From 86a6a16a289e38c0c4f2b7f302d9a2b33414d47e Mon Sep 17 00:00:00 2001 From: FoleyLab Date: Sun, 6 Dec 2020 20:36:45 -0500 Subject: [PATCH] fixed validatre cooling bad output --- example/Cooling_Opt.ipynb | 2 +- example/Validate_Cooling.ipynb | 134 +++++++++++---------------------- 2 files changed, 47 insertions(+), 89 deletions(-) diff --git a/example/Cooling_Opt.ipynb b/example/Cooling_Opt.ipynb index dc9b425..890df78 100644 --- a/example/Cooling_Opt.ipynb +++ b/example/Cooling_Opt.ipynb @@ -188,7 +188,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.6" } }, "nbformat": 4, diff --git a/example/Validate_Cooling.ipynb b/example/Validate_Cooling.ipynb index 3fe5f17..5284abc 100644 --- a/example/Validate_Cooling.ipynb +++ b/example/Validate_Cooling.ipynb @@ -27,86 +27,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "pUu4pD7JO3b2" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:root:code for hash md5 was not found.\n", - "Traceback (most recent call last):\n", - " File \"/usr/local/Cellar/python@2/2.7.15_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/hashlib.py\", line 147, in \n", - " globals()[__func_name] = __get_hash(__func_name)\n", - " File \"/usr/local/Cellar/python@2/2.7.15_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/hashlib.py\", line 97, in __get_builtin_constructor\n", - " raise ValueError('unsupported hash type ' + name)\n", - "ValueError: unsupported hash type md5\n", - "ERROR:root:code for hash sha1 was not found.\n", - "Traceback (most recent call last):\n", - " File \"/usr/local/Cellar/python@2/2.7.15_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/hashlib.py\", line 147, in \n", - " globals()[__func_name] = __get_hash(__func_name)\n", - " File \"/usr/local/Cellar/python@2/2.7.15_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/hashlib.py\", line 97, in __get_builtin_constructor\n", - " raise ValueError('unsupported hash type ' + name)\n", - "ValueError: unsupported hash type sha1\n", - "ERROR:root:code for hash sha224 was not found.\n", - "Traceback (most recent call last):\n", - " File \"/usr/local/Cellar/python@2/2.7.15_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/hashlib.py\", line 147, in \n", - " globals()[__func_name] = __get_hash(__func_name)\n", - " File \"/usr/local/Cellar/python@2/2.7.15_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/hashlib.py\", line 97, in __get_builtin_constructor\n", - " raise ValueError('unsupported hash type ' + name)\n", - "ValueError: unsupported hash type sha224\n", - "ERROR:root:code for hash sha256 was not found.\n", - "Traceback (most recent call last):\n", - " File \"/usr/local/Cellar/python@2/2.7.15_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/hashlib.py\", line 147, in \n", - " globals()[__func_name] = __get_hash(__func_name)\n", - " File \"/usr/local/Cellar/python@2/2.7.15_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/hashlib.py\", line 97, in __get_builtin_constructor\n", - " raise ValueError('unsupported hash type ' + name)\n", - "ValueError: unsupported hash type sha256\n", - "ERROR:root:code for hash sha384 was not found.\n", - "Traceback (most recent call last):\n", - " File \"/usr/local/Cellar/python@2/2.7.15_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/hashlib.py\", line 147, in \n", - " globals()[__func_name] = __get_hash(__func_name)\n", - " File \"/usr/local/Cellar/python@2/2.7.15_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/hashlib.py\", line 97, in __get_builtin_constructor\n", - " raise ValueError('unsupported hash type ' + name)\n", - "ValueError: unsupported hash type sha384\n", - "ERROR:root:code for hash sha512 was not found.