From 087e0ee9d7237f9fe424ddfd166402458c5c78ee Mon Sep 17 00:00:00 2001 From: krd2rng Date: Thu, 2 Oct 2025 15:18:58 +0200 Subject: [PATCH] first set up of pyprojetc.toml and corresponding action Signed-off-by: krd2rng --- .github/workflows/pypi-deploy.yml | 73 ++--- .../Introduction_Tutorial_PINNs.ipynb | 251 ++++-------------- pyproject.toml | 99 ++++++- setup.cfg | 138 ---------- setup.py | 16 +- .../utils/data/deeponet_dataloader.py | 74 +++--- 6 files changed, 219 insertions(+), 432 deletions(-) delete mode 100644 setup.cfg diff --git a/.github/workflows/pypi-deploy.yml b/.github/workflows/pypi-deploy.yml index 55dd2a5a..0c3bea58 100644 --- a/.github/workflows/pypi-deploy.yml +++ b/.github/workflows/pypi-deploy.yml @@ -5,41 +5,37 @@ on: release: types: [published] -env: - CIBW_BUILD: cp37-* cp38-* cp39-* cp310-* - CIBW_BEFORE_BUILD: pip install cython - CIBW_MANYLINUX_X86_64_IMAGE: manylinux2014 - CIBW_MANYLINUX_I686_IMAGE: manylinux2014 + push: + branches: [ci-debug] + + pull_request: + branches: + - master + - develop jobs: build_wheels: - name: Build wheels on [ ubuntu-latest ] - runs-on: [ ubuntu-latest ] + name: Build wheels on ${{ matrix.os }} + runs-on: ${{ matrix.os }} strategy: matrix: - os: [ubuntu-latest] #, windows-latest] + os: [ubuntu-latest, windows-latest] + steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - - uses: actions/setup-python@v5 - name: Install Python - with: - python-version: '3.10' - - - name: Install cibuildwheel - run: | - # python -m pip install cibuildwheel - pip install -U setuptools setuptools_scm wheel - name: Build wheels - run: | - # python -m cibuildwheel --output-dir wheelhouse - python setup.py bdist_wheel + uses: pypa/cibuildwheel@v2.21.1 + env: + CIBW_BUILD: cp39-* cp310-* cp311-* cp312-* cp313-* + CIBW_MANYLINUX_X86_64_IMAGE: manylinux2014 + - uses: actions/upload-artifact@v4 with: - name: artifact-wheel-${{ matrix.os }} - path: ./dist/*.whl + name: artifact-wheel-${{ matrix.os }} + path: ./wheelhouse/*.whl build_sdist: @@ -53,14 +49,13 @@ jobs: - uses: actions/setup-python@v5 name: Install Python with: - python-version: '3.10' + python-version: '3.12' - name: Install setuptools run: | - pip install -U setuptools setuptools_scm wheel - + pip install -U setuptools setuptools_scm wheel build - name: Build sdist - run: python setup.py sdist + run: python -m build . --sdist - uses: actions/upload-artifact@v4 with: @@ -70,24 +65,34 @@ jobs: upload_pypi: needs: [build_wheels, build_sdist] runs-on: ubuntu-latest - if: github.event_name == 'release' && github.event.action == 'published' + # Specifying a GitHub environment is optional, but strongly encouraged + environment: release + permissions: + # IMPORTANT: this permission is mandatory for Trusted Publishing + id-token: write steps: - uses: actions/download-artifact@v4 with: name: artifact-sdist path: dist - # - uses: actions/download-artifact@v4 - # with: - # name: artifact-wheel-windows-latest - # path: dist + - uses: actions/download-artifact@v4 + with: + name: artifact-wheel-windows-latest + path: dist - uses: actions/download-artifact@v4 with: name: artifact-wheel-ubuntu-latest path: dist - - uses: pypa/gh-action-pypi-publish@v1.10.2 + - name: Publish package distributions to PyPI + if: github.event_name == 'release' && github.event.action == 'published' + uses: pypa/gh-action-pypi-publish@release/v1 + + - name: Publish package distributions to PyPI test + if: github.event_name != 'release' + uses: pypa/gh-action-pypi-publish@release/v1 with: - user: __token__ - password: ${{ secrets.upload_pypi }} + verbose: true + repository-url: https://test.pypi.org/legacy/ diff --git a/examples/tutorial/Introduction_Tutorial_PINNs.ipynb b/examples/tutorial/Introduction_Tutorial_PINNs.ipynb index 1dc7c0cc..221241a1 100644 --- a/examples/tutorial/Introduction_Tutorial_PINNs.ipynb +++ b/examples/tutorial/Introduction_Tutorial_PINNs.ipynb @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "d6b5fdd2-67c1-4f7e-a185-9d515fb9f3f8", "metadata": { "colab": { @@ -51,18 +51,7 @@ "id": "d6b5fdd2-67c1-4f7e-a185-9d515fb9f3f8", "outputId": "b6f2faea-b0e8-4af0-dc49-e8312374062b" }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "u_0 = 16 # initial temperature\n", "u_heater_max = 40 # maximal temperature of the heater\n", @@ -147,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "6af0dba0-d481-4566-a8b7-244098eee713", "metadata": { "id": "6af0dba0-d481-4566-a8b7-244098eee713" @@ -166,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "1efe92cb-daab-4d21-8a43-5008e3e9248a", "metadata": { "colab": { @@ -176,18 +165,7 @@ "id": "1efe92cb-daab-4d21-8a43-5008e3e9248a", "outputId": "cdb09abd-279c-42b4-b96c-7f244d36d305" }, - "outputs": [ - { - "data": { - "image/png": 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kPoRwYwKR5VQgx56y6hbmIy2UtHtN97ps+h5qWhsAqGkMYE9ZNYKhMBZvkK+WzXoTs4rC0nLc+twniu9Mmt/3De9GeEeyvnGSRpdnE7yRfoyNlgLMmaEF/CHMUiaIdTqNxI3SPq+OhLxhZLFV0u7puZfZe5y9cMmVyQvlkMZy8dEKvKERBp8Vkehw3d5vNE0XeT/NwKpPTmq2wUkaXZ5N8Hr6UVqt5uf3ll273BYY0ZoRwo1JJGl/yYYjABqaP3daGLSW4GIk8qO1CkN6F1s1Wz7pvdKTvabv0TElketNjNZ40hNVc01yAj793S+Q0KZJyU0SQTfsZ9e4zn+DZxO8lr9MJJmpiQAaMKZPpuI1rCOErFo37bwvDwjhhgITcrIxsuc12Fy4Cc/+KldXhmIeBAAtwcWII6Fbw2BJ0LPYAuraPZKEZADw+ztyNO9BstmSmhXs3sRojSelsazEuYbL2H+6pnmzI83p5LbEljw73KqlbZB4YHg3jOmbhYGdU7C5cJPdTSTGqnXT7vs+NamX4XvSQvjcUEJaqCbekE0cBs1DWKVWyOrGL8p1OxK6NQyWFDXnxEiyCWz50r3U7vPvI7qr5srR4yzJox8ZyXhSKwgqQSOqBmgScHY8NgprZwzDyrsGYO2MYdjx2Kio9+g2/w2tMR0GW4FNqb+z0xLx8q8H4cnb+nGfnsKqdZPFfee8V2LonjQRmhtGsAirjNUSDe7aQTNk9ckPS3FOxaE11gdDJDZsQumEf01yAu4YcC3G9s0i1tKp3WvxHTlEWY5JNQ60/MhoaSRJxtP8vx3Gwo+OoqJO/VRKI6pGgsR04Tb/DWkMzf/b4RYO6u2TyCLJrMTJ/W3VusnqvpHXsRoZQrhhAAsBQE59mJ7sVY3qCQOqgk0k0um2tTikkkBzsaVxr7F9s5CS6MWuk+cAhJH30wwMkznNmvUjo6kCJyod0RgAED2O5Q4Jev2EaJhaWPtvWEGtTORdbWOAi3Izkf3Nqy+IHFatmyzvCwD7T9dg+PXK/k1WIoQbBtgtAChpifSGK6shnW55c0hl7dNEc3NTu5cRh/C/HvhOUeAw6kdGWyNpdJzIHRKMRNU4zTfGSpykleXZF0QOq9ZN1vetqvfrui9NmPrcvPTSS8jNzUVqaipSU1ORl5eHTZuUnb3WrFkDj8cT9V9ionPCKSXsFACM+hlEopXcLNIHg6eoio1flGPIH4q5SxVPGy3fLaM2d71+ZFYkesto5yO+Vu43I3OvaPkTReJU3xgr4TnXTSS8+4LIYdW6yfq+ZuavWZhqbjp37oylS5eiZ8+eCIfDeOutt3DHHXfg4MGD6Nevn+x3UlNTcfz48ea/PR7nnarsFACM+hkAV9Xy/5Kbhdf+3ynF6yJPtySRQunJXlTUXcLOE1VAGKhq8FPXqizZeFS2ZEE5JZ8m1hohCS1NyQvTBmLxhmO2nLbNaiRj+7SmwY9nPpZPKKgH6ZCgVQw1DOD+4d10+UO1JnjTysrhBF8QOayKRmN5X6Ap8zcrmAo3t912W9Tff/jDH/DSSy9h9+7disKNx+NBVlaWHc2zDDvDKo0uNNLgvL1/Nl5VqWv0mxHdo4QEkrDM6oaA7AmKVqj4xi++N1WLSQt5/6UE/J7QuZcWJGaC2CrictfRMoGa2fz05J7RS+QhYUJONl6YNujHfrnqT+a0vFQs4Ekrq4QTfEHkkNbNh945IPvvRqPRtAR6WHhf6TpWcONzEwwG8f7776OhoQF5eXmK19XX16Nr164IhUIYNGgQ/vjHPyoKQgDg9/vh91+1+9XV1QEAAoEAAgF6PifSvUjv+dSkXs0bvNzAeGpSL4SCVxAKmmtXRlIb+OK1zQAdkhJQ0xix2KcmYt743nh285dIUPi+B0Dh4e8xZ3SPqEE8ulcGXpzWH0s3fRkVwaJFTf1FzF67H8/fOQDtk7yoqvcjo50Pg7t2iLq/Wl8HQ2E88/fDms9cXX8Ru0+c1S1AFh+rxJz3ShAG4Iu/+nnDJT/mvLcfh7/pirnjeuu6p1H2lFWjuv5iVDtiabh0WfXfJc7WNiAQSI36TO+YJh1rGUltou6p1KdaZKb44L8SQu3FgOIhITM1EQM7pzT/XvGxSizb9CUaLvmbf6tDkhdP5F+P0b0yqK4JpOjtZ1YM7JyCrh18qKxTPpTF9rfdnK1tUB2Dvrimf6uqa+Suv8OhoGrbw6GgoTYrrcdZqYmYn9/b8LhXu+9j43sicLqEeh/ruZ8nHA4zLRJz+PBh5OXl4dKlS2jXrh0KCgowceJE2Wt37dqFr7/+Grm5uaitrcWf/vQnbN++HUeOHEHnzp1lv7Nw4UIsWrSoxecFBQVISkqi+iwCgUAgEAisobGxEdOmTUNtbS1SU1NVr2Uu3Fy+fBlnzpxBbW0t/vKXv+D111/Hp59+ir595SsTRxIIBNCnTx9MnToVixcvlr1GTnPTpUsXVFVVaXaOHgKBAIqKijB27Fh4veTW3GAojP2naxS1FDSQTsaAvJZo+ZQBsunINx4ux7y/fqF5/2d/lYuJNyir8/eUVeP+t/bqaHE0se1U62vSNgPA6ulDdGluSJ+jQ5IX2377C8tVsuTtScD5xsuqp+3Ns0e0aK+RMa13rJkZG9K4Kz5WqXgqlX4rGApj/IrtippEtX6wGqNrBytI+psV0ntW0i4lxoXxzI0hjB4zBr6EBNvbpwTpPFBbs+zYS0ixakzX1dUhIyODSLhhbpZKSEhAjx49AACDBw/G3r17sXLlSrzyyiua3/V6vRg4cCBOnDiheI3P54PP19Jj2+v1WrKQ6L2vF7Dc9puf2xmeuHjduUc6piXDH9SeHB3TklWfuarxCtF91PAAeGbDcYzL6dTsCCjX16RtviY5AcN6dNQ1+Umfo+LCFRz89oLl+U2G9eiI9HZtNX23npzUFzMLmmz5cgLHgkn9kOhTXuj1jGm9Y83M2JDGXX5uZ4zL6aTq4L3v5DmcrvFDLWf06Rq/Le9NCavWJNqQ9DcrvGgazw+/Iz/epRb6EhK46mvSeVDVeEW23byWu6E9pvXci7lwE0soFIrStKgRDAZx+PBhRTOW4CpGksDRcnym4WAY6fh643XKEjtpLabFKrWYlNDzHHZEjJA6C07IycZLcdrZiWmhZ6wZGRty404rn5ATIn2cBM/JCdWycT81qRcul+1n2Dp5zDhrs8h27wSYCjcLFixAfn4+rrvuOly4cAEFBQXYtm0bNm/eDAC455570KlTJyxZsgQA8Mwzz2DYsGHo0aMHzp8/j+eeew6nT5/Ggw8+yPIxTGNXWLHeBYmWp73eQpJqNG0+ysJNZJuVfqupFpP+yT60ezrSkxOiomyUsCtihLSsgt2p6UnHmt6xETnuAGDXyXNEz+OESB8BPZTGeyh4BRuVAymZYfQg6aTEinbDVLg5e/Ys7rnnHpSXlyMtLQ25ubnYvHkzxo4dCwA4c+YM4uKu5hmsqanBjBkzUFFRgQ4dOmDw4MH47LPPiPxzeIVXdaIE6eapBkl4OCkkm49Sm9OTvT+GaysXmVQjPs6D39+Rg0cK5MM1JewuLKkmuPCSj0cJvWNDGncAcMuyrcTzhueq1gJrkBOwzUafWoXRg6Qod6MMU+HmjTfeUP33bdu2Rf29fPlyLF++3MIW2YtT1Ik0Tv1KAkckcR5ALXmtJDSEgldsabMcE3Oz8e/fdlfMo+MBm5T9cgs574KzhNLYyP7RX6hDckLUOyw6WqF73liV70MgoIWRgyQNcyvvByCjcOdz01pwmjqRho09VuDIaOeLylBc0+DHzIKDiqf32/tnIz7OQ3z6ssovYMHEvujfuX2LBHk8CQ5OEZwlSIVRM/OGhhZS0Pqwc/PXeygza25VOwA5tcK6hBBuGNFa1YlaAsdvvj2vqBV5dXsZBl7XAaN7ZVjVPGIm5l6L8TnZXE5+pwnOEiTCqNl5Y7fvkcDZsNB+6jmUmTG3qh2AHnrnANoneXG+kc/DGwlMC2e2ZkT0RkuCoTA+OqRc1DIMYP7fDmP3yXP2NUoFaRG6Y0AnosKSdmFlgcNgKIxdJ8/hw5LvsOvkOV1FMGlAY97w+t54hvV7Z4HRgrN2IplbgZZJDtTMrSQ1uCIFG4Cv5yZBaG4YIaI3WkJS5PN8YwAP/nkfnh3alEwsP1c+M3VrxirB2egplqZaX8wb+3GK7xZNeNJ+as0fI+ZWIwWVSZ67ua21Dc1/s8omJIQbRojojZbo3WznvFcCT1y8axdYo1ghABj14aG9MYp5Yy9O892iBS9uA6TzR6+51ahFQO25I9vqiw/j2aHA+BXbsWBSPyZjRJilKLOnrJpIdWtUnehmjJy2F/39aKtQketBEgCURo4H+sLVSVTYcu/BCrW+mDf2YfS9uwEe3Ab0zh8lc2swFMbOE1X40+bj+NPmL7Hz66qmYA4TxD63Ulsr69iZsoRwQ4niY5UAgPvf2otH15Vg6mu7ccuyraovVVInZqVFb+pZaYmuPRGpobUpx2