/RNEc6ZF8HCHodGTPmIDRCAa3jKE6U5FC+jAk0eU3rgYX7Tvb6TrYldZZvBDCBsMhQnqkwbA/Cka1XQj4i426TOK5MzkXVSKJg/COSvigBh2oXjUQwxLccYHQQU68k27DaVmyJqTn5fWOXY5IdTXqp7VAsYh2yZsCBXVydYxEkm6kACr6Or/7nYjH6+4WalOsSbwY26tjywKbpTIjfVgQtASbAwfEKkiLJWIms0SSKoaryzwRwlclBrtMMYGn3YO5xIwhR8gM4S7TvoR0AFuGENI9kfIDI5FouymRSPokUj0EIO7ioOGTboqiYCu3iVUsjW4x2aYDn8uHo8rRy4jV3/dPlLgc6X4URXRJDSf9Qrph5LTxkviQQvowIZK4Fc/yG+cpJ85PnSgmhaw5WVinWfHavbS/307n/k5Gf2s0mTMzU9/JCxsMO+xCILf6OiNWaNeiYFS9EM89KNg1IR1kLqpEEoPwjkrwoEYxK+ADRa/gXynbyy2kDXJcDS6a/9gc99K5WDFVnR91DpkBpFZoNK58dP/qGNUpJvk8KLS6rHJ1jEQyUMQrgI5E92F1NTz+uCjUPm5czyRCtEm8PrZXr+sppu31ZyicMUI6BJZgj7j/E8mQZji6zBMhfFWiaawJY54R53EnGMBgM2Att4ISOe6vq0vsJyPGR2Qz++P11Diu1UTbTYlEEhfhK56Diac4aKQoUGOhkex52dir7XQd6cKQbUB1qRGNWP19/0SJ25Hup6AA9CelI/1MRQrpw4BEcqGCl7+YSk2ox1RUVFSvin2/HX2mcAN4Wjx0HenC/qGdzFmZ2CpsqXeoBw2GHR8K52uGuz2yG8gfBaM6xbI/Dwp2v4tdnJjMRZVIohHeUQleOmcoMODt9mLIE8tvfT5fiFtIG+SYS800/qYRd3NiS+eCB4z2ajvHHjqW9gy7RAl2pPeJtjrGOA0Q4tPpLr9tSq6OkUgGhr4E0JHoPvRP4tHUFBprE20SL47tfX/8M3A+PsCXhoz0YYFON2Tb6qeeeoof//jH1NfXM3PmTJ544gnmzp0bdftXXnmFu+++m5qaGsrKynjwwQe59NJLB/CIJWc6mrFL9agUfqMwsOJZZ9ahKAqKXsE23YZhlAFPmydi3F9cjnSzGDf26UhPtN2USCRxE77iOZxYxUEhehSoYlIw5BnILcml8FuFUU2d/X3/REnEkQ5+Id3vUtMZZfsy2CTap+ovafuLP/XUU0ycOBGLxcK8efPYtWtXzO1feeUVysvLsVgszJgxg9dffz3keVVVWbt2LSUlJWRkZLBo0SI++eSTkG2+/vWvM378eCwWCyUlJXznO9/hxIkTKT+3SKg+la6aLjo/6qSrpgvVp/b9ojjQhPGOvR0YCgxYp1kxFBjo2NvB8cePY6+2hxxDyPIXVcHd4saYb8Q02oTX4aVjTwftO9tx1bsw5gmhWmfSRdxfSqiogFWraJ46H4C8K74Ad97Ze6Drj4JRO8T7e9D1RLtoUTAVFSM/F1UiSQKto2IsMOKocuBt92Its6IYFJyfOtEZdWSdnxX4XTEoZJRl4Gn34KhyYMw3ggruZtF2GLINKHoFQ7aBjIoMuj/r5kTlCbo+jd62JdJWDTQJOdL9q2N8iphn9qDQ2h002rPZxChQro6RSNKLJoDOmCHug0X0xx8XbsOCAiGQaIXJH39cPD8c8U/iMW5c7wyq8Em8OLdXDx4GQsWoM8qRPoR5+eWXWblyJevWreP9999n5syZLF68mFOnTkXcfvv27VxzzTXcdNNN7N27l6VLl7J06VI+/vjjAT5yyZmKvdpO7QO11KytoWZDDQ0vNIAKRdcVMeXHUyj7rzJGXzMagK5DXXiaPWTNzmLs98eGukzjENKtfke629eHkJ5ouymR+DnTtKpkCF7xHIloxUGD0aJAs87LwtPsCbQN2XOymbBmAoVXFJIxMaPPaJhk3z8RknGkGxCTfjLaZXBJtE+VCtIipKejc/jQQw/x+OOPU1lZyc6dO7HZbCxevJjuoBHBxRdfzK9//WsOHjzIb3/7W44cOcI3v/nNdJxiCOEdi5q1NdQ+UNtv4ShcGA8Wt6zTrbib3DRtbgoIW+HLX4JzkhVFQZ+lp7umG0+bB2OhUTQ6PpGXHGl/KUOno6FTdKCKKvIiOwL8UTA+oxXwFxt1GUWLFikKRiKRhBDeUfG0eMiYlIH1bCsZkzJQVCXkd0+LJzDIKfxmIZ4WT6+lc+5GN+3vtNN9tJumzU0c/tHhiG1bom1V+GvTMQkZTEKOdG11jL94jBeFVmfQaE+ujpFIBo9w92F2tqhMp7kPm5qE+9DnG+wjTRz/JB62KKt3wifx4the7RYF3r1SSB8QOjo6aG9vD9ycMez/P/nJT1i+fDk33ngj06dPp7KyEqvVyrPPPhtx+8cee4wlS5Zw++23U1FRwYYNG5g9ezZPPvlkuk5HIgkQzSzR+UEnjb9tRDEo5C3KY8LqCUxcP5EJd4v78XeO77UiUYt2iSWkZ5hEH8zVl5CeaLspkXDmaVXJoq14dh5zoqqh4zNtRbO1whpSHDQStgob41eN77NtSOX7JzO+7I8jXUa7DC6J9qlSQVqiXYJPBKCyspLXXnuNZ599llWrVvXaPrhzCLBhwwa2bt3Kk08+SWVlJaqq8uijj7JmzRquuOIKAF544QWKiorYvHkzV199NQA/+MEPAvucMGECq1atYunSpbjdboxGY6/3TQXpqiSs+lTatrfRtqMN02gTCn3nQoUvf+mVk6yCr8uHcbQxILRrOcnpyJkKRvteKiqKsVFFBWpZGxzqFkK6QycUMFkYSiKJi1iZ5dF+t4y3YN9v77V0zt3opn1nO16HF322HhSxhC5S25Zshl0isVX9ISFHurY65g8eQDg5T3f5jzlSoWSJRDJwJOI+HKJxHlHxT+Jht0e2Q4VP4sWxvWoUbZcU0geG6dOnh/y+bt067rnnnl7buVwu9uzZw+rVqwOP6XQ6Fi1axI4dOyLue8eOHaxcuTLkscWLF7N58+Z+H7dEEotEC/71NYbU2qBYGelWU5yO9ETbTYmE4aNVOZ3OkAnZjgGeEIoWzeK1eyPGNvW1r1htQ6R6gMm+f8LjS3/Nnbaj2UAe2Vk+4vEbC0e6FNLTiWZQ0DCbzZjNoVE/yfSpUkHK7b3aiSxatKjnTeLoHAZvD6JzqG1/9OhR6uvrQ7bJyclh3rx5UffZ0tLCiy++yIIFC6KK6E6nM8Q5kmjj1B8XZiw0h3vtg7V07uukfVc7bf9ow93oDtlOb9Pj6/YFcqHCl79oOcmeNg+qquLrFg4tnUWHqqp42j0YC4yBqurh+0slDQ3ifvTo2NupVtHR8aDQaciB9esjR8FIJJKIaB2VzBmZZEzMQGfQxfxd0Sm92g5VVbEfsON1eDEWiok3nVGHMd8YsW3TJvH0tshL6yK1LQMZBZOQI92/OkbVie8NDwqtXWa5OkYiGQqMZPehfxKPY8fEpF0wkSLu4thenXgWIOLyNB3pjMtIH0Cqqqpoa2sL3IIHdcE0NTXh9XopCnOXFBUVUV9fH/E19fX1CW2fDgYjCuH+++9nwYIFWK1WcnNzI75PbW0tl112GVarldGjR3P77bfj8Xj6da6SHhIxS8S1vziiXSwG0aY5vX30tRJtNyVJkei1P5QZTlrVxo0bycnJCdzCJ2sHgmjRLJFim5IlVrpDou+f8PiyuhoeeADWrqV9l4gGzNn1RlwxgYWF0pGebqZPnx5yDWzcuLHXNsn0qVJBypWAdHQOtft49nnnnXdis9nIz8+ntraW3//+91GPtb+NU6o7FhB68RtHGzGMMqAYFZwnnbTvbA8R08NzocKXvyiKgq3cht6qx93oxtPuQZehQ3WruBvd6K36QFX1SPtLFW53j5AV05EOqB5/lXYU7F161AkTpWAlkaSZ8LbD2+bF3eQOTLIFT7pFatsSzbBL1yRkNBJypINYHZMlVHcvCq3tQatjvv99ObE3xBlJAz5JGMHuw0gMZ/ehfxKPggIxadfWBh5P9Em8OLb3ffFLgOhTaUuVpSM9fWRlZZGdnR24hbumhjODFYXgcrlYtmwZt9xyS8T38Xq9XHbZZbhcLrZv387zzz/Pc889x9q1a1P7AZzBJGOWiEU80S4WozB/Ob19iFOJtpuShBmM7OF0Mpy0qtWrV4dMzlZVVcV3kikm2WiWeIhH+I73/RMeX4bV3GkzFQKQ3fBJXDV3QhzpMiM9LcRrUBgMRty3yu23387evXv5y1/+gl6v57rrruuVqaTR38Yp1R2L8IvfXGrGWGjE1+3DUGDA6/DiOOAANXIuVHjBQU+bB8MoA9azxVI7nVGHPluPp8WDqdhE9rxsjIViBjSRnKtEaWwU9zpd30JWTy6xDq8XXK6UHopEIolAeNvhbnajulRUb+RJt/C2LdEMu3RMQkbE54OaGpprheiWlxt/brJWbNSLjtPmIrk6Zpgw0gZ8kjBGuvuwokJM1p13npi8O3Qo9iReH9ur4ycCYkJQM/NKIX3wKSgoQK/X06At1/TT0NBAcXFxxNcUFxcntH2qSXWme3gUwrnnnssLL7zAiRMnQuJq7r33Xn7wgx8wY8aMiO/zl7/8haqqKn71q18xa9YsvvrVr7JhwwaeeuopXHIQkRJSXfAvnmiXgCPdHYc4lWi7KUlrPQdJbBLRqsxmc8jkbNYgmgTCVzzHE+fSF4kI3/G8f0Ljywg1d9pdYpyaMzkvrpo7wRnpOuOIk1WHBPEYFJLpU6WClP/F09E51O7j2WdBQQFTp07ly1/+Mi+99BKvv/467777bsT37W/jlPKORdjFH+wo9zR50Jl1uE656D4ucqIi5UJFWv6iqAqjrxnN1J9NZcqPp5DzhRyMeUYUk4LqUfG0eaLuLxVof7bCQlETLBY+t2istDzPzs6UHoqkH0iX58gmuO3w2X34HEIoN5eYQybdoHfbFmkSL1bbkupJyIgELdVrOSIs6fl/+d+4lupB6OqY1k6jyFuWjqYhjxzwjXDOBPdhRQWsWiUm7+6+u+9JvBjba+aEYEe6jHYZfEwmE3PmzGHbtm2Bx3w+H9u2bWP+/PkRXzN//vyQ7QG2bt0adftUMlSiEKK9z4wZM0KcoIsXL6a9vZ39+/dHfE1/oz3PNFJVcFAjnmgXs168T7cnzjFpou3mGU48cQmQ3LU/1BlOWtVIJ9XGqoTGlxFq7rR1i0Yp2+wKrbkTBeFIF9qVjHYZPJLpU6WClI800tE5nDRpEsXFxSHbtLe3s3Pnzpgfjs8/gxRrlrU/pLpjEeniNxYayZ6XjanEJISpVg/uU+6YuVSRlr9MWD2BvEV5FF5RyIT/mEDW7PTlXIWjfaf0FesCPY50/Mtjoq3elgws0uV5ZqC1HZMfnkz+0nwskyxkfz5URI/WtsWbYaf6hMDu6/LhOuGCCCaMfsdMhS3Va/bmApBXuy+upXpAiADV3S1dnINNPO6pkTjgk0TgTHAf6nRi8m7GjPgm8aJs37PKr8eR7vWKuQfJ4LJy5Up+/vOf8/zzz1NdXc0tt9yC3W4PFL+77rrrQpYw33rrrWzZsoVHHnmEAwcOcM8997B7925WrFiR9mMd7CiEWER7n+D3CGco5A4PJxI1S8RCVeMT0k1+Ib0rXiEdEm83z2DSWc9hqHMmaVVDnVQbqxIyuUaoudPuFG7nHIszrpo7wdEuyGiXQaWvPlU6MKRjpytXruT666/n/PPPZ+7cuTz66KO9Oodjx44NzH7eeuutXHjhhTzyyCNcdtllvPTSS+zevZtnnnkGEDNSt912G/fddx9lZWVMmjSJu+++mzFjxrB06VIAdu7cyXvvvcfChQsZNWoUR44c4e6772bKlClpm4lIZSVjCL34Ddk9fxpjoZGcghycx5y4G92Mv3M8OQtyYu43VmVkW4UN6zRrxMrI6UDTWhMR0g0WBTqlkD5USLS6uWT4ougUrJOtjP3eWI4/fhxHdfxtW19ti1ZF3V5tp7umG/uHdixTLNgqbAGx3ufz4TjowFpmBR+B5XxxE7ZUT0WhpcsKQP7ZxVD3rliqN21azEGW1hb5/KtjWlthgFbQSyIQLnSsW7eOe+65J+SxWAO+AwcOpPsQJQNJRYW4hmtrxSAnK0vEuUjhJISeCUEdwXUau7shM3NwjkkiuOqqq2hsbGTt2rXU19cza9YstmzZEmi/amtr0QX9Py9YsIBNmzaxZs0a7rrrLsrKyti8eTPnnHPOYJ3CsGX16tWsXLky8HtdXZ0U0/tAM0s0/a4JxwEHrjoXOouOrNlZFCwtiNuIFawXxop2MfujXbpdUpxKB1pcwpnKmaJVDXWiaV8aiRqrNJNrx94O9NP1IS53zQiWNTtLGMFqg2ru+K+FNk1IN3fHVXMnP78n2qXLKduqwaSvPlU6SIuQno7O4R133IHdbufmm2+mtbWVhQsXsmXLFiz+6Wyr1cqrr77KunXrsNvtlJSUsGTJEtasWZPWYj+p6lhA7IsfxKxdzoKcPkX0eIgltKeahBzp/jgFg1kI6TLaJb1oLk8Ns9nc63rRXJ7BbgXp8hz5JNu2hbctqk+lq6aLzg86aXylEV+3D/N4MzkLc2h7p42uQ124m9zkLMjB2+Wlc08nqksFH9TcU4O13ErBlQm0pWFL9TqdJtw+0QHLs3aHLtWbODHqbjQBKiNLgQ44fVoK6YNJVVUVY8eODfw+kor4SZJEcx9KohIcUaVFu8AwE9J9vhE7YbJixYqojvK3336712PLli1j2bJlaT6q3qQ7CqGkpCRkm1mzZsV9bMXFxb2iBrX3jXZs4X3d4H6wJDqpMGIFr+6L5Ug3KqLtcrhGxrU+XBms7OF0cyZpVYOF6lP7bCsSEr7j3H/cJlet5s7evTB9Oj50dPiF9GxTN9QeFysdY9TcsVr9bZUK7Q4ppA82sfpU6SAtQjqkvnOoKArr169n/fr1EZ+fMWMGb775ZlLH2l9S5fBOtcM9EeJp7JJF++4dPTqO4/CLV6YMGe0yEEiXpyQW/W3bgh3onfs68bZ7sUyxYCoyYSoykfuFXOzVdrqOdHF622lQQGfSkT0/G/N4M167l469HXQf66b0+6XxielhS/VauoSob9Z7sBrdoLNBXV3MpXrQI0DlFuigQ7RjIyExYrgSj3tqpA74JJJkCa47Y7OBwSBiXYbNKvLqarHC6MAB0a5bLGLge+WVskEeQIKjEDR3pRaFEG2sp0Uh3HbbbYHHokUhaMK5FoVwyy23xH1s8+fP5/777+fUqVOM9g80tm7dSnZ2tnSZp4H+GrEcdpUiurHhxVunR50QuU9p1PnwAY5YjvQRPMk2VEjm2h8unEla1UCjjf8cBxwiusWij2iMiqV9dR/rRm/WY50uxqGRVjhH239cRjCt5s6xY1BVRWfhFFT/KuSco/ugKL6aOyadCl5o65Rtz5lG2oT0M41UObxT6XCPl3gau/4I7Yk40rVBnzFDNEZSSE8v0uUp6Ytk2zZ7tZ3jjx/H3eRGn60HBQz5Blz1Lrzt3kAB05yCHMwTzLTvaMdcbCbniz0rbgzZBnQVOjp3d3Ki8gSlt5ZimdhH25MVulSv2R/rkpfRJWrJxLFUD3raopKxChyNWWtGMkQYyQM+iSQZgjPSLRbR9HV2DpOaD1qti6YmscLIZhPt9969YuA7UvLwhwmDEYUAwhna0tJCbW0tXq+Xffv2AXDWWWeRmZnJV77yFaZPn853vvMdHnroIerr61mzZg3/7//9P9mnHWLYq+00PNvEjTjI0HmpWRdZXAPh8nQCjmhxCXKSbcDo69qXSIIJHv+Zx5kx22IboyJpXz6nD2+3F9zQ8GIDTb9tCrQVQFz7j8sIptXc+d3vaN8jBCujzoP5/Blw5dK42hKjzgdeaO2UjvQzDSmkD0EGMsM8nsYOiGtWMRrJFBs1WcW5ytWW6UW6PCXpQPWpNP2uCXeTG+t0K+5TblSviiHXgD5Lj7vRjeOAg5yCHBRFwWAzoHapomp7UDvnbnRjP2DHdcKFY7+D7mPdZJ+fHbvtCVuqpznS860OUeXqeN9L9VRVuAsASsaL4/nss9R8NpL0Igd8EkkPwUWTh5WQHlbrAm25d3a2+L2qKq5aF5LUMRhRCABr167l+eefD/x+3nnnAfDWW29x0UUXodfr+dOf/sQtt9zC/PnzsdlsXH/99VFdoZLBQRtvdn/qpg0z7UYzhoLo4prBL6TbIwnpcpJtQBmM7GHJ8CR8/KdFtRiyDein63FUOWja3IR1mjVkvBesfWlRoIpRwTy+x6HesbeDrtou9BZ93PuPywjmr7nT9sZJeBVychWUVXfG3bcw+GOoTndIIf1MQwrpQ5SByDCPp7E78cwJfF0+3M3xzSpGIpmM9Jx8cSwnTyZ/fpLUIF2ekkTpru3GccAhhHFFQWfWoRgUVLeKzqzDkG3A3eTG0+bBkGvA0+YBwJDb85XkbnTTvrMdr8MbcLTrbfq+256wpXrN9skA5Bs7hPhS0PdSPa0dAhgrhfRhhRzwSSQ99GSk6wJCOgwDIT2s1kUIihJ3rQtJahnoKASA5557jueeey7mcU2YMIHXX3895jaSwUGrk1NXWUf3Z934xmfhQCHLGFtcM/gL+Dm9Ck4nBBYXyEm2QWGgs4clw5Pw8V8wiqJgLjXjqHbQXdvdS+dSdAqW8RZObTqFz+nDenZvbarjvQ5cJ13kXpTba/8gipe2bW+jbXtbYjUFdTras8QK/excPSTQdGht1el2KaSfaUgh/QwmVmMHoMvU0fzn5ohxC7FmFcM5dUrcJ+JIzx8t9ldXl+BJSdKCdHmOXNJRH8Hb4cXb7cVsEyMffY4eY4ER10kXxkIjiklB7VDxOX2oqoq72Y0hz4BiEO+rqir2A3a8Di/GQiOqU0Vn1GHMN6LPiaPtCVqq1/J70abk6VqFE33p0j6dSlo7BFA6UexfRrsMH+SATyIRBEe7WC09YtSQz0gPq3XRC1t8tS4kEsngoUWHduzuoH1PO3qrHneDSg5WdAYjEF1c06la26WjoyNISJeTbBLJkCV8/BeO3qbHVefC2+GN+HxfQrwx34hjvyPE8AQ9K5jdp9x4Wj3UPlhLzvycuNMTANraxH1wYfZ40PuF9BYppJ9xSCH9DCZaY6c1Rs7jTpzHnKhOFeUdBWu5FWNh7I5POD4fNDaKnxMR0guLRWN04kSSJydJKdLlOTKJtxhMouiz9OgtYimeIduAoijYym1427y4G93ozDrQgc/lw1HlEFXbp1pxHneiz9aL7ZrcGHLEV5Sn3YO5xCx+V4ir7dGW6jU3t8EuyF8wDe6cHZdDKVhIHzdROtIlEsnwRKv1EBztAsPAkR5W66IXcda6kEgkg0NwdKguU4fepkefpUetd1KBhwYlGxBjyojimqen7WpvF4sJATnJJpEMYcLHf+F47V50Fh36LH3E1/clxAfGha0ejPmi/Qhewawz6zDkGjCNNiWUngA9ccJ9JN72QucTY8am01JIP9OQa57OYIIbOw2tMXKddKEoCopRQZ+tx3nSSfvOdtyN7p7X2/T4un1RZxUBmpvB63+6sLDvY9IGfYUl4l9TOtKHDitWrOCzzz7D6XSyc+dO5s2bN9iHJOkH2iCnY28HhgID1mlWDAUGOvZ2cPzx49irk6/0axlvwVpuFRNxfleRsdBI9rxsTMUm3C2iHVFdKlmzsyi9tZQx3x2DscCIo8qBu9mN6lJRvSruRjd6qxD4/cXU42p7ANDpaPaNAiBvQlbcy3y1dghg/GTxmtpaEbEukUgkw4VIxUZhGAjpWq2LY8d6N7xarYuKipi1LiQSyeAQHh1qzDOiGBUUnYIv24gFLyVuB34jZ0RxTXOcelFCNfHgSbZIyEk2iWTQiDT+01BVFedxJ9YKK5bxloivj6RNhWAAQ55BjBNVNWQFs6HAgM/pwzTahLnULGp0Nblp2tyE6lMDMVOdH3XSVdOF6gs9vmQd6QEh/aQTPvoIamqEk1Qy4pGO9DhJR/zBYKM1dh17O9BPF52X4MbIdcKFPkOPPkOPkquEFAhUUXGecOLt8uJp8+Dz+HAed/b6fLR89Lw8MBr7PiZt0De6REa7SCTpItliMPGi6BQKriyg+1g3jioH5lJRLEYxKRjyDOSW5FL4rUIyZ2aGtKVa1faO3R34HKITYh5jDlkNA307GoJpaRH3+fnxH39gyaAC4yYoKIoQnhobYfTo+PcjkUgkg0lPRrqC2TyMol3Cal1QWtpTUPD48bhqXUgkksEhPJ4hON7PhxE7BvJ8ok6OPkeP87iTrNlZIeJacKHkECE9rKB8SLxLnAXlJRJJeog2/vPavTiPOzEVmChYWhB1bBmuTQXHu6iqiqtO5KP7usSKZn2WHvcpsdLZ0+QJMV4p9KQnnH7zNB27OmKuwE7Gka6qKoompFedhA0bxEReebnow8iixyMa2QONA3u1ndoHaqlZW0PNhhpq1tZQ+0BtvxybQwGtsdNcoM5jzpDGyJBjwDLRgqfdXwjQXyCw62gXbX9v4/S203TXdFP7UC0fXvohh2893OvzSaTQKPQM+orG9gjp0gUqkaSWRIrBJIutwkbp90vJOi8LT7OHrkNdeJo9ZM/JZsKaCRReUUjGxIxeVdvHrxrP5Icnk780H8skC9mfzw4R0eNxNASjtUGBZcFxoA3gFKOCyQQlJeJxmZMuSQdPPfUUEydOxGKxMG/ePHbt2hVz+1deeYXy8nIsFgszZszoVWRPVVXWrl1LSUkJGRkZLFq0iE8++SRkm5aWFq699lqys7PJzc3lpptuorOzM+L7HT58mKysLHJzc/t1nkMOn084h0awg2jYOtKhp9bFeeeJ5Y2HDon72bPF43KAKpEMSbR4Br1NmB20eD+9VY/a5kZBxaD6cDe7cVQ5IoprwUK6JnABPZNsBQVikq2tDTwecR9nQfm4OQO+IySSVBNt/Jc1O4ux3x8bM2YlXJvytHlQPSqeNk+grRhz8xhKbxX7dzeKTHTVo2IuMZM9L3TMqLfpcZ1ycfKZk32uwE7Gka5+VB34udGTI4ocFxSIib7HHxf1GiQjFulI74PgjDfzODNmmxmv3Ztw7tJQRWvsmn7XRNuONjytHgy5BswlwgUKBCJd9Fl6PB0e2t9tx+fwYcgzYK2w4qh24GnxYMgzkLMgB51VF/h8mieXArb4hXR/x6lojOhMOZ3CUZqIm1QikcSmv8Vg4sVWYcM6zZrQah5Fp2CdbGXs98Zy/PHjOKoTdzQE8+mn4n7SpPiPOyCk+4ufTpgg6jV89hmcf378+5FI+uLll19m5cqVVFZWMm/ePB599FEWL17MwYMHGR1h+cP27du55ppr2LhxI5dffjmbNm1i6dKlvP/++5xzzjkAPPTQQzz++OM8//zzTJo0ibvvvpvFixdTVVWFxa+kXnvttZw8eZKtW7fidru58cYbufnmm9m0aVPI+7ndbq655hq+8IUvsH379vR/IANFdTX87neiaF1394h1EPWIUbrhJ6RDoNYFtbUi8zgrSzhNg0Uyny/28xKJZECJlJOsxfud2mbHiguT6sNr95J9QTYFS3vX5emZBNT1jjsPKijPgQPCdWWxxF1QPi7OkO8IiSQdJDP+C36tpk05Djhw1bnQWXRkzc4KaSus06y0bW+j9sHaQJwLYbv3dHpwNbhAB1kXZMVcgd3uLxYatyPd50P93R+AzwFwypkFer3YwfTpYmJv82bRh5F9khGJFNJjkO74g6GC1thFa4yy52WL4qN1TjwtHnQ2HdZpVqzlVhwHReVk82QzniYPXZ90kbMwJ/D5eI41oWClqCi+z0frOJkzFQoKoKlJ9I+kkC6RpI7+FoNJBEWnRC8IGoN4O1Kx8HqFiQhgypT431tbGaMYRbs1fjzs2CELjkpSz09+8hOWL1/OjTfeCEBlZSWvvfYazz77LKtWreq1/WOPPcaSJUu4/fbbAdiwYQNbt27lySefpLKyElVVefTRR1mzZg1XXHEFAC+88AJFRUVs3ryZq6++murqarZs2cJ7773H+f6ZoSeeeIJLL72Uhx9+mDFjxgTeb82aNZSXl3PJJZeMHCG9ulo4hZqaYNy4nsiQvXtFlMgIcjtr9R6GpSNdQ6eDiRMjPyfFLolkyBEtnsFYaKS5JJv6g53YJ1n5ysOlWCZGFteCM9JDHOka8UyyJctQ/Y6Qk4aSYUSy4z+IT4hXdAo5C3LImZ9Dx94OTJhQgpR0VVXpOtQFCNG9rxXY9fXiWOOp6QdAbS1q9SdoQnpjtxWvT0GvU0XkVGmpaEtqa6P3YSTDGimkxyCR+INkG4qhQqzGyFhoJDs/m7a/taEoCjnzczCPM+Np8+BucmPIMaDT6QLRL542v6u91Iy61cFouikq6vvzUVU10HHSGXWMHdsjpJ97blpPXyI5o+grgy5SXuVgEKkjZS414zzupPOjzj4dDseOgdst6jOMHRv/+2oTejqjGKBMmCAel9EuklTicrnYs2cPq1evDjym0+lYtGgRO3bsiPiaHTt2sHLlypDHFi9ezObNmwE4evQo9fX1LFq0KPB8Tk4O8+bNY8eOHVx99dXs2LGD3NzcgIgOsGjRInQ6HTt37uTKK68E4M033+SVV15h3759vPrqq32ej9PpxBkUvt3Ry0Y4BPD5hPDa1BSarztCHUTBGekWyzDKSI+HoSp2SSRnOLFykjnmpIEMvAvHkDE5+thQmwTslZEeTKxJtmQZqt8RctJQcoYRjxDfVya7IdMARaDPjGwMC16BrRmvtDFfn3R0oHa5Ar960HG6O4MCq0M8YLMJEWso9oUlKUEK6TEYqPiDoUJfjZF5tBm9RY9prAkU8Dl9qB414NpUTApqh4rPKTo/epset92FFW9c0S6qtycMXTEqjB0LH3wgC45KJKmmv8VgBvpYtY6UvdrOsYeOxSwWE8yRI+J+0iSx2i5etAGcFu2i1aySjnRJKmlqasLr9VIU9gVZVFTEgQMHIr6mvr4+4vb19fWB57XHYm0THhtjMBjIy8sLbNPc3MwNN9zAr371K7LjXOe6ceNG7r333ri2HTRqa4UQMW5caJE6GJEOomGdkR6LoSp2SUYkHXs7OPmLkyh6BUWvgJ7Az5F+Vwz+xwziZ+2xwM3Y87POqOv53aigM/l/1342Bd2bdejMOvGeQ5xoqwqPmbN4lQJ+NCP2qsKoxUbTzVD8jpCThhJJVGKtYM66IIuGFxriWoGtjfHivqyzsvCZrIFffUCj3dojpNvtotOVldWv85MMXaSQHoOBjD8YKiTSGOnMOhSDgupWUcwKqksVnUKzGLR47V46XToc6IkQ9doLrdMEPUI6SCFdIkkHqYhOGUiSqVeh5aMnEusCocVGQTrSJWcey5cv59vf/jZf/OIX437N6tWrQ9zydXV1TJ8+PR2HlzwdHUJFtkVp39LlIBqkJfnBYtSIEtKHotglGbF0He7ixH+dGOzD6EGPENUtQbcMHfoMPboMHTqrDr1Vj96mR2fToc/Uo8/Ui2jSLP99jh5DrgHjKCOGUQYMeQZ0htS2SZFWFb52jYVaFIISxCLSZ7RLuhis74hoyElDSQI0vNjA6TdPh0zY6Yy6nt9NQZN3QRN3OnPYpJ1Jh2LumbzTWXS92hzFpPRKbBgsokXBAHTs6uhzBTajLfh9JPE70sePR51cBog+Fig0OazazuH4cVG3QXNjSUYcUkiPwXCJP0g18TZG+hw9xgIjrpMuDAUGPO0ezCVmDDmGwOfzGVmcwhKfIz1YSDdIIV0iSTf9KQYzkCRbr0JzpCcspEfISAfpSJekloKCAvR6PQ0NDSGPNzQ0UFxcHPE1xcXFMbfX7hsaGigpKQnZZtasWYFtTp06FbIPj8dDS0tL4PVvvvkmf/jDH3j44YcB0efx+XwYDAaeeeYZ/uVf/qXXsZnNZszmnhV87QOqfsRJVpZQk+32yBWl0uEgGsQl+cO+2Gg0hprYJRnRWCusTLh7glg56xMraLUb3gi/e0RUpeoNuncH/e723/zb+dy+nsfc/t9d/p9d4ucQvOBz+PA5fCk9T0OeAdNoE8ZCI6YSkzAtlJqxjLNgmWQhY2qGiEpIgPB4hhN+sSro6ykig+ZIH4zviFjISUNJArRtb6P+2fqBeTMFMXGXISbudFb/ZJ5Nh94mJvK0STx9th5Dln8iL8eAITfoNsqAMd+IzqrrlzAfLQomnhXYx46L97XZIC8vzjfU6VAXLYGHmtBa4qYOM7S1CRG9oEAUP5YTXCMWKaTHYDjFH6SaeBsja5kVd6Mb56dODHkGMsoy8LR7Ap/P37wFqCiJC+nSkS6RDAj9KQYzUCRbryJpIT2KI725WYyhomk3EkkimEwm5syZw7Zt21i6dCkAPp+Pbdu2sWLFioivmT9/Ptu2beO2224LPLZ161bmz58PwKRJkyguLmbbtm0B4by9vZ2dO3dyyy23BPbR2trKnj17mDNnDiCEc5/Px7x58wCRxe719sTW/f73v+fBBx9k+/btjE2k4MBQY/x4IWLv3Rvq7gPh/Dt4EMrKxM8+X/8HQIO8JN8bFu0yYjLSh5rYJRnRZJ6TSeY5mYP2/qrqF9WdPhGr6VTxdfsCv/u6ffi6xM3r8Ip7uxev3YvP7v+5w4unw4O33YunTdy7T7vxnBY/A3haPHhaPBA5WQwA01gT1mlWMmdmkj0/m5wFOZjHRo5A7X0ecPKk+DleId2LbmCF9FjfEal2mcazUklOGkoSoOCKAlGjzhM6aRdxss6torr8k3VBk3aBdkb7uTu03Qmg9kzoeZo9/T52xaxgzDeKibxiE6YiE6ZiE+YxZjGpN15M6hlHGxMS3KOtwM6clUn2vGxUj8pnu7tQsDBhgtJrvioW6oTJQBM+HeCDpppOsDaLNmLpUhm5NMKRQnofDLf4g3QT/nn4un1kTMrAWyLyij0tnsDnk39FAfueFZ9PXEK6J0hI10shXSKRCJKtV9FvId2fkZ6TI25tbWLMI/tFklSxcuVKrr/+es4//3zmzp3Lo48+it1u58YbbwTguuuuY+zYsWzcuBGAW2+9lQsvvJBHHnmEyy67jJdeeondu3fzzDPPAGJi6bbbbuO+++6jrKyMSZMmcffddzNmzJiAWF9RUcGSJUtYvnw5lZWVuN1uVqxYwdVXX80Y/3r7irB/8t27d6PT6TjnnHMG6JNJEzqdcIIfOyaWxJeWCiHi2DHYswdcLiFu3HNP/13jQ2BJvsc/6B1xGekDKXZJJP2ln9FOiqIE4hZIw9yQz+PDc9qD+5QbV6MLd4Mb50knzuNOnMfEretwF+5GN646F646F61vtsJPxevN48zkLc5j9LWjyf1iblSD2enTPZN4fQnpwcVGB3RxU7TvCLs9tS7TeFcqyUlDSQLkfSWPvK/Ea6lOHFUV4ru3y9szgefwid8d/sm8Tm9gIs/b6b+1B03ktXvwtAbdWjxC1HequE64cJ1wYf/AHvUYdDYd1jIrGVMzsE61YpthI2tOFpbJlqgCe/gKbFeDi/ad7SKyuNtLV62O5ZgwZ+TQVZMZ9+rsgAnUqAcnNH3+CvjB4gGL75MMLlJIj4PhEn8wUET6PMylZpzHnSGfT1u7gstfzDgeIT1Q4M8oMrekkC6RSCC5ehWq2v9oF52xpxM0fjx89JGId5FCuiRVXHXVVTQ2NrJ27Vrq6+uZNWsWW7ZsCRQLra2tRRfUGV+wYAGbNm1izZo13HXXXZSVlbF58+YQgfuOO+7Abrdz880309raysKFC9myZQsWTUUFXnzxRVasWMEll1yCTqfjG9/4Bo8//vjAnfhgUlEhnOCaiFFdLQoqmEwwf7642FPhGh8CS/I93T3xCGbzCBLSB0rskkj6yyBGO8WLzqDDVGjCVGjCRnSDmPu0G8dBB45qBx3vddC+o53ODztxHnNy8hcnOfmLk5hLTYy+1MKYpToyKnJDBCXNjT5qVE9bFI3gjPQBN1uHf0fU1YkDTpXLNJGVSnLSUDKEUBQlkJ2eKlRVxdvpxd3sxtPswXXKhavBhavef6tz0X2sG+cxJ1HJ3O4AALgOSURBVK6TLnx2H537Ounc1xmyH32OnqzZWeRemMuoxaPIviA7pDCztgLbXm2n8beNgZpbui4d3h12LqYV26FGDt8qnOoFV/ZtmA2YQA06IaQbimFiyj4ayRBHCulxMhziDwaSSJ9H8O+qT6VuTxeT8KLP1GM2WYDYEw/hLlBNSG9qEg4Gc3wrByWSuHnqqaf48Y9/TH19PTNnzuSJJ55g7ty5Ubd/5ZVXuPvuu6mpqaGsrIwHH3yQSy+9NPC8qqqsW7eOn//857S2tvL5z3+en/3sZ5SViWIkNTU1bNiwgTfffJP6+nrGjBnDP//zP/Mf//EfmEymtJ/vcCVavQpVVfG0enBUO8iclYm5tKeRaG4m4GKaNCmx9wue1NOYMEEI6bLgqCTVrFixImqUy9tvv93rsWXLlrFs2bKo+1MUhfXr17N+/fqo2+Tl5bFp06a4j/GGG27ghhtuiHv7IU9FhXCC19TAY48Joef883uE11S4xofAknxNSPfpFAyGAYp2GajCqukWuySS/jLI0U6pxjjKSM7ncsj5XA4lNwpLuafTQ/v2dk79+hSNv27AedzFsWdc1P23m0mz/o/Sr7tRFnwOioo4+UEhUNynGx1CM9IHpdyG9h2R6rYs0ZVKctJQMsJRFAVDlgFDlqFPEdrn8tF9tBvHIQddh7pwHHAIUf3DTrxtXlrfaqX1rVZq7qnBMMrAqEWjKLq2iPzL81H0Sq+aW54mDx3vdUC7l2ZMZJs9uJvcdLzfQfexbkq/XxpTTA+MF/3aVVNTqj4VyXBACumSlGOvttP0uyYa/+rgO3ixKHpqH7D2ObMXnkuclycGfU4nnDiRuBgmkcTi5ZdfZuXKlVRWVjJv3jweffRRFi9ezMGDBxk9enSv7bdv384111zDxo0bufzyy9m0aRNLly7l/fffD7hBH3roIR5//HGef/75QKTC4sWLqaqqwmKxcODAAXw+H08//TRnnXUWH3/8McuXL8dutweK+kl6E6leha/Lh/0jO93Hu9EZRIGbYw8dC7Qzmht97FjISHAONHxSD2TBUYlkxKGJFKdPC8dfuBDRX9f4EFiS73VqbZk4t7Q70gfafZsusUsi6S+pinYaqImpJDFkGkScxLgGypRf07I/k+PHLqDteAFH9syjseoo0wo3YKvI4GTnUuB7lGTbIYbzHQax2GgwOl3qVwsls1JJThpKJADoTDqs06xYp1lDHve5fdj32+nY1cHprac5/cZpPKc9NL7SSOMrjZjHmxnzvTGM+sqoQM0tAPsBO16Hl1bViBsFQ66Ct9OLaawJ1wkXTZubsE6zRk2h0NopnVEK6WciUkiXpBR7tZ3jjx/H3eTGYTBzDDNTMr107O17Zi9cSFcUGDMGjh4VfQYppEtSyU9+8hOWL18eyCKurKzktdde49lnn2XVqlW9tn/sscdYsmQJt99+OwAbNmxg69atPPnkk1RWVqKqKo8++ihr1qzhiiuuAOCFF16gqKiIzZs3c/XVV7NkyRKWLFkS2OfkyZM5ePAgP/vZz6SQ3gfB9Rnad7Zj/9iO6lGxlFqwzbChs+pC2pkjR0Q7k2isC/Rui6Cn4Kh0pEskI4h0usaHwJJ8ryt0kJdWIX2w3LfpELskkv6SiminYRALAwQmDfStpyj8cgEFfMzJv2Vy5G9n0941id21a5ia8RonC8VS45Lmj6E6O+o5aMVVYRCKjaabZL9z5KShRBIVnVFH1qwssmZlMebmMfg8Pjre66Dpd02cfPYkzlonR+86Ss26GixnWShYWoC3zYu7yY0hx0D3EdFGm2xKoAirudSMo9pBd2131FSKQBSoSby+sXFgzlcyNJBCegKcfvM0nR90ohiUnptR3OuMup7f/TedUSd+Nvl/9heKCdybxb3OrAvJcBquhC+X6XxPwQcYcgxYp+txVDlizuxFyiUeO7ZHSJdIUoXL5WLPnj2sXr068JhOp2PRokXs2LEj4mt27NjBypUrQx5bvHgxmzdvBuDo0aPU19ezaNGiwPM5OTnMmzePHTt2cPXVV0fcb1tbG3l5sQvDOJ1OnEFr8Tv6GFV0HelC9anozDp0Fl3gXjEpCVU6H2rYKmxklGXw6epP8XZ5sVZYMeYaA6lR+qB25lO3FVCYPDnx99HaomAhXTrSJZIRSDTXuKqK6sLNzeDxRBc9YjEEluR7naExVWkT0odAYdWRSktLC//+7//OH//4x0A9g8cee4zMzMyor7nooov461//GvLYd7/7XSorK9N9uBIQ18OBA1BfLyqVq2pvMb2vSbrhFAsTNmmgqCpjdH8mb/KrfNJyPc0t0/jk0KW06USE4RjlJGx+M3p74Ov50YuCYzCiXSKRitUB/VmpJCcNJZK40Bl05MzPIWd+DhPXT6Tx5Ubqnqqj470Ouqq7qG+uJ+fiHDHe0ys4/TX9zHoVRRUZ8HqbHledC2+HN+r7aBN+BrN0pJ+JSCE9ARpfbeTEUyfSsm/F4C/eYNGF3PQZenQZOnQZOvRWPTqr/94mLnB9pv+WpceQZUCfrceQYxC3XAOGUQZR5X0A6K7tDiyXURQFu7/gcmamyL/qa2YvUi6xLDgqSQdNTU14vd5AQT+NoqIiDhw4EPE19fX1Ebevr68PPK89Fm2bcA4fPswTTzzRpxt948aN3HvvvTG3CebjKz/G/lHkiuchbYw1qG3J8LcpNn87o7UttqA2JtvfxmQb0OfoRRuTa0Bv0w+YQO887sTd4MZ2jq1X4dHgdqbe0Q1k9MuRHjypJx3pEskIJJJrvLFRiEKNjdDSAoWF8L//C//0T4kLV4O8JD/ckZ62jPQhUFh1pHLttddy8uRJtm7ditvt5sYbb+Tmm2/us8bB8uXLQ+okWK3WGFtLUobmIt+9Gw4eFKL3mDGinSks7NkulmA63Camwl3WbW3Q1ISlwMo5Y/6XDz/8Dqdbp1BRowdUSorVmO2BNh4EEe3idILLJepBDxqpWh0wBFYqSSRnEnqLnuLriym+vphTL5/iwI0HcJ9y0/SbJoxFRjxGPaBHh4quy4OxxIwhx4Cn3SO0uCx91H0HhHSLaIelkH5mIYX0BMi+IBvPtR5Uj1hyFnLv/9nn9onH3GG/u1R8Ll/IfTDaPnx2X5R3Tx6dTYcx34gxz4ix0IipyIRxtLg3jTFhGWfBPM6Meay5X1WYvR1evN1ezDYxUuv0F1PW+lV9zexFyiXWhPQT6Zm/kEgGjbq6OpYsWcKyZctYvnx5zG1Xr14d4oavq6tj+vTpUbfX2/Tos/X4nD5UZ2hb4+v24etOcTujF0WoDPkG0dbk925rzGPMmMaaMI81o8+I3inpi/B2JhhtObCrwYW9zoGChSlTEhf4w4vHQI+Qfvy4MKga5LenRDL8CXeNW62iqrDmEh09GmbMgH37xMWfjAt0EJfk+1wqCj3LjtPmSB8ChVWHAh0dHbQHVUY0m82Yzb2/q+KlurqaLVu28N5773H++ecD8MQTT3DppZfy8MMPM2bMmKivtVqtFBcXJ/3ekiQIdpFPmCDqL9TViUFMWxvMmyfE9L4E0+E2MRXusnY6RUfJaERRYOqE3/Je222UdplYQj0l+U7RXkRpD7TxIAghHcSm+fkDcja9SeXqgCGwUkkiOVMZfdVojMVGqv+5GtdxF+4TbuhQMWJhlNGL3qrHWm5FRcV53EnW7Cws4y1R96eNFw2WnnbK6ewxLUhGNlIKSABtNisVqKpfOO/2BcQun9MXELkCty4f3i4vPof/Z4cXr92Lz+7Da/fi7fTfOrx4Ojx427142jx4Wj1CsFbBZ/fhtDtx1vZtQTKXmsmYmoF1qpWMqRlknptJ5uxMjKOMfb5Wn6VHb9HjtXsxZBtCHOkAXrs35sxepFxi6UiXpIOCggL0ej0NDQ0hjzc0NEQdeBYXF8fcXrtvaGigpKQkZJtZs2aFvO7EiRNcfPHFLFiwgGeeeabP4w0fjAcP1CMxe8fswM+q6p/IC2pXvF1efF2+wE1rY7z2nvvw9iXQzrR78LT525rTYmIRL7ib3Lib3HTR1ef5GPIMWCZayJiSgWWyuLeWW7GdbcOYF7utCW9nNNyNbuwH7LhOuPA5fFQ0nOAaOpmkK6CvolbhRIp2KS4GoxHcbjEmlmYhiWSEoLnGf/tbcWtqglGjhOCluUhVtX8u0EFaku9zq+ghsDIxbUL6ECisOhQIn+Bet24d99xzT9L727FjB7m5uQERHWDRokXodDp27tzJlVdeGfW1L774Ir/61a8oLi7ma1/7Gnfffbd0paeTSC7y6dOFsuJwCCG9qgpmzhSDmliC6XCbmAp3WZvNwm3gdoPJRIarholT/sqnh7/MLRwhx30aMqO3B1ofDMBoVnA5B1FIj7U6oKJCrDyorIRbbxVtfDzfDbJ4qEQyaIy6cBTn/t+5fHr7p7S83gIdHkpw0mnNIOt8G4pJwVHlwFRgomBpQdRCo9DTVhktCno9eL0iETDGHLdkBCGF9EFCUXpy1EnTuEL1qUJUb/HgbnHjbnbjPuXG1eAS9/UunHVOnMecOI878XX7cB4XP7e+2RqyL8sUC1nnZzHq4lHkXZqHZVzv2TnLeAvWcisdezvQT9fT2SkaHptNiHl9zexFEq+kkC5JByaTiTlz5rBt2zaWLl0KgM/nY9u2baxYsSLia+bPn8+2bdu47bbbAo9t3bqV+fPnAzBp0iSKi4vZtm1bQDhvb29n586d3HLLLYHX1NXVcfHFFzNnzhx++ctfokuz60RR/LFRZh3kpHbfqqri6/LhOd3TxniaPaKtaRRtTeB2wiXamS4fnhYPnS2ddL7f2WufphITtnNsZF2QRc6CHLI/l40xv0dcD29nFEXB3eimfWc7XocX1aNinGjhs1oLZXRg3dKN/dzoRY4jnleEST2dThiRPv1UmMCkkC6R9JNU5M2miooK+Pa34f334ZxzhGKTk9MjmgxFF2gcaEK6PsyRnvJoFxlXAEBVVRVjtY4r9MuNDiIybvTo0SGPGQwG8vLyokbGAXz7299mwoQJjBkzhg8//JA777yTgwcP8uqrr/breCQxiOQiLywULvQDB8QM/Kefikm6Cy6ILZjGmphSVbGvri4hzvt8g+9eDndZjx0LeXniujcYwGaj9Pw2/nLYxlnYsezOhO9XRG0Pgh3ptmwFe+MgzhlEWx2gRYCdOAH794tzP//8+KNeZPFQiWTQyJyeyYw/zuDADQdo+J8GrHjpyDXiaRFxLlmzsyhYWtDn2DF4vJifD6dOiTk3KaQPbe6//35ee+019u3bh8lkorW1Nan9SCF9BKPoFIyjjBhHGcmYErnasIaqqrgb3XQd6aLrUBeOQw4cBxx07u2k+2g33UfErfFlUY7YNsNG3qV5FF9fHGhkFJ1CwZUFdB/rxlHlwNthRoceK14cVc4+Z/Yi5RJLIV2SLlauXMn111/P+eefz9y5c3n00Uex2+3ceOONAFx33XWMHTuWjRs3AnDrrbdy4YUX8sgjj3DZZZfx0ksvsXv37oCjXFEUbrvtNu677z7KysqYNGkSd999N2PGjAmI9XV1dVx00UVMmDCBhx9+mMag8t7DcQm2oijorXr0Vj3msX0LBqqq4mn14DzmpLumm65Pu+j+tJuuw13Yq+w4P3PiOunCddLF6a2nA6/LmJZB3pI8Cq4oIOcLOSHtjGmsCXuVHU+bBwyiuLFnjA07BrwmPbqO2EWOIx5nhJgpEGOcTz8VBUcXLkzgg5JIJKGkKm82ldjtQvSZOBH0EVbODTUXaByo/mXHenNoRnrKHekyrgCArKwssiM58sNYtWoVDz74YMxtqqurkz6Om2++OfDzjBkzKCkp4ZJLLuHIkSNMSaZwiKRvornICwvF/39zM3zyCdx8M3zlK7GvhWgTU42Nou08ckQI7E8/DX/72+C2mxrhLmuzWRy3Xg/Tp9OZVcSDlFPJHrrqx9GUO4OCKJ9BcB8sK1vhVCP0sRAzfUT6uzY2ws6dYqVBdrY4T5st8agXWTxUIhk0FJ3C1GemUv1aJ3ktdnJVN+PvKsOQa8Ay3hLXmDFYSC8oEEJ60NBeMkRxuVwsW7aM+fPn89///d9J70cK6RJACGKm0SZMo03kzA+1rbqb3XS830H7u+20bGmh/d127B/ZsX9k59iDx8i/PJ9xPxpHzhdzsFXYKP1+KU2/a8LwOwfjcGFx6cia2/fMXqRc4mAhPVLRe4kkWa666ioaGxtZu3Yt9fX1zJo1iy1btgSKhdbW1oa4xRcsWMCmTZtYs2YNd911F2VlZWzevJlzzjknsM0dd9yB3W7n5ptvprW1lYULF7JlyxYsfhvg1q1bOXz4MIcPH6a0tDTkeFQ1NMt8JKIoPZN7medm9nre0+HBUeWg84NO2t9tp217G10Hu+g62EXdwTrqHqvDMMpA/uX5jPrKKLo/66ZzTyfdn3ajs+owjzFjLbdytEU42EflKZjHxS5yHIlIjnSQBUclkpSQyrzZVDIC40kChbDMac5IBxlXkAA//OEPueGGG2JuM3nyZIqLizl16lTI4x6Ph5aWloQm3+fNmweIAudSSE8TsdoPRRHZcEVFQiAPFpCjrcwJn5jq6oLt20UB5Lw8WLBA1HQY7HYzmHCXdUODEJwPHuTkx00cJovf60v4J+9JTvxBR8EtkXcTGA8alUBzO2jzl+F/V1UV7ZvDISZJnE7xt9VWMQ21QrASiSQqeouel8rO5sadu8mpbafl9RYm3DUh7tcHT/qNGycu/08/hUsuSdcRn3mkuvYMwL333gvAc88916/9SCFd0ifGfCN5X84j78t5TLx7Iu5mNy3/18Kpl0/R/Mdmmv8kblkXZFH2RBnZ87JRS638/D+6seLl3zfoyZ/e98xeJPFKWxrjdIq+46AVmpGMSFasWBE1yuXtt9/u9diyZctYtmxZ1P0pisL69etZv359xOdvuOGGPgfPZzKGLAPZ87LJnpfNmJvFxe9udtP6t1aa/9BM0x+b8DR7aPifBhr+p4GseVmMumQU7g43tnJ/vroCp4+I/eXl9V3kOBJazFTw6hjoWYX82Wf9P1eJ5IwkVt7s9OmDK0KMwHgSrS3TD4SQDjKuIE4KCwspLCzsc7v58+fT2trKnj17mDNnDgBvvvkmPp8vII7Hw759+wBC6rdIUkwy7UdfK3O0ianqalHwuL0dpk4Vz2n/P+luNxON4Ap2Wc+YAV/6EtTWcvJNH2yBw2OK4NhJ7NX2qLsIRH0alMCcxKA50sP/rm1t4vsrJ6fnwEpKeqLAhmEEmERyJvN+k5V2prKKAxxde5RRi0aRPbfvlWUQOl6cXgb/93+iOZakjlTXnkklaevZPvXUU0ycOBGLxcK8efPYtWtXzO1feeUVysvLsVgszJgxg9dffz3keVVVWbt2LSUlJWRkZLBo0SI++eSTwPM1NTXcdNNNTJo0iYyMDKZMmcK6detwuVxpOb8zGWO+kaJvFzHj9zOYWz2Xku+WoJgVOt7rYO8X93Lyv09yqlGhgQzqMzLJPzsj4eUxGmazWBEJMt5FIjkTMeYbKbyykPJflrOgfgGz/jqLkn/1tzk7O6j9z1o63+3EecwJ/qajpUXcjxrVd5HjSAS7oYLRHOlSSJdIkiRa3iz0FiEGGs0FWlAgRkJtbeDx9BQJHIbxJMGFsKAn2iXlGenBaELajBnxF9+TRKSiooIlS5awfPlydu3axTvvvMOKFSu4+uqrGeN3mtTV1VFeXh4YZx05coQNGzawZ88eampq+MMf/sB1113HF7/4Rc4999zBPJ2RTaLth7YyZ+9e8dy0aeJ+717xeHW1EMxXrYLvfldcS4sWwRe+0COiQ3rbzepqeOABWLsWNmwQ9w88IB6PF397cDJjsvh9nCh46/zMidce2eAQPB4cdEd6+N+1uRlcLlFVsLFRrAooL+/5PrPZxKTIMIoASxX3338/CxYswGq1kpubO9iH0ydSq5Jo84T/RxG2ywvBCyeePhH/64PGi5re249UNkkEqqqqaGtrC9xWr1492IcUIC2925dffpmVK1eybt063n//fWbOnMnixYt7LU/U2L59O9dccw033XQTe/fuZenSpSxdupSPP/44sM1DDz3E448/TmVlJTt37sRms7F48WK6/baaAwcO4PP5ePrpp9m/fz8//elPqays5K677krHKUr8WKdZmVY5jfm18ym4sgDVpXLwXw9y/PZD6PFRNMqF8lmNaKn6IFKxUZA56RKJRKAz6Mj9Yi7Tfj6N+Z/NZ8LaCRjyDXg7vTT/vpnmPzejelRO++PV83J9OA46MIwygE8UYI6HaBnpMtpFIukn0XKENQZbhNBcoOedJwSTQ4fE/ezZQyM6IVH8/SqDRXT3NUe6xyNukqHPiy++SHl5OZdccgmXXnopCxcuDNRmAXC73Rw8eBCHwwGIYupvvPEGX/nKVygvL+eHP/wh3/jGN/jjH/84WKdw5hBv+xG+Mic7W2SJaytzmpqEw1wrJJqTAxkZYplupIzLdLSb8Qj9CXDCr03lTjBhLBDxe45DjojbBgvpmiN9UHXp4L+r3S5iXTo6hBN93rzQiY2BjADz+aCmBj76SNzHMdZOJ1ru8C23RMnsGUJIrUoCcPIkuN2g1ytM+ncxOd3yektS40WteZeO9NSi1Z7RbtFiXVatWoWiKDFvBw4cSOmxpSXa5Sc/+QnLly8PFO2rrKzktdde49lnn2XVqlW9tn/sscdYsmQJt99+OwAbNmxg69atPPnkk1RWVqKqKo8++ihr1qzhiiuuAOCFF16gqKiIzZs3c/XVV7NkyRKWLFkS2OfkyZM5ePAgP/vZz3j44YfTcZqSIEyjTZz9m7P57P7PqFlbg+/VEzyCnd87MlDv/k+6i2fiXfhl9DPLohZwiFRsFGDsGJUPPlCo23kcKjxymbBEIsFUZGLSvZMYf+d4Dt1yiIYXGujc1Un3kW487gKK8JH7cSfdJhV8UHNPDdZyKwVXxlGFPcqkXnC0i6zZMPxJVdV2SQIMhxzykRRP4hHCisESGu0CwpVukAGPQ568vDw2bdoU9fmJEyeG1FgZN24cf/3rXwfi0CSRiKf9SGRlzsSJA99upiGC6+RJcV9SAtYKK21/b8NR7SDrvN7HHDwe1E5p0KJdNLS/a00NPPaYKBx7/vmh5z+QEWD9LNg9lHOHBwKpVUmgZ4VxaSnkXZSDPlOPq95F575Osmb33Z4Gjxe1y+7YMeho85F1egT0IYcR8daeSSUp/4u6XC727NnDokWLet5Ep2PRokXs2LEj4mt27NgRsj3A4sWLA9sfPXqU+vr6kG1ycnKYN29e1H0CtLW1kZeXF/V5p9NJe3t74NZxBi7DSiWKTmHiN7s4Z8nf8eq8zKSN77haqP10PjW/tlGzch81t+6j9oHaiNl4kYqNUl3N2MZ9ANS9sj25ZYUSiWTEorfqqXi+grLKMnQ2HZ5mD19or2cOrZjNkD0/m6wLsjAUGOjY28Hxx4/HzOaE6MVGx40T93Z7T3yMZPgynNxTIwYtb/bYMSE6BKOJEBUVqREh+uPWGyHxJIo3crQLpDEnXSI50+mr/Uh0Zc5AtpuQlgiuECG9XMS7OA5EcaQHZaRrCSHNzQmdQXrQ6WDyZPje98QSxerqwYkAS8FqgenTp5OTkxO4bdy4MX3HO8SQWpVEQxPSJ0wAnUnHqC+PAqD5tfganODxYl6eqCcNcGD18/2LxJIkTGFhIeXl5TFvJpMppe+Z8la+qakJr9dLkfaf5KeoqIj6+vqIr6mvr4+5vXafyD4PHz7ME088wXe/+92ox7px48aQL5HwMHtJgvgdDAWZH/LBTAUfMMnhoqWuCEOJFaupAUPTUTrejyxm9RKv/B2FsY5DANSZJ/VrWaFkYBhuGXmSkcHY745l3ifzMM/NQQfk4Sb/y7lYJllQ9AqGbAPW6VbcTW6aNjfFXLYXLdolI0OMH0Eu3RtoNPeUdnOmIOT53nvv5Qc/+AEzZsxIwRFK4mKgcshTke07EggT0g0GkSABac5Jl0gk0Ql2mEci3GE+kPUbfD4hotfXi8yDcOEekoqSCXekAziqIwvpwbnDU6aIxw4fjv8U0s5gRoAlEgsUg6GcO5xupFYl0aipEfdaXeC8S8WkRqJCupamMH2iaNOr3rOnJBJLkh5qa2vZt28ftbW1eL1e9u3bx759++js7ExoP8PTYtMHdXV1LFmyhGXLlrF8+fKo261evTrkS6RKKiP9I8jB8IF3FIfIBKCrxYbe7EPJycLQ2YB1rCuimBUSpxDUURg7WaxFruvMSbijIBl4pMtTMliYi4x0fSuPNgwYUGl5tSnQrgAoioK51Iyj2kF3bXQ7pjaIC4+ZArjgAnHfR00iSYo5k91TI450ixApzvYdlvjd+Io/2sUYFOmixbtIR7pEMgj4fOI2apQYM4WPY6I5zAdCvNUmIJ95Bg4ehDfegH/8QxTVDCaJKBktI33MmB5HerTVgcHGqqlTxWOHDiV2KmlHKwS7fj3cfbe4v/PO9NfRSNFqgaGcO3wmILWqoUGwIx0g/9J8ADp2deBq7LsIbEiags9HBeJaqDbNTHqSS5J+1q5dy3nnnce6devo7OzkvPPO47zzzmP37t0J7Sfl6YgFBQXo9XoaGhpCHm9oaKC4uDjia4qLi2Nur903NDRQUlISss2sWbNCXnfixAkuvvhiFixYEFKMJxLheWDtgx7ANswJWqpYeyKfibQzTenA1WHGccqGrdAHHR0oLjfm0qyAmJUxMQMIc6QHdRTGNgjHQ127PxcwUn6gJCnO9Iw8yQjCnxd57OVx1HEuF3AaTrpoefUE+d8aG9hMb9PjqnPh7fBG3VW0jHR8PuaWtfE7RrHrLTv8IGPYRj4MN6qqqhg7tufv2N92SjLIpCuHPA3ZvsOOoOxcnedfADB9uBOqx0BFRcAIO2hCus83MvLnJZJECc61PnUKPv1UXAtaprbdLkT0aA7zdNZv0CYgm5qEqnT6NNTVCQW8ra2nqGaSOeAhjnSLENK7PunC5/GhM4Qef/CqQE1IP3pUGOSNxv6fasrQInwGknhigerqUladdTByh9ON1KokGuFCunmMmcxZmXTu66RlSwvF34n8/6ARMl6srWW6+jEwh6qm0aEbSu1qSPHcc8+lRKdKec/VZDIxZ84ctm3bFnjM5/Oxbds25s+fH/E18+fPD9keYOvWrYHtJ02aRHFxccg27e3t7Ny5M2SfdXV1XHTRRcyZM4df/vKX6GTHfGDxL1V0tXdT15wLgGWMWCJx+ugofF1usa7YbEJv0+Pr9oWIWSFxCkEdhbHZfiG9I8j5kI4K9Wcg0uUpGREEOVBrGEMHJhqyAVQ6qz10/u1kYFOv3YvOokOfpY+6u4gZ6X6n1tz3KwF476/2MzMqYpCQ7qkRSDpyyNOQ7TusCHPja5+Aqel4wI2vOdIHJdpFRu5IRgLJ1F8IXylzwQWgjWHffRd2747PYZ6OdjN8AjInpyc2RFF6ImRaW5OKknE4eoqFlpSAZbwFXYYO1aXSfbT3jF6wOFVSIoZ8Xq8Q01NKf+poDBaJxgL1k8HIHU43UquSaIRHu0BPvEvL630XwwoZL3Z0MD1D7LC6qaD3xlK7GnGk3JEOsHLlSq6//nrOP/985s6dy6OPPordbg9URr7uuusYO3ZsQLS79dZbufDCC3nkkUe47LLLeOmll9i9e3dglk5RFG677Tbuu+8+ysrKmDRpEnfffTdjxoxh6dKlQE/DNGHCBB5++GEag5ahRZtdlKQYfzGcqrdO064aQaeSP6aNhqYMvE4DHZ9ZyZlhhZwcvO29xayQOIWgjsLYLNH7anLYcHr0mA3e1FeoP0ORLk/JsCdsALjng7FkkcG8DA85o9pp+yyP5r86MZW7MBYacR53kjU7C8t4S9Rd9spID3JqzZlahvKGj6Odo2l89wiFxx5PfyamJG5GontKkgAD7NYbUkRw4+v8yVamMXmBZcVmczmgDLwjPdjxOm6c+FvY7UJYPHZMtqOS4UGwq7y7W4xFystFhnm0/99oK2UmTRJWyN27oawMbr11cIobR5qALCwULvQDB4Qr/dNPRRzNBRcIET2Ba1Vzo2dkaNq8gnWalc59nTgOOLCWWUO2D84dVhTx0ezbJ+JdNId6XMRa/ZLM33EooBWe3bs39H8Jkl4tkCpqa2tpaWkJyR0GOOuss8jMzBzw44mF1KokqtrbkQ6Qf1k+tf9ZS8uWlogrZkL2ETxezMqiorAJgE9Pj6LLbSDD6OnZWGpXI460COlXXXUVjY2NrF27lvr6embNmsWWLVsCBRhqa2tDZuAWLFjApk2bWLNmDXfddRdlZWVs3ryZc845J7DNHXfcgd1u5+abb6a1tZWFCxeyZcsWLH5rzdatWzl8+DCHDx+mVKsG50eNVChFknr8xXD2bd3FKSx4M924Oozkjj1F86djaGseg21cJnqIKGaFzOoFdRTyKrIw6z04vQZOdGQxKff0oHYURhKay7MvVq1axYMPPhhzm+rqasrLy1N1aBJJfAQNAH3o2H58PDZUbhm1FwMezFl2nB022t9qIuOcHEwFJgqWFqDolKi7jFavgenTyVEUphU0c6CpkPcyL+bSpt/3REVoxyMjCwaNwsJCCgsLB/swJINFsFsv0nfbSB7IhIlhqtqz7NRs9ATc+Ba9GzANrJA+kJE7MjpGki6SnQyKtVJGpxP/983N4ufB+F+NNgFZWCjc583N8MkncPPN8JWvJHyMmpA+ZkzP6VvL/UJ6tQO+Frp9T7FRoKaGqUW57COXQwd9cHmc7x1LKIfhO6mnFZ49dky0m6WlPccfKxZoAFi7di3PP/984PfzzjsPgLfeeouLLrpowI8nFlKrkjSd8tHVJf7G47w14BN9hex52RjyDHhaPLS/207uwtyo+wgZL44fT9GsEkb9vpPT7kwONeczs9gfBzTIk1yS9JAWIR1gxYoVrFixIuJzb7/9dq/Hli1bxrJly6LuT1EU1q9fz/r16yM+f8MNN/TpQpOkkGgDlYoKPhg/GhUF8pswuhpxuW0YM124O020HzJgtjsiilkhjVFQR0GprmKMrY2j7fnUndQx6USKK9RL+kS6PCVDlqAB4IGmApq7rDgMbmZc9Amth/LxdulxdthwHHFT+O1MCr9ZiK0iilvVT0gV9ggD4Llj6zjQVMiuE6Vcep4/KuLNN0UF0uHmbjqDGU7uKUmcDGG3XtoJE8NUX8+5m42egBvfYhCRegMqpCcSudOf7NDh6jKVDH36MxmUzpUyqZg4ijUBqSgimLyoSFxL4fuO4/21QqNB0dFYK4QL3XHA0etwAuPBumOw9lGm1l4BfINDL++DSzP6vpZjTXjU1opzHc51NLTCs1pbV1cnzmn27IRXC6SSVOUODxRSqzqDqa6m5ol3gRspsbRgvn9toK+gVFSQtziPU/97ivrn6jGXmrGMt0Q0YIWMF3U6lH+6koonGtjekEl1rY2ZBZ4hMcklSQ9pE9IlI5g+Bir7akXF47P+bTalZYU0/UPB+ZYH955OXMddFHytgIKlBb3ErF65xEEdhbPeOcHR9nyqajNZeMXgdhTORKTLUzJkCRoA/qNWiGOfKz1OTpGd7NF2Cse52PvSF3A7M7CdY+tTRIewKuwRBsBzx9Txwgez2FU3Bs5xw5Ej8NOfinXL48cPL3fTGcxwck9J4mQIu/XSTpgYFiKkmzwBN745Qzw+oBnpAxG5I6NjJOmkP5NB6Vopk6qJo2QnION8/+BCoxrWcr+QXh1BSP+sDgDF3gYFBUwtU6EaDtWYxDUe61rua8LjvffEAV10UXon9dJNOgvPSiQjGX9f4bN9ZwEwsaBT9A39fQX7Zf+G6hHXUeNvGlFdKtZyKwVX9tauelbP9GhX079wmu2/garj2ZB5aEhMcknSgxTSJYnRx0BF/ffvs2+faCRmXlKA7bwCrF9TMb/UwIFrRYG38XeOjzmrF8glhkBH4YK6drb+F+yasIyb77TJjsIQRro8JQNK0ADwH7XjAFg4XhQSVFCx2j+h6IJzOP6PDBpebKDwyr4nhEIm9SIMgOeOFYO8XZ8VoW59A6WhXrSF06YJ11Z29vByN52hDDf3lCROhqhbL+2EiWGqt6cvZTG4AmKY5biohTKgjvR0R+4MZHSM5MykP5NB6Vgpk8qJo0QmIDUH+gcfwCuviM+kDwNBRCE9yJGuqiqK9pn4fKg73wfOQsm2QnY2ZYWtABzqHheo9RD1Wu5rwiM/H/bvB4+n92tVFdxuaGgQ+xjqwrRWeFYikcRHUF/hs6ylAEzIbQv0FezvnuT4hv14R08HwNvmBTN07O2g+1g3pd8vDRHTI2lXFfNHwW+guvTLcPcUOck1gpFCuiR+4hioHHv+TVpbKzAYxEMAik4hf7FwqTuPOfF2ejFk9/7XCyk2GoxOx9yv5MJ/wa7qrJ7QT8mQRLo8JQNK0ADwH38Wo7SFY49CW1tgAFj87XM4/o8Wmv/YTMcHHSg6BX2WPvpSvbDMu/AB8Ln6/ZgUF83uHI52FjLZ0gqjR0N9PbS3iwJdhYXDy90kkYwkzkS3XpgY5svviVsznzgM04UYZvm7aPMGVEhPd+TOQEXHSM5c+jMZlOqVMumYOIpnAlJzoFdXi+qf7e0wZUqfBoLgjHSNjLIM0IGn1YOrwYW5WEzwUVuLeqIBOAudXvTFpuY3A1DXkYO9aDK2WNdyXxMeOTnivrVViOoajY09hVUdDnjmGXj/fRkLJZGMJIL6CtVHhLFqcu5pAFQUmjpn4a7vIvNzHuylZpzHnbgb3GSel4mjykHT5ias06yBsWPIeNGPpn9VfWaDGTMG8OQkA40U0iXxE8dAZd8uFyAaEbO552ljvhGzv0Hq/LAzYuGGSI2Rxty54n7/fujsBGlsHrpIl6dkwKmooO6qlRytLEKn+Phc11vQrAYGgJkVFWRM3UnXoS4OffcQ5vFm9BZ91KV6IY708AHw2LGYD37ILFM1u5wz2aXMY3LmSTF4zs7uGYwVFIh2MRWRBRKJJHHORLdekBimvv8ZAF7Ads4UWPFtqKjAX/dsYIX0dEfuDER0jOTMpr+TQalcKZOuiaNYE5DBDvjs7B53dxwGgkgZ6XqLHsskC91HunEccPQI6R0dqC5Rx0HRib5YXkYX+RkOmrusHHaOY2b30ejXcl8THgYD5OWJAqqTJ4vjbWyEnTuFgO7xiMcnTJCxUBLJSCOor/DOMbGK+XOlxwHobrPgaM/FbD6F4nJjGmPCedyJp8WDoiiYS804qh1013aTMTEDiBBLTI+Q/sknYoGL0TiA5ycZUKSQLomfOAYq+xqF3WDmzAhPz7ThPO7E/oE9spAeoTHSKCkR/bLjx4VB4ItfTPosJBLJCOSdU2UAzJzuJnv9j0IGgPZqO4Y88XXnPOZk1JdH4bV741+qFzwA3r0bPv2UuRkfCSE96xKuNm0VvSWzWQzcmpqEIz43t/+RBRKJRJIIfjFM3fEp/PY4XhQsN18HFUKk1kwOA5qRrh1XuiJ30h0dI5GkYjIoVStl0jlxFGkCMtwBf+oUeL2ij5OV1aeBIFK0C4CtwiaE9GoHoy4aJR7MysKnNwE9QjoIV/qO41YOnchkZn6Ma7mvCY+6OpGP3tUVMEdQVSX6bAaDcKxPny7us7NlLJREMpLw9xWam1QONAlH+oJxxwDwOvV4naroI5lN6LNF2+FpFzFQepseV50Lb4c3sLuQYqN+gpO2jhwRzZFkZCKFdEn8xDFQ+aBDLCWeNav305kzM2l5rYXOfZ0Rdx8xIz2IuXNFX3XXrjAhPRUV6yUSyfAj6Nr/x+sTgGwWfskcspRO9ak0/a4pIKS7TrjwtHswjjKin66PuVQvJGZKGwD/5S/wk58wV/XBG/CefTqMKRAjxcJCMJlEW+R0piayQCKRSBJFp0MtGQsIId2c0dOWDYojXSNdkTvpjo6RSCA1k0GpWCkz0BNH4Q54s1mIznEaCKIJ6dZyK81/ag4tODp+PGpBMQCKzhd4uCy/mR3Hx3HoMzMsrIh+Lccz4XHzzWLbIHMEVqvInikvF305GH6xUHI8LJHExt9X2P6auC7KCxrJt3YBoDd50Ds78BaOxpCTgyFHtEveNiGce+1edBYd+ix9YHeBYqNB2pWiiK+C3btFEySF9JGLFNIl8RPHQGVfp6iAHMmRnjlT5LF0ftCHkB7BkQ5CSH/1VSGkB0hVxXqJRDK8CLv2/7HtISCbhROPA6WBzbprxbJh6zQrXdVddB/txv6Rndwv5kZdqterCruGTifal+Ji5upEwdE9J8fguWw6hrY24coym8V2LpfoQfU3skAikUiSQOtTedAFxHMYZCEd0hO5k+7oGIlEYyjUXxjoiaNwB3xOjrim4jAQOJ3Q0iJeFpyRDqEFRwPodKjTzoEtnSjtp4U4b7Mx1VYHzOKQYyxMt4rPP9rnHu+ER5A5gmnTRORLeFTOcImFkuNhiaRv/H2F7b8R0XcLio6IOCe7HUvdcazFC+nInIQe0OcIwdzT5kFVVZzHnWTNzsIyvqdDFS2WWBPSq6sH5rQkg4MU0iXx08dApT27lE9bxNK8WEK6/WM7qldF0Yc2OlGLjfrRctIDQnoqK9ZLJJLhQ9i1327I44PTYsD4+b1PQvX1gWvf2+HF2+3FbDNjO9cmhPQP7eR8IQdFUWIu1Yu4OsY/gC17fy855i7anBnsV6czc55PHNeRI8Kd5XKJpTnz5olOWk2NdAdJJJIBw+cSfSoPSkQhfcCjXdJNOqNjJJJgBrv+wkBPHIU74BVFiLRxGAjq68UuzGYYNSp0t9Zyv5Ae7EgH1LwCoBOl0J9lXlfHVFcWcBmftI6GF5+E3/42tlAcz4RHkDkCo7G3iA6JuftT7QiPd39yPCyRxE9FBe/oJgDw+eyP4dAhsFhQ5sym4IYv0f2aCUeVA0OOkEm9HV7s++2YC80ULC0IrF6G6CbQQMHRqgE4H8mgIYV0SWLEGKh8OO4aeFb05woKer8046wMdBk6fF0+HJ84sJWHFfiLUWwUYM4c0cf57DNoOOmjKNUV6yUSydAnPKtTUXj3yAR8qo5JuS2M7T4Scu3rs/ToLXq8di/WCistr7XgafbgOuHCPNYccalezNUx/gGs7tgxLsj5hDdOncuuYyXMnHpIuJlKSuBb3xIDmZ074YUXpDtIIpEMOE675kgPFdK1jPRBc6Snk6HgFpZIBoKBnDiK5IAvLBRGgXADQdj7a4VGi4t769SakO487sTT4cGQJWSJQO7w3Nnwo8/BBx8w9ZmdABzqKhXXeDxCcTwTHqly96faER7v/iL0iQE5HpZIouBywXv7Rdvz+Q1LYPS8QF/BptNROsVO0++asFfZQQF8YD3LSvH1xSH1tCC68Uq7RKUjfWQjW1RJ4lRUwKpVsH493H23uL/zTva1TgQiu9EBFL2CbYZogOwf2Hs931e0S3Z2T8P03uuN8Vesl0gkI4fwrE7gH7VigLNw/LFe175lvAVruRXnMSeKScEyWShKzuPOwFI9a4U1rqV6AfwD2LkVYqnvruos4ZqaMwfWrIGpU4Vbat8+Mas4bZq437tXuIZkz0oikaQZZ5dox7xRHOkjUkiHHvFsxgxxL8WjlPHUU08xceJELBYL8+bNY1dI1mJvXnnlFcrLy7FYLMyYMYPXX3895HlVVVm7di0lJSVkZGSwaNEiPvnkk5BtWlpauPbaa8nOziY3N5ebbrqJzs6eiMiamhoURel1e/fdd1N34n3h84lVZx99JO59vr5ekRqijMd6Ca39PTbNAV9Q0FOY0+MRkS55ebBwIdx3H2zY0Ov960QKXq98dABjnhHjaCMAjoM9rvRAvJ5JJwTs/fs5yyT6dM3dmTQ7M3uE4qYmIRQn+5lHO7e2tvjj+TRH+N69qenzJbK/CH3iAHI8LJH04v33Rf+noACmfqm0V1/BVmFj/KrxTLpvEqZiUfi44OsFvUR0iD5e1BzpBw4M3NeBZOCRvUtJcoQPVIB9fxei0qyJrVFbjcxZ0XPS+yo2CkHxLu/Rd8X67u6hn2knkUhC6WvQF57VSbCQXtvr2ld0CgVXFmAsMOKocqDPFs5z1wkXjioHpgJT1KV60WKmAKio4ILvzwdgl3FBzwB22rRQd1B2Nuj1qRv0SSQSSRxojnQvCkZjz+MjXkiXpIWXX36ZlStXsm7dOt5//31mzpzJ4sWLOXXqVMTtt2/fzjXXXMNNN93E3r17Wbp0KUuXLuXjjz8ObPPQQw/x+OOPU1lZyc6dO7HZbCxevJjuoH/Oa6+9lv3797N161b+9Kc/8be//Y2btWKRQbzxxhucPHkycJszZ07qP4RIVFfDAw/A2rVCSF67Vvw+UBPmsSaOUnlsmgP+vPOEceDQoVADwRVXRJy42r1b3J9zTuTdRspJD4hTBiUgFNsmjWZsVjsAn7Tkiw1TJRRHO7fZs/uORQl3hAf3+SoqxDLqykpR0DSefl+s/UXqQ0boE4cgx8MSSQjvvCPuFyyInOYEYuyYMTEjxHwViWjjxUmTxOq/ri7RBEhGJjLaRdJ//MvPPth2NZDFrI/+Bx7oiLicLVbB0b4c6SCE9Oeeg13V2TBhACvWSySS9BPPUtawrE6XV8+7x0Vx0YXjayNe+7YKG6XfL6Xpd024TrgAcDW4GH31aAqW9nYZRKrCHom5nxMdp48PZ9CeN5FsHUL8j9cdNJgZqxKJZEQTENIVXUhTpEW7jLiMdEla+clPfsLy5cu58cYbAaisrOS1117j2WefZdWqVb22f+yxx1iyZAm33347ABs2bGDr1q08+eSTVFZWoqoqjz76KGvWrOGKK64A4IUXXqCoqIjNmzdz9dVXU11dzZYtW3jvvfc4//zzAXjiiSe49NJLefjhhxkTVL0yPz+f4uLidH8MoQzlbOp0HFsS0Unbt4v7z38+8vPWcittf20LyUkPGQ8GCcVT85up68jmUHM+nys9LjZOVTHQZGOhojnCG/0rp0+cgP37xWd+/vl9R70k4jCfOLF3fn04cjwskYTQV5sUjGW8hfZ32nEei9xhCowXw7QrgwHOPlu437dvF8K6ZOQhHemS/uHvqHn2fMBHreMAmDm5I+pytoCQvi+CkO7p2wUacKR/ZEGdVi46JqoatiN/pl1FReoq1kskkvQS71JWLc/Sf+3vPVlMl8dIXoaD8vzGqNe+tlRv3O2inUKF8XeOT2ipXjhjxogUF58PXnvN/6B0B0kkkiGAyyEGeD5daDsmHenDi/vvv58FCxZgtVrJzc2N6zXxRKYkgsvlYs+ePSxatCjwmE6nY9GiRezYsSPia3bs2BGyPcDixYsD2x89epT6+vqQbXJycpg3b15gmx07dpCbmxsQ0QEWLVqETqdj586dIfv++te/zujRo1m4cCF/+MMfYp6P0+mkvb09cOtI5vs4UefwQJLOY0sgOsnlgvfeEz8vWBB5m4yyDAA63u+gq6YL1aeGCulBQvHU/GYAPmnO69lBKoXiZGKhIvX5GhtFjZyTJ8VnbrOJWzxRL4n2IcP6xCHI8bBEEoKq9jjS4xHSzeOE86C7NnKHKVaawle+Iu7//OfEj1MyPJBCuiR5gjpqB0d/AafXiM3oYsp4d9SOmu1c0TFwnXDhanKF7i7KrF4wM2YIN9Xp0wpH5nyrf5l2EolkaJDIoC8sz/Ifh0YDsLDkCLrq/TGvfUWnMOriUQC4T7nxdngjHk48q2M0li0T96+84n8g2B0UCekOkkgkA4DLn5GuKlJIH864XC6WLVvGLbfcEvdr4olMSYSmpia8Xi9FRUUhjxcVFVFfXx/xNfX19TG31+772mb06NEhzxsMBvLy8gLbZGZm8sgjj/DKK6/w2muvsXDhQpYuXRpTTN+4cSM5OTmB23Qt0DYRhnI29RA5tr17RTuTnw9lZb2ft1fb6dgpBOHO9zupWVtD7QO1uBrF+FBn1IUIxVPzmgA4pEW7DAWhOLzPp6ris3c4REFWRQGjUXwI8UxiJNqHTEXGu0RyhvDpp9DQIMo7xJP+pdXPiuZIj2W8+upXxf2WLeCNPNyUDHNkqypJnqCO2genRBWZmcX16BQ1akfNkGUI5E2FFxyNJyPdZBIRdgC7Ws5KPtNOIpEMHRId9AXlWf7+4DQALhz1UVzXviHHgKlEFI8JLm4VTDxtkca3viXuX3/dbxCS7iBJggzFAn5vv/02V1xxBSUlJdhsNmbNmsWLL76YupOWpB2Xwy+k66WQPpy59957+cEPfsCMGTPi2j48MuXcc8/lhRde4MSJE2zevDm9BzsIFBQUsHLlSubNm8cFF1zAAw88wD//8z/z4x//OOprVq9eTVtbW+BWVVUV+00i1W5J1+qzVBQHTeTY0lgoVYtQiJRFbK+2c/zx4zhP9ghUhgIDHXs7sH8kxoeKQQkRiqfa9wJwqClv6AjF4X2+tjYhlufkiOfb28Ux5uTEN4mRTB+yPxnvEsmZgs/HO5sbAZhzTjcWU99tXdyO9AhC+vz54rJvbu6pFSEZWciMdEnyBHXU/lozAYDZxSd7no+SW5c5M5PuT7vp/KCTUZeMCjwerwt07gUq776rsOv1Jr69IAPuuEN0LBLJtJNIJEOHeAZ94W1JRQVHTdP4+3/oUBSVbz3xBZh3dVzXvrXciuukC8cBB9lze2dKJuJInzFDpNAcPAh//CN8+9v+Qd+xY2KQV1rak016/PjgD/okQwqtgF9lZSXz5s3j0UcfZfHixRw8eLCXExN6Cvht3LiRyy+/nE2bNrF06VLef/99zvFXc9PcqM8//zyTJk3i7rvvZvHixVRVVWHxq6jXXnstJ0+eZOvWrbjdbm688UZuvvlmNm3aFHifc889lzvvvJOioiL+9Kc/cd1115GTk8Pll18+cB+QJGk0R3p4tIvMSE8fHR0dtLe3B343m82YtQ98gOgrMuXqq69OeJ8FBQXo9XoaGhpCHm9oaIiaS15cXBxze+2+oaGBkpKSkG1mzZoV2Ca8mKnH46GlpSVmHvq8efPYunVr1OfD/y7Bf7NeRKvdMndu6rOp46kTEw/x5mY3NIiOS3/fLwrRsohVn0rT75pwN7mxVlhp/0c7vi4fhmwD+ul6Onb5+3qaSuEXisue+StsF0K62tSMMnu26E8NplCsC+vzWSwi08ZsFhEvVqv4TLWZhL4y3cP3F28fMtmMd4nkTMDftr7z3OeAL/H57jfhgX19tnXm8eJ7IqojPUqxURALUb78ZfjNb4TZat68fp+FZIghW1dJ8vg7at4OB5sPlgPwtWmHep6P0onMnBW54Ghc4lV1NXPrxXLNXVuaRQX6hx4SZZG1TDtIm7tCIpGkgSTjUH61SXyFfelLCqXzx8U9YLBWWAFCiltpqKoaV70GDUXpiXf59a/9D0p3kCROggv4TZ8+ncrKSqxWK88++2zE7YML+FVUVLBhwwZmz57Nk08+CcTnRtUK+P3iF79g3rx5LFy4kCeeeIKXXnqJEydOAHDXXXexYcMGFixYwJQpU7j11ltZsmQJr776atRzSUnusCRluP1COvrQdkw60tPH9OnTQyJDNm7cOODHEE9kSqKYTCbmzJnDtm3bAo/5fD62bdvG/PnzI75m/vz5IdsDbN26NbD9pEmTKC4uDtmmvb2dnTt3BraZP38+ra2t7NmzJ7DNm2++ic/nY14MVWLfvn0h4nzSxKrd8pvfQF5e6lafxVsnJh7icTXn54tzSMX7RSA4izg8H727thvHAQfmcWb0Nj0AXrsXVVVRFAWdWbRZIfF7FRVM2ngzer2Kw2vhxP+7H+68c2j0p4L7fHa7iHXp6ICSEqGeFRb2bBvPBEuyfchkMt4lkpFOUNu6vUVcO5+fUh9XW2cZJzpM7lNuvN2981kC0S5RVjBfeqm4//OfValNjUCkI12SPP6O2j/+4uSUPZNRli4unnhUPKd11GbP7tWJDBQcDRfS+xKv/A3h3HYDcAXvt07GPWo0xuAK9JAaN4dEIhk4tEHf3r0iQzJ4DXCUtkRV4YUXxM/XXZfY21nL/UL6gQhCurdn0BmPIx1EvMt994kcvPZ2vwFMuoMkfaAV8Fu9enXgsXgK+K1cuTLkscWLFwdE8njcqH0V8LvyyisjvndbWxsVMb5HN27cyL333tvneUsGBne3ihEZ7TKQVFVVMXbs2MDv0dzoq1at4sEHH4y5r+rqasrLy1N6fP1h5cqVXH/99Zx//vnMnTuXRx99FLvdzo033gjAddddx9ixYwOTB7feeisXXnghjzzyCJdddhkvvfQSu3fv5plnngFAURRuu+027rvvPsrKygKrZ8aMGcPSpUsBqKioYMmSJSxfvpzKykrcbjcrVqzg6quvZsyYMQA8//zzmEwmzvPnPr766qs8++yz/OIXv+jfCYfXbtH6JVrtFs0tnJ/f/9Vn8bzX5s2iTxHP/vpyNefni05Uc3Nq3i8Cn30mam0aDBD0VQMIgdzb7cVsM6P6/H0uH6guFcWsBGx+2rhQw2TRMWkSHD4Mh7rGMXYodae0Pl9NDTz2GHzyiTjx4M8vxtg46v5kH1IiSZ6gtrV18mz2/0asZFowrRmsfbd1hjwDOqsOn8OH87gT61nW0N33Ud9vyRJx/957Cqdu/zGjlUapTY0gpJAuSR5/R+23/yscLl+f9BFG1QVtsTuRthkivsGx34H9kB3rWVYUnRK7MQpqCM+aO53cv3bR2p3B+/ZpzJtuEw3hM88IZ3pzs8ha1jqNwUK7bLAkkqFHEktZ331XDKasVvinf0rs7WIK6e4gIT2OjHSAc84RfaIDB8Qq6WuvDTovbZWMRBJGrAJ+Bw4ciPiagSrgF86vf/1r3nvvPZ5++umo57N69eoQkb+uri65In6SlODu8jueogjpMtol9WRlZZEdKUojjB/+8IfccMMNMbeZPHlyUscQT2RKMlx11VU0Njaydu1a6uvrmTVrFlu2bAm0NbW1teiCvqMXLFjApk2bWLNmDXfddRdlZWVs3rw5EEEFcMcdd2C327n55ptpbW1l4cKFbNmyJRBBBfDiiy+yYsUKLrnkEnQ6Hd/4xjd4/PHHQ45tw4YNfPbZZxgMBsrLy3n55Zf55je/mfS5+k+o79otzc1iJn/XLrFtXZ24wBKNHEmkTky8fQrN1ayZi4KP7YILhBMhle8XhhbrMns2ZGSEPqfP0qO36PHavRiyDShGBdWt4rV70Zl1gX6YPlPfa79Tp/qF9ENw8cVJHVr60Olg8mT43vd6nK79mWCRfUiJpH8Eta07jo9DRaEsr5nRNjvQd1unKArmcWa6DnbhrO0tpPeVplDSWs2s/Ez2NY/j/zoW8J0LDkhtagQhhXRJv/BNq+DVxrMA+EbRP0TPJkYn0l5tp/HVxkCn6cgPj5AzP4eCKwtiF/gLaggVncKXJ3/KK1Vn80rV2cwrrYOxY+Htt8UyugsuSIu7QiKRpJFYg74Ibcn//I+4/6d/gszMxN5KE9K7Dnfhc/tCVsEEO6DidaRr8S4bNoh4l4CQPtD4fNK9JEk5b731FjfeeCM///nPOfvss6Nul1DusCTteLr9bVlYn0r7E0lH+uBRWFhIYXDcQwoJjkzRhHMtMuWWW27p175XrFjBihUrIj739ttv93ps2bJlLNOyzyKgKArr169n/fr1UbfJy8sL1G6IxPXXX8/1118f/aCTJd7aLUVFsGpV/757k6kTEw/RXM3796fn/YKIlo8OYBlvwVpupWNvB/rpenRWHd42Lz6HD3WUENQBTEWmXq895xyRN/zOO/Dd7yZ9eOklwf6sRCJJE0Ft6/8dEXrV58cHFfqNo62zjLcIIT1CTnpM7cpvAr20sJx9zeN4ve5cvvO5T6Q2NYKQQrqkX+zaBXUNRrKyVL5c+Q1wfTlqJ1Kr0O5ucmMcbcRV50J1q3Ts7aD7WDe+7hiO9LBO5rUzPuSVqrPZ9NEMHly0Fb3HAy0tooeVJneFRCJJM3EuZXU64aWXxM+JxroAmEvN6Gw6fHYf3Z92Y53W4zAIcaTHKaSDiHfZsEHEu7S1iUrtA0qqipRJBozhUMDvr3/9K1/72tf46U9/ynXJXGySQSOakC6jXYYXtbW1tLS0UFtbi9frZd++fQCcddZZZPpnkcvLy9m4cSNXXnllXJEpkjiIt2BnVlb/ncOJvFdfRJpQDz+2VL5fFKLlowMoOoWCKwvoPtaNo8qBzqzDixd3kxtPmycgSmlZ6cF89auiNNbrr4PXC/repvWhgYxmkUjSSzzmIX9b52rv5sWPZgDwzYqqnufjaOvM44T7oLu2d6cpkJEeabzoN4F+9dws/vMA/N/hs/D6FPQ6tefYtm8XtwULZNswQNTU1LBhwwbefPNN6uvrGTNmDP/8z//Mf/zHf2Ay9Z68jYUU0iX94je/EfeXX65gmTYh6nYhFdqnW+k+0o2rzoXP6SNrXhaOKge+rhhCelin76tlh8nLcHCyM4u3aiaxSD0stsvNjXwAKXBXpAzpGpVIohPHgPT11+H0aRgzBr70pcTfQlEUrOVWOvd0Yq+2RxfS9fEL6WefLcZN1dUi3uWf/znx40oarZBOU5OMtRpGBBfw0wQurYBfNNenVsDvtttuCzwWrYBfNDdqcAG/OXPmAJEL+L399ttcfvnlPPjgg9x8880pPntJuvG4NKeULDY6nFm7di3PP/984HctC/ytt97ioosuAuDgwYO0tbUFtoknMkXSB0nUbhn094p3Qj3N59bRAR9+KH6OJKQD2CpslH6/lKbfNdG+Xaxecje6yb88H2eNE1edK6LL8/Ofh1GjRKrOjh2wcGFShzgwyGgWiSQ9JNjWvf4nA00OG8WZHSw+64h4Ls62zjJefG+GO9JVVQ2MGSPW9/ObQD9X1kSupYvT3RnsrCtlgeV9cdynTkFrKzz4IMyfL41PA8SBAwfw+Xw8/fTTnHXWWXz88ccsX74cu93Oww8/nNC+pHonSRpVhd/+Vvz8jW/E3ja4QruiKOiz/FXaO70if6rUHLsxCqtAb9J7+db0/QC8+OEM0aPKyxNVbSKRAndFSqiuhgcegLVrhX117Vrxe4yK0RKJJBStyOi11ybvRoqWkx6o1WBQUMJXt8RAi3cBEe8SEZ8vdtX2vp6Pts/gImXZ2eJD0ZYONjWJpYOJVohP5lgkCbNy5Up+/vOf8/zzz1NdXc0tt9zSq4BfcDHSW2+9lS1btvDII49w4MAB7rnnHnbv3h0Q3oPdqH/4wx/46KOPuO6666IW8Nu1axfvvPNOrwJ+b731Fpdddhnf//73+cY3vkF9fT319fW0tLQM7AckSRqvM/KSYy3aRWakDw+ee+45MWAPu2kiOogBfXDmuhaZUl9fT3d3N2+88QZTp04d+IMfzmi1WwoKxBL8tjbweMR9VVViWdcD8V7ahPrevWL7adPE/d69PXndA3Ruu3aJLsOECcLwEA1bhY3xq8aTNVeMzXIvzGX8neNRTKLNimSsMhqFKx3gD39I6vAkEslwJom27pcnFwPwnWnvYfC5EmrrojnSVW8fK5j9JlBDVwdfmSLE+z9/MAZ27uypxJybC6NHRz52SVpYsmQJv/zlL/nKV77C5MmT+frXv86PfvQjXn311YT3JR3pkqTZu1foKxkZPVWJoxFcoR16Csh4O0UOnt6mD1Ruj5gzFaEY4bVn76NyzwX8dv80npozBevUqWJmMTs7vc6RZJGuUcmZSIpXYDQ3w2uviZ/7kzQRTUiPuUyvD771LVi/XsS7nDwpSjYEiOSemDoVPvc5kbHa0CA6VwcPJhbNEqtIGSS3dFDGxAwYQ7WA3/PPP4/D4WDjxo1s3Lgx8PiFF14YMQtZMvTwOiOv8tP+DdzuIR6NIJEMNgOZdd2f9wqfUI+nTlQaz02LdYmUjx6OolPImCiqkapeFUWn9FnA72tfg02bxOq/hx5K+jAlEslwI4m2riGvgteOiTblhjF/gUOfJtTWmccL7aqXIz14BXMk7Spo5c+lZx3i1/vP4fVDU9hQ6hACflOTGCiWlortZWZ6RDo6OkJqLoXXY0oFbW1t5OXlJfw6KaRLkkZzo3/1q9Hr1WiEV2gPF9K9di/426OoAlZYp29B1xtMtC2lxl7IH8++k6u+2iGEar/QnnSV9FSjOTsrK+Gzz+D883uOQxacGFSeeuopfvzjH1NfX8/MmTN54oknmDt3btTtX3nlFe6++25qamooKyvjwQcf5NJLLw08r6oq69at4+c//zmtra18/vOf52c/+xllZWWBbe6//35ee+019u3bh8lkorW1NZ2nOLikQZDdtEkIQLNmiZIIyRJVSO9jABeLs88WWvX27fDww/DII/4nIk2i1daKoPcXXhBuhFOnwGSCOXNEOxDvJFu0ImWNjcktHZQTfgPOUCzg99xzz/Hcc89FfV4y9NEc6booQjoIV7rVikQiicZAZl0n+16xJtRj1YlK07lphUajxbqEYyw0AuBucgPEXqGMMG8ZDOKUDx+Gs87q1+FKJJLhQhJt3YsvgterMHeuyvTHv5dwW2cZ5492qXWiqmpgtbJmvIIoY8YgE+iS468D/8T7HVOpU0oZ23RcdL7Ky3vOQ9bzi8j06dNDfl+3bh333HNPyvZ/+PBhnnjiiYRjXUBGu0ii0cey/kRiXaCnQrvzmGiEgoV0VVXpPtbdt5AOotO3ahWsX49u7Rq+fb3ofL34dmmP0H7eecK2euiQuJ89e/DEHy3K5Uc/EkL50aPCqtHY2LNNeMMvGRBefvllVq5cybp163j//feZOXMmixcv7lWET2P79u1cc8013HTTTezdu5elS5eydOlSPv7448A2Dz30EI8//jiVlZXs3LkTm83G4sWL6Q4Ko3W5XCxbtiyQVzzkSTbiI5Gld3HS1QUPPCAain+9tK5fkSO2CiE8Ow44UNWezlDMCuxxcPfd4v5nPxMadsTolZYW2L9fPOfzwYkTolH1esXjLS3xRbP4fGJ5YldXzz5AtC/JLB1MV0yMRCIZcLz+jHSdKbqQLnPSJZI40LKuZ8wQ9+k0vCTzXtEm1DVsNvF8R0fvPh2k9Nx8PpFdDgkI6QVhQrondj8sNxe++EXx8x//mPShSlJITU0NN910E5MmTSIjI4MpU6awbt06XC7XYB+aZCSRSFuHGBb98pfiqRtvVJJq67RoF2+nF0+bJ/B4iCO9DxNo0fzJLMwTkcQ/a/ymcKLPmweFhVGPXSKoqqqira0tcAuOuwxm1apVKIoS83bgwIGQ19TV1bFkyRKWLVvG8uXLEz426UiX9KavCIKsLKo6xnPwoA6TCS6/vO9dhldoD2Skd3ix77djyuupktunEzSoeMu1/w/+87/gz38WGk/BUKqSHuzszMwUDWRWlhC32tpCG9ChVAz1DOEnP/kJy5cvD2QRV1ZW8tprr/Hss8+yatWqXts/9thjLFmyhNtvvx2ADRs2sHXrVp588kkqKytRVZVHH32UNWvWcMUVVwDwwgsvUFRUxObNm7n66qsBuPfeewEScns6nU6cQYG2HQP1f5KsozyZZcax9uW/np98wsKJE2WMtzXxrwduh7WGpB3uGWdlgA68bV5cDS7MxaKj1J9oF4DFi+GCC+C994Qj/cFbwtwTqip+dzjE9d/eLgazEyeKz0dzkhcUxHaSaX+b6mrx+g8/hClTxOdx8KDYf6JLB5N1tUkkkiGHzxXZkW4wiEvf55M56RLJiMCfxYvdLvoR4Wh1ohoahPKcxti2/ftFt8ZmE3pVPAQc6Y1CSA/UqonRD/va1+DNN8Xp/OAH/TtmSf9JZQE/SYpIcbTmoBNuHiot7T1WCa6J5/Px/mv1fPzxGMwmlau/pZKMh1hv1WPIN+Bp9uCsdWLMFe1ViJCu78MEOm0aP1AP8o818LP2b7N67ilsZk/odkOlnt8QIysri+xI32th/PCHPwypFROJyZMnB34+ceIEF198MQsWLOCZZ55J6tiG8dUkSQuRXKSKIiII/u3f4PbbYe1anvnuHgC+8pXIfbZIaBXas87LQu32Nz5esE23UfLdnjDhaEv5IjF9ujCcezxBBf4G0jkSjXAhMS9PVMjR6YR45nCIjqzmIJWN54DicrnYs2cPixYtCjym0+lYtGgROzQrTRg7duwI2R5g8eLFge2PHj1KfX19yDY5OTnMmzcv6j7jZePGjeTk5ARu4cuc0kJ/HOWJCLJ9HYO/OG/rbfew8b/FxNO9M3+HefqUfjncdWYdGZNFLqejuifepa8lxX2hKKKGMMBTT0FTrSPUPdHWJtqFnByxsU4nnOiKIm7Z2eL5tjaxfSSHQvDfprAQFi4U+zt0SIwsjx0TFQWbmkKXDvb12Sfo9BjOSPeUZKTj09oyc++2THOlS0e6RDIC0LJ4jx3rGVdoaHWi8vPhN79J6SrBSGiJYV/8opi0i4dejvQ4Iva+9jVx/7e/wenTyR3rmYyWO6zdnP2cVU1lAT9JCggaP7Fhg7h/4IHhW8xSO5+nnxbmoTfegL//PXSFv9bWVVQIXeWBB/jl7cIFfuWYd8mtTP78LeP98S5BOenBK2eU8LFuODodV9wxjSl5LbQ4M3nug/NCnw8+9sGs5zeMKSwspLy8PObNZBLG3bq6Oi666CLmzJnDL3/5y5BaVImQNoXxqaeeYuLEiVgsFubNm8euXbtibv/KK69QXl6OxWJhxowZvP766yHPq6rK2rVrKSkpISMjg0WLFvHJJ5+EbHP//fezYMECrFYrubm5qT6lkU9fEQSqCk4ndebJPL1zJgC3Lv0sobfQKrRP+s9JAVd60dVFZEzJCGyTaKTCtdeK+xdfTOhl6SVcSMzJER1WTRwLFstiNZ7JxmpIYtLU1ITX6w0U9NMoKiqivr4+4mvq6+tjbq/dJ7LPeFm9enXIsqaqqqp+7a9P+hvxkQpBNlgszs/n4cNLOe3LpcJ0mO90/zz++JMYRMpJDzihkox2AbjsMpEwZbfDT18e0+MUA2EB9XjExBqIY9brewa/JpN4XhvUhE+yRfrbFBXBF74gVg61tQnXmdud+NLBYFdbJGJN+A2ztirYPbV//35++tOfUllZyV133TXYhyaRpASfS1yDelPvtkwK6RLJCELL4i0oEKvO2tpEP6KtTfyeny/6GM3NaY1tczrhF78QPyeySj7ckR5PxN6UKeLQvV5R4F2SGNOnTw8x6AQXFU8VyRbwk/STNERrDiqxzEN//zvU1/e0dQUFwkj55JM4d3/EphpR8fjG2R/06/y1eJfu2p5OUzwrZ4LRG3Xcdosw6/z0b3Pwnm4PbacHs57fGYQmoo8fP56HH36YxsZG6uvrk9Jq0vKXktnDw5Rw8Tc8giA/H1paeOC9L+H0mViYX80lDZsS7nhpFdpNY8SskKvBFV/OVBSuvlq0Odu3i3ZoSBAuJCqKcItYrWL2VFXB5RKd2miN5zCcTZYuz/RgNpvJzs4O3LLSvXKhv47y/giy0Essbui08dNj3wTg/rOeQ9/V2bOiox81BiIJ6f0pNqqhKD1Z6U/8Tw6nJ8zqcYqZzcKm5XaL37u7xYoVp7OnXTAYxHaRJtmi/W0KC4WYvnCh+FzPPlv8HCyiQ+zPPh5XW6QJvwFoq6R7SiJJDJ/fLaU3RxfSZbSLRDJCiFUn6pvfFOaD/q4S7IPf/EZ028aO7XGMx4PmSPe0evC5fXFH7GnvIXPSEyfe3OFk0Qr4ffe7303pflPJiDR9jrRaR/GYh955Rzw/ezasWCGiLpua+C3f4LTTSml2G5ec3dCv84/oSE9ivHjj6mJGZXs5Yi/mDx9MGBr1/M4wtm7dyuHDh9m2bRulpaWUlJQEbomSFiE9OHt4+vTpVFZWYrVaefbZZyNuH5w9XFFRwYYNG5g9ezZPPvkkQK/s4XPPPZcXXniBEydOsHnz5sB+7r33Xn7wgx8wI95QtuFMOtx/4eJveASBycTxrnye+Wg+APde9CbKgeQ7XuYSMbvnqnf1VD7WCaE9EcaMAX8kNQNiJozns48kJBYWCndoSYkIEHQ4xPORGs9hOps8XFyeBQUF6PV6GhoaQh5vaGiguLg44muKi4tjbq/dJ7LPIUt/HeXJCrIaYWLxfe9egkPNYG5WFUsL/hFf/EkcZEwTK2E6dnfQVdOF6lP7nZGuccUVwhTR0aHwWMt3epxiIITzpiZRjdRmg7lzeybZmpvF8yBWA5lMovNXWyvamlh/G0URHctRoyKvde7rs+/L1RZtwm8A2irpnpJIEkMb5BkiONLNovslHekSyUiiogJWrYL168Vs/vr1cOedQngagNi2//ovcf/d78Yf6wJgHGUEfzPlbnbHHbGnCel//rPwJkjiR8sd1m5m7UshjMEo4DcQjFjTZ3+MUENxZWlf5qFLLhExvt/9rmjrbDY4cABnyUTufvtLACyf/T56Xf+MV5Ec6fGsnAnHZoNbVohEhoc7vxvaTvdVeyz4b+PxDL2/1TDhhhtuQFXViLdESXmxUS17OHhmM57s4ZUrV4Y8tnjx4oBI3lf2sFbEL1EGrYBff0m2AGBfhBerCY8gcLnY2PJvuHxGLpxQw8XTTsInyXe8TMV+R3q9q98u0P/8T/jDH+D3vxerfL7whRgb96f4RryfvSYk7t0bWmxRc/bv3g1lZXDrrb1z3FNZqHGAWbJkCUuWLAn8PnnyZA4ePMjPfvazIVVsxmQyMWfOHLZt28bSpUsB8Pl8bNu2jRUrVkR8zfz589m2bRu33XZb4LGtW7cyf76YWJo0aRLFxcVs27aNWbNmAdDe3s7OnTuH3yqZeAtXRXOUa4LssWPi/7W0VPQe7HYh5Pa1fC1ILD56Openq8QFvbH0v8TlYDKJbaLFn8SBvdpOx3ui7erc10nN2hqs5VaMo0V7118hXacT/aNvfQt++qtCbvrdSsa9+4poO8xmcV3r9eKaHj9enNOePeJxsxmOHhWfgdstcqt++1vRpsydG/tv43DA5Mnis0jms9dcbVo7V1cn3m/2bPG64HZuANuqqqoqxo4dG/g92qAvWTT31FBqpySS/qD1q2I50qWQLpGMMLQ6UcH0t08XB/v2iZXBBgP8678m9lpFr2DMN+JucuNucscdmfC5z/XUVP/73+FLX0ry4CVRGYwCfgNBsOkToLKyktdee41nn32WVatW9do+2PQJsGHDBrZu3cqTTz5JZWVlL9MnwAsvvEBRURGbN28OaFX33nsvAM8991xcx5mwVhWPEaqurrd2ky5tqb/0ZR4aMwY6O4XpU6cLbP9E1SV8ejqPkswOVs4P0h+jnX8fmEvFmMNxwEFXTReW8ZakjVcrVsDDD8P2PRbetc/gc335f8P/Nk6nKLiakSHGi0Plb3UGknIhPVb2cPiMpcZgZQ9v3Lgx0KANGzT3X1OTmJ3TBJK9e4Vo1Z9lIeHib3AEgclEbbONn7d8A4B7L3oLxdG/jldEIT3JXOLyctFxe/ppUQ91x47eE7FA/74oEvns+xISJ0yA731PCF7hJDKbHN5ZTnCSQItL0DCbzSkXqIaqy3PlypVcf/31nH/++cydO5dHH30Uu90e6FRdd911jB07NuB8vfXWW7nwwgt55JFHuOyyy3jppZfYvXt3oKOoKAq33XYb9913H2VlZUyaNIm7776bMWPGBMR6gNraWlpaWqitrcXr9bJv3z4AzjrrLDIzMwf0M4hKtIkg6HE1z54duyBKIoJsOP5Bn6/TwfI/3ojbZ2DRqD18iTdBLYwcf9LX8QRhr7Zz/PHjOE+KzqnP4UOXraNjbwdeuxfoX0a6xje+IRah7NwJNz1Qxv/9eRXKMf/12dAgnjh4UCzts1jgmmvEC+x2eOUVMYk5fnxoW1NbKxzrx45F/9t87nPw9a+LmcVEP3sIVJjvsy3pT1uVIPFWbV+1ahUPPvhgzG2qq6spLy8P/D4c3FMSScL4B3kGiyw2KpGc0aSiT9cHP/uZuP+nfxILbxPFWNAjpMc7JtTrRVfn2WfhiSekkJ4OCgsLKQyPCIxCXV0dF198cb8L+KWb4WT6TFirSmbSLJ3aUn9J9HyysmikkA1/vxCA+7+0jUyTK/r2cWCvttP611YAHNWOgPEqY6pY1dzXyplwSkpEfb9f/hIeeUQM93qh6TkffCA26O4W7XNXl4iyaWkRY8EFC8SK5qHwtzoDSbmQPpxYvXp1SKNYV1fH9OnTB/GI+iAR9x/EFkGiCa7B4u/YseIiPX4cDAb+s/U+3KqRiyce5cIJNVDVv46XqcQvpJ90BdwHiTZGwdxzD/zqV0Kb+u1vRSRgCP35okjGeZmskDiAs8nh/+/r1q3jnnvuify+STCUXZ5XXXUVjY2NrF27lvr6embNmsWWLVsCE3a1tbUhncAFCxawadMm1qxZw1133UVZWRmbN2/mnHPOCWxzxx13YLfbufnmm2ltbWXhwoVs2bIFi6ZaAGvXruX5558P/H7eeaJy91tvvcVFF12U5rOOk/46yjXiFWTD8Q/6fvy/49l2dDJWo4snlrwGh/zxJ263OCZIuECL6lNp+l0T7iY3medl0rqtFZ/Dh+pWsU63cvoNEYnSX0c6iMN5/nmYNQu2boWnf67je9+bKJ6cMUOM+sI/GxD54k6nyDmP1NaUloqVLbH+NhUV4pbs6ptIrrZwkm2roH8rg2IwUt1TEknCeES/ymCJHu0iM9IlkjOAVPXpotDWJsZfAP/2b8kdorHQCAdEwdFEnJ4/+hE895wYgu3eDeefn9z7S/qHVsBvwoQJgQJ+GkMt3nI4mT4T1qoSnTQbyqvgfT5xGzVKaBvnnx96DJHOZ/x47qn9F9pdGcwqPsl1Mz+IvX0faMar7uPCdeDr9qHP09Oxt4P23cKImMx4ceVKIaS/+qr4U/mlAIGm51RXi6U+7e2iuvLo0cJ45fEII2ZTE3zyiaiHNdh/qzOUlAvp6c4eDg6Cb2hoCEQoJEO4+zbYmTskidf99+absGtXdEG1L8E1WPz1RxDUeMfxbItYqnTvBX9KSXXhVEa7ABQXww9/KKKmVq8WLgWTyf9kf78oknVeJiMkDuBscrxxCSPV5blixYqoUS5vv/12r8eWLVvGsmXLou5PURTWr1/P+vXro27z3HPPxb2kb1Dpj6M8mHgE2Qiv2TXlGtZUiY7OExf9lvLJLjCeHRp/0tKS8PF013bjOODAPM6MoigYC4w4a514mjyYS8wiqxPAm9ghR2PaNKGL33abGPB9+cuiP6SdZ6/Ppqam77amuRmuu66nnY/2t0nms0+EZJeLp3EJ6Uh0T0kkSeEV/SpjBCFdOtIlkjOMVPXpIvDCCyJVbvp0+OIXk9uHVnA0xJEex5iwokK4O//nf0Sd87D6j5IBQivgd/jwYUo1o4ufZLKHJYKEtapEJ80S0TfGj0+LASYiweOEU6fg00/Fe2sieJTzqTqg4+ndswH4ydSn0Xe0JT1pGGK8mp3J6T+fBp+IorJOt9L2N1GnK5kVzOecI6I/f/1r+Jd/gV3v+jCeDHOgazUK8/Ohvl4YyVwuYXLV6ULrheXmpnQVcL/oyyiVJiPVYJByIV1mD6eReNx/1dXwzDNi1i2SoHrZZfDaa30LrkHir/dEAzd8r0zEK4z+kC9Y3oOK/ne8QoT0FBX4+9GPoLISDh8WH0PgXy7WFwWIC3n7dnFbsKD3Bd0f52VfYlZwg2KzJT772o9JgnjjEqTL8wwlnomgNHwhtrfDNXdNwuODb82o4saiP8Oh7tD4k6KipN7P2+HF2+3FbBMdU0OeAWetE/dpUakq0Ab135Ae4N//XVyif/0r3HgjvPWWWJIckXjbmqIiUVQs3s8+HR2XZJaLD5ElpMPJPSWRJIVXi3aRQrpEIiH5VYIxUNWeIqP/9m9RYjXjwFgohHRXgwv8umu8q5TXrYNNm0TRUW0YJxlYbrjhhj7HiUOF4WT6TIpEJs3iHXN88IG4yAYiQz18nDBhghjz7NkD774rhPXRoyOez+23g9ercMWXOrj4EhMcaE560jDYeKXT69Bn6fG2e/G2eTFkGTDmG/t1mo8/Dm+8IUznP/7a37ir6L9DHegGA3i9QiTPyhJjpM5Oce7Qu15YsBY1WGJ1X0apoZrFnyRpiXaR2cNpoi/3X2enyN3V6eCCC3oLqvv3ixC5rKzokQHBgqtf/N34+xn8tQYybT7+67/NcM76lFyQwdEuqXCkgzi1e+4Rnbl774WrroLCfJ+4YOvrxeyeqvace2Njz2xnays8+CDMn9/7go712asqnDghcqva2kTjlUzx0lOnxN8PRGN46lRcs68DkVMsXZ7DmES/TCNtH+3/Jk1fiP/v/wnzwYQJ8PTb5Sjt61PWGdBn6dFb9HjtXgzZhoAD3XPaA4i8dACdOXX/tzqdWMJ37rmiINajj4rVMxFJpK2B+K7pSH+nqVNFlnqSExKBE0vE+TKElpBK95RkpKNojvSM6EK6jHaRSM4wUrxS7Te/EV0Lmw2+853k96M50l0ne/KM43V6TpkiTAq/+IUo8r5tW/LHMaiMIKfmUOaMMH3GO2kWz8pSp1O4pJ3O9Btgoo0TJk0Sg8Ldu6GsDG69VbRjQeezebNYkWIwwI8rs2BKAmajCPQyXuUY8LZ78bR5MJeaUUz+Y0vyEi0qgsfuqOM7q8Zy77bPc+XSN6hQPgh1oHs8Is7UbBYa1unTPcVVw+uFaePDDz6AP/xB1OBKdmyeTFvUl1EqXjPvMCItQvoZmT08EF9+sdx/Pp+YxeruFhknkVAUIaZ//vORn4sguL7zjhCmAf7rZzrKLp+WstPRHOnuJjdeR+oK/P3rv8KTTwpN5ttXdLLl0ifQv/+eaFCOHRMVnrUIkp07xXpEs1nM+I0eHfmCjvbZNzaKz+zIEfEF9PTT8Le/JV681OrPfrbbe4T+igqx3z5mX/vllk8x0uU5xEhU6E5k+zQ5i598UmRt6vXC/JCbp4O8icmdfwQs4y1Yy6107O1AP12PIU98DXpaPKiqirv5/7d33uFNVu0f/yZpk+5FJ7SlzNKyREYpAgJliQNwIIggQ1FfcQ9QGeJ4EUQFAUVUXsCfiuDAgaLIkFUqu0DLFGgZbWlLS+lOc35/3H2ymqRJk3Ten+t6rrTJkyfJk5P7Oed7vue+yZmu8DZnGa8ZrVoBH3wATJtGRvKOHYHhw03s6OhYY+p7SksD1q2j9ditW1NsqekEiC3Ol1osTlodDck9xTA1QRLSle5V+6LSSnF2pDMMU1Oys8n4AJA5wIoFrmbROtL1hXQbzFWzZ1OXZts2WvU3cGDN30ud0MicmvWdJmH6tGZFfHWr4NPTqT26ulpnwrQHjYaWlCQm6lzXxp8nOprSW8rlBq935gzwyCP09/PPk9YO2DdpaGy8cvFzQWl6KdS5RsYrZQ0/t0aD8eo1WBc6DJsyumPKninYHfATFH5epCVmZZGQnp8PBAUBXl70PeTn0+M3blDl0rIycmmdO0cu9TffpNvu3el82To2r0ksqs4oZauZt4HgtGKjTSr3cG1d/My5/9LTabmLtLxj/37g4kV6D0FBOtd1WhoFn0OHqKFLj0sYCa55ecBDD9Gqkocfts9pYArXZq6Qucgg1AJll6njZE+xUe1xXSnnVK+eGvyV6IW519vj7eFZNIt3+TLN2OXl0Y5FReSazM6mYKRfwFD/B23q3BcXU8CvSeVk/YATE0MzFsXFulL3167RjOPo0fTdmpl9BVDzPMVOgF2e9QhbhW5b9rfHWWxh0nH1akqDAgBvv+2c5bkyuQyBowNRkl6CopQiraOgPLscRSlFUHiSgF7jjpEFHn2U0rt89RUVQ96+nRYPGeDIWGPqe7p2jTo0Gg11kktLyf1gzwSItc6XejTpxzCNHZlGEtI5tQvDMI7n2WepS9GpE/D66/Ydy6Qj3QYhPTISeOwxYPlyEtV37ap5mplap56kvGtKNEnTpz7W5iBXqcgRHRlpvwHGePwXHk6vUVBAK/KTksg8eOQImRsvXKhWqwJ0csmNG+QVffttx5yiKsarQJJNy3PKIYRA2bVK7cqzhuPFtDTITp3EirvK0fHLjtiX2RpL5RPwnO+Phg50FxcKtNJ9CgV9XwEBpGHt3k3jQ39/wN2dNK6KChrreXnR+bNWrDYXiw4douONGQN07Vp1jFedUcrHh44xcGCdG6kcidOE9CZDbV/8jN1/qan0Y1IqaSbxzBn6wV29SjNW7drRfUVFJByrVLSv9HhcnC5A6QmuQpBzMi2NlswtX+64jyAhk8vgGuKKsstlKEmn0Zy9qV0kOsZo8NndP2P8+lF45+R96N2tFHfFllHgLSqiCYWSEhKuJUd4hw66H7epH7T+udevpNy+PT0mnUdbi5feuEHvQSoqAegKSBQUmJ191VKTPMVOgl2e9QRLQndMDC2NW7FCNzkD2CaM17TmgIVJx/XHYjB1Ku323HPAjBnOOz2eMZ4IfyYc2T9m4+bRmwDIWeDZyROiXCB/Z75DVscYI5MBq1ZRf+jPP4ERI2gOrX17ox0dFWuMvych6P+iIjpGaSl1voQw3S5syYVv7HzRaKgTrL9/PZr0Y5jGjBCATENuKRbSGYZxND//TKsG5XLq1yiV9h1PEtJLr+jyTdnaD3vtNeCLL6hf9f33ZFio99SjlHdNjSZl+tTHlhzksbHk/rHXAGM8/istJZOQuzv9LWlZ0dEkCOtrWWa0KoD6OlOnksYbFkYZaOyNRRLGxiuFBxmtyjLKUJRSBBdPklFrbAKtNBeFt1TjvSFb8Pivd+PVzGcRrzqEuMir9EFcXOg7yMrSrUhu1YrOmUpF8UEaH7ZoASQnkzlKqdQZaQMDrROrzcUiaax47hwd/5ZbaMyobxiuzijl4kITMuYKhDVQIxUL6fZQVxc/yf134QKwZAkdu0cPev28PAo8gYH0A/rnH/qhSf83a0ZRR3JhSz8wISj1Sbt2gEaDeW8IbNggg4sL8M039i3Xs4QyVImyy2UoTaeOk6OEdKSl4SHVD0i8pRmWHemHCT+OxsFp19A6DvSZ//2XAo+fHwUeK2Y8AejO/d69lE89OJgCk76YaE2w0g842dm0dMdVr2iFfgGJZs3MBxdJ3OrYkdrbiROGEzo2VqhmGgnmhG7ponrlCrWV9HSKHb162ZZyw9wF01LNAcDspOOvu30xfksHaDQyPPoopUBxtpPIM8YTHtEeKEkrwfU/r0Odp0bgyEDkbc+jj+2oWGSEUkk5RQcOpD7rsGG0irFK5iNHxBrj7yk/33DSToozGRnAsWNV20VNi8OY23/kyHoz6ccwjRm1GnCprNin9OAc6QzDOI7r14EnnqC/X37ZxMq6GqAMqqyblVnpSFeQMGkLzZtTipl33iFxrUsXE0aFusSUIaEepbxjmgC25CCPjCRhvbiYxgfGYxDAOgOMsXBfXEyzXfouaoBc1BcukLnxxo2qWhVQZZzw/vuUhcDVlcZWenVeHYK+8erGvhsAKBWoVzcvyJVyXN9yvebjRT1z0WO3HsTPp6Kx6Ux73Jm+Anvl49DeJ4O0G09Pcp+Hhekc4eHh9N3ojw+lVDCurjoXeHY2jf18fUnIzsyk86m/GsBSLLp2TZcGOSBAd/zdu8nkNW0aMGAAvYa5diIEXTSEoP3CwmrWjuohLKTbQ01dmdZgjftPLqeG2aGD7rEOHXRiiUxGTuYWLeh/T0+aRTpzhv5XqehHd/w4iehlZYBGg7fuPYx5R1sDAD780DEdJHNIedK1QrqjXKCVAtL7d2zFgewo7LsUgXu/fRDbH1kN/76BdE7++osuIp072/aDlsspILm7U6/N1Hdf3cyavjNTpdLN1EmJS/ULSJh7L+Zmd//9l55XgwrVTCPBlNCtfzH08aF26+lJoqbkeG7Z0vTxjNuzKWex/vGNaw6kpdH+JiYdf5Tfi3F/3Ae1RoaHxgmsWCGrteW4MrkM7lHucG/vjoJ/ClDybwmE2jGFjy3h7U0Fcfr0oQn+hAT6v8rptzfWGH9PpaWGk3ZlZfR/SgrdGreLmhSHsabYjLXFSRmGqRElJYCiUkhXeVaNG5wjnWGYmvLCC+TZio4G5s51zDElRzqoZFaNXZ5z51L5mF27gHvvJa3J0amna4Q5g0HHjpzyjqk9LGlX+jnI//2XaimlppK4nZxMKQr0V8VaY4AxFu4BKoapVlONpitXaPwYFUUdk2vXaIzi4WGoVV26pBPXK8cJ33+vW728eDHQp7cGuFBNiskaIBmvis4UIWdTDkS5QOjEUORtywNgh3all1FAFhuLdfd/h4FrHsGBKy0w7Moq7C29B2H+ahqrde9eVc8xHh8a60mmzFJFRcCiReRYc3fX6UWmYpHxKmapoOmJE6Q35ebScSMi6Bim2olU3+vsWfrOExPpPlvbUT2FhXR7qIkr0xpB09TFtn17oHdvWnojBQdTrx8UREtgpJzopaX0A5R+rEFBNKMkvb9r1ygweXsD8fH4b9rDmHN0CABgYe8fMD0hBoDzRFhVGI3mHO5IrxSQlCU3sOGBDei+chqOZoZi4JpJ+HPClwgOCKBzaWoUac0P2t4UBfrpWGJi6MJw9aouqEgFJHx8qD0YvxdzYlVaGgVFczmsmMaPRlN1ZhiomtLD1ZVWO/j6Ul2FzExdJXBjjNuzcToh/eObqjmwfz+17wEDtB03IYC3d/bHnB2DAACjIg5g9bxAKBRRzjs3ZnBvS0J68bliiHLnC+kAzTH88QfQvz9pyr17A7/+Sn0lA+yJNcbfk34nS6mkdqJW01I7U+3C1uIw1qzSOnYMmD4d+Omn6ouTMgxTI/SFdKVH1T4Ap3ZhGKYmLF1K9WykVHWSmdRepGKjEjXtg0l1sm69lbowjz5KK6vrNF+6JYPBiRPU9+KUd0xtYE2totRUYOVKGqhFRAB9+5KD/PRpasN9+tAP3xoDjLFwn5enWxkrl5Ngfu0ajR8kF3VxMWkYly/rtLSsLHrdynHCmjXAlCn0tClTgCcHpALvOq9eoUwug2e0J9xbu6P4bDGKTxfbb7wyqonlFR6OTWPW4rbVj+JsXhhGFG3A3++dgE98R9N6jvH40NfXUE8yNkup1aR9ZWWZrrllHIuMVzHn5ZGBVybTZbO4dInuCw6mcV5qqq6dSCuapdeS/q9JO6qnNKx3W9/Qb8ASkivz6lUSLPRdmR99RA3IEtLF9vBhalTR0dRg160D/vMfWkM3Zw7w7rskfBm/PkA/nr59yX3erBn1Jvr21Ym00uM9etD7a9UKuPdeLLj8MF7fQSL6/EFb8HL4NySSVObZdAaSI70kjUZzjig2CkAnIKWnI9w7H39NWIsQz5s4mhmK21dPwuWzxSTqRUZSgJEEpfx8+l//By3l+j12jG41GoPjw7iIphRYYmLMC/FS8AwMpO88PJy+yytXaHNzI9d8amrV4GIsVvn4kBDm40NiV1kZfQYW0ZseqakUGz79lNrqX3+RLefff3UXQ0A3qy/9HxJCHZfDh6v+3k21Z/32m5JCv4OsLBJqjWsOyGQUh3Jz6TcGoKjcFWO/v18roj/TZTs2RM+G67mTTo035nBvQyPB4rN6QroTcqQb06YNuaU6dybDQP/+wC+/GO1kT6wx/p4A6sxkZ9P35eJCm6l2IXVoz5wxrN8gYbzkGLB+ldb168Arr1Bl+dmz6XbGDBbRGcZBlJToUrvITQzyWEhnGMZWvviCFqEB5Px2ZEF4hYcCcnfdmMWePlhoKOVKdnEBvv2WsqDWGZbGbLGxJFyVlFD/qSbjSYaxBVPalT43b+qMVVJ7DQkB+vUjU2d+Ponq2dmkL1mqBajR0JggI4MMPEJUXRlr3BlRKulxT0/Sqnr2JD1rxgztOOHjj4FJk3Qi+srnUyFbaqSdBQZar73ZgEeMBwCg6GSRdrxol3Yl1cTq1g3IyUHw5cP4Y8C7CPYsxJHrURi58k7cCIiyXCNPGh/KZPS/NDmRnU3nvbiYHvfxoeNIqwHUahrjeXubjkX635VGQ9qmQkFGOcntXlFBepVaTa/Xty+1k7w84O+/6bZ9e2o/MTE1a0f1GHak1xSNhjZ/fwoSUo5yS67M6nKmm3LzXbtGM0Qaja5RN2umS5cQEEA/ION8swDt37EjNXJTZGYC7u4o7dwDz/9+Nz45QDlc3hq4DTP77QHynZ+XTRlGQro6lwQ2h7lAjWb5OoffwM4JnyHh/ybhZHYQ+u96G1ufy0dUi3Kd+9+UM9JSbmC94xukKEhPJ0ExNpbOnTlB27hwbHCwrhMVHEy3plyanE+PMYWx40XfQXDpEsUBadmcJHRLueekGf/iYt1FTb9qu6mZYv32m5hIz/fzo5hnXHNAEmrz8nAG7TD2+/tx6GpzuMrV+Lj9Ejxa9jlwuogcEIcOOcxBYC3ubXVCujKYYpLDJvWqISKCUs3dfz+wZQud5oULgeefrzzdRrHM5nQoxnFGpaI4oVBQfDh7ltqGfruQ4oqtxWFqskqrc2e7zyHDMIaQkE6Tkqb6VVJqF86RXv955513sGnTJhw5cgRKpRJ5eXnVPmfSpElYs2aNwX3Dhg3D5s2bnfQumcbO118Djz1Gf7/wAnm6HI1rkCtK0xyzQvm22yh7wTPPAC+9RF3ae+91xLu0kerGbBERZHZxc+OUd4xzMaVd6bcrIWjMCOiMnBJBQdQWo6KoT//445bTFkv6yYEDlD44PZ1SkDRvbph+RCYjV7Ik9uqntgVoXNGnD9CnD4RMjoULgJkz6aFnnwU+WKSBfGHt1Sv06OCBnF9yUHSyCO7taexot3Yl1cSqTOnc2tsbv11zx4BBwI4dFMt+/dVM+k/j8aG/P2l/Bw/S2K60lMbg0rlPTtatBtDPoe7nVzUWeXvTfgUFOtE8PJzuKymhyRalkoR2V1c6VufOJJY3a0baQHy84bjS1nZUz2EhvSboi6tZWdTo0tJohsWcKxOoXuA0vtga5yaSquYKoQsO4eHUWE1dfIOCaMpu0ybTj3t5Ic2nEx744Vn8cyUCAPDfQX/h1X676f3UQl42yZEu4dB0CkYCUvuSy9g1JB0Jf8/Gv7lB6DMxCF9/DQyYGW06H311uX6fecZQoLp8WTebV15OVa6//97y0iKj4KkVoAoLzef3smZZFufTa1qYmoTz8aGLWWoqdWIKC+mC17w5tUnAMKd5UBDFsFOnqlZtN5dyw5qCmADg4oIK/0B8dKAfXjs7GSVqVwS6FeD7yBfQX7YHKK+cHW/Z0nzubSeiFdLPFcO7Jy2fdXZqF318fChMP/kkOb5eegnYvBn43/8q52GNxXBb06EYx5nMTPruDx6k7x/QtQv9CRDJiWBqMlbKlVdcTJ0wjcb23Pm1/D0zTFOhuFiX2sVULGNHesOhrKwMDzzwAOLj4/HFF19Y/bzhw4fjf//7n/Z/lSRMMIyN/PADMHEiXfafeIJS7DojVYproOOEdICyyB04AKxdS2aFjz/WFUmtNawZs6lUwAMPkHGOU94xzsCcdmVsnPLyIge6qcICMhmNFaQ0oJZEdEk/admSVqFevkxjhrw8Es7z80lQLSggTUytpvFCebmhAbVyIqmgUI4nnyRpBQBmzaLFrLKLtWsu9Oigc6S7taaOlENWMMvlBu+vexSJ6HffTaUMe/UCfv6ZsjcbYG58OG4cjbU2bKDxX0CAYTFSQOcqlxwVxrFIcvLn5lKbAHTGOHVl7vaAAHqOELpjyWQk6Mtk5lc0W9OOGgAspNuKsbjasiU1roMHaSstJSHClCtTX+A0VUzU+GJrnJtIv8FLwSEnh3o3//xj/uLbpo1JAWaLfBjGzW2HnFJv+LsV4//u/QEj2p3Rvd9ayMtWRUh3dDoFIwEpytsbu1x8MXQ4xYiEBGD2bDlmz44yNF1ak+t340ZaZjRzJh3/6FEKWK6u9H1WV5RPwih4Vou9+dmZxoc5x4s089uyJc0Mh4TQ1LZMRjZo49UznTrRTLZ+1faoKMsXObmcZpPj46mtGyMETp/UYHLWd9h7tRUAICHiFFa1mIPI7EOAvDK1SGws3fr4ONxBUB1SapfStFJU3CTRuDZSu+jj6gp89hmF7pdeoqw8nTsDK1YADz6IqmK4rYV09ONM587AoEGU/mfJElraZ8qZcuMGtYP8fMMJEql4zLlz9H19+ilV9xo50rbc+bX8PTNMU+GPP4AoFtIbBfPmzQMArF692qbnqVQqhIaGOuEdMU0FIcjVPWMGzadPnAgsX+68fOPagqNwzKpAKY+7mxsteHzySeBqah7emJoOmY/jihFaxNoxW9eupJrVtI/HMOawpF0ZG6d69qSZp5pqDKb0k9hYatNFRTSu8Pcn1/m//5IQe8stNPt/8KCuaGZurlbLOlQcgwdvpQW0CgXw3nu0ahdArZsLtUJ6ahH8h/gDcJ7xqnt3kvfuuoskpgEDKJ6NG2e0o7nxYVoapXd1dTVdjFTf/a9vjmrWjNJvXrqk07Zycgw1yLw8Oq6kUZaWGq4ksGTEAhqNVsVCui2YE1dbtaKgtHMnOf169jQ9MyY1msxMSoRrnC6kVy/Di61xHinj5S5ScAgJ0Ym5pi6+Rj+w3ApfzFwegc8+p/fX3f8cvpvwM6L883XvtZYq6EqpXSScEoyMhOrmIIPk00+T43PePGD7dprlDA8Hfc9795LwKKVYMXiTJmY4IyNp3WNpqXVF+ezBuICgfjtrwJWPGTuw1JGQySh/WVSUrnitt7flnOZS1Xa53Lr2aib9SF5OBd7dHofFZ+9EaYUrvD0rsGjwH3hM9jlkhw7S6xo7oesgPZFrsCsUXgpU3KxA8Zliehu16EiXkMmoFEZCAjBhAtVoHTuWcnwuWgS0bm3jpJsl5HJaBfDEE7ocgtasaioupvhoqlBNejpw5526dmCpnQGchophnIBaTQUBl0hCuolJQUlI59QujqWgoAA3btzQ/q9SqerMCb5jxw4EBwfD398fgwYNwttvv41mzZrVyXthGh7XrwOTJ1NdcID6JF984VxdV7/gqKPMDAoFGRLCXK5h3sdBePMjP1z95QCWxX8AZcd2zk8laMuYzVZjFcNUR3XalbFxCiD1tqYagyljV1AQWalPniSxNjOTXjMsjDojubk6F3VcHOla3t6oaBGJpcvleOUV0n4jIqhw8G236b1eLZsLJSG99FIp1NcdnJbYBOHh5HsbN47Suzz0EEmIS5eS3q3FVOwwjj36xUgDA2lSIyyM9MVdu6qao0aPJoNU+/aUYSEjg74/f386tnQMjUZ3LF9fy0YsoFFpVTzNaQuW8pzJ5TSj5uZGDc0YqdE0awZ8953pggjffafLeS6E4cyR1Cj1CwTqBwfpB9S5s2kHqVwOTWQU/negM6KHRGpF9MfHXMfu+xYj6speywU3nYQyxFBIr628xJ6eNKv3f/9Hq5d27iT9e/GrGSh/ZyGlqThyhC4mu3eTA9P4ACUluhlOW/KW24txAcE6+N6YekZ1xWMKC2lSaNo0Kmhy7RrNJqvVdOGLi6u6eka/fVuDXsGU0qx8LP61Ldqseh0LTo1CaYUrhg4FjqcoMO2H4ZA9Po1i3+DBhoWQ7Xl9O5DJZNr0LkUnKdVJXQjpEtHRlN7+jTdoEPjjj3R6Z8ygy4BDMSp0g9On6VYq/nLXXbrHs7PpjeXn64rHhIbqJgyzs6ko8/TpzmtnDMNY5McfqRvpKjNfCEvSdtmR7lhiY2Ph6+ur3ebPn18n72P48OFYu3Yttm7digULFuDvv//GHXfcgQpz7jCG0ePAAXJD/vQTLYb++GNgzRoakjoTfUe6I/tgspOpeEMzB590WwkZNPjs/GD0/PMdHN523eHFCKvAYzamLqlOu4qOplkzyThlb3s1Z+wKCqLxXkICibsvvUQmnSVLgNmzKU/Lq6/SuLBzZyRejUJcvBzPP08y2KhRJMsYiOhA1YKb+jihWK9rgCtcgylOFR6nMbezVzB7eZEX8/XX6bR/8w0NuX78sZonGn+XkrgtrQZwcaHHdu+msZ+vL53goCDDQq0xMcBrr9FSgPh40rS6dCHdU/9Y7drRa6Sk0DGefppuG3HcY0e6tehXHpZmW4wDkn5eKVM5yZs1o+fl5FRNFxITQz2XZs2o13LiBD0/IICe6+JCx5LcfDbM5ghBeZZmzSIjIUAZHD7+GOjXzx9I/U/Nc+/aicJDAYWPAhU3KtMp1LJ4NX48LQQYP57cn8+/G4qV3g9jSR9XDPG/SOf96lX64euLQMYznLW1tEhKCaRWA/fdR9b6U6c4n15Tx1rHy6BBtFWX07yGM/iFkTH4n3c03t9ZgQuXqKMREyOwcKEMd94pvYyc3mtoqG65WZUD1f6SL7c2brh55KY2P2dtTeqZw9UVmDuXfubPP0+pXhYuBFavps7Uo4+SwdshVJc2xppc+PoThuPG0SopJ7UzhmHMs3gx3bq5aIBy0/2qwEC6PXqUurWcAcQxpKSkoEWLFtr/zbnRZ86ciQULFlg8VmpqKjpI9UxsZOzYsdq/O3fujC5duqBNmzbYsWMHEhISanRMpvGTm0v9jk8+oRX5rVrRqv7u3Wvn9ZVBOnOVw8aDeo7cJ+6+jBbR6zDl55FIzm6OXtvfxetXvsdrzX+G8nUnppizt84Nw1iDNWmDjTGlT9jTXi05xGUyGtyEhNA40MWlios6I4NMQ2vX0v8+PsC779LiWZMppcysiHZmsV6PDh7Iz8rXCum1MV5UKIC33yaD+KRJ9FHvvZfSmc+fT1mcTWL8XZaUUGAPCyNHhSSwt29P+0o6l6lsCoMH0wp3U8fSX1lgRXrpxhL3WEi3BnOVh41zoEuuT3M5y6W8U8azgteu6Za7nDhBMzpyOXD+vK6isUJBjdrfn0RdK4KDEMDWrTTJt2sX3efpSS7HZ5/VZYyxO/eunSjDlCi+UZlOoZbzEgN0uhP3aLBqzGa89ltfpBaEY+gfL+LOZrdhlvdi9I64Qk7Lkyd1o0/jSYzaWFqkXyhESgnUvj21t8plUJxPr4lia0eimpzmti65unYNWLaMcmfm5MgByBEWRrFn0iRZVRdTPUxPJDnSJeoiFpmiUyfgzz/JuPHii2QaePZZ6lA9/zylgpEWKdlFdUuK5XJ6IXd3uv6Z6tHqd8ityJ3fWJb2MUx94Z9/aP7K1RVwlQsImBak+vWjLun+/ZQKUxq0Mvbh7e0NH1N9QCNefPFFTJo0yeI+rVu3dtC7omMFBgbi7NmzLKQzVVCraTX/nDmkhQAk0KxcSfXBawunONKNHLl3R5/Gif98jP9suhPfp8ZiXuoY/PhhGhZGZmHoxFCn5X+v67E208gxpRGYShtsjDl9oqbttYbju6tXqR7DJ5/oFldPnkwisVTn0iy1PFHlEeOB/J35KDlPS/pq0wTasyelkp83j8xVGzbQx37iCTLMmjxXpr7L8HDKj2+NOUo//aa5Y126ZFV66cYW91hIrw5LlYf1XcqmXJ/GjebEiaqzgteukau4qIgCnExGMzvFxSSiT5lC+0vO49Onqw0OJSWUJWb5cvqNAGRyf+wxMglKdd4MqMO8bMpQJYpP1V1eYgBQXE7DY97rcP/UPZh36G4s+6cXNuX0xqacdRiYtQ+vhq3G4Mw9kF26pEuxoy9MOlsYNC4UIomkR49Se3zmGc6r19SxpSNhzwx+peNBk1+AbceC8MWvIfhxo0yba7d1a+CFF6gDZNYxXQcOguqoIqTXYWoXY2QyyrAydCjlKF24kOqEvvYaOTWmTAEef5xCkFOxdcKwHn7PDNOYkdzo48YB4v/M50iXy6mPGBcHfPkl9Q/79avFN9rECQoKQpBxSjMncunSJeTk5CAsLKzWXpOp/5SUUH2o996jISZQmeZyMZkPaxtn5Eg35cgN9izEhgfWY/2JjnjqtxFIvh6J4ZOAfl8A77zjxFjIOdAZZ2BOIzh8mLQoKW2wrfpETdqrjf3+CxdoTLNqla5mS69elAO8Vy8bXrcWBVspT7pEbY8X3dxogmHsWNL1Nm8mM9v//kcywPTp5HcywNR3aYs5qrpjVWfEaqRxj4V0S1RXeVjK89O1KzU0Y1HAuNEYixBCkOBVVERifGkp2YiaNaPGnZJC24wZpoV5o+Bw5gzw2WcUjHJy6D43N0qL/MortBqjPqIMdcJSPlup7Gj5t5Rj8fDN+E/P/Viw+zZ8mdwF24t6Y/u53ujimoLHsBMP31sEv3F3OE6YrA5zhUKcUciUadjY0pGoyQx+aipOrtyJ9dsD8b8z/XChSFeMt0cP4OWXaamZVXk069lSV/c29VdIl1AqgSefpLQu335LHamUFBr0Ll4M9O9Pgvro0dQ3cjg1mTCsZ98zwzRWLl0idxIAPPO0QEGly9xcLOvZk2LJZ58BTz0FHDrk/BzIjO2kpaUhNzcXaWlpqKiowJEjRwAAbdu2hZeXFwCgQ4cOmD9/PkaPHo2bN29i3rx5uO+++xAaGopz587hlVdeQdu2bTFs2LA6/CRMfSEnhwpwLl1Kdf8A0trefJP6EHUVB/Qd6Q5Ll2DGACCTAQ92OoFBzY5i/q6++PjcMOzaJUP//jSJ8MwzwIgRtCCcYeot1mgE4eGkLTnb0GJl+ll1uxj8/gutgvn9d3oaQItYX38duOMOM2lcqqOWBNsqQnodrWDu2pXO37ZtJBUeOEDjwkWLSGR//nkqV2WWWi7U2hjhLrMlrKk8/O+/lG6lZ8/qRQFjESI/nwKftC5fv+KtqSUVJoJDejqwfj2wbh39gCQiIkhAf/TR+p/7UhWmyyFZZ3mJjYJJ+2Y5+GLkz3hjwA68nxiPlQduRXJ5LJ4+EYuXzwk8cEGGRx4Bbr9dr8PpLMHIlkKmjXTGj7EBWzoSVgjv0nzfd59cw4avvHEs93HtYz7KYowP34mp3Q7h1jdHQRZrYxuvR0u+6mtqF1O4ugIPP0zV2zdvps7or79S0eSdO+k0jhpFHakhQ/TSeNlLTScM69H3zDCNlY8/pvFr//5At84COyvvt9Sv+u9/ge+/pxrBH39MXRimfjFnzhysWbNG+3+3ypHx9u3bMWDAAADAqVOnkJ+fDwBQKBRITk7GmjVrkJeXh+bNm2Po0KF46623zOZsZxo/ZWUkuqxdS/2FsjK6PyICeO45Gi9akZXIqRg40h1lZqjGABCUewofTPDEiw8Pxdv/leHzz6kmzV9/0VOnTQOmTq3/Y2mmiWKNRpCTYz7tsD36hH5O9sxMnXBuIv2s8PLGiYJIrPtKjtWr6S1IDB1KK2z796+hgF7L1LUj3ZhBg+ir3bgReP99YM8eWmn45Zc0OfHII8CYMSbSdNXDNKsNDRbSLWGp8nBgIAWmM2foKjt0aPWigLEI4eZGPRmVilK8eHjoiokCJpdUCEFVizdtok1K3QLQrPnQoZQnacSIhuMsqheOdDPBJML3BhYP24y5IZ/i/zQP4bOLQ3DsmEwboIKC6CsdM6ZSVHeGYFRbhUyZpokJ4b24mAoUb9oE/PYblWsAaBm6i7wCQ1r/i3GdjuG+2FR4uJRRPPsJQIcarIqoJ0u+VC1UkKlkEKWV6RDqoSPdGLmcYv2IEdTf+eILWpGUlqbrRAUEUFqYu++m64PdA+WaThjWk++ZYRojycnkMAVIFBNqoX3MUiwLDCQx/YkngNmzgQcftCIfKVOrrF69GqtXr7a4jxC679vd3R1//PGHk98V0xAoLia34s8/04SZtFoZoEv2iy9SLnSHTbbbiUGOdEeZGaw0ALSIkOOTT2gF9yefUH8qLY3yDs+ZAwwcSGO90aMNy6MxTJ1irUYQEkJ5QBylT+jnZM/KImOpUkmViaOjgcJCiCNHcTxZ4PugJ7B+exBSU3VPDwykFKCPPkp6e0PCLdINcnc5NMVkpa8zE6geMhnFptGjqfbNhx+S0XbvXtqefZaGaOPGkcHK3R2cftMBNBCptY6wpfKwtY1MX4Q4cIDSugDmi5e6uSG9wA87vgS2bwf++IOM8Ppvo18/ch7edx/VC2hoGAjpdeUCrSaY+DcPxNPPRGB6Bxn++Yc6WD/8QPMfK1fS5utLwemOO+QYNiwKLTo76L3x0hvGyZSWkpFg+3baEhN1biUAUCoFEgKT8UCnFIy8JQ0B7sV6z24cqyJkchncW7ujKJVicn3oGNlCeDgwdy6JYfv2Ad98Q52orCxyoK1dS5es/v1JUE9IAG65pYbLltlhzjD1Ao2G0jq9+irF7NhY4J57AE2BRrtPdf0qKb3LwYP03JUrackwwzANCyHIELpjB7nPt2whMV0iLAwYPx6YMAHo0qXO3qZZXANcARkA4WAzgw0GgFatKGfzm29SqqxPPqE+8dattP3nP9SPGj4cGDaMzmNDcNEyjRRbNAJHGVr0c7KHh5N2AgAVFbiZ/C+2Z/fGpsye+O1MW6Tf8NM+TamktC3jxtHPrqEukJLJZfCI9sDNIzfp/3pmvOrZE/j6a0rx8tVXwJo1VKZx3TraPDwofo0eDdxxRwyacfrNGsNCuiWcteRBEiEuXACWLCFXe48egFyOCo0Mx7OCse9SOPYd98Kugltw7kt/g6d7eJAIcued5DSsr7nPrUUZVg8c6YBVHS0ZKLNPXBwV6tqxQ1cxOTubirx+9x0drkMHcqn370+3Nf6eeOkN40CEoKadlESDg717SUDRF84B6hvdeSe5nRNCTsDz/XcobplSXhvJqgj3tjohvb51jKxFLqelfH36kCNh925axv3LL1SrWhoMApSVbOBAmoy97TYS1q12prHDnGHqlMuXgUmTKAUBQCL4559TiK4ot86RDtD+n35K/ZR//qHuxH/+A7z1lomlwAzD1BvKymg1SlISXet37AAyMgz3iYigFWmjRlEKgPqc81umkMElwAXqHLXj+2A2GgDc3GjCYcIEMttu2EDmhEOHdKaTGTNociIhgfpRffva5q1jGLupbY3AKCd7wbUS7L3YATtKJ+Pvm7di/41oqI/oBhJuijIkhKZgzPMtMPLRIG0244aORwc9Ib2epgJt3pxql730EsWtL78kE2h6Ot3+8AM1l+7dYzBkcAcMHZGJ3m2z4Rbo1ejNUffccw+OHDmCrKws+Pv7Y/DgwViwYAGaV6nSahkW0i3hzCUPcjnKI1rj5KBncejYLhz+JhCHijvgUFY4CstVxruiRw9gwADqBN1+O13gGwuuwbqAW3GzAkIjIJPXUVCyoaPl6koO9CFDyLFw4ADlK/79d10aspMnaYAKUGc2Lo6qUMfFkWhlVZoFXnrD1BCNhtKyJCdTH+vgQdqk4lL6BAeTqCpt7drp9ccueDWJVRH6BUfra8fIFlxc6LoxYAA5E06fpvi0dSsNuK9f13WmAFrq16sXbT170nUnKordVgxTXygqopRb69bRbWkpmSs+/BB47DHdb1VIQroCkFnxA+7enboXL71EgtGyZVTQ+IknSKC/9VbuYjBMXVJcDBw/Dhw9StvBgySOlJYa7qdS0UT6oEEkoDc0x7RroCvUOWrnrAqsoQGgdWsSzWfMAM6do5SHmzdTP+rqVeD//o82gNLp9e5NMbVHD7pt3rxhfQdMA6IWNYLyciD1r6s48Gsw9hW8gH07W+N4VhAEDI/dSnkJIzqcx4hOaRgQfhYe508AQ2cDvo0nJ5J+nvT6brwisZy2Dz+k68bGjcBPP1F9nAMHgAMHZJiPUCiVoejZUzcxGBdHTaixMXDgQLz22msICwvD5cuX8dJLL+H+++/H3r17bToOC+nV4YACkiUlwJlTGpzak42TqRocv+CNExc8cOqUDOXlrQG0Ntjf27UYvSIzED/EG/F3B6Jv37ovAOMsClMLcW39Ne3/edvzkPZuGgJHB8Izxky+L2dTg46WQqFzqs+dC+Tmkjvk779pO3yYrnHp6TrHOgC0aUMVlbt2BTp1Ajp2pA5bFceIswqZMo2Sp5+mC+OxY9SXMkahoLbWpw8QH0+3bdpY6Og3kVURbm10M5Tl18vrdlLPCbRvT9uzz1JRwgMHaCC4Zw+tTMjN1cUsiWbNKD5JW5cu1BTc3c2+DMMwDiI3lwSzAwco7+WffxrG9N69admucY5RSUi3RYyKjCTH5datdA1JTSVX+ltvkRB01120cqVrV+pyKJXVH5NhGOsRglJGnjtHE9+pqbrt3DkyRxjj768bfwwYQDGhIZutXINcUXyqGBU3K1B8oRhukW71qh/Wpg3Fx6efpkkMaay3ezel1cvNJaH9t990zwkKojFe5850K3m2AgNZYGccgBM0gpwcmrg7fpzMWIcO0ZiytLQFgEcN9o1yvYwBfocxIOAYbvc5jKjCE0Cf/pT+OD+/URitjHGP1g2C1DfUDWa8qC+qv/UWpYv+6y9KA/bXX7Siac8e2iSioshc1bOnTrOqzToRBQUFuHHjhvZ/lUpld+H0559/Xvt3y5YtMXPmTIwaNQrl5eVwtaFoCAvp1lCNS1kIIC+PMrWcP0+dHWk7exa4eFFACDmAqgnMvb2pUXa7RaBbZDZujbqO2FuUULRq2ejtP4Wphbj00SWUXSvT5sSTe8tRcLgAJeklCH8mvO7EdDsJCCAX1z330P8FBTQYTkoit/o//5D2KLUTfXHdzY2EquhocgVL4lebNjFoNiMasnTOS8xYZudO6vgA5E7q2JEEUMkd06ULuRitpgmsiihMLUTBQV1qmmvfXoM6R123k3pOxMWFBty9e9P/Gg31vxMTdaJdcjJ1prdto01CJqMJv9hY2tq3p3jVvj0PDBnGWioqSHS5epUGL1evUog9d45SCZw7R+NhY6KiqC7O2LHm3aZSsdGarKxJSCDH67ffkmtp82YabEn1YABakRcbC7RtC7RsSe+pZUtKcxASQiucGrKYxzDOQAj6zUvGmgsXdNv58zRmtJQhLyiIRIxbbqGtVy/6DTaWa25haiHUOWrt3xfmXIBHB4962w9TqSheJiTQ/+XlJDju369bAXriBE2OSOlg9PH3p35Tu3bUp5K2li1p8tKFVRrGWmpQu6i8nGLP2bNUW+HkSd2tcYooCW8vDbp5nEZ85GX0bnMNcc3TEXZ0M3VggoJodsnFhX4cjchopU9haiFu7NUJuzm/5kAmk9XbOGWJ5s2BiRNpE4L6nbt20bZnD03oSteoDRt0zwsNpWtRx466sWBMjHPSAcbGxhr8P3fuXLzxxhsOO35ubi6++uor9OnTxyYRHWAh3Xrkchy+HoWjRykmpKfrbi9cqC41sAy+roWIbpaNDkG56Oh7CZ3EMXSMKkTkaw9DFhsDUpKDKrfGj9AIZP+YjfLscnh29ETen3moKKiAwkMBj1gPFKUUIXtjNjyiPRrEDF91eHvrUixIZGfTYPXIEdpSUsh1Ulysu6/qceRo3TpK29GStshISh0TGNigtUzGQbz+Ol0Qu3ShDrpDOuONeFWEdlIvQ5coXuGnaBSTetYil+s6Q1On0n2lpYZLyY8eJXH9+nXdJOAvvxgex8+PHFutW+tuo6Joi4xsuMWFnMny5cvx3nvvISMjA127dsXSpUvRq1cvs/tv2LABs2fPxoULF9CuXTssWLAAI0aM0D4uhMDcuXPx2WefIS8vD7fddhs++eQTtGvXTrtPbm4unn76afzyyy+Qy+W47777sGTJEnh5eWn3SU5OxlNPPYX9+/cjKCgITz/9NF555RWHfe7y6+U4Ovho1QeMLvlVUpNU1yUwftzc8WSmH5f+l8lkEAIQoIkm6VYjACFk0GiAisr/NRVAhSBxvEIjq7wF1BWAWi2DugIoVwNlahnKyoDScqC8XAYNAA1kdOzK2zDIEAwZelXe5+UrQ2CIDMGhMrRoKUOLKDnkbnLIt8pxdZ8CCh8FXHxcoPBVwC3SDaoIFTTlZF2t6ZJjV1fg4YdpKymhlSubN9OipKNHyWQmxQRz+PiQqcDfX3fr7U33e3vT5ulJm4cH3bq50WoXaVOp6D6Vijalkq5njUU4ZBoHZWXUp8/KotR50nb1Km1XrtB2+bJh8U9TyGTUn2/blkw1kkARE0PiRWNt+1I/rKKkAgDg4ucCl0CXBtUPc3XVrQ6QKCqisd3x4+ToPX6cutBpadSXSkqizRiFgmprRUaSd6VFC93WvDm1hbAwipsMA6DKinq1GrhyidraxYuGk3bnztH9pla5SLRqRUJpp0401OvWDWgdBcgX/kCdgejKFcodOlCn4No1UufDw+kAKSmNwmilj3a8mKk3XvRpHONFmYyuO23bApMn0315eTQhKE0OHj1KEy8ZGbT98YfhMYKCdCZQaYKwVSu6ranRKiUlBS30Cg3a60aXmDFjBpYtW4aioiL07t0bv/76q83HYCHdBpYvB774wvzjwcEkarZpU7m10qDNvq8QnfE3gru1MBSEhQBSTgM/bQQ6RDeaAGMtJWklKDpZBFWECjKZDAovBSoKKiCTyyCTyaAKV6EotQglaSVwj2qcOQQCAw2dDAANwi9coGvPmTM0EyhtUi1HS4NXpZI6WeHhwKxZwNChtfJRmHrGmDFOOnANHA/1Hf1JPa/uXrj+x3VAAAovBdxauTW6ST1bUKl0SwAlhCCxICVFN/l3+jS5WNLSdJ2ugwdNHzM0VDfxJ22TJ5PI1hT59ttv8cILL2DFihWIi4vD4sWLMWzYMJw6dQrBwVVXse3duxfjxo3D/Pnzcdddd+Hrr7/GqFGjcOjQIXTq1AkAsHDhQnz00UdYs2YNWrVqhdmzZ2PYsGFISUmBW6VNePz48bh69Sq2bNmC8vJyTJ48GdOmTcPXX38NALhx4waGDh2KwYMHY8WKFTh27BimTJkCPz8/TJs2zSGfvfCGwM1DNx1yrLpEXrk5rUOdX7mdBtQALlazu4ufC9yi6Ht2RO5ONzdg+HDaAIoBFy+SKKTvqE1Lo4FVZiaNpW/coO3CBbvfggEyGfV1lEoSrqRb/c3FRXdrvCkUulvjvxUKupyZ+j8sDHjxRcd+FqZhcOIErc7IydFtubmkG127RhqSLQQHUz9dmmSWtrZtSXRoaqs59PthqhYqlJ4vhcxFRhOEsYoG3Q/z8KjajwJoQuXMGeo7/fuvbhXSv/+SSU+tppialmb5+F5e1J70t4QEWrFU2ziqgB9Tcz74gJzD6ek0iWdJKAdowrpNGxraRUeTJt6hAw339HwVephYoezvT4r7wYN0gVapKEA2AqOVPgYm0M6eyN+RD/V1NVy8XBqlCRQgc5SxVnXzpi7lT2qqbjx46ZLummgq1binp878Kd2OHElNxxLe3t7wsSLH9cyZM7FgwQKL+6SmpqJDhw4AgJdffhlTp07FxYsXMW/ePEycOBG//vqrVXWFJFhIt4Hu3emCFhFBcUO6lZx2VVIlXEgD/t4CtAsEjH9QMhk9OTWVDlqD4icNmYqCClSUVEDlSbNKCl8FcBWQqeg8KTwVKLtchoqCirp8m7WOQqGbiDGmpIQGpFJHS5phvniR/s7MJFfM+fO0lZTU+ttnmgI1LNZUX9Gf1JO7yOHSzAXqbDXkKnmTmdSzBZmMUjeEhFBRWn2Kiw1Tm0mDQ8kJU1SkczH884/ueePH1+pHqFd88MEHeOyxxzC50v6xYsUKbNq0CatWrcLMmTOr7L9kyRIMHz4cL7/8MgDgrbfewpYtW7Bs2TKsWLECQggsXrwYs2bNwsiRIwEAa9euRUhICDZu3IixY8ciNTUVmzdvxv79+9GjRw8AwNKlSzFixAgsWrQIzZs3x1dffYWysjKsWrUKSqUSHTt2xJEjR/DBBx+YFdJLS0tRqlf5rsDyUj2UurhgBjpb3KcacznIJ279/uaeX405XbufiwJQSQKuK6B0FXRbKeyqlICbSsCt0kHtpqp0V6uE9tZLcmB7AB7uAgo5DdCgAUSFgKgQQAXdJ9T0vyjXbZpSjW4r1ECdr4b6hhrqPDVKL5ZCnafGzSM0QeHi6/huvkymE/5MIaU7zMoix2VuLt1ev07zrwUFJLAXFFB2sKIi3W1xsW4rKaFVMcXFdEz945eWVi2y6Gw6dmQhvaly/DiZUywhl5NBRro+hoSQczgsTHcrOYt5ZZYh+v0w10BXlF4shWcncnU21n6YuzutGu3SpepjFRU0ppPGeZcv03bpEt1KqcCKikjUunmT+loSbm51I6Q7qoAfU3PS0ihXv4SrK8Uc/Qk7fdNnWFgNXMLmViiPG0fLMUJCGoXRyhhjE6iqpYqE9ACXRhunTOHlZZgaVKKggNzqp0/TJOGZM6RH/fsvNZHCQp3oLtG6dfVCurW8+OKLmDRpksV9WrfW1aUMDAxEYGAg2rdvj5iYGERERGDfvn2Ij4+3+jVZSLeBJ5+kzWoKCmgkYG7dlaenzmbcxFB4K6BwU6CisAIuPi7wG+QHZZhSWwW5orACcjc5FN7GVTebLlLu9MqJtCqUl9PS0UuXaLOQGYBhmEqMJ/Wa3d0MZZfLoGxOlfSa6qReTXB3pyWglcZoA4Sgpe9pabr8sFKKtNosWqNPXbunysrKcPDgQbz66qva++RyOQYPHozExESTz0lMTMQLL7xgcN+wYcOwceNGAMD58+eRkZGBwYMHax/39fVFXFwcEhMTMXbsWCQmJsLPz08rogPA4MGDIZfLkZSUhNGjRyMxMRH9+/eHUq+i5LBhw7BgwQJcv34d/iaWEMyfPx/z5s2z+vN7+srR7fFmkMuh3WQynQvY+H9Lt/qbscNY34msUFR1LOvf6jucpVsppYira/1Pq6Ap1aDoZBFuHr2JopNF8B9a+0s9ZDIyqDlqlYkQ5M4sLSWzgP5WXm74t1pd9Vb6u6KCNuk+6X/pvooKcu/p3y/9r9GQ05NpmnToAEyZQsW3pS0ggK5dQUEkoPv7U3xhbEe/H+bi44LQKaEGjze1fphCQZMvzZtXFav0KSggUT0ri1ygWVm06V3aLTy3/hbwY2rOxIlA//46w2dIiJO07Ea4Qrk6qowX72oGv4F+cPEhObWpxSljtHUfu1V9rKREZ/yUbtPSTI8Xa0pQUBCCajig1FQu3Si10aHBQroz8fYm9bOwkJJCGlNY2CgrGVuDW6QbPDp4oOBwARSxCiiDlFAG0YBdCIHSS6XwvtUbbpFNbH2jHbi66nKmMwxjHcaTem6RbgZxhyf1HINMphMdjJc41xV17Z7Kzs5GRUUFQkJCDO4PCQnByZMnTT4nIyPD5P4ZlZWhpNvq9jFOG+Pi4oKAgACDfVq1alXlGNJjpoT0V1991UDkv3z5cpUiQfp4eQErVph9mKkBcpUcXl294NXV5JrsBolMppvkYJi6oGtXy6k9Gfsw7ocZw/0w00i1JvTKn1hNfS7gx9ScW2+lrVZoZCuUq8M4TskUMoN4xXHKPG5uuvRBdU1SUhL279+Pvn37wt/fH+fOncPs2bPRpk0bm9zoAKV1ZJxFZCTZGNLTDdelArpKxjExjaqSsbXI5FTd2DXQFUUpRVDnqyHUAup8NYpSiqAMVCJwVGCjyTHFMEz9RJrUK00vhTCK09KknkeMB0/qNUKef/559O7dGy1btkSfPn0wc+ZM7Nu3D+Xl5XX91hokKpUKPj4+2s27CZoEGKYxs3z5ckRFRcHNzQ1xcXH4Rz9Hlwk2bNiADh06wM3NDZ07d8Zvv/1m8LgQAnPmzEFYWBjc3d0xePBgnDlzxmCf3NxcjB8/Hj4+PvDz88PUqVNx86ZhbYXk5GT069cPbm5uiIiIwMKFCx3zgZlagfthtU9KSgry8/O1m/7KOHuYMWMGPD090axZM6SlpeGnn35yyHEZpq7hONU48PDwwA8//ICEhARER0dj6tSp6NKlC/7++2+bV+WwkO5M5JUFGQIDKSFQfj6tH83Pb5SVjG3FM8YT4c+Ew7ubN9Q5ahSfLoY6Rw3vW73R4pkWDbbqcVPnnnvuQWRkJNzc3BAWFoYJEybgypUrdf22GMYkPKnXMJCWIUubrcvvqqMu3FOBgYFQKBTIzMw0uD8zMxOhoaEmnxMaGmpxf+m2un2ysrIMHler1cjNzTXYx9Qx9F+DYZimg1QYee7cuTh06BC6du2KYcOGVYklElJh5KlTp+Lw4cMYNWoURo0ahePHj2v3kQojr1ixAklJSfD09MSwYcNQolfkZ/z48Thx4gS2bNmCX3/9FTt37jSo0yAVRm7ZsiUOHjyI9957D2+88QZWrlzpvJPBOBTuh9U+UgE/aTMnIM2cORMymczipr+C7uWXX8bhw4fx559/QqFQYOLEiVVER4ZpiHCcahx07twZ27ZtQ05ODkpKSnD+/Hl88sknaNGihc3HcpqCW19dC7WOVJChWzcq8376NN3eeivd30gqGdcUzxhPRM6MRNSbUWg5uyWi3oxC5IxIFtEbMAMHDsT69etx6tQpfP/99zh37hzuv//+un5bDGMWntSr/8TGxsLX11e7zZ8/3yHHrUv3lFKpRPfu3bF161btfRqNBlu3bjW7vDA+Pt5gfwDYsmWLdv9WrVohNDTUYJ8bN24gKSlJu098fDzy8vJw8OBB7T7btm2DRqNBXFycdp+dO3cauPO3bNmC6Ohok2ldGIZp3OgXRo6NjcWKFSvg4eGBVatWmdxfvzByTEwM3nrrLdx6661YtmwZAFQpjNylSxesXbsWV65c0dZ8kAojf/7554iLi0Pfvn2xdOlSrFu3TmvQ0C+M3LFjR4wdOxbPPPMMPvjgg1o5L4xj4H5Y/eTFF19Eamqqxc24gF/79u0xZMgQrFu3Dr/99hv26Ve/ZJgGDMcpxgDhBNatWyeUSqVYtWqVOHHihHjssceEn5+fyMzMNLn/nj17hEKhEAsXLhQpKSli1qxZwtXVVRw7dky7z7vvvit8fX3Fxo0bxdGjR8U999wjWrVqJYqLi7X7DB8+XHTt2lXs27dP7Nq1S7Rt21aMGzfO6vednp4uAIj09PSaf3hzVFQIcf68EMnJdFtR4fjXYBgrkdp6SkqKyM/P124lJSUOf62ffvpJyGQyUVZW5vBjN1WcGquaMJoKjSg6XyQKkgtE0fkioanQ1PVbavLYGqtmzJghAFjcUlNTtftfu3ZNnDp1Svz555/itttuEyNGjBAaTe197+vWrRMqlUqsXr1apKSkiGnTpgk/Pz+RkZEhhBBiwoQJYubMmdr99+zZI1xcXMSiRYtEamqqmDt3rsn+kp+fn/jpp59EcnKyGDlypMn+Urdu3URSUpLYvXu3aNeunUF/KS8vT4SEhIgJEyaI48ePi3Xr1gkPDw/x6aefWv3ZOE4xTYXG3tZLS0uFQqEQP/74o8H9EydOFPfcc4/J50RERIgPP/zQ4L45c+aILl26CCGEOHfunAAgDh8+bLBP//79xTPPPCOEEOKLL74Qfn5+Bo+Xl5cLhUIhfvjhByEExciRI0ca7LNt2zYBQOTm5pp8byUlJQbXk5SUlEb9/TUkuB/mXGozVl28eFEAENu3b3f6a9WEZcuWiZYtWwqVSiV69eolkpKSLO6/fv16ER0dLVQqlejUqZPYtGmTweMajUbMnj1bhIaGCjc3N5GQkCBOnz5tsE9OTo546KGHhLe3t/D19RVTpkwRBQUFVr/nxn6taShwnHI+DaGtO0VI79Wrl3jqqae0/1dUVIjmzZuL+fPnm9x/zJgx4s477zS4Ly4uTjz++ONCCApMoaGh4r333tM+npeXJ1Qqlfjmm2+EEELbCdq/f792n99//13IZDJx+fJlq953Q/jCGMYRSG3deJs7d65DXycnJ0eMGTNG3HbbbQ49rqNoiJ0oIThWMU0HW9t6VlaWSE1NtbiVlpZafK29e/c68iNUy9KlS0VkZKRQKpWiV69eYt++fdrHbr/9dvHII48Y7L9+/XrRvn17oVQqRceOHc3GoZCQEKFSqURCQoI4deqUwT45OTli3LhxwsvLS/j4+IjJkydXiUNHjx4Vffv2FSqVSrRo0UK8++67Nn0ujlNMU6Gxt/XLly+bjI0vv/yy6NWrl8nnuLq6iq+//trgvuXLl4vg4GAhBE0KAhBXrlwx2OeBBx4QY8aMEUII8c4774j27dtXOXZQUJD4+OOPhRBCDBkyREybNs3g8RMnTmgnYE0xd+5ck33gxvr9MYyEs2LVvn37xNKlS8Xhw4fFhQsXxNatW0WfPn1EmzZtnGLSshc2fTJM/aYhtHWHC+n12bVgDDsSmKaKsx3pr7zyivDw8BAARO/evUV2drZDjutIGmonSoiGcXFhGEfA7qmGC8cppqnQ2Nt6YxPSefzHNFWcFauSk5PFwIEDRUBAgFCpVCIqKko88cQT4tKlSw59HUfBpk+Gqd80hLbu8Bzp2dnZqKioQEhIiMH9ISEhyMjIMPmcjIwMi/tLt9XtExwcbPC4i4sLAgICzL7u/PnzDXKuxsbGWvkpGaZx0JSLzdTXfJ8MwzifpKQkLFu2DEeOHMHFixexbds2jBs3Dm3atDGbn5xhGKYp0tgKI6tUKoO+r7e3t+kPzjCMVTiygJ+zKSsrw8GDBzF48GDtfXK5HIMHD0ZiYqLJ5yQmJhrsDwDDhg3T7n/+/HlkZGQY7OPr64u4uDjtPomJifDz80OPHj20+wwePBhyuRxJSUkmX7e0tBQ3btzQbgUFBTX70AzDOByXun4Ddcmrr76KF154Qft/eno6OnXqhKtXr9bhu2IY5yO1cY1GY9X+L774IiZNmmRxH+NiM1LBmZiYGERERGDfvn31RqCSOlGvvvqq9j5rOlH68QKgTpQkklfXiRo7dmy1najRo0ebfO3S0lKUlpZq/8/PzwcAjlVMo8fWWGUtHh4e+OGHHzB37lwUFhYiLCwMw4cPx6xZs8xOKDK2IX1nHKeYxo6z4lR9Qb8w8qhRowDoCiNPnz7d5HOkwsjPPfec9j5zhZFvueUWALrCyE8++aT2GFJh5O7duwMwXRj59ddfR3l5OVxdXbWvY0thZI5VTFOhsccqa7Bk+tQ3helTl6bPefPmVbmfYxXT2GkIscrhQrqzXQthYWEG+0idL2tcC8aoVCqDAXNRUREAoFevXtV9TIZpFGRmZiIyMrLa/YKCghAUFFSj15ACoL4QXNc0pE4UYL4jxbGKaSpYG6usRXJPMc5D6tdxnGKaCo6OU/WJF154AY888gh69OiBXr16YfHixSgsLMTkyZMBABMnTkSLFi0wf/58AMCzzz6L22+/He+//z7uvPNOrFu3DgcOHMDKlSsBADKZDM899xzefvtttGvXDq1atcLs2bPRvHlzrVgfExOD4cOH47HHHsOKFStQXl6O6dOnY+zYsWjevDkA4KGHHsK8efMwdepUzJgxA8ePH8eSJUvw4YcfWv3ZOFYxTY3GHKsaE8amz4MHD2LQoEEcq5gmQ32OVQ4X0uuza6E6unXrhn/++QchISGQyx2e9cZqCgoKEBsbi5SUFF5uWEP4HFpGo9EgMzMT3bp1c+hxk5KSsH//fvTt2xf+/v44d+4cZs+ezekS7MS4I6VWq5GamoqIiAiOVQ0cPoeWcVasYpxPfelTAfw7cwR8Ds3TFOLUgw8+iGvXrmHOnDnIyMjALbfcgs2bN2vNA2lpaQa/8z59+uDrr7/GrFmz8Nprr6Fdu3bYuHEjOnXqpN3nlVdeQWFhIaZNm4a8vDz07dsXmzdvhpubm3afr776CtOnT0dCQgLkcjnuu+8+fPTRR9rHfX198eeff+Kpp55C9+7dERgYiDlz5mDatGlWfzaOVY0HPn+WaQqxqjoasumzX79+9SJW8e/MfvgcWqZBxCpnJF5ft26dUKlUYvXq1SIlJUVMmzZN+Pn5iYyMDCGEEBMmTBAzZ87U7r9nzx7h4uIiFi1aJFJTU8XcuXNNFvHz8/MTP/30k0hOThYjR440WcSvW7duIikpSezevVu0a9fO5iJ+9YH8/HwBQOTn59f1W2mw8DmsGxpKsZmGVBS5PsO/M/vhc8gwzod/Z/bD55BhnA//zuyDzx9jDb169RLTp0/X/l9RUSFatGhhsdjoXXfdZXBffHx8lWKjixYt0j6en59vstjogQMHtPv88ccfNhUbrS/w78x++Bw2fJySI72+uhYYhnEuDSVdQkNeOcMwDMMwDMMwDMPYTn1NVcUwTMNBJoQQdf0mGENu3LgBX19f5Ofnw8fHp67fToOEzyFTHd9++y0eeeQRfPrpp9pO1Pr163Hy5EmEhIRU6UTt3bsXt99+O959911tJ+q///0vDh06pJ30W7BgAd59912sWbNG24lKTk5GSkqKdtLvjjvuQGZmprYTNXnyZPTo0QNff/11nZ2LmsK/M/vhc8gwzod/Z/bD55BhnA//zuyDzx9jLcuWLcN7772nNX1+9NFHWlPTgAEDEBUVhdWrV2v337BhA2bNmoULFy6gXbt2WLhwIUaMGKF9XAiBuXPnYuXKlVrT58cff4z27dtr98nNzcX06dPxyy+/GJg+vby8au1zOwL+ndkPn8OGj1Mc6Yx9qFQqzJ071yAnFmMbfA6Z6uCVM/bDvzP74XPIMM6Hf2f2w+eQYZwP/87sg88fYy3Tp083uwp5x44dVe574IEH8MADD5g9nkwmw5tvvok333zT7D4BAQEN0jhlDP/O7IfPYcOHHekMwzAMwzAMwzAMwzAMwzAMY4G6LU3OMAzDMAzDMAzDMAzDMAzDMPUcFtIZhmEYhmEYhmEYhmEYhmEYxgIspDMMwzAMwzAMwzAMwzAMwzCMBVhIZxiGYRiGYRiGYRiGYRiGYRgLsJDOMAzDMAzDMAzDMAzDMAzDMBZgIb2W+OSTT9ClSxf4+PjAx8cH8fHx+P333w32SUxMxKBBg+Dp6QkfHx/0798fxcXF2sdzc3Mxfvx4+Pj4wM/PD1OnTsXNmzdr+6PUGdWdw4yMDEyYMAGhoaHw9PTErbfeiu+//97gGE39HDJMdXCssh+OVQzjfDhW2Q/HKoZxLhyn7IfjFMM4H45V9sOxqokhmFrh559/Fps2bRKnT58Wp06dEq+99ppwdXUVx48fF0IIsXfvXuHj4yPmz58vjh8/Lk6ePCm+/fZbUVJSoj3G8OHDRdeuXcW+ffvErl27RNu2bcW4cePq6iPVOtWdwyFDhoiePXuKpKQkce7cOfHWW28JuVwuDh06pD1GUz+HDFMdHKvsh2MVwzgfjlX2w7GKYZwLxyn74TjFMM6HY5X9cKxqWrCQXof4+/uLzz//XAghRFxcnJg1a5bZfVNSUgQAsX//fu19v//+u5DJZOLy5ctOf6/1Ff1z6OnpKdauXWvweEBAgPjss8+EEHwOGaamcKyyH45VDON8OFbZD8cqhnEuHKfsh+MUwzgfjlX2w7Gq8cKpXeqAiooKrFu3DoWFhYiPj0dWVhaSkpIQHByMPn36ICQkBLfffjt2796tfU5iYiL8/PzQo0cP7X2DBw+GXC5HUlJSXXyMOsX4HAJAnz598O233yI3NxcajQbr1q1DSUkJBgwYAIDPIcPYCscq++FYxTDOh2OV/XCsYhjnwnHKfjhOMYzz4VhlPxyrGj8udf0GmhLHjh1DfHw8SkpK4OXlhR9//BGxsbHYt28fAOCNN97AokWLcMstt2Dt2rVISEjA8ePH0a5dO2RkZCA4ONjgeC4uLggICEBGRkZdfJw6wdw5BID169fjwQcfRLNmzeDi4gIPDw/8+OOPaNu2LQDwOWQYK+FYZT8cqxjG+XCssh+OVQzjXDhO2Q/HKYZxPhyr7IdjVdOBhfRaJDo6GkeOHEF+fj6+++47PPLII/j777+h0WgAAI8//jgmT54MAOjWrRu2bt2KVatWYf78+XX5tusV5s5hbGwsZs+ejby8PPz1118IDAzExo0bMWbMGOzatQudO3eu67fOMA0GjlX2w7GKYZwPxyr74VjFMM6F45T9cJxiGOfDscp+OFY1HVhIr0WUSqV2xql79+7Yv38/lixZgpkzZwKAdrZKIiYmBmlpaQCA0NBQZGVlGTyuVquRm5uL0NDQWnj39QNz5/CVV17BsmXLcPz4cXTs2BEA0LVrV+zatQvLly/HihUr+BwyjJVwrLIfjlUM43w4VtkPxyqGcS4cp+yH4xTDOB+OVfbDsarpwDnS6xCNRoPS0lJERUWhefPmOHXqlMHjp0+fRsuWLQEA8fHxyMvLw8GDB7WPb9u2DRqNBnFxcbX6vusT0jksKioCAMjlhk1aoVBoZ1H5HDJMzeBYZT8cqxjG+XCssh+OVQzjXDhO2Q/HKYZxPhyr7IdjVSOmrqudNhVmzpwp/v77b3H+/HmRnJwsZs6cKWQymfjzzz+FEEJ8+OGHwsfHR2zYsEGcOXNGzJo1S7i5uYmzZ89qjzF8+HDRrVs3kZSUJHbv3i3atWsnxo0bV1cfqdaxdA7LyspE27ZtRb9+/URSUpI4e/asWLRokZDJZGLTpk3aYzT1c8gw1cGxyn44VjGM8+FYZT8cqxjGuXCcsh+OUwzjfDhW2Q/HqqYFC+m1xJQpU0TLli2FUqkUQUFBIiEhQRuYJObPny/Cw8OFh4eHiI+PF7t27TJ4PCcnR4wbN054eXkJHx8fMXnyZFFQUFCbH6NOqe4cnj59Wtx7770iODhYeHh4iC5duoi1a9caHKOpn0OGqQ6OVfbDsYphnA/HKvvhWMUwzoXjlP1wnGIY58Oxyn44VjUtZEIIUdeueIZhGIZhGIZhGIZhGIZhGIapr3COdIZhGIZhGIZhGIZhGIZhGIaxAAvpDMMwDMMwDMMwDMMwDMMwDGMBFtIZhmEYhmEYhmEYhmEYhmEYxgIspDMMwzAMwzAMwzAMwzAMwzCMBVhIZxiGYRiGYRiGYRiGYRiGYRgLsJDOMAzDMAzDMAzDMAzDMAzDMBZgIZ1hGIZhGIZhGIZhGIZhGIZhLMBCOsMwDMMwDMMwDMMwDMMwDMNYgIV0hmEYhmEYhmEYhmEYhmEYhrEAC+kMwzAMwzAMwzAMwzAMwzAMYwEW0hmGYRiGYRiGYRiGYRiGYRjGAv8PxXBxKOyp/UkAAAAASUVORK5CYII=\n" }, "metadata": {} } ], "source": [ "fig, axes = plt.subplots(nrows=5, ncols=5, figsize = (15,15))\n", "for ind, ax in enumerate(axes.flat):\n", " ind_plot = [np.random.randint(low=0, high = beps_small.shape[0]),np.random.randint(low=0, high = beps_small.shape[1]),\n", " np.random.randint(low=0, high = beps_small.shape[3]),np.random.randint(low=0, high = beps_small.shape[4])]\n", "\n", " ydata = np.array(beps_small[ind_plot[0], ind_plot[1], :, ind_plot[2], ind_plot[3]])\n", " fit_parms = fit_parameters_mat[ind_plot[0], ind_plot[1], ind_plot[2], ind_plot[3], :]\n", "\n", " ydata_fit = SHO_fit_flattened(freq_vec, *fit_parms)\n", " complex_output = ydata_fit[:len(ydata_fit)//2] + 1j*ydata_fit[(len(ydata_fit)//2):]\n", " amp_fit = np.abs(complex_output)\n", " phase_fit = np.angle(complex_output)\n", "\n", " ax.plot(freq_vec/1E3, np.abs(ydata), 'ro', alpha = 0.5)\n", " ax.plot(freq_vec/1E3, amp_fit, 'b-')\n", " fit_parms_labels = [np.round(fit_parms[0]*1E3,2), np.round(fit_parms[1]/1E3 , 0), np.round(fit_parms[2],0), np.round(fit_parms[3],2)]\n", " ax.set_title(str(fit_parms_labels))\n", " ax2 = plt.twinx(ax=ax)\n", " ax2.plot(freq_vec/1E3, np.angle(ydata), 'mo', alpha = 0.5)\n", " ax2.plot(freq_vec/1E3, phase_fit, 'm-')\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "code", "execution_count": null, "id": "e531b402-c6f1-4086-92b6-ece352acd0fa", "metadata": { "id": "e531b402-c6f1-4086-92b6-ece352acd0fa" }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" }, "colab": { "provenance": [] }, "gpuClass": "standard" }, "nbformat": 4, "nbformat_minor": 5 }sidpy-0.12.3/notebooks/01_parallel_computing/index.rst000066400000000000000000000003561455261647000227700ustar00rootroot00000000000000Parallel computing ================== | This folder contains notebooks describing approaches to parallel computing | `Parallel Computing `_ .. toctree:: :maxdepth: 1 :hidden: parallel_compute.ipynb sidpy-0.12.3/notebooks/01_parallel_computing/parallel_compute.ipynb000066400000000000000000000515251455261647000255260ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Speed up computations with parallel_compute()\n\n**Suhas Somnath, Chris R. Smith**\n\n9/8/2017\n\n**This document will demonstrate how ``sidpy.proc.comp_utils.parallel_compute()`` can significantly speed up data processing by\nusing all available CPU cores in a computer**\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\nQuite often, we need to perform the same operation on every single component in our data. One of the most popular\nexamples is functional fitting applied to spectra collected at each location on a grid. While, the operation itself\nmay not take very long, computing this operation thousands of times, once per location, using a single CPU core can\ntake a long time to complete. Most personal computers today come with at least two cores, and in many cases, each of\nthese cores is represented via two logical cores, thereby summing to a total of at least four cores. Thus, it is\nprudent to make use of these unused cores whenever possible. Fortunately, there are a few python packages that\nfacilitate the efficient use of all CPU cores with minimal modifications to the existing code.\n\n``sidpy.proc.comp_utils.parallel_compute()`` is a very handy function that simplifies parallel computation significantly to a\n**single function call** and will be discussed in this document.\n\n## Example scientific problem\nFor this example, we will be working with a ``Band Excitation Piezoresponse Force Microscopy (BE-PFM)`` imaging dataset\nacquired from advanced atomic force microscopes. In this dataset, a spectra was collected for each position in a two\ndimensional grid of spatial locations. Thus, this is a three dimensional dataset that has been flattened to a two\ndimensional matrix in accordance with **Universal Spectroscopy and Imaging Data (USID)** model.\n\nEach spectra in this dataset is expected to have a single peak. The goal is to find the positions of the peaks in each\nspectra. Clearly, the operation of finding the peak in one spectra is independent of the same operation on another\nspectra. Thus, we could in theory divide the dataset in to N parts and use N CPU cores to compute the results much\nfaster than it would take a single core to compute the results. There is an important caveat to this statement and it\nwill be discussed at the end of this document.\n\n**Here, we will learn how to fit the thousands of spectra using all available cores on a computer.**\nNote, that this is applicable only for a single CPU. Please refer to another advanced example for multi-CPU computing.\n\n

Note

In order to run this document on your own computer, you will need to:\n\n 1. Download the document as a Jupyter notebook using the link at the bottom of this page.\n 2. Save the contents of `this python file `_ as ``peak_finding.py`` in the\n same folder as the notebook from step 1.

\n\nEnsure python 3 compatibility:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import division, print_function, absolute_import, unicode_literals\n\n# The package for accessing files in directories, etc.:\nimport os\n\n# Warning package in case something goes wrong\nfrom warnings import warn\nimport subprocess\n\n\ndef install(package):\n subprocess.call([sys.executable, \"-m\", \"pip\", \"install\", package])\n# Package for downloading online files:\ntry:\n # This package is not part of anaconda and may need to be installed.\n import wget\nexcept ImportError:\n warn('wget not found. Will install with pip.')\n import pip\n install(wget)\n import wget\n\n# The mathematical computation package:\nimport numpy as np\n\n# The package used for creating and manipulating HDF5 files:\nimport h5py\n\n# Packages for plotting:\nimport matplotlib.pyplot as plt\n\n# Parallel computation library:\ntry:\n import joblib\nexcept ImportError:\n warn('joblib not found. Will install with pip.')\n import pip\n install('joblib')\n import joblib\n\n# Timing\nimport time\n\n# A handy python utility that allows us to preconfigure parts of a function\nfrom functools import partial\n\n# Finally import sidpy:\ntry:\n from sidpy.proc.comp_utils import parallel_compute\nexcept ImportError:\n warn('sidpy not found. Will install with pip.')\n import pip\n install('sidpy')\n from sidpy.proc.comp_utils import parallel_compute\n\n# import the scientific function:\nimport sys\nsys.path.append('./supporting_docs/')\nfrom peak_finding import find_all_peaks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load the dataset\nIn order to demonstrate parallel computing, we will be using a real experimental dataset that is available on the\npyUSID GitHub project. First, lets download this file from Github:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "h5_path = 'temp.h5'\nurl = 'https://raw.githubusercontent.com/pycroscopy/pyUSID/master/data/BELine_0004.h5'\nif os.path.exists(h5_path):\n os.remove(h5_path)\n_ = wget.download(url, h5_path, bar=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, lets open this HDF5 file. The focus of this example is not on the data storage or arrangement but rather on\ndemonstrating parallel computation so lets dive straight into the main dataset that requires fitting of the spectra:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Open the file in read-only mode\nh5_file = h5py.File(h5_path, mode='r')\n# Get handle to the the raw data\nh5_meas_grp = h5_file['Measurement_000']\n\n# Accessing the dataset of interest:\nh5_main = h5_meas_grp['Channel_000/Raw_Data']\nprint('\\nThe main dataset:\\n------------------------------------')\nprint(h5_main)\n\nnum_cols = 128\ncores_vec = list()\ntimes_vec = list()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The operation\nThe scipy package has a very handy function called *find_peaks_cwt()* that facilitates the search for one or more\npeaks in a spectrum. We will be using a function called *find_all_peaks()* that uses *find_peaks_cwt()*.\nFor the purposes of this example, we do not be concerned with how this\nfunction works. All we need to know is that this function takes 3 inputs:\n\n* ``vector`` - a 1D array containing the spectra at a single location\n* ``width_bounds`` - something like [20, 50] that instructs the function to look for peaks that are 20-50\n data-points wide. The function will look for a peak with width of 20, then again for a peak of width - 21 and so on.\n* ``num_steps`` - The number of steps within the possible widths [20, 50], that the search must be performed\n\nThe function has one output:\n\n* ``peak_indices`` - an array of the positions at which peaks were found.\n\n.. code-block:: python\n\n def find_all_peaks(vector, width_bounds, num_steps=20, **kwargs):\n \"\"\"\n This is the function that will be mapped by multiprocess. This is a wrapper around the scipy function.\n It uses a parameter - wavelet_widths that is configured outside this function.\n\n Parameters\n ----------\n vector : 1D numpy array\n Feature vector containing peaks\n width_bounds : tuple / list / iterable\n Min and max for the size of the window\n num_steps : uint, (optional). Default = 20\n Number of different peak widths to search\n\n Returns\n -------\n peak_indices : list\n List of indices of peaks within the prescribed peak widths\n \"\"\"\n # The below numpy array is used to configure the returned function wpeaks\n wavelet_widths = np.linspace(width_bounds[0], width_bounds[1], num_steps)\n\n peak_indices = find_peaks_cwt(np.abs(vector), wavelet_widths, **kwargs)\n\n return peak_indices\n\n## Testing the function\nLet\u2019s see what the operation on an example spectra returns.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "row_ind, col_ind = 103, 19\npixel_ind = col_ind + row_ind * num_cols\nspectra = h5_main[pixel_ind]\n\npeak_inds = find_all_peaks(spectra, [20, 60], num_steps=30)\n\nfig, axis = plt.subplots()\naxis.scatter(np.arange(len(spectra)), np.abs(spectra), c='black')\naxis.axvline(peak_inds[0], color='r', linewidth=2)\naxis.set_ylim([0, 1.1 * np.max(np.abs(spectra))]);\naxis.set_title('find_all_peaks found peaks at index: {}'.format(peak_inds), fontsize=16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before we apply the function to the entire dataset, lets load the dataset to memory so that file-loading time is not a\nfactor when comparing the times for serial and parallel computing times:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "raw_data = h5_main[()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Note

This documentation is being generated automatically by a computer in the cloud whose workload cannot be controlled\n or predicted. Therefore, the computational times reported in this document may not be consistent and can even be\n contradictory. For best results, we recommend that download and run this document as a jupyter notebook.

\n\n## Serial computing\nA single call to the function does not take substantial time. However, performing the same operation on each of the\n``16,384`` pixels sequentially can take substantial time. The simplest way to find all peak positions is to simply loop\nover each position in the dataset:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "serial_results = list()\n\nt_0 = time.time()\nfor vector in raw_data:\n serial_results.append(find_all_peaks(vector, [20, 60], num_steps=30))\ntimes_vec.append(time.time()-t_0)\ncores_vec.append(1)\nprint('Serial computation took', np.round(times_vec[-1], 2), ' seconds')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## sidpy.proc.comp_utils.parallel_compute()\n\nThere are several libraries that can utilize multiple CPU cores to perform the same computation in parallel. Popular\nexamples are ``Multiprocessing``, ``Mutiprocess``, ``Dask``, ``Joblib`` etc. Each of these has their own\nstrengths and weaknesses. Some of them have painful caveats such as the inability to perform the parallel computation\nwithin a jupyter notebook. In order to lower the barrier to parallel computation, we have developed a very handy\nfunction called ``sidpy.proc.comp_utils.parallel_compute()`` that simplifies the process to a single function call.\n\nIt is a lot **more straightforward** to provide the arguments and keyword arguments of the function that needs to be\napplied to the entire dataset. Furthermore, this function intelligently assigns the number of CPU cores for the\nparallel computation based on the size of the dataset and the computational complexity of the unit computation.\nFor instance, it scales down the number of cores for small datasets if each computation is short. It also ensures that\n1-2 cores fewer than all available cores are used by default so that the user can continue using their computer for\nother purposes while the computation runs.\n\nLets apply this ``parallel_compute`` to this problem:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cpu_cores = 2\nargs = [[20, 60]]\nkwargs = {'num_steps': 30}\n\nt_0 = time.time()\n\n# Execute the parallel computation\nparallel_results = parallel_compute(raw_data, find_all_peaks,\n cores=cpu_cores, func_args=args,\n func_kwargs=kwargs,\n joblib_backend='multiprocessing')\n\ncores_vec.append(cpu_cores)\ntimes_vec.append(time.time()-t_0)\nprint('Parallel computation with {} cores took {} seconds'.format(cpu_cores, np.round(times_vec[-1], 2)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare the results\nBy comparing the run-times for the two approaches, we see that the parallel computation is substantially faster than\nthe serial computation. Note that the numbers will differ between computers. Also, the computation was performed on\na relatively small dataset for illustrative purposes. The benefits of using such parallel computation will be far\nmore apparent for much larger datasets.\n\nLet's compare the results from both the serial and parallel methods to ensure they give the same results:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print('Result from serial computation: {}'.format(serial_results[pixel_ind]))\nprint('Result from parallel computation: {}'.format(parallel_results[pixel_ind]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simplifying the function\nNote that the ``width_bounds`` and ``num_steps`` arguments will not be changed from one pixel to another. It would be\ngreat if we didn't have to keep track of these constant arguments. We can use a very handy python tool called\n``partial()`` to do just this. Below, all we are doing is creating a new function that always passes our preferred\nvalues for ``width_bounds`` and ``num_steps`` arguments to find_all_peaks. While it may seem like this is unimportant,\nit is very convenient when setting up the parallel computing:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "find_peaks = partial(find_all_peaks, num_steps=30, width_bounds=[20, 60])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that even though ``width_bounds`` is an argument, it needs to be specified as though it were a keyword argument\nlike ``num_steps``.\nLet's try calling our simplified function, ``find_peaks()`` to make sure that it results in the same peak index for the\naforementioned chosen spectra:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print('find_peaks found peaks at index: {}'.format(find_peaks(h5_main[pixel_ind])))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## More cores!\nLets use ``find_peaks()`` instead of ``find_all_peaks`` on the entire dataset but increase the number of cores to 3.\nNote that we do not need to specify ``func_kwargs`` anymore. Also note that this is a very simple function and the\nbenefits of ``partial()`` will be greater for more complex problems.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cpu_cores = 3\n\nt_0 = time.time()\n\n# Execute the parallel computation\nparallel_results = parallel_compute(raw_data, find_peaks,\n cores=cpu_cores,\n joblib_backend='multiprocessing')\n\ncores_vec.append(cpu_cores)\ntimes_vec.append(time.time()-t_0)\nprint('Parallel computation with {} cores took {} seconds'.format(cpu_cores, np.round(times_vec[-1], 2)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scalability\nNow lets see how the computational time relates to the number of cores.\nDepending on your computer (and what was running on your computer along with this computation), you are likely to see\ndiminishing benefits of additional cores beyond 2 cores for this specific problem in the plot below. This is because\nthe dataset is relatively small and each peak-finding operation is relatively quick. The overhead of adding additional\ncores quickly outweighs the speedup in distributing the work among multiple CPU cores.\n\n

Note

This documentation is being generated automatically by a computer in the cloud whose workload cannot be controlled\n or predicted. Therefore, the computational times reported in this document may not be consistent and can even be\n contradictory. For best results, we recommend that download and run this document as a jupyter notebook.\n\n If everything ran correctly, you should see the computational time decrease substantially from 1 to 2 cores but\n the decrease from 2 to 3 or 3 to 4 cores should be minimal or negligible.

