diff --git a/.gitignore b/.gitignore index 508562dc..a2690e60 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,164 @@ -.DS_Store -.vscode/ -__pycache__/ \ No newline at end of file +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# 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/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +.vscode/ \ No newline at end of file diff --git a/notebooks/demo.ipynb b/notebooks/demo.ipynb index aa0c5cee..d4ae558f 100644 --- a/notebooks/demo.ipynb +++ b/notebooks/demo.ipynb @@ -6,14 +6,24 @@ "metadata": {}, "outputs": [], "source": [ - "# Add relevant Jupyter notebook extensions " + "# Add relevant Jupyter notebook extensions \n", + "%load_ext autoreload\n", + "%autoreload 2" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['/workspaces/b1-coding-practical-mt24', '/home/codespace/.python/current/lib/python312.zip', '/home/codespace/.python/current/lib/python3.12', '/home/codespace/.python/current/lib/python3.12/lib-dynload', '', '/workspaces/b1-coding-practical-mt24/.venv/lib/python3.12/site-packages']\n" + ] + } + ], "source": [ "# You can double-check your Python path like this...\n", "import sys \n", @@ -30,16 +40,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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/+w5LV66ipKKaszeexx///DAAbW1tSE4Pg0NDfPijNyM5Pfz6N/cDsGfvXq659q2UV9cxb/4i3v/BDzEwMJh6z9dd+UZu/eSn+M9Pf4b6efO5+s3XTvl1t33qdj77+S9S29hE04LFfPXrd6X9PF6vl5s/divz5i/CU17FurM38o9//iu1/7nnnuOCCy5AkiQaGhr42Mc+RugUPr+poAcqOjoFJOiPpDxR7G5nUlDrGz9tHA5GwFKK0WhI32Fy6KZvU0CW43R3q4SsF+C3bKI7ej6He8/k4EEDwwPBQi9P5yTE6rpx/1nefX3ascL8ReMea732renHrlg15phssGfvXl588SUslmR249v//R3u/78H+eH3vssrL73ALTd9lPd/4EM8+9xz1NfX03L4AC6Xi29/8y5aDh/grdddi9fr5co3vokzV61iy+anePhPv6evr5/3vPd9ae91/wP/h9Vi5cnH/skPv/fdabzuQex2G888+QRf+8qdfP0b3+KJJ48HVtdc+za2vvAiv/jZT3j15Rf5yp3/hcmUnMx+5MgRXv/613Pdddexa9cufvvb3/Lcc89x8803Z+XzGw+99KOjU0AigSgKJVhFAaPJTEyRCPoilJTZMx4fCiQwiaVjd+imb1Oip32YULyc+WctwyJYgGT57fCzx4iE9M9PZ/r849F/Ul5dRyKRIBqNYjQa+c5/f4toNMq3/t93+Ntf/sw5G84GoLm5iee3vsDPfnEfF5x/PtVVVRgM4Ha5qK6qAuD7P/wfzjxzFV/+0hdT7/HjH/0Pi5au4NChwyxatBCAhQvm8/Wvfjl1zDe+9e0pvW7lihV87o7PJM+xcAH3/OSnPPX0Zi679BKefOpptm3fzo5tL6WOb25uQolFAbjrrrt497vfza233grAokWL+MEPfsBFF13EPffcgyjmZjCqHqjo6BSQcFAGazLwMFtMYCklFGgf9/hQCATX2CDGJNiRI3rpYjK6j/kxOFamghQAk8kIZhdyuLOAK9PJhNwzwe9k5Cl/lOjRQ+Mfa0wvHkT37DqVZaVx0YUX8IPvfodQOMQP/+dHmM1m3nLNNezdt49wOMxV17wl7fhYLMaZZ64a93y7dr/G5meepTxDludoS0sqgFizevWMXnfGyhVp+2qqqujv7wdg567d1NXVpo49mZ07d7Jr1y7uv//+1DZN01BVlZaWFpYtWzbuz3Uq6IGKjk4BCQUVzKLn+AahnHAg8wU3Gk0gx8yI9rGBikXUTd8mw+8N4/WJlC2oH7vT7CESOpr/RelMTIbvet6PnQSbzcaCBfMB+N977ubsjedz3y9/xfLlywH40+9/S21NbdprrML4wtdQKMQbrnw9X/vyWDFudXXV8fe122b0OrPlpNu+wYCqJh9yJGnijEgwGOTDH/4wH/vYx8bsa2xsnPC1p4IeqOjoFJBQGISS4xdNyeEkOI4uLegb0bM4HWP2WQSBqC9Xq5wbdLUNEVGrqa8qH7PPLDnGaDN1dKaL0WjkU/95G5+543PsenUbgiDQ0XGMC84/f8rnWH3mmfz54b8wb14jZvPUb9Ezfd2JnLFyBZ2dXWmlohNZu3Yte/fuZeHCzBmXXKGLaXPA/p3HCEzBYVTn9CYSiRGLWRAdxwMV0eEgEjERjY41bwsFosnWZOfYp0GrJBJXzBlfp5M0yuvtlhHLFiZLPSchSDbC4akNhdTRmYjr3vJmjCYTP/vFfdz6sVu4/TOf5Tf3P8DRoy28umMHP/rx//Kb+x8Y9/Uf/tAHGB4e5vobbmTb9lc4erSFxx5/gg/9x0dRlPE7Amf6uhO54PzzOf+8c/m391zPE08+RWtrK//812M89sSTAHz605/m+eef5+abb2bHjh0cOnSIhx9+OOdiWj1QyTIBv0x7a5SOw72FXopOkRPyRYlr6RkSm9OREtSeTDgog7kk443WKogomoWIbvqWkYEeP/6wk7K62oz7rZJELGYiFtMDPZ1Tw2w28x8f+iDf/d73+dQnP8FnPv0pvv3f32X1WWdzzVveyqP//BdN8+aN+/ramhqefOyfKIrC1W9+C2edcy6f+vQduD1ujMbxb9kzfd3J/N9vfs26tWt47w03smb9OXzuC/+VCnRWrVrF5s2bOXjwIBdccAFr1qzhi1/8IrW1mf+usoVB07RZ/Rjh9/txu934fD5cLldWz91z8FU6dj5Paf2CKb+m4+gA27ab8dgGueCKZsxmPRbMhKppGA2GyQ+cw7Qd7ueVHSILL7guFXwkO1D+wNrVMvMWVqQd/9LTh+iMnMeS9WvHnEsOyxx7+fdsOMdCVZ17zP7TnV0vtXK4azFLz780437/oI/BPX/gvEs8eEpsGY/RyQ0KJmSpjHkNDQgTaDd0CocSi2K1ObCI0//bkGWZlpYWmpubx3QFTfX+rd9Fs4x/KETCUEZA9tDX5S30cooOVdM4vLebzX/bh3c4XOjlFJRIUAZLeobEZDKCtYyQPz2jMtQfpH/YRtk47pVWUUBRrbqXSgZisQR9vSrOquZxj5EcNhKqSDgQzePKdHR0poIeqGQZ73ACW1k9irWJng5voZdTVEQiMV597ih79lrxRhrw9o9vsKUoKi8/vZ/hwdw6HhaScDAG1rIx201iGaFAesDRcbifqKGJ0urKjOcyGg1gdhCNFH/pZ3gwRG9n/pS/vce8hGKllNXVjHtM0t3XjhzWAxUdnWJDD1SyiCzHCYTM2D0e3DXzGBgwEpkFN4580N/j56Wn2mjrn0/F8teDWEPAO34Q4vdG8PrMDPcF8rjK/BIMkjZccBTR7kjr/An4Zbp7jXjql4x1pD0Rs3NW2Ogf2dvDoT39eXu/ng4fqtiMaJvEjMri1gc76ugUITkNVO655x5WrVqFy+XC5XKxceNG/vGPf6T2y7LMTTfdRFlZGQ6Hg+uuu47e3tkrQh0eCBJTHLjKSiirqSIUL6OnfbjQyyo47UcG2L7Vy5B2NvPPvhRPZRlWRxl+3/gGZf7hMOF4aVJAOgeJJxTkqBEhg5+D6HQQjVpSQW77oV7Caj0VDRML1gwWR8aun4BfRi0SKVoslmB42EAonJ8OpYBfZnDYQkntFDweLG7kcPEHejo6pxs5DVTq6+v5xje+wfbt29m2bRuXXnop11xzDXv27AHgE5/4BH/961956KGH2Lx5M11dXVx77bW5XFJO8Q2GUIzliDYRi2DB6FxEz7G5mxGYKn2dPiLCWSxafzZWUQBAcroIhY3jdlkEhkOEY05Cwam11c02kq3GyWGEJ2N3O4mPzPyR5ThdnQqOqqUZu31OxCKIY2av+b1htj19mJ6O4giYB/sCROIuYoodf441Soqisv/VY4TU8UtmJyLa7YRPb9lUQdEojmBaJ7tko18np4HK1VdfzRve8AYWLVrE4sWL+drXvobD4eCFF17A5/Px85//nO985ztceumlrFu3jnvvvZfnn3+eF154IZfLyhm+oSgm+/E6eFltHcN+8bQXjYZCIDmdaWULh8dFNGEj4B3bhgvg9ymoCITm6EcXDsgkVAlbBjt8QZJIYCfoi9BxuJ9grJqqpskzAlZRJHqSxOLovh6G5cacBwVTZbjPT8JUQwIXgRyv6ei+HroGyqlbcXZyPMEkWCWJiGwomuzT6YIRFVQVWdb1QXOR8Ej0b7FYJjlyfPLmTKsoCg899BChUIiNGzeyfft24vE4mzZtSh2zdOlSGhsb2bp1K+ecc07G80SjUaInXI39fn/O1hwMQjgMGUbAjSGeUPAHDNhrSlLbPFXl9B2qoae9+7RteUwkVOSoAWtF+s9vczlI4MQ/HKCs0pm2LxZLEAwZsZdWEJNbiURiSNLcalsM+WVUY9VYO2tGhLHWcgLeHoYGVYTyc9Nm04yHRRSJJYzEEwoWswnvcJieXjOyUkrInz9NyET098URPXVEQz4C3tzN1hnoCXDkcBypej2uMs+UXiPaJAKqFTkUw+YQcrY2nXQMaJjiYfoHBgAQRQEDp7d1QbGhxOOopijKNHIbmqYRDofp6+vD4/GkJjDPhJwHKrt372bjxo3IsozD4eBPf/oTy5cvZ8eOHVitVjweT9rxVVVV9PT0jHu+u+66izvvHDvLIBd09jjYu9+EwTpIXdPY7owT8Q2EkRN2qko8qW1GowGxdCG9XUdZtFKdNHU/F4mEYyQ0AadNSttuNBpAqCLoG6tJ8g9FiCp2Smtr8R0WCPmicy5QSQ4jHP87ZRZLGOwL4os20rBifHOoE7EKAmHVihyOY3GZaNvfS0hbRGn9PPyBA9la+ozxe8MEIxIljRX4Boz4fLnJXESjCfbt7CZiPptFi+ZP+XWCTWJItRIO6oFKvhHUMFEZ+nqVMQMEdQqPqiQwWwRMlulfhz0eD9XV1af0/jkPVJYsWcKOHTvw+Xz8/ve/573vfS+bN2+e8fnuuOMObrvtttT/+/1+GhoasrHUMdgqFtIXk1FffYF4TKFp8fh1bu9QkIRWis2V3sVRXl9L7y43Az2B09KIKxyIoqhWBLs0Zp/VXppRUOsfDpHQPJRUVTB4UCIcigLOMcfNZkIhDYs0vsGR5HQSDXowOJciZfjsMmGVBBTVghyOoSgq3T1mSuctx2w2ExqwEA7FsNkLF/ANdPuJqaU4y0qIx+OEhyw5yZbtf6WD/uB8mtavnrhL6iQEm0RCFYiE5973rdgxAKIaRotGUPVm1KLD19dOxbL1lDWO70WUCYvFckqZlFFyHqhYrdbUAKN169bx8ssv8/3vf593vOMdxGIxvF5vWlalt7d3wuhLEAQEIT9POwaDAUftGWgJgT2vPUc81sWilZk7L/xDYZDOGHNhdHicHDM10tOx77QMVCLhKIpmQ8gwldPmdhPqSApqrdbjX0W/NwziiHjUXEI42JbPJeccVdMIh0GsHH+Cq93jprWljgVnTN0VOXmjtRCNxOk8OkDYsIT6+hpicpRh1YbfGy5ooDLYFwLbGZhMRhweNz5Fwj8UQarL3praDvfT3m2nYtHZk7cjn0Ty++YmEspdSUpnYgxomJibAvpZTVzGYjKMcZbNF3kPXVVVJRqNsm7dOiwWC0888URq34EDB2hvb2fjxo35XtaEzFu+GGvNJew/YGHP9rYxYjtV0xj2gt2TWc3irJzHQL+KoozfjjtXkcMxMLsyPtnaXU6iCRv+oXRBrc+nIrlGyiJCGZHQ3PK2CAejxBMCkmP8QMVdXsKyCy7H4Zn6k73JZASTncEeLz19VkrnLU+WH20iisGVcX5QvojFEgz7jLjKk1lJyS6hGDz4J/DSmS6KotJy0AuuNZTVTt7lkxGzZ1aY5unonE7kNKNyxx13cOWVV9LY2EggEOCBBx7g6aef5p///Cdut5sbb7yR2267jdLSUlwuF7fccgsbN24cV0hbSOoWNdNjsXK0bTNWsZtFK45nVvzeCHJcorS0JONrnaUlDPZI+L0RSsrGvznNRaLhKFg8GfeNCmoDvgDl1ckbciQSIxKxYqtNlkVEu4NQcG51YYT8o1OQx5q9nYhVnEGmwexguN9PSDub+roTMpNCJSH/semfL0sM9ASQ4y7qKsuPbxSrCfo6svoeAdlN1eKmGZ/DLDkIhU6/BwodnWImp4FKX18f119/Pd3d3bjdblatWsU///lPLr/8cgC++93vYjQaue6664hGo1xxxRX86Ec/yuWSTonqpjqORTdw5NATeMr8VFQnb6begSAxxYmjJHNpx+5x0a048Q2FTrtAJRwBs5j5Z052t1QR8B4X1HoHQ8RUO5We5Gcp2GyEh5PdQ3NlwGPAF0ahdNqliSlhchKOVVDWtDwtiyXYPQT8hQv4hvv8KOZFaXobyenBn0VBbV/nEHHjkmlloU5GECUi+XP319HRmQI5DVR+/vOfT7hfFEXuvvtu7r777lwuI6vULprPIe8Ae199ibMvFpEkK/6hEAgLxu3qMZmMINQQGN6d59UWnkgEhJLxxaBWRykB//En2MBwGMVYk7qJSw47Q6pEOCjj8syNFu+ANwLi+HNnTgXB7iISX0JjXbrOS3K5CGfQA+UDVdMYGIgjltSnbbe73YQGsyPyTSRU+npVHBVT65AaD6vNRrTfVJDPSUdHJzNz4xE1jxiNBprPXMNAZBGvvdSBoqh4h1VEd8WErxOc5fi8p5dILBpNEIubEOzjBxg2tzvNoTbgldNu4pLDTkIRCPrnjhmU36chuTKXCU+VxuWLWbzxvDGaILvLSUyxEfTlfySB3xshGLHhLi9P2273JB1qfcOnrlPp6/IRirkprT21NkjRZiOhWQnPMV2Ujs5sRg9UZoBVtFK7fANdA2Xs3d5GOGLFcZIfzMnYSzwEw5bTakhhOBhFUQVE2/iBit3lJKokBbWqpuHzJ5+0R7GKVlSjc87M/AkGZCJRYdLvy0wxGg0ZM3uS0058pPMn3wz1jLYle9K2izYRxVhCcBx34unQ1zmMYpmHzXlqpVXJYSOhCERCcycw1tGZ7eiBygxxl5dgq91AZ6cJeWQQ4UQ4SzzEFDvewex1ORQ7kVCMhCog2sYv/dhcDhKqk4AvnJxtExNxeE7S+ljLiMyRQMU3GCKq2HGWevL6viaTEawVhAP57/wZ7AthsM/LXBoVq8YdozBVYrEE/QMGnJWn7qdkESyoBgeRoB6o6OgUC3qgcgrULGhCltahWesntTgXbSKKqSzpt3KaIIejqAb7hJ/NqENtwBvGPxwmmrCNESWbJfec6cQIeMOoxrIpWeJnG7NUht+X+4nFJxKLJRj2GnGVZy6N2l0efKc4BaP3mJdwzHPKZZ8UFneyrV5HR6co0NVip4DRaGDh2rWoU/RHMYg1+IeP5nhVxYMcjoFlfPfVUQRnGQG/itkSAuvCMfNvRJud8FCuVplffMMxTPYs3VCnieRyEepL+o3ka5xD0CcTU2x4Ts6SjWBzuQj2CwQDMg7nxF1QsVgCs8WE0ZCuv+nr8qEK67LXRWVxI4fj2TmXjo7OKaNnVE4Ro9EwpcmsAA6PB7+f08b4TQ7HwTK5G6/kchEKGxkelDHbxj55i3Y7sdjs1/ckEir+gAFbjvQpk2FzOpETEqFA/soaciSGolkRxxkD4ChxJ03/hicu/wz1B9nyryPs2HI0JbwGkOU4g4Mm3NX1E7x6egiSjUjhvPF0dHROQg9U8oijxIOcSBq/nQ5EIiBIk4sbHR4XUcVGKGzKeBOXnHbimkjIN7t1A/7hMDHFhvOEwZX5xO52klAlAnkU1MrhGCq2jFOiYUQsbS6dcE1D/UFefbGXYW09bb0NbHumhfCIhqSnY5hwopSymqqsrVmw2wiHGeNAraOjUxj0QCWPJNsxk8Zvcx1V04jIBqwTCGlHkRx2EpqLuGofK6RldIaNRGiWCGrHy5j5h8PEVOeYwZX5wiJYUE2lBP35+xyjkThYJjFgE6oIjNM2PdQfZMdLvfi1tSw8awP1ay6lO7CMl59tZ6g/SH9XAE2an1XNj2iTUDQB+TRpUY4nFAZ6AoVeho7OuOiBSh45bvw29wMVORwnkbAgSpMHKkmH2kpiiiPjTTw5LM4zKzp/2g7388IT+zMGK/7hEFir86YPyYhQQSivgUoMTBMHKna3G79/bAZjeDDEzpd68alrWXDW2ZgtJuwuBwvWX8hQ4iy2v9DP4LCFkpq6rK5ZsEnEVSvh4OkRqBzZ082ulzpIJE6PkrTO7EMX0+aZ08X4LRKKEtesCONoE07GWVFHyGcf/yYulBEOHcziCnNDyB/GGyyhp2OYuqaytH0+r4rgLB/nlfnB5nQRyOPDsxwFszixo7DD48bbK9BxZACj0UAirpCIK3S0hUeClPVpOjCraGXh2Rto2WUn6u2mtnpis8XpkszgCUTCUWDmdvyzgXhCoftYlIjimVPuzzpzCz1QyTP2Eg/BlqQwVJKyN96+2IiEYiQUAXECV9oTqV3YNOF+0e4gGMzCwnKMHEnglRs41nI0LVCJRGKEIhbs1ZOLi3OJ5HIS7s/f9y8SAcE9cTeO3e2iK1HJjp0BVM0EBjuYRAxiBQvWrs6obzGZjCxcswpVPSPjZO5TIZnBcyGHu7N63mKkq3UIv1wGmkIoGNUDFZ2iRA9U8oyzxIPvcNL4Taqfw4FKOArmqqyVOQSbHXnYkLfhhIqi0n64n4YFFdN6PzkCFtHFgNfGYF+AssrkE7lvKCmkrSyQkHYUu9uFT5XwD0WQ6nL7/UskVOJxI5ZJyn8WwcKic68EwGQxTyvwyHaQcnxRJcjh1tycu0hQNY3OVh+a/Vy08DEigTniAaAz59A1KnnmdDF+i4ZjYPFk7Xw2p524KubNSn+oP0jLgQEGe6dXJ4nIUFpbj8w8Oo70p7b7h0IoxhxNTJ4Gkl0igYuQP73zTFFUZDlOMCDj94YZ6g+est1+JBwjoVoRxMl/ZotgwSJYchd4TBOL5MLnVeZ0589gb4Bhv53Kxiawls2ZMRU6cw89o1IATgfjNzmiYLJmr76fHE4oEvTnJz092OsnlKgY0SlMDVmOE0+YsdkkPPWL6es+RDgYxeYQ8J80bLGgWCsJeI8x2BdgqD+AbyiC1wsJxYKmGVExoqombFY/F1zRPOMpwnI4hqJaEeyFDc5mQlltDUN77Qz3B1NZsblG59EBoqbleCpLGeyeO+7Pk5FPw0Od7KAHKgXA4fHg75/bfzDhCFNqTZ4qFsEyMpxwIGvnnIjhAZlwrAI53D/5wSNEQiM3ZknCU1HOkfZa2o/0sXhVHT4vOMo9OVvvdBCcJXT3WujoDhPXysDWgLOiFJtNwmw2YzSZ0TSVvj1/Z6DHT21j6YzeRw7HSGgWrFPIqBQb7vISug0NdLcenpOBSjAg09tnxFO/EEi6P4eG5m72aJSAX+bVZw5w1sVLsTmEQi9HZ4rogUoBcJR4GOxOGr+VlJ3atNdiJJFQkWUD1rIsZz4spUSCx7J7zgxEIjF8AQuKJhKdTkYlMlLqkETMFhNixVK6j3VQWRtCTkiUFlifMkpV8zy8fVdSWuLB5nKMW27pMdcy3Hdw5oFKJAamklkbjDsqm+ntO8jiWGLGWaVi5djRAUKJWhbWJbN8gt1GuGfui/y9A0Giip2AT9YDlVnE7LyCzHKSxm8u+jq9hV5KToiEY8QnmZo8E8y2/KSnB3sCRBUX9tIqwvLUNRPRcBwVCauYvNBXNTXii5Rx+LUOYoodu3vyuUf5QLJL1DQ34PA4J9SEiCV1DAzEZ6zTiEXiYC6On3kmlDfUEYyW0tPhLfRSsko8odDdGUWqXJpq+7Y5HRO6PyuKSvexYeKJ2W2tEPRFkBPuZBCtM2vQA5UCYDIZsVWv5vARA0f39RR6OVknEoqOaBOyG6iINjvhPGiQh/v9qJY6bC43kfDUb9JyOJo220i0iRicSwkFFBCqpzwTqlhwl5cTjNhmPPJBjsQxWArjwpsNRJuIKi2k95iv0EuZMa0H+3j674fYsfUo7UcGCIdidLcN44+UUTmvIXWcYJNIKOK47s89HcPsfDnAln8d5fDe7rR5S7OJoD+GnHAQk/Whk7MJPVApEA1LF2KuuJB9+zQO7ekq9HKySjgYRdFEBCm72gSrTSIWNyPn8CKjahpDgwpSSU3q/aLRqV2Uo5HYmAxCVVMTvlgdFnt2TcnygbPMQ0wpYajHP+4xE/0uZNmAVcxusJpvSuvqGRyyztr5XF3tfvqja2kZOJPtr5h55l89HN47hGpbhHTCg4TJZARLCZFQ5oyKbyhESGsiJG1i914HWx5r4+DuzlkVsKiahj8AccWa/FvVmTXogUoBaVi6EEv1RRw4YOTg7s5CLydryOEYmN1ZbzWV7HYUTSCSwxksfm+EoGzDVVaWfD/VmhqANxkReawLq6vMg1h1FuV12bV5zwcmkxHsjQz2ZR750H1smBce2094nN+HLDMrhbQnUlpdSVitoattsNBLmTZ+bwRfQKR6wSKWbDyP+edehzT/TUQcl1O3aPHYFwhl437X/d4YVmcNzWcso/mcNyE7L2fPwVJ2v9g24RrCwWjRBHmhQJRoXMBgtiHLs7uEdbqhByoFpn7xfMTaizlwyMyBXXMjWImGo1n1UBlFtI9Ym4/z1JcNhnr9xBQPzjIPgiSOvN/UAqNIhIyeIY3LFuEqK6wj7UxxV1Qx7DNmfHJuPzRIIFaNL8Psqmg0QVwxY8lyVi3fmExGxLKF9HZFxsxv8nvD9E+QbSo0vZ3DyGoFnqrk2AaLYKGirprmM5bh8IztZBLtDsIZYtJ4QsEfMGIvKQHAKgrMW76YskXn0jMgMtSf2TJa1TR2vtjBvh3FcV0LeMPEFRuOsgqiumXMrEIPVIqA2oVNWCvPo60lNuUyQzGTzCxkv5vJbDGjGuxTznDMhOH+ENgaMZmMyZZogx15CoFRPKEQjxux2uaWBbm7ogw57hozXbe/x8/AsI2o4