\n", - "Traceback (most recent call last):\n", - " File \"/usr/local/Cellar/python@2/2.7.15_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/hashlib.py\", line 147, in \n", - " globals()[__func_name] = __get_hash(__func_name)\n", - " File \"/usr/local/Cellar/python@2/2.7.15_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/hashlib.py\", line 97, in __get_builtin_constructor\n", - " raise ValueError('unsupported hash type ' + name)\n", - "ValueError: unsupported hash type sha512\n", - "Traceback (most recent call last):\n", - " File \"/usr/local/bin/pip\", line 11, in \n", - " load_entry_point('pip==18.0', 'console_scripts', 'pip')()\n", - " File \"/usr/local/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 484, in load_entry_point\n", - " return get_distribution(dist).load_entry_point(group, name)\n", - " File \"/usr/local/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 2714, in load_entry_point\n", - " return ep.load()\n", - " File \"/usr/local/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 2332, in load\n", - " return self.resolve()\n", - " File \"/usr/local/lib/python2.7/site-packages/pkg_resources/__init__.py\", line 2338, in resolve\n", - " module = __import__(self.module_name, fromlist=['__name__'], level=0)\n", - " File \"/usr/local/lib/python2.7/site-packages/pip/_internal/__init__.py\", line 20, in \n", - " from pip._vendor.urllib3.exceptions import DependencyWarning\n", - " File \"/usr/local/lib/python2.7/site-packages/pip/_vendor/urllib3/__init__.py\", line 8, in \n", - " from .connectionpool import (\n", - " File \"/usr/local/lib/python2.7/site-packages/pip/_vendor/urllib3/connectionpool.py\", line 29, in \n", - " from .connection import (\n", - " File \"/usr/local/lib/python2.7/site-packages/pip/_vendor/urllib3/connection.py\", line 39, in \n", - " from .util.ssl_ import (\n", - " File \"/usr/local/lib/python2.7/site-packages/pip/_vendor/urllib3/util/__init__.py\", line 6, in \n", - " from .ssl_ import (\n", - " File \"/usr/local/lib/python2.7/site-packages/pip/_vendor/urllib3/util/ssl_.py\", line 8, in \n", - " from hashlib import md5, sha1, sha256\n", - "ImportError: cannot import name md5\n" - ] - } - ], + "outputs": [], "source": [ "!pip install wptherml" ] @@ -121,15 +48,37 @@ }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'wptherml'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m### Import WPTHERML class!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mwptherml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwpml\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmultilayer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mwptherml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatalib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatalib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'wptherml'" + "name": "stdout", + "output_type": "stream", + "text": [ + " Gradient will be taken with respect to all layers! \n", + " Temperature not specified!\n", + " Proceeding with default T = 300 K\n" ] + }, + { + "data": { + 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\n", 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5k/r16yfsmtqdpdH4IC8vj+LiYrZv355sUTQaR+rXr09eXl7CrqeViEbjg6ysLPLz85MthkaTMmh3lkaj0WhiJjQlIoT4pxBimxDimwjHhRDiYSFEoRBimRBiUFiyaDQajSYcwrREngYmuRw/Cehu/K4Eag6N1Wg0Gk1KE5oSkVJ+BPzgUmQq8KxULASaCSHahSWPRqPRaIInmYH1DkCRZbvY2LfFXlAIcSXKWqFTp04JEU6TPOYBy4AWwGHga2AbcDPQK6BrbAcOAq2Na3QDrInSBwJfGesNgQbATsvxMcbve6C5UaYXUG78coE2xt/QBvW1ptMOamojyVQiTvmjHTvfSykfBx4HlTsrTKE0wXEPymfZB+Wz7A+8BrwPHGeU2QXsA8xPgxlApATr83D4wvCIRH2ldDSuf0KU8l9Z1g+hFI6VBcYvB8h0OO6EAE41zmsO9AYWA/2AC4FxQDugBKWUNJp0IJm9s8x32iQP2JwkWTQBsxf4LVCAamTXohQIwPHG8i7U13ln4Fljn9sMHVtRjfsuYL9PeR5EKaqviaxAVkbYf8Cl3sPG8UMeZGgNvImSfy3wFkop/helRDqivuoaoRTOucDfgFIPdWs0ySKZSmQ2cJHRS2sEsEdKGeuHpibFeC7K8V3ADZbtiz3WOxileHr6lGeOsYw0KeuzKHfUt7b956BcWVayHc6vD+ygun/Wyk+AsZbtI7bj4xzOeQn4P+N6ArgEKHMop9Ekk9DcWUKIF1HvRishRDHKpZ0FIKV8DHgHOBkoRHkD9EQDtYhtUY7fHGO9pi/Tr8m6w+VYF5QlANWV0wTgRWPdfFjNupo41NPS5RoS+A0wy9jOsh2/zzg2AWWR9HCo4xlgKLAJeBLlEpwMDEC5xjSaZBCaEpFSnhvluER9aGlqGRK4PUqZvyRCEIMSVOzBzk3ArSgrwglrQ3+sZT3WrES5EfZfiIoXDbbsawrscSh7lWX9JeMHsBytSDTJQY9Y1wTOR0BFsoWwUBJhvzknn/sM4QqrCyvWL69IyudZhzr99h7pg4r7bI3hXI0mHrQS0QTOrhjPe8Jn+Tl4azCdyqynyiUVSYlY92dF2O+VBqieXFbmEtmtF4simIEa3ZtBVe83jSZstBLRBM5hn+XNh/AKn+dNInoAH9S4DTtOjbodaxDeSXFc7uHaANcDdzpcbxxwi8c6vLLUWM5FjU/Z6VJWowkCncVXEzh+u6RmELv7yy0lgkk0JWJXEHtQgfQzXer0Yyn8yVh6GUsSS/2R2IbqTr0kgLo0mkhoS0STdDJdjr3ocgzU4LxoRFIiJnYl0gTVtTfoL6xolk8YLI1eRBMAkmDigOkYz9JKRBM4fl8Et4fwGKq7x+zWQbSGuZiaYzIySY4JnolyYc2KUg783UOvY2w04fA9MIXq44CiUQIstGzvB65Dxd7eDE60hKCViCbpuFkimVRXFI/YjjtZGSZbUWMuJtj2mwqktbGcGE1ACx18lHViLu5uMhM/SiSaQlzloy6NP+4C2qKyDyzA+2DQu4CRwHvGdi5qrFA58POAZQwbrUQ0gROpAbwXuMxhv9tDaD/WChht2XZTIuYEtvZG1Ozy2x5Yh3qhvbAGlTYlWbSNsN9NCQNcGrQgmkpusG0/bVl3ezafN5ZOz1MxyrpJF7QS0SSMc3HujeTWZdbeQGaiuuealFuW1wIbfcrUBe+ura6oxImJwEkRj4tQNpoS+QT3/F+a4LgClTdOAM2o+X88jHKvrja2I41h8tJhJFXQSkQTOJEskWxUls0/+qjLfEC/Bf5trG+yHDeVyGfA/dSe+IDTPYykbKMpEYBRccii8YeZHmc/1btYl6AyEVgTgH6O8//aHsdLZbQS0SQMc8Cen4fObCB7Aqc7HDeVSJltme6c5aOseY/c8pEti0MWjT8WWdaLLeu7UcphvmXfa1Rlt7byXQhyhYVWIprAiWSJZNqWJm6B7WgPqKlEzGtay8cysjxVeBKVJv5OD2XNv71ZeOLUaf6NGjdkJ9J4KKuLyupG3Beh/BkO+37sQS5QSmmtx7JhoZWIxjOTia9hNntZWR+6ZVRNSOWEk6tmCfBnY91UImYf/XRWHFayUIH0G4CrjX2R/jbzb9cvczicCZxi29cF96zNJtZ5ZiIpEXu9fpiOmpUzmXNo6OdO45m3PJZzskT+SVUSQ6tiyMC94Xd6QPujgvRQ0xKpLUrEDzeixinUlnhQqvKNZX0D3pSCXyVinyIgGk8Zyy99nhckWoloEsJRlvUM27qf3ln2/XZLpC490OcYy7bAG6igrRu7gQ9DlSi9eR9ldVg/gqyj0P8VQ51WJeJlDpzWEfYX4T4ifqtniYKnLr1zmoCI1P9donz5TgkYrYoimiUyyHbcCbsScbJEaptVYv97XqDm/8ItieU0VDfhvQHKVJs4GRX/sD6/r1rWY2morUpkte2YvaPIGJw7R8xFuXz/bNtvVSrbSR5aiWh8E2l+8/dQmW2vjXK+3RKxE0nh4LDfzRJJxzxEXrkSdZ/s9+/vRM439pWx1HO2O2OOFzrksA9iG7vxOVXdfO1jQlrZtqfbrveRsXzDWL5vK299D5PZK1ErEY1vDkXYbwbO47VEoikZqHrZTCViNowCpTwexZsPOp2w3qe/u5Q5IcIxbYG4Yz5TB1Fzdj9H9cGl5jw5fhIt/o2qKZftDb0971sGakCriTkbp5n92f5RZH2+kzkJnE4Fr/FNtC68TlgbwGgxkcwI605lzBfTVCIZqC+4nwMDXeSpzdi/cDXeMJ+pQ0BfVPfZuZbj5rPmJ6U/VFkidtdjtm1boHpnzUF1ezdjXKYFs8lW3mqJJFOJaEtE45v7I+z36iaxPvCxWiJ2d5Y5wjfDsp7MYGMqU9tiRfHyMCq/mtUSMZ8h6weTed9iTSHjRYkADDGWe41znjW299jKWy0brUQ0acV9Efa7+WWtDZdV2ViVSHuHsm6WSIfiYtp98glUVFS60OpR1U1S+/410ShFjcPpRVVw2mppODXOsSoR+/thVyImucZyL9WfYbfrJjP+p5WIJm4Oo3qVuMUgvCiR1pZ9OKxbyfjgA1Z3786lo0fDtGmUHFHfjllEViJeJrBKRbTlEB5O8Ttr3qoXHI6HZYmYZKE+hg5TXTnYO7TYuyKfgBqPlWi0EtHEzV+AW4F7PJa3vkxWJWIuo8ZEKioQP/85Gzp35oNbboE336TPvfcC7krkU4/ypRp1eSBl2Dhl0bU+n9ZGOZI7azLwiodr2ZWI24Rq2ShlZrWESomcmLEE1XvLaaoFcFaWQaED65q4Mc3/SF1/oXoDOMK2v6Gxbi6jWiIffACrVnHHc8/R5YILGLF0KQP/9CdaX3EFWUcdFVGJeMl2m8poJRI8To3rTod9VuxK5GaqXFBueHVnmcdKqelOO2A5z2qJRJt/5Kqo0sWOtkQ0MbMfZYGYjbXbJDzWBtA+jeg1wB+MJXhIovjGG9CwIf8+80wkMPlPfyLn0CGueuQR15hIuiuRoNDKqAonJRIt+aG9d1Y9vN1Tr+4sUM+w3RKByB9q0UbTfxzleDyEqkSEEJOEEKuEEIVCiOsdjncSQswVQnwlhFgmhDjZqR5N6jENNSf0zVQ9wG5KxE4bYymB+ihl1MDYF1WJvPsuHHccpfXrI4EFPXvyvxNP5OJnnkFUVETtFqzRmESaFMqJT1ADDu2WiFclUmYrF6l3lnnMqkRM692qRPwE03dFLxIzoSkRIUQm8FfgJKA3cK4Qoret2O+BmVLKgahUQH8LSx5NsLxBVUJGc/ChV0vEadu6z/Wh3LwZCgvhhBMQVL1kz1x8MZ03bqTHh5GzQyVKiXyDc+rweNEWRPD4jRVci7MS8UK5rawfd1YTY+nmMnYjzEmuwrREhgGFUsq1UsojwEvAVFsZSdX9aYq3HGWaFMFs1MptS7eyXnB9KJcsUctBgypHpwO8MXUqh+rXZ8Abb0TMNpwoJdIH9eWUqmhlVIUfSwTUB5NdiWTi3Z1lVSJuGXtNd5b5fJsxFy89w5zew3RVIh1QySdNio19Vm4BLhBCFKM+3n4RojyagDEfHvNryU/+nqHGsr5ln/nwuz6US5eqZb9+ZFD1kh1q2JC548eT/847/CaKvOmKbvyDx+8gvTKcY21e3VlWJeL2UWN3ZzlZIpHcWU5pidJViTjdV/vffS7wtJQyD5VE8zkhRA2ZhBBXCiEWCSEWbd+ezHyVGit2S6Q4UkFqPgwvoLrcWnMTeUrnvnQpdO4MzZpVc2cBvDtpEj1Wr6bLunWOp+qYiKKE2jONcLz4HaRXRs0vfS9KpIKaloibG8zuzjLjhV4sJ7sSkYQ78DZMJVIMdLRs51HTXXUZMBNASvkp6sO0RuofKeXjUsohUsohrVtHyrivCZObHPaZL06kr7k/OpQ1aUz1rr7gPMVtDZYuhf79K+uUVH1lfXDccQCMmzfP8VStRBRtqXnvNd5wUiIQXYmY53m1REx3lpk+3oyfWJW/V0sk7MwNYSqRL4DuQoh8IUQ2KnA+21ZmI3A8gBDiGJQS0aZGilEC3OawP5oSsXfljUZUS6S0FFavhr59K8tZX6TlffqwvVWrWqtEgnRnLY5eRONAOTWViMCbJWJ3Z0WzRI4AP7KcD94Ugl2JhOnKghCViJSyDDXGZQ6wEtULa7kQ4lYhxBSj2LXAFUKIpahpEC6RUtbmaSDSkkhKwh4TseN3gqioMZGiIigvh27dKuusdm0hmDduHOPnznU6O+2ViCZ4gnBneXm2Jf4sEdOdZd3Gts+rJRK2Egl1xLqU8h1svR2llDdZ1lcAo8OUQRM/0V60SEqkpWXdy4sW1RJZv14tu3SprNMu28ejR3PWK69w1Pffs61Nm2rHdGBaEy+xurPAX0wki+rzv5hlvVgi9m7LaWuJaGoPkZRIobGM1LX3KPzNbRFViZgB8/z8ynJ22ZYa8ZL+Zi8uC+muRNJd/lTEryUSqztLElvvLHtZqxKJNBbJrmjSOSaiqSVE6wbp5s7Kt6x7vY6rEsnMhI4dK+u0X9tUIgPM8SQ2BgFPeZBFo3EiSEvErkTsI9ZLHcpa9/0+wnXslkfYc41oJaKJSrSvNS9fc36USMQvtPXrIS8P6tWrrNN+7V0tWrCxY0dHSwRUQPkSD7LUFiI1NBpFUDGRWCyRaO4sqzIwy3rpmm23PMIOMkdVIkKIq4UQTYTiSSHEl0KICSHLpUkhYn0I/bpfoloimzZVWiFmOSfZlvbvX0OJONsl6UE8qeDd0o1r/BOPO8tvYN1JiZgKwm1MVsopEeBSKeVeYAJq3qCfAHeFKpUmpQjiIfRjiUQsu3kztG9fuenkzgJYMmAAvb79lpzDVSHGLp6kTG1iUSI6jhIspiUSiwsnlsGG9rLmPrfU76WodPaL/AoYI17uhfkcngw8JaVcin426xQ10i/HQCAxEQclEskSqVdeTp/ly33JWBvR/eXd8Xt/nCyKoALrTS3rdneWeQ1TiTilNjE5AoykKrVQKlgii4UQ/0UpkTlCiFySOy+8JsH8Pcbz/H5pnAb0I4LS2rcP9u/3rESgenC9Nnz11Ia/Id2JVYl85HCedf1M4ETLtt2dJVGKxVQibtmHS6ka6S4JX4l4GSdyGTAAWCulPCiEaIlyaWk0nvHSALYElkU6uNnImGNRIhk4f82s6daN/Y0aVYuL1NUGuK7+3V4JwhKB6Pf5VKAnkS2RC2112C0RaZQ3n3c3S8TqBisnNSyR/0kpv5RS7gaQUu4EHghXLE1twO+IdVdMJdKhKhG0wLm7pczIYFlBQcRuvumKVgix0QmVnj8IzESKVgXgxRIBd3eWvSG2KgxQisD60eRViZSQREtECFEfNe11KyFEc6ruUxOgfaTzNJogeBvYZt3hYIlECqyDcmmd98ILICUIoRvgOkyRyzG3BrY+Nd1GTtl4vT5bbu4sJyVi/UAylYi5z62r7xHbejItkZ+iutX3Ar401hejJrX7a8hyaWoB8VgiJ2Mbz7Fpk1q2a1e5y/pS2VlWUEDTvXvpYJ6n0fjEqXGM5M7yglsXX/u17K5auzvL7X2yWyJhE/FeSCkfAh4SQvxCSvmXBMii0URm82bIzVU/g0juLIBve/UCoJKFdV8AACAASURBVNe337IpL69WWCK14W9IJ5wax3gsEbfBhvaeWvZtuzvLDWuZRLizIloiQojjjNVNQojT7b+Q5dLUAgJt9Gzde836I71UViUSuCxJQnfX9Uc5sCJKGbd76jQYMNbeWaY81jqjWSJWKqiuRNyuZ30nviO57ixzOojJDr9TQ5ZLU8sIJLBucWWBuztra9u27GnSpFKJpDPx3LtowctvjPr/G8c1UpWbiC+gHsmdVeFwLN4svk4xEft1Y1EiE0hiYF1KebOx1N15NTFhfdDjfpC3boVhw2rUH0mJIATf9upVqyyRWP6GS4BLXY7PN5avoiZAyqL2zLvyiYcybs+lU+NojruwNvp+emdlWbbtPbys2JWKPbDuht06T/o4ESFEDnAGKnNEZXkp5a3hiaXR2Pj+e3CYH8Ttpfq2Vy+Of//9yrLpSv09exi5fDnH7N9Pi8GD+aFly6jnnA7swN/f3QA1bfG+2MRMeU5B9Qy6HDVTZ7R746RMK6ipRPBQF7i7s6JduwHeA+v2dyIVxom8AUxFKdIDlp9G40pg40QOHlSj1Y86qtruaF9m3/bqRd6mTTTel8bN4ty5/K5nTz4ZPZqrJ05k5THHMHm2fZbpmvwb+DCGy+2n+mRItYl3UDmn7qDKfefWwDo1jk6NeKyBdbs143TtXsbyl8QWWIfUUCJ5UsqzpZT3SCn/bP5ClktTy4jrQd5mjBiJwRIB6LlqVXpaIq+/DiecwKFmzZj6+uv85T//YVOHDsyeOhUeeyyQSzjdl0R0Cw2bJ1FzckfCLW2ISaTAuqSmEoklJuKmiDIty+NQ2ZitSsTtfUp0TiovSuQTIUS/0CXR1DoCa7i/N3KW2iwRt95ZULOHVlqxbh1ccgkMHsx9ixYxe+pUVkyaxPDPPuPdiRPhV7+CVasCu5y1UaoNifEuxz3TrRciWSLxuLPcgulO1y631B2rEkkFS2QMKgnjKiHEMiHE10KIiCmONJrAiWCJRHNnrenWjbLMTHp9+216WSKlpXDOOWr95Zc50rgxYGRxzc7mJ089BQ0awBVXQEV8Tb7TfakLXYmlbelEpN5ZsQbWK/DuzjItkTJLOevz7ia3/Z0Ie451LwMvTwpZBk0K8gEq59DRcdQRuCXi051Vmp3N2q5d00+JPPUUfP45vPRS5XzyUHU/t7ZrB/feC5ddBs8/DxdeGPcla5slEgROSmQLKkhcYNnnVYmA90GK5rXLLOWsgXU/lkjY7kkvloiM8NPUYo4HugdQTyCNt6lEWreuUXe0Lo/Wbr5pQUkJ3H47DB8OP/5x5HKXXAIFBUqZyNhexzeA6Q7768rLvRWY5HLc7dmN9bmOxRJxcme5kYpK5G3gLWP5PrAW+E+YQmlqD4E0SNu2QZMmyoVjIVpMBOCbvn1VYL0kTcLFTz4JRUVKkYjqTUu1rYwMuPpq+PprmDs3tktF2F9XLJHFUY67NY6xBNbBe2DdaolY3VlpaYlIKftJKQuMZXdgGLAgZLk0tYDAuvhu21YjqA7eBl8t7d+frLIyxEq3vjrhcAnwKz8nlJfD/ffDiBFw/PHRy593HrRqBQ89FJN8WyLsrwuWyL34GyfiFkiPVYl4uXYkS8SPEnnB4zVjxfdUwVLKL6maeVGj8URcDZPDQEPw5s4yZzkUlgmqEsVTgK++8G+/DWvWwIwZNawQR+rXh+nT4c031Xk+sc7BXddiIp8AG6KUsY4uj9YbK14lEskSiaV3lv2deM6jbLESVYkIIX5l+f1aCPECsD1kuTS1gMCC2REsES/urNXdu3OwQYOkKBHfPPggdOwIpzvnN3W8nz/9qYqJvPRSYGLUBUsEov+dViXipjTCtkT89M7yGjcJEi+WSK7ll4OKjUz1UrkQYpLRNbhQCOE4dbYQ4sdCiBVCiOWGgtIkmTAewrgUyvffx+zOqsjM5Ju+fSHVlUhhoYptTJ8O9ao3NUOMZW+n8/LyYORIePXVuC7/g2U93S0Rr0ow2jPpZonY6wnLEvHbOysT9wmrwiCqYpRS/jGWioUQmajJq04EioEvhBCzpZQrLGW6AzcAo6WUu4QQNVsKTcKxTmoTTx/zQBIwlpfDDz9EtES8JKRb2r8/Q197rXKWw5Tk6adVsPzii2scugAYgUtDdfrpcN11aoCipUuwH6wqKN0tkTCUSKzuKzthx0QyCX9ciB3fMREfDAMKpZRrpZRHgJeoacFcAfxVSrkLQEq5DU3SsSqRa5MmhcHu3WpAnUPSQT9KROzcWTXFbqpRXg7PPAMTJ1abQ95EoLpbuyoRgNdeC0QcbYkosi3rYQfW/fbOikQGtUuJdKD69MbFxj4rPYAeQoiPhRALhRCO3baFEFcKIRYJIRZt367DMWFjVSL2bpB+vsAi9s46fNh7IHjHDrVs1arGIa+psc3gesq6tN5/H4qL4ScxzrrQtSsMGAD//ncg4tQVS+SGKMe9BtbDUCKmJWLN01UXLREvGRXqoT6yxgHnAk8IIZrVOEnKx6WUQ6SUQ1rbBpxpgsftIYz1gan8x69ZA716wdFHw+WXRx8ot3OnWjooEa+WyLICY3xxqiqR55+HZs1gypTY6zj9dPjkE9gSqeOud9LdEvEq/64ox73GRCD4wLrTQEQvgfVMqn8EJoKY2gQhhJeZDYuBjpbtPMDuTygG3pBSlkop1wGrCGagtCYOrA/hp7ZjfiYscnyxrroKdu1SqTqefBJmzXKvxMUS8dI7C2Bv06bQpUtqKpEjR+CNN2DqVMjJcS3q2lCddppavvlm3CLVFUskGsm0RJyUiNfAerpYIl7GiXwBdBdC5AshsoFzAPtECK8D4wGEEK1Q7q21McqkCQi33h2xBhQFwDffwLvvwu9+p/JD9esHt9zibo24KBFfX139+8OSJb5kTgjvvw979sBZZ8VXT58+ag56YxKueEh3SyQoJWKNiUQLrAdtiVjr8zNiPYM0sUTMqXOjlCkDrgLmoFL7z5RSLhdC3CqEMO32OcBOIcQKYC5wnZRyZywyaYLDb