\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig, axis = plt.subplots(figsize=(3.5, 3.5))\naxis.scatter(cores_vec, times_vec)\naxis.set_xlabel('CPU cores', fontsize=14)\naxis.set_ylabel('Compute time (sec)', fontsize=14)\nfig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Best practices for parallel computing\n --------------------------------------\n\n While it may seem tempting to do everything in parallel, it is important to be aware of some of the trade-offs and\n best-practices for parallel computing (multiple CPU cores) when compared to traditional serial computing (single\n CPU core):\n\n * There is noticeable time overhead involved with setting up each compute worker (CPU core in this case).\n For very simple or small computations, this overhead may outweigh the speed-up gained with using multiple cores.\n * Parallelizing computations that read and write to files at each iteration may be actually be noticeably *slower*\n than serial computation since the cores will compete for rights to read and write to the file(s)\n and these input/output operations are by far the slowest components of the workflow. Instead, it makes sense to\n read large amounts of data from the necessary files once, perform the computation, and then write to the files once\n after all the computation is complete. In fact, this is what we automatically do in the ``Process`` class in pyUSID or pyNSID.\n Please see `another example <./plot_process.html>`_ on how to write a Process class to formalize data processing.\n\n .. note::\n ``parallel_compute()`` will revert to serial processing when called within the message passing interface (MPI)\n context in a high-performance computing (HPC) cluster. Due to conflicts between MPI, numpy, and joblib, it is\n recommended to use a pure MPI approach for computing instead of the MPI + OpenMP (joblib) paradigm.\n\n#######################################################################################################################\n Cleaning up\n ~~~~~~~~~~~\n Lets not forget to close and delete the temporarily downloaded file:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "h5_file.close()\nos.remove(h5_path)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 0 }sidpy-0.12.3/notebooks/02_visualization/000077500000000000000000000000001455261647000201445ustar00rootroot00000000000000sidpy-0.12.3/notebooks/02_visualization/README.rst000066400000000000000000000000121455261647000216240ustar00rootroot00000000000000index.rst sidpy-0.12.3/notebooks/02_visualization/index.rst000066400000000000000000000012661455261647000220120ustar00rootroot00000000000000Visualization ================== This folder contains notebooks demonstrating the various plotting functions in sidpy. Some of the functions in ``sidpy.viz.plot_utils`` fill gaps in the default matplotlib package, others were developed for scientific applications. These functions have been developed to substantially simplify the generation of high quality figures for journal publications. * `1D / curve plotting `_ * `2D / image plotting `_ * `Color map utilities `_ * `Miscellaneous plotting utilities `_ .. toctree:: :maxdepth: 1 :hidden: plot_1d.ipynb plot_2d.ipynb plot_cmap.ipynb plot_misc.ipynb sidpy-0.12.3/notebooks/02_visualization/plot_1d.ipynb000066400000000000000000022175041455261647000225640ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "1D plotting utilities\n", "===============\n", "\n", "**Suhas Somnath**\n", "\n", "8/12/2017\n", "\n", "### Table of contents:\n", "* [use_scientific_ticks](#use_scientific_ticks)\n", "* [plot_curves](#plot_curves)\n", "* [plot_line_family](#plot_line_family)\n", "* [plot_complex_spectra](#plot_complex_spectra)\n", "* [rainbow_plot](#rainbow_plot)\n", "* [plot_scree](#plot_scree)\n", "\n", "#### Import necessary packages:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from __future__ import division, print_function, absolute_import, unicode_literals\n", "import numpy as np\n", "from warnings import warn\n", "import matplotlib.pyplot as plt\n", "import subprocess\n", "import sys\n", "\n", "\n", "def install(package):\n", " subprocess.call([sys.executable, \"-m\", \"pip\", \"install\", package])\n", "# Package for downloading online files:\n", "try:\n", " import sidpy\n", "except ImportError:\n", " warn('sidpy not found. Will install with pip.')\n", " import pip\n", " install('sidpy')\n", " import sidpy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "use_scientific_ticks()
\n", "------------------------\n", "Often scientific plots look ugly because of the way tick marks are formatted for small or large values.\n", "use_scientific_ticks() is a handy function that can convert tick marks on 1D plots to scientific form" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(ncols=2, figsize=(8, 4))\n", "x_vec = np.linspace(0, 2*np.pi, 128)\n", "for axis, title in zip(axes.flat, ['Default', 'Scientific']):\n", " axis.plot(x_vec, np.sin(x_vec) * 1E-6)\n", " axis.set_title(title)\n", " \n", "# Changing how the tick marks on the Y axis are formatted only on the second axis:\n", "sidpy.viz.plot_utils.use_scientific_ticks(axes[1], is_x=False)\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "plot_curves() \n", "---------------\n", "This function is particularly useful when we need to plot a 1D signal acquired at multiple locations.\n", "The function is rather flexible and can take on several optional arguments that will be alluded to below\n", "In the below example, we are simply simulating sine waveforms for different frequencies (think of these as\n", "different locations on a sample)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_vec = np.linspace(0, 2*np.pi, 256)\n", "# The different frequencies:\n", "freqs = np.linspace(0.5, 5, 9)\n", "# Generating the signals at the different \"positions\"\n", "y_mat = np.array([np.sin(freq * x_vec) for freq in freqs])\n", "\n", "sidpy.viz.plot_utils.plot_curves(x_vec, y_mat);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Frequently, we may need to compare signals from two different datasets for the same positions\n", "The same plot_curves function can be used for this purpose even if the signal lengths / resolutions are different" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_vec_1 = np.linspace(0, 2*np.pi, 256)\n", "x_vec_2 = np.linspace(0, 2*np.pi, 32)\n", "freqs = np.linspace(0.5, 5, 9)\n", "y_mat_1 = np.array([np.sin(freq * x_vec_1) for freq in freqs])\n", "y_mat_2 = np.array([np.cos(freq * x_vec_2) for freq in freqs])\n", "\n", "sidpy.viz.plot_utils.plot_curves([x_vec_1, x_vec_2], [y_mat_1, y_mat_2],\n", " title='Sine and Cosine of different resolutions');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "plot_line_family() \n", "-------------------\n", "Often there is a need to visualize multiple spectra or signals on the same plot. plot_line_family\n", "is a handy function ideally suited for this purpose and it is highly configurable for different styles and purposes\n", "A few example applications include visualizing X ray / IR spectra (with y offsets), centroids from clustering\n", "algorithms" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_vec = np.linspace(0, 2*np.pi, 256)\n", "freqs = range(1, 5)\n", "y_mat = np.array([np.sin(freq * x_vec) for freq in freqs])\n", "freq_strs = [str(_) for _ in freqs]\n", "\n", "fig, axes = plt.subplots(ncols=3, figsize=(12, 4))\n", "sidpy.viz.plot_utils.plot_line_family(axes[0], x_vec, y_mat)\n", "axes[0].set_title('Basic line family')\n", "\n", "# Option suitable for visualizing spectra with y offsets:\n", "sidpy.viz.plot_utils.plot_line_family(axes[1], x_vec, y_mat, \n", " line_names=freq_strs, label_prefix='Freq = ', label_suffix='Hz',\n", " y_offset=2.5)\n", "axes[1].legend()\n", "axes[1].set_title('Line family with legend')\n", "\n", "# Option highly suited for visualizing the centroids from a clustering algorithm:\n", "sidpy.viz.plot_utils.plot_line_family(axes[2], x_vec, y_mat, \n", " line_names=freq_strs, label_prefix='Freq = ', label_suffix='Hz',\n", " y_offset=2.5, show_cbar=True)\n", "axes[2].set_title('Line family with colorbar');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "plot_complex_spectra() \n", "-----------------------\n", "This handy function plots the amplitude and phase components of multiple complex valued spectra\n", "Here we simulate the signal coming from a simple harmonic oscillator (SHO). " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "num_spectra = 4\n", "spectra_length = 77\n", "w_vec = np.linspace(300, 350, spectra_length)\n", "amps = np.random.rand(num_spectra)\n", "freqs = np.random.rand(num_spectra)*35 + 310\n", "q_facs = np.random.rand(num_spectra)*25 + 50\n", "phis = np.random.rand(num_spectra)*2*np.pi\n", "spectra = np.zeros((num_spectra, spectra_length), dtype=complex)\n", "\n", "\n", "def sho_resp(parms, w_vec):\n", " \"\"\"\n", " Generates the SHO response over the given frequency band\n", " Parameters\n", " -----------\n", " parms : list or tuple\n", " SHO parae=(A,w0,Q,phi)\n", " w_vec : 1D numpy array\n", " Vector of frequency values\n", " \"\"\"\n", " return parms[0] * np.exp(1j * parms[3]) * parms[1] ** 2 / \\\n", " (w_vec ** 2 - 1j * w_vec * parms[1] / parms[2] - parms[1] ** 2)\n", "\n", "\n", "for index, amp, freq, qfac, phase in zip(range(num_spectra), amps, freqs, q_facs, phis):\n", " spectra[index] = sho_resp((amp, freq, qfac, phase), w_vec)\n", "\n", "fig, axis = sidpy.viz.plot_utils.plot_complex_spectra(spectra, w_vec, title='Oscillator responses')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "rainbow_plot() \n", "---------------\n", "This function is ideally suited for visualizing a signal that varies as a function of time or when \n", "the directionality of the signal is important" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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mhNgnhCgQQjx8jOdvFEJUCyG2tX3d2uG5G4QQB9q+blDhjz/wSCfrKh7E47UzpsffMWg7Lzi6rvoAV373EptrD/Fw9gX8e9iNxAeFd9r4pzOxQSG8OvoqHht8Lptqirho+WtsqinqtPFHRM2gX+gYVlS+R1FLfqeN29WctLB0aLE6HegHXCWE6HeMSz+SUg5u+3qj7bWR+LoijsDXUfGxtsr9pxw7av5JnWMXw2P/TKihZ6eM6ZVeXj+wgrs2zSbCYGHOmDu5InVEt5hSdyZCCK7qmcvHE28hWG/ixu9mM7tgA50RBhBCcEHi3UQYevDZkX9gczf5fcxTARUzlu9brEopnUB7i9Wfw1RgqZSyTkpZDywFTrlz6CXNyyiwfkTvsGtJDJ7UKWO2uB08sOUDXj2wgukJA5k9+g56hQQamp8MfcLimDvxVib06M1fdizh0a1f4vJ6jv/Ck8SoNXNJ8gM0expYUPZSpwhaV6NCWI7VYvVYKZCXCiF2CCHmCiHaG5z93Nd2GS2ucjZVP0GksT8Dou7qlDErbA3cvO4/fFe1jwf7nceTgy4jSGfolLG7O8F6Iy+OvII7+ozlk8NbuX3tBzS7HH4fNyEog0mx17LXuo6dDd/6fbyuprMifwuANCnlQHyzkndP1IAQYpYQIk8IkVddXa3cwWMhpYeNVY+ChJFxf+mUYO1+awU3rnudcls9L+Zez1VpowJLH8VohOC32ZN4esgMNlQf4vpV71Jrb/H7uCOjLyTZ3I9F5a9jddX4fbyuRIWwHLfFqpSyVkrZflt4Axj6c1/bwcbrUspcKWVuTEyMArePz76G96ixbyEn5iEsev9PpLbWHebW9f9BAG+OvI2RMRl+H/NM5tK0wbwy6koKm2u4dtU7VNqsfh2vvdSCV7r5quyVbr0kUiEs37dYFUIY8HU8nN/xAiFEx9TDGcCetp+XAOcIISLagrbntD3W5Vidh9hV9yqJlkmkBp/n9/HWVxfw643vEm0M4e1Rs8gMPT1r455unNUjkzfGXEOVvYnrV832u7hEGuOZEHcNB5o2sce6xq9jdSUnLSxSSjfQ3mJ1D/Bxe4tVIUT7Ec/fCCF2CSG2A78Bbmx7bR3wJD5x2gQ80fZYlyKlh01Vf0anMTMk+mG/L0XWVR/gt5vnkGyJ4o2Rt9IjsJXcqeRGp/LGmGuocTRzw3fv+T3XZUTUBcSbMlhU/jp2j/+XYF1BIPP2GBxo/JBtNX9jeOyTpIac67dxAPJqC7l702xSLdG8OuJmwg2nbuq3lJIKazNFdQ2UNlqpbmqhvtVGk8OBw+3G7fUiEBh1WswGA6EmIzHBFnqEBpMUEUZaZAQm/albqWNLbTG3rH6f1OBI3h13PWEG/xXBKrMd4I2DDzAiagZT42/x2ziqCdRj+YXY3NXk175MXNAoUoL9W1oyv6GE3+bNIdEcySsjbjrlRKWupZWNRSVsOVLGzrJK9lZW0+r830r9Zr2eYJOBIL0erUaDV0pcHg8tDidWuwNvhxuXAFKjIugfH0tOUgLDUpPoHRt1ygSnh0Sl8OLIK/jD5vlsrytlREwaRq1/PiIJQZkMiZjCxtoF5ERMIbab9YcOzFiOYn3l7ylt+ZapyR8TrE8+/gt+IUXNNdy0/nWCdUbeHDmLGFOI38b6uUgp2VtZzdd7ClhZcIhd5b7C1iadjn7xsfTtEUNmTBRpkREkhocSGxL8kzMQr5TUtbRSbm3iSH0jB6vr2FtZzY6yCqqafEuAaIuZszLTOScrgzE9UzDouv5eV2Vr4rxFbzIuvid/H3mB34Sv1W3lxf23k2zuy9Vpj/plDNUEZiy/gGrbZo40f02/iNv9Kir1jhbuznsXgeClYTd2uahUNTXz2fbdfL5jD4U1dWiEICcpnnsnjmFUejL94mPRa0+8fYlGCKKDLUQHWxiQ8P+D0VJKShutbDhcwuqDh1m6p4BPt+0izGRkenYfrhjSn+z4rksGjA0K4freuTy/cxWZYdHc2W+0X8Yx60IZF3M5yyrfobB5Oz2DB/llnK4gMGNpQ0oPy0quw+FtYFryPHR+ai7m9Li5Y+Pb7Gks5fWRtzAg3H8Cdjx2lFbw9vrNLNl9AI+U5KYkMmNAFlOyMoi0dN6yzOnxsK6wmAX5e1m6pwC7283gxHhuGjWEKVkZaLugKr+UknvWfsFXxbt5e8KVnBXvn2Mcbq+Tfx/4FWZtKLf2+vspsyz8MQIzlhPkcNNXNDj3MSL2L34TFYDndn/Jtvoinhk8s8tEZVNRCS+tXM/6w0cIMRq5YcQQZg4dQFpU1xzTMmi1jM9MZ3xmOtbpdj7fvof3Nm7lnrlfkR4VwW/OGsW07N5oNJ33oRNC8OyIcznQWM1v137Bgmk3k2gJUz6OTmNgfOxVzC/9F/uaNpAVOlL5GF1BYMYCeLwOFhVfjEkXzdmJ7/rtrvH5kTye2Pk5N/U6i7v7nOOXMX6KfZU1/N/y71hVcJiYYDO3jMrliqEDsBhOveMCHq+XJXsO8M76LewrqiIzNpqHzxtPbnpSp/pxuKmOGYvfok94LB+cfa1fehp5pYdXDtyFVui4PeNfiFO4FEagHssJUGD9GJunkoGRd/lNVPZbK3h215eMiOrFr3pP9ssYP0aTw8ETy77hd/MXs62knAcnj2PZ3bdw06ihp6SoAGg1Gs7N7sOHN13JU5ecQ12Ljev/8wkPfbyIuubWTvMjLSSSp4ZNZ3NNCf/e5Z+ENo3QMi72CqocRexv2uSXMTqbM15Y3N5W9ta/Q1zQCGLNw/0yhs3t5OGtHxGqN/H04Ms7tTjTNwcLmfbGbN7bvI2JWb1YevfN3Do695TOJ+mIRiO4YHBfvrr3Bm6fMJzFO/dz/j/f5ev8A53mw4y0bC5K689Lu1azvbbML2P0DzuLcH0s31V/0i1S/c94YTnYOBent4HsyDv8NsY/9y6mqKWGJwddTqQx2G/jdKTV6eIPi5Zy29wvCDMZmXvdVdwzbhThQf7rxexPggx67jlnDPN+fQ1JEWH89r9f8sd5X/8gr8ZfPD70HGJMwfxuw1c4PepLLWiEltHRl1Bm209x6y7l9jubM1pY3F4b+xpmExc0gijTQL+Msbb6AJ8Ub+Sa9NGMiO7llzGOZn91DRe/+18+2ZHP7SOH8dkNVzMooXucPcrsEc37d8zk9gnD+WzLLq565QOKahv8Pm6owcSTw6axv7Ga1/es88sYgyLOJkgbwoba+ce/+BTnjBaWw00LcHjr6Rtx6/Ev/gW0uB08tfNz0oNj+HUnxVUW7zvAZe99SKPDzuwrL+PB8WMxngJJZyrRa7Xcc84YXr/xEqqsLcx8+b9sLDxy/BeeJGcnZnJuchb/3r2W4uZ65fb1GiNDI6ex17qBemeFcvudyRkrLFJ62N8wh0hjf6JNOX4Z46V9S6m0W3l0wMUYtf6t5SKl5OV1G7nr8y/pHRPN/BuvYVRq1+XIdAZjMlP5+NdXERNi4da3P2Xh9n1+H/OPQ6agFYKntizzi/3cyOkIBFvqTolD/r+YM1ZYSltW0uIupU/49X7ZCdrdWMrHRRu4InWE33v+eLxeHvt6Bf9YtYYZ/bJ4/6rLiA3unFhOV5McGc77t89kcHI8D368kE827fTreD3MIdyVPZZlpQdYW3FYuf1QfTS9Q4axrX4ZHm/nxI/8wRkrLAWNH2LWxZNomaDctld6eXbXAiINFr9vLbu9Xh74cjH/3baDWSNy+fv507rd0ud4hAaZeP2mSxjXO53HPlvGxxt3+HW8m/oMI9ESxjPblv/PIUtVDImcRounkUMt/v13+JMzUlgaHQVU2zfTK/RyfE0G1LKobAf5DSXckzWVEL3/dmE8baKyYM8+Hhg/hocmjDvlU8L9hUmv44VrzuesPun8+YvlfLV9r9/GMmp13DfgLHbVV7L4iPpxegUPZlavf5IRMvT4F5+inJHCUmj9FI0wkB464/gXnyB2j4uX9n1N39AEzk3036EyKSWPLF7Gl3v28dCEsdwx0j85OKcTBp2Of159PrlpSfx+7hLWHyz221gzUrPJDI3mXzu/Uz5r0QgtDa5mnt59H02uRqW2O4szTlg8XjtFzQtJtEzEqFV/Nubjog1U2q3c23caGj8mwv3ju7XM3bmLu0aPYNaIYX4b53TDpNfx4rUXkBoVzm/f/5LDNep3b8CXGfzr7DEcsNbwdYn6oHGIPowqRzk7Gk/PTNwzTlhKW77B5W2iZ8hFym23uB28fXAlI6MzyI3yX1OzuTt28cq6jVw5aAD3jB3lt3FOV0KDTLxy/UVoNIK758ynxeH0yzjnpfQlNTiCV3evU54tm2BKIdaYwNZ6/+TM+JvOarF6nxBid1tfoeVCiNQOz3k6tF71e2ZQUdNCzLp4YoKOe47qhPm4aD2NLptfc1a2lJbxpyXLGJOawuPnTDpjYyrHIykyjP+78lwOVdfz1PwVfhlDq9FwS9ZwdtSVk1dTotS2EIKciJEUNO85LZdDndVidSuQ29ZXaC7w1w7P2Tq0XlUf9OiA3V1HpW0DKcHTlJ8gtbmdvHdoDaNjMskO988J3NrWVu7+/Et6hIbwwkXn+eWkbXdidEYqt08czhdb97Boh39yXC5JH0CYwcTs/eorGg4Iy0Ui2W3dpty2v+mUFqtSym+klO1HUtfj6x/U6ZS0LEXiISVEfS3bcnsD4XoLt2VMVG4bfGUeH/xyCfU2O/++6HzCTKfnmZ/O5s6JIxmY3IMnvlhBdZP6ivhmnYFL0wey5Mg+qm1qq/snBaURpo8kv3GzUrudgYqEh2O1SR3xE9ffAizq8LtJCJEHuIFnpZSfK/DpmJQ0LydU34swg9ozOx7p5ePib3l80AUMDPdPMtz7W7ez6tBhHpsykX5xsX4Z4+dS39DCrgMVFBZVU1LRQE1dM00tdlwuDxqNBrNJT3iYGXuzg0mj+9A7swfpqdFotZ0/w9JpNfzl0qlc/OIcnv3yW/5+lfoeUVdn5PDWvo18emgnt/dTF/MSQpAdmsPm+rV4pButOH3ykzrVUyHEtUAuML7Dw6lSylIhRE9ghRBip5Ty4DFeOwuYBZCScuIfXru7jmr7VvpFqG+1sLF2HwvLNzE8qo9y2wDFDQ389dvvOCs9jWtzOr8uqpSSfYWVrFizj3WbCzl0pPb756IiLMREBhMaEoRep0VKSavNScHhKirKGsjbeAiA4GAjw4akM35sH0aN6IXR0HlvvZ6xkcyaMIx/L1/PZcMGMCpDrfj3DI1iaHQS8w7tYFbfkUrjXn1CBrC2djlFLQfpGeyf95c/UPG/+7PapAohJgOPAOM7tFtFSlna9r1QCPEtkAP8QFiklK8Dr4OvgtyJOlneugrwkmhRv1T5vGQdkYYQxsVkK7ctpeRPi5ejERqenja5U4O1DoeLxSt3M2/hVgqLa9BqNeRkJzNtQjb9sxLITIvFHPTjhaK8XklZeT2795WzdVsx6zcd5JtVewkNMXHetEFcdtFQoiI75+jBrWcN44stu3lu4Urm3XWN8jq6l6QP4JFNi9jTUEW/CHWFwDNDshEI9jXtPOOE5fsWq/gE5Urg6o4XCCFygNeAaVLKqg6PRwCtUkqHECIaGMP/BnaVUd66GrOuB2GG3krtVtobWF+7l+vSJqHXqL8LL9y7nzVFxTw2ZSLxoZ1Tzd/t9vDF0h3M/mQ9tQ0t9E6P5YHbpzBpdG9CQ35+PWCNRpCUGElSYiTnTMrG4/GyZVsRCxZt56N5G5n3xWYunTGEa68chcVi9OO/CIx6HfdOHcv9Hy5kwbY9XDRE7U1ganIfHstbwoKi3UqFxaILJjEolYPNe45/8SnESX8SpJRuIUR7i1Ut8FZ7i1UgT0o5H/gbEAx80nbHLW7bAeoLvCaE8OILJD8rpdx9sj4djVe6qGzdSErINOV3/MXleUgk5yaoz3y1uVw8880qsuNiuXqwf+rFHM323SX87dWvOVxSx+DsJB679zxy+icr+btptRqGDU1n2NB0Ssrqeff9NXwwdyNfr9jFfXefw5iRmQr+BT/OtAG9eWNVHi8vX8/5g/qiUxjziTSaGRWXytcl+/jdYLWz4p7BWayrWYHb60bnh5uXP1Dyl5VSLpRS9pZS9pK/lBzlAAAgAElEQVRSPt322KNtooKUcrKUMu7obWUp5Vop5QAp5aC272+q8OdoauzbccsWepjV9oeRUrKkfDM54b1ICIpUahvgnbytVDQ188jZ4/3eAsPpcvPi29/w6z9+iMPp5tmHL+LFJ2YyZECKX5ZfSQkRPPLg+bzyz+sIDzPzyJ8/4/mXvsbpdCsfqx0hBL8+eyQl9VYW7VS//Tw5qTeHmuo4aK1RareXJQuXdFJqO6zUrj85IxIhqmybEGiJVZwUt7+plBJbDVN6qK/n0mi38/qGPCZl9GR4sn9356tqmvj1Ix/y0YLNXDxtMLP/eSNjh2d0Sjynb594Xv3X9VxxyTC++Gob9/3+IxqtNr+NN6FPT3rFRvLO6i3Ks2UnJWQA8G1ZoVK7KRbfLmZxq1q7/uSMEZYIYxZ6jdpA4YrKbWiFhrNiByi1C77ZSpPDwb3j/NOFr50Dh6q47XdzKCqt4+mHLuT+WZMJMnVu5X69XsuvbpvIow9fwL4DFfzmgf9SW6c2J6QdjUZw3egc9pRVsbVYbWHsREsYvUKj+K5crQBE6KMI1oVS3PqDPY1Tlm4vLG6vjTr7LuUp/FJKVlXvYmhEBqF6tV0DW5xOZm/eyuTMXvSNjVFquyP5+8q4608fotVqePUvVzHezzGO4zFpfF/+9tTlVFZbuf/3H2Ft8s/M5bxBWViMBuZuyldue0xcGnk1R3B51RXcFkKQHJROqa1ImU1/0+2Fpd6xB4mbaNNgpXaLWqootdUw1g9bzPN27qbR7mDWCPXnmdrZd7CS+5+cS0SYmVf+chU9U/0nYCfC4IEpPPPnSyktb+CPT3yGy6W+Ir7FaGD6gN4syT+gvMr/8NgUWt0u8uvU1qyND0qmwl6CR/ovBqWSbi8stXZfFa4ok9rlyvpaX4GfUdF9ldqVUvLelm0Miu/BkMQEpbbb2banhPuenEuIxcQLf55JXHSoX8b5peQMTOF3905nR34Jr775rV/GOH9wFjani5V71S5bhkb74mFba36QynVSJASl4JEequzlSu36i+4vLI6dBOtTlNde2Vi3jzRLHHEmtXbXF5dwqK6ea4f4J8PW7nDx7FtLcWi9/N8fLyE2unNyY06UyRP7cemFQ5n3xWY2bT6k3P7QtESiLGaW7SpQajfOHEK8OYRtihubxRp9N5kqx+lRvb/bC0uDYy8RRrWzCqfXzY6GQ+RGqo9JzN2ZT4jRyPQ+ahP52vnH7G8oKq/jyd+cT1pytF/GUMWsm8eTkhzJ/72wBLtd7ZJFq9EwPiud1QeKcHu8Sm33j4hnT0OlUpsxRl9fqBpHYMbS5Tg8DbS6Kwg3qE2F3tNYjNPrJidC7WHGVqeLr/cXcG5Wpl9aoK7aXMD8b3dy3fnDGTUoXbl91RgNOu676xwqq6x8/Jn6SmpjMlNpsjvIL1U7C8iKiOVQUx12tzoxNOssWHQhVDvUCpa/6NbC0uj09fcNN6q9++9sPAzAwHC1H85vCw9hc7k5v2+WUrsALTYnf3t7ORkpMdx2mX+3sFUyeGAKY0dl8uHcjdTUNim1PaKn74jbpkNqizT1DovGKyWHmuqU2o3UR9PgrD3+hacA3VpYrE7f2lx1mYRdjUUkm2MI01uU2l26v4CIoCCGJycqtQvw9ufrqa5v5uFbpqDXqe9M4E+uuDgXu83JnDlrldqNDDaTHh3BtiK1y4ueIVEAFDapFYFwQxT1roCwdDlWZyE6jQWTVu1W6h7rEfqFqu0y6PZ6WVl4mEm90pWn71fVNvHxki2cO64f/TPildruDAYOSGZQZjx56w/i9arNls1OimNXmdrlRUqwL6B/pFltSclwfSSNLv8UB1dNtxaWJlcRofo0panpdY4m6pxNZIaoTbPfUV6B1eHgrJ5pSu0CzF6wEa9XctulY5Tb7izOPS+H8vIGdu9Su2zp0yOGKmsLDa12ZTYtegPhhiBKW9QKS7AuFJunBbf31M9l6dbC0uIqxaJXKwAFzb5txIxgtXf+9cW+D8yoVLVFiBqbbCxYmc+0sX2Jjzm18lVOhNFjMtHpNKxZc0Cp3YxY3+HRwzVq4yFxQcFUKS5VGazz/f+1eKxK7fqDbissXumm1V2BRac2XnG4xVdOpmdwD6V2846UkhkdRaT559c7+Tl8uWoXDqebK6edvl31ACwWI9n9k9iiOKclNdq3bDlQoTZ2EW2yUGNXW2PXrPOddbO5W49zZdfTbYXF5q5G4sGiVzuzKG6tIlRnJtyg7kCjlJLt5eXkJKj1VUrJlyvzGZAZT0bKqZGyfzIMGJBM4cEqpTktMcEWNHaob1T7YY0wmql3qj3rZNL4bjp2r/9Of6vi9Kga8wuwe6oBCNKqLTxd2lpDslltYllJo5VGu4MB8eoqjwEcPFLDodJaHrzpbKV2j6apvoWtq/ZSWljF9u/2kjshiylXjyVMcVZvr4w4vF5J0eEa+mSpEWGLyYDFqKe+Re2HNcRgpMmpLm4DYNT6OjPYPQFh6TJs7jZh0am9U1fY68lSvCO0t9pXGEj1SeZVm33H7Cfk+ufUckNNE+8+O59lH2/A3aFA09blO3nn8blccvdUrv/jJegUJfslJvniIRUVDcqEBSDMbMKqMHgLEKwz0OJW24FRr9ED4D4NDiJ2W2Gxe3xrZpM2SplNr/RSZW9gfKzaMpEHa32+ZkSp8xVg487DZKXHERWuNt8GYO+WQzxxw2s0NbQw9erRTJ45kp7ZSUgpOZx/hAX/WcFHf/+KsoNVPPLer5XszEVF+f4ddYprtZgNelodao8MGLRanF4PUkplu5Lt7T9OhxPO3VZYnJ4GAAzaMGU2m9w2XNJDtFHt7srhugZigy0EG9UVWHI43ew6WMEVU9VXtyvYeYQ/XP4CYdEhPP3x3aT3/d8AeZ/cXvTJ7UVKVgJv/uljVn+ex7iLT75xvTnIV3DbZlMrAnqdFrdX7XkhndDilRIJqEp20OJLbPRK9aUkVNNZvZuNQoiP2p7fIIRI6/Dc79se3yeEmKrCHwCn14pOWNAIvSqT1Dl8KeURCgO3AKVWK4mhasWq4Eg1LreHbMUJcXWVjfz5xtewhAXxt8/v+4GodOSye6ZjDNLz6kPvKRlbaHwfUdUlJQVCvU0/VPX00u7jqd+vu7N6N98C1EspM4DngefaXtsPX7uQbGAa8HKbvZPG6bGi16oVgEaXb+cgXHEqf0VTMz1C1PpaUOyLMfVOVRu8fvmxz6ipszHricuJjg//yWs1Gg1RsSGYTGrE3dG2G2Q0qrtZgC/rWXW2s6dNqNRKQLvNM0BY+Bm9m9t+f7ft57nA2cK38LwQ+FBK6ZBSHgIK2uydNB5pRyfUloxsdvui8cE6tbkmNS2txASrFavi8nr0Oq3SpDiH3cX2DQfJGdubcef/9BKrrqKedQvyqCgoJ3eymiJb9Q2+vJCwMLV/f5vTRZBBrVg5PG6MWp3SrO/22IpWo/asl3QfRrbtoqpChbAcq3fz0fPj76+RUrqBRiDqZ772F+GWdnQatW/AFrdv58CiU9eQ3eXxMD4zjX491M4sKmubiIsKUXon3rxqH82NNi6bdfy+Oa8/9B6PXvgccWmxXPnwRUrGLy/zxc3ieqiLmwFYW+2EBKltmGZzuwjSqhUrp9e3y6QXaoudy/qbkU1q+wSeNsHbE+3d7PHa0Qq1bxaHt20qrlH3hrG5Xcwv2MugBLWZvHWNrcp3g3asP4jRpGfgyOOfFp9x51QmXDGGnLP7Y1T0oS0s9GU9p6aqyyNyuT1YbQ4ig9XObq1OOyF6te8/V7uwaBR3UZCtINS+Vzqrd3P7NSVCCB0QBtT+zNcCJ967WeJBKNZNZ9vhL4NWnV27u92m2ults81BTITauM2Rg5WkZMah0x/f136j1PcZ3pVfQmJiBGFh6kSgstG3dR0XpvZv1eC0E2ZQN7MFsHl8Mb4grVoRxNsKGrU2VcyTv+/dLIQw4AvGzj/qmvnADW0/XwaskL4w/HzgyrZdo3QgE9iowCek9KIoDvw93raAnEbhSQhXW1lEneLgodPpxqi4Cp21voXwqM5p4n40TqebrVuKyBmSptRuSa3vBHJilNpduVp7C9FBamcBNo8vxmTWqrMrpQewI4TasEFn9W5+E3hPCFEA1OETH9qu+xjYDbiBX0upZpNe4j0touenE75t2a4Ze+OGg9hsTsaMVVsNsLDSd6o5PVZti9xKWxN9FTaHB2hx+2ZXQQqFBdlWlU+oPX6h5JYmpVwILDzqsUc7/GwHLv+R1z4NPK3Cj44IoUWiNpFI0xbh96Iumap9puJR/InV67Q43WozNMOigqmvUVse8uey8KttREZaGDpUbTnQ/WXVhJlNxISq+7A6PG6q7S0kWtTOgprcDRg1Qd+fGVKCt61wlEZtt4lue7pZgw6v4tRnfdvSyqWw0I6xrUykQ7EIWIIMtLSqPauSkhHLkYJKXH5s3H4sCgur2LD+IBfMGIJWp/Ytm3+kkr5JsUq3hUtafLtXyZafzvM5UayuBsL0am3i9fmKRq3d7issQo9Xqj7/4dsNcioUFoveF+FvdqoVgYhQM3VWtaUA+g/vidPhJn+T+j4/P8Wb//kWi8XIRZeo7QzZbHdwoLyGQalqs5MLrb7lVc9QtWe/Glx1hOnVzizwttWhCcxYfh46TRBuqfZ4uUXr2z5sz2dRgV6rxaLX02BXe7o2LiqEylqr0lT1nLG9CbIYWf5pnjKbx2PB53msX1fA+RfkEBqqNsC4pbAUr5QMy1BbZfBAo++0enqI2rhNraOKSIPiujretnq/GrXpDt1XWIQZt1ftHdvSlnGrUlgAosxmam1qfU3qEYHd4aa6Xt1JYFOQgcmX5rJi4XZ2bzmszO6PUV/bzJxXV5ISE8Y116mv17t6bxFBBh056Wpb2e5tqCLJEkaowu1mp9eJ1d2gXFikpxLQgkbt7Kr7CovGjMurtjRgmN6319/gUms3zhJMRbPaoGivJF8SWUFxjVK7Z1+Si9RreeuFpcoP7nXE6XTz5MOf0Nri4NFnLsNiUZtsJqXkm/yDjMhMwaBTuy2fX19BP8U7QjVtrVVjTYq7LHgqQBOjPDWj2wqLQRuGy9uEot1r4P+faq53qq0HkhASQolVbYHk3mmxCAG7DqrtmdNnYDK33j+d/M2HWfDhBqW223G5PPzlkXnkbz/C/Y9eSHqG2g8pwI6iCioampg8MEOp3QaHjcNNdQyKUjsLqrT7iri393BWhucIaNUuBaEbC4uvCbzE6VX3gQ3TW9AKDTUOtSKQGh5OeVOT0p0hS5CBzJQYtu87ZiLzSXHxdaMZMb4Prz73FauX7VJqu6XZwWMPfMjalfv41f3TmDAlW6n9dr7cvAeDTsvZA9QKS2lLI9EmC4Mi1QpAhb0EgfDDjOUI6NR2hoDuLCxtUW6HR12DJ51GS7QxlCp7gzKbAD0jIpFAYb3aZlTpKdHsq6ih1a52x0mj0fDwX2fSOzuRvzzwIQs+XK9kWVSwr5y7b3qDLRsLufcP53PRTCUH3X+Azeniqy17mTwwk2CT2iXWxooSLkjsz4hYtR/WUlsRscZ4DBp1/kppA28VQhsQlp9Ne63b9tq3qogzRlBhVysAWdG+eMi+WrW+nj++P/UtNtblH1ZqFyDIbOSZ/9xE7phM/v30Ap64532qyn+Z4Fobbbz2z6+568Y3aG1x8NxL1zH9oiGKPf7/LNyylyabg8tGqinn0JG5BfnsrK1UXt+l1HaYxKBUpTZxt6UNaNPU2qVbC4uvDIHNrbZ9ZpI5mpJWtQHRnhGRmHQ6dlZWKbWb0zuJiJAglm7ar9RuO0FmI4+/eC233j+NLesKuOX853nhic8p2FN23BmMlJKD+yt45R9LuP6if/HpB+s557zBvP7BnQwamuYXfwG8XsnslVvokxBDbi+1PaeqbS3sqqtkfKLa7OAmVyN1zhqSzGrt4i7wfdepL7Z+2pRNOFGC2vo1t/pBWBaWb6LVbcesqC6LTqMhOyaWbRVqA606rYZzhvfh05U7aWi2ER6sNg8EfMuiy24cx1lTB/DBa9+w9IutLPxkE7EJ4fQfkkZqr1gMJj3NrQ5i4yOorW6iqLCa/O3FVFda0ek0jJvUjytvHOOXIO3RLN9ZQGFlHc9dO11pti3ANyVtXRESeyq1W9TqE4A0i1oBkO4DgA50imdCdGNh0WqMmLQxtLjVBi9TLb6Z0OGWKvqFqVub5iYk8tbWzdhcLoL06uq9XHzWAD5avo35q/O5ftrJF7T+MWLjw7nn8Yu56bdTWb00n7w1B9i24SArvtyG1GiQHcpJxsSFkpWdyDW3nMWYCVmEhSsuA/AjeLxeXl6yjrTYCKYOVnuYEeDr4gMkWkLpF6m2aNehlv1o0JKsfMayH3TpCMWFo6AbCwtAsD6ZZteR4194AvQK9kX7DzaXKxWW4YmJvLZ5E3llZYxLVXcHyUiKYWifJD5atpWrJg9Br1Obr3A0oeFmzr18OOde7gu8trY4qCitp7GhhR6JkUREBSurgXuifL5xFwUVtfz9hvOUx0AaHXZWlR7i2qwc5TOhguY9pFh6Kg3cAuDaBYZRam220W1jLOAfYelhCseiNbG/qUSp3RFJyRi0Wr4rPqzULsAN04dRWd/MgjVqt4Z/DmaLkZ69e5AzvBfxiRFdJiqNrXZeWLiWnPQEpgxUH1P4uvgATq+HC9L7KrXr8NgpbikkI/jo+vQnh/RU+XaE9P2V2m2nWwtLSvA5ZEXcrDRDVCM0ZIUmsdeqVljMej3DEhJZcahQqV2AUf3TGNAznjcWrMeuuDHX6cLzX35HQ4uNP1wyUfmMAmBuwU7SQiIYHK02z6SgeQ9ePGQqFhZc+b7vesV22+jWwhJnHkmv0EvwSrVne/qGplDQXIbDo/ZDOrlnLwrr6zlYV6fUrhCC31w+jqr6Zt5dtEmp7dOBtfuKmLc+nxsmDCErUW38A+CwtZ4NlUe4LKO/ctHaY92GQWOkV3CWUrvStQXQQWDGcuI43FWsLsqhsvkzpXYHhKfhkV52NRYptTs1IwMBfHVgn1K74Nt6njqiD+8s2sThcrXCdSpT19zKHz9YQs+4SH41dbRfxnh/31a0QnB5ptq8GCkle6zbyQjuq76AtnMr6PsqL0nZTrcWFoM2Bq3GQpMzX6nd/mFpCATbGtQuW3oEhzA8MYkv9u71ywG/e2dOIMig4/G3FuP2qG0peiri8Xr5/fuLaWy189y10zEZ1O9VtLqcfHxgJ1NTehNnVlvesdxeQo2zkuwwtcmCUjrBtQP0Q5Xa7chJCYsQIlIIsVQIcaDt+w+qxQghBgsh1gkhdgkhdgghZnZ47h0hxCEhxLa2r8En488xxibEMIAmh1phCdEH0SckkS11BUrtAlzStx+HGurZXF6m3HZ0mIWHr5tMfmEFr32xVrn9U41/fbWatfuK+P3FE/2yBAKYdzCfRqedm/upLUIFsLPRt2wdEKbYtms74EAY/Jd+cLIzloeB5VLKTGB52+9H0wpcL6Vsb6P6TyFExzp4D0opB7d9bTtJf35AiLE/La4DeLxq4yzDonqzy1qkvDbLuZm9CTYY+GDnDqV22zlneB9mjM3m7a82snLbQb+McSrw8dodvP3NZmaOGcRlo9Sn7gO4vB5ez9/I4Oh4hsaqzeIF2Fa/gTRLpvKqcdKxFtCAYYRSux05WWHp2Dr1XeAHLe+klPullAfafi4DqgDFZbB+nFDjYMBDk3OnUrvDIvvgkV421apNl7cYDFyU1ZevDuynplVt8ad2HrpmEn1T4/jT6wvZV6z2GMGpwJJt+3l63grG9U3n4Ysm+G2cLwp3c6S5kbsGjVYetK2wlVBmL2ZIuB/iQs51oM9GaNQW++7IyQpLnJSyPQ+9AvjJnGwhxHDAAHS8VT7dtkR6XgjFrQuBUKOvx3CjXW05xf5hqYTqzKypUZ8bcuPgHJweD3N2KJ/AAWAy6PnH3RcSYjHxm+c/pbhS7aHKruTr7ft5eM4iBqfF83/Xn4dO658wotPj4YXta8mOjOPspON3hjxRNtevQSDIiRip1K70NvqWQgb/BLLbOe5fXQixTAiRf4yv/2n83taA7EcjjkKIeOA94CYpZXvk8PdAFjAMiAR+9xOvnyWEyBNC5FVX//xTwHptOGZ9hnJh0Wm0jIruy9qaPbi9atuM9IyI5Oz0nszevo0WxUW224mJCOaley/B45Xc+bdPuoW4fLYhnwdnL2RAag/+fdtFmI3+S8b76MAOipsaeGDIOOWzFa/0srHuO/qEDCBUdVV+x2rAgzAev//2yXBcYZFSTpZS9j/G1xdAZZtgtAvHMefVQohQ4CvgESnl+g62y6UPB/A28KMFOKSUr0spc6WUuTExJ7aSCjeNoNGRh1eq/ZBOiB1Ik9tGXt0BpXYBfjVsOA12O+/5adYCkJ4QxcsPXIbD5eG2Zz9ib9HpuSzyeiX//Pw7nvnsG0b2TuHVWRcrr7PSEavTwT+3rWZ4XLLyA4cA+5vyaXDVMjJK/YdfOr4FEQ76Qcptd+Rk54kdW6feAHxx9AVtbVc/A2ZLKece9Vy7KAl88Rm12zdtRJhG45U2rI7tSu0Oi+pNsM7E8kr1H/6c+ATGp6bx+uY8rA6Hcvvt9E6O4Y2HZ6LTabntuY/4dov6nS5/0tRq5/7/LODdpXlM7pfBS7dciNmo/lBdR17cvoZaeyt/GjbJL1m8a2qWY9EGMyBM7XawlC5wrATjWcpr3B7NyQrLs8AUIcQBYHLb7wghcoUQb7RdcwVwFnDjMbaV3xdC7AR2AtHAUyfpzzEJN40ANNTb1ii1a9DomBg7iFXVO2l1q//w3z96DA12O6/mKWln/aOkxUfy7iNXkZ4QyQP/ns8Ln6zC7Va7vPMH2w6WcuWz77M6/xAPXjaep6+b5vdDlvvrq3lrdx5X9R7EgGi1LTMAGpy15DfmMTJqIjqN4qWccyPIBoRpqlq7x+CkhEVKWSulPFtKmdm2ZKprezxPSnlr289zpJT6DlvK328rSyknSSkHtC2trpVSqq1S3YZOG0qoMYc627fKbU+LH4rN42Rllfrt4f6xcVyc1Ze3tm6huFFtOcyjiQ4P5j+/m8nF4wcwe3EeNz/zIQdL1Ra0UkWr3cnf563k5uc/RiMEb953BVdPHOKX2UNHPF4vD69dTIjByINDzvLLGGtqliGRjImerNy2tC8BYQbjOOW2j6ZbZ952JMo8kWbnbhzuCqV2+4elkWKOYX6ZfyrWPzhmLDqNhidXfusX+x0x6nU8cv0UnrvzfMpqrFzz5zm8NO875TVzfyler2RR3l4uefJd5qzYwqVjBvLh769lYLriAtM/wjt7NrOluozHhk8m0qS+hozDY2d1zTL6hw0lyqg2oc+3DPoajOMRQmHv5x/hzBGWoEkA1LZ+o9SuEIIZiSPZ1VhEQZP6bNkewSHcM2IUyw8VsrhAfZD4WJyd25tPnrqBaSOyeGfhJi7+/Vt8tHwrDlfn9mxux+uVfLvjINf+9b/84e1FRISYefu+K3jkqrOxmPwbT2mnoKGGv25ZxdlJvbiop39OBG+oW0mrp5mzYy9Qb9y5Brx1CNMM9baPwRkjLGZ9L4J06VS3LlZue1p8LkaNnnklamM47dyUM4TsmFge/WY59Ta1bWN/jIgQM4/fMo23/3AlKXER/O2/3zDjd2/y5pcbqG1U27Dtx2i2Ofjku+1c/vRs7n1tPk02B09eP5U5D13FYMX1an8Ku9vNb1YtwKzT88zoaX5Zcrm9blZUfkm6pTfpweqr20nbfN9uUCcsg6CbV5DriBCCGMs0ihtfw+mpw6BV11c3VG9mWvxQFpXncVvPaUQa1R5G02k0/HXKVC768H3+9M1yXpx+nt/jCe0M6JXA67+7gry9R3h30SZe+WwNr89fx9gB6UwcmsGYAelEhKhbFticLtbvKWbplv18s6MAu9NNVnIsT90wjXOG9kav9W9w9lg8k/cNu+uqePPsS4k1B/tljE11q6h31TAz5RbltqW3CezLIOhiv5ShPBZnjLAAbcLyCjUtS0gIvUqp7cuTxzG/dAPzStZwW69pSm0D9I2J4d5Ro/nrmtWMS0llZn//nH85FkIIhvVNYVjfFA6X1/H5qp0s3rCX1bsP4dULBqbHk5uZRP+0ePokxRATZvlZwielpNbayv7SanYermBLQQnbDpbhdHsINRs5b1hfZozKZkBaj04T0qP5onA37+7dwi39cjk7WW1zs3bcXjdfV35OirkXWSF+yC+xLwDsCPOl6m3/CGeUsFj0WZj1GVS2fKFcWFIssYyLyeazkjVclTqeYJ36Ohezhg5jTXExf175DQPjetD3BBMFVZAWH8lvZ47n7svHsXJ7ITuLytmwt5g3Fm/E21bqIdhkID4qlJhQC6EWE0EGPRqNwOOV2J0urC12qq0tlNdaaW4LDAsBmQkxXD5uIGOz0xmameT3rePjkV9bwe/WLGJ4XBIP507w2zhra5dT56xmZvKtfhFQ2fox6LJA13k3ozNKWIQQxFku5FDD37G5ignSq+0Ad13a2ayqzmfekdXckD5FqW0AjRD8Y+p0Znwwhzu/ms9nM68mIsg/hXqOh1ajYVJOBpNyfHfxFruTPUeqOFBaTVFVA+V1VmqtLRypacTmdOH1SjQaQZBeR6jFRHxECEN6JZISG0FmYjRZybGEBPkvW/ZEqWhp4tbl84gymfn3hIvQa/wjcnaPjSUVn5IR3I8+Ieo/+NK1A9y7ESGPduqs74wSFoDY4BkcavgHFc2fkh7xW6W2+4QmMSa6Hx8Vr+KSpDGE6NVvScZYLLx83gVcNe8T7lr4Je9cdEmXxB2OxmIykJuZRG6m+gbjnY3V6eDGZZ/Q7HLyyfRriAmy+G2spZVf0Oy2MiPhKv/MVlreA2GBoB8UHvArZ8yuUDsmXTyRQeOoaJ6LlOq3T+r5qVMAAB9TSURBVG/tOZUWt4P3i75VbrudnPgEnjl7CutKjvD75Uv9Um3uTMXmdnHLsrkUNNTy6sSL6au4R1BHah3VfFu1kNyIsaRa1MdvpKca7Ash6BKExj9B5x/jjBMWgPjgmTg9VdT6IRO3V0gCU3rkMPfId1TY/Hdi+OK+/fjtyFF8umc3z65eFRAXBdjdLmat+JTN1aX8a/wFjE1I8+t4n5XORiM0nJ9wpV/sy9YPABfCfK1f7P8UZ6SwRJonYNDGUmZ93y/2b+s1HYDXDy7yi/127h4+kusGDuI/Wzbzwob1x39BgB/F5nZx24pPWV12mOdGT+e8NLVV8Y8mv3EzOxvzmNbjUiIMUcrtS28rtM4B4ySETnEHxZ/BGRdjAdAIPQkh13C44XlanAVYDGqnoXGmcG7tOQ2LzoTL60av8c+fWQjBYxMm0epy868N65BI7hkxqsu2Zk9XrE4HtyybS15VCc+Nma682v7R2D2tfHLkbXqYkpgQO90/g9jm+g4cWm7zj/3jcEbOWADiQ2aiEUZKre8e/+JfwJWp43HJJh7Y9ndcXv+lwmuE4NnJU7isXzYvbFjP09+t/H7bN8DxqWhp4opF77OtpowXx8/gisyBfh9zftkHNLrquCplFlqh/qYjpRPZ8hbohyAM/qvE/1OcscJi0EYSZ7mQiubPcHr8c4o3MSiOwpYSPi1Z7hf77Wj/X3tnHt90ff/x5yd30rRN74P7kJtxCJ4T7wHKRBzz2OYxdUzdnM6fTjZ36dymzqnTOe8bFQW3yVRkMgXcFBE55CpHuVra0rtpmjt5//5I2CpSWmi+SUq/z8ejjybffJLP+5Omr3zO98tg4J5zvsZV4yfw7No13PzuOwTCqTnX05PY2FDDrLdfotLTwrPnzGZGgu1RD0Wldzf/qV/K6QXTGZiReKtXAHx/hWgVynmDNq/fBXqtsAD0zb4WIUSlRr2WE/PG8tX8Cczf+y4V3sSeqj4Yg1L8YsoZ3HbKV3lr21a+89eF1LUl50xPT+TNnZuZ/c7LKAWvT/82p5VqPw/hC/vINOVy5cAfcV7JNzWpQySIeB4D83iwJOdc0KHo1cLiMA+kwDGNKvfLhCNuTeqYM2Q2dqOVB7a+lPDcuAejlOL6ySfwyPTz2VRXy/VvLWJbY3rmVEkVgUiYX618j5tW/IOxecW8OeNKRmm4pHwAEeGZXS/wq013MzprIlajRqkLvK9DtBrl/FFK59p6tbAA9M/+PhHxUOl+TpPXz7FkccPQS9jh2cvrFUs0qeNgzh82nNdmX8KEfiWcv/BFnl6/Wp93IZb6YNbbL/337M8r0y7VdPNbez6oW8Enjas5q/AMrEZtdhhL1IO0/RnMk8FyqiZ1dJVeuSrUHqd1FPmOqVS6n6dP1pWYjQnOig58tWACnzRO5rW97zLONYzR2docZmvP2KIi+mVnscfdzN0fL2PZ3l3cd+ZUSp3aecmkK5FolGc3r+YPa1aQYbbwzNnf0OxA4aHY07aXebvnMyZrFDNKE39A9QDS9kws54rriZSvDPb6HgvAQNeNRMRLRctTmtVx/ZCLKbLlcX/ZC7SENMnA+SVcNjtPTJ3Jb6ecy5r9VUx9/XnmbVrXq3ovGxv2M+vtl/jt6g+Y0mcQ/7zwmqSKiifs4aHtf8FpcnLd0GswKG3+5SRSA97nwDYdZdE2A39X0Ny7OV4u0i6R9qJ21wcppT5RSu1QSr2mkpUs4iAyLMMozPg6+1pfxB+u7vwJR4HDZOMnI75Lc8jD/WUvEJHkmLIrpfj2qHEs/uaVjC0o5ucfLuUbf3+FDXXaTianmka/l59//E8ueOsFqtrcPHL6BTx11kVJG/oARCTCo9ufpDnYzI+GXU+2OVuzuqT1fpAwyvl/mtVxJCTDuxnA1y6RdvvcePcCD4rIUKAJSHyWmy4yKOfHiETZ3fSQZnUMzezPdUNms665jJf3vKVZPYdiQLaLl2d8k/vPnEaFu4UL3pjHLe+/w75WbSatU4U3FOTRzz/m9Dee4JVt67hixET+Net7fH3QyKQPD+btmc9G92auGvgdhjoT7z90AAmuAf8iyLgaZUrsif2jRXXnjIlSaitwhohUxz2ClonI8EOU84iI86BrCqgDikUkrJQ6Gfi1iHTqTTBp0iRZvTqxzoYA5Y33Uel+hoklC8m0arP7UkR4dMd8ltR8xP8Nv5IzCidpUs/hcAcCPLp2Jc9tWAMCl44cy/fHn0CfzJ47/9IaDPDy1nU8tWkVDX4vZ/cdwtxJZ3CcKz8l8SypWcq8PfM5r2Qql/XXZmkZQCSMNHwDog2o/CUog7Y9MqXUZyLS6Ye2u8LSLCKu+G0FNB24f1C5MLAOCAP3iMjflVL5wMp4bwWlVD9gsYiM6aCuOcAcgP79+x+/Z8+eo467I8JRD6sqv4bd3I/xxa+iNBoPh6JhfrnxUba6d3P32BsZla3dt9nhqPK4eeSzlSzYGvOJu2DoCK4eezxjCg5rwZ1WVHpaeKlsDa9uW487GOC00oHcPP6rHF+YvJy4B/NJw6c8uuNJjs8Zz43HXa/ZvAqAtD2LtN6Dcj2SFL+ghAmLUmopcChnpjuAF9oLiVKqSUS+NM+ilOojIvuUUoOB94GzgRaOQFjao1WPBaCm9a9sbZjL8Px7KHZepEkdAO5QGz9Z/wDukId7x/2Yfo7Em191lX2tbp5av5rXyzbgDYeYUFTCZSO/wvTBw8i0pE/ypQOICO9XlvNS2VqW79uJQSmmDRjGnDEnMi4/OVYgHbGxZTN/3Powg52DuH3Ej7EYtJs2lEgVUn8eWE5I2kpQsnosXRoKHfSc54G3gDdIs6EQgEiUdTWX4QvtZnKfxZgTmHT7YGp89dy2/gHMBhP3jvsxBdZDzn0njZaAn4VbN/HK5vWUNzdiNZo4Z8Bgpg8exun9B6VUZHyhEB9V7WXxzm18tG8vx/ctZlVtJRcPHculw8bTJw2W0be2bue+sgcpshXys5G34jRplwNFRJCmayG0GpX3NsqUnARbyRKWPwANInKPUmoukCsiPzmoTA7gFZFAfPjzMTBTRDYrpRYAb4jIfKXU48DnIvKXzurVUlgAPMGtrKmaRZFzJsPzf69ZPQDlngp+9vnD5Fiy+P1XbiLHkvp/EBFhbW01f9u2mcU7t1Hv82I2GJhc0pdT+wzgpNJ+jCkoxGrUbhtUKBJhc0Mtn1RV8p99e1hZVUkgEibTYuXcgUO4bvwJDHblYjKkx46Jba07+EPZQ+RYXNwx6jZNV4AAxLsAcd+ByvwFKuNyTetqT7KEJQ94HegP7AEuFpFGpdQk4DoRuVYpdQrwBBAltgr1kIg8E3/+YGA+kAusBb4jIp2aIGstLAA7m+6nouVJxhY9R65d212Mm1rK+dXGv1Boy+V3Y3+Ey5JY+5DuEIlGWV2zj/f37mR5xW7KGuoAsBiNjMwtYFR+IcNy8xniyqV/VjalziwsR5AqMxiJUO1ppaK1hZ3NjWxrrGdTQy1bGurwxw9SDnHlclrfgZw1YDAnlfY7otdPBltbt3N/2UO4LC5+OvJWci3a9jxjQ6AZYB6NynlBs7nAQ5EUYUkVyRCWSNTPmuoLiUR9TOrzNiaNU/ttaN7Orzc9RpEtj7vH/pBci7bfeEdLva+N1TVVrKnZx4a6/Wyqr8Ud/OJ3QY7NTo7NTrbVit1kxmo0YlCKqAiBSARfOERLIECT30eT/4sGbJkWCyPzChmTX8TxxaVMKu5DUUZy0yoeCZ83b+RP2/9CniWXn468lRxL4ndut0ckgjReEUuQnfdm0peXdWFJAO7AetZWX0Kx8yKG5/9O8/o+b97GbzY9QY4li9+M/SFFtsRnFks0IkKdt43y5kYqW91UedzUetto9vtoCQTwR8IEImFEYhYfVqMJm9FEttWKy2anwJFBqTOTfpnZDHblUujomidROrCyYRWPlz9DX3spt424WfPhD4B4Hkc8D6Cy70XZZ2le38HowpIgdjb9kYqWJxhV8AgFGdov55W5d/HrTY9hNVj49ZjrGZSRumVTnUMjIrxTvYT5FQsZlnkctwy7kQxT4h0ZvlRvcC3S+G2wfQ2V/WBKBLirwpIeM19pzEDXjWRaxrCt4ecEwtpvgx+RNYh7vnIzCsXc9Q+xrqlM8zp1uk44Gub53fOYX7GQE3IncfuIW5IjKtFGpPkmMJagsu5K+16dLiydYFAWRhQ8QFRCbKm7RRPLkIMZmFHK/eNvodCay682PsbbVSs0r1Onc9yhVu4te4D3a5czo2QaPxg6B4vBrHm9IlGk+bbY7lrXwyhD6lcOO0MXli7gMA9kWN6dtARWs6vpwaTUmW/N4d5xNzMxdySPly/gz9vnE4qGklK3zpfZ0VrOLzbeRblnF9cP+R6X9J+t6Y7a9ojnTxD8EJV1B8o8Oil1dhddWLpIkXMmJc5LqXA/Rb1X2xy2B3CY7Px81Bxm9z2XJTX/4fb1D1Hrb0xK3ToxRIQlNUu5e8t9GJWRX46eyyn5Jyavfv+70PYY2GeDPbF+41qiT94eAdFogHU1l+EN7WJCyYKE24Ycjo/r1/PgtnkYlOLG477Fqfnjk1Z3b6Ul5Oapnc+xvnkDE13jmTPku2SYkpd2QUKbkcZvgWkYKnceKcoq8gX0VSGNCIRr+KzqIkyGDCaULMRsTN5+k2pfHX8oe57tnr2cW3QS1w6+CIcpNabwxzqfNn7Gc7vm4Y/4uKz/xZxTdGZSJ0wlUoM0zAaMqLwFKKP2eXm7gi4sGtLiX8P6msvJth3P2KKnMSTxmyQUDfPq3nd4o2Ip+dYcfnjcZUzI0da1rzfREmph3p7XWNmwioGO/swZcg39HMld8peoJ9ZTiVSgcl9FmdPn76sLi8bs97xJWf1tFGXMYnj+PUlf/itz7+KhbfPY56vlrMITuHrwLLLN6btDNd2JSpQVdf/m1b0LCUaDXFB6Pl8vnY5JIxfLjhAJIk3fg+AqVM4TKOuUpNbfGV0Vll6fTPtoKXLOxBeuYE/zw1hNpQzKuSmp9Y/IGsTDE+cyf++7/LVyKasaN3L5gBlMLTkVYxLPjhwL7Ggt58U9r7KrbTfDM4dx9aDLKbUnP/2CSARpvhWCH8d21qaZqBwJurB0gwHZPyAQrmJvy6OYjTn0zboiqfVbDGauGPh1Ti+YxBPlC3is/HUW1/ybqwfN0odHXaDWX8eCyr+xsmEVLnM21w25hlPyTkrJ5jORKOL+JQTeRWXOTcl2/USiD4W6iUiYzXU3Ue99jxH591HkvDBFcQj/rl/LC7sXsd/fwHjXCC4feD7DMgemJJ50pjHQyKKqxSyrW4FRGZlWfC4zSqdj18pErBNEBGm9E7yvQMYNGDJvTkkcXUGfY0ki0WiADbVzaPZ/wsj8+yl0zkhZLKFoiLerP2TB3vdwhz1Myh3NJf2mMiJLewvRdKfWX8fimn+yvPZDoghTCk5lVp8LND+RfDhiovI78L4AGdeinLel9XZ9XViSTCTqZcP+79ESWMOoggcoyJie0ni8YT//qFrOm/vepzXsZXTWUC7seyaTc8f0qjkYEaHcs5Ml+//FqobVKKU4Lf8ULuhzPgXW1CTa/l9sUcR9J/heBcdVqMyfprWogC4sKSESbePz/dfgDqxnRP69FDkv6PxJGuOLBFhS/R/erFpGfaCJEls+00pO5ezCE8lOo4RSicYf8bOy4VM+qF3Ozrbd2I12zig4jWkl52qeiKkriIQR9y/A9wZkfA/lvDXtRQV0YUkZkWgbG2qvo8W/imF5v6Ek8+JUhwTEzLM+ql/PW1XL2ezeiUkZmZw7hrOKTuD4nFGYk7ysqgVRiVLm3sZ/GlayqmE1/qifUlsJ5xSdyVcLTknZHMrBiPiR5lsgsBQyfohy3tgjRAV0YUkpkaifTXU/oMn3IYNct9Av+/tp9cHZ01bNP2s+YkXdZzSHWnGaHJyYN5ZT8sYzzjUMqzH1W8e7SjgaZmvrdlY3reGzxrU0hZqxGWxMzp3IGYWncZxzaFq99xJ1I03Xx5JgZ96BykjuSmJ3SVbO21zgNWAgsJtYztumg8qcCbQ/EjwCuDTuLfQ8cDoxKxCAq0RkXWf1pruwAEQlyNb6udS2vUWfzMsZkvszlEqvXK3haIR1zWV8WLeGTxo20BbxYTGYGecazgTXCMbnDKevvSi9/jFF2B+oZbO7jI3Nm9no3owv4sNisPCV7DGcmDeJCa5xWI1paFsSrkCavw/hPfEMcKmb5D9akiUs9wGN7bL054jI7YcpnwvsAPqKiPeAFYiILDySenuCsEBscq686R72uZ8nz342Iwv+iNGgfVKgoyEUDbGhZQefNm5ideMmavz1ALjMmYzIGsTIrEEMcfZjSEY/nObktcEb9rLXW8mutt3s8Oxke2s5TaHYd1eOOYdxrjGMc41lTPZobGkoJgeQ4Dqk+bqYv7LrUZQ1eSekE0la+grF3QxPF5Fvx+8/zzEsLAfY536RHY2/w2kZwZjCx7GaUmdO1lVq/PWsb97G5pZytrh3Uh0XGoB8i4t+jmL6OoootuVTZMsjz+oi15JFttmJ8Qh6ZhGJ0BpqpSXkpiHYSH2ggdpAHdW+Gqp81dQHG/5XrzWfoc7BDM88jtFZIym2pVdvqiPE+wbi/hUYi1E5T6JMqXG+TARpZbHarvz7wAMi8lb8/vPAyUCAuKl8uth/JJoG7zK21P0Yg7IzuvBhsm3J92zuDi3BVsrbKin3VLDXW0OFt4Z93v34o8EvlbUbbThNdm4bcRUf1i1jn68aRUwAwhImLGH8ET/eiA9/xI/wxc+gxWChxFZMib2Yfva+DMjoxwBHf1xp6lzQESKh+B6Vl8FyMsr1IMqgnQFeMkgri9X4YyXA50CpiITaXasBLMCTQLmI3NXB8zX3btaatuB2NtX+AH+4kiG5P6M089s94hu3I0QEd8jD/kADDYEWGoMtuEMePGEfnrCXi/tN5f3af1Ht/1+uYJMyYTKYsBms2I12HCYH2eYsss1Z5FpyyLfmk2XK7NHvC8S9f5pvhtA6cFyNyrwVpXr+ylvaDYWUUjcBo0VkTgePnwHcKiKdzmj1xB7LAcIRN1vqb6PR9wEFjukMy/+t5p5FOslF/B8gLbcDIVTW3Sj7+akOKWEkK0v/IuDK+O0rgTcPU/Yy4NX2F+JidGAYdSGwsZvxpD0mYxZjCh9jUM6t1Hn/yWdVF9Ia2JDqsHQSgIifqPuu2MqPsQSV97djSlSOhO4Kyz3AuUqp7cA58fsopSYppZ4+UEgpNRDoByw/6PkvK6U2ABuAfODubsbTI1DKQP/sOYwvnodIkLXVl7Cn+S9JcQDQ0QYJbUbqZ4F3Xmx7ft7rKNPAVIeVMvQNcikmFGlhR+Od1La9RZZ1AsPzfofDMiTVYekcIdGWX0NgaTyPirZe36lENyzrIZiN2YwseICR+Q/gDe1kddVM9rY8qfdeegDewKfsrr2IUGQ/KvMnqPx/HNOiciTowpImFDpnMLn0HfIcZ7Kr6X4+q7qIFv/aVIelcwjCkUaqGm9jd+2FBMMVhML7UAYHypD6w43pgi4saYTFVMDowkcYVfAI4Wgz62ouYVv9zwlGdC+hdEAkTKPnBcprptDc9hp5mdcxtHgZDuvEVIeWdvT8hfVjkIKMqeTYT2VP86NUup+ntm0xA1w30Cfr8qQ6Auj8D49/Bfub7yQQKsNhPZli113YLKNSHVbaovdY0hSTwcmQ3NuZVPoPsm0T2dl0L5/um8Z+zyJEoqkOr9fgC6xjT+0l7K27jGjUS9+8pxhQsEAXlU7QhSXNybAMZWzRU4wtegajclJWfyufVc2kvu09XWA0xBfcSEX9NeyqPR9/aDNFrjsZUrKMLMd5PX5XcDLQh0I9hFz7aeSUnkpd2zvsbv4Tm+p+QIZ5OANcN5DvmIrqRekmtcQbWE29+894/O9hUFnkZ91CXuYcjIZjN9ueFujC0oNQykChcwYFGdOobXubvS2PsbnuJgbnzKVv1ncRghhU+qYOSFdEInj8S6l3P4Yv+CkGg4uCrFvJzbwGoyEr1eH1SHRh6YEoZaLIOZPCjBnUeZeQYzsZX3AtFfVX4Mq4lBznd7D04l2fXSUcaaS5bT5NnhcJRSowG/tR5LqLnIxLMRiSZ/5+LKILSw9GKSOFGecBEInU4LCeTEPrkzS0PkaG7QxcGZeQaZ+q92LaIRKhLfBvmtteo9X7LkIAh/Vkilx3kGmffkycQE4H9HfxGMFmGUm//KcIhatpanuF5rZX2NdwPQaDi2zHTLIcM3FYJvfKuRgRwR/ahNv7d1q8bxKOVGEwuHA5v0VOxnewWXTXyESjnxU6RvnCN7NvCSJ+TMZisuznk2mfisN64jH97SwSxRdcR6tvCa2+xQTD5YAJp20K2RnfJNP+NQwqPbL29yR0U/hejlJGnLbTcdpOJxpto9X3Hi3eN2nyzKPR8wwGgwun7Uyctilk2KZgNqZ/uszOCEcaaQt8iMe3jDb/csLR/YARh/Uk8jLnkGk/D5OxZ2dw6ynowtILMBgyyM64kOyMC4lG2/D4l9PqW4LHvwy3928AWEzHkWE9EYf1ROzWyZiNfdN6v4aIEI5U4QuuoS3wCd7ASgKhLQAx0bSeRqb9azjtZ2E0pM5CtbeiC0svw2DIIMtxHlmO8xCJEghtweNfgTfwUaxH0zYPAKMhD7tlHDbLWKzm4djMI7GYBqGUOekxi4QIhvcQCG3BHyrDH9yEP7iecLQWAKUcOCyTyMqaQYZtCnbLuLSzWult6MLSi1HKgM0yGptlNHA9IhECoS14g5/hD67HF1yPx70ciMSfYcJi6ovZNAiLqT9mYykmYwlmYzFGQy5GYy4mQw7qCM4zRSVANNpCONJAJNpAOFJLKFJNKFJFKLyXYHgXwXAFcCCNhAGLaXBcQMZjt4zHZhmTEsHT6RhdWHT+i1JGbJYx2Cxj/nstKn6CoXL8oTKCoe0Ew7sJhnfTElhDVFoO/TpYUAYHBmVHYY73HoxANOZZTAgRH5FoGxA65GsYVDZmUx+s5lFk2s/Hah6M1TwKq2koBoM98Y3XSSi6sOgcFoOytevVfJFo1EsoUk04UkMk2kQ42kgk2kQ06iUqbYh440ISAQmDMqIwoZQJpewYlBODwYFRZWMy5mE05GEyFmA2luob1Ho4urDoHDUGgwOrYQhWs55KU+eLdGu3lFLqm0qpTUqpqFKqw7VtpdQ0pdRWpdSOuBXrgeuDlFKfxK+/po5kcK6jo5O2dHcb5kbgImBFRwVUbID9KDAdGAVcppQ6kMziXuBBERkKNAHXdDMeHR2dNKBbwiIiW0RkayfFTgB2iMhOEQkC84GZcS+hs4ADvs0vEPMW0tHR6eEk4+BIH6Ci3f3K+LU8oFn+l47+wPVDopSao5RarZRaXVdXp1mwOjo63afTydvDeTeLyOGcDxOKiDxJzN+ZSZMm9bwDTjo6vYhOhUVEzulmHfuIuSAeoG/8WgPgUkqZ4r2WA9d1dHR6OMkYCn0KHBdfAbIAlwKLJHas+gNgdrxcZ97POjo6PYTuLjfPUkpVAicDbyullsSvlyql3gGI90Z+CCwBtgCvi8im+EvcDtyilNpBbM7lme7Eo6Ojkx70uHwsSqk5wG+BPamOJcHkA/WpDiLB6G3qOXS1XQNEpKCzQj1RWFZ3JdFMT+NYbJfepp5DotvV+/IU6ujoaI4uLDo6OgmnJwrLk6kOQCOOxXbpbeo5JLRdPW6ORUdHJ/3piT0WHR2dNCfthaW7qRnSFaVUrlLqPaXU9vjvnA7KRZRS6+I/i5IdZ1fo7L1XSlnjaTF2xNNkDEx+lEdGF9p0lVKqrt3f5tpUxHkkKKWeVUrVKqU2dvC4Uko9HG/z50qpiUddmYik9Q8wEhgOLAMmdVDGCJQDgwELsB4YlerYO2nXfcDc+O25wL0dlPOkOtZO2tHpew/cADwev30p8Fqq405Am64C/pzqWI+wXVOAicDGDh4/D1gMKOAk4JOjrSvteyzSjdQM2kfXLWYSSxUBPTtlRFfe+/ZtXQicrdLZW6Rnfp46RURWAI2HKTITeFFirCR2lq/kaOpKe2HpIh2lZkhnikSkOn67BijqoJwtni5ipVIqHcWnK+/9f8tI7IhHC7EjHOlKVz9P34gPGRYqpfod4vGeRsL+j9Ii5226pGZINIdrV/s7IiJKqY6W5waIyD6l1GDgfaXUBhEpT3SsOkfMP4BXRSSglPo+sR7ZWSmOKW1IC2ER7VIzpJTDtUsptV8pVSIi1fHuZm0Hr7Ev/nunUmoZMIHY+D9d6Mp7f6BMpYoZRmcTS5uRrnTaJhFpH//TxObMejoJ+z86VoZCh0zNkOKYOmMRsVQR0EHKCKVUjlLKGr+dD5wKbE5ahF2jK+99+7bOBt6X+GxhmtJpmw6ae7iA2Mn9ns4i4Ir46tBJQEu74fqRkeqZ6i7MZM8iNtYLAPuBJfHrpcA7B81obyP2bX5HquPuQrvygH8B24GlQG78+iTg6fjtU4ANxFYlNgDXpDruDtrypfceuAu4IH7bBiwAdgCrgMGpjjkBbfo9sCn+t/kAGJHqmLvQpleBamIucZXEktdfB1wXf1wRS3xfHv+8HXIVtis/+s5bHR2dhHOsDIV0dHTSCF1YdHR0Eo4uLDo6OglHFxYdHZ2EowuLjo5OwtGFRUdHJ+HowqKjo5NwdGHR0dFJOP8PjP0Xgb7t3k4AAAAASUVORK5CYII=", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "num_pts = 1024\n", "t_vec = np.linspace(0, 10*np.pi, num_pts)\n", "\n", "fig, axis = plt.subplots(figsize=(4, 4))\n", "sidpy.viz.plot_utils.rainbow_plot(axis, np.cos(t_vec)*np.linspace(0, 1, num_pts),\n", " np.sin(t_vec)*np.linspace(0, 1, num_pts),\n", " num_steps=32)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "plot_scree() \n", "------------\n", "One of the results of applying Singular Value Decomposition is the variance or statistical significance\n", "of the resultant components. This data is best visualized via a log-log plot and plot_scree is available\n", "exclusively to visualize this kind of data" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "scree = np.exp(-1 * np.arange(100))\n", "sidpy.viz.plot_utils.plot_scree(scree, color='r');" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.16 (main, Jan 11 2023, 16:16:36) [MSC v.1916 64 bit (AMD64)]" }, "vscode": { "interpreter": { "hash": "838e0debddb5b6f29d3d8c39ba50ae8c51920a564d3bac000e89375a158a81de" } } }, "nbformat": 4, "nbformat_minor": 2 } sidpy-0.12.3/notebooks/02_visualization/plot_2d.ipynb000066400000000000000000042633221455261647000225660ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "2D plotting utilities\n", "===============\n", "\n", "\n", "**Suhas Somnath**\n", "\n", "8/12/2017 \n", "\n", "**This is a short walk-through of useful plotting utilities available in sidpy**\n", "\n", "\n", "Introduction\n", "--------------\n", "Some of the functions in ``sidpy.viz.plot_utils`` fill gaps in the default matplotlib package, some were\n", "developed for scientific applications, These functions have been developed\n", "to substantially simplify the generation of high quality figures for journal publications.\n", "\n", "### Table of contents:\n", "* [plot_map](#plot_map)\n", "* [plot_map_stack](#plot_map_stack)\n", "* [plot_complex_spectra](#plot_complex_spectra)\n", "\n", "\n", "#### Import necessary packages:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "from __future__ import division, print_function, absolute_import, unicode_literals\n", "import numpy as np\n", "from warnings import warn\n", "import matplotlib.pyplot as plt\n", "import subprocess\n", "import sys\n", "\n", "\n", "def install(package):\n", " subprocess.call([sys.executable, \"-m\", \"pip\", \"install\", package])\n", "# Package for downloading online files:\n", "try:\n", " import sidpy\n", "except ImportError:\n", " warn('sidpy not found. Will install with pip.')\n", " import pip\n", " install('sidpy')\n", " import sidpy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# plot_map()\n", "\n", "This function adds several popularly used features to the basic image plotting function in matplotlib including:\n", "\n", "* easy addition of a colorbar\n", "* custom x and y tick values\n", "* clipping the colorbar to N standard deviations of the mean\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x_vec = np.linspace(0, 6*np.pi, 256)\n", "y_vec = np.sin(x_vec)**2\n", "\n", "atom_intensities = y_vec * np.atleast_2d(y_vec).T\n", "\n", "fig, axes = plt.subplots(ncols=2, figsize=(10, 5))\n", "\n", "# Standard imshow plot for reference\n", "axes[0].imshow(atom_intensities, origin='lower')\n", "axes[0].set_title('Standard imshow')\n", "\n", "\n", "# Now plot_map with some options enabled:\n", "sidpy.viz.plot_utils.plot_map(axes[1], atom_intensities, stdevs=1.5, num_ticks=4,\n", " x_vec=np.linspace(-1, 1, atom_intensities.shape[0]),\n", " y_vec=np.linspace(0, 500, atom_intensities.shape[1]),\n", " cbar_label='intensity (a. u.)', tick_font_size=16)\n", "axes[1].set_title('plot_map')\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# plot_map_stack()\n", "\n", "One of the most popular operations in scientific research is the visualization of a stack of images. \n", "This function is built specifically for that purpose. \n", "Here we simply simulate some images using sinusoidal functions for demonstration purposes." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def get_sine_2d_image(freq):\n", " x_vec = np.linspace(0, freq*np.pi, 256)\n", " y_vec = np.sin(x_vec)**2\n", " return y_vec * np.atleast_2d(y_vec).T\n", "\n", "\n", "frequencies = [0.25, 0.5, 1, 2, 4 ,8, 16, 32, 64]\n", "image_stack = [get_sine_2d_image(freq) for freq in frequencies]\n", "image_stack = np.array(image_stack)\n", "\n", "fig, axes = sidpy.viz.plot_utils.plot_map_stack(image_stack, reverse_dims=False, title_yoffset=0.95)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# plot_complex_spectra()\n", "\n", "This function plots the amplitude and phase components of a stack of complex valued 2D images. \n", "Here we simulate the data using sine and cosine components" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def get_complex_2d_image(freq):\n", " # Simple function to generate images\n", " x_vec = np.linspace(0, freq*np.pi, 256)\n", " y_vec_1 = np.sin(x_vec)**2\n", " y_vec_2 = np.cos(x_vec)**2\n", " return y_vec_2 * np.atleast_2d(y_vec_2).T + 1j*(y_vec_1 * np.atleast_2d(y_vec_1).T)\n", "\n", "\n", "# The range of frequences over which the images are generated\n", "frequencies = 2 ** np.arange(4)\n", "image_stack = [get_complex_2d_image(freq) for freq in frequencies]\n", "\n", "fig, axes = sidpy.viz.plot_utils.plot_complex_spectra(np.array(image_stack), figsize=(3.5, 3))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.11" } }, "nbformat": 4, "nbformat_minor": 4 } sidpy-0.12.3/notebooks/02_visualization/plot_cmap.ipynb000066400000000000000000012473471455261647000232070ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Utilities for color maps\n", "===============\n", "\n", "\n", "**Suhas Somnath**\n", "\n", "8/12/2017 \n", "\n", "**This is a short walk-through of useful plotting utilities available in sidpy**\n", "\n", "Introduction\n", "--------------\n", "Some of the functions in ``sidpy.viz.plot_utils`` fill gaps in the default matplotlib package, some were\n", "developed for scientific applications, These functions have been developed\n", "to substantially simplify the generation of high quality figures for journal publications.\n", "\n", "#### Import necessary packages:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Ensure python 3 compatibility:\n", "from __future__ import division, print_function, absolute_import, \\\n", " unicode_literals\n", "import numpy as np\n", "from warnings import warn\n", "import matplotlib.pyplot as plt\n", "import subprocess\n", "import sys\n", "\n", "\n", "def install(package):\n", " subprocess.call([sys.executable, \"-m\", \"pip\", \"install\", package])\n", "\n", "\n", "# Package for downloading online files:\n", "try:\n", " import sidpy\n", "except ImportError:\n", " warn('sidpy not found. Will install with pip.')\n", " import pip\n", "\n", " install('sidpy')\n", " import sidpy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# plot_utils has a handful of colormaps suited for different applications.\n", "\n", "# cmap_jet_white_center()\n", "\n", "This is the standard jet colormap with a white center instead of green. This is a good colormap for images with\n", "divergent data (data that diverges slightly both positively and negatively from the mean).\n", "One example target is the ronchigrams from scanning transmission electron microscopy (STEM)\n", "\n", "## cmap_hot_desaturated()\n", "\n", "This is a desaturated version of the standard jet colormap\n", "\n", "## discrete_cmap()\n", "\n", "This function helps create a discretized version of the provided colormap. This is ideally suited when the data\n", "only contains a few discrete values. One popular application is the visualization of labels from a clustering\n", "algorithm" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x_vec = np.linspace(0, 2*np.pi, 256)\n", "y_vec = np.sin(x_vec)\n", "\n", "test = y_vec * np.atleast_2d(y_vec).T\n", "\n", "fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(10, 10))\n", "for axis, title, cmap in zip(axes.flat,\n", " ['Jet',\n", " 'Jet with white center',\n", " 'Jet desaturated',\n", " 'Jet discretized'],\n", " [plt.cm.jet,\n", " sidpy.viz.plot_utils.cmap_jet_white_center(),\n", " sidpy.viz.plot_utils.cmap_hot_desaturated(),\n", " sidpy.viz.plot_utils.discrete_cmap(8, cmap='jet')]):\n", " im_handle = axis.imshow(test, cmap=cmap)\n", " cbar = plt.colorbar(im_handle, ax=axis, orientation='vertical',\n", " fraction=0.046, pad=0.04, use_gridspec=True)\n", " axis.set_title(title)\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# make_linear_alpha_cmap()\n", "\n", "On certain occasions we may want to superimpose one image with another. However, this is not possible\n", "by default since colormaps involve solid colors. This function allows one to plot multiple images using\n", "a transparent-to-solid colormap. Here we will demonstrate this by plotting blobs representing atomic columns\n", "over some background intensity." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "num_pts = 256\n", "\n", "fig, axis = plt.subplots()\n", "\n", "# Prepare some background signal\n", "x_mat, y_mat = np.meshgrid(np.linspace(-0.2*np.pi, 0.1*np.pi, num_pts), np.linspace(0, 0.25*np.pi, num_pts))\n", "background_distortion = 0.2 * (x_mat + y_mat + np.sin(0.25 * np.pi * x_mat))\n", "\n", "# plot this signal in grey\n", "axis.imshow(background_distortion, cmap='Greys')\n", "\n", "# prepare the signal of interest (think of this as intensities in a HREM dataset)\n", "x_vec = np.linspace(0, 6*np.pi, num_pts)\n", "y_vec = np.sin(x_vec)**2\n", "atom_intensities = y_vec * np.atleast_2d(y_vec).T\n", "\n", "# prepare the transparent-to-solid colormap\n", "solid_color = plt.cm.jet(0.8)\n", "translucent_colormap = sidpy.viz.plot_utils.make_linear_alpha_cmap('my_map', solid_color,\n", " 1, min_alpha=0, max_alpha=1)\n", "\n", "# plot the atom intensities using the custom colormap\n", "im_handle = axis.imshow(atom_intensities, cmap=translucent_colormap)\n", "cbar = plt.colorbar(im_handle, ax=axis, orientation='vertical',\n", " fraction=0.046, pad=0.04, use_gridspec=True)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# get_cmap_object()\n", "\n", "This function is useful more for developers writing their own plotting functions that need to manipulate the\n", "colormap object. This function makes it easy to ensure that you are working on the colormap object and not the\n", "string name of the colormap (both of which are accepted by most matplotlib functions).\n", "Here we simply compare the returned values when passing both the colormap object and the string name of the colormap \n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sidpy.viz.plot_utils.get_cmap_object('jet') == sidpy.viz.plot_utils.get_cmap_object(plt.cm.jet)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# cmap_from_rgba()\n", "This function is handy for converting a Matlab-style colormap instructions (lists of [reg, green, blue, alpha]) to\n", "matplotlib's style:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "hot_desaturated = [(255.0, (255, 76, 76, 255)),\n", " (218.5, (107, 0, 0, 255)),\n", " (182.1, (255, 96, 0, 255)),\n", " (145.6, (255, 255, 0, 255)),\n", " (109.4, (0, 127, 0, 255)),\n", " (72.675, (0, 255, 255, 255)),\n", " (36.5, (0, 0, 91, 255)),\n", " (0, (71, 71, 219, 255))]\n", "\n", "new_cmap = sidpy.viz.plot_utils.cmap_from_rgba('hot_desaturated', hot_desaturated, 255)\n", "\n", "x_vec = np.linspace(0, 2*np.pi, 256)\n", "y_vec = np.sin(x_vec)\n", "\n", "test = y_vec * np.atleast_2d(y_vec).T\n", "\n", "fig, axes = plt.subplots(ncols=2, figsize=(10, 5))\n", "for axis, title, cmap in zip(axes.flat,\n", " ['Jet', 'Jet desaturated'],\n", " [plt.cm.jet, new_cmap]):\n", " im_handle = axis.imshow(test, cmap=cmap)\n", " cbar = plt.colorbar(im_handle, ax=axis, orientation='vertical',\n", " fraction=0.046, pad=0.04, use_gridspec=True)\n", " axis.set_title(title)\n", "fig.tight_layout()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 } sidpy-0.12.3/notebooks/02_visualization/plot_misc.ipynb000066400000000000000000013202611455261647000232050ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Misc Plotting utilities\n", "===============\n", "\n", "\n", "**Suhas Somnath**\n", "\n", "8/12/2017 \n", "\n", "**This is a short walk-through of useful plotting utilities available in sidpy**\n", "\n", "Introduction\n", "--------------\n", "Some of the functions in ``sidpy.viz.plot_utils`` fill gaps in the default matplotlib package, some were\n", "developed for scientific applications, These functions have been developed\n", "to substantially simplify the generation of high quality figures for journal publications.\n", "\n", "#### Import necessary packages:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Ensure python 3 compatibility:\n", "from __future__ import division, print_function, absolute_import, \\\n", " unicode_literals\n", "import numpy as np\n", "from warnings import warn\n", "import matplotlib.pyplot as plt\n", "import subprocess\n", "import sys\n", "import sidpy\n", "\n", "def install(package):\n", " subprocess.call([sys.executable, \"-m\", \"pip\", \"install\", package])\n", "\n", "\n", "# Package for downloading online files:\n", "try:\n", " import sidpy\n", "except ImportError:\n", " warn('sidpy not found. Will install with pip.')\n", " import pip\n", "\n", " install('sidpy')\n", " import sidpy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# General Utilities\n", "# ==================\n", "## set_tick_font_size()\n", "# ---------------------\n", "Adjusting the font sizes of the tick marks is often necessary for preparing figures for journal papers.\n", "However, adjusting the tick sizes is actually tedious in python and this function makes this easier." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Subfigures\tFewer Rows\tFewer Columns\n", "1\t\t(1, 1)\t\t(1, 1)\n", "2\t\t(1, 2)\t\t(2, 1)\n", "3\t\t(1, 3)\t\t(3, 1)\n", "4\t\t(2, 2)\t\t(2, 2)\n", "5\t\t(2, 3)\t\t(3, 2)\n", "6\t\t(2, 3)\t\t(3, 2)\n", "7\t\t(2, 4)\t\t(4, 2)\n", "8\t\t(2, 4)\t\t(4, 2)\n", "9\t\t(3, 3)\t\t(3, 3)\n", "10\t\t(3, 4)\t\t(4, 3)\n", "11\t\t(3, 4)\t\t(4, 3)\n", "12\t\t(3, 4)\t\t(4, 3)\n", "13\t\t(3, 5)\t\t(5, 3)\n", "14\t\t(3, 5)\t\t(5, 3)\n", "15\t\t(3, 5)\t\t(5, 3)\n", "16\t\t(4, 4)\t\t(4, 4)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "test = np.random.rand(10, 10)\n", "\n", "fig, axes = plt.subplots(ncols=2, figsize=(8, 4))\n", "for axis, title in zip(axes, ['Default', 'Custom']):\n", " axis.imshow(test)\n", " axis.set_title(title + ' tick size')\n", "# only changing the tick font size on the second plot:\n", "sidpy.viz.plot_utils.set_tick_font_size(axes[1], 24)\n", "fig.tight_layout()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## get_plot_grid_size()\n", "# ---------------------\n", "\n", "This handy function figures out the layout for a 2D grid of sub-plots given a desired number of plots" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Subfigures\tFewer Rows\tFewer Columns\n", "1\t\t(1, 1)\t\t(1, 1)\n", "2\t\t(1, 2)\t\t(2, 1)\n", "3\t\t(1, 3)\t\t(3, 1)\n", "4\t\t(2, 2)\t\t(2, 2)\n", "5\t\t(2, 3)\t\t(3, 2)\n", "6\t\t(2, 3)\t\t(3, 2)\n", "7\t\t(2, 4)\t\t(4, 2)\n", "8\t\t(2, 4)\t\t(4, 2)\n", "9\t\t(3, 3)\t\t(3, 3)\n", "10\t\t(3, 4)\t\t(4, 3)\n", "11\t\t(3, 4)\t\t(4, 3)\n", "12\t\t(3, 4)\t\t(4, 3)\n", "13\t\t(3, 5)\t\t(5, 3)\n", "14\t\t(3, 5)\t\t(5, 3)\n", "15\t\t(3, 5)\t\t(5, 3)\n", "16\t\t(4, 4)\t\t(4, 4)\n" ] } ], "source": [ "print('Subfigures\\tFewer Rows\\tFewer Columns')\n", "for num_plots in range(1, 17):\n", " print('{}\\t\\t{}\\t\\t{}'.format(num_plots,\n", " sidpy.viz.plot_utils.get_plot_grid_size(num_plots, fewer_rows=True),\n", " sidpy.viz.plot_utils.get_plot_grid_size(num_plots, fewer_rows=False)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## make_scalar_mappable()\n", "# ---------------------\n", "\n", "This is a low-level function that is used by ``cbar_for_line_plot()`` to generate the color bar manually.\n", "Here we revisit the example for plot_line_family() but we generate the colorbar by hand using\n", "``make_scalar_mappable()``. In this case, we make the colorbar horizontal just as an example." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_vec = np.linspace(0, 2*np.pi, 256)\n", "freqs = range(1, 5)\n", "y_mat = np.array([np.sin(freq * x_vec) for freq in freqs])\n", "\n", "fig, axis = plt.subplots(figsize=(4, 4.75))\n", "sidpy.viz.plot_utils.plot_line_family(axis, x_vec, y_mat)\n", "\n", "num_steps = len(freqs)\n", "\n", "sm = sidpy.viz.plot_utils.make_scalar_mappable(1, num_steps+1)\n", "\n", "cbar = plt.colorbar(sm, ax=axis, orientation='horizontal',\n", " pad=0.04, use_gridspec=True)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## cbar_for_line_plot()\n", "# ---------------------\n", "Note that from the above plot it may not be clear if the signal is radiating outwards or spiraling inwards.\n", "In these cases it helps to add a colorbar. However, colorbars can typically only be added for 2D images.\n", "In such cases we can use a handy function: ``cbar_for_line_plot()``" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "num_pts = 1024\n", "t_vec = np.linspace(0, 10*np.pi, num_pts)\n", "\n", "fig, axis = plt.subplots(figsize=(4.5, 4))\n", "sidpy.viz.plot_utils.rainbow_plot(axis, np.cos(t_vec)*np.linspace(0, 1, num_pts),\n", " np.sin(t_vec)*np.linspace(0, 1, num_pts),\n", " num_steps=32)\n", "\n", "cbar = sidpy.viz.plot_utils.cbar_for_line_plot(axis, 10)\n", "cbar.set_label('Time (sec)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## use_nice_plot_params()\n", "# ----------------------\n", "This function changes the default plotting parameters so that the figures look nicer and are closer to publication-\n", "ready figures. Note that all subsequent plots will be generated using the new defaults.\n", "\n", "## reset_plot_params()\n", "# -------------------\n", "This function resets the plot parameters to matplotlib defaults.\n", "The following sequence of default >> nice >> default parameters will illustrate this.\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_vec = np.linspace(0, 2*np.pi, 256)\n", "freqs = range(1, 5)\n", "y_mat = np.array([np.sin(freq * x_vec) for freq in freqs])\n", "\n", "for nice in [False, True, False]:\n", " if nice:\n", " sidpy.viz.plot_utils.use_nice_plot_params()\n", " else:\n", " sidpy.viz.plot_utils.reset_plot_params()\n", " fig, axis = plt.subplots(figsize=(4, 4))\n", " sidpy.viz.plot_utils.plot_line_family(axis, x_vec, y_mat)\n", " axis.set_xlabel('Time (sec)')\n", " axis.set_ylabel('Amplitude (a. u.)')\n", " if nice:\n", " axis.set_title('Nice')\n", " else:\n", " axis.set_title('Default')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 } sidpy-0.12.3/notebooks/03_hdf5/000077500000000000000000000000001455261647000160725ustar00rootroot00000000000000sidpy-0.12.3/notebooks/03_hdf5/h5py_primer.ipynb000066400000000000000000000617761455261647000214210ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Primer to HDF5 and h5py\n", "\n", "\n", "**Suhas Somnath**\n", "\n", "4/18/2018\n", "\n", "**This document serves as a quick primer to HDF5 files and the h5py package used for reading and writing to such files**\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Introduction\n", "-------------\n", "We create and consume digital information stored in various file formats on a daily basis such as news presented in\n", "HTML files, scientific journal articles in PDF files, tabular data in XLSX spreadsheets and so on. Commercially\n", "available scientific instruments generate data in a variety of, typically proprietary, file formats. The proprietary\n", "nature of the data impedes scientific research of individual researchers and the collaboration within the scientific\n", "community at large. Hence, pycroscopy stores all relevant information including the measurement data, metadata etc.\n", "in the most popular file format for scientific data - Hierarchical Data Format (HDF5) files.\n", "\n", "HDF5 is a remarkably straightforward file format to understand since it mimics the familiar folders and files paradigm\n", "exposed to users by all operating systems such as Windows, Mac OS, Linux, etc. HDF5 files can contain:\n", "\n", "* ``Datasets`` - similar to spreadsheets and text files with tabular data.\n", "* ``Groups`` - similar to folders in a regular file system\n", "* ``Attributes`` - small metadata that provide additional information about the Group or Dataset they are attached to.\n", "* other advanced features such as hard links, soft links, object and region references, etc.\n", "\n", "h5py is the official software package for reading and writing to HDF5 files in python. Consequently, Pycroscopy relies\n", "entirely on h5py for all file related operations. While there are several high-level functions that simplify the\n", "reading and writing of Pycroscopy stylized data, it is still crucial that the users of Pycroscopy understand the\n", "basics of HDF5 files and are familiar with the basic functions in h5py. There are several tutorials available\n", "elsewhere to explain h5py in great detail. This document serves as a quick primer to the basics of interacting with\n", "HDF5 files via h5py.\n", "\n", "\n", "Import all necessary packages\n", "-------------------------------\n", "For this primer, we only need some very basic packages, all of which come with the standard Anaconda distribution:\n", "\n", "* ``os`` - to manipulate and remove files\n", "* ``numpy`` - for basic numerical work\n", "* ``h5py`` - the package that will be the focus of this primer" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from __future__ import print_function, division, unicode_literals\n", "import os\n", "import numpy as np\n", "import h5py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating a HDF5 files using h5py is similar to the process of creating a conventional text file using python. The File\n", "class of h5py requires the path for the desired file with a .h5, .hdf5, or similar extension.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "h5_path = 'hdf5_primer.h5'\n", "h5_file = h5py.File('hdf5_primer.h5', mode='w')\n", "print(h5_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At this point, a file in the path specified by h5_path has been created and is now open for modification. The returned\n", "value - h5_file is necessary to perform other operations on the file including creating groups and datasets.\n", "\n", "Groups\n", "===========\n", "create_group()\n", "----------------\n", "We can use the ``create_group()`` function on an existing object such as the open file handle (``h5_file``) to create a\n", "group:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "h5_group_1 = h5_file.create_group('Group_1')\n", "print(h5_group_1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output of the above print statement reveals that a group named ``Group_1`` was successfully created at location: '/'\n", "(which stands for the root of the file). Furthermore, this group contains 0 objects or members.\n", ".name\n", "-------\n", "One can find the full / absolute path where this object is located from its ``name`` property:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(h5_group_1.name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Groups in Groups\n", "----------------\n", "Much like folders in a computer, these groups can themselves contain more groups and datasets.\n", "\n", "Let us create a few more groups the same way. Except, let us create these groups within the newly created. To do this,\n", "we would need to call the ``create_group()`` function on the h5_group_1 object and not the h5_file object. Doing the\n", "latter would result in groups created under the file at the same level as ``Group_1`` instead of inside ``Group_1``.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "h5_group_1_1 = h5_group_1.create_group('Group_1_1')\n", "h5_group_1_2 = h5_group_1.create_group('Group_1_2')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, when we print h5_group, it will reveal that we have two objects - the two groups we just created:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(h5_group_1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets see what a similar print of one of the newly created groups looks like:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(h5_group_1_1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above print statement shows that this group named ``Group_1_1`` exists at a path: ``\"/Group_1/Group_1_1\"``. In other\n", "words, this is similar to a folder contained inside another folder.\n", "\n", ".parent\n", "---------\n", "The hierarchical nature of HDF5 allows us to access datasets and groups using relationships or paths. For example,\n", "every HDF5 object has a parent. In the case of 'Group_1' - its parent is the root or h5_file itself. Similarly, the\n", "parent object of 'Group_1_1' is 'Group_1':\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print('Parent of \"Group_1\" is {}'.format(h5_group_1.parent))\n", "print('Parent of \"Group_1_1\" is {}'.format(h5_group_1_1.parent))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In fact the .parent of an object is an HDF5 object (either a HDF5 group or HDF5 File object). So we can check if the\n", "parent of the h5_group_1_1 variable is indeed the h5_group_1 variable:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(h5_group_1_1.parent == h5_group_1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Accessing H5 objects\n", "----------------------\n", "Imagine a file or a folder on a computer that is several folders deep from where one is (e.g. -\n", "/Users/Joe/Documents/Projects/2018/pycroscopy).One could either reach the desired file or folder by opening one folder\n", "after another or directly by using a long path string. If you were at root (/), you would need to paste the entire\n", "path (absolute path) of the desired file - ``/Users/Joe/Documents/Projects/2018/pycroscopy``. Alternatively, if you\n", "were in an intermediate directory (e.g. - ``/Users/Joe/Documents/``), you would need to paste what is called the\n", "relative path (in this case - ``Projects/2018/pycroscopy``) to get to the desired file.\n", "\n", "In the same way, we can also access HDF5 objects either through ``relative paths``, or ``absolute paths``. Here are a few\n", "ways one could get to the group ``Group_1_2``:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(h5_file['/Group_1/Group_1_2'])\n", "print(h5_group_1['Group_1_2'])\n", "print(h5_group_1_1.parent['Group_1_2'])\n", "print(h5_group_1_1.parent.parent['Group_1/Group_1_2'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let us look at how one can iterate through the datasets and Groups present within a HDF5 group:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for item in h5_group_1:\n", " print(item)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ".items()\n", "----------\n", "Essentially, h5py group objects contain a dictionary of key-value pairs where they key is the name of the object and\n", "the value is a reference to the object itself.\n", "\n", "What the above for loop does is it iterates only over the keys in this dictionary which are all strings. In order to\n", "get the actual dataset object itself, we would need to use the aforementioned addressing techniques to get the actual\n", "Group objects.\n", "\n", "Let us see how we would then try to find the object for the group named 'Group_1_2':\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for key, value in h5_group_1.items():\n", " if key == 'Group_1_2':\n", " print('Found the desired object: {}'.format(value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Datasets\n", "===========\n", "create_dataset()\n", "----------------\n", "We can create a dataset within ``Group_1`` using a function that is similar to ``create_group()``, called\n", "``create_dataset()``. Unlike create_group() which just takes the path of the desired group as an input,\n", "``create_dataset()`` is highly customizable and flexible.\n", "\n", "In our experience, there are three modes of creating datasets that are highly relevant for scientific applications:\n", "\n", "* dataset with data at time of creation - where the data is already available at the time of creating the dataset\n", "* empty dataset - when one knows the size of data but the entire data is not available\n", "* resizable dataset - when one does not even know how large the data can be. *This case is rare*\n", "\n", "Creating Dataset with available data:\n", "-------------------------------------\n", "Let as assume we want to store a simple greyscale (floating point values) image with 256 x 256 pixels. We would create\n", "and store the data as shown below. As the size of the dataset becomes very large, the precision with which the data is\n", "stored can significantly affect the size of the dataset and the file. Therefore, we recommend purposefully specifying\n", "the data-type (via the ``dtype`` keyword argument) during creation.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "h5_simple_dataset = h5_group_1.create_dataset('Simple_Dataset',\n", " data=np.random.rand(256, 256),\n", " dtype=np.float32)\n", "print(h5_simple_dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Accessing data\n", "----------------\n", "We can access data contained in the dataset just like accessing a numpy array. For example, if we want the value at\n", "row ``29`` and column ``167``, we would read it as:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(h5_simple_dataset[29, 167])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, just as before, we can address this dataset in many ways:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(h5_group_1['Simple_Dataset'])\n", "print(h5_file['/Group_1/Simple_Dataset'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating (potentially large) empty datasets:\n", "--------------------------------------------\n", "In certain situations, we know how much space to allocate for the final dataset but we may not have all the data at\n", "once. Alternatively, the dataset is so large that we cannot fit the entire data in the computer memory before writing\n", "to the HDF5 file. Another possible circumstance is when we have to read N files, each containing a small portion of\n", "the data and then write the contents into each slot in the HDF5 dataset.\n", "\n", "For example, assume that we have 128 files each having 1D spectra (amplitude + phase or complex value) of length 1024.\n", "Here is how one may create the HDF5 dataset to hold the data:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "h5_empty_dataset = h5_group_1.create_dataset('Empty_Dataset',\n", " shape=(128, 1024),\n", " dtype=np.complex64)\n", "print(h5_empty_dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that unlike before, this particular dataset is empty since we only allocated space, so we would be reading zeros\n", "when attempting to access data:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(h5_empty_dataset[5, 102])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "populating with data\n", "----------------------\n", "One could populate each chunk of the dataset just like filling in a numpy array:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "h5_empty_dataset[0] = np.random.rand(1024) + 1j * np.random.rand(1024)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "flush()\n", "--------\n", "It is a good idea to ensure that this data is indeed committed to the file using regular flush() operations. There are\n", "chances where the data is still in the memory / buffer and not yet in the file if one does not flush():\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "h5_file.flush()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating resizeable datasets:\n", "-----------------------------\n", "This solution is relevant to those situations where we only know how large each unit of data would be but we don't\n", "know the number of units. This is especially relevant when acquiring data from an instrument.\n", "\n", "For example, if we were acquiring spectra of length 128 on a 1D grid of 256 locations, we may have created an empty 2D\n", "dataset of shape (265, 128) using the aforementioned function. The data was being collected ordinarily over the first\n", "13 positions but a change in parameters resulted in spectra of length 175 instead. The data from the 14th positon\n", "cannot be stored in the empty array due to a size mismatch. Therefore, we would need to create another empty 256 x 175\n", "dataset to hold the data. If changes in parameters cause 157 changes in spectra length, that would result in the\n", "creation of 157 datasets each with a whole lot of wasted space since datasets cannot be shrunk easily.\n", "\n", "In such cases, it is easier just to create datasets that can expand one pixel at a time. For this specific example,\n", "one may want to create a 2D dataset of shape (1, 128) that could grow up to a maxshape of (256, 128) as shown below:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "h5_expandable_dset = h5_group_1.create_dataset('Expandable_Dataset',\n", " shape=(1, 128),\n", " maxshape=(256, 128),\n", " dtype=np.float32)\n", "print(h5_expandable_dset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Space has been allocated for the first pixel, so the data could be written in as:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "h5_expandable_dset[0] = np.random.rand(128)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the next pixel, we would need to expand the dataset before filling it in:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "h5_expandable_dset.resize(h5_expandable_dset.shape[0] + 1, axis=0)\n", "print(h5_expandable_dset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice how the dataset has increased in size in the first dimension allowing the second pixel to be stored. The second\n", "pixel's data would be stored in the same way as in the first pixel and the cycle of expand and populate-with-data\n", "would continue.\n", "\n", "It is very important to note that there is a non-trivial storage overhead associated with each resize operation. In\n", "other words, a file containing this resizeable dataset that has been resized 255 times will certainly be larger than\n", "a similar file where the dataset space was pre-allocated and never expanded. Therefore this mode of creating datasets\n", "should used sparingly.\n", "\n", "Attributes\n", "===========\n", "* are metadata that can convey information that cannot be efficiently conveyed using Group or Dataset objects.\n", "* are almost exactly like python dictionaries in that they have a key-value pairs.\n", "* can be stored in either Group or Dataset objects.\n", "* are not appropriate for storing large amounts of information. Consider datasets instead\n", "* are best suited for things like experimental parameter such as beam intensity, scan rate, scan width, etc.\n", "\n", "Writing\n", "---------\n", "Storing attributes in objects is identical to appending to python dictionaries. Lets store some simple attributes in\n", "the group named 'Group_1':\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "h5_simple_dataset.attrs['single_num'] = 36.23\n", "h5_simple_dataset.attrs.update({'list_of_nums': [1, 6.534, -65],\n", " 'single_string': 'hello'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reading\n", "----------\n", "We would read the attributes just like we would treat a dictionary in python:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for key, val in h5_simple_dataset.attrs.items():\n", " print('{} : {}'.format(key, val))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets read the attributes one by one and verify that we read what we wrote:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print('single_num: {}'.format(h5_simple_dataset.attrs['single_num'] == 36.23))\n", "print('list_of_nums: {}'.format(np.all(h5_simple_dataset.attrs['list_of_nums'] == [1, 6.534, -65])))\n", "print('single_string: {}'.format(h5_simple_dataset.attrs['single_string'] == 'hello'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Caveat\n", "--------\n", "While the low-level attribute writing and reading does appear to work and is simple, it does not work for a list of\n", "strings in python 3. Hence the following line will not work and will cause problems.\n", "\n", ".. code-block:: python\n", "\n", " h5_simple_dataset.attrs['list_of_strings'] = ['a', 'bc', 'def']\n", "\n", "Instead, we recommend writing lists of strings by casting them as numpy arrays:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "h5_simple_dataset.attrs['list_of_strings'] = np.array(['a', 'bc', 'def'], dtype='S')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the same way, reading attributes that are lists of strings is also not straightforward:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print('list_of_strings: {}'.format(h5_simple_dataset.attrs['list_of_strings'] == ['a', 'bc', 'def']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A similar decoding step needs to be taken to extract the actual string values.\n", "\n", "To avoid manual encoding and decoding of attributes (different strategies for different versions of python), we\n", "recommend:\n", "\n", "* writing attributes using: ``pycroscopy.hdf_utils.write_simple_attrs()``\n", "* reading attributes using: ``pycroscopy.hdf_utils.get_attr() or get_attributes()``\n", "\n", "Both these functions work reliably and consistently across all python versions and fix this problem in h5py.\n", "\n", "Besides strings and numbers, we tend to store references to datasets as attributes. Here is how one would link the\n", "empty dataset to the simple dataset:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "h5_simple_dataset.attrs['Dataset_Reference'] = h5_empty_dataset.ref\n", "print(h5_simple_dataset.attrs['Dataset_Reference'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is how one would get a handle to the actual dataset from the reference:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Read the attribute how you normally would\n", "h5_ref = h5_simple_dataset.attrs['Dataset_Reference']\n", "# Get the handle to the actual dataset:\n", "h5_dset = h5_file[h5_ref]\n", "# Check if this object is indeed the empty dataset:\n", "print(h5_empty_dataset == h5_dset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we are done reading or manipulating an HDF5 file, we need to close it to avoid and potential damage:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "h5_file.close()\n", "os.remove(h5_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As mentioned in the beginning this is not meant to be a comprehensive overview of HDF5 or h5py, but rather just a\n", "quick overview of the important functionality we recommend everyone to be familiar with. We encourage you to read more\n", "about h5py and HDF5 if you are interested.\n", "\n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.5" } }, "nbformat": 4, "nbformat_minor": 1 } sidpy-0.12.3/notebooks/03_hdf5/hdf_utils_read.ipynb000066400000000000000000002310541455261647000221160ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Utilities for reading h5USID files\n", "\n", "**Suhas Somnath**\n", "\n", "4/18/2018\n", "\n", "**This document illustrates the many handy functions in sidpy.hdf.hdf_utils and pyUSID.hdf_utils that significantly simplify reading data\n", "and metadata in Universal Spectroscopy and Imaging Data (USID) HDF5 files (h5USID files)**\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note**: Most of the functions demonstrated in this notebook have been moved out of ``pyUSID.hdf_utils`` and into ``sidpy.hdf``\n", "
\n", "\n", "## Introduction\n", "The USID model uses a data-centric approach to data analysis and processing meaning that results from all data analysis\n", "and processing are written to the same h5 file that contains the recorded measurements. **Hierarchical Data Format\n", "(HDF5)** files allow data, whether it is raw measured data or results of analysis, to be stored in multiple datasets within\n", "the same file in a tree-like manner. Certain rules and considerations have been made in pyUSID to ensure\n", "consistent and easy access to any data.\n", "\n", "The h5py python package provides great functions to create, read, and manage data in HDF5 files. In\n", "``pyUSID.hdf_utils``, we have added functions that facilitate scientifically relevant, or USID specific\n", "functionality such as checking if a dataset is a Main dataset, reshaping to / from the original N dimensional form of\n", "the data, etc. Due to the wide breadth of the functions in ``hdf_utils``, the guide for hdf_utils will be split in two\n", "parts - one that focuses on functions that facilitate reading and one that facilitate writing of data. The following\n", "guide provides examples of how, and more importantly when, to use functions in ``pyUSID.hdf_utils`` for various\n", "scenarios.\n", "\n", "## Recommended pre-requisite reading\n", "* [Universal Spectroscopic and Imaging Data (USID) model](https://pycroscopy.github.io/USID/usid_model.html)\n", "* [Crash course on HDF5 and h5py](./h5py_primer.html)\n", "\n", "\n", "## Import all necessary packages\n", "\n", "Before we begin demonstrating the numerous functions in ``pyUSID.hdf_utils``, we need to import the necessary\n", "packages. Here are a list of packages besides pyUSID that will be used in this example:\n", "\n", "* ``h5py`` - to open and close the file\n", "* ``wget`` - to download the example data file\n", "* ``numpy`` - for numerical operations on arrays in memory\n", "* ``matplotlib`` - basic visualization of data\n", "* ``sidpy`` - basic scientific hdf5 capabilities" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from __future__ import print_function, division, unicode_literals\n", "import os\n", "# Warning package in case something goes wrong\n", "from warnings import warn\n", "import subprocess\n", "import sys\n", "\n", "\n", "def install(package):\n", " subprocess.call([sys.executable, \"-m\", \"pip\", \"install\", package])\n", "# Package for downloading online files:\n", "\n", "try:\n", " # This package is not part of anaconda and may need to be installed.\n", " import wget\n", "except ImportError:\n", " warn('wget not found. Will install with pip.')\n", " import pip\n", " install(wget)\n", " import wget\n", "import h5py\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# import sidpy - supporting package for pyUSID:\n", "try:\n", " import sidpy\n", "except ImportError:\n", " warn('sidpy not found. Will install with pip.')\n", " import pip\n", " install('sidpy')\n", " import sidpy\n", "\n", "# Finally import pyUSID.\n", "try:\n", " import pyUSID as usid\n", "except ImportError:\n", " warn('pyUSID not found. Will install with pip.')\n", " import pip\n", " install('pyUSID')\n", " import pyUSID as usid" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to demonstrate the many functions in hdf_utils, we will be using a h5USID file containing real\n", "experimental data along with results from analyses on the measurement data\n", "\n", "### This scientific dataset\n", "\n", "For this example, we will be working with a **Band Excitation Polarization Switching (BEPS)** dataset acquired from\n", "advanced atomic force microscopes. In the much simpler **Band Excitation (BE)** imaging datasets, a single spectrum is\n", "acquired at each location in a two dimensional grid of spatial locations. Thus, BE imaging datasets have two\n", "position dimensions (``X``, ``Y``) and one spectroscopic dimension (``Frequency`` - against which the spectrum is recorded).\n", "The BEPS dataset used in this example has a spectrum for **each combination of** three other parameters (``DC offset``,\n", "``Field``, and ``Cycle``). Thus, this dataset has three new spectral dimensions in addition to ``Frequency``. Hence,\n", "this dataset becomes a 2+4 = **6 dimensional dataset**\n", "\n", "### Load the dataset\n", "First, let us download this file from the pyUSID Github project:\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Working on:\n", "temp.h5\n" ] } ], "source": [ "url = 'https://raw.githubusercontent.com/pycroscopy/pyUSID/master/data/BEPS_small.h5'\n", "h5_path = 'temp.h5'\n", "_ = wget.download(url, h5_path, bar=None)\n", "\n", "print('Working on:\\n' + h5_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, lets open this HDF5 file in read-only mode. Note that opening the file does not cause the contents to be\n", "automatically loaded to memory. Instead, we are presented with objects that refer to specific HDF5 datasets,\n", "attributes or groups in the file\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "h5_path = 'temp.h5'\n", "h5_f = h5py.File(h5_path, mode='r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, ``h5_f`` is an active handle to the open file\n", "\n", "## Inspect HDF5 contents\n", "\n", "The file contents are stored in a tree structure, just like files on a contemporary computer. The file contains\n", "groups (similar to file folders) and datasets (similar to spreadsheets).\n", "There are several datasets in the file and these store:\n", "\n", "* The actual measurement collected from the experiment\n", "* Spatial location on the sample where each measurement was collected\n", "* Information to support and explain the spectral data collected at each location\n", "* Since the USID model stores results from processing and analyses performed on the data in the same h5USID file,\n", " these datasets and groups are present as well\n", "* Any other relevant ancillary information\n", "\n", "### print_tree()\n", "Soon after opening any file, it is often of interest to list the contents of the file. While one can use the open\n", "source software HDFViewer developed by the HDF organization, ``pyUSID.hdf_utils`` also has a very handy function -\n", "``print_tree()`` to quickly visualize all the datasets and groups within the file within python.\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Contents of the H5 file:\n", "/\n", "├ Measurement_000\n", " ---------------\n", " ├ Channel_000\n", " -----------\n", " ├ Bin_FFT\n", " ├ Bin_Frequencies\n", " ├ Bin_Indices\n", " ├ Bin_Step\n", " ├ Bin_Wfm_Type\n", " ├ Excitation_Waveform\n", " ├ Noise_Floor\n", " ├ Position_Indices\n", " ├ Position_Values\n", " ├ Raw_Data\n", " ├ Raw_Data-SHO_Fit_000\n", " --------------------\n", " ├ Fit\n", " ├ Guess\n", " ├ Spectroscopic_Indices\n", " ├ Spectroscopic_Values\n", " ├ Spatially_Averaged_Plot_Group_000\n", " ---------------------------------\n", " ├ Bin_Frequencies\n", " ├ Mean_Spectrogram\n", " ├ Spectroscopic_Parameter\n", " ├ Step_Averaged_Response\n", " ├ Spatially_Averaged_Plot_Group_001\n", " ---------------------------------\n", " ├ Bin_Frequencies\n", " ├ Mean_Spectrogram\n", " ├ Spectroscopic_Parameter\n", " ├ Step_Averaged_Response\n", " ├ Spectroscopic_Indices\n", " ├ Spectroscopic_Values\n", " ├ UDVS\n", " ├ UDVS_Indices\n" ] } ], "source": [ "print('Contents of the H5 file:')\n", "sidpy.hdf_utils.print_tree(h5_f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, ``print_tree()`` presents a clean tree view of the contents of the group. In this mode, only the group names\n", "are underlined. Alternatively, it can print the full paths of each dataset and group, with respect to the group / file\n", "of interest, by setting the ``rel_paths``\n", "keyword argument. ``print_tree()`` could also be used to display the contents of and HDF5 group instead of complete HDF5\n", "file as we have done above. Lets configure it to print the relative paths of all objects within the ``Channel_000``\n", "group:\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Measurement_000/Channel_000\n", "Bin_FFT\n", "Bin_Frequencies\n", "Bin_Indices\n", "Bin_Step\n", "Bin_Wfm_Type\n", "Excitation_Waveform\n", "Noise_Floor\n", "Position_Indices\n", "Position_Values\n", "Raw_Data\n", "Raw_Data-SHO_Fit_000\n", "Raw_Data-SHO_Fit_000/Fit\n", "Raw_Data-SHO_Fit_000/Guess\n", "Raw_Data-SHO_Fit_000/Spectroscopic_Indices\n", "Raw_Data-SHO_Fit_000/Spectroscopic_Values\n", "Spatially_Averaged_Plot_Group_000\n", "Spatially_Averaged_Plot_Group_000/Bin_Frequencies\n", "Spatially_Averaged_Plot_Group_000/Mean_Spectrogram\n", "Spatially_Averaged_Plot_Group_000/Spectroscopic_Parameter\n", "Spatially_Averaged_Plot_Group_000/Step_Averaged_Response\n", "Spatially_Averaged_Plot_Group_001\n", "Spatially_Averaged_Plot_Group_001/Bin_Frequencies\n", "Spatially_Averaged_Plot_Group_001/Mean_Spectrogram\n", "Spatially_Averaged_Plot_Group_001/Spectroscopic_Parameter\n", "Spatially_Averaged_Plot_Group_001/Step_Averaged_Response\n", "Spectroscopic_Indices\n", "Spectroscopic_Values\n", "UDVS\n", "UDVS_Indices\n" ] } ], "source": [ "sidpy.hdf_utils.print_tree(h5_f['/Measurement_000/Channel_000/'], rel_paths=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, ``print_tree()`` can also be configured to only print USID Main datasets besides Group objects using the ``main_dsets_only`` option. \n", "\n", "**Note**: only ``pyUSID`` has this capability unlike ``sidpy``:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/\n", "├ Measurement_000\n", " ---------------\n", " ├ Channel_000\n", " -----------\n", " ├ Raw_Data\n", " ├ Raw_Data-SHO_Fit_000\n", " --------------------\n", " ├ Fit\n", " ├ Guess\n", " ├ Spatially_Averaged_Plot_Group_000\n", " ---------------------------------\n", " ├ Spatially_Averaged_Plot_Group_001\n", " ---------------------------------\n" ] } ], "source": [ "usid.hdf_utils.print_tree(h5_f, main_dsets_only=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Accessing Attributes\n", "\n", "HDF5 datasets and groups can also store metadata such as experimental parameters. These metadata can be text,\n", "numbers, small lists of numbers or text etc. These metadata can be very important for understanding the datasets\n", "and guide the analysis routines.\n", "\n", "While one could use the basic ``h5py`` functionality to access attributes, one would encounter a lot of problems when\n", "attempting to decode attributes whose values were strings or lists of strings due to some issues in ``h5py``. This problem\n", "has been demonstrated in our `primer to HDF5 and h5py <./plot_h5py.html>`_. Instead of using the basic functionality of ``h5py``, we recommend always\n", "using the functions in pyUSID that reliably and consistently work for any kind of attribute for any version of\n", "python:\n", "\n", "### get_attributes()\n", "\n", "``get_attributes()`` is a very handy function that returns all or a specified set of attributes in an HDF5 object. If no\n", "attributes are explicitly requested, all attributes in the object are returned:\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "current_position_x : 4\n", "experiment_date : 26-Feb-2015 14:49:48\n", "data_tool : be_analyzer\n", "translator : ODF\n", "project_name : Band Excitation\n", "current_position_y : 4\n", "experiment_unix_time : 1503428472.2374\n", "sample_name : PZT\n", "xcams_id : abc\n", "user_name : John Doe\n", "comments : Band Excitation data\n", "Pycroscopy version : 0.0.a51\n", "data_type : BEPSData\n", "translate_date : 2017_08_22\n", "project_id : CNMS_2015B_X0000\n", "grid_size_y : 5\n", "sample_description : Thin Film\n", "instrument : cypher_west\n", "grid_size_x : 5\n" ] } ], "source": [ "for key, val in sidpy.hdf_utils.get_attributes(h5_f).items():\n", " print('{} : {}'.format(key, val))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "``get_attributes()`` is also great for only getting selected attributes. For example, if we only cared about the user\n", "and project related attributes, we could manually request for any that we wanted:\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "user_name : John Doe\n", "project_name : Band Excitation\n", "project_id : CNMS_2015B_X0000\n" ] } ], "source": [ "proj_attrs = sidpy.hdf_utils.get_attributes(h5_f, ['project_name', 'project_id', 'user_name'])\n", "for key, val in proj_attrs.items():\n", " print('{} : {}'.format(key, val))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### get_attr()\n", "\n", "If we are sure that we only wanted a specific attribute, we could instead use ``get_attr()`` as:\n", "\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "John Doe\n" ] } ], "source": [ "print(sidpy.hdf_utils.get_attr(h5_f, 'user_name'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### check_for_matching_attrs()\n", "Consider the scenario where we are have several HDF5 files or Groups or datasets and we wanted to check each one to\n", "see if they have the certain metadata / attributes. ``check_for_matching_attrs()`` is one very handy function that\n", "simplifies the comparision operation.\n", "\n", "For example, let us check if this file was authored by ``John Doe``:\n", "\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "print(sidpy.hdf.prov_utils.check_for_matching_attrs(h5_f, \n", " new_parms={'user_name': 'John Doe'}))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Finding datasets and groups\n", "\n", "There are numerous ways to search for and access datasets and groups in H5 files using the basic functionalities\n", "of h5py. pyUSID.hdf_utils contains several functions that simplify common searching / lookup operations as part of\n", "scientific workflows.\n", "\n", "### find_dataset()\n", "\n", "The ``find_dataset()`` function will return all datasets that whose names contain the provided string. In this case, we\n", "are looking for any datasets containing the string ``UDVS`` in their names. If you look above, there are two datasets\n", "(UDVS and UDVS_Indices) that match this condition:\n", "\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n" ] } ], "source": [ "udvs_dsets_2 = usid.hdf_utils.find_dataset(h5_f, 'UDVS')\n", "for item in udvs_dsets_2:\n", " print(item)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you might know by now, h5USID files contain three kinds of datasets:\n", "\n", "* ``Main`` datasets that contain data recorded / computed at multiple spatial locations.\n", "* ``Ancillary`` datasets that support a main dataset\n", "* Other datasets\n", "\n", "For more information, please refer to the documentation on the USID model.\n", "\n", "### check_if_main()\n", "``check_if_main()`` is a very handy function that helps distinguish between ``Main`` datasets and other objects\n", "(``Ancillary`` datasets, other datasets, Groups etc.). Lets apply this function to see which of the objects within the\n", "``Channel_000`` Group are ``Main`` datasets:\n", "\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Main Datasets:\n", "----------------\n", "Raw_Data\n", "\n", "Objects that were not Main datasets:\n", "--------------------------------------\n", "Bin_FFT\n", "Bin_Frequencies\n", "Bin_Indices\n", "Bin_Step\n", "Bin_Wfm_Type\n", "Excitation_Waveform\n", "Noise_Floor\n", "Position_Indices\n", "Position_Values\n", "Raw_Data-SHO_Fit_000\n", "Spatially_Averaged_Plot_Group_000\n", "Spatially_Averaged_Plot_Group_001\n", "Spectroscopic_Indices\n", "Spectroscopic_Values\n", "UDVS\n", "UDVS_Indices\n" ] } ], "source": [ "h5_chan_group = h5_f['Measurement_000/Channel_000']\n", "\n", "# We will prepare two lists - one of objects that are ``main`` and one of objects that are not\n", "\n", "non_main_objs = []\n", "main_objs = []\n", "for key, val in h5_chan_group.items():\n", " if usid.hdf_utils.check_if_main(val):\n", " main_objs.append(key)\n", " else:\n", " non_main_objs.append(key)\n", "\n", "# Now we simply print the names of the items in each list\n", "\n", "print('Main Datasets:')\n", "print('----------------')\n", "for item in main_objs:\n", " print(item)\n", "print('\\nObjects that were not Main datasets:')\n", "print('--------------------------------------')\n", "for item in non_main_objs:\n", " print(item)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above script allowed us to distinguish Main datasets from all other objects only within the Group named\n", "``Channel_000``.\n", "\n", "### get_all_main()\n", "What if we want to quickly find all ``Main`` datasets even within the sub-Groups of ``Channel_000``? To do this, we have a\n", "very handy function called - ``get_all_main()``:\n", "\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "located at: \n", "\t/Measurement_000/Channel_000/Raw_Data \n", "Data contains: \n", "\tCantilever Vertical Deflection (V) \n", "Data dimensions and original shape: \n", "Position Dimensions: \n", "\tX - size: 5 \n", "\tY - size: 5 \n", "Spectroscopic Dimensions: \n", "\tFrequency - size: 87 \n", "\tDC_Offset - size: 64 \n", "\tField - size: 2 \n", "\tCycle - size: 2\n", "Data Type:\n", "\tcomplex64\n", "--------------------------------------------------------------------\n", "\n", "located at: \n", "\t/Measurement_000/Channel_000/Raw_Data-SHO_Fit_000/Fit \n", "Data contains: \n", "\tSHO parameters (compound) \n", "Data dimensions and original shape: \n", "Position Dimensions: \n", "\tX - size: 5 \n", "\tY - size: 5 \n", "Spectroscopic Dimensions: \n", "\tDC_Offset - size: 64 \n", "\tField - size: 2 \n", "\tCycle - size: 2\n", "Data Fields:\n", "\tPhase [rad], R2 Criterion, Quality Factor, Amplitude [V], Frequency [Hz]\n", "--------------------------------------------------------------------\n", "\n", "located at: \n", "\t/Measurement_000/Channel_000/Raw_Data-SHO_Fit_000/Guess \n", "Data contains: \n", "\tSHO parameters (compound) \n", "Data dimensions and original shape: \n", "Position Dimensions: \n", "\tX - size: 5 \n", "\tY - size: 5 \n", "Spectroscopic Dimensions: \n", "\tDC_Offset - size: 64 \n", "\tField - size: 2 \n", "\tCycle - size: 2\n", "Data Fields:\n", "\tPhase [rad], R2 Criterion, Quality Factor, Amplitude [V], Frequency [Hz]\n", "--------------------------------------------------------------------\n" ] } ], "source": [ "main_dsets = usid.hdf_utils.get_all_main(h5_chan_group)\n", "for dset in main_dsets:\n", " print(dset)\n", " print('--------------------------------------------------------------------')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The datasets above show that the file contains three main datasets. Two of these datasets are contained in a HDF5\n", "Group called ``Raw_Data-SHO_Fit_000`` meaning that they are results of an operation called ``SHO_Fit`` performed on the\n", "``Main`` dataset - ``Raw_Data``. The first of the three main datasets is indeed the ``Raw_Data`` dataset from which the\n", "latter two datasets (``Fit`` and ``Guess``) were derived.\n", "\n", "The USID model allows the same operation, such as ``SHO_Fit``, to be performed on the same dataset (``Raw_Data``),\n", "multiple\n", "times. Each time the operation is performed, a new HDF5 Group is created to hold the new results. Often, we may\n", "want to perform a few operations such as:\n", "\n", "* Find the (source / main) dataset from which certain results were derived\n", "* Check if a particular operation was performed on a main dataset\n", "* Find all groups corresponding to a particular operation (e.g. - ``SHO_Fit``) being applied to a Main dataset\n", "\n", "``hdf_utils`` has a few handy functions for many of these use cases.