LGd8TN0BYA9p+u0e3DY+XGKOaNPVjpu8U7rM2fwVAYCz86Ynr+FJaWY/Dvi3D3659j1ScnsOqTk7j7jc/xyLv70T7JS7zWxhL53LwKwcIsRYHC0nLMea8Ey4ZGf06iuhXRG1cxmuzPCU7XdueSoBn2XFVPVg4l8j1YrdYX88Z6eNBesIK1+XPV1hOoqFOedyTzp7C0HA+9I59wtPbi1VxhetZauefmxYQXixBuTELD8cxMPha1TdOJyZlIkv3FwrvzKCuHTFoCAKkKO/I92LEx8lzfyA2w1l6whGXSx8LSciwv/oroWqX506T5Oar5/fZt2yDR2yaqwnuHJC9qGgPEz82rECyEG5NIUqsvXv7frZRa1TZNAI6NcJA25d3/PIeZ7x7A+YsB2ev0+o6wgLVDJg0BYHDXDrpPsa15Y3QLrLUXrGGR9FE6LJOiNH/2lFVHCSxKnL94Be9OG4y4OE+LLOCkz83rXBfCjUlYSa1aCZjkcFKEQ3ycB8N7ZGDpr27Awz8+j9wCy7PzKE/hpGYwcopt7RujGxAlK+w3f+oJ0VY72OnZb6oa/LhjQKeoz/Q8N69zXTgUm4SF1EriwCUHC+cus8m/lJxHAWD5lAFcC2lucsjU68Tr9Kim1pi0Tg7hvG1v0kc9Qona/NGz3yhdS/rcvM51obkxiSS11tRflP13EqlVr2+MkQRMEnY6d9HyNWlRkyqpDaqO7caYPplWNNswse+RRC0MOMchU+8p1qm1nFpj0jo1hPO2cfSu7aRCyZwxPVXH4tDu6chK1V6DslJ9VDQqSnM9MzWRWZ4bIdyYRJJaZ6/d3+LfSKRWIwspjc3Q6g2Vtq9JpO9IIBDAxmP622Slg7Xce0xPJsvN6SS/E70+PE7bGFn7SPGKcN7Wj5G1XcvEI91j1qieqr8dH+fBwtv7KrooSCy8vR+1uRg112sbgG8OYvPsEUj0JVC5v16EWYoCE3KysXzKgBafa6lujSY5o7EZWrmh8pj3oLC0HLcs24qpr+0mzkOk595y7zGyYrgcTnCIpoFTajnxOG4FzsTo2q5l4vGA3MQzIScbL/96ENontTxktU/y4mULBHVprk+8Ibv5b1YIzQ0lxvTJxMYyYPX0IahqvKJ5QjXjbEoi3Sthh3MXb3kPrDyNq73HSFqrQ6aT4G3cCpyJ2UACmubcyMjTXSfPAQgj76cZGMbxIYMWQrihzNDu6fB6tc0RZhZSrSiGsMz/l/4GrN9Qecp7YHXEEqn/U4fkBFQ3XG7+m3e/k9YIT+NW4FxoCMk0zblS5OnwHhm6v+tkhHDDCLMLqZZ0D7TMc2PFhirnx8JT3gOrT+Ok7/HJSX2QldbWEX4nrRUa49aJiTMFdKElJAs/J3MI4YYRNBZSLeneakdOJYe5Jyf15SbvgdWncdL3mJXWVixUHKAmfJjN1yGirAQAv0ntWhtCuGEErcRHatK9lZK/mh/LzIID+M2I7nh1exlzXxOrFxpeE1ip0Vq1C1rCh5mkdSLKSiDhxDXBjYhoKUbwmvgI0E5gRhJV8tGhcrwwbSDz5F9alcbNRixZ+R6tSCRnZdSYGaxOmkcavWIkaZ3WfAhDRFm1JmitCSKRpDmE5oYhPCY5I1Gtk/qxdEj2Ycdjo5hqCexIIW/kPWppT6wwcfCqXbDanKPXqVyvMyeJU3l57SXs/uc5xHk8hueCUzVuTm23GZTWhPTkBCy+I0dzXAsTp3mEcMMYnpKckW5+evxYeHCKs0OI1PMetRYuGkJI7IYyuGsHLutc2SFwGXEq1zNuSefDjLf3ofFysPlv6Z2P7qUdxeLUzc6p7abBhJxshEJhPPFhaXPOq3MNl7F4w1HExUEz/xlvhxCnIYQbDuBBANBzunWiw5wdQiTJe9RauF6YNgiLN5gTQuSzJUeHosvd2+4cLnYVFuXFqTxSsAGuvvMXp/VX/Z5TNzuntpsWhaXlmFlwUNfzu6XYLg8InxsBAH2nW6v9WKyCdaZcEl+lJz8sNVVsUzlbsrJgE4mdOVzsKixqh1M5aamNSKR3vnTTl4rXkPjzPP7BYfx1/7d44//9Ex8c5MM/o7Vnezb6/G4qtssaoblxAMFQ2PIMk3pNTVb7sbgRkoXrnAkhhDRbshp2atvsSppndfRKfJwH/zqgE97YeUr3d8OAanFDEn+e6oYA/vP9Q1GfsTb96D0s8WCWp4leU6hkRt5E6NgvEklqI4QbziksLcf8vx3G+cardYpWfXIS7ZO8WPrLG6gtXnpPtzw6Q/MOzQVJ7n2ZqRbPIjzVSo1KrM/Rk5P6YGbBQcuE8TF9swwJN1oYHTPljE0/pO0uPlqBuf9b4jqfHD2Cu5wZWQueTP68IoQbjiksLVes6nq+MYCH3jlArfiZkdOtGT+W1hhBQbogpSd7UdMQ0K1lMLoRstK2WaVRUXJi/c2I7vjoULmiMG5mTJqp9yaH1JavK+tN3YeVfwbpWJcTCN3gk0P6/KeqGrCi+GviMSNy5JAjhBtOCYbCWPjRUc3rFn50hMriZdTUZMQZurVGUJBu5ka1DHqEp8iK5ay0bVaYN9WcWF/dXoYXpg1Ch+SEFgKM2TGp9ixqeABkpSYCaIh6Br0neTlYFvokGeseDyDncuMGx1nSub52zxldYwUQJn9ShEMxp+wpq1a1xUtU1Pl1O5cpJYcyksBML6TJ1NwIaXKvibnXGnoPpI7euxeMwdoZw7DyrgFYO2MYdjw2iplQSXPMkThxLt5wFEO7p0c5ldMak0rP0iGpydlY6Z3Pz+/d/JlSW8zAwj9Da6yHIS/YSDjdcZZkrt815DpU1PmJ72l3AlSnIzQ3nKJnQdJzrdYJ1cqQaRHmSO6rZOQ9kGpCEtrEMU89EAmtMWcknw3tMan0LEVHKxTf+eheGdhYRschXA5W/hlqYz0/JwurCXyUnOw4qzXX/VdCRPe5J68r8nOyW4XpniZCuOEUPQsS6bWkeSesyrtjdYVup0C6mRt5D0519KYx5oxEX1kxJuWeRe2dBwJNJsL9p2uINDaPjPwp1u39VjO8nwf/DKXn3lNWTSTcON1xVu29N0W/apOfk+3q9dAqmAo3L730El566SWcOnUKANCvXz889dRTyM/PV/zO+++/jyeffBKnTp1Cz549sWzZMkycONGmFtvH0O7pyEpN1DRNZaX6iBYvHrQmdoX+OgErEzfylPXaToxEX9k5JrXeeVU9mYmiV1Yq/vivObIHlVh48M+Qe27JhKomzPGYK8sISu9dFNi0FqY+N507d8bSpUuxf/9+7Nu3D6NGjcIdd9yBI0eOyF7/2WefYerUqXjggQdw8OBBTJ48GZMnT0ZpaanNLbee+DgPFt7eV/O6hbf3I1q8eEgORbr5ZCT7LGtDa4F1wkIWGEkuaXU4up7ChxntyMZ9x5TEZg1ddpp8u7I598+Ij/Pg9v7qbbu9f7arxy3PxZPdAFPNzW233Rb19x/+8Ae89NJL2L17N/r169fi+pUrV2LChAn43e9+BwBYvHgxioqKsGrVKrz88suyv+H3++H3Xz0R1dXVAQACgUCzOpgG0r1o3nN0rwy8NK0/Fn50BOcvRt+3fVsvFt7eD6N7ZRD95tnaBvjita35Z2sbEAikGm6zGgM7p6BrBx8q69TDZRf8tQQLJvbGmD6Zsv9uRV8LWuLEfn5qUi/Mea8EgLzP0VOTeiEUvILQj5UQtMakB0BmaiIGdk7R1Q/FxyqxdNOXUZrXrNREzM9vOa6l+/bv1E5zfmRFtGV0rwyM7Plz7D9dg7N1l1DTeBkdkn3omOLD4K4dokxevBEMhVF4+HvVNanw8PeYM7oH1c2dtzE9ulcGXpzWX3GskK7vvGFVP+u5nyccDnOR/zoYDOL999/H9OnTcfDgQfTt21Jrcd1112Hu3LmYPXt282dPP/001q9fj0OHDrW4HgAWLlyIRYsWtfi8oKAASUlJ1NovEAgEAoHAOhobGzFt2jTU1tYiNVX9EM7cofjw4cPIy8vDpUuX0K5dO3zwwQeygg0AVFRUIDMz+tSTmZmJiooKxfsvWLAAc+fObf67rq4OXbp0wbhx4zQ7h5TiY5X4783HMOv6Rjy5Lw7+kEfxlMaKYCiM8Su2a55QN88eYbkatPhYJZZsPIbKC8o+BmrtCQQCKCoqwtixY+H16q/pIyBD6ufRY8bg0Hf1qKr3I6PdVa0AzwRDYew/XUPcZj2aFq3fHb9iu6KvnNy4jh3P//2PL7H6s9OK318+ZQA364pRNh4ux7y/fqF53bO/ysXEG+iZ1sTaYQ9W9bNkeSGBuXDTq1cvlJSUoLa2Fn/5y18wffp0fPrpp4oCjl58Ph98vpa2bK/XS6XTC0vL8UjBIST8qF71hzzwBz04U+PHIwWHuLF7ewEsmNQPD/+Y8VhOZb9gUj8k+hIsb0t+bmekJiXi7tc/V73udI0fB7+9oOiESesdCtSZtGoXTtdcFUSdkHDRC2D49eQCQH5uZ4zL6WTaCXvfyXM/9pXy95TGtdfrRVx8G6z/4iz8QfnvewA8s+E4xuV0Yi5gmsno3DEtWfEZY6+zYo4bWTtaY1Z1s9Beo/Xci7lwk5CQgB49egAABg8ejL1792LlypV45ZVXWlyblZWFysrKqM8qKyuRlZVlS1tj4SECSQ+kYcJ2TGLSyBBWkVNiIWvSZgBSYcerz66UHt/pfcYqHF0iGArj7d1ljkiXYDajs9MihVprVnU1eJ/vzIWbWEKhUJQDcCR5eXnYsmVLlM9NUVER8vLybGpdNE7M26IVJmzXJLYySsUsYiFrWriWbvoSc3u3/Dc5wV30WRNmxvX4FdujNGRqsEyXQJovSw0rSm9YBY3ndRtOmO9MQ8EXLFiA7du349SpUzh8+DAWLFiAbdu24e677wYA3HPPPViwYEHz9Y8++igKCwvx/PPP48svv8TChQuxb98+zJo1i0n7nZq3RSlM2M7SCEbCdmmhFqJrtA/0hv3yjlb5j0jBvTWX1IjFyLiO1pCRwSq5HUmJi0V/P0o0/u0o92IWms/rFpwy35lqbs6ePYt77rkH5eXlSEtLQ25uLjZv3oyxY8cCAM6cOYO4uKvy180334yCggI88cQTePzxx9GzZ0+sX78eOTk5TNrPs/ZBDTl1IgBbTWysTm5qJ46xfbMM9YHcPdu39eK+4d0wa1RPLk6feiEVyCtqL+LZzccdY5q1Gr3jWk1DJgdrcw1tbTXvCSedqJ23ElJXjJE9f25zy1rCVLh54403VP9927ZtLT678847ceedd1rUIn1E2o3loLkQ0bJvKm3udw25zvZJbHepAC318uwxPXX3gdI9z18MYHnx13jzs1NY+ssbuDiF6oFUIK9uuCwW/xi0xvXYvlnYdfIczl64hKoLfmKNDQ/mGiu01VZm6zaLU7XzVkEq7O0/XWNfoxTgzufGScSe0iKhuRDRsm+qbe7Li78iugftSWzXyY3kxPEmQa0b4GofkBQ6PN8YsMQub7Uzn1T+A2iQ/XdJcE8nzKrbWhZ/CbUCmrcs2xo1l33xZPfkoT6YU7XVRmltz6sF6TwmDRixEiHcmEQ6pS3ZcASRGwGthcioM1vs5je4awdN2zEJVkxiO05uJCeO2CzQSkh9oHXPyHtLphnpe2aEEjuc+eLjPJif3xuXy/arCu5pbclSB7SWxT+S2HGtNJdJeHJSH9w7vDtzc43TopzMwuPzsoxSIi6h086HKovbooUQbigwIScbI3teg82Fm/Dsr3LRMS2ZyoAzGmout/mlJ3tR3WA8FbbTFy3SE0f7tl7UXgwQLWR6tBHltZewauvXWLf3G1NCiZ2RG2P6ZGJjWVPSucgonkjBPRgKc7f48wiJlk8Oqf94EGwAZ0U50YC352UdpUQq7A3u2gGbj1neHFWYRku5CWlwT7whm1qhQiPFLpU82fUINm4s4kZ64rhveHcAZH2gVxuxvPhrUxEGrCI3Ns8egbUzhmHlXQOwdsYw7HhsVPNCqlb8T2rXk5P6OHbc0IJUyxeJnnlnZ7SeE6KcaMLyeSPf68rir/AQ4yglJxX7FJobjtHrzGb0dBjJnDE9W2gXeLD1m4X0xDFrVA/0ympH5OQs3VPvphWJnogiVpEbWmZDJQdaicUbjiEuztOs6eE1MsZKjPgckc47Fqd53qOcaMPieeXeqxx2RyWSBILwUOxTCDcco9eZzcjpUOLq5t4Ts0b1pD6JWW9qetTLpAuZdM+HfixpYRRSoYTnyI0JOdkIhYBHClr2RfmPJ8vfjOiOjw6Vc534yypI5/KTk/ogI8VHPEdYJpjjOcrJCux8Xr3+WXZHJTpBuBXCDcfodWYzuqnJqRNpThDWdmIJPaHnpAvZhJxsvPzrQZj/t8M439jytBIrSKmh9f54jtwIhsJYvOGo4r+HAbyyvazF560ly6vWXAaaCnXq8a1xWvkXARlmNPB2Hmx4F26FcMMxep3ZSDe19OQEVDdcbv7bSrMTb6nLrThxSPdctfUE3txZFhV1lZWWiCk3dsGKLV9r3icjWT2smmyD9DFx3jWqNZQ24f/6oBQXLweRldaW+QnQCi0jyVyen99b1++IBHPORmmcmdHAt8aoRCWEcMMYrYVUj7aBVNPz6e9+gf2nayxXJxo9WVptwrLixBEf58GjY3pi1qgeLdq++5/nyG6i8YhqG6TEpSshFB2tsF0LYubEGAZwruEy5vzvIQDyWj27zJpWahmV5nLmj/mExvQhr2IO8G2mFKijNs78V0K67yeiElsihBuGkC6kenxAbu+fLav+l3j6tr5IaBNny0nOyMmSFxOWUeQEJ9KEViTXSRukkhms1qKkgVrQPDFGavWaNGJf482dp6I0YlaMCTu0jHJzeWDnFGwu3KT7XjybKQXKKI0zyTdt9pjrdd2PtyglXhCh4IzQW3xMqdhl7D1fVRFsfjOiu60bnt6TpVMKsumF9iY0tm8WEtvIp7VlVcxvaPd0tG/rpXIvqdXz/3YYg39fhOXFX7dIsEh7TNgZZk8yl0lgWXzWLtxWkFbLnyYMYN3eM8hK9Wkpcptxawi+WYRwwwArFlISJ7SPDpXbujjo2dTdXH2X9iakp2K3XcTHeZpzBNEgjKbSFXLaKenfAXpjwkhOKdY4KeeIEQpLy3HLsq2Y+tpuPLquBFNf241blm117CEHIPNNK6+9hKlDrwMg/149AOaMuV4275TgKkK4YYAVCynppLFzcdazqTtxcyGF9iZkha8FjRPyrFE90D6JjvaGBD1jQuv5nOq/4taEem7V4lbUXiS67rr0JNn3mpnqw+wxPdEtI4nL8GueED43DLBiIeVxcdYT7aWv/amUW2o9NCug0zZzKfk5PTWpF3GbgKb3vfSXN8jm/ZHef/skr6I2xihaY4fEj8vJ/itOyDmih2AojIUfuTPEvar+svZFP143Y8RPo97rqapGrN1zBsuLr0ZeOskf0W6EcMMAKxZSXhdn0k2d1/bThNYmRLOYn5oT7Zz3SrBsqK6mNef9UXrf0vNX1F3C4o+PmKp3JqE2JkidhHkskKgH3nOO6Il2W7X1a2KzK8/PLMf5RjLhRrpOeq+FpeVYUfwVNyk1nIAQbhhgxULK8+JMsqnraX8oeMW2ttOGxiZEq5gfiZ+TdF2cjs1J631Lz9/WG4eHf9TyGPWaUfNV0puKgKcCiRKsM3vTQE8EZGFpeZRmQg3eTIQkeAhfXeR1ViVrdMPYUkMINwywYiHldXGW0NrU9bQ/FLSypc6AhpmLxM8JAF7d/k+s3fedrvB8EiFO8RlSfbh0JYTaRvnq7JGojWlSP641O8tw7/DuVE2HNHB6WgRAX3i9tImT4kQtbt5PM7Dqk5NE10lYkazRDWNLCyHcMMKKhZS3xVkvTm+/3Zg1c5GefF/YdgL+YPQ9aanDlZ6h6GiFarLC9kleLP3lDaq/Tfp8izccw+s7yprHGA/+K7xl9jaCXo2Dnsy8Tg1xH/azazT9ztoneTEsQkih7U/phrFFghBuGGJlKQDWi7NRnN5+uzFj5jJz8qXp2Cn3DEqCbvskL+67uTtmjeqh+Zt6ni92YWfpy+GWmlF6NQ56zEy0tdB2mWjUnO4llv7yhqjfpumP6JaxRYIQbhhjVSkApznaReL09jsFklpValjt2GlW0NXzfDwt7G6pGaVX40C6ic8Zc71pzcKesmpUNV5Bx5RE1DRcxuIN9ploJKf7hR8dQUXd1azkWak+LLy9X4vfpOlP6ZaxRYIQbjgn9kQxuGsHW+pCCZRh7YhH6/dJ/JxIsNKx04ygS1KLKxJeFnYe0zoYQa/GgbQw7KxRPQy3qfhYJQDg/rf2tjC1RmK1iUaP4E7Tn9ItY4sEIdxwjJzTV5wHiMw/5jYnMN5h7YhH+/fV/Jym3ngt0PiV5j14duxUej41WC/sbkmLoFfjQLKJL7y9n+GDRGFpOXF6Azs0eXoEd1r+iG4ZWyQI4YZTlJy+YhPHus0JjGdYO+JZ9ftKp8hAIIDNhV+pZpjmOfeLhPR8a3aWYfGGY5rXs17YeU7roAcjGgerggpIytPEwosmT4KGP6JbxhYJQrjhED0TkSdfATfD2hHP6t+XO0WGIu7DY3oBPcTHeXDv8O54fUcZ9ws772kd9GBEWLEiqEDyNfHJ15tVhbUmLxKz/ohuGltaCOGGMXI+NX/edYpYhQ7wd8JwI6wd8Vj+/vIpA/DMhuOqmxNrPyQSnLSwuyktghFhhXZQgRkBhbUmjybBUBhpbRNw//Bu+KDku6gM4U4cW2oI4YYhJD41euDphOE2WDvisfz9MX0yMS6nk+LmxNoPSQ9WCA1WCXZuSovAOgLSiIAiafJCoTA+LPnO0f0PyM/T9OQETB5wLcb2zXL0s8khhBtGkPrU6MFNJwzeYO2Ix/r3lTYn1n5IRqApNFgt2LEWCtyC5GtSU09WlVvS7F0MBHH3G583f86r0K6F0jytabiMN3eecp1gAwBxrBvQGjHi3KaGB87N2OkUpMVRzbnWynfA+vflIKlNtejvRxE0I7FbhCQ03DGgE/J+do1hwebhdw60MBdKgl1haTmt5gpMIpkkSWmf5AWAFpmEnfhunTxPzSCEGwboSTOuBW++Am4lcnGM7WU73gHr35dDjx+Q22itG4aTmZCTjeVTBrT4vH2SF2lto40YSuURnPhuW+s8FcINA8z4RcTuXVlpiVyq/kkIhsLYdfIcPiz5DrtOnuN+sZD8NbLSok0/dr0D1r8fC2s/JJa01g3D6YzpkwkAWD19CFbeNQBzxlyP2sYAai9eibpObSVy2rttrfNU+NwwwKhfxJOT+uDf8rq5IkOxk5xQI2Ht5Mn69yOx2g/I6ggsM/dvrRuGWxjaPR1x8W1wy7Ktht0DnPJuWfvrsYKpcLNkyRL87W9/w5dffom2bdvi5ptvxrJly9CrVy/F76xZswb33Xdf1Gc+nw+XLjljoAH6a/pIXvv3Du/uCgdDJzqhRsL6HbD+fQkrE4JZLfyavX9r3TDchFn3AKe829aUuC8SpmapTz/9FDNnzsTu3btRVFSEQCCAcePGoaGhQfV7qampKC8vb/7v9OnTNrWYDmr+E7G4zadG+Cq4B6v8gKx21DVy/1gT6uCuHbhz8HYbVputzWhe2rf1IhQOO2Kd4tFfzw6Yam4KCwuj/l6zZg06duyI/fv3Y8SIEYrf83g8yMrKsrp5lqKUbyM2z42TEiuRqPlZJ8MT0IV23hirMzEbub+Sluf2/tl4dXuZ4YSAzfOltqH5b6/uJ3IndpitzWhezl8M4O7XP3eEKR0gn6dOSMZJClc+N7W1tQCA9HT10059fT26du2KUCiEQYMG4Y9//CP69esne63f74fff7WsfF1dHQAgEAggEJD3iDeCdC899xzdKwMje/4c+0/XoKrej4x2Pgzo0h4l35xv/ntw1w6Ij/NQbasVFB+rxNJNX6KiLmLipCZifn7vZic+ADhb2wBfvPZp52xtAwKBVNl/M9LXdhMMhaPeq/QenQRpP8uNY6Pjdk9ZNarrL6qmya+uv4jdJ84a0orovX/xsUrMea8EYSDqOzX1F/HWzn/ioZ93xcbSStlxP7pXhuLzR84XX1wYi28EbvufTzF3fJ+o+dIaUevz2Wv3Y/mUAcR9FDsP+3dqB6BpTA/snIKuHXyorFN2D9BKqmqkTazQmqekazgJVq3Reu7nCYfDXOjVQqEQbr/9dpw/fx47duxQvG7Xrl34+uuvkZubi9raWvzpT3/C9u3bceTIEXTu3LnF9QsXLsSiRYtafF5QUICkpCSqzyAQCAQCgcAaGhsbMW3aNNTW1iI1Vf7wK8GNcPPwww9j06ZN2LFjh6yQokQgEECfPn0wdepULF68uMW/y2luunTpgqqqKs3O0UMgEEBRURHGjh0Lr5d/5TItKT0YCmP8iu1R94nEAyAzNRGbZ49AfJyn+Xql01Ls9XLw3NeRp85IpCdxwglPwqp+Vht7qYle3P/WXs17rJ4+xLDmhvT+AKi3RW6+NGluQnhyXxwuhzya49/N6Hk/an2uNA8T48J45sYQvF0HYGy/a5uvVVsLabWJV/Su4SRYtXbU1dUhIyODSLjhwiw1a9YsfPzxx9i+fbsuwQYAvF4vBg4ciBMnTsj+u8/ng8/nk/2eFRujVfelSWFpOR4pOPTjxL86WM/U+PFIwSFd0Ur7Tp7D6Ro/1FyjT9f4cfDbC8j72TXwAlgwqR8efucAAHlfhQWT+iHRl6D523J9zdJmHAyF8cyG47gUlP89D4BnNhzHuJxOjtq4aI5prbH3wrRBSG/XVjOyY1iPjob6cFiPjsT3//iL7+FXeJeRVDVeIe4ftfniD3ngD3qi5ktro6rxiuk+15qHALBs89cYn3sd4uM8yM/tjNF9r8Wfd53C6epGdE1Pwr/ldUNCmzhqbeIZvWu4Hmjvh3ruxTRaKhwOY9asWfjggw+wdetWdO/eXfc9gsEgDh8+jOxsvh26eIF2tJKRfB9WJaMrLC3HLcu2Yupru/HouhJMfW03blm21bZU6SKxmzokY2/xhqN4clIfAPKRHWEAdw3pgo+/+N5QBI2eyBErwr1Ffhx1aPQ5SYh3Rd3VeVhYWo5bn/sEizccw9u7TmPxhmO49blPmtcNt4f9u3VMMtXczJw5EwUFBfjwww+RkpKCiooKAEBaWhratm0LALjnnnvQqVMnLFmyBADwzDPPYNiwYejRowfOnz+P5557DqdPn8aDDz7I7Dl4QktzQTtayejEp52MjofcOW5dJGhBOvY6JPtkIzvSfqz3s7z46+bPjESrkEaOWJEfRM98cVPkCik0+lzPPCRZN8b2zeI2TwyNMeJW4Y2pcPPSSy8BAEaOHBn1+Ztvvol7770XAHDmzBnExV1VMNXU1GDGjBmoqKhAhw4dMHjwYHz22Wfo25e8KJpbIQmfpL0Bm1mMaCWjoxE+LBYJ69Ez9u4Y0ClK+D1V1YgVxV9RE15JhGtJy/PwOwcMh3vHQjpfahou45ZlWx2XwdssNPqcdH5ltPPht+8fIlo3aI8DGtAKl3drkj/mZim5/yTBBgC2bduGNWvWNP+9fPlynD59Gn6/HxUVFdiwYQMGDhxof+M5gzQxGe0NmIcEUWbNQbTMWTxW7uYJvWNPEn7/JfdarNt7hnriR5LK4LRNqCTz5fb+2ZhZQCeJodPqtwHm+1xrHgJNDsMIg3jd4K2uG81Elzys4VbAhUOxwBx6NBdWSOm0E7npxYw2iqY5y4qTvpswOvZIhdfd/zyHOI+HuhmHtglVab5kpibisfy+WLzhGJUkhlYnwrPSbGamz0nm4fz83qhq8Mt8uyXSusFLXTcrEl2yXsOtQAg3LkCvH40VG7DaxLfad8CoNkosEvZiVPgjFV5nvnsA5y9eTfJFcyOnXc8rar7UNgDfHMTm2SNw8NsLVHzirPZBsyODsJk+VxMggQaM6ZOJfWfqiO4VuW7wUNfNqizvvAhvtBDCjQvQq7mwagOWm/h2LII1DZdVM4ma1Qi09kWCJkbGHqnwGinYAPwXYpXmSyCQio3fHER8nIeKT5zVJSx4cN4nQW4eDuycgs2FmwA419fEysAFHoQ3WgjhxgUY0VzYsQHbsQgWlpZjZkHL34jFjEagtS8StNE79rQ2ISVobORm0au1tCMU2kz9NqsFJ9rEzsPI9P1ONSOLwAUyhHDjAoyeQKzcgO1YBNV+QyLOA6yaOtCURqC1LxJWoGfsqW1CWrAsxGpEa2l3KLRe3Fb41olmZNIxMrhrB+w6ea7Vao+FcOMCeDyB2LEI7j9do5msKxQGOiS3zFANOFct3RpR2oTaJ3lxvlG7mJ7duYWMai2lufzQjxm8YwmDXii0EaHdjbmcnGZGjlzvY4mMuLv1uU9aXSqBSIRw4xJ4O4HYsQhW1euLdoiFR6FQoIzcJhQKhXH3G59rftdO7Rtr042VQrtbtZ1ONCOnyQj27ZO8+L83dsar28u494myGiHcuAieTiB2LIIZ7eQ1MqS/EQyFkdY2AfcP74YPSr5DdcPVhYJntXRrJnYTCobC3GnfzGgtJcFICRLByEqhXWg72aOkFQSAmsYA3tv3rWN8oqxECDcug5cTiB2L4OCuHQz/hpw/RHpyAiYPuLY5H5DbJ78b4FH7ZkZrScuca2VEpJLZTGg7rYfEz1DNTOs0nygzCOFGYAl2bDpGf0Pp5FPTcBlv7jwlBBvO0Io44s0ka0ZrSdOca6UmV87XKS3Ji6W/vMGx2k4n1PIiKQpKgpN8oowihBuLccKEsQo7Nh29v8HaH0LQBOm8II044skka0ZraUV5FJondDWTSC2BYzev6IlsY7mm0xJKnOYTZQQh3FiIHQnseMeOTUfPb7gtlNWJwjPpvNAbccSLSdaM1pJnnxYSk4gTDwZ6xhnrNd2sUNKafKKYFs50MzQLm6nhhMJ4JAUK7foNN4Wy0ir4aSek8yIYCmPhR0eoF8u0C6OFFnkuYmi2QC2PaGlygavjzK41XQ2S4rztk7zwgL/xYzdCc2MBdpk+WJ8inIhbQlmdkgI/Ej3zYtXWE6ioUw71Z6lhI9WWGdVa0jTn0tTsuelgIEFclPXkOS7M2SRawaW/vAH4sT08+KCxQgg3FmCH6cOJmxsP8Kz2J8WpfkOk82LV1hNYXvwV0T2t2kiVhAK9BwqjpjIa5lzahx+3HAwiIR0/u/5ZZas5W00oJRV+efFBY4UQbizA6hOOUzc3HuAxdFgvvPgN6dUKkI73N3eWEbfBio1USSi4vX+2rcnRzPgQWXH4ccPBIBby8UO2HtAQtuXGX1aqD1OHXoduGcnomJKIsX2zNIUXXnzQWCGEGwuw+oTDy+YmhxMcXHkLHdYLD+YBI1oBo9W9lci2YCNVEwpe2S4vdPF2oLDq8OOGg0EspAJb3s+uwapPTmjez6ywrTj+6vxYXvx189/C/UAbIdxYgNUnHB42Nzmc5APEU+iwXlibB4xqBUjmRVpbL7FwQ3sjJXEuVYKnKDsrDz9OPxjEQiqwDfvpNZau6cFQGLtPnsP8vx4mKgwr3A+0EdFSFmB1xAPrzU0OHiIJ9GJHFJcVkERMWKHVAPRFl8RCMi/uG96NqB1zxvSkvqjTSJBWUXuRefSi1YefCTnZ2PHYKKydMQwr7xqAtTOGYcdjoxy7yZJEtlm5pktRj3e/8TmxYO+EiEHWCM2NRVh5whnctQPSk71RtZAisdv2zaMPkBPMY0ZhaR4wqxXQmhdj+2Zh3d5vFE/IQJPgNmtUT5NP0hIams7FG46huuFy898sNJd2HH7c5s9Bosm1Yk1XS4qoBU/aQh4Rwo2FWGH6kEw/SoKNhJ22b958gJxkHjMKK/MADa2A1rxgJbjR0HRGCjaAuvmgWQCvbWj+22u6Be50/LUDEoGN5ppOkhSRBCeF3tuJEG4shuYJh1TKT0uisUSSw5MPEA8h8nZpjVj4DdmhFWAluGkJBUZQ0lxGCuC++DCeHQqMX7EdCyb1M/18bnT85QlaazqtOlFOCr23EyHcOAQ9Un5tY8BWZzNefIB4MI/ZrTWy2zxAQyuw8YtyPPFhqar5hoXgRiIU/GZEd3x0qDymmryyiRhoqblUEsAr6+gJ4G5z/CXBaaZos4c9oYFTRwg3lNlTVo2qxivUJ5ceKd9uPxde1OCszWMstEZ2L+hmtQJLNh6VDakul+kjFn4dJELBvAl9ovq8ou4S5rxXonnvsxcuETtk05i3To4I1IsTTdFmDntCA6eNEG4oUXysEgBw/1t74Q82DTaak0uvlG+nnwsvanDSPtr0Y+QWzYWehdaI1YJuVCuw8YvvFXPFAE39xEOuGC2hIFbo2nXyHNF9O6Yk2i6AWyUg8qQl4cEUbQQSM2j7tl7ce3O3Jif7utahgaOFEG4oUFhajjnvlWDZ0OjPaU4uo1K+Xc5mPKjBSfvo7V2n8fau01QFAbs3LdYLul6tQDAUxhMflmrel5foDz1CgR7N5cdffE90T56dRHnSkvBgijYKUZ2oX92ACTnZ+I/RPbkRJp2CEG5MYtfkMursaKezGWs1uN4+oikI2OlUzcuCrkcA2FNWrRnhJ8Hzxi6HHs0lL/5pRjEjVFuh7WFtijYL6aHQbaH3diCEG5NIk8sXL//vtCaX2gIqBytnM5aTUG8f0RQE7Ny0nLig6xFYeN3Y1SDdpHjxTzOCGaHaKm0PT5GaRgiGwkhrm4B5E3qjut6P9OQEZKW1dZxmJlZwHdg5hXWT9Ak3hw4dwt///nekp6fj//7f/4uMjIzmf6urq8Ps2bOxevVq6o3kGTsnl9ICqkRrdDbT20e0BAE7Ny0nLuikAss1yQlcbuwkkGguefFPM4JRodpKE6qTNWFqAh+P718Juefo2sGHub0ZNgo6yi/84x//wNChQ7Fu3TosW7YMvXv3xieffNL87xcvXsRbb71lSSN5xu7JFZv6fM6Ynkhr21JGtTvXDU9E9tE9eV2JvmNWELC65EYkRsZcMBRmWhZAEv60WHxHjmYfsX4WNUhKeiil+89MTeTW+RUwJlSbKddBAstSJGZwYrkaOZSeo/JH52cp0IYFxJqbhQsX4re//S3+8Ic/IBwO47nnnsPtt9+O999/HxMmTLCyjVwjTa6a+ouy/26FmjnS9FNYWo7ai1daXGM01w1PURBmiOyjt3ed1ryehvBpl1O1Xi0RDw6gkRoLpW3s30d0x8Rc9fbw8Cw0iNLy1DYA3xzE5tkjkOhLYN00RYwI1VabUJ2oCePFZ84swVAYCz86oiq4Lt30JcbldGLyHMSamyNHjuD+++8HAHg8HsybNw+vvPIK/s//+T/4+OOPDf34kiVLMGTIEKSkpKBjx46YPHkyjh8/rvm9999/H71790ZiYiJuuOEGbNy40dDv0yDyxB6L1ZNLmiRyGDkVSQXcpr62G4+uK8HU13bjlmVbHXOKkMPuk50dRQX1aIl4OiFKwl+sBic92YsXpw3Egony80iCp2ehgSSAT7zhqtMozxiZS3aYUEkKX/KEHoGPZ1ZtPYGKOr/qNRV17J6DWHPj8/lw/vz5qM+mTZuGuLg4TJkyBc8//7zuH//0008xc+ZMDBkyBFeuXMHjjz+OcePG4ejRo0hOTpb9zmeffYapU6diyZIl+Jd/+RcUFBRg8uTJOHDgAHJycnS3gQYTcrKxfMoAXC7bH/W51WHQNE9FrEOLrYLFyc4Op2oSLRGPJ0SjEXU8PotbINXWGplLdpntWUdq6sGJPnOxFJaWY3nxV0TXsnoOYuFmwIAB+OSTTzB48OCoz++66y6Ew2FMnz5d948XFhZG/b1mzRp07NgR+/fvx4gRI2S/s3LlSkyYMAG/+93vAACLFy9GUVERVq1ahZdffll3G2gxpk8mNpYBq6cPsSRDsRy0JonbNw4ecvBYwYScbIzqnYk/7zqF09WN6JqehH/L64aENk0KWV6jqowIf7w+i9PRa+bTO5fsdLR3Sri0k52gAXWLgRysnoNYuHn44Yexfft22X+bOnUqwuEwXnvtNVONqa2tBQCkpysP9F27dmHu3LlRn40fPx7r16+Xvd7v98Pvv6o6q6urAwAEAgEEAmR5N0iQ7jWwcwq83iZn3lDwCkJBaj/RgoykNvDFa5ucMpLaqD7rnrJqVNdfVAxnB4Dq+ovYfeIsF4550rPoeX+je2VgZM+fY//pGlTV+5HRzofBXTsgPs5DdRzYSfGxSizd9GVU5tK3P/sn5uf3xpg+mThb20A0Ps7WNiAQSG3xuZF+tgrSZ9n1dQUGdk5xlBDOqp+Lj1ViznslCANRc7+m/iJmr92P5VMGYEyfzBbf0zuXnprUq7k8hZy256lJvSxfK4OhcFN76xoBAP7LlzW+YR0DO6egawcfKuuUBb7M1KZwah7mXiwk+4UvrunJrmvvo/oceu7jCYfDloQarF27FrfffruieSmWUCiE22+/HefPn8eOHTsUr0tISMBbb72FqVOnNn/24osvYtGiRaisbOmZvXDhQixatKjF5wUFBUhKSiJqm0AgEAgEArY0NjZi2rRpqK2tRWpqywNZJJYl8fv3f/933HTTTfjpT39KdP3MmTNRWlqqKtgYYcGCBVGanrq6OnTp0gXjxo3T7Bw9BAIBFBUVYezYsc2aGzuQTl+A/KlI6fQVyZ6yatz/1l7N31o9fQg3mhsWfc0LwVAY41dsj9LYxJKVmoiN/9/PMfF//p/mCXHz7BGymg6e+ll6ZqVniUTP2OcBFv1MOucjyUpNbNYKGqFZexKj7TGD1j0jtVNAk0Zh8Y0hPLUvDv6Qh+kYkdO8mu1jOyAZO1I/0x7TkuWFBMuEGz0KoVmzZuHjjz/G9u3b0blzZ9Vrs7KyWmhoKisrkZWVJXu9z+eDz+dr8bnX67VkIbHqvkrk53aGJy7eVHjssB4dkd6uraZdfFiPjlyp++3ua17Yd/IcTtf40TJW6iqna/z44vt6LJjUDw+/cwCAvPC7YFI/zfBjHvrZCyg+ixweAM9sOM4sDNUIdvZzVeOV5gK/pJyp8eORgkO6ggtinZVpriFa/kLBUBjPbDiOSzLPeSnkweWgh+kYyc/tjHE5nRzhBB2J1n4BNAlpQAP1Ma3nXkzLL4TDYfzHf/wHPvjgA2zbtg3du3fX/E5eXh62bNmC2bNnN39WVFSEvLw8C1vKN2YjBZyYK6I1oBTFoqaxiaSi7hL+dWAnxztTS/3gvxLC7DE9sXbPGc0QVMnBeM3OMtw7vLsYuzEYcfLUG1xgZU4ikujOtLYJ3DuhO8UJOhKS/WJ+fu8W0cN2w1S4mTlzJgoKCvDhhx8iJSUFFRUVAIC0tDS0bdsWAHDPPfegU6dOWLJkCQDg0Ucfxa233ornn38ekyZNwrp167Bv3z68+uqrzJ6DB8xOErdGFDkVtY2hul59Y5eQrnNSmGwscv2QlZqI/JwsbCqt0Pz+4g3H8PqOMjGGYzBaiDdWIFASwK1MLUEa3TlvfC+i+/Eccs0rWvvF6F4Z2FjGsIFgLNy89NJLAICRI0dGff7mm2/i3nvvBQCcOXMGcXFXcw3efPPNKCgowBNPPIHHH38cPXv2xPr165nluKGF3szAVmQSdvIm6Ca0NoZ7byYrKZGefNXc5MQTolI/VNZdIhJsJJyeq8kK9BaZjeXshUuKAviTk/pg8YZjlqWWIE0LUN1AFhHFa8g176jtFzxEeTE3S2mxbdu2Fp/deeeduPPOOy1oERv0qm+tVPfavQm6pdwDLUhOpR8e+p7oXllpbWk2zVZI+sHjAUiSb7shV5MV6C0yG8mpqgasKP5aVgB/pOCg6nfNmoNINS3p7XyOrcDuFHg+NBGXX5CILJYZyyuvvNL8/7t27crcCdEJ6E0p76YU9G4s92AWklNpdUMgSisjR3ZaIgZ37cBtgUktSPpBehwSUcUpKe3tJrZcyLsP3ISsVO0SC2v3nFGtKUSCUXMQqaYlKzXRlmK2PBdybc3oFm6k7MCRaqeqqircdtttmD9/fvNnpaWl6NKlC51WuhS9FXOtrrBrJ24S0mhCuuBPHnCt6gZ0e/9s3PrcJ44VHEn74YHh3VrUFKJx39ZEZCXz4T0zsPB2dYHgriHXaTp0k2DUHKSnxpXVFdjFAY1fDGluPvjgAwwZMgRHjx7Fhg0bkJOTg7q6OpSUlFjQRPeit4CaWwquuUlIow3pgj+2b5ZsEcrstET8ZkR3vLq9zNGCI2k/jOmbhR2PjcKTk/pQvW9rRqsQZd1Fc9l9zRar1VM4FojWTj37q1wAwObZI6hFbPE+z1qrZkm3z83NN9+MkpISPPTQQxg0aBBCoRAWL16MefPmweMRtmw96K0N5YaCa4CoE6SGnlo88XGeFg59g7t2wK3PfULVmTMYCmPfyXO2+kXp7Yd7h3fH6zvKhH8FJZScRQHg8Q8OE9/HqtQSpIVjY9sfCqZi4zcHqZiieK/HFwyFsWrr13hz5ymcv3jV0kLin+kGX0hDDsVfffUV9u3bh86dO+P777/H8ePH0djYSFxqQdCE3gJqTi+4JuEWIQ2gvwjozTkU69C36+Q56oLj+BXbf0wa2AQt53U1jPSDyNVEl9ixFQyFsWZnGaobtCNhrklOwOI7crB4g3