2bTSNTonfXUU5CVpRIHZmaqtB0rV8JilwQULkqkHj5emNGjVer09eu9npEYZs1SKV1OOCFqUdeGSgg1yv2DD+LO7JvOlsga1ORTQeDHEvFKJEs+2jgUUwYv7qyUs0SEEPWNgYavCiH+LYSYIYSo76VyKeU7UsoeUspuUso7jH03SSlnG+tSSvkrKWVvI61KcCOmNDHj1oj4nX+72ssxezaceGKVQjj9dKVUXn45cgU7dig3T8OGjrJ4ViJnnqmW0dxniaS0VE08NWVKVFeWJ44/Xt2vr7+Oq5p0tkSORs3lHQRe5/7w486KVI8XJeLlGqnaO+tZoA/wF+AR4BjCH0mvSSJBWiJmXTmFhWpA3UmWmQWaN4cJE1TDHsmltWOHUjoO4zt8WSL5+TB0KMyc6f0PCJu5c1V8yFRw8WLm24rTpZXOlkiQOLmUnI45bXupM5ZrR5sHJSUtEaCnlPIyKeVc43clKnahqYPEGkRr8sEHamXChOoHTj4ZNmyAtRFCYUVFalS2A76UCKjU6osWRb5Wopk1Cxo3VuNDPBC1AcrLg549tRIJCDclEqlcNOK1RLwokVSMiXwlhBhhbgghhgMfhyeSJtkEaYmYL0Djzz5TAwa72zrfHXecWkZq+IqKVD4pB3wrEfOL/1//8nNWOJSVqcGBkyerRIpBcfzx8OGHqtdXjKSzOyss7M+99R2JdeyU1+s5ubPSwhKxTIM7HDXP+nohxDpUr88fJUpATWrh1xIxH/bGn30Gw4bVdEv17Oneq2jLFmjXzvFQPXzmCerSRTXa999fFbAvL1fdYi++WPXgGj4crr1WKa8w+fBDNQbmjKA8+AbHHw8HDqiZEWNEKxGFW4Mfq7UWqU43S8SPOyvVYiKnApNRk3/lA2NRgwLzgVNCl0yTNILunZW7dy8NVqxQDXSNQgLGjYMFDlPUHD4Me/c6poEH/0F+AP70J9i/X8VHrrhCDXqcMgXeegs6dYJGjeAvf4EePcK1WGbNUp0FTvI++7Snr9hx49Ry3rwYhFJoJVIT+zsRtCXiN7CeFpaIlHIDKm3J21LKDfZf4kTUJJqglAioB3/gV18hpFQNtxPDh6u8VsXF1fdHmFvdxGuf+2r06aOsnnr14JVXlBKZNUtd6803VRfZ1auVTBdeCHffHctV3CkrU1l3Tz3VsddZXLRoAb17w6f2YaLeSRcl8nugZ4j1Bx0sj7VOJ+WRSkrE9T2UUlYIIZYKITpJKTcmSihN6hKLO6vP8uVqw0w/Yse0UD77rHoQPQwlAjB2LHz3XeSMvp07w//+BxddBNdfr5IiXnBBrFeryUcfwfbt8Q8wjMTo0UoxVlSozMA+SRclcoex3A6EnQzJ/qTE6s6KJbCeYdnndN3XACNfQcoG1tsBy4UQ7wshZpu/sAXTJI+gxomYL0Kf5cspa9LEMUMtoGIRWVlKiVgJS4lA9JTwWVkqPfu4ccrtZSrCIHjlFWWBnHyyr9M8f/GOGqWyH3/7rW/RIH2UiInjPCsBYL3f9v5zYQfW/bizrPmtkxET8fIexjSfiCb9+BTYCAxwKROLO6v3ihUc6t2b3EgNd/36KgutPRhsKpG2bR1Pi0uJeCEnR80Y2K+fms/8s8/i70lVXq5cWaecErwry2TUKLX85BPl2vKJqUTORc3fkOpdfneEVK/1ab0LWAJ8GWCdbvudemcBlAD7iKzEkjEpVdQ2QUr5ofWHkvHH4YumSTSjUAnOgh5s2HvFCg716eNeePhwNYaj3JKX11QiDhNSQYyBdb+0aaPStSxbBn8M4Htq/nz1dwU1wNCJ7t3VAM1PPonpdFOJ1MX0EZEa+XpAF8t2MnpnCWAp0MTl+l6nRwgST22CEGKAEOIeIcR64HZULixNLSXIL8+mO3bQZts2DkX7Ih4+XHVN/eabqn2bNqlR7RG+/kO3RExOOQUuuQTuuy9+t9bLL0ODBqpOn3h2mwihrJEYlUiiG6FUIlJAuz7+3FZe6vcrh9feWSljiQghegghbhJCrESlOykChJRyvJTykYRJqEkp/PrLu65QsyFHtURGGONZFy6s2rd2LXTrFvGUhCkRgHvvVYkSp0+PPcFhSYlSItOmqa7EYTJqlEo4ucO/syfRjVCqcznQwLYvaBefly6+ka5vd2cl2v3oZol8CxwPTJZSjpFS/oW6/ZFSZ3B7CP02n12M4O6hY45xL9itm3LBWLumrlmjZu2LQEKVSKtWSpEsWADPPhtbHf/5T9VcKjHg60vYjIvE0NW3LisRp8b7KIdjQbuz3MplOOyLREJcvDbclMgZwFZgrhDiH0KI4wnGotOkMX4fgPZr11KSnc2RCPmvqioWMHJkVaNXVqbStqeKJQLKpTVyJPz2t2oOEL8895yKsZx4YuCi1WDIEDUWJgaXVl1WIlbcrINExkT8WCJhTlUbCbfBhq9JKc8GegHzgBlAGyHEo0KICZHO06Q/bi+IbyWybh0bOnf2Nl5h5Eg1fmPnTpV2pKwstZRIRgY88oga43HLLf7O3bVLjYo/91zVuIdNgwYwaJC2RHzi9Hx7TcX+ekhy+I2JJBovvbMOSCmfl1KeipridglwfeiSaZJGkEqk47p1rMvP99bgjxyplgsXKlcWuLqzkvHCMGgQ/PSnKjWKn3k7Zs5USRFjdGVBDG6AoUNh8WIyyv15oRM9WC2ViHUeD4CpVJ8PPFr9bvv9JGC0x0QSjS/rR0r5g5Ty71LK48ISSJPa+G3IeqxbR5v8fAZ5KTx0qJrxcMGCKiWSSpaIye23Q9Om8ItfuE/tayIl/PWvarzJwIHhy2cydCjs30/PVat8naYtEed9yXJnOTXSaWWJaOoebt+tvpTIvn1k7NjBgPx8b+c1aqQCwnPmwFdfQW5uxLlEIIlKpGVLlcjxww/dZ2U0mTNHWS3XXht9pHyQDBsGwNAvvvB1Wl1WIlbCiInEcu16DvvcxokkGq1ENDVwc2f4agLXrVPL/Hzv55x8slIgf/+7mqzJJZaSNCUCap74wYOVYti/373sPfeolC/nnhvXJX2rn549ITfXtxKpi+6sfsbSzZ2VLEsk02Ff2rqzNHWDpCqRs8+uWr/sMteiSVUimZkqyL55M/z615HLzZunpsG9+mrIzk6YeIBSwIMHM8zn3CJ10RKZZizd3FlBEEsXXz/PubZENClB4ErEJTheg/x8la/q0kvhhBNciyYlsG5lxAj4zW+U1TRrVs3jhw6p5I1du8LPf554+QCGDqX/0qVk+ZjpsC4qETeCtESiXcNp248lkowPK61ENDUIVInk5qo5Lvxw9tnw5JNRu8Im1RIxuf12lbLlootqTgR1881QWAj/+EcgI9Rj+iIeOpScI0coWLbM8ynpqEQk8GIc53vpieVFiUT7H8XSO8vpY0kH1jUpjVsj4luJ5OeHFkxOCSWSlaUms+rWTcVz7rwTvvxSpUe5915liRyXxM6MMQTX0zEm8jZwXkh1RxvsF/Y1nSyRighltTtLkxIENk5k40Y15WxIpIQSAWjdWs2WOGEC3HijCrg//jhcd50aT5JMOnViW+vWvpRIOloiQc2Y52Rt+B0v4rV+t/3RemdF6kGZjAY9Zd5DTerglh/L14tUXFyVwykErA/vHqBpaFfyQJs28PrrygopKoIuXdRkWwESUyMmBF8MHVrrlUgMSWiq4aYoEtE7y62ck4tKKxFNShOEJVL/0CGVviRazqw4sL5cuaFdxSeDBqlfCvHF0KFMevddGh44wEEPsZl0m9kQYHcCrhFETMRL3fZtJ3dWeYSytc6dJYSYJIRYJYQoFEJETJUihDhTCCGFEEPClEfjjSAmpeqwaZNaCVGJWL+A6kJm0Fj/xi+GDiWzooJBX3qbly/VZzJ0IijryWsX32SPE4mk6GtVYF0IkQn8FTgJNQ3yuUKIGjMTCSFygV8Cn9mPaZJDEJZIXnGxsZIYJaKJzBdDhwJ4Hi+SjpZIvB8Rft1ZQVkcXso5Ped1xRIZBhRKKddKKY+gZtuc6lDuNuAe4HCIsmh8EEQjopVI6rD9qKNY37mz57hIOiqRMAkysB7tGiZOXXy9BNaTYZGHqUQ6oGZDNCk29lUihBgIdJRSvuVWkRDiSiHEIiHEou3btwcvqaYaQQTWK5VIhw7uBeOgrimReBoIP8H1uqhE/I4Tifc6fspFC6zXZkvE1ZUohMgAHgCujVaRlPJxKeUQKeWQ1q1bByiixoktLsd8KZHmzUOdBrauKZF4+GLoULqtXUuLnTujlk3HmEhQX+BuXXyDrt9tf6xdfGubJVJM9fT6ecBmy3Yu0BeYJ4RYD4wAZuvgevL5P5djvpRIiK4sSIG0J2nE58agwyGLFkUtWwFsClmedMKPOysZvbOCuH48hKlEvgC6CyHyhRDZwDnAbPOglHKPlLKVlLKLlLILsBCYIqWM/pRrkkYqKZG6ZonE00AsHjyYCiE8BdcrUF986UQYgXW3hj3e6/gp5/SxFMnlWKuUiJSyDLgKmAOsBGZKKZcLIW4VQkwJ67qacNFKJD3Zn5vLit69PSuRVOd723ai3VnJTsCYSpZIqO+hlPId4B3bvpsilB0XpiyaYPDy1ZF15Ahtv/9eK5EU4/Nhwzjl7bfVTIsu+czSISZyVcD1hR1Q91tXhsN6qqY90bmzNL7w8hK032yEvrQSCZR4G7Mvhg6lzbZtdNronmkqHSyRdSHV63WwodcAeaznRRuT4tcSmRZhfxBoJaLxhZeGLBHde0EH1v1iBtejubTSQYmEEa+IVk8iLTQnJRKPOyvMhl4rEY0vfCkRbYkESrwN5df9+nE4JyeqEkkHd5addImJBGWJROo9F6uFFA9aiWgAWOOxnFYi6UtpdjZfDRxYKy2RMOtL9jgRJ0vkgzjrDxKtRDQA7PVYzqsS2de4MTRpEo9Igciiqc7nw4YxZNEiMssipyxMByViJ0x3VjKes2hKJBKRGnTtztKkFXnFxRTn5bn2AAoCe2NX2x/meO/mnSgl0ujgQY5ZuTJiuXRUIvHi5d4G4eYLyp0Vb/1BUtvfO03AeLVENoUcVIe6p0TiJQdvwXV7Y5mKMZJEBtbNfTJKuSDlsD7LQYyY15aIJnS8NhReHpi84mKKOnaMXjBO7EpEu7eiU3j00fzQvDnDP4s884L9vqaiErETxoj1VA6sx1t/kOjYpMYX0R7GzLIy2m3ZotxZIdMLaAScZWzX9i+iQBoCIVg4YgSjP/44YpF0cGcl8oPByRKJt65o+6PFROrhPBFXpHdAu7M0KUO0h7Hdli1kVlQkxBLJAvYDTxnb+mH2xvxjj6XPihURM/qmgyUSVqMYqwUQzzle5bArET/X1+4sTcoQ7SUxu/cmwhKxowcfemPBmDEAEa2RdIiJ2En0OJFY5UjWiHVtiWhShlRWIrX9YQ6qIfhi6FBKsrMZs2CB4/F0cGcFTVDjRGJVIm7lghixrpWIJlTWAIM9lk1lJeIkW/OES5H6lNSvz6IhQzwrkVS0RILuneWlB5SX+xCrHPbzovXO8psKXruzNKEy00fZaC9Jx6IiDjRsyK7miW++nR7mQmB9guVIB+YfeyxDFi2i/qFDNY6lgxKxkyo98xLlzoqEDqxrUp5oD0yiBho64RQTaQF0TrQgIRHkHV0wZgzZpaWO40XqotLwMxbDSz2xHncqF8Q4Ea1ENCmDZyWSBOyyPZ8UKdKDT0aNAnB0af3Ptp0OSiXRRLonQbmzgh4not1ZmrQhUQMNnbA/zF2TIkV4xPs1aW34drVowdd9+zoqkd0u56UqYY5YN7Hfh7ExyKEtEU2dx+2BySgvp/3mzUmzROwvSjo0fslkwZgxjPrkEzLKI/X1SV0SEVh3e34E8B5wOAY5/gJ8GOU8rUQ0tRa3h7Ht1q3UKy9PGXeWViLuLBgzhqZ799Lv669dy9Wl++iWO8tOPVQ+Mi9lrcevAn4U5Tw/7izr/0dn8dWkPG4PTDK790Ltf5iD/pqcf+yxAIybN8+1XKopkRLAPkwyzC9tp78/1piIVzmDTsCoLRFNyuBFiSQrJqLxR1GnTqzq0YMJ//1vskXxRRgdJvyOEwm6sQ7bnaUtEU3K4PYgdywqApJniaTKWIF0Ys7EiYybN4+cw3bvfhWpZok4RXCCktGPO8vr+bHUFXTvLG2JaFKGaJbIofr1+aFFi4TJY6W2K5Ew/r7/TphAw0OHXLP6ppoScSJeGZ3ubbTAup/9fs/TgXVNrcXtYbQONDwqYRJp4mHeuHEcycpi4pw5yRYlLsK0RJyuEXZjHfSIde3O0oSKnxfQ/kA/bFlP5hgR0F18o+F0Pw40bszHo0e7KpF0uI9hyBiLQtCWiEYTBfvDOMCy3rGoKGnxENDurFiZM3Ei/Zcto+2WLSFdIXxejPN8v+NEotXj97jfBIzx1h8koSoRIcQkIcQqIUShEOJ6h+O/EkKsEEIsE0K8L4SoLWmOai32B8Z8OJM90BDgQaBN0q6evsyZOBEgYi+tdLBECgOqx2tgPZHurCDOS0t3lhAiE/grcBLQGzhXCNHbVuwrYIiUsgB4BbgnLHk0kYnH5yqAW4Gjtm0jq6wsqUpkCrDVsp0OjV8qsLR/f4ry8jj91Vcdj6fafQzjq9pLnV5iIrFeR7uznBkGFEop10opjwAvAVOtBaSUc6WUB43NhUDyWiCNJ5zM7j8AA/QYkdAJqyGQGRn8+4wzmDhnDrl794Z0leAIU6kFlT4lXmJVIrUtFXwHoMiyXWzsi8RlwH+cDgghrhRCLBJCLNq+fXuAImr8EumLqX2Sx4hoInMmcFqUMjN//GPql5Qw+c03axxLNUskTMJQIs2jHHfaH/Q4kbR0Z+Gj67UQ4gJgCHCv03Ep5eNSyiFSyiGtW7cOUESNXyLFRLqsWQPAuvz8hMpTl4i1gZsFODuqqlg4YgTFHTpw1qxZNY6lmhIJ86vaTwJGr/sfiPE8+7G66M4qBqy+jTxgs72QEOIE4EZgipSyJER5NBHw00iMtG2bD1DnNWvY0bIle5o1C0iq+Em1xi+VkRkZvHLmmUx69920cGkFjd+YiJ96Eukiq21K5AuguxAiXwiRDZwDzLYWEEIMBP6OUiDbQpRFExBnAVaHh/lw5hcWUnj00UmQSBMUs846y9GlVZeUcdiDDcO2RGrVYEMpZRkq6/EcYCUwU0q5XAhxqxBiilHsXqAxMEsIsUQIMTtCdZoUQQCdbNsAeevXs75Ll8QLVIcIexzMpyNHsq5LFy578slq++uCEvHSiHspb+4/ymFftOtEu35ddGchpXxHStlDStlNSnmHse8mKeVsY/0EKWUbKeUA4zfFvUZNGMRjLmcAVFTQrriYjZ06OZyRPOpC4+eHaPdDZmTw2PTpHDd3Lr1WrkyITKmGV0sk2vmveKzTS5m6HFjX1EIEDl9W27dTv6SkUolkogf8pCv/vPRSSrKz+dmjj1buqwvK2M/Xvpd6/M43ogPrmjqDY1fEjRuBqjEiZcDFiRSqjpCItC47Wrdm5o9/zMXPPEOj/fuB1FMiiUpvk8gsvl7O0UpEk7L4TcBofSAzAJYuBWDlMccEJ5Qmafzt5z+n6d69XPjcc8kWJeE4dfH100hGs0S8XNfPMTu1KrCuqZ04jhNZsYJDDRro3lm1hIUjRvDpiBH8/vbbaXjgQMpZImFibbArHPY5lXPaH+v0uW7naEtEUytwDKyvXcuGrl2RGanxOB1rLGtb45coNw5CcO2f/0yHzZv59X331br76ITbyOgMhzJ+lEis40u8HLPXr5WIJuVxDKyvWcOGbt2SI5ADCWtsazGfjhrFrDPP5Df33EPG5hpjhGstbpaIl8YyVS0R7c7ShEo8XQhFeTmsWcN67coKnUQrx+vvuous0lIa33BDgq+ceJwaaulyLFo9qdY7K9PDubGilYjGF3ZLJGf1NKwMLwAAFSVJREFUajh0iJUFBckSSRMSa7t1497rrqPBs89CHQyym5aI1Z2VUV5Og4MHyd29