iCY4Yk5Eo6haBaEWV76ASivr8Mf9qSCEkVRObSnixc297Hnld4Cr258+rvDmFzNU+66Emx2whHDmOGEvoEw0cTYyeal1ZVEjfNpPZD5M+hqHWLA6yYUKg4Tv4A3gmoqQXI4kfVAZVahBypFgruigrgqEAnP/tppOAzCFKYmzwhLSdL1dhoM9gUITGFicCKhMjRswFl2gp7E4kaOTB6oRILJTqds63IKjWSXUMw1DPelZw46DvcTMy/EZK/LOI05afYmIM5Cs7eTcXicJCxN9LQN4feGeWnzUfbssxEyr0eOCTnVTM0UvzeMLyBSWj11k0HJYcvo/uwfDpHAPWayudFooLRxKb39AsOD6akYRVFpPTRESKkjFku2PReaoF8GsQqrJBJXcqt108kueqBSJOSjrJEPYrEEsbgJa44CFZPgIhyaXtZp76vd7N3eMelxQ/0B5LgDd8XxYYIGq5tIaPILWtLszYKQ5ZbsYkBwp7cp+71h+vqtlDQsQnR68PvHdkbJ4RiKJmC2zg1PDldNE339Rl54ppfuwEpqznwD9cuWEFeFjIFaoek95kXWKnBXlk1+8Agnuj+fiH84DGJNRs1ZeV01EUMzbSdlVdoP9zMYqKBu6coJ257zid+fnB5uFUQUzYIc0QOV2YIeqBQJVtGKioScQ6FoPggFkq3JYo5KIFbJRmQa2rxwMEooLNE/7KJnEt+aob4ACWMF9hOeHAWbnUhk8tS1HElmEKzi3DOR8lSmtym3HewjpDVSXleN5HQSlk1jSpbREQ+VYpndc6qU11YT0Jahlmxi0TkX4ixxIdptJBSJUKDwN+GT6esOY3JOvewDI9cgo2NMRsXrVZFcmU3/jEYDJY3L6Om14B1OegfEYgnajvgxulfiqSqfsO05X4RDMaJRCzaXC9EuoqhCUvSvMyvQA5Viwuye9W1z4YBMQpOQHLkLVOTo2Dr6eAwPBImqDqKmBbQcGJjQvGxoQMbsakjbZpUkIhEmNT2LRmJgmTs35hNxlpUQU5NtyuFQjO5ucNctxWg04PC4iKu25FP3CcTksa3asxmLYGHZ+RfRuGxR6uY/2tJbbMP8/N4wvuD0yj4pLGVETvh5ggEZOSbi8HjGfUlFfQ1hjmdVWg/0MhyppW7xwuNtzwX+jPzeMDHVjt3jwmy1omhWPVCZReiBSjFhcc/6dGQoIKMa3VgES07OL9pGSmRTvMj4hkKopkpql6xgwOsc12U0EonhD1hwlqanykWbhKIJk2a6opEYmGZnZ89kmExGsCXblNsP9xGM11LZWA+QzCpoTgInTVqOhFVM1rklLM7IBC292SQWS7D7pZYp6Sp6j3mJTrPsM8rJ7s++wRBRxY6z1DPua4xGA56GZXT3mOjv8dPRFkWqXHV8UrhQNm4Le74IeMMoBjeiTUw+TJiduunbLEIPVIoIq2QnPA0n1GIkHIiCkDtzM9FhI6Fak+8zBbzDMczOKtzlJSSEpbQezJxVGewJICdceCrSL+6CTSKuWgkHJ77QRsIqZnH2urBOhqu8kmGvgc72KLaqZSmXXaPRANZKQv70epwskzOdUjEh2h0EM3fnZhXvYIjubpVjRycfytnXHcbsXjCjGUsnuz/7h8Oo5opJHzwqGmoIa03s23YUX7SR2oXNx8+Zp89oIkI+GYTK4xvMrlmfvT6d0AOVIsIqitPSXxQjwaCGYMtdyt8qCiiaRGQKwwJjsQTBoAm7O+n/ULNwKYM+Dz0dw2nHRaMJutoG0ax1qTk9owg2iYRqnfT95ChYZrm52UR4KsuREy780UqqmxvT9lkdpfj9x5/CFUUlGjOcFoHKqIZpqqXImRIORAlGK+g5FpywDJks+0iUVlfP6H1Eu51Y/HiXjm84itlWNenrTCYj7vplDEaacNWtxGw5PsQxX5/RRPj9GqLTk/p/k9WOHCncenSmhx6oFBFWm0Q8MXXL9mIjkVAJhQ2IjtxlFpJpW/eU6svewRDRhANXmQcAV5mbhLSMlgODqYv9YF+Al54+yrHB+VQtXDnmHCaTcdL3UxR1zmcQJLtEwjwPa9nKMYJhyekkFDKkBtZF5QSKYsEqzD1h8cnYnHYSmkgwkFsNRiQUJYGD4aCLgQlM5nqPeYmqFbjKZzbxWnLaiavJLp1EQiUQNGKbQJ9yIlXz6ilbcglVJwWyo59RobQ80WiCsGxGch43ubOIEtHi00DrjIMeqBQRos2GolryUvPOBQFfhIQqYnPmuARi8UwpUPENhkgYSrE5j5vP1S5azKDfQ2fLIIf2dLHt+SEGYutpWr8JT+U4F3ezJzlwcBwiI54hc81D5WQWnXMe81YsHbPd7k4KagPDyXSgHI6R0KxY5/jnASMtvapEOMeBSjgUw+KsIW6aR3fr0LjH9XWHMXvmz6jsAyOaI1UiFJTxDYWQ43acJZ4pvdZoNFBeWzXmvcdre84X/uEwcUXC4Tme6bWIInJ0cpG8TnGgBypFhGiTSGjCrB1BHvLLxBQJmyv7rrQnYrU5pjRF2TccwWCrTdvmLHGh2ldw6LU+9u4XofwyFm0457jwLwNmwUEkMv4FTQ7FSWjWOemhciImkzFjV5PN5SCWsBHwjQQqkRiKZkXM8vTsYsQiWFCNLkI5DlRCQZAcDlw18+ntM2Q0UOtsHWQo4KSspjbDGaaG0WhIden4BkMkNNcYo7fpMvoZFSqjEvCGSWhORPtxcbdVFEgoFmKzNHt9uqEHKkWERbCgYpu1XiqhgAyWkrT6dC4QJIlIWJvwaUhRVHw+cHhKxuyrW7wYn/E8Kpa9noalCydtKU62KE8QqERiJJS5H6iMh8lkBKGCkD8ZPcrhGCq2nH8PigZrGZEcZkHjCQU5akSw26moqyGUqBiTVYlGExzeN4RqP3P8zOBUsZYSDsaSLee22uy03FtKC9aiHPRFQKhK+zkESUJRLTkdcKqTPfRApdiwTE1/UYyEg1Gwluf8fZJaHgvRCdoL/d4IckzCkSFtbXc5WHLOhilf0K02G9GoMaXBOJlIOAomx4zT7XMBs1SO35f8fUQjcbCMHXo3V7FILkI57NYLBaLEVQHJbsciWDC7F9PVEUgL1A/uPMZgqIl5K5ad8vuJdgfB0IjRm/MUg54RzDZXwToaA34VqyP9gUWQRBKaddZmr083Tt8ra7Fidicv9LOQQEBDdOTe5Eu02UhoApEJWoZ9gyFimhu759TXI9qk5Bymcd4vFomDZW56qEwVyeUiGDQkO37kOJhPn89DtNsJByfO8J0K4YBMQj1eUq1oaMAbdDHYmxwU2dfto6PTQsn8dVlxRh7t0pGjwoRGb9NBsjsI5vAzGo94QiEUNiI5068DVtGKqglEw7PzWnu6oQcqRYZVshEep0U5nlA4vLd7zLj5fKFq2rjGTfGEgiwbc9rxM4potxFXrBOKjv3DIbDWZiXLIdgkFNVKeJw5TLKcwGjNfwahxT/Mcz1t7Bvupy8SIqEWrt3S5nQSTUiEAjKRiIZZmPtC2lEkh51YQsiZCD4UkFENrlQpzVXmIW6eR1frIPGEwsHdfUStK6lqnLk25URGu3SiqmNCo7fpINhsxONW5DwHBoHhCLGThLQpzK4Js7I6xcNpUkSePQiSRKQ/81NHf5eP9sNDlFW5KCnLrWA1Ez0dXg7t6mDDpmWIYroBVNAnE1dE3Lnu+IGk2ZjZRSScebw8wPCwiuTJThlKtIkomogcyqzgjYTBIuRXn+KLyvzgtRdIaMeDEwNQIkhc27ycM8tm5qMxUxwlLvoUiYA3giyD4D59AhXRYWdQFQn5ozic2f+55VAULOklGGf1fHp79mF8pZ3+QC2NZ52Rtfcb7dJRzeVZc5iWnHa8qkAoIGOz529Qpd8bJq46kJwZrpcmZ3LUg07Ro2dUigzLyAjyTF4q/uEwEaWkYAKwcFAmHC9loHusj0PQFyGmSkiOPAVQFk/yAp6BYEAmImcvbQ2MeKmMfT9V05Cj5L0V98muoyQ0FbdVoMbmwGo0oQFD0QgPt+7Le4rdbDGjmcvwDQWJx41YczSUshiR7BKKZstZV0sopGI+yUSxoq6GULySzi4jzrp1SPbsfd4WwUKcUsz27AW7yVELtpx3R51MyBcBa0XGzKpJsBOVddO32YCeUSkyRJuNoJosawhC+q8n6IsixyuJRqbQm5sDYnKMQLSSob4e6pvTreZDARnMjSlr9VxjsjrHdZb0DoSQE3ZqspS2BpKBUQbtUFSOk1As2PNo9haKx3iupx2Af1u4ihUllWiahjcmc9erz9Avh9nn7WdFSeUkZ8oyQgVD/buJqx4cp4HZWxpCGZFQf9ZPq2oaoRBI5ekPABbBgrV0OZGwl0Xz52X9fWuWnZXVLrbjbc/Dkx+cRXzeOGZ75syqVZKmZHOgU3hymlG56667WL9+PU6nk8rKSt785jdz4MCBtGNkWeamm26irKwMh8PBddddR2/v+Cn9uY44atl+UtZktN02rhRu6qccThBLSAwOqGN0MskZP7nv+BlFsNnGvcj4h4NolqqsDkZMereMzVLI4ThKnj1UNne3ElMV6u0ulnuSc5UMBgMlgsSGyuT052e6W/O2nlHsLheybEFVrQj206f0A2ASPISD2ffkkMNx4nEron1sprL5jGUs2bAxJxO7S6rK04wSs4K1NKOOJ5FQ6Wwd5ODuTnZuPcKWx47w5F8P0n3s1IKaSCSGP2jBWZq5c8kiikRl3fTtZBRFZai/wMOZTiKngcrmzZu56aabeOGFF3jssceIx+O87nWvIxQKpY75xCc+wV//+lceeughNm/eTFdXF9dee20ul1XUWAQLqsE2ZmBWwC8jJyQ0o7NgAjBZBsldSijqYLg/lLYvGATJkT9BqdVmIxrL3DLsHU5gdkw+n2Q6JL1bxm6PhKJJD5U8lX6iSoLNI0HI5fULMBjSb1IX1CSfrvcN99MfCZ388pxic7lIqBIJzYJ1Ds89ysRoS2+2CQVk4qqA6Jj9pTTJ4SAUGhtU7X2lnW3bYM+RWo4OnYXPchnDsYX4h04t3THUGyCacI4ZNDqKVRBQtIltDk5Heju97NjaxvBgfq8fE5HT0s+jjz6a9v/33XcflZWVbN++nQsvvBCfz8fPf/5zHnjgAS699FIA7r33XpYtW8YLL7zAOeeck8vlFS8WN3I4fUqqfyhMTHHgKK8kHN5XkGVFZHBXVBLoHmCgt4vy6mRgEosliMhGxKr8TQ8WbRJhVSASjGLxHL+IJwcRGnE0jDV6OxWsNomwYkGW42lCYjkcQzWUZDV7MxFbetoJJ+JUiHZWl9WM2V8p2VnmqWCft59ne9q4tnl5XtYFYHM7GVJtYHKedp4ygsNOpD+pLTu5ZHsqhAIyCU1Kc1WdrQh2O/JQ8uHCYk6WiAN+ma4ucMy7kJrmhtSxB170I4daTun9vANBVMviMYNGU+uxSfi1ZCeSJOVP4FvshPwyvmg9HYf7C9K0kYm8Xk18Ph8ApSOpuO3btxOPx9m0aVPqmKVLl9LY2MjWrVszniMajeL3+9P+zTkyzLIJDIdAqEK02ZELYPAYjSaIJ8xYbRJmVyODfccXEfTJxFUp9zN+TkBy2FBUYUy79FB/EPmEQYTZQrTZULSxJbl8mpvFVYUnu44CcHn9fIyGzCn/i2qaAHiht4Ookj+LcNEmohg8YM69l06xIdntJFSRkD+7f5yRYLLjJxflnXwj2e3EVYFQ4Hj5p21/DyFlHpWNdWnHWm1OxmmymzJDg3EE99hgfhRBEkkoujvtyYQDMpGEh55eimbuXN4CFVVVufXWWznvvPNYuTI5pbanpwer1YrnpO6Mqqoqenp6Mp7nrrvuwu12p/41NDRkPG42YxUlInL6hcnnS2C2lWO1ScTiprxPWE4O3rMgSBLuigoCQSE1MTbol4mrtswtgDnCKgooSGnjBlRNo+1gP4q1OatdEACiXSKhCkRO6jSSw7G8mZu91NeJLxbFYxVZX1E/7nHLSiooF21ElATb+rvysrZRjPY6zLbsuJnOJlJTh7Pc1RIJxcA6Nz5Pmyt9gGMwINPVDe765WMycKOl1pnqR4IBmVBYGFefAqNldonYSaWfeELh1S2HCWQ56JwtBIMazsp6QvF62g/3FXo5QB4DlZtuuonXXnuNBx988JTOc8cdd+Dz+VL/Ojo6srTC4kE4abZMPKEQDBqxuz3Hn+zzLKhNTsRNTgj2VJQTUTypNuVwIAKW0vyn+83utMChp2OYviE31YtWZP+tLGZUg33MHKaInBwZn2sUTeXxziMAXFo3H7Nx/M/aaDBwQXVSq/JMdytaHsWCC9acyYIzs//5FzsmkxHMJVkPVIKhpLZjLmC2mNMGOLYd7CWoNFDZODboFmw24gnLjA3ihvqCRBQX7opJgjyTY4yNfnfbMN19Ij3t40+pnqsoiko4YsDudiNVLqHzWIxYrPCDG/NyZ7n55pt55JFHeOqpp6ivP/6lrK6uJhaL4fV6047v7e2lujpzD78gCLhcrrR/c43kLJvjXiq+gTBRxYbD40qmKzM82ecaORRDRcIqWpMtyLamlIV3KBADa2bBWk6xlqRKZIqicnT/IAnbCtzl2dWnpLB4kCPJz12W48kyk5wMLHPNjoEeBuQwdrOFc6smzyKeU9WAxWikKxzgiD9/F9y5UKKYMdbSrJYREgmVSMSAVZr9+pQUluQAx3AoRucxBWfN8oyWBqLDRlwTkt2EM8A7EAChbvLBmGbXGNO3rnYvw+FaBvtOv97lUCBKTBGRHHaqmxvxy5UcOzIw+QtzTE4DFU3TuPnmm/nTn/7Ek08+SXNzc9r+devWYbFYeOKJJ1LbDhw4QHt7Oxs3bszl0oqa5GTP4xbxfm+IhJoct56cUSHlfcKyHImB6bgWw1lexdCwYSTbU5inPkE63qLcfrifwUAF9UuW5Oz9jFYXfd1hnvzbEZ5+tIutzwUZDlVjd+c+WB7t9LmwpgnBNLlY02a2sL6iLu21OrlFsDkJBbOXvQoGZBKamP024QJilpyEQmoym5JopKqpMeNxot1GQhWTAz+niappDA0qSJ7JO//Mgh35hOz18GCIQa+Eq7IBr9867siQuUrQL5MYCVSsooDZs5SOtgBqgca2jJLTrp+bbrqJBx54gIcffhin05nSnbjdbiRJwu12c+ONN3LbbbdRWlqKy+XilltuYePGjadvxw+jXioCkVCMkjI7geEwiEuPP61a3EQjg3ldUzQSSxu8V1JVTmeHi67WISJRM2J1/gMVqyQRCSSFvq2HA5hK1mN35W4dFQ2NDHa/DtFmw2a3IdpsiHZbzk3uukIBWgLDGA0GzqvOfGHPxIU1TTzf28GuwV78MRmX9fRqGc43osNOuNNAIqFiNp/6M2A4cPymMVeQ7HbC/RAMJXBULxv3bydZSnMTDnZO+z2CvgihqERJ0+TaHosoIp/QhdvVOoCsNbFw+VKObt1Lf7ePeQsrpr2G2UooEEE1VqY6parnN9P96i6G+jtpnuS1uSSnGZV77rkHn8/HxRdfTE1NTerfb3/729Qx3/3ud7nqqqu47rrruPDCC6muruaPf/xjLpdV9Ix6qYxatvt8GqLrhD86izujS2oukWUwi8dT0JLdhmKupbu1l7gq5rXjZxTRZiORsHJgRzteuZ76xQty+n6eylIWnLmCukXNlNdW4fA48+LEu6W3DYAzSqtwTyPYqLO7qLe7UNE45Dv96u35RnIkBbXZstIPB6OoRlfeWt/zgeR0EFcEgrFaqidz1LWUIoem/1kO9QWJKW4cJZ5Jj7WIItGYAVXTiMUS9HQncFYtTH7m4jyG+gLTfv/ZTLLL7HgZ3+5yoEhLGBgqrIl9zks/mf69733vSx0jiiJ33303Q0NDhEIh/vjHP46rTzmtsLiIRuJEIjHCsgW753g2wyLaiORZkC7LGtaTJuKKJfWEwmYSee74GUWwS8Q1Kz09GvbqM7Iy4r7YiCkKL/clnyrPq5p6NmWUBSMBbj51KqcrNqeDhCISzFK3SCQojxlGONsRHXb8chm2qhWT6kcskoOwPL7mKRKJkUiMLUl4B4Ig1U/pIUIQRRQlKdrtbh8mIJdR3pAsmTrLqxkc1DK+x2xF1bQJO0ZDQRWLPb2LsappPsOhSoYKeAk5vVyZZhMjXiq+oTAxxY7jhEAl2bqXv06OREJFlg1juls8lRXIigssZQUx+BLtNhKKQCAxn+r5TXl//3zwykAXESVBuWhjyQymQc93JYXFLYH8zlg5HbEIFlRTGb4sOXpmGkY425HsEjXLz6N20fxJjxVtNsKh8a9zrzzbyivPHU0LJFRNY2hYw+6ZWrnGKokkNAtyOEZ3uw8cixFtyQcyT1UFkZiLgd6549XVcWSAl57cP2YECozMlQozZlyDp7KUqGkhQ97ClY71QKVIESQb4Qj4h0IoxpLUHw8k05XxuClvbWNyJIaiWdPWAOAs9RBRKjGKhXnqM5mMGF1LqVi0Nm/DEPPNlpHhgxurGsY1eJuI+c7k76Yz5CeS0K3Cc41QMo++3mhW5seEgiDa5o4+ZZSy2sopPdiMtihHImMFrZFIjEBI5FhfJXu3t6c+b99QmEjUhqt8al2IgiSiqFb6OocZ9NmoaDietbQ57cSN1Qz1+qb4kxU/geEQQdmDL8N4gnAwSjwhZCzjG0o3YKtYlI8lZkQPVIoUqygiy+D3yiCml8JEm42ENnNFeiQSY+8rbSnDtkmPD8VQVCuCPT2jYjQaqF6ygZoFudWGTMSidaspr83uXJ9i4VjIT2vQi0VTOafyeEuyZRoTaD2CSKkgoQGtAW/2F6mTRkl1FcGwDe/AqWVVIpEYccWScRjh6YLosBFXM7coewdDxFQ7rqZzaD3m4MiebgCG+wLEtBIcJVMzYRw1fes7NoCs1eM+aS6QWNLAQP/cCfCDQYVQ3MNQ/1jtTdK4U0LMIN4+eaZYvtEDlSIl6aViwedVsbs9aftEu4SiCjOaojw8GGLbM60cOuqke4qGRkmzN2vGQXOlNRU4PPkbRnjaoGkMPfd77vvdf/HjJ3+Ky5rU39h6jnL5++pY9T8fROqZ2iyU0fLPUb38k3OcpR5ihmr6uryndJ6QL0pcFQui/SoWki3KQkYb98BwGMVYQu38RqTa8zh0SOVYyyDDAyGQ6qfn52NyICtOnNULx7zOU1lBIGzDOzz7PVUURSUQMBCNO/AOZsio+GUUHGMy58WAHqgUKaLNhqJaiCZsafoUAKtoHWMfPxWOtQyybUsvA7G1JKxNBH1Ty6iMeqicboPmCoUpGmbD5y/jc3ffyMUt27lw52OpLEr11j9hikWY96+fcelHFrPip7eCOrHYb8FI+eeoLqjNOUajAYu7if6eyCmdJxSUSahzYxjhTBltUc5kbpnMNCfn+NQtakZxbmD/a36GveAsnaaWy+ImnKgcM28IwFlWQkwtZaB79pd/QgGZmCJiL6vC62WMSDgclAtj3DkF9DtPkTLqQBtN2DKnMU3OMdbP46FqGvt3HmPnjiiy7QIWnn0ujtJKvMNTU7PHIvHTctBcoVh276eo3P0UstnKQ2ddw9M/3E3cMZIVecsnee6bW+hb8zqMSoL5f/0+C//4rQnPN5pRaQ14UbS508FQrJRWV+EP2xk+BVFtJCiD2XN6O/0CmEvGBCqqpuHzgeOEGXFNZ6wgYFhDIFI+ZX3KKK7KGoSK1Rm7Bk0mI0ZHE4N92RFIF5KAN0JckahqakKOO8aUJ4NBBbPNU5jFTYIeqBQpydqpHaxVmdv4LB6iU3RtPLqvhwOH7Qi1l7Fg9RmYTEbsbjdyTJySTkWWExitenknH1Ru+zvNf/8RAB+49gs89/7/Rq5dmHbM8LJzefHOf7LrI/cAsOQ3n6d073PjnrPa5kQymYmpCp2h08sXohC4ykuJqRX0n0L5JxyMgVCcT7f5xCw5CZ/U4Rj0RZDjInb38Qc4k8nI/DXrKFt68bRL0TXNDcxbvnjc/a6KSrxec0ZR72wi6JfRzCU4Sz3EKWV44Pi1QNU0QiGQ7MU5V0oPVIoZazlmR2ahqEWyT9lLJeQLo4oLqF1w3GDJWeohptqn1EopR8Ai5H6ezemO1dvH6u/fAMDPz7qGrfPXsqFq/CnJba//MMcuejdGVWHt//s3rP7MMzmMBgPNozoVvfyTc4xGA2ZP8ymVf/x+sDn1LGayRTl9m28oTFyx4yhJ/3wsgiUnwvqSynLkE4awzlZCfhmESoxGAwZbPd7B499PORQjHrcWrXhbD1SKmOY1ZzFveebZNVZRJDLF62BENqS5ysJIxsZYRsA7sUhM1TRkOek3oJNbHJ0HMChx2qrm862L3sv6ijqclglM7AwGdn3kHoJ1S+jZ8GYS0vhPkvOdo4GKLqjNByWVVfiCIv5J/r4yEQ7FkKMWbHNw4Op0Eew24kp6i3LAG0KzVk4+cDBLWEUrqrWB/i5vUUwSnimBAEiO5DXCUVqG12dI6VSCATkp3s7hCJJTQQ9UihirKIz7x2iVJGIx45T+cKLRsa6yACZ7NX7vxOnMWDRBPGHRA5U8MLTiAn77za28/6pPETNbubx+8rZvxebkmf9+mdc+/EPUCYKa+SMOtUcDQ2hZ8PjQmRh3ZRlRtYLeTu+0X+sbTrbe2j3ZD1T+3LKPO7c/xbHg7BCHijYbcUUgEjx+nfL7EpjtlXldR0n9fNp6Knj2X23se7WDQJbch/OFLMeJRC1IrmSg4i4vRU44GB4IAslsS0KzIxTpdV4PVGYpok0ioU3eoqwoKtHoWFdZAJvHg89vyOhSOEokFEPBimg7fbsP8snDkTBHyhtYXVZDpTS1pxvFdkImRVGQelvHHDPP4cFoMOCLRRmKnlpHis7kjIowB3qmn1EJeiMoRk/W20QH5DBPdh1lQA5zz96XZ8X3QHLa01qUE4lki+3Jlg25prKhhnlnX0Pc/Tr2tdSy5Yk+XtlylP6e2VEOSgppRewjgYrd5SBBGcMjfiqRkAxCWdGKt/VAZZYy6qUymelbJDxi1iaNfdp2uN3EEhJ+7/gXrEgoRkKxFm2kPRc48wc3UvXiXxiQw7zSnzSumko25WSsw71s/MJlrL/r2jEty1aTiYaRGR56+Sc/lNRU4/ML0376DngjIGRfa/F0VwujuTR/PMqP975EuMjdik0mI1iOtyj7h8MZLRvygWgTaVy2iEXnXYkw7yraBs/gpeeDvPT0YXo7iztDFfCGSeBMa3c32usYHtGpBP0JTEJJoZY3KXqgMkuxigKKJkzqpRKNxEloFgTb2IyK3eMkpjonFNRGIzFUgzSnJrgWE46OfTQ+/gvW3/UWtu99ARWNZZ4KGhwzuBCbTHiObMd99FVqXvjTmN3Hjd90QW0+8FSUE1HK6J9m+cfvB5vLk9W1hOIxtvZ2APCuhatwWwW6w0F+tn87cVXJ6ntlHUvJ8UnyQyHimgtbAbUUJpORqsZalpx7AZ4lb6IzsIaXX4zwwlOH6StSv5WQPwLWirSMibOsHJ8vKR/INOOnmNADldmM2T2pl4ocTmZEMrnKmkxGEKoJeCcOVHQPldwx/+HvAnDsrDfyqJy8GM8kmwIQc5Vz9E2fAGDJ/V8AJf0GlApU9IxKXjBbTBid8+nvCU75NbkS0m7pbSemKtTZXZxTWc9/LFuPYDRxyDfIA4d3FbVuySweb1H2D4dAqC6aEkVJVTlLNp5H6dI30R1ax+5t/cQTxRf4BQMqZlt6xsRVVkI04aT3mJdo1IKYYcZPsaAHKrMZi3tSL5VoJA4m+7hD+wRnOT7v+H9YeqCSO6zePuqf+hUA9298BwlNpdnpYaFr5kMej7z5k8QcJTg79lH/zANp+0YHFHaHA0Wf8p8rlNTUMOQVp2z+lgshbVxV2NzVCsBltc0YDAbqHW5uXLoOo8HAtv4uHu04nLX3yzaizUZoZIqy36ciOIrPX8ZTWUrDilWEY24Ge4vLq0hRVIIhw5jg1+a0kzCW0d3WT1wTMw4jLBb0QGUWYxbtyNGJnyzkSAzM47et2txugmEz0Wjm7iE5Ora1WSc7NP3jHkzxKIMLz+I+W/Lie3n9wlMaAJawuzly7e0ALP6/L2E4ISBxWQXKRdvIgEI9q5IPSqsridBE+6G+KR2fCyHt9v4u/PEoHqvI2vLa1PZlJRW8c8FKAJ7sOkpMKb5MACRblGNxC35vmFDEgn2KAwfzTfLGX4k3w8C/QhLwJ63zMwUiJns9oWC86Mc16IHKLEaQpDGujScTncT+3lnqJq7Yx532GoloCBnKRjqnhjEaoenvdwPw8IXvQVYVamxOVpScettly1W3EHVXYu85SuPjv0jbl2pT1ss/ecFoNOCuX0JPj2FKLtDZFtJqmsYTnUcBuLi2CZMx/ZK/obKBEkFCVhLs9U4tmMo3os1GQrXS0zFMTLEXREg7VcyueoYGpuYYni+C3gixhITdPfaB1VFaRlx1gqV4O35AD1RmNVZJIh43TlgTlcdpTR5FsttIGEqStd+TSCRUYjEjVr01OevUP/0bBF8/oYpGvl+dNPW7vG4BxiyMU1dEO4fe9lkAarY8lLZv1PjtkH/wlN9HZ2pUNtYRUhumlFXJtpB2r7efnkgQ0WTm3KrGMfuNBgNry5PD/UY7zooN0W4jrogM9gyjGEqQ7MXrku0sLcUftE7ajZlPgv4IWMoyenK5K0qJJDwYxZmXm/OBHqgUAZFEnO/sep6Hjr42rdeJNom4KhDJMAZ9FFnWMgpp009UnXySO3ld4RhxVW9NzgXhyiaGlpzDkxf9Oz5FoVSQWFtRk7Xzt73+w7xy22948UuPpm1f6klOlm31e3WdSp4wmYw4qpbSeSyBLI//medCSDuaTTm3qgHJnLlzb91IOei14V6iSvE5r5otJjC7ktlhqbrQy5kQT0UZcsLJYG/x+KuEAlEQKjLuk+wScfM87CV6oKIzCa8OdNMSGOaZ7jaOTGMWi1USUVQrkVDmi188oRCPG7FKEz+B2N0leH0a6knKfzkcQ1WtuittDhhYcznPfGsLX156EQCX1c3HZMjen6NqFem8+N1opvSnqDLRRrXkQEVj33B/1t5PZ2KqmhoJxmomzKpkW0h7LOjjkG8Qo8HAxbXN4x5Xb3dRIdqIqyq7h3qz8t5Zx1xCQrNjz3LbdraxilY0oR7vQPHoVAJ+sDnH1ykuP+9cauePzbYVE3qgUgS8Ong85fqXtv1TbhVMeqmIRMbp/IkEY8RVYdKMiN3jJhoTk5H3ia8PxUhoQsbx5zqnzqsD3fTHYzjMVs6pbMjZ+xjiMRzH9qf+f1QHs2e4ODUJcxGLYEEoX05nhzxuqTbbQtrR3+/KkkpKJhgqajAYUiLbVwaKs/xjtjmJJNw4SjyFXsqkSJ5qhgaVMQ9+hSASSWbpRq3zZyt6oFJgQvEYB71JvYDJYOCof5i93qk96RqNBjC7xrXRj8pxVNWCNYMr7Yk4PG6iythJytGRjiGTSf+aZAtH+14WPnQX5sAQj4+k5S+qbcJqytw+fqq4ju7gsg82s+FLV2IYSeuvKE0GKvuG+4viYjolNA1hsAt71yHMQW+hVzMjqufPwxep4NiRzFOusy2kPTrS2bXIPXk777qKZKCyd7ivKEuCgmQjFC3B7i5+qwRXWSlB2UbQV/gRBQFvhLiaWUg7m9DvQAVm11AvKhq1NmcqPftI24Gp30Am8FKRwzESqmVSjYpFsKBZKscIanUPlezT8MR9LPv1Z2n6/g10hvxYjSYuqJ6Xs/cL1i3BmIhh62ul5vk/AElBrWQyE0zEaJ8FN31x4Bjn3nEhr7uhjkv/Y3Ga664xHsV9aFsBVzd1RJuIyb2MjhZ/xvla2RLSGuIxGv/+IxpfSeqT5rtKQVUn/JxqbE5qbE4UTWPXYM8pryHbVDbWU7tyw6xwyHaWlRBTi8NPJeiLkMCJVMStx1NBD1QKzKsjqdY15TVcXrcA0WTmWMif2j4Zgt1NcBzjS3kaGRGzrZLhQZnO1kG8w2ESCRVZVjBZ82sCpGkaveEgT3W18ETn0dnzxD8VFIW6ERO2BxduBOC86kbsFmvO3lIVJFrfeDMAC/70bdA0TEYjSz1Jcd2eoeIu/5TvfIILP7GWsr3PoRmNxG3pgfOKn36c82/fSPNffwCz4LtSPb+Z4VApx1rSu66yIaQ1KAkaHr+XSz+yhDN/fBP/+fhPEYE6m5PK7f/gwk+u57xPn0/ly3/L+Pp1I90/2we6ZryGXGEVrZTXZn/+US4wmYwgNTLcP3VH4lwxap0/2xnbr6STN8KJOAd8yTTw6rIa7BYrl9XN52/tB/lb+0FWl1WP8T04GcnlItSRbFG2mNPLB7FJPFROpLS2ht6DS+jfNoTF5MVijGAwGLB48tMKuN87wK7BHvZ5+xmQj0+cDSVivGne0rysIdeUv/Y00mAnst3D/9UsxWQwcMkEIsds0frGm1j4h2/gObydstc2M3jGxaworeTVwW72DPfxxnlLcr6GaaOqLPzDN1l6/+cxqCq+5tVsu+MPhKvnpw4xKAnMYT9GJcHKn34cx/6tPP7+7xAX7Yw2eRuActGes9LadLG7HBg9qzi07xlcJTZKypLzVUaFtJUzFNIaEnE2fmETZXueAcDvquBXa99Ik8ONyWjE0XkAxWyldN8WNnzlKl699Zccu/T6tHOsLa/lkfaDHPQOEohHcVp0bdpMcZZWMNyfdIUtZOk84Fex2It32OBU0TMqBWTXYA+qplFjc1JtS2YuLq5pxmGx0i+HeLHv2KTnsLucxBSJwPDYeqgciWOwTC0jUlJVztILrmD+eW+jbPm1mOuuIeHZRHl97eQvPkWe7mrh7j0v8mxPGwNyGLPBSPOI38djx44UZSp6JtQ9/RsAnlp5KTGzhfUVdROKHLNFzFVOx2U3ACNZFWDZSEalI+THF5vedN98sPQ3n2fZrz+LQVVpv+wGnvvW82lBSlxVeLK3g49f9Z/84A0fI2400fTsg5z/qY384rmH+caOZ/nGjme5a8ezfGn7U0XV4dS0cgUBVrPr5a6U38apCmkXP3gnZXueIS452XPDt/noHX/hV+uuZt7I7/nom2/jiZ+10nb5jQCc8eOPYu88mHaOCslOg92NisbOgbnxN1coXOVlhOMOhgqYVVEUlVDYgDRBx89sQQ9UCshot8+asuP+GaLZzOvqFwLwj45Dk042lZx24qodvzc8Zp8sG7BOYPaWCbPFjKvMQ3VTPfOWL8ae4ymlvqjMI20HgKSfw4eWnsU3NlzObavO5eKaJgB+fWgnfZGpzUopVkzRcEoj8ouFG4BkS3K+OHLNbWgGA1Xb/o6jfQ8uq0DjyITmvUXY/dN25X8QKatj580/ZefHf4F6QkA3JIf5/u4X+GPLXrYPdvOdM17Hu975NfrsJSwdaOP//etHuMxWnBYB0WQmEI/yo70v8XDrPhLqWG1IvjGZjDSvOYuB8GJ2vthOPKGckpC29LVnWPTQ1wHYecvPOfqW/+SAnAw+RwN+gGhpDbs++r8MnHExZjnEum+/E2M8Xd+2rqJ4yz+zCYfHSYJyhgtopx8OxYgr1lmvTwE9UCkY4UScA95k2WdNebqJ0fnVjZRYRbwxmZf6Oic8j8lkBGsloQwKcznK5GZvBebPrfuIqgpNDg/XL17NGWVVCCPeH29uWsZ8ZwmykuBn+7cXpRnVVKl68S9YIgEGy+rZVreMxe4yqm35e9IJ1y6ke+O1ybVs+ztwYpty8WQbRolUNPLkjw/R/roPpG3fM9zHN3c+R1vQi81s4arGJXxg6To2Xf1Rdty1GcVs5aJDL3BvdJCvn72Jr63fxPnVSY+IxzuP8r3dW9NKi4XCKgo0rDqXnuF69r7cfkpC2totD2HQNNovu4Hu899GIBalX04G9s2uk9L+JhOv3HY/UVc57qOvsuy+T6ftXjPSpnzEP4Q3WnyZttmE0V7H0EDhOn/CgSgJVUR02Au2hmyhByoFYvdQL4qmUS05xtywLEYT5410gkzF68JsLyMQSL+JR6MJ4gkzliI2azvkG2TbQBcG4O0LVo6xjzcZjbx/6VqcFoHucIAHD+8u6nH0E2HrbUExW3l01SY0g5EzSvMvDDzwnq+y+buvcOTaTwFJfw2AA96Bosg0uI68Svmrj6X+/8QsiqppPNJ2gB/vfZlwIk6jw83tZ57PFQ0LObOsmlq7k2jTGRx6++eJuspRzUmBstVk4h0LzuDGpWuRTGbagl6+ueNZWopgKKPD46Ri8Xm0drlPSUj72od+wCuf+DWvffD7AKmfrVpyYMvgRhstq2XHx+9DsUoEG5al7SsVJOY7S9CAV/SsyinhLKvA7zeNO/A110RCURRNnBM+WHqgUiB2nNDtk4nlJcna8sEp3EQkp4uAn7SWx0g4hqJZEKZZ+skXiqryuyPJkQHnV8+jwZF50JjbKvL+JWswYmDbQBfP9rTlc5lZ4/Db7uAvv+jgO6uuAGB5FoYPTpdg/VL8C9ak/r/e4cZpEZCVxLQckXOBOeTjrG++lXO+dAW1zzyYtk/VNH5x4BX+eewwABdUz+PWMzZSlmGq9+HrPs3Td++l59xr07avLqvhM2supHkkQ/fX1v1jXlsISmsqsNefhzdah7N0hsP2DAY6L3kPysgDz2igMv/kbMoJ9K1/I4//tIW21394zL5RT5WX+yfO5upMjKeyjEjCVTA7/UgoCpaSoh42OFX0QKUARBJx9qfKPpkDlTq7C6fFSlRVJn36c7hdxFSJgP94qlYOx1BUYVKzt0KxubuVnkgQh9nKVZN0nSx0l3FNU7Lz52/tB2dtCeg1RWFQdFIh2qmUCpuOFYa6sUYCqYC4oC61msbq79+AvecokYpG+te8Lm3339sPsnOwB7PByHsXr+btC1ZiMWbu4tEsVmLuE9oxT8jAlQoSN4wEvYf8QxwL+nLy40yX2gXzWH7R66f15Cv1tHDmD27MaH43Ohn7RH1KJmIlJ2T1Tvic1pXXYjQYOBby0x0uvBfIbEW0iaimCvzDhSk1RsIxsM7+jh/QA5WC8NpQLwlNpUqyUy1lFqsaDYaU18XeSTQENpeTWMJG4IQ/iGgkjqJZizLt54vK/L092XHwpqalGdPTJ3NxbTMVoo1wIs6WnvZcLzGrSP3J9Y56lqwoQDblRBb+/htc9oEmmh75n+M6lQL6qTT/5fvUvPAnVLOFbZ9+iLjz+IC07f1dqUzKuxaewVkVdVM7qaZRs+X3nP+fGzCdcLMtESRWj2jCnupqyd4PcYqYLdNrn156/xdofPwXrLrnP9K2x1WF9pEAbL5raoPmap7/Axd+fDWeAy8CYLdYU9+LlyfRyOlMgsUzriFnrgmHQCjwA1G20AOVArBzZPDXmrIaDIbx03KjLaT7J7HUN1tMYC1Ps2wetb8vxrRfSkDr9LChsn5KrzEaDFxevwCAJ7uOTtoNVSzYOw+y6cZ5nPvp8zkw0ma9orSwBkyRsnpMiRjz//o9Vkg2jAYDfXKIvkj+Wykdx/az7JdJQeee938H36L1qX0dQR/3H94JJDuk1k/xuwJJx9pl991OyaGXWfarO9L2jXrXbB/oKsrW7Mmw9Ryl7tn/A+DIWz6Vtu9Y0E9CU3GYrVRkKI1lovLlR3C37kp1DgGsHwkIt/V3zi3TxTxjFm1ECvAVUxSViGxAmAMdP6AHKgVh9IlniWfiG9bSknIMwLEpeF2YpApCgeMzf5KBygxr3jkknIinWh/fPn+sgHYi1lfU47GK+GLRSbuhioX6Ee+UoGBnWElgNZpYMMUn3VzRdeE7CVc2Ifj6WfzUr1niLgfg0Y7D+V2IqrLqfz6EKRGjb+3raX3jTald/liUn+zbRlxVWV5SMW3TP9UqsuvmnwLQ9Og92HqOpvY1OUtodnpQNI1nu2ef5mnhH76JQVXpW/t6fAvXpe0bne/T7CqZ8CHoRA5f9xk0g4Hql/6Cs3U3ACtLKxFNZoZjcsH1S7MZQZSIFKDxJxKOkZgjrcmgByp5J5KIMxxNfnNr7RO3pzotAvX2ZLAxqmkZD8npwucj9fQjR1RM1uL7kh7xDaEBVZJ9XAHteJiNRi4d8R55vPMIilb4TpUJ0TTqNict859cnRTRLvGUj6uvyBeayczhkc6fBX/6Nm8ayTC83N9Jiz9/3TBV2/5G2d5nSYh2dn30xzByY42rCj/bvx1vTKZSsvO+xWumFdCOMnDmZfStuQKDqrLgT/8vbd8ltcnv0ZaedmLK7MjOAYiDndQ/cR8Ah97+uTH7j44EFfMn0aecSKh+Cd3nvhUglVWxGE0pfye9/DNzrDYb8bhx3InZuSIcjBJXBT2jojMzesLJ9LrHKk5JmzEqdpzMWTOpUxEIBZL10IgMliLs+DnkT844WeiafKJrJs6tasButjAgh9lR5O6Z7qOvYu85gmIV+V3DGUDh9SmjdFx2A7KnClt/O+e88nfOGSmr/L5lT95S/b3rr+KVT97Png98j0jl8cGMT3a20BIYRjKZ+fCys5Cm8HcyHoffliz7NDz+C4Th49+XVWVVlAoSwURsVnW3LPjT/8OUiDG4/AKGlp+ftk/TNFoCXiCDf8okHHrbZwGo3fI77F2HAFhfmSz/vDrYPWtKrcWGIInEVYFIMPOE+1wRDkRRNAmhiO0ppoMeqOSZrhFhX80Uzb6WlRzXqUx0A7G7XcQUGwFvGFXTiMpgKUKzt0O+ZKAyldHzmRBM5tSU6X8dO1zUvio1z/0OgM61V7I/lrxQFUugogoSR6/5BAAL//hNrm5YhGgy0x708dIURjdkBYOBzovelWbq5o/JPDYinn3b/JVUjiM2nyqDKy5kaMk5mOJRmv/y/dR2k8HIhSPOx093tRT192gUq6+fxn/+BMicTRmMRgjEo5gMhpTr8FTxz19N71lvTGaf/vBNABa4SimxishKgteKfHhlsSLYJBTVSiSU30BFDs+d1mTQA5W8050KVKZ2AW5yeBBNZkKJOB0TtFNaRSuquYSgL0JUjo94qBRXoBJOxOkMJT0FFrpnrtO4sKYJwWiiKxwobFvtRGgatSOBytbVr0Mj2XLuEYrnd9J25UeI293Yelup6z7MFSOjG/7SdoBIIp6z93Uf2oYlmLnE9Pf2Q0RVhUaHO+XncUoYDBx+azKr0vSPH2EOH/e0OLeqAcFooicSnLS0Wiy0X34jg8svGNPCDcfLPg0O94zKi6PBT8NTv0Ic7MRoMKS6rGZT1qmYsIoCiiYkJ9nnkUg4Pmdak0EPVPJO9zQzKiajkSWepNhx3yTdPwiVBH0ycjjZmizYiqv0c8Q/hEFV+PRLf+INX9jEpR9awOXXV0/+wpOwmS2cX5MsFfzr2JGifBp2H96OvbeFhGDjzw2rAFhRUlzj1hM2F9tuf4jHf9ZKoHkVF9U2USHaCMSj/OtYboS15pCP9V+7hos/ugxXy860fV0hP8/3Jlu5r21ePiNdSiZ6119F2xUf4uXPPUxCOv53J5ktnFPVABRXq/J4xNwV7PnQD3j+60+n9DwnMuq3NJl/yngML93I0Tfdyst3/Am5NBkkjpZ/9gz3EYzn92Y7FzAaDWB2I4fzXPoJaXOmNRlyHKg888wzXH311dTW1mIwGPjzn/+ctl/TNL74xS9SU1ODJEls2rSJQ4cO5XJJBWc0UKmdxpyXZVP0U5EcLvx+jUgoRkKxFF19smWgi/95+Jt85KmfU7pvC/aeo4jeXkwjc0lQVc666zrqn/p1mgFVJi6pbcZsMNISGE51OhQTwcblbLv9d+x791fYEUr+zoul7HMiA2suJ+ZJrstiNHFt83IgeePOxSDI5ffdjjTURUJyEqxdnNquaRp/at2HBqwuq85uZ5TRyK6b/pfBMy4ec4O/qKYJA8mHAP9saVU2Zr5sjxq9TeRIOxl7PvBd+ta/MfU51dic1NtdqJqmW+rPFIuLmJy/QEVRVMKRudOaDDkOVEKhEGeeeSZ33313xv3f+ta3+MEPfsCPf/xjXnzxRex2O1dccQWyPEsuGNMkEIsSiMcwwLQG0o3qVFoDw4QnSMnb3C7kqBX/cAgVCYswcxFitrEEh7nphzfwhoPPkzBbee2D3+e5bzzHEz85gmJNZn7qnvk/arb+kTXfvZ61337nuOUBSFrrj3o9FKMBnCLY6D7/bTxx6fuIKAlsZgtNM3zSzRflOx5npcPDMk8Fiqbx2yO7szoDqOLVfzFvRGOx65afpc3y2evtZ793ALPBOO1W5Glzws9UIdmptSdn7BzJY8fTdDDGZNZ8599ThmyZCMVjKf3bdDp+psLo35ne/TMzzKKDcCh/HYpzrTUZchyoXHnllXz1q1/lLW95y5h9mqbxve99j89//vNcc801rFq1il/96ld0dXWNybzMFUYvJOWiDatp6jXkUkGiWnKgQWricibsI1b63n4fmPM3mXcyjNEIGz99PqvbduIX7Dz5+b/ScvXHGF5+HuHq+aknxK4L3sH+93wV1WSm7rnfceHHVyMOjn9xPG9kKu6rA92EijQtPer4utxTkbVSRi5Y9cMPsPGLl7PgkR9wbfNyLEYjB32D/PLgq1lpAzeHfJz5w6Ro9uhVtzC48qLUPkVT+XPLPgAurJlHRY5S1uaQj2X33s6Fn1iL4YQxDAtHsjeHRzrSio2aLb+n/unfcNY33wbjtFKPdtNVSw5c1lPLpJpDPpb85gucd/u5oCisq0ha6rcGvbSOdBXpTB2rIDLRs/eBXZ20Hsye1i4cjJKYQ63JUECNSktLCz09PWzatCm1ze12s2HDBrZu3Tru66LRKH6/P+3fbGG6+pQTGbXTn0inItklFIOLaCQKluIxe1MFiZ1nXUW3o4wPvf+HRNeOFQJC0t/j0Ns/x3Pfep5Q9QJs/e2s/u71416cGx1u6u0uEppaVGK/5oe/x6IHv4LU15b6fRViCOF0GG11XfzbLzNPDvDBpWdhNhjZMdjDbw7tPOWW5eX3/ifSQAeh6gXsv/6utH1bezroiQSxmy1c0bDolN5nIjSzhYYn78PdsjMldAZSZaZizag0/eMeANqu+BCM84Bzqt10J6KarTT9/W5K92+lYsdjuK0i68qTmpUnO49O8mqdk7HabMhRA4lE5oC/t0um9bAvbajsqRAORknModZkKGCg0tOT9DSoqkofd19VVZXal4m77roLt9ud+tfQ0JDTdWaTUwlUTvRTmVA8KlShahbMU7TPzhe/vOi9XPn+H2JYtG7SY32L1vPif/2dhGinYteTLPzTtzMeZzAY2Dgihny+t704RLWqyoKHv8PSB76IdGhbqsspGzeQXHLskusZXrwBcyTIsl9+hmUlFbx/yVqMBgPb+rv47ZHdM/58K179F/P+9TMAdnzsFyji8YxJJBHnbx3JuU9XNi6ekrfQTFEEGy1vvAWApr/9T2r7aKDSFfJPWFotBM6WXZTufx7VZKb98hvHPW40UFmcjUBFkDh2yfUAqVLdZSNGizsGuxmQCzNkb7YiiCKKJiSHBJ5ENJogLJvwR0ro7fRm5f3kUBQsnjnTmgyzsOvnjjvuwOfzpf51dHQUeklT5lQClQWuUkwGA96YzGB0fE9mwV5CTHVisRZ+GGHZrqcwRZMXtUOBIbySi0VTNHoL1S3mtQ/9EIBFD30Nyzhp+fUVdViMRrrDwUmnTOeDkoMvIg10kJAcvLjobDSgTJCKqi05I0Yjuz+c/LwbnvoVJfue54yyKq5ftBoD8HxvB39o2TujYMW7aD0dl76Xo1d/jKGVF6bte6LzKMF4jErRzvlVjdn4SSak/YoPoprMlO7fiuvIqwC4rAKVoh2N4y2+xcK8f/4vAD0b3ky0NPOkdX8sSveIkeTCLAXEbVd8EICql/6CMNRNnd3FMk8FGnpWZboI9lEvlbHDCQPDEeKqhKzV0t2WnetXsjW5sGM6sk3BApXq6mRbam9vb9r23t7e1L5MCIKAy+VK+zcb0DQtdTGZzDo/E1aTiYYRO/2JbM5tbhfhuBurVNjWZHvnQTZ8+Q1cdMsqtL72lAfMdPxTOi57H4feegfPfWML8XECHMlsSaWlt/QWPmgdLSn0nH0NB+VkQDnVKbaFxrdoPe2b3g/Ayp9+LKVPePeiMwHY3N3K3zum35UXd5Sw49b72HPjd9O2e6MyT3Ylb3pvalqKaZxulmwSLamm+9zrgOMlFYAF7lGdSvEEKqZIMNkBB7Rd+R/jHjeaTam1OXFYrFl572DjCoaWnotRVWgYsewfzaq80NehtypPA0ESSWgCcnhsti7oj5BQ7ZQ3LaF/0EIwcOqNJMnW5OLKqJ8qBQtUmpubqa6u5oknnkht8/v9vPjii2zcuLFQy8oZwzEZWUlgMhioFGcmFhy1xZ4oc+DwuIjE3Yi2An5RVZUz/+eDmGIyoZoF7DUJaCRFxCXCNAIog4H913+dQPOqCQ87NyWq7Sps6l5VqdnyEABd57/9+NyVU2gXzTf7/v3rxG0uPIe30/jEvQBsqKzn7fNXAvBox6EpD/Ir3/lEur7opEDkb+0HiKsq850lrCqtIl+0XvlRAOo23485NBJAj+pUfMUTqNRtfgBLJECwbjEDqy4d97hsln1OpO2KDwHQ+K+fgqqy2F1Gvd1FXFV5rmf2DXMsFEkvFVfSLfYkwsEICBWU11UTUaroaj01QbeiqITDINjnjocK5DhQCQaD7Nixgx07dgBJAe2OHTtob2/HYDBw66238tWvfpW//OUv7N69m+uvv57a2lre/OY353JZBaHHN8D5La/yrSd+ymU3r8A4A8+GUSOniQIVm9POonMuxFNZuKf4ef/8CWV7nhkZNve/HAokL/5TLfuMh+fgS1S98PCY7U0ODzU2J3FVZVsBRbWl+59HGuwkbnPRs/ryVIdEoaclT4dYSRUH/u1O5NIahpYef2C4oGYeV44IXR86+hqvDnRPeJ66zQ+w8QubWPftd6Z12IzSGfLz4ohV/5ubl0150m82GFpxAf7GFZijYeqf+hVwPFBpD/mIZlhvIVBEO6HqBbRd8eGMBm+jHA9UyrP6/t3nv4243Y29t4XynU9gMBhSWZXN3a2zaphjwTG5kxPtTyLgVzCLpZgtJqyli+nujJySqDYSjpFQBcQiM/s8Vcy5PPm2bdu45JJLUv9/2223AfDe976X++67j9tvv51QKMSHPvQhvF4v559/Po8++ihikVm/nwrmsJ8VP/sEl215CFskkNpevutJ+s56AwBSXxuRisYJL0Zw3B+hM+RHTiQQzZl/fXbXqc1HORXE/g6W3Xc7APuuv4tIVROHd24BTs02v2TvFs793MXEHSU8sepSlBN0PgaDgfOqGvh9y1629LRzQfW8vN74Rql7+n4Aes55C+2xKDFVwWa2UHWK82ryTesbb6Ln3GuT38kTuLJhEf5YlC297fzq4A4cFmtGkXD5q4+x+vvvA0Aur0fLYOf+cOt+NGBNWc2MnVRnjMHAkbd8ClfrLvrWJf8GS0UbJVaR4ZhMa8CbcoMuJJ0Xv5vOC/8tY6A3ii8q0yeHMHC8fJUtFMFG65UfxRIcSn0X1pTX8Ne2AwxFI7zUf4zzq+dNchYdSHqpRMLpAYiqaQQDGlJF8lpW0VBP7043fd0+aupn9jcx2posOvSMypS5+OKL0TRtzL/77rsPSN5gvvzlL9PT04Msyzz++OMsXrx44pPOJlSVNd+9nsbHf4EtEqDf7mHLee/gxS88kkrlmsIBzr99I+fecSHOttcmPJ1bECkVJDSgNejN/fqni6ax6p6PYIkEGFp6Lq1XfpTICTOKTkXo5118NuGqZgRff8YuoPWV9ViMRrrCAdoK9NkogkRCcnDs4vdwZLTs4ywpav+UTGhmS1qQUrb7aRzH9mMwGHj7gpWcWVZNQlP5yb5tHDtp/pT78HbWf+NajIk4nRe8kz3v/+8xAfh+bz/7vP2YDAaunrckHz/SGI5d9l723vjfhGsWpLYVo04FoxFtAt3JwZFsSr3dnZOOqf3Xf53dH/0xofrk78lkMHLJyFDQJztb8jZpe7ZjEUXkkyo/oUCUaELA5koGKg6Pk4S1me7WmX//5mJrMszCrp/ZxMI/fovqFx9GMVv5z/d8i7Nv+hWbP/B9+ta/EXXElMlzeBvmkI+yvc9x7mcvQuqbuPY7mlWZSFBbKCp2PEbVtr+hmK3svOVnYDJxNDCMika5aKN0OvqUk9DMFvZd/w0A5v/5vxEG0+28bWYLq8uSXRGFcqrde+N3+Nevehk445KUrX8uhbSRUISD23YQCY3fBXaqlO16ig1fej3nfGETUm8rRoOB9y5ezUJXKbKS4Ed7X05Z7XsOvMiGO9+AORKk/8zL2HHrfWN0Kaqm8XDrfgDOr86dudtMWDhSmjziK6zxmyUwRN3T92OMj9U0nEyu9CkTsbGqAZvZQr8cyt+k7VmOVbIhy6SVdQLeCHFFSgUqAO6aJvoHTISDk//uMyGHomB2z6nWZNADlZxi9SXNvnZ9+If8uX4lmsFIzUkdP4OrLuGpH+3Hu2At1sAQ6775tgkvUFMR1BaKcMU8us59K61vvIlgwzLguDhxYRZu2D0b38LQ0o2Yo2GW/N9/jdl/7kh7647BHuJqYernimBDMxqzMndlwvdRVNp3vYIt+jzdR3I3UC/QdAbh6gVIg51s/MJl1D9xHw5fPx9cdhZ1dheBeJT/ee0Fah/8Mhd86hwEXx+++WvYdscfUS1jW+Sf7mrhWMiPaDLz+hyau02Vsl1Pse6bb0cY7EppiVqD3oJ9fwAanvwla7/zHs6+8w2THjvqSJtTnx5Nw7P/BZbd+ylQVQSTmYtrklmVB4/s5rWh3klOoCPaRBQ13Usl5I+gmjxYxeMZs/K6GsJKNZ0zFNVGQrE515oMeqCSU/be+N88+62tbL/gXSQ0FavRlDGrIFc0sO0zfyDmKKHk0Mss//knxz3niYLaYku7huqXsP0zD7H3hv+X2jZahsnKnBuDIXXuxsd/gaN9T9ru+a4SPFYRWUmwf4JRA9nGFA4kPTlGfh8DcphAPIrZYKTRkRuH4GMHDmNXd1FVqREf3pezoWcxVzlbv/wYoer52HuOsub7N/C699bw+k+ezY93/YNK0cZwTOb/uWpRTWY6Ln0vL9z5TxK2sbYB2/u7+HNr0ir/qsbFWWulPRWWPPBFarc8RONjP6NKsuOwWImraqpcmXc0jXmP/hiA7vPeNuGhQ3KYATmMEUNOBdumWISN/3U5C//0/yjb+ywAVzQsZG15DYqm8fP9r0w42kMn6aWSUK3JQGKEUEAGa/pEdbPFhNmziO5joRmJasNhEGzFk6XMFnqgkmWM8SiGE1pkvUvPSTN6G0+vEKlq4tVPJD0Tmv9+N3WbH8h4XK3didVoQlYS9Iz4shQdI+l+VdNoH7ngz3N6snLq4WXn0r3xWgyqyrJffib9bQ0GzixLevDsGBzf3Tjb1Gz9Axd9Yi3rv56caTWqT2l0uLFkEJKeKkPd/ST6X2TRUhtL187DJfTS25q7cle0rJYt39zCwXd8Ae/CswBwt+ygdu+z3LzyHEoFia3uGq6+7fdsueknxNwVY86x39vPrw/tQCM5sfjCmqacrXc6jLYqz3v0fzEqidQNv1A6lbJdT+HoPJjUOl307gmPHdWnNDrd4wrrs4Ei2Oi84J0ANDz2CyD5t3b9otWcUVqV0isdKSZtT5FhFUe9VI4HKsGAhmAf+yBTNa+RwUAFe7e3TytYUTWNcDhp2T/X0AOVLLP857ex8fOXIQwfv1F2pQKVibs/+ta/kYNv/xwA8x/+btqU11FMBiNNIzf9Yin/VD//R878wY1Iva1p2/sjIWQlgcVonPRnnw77rr8LuaSa/jWvS2UxRhnVqewe7Mnq5N+JqH/6N0DSgRU4oeyT/adcOSzTd/BFGmoCzFtciSCYqa2zEOrdTyKeu3JFtKSaA+/+Ms9+52X++ateXvnErzny5v+kRJC4ecUGXBaBPUYz9+x9mZ5wMM3Btj3g5Wf7tqNoGmvKari2eXlBurIy0XPutUTdlUhDXVS/+PBxP5UC3XSbRrIpxy56T1pnWyayOd9nMto3Je37a7c8lPKeMRmN3LBkDcs8FcRUhR/vfZk2fWhhRkwmI5icqUAlFksQjhiRXGN/xw6Pk9LFF3Ok3cPul1qnHKyMdvzMpanJo+iBShaRelpoevTHlO19FlfrrtT26VjnH/i3O9n371/n+a8+OUaIOMqooLYo7L4VhaX3f57Gx39Bw4hB2CijZZ96uxuTIXtftVDdYh7/WRutV90ypqNkvqsEp0UgoiQ46Mt9Oloc7KR815MAHLvwXQAcGfGNWZBlfYqqarTtfIUKx1GWrWtMZecaFlZgNx2j/1jXJGfIDjFPJZ2XvIfec64BoEKyc9OKDdjMFtqCXr726ma+/MrTPHT0Nbb1d3LPvpeJqgqL3WX8++Izi6oLSrUIKWOzpr/fncqoHPXnv7QqDHVT/cKfgImdaCHpdJ1PIa13yQYCDcswxSLUPvvb1HaL0cQHlq5j0Yi4+p69L+GbgUfUaYHFnTJ9C/pkYoqEPUOgAlBRV03Z4ktp6axg94tTC1YioREPFYceqOhMwIKHv4NBVelffXnyaX+E0YzKlKzzTSYOv+2OCZ+miklQW/fsgzg79hFzlHD0mtvS9qXKPjnQaYzXspks/yRdTvNR/ql95kEMmsbQsvOIVDcTiEdTXTDZ9gfpPHgEu7qLFWtrEYTjqX6HU6S6WsN77ACqWhjdUq3dyS0rNrDUU47ZYGRADvNMdxu/PLiDYDxGg93NB5eelZNS2KnS9voPoxpNlO9+mqUD7YgmM7KSSA2UzBeNj/8Co5JgaOlG/M1nTnjsgBxmOCZjMhiY78yDeNJgSGVVGh//edouq8nEh5evp97uIpSI8+DhmQ+wnMuYrE7kSDLgCPgixBUbknN8PUlZbSWVyy6hpbuKnVtbxp2+PEooIJPQRIQCj0/JBXqgkiWs/gEaH0v+AR++7tOp7XFVoX/kxjXtYYSKQuM/f4qjY1/a5lFhar8cJhCbWRtbNjAk4iz+vy8BcOQtnyJxUr11NKPS6PDkZgGqSs2W33PW169Ns2ofLf/sGuxB0XJb/hkt+xy7+D3A8bbxGpsDe5bFouH+I9Q3GCmtGFtGa1xUiUQrQz19WX3P6VDvcHPTig18Y8PlfGjpWZxX1UiJIFFvd/GR5etzqqMASMQT7N+6le6W6c18ksvr6TnnzQDM//uPUp1a+S7/OI4l27bbrvzIpMeO6lPmOT1YTfkJ/o5d8u+oJjMlB18a4/kkmMxcv3g1ZoOR14b7Uq7DcwFN0+gNB1FOsZRslSQiI04CIX8EhPJkSWgCSqrKqVp2CW09Nbz09BE6jg6MCVii0QSHXuvi6MEACNVzrjUZcuxMezrR9Le7McUieBesTZvL0RsOopH0+XBlaNeciOX33c6Ch79D35rX8eKXHk2VOWxmCzU2R2pi8Kqy8Yc45pL6J3+Fo/swUXcFLVfdkrZPUdXUE2m2hLQnY5aDrLr7Q1iDw9Q98390XpIMFha6S7GbLYQScQ77hnLmMupo34O7ZQeq2ULXSIdGSp+S5afcSCiCSe2ntCrzEM6SMjsVpT0caztMeW3+5uZkQjCZOaOsijPK8rcOVdU4unMnDvUVQsN2aG6Y1utb33gzrrbd+OavYYGrlL3D/RzxD3HxiLlZPnj1tl9z6G2fJVzZNOmxOwaTIwyWe8YKl3NFzFNJ7/qrcR99BXGwk8C8lWn7a2xO3ti4mIfb9vOHlr0s9pSfkndSIdE0jWMhP9sHunh1oJuhaIQam5P3LV5NrX1mg3CtkkR02JCcxxOMYxKnVrLzVJZismyi+8gh+l85gOfAUWobbFTVl9B7bJiOtgi+SA1i5fk0z2+a0dqKHT2jkgVMcojmR34IjGRTTqjBj+pTam3OaQsIW9/wURSzlcpX/0XVS39N2zd6IyxU+ceQiLPooa8BcPjaT6OcZBPfFQ4QV1Ukk5kKMTc104TNxZG3fAqAxQ/emeq2MhmMqeBt9IKeC2q3/B6AvrVXpqY7j+pTsu2f4usfQjQHKCkfX5TcsLAMc+wwvoHClwTzTcf+Q4jyNlz2CEp8+gZ4gysv4qm799H++g/RNJIBbC9Ai3KwYRnqJDf3QCzKQW8yo7J2ZHJ4vth5y8944idH6V97Rcb9l9bNp9npQVYSPHBoV9FZKEyFLT3tfOWVp/nWzud4ovMoQ9Hk96k7HODbO7fwdNfMHHkFSSShWImEY/gDIDqmnmF3lrhYfNY66tZdQ0jaxGv7S3j+qX5eO+BGtm+i+ZyrmLd8cZony1xCD1SyQP1Tv8YaGCRUPZ+ejdem7esaaSGedtkHCNcsSOk+Vvz8tjQjuFGdytECBSp1m+/H3ttC1F1J2xvGpqpPLPvkssOj5apbiLorcHQfpv7JX6W2rxkp/+wc7M3ZxfLQ2z/HC//1Dw699Q4AYoqS8t/IdsdPaHgIl1NJ06acTGWtmzJ3gN7W3BnAFSO97V0kBrayZJmIp9wNiRm07RsMKfF6g8ONARiKRvJSWrX6B7B6p16ye3WwGxWNRoc7786+cWfpuCJ/SGrE3rNoNRajkQO+gVk3ZXlzdysPHtlNvxzGYjSyuqyaG5es5c51l7CipJKEpvKHlr38eO/L0xYNCzaJhGZluD9ILC5gc07/nmBz2mlauZQF512NNP9NNG+4iqaVS+ZsgDKKHqhkgY7L3sfOm37C/n//Opop/UbSFU6WP2pnEKgAHHrbZ5FLa7D3HKH5L99LbR8VarYHfAVx0exdfzUH3/EFDvzbl1CEsRmT9kDyht3ozI3h2SiK5ODwdUk/lcW/+wqGeLL9b5G7DMlkJhCP5qw7SjOZ6V/3erxLzwGSwZmiabitAmVZTnkngp14yiae32E0GKiosaGFc5dFKjZ8A8N4j26huVGmeUkVVsECSmjG5zNGIyx++jds6m8FyMvcqPkPf5fL31/Pwt9/Y0rHb+9Pdnety3M25UQM8Rile5/LuK9SsnPNvKVAcvjkqEav2NnW38nvjyZNJDfVLeDr6y/nxqXrWF1eQ6lo48PLzuJt81dgMRrZ5+3nmzuem1awItgkEoqVoV4fMUXC5p7ZPQHAbDFTUVeNVZyenGC2ogcqWUC1irRf8UG6LnjHmH3doxmVqXT8ZECxOVMzbhb/7qsIQ8mbUIVow2G2ktBUjuW5OwEg7irjwLu/nDGbAscv8PNyJaQ9gdYrP4JcWoOtry0laDYbjZyRQ/O3TGMORttFF7hKs5pFioTCmNR+PBOUfUaxOyWMqi9nTrXFRCQUoXvv89RX9LFsTVKTYhUtoIRn5OoJsOSB/2L1D97Pf7zwOwDaclz+McRjNDz2c4yJOKHayUcKDEUjHA0MY6BwgYo56OXy9zdw7mcvQhzszHjMBTVNLHKXEVMV/t5+MM8rnD57hvr49aGdQNKQ8E3zlowRfxsMBi6saeJTZ55PhWgnEI/yQu/UhdsmkxHMTvzDARSDE9E2twYH5hI9UDkVNC2t2+RkIok4wyP1zZmUfkY5dvF7GF68AXMkmHJjNRgMx8s/+RxQqGljTNZOJqYoKdfcXFnIn4gqSBx662cBWPS7r2IcecpZMxKo7BzsyWr5x7P/BTbdUM+i330tbXuuDLh8/cOI5mDGbp+TcbhEzKYIYX+RuhZnka5DhymzHWXVOfNS3RNW0YzZGCd28qjaKdL+ug8AsGbfszR4e2jPcUal/unfIHp7kUtr6Dn7TZMe/8pINmWhqxS3UJgbXcLhIVi3BIOqppxqT8ZoMKSyKjuHeoic4NZdbBz1D/HzA9tRNY2zKmonNSSssTl5XX1y6vbL/Z3Ta8U2u1AVFayVp7rs0wo9UDkFync+wSU3LUvTRpzI6M3aYxVPbQS70cjuD/+QweUX0PqGj6Y2jxqK5cPYbJTa537HuXdcRNnup8c95ljIh4qGyyLgsebnYtp+xQfp2XANu275WWoY3hJPOaLJjDcmZ9Uxc/5fv4fgH8DedSi1LaYotI68x2J3druMgkODuFwKVuvkTXp2p4BgjhIOzu1ARQ7LKL59NDa70nQ7omTBaIwTn2GgEqpbTN+aKzBqGu/b/lfaAt7ceYIoCgv/kMyWHrnmk2hTuEZsH0gGKmsrClf2AVImefMe/XHayJATaXS4qZYcxFWVVweKrxypaRq7B3v58d6XiasqK0oqec/CqRkSnllWjcVopDcSmlZG2yS4SKgSQh4yzXMJPVA5BRqe/CWOrkOUHHgh4/6uaTjSToZv0Xqe/8YzeJdsSG1bOtKaeNg3lB+diqqy6LdfoWzvs5TueWbcw9pOmO+TL6t01SLw8uf+TN+6K1NdVxajiZWlySeXl/qz4+sg9ndQM9Ltc/RNt6a2twSGSWgqHquY9S4nJdSJp3RqAZ/JZMTuAHmOByo9R1txi33UL0gPCq2iBZMhQSw6cxHs6O/1XTsexebtYzA6/S6iqVD7/O9xdB0i5iyl7fUfnvT4nnCQYyE/RoMhJRYvFN3nvw3ZU4U01EXN1j9mPMZgMLChsh6gYL4qhngMZ+tuap/9LYsf+C/WffPtLP/Ff9LdeYTvv/YCP9m/jYiSYL6rhPcvWYtpAqHwiUhmCytLku33L/dnLn9lwiJKyKob0ZG9kSKnA3qgMkNMcihld91xyfUZjzmxNTnbGJQEtTYnLotATFXyUv6peeFPuNr3ELe5aLnqY+MeN5q9yEfZZzxM0TAA51Y1AvByXydyInHK5236+90YVYWBMy7GP391avtoVmuRuyyrwVk4EMKsDlBSPvXvkMNuIB6a3S3KA129HNz2akatSTwaJzqwl/p5NizmdLMzq2DGZIgTj85co9O/9gqGlpyDlIjyHy/+PjfzazSNhQ99HYCjV398THt/JkazKUs95Vk3E5wuqkVIBVej1gyZWF9Rh4Fkd2IhRLVlr23m4o+tYt2338mSB79M7ZaHWPDn/+a629ayZsvvsBgMXF63gI8uP3vaxnnrK+qApLh5qqVlqyQRjnpm1PFzOqMHKjOk6qW/YpZDhKqa07IcJ5LKqMxQSJsJUzjAsvs+zYW3rsWoJFhaksyq7Pf2Z+09MqJpLPrtVwBoufrjJCZIXbbnUUg7BlVlwR++yaYb6nF07GOhq5RKyU5UVdg2MPUnn0yY5BDz/vkTAI6+6RNp+3I1d8U/OIxgDlJSMfU2VLtTglj+yoG5wNfbhyi/TMe+sULMnpY2XEI3DQvHmp0ZDQYEEeLyKcybMRg4+K47AXj3jn/g7Tw0yQumj6PzAPaeIyQkB61vvHnS4zVNS3X7nFXAbp8TaXv9f6CazJTu24L7yCsZj3ELYirzm++syq7BHr40PIRfsLO9dikPrnod37zovewvn0dZxP//27vvODnu+uDjnyk7M9v3ej/p1C1ZcpGbXDE42GBCeQgJxhSDqTGhmBBMINTHMQFCQksIeWhJIAZCSXDAYGxjYyN3y7asXu90vW3fnZndneePvTvpdG3vbvd2V/69Xy+9Xvbu7N3cSLf7nd/vW/jiL/+Rf1KzvHL1JnRl8b1Pz6ppwKO6iNrmVKfghQTqalB8HXhDIlBZDBGoLFHbgz8AoPeqN8wYjDdpMcMICyXh0HHvdwkcf45Vd3+Dsya6ru4Nl/aDqeXh/yJ49Bkybt+0LY/TJTM2w+n8akZZVlRkmdq9f0CLj7Pl/30ACbhsYlXloYHuZeUbtN/3b2jxcRLNaxm84Pqpx9OZzNR2V7ETaWOjI4SCuYLyUyZ5AgYKcdLJ6h0Ol0mPE3BHyI49ynDvyaqtjJ0lMbiP1nYNw5g9p0PTwF5m/5Phc/+I4+su5JGOrYwuosdJoeLtm/jt/zvOE7f9JN+bZAE9iSjD6QQuWWZrbXk6UZ/OrG2Z6sjc8NSv5zxucvvnseHekjeA8x97jvO/cANmKs6dh3ezX/Oy7f138to3fZFPXv9Bfvyit/DxD/2QR278LP07/g/pC1+x5O+lyjLn1+e34J4ocPvHG/Cx6ZILF2ydL0wnWugvgSs6SuOTvwKg96obZz0mZpnEbQsJaC5gWbdQGU+A/W/4NNv++T1s/M9Pc/blf4oE9CaiRK00gRIkr8q2yVnfy88vOvzqv5z3jXVyNaXe8JRtefr5t/09DU/dne/o+/hdXHzetfzi+H56E1GOx8NTs5IWxXHouusrABx55fvhlGXiw7Exco5Dre6mrsj5KblkH8GWxf2d+gIGLnmEVCxelSWQuZwD5ggtGxrxjic5cvhRfKE/wu31MNR9Ap96glXr587RMAyJzPgyG7VJEvd9/H/49N4n0WSF1zq5ok4Ah3yJ/6nDS+fz5MQH4dk1TSWfmbQYB17/SQ6/9iPzDlHcWtuEW1EZN1McjIyWbKRFy0M/5twv34RqJvHoPmLbX02D28ctWy7C79Knbe0Mb7mYYceZusmUshlc0VGsmsWNfbigoY2HBrp5ZnSAP11z9orNXXqhEWHdErT84SfI2QyRrnOJd5w16zGT2z71hqfo/3i7X/p2Yh1nocVGOe9nf0/7xDDAUq2qtD3wA7yDR0nXtnD4NX8577HHJxu9lTGrPdm6jiOvym/NbPl/H8SPM3Xn8/BA99K+qCTxxEd/ytHr30vPi2+a9lSptn3y+Smj1BRQlnwqj09Hc5mkYtWZUGumUqhSAm/A4KzzO2j099D9zJNk7AzRvn20tkp4fHM3utJ0BTKxZZ9HbU0ThqJi5U6W2xdD4Ogzizo+nclM5adsL3O1z+kS7RsXnPSsKcpUq//HSrT90/yHn3LB5/8U1UzSu+3FfGLLNQC8puss6uZ6D54IUtzD3Vz60au4+DMvn2oYWagufw21upt0NsPu8cFl/xzC7ESgsgSxzi30XvF6uv/obXMeU4ptn0mOorLnpi8A0PWLL3N5Ln/3uG+8NHkqPS9+C0998N/Z/fYvkzXmz5U42eitfIm0AAdf97FpHX0vb14F5BMSk0vs6RDvOIvd7/oq2dP+TksVqERGxtDV2KIDFVmS8PkkUonqDFSSkTguOYUv4EbTVLZc0IpP2s3+Rx7Bw3E6188/iM+1zO60k2RJosMXpCE+xtnfuhVjeHFTmWcTOvAYV73/XHZ87MXz9mCalHMcvnfgaSKWSVDT2VyzckMIF0uLDCNlZ09Yn9z+2TU6UJSk9lMFDz3J+V/KDyQ9/tJ38Oevv50Rw8emUD1n1yzcr8SRFXy9+wgdfoqNP/jEor63LElcMBE8Pj60vBw4YW4iUFmC8c2X8dSH/5Njp00MPlVfCSt+AIYueDnD57wEJWPx9p9/HhyHfZGR0uwByzK9V7+R/stft+Ch3afM+Cmn0zv6brSStHr82Lncou/q/Meem/O5ZMaemu+zvgT9U4LB3IzKlkL4fAqZZGlGB5RaKh5H02w83vzWYajGw4azfHjZR3NThkBo/u01TXdBJpHfQlqmVb4QX/zlP3L5b7/F+v+6Y3lfLJtl83fyQzSTjaumbR/O5efH9rJ7fAiXLPP2TdtxyZW5tbD523/JNW/roPmRn8/6/Gp/iAbDi5XLFnVQqDHay4X/95UoVorB7S/jxzd8hmcio8hICzZum5Sua+OZ9/4/ANb99PPz9oiazQUT1T97wsPEF7kiIxRGBCol0l+Cip9pJInd7/gyOdVF5/6drIsOEbcteovYTl8bH0RJF35nOm6miFgmEtDhW9oo9GKa7Ogr2Rah489yWXM+qfbhRSTVNj75K170vm1s++rbZ+3IezgyhgM0Gl5CRe4Umkv0UlO3tJlBHr8B5khRPqxXWjoex3fawt2qdQ2cvc1gw7a2BV+vu10osk3GWv6HxipfkH+6JB+gd97z/+YNWhey9ud/T93zD5IxvBz8s79Z8PiHB7q5vy8/YPLGdecsLbdqhWQ1N4ptzlmqXJKeKo7D9s//Ge6xPqKdW3j8L3/AT7r3A3BFy6pFrWYP7HgNx1/6diTH4bx/eBOueOHl/S0eP+3eADnHqcjGdmcCEagsUtcvvoKve8+8xziOQ3+idFs/k+KdW3j6g//O7772PO6ubQDsLWKZ8tZ/uYUXv3s9DU//pqDjD01sgXT4gksq9ys6WebpD/4bj/zfexna/jIubGhDkxUGUnEOFzCoUI2H8wEKkHX7Z63uOrV/SjElonFUZ7Sg+T6z8QYMVCmBmSpNs7JSyqbH8AVm/vvpXFs/b27KJN3IByrFmHe0yh/i0c6t3Lv2IuSMzQWfey1qcvE3A4HDT7Pp+x8HYPfbv0yyec28x+8Pj/CjI7sBeHnHhorLTTnd8ZflS5Xrnn+Qumfvn/WYixrzPVUORccYShVhW1KS2P32fyDSdQ6P/c1dPBAN05+M41FdvKxj4blJp3v+5n8g3rIO98gJtn39XQuOCjnVZE+VpyZyic4k7uFu3LHiV74thghUFsHXs5ez//X9XPX+c+aNuMfNFGYuiyJJNC6Q07FcfVf8GemGDjZNlikXKU+lZs/DtP7hJ+jhQdK1hb1JHozkP/zXB4r7ob0cibYNjG2+HMh3k7wsEAIKS6rd8q0P4h7rI966nn1vun3WYyb7J2wIFTlQicTQ1QShuqX9+/GH3LiUFMlIdeWp5HIO2KN4fEufPq0bLhRsrNTyy7NDmoHfpfOXL38/sbo2fH0HOffLb13Uh5hiJjn/SzciZ2z6L3kNPfPktgEMJGN8e/9T5ByH7fWtXNexbrk/Rsml69o4ft27Adjy7Q9BbmajvhrdzZaJnJF7e48U5ftG1l/Ig//4NCO1rfzvxPDD6zs3LKniMOv28dRf/oCcotL68I8XtQU02QH7eDxMdpafvVoYwz1cetsVuAeOTj226lffYP3un5XxrESgsihtD+R7pwydfx22b+5l2Mn8lCa3r+CWzMt1VqiBK44+Rfsz9yw7WU1JJ9j2z/k3ne5rbia26uyCXncwWpqhfMXiP/osX/ni6/iT537L06P9jE30e5lN4+P/S+e938WRJHa97ztk9Zl5EXHbmvq7LnZwlo7HMfTF9U85ldutoWs2qSprpZ9OJFHJV/wslW6oyLKFtYzutJMkSWKVL8i4J8i33v41cqqLlp0/Zc3Pv1Tw19j0vdvw9+wlXdvCs+/95px9lyCfmP0Pz+0kmbFZ7Q9x4/ptKzaGYrkO3PBJbE+A4JGnab//32c95pqJYX6PDfUSsZYWSDY+fheBw0+ffECSuLvnIMmMTYvHN7XFuxSR9Rdy/Np3MbZpB7lFtHqoN7y4FRU7l5va9q82xsgJLv3Y1dTteQgjfLJ30UhtKyfsUTJlDMBEoFIox6Htwf8EoPfKN8x7aKkTaWez7em7+fcffYLP/+8/0tN/eOlfyHHY9vV3Eji+m3SoiX03fragl42bKUbSSSRgTaAy99KbH/05/tET3PHrr3HJ0af59YlDM46RbZO1P/0C53/xBgCOvOpWxjdfNuvXm6z2afH48GsLb0ksRioeY7njQPw+SMer600zGYvjktP4gksPVBRFxuWCjFmchner/CEAHqpfxe63fxmAzt9+u+BS1t6rbiTeso5d7/8uVmDuhOudg9187flH80GKL8Q7z7qgYpNnZ2MF6jn4uo8BsOk/PjY1xuJUawO1rPHXkHFy/K7v6IznF+Lrfp7tX3g9l3/kUoIHnwDyM5AeHDgOwP/p2rzsfjd7bv57Hv67hxnftKPg18iSNFVAMNn8sZoYIye49K9fhHfgMImmLszQyX4yX9pwOZ8+6xz+6fGl52ctlwhUCuQ/9hzegcNkNYPBi/543mNLnkg7i8ELrqe3sYvGxDgXfvevlvx1Vv/v12h/4AfkZIUnP/LjghsgHTwlP8W9nEnRJXTwTz9O7xV/hiub4fs//Di3/v3rqfvFV6dt48m2ydqffQFXKsbYWZfNG6idLEsuQQMrcwSPf3nBj8fnIpuursqfdCyOrtu43ctrFqjrEnYRkmnh5CiI4/EIx1/2bp5719d46As7cebbXnCcqVLd8MaL+d3X98zZ3C3nOPz82F5+cOg5co7D+fWt/MXZl+B3FTf4XQlH//h9JBtXoSaj01c9TjG5qvLQQPeiWgWo8TAX3v5q1HSCsbMum5q19bNje8g5DmfXNE6161+OnEufd9VrLqv8+ZYMJZkNVULGaC87Pnb1VJCy829/N5VD1R2PTA1dfPn6VWU7RxGoFKj50Z8DMHzuSxccINY/0RyqlIm0p8tpBve8/cvkkLji8f9hww8+uah9dMj3eNjyrVsB2PPWLzK25YqCX1uqXiJFJcvsev936b3i9WQlmQt693Lpv76PP3pLC2v++x+AfOff3W//Mk+//zs8fMeD5PTZcyWyTo494/kEs2JvdWWzOciM4/Uvr4rI4zfAqq7Kn3Qijr8IjZwN3cEu0orK5CiI4XSCRMbm2PW3kPHkq9qUZIwt33zftGqg4MHH2fHxF08NHQRw5gjerWyWb+17cipn42Ud67lpw7lV2+E0pxk8+Vc/4r5/OTTnSuSWmkZaPD7S2QwPTayELPyFc5z/pRvx9R8i2biKpz58Z76f1PgQe8aHUSSJ13TN3nxzqVyxMTb856fxH322oONPBrThop5HKU0GKfnrupqdt99PqiG/deY4Dj8/mi8c2ai3sa42VLbzFIFKgVom+gMMXPLqeY/LOjkGyxCoAHguuI47rs4n6m288zNs+vePLSpYia06m94rb6D3itdz9JXvX9T3PjRRRbOukgMV8m+kT334P/nuV3fzf69+G/saVqPYJvHWk1UCfVfdwImX3ATz5Bc9MniCUTOFV3WxscgrKqlYAk1JLTtQ8fndqHKSVHzlp9YuVTY9hse3/IoxTVdwMnPnIC2G16XR5M4nNe87rftz++/+gzV3fZUXvW8bl33kci6447Vc+aGLqH/ud6z5xVeQ58nDMLMZ/mXv4zw7Nogqybxlw7m8vHND1eSkzCW84SKs0NyN1mRJ4iVt+VWV3/Udw84t3Phuw52fpumJX5LVDJ746E+xAvVkczl+dnQvAFe2rKaxiKNKALZ861Y2/uenWP/j2RPpTze5RdifjGHO0fiuojgO53/hBnx9B0k2ruIPt99PqvHkqsnu8SEORsdQJZkd3o1lPFERqBTEFR/HM3gER5YZXGCI1XAqScbJockKtXPcjZeKW3Xx5Mtv4TMvfgcA6//rjnyDqYWClYnns7qHXR/4Hk9/4HuLWvocm8hPkZFYW8CAtUrQ2HkWO699N9e97Wt8+IN3MnzetQW/1sxm+OVEhcF1HeuLPnsln6eRWlaeBoC/xo1LTpOsklb6uZwD1vIqfia5dFdR2uhP2jYxCPD0ZmWRNefRd+lryckKtXsfpmXnT3EkiZ6r38SD//DknAmZqYzNP+15jAORUXRZ4c+3XDTVOOxM0vD0b2Zt53BBfSs1mkHMNhfsq9Jxz7fZeOdnAHj2lm8SWXs+AA8NdjOQiuNVXVy3hHLkhRx+9YcAaH34x3hP7F/w+KBmENIMHKAnXrx+VqV09I/fR3TV2ez87L2kmlZPPZ7N5fj5sXwQeHVrF35lZT/LTicClQLYvhp+/W9DPPR3f8AKzr8H2n9KIq1chjujq1u7+PaFr+KTf/QeAMxgw5xBh5TNsOpX3+DSv34R0uResSTNv/c+i8ltn05fsKIGpi3k5Z0bAPgvzcfAIqbt3t93lKhtUm94plrzF9NyK34m6bqK28iQrpJAJZ1IosrJZVX8TNINF2SLs6ICcG5dPlDZMz6MdUrr+/CmS3jytv/it9/qZt8bPsPRl/85D/7j0+z64L9Nuzs9VTJj8/XnH+NIdBxDUblly8UVWym3HOt+fAeXfPJaLrzjNWinTaBWZJkXt+XzIO7tPTJ3R23Hof7ZewE49Jq/5MTVbwIgYVtTNwvXd27EU4K8uNjqrQxc9Eokx2HdTz5X0GsmtwmrYvtHkui/7E944CvPkmxZO+2phwe7GUol8Lk0/qh97RxfYOWIQKVAjksjvPHiBY/rK+GMn0Ks9odYE6jhe+dfz//9q59y+LUfmXpu/Y9up+M338Lbe4DGx/+Xq963jW3//B7qnn9wznLCQkw2eqv0bZ/TdfqCbK1twgHu7jlY0GuilslvT+Srql7RuRG1BOXnxaj4meTzQTJWHZU/xaj4maQZKrKTJmMXZwm+wxekVndj5bKzNlU061o5+Pq/Yfe7vz7vkL64bfHV3Y9wPB7Go7r4i7MvoatCq+SW68SL30yyoRNf7wEu+eRLZ/Se2tHUgUd1MZJOzt1WX5KmVnn33vR5IJ98/JOjeybKkf1c2txRsp/h4J/mq5ja7/933IPHFjx+cvun4hNqTy01Pu1GNpWx+VV3/v3wZR3rK6I4QgQqC5By2UXleZSj4ud0L27N36n8wOWduvuTrTTr7/wM537t7bz4PRu5+LOvwN+zF8tfx3Pv/MrUncpSTDY9Wx+sjm2fU718Ysn4yZE+ThRQVnh3z0HMXJZOX5DzJiYyL1bGzhIemqcapwgVP5O8Pg3M0kzVLrZ0LI6uLb/iB/IrKrJsYxah6Rvk+6mcM7Gq8szowAJHz85xHP7twC5OJKL4XBrvO/uSqTvwM1G6ro1HPvtb0qEmgkef4aJPvxzllI60uqJyVctqAH5yZA/j5skuyrV7Hpr6MHUUlRMvfnN+tddx+PGR3Tw+3IsE/EkRypHnE95wEcPnXIOcy7L2Z19c8PjJhNruCl5RcUVHufrPN7Hql/806xDJe04cJp6xaHJ7uaxp6T1pikkEKgvY9PQPePG71tP1P19e8NhsLsexWP6uoVwrKgBba5uoNzwkM/bUAD7ZSnPkNX/J6OYryKoaWVXj0Gv+knv/5RDHXvEXc1YlLGQ0nWTMTOXzUwLVF6i0+4KcW9eMA3x59yM8MTz3BNShVHyqo+2rV5+15K29kd5+up95mHRy5odosSp+JnkDbsiM5b9uhUsnZs74WSrd7UKRbOwitNGfNBmo7B4bXFLzqz3jw+wN5ytU/mLLJbR5yz8Pq9QSret55DP3YPlqqN3/CBfe/qppCcYvbl1Di8dP1Db5xp7HsRIx1vzsi1z60Ss556s3T5sw7UyspDw00I0EvHH9OWwIlaA1wGkOTaxKt9//b9MCrdlMBp6jZoqYXfh28kra8MPP4Os7yKq7/wWH6e9hJ+KRqflSr1p91oo1LF1IZZxFBVu3++d4Bw6jxUYXPPaJkT4ilonfpbOmjAPEZEmaulO5v+8oOcch4wux702384fPPcjdd0a4+84Ie9/6BTLLnHI8We3T6S/9fJ/oaIQT+5fRzG4Or1uzhTX+GtLZDN87sIt/O7CL1Cz9HX5xfD85HLbUNC4rp8BKpfDqY0RGZq6qpGIJXPLyK34m+YJuNCVFKlb5lT/Z9Chef3GWmXW3C1W2sczifVh0+WsIuHRS2czUjKdCZXM5fnYsn1T6opYuWsu44rrSYqu38uin7ibj9tHw7H2cdUqfJ0NVefdZF9CSzfDqe7/Fi9+xii3f+TCS45BzGVPbEo7j8LNje3mg/xgAb1i3jYsmhhyW2si2FxNZvY2BS16Dmpp/G9WtuqYqxLpjldf4zdt7gNW//CcA9rzt76dN8A6baf5l7xNknBybaxo4u2buyq2VVj2Zj2UgpVN07bsbWLgsOec43DPR6fTFrV1l74NwSWMHv+w+wFA6wZ7xIc6uPdm4bTGtoRcyte1T4vk+uZxD376nkaxhMmtWo7qKd30DmsH7tl7Cb3oOc3fPQR4f7uVIdIw/al9HzDYZS6cYNZMciIwiAa9ctWlZ389Op/GoY8RHR2nqnD5H6WTFT3HuFH1BA5c8RCIawxeq3A/HqYqfIgVoLlVBkXNkihioyJLEtromHhroZtfoAJsX8Ub+0MBxBieSE6+tgtk9xRbecBGP/c1dXPyp64i3n/z98Qwc4cJv3crrn70PfSIIGK5ppe/PPk73de8CWSZmm9xz4vDUnf7r127lkqbS5aXMIMs8+A9PTftQn88qX4jBVILj8TBbaivnwx5g83f/CjmbYeDCVzBy7jVTj0+WyoetNM1uH2/ZcF5FlcmLQGUegcfvxWUnSdV3EFlz3rzHPjs2wGAqgVtRlzVrolgMVeXS5k7u7T3CfX1HpwUqxXRoKj+ltIHKSO8A7twhcqpMIhIlWF/cFStFknlZ53o2hur43oFdjJop7jw8s2X0pU2dy74bzlgpVClFJt4DbJ32XDoex2Msv+JnkktV8PtyjIyPzwiKKkkqnkCVk/j8M2cqLZVhQCRd3OX3c+paeGigm+dGB8muzRWUH5GwLX45kax9fceGikhOLIfRs6/ioc/vnFY52bDrHpof/W8Ahls3cMd5r+B/zrqSqzo2kDu+jwORUXoTJ0t9X7dmS3neXxdx49npD/HYcG/FVf7U7NtJ86P/TU5W2PvWL0w9nnMcvrv/6ancqXdtvrAkVVTLURFbP1//+tdZvXo1hmFw8cUX89hjj5X7lAAIPfBzAAYuftW8fUUcx+E3PfktiataVlfMG9FVLauRkTgYGS0oUXSxpvJTJKmk832y2Ryjx56nqdHEUFMkwqVbUl0TqOW2c6/gqpbVbAjWcUljOy/v2MCb1p/DB7fu4HVrtyz/m2Ri6G4dNTdCIjp9z7uYFT+TgjUqmcRgcb9okU1V/ISKt9qnaZBZRNl5IdYHavGoLuIZiyPRuSeon+rkwDw/O0pYoVINomvOJV13sl9MpOtcDr7ur3nsY//NI/+0l+z1f05GUbm37wj39x2dClJaPH7euP4crpzY0i6XwNFnaP39D+c9ZiqhNhbBWWR38FKaHKTZ85Kbpq1q/ezoXnaPD6FKMu/cdAH1RvFuFoql7CsqP/zhD7n11lv5xje+wcUXX8w//uM/cu2117J//34aG8u4bJbNEnrwf4CFt332hUfoSUTQZIWrWrtW4OQKU6O7Obe+hadG+vjf7gO886wLirqcN9k/ZZWvtPkpwz19eKUjrNvSwr5d/QxES7v361Zd/MmaIgQkc8nEqWupIZZKEBkZwxs4JTIxR/G0FnfGS6DGh3RkENu0843QKlA6FsfQMhhG8c7PcMtkY8XrpQL5/h/bapt4ZOgEu0b7F1xJHJw2MO+sklaolFJ4aIy+g/vYcNHFRd12DW+8eFrbh5e0rSGRsdk9NsiaQA0bgvWsD9YRKPLQz6Wo2beTy//qUmxPgMGL/njWieoAbV4/iiQRz1iMmqmK+ODXxwdofuRnABx95QemHn9o4Di/689vqb1pwzkVWypf9t+aL33pS7zjHe/grW99K5s3b+Yb3/gGHo+Hb3/722U9r70//TWu8WESRoD/qmnnvt4j3Nd7hOfGBmdEyb+ZyE25tKkT3yKbpZXate3rUCSJ3eNDPLJAB8jFmuwnUcr+KdlsjvHjz9HWmiMQ8hAIaWSTQwu/sEJl7CySk8Tj1akJ5YiNnuzJka/4GcPjK96qAkCo3ouuJIiXcCVquVLxOL4ip9C4NBWyxU8iPrVMec5GZRN+fmxvUQfmlYOVNunfuxM9e7Dk4xgkSeJVqzfxsfOv4oZ129je0FoRQQrA+IaLSTSvwZWM0vLQj+Y8ziUrUxVdlVKmbNY08/u/f5w9b/k7YqvOBuBEIspPjuQTvF/RuYHz6yt3a7isgYplWTz55JNcc83JpB5ZlrnmmmvYuXPnrK8xTZNoNDrtTyk8PO7w/XOv4z/Pvpqf9BziZ8f28rNje/nm3if4h+f+MNXQ50h0jEPRMRRJ4sVtlbOaMqnV6+f6zvychp8ceZ6RdHHuMGO2ybOj+e2Ecyfai5fC4NFu/Opxus7Kfw9f0IOUGcE2C5+6WkmsdBpFtjA8GqE6DyRPTA0NnKz48RWhM+upfH4Dt54mNlbYVkU5OOZovudLEWm6CzLF78q7MVSPoahELHPePISdg93sHh9CliRevbq4A/NWSi7ncPTZZ6hzH8Ylp6pqblTRyTLdL82PJ1n162/Oe+jUgMIKavwWXXseh1+br7gysxm+u/8pMk6Os2saeWl7ZSd4lzVQGRkZIZvN0tQ0PdGzqamJgYHZmyrdcccdBIPBqT8dHaXZ822+5Dzufsff8uOXf5ILGlq5oKGV8+tb0GSFo7EwX3z2Yf7j4DP870Qb54sa26lZ4dk+hXpJ2xrWBmoxc1n+/cCuBe8CC/Ho4AkyTo5OX5DOiW6MxZaxM0R7d9PWLuGbqAYJ1HjQlCSJSHXM0jidlUqjSja620VdUwBNGiM+nl/pKNaMn9mEamRS0YVL7Mshm80VteJnkma4IJcqeg8Zl6ywZaLiZ+dAz6x5CM+PD3Hnod0AXNe+niZPkROPVsjAkeO4zWfYfF4TupbFSqUWftEZrOclN5FTVGr37Zw2Mft0Ux1qS5AbuGjZmUMff3J0D4OpBEFN58b151RUhc9syr71s1gf/ehHiUQiU396enpK8n1esa2V9190Hi8NnMdbNuT/vHXj+Xzi/Bdx0cTwsEeHTkyVrF7TVv55CHORJYk3rT8HQ1E5Ehvnt73L60WScxweHsw3PivFrJtJ/YePEdC6WbP5ZAdYX9BAd6UrehtjPmbaRJFtDLdGsNaDR08SHckHEOl4HLe7eBU/pwrUeCDVN7V6U0miI2NoaoxgXZG6vU2Y6qVS5MofgAsn3gN2DvXwbwd3TZv/0x2P8O19T5HD4aKGNq6r0nLk2HiU+IlH6Vor09gSxO0GM1XcnJ9qY9Y054srgM7f/Oucx62aaPzWE4+QdcrYbDGX48pbt7Ptq29HG8+vgD810sfOwR4k4M3rz624dIXZlDVQqa+vR1EUBgenVyQMDg7S3Dz7doKu6wQCgWl/VlJQN3jThnO5ddulU8t7FzW20+gu7ptssdUZHl7btRmAX3YfoGcZkf7+8Agj6SRuReX8JbaRX0g2myMx8Dztndq0luqyJBEMQrJEW36lZqfTuFw5VFVGliRqaiUSY/mcm1Q8jtdTmkAiVOdDU6IkIpU39yc8NIzfnSRUU9ykQ91QkWUbuwSByuaaBv6kazMyEk8M9/GPz/1haor4N/Y8jpXLsjFYzw3rtlX83epsMnaGE7sfo7V+mHVb8r/jXq9MJll5/35WWve17wTy838Uc/bArdHtw1BUrFyWwWT5hoI2PnU3waPP0Prwj8nqHkbTSe48lF8Jemn7uhXp7FsMZQ1UNE1j+/bt3HvvvVOP5XI57r33Xnbs2FHGM1tYl7+GW7ddykfOvYIb1m5d+AUV4OLGdrbVNpGdmDli52YuCRbioYkqhosa20tW7ZOKJdDkMI2toRnP+QI6Trqyy23nYpsm+im5gaF6H5gnyNhZMEfwFmnGz+mCtfkts0rMU7HC3TQ0FX+7S3drKFJxu9NOkiSJq1q7eO/ZF+NTNXoSUb7wzEP80/OPErNNWj1+bt50fkmGVpZaIhrn8FO7qNH2s3l7O4qS/xl0twbZcHlPrgIMn3MNycbV5Fw6vhP7Zj1GlqSpdvpHYuX7nev6RX70S/cfvR3L7eV7B54mlc3Q5Q/xsok5Z9Wg7L9Ft956K//6r//K9773Pfbu3ct73vMeEokEb33rW8t9aguSJYl2b6Bi5iEsRJIkbli3Db9LZyAV59c9hxb9NcbNFLsnVgBK2XgplUigyim8sySWBmo8yJlRrCLOcVkpVjqFccqPVNvgx1AiRIZH8xU//tLkOSmKTCjokAhXVqASD8dQnUHqmou/MurSFBQ5U5Ktn0nrg3V8+JzLaPcGiNsWw+kkNZrBezZfVDH9lAqRyzmM9Q+z/9FH6X/6v6mVd7L53Lqp3DAAt1cHO1IVc6NKSpbZ+Zl7+O23e4isPX/OwyZnn022cVhpvu49ND79GxxZ5uj17+XJ4T6OxsIYispbNpxXNZ9bUAGByp/92Z/xxS9+kU984hOce+657Nq1i7vvvntGgq1QHD6Xxp+tzZen3dN7eFrXx0LsHOwhh8O6QG1JBy/m+2pk0fWZKzaBGg+6WqUJtZk4hvvkzxQIufG6TUZ6evINz4pc8XOqQI1BLtlfsq+/FOHBITyuGHWNxf+3JEsSukZR2+jPptbw8MGtl7KjqYM2b4B3b76IkF66v8dii4dj7H/4XiKHfkGr5zG2b4fLX7qW5rbQtOM8Ph1VMUknXth5KgDJ1nULDnLdMDEG42BktCyN37ru+goAAxe9ilRzF7+fWAl/Sdsa6iqgt8tilD1QAXjve9/L8ePHMU2TRx99lIsvvnjhFwlLdk5dM+fUNZNzHH5w6NmCq4CyTo4/rEASLYCZTOCdI+3H5zfQNbOkHWpLJhOb0XSttlbBjPaVrOJnUqDWg5Ibm3Vqc7kkxvqpq5emtheKTTck7CJ3p52Npii8Yd02bjv3iqobONh36ADNvj3suDzAxVevp2113ax/H26vjksyScdFoDIlm8U9cHTWp1b7Q7hkmZhtMbDA1OViUxMR2u//dwCOvvL99MQjHIuFUSSJS1dyTlKRVESgIqy813Vtwa2odMcj/K5v9l+00+0eGyJimfhcGtvqSrvilU2P4fXPnf8SDEAiGi7pORRbLudANoFuTM+yr2n049biGEa2JBU/U9+n3oemxImOVsb2j5U2ka0e6puDJfseuuaUfEWlmiVjCaT4PjrX1lLbMH8JteFx4VJtzBd4ifIk/7HnuOYdq7n041dDbuZ2mCrLU9s/B8KLm7a9XO33/zuqmSTauYXRLVdOraacU9dCoIhDaVeKCFReoIK6MdWE6q7u/QU1gptMot3R2IFLLt106FzOAXsMj2/ufA1/yA1VllBrpU0UyUI/rU18bZMfXYkWfcbP6QzDhc9jk4xUxkrU2MAIbjVMfUvpKvc0wwUZUakyl6HjPYS847R01i54rCxJGG4wk2JFBSDRsg41FcMzdJz65+6f9ZjJEQsHVjhPpf/S17LvDZ/h0GtvI5nN8MRwLwBXlHglvFREoPICtqOpg/WBWuxcjjsPPTfnPmo2l+PXPYfYFx5BAi4t8fRSM5VCJYlnngoYX8iNkhuvqG2MhVgpE0W2cHunByput0Zt0MRfwm2fSaEaFTNWGSMIIsMD1IRyRZ3vczqXXpo2+meCjJ3BHNlLa4cHVS3so8DrlbBS4noC5HQ3vVfcAEDHb2cf+XJqnkoxGm0Wyqxt4eDr/4beq9/Io0MnsHM5Wj1+1lboLJ+FiEDlBUySJF6/bhsuWWZ/ZIRfdO9n3Jy+rHsiHuGLzz7MXd37gXylT6mHbKWiiYnE0rlXVII1XrQKn19zOjOdQpHsfJnnac67Yi1rN5emJ82pArU+MPvz5dBllM3mIHGcusbS9h/SDQ0yiYpsdFduQ8dP4NUGaF9beC8Nw6OBVRlbh5Wg+4/eBkDLzp+izjJOocMXwFBUUtnMogsXiiHnOPy+P78SfkXzqqrs6QMiUFkRuZxTsSV9jW4vL+vYAMA9Jw7ziSfu44vPPMw9Jw5z1/H9fOHZhzmRiOJRXbx5/bn86ZqzS35OqXgcl2pheOa+0/Z4Ndy6TbKKKn/stIlLycxayeRSlZIllJ4qVOdBU+JTbfvLJTI0iqGMUd9SuvwUAM1QUWQLM1U9K28rIZdziPQdoKVZmtZQcSGGR4NMWAR+EyLrLiDauQXFStP2+ztnPK9IMusm81Qipc9T0aIjXPypl9Hyh5+A43AgMspwOoGhqFww0U25GolAZQUMdfey5/cPlvs05vSStjX86ZqzWeOvQQKOx8P8z/F9/PrEIXKOw7l1zXzsvKu4sLFtRSLydDKB15ffE59PICiRqqChXws5vdlbOfiCbjxamvh4ee+Kw8PD+L1pAqHSzseqbfDh1mKM91dXPlOpjQ0M4ZZ6aF+7uInOHp+OKqVF4DdJkui5Jr+q0nHvd2Y9ZCXzVNrv/S6NT93Nuv+6AySJ3/cfA+CixjYMtXSJ+qVWvWdeRVKxGK7cMFbaRDMqY2T5qWRJ4oqWVVzRsoqIlea50UGeGRsgYdv8UftazitRm/y5ZFIRvI0Lx9C+oAFzDK+sRHY6XfZAZXIEwfFIeQMVO9JNfVfpL4amqTQ2wOHBY7SuW13y71eIeDiGZuhoRvlmrIx2H6W9Nk3NIucrub06ihzDTCRxeytzCOtKO/GiN3LW9z5CzYHH8J3YR7x907TnJ9vUH4qMkc3lStdoLZdj1d3/AsDx697NuJniubF8gF7qdhKlJgKVFWClU+iuGMlovCIDlVMFNYPLW1ZxeUsZ/2GbI3gL6NAaCHlRnTCpRBK3t/IbGGXNBEao/IuY/ho3DJUvwIuHY6i5QepKWJZ8qqaOGrp7u4mHY/hC5e1xkkqkOP7U7/A2rmf12ZsWfkEJxMMxVPMgbdsWrvQ5ncerocom6aQoUZ5khRrZ/Y6vEFl7PvG2jTOeb/X48aouEhmb4/EwawKLv+6FqH/mXnz9h7A9AXqvvIGHB7pxgPUlbs65Esr/rvlCYEdwyUmSsfINp6oWVtpEJo67gJk3wToPLiVJPFwleSrZmc3eysHrN5CdGBk7U5bvPz4wiM+IL9i3o1gamgMEPFFGTvStyPebT/fzz1NnHMRMlK9kevDYMUK+cZraFh8oKoqMYYgS5dMdf/l7CG+8GGbZrpYlaUW2f1bf/Q0ATlz9ZlIuY2rC/RUtq0v2PVeKCFRWgh3DJaVJxUWgspDkRMWP17dwqa5huHAbVZRQm4ljLCJxsVTcXj1/V5woz11xMjxMbW3putGeTlFkmlp0zPHDZU0CHe4dQDOfxesD7PJsveVyDpnwQVo6/Eu+/l4vWCkRqMxpljLkyUClVHN/9NE+mh79bwCOX/cuHhnqIW5b1OrukjfnXAkiUCmxjJ1FchIoik0mWT2ltOWSSkwEKgVOEa6pUUhHx0p8VstnmzYy6XwDsjJzezUUySJdhrvijJ2F9AmC9SuzmjKpqaMWjYH88McysE2bkUNP0t5s0tRRD5lIWYImM5VGlRIEapa+Ver2qOTMcPFO6gyhj/ax7evvYsfHXzLjucl+Kkei40ueWj+f1Xd/AzmXZXTz5YQ7NnNv7xEgXyihSNX/MV/9P0GFM1MpFMnCH/KDNVzu06l46Xgcj5eC7/Z8QTek+yu+XNJMpVFkC8Nd/kDFMFy4FBuzDMPl4uNhNCW6Yts+k2rqvNT4k4ycOLGi33dS99791BjH2HBuW1krZ8xEElU28fiWnitneHTRS2UWjqLSfu93qX/ufoIHn5j2XJPbS8Clk3FyHI0W/9pF1p5PeN12jr7ifTw10s+YmcLn0riksfrm+sxGBColZqXSuGSLmoYAMvGSjpw/E1jJKL5FFCIEQl5UOUoqXtndMm3TRJXsighUADw+qSwzW6KjY/iMdD7AXGHNbT5y8cMrnpsTGRnHiexizQY/breWD1RksyyBYiqRRFUs3J6lb0G6fTqylMRKW0U8s+pnhRrpv+x1AKz+5denPSdJEhtCpctTGbjk1fz+7x+n79LXck/vYQBe1NKFppRu1MlKEoFKiZmpNLJsUdvowyWnSUZFnsq8rFE8BVT8TArUutGVJLGxyt5Wm/x3oFdKoGI42OmVD+5SkRFqauUFe+SUQnNnLR5lmJHeleupks3m6N/3NC31ETrX5XuWuD0aqmKRKkOgYqVSuA1nWflBJ3OcRJ7K6Y5dfwsAbb+/E1d0ekCyYSqhtkSN3ySJ58PD9CdjGIrKFeWs3CwyEaiUmJVKYej5LQpNNUlW+J1/OWXsDFI2sqhlaU1T8XlzJKOVHahM9lBZqQTShehlWL6fzE8J1Ja2bf5cPF6N+roskYGeFfuewz19eKVDbDynZSo4UxQZt+FglWFFK51I4FlmJb/Xr+MSJcqzGt94CZE156FYaTpPm/+zcSJP5VgsPGNUyVLV7X6ANT/7e9RE/v1vcjXlsuZOPGpl3BQVQ2W8a57B7HQatztfoub1OKRF5c+c8hU/KTz+xQ3nCwQV7ER5kiQLZZkmhlE5czbcXn3FEzrLlZ9yqqb2EHL6CKkVqniKj40RCtgEQtOjA48nHzSsOHsct3d5lWcuVUFz5TDLcf6VTpI4+vL8qsqqX/0zZE8mztYaHtYHanGAR4eKkyu1/ke3s+U7f8n6H93O4egYR6LjqJLM1S1dRfn6lUIEKiWWMRMY7vxl9vpk7ERl3/kvl5U2OfjkLhJL2OJKJRK4FDPfcXYRfCEPmAMVO08JIGOWvyvtqdxebcUTOsuZnzKpqSOEzwgzukI9VbKJfoK1M//iDY8L7PCKnMM0VjgfpC6Tx0NZVoSqQd9VN2D5avAOHqXxqbunPbejKZ/cunOwZ9nTlP3HnqNh1z04ssyx62/hnhP51ZSLGtsI6qWfxL6SRKBSapnoVKtsj98Au/SDqcppbGAEPfk4PU//juhoeFGvNRMJDCOHS11cAliw1osmx0iEy9dEa0GZKNoswwjLxePTUWQTcwV7qZQzP2WSS1Woq4NEuPQl7elkGjk3QqBm5laX26uveKCSTqZRpBTGMldUANwemYxZJf2LVlhW93D4NR9m/+s/SWTt+dOeO6euBbeiMmamlp1Uu+a//wGA/h2v5ZC3hufHh5CAl7StXdbXrUQiUCmhXM6BTHQqgdIbMJCd6BmdLZ+KRfF7TTrqj9O/+z7G+gsvyU7FY/iWsCsQCLnR1DTxSAWvVmViFdFDZZLbM9kKfWUSIsudn3Iqw+2CTOk/ZGPjYXQlQU3DzJ/Z8GrIJFe0CtBMplBkE08RVlSMMgRa1eTQ6z7KgTd8CrN2+pw0TVHY3tAK5FdVlkofH6Dtge8DcPhVt/I/x/cBcG5dC43u8v+OFZsIVEooY1kokpkfjQ74Au4zvvLHSowRCCmcd1kXa1oHGT1wLwPHegt7sTmKZwl3e4oiE/A7pCo0UMlmc5BNVkRX2kmTrdBXavm+EvJTJuluDezS/1tJjIfxui3cs/y9e30r3x04FU8uqpnifDxeHSkbzQegwvxO2+LZ0dQJwLOjAyTspd20rv7lP6FkLMY27WBn01r2jA8jSxKvWDVz1tCZQAQqJZROpCfuYCa2frwaLsUkeYYm1OZyDpiD+ENeVFVm6yWrWd8VJXH8XvoOHZv3tdlsDjJjiypNPlUgpJFJrFzZ6WJY6TSqbFdMafKkfJ7ByqyoVEJ+yiTDqyFLqZKvbJrRYUI1s29jun35ypmVLFG2UkkMfXmlyZNEiXJh6nY/wOUf3kHg8NNTj3V4A7R5A2ScHE8MF3gTdwpXbGyqT8uhV36Qnx/bC8CVzavPyNUUEIFKSZnpfFfayRUVRZHxepde+TPSN8iehx/GNu1inmbRJKNxVCmGP5T/MJIlic3nd7JhvU2i9w+MD86dn5OKJVClpd/t+UMesIfLNmhvPlbKRJZt9Ara+gHweFWy5sqsQlVCfsokw62hSmZJE4kzdhasfgK1s68guVQFXVvZyhkzmcBTpM8xj19HlcszhqGadN79L9QceJTN3/vI1GOSJLGjsR2AnUMncBaZVCvlsgyd/zKiq7by313b6UvG8KgurutYV9RzryQiUCkhO5XGpWTRtJNJlL4lVv4M9fQzfvB+PLl9xMOVucWRiMbQleRUoDJp/dZWOprGGdz3yJxlofmKnzTewNKy1QM1HnQ1SXy88q5Nfr6Kma/0qCCGe2XyDCopPwUmE4ntkn7IxscjaEqcmvq5f+b8cL+V2/rJWRE83uIkdJdzDEM12ffG28mpLhp23UPD07+ZevyChjZUSaY3EaUnsbh8KSvYwNMf+g9+e8fv+cXETJ9r29fhdVXO1nKxiUClhKx0GuO0le585c/isr0Hjp0geuQ+1q6KoKnpim0Xn4pG8Xpy0wIzyK+sbLlwFY3+o3Q/++SsZcSpaAxdy2AscdXBFzQwtHRFBnEZ00RVZl6XcjO8GrKTKPkK3cn8FH9Jv0+hdF3FpWawS7iiEh8fx62l5t3qMtzKiq1oAWCF83N6isTjpSxjGKpJqrmLo9e/F4CzvvtXU31VvC6NbXXNAOwc7F7S1/716ABR26TB8HBly+qinG+lEoFKCZmpFO7TAxWfgZyLFLw/3nfoGInj97F+bZLN2zvxeCTMCl1uNePj+AOz/5PSdZUt21vx8RzHdu+dejyVSHHwyaexh3fS0Lj0FQdZkggEIFHmhNroaJjDzzw/rZrDSqcxKrCtgdu7MsPxTuanVM5FMIz830upJCLjhILMu9W1kpUzVtpCJo57GcMIT+c2wE6/MAKVoZ5+Thw4sqTXHnzdx7C9QYJHn6F9olIHTvZUeXK4Dyu7cFJy6wP/yfbPvQ730HHGzRT39eXP55WrN6HKZ/ZH+Zn905WbHcVwT7+L9gUMVKWwyp/eg0cx+3/Hxg02Z53bMdXd1kpWXv+CXM6B9MC8d5A1dV42bvYjRR5n4NgJTuw/TPcTvyTkPMT55yts3r68SZ/+oAHmwLK+xnKNDw6hxx/kyKP3MNKXT+61zZkra5UgvwVikSpxnkQl5adM8rjBLFEicS7nQKqXQO38veo9Ph3ZiRctryoejnHgiV0kYzP/PtMTU5O9RQxUNEOFTAX3LiqSvkPHiB35LYm+Z5b0ejtQx8E/+WsANv3Hx5En2udvCNZRq7tJZTNTQcdclFSczd/9MK1/+C+a7/s3/uvI89i5HGsDtZxT27yk86omIlAppVOavU3y+nW0Aip/MnaWeO8u1nbZbNjaNvW422eAXfpmVQDR0UjB2wL5N8JYPql1Hp1r61ndYRI79iCM/obN68Nces0a2lbXLfuDLFDrQc6OlXVCdca28PsyrG3tIXrobg49/SzZdBRdr7wpprquorkyWCWc2VJp+SmTdLerZCXK+aTyMMEFfubJFa1ilCgPHDtB7zO/wWM+zMgsXXfTyRQu2SzqiopLd0G2Mrehi+XE/sOY/b+jtXEcRYovefTC0Vf8Ban6DtwjPVOrKrIk8eK2NQD8b/cBfnF835yJtet//Le4R3sZq+/krau28+zYIBLwmtVnIVXQDUCpVNam+RkkY2chG5/RO0NRZLweh/EF7mLHBoZwq8O0r22c9rjHZ4AdJmNnUV2l+/DLZnP0PPsgnvp1dG09a8Hj4+EompwkUFO74LGbzuvA8A7S1N6Cf4nJs7MJ1njR5QFiYxHqWhsXfkEJZEwTd0jmnB1d1B0d5cCe3xOx6iuq2dup3G4YKeFWYnRkDEOJUNtQV7LvsRSGWwN7cSuTfYeO0dDRlv+Ankc8HEFXEoTq6+c97tQVLV9oafk7GTvD0Wd3oyafYsNqm4wFR0YGgPXTjjMTCQx98V2f56MbGmQT5HIOsnzmfVge33OA3OhDnLVJprG9k6HfjpCOJ3B7F788mtPd7H7nV1DMJL1XvH7q8SubV5HOZLirez+/OXGYhG3zp2vPnnbT5j2xnzU//3sAPnLlmxnOOdQbHv6kawur/KFl/5zVQAQqJWKl06iKheGeucLg9UkMjs5/Nzfe30t7jY3vtAF93oCBpsZIJ5JLfnMrRGx0HI86hDmukMttWvCNKBmJ4ndn0AtoE6+qMus2tyx43GJ5vBpuwyYRKV+gQjaJayJptr2rjtoGH4f39FHf3FCe81mAxw2ZsdLdFYeHhwl50wRClbX3NdkZNmNnUF0L/5tNxhKMHtsFkkTr2lXzHpsYH6fBv3BQoOsquiuz5MqZ6GiEvj2PU6PtZ+P2Glo7W+k9NsqxvhNYaRPNOLl6YiaT1C1zavLpNENFkSwyljXte50JDj/zPK7oH9iyRWf1hkZyjoOmmqRicWqa5g9A5zJwyaunP+A4SJLEtR3r8Lk0fnj4OR4e7CaRsXj92q0ci4dJ736Iv/jnd6BkLB7oOp8HNlzKKzrW8+K2Llxy5a3SlorY+ikRK5VGlWwM78y7L4/fAHPuniJW2oLEIRrbAjOe8/rzy8XJWGmbxkVGRjGUGDqDBZX8molxAoHy31UFgxKpWLh8J5BN4TolWPP4dLZe1FURHVlnY3iNkiZ0WuFj1DdVThLtJMOtoUiFd4aNh6P49RGSBSRrZ+L9BGsK++B2exafK5PN5ji+5wCDu39Fe80+LryyjdbO/EpmXXMAQwkzPjS9sjBrRnB7intfqhsuFMnGTJVvq7UUuvcezAcp57hZvSF/wyNLEh4vRcvnckVHuey2K6h75j4ALmvu5G0bz0eVZHaNDnDbY/fw6D3/zl9++Y3UxcfY27Caf7vxb/n49hdxbce6F1SQAiJQKZl0Mt+V1u2ZpX22342cm3vmz0jfAF5tjOaOmhnPaZqKrmVL3igqHe6nsVnH50kw1l9Agmo635G23HwhN6RWZjLurE5ZUakGhkcDO5xPAC2y2HgUjWHqm4JF/9rL5fZpqLKFWWB+TjoeR5djZBPz/y5YaRM5O0KwrrDfBa9XJpMu/KYjOhrmwB/uxxW5l62b02y/Ys20VVfDcBEMZokOD01/oT2OPst70XIYnnygYpcxJ6zYhnr6sYd3smGTi44101dOfD6ZTLI4eU3rf/y31O59mIv+7x9Tt/sBAM6tb+Hdmy/EUPLvH2NtGxira+fIpkt59ouP8OqLrqVGr6yVyZVSPe+oVcZOpzA0Zm1Xna/8GScVT8xItgWIDXbT1cCc2yheL0STpQtUrLSFbPdR0xBAUROMHj8ObJrz+FQiiUo0HySUmS/oRiaOlbZmvballM3mIJeuqkDFMzFzxkylcHvn3xvIZnNIklRwPsL4wCBeI05NQ+Vte+mGC1WxMQssUU7FIrgVEzkzim3ac+apRMfC6GqcUF1hlRj5uUPjCx6XzeY4sf8Q1vCTdNSNsunctjm30+oaPPTv7yGXOwdZlrBNG9lJ4CliIi3kb5pk2cYyz4xAJTIyzvjhh1i/OsWas2Zu77l9BpxY3sTjSfvedDv+nj00PnU3F3/qZYTXbcf2hjjPE+SyrS/iwFU3Uqu72bvlYjLeIB7XmbW1tlhiRaVELHPu3hn5yp80icjMZL5kLIFiHaexPTTn1/b6VbJmuDgnOovw8Ci6EqWu2U9jaw2aM0BsfO7Ew3g4iktJEqgp8ib4EuiGC1WySt4bZDa2aaHImaoKVNze/BRls4AtkAOP7OTE/kMFf+3UeB/19XJRZssUmyxJuA0Kr3gyh6hpCKDJiXmbCsbHwrgNq+Dhmm6vDpnIrE0QJ9mmzaHHH0ON3MfWLSYXXLV23pyfuqYAmjRCfOJ3Np1MoUrp/PcqIkWR0Vz5BPJql4wl6H/+YTqahjjrvNnbJOR7YM29Er4YOc3g8b/+GUPnXYtipajb8xDNj99F+wPfp/nILmonVk6sUCO5F3iQAmJFpWQy6QTu+tnvPBVFpqFB4siJ56hpbpyWRT7a24/PCNPY2jXn13Z7DbBKV6IcGxulzm/jdmvohgufMcj4wCD+mpk5M5DvKusxlt5VtpjcXg1FtjFTqTnPt1Qylo0iZ9AqsWnKHNweDVWxSC/wgW2bNrLVTWo8x+kVJbNJJVLI9gnqmkPFOdESMNwwUkCAZqUt5GyY+pYQo6PjxMfDcyZUmvER2hsKzx9we3RUOUk6kcQbmJnHlIwlOL7rEeqNPWy9oKWgXKdQvRePMUhkeJhAXZBUPJHvoVKEqcmn0w2IV0igkrEzDHX30tjZPm9FZDqZJpfNIskykiThOA7dzzxCU+AoWy/qmjOwXmglfLFymsGjn/hfavf9AT0yhBoP40qEiXads+yvfaYRgUqp2JF5/zGfdX4Hidgxju96nPWXXD71yxEfPsbGNgVVnfsu1O3TUUiQTqYxPMVPVMxEe6hdk/+6siTR2Oxi3/FjzPUBlYqO01xT/kRaODmDxCrDikrGspAprPKpUiiKjK47JBYoUU5EouhqAtMaIJvNLbhKMj44jMcVob65s5inW1SGoZKbZ6VwUjIaw6Uk8YfqCQbHOR4Jz3pcxs5Aum9RPWM8fh2XNI6ZSM0IVMJDYwzseZiW0HG2XdwxowJwLrIkUVcnc3hoAFiHlUyiadmSjHDQNQc7tvK/a7MZGxgmeeL3HB7qouu8C2e8/+ZyDt179mOPPIUsZ8CRcMj/qXWPcc4lHfP+7k6uhCdjcYL1M/MHl0RRGNtyRXG+1hmset5RK5SVtsjYNh7/aW9OmeiMHiqn0nWVLRe2knroOY49V8Pac7cSHY2gO700dc7fi8QXMFDlfE1/sQOVZCyBmhumpv5k6XN9SwjXsX7i4djsJdHmIN4Ka48eKUOgYpkWimzjMqrr18rrgfEFApX4eBhNTuIgkwhHCdSF5j0+NtzPqjoqbr7RqXRPYb1UktEYuiu/IuEPuWGwf9bjxodGcKvjNLY0FXwOhseFS505IHGwu4/I0YdY1TTM1otXL/o61jT4kXp6sNIWZjrFPA2jl0U3XDBeGU3f7HQarzaOqo5z+PE0q8+/dGq12kpbHH3mKbyZXZy11Y0/6MHBwXEccjmHQLBzwRweRZHxuB0iKzjxWsirvM3jKtN78DBHn/jdtA6uVtpClkyMBfapQzUeNp0dQo7lW8qP9ffjd0epa5q/P4rbo+GSTZIlGE4YHh7F0BLUNp28u6tr9OMzYrNW/6QSKRQnsmBH2pVkGGCbKz+DJGNZKHJxm2qtBLdXJWfN/4GdiIYJ1coYaor4eHjeYzN2BlLd1DZWZkn2JMOtQTaWb844j2Q0it83MU8q5EV1wqRm6X0SGRoi6LcWlbQqSxKGm2nzu/oOHSN+9F7Wrx7n3Eu7lhTs1bcEMNQo4eFRMqkoHm9p3uo1wwXZ0rZKKJRtmrjdMtsv76DZu4fjT/6OeDhGPBzjyOO/o1Z+nO076uja2ER9s5+G5gCNLUGa20IF/535fBLmIqcdC8snApVlstIparQj9B48PPWYmUqjSma+9HMB7V11dK3KEjn2B1Ijh2hqdS/YSl5R5PyI+BJ0FI2NDlMTmv5hqygyDfUKqbGeGccnIvmOtMEF5pqsJN1QyFkr/+aZsSxc5U/TWTTDrYO1QOVJaoBgjYdAAOLh8LyHjg+N4FbGaWypvLLkU7m9+fychSYAZ9Oj+AL5v9hQvRdNSRAbm55Qm8s5ZKPHqWtc/NKF1yNhpfI3HT37Dk3M97LYsn3VkhOR3W6NUMAmMjQE9nh+AGIJuHQVMpURqFjpFIYbfH6D7Veupq3mEL3P3EvvM7+hLbSfC69cRV3j8ppkun062MWp/BEKJwKV5cok0F0JrNFnp4aBmakUimzP2kNlNhvPaaO9YQif2k1Te2F7n16vhJks7kCwXM6BRDe19TP32OvbQqiZXhKnDVNMRqK4jXzibaXQ3RpkVv6uJ2PbuCrnMhTM8OrITmLOuU75VbMx/DUeAjUGTmr2rY9J4YFBQsHFrSyUQ35lcv58pmw2B9YI3kA+ANF1Fa8nM6PxW2wsjCYP09AaWvR5GF4NrHGO7d5HZvgBzjqLafO9lqq2wU023gOZKJ4SBSq624UsWUWphFkux45P5ZgYhovzr1jDmrYBNqweZfsVawuuxJqP128gZcJFGyQpFKZkgcrtt9/OpZdeisfjIRQKzXpMd3c3119/PR6Ph8bGRj784Q+TyVTZP4BsnIa2eup8A5zYvx/Id6V1KYUnVSqKzNaLV7H1/BA1BTaKMrw6WMWN7GNjYTRlnJpZ7jrqmwJ4tCjj/fmJwNlsjvDQKLGRQQLBykiknaS7NcjGS9LEbD4Zy0SvwkBlskR5rsqf/L+LJKE6b37wY26MdHL2D/dcziEXP05dY+WssM1Fd7tQ5PkrnlKxBJqSwH9KkkcwpGDGp//uhYeG8RmJJa0sGh4NxxpHDj/I2Vs11mwqzjTcyTJll2IWvTR5kq7nm76VcxDolGx82kwtl6pwziWr2bK9c97ihMXw+A1ccopkVOSprKSSZbpZlsXrXvc6duzYwbe+9a0Zz2ezWa6//nqam5v5wx/+QH9/P29+85txuVz87d/+balOq/gyCQyPxpqNbsJP7iYy0oWVSrPY3FLDcE21wS6Ex6cXfThhdGQUj54iWDtzDo+qyjQ0KezvOUIiPALpE2hymEZ3ipZVS5t9USqGx4UqmZip9JIGiC2VY6dxadW3SJkfjmeSTiZnLelORiL43TaG4SJUlx/8GA9HZk3kjo6Mo8kj1Ff4tg+crHhKztP0LR6O4pJT+GtONq3zh7zQ3T+t+ik11kNnu7akCeDBWi817mNs2NpM2+riDW8M1Xnx6ENYtgtPCUqTAQy3C1XOYJsmULrZYwvJ5RywYyUf/ukLGriUIVKJBIG6yv83fqYoWaDy6U9/GoDvfve7sz7/m9/8hj179vDb3/6WpqYmzj33XD772c/ykY98hE996lNoWuXfmtqmjYyJ7nbR1Bai8egh+g7sRtJ8GN7S3s17/QYuJV7U4YTJ8CAtDfKcb7YtnbWMjewhEHRR2+CjtrGGQGj5S9TFZrg1ZDmBtcKBCrnqap8/SddVDM0mFY1B28y7eTM2SntjPhh2uzU8bptEOEJ968zqlvDQMD53ctZgtxK5DQjPM2snHY8R9EzP2QrWedHkKMloHH9NIF8pl+2jbonBWU2dlytfflbRG+MpikxtrUR4LF2yHke624WEhWWWd+vHSpsosoXuLm2g4lIVDD1HIl4ZeTkvFGW7/du5cydbt26lqenkm921115LNBrl+eefn/N1pmkSjUan/SkXM5VGkTPougtZkli3pRk9e4DUeB9GiX9hvAEDVTaLNiQrX6nRQ6h+7qCnoTnAi16+kfMvW8PqDY0EKqjS51RuX74qasW702aTaHO0Vq90jc0ayZEjM7bL8jkaAwRqTm5JBkMS6cjMbcdsNkd67DCNTfqSVhbKwXDLZOdJvDbjYXz+6T+LP+hGU1PEJqqfwoPDeLTYgtV68ylV997OdY2sXh8qydeGie60moNd4CiCUrFS+UClkAKG5fL5IJ0QgcpKKlugMjAwMC1IAab+f2Bg7sFfd9xxB8FgcOpPR8fs7Y5XQsayUCQbbaJvRl2jn7ZmC0ONopU4UNF1FV3LYBapRDk8PIpbjVZ8SWkhXKqCS81hLVDNUUy5nAPZFGoVrqgANHfUodNLdGR69U8yGkeTY9PGIwRqvGD1zwhqxgeHcUt9tBRx+6LUDI8O9jyD5qzBqUTaSaoq4/fnSE0k1MaG+6mroyLL0msbfFMTgEtF1yUyVplXVMw0ipQp+Q0i5Jv0kR4p+fcRTlpUoHLbbbchSdK8f/bt21eqcwXgox/9KJFIZOpPT8/MktmVMtng69TGbmu2tFDj7sVbYBfJ5fB6IF2kFZX42Dhej1lw98tKpxtM7JuvjKydQZZsXHrlfVgVorbBR8ibYKT3xLTH4+EI2mlznIK1J7c+TjXac4z6WpNQBcx8KpThyfdSmW3WTr7aKYY/OPPnCYZ0MsnBfKVU+jh1TSs7rqGS6FoO2yzzikraRFVstBXoCu3xGZANzzufSSiuRf2tfuhDH+Kmm26a95g1a9YU9LWam5t57LHHpj02ODg49dxcdF1H1yuj7NFOp1HV3LSMcn/A4OKXbMh3bCwxr08l21+cseNmdJDOlurctpiN24DB6MqtqFimiSxn0LTq+ZA+XVO7j6G9h8nYW6YStJPhMA1+Z9q/8UCNB00ZIjYensqPSsYSyOlDtGwOlePUl8zwaKiSiZVOz5genYzE0OQk/tDM96NAjQfp8DDDvf24XREaWqsjJ6cUdEMlFylvFUzGNPForMiWo9evo0pJUrFE0fIDhfktKlBpaGigoUgj23fs2MHtt9/O0NAQjY35pcl77rmHQCDA5s2bi/I9Ss02LWbbEl2pniJun16U4YQZOwNmP4Ha6t/2maS7XTC6cvlLGctGlrIlrzoopdbOWg4dGGC0f5CmzlYAMolBAp3Tf6bJrY/hcBjIb70O9/QSMMZp7ijsRqVSGB4NRY5iJlIzApVULIau27P23wjUetGVEYaPHaarobL6CK00l+4qe9M3K5WifoXy5n1BNy5ljFRCBCorpWQ5Kt3d3ezatYvu7m6y2Sy7du1i165dxCeypV/60peyefNm3vSmN/HMM8/w61//mo9//OPccsstFbNispCMlUYv406Jx2+gEF92D4PYWBhDjVNTwGTWamGscNM3ezJfqYoGEp7O49Opq8kQ7s9vp9qmjZQZnpZIO2ly6wPySbSp4QO0tBtF61exUtweDVW2Zu0Lk4hG8c/xOeT16xi6iYvRqugZU0q64YJseVdUslYS3ViZf3v5KrkM6ZhIqF0pJfub/cQnPsF5553HJz/5SeLxOOeddx7nnXceTzzxBACKonDXXXehKAo7duzgjW98I29+85v5zGc+U6pTKrqslULXy/fG7PUbKHJ62c2HYmPjeAwTf+DMyE+BiRWVbGLBOS7Fkl9RyeDSqjNHZVJzRxA5fYR0Mp0fj6AkCM7ShDBQ40HODGObNqN9g3iUPtq6KqufTiFUVc5PAE7Psk1oDuMLzH7TJEsSwQB4XOEldaM9k2iGiuyky9utNRPLr+ysEK8XzKRo+rZSSnb7993vfnfOHiqTVq1axS9/+ctSnULprfAvx+k8Xg1NyZcohyi8WdzpUpFRmuuqo5y0UIZHQ1WSmKkUqqv0K0UZ00RboT3yUmpqD+F7/jgjJ/rAcXDr+anBpwvW5bc+YmNhxnuP0VFnV22g63ZD9LQKsYydAXsUX2Du/YRQvRczHSZU017qU6xouuFClpP5GWcr8Ls2q2x8RfICJ3n9KtnI8rfdhcJU1zptpckmy7rUPzl23FxG5U82mwOzl2BtYa37q