WpJdSidZSCLp6aRBYergXvB7TC0nLM/9thnG9s+a60ogetDFixE91mqaVLlyIvLw9jx45FaWkp9uzZg4MHDyI3Nxe7du2yoo2uRY/t2Mj1vOIWIc0qe7uWWUBtgaEpOBYfa8oEHps00C61u95+MNNvAnWksb54wzGi6+8YcC0m5kY7K6+dMQw7HhtF9T1E+gvl/eyaqBw7cuYiqVSNWXg+oEnPLyfYAOqmf6eY2kjQrblZuXIl1q9fj/z8fABATk4O9uzZg8cffxwjR46MqsAtUKe1nk71mBx4xcokZYDxnEO0BMdgKIylm77E3N4t/81OtbvefhC5muijNNbVGNu3qRwOi9QSWv580nVmYnl5PaCpPX8kcpolJ5ja9KBbc3P48OFmwUbC6/Xiueeewz/+8Q9qDWstKJ0205MTcN/wbkhrmxAlXTv9dCqZcfJzsponTCROENLscoiWO5VqQUu7t6esWrXMg53O63r7wUi/CeQh3SwjYak9JjEXAcD+0zWmfodXLbrW88cSqVlyS8CKhG7NTUZGhuK/3XrrraYa01qJPG0WH63AByXf4VzDZazeeQqrd55qYe906ulUzpbr8QCRuRydUO6BZ3s7Le0ez2p3gX3o2Sx5OJiQjscqjRImWr50vGrR9c7HSM2S2+Y80wzFgqvEx3lQe7FJoCExdfCcGVIOJdW2pNx4YHg3jOmb5QghjbdFIHYhlsLEYwXJzFQfpg69Dv4rIew6eU61r3lVuwvsQRpTm3T4WPBwMCEdjxntfIr/RupQy2M9Pj3zMVazRPrdqgt+BENh7tdpIdxwgtvsnZFoqbY9ADaWVuDxSfyaoiLhaeNXW4h3PDaqWeg5VdWItXvOYHnx1y2uk1uEh3ZPR1ZqIoAG2d91gl8UbzglvFZuTGnx5KQ+XFRfJ/HnA4DBXTvIfl+vL52WFt3ud66nIGqsZon0u04pRqvb50ZgDW6zd0bitmfjxd6uFdlQdLQCeT+7Br42cVhR/JWuqKf4OA/m5zd5EzvRL4o3nJLJVmlMKSGNdR4EG4AswZ90XSxGfemUfLxYvHO155don+TFyzL+mSTflXBC9JQQbjiBN1MHTdz2bHozpFoB6UJ8+UrIsPPzmD6ZAJpS1UdixHm9tWZJBbSF0H8caQq533i4nGnf6HUe5lXIVQu6WD5lgOL3aB7CrAqpJplHSs/fPsmLOWOux/4nxirOXaXvxuKETPLCLMUJPJk6aOPGZ2NtbyddiP+865Rp5+fNs0fg4LcXDKvW3ZIUzAgkQuhv/1KCpUOAeX/9Av6gh1nf6I204cHHRgklc1EoeAUby+S/Q+sQZpWLgZ55ZCboRPrump1lqnmNlMLJ95RV42xtQ/PfrMpnC+GGE9yQ+0UJWs/G08QBrI1a07LVky7Ep6sbia5Tu58Z53Wr8wHxDonAEHvwZdU3pGPqnryuyM/J5tZnSEJu3IaCytfTOoRZEU1pZB6ZmbfxcR5kpCg7XUcijZtI4csXH8azQ5uymy+Y1I/JHBdmKU7gwdRhFTSeLdJ+Pe+vXwBomjisbb5W5FQhsdWTLsRd05OIrrNCayYKpBoztbLqG9IxkJ+T7cr8QbR86Wib4VnNIz3CnpIZrrKOnW+OEG44wukJ+tQw82w8ThyrILXVky7E/5bXjZnzs9scyY1gVGhk0Te8OMqzgtYBk7YZntU8Ih0Pg7t24PIQI8xSnOHUBH0kGHk20lOLE0PkY9FrqydJIpbQJo5ZsjG3OZIbQU9orhx29g2viensRMuXbmzfLOw6eU51/aLtYsBqHpGOh/2na7hMaiqEGw5xWoI+Peh9NiuzAfOWd0Tvs5I6NbNyfnajI7le1DYIEuzuG9aO8jygdAgrOlqBW5Zt1XTopS0kspxHJOPhw5LviO5l9yFGCDcCrrHq1MJjBI+RZyXVhrHQCLrZSV4PShtEnKelM7EEy77hQXvM+uARewgzktyPlpDIeh5pjQdeDzFCuOEA1hOZZ6yYOCwjeNTetdFnJdWG2a0RFGaOq8htEDUNfswsOMhlAAFL7TFvBw+jod20hEQe5pHaeGAtfCkhhBvG8DaReYP2xGFZ5kLrXVuxSLAWnIWZ4ypyG8RLcR4s2XAEkWUuWmPfSPCYOsCMaZyWkCjNo4UfHUFF3dWin5mpPiy8nU2otQQPwpccQrhhCI8TmTdoTxzShWrNzjKqKeVJ3zXNZ+VFcI49wWa08wFhoKrBr1nA0+1MyMnGyJ7XYHPhJjz7q1x0TEtutf3Ba309vhzj1YpKsEPpEJOZmijy3LQ2RA4QcpTCyDNT9YfIky5Aizcco1YHRs+7ppUOwKr070aRTrC+NnH47fuHcPcbn3NdY8nOchHSRj3xBnfmjyGF19QBPPiUSPM5tj4cT+kwJuRkY8djo7B2xjA8+6tcAE3ZzVkd0IXmhhFWRgG5kajTf20D8M1BbJ49Aom+BF330bMA0dKgGYmCMmOr5/UE7BRNJS8ar9YGXxqSq7D2KeF1PsshHWICgVRs/OYg0/YIzQ0jWExkpxcvlCbOxBuym//Wi1ZiqkhoadCMvGszmY95PAE7RVPJm8arNcGDhkQO1tnjeZzPTkAIN4yweyKTpPRvDagtVHLQWDjsftc8noCdsEA7RQBzKzxnSGaZPZ7H+ewEhFmKEXaqOq00B7COxjGCkvObGmYWDrvV2jyegJ2wQAtTcROs5jSvUTcSZszFZvqUx/nsBIRwwwi7JrKV9lrWvgnBUBj7NFKhKyEtVGt2lmHxhmOa15tZOOxetFn7CMjhhAXaCQKY1bCe07ynDjAS2m22T3mcz05AmKUYYoeqU0/osx51Ow++CeNXbDdlZouP8+De4d1tUYXbqdZm7SMg59vFs8lBwgkCmJXwMKeB6KiblXcNwNoZw7DjsVHMBRsj0OhTO+dz7Ny9fCXkWD9NoblhjKRB2P3Pc9h18hyAMPJ+moFhlNTeekKfX99RRnSaYO29X3ysEgB+DIu8en8jZjY7tSp2prVndQJWO6XybHIAWvcJmfWcjsUN9fWCoTDm/+0wlT61Yz7Lzd3YEiFOihoUwg0HFB2tiBpUqz45SW0QWRH6bLdvQqS9OqOdD0s2HsN/9pH/XSOLsJ2CgJ2Ltt01gkh8u3g3OfAugFmF8Deiz6qtX+N8Y0Dx3/X2Kc35HOsDJJUCiZ27sYoa3tI2qMFUuNm+fTuee+457N+/H+Xl5fjggw8wefJkxeu3bduGX/ziFy0+Ly8vR1ZWloUttQ6rc39onUYjIRUO7PRNkDtN+OKVn8ToIsxDsUArsEqYil0cB3ftQHTy3/HYKK77mXefD6sQ/kZ0CYbCeHPnKaJr9fQpjfmspKEhMTjxlldHDabCTUNDA/r374/7778fv/zlL4m/d/z4caSmpjb/3bFjRyuaZzl2qILVTqNykAgHdvkmKAl+JBhZhN2gCrcDucUxPdmL6gbyUyrP/cxK0N1TVo2qxitMBL7W7m9Emz1l1Th/UXk+RGJnnyqtqXpcaZyixWMq3OTn5yM/P1/39zp27Ij27dsTXev3++H3Xy00VldXBwAIBAIIBMgGHwnSvfTcc09ZNarrL8IXr3xNdf1F7D5x1pSdf3SvDLw4rT+WbvqyRfpuJc7WNiAQSJX9t4GdU9C1gw+Vdcq+CZmpiRjYOcVwHwdDYSzZcAQJMloaX1w46n/lyEhqY+r9BkNh7D9dg6p6PzLa+TC4aweuTylWIDemi49VYs57JQgDUeO24dJl1XEsoTaueOPG61IBNLU1FLyCUNCa3yk68j0A4OE/74E/1DTGslITMT+/N8b0ybTmR2OQ5rTW+lB94SLVddNu5Ma0FXP9bG2DqoZZon1br6l1EiBvv9qaagS1uWxkPyRBz/084XCYC/dnj8dDbJbq2rUr/H4/cnJysHDhQgwfPlzxOwsXLsSiRYtafF5QUICkpCQaTRcIBAKBQGAxjY2NmDZtGmpra6OsN3I4Srg5fvw4tm3bhhtvvBF+vx+vv/46/vznP+Pzzz/HoEGDZL8jp7np0qULqqqqNDtHD4FAAEVFRRg7diy8Xi/Rd/aUVeP+t/ZqXrd6+hBqERrBUBjjV2zX1Lpsnj1C8/RSfKyyhTaI1olz4+FyzPvrF7L/5osLY/GNITy5L675pCu1HQCWTxlg+PcjNROR0Lg3DezUKMWOadLxKoeecdVakOZiTf1FxfFM0me05iGL9chuIsf0pyeqLZvrWutsJEbXTL1rldqaqgeScWlkPyShrq4OGRkZRMKNo6KlevXqhV69ejX/ffPNN+PkyZNYvnw5/vznP8t+x+fzwefztfjc6/VS7XQj9x3WoyPS27XVDD0d1qMjtQ3BC2DBpH54+J0DAOQjQhZM6kdUkDI/tzPG5XSyxDehY1oy/EH1+/hDnqhrzEaYBUNhPLPhOC4p/K4HwDMbjmNcTicmGzSrBGvSmK5qvKL5TuTQO65aC/tOnsPpGj988U09FDueAeB0jR8Hv72g6NtQWFqORwoO/TiPr373TI0fjxQcUg1IiHUKP1sfIHq/VY1XLFk77SQuvo2lc11tnY2F5F3FYmStIllTtdA7l2nvs3ru5fgkfkOHDsWJEydYN8MQrJKt0UwoZ6bAoxokBS6zUhPx7gM3UUv0xXP9Ix4SrJE6PqYnRy96dtTfcSKkTu87T1TJJk8zUwtLrtYcSaZuwB1OxftP11g+15XWWbnfAvTVLTOyVukpGiwRe62T5rKjNDdylJSUIDub/45WglXoqVJECADsMljSIBKz9WlIco7Mz++N4T0zdLdNCV7DYXlJsEaa5O7T3/0C+0/XcBnqzROkQsKqT07grwe+bbEeGM1NoxQxU9NwWbUdTktiKLcGSVTV+1W+eRWzc520zIveCCQja1XkmkrK2/cPRZv4OEfOZabCTX19fZTWpaysDCUlJUhPT8d1112HBQsW4LvvvsPbb78NAFixYgW6d++Ofv364dKlS3j99dexdetW/OMf/2D1CFRgFXoaG/pMy+xB6z5Kgl9maiKABuq+L7yGw/KSYI00yV1CmziuQ0R5QRIWa+oval4rl/fKyAZHou2Rw2lJDJXWoKcmNbk1ZLRr6aogB425Hh/nQUYK2e+RvlOja5W0pi786KhqZJwkyN7cI8MR71sOpmapffv2YeDAgRg4cCAAYO7cuRg4cCCeeuopAE3J+c6cOdN8/eXLl/Gf//mfuOGGG3Drrbfi0KFDKC4uxujRo5m0nyZWmXdIoWX2oG0+kaszs3n2CF33IIXX+kc8aZTsrJHldiLN0lrImS6MbHBagrJEenK0b4OT3q/aGjTnvRIAwIAu7Vs8YyS05zrtg5OZtWpCTjZ2zh+FOWN6Kn4XcI4gqwRTzc3IkSOhFqy1Zs2aqL/nzZuHefPmWdyq1gctswfJqXD+3w4jxefFMB0CXKyGyao8G/FxHtzePxuvbC9TvIbFhOdNo+TWbM4smJCTjeVTBuBy2X7Na2M1dEZqYZEKwE/+Sz9kpSY67v2SaqbyV/4/xaSTVmzutOuWmS0VEh/nwaNjrkevrBTXZuN2vM+NwDy0zB4kp8LzjQHc/cbnXBZgKywtx6sqgs1vRnRn0l4eCzqKbM70GNMnExvLgH//eXf8z7ZTmtdLAoqRDY5UAM5KTXTk+yVZywCg8kJ00d1IrKopp5YpPgzgriFddN2Thr+mmw8qjo+WciKxZeVZl5GnZfbQYxaxM8qHBLUTn8RHh8qZvCtWUXUCe7npp2TO8ZECil4zIa+mV1qYNc1ek5yAT3/3C0sOMVrRU8uLv8Yty7bqWhPlzPZ6o0ZZu0RYhdDc2AyrXCVq0DJ76DGL8FaAjUTrxLKeitIpLT05AXcMuBZpbRMQDIWZ96PAOIO7djCkodNz+nZ75XOzptlzDZex/3SNZXNcelertp7A8uKvWvy7kYLJQosqj9Dc2AgPuUrkoHWa05tHgXbeGDMaMRZOu3rbG3lKe2B4N6Qne3Gu4TJW7zyFqa/t1n3qE/CFGQ2dntO3m53CjeRyicXMHCed0+v2npH93EjOGzvgzdpAgtDc2AQvuUrkoHWa01uBXIKGwGBWI2a3067R9sbHeVB7sUmgie1fI6c+AV/YlffKrb4WRtegSIzOcdI5zUtqB1J4tDaQIDQ3NsFz9luA3mmONCtnJGYFBhoaMTt9Ecy0V0tIDgN4/IPD+OCgc05Ygmho+FGQ4FZfC8W1LNWH9m2tCf/WM6d5Su2gBa/WBhKE5sYmnDCgaZ3mpPvs/uc5zHz3AM5fVA65NBvlQ0sjZpcvgtn2kvgGVTcEmvN5OOGEJWiJG/wozGYpN4PSWvaP0u9wuWw/Vcd8vXOat9QOSvBsbSBBCDc2YfWAprWQ0FpU4+M8GN4jA0t/dYNqkU6zAgNNFa8dJgGz7dUr/CqZqlhuPAJnYWSs8GDKkFvLpLD7zNREnK65WoLBzBzXO6d5TO0gh9PMZ7EI4cYmrBzQPCwkSlgtMNDWiFnti2C2vXqFX7kTFs/jRUCOltBBQ4A1MlaUalfx5BO2efYIHPz2Qou+MdJneue0UyLWnGBtUEMINzZh1YB2wkJipcBghUbMSpOA2fZqCclyRJ6wai9e5n68CLSREzrSkxPw+ztyMDE3m4oAa2RtcYopQ26OG+0zI3OaVcFkPTjFfKaEcCi2EdohmCSpxnkJKbTKedFpScnMtlctXFiLirpLjhkvAmWUnDyrGy7jkYIDmPH2XtNOoEbXFt4DJ5Qw4zhrdE7HOo6/++BN+NP/6Q//lRAXwQBOW1tjEcKNRcjlBQiGwkhrm4B543vhyUl9sHyKuUgIpy4kNHFa9l4a7TUSkQYA1fX+Vj9enA5JJu2io2dNC7BG1xYnmjLMHhJp5CfytYnDb98/hLvf+ByPriuhmrfKaI4ap62tsQizlAXIqTfbJzWFIJ5vvBo5JKk8jQ4OJy4kVuAEFW8ktGvCVNRexOINx1DTcFnVnys9OYGofW4fL06GtKq3EqROoEbXloxkH9H3SK+zAxqOs2bmtJWuBWbNk0aeS/JbApr6dliPjkwEICHcUKb4WCUeKTjUYqBGCjUSZgev022iNHFaUjIa7Y30G2ibEK/pz5XWlky4aQ3jxanQEjy17mN4bSEdvjLXsYrgo3VINDKnrfRRoiU06XkuSZiqrr+IZ4cC97+1F+nt2jI5ZArhhjJLN32py9HTzOB1SkihXTgtNwjN9pKcsIKhsBgvDoeW4Kl1H6NrS1W9X+bqlsRexzKCj+YhUe+ctircmrbQRPJckcKUL/7q56yCFYTPDWUq6vSdrMz4OTjdJtpasapOi1ZmWzFenM/Q7unE5kU5SJ1AjY4VI4IC6yy4LB1nrXItsNsfk8fgFiHccIJRdbObi+C5kcLSctyybCumvrabuuMgoB2VJsaLs4mP8+D3d+QQXWtWgDUyVvQKCjxsiiyFfqtcC+z2x+QxuEWYpTjBjLrZaf4mrRVechKJ8eJsJuZm49+/7Y5XtpfJ/rsHwG9GdMdHh8pNO9jLjZXBXTtg/+kafFjyXYuxozefFy9ZcFkEJQRDYYTCYbRv66VeosZuf0weg1uEcEOZrNREnKnxE/vdkAxeEkc7p/mbtDZ4S25mxXgRJR3sY8HEvujfuT2e+LAU1Q0tIzAn5GRj3oQ+1EuyFJaW49bnPlH1jdEjKPC0Kdop9Mv5GMViRmtktz8mj8EtQrihzPz83nik4FCLU4scJINXpMp3B7ycUK1CjFP7mZh7LcbnZCtuxrQFWD2aR1JBgbdN0Y5DolI/xmJGa2R3iQceg1uEzw1lxvTJlLVTd0jyNue6kdDyc2DtaCegB08nVNqIccoO2pm/lZzdjfjGkLTN6Vlw9UKShLF9khfvPnCT4eSuEnb61/EYrCA0NxagdGoBQKzy5M2MITAHbydUWohx6h7UtG9pbRMs0Tw6pYgkLUiSMJ5vDCAuzkNFUE1rm4B5E3qjut6P9OQEZKW1tczUFmmOrK6/2Pw5q2SqQrixCCX1JunEd7sZo7XBo9qWBmKcugMtk9N9w7sR3ceI5tFpGcbNYJcGV01QtVJQlA72u0+cRdWx3Vg9fYjIUCyIxs1mDJrw5sSq1B63nlDFOG2J3WPS7O+RaN8+LPme6F5GNY88R/DRfJ92aHBZR2XGx3kwtHs6Nh4D03cohBtOcasZgybFxyrxzIbjupxYrdx4tJxq3XhCFeM0Grsdq2n8Hon27VzDZaQne1HTELBM88hjxCft92m1BleYia8ihBvK7CmrRlXjFdMbp1vNGDSZ814JLgWj+1ftdGLlxkN6WuL5hGoEMU6vYveJmdbvkWrV/nVAJ6zeecpVmkc1lPq33MT7tFqDK8zEVxHRUpQoPlYJoKlQGI3Mszx6n/OCFJGhJ3LDyogevZEktCNc1NplRZmHSMQ4bcLuTLs0f49Uqzamb1aryW6tFdUUBrDgb4cNvU8ro5iEmfgqQnNDgcLScsx5rwTLhkZ/bvbE5kQzhh3+BvtP16j+e+zpxGpVLelpac3OMtw7vLstG72d5hEnjtNYzI5bK0/Mcm2j9XvBUBihEHmW3Pg4j6s0j0qQRDXVNAawauvXeHTM9brvb5UGV5iJryKEG5NYvXE6yYxh14ZKWnlYOp1YvfHsPPED0bWLNxzD6zvKLN/wWTgUOmmcxkJj3Fp1YlZqW35OlunfM5oll0ffGNqQvqc3d57CrFE9TWd+poUwE19FmKVMYkfBMLvMGGawM5FbRjsf0XXS6cTKjeeWZVux6pOTxN+xOrEdy0KEThinsdAat1acmNXatnrnKVO/p3TvWNxociLhVFUD0XXnLwZsLQaphTATX0UINyYRNk77N9TBXTsAaDl5JWKzmtq58WhhtYDBY3VeXqE5bmln2iXRCKvtT2q/R5Qlt60X7z5oPkuuEyksLcfy4q+Jr+dtbbczMzHPMBVutm/fjttuuw3XXnstPB4P1q9fr/mdbdu2YdCgQfD5fOjRowfWrFljeTvVaK02zkhn1TU7y2zdUCNPHSSnEyMbj5ozLsnmoIaVAoYQtsmhKQjSPjGTtE0aknp/jyhL7sUA4jzms+Q6DWlu64HHtX1CTjZ2PDYKa2cMw8q7BmDtjGGtTlBl6nPT0NCA/v374/7778cvf/lLzevLysowadIkPPTQQ3j33XexZcsWPPjgg8jOzsb48eNtaHFLpI2zJiLddCRutHGS2OrloL2hLp8yoEWeGzknVr3hl1o+GCSbAwlWCBitVdg2Am1BkKZjNelvPjC8GzaWVuj6PScIwKySc+qZ27yv7a3BN0oNpsJNfn4+8vPzia9/+eWX0b17dzz//PMAgD59+mDHjh1Yvnw5M+FG2jgfXbtf9t/DcJeNk7SirRxmNtTIxS4jqWnYjumTiXE5nYgWQdKNh8QZ138lZPg5IrFCwKhpuIw4z9VTfSw8L8jSO66ovYjqhstIb+dDVqp1G5sVgiAtx2o94dmPT+qr6/d4F4BZVpjXK9C5aW13G46Kltq1axfGjBkT9dn48eMxe/Zsxe/4/X74/Veja+rq6gAAgUAAgYB86KNewqEgfHFNu4n0v7H/Tuu3WBIMhbFkwxEkxOsTbTwAMlMTMbBziqF+KD5WiaWbvkRFXdPC44sLY/GNQNGR7zG237W48bpUAKkAgFDwCkJB+fuM7pWBkT1/jv2na1BV70dGOx8Gd+2A+DgPAoGA5vN5ACzZcAS/vyMHPp19EHsfM/2hRPGxSsx9rwReFWOzB8BTk3qp9pOE1DY7xm7sO44kKzUR8/N7Y0yfTKq/ObBzCrp28KGyTjmyxOh7Ih2TgHw/62lbKHhF1+9Z+dxmKT5WiTnvlSAMwBd/9fOa+ouYvXY/lk8ZYGocaI3pjKQ2RHO7Q5IXT9/WD6N7ZbhibaeNVWuHnvt5wuEwfa9GA3g8HnzwwQeYPHmy4jXXX3897rvvPixYsKD5s40bN2LSpElobGxE27ZtW3xn4cKFWLRoUYvPCwoKkJSURKXtAoFAIBAIrKWxsRHTpk1DbW0tUlNTVa91lObGCAsWLMDcuXOb/66rq0OXLl0wbtw4zc4hYU9ZNe5/a++P2oQQntwXB3+opZpy9fQhXJoC9LDxcDnm/fULXd8xc+oOhsIYv2J7i9O81NdP7YtD+3ZtsXn2CFXVsJxWQK5dpM/37K9ykdAmDnPeKwEg78OzfMoAACD6XRpI41ALPeMwEAigqKgIY8eOhdfrNdtEWZTecSySNkHrXRuBdHxYhVo/W9k21s8dixVjOBaSMS1pjwDluc2if5yEVWuHZHkhwVHCTVZWFiorK6M+q6ysRGpqqqzWBgB8Ph98vpZ5UbxeL5VOr2q8An9EfSN/yBP1d+R1Vm0QdtExLVn22WJ5clIfZKT4TDsC7jt5Dqdr/FAK+r4U8uB0jR8Hv72g6DhXWFqORwoO/bhIXb3PmRo/Hik4FBUaSfp8HdOSkfeza+CJi9f0DSD1CTJL7DhUu07vOKQ1V+TQeseRaL1ro+TndrbtPakh189Wto2X55awcgzHojam83M7E81tgTa01w4993KUcJOXl4eNGzdGfVZUVIS8vDxGLeLfOY8mpNkvaZUYMBvVoTd7tN7snlZl5TUSKeLUcajXgdNsBI9S3/IcWWJl23h6bp7GMM25zSryq7XDVLipr6/HiRMnmv8uKytDSUkJ0tPTcd1112HBggX47rvv8PbbbwMAHnroIaxatQrz5s3D/fffj61bt+J///d/sWHDBlaP0KpCwa2uaBuL2cVOb9kFI8+ntjkYifowGini1LTrejcqMxsbyygcgTa8jWEagp8Yc+xgmsRv3759GDhwIAYOHAgAmDt3LgYOHIinnnoKAFBeXo4zZ840X9+9e3ds2LABRUVF6N+/P55//nm8/vrrzMLAgejkXbG4Md21ndkvzWZ9NaL5ofV8RtL6mykF4NS069I71kJvht9Y7CwP4ibsqCwv4dQxrITSmCuvvYSH3jmAjV+IMWclTDU3I0eOhFqwllz24ZEjR+LgwYMWtko/E3KysXzKAFwui85146SqyHqgpbLVUteqaVIk1BY7o5ofs89npJgqjQKsTqzOHfmOtbZNoxub1cVt3QoLrYMTx7AcJFnMZ609gFUYiIm519rWrtaEo3xueGZMn0xsLGvy5K9qvOJ626pZlS3pwqm02AFNUQtWmWrMPJ+RKuRGK5fHCohj+2Y5rjq32jsGzG+oVlaFdyssKstLOLnCvARJpuNQGHik4CBejvM4RmhTQy7RKkvYt8BlDO2e7vioKCuIHPinqhqxovgr4oUzdrHLSGqDqmO7NcMx7fYRkjBiDjPyHTfZ8yPfMe0MxU4oN8ATPGi6eHJ0NoKeseQGrWHsWuSLD+PZoU1h9fm5nZm0SQg3AsshrUWltnBGLnaBQAAbj139npp5i4Wa24g5TO93WJ6srcKqDY2nKBwnIDRd5tEzlsprL2F50XEM7/ETx2moAPWSPHPeK4EnLp7JWiSEG4Gl6K1FpXfhJNFe2K3mNmIO0/MdHk7WToK3KBzeEZou80hjjrQI56pPTmLVJyeZaF7NhKqT+BaxWouYRksJ3A3JwFeCZOEsPlZJHAEjaQXuGNCpOezbCCTRI0aiPvR8R8/JWuC+KByrEZou86hF0aphd/ReYWk5blm2FVNf241H15Vg6mu7ccuyrcS/z/NaJIQbgWWQONUpQbJwLt30paL2Amg6MdAMXdWzEBgJKSf9jjhZ68fOFAZOx2wKBlLsDDNnwYScbLw4bRD0yMwkaxetfqORHoHntUiYpQSWYWRA6zERNNXEkV85aPsFGPFxMWIOI/mOOFkbww1ROHZghyO+m5zh1ZiYm41VGIhHCsjTl6itXbT6jZZpm+e1SGhuBJahd0BbYSKgcWLQWggA5ZOWEXOY1nfsOlm7EVrmSVKcqp2wUtPlpISKNN7fxNxr8fKvBxElq4wkdu2i2W+0zEk8r0VCcyOwDC1HzlisiGCicWIwEj1iZT0ZViHuAn04XTthhabLSc7wNN9fZF/uPFGFVZ+c0PxO5NpFu99omZPMJlq1EiHcUEKS6DceLkfHtGSh7ob2JhwGMGdMT3TLSDa0cGalJuJMjd/yCBi9C4Edm5pbMrm6FatC9e0uwkg7PJ/3MHOpf4uPVuCNnada/LuZ9yf15dDu6fjrgW91Re/R7jea5iQziVatRAg3FCgsLceSDUcwtzcw769fwB/0OOqEZiVqm/CTk/qgQ7LPsOlofn5vPFJwyHLthZ6FwM78M8KHhE+s0k44XRME8O2ASpKPi4Z2yYjmlXa/0U6PYDTRqpUI4cYk0maWEB89RJycTI02cptwTcNlLN5gbqEe0yfTFu0F6UIwuGsH3PrcJ7aq3K1IfNesHahtaP5b5NwmxwrthFuSNvLqgKonHxcN7ZJezSvtfrPCtK2WaJUFQrgxgZPsx6yJHPiFpeWYWUBnobZDe0G6EOw/XcO1yp2EyNOrlEJ9/IrtWDCpnyM2Tx6gfcp20zrDY0JFo/m4zGqX9KxdRvuNt+ztdiKEGxOwsh/bbXeniRULtR11aEgWgg9LviO6184TVVy+O6XTa2Wds7QDrKF9yubdT0UPPDrDG83HRUO7RLp2Gek3HrO324kQbkzAwn7Mq92dVOBy8kKttRCQLnaRkRI8vDuAPNzdCdoB1tDWTvDsp2IE3jQGevuNVbkOLf/FtLYJ+LDkux/N/n7MLDhIpB13epFSJYRwYwK77ce82t31CFxOX6jVFgK9oe8A+3cn4WShkzdoayd49VMxA08aAyP9xiq8mdR/Mc4jH5btNDOmGUQSPxPYmcDITCI5K9GbWMpNC3Vsgi8AijWMlGD57iJxutDJGzST4PGcKM0MdidUVEKrfyOJ8wC/GdGd6UEkst9qL17GzIKW66/aUtJaas8JzY0JYk9okdC2H/N4sjbiP8OjQ6ER1LRVSjkflOBBK+ImoZMXaGknePRTcRMkiegkwmHg1e1lGHhdB65NySS4/aAiNDcmkU5omanWFuTj8WRtJIW3Gyo0a2mrAGDHY6OwdsYwrLxrAGb94mdE92W52LhVO8AaWtqJsX2zMHvM9UhrGx2ULwp/0kFJ0xYLL5pWwFxhYsD9BxWhuaHAhJxsjOx5DTYXbsKzv8ptkaGYRnQTjydrowIXbw6FetCjrZK0MLtOnsOqT05q3pvlYiMJnQ/9KJxFYrXQ6eToPzuQ0xK2b+vFfcO7YdaonqKvKCFp2tbsLMPiDcpJWnjQtALGD0NO0Y6bRQg3lJAWmIk3ZMPrvXq6ohXdZMScY/WmYUbg4smhUA9GzINOMsW1T/LifGMg6rO0tl4smpxridBpZfSfG4QmpSCC2osBrCj+Gr2yUiw9DLDoQ5bvLT7Og4wUH9G1rM06Rg9DYfCvHaeBEG4shGZ0k167ux0h42Y3bSeGIBrRVjnBZ0ItQ2vtxYDMp9b9Jo0IMl5TJuiBdfI+Fn3Iw3vjUUsuh5HoTKCpnp9T5oAZhM+NRVgR3UQagaE3gskobvCf0YvRhY9m9AxtSBwTafsYWBn9Z9f4txojPm20YNGHvLw3p/ifqa2/anTLSDb0e5HRoTtPVGHn11XNkaKs/Y/kEJobi7AquknLnKPntCe1M/I+cp+pCSdO9p8xghltFa+mOBaReFb9JmttB01YBRGw6EOe3psTNK0SahW5lTCicdIqKsqjVlQINxZh5cKkZs4h3TRWbT2BdXvPRDspJjX5CkX6XJAMWl43bSswu/DxaIpjsYkWHa2w5Dd5TJlgFFbmETcJu0Zx0qFNWn93nzyHmQUHcF7BjGzUt4+kqCgvyUgjEcKNRbBamEg3g+XFX7X4LNaRFCAftDxu2lbhpIWPBDvHajAUxqqtJ7B65ylLfpPHlAlGCIbCCIXDaN/WS32z0oJFH/L43px0aIuP82B4zwws/dUNzekoaGicSHPpyFkFWCOEG4tgFSFDW1hymirfLpy08GlBMlZp+BgUlpZj4UdHUFHnJ7reyG86xRlUDS0TAGCteYRFH/L63px2aKN98NKTSydSu3bjdam6fscKhHBjEazstkY96NVwkirfTkgXPt5DkkkytJodqySqbRq/6aSw++ZxUdvQ/PcWwn6yUkvIog+d9N54h+bBy4imrOk7QrhxNSzMF1pClRmBh3dVvlmsEEJ4CG0lQc0xcfmUAabaaiRN/APDuxH9ptw7c4IzaOS48MWH8exQYNzy7ai7HFLtp7S2bTDrFz2RkeJDWtsEBENh6s/C4mDmJCdeK6C99tDSOBnRlPGiFRXCjcWwMF+oCVV3DemC5cVfG7ovL4PWCqwQQnit4q5E7FjNSGqDqmO7MaZPpqn7GkkTP4bAbq+3vhcPPlFNPkdfy87ByguX4A+qrwu1F6/gDxuvZs+1SlBmcTCz4jdjhYaBnVNoNpkKrA9AaoKVHktApHYtFLxiebu1EMKNDbCw2yoJVQCwbu83usxWblcJWyGE8BTaqofIsRoIBLBROQs9MXo0fqRjjeSd7XhsFFfmwCafo6OoqKOnAbVSUJZbQwZ37YD9p2vwYcl3lvQpzcOgnNDQtYMPc3tTa65pWB+AtAQr0qKisdq1UNCyJhMjhBsXoyRUkVbABdyvErZKCOEttJUlejV+WmPNSH0v1hjxOSLBakE5cg0pLC3Hrc99YrmGgcZhUKm/K38ULIuPVSI/t7Op3zALywOQFLUoFzUbK1iR5NLhQSsaCxcZil944QV069YNiYmJuOmmm7Bnzx7Fa9esWQOPxxP1X2Kie80lVqCULbd9krc5142EkQy6kZksec1eKWFVFlgeQ1tZoZXxVSKbcKyxzNxrBCM+R3qw43l5yR5MAkn266WbvmS+LrEax4Wl5Ri+dIusYCP9LhCdIXxCTjZ2PDYKa2cMw8q7BuDdB2/Cuw/chJV3DcDaGcOw47FRXAk2AAeam/feew9z587Fyy+/jJtuugkrVqzA+PHjcfz4cXTs2FH2O6mpqTh+/Hjz3x6P+zQKVqNmtjKjEmZtP9aLVUIIr6GtLCBRbc8Z05O4wjUPgqMeB1AjPkdGsOp5nWZiJenvijr2WlMW45hUgyinWXZaWDxz4ea///u/MWPGDNx3330AgJdffhkbNmzA6tWrMX/+fNnveDweZGWRJQry+/3w+6/m1airqwPQ5E8QCNArCCjdi+Y97aApH0FT2J7kBBb7Gan9tPhYJea8V4IwAF/81c9r6i9i9tr9WD5lgGnnVIBuX2cktYEvXvsEl5HURtfvDeycgq4dfKisUw5tzUxtcnDkdczQ7OfRvTLw4rT+WLrpyyifk6zURMzP740xfTKJx5pV74yU4mOVqs8Ry9naBtX2+uLCzf/rAdC+rRcJbeJQeYEsH5CEVc+7p6wa1fUXo+Z0LNX1F7H7xFku/PLU+juyr8/WNiAQYBeybPc4DobCWLLhCBIIflOi4nwDdn51BVX1fmS082Fw1w5EAqxV+6Ge+3nC4TAz3dzly5eRlJSEv/zlL5g8eXLz59OnT8f58+fx4YcftvjOmjVr8OCDD6JTp04IhUIYNGgQ/vjHP6Jfv36yv7Fw4UIsWrSoxecFBQVISkqi9iwCgUAgEAiso7GxEdOmTUNtbS1SU9UFU6aam6qqKgSDQWRmRp92MjMz8eWXX8p+p1evXli9ejVyc3NRW1uLP/3pT7j55ptx5MgRdO7c0kFswYIFmDt3bvPfdXV16NKlC8aNG6fZOXoIBAIoKirC2LFj4fV6tb/gMvaUVeP+t/ZqXrd6+hBDp7tgKIz9p2tQVe9Hett4VH+1j1pfSxonQD6/hhmNk95TPk/wPKaV3pnECkpawkiCoTDGr9iuGO0kaeM2zx4RdbqVvqekxfPFhbH4xhD+f8fb4j8n9G1ut9b3In+XllZUDqvnNm3U+k3q61VfJeHv/9+tzM1oVq49sWw8XI55f/3C1D1I22XV2iFZXkhgbpbSS15eHvLy8pr/vvnmm9GnTx+88sorWLx4cYvrfT4ffD5fi8+9Xq8lC7ZV9+WdqsYrmjk6pOv09k+sH4+U9OzTE9VUIh7yczvDExdvia9Qfm5njMvpZEtIslWZkHkc09I7m/+3wy1qorVP8sITF0+9zftOnsPpGj+g4hp9usaPg99eiPJN8AJYMKmfbM2fSD5+dCQSfQm6vtchyYslv7zBUn+2YT06Ir1dW83swcN6dGQuLADq/Sa1bu74PlF9zQor155YOqYlE63RJCz6+EuMy+mk+b5prx167sVUuMnIyEB8fDwqKyujPq+srCT2qfF6vRg4cCBOnDhhRRNbNXo2S6scaNUc4Oa8VwJPXDyVBcDKZIt2OOI5zZGbFrUyxV5rGwOW5Agx4wCqFE6bnZaIpyb1wuWy/bJjTel77dt6cd/wbsRO2GZwYvZgpX7LTE0E0MCV1tSuRK+kCfnSkxNQ3XBZ9V4VdX6s2noCj47pSbWNNGEq3CQkJGDw4MHYsmVLs89NKBTCli1bMGvWLKJ7BINBHD58GBMnTrSwpfxi1Wld72ZpRW0YkhBamlEaTosGkGCdCIwFLCJ4zArwSptYKHgFG8uU78dDkVYWGYvNItdvAzunYHPhJtZNa4Edaw9p1OJ16UmY87+HNO+3vPgr9Mpqx+W7BzgwS82dOxfTp0/HjTfeiKFDh2LFihVoaGhojp6655570KlTJyxZsgQA8Mwzz2DYsGHo0aMHzp8/j+eeew6nT5/Ggw8+yPIxmGDVaZ10s4wVrJ6c1AczCw5SO92JRHjamN3keS/qqQSLsUFDgJfbxEgixHgQvHkQsvQS22+8RibahZoGUdo3dp08R3y/RX8/ilG9M7H/dE2LlCKsYS7cTJkyBT/88AOeeuopVFRUYMCAASgsLGx2Mj5z5gzi4q7mGqypqcGMGTNQUVGBDh06YPDgwfjss8/Qt29fVo/ABKtO66SbZSgELN7QcoL8ZkR3fHSonMrpzqgZwKkbthHMbPJONmWxyBHiRPMMbXgQsgTm0BJSJSGeJDdTee0lDFtSjOqGq0KjZGplDXPhBgBmzZqlaIbatm1b1N/Lly/H8uXLbWgVv1ipkifdLB8pONDi3ypqL+HV7WV4YdogdEhOMC1cGDEDOHnDNoLRTZ5EOB7dK4NSK+nDKkmiE80zgqY1c/fJc/j8ZCV+BmD3P8/h5p6ZrhZE1VATUiUh/qF3Wq7xckQKNkDTGjLnvRIsG2q6mabgQrgR6MNKlbyZk64kWC3ecBQ7HhtleuEgMQNkR5gBWqPviZFNnlQ4Htnz51TaaAVW+HiR4kTzTGumsLS8OapOirR88O19aOtLwFKLI82swmrt9IScbMwZ01O2er0WkfMxGAqDVZwlF7WlBPqwUiVv9qRLsx6KdIIAlINvJTMAST2ZyFopbkGrblOsAAiQC8f7T9dQbSstpIV9Yk6WomADWGsikk6+dwzohLyfXcO1YOOkWm+0KSwtx0PvHGiRLgAAzjcG8BBndbFIKCwtxy3LtmLqa7vx6LoSTH1tN25ZtpX6c8wa1RNZqcb2A2mEsVxDhHDjQKxUyZMWOdSClq+DUpFPoCmRlHTqcloxRVqoCYBKmzzpu6mq15f+3w4iF/Y3dp4CAMTKFUaKvboVuzZCHgmGwlj40RHN65x06LGzgGl8nAcLb+8LD9QyO6nDcg0Rwo0DMXJaJ4VksySBpq9DbEXa1dOHAEBUrgoeiimyQkkAVNrkSd9NRruWyS9ZorSwSwVk7h/ejdsKxSxwUiVvK9hTVo2KOu3N1SmHHhbaaaW15ZpksgSILNcQ4XPjQKyO2lBzmnxyUh8s3nDMdl+HSAe4QCCAjcei/721V+HW4wdC6q8yuGsHbD4mc4GNSCaoitqLWLzhmKqf0KbSCvzXJGujlZwSiee0St5WoOcg44RDD6vUGHJry+CuHXDrc59oJgQc3LUDtXboRQg3DiQYCiOtbQLuG94N60u+j8omSStqQ22zjIvzcBcOy9LBlBdIw3SdEtIsF/mmhB05j5wUiSdyROk7yDjh0GOHdlpJeJdbW6Q1RA2Wa4gQbhyG3AKbnuzFvw7ohDF9s6ieJJU2Sx7DYZ2yYfMCyTtkmfBMreyGGladwJ0WideazbQSQ7unIzMlAZUX1EsJGDXh243V2mm9wvuEnGz8ZkR3vLJdOb128bFKKvX/jCCEGwehtMDWNASweucpDLFRRc5jOCyPQhcrSMwnPL5DgKzshhJWnMBZmXjMmMBau5kWAIqOVsAf1B5FTjn0WKmdNiK8B0NhfHRI3W9r6SayAptWIIQbh8CjDZ3HbKW8bth2oucExuM71DKpyGGl2ZGFicesCay1m2lJNH92VFSniVXaaaN7C8k8rahjZ/oU0VIOobWGOscinWaBpj6RiwxwUg4S2rghQkavqcRqs6PdJh4a79BIigC3QKL565DkxeePj3GMYCOhNzKSBKN7C++mT6G5oYS0yW48XI6OacnUtQWkA2TTjwufG7UV0mm2uv4inh0K3P/WXqS3a2va5OSUCBgteNTuGUGvqcRqs6OdJh6a77C1mmlJNAo1jQHsP13DndaSBNraaaNCCu+mTyHcUKCwtBxLNhzB3N7AvL9+AX/QQz2KgnSAvL3rNN7edZrbKA