\n", "\n", "### find_results_groups()\n", "First, lets show that ``find_results_groups()`` finds all Groups containing the results of a ``SHO_Fit`` operation applied\n", "to ``Raw_Data``:\n", "\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Instances of operation \"SHO_Fit\" applied to dataset named \"/Measurement_000/Channel_000/Raw_Data\":\n", "[]\n" ] } ], "source": [ "# First get the dataset corresponding to Raw_Data\n", "h5_raw = h5_chan_group['Raw_Data']\n", "\n", "operation = 'SHO_Fit'\n", "print('Instances of operation \"{}\" applied to dataset named \"{}\":'.format(operation, h5_raw.name))\n", "h5_sho_group_list = usid.hdf_utils.find_results_groups(h5_raw, operation)\n", "print(h5_sho_group_list)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, the ``SHO_Fit`` operation was performed on ``Raw_Data`` dataset only once, which is why\n", "``find_results_groups()`` returned only one HDF5 Group - ``SHO_Fit_000``.\n", "\n", "### check_for_old()\n", "\n", "Often one may want to check if a certain operation was performed on a dataset with the very same parameters to\n", "avoid recomputing the results. ``hdf_utils.check_for_old()`` is a very handy function that compares parameters (a\n", "dictionary) for a new / potential operation against the metadata (attributes) stored in each existing results group\n", "(HDF5 groups whose name starts with ``Raw_Data-SHO_Fit`` in this case). Before we demonstrate ``check_for_old()``, lets\n", "take a look at the attributes stored in the existing results groups:\n", "\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parameters already used for computing SHO_Fit on Raw_Data in the file:\n", "machine_id : mac109728.ornl.gov\n", "timestamp : 2017_08_22-15_02_08\n", "SHO_guess_method : pycroscopy BESHO\n", "SHO_fit_method : pycroscopy BESHO\n" ] } ], "source": [ "print('Parameters already used for computing SHO_Fit on Raw_Data in the file:')\n", "for key, val in sidpy.hdf_utils.get_attributes(h5_chan_group['Raw_Data-SHO_Fit_000']).items():\n", " print('{} : {}'.format(key, val))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let us check for existing results where the ``SHO_fit_method`` attribute matches an existing value and a new value:\n", "\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Checking to see if SHO Fits have been computed on the raw dataset:\n", "\n", "Using \"pycroscopy BESHO\":\n", "[]\n", "\n", "Using \"alternate technique\"\n", "[]\n" ] } ], "source": [ "print('Checking to see if SHO Fits have been computed on the raw dataset:')\n", "print('\\nUsing \"pycroscopy BESHO\":')\n", "print(usid.hdf_utils.check_for_old(h5_raw, 'SHO_Fit',\n", " new_parms={'SHO_fit_method': 'pycroscopy BESHO'}))\n", "print('\\nUsing \"alternate technique\"')\n", "print(usid.hdf_utils.check_for_old(h5_raw, 'SHO_Fit',\n", " new_parms={'SHO_fit_method': 'alternate technique'}))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clearly, while find_results_groups() returned any and all groups corresponding to ``SHO_Fit`` being applied to\n", "``Raw_Data``, ``check_for_old()`` only returned the group(s) where the operation was performed using the same specified\n", "parameters (``sho_fit_method`` in this case).\n", "\n", "Note that ``check_for_old()`` performs two operations - search for all groups with the matching nomenclature and then\n", "compare the attributes. ``check_for_matching_attrs()`` is the handy function, that enables the latter operation of\n", "comparing a giving dictionary of parameters against attributes in a given object.\n", "\n", "### get_source_dataset()\n", "``hdf_utils.get_source_dataset()`` is a very handy function for the inverse scenario where we are interested in finding\n", "the source dataset from which the known result was derived:\n", "\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Datagroup containing the SHO fits:\n", "\n", "\n", "Dataset on which the SHO Fit was computed:\n", "\n", "located at: \n", "\t/Measurement_000/Channel_000/Raw_Data \n", "Data contains: \n", "\tCantilever Vertical Deflection (V) \n", "Data dimensions and original shape: \n", "Position Dimensions: \n", "\tX - size: 5 \n", "\tY - size: 5 \n", "Spectroscopic Dimensions: \n", "\tFrequency - size: 87 \n", "\tDC_Offset - size: 64 \n", "\tField - size: 2 \n", "\tCycle - size: 2\n", "Data Type:\n", "\tcomplex64\n" ] } ], "source": [ "h5_sho_group = h5_sho_group_list[0]\n", "print('Datagroup containing the SHO fits:')\n", "print(h5_sho_group)\n", "print('\\nDataset on which the SHO Fit was computed:')\n", "h5_source_dset = usid.hdf_utils.get_source_dataset(h5_sho_group)\n", "print(h5_source_dset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the source dataset is always a ``Main`` dataset, ``get_source_dataset()`` results a ``USIDataset`` object instead of\n", "a regular ``HDF5 Dataset`` object.\n", "\n", "Note that ``hdf_utils.get_source_dataset()`` and ``find_results_groups()`` rely on the USID rule that results of an\n", "operation be stored in a Group named ``Source_Dataset_Name-Operation_Name_00x``.\n", "\n", "### get_auxiliary_datasets()\n", "\n", "The association of datasets and groups with one another provides a powerful mechanism for conveying (richer) information. One way to associate objects with each other is to store the reference of an object as an attribute of another. This is precisely the capability that is leveraged to turn Central datasets into USID Main Datasets or ``USIDatasets``. USIDatasets need to have four attributes that are references to the ``Position`` and ``Spectroscopic``\n", "``ancillary`` datasets. Note, that USID does not restrict or preclude the storage of other relevant datasets as attributes of another dataset. For example, the ``Raw_Data`` dataset appears to contain several attributes whose keys / names match the names of datasets we see above and values all appear to be HDF5 object references:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Bin_Frequencies : \n", "Position_Indices : \n", "Excitation_Waveform : \n", "out_of_field_Plot_Group : \n", "Bin_Indices : \n", "Spectroscopic_Indices : \n", "UDVS : \n", "Bin_Wfm_Type : \n", "units : V\n", "Bin_Step : \n", "Bin_FFT : \n", "Spectroscopic_Values : \n", "UDVS_Indices : \n", "Position_Values : \n", "in_field_Plot_Group : \n", "Noise_Floor : \n", "quantity : Cantilever Vertical Deflection\n" ] } ], "source": [ "for key, val in sidpy.hdf_utils.get_attributes(h5_raw).items():\n", " print('{} : {}'.format(key, val))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As the name suggests, these HDF5 object references are references or addresses to datasets located elsewhere in the\n", "file. Conventionally, one would need to apply this reference to the file handle to get the actual HDF5 Dataset / Group\n", "object.\n", "\n", "``get_auxiliary_datasets()`` simplifies this process by directly retrieving the actual Dataset / Group associated with\n", "the attribute. Thus, we would be able to get a reference to the ``Bin_Frequencies`` Dataset via:\n", "\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "True\n" ] } ], "source": [ "h5_obj = sidpy.hdf_utils.get_auxiliary_datasets(h5_raw, 'Bin_Frequencies')[0]\n", "print(h5_obj)\n", "# Lets prove that this object is the same as the 'Bin_Frequencies' object that can be directly addressed:\n", "print(h5_obj == h5_f['/Measurement_000/Channel_000/Bin_Frequencies'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Accessing Ancillary Datasets\n", "One of the major benefits of h5USID is its ability to handle large multidimensional datasets at ease. ``Ancillary``\n", "datasets serve as the keys or legends for explaining the dimensionality, reshape-ability, etc. of a dataset. There are\n", "several functions in hdf_utils that simplify many common operations on ancillary datasets.\n", "\n", "Before we demonstrate the several useful functions in hdf_utils, lets access the position and spectroscopic ancillary\n", "datasets using the ``get_auxiliary_datasets()`` function we used above:\n", "\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "dset_list = sidpy.hdf_utils.get_auxiliary_datasets(h5_raw, ['Position_Indices', 'Position_Values',\n", " 'Spectroscopic_Indices', 'Spectroscopic_Values'])\n", "h5_pos_inds, h5_pos_vals, h5_spec_inds, h5_spec_vals = dset_list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As mentioned above, this is indeed a six dimensional dataset with two position dimensions and four spectroscopic\n", "dimensions. The ``Field`` and ``Cycle`` dimensions do not have any units since they are dimensionless unlike the other\n", "dimensions.\n", "\n", "### get_dimensionality()\n", "Now lets find out the number of steps in each of those dimensions using another handy function called\n", "``get_dimensionality()``:\n", "\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Size of each Position dimension:\n", "X : 5\n", "Y : 5\n", "\n", "Size of each Spectroscopic dimension:\n", "Frequency : 87\n", "DC_Offset : 64\n", "Field : 2\n", "Cycle : 2\n" ] } ], "source": [ "pos_dim_sizes = usid.hdf_utils.get_dimensionality(h5_pos_inds)\n", "spec_dim_sizes = usid.hdf_utils.get_dimensionality(h5_spec_inds)\n", "pos_dim_names = sidpy.hdf_utils.get_attr(h5_pos_inds, 'labels')\n", "spec_dim_names = sidpy.hdf_utils.get_attr(h5_spec_inds, 'labels')\n", "\n", "print('Size of each Position dimension:')\n", "for name, length in zip(pos_dim_names, pos_dim_sizes):\n", " print('{} : {}'.format(name, length))\n", "print('\\nSize of each Spectroscopic dimension:')\n", "for name, length in zip(spec_dim_names, spec_dim_sizes):\n", " print('{} : {}'.format(name, length))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### get_sort_order()\n", "\n", "In a few (rare) cases, the spectroscopic / position dimensions are not arranged in descending order of rate of change.\n", "In other words, the dimensions in these ancillary matrices are not arranged from fastest-varying to slowest.\n", "To account for such discrepancies, ``hdf_utils`` has a very handy function that goes through each of the columns or\n", "rows in the ancillary indices matrices and finds the order in which these dimensions vary.\n", "\n", "Below we illustrate an example of sorting the names of the spectroscopic dimensions from fastest to slowest in\n", "the BEPS data file:\n", "\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rate of change of spectroscopic dimensions: [0 2 1 3]\n", "\n", "Spectroscopic dimensions arranged as is:\n", "['Frequency' 'DC_Offset' 'Field' 'Cycle']\n", "\n", "Spectroscopic dimensions arranged from fastest to slowest\n", "['Frequency' 'Field' 'DC_Offset' 'Cycle']\n" ] } ], "source": [ "spec_sort_order = usid.hdf_utils.get_sort_order(h5_spec_inds)\n", "print('Rate of change of spectroscopic dimensions: {}'.format(spec_sort_order))\n", "print('\\nSpectroscopic dimensions arranged as is:')\n", "print(spec_dim_names)\n", "sorted_spec_labels = np.array(spec_dim_names)[np.array(spec_sort_order)]\n", "print('\\nSpectroscopic dimensions arranged from fastest to slowest')\n", "print(sorted_spec_labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### get_unit_values()\n", "\n", "When visualizing the data it is essential to plot the data against appropriate values on the X, Y, or Z axes.\n", "Recall that by definition that the values over which each dimension is varied, are repeated and tiled over the entire\n", "position or spectroscopic dimension of the dataset. Thus, if we had just the bias waveform repeated over two cycles,\n", "spectroscopic values would contain the bias waveform tiled twice and the cycle numbers repeated as many times as the\n", "number of points in the bias waveform. Therefore, extracting the bias waveform or the cycle numbers from the ancillary\n", "datasets is not trivial. This problem is especially challenging for multidimensional datasets such as the one under\n", "consideration. Fortunately, ``hdf_utils`` has a very handy function for this as well:\n", "\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Position unit values:\n", "Y : [0. 1. 2. 3. 4.]\n", "X : [0. 1. 2. 3. 4.]\n" ] } ], "source": [ "pos_unit_values = usid.hdf_utils.get_unit_values(h5_pos_inds, h5_pos_vals)\n", "print('Position unit values:')\n", "for key, val in pos_unit_values.items():\n", " print('{} : {}'.format(key, val))\n", "spec_unit_values = usid.hdf_utils.get_unit_values(h5_spec_inds, h5_spec_vals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the spectroscopic dimensions are quite complicated, lets visualize the results from ``get_unit_values()``:\n", "\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(6.5, 6))\n", "for axis, name in zip(axes.flat, spec_dim_names):\n", " axis.set_title(name)\n", " axis.plot(spec_unit_values[name], 'o-')\n", "\n", "fig.suptitle('Spectroscopic Dimensions', fontsize=16, y=1.05)\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reshaping Data\n", "\n", "### reshape_to_n_dims()\n", "\n", "The USID model stores N dimensional datasets in a flattened 2D form of position x spectral values. It can become\n", "challenging to retrieve the data in its original N-dimensional form, especially for multidimensional datasets such as\n", "the one we are working on. Fortunately, all the information regarding the dimensionality of the dataset are contained\n", "in the spectral and position ancillary datasets. ``reshape_to_n_dims()`` is a very useful function that can help\n", "retrieve the N-dimensional form of the data using a simple function call:\n", "\n" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Succeeded in reshaping flattened 2D dataset to N dimensions\n", "Shape of the data in its original 2D form\n", "(25, 22272)\n", "Shape of the N dimensional form of the dataset:\n", "(5, 5, 87, 64, 2, 2)\n", "And these are the dimensions\n", "['X' 'Y' 'Frequency' 'DC_Offset' 'Field' 'Cycle']\n" ] } ], "source": [ "ndim_form, success, labels = usid.hdf_utils.reshape_to_n_dims(h5_raw, get_labels=True)\n", "if success:\n", " print('Succeeded in reshaping flattened 2D dataset to N dimensions')\n", " print('Shape of the data in its original 2D form')\n", " print(h5_raw.shape)\n", " print('Shape of the N dimensional form of the dataset:')\n", " print(ndim_form.shape)\n", " print('And these are the dimensions')\n", " print(labels)\n", "else:\n", " print('Failed in reshaping the dataset')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### reshape_from_n_dims()\n", "The inverse problem of reshaping an N dimensional dataset back to a 2D dataset (let's say for the purposes of\n", "multivariate analysis or storing into h5USID files) is also easily solved using another handy\n", "function - ``reshape_from_n_dims()``:\n", "\n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of flattened two dimensional form\n", "(25, 22272)\n" ] } ], "source": [ "two_dim_form, success = usid.hdf_utils.reshape_from_n_dims(ndim_form, h5_pos=h5_pos_inds, h5_spec=h5_spec_inds)\n", "if success:\n", " print('Shape of flattened two dimensional form')\n", " print(two_dim_form.shape)\n", "else:\n", " print('Failed in flattening the N dimensional dataset')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Close and delete the h5_file\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "h5_f.close()\n", "os.remove(h5_path)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.5" } }, "nbformat": 4, "nbformat_minor": 1 } sidpy-0.12.3/notebooks/03_hdf5/index.rst000066400000000000000000000004671455261647000177420ustar00rootroot00000000000000HDF5 Tools ========== | This folder contains notebooks on reading, writing, and operating on information in HDF5 files. | `HDF5 and h5py Primer <./h5py_primer.ipynb>`_ | `HDF5 reading utilities <./hdf_utils_read.ipynb>`_ .. toctree:: :maxdepth: 1 :hidden: h5py_primer.ipynb hdf_utils_read.ipynb sidpy-0.12.3/server_user_id.txt000066400000000000000000000000441455261647000165160ustar00rootroot00000000000000493f6874-4130-4543-9c4c-c65a886cd2dcsidpy-0.12.3/setup.cfg000066400000000000000000000005611455261647000145620ustar00rootroot00000000000000[bdist_wheel] # This flag says that the code is written to work on both Python 2 and Python # 3. If at all possible, it is good practice to do this. If you cannot, you # will need to generate wheels for each Python version that you support. # Uncomment after checking compatibility with python 3 universal=1 [aliases] test=pytest [tool:pytest] testpaths = tests docssidpy-0.12.3/setup.py000066400000000000000000000073011455261647000144520ustar00rootroot00000000000000from codecs import open import os from setuptools import setup, find_packages here = os.path.abspath(os.path.dirname(__file__)) with open(os.path.join(here, 'README.rst')) as f: long_description = f.read() with open(os.path.join(here, 'sidpy/__version__.py')) as f: __version__ = f.read().split("'")[1] # TODO: Move requirements to requirements.txt requirements = ['numpy>=1.10', 'toolz', # dask installation failing without this 'cytoolz', # dask installation failing without this 'dask', 'h5py>=2.6.0', 'matplotlib>=2.0.0', 'distributed>=2.0.0', 'psutil', 'six', 'joblib>=0.11.0', 'ipywidgets', 'ipykernel', 'ipython', # Beginning with IPython 6.0, Python 3.3 and above is required. 'scikit-learn', 'scipy', 'ase', 'ipympl' ] setup( name='sidpy', version=__version__, description='Python utilities for storing, visualizing, and processing Spectroscopic and Imaging Data (SID)', long_description=long_description, classifiers=[ 'Development Status :: 2 - Pre-Alpha', 'Environment :: Console', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: MIT License', 'Natural Language :: English', 'Operating System :: OS Independent', 'Programming Language :: Cython', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Programming Language :: Python :: 3.10', 'Programming Language :: Python :: Implementation :: CPython', 'Topic :: Scientific/Engineering :: Information Analysis'], keywords=['imaging', 'spectra', 'multidimensional', 'scientific', 'visualization', 'processing', 'storage', 'hdf5'], packages=find_packages(exclude=["*.tests", "*.tests.*", "tests.*", "tests"]), url='https://pycroscopy.github.io/sidpy/about.html', license='MIT', author='Pycroscopy contributors', author_email='pycroscopy@gmail.com', install_requires=requirements, tests_require=['unittest2;python_version<"3.0"', 'pytest'], platforms=['Linux', 'Mac OSX', 'Windows 10/8.1/8/7'], # package_data={'sample':['dataset_1.dat']} test_suite='pytest', # dependency='', # dependency_links=[''], include_package_data=True, # https://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-dependencies extras_require={ 'MPI': ["mpi4py"], 'File_Widgets': ['pyqt5'] }, # If there are data files included in your packages that need to be # installed, specify them here. If using Python 2.6 or less, then these # have to be included in MANIFEST.in as well. # package_data={ # 'sample': ['package_data.dat'], # }, # Although 'package_data' is the preferred approach, in some case you may # need to place data files outside of your packages. See: # http://docs.python.org/3.4/distutils/setupscript.html#installing-additional-files # noqa # In this case, 'data_file' will be installed into '/my_data' # data_files=[('my_data', ['data/data_file'])], # To provide executable scripts, use entry points in preference to the # "scripts" keyword. Entry points provide cross-platform support and allow # pip to create the appropriate form of executable for the target platform. # entry_points={ # 'console_scripts': [ # 'sample=sample:main', # ], # }, ) sidpy-0.12.3/sidpy/000077500000000000000000000000001455261647000140675ustar00rootroot00000000000000sidpy-0.12.3/sidpy/__init__.py000066400000000000000000000015171455261647000162040ustar00rootroot00000000000000""" The sidpy package """ from .__version__ import version as __version__ from . import base, hdf, io, proc, sid, viz from .base import * from .hdf import * from .io import * from .proc import * from .sid import * from .viz import plot_utils from .viz import jupyter_utils __all__ = ['__version__'] # Traditional hierarchical approach - importing submodules __all__ += base.__all__ __all__ += hdf.__all__ __all__ += io.__all__ __all__ += proc.__all__ __all__ += sid.__all__ __all__ += viz.__all__ # Making things easier by surfacing all low-level modules directly: __all__ += ['dict_utils', 'num_utils', 'string_utils'] __all__ += ['hdf_utils', 'reg_ref', 'dtype_utils', 'prov_utils'] __all__ += ['interface_utils'] __all__ += ['comp_utils'] __all__ += ['Dimension', 'Translator', 'Dataset', 'Reader'] __all__ += ['plot_utils', 'jupyter_utils'] sidpy-0.12.3/sidpy/__version__.py000066400000000000000000000000601455261647000167160ustar00rootroot00000000000000version = '0.12.3' time = '2024-01-09 10:30:00' sidpy-0.12.3/sidpy/base/000077500000000000000000000000001455261647000150015ustar00rootroot00000000000000sidpy-0.12.3/sidpy/base/__init__.py000066400000000000000000000002221455261647000171060ustar00rootroot00000000000000""" General python helper functions """ from . import num_utils, string_utils, dict_utils __all__ = ['num_utils', 'string_utils', 'dict_utils'] sidpy-0.12.3/sidpy/base/dict_utils.py000066400000000000000000000163511455261647000175240ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Utilities that assist in dictionary manipulation Created on Thu Jul 7 21:14:25 2020 @author: Suhas Somnath """ from __future__ import division, print_function, unicode_literals, absolute_import import sys from warnings import warn import math if sys.version_info.major == 3: unicode = str from collections.abc import MutableMapping else: from collections import MutableMapping from sidpy.base.string_utils import validate_single_string_arg def flatten_dict(nested_dict, separator='-'): """ Flattens a nested dictionary Parameters ---------- nested_dict : dict Nested dictionary separator : str, Optional. Default='-' Separator between the keys of different levels Returns ------- dict Dictionary whose keys are flattened to a single level Notes ----- Taken from https://stackoverflow.com/questions/6027558/flatten-nested- dictionaries-compressing-keys """ if not isinstance(nested_dict, dict): raise TypeError('nested_dict should be a dict') separator = validate_single_string_arg(separator, 'separator') def __flatten_dict_int(nest_dict, sep, parent_key=''): items = [] if sep == '_': repl = '-' else: repl = '_' for key, value in nest_dict.items(): if not isinstance(key, str): key = str(key) if sep in key: key = key.replace(sep, repl) new_key = parent_key + sep + key if parent_key else key if isinstance(value, MutableMapping): items.extend(__flatten_dict_int(value, sep, parent_key=new_key).items()) # nion files contain lists of dictionaries, oops elif isinstance(value, list): for i in range(len(value)): if isinstance(value[i], dict): for kk in value[i]: items.append(('dim-' + kk + '-' + str(i), value[i][kk])) else: if type(value) != bytes: items.append((new_key, value)) else: if type(value) != bytes: items.append((new_key, value)) return dict(items) return __flatten_dict_int(nested_dict, separator) def nested_dict_from_flattened_key(single_pair, separator='-'): """ Converts a dictionary with a single key: value pair to a nested dictionary Parameters ---------- single_pair : dict Dictionary with a single key-value pair separator : str, optional. Default = '-' Separator used to delimit the levels in the keys Returns ------- nested_dict : dict Nested dictionary Notes ----- Converts {'A|B|C': value}... to {'A': 'B': {'C': value_1, } } } """ if not isinstance(single_pair, dict): raise TypeError('Expected dict. Provided object of type: {}' ''.format(type(single_pair))) if len(single_pair) > 1: warn('This function only works on one key-value pair. Provided dict' 'has {} pairs'.format(len(single_pair))) key = list(single_pair.keys())[-1] if not isinstance(key, (str, unicode)): raise TypeError('Key for provided dict not string and is instead: ' '{}'.format(type(key))) value = single_pair[key] # break up the key by separator hierarchy = key.split(separator) # set actual value to the last item in the hierarchy discovered above: nested_dict = {hierarchy[-1]: value} # build the tree above by iterating in reverse, excepting the last leaf for parent in hierarchy[:-1][::-1]: nested_dict = {parent: nested_dict} return nested_dict def nest_dict(flat_dict, separator='-'): """ Generates a nested dictionary from a flattened dictionary Parameters ---------- flat_dict : dict Dictionary whose keys are flattened to a single string with a separator separator : str, optional. Default = '-' Separator used to delimit the levels in the keys Returns ------- nested_dict : dict Nested dictionary Notes ----- flat_dict should look like {'A|B|C': V1, 'A|B|D': V2, ...} """ nested_dict = dict() conflict_items = dict() for key, val in flat_dict.items(): this_dict = nested_dict_from_flattened_key({key: val}, separator=separator) try: # merge the nested dictionaries: nested_dict = merge_dicts(nested_dict, this_dict) except ValueError as exp: warn(exp) conflict_items.update({key: val}) if len(conflict_items) > 0: return nested_dict, conflict_items return nested_dict def print_nested_dict(nested_dict, level=0): """ Prints a nested dictionary in a nested manner Parameters ---------- nested_dict : dict Nested dictionary level : uint, internal variable. Leave unspecified Current depth of nested dictionary Returns ------- None """ if not isinstance(nested_dict, dict): raise TypeError('nested_dict should be a dict. Provided object was: {}' ''.format(type(nested_dict))) for key, val in nested_dict.items(): if isinstance(val, dict): print('\t'*level + str(key) + ' :') print_nested_dict(val, level=level+1) else: print('\t'*level + '{} : {}'.format(key, val)) def merge_dicts(left, right, path=None): """ Merges two nested dictionaries (right into left) Parameters ---------- left : dict First dictionary right : dict Second dictionary path : str, internal variable. Do not assign Internal path to current key Notes ----- https://stackoverflow.com/questions/7204805/how-to-merge-dictionaries-of-dictionaries """ if path is None: path = [] for key in right: if key in left: if isinstance(left[key], dict) and isinstance(right[key], dict): merge_dicts(left[key], right[key], path + [str(key)]) elif all([isinstance(this_dict[key], float) for this_dict in [left, right]]) and math.isnan(left[key]) and math.isnan(right[key]): pass elif left[key] == right[key]: pass # same leaf value elif isinstance(left[key], dict) and not isinstance(right[key], dict): merge_dicts(left[key], {'value': right[key]}, path + [str(key)]) elif not isinstance(left[key], dict) and isinstance(right[key], dict): left[key] = {'value': left[key]} merge_dicts(left[key], right[key], path + [str(key)]) else: mesg = 'Left value: {} of type: {} cannot be merged with Right: value: {} of type: {}' \ ''.format(left[key], type(left[key]), right[key], type(right[key])) raise ValueError('Conflict at %s' % '|'.join( path + [str(key)]) + '. ' + mesg) else: left[key] = right[key] return left sidpy-0.12.3/sidpy/base/num_utils.py000066400000000000000000000200751455261647000173760ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Utilities that assist in writing scientific data to HDF5 files Created on Thu Sep 7 21:14:25 2017 @author: Suhas Somnath, Chris Smith """ from __future__ import division, print_function, unicode_literals, absolute_import import sys from itertools import groupby import numpy as np if sys.version_info.major == 3: from collections.abc import Iterable unicode = str xrange = range else: from collections import Iterable __all__ = ['get_slope', 'to_ranges', 'contains_integers', 'integers_to_slices', 'get_exponent', 'build_ind_val_matrices'] def get_slope(values, tol=1E-3): """ Attempts to get the slope of the provided values. This function will be handy for checking if a dimension has been varied linearly or not. If the values vary non-linearly, a ValueError will be raised Parameters ---------- values : array-like List of numbers tol : float, optional. Default = 1E-3 Tolerance in the variation of the slopes. Returns ------- float Slope of the line """ if not isinstance(tol, float): raise TypeError('tol should be a float << 1') if len(values)==1: step_size=[0] else: step_size = np.unique(np.diff(values)) if len(step_size) > 1: # often we end up here. In most cases, step_avg = step_size.max() step_size -= step_avg var = np.mean(np.abs(step_size)) if var / step_avg < tol: step_size = [step_avg] else: # Non-linear dimension! - see notes above raise ValueError('Provided values cannot be expressed as a linear trend') return step_size[0] def to_ranges(iterable): """ Converts a sequence of iterables to range tuples From https://stackoverflow.com/questions/4628333/converting-a-list-of-integers-into-range-in-python Credits: @juanchopanza and @luca Parameters ---------- iterable : collections.Iterable object iterable object like a list Returns ------- iterable : generator object Cast to list or similar to use """ iterable = sorted(set(iterable)) for key, group in groupby(enumerate(iterable), lambda t: t[1] - t[0]): group = list(group) if sys.version_info.major == 3: yield range(group[0][1], group[-1][1]+1) else: yield xrange(group[0][1], group[-1][1]+1) def contains_integers(iter_int, min_val=None): """ Checks if the provided object is iterable (list, tuple etc.) and contains integers optionally greater than equal to the provided min_val Parameters ---------- iter_int : :class:`collections.Iterable` Iterable (e.g. list, tuple, etc.) of integers min_val : int, optional, default = None The value above which each element of iterable must possess. By default, this is ignored. Returns ------- bool Whether or not the provided object is an iterable of integers Examples -------- >>> item = [1, 2, -3, 4] >>> print('{} : contains integers? : {}'.format(item, sidpy.base.num_utils.contains_integers(item))) [1, 2, -3, 4] : contains integers? : True >>> item = [1, 4.5, 2.2, -1] >>> print('{} : contains integers? : {}'.format(item, sidpy.base.num_utils.contains_integers(item))) [1, 4.5, 2.2, -1] : contains integers? : False >>> item = [1, 5, 8, 3] >>> min_val = 2 >>> print('{} : contains integers >= {} ? : {}'.format(item, min_val, sidpy.base.num_utils.contains_integers(item, min_val=min_val))) [1, 5, 8, 3] : contains integers >= 2 ? : False """ if not isinstance(iter_int, Iterable): raise TypeError('iter_int should be an Iterable') if len(iter_int) == 0: return False if min_val is not None: if not isinstance(min_val, (int, float)): raise TypeError('min_val should be an integer. Provided object was of type: {}'.format(type(min_val))) if min_val % 1 != 0: raise ValueError('min_val should be an integer') try: if min_val is not None: return np.all([x % 1 == 0 and x >= min_val for x in iter_int]) else: return np.all([x % 1 == 0 for x in iter_int]) except TypeError: return False def integers_to_slices(int_array): """ Converts a sequence of iterables to a list of slice objects denoting sequences of consecutive numbers Parameters ---------- int_array : :class:`collections.Iterable` iterable object like a :class:`list` or :class:`numpy.ndarray` Returns ------- sequences : list List of :class:`slice` objects each denoting sequences of consecutive numbers """ if not contains_integers(int_array): raise ValueError('Expected a list, tuple, or numpy array of integers') def integers_to_consecutive_sections(integer_array): """ Converts a sequence of iterables to tuples with start and stop bounds @author: @juanchopanza and @luca from stackoverflow Parameters ---------- integer_array : :class:`collections.Iterable` iterable object like a :class:`list` Returns ------- iterable : :class:`generator` Cast to list or similar to use Note ---- From https://stackoverflow.com/questions/4628333/converting-a-list-of-integers-into-range-in-python """ integer_array = sorted(set(integer_array)) for key, group in groupby(enumerate(integer_array), lambda t: t[1] - t[0]): group = list(group) yield group[0][1], group[-1][1] sequences = [slice(item[0], item[1] + 1) for item in integers_to_consecutive_sections(int_array)] return sequences def get_exponent(vector): """ Gets the scale / exponent for a sequence of numbers. This is particularly useful when wanting to scale a vector for the purposes of plotting Parameters ---------- vector : array-like Array of numbers Returns ------- exponent : int Scale / exponent for the given vector """ if not isinstance(vector, np.ndarray): raise TypeError('vector should be of type numpy.ndarray. Provided object of type: {}'.format(type(vector))) if np.isnan(vector).any(): raise TypeError('vector should not contain NaN values') if np.max(np.abs(vector)) == np.max(vector): exponent = np.log10(np.max(vector)) else: # negative values exponent = np.log10(np.max(np.abs(vector))) return int(np.floor(exponent)) def build_ind_val_matrices(unit_values): """ Builds indices and values matrices using given unit values for each dimension. This function is originally from pyUSID.io Unit values must be arranged from fastest varying to slowest varying Parameters ---------- unit_values : list / tuple Sequence of values vectors for each dimension Returns ------- ind_mat : 2D numpy array Indices matrix val_mat : 2D numpy array Values matrix """ if not isinstance(unit_values, (list, tuple)): raise TypeError('unit_values should be a list or tuple') if not np.all([np.array(x).ndim == 1 for x in unit_values]): raise ValueError('unit_values should only contain 1D array') lengths = [len(x) for x in unit_values] tile_size = [np.prod(lengths[x:]) for x in range(1, len(lengths))] + [1] rep_size = [1] + [np.prod(lengths[:x]) for x in range(1, len(lengths))] val_mat = np.zeros(shape=(len(lengths), np.prod(lengths))) ind_mat = np.zeros(shape=val_mat.shape, dtype=np.uint32) for ind, ts, rs, vec in zip(range(len(lengths)), tile_size, rep_size, unit_values): val_mat[ind] = np.tile(np.repeat(vec, rs), ts) ind_mat[ind] = np.tile(np.repeat(np.arange(len(vec)), rs), ts) val_mat = val_mat.T ind_mat = ind_mat.T return ind_mat, val_matsidpy-0.12.3/sidpy/base/string_utils.py000066400000000000000000000334171455261647000201110ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Utilities for formatting strings and other input / output methods Created on Tue Nov 3 21:14:25 2015 @author: Suhas Somnath, Chris Smith """ from __future__ import division, print_function, absolute_import, unicode_literals import sys from numbers import Number from time import strftime import numpy as np if sys.version_info.major == 3: unicode = str from collections.abc import Iterable else: from collections import Iterable def format_quantity(value, unit_names, factors, decimals=2): """ Formats the provided quantity such as time or size to appropriate strings Parameters ---------- value : number value in some base units. For example - time in seconds unit_names : array-like List of names of units for each scale of the value factors : array-like List of scaling factors for each scale of the value decimals : uint, optional. default = 2 Number of decimal places to which the value needs to be formatted Returns ------- str String with value formatted correctly Examples -------- >> # If ``sidpy.string_utils.format_time()`` were not available, we could >> # get the same functionality via: >> import sidpy >> units = ['msec', 'sec', 'mins', 'hours'] >> factors = [0.001, 1, 60, 3600] >> time_value = 14497.34 >> str_form = sidpy.string_utils.format_quantity(time_value,units,factors) >> print('{} seconds = {}'.format(14497.34, str_form)) See Also -------- sidpy.string_utils.format_size sidpy.string_utils.format_time """ # assert isinstance(value, (int, float)) if not isinstance(unit_names, Iterable): raise TypeError('unit_names must an Iterable') if not isinstance(factors, Iterable): raise TypeError('factors must be an Iterable') if len(unit_names) != len(factors): raise ValueError('unit_names and factors must be of the same length') unit_names = validate_list_of_strings(unit_names, 'unit_names') index = None for index, val in enumerate(factors): if value < val: index -= 1 break index = max(0, index) # handles sub msec return '{} {}'.format(np.round(value / factors[index], decimals), unit_names[index]) def format_time(time_in_seconds, decimals=2): """ Formats the provided time in seconds into seconds, minutes, or hours Parameters ---------- time_in_seconds : number Time in seconds decimals : uint, optional. default = 2 Number of decimal places to which the time needs to be formatted Returns ------- str String with time formatted correctly Examples -------- >>> import sidpy >>> num_secs = 14497.34 >>> time_form = sidpy.string_utils.format_time(num_secs) >>> print('{} seconds = {}'.format(num_secs, time_form)) """ units = ['msec', 'sec', 'mins', 'hours'] factors = [0.001, 1, 60, 3600] return format_quantity(time_in_seconds, units, factors, decimals=decimals) def format_size(size_in_bytes, decimals=2): """ Formats the provided size in bytes to kB, MB, GB, TB etc. Parameters ---------- size_in_bytes : number size in bytes decimals : uint, optional. default = 2 Number of decimal places to which the size needs to be formatted Returns ------- str String with size formatted correctly Examples -------- >>> # using the function to print available memory / RAM in system: >>> import sidpy >>> mem_in_bytes = sidpy.comp_utils.get_available_memory() >>> print('Available memory in this machine: {}' >>> ''.format(sidpy.string_utils.format_size(mem_in_bytes))) See Also -------- sidpy.comp_utils.get_available_memory """ units = ['bytes', 'kB', 'MB', 'GB', 'TB'] factors = 1024 ** np.arange(len(units), dtype=np.int64) return format_quantity(size_in_bytes, units, factors, decimals=decimals) def formatted_str_to_number(str_val, magnitude_names, magnitude_values, separator=' '): """ Takes a formatted string like '4.32 MHz' to 4.32 E+6. This function provides the inverse functionality of ``sidpy.string_utils.format_quantity`` Parameters ---------- str_val : str / unicode String value of the quantity. Example '4.32 MHz' magnitude_names : Iterable List of names of units like ['seconds', 'minutes', 'hours'] magnitude_values : Iterable List of values (corresponding to magnitude_names) that scale the numeric value. Example [1, 60, 3600] separator : str / unicode, optional. Default = ' ' (space) The text that separates the numeric value and the units. Returns ------- number Numeric value of the string Examples -------- >>> import sidpy >>> unit_names = ["MHz", "kHz"] >>> unit_magnitudes = [1E+6, 1E+3] >>> str_value = "4.32 MHz" >>> num_value = sidpy.string_utils.formatted_str_to_number(str_value, >>> unit_names, >>> unit_magnitudes, >>> separator=' ') >>> print('formatted_str_to_number: {} = {}'.format(str_value, num_value)) See Also -------- sidpy.string_utils.format_quantity """ [str_val] = validate_string_args(str_val, 'str_val') magnitude_names = validate_list_of_strings(magnitude_names, 'magnitude_names') if not isinstance(separator, (str, unicode)): raise TypeError('separator must be a string') if not isinstance(magnitude_values, (list, tuple)): raise TypeError('magnitude_values must be an Iterable') if not np.all([isinstance(_, Number) for _ in magnitude_values]): raise TypeError('magnitude_values should contain numbers') if len(magnitude_names) != len(magnitude_values): raise ValueError('magnitude_names and magnitude_values should be of ' 'the same length') components = str_val.split(separator) if len(components) != 2: raise ValueError('String value should be of format ' '"123.45Unit') for unit_name, scaling in zip(magnitude_names, magnitude_values): if unit_name == components[1]: # Let it raise an exception. Don't catch return scaling * float(components[0]) def validate_single_string_arg(value, name): """ This function is to be used when validating a SINGLE string parameter for a function. Trims the provided value. Errors in the string will result in Exceptions Parameters ---------- value : str Value of the parameter name : str Name of the parameter Returns ------- str Cleaned string value of the parameter """ if not isinstance(value, (str, unicode)): raise TypeError(name + ' should be a string') value = value.strip() if len(value) <= 0: raise ValueError(name + ' should not be an empty string') return value def validate_list_of_strings(str_list, parm_name='parameter'): """ This function is to be used when validating and cleaning a list of strings. Trims the provided strings. Errors in the strings will result in Exceptions Parameters ---------- str_list : array-like list or tuple of strings parm_name : str, Optional. Default = 'parameter' Name of the parameter corresponding to this string list that will be reported in the raised Errors Returns ------- array-like List of trimmed and validated strings when ALL objects within the list are found to be valid strings """ if isinstance(str_list, (str, unicode)): return [validate_single_string_arg(str_list, parm_name)] if not isinstance(str_list, (list, tuple)): raise TypeError(parm_name + ' should be a string or list / tuple of ' 'strings') return [validate_single_string_arg(x, parm_name) for x in str_list] def validate_string_args(arg_list, arg_names): """ This function is to be used when validating string parameters for a function. Trims the provided strings. Errors in the strings will result in Exceptions Parameters ---------- arg_list : array-like List of str objects that signify the value for a position argument in a function arg_names : array-like List of str objects with the names of the corresponding parameters in the function Returns ------- array-like List of str objects that signify the value for a position argument in a function with spaces on ends removed """ if isinstance(arg_list, (str, unicode)): arg_list = [arg_list] if isinstance(arg_names, (str, unicode)): arg_names = [arg_names] cleaned_args = [] if not isinstance(arg_list, (tuple, list)): raise TypeError('arg_list should be a tuple or a list or a string') if not isinstance(arg_names, (tuple, list)): raise TypeError('arg_names should be a tuple or a list or a string') for arg, arg_name in zip(arg_list, arg_names): cleaned_args.append(validate_single_string_arg(arg, arg_name)) return cleaned_args def clean_string_att(att_val): """ Replaces any unicode objects within lists with their string counterparts to ensure compatibility with python 3. If the attribute is indeed a list of unicodes, the changes will be made in-place Parameters ---------- att_val : object Attribute object Returns ------- att_val : object Attribute object Notes ----- The ``h5py`` package used for reading and manipulating HDF5 files has issues which necessitate the encoding of attributes whose values are lists of strings. This method encodes lists of strings correctly so that they can directly be written to HDF5 without causing any errors. All other kinds of simple attributes - single strings, numbers, lists of numbers are unmodified by this function. Examples -------- >>> import sidpy >>> expected = ['a', 'bc', 'def'] >>> returned = sidpy.string_utils.clean_string_att(expected) >>> print('List of strings value: {} encoded to: {}'.format(expected, returned)) >>> >>> expected = [1, 2, 3.456] >>> returned = sidpy.string_utils.clean_string_att(expected) >>> print('List of numbers value: {} returned as is: {}'.format(expected, returned)) """ try: if isinstance(att_val, Iterable): if type(att_val) in [unicode, str]: return att_val elif np.any([type(x) in [str, unicode, bytes, np.str_] for x in att_val]): return np.array(att_val, dtype='S') elif isinstance(att_val, (list, tuple)): # Not sure how to do this elegantly, for item in att_val: if not isinstance(item, (str, unicode, bytes, np.str_, Number, list)): raise TypeError('Provided object was a list or tuple ' 'whose element was not a string or ' 'number but was of type: {}' ''.format(type(item))) if type(att_val) == np.str_: return str(att_val) return att_val except TypeError: raise TypeError('Failed to clean: {}'.format(att_val)) def get_time_stamp(): """ Returns the current date and time as a string formatted as: Year_Month_Day-Hour_Minute_Second Returns ------- str Examples -------- >>> import sidpy >>> stamp = sidpy.string_utils.get_time_stamp() >>> print('Current time is: {}'.format(stamp)) """ return strftime('%Y_%m_%d-%H_%M_%S') def str_to_other(value): """ Casts a single value encoded in a string to the appropriate python object. Useful when parsing numbers, boolean, etc. in text files Parameters ---------- value : str / unicode String to be cast into other appropriate python object """ if not isinstance(value, (str, unicode)): raise TypeError('Expected object of type str. Provided object was: {}' ''.format(type(value))) if len(value.split(' ')) > 1: raise ValueError('Expected a string without spaces. Got: "{}"' ''.format(value)) value = value.strip() try: return int(value) except ValueError: try: return float(value) except ValueError: if value.lower() == 'true': value = True elif value.lower() == 'false': value = False return value def remove_extra_delimiters(line, separator=' '): """ Removes extra spaces (or other delimiters) between words in a line. Useful when parsing parameters (written by hand) Parameters ---------- line : str / unicode Line to be cleaned separator : str / unicode, Optional. Default = ' ' Separator between tokens Returns ------- line : str Line with extra separators removed """ if not isinstance(line, (str, unicode)): raise TypeError('line should be a string') if not isinstance(separator, (str, unicode)): raise TypeError('separator should be a string') if len(separator) == 0: raise ValueError('separator should not be empty') items = line.split(separator) real = list() for item in items: item = item.strip() if len(item) > 0: real.append(item) line = separator.join(real) return line sidpy-0.12.3/sidpy/hdf/000077500000000000000000000000001455261647000146305ustar00rootroot00000000000000sidpy-0.12.3/sidpy/hdf/__init__.py000066400000000000000000000002531455261647000167410ustar00rootroot00000000000000""" Tools to read, write data in HDF5 files """ from . import hdf_utils, prov_utils, reg_ref, dtype_utils __all__ = ['hdf_utils', 'prov_utils', 'reg_ref', 'dtype_utils'] sidpy-0.12.3/sidpy/hdf/dtype_utils.py000066400000000000000000000650531455261647000175600ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Utilities for transforming and validating data types Given that many of the data transformations involve copying the data, they should ideally happen in a lazy manner to avoid memory issues. Created on Tue Nov 3 21:14:25 2015 @author: Suhas Somnath, Chris Smith """ from __future__ import division, absolute_import, unicode_literals, print_function import sys from warnings import warn import h5py import numpy as np import dask.array as da __all__ = ['flatten_complex_to_real', 'get_compound_sub_dtypes', 'flatten_compound_to_real', 'check_dtype', 'stack_real_to_complex', 'validate_dtype', 'is_complex_dtype', 'stack_real_to_compound', 'stack_real_to_target_dtype', 'flatten_to_real'] from sidpy.hdf.hdf_utils import lazy_load_array if sys.version_info.major == 3: unicode = str def flatten_complex_to_real(dataset, lazy=False): """ Stacks the real values followed by the imaginary values in the last dimension of the given N dimensional matrix. Thus a complex matrix of shape (2, 3, 5) will turn into a matrix of shape (2, 3, 10) Parameters ---------- dataset : array-like or :class:`numpy.ndarray`, or :class:`h5py.Dataset`, or :class:`dask.array.core.Array` Dataset of complex data type lazy : bool, optional. Default = False If set to True, will use lazy Dask arrays instead of in-memory numpy arrays Returns ------- retval : :class:`numpy.ndarray`, or :class:`dask.array.core.Array` real valued dataset Examples -------- >>> import numpy as np >>> import sidpy >>> length = 3 >>> complex_array = np.random.randint(-5, high=5, size=length) + 1j * np.random.randint(-5, high=5, size=length) >>> print('Complex value: {} has shape: {}'.format(complex_array, complex_array.shape)) Complex value: [2.-2.j 0.-3.j 0.-4.j] has shape: (3,) >>> stacked_real_array = sidpy.dtype_utils.flatten_complex_to_real(complex_array) >>> print('Stacked real value: {} has shape: ' >>> '{}'.format(stacked_real_array, stacked_real_array.shape)) Stacked real value: [ 2. 0. 0. -2. -3. -4.] has shape: (6,) """ if not isinstance(dataset, (h5py.Dataset, np.ndarray, da.core.Array)): raise TypeError('dataset should either be a h5py.Dataset or numpy / dask array') if not is_complex_dtype(dataset.dtype): raise TypeError("Expected a complex valued dataset") if isinstance(dataset, da.core.Array): lazy = True xp = np if lazy: dataset = lazy_load_array(dataset) xp = da axis = xp.array(dataset).ndim - 1 if axis == -1: return xp.hstack([xp.real(dataset), xp.imag(dataset)]) else: # along the last axis return xp.concatenate([xp.real(dataset), xp.imag(dataset)], axis=axis) def flatten_compound_to_real(dataset, lazy=False): """ Flattens the individual components in a structured array or compound valued hdf5 dataset along the last axis to form a real valued array. Thus a compound h5py.Dataset or structured numpy matrix of shape (2, 3, 5) having 3 components will turn into a real valued matrix of shape (2, 3, 15), assuming that all the sub-dtypes of the matrix are real valued. ie - this function does not handle structured dtypes having complex values Parameters ---------- dataset : :class:`numpy.ndarray`, or :class:`h5py.Dataset`, or :class:`dask.array.core.Array` Numpy array that is a structured array or a :class:`h5py.Dataset` of compound dtype lazy : bool, optional. Default = False If set to True, will use lazy Dask arrays instead of in-memory numpy arrays Returns ------- retval : :class:`numpy.ndarray`, or :class:`dask.array.core.Array` real valued dataset Examples -------- >>> import numpy as np >>> import sidpy >>> num_elems = 5 >>> struct_dtype = np.dtype({'names': ['r', 'g', 'b'], >>> 'formats': [np.float32, np.uint16, np.float64]}) >>> structured_array = np.zeros(shape=num_elems, dtype=struct_dtype) >>> structured_array['r'] = np.random.random(size=num_elems) * 1024 >>> structured_array['g'] = np.random.randint(0, high=1024, size=num_elems) >>> structured_array['b'] = np.random.random(size=num_elems) * 1024 >>> print('Structured array is of shape {} and have values:'.format(structured_array.shape)) >>> print(structured_array) Structured array is of shape (5,) and have values: [(859.62445, 54, 1012.22256219) (959.5565 , 678, 296.19788769) (383.20737, 689, 192.45427816) (201.56635, 889, 939.01082338) (334.22015, 467, 980.9081472 )] >>> real_array = sidpy.dtype_utils.flatten_compound_to_real(structured_array) >>> print("This array converted to regular scalar matrix has shape: {} and values:".format(real_array.shape)) >>> print(real_array) This array converted to regular scalar matrix has shape: (15,) and values: [ 859.62445068 959.55651855 383.20736694 201.56634521 334.22015381 54. 678. 689. 889. 467. 1012.22256219 296.19788769 192.45427816 939.01082338 980.9081472 ] """ if isinstance(dataset, h5py.Dataset): if len(dataset.dtype) == 0: raise TypeError("Expected compound h5py dataset") if lazy: xp = da dataset = lazy_load_array(dataset) else: xp = np warn('HDF5 datasets will be loaded as Dask arrays in the future. ie - kwarg lazy will default to True in future releases of sidpy') return xp.concatenate([xp.array(dataset[name]) for name in dataset.dtype.names], axis=len(dataset.shape) - 1) elif isinstance(dataset, (np.ndarray, da.core.Array)): if isinstance(dataset, da.core.Array): lazy = True xp = np if lazy: dataset = lazy_load_array(dataset) xp = da if len(dataset.dtype) == 0: raise TypeError("Expected structured array") if dataset.ndim > 0: return xp.concatenate([dataset[name] for name in dataset.dtype.names], axis=dataset.ndim - 1) else: return xp.hstack([dataset[name] for name in dataset.dtype.names]) elif isinstance(dataset, np.void): return np.hstack([dataset[name] for name in dataset.dtype.names]) else: raise TypeError('Datatype {} not supported'.format(type(dataset))) def flatten_to_real(ds_main, lazy=False): """ Flattens complex / compound / real valued arrays to real valued arrays Parameters ---------- ds_main : :class:`numpy.ndarray`, or :class:`h5py.Dataset`, or :class:`dask.array.core.Array` Compound, complex or real valued numpy array or HDF5 dataset lazy : bool, optional. Default = False If set to True, will use lazy Dask arrays instead of in-memory numpy arrays Returns ---------- ds_main : :class:`numpy.ndarray`, or :class:`dask.array.core.Array` Array raveled to a float data type Examples -------- >>> import numpy as np >>> import sidpy >>> num_elems = 5 >>> struct_dtype = np.dtype({'names': ['r', 'g', 'b'], >>> 'formats': [np.float32, np.uint16, np.float64]}) >>> structured_array = np.zeros(shape=num_elems, dtype=struct_dtype) >>> structured_array['r'] = np.random.random(size=num_elems) * 1024 >>> structured_array['g'] = np.random.randint(0, high=1024, size=num_elems) >>> structured_array['b'] = np.random.random(size=num_elems) * 1024 >>> print('Structured array is of shape {} and have values:'.format(structured_array.shape)) >>> print(structured_array) Structured array is of shape (5,) and have values: [(859.62445, 54, 1012.22256219) (959.5565 , 678, 296.19788769) (383.20737, 689, 192.45427816) (201.56635, 889, 939.01082338) (334.22015, 467, 980.9081472 )] >>> real_array = sidpy.dtype_utils.flatten_to_real(structured_array) >>> print('This array converted to regular scalar matrix has shape: {} and values:'.format(real_array.shape)) >>> print(real_array) This array converted to regular scalar matrix has shape: (15,) and values: [ 859.62445068 959.55651855 383.20736694 201.56634521 334.22015381 54. 678. 689. 889. 467. 1012.22256219 296.19788769 192.45427816 939.01082338 980.9081472 ] """ if not isinstance(ds_main, (h5py.Dataset, np.ndarray, da.core.Array)): ds_main = np.array(ds_main) if is_complex_dtype(ds_main.dtype): return flatten_complex_to_real(ds_main, lazy=lazy) elif len(ds_main.dtype) > 0: return flatten_compound_to_real(ds_main, lazy=lazy) else: return ds_main def get_compound_sub_dtypes(struct_dtype): """ Returns a dictionary of the dtypes of each of the fields in the given structured array dtype Parameters ---------- struct_dtype : :class:`numpy.dtype` dtype of a structured array Returns ------- dtypes : dict Dictionary whose keys are the field names and values are the corresponding dtypes Examples -------- >>> import numpy as np >>> import sidpy >>> struct_dtype = np.dtype({'names': ['r', 'g', 'b'], >>> 'formats': [np.float32, np.uint16, np.float64]}) >>> sub_dtypes = sidpy.dtype_utils.get_compound_sub_dtypes(struct_dtype) >>> for key, val in sub_dtypes.items(): >>> print('{} : {}'.format(key, val)) g : uint16 r : float32 b : float64 """ if not isinstance(struct_dtype, np.dtype): raise TypeError('Provided object must be a structured array dtype') dtypes = dict() for field_name in struct_dtype.fields: dtypes[field_name] = struct_dtype.fields[field_name][0] return dtypes def check_dtype(h5_dset): """ Checks the datatype of the input HDF5 dataset and provides the appropriate function calls to convert it to a float Parameters ---------- h5_dset : :class:`h5py.Dataset` Dataset of interest Returns ------- func : callable function that will convert the dataset to a float is_complex : bool is the input dataset complex? is_compound : bool is the input dataset compound? n_features : Unsigned int Unsigned integer - the length of the 2nd dimension of the data after `func` is called on it type_mult : Unsigned int multiplier that converts from the typesize of the input :class:`~numpy.dtype` to the typesize of the data after func is run on it Examples -------- >>> import numpy as np >>> import h5py >>> import sidpy >>> struct_dtype = np.dtype({'names': ['r', 'g', 'b'], >>> 'formats': [np.float32, np.uint16, np.float64]}) >>> file_path = 'dtype_utils_example.h5' >>> if os.path.exists(file_path): >>> os.remove(file_path) >>> with h5py.File(file_path, mode='w') as h5_f: >>> num_elems = (5, 7) >>> structured_array = np.zeros(shape=num_elems, dtype=struct_dtype) >>> structured_array['r'] = 450 * np.random.random(size=num_elems) >>> structured_array['g'] = np.random.randint(0, high=1024, size=num_elems) >>> structured_array['b'] = 3178 * np.random.random(size=num_elems) >>> _ = h5_f.create_dataset('compound', data=structured_array) >>> _ = h5_f.create_dataset('real', data=450 * np.random.random(size=num_elems), dtype=np.float16) >>> _ = h5_f.create_dataset('complex', data=np.random.random(size=num_elems) + 1j * np.random.random(size=num_elems), >>> dtype=np.complex64) >>> h5_f.flush() >>> # Now, lets test the the function on compound-, complex-, and real-valued HDF5 datasets: >>> def check_dataset(h5_dset): >>> print('\tDataset being tested: {}'.format(h5_dset)) >>> func, is_complex, is_compound, n_features, type_mult = sidpy.dtype_utils.check_dtype(h5_dset) >>> print('\tFunction to transform to real: %s' % func) >>> print('\tis_complex? %s' % is_complex) >>> print('\tis_compound? %s' % is_compound) >>> print('\tShape of dataset in its current form: {}'.format(h5_dset.shape)) >>> print('\tAfter flattening to real, shape is expected to be: ({}, {})'.format(h5_dset.shape[0], n_features)) >>> print('\tByte-size of a single element in its current form: {}'.format(type_mult)) >>> with h5py.File(file_path, mode='r') as h5_f: >>> print('Checking a compound-valued dataset:') >>> check_dataset(h5_f['compound']) >>> print('') >>> print('Checking a complex-valued dataset:') >>> check_dataset(h5_f['complex']) >>> print('') >>> print('Checking a real-valued dataset:') >>> check_dataset(h5_f['real']) >>> os.remove(file_path) Checking a compound-valued dataset: Dataset being tested: Function to transform to real: is_complex? False is_compound? True Shape of dataset in its current form: (5, 7) After flattening to real, shape is expected to be: (5, 21) Byte-size of a single element in its current form: 12 - - - - - - - - - - - - - - - - - - Checking a complex-valued dataset: Dataset being tested: Function to transform to real: is_complex? True is_compound? False Shape of dataset in its current form: (5, 7) After flattening to real, shape is expected to be: (5, 14) Byte-size of a single element in its current form: 8 - - - - - - - - - - - - - - - - - - Checking a real-valued dataset: Dataset being tested: Function to transform to real: is_complex? False is_compound? False Shape of dataset in its current form: (5, 7) After flattening to real, shape is expected to be: (5, 7) Byte-size of a single element in its current form: 4 """ if not isinstance(h5_dset, h5py.Dataset): raise TypeError('h5_dset should be a h5py.Dataset object') is_complex = False is_compound = False in_dtype = h5_dset.dtype # TODO: avoid assuming 2d shape - why does one even need n_samples!? We only care about the last dimension! n_features = h5_dset.shape[-1] if is_complex_dtype(h5_dset.dtype): is_complex = True new_dtype = np.real(h5_dset[0, 0]).dtype type_mult = new_dtype.itemsize * 2 func = flatten_complex_to_real n_features *= 2 elif len(h5_dset.dtype) > 0: """ Some form of structured numpy is in use We only support real scalars for the component types at the current time """ is_compound = True # TODO: Avoid hard-coding to float32 new_dtype = np.float32 type_mult = len(in_dtype) * new_dtype(0).itemsize func = flatten_compound_to_real n_features *= len(in_dtype) else: if h5_dset.dtype not in [np.float32, np.float64]: new_dtype = np.float32 else: new_dtype = h5_dset.dtype.type type_mult = new_dtype(0).itemsize func = new_dtype return func, is_complex, is_compound, n_features, type_mult def stack_real_to_complex(ds_real, lazy=False): """ Puts the real and imaginary sections of the provided matrix (in the last axis) together to make complex matrix Parameters ------------ ds_real : :class:`numpy.ndarray`, :class:`dask.array.core.Array`, or :class:`h5py.Dataset` n dimensional real-valued numpy array or HDF5 dataset where data arranged as [instance, 2 x features], where the first half of the features are the real component and the second half contains the imaginary components lazy : bool, optional. Default = False If set to True, will use lazy Dask arrays instead of in-memory numpy arrays Returns ---------- ds_compound : :class:`numpy.ndarray` or :class:`dask.array.core.Array` 2D complex array arranged as [sample, features] Examples -------- >>> import numpy as np >>> import sidpy >>> real_val = np.hstack([5 * np.random.rand(6), >>> 7 * np.random.rand(6)]) >>> print('Real valued dataset of shape {}:'.format(real_val.shape)) >>> print(real_val) Real valued dataset of shape (12,): [3.59249723 1.05674621 4.41035214 1.84720102 1.79672691 4.7636207 3.09574246 0.76396171 3.38140637 4.97629028 0.83303717 0.32816285] >>> comp_val = sidpy.dtype_utils.stack_real_to_complex(real_val) >>> print('Complex-valued array of shape: {}'.format(comp_val.shape)) >>> print(comp_val) Complex-valued array of shape: (6,) [3.59249723+3.09574246j 1.05674621+0.76396171j 4.41035214+3.38140637j 1.84720102+4.97629028j 1.79672691+0.83303717j 4.7636207 +0.32816285j] """ if not isinstance(ds_real, (np.ndarray, da.core.Array, h5py.Dataset)): if not isinstance(ds_real, (tuple, list)): raise TypeError("Expected at least an iterable like a list or tuple") ds_real = np.array(ds_real) if len(ds_real.dtype) > 0: raise TypeError("Array cannot have a compound dtype") if is_complex_dtype(ds_real.dtype): raise TypeError("Array cannot have complex dtype") if ds_real.shape[-1] / 2 != ds_real.shape[-1] // 2: raise ValueError("Last dimension must be even sized") half_point = ds_real.shape[-1] // 2 if isinstance(ds_real, da.core.Array): lazy = True if lazy and not isinstance(ds_real, da.core.Array): ds_real = lazy_load_array(ds_real) return ds_real[..., :half_point] + 1j * ds_real[..., half_point:] def stack_real_to_compound(ds_real, compound_type, lazy=False): """ Converts a real-valued dataset to a compound dataset (along the last axis) of the provided compound d-type Parameters ------------ ds_real : :class:`numpy.ndarray`, :class:`dask.array.core.Array`, or :class:`h5py.Dataset` n dimensional real-valued numpy array or HDF5 dataset where data arranged as [instance, features] compound_type : :class:`numpy.dtype` Target complex data-type lazy : bool, optional. Default = False If set to True, will use lazy Dask arrays instead of in-memory numpy arrays Returns ---------- ds_compound : :class:`numpy.ndarray` or :class:`dask.array.core.Array` N-dimensional complex-valued array arranged as [sample, features] Examples -------- >>> import numpy as np >>> import sidpy >>> struct_dtype = np.dtype({'names': ['r', 'g', 'b'], >>> 'formats': [np.float32, np.uint16, np.float64]}) >>> num_elems = 5 >>> real_val = np.concatenate((np.random.random(size=num_elems) * 1024, >>> np.random.randint(0, high=1024, size=num_elems), >>> np.random.random(size=num_elems) * 1024)) >>> print('Real valued dataset of shape {}:'.format(real_val.shape)) >>> print(real_val) Real valued dataset of shape (15,): [276.65339095 527.80665658 741.38145798 647.06743252 710.41729083 380. 796. 504. 355. 985. 960.70015068 567.47024098 881.25140299 105.48936013 933.13686734] >>> comp_val = sidpy.dtype_utils.stack_real_to_compound(real_val, struct_dtype) >>> print('Structured array of shape: {}'.format(comp_val.shape)) >>> print(comp_val) Structured array of shape: (5,) [(276.65338, 380, 960.70015068) (527.80664, 796, 567.47024098) (741.3815 , 504, 881.25140299) (647.06744, 355, 105.48936013) (710.4173 , 985, 933.13686734)] """ if lazy or isinstance(ds_real, da.core.Array): raise NotImplementedError('Lazy operation not available due to absence of Dask support') if not isinstance(ds_real, (np.ndarray, h5py.Dataset)): if not isinstance(ds_real, (list, tuple)): raise TypeError("Expected at least an iterable like a list or tuple") ds_real = np.array(ds_real) if len(ds_real.dtype) > 0: raise TypeError("Array cannot have a compound dtype") elif is_complex_dtype(ds_real.dtype): raise TypeError("Array cannot have complex dtype") if not isinstance(compound_type, np.dtype): raise TypeError('Provided object must be a structured array dtype') new_spec_length = ds_real.shape[-1] / len(compound_type) if new_spec_length % 1: raise ValueError('Provided compound type was not compatible by number of elements') new_spec_length = int(new_spec_length) new_shape = list(ds_real.shape) # Make mutable new_shape[-1] = new_spec_length xp = np kwargs = {} """ if isinstance(ds_real, h5py.Dataset) and not lazy: warn('HDF5 datasets will be loaded as Dask arrays in the future. ie - kwarg lazy will default to True in future releases of sidpy') if isinstance(ds_real, da.core.Array): lazy = True if lazy: xp = da ds_real = lazy_load_array(ds_real) kwargs = {'chunks': 'auto'} """ ds_compound = xp.empty(new_shape, dtype=compound_type, **kwargs) for name_ind, name in enumerate(compound_type.names): i_start = name_ind * new_spec_length i_end = (name_ind + 1) * new_spec_length ds_compound[name] = ds_real[..., i_start:i_end] return ds_compound.squeeze() def stack_real_to_target_dtype(ds_real, new_dtype, lazy=False): """ Transforms real data into the target dtype Parameters ---------- ds_real : :class:`numpy.ndarray`, :class:`dask.array.core.Array` or :class:`h5py.Dataset` n dimensional real-valued numpy array or HDF5 dataset new_dtype : :class:`numpy.dtype` Target data-type Returns ---------- ret_val : :class:`numpy.ndarray` or :class:`dask.array.core.Array` N-dimensional array of the target data-type Examples -------- >>> import numpy as np >>> import sidpy >>> struct_dtype = np.dtype({'names': ['r', 'g', 'b'], >>> 'formats': [np.float32, np.uint16, np.float64]}) >>> num_elems = 5 >>> real_val = np.concatenate((np.random.random(size=num_elems) * 1024, >>> np.random.randint(0, high=1024, size=num_elems), >>> np.random.random(size=num_elems) * 1024)) >>> print('Real valued dataset of shape {}:'.format(real_val.shape)) >>> print(real_val) Real valued dataset of shape (15,): [276.65339095 527.80665658 741.38145798 647.06743252 710.41729083 380. 796. 504. 355. 985. 960.70015068 567.47024098 881.25140299 105.48936013 933.13686734] >>> comp_val = sidpy.dtype_utils.stack_real_to_target_dtype(real_val, struct_dtype) >>> print('Structured array of shape: {}'.format(comp_val.shape)) >>> print(comp_val) Structured array of shape: (5,) [(276.65338, 380, 960.70015068) (527.80664, 796, 567.47024098) (741.3815 , 504, 881.25140299) (647.06744, 355, 105.48936013) (710.4173 , 985, 933.13686734)] """ if is_complex_dtype(new_dtype): return stack_real_to_complex(ds_real, lazy=lazy) try: if len(new_dtype) > 0: return stack_real_to_compound(ds_real, new_dtype, lazy=lazy) except TypeError: return new_dtype(ds_real) # catching all other cases, such as np.dtype('>> import numpy as np >>> import sidpy >>> for item in [np.float16, np.complex64, np.uint8, np.int16]: >>> print('Is {} a valid dtype? : {}'.format(item, sidpy.dtype_utils.validate_dtype(item))) Is a valid dtype? : True Is a valid dtype? : True Is a valid dtype? : True Is a valid dtype? : True # This function is especially useful on compound or structured data types: >>> struct_dtype = np.dtype({'names': ['r', 'g', 'b'], >>> 'formats': [np.float32, np.uint16, np.float64]}) >>> print('Is {} a valid dtype? : {}'.format(struct_dtype, sidpy.dtype_utils.validate_dtype(struct_dtype))) Is [('r', '>> import numpy as np >>> import sidpy >>> for dtype in [np.float32, np.float16, np.uint8, np.int16, bool]: >>> print('Is {} a complex dtype?: {}'.format(dtype, (sidpy.dtype_utils.is_complex_dtype(dtype)))) Is a complex dtype?: False Is a complex dtype?: False Is a complex dtype?: False Is a complex dtype?: False Is a complex dtype?: False >>> struct_dtype = np.dtype({'names': ['r', 'g', 'b'], >>> 'formats': [np.float32, np.uint16, np.float64]}) Is [('r', '>> for dtype in [complex, np.complex64, np.complex128, np.complex256]: >>> print('Is {} a complex dtype?: {}'.format(dtype, (sidpy.dtype_utils.is_complex_dtype(dtype)))) Is a complex dtype?: True Is a complex dtype?: True Is a complex dtype?: True Is a complex dtype?: False """ validate_dtype(dtype) if dtype in [complex, np.complex64, np.complex128]: return True return False sidpy-0.12.3/sidpy/hdf/hdf_utils.py000066400000000000000000000743251455261647000171760ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Simple yet handy HDF5 utilities, independent of the data model Created on Tue Nov 3 21:14:25 2015 @author: Suhas Somnath, Chris Smith """ from __future__ import division, print_function, absolute_import, unicode_literals import socket import sys from warnings import warn from platform import platform from enum import Enum import h5py import numpy as np from dask import array as da from sidpy.__version__ import version as sidpy_version from sidpy.base.string_utils import validate_single_string_arg, \ validate_list_of_strings, clean_string_att, get_time_stamp # from sidpy.base.dict_utils import flatten_dict if sys.version_info.major == 3: unicode = str def print_tree(parent, rel_paths=False): """ Simple function to recursively print the contents of a hdf5 group Parameters ---------- parent : :class:`h5py.Group` HDF5 (sub-)tree to print rel_paths : bool, optional. Default = False True - prints the relative paths for all elements. False - prints a tree-like structure with only the element names """ # TODO: Accept callables where the user could filter out group / datasets # based on some condition. This will simplify print_tree extensions in # pyUSID and pyNSID if not isinstance(parent, (h5py.File, h5py.Group)): raise TypeError('Provided object is not a h5py.File or h5py.Group ' 'object') def __print(name, obj): if rel_paths: print(name) else: levels = name.count('/') curr_name = name[name.rfind('/') + 1:] print(levels * ' ' + '├ ' + curr_name) if isinstance(obj, h5py.Group): print((levels + 1) * ' ' + len(curr_name) * '-') print(parent.name) parent.visititems(__print) def get_auxiliary_datasets(h5_object, aux_dset_name=None): """ Returns auxiliary dataset objects associated with some DataSet through its attributes. Note - region references will be ignored. Parameters ---------- h5_object : :class:`h5py.Dataset`, :class:`h5py.Group` or :class:`h5py.File` Dataset object reference. aux_dset_name : str or :class:`list` of str, optional. Default = all Name of auxiliary :class:`h5py.Dataset` objects to return. Returns ------- list of :class:`h5py.Reference` of auxiliary :class:`h5py.Dataset` objects. """ if not isinstance(h5_object, (h5py.Dataset, h5py.Group, h5py.File)): raise TypeError('h5_object should be a h5py.Dataset, h5py.Group or h5py.File object') if aux_dset_name is None: aux_dset_name = h5_object.attrs.keys() else: aux_dset_name = validate_list_of_strings(aux_dset_name, 'aux_dset_name') data_list = list() curr_name = None try: h5_file = h5_object.file for curr_name in aux_dset_name: h5_ref = h5_object.attrs[curr_name] if isinstance(h5_ref, h5py.Reference) and isinstance(h5_file[h5_ref], h5py.Dataset) and not \ isinstance(h5_ref, h5py.RegionReference): data_list.append(h5_file[h5_ref]) except KeyError: raise KeyError('%s is not an attribute of %s' % (str(curr_name), h5_object.name)) return data_list def get_attr(h5_object, attr_name): """ Returns the attribute from the h5py object Parameters ---------- h5_object : :class:`h5py.Dataset`, :class:`h5py.Group` or :class:`h5py.File` object whose attribute is desired attr_name : str Name of the attribute of interest Returns ------- att_val : object value of attribute, in certain cases (byte strings or list of byte strings) reformatted to readily usable forms """ if not isinstance(h5_object, (h5py.Dataset, h5py.Group, h5py.File)): raise TypeError('h5_object should be a h5py.Dataset, h5py.Group or h5py.File object') attr_name = validate_single_string_arg(attr_name, 'attr_name') if attr_name not in h5_object.attrs.keys(): raise KeyError("'{}' is not an attribute in '{}'".format(attr_name, h5_object.name)) h5py_major = int(h5py.__version__.split('.')[0]) att_val = h5_object.attrs.get(attr_name) if isinstance(att_val, np.bytes_) or isinstance(att_val, bytes): att_val = att_val.decode('utf-8') elif isinstance(att_val, np.ndarray): if sys.version_info.major == 3: if att_val.dtype.type in [np.bytes_]: att_val = np.array([str(x, 'utf-8') for x in att_val]) elif att_val.dtype.type in [np.object_] and h5py_major < 3: att_val = np.array([str(x, 'utf-8') for x in att_val]) return att_val def get_attributes(h5_object, attr_names=None, strict=False): """ Returns attribute associated with some DataSet. Parameters ---------- h5_object : :class:`h5py.Dataset` Dataset object reference. attr_names : str or :class:`list` of str, optional. Default = all Name of attribute object to return. strict : bool, optional. Default = False If True - raises a KeyError if desired keys are not found. Else, raises warning instead. This is especially useful when attempting to read attributes with invalid names such as spaces on either sides of text. Returns ------- att_dict : dict Dictionary containing (name,value) pairs of attributes """ if not isinstance(h5_object, (h5py.Dataset, h5py.Group, h5py.File)): raise TypeError('h5_object should be a h5py.Dataset, h5py.Group or h5py.File object') if attr_names is None: attr_names = h5_object.attrs.keys() else: attr_names = validate_list_of_strings(attr_names, 'attr_names') # Set strict to True since user is looking for specific attributes strict = True att_dict = {} for attr in attr_names: try: att_dict[attr] = get_attr(h5_object, attr) except KeyError: message = '"{}" is not an attribute of {}'.format(attr, h5_object.name) if strict: raise KeyError(message) else: warn(message) return att_dict def get_h5_obj_refs(obj_names, h5_refs): """ Given a list of H5 references and a list of names, this method returns H5 objects corresponding to the names Parameters ---------- obj_names : string or List of strings names of target h5py objects h5_refs : H5 object reference or List of H5 object references list containing the target reference Returns ------- found_objects : List of HDF5 dataset references Corresponding references """ obj_names = validate_list_of_strings(obj_names, 'attr_names') if isinstance(h5_refs, (h5py.File, h5py.Group, h5py.Dataset)): h5_refs = [h5_refs] if not isinstance(h5_refs, (list, tuple)): raise TypeError('h5_refs should be a / list of h5py.Dataset, h5py.Group or h5py.File object(s)') found_objects = [] for target_name in obj_names: for h5_object in h5_refs: if not isinstance(h5_object, (h5py.File, h5py.Group, h5py.Dataset)): continue if h5_object.name.split('/')[-1] == target_name: found_objects.append(h5_object) return found_objects def validate_h5_objs_in_same_h5_file(h5_src, h5_other): """ Checks if the provided objects are in the same HDF5 file. If not, it throws a ValueError Parameters ---------- h5_src : h5py.Dataset, h5py.File, or h5py.Group object First object to compare h5_other : h5py.Dataset, h5py.File, or h5py.Group object Second object to compare """ if not isinstance(h5_src, (h5py.Dataset, h5py.File, h5py.Group)): raise TypeError('h5_src should either be a h5py Dataset, File, or ' 'Group') if not isinstance(h5_other, (h5py.Dataset, h5py.File, h5py.Group)): raise TypeError('h5_other should either be a h5py Dataset, File, or' ' Group') if h5_src.file != h5_other.file: raise ValueError('Cannot link h5 objects across files. ' '{} is present in file: {}, while {} is in file :' '{}'.format(h5_src.name, h5_src.file, h5_other.name, h5_other.file)) def __link_h5_obj(h5_src, h5_other, alias=None): validate_h5_objs_in_same_h5_file(h5_src, h5_other) if alias is None: alias = h5_other.name.split('/')[-1] h5_src.attrs[alias] = h5_other.ref def link_h5_objects_as_attrs(src, h5_objects): """ Creates Dataset attributes that contain references to other Dataset Objects. Parameters ----------- src : Reference to h5.object Reference to the object to which attributes will be added h5_objects : list of references to h5.objects objects whose references that can be accessed from src.attrs Returns -------- None """ if not isinstance(src, (h5py.Dataset, h5py.File, h5py.Group)): raise TypeError('src should either be a h5py Dataset, File, or Group') if isinstance(h5_objects, (h5py.Dataset, h5py.Group)): h5_objects = [h5_objects] for itm in h5_objects: if not isinstance(itm, (h5py.Dataset, h5py.Group)): raise TypeError('h5_objects should only contain h5py. Dataset and Group objects') __link_h5_obj(src, itm) def link_h5_obj_as_alias(h5_main, h5_ancillary, alias_name): """ Creates Dataset attributes that contain references to other Dataset Objects. This function is useful when the reference attribute must have a reserved name. Such as linking 'SHO_Indices' as 'Spectroscopic_Indices' Parameters ------------ h5_main : h5py.Dataset Reference to the object to which attributes will be added h5_ancillary : h5py.Dataset object whose reference that can be accessed from src.attrs alias_name : String Alias / alternate name for trg """ if not isinstance(h5_main, (h5py.Dataset, h5py.File, h5py.Group)): raise TypeError('h5_main should either be a h5py Dataset, File, or Group') if not isinstance(h5_ancillary, (h5py.Dataset, h5py.Group)): raise TypeError('h5_ancillary should be a h5py. Dataset or Group object') alias_name = validate_single_string_arg(alias_name, 'alias_name') __link_h5_obj(h5_main, h5_ancillary, alias=alias_name) def is_editable_h5(h5_obj): """ Returns True if the file containing the provided h5 object is in w or r+ modes Parameters ---------- h5_obj : h5py.File, h5py.Group, or h5py.Dataset object h5py object Returns ------- mode : bool True if the file containing the provided h5 object is in w or r+ modes """ if not isinstance(h5_obj, (h5py.File, h5py.Group, h5py.Dataset)): raise TypeError('h5_obj should be a h5py File, Group or Dataset object but is instead of type ' '{}t'.format(type(h5_obj))) try: file_handle = h5_obj.file except RuntimeError: raise ValueError('Encountered a RuntimeError possibly due to a closed file') # file handle is actually an open hdf file if file_handle.mode == 'r': return False return True def write_book_keeping_attrs(h5_obj): """ Writes basic bookkeeping and posterity related attributes to groups created using sidpy such as machine id, version, timestamp. Parameters ---------- h5_obj : :class:`h5py.Dataset`, :class:`h5py.Group`, or :class:`h5py.File` Object to which basic bookkeeping attributes need to be written """ if not isinstance(h5_obj, (h5py.Group, h5py.File, h5py.Dataset)): raise TypeError('h5_obj should be a h5py.Group, h5py.File, or h5py.Dataset object') write_simple_attrs(h5_obj, {'machine_id': socket.getfqdn(), 'timestamp': get_time_stamp(), 'platform': platform(), 'sidpy_version': sidpy_version}, verbose=False) def write_simple_attrs(h5_obj, attrs, force_to_str=True, verbose=False): """ Writes attributes to a h5py object Parameters ---------- h5_obj : :class:`h5py.File`, :class:`h5py.Group`, or h5py.Dataset object h5py object to which the attributes will be written to attrs : dict Dictionary containing the attributes as key-value pairs force_to_str : bool, optional. Default = True Whether or not to cast keys or values to string when they do not have the correct types verbose : bool, optional. Default=False Whether or not to print debugging statements """ if not isinstance(attrs, dict): raise TypeError('attrs should be a dictionary but is instead of type ' '{}'.format(type(attrs))) if not isinstance(h5_obj, (h5py.File, h5py.Group, h5py.Dataset)): raise TypeError('h5_obj should be a h5py File, Group or Dataset object' ' but is instead of type ' '{}t'.format(type(h5_obj))) for key, val in attrs.items(): if not isinstance(key, (str, unicode)): if force_to_str: warn('Converted key: {} from type: {} to str' ''.format(key, type(key))) key = str(key) else: warn('Skipping attribute with key: {}. Expected str, got {}' ''.format(key, type(key))) continue # Get rid of spaces in the key key = key.strip() if val is None: continue if isinstance(val, Enum): if verbose: print('taking the name: {} of Enum: {}'.format(val.name, val)) val = val.name if isinstance(val, list): dictionaries = False for item in val: if isinstance(item, dict): dictionaries = True break if dictionaries: new_val = {} for key2, item in enumerate(val): new_val[str(key2)] = item val = new_val if isinstance(val, dict): if isinstance(h5_obj, h5py.Dataset): raise ValueError('provided dictionary was nested, not flat. ' 'Flatten dictionary using sidpy.base.dict_utils.' 'flatten_dict before calling sidpy.hdf.hdf_utils.' 'write_simple_attrs') else: new_object = h5_obj.create_group(str(key)) write_simple_attrs(new_object, val, force_to_str=True, verbose=False) if verbose: print('Writing attribute: {} with value: {}'.format(key, val)) if not (isinstance(val, dict)): # not sure how this can happen if verbose: print(key, val) clean_val = clean_string_att(val) if verbose: print('Attribute cleaned into: {}'.format(clean_val)) try: h5_obj.attrs[key] = clean_val except Exception as excp: if verbose: if force_to_str: warn('Casting attribute value: {} of type: {} to str'.format(val, type(val))) h5_obj.attrs[key] = str(val) else: raise excp('Could not write attribute value: {} of type: {}'.format(val, type(val))) if verbose: print('Wrote all (simple) attributes to {}: {}\n' ''.format(type(h5_obj), h5_obj.name.split('/')[-1])) def lazy_load_array(dataset): """ Loads the provided object as a dask array (h5py.Dataset or numpy.ndarray) Parameters ---------- dataset : :class:`numpy.ndarray`, or :class:`h5py.Dataset`, or :class:`dask.array.core.Array` to load as dask array Returns ------- :class:`dask.array.core.Array` Dask array with appropriate chunks """ if isinstance(dataset, da.core.Array): return dataset elif not isinstance(dataset, (h5py.Dataset, np.ndarray)): raise TypeError('Expected one of h5py.Dataset, dask.array.core.Array, or numpy.ndarray' 'objects. Provided object was of type: {}'.format(type(dataset))) # Cannot pass 'auto' for chunks for python 2! chunks = "auto" if sys.version_info.major == 3 else dataset.shape if isinstance(dataset, h5py.Dataset): chunks = chunks if dataset.chunks is None else dataset.chunks return da.from_array(dataset, chunks=chunks) def copy_attributes(source, dest, skip_refs=True, verbose=False): """ Copy attributes from one h5object to another Parameters ---------- source : h5py.Dataset, :class:`h5py.Group`, or :class:`h5py.File` Object containing the desired attributes dest : h5py.Dataset, :class:`h5py.Group`, or :class:`h5py.File` Object to which the attributes need to be copied to skip_refs : bool, optional. default = True Whether or not the references (dataset and region) should be skipped verbose : bool, optional. Default = False Whether or not to print logs for debugging """ message = 'should be a h5py.Dataset, h5py.Group,or h5py.File object' if not isinstance(source, (h5py.Dataset, h5py.Group, h5py.File)): raise TypeError('source ' + message) if not isinstance(dest, (h5py.Dataset, h5py.Group, h5py.File)): raise TypeError('dest ' + message) skip_dset_refs = skip_refs try: validate_h5_objs_in_same_h5_file(source, dest) except ValueError: if not skip_refs: warn('Dataset references will not be copied since {} and {} are ' 'in different files'.format(source, dest)) skip_dset_refs = True for att_name in source.attrs.keys(): # print(att_name) if att_name not in ['DIMENSION_LIST']: att_val = get_attr(source, att_name) """ Don't copy references unless asked """ if isinstance(att_val, h5py.Reference) and not isinstance(att_val, h5py.RegionReference): if not skip_dset_refs: if verbose: print('dset ref copying ' + att_name) dest.attrs[att_name] = att_val elif isinstance(att_val, h5py.RegionReference): # handled in dedicated if condition below continue else: # everything else if verbose: print('simple copying ' + att_name) dest.attrs[att_name] = clean_string_att(att_val) if not skip_refs: # This can be copied across files without problems mesg = 'Could not copy region references to {}.'.format(dest.name) if isinstance(dest, h5py.Dataset): try: if verbose: print('requested reg ref copy') # copy_region_refs(source, dest) pass # TODO: activate again except TypeError: warn(mesg) else: warn('Cannot copy region references to {}'.format(type(dest))) return dest def copy_dataset(h5_orig_dset, h5_dest_grp, alias=None, verbose=False): """ Copies the provided HDF5 dataset to the provided destination. This function is handy when needing to make copies of datasets to a different HDF5 file. Notes ----- This function does NOT copy all linked objects such as ancillary datasets. Call `copy_linked_objects` to accomplish that goal. Parameters ---------- h5_orig_dset : h5py.Dataset h5_dest_grp : h5py.Group or h5py.File object : Destination where the duplicate dataset will be created alias : str, optional. Default = name from `h5_orig_dset`: Name to be assigned to the copied dataset verbose : bool, optional. Default = False Whether or not to print logs to assist in debugging Returns ------- """ if not isinstance(h5_orig_dset, h5py.Dataset): raise TypeError("'h5_orig_dset' should be a h5py.Dataset object") if not isinstance(h5_dest_grp, (h5py.File, h5py.Group)): raise TypeError("'h5_dest_grp' should either be a h5py.File or " "h5py.Group object") if alias is not None: validate_single_string_arg(alias, 'alias') else: alias = h5_orig_dset.name.split('/')[-1] if alias in h5_dest_grp.keys(): if verbose: warn('{} already contains an object with the same name: {}' ''.format(h5_dest_grp, alias)) h5_new_dset = h5_dest_grp[alias] if not isinstance(h5_new_dset, h5py.Dataset): raise TypeError('{} already contains an object: {} with the desired' ' name which is not a dataset'.format(h5_dest_grp, h5_new_dset)) da_source = lazy_load_array(h5_orig_dset) da_dest = lazy_load_array(h5_new_dset) if da_source.shape != da_dest.shape: raise ValueError('Existing dataset: {} has a different shape ' 'compared to the original dataset: {}' ''.format(h5_new_dset, h5_orig_dset)) if not da.allclose(da_source, da_dest): raise ValueError('Existing dataset: {} has different contents' 'compared to the original dataset: {}' ''.format(h5_new_dset, h5_orig_dset)) else: kwargs = {'shape': h5_orig_dset.shape, 'dtype': h5_orig_dset.dtype, 'compression': h5_orig_dset.compression, 'chunks': h5_orig_dset.chunks} if h5_orig_dset.file.driver == 'mpio': if kwargs.pop('compression', None) is not None: warn('This HDF5 file has been opened wth the ' '"mpio" communicator. mpi4py does not allow ' 'creation of compressed datasets. Compression' ' kwarg has been removed') if verbose: print('Creating new HDF5 dataset named: {} at: {} with' ' kwargs: {}'.format(alias, h5_dest_grp, kwargs)) h5_new_dset = h5_dest_grp.create_dataset(alias, **kwargs) if verbose: print('dask.array will copy data from source dataset ' 'to new dataset') da.to_hdf5(h5_new_dset.file.filename, {h5_new_dset.name: lazy_load_array(h5_orig_dset)}) if verbose: print('Copying simple attributes of original dataset: {} to ' 'destination dataset: {}'.format(h5_orig_dset, h5_new_dset)) copy_attributes(h5_orig_dset, h5_new_dset, skip_refs=True) # TODO: reinstate copy all region_refs() # copy_all_region_refs(h5_orig_dset, h5_new_dset) return h5_new_dset def copy_linked_objects(h5_source, h5_dest, verbose=False): """ Recursively copies datasets linked to the source h5 object to the destination h5 object that are in different HDF5 files. This is for copying ancillary datasets to a target dataset that is missing ancillary datasets. It is not meant for copying to a Group, but that is supported. Notes ----- We anticipate this function being used to copy over ancillary datasets Parameters ---------- h5_source : h5py.Dataset or h5py.Group object Source object h5_dest : h5py.Dataset or h5py.Group object Destination object verbose : bool, optional. Default: False Whether or not to print logs for debugging purposes """ try: # The following line takes care of object validation validate_h5_objs_in_same_h5_file(h5_source, h5_dest) same_file = True except ValueError: same_file = False if same_file: warn('{} and {} are in the same HDF5 file. Consider copying references' ' instead of copying linked objects'.format(h5_source, h5_dest)) return if isinstance(h5_dest, h5py.Group): h5_dest_grp = h5_dest else: h5_dest_grp = h5_dest.parent # Now we are working on other files for link_obj_name in h5_source.attrs.keys(): h5_orig_obj = get_attr(h5_source, link_obj_name) if isinstance(h5_orig_obj, h5py.Reference) and not \ isinstance(h5_orig_obj, h5py.RegionReference): h5_orig_obj = h5_source.file[h5_orig_obj] if verbose: print('Attempting to copy object linked to source: {} as {}' ''.format(h5_orig_obj, link_obj_name)) # Check to see if such a dataset already exist if link_obj_name in h5_dest_grp.keys(): h5_new_obj = h5_dest_grp[link_obj_name] warn('An object with the same name: {} already exists in the ' 'destination group: {}'.format(h5_new_obj, h5_dest_grp.name)) if type(h5_dest_grp[link_obj_name]) != type(h5_orig_obj): mesg = 'Destination parent: {} already has a child named' \ ' {} that is of type: {} which does not match ' \ 'with that of the object linked with the source ' \ 'dataset: {}'.format(h5_dest_grp, link_obj_name, type(h5_orig_obj), type(h5_new_obj)) raise TypeError(mesg) elif isinstance(h5_new_obj, h5py.Dataset): _ = copy_dataset(h5_orig_obj, h5_dest_grp, alias=link_obj_name, verbose=verbose) h5_dest.attrs[link_obj_name] = h5_new_obj.ref continue elif isinstance(h5_new_obj, h5py.Group): raise ValueError('Destination already contains another ' 'HDF5 group: {} with the same name as ' 'the source: {}'.format(h5_new_obj, h5_orig_obj)) else: raise NotImplementedError('Unable to copy {} objects yet' '. Contact developer if you need' ' this' ''.format(type(h5_orig_obj))) else: if isinstance(h5_orig_obj, h5py.Dataset): h5_new_obj = copy_dataset(h5_orig_obj, h5_dest_grp, alias=link_obj_name, verbose=verbose) h5_dest.attrs[link_obj_name] = h5_new_obj.ref else: raise NotImplementedError('Unable to copy {} objects yet' '. Contact developer if you need' ' this'.format(type(h5_orig_obj))) def find_dataset(h5_group, dset_name): """ Uses visit() to find all datasets with the desired name Parameters ---------- h5_group : :class:`h5py.Group` Group to search within for the Dataset dset_name : str Name of the dataset to search for Returns ------- datasets : list List of [Name, object] pairs corresponding to datasets that match `ds_name`. """ if not isinstance(h5_group, (h5py.File, h5py.Group)): raise TypeError('h5_group should be a h5py.File or h5py.Group object') dset_name = validate_single_string_arg(dset_name, 'dset_name') # print 'Finding all instances of', ds_name datasets = [] def __find_name(name, obj): if dset_name in name.split('/')[-1] and isinstance(obj, h5py.Dataset): datasets.append(obj) return h5_group.visititems(__find_name) return datasets def write_dict_to_h5_group(h5_group, metadata, group_name): """ If the provided metadata parameter is a non-empty dictionary, this function will create a HDF5 group called group_name within the provided h5_group and write the contents of metadata into the newly created group Parameters ---------- h5_group : h5py.Group Parent group to write metadata into metadata : dict Dictionary that needs to be written into the group group_name : str Name of the group to write attributes into Returns ------- h5_metadata_grp : h5py.Group Handle to the newly create group containing the metadata Notes ----- Writes now (sidpy version 0.0.6) nested dictionaries to HDF5 files. Use h5_group_to_dict to read from HDF5 file. """ if not isinstance(metadata, dict): raise TypeError('metadata is not a dict but of type: {}' ''.format(type(metadata))) if len(metadata) < 1: return None if not isinstance(h5_group, (h5py.Group, h5py.File)): raise TypeError('h5_group is neither a h5py.Group or h5py.File object' 'and is of type: {}'.format(type(h5_group))) validate_single_string_arg(group_name, 'group_name') group_name = group_name.replace(' ', '_') h5_md_group = h5_group.create_group(group_name) # flat_dict = flatten_dict(metadata) write_simple_attrs(h5_md_group, metadata) return h5_md_group def h5_group_to_dict(group_iter, group_dict={}): """ Reads a hdf5 group into a nested dictionary Parameters ---------- group_iter: hdf5.Group starting group to read from group_dict: dict group dictionary; mostly needed for recursive reading of nested groups but can be used for initialization Returns ------- group_dict: dict """ if not isinstance(group_iter, h5py.Group): raise TypeError('we need a h5py group to read from') if not isinstance(group_dict, dict): raise TypeError('group_dict needs to be a python dictionary') group_dict[group_iter.name.split('/')[-1]] = dict(group_iter.attrs) for key in group_iter.keys(): h5_group_to_dict(group_iter[key], group_dict[group_iter.name.split('/')[-1]]) return group_dict sidpy-0.12.3/sidpy/hdf/prov_utils.py000066400000000000000000000352751455261647000174240ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Tools for tracking provenance within HDF5 files Created on Tue Nov 3 21:14:25 2015 @author: Suhas Somnath, Chris Smith """ from __future__ import division, print_function, absolute_import, \ unicode_literals import sys from warnings import warn import h5py import numpy as np if sys.version_info.major == 3: from collections.abc import Iterable unicode = str else: from collections import Iterable from sidpy.base.string_utils import validate_single_string_arg from sidpy.hdf.hdf_utils import get_attr, write_book_keeping_attrs, \ write_simple_attrs def assign_group_index(h5_parent_group, base_name, verbose=False): """ Searches the parent h5 group to find the next available index for the group Parameters ---------- h5_parent_group : :class:`h5py.Group` object Parent group under which the new group object will be created base_name : str or unicode Base name of the new group without index verbose : bool, optional. Default=False Whether or not to print debugging statements Returns ------- base_name : str or unicode Base name of the new group with the next available index as a suffix """ if not isinstance(h5_parent_group, h5py.Group): raise TypeError('h5_parent_group should be a h5py.Group object') base_name = validate_single_string_arg(base_name, 'base_name') if len(base_name) == 0: raise ValueError('base_name should not be an empty string') if not base_name.endswith('_'): base_name += '_' temp = [key for key in h5_parent_group.keys()] if verbose: print('Looking for group names starting with {} in parent containing items: ' '{}'.format(base_name, temp)) previous_indices = [] for item_name in temp: if isinstance(h5_parent_group[item_name], h5py.Group) and item_name.startswith(base_name): previous_indices.append(int(item_name.replace(base_name, ''))) previous_indices = np.sort(previous_indices) if verbose: print('indices of existing groups with the same prefix: {}'.format(previous_indices)) if len(previous_indices) == 0: index = 0 else: index = previous_indices[-1] + 1 return base_name + '{:03d}'.format(index) def create_indexed_group(h5_parent_group, base_name): """ Creates a group with an indexed name (eg - 'Measurement_012') under ``h5_parent_group`` using the provided ``base_name`` as a prefix for the group's name Parameters ---------- h5_parent_group : :class:`h5py.Group` or :class:`h5py.File` File or group within which the new group will be created base_name : str or unicode Prefix for the group name. This need not end with a '_'. It will be added automatically """ if not isinstance(h5_parent_group, (h5py.Group, h5py.File)): raise TypeError('h5_parent_group should be a h5py.File or Group object') base_name = validate_single_string_arg(base_name, 'base_name') group_name = assign_group_index(h5_parent_group, base_name) h5_new_group = h5_parent_group.create_group(group_name) write_book_keeping_attrs(h5_new_group) return h5_new_group def create_results_group(h5_main, tool_name, h5_parent_group=None): """ Creates a h5py.Group object auto-indexed and named as 'DatasetName-ToolName_00x' Parameters ---------- h5_main : h5py.Dataset object Reference to the dataset based on which the process / analysis is being performed tool_name : string / unicode Name of the Process / Analysis applied to h5_main h5_parent_group : h5py.Group, optional. Default = None Parent group under which the results group will be created. Use this option to write results into a new HDF5 file. By default, results will be written into the same group containing `h5_main` Returns ------- h5_group : :class:`h5py.Group` Results group which can now house the results datasets """ # TODO: Revise significantly. Avoid parent dataset name # Consider embedding refs to source datasets as attributes of group warn('The behavior of create_results_group is very likely to change soon ' 'and significantly. Use this function with caution', FutureWarning) if not isinstance(h5_main, h5py.Dataset): raise TypeError('h5_main should be a h5py.Dataset object') if h5_parent_group is not None: if not isinstance(h5_parent_group, (h5py.File, h5py.Group)): raise TypeError("'h5_parent_group' should either be a h5py.File " "or h5py.Group object") else: h5_parent_group = h5_main.parent tool_name = validate_single_string_arg(tool_name, 'tool_name') if '-' in tool_name: warn('tool_name should not contain the "-" character. Reformatted name from:{} to ' '{}'.format(tool_name, tool_name.replace('-', '_'))) tool_name = tool_name.replace('-', '_') group_name = h5_main.name.split('/')[-1] + '-' + tool_name + '_' group_name = assign_group_index(h5_parent_group, group_name) h5_group = h5_parent_group.create_group(group_name) write_book_keeping_attrs(h5_group) # Also add some basic attributes like source and tool name. This will allow relaxation of nomenclature restrictions: # this are NOT being used right now but will be in the subsequent versions of pyNSID write_simple_attrs(h5_group, {'tool': tool_name, 'num_source_dsets': 1}) # in this case, there is only one source if h5_parent_group.file == h5_main.file: for dset_ind, dset in enumerate([h5_main]): h5_group.attrs['source_' + '{:03d}'.format(dset_ind)] = dset.ref return h5_group def find_results_groups(h5_main, tool_name, h5_parent_group=None): """ Finds a list of all groups containing results of the process of name ``tool_name`` being applied to the dataset Parameters ---------- h5_main : h5 dataset reference Reference to the target dataset to which the tool was applied tool_name : String / unicode Name of the tool applied to the target dataset h5_parent_group : h5py.Group, optional. Default = None Parent group under which the results group will be searched for. Use this option when the results groups are contained in different HDF5 file compared to `h5_main`. BY default, this function will search within the same group that contains `h5_main` Returns ------- groups : list of references to :class:`h5py.Group` objects groups whose name contains the tool name and the dataset name """ warn('The behavior of find_results_group is very likely to change soon ' 'and significantly. Use this function with caution', FutureWarning) if not isinstance(h5_main, h5py.Dataset): raise TypeError('h5_main should be a h5py.Dataset object') tool_name = validate_single_string_arg(tool_name, 'tool_name') if h5_parent_group is not None: if not isinstance(h5_parent_group, (h5py.File, h5py.Group)): raise TypeError("'h5_parent_group' should either be a h5py.File " "or h5py.Group object") else: h5_parent_group = h5_main.parent dset_name = h5_main.name.split('/')[-1] groups = [] for key in h5_parent_group.keys(): if dset_name in key and tool_name in key and isinstance(h5_parent_group[key], h5py.Group): groups.append(h5_parent_group[key]) return groups def check_for_old(h5_base, tool_name, new_parms=None, target_dset=None, h5_parent_goup=None, verbose=False): """ Check to see if the results of a tool already exist and if they were performed with the same parameters. Parameters ---------- h5_base : h5py.Dataset object Dataset on which the tool is being applied to tool_name : str process or analysis name new_parms : dict, optional Parameters with which this tool will be performed. target_dset : str, optional, default = None Name of the dataset whose attributes will be compared against new_parms. Default - checking against the group h5_parent_goup : h5py.Group, optional. Default = None The group to search under. Use this option when `h5_base` and the potential results groups (within `h5_parent_goup` are located in different HDF5 files. Default - search within h5_base.parent verbose : bool, optional, default = False Whether or not to print debugging statements Returns ------- group : list List of all :class:`h5py.Group` objects with parameters matching those in `new_parms` """ warn('The behavior of check_for_old is very likely to change soon ' '. Use this function with caution', FutureWarning) if not isinstance(h5_base, h5py.Dataset): raise TypeError('h5_base should be a h5py.Dataset object') tool_name = validate_single_string_arg(tool_name, 'tool_name') if h5_parent_goup is not None: if not isinstance(h5_parent_goup, (h5py.File, h5py.Group)): raise TypeError("'h5_parent_group' should either be a h5py.File " "or h5py.Group object") else: h5_parent_goup = h5_base.parent if new_parms is None: new_parms = dict() else: if not isinstance(new_parms, dict): raise TypeError('new_parms should be a dict') if target_dset is not None: target_dset = validate_single_string_arg(target_dset, 'target_dset') matching_groups = [] groups = find_results_groups(h5_base, tool_name, h5_parent_group=h5_parent_goup) for group in groups: if verbose: print('Looking at group - {}'.format(group.name.split('/')[-1])) h5_obj = group if target_dset is not None: if target_dset in group.keys(): h5_obj = group[target_dset] else: if verbose: print('{} did not contain the target dataset: {}'.format(group.name.split('/')[-1], target_dset)) continue if check_for_matching_attrs(h5_obj, new_parms=new_parms, verbose=verbose): # return group matching_groups.append(group) return matching_groups def check_for_matching_attrs(h5_obj, new_parms=None, verbose=False): """ Compares attributes in the given H5 object against those in the provided dictionary and returns True if the parameters match, and False otherwise Parameters ---------- h5_obj : h5py object (Dataset or :class:`h5py.Group`) Object whose attributes will be compared against ``new_parms`` new_parms : dict, optional. default = empty dictionary Parameters to compare against the attributes present in h5_obj verbose : bool, optional, default = False Whether or not to print debugging statements Returns ------- tests: bool Whether or not all paramters in new_parms matched with those in h5_obj's attributes """ if not isinstance(h5_obj, (h5py.Dataset, h5py.Group, h5py.File)): raise TypeError('h5_obj should be a h5py.Dataset, h5py.Group, or h5py.File object') if new_parms is None: new_parms = dict() else: if not isinstance(new_parms, dict): raise TypeError('new_parms should be a dictionary') tests = [] for key in new_parms.keys(): if verbose: print('Looking for new attribute named: {}'.format(key)) # HDF5 cannot store None as an attribute anyway. ignore if new_parms[key] is None: continue try: old_value = get_attr(h5_obj, key) except KeyError: # if parameter was not found assume that something has changed if verbose: print('New parm: {} \t- new parm not in group *****'.format(key)) tests.append(False) break if isinstance(old_value, np.ndarray): if not isinstance(new_parms[key], Iterable): if verbose: print('New parm: {} \t- new parm not iterable unlike old parm *****'.format(key)) tests.append(False) break new_array = np.array(new_parms[key]) if old_value.size != new_array.size: if verbose: print('New parm: {} \t- are of different sizes ****'.format(key)) tests.append(False) else: try: answer = np.allclose(old_value, new_array) except TypeError: # comes here when comparing string arrays # Not sure of a better way answer = [] for old_val, new_val in zip(old_value, new_array): answer.append(old_val == new_val) answer = np.all(answer) if verbose: print('New parm: {} \t- match: {}'.format(key, answer)) tests.append(answer) else: """if isinstance(new_parms[key], collections.Iterable): if verbose: print('New parm: {} \t- new parm is iterable unlike old parm *****'.format(key)) tests.append(False) break""" answer = np.all(new_parms[key] == old_value) if verbose: print('New parm: {} \t- match: {}'.format(key, answer)) tests.append(answer) if verbose: print('') return all(tests) def get_source_dataset(h5_group): """ Find the name of the source dataset used to create the input `h5_group`, so long as the source dataset is in the same HDF5 file Parameters ---------- h5_group : :class:`h5py.Group` Child group whose source dataset will be returned Returns ------- h5_source : NSIDataset object Main dataset from which this group was generated """ if not isinstance(h5_group, h5py.Group): raise TypeError('h5_group should be a h5py.Group object') h5_parent_group = h5_group.parent group_name = h5_group.name.split('/')[-1] # What if the group name was not formatted according to Pycroscopy rules? name_split = group_name.split('-') if len(name_split) != 2: raise ValueError("The provided group's name could not be split by '-' as expected in " "SourceDataset-ProcessName_000") h5_source = h5_parent_group[name_split[0]] if not isinstance(h5_source, h5py.Dataset): raise ValueError('Source object was not a dataset!') return h5_source sidpy-0.12.3/sidpy/hdf/reg_ref.py000066400000000000000000000470061455261647000166220ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Tue Nov 3 21:14:25 2015 @author: Chris Smith, Suhas Somnath """ from __future__ import division, print_function, absolute_import, \ unicode_literals import sys from warnings import warn import h5py import numpy as np from sidpy.base.string_utils import clean_string_att if sys.version_info.major == 3: from collections.abc import Iterable unicode = str else: from collections import Iterable __all__ = ['get_region', 'clean_reg_ref', 'attempt_reg_ref_build', 'copy_reg_ref_reduced_dim','create_region_reference', 'get_indices_for_region_ref', 'simple_region_ref_copy', 'write_region_references'] def get_region(h5_dset, reg_ref_name): """ Gets the region in a dataset specified by a region reference Parameters ---------- h5_dset : h5py.Dataset Dataset containing the region reference reg_ref_name : str / unicode Name of the region reference Returns ------- value : np.ndarray Data specified by the region reference. Note that a squeeze is applied by default. """ if not isinstance(reg_ref_name, (str, unicode)): raise TypeError('reg_ref_name should be a string') if not isinstance(h5_dset, h5py.Dataset): raise TypeError('h5_dset should be of type h5py.Dataset') # this may raise KeyErrors. Let it reg_ref = h5_dset.attrs[reg_ref_name] return np.squeeze(h5_dset[reg_ref]) def clean_reg_ref(h5_dset, reg_ref_tuple, verbose=False): """ Makes sure that the provided instructions for a region reference are indeed valid. This method has become necessary since h5py allows the writing of region references larger than the maxshape Parameters ---------- h5_dset : h5.Dataset instance Dataset to which region references will be added as attributes reg_ref_tuple : list / tuple The slicing information formatted using tuples of slice objects. verbose : Boolean (Optional. Default = False) Whether or not to print status messages Returns ------- new_reg_refs : tuple Instructions for the corrected region reference """ if not isinstance(reg_ref_tuple, (tuple, dict, slice)): raise TypeError('slices should be a tuple, list, or slice but is ' 'instead of type {}'.format(type(reg_ref_tuple))) if not isinstance(h5_dset, h5py.Dataset): raise TypeError('h5_dset should be a h5py.Dataset object but is ' 'instead of type {}'.format(type(h5_dset))) if isinstance(reg_ref_tuple, slice): # 1D dataset reg_ref_tuple = [reg_ref_tuple] if len(reg_ref_tuple) != len(h5_dset.shape): raise ValueError('Region reference tuple did not have the same ' 'dimensions as the h5 dataset') if verbose: print('Comparing {} with h5 dataset maxshape of {}' ''.format(reg_ref_tuple, h5_dset.maxshape)) new_reg_refs = list() for reg_ref_slice, max_size in zip(reg_ref_tuple, h5_dset.maxshape): if not isinstance(reg_ref_slice, slice): raise TypeError('slices should be a tuple or a list but is instead' ' of type {}'.format(type(reg_ref_slice))) # For now we will simply make sure that the end of the slice is # <= maxshape if max_size is not None and reg_ref_slice.stop is not None: reg_ref_slice = slice(reg_ref_slice.start, min(reg_ref_slice.stop, max_size), reg_ref_slice.step) new_reg_refs.append(reg_ref_slice) if verbose: print('Region reference tuple now: {}'.format(new_reg_refs)) return tuple(new_reg_refs) def attempt_reg_ref_build(h5_dset, dim_names, verbose=False): """ Attempts to build region references Parameters ---------- h5_dset : h5.Dataset instance Dataset to which region references need to be added as attributes dim_names : list or tuple List of the names of the region references (typically names of dimensions) verbose : bool, optional. Default=False Whether or not to print debugging statements Returns ------- labels_dict : dict The slicing information must be formatted using tuples of slice objects For example {'region_1':(slice(None, None), slice (0,1))} """ if not isinstance(h5_dset, h5py.Dataset): raise TypeError('h5_dset should be a h5py.Dataset object but is ' 'instead of type {}.'