{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Offline plotting with categorical data\n", "\n", "This notebook is a collection of plotting examples using the `plot_dataset` function and caterogical (string-valued) data. The notebook should cover all possible permutations of categorical versus numerical data." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from pathlib import Path\n", "\n", "import numpy as np\n", "\n", "from qcodes.dataset import (\n", " Measurement,\n", " initialise_or_create_database_at,\n", " load_or_create_experiment,\n", " plot_dataset,\n", ")\n", "from qcodes.parameters import Parameter" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "initialise_or_create_database_at(Path.cwd() / \"offline_plotting_example_categorical.db\")\n", "exp = load_or_create_experiment('offline_plotting_experiment', 'nosample')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1D plotting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Category is the independent parameter\n", "\n", "With the category as the independent parameter, `plot_dataset` will default to a bar plot as long as there is\n", "at most one value per category. If more than one value is found for any category a bar plot is not possible, and the `plot_dataset` falls back to a scatter plot." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 752\n" ] } ], "source": [ "voltage = Parameter('voltage',\n", " label='Voltage',\n", " unit='V',\n", " set_cmd=None,\n", " get_cmd=None)\n", "fridge_config = Parameter('config',\n", " label='Fridge configuration',\n", " set_cmd=None,\n", " get_cmd=None)\n", "\n", "meas = Measurement(exp=exp)\n", "meas.register_parameter(fridge_config, paramtype='text')\n", "meas.register_parameter(voltage, setpoints=(fridge_config,))\n", "\n", "with meas.run() as datasaver:\n", "\n", " configurations = ['open', 'outer chamber closed',\n", " 'pumping', 'closed']\n", "\n", " for configuration in configurations:\n", " datasaver.add_result((fridge_config, configuration),\n", " (voltage, np.random.rand()))\n", "\n", "dataset = datasaver.dataset" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "_ = plot_dataset(dataset)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 753\n" ] }, { "data": { "image/png": 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tyYnGOt3CpcvbVG5m3YsTjXW6AX1b2lRuZt2LE411upNGDaOlT681ylr69OKkUcM6KSIza09+GMA6XeWGv586M+uZnGisSxg9YqATi1kP5URjZtZNdNffm22Qiaa77iwz23B159+blfYwgKRhku4vvP4t6QRJ/STdLunR/L5NHl+SfiJpjqQZknYvI67KzlqwdDnB6p01afqCMhZnZtYuuvPvzUpLNBExOyKGR8RwYA/gBWAi8A3gjogYCtyR+wEOAobm1zjg/DLi6s47y8w2XN3592Yd9XjzAcDfI+Jx4BBgQi6fAIzO3YcAl0VyD9BX0o7tHUh33llmtuHqzr8366hEcwRwZe7eISKeAMjv2+fygcC8wjTzc9kaJI2TNFXS1MWLF7c5kO68s8xsw9Wdf29WeqKRtDHwIeDa1katURZrFURcGBEjI2Jk//792xxPd95ZZrbhGj1iIKcf+mYG9m1BwMC+LZx+6Ju7/IMA0DFPnR0E3BcRi3L/Ikk7RsQTuWrsyVw+HxhcmG4QsLC9g/GPA82su+quvzfriERzJKurzQBuAsYCZ+T3GwvlX5R0FbAX8Gyliq29ddedZWbWHZWaaCRtBrwX+Gyh+AzgGknHAP8EPprLbwYOBuaQnlA7uszYzMysY5SaaCLiBWDbqrKnSU+hVY8bwBfKjMfMzDqeW282M7NSOdGYmVmpnGjMzKxUTjRmZlYqJxozMyuVE42ZmZXKicbMzErlRGNmZqVyojEzs1I50ZiZWamcaMzMrFRONGZmVionGjMzK5UTjZmZlcqJxszMSuVEY2ZmpXKiMTOzUjnRmJlZqZxozMysVE40ZmZWKicaMzMrlRONmZmVyonGzMxK5URjZmalcqIxM7NSlZpoJPWVdJ2kRyTNkrSPpH6Sbpf0aH7fJo8rST+RNEfSDEm7lxmbmZl1jLKvaH4M3BIRbwDeCswCvgHcERFDgTtyP8BBwND8GgecX3JsZmbWAUpLNJK2At4JXAwQES9HxFLgEGBCHm0CMDp3HwJcFsk9QF9JO5YVn5mZdYwyr2heCywGLpE0XdJFkjYHdoiIJwDy+/Z5/IHAvML083PZGiSNkzRV0tTFixeXGL6ZmbWHMhNNb2B34PyIGAE8z+pqslpUoyzWKoi4MCJGRsTI/v37t0+kZmZWmjITzXxgfkTcm/uvIyWeRZUqsfz+ZGH8wYXpBwELS4zPzMw6QGmJJiL+BcyTNCwXHQA8DNwEjM1lY4Ebc/dNwJj89NnewLOVKjYzM+u+epc8/+OAKyRtDDwGHE1KbtdIOgb4J/DRPO7NwMHAHOCFPK6ZmXVzpSaaiLgfGFlj0AE1xg3gC2XGY2ZmHc8tA5iZWamcaMzMrFRONGZmVionGjMzK5UTjZmZlcqJxszMSuVEY2ZmpXKiMTOzUpXdMoCZdVOTpi/g7Ftns3Dpcgb0beGkUcMYPWKtBtXNWuVEY2ZrmTR9ASffMJPlK1YBsGDpck6+YSaAk421mavOzGwtZ986+9UkU7F8xSrOvnV2J0Vk3ZkTjZmtZeHS5W0qN2vEicbM1jKgb0ubys0acaIxs7WcNGoYLX16rVHW0qcXJ40aVmcKs/oaPgwgaRBwBPAOYACwHHgQ+C3wu4h4pfQIzazDVW74+6kzaw9KfwNTY4B0CTAQ+A0wlfSXy5sCrwfeBewBfCMi7u6YUNc2cuTImDp1amct3sysW5I0LSJq/VdYKRpd0ZwTEQ/WKH8QuCH/a+ZO5YRlZmY9RaN7NO/LVWc1RcTLETGnhJjMzKwHaZRoBgJ/kXS3pM9J2q6jgjIzs56jbqKJiC+Tqsa+BbwFmCHpd5LGSNqyowI0M7PureHjzZHcFRGfAwYDPwK+DCzqiODMzKz7a6qtM0lvJj3mfDjwNHBKmUGZmVnPUTfRSBpKSi5HAquAq4ADI+KxDorNzMx6gEZXNLcCVwKHR8TMDorHzMx6mLqJJiJeW+yXtFVx/Ih4prWZS5oLPEe6IloZESMl9QOuBoYAc4GPRcQSSQJ+DBwMvAAcFRH3tXF9zMysi2m1rTNJn5W0CJgBTMuvtvwc/10RMbzwK9RvAHdExFDgjtwPcBAwNL/GAee3YRlmZtZFNfMwwInAbhHxVDst8xBg/9w9AZgMfD2XXxapTZx7JPWVtGNEPNFOyzUzs07QTOvNfydVZa2LAG6TNE3SuFy2QyV55Pftc/lAYF5h2vm5bA2SxkmaKmnq4sWL1zEsMzPrKM1c0ZwM/FnSvcBLlcKIOL6Jad8eEQslbQ/cLumRBuOqRtlaLX5GxIXAhZAa1WwiBjMz60TNJJqfA38AZgJt+luAiFiY35+UNBHYE1hUqRKTtCOpVWhIVzCDC5MPAha2ZXlmZtb1NJNoVkbEV9o6Y0mbAxtFxHO5+0Dgu8BNwFjgjPx+Y57kJuCLkq4C9gKe9f0ZM7Pur5lEc2e+v/K/rFl11trjzTsAE9NTy/QGfh0Rt0j6K3CNpGOAfwIfzePfTHq0eQ7pntDRbVkRMzPrmur+8dmrI0j/qFEc1b+z6Qz+4zMzs7brSn98BkBEvKYjAjEzs56p7uPNkvZtNKGkrSS9qf1DMjOznqTRFc1HJJ0F3EJqDWAxsCnwOuBdwM7AV0uP0MzMurVGbZ19WdI2wGGkG/Y7AsuBWcDPI+KPHROimZl1Zw3v0UTEEuAX+WVmZtZmzTRBY2Zmts6caMzMrFRONGZmVqpm/o9mM0nfkvSL3D9U0gfKD83MzHqCZq5oLiE1PbNP7p8PnFpaRGZm1qM0k2h2iYizgBUAEbGc2k36m5mZraWZRPOypBbyf8NI2oVC45pmZmaNNNN683+TWgcYLOkK4O3AUWUGZWZmPUczjWreLuk+YG9SldmXIuKp0iMzM7MeodVEI2n33Fn5E7KdJG0NPB4RK0uLzMzMeoRmqs5+BuwOzCBd0bwpd28r6diIuK3E+MzMrJtr5mGAucCIiBgZEXsAI4AHgfcAZ5UYm5mZ9QDNJJo3RMRDlZ6IeJiUeB4rLywzM+spmqk6my3pfOCq3H848DdJm5B/W2NmZlZPM1c0RwFzgBOALwOP5bIVpD9AMzMzq6uZx5uXA+fkV7Vl7R6RmZn1KM083jwUOB14I+mvnAGIiNeWGJeZmfUQzTaqeT6wklRVdhnwqzKDMjOznqOZRNMSEXcAiojHI2I88O5ywzIzs56imafOXpS0EfCopC8CC4Dtyw3LzMx6imauaE4ANgOOB/YAPgGMaXYBknpJmi7pN7n/NZLulfSopKslbZzLN8n9c/LwIW1dGTMz63qaSTRDImJZRMyPiKMj4iPATm1YxpeAWYX+M4FzI2IosAQ4JpcfAyyJiNcB5+bxzMysm2sm0ZzcZNlaJA0C3g9clPtFur9zXR5lAjA6dx+S+8nDD8jjm5lZN1b3Ho2kg4CDgYGSflIYtBXpCbRm/Aj4GrBl7t8WWFpo9Xk+MDB3DwTmAUTESknP5vHX+EsCSeOAcQA77dSWCyszM+sMja5oFgLTgBfze+V1EzCqtRlL+gDwZERMKxbXGDWaGLa6IOLC3MDnyP79+7cWhpmZdbK6VzQR8QDwgKTL1/F/Z94OfEjSwaQfem5FusLpK6l3nucgUkKDdHUzGJgvqTewNfDMOizXzMy6kEZVZzPJVxS1bpVExFsazTgiTibfy5G0P3BiRHxc0rXAYaRGOscCN+ZJbsr9f8nD/xARa13RmJlZ99LodzQfKGmZXweuknQqMB24OJdfDPxK0hzSlcwRJS3fzMw6UKOqs8cr3ZJ2AN6We6dExJNtWUhETAYm5+7HgD1rjPMi8NG2zNfMzLq+Vh9vlvQxYAopCXwMuFfSYWUHZmZmPUMzTdB8E3hb5SpGUn/g96z+LYyZmVldzfxgc6OqqrKnm5zOzMysqSuaWyTdClyZ+w8Hbi4vJDMz60ma+YfNkyQdCuxL+lHlhRExsfTIzMysR2j0O5rzgF9HxJ8j4gbgho4Ly8zMeopG91oeBc6RNFfSmZKGd1RQZmbWc9RNNBHx44jYB9iP9APKSyTNkvRtSa/vsAjNzKxba/Xpsfz3zWdGxAjgP4EPs+b/y5iZmdXVzA82+0j6oKQrgN8BfwM+UnpkZmbWIzR6GOC9wJGkPy6bQmoEc1xEPN9BsZmZWQ/Q6PHmU4Bfk1pddnP9Zma2Tho1qvmujgzEzMx6JjclY2ZmpXKiMTOzUjnRmJlZqZxozMysVE40ZmZWKicaMzMrlRONmZmVyonGzMxK5URjZmalcqIxM7NSOdGYmVmpnGjMzKxUpSUaSZtKmiLpAUkPSfpOLn+NpHslPSrpakkb5/JNcv+cPHxIWbGZmVnHKfOK5iXg3RHxVmA48D5JewNnAudGxFBgCXBMHv8YYElEvA44N49nZmbdXGmJJpJlubdPfgXwbuC6XD4BGJ27D8n95OEHSFJZ8ZmZWcco9R6NpF6S7geeBG4H/g4sjYiVeZT5wMDcPRCYB5CHPwtsW2Oe4yRNlTR18eLFZYZvZmbtoNREExGrImI4MAjYE9i11mj5vdbVS6xVEHFhRIyMiJH9+/dvv2DNzKwUHfLUWUQsBSYDewN9JVX+2XMQsDB3zwcGA+ThWwP+C2kzs26uzKfO+kvqm7tbgPcAs4A7gcPyaGOBG3P3TbmfPPwPEbHWFY2ZmXUvvVsfZZ3tCEyQ1IuU0K6JiN9Iehi4StKpwHTg4jz+xcCvJM0hXckcUWJsZmbWQUpLNBExAxhRo/wx0v2a6vIXgY+WFY+ZmXUOtwxgZmalcqIxM7NSOdGYmVmpnGjMzKxUTjRmZlYqJxozMyuVE42ZmZXKicbMzErlRGNmZqVyojEzs1I50ZiZWamcaMzMrFRONGZmVionGjMzK5UTjZmZlcqJxszMSuVEY2ZmpXKiMTOzUjnRmJlZqZxozMysVE40ZmZWKicaMzMrlRONmZmVyonGzMxKVVqikTRY0p2SZkl6SNKXcnk/SbdLejS/b5PLJeknkuZImiFp97JiMzOzjlPmFc1K4KsRsSuwN/AFSW8EvgHcERFDgTtyP8BBwND8GgecX2JsZmbWQUpLNBHxRETcl7ufA2YBA4FDgAl5tAnA6Nx9CHBZJPcAfSXtWFZ8ZmbWMTrkHo2kIcAI4F5gh4h4AlIyArbPow0E5hUmm5/Lquc1TtJUSVMXL15cZthmZtYOSk80krYArgdOiIh/Nxq1RlmsVRBxYUSMjIiR/fv3b68wzcysJKUmGkl9SEnmioi4IRcvqlSJ5fcnc/l8YHBh8kHAwjLjMzOz8pX51JmAi4FZEfHDwqCbgLG5eyxwY6F8TH76bG/g2UoVm5mZdV+9S5z324FPAjMl3Z/LTgHOAK6RdAzwT+CjedjNwMHAHOAF4OgSYzMzsw5SWqKJiD9S+74LwAE1xg/gC2XFY2ZmncMtA5iZWanKrDrrsiZNX8DZt85m4dLlDOjbwkmjhjF6xFpPUpuZWTvY4BLNpOkLOPmGmSxfsQqABUuXc/INMwGcbMzMSrDBVZ2dfevsV5NMxfIVqzj71tmdFJGZWc+2wSWahUuXt6nczMzWzwaXaAb0bWlTuZmZrZ8NLtGcNGoYLX16rVHW0qcXJ40a1kkRmZn1bBvcwwCVG/5+6szMrGNscIkGUrJxYjEz6xgbXNWZmZl1LCcaMzMrlRONmZmVyonGzMxK5URjZmalcqIxM7NSOdGYmVmpnGjMzKxUTjRmZlYqJxozMyuVIqKzY1hnkhYDj6/HLLYDnmqncKx9eJ90Pd4nXc/67pOdI6J/ewXTmm6daNaXpKkRMbKz47DVvE+6Hu+Trqe77RNXnZmZWamcaMzMrFQbeqK5sLMDsLV4n3Q93iddT7faJxv0PRozMyvfhn5FY2ZmJXOiMTOzUjnRbEAkHSVpQEnzXlbSfOdK2q6MeReWcamkw0qcf+nr0FkkHStpTGfH0RNIGi/pxBLnP1lSpzwS3bszFmqd5ijgQWBhsxNI6h0RK0uLqETdOfbuIiIu6OwYrOvrcVc0kr4i6cH8OkHSEEmPSJogaYak6yRtlsfdQ9JdkqZJulXSjrl8sqQzJU2R9DdJ7+jctaqtel1z2RBJDxbGOTF/UzoMGAlcIel+SS2trP/3JWW8O80AAAk0SURBVN0FfKlqmVtIukTSzLw9P1IYdpqkByTdI2mHXPZBSfdKmi7p94Xy8Xmf3Ja/8R8q6aw831sk9Sks9qS8L6ZIel2evr+k6yX9Nb/eXpjvhZJuAy6rsc2+lpfxgKQzagw/IMc6U9IvJW2Sy8+Q9HBe5x+0EsO2eb2mS/o5oLbu23VV73gvXlVJGilpcu5uaj/kYZXPRHE/vPotvN7nJi//mhzP1fl46DY/NiyLpDF5mzwg6VdVw4bnz9EMSRMlbZPLjy8ch1flss3zsfrXfMwdkstbJF1V2e5AS4evZEVE9JgXsAcwE9gc2AJ4CBgBBPD2PM4vgROBPsCfgf65/HDgl7l7MnBO7j4Y+H1nr1sb1nUI8GBhvBOB8YX1Gpm7W1v/n9VZ7pnAjwr92+T3AD6Yu88C/qsynNVPN366sF3HA3/McbwVeAE4KA+bCIzO3XOBb+buMcBvcvevgX1z907ArMJ8pwEtNWI/KK/zZrm/X36/FDgM2BSYB7w+l18GnAD0A2YX1qNvKzH8BPh27n5/3jbbddBxMYTax/vcSgykLxyT22k/jAdObPS5ycv/ee5+E7CSfBxuqC9gt3xMVfZJv6ptOQPYL3d/l/yZI9VGbFJ1HH4f+ESlDPgb6bzwFVZ/pt/Smdu9p1Wd7QtMjIjnASTdALwDmBcRf8rjXA4cD9xCOuhvlwTQC3iiMK8b8vs00oe3q6m3rjc1Of0wGq//1XWmew9wRKUnIpbkzpeB3+TuacB7c/cg4Op8tbQx8I/CvH4XESskzczLvyWXz2TNbX5l4f3cQhxvzLEDbCVpy9x9U0QsrxP7JRHxQo79marhw4B/RMTfcv8E4AvAecCLwEWSfltYz3oxvBM4NC/jt5Iq26ij1DreG1mf/VCt1udmX+DHABHxoKQZTaxDT/du4LqIeArSsVg5jiRtTUoid+VxJwDX5u4ZpFqJScCkXHYg8CGtvr+zKemLzztJX3qIiBmdud17WqKpV0VR/WOhyOM+FBH71Jnmpfy+iq65neqt60rWrBLdtMH0jdb/+QbT1frx1YrIX51Yc5v9FPhhRNwkaX/St7aKlwAi4hVJxelfYc1tHjW6NwL2qU4o+cPa1tiLw9cSESsl7QkcQEqyXySdKBrF0Jk/UKt1vBePi+pjYn32Q7Van5sOqzrsRlo7Fut5PymBfAj4lqTd8rw+EhGz11hA5x+Hr+pp92juBkbnOuHNgQ8D/wfsJKlyQj2SVFUwG+hfKZfUJ++07qLeui4Cts/3CTYBPlCY5jmg8q1/Xdf/NtKJljzdNq2MvzWwIHePbWL+tRxeeP9LnTiGNzGf24BPafU9un5Vwx8BhlTuPwCfBO6StAWwdUTcTKpKG16YX60Y7gY+nssOIlUfdqRax/tcUnUrwEdqTdSEWvuhGX8EPgYg6Y3Am9dx+T3JHcDHJG0Lax6LEfEssESr7w1XjsONgMERcSfwNVI12RbArcBxyplF0og8XfE4fBOp+qxTdMVv6ussIu6TdCkwJRddBCwBZgFj843ZR4HzI+JlpRvkP8mXqr2BH5HudXR5tdY1IqYDSPoucC+pmuqRwmSXAhdIWg7sQ7ov0db1PxX4H6UHDlYB32F1dUkt44FrJS0A7gFe0+QqFm0i6V7SF6Mjc9nxOY4ZOfa7gWMbzSQibsnJYKqkl4GbgVMKw1+UdHSOtzfwV+ACUv35jZI2JX17/HIrMXwHuFLSfcBdwD/XYZ3Xx1rHO+k4uVjSKaRjY13U2g/N+BkwIW+n6aTqn2fXMYYeISIeknQaKYGsIm2XuYVRxpI+q5sBjwFHk6o1L8+fVwHnRsRSSd8jfXZn5GQzl/QF83zgkrzd72f1uaLD9fgmaCQNId24fFMnh2JWurKOd0lzSTeS2/wfKJJ6AX1yIt+F9G3+9RHxcnvGaF1Xj7qiMbMuaTPgTqVHpQV8zklmw9Ljr2jMzKxz9bSHAczMrItxojEzs1I50ZiZWamcaKzLkbRKqT22ymtIjXEGSLquzvSd1kptLZI2UWrn7X5Jh0u6KP+epOzlnlLV/+eyl2lWix8GsC5H0rKI2KLB8IatMis1GHliREwtI762krQ3cGZE7NfO8+0VEasaDG+4Hc06iq9orFtQ+i+dayX9L3CbCq1Uq0ErtZKOUWpJeLKkX0g6L5fXbHm5apm9JP1Aq1uqPi6X12vhea6k70i6Lw97g6TtSe2NDc9XNLsUr7gaxLfGf+Qo/9+PpP0l3Snp16S2yJA0SakF7ockjctlZwAteZlXVM1Dks5WavV7pqTDC/OerNTi8yOSrsg/ADRbP53VeqlfftV7kVocuD+/Juayo4D5rG5xeQi5lWrqtFILDCD9SrofqXXi/wPOy+PVbHm5Ko7PAdcDvXN/P+q08Jy75wLH5e7Pk1prANif3Npx7p/cRHyXAocVpllWmNfzwGsKwyrbpIX0f0PbFqepMY+PALeTfmm+A6nlgh3zvJ8lNYS6EamZmX07+3jwq/u/fEVjXdHyiBieXx8ulN8ea7e4DKmRwcshtVJLauIEYE/groh4JiJWsLoFXEgtL58n6X5Si9fF1p+L41wQuZouL7tWC8/vLEzTlla/G8XXyJSIKLaCfbykB0hN/AwGhrYy/b7AlRGxKiIWkZrJeVth3vMj4hVSom9tHcxa5ZYBrDup1yoz1G6ltlG1T82Wl2tMXz3f1qqS2tLqd6N5vdracq6+2rgw7NXtoNQi9ntI6/JCvj9Vr8XuZpb7UqG7q7Zcbt2Mr2isJ6jXSu0UYD9J2yg1kllstbiZ1p9vA47N01Za2K3ZwvM6xt0ovrmsbm35EFLVWi1bA0tyknkDsHdh2Aqt+U+lFXcDh+d7UP1JV2Sd1uCi9XxONNYTnA9skVup/Rr5pBkRC0j/Pngv8HvgYVa3Gnw8MDLf5H+Y2i0/X0S6fzEjV039Z0S8SGpJ91qlPwp7hdTCc5u1Et8vSEloCrAX9a/mbgF653X/Hqn6rOLCHPsVVdNMJFUvPgD8AfhaRPxrXdbBrBl+vNl6NElbRMSyfMUwkfTQwMTOjquiq8dn1h58RWM93fh8w/9B0v/zTGpl/I7W1eMzW2++ojEzs1L5isbMzErlRGNmZqVyojEzs1I50ZiZWamcaMzMrFT/H/F3XC5g79zvAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "with meas.run() as datasaver:\n", "\n", " configurations = ['open', 'outer chamber closed',\n", " 'pumping', 'closed']\n", "\n", " for configuration in configurations:\n", " datasaver.add_result((fridge_config, configuration),\n", " (voltage, np.random.rand()))\n", "\n", " datasaver.add_result((fridge_config, 'open'),\n", " (voltage, np.random.rand()))\n", "\n", "dataset = datasaver.dataset\n", "\n", "_ = plot_dataset(dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Category is the dependent parameter\n", "\n", "With the categories as the dependent variable, i.e., the _outcome_ of a measurement, the `plot_dataset` defaults to a scatter plot.