6jRKqZffFXPzfr0OmkCBgt3BIho2VSAZqc6J/8l37ISrVeGLXTxEP7HbZGMy3vGgUa0DQnGxVSSOapND9ZIMxSJpE23Yo6a80AejMHO8kMoYVVyauU1P/ltZfw0DsHsPELZ/WdWxZ1LZOKB8Af//UG/OtAe8yOdpp4rHiHrc1My7tGgTeMJoUlmRfz83szG29CuDGBnRkjSeosWfn7LLHC34jELj9r7QFs/OJ78oYyxk2LuhW+BU5oj5veIStIDoIsNQq8YUZ4V5oXmT/WpIrMIm83wixlArvNAEo2dLt+nxVWnGZJ7PKhMPBIwUG8HOdxhInKbREyvJlU7GiP294hC0iiimhpFNzir2fGP0tuXgzsnILNhZvsaLoiQrgxAQszQORA2lRajrd3nbb191lgxWlWT584wQkXcGciQ95C1a1ujxvfIQuUNusOSQkALiI10YtgKGyqH93krweYE95j5wXLBKASwixlAlYqZGkg5RNOIKersK0oFKqnT5wUYs+bOUegH6vfYTAUxq6T5/BhyXfYdfKc483WSkQW3H1geDekJ3tR3diUrfj+t/aaqo7uhpQLcrjJP0tobkwQqUKWw2oVcmtRYceeZiMxepqV+o40WZyTtF+8mXME+rHqHbpJ20BiEoqP86D24mWs3nmKWqQlDxmrM5J9gAeoqveL+a2AEG5MYMWma+b33azCjlQzV9dfbP7caM4Oqe8e0ij8JuE07Rdv5hyBfmi/Q6fVx1KDVEizQhDhJWN1JE4VUK1EmKVMIm26kne4hF1mgNZkhpDUzKunDwEArJ4+BDseG2X4GSfkZOPFaYOgtqYZMXkJ2NNaTC+k2BnZaTV6TEJWRFrykrE6Eqebw6xAaG4oMCEnGyN7XoPNhZvw7K9yLclQrPX7rcUMER/nwdDu6dh4jE4W5om52ViFgXik4GCLf3Ob9qu14CbTCy3ckuBRrybGCkGEl4zVkTgpA7ldCM0NJaTBNPGGbCaOWG5yBLObibnX4uVfD0I2J9ovoXUwjlsdPc3ilgSPejUxVggipAEOoVDY9BzWU0S2tdQXJEVobgQC8KP9EloH47iltpYVuCU5oF4hzYqgCy1fxzCAi4Eg7n7j8+bPjc5hI8Im7wKqXQjNjUDwI6y1X0LrYA4r/CvcghXpFFigV0izqnSGkq9j+yQvAOB8Y3SeF6Nz2IiwybuAahdCuGlF8Gzu4LltduAmh09WuMX0YgV21seyEiNCmlVBF5F5dFbeNQDvPngTfG3kt1Sjc3ho93S0b+slvt4JAqpdCLNUK8Fuc4eetOTCFOMeh0+WuMX0YhVmUuzzgtH0F5LZefeJs6g6thurpw/BsB4dTQtzkeH6u06eQ0WdX/FapTmstlbGx3lw3/DuWF78FVF7nCCg2oUQbloBdue30COsuCn3hhmE1sE8rSWppRmM+JbxVj/JqJBGO9IyFiNzmGStnDWqB978rKyFqSuSOA+waqr2Wsnbu7QSIdy4HLudLPUIK8IB9CpC62Ce1pDUksbmpCc5IK9aVV4CACLRO4dJ18r4OA+W/vIG1YSjq6YOxMRc9ffB67u0CuFz43LsdLLU6zciHECv4haHT9awSGppl79YYWk5blm2FVNf241H15Vg6mu7TdVHIvk9nh3cWQcAxKJnDutdKyfkZMumq8hOS8TLvx6EibnXqraN93dpBUJz43LsNHfo9RsRppgmpNP4xJwsvLHzVIt/d4vWwS7sPNXbdRq223wrtKr60aM53HXynG4fO6PjurW+SyHcuBw7zR16hRVhipHfHOM8QOTh30kOn7xgR20tuwQOFpuTcHA3Bqk/kNGDnZFx3VrfJRdmqRdeeAHdunVDYmIibrrpJuzZs0f1+vfffx+9e/dGYmIibrjhBmzcuNGmljoPO80deoWV1m6KUVIVh3/cxe4f3g1rZwwzVT9LYA12hu6zMN8KrapxYkPE5eYwz4dOt8BcuHnvvfcwd+5cPP300zhw4AD69++P8ePH4+zZs7LXf/bZZ5g6dSoeeOABHDx4EJMnT8bkyZNRWlpqc8udgZ35LfQKK27JvWEEktP4ptIK5k6SAnnsFDhYbE5Cq2oOLX8gng+dboG5cPPf//3fmDFjBu677z707dsXL7/8MpKSkrB69WrZ61euXIkJEybgd7/7Hfr06YPFixdj0KBBWLVqlc0tdw52OVkaEVZaU1XzSIQztbOxU+BgsTm1dq2q1fB86HQLTH1uLl++jP3792PBggXNn8XFxWHMmDHYtWuX7Hd27dqFuXPnRn02fvx4rF+/XvZ6v98Pv/9qYqW6ujoAQCAQQCCgnDdAL9K9aN6TJqN7ZWBkz59j/+kaVNX7kdHOh8FdOyA+zkO1zaN7ZeDFaf2xdNOXqKiLsDmnJmJ+fm+M7pXR4vf0to33vibhbG0DfPHaJouztQ0IBFJtaFFL3NDPVpGR1Ibo/WUktdHsP61+Htg5BV07+FBZp5y/JzM1EQM7p1B9V09N6oU575UAkHeOfWpSL4SCVxAKUvtJy+FpTBtZK41i97u0qp/13M8TDoetiVsk4Pvvv0enTp3w2WefIS8vr/nzefPm4dNPP8Xnn3/e4jsJCQl46623MHXq1ObPXnzxRSxatAiVlZUtrl+4cCEWLVrU4vOCggIkJSVRehKBQCAQCARW0tjYiGnTpqG2thapqeqHPtdHSy1YsCBK01NXV4cuXbpg3Lhxmp2jh0AggKKiIowdOxZeL3ktEIF+3NDXwVAY41ds1zyNb549gpnPjRv62UqKj1WqnoaXTxmAMX0yNe9D2s/FxyoVT/kkv2OUYCgsq1V1Iq19TNv1Lq3qZ8nyQgJT4SYjIwPx8fEtNC6VlZXIysqS/U5WVpau630+H3w+X4vPvV6vJYPbqvsKWuLkvvYCWDCpHx7+Meuo3Oa4YFI/JPoS7G5aC5zcz1aSn9sZnrh4anlutPo5P7czxuV0sj0rrxfA8OutE55Y0FrHtN3vknY/67kXU+EmISEBgwcPxpYtWzB58mQAQCgUwpYtWzBr1izZ7+Tl5WHLli2YPXt282dFRUVRZi2BwAm4oZBha8fuMgB25O8RCNwAc7PU3LlzMX36dNx4440YOnQoVqxYgYaGBtx3330AgHvuuQedOnXCkiVLAACPPvoobr31Vjz//POYNGkS1q1bh3379uHVV19l+RgCgSF4rJEj0IcQOAQC/mAu3EyZMgU//PADnnrqKVRUVGDAgAEoLCxEZmaT6uzMmTOIi7sasX7zzTejoKAATzzxBB5//HH07NkT69evR05ODqtHEAhMITZHgUAgoAtz4QYAZs2apWiG2rZtW4vP7rzzTtx5550Wt0ogEAgEAoETYZ7ETyAQCAQCgYAmQrgRCAQCgUDgKoRwIxAIBAKBwFUI4UYgEAgEAoGrEMKNQCAQCAQCVyGEG4FAIBAIBK5CCDcCgUAgEAhchRBuBAKBQCAQuAoukvjZSTjcVKJQT3VREgKBABobG1FXV9cqC7LZiehrexD9bA+in+1D9LU9WNXP0r4t7eNqtDrh5sKFCwCALl26MG6JQCAQCAQCvVy4cAFpaWmq13jCJCKQiwiFQvj++++RkpICj4deccK6ujp06dIF33zzDVJTU6ndV9AS0df2IPrZHkQ/24foa3uwqp/D4TAuXLiAa6+9NqrmpBytTnMTFxeHzp07W3b/1NRUMWlsQvS1PYh+tgfRz/Yh+toerOhnLY2NhHAoFggEAoFA4CqEcCMQCAQCgcBVCOGGEj6fD08//TR8Ph/rprge0df2IPrZHkQ/24foa3vgoZ9bnUOxQCAQCAQCdyM0NwKBQCAQCFyFEG4EAoFAIBC4CiHcCAQCgUAgcBVCuBEIBAKBQOAqhHBDiRdeeAHdunVDYmIibrrpJuzZs4d1k1zH9u3bcdttt+Haa6+Fx+PB+vXrWTfJlSxZsgRDhgxBSkoKOnbsiMmTJ+P48eOsm+U6XnrpJeTm5jYnOsvLy8OmTZtYN8v1LF26FB6PB7Nnz2bdFNexcOFCeDyeqP969+7NpC1CuKHAe++9h7lz5+Lpp5/GgQMH0L9/f4wfPx5nz55l3TRX0dDQgP79++OFF15g3RRX8+mnn2LmzJnYvXs3ioqKEAgEMG7cODQ0NLBumqvo3Lkzli5div3792Pfvn0YNWoU7rjjDhw5coR101zL3r178corryA3N5d1U1xLv379UF5e3vzfjh07mLRDhIJT4KabbsKQIUOwatUqAE31q7p06YL/+I//wPz58xm3zp14PB588MEHmDx5MuumuJ4ffvgBHTt2xKeffooRI0awbo6rSU9Px3PPPYcHHniAdVNcR319PQYNGoQXX3wRv//97zFgwACsWLGCdbNcxcKFC7F+/XqUlJSwborQ3Jjl8uXL2L9/P8aMGdP8WVxcHMaMGYNdu3YxbJlAQIfa2loATRuvwBqCwSDWrVuHhoYG5OXlsW6OK5k5cyYmTZoUtVYL6PP111/j2muvxU9/+lPcfffdOHPmDJN2tLrCmbSpqqpCMBhEZmZm1OeZmZn48ssvGbVKIKBDKBTC7NmzMXz4cOTk5LBujus4fPgw8vLycOnSJbRr1w4ffPAB+vbty7pZrmPdunU4cOAA9u7dy7opruamm27CmjVr0KtXL5SXl2PRokX4+c9/jtLSUqSkpNjaFiHcCAQCRWbOnInS0lJmdnO306tXL5SUlKC2thZ/+ctfMH36dHz66adCwKHIN998g0cffRRFRUVITExk3RxXk5+f3/z/c3NzcdNNN6Fr16743//9X9tNrUK4MUlGRgbi4+NRWVkZ9XllZSWysrIYtUogMM+sWbPw8ccfY/v27ejcuTPr5riShIQE9OjRAwAwePBg7N27FytXrsQrr7zCuGXuYf/+/Th79iwGDRrU/FkwGMT27duxatUq+P1+xMfHM2yhe2nfvj2uv/56nDhxwvbfFj43JklISMDgwYOxZcuW5s9CoRC2bNkibOcCRxIOhzFr1ix88MEH2Lp1K7p37866Sa2GUCgEv9/PuhmuYvTo0Th8+DBKSkqa/7vxxhtx9913o6SkRAg2FlJfX4+TJ08iOzvb9t8WmhsKzJ07F9OnT8eNN96IoUOHYsWKFWhoaMB9993Hummuor6+PuoEUFZWhpKSEqSnp+O6665j2DJ3MXPmTBQUFODDDz9ESkoKKioqAABpaWlo27Yt49a5hwULFiA/Px/XXXcdLly4gIKCAmzbtg2bN29m3TRXkZKS0sJfLDk5Gddcc43wI6PMb3/7W9x2223o2rUrvv/+ezz99NOIj4/H1KlTbW+LEG4oMGXKFPzwww946qmnUFFRgQEDBqCwsLCFk7HAHPv27cMvfvGL5r/nzp0LAJg+fTrWrFnDqFXu46WXXgIAjBw5MurzN998E/fee6/9DXIpZ8+exT333IPy8nKkpaUhNzcXmzdvxtixY1k3TSAwxLfffoupU6fi3Llz+MlPfoJbbrkFu3fvxk9+8hPb2yLy3AgEAoFAIHAVwudGIBAIBAKBqxDCjUAgEAgEAlchhBuBQCAQCASuQgg3AoFAIBAIXIUQbgQCgUAgELgKIdwIBAKBQCBwFUK4EQgEAoFA4CqEcCMQCAQCgcBVCOFGIBAIBAKBqxDCjUAgcDXl5eWYNm0arr/+esTFxWH27NmsmyQQCCxGCDcCgcDV+P1+/OQnP8ETTzyB/v37s26OQCCwASHcCAQC7vnhhx+QlZWFP/7xj82fffbZZ0hISMCWLVtUv9utWzesXLkS99xzD9LS0qxuqkAg4ABRFVwgEHDPT37yE6xevRqTJ0/GuHHj0KtXL/zbv/0bZs2ahdGjR7NunkAg4Awh3AgEAkcwceJEzJgxA3fffTduvPFGJCcnY8mSJaybJRAIOESYpQQCgWP405/+hCtXruD999/Hu+++C5/Px7pJAoGAQ4RwIxAIHMPJkyfx/fffIxQK4dSpU6ybIxAIOEWYpQQCgSO4fPkyfv3rX2PKlCno1asXHnzwQRw+fBgdO3Zk3TSBQMAZQrgRCASO4L/+679QW1uL//mf/0G7du2wceNG3H///fj44481v1tSUgIAqK+vxw8//ICSkhIkJCSgb9++FrdaIBCwwBMOh8OsGyEQCARqbNu2DWPHjsUnn3yCW265BQBw6tQp9O/fH0uXLsXDDz+s+n2Px9Pis65duwrTlkDgUoRwIxAIBAKBwFUIh2KBQCAQCASuQgg3AoHA0fTr1w/t2rWT/e/dd99l3TyBQMAAYZYSCASO5vTp0wgEArL/lpmZiZSUFJtbJBAIWCOEG4FAIBAIBK5CmKUEAoFAIBC4CiHcCAQCgUAgcBVCuBEIBAKBQOAqhHAjEAgEAoHAVQjhRiAQCAQCgasQwo1AIBAIBAJXIYQbgUAgEAgEruL/D/Jgtx9x4QvRAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# The domain can be visualized by creating a sampler object, see also step 2, and use the scatter plot function from tp.utils.\n", "Omega_sampler = tp.samplers.RandomUniformSampler(Omega, n_points=500)\n", @@ -211,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "d428cf7f-89ee-4f3f-a1bf-822b82550a7e", "metadata": { "id": "d428cf7f-89ee-4f3f-a1bf-822b82550a7e" @@ -233,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "d020f7f4-c286-466f-928d-1f80ee64c53f", "metadata": { "id": "d020f7f4-c286-466f-928d-1f80ee64c53f" @@ -255,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "3a1ee851-1bd4-4ee2-83e4-7dca3f883c0f", "metadata": { "id": "3a1ee851-1bd4-4ee2-83e4-7dca3f883c0f" @@ -281,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "e780f5fa-5ebf-4731-8568-77116ea039f6", "metadata": { "id": "e780f5fa-5ebf-4731-8568-77116ea039f6" @@ -304,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "b627951a-a12b-4333-b965-35a56b8fc396", "metadata": { "id": "b627951a-a12b-4333-b965-35a56b8fc396" @@ -317,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "c23a19e6-4167-4785-8323-984c319e2cb4", "metadata": { "colab": { @@ -327,18 +305,7 @@ "id": "c23a19e6-4167-4785-8323-984c319e2cb4", "outputId": "f47cb4e2-7c58-41e3-8d5b-445b02ec45fa" }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Visualization of sampled points at the boundary\n", "plot = tp.utils.scatter(X, sampler_boundary_condition)" @@ -363,7 +330,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "c29f3f92-d613-470f-ab74-9369e071ea04", "metadata": { "id": "c29f3f92-d613-470f-ab74-9369e071ea04" @@ -386,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "65954de9-4c80-4d2a-be6e-0cd16ab82596", "metadata": { "id": "65954de9-4c80-4d2a-be6e-0cd16ab82596" @@ -418,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "c97e8bfe-1580-4bb8-bb1b-d4c874ef6244", "metadata": { "id": "c97e8bfe-1580-4bb8-bb1b-d4c874ef6244" @@ -441,7 +408,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "17d5e293-57bd-4739-9518-a014f6df2b79", "metadata": { "id": "17d5e293-57bd-4739-9518-a014f6df2b79" @@ -466,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "4864c6ed-6f2b-4f80-bd6f-cd8ff3d8a809", "metadata": { "id": "4864c6ed-6f2b-4f80-bd6f-cd8ff3d8a809" @@ -501,7 +468,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "bdef3d80-90e6-47aa-95ce-6d735fd03f36", "metadata": { "id": "bdef3d80-90e6-47aa-95ce-6d735fd03f36" @@ -523,7 +490,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "fa15606a-a2c7-40bf-9e41-920c8f6a1bc9", "metadata": { "id": "fa15606a-a2c7-40bf-9e41-920c8f6a1bc9" @@ -545,7 +512,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "9b838d6f-1b90-4667-8ecb-9f54b4ec627e", "metadata": { "id": "9b838d6f-1b90-4667-8ecb-9f54b4ec627e" @@ -572,7 +539,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "008c09a7-81f8-41b5-8c10-3892812740ad", "metadata": { "id": "008c09a7-81f8-41b5-8c10-3892812740ad" @@ -632,7 +599,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "bb76e892-bf53-4a01-adc5-74dddb770525", "metadata": { "colab": { @@ -641,15 +608,7 @@ "id": "bb76e892-bf53-4a01-adc5-74dddb770525", "outputId": "90104470-f0c5-40ab-e922-3c9915202fd6" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GPU available: False\n" - ] - } - ], + "outputs": [], "source": [ "import pytorch_lightning as pl\n", "import os\n", @@ -670,7 +629,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "ea27b608-e319-4fac-85c1-5984f2d043c6", "metadata": { "id": "ea27b608-e319-4fac-85c1-5984f2d043c6" @@ -692,7 +651,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "b1848d26-ea33-400c-84be-2291429e8065", "metadata": { "id": "b1848d26-ea33-400c-84be-2291429e8065" @@ -714,7 +673,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "4ea2cb3f-087c-4e03-aeb0-40318f556062", "metadata": { "id": "4ea2cb3f-087c-4e03-aeb0-40318f556062" @@ -736,7 +695,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "9ea9431a-9ea4-4312-8869-af4c8c4733a4", "metadata": { "colab": { @@ -770,64 +729,7 @@ "id": "9ea9431a-9ea4-4312-8869-af4c8c4733a4", "outputId": "6e93ce9c-379c-4f4f-b946-16cf8b9652a5" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:pytorch_lightning.utilities.rank_zero:GPU available: False, used: False\n", - "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n", - "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n", - "INFO:pytorch_lightning.callbacks.model_summary:\n", - " | Name | Type | Params | Mode \n", - "--------------------------------------------------------\n", - "0 | train_conditions | ModuleList | 9.5 K | train\n", - "1 | val_conditions | ModuleList | 0 | train\n", - "--------------------------------------------------------\n", - "9.5 K Trainable params\n", - "0 Non-trainable params\n", - "9.5 K Total params\n", - "0.038 Total estimated model params size (MB)\n", - "20 Modules in train mode\n", - "0 Modules in eval mode\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c0c85ee450fa4382b8ba6d076bbe15ab", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: | | 0/? [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "vmin = 15 # limits for the axes\n", "vmax = 42\n", @@ -953,7 +836,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "e9e54d6e-f7a2-4746-a05e-681e3dbee8b7", "metadata": { "colab": { @@ -963,18 +846,7 @@ "id": "e9e54d6e-f7a2-4746-a05e-681e3dbee8b7", "outputId": "bc9feda3-d8bc-4e0a-8e50-7be9a6d8487d" }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_sampler = tp.samplers.PlotSampler(plot_domain=Omega, n_points=600, data_for_other_variables={'t':4.})\n", "fig = tp.utils.plot(model, lambda u : u,\n", @@ -984,7 +856,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "10a7c785-90da-4b62-964f-af7d816ed1bd", "metadata": { "colab": { @@ -994,18 +866,7 @@ "id": "10a7c785-90da-4b62-964f-af7d816ed1bd", "outputId": "249db53f-3a95-4bca-aeff-019f9a9d4dce" }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_sampler = tp.samplers.PlotSampler(plot_domain=Omega, n_points=600, data_for_other_variables={'t':8.})\n", "fig = tp.utils.plot(model, lambda u : u,\n", @@ -1015,7 +876,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "c3e6a8cf-6bd5-42d6-a3ac-16c4a64eb22b", "metadata": { "colab": { @@ -1025,18 +886,7 @@ "id": "c3e6a8cf-6bd5-42d6-a3ac-16c4a64eb22b", "outputId": "ca72f816-5fba-4edb-8f3c-62a8b624cae6" }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_sampler = tp.samplers.PlotSampler(plot_domain=Omega, n_points=600, data_for_other_variables={'t':12.})\n", "fig = tp.utils.plot(model, lambda u : u, plot_sampler, plot_type='contour_surface',\n", @@ -1065,7 +915,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "9ccbb9b3-6f6a-4a29-8dc7-c2360b2df7c9", "metadata": { "id": "9ccbb9b3-6f6a-4a29-8dc7-c2360b2df7c9" @@ -1093,7 +943,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "67c99cdd-70db-4465-9ec0-8278b7381fa6", "metadata": { "id": "67c99cdd-70db-4465-9ec0-8278b7381fa6" @@ -1115,7 +965,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "854b969a-96f2-4088-b045-d1ca5cf0db64", "metadata": { "id": "854b969a-96f2-4088-b045-d1ca5cf0db64" @@ -1128,7 +978,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "70d30023-ca42-460a-9906-2bcc736016ce", "metadata": { "colab": { @@ -1138,18 +988,7 @@ "id": "70d30023-ca42-460a-9906-2bcc736016ce", "outputId": "a7a3ce2b-18e1-4fce-e1c5-e370e258cc85" }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.imshow(np.rot90(output[:, :]), 'gray', vmin=vmin, vmax=vmax)\n", "plt.show()" @@ -1157,7 +996,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "9840aad9", "metadata": { "id": "9840aad9" @@ -1171,7 +1010,7 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -1185,7 +1024,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.13.3" } }, "nbformat": 4, diff --git a/pyproject.toml b/pyproject.toml index 2c63dbb2..f85ba9be 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,8 +1,105 @@ [build-system] -# AVOID CHANGING REQUIRES: IT WILL BE UPDATED BY PYSCAFFOLD! requires = ["setuptools>=46.1.0", "setuptools_scm[toml]>=5", "wheel"] build-backend = "setuptools.build_meta" [tool.setuptools_scm] # See configuration details in https://github.com/pypa/setuptools_scm version_scheme = "no-guess-dev" + +[project] +name = "torchphysics" +description = "PyTorch implementation of Deep Learning methods to solve differential equations" +authors = [ + {name = "Nick Heilenkötter", email = "nick7@uni-bremen.de"}, + {name = "Tom Freudenberg", email = "tomfre@uni-bremen.de"} +] +license = {text = "Apache-2.0"} +classifiers = [ + "Development Status :: 5 - Production/Stable", + "Programming Language :: Python", +] +requires-python = ">=3.8" +dependencies = [ + "torch>=2.0.0", + "pytorch-lightning>=2.0.0", + "numpy>=1.20.2, <2.0", + "matplotlib>=3.0.0", + "scipy>=1.6.3", + "importlib-metadata", + "jupyter", +] +dynamic = ["version"] + +[project.readme] +file = "README.rst" +content-type = "text/x-rst; charset=UTF-8" + +[project.urls] +Homepage = "https://github.com/boschresearch/torchphysics" +Documentation = "https://boschresearch.github.io/torchphysics/index.html" +Source = "https://github.com/boschresearch/torchphysics" +Changelog = "https://github.com/boschresearch/torchphysics/blob/main/CHANGELOG.rst" + +[project.optional-dependencies] +all = [ + "networkx>=2.5.1", + "trimesh>=3.9.19", + "shapely>=1.7.1", + "rtree>=0.9.7", +] +testing = [ + "setuptools", + "pytest", + "pytest-cov", +] + +[tool.setuptools] +zip-safe = false +include-package-data = true +package-dir = {"" = "src"} +platforms = ["any"] + +[tool.setuptools.packages.find] +where = ["src"] +exclude = [ + "tests", + "examples", + "docs", + "experiments", +] + +[tool.pytest.ini_options] +# Specify command line options as you would do when invoking pytest directly. +# e.g. --cov-report html (or xml) for html/xml output or --junitxml junit.xml +# in order to write a coverage file that can be read by Jenkins. +# CAUTION: --cov flags may prohibit setting breakpoints while debugging. +# Comment those flags to avoid this py.test issue. +addopts = [ + "--cov=torchphysics", + "--cov-report=term-missing", + "--verbose", +] +norecursedirs = [ + "dist", + "build", + ".tox", +] +testpaths = ["tests"] + +[tool.distutils.bdist_wheel] +# Use this option if your package is pure-python +universal = 1 + +[tool.flake8] +# Some sane defaults for the code style checker flake8 +max-line-length = 88 +extend-ignore = ["E203", "W503"] +# ^ Black-compatible +# E203 and W503 have edge cases handled by black +exclude = [ + ".tox", + "build", + "dist", + ".eggs", + "docs/conf.py", +] diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index b99499a9..00000000 --- a/setup.cfg +++ /dev/null @@ -1,138 +0,0 @@ -# This file is used to configure your project. -# Read more about the various options under: -# http://setuptools.readthedocs.io/en/latest/setuptools.html#configuring-setup-using-setup-cfg-files - -[metadata] -name = torchphysics -description = PyTorch implementation of Deep Learning methods to solve differential equations -author = Nick Heilenkötter, Tom Freudenberg -author_email = nick7@uni-bremen.de, tomfre@uni-bremen.de -license = Apache-2.0 -long_description = file: README.rst -long_description_content_type = text/x-rst; charset=UTF-8 -url = https://github.com/boschresearch/torchphysics -# Add here related links, for example: -project_urls = - Documentation = https://boschresearch.github.io/torchphysics/index.html - Source = https://github.com/boschresearch/torchphysics - Changelog = https://github.com/boschresearch/torchphysics/blob/main/CHANGELOG.rst -# Tracker = https://github.com/pyscaffold/pyscaffold/issues -# Conda-Forge = https://anaconda.org/conda-forge/pyscaffold -# Download = https://pypi.org/project/PyScaffold/#files -# Twitter = https://twitter.com/PyScaffold - -# Change if running only on Windows, Mac or Linux (comma-separated) -platforms = any - -# Add here all kinds of additional classifiers as defined under -# https://pypi.python.org/pypi?