4Xbo6FK889xmU82m+PIs3vmnH9zuoxto1VvesoUTVNpbFKIDx0lEQ0TDMwefPn8Bm7dZLSvF8U8ROuqpQfK5eb2yGSs6b9DiUgMTU0RqJk7UFm9sYkLX7Sh1KdX8XR3vo2+XaZ5Pxk7g+ykSt7s7VQenxus0RUf0/FCJQKVJcrYWcglVzSKn43XJ2HGw0t+fSIcRZMihOrPnPwUmJzjYmKmlpa/Ex+PkBrez9jgcEHHV+vk5Nk0ddTgyp4gG+/BH5r7gzoYlIiP9BH0hGlqq9524pqhgR2e9lgiGkNT0vO2GZAlqepyckpBd7tQZRtrBdsBnMpMpZGl6W0iSs0bMFCl1IKTt4XiEL9lS2RbJqqcKXuVR11TANk8OueQuIXExsYxtBSBeT6QqpGiyOgas+ceFCAVT+DVxmZMyZ1L1k6jG9W97TOpvjlAwBudaPQ290pboMZDwD1MS7unZJ1VV4Lh0SATI5dzsNImvQePEu7Zj9+Xq+qfa6W4VAVVzZEpU6BipdKosr0izd4mef0GLjlNMiISaleC+C1cIjttIcs2ulHeKo+m9hBebSyfU7AE8fAYoWDpWniXk+GWsJbYiMpMJtGVCFa8wA6U2SSqq3orfk4lSxLNrR686gjBurkrWmoa/QSNIdqrMIn2VIZbQ5FMDj72OEd3/gyGf8XGjsNsPr+13KdWNXSNsk1QNlMrM+fnVB6vhku1MJNzz4kSiufMeGctAzOdRpFs9DL3T5jMKTjUdww2LKGHRaqPQOeZtZoyyTAgM7q0N5J0Io5HMsEcmDYld065VFUOJJzL6o1N+EPueYfZ1dR5ufxlZ1d9ArE/aODT+nF7wrRsDNC6qhPtDPq7XAm6AZmVnq01IWOm0TRnxW+2dB1iZR4d8EIhfhuXyDYtFLkyylGb2mo4fqKH2HgUf03hrbwT0Tgq44Tqzqz8lEm6W5t/jst8rDE8ITfx0TiJcGzhke7Z6pycPBddV2ntXDhBttqDFMj3j7niZesrclZPtdB1mWxk5jZr/9EerFSKVZtLl3RsmSazFKaVnK7DWJmHMZZKKpFa2cnzCzjz1vtXSMY00SukHLW+2U/AE2O0d3HbP7GxMLoSJ1R/ZlX8TMo3fYst+nW5nAP2OPXNNWhqivgCeSoZO4vkWFPDKYXqI4KU5cl3p535uxY5sQdr6EmSsdL1HLHTKYxyBCqGRNY+8wKVwe4+Dv7htySilZN/IwKVJbIts2KSJxVFpqnFwBw7vKhyufjYGIFA7ox9k9Y9LmRSBZcYTzJTKVQpjS/oJuB3pqbkzsW2TBQpc0atqAjCYmi6OqM7bTwcw+UM4NcG6D98tHTfPBNb0fyUSdocwVklyyeMz79dFe49ToP3MP2Hj6zQWS1MBCpLlF9RqZwa+ubOWnRpgPBg4ePHs4l+QrXV2aSrEIZbQ1VszEXunaeiCVxyGq9PJxDSplrFz8VO28iyjauK2+cLwnLohgvJSU3rBD0+MIhPj9O5LkQmvGfJlYkwMaYhMUe+WSZelupLTXdBtnJWHQox1N3Lvofvm/PmLZVIIZtHCAYlcpE9pBKVUX4tApWlysTKXpp8qlCNh5pAitG+3oKOTyfTKLmRWdujnykMjwtFsjCTi/tlSycSuFQLw6vhD3mQ7OF5V2UyloVCpuwVYIJQLrqh5Zu+WScrfxKjJ6hvkOlc10DQPcTAkeNL/vpD3b0c2PngjJEY2WwOcivblXaSZrggm8qfQ5Uwk0n8Wh9D3SdmfX70RB8+I8zZF68h5Blm4MixlT3BOYhAZamyiYq7g25q85GLHSpo5kZ0dBxNiVFzhjV6O5VuuFCVDOYiE97SySQez8Q8lzovmpIgEZl7wKFt20hSRlSKCC9YmqEiyxmsiQaLqUQSNdNLXXMITVNp73RjjhTe6fl06Xgcn2uY2Nj4tMettIkq2eXZ+jHUfKO7MpVlL4WdTuNRRoj2H5g1TSA+fIzGRhnDcNG2yos1tmfBraKVIAKVJcjlHMgk0I3KGu3e3FGLRx1mpHf+rQqARDiM35udt/y02smShKE72IsMVOxkFI83n3/k9esYmkk8PHeeSsY0cblWvjxSECqF4XGhStZUd9rx/iHcrggNrfkqxM71jfj1PgaPdS/p61upJG51lMjw6GmPp5Hlle1KO8lwu5BlG7uKApWsncLjVfFIPYz2T/+ciI5GcGVP0NxZB0DnukYC+gADR5e+ElYs4p11CTKWhSLbFVfl4fFq1NfliAz0LHisFR0gVFNZ518KhnsJ3Wkz43h8+dydySm5iUh47sPPkDk/grBUmqaiKtmpD+3YcD/19SerqQzDRVu7Rnxg79ImmtsRXHISMzow7WEzlUaVLAxPebZ+FClTttEBS5KJE6zz01BvMtZ9eNpTo319BH0x6pr8QL5FQVuHTmpoD9lyTsZGBCpLYqUtFMkq+5yf2TS1h5DTh+dNgrJNG8keIHCGDSKcja7LZM3CE94ydhbsMG7vyXpHf8iA9MA8rzlz5vwIwlLpOtimiZW2kMzj1DdP7+m0an0jPvXEnPkR87LDGF4Dyeqbtn2UMU1UNVuWbVdNV1GqbOuHbAJNd9HWVYtqHyY2nt/SzmZzmGOHaWpxT2u5sWpDI36tj1R44ZvfUhKByhJYqTSKlKnMQKUjhEeLEB6au/onOjqOocSpa/Sv4JmVh+HRITt3fsnp0okkmmriDZyshgrUelFy43NWLWQsq2JK1QWhXDQtHziMDQzjcY3T0Dq9SaLHp9PSAtG+fYtKQLXSFrKUoqm9HkOJER4ZPeU5syw9VCC/2qpP/MzVIJvNQTaJZrhoagtR44swcCRfNh4eHMEtD9DSWTPtNW63RkurjGHvwcmVL2lYBCpLYJkWsmxVZJWHS1UIBR1io6NzHhMbG8frMfH4yvQbvoI0wwV2rOD+MslYHEVKT7s2wVoPmpyYO08lm8TlOjN70QhCoQy3RMZKERkeoCaUmzX/bdWGRjx0M17gVHKAdCKFKpuEGnz4fTax0bGp5zJWGr2MHRZ0Q5pW6VTJrLSJOpGyIEsSbasCOPH9pJNpxvr7qAmmCYRmzvbqXN9EUO/BjCxtnlwxiEBlCTJmGpercgf5hercOIkTc344m9EBamorL8gqBcOtochWwcuzVjKJoWfRT6nocrs1PG6b5FyVP5kkqqj4EV7gXJoLx4pA4ij1zbNXEwZCHnxec1qwsZB0MokqWXi8GjV1Knb05Adm1oyj6+V7H9Y1h0yVtNGfHKQ7OWW6rasOv5YvQc7FDtLUNvsKuz9g0NGWxauPz/r8SqjMT9oKZ1sWul65S/019X5UaZTkLC2QbdNGtvsJnqHzfU7n9rrygUqBTd/SiQTeWQYGB4ISqegcq1TZVEXMfBKEctINFzk7iluZue1zqppaF1Z07pyv01mpFJqWz0MJ1QeQs4Mnt2GzsbIOhs33UqmOpm+WmU9ZmOz/pWkqrRMJzl5tlObTtn1O1dgETU0rdaYziUBlCWwzjaFXTlfa04XqvbhdCaIjMz9Yo6Pj6HLsBZGfAmB4NFTJIl1g07esGcHjnbk6Egh5ID0wY5Uqm82Bc2ZNThaEpchXwdgEAyY+/9z7McE6H5I9UHBPFTOVxOPJ3xjWNvowlCiRkYkVmTJ1pZ3k0lXIVEegYpsWimTnxx1MaF/bQNDop742i7uMAd9CRKCyBE4miaZX7h20qsqEQg6x8ZmBygspPwUmyibVbOG9VKyxaRU/kwI1XlQpSio+fZ5J1rbzdyl65SVWC8JK0gwVjytMfdMsS5KnqG3wYSgxIgVu/2TSCYyJuMcwXAQDWeKjoxNJtmZZmr1N0g0NcslFzVgrFzudz+c5tarHHzDYuNlH11nNZTyzhYlAZSkyify00AoWqvNAsmfGL9ALKT9lktcL0f7D9B0+Pu9dnJW2kInjnmVmfKDWja4kiIejM16jyJmK66kjCCvN5zdwqxGa2kPzHufx6Xg9FvGxAnMe7Ei+em9CqM4gEz8xlRxajmZvkzRDRZEKz4Erp7lSFlZvaKSmwkepiEBlKTLlmS2xGLUNfjRpnPj4yQ/WF1p+yqTN57Wwvu0QucFfcuSR/+HwrueIh2dOPU3FE6iyidc3c9la01R83hyJcHja4xnLQiYjclSEFzyPT+fy67fNWjlyuppaFTO6cAftfBfwCIbnZDBSU+9HzY0QHR1DoTzN3ibpE9tdtln+NvMLyZhp9CpdSBeByiJl7AyyZFb8HXSw1oPhShI9pUz5hZafMikQ8rDt4tVc8UdtbD0rSih7P727fkV0dHq5cSqewCWn5twWCwQVzEj/tFUqy7SQT9v3FYQXqlO3FeYTrPMhWf0L5qmYqTSKlMbtPRmo1Db5MFwxxvt7UZTy9rPKDz61C07WL6tsoqJTFuYjApVFstImcoV2pT2VosjU1kkkxk/2K3ih5aeczu3WWLe5hUuvWUdTaJD+w/unPW8mk3jc+Ryf2bStacDLEYZP9E89lrFtFCUnBhIKwiLUTOSpREfn3/4xEykU2ZqWNzbZK8qMR9F1p+DgqBR0Y2Lej1X5Kypk4riqdNaHCFQWyUqZ00q8Klmozgvp3qkukC/E/JTZKIrM6g31qOm9REZOvlGmE3E882zV1tR5aWnOMHZs99Q1zZgmWuUmywtCRfL5Ddxuc8Y05NOlkylUycTjnf5LFqpzo6vJsreJmOxOu9jBpystv4VW3gqp5RCByiJZpjkx56fyP/BrGv1ocpj4eOQFm58yl+aOEPWhGAMH95180Bqf8YZ4uq6zmvHKRxg8np9XkrFtXFrl9tQRhEpVW6tgRofmPcZKJXG7Z04mr6n349EiFdEmQtOp+BWVjGUjy3ZZK6SWQwQqi5QfglUdS/2BkBuPniI6MvqCzU+ZiyxJrNnYgJbZz/jgSP6Owx7DmKU0+VSBkJv2donoiefI2FmytoWmlm8GhiBUq2CdH6w+MvNM5rVSKTzumY/XNHjxuMbRjPIvZxp6vrdWJZuskKr0lIW5iEBlkWzLQi//70ZBZEmipkYiOT78gs9PmU1TW5CGmjhDR/aRTiRRpTTeeRpVTVqzqZmAqzvfetpOV22CmiCU02SeSmwsPOcxWTOGbsz8mFIUmQ1bm2jrqivhGRZG01XIJBY+sIxsM5+yoFdp0r8IVBbJTldXiVeo3gfmCVLhPpGfMouuTY1omQMMHD2OSy4sUPH4dNo7VBKDu8lZEdGVVhCWwB8w8LjT8+epZMLTeqicqm11XUGl0KVWDW30LdNCwhJbPy8UWTuNblRPTkJtgx9diaJkBkR+yiwamgM01aeIDRzBpRbek2H1pmYCrhPkrIQYSCgISxQKyaQis+epZLM5yMSm9VCpRK5qWFFJp9G0mbk+1aI6z7qcqqzEKxBy43WnMZS4yE+Zw5pNTQTdg3g8hfeBMAwXnV1ufPqYWFERhCUK1vkg3UfGzs54Lp1Ioiom7jlWVCqF7nYhY2KlKzehNmNZVV2dKAKVxcpWflfa09XWyvg8czcye6GrbfDR2mwRrFnc3+uqDY001CYJ1JR/+VkQqlFtgw9diRIfD894zkykcEkWnllGWlQSXXehyHZFt9G3zfTUvKRqJG4FFyGbzUEumV/qqyLrtrZhm3Nn1gtw7o6uRb9G01QuuGpDCc5GEF4YfEE3Hr2P6OgYocbpibHpVL7ZWzlb5BfCcLtQpAy2aQKVuWqds5NoWvWuS1TvmZfByTHZlf2Lczq3W6uIpDNBEIRT5SsTZVLh4RnPWckUbnfh27HlortdyJKFVcnzfjLxiijlXioRqCxCPlARk3IFQRCKpbYpgGweJZVITnvcSuebvVU6RZHRNKeyu9NmE1U9j0wEKouQsSxkKVu1teiCIAiVpqWzlqBnjMGj3dMed6wIbm91rF5rWv7zoRJl7AySk67a0mQQgcqi2JaNItu4xIqKIAhCUaiqTGuHB3N0//QutXYEw13ZibSTDKNyu9NaaRNFttGqqFr1dCJQWYSMZSHLOVyq6EQqCIJQLO1rGvCq/Qz19AHkZ5ORxFhg9lal0DSZnFWZvVRs00KWbPQKT0qejwhUFiFr27iq9+9aEAShInm8Gk1NDtG+g+RyDmYqjSqbuBeYvVUpNEODbGUGKmYqjSrZVTFIdy4lC1SOHTvGzTffTFdXF263m7Vr1/LJT34S67R9vGeffZYrrrgCwzDo6Ojg85//fKlOadkyto3mquwMdEEQhGrUvqYe3TlOZHiUdDKJIlm4q2RFRTdckKnMNvoZy0JVslUxSHcuJTvzffv2kcvl+Jd/+RfWrVvH7t27ecc73kEikeCLX/wiANFolJe+9KVcc801fOMb3+C5557jbW97G6FQiHe+852lOrUly9oWrirIQhcEQag2dY1+6kKD9B8/ir+2AZdqY1RJc03NUJElk4ydQXVVVkBgmyYV3jNvQSW7otdddx3XXXfd1P+vWbOG/fv388///M9Tgcr3v/99LMvi29/+NpqmsWXLFnbt2sWXvvSlCg1UTLSgWFERBEEohfbVNQw+dYDoiESzu3rea3XDhSwlsdJmxQUqGdOs6vb5sMI5KpFIhNra2qn/37lzJ1deeSXaKVfx2muvZf/+/YyPzz5R0zRNotHotD8rJpusuH+EgiAIZ4qWzhpCnjFSkWE8VdSjUjPy3WmtVOW10c9YKfQqX1FZsUDl0KFDfPWrX+Vd73rX1GMDAwM0NTVNO27y/wcGBmb9OnfccQfBYHDqT0dHR+lO+nS5lBhAJwiCUCKKItPa4cWnj2FUUd8Pw+NCkSwss/ICFbLV3ZUWlhCo3HbbbUiSNO+fffv2TXtNb28v1113Ha973et4xzvesawT/uhHP0okEpn609PTs6yvtyhZEagIgiCUUvvaegJ6P0aVVPwAuFQFVc2RqcRAJVN9g3RPt+hP3Q996EPcdNNN8x6zZs2aqf/u6+vj6quv5tJLL+Wb3/zmtOOam5sZHByc9tjk/zc3N8/6tXVdRy/DOlbGziI5Ji5N9FARBEEoFbdbY/sVa6tu2ruuQ7TCJihX6yDd0y367BsaGmhoaCjo2N7eXq6++mq2b9/Od77zHWR5+gLOjh07+NjHPoZt27gmGpTcc889bNy4kZqamsWeWknZloUsZXDp1b2EJgiCUOkCIXe5T2HRDB3GKmzez9R8uiobpHu6kuWo9Pb28qIXvYjOzk6++MUvMjw8zMDAwLTckze84Q1omsbNN9/M888/zw9/+EO+/OUvc+utt5bqtJYsa2dQpIzY+hEEQRBm0A2ZrJUq92lMY6VMFMmq6q60UMLy5HvuuYdDhw5x6NAh2tvbpz3nOPleJMFgkN/85jfccsstbN++nfr6ej7xiU9UZGmybZpiIKEgCIIwK5emQraymr7ZpokiZaq6Ky2UMFC56aabFsxlAdi2bRu///3vS3UaRWNbNrIkBhIKgiAIM2mGCzKV1Ubftixk2a76ZFox66dAGdtGUcRAQkEQBGEmw6MjO3GstLXwwSvETqfRdZCl6mmeNxsRqBQoa1mIXm+CIAjCbHwBA1UxSUYrZ/vHtqyq70oLIlApWMa2cZ0Bf+GCIAhC8Xn9OpqSJpWonO0f20xjVFeV96xEoFKgrH1mRKaCIAhC8SmKjMfjkI5XzooKmQTaGVAAIgKVAmVtE02r7n0+QRAEoXR8PgkrESn3aZyUjeeTfKucCFQKJQYSCoIgCPPw+A2wR8t9GgDYpg2ZeNV3pQURqBRODCQUBEEQ5uH1G8jZSD5IWIZUIrnsc+k9eJiAMURTe2jZX6vcRKBSKDGQUBAEQZhHvvInTTK29DyVeDjGvod/RzwcW/LXSETjWCO76Frrxe2u/uRKEagUIGNnIScGEgqCIAhz8/qNfOVPfOmVP8lYHK8WITYeXvLXOLFvDw2BYVZtaFzy16gkIlApQMa2UeTMGbHXJwiCIJSGosh43Mur/DETCdzqOMlodEmvH+sfRjP3sGZzPYpyZnzEnxk/RYllLFsMJBQEQRAW5PNJmImlBRkAViqFJsfJJMcW/dpsNsfQ4Wdpqk/Q0l6z5HOoNCJQKYAYSCgIgiAUwuM3wBpZ8usz6SiynAFzkFzOWdRrB49245MOsWFry5K/fyUSgUoBxEBCQRAEoRDegIGcW0blTyaML+hDlROLasdvpS1ivc/Q3iERCHmW9r0rlAhUCpCxbWRZDCQUBEEQ5ufxG6hyakkJtRk7C5kodU0hdCVJIlp45c/A0eMEjROs3dK66O9b6USgUoCsZaFVf3M/QRAEocR8fgOXvLQS5XQiiUuxCNR48LhzpBaRUJuKhamtkc7IFAURqBQgP5BQtM8XBEEQ5qeqMl6PQ3oJKypmMoUqmbh9Gv6AhBkbX8SLh/H4z4AJhLMQgUoB8gMJF5fUJAiCILwweb0SZnLxlT9mMolLtdENF/6gB6yhgl5nmzZyLoIv6F7096wGIlApgBhIKAiCIBTK49eXVPljplK4PRKyJOELuVGJFtROPxmLo8opfAFjKadb8USgUggxkFAQBEEokNdvIGXCZOzMol5npRJ4Jgp2AjUeXEqKeHjhlZlENIampPH6RaDywiUGEgqCIAgF8gbduOQUyegi81SscQxPfjaPYbhwGzapAip/0vE4Xq9zxnSiPd2Z+VMVW0YEKoIgCEJhfH4DdZGVP7mcA5nIVKACEAhIpKLhBV9rJ8bx+8/cj/Mz9ycrklw2C44YSCgIgiAU5mTlz2IatpkoUgq352Tlji9gFJZQa43g9Z+ZibQgApUFZSwxkFAQBEFYHK8XzGThDdvS8SSqbE0rMfaF3Mi5MFbanPt1yTQKMTxnaCItiEBlQRlbDCQUBEEQFic/82e04OPTyRSqnMbjPbn1Ewx50JUU8fG5E2qTkXwibeAMLU0GEagsKGtbYiChIAiCsChevwH2eL4tfgGsVBJDZ1pCrMeno2smyXk61KbicVyKiXFKgHOmEYHKApycJQYSCoIgCIviDRhoSuEzf6xUEo935uN+PyTmC1RiMXx+B1k6c3t9iUBlITkxkFAQBEFYHF/QwKUUXvmTNSO43TNviH0BHdJzJ9Rm02P4A2f2MDoRqCxAciy0M3dFTRAEQSgBl6rgcWeJj48V9gI7jNs7c1aPP+hGyo5hm/aM53I5B6wRPL4zNz8FRKCyMMfG5Tpzl9QEQRCE0mjt8JEZ3ztv1Q5Axs4gO4lZ80z8IQ8uJUkiMrOCKBVPoMoJfAERqLywOWIgoSAIgrB47WvrCehDDBztnve4dCKFKqVnXVHxBQ0MNT1rQm0yFsclp/GFztzSZBCByoKknBhIKAiCICyepqm0deqkhvbOO/cnlUigyBYe38xARZYk/H5mDVRS0RhuPYNhiByVFzRVEgMJBUEQhKXpXNeIV+1l6PiJOY+xkik0V2bONhg19W6yscMztpBS8Tg+35m/4i8ClQUoiDk/giAIwtJ4vBqtLRLR/v1ks7lZj0knk1NTk2fTub6RoNFL36Gj058wh/GewR1pJ4lAZQEiUBEEQRCWo3NDIx7pOCO9A7M+n0nHcbvnTjHQdZXVa3yYI8+RSqTyr7GzYI/lG8ud4USgMg8nl0WRxUBCQRAEYekCIQ+NDRnGe/bPfoA9juGZmZ9yqo71DdR6B+g9cBDIV/xoSgrfGdw6f5IIVOaRy9rIkhhIKAiCICzPqvUN6LmjjPUPT3s8l3MgE5214udULlVh1bogTuR5EtE4iUgMVU7hC4oVlRc0J2sjS1mx9SMIgiAsS12jn8baFMPHDk573ExNliYv3Fm0Y009dYEReg8cIB2P4/U4L4iu6SJQmUcua6FIc2diC4IgCEKhOtfV487u5tDTz04l1qbjSVTZKihQURSZrg21KInniY304fe/MFpniEBlHk42P5BQFYGKIAiCsExNbUG2nuvFnX6YQ489ipW2SCdTqIqF21PYrJaWVbXUhyIouVE8L4BEWihxoPLKV76Szs5ODMOgpaWFN73pTfT19U075tlnn+WKK67AMAw6Ojr4/Oc/X8pTWpRc1kaWHTTXmb+0JgiCIJRe2+o6tl/SQK36BIcff5DY6Ahuw0FRCvs4liWJrk311Bg9eM/w1vmTShqoXH311fzoRz9i//79/OQnP+Hw4cP8yZ/8ydTz0WiUl770paxatYonn3ySL3zhC3zqU5/im9/8ZilPq2BO1sLlOvOb6QiCIAgrp7bBx/bLO2jx7yUTOTpvD5XZNLeF2LK9haa2YGlOsMKUdE/jgx/84NR/r1q1ittuu41Xv/rV2LaNy+Xi+9//PpZl8e1vfxtN09iyZQu7du3iS1/6Eu985ztn/ZqmaWKaJ7vzRWdpK1wsuYyNJgYSCoIgCEXm8xtccGUX+54+QbAusOjXN7eFin9SFWrFki/Gxsb4/ve/z6WXXorLlZ9LsHPnTq688ko07eTe3LXXXsvf/d3fMT4+Tk1NzYyvc8cdd/DpT396Rc7ZyVqocpqx3iMr8v0EQRCEF5b2doAYY70zpyNXiqw1//TnUit5oPKRj3yEr33taySTSS655BLuuuuuqecGBgbo6uqadnxTU9PUc7MFKh/96Ee59dZbp/4/Go3S0dFRknPv2tRMY8PV1NWX5MsLgiAIQlXw1bWU7XsvOlC57bbb+Lu/+7t5j9m7dy+bNm0C4MMf/jA333wzx48f59Of/jRvfvObueuuu5CkpW2p6LqOrs/fGKdYVm9sAppW5HsJgiAIgjDTogOVD33oQ9x0003zHrNmzZqp/66vr6e+vp4NGzZw1lln0dHRwSOPPMKOHTtobm5mcHBw2msn/7+5uXmxpyYIgiAIwhlm0YFKQ0MDDQ0NS/pmuVy+wc1kMuyOHTv42Mc+NpVcC3DPPfewcePGWbd9BEEQBEF4YSlZefKjjz7K1772NXbt2sXx48e57777uOGGG1i7di07duwA4A1veAOapnHzzTfz/PPP88Mf/pAvf/nL03JQBEEQBEF44SpZoOLxePjpT3/KS17yEjZu3MjNN9/Mtm3beOCBB6ZyTILBIL/5zW84evQo27dv50Mf+hCf+MQn5ixNFgRBEAThhUVyHKeqO5pFo1GCwSCRSIRAYPG16IIgCIIgrLxCP7/FrB9BEARBECqWCFQEQRAEQahYIlARBEEQBKFiiUBFEARBEISKJQIVQRAEQRAqlghUBEEQBEGoWCJQEQRBEAShYolARRAEQRCEirXoWT+VZrJfXTQaLfOZCIIgCIJQqMnP7YX6zlZ9oBKLxQDo6Ogo85kIgiAIgrBYsViMYDA45/NV30I/l8vR19eH3+9HkqSifu1oNEpHRwc9PT2iPX+JiWu9csS1XjniWq8cca1XTrGuteM4xGIxWltbkeW5M1GqfkVFlmXa29tL+j0CgYD4h79CxLVeOeJarxxxrVeOuNYrpxjXer6VlEkimVYQBEEQhIolAhVBEARBECqWCFTmoes6n/zkJ9F1vdyncsYT13rliGu9csS1XjniWq+clb7WVZ9MKwiCIAjCmUusqAiCIAiCULFEoCIIgiAIQsUSgYogCIIgCBVLBCqCIAiCIFQsEagIgiAIglCxRKAyh69//eusXr0awzC4+OKLeeyxx8p9SlXvjjvu4MILL8Tv99PY2MirX/1q9u/fP+2YdDrNLbfcQl1dHT6fj9e+9rUMDg6W6YzPHJ/73OeQJIkPfOADU4+Ja108vb29vPGNb6Surg63283WrVt54oknpp53HIdPfOITtLS04Ha