G3bvhr17Yd8+OHAADh0iu6SEeqWlyPLyynq8uLPcGuJUD6zXC7FuTZoQz/S49ZctA2BF//6ByRMUdcGXHzY3//GP/PqTT8i58kro2xcGDky2SDEhKipo8cMPtPn+e47ato02339P6+3babZ7N8127+a4PXuYuWcPeXv2kLFnD0337KHVwYPcU1JCsyNHoKSEp48c4TmLcnDCmoa8JCuLA40akdOwISMaNeJAo0a0bdgQGjWC3Fxo1Yo7WrZkZ8uWZLZsCS1bQqtWatmmDTRpAkL4skQiWRxhWiJaiWh8kYHNElm6FOrVY7Xu3hs68bqzYlGq5fXqsevll2k7aBCcfjrMnQsplt4mo7yctlu30nnDBjpt3Fjt17GoqFJhZJWVOZ5/oGFDaNoU0bQppU2bsqVZMzZ07kzbRo1YkZPDuOxseuXk8HZODouysynJyaFNdja/EgIqKtRPSqio4J8VFaypqOAn5eXMOnSIRgcOcNLBgyw+cICGBw9yzIEDsH8/bNoEO3dy3c6dEeXa27gxxXl5dOzYEfLymNCxI7vz8ijq2JHivDyK8/LY07QpiKonI5Ky0JaIJmWwN2Q5S5dCr14cyclJijya8Kk46ih47TWYMAGGD4c33oARIxIrxJEjsHYtfPcd/b77jr9/9x09vvuOzhs2kFdcXKMh3tWsGRs7daKoY0cWDx7M923asO2oo/i+TZvK37ajjmJP06aUZWXxCCrRX39gqVHHuai52/8F9AJeA542jg0FfuUg5svAf4EfAb8z9jUFLjHWN1I9tXm2lOTu28feHTtg586q39atPFFcTMeiIroUF8OcORy/ZQsnyuqfAodzcqB9e+Z17MjGTp0o6NSJq9q25UCjRqzLz2d5nz7sbtaMVsZUDWGglYjGF9WUiJTkLF4MJ5xQueuzhEtUk1gHfGmckQBDh8Knn8Ipp8C4cXDjjfDLX0LTpsFdqKICiorgu+9q/tavV8dRU6bmt2rF6u7d+Xj0aDZ26lT529C5M0UdO7KvSRNflw4zsO7aNVgIJWuTJtC1a7VDppJaiVJiN5aW8vyWLXQsKiKvuJi84mLafP89l23ahCgq4tj58+lUXMxfbC63CiHIkJLzPcgfC1qJaHxhDaznr1tHva1bYdSoyn2p8EDV1i6+yfq7KhvEXr3gs8/g8svhppvg/vvhqqvg1FNh0CDIyopeWUUFbN4MhYVVP1NRFBZCiWVKocaNoXt3pcDOPx969IAePfhX9+5c2Lx55GvEgVVhuI0TiYZViVhdSfF08a3IyqKoUyeKbIlOxwNjjfXXy8q4fNcuGu/fz9GFhfRZvpwWP/zAsd27w0UXxXD16KTCO69JMrF28R398cfGyugaX22aWkqrVvD66/Dll3DbbXD77erXqBEMGABHHQWtWyuFUlKifnv2wNatsGWLWpaWVtWXlQXduikFcdJJlYqCHj2gbdtq/n6TIyH+eVZLwj5OxMtgw4eBa4Bxln3xBrV9WUL16rGjdWt2tG7N+vx83jvxRAD+AVqJaFIDqyUy+uOPqWjalIw+faodTzYXAvOAHkmWo7bg6BYcNEjFSbZuhY8+gg8/hOXLlUXx8cdQVgY5OVC/vuqJ1K4dHHOMUgz5+UpxHH00dOwImWH2HfKG03Nr7+LrxaLoAbxj22f968IebKh7Z2mSgp/YgfVFGrNgAUdGjqR+ZmZKKA+TS41fbSPp7iwn2raFH/9Y/WoB1gbbaZxILMTbM8rPdZPRO0sPNtT4wnyg22zdSt/lyzly7LGOxzWadMctsB5rwx6GJWLtFxmpQdeWiCalEMCU2Wom45LJk5MrjCZ06kIvN7feWUFaIkEqkUuB4YA1V4S2RDRpw/nPP8+3PXtS2rdvtf3aEgmPlHRn1TLiTcDoRFCBdTvNgCs9XkvnztKkFFnr1zP2o4947sILa/Se0UokdalLysAvQaWCdyIoS8T+/9sX5Vpe9geBViIa3zR75BHKMzL41wUX6IYpgWhLRBGmPG6WSLKViB2nZCnaEtGkPlu20PRvf+P5889nY+fOlbvNTr4ehptp0oy6oEScxjnFMmLdicwI616JZImU2gu61K8tEU1KICoq4NJLQUpu+8MfgKoHeybwLNAzWcLVIcLIl/zrEOoMiyCUyM1R6nTq4hurRWE9L9fHefZr2T/QnJRIMnpn1U4lcvCgyvMvU+0bKo2Rkj/dcAO8+y477r+fwu7d1W7jcFPUID8dEwmX+cD7IdR7lcuxVHuLKqIXiUrvCHU6jViPtXfWG8Afqd6AZ/usA8v59hSnqWKJ1I4uvjt2qBTV77+vfoWFan+TJjBkCJx4okqpUFDgmEZB407n9et5YMYMTnv9dZg+nX3Tp1ce03czsYxJwjVTTYkEIY+9Djd3VqwN8BTjNz/G800aGUu7EkmVmEj6KZGKCvj6a1i5EhYsgPnzYelSZXXk5sLYsXDJJZCdDRs2qOM33KB+HTrAySerTKSjR6s8QJoa1Cst5ZiVKxn6xRdMfvNNTn7nHSoyMrj2vvv4869+VU0Rd0iinBp//MjlWKopCjdilfUh4OoIdbi5s4IcbBgLDY2lF0uk1s1sKISYhPrfZQJPSCnvsh3PQbnSBwM7gbOllOtdK/3qK2VRADRsqOY3+OMfVTryIUOcM4lu2QL/+Q+88w689BL84x9qf16eShpn5vDp2BHat4dmzdSvaVOVWC7VrBcpVW6ikhI1z4J9ad9XUqJcfPv3q2k8zZ+xPfXAAfrt20e7LVvosGkTbb7/ngzDFbipfXv+8otf8MCMGWzKy+PPVL1AmWhLJJ0YCZRQszGKRqopmFjdWdbk8Pa/yXQzeQ2sxxIT6exaKjJmi1bnLBEhRCbwV+BEoBj4QggxW0q5wlLsMmCXlPJoIcQ5wN3A2a4Vd+igUlAffTT06+ct/XS7diogfOmlqmH99FNYtAiWLFG/uXNVo+pEZqZSJNnZ6peTU329Xj2lZKw/dQOc95WXe/9VVFStl5ZWVxDxkpGh/q7GjWnXqBFHGjdmS7t2LBkwgE0dOvBdjx58MXQohUcfjcxw/o6pnQG12k0kn3xYPZ6cfl6OlQI/oL4s7cuPfMpxEepLNRL/A76PID84B9b9YDbgrR2OvQ7s9ViPXYk0c7mWnXS1RIYBhVLKtQBCiJeAqYBViUwFbjHWXwEeEUIIKSNHxL9q25bceJK9ZWcrl9fYsVX7pKTZ7t3kbdxIm61baWLMsdx0926a7t5Ng4MHyT5yRP1KSsg+coQcY1mvtBQhZeUPqLYtpATLenlmJuU5OWqZmUmFsXT6yYyMyvWyevUoycnhSE4OR4wpOkuN5ZHs7Grr9jJHsrM50KgRBxs14kDjxhxo1IiSnJxK5XYIsM8c/VNgdYRb2NhY9olwXJPaPINqiKegemWtxr1rdgFV82t4UQZhInBuPN0Yj1Ii1twK5t97FXACKggO0M5YDkQ93/MA0+ndwnL+UT6un2cspzgcm+qjHvv0X9Ys1TkoK9Ns0JsDuyzH/d4zPwiX9jq+ioU4E5gkpbzc2L4QGC6lvMpS5hujTLGxvcYos8NW15VUjfDvCawKRejItAJ2RC2VeLRc/klV2bRc/khVuSB1ZesppYyll7ErYVoibpkE/JRBSvk48HgQQsWCEGKRlHJIsq4fCS2Xf1JVNi2XP1JVLkhd2YQQi8KoN0xXWTHV56TPAzZHKiOEqIey2H4IUSaNRqPRBEiYSuQLoLsQIl8IkQ2cA8y2lZkNXGysnwl84BYP0Wg0Gk1qEZo7S0pZJoS4CpiD6jTwTynlciHErcAiKeVs4EngOSFEIcoCOScseeIkaa60KGi5/JOqsmm5/JGqckHqyhaKXKEF1jUajUZT+9Fd/TUajUYTM1qJaDQajSZm6rQSEUJkCiG+EkK8ZWznCyE+E0KsFkK8bHQIQAiRY2wXGse7WOq4wdi/SggxMQCZmgkhXhFCfCuEWCmEGCmEaCGE+J8h1/+EEM2NskII