%3Aaction=list_classifiers -classifiers = - Development Status :: 5 - Production/Stable - Programming Language :: Python - - -[options] -zip_safe = False -packages = find_namespace: -include_package_data = True -package_dir = - =src - -# Require a min/specific Python version (comma-separated conditions) -# python_requires = >=3.8 - -# Add here dependencies of your project (line-separated), e.g. requests>=2.2,<3.0. -# Version specifiers like >=2.2,<3.0 avoid problems due to API changes in -# new major versions. This works if the required packages follow Semantic Versioning. -# For more information, check out https://semver.org/. -install_requires = - torch>=2.0.0 - pytorch-lightning>=2.0.0 - numpy>=1.20.2, <2.0 - matplotlib>=3.0.0 - scipy>=1.6.3 - importlib-metadata - jupyter - -[options.packages.find] -where = src -exclude = - tests - examples - docs - experiments - -[options.extras_require] -# Add here additional requirements for extra features, to install with: -# `pip install torchphysics[all]` like: -all = - networkx>=2.5.1 - trimesh>=3.9.19 - shapely>=1.7.1 - rtree>=0.9.7 - -# Add here test requirements (semicolon/line-separated) -testing = - setuptools - pytest - pytest-cov - -[options.entry_points] -# Add here console scripts like: -# console_scripts = -# script_name = torchphysics.module:function -# For example: -# console_scripts = -# fibonacci = torchphysics.skeleton:run -# And any other entry points, for example: -# pyscaffold.cli = -# awesome = pyscaffoldext.awesome.extension:AwesomeExtension - -[tool:pytest] -# Specify command line options as you would do when invoking pytest directly. -# e.g. --cov-report html (or xml) for html/xml output or --junitxml junit.xml -# in order to write a coverage file that can be read by Jenkins. -# CAUTION: --cov flags may prohibit setting breakpoints while debugging. -# Comment those flags to avoid this py.test issue. -addopts = - --cov torchphysics --cov-report term-missing - --verbose -norecursedirs = - dist - build - .tox -testpaths = tests -# Use pytest markers to select/deselect specific tests -# markers = -# slow: mark tests as slow (deselect with '-m "not slow"') -# system: mark end-to-end system tests - -[bdist_wheel] -# Use this option if your package is pure-python -universal = 1 - -[devpi:upload] -# Options for the devpi: PyPI server and packaging tool -# VCS export must be deactivated since we are using setuptools-scm -no_vcs = 1 -formats = bdist_wheel - -[flake8] -# Some sane defaults for the code style checker flake8 -max_line_length = 88 -extend_ignore = E203, W503 -# ^ Black-compatible -# E203 and W503 have edge cases handled by black -exclude = - .tox - build - dist - .eggs - docs/conf.py - -[pyscaffold] -# PyScaffold's parameters when the project was created. -# This will be used when updating. Do not change! -version = 4.0.1 -package = torchphysics -extensions = diff --git a/setup.py b/setup.py index 77b7d7bf..e2889240 100644 --- a/setup.py +++ b/setup.py @@ -1,21 +1,7 @@ """ Setup file for torchphysics. - Use setup.cfg to configure your project. - - This file was generated with PyScaffold 4.0.1. - PyScaffold helps you to put up the scaffold of your new Python project. - Learn more under: https://pyscaffold.org/ """ from setuptools import setup if __name__ == "__main__": - try: - setup(use_scm_version={"version_scheme": "no-guess-dev"}) - except: # noqa - print( - "\n\nAn error occurred while building the project, " - "please ensure you have the most updated version of setuptools, " - "setuptools_scm and wheel with:\n" - " pip install -U setuptools setuptools_scm wheel\n\n" - ) - raise + setup() diff --git a/src/torchphysics/utils/data/deeponet_dataloader.py b/src/torchphysics/utils/data/deeponet_dataloader.py index d839c463..c57a20a3 100644 --- a/src/torchphysics/utils/data/deeponet_dataloader.py +++ b/src/torchphysics/utils/data/deeponet_dataloader.py @@ -5,29 +5,30 @@ class DeepONetDataLoader(torch.utils.data.DataLoader): - """ - A DataLoader that can be used in a condition to load minibatches of paired data - points as the input and output of a DeepONet-model. + r""" + A DataLoader that can be used in a condition to load minibatches of paired + data points as the input and output of a DeepONet-model. Parameters ---------- branch_data : torch.tensor - A tensor containing the input data for the branch network. Has to be of the shape: - [number_of_functions, discrete_points_of_branch_net, function_space_dim] - For example, if we have a batch of 20 vector-functions (:math:`f:\R \to \R^2`) and - use 100 discrete points for the evaluation (where the branch nets evaluates f), - the shape would be: [20, 100, 2] + A tensor containing the input data for the branch network. Has to be of + the shape: [number_of_functions, discrete_points_of_branch_net, + function_space_dim]. For example, if we have a batch of 20 + vector-functions (:math:`f:\R \to \R^2`) and use 100 discrete points + for the evaluation (where the branch nets evaluates f), the shape would + be: [20, 100, 2] trunk_data : torch.tensor - A tensor containing the input data for the trunk network. There are two different - possibilites for the shape of this data: - 1) Every branch input function uses the same trunk values, then we can pass in - the shape: [number_of_trunk_points, input_dim_of_trunk_net] - This can speed up the trainings process. - 2) Or every branch function has different values for the trunk net, then we - need the shape: - [number_of_functions, number_of_trunk_points, input_dim_of_trunk_net] - If this is the case, remember to set 'trunk_input_copied = false' inside - the trunk net, to get the right trainings process. + A tensor containing the input data for the trunk network. There are two + different possibilities for the shape of this data: + 1) Every branch input function uses the same trunk values, then we + can pass in the shape: [number_of_trunk_points, + input_dim_of_trunk_net]. This can speed up the training process. + 2) Or every branch function has different values for the trunk net, + then we need the shape: [number_of_functions, + number_of_trunk_points, input_dim_of_trunk_net]. If this is the + case, remember to set 'trunk_input_copied = false' inside the + trunk net, to get the right training process. output_data : torch.tensor A tensor containing the expected output of the network. Shape of the data should be: @@ -45,9 +46,11 @@ class DeepONetDataLoader(torch.utils.data.DataLoader): shuffle_trunk : bool Whether to shuffle the order of the trunk points at initialization. num_workers : int - The amount of workers used during data loading, see also: the PyTorch documentation + The amount of workers used during data loading, see also: the PyTorch + documentation pin_memory : bool - Whether to use pinned memory during data loading, see also: the PyTorch documentation + Whether to use pinned memory during data loading, see also: the PyTorch + documentation """ def __init__( @@ -67,22 +70,16 @@ def __init__( ): assert len(branch_data.shape) == 3, "Branch data has the wrong shape" assert branch_data.shape[-1] == branch_space.dim, ( - "Branch data dimension is not correct, is " - + str(branch_data.shape[-1]) - + " but expected " - + str(branch_space.dim) + f"Branch data dimension is not correct, is " + f"{branch_data.shape[-1]} but expected {branch_space.dim}" ) assert trunk_data.shape[-1] == trunk_space.dim, ( - "Trunk data dimension is not correct, is " - + str(trunk_data.shape[-1]) - + " but expected " - + str(trunk_space.dim) + f"Trunk data dimension is not correct, is " + f"{trunk_data.shape[-1]} but expected {trunk_space.dim}" ) assert output_data.shape[-1] == output_space.dim, ( - "Solution data dimension is not correct, is " - + str(output_data.shape[-1]) - + " but expected " - + str(output_space.dim) + f"Solution data dimension is not correct, is " + f"{output_data.shape[-1]} but expected {output_space.dim}" ) if len(trunk_data.shape) == 3: super().__init__( @@ -126,7 +123,8 @@ def __init__( class DeepONetDataset_Unique(torch.utils.data.Dataset): """ - A PyTorch Dataset to load tuples of data points, used in the DeepONetDataLoader. + A PyTorch Dataset to load tuples of data points, used in the + DeepONetDataLoader. Is used when every branch input has unique trunk inputs -> Ordering of points is important. """ @@ -207,7 +205,7 @@ def __getitem__(self, idx): idx : int The index of the desired point. """ - # frist slice in branch dimension (dim 0): + # first slice in branch dimension (dim 0): branch_idx = int(idx / self.branch_batch_len) a = (branch_idx * self.branch_batch_size) % len(self.branch_data_points) b = ((branch_idx + 1) * self.branch_batch_size) % len(self.branch_data_points) @@ -246,8 +244,8 @@ def __getitem__(self, idx): class DeepONetDataset(torch.utils.data.Dataset): """ - A PyTorch Dataset to load tuples of data points, used via DeepONetDataLoader. - Used if all branch inputs have the same trunk points. + A PyTorch Dataset to load tuples of data points, used via + DeepONetDataLoader. Used if all branch inputs have the same trunk points. """ def __init__( @@ -297,8 +295,8 @@ def __init__( def __len__(self): """Returns the number of points of this dataset.""" - # the least common multiple of both possible length will lead to the correct distribution - # of data points and hopefully managable effort + # the least common multiple of both possible length will lead to the + # correct distribution of data points and hopefully manageable effort return int( np.lcm( int(