7ueaaazh48GAZz7g6ZbNZ/uZv/oauri7cbjdr167ls5/97LShduJaL82DDz7IH//xH9Pa2ookSfz85z+f9nwh13VsbIwbb7yRQCBAKBTi5ptvJh6PL//kHGGGO++809E0zfn2t7/tPP/888473vEOJxQKOYODg+U+tap27bXXOt/5znec3bt3O7t27XJe/vKXO52dnU48Hp865t3vfrfT0dHh3Hvvvc4TTzzhXHLJJc6ll15axrOufo899pizevVqZ9u2bc773//+qcfFtS6OsbExZ9WqVc5NN93kPProo86RI0ecX//6186hQ4emjvnc5z7nBINB5+c//7nzzDPPOK985Sudrq4uJ5VKlfHMq8/tt9/u1NXVOXfddZdz9OhR58c//rHj8/mcL3/5y1PHiGu9NL/85S+dj33sY85Pf/pTB3B+9rOfTXu+kOt63XXXOeecc47zyCOPOL///e+ddevWOTfccMOyz00EKrO46KKLnFtuuWXq/7PZrNPa2urccccdZTyrM8/Q0JADOA888IDjOI4TDocdl8vl/PjHP546Zu/evQ7g7Ny5s1ynWdVisZizfv1655577nGuuuqqqUBFXOvi+chHPuJcfvnlcz6fy+Wc5uZm5wtf+MLUY+Fw2NF13fnP//zPlTjFM8b111/vvO1tb5v22P/5P//HufHGGx3HEde6WE4PVAq5rnv27HEA5/HHH5865le/+pUjSZLT29u7rPMRWz+nsSyLJ598kmuuuWbqMVmWueaaa9i5c2cZz+zME4lEAKitrQXgySefxLbtadd+06ZNdHZ2imu/RLfccgvXX3/9tGsK4loX0//8z/9wwQUX8LrXvY7GxkbOO+88/vVf/3Xq+aNHjzIwMDDtWgeDQS6++GJxrRfp0ksv5d577+XAgQMAPPPMMzz00EO87GUvA8S1LpVCruvOnTsJhUJccMEFU8dcc801yLLMo48+uqzvX/XTk4ttZGSEbDZLU1PTtMebmprYt29fmc7qzJPL5fjABz7AZZddxtlnnw3AwMAAmqYRCoWmHdvU1MTAwEAZzrK63XnnnTz11FM8/vjjM54T17p4jhw5wj//8z9z66238td//dc8/vjjvO9970PTNN7ylrdMXc/Z3lPEtV6c2267jWg0yqZNm1AUhWw2y+23386NN94IIK51iRRyXQcGBmhsbJz2vKqq1NbWLvvai0BFKItbbrmF3bt389BDD5X7VM5IPT09vP/97+eee+7BMIxyn84ZLZfLccEFF/C3f/u3AJx33nns3r2bb3zjG7zlLW8p89mdWX70ox/x/e9/nx/84Ads2bKFXbt28YEPfIDW1lZxrc9gYuvnNPX19SiKMqP6YXBwkObm5jKd1Znlve99L3fddRf3338/7e3tU483NzdjWRbhcHja8eLaL96TTz7J0NAQ559/PqqqoqoqDzzwAF/5yldQVZWmpiZxrYukpaWFzZs3T3vsrLPOoru7G2Dqeor3lOX78Ic/zG233cbrX/96tm7dypve9CY++MEPcscddwDiWpdKIde1ubmZoaGhac9nMhnGxsaWfe1FoHIaTdPYvn07995779RjuVyOe++9lx07dpTxzKqf4zi8973v5Wc/+xn33XcfXV1d057fvn07Lpdr2rXfv38/3d3d4tov0kte8hKee+45du3aNfXnggsu4MYbb5z6b3Gti+Oyyy6bUWZ/4MABVq1aBUBXVxfNzc3TrnU0GuXRRx8V13qRkskksjz9Y0tRFHK5HCCudakUcl137NhBOBzmySefnDrmvvvuI5fLcfHFFy/vBJaVinuGuvPOOx1d153vfve7f47y0QAAAh5JREFUzp49e5x3vvOdTigUcgYGBsp9alXtPe95jxMMBp3f/e53Tn9//9SfZDI5dcy73/1up7Oz07nvvvucJ554wtmxY4ezY8eOMp71mePUqh/HEde6WB577DFHVVXn9ttvdw4ePOh8//vfdzwej/Mf//EfU8d87nOfc0KhkPPf//3fzrPPPuu86lWvEiWzS/CWt7zFaWtrmypP/ulPf+rU19c7f/VXfzV1jLjWSxOLxZynn37aefrppx3A+dKXvuQ8/fTTzvHjxx3HKey6Xnfddc55553nPProo85DDz3krF+/XpQnl9JXv/pVp7Oz09E0zbnoooucRx55pNynVPWAWf985zvfmTomlUo5f/7nf+7U1NQ4Ho/Hec1rXuP09/eX76TPIKcHKuJaF88vfvEL5+yzz3Z0XXc2bdrkfPOb35z2fC6Xc/7mb/7GaWpqcnRdd17ykpc4+/fvL9PZVq9oNOq8//3vdzo7Ox3DMJw1a9Y4H/vYxxzTNKeOEdd6ae6///5Z35/f8pa3OI5T2HUdHR11brjhBsfn8zmBQMB561vf6sRisWWfm+Q4p7T0EwRBEARBqCAiR0UQBEEQhIolAhVBEARBECqWCFQEQRAEQahYIlARBEEQBKFiiUBFEARBEISKJQIVQRAEQRAqlghUBEEQBEGoWCJQEQRBEAShYolARRAEQRCEiiUCFUEQBEEQKpYIVARBEARBqFj/H+VjNwBmyfJeAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Import relevant modules\n", + "from uuv_mission import *\n", "\n", "sub = Submarine()\n", "# Instantiate your controller (depending on your implementation)\n", + "controller = PD_Controller(Kp=0.08, Kd=0.7)\n", "closed_loop = ClosedLoop(sub, controller)\n", - "mission = Mission.from_csv(\"path/to/file\") # You must implement this method in the Mission class\n", + "mission = Mission.from_csv(\"/workspaces/b1-coding-practical-mt24/data/mission.csv\")\n", "\n", "trajectory = closed_loop.simulate_with_random_disturbances(mission)\n", "trajectory.plot_completed_mission(mission)" @@ -48,7 +71,7 @@ ], "metadata": { "kernelspec": { - "display_name": "first-venv", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -62,7 +85,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.12.1" } }, "nbformat": 4, diff --git a/uuv_mission/__init__.py b/uuv_mission/__init__.py index e69de29b..47570686 100644 --- a/uuv_mission/__init__.py +++ b/uuv_mission/__init__.py @@ -0,0 +1,4 @@ +from .dynamic import Submarine +from .dynamic import Mission +from .dynamic import ClosedLoop +from .control import PD_Controller diff --git a/uuv_mission/control.py b/uuv_mission/control.py new file mode 100644 index 00000000..697a0a67 --- /dev/null +++ b/uuv_mission/control.py @@ -0,0 +1,7 @@ +class PD_Controller: + def __init__(self, Kp, Kd): + self.proportional_gain = Kp + self.derivative_gain = Kd + + def get_action(self, error, error_dot): + return self.proportional_gain * error + self.derivative_gain * error_dot diff --git a/uuv_mission/dynamic.py b/uuv_mission/dynamic.py index c7c7ad53..01743789 100644 --- a/uuv_mission/dynamic.py +++ b/uuv_mission/dynamic.py @@ -1,9 +1,11 @@ -from __future__ import annotations +# from __future__ import annotations from dataclasses import dataclass import numpy as np +import pandas as pd import matplotlib.pyplot as plt from .terrain import generate_reference_and_limits + class Submarine: def __init__(self): @@ -11,14 +13,13 @@ def __init__(self): self.drag = 0.1 self.actuator_gain = 1 - self.dt = 1 # Time step for discrete time simulation + self.dt = 1 # Time step for discrete time simulation self.pos_x = 0 self.pos_y = 0 - self.vel_x = 1 # Constant velocity in x direction + self.vel_x = 1 # Constant velocity in x direction self.vel_y = 0 - def transition(self, action: float, disturbance: float): self.pos_x += self.vel_x * self.dt self.pos_y += self.vel_y * self.dt @@ -29,38 +30,16 @@ def transition(self, action: float, disturbance: float): def get_depth(self) -> float: return self.pos_y - + def get_position(self) -> tuple: return self.pos_x, self.pos_y - + def reset_state(self): self.pos_x = 0 self.pos_y = 0 self.vel_x = 1 self.vel_y = 0 - -class Trajectory: - def __init__(self, position: np.ndarray): - self.position = position - - def plot(self): - plt.plot(self.position[:, 0], self.position[:, 1]) - plt.show() - def plot_completed_mission(self, mission: Mission): - x_values = np.arange(len(mission.reference)) - min_depth = np.min(mission.cave_depth) - max_height = np.max(mission.cave_height) - - plt.fill_between(x_values, mission.cave_height, mission.cave_depth, color='blue', alpha=0.3) - plt.fill_between(x_values, mission.cave_depth, min_depth*np.ones(len(x_values)), - color='saddlebrown', alpha=0.3) - plt.fill_between(x_values, max_height*np.ones(len(x_values)), mission.cave_height, - color='saddlebrown', alpha=0.3) - plt.plot(self.position[:, 0], self.position[:, 1], label='Trajectory') - plt.plot(mission.reference, 'r', linestyle='--', label='Reference') - plt.legend(loc='upper right') - plt.show() @dataclass class Mission: @@ -70,13 +49,56 @@ class Mission: @classmethod def random_mission(cls, duration: int, scale: float): - (reference, cave_height, cave_depth) = generate_reference_and_limits(duration, scale) + (reference, cave_height, cave_depth) = generate_reference_and_limits( + duration, scale + ) return cls(reference, cave_height, cave_depth) @classmethod def from_csv(cls, file_name: str): - # You are required to implement this method - pass + data = pd.read_csv(file_name, header=0) + npData = data.to_numpy() + reference = npData[:, 0].astype(float) + cave_height = npData[:, 1].astype(float) + cave_depth = npData[:, 2].astype(float) + + return cls(reference, cave_height, cave_depth) + + +class Trajectory: + def __init__(self, position: np.ndarray): + self.position = position + + def plot(self): + plt.plot(self.position[:, 0], self.position[:, 1]) + plt.show() + + def plot_completed_mission(self, mission: Mission): + x_values = np.arange(len(mission.reference)) + min_depth = np.min(mission.cave_depth) + max_height = np.max(mission.cave_height) + + plt.fill_between( + x_values, mission.cave_height, mission.cave_depth, color="blue", alpha=0.3 + ) + plt.fill_between( + x_values, + mission.cave_depth, + min_depth * np.ones(len(x_values)), + color="saddlebrown", + alpha=0.3, + ) + plt.fill_between( + x_values, + max_height * np.ones(len(x_values)), + mission.cave_height, + color="saddlebrown", + alpha=0.3, + ) + plt.plot(self.position[:, 0], self.position[:, 1], label="Trajectory") + plt.plot(mission.reference, "r", linestyle="--", label="Reference") + plt.legend(loc="upper right") + plt.show() class ClosedLoop: @@ -84,24 +106,38 @@ def __init__(self, plant: Submarine, controller): self.plant = plant self.controller = controller - def simulate(self, mission: Mission, disturbances: np.ndarray) -> Trajectory: + def simulate(self, mission: Mission, disturbances: np.ndarray) -> Trajectory: T = len(mission.reference) if len(disturbances) < T: - raise ValueError("Disturbances must be at least as long as mission duration") - + raise ValueError( + "Disturbances must be at least as long as mission duration" + ) + positions = np.zeros((T, 2)) actions = np.zeros(T) + errors = np.zeros(T) self.plant.reset_state() for t in range(T): + # getting the current position positions[t] = self.plant.get_position() - observation_t = self.plant.get_depth() - # Call your controller here + + # calculating the error and error_dot + errors[t] = mission.reference[t] - self.plant.get_depth() + error = errors[t] + error_dot = 0 if t == 0 else errors[t] - errors[t - 1] + + # calling the controller to get the required action + actions[t] = self.controller.get_action(error, error_dot) + + # applying the action to the plant self.plant.transition(actions[t], disturbances[t]) return Trajectory(positions) - - def simulate_with_random_disturbances(self, mission: Mission, variance: float = 0.5) -> Trajectory: + + def simulate_with_random_disturbances( + self, mission: Mission, variance: float = 0.5 + ) -> Trajectory: disturbances = np.random.normal(0, variance, len(mission.reference)) return self.simulate(mission, disturbances) diff --git a/uuv_mission/terrain.py b/uuv_mission/terrain.py index a69f4932..91efd4f9 100644 --- a/uuv_mission/terrain.py +++ b/uuv_mission/terrain.py @@ -2,54 +2,59 @@ import matplotlib.pyplot as plt import pandas as pd + def generate_random_multisine_timeseries(length=100): t = np.linspace(0, 2 * np.pi, length) - - frequncies = np.array([0.05,0.1,0.5,1]) + + frequncies = np.array([0.05, 0.1, 0.5, 1]) # Generate a multisine time series by summing sine waves with random frequencies and amplitudes multisine_series = np.zeros(length) for frequency in frequncies: amplitude = np.random.uniform(0.1, 1) # Random amplitude between 0.1 and 1 multisine_series += amplitude * np.sin(frequency * t * 2 * np.pi) - + return t, multisine_series + def generate_reference_and_limits(duration, scale): - reference = scale*generate_random_multisine_timeseries(duration)[1] + reference = scale * generate_random_multisine_timeseries(duration)[1] - bound = 3*scale - upper_margin = bound*np.ones(duration) - lower_margin = bound*np.ones(duration) + bound = 3 * scale + upper_margin = bound * np.ones(duration) + lower_margin = bound * np.ones(duration) alpha = 0.6 - for t in range(duration-1): - upper_margin[t+1] = alpha*upper_margin[t] + (1-alpha)*np.random.uniform(0.5, bound) - lower_margin[t+1] = alpha*lower_margin[t] + (1-alpha)*np.random.uniform(0.5, bound) + for t in range(duration - 1): + upper_margin[t + 1] = alpha * upper_margin[t] + (1 - alpha) * np.random.uniform( + 0.5, bound + ) + lower_margin[t + 1] = alpha * lower_margin[t] + (1 - alpha) * np.random.uniform( + 0.5, bound + ) upper = reference + upper_margin lower = reference - lower_margin return reference, upper, lower - + + def plot_reference_and_terrain(reference, upper, lower): - plt.plot(reference, 'b') - plt.plot(upper, 'r') - plt.plot(lower, 'r') + plt.plot(reference, "b") + plt.plot(upper, "r") + plt.plot(lower, "r") plt.show() + def write_mission_to_csv(mission, file_name): # Create a DataFrame from the Mission object's attributes data = { - 'reference': mission.reference, - 'cave_height': mission.cave_height, - 'cave_depth': mission.cave_depth + "reference": mission.reference, + "cave_height": mission.cave_height, + "cave_depth": mission.cave_depth, } df = pd.DataFrame(data) - + # Write the DataFrame to a CSV file df.to_csv(file_name, index=False) - - - \ No newline at end of file