8bBx/WVCiEGWei42yq8WQlwc+Yq+ZJshhFguhPhGCPGiEKJ+Mu6ZEOKfQohtxjgjc19g90gIMVgI8bVxzsNCeMt7E0Gue43/5TIhxGtCiGaWY473QQgxydhXKIS43rLf8V7HKpvl2K+FEFII0SoV7pmx/xfGPVguhLgn0fcswv9ygBBioRBiiRBikRBiWBLuV0chxFyh2oblQoirjf3Je/6llHX2B/wKeAF4y9ieCZxjrD8G/MxY/znwmLF+DvCysd4bWIoaMJoPrAEy45TpGeByYz0bNdj0HuB6Y9/1wN3G+snAf1DjbUYAnxn7WwBrjWVzY715nHJ1ANYBDSz36pJk3DNULsFBwDeWfYHdI+BzVKopYZx7UhxyTQDqGet3W+RyvA/Gbw3Q1fj/LwV6uz2fscpm7O+I6vyyAWiVIvdsPPAekGNsH5XoexZBrv+af5dxj+Yl4X61AwYZ67nAd8Z9Sdrzn/SGPFk/1LiV94HjgLeMG7aDqhd+JDDHWJ8DjDTW6xnlBHADcIOlzspyMcrUBNVQC9v+VUA7y0O0ylj/O3CuvRxwLvB3y/5q5WKUrQNQZDx09Yx7NjFZ9wzoYnvBA7lHxrFvLfurlfMrl+3YacDzxrrjfbDeQ2s5t+czHtlQ6Yb6A+upUiJJvWeohv8Eh3IJvWcOcs1BJYk1/8YXknG/bDK+gcpPmLTnvy67sx4EfkNVUtCWwG4ppZkcs5iqTOdmA4pxfI9RvnK/wzmx0BXYDjwllJvtCSFEI6CNlHKLcf0tVKXuiXT9oOVCSrkJuA/YCGxB3YPFJP+emQR1jzoY60HLB3Ap6ssuFrncns+YEEJMATZJKZfaDiX7nvUAjjXcUB8KIYbGKFfQ9+wa4F4hRBHqXbghRrkCuV9CuYgHAp+RxOe/TioRIcSpwDYp5WLrboeiMsoxT2lbfFAPZUI/KqUcCBxAmaaRSJRcGD7WqSg3QnvUXDknuVwnYbJFwa8cocgnhLgRlb37+VSQSwjRELgRuMnpcDJlQ70HzVHul+uAmYZfPtly/QyYIaXsCMxAjXMjGXIJIRoD/waukVK6JQIOXbY6qUSA0cAUIcR64CWUS+tBoJlQ6VegepqWSOlZvKR28UMxUCyl/MzYfgWlVL4XQrQzrt8O2GaXy3b9oOUClex0nZRyu5SyFHgVGEXy75lJUPeomKrEq4HIZwQtTwXOl4aPIAa5dhD5XsdCN9QHwVLjPcgDvhRCtI1BtqDvWTHwqlR8jvIWtIpBrqDv2cWo5x5gFipTuSlvwu6XECILpUCel1Ka8iTv+Y/FD1ebfsA4qgLrs6gehPu5sf5/VA8SzzTW+1A90LeW+APr81HZNkGlyb/X+FmDZvcY66dQPWj2ubG/BSq20tz4rQNaxCnXcGA5aqI1geoA8Itk3TNq+qsDu0eolD0jqAosnhyHXJNQ0x+0tpVzvA+or/C1xj4zSNzH7fmMVTbbsfVUxUSSfc+mA7ca6z1QbheR6HvmINdKYJyxfjywONH3yyj/LPCgbX/Snv+ENNSp/KO6EumK6plQaDx8Zu+Q+sZ2oXG8q+X8G1E9Q1bhsYdFFHkGAIuAZf/f3t2EWFWHcRz//lxoggoW2YtUbgTLoiE1skQwyIUtIpIM3JhuokVFFhQtyoiihbQp6I2CilIKscAgwRJBJ6h8mxE0tFdKiNBsXOTo+LR4/jePtztvdw7ODP4+MMw95/zv/5x7Zu557rn/c56HrFkznfxudxtZ+mFb5Y8tsvDXEaALmF/pZ3XZ3sPAgzXtq3XAQaAbeL+8mS/4PgM+IsdlTpOfnNbUuY+A+eU1HgFepelCh2Fu12HyILi3/Lw+2H4gr6j5vix7pjK/5b5ud9ualv/EuSAy2vtsIvBB6W83cOeF3mf9bNcichxwHzkOMW8U9tci8uul/ZX/qWWM4v+/056YmVnbLtYxETMzq4GDiJmZtc1BxMzM2uYgYmZmbXMQMTOztjmI2Jgm6RVJj1Wmv5D0dmV6vaTHa17nyTr7K312SFpWmX5O0hNDeJ4kfSlpWg3bMFHSjsrNd2Yj5iBiY90u8s54JE0g71yeW1l+O7BzFLZruDrI6/mHaxmwLwZObTEkEdFL3kOwYqR9mTU4iNhYt5MSRMjg0Q30SJouaRJwPbBH0hRJ2yTtLrUQ7gGQ9LKkhxudlTOAteXxk5K+KXUW1rVaeas2kmaVeg5vlZoOWyVNLssWlLadyloi3coaFs8DK5S1KBoH8RskbZf0g6RH+nn9K8lMrYOtd3s5a9tR2iyQtKnUinih0t/m0qdZLRxEbEyLiN+BM5KuJYNJJ3m38ELyztr95RP2P8C9EXELWY9ifUnat4HzP3nfD3wsaSkwm8x/1AHMk7S4uu5B2swGXouIucBfwH1l/rvAQxGxEOgrr6GXTHS4MSI6ImJjaTuHTKd/K/BsyYnU7A7yLumG/tYL0BsRi8n0Hp+SqWduBFZJuqy06QYWYFYTBxEbDxpnI40g0lmZ3lXaCHhR0n6yoNFMMj32HmCGpKsl3Qwcj4hfyGJRS4E9ZGqNOeQBumqgNj9GxN7y+DtglrJq4dSIaGzTh4O8ri0RcSoi/iQT5l3Ros2lEdFTmf7feivLPiu/u4ADEXE0Ik6ReaWuAYiIPqBX0tRBts1sSDzAZuNBY1zkJvKT9K/AWuBv4J3SZiVwOZnP6HTJTHtJWfYJsBy4kjwzgQw6L0XEGwOst2WbUsfhVGVWHzCZ1mm0B9LcR6v34xlJEyLibD/Pmdyiv7NN7c429T2JPHMzGzGfidh4sJNMpX4sIvoi4hhZNngheVYCmWr+jxJAlgDXVZ6/gcwkvJwMKJBV6laXugxImilpBucbSpv/RMRxcrzmtjLrgcriHrKc6XAdIpMI1qJ8rdVI5282Yg4iNh50kVdlfd0070T5Kgiy2NN8Sd+SZyUHGw0j4gB5AP8tzlV/20p+3dQpqYsMLucd5IfSpoU1wJuSOskzkxNl/lfkQHp1YH0otpCZpuuyBPi8xv7sIucsvmY1kjQlIk6Wx0+Rda8fHUF/VwHvRcRdNW3fJrJO+aE6+jPzmIhZve6W9DT53voZWDWSziLiaLmkd9pI7xUplxpvdgCxOvlMxMzM2uYxETMza5uDiJmZtc1BxMzM2uYgYmZmbXMQMTOztv0LMyHqQFRS6J4AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ @@ -197,13 +146,22 @@ }, { "cell_type": "code", - "execution_count": 0, + "execution_count": 2, "metadata": { "colab": {}, "colab_type": "code", "id": "OBIE6kPDOsZo" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Radiative Power (cooling) is 450.4891874450116 W/m^2\n", + "Stefan-Boltzmann Law is 459.29972699999996 W/m^2\n" + ] + } + ], "source": [ "\n", "### now make structure a blackbody emitter at all angles\n", @@ -239,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 0, + "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", @@ -272,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 0, + "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", @@ -303,7 +261,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.6" } }, "nbformat": 4,