.format(type(h5_dset))) if not isinstance(dim_names, (list, tuple)): raise TypeError('slices should be a list or tuple but is instead of ' 'type {}'.format(type(dim_names))) if len(h5_dset.shape) != 2: return dict() if not np.all([isinstance(obj, (str, unicode)) for obj in dim_names]): raise TypeError('Unable to automatically generate region references ' 'for dataset: {} since one or more names of the region' ' references was not a string'.format(h5_dset.name)) labels_dict = dict() if len(dim_names) == h5_dset.shape[0]: if verbose: print('Most likely a spectroscopic indices / values dataset') for dim_index, curr_name in enumerate(dim_names): labels_dict[curr_name] = (slice(dim_index, dim_index + 1), slice(None)) elif len(dim_names) == h5_dset.shape[1]: if verbose: print('Most likely a position indices / values dataset') for dim_index, curr_name in enumerate(dim_names): labels_dict[curr_name] = (slice(None), slice(dim_index, dim_index + 1)) if len(labels_dict) > 0: warn('Attempted to automatically build region reference dictionary for' ' dataset: {}.\nPlease specify region references as a tuple of ' 'slice objects for each attribute'.format(h5_dset.name)) else: if verbose: print('Could not build region references since dataset had shape:' '{} and number of region references is {}' ''.format(h5_dset.shape, len(dim_names))) return labels_dict def get_indices_for_region_ref(h5_main, ref, return_method='slices'): """ Given an hdf5 region reference and the dataset it refers to, return an array of indices within that dataset that correspond to the reference. Parameters ---------- h5_main : HDF5 Dataset dataset that the reference can be returned from ref : HDF5 Region Reference Region reference object return_method : {'slices', 'corners', 'points'} slices : the reference is return as pairs of slices corners : the reference is returned as pairs of corners representing the starting and ending indices of each block points : the reference is returns as a list of tuples of points Returns ------- ref_inds : Numpy Array array of indices in the source dataset that ref accesses """ if not isinstance(h5_main, h5py.Dataset): raise TypeError('h5_main should be a h5py.Dataset object') if not isinstance(ref, h5py.RegionReference): raise TypeError('ref should be a h5py.RegionReference object') if return_method is not None: if not isinstance(return_method, (str, unicode)): raise TypeError('return_method should be a string') if return_method == 'points': def __corners_to_point_array(start, stop): """ Convert a pair of tuples representing two opposite corners of an HDF5 region reference into a list of arrays for each dimension. Parameters ---------- start : Tuple the starting indices of the region stop : Tuple the final indices of the region Returns ------- inds : Tuple of arrays the list of points in each dimension """ ranges = [] for i in range(len(start)): if start[i] == stop[i]: ranges.append([stop[i]]) else: ranges.append(np.arange(start[i], stop[i] + 1, dtype=np.uint)) grid = np.meshgrid(*ranges, indexing='ij') ref_inds = np.asarray(zip(*(x.flat for x in grid))) return ref_inds return_func = __corners_to_point_array elif return_method == 'corners': def __corners_to_corners(start, stop): return start, stop return_func = __corners_to_corners elif return_method == 'slices': def __corners_to_slices(start, stop): """ Convert a pair of tuples representing two opposite corners of an HDF5 region reference into a pair of slices. Parameters ---------- start : Tuple the starting indices of the region stop : Tuple the final indices of the region Returns ------- slices : list pair of slices representing the region """ slices = [] for idim in range(len(start)): slices.append(slice(start[idim], stop[idim])) return slices return_func = __corners_to_slices region = h5py.h5r.get_region(ref, h5_main.id) reg_type = region.get_select_type() if reg_type == 2: """ Reference is hyperslabs """ ref_inds = [] for start, end in region.get_select_hyper_blocklist(): ref_inds.append(return_func(start, end)) ref_inds = np.array(ref_inds).reshape(-1, len(start)) elif reg_type == 3: """ Reference is single block """ start, end = region.get_select_bounds() ref_inds = return_func(start, end) else: warn('No method exists for converting this type of reference') ref_inds = np.empty(0) return ref_inds def copy_reg_ref_reduced_dim(h5_source, h5_target, h5_source_inds, h5_target_inds, key): """ Copies a region reference from one dataset to another taking into account that a dimension has been lost from source to target Parameters ---------- h5_source : HDF5 Dataset source dataset for region reference copy h5_target : HDF5 Dataset target dataset for region reference copy h5_source_inds : HDF5 Dataset indices of each dimension of the h5_source dataset h5_target_inds : HDF5 Dataset indices of each dimension of the h5_target dataset key : String Name of attribute in h5_source that contains the Region Reference to copy Returns ------- ref_inds : Nx2x2 array of unsigned integers Array containing pairs of points that define the corners of each hyperslab in the region reference """ for param, param_name in zip([h5_source, h5_target, h5_source_inds, h5_target_inds], ['h5_source', 'h5_target', 'h5_source_inds', 'h5_target_inds']): if not isinstance(param, h5py.Dataset): raise TypeError(param_name + ' should be a h5py.Dataset object') if not isinstance(key, (str, unicode)): raise TypeError('key should be a string') key = key.strip() ''' Determine which dimension is missing from the target ''' lost_dim = [] for dim in h5_source_inds.attrs['labels']: if dim not in h5_target_inds.attrs['labels']: lost_dim.append(np.where(h5_source_inds.attrs['labels'] == dim)[0]) ref = h5_source.attrs[key] ref_inds = get_indices_for_region_ref(h5_source, ref, return_method='corners') ''' Convert to proper spectroscopic dimensions First is special case for a region reference that spans the entire dataset ''' if len(ref_inds.shape) == 2 and all(ref_inds[0] == [0, 0]) and all(ref_inds[1] + 1 == h5_source.shape): ref_inds[1, 1] = h5_target.shape[1] - 1 ref_inds = np.expand_dims(ref_inds, 0) else: ''' More common case of reference made of hyperslabs ''' spec_ind_zeroes = np.where(h5_source_inds[lost_dim] == 0)[1] ref_inds = ref_inds.reshape([-1, 2, 2]) for start, stop in ref_inds[:-1]: start[1] = np.where(start[1] == spec_ind_zeroes)[0] stop[1] = np.where(stop[1] == spec_ind_zeroes - 1)[0] - 1 ref_inds[-1, 0, 1] = np.where(ref_inds[-1, 0, 1] == spec_ind_zeroes)[0] stop = np.where(ref_inds[-1, 1, 1] == spec_ind_zeroes - 1)[0] if stop.size == 0: stop = len(spec_ind_zeroes) ref_inds[-1, 1, 1] = stop - 1 ''' Create the new reference from the indices ''' h5_target.attrs[key] = create_region_reference(h5_target, ref_inds) return ref_inds def create_region_reference(h5_main, ref_inds): """ Create a region reference in the destination dataset using an iterable of pairs of indices representing the start and end points of a hyperslab block Parameters ---------- h5_main : HDF5 dataset dataset the region will be created in ref_inds : Iterable index pairs, [start indices, final indices] for each block in the hyperslab Returns ------- new_ref : HDF5 Region reference reference in `h5_main` for the blocks of points defined by `ref_inds` """ if not isinstance(h5_main, h5py.Dataset): raise TypeError('h5_main should be a h5py.Dataset object') if not isinstance(ref_inds, Iterable): raise TypeError('ref_inds should be a list or tuple') h5_space = h5_main.id.get_space() h5_space.select_none() for start, stop in ref_inds: block = stop - start + 1 h5_space.select_hyperslab(tuple(start), (1, 1), block=tuple(block), op=1) if not h5_space.select_valid(): warn('Could not create new region reference.') return None new_ref = h5py.h5r.create(h5_main.id, b'.', h5py.h5r.DATASET_REGION, space=h5_space) return new_ref def simple_region_ref_copy(h5_source, h5_target, key): """ Copies a region reference from one dataset to another without alteration Parameters ---------- h5_source : HDF5 Dataset source dataset for region reference copy h5_target : HDF5 Dataset target dataset for region reference copy key : String Name of attribute in h5_source that contains the Region Reference to copy Returns ------- ref_inds : Nx2x2 array of unsigned integers Array containing pairs of points that define the corners of each hyperslab in the region reference """ for param, param_name in zip([h5_source, h5_target], ['h5_source', 'h5_target']): if not isinstance(param, h5py.Dataset): raise TypeError(param_name + ' should be a h5py.Dataset object') if not isinstance(key, (str, unicode)): raise TypeError('key should be a string') ref = h5_source.attrs[key] ref_inds = get_indices_for_region_ref(h5_source, ref, return_method='corners') ref_inds = ref_inds.reshape([-1, 2, 2]) ref_inds[:, 1, 1] = h5_target.shape[1] - 1 target_ref = create_region_reference(h5_target, ref_inds) h5_target.attrs[key] = target_ref return ref_inds def copy_all_region_refs(h5_source, h5_target): """ Copies only region references from the source dataset to the target dataset Parameters ---------- h5_source : h5py.Dataset Dataset from which to copy region references h5_target : h5py.Dataset Dataset to which to copy region references to """ if not isinstance(h5_source, h5py.Dataset): raise TypeError("'h5_source' should be a h5py.Dataset object") if not isinstance(h5_target, h5py.Dataset): raise TypeError("'h5_target' should be a h5py.Dataset object") for key in h5_source.attrs.keys(): if not isinstance(h5_source.attrs[key], h5py.RegionReference): continue simple_region_ref_copy(h5_source, h5_target, key) def write_region_references(h5_dset, reg_ref_dict, add_labels_attr=True, verbose=False): """ Creates attributes of a h5py.Dataset that refer to regions in the dataset Parameters ---------- h5_dset : h5.Dataset instance Dataset to which region references will be added as attributes reg_ref_dict : dict The slicing information must be formatted using tuples of slice objects . For example {'region_1':(slice(None, None), slice (0,1))} add_labels_attr : bool, optional, default = True Whether or not to write an attribute named 'labels' with the verbose : Boolean (Optional. Default = False) Whether or not to print status messages """ if not isinstance(reg_ref_dict, dict): raise TypeError('slices should be a dictionary but is instead of type ' '{}'.format(type(reg_ref_dict))) if not isinstance(h5_dset, h5py.Dataset): raise TypeError('h5_dset should be a h5py.Dataset object but is ' 'instead of type {}'.format(type(h5_dset))) if verbose: print('Starting to write Region References to Dataset', h5_dset.name, 'of shape:', h5_dset.shape) for reg_ref_name, reg_ref_tuple in reg_ref_dict.items(): if verbose: print('About to write region reference:', reg_ref_name, ':', reg_ref_tuple) reg_ref_tuple = clean_reg_ref(h5_dset, reg_ref_tuple, verbose=verbose) h5_dset.attrs[reg_ref_name] = h5_dset.regionref[reg_ref_tuple] if verbose: print('Wrote Region Reference:%s' % reg_ref_name) ''' Next, write these label names as an attribute called labels Now make an attribute called 'labels' that is a list of strings First ascertain the dimension of the slicing: ''' if add_labels_attr: found_dim = False dimen_index = None for key, val in reg_ref_dict.items(): if not isinstance(val, (list, tuple)): reg_ref_dict[key] = [val] for dimen_index, slice_obj in enumerate(list(reg_ref_dict.values())[0]): # We make the assumption that checking the start is sufficient if slice_obj.start is not None: found_dim = True break if found_dim: headers = [None] * len(reg_ref_dict) # The list that will hold all the names for col_name in reg_ref_dict.keys(): headers[reg_ref_dict[col_name][dimen_index].start] = col_name if verbose: print('Writing header attributes: {}'.format('labels')) # Now write the list of col / row names as an attribute: h5_dset.attrs['labels'] = clean_string_att(headers) else: warn('Unable to write region references for {}' ''.format(h5_dset.name.split('/')[-1])) if verbose: print('Wrote Region References of Dataset {}' ''.format(h5_dset.name.split('/')[-1]))sidpy-0.12.3/sidpy/io/000077500000000000000000000000001455261647000144765ustar00rootroot00000000000000sidpy-0.12.3/sidpy/io/__init__.py000066400000000000000000000001361455261647000166070ustar00rootroot00000000000000""" User interface utilities """ from . import interface_utils __all__ = ['interface_utils'] sidpy-0.12.3/sidpy/io/interface_utils.py000066400000000000000000000272741455261647000202440ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Utilities for user interfaces Created on Tue Aug 3 21:14:25 2020 @author: Gerd Duscher, Suhas Somnath, Chris Smith """ from __future__ import division, print_function, absolute_import, unicode_literals import os import sys import warnings import numpy as np import ipywidgets as widgets from IPython.display import display if sys.version_info.major == 3: unicode = str if sys.version_info.minor < 6: ModuleNotFoundError = ValueError class open_file_dialog(object): """Widget to select directories or widgets from a list Works in google colab. The widget converts the name of the nion file to the one in Nion's swift software, because it is otherwise incomprehensible Attributes ---------- dir_name: str name of starting directory extension: list of str extensions of files to be listed in widget Methods ------- get_directory set_options get_file_name Example ------- >>from google.colab import drive >>drive.mount("/content/drive") >>file_list = pyTEMlib.file_tools.FileWidget() next code cell: >>dataset = pyTEMlib.file_tools.open_file(file_list.file_name) """ def __init__(self, dir_name='.', extension=['*']): self.save_path = False self.dir_dictionary = {} self.dir_list = ['.', '..'] self.display_list = ['.', '..'] if os.path.isdir(dir_name): self.dir_name = dir_name else: self.dir_name = '.' self.get_directory(self.dir_name) self.dir_list = ['.'] self.extensions = extension self.file_name = '' self.select_files = widgets.Select( options=self.dir_list, value=self.dir_list[0], description='Select file:', disabled=False, rows=10, layout=widgets.Layout(width='70%') ) display(self.select_files) self.set_options() self.select_files.observe(self.get_file_name, names='value') def get_directory(self, directory=None): self.dir_name = directory self.dir_dictionary = {} self.dir_list = [] self.dir_list = ['.', '..'] + os.listdir(directory) def set_options(self): self.dir_name = os.path.abspath(os.path.join(self.dir_name, self.dir_list[self.select_files.index])) dir_list = os.listdir(self.dir_name) file_dict = update_directory_list(self.dir_name) sort = np.argsort(file_dict['directory_list']) self.dir_list = ['.', '..'] self.display_list = ['.', '..'] for j in sort: self.display_list.append(f" * {file_dict['directory_list'][j]}") self.dir_list.append(file_dict['directory_list'][j]) sort = np.argsort(file_dict['display_file_list']) for i, j in enumerate(sort): if '--' in dir_list[j]: self.display_list.append(f" {i:3} {file_dict['display_file_list'][j]}") else: self.display_list.append(f" {i:3} {file_dict['display_file_list'][j]}") self.dir_list.append(file_dict['file_list'][j]) self.dir_label = os.path.split(self.dir_name)[-1] + ':' self.select_files.options = self.display_list def get_file_name(self, b): if os.path.isdir(os.path.join(self.dir_name, self.dir_list[self.select_files.index])): self.set_options() elif os.path.isfile(os.path.join(self.dir_name, self.dir_list[self.select_files.index])): self.file_name = os.path.join(self.dir_name, self.dir_list[self.select_files.index]) def add_to_dict(file_dict, name): full_name = os.path.join(file_dict['directory'], name) basename, extension = os.path.splitext(name) size = os.path.getsize(full_name) * 2 ** -20 display_name = name if len(extension) == 0: display_file_list = f' {name} - {size:.1f} MB' elif extension[0] == 'hf5': if extension in ['.hf5']: display_file_list = f" {name} - {size:.1f} MB" else: display_file_list = f' {name} - {size:.1f} MB' file_dict[name] = {'display_string': display_file_list, 'basename': basename, 'extension': extension, 'size': size, 'display_name': display_name} def update_directory_list(directory_name): dir_list = os.listdir(directory_name) file_dict = {'directory': directory_name} # add new files file_dict['file_list'] = [] file_dict['display_file_list'] = [] file_dict['directory_list'] = [] for name in dir_list: if os.path.isfile(os.path.join(file_dict['directory'], name)): if name not in file_dict: add_to_dict(file_dict, name) file_dict['file_list'].append(name) file_dict['display_file_list'].append(file_dict[name]['display_string']) else: file_dict['directory_list'].append(name) return file_dict def check_ssh(): """ Checks whether or not the python kernel is running locally (False) or remotely (True) Returns ------- output : bool Whether or not the kernel is running over SSH (remote machine) Notes ----- When developing workflows that need to work on remote or virtual machines in addition to one's own personal computer such as a laptop, this function is handy at letting the developer know where the code is being executed Examples -------- >>> import sidpy >>> mode = sidpy.interface_utils.check_ssh() >>> print('Running on remote machine: {}'.format(mode)) """ return 'SSH_CLIENT' in os.environ or 'SSH_TTY' in os.environ def get_QT_app(): """ Starts pyQT app if not running Returns: QApplication ------- instance : ``QApplication.instance`` """ try: from PyQt5.Qt import QApplication except ImportError: raise ModuleNotFoundError('Required package PyQt5 not available') # start qt event loop _instance = QApplication.instance() if not _instance: # print('not_instance') _instance = QApplication([]) return _instance def openfile_dialog_qt(file_types="All files (*)", multiple_files=False, file_path='.', caption="Select a file..."): """ Opens a File dialog which is used in open_file() function This function uses pyQt5. Parameters ---------- file_types : str, optional. Default = all types of files accepted multiple_files : bool, optional. Default = False Whether or not multiple files can be selected file_path: str, optional. Default = '.' path to starting or root directory caption: str, optional. Default = "Select a file..." caption of the open file dialog Returns ------- filename : str full filename with absolute path and extension Notes ----- In jupyter notebooks use ``%gui Qt`` early in the notebook. Examples -------- >> import sidpy as sid >> filename = sid.io.openfile_dialog() >> print(filename) """ # Check whether QT is available try: from PyQt5 import QtGui, QtWidgets, QtCore except ImportError: raise ModuleNotFoundError('Required package PyQt5 not available') # try to find a parent the file dialog can appear on top try: get_QT_app() except: pass for param in [file_path, file_types, caption]: if param is not None: if not isinstance(param, (str, unicode)): raise TypeError('param must be a string') parent = None if multiple_files: func = QtWidgets.QFileDialog.getOpenFileNames fnames, file_filter = func(parent, caption, file_path, filter=file_types) if len(fnames) > 0: fname = fnames[0] else: return else: func = QtWidgets.QFileDialog.getOpenFileName fname, file_filter = func(parent, caption, file_path, filter=file_types) if multiple_files: return fnames else: return str(fname) def savefile_dialog_qt(initial_file='*.hf5', file_path='.', file_types=None, caption="Save file as ..."): """ Produces a window / dialog to allow users to specify the location and name of a file to save to. Parameters ---------- initial_file : str, optional. Default = ``*.hf5`` File extension? @gduscher to clarify file_path : str, optional. Default = '.' path to starting or root directory file_types : str, optional. Default = None Filters for kinds of files to display in the window caption: str, optional. Default = "Save file as..." caption of the save file dialog Returns ------- fname : str path to desired file Notes ----- In jupyter notebooks use ``%gui Qt`` early in the notebook. """ # Check whether QT is available try: from PyQt5 import QtGui, QtWidgets, QtCore except ImportError: raise ModuleNotFoundError('Required package PyQt5 not available') else: for param in [file_path, initial_file, caption]: if param is not None: if not isinstance(param, (str, unicode)): raise TypeError('param must be a string') if file_types is None: file_types = "All files (*)" try: get_QT_app() except: pass func = QtWidgets.QFileDialog.getSaveFileName fname, file_filter = func(None, caption, file_path + "/" + initial_file, filter=file_types) if len(fname) > 1: return fname else: return None # Compatibility, should be depreciated openfile_dialog_QT = openfile_dialog_qt savefile_dialog = savefile_dialog_qt try: from PyQt5 import QtWidgets class ProgressDialog(QtWidgets.QDialog): """ Simple dialog that consists of a Progress Bar and a Button. Clicking on the button results in the start of a timer and updates the progress bar. """ def __init__(self, title=''): super().__init__() self.initUI(title) def initUI(self, title): self.setWindowTitle('Progress Bar: ' + title) self.progress = QtWidgets.QProgressBar(self) self.progress.setGeometry(10, 10, 500, 50) self.progress.setMaximum(100) self.show() def set_value(self, count): self.progress.setValue(count) except ImportError: pass def progress_bar(title='Progress', start=0, stop=100): """ Opens a progress bar window Parameters ---------- title: str, optional. Default = 'Progress' Title for the progress window start: int, optional. Default = 0 Start value stop: int, optional. Default = 100 End value Returns ------- progress : QtWidgets.QProgressDialog Progress dialog Examples -------- >>> import sidpy >>> progress = sidpy.interface_utils.progress_bar('progress', 1,50) >>> for count in range(50): >>> progress.setValue(count) """ # Check whether QT is available warnings.warn("progress_bar() is deprecated; use tqdm package instead", warnings.DeprecationWarning) try: from PyQt5 import QtGui, QtWidgets, QtCore except ImportError: raise ModuleNotFoundError('Required package PyQt5 not available') try: get_QT_app() except: pass progress = QtWidgets.QProgressDialog(title, "Abort", 0, 100) progress.setWindowFlags(QtCore.Qt.WindowStaysOnTopHint) progress.show() return progress sidpy-0.12.3/sidpy/proc/000077500000000000000000000000001455261647000150325ustar00rootroot00000000000000sidpy-0.12.3/sidpy/proc/__init__.py000066400000000000000000000001221455261647000171360ustar00rootroot00000000000000""" Basic computational utilities """ from . import fitter __all__ = ['fitter'] sidpy-0.12.3/sidpy/proc/fitter.py000066400000000000000000000633341455261647000167120ustar00rootroot00000000000000""" :class:`~sidpy.proc.fitter.SidFitter` class that fits the specified dimension of a sidpy.dataset using the user-specified fit function. An extension of scipy.optimise.curve_fit that works on sidpy.dataset Created on Mar 9, 2022 @author: Rama Vasudevan, Mani Valleti """ from xml.dom import NotFoundErr from dask.distributed import Client import numpy as np import dask import inspect from ..sid import Dimension, Dataset from ..sid.dimension import DimensionType from ..viz.dataset_viz import SpectralImageFitVisualizer from ..sid.dataset import DataType try: from scipy.optimize import curve_fit except ImportError: curve_fit = None try: from sklearn.cluster import KMeans except ModuleNotFoundError: KMeans = None class SidFitter: # An extension of the Process Class for Functional Fitting def __init__(self, sidpy_dataset, fit_fn, xvec=None, ind_dims=None, guess_fn=None, num_fit_parms=None, km_guess=False, n_clus=None, return_cov=False, return_std=False, return_fit=False, fit_parameter_labels=None, num_workers=2, threads=2): """ Parameters ---------- sidpy_dataset: (sidpy.Dataset) Sidpy dataset object to be fit fit_fn: (function) Function used for fitting. Should take xvec as the first argument and parameters as the rest of the arguments. Should return the function value at each of the points in the xvec xvec: (numpy ndarray or list of numpy ndarrays) (Optional) Independent variable for fitting. Should be an array If NOT provided, the dimension arrays are assumed to be xvecs ind_dims: (tuple) (Optional) Tuple with integer entries of the dimensions over which to parallelize. These should be the independent variable for the fitting. If NOT provided, it is assumed that all the non-spectral dimensions are independent dimensions. guess_fn: (function) (optional) This optional function should be utilized to generate priors for the full fit It takes (xvec,yvec) as inputs and should return the fit parameters. If the guess_fn is NOT provided, then the user MUST input the num_fit_parms. num_fit_parms: (int) Number of fitting parameters. This is needed IF the guess function is not provided to set the priors for the parameters for the curve_fit function. km_guess: (bool) (default False) When set to True: Divides the spectra into clusters using sklearn.optimize.kMeans, applies the fitting function on the cluster centers, uses the results as priors to each spectrum of the cluster. n_clus: (int) (default None) Used only when km_guess is set to True. Determines the number of clusters to be formed for sklearn.optimize.kmeans. If not provided then n_clus = self.num_computations/100 return_std: (bool) (default False) Returns the dataset with estimated standard deviation of the parameter values. Square roots of the diagonal of the covariance matrix. return_cov: (bool) (default False) Returns the estimated covariance of fitting parameters. Confer scipy.optimize.curve_fit for further details return_fit: (bool) (default False) Returns the fitted sidpy dataset using the optimal parameters when set to true fit_parameter_labels: (list) (default None) List of parameter labels num_workers: (int) (default =2) Number of workers to use when setting up Dask client threads: (int) (default =2) Number of threads to use when setting up Dask client Returns: ------- sidpy.dataset: if return_cov and return_fit are both set to False List: containing sidpy.dataset objects, if either of return_cov or return fit is set to True If multiple datasets are expected, the order of the returned datasets is [sidpy.dataset with mean parameter values, sidpy.dataset with estimated covariances of the fitting parameters, sidpy.dataset that is fit with the parameters obtained after fitting] """ if guess_fn is None: if num_fit_parms is None: raise ValueError("You did not supply a guess function, you must at least provide number of fit " "parameters to set the priors for scipy.optimize.curve_fit") self.dataset = sidpy_dataset # Sidpy dataset self.fit_fn = fit_fn # function that takes xvec, *parameters and returns yvec at each value of xvec self.num_fit_parms = num_fit_parms # int: number of fitting parameters self._complex_data = False # if data is complex. Will be checked during guess/fit as needed. if ind_dims is not None: self.ind_dims = tuple(ind_dims) # Tuple: containing indices of independent dimensions else: # All the dimensions that are not spectral will be considered as independent dimensions ind_dims = [] for i, dim in self.dataset._axes.items(): if dim.dimension_type != DimensionType.SPECTRAL: ind_dims.extend([i]) self.ind_dims = tuple(ind_dims) # Make sure there is at least one spectral dimension if len(self.ind_dims) == len(self.dataset.shape): raise NotImplementedError('No Spectral (dependent) dimensions found to fit') # Let's get the dependent dims here dep_dims = [] # Tuple: contains all the dependent dimensions. ind_dims+dep_dims = all_dims for d in np.arange(len(self.dataset.shape)): if d not in self.ind_dims: dep_dims.extend([d]) self.dep_dims = tuple(dep_dims) # xvec is not provided if xvec is None: # 1D fit if len(self.dep_dims) == 1: dep_vec = np.array(self.dataset._axes[self.dep_dims[0]]) # Multidimensional fit else: dep_vec = [] for d in self.dep_dims: dep_vec.append(np.array(self.dataset._axes[d])) # xvec is provided if xvec is not None: # 1D fit if len(self.dep_dims) == 1: if isinstance(xvec, np.ndarray): dep_vec = xvec elif isinstance(xvec, list): dep_vec = np.array(xvec) else: raise TypeError('Please provide a np.ndarray or a list of independent vector values') # Multidimensional fit else: if isinstance(xvec, list) and len(xvec) == len(self.dep_dims): dep_vec = xvec elif isinstance(xvec, list) and len(xvec) != len(self.dep_dims): raise ValueError('The number of independent dimensions provided in the xvec do not match ' 'with the number of dependent dimensions of the dataset') else: raise TypeError('Please provide a list of value-arrays corresponding to each dependent dimension') # Dealing with the meshgrid part of multidimensional fitting if len(self.dep_dims) > 1: self.dep_vec = [ar.ravel() for ar in np.meshgrid(*dep_vec, indexing='ij')] else: self.dep_vec = dep_vec self.km_guess = km_guess if self.km_guess: self.km_priors = None self.km_labels = None self.n_clus = n_clus self._setup_calc() self.guess_fn = guess_fn self.prior = None # shape = [num_computations, num_fitting_parms] self.fit_labels = fit_parameter_labels self.num_workers = num_workers self.threads = threads self.guess_completed = False self.return_std = return_std self.return_cov = return_cov self.return_fit = return_fit self.fitted_dset = None self.mean_fit_results = [] if self.return_cov: self.cov_fit_results = None if self.return_std: self.std_fit_results = None if 'complex' in self.dataset.dtype.name: self._complex_data = True # set up dask client self.client = Client(threads_per_worker=self.threads, n_workers=self.num_workers) def _setup_calc(self): self.fold_order = [[]] # All the independent dimensions go into the first element and will be collapsed self.num_computations = 1 # Here we have to come up with a way that treats the spatial dimensions as the independent dimensions # In other words make the argument 'ind_dims' optional # if self.ind_dims is not None: for i in np.arange(self.dataset.ndim): if i in self.ind_dims: self.fold_order[0].extend([i]) self.num_computations *= self.dataset.shape[i] else: self.fold_order.append([i]) self.folded_dataset = self.dataset.fold(dim_order=self.fold_order) self.folded_dataset_numpy = np.array(self.folded_dataset) self.dep_vec = np.array(self.dep_vec) # Here is the tricky part, dataset.unfold is designed to get back the original dataset with minimal loss of # information. To do this, unfold utilizes the saved information while folding the original dataset. # Here, we are going to tweak that information and use the unfold method on the dataset with fitted parameters. self._unfold_attr = { 'dim_order_flattened': list(np.arange(len(self.fold_order[0]))) + [len(self.fold_order[0])], 'shape_transposed': [self.dataset.shape[i] for i in self.fold_order[0]] + [-1]} axes, j = {}, 0 for i, dim in self.dataset._axes.items(): if not i in self.dep_dims: axes[j] = dim j += 1 self._unfold_attr['_axes'] = axes def do_guess(self): """ If a guess_fn is provided: Applies the guess_fn to get priors for the fitting parameters. self.prior is set as the output of guess function at each of the ind_dims Returns: None ------- """ guess_results = [] for ind in range(self.num_computations): ydata = self.folded_dataset_numpy lazy_result = dask.delayed(self.guess_fn)(self.dep_vec, ydata[ind, :]) guess_results.append(lazy_result) guess_results = dask.compute(*guess_results) self.prior = np.squeeze(np.array(guess_results)) self.num_fit_parms = self.prior.shape[-1] self.guess_completed = True def do_fit(self, **kwargs): """ Perform the fit. **kwargs: extra parameters passed to scipy.optimize.curve_fit, e.g. bounds, type of lsq algorithm, etc. """ if self.guess_fn is not None: guess_function_str = inspect.getsource(self.guess_fn) else: guess_function_str = 'Not Provided' fit_results = [] if not self.km_guess: if not self.guess_completed and self.guess_fn is not None: self.do_guess() for ind in range(self.num_computations): if self.prior is None: p0 = np.random.normal(loc=0.5, scale=0.1, size=self.num_fit_parms) else: p0 = self.prior[ind, :] ydata = self.folded_dataset_numpy[ind, :] if self._complex_data: ydata = np.array(np.hstack([np.real(ydata), np.imag(ydata)])) lazy_result = dask.delayed(SidFitter.default_curve_fit)(self.fit_fn, self.dep_vec, ydata, self.num_fit_parms, return_cov=(self.return_cov or self.return_std), p0=p0, **kwargs) fit_results.append(lazy_result) fit_results_comp = dask.compute(*fit_results) self.client.close() else: self.get_km_priors(**kwargs) for ind in range(self.num_computations): ydata = self.folded_dataset_numpy[ind, :] if self._complex_data: #ydata = ydata.flatten_complex() ydata = np.array(np.hstack([np.real(ydata), np.imag(ydata)])) lazy_result = dask.delayed(SidFitter.default_curve_fit)(self.fit_fn, self.dep_vec, ydata, self.num_fit_parms, return_cov=(self.return_cov or self.return_std), p0=self.km_priors[self.km_labels[ind]], **kwargs) fit_results.append(lazy_result) fit_results_comp = dask.compute(*fit_results) self.client.close() if self.return_cov or self.return_std: # here we get back both: the parameter means and the covariance matrix! self.mean_fit_results = np.squeeze( np.array([fit_results_comp[ind][0] for ind in range(len(fit_results_comp))])) self.cov_fit_results = np.squeeze( np.array([fit_results_comp[ind][1] for ind in range(len(fit_results_comp))])) else: # in this case we can just dump it to an array because we only got the parameters back self.mean_fit_results = np.squeeze(np.array(fit_results_comp)) # Here we have either the mean fit results or both mean and cov arrays. We make 2 sidpy dataset out of them # Make a sidpy dataset mean_sid_dset = Dataset.from_array(self.mean_fit_results, title='Fitting_Map') mean_sid_dset.metadata['fold_attr'] = self._unfold_attr.copy() mean_sid_dset = mean_sid_dset.unfold() # Set the data type mean_sid_dset.data_type = 'image_stack' # We may want to pass a new type - fit map # We set the last dimension, i.e., the dimension with the fit parameters fit_dim = Dimension(np.arange(self.num_fit_parms), name='fit_parms', units='a.u.', quantity='fit_parameters', dimension_type='temporal') mean_sid_dset.set_dimension(len(mean_sid_dset.shape) - 1, fit_dim) fit_parms_dict = {'fit_parameters_labels': self.fit_labels, 'fitting_function': inspect.getsource(self.fit_fn), 'guess_function': guess_function_str, 'ind_dims': self.ind_dims } mean_sid_dset.metadata = self.dataset.metadata.copy() mean_sid_dset.metadata['fit_parms_dict'] = fit_parms_dict.copy() mean_sid_dset.original_metadata = self.dataset.original_metadata.copy() cov_sid_dset, std_fit_dset, fit_dset = None, None, None # Here we deal with the covariance dataset if self.return_cov: # Make a sidpy dataset cov_sid_dset = Dataset.from_array(self.cov_fit_results, title='Fitting_Map_Covariance') fold_attr = self._unfold_attr.copy() fold_attr['dim_order_flattened'] = fold_attr['dim_order_flattened'] + [ len(fold_attr['dim_order_flattened'])] fold_attr['shape_transposed'] = fold_attr['shape_transposed'][:-1] + [self.num_fit_parms] + \ [self.num_fit_parms] cov_sid_dset.metadata['fold_attr'] = fold_attr cov_sid_dset = cov_sid_dset.unfold() # Set the data type cov_sid_dset.data_type = 'IMAGE_4D' # We may want to pass a new type - fit map cov_dims = [Dimension(np.arange(self.num_fit_parms), name='fit_cov_parms_x', units='a.u.', quantity='fit_cov_parameters', dimension_type='spectral'), Dimension(np.arange(self.num_fit_parms), name='fit_cov_parms_y', units='a.u.', quantity='fit_cov_parameters', dimension_type='spectral')] for i, dim in enumerate(cov_dims): cov_sid_dset.set_dimension(i - 2 + len(cov_sid_dset.shape), dim) cov_sid_dset.metadata = self.dataset.metadata.copy() cov_sid_dset.metadata['fit_parms_dict'] = fit_parms_dict.copy() cov_sid_dset.original_metadata = self.dataset.original_metadata.copy() # Here is the std_dev dataset if self.return_std: self.std_fit_results = np.diagonal(self.cov_fit_results, axis1=-2, axis2=-1) std_fit_dset = Dataset.from_array(self.std_fit_results, title='Fitting_Map_std_dev') std_fit_dset.metadata['fold_attr'] = self._unfold_attr.copy() std_fit_dset = std_fit_dset.unfold() # Set the data type std_fit_dset.data_type = 'image_stack' # We may want to pass a new type - fit map # We set the last dimension, i.e., the dimension with the fit parameters fit_dim = Dimension(np.arange(self.num_fit_parms), name='std_dev', units='a.u.', quantity='std_dev_fit_parms', dimension_type='temporal') std_fit_dset.set_dimension(len(std_fit_dset.shape) - 1, fit_dim) std_fit_dset.metadata = self.dataset.metadata.copy() std_fit_dset.metadata['fit_parms_dict'] = fit_parms_dict.copy() std_fit_dset.original_metadata = self.dataset.original_metadata.copy() # Fitted dset if self.return_fit: fit_dset = self.get_fitted_dataset() fit_dset.metadata['fit_parms_dict'] = fit_parms_dict.copy() results = [mean_sid_dset, cov_sid_dset, std_fit_dset, fit_dset] inds = [True, self.return_cov, self.return_std, self.return_fit] results = [results[i] for i in range(len(inds)) if inds[i]] if len(results) == 0: return results[0] else: return results def get_fitted_dataset(self): """This method returns the fitted dataset using the parameters generated by the fit function""" fitted_dset = self.dataset.like_data(np.zeros_like(self.dataset.compute()), title_prefix='fitted_') fitted_dset_fold = fitted_dset.fold(dim_order=self.fold_order) output_shape = np.prod(fitted_dset_fold.shape[1:]) user_folding = False ydata_fit = self.fit_fn(self.dep_vec, *self.mean_fit_results[0]) # print(r"ydata shape is {} and squeezed is {}".format(ydata_fit.shape, ydata_fit.squeeze().shape)) if ydata_fit.squeeze().shape[0] != output_shape: print('Shapes of output of fitting function is {} and original data is {} \ Reshaping output dataset. You are responsible for reshaping'.format(ydata_fit.shape[0], output_shape, )) fitted_dset_fold = self.dataset.like_data(np.zeros((fitted_dset_fold.shape[0], ydata_fit.shape[0])), title_prefix='fitted_') user_folding = True # Here we make a roundtrip to numpy as earlier versions of dask did not support the assignments # of the form dask_array[2] = 1 np_folded_arr = fitted_dset_fold.compute() for i in range(np_folded_arr.shape[0]): # ydata_fit = self.fit_fn(self.dep_vec, *self.mean_fit_results[i]) # print('dep vec is {} and mean fit results are {}'.format(self.dep_vec,self.mean_fit_results[i])) fit_output = self.fit_fn(self.dep_vec, *self.mean_fit_results[i]) # print('ydata output from fitting fn is {}'.format(fit_output)) if fit_output.shape != np_folded_arr[i].shape: try: np_folded_arr[i] = fit_output.reshape(np_folded_arr[i].shape) except: print("Cannot reshape function output to retrieve fitted dataset") else: np_folded_arr[i] = fit_output if not user_folding: fitted_sid_dset_folded = fitted_dset_fold.like_data(np_folded_arr, title=fitted_dset_fold.title) fitted_sid_dset = fitted_sid_dset_folded.unfold() fitted_sid_dset.original_metadata = self.dataset.original_metadata.copy() else: fitted_sid_dset = fitted_dset_fold.like_data(np_folded_arr, title=fitted_dset_fold.title) fitted_sid_dset.original_metadata = self.dataset.original_metadata.copy() self.fitted_dset = fitted_sid_dset return fitted_sid_dset def get_km_priors(self, **kwargs): kwargs['maxfev'] = 100 # give a large number of tries for fitting the kmeans cluster centers shape = self.folded_dataset.shape # We get the shape of the folded dataset # Our prior_dset will have the same shape except for the last dimension whose size will be equal to number of # fitting parameters dim_order = [[0], [i + 1 for i in range(len(shape) - 1)]] # We are using the fold function in case we have a multidimensional fit. # In that case we need all the spectral dimensions collapsed into a single dimension for kMeans # In case of a 1D fit the next line essentially does nothing. km_dset = self.folded_dataset.fold(dim_order) if self._complex_data: print('Warning: complex dataset detected. For Kmeans priors, we will treat real part only') km_dset = km_dset.real if KMeans is None: raise ModuleNotFoundError("sklearn is not installed") else: if self.n_clus is None: self.n_clus = int(self.num_computations / 100) km = KMeans(n_clusters=self.n_clus, random_state=0).fit(km_dset.compute()) self.km_labels, self.km_centers = km.labels_, km.cluster_centers_ if self._complex_data: km_dset = np.array(self.folded_dataset.fold(dim_order)) self.km_centers = [] # in the case of complex data, the centers have to be recomputed based on the labels for ind_l in range(self.n_clus): cent = km_dset[self.km_labels == ind_l, :] centroid = cent.real.mean(axis=0) + 1j*cent.imag.mean(axis=0) self.km_centers.append(centroid) self.km_centers = np.array(self.km_centers) print('---Finished KMeans, onto fiting each KM Center---') km_priors = [] for i, cen in enumerate(self.km_centers): print('Fitting center {}'.format(i)) num_start = 100 #number of times to restart the fit. For now this is fixed. if self.guess_fn is not None: p0 = self.guess_fn(self.dep_vec, cen) else: p0 = np.random.normal(loc=0.5, scale=0.1, size=self.num_fit_parms) if self._complex_data: cen = np.hstack([np.real(cen), np.imag(cen)]) residuals = [] for _ in range(num_start): popt = SidFitter.default_curve_fit(self.fit_fn, self.dep_vec, cen, self.num_fit_parms, return_cov=False, p0 = p0, **kwargs) temp_fit = self.fit_fn(self.dep_vec, *popt) #temp_fit = temp_fit[:len(temp_fit)//2] + 1j* temp_fit[len(temp_fit)//2 :] #temp_fit = np.hstack([np.real(cen), np.imag(cen)]) #print(cen, temp_fit, cen.shape, temp_fit.shape) resid = cen - temp_fit resid_ss = np.sum(np.abs(resid@resid)) residuals.append((popt, resid_ss)) residuals = np.array(residuals, dtype = object) self.residuals = residuals min_idx = np.argmin(residuals[:,1]) best_popt = residuals[min_idx,0] km_priors.append(best_popt) self.km_priors = np.array(km_priors) self.num_fit_parms = self.km_priors.shape[-1] def visualize_fit_results(self, figure=None, horizontal=True): ''' Calls the interactive visualizer for comparing raw and fit datasets. Inputs: - figure: (Optional, default None) - handle to existing figure - horiziontal: (Optional, default True) - whether spectrum should be plotted horizontally ''' dset_type = self.dataset.data_type supported_types = ['SPECTRAL_IMAGE'] if self.fitted_dset == None: raise NotFoundErr("No fitted dataset found. Re-run with return_fit=True to use this feature") if dset_type == DataType.SPECTRAL_IMAGE: visualizer = SpectralImageFitVisualizer(self.dataset, self.fitted_dset, figure=figure, horizontal=horizontal) else: raise NotImplementedError( "Data type is {} but currently we only support types {}".format(dset_type, supported_types)) return visualizer @staticmethod def default_curve_fit(fit_fn, xvec, yvec, num_fit_parms, return_cov=True, **kwargs): yvec = np.array(yvec).ravel() if curve_fit is None: raise ModuleNotFoundError("scipy is not installed") else: try: popt, pcov = curve_fit(fit_fn, xvec, yvec, **kwargs) except: popt = np.zeros(num_fit_parms) pcov = np.zeros((num_fit_parms, num_fit_parms)) if return_cov: return popt, pcov else: return popt sidpy-0.12.3/sidpy/sid/000077500000000000000000000000001455261647000146465ustar00rootroot00000000000000sidpy-0.12.3/sidpy/sid/__init__.py000066400000000000000000000005261455261647000167620ustar00rootroot00000000000000""" Spectroscopy and Imaging Data related classes """ from .dimension import Dimension, DimensionType from .translator import Translator from .dataset import Dataset, DataType, convert_hyperspy from .reader import Reader __all__ = ['Dimension', 'DimensionType', 'Dataset', 'DataType', 'Reader', 'Translator', 'convert_hyperspy'] sidpy-0.12.3/sidpy/sid/dataset.py000066400000000000000000002310201455261647000166430ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Abstract :class:`~sidpy.io.dataset.Dataset` base-class Created on Tue Nov 3 15:07:16 2015 @author: Gerd Duscher Modified by Mani Valleti. Look up dask source code to understand how numerical functions are implemented starting code from: https://scikit-allel.readthedocs.io/en/v0.21.1/_modules/allel/model/dask.html """ from __future__ import division, print_function, absolute_import, unicode_literals from hashlib import new from functools import wraps from re import A import sys from collections.abc import Iterable, Iterator, Mapping import warnings import ase import dask.array.core import numpy as np import matplotlib.pylab as plt import string import dask.array as da import h5py from enum import Enum from numbers import Number from .dimension import Dimension, DimensionType from ..base.num_utils import get_slope from ..base.dict_utils import print_nested_dict from ..viz.dataset_viz import CurveVisualizer, ImageVisualizer, ImageStackVisualizer from ..viz.dataset_viz import SpectralImageVisualizer, FourDimImageVisualizer, ComplexSpectralImageVisualizer from ..viz.dataset_viz import PointCloudVisualizer # from ..hdf.hdf_utils import is_editable_h5 from .dimension import DimensionType from copy import deepcopy, copy from sidpy.base.string_utils import validate_single_string_arg import logging def is_simple_list(lst): if isinstance(lst, list): return any(hasattr(item, '__getitem__') for item in lst) return False class DataType(Enum): UNKNOWN = -1 SPECTRUM = 1 LINE_PLOT = 2 LINE_PLOT_FAMILY = 3 IMAGE = 4 IMAGE_MAP = 5 IMAGE_STACK = 6 # 3d SPECTRAL_IMAGE = 7 IMAGE_4D = 8 POINT_CLOUD = 9 def view_subclass(dask_array, cls): """ View a dask Array as an instance of a dask Array sub-class. Parameters ---------- dask_array cls Returns ------- cls: sidpy.Dataset """ return cls(dask_array.dask, name=dask_array.name, chunks=dask_array.chunks, dtype=dask_array.dtype, shape=dask_array.shape) class Dataset(da.Array): """ ..autoclass::Dataset To instantiate from an existing array-like object, use :func:`Dataset.from_array` - requires numpy array, list or tuple This dask array is extended to have the following attributes: -data_type: DataTypes ('image', 'image_stack', spectral_image', ... -units: str -quantity: str what kind of data ('intensity', 'height', ..) -title: title of the data set -modality: character of data such as 'STM, 'AFM', 'TEM', 'SEM', 'DFT', 'simulation', ..) -source: origin of data such as acquisition instrument ('Nion US100', 'VASP', ..) -_axes: dictionary of Dimensions one for each data dimension (the axes are dimension datasets with name, label, units, and 'dimension_type' attributes). -metadata: dictionary of additional metadata -original_metadata: dictionary of original metadata of file, -labels: returns labels of all dimensions. -data_descriptor: returns a label for the colorbar in matplotlib and such functions: -from_array(data, title): constructs the dataset form an array like object (numpy array, dask array, ...) -like_data(data,title): constructs the dataset form an array like object and copies attributes and metadata from parent dataset -copy() -plot(): plots dataset dependent on data_type and dimension_types. -get_extent(): extent to be used with imshow function of matplotlib -set_dimension(axis, dimensions): set a Dimension to a specific axis -rename_dimension(dimension, name): renames attribute of dimension -view_metadata: pretty plot of metadata dictionary -view_original_metadata: pretty plot of original_metadata dictionary """ def __init__(self, *args, **kwargs): """ Initializes Dataset object which is essentially a Dask array underneath Attributes ---------- self.quantity : str Physical quantity. E.g. - current self.units : str Physical units. E.g. - amperes self.data_type : enum Type of data such as Image, Spectrum, Spectral Image etc. self.title : str Title for Dataset self._structures : dict dictionary of ase.Atoms objects to represent structures, can be given a name self.view : Visualizer Instance of class appropriate for visualizing this object self.data_descriptor : str Description of this dataset self.modality : str character of data such as 'STM', 'TEM', 'DFT' self.source : str Source of this dataset. Such as instrument, analysis, etc.? self.h5_dataset : h5py.Dataset Reference to HDF5 Dataset object from which this Dataset was created self._axes : dict Dictionary of Dimension objects per dimension of the Dataset self.meta_data : dict Metadata to store relevant additional information for the dataset. self.original_metadata : dict Metadata from the original source of the dataset. This dictionary often contains the vendor-specific metadata or internal attributes of the analysis algorithm """ # TODO: Consider using python package - pint for quantities super().__init__() self._units = '' self._quantity = '' self._title = '' self._data_type = DataType.UNKNOWN self._modality = '' self._source = '' self._structures = {} self._h5_dataset = None self._metadata = {} self._original_metadata = {} self._axes = {} self.view = None # this will hold the figure and axis reference for a plot self.__protected = set() # a set to keep track of protected attributes self.point_cloud = None # attribute to store coordinates and base_image for point_cloud datatype self._variance = None # to save variance dask.array def __repr__(self): rep = 'sidpy.Dataset of type {} with:\n '.format(self.data_type.name) rep = rep + super(Dataset, self).__repr__() rep = rep + '\n data contains: {} ({})'.format(self.quantity, self.units) rep = rep + '\n and Dimensions: ' for key in self._axes: rep = rep + '\n' + self._axes[key].__repr__() if hasattr(self, 'metadata'): if len(self.metadata) > 0: rep = rep + '\n with metadata: {}'.format(list(self.metadata.keys())) return rep def hdf_close(self): if self.h5_dataset is not None: self.h5_dataset.file.close() print(self.h5_dataset) def __setattr__(self, key, value): if not hasattr(self, '_Dataset__protected'): super().__setattr__(key, value) else: # if key is in __protected, only Dimension and numpy.ndarray instances are allowed to be set if key != 'none' and key in self._Dataset__protected: if not isinstance(value, Dimension): raise AttributeError('The attribute "{}" is reserved to represent a dimension'.format(key)) else: if getattr(self, key).name == value.name and len(getattr(self, key)) == len(value): cur_ind = [i for i in self._axes if self._axes[i].name == key][0] self.del_dimension(cur_ind) self._axes[cur_ind] = value self.__dict__[key] = value self.__dict__['dim_{}'.format(cur_ind)] = value self.__protected.add(key) self.__protected.add('dim_{}'.format(cur_ind)) else: raise NotImplementedError("The new dimension's name or length does not " "match with the existing dimension.") else: super().__setattr__(key, value) @classmethod def from_array(cls, x, title='generic', chunks='auto', lock=False, datatype='UNKNOWN', units='generic', quantity='generic', modality='generic', source='generic', coordinates=None, variance=None, **kwargs): """ Initializes a sidpy dataset from an array-like object (i.e. numpy array) All meta-data will be set to be generically. Parameters ---------- x: array-like object the values which will populate this dataset chunks: optional integer or list of integers the shape of the chunks to be loaded title: optional string the title of this dataset lock: boolean datatype: str or sidpy.DataType data type of set: i.e.: 'image', spectrum', .. units: str units of dataset i.e. counts, A quantity: str quantity of dataset like intensity modality: str modality of dataset like source: str source of dataset like what kind of microscope or function coordinates: numpy array, optional coordinates for point cloud point_cloud: dict or None dict with coordinates and base_image for point_cloud data_type variance: array-like object the variance values of the x array Returns ------- sidpy dataset """ # create vanilla dask array if isinstance(x, da.Array) and not np.any(np.isnan(x.shape)): dask_array = x else: dask_array = da.from_array(np.array(x), chunks=chunks, lock=lock) # view as subclass sid_dataset = view_subclass(dask_array, cls) sid_dataset.data_type = datatype sid_dataset.units = units sid_dataset.title = title sid_dataset.quantity = quantity sid_dataset.modality = modality sid_dataset.source = source sid_dataset._axes = {} for dim in range(sid_dataset.ndim): # TODO: add parent to dimension to set attribute if name changes sid_dataset.set_dimension(dim, Dimension(np.arange(sid_dataset.shape[dim]), string.ascii_lowercase[dim])) sid_dataset.metadata = {} sid_dataset.original_metadata = {} sid_dataset.variance = variance # add coordinates for point_cloud datatype if coordinates is not None: sid_dataset.point_cloud = {'coordinates': coordinates} else: sid_dataset.point_cloud = None return sid_dataset def like_data(self, data, title=None, chunks='auto', lock=False, coordinates=None, variance=None, **kwargs): """ Returns sidpy.Dataset of new values but with metadata of this dataset - if dimension of new dataset is different from this dataset and the scale is linear, then this scale will be applied to the new dataset (naming and units will stay the same), otherwise the dimension will be generic. -Additional functionality to override numeric functions Parameters ---------- data: array like values of new sidpy dataset title: optional string title of new sidpy dataset chunks: optional list of integers size of chunks for dask array lock: optional boolean for dask array coordinates: array like coordinates for point cloud variance: numpy array, optional variance of dataset Returns ------- sidpy dataset """ title_suffix = kwargs.get('title_suffix', '') title_prefix = kwargs.get('title_prefix', '') reset_quantity = kwargs.get('reset_quantity', False) reset_units = kwargs.get('reset_units', False) checkdims = kwargs.get('checkdims', True) # if coordinates is None: # coordinates = self.point_cloud['coordinates'] new_data = self.from_array(data, chunks=chunks, lock=lock, variance =variance) new_data.data_type = self.data_type # if variance is None: # if new_data.shape == self.shape: # new_data.variance = self.variance # units if reset_units: new_data.units = 'generic' else: new_data.units = self.units if title is not None: new_data.title = title else: if title_prefix or title_suffix: new_data.title = self.title else: new_data.title = self.title + '_new' new_data.title = title_prefix + new_data.title + title_suffix # quantity if reset_quantity: new_data.quantity = 'generic' else: new_data.quantity = self.quantity new_data.modality = self.modality new_data.source = self.source if checkdims: for dim in range(new_data.ndim): # TODO: add parent to dimension to set attribute if name changes if len(self._axes[dim].values) == new_data.shape[dim]: new_data.set_dimension(dim, self._axes[dim]) else: # assuming the axis scale is equidistant try: scale = get_slope(self._axes[dim]) # axis = self._axes[dim].copy() axis = Dimension(np.arange(new_data.shape[dim]) * scale, self._axes[dim].name) axis.quantity = self._axes[dim].quantity axis.units = self._axes[dim].units axis.dimension_type = self._axes[dim].dimension_type new_data.set_dimension(dim, axis) except ValueError: print('using generic parameters for dimension ', dim) new_data.metadata = dict(self.metadata).copy() new_data.original_metadata = {} return new_data def __reduce_dimensions(self, new_dataset, axes, keepdims=False): new_dataset.del_dimension() if not keepdims: i = 0 for key, dim in self._axes.items(): new_dim = dim.copy() if key not in axes: new_dataset.set_dimension(i, new_dim) i += 1 if keepdims: for key, dim in self._axes.items(): new_dim = dim.copy() if key in axes: new_dim = Dimension(np.arange(1), name=new_dim.name, quantity=new_dim.quantity, units=new_dim.units, dimension_type=new_dim.dimension_type) new_dataset.set_dimension(key, new_dim) return new_dataset def __rearrange_axes(self, new_dataset, new_order=None): """Rearranges the dimension order of the current instance Parameters: new_order: list or tuple of integers All the dimensions that are not in the new_order are deleted """ new_dataset.del_dimension() for i, dim in enumerate(new_order): new_dataset.set_dimension(i, self._axes[dim]) return new_dataset def copy(self): """ Returns a deep copy of this dataset. Returns ------- sidpy dataset """ dataset_copy = Dataset.from_array(self, self.title, self.chunks) dataset_copy.title = self.title dataset_copy.units = self.units dataset_copy.quantity = self.quantity dataset_copy.data_type = self.data_type dataset_copy.modality = self.modality dataset_copy.source = self.source dataset_copy.point_cloud = self.point_cloud dataset_copy.variance = self.variance dataset_copy.del_dimension() for dim in self._axes: dataset_copy.set_dimension(dim, self._axes[dim]) dataset_copy.metadata = dict(self.metadata).copy() return dataset_copy def __validate_dim(self, ind, name): """ Validates the provided index for a Dimension object Parameters ---------- ind : int Index of the dimension Raises ------- TypeError : if ind is not an integer IndexError : if ind is less than 0 or greater than maximum allowed index for Dimension ValueError: if name is not 'none' and is already used. """ if not isinstance(ind, int): raise TypeError('Dimension must be an integer') if (0 > ind) or (ind >= self.ndim): raise IndexError('Dimension must be an integer between 0 and {}' ''.format(self.ndim - 1)) for key, dim in self._axes.items(): if key != ind: if name != 'none' and name == dim.name: raise ValueError('name: {} already used, but must be unique'.format(name)) def rename_dimension(self, ind, name): """ Renames Dimension at the specified index Parameters ---------- ind : int Index of the dimension name : str New name for Dimension """ self.__validate_dim(ind, name) if not isinstance(name, str): raise TypeError('New Dimension name must be a string') if hasattr(self, self._axes[ind].name): delattr(self, self._axes[ind].name) if self._axes[ind].name in self.__protected: self.__protected.remove(self._axes[ind].name) if hasattr(self, 'dim_{}'.format(ind)): delattr(self, 'dim_{}'.format(ind)) self.__protected.remove('dim_{}'.format(ind)) self._axes[ind]._name = validate_single_string_arg(name, 'name') # protected attribute name setattr(self, name, self._axes[ind]) self.__protected.add(name) setattr(self, 'dim_{}'.format(ind), self._axes[ind]) self.__protected.add('dim_{}'.format(ind)) def set_dimension(self, ind, dimension): """ sets the dimension for the dataset including new name and updating the axes dictionary Parameters ---------- ind: int Index of dimension dimension: sidpy.Dimension Dimension object describing this dimension of the Dataset Returns ------- """ if not isinstance(dimension, Dimension): raise TypeError('dimension needs to be a sidpy.Dimension object') self.__validate_dim(ind, dimension.name) if len(dimension.values) != self.shape[ind]: raise ValueError('The length of the dimension array does not match the shape of the ' 'dataset at {}th dimension. {} != {}'.format(ind, len(dimension.values), self.shape[ind]) ) dim = dimension.copy() try: if hasattr(self, self._axes[ind].name): delattr(self, self._axes[ind].name) if self._axes[ind].name in self.__protected: self.__protected.remove(self._axes[ind].name) except KeyError: pass setattr(self, dimension.name, dim) self.__protected.add(dimension.name) if hasattr(self, 'dim_{}'.format(ind)): delattr(self, 'dim_{}'.format(ind)) if 'dim_{}'.format(ind) in self.__protected: self.__protected.remove('dim_{}'.format(ind)) # we don't need this. But I am trying to be consistent setattr(self, 'dim_{}'.format(ind), dim) self._axes[ind] = dim self.__protected.add('dim_{}'.format(ind)) def del_dimension(self, ind=None): """ Deletes the dimension attached to axis 'ind'. """ if isinstance(ind, int): ind = [ind] elif ind is None: ind = list(np.arange(self.ndim)) else: ind = list(ind) for i in ind: # Delete the attribute with the format dim_0 if hasattr(self, 'dim_{}'.format(i)): delattr(self, 'dim_{}'.format(i)) if 'dim_{}'.format(i) in self.__protected: self.__protected.remove('dim_{}'.format(i)) if i in self._axes.keys(): # Deleting the dataset attribute that has the dimension's name if hasattr(self, self._axes[i].name): delattr(self, self._axes[i].name) if self._axes[i].name in self.__protected: self.__protected.remove(self._axes[i].name) # Deleting the key-value pair from the _axes dictionary del self._axes[i] def view_metadata(self): """ Prints the metadata to stdout Returns ------- None """ if isinstance(self.metadata, dict): print_nested_dict(self.metadata) def view_original_metadata(self): """ Prints the original_metadata dictionary to stdout Returns ------- None """ if isinstance(self.original_metadata, dict): print_nested_dict(self.original_metadata) def plot(self, verbose=False, figure=None, **kwargs): """ Plots the dataset according to the - shape of the sidpy Dataset, - data_type of the sidpy Dataset and - dimension_type of dimensions of sidpy Dataset the dimension_type 'spatial' or 'spectral' determines how a dataset is plotted. Recognized data_types are: 1D: any keyword, but 'spectrum' or 'line_plot' are encouraged 2D: 'image' or one of ['spectrum_family', 'line_family', 'line_plot_family', 'spectra'] 3D: 'image', 'image_map', 'image_stack', 'spectrum_image' 4D: not implemented yet, but will be similar to spectrum_image. Parameters ---------- verbose: boolean kwargs: dictionary for additional plotting parameters additional keywords (besides the matplotlib ones) for plotting are: - scale_bar: for images to replace axis with a scale bar inside the image figure: matplotlib figure object define figure to which this datset will be plotted Returns ------- self.view.fig: matplotlib figure reference """ if verbose: print('Shape of dataset is: ', self.shape) if self.data_type.value < 0: raise NameError('Datasets with UNKNOWN data_types cannot be plotted') if len(self.shape) == 1: if verbose: print('1D dataset') self.view = CurveVisualizer(self, figure=figure, **kwargs) # plt.show() elif len(self.shape) == 2: # this can be an image or a set of line_plots if verbose: print('2D dataset') if self.data_type == DataType.IMAGE: self.view = ImageVisualizer(self, figure=figure, **kwargs) elif self.data_type.value <= DataType['LINE_PLOT'].value: # self.data_type in ['spectrum_family', 'line_family', 'line_plot_family', 'spectra']: self.view = CurveVisualizer(self, figure=figure, **kwargs) elif self.data_type == DataType.POINT_CLOUD: self.view = PointCloudVisualizer(self, figure=figure, **kwargs) else: raise NotImplementedError('Datasets with data_type {} cannot be plotted, yet.'.format(self.data_type)) elif len(self.shape) == 3: if verbose: print('3D dataset:', self.data_type) if self.data_type == DataType.IMAGE: self.view = ImageVisualizer(self, figure=figure, **kwargs) elif self.data_type == DataType.IMAGE_MAP: pass elif self.data_type == DataType.IMAGE_STACK: self.view = ImageStackVisualizer(self, figure=figure, **kwargs) elif self.data_type == DataType.SPECTRAL_IMAGE: if 'complex' in self.dtype.name: self.view = ComplexSpectralImageVisualizer(self, figure=figure, **kwargs) else: self.view = SpectralImageVisualizer(self, figure=figure, **kwargs) elif self.data_type.name == 'SPECTRAL_IMAGE': print('spec3') if 'complex' in self.dtype.name: self.view = ComplexSpectralImageVisualizer(self, figure=figure, **kwargs) else: self.view = SpectralImageVisualizer(self, figure=figure, **kwargs) elif self.data_type == DataType.POINT_CLOUD: self.view = PointCloudVisualizer(self, figure=figure, **kwargs) else: raise NotImplementedError('Datasets with data_type {} cannot be plotted, yet.'.format(self.data_type)) elif len(self.shape) == 4: if verbose: print('4D dataset') if self.data_type == DataType.IMAGE: self.view = ImageVisualizer(self, **kwargs) plt.show() elif self.data_type == DataType.IMAGE_MAP: pass elif self.data_type == DataType.IMAGE_STACK: self.view = ImageStackVisualizer(self, figure=figure, **kwargs) plt.show() elif self.data_type == DataType.SPECTRAL_IMAGE: if 'complex' in self.dtype.name: self.view = ComplexSpectralImageVisualizer(self, figure=figure, **kwargs) else: self.view = SpectralImageVisualizer(self, figure=figure, **kwargs) plt.show() elif self.data_type == DataType.IMAGE_4D: self.view = FourDimImageVisualizer(self, figure=figure, **kwargs) plt.show() if verbose: print('4D dataset') else: raise NotImplementedError('Datasets with data_type {} cannot be plotted, yet.'.format(self.data_type)) else: raise NotImplementedError('Datasets with data_type {} cannot be plotted, yet.'.format(self.data_type)) return self.view.fig def set_thumbnail(self, figure=None, thumbnail_size=128): """ Creates a thumbnail which is stored in thumbnail attribute of sidpy Dataset Thumbnail data is saved to Thumbnail group of associated h5_file if it exists Parameters ---------- thumbnail_size: int size of icon in pixels (length of square) Returns ------- thumbnail: numpy.ndarray """ import imageio # Thumbnail configurations for matplotlib kwargs = {'figsize': (1, 1), 'colorbar': False, 'set_title': False} view = self.plot(figure=figure, **kwargs) for axis in view.axes: axis.set_axis_off() # Creating Thumbnail as png image view.savefig('thumb.png', dpi=thumbnail_size) self.thumbnail = imageio.imread('thumb.png') # Writing thumbnail to h5_file if it exists if self.h5_dataset is not None: if 'Thumbnail' not in self.h5_dataset.file: thumb_group = self.h5_dataset.file.create_group("Thumbnail") else: thumb_group = self.h5_dataset.file["Thumbnail"] if "Thumbnail" in thumb_group: del thumb_group["Thumbnail"] thumb_dset = thumb_group.create_dataset("Thumbnail", data=self.thumbnail) return self.thumbnail def get_extent(self, dimensions): """ get image extents as needed i.e. in matplotlib's imshow function. This function works for equi- or non-equi spaced axes and is suitable for subpixel accuracy of positions Parameters ---------- dimensions: list of dimensions Returns ------- list of floats """ extent = [] for ind, dim in enumerate(dimensions): temp = self._axes[dim].values start = temp[0] - (temp[1] - temp[0]) / 2 end = temp[-1] + (temp[-1] - temp[-2]) / 2 if ind == 1: extent.append(end) # y-axis starts on top extent.append(start) else: extent.append(start) extent.append(end) return extent def get_dimension_slope(self, dim): axis = None if isinstance(dim, int): axis = self._axes[dim] elif isinstance(dim, Dimension): axis = dim return get_slope(axis) def get_dimension_by_number(self, dims_in): if isinstance(dims_in, int): dims_in = [dims_in] for i in range(len(dims_in)): if not isinstance(dims_in[i], int): raise ValueError('Input dimensions must be integers') out_dim = [] for dim in dims_in: out_dim.append(self._axes[dim]) return out_dim def get_dimensions_types(self): out_types = [] for dim, axis in self._axes.items(): out_types.append(axis.dimension_type) return out_types def get_dimensions_by_type(self, dims_in, return_axis=False): """ get dimension by dimension_type name Parameter --------- dims_in: dimension_type/str or list of dimension_types/string Returns ------- dims_out: list of [index] the kind of dimensions specified in input in numerical order of the dataset, not the input! """ if isinstance(dims_in, (str, DimensionType)): dims_in = [dims_in] for i in range(len(dims_in)): if isinstance(dims_in[i], str): dims_in[i] = DimensionType[dims_in[i].upper()] dims_out = [] for dim, axis in self._axes.items(): if axis.dimension_type in dims_in: if return_axis: dims_out.append(axis) else: dims_out.append(dim) # , self._axes[dim]]) return dims_out def get_image_dims(self, return_axis=False): """Get all spatial dimensions""" return self.get_dimensions_by_type(DimensionType.SPATIAL, return_axis=return_axis) def get_spectral_dims(self, return_axis=False): """Get all spectral dimensions""" return self.get_dimensions_by_type(DimensionType.SPECTRAL, return_axis=return_axis) def _griddata_transform(self, **kwargs): """ Interpolate unstructured point cloud for the visualization to 3D/4D sidpy.Dataset Parameters ---------- kwards: parameters to reduce dataset dimentions to 2D (number of point, spectral data) Returns ------- sidpy.Dataset with data_type = SPECTRAL_IMAGE """ from scipy.interpolate import griddata if 'coordinates' in self.metadata.keys(): coord = self.metadata['coordinates'] else: raise NotImplementedError('Datasets with data_type POINT_CLOUD must contain coordinates in metadata.') if 'spacial_units' in self.metadata.keys(): sp_units = self.metadata['spacial_units'] else: sp_units = 'a.u.' im_size = max(50, coord.shape[0]) _x0, _x1 = np.min(coord, axis=0)[0], np.max(coord, axis=0)[0] _y0, _y1 = np.min(coord, axis=0)[1], np.max(coord, axis=0)[1] _delta_x = _x1 - _x0 _delta_y = _y1 - _y0 # to extend filed of view _x0, _x1 = _x0 - 0.05*_delta_x, _x1 + 0.05*_delta_x _y0, _y1 = _y0 - 0.05*_delta_y, _y1 + 0.05 * _delta_y _px_x = np.array((coord[:, 0] - _x0) * im_size/(_x1 - _x0)).astype(int) _px_y = np.array((coord[:, 1] - _y0) * im_size/(_y1 - _y0)).astype(int) grid_x, grid_y = np.mgrid[_x0: _x1: (_x1 - _x0)/im_size, _y0: _y1: (_y1 - _y0)/im_size] grid_z = griddata(coord, self, (grid_x, grid_y), method='nearest') # transpform to 3D _dset = Dataset.from_array(grid_z) _dset.data_type = 'point_cloud' _dset.units = self.units _dset.quantity = self.quantity _dset.title = self.title _dset.set_dimension(0, Dimension(grid_x[:, 0], 'x')) _dset.x.dimension_type = 'spatial' _dset.x.units = sp_units _dset.x.quantity = 'distance' _dset.set_dimension(1, Dimension(grid_y[0], 'y')) _dset.y.dimension_type = 'spatial' _dset.y.units = sp_units _dset.y.quantity = 'distance' _dset.set_dimension(2, self.get_dimension_by_number(1)[0]) if len(self.shape) == 3: _dset.set_dimension(3, self.get_dimension_by_number(2)[0]) _dset.metadata = {'coord': np.array([_px_x, _px_y]).T} if 'variance' in self.metadata.keys(): grid_z_var = griddata(coord, self.metadata['variance'], (grid_x, grid_y), method='nearest') _dset.metadata['variance'] = grid_z_var return _dset @staticmethod def _min_dist(array): _sort_ar = np.sort(array) return np.min(_sort_ar[1:] - _sort_ar[:-1]) @staticmethod def _closest_point(array_coord, point): diff = array_coord - point return np.argmin(diff[:, 0]**2 + diff[:, 1]**2) @property def labels(self): labels = [] for key, dim in self._axes.items(): labels.append('{} ({})'.format(dim.quantity, dim.units)) return labels @property def title(self): return self._title @title.setter def title(self, value): if isinstance(value, str): self._title = value else: raise ValueError('title needs to be a string') @property def structures(self): return self._structures def add_structure(self, atoms, title=None): if isinstance(atoms, ase.Atoms): if title is None: title = atoms.get_chemical_formula() self._structures.update({title: atoms}) else: raise ValueError('structure not an ase.Atoms object') @property def units(self): return self._units @units.setter def units(self, value): if isinstance(value, str): self._units = value else: raise ValueError('units needs to be a string') @property def quantity(self): return self._quantity @quantity.setter def quantity(self, value): if isinstance(value, str): self._quantity = value else: raise ValueError('quantity needs to be a string') @property def data_type(self): return self._data_type @data_type.setter def data_type(self, value): if isinstance(value, str): if value.upper() in DataType._member_names_: self._data_type = DataType[value.upper()] else: self._data_type = DataType.UNKNOWN raise Warning('Supported data_types for plotting are only: ', DataType._member_names_) elif isinstance(value, DataType): self._data_type = value else: raise ValueError('data_type needs to be a string') @property def modality(self): return self._modality @modality.setter def modality(self, value): if isinstance(value, str): self._modality = value else: raise ValueError('modality needs to be a string') @property def source(self): return self._source @source.setter def source(self, value): if isinstance(value, str): self._source = value else: raise ValueError('source needs to be a string') @property def h5_dataset(self): return self._h5_dataset @h5_dataset.setter def h5_dataset(self, value): if isinstance(value, h5py.Dataset): self._h5_dataset = value elif value is None: self.hdf_close() else: raise TypeError('h5_dataset needs to be a hdf5 Dataset') @property def metadata(self): return self._metadata @metadata.setter def metadata(self, value): if isinstance(value, dict): if sys.getsizeof(value) < 64000: self._metadata = value else: raise ValueError('metadata dictionary too large, please use attributes for ' 'large additional data sets') else: raise ValueError('metadata needs to be a python dictionary') @property def original_metadata(self): return self._original_metadata @original_metadata.setter def original_metadata(self, value): if isinstance(value, dict): if sys.getsizeof(value) < 64000: self._original_metadata = value else: raise ValueError('original_metadata dictionary too large, please use attributes for ' 'large additional data sets') else: raise ValueError('original_metadata needs to be a python dictionary') @property def data_descriptor(self): return '{} ({})'.format(self.quantity, self.units) @property def variance(self): return self._variance @variance.setter def variance(self, value): if value is None: self._variance = None else: if np.array(value).shape != np.array(self).shape: raise ValueError('Variance array must have the same dimensionality as the dataset') if isinstance(value, da.Array) and not np.any(np.isnan(value.shape)): self._variance = value else: self._variance = da.from_array(np.array(value)) def fft(self, dimension_type=None): """ Gets the FFT of a sidpy.Dataset of any size The data_type of the sidpy.Dataset determines the dimension_type over which the fourier transform is performed over, if the dimension_type is not set explicitly. The fourier transformed dataset is automatically shifted to center of dataset. Parameters ---------- dimension_type: None, str, or sidpy.DimensionType - optional dimension_type over which fourier transform is performed, if None an educated guess will determine that from dimensions of sidpy.Dataset Returns ------- fft_dset: 2D or 3D complex sidpy.Dataset (not tested for higher dimensions) 2 or 3 dimensional matrix arranged in the same way as input Example ------- >> fft_dataset = sidpy_dataset.fft() >> fft_dataset.plot() """ if dimension_type is None: # test for data_type of sidpy.Dataset if self.data_type.name in ['IMAGE_MAP', 'IMAGE_STACK', 'SPECTRAL_IMAGE', 'IMAGE_4D']: dimension_type = self.dim_2.dimension_type else: dimension_type = self.dim_0.dimension_type if isinstance(dimension_type, str): dimension_type = DimensionType[dimension_type.upper()] if not isinstance(dimension_type, DimensionType): raise TypeError('Could not identify a dimension_type to perform Fourier transform on') axes = self.get_dimensions_by_type(dimension_type) if dimension_type.name in ['SPATIAL', 'RECIPROCAL']: if len(axes) != 2: raise TypeError('sidpy dataset of type', self.data_type, ' has no obvious dimension over which to perform fourier transform, please specify') if dimension_type.name == 'SPATIAL': new_dimension_type = DimensionType.RECIPROCAL else: new_dimension_type = DimensionType.SPATIAL elif dimension_type.name == 'SPECTRAL': if len(axes) != 1: raise TypeError('sidpy dataset of type', self.data_type, ' has no obvious dimension over which to perform fourier transform, please specify') new_dimension_type = DimensionType.SPECTRAL else: raise NotImplementedError('fourier transform not implemented for dimension_type ', dimension_type.name) fft_transform = np.fft.fftshift(da.fft.fftn(self, axes=axes)) fft_dset = self.like_data(fft_transform) fft_dset.units = 'a.u.' fft_dset.modality = 'fft' units_x = '1/' + self._axes[axes[0]].units fft_dset.set_dimension(axes[0], Dimension(np.fft.fftshift(np.fft.fftfreq(self.shape[axes[0]], d=get_slope(self._axes[axes[0]].values))), name='u', units=units_x, dimension_type=new_dimension_type, quantity='reciprocal')) if len(axes) > 1: units_y = '1/' + self._axes[axes[1]].units fft_dset.set_dimension(axes[1], Dimension(np.fft.fftshift(np.fft.fftfreq(self.shape[axes[1]], d=get_slope(self._axes[axes[1]].values))), name='v', units=units_y, dimension_type=new_dimension_type, quantity='reciprocal_length')) return fft_dset def flatten_complex(self): """ This function returns a dataset with real and imaginary components that have been flattened This is necessary for scenarios such as fitting of complex functions Must be a 2D or 1D dataset to begin with Output: - ouput_arr: sidpy.Dataset object """ assert self.ndim < 3, "flatten_complex() only works on 1D or 2D datasets, current dataset has {}".format( self.ndim) # Only the second dimension needs to be changed # Because we are stacking real and imaginary, this means we just tile the existing axis values if len(self._axes) == 1: index_ax = 0 elif len(self._axes) == 2: index_ax = 1 new_ax_values = np.tile(self._axes[index_ax].values, 2) output_arr = self.like_data(dask.array.hstack([self.real, self.imag])) output_arr.set_dimension(index_ax, Dimension(new_ax_values, name=output_arr._axes[index_ax].name, units=output_arr._axes[index_ax].units, dimension_type=output_arr._axes[index_ax].dimension_type, quantity=output_arr._axes[index_ax].quantity)) return output_arr # ##################################################### # Original dask.array functions replaced # ################################################## def __eq__(self, other): # TODO: Test __eq__ if not isinstance(other, Dataset): return False # if (self.__array__() == other.__array__()).all(): if (self.__array__().__eq__(other.__array__())).all(): if self._units != other._units: return False if self._quantity != other._quantity: return False if self._source != other._source: return False if self._data_type != other._data_type: return False if self._modality != other._modality: return False if self._axes != other._axes: return False if (self._variance is not None) and (other._variance is not None): if not (self._variance.__eq__(other._variance)).all(): return False elif (self._variance is not None) or (other._variance is not None): return False return True return False @property def T(self): return self.transpose() def abs(self): return self.like_data(super().__abs__(), title_suffix='_absolute_value') ###################################################### # Original dask.array functions handed through ################################################## @property def real(self): result = self.like_data(super().real) if self._variance is not None: result._variance = self._variance.real return result @property def imag(self): result = self.like_data(super().imag) if self._variance is not None: result._variance = self._variance.image return result # This is wrapper method for the methods that reduce dimensions def reduce_dims(original_method): @wraps(original_method) def wrapper_method(self, *args, **kwargs): result, arguments = original_method(self, *args, **kwargs) axis, keepdims = arguments.get('axis'), arguments.get('keepdims', False) if axis is None and not keepdims: return result.compute() if axis is None: axes = list(np.arange(self.ndim)) elif isinstance(axis, int): axes = [axis] else: axes = list(axis) return self.__reduce_dimensions(result, axes, keepdims) return wrapper_method @reduce_dims def all(self, axis=None, keepdims=False, split_every=None, out=None): result = self.like_data(super().all(axis=axis, keepdims=keepdims, split_every=split_every, out=out), title_prefix='all_aggregate_', checkdims=False) if self._variance is not None: result._variance = self._variance.all(axis=axis, keepdims=keepdims, split_every=split_every, out=out) return result, locals().copy() @reduce_dims def any(self, axis=None, keepdims=False, split_every=None, out=None): result = self.like_data(super().any(axis=axis, keepdims=keepdims, split_every=split_every, out=out), title_prefix='any_aggregate_', checkdims=False) if self._variance is not None: result._variance = self._variance.any(axis=axis, keepdims=keepdims, split_every=split_every, out=out) return result, locals().copy() @reduce_dims def min(self, axis=None, keepdims=False, split_every=None, out=None): result = self.like_data(super().min(axis=axis, keepdims=keepdims, split_every=split_every, out=out), title_prefix='min_aggregate_', checkdims=False) if self._variance is not None: if axis is not None: _min_ind_axis = super().argmin(axis=axis, split_every=split_every, out=out) _coords = np.array(list(np.ndindex(_min_ind_axis.shape))) #list? _inds = np.insert(_coords, axis, np.array(_min_ind_axis).flatten(), axis=1) _extracted_points = da.take(self._variance.flatten(), np.ravel_multi_index(_inds.T, (self._variance.shape))) result._variance = _extracted_points.reshape(result.shape).rechunk(result.chunksize) else: _ind = np.unravel_index(super().min(), self._variance.shape) result._variance = self._variance[_ind] return result, locals().copy() @reduce_dims def max(self, axis=None, keepdims=False, split_every=None, out=None): result = self.like_data(super().max(axis=axis, keepdims=keepdims, split_every=split_every, out=out), title_prefix='max_aggregate_', checkdims=False) if self._variance is not None: if axis is not None: _max_ind_axis = super().argmax(axis=axis, split_every=split_every, out=out) _coords = np.array(list(np.ndindex(_max_ind_axis.shape))) #list? _inds = np.insert(_coords, axis, np.array(_max_ind_axis).flatten(), axis=1) _extracted_points = da.take(self._variance.flatten(), np.ravel_multi_index(_inds.T, (self._variance.shape))) result._variance = _extracted_points.reshape(result.shape).rechunk(result.chunksize) return result, locals().copy() @reduce_dims def sum(self, axis=None, dtype=None, keepdims=False, split_every=None, out=None): result = self.like_data(super().sum(axis=axis, dtype=dtype, keepdims=keepdims, split_every=split_every, out=out), title_prefix='sum_aggregate_', checkdims=False) if self._variance is not None: result._variance = abs(self._variance).sum(axis=axis, dtype=dtype, keepdims=keepdims, split_every=split_every, out=out) #TODO imaginary return result, locals().copy() @reduce_dims def mean(self, axis=None, dtype=None, keepdims=False, split_every=None, out=None): result = self.like_data(super().mean(axis=axis, dtype=dtype, keepdims=keepdims, split_every=split_every, out=out), title_prefix='mean_aggregate_', checkdims=False) if (self._variance is not None) and (axis is not None): if type(axis) is tuple: sh = np.prod(np.array(self._variance.shape, dtype=int)[list(axis)]) else: sh = axis result._variance = self._variance.sum(axis=axis, dtype=dtype, keepdims=keepdims, split_every=split_every, out=out)/sh**2 return result, locals().copy() @reduce_dims def std(self, axis=None, dtype=None, keepdims=False, ddof=0, split_every=None, out=None): result = self.like_data(super().std(axis=axis, dtype=dtype, keepdims=keepdims, ddof=0, split_every=split_every, out=out), title_prefix='std_aggregate_', checkdims=False) return result, locals().copy() @reduce_dims def var(self, axis=None, dtype=None, keepdims=False, ddof=0, split_every=None, out=None): result = self.like_data(super().var(axis=axis, dtype=dtype, keepdims=keepdims, ddof=ddof, split_every=split_every, out=out), title_prefix='var_aggregate_', checkdims=False) return result, locals().copy() @reduce_dims def argmin(self, axis=None, split_every=None, out=None): result = self.like_data(super().argmin(axis=axis, split_every=split_every, out=out), title_prefix='argmin_aggregate_', reset_units=True, reset_quantity=True, check_dims=False) return result, locals().copy() @reduce_dims def argmax(self, axis=None, split_every=None, out=None): result = self.like_data(super().argmax(axis=axis, split_every=split_every, out=out), title_prefix='argmax_aggregate_', reset_units=True, reset_quantity=True, check_dims=False) return result, locals().copy() def angle(self, deg=False): result = self.like_data(da.angle(self, deg=deg), reset_units=True, reset_quantity=True, title_prefix='angle_', checkdims=True) if deg: result.units = 'degrees' else: result.units = 'radians' return result def conj(self): return self.like_data(super().conj(), reset_units=True, reset_quantity=True, title_prefix='conj_', checkdims=True) def astype(self, dtype, **kwargs): return self.like_data(super().astype(dtype=dtype, **kwargs), variance=self._variance) def flatten(self): result = self.like_data(super().flatten(), title_prefix='flattened_', check_dims=False) if self._variance is not None: result.variance = self._variance.flatten() return result def ravel(self): return self.flatten() def clip(self, min=None, max=None): return self.like_data(super().clip(min=min, max=max), reset_quantity=True, title_prefix='clipped_') def compute_chunk_sizes(self): return self.like_data(super().compute_chunk_sizes()) def cumprod(self, axis, dtype=None, out=None, method='sequential'): if axis is None: self = self.flatten() axis = 0 return self.like_data(super().cumprod(axis=axis, dtype=dtype, out=out, method=method), title_prefix='cumprod_', reset_quantity=True) def cumsum(self, axis, dtype=None, out=None, method='sequential'): if axis is None: self = self.flatten() axis = 0 return self.like_data(super().cumsum(axis=axis, dtype=dtype, out=out, method=method), title_prefix='cumsum_', reset_quantity=True) # What happens to the dimensions?? def dot(self, other): return self.from_array(super().dot(other)) def squeeze(self, axis=None): result = self.like_data(super().squeeze(axis=axis), title_prefix='Squeezed_', checkdims=False) if self._variance is not None: result._variance = self._variance.squeeze(axis=axis) if axis is None: shape_list = list(self.shape) axes = [i for i in range(self.ndim) if shape_list[i] == 1] elif isinstance(axis, int): axes = [axis] else: axes = list(axis) return self.