\n", "\n", "Here is an example with made-up parameters and random values.\n", "\n", "**UNRESOLVED**: How do we ensure the y-axis order?\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 754\n" ] } ], "source": [ "voltage = Parameter('voltage',\n", " label='Voltage',\n", " unit='V',\n", " set_cmd=None,\n", " get_cmd=None)\n", "response = Parameter('response',\n", " label='Sample response',\n", " set_cmd=None,\n", " get_cmd=None)\n", "\n", "meas = Measurement(exp=exp)\n", "meas.register_parameter(voltage)\n", "meas.register_parameter(response, paramtype='text', setpoints=(voltage,))\n", "\n", "\n", "with meas.run() as datasaver:\n", "\n", " for volt in np.linspace(0, 1, 50):\n", " coinvalue = volt + 0.5*np.random.randn()\n", " if coinvalue < 0:\n", " resp = 'Bad'\n", " elif coinvalue < 0.8:\n", " resp = 'Good'\n", " else:\n", " resp = 'Excellent'\n", "\n", " datasaver.add_result((voltage, volt),\n", " (response, resp))\n", "\n", "dataset = datasaver.dataset" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "_ = plot_dataset(dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Both variables are categorical\n", "\n", "For both variables being categorical, the `plot_dataset` defaults to a scatter plot.\n", "\n", "This case would typically be some summary of a large number of measurements." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 755\n" ] } ], "source": [ "sample = Parameter('sample',\n", " label='Sample',\n", " unit='',\n", " set_cmd=None,\n", " get_cmd=None)\n", "feature = Parameter('feature',\n", " label='Physical feature',\n", " set_cmd=None,\n", " get_cmd=None)\n", "\n", "meas = Measurement(exp=exp)\n", "meas.register_parameter(sample, paramtype='text')\n", "meas.register_parameter(feature, paramtype='text', setpoints=(sample,))\n", "\n", "\n", "with meas.run() as datasaver:\n", "\n", " features = ['superconducting', 'qubit',\n", " 'clean states', 'high bandwidth']\n", "\n", " for samp in ['Nanowire', 'Silicon Chip', 'SQUID', 'Membrane']:\n", "\n", " feats = np.random.randint(1, 5)\n", " for _ in range(feats):\n", "\n", " datasaver.add_result((sample, samp),\n", " (feature, features[np.random.randint(0, 4)]))\n", "\n", "dataset = datasaver.dataset" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "_ = plot_dataset(dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2D plotting\n", "\n", "Naming convention: the x-axis is horizontal, the y-axis is vertical, and the z-axis is out-of-plane." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Categorical data on the x-axis\n", "\n", "Here is an example where different samples are tested for conductivity. The longer the name of the sample, the higher the conductivity." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 756\n" ] } ], "source": [ "sample = Parameter('sample',\n", " label='Sample',\n", " unit='',\n", " set_cmd=None,\n", " get_cmd=None)\n", "gate_voltage = Parameter('gate_v',\n", " label='Gate voltage',\n", " unit='V',\n", " set_cmd=None,\n", " get_cmd=None)\n", "conductance = Parameter('conductance',\n", " label='Conductance',\n", " unit='e^2/hbar',\n", " set_cmd=None,\n", " get_cmd=None)\n", "\n", "meas = Measurement(exp=exp)\n", "meas.register_parameter(sample, paramtype='text')\n", "meas.register_parameter(gate_voltage)\n", "meas.register_parameter(conductance, setpoints=(sample, gate_voltage))\n", "\n", "\n", "with meas.run() as datasaver:\n", "\n", " for samp in ['Nanowire', 'Silicon Chip', 'SQUID', 'Membrane']:\n", "\n", " gate_vs = np.linspace(0, 0.075, 75)\n", "\n", " for gate_v in gate_vs:\n", " datasaver.add_result((sample, samp),\n", " (gate_voltage, gate_v),\n", " (conductance, len(samp)*gate_v))\n", "\n", "dataset = datasaver.dataset" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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feVanRRgwDN/g0X6PJ3n2ucXc/YdyXWbDN3h0vu0d6223/bCk75DC48wjDapcVG9/ag8AbUtfSZjLgiAI2oCBJSX/SuWXQmNtb/vNpNBQjwIzKmaw/P/pvPs0lo0YUS9SQNMJJRMEQdAGjFnoxaWWMhQiW2xECnp6MWkA5iF5l0NIHonk9IOzl9muwJyKWa3VDFlzGYbF7k8IoWAwonC2XMpLNQeSB/2hbCulJJfnPpmFwJG2Z0k6GfiFpMNJcxRVwkVdTeq3eYwU+LNtfQVDV8kEQRC0EWMWN3HIiO031Uh7lhS5vjrdwJFNO3kDhJIJgiBoE0va09c+oBiySsaIxUuiSyqooqnWjO7mJffmrBQ0ioHFoWSCIAiCVhEtmSAIgqAlGFg4BMN4DVklY8PixWEuC5YlvMt6eKGkK21QDuMwlwVBEAQtwrB46OmYoa1kFi+JcTLBsig6/pcyfwiadlpJGvE/9Og6e5GkzSXdX1j+I+nYevMoBEEQDAzE4pLLYKLrlIztR2xvm2ew3IE0evUK4ATgetubAdfn9SAIggFB6vhXqWUw0e3msj2Bf9qeImkCsHtOvwC4CTi+3oFGLIlxMkEVI8JCtJTnPbzTIgwq0jiZwaVAytDtSuYAUlA4qD+PwlIkHQEcATBinZhLJgiC9rJkkLVSytC1n/KSRgLvBn5Z9hjbE23vaHvH4Wut1jrhgiAIqqi0ZIZan0w3t2T2Ae6zPSOvz5C0QW7FFOdRqI1hSXiXBdWEuWwp893N1cPAw4jF3ftdv8J08xUfSI+pDOrPoxAEQTAgWGKVWgYTXfmpImlV4O3AJwrJ9eZRCIIg6DhGLBiCzhRdqWRsvwC8rCqt5jwKvRHmsqCaGIzZwwtLRnZahEFFGozZPOORpOOAj+WsHyRNRLYBcAkwBrgP+IjtBZJGAReShn08C+xve3LThOmFbjaXBUEQdBXN6viXNBY4GtjR9lbAcJK37XeAU/J4wVnA4fmQw4FZtjcFTsn7tYVQMkEQBG3AFos9rNRSkhHAKpJGAKsC04G3Apfl7RcA++XfE/I6efuektpiyulKc1kzsMExGDOoJrzLlhLeZc1nSZPck23/W9L/kfqfXwSuBe4FZttLZ5ubBozNv8cCU/OxiyTNIXU5zGyKQL0QpSgIgqANpI7/0lXuOpImFdYn2p5YWcmxGScArwBmk8YL7lPztPmQXra1lFAyQRAEbaDBjv+ZtnfsZfvbgCdsPwMg6VfAG4DRkkbk1sw44Mm8/zRgPDAtm9fWAp5r/CoaZwgrGbFkcXiXBcsSk5b18PySUZ0WYdCxuHljYP4F7JqHc7xI8qydBNwIvJ/kYVYcL1gZR3hH3n6D3Z65HIawkgmCIGgfzRzxb/suSZeR3JQXAX8GJgK/Ay6R9M2cdk4+5Bzgp5IeI7VgDmiKICUIJRMEQdAmlpT3HOsT218FvlqV/Diwc41959OhAepDV8kYHIMxgyrCXNbDfMdgzGaSAmQOPY/WoatkgiAI2ogRCyOsTBAEQdAKbBoZaDloGNpKZpBFOw2aQMQuW0rELms2atpgzG5iaCuZIAiCNmGiJRMEQRC0kOj4H0oYiMGYQRXhXdbD/CUrdVqEQYXp3gnJJK0GzLe9uNFjh66SCYIgaCMGFnZJ0FFJw0gDNg8CdgJeAkZJega4mhRL7dEyeQ29tlsQBEFHKDeXTJn5ZNrAjcCrgC8CL7c93vZ6wJuAO4GTJX24TEbdoVZbhMOTKKgiZsbsYb7DXNZMTHNH/LeYt5HC1Yyze2pK288BlwOXSypVQLrmioMgCLqdbmnJ2F6YA2j+urd9yuQ1pFsyirAyQTXR8b+UFxZHFOZmYqubWjIV7pS0k+17VjSDrlQykkYDPwG2IlULHwUeAS4FNgEmAx+0PatDIgZBECxD6vjvurAyewCfkDQFeJ40+Zltb102g65UMsBpwDW23y9pJGl+6y8B19s+WdIJwAnA8Z0UMgiCoAd142DMWrNtNkTXKRlJawJvBg4FsL0AWCBpArB73u0C4CZ6UzIWhLksqCLGyfTw0pKuqx4