__reduce_dimensions(result, axes, keepdims=False) def swapaxes(self, axis1, axis2): result = self.like_data(super().swapaxes(axis1, axis2), title_prefix='Swapped_axes_', checkdims=False) if self._variance is not None: result._variance = self._variance.swapaxes(axis1, axis2) new_order = np.arange(self.ndim) new_order[axis1] = axis2 new_order[axis2] = axis1 return self.__rearrange_axes(result, new_order) def transpose(self, *axes): result = self.like_data(super().transpose(*axes), title_prefix='Transposed_', checkdims=False) if self._variance is not None: result._variance = self._variance.transpose(*axes) if not axes: new_axes_order = range(self.ndim)[::-1] elif len(axes) == 1 and isinstance(axes[0], Iterable): new_axes_order = axes[0] else: new_axes_order = axes return self.__rearrange_axes(result, new_axes_order) def round(self, decimals=0): return self.like_data(super().round(decimals=decimals), title_prefix='Rounded_') def reshape(self, shape, merge_chunks=True, limit=None): # This somehow adds an extra dimension at the end # Will come back to this warnings.warn('Dimensional information will be lost.\ Please use fold, unfold to combine dimensions') if len(shape) == 1 and isinstance(shape[0], Iterable): new_shape = shape[0] else: new_shape = shape return super().reshape(*new_shape, merge_chunks) @reduce_dims def prod(self, axis=None, dtype=None, keepdims=False, split_every=None, out=None): result = self.like_data(super().prod(axis=axis, dtype=dtype, keepdims=keepdims, split_every=split_every, out=out), title_prefix='prod_aggregate', reset_units=True, reset_quantity=True, checkdims=False) return result, locals().copy() @reduce_dims def trace(self, offset=0, axis1=0, axis2=1, dtype=None): if self.ndim == 2: axes = None result = (super().trace(offset=offset)) else: axes = [axis1, axis2] result = self.like_data(super().trace(offset=offset, axis1=axis1, axis2=axis2, dtype=None), title_prefix='Trace_', checkdims=False) local_args = locals().copy() local_args['axis'] = axes return result, local_args def repeat(self, repeats, axis=None): result = self.like_data(super().repeat(repeats=repeats, axis=axis), title_prefix='Repeated_', checkdims=False) # result._axes = {} for i, dim in self._axes.items(): if axis != i: new_dim = dim.copy() else: new_dim = Dimension(np.repeat(dim.values, repeats=repeats), name=dim.name, quantity=dim.quantity, units=dim.units, dimension_type=dim.dimension_type) result.set_dimension(i, new_dim) return result @reduce_dims def moment(self, order, axis=None, dtype=None, keepdims=False, ddof=0, split_every=None, out=None): result = self.like_data(super().moment(order=order, axis=axis, dtype=dtype, keepdims=keepdims, ddof=0, split_every=split_every, out=out), title_prefix='moment_aggregate_', checkdims=False) return result, locals().copy() def persist(self, **kwargs): return self.like_data(super().persist(**kwargs), title_prefix='persisted_') def rechunk(self, chunks='auto', threshold=None, block_size_limit=None, balance=False): return self.like_data(super().rechunk(chunks=chunks, threshold=threshold, block_size_limit=block_size_limit, balance=balance), title_prefix='Rechunked_') def fold(self, dim_order=None, method=None): """ This method collapses the dimensions of the sidpy dataset """ """ Parameters ---------- dim_order: List of lists or tuple of tuples -Each element corresponds to the order of axes in the corresponding new axis after the collapse -Default: None method: str -'spaspec': collapses the original dataset to a 2D dataset, where spatail dimensions form the zeroth axis and spectral dimensions form the first axis -'spa': combines all the spatial dimensions into a single dimension and the combined dimension will be first -'spec': combines all the spectral dimensions into a single dimension and the combined dimension will be last -Uses the user defined dim_order when set to None -Default: None Returns ------- Collapsed sidpy.Dataset object whose number of dimensions equals two if method=='spaspec' or len(dim_order) """ if method is None: if dim_order is None: raise NotImplementedError("Specify the dim_order or set the\ method to 'spaspec'") if not (isinstance(dim_order, list) or isinstance(dim_order, tuple)): raise NotImplementedError("dim_order should be a List or a Tuple") dim_order_list = [list(x) for x in dim_order] # Book-keeping for unfolding fold_attr = {'_axes': self._axes.copy()} if method == 'spaspec': dim_order_list = [[], []] for dim, axis in self._axes.items(): if axis.dimension_type == DimensionType.SPATIAL: dim_order_list[0].extend([dim]) elif axis.dimension_type == DimensionType.SPECTRAL: dim_order_list[1].extend([dim]) else: warnings.warn('One of the dimensions is neither Spatial\ nor Spectral Type and is considered to be a \ part of the last collapsed dimension') dim_order_list[1].extend([dim]) if method == 'spa': dim_order_list = [[]] for dim, axis in self._axes.items(): if axis.dimension_type == DimensionType.SPATIAL: dim_order_list[0].extend([dim]) else: dim_order_list.append([dim]) if len(dim_order_list[0]) == 0: raise NotImplementedError("No spatial dimensions found and the method is set to 'spa' ") if len(dim_order_list[0]) == 1: warnings.warn('Only one spatial dimension found\ Folding returns the original dataset') if method == 'spec': dim_order_list = [[]] for dim, axis in self._axes.items(): if axis.dimension_type == DimensionType.SPECTRAL: dim_order_list[-1].extend([dim]) else: dim_order_list.insert(-1, [dim]) if len(dim_order_list[-1]) == 0: raise NotImplementedError("No spectral dimensions found and the method is set to 'spec'") if len(dim_order_list[-1]) == 1: warnings.warn('Only one spatial dimension found\ Folding returns the original dataset') # We need the flattened list to transpose the original array dim_order_flattened = [item for sublist in dim_order_list for item in sublist] # Check if all the dimensions are accounted for, if len(dim_order_flattened) != len(self.shape): warnings.warn('All the dimensions that are not present in the dim_order \ are considered to be a part of last collapsed dimension') left_dims = set(np.arange(0, self.ndim)) - set(dim_order_flattened) dim_order_list[-1].extend(list(left_dims)) dim_order_flattened.extend(list(left_dims)) fold_attr['dim_order_flattened'] = dim_order_flattened fold_attr['dim_order'] = dim_order_list # Get the shape of the collapsed array new_shape = np.ones(len(dim_order_list)).astype(int) for i, dim in enumerate(dim_order_list): for d in dim: new_shape[i] *= self.shape[d] # Collapsed dask array transposed_dset = self.transpose(dim_order_flattened) folded_dset = self.like_data(da.reshape(transposed_dset, tuple(new_shape), merge_chunks=True), title_prefix='folded_', checkdims=False) fold_attr['shape_transposed'] = [self.shape[i] for i in dim_order_flattened] # Setting the dimensions for spaspec method if method == 'spaspec': folded_dset._axes[0].dimension_type = DimensionType.SPATIAL folded_dset._axes[1].dimension_type = DimensionType.SPECTRAL folded_dset.metadata['fold_attr'] = fold_attr # Setting the dimensions for a general case for i, dim in enumerate(dim_order_list): dim_types = [self._axes[d].dimension_type for d in dim] if dim_types.count(dim_types[0]) == len(dim_types): folded_dset._axes[i].dimension_type = dim_types[0] return folded_dset def unfold(self): try: shape_transposed = self.metadata['fold_attr']['shape_transposed'] dim_order_flattened = self.metadata['fold_attr']['dim_order_flattened'] old_axes = self.metadata['fold_attr']['_axes'] except: raise NotImplementedError('unfold only works on the dataset that was collapsed/folded by' ' the fold method') reshaped_dset = da.reshape(self, shape_transposed, merge_chunks=True) old_order = [dim_order_flattened.index(d) for d in range(len(dim_order_flattened))] unfolded_dset = self.like_data(da.transpose(reshaped_dset, old_order), title=self.title.replace('folded_', ''), checkdims=False) unfolded_dset._axes = {} for i, dim in old_axes.items(): unfolded_dset.set_dimension(i, dim.copy()) del unfolded_dset.metadata['fold_attr'] return unfolded_dset # Following methods are to be edited def adjust_axis(self, result, axis, title='', keepdims=False): if not keepdims: dim = 0 dataset = self.from_array(result) if isinstance(axis, int): axis = [axis] # for ax, dimension in self._axes.items(): # if int(ax) not in axis: # delattr(self, dimension.name) # delattr(self, f'dim_{ax}') # del self._axes[ax] for ax, dimension in self._axes.items(): if int(ax) not in axis: dataset.set_dimension(dim, dimension) dim += 1 else: dataset = self.like_data(result) dataset.title = title + self.title dataset.modality = f'sum axis {axis}' dataset.quantity = self.quantity dataset.source = self.source dataset.units = self.units return dataset def choose(self, choices): return self.like_data(super().choose(choices)) def __abs__(self): return self.like_data(super().__abs__(), title_suffix='_absolute_value') def __add__(self, other): return self.like_data(super().__add__(other)) def __radd__(self, other): return self.like_data(super().__radd__(other)) def __and__(self, other): return self.like_data(super().__and__(other)) def __rand__(self, other): return self.like_data(super().__rand__(other)) def __div__(self, other): return self.like_data(super().__div__(other)) def __rdiv__(self, other): return self.like_data(super().__rdiv__(other)) def __gt__(self, other): return self.like_data(super().__gt__(other)) def __ge__(self, other): return self.like_data(super().__ge__(other)) def __getitem__(self, idx): # Here we need to modify the dimensions of the sliced dataset using the argument index if not isinstance(idx, tuple): # This comes into play when slicing is done using 'None' or just integers. # For example: dset[4] or dset[None] idx = tuple([idx]) # The following line creates a new sidpy dataset with generic dimensions and .. # all the other attributes copied from 'self' aka parent dataset. sliced = self.like_data(super().__getitem__(idx), checkdims=False) # Delete the dimensions created by like_data sliced.del_dimension() old_dims = copy(self._axes) j, k = 0, 0 # j is for self (old_dims) and k is for the sliced dataset (new dimensions) for ind in idx: if ind is None: # Add a new dimension sliced.set_dimension(k, Dimension(1)) k += 1 elif isinstance(ind, (int, np.integer)): j += 1 elif isinstance(ind, (slice, list)): old_dim = old_dims[j] sliced.set_dimension(k, Dimension(old_dim[ind].values, name=old_dim.name, quantity=old_dim.quantity, units=old_dim.units, dimension_type=old_dim.dimension_type)) j += 1 k += 1 elif isinstance(ind, (np.ndarray, da.Array)): if not ind.ndim == 1: raise NotImplementedError('Multi Dimensional Slicing of sidpy Dataset' 'is not available at this moment, please' 'raise an issue on out GitHub page') old_dim = old_dims[j] sliced.set_dimension(k, Dimension(old_dim[np.array(ind)].values, name=old_dim.name, quantity=old_dim.quantity, units=old_dim.units, dimension_type=old_dim.dimension_type)) j += 1 k += 1 elif ind is Ellipsis: start_dim = idx.index(Ellipsis) ellipsis_dims = sliced.ndim - (len(idx) - 1) stop_dim = start_dim + ellipsis_dims for l in range(start_dim, stop_dim): old_dim = old_dims[j] sliced.set_dimension(k, old_dim) j += 1 k += 1 # Adding the rest of the dimensions for k in range(k, sliced.ndim): old_dim = old_dims[j] sliced.set_dimension(k, Dimension(old_dim.values, name=old_dim.name, quantity=old_dim.quantity, units=old_dim.units, dimension_type=old_dim.dimension_type)) j += 1 k += 1 return sliced def __invert__(self): return self.like_data(super().__invert__()) def __lshift__(self, other): return self.like_data(super().__lshift__(other)) def __rlshift__(self, other): return self.like_data(super().__rlshift__(other)) def __lt__(self, other): return self.like_data(super().__lt__(other)) def __le__(self, other): return self.like_data(super().__lt__(other)) def __mod__(self, other): return self.like_data(super().__lshift__(other)) def __rmod__(self, other): return self.like_data(super().__rmod__(other)) def __mul__(self, other): return self.like_data(super().__mul__(other)) def __rmul__(self, other): return self.like_data(super().__rmul__(other)) def __ne__(self, other): return self.like_data(super().__ne__(other)) def __neg__(self): return self.like_data(super().__neg__()) def __or__(self, other): return self.like_data(super().__or__(other)) def __ror__(self, other): return self.like_data(super().__ror__(other)) def __pos__(self): return self.like_data(super().__pos__()) def __pow__(self, other): return self.like_data(super().__pow__(other)) def __rpow__(self, other): return self.like_data(super().__rpow__(other)) def __rshift__(self, other): return self.like_data(super().__rshift__(other)) def __rrshift__(self, other): return self.like_data(super().__rrshift__(other)) def __sub__(self, other): return self.like_data(super().__sub__(other)) def __rsub__(self, other): return self.like_data(super().__rsub__(other)) def __truediv__(self, other): return self.like_data(super().__truediv__(other)) def __rtruediv__(self, other): return self.like_data(super().__rtruediv__(other)) def __floordiv__(self, other): return self.like_data(super().__floordiv__(other)) def __rfloordiv__(self, other): return self.like_data(super().__rfloordiv__(other)) def __xor__(self, other): return self.like_data(super().__xor__(other)) def __rxor__(self, other): return self.like_data(super().__rxor__(other)) def __matmul__(self, other): return self.like_data(super().__matmul__(other)) def __rmatmul__(self, other): return self.like_data(super().__rmatmul__(other)) def __array_ufunc__(self, numpy_ufunc, method, *inputs, **kwargs): out = kwargs.get("out", ()) if method == "__call__": # if numpy_ufunc is np.matmul: # from dask.array.routines import matmul # # # special case until apply_gufunc handles optional dimensions # return self.like_data(matmul(*inputs, **kwargs)) if numpy_ufunc.signature is not None: from dask.array.gufunc import apply_gufunc return self.like_data(apply_gufunc( numpy_ufunc, numpy_ufunc.signature, *inputs, **kwargs)) if numpy_ufunc.nout > 1: from dask.array import ufunc try: da_ufunc = getattr(ufunc, numpy_ufunc.__name__) except AttributeError: return NotImplemented return self.like_data(da_ufunc(*inputs, **kwargs)) else: return self.like_data(dask.array.core.elemwise(numpy_ufunc, *inputs, **kwargs)) elif method == "outer": from dask.array import ufunc try: da_ufunc = getattr(ufunc, numpy_ufunc.__name__) except AttributeError: return NotImplemented return self.like_data(da_ufunc.outer(*inputs, **kwargs)) else: return NotImplemented def convert_hyperspy(s): """ imports a hyperspy signal object into sidpy.Dataset Parameters ---------- s: hyperspy dataset Return ------ dataset: sidpy.Dataset """ try: import hyperspy.api as hs except ModuleNotFoundError: raise ModuleNotFoundError("Hyperspy is not installed") if not isinstance(s, (hs.signals.Signal1D, hs.signals.Signal2D)): raise TypeError('This is not a hyperspy signal object') dataset = Dataset.from_array(s, name=s.metadata.General.title) # Add dimension info axes = s.axes_manager.as_dictionary() if isinstance(s, hs.signals.Signal1D): if s.data.ndim < 2: dataset.data_type = 'spectrum' elif s.data.ndim > 1: if s.data.ndim == 2: dataset = Dataset.from_array(np.expand_dims(s, 2), title=s.metadata.General.title) dataset.set_dimension(2, Dimension([0], name='y', units='pixel', quantity='distance', dimension_type='spatial')) dataset.data_type = DataType.SPECTRAL_IMAGE for key, axis in axes.items(): if axis['navigate']: dimension_type = 'spatial' else: dimension_type = 'spectral' dim_array = np.arange(axis['size']) * axis['scale'] + axis['offset'] if axis['units'] == '': axis['units'] = 'frame' dataset.set_dimension(int(key[-1]), Dimension(dim_array, name=axis['name'], units=axis['units'], quantity=axis['name'], dimension_type=dimension_type)) elif isinstance(s, hs.signals.Signal2D): if s.data.ndim < 4: if s.data.ndim == 2: dataset.data_type = 'image' elif s.data.ndim == 3: dataset.data_type = 'image_stack' for key, axis in axes.items(): if axis['navigate']: dimension_type = 'temporal' else: dimension_type = 'spatial' dim_array = np.arange(axis['size']) * axis['scale'] + axis['offset'] if axis['units'] == '': axis['units'] = 'pixel' dataset.set_dimension(int(key[-1]), Dimension(dim_array, name=axis['name'], units=axis['units'], quantity=axis['name'], dimension_type=dimension_type)) elif s.data.ndim == 4: dataset.data_type = 'IMAGE_4D' for key, axis in axes.items(): if axis['navigate']: dimension_type = 'spatial' else: dimension_type = 'reciprocal' dim_array = np.arange(axis['size']) * axis['scale'] + axis['offset'] dataset.set_dimension(int(key[-1]), Dimension(dim_array, name=axis['name'], units=axis['units'], quantity=axis['name'], dimension_type=dimension_type)) dataset.metadata = dict(s.metadata) dataset.original_metadata = dict(s.original_metadata) dataset.title = dataset.metadata['General']['title'] dataset.units = dataset.metadata['Signal']['quantity '].split('(')[-1][:-1] dataset.quantity = dataset.metadata['Signal']['quantity '].split('(')[0] dataset.source = 'hyperspy' return dataset sidpy-0.12.3/sidpy/sid/dimension.py000066400000000000000000000171161455261647000172130ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Objects that represent dimensions or axes of scientific datasets Created on Thu Jul 7 21:14:25 2020 @author: Gerd Duscher, Suhas Somnath """ from __future__ import division, print_function, unicode_literals, \ absolute_import from warnings import warn import sys import numpy as np from enum import Enum from sidpy.base.string_utils import validate_single_string_arg import copy __all__ = ['Dimension', 'DimensionType'] if sys.version_info.major == 3: unicode = str class DimensionType(Enum): """ Physical type of Dimension object. This information will be used for visualization and processing purposes. """ UNKNOWN = -1 SPATIAL = 1 RECIPROCAL = 2 SPECTRAL = 3 TEMPORAL = 4 CHANNEL = 5 POINT_CLOUD = 6 class Dimension(np.ndarray): """ """ def __new__(cls, values, name='none', quantity='generic', units='generic', dimension_type=DimensionType.UNKNOWN, *args, **kwargs): """ Parameters ---------- name : str or unicode Name of the dimension. For example 'X' quantity : str or unicode Quantity for this dimension. For example: 'Length' units : str or unicode Units for this dimension. For example: 'um' values : array-like or int Values over which this dimension was varied. A linearly increasing set of values will be generated if an integer is provided instead of an array. dimension_type : str or sidpy.sid.dimension.DimensionType For example: 'spectral', 'spatial', 'reciprocal', 'channel', or 'UNKNOWN', 'time', 'frame', 'reciprocal' This will determine how the data are visualized. 'spatial' are image dimensions. 'spectral' indicate spectroscopy data dimensions. Attributes ---------- self.name : str Name of the dimension self.quantity : str Physical quantity. E.g. - current self.units : str Physical units. E.g. - amperes self.dimension_type : enum Type of dimension. E.g. - Spectral, Spatial, etc. self.values : array-like Values over which this dimension was varied """ if isinstance(values, int): if values < 1: raise TypeError("When specifying the size of a Dimension, " "values should at be integers > 1") values = np.arange(values) elif len(np.array(values)) < 1: raise TypeError("When specifying values over which a parameter is " "varied, values should not be an empty array") if np.array(values).ndim != 1: raise ValueError('Dimension can only be 1 dimensional') new_dim = np.asarray(values, dtype=float).view(cls) new_dim._name = validate_single_string_arg(name, 'name') new_dim.quantity = quantity new_dim.units = units new_dim.dimension_type = dimension_type return new_dim def __array_finalize__(self, obj): # see InfoArray.__array_finalize__ for comments if obj is None: return self._name = validate_single_string_arg(getattr(obj, '_name', 'generic'), 'name') self.quantity = getattr(obj, '_quantity', 'generic') self.units = getattr(obj, '_units', 'generic') self.dimension_type = getattr(obj, '_dimension_type', 'UNKNOWN') def __array_wrap__(self, out_arr, context=None): # just call the parent super(Dimension, self).__array_wrap__(self, out_arr, context) # return correct values return out_arr def __repr__(self): return '{}: {} ({}) of size {}'.format(self.name, self.quantity, self.units, self.shape) def __str__(self): return '{}: {} ({}) of size {}'.format(self.name, self.quantity, self.units, self.shape) # def __copy__(self): # new_dim = Dimension(np.array(self), name=self.name, quantity=self.quantity, units=self.units) # new_dim.dimension_type = self.dimension_type # return new_dim def __copy__(self): # Create a new instance of Dimension new_instance = Dimension( copy.copy(self.values), copy.copy(self.name), copy.copy(self.quantity), copy.copy(self.units), copy.copy(self.dimension_type) ) return new_instance def __deepcopy__(self, memo): # For now this is what chatGPT came up with and it does not break any tests # Create a new instance of Dimension new_instance = Dimension( copy.deepcopy(self.values, memo), copy.deepcopy(self.name, memo), copy.deepcopy(self.quantity, memo), copy.deepcopy(self.units, memo), copy.deepcopy(self.dimension_type, memo) ) return new_instance # TODO: Implement equality # TODO: Find out how to get rid of this def copy(self): # Not sure why __copy__() would not be called by itself new_dim = self.__copy__() return new_dim @property def info(self): return '{} - {} ({}): {}'.format(self.name, self.quantity, self.units, self.values) @property def name(self): return self._name @name.setter def name(self, value): raise AttributeError("Cannot change the name of the dimension. " "If the dimension is associated with the dataset, please try " "dataset.rename_dimension") # # self._name = validate_single_string_arg(value, 'name') @property def quantity(self): return self._quantity @quantity.setter def quantity(self, value): self._quantity = validate_single_string_arg(value, 'quantity') @property def units(self): return self._units @units.setter def units(self, value): self._units = validate_single_string_arg(value, 'units') @property def dimension_type(self): return self._dimension_type @dimension_type.setter def dimension_type(self, value): if isinstance(value, DimensionType): self._dimension_type = value else: dimension_type = validate_single_string_arg(value, 'dimension_type') if dimension_type.upper() in [member.name for member in DimensionType]: self._dimension_type = DimensionType[dimension_type.upper()] elif dimension_type.lower() in ['frame', 'time', 'stack']: self._dimension_type = DimensionType.TEMPORAL else: self._dimension_type = DimensionType.UNKNOWN warn('Supported dimension types for plotting are only: {}' ''.format([member.name for member in DimensionType])) warn('Setting DimensionType to UNKNOWN') @property def values(self): return np.array(self) # @values.setter # def values(self, value): # isinstance(np.ndarray) def __eq__(self, other): if not isinstance(other, Dimension): return False if self.name != other.name: return False if self.units != other.units: return False if self.quantity != other.quantity: return False if len(self.values) != len(other): return False if not (np.array(self) == np.array(other)).all(): return False if not (self.values == other.values).all(): return False return True sidpy-0.12.3/sidpy/sid/reader.py000066400000000000000000000034031455261647000164620ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Abstract :class:`~sidpy.Reader` base-class Created on Sun Aug 22 11:07:16 2020 @author: Suhas Somnath """ from __future__ import division, print_function, absolute_import, unicode_literals import warnings import abc import sys import os from sidpy.base.string_utils import validate_single_string_arg, \ validate_list_of_strings if sys.version_info.major == 3: unicode = str else: FileNotFoundError = ValueError class Reader(object): """ Abstract class that defines the most basic functionality of a data format Reader. A Reader extracts measurement data and metadata from binary / proprietary data formats into a single or set of sipy.Dataset objects """ __metaclass__ = abc.ABCMeta def __init__(self, file_path, *args, **kwargs): """ Parameters ----------- file_path : str Path to the file that needs to be read Attributes ---------- self._input_file_path : str Path to the file that will be read Notes ----- * This method will check to make sure that the provided file_path is indeed a string and a valid file path. * Consider calling ``can_read()`` within ``__init__()`` for validating the provided file Raises ------ FileNotFoundError """ file_path = validate_single_string_arg(file_path, 'file_path') if not os.path.exists(file_path): raise FileNotFoundError(file_path + ' does not exist') self._input_file_path = file_path @abc.abstractmethod def can_read(self): warnings.warn("The 'can_read' method has been deprecated.", DeprecationWarning, stacklevel=2) return None sidpy-0.12.3/sidpy/sid/translator.py000066400000000000000000000064511455261647000174170ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Abstract :class:`~sidpy.io.translator.Translator` base-class Created on Tue Nov 3 15:07:16 2015 @author: Suhas Somnath """ from __future__ import division, print_function, absolute_import, unicode_literals import abc import sys import os from warnings import warn from sidpy.base.string_utils import validate_single_string_arg, \ validate_list_of_strings if sys.version_info.major == 3: unicode = str else: FileNotFoundError = ValueError class Translator(object): """ Abstract class that defines the most basic functionality of a data format translator. A translator converts experimental data from binary / proprietary data formats to a single standardized HDF5 data file """ __metaclass__ = abc.ABCMeta def __init__(self, *args, **kwargs): """ Parameters ----------- Returns ------- Translator object """ warn('Consider using sidpy.Reader instead of sidpy.Translator if ' 'possible and contribute your reader to ScopeReaders', FutureWarning) pass @abc.abstractmethod def translate(self, *args, **kwargs): """ Abstract method. """ raise NotImplementedError('The translate method needs to be ' 'implemented by the child class') @staticmethod def is_valid_file(file_path, *args, **kwargs): """ Checks whether the provided file can be read by this translator. This basic function compares the file extension against the "extension" keyword argument. If the extension matches, this function returns True Parameters ---------- file_path : str Path to raw data file Returns ------- file_path : str Path to the file that needs to be provided to translate() if the provided file was indeed a valid file Else, None """ file_path = validate_single_string_arg(file_path, 'file_name') if not os.path.exists(file_path): raise FileNotFoundError(file_path + ' does not exist') targ_ext = kwargs.get('extension', None) if not targ_ext: raise NotImplementedError('Either is_valid_file() has not been ' 'implemented by this translator or the ' '"extension" keyword argument was ' 'missing') if isinstance(targ_ext, (str, unicode)): targ_ext = [targ_ext] targ_ext = validate_list_of_strings(targ_ext, parm_name='(keyword argument) ' '"extension"') # Get rid of any '.' separators that may be in the list of extensions # Also turn to lower case for case insensitive comparisons targ_ext = [item.replace('.', '').lower() for item in targ_ext] file_path = os.path.abspath(file_path) extension = os.path.splitext(file_path)[1][1:] # Ensure extension is lower case just like targets above extension = extension.lower() if extension in targ_ext: return file_path else: return None sidpy-0.12.3/sidpy/viz/000077500000000000000000000000001455261647000146775ustar00rootroot00000000000000sidpy-0.12.3/sidpy/viz/__init__.py000066400000000000000000000005111455261647000170050ustar00rootroot00000000000000""" Tools for static and interactive visualization of scientific imaging and spectroscopy data Submodules ---------- .. autosummary:: :toctree: _autosummary plot_utils jupyter_utils dataset_viz """ from . import plot_utils, jupyter_utils, dataset_viz __all__ = ['plot_utils', 'jupyter_utils', 'dataset_viz'] sidpy-0.12.3/sidpy/viz/dataset_viz.py000066400000000000000000002525071455261647000176010ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Utilities for generating static image and line plots of near-publishable quality Created on Thu May 05 13:29:12 2020 @author: Gerd Duscher """ from __future__ import annotations from typing import TYPE_CHECKING if TYPE_CHECKING: import sidpy import sidpy import sys import numpy as np import matplotlib.pyplot as plt import matplotlib.patches as patches import ipywidgets from IPython.display import display import scipy # import matplotlib.animation as animation from ..hdf.dtype_utils import is_complex_dtype if sys.version_info.major == 3: unicode = str default_cmap = plt.cm.viridis class CurveVisualizer(object): def __init__(self, dset, spectrum_number=0, figure=None, **kwargs): scale_bar = kwargs.pop('scale_bar', False) colorbar = kwargs.pop('colorbar', True) set_title = kwargs.pop('set_title', True) if not isinstance(dset, sidpy.Dataset): raise TypeError('dset should be a sidpy.Dataset object') if dset.data_type.name != 'SPECTRUM': raise TypeError("sidpy.Dataset should have DataType 'Spectrum' ") fig_args = dict() temp = kwargs.pop('figsize', None) if temp is not None: fig_args['figsize'] = temp if figure is None: self.fig = plt.figure(**fig_args) else: self.fig = figure self.dset = dset self.selection = [] self.spectral_dims = [] for dim, axis in dset._axes.items(): if axis.dimension_type == sidpy.DimensionType.SPECTRAL: self.selection.append(slice(None)) self.spectral_dims.append(dim) else: if spectrum_number <= dset.shape[dim]: self.selection.append(slice(spectrum_number, spectrum_number + 1)) self.spectral_dims.append(dim) else: self.spectrum_number = 0 self.selection.append(slice(0, 1)) self.spectral_dims.append(dim) # Handle the simple cases first: fig_args = dict() temp = kwargs.pop('figsize', None) if temp is not None: fig_args['figsize'] = temp self.dim = self.dset._axes[self.spectral_dims[0]] if is_complex_dtype(dset.dtype): # Plot real and imaginary self.fig, self.axes = plt.subplots(nrows=2, **fig_args) self.axes[0].plot(self.dim.values, self.dset.squeeze().abs().compute(), **kwargs) if set_title: self.axes[0].set_title(self.dset.title + '\n(Magnitude)', pad=15) self.axes[0].set_xlabel(self.dset.labels[0]) self.axes[0].set_ylabel(self.dset.data_descriptor) self.axes[0].ticklabel_format(style='sci', scilimits=(-2, 3)) if set_title: self.axes[1].set_title(self.dset.title + '\n(Phase)', pad=15) self.axes[1].set_ylabel('Phase (rad)') self.axes[1].set_xlabel(self.dset.labels[0]) # + x_suffix) self.axes[1].ticklabel_format(style='sci', scilimits=(-2, 3)) self.fig.tight_layout() self.fig.canvas.draw_idle() else: self.axis = self.fig.add_subplot(1, 1, 1, **fig_args) self.axis.plot(self.dim.values, self.dset.compute(), **kwargs) if self.dset.variance is not None: self.axis.fill_between(self.dim.values, self.dset.compute()-self.dset.variance, self.dset.compute()+self.dset.variance, alpha = 0.3, **kwargs) if set_title: self.axis.set_title(self.dset.title, pad=15) self.axis.set_xlabel(self.dset.labels[self.spectral_dims[0]]) self.axis.set_ylabel(self.dset.data_descriptor) self.axis.ticklabel_format(style='sci', scilimits=(-2, 3)) self.fig.canvas.draw_idle() class ImageVisualizer(object): """ Interactive display of image plot The stack can be scrolled through with a mouse wheel or the slider The usual zoom effects of matplotlib apply. Works on every backend because it only depends on matplotlib. Important: keep a reference to this class to maintain interactive properties so usage is: >>view = plot_stack(dataset, {'spatial':[0,1], 'stack':[2]}) Input: ------ - dset: NSIDask _dataset - figure: optional matplotlib figure - image_number optional if this is a stack of images we can choose which one we want. kwargs optional additional arguments for matplotlib and a boolean value with keyword 'scale_bar' """ def __init__(self, dset, figure=None, image_number=0, **kwargs): """ plotting of data according to two axis marked as SPATIAL in the dimensions """ if not isinstance(dset, sidpy.Dataset): raise TypeError('dset should be a sidpy.Dataset object') fig_args = dict() temp = kwargs.pop('figsize', None) if temp is not None: fig_args['figsize'] = temp if figure is None: self.fig = plt.figure(**fig_args) else: self.fig = figure self.dset = dset self.image_number = image_number self.selection = [] self.image_dims = [] for dim, axis in dset._axes.items(): if axis.dimension_type in [sidpy.DimensionType.SPATIAL, sidpy.DimensionType.RECIPROCAL]: self.selection.append(slice(None)) self.image_dims.append(dim) else: if image_number <= dset.shape[dim]: self.selection.append(slice(image_number, image_number + 1)) else: self.image_number = 0 self.selection.append(slice(0, 1)) if len(self.image_dims) != 2: raise TypeError('We need two dimensions with dimension_type SPATIAL or RECIPROCAL to plot an image') if is_complex_dtype(self.dset.dtype): self.plot_complex_image(**kwargs) else: self.axis = self.fig.add_subplot(1, 1, 1) self.plot_image(**kwargs) if self.dset.variance is not None: if self.dset.variance.shape != self.dset.shape: raise ValueError('Variance array must have the same dimensionality as the dataset') self._variance_button = ipywidgets.widgets.Dropdown(options=[('z', 1), ('σ', 2), ('z + σ', 3), ('z - σ', 4)], value=1, description='Image', tooltip='What to plot: image data (z), variance of z (σ), etc.', layout=ipywidgets.Layout(width='20%', height='40px', )) self._variance_button.observe(self._var_button_event, 'value') # pixel or unit wise self.fig.canvas.draw_idle() drop_down_menu = ipywidgets.HBox([self._variance_button]) display(drop_down_menu) def plot_image(self, **kwargs): from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar scale_bar = kwargs.pop('scale_bar', False) self.colorbar = kwargs.pop('colorbar', True) set_title = kwargs.pop('set_title', True) rgb = False if set_title: self.axis.set_title(self.dset.title) if len(self.dset.shape) > 2: if self.dset.shape[2] > 4: self.axis.set_title(self.dset.title + '_image {}'.format(self.image_number)) else: rgb = True if rgb: self.img = self.axis.imshow(self.dset, extent=self.dset.get_extent(self.image_dims), **kwargs) else: self.img = self.axis.imshow(self.dset[tuple(self.selection)].squeeze().T, extent=self.dset.get_extent(self.image_dims), **kwargs) self.axis.set_xlabel(self.dset.labels[self.image_dims[0]]) self.axis.set_ylabel(self.dset.labels[self.image_dims[1]]) if scale_bar: plt.axis('off') extent = self.dset.get_extent(self.image_dims) size_of_bar = int((extent[1] - extent[0]) / 10 + .5) if size_of_bar < 1: size_of_bar = 1 scalebar = AnchoredSizeBar(plt.gca().transData, size_of_bar, '{} {}'.format(size_of_bar, self.dset._axes[self.image_dims[0]].units), 'lower left', pad=1, color='white', frameon=False, size_vertical=.2) plt.gca().add_artist(scalebar) if self.colorbar: cbar = self.fig.colorbar(self.img) cbar.set_label(self.dset.data_descriptor) self.axis.ticklabel_format(style='sci', scilimits=(-2, 3)) self.fig.tight_layout() self.img.axes.figure.canvas.draw_idle() def plot_complex_image(self, **kwargs): from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar scale_bar = kwargs.pop('scale_bar', False) self.colorbar = kwargs.pop('colorbar', True) self.axes = [] # magnitude self.axes.append(self.fig.add_subplot(121)) self.img = self.axes[0].imshow(self.dset[tuple(self.selection)].abs().squeeze().T, extent=self.dset.get_extent(self.image_dims), **kwargs) self.axes[0].set_xlabel(self.dset.labels[self.image_dims[0]]) self.axes[0].set_ylabel(self.dset.labels[self.image_dims[1]]) self.axes[0].set_title(self.dset.title + '\n(Magnitude)', pad=15) if self.colorbar: cbar = self.fig.colorbar(self.img) cbar.set_label("{} [{}]".format(self.dset.quantity, self.dset.units)) self.axes[0].ticklabel_format(style='sci', scilimits=(-2, 3)) # phase self.axes.append(self.fig.add_subplot(122, sharex=self.axes[0], sharey=self.axes[0])) self.img_c = self.axes[1].imshow(self.dset[tuple(self.selection)].squeeze().angle().T, extent=self.dset.get_extent(self.image_dims), **kwargs) self.axes[1].set_xlabel(self.dset.labels[self.image_dims[0]]) self.axes[1].set_ylabel(self.dset.labels[self.image_dims[1]]) self.axes[1].set_title(self.dset.title + '\n(Phase)', pad=15) if self.colorbar: cbar_c = self.fig.colorbar(self.img_c) cbar_c.set_label(self.dset.data_descriptor) self.axes[1].ticklabel_format(style='sci', scilimits=(-2, 3)) if scale_bar: for ax in self.axes: ax.axis('off') extent = self.dset.get_extent(self.image_dims) size_of_bar = int((extent[1] - extent[0]) / 10 + .5) if size_of_bar < 1: size_of_bar = 1 scalebar = AnchoredSizeBar(ax.transData, size_of_bar, '{} {}'.format(size_of_bar, self.dset._axes[self.image_dims[0]].units), 'lower left', pad=1, color='white', frameon=False, size_vertical=.2) ax.add_artist(scalebar) self.fig.tight_layout() def _var_button_event(self, event): disp = event.new self._update_image(disp) def _update_image(self, event_value, **kwargs): _data = {1: self.dset[tuple(self.selection)].squeeze().T, 2: self.dset.variance[tuple(self.selection)].squeeze().T, 3: self.dset.variance[tuple(self.selection)].squeeze().T + self.dset[tuple(self.selection)].squeeze().T, 4: self.dset[tuple(self.selection)].squeeze().T - self.dset.variance[tuple(self.selection)].squeeze().T} if is_complex_dtype(self.dset.dtype): _dat = np.array(_data[event_value] + 0*1j) self.img.set_data(np.abs(_dat)) self.img.set_clim(np.abs(_dat).min(), np.abs(_dat).max()) self.img_c.set_data(np.angle(_dat)) self.img_c.set_clim(np.angle(_dat).min(), np.angle(_dat).max()) else: self.img.set_data(_data[event_value]) self.img.set_clim(_data[event_value].min(), _data[event_value].max()) class ImageStackVisualizer(object): """ Interactive display of image stack plot The stack can be scrolled through with a mouse wheel or the slider The usual zoom effects of matplotlib apply. Works on every backend because it only depends on matplotlib. Important: keep a reference to this class to maintain interactive properties so usage is: >>kwargs = {'scale_bar': True, 'cmap': 'hot'} >>view = ImageStackVisualizer(dataset, **kwargs ) Input: ------ - dset: sidpy Dataset - figure: optional matplotlib figure - kwargs: optional matplotlib additional arguments like {cmap: 'hot'} """ def __init__(self, dset, figure=None, **kwargs): if not isinstance(dset, sidpy.Dataset): raise TypeError('dset should be a sidpy.Dataset object') fig_args = dict() temp = kwargs.pop('figsize', None) if temp is not None: fig_args['figsize'] = temp self.set_title = kwargs.pop('set_title', True) if figure is None: self.fig = plt.figure(**fig_args) else: self.fig = figure if dset.ndim < 3: raise TypeError('dataset must have at least three dimensions') self.stack_dim = -1 self.image_dims = [] self.selection = [] for dim, axis in dset._axes.items(): if axis.dimension_type in [sidpy.DimensionType.SPATIAL, sidpy.DimensionType.RECIPROCAL]: self.selection.append(slice(None)) self.image_dims.append(dim) elif axis.dimension_type == sidpy.DimensionType.TEMPORAL or len(dset) == 3: self.selection.append(slice(0, 1)) self.stack_dim = dim else: self.selection.append(slice(0, 1)) if len(self.image_dims) != 2: raise TypeError('We need two dimensions with dimension_type spatial to plot an image') if self.stack_dim < 0: raise TypeError('We need one dimensions with dimension_type stack, time or frame') if len(self.image_dims) < 2: raise TypeError('Two SPATIAL dimension are necessary for this plot') self.dset = dset # self.axis = self.fig.add_axes([0.0, 0.2, .9, .7]) self.ind = 0 self.plot_fit_labels = False self.number_of_slices = self.dset.shape[self.stack_dim] if self.set_title: if 'fit_dataset' in dir(dset): if dset.fit_dataset: if dset.metadata['fit_parms_dict']['fit_parameters_labels'] is not None: self.plot_fit_labels = True img_titles = dset.metadata['fit_parms_dict']['fit_parameters_labels'] self.image_titles = ['Fitting Parm: ' + img_titles[k] for k in range(len(img_titles))] else: self.image_titles = 'Fitting Maps: ' + dset.title + '\n use scroll wheel to navigate images' else: self.image_titles = 'Fitting Maps: ' + dset.title + '\n use scroll wheel to navigate images' else: self.image_titles = 'Image stack: ' + dset.title + '\n use scroll wheel to navigate images' self.axis = None self.plot_image(**kwargs) self.axis = plt.gca() # self.axis.set_title(image_titles) self.img.axes.figure.canvas.mpl_connect('scroll_event', self._onscroll) self.play = ipywidgets.Play(value=0, min=0, max=self.number_of_slices, step=1, interval=500, description="Press play", disabled=False) self.slider = ipywidgets.IntSlider(value=0, min=0, max=self.number_of_slices, continuous_update=False, description="Frame:") # set the slider function ipywidgets.interactive(self._update, frame=self.slider) # link slider and play function ipywidgets.jslink((self.play, 'value'), (self.slider, 'value')) # We add a button to average the images self.button = ipywidgets.widgets.ToggleButton(value=False, description='Average', disabled=False, button_style='', tooltip='Average Images of Stack') self.average = False self.button.observe(self._average_slices, 'value') if self.dset.variance is not None: if self.dset.variance.shape != self.dset.shape: raise ValueError('Variance array must have the same dimensionality as the dataset') self._variance_button = ipywidgets.widgets.Dropdown(options=[('z', 1), ('σ', 2), ('z + σ', 3), ('z - σ', 4)], value=1, tooltip='What to plot: image data (z), variance of z (σ), etc.',) self._variance_button.observe(self._var_button_event, 'value') widg0 = ipywidgets.HBox([self.play, self.slider]) widg1 = ipywidgets.HBox([self.button, self._variance_button]) widg = ipywidgets.VBox([widg0, widg1]) self.display = 1 # 0 - without var, 1 z, 2 sigma, 3 z-sigma, 4 z+sigma else: widg = ipywidgets.HBox([self.play, self.slider, self.button]) self.display = 0 display(widg) # self.anim = animation.FuncAnimation(self.fig, self._updatefig, interval=200, blit=False, repeat=True) self._update() def plot_image(self, **kwargs): from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar scale_bar = kwargs.pop('scale_bar', False) colorbar = kwargs.pop('colorbar', True) self.axis = plt.gca() if self.set_title: self.axis.set_title(self.dset.title) self.img = self.axis.imshow(self.dset[tuple(self.selection)].squeeze().T, extent=self.dset.get_extent(self.image_dims), **kwargs) self.axis.set_xlabel(self.dset.labels[self.image_dims[0]]) self.axis.set_ylabel(self.dset.labels[self.image_dims[1]]) if scale_bar: plt.axis('off') extent = self.dset.get_extent(self.image_dims) size_of_bar = int((extent[1] - extent[0]) / 10 + .5) if size_of_bar < 1: size_of_bar = 1 scalebar = AnchoredSizeBar(plt.gca().transData, size_of_bar, '{} {}'.format(size_of_bar, self.dset._axes[self.image_dims[0]].units), 'lower left', pad=1, color='white', frameon=False, size_vertical=.2) plt.gca().add_artist(scalebar) if colorbar: cbar = self.fig.colorbar(self.img) cbar.set_label(self.dset.data_descriptor) self.axis.ticklabel_format(style='sci', scilimits=(-2, 3)) self.fig.tight_layout() self.img.axes.figure.canvas.draw_idle() def _average_slices(self, event): self.average = event.new self._update(self.ind) # if event.new: # if len(self.dset.shape) == 3: # image_stack = self.dset # else: # stack_selection = self.selection.copy() # stack_selection[self.stack_dim] = slice(None) # image_stack = self.dset[stack_selection].squeeze() # # self.img.set_data(image_stack.mean(axis=self.stack_dim).T) # self.fig.canvas.draw_idle() # elif event.old: # self.ind = self.ind % self.number_of_slices # self._update(self.ind) def _onscroll(self, event): if event.button == 'up': self.slider.value = (self.slider.value + 1) % self.number_of_slices else: self.slider.value = (self.slider.value - 1) % self.number_of_slices self.ind = int(self.slider.value) def _var_button_event(self, event): self.display = event.new self._update(self.ind) def _update(self, frame=0): if self.display == 2: _dset = self.dset.variance elif self.display == 3: _dset = self.dset + self.dset.variance elif self.display == 4: _dset = self.dset - self.dset.variance else: _dset = self.dset if self.average: if len(self.dset.shape) == 3: image_stack = _dset else: stack_selection = self.selection.copy() stack_selection[self.stack_dim] = slice(None) image_stack = self.dset[stack_selection].squeeze() self.img.set_data(image_stack.mean(axis=self.stack_dim).T) self.fig.canvas.draw_idle() else: self.ind = frame self.selection[self.stack_dim] = slice(frame, frame + 1) self.img.set_data((_dset[tuple(self.selection)].squeeze()).T) self.img.set_clim(_dset[tuple(self.selection)].min(), _dset[tuple(self.selection)].max()) self.img.axes.figure.canvas.draw_idle() if self.plot_fit_labels: self.axis.set_title(self.image_titles[frame]) else: self.axis.set_title(self.image_titles) class SpectralImageVisualizerBase(object): """ ### Interactive spectrum imaging plot If there is a 4D dataset, and one of them is named 'channel', then you can plot the channel spectra too """ def __init__(self, dset, figure=None, horizontal=True, **kwargs): if not isinstance(dset, sidpy.Dataset): raise TypeError('dset should be a sidpy.Dataset object') scale_bar = kwargs.pop('scale_bar', False) colorbar = kwargs.pop('colorbar', True) self.set_title = kwargs.pop('set_title', True) fig_args = dict() temp = kwargs.pop('figsize', None) if temp is not None: fig_args['figsize'] = temp if figure is None: self.fig = plt.figure(**fig_args) else: self.fig = figure self.image_dims = [] self.energy_axis = [] self.channel_axis = [] self.dset = dset self.verify_dataset() self.horizontal = horizontal self.x = 0 self.y = 0 self.bin_x = 1 self.bin_y = 1 self.set_dataset() if horizontal: self.axes = self.fig.subplots(ncols=2) else: self.axes = self.fig.subplots(nrows=2, **fig_args) if self.set_title: self.fig.canvas.manager.set_window_title(self.dset.title) self.set_image(**kwargs) self.set_spectrum() self.fig.tight_layout() self.cid = self.axes[1].figure.canvas.mpl_connect('button_press_event', self._onclick) self.fig.canvas.draw_idle() def verify_dataset(self): dset = self.dset if len(dset.shape) < 3: raise TypeError('dataset must have at least three dimensions') if len(dset.shape) > 4: raise TypeError('dataset must have at most four dimensions') # We need one stack dim and two image dimes as lists in dictionary selection = [] image_dims = [] spectral_dim = [] channel_dim = [] for dim, axis in dset._axes.items(): if axis.dimension_type in [sidpy.DimensionType.SPATIAL, sidpy.DimensionType.RECIPROCAL]: selection.append(slice(None)) image_dims.append(dim) elif axis.dimension_type == sidpy.DimensionType.SPECTRAL: selection.append(slice(0, 1)) spectral_dim.append(dim) elif axis.dimension_type == sidpy.DimensionType.CHANNEL: channel_dim.append(dim) else: selection.append(slice(0, 1)) if len(image_dims) != 2: raise TypeError('We need two dimensions with dimension_type SPATIAL: to plot an image') if len(channel_dim) >1: raise ValueError("We have more than one Channel Dimension, this won't work for the visualizer") if len(spectral_dim)>1: raise ValueError("We have more than one Spectral Dimension, this won't work for the visualizer...") if self.dset.variance is not None: if self.dset.variance.shape != self.dset.shape: raise ValueError('Variance array must have the same dimensionality as the dataset') if len(dset.shape) == 4: if len(channel_dim) != 1: raise TypeError("We need one dimension with type CHANNEL \ for a spectral image plot for a 4D dataset") elif len(dset.shape)==3: if len(spectral_dim) != 1: raise TypeError("We need one dimension with dimension_type SPECTRAL \ to plot a spectra for a 3D dataset") self.image_dims = image_dims self.energy_axis = spectral_dim[0] if len(channel_dim)>0: self.channel_axis = channel_dim return True def set_dataset(self): size_x = self.dset.shape[self.image_dims[0]] size_y = self.dset.shape[self.image_dims[1]] self.energy_scale = self.dset._axes[self.energy_axis].values self.extent = [0, size_x, size_y, 0] self.rectangle = [0, size_x, 0, size_y] self.scaleX = 1.0 self.scaleY = 1.0 self.analysis = [] self.plot_legend = False self.extent_rd = self.dset.get_extent(self.image_dims) def set_image(self, **kwargs): if len(self.channel_axis)>0: self.image = self.dset.mean(axis=(self.energy_axis,self.channel_axis[0])) else: self.image = self.dset.mean(axis=(self.energy_axis)) self.axes[0].imshow(self.image.T, extent=self.extent, **kwargs) if 1 in self.dset.shape: self.axes[0].set_aspect('auto') self.axes[0].get_yaxis().set_visible(False) else: self.axes[0].set_aspect('equal') self.axes[0].set_xticks(np.linspace(self.extent[0], self.extent[1], 5)) self.axes[0].set_xticklabels(np.round(np.linspace(self.extent[0], self.extent[1], 5),2)) self.axes[0].set_yticks(np.linspace(self.extent[2], self.extent[3], 5)) self.axes[0].set_yticklabels(np.round(np.linspace(self.extent[2], self.extent[3], 5),1)) self.axes[0].set_xlabel('{} [{}]'.format(self.dset._axes[self.image_dims[0]].quantity, 'px')) self.axes[0].set_ylabel('{} [{}]'.format(self.dset._axes[self.image_dims[1]].quantity, 'px')) self.rect = patches.Rectangle((0, 0), self.bin_x, self.bin_y, linewidth=1, edgecolor='r', facecolor='red', alpha=0.2) self.axes[0].add_patch(self.rect) def set_spectrum(self): self.intensity_scale = 1. self.spectrum = self.get_spectrum() if len(self.energy_scale)!=self.spectrum.shape[0]: self.spectrum = self.spectrum.T self.axes[1].plot(self.energy_scale, self.spectrum.compute()) # add variance shadow graph if self.variance is not None: #3d - many curves if len(self.variance.shape) > 1: for i in range(len(self.variance)): self.axes[1].fill_between(self.energy_scale, self.spectrum.compute().T[i] - self.variance[i], self.spectrum.compute().T[i] + self.variance[i], alpha=0.3) # , **kwargs) # 2d - one curve at each point else: self.axes[1].fill_between(self.energy_scale, self.spectrum.compute() - self.variance, self.spectrum.compute() + self.variance, alpha=0.3) # , **self.kwargs) self.axes[1].set_title('spectrum {}, {}'.format(self.x, self.y)) self.xlabel = self.dset.labels[self.energy_axis] self.ylabel = self.dset.data_descriptor self.axes[1].set_xlabel(self.dset.labels[self.energy_axis]) # + x_suffix) self.axes[1].set_ylabel(self.dset.data_descriptor) self.axes[1].ticklabel_format(style='sci', scilimits=(-2, 3)) self.fig.tight_layout() self.cid = self.axes[1].figure.canvas.mpl_connect('button_press_event', self._onclick) self.button = ipywidgets.widgets.Dropdown( options=[('Pixel Wise', 1), ('Units Wise', 2)], value=1, description='Image', tooltip='How to plot spatial data: Pixel Wise (by px), Units wise (in given units)', layout = ipywidgets.Layout(width='30%', height='50px',)) self.button.observe(self._pw_uw, 'value') #pixel or unit wise self.fig.canvas.draw_idle() widg = ipywidgets.HBox([self.button]) display(widg) def _update_image(self, event_value): #pixel wise or unit wise listener if event_value==1: self.axes[0].set_xticks(np.linspace(self.extent[0], self.extent[1], 5)) self.axes[0].set_xticklabels(np.round(np.linspace(self.extent[0], self.extent[1], 5),2)) self.axes[0].set_yticks(np.linspace(self.extent[2], self.extent[3], 5)) self.axes[0].set_yticklabels(np.round(np.linspace(self.extent[2], self.extent[3], 5),2)) self.axes[0].set_xlabel('{} [{}]'.format(self.dset._axes[self.image_dims[0]].quantity, 'px')) self.axes[0].set_ylabel('{} [{}]'.format(self.dset._axes[self.image_dims[1]].quantity, 'px')) else: self.axes[0].set_xlabel('{} [{}]'.format(self.dset._axes[self.image_dims[0]].quantity, self.dset._axes[self.image_dims[0]].units)) self.axes[0].set_ylabel('{} [{}]'.format(self.dset._axes[self.image_dims[1]].quantity, self.dset._axes[self.image_dims[1]].units)) self.axes[0].set_xticks(np.linspace(self.extent[0], self.extent[1], 5),) self.axes[0].set_xticklabels(np.round(np.linspace(self.extent_rd[0], self.extent_rd[1], 5), 2)) self.axes[0].set_yticks(np.linspace(self.extent[2], self.extent[3], 5),) self.axes[0].set_yticklabels(np.round(np.linspace(self.extent_rd[2], self.extent_rd[3], 5), 2)) self.axes[0].set_xlabel('{} [{}]'.format(self.dset._axes[self.image_dims[0]].quantity, self.dset._axes[self.image_dims[0]].units)) self.axes[0].set_ylabel('{} [{}]'.format(self.dset._axes[self.image_dims[1]].quantity, self.dset._axes[self.image_dims[1]].units)) if self.dset._axes[self.image_dims[0]].units =='m': scaled_values_y = self.dset._axes[self.image_dims[1]].values*1E9 scaled_values_x = self.dset._axes[self.image_dims[0]].values*1E9 if scaled_values_x.mean() >=0.1 and scaled_values_x.mean() <=1000: self.axes[0].set_xticks(np.linspace(self.extent[0], self.extent[1], 5),) self.axes[0].set_xticklabels(np.round(np.linspace(scaled_values_x[0], scaled_values_x[-1], 5), 2)) self.axes[0].set_yticks(np.linspace(self.extent[2], self.extent[3], 5),) self.axes[0].set_yticklabels(np.round(np.linspace(scaled_values_y[0], scaled_values_y[-1], 5), 2)) self.axes[0].set_xlabel('{} [{}]'.format(self.dset._axes[self.image_dims[0]].quantity, 'nm')) self.axes[0].set_ylabel('{} [{}]'.format(self.dset._axes[self.image_dims[1]].quantity, 'nm')) return def set_bin(self, bin_xy): old_bin_x = self.bin_x old_bin_y = self.bin_y if isinstance(bin_xy, list): self.bin_x = int(bin_xy[0]) self.bin_y = int(bin_xy[1]) else: self.bin_x = int(bin_xy) self.bin_y = int(bin_xy) if self.bin_x > self.dset.shape[self.image_dims[0]]: self.bin_x = self.dset.shape[self.image_dims[0]] if self.bin_y > self.dset.shape[self.image_dims[1]]: self.bin_y = self.dset.shape[self.image_dims[1]] self.rect.set_width(self.rect.get_width() * self.bin_x / old_bin_x) self.rect.set_height((self.rect.get_height() * self.bin_y / old_bin_y)) if self.x + self.bin_x > self.dset.shape[self.image_dims[0]]: self.x = self.dset.shape[0] - self.bin_x if self.y + self.bin_y > self.dset.shape[self.image_dims[1]]: self.y = self.dset.shape[1] - self.bin_y self.rect.set_xy([self.x * self.rect.get_width() / self.bin_x + self.rectangle[0], self.y * self.rect.get_height() / self.bin_y + self.rectangle[2]]) self._update() def get_spectrum(self): if self.x > self.dset.shape[self.image_dims[0]] - self.bin_x: self.x = self.dset.shape[self.image_dims[0]] - self.bin_x if self.y > self.dset.shape[self.image_dims[1]] - self.bin_y: self.y = self.dset.shape[self.image_dims[1]] - self.bin_y selection = [] for dim, axis in self.dset._axes.items(): if axis.dimension_type == sidpy.DimensionType.SPATIAL: if dim == self.image_dims[0]: selection.append(slice(self.x, self.x + self.bin_x)) else: selection.append(slice(self.y, self.y + self.bin_y)) elif axis.dimension_type == sidpy.DimensionType.SPECTRAL: selection.append(slice(None)) elif axis.dimension_type == sidpy.DimensionType.CHANNEL: selection.append(slice(None)) else: selection.append(slice(0, 1)) self.spectrum = self.dset[tuple(selection)].mean(axis=tuple(self.image_dims)) if self.dset.variance is not None: self.variance = self.dset.variance[tuple(selection)].mean(axis=tuple(self.image_dims)) else: self.variance = None # * self.intensity_scale[self.x,self.y] return self.spectrum.squeeze() def _onclick(self, event): self.event = event if event.inaxes in [self.axes[0]]: x = int(event.xdata) y = int(event.ydata) x = int(x - self.rectangle[0]) y = int(y - self.rectangle[2]) if x >= 0 and y >= 0: if x <= self.rectangle[1] and y <= self.rectangle[3]: self.x = int(x / (self.rect.get_width() / self.bin_x)) self.y = int(y / (self.rect.get_height() / self.bin_y)) if self.x + self.bin_x > self.dset.shape[self.image_dims[0]]: self.x = self.dset.shape[self.image_dims[0]] - self.bin_x if self.y + self.bin_y > self.dset.shape[self.image_dims[1]]: self.y = self.dset.shape[self.image_dims[1]] - self.bin_y self.rect.set_xy([self.x * self.rect.get_width() / self.bin_x + self.rectangle[0], self.y * self.rect.get_height() / self.bin_y + self.rectangle[2]]) self._update() else: if event.dblclick: bottom = float(self.spectrum.min()) if bottom < 0: bottom *= 1.02 else: bottom *= 0.98 top = float(self.spectrum.max()) if top > 0: top *= 1.02 else: top *= 0.98 self.axes[1].set_ylim(bottom=bottom, top=top) def _update(self, ev=None): xlim = self.axes[1].get_xlim() ylim = self.axes[1].get_ylim() self.axes[1].clear() self.get_spectrum() if len(self.energy_scale)!=self.spectrum.shape[0]: self.spectrum = self.spectrum.T self.axes[1].plot(self.energy_scale, self.spectrum.compute(), label='experiment') if self.dset.variance is not None: #3d - many curves if len(self.variance.shape) > 1: for i in range(len(self.variance)): self.axes[1].fill_between(self.energy_scale, self.spectrum.compute().T[i] - self.variance[i], self.spectrum.compute().T[i] + self.variance[i], alpha=0.3) # 2d - one curve at each point else: self.axes[1].fill_between(self.energy_scale, self.spectrum.compute() - self.variance, self.spectrum.compute() + self.variance, alpha=0.3) self.axes[1].set_title('spectrum {}, {}'.format(self.x, self.y)) self.axes[1].set_xlim(xlim) #self.axes[1].set_ylim(ylim) self.axes[1].set_xlabel(self.xlabel) self.axes[1].set_ylabel(self.ylabel) self.fig.canvas.draw_idle() def set_legend(self, set_legend): self.plot_legend = set_legend def get_xy(self): return [self.x, self.y] @staticmethod def _closest_point(array_coord, point): diff = array_coord - point return np.argmin(diff[:,0]**2 + diff[:,1]**2) class SpectralImageVisualizer(SpectralImageVisualizerBase): def __init__(self, dset, figure=None, horizontal=True, **kwargs): super().__init__(dset, figure, horizontal, **kwargs) self.button = ipywidgets.widgets.Dropdown( options=[('Pixel Wise', 1), ('Units Wise', 2)], value=1, description='Image', tooltip='How to plot spatial data: Pixel Wise (by px), Units wise (in given units)', layout = ipywidgets.Layout(width='30%', height='50px',)) self.button.observe(self._pw_uw, 'value') #pixel or unit wise def _pw_uw(self, event): pw_uw = event.new self.update_image(pw_uw) def update_image(self, event_value): #pixel wise or unit wise listener if event_value==1: self.axes[0].xaxis.set_ticks(ticks=list(np.linspace(self.extent[0], self.extent[1], 5)), labels=list(np.round(np.linspace(self.extent[0], self.extent[1], 5),2))) self.axes[0].yaxis.set_ticks(list(np.linspace(self.extent[2], self.extent[3], 5)), list(np.round(np.linspace(self.extent[2], self.extent[3], 5),1))) self.axes[0].set_xlabel('{} [{}]'.format(self.dset._axes[self.image_dims[0]].quantity, 'px')) self.axes[0].set_ylabel('{} [{}]'.format(self.dset._axes[self.image_dims[1]].quantity, 'px')) else: self.axes[0].set_xlabel('{} [{}]'.format(self.dset._axes[self.image_dims[0]].quantity, self.dset._axes[self.image_dims[0]].units)) self.axes[0].set_ylabel('{} [{}]'.format(self.dset._axes[self.image_dims[1]].quantity, self.dset._axes[self.image_dims[1]].units)) self.axes[0].xaxis.set_ticks(np.linspace(self.extent[0], self.extent[1], 5), list(np.round(np.linspace(self.extent_rd[0], self.extent_rd[1], 5), 2)), minor=False) self.axes[0].yaxis.set_ticks(np.linspace(self.extent[2], self.extent[3], 5), list(np.round(np.linspace(self.extent_rd[2], self.extent_rd[3], 5), 2)), minor=False) self.axes[0].set_xlabel('{} [{}]'.format(self.dset._axes[self.image_dims[0]].quantity, self.dset._axes[self.image_dims[0]].units)) self.axes[0].set_ylabel('{} [{}]'.format(self.dset._axes[self.image_dims[1]].quantity, self.dset._axes[self.image_dims[1]].units)) if self.dset._axes[self.image_dims[0]].units =='m': scaled_values_y = self.dset._axes[self.image_dims[1]].values*1E9 scaled_values_x = self.dset._axes[self.image_dims[0]].values*1E9 if scaled_values_x.mean() >=0.1 and scaled_values_x.mean() <=1000: self.axes[0].set_xticks(np.linspace(self.extent[0], self.extent[1], 5), list(np.round(np.linspace(scaled_values_x[0], scaled_values_x[-1], 5), 2))) self.axes[0].set_yticks(np.linspace(self.extent[2], self.extent[3], 5), list(np.round(np.linspace(scaled_values_y[0], scaled_values_y[-1], 5), 2))) self.axes[0].set_xlabel('{} [{}]'.format(self.dset._axes[self.image_dims[0]].quantity, 'nm')) self.axes[0].set_ylabel('{} [{}]'.format(self.dset._axes[self.image_dims[1]].quantity, 'nm')) return class PointCloudVisualizer(object): """ Interactive point cloud visualization """ def __init__(self, dset, base_image = None, figure=None, horizontal=True, **kwargs): if not isinstance(dset, sidpy.Dataset): raise TypeError('dset should be a sidpy.Dataset object') self.dset = dset if self.dset.variance is not None: if self.dset.variance.shape != self.dset.shape: raise ValueError('Variance array must have the same dimensionality as the dataset') #kwargs parsing scale_bar = kwargs.pop('scale_bar', False) self.set_title = kwargs.pop('set_title', True) fig_args = dict() temp = kwargs.pop('figsize', None) if temp is not None: fig_args['figsize'] = temp #initial checks if len(dset.shape) < 2: raise TypeError('dataset must have at least two dimensions') if len(dset.shape) > 3: raise TypeError('dataset must have at most tree dimensions') if dset.point_cloud is None: raise TypeError(r'''must contain dataset.point_cloud attribute''') selection = [] point_dims = [] spectral_dim = [] channel_dim = [] for dim, axis in dset._axes.items(): if axis.dimension_type == sidpy.DimensionType.POINT_CLOUD: selection.append(slice(None)) point_dims.append(dim) elif axis.dimension_type == sidpy.DimensionType.SPECTRAL: selection.append(slice(0, 1)) spectral_dim.append(dim) elif axis.dimension_type == sidpy.DimensionType.CHANNEL: channel_dim.append(dim) else: selection.append(slice(0, 1)) #checking dimension types if len(channel_dim) >1: raise ValueError("We have more than one Channel Dimension, this won't work for the visualizer") if len(spectral_dim)>1: raise ValueError("We have more than one Spectral Dimension, this won't work for the visualizer...") if len(dset.shape)==3: if len(channel_dim)!=1: raise TypeError("We need one dimension with type CHANNEL \ for a spectral image plot for a 4D dataset") elif len(dset.shape)==2: if len(spectral_dim) != 1: raise TypeError("We need one dimension with dimension_type SPECTRAL \ to plot a spectra for a 3D dataset") #figure creation if figure is None: self.fig = plt.figure(**fig_args) else: self.fig = figure if horizontal: self.axes = self.fig.subplots(ncols=2) else: self.axes = self.fig.subplots(nrows=2, **fig_args) if self.set_title: self.fig.canvas.manager.set_window_title(self.dset.title) #pull base_image if base_image is not None: self.image, self.px_coord = self._base_image(base_image) else: if len(channel_dim) > 0: self.cloud= dset.mean(axis=(spectral_dim[0], channel_dim[0])) else: self.cloud = dset.mean(axis=(spectral_dim[0],)) self.image, self.px_coord = self._mask_image() self.x = 0 self.y = 0 size_x, size_y = self.image.shape self.extent = [0, size_x, size_y, 0] self.rectangle = [0, size_x, 0, size_y] if 'quantity' in self.dset.point_cloud: _quantity = self.dset.point_cloud['quantity'] if isinstance(_quantity, str): _quantity = (_quantity, _quantity) elif not (isinstance(_quantity, list) or isinstance(_quantity, tuple)): raise ValueError('Quantity in Dataset.point_cloud should be str or list, or tuple.') else: _quantity = ('distance', 'distance') self.axes[0].imshow(self.image.T, extent=self.extent, **kwargs) self.axes[0].set_xticks(np.linspace(self.extent[0], self.extent[1], 5),) self.axes[0].set_xticklabels(np.round(np.linspace(self.extent[0], self.extent[1], 5),1)) self.axes[0].set_yticks(np.linspace(self.extent[2], self.extent[3], 5),) self.axes[0].set_yticklabels(np.round(np.linspace(self.extent[2], self.extent[3], 5),1)) self.axes[0].set_xlabel('{} [{}]'.format(_quantity[0], 'px')) self.axes[0].set_ylabel('{} [{}]'.format(_quantity[1], 'px')) self.axes[0].scatter(self.px_coord[:,0], self.px_coord[:,1], color='red', s=1) if scale_bar: self._scale_bar() #---spectral part---- #find closest spectrum #self.tree = cKDTree(self.px_coord) _point_number = self.tree.query(np.array([self.x, self.y]))[1] self.sel_point = self.axes[0].scatter(self.px_coord[_point_number, 0], self.px_coord[_point_number, 1], color='red', s=10, edgecolors='darkred') self.spectrum, self.variance = self.get_spectrum(_point_number) self.energy_axis = spectral_dim[0] if len(channel_dim)>0: self.channel_axis = channel_dim self.energy_scale = self.dset._axes[self.energy_axis].values self.spectrum_plot = [] #list is required for the case of several channels if len(self.spectrum.shape) > 1: for i in range(len(self.spectrum)): _spectrum_plot, = self.axes[1].plot(self.energy_scale, self.spectrum.compute()[i]) self.spectrum_plot.append(_spectrum_plot) else: _spectrum_plot, = self.axes[1].plot(self.energy_scale, self.spectrum.compute()) self.spectrum_plot.append(_spectrum_plot) self.fill_between = [] if self.variance is not None: #3d - many curves if len(self.variance.shape) > 1: for i in range(len(self.variance)): _fill_between = self.axes[1].fill_between(self.energy_scale, self.spectrum[i] - self.variance[i], self.spectrum[i] + self.variance[i], alpha=0.3, **kwargs) self.fill_between.append(_fill_between) # 2d - one curve at each point else: _fill_between = self.axes[1].fill_between(self.energy_scale, self.spectrum - self.variance, self.spectrum + self.variance, alpha=0.3, **kwargs) self.fill_between.append(_fill_between) self.axes[1].set_title('point {}'.format(_point_number)) self.axes[1].set_xlabel(self.dset.labels[self.energy_axis]) self.axes[1].set_ylabel(self.dset.data_descriptor) self.axes[1].ticklabel_format(style='sci', scilimits=(-2, 3)) self.fig.tight_layout() self.cid = self.axes[1].figure.canvas.mpl_connect('button_press_event', self._onclick) self.button = ipywidgets.widgets.Dropdown( options=[('Pixel Wise', 1), ('Units Wise', 2)], value=1, descrption='Image', tooltip='How to plot spatial data: Pixel Wise (by px), Units wise (in given units)', layout = ipywidgets.Layout(width='30%', height='50px',)) self.button.observe(self._pw_uw, 'value') #pixel or unit wise self.fig.canvas.draw_idle() widg = ipywidgets.HBox([self.button]) display(widg) def _pw_uw(self, event): pw_uw = event.new self._update_image(pw_uw) def _update_image(self, event_value): # pixel wise or unit wise listener if 'spacial_units' in self.dset.point_cloud: _sp_units = self.dset.point_cloud['spacial_units'] if isinstance(_sp_units, str): _sp_units = (_sp_units, _sp_units) elif not (isinstance(_sp_units, list) or isinstance(_sp_units, tuple)): raise ValueError('Spacial units in Dataset.point_cloud should be str or list, or tuple.') if 'quantity' in self.dset.point_cloud: _quantity = self.dset.point_cloud['quantity'] if isinstance(_quantity, str): _quantity = (_quantity, _quantity) elif not (isinstance(_quantity, list) or isinstance(_quantity, tuple)): raise ValueError('Quantity in Dataset.point_cloud should be str or list, or tuple.') else: _quantity = ('distance', 'distance') if event_value == 1: self.axes[0].set_xticks(np.linspace(self.extent[0], self.extent[1], 5),) self.axes[0].set_xticklabels(np.round(np.linspace(self.extent[0], self.extent[1], 5), 1)) self.axes[0].set_yticks(np.linspace(self.extent[2], self.extent[3], 5),) self.axes[0].set_yticklabels(np.round(np.linspace(self.extent[2], self.extent[3], 5), 1)) self.axes[0].set_xlabel('{} [{}]'.format(_quantity[0], 'px')) self.axes[0].set_ylabel('{} [{}]'.format(_quantity[1], 'px')) else: self.axes[0].set_xticks(np.linspace(self.extent[0], self.extent[1], 5),) self.axes[0].set_xticklabels(np.round(np.linspace(self.real_extent[0], self.real_extent[1], 5), 2)) self.axes[0].set_yticks(np.linspace(self.extent[2], self.extent[3], 5),) self.axes[0].set_yticklabels(np.round(np.linspace(self.real_extent[2], self.real_extent[3], 5), 2)) if 'spacial_units' in self.dset.point_cloud: self.axes[0].set_xlabel('{} [{}]'.format(_quantity[0], _sp_units[0])) self.axes[0].set_ylabel('{} [{}]'.format(_quantity[1], _sp_units[1])) else: self.axes[0].set_xlabel('{}'.format(_quantity[0])) self.axes[0].set_ylabel('{}'.format(_quantity[1])) def _base_image(self, base_image): if not isinstance(base_image, sidpy.Dataset): raise TypeError('base_image should be a sidpy.Dataset object') if base_image.data_type.value != sidpy.DataType.IMAGE.value: raise TypeError(f'base_image expected to be IMAGE') if 'coordinates' in self.dset.point_cloud: coord = self.dset.point_cloud['coordinates'] else: raise NotImplementedError('Datasets with data_type POINT_CLOUD must contain coordinates\ in point_cloud attribute') image_dims = [] selection = [] for dim, axis in base_image._axes.items(): if axis.dimension_type in [sidpy.DimensionType.SPATIAL, sidpy.DimensionType.RECIPROCAL]: image_dims.append(dim) selection.append(slice(None)) else: selection.append(slice(0, 1)) if len(image_dims) != 2: raise TypeError('We need two dimensions with dimension_type SPATIAL or RECIPROCAL to plot an image') self.image = base_image[tuple(selection)].squeeze() im_size = self.image.shape _x0, _x1, _y1, _y0 = base_image.get_extent(image_dims) _delta_x = _x1 - _x0 _delta_y = _y1 - _y0 self.real_extent = [_x0, _x1, _y1, _y0] self.dset.point_cloud['spacial_units'] = (base_image._axes[image_dims[0]].units, base_image._axes[image_dims[1]].units) self.dset.point_cloud['quantity'] = (base_image._axes[image_dims[0]].quantity, base_image._axes[image_dims[1]].quantity) _px_x = np.array((coord[:,0] - _x0)*im_size[1]/_delta_x).astype(int) _px_y = np.array((coord[:, 1] - _y0) * im_size[0]/_delta_y).astype(int) _px_coord = np.array([_px_x, _px_y]).T self.tree = scipy.spatial.cKDTree(_px_coord) return self.image, _px_coord def _scale_bar(self): from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar self.axes[0].axis('off') extent = self.extent size_of_bar = int((extent[1] - extent[0]) / 10 + .5) if 'units' in self.dset.point_cloud: _units = self.dset.point_cloud['units'] else: _units = 'px' if size_of_bar < 1: size_of_bar = 1 scalebar = AnchoredSizeBar(self.axes[0].transData, size_of_bar, '{} {}'.format(size_of_bar, _units), 'lower left', pad=1, color='white', frameon=False, size_vertical=size_of_bar/5) self.axes[0].add_artist(scalebar) def _onclick(self, event): self.event = event if event.inaxes in [self.axes[0]]: self.x = round(event.xdata) self.y = round(event.ydata) _point_number = self.tree.query(np.array([self.x, self.y]))[1] self.spectrum, self.variance = self.get_spectrum(_point_number) if len(self.spectrum.shape) > 1: for i in range(len(self.spectrum)): self.spectrum_plot[i].set_data(self.energy_scale, self.spectrum.compute()[i]) else: self.spectrum_plot[0].set_data(self.energy_scale, self.spectrum.compute()) if self.variance is not None: # 3d - many curves if len(self.variance.shape) > 1: for i in range(len(self.variance)): _c = self.fill_between[i].get_facecolor()[0] self.fill_between[i].remove() self.fill_between[i] = self.axes[1].fill_between(self.energy_scale, self.spectrum[i] - self.variance[i], self.spectrum[i] + self.variance[i], color= _c) else: _c = self.fill_between[0].get_facecolor()[0] self.fill_between[0].remove() self.fill_between[0] = self.axes[1].fill_between(self.energy_scale, self.spectrum - self.variance, self.spectrum + self.variance, color=_c) self.axes[1].set_title('point {}'.format(_point_number)) self.sel_point.set_offsets(np.column_stack((self.px_coord[_point_number, 0], self.px_coord[_point_number, 1]))) self.fig.canvas.draw_idle() else: if event.dblclick: bottom = float(self.spectrum.min()) if bottom < 0: bottom *= 1.02 else: bottom *= 0.98 top = float(self.spectrum.max()) if top > 0: top *= 1.02 else: top *= 0.98 self.axes[1].set_ylim(bottom=bottom, top=top) def get_spectrum(self, point_number): ''' Getting the spectrum by the point number in the point cloud. Parameters ---------- point_number: int Returns ------- self.spectrum: sidpy.array ''' selection = [] for dim, axis in self.dset._axes.items(): if axis.dimension_type == sidpy.DimensionType.POINT_CLOUD: selection.append(point_number) elif axis.dimension_type == sidpy.DimensionType.SPECTRAL: selection.append(slice(None)) elif axis.dimension_type == sidpy.DimensionType.CHANNEL: selection.append(slice(None)) else: selection.append(slice(0, 1)) self.spectrum = self.dset[tuple(selection)].squeeze() if self.dset.variance is not None: self.variance = self.dset.variance[tuple(selection)].squeeze() else: self.variance = None return self.spectrum, self.variance def _mask_image(self): ''' Griddata transformation of the unstructured point cloud to the numpy 2D array Returns ------- 2D np.array - image data 2D np.array - coordinate data ''' if 'coordinates' in self.dset.point_cloud: coord = self.dset.point_cloud['coordinates'] else: raise NotImplementedError('Datasets with data_type POINT_CLOUD must contain coordinates\ in point_cloud attribute') # minimal image size in 50x50px or equal to the number of point for dimensions im_size = max(50, coord.shape[0]) _x0, _x1 = np.min(coord, axis=0)[0], np.max(coord, axis=0)[0] _y0, _y1 = np.min(coord, axis=0)[1], np.max(coord, axis=0)[1] _delta_x = _x1 - _x0 _delta_y = _y1 - _y0 #to extend filed of view _x0, _x1 = _x0 - 0.05*_delta_x, _x1 + 0.05*_delta_x _y0, _y1 = _y0 - 0.05*_delta_y, _y1 + 0.05 * _delta_y self.real_extent = [_x0, _x1, _y1, _y0] _px_x = np.array((coord[:,0] - _x0)*im_size/(_x1-_x0)).astype(int) _px_y = np.array((coord[:, 1] - _y0) * im_size/ (_y1-_y0)).astype(int) _px_coord = np.array([_px_x, _px_y]).T self.tree = scipy.spatial.cKDTree(_px_coord) grid_x, grid_y = np.mgrid[0:im_size, 0:im_size] mask = scipy.interpolate.griddata(_px_coord, self.cloud, (grid_x, grid_y), method='nearest') return mask, _px_coord def get_xy(self): return [self.x, self.y] class FourDimImageVisualizer(object): """ ### Interactive 4D imaging plot Either you specify only two spatial dimensions or you specify scan_x and scan_y image_4d_x, image_4d_y If none of the keywords are specified, it is assumed that the order is slowest to fastest dimension. """ def __init__(self, dset, figure=None, horizontal=True, **kwargs): if not isinstance(dset, sidpy.Dataset): raise TypeError('dset should be a sidpy.Dataset object') scale_bar = kwargs.pop('scale_bar', False) colorbar = kwargs.pop('colorbar', True) self.set_title = kwargs.pop('set_title', True) fig_args = dict() temp = kwargs.pop('figsize', None) if temp is not None: fig_args['figsize'] = temp if figure is None: self.fig = plt.figure(**fig_args) else: self.fig = figure if len(dset.shape) < 4: raise TypeError('dataset must have at least four dimensions') # Find scan and 4D_image dimension scan_x = kwargs.pop('scan_x', None) scan_y = kwargs.pop('scan_y', None) image_x = kwargs.pop('image_4d_x', None) image_y = kwargs.pop('image_4d_y', None) self.gamma = kwargs.pop('gamma', False) for dim, axis in dset._axes.items(): if axis.dimension_type in [sidpy.DimensionType.SPATIAL]: if scan_y is None: scan_y = dim elif scan_x is None: scan_x = dim # We assume slow scan first order if scan_y is None or scan_x is None: scan_y = 0 scan_x = 1 if image_y is None: for dim in range(4): if dim not in [scan_x, scan_y]: image_y = dim break if image_x is None: for dim in range(4): if dim not in [image_y, scan_x, scan_y]: image_x = dim break image_dims = [scan_x, scan_y] dims_4d = [image_x, image_y] if len(image_dims) != 2: raise TypeError('We need two dimensions with dimension_type SPATIAL: to plot an image') if len(dims_4d) != 2: raise TypeError('We need two dimension with dimension_type other than spatial for a 4D image plot') self.horizontal = horizontal self.x = 0 self.y = 0 self.bin_x = 1 self.bin_y = 1 image_dims = [scan_x, scan_y] size_x = dset.shape[image_dims[0]] size_y = dset.shape[image_dims[1]] self.dset = dset self.extent = [0, size_x, size_y, 0] self.rectangle = [0, size_x, 0, size_y] self.scaleX = 1.0 self.scaleY = 1.0 self.analysis = [] self.plot_legend = False self.image_dims = image_dims self.dims_4d = dims_4d if is_complex_dtype(dset.dtype): number_of_plots = 3 else: number_of_plots = 2 self.number_of_plots = number_of_plots if horizontal: self.axes = self.fig.subplots(ncols=number_of_plots) else: self.axes = self.fig.subplots(nrows=number_of_plots, **fig_args) if self.set_title: self.fig.canvas.manager.set_window_title(self.dset.title) self.image = np.array(dset).mean(axis=tuple(dims_4d)) if is_complex_dtype(dset.dtype): self.image = np.abs(np.array(dset)).mean(axis=tuple(dims_4d)) self.axes[0].imshow(self.image.T, extent=self.dset.get_extent(self.image_dims), **kwargs) #if horizontal: self.axes[0].set_xlabel('{} [{}]'.format(self.dset._axes[image_dims[0]].quantity, self.dset._axes[image_dims[0]].units)) #else: self.axes[0].set_ylabel('{} [{}]'.format(self.dset._axes[image_dims[1]].quantity, self.dset._axes[image_dims[1]].units)) self.axes[0].set_aspect('equal') # self.rect = patches.Rectangle((0,0),1,1,linewidth=1,edgecolor='r',facecolor='red', alpha = 0.2) self.rect = patches.Rectangle((0, 0), self.bin_x, self.bin_y, linewidth=1, edgecolor='r', facecolor='red', alpha=0.2) self.axes[0].add_patch(self.rect) self.intensity_scale = 1. self.image_4d = self.get_image_4d() if is_complex_dtype(dset.dtype): self.image_4d = np.abs(self.image_4d) if self.gamma: self.image_4d = np.log(1+self.image_4d) self.reciprocal_extent = None if len(self.dset.get_extent(self.dset.get_spectral_dims()))==4: self.reciprocal_extent = self.dset.get_extent(self.dset.get_spectral_dims()) self.axes[1].imshow(self.image_4d, extent = self.reciprocal_extent) if self.set_title: self.axes[1].set_title('set {}, {}'.format(self.x, self.y)) self.xlabel = self.dset.labels[self.dims_4d[0]] self.ylabel = self.dset.labels[self.dims_4d[1]] self.axes[1].set_xlabel(self.xlabel) # + x_suffix) self.axes[1].set_ylabel(self.ylabel) self.axes[1].ticklabel_format(style='sci', scilimits=(-2, 3)) if is_complex_dtype(dset.dtype): self.axes[2].imshow(np.angle(np.array(self.image_4d))) if self.set_title: self.axes[1].set_title('power {}, {}'.format(self.x, self.y)) self.axes[2].set_title('phase {}, {}'.format(self.x, self.y)) self.axes[2].set_xlabel(self.xlabel) # + x_suffix) self.axes[2].set_ylabel(self.ylabel) self.axes[2].ticklabel_format(style='sci', scilimits=(-2, 3)) self.fig.tight_layout() self.cid = self.axes[1].figure.canvas.mpl_connect('button_press_event', self._onclick) self.fig.canvas.draw_idle() def set_bin(self, bin_xy): old_bin_x = self.bin_x old_bin_y = self.bin_y if isinstance(bin_xy, list): self.bin_x = int(bin_xy[0]) self.bin_y = int(bin_xy[1]) else: self.bin_x = int(bin_xy) self.bin_y = int(bin_xy) if self.bin_x > self.dset.shape[self.image_dims[0]]: self.bin_x = self.dset.shape[self.image_dims[0]] if self.bin_y > self.dset.shape[self.image_dims[1]]: self.bin_y = self.dset.shape[self.image_dims[1]] self.rect.set_width(self.rect.get_width() * self.bin_x / old_bin_x) self.rect.set_height((self.rect.get_height() * self.bin_y / old_bin_y)) if self.x + self.bin_x > self.dset.shape[self.image_dims[0]]: self.x = self.dset.shape[0] - self.bin_x if self.y + self.bin_y > self.dset.shape[self.image_dims[1]]: self.y = self.dset.shape[1] - self.bin_y self.rect.set_xy([self.x * self.rect.get_width() / self.bin_x + self.rectangle[0], self.y * self.rect.get_height() / self.bin_y + self.rectangle[2]]) self._update() def get_image_4d(self): from sidpy import DimensionType if self.x > self.dset.shape[self.image_dims[0]] - self.bin_x: self.x = self.dset.shape[self.image_dims[0]] - self.bin_x if self.y > self.dset.shape[self.image_dims[1]] - self.bin_y: self.y = self.dset.shape[self.image_dims[1]] - self.bin_y selection = [] for dim, axis in self.dset._axes.items(): # print(dim, axis.dimension_type) if dim == self.image_dims[0]: selection.append(slice(self.x, self.x + self.bin_x)) elif dim == self.image_dims[1]: selection.append(slice(self.y, self.y + self.bin_y)) elif dim in self.dims_4d: selection.append(slice(None)) else: selection.append(slice(0, 1)) self.image_4d = self.dset[tuple(selection)].mean(axis=tuple(self.image_dims)) # * self.intensity_scale[self.x,self.y] return self.image_4d.squeeze() def _onclick(self, event): self.event = event if event.inaxes in [self.axes[0]]: x = int(event.xdata) y = int(event.ydata) x = int(x - self.rectangle[0]) y = int(y - self.rectangle[2]) if x >= 0 and y >= 0: if x <= self.rectangle[1] and y <= self.rectangle[3]: self.x = int(x / (self.rect.get_width() / self.bin_x)) self.y = int(y / (self.rect.get_height() / self.bin_y)) if self.x + self.bin_x > self.dset.shape[self.image_dims[0]]: self.x = self.dset.shape[self.image_dims[0]] - self.bin_x if self.y + self.bin_y > self.dset.shape[self.image_dims[1]]: self.y = self.dset.shape[self.image_dims[1]] - self.bin_y self.rect.set_xy([self.x * self.rect.get_width() / self.bin_x + self.rectangle[0], self.y * self.rect.get_height() / self.bin_y + self.rectangle[2]]) self._update() def _update(self, ev=None): xlim = self.axes[1].get_xlim() ylim = self.axes[1].get_ylim() self.axes[1].clear() self.get_image_4d() if is_complex_dtype(self.dset.dtype): self.axes[2].clear() self.image_4d = np.abs(self.image_4d) self.axes[2].imshow(np.angle(self.image_4d)) if self.set_title: self.axes[1].set_title('power {}, {}'.format(self.x, self.y)) self.axes[2].set_title('phase {}, {}'.format(self.x, self.y)) self.axes[2].set_xlabel(self.xlabel) # + x_suffix) self.axes[2].set_ylabel(self.ylabel) self.axes[2].ticklabel_format(style='sci', scilimits=(-2, 3)) else: if self.set_title: self.axes[1].set_title('set {}, {}'.format(self.x, self.y)) if self.gamma: self.image_4d = np.log(1+self.image_4d) self.axes[1].imshow(self.image_4d, extent = self.reciprocal_extent) self.axes[1].set_xlim(xlim) self.axes[1].set_ylim(ylim) self.axes[1].set_xlabel(self.xlabel) self.axes[1].set_ylabel(self.ylabel) self.fig.canvas.draw_idle() def set_legend(self, set_legend): self.plot_legend = set_legend def get_xy(self): return [self.x, self.y] class ComplexSpectralImageVisualizer(object): """ ### Interactive spectrum imaging plot for Complex Data ## 4D and complex data also works """ def __init__(self, dset, figure=None, horizontal=True, **kwargs): if not isinstance(dset, sidpy.Dataset): raise TypeError('dset should be a sidpy.Dataset object') scale_bar = kwargs.pop('scale_bar', False) colorbar = kwargs.pop('colorbar', True) self.set_title = kwargs.pop('set_title', True) fig_args = dict() temp = kwargs.pop('figsize', None) if temp is not None: fig_args['figsize'] = temp if figure is None: self.fig = plt.figure(**fig_args) else: self.fig = figure if len(dset.shape) > 4: raise TypeError('dataset must have four dimensions at max') if 'complex' not in dset.dtype.name: raise TypeError('This visualizer is only for Complex Data, data type is {}'.format(dset.dtype)) # We need one stack dim and two image dimes as lists in dictionary selection = [] image_dims = [] spectral_dim = [] channel_dim = [] for dim, axis in dset._axes.items(): if axis.dimension_type in [sidpy.DimensionType.SPATIAL, sidpy.DimensionType.RECIPROCAL]: selection.append(slice(None)) image_dims.append(dim) elif axis.dimension_type == sidpy.DimensionType.SPECTRAL: selection.append(slice(0, 1)) spectral_dim.append(dim) elif axis.dimension_type == sidpy.DimensionType.CHANNEL: channel_dim.append(dim) else: selection.append(slice(0, 1)) if len(image_dims) != 2: raise TypeError('We need two dimensions with dimension_type SPATIAL: to plot an image') if len(channel_dim) >1: raise ValueError("We have more than one Channel Dimension, this won't work for the visualizer") if len(spectral_dim)>1: raise ValueError("We have more than one Spectral Dimension, this won't work for the visualizer...") if len(dset.shape)==4: if len(channel_dim)!=1: raise TypeError("We need one dimension with type CHANNEL \ for a spectral image plot for a 4D dataset") elif len(dset.shape)==3: if len(spectral_dim) != 1: raise TypeError("We need one dimension with dimension_type SPECTRAL \ to plot a spectra for a 3D dataset") self.horizontal = horizontal self.x = 0 self.y = 0 self.bin_x = 1 self.bin_y = 1 size_x = dset.shape[image_dims[0]] size_y = dset.shape[image_dims[1]] self.dset = dset self.energy_axis = spectral_dim[0] if len(channel_dim)>0: self.channel_axis = channel_dim self.energy_scale = dset._axes[self.energy_axis].values self.extent = [0, size_x, size_y, 0] self.rectangle = [0, size_x, 0, size_y] self.scaleX = 1.0 self.scaleY = 1.0 self.analysis = [] self.plot_legend = False self.ri_ap = 'Real and Imaginary' #real/imaginary of amplitude/phase plotting self.image_dims = image_dims self.spec_dim = spectral_dim[0] if horizontal: self.axes = self.fig.subplots(ncols=3) else: self.axes = self.fig.subplots(nrows=3, **fig_args) if self.set_title: self.fig.canvas.manager.set_window_title(self.dset.title) if len(channel_dim)>0: self.image = dset.mean(axis=(spectral_dim[0],channel_dim[0])) else: self.image = dset.mean(axis=(spectral_dim[0])) if 1 in self.dset.shape: self.image = dset.squeeze() self.axes[0].set_aspect('auto') else: self.axes[0].set_aspect('equal') #self.axes[0].imshow(np.abs(self.image.T), extent=self.extent, **kwargs)# throwing an error self.axes[0].imshow(np.abs(np.array(self.image)).T, extent=self.extent, **kwargs) if horizontal: self.axes[0].set_xlabel('{} [pixels]'.format(self.dset._axes[image_dims[0]].quantity)) else: self.axes[0].set_ylabel('{} [pixels]'.format(self.dset._axes[image_dims[1]].quantity)) if 1 in self.dset.shape: self.axes[0].set_aspect('auto') self.axes[0].get_yaxis().set_visible(False) else: self.axes[0].set_aspect('equal') self.rect = patches.Rectangle((0, 0), self.bin_x, self.bin_y, linewidth=1, edgecolor='r', facecolor='red', alpha=0.2) self.axes[0].add_patch(self.rect) self.intensity_scale = 1. self.spectrum = self.get_spectrum() if len(self.energy_scale)!=self.spectrum.shape[0]: self.spectrum = self.spectrum.T self.axes[1].plot(self.energy_scale, np.real(self.spectrum.compute()), label = 'Real') self.axes[2].plot(self.energy_scale, np.imag(self.spectrum.compute()), label = 'Imaginary') for ax_ind in [1,2]: self.axes[ax_ind].set_title('spectrum {}, {}'.format(self.x, self.y)) self.xlabel = self.dset.labels[self.spec_dim] self.ylabel = self.dset.data_descriptor self.axes[ax_ind].set_xlabel(self.dset.labels[self.spec_dim]) # + x_suffix) self.axes[ax_ind].set_ylabel(self.dset.data_descriptor) self.axes[ax_ind].ticklabel_format(style='sci', scilimits=(-2, 3)) leg = self.axes[ax_ind].legend(loc = 'best') leg.get_frame().set_linewidth(0.0) self.fig.tight_layout() self.cid = self.axes[1].figure.canvas.mpl_connect('button_press_event', self._onclick) self.button = ipywidgets.Dropdown(options=['Real and Imaginary', 'Amplitude and Phase'], description='Plot', disabled=False, tooltip='How to plot complex data') self.button.observe(self._ri_ap, 'value') #real/imag or amp/phase widg = ipywidgets.HBox([self.button]) display(widg) self.fig.canvas.draw_idle() def _ri_ap(self, event): self.ri_ap = event.new self._update() def set_bin(self, bin_xy): old_bin_x = self.bin_x old_bin_y = self.bin_y if isinstance(bin_xy, list): self.bin_x = int(bin_xy[0]) self.bin_y = int(bin_xy[1]) else: self.bin_x = int(bin_xy) self.bin_y = int(bin_xy) if self.bin_x > self.dset.shape[self.image_dims[0]]: self.bin_x = self.dset.shape[self.image_dims[0]] if self.bin_y > self.dset.shape[self.image_dims[1]]: self.bin_y = self.dset.shape[self.image_dims[1]] self.rect.set_width(self.rect.get_width() * self.bin_x / old_bin_x) self.rect.set_height((self.rect.get_height() * self.bin_y / old_bin_y)) if self.x + self.bin_x > self.dset.shape[self.image_dims[0]]: self.x = self.dset.shape[0] - self.bin_x if self.y + self.bin_y > self.dset.shape[self.image_dims[1]]: self.y = self.dset.shape[1] - self.bin_y self.rect.set_xy([self.x * self.rect.get_width() / self.bin_x + self.rectangle[0], self.y * self.rect.get_height() / self.bin_y + self.rectangle[2]]) self._update() def get_spectrum(self): if self.x > self.dset.shape[self.image_dims[0]] - self.bin_x: self.x = self.dset.shape[self.image_dims[0]] - self.bin_x if self.y > self.dset.shape[self.image_dims[1]] - self.bin_y: self.y = self.dset.shape[self.image_dims[1]] - self.bin_y selection = [] for dim, axis in self.dset._axes.items(): # print(dim, axis.dimension_type) if axis.dimension_type == sidpy.DimensionType.SPATIAL: if dim == self.image_dims[0]: selection.append(slice(self.x, self.x + self.bin_x)) else: selection.append(slice(self.y, self.y + self.bin_y)) elif axis.dimension_type == sidpy.DimensionType.SPECTRAL: selection.append(slice(None)) elif axis.dimension_type == sidpy.DimensionType.CHANNEL: selection.append(slice(None)) else: selection.append(slice(0, 1)) self.spectrum = self.dset[tuple(selection)].mean(axis=tuple(self.image_dims)) # * self.intensity_scale[self.x,self.y] return self.spectrum.squeeze() def _onclick(self, event): self.event = event if event.inaxes in [self.axes[0]]: x = int(event.xdata) y = int(event.ydata) x = int(x - self.rectangle[0]) y = int(y - self.rectangle[2]) if x >= 0 and y >= 0: if x <= self.rectangle[1] and y <= self.rectangle[3]: self.x = int(x / (self.rect.get_width() / self.bin_x)) self.y = int(y / (self.rect.get_height() / self.bin_y)) if self.x + self.bin_x > self.dset.shape[self.image_dims[0]]: self.x = self.dset.shape[self.image_dims[0]] - self.bin_x if self.y + self.bin_y > self.dset.shape[self.image_dims[1]]: self.y = self.dset.shape[self.image_dims[1]] - self.bin_y self.rect.set_xy([self.x * self.rect.get_width() / self.bin_x + self.rectangle[0], self.y * self.rect.get_height() / self.bin_y + self.rectangle[2]]) self._update() else: if event.dblclick: bottom = float(self.spectrum.min()) if bottom < 0: bottom *= 1.02 else: bottom *= 0.98 top = float(self.spectrum.max()) if top > 0: top *= 1.02 else: top *= 0.98 self.axes[1].set_ylim(bottom=bottom, top=top) def _update(self, ev=None): xlim_ax1 = self.axes[1].get_xlim() ylim_ax1 = self.axes[1].get_ylim() xlim_ax2 = self.axes[2].get_xlim() ylim_ax2 = self.axes[2].get_ylim() xlims = [xlim_ax1,xlim_ax2] ylims = [ylim_ax1, ylim_ax2] self.axes[1].clear() self.axes[2].clear() self.get_spectrum() if len(self.energy_scale)!=self.spectrum.shape[0]: self.spectrum = self.spectrum if self.ri_ap == 'Real and Imaginary': self.axes[1].plot(self.energy_scale, np.real(self.spectrum.compute()), label='Real') self.axes[2].plot(self.energy_scale, np.imag(self.spectrum.compute()), label='Imaginary') else: self.axes[1].plot(self.energy_scale, np.abs(self.spectrum.compute()), label='Amplitude') self.axes[2].plot(self.energy_scale, np.angle(self.spectrum.compute()), label='Phase') for ind,ax_ind in enumerate([1,2]): if self.set_title: self.axes[ax_ind].set_title('spectrum {}, {}'.format(self.x, self.y)) self.axes[ax_ind].set_xlim(xlims[ind]) self.axes[ax_ind].set_xlabel(self.xlabel) self.axes[ax_ind].set_ylabel(self.ylabel) leg = self.axes[ax_ind].legend(loc = 'best') leg.get_frame().set_linewidth(0.0) self.fig.canvas.draw_idle() self.fig.tight_layout() def set_legend(self, set_legend): self.plot_legend = set_legend def get_xy(self): return [self.x, self.y] class SpectralImageFitVisualizer(SpectralImageVisualizer): def __init__(self, original_dataset, fit_dataset, figure=None, horizontal=True): ''' Visualizer for spectral image datasets, fit by the Sidpy Fitter This class is called by Sidpy Fitter for visualizing the raw/fit dataset interactively. Inputs: - original_dataset: sidpy.Dataset containing the raw data - fit_dataset: sidpy.Dataset with the fitted data. This is returned by the Sidpy Fitter after functional fitting. - figure: (Optional, default None) - handle to existing figure - horiziontal: (Optional, default True) - whether spectrum should be plotted horizontally ''' super().__init__(original_dataset, figure, horizontal) self.fit_dset = fit_dataset self.axes[1].clear() self.get_fit_spectrum() self.axes[1].plot(self.energy_scale, self.spectrum, 'bo') self.axes[1].plot(self.energy_scale, self.fit_spectrum, 'r-') def get_fit_spectrum(self): if self.x > self.dset.shape[self.image_dims[0]] - self.bin_x: self.x = self.dset.shape[self.image_dims[0]] - self.bin_x if self.y > self.dset.shape[self.image_dims[1]] - self.bin_y: self.y = self.dset.shape[self.image_dims[1]] - self.bin_y selection = [] for dim, axis in self.dset._axes.items(): if axis.dimension_type == sidpy.DimensionType.SPATIAL: if dim == self.image_dims[0]: selection.append(slice(self.x, self.x + self.bin_x)) else: selection.append(slice(self.y, self.y + self.bin_y)) elif axis.dimension_type == sidpy.DimensionType.SPECTRAL: selection.append(slice(None)) else: selection.append(slice(0, 1)) self.spectrum = np.array(self.dset[tuple(selection)].mean(axis=tuple(self.image_dims))) self.fit_spectrum = np.array(self.fit_dset[tuple(selection)].mean(axis=tuple(self.image_dims))) # * self.intensity_scale[self.x,self.y] return self.fit_spectrum.squeeze(), self.spectrum.squeeze() def _update(self, ev=None): xlim = self.axes[1].get_xlim() ylim = self.axes[1].get_ylim() self.axes[1].clear() self.get_fit_spectrum() self.axes[1].plot(self.energy_scale, self.spectrum, 'bo', label='experiment') self.axes[1].plot(self.energy_scale, self.fit_spectrum, 'r-', label='fit') if self.set_title: self.axes[1].set_title('spectrum {}, {}'.format(self.x, self.y)) self.axes[1].set_xlim(xlim) #self.axes[1].set_ylim(ylim) self.axes[1].set_xlabel(self.xlabel) self.axes[1].set_ylabel(self.ylabel) self.fig.canvas.draw_idle() sidpy-0.12.3/sidpy/viz/jupyter_utils.py000066400000000000000000000434531455261647000202040ustar00rootroot00000000000000""" Utilities for interactive visualization of data in Jupyter notebooks Created on 11/11/16 10:08 AM @author: Suhas Somnath, Chris Smith """ from __future__ import division, print_function, unicode_literals, absolute_import import os import matplotlib.pyplot as plt import ipywidgets as widgets import numpy as np import sys from sidpy.viz.plot_utils.image import plot_map from sidpy.viz.plot_utils.misc import export_fig_data from sidpy.sid.dimension import Dimension if sys.version_info.major == 3: unicode = str def simple_ndim_visualizer(data_mat, pos_dims, spec_dims, spec_xdim=None, pos_xdim=None, verbose=False): """ Generates a simple visualizer for visualizing simple datasets (up to 4 dimensions). The visualizer will ONLY work within the context of a jupyter notebook! The visualizer consists of two panels - spatial map and spectrograms. slider widgets will be generated to slice dimensions. The data matrix can be real, complex or compound valued Parameters ---------- data_mat : numpy.array object Data to be visualized pos_dims : list / tuple List of Dimension objects specifying all position dimensions in the same order as in data_mat spec_dims : list / tuple List of Dimension objects specifying all position dimensions in the same order as in data_mat spec_xdim : str, optional Name of dimension with respect to which the spectral data will be plotted for 1D plots pos_xdim : str, optional Name of dimension with respect to which the position data will be plotted for 1D plots verbose : bool, optional Whether or not to print log statements """ label_fontsize = 12 subtitle_fontsize = 13 # ############################# VALIDATING ALL INPUTS ############################################################## for parm, parm_name in zip([pos_dims, spec_dims], ['pos_dims', 'spec_dims']): if not isinstance(parm, (list, tuple)): raise TypeError('Expected {} to be of type: Iterable - example list or tuple'.format(parm_name)) for item in parm: if not isinstance(item, Dimension): raise TypeError('Expected items in {} to be of type: Dimension'.format(parm_name)) if len(parm) > 2: raise NotImplementedError('Currently not able to handle more than 2 position or spectroscopic dimensions.' ' {} contains {} dimensions'.format(parm_name, len(parm))) elif len(parm) < 1: raise ValueError('{} contains too few ({}) dimensions'.format(parm_name, len(parm))) if len(pos_dims) + len(spec_dims) != data_mat.ndim: raise ValueError('Lengths of pos_dims: {} and spec_dims: {} not matching with that of the dimensions of ' 'data_mat: {}'.format(len(pos_dims), len(spec_dims), data_mat.ndim)) # now check if the dimension matches with that of the N dimensional dataset for parm, dim_type in zip([pos_dims, spec_dims], ['Position', 'Spectroscopic']): offset = 0 if dim_type == 'Spectroscopic': offset += len(pos_dims) for ind, item in enumerate(parm): actual_ind = ind + offset if item.values is None: # Let's take this opportunity to fill in the values: item.values = np.arange(data_mat.shape[actual_ind]) if verbose: print('automatically generated reference {} values for dimension: {}'.format(dim_type, item.name)) else: if len(item.values) != data_mat.shape[actual_ind]: raise ValueError( '{} dimension {} of size {} in the dataset does not have values of the same length: {}' '.'.format(dim_type, item.name, data_mat.shape[actual_ind], len(item.values))) # Is there anything worth visualizing interactively at all in positions or spectroscopic? pos_size = np.prod([len(item.values) for item in pos_dims]) spec_size = np.prod([len(item.values) for item in spec_dims]) if pos_size == 1 or spec_size == 1: raise ValueError('Too few position: {} or spectroscopic: {} values to visualize interactively. ' 'Consider using alternate visualization approaches'.format(pos_size, spec_size)) # create a dictionary that will allow lookup of values and units by name: pos_dims_dict = {} for dimension in pos_dims: pos_dims_dict[dimension.name] = dimension spec_dims_dict = {} for dimension in spec_dims: spec_dims_dict[dimension.name] = dimension if spec_xdim is not None: if not isinstance(spec_xdim, (str, unicode)): raise TypeError('spec_xdim should have been a string') if spec_xdim not in spec_dims_dict.keys(): raise KeyError('{} not among the provided spectroscopic dimensions'.format(spec_xdim)) if pos_xdim is not None: if not isinstance(pos_xdim, (str, unicode)): raise TypeError('spec_xdim should have been a string') if pos_xdim not in pos_dims_dict.keys(): raise KeyError('{} not among the provided position dimensions'.format(pos_xdim)) pos_plot_2d = len(pos_dims) > 1 and pos_xdim is None spec_plot_2d = len(spec_dims) > 1 and spec_xdim is None if verbose: print('Plot 2D: Positions: {}, Spectroscopic: {}'.format(pos_plot_2d, spec_plot_2d)) if not spec_plot_2d and spec_xdim is None: # Take the largest dimension you can find: max_ind = np.argmax([len(item.values) for item in spec_dims]) spec_xdim = spec_dims[max_ind].name if verbose: print('automatically chose X axis for 1D Spectroscopic plot as {}'.format(spec_xdim)) if not pos_plot_2d and pos_xdim is None: # Take the largest dimension you can find: max_ind = np.argmax([len(item.values) for item in pos_dims]) pos_xdim = pos_dims[max_ind].name if verbose: print('automatically chose X axis for 1D Position plot as {}'.format(pos_xdim)) pos_dim_names = [item.name for item in pos_dims] spec_dim_names = [item.name for item in spec_dims] # ################################## HELPER FUNCTIONS ############################################################## def check_data_type(data_mat): if data_mat.dtype.names is not None: return 2, list(data_mat.dtype.names), None if data_mat.dtype in [np.complex64, np.complex128, complex]: return 1, ['Real', 'Imaginary', 'Amplitude', 'Phase'], [np.real, np.imag, np.abs, np.angle] else: return 0, None, None def get_clims(data, stdev=2): avg = np.mean(data) std = np.std(data) return avg - stdev * std, avg + stdev * std def get_slice_string(slice_dict, dim_list): slice_str = '' for dimension in dim_list: assert isinstance(dimension, Dimension) if dimension.name in slice_dict.keys(): # TODO: Format to only have 1-2 digits of precision / use scientific notation slice_str += '{} = {} {}\n'.format(dimension.name, dimension.values[slice_dict[dimension.name]], dimension.units) slice_str = slice_str[:-1] return slice_str def get_slicing_tuple(slice_dict): slice_list = [] for dim_name in pos_dim_names + spec_dim_names: cur_slice = slice(None) if slice_dict[dim_name] is not None: cur_slice = slice(slice_dict[dim_name], slice_dict[dim_name] + 1) slice_list.append(cur_slice) return tuple(slice_list) def naive_slice(data_mat, slice_dict): return np.squeeze(data_mat[get_slicing_tuple(slice_dict)]) def get_spatmap_slice_dict(slice_dict={}): spatmap_slicing = {} for name in pos_dim_names: spatmap_slicing[name] = None for ind, name in enumerate(spec_dim_names): spatmap_slicing[name] = slice_dict.get(name, data_mat.shape[ind + len(pos_dim_names)] // 2) return spatmap_slicing def get_spgram_slice_dict(slice_dict={}): spgram_slicing = {} for ind, name in enumerate(pos_dim_names): spgram_slicing[name] = slice_dict.get(name, data_mat.shape[ind] // 2) for name in spec_dim_names: spgram_slicing[name] = None return spgram_slicing def plot_1d(axis, image_mat, dim_name, dim_dict, component_title): axis.set_xlabel(dim_name + ' (' + dim_dict[dim_name].units + ')', fontsize=label_fontsize) axis.set_ylabel(component_title, fontsize=label_fontsize) if image_mat.shape[0] != dim_dict[dim_name].values.size: image_mat = image_mat.T img_handle = axis.plot(dim_dict[dim_name].values, image_mat) if image_mat.ndim > 1: other_dims = list(dim_dict.keys()).copy() other_dims.remove(dim_name) other_dims = other_dims[0] axis.legend(dim_dict[other_dims].values) # , fontsize=14) # set_tick_font_size(axis, 14) return img_handle def plot_2d(axis, image_mat, clims, dim_list): if verbose: print('image shape: {}, x_vec: {}, y_vec: {}'.format(image_mat.shape, dim_list[1].values.shape, dim_list[0].values.shape)) img, cbar = plot_map(axis, image_mat, aspect='auto', clim=clims, x_vec=dim_list[1].values, y_vec=dim_list[0].values) axis.set_xlabel(dim_list[1].name + ' (' + dim_list[1].units + ')', fontsize=label_fontsize) axis.set_ylabel(dim_list[0].name + ' (' + dim_list[0].units + ')', fontsize=label_fontsize) return img, cbar def update_image(axis, img_handle, data_mat, slice_dict, twoD=True): if twoD: img_handle.set_data(naive_slice(data_mat, slice_dict)) else: y_mat = naive_slice(data_mat, slice_dict) if y_mat.ndim > 1: if y_mat.shape[0] != len(img_handle): y_mat = y_mat.T for line_handle, y_vec in zip(img_handle, y_mat): line_handle.set_ydata(y_vec) axis.set_ylim([np.min(y_mat), np.max(y_mat)]) # ########################## VISUALIZATION BEGINS ############################################################## data_type, data_names, data_funcs = check_data_type(data_mat) sub_data = data_mat component_name = 'Real' if data_type == 1: if verbose: print('Data found to be of type: complex') sub_data = data_funcs[0](data_mat) component_name = data_names[0] elif data_type == 2: if verbose: print('Data found to be of type: compound') component_name = data_names[0] sub_data = data_mat[component_name] else: if verbose: print('Data found to be of type: scalar / real') component_title = 'Component: ' + component_name if verbose: print('default component name: {}'.format(component_name)) clims = get_clims(sub_data) if verbose: print('Default clims: {}'.format(clims)) spatmap_slicing = get_spatmap_slice_dict() spgram_slicing = get_spgram_slice_dict() if verbose: print('Slicing: Spatial: {}, Spectrogram: {}'.format(spatmap_slicing, spgram_slicing)) current_spatmap = naive_slice(sub_data, spatmap_slicing) current_spgram = naive_slice(sub_data, spgram_slicing) if verbose: print('Spatial map data shape: {}, Spectrogram data shape: {}'.format(current_spatmap.shape, current_spgram.shape)) fig, axes = plt.subplots(ncols=2, figsize=(8, 3.5)) # axes[0].hold(True) spec_titles = get_slice_string(spatmap_slicing, spec_dims) axes[0].set_title('Spatial Map for\n' + component_title + '\n' + spec_titles, fontsize=subtitle_fontsize) if pos_plot_2d: img_spat, cbar_spat = plot_2d(axes[0], current_spatmap, clims, pos_dims) main_vert_line = axes[0].axvline(x=spgram_slicing[pos_dims[1].name], color='k') main_hor_line = axes[0].axhline(y=spgram_slicing[pos_dims[0].name], color='k') else: img_spat = plot_1d(axes[0], current_spatmap, pos_xdim, pos_dims_dict, component_title) pos_titles = get_slice_string(spgram_slicing, pos_dims) axes[1].set_title('Spectrogram for\n' + component_title + '\n' + pos_titles, fontsize=subtitle_fontsize) if spec_plot_2d: img_spec, cbar_spec = plot_2d(axes[1], current_spgram, clims, spec_dims) else: img_spec = plot_1d(axes[1], current_spgram, spec_xdim, spec_dims_dict, component_title) fig.tight_layout() slice_dict = {} for dim_ind, dim_name in enumerate([item.name for item in pos_dims]): slice_dict[dim_name] = (0, sub_data.shape[dim_ind] - 1, 1) for dim_ind, dim_name in enumerate([item.name for item in spec_dims]): slice_dict[dim_name] = (0, sub_data.shape[dim_ind + len(pos_dims)] - 1, 1) if data_type > 0: slice_dict['component'] = data_names # stupid and hacky way of doing this: global_vars = {'sub_data': sub_data, 'component_title': component_title} def update_plots(**kwargs): component_name = kwargs.get('component', None) if component_name is not None: if component_name != slice_dict['component']: # update the data and title: if data_type == 1: func_ind = data_names.index(component_name) sub_data = data_funcs[func_ind](data_mat) elif data_type == 2: sub_data = data_mat[component_name] component_title = 'Component: ' + component_name # sub data and component_title here are now local, update gobal vars! global_vars.update({'sub_data': sub_data, 'component_title': component_title}) clims = get_clims(sub_data) update_image(axes[0], img_spat, sub_data, spatmap_slicing, twoD=pos_plot_2d) if pos_plot_2d: img_spat.set_clim(clims) else: axes[0].set_ylabel(component_title, fontsize=label_fontsize) update_image(axes[1], img_spec, sub_data, spgram_slicing, twoD=spec_plot_2d) if spec_plot_2d: img_spec.set_clim(clims) else: axes[1].set_ylabel(component_title, fontsize=label_fontsize) spec_titles = get_slice_string(spatmap_slicing, spec_dims) axes[0].set_title('Spatial Map for\n' + component_title + '\n' + spec_titles) pos_titles = get_slice_string(spgram_slicing, pos_dims) axes[1].set_title('Spectrogram for\n' + component_title + '\n' + pos_titles) # print('Updated component!') # Check to see if spectrogram needs to be updated: update_spgram = False for dim_name in [item.name for item in pos_dims]: if kwargs[dim_name] != slice_dict[dim_name]: update_spgram = True break if update_spgram: # print('updating spectrogam + crosshairs') spgram_slicing.update(get_spgram_slice_dict(slice_dict=kwargs)) update_image(axes[1], img_spec, global_vars['sub_data'], spgram_slicing, twoD=spec_plot_2d) pos_titles = get_slice_string(spgram_slicing, pos_dims) axes[1].set_title('Spectrogram for\n' + global_vars['component_title'] + '\n' + pos_titles, fontsize=subtitle_fontsize) if pos_plot_2d: main_vert_line.set_xdata(spgram_slicing[pos_dims[1].name]) main_hor_line.set_ydata(spgram_slicing[pos_dims[0].name]) update_spatmap = False for dim_name in [item.name for item in spec_dims]: if kwargs[dim_name] != slice_dict[dim_name]: update_spatmap = True break if update_spatmap: # print('updating spatial map') spatmap_slicing.update(get_spatmap_slice_dict(slice_dict=kwargs)) update_image(axes[0], img_spat, global_vars['sub_data'], spatmap_slicing, twoD=pos_plot_2d) spec_titles = get_slice_string(spatmap_slicing, spec_dims) axes[0].set_title('Spatial Map for\n' + global_vars['component_title'] + '\n' + spec_titles, fontsize=subtitle_fontsize) slice_dict.update(kwargs) widgets.interact(update_plots, **slice_dict) return fig def save_fig_filebox_button(fig, filename): """ Create ipython widgets to allow the user to save a figure to the specified file. Parameters ---------- fig : matplotlib.Figure The figure to be saved. filename : str The filename the figure should be saved to Returns ------- widget_box : ipywidgets.HBox Widget box holding the text entry and save button """ filename = os.path.abspath(filename) file_dir, filename = os.path.split(filename) name_box = widgets.Text(value=filename, placeholder='Type something', description='Output Filename:', disabled=False, layout={'width': '50%'}) save_button = widgets.Button(description='Save figure') def _save_fig(*args): filename = name_box.value save_path = os.path.join(file_dir, filename) _, ext = os.path.splitext(filename) if ext == '.txt': export_fig_data(fig, save_path, True) else: fig.savefig(save_path, dpi='figure') print('Figure saved to "{}".'.format(save_path)) widget_box = widgets.HBox([name_box, save_button]) save_button.on_click(_save_fig) return widget_box sidpy-0.12.3/sidpy/viz/plot_utils/000077500000000000000000000000001455261647000170755ustar00rootroot00000000000000sidpy-0.12.3/sidpy/viz/plot_utils/__init__.py000066400000000000000000000002301455261647000212010ustar00rootroot00000000000000""" Utilities for simple 1D and 2D static plots using matplotlib """ from .misc import * from .cmap import * from .image import * from .curve import * sidpy-0.12.3/sidpy/viz/plot_utils/cmap.py000066400000000000000000000176131455261647000203770ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Utilities for generating static image and line plots of near-publishable quality Created on Thu May 05 13:29:12 2016 @author: Suhas Somnath, Chris R. Smith """ from __future__ import division, print_function, absolute_import, unicode_literals from numbers import Number import sys import matplotlib as mpl import numpy as np from dask import array as da from matplotlib import pyplot as plt from matplotlib.colors import LinearSegmentedColormap if sys.version_info.major == 3: unicode = str default_cmap = plt.cm.viridis def get_cmap_object(cmap): """ Get the matplotlib.colors.LinearSegmentedColormap object regardless of the input Parameters ---------- cmap : String, or matplotlib.colors.LinearSegmentedColormap object (Optional) Requested color map Returns ------- cmap : matplotlib.colors.LinearSegmentedColormap object Requested / Default colormap object """ if cmap is None: return default_cmap elif type(cmap) in [str, unicode]: return plt.get_cmap(cmap) elif not isinstance(cmap, mpl.colors.Colormap): raise TypeError('cmap should either be a matplotlib.colors.Colormap object or a string') return cmap def cmap_jet_white_center(): """ Generates the jet colormap with a white center Returns ------- white_jet : matplotlib.colors.LinearSegmentedColormap object color map object that can be used in place of the default colormap """ # For red - central column is like brightness # For blue - last column is like brightness cdict = {'red': ((0.00, 0.0, 0.0), (0.30, 0.0, 0.0), (0.50, 1.0, 1.0), (0.90, 1.0, 1.0), (1.00, 0.5, 1.0)), 'green': ((0.00, 0.0, 0.0), (0.10, 0.0, 0.0), (0.42, 1.0, 1.0), (0.58, 1.0, 1.0), (0.90, 0.0, 0.0), (1.00, 0.0, 0.0)), 'blue': ((0.00, 0.0, 0.5), (0.10, 1.0, 1.0), (0.50, 1.0, 1.0), (0.70, 0.0, 0.0), (1.00, 0.0, 0.0)) } return LinearSegmentedColormap('white_jet', cdict) def cmap_from_rgba(name, interp_vals, normalization_val): """ Generates a colormap given a matlab-style interpolation table Parameters ---------- name : String / Unicode Name of the desired colormap interp_vals : List of tuples Interpolation table that describes the desired color map. Each entry in the table should be described as: (position in the colorbar, (red, green, blue, alpha)) The position in the color bar, red, green, blue, and alpha vary from 0 to the normalization value normalization_val : number The common maximum value for the position in the color bar, red, green, blue, and alpha Returns ------- new_cmap : matplotlib.colors.LinearSegmentedColormap object desired color map """ if not isinstance(name, (str, unicode)): raise TypeError('name should be a string') if not isinstance(interp_vals, (list, tuple, np.array)): raise TypeError('interp_vals must be a list of tuples') if not isinstance(normalization_val, Number): raise TypeError('normalization_val must be a number') normalization_val = np.round(1.0 * normalization_val) cdict = {'red': tuple([(dist / normalization_val, colors[0] / normalization_val, colors[0] / normalization_val) for (dist, colors) in interp_vals][::-1]), 'green': tuple([(dist / normalization_val, colors[1] / normalization_val, colors[1] / normalization_val) for (dist, colors) in interp_vals][::-1]), 'blue': tuple([(dist / normalization_val, colors[2] / normalization_val, colors[2] / normalization_val) for (dist, colors) in interp_vals][::-1]), 'alpha': tuple([(dist / normalization_val, colors[3] / normalization_val, colors[3] / normalization_val) for (dist, colors) in interp_vals][::-1])} return LinearSegmentedColormap(name, cdict) def make_linear_alpha_cmap(name, solid_color, normalization_val, min_alpha=0, max_alpha=1): """ Generates a transparent to opaque color map based on a single solid color Parameters ---------- name : String / Unicode Name of the desired colormap solid_color : List of numbers red, green, blue, and alpha values for a specific color normalization_val : number The common maximum value for the red, green, blue, and alpha values. This is 1 in matplotlib min_alpha : float (optional. Default = 0 : ie- transparent) Lowest alpha value for the bottom of the color bar max_alpha : float (optional. Default = 1 : ie- opaque) Highest alpha value for the top of the color bar Returns ------- new_cmap : matplotlib.colors.LinearSegmentedColormap object transparent to opaque color map based on the provided color """ if not isinstance(name, (str, unicode)): raise TypeError('name should be a string') if not isinstance(solid_color, (list, tuple, np.ndarray, da.core.Array)): raise TypeError('solid_color must be a list of numbers') if not len(solid_color) == 4: raise ValueError('solid-color should have fourth values') if not np.all([isinstance(x, Number) for x in solid_color]): raise TypeError('solid_color should have three numbers for red, green, blue') if not isinstance(normalization_val, Number): raise TypeError('normalization_val must be a number') if not isinstance(min_alpha, Number): raise TypeError('min_alpha should be a Number') if not isinstance(max_alpha, Number): raise TypeError('max_alpha should be a Number') if min_alpha >= max_alpha: raise ValueError('min_alpha must be less than max_alpha') solid_color = np.array(solid_color) / normalization_val * 1.0 interp_table = [(1.0, (solid_color[0], solid_color[1], solid_color[2], max_alpha)), (0, (solid_color[0], solid_color[1], solid_color[2], min_alpha))] return cmap_from_rgba(name, interp_table, 1) def cmap_hot_desaturated(): """ Returns a desaturated color map based on the hot colormap Returns ------- new_cmap : matplotlib.colors.LinearSegmentedColormap object Desaturated version of the hot color map """ hot_desaturated = [(255.0, (255, 76, 76, 255)), (218.5, (107, 0, 0, 255)), (182.1, (255, 96, 0, 255)), (145.6, (255, 255, 0, 255)), (109.4, (0, 127, 0, 255)), (72.675, (0, 255, 255, 255)), (36.5, (0, 0, 91, 255)), (0, (71, 71, 219, 255))] return cmap_from_rgba('hot_desaturated', hot_desaturated, 255) def discrete_cmap(num_bins, cmap=None): """ Create an N-bin discrete colormap from the specified input map specified Parameters ---------- num_bins : unsigned int Number of discrete bins cmap : matplotlib.colors.Colormap object Base color map to discretize Returns ------- new_cmap : matplotlib.colors.LinearSegmentedColormap object Discretized color map Notes ----- Jake VanderPlas License: BSD-style https://gist.github.com/jakevdp/91077b0cae40f8f8244a """ if cmap is None: cmap = default_cmap.name elif isinstance(cmap, mpl.colors.Colormap): cmap = cmap.name elif not isinstance(cmap, (str, unicode)): raise TypeError('cmap should be a string or a matplotlib.colors.Colormap object') if not isinstance(num_bins, int): raise TypeError('num_bins must be an unsigned integer') return plt.get_cmap(cmap, num_bins)sidpy-0.12.3/sidpy/viz/plot_utils/curve.py000066400000000000000000000542411455261647000206010ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Utilities for generating static image and line plots of near-publishable quality Created on Thu May 05 13:29:12 2016 @author: Suhas Somnath, Chris R. Smith """ from __future__ import division, print_function, absolute_import, \ unicode_literals from numbers import Number import sys import h5py import matplotlib as mpl import numpy as np from dask import array as da from matplotlib import pyplot as plt from sidpy.viz.plot_utils.misc import get_plot_grid_size, make_scalar_mappable from sidpy.viz.plot_utils.cmap import default_cmap, get_cmap_object, discrete_cmap from sidpy.viz.plot_utils.image import plot_map if sys.version_info.major == 3: unicode = str def cbar_for_line_plot(axis, num_steps, discrete_ticks=True, **kwargs): """ Adds a colorbar next to a line plot axis Parameters ---------- axis : matplotlib.axes.Axes Axis with multiple line objects num_steps : uint Number of steps in the colorbar discrete_ticks : (optional) bool Whether or not to have the ticks match the number of number of steps. Default = True """ if not isinstance(axis, mpl.axes.Axes): raise TypeError('axis must be a matplotlib.axes.Axes object') if not isinstance(num_steps, int) and num_steps > 0: raise TypeError('num_steps must be a whole number') assert isinstance(discrete_ticks, bool) cmap = get_cmap_object(kwargs.pop('cmap', None)) cmap = discrete_cmap(num_steps, cmap=cmap.name) sm = make_scalar_mappable(0, num_steps, cmap=cmap) if discrete_ticks: kwargs.update({'ticks': np.arange(num_steps)}) cbar = plt.colorbar(sm, ax=axis, orientation='vertical', pad=0.04, use_gridspec=True, **kwargs) return cbar def rainbow_plot(axis, x_vec, y_vec, num_steps=32, **kwargs): """ Plots the input against the output vector such that the color of the curve changes as a function of index Parameters ---------- axis : matplotlib.axes.Axes object Axis to plot the curve x_vec : 1D float numpy array vector that forms the X axis y_vec : 1D float numpy array vector that forms the Y axis num_steps : unsigned int (Optional) Number of discrete color steps """ if not isinstance(axis, mpl.axes.Axes): raise TypeError('axis must be a matplotlib.axes.Axes object') if not isinstance(x_vec, (list, tuple, np.ndarray, da.core.Array)): raise TypeError('x_vec must be array-like of numbers') if not isinstance(x_vec, (list, tuple, np.ndarray, da.core.Array)): raise TypeError('x_vec must be array-like of numbers') x_vec = np.array(x_vec) y_vec = np.array(y_vec) assert x_vec.ndim == 1 and y_vec.ndim == 1, 'x_vec and y_vec must be 1D arrays' assert x_vec.shape == y_vec.shape, 'x_vec and y_vec must have the same shape' if not isinstance(num_steps, int): raise TypeError('num_steps must be an integer < size of x_vec') if num_steps < 2 or num_steps >= len(x_vec) // 2: raise ValueError('num_steps should be a positive number. 1/4 to 1/16th of x_vec') assert num_steps < x_vec.size, 'num_steps must be an integer < size of x_vec' assert isinstance(kwargs, dict) cmap = kwargs.pop('cmap', default_cmap) cmap = get_cmap_object(cmap) # Remove any color flag _ = kwargs.pop('color', None) pts_per_step = len(y_vec) // num_steps for step in range(num_steps - 1): axis.plot(x_vec[step * pts_per_step:(step + 1) * pts_per_step], y_vec[step * pts_per_step:(step + 1) * pts_per_step], color=cmap(255 * step // num_steps), **kwargs) # plot the remainder: axis.plot(x_vec[(num_steps - 1) * pts_per_step:], y_vec[(num_steps - 1) * pts_per_step:], color=cmap(255 * num_steps / num_steps), **kwargs) def plot_line_family(axis, x_vec, line_family, line_names=None, label_prefix='', label_suffix='', y_offset=0, show_cbar=False, **kwargs): """ Plots a family of lines with a sequence of colors Parameters ---------- axis : matplotlib.axes.Axes object Axis to plot the curve x_vec : array-like Values to plot against line_family : 2D numpy array family of curves arranged as [curve_index, features] line_names : array-like array of string or numbers that represent the identity of each curve in the family label_prefix : string / unicode prefix for the legend (before the index of the curve) label_suffix : string / unicode suffix for the legend (after the index of the curve) y_offset : (optional) number quantity by which the lines are offset from each other vertically (useful for spectra) show_cbar : (optional) bool Whether or not to show a colorbar (instead of a legend) """ if not isinstance(axis, mpl.axes.Axes): raise TypeError('axis must be a matplotlib.axes.Axes object') if not isinstance(x_vec, (list, tuple, np.ndarray, da.core.Array)): raise TypeError('x_vec must be array-like of numbers') x_vec = np.array(x_vec) assert x_vec.ndim == 1, 'x_vec must be a 1D array' if not isinstance(line_family, list): line_family = np.array(line_family) if not isinstance(line_family, (np.ndarray, da.core.Array)): raise TypeError('line_family must be a 2d array of numbers') assert line_family.ndim == 2, 'line_family must be a 2D array' # assert x_vec.shape[1] == line_family.shape[1], \ # 'The size of the 2nd dimension of line_family must match with of x_vec, but line fam has shape {} whereas xvec has shape {}'.format(line_family.shape, x_vec.shape) num_lines = line_family.shape[0] for var, var_name in zip([label_suffix, label_prefix], ['label_suffix', 'label_prefix']): if not isinstance(var, (str, unicode)): raise TypeError(var_name + ' needs to be a string') if not isinstance(y_offset, Number): raise TypeError('y_offset should be a Number') assert isinstance(show_cbar, bool) if line_names is not None: if not isinstance(line_names, (list, tuple)): raise TypeError('line_names should be a list of strings') if not np.all([isinstance(x, (str, unicode)) for x in line_names]): raise TypeError('line_names should be a list of strings') if len(line_names) != num_lines: raise ValueError('length of line_names not matching with that of line_family') cmap = get_cmap_object(kwargs.pop('cmap', None)) if line_names is None: # label_prefix = 'Line ' line_names = [str(line_ind) for line_ind in range(num_lines)] line_names = ['{} {} {}'.format(label_prefix, cur_name, label_suffix) for cur_name in line_names] for line_ind in range(num_lines): axis.plot(x_vec, line_family[line_ind] + line_ind * y_offset, label=line_names[line_ind], color=cmap(int(255 * line_ind / (num_lines ))), **kwargs) if show_cbar: # put back the cmap parameter: kwargs.update({'cmap': cmap}) _ = cbar_for_line_plot(axis, num_lines, **kwargs) def plot_curves(excit_wfms, datasets, line_colors=[], dataset_names=[], evenly_spaced=True, num_plots=25, x_label='', y_label='', subtitle_prefix='Position', title='', use_rainbow_plots=False, fig_title_yoffset=1.05, h5_pos=None, **kwargs): """ Plots curves / spectras from multiple datasets from up to 25 evenly spaced positions Parameters ----------- excit_wfms : 1D numpy float array or list of same Excitation waveform in the time domain datasets : list of 2D numpy arrays or 2D hyp5.Dataset objects Datasets containing data arranged as (pixel, time) line_colors : list of strings Colors to be used for each of the datasets dataset_names : (Optional) list of strings Names of the different datasets to be compared evenly_spaced : boolean Evenly spaced positions or first N positions num_plots : unsigned int Number of plots x_label : (optional) String X Label for all plots y_label : (optional) String Y label for all plots subtitle_prefix : (optional) String prefix for title over each plot title : (optional) String Main plot title use_rainbow_plots : (optional) Boolean Plot the lines as a function of spectral index (eg. time) fig_title_yoffset : (optional) float Y offset for the figure title. Value should be around 1 h5_pos : HDF5 dataset reference or 2D numpy array Dataset containing position indices Returns --------- fig, axes """ for var, var_name in zip([use_rainbow_plots, evenly_spaced], ['use_rainbow_plots', 'evenly_spaced']): if not isinstance(var, bool): raise TypeError(var_name + ' should be of type: bool') for var, var_name in zip([x_label, y_label, subtitle_prefix, title], ['x_label', 'y_label', 'subtitle_prefix', 'title']): if var is not None: if not isinstance(var, (str, unicode)): raise TypeError(var_name + ' should be of type: str') else: var = '' if fig_title_yoffset is not None: if not isinstance(fig_title_yoffset, Number): raise TypeError('fig_title_yoffset should be a Number') else: fig_title_yoffset = 1.0 if h5_pos is not None: if not isinstance(h5_pos, h5py.Dataset): raise TypeError('h5_pos should be a h5py.Dataset object') if not isinstance(num_plots, int) or num_plots < 1: raise TypeError('num_plots should be a number') for var, var_name, dim_size in zip([datasets, excit_wfms], ['datasets', 'excit_wfms'], [2, 1]): mesg = '{} should be {}D arrays or iterables (list or tuples) of {}D arrays' \ '.'.format(var_name, dim_size, dim_size) if isinstance(var, (h5py.Dataset, np.ndarray, da.core.Array)): if not len(var.shape) == dim_size: raise ValueError(mesg) elif isinstance(var, (list, tuple)): if not np.all([isinstance(dset, (h5py.Dataset, np.ndarray, da.core.Array)) for dset in datasets]): raise TypeError(mesg) else: raise TypeError(mesg) # modes: # 0 = one excitation waveform and one dataset # 1 = one excitation waveform but many datasets # 2 = one excitation waveform for each of many dataset if isinstance(datasets, (h5py.Dataset, np.ndarray, da.core.Array)): # can be numpy array or h5py.dataset num_pos = datasets.shape[0] num_points = datasets.shape[1] datasets = [datasets] if isinstance(excit_wfms, (np.ndarray, h5py.Dataset, da.core.Array)): excit_wfms = [excit_wfms] elif isinstance(excit_wfms, list): if len(excit_wfms) == num_points: excit_wfms = [np.array(excit_wfms)] elif len(excit_wfms) == 1 and len(excit_wfms[0]) == num_points: excit_wfms = [np.array(excit_wfms[0])] else: raise ValueError('If only a single dataset is provided, excit_wfms should be a 1D array') line_colors = ['b'] dataset_names = ['Default'] mode = 0 else: # dataset is a list of datasets # First check if the datasets are correctly shaped: num_pos_es = list() num_points_es = list() for dataset in datasets: if not isinstance(dataset, (h5py.Dataset, np.ndarray, da.core.Array)): raise TypeError('datasets can be a list of 2D h5py.Dataset or numpy array objects') if len(dataset.shape) != 2: raise ValueError('Each datset should be a 2D array') num_pos_es.append(dataset.shape[0]) num_points_es.append(dataset.shape[1]) num_pos_es = np.array(num_pos_es) num_points_es = np.array(num_points_es) if np.unique(num_pos_es).size > 1: # or np.unique(num_points_es).size > 1: raise ValueError('The first dimension of the datasets are not matching: ' + str(num_pos_es)) num_pos = np.unique(num_pos_es)[0] if len(excit_wfms) == len(datasets): # one excitation waveform per dataset but now verify each size if not np.all([len(cur_ex) == cur_dset.shape[1] for cur_ex, cur_dset in zip(excit_wfms, datasets)]): raise ValueError('Number of points in the datasets do not match with the excitation waveforms') mode = 2 else: # one excitation waveform for all datasets if np.unique(num_points_es).size > 1: raise ValueError('Datasets don not contain the same number of points: ' + str(num_points_es)) # datasets of the same size but does this match with the size of excitation waveforms: if len(excit_wfms) != np.unique(num_points_es)[0]: raise ValueError('Number of points in dataset not matching with shape of excitation waveform') excit_wfms = [excit_wfms] mode = 1 for var, var_name in zip([dataset_names, line_colors], ['dataset_names', 'line_colors']): if not isinstance(var, (list, tuple)) or not np.all([isinstance(x, (str, unicode)) for x in var]): raise TypeError(var_name + ' should be a list of strings') if len(var) > 0 and len(var) != len(datasets): raise ValueError(var_name + ' is not of same length as datasets: ' + len(datasets)) # Next the identification of datasets: if len(dataset_names) == 0: dataset_names = ['Dataset' + ' ' + str(x) for x in range(len(dataset_names), len(datasets))] if len(line_colors) == 0: # TODO: Generate colors from a user-specified colormap or consider using line family color_list = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'pink', 'brown', 'orange'] if len(datasets) < len(color_list): remaining_colors = [x for x in color_list if x not in line_colors] line_colors += remaining_colors[:len(datasets) - len(color_list)] else: raise ValueError('Insufficient number of line colors provided') # cannot support rainbows with multiple datasets! use_rainbow_plots = use_rainbow_plots and len(datasets) == 1 if mode != 2: # convert it to something like mode 2 excit_wfms = [excit_wfms[0] for _ in range(len(datasets))] if mode != 0: # users are not allowed to specify colors _ = kwargs.pop('color', None) num_plots = min(min(num_plots, 49), num_pos) nrows, ncols = get_plot_grid_size(num_plots) if evenly_spaced: chosen_pos = np.linspace(0, num_pos - 1, nrows * ncols, dtype=int) else: chosen_pos = np.arange(nrows * ncols, dtype=int) fig, axes = plt.subplots(nrows=nrows, ncols=ncols, sharex=True, figsize=(12, 12)) if type(axes)==np.ndarray: axes_lin = axes.flatten() else: axes_lin = [axes] for count, posn in enumerate(chosen_pos): if use_rainbow_plots: rainbow_plot(axes_lin[count], excit_wfms[0], datasets[0][posn], **kwargs) else: for dataset, ex_wfm, col_val in zip(datasets, excit_wfms, line_colors): axes_lin[count].plot(ex_wfm, dataset[posn], color=col_val, **kwargs) if h5_pos is not None: # print('Row ' + str(h5_pos[posn,1]) + ' Col ' + str(h5_pos[posn,0])) # TODO: Do NOT assume 2 pos dims. Also format with low precision, use correct dim name, units as well axes_lin[count].set_title('Row ' + str(h5_pos[posn, 1]) + ' Col ' + str(h5_pos[posn, 0]), fontsize=12) else: axes_lin[count].set_title(subtitle_prefix + ' ' + str(posn), fontsize=12) if count % ncols == 0: axes_lin[count].set_ylabel(y_label, fontsize=12) if count >= (nrows - 1) * ncols: axes_lin[count].set_xlabel(x_label, fontsize=12) axes_lin[count].axis('tight') axes_lin[count].set_aspect('auto') axes_lin[count].ticklabel_format(style='sci', axis='y', scilimits=(0, 0)) if len(datasets) > 1: axes_lin[count].legend(dataset_names, loc='best') if title: fig.suptitle(title, fontsize=14, y=fig_title_yoffset) plt.tight_layout() return fig, axes def plot_complex_spectra(map_stack, x_vec=None, num_comps=4, title=None, x_label='', y_label='', evenly_spaced=True, subtitle_prefix='Component', amp_units=None, stdevs=2, **kwargs): """ Plots the amplitude and phase components of the provided stack of complex valued spectrograms (2D images) Parameters ------------- map_stack : 2D or 3D numpy complex matrices stack of complex valued 1D spectra arranged as [component, spectra] or 2D images arranged as - [component, row, col] x_vec : 1D array-like, optional, default=None If the data are spectra (1D) instead of spectrograms (2D), x_vec is the reference array against which num_comps : int Number of images to plot title : str, optional Title to plot above everything else x_label : str, optional Label for x axis y_label : str, optional Label for y axis evenly_spaced : bool, optional. Default = True If True, images will be sampled evenly over the given dataset. Else, the first num_comps images will be plotted subtitle_prefix : str, optional Prefix for the title over each image amp_units : str, optional Units for amplitude stdevs : int Number of standard deviations to consider for plotting **kwargs will be passed on either to plot_map() or pyplot.plot() Returns --------- fig, axes """ if not isinstance(map_stack, (np.ndarray, da.core.Array)) or not map_stack.ndim in [2, 3]: raise TypeError('map_stack should be a 2/3 dimensional array arranged as [component, row, col] or ' '[component, spectra') if x_vec is not None: if not isinstance(x_vec, (list, tuple, np.ndarray, da.core.Array)): raise TypeError('x_vec should be a 1D array') x_vec = np.array(x_vec) if x_vec.ndim != 1: raise ValueError('x_vec should be a 1D array') if x_vec.size != map_stack.shape[1]: raise ValueError('x_vec: {} should be of the same size as the second dimension of map_stack: ' '{}'.format(x_vec.shape, map_stack.shape)) else: if map_stack.ndim == 2: x_vec = np.arange(map_stack.shape[1]) if num_comps is None: num_comps = 4 # Default else: if not isinstance(num_comps, int) or not num_comps > 0: raise TypeError('num_comps should be a positive integer') for var, var_name in zip([title, x_label, y_label, subtitle_prefix, amp_units], ['title', 'x_label', 'y_label', 'subtitle_prefix', 'amp_units']): if var is not None: if not isinstance(var, (str, unicode)): raise TypeError(var_name + ' should be a string') if amp_units is None: amp_units = 'a.u.' if stdevs is not None: if not isinstance(stdevs, Number) or stdevs <= 0: raise TypeError('stdevs should be a positive number') num_comps = min(24, min(num_comps, map_stack.shape[0])) if evenly_spaced: chosen_pos = np.linspace(0, map_stack.shape[0] - 1, num_comps, dtype=int) else: chosen_pos = np.arange(num_comps, dtype=int) nrows, ncols = get_plot_grid_size(num_comps) figsize = kwargs.pop('figsize', (4, 4)) # Individual plot size figsize = (figsize[0] * ncols, figsize[1] * nrows) fig, axes = plt.subplots(nrows * 2, ncols, figsize=figsize) fig.subplots_adjust(hspace=0.1, wspace=0.4) if title is not None: fig.canvas.manager.set_window_title(title) fig.suptitle(title, y=1.025) title_prefix = '' for comp_counter, comp_pos in enumerate(chosen_pos): ax_ind = (comp_counter // ncols) * (2 * ncols) + comp_counter % ncols cur_axes = [axes.flat[ax_ind], axes.flat[ax_ind + ncols]] funcs = [np.abs, np.angle] labels = ['Amplitude (' + amp_units + ')', 'Phase (rad)'] for func, comp_name, axis, std_val in zip(funcs, labels, cur_axes, [stdevs, None]): y_vec = func(map_stack[comp_pos]) if map_stack.ndim > 2: kwargs['stdevs'] = std_val _ = plot_map(axis, y_vec, **kwargs) else: axis.plot(x_vec, y_vec, **kwargs) if num_comps > 1: title_prefix = '%s %d - ' % (subtitle_prefix, comp_counter) axis.set_title('%s%s' % (title_prefix, comp_name)) axis.set_aspect('auto') if ax_ind % ncols == 0: axis.set_ylabel(y_label) if np.ceil((ax_ind + ncols) / ncols) == nrows: axis.set_xlabel(x_label) fig.tight_layout() return fig, axes def plot_scree(scree, title='Scree', **kwargs): """ Plots the scree from SVD Parameters ------------- scree : 1D real numpy array The scree vector from SVD title : str Figure title. Default Scree Returns --------- fig, axes """ if isinstance(scree, (list, tuple)): scree = np.array(scree) if not (isinstance(scree, (np.ndarray, da.core.Array)) or isinstance(scree, h5py.Dataset)): raise TypeError('scree must be a 1D array or Dataset') if not isinstance(title, (str, unicode)): raise TypeError('title must be a string') if h5py.__version__ >= '3' and isinstance(scree, h5py.Dataset): scree = scree[()] fig = plt.figure(figsize=kwargs.pop('figsize', (6.5, 6))) axis = fig.add_axes([0.1, 0.1, .8, .8]) # left, bottom, width, height (range 0 to 1) kwargs.update({'color': kwargs.pop('color', 'b')}) kwargs.update({'marker': kwargs.pop('marker', '*')}) axis.loglog(np.arange(len(scree)) + 1, scree, **kwargs) axis.set_xlabel('Component') axis.set_ylabel('Variance') axis.set_title(title) axis.set_xlim(left=1, right=len(scree)) axis.set_ylim(bottom=np.min(scree), top=np.max(scree)) fig.canvas.manager.set_window_title(title) return fig, axis sidpy-0.12.3/sidpy/viz/plot_utils/image.py000066400000000000000000000415641455261647000205430ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Utilities for generating static image and line plots of near-publishable quality Created on Thu May 05 13:29:12 2016 @author: Suhas Somnath, Chris R. Smith """ from __future__ import division, print_function, absolute_import, \ unicode_literals import inspect import sys from numbers import Number import matplotlib as mpl import numpy as np from dask import array as da from matplotlib import pyplot as plt from mpl_toolkits.axes_grid1 import ImageGrid from sidpy.base.num_utils import get_exponent from sidpy.viz.plot_utils.misc import get_plot_grid_size, set_tick_font_size from sidpy.viz.plot_utils.cmap import default_cmap if sys.version_info.major == 3: unicode = str def plot_map(axis, img, show_xy_ticks=True, show_cbar=True, x_vec=None, y_vec=None, num_ticks=4, stdevs=None, cbar_label=None, tick_font_size=None, infer_aspect=False, **kwargs): """ Plots an image within the given axis with a color bar + label and appropriate X, Y tick labels. This is particularly useful to get readily interpretable plots for papers Parameters ---------- axis : matplotlib.axes.Axes object Axis to plot this image onto img : 2D numpy array with real values Data for the image plot show_xy_ticks : bool, Optional, default = None, shown unedited Whether or not to show X, Y ticks show_cbar : bool, optional, default = True Whether or not to show the colorbar x_vec : 1-D array-like or Number, optional if an array-like is provided, these will be used for the tick values on the X axis if a Number is provided, this will serve as an extent for tick values in the X axis. For example x_vec=1.5 would cause the x tick labels to range from 0 to 1.5 y_vec : 1-D array-like or Number, optional if an array-like is provided - these will be used for the tick values on the Y axis if a Number is provided, this will serve as an extent for tick values in the Y axis. For example y_vec=225 would cause the y tick labels to range from 0 to 225 num_ticks : unsigned int, optional, default = 4 Number of tick marks on the X and Y axes stdevs : unsigned int (Optional. Default = None) Number of standard deviations to consider for plotting. If None, full range is plotted. cbar_label : str, optional, default = None Labels for the colorbar. Use this for something like quantity (units) tick_font_size : unsigned int, optional, default = None Font size to apply to x, y, colorbar ticks and colorbar label infer_aspect : bool, Optional. Default = False Whether or not to adjust the aspect ratio of the image based on the provided x_vec and y_vec The values of x_vec and y_vec will be assumed to have the same units. kwargs : dictionary Anything else that will be passed on to matplotlib.pyplot.imshow Returns ------- im_handle : handle to image plot handle to image plot cbar : handle to color bar handle to color bar Note ---- The origin of the image will be set to the lower left corner. Use the kwarg 'origin' to change this """ if not isinstance(axis, mpl.axes.Axes): raise TypeError('axis must be a matplotlib.axes.Axes object') if not isinstance(img, (np.ndarray, da.core.Array)): raise TypeError('img should be a numpy array') if not img.ndim == 2: raise ValueError('img should be a 2D array') if not isinstance(show_xy_ticks, bool): raise TypeError('show_xy_ticks should be a boolean value') if not isinstance(show_cbar, bool): raise TypeError('show_cbar should be a boolean value') # checks for x_vec and y_vec are done below if num_ticks is not None: if not isinstance(num_ticks, int): raise TypeError('num_ticks should be a whole number') if num_ticks < 2: raise ValueError('num_ticks should be at least 2') if tick_font_size is not None: if not isinstance(tick_font_size, Number): raise TypeError('tick_font_size must be a whole number') if tick_font_size < 0: raise ValueError('tick_font_size must be a whole number') if stdevs is not None: if not isinstance(stdevs, Number): raise TypeError('stdevs should be a Number') data_mean = np.mean(img) data_std = np.std(img) kwargs.update({'clim': [data_mean - stdevs * data_std, data_mean + stdevs * data_std]}) kwargs.update({'origin': kwargs.pop('origin', 'lower')}) if show_cbar: if np.isnan(img).any(): _img = img[np.where(~np.isnan(img))] y_exp = get_exponent(np.squeeze(_img)) else: y_exp = get_exponent(np.squeeze(img)) z_suffix = '' if y_exp < -2 or y_exp > 3: img = np.squeeze(img) / 10 ** y_exp z_suffix = ' x $10^{' + str(y_exp) + '}$' assert isinstance(show_xy_ticks, bool) ######################################################################################################## def set_ticks_for_axis(tick_vals, is_x): if is_x: tick_vals_var_name = 'x_vec' tick_set_func = axis.set_xticks tick_labs_set_func = axis.set_xticklabels else: tick_vals_var_name = 'y_vec' tick_set_func = axis.set_yticks tick_labs_set_func = axis.set_yticklabels img_axis = int(is_x) img_size = img.shape[img_axis] chosen_ticks = np.linspace(0, img_size - 1, num_ticks, dtype=int) if tick_vals is not None: if isinstance(tick_vals, (int, float)): if tick_vals > 0.01: tick_labs = [str(np.round(ind * tick_vals / img_size, 2)) for ind in chosen_ticks] else: tick_labs = ['{0:.1E}'.format(ind * tick_vals / img_size) for ind in chosen_ticks] print(tick_labs) tick_vals = np.linspace(0, tick_vals, img_size) else: if not isinstance(tick_vals, (np.ndarray, list, tuple, range, da.core.Array)) or \ len(tick_vals) != img_size: raise ValueError( '{} should be array-like with shape equal to axis {} of img'.format(tick_vals_var_name, img_axis)) if np.max(tick_vals) > 0.01: tick_labs = [str(np.round(tick_vals[ind], 2)) for ind in chosen_ticks] else: tick_labs = ['{0:.1E}'.format(tick_vals[ind]) for ind in chosen_ticks] else: tick_labs = [str(ind) for ind in chosen_ticks] tick_set_func(chosen_ticks) tick_labs_set_func(tick_labs) if tick_font_size is not None: set_tick_font_size(axis, tick_font_size) return tick_vals ######################################################################################################## if show_xy_ticks is True or x_vec is not None: x_vec = set_ticks_for_axis(x_vec, True) else: axis.set_xticks([]) if show_xy_ticks is True or y_vec is not None: y_vec = set_ticks_for_axis(y_vec, False) else: axis.set_yticks([]) if infer_aspect: # Aspect ratio determined by this function will take precedence. _ = kwargs.pop('infer_aspect', None) """ At this stage, if x_vec and y_vec are not None, they should be arrays. This will be very useful when one dimension is coarsely sampled while another is finely sampled and we want to visualize the image with the physically correct aspect ratio. This CANNOT be performed automatically due to potentially incompatible units which are unknown to this func. """ if x_vec is not None or y_vec is not None: x_range = x_vec.max() - x_vec.min() y_range = y_vec.max() - y_vec.min() kwargs.update({'aspect': (y_range / x_range) * (img.shape[1] / img.shape[0])}) im_handle = axis.imshow(img, **kwargs) cbar = None if not isinstance(show_cbar, bool): show_cbar = False if show_cbar: cbar = plt.colorbar(im_handle, ax=axis, orientation='vertical', fraction=0.046, pad=0.04, use_gridspec=True) # cbar = axis.cbar_axes[count].colorbar(im_handle) if cbar_label is not None: if not isinstance(cbar_label, (str, unicode)): raise TypeError('cbar_label should be a string') if tick_font_size is not None: cbar.set_label(cbar_label + z_suffix) else: cbar.set_label(cbar_label + z_suffix, fontsize=tick_font_size) else: if z_suffix != '': cbar.set_label(z_suffix) if tick_font_size is not None: cbar.ax.tick_params(labelsize=tick_font_size) return im_handle, cbar def plot_map_stack(map_stack, num_comps=9, stdevs=2, color_bar_mode=None, evenly_spaced=False, reverse_dims=False, subtitle='Component', title=None, colorbar_label='', fig_mult=(5, 5), pad_mult=(0.1, 0.07), x_label=None, y_label=None, title_yoffset=None, title_size=None, **kwargs): """ Plots the provided stack of maps Parameters ------------- map_stack : 3D real numpy array structured as [component, rows, cols] num_comps : int, Optional Number of components to plot stdevs : int, Optional Number of standard deviations to consider for plotting. Set to None if no clipping is desired color_bar_mode : String, Optional Options are None, single or each. Default None evenly_spaced : bool, Optional. Default = False If set to True - The slices / component will be selected at intervals from the first to last If set to False - The first ``num_comps`` images will be plotted instead reverse_dims : bool, Optional. Default = False Set this to True to accept data structured as [rows, cols, component] subtitle : String or list of strings The titles for each of the plots. If a single string is provided, the plot titles become ['title 01', title 02', ...]. if a list of strings (equal to the number of components) are provided, these are used instead. title : str, Optinal Title for the plot grid that will appear at the top colorbar_label : str, Optional label for colorbar. Default is an empty string. fig_mult : length 2 array_like of uints Size multipliers for the figure. Figure size is calculated as (num_rows*`fig_mult[0]`, num_cols*`fig_mult[1]`). Default (4, 4) pad_mult : tuple, list, array-like, Optional Array-like of floats of length 2. Multipliers for the axis padding between plots in the stack. Padding is calculated as (pad_mult[0]*fig_mult[1], pad_mult[1]*fig_mult[0]) for the width and height padding respectively. Default (0.1, 0.07) x_label : str, Optional X Label for all plots y_label : (optional) String Y label for all plots title_yoffset : float Offset to move the figure title vertically in the figure title_size : float Size of figure title kwargs : dictionary Keyword arguments to be passed to either matplotlib.pyplot.figure, mpl_toolkits.axes_grid1.ImageGrid, or pyUSID.viz.plot_utils.plot_map. See specific function documentation for the relavent options. Returns --------- fig, axes """ # plt.rcParams["mpl_toolkits.legacy_colorbar"] = False if not isinstance(map_stack, (np.ndarray, da.core.Array)) or not map_stack.ndim == 3: raise TypeError('map_stack should be a 3 dimensional array arranged as [component, row, col]') if num_comps is None: num_comps = 4 # Default else: if not isinstance(num_comps, int) or num_comps < 1: raise TypeError('num_comps should be a positive integer') for var, var_name in zip([title, colorbar_label, color_bar_mode, x_label, y_label], ['title', 'colorbar_label', 'color_bar_mode', 'x_label', 'y_label']): if var is not None: if not isinstance(var, (str, unicode)): raise TypeError(var_name + ' should be a string') if title is None: title = '' if colorbar_label is None: colorbar_label = '' if x_label is None: x_label = '' if y_label is None: y_label = '' if color_bar_mode not in [None, 'single', 'each']: raise ValueError('color_bar_mode must be either None, "single", or "each"') for var, var_name in zip([stdevs, title_yoffset, title_size], ['stdevs', 'title_yoffset', 'title_size']): if var is not None: if not isinstance(var, Number) or var <= 0: raise TypeError(var_name + ' of value: {} should be a number > 0'.format(var)) for var, var_name in zip([evenly_spaced, reverse_dims], ['evenly_spaced', 'reverse_dims']): if not isinstance(var, bool): raise TypeError(var_name + ' should be a bool') for var, var_name in zip([fig_mult, pad_mult], ['fig_mult', 'pad_mult']): if not isinstance(var, (list, tuple, np.ndarray, da.core.Array)) or len(var) != 2: raise TypeError(var_name + ' should be a tuple / list / numpy array of size 2') if not np.all([x > 0 and isinstance(x, Number) for x in var]): raise ValueError(var_name + ' should contain positive numbers') if reverse_dims: map_stack = np.transpose(map_stack, (2, 0, 1)) num_comps = abs(num_comps) num_comps = min(num_comps, map_stack.shape[0]) if evenly_spaced: chosen_pos = np.linspace(0, map_stack.shape[0] - 1, num_comps, dtype=int) else: chosen_pos = np.arange(num_comps, dtype=int) if isinstance(subtitle, list): if len(subtitle) > num_comps: # remove additional subtitles subtitle = subtitle[:num_comps] elif len(subtitle) < num_comps: # add subtitles subtitle += ['Component' + ' ' + str(x) for x in range(len(subtitle), num_comps)] else: if not isinstance(subtitle, str): subtitle = 'Component' subtitle = [subtitle + ' ' + str(x) for x in chosen_pos] fig_h, fig_w = fig_mult p_rows, p_cols = get_plot_grid_size(num_comps) if p_rows * p_cols < num_comps: p_cols += 1 pad_w, pad_h = pad_mult ''' Set defaults for kwargs to the figure creation and extract any non-default values from current kwargs ''' figkwargs = dict() if sys.version_info.major == 3: inspec_func = inspect.getfullargspec else: inspec_func = inspect.getargspec for key in inspec_func(plt.figure).args: if key in kwargs: figkwargs.update({key: kwargs.pop(key)}) fig = plt.figure(figsize=(p_cols * fig_w, p_rows * fig_h), **figkwargs) ''' Set defaults for kwargs to the ImageGrid and extract any non-default values from current kwargs ''' igkwargs = {'cbar_pad': '1%', 'cbar_size': '5%', 'cbar_location': 'right', 'direction': 'row', 'share_all': False, 'aspect': True, 'label_mode': 'L'} # 'add_all': True} for key in igkwargs.keys(): if key in kwargs: igkwargs.update({key: kwargs.pop(key)}) axes = ImageGrid(fig=fig, rect=111, nrows_ncols=(p_rows, p_cols), cbar_mode=color_bar_mode, axes_pad=(pad_w * fig_w, pad_h * fig_h), **igkwargs) try: fig.canvas.set_window_title(title) except: fig.canvas.manager.set_window_title(title) # These parameters have not been easy to fix: if title_yoffset is None: title_yoffset = 0.9 if title_size is None: title_size = 16 + (p_rows + p_cols) fig.suptitle(title, fontsize=title_size, y=title_yoffset) # plt.rcParams["mpl_toolkits.legacy_colorbar"] = False for count, index, curr_subtitle in zip(range(chosen_pos.size), chosen_pos, subtitle): im, im_cbar = plot_map(axes[count], map_stack[index], stdevs=stdevs, show_cbar=False, **kwargs) axes[count].set_title(curr_subtitle) if color_bar_mode == 'each': cb = axes[count].cax.colorbar(im) if count % p_cols == p_cols-1: cb.set_label(colorbar_label) if count % p_cols == 0: axes[count].set_ylabel(y_label) if count >= (p_rows - 1) * p_cols: axes[count].set_xlabel(x_label) # With cbar_mode="single", cax attribute of all axes are identical. if color_bar_mode == 'single': cb = axes[0].cax.colorbar(im) cb.set_label(colorbar_label) return fig, axes sidpy-0.12.3/sidpy/viz/plot_utils/misc.py000066400000000000000000000224461455261647000204120ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Utilities for generating static image and line plots of near-publishable quality Created on Thu May 05 13:29:12 2016 @author: Suhas Somnath, Chris R. Smith """ from __future__ import division, print_function, absolute_import, unicode_literals import os import sys from numbers import Number import numpy as np import matplotlib as mpl from matplotlib import ticker as mtick, pyplot as plt from matplotlib.colors import LinearSegmentedColormap from sidpy.viz.plot_utils.cmap import default_cmap if sys.version_info.major == 3: unicode = str def reset_plot_params(): """ Resets the plot parameters to matplotlib default values Adapted from: https://stackoverflow.com/questions/26413185/how-to-recover-matplotlib-defaults-after-setting-stylesheet """ mpl.rcParams.update(mpl.rcParamsDefault) # Also resetting ipython inline parameters inline_rc = dict(mpl.rcParams) mpl.rcParams.update(inline_rc) def use_nice_plot_params(): """ Resets default plot parameters such as figure size, font sizes etc. to values better suited for scientific publications """ # mpl.rcParams.keys() # gets all allowable keys # mpl.rc('figure', figsize=(5.5, 5)) mpl.rc('lines', linewidth=2) mpl.rc('axes', labelsize=16, titlesize=16) mpl.rc('figure', titlesize=20) mpl.rc('font', size=14) # global font size mpl.rc('legend', fontsize=16, fancybox=True) mpl.rc('xtick.major', size=6) mpl.rc('xtick.minor', size=4) # mpl.rcParams['xtick.major.size'] = 6 def set_tick_font_size(axes, font_size): """ Sets the font size of the ticks in the provided axes Parameters ---------- axes : matplotlib.pyplot.axis object or list of axis objects axes to set font sizes font_size : unigned int Font size """ assert isinstance(font_size, Number) font_size = max(1, int(font_size)) def __set_axis_tick(axis): """ Sets the font sizes to the x and y axis in the given axis object Parameters ---------- axis : matplotlib.axes.Axes object axis to set font sizes """ for tick in axis.xaxis.get_major_ticks(): tick.label1.set_fontsize(font_size) for tick in axis.yaxis.get_major_ticks(): tick.label1.set_fontsize(font_size) mesg = 'axes must either be a matplotlib.axes.Axes object or an iterable containing such objects' if hasattr(axes, '__iter__'): for axis in axes: assert isinstance(axis, mpl.axes.Axes), mesg __set_axis_tick(axis) else: assert isinstance(axes, mpl.axes.Axes), mesg __set_axis_tick(axes) def use_scientific_ticks(axis, is_x=True, formatting='%.2e'): """ Makes the desired axis use scientific notation for its tick labels. This is applicable only for 1D plots at the moment. Parameters ---------- axis : matplotlib.pyplot.axis object Axis handle is_x : bool, optional. Default = True If set to true, scientific notation will be applied only to the X axis. If set to False, scientific notation will be applied only to the Y axis. formatting : str / unicode, optional. Default = 2 digits of precision Precision for the tick labels """ if not isinstance(axis, mpl.axes.Axes): raise TypeError('axis must be a matplotlib.axes.Axes object') if not isinstance(is_x, bool): raise TypeError('is_x should be a boolean to avoid confusion') if not isinstance(formatting, (str, unicode)): raise TypeError('formatting must be a string') if is_x: ax_hand = axis.xaxis else: ax_hand = axis.yaxis ax_hand.set_major_formatter(mtick.FormatStrFormatter(formatting)) def make_scalar_mappable(vmin, vmax, cmap=None): """ Creates a scalar mappable object that can be used to create a colorbar for non-image (e.g. - line) plots Parameters ---------- vmin : Number Minimum value for colorbar vmax : Number Maximum value for colorbar cmap : colormap object Colormap object to use Returns ------- sm : matplotlib.pyplot.cm.ScalarMappable object The object that can used to create a colorbar via plt.colorbar(sm) Adapted from: https://stackoverflow.com/questions/8342549/matplotlib-add-colorbar-to-a-sequence-of-line-plots """ assert isinstance(vmin, Number), 'vmin should be a number' assert isinstance(vmax, Number), 'vmax should be a number' assert vmin < vmax, 'vmin must be less than vmax' if cmap is None: cmap = default_cmap else: assert isinstance(cmap, (mpl.colors.Colormap, str, unicode)) sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax)) # fake up the array of the scalar mappable sm._A = [] return sm def get_plot_grid_size(num_plots, fewer_rows=True): """ Returns the number of rows and columns ideal for visualizing multiple (identical) plots within a single figure Parameters ---------- num_plots : uint Number of identical subplots within a figure fewer_rows : bool, optional. Default = True Set to True if the grid should be short and wide or False for tall and narrow Returns ------- nrows : uint Number of rows ncols : uint Number of columns """ assert isinstance(num_plots, Number), 'num_plots must be a number' # force integer: num_plots = int(num_plots) if num_plots < 1: raise ValueError('num_plots was less than 0') if fewer_rows: nrows = int(np.floor(np.sqrt(num_plots))) ncols = int(np.ceil(num_plots / nrows)) else: ncols = int(np.floor(np.sqrt(num_plots))) nrows = int(np.ceil(num_plots / ncols)) return nrows, ncols def export_fig_data(fig, filename, include_images=False): """ Export the data of all plots in the figure `fig` to a plain text file. Parameters ---------- fig : matplotlib.figure.Figure The figure containing the data to be exported filename : str The filename of the output text file include_images : bool Should images in the figure also be exported Returns ------- """ # Get the data from the figure axes = fig.get_axes() axes_dict = dict() for ax in axes: ax_dict = dict() ims = ax.get_images() if len(ims) != 0 and include_images: im_dict = dict() for im in ims: # Image data im_lab = im.get_label() im_dict['data'] = im.get_array().data # X-Axis x_ax = ax.get_xaxis() x_lab = x_ax.label.get_label() if x_lab == '': x_lab = 'X' im_dict[x_lab] = x_ax.get_data_interval() # Y-Axis y_ax = ax.get_yaxis() y_lab = y_ax.label.get_label() if y_lab == '': y_lab = 'Y' im_dict[y_lab] = y_ax.get_data_interval() ax_dict['Images'] = {im_lab: im_dict} lines = ax.get_lines() if len(lines) != 0: line_dict = dict() xlab = ax.get_xlabel() ylab = ax.get_ylabel() if xlab == '': xlab = 'X Data' if ylab == '': ylab = 'Y Data' for line in lines: line_dict[line.get_label()] = {xlab: line.get_xdata(), ylab: line.get_ydata()} ax_dict['Lines'] = line_dict if ax_dict != dict(): axes_dict[ax.get_title()] = ax_dict ''' Now that we have the data from the figure, we need to write it to file. ''' filename = os.path.abspath(filename) basename, ext = os.path.splitext(filename) folder, _ = os.path.split(basename) spacer = r'**********************************************\n' data_file = open(filename, 'w') data_file.write(fig.get_label() + '\n') data_file.write('\n') for ax_lab, ax in axes_dict.items(): data_file.write('Axis: {} \n'.format(ax_lab)) if 'Images' not in ax: continue for im_lab, im in ax['Images'].items(): data_file.write('Image: {} \n'.format(im_lab)) data_file.write('\n') im_data = im.pop('data') for row in im_data: row.tofile(data_file, sep='\t', format='%s') data_file.write('\n') data_file.write('\n') for key, val in im.items(): data_file.write(key + '\n') val.tofile(data_file, sep='\n', format='%s') data_file.write('\n') data_file.write(spacer) if 'Lines' not in ax: continue for line_lab, line_dict in ax['Lines'].items(): data_file.write('Line: {} \n'.format(line_lab)) data_file.write('\n') dim1, dim2 = line_dict.keys() data_file.write('{} \t {} \n'.format(dim1, dim2)) for val1, val2 in zip(line_dict[dim1], line_dict[dim2]): data_file.write('{} \t {} \n'.format(str(val1), str(val2))) data_file.write(spacer) data_file.write(spacer) data_file.close()sidpy-0.12.3/tests/000077500000000000000000000000001455261647000141015ustar00rootroot00000000000000sidpy-0.12.3/tests/__init__.py000066400000000000000000000000001455261647000162000ustar00rootroot00000000000000sidpy-0.12.3/tests/base/000077500000000000000000000000001455261647000150135ustar00rootroot00000000000000sidpy-0.12.3/tests/base/__init__.py000066400000000000000000000000001455261647000171120ustar00rootroot00000000000000sidpy-0.12.3/tests/base/test_dict_utils.py000066400000000000000000000036741455261647000206010ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Mon Sep 28 15:07:16 2020 @author: Suhas Somnath """ from __future__ import division, print_function, unicode_literals, \ absolute_import import unittest import sys sys.path.append("../../sidpy/") from sidpy.base.dict_utils import * if sys.version_info.major == 3: unicode = str class TestFlattenDict(unittest.TestCase): def test_already_flat(self): pass def test_two_level(self): pass def test_five_level(self): pass def test_non_str_keys(self): pass def test_invalid_separator(self): pass def test_not_dict_at_all(self): pass def test_value_is_list(self): # Going by what @gduscher added pass class TestMergeDicts(unittest.TestCase): def test_blah(self): pass class TestNestDict(unittest.TestCase): def test_not_dict(self): pass def test_invalid_separator(self): pass def test_empty_separator(self): pass def test_incorrect_separator(self): pass def test_already_nested_dict(self): pass def test_partially_nested_dict(self): pass def test_typical_flat_dict(self): pass def test_keys_are_not_str(self): pass class TestNestedDictFromFlattenedKey(unittest.TestCase): def test_nothing_to_flatten(self): pass def test_multiple_key_val(self): pass def test_five_level_key(self): pass def test_not_a_dict_at_all(self): pass def test_invalid_sep(self): pass def test_wrong_separator(self): pass def test_key_is_not_str(self): pass class TestPrintNestedDict(unittest.TestCase): def test_not_dict(self): pass def test_invalid_level_type(self): pass def test_flat_dict(self): pass def test_typical_nested_dict(self): pass if __name__ == '__main__': unittest.main() sidpy-0.12.3/tests/base/test_num_utils.py000066400000000000000000000144601455261647000204500ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Tue Nov 3 15:07:16 2017 @author: Suhas Somnath, Rama Vasudevan """ from __future__ import division, print_function, unicode_literals, absolute_import import unittest import sys import numpy as np sys.path.append("../../sidpy/") from sidpy.base.num_utils import * if sys.version_info.major == 3: unicode = str xrange = range class TestGetSlope(unittest.TestCase): def test_linear(self): expected = 0.25 actual = get_slope(np.arange(-1, 1, expected)) self.assertEqual(expected, actual) def test_linear_dirty(self): # When reading from HDF5, rounding errors can result in minor variations in the diff expected = 0.25E-9 vector = np.arange(-1E-9, 1E-9, expected) round_error = np.random.rand(vector.size) * 1E-14 vector += round_error actual = get_slope(vector, tol=1E-3) self.assertAlmostEqual(expected, actual) def test_invalid_tolerance(self): with self.assertRaises(TypeError): _ = get_slope(np.sin(np.arange(4)), tol="hello") def test_non_linear(self): with self.assertRaises(ValueError): _ = get_slope(np.sin(np.arange(4))) def test_invalid_inputs(self): with self.assertRaises(BaseException): _ = get_slope("hello") class TestToRanges(unittest.TestCase): def test_valid(self): actual = to_ranges([0, 1, 2, 3, 7, 8, 9, 10]) actual = list(actual) if sys.version_info.major == 3: expected = [range(0, 4), range(7, 11)] self.assertTrue(all([x == y for x, y in zip(expected, actual)])) else: expected = [xrange(0, 4), xrange(7, 11)] for in_x, out_x in zip(expected, actual): self.assertTrue(all([x == y for x, y in zip(list(in_x), list(out_x))])) class TestContainsIntegers(unittest.TestCase): def test_typical(self): self.assertTrue(contains_integers([1, 2, -3, 4])) self.assertTrue(contains_integers(range(5))) self.assertTrue( contains_integers([2, 5, 8, 3], min_val=2)) self.assertTrue(contains_integers(np.arange(5))) self.assertFalse( contains_integers(np.arange(5), min_val=2)) self.assertFalse( contains_integers([1, 4.5, 2.2, -1])) self.assertFalse( contains_integers([1, -2, 5], min_val=1)) self.assertFalse( contains_integers(['dsss', 34, 1.23, None])) self.assertFalse(contains_integers([])) with self.assertRaises(TypeError): _ = contains_integers(None) with self.assertRaises(TypeError): _ = contains_integers(14) def test_illegal_min_val(self): with self.assertRaises(TypeError): _ = contains_integers([1, 2, 3, 4], min_val='hello') with self.assertRaises(TypeError): _ = contains_integers([1, 2, 3, 4], min_val=[1, 2]) with self.assertRaises(ValueError): _ = contains_integers([1, 2, 3, 4], min_val=1.234) class TestIntegersToSlices(unittest.TestCase): def test_illegal_inputs(self): with self.assertRaises(TypeError): integers_to_slices(slice(1, 15)) with self.assertRaises(ValueError): integers_to_slices( [-1.43, 34.6565, 45.344, 5 + 6j]) with self.assertRaises(ValueError): integers_to_slices( ['asdds', None, True, 45.344, 5 + 6j]) def test_positive(self): expected = [slice(0, 3), slice(7, 8), slice(14, 18), slice(22, 23), slice(27, 28), slice(29, 30), slice(31, 32)] inputs = np.hstack([range(item.start, item.stop) for item in expected]) ret_val = integers_to_slices(inputs) self.assertEqual(expected, ret_val) def test_negative(self): expected = [slice(-7, -4), slice(-2, 3), slice(14, 18), slice(22, 23), slice(27, 28), slice(29, 30)] inputs = np.hstack([range(item.start, item.stop) for item in expected]) ret_val = integers_to_slices(inputs) self.assertEqual(expected, ret_val) class TestGetExponent(unittest.TestCase): def test_negative_small(self): expected = -7 self.assertEqual(expected, get_exponent(np.arange(5) * -10 ** expected)) def test_positive_large(self): expected = 4 self.assertEqual(expected, get_exponent(np.arange(6) * 10 ** expected)) def test_mixed_large(self): expected = 4 self.assertEqual(expected, get_exponent(np.random.randint(-8, high=3, size=(5, 5)) * 10 ** expected)) def test_illegal_type(self): with self.assertRaises(TypeError): _ = get_exponent('hello') _ = get_exponent([1, 2, 3]) _ = get_exponent([0, 1, np.nan]) class TestBuildIndValMatrices(unittest.TestCase): '''Testing the build_ind_val_matrices function''' def test_not_list_or_tuple(self): with self.assertRaises(TypeError): #try putting in a dictionary unit_values = {'values':(0,1,2)} _,_ = build_ind_val_matrices (unit_values) #try a numpy array unit_values = np.array([0,1,2,3]) _, _ = build_ind_val_matrices(unit_values) def test_not_1D(self): with self.assertRaises(ValueError): # try a 2D matrix unit_values = [np.random.normal(loc=1,scale=1,size=(5,5))] _, _ = build_ind_val_matrices(unit_values) def test_standard_case(self): #here we want to assert that a standard case works #two spectroscopic dimensions - [[0,1], [10,20]] unit_values = [[0,1], [10,20]] ind_mat, val_mat = build_ind_val_matrices(unit_values) ind_mat_true = np.array([[0,0],[1,0], [0,1],[1,1]]) val_mat_true = np.array([[0., 10.], [1., 10.], [0., 20.], [1., 20.]]) self.assertTrue(np.isclose(ind_mat, ind_mat_true).all() == True) self.assertTrue(np.isclose(val_mat, val_mat_true).all() ==True) if __name__ == '__main__': unittest.main()sidpy-0.12.3/tests/base/test_string_utils.py000066400000000000000000000215241455261647000211560ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Tue Nov 3 15:07:16 2017 @author: Suhas Somnath """ from __future__ import division, print_function, unicode_literals, absolute_import import unittest import sys sys.path.append("../../sidpy/") from sidpy.base.string_utils import * if sys.version_info.major == 3: unicode = str class TestCleanStringAtt(unittest.TestCase): def test_float(self): expected = 5.321 self.assertEqual(expected, clean_string_att(expected)) def test_str(self): expected = 'test' self.assertEqual(expected, clean_string_att(expected)) def test_num_array(self): expected = [1, 2, 3.456] self.assertEqual(expected, clean_string_att(expected)) def test_str_list(self): expected = ['a', 'bc', 'def'] returned = clean_string_att(expected) expected = np.array(expected, dtype='S') for exp, act in zip(expected, returned): self.assertEqual(exp, act) def test_str_tuple(self): expected = ('a', 'bc', 'def') returned = clean_string_att(expected) expected = np.array(expected, dtype='S') for exp, act in zip(expected, returned): self.assertEqual(exp, act) class TestFormattedStrToNum(unittest.TestCase): def test_typical(self): self.assertEqual( formatted_str_to_number("4.32 MHz", ["MHz", "kHz"], [1E+6, 1E+3]), 4.32E+6) def test_wrong_types(self): with self.assertRaises(TypeError): _ = formatted_str_to_number("4.32 MHz", ["MHz", "kHz"], [1E+6, 1E+3], separator=14) with self.assertRaises(TypeError): _ = formatted_str_to_number({'dfdfd': 123}, ["MHz"], [1E+6]) with self.assertRaises(TypeError): _ = formatted_str_to_number("dfdfdf", ["MHz"], 1E+6) with self.assertRaises(TypeError): _ = formatted_str_to_number("jkjk", ["MHz", 1234], [1E+6, 1E+4]) with self.assertRaises(TypeError): _ = formatted_str_to_number("4.32 MHz", ["MHz", "kHz"], [{'dfdfd': 13}, 1E+3]) def test_invalid(self): with self.assertRaises(ValueError): _ = formatted_str_to_number("4.32 MHz", ["MHz"], [1E+6, 1E+3]) with self.assertRaises(ValueError): _ = formatted_str_to_number("4.32 MHz", ["MHz", "kHz"], [1E+3]) with self.assertRaises(ValueError): _ = formatted_str_to_number("4.32-MHz", ["MHz", "kHz"], [1E+6, 1E+3]) with self.assertRaises(ValueError): _ = formatted_str_to_number("haha MHz", ["MHz", "kHz"], [1E+6, 1E+3]) with self.assertRaises(ValueError): _ = formatted_str_to_number("1.2.3.4 MHz", ["MHz", "kHz"], [1E+6, 1E+3]) with self.assertRaises(ValueError): _ = formatted_str_to_number("MHz", ["MHz", "kHz"], [1E+6, 1E+3]) class TestFormatQuantity(unittest.TestCase): def test_typical(self): qty_names = ['sec', 'mins', 'hours', 'days'] qty_factors = [1, 60, 3600, 3600*24] ret_val = format_quantity(315, qty_names, qty_factors) self.assertEqual(ret_val, '5.25 mins') ret_val = format_quantity(6300, qty_names, qty_factors) self.assertEqual(ret_val, '1.75 hours') def test_unequal_lengths(self): with self.assertRaises(ValueError): _ = format_quantity(315, ['sec', 'mins', 'hours'], [1, 60, 3600, 3600 * 24]) with self.assertRaises(ValueError): _ = format_quantity(315, ['sec', 'mins', 'hours'], [1, 60]) def test_incorrect_element_types(self): with self.assertRaises(TypeError): _ = format_quantity(315, ['sec', 14, 'hours'], [1, 60, 3600 * 24]) def test_incorrect_number_to_format(self): with self.assertRaises(TypeError): _ = format_quantity('hello', ['sec', 'mins', 'hours'], [1, 60, 3600]) def test_not_iterable(self): with self.assertRaises(TypeError): _ = format_quantity(315, 14, [1, 60, 3600]) with self.assertRaises(TypeError): _ = format_quantity(315, ['sec', 'mins', 'hours'], slice(None)) class TestTimeSizeFormatting(unittest.TestCase): def test_format_time(self): ret_val = format_time(315) self.assertEqual(ret_val, '5.25 mins') ret_val = format_time(6300) self.assertEqual(ret_val, '1.75 hours') def test_format_size(self): ret_val = format_size(15.23) self.assertEqual(ret_val, '15.23 bytes') ret_val = format_size(5830418104.32) self.assertEqual(ret_val, '5.43 GB') class TestValidateStringArgs(unittest.TestCase): def test_empty(self): with self.assertRaises(ValueError): _ = validate_string_args([' '], ['meh']) def test_spaces(self): expected = 'fd' [ret] = validate_string_args([' ' + expected + ' '], ['meh']) self.assertEqual(expected, ret) def test_single(self): expected = 'fd' [ret] = validate_string_args(expected, 'meh') self.assertEqual(expected, ret) def test_multi(self): expected = ['abc', 'def'] returned = validate_string_args([' ' + expected[0], expected[1] + ' '], ['meh', 'foo']) for exp, ret in zip(expected, returned): self.assertEqual(exp, ret) def test_not_string_lists(self): with self.assertRaises(TypeError): _ = validate_string_args([14], ['meh']) with self.assertRaises(TypeError): _ = validate_string_args(14, ['meh']) with self.assertRaises(TypeError): _ = validate_string_args({'dfdf': 14}, ['meh']) def test_name_not_string(self): actual = ['ghghg'] ret = validate_string_args(actual, [np.arange(3)]) self.assertEqual(ret, actual) def test_unequal_lengths(self): expected = ['a', 'b'] actual = validate_string_args(expected + ['c'], ['a', 'b']) for exp, ret in zip(expected, actual): self.assertEqual(exp, ret) def test_names_not_list_of_strings(self): with self.assertRaises(TypeError): _ = validate_string_args(['a', 'v'], {'a': 1, 'v': 43}) class TestStrToOther(unittest.TestCase): def test_invalid_input_obj_type(self): for val in [1.23, {'1we': 123}, ['dssd'], True, None]: with self.assertRaises(TypeError): str_to_other(val) def base_test(self, inputs, out_type): for val in inputs: ret = str_to_other(str(val)) self.assertEqual(val, ret) self.assertIsInstance(ret, out_type) def test_int(self): self.base_test([23, -235457842], int) def test_float(self): self.base_test([23.45643, -2354.57842], float) def test_exp(self): self.base_test([3.14E3, -4.3E-5], float) def test_str(self): self.base_test(['hello', '1fd353'], str) def test_bool(self): for val in ['true', 'TRUE', 'True']: ret = str_to_other(val) self.assertEqual(ret, True) self.assertIsInstance(ret, bool) for val in ['false', 'FALSE', 'False']: ret = str_to_other(val) self.assertEqual(ret, False) self.assertIsInstance(ret, bool) class TestRemoveExtraDelimiters(unittest.TestCase): def test_invalid_sep_type(self): for sep in [14, {'fdfd': 45}, [' ', ', '], True, (23, None)]: with self.assertRaises(TypeError): remove_extra_delimiters('fddfdf dfref', separator=sep) def test_invalid_line_type(self): for line in [14, {'fdfd': 45}, [' ', ', '], True, (23, None)]: with self.assertRaises(TypeError): remove_extra_delimiters(line, separator='-') def test_empty_delim(self): with self.assertRaises(ValueError): remove_extra_delimiters('this is a test', '') def typical_case(self, pad=False): words = ['this', 'is', 'a', 'test'] for sep in [' ', '-']: line = sep.join(words) if pad: dirty = sep * 4 + line + sep * 3 else: dirty = line clean = remove_extra_delimiters(dirty, separator=sep) self.assertEqual(line, clean) self.assertIsInstance(clean, str) def test_single_delim(self): self.typical_case(pad=False) def test_delims_before_or_after(self): self.typical_case(pad=True) def test_multiple_consecutive_delims(self): line = 'this is a test sentence' words = ['this', 'is', 'a', 'test', 'sentence'] clean = remove_extra_delimiters(line, separator=' ') self.assertEqual(clean, ' '.join(words)) line = 'this====is=a==test=========sentence' clean = remove_extra_delimiters(line, separator='=') self.assertEqual(clean, '='.join(words)) if __name__ == '__main__': unittest.main() sidpy-0.12.3/tests/dask-worker-space/000077500000000000000000000000001455261647000174235ustar00rootroot00000000000000sidpy-0.12.3/tests/dask-worker-space/global.lock000066400000000000000000000000001455261647000215230ustar00rootroot00000000000000sidpy-0.12.3/tests/dask-worker-space/purge.lock000066400000000000000000000000001455261647000214050ustar00rootroot00000000000000sidpy-0.12.3/tests/hdf/000077500000000000000000000000001455261647000146425ustar00rootroot00000000000000sidpy-0.12.3/tests/hdf/__init__.py000066400000000000000000000000001455261647000167410ustar00rootroot00000000000000sidpy-0.12.3/tests/hdf/data_utils.py000066400000000000000000000306201455261647000173460ustar00rootroot00000000000000from __future__ import division, print_function, unicode_literals, absolute_import import os import sys import socket import h5py import numpy as np from io import StringIO from contextlib import contextmanager from platform import platform sys.path.append("../../sidpy/") from sidpy import __version__ from sidpy.base.string_utils import get_time_stamp std_beps_path = 'test_hdf_utils.h5' if sys.version_info.major == 3: unicode = str def delete_existing_file(file_path): if os.path.exists(file_path): os.remove(file_path) def write_safe_attrs(h5_object, attrs): for key, val in attrs.items(): h5_object.attrs[key] = val def write_string_list_as_attr(h5_object, attrs): for key, val in attrs.items(): h5_object.attrs[key] = np.array(val, dtype='S') def write_aux_reg_ref(h5_dset, labels, is_spec=True): for index, reg_ref_name in enumerate(labels): if is_spec: reg_ref_tuple = (slice(index, index + 1), slice(None)) else: reg_ref_tuple = (slice(None), slice(index, index + 1)) h5_dset.attrs[reg_ref_name] = h5_dset.regionref[reg_ref_tuple] def write_main_reg_refs(h5_dset, attrs): for reg_ref_name, reg_ref_tuple in attrs.items(): h5_dset.attrs[reg_ref_name] = h5_dset.regionref[reg_ref_tuple] write_string_list_as_attr(h5_dset, {'labels': list(attrs.keys())}) @contextmanager def capture_stdout(): """ context manager encapsulating a pattern for capturing stdout writes and restoring sys.stdout even upon exceptions https://stackoverflow.com/questions/17067560/intercept-pythons-print-statement-and-display-in-gui Examples: >>> with capture_stdout() as get_value: >>> print("here is a print") >>> captured = get_value() >>> print('Gotcha: ' + captured) >>> with capture_stdout() as get_value: >>> print("here is a print") >>> raise Exception('oh no!') >>> print('Does printing still work?') """ # Redirect sys.stdout out = StringIO() sys.stdout = out # Yield a method clients can use to obtain the value try: yield out.getvalue finally: # Restore the normal stdout sys.stdout = sys.__stdout__ def verify_book_keeping_attrs(test_class, h5_obj): time_stamp = get_time_stamp() in_file = h5_obj.attrs['timestamp'] test_class.assertEqual(time_stamp[:time_stamp.rindex('_')], in_file[:in_file.rindex('_')]) test_class.assertEqual(__version__, h5_obj.attrs['sidpy_version']) test_class.assertEqual(socket.getfqdn(), h5_obj.attrs['machine_id']) test_class.assertEqual(platform(), h5_obj.attrs['platform']) def make_beps_file(rev_spec=False): if os.path.exists(std_beps_path): os.remove(std_beps_path) with h5py.File(std_beps_path, mode='w') as h5_f: h5_raw_grp = h5_f.create_group('Raw_Measurement') write_safe_attrs(h5_raw_grp, {'att_1': 'string_val', 'att_2': 1.2345, 'att_3': [1, 2, 3, 4]}) write_string_list_as_attr(h5_raw_grp, {'att_4': ['str_1', 'str_2', 'str_3']}) _ = h5_raw_grp.create_group('Misc') num_rows = 3 num_cols = 5 num_cycles = 2 num_cycle_pts = 7 source_dset_name = 'source_main' tool_name = 'Fitter' # Per USID, dimensions are arranged from fastest to slowest source_pos_data = np.vstack((np.tile(np.arange(num_cols), num_rows), np.repeat(np.arange(num_rows), num_cols))).T pos_attrs = {'units': ['nm', 'um'], 'labels': ['X', 'Y']} h5_pos_inds = h5_raw_grp.create_dataset('Position_Indices', data=source_pos_data, dtype=np.uint16) write_aux_reg_ref(h5_pos_inds, pos_attrs['labels'], is_spec=False) write_string_list_as_attr(h5_pos_inds, pos_attrs) # make the values more interesting: cols_offset = -750 cols_step = 50 rows_offset = 2 rows_step = 1.25 source_pos_data = np.vstack((cols_offset + source_pos_data[:, 0] * cols_step, rows_offset + source_pos_data[:, 1] * rows_step)).T _ = h5_raw_grp.create_dataset('X', data=cols_offset + cols_step * np.arange(num_cols)) _ = h5_raw_grp.create_dataset('Y', data=rows_offset + rows_step * np.arange(num_rows)) h5_pos_vals = h5_raw_grp.create_dataset('Position_Values', data=source_pos_data, dtype=np.float32) write_aux_reg_ref(h5_pos_vals, pos_attrs['labels'], is_spec=False) write_string_list_as_attr(h5_pos_vals, pos_attrs) if rev_spec: source_spec_data = np.vstack((np.repeat(np.arange(num_cycles), num_cycle_pts), np.tile(np.arange(num_cycle_pts), num_cycles))) source_spec_attrs = {'units': ['', 'V'], 'labels': ['Cycle', 'Bias']} else: source_spec_data = np.vstack((np.tile(np.arange(num_cycle_pts), num_cycles), np.repeat(np.arange(num_cycles), num_cycle_pts))) source_spec_attrs = {'units': ['V', ''], 'labels': ['Bias', 'Cycle']} h5_source_spec_inds = h5_raw_grp.create_dataset('Spectroscopic_Indices', data=source_spec_data, dtype=np.uint16) write_aux_reg_ref(h5_source_spec_inds, source_spec_attrs['labels'], is_spec=True) write_string_list_as_attr(h5_source_spec_inds, source_spec_attrs) # make spectroscopic axis interesting as well bias_amp = 2.5 bias_period = np.pi bias_vec = bias_amp * np.sin(np.linspace(0, bias_period, num_cycle_pts, endpoint=False)) _ = h5_raw_grp.create_dataset('Bias', data=bias_vec) _ = h5_raw_grp.create_dataset('Cycle', data=np.arange(num_cycles)) if rev_spec: source_spec_data = np.vstack((np.repeat(np.arange(num_cycles), num_cycle_pts), np.tile(bias_vec, num_cycles))) else: source_spec_data = np.vstack((np.tile(bias_vec, num_cycles), np.repeat(np.arange(num_cycles), num_cycle_pts))) h5_source_spec_vals = h5_raw_grp.create_dataset('Spectroscopic_Values', data=source_spec_data, dtype=np.float32) write_aux_reg_ref(h5_source_spec_vals, source_spec_attrs['labels'], is_spec=True) write_string_list_as_attr(h5_source_spec_vals, source_spec_attrs) main_nd = np.random.rand(num_rows, num_cols, num_cycles, num_cycle_pts) h5_nd_main = h5_raw_grp.create_dataset('n_dim_form', data=main_nd) write_string_list_as_attr(h5_nd_main, {'dims': ['Y', 'X', 'Cycle', 'Bias']}) if rev_spec: # This simulates things like BEPS where Field should actually be varied slower but is varied faster during acquisition main_nd = main_nd.transpose(0, 1, 3, 2) source_main_data = main_nd.reshape(num_rows * num_cols, num_cycle_pts * num_cycles) # source_main_data = np.random.rand(num_rows * num_cols, num_cycle_pts * num_cycles) h5_source_main = h5_raw_grp.create_dataset(source_dset_name, data=source_main_data) write_safe_attrs(h5_source_main, {'units': 'A', 'quantity': 'Current'}) write_main_reg_refs(h5_source_main, {'even_rows': (slice(0, None, 2), slice(None)), 'odd_rows': (slice(1, None, 2), slice(None))}) # Now need to link as main! for dset in [h5_pos_inds, h5_pos_vals, h5_source_spec_inds, h5_source_spec_vals]: h5_source_main.attrs[dset.name.split('/')[-1]] = dset.ref _ = h5_raw_grp.create_dataset('Ancillary', data=np.arange(5)) # Now add a few results: h5_results_grp_1 = h5_raw_grp.create_group(source_dset_name + '-' + tool_name + '_000') write_safe_attrs(h5_results_grp_1, {'att_1': 'string_val', 'att_2': 1.2345, 'att_3': [1, 2, 3, 4]}) write_string_list_as_attr(h5_results_grp_1, {'att_4': ['str_1', 'str_2', 'str_3']}) num_cycles = 1 num_cycle_pts = 7 results_spec_inds = np.expand_dims(np.arange(num_cycle_pts), 0) results_spec_attrs = {'units': ['V'], 'labels': ['Bias']} h5_results_1_spec_inds = h5_results_grp_1.create_dataset('Spectroscopic_Indices', data=results_spec_inds, dtype=np.uint16) write_aux_reg_ref(h5_results_1_spec_inds, results_spec_attrs['labels'], is_spec=True) write_string_list_as_attr(h5_results_1_spec_inds, results_spec_attrs) results_spec_vals = np.expand_dims(2.5 * np.sin(np.linspace(0, np.pi, num_cycle_pts, endpoint=False)), 0) h5_results_1_spec_vals = h5_results_grp_1.create_dataset('Spectroscopic_Values', data=results_spec_vals, dtype=np.float32) write_aux_reg_ref(h5_results_1_spec_vals, results_spec_attrs['labels'], is_spec=True) write_string_list_as_attr(h5_results_1_spec_vals, results_spec_attrs) # Let this be a compound dataset: struc_dtype = np.dtype({'names': ['r', 'g', 'b'], 'formats': [np.float32, np.float16, np.float64]}) num_elems = (num_rows, num_cols, num_cycles, num_cycle_pts) results_1_nd = np.zeros(shape=num_elems, dtype=struc_dtype) for name_ind, name in enumerate(struc_dtype.names): results_1_nd[name] = np.random.random(size=num_elems) h5_results_1_nd = h5_results_grp_1.create_dataset('n_dim_form', data=results_1_nd) write_string_list_as_attr(h5_results_1_nd, {'dims': ['Y', 'X', 'Cycle', 'Bias']}) results_1_main_data = results_1_nd.reshape(num_rows * num_cols, num_cycle_pts * num_cycles) h5_results_1_main = h5_results_grp_1.create_dataset('results_main', data=results_1_main_data) write_safe_attrs(h5_results_1_main, {'units': 'pF', 'quantity': 'Capacitance'}) # Now need to link as main! for dset in [h5_pos_inds, h5_pos_vals, h5_results_1_spec_inds, h5_results_1_spec_vals]: h5_results_1_main.attrs[dset.name.split('/')[-1]] = dset.ref # add another result with different parameters h5_results_grp_2 = h5_raw_grp.create_group(source_dset_name + '-' + tool_name + '_001') write_safe_attrs(h5_results_grp_2, {'att_1': 'other_string_val', 'att_2': 5.4321, 'att_3': [4, 1, 3]}) write_string_list_as_attr(h5_results_grp_2, {'att_4': ['s', 'str_2', 'str_3']}) # Let these results be a complex typed dataset: results_2_nd = np.random.random(size=num_elems) + \ 1j * np.random.random(size=num_elems) h5_results_2_nd = h5_results_grp_2.create_dataset('n_dim_form', data=results_2_nd) write_string_list_as_attr(h5_results_2_nd, {'dims': ['Y', 'X', 'Cycle', 'Bias']}) results_2_main_data = results_2_nd.reshape(num_rows * num_cols, num_cycle_pts * num_cycles) h5_results_2_main = h5_results_grp_2.create_dataset('results_main', data=results_2_main_data) write_safe_attrs(h5_results_2_main, {'units': 'pF', 'quantity': 'Capacitance'}) h5_results_2_spec_inds = h5_results_grp_2.create_dataset('Spectroscopic_Indices', data=results_spec_inds, dtype=np.uint16) write_aux_reg_ref(h5_results_2_spec_inds, results_spec_attrs['labels'], is_spec=True) write_string_list_as_attr(h5_results_2_spec_inds, results_spec_attrs) h5_results_2_spec_vals = h5_results_grp_2.create_dataset('Spectroscopic_Values', data=results_spec_vals, dtype=np.float32) write_aux_reg_ref(h5_results_2_spec_vals, results_spec_attrs['labels'], is_spec=True) write_string_list_as_attr(h5_results_2_spec_vals, results_spec_attrs) # Now need to link as main! for dset in [h5_pos_inds, h5_pos_vals, h5_results_2_spec_inds, h5_results_2_spec_vals]: h5_results_2_main.attrs[dset.name.split('/')[-1]] = dset.ref sidpy-0.12.3/tests/hdf/test_dtype_utils.py000066400000000000000000000642531455261647000206320ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Tue Nov 3 15:07:16 2017 @author: Suhas Somnath """ from __future__ import division, print_function, unicode_literals, absolute_import import unittest import sys import os import numpy as np import dask.array as da import h5py sys.path.append("../../sidpy/") from sidpy.hdf import dtype_utils struc_dtype = np.dtype({'names': ['r', 'g', 'b'], 'formats': [np.float32, np.uint16, np.float64]}) file_path = 'test_dtype_utils.h5' def compare_structured_arrays(arr_1, arr_2): """ if not isinstance(arr_1, np.ndarray): raise TypeError("arr_1 was not a numpy array") if not isinstance(arr_2, np.ndarray): raise TypeError("arr_2 was not a numpy array") """ if arr_1.dtype != arr_2.dtype: return False if arr_1.shape != arr_2.shape: return False tests = [] for name in arr_1.dtype.names: tests.append(np.allclose(arr_1[name], arr_2[name])) return np.all(tests) class TestDtypeUtils(unittest.TestCase): def setUp(self): if not os.path.exists(file_path): with h5py.File(file_path, mode='w') as h5_f: num_elems = (5, 7) structured_array = np.zeros(shape=num_elems, dtype=struc_dtype) structured_array['r'] = 450 * np.random.random(size=num_elems) structured_array['g'] = np.random.randint(0, high=1024, size=num_elems) structured_array['b'] = 3178 * np.random.random(size=num_elems) _ = h5_f.create_dataset('compound', data=structured_array) _ = h5_f.create_dataset('real', data=450 * np.random.random(size=num_elems)) _ = h5_f.create_dataset('real2', data=450 * np.random.random(size=(5, 7, 6))) _ = h5_f.create_dataset('complex', data=np.random.random(size=num_elems) + 1j * np.random.random(size=num_elems), dtype=np.complex64) h5_f.flush() return def tearDown(self): os.remove(file_path) class TestStackRealToComplex(unittest.TestCase): def test_single(self): expected = 4.32 + 5.67j real_val = [np.real(expected), np.imag(expected)] actual = dtype_utils.stack_real_to_complex(real_val) self.assertTrue(np.allclose(actual, expected)) def test_1d(self): expected = 5 * np.random.rand(6) + 7j * np.random.rand(6) real_val = np.hstack([np.real(expected), np.imag(expected)]) actual = dtype_utils.stack_real_to_complex(real_val) self.assertTrue(np.allclose(actual, expected)) def test_2d(self): expected = 5 * np.random.rand(2, 8) + 7j * np.random.rand(2, 8) real_val = np.hstack([np.real(expected), np.imag(expected)]) actual = dtype_utils.stack_real_to_complex(real_val) self.assertTrue(np.allclose(actual, expected)) def base_nd(self, lazy_in, lazy): expected = 5 * np.random.rand(2, 3, 5, 8) + 7j * np.random.rand(2, 3, 5, 8) real_val = np.concatenate([np.real(expected), np.imag(expected)], axis=3) if lazy_in: real_val = da.from_array(real_val, chunks=real_val.shape) actual = dtype_utils.stack_real_to_complex(real_val, lazy=lazy) if lazy: self.assertIsInstance(actual, da.core.Array) actual = actual.compute() self.assertTrue(np.allclose(actual, expected)) def test_nd_numpy_not_lazy(self): self.base_nd(False, False) def test_nd_numpy_lazy(self): self.base_nd(False, True) def test_nd_dask(self): self.base_nd(True, True) def test_invalid_inputs(self): with self.assertRaises(TypeError): _ = dtype_utils.stack_real_to_complex("Hello") with self.assertRaises(TypeError): _ = dtype_utils.stack_real_to_complex([1, {'a': 1}, 'a', True]) class TestStackRealToComplexHDF5(TestDtypeUtils): def base_h5_legal(self, lazy): with h5py.File(file_path, mode='r') as h5_f: h5_real = h5_f['real2'] expected = h5_real[:, :, :3] + 1j * h5_real[:, :, 3:] actual = dtype_utils.stack_real_to_complex(h5_real, lazy=lazy) if lazy: self.assertIsInstance(actual, da.core.Array) actual = actual.compute() else: self.assertIsInstance(actual, np.ndarray) self.assertTrue(np.allclose(actual, expected)) def test_h5_as_numpy(self): self.base_h5_legal(False) def test_h5_as_dask(self): self.base_h5_legal(True) def test_h5_illegal(self): with h5py.File(file_path, mode='r') as h5_f: with self.assertRaises(TypeError): _ = dtype_utils.stack_real_to_complex(h5_f['complex']) with self.assertRaises(TypeError): _ = dtype_utils.stack_real_to_complex(h5_f['compound']) def test_illegal_odd_last_dim(self): expected = 5 * np.random.rand(2, 3, 5, 7) + 7j * np.random.rand(2, 3, 5, 7) real_val = np.concatenate([np.real(expected), np.imag(expected)[..., :-1]], axis=3) with self.assertRaises(ValueError): _ = dtype_utils.stack_real_to_complex(real_val) class TestFlattenComplexToReal(unittest.TestCase): def test_1d_array(self): complex_array = 5 * np.random.rand(5) + 7j * np.random.rand(5) actual = dtype_utils.flatten_complex_to_real(complex_array) expected = np.hstack([np.real(complex_array), np.imag(complex_array)]) self.assertTrue(np.allclose(actual, expected)) def test_2d_array(self): complex_array = 5 * np.random.rand(2, 3) + 7j * np.random.rand(2, 3) actual = dtype_utils.flatten_complex_to_real(complex_array) expected = np.hstack([np.real(complex_array), np.imag(complex_array)]) self.assertTrue(np.allclose(actual, expected)) def test_nd_array_numpy(self): complex_array = 5 * np.random.rand(2, 3, 5, 7) + 7j * np.random.rand(2, 3, 5, 7) actual = dtype_utils.flatten_complex_to_real(complex_array) expected = np.concatenate([np.real(complex_array), np.imag(complex_array)], axis=3) self.assertTrue(np.allclose(actual, expected)) def test_nd_array_dask(self): complex_array = 5 * np.random.rand(2, 3, 5, 7) + 7j * np.random.rand(2, 3, 5, 7) da_comp = da.from_array(complex_array, chunks=complex_array.shape) actual = dtype_utils.flatten_complex_to_real(da_comp) self.assertIsInstance(actual, da.core.Array) actual = actual.compute() expected = np.concatenate([np.real(complex_array), np.imag(complex_array)], axis=3) self.assertTrue(np.allclose(actual, expected)) def test_real_in(self): with self.assertRaises(TypeError): _ = dtype_utils.flatten_complex_to_real(np.arange(4)) def test_compound_illegal(self): num_elems = 5 structured_array = np.zeros(shape=num_elems, dtype=struc_dtype) structured_array['r'] = np.random.random(size=num_elems) structured_array['g'] = np.random.randint(0, high=1024, size=num_elems) structured_array['b'] = np.random.random(size=num_elems) with self.assertRaises(TypeError): _ = dtype_utils.flatten_complex_to_real(structured_array) class TestFlattenComplexToRealHDF5(TestDtypeUtils): def base_h5_legal(self, lazy): with h5py.File(file_path, mode='r') as h5_f: h5_comp = h5_f['complex'] actual = dtype_utils.flatten_complex_to_real(h5_comp, lazy=lazy) if lazy: self.assertIsInstance(actual, da.core.Array) actual = actual.compute() expected = np.concatenate([np.real(h5_comp[()]), np.imag(h5_comp[()])], axis=len(h5_comp.shape) - 1) self.assertTrue(np.allclose(actual, expected)) def test_h5_legal_not_lazy(self): self.base_h5_legal(False) def test_h5_legal_lazy(self): self.base_h5_legal(True) def test_h5_illegal(self): with h5py.File(file_path, mode='r') as h5_f: with self.assertRaises(TypeError): _ = dtype_utils.flatten_complex_to_real(h5_f) def test_h5_illegal_dtype(self): with h5py.File(file_path, mode='r') as h5_f: with self.assertRaises(TypeError): _ = dtype_utils.flatten_complex_to_real(h5_f['real']) with self.assertRaises(TypeError): _ = dtype_utils.flatten_complex_to_real(h5_f['compound']) class TestGetCompoundSubTypes(unittest.TestCase): def test_legal(self): self.assertEqual({'r': np.float32, 'g': np.uint16, 'b': np.float64}, dtype_utils.get_compound_sub_dtypes(struc_dtype)) def test_illegal(self): with self.assertRaises(TypeError): _ = dtype_utils.get_compound_sub_dtypes(np.float16) with self.assertRaises(TypeError): _ = dtype_utils.get_compound_sub_dtypes(16) with self.assertRaises(TypeError): _ = dtype_utils.get_compound_sub_dtypes(np.arange(4)) class TestStackRealToCompound(unittest.TestCase): def test_single(self): num_elems = 1 structured_array = np.zeros(shape=num_elems, dtype=struc_dtype) structured_array['r'] = r_vals = np.random.random(size=num_elems) structured_array['g'] = g_vals = np.random.randint(0, high=1024, size=num_elems) structured_array['b'] = b_vals = np.random.random(size=num_elems) real_val = np.concatenate((r_vals, g_vals, b_vals)) actual = dtype_utils.stack_real_to_compound(real_val, struc_dtype) self.assertTrue(compare_structured_arrays(actual, structured_array[0])) def test_1d(self): num_elems = 5 structured_array = np.zeros(shape=num_elems, dtype=struc_dtype) structured_array['r'] = r_vals = np.random.random(size=num_elems) structured_array['g'] = g_vals = np.random.randint(0, high=1024, size=num_elems) structured_array['b'] = b_vals = np.random.random(size=num_elems) real_val = np.concatenate((r_vals, g_vals, b_vals)) actual = dtype_utils.stack_real_to_compound(real_val, struc_dtype) self.assertTrue(compare_structured_arrays(actual, structured_array)) def test_1d_list(self): num_elems = 5 structured_array = np.zeros(shape=num_elems, dtype=struc_dtype) structured_array['r'] = r_vals = np.random.random(size=num_elems) structured_array['g'] = g_vals = np.random.randint(0, high=1024, size=num_elems) structured_array['b'] = b_vals = np.random.random(size=num_elems) real_val = np.concatenate((r_vals, g_vals, b_vals)) actual = dtype_utils.stack_real_to_compound(list(real_val), struc_dtype) self.assertTrue(compare_structured_arrays(actual, structured_array)) def base_nd(self, in_lazy, out_lazy): num_elems = (2, 3, 5, 7) structured_array = np.zeros(shape=num_elems, dtype=struc_dtype) structured_array['r'] = r_vals = np.random.random(size=num_elems) structured_array['g'] = g_vals = np.random.randint(0, high=1024, size=num_elems) structured_array['b'] = b_vals = np.random.random(size=num_elems) real_val = np.concatenate((r_vals, g_vals, b_vals), axis=len(num_elems) - 1) if in_lazy: real_val = da.from_array(real_val, chunks=real_val.shape) actual = dtype_utils.stack_real_to_compound(real_val, struc_dtype, lazy=out_lazy) if out_lazy: self.assertIsInstance(actual, da.core.Array) actual = actual.compute() self.assertTrue(compare_structured_arrays(actual, structured_array)) def test_nd_dask_in(self): with self.assertRaises(NotImplementedError): self.base_nd(True, True) def test_nd_numpy_in_not_lazy(self): self.base_nd(False, False) def test_nd_numpy_in_lazy(self): with self.assertRaises(NotImplementedError): self.base_nd(False, True) def test_illegal(self): num_elems = (3, 5) r_vals = np.random.random(size=num_elems) with self.assertRaises(TypeError): _ = dtype_utils.stack_real_to_compound(r_vals, np.float32) with self.assertRaises(ValueError): _ = dtype_utils.stack_real_to_compound(r_vals, struc_dtype) with self.assertRaises(ValueError): _ = dtype_utils.stack_real_to_compound([1, 'a', {'a': 1}, False], struc_dtype) class TestFlattenCompoundToReal(unittest.TestCase): def test_single(self): num_elems = 1 structured_array = np.zeros(shape=num_elems, dtype=struc_dtype) structured_array['r'] = r_vals = np.random.random(size=num_elems) structured_array['g'] = g_vals = np.random.randint(0, high=1024, size=num_elems) structured_array['b'] = b_vals = np.random.random(size=num_elems) expected = np.concatenate((r_vals, g_vals, b_vals)) actual = dtype_utils.flatten_compound_to_real(structured_array[0]) self.assertTrue(np.allclose(actual, expected)) def test_1d(self): num_elems = 5 structured_array = np.zeros(shape=num_elems, dtype=struc_dtype) structured_array['r'] = r_vals = np.random.random(size=num_elems) structured_array['g'] = g_vals = np.random.randint(0, high=1024, size=num_elems) structured_array['b'] = b_vals = np.random.random(size=num_elems) expected = np.concatenate((r_vals, g_vals, b_vals)) actual = dtype_utils.flatten_compound_to_real(structured_array) self.assertTrue(np.allclose(actual, expected)) def base_nd(self, lazy_in, lazy): num_elems = (5, 7, 2, 3) structured_array = np.zeros(shape=num_elems, dtype=struc_dtype) structured_array['r'] = r_vals = np.random.random(size=num_elems) structured_array['g'] = g_vals = np.random.randint(0, high=1024, size=num_elems) structured_array['b'] = b_vals = np.random.random(size=num_elems) if lazy_in: structured_array = da.from_array(structured_array, chunks=structured_array.shape) expected = np.concatenate((r_vals, g_vals, b_vals), axis=len(num_elems) - 1) actual = dtype_utils.flatten_compound_to_real(structured_array, lazy=lazy) if lazy: self.assertIsInstance(actual, da.core.Array) actual = actual.compute() self.assertTrue(np.allclose(actual, expected)) def test_numpy_nd_not_lazy(self): self.base_nd(False, False) def test_numpy_nd_lazy(self): self.base_nd(False, True) def test_dask_nd(self): self.base_nd(True, True) def test_illegal(self): num_elems = (2, 3) r_vals = np.random.random(size=num_elems) with self.assertRaises(TypeError): _ = dtype_utils.flatten_compound_to_real(r_vals) with self.assertRaises(TypeError): _ = dtype_utils.flatten_compound_to_real(14) class TestFlattenCompoundToRealHDF5(TestDtypeUtils): def base_nd_h5_legal(self, lazy): with h5py.File(file_path, mode='r') as h5_f: h5_comp = h5_f['compound'] actual = dtype_utils.flatten_compound_to_real(h5_comp, lazy=lazy) if lazy: self.assertIsInstance(actual, da.core.Array) actual = actual.compute() expected = np.concatenate([h5_comp['r'], h5_comp['g'], h5_comp['b']], axis=len(h5_comp.shape) - 1) self.assertTrue(np.allclose(actual, expected)) def test_h5_legal_not_lazy(self): self.base_nd_h5_legal(False) def test_h5_legal_lazy(self): self.base_nd_h5_legal(True) def test_nd_h5_illegal(self): with h5py.File(file_path, mode='r') as h5_f: with self.assertRaises(TypeError): _ = dtype_utils.flatten_compound_to_real(h5_f['real']) with self.assertRaises(TypeError): _ = dtype_utils.flatten_compound_to_real(h5_f['complex']) class TestStackRealToCompoundHDF5(TestDtypeUtils): def base_nd_h5_legal(self, lazy): with h5py.File(file_path, mode='r') as h5_f: h5_real = h5_f['real2'] structured_array = np.zeros(shape=list(h5_real.shape)[:-1] + [h5_real.shape[-1] // len(struc_dtype.names)], dtype=struc_dtype) for name_ind, name in enumerate(struc_dtype.names): i_start = name_ind * structured_array.shape[-1] i_end = (name_ind + 1) * structured_array.shape[-1] structured_array[name] = h5_real[..., i_start:i_end] actual = dtype_utils.stack_real_to_compound(h5_real, struc_dtype, lazy=lazy) if lazy: self.assertIsInstance(actual, da.core.Array) actual = actual.compute() self.assertTrue(compare_structured_arrays(actual, structured_array)) def test_nd_h5(self): self.base_nd_h5_legal(False) def test_nd_h5_lazy(self): with self.assertRaises(NotImplementedError): self.base_nd_h5_legal(True) def test_nd_h5_illegal(self): with h5py.File(file_path, mode='r') as h5_f: with self.assertRaises(TypeError): _ = dtype_utils.stack_real_to_compound(h5_f['compound'], struc_dtype) with self.assertRaises(TypeError): _ = dtype_utils.stack_real_to_compound(h5_f['complex'], struc_dtype) class TestFlattenToRealNumpy(unittest.TestCase): def test_real_nd(self): real_array = 5 * np.random.rand(2, 3, 5, 7) actual = dtype_utils.flatten_to_real(real_array) self.assertTrue(np.allclose(actual, real_array)) def test_complex_nd(self): complex_array = 5 * np.random.rand(2, 3, 5, 7) + 7j * np.random.rand(2, 3, 5, 7) actual = dtype_utils.flatten_to_real(complex_array) expected = np.concatenate([np.real(complex_array), np.imag(complex_array)], axis=3) self.assertTrue(np.allclose(actual, expected)) def test_complex_single(self): complex_val = 4.32 + 5.67j expected = [np.real(complex_val), np.imag(complex_val)] actual = dtype_utils.flatten_to_real(complex_val) self.assertTrue(np.allclose(actual, expected)) def test_compound_nd(self): num_elems = (5, 7, 2, 3) structured_array = np.zeros(shape=num_elems, dtype=struc_dtype) structured_array['r'] = r_vals = np.random.random(size=num_elems) structured_array['g'] = g_vals = np.random.randint(0, high=1024, size=num_elems) structured_array['b'] = b_vals = np.random.random(size=num_elems) expected = np.concatenate((r_vals, g_vals, b_vals), axis=len(num_elems) - 1) actual = dtype_utils.flatten_to_real(structured_array) self.assertTrue(np.allclose(actual, expected)) def test_compound_single(self): num_elems = 1 structured_array = np.zeros(shape=num_elems, dtype=struc_dtype) structured_array['r'] = r_vals = np.random.random(size=num_elems) structured_array['g'] = g_vals = np.random.randint(0, high=1024, size=num_elems) structured_array['b'] = b_vals = np.random.random(size=num_elems) expected = np.concatenate((r_vals, g_vals, b_vals)) actual = dtype_utils.flatten_to_real(structured_array[0]) self.assertTrue(np.allclose(actual, expected)) class TestStackRealToTargetDtypeNumpy(unittest.TestCase): def test_complex_single(self): expected = 4.32 + 5.67j real_val = [np.real(expected), np.imag(expected)] actual = dtype_utils.stack_real_to_target_dtype(real_val, complex) self.assertTrue(np.allclose(actual, expected)) def test_complex_nd(self): expected = 5 * np.random.rand(2, 3, 5, 8) + 7j * np.random.rand(2, 3, 5, 8) real_val = np.concatenate([np.real(expected), np.imag(expected)], axis=3) actual = dtype_utils.stack_real_to_target_dtype(real_val, complex) self.assertTrue(np.allclose(actual, expected)) def test_compound_single(self): num_elems = 1 structured_array = np.zeros(shape=num_elems, dtype=struc_dtype) structured_array['r'] = r_vals = np.random.random(size=num_elems) structured_array['g'] = g_vals = np.random.randint(0, high=1024, size=num_elems) structured_array['b'] = b_vals = np.random.random(size=num_elems) real_val = np.concatenate((r_vals, g_vals, b_vals)) actual = dtype_utils.stack_real_to_target_dtype(real_val, struc_dtype) self.assertTrue(compare_structured_arrays(actual, structured_array[0])) def test_compound_nd(self): num_elems = (2, 3, 5, 7) structured_array = np.zeros(shape=num_elems, dtype=struc_dtype) structured_array['r'] = r_vals = np.random.random(size=num_elems) structured_array['g'] = g_vals = np.random.randint(0, high=1024, size=num_elems) structured_array['b'] = b_vals = np.random.random(size=num_elems) real_val = np.concatenate((r_vals, g_vals, b_vals), axis=len(num_elems) - 1) actual = dtype_utils.stack_real_to_target_dtype(real_val, struc_dtype) self.assertTrue(compare_structured_arrays(actual, structured_array)) def test_compound_nd(self): num_elems = (2, 3, 5, 7) structured_array = np.zeros(shape=num_elems, dtype=struc_dtype) structured_array['r'] = r_vals = np.random.random(size=num_elems) structured_array['g'] = g_vals = np.random.randint(0, high=1024, size=num_elems) structured_array['b'] = b_vals = np.random.random(size=num_elems) real_val = np.concatenate((r_vals, g_vals, b_vals), axis=len(num_elems) - 1) actual = dtype_utils.stack_real_to_target_dtype(real_val, struc_dtype) self.assertTrue(compare_structured_arrays(actual, structured_array)) def test_cast_std_type(self): uint_array = np.random.randint(0, high=15, size=(3, 4, 5), dtype=np.uint16) actual = dtype_utils.stack_real_to_target_dtype(uint_array, np.float32) self.assertTrue(np.allclose(actual, np.float32(uint_array))) def test_cast_str_type(self): uint_array = np.random.randint(0, high=15, size=(3, 4, 5), dtype=np.uint16) actual = dtype_utils.stack_real_to_target_dtype(uint_array, np.dtype(' 1: cycles_dim = np.arange(cycles) data_set.set_dimension(3, sid.Dimension(cycles_dim, name='Cycle', units='#', quantity='Cycle', dimension_type='spectral')) # append metadata data_set.metadata = parms_dict return data_set, xvec def gauss_2D(fitting_space, *parms): x = fitting_space[0] y = fitting_space[1] amplitude, xo, yo, sigma, offset = parms xo = float(xo) yo = float(yo) r = ((x - xo) ** 2 + (y - yo) ** 2) ** .5 g = amplitude * np.exp(-(r / sigma) ** 2) + offset return g.ravel() def make_4D_dataset(shape=(32, 16, 64, 48)): dataset = np.zeros(shape=shape, dtype=np.float64) xlen, ylen, kxlen, kylen = shape kx, ky = np.meshgrid(np.linspace(0, 11, kxlen), np.linspace(0, 7, kylen), indexing='ij') true_parms = np.zeros((dataset.shape[0], dataset.shape[1], 5)) for row in range(xlen): for col in range(ylen): amp = np.sqrt(row * col // xlen * ylen) + 3.5 sigma = np.random.normal(loc=2.5, scale=0.34) offset = 0.1 # col xo = np.random.uniform(low=0, high=6) yo = np.random.uniform(low=0, high=5) cur_parms = [amp, xo, yo, sigma, offset] true_parms[row, col, :] = cur_parms # amplitude, xo, yo, sigma, offset gauss_mat = gauss_2D([kx.ravel(), ky.ravel()], *cur_parms) gauss_mat += np.mean(gauss_mat) / 2 * np.random.normal(size=len(gauss_mat)) gauss_mat = gauss_mat.reshape([kxlen, kylen]) dataset[row, col, :, :] = gauss_mat # Now make it a sidpy dataset parms_dict = {'info_1': np.linspace(0, 5.6, 30), 'instrument': 'opportunity rover AFM'} # Let's convert it to a sidpy dataset # Specify dimensions x_dim = np.linspace(0, 1E-6, dataset.shape[0]) y_dim = np.linspace(0, 2E-6, dataset.shape[1]) kx_dim = np.linspace(0, 11, kxlen) ky_dim = np.linspace(0, 5, kylen) # Make a sidpy dataset data_set = sid.Dataset.from_array(dataset, name='4D_STEM') # Set the data type data_set.data_type = sid.DataType.IMAGE_4D # Add quantity and units data_set.units = 'nA' data_set.quantity = 'Current' # Add dimension info data_set.set_dimension(0, sid.Dimension(x_dim, name='x', units='m', quantity='x', dimension_type='spatial')) data_set.set_dimension(1, sid.Dimension(y_dim, name='y', units='m', quantity='y', dimension_type='spatial')) data_set.set_dimension(2, sid.Dimension(kx_dim, name='Intensity KX', units='counts', quantity='Intensity', dimension_type='spectral')) data_set.set_dimension(3, sid.Dimension(ky_dim, name='Intensity KY', units='counts', quantity='Intensity', dimension_type='spectral')) # append metadata data_set.metadata = parms_dict return data_set, [kx.ravel(), ky.ravel()] def make_4D_dataset_spectral_image(shape=(32, 16, 64, 48)): dataset = np.zeros(shape=shape, dtype=np.float64) xlen, ylen, kxlen, kylen = shape kx, ky = np.meshgrid(np.linspace(0, 11, kxlen), np.linspace(0, 7, kylen), indexing='ij') true_parms = np.zeros((dataset.shape[0], dataset.shape[1], 5)) for row in range(xlen): for col in range(ylen): amp = np.sqrt(row * col // xlen * ylen) + 3.5 sigma = np.random.normal(loc=2.5, scale=0.34) offset = 0.1 # col xo = np.random.uniform(low=0, high=6) yo = np.random.uniform(low=0, high=5) cur_parms = [amp, xo, yo, sigma, offset] true_parms[row, col, :] = cur_parms # amplitude, xo, yo, sigma, offset gauss_mat = gauss_2D([kx.ravel(), ky.ravel()], *cur_parms) gauss_mat += np.mean(gauss_mat) / 2 * np.random.normal(size=len(gauss_mat)) gauss_mat = gauss_mat.reshape([kxlen, kylen]) dataset[row, col, :, :] = gauss_mat # Now make it a sidpy dataset parms_dict = {'info_1': np.linspace(0, 5.6, 30), 'instrument': 'opportunity rover AFM'} # Let's convert it to a sidpy dataset # Specify dimensions x_dim = np.linspace(0, 1E-6, dataset.shape[0]) y_dim = np.linspace(0, 2E-6, dataset.shape[1]) kx_dim = np.linspace(0, 11, kxlen) ky_dim = np.linspace(0, 5, kylen) # Make a sidpy dataset data_set = sid.Dataset.from_array(dataset) # Set the data type data_set.data_type = sid.DataType.SPECTRAL_IMAGE # Add quantity and units data_set.units = 'nA' data_set.quantity = 'Current' # Add dimension info data_set.set_dimension(0, sid.Dimension(x_dim, name='x', units='m', quantity='x', dimension_type='spatial')) data_set.set_dimension(1, sid.Dimension(y_dim, name='y', units='m', quantity='y', dimension_type='spatial')) data_set.set_dimension(2, sid.Dimension(kx_dim, name='Intensity KX', units='counts', quantity='Intensity', dimension_type='spectral')) data_set.set_dimension(3, sid.Dimension(ky_dim, name='Intensity KY', units='counts', quantity='Intensity', dimension_type='channel')) # append metadata data_set.metadata = parms_dict return data_set, [kx.ravel(), ky.ravel()] if __name__ == '__main__': unittest.main() sidpy-0.12.3/tests/sid/000077500000000000000000000000001455261647000146605ustar00rootroot00000000000000sidpy-0.12.3/tests/sid/__init__.py000066400000000000000000000000001455261647000167570ustar00rootroot00000000000000sidpy-0.12.3/tests/sid/test_dataset.py000066400000000000000000001113701455261647000177210ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri Sep 18 17:07:16 2020 @author: Suhas Somnath, Gerd Duscher """ from __future__ import division, print_function, unicode_literals, \ absolute_import import unittest import numpy as np import dask.array as da import string import ase.build import sys from copy import deepcopy sys.path.insert(0, "../../sidpy/") from sidpy.sid.dimension import Dimension from sidpy.sid.dataset import DataType, Dataset if sys.version_info.major == 3: unicode = str generic_attributes = ['title', 'quantity', 'units', 'modality', 'source'] def validate_dataset_properties(self, dataset, values, title='generic', quantity='generic', units='generic', modality='generic', source='generic', dimension_dict=None, data_type=DataType.UNKNOWN, variance=None, metadata={}, original_metadata={}, ): self.assertIsInstance(self, unittest.TestCase) self.assertIsInstance(dataset, Dataset) # DONE: Validate that EVERY property is set correctly values = np.array(values) self.assertTrue(np.all([hasattr(dataset, att) for att in generic_attributes])) expected = values.flatten() actual = dataset.compute().flatten() self.assertTrue(np.allclose(expected, actual, equal_nan=True, rtol=1e-05, atol=1e-08)) # self.assertTrue(np.all([x == y for x, y in zip(expected, actual)])) this_attributes = [title, quantity, units, modality, source] dataset_attributes = [getattr(dataset, att) for att in generic_attributes] for expected, actual in zip(dataset_attributes, this_attributes): self.assertTrue(np.all([x == y for x, y in zip(expected, actual)])) if variance is None: self.assertEqual(dataset.variance, None) else: self.assertTrue(isinstance(dataset.variance, da.core.Array)) expected_var = np.array(variance).flatten() actual_var = dataset.variance.compute().flatten() self.assertTrue(np.allclose(expected_var, actual_var, equal_nan=True, rtol=1e-05, atol=1e-08)) self.assertEqual(dataset.data_type, data_type) self.assertEqual(dataset.metadata, metadata) self.assertEqual(dataset.original_metadata, original_metadata) if dimension_dict is None: for dim in range(len(values.shape)): self.assertEqual(getattr(dataset, string.ascii_lowercase[dim]), getattr(dataset, 'dim_{}'.format(dim))) else: for dim in range(len(values.shape)): self.assertEqual(getattr(dataset, dimension_dict[dim].name), getattr(dataset, 'dim_{}'.format(dim))) self.assertEqual(dataset._axes[dim], dimension_dict[dim]) # Make sure we do not have too many dimensions self.assertFalse(hasattr(dataset, 'dim_{}'.format(len(values.shape)))) # self.assertFalse(hasattr(dataset, string.ascii_lowercase[len(values.shape)])) # Following 4 methods are used in testing the methods that reduce dimensions of the dataset def single_axis_test(self, func, **kwargs): dset_np = np.random.rand(4, 1, 5) dset = Dataset.from_array(dset_np, title='test') sid_func = getattr(dset, func) np_func = getattr(dset_np, func) dset_1 = sid_func(axis=0, keepdims=False) dim_dict = {0: dset._axes[1].copy(), 1: dset._axes[2].copy()} title_prefix = kwargs.get('title_prefix') validate_dataset_properties(self, dset_1, np_func(axis=0, keepdims=False), title=title_prefix + dset.title, modality=dset.modality, source=dset.modality, dimension_dict=dim_dict, data_type=DataType.UNKNOWN, metadata={}, original_metadata={} ) def multiple_axes_test(self, func, **kwargs): dset_np = np.random.rand(1, 6, 4) dset = Dataset.from_array(dset_np, title='test') sid_func = getattr(dset, func) np_func = getattr(dset_np, func) dset_1 = sid_func(axis=(0, 1), keepdims=False) dim_dict = {0: dset._axes[2].copy()} title_prefix = kwargs.get('title_prefix') validate_dataset_properties(self, dset_1, np_func(axis=(0, 1), keepdims=False), title=title_prefix + dset.title, modality=dset.modality, source=dset.modality, dimension_dict=dim_dict, data_type=DataType.UNKNOWN, metadata={}, original_metadata={} ) # The following two tests are for when keep_dims is set to True def keepdims_test(self, func, **kwargs): dset_np = np.random.rand(2, 1, 4) dset = Dataset.from_array(dset_np, title='test') sid_func = getattr(dset, func) np_func = getattr(dset_np, func) dset_1 = sid_func(axis=0, keepdims=True) dim_dict = dset._axes.copy() dim_dict[0] = Dimension(np.arange(1), name=dset._axes[0].name, quantity=dset._axes[0].quantity, units=dset._axes[0].units, dimension_type=dset._axes[0].dimension_type) title_prefix = kwargs.get('title_prefix') validate_dataset_properties(self, dset_1, np_func(axis=0, keepdims=True), title=title_prefix + dset.title, modality=dset.modality, source=dset.modality, dimension_dict=dim_dict, data_type=DataType.UNKNOWN, metadata={}, original_metadata={} ) def keepdims_multiple_axes_test(self, func, **kwargs): dset_np = np.random.rand(1, 5, 4) dset = Dataset.from_array(dset_np, title='test') sid_func = getattr(dset, func) np_func = getattr(dset_np, func) title_prefix = kwargs.get('title_prefix') dset_1 = sid_func(axis=(0, 1), keepdims=True) dim_dict = dset._axes.copy() dim_dict[0] = Dimension(np.arange(1), name=dset._axes[0].name, quantity=dset._axes[0].quantity, units=dset._axes[0].units, dimension_type=dset._axes[0].dimension_type) dim_dict[1] = Dimension(np.arange(1), name=dset._axes[1].name, quantity=dset._axes[1].quantity, units=dset._axes[1].units, dimension_type=dset._axes[1].dimension_type) validate_dataset_properties(self, dset_1, np_func(axis=(0, 1), keepdims=True), title=title_prefix + dset.title, modality=dset.modality, source=dset.modality, dimension_dict=dim_dict, data_type=DataType.UNKNOWN, metadata={}, original_metadata={} ) class TestDatasetFromArray(unittest.TestCase): def test_std_inputs(self): # verify generic properties, dimensions, etc. values = np.random.random([4, 5, 6]) descriptor = Dataset.from_array(values) validate_dataset_properties(self, descriptor, values) def test_dset_with_variance(self): values = np.random.random([4, 5, 6]) variance = np.random.random([4, 5, 6]) descriptor = Dataset.from_array(values, variance=variance) validate_dataset_properties(self, descriptor, values, variance=variance) class TestDatasetConstructor(unittest.TestCase): def test_minimal_inputs(self): """ test minimum input requirement of an array like object """ with self.assertRaises(TypeError): Dataset.from_array() descriptor = Dataset.from_array(np.arange(3)) validate_dataset_properties(self, descriptor, np.arange(3)) def test_all_inputs(self): descriptor = Dataset.from_array(np.arange(3), title='test') validate_dataset_properties(self, descriptor, np.arange(3), title='test') def test_user_defined_parms(self): descriptor = Dataset.from_array(np.arange(3), title='test') for att in generic_attributes: setattr(descriptor, att, 'test') test_dict = {0: 'test'} descriptor.metadata = test_dict.copy() descriptor.original_metadata = test_dict.copy() validate_dataset_properties(self, descriptor, np.arange(3), title='test', quantity='test', units='test', modality='test', source='test', dimension_dict=None, data_type=DataType.UNKNOWN, metadata=test_dict, original_metadata=test_dict ) def test_invalid_main_types(self): """ anything that is not recognized by dask will make an empty dask array but name has to be a string """ # TODO: call validate_dataset_properties instead descriptor = Dataset.from_array(DataType.UNKNOWN) self.assertEqual(descriptor.shape, ()) descriptor = Dataset.from_array('test') self.assertEqual(descriptor.shape, ()) descriptor = Dataset.from_array(1) self.assertEqual(descriptor.shape, ()) with self.assertRaises(ValueError): Dataset.from_array(1, 1) # TODO: Should be TypeError def test_numpy_array_input(self): x = np.ones([3, 4, 5]) descriptor = Dataset.from_array(x, title='test') self.assertEqual(descriptor.shape, x.shape) # TODO: call validate_dataset_properties instead def test_dask_array_input(self): x = da.zeros([3, 4], chunks='auto') descriptor = Dataset.from_array(x, chunks='auto', title='test') self.assertEqual(descriptor.shape, x.shape) # TODO: call validate_dataset_properties instead def test_list_input(self): x = [[3, 4, 6], [5, 6, 7]] descriptor = Dataset.from_array(x, title='test') self.assertEqual(descriptor.shape, np.array(x).shape) # TODO: call validate_dataset_properties instead def test_1d_main_data(self): values = np.ones([10]) descriptor = Dataset.from_array(values) self.assertTrue(np.all([x == y for x, y in zip(values, descriptor)])) # TODO: call validate_dataset_properties instead # Move such validation to validate_dataset_properties for dim in range(len(values.shape)): self.assertEqual(getattr(descriptor, string.ascii_lowercase[dim]), getattr(descriptor, 'dim_{}'.format(dim))) self.assertFalse(hasattr(descriptor, 'dim_{}'.format(len(values.shape)))) self.assertFalse(hasattr(descriptor, string.ascii_lowercase[len(values.shape)])) def test_2d_main_data(self): values = np.random.random([4, 5]) descriptor = Dataset.from_array(values) for expected, actual in zip(values, descriptor): self.assertTrue(np.all([x == y for x, y in zip(expected, actual)])) for dim in range(len(values.shape)): self.assertEqual(getattr(descriptor, string.ascii_lowercase[dim]), getattr(descriptor, 'dim_{}'.format(dim))) self.assertFalse(hasattr(descriptor, 'dim_{}'.format(len(values.shape)))) self.assertFalse(hasattr(descriptor, string.ascii_lowercase[len(values.shape)])) def test_3d_main_data(self): values = np.random.random([4, 5, 6]) descriptor = Dataset.from_array(values) for expected, actual in zip(values, descriptor): self.assertTrue(np.all([x == y for x, y in zip(expected, actual)])) for dim in range(len(values.shape)): self.assertEqual(getattr(descriptor, string.ascii_lowercase[dim]), getattr(descriptor, 'dim_{}'.format(dim))) self.assertFalse(hasattr(descriptor, 'dim_{}'.format(len(values.shape)))) self.assertFalse(hasattr(descriptor, string.ascii_lowercase[len(values.shape)])) def test_4d_main_data(self): values = np.random.random([4, 5, 7, 3]) descriptor = Dataset.from_array(values) for expected, actual in zip(values, descriptor): self.assertTrue(np.all([x == y for x, y in zip(expected, actual)])) for dim in range(len(values.shape)): self.assertEqual(getattr(descriptor, string.ascii_lowercase[dim]), getattr(descriptor, 'dim_{}'.format(dim))) self.assertFalse(hasattr(descriptor, 'dim_{}'.format(len(values.shape)))) self.assertFalse(hasattr(descriptor, string.ascii_lowercase[len(values.shape)])) def test_dimensions_not_matching_main(self): pass def test_unknown_data_type(self): values = np.random.random([4]) descriptor = Dataset.from_array(values) expected = "Supported data_types for plotting are only:" with self.assertRaises(Warning) as context: descriptor.data_type = 'quark' self.assertTrue(expected in str(context.exception)) def test_enum_data_type(self): values = np.random.random([4]) descriptor = Dataset.from_array(values) for dt_type in DataType: descriptor.data_type = dt_type self.assertTrue(descriptor.data_type == dt_type) def test_string_data_type(self): values = np.random.random([4]) descriptor = Dataset.from_array(values) for dt_type in DataType: descriptor.data_type = str(dt_type.name) self.assertTrue(descriptor.data_type == dt_type) class TestDatasetRepr(unittest.TestCase): def test_minimal_inputs(self): values = np.arange(5) descriptor = Dataset.from_array(values) actual = '{}'.format(descriptor) out = 'generic' da_array = da.from_array(values, chunks='auto') expected = 'sidpy.Dataset of type {} with:\n '.format(DataType.UNKNOWN.name) expected = expected + '{}'.format(da_array) expected = expected + '\n data contains: {} ({})'.format(out, out) expected = expected + '\n and Dimensions: ' expected = expected + '\n{}: {} ({}) of size {}'.format('a', out, out, values.shape) """ for exp, act in zip(expected.split('\n'), actual.split('\n')): print('Expected:\t' + exp) print('Actual:\t' + act) print(exp == act) """ self.assertEqual(actual, expected) def test_fully_configured(self): values = np.arange(5) descriptor = Dataset.from_array(values) for att in generic_attributes: setattr(descriptor, att, 'test') descriptor.metadata = {0: 'test'} actual = '{}'.format(descriptor) out = 'test' da_array = da.from_array(values, chunks='auto') expected = 'sidpy.Dataset of type {} with:\n '.format(DataType.UNKNOWN.name) expected = expected + '{}'.format(da_array) expected = expected + '\n data contains: {} ({})'.format(out, out) expected = expected + '\n and Dimensions: ' expected = expected + '\n{}: {} ({}) of size {}'.format('a', 'generic', 'generic', values.shape) expected = expected + '\n with metadata: {}'.format([0]) """ for exp, act in zip(expected.split('\n'), actual.split('\n')): print('Expected:\t' + exp) print('Actual:\t' + act) print(exp == act) """ self.assertEqual(actual, expected) def test_user_defined_parameters(self): # self.blah = 14. Will / should this get printed pass class TestLikeData(unittest.TestCase): def test_minimal_inputs(self): values = np.ones([4, 5]) source_dset = Dataset.from_array(values) values = np.zeros([4, 5]) descriptor = source_dset.like_data(values) self.assertTrue(descriptor.shape == values.shape) self.assertIsInstance(descriptor, Dataset) def test_all_customized_properties(self): values = np.ones([4, 5]) source_dset = Dataset.from_array(values) for att in generic_attributes: setattr(source_dset, att, 'test') source_dset.metadata = {0: 'test'} values = np.zeros([4, 5]) descriptor = source_dset.like_data(values) self.assertEqual(descriptor.title, 'test_new') descriptor.title = 'test' self.assertTrue(np.all([getattr(descriptor, att) == 'test' for att in generic_attributes])) self.assertEqual(descriptor.metadata, source_dset.metadata) self.assertEqual(descriptor.original_metadata, source_dset.original_metadata) def test_changing_size(self): values = np.ones([4, 5]) source_dset = Dataset.from_array(values) source_dset.a *= 0.5 source_dset.quantity = 'test' values = np.zeros([3, 5]) descriptor = source_dset.like_data(values) # self.assertEqual(descriptor.a.values), np.arange(3)*.5) expected = descriptor.a.values actual = np.arange(3) * .5 self.assertTrue(np.all([x == y for x, y in zip(expected, actual)])) def test_variance(self): values = np.ones([4, 5]) var = np.random.normal(size=(4, 5)) source_dset = Dataset.from_array(values, variance=var) descriptor = source_dset.like_data(values) self.assertEqual(descriptor.variance, None) descriptor = source_dset.like_data(values, variance=var) self.assertEqual(descriptor.variance.all(), source_dset.variance.all()) class TestCopy(unittest.TestCase): def test_minimal_inputs(self): values = np.random.random([4, 5]) dataset = Dataset.from_array(values) descriptor = dataset.copy() self.assertIsInstance(descriptor, Dataset) for expected, actual in zip(dataset, descriptor): self.assertTrue(np.all([x == y for x, y in zip(expected, actual)])) self.assertTrue(np.all([hasattr(descriptor, att) for att in generic_attributes])) self.assertTrue(np.all([getattr(descriptor, att) == 'generic' for att in generic_attributes])) self.assertEqual(descriptor.data_type, DataType.UNKNOWN) self.assertEqual(descriptor.metadata, {}) self.assertEqual(descriptor.original_metadata, {}) for dim in range(len(values.shape)): self.assertEqual(getattr(descriptor, string.ascii_lowercase[dim]), getattr(descriptor, 'dim_{}'.format(dim))) self.assertFalse(hasattr(descriptor, 'dim_{}'.format(len(dataset.shape)))) self.assertFalse(hasattr(descriptor, string.ascii_lowercase[len(dataset.shape)])) def test_all_customized_properties(self): values = np.random.random([4, 5]) dataset = Dataset.from_array(values) dataset.rename_dimension(0, 'x') dataset.quantity = 'test' descriptor = dataset.copy() self.assertIsInstance(descriptor, Dataset) self.assertEqual(descriptor.quantity, dataset.quantity) self.assertTrue(hasattr(descriptor, 'x')) class TestRenameDimension(unittest.TestCase): def test_valid_index_and_name(self): values = np.zeros([4, 5]) descriptor = Dataset.from_array(values) descriptor.rename_dimension(0, 'v') self.assertEqual(descriptor.v, descriptor.dim_0) def test_invalid_index_object_type(self): values = np.zeros([4, 5]) descriptor = Dataset.from_array(values) with self.assertRaises(TypeError): descriptor.rename_dimension('v', 'v') def test_index_out_of_bounds(self): values = np.zeros([4, 5]) descriptor = Dataset.from_array(values) with self.assertRaises(IndexError): descriptor.rename_dimension(3, 'v') def test_invalid_name_object_types(self): values = np.zeros([4, 5]) descriptor = Dataset.from_array(values) with self.assertRaises(TypeError): descriptor.rename_dimension(0, 1) def test_empty_name_string(self): values = np.zeros([4, 5]) descriptor = Dataset.from_array(values) with self.assertRaises(ValueError): descriptor.rename_dimension(0, '') def test_existing_name(self): values = np.zeros([4, 5]) descriptor = Dataset.from_array(values) with self.assertRaises(ValueError): descriptor.rename_dimension(0, 'b') class TestSetDimension(unittest.TestCase): def test_valid_index_and_dim_obj(self): values = np.zeros([4, 5]) descriptor = Dataset.from_array(values) descriptor.set_dimension(0, Dimension(np.arange(4), 'x', quantity='test', units='test')) self.assertIsInstance(descriptor.x, Dimension) def test_invalid_dim_object(self): values = np.zeros([4, 5]) descriptor = Dataset.from_array(values) with self.assertRaises(TypeError): descriptor.set_dimension(3, "New dimension") with self.assertRaises(TypeError): descriptor.set_dimension('2', {'x': np.arange(4)}) with self.assertRaises(TypeError): descriptor.set_dimension(2, np.arange(4)) # validity of index tested in TestRenameDimension class TestHelperFunctions(unittest.TestCase): def test_get_image_dims(self): values = np.zeros([4, 5]) descriptor = Dataset.from_array(values) descriptor.set_dimension(0, Dimension(np.arange(4), 'x', quantity='test', dimension_type='spatial')) image_dims = descriptor.get_image_dims() self.assertEqual(len(image_dims), 1) self.assertEqual(image_dims[0], 0) descriptor.dim_1.dimension_type = 'spatial' image_dims = descriptor.get_image_dims() self.assertEqual(len(image_dims), 2) self.assertEqual(image_dims[1], 1) def test_get_dimensions_by_type(self): values = np.zeros([4, 5]) descriptor = Dataset.from_array(values) descriptor.set_dimension(0, Dimension(np.arange(4), 'x', quantity='test', dimension_type='spatial')) image_dims = descriptor.get_dimensions_by_type('spatial') self.assertEqual(len(image_dims), 1) self.assertEqual(image_dims[0], 0) descriptor.dim_1.dimension_type = 'spatial' image_dims = descriptor.get_dimensions_by_type('spatial') self.assertEqual(len(image_dims), 2) self.assertEqual(image_dims[1], 1) def test_get_spectral_dims(self): values = np.zeros([4, 5]) descriptor = Dataset.from_array(values) descriptor.set_dimension(0, Dimension(np.arange(4), 'x', quantity='test', dimension_type='spatial')) spec_dims = descriptor.get_spectral_dims() self.assertEqual(len(spec_dims), 0) descriptor.x.dimension_type = 'spectral' spec_dims = descriptor.get_spectral_dims() self.assertEqual(len(spec_dims), 1) self.assertEqual(spec_dims[0], 0) descriptor.dim_1.dimension_type = 'spectral' spec_dims = descriptor.get_spectral_dims() self.assertEqual(len(spec_dims), 2) self.assertEqual(spec_dims[1], 1) def test_get_extent(self): values = np.zeros([4, 5]) descriptor = Dataset.from_array(values) descriptor.set_dimension(0, Dimension(np.arange(4), 'x', quantity='test', dimension_type='spatial')) descriptor.dim_1.dimension_type = 'spatial' descriptor.set_dimension(0, Dimension(np.arange(4), 'x', quantity='test', dimension_type='spatial')) extent = descriptor.get_extent([0, 1]) self.assertEqual(extent[0], -0.5) self.assertEqual(extent[1], 3.5) def test_get_labels(self): values = np.zeros([4, 5]) descriptor = Dataset.from_array(values) labels = descriptor.labels self.assertEqual(labels[0], 'generic (generic)') def test_empty_structure(self): values = np.zeros([4, 5]) descriptor = Dataset.from_array(values) structures = descriptor.structures self.assertEqual(len(structures), 0) def test_add_structure(self): values = np.zeros([4, 5]) a = 5.14 # A atoms = ase.build.bulk('Si', 'diamond', a=a, cubic=True) descriptor = Dataset.from_array(values) descriptor.add_structure(atoms) descriptor.add_structure(atoms, 'reference') self.assertEqual(len(descriptor.structures), 2) self.assertTrue('reference' in descriptor.structures.keys()) def test__equ__(self): values = np.zeros([4, 5]) descriptor1 = Dataset.from_array(values) descriptor2 = Dataset.from_array(values) # TODO: why does direct comparison not work self.assertTrue(descriptor1.__eq__(descriptor2)) self.assertFalse(descriptor1.__eq__(np.arange(4))) descriptor1.set_dimension(0, Dimension(np.arange(4), 'x', quantity='test', dimension_type='spatial')) self.assertFalse(descriptor1.__eq__(descriptor2)) descriptor2.modality = 'nix' self.assertFalse(descriptor1.__eq__(descriptor2)) descriptor2.data_type = 'image' self.assertFalse(descriptor1.__eq__(descriptor2)) descriptor2.source = 'image' self.assertFalse(descriptor1.__eq__(descriptor2)) descriptor2.quantity = 'image' self.assertFalse(descriptor1.__eq__(descriptor2)) descriptor2.units = 'image' self.assertFalse(descriptor1.__eq__(descriptor2)) def test_h5_dataset(self): values = np.ones([4, 5]) source_dset = Dataset.from_array(values) class TestViewMetadata(unittest.TestCase): def test_default_empty_metadata(self): values = np.zeros([4, 5]) descriptor = Dataset.from_array(values) descriptor.view_metadata() # self.assertEqual('{}'.format(descriptor.view_metadata()),'None') def test_entered_metadata(self): values = np.zeros([4, 5]) descriptor = Dataset.from_array(values) descriptor.metadata = {0: 'test'} print('{}'.format(descriptor.view_metadata())) # self.assertEqual(descriptor.view_metadata(), '0 : test') class TestViewOriginalMetadata(unittest.TestCase): def test_default_empty_metadata(self): pass def test_entered_metadata(self): pass class Testallmethod(unittest.TestCase): def test_all_single_axis(self): single_axis_test(self, 'all', title_prefix='all_aggregate_') def test_all_multiple_axes(self): multiple_axes_test(self, 'all', title_prefix='all_aggregate_') def test_all_keepdims(self): keepdims_test(self, 'all', title_prefix='all_aggregate_') def test_all_keepdims_multiple_axes(self): keepdims_multiple_axes_test(self, 'all', title_prefix='all_aggregate_') class Testanymethod(unittest.TestCase): def test_any_single_axis(self): single_axis_test(self, 'any', title_prefix='any_aggregate_') def test_any_multiple_axes(self): multiple_axes_test(self, 'any', title_prefix='any_aggregate_') def test_any_keepdims(self): keepdims_test(self, 'any', title_prefix='any_aggregate_') def test_any_keepdims_multiple_axes(self): keepdims_multiple_axes_test(self, 'any', title_prefix='any_aggregate_') class TestMinMethod(unittest.TestCase): def test_min_single_axis(self): single_axis_test(self, 'min', title_prefix='min_aggregate_') def test_min_multiple_axes(self): multiple_axes_test(self, 'min', title_prefix='min_aggregate_') def test_min_keepdims(self): keepdims_test(self, 'min', title_prefix='min_aggregate_') def test_min_keepdims_multiple_axes(self): keepdims_multiple_axes_test(self, 'min', title_prefix='min_aggregate_') class TestMaxMethod(unittest.TestCase): def test_max_single_axis(self): single_axis_test(self, 'max', title_prefix='max_aggregate_') def test_max_multiple_axes(self): multiple_axes_test(self, 'max', title_prefix='max_aggregate_') def test_max_keepdims(self): keepdims_test(self, 'max', title_prefix='max_aggregate_') def test_min_keepdims_multiple_axes(self): keepdims_multiple_axes_test(self, 'max', title_prefix='max_aggregate_') class TestSumMethod(unittest.TestCase): def test_sum_single_axis(self): single_axis_test(self, 'sum', title_prefix='sum_aggregate_') def test_sum_multiple_axis(self): multiple_axes_test(self, 'sum', title_prefix='sum_aggregate_') def test_sum_keepdims(self): keepdims_test(self, 'sum', title_prefix='sum_aggregate_') def test_sum_keepdims_multiple_axis(self): keepdims_multiple_axes_test(self, 'sum', title_prefix='sum_aggregate_') def test_sum_dtype(self): # Have to take care of complex datasets when asked about the sum of the entire dataset pass class TestMeanMethod(unittest.TestCase): def test_mean_single_axis(self): single_axis_test(self, 'mean', title_prefix='mean_aggregate_') def test_mean_multiple_axis(self): multiple_axes_test(self, 'mean', title_prefix='mean_aggregate_') def test_mean_keepdims(self): keepdims_test(self, 'mean', title_prefix='mean_aggregate_') def test_mean_keepdims_multiple_axis(self): keepdims_multiple_axes_test(self, 'mean', title_prefix='mean_aggregate_') def test_mean_dtype(self): # Have to take care of complex datasets when asked about the sum of the entire dataset pass class TestSlicing(unittest.TestCase): np.random.seed(0) values = np.random.rand(3, 4, 6, 5) dset = Dataset.from_array(values, title='4D_STEM', units='nA', quantity='Current', modality='modality', source='source') dset.data_type = DataType.IMAGE_4D dset.metadata = {'info_1': np.linspace(0, 5.6, 30), 'instrument': 'opportunity rover AFM'} x_dim = np.linspace(0, 1E-6, dset.shape[0]) y_dim = np.linspace(0, 2E-6, dset.shape[1]) kx_dim = np.linspace(0, 12, dset.shape[2]) ky_dim = np.linspace(0, 10, dset.shape[3]) dset.set_dimension(0, Dimension(x_dim, name='x', units='m', quantity='x', dimension_type='spatial')) dset.set_dimension(1, Dimension(y_dim, name='y', units='m', quantity='y', dimension_type='spatial')) dset.set_dimension(2, Dimension(kx_dim, name='Intensity KX', units='counts', quantity='Intensity', dimension_type='spectral')) dset.set_dimension(3, Dimension(ky_dim, name='Intensity KY', units='counts', quantity='Intensity', dimension_type='spectral')) def test_getitem_integer(self): # Create a sample Dask array old_dset = self.dset sliced = self.dset[:, 2] dim_dict = {0: old_dset._axes[0].copy(), 1: old_dset._axes[2].copy(), 2: old_dset._axes[3].copy()} validate_dataset_properties(self, sliced, self.dset.compute()[:, 2], title=self.dset.title, quantity=self.dset.quantity, units=self.dset.units, modality=self.dset.modality, source=self.dset.source, dimension_dict=dim_dict, data_type=self.dset.data_type, metadata=self.dset.metadata, original_metadata=self.dset.original_metadata) def test_getitem_NoneandEllipsis1(self): old_dset = self.dset sliced = self.dset[..., None, :] dim_dict = {0: old_dset._axes[0].copy(), 1: old_dset._axes[1].copy(), 2: old_dset._axes[2].copy(), 3: Dimension(1), 4: old_dset._axes[3].copy()} validate_dataset_properties(self, sliced, self.dset.compute()[..., None, :], title=self.dset.title, quantity=self.dset.quantity, units=self.dset.units, modality=self.dset.modality, source=self.dset.source, dimension_dict=dim_dict, data_type=self.dset.data_type, metadata=self.dset.metadata, original_metadata=self.dset.original_metadata) def test_getitem_NoneandEllipsis2(self): old_dset = self.dset sliced = self.dset[None, ..., None] dim_dict = {0: Dimension(1), 1: old_dset._axes[0].copy(), 2: old_dset._axes[1].copy(), 3: old_dset._axes[2].copy(), 4: old_dset._axes[3].copy(), 5: Dimension(1)} validate_dataset_properties(self, sliced, self.dset.compute()[None, ..., None], title=self.dset.title, quantity=self.dset.quantity, units=self.dset.units, modality=self.dset.modality, source=self.dset.source, dimension_dict=dim_dict, data_type=self.dset.data_type, metadata=self.dset.metadata, original_metadata=self.dset.original_metadata) def test_getitem_slice1(self): old_dset = self.dset sliced = self.dset[0:1] dim_dict = {0: deepcopy(old_dset._axes[0][0:1]), 1: deepcopy(old_dset._axes[1]), 2: deepcopy(old_dset._axes[2]), 3: deepcopy(old_dset._axes[3])} validate_dataset_properties(self, sliced, self.dset.compute()[0:1], title=self.dset.title, quantity=self.dset.quantity, units=self.dset.units, modality=self.dset.modality, source=self.dset.source, dimension_dict=dim_dict, data_type=self.dset.data_type, metadata=self.dset.metadata, original_metadata=self.dset.original_metadata) def test_getitem_slice2(self): old_dset = self.dset sliced = self.dset[0:3] dim_dict = {0: deepcopy(old_dset._axes[0][0:3]), 1: deepcopy(old_dset._axes[1]), 2: deepcopy(old_dset._axes[2]), 3: deepcopy(old_dset._axes[3])} validate_dataset_properties(self, sliced, self.dset.compute()[0:3], title=self.dset.title, quantity=self.dset.quantity, units=self.dset.units, modality=self.dset.modality, source=self.dset.source, dimension_dict=dim_dict, data_type=self.dset.data_type, metadata=self.dset.metadata, original_metadata=self.dset.original_metadata) def test_getitem_nparray(self): old_dset = self.dset inds = np.array([True, False, True, False, True, False]) sliced = old_dset[:, :, inds, :] dim_dict = {0: deepcopy(old_dset._axes[0]), 1: deepcopy(old_dset._axes[1]), 2: deepcopy(old_dset._axes[2])[inds], 3: deepcopy(old_dset._axes[3])} validate_dataset_properties(self, sliced, self.dset.compute()[:, :, inds, :], title=self.dset.title, quantity=self.dset.quantity, units=self.dset.units, modality=self.dset.modality, source=self.dset.source, dimension_dict=dim_dict, data_type=self.dset.data_type, metadata=self.dset.metadata, original_metadata=self.dset.original_metadata) def test_getitem_daarray(self): np.random.seed(0) old_dset = self.dset inds = da.array(np.array([True, False, True, False, True])) sliced = old_dset[..., inds] dim_dict = {0: deepcopy(old_dset._axes[0]), 1: deepcopy(old_dset._axes[1]), 2: deepcopy(old_dset._axes[2]), 3: deepcopy(old_dset._axes[3])[np.array(inds)]} validate_dataset_properties(self, sliced, self.dset.compute()[..., inds], title=self.dset.title, quantity=self.dset.quantity, units=self.dset.units, modality=self.dset.modality, source=self.dset.source, dimension_dict=dim_dict, data_type=self.dset.data_type, metadata=self.dset.metadata, original_metadata=self.dset.original_metadata) if __name__ == '__main__': unittest.main() sidpy-0.12.3/tests/sid/test_dimension.py000066400000000000000000000166531455261647000202710ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Tue Nov 3 15:07:16 2017 @author: Suhas Somnath """ from __future__ import (absolute_import, division, print_function, unicode_literals) import sys import unittest import warnings import numpy as np from numpy.testing import assert_array_equal from sidpy.sid.dimension import Dimension sys.path.insert(0, "../../sidpy/") if sys.version_info.major == 3: unicode = str class TestDimension(unittest.TestCase): def test_values_as_array(self): name = 'Bias' values = np.random.rand(5) descriptor = Dimension(values, name) for expected, actual in zip([name, values], [descriptor.name, descriptor.values]): self.assertTrue(np.all([x == y for x, y in zip(expected, actual)])) def test_values_as_length(self): name = 'Bias' units = 'V' values = np.arange(5) descriptor = Dimension(len(values), name, units=units) for expected, actual in zip([name, units], [descriptor.name, descriptor.units]): self.assertTrue(np.all([x == y for x, y in zip(expected, actual)])) self.assertTrue(np.allclose(values, descriptor.values)) def test_copy(self): name = 'Bias' units = 'V' values = np.arange(5) descriptor = Dimension(values, name, units=units) copy_descriptor = descriptor.copy() for expected, actual in zip([copy_descriptor.name, copy_descriptor.units], [descriptor.name, descriptor.units]): self.assertTrue(np.all([x == y for x, y in zip(expected, actual)])) self.assertTrue(np.allclose(copy_descriptor.values, descriptor.values)) copy_descriptor.units = 'eV' self.assertFalse(copy_descriptor.units == descriptor.units) copy_descriptor = descriptor + 1 self.assertFalse(np.allclose(copy_descriptor.values, descriptor.values)) def test_repr(self): name = 'Bias' values = np.arange(5) descriptor = Dimension(values, name) actual = '{}'.format(descriptor) quantity = 'generic' units = 'generic' expected = '{}: {} ({}) of size {}'.format(name, quantity, units, values.shape) self.assertEqual(actual, expected) def test_change_name(self): name = 'Bias' values = np.arange(5) descriptor = Dimension(values, name) with self.assertRaises(AttributeError): descriptor.name = 'Voltage' def test_inequality_req_inputs(self): name = 'X' quantity = "Length" units = 'nm' self.assertTrue(Dimension(5, name) == Dimension(5, name)) self.assertFalse(Dimension(5, 'Y') == Dimension(5, name)) self.assertFalse(Dimension(4, name) == Dimension(5, name)) self.assertTrue( Dimension(5, units=units) == Dimension(5, units=units)) self.assertFalse( Dimension(5, units='pm') == Dimension(5, units=units)) self.assertTrue( Dimension(5, quantity=quantity) == Dimension(5, quantity=quantity)) self.assertFalse( Dimension(5, quantity='Bias') == Dimension(5, quantity=quantity)) self.assertFalse( Dimension(np.arange(5)) == Dimension(np.arange(5) + 1)) def test_dimensionality(self): vals = np.ones((2, 2)) expected = 'Dimension can only be 1 dimensional' with self.assertRaises(Exception) as context: _ = Dimension(vals, "x", ) self.assertTrue(expected in str(context.exception)) def test_info(self): expected = "X - Bias (mV): [0. 1. 2. 3. 4.]" dim = Dimension(np.arange(5), "X", "Bias", "mV") self.assertTrue(dim.info, expected) def test_values_smaller_than_min_size(self): with self.assertRaises(TypeError) as context: _ = Dimension(0, name="x") self.assertTrue("When specifying the size of a Dimension, values " "should at be integers > 1" in str(context.exception)) def test_empty_array_values(self): with self.assertRaises(TypeError) as context: _ = Dimension([], name="x") self.assertTrue("When specifying values over which a parameter is " "varied, values should not be an empty array" "" in str(context.exception)) def test_dimension_size_1(self): dim = Dimension(1) self.assertIsInstance(dim, Dimension) assert_array_equal(np.array(dim), [0]) def test_single_valued_dimension(self): dim = Dimension([1.23]) self.assertIsInstance(dim, Dimension) assert_array_equal(np.array(dim), [1.23]) def test_conv2arr_values(self): arr = np.arange(5) vals = [5, arr, arr.tolist(), tuple(arr)] vals_expected = arr for v in vals: dim = Dimension(v, "x") self.assertIsInstance(dim, Dimension) assert_array_equal(np.array(dim), vals_expected) def test_dimension_type(self): dim_types = ["spatial", "Spatial", "reciprocal", "Reciprocal", "spectral", "Spectral", "temporal", "Temporal", "frame", "Frame", "time", "Time", "stack", "Stack"] dim_vals_expected = [1, 1, 2, 2, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4] dim_names_expected = ["SPATIAL", "SPATIAL", "RECIPROCAL", "RECIPROCAL", "SPECTRAL", "SPECTRAL", "TEMPORAL", "TEMPORAL", "TEMPORAL", "TEMPORAL", "TEMPORAL", "TEMPORAL", "TEMPORAL", "TEMPORAL"] for dt, dv, dn in zip(dim_types, dim_vals_expected, dim_names_expected): dim = Dimension(5, "x", dimension_type=dt) self.assertEqual(dim.dimension_type.value, dv) self.assertEqual(dim.dimension_type.name, dn) def test_dimension_type(self): dim_types = ["spatial", "Spatial", "reciprocal", "Reciprocal", "spectral", "Spectral", "temporal", "Temporal", "frame", "Frame", "time", "Time", "stack", "Stack"] dim_vals_expected = [1, 1, 2, 2, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4] dim_names_expected = ["SPATIAL", "SPATIAL", "RECIPROCAL", "RECIPROCAL", "SPECTRAL", "SPECTRAL", "TEMPORAL", "TEMPORAL", "TEMPORAL", "TEMPORAL", "TEMPORAL", "TEMPORAL", "TEMPORAL", "TEMPORAL"] for dt, dv, dn in zip(dim_types, dim_vals_expected, dim_names_expected): dim = Dimension(5, "x", dimension_type=dt) self.assertEqual(dim.dimension_type.value, dv) self.assertEqual(dim.dimension_type.name, dn) def test_unknown_dimension_type(self): dim_type = "bad_name" expected_wrn = ["Supported dimension types for plotting are only: [", "Setting DimensionType to UNKNOWN"] with warnings.catch_warnings(record=True) as w: _ = Dimension(5, "x", dimension_type=dim_type) self.assertTrue(expected_wrn[0] in str(w[0].message)) self.assertTrue(expected_wrn[1] in str(w[1].message)) def test_add(self): name = 'Bias' units = 'V' values = np.arange(5) descriptor = Dimension(values, name, units=units) descriptor = descriptor + 3. self.assertIsInstance(descriptor, Dimension) if __name__ == '__main__': unittest.main() sidpy-0.12.3/tests/sid/test_processing.py000066400000000000000000000426111455261647000204510ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Fri March 26 17:07:16 2021 @author: Gerd Duscher """ from __future__ import division, print_function, unicode_literals, \ absolute_import import unittest import numpy as np import sys sys.path.insert(0, "../../sidpy/") import sidpy if sys.version_info.major == 3: unicode = str class TestUFunctions(unittest.TestCase): def test_add(self): input_spectrum = np.zeros([512]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset+3. new_dataset.compute() self.assertIsInstance(new_dataset, sidpy.Dataset) self.assertEqual(np.array(new_dataset)[0], 3) input_spectrum = np.zeros([3, 3, 512]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset + 3. self.assertIsInstance(new_dataset, sidpy.Dataset) self.assertEqual(np.array(new_dataset)[0, 0, 0], 3) def test_sub(self): input_spectrum = np.zeros([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset-3. self.assertIsInstance(new_dataset, sidpy.Dataset) self.assertEqual(np.array(new_dataset)[0, 0, 0], -3) def test_mul(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset*3. self.assertIsInstance(new_dataset, sidpy.Dataset) self.assertEqual(np.array(new_dataset)[0, 0, 0], 3) def test_min(self): input_spectrum = np.zeros([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) min_dataset = dataset.min() self.assertIsInstance(min_dataset, float) self.assertEqual(min_dataset, 0) def test_max(self): input_spectrum = np.zeros([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) max_dataset = dataset.max() self.assertIsInstance(max_dataset, float) self.assertEqual(max_dataset, 0) def test_abs(self): input_spectrum = np.ones([3, 3, 3]) * -1 dataset = sidpy.Dataset.from_array(input_spectrum) abs_dataset = dataset.abs() self.assertIsInstance(abs_dataset, sidpy.Dataset) self.assertEqual(abs_dataset[0, 0, 0].compute(), 1) new_dataset = dataset.__abs__() self.assertIsInstance(new_dataset, sidpy.Dataset) def test_angle(self): input_spectrum = np.ones([3, 3, 3]) * -1 dataset = sidpy.Dataset.from_array(input_spectrum) angle_dataset = dataset.angle() self.assertIsInstance(angle_dataset, sidpy.Dataset) self.assertEqual(float(angle_dataset[0, 0, 0]), np.pi) def test_dot(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.dot(dataset) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_t(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.T self.assertIsInstance(new_dataset, sidpy.Dataset) def test_transpose(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.transpose() self.assertIsInstance(new_dataset, sidpy.Dataset) def test_ravel(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.ravel() self.assertIsInstance(new_dataset, sidpy.Dataset) def test_choose(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.choose([2, 3, 1, 0]) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_radd(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset+3. self.assertIsInstance(new_dataset, sidpy.Dataset) def test_and(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.__and__(dataset) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_rand(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.__rand__(dataset) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_div(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset/dataset self.assertIsInstance(new_dataset, sidpy.Dataset) def test_rdiv(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset/np.array(dataset) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_gt(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset > 3 self.assertIsInstance(new_dataset, sidpy.Dataset) def test_ge(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset >= 3 self.assertIsInstance(new_dataset, sidpy.Dataset) def test_invert(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = ~dataset self.assertIsInstance(new_dataset, sidpy.Dataset) def test_lshift(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.__lshift__(1) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_lt(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset < 3 self.assertIsInstance(new_dataset, sidpy.Dataset) def test_le(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset <= 3 self.assertIsInstance(new_dataset, sidpy.Dataset) def test_mod(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset % 3 self.assertIsInstance(new_dataset, sidpy.Dataset) def test_rmod(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.__rmod__(2) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_rmul(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.__rmul__(2) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_ne(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset != 2 self.assertIsInstance(new_dataset, sidpy.Dataset) def test_neg(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.__neg__() self.assertIsInstance(new_dataset, sidpy.Dataset) def test_or(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.__or__(dataset) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_ror(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.__ror__(dataset) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_pos(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.__pos__() self.assertIsInstance(new_dataset, sidpy.Dataset) def test_pow(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset**2 self.assertIsInstance(new_dataset, sidpy.Dataset) def test_rpow(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.__rpow__(2) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_rshift(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.__rshift__(2) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_rrshift(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.__rrshift__(2) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_rsub(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.__rsub__(2) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_rtruediv(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.__rtruediv__(2) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_rfloordiv(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.__rfloordiv__(2) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_xor(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.__xor__(2) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_rxor(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.__rxor__(2) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_matmul(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset @ dataset self.assertIsInstance(new_dataset, sidpy.Dataset) def test_rmatmul(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.__rmatmul__(dataset) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_divmod(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset, _ = dataset.__divmod__(2) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_rdivmod(self): input_spectrum = np.ones([3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset, _ = dataset.__rdivmod__(8) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_real(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.real self.assertIsInstance(new_dataset, sidpy.Dataset) def test_imag(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.imag self.assertIsInstance(new_dataset, sidpy.Dataset) def test_conj(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.conj() self.assertIsInstance(new_dataset, sidpy.Dataset) def test_clip(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.clip() self.assertIsInstance(new_dataset, sidpy.Dataset) def test_sum(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.sum(axis=1) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_mean(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.mean(axis=1) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_squeeze(self): input_spectrum = np.ones([3, 1, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.squeeze() self.assertIsInstance(new_dataset, sidpy.Dataset) def test_swapaxes(self): input_spectrum = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_spectrum) new_dataset = dataset.swapaxes(0, 1) self.assertIsInstance(new_dataset, sidpy.Dataset) def test_ufunc(self): # Todo: More testing for better coverage input_image = np.ones([3, 3, 3]) dataset = sidpy.Dataset.from_array(input_image) new_dataset = np.sin(dataset) self.assertIsInstance(new_dataset, sidpy.Dataset) new_dataset = dataset @ dataset self.assertIsInstance(new_dataset, sidpy.Dataset) class TestFftFunctions(unittest.TestCase): def test_spectrum_fft(self): input_spectrum = np.zeros([512]) x = np.mgrid[0:32] * 16 input_spectrum[x] = 1 dataset = sidpy.Dataset.from_array(input_spectrum) dataset.data_type = 'spectrum' dataset.units = 'counts' dataset.quantity = 'intensity' with self.assertRaises(NotImplementedError): dataset.fft() with self.assertRaises(TypeError): dataset.fft(dimension_type='spectral') dataset.set_dimension(0, sidpy.Dimension(np.arange(dataset.shape[0]) * .02, 'x')) dataset.x.dimension_type = 'spectral' dataset.x.units = 'nm' dataset.x.quantity = 'distance' fft_dataset = dataset.fft() self.assertEqual(np.array(fft_dataset)[0], 32+0j) def test_image_fft(self): input_image = np.zeros([512, 512]) x, y = np.mgrid[0:32, 0:32] * 16 input_image[x, y] = 1 dataset = sidpy.Dataset.from_array(input_image) dataset.data_type = 'image' dataset.units = 'counts' dataset.quantity = 'intensity' with self.assertRaises(NotImplementedError): dataset.fft() with self.assertRaises(TypeError): dataset.fft(dimension_type='spatial') dataset.set_dimension(0, sidpy.Dimension(np.arange(dataset.shape[0]) * .02, 'x')) dataset.x.dimension_type = 'spatial' dataset.x.units = 'nm' dataset.x.quantity = 'distance' with self.assertRaises(TypeError): dataset.fft() dataset.set_dimension(1, sidpy.Dimension(np.arange(dataset.shape[1]) * .02, 'y')) dataset.y.dimension_type = 'spatial' dataset.y.units = 'nm' dataset.y.quantity = 'distance' fft_dataset = dataset.fft() self.assertEqual(np.array(fft_dataset)[0, 0], 1024+0j) def test_image_stack_fft(self): input_stack = np.zeros([3, 512, 512]) x, y = np.mgrid[0:32, 0:32] * 16 input_stack[:, x, y] = 1. dataset = sidpy.Dataset.from_array(input_stack) dataset.data_type = 'image_stack' dataset.units = 'counts' dataset.quantity = 'intensity' with self.assertRaises(NotImplementedError): dataset.fft() with self.assertRaises(TypeError): dataset.fft(dimension_type='spatial') dataset.set_dimension(0, sidpy.Dimension(np.arange(dataset.shape[0]), 'frame')) dataset.frame.dimension_type = 'time' dataset.set_dimension(1, sidpy.Dimension(np.arange(dataset.shape[1]) * .02, 'x')) dataset.x.dimension_type = 'spatial' dataset.x.units = 'nm' dataset.x.quantity = 'distance' with self.assertRaises(NotImplementedError): dataset.fft() dataset.set_dimension(2, sidpy.Dimension(np.arange(dataset.shape[2]) * .02, 'y')) dataset.y.dimension_type = 'spatial' dataset.y.units = 'nm' dataset.y.quantity = 'distance' fft_dataset = dataset.fft() self.assertEqual(np.array(fft_dataset)[0, 0, 0], 1024 + 0j) def test_spectrum_image_fft(self): input_si = np.zeros([3, 3, 512]) x = np.mgrid[0:32] * 16 input_si[:, :, x] = 1. dataset = sidpy.Dataset.from_array(input_si) dataset.data_type = 'spectral_image' dataset.units = 'counts' dataset.quantity = 'intensity' with self.assertRaises(NotImplementedError): dataset.fft() with self.assertRaises(TypeError): dataset.fft(dimension_type='spectral') dataset.set_dimension(0, sidpy.Dimension(np.arange(dataset.shape[0]) * .02, 'x')) dataset.x.dimension_type = 'spatial' dataset.x.units = 'nm' dataset.x.quantity = 'distance' with self.assertRaises(NotImplementedError): dataset.fft() dataset.set_dimension(1, sidpy.Dimension(np.arange(dataset.shape[1]) * .02, 'y')) dataset.y.dimension_type = 'spatial' dataset.y.units = 'nm' dataset.y.quantity = 'distance' dataset.set_dimension(2, sidpy.Dimension(np.arange(dataset.shape[2]) * .02, 'spec')) dataset.spec.dimension_type = 'spectral' dataset.spec.units = 'i' dataset.spec.quantity = 'energy' fft_dataset = dataset.fft() self.assertEqual(np.array(fft_dataset)[0, 0, 0], 32+0j) if __name__ == '__main__': unittest.main() sidpy-0.12.3/tests/sid/test_reader.py000066400000000000000000000035731455261647000175430ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Tue Nov 3 15:07:16 2017 @author: Suhas Somnath """ from __future__ import division, print_function, unicode_literals, \ absolute_import import unittest import sys import os sys.path.append("../../sidpy/") from sidpy.sid import Reader class TestCanReadNotImplemented(unittest.TestCase): def setUp(self): class DummyReader(Reader): def read(self): pass self.reader_class = DummyReader def test_bad_init_obj(self): with self.assertRaises(TypeError): _ = self.reader_class(['haha.txt']) def test_file_does_not_exist(self): with self.assertRaises(FileNotFoundError): _ = self.reader_class('haha.txt') def test_valid_file(self): file_path = os.path.abspath('blah.txt') with open(file_path, mode='w') as file_handle: file_handle.write('Nothing') reader = self.reader_class(file_path) with self.assertWarns(DeprecationWarning): reader.can_read() os.remove(file_path) def test_invalid_file_object(self): with self.assertRaises(TypeError): _ = self.reader_class(1.23234) with self.assertRaises(TypeError): _ = self.reader_class({'fdfdfd': 33}) with self.assertRaises(TypeError): _ = self.reader_class([1, '2343434', False]) def test_empty_file_path(self): with self.assertRaises(ValueError): _ = self.reader_class(' ') def test_space_in_file_path(self): file_path = './blah.txt' with open(file_path, mode='w') as file_handle: file_handle.write('Nothing') reader = self.reader_class(' ' + file_path + ' ') self.assertEqual(reader._input_file_path, file_path) os.remove(file_path) if __name__ == '__main__': unittest.main() sidpy-0.12.3/tests/sid/test_translator.py000066400000000000000000000070761455261647000204740ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ Created on Tue Nov 3 15:07:16 2017 @author: Suhas Somnath """ from __future__ import division, print_function, unicode_literals, \ absolute_import import unittest import sys import os sys.path.append("../../sidpy/") from sidpy.sid import Translator class TestIsValidFile(unittest.TestCase): def setUp(self): class DummyTranslator(Translator): def translate(self, *args, **kwargs): pass self.translator = DummyTranslator() self.file_path = os.path.abspath('blah.txt') with open(self.file_path, mode='w') as file_handle: file_handle.write('Nothing') def tearDown(self): os.remove(self.file_path) def test_file_does_not_exist(self): err_type = ValueError if sys.version_info.major == 3: err_type = FileNotFoundError with self.assertRaises(err_type): _ = self.translator.is_valid_file('dfdfd.txt', extension='.txt') def test_no_extension_kwarg(self): with self.assertRaises(NotImplementedError): _ = self.translator.is_valid_file(self.file_path) def test_single_ext(self): self.assertEqual(self.translator.is_valid_file(self.file_path, extension='txt'), self.file_path) def test_case_insensitive(self): # self.assertTrue(self.translator.is_valid_file('blah.TXT', # extension='txt')) self.assertEqual(self.translator.is_valid_file(self.file_path, extension='TXT'), self.file_path) def test_with_dot_in_ext(self): self.assertEqual(self.translator.is_valid_file(self.file_path, extension='.txt'), self.file_path) def test_multi_ext(self): self.assertEqual(self.translator.is_valid_file(self.file_path, extension=['txt', '.png']), self.file_path) def test_diff_extension(self): self.assertEqual(self.translator.is_valid_file(self.file_path, extension=['jpeg', '.png']), None) def test_wrong_type(self): with self.assertRaises(TypeError): _ = self.translator.is_valid_file({'hello': 3}, extension=['jpeg', '.png']) with self.assertRaises(TypeError): _ = self.translator.is_valid_file('blah.txt', extension=[14, '.png']) def test_folder(self): self.assertEqual(self.translator.is_valid_file(os.path.abspath( './sidpy'),extension='.txt'), None) class TestTranslateNotImplemented(unittest.TestCase): def test_empty_translator(self): if sys.version_info.major == 2: # Unable to assert TyperError when instantiating empty Translator return """ with self.assertRaises(TypeError): _ = Translator() return """ tran = Translator() with self.assertRaises(NotImplementedError): tran.translate('blah.txt') if __name__ == '__main__': unittest.main() sidpy-0.12.3/tests/sink.h5000066400000000000000000000040241455261647000153030ustar00rootroot00000000000000HDF  `TREE0HEAPXotherH F aSNOD sidpy-0.12.3/tests/source.h5000066400000000000000000000040241455261647000156370ustar00rootroot00000000000000HDF  `TREE0HEAPXmainH F aSNOD sidpy-0.12.3/tests/viz/000077500000000000000000000000001455261647000147115ustar00rootroot00000000000000sidpy-0.12.3/tests/viz/__init__.py000066400000000000000000000000001455261647000170100ustar00rootroot00000000000000sidpy-0.12.3/tests/viz/test12.h5000066400000000000000000000041401455261647000162700ustar00rootroot00000000000000HDF  ``TREE0HEAPXtest12H `;cSNOD  sidpy-0.12.3/tests/viz/test_dataset_plot.py000066400000000000000000000373101455261647000210110ustar00rootroot00000000000000""" Created on Thurs Dec 10 2021 @author: Gerd Duscher """ import unittest import sys import os import ipywidgets import matplotlib as mpl if os.environ.get('DISPLAY', '') == '': print('no display found. Using non-interactive Agg backend') mpl.use('Agg') import numpy as np sys.path.insert(0, "../../sidpy/") import sidpy def get_spectrum(dtype=float): x = np.array(np.random.normal(3, 2.5, size=1024), dtype=dtype) dset = sidpy.Dataset.from_array(x) # dataset metadata dset.data_type = 'spectrum' dset.title = 'random' dset.quantity = 'intensity' dset.units = 'a.u.' scale = .5 offset = 390 dset.set_dimension(0, sidpy.Dimension(np.arange(dset.shape[0]) * scale + offset, 'energy')) dset.dim_0.dimension_type = 'spectral' dset.energy.units = 'eV' dset.energy.quantity = 'energy' return dset, x def get_image(dtype=float): x = np.array(np.random.normal(3, 2.5, size=(512, 512)), dtype=dtype) dset = sidpy.Dataset.from_array(x) dset.data_type = 'image' dset.units = 'counts' dset.quantity = 'intensity' dset.title = 'random' dset.set_dimension(0, sidpy.Dimension(np.arange(dset.shape[0]) * .02, 'x')) dset.x.dimension_type = 'spatial' dset.x.units = 'nm' dset.x.quantity = 'distance' dset.set_dimension(1, sidpy.Dimension(np.arange(dset.shape[1]) * .02, 'y')) dset.y.dimension_type = 'spatial' dset.y.units = 'nm' dset.y.quantity = 'distance' return dset, x def get_image_stack(dtype=float): x = np.array(np.random.normal(3, 2.5, size=(25, 512, 512)), dtype=dtype) dset = sidpy.Dataset.from_array(x) dset.data_type = 'image_stack' dset.units = 'counts' dset.quantity = 'intensity' dset.set_dimension(0, sidpy.Dimension(np.arange(dset.shape[0]), 'frame')) dset.frame.dimension_type = 'temporal' dset.set_dimension(1, sidpy.Dimension(np.arange(dset.shape[1]) * .02, 'x')) dset.x.dimension_type = 'spatial' dset.x.units = 'nm' dset.x.quantity = 'distance' dset.set_dimension(2, sidpy.Dimension(np.arange(dset.shape[2]) * .02, 'y')) dset.y.dimension_type = 'spatial' dset.y.units = 'nm' dset.y.quantity = 'distance' return dset, x def get_spectral_image(dtype=float): x = np.array(np.random.normal(3, 2.5, size=(25, 512, 512)), dtype=dtype) dset = sidpy.Dataset.from_array(x) dset.data_type = 'spectral_image' dset.units = 'counts' dset.quantity = 'intensity' dset.set_dimension(0, sidpy.Dimension(np.arange(dset.shape[0]), 'energy')) dset.energy.dimension_type = 'spectral' dset.energy.units = 'eV' dset.energy.quantity = 'energy' dset.set_dimension(1, sidpy.Dimension(np.arange(dset.shape[1]) * .02, 'x')) dset.x.dimension_type = 'spatial' dset.x.units = 'nm' dset.x.quantity = 'distance' dset.set_dimension(2, sidpy.Dimension(np.arange(dset.shape[2]) * .02, 'y')) dset.y.dimension_type = 'spatial' dset.y.units = 'nm' dset.y.quantity = 'distance' return dset, x def get_point_cloud(dtype=float): data = np.array(np.random.normal(3, 2.5, size=(20, 10)), dtype=dtype) data_var = np.array(np.random.normal(10, 2.5, size=(20, 10)), dtype=dtype) coordinates = np.array(np.random.rand(20, 2) + 10, dtype=dtype) dset = sidpy.Dataset.from_array(data, coordinates=coordinates) dset.data_type = 'point_cloud' dset.variance = data_var dset.point_cloud['spacial_units'] = 'um' dset.point_cloud['quantity'] = 'Distance' dset.set_dimension(0, sidpy.Dimension(np.arange(data.shape[0]), name='point number', quantity='Point number', dimension_type='point_cloud')) dset.set_dimension(1, sidpy.Dimension(np.arange(data.shape[1]), name='X', units='a.u.', quantity='X', dimension_type='spectral')) dset.units = 'a.u.' dset.quantity = 'Intensity' return dset, data def get_4d_image(dtype=float): data = np.array(np.random.random([5, 5, 10, 10]), dtype=dtype) for i in range(5): for j in range(5): data[i, j] += (i+j) dataset = sidpy.Dataset.from_array(data) dataset.data_type = 'Image_4d' dataset.title = 'random' dataset.set_dimension(0, sidpy.Dimension(np.arange(dataset.shape[0]) * .02, 'u')) dataset.u.dimension_type = 'reciprocal' dataset.u.units = '1/nm' dataset.u.quantity = 'frequency' dataset.set_dimension(1, sidpy.Dimension(np.arange(dataset.shape[1]) * .02, 'v')) dataset.v.dimension_type = 'reciprocal' dataset.v.units = '1/nm' dataset.v.quantity = 'frequency' dataset.set_dimension(2, sidpy.Dimension(np.arange(dataset.shape[2]) * .02, 'x')) dataset.x.dimension_type = 'spatial' dataset.x.units = 'nm' dataset.x.quantity = 'distance' dataset.set_dimension(3, sidpy.Dimension(np.arange(dataset.shape[3]) * .02, 'y')) dataset.y.dimension_type = 'spatial' dataset.y.units = 'nm' dataset.y.quantity = 'distance' return dataset, data class TestSpectrumPlot(unittest.TestCase): def test_spectrum(self): # dimension with metadata dset, x = get_spectrum() view = dset.plot(verbose=True) x_y = view.axes[0].lines[0].get_xydata() self.assertTrue(np.allclose(x_y[:, 1], x)) self.assertTrue(np.allclose(x_y[:, 0], dset.energy)) self.assertEqual(dset.title, view.axes[0].get_title()) self.assertEqual(f"{dset.energy.quantity} ({dset.energy.units})", view.axes[0].get_xlabel()) def test_false_type(self): x = np.zeros(5) with self.assertRaises(TypeError): sidpy.viz.dataset_viz.CurveVisualizer(x) def test_false_dim(self): dset, x = get_image() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.CurveVisualizer(dset) def test_generic(self): x = np.random.normal(3, 2.5, size=(4, 1024)) dset = sidpy.Dataset.from_array(x) dset.data_type = 'spectrum' dset.plot() def test_complex(self): dset, x = get_spectrum(dtype=complex) view = dset.plot(verbose=True) x_y = view.axes[0].lines[0].get_xydata() self.assertEqual(len(view.axes), 2) self.assertTrue(np.allclose(x_y[:, 1], np.abs(x))) self.assertTrue(np.allclose(x_y[:, 0], dset.energy)) # self.assertEqual(dset.title, view.axes[0].get_title()) self.assertEqual(f"{dset.energy.quantity} ({dset.energy.units})", view.axes[0].get_xlabel()) class TestImagePlot(unittest.TestCase): def test_image(self): dset, x = get_image() view = dset.plot() data = view.axes[0].images[0].get_array().data self.assertTrue(np.allclose(data.shape, x.shape)) self.assertEqual(dset.title, view.axes[0].get_title()) self.assertEqual(f"{dset.x.quantity} ({dset.x.units})", view.axes[0].get_xlabel()) def test_false_type(self): x = np.zeros(5) with self.assertRaises(TypeError): sidpy.viz.dataset_viz.ImageVisualizer(x) def test_false_dim(self): dset, x = get_spectrum() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.ImageVisualizer(dset) def test_generic(self): x = np.random.normal(3, 2.5, size=(6, 1024)) dset = sidpy.Dataset.from_array(x) dset.data_type = 'image' dset.dim_0.dimension_type = 'spatial' dset.dim_1.dimension_type = 'spatial' dset.data_type = 'image' dset.plot() def test_complex(self): dset, x = get_image(dtype=complex) view = dset.plot(verbose=True) x_y = view.axes[0].images[0].get_array().data self.assertEqual(len(view.axes), 4) self.assertEqual(x_y.shape, x.shape) # self.assertEqual(dset.title, view.axes[0].get_title()) self.assertEqual(f"{dset.x.quantity} ({dset.x.units})", view.axes[0].get_xlabel()) def test_image_scale(self): dset, x = get_image() kwargs = {'scale_bar': True, 'cmap': 'hot'} # or 'cmap': 'gray' view = dset.plot(verbose=True, **kwargs) data = view.axes[0].images[0].get_array().data self.assertTrue(np.allclose(data.shape, x.shape)) self.assertEqual(dset.title, view.axes[0].get_title()) self.assertEqual(f"{dset.x.quantity} ({dset.x.units})", view.axes[0].get_xlabel()) def test_image_stack(self): dset, x = get_image_stack() view = sidpy.viz.dataset_viz.ImageVisualizer(dset) data = view.fig.axes[0].images[0].get_array().data self.assertTrue(np.allclose(data.shape, x.shape[1:])) self.assertEqual('generic_image 0', view.fig.axes[0].get_title()) self.assertEqual(f"{dset.x.quantity} ({dset.x.units})", view.fig.axes[0].get_xlabel()) class TestImageStackPlot(unittest.TestCase): def test_plot(self): dset, x = get_image_stack() view = dset.plot() data = view.axes[0].images[0].get_array().data self.assertTrue(np.allclose(data.shape, x.shape[1:])) self.assertEqual(view.axes[0].get_title(), 'Image stack: generic\n use scroll wheel to navigate images') self.assertEqual(f"{dset.x.quantity} ({dset.x.units})", view.axes[0].get_xlabel()) def test_scalebar(self): dset, x = get_image_stack() kwargs = {'scale_bar': True, 'cmap': 'hot'} # or 'cmap': 'gray' view = dset.plot(verbose=True, **kwargs) data = view.axes[0].images[0].get_array().data self.assertTrue(np.allclose(data.shape, x.shape[1:])) def test_false_type(self): x = np.zeros(5) with self.assertRaises(TypeError): sidpy.viz.dataset_viz.ImageStackVisualizer(x) def test_false_dim(self): dset, x = get_spectrum() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.ImageStackVisualizer(dset) def test_button_up(self): dset, x = get_image_stack() viz = sidpy.viz.dataset_viz.ImageStackVisualizer(dset) viz.slider.value = 2 data = viz.fig.axes[0].images[0].get_array().data self.assertTrue(np.allclose(data.shape, x.shape[1:])) def test_button_average(self): dset, x = get_image_stack() viz = sidpy.viz.dataset_viz.ImageStackVisualizer(dset) viz.button.value = True data = viz.fig.axes[0].images[0].get_array().data self.assertTrue(np.allclose(data.shape, x.shape[1:])) def test_button_average2(self): dset, x = get_image_stack() viz = sidpy.viz.dataset_viz.ImageStackVisualizer(dset) viz.button.value = True viz.button.value = False data = viz.fig.axes[0].images[0].get_array().data self.assertTrue(np.allclose(data.shape, x.shape[1:])) class TestSpectralImagePlot(unittest.TestCase): def test_plot(self): dset, x = get_spectral_image() view = dset.plot() self.assertEqual(len(view.axes), 2) def test_bin(self): dset, x = get_spectral_image() view = dset.plot() dset.view.set_bin([20, 20]) self.assertEqual(len(view.axes), 2) dset.view.set_bin(10) self.assertEqual(len(view.axes), 2) def test_false_type(self): x = np.zeros(5) with self.assertRaises(TypeError): sidpy.viz.dataset_viz.SpectralImageVisualizer(x) def test_false_dim(self): dset, x = get_spectrum() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.SpectralImageVisualizer(dset) dset, x = get_image() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.SpectralImageVisualizer(dset) dset, x = get_4d_image() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.SpectralImageVisualizer(dset) class TestPointCloudPlot(unittest.TestCase): def test_plot_basic(self): dset, x = get_point_cloud() view = dset.plot() self.assertEqual(len(view.axes), 2) self.assertEqual(view.axes[0].get_xlabel(), 'Distance [px]') self.assertEqual(view.axes[0].get_ylabel(), 'Distance [px]') self.assertEqual(view.axes[1].get_xlabel(), 'X (a.u.)') self.assertEqual(view.axes[1].get_ylabel(), 'Intensity (a.u.)') x_y = view.axes[1].lines[0].get_xydata() self.assertEqual(x_y.shape, (10, 2)) def test_false_type(self): x = np.zeros(5) with self.assertRaises(TypeError): sidpy.viz.dataset_viz.PointCloudVisualizer(x) def test_false_dim(self): dset, x = get_spectrum() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.PointCloudVisualizer(dset) dset, x = get_image() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.PointCloudVisualizer(dset) dset, x = get_4d_image() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.PointCloudVisualizer(dset) dset, x = get_spectral_image() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.PointCloudVisualizer(dset) def test_units_button(self): dset, x = get_point_cloud() view = dset.plot() self.assertIsInstance(dset.view.button, ipywidgets.Dropdown) self.assertEqual(dset.view.button.value, 1) dset.view.button.value = 2 self.assertEqual(view.axes[0].get_xlabel(), 'Distance [um]') self.assertEqual(view.axes[0].get_ylabel(), 'Distance [um]') def test_point_selection(self): dset, x = get_point_cloud() view = dset.plot() event = mpl.backend_bases.MouseEvent( name='button_press_event', canvas=dset.view.fig.canvas, x=0, # x-coordinate of the click (adjust as needed) y=0, # y-coordinate of the click (adjust as needed) button=1, # button number (1 for left button) ) xpos, ypos = 25, 25 event.inaxes = dset.view.axes[0] event.xdata = xpos event.ydata = ypos dset.view._onclick(event) selected_point = dset.view.tree.query(np.array([xpos, ypos]))[1] spectrum_title = dset.view.axes[1].get_title() self.assertEqual(spectrum_title, 'point {}'.format(selected_point)) actual = dset.view.axes[1].lines[0].get_ydata() expected = dset[selected_point].compute() self.assertTrue(np.allclose(actual, expected, equal_nan=True, rtol=1e-05, atol=1e-08)) class Test4DImageStackPlot(unittest.TestCase): def test_plot(self): dataset, data = get_4d_image() view = dataset.plot() self.assertEqual(len(view.axes), 2) def test_bin(self): dset, x = get_4d_image() view = dset.plot() dset.view.set_bin([20, 20]) self.assertEqual(len(view.axes), 2) dset.view.set_bin(10) self.assertEqual(len(view.axes), 2) def test_scan_directions(self): dataset, data = get_4d_image() view = dataset.plot(scan_x=3,scan_y=2, image_4d_x=1, image_4d_y=0) self.assertEqual(len(view.axes), 2) def test_false_type(self): x = np.zeros(5) with self.assertRaises(TypeError): sidpy.viz.dataset_viz.FourDimImageVisualizer(x) def test_false_dim(self): dset, x = get_image() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.FourDimImageVisualizer(dset) dset, x = get_spectral_image() with self.assertRaises(TypeError): sidpy.viz.dataset_viz.FourDimImageVisualizer(dset) def test_plot_complex(self): dataset, data = get_4d_image(dtype=complex) view = dataset.plot() self.assertEqual(len(view.axes), 3) if __name__ == '__main__': unittest.main() sidpy-0.12.3/tests/viz/test_plot_utils.py000066400000000000000000000500621455261647000205230ustar00rootroot00000000000000""" Created on Thurs Jun 27 2019 @author: Emily Costa """ from __future__ import division, print_function, unicode_literals, absolute_import import unittest import sys import os import matplotlib as mpl if os.environ.get('DISPLAY', '') == '': print('no display found. Using non-interactive Agg backend') mpl.use('Agg') import numpy as np import matplotlib.pyplot as plt sys.path.append("../../sidpy/") from sidpy.viz import plot_utils import h5py """ class TestGridDecoration(unittest.TestCase): #plot_utils.get_plot_grid_size def test_get_plot_grid_size(self): pass def test_get_plot_grid_size_num_plots_error(self): #with self.assertRaises(ValueError): #num_plots should be < 0 pass def test_get_plot_grid_size_fewer_rows_false(self): #tall and narrow grid pass #plot_utils.set_tick_font_size def test_fontsize_not_num(self): pass def test_fontsize(self): pass #plot_util.set_tick_font_size.__set_axis_tick def test_not_axes(self): pass def test_complete(self): pass class TestPlotParams(unittest.TestCase): def test_reset_plot_params(self): pass def test_use_nice_plot_params(self): pass def test_is_x_true(self): fig, axis = plt.subplots(figsize=(4, 4)) plot_utils.use_scientific_ticks(axis, is_x=True) def test_is_x_false(self): fig, axis = plt.subplots(figsize=(4, 4)) plot_utils.use_scientific_ticks(axis, is_x=False) """ class TestUseScientificTicks(unittest.TestCase): def test_axis_not_axes(self): not_axis = 1 with self.assertRaises(TypeError): plot_utils.use_scientific_ticks(not_axis) """ def test_is_x_not_boolean(self): not_bool = 'hello' fig, axis = plt.subplots(figsize=(4, 4)) with self.assertRaises(TypeError): plot_utils.use_scientific_ticks(axis, is_x=not_bool) def test_formatting_not_string(self): notStr = 55 fig, axis = plt.subplots(figsize=(4, 4)) with self.assertRaises(TypeError): plot_utils.use_scientific_ticks(axis, formatting = notStr) """ class TestMakeScalarMappable(unittest.TestCase): def test_vmin_not_num(self): notNum = 'hello' with self.assertRaises(AssertionError): plot_utils.make_scalar_mappable(notNum, 5) def test_vmax_not_num(self): notNum = 'hello' with self.assertRaises(AssertionError): plot_utils.make_scalar_mappable(5, notNum) def test_vmin_more_vmax(self): with self.assertRaises(AssertionError): plot_utils.make_scalar_mappable(5, 3) def test_cmap_not_none_wrong_input(self): with self.assertRaises(ValueError): plot_utils.make_scalar_mappable(3, 5, cmap='hello') """ def test_cmap_none(self): plot_utils.make_scalar_mappable(3, 5, cmap=None) def test_cmap_not_none(self): jet = plt.get_cmap('jet') plot_utils.make_scalar_mappable(3, 5, cmap=jet) """ class TestCmapFromRGBA(unittest.TestCase): def test_name_not_string(self): hot_desaturated = [(255.0, (255, 76, 76, 255)), (218.5, (107, 0, 0, 255)), (182.1, (255, 96, 0, 255)), (145.6, (255, 255, 0, 255)), (109.4, (0, 127, 0, 255)), (72.675, (0, 255, 255, 255)), (36.5, (0, 0, 91, 255)), (0, (71, 71, 219, 255))] with self.assertRaises(TypeError): plot_utils.cmap_from_rgba(5, hot_desaturated, 255) def test_interp_vals_not_tuple(self): with self.assertRaises(TypeError): plot_utils.cmap_from_rgba('cmap', 'hello', 255) def test_normalization_val_not_number(self): hot_desaturated = [(255.0, (255, 76, 76, 255)), (218.5, (107, 0, 0, 255)), (182.1, (255, 96, 0, 255)), (145.6, (255, 255, 0, 255)), (109.4, (0, 127, 0, 255)), (72.675, (0, 255, 255, 255)), (36.5, (0, 0, 91, 255)), (0, (71, 71, 219, 255))] with self.assertRaises(TypeError): plot_utils.cmap_from_rgba('cmap', hot_desaturated, 'hi') class TestMakeLinearAlphaCmap(unittest.TestCase): """ def test_make_linear_alpha_cmap(self): solid_color = plt.cm.jet(0.8) plot_utils.make_linear_alpha_cmap('my_map', solid_color, 1, min_alpha=0, max_alpha=1) """ def test_name_not_str(self): solid_color = plt.cm.jet(0.8) with self.assertRaises(TypeError): plot_utils.make_linear_alpha_cmap(5, solid_color, 1, min_alpha=0, max_alpha=1) def test_solid_color_not_tuple(self): with self.assertRaises(TypeError): plot_utils.make_linear_alpha_cmap('cmap', 'hello', 1, min_alpha=0, max_alpha=1) def test_solid_color_len_wrong(self): solid_color = [0, 255, 45] with self.assertRaises(ValueError): plot_utils.make_linear_alpha_cmap('cmap', solid_color, 1, min_alpha=0, max_alpha=1) def test_solid_color_list_not_nums(self): solid_color = [0, 255, 'hello', 55] with self.assertRaises(TypeError): plot_utils.make_linear_alpha_cmap(5, solid_color, 1, min_alpha=0, max_alpha=1) def test_solid_normalization_val_not_num(self): solid_color = plt.cm.jet(0.8) with self.assertRaises(TypeError): plot_utils.make_linear_alpha_cmap('cmap', solid_color, 'hello', min_alpha=0, max_alpha=1) def test_min_alpha_not_num(self): solid_color = plt.cm.jet(0.8) with self.assertRaises(TypeError): plot_utils.make_linear_alpha_cmap('cmap', solid_color, 1, min_alpha='hello', max_alpha=1) def test_max_alpha_not_num(self): solid_color = plt.cm.jet(0.8) with self.assertRaises(TypeError): plot_utils.make_linear_alpha_cmap('cmap', solid_color, 1, min_alpha=0, max_alpha='hello') def test_max_less_than_min_alpha(self): solid_color = plt.cm.jet(0.8) with self.assertRaises(ValueError): plot_utils.make_linear_alpha_cmap('cmap', solid_color, 1, min_alpha=1, max_alpha=0) class TestDiscreteCmap(unittest.TestCase): """ def test_cmap_is_None(self): plot_utils.discrete_cmap(num_bins=5) def test_cmap_is_not_None(self): plot_utils.discrete_cmap(num_bins=5, cmap=plt.get_cmap('jet')) """ def test_numbins_is_not_uint(self): with self.assertRaises(TypeError): plot_utils.discrete_cmap(num_bins='hello') def test_cmap_not_str(self): with self.assertRaises(ValueError): plot_utils.discrete_cmap(num_bins=1, cmap='hello') class TestGetCMapObject(unittest.TestCase): def test_cmap_not_cmap(self): with self.assertRaises(ValueError): plot_utils.get_cmap_object(cmap='hello') def test_none(self): self.assertEqual(plt.cm.viridis, plot_utils.get_cmap_object(None)) def test_string_name(self): self.assertEqual(plt.cm.jet, plot_utils.get_cmap_object(plt.get_cmap('jet'))) def test_wrong_dtype(self): with self.assertRaises(TypeError): plot_utils.get_cmap_object(5) class TestRainbowPlot(unittest.TestCase): def test_axis_not_axis(self): notAxis = 5 num_pts = 1024 t_vec = np.linspace(0, 10 * np.pi, num_pts) with self.assertRaises(TypeError): plot_utils.rainbow_plot(notAxis, np.cos(t_vec) * np.linspace(0, 1, num_pts), np.sin(t_vec) * np.linspace(0, 1, num_pts), num_steps=32) """ def test_xvec_not_array(self): num_pts = 1024 t_vec = np.linspace(0, 10 * np.pi, num_pts) fig, axis = plt.subplots(figsize=(4, 4)) with self.assertRaises(TypeError): plot_utils.rainbow_plot(axis, 'hello', np.sin(t_vec) * np.linspace(0, 1, num_pts), num_steps=32) def test_yvec_not_a1darrray(self): num_pts = 1024 t_vec = np.linspace(0, 10 * np.pi, num_pts) fig, axis = plt.subplots(figsize=(4, 4)) with self.assertRaises(AssertionError): plot_utils.rainbow_plot(axis, np.cos(t_vec) * np.linspace(0, 1, num_pts), np.arange(100).reshape(10,10), num_steps=32) def test_xvec_not_a1darrray(self): num_pts = 1024 t_vec = np.linspace(0, 10 * np.pi, num_pts) fig, axis = plt.subplots(figsize=(4, 4)) with self.assertRaises(AssertionError): plot_utils.rainbow_plot(axis, np.arange(100).reshape(10,10), np.cos(t_vec) * np.linspace(0, 1, num_pts), num_steps=32) def test_yvec_not_same_xvec(self): num_pts = 1024 t_vec = np.linspace(0, 10 * np.pi, num_pts) fig, axis = plt.subplots(figsize=(4, 4)) with self.assertRaises(ValueError): plot_utils.rainbow_plot(axis, np.cos(t_vec) * np.linspace(0, 1, num_pts-1), np.sin(t_vec) * np.linspace(0, 1, num_pts), num_steps=32) def test_num_steps_not_num(self): num_pts = 1024 t_vec = np.linspace(0, 10 * np.pi, num_pts) fig, axis = plt.subplots(figsize=(4, 4)) with self.assertRaises(TypeError): plot_utils.rainbow_plot(axis, np.cos(t_vec) * np.linspace(0, 1, num_pts), np.sin(t_vec) * np.linspace(0, 1, num_pts), num_steps='hello') def test_base(self): num_pts = 1024 t_vec = np.linspace(0, 10 * np.pi, num_pts) fig, axis = plt.subplots(figsize=(4, 4)) plot_utils.rainbow_plot(axis, np.cos(t_vec) * np.linspace(0, 1, num_pts), np.sin(t_vec) * np.linspace(0, 1, num_pts), num_steps=32) """ class TestPlotLineFamily(unittest.TestCase): """ def test_base(self): x_vec = np.linspace(0, 2 * np.pi, 256) freqs = range(1, 5) y_mat = np.array([np.sin(freq * x_vec) for freq in freqs]) freq_strs = [str(_) for _ in freqs] fig, axis = plt.subplots(figsize=(12, 4)) plot_utils.plot_line_family(axis, x_vec, y_mat, line_names=freq_strs, label_prefix='Freq = ', label_suffix='Hz', y_offset=2.5, show_cbar=True) """ def test_plot_line_family_not_axis(self): x_vec = np.linspace(0, 2 * np.pi, 256) freqs = range(1, 5) y_mat = np.array([np.sin(freq * x_vec) for freq in freqs]) freq_strs = [str(_) for _ in freqs] notAxis = 'hello' with self.assertRaises(TypeError): plot_utils.plot_line_family(notAxis, x_vec, y_mat, line_names=freq_strs, label_prefix='Freq = ', label_suffix='Hz', y_offset=2.5, show_cbar=True) """ def test_plot_line_family_not_xvec(self): x_vec = 'hello' freqs = range(1, 5) y_mat = np.array([freq for freq in freqs]) freq_strs = [str(_) for _ in freqs] fig, axis = plt.subplots(figsize=(12, 4)) with self.assertRaises(TypeError): plot_utils.plot_line_family(axis, x_vec, y_mat, line_names=freq_strs, label_prefix='Freq = ', label_suffix='Hz', y_offset=2.5, show_cbar=True) def test_plot_line_family_not_ymat(self): x_vec = np.linspace(0, 2 * np.pi, 256) freqs = range(1, 5) y_mat = np.zeros_like(x_vec) freq_strs = [str(_) for _ in freqs] fig, axis = plt.subplots(ncols=2, figsize=(12, 4)) with self.assertRaises(TypeError): plot_utils.plot_line_family(axis, x_vec, y_mat, line_names=freq_strs, label_prefix='Freq = ', label_suffix='Hz', y_offset=2.5, show_cbar=True) def test_plot_line_family_not_freqstrs(self): x_vec = np.linspace(0, 2 * np.pi, 256) freqs = range(1, 5) y_mat = np.array([np.sin(freq * x_vec) for freq in freqs]) freq_strs = 5 fig, axis = plt.subplots(figsize=(12, 4)) with self.assertRaises(TypeError): plot_utils.plot_line_family(axis, x_vec, y_mat, line_names=freq_strs, label_prefix='Freq = ', label_suffix='Hz', y_offset=2.5, show_cbar=True) def test_plot_line_family_not_labelprefix(self): x_vec = np.linspace(0, 2 * np.pi, 256) freqs = range(1, 5) y_mat = np.array([np.sin(freq * x_vec) for freq in freqs]) freq_strs = [str(_) for _ in freqs] fig, axis = plt.subplots(figsize=(12, 4)) with self.assertRaises(TypeError): plot_utils.plot_line_family(axis, x_vec, y_mat, line_names=freq_strs, label_prefix= 6, label_suffix='Hz', y_offset=2.5, show_cbar=True) """ class TestPlotMap(unittest.TestCase): def test_plot_map(self): x_vec = np.linspace(0, 6 * np.pi, 256) y_vec = np.sin(x_vec) ** 2 atom_intensities = y_vec * np.atleast_2d(y_vec).T fig, axis = plt.subplots() plot_utils.plot_map(axis, atom_intensities, stdevs=1.5, num_ticks=4, x_vec=np.linspace(-1, 1, atom_intensities.shape[0]), y_vec=np.linspace(0, 500, atom_intensities.shape[1]), cbar_label='intensity (a. u.)', tick_font_size=16) def test_plot_map_with_nan(self): x_vec = np.linspace(0, 6 * np.pi, 256) y_vec = np.sin(x_vec) ** 2 atom_intensities = y_vec * np.atleast_2d(y_vec).T rand_nan = np.where(np.random.rand(256,256) < 0.2) atom_intensities[rand_nan] = np.nan fig, axis = plt.subplots() plot_utils.plot_map(axis, atom_intensities, stdevs=1.5, num_ticks=4, x_vec=np.linspace(-1, 1, atom_intensities.shape[0]), y_vec=np.linspace(0, 500, atom_intensities.shape[1]), cbar_label='intensity (a. u.)', tick_font_size=16) class TestPlotCurves(unittest.TestCase): pass """ def test_plot_curves(self): x_vec = np.linspace(0, 2 * np.pi, 256) freqs = np.linspace(0.5, 5, 9) y_mat = np.array([np.sin(freq * x_vec) for freq in freqs]) plot_utils.plot_curves(x_vec, y_mat) """ class TestPlotComplexSpectra(unittest.TestCase): @staticmethod def get_complex_2d_image(freq): # Simple function to generate images x_vec = np.linspace(0, freq * np.pi, 256) y_vec_1 = np.sin(x_vec) ** 2 y_vec_2 = np.cos(x_vec) ** 2 return y_vec_2 * np.atleast_2d(y_vec_2).T + 1j * (y_vec_1 * np.atleast_2d(y_vec_1).T) """ def test_plot_complex_spectra(self): # The range of frequences over which the images are generated frequencies = 2 ** np.arange(4) image_stack = [self.get_complex_2d_image(freq) for freq in frequencies] plot_utils.plot_complex_spectra(np.array(image_stack)) """ def test_not_map_stack(self): with self.assertRaises(TypeError): plot_utils.plot_complex_spectra('wrongthing') def test_not_x_vec(self): frequencies = 2 ** np.arange(4) image_stack = [self.get_complex_2d_image(freq) for freq in frequencies] with self.assertRaises(TypeError): plot_utils.plot_complex_spectra(np.array(image_stack), x_vec='notvec') def test_is_2d_x_vec(self): frequencies = 2 ** np.arange(4) image_stack = [self.get_complex_2d_image(freq) for freq in frequencies] with self.assertRaises(ValueError): plot_utils.plot_complex_spectra(np.array(image_stack), [[1]]) def test_is_not_dim_x_vec(self): frequencies = 2 ** np.arange(4) image_stack = [self.get_complex_2d_image(freq) for freq in frequencies] with self.assertRaises(ValueError): plot_utils.plot_complex_spectra(np.array(image_stack), [1]) def test_is_x_vec(self): frequencies = 2 ** np.arange(4) image_stack = [self.get_complex_2d_image(freq) for freq in frequencies] ran_arr = np.zeros_like(image_stack) with self.assertRaises(ValueError): plot_utils.plot_complex_spectra(np.array(image_stack), ran_arr) """ def test_num_comps(self): frequencies = 2 ** np.arange(4) image_stack = [TestPlotFeatures.get_complex_2d_image(freq) for freq in frequencies] plot_utils.plot_complex_spectra(np.array(image_stack), num_comps=None) """ def test_num_comps_not_int(self): frequencies = 2 ** np.arange(4) image_stack = [self.get_complex_2d_image(freq) for freq in frequencies] with self.assertRaises(TypeError): plot_utils.plot_complex_spectra(np.array(image_stack), num_comps='wrong') def test_not_str(self): frequencies = 2 ** np.arange(4) image_stack = [self.get_complex_2d_image(freq) for freq in frequencies] with self.assertRaises(TypeError): plot_utils.plot_complex_spectra(np.array(image_stack), title=1) def test_not_stdevs(self): frequencies = 2 ** np.arange(4) image_stack = [self.get_complex_2d_image(freq) for freq in frequencies] with self.assertRaises(TypeError): plot_utils.plot_complex_spectra(np.array(image_stack), stdevs=-1) class TestPlotScree(unittest.TestCase): """ def test_simple(self): scree = np.exp(-1 * np.arange(100)) plot_utils.plot_scree(scree, color='r') """ def test_title_wrong(self): scree = np.exp(-1 * np.arange(100)) with self.assertRaises(TypeError): plot_utils.plot_scree(scree, title=1) def test_scree_wrong(self): scree = 'string' with self.assertRaises(TypeError): plot_utils.plot_scree(scree) """ def test_scree_h5py_dataset(self): h5_f = h5py.File('test12.h5', 'a') scree = h5_f.create_dataset("test12", data=np.arange(1,25)) plot_utils.plot_scree(scree) def test_scree_list(self): scree = np.arange(5) plot_utils.plot_scree(scree, color='r') def get_sine_2d_image(freq): x_vec = np.linspace(0, freq*np.pi, 256) y_vec = np.sin(x_vec)**2 return y_vec * np.atleast_2d(y_vec).T """ class TestMapStack(unittest.TestCase): pass """ def test_map_stack(self): def get_sine_2d_image(freq): x_vec = np.linspace(0, freq*np.pi, 256) y_vec = np.sin(x_vec)**2 return y_vec * np.atleast_2d(y_vec).T frequencies = [0.25, 0.5, 1, 2, 4 ,8, 16, 32, 64] image_stack = [get_sine_2d_image(freq) for freq in frequencies] image_stack = np.array(image_stack) fig, axes = plot_utils.plot_map_stack(image_stack, reverse_dims=False, title_yoffset=0.95) """ class TestCbarForLinePlot(unittest.TestCase): def test_not_axis(self): with self.assertRaises(TypeError): plot_utils.cbar_for_line_plot(1, 2) """ def test_neg_num_steps(self): fig, axis = plt.subplots(figsize=(4, 4)) with self.assertRaises(ValueError): plot_utils.cbar_for_line_plot(axis, -2) def test_not_int_num_steps(self): fig, axis = plt.subplots(figsize=(4, 4)) with self.assertRaises(TypeError): plot_utils.cbar_for_line_plot(axis, 'hello') def test_ticks_not_boolean(self): fig, axis = plt.subplots(figsize=(4, 4)) with self.assertRaises(AssertionError): plot_utils.cbar_for_line_plot(axis, 2, discrete_ticks='hello') def test_complete_func(self): fig, axis = plt.subplots(figsize=(4, 4)) plot_utils.cbar_for_line_plot(axis, 2) """ if __name__ == '__main__': unittest.main()