GNKnjv7vqHNtT8lTPmwErFzZNKZtHN5aiVwLPAOdJ2ga4FzgGWN/2dADb0yWtV32gpCOAIwCGjxndPomDIAjovhH/kj5Gql/HAfcDu5Jm13xr2Ty664oTI4DtgbNsb0eyE55Q5kDbE23vaHvH4auv3koZgyAIlqEy4r/M0heSNpd0f2H5j6RjJY2RdJ2kR/P/tfP+knS6pMckPSBp+5JiH0MajDnF9h7AdqSP/NJ0Y0tmGjDN9l15/TKSkpkhaYPcitkAeLrPnBoOkBAMemKczFIiCnPzWdK86ZcfAbYFkDQc+DdwBakurNU3vQ/J5LUZsAtwVv7fF/Ntz5eEpFG2/y5p80Zk7bqWjO2ngKmFC90T+BtwFXBITjsEuLID4gVBENTEhoVLhpVaGmRP4J+2pwATSH3S5P/75d8TgAuduBMYnT/G+2Ja9ub9NXCdpCuBJxsRrhtbMgCfAS7KnmWPA4eRFOYvJB0O/IsOzWcdBEFQi2QuK61A1pE0qbA+0fbEOvseAFycf9frmx4LTC0cMy2nTe9VZvs9+eeJkm4E1gKuKXsR0KVKxvb9wI41Nu1ZPpMYjBksT3iX9RDeZc2ngdH8M23XquOWIX9ov5sUY6zXXWukNVTabd/cyP4VohQFQRC0gRa5MO8D3Gd7Rl6v1zc9DRhfOG4cJcxeklYGPg3sRrqEW0lOV/PLCth1fTJBEATdSTKXlVka4EB6TGVQv2/6KuDg7GW2KzCnYlbrgwuBLYEfAGcAWwA/bUTAod2SCdNIUIWWRKGo8OLiiMLcbJY0MfilpFWBtwOfKCSfTO2+6auBfYHHgBdI/dhl2Nz2NoX1GyX9pRE5h7aSCYIgaBPJu6x5sctsvwC8rCrtWWr0TeeIykeuwGn+LGnX7JGGpF2A2xrJIJRMEARBG+im6ZclPUiy9axEMrP9K2/aiDRkpDRDV8kYtLg7HnjQRsJatpQF4V3WdJppLmsx72xWRlGKgiAI2kA3BcjMAzuRNAp4H2kKlaK++HrZvELJBEEQtIkunLTsSmAOKRDxSyuSwZBWMjGfe1BNlIke5od3WVOxxaLuUzLjbO/dnwy67oqDIAi6lWZFYW4jt0t6XX8yGNItmSAIgnbRTX0yBe+yEcBhkh4nmcuGzPTLzSFMI0EVEbushwWLu24++gFPtygZ0gDP6TShlhzaSiYIgqBNdNM4GeBgYGfgH6Soy9fkaVYaJpRMEARBm+iWcTK2Pwkg6TWkIJznS1oLuJGkdG6zXWraxyGrZBSDMYMaROyyHuYvHrLVQ0uwYVHjE5J1FNt/B/4OnCJpFWAPUjy071N7upXl6EgpkjSONNHOm4ANgReBh4DfAb+3Hb0lQRAMOrrIXLYctl8kBdq8upHj2q5WJZ0HnAssAL5DClX9aeCPwN7ArZLe3G65giAIWkmlT6ZbXJglvUbSnpJWr0pvaNxMJ1oy37P9UI30h4Bf5ZneNmqHIDHwLliOsJYtZWF4lzUdDxAF0heSjiZFbX4YOEfSMbYrc9N8iwamYO6Ektlb0mzb02pttL2ANOdBEATBoKJbOv6BjwM72J4naRPgMkmb2D6N2lM516UTSmYscIekJ0gzuv3S9sxGMpA0GZgLLAYW2d5R0hjgUlIgt8nAB23PqpuJoyUTLE+Mk+nhpej4byp2c/tkJI0GfgJsRWqDfxR4hBr1oCQBp5EmLnsBONT2fb1kP9z2vCS3J0vanaRoNqZBJdP2Phnbx5HMYf8DbA08IOn3kg6WtEYDWe1he1vbFQ+HE4DrbW8GXJ/XgyAIBghi8ZJhpZaSnEYav/IaYBuSaatePbgPsFlejgDO6iPvpyRtW1nJCuedwDpAQ2FmOuJP58TNtj8FjAdOBY4DZvQj2wnABfn3BcB+/ZMyCIKgudgqtfSFpDWBNwPnpHy9wPZs6teDE4ALc917JzBa0ga9nOJgYJnBl7YX2T44n7c0HW0P58BrBwD7A88CXyp5qIFrJRn4ke2JwPq2pwPYni5pvRrnO4KkxRmx1tphLguWI8bJ9LBocXeN6RjoNBi7bB1JkwrrE3M9V+GVwDPAeZK2IYXiP4b69eBYYGrh+Gk5bXpNWXOfuaQ1bM+t2jawp1+WtBlJsRxI6lO5BNjL9uMNZPNG20/mG3idpL+XOSg/pIkAK48dH7VJEATtw6lfpiQzC10BtRgBbA98xvZdkk6j9y6CWtqtV2kkjQV+DrylL2F7oxOfKn8AVgb2t/062yc1qGCw/WT+/zRwBSnGzoxK8y//f7q5YgdBEPSPJajUUoJpwDTbd+X1y0hKp149OI3UNVFhHPBkvcwlbUlyUz6+oQusQdtbMrZfWVzPtsURhe3P9Xa8pNWAYbbn5t97kaYCvQo4BDg5/7+yfi6Ed1lQm2jfLmXhkhgn00ycO/6bkpf9lKSpkja3/QiwJ/C3vNSqB68CjpJ0CbALMKdiVqvDjcCE3H/TLzrWJyPpEyTl8CI9r7ZJtsbeWB+4InnkMQL4ue1rJN0D/ELS4cC/SPF1giAIBgwNmMvK8BngojyA/XHgMJJ1qlY9eDXJffkxkgvzYX3kfQ/wPuCO/grZyY7/zwNbNjpGJpvWtqmR/ixJmwdBEAxImjni3/b91A5SuVw9aNukEfxleTdwlqTv2v7CCooIdFbJ/JOkUTtHmMuCKlQqePnQYOGiMJc1E7t7wsrkMP5HSPpKf/PqpJL5Imn+6LtI03oCYPvozokUBEHQOgZK8Muy2P56f/PopJL5EXAD8CDRpgiCYAjQ5D6ZlpPD0RwEvNL21yVtBLzc9t1l8+ikkllk+7OdOrkI77JgeSJ2WQ+LYzBmUzFiSZdNWgacSWoEvJXkqDUXuBzYqWwGnVQyN+YR+L9hWXNZry7MQRAE3UoXfsPsYnt7SX8GyME2RzaSQSeVzIfy/y8W0sq4MAdBEHQfXdTxX2ChpOFk/ShpXRrs3uiYkrH9ik6dOwkQnkTB8kTssh7CXNYCuq94nU6KqrKepJOA9wP/3UgGnYhdtpvtW3vZviawUZ3ZM4MgCLqWbmvJ2L5I0r2ksTcC9rP9cCN5dKIl8z5J3yXFxbmXFEl0ZWBTYA9gY+BzHZArCIKgZRhYsqS7lAyA7b8DpYIQ16ITscuOk7Q2qdn1AWADUmiZh0lh++u2cppNeJcFy9F95oyWsSTMZc3FQJe1ZJpBR/pk8rTIP85LEATBkKDbxsk0g5jEOwiCoF2EkhlCRKj/oAbhXdbDkkVDz7TTWspNrdxpJH0S2AG4Hvgw8DvbZ61ofkNXyQRBELSb7viGeSuwP/An27tJOrs/mXWsZ0/SqpL+R9KP8/pmkt7ZKXmCIAhaisFLVGrpMM/mqQG+k9df6m3nvuhkS+Y8kgvz6/P6NOCXwG/bJUAMxgyqidhlPbj74mx1Ac1TIJImk2KJLSbFgtxR0hjgUmATYDLwwRwKRsBppInLXgAOtX1fnaxPA7D9m7z+q/7I2clS9Crb3wUWAth+kWY+gSAIgoGGSy7l2cP2trYrk5edAFxvezNSn8oJOX0fYLO8HAHU7WPJ42KQtE5ev7khiaropJJZIGkVemLivIp+NsuCIAgGNM1XMtVMAC7Ivy8A9iukX+jEncBoSRv0kde5/ZIk00lz2VdJo/7HS7oIeCNwaLtOLodpJKhBlImlOLzLmktjgzHXkTSpsD7R9sQaOV4ryaSB7BOB9W1PB7A9XdJ6ed+xwNTCsdNy2vReZGhKAehkgMzrJN0H7Eq6mGNsz+yUPEEQBK2mgcGYMwsmsHq80faTWZFcJ6m30C+1FEZf0jTlk6tjSkbS9vlnRZNuJGktYIrtRX0cOxyYBPzb9jslvQK4BBgD3Ad8xPaCPmWIcTJBFVocTZkKWhwtmabTRM8x20/m/09LugLYGZghaYPcitkAeDrvPg0YXzh8HPBkH6doirCd7JM5E7gTmEgKL3MHSVH8Q9JefRx7DCnWWYXvAKfkzq5ZwOHNFzcIgqB/VMz0fS195iOtJmmNym9gL+Ah4CrgkLzbIcCV+fdVwMFK7ArMqZjVeuGLfWwvRSeVzGRgO9s72t4B2CvhiWcAAB1xSURBVI50k94GfLfeQZLGAf8F/CSvizR46LK8S7GzKwiCYGBQttO/XGN6feBWSX8B7iaNyr8GOBl4u6RHgbfndYCrgceBx0gf9Z8ucY5Fko6XdLqk0/LvLUpJV6CTHf+vsf3Xyortv0nazvbjSW/U5VTgC8Aaef1lwOyCia3SobUcebrnIwBWWn3tGCcTLE9Yy3qIjv8mo6ZFYbb9OLBNjfRnSXO/VKcbOLJs/pKOBw4kWZfuzsnjgIslXWL75LoHV9FJJfOIpLNIFwEpjME/JI0ij52pJkcEeNr2vZJ2ryTX2LVmVZG9LyYCrLre+KhOgiBoL91T6xwObGl7mbpY0veBv9LTQuqTTiqZQ0lNtmNJiuJW4PMkBbNHnWPeCLxb0r6kic7WJLVsRksakVszZTq0giAI2k/3OBstATYEplSlb0CDV9FJF+YXge/lpZp5dY75IrkzKrdkPm/7IEm/JE2CdgnLdnb1SniXBdVEFOYewrusyXTXpGXHAtfnvp3K+JqNSDMYH9VIRp10Yd4M+DbwWlKrBADbr1yB7I4HLpH0TeDPwDlNETIIgqCJdMsAcNvXSHo1yS16LMnaNA24x3ZDvdmdDpD5VeAUknnsMBrwy7Z9E3BT/v046WYEQRAMXLpEyQDYXkIaZtIvOqlkVrF9vSTZngKcKOlPJMXTFsJcFlTTLV+a7SDej6AWkg6zfV7Z/Ts5Tma+pGHAo5KOkvQeYL2+DgqCIOhWmjUYs8N8rZGdO9mSORZYFTga+AbJZHZwB+UJgiBoHaapYWVaiaQH6m0iDQQtTSeVzCa27yF5kh0GIOkDwF1tObvpJnfCoE2Ed1kPisGYzad7itf6wDtIYbqKCLi9kYw6aS6rFRenKbFygiAIBiJdZC77LbC67SlVy2Syw1VZ2t6SkbQPaQrQsZJOL2xaE+g1+nIQBEFXMzAUSJ/Yrhtk2PaHGsmrE+ayJ4F7gXfn/xXmAse1SwgBwyKse1BNmFCXErH9WkCXVDnZ67dXacvsAx1QMrb/AvxF0s/6mjcmCIJgsDCATGFluFHS5cCVtv9VSZQ0EtiNFFnlRuD8vjLqhLnsQbI+rxVt2fbW7ZYpCIKgLXSJdxmwN/BRUtTlVwCzSZFZhgPXkubvur9MRp0wl72zA+dcHsdgs2B51MD8uIOd8C5rPt3SkrE9nzSx5JmSVgLWAV60PbvRvNruXVb0VADmA6/Ly4s5LQiCYHDSvEnLkDRc0p8l/Tavv0LSXZIelXRpNm0haVRefyxv36Qhke2FtqeviIKBDrowS/ogaTKcDwAfBO6S9P5OyRMEQdBSSrovN9DaKTsN/eHALNubkmJFfqc5F1SOTg7G/DKwk+2nASStC/yRnmmUW06Yy4LliMGYSwnvshbQpOJVmIb+JOCzhWnoK+7FFwAnAmcBE/JvSPXrGWU9w5pBJwdjDqsomMyzdFaeIAiClqIl5RZgHUmTCssRVVlVpqGvfCr3Ng39WPKcMHn7nLx/3/ImPizpK3l9I0kNRbzvZEvmGkl/AC7O6/sDV3dQniAIgoHCTNs71tqwAtPQl56ivgZnkhTZW4Gvk8YzXg7sVPL4js6M+f8kvZfkcy1gou0r2idAmAOC5ekW7592MCzej+bTnPLV6DT004DxwDRJI4C1gOdKnmsX29tL+jOA7VkVh4KydLLj/zjgbtuftX1cWxVMEARBu2lSx7/tL9oeZ3sT4ADgBtsHkQZHVpynitPQX5XXydtvaKA/ZqGk4fSMbVyXBuNidLIPZE3gD5L+JOlISQ2Fjw6CIOg6mujCXIPjSU4Aj5H6XCrT0J8DvCynfxY4oYE8TweuANaTdBJwK/DtRoTqpLnsa8DXJG1N6o+5WdI0229rx/lFhHUPlkcRz24pYU5uAU0uXmWmoc8DKz+wgvlfJOleYE9Stbmf7Yf7OGwZBoI319PAUyTvsj5nxpS0sqS7Jf1F0l8lfS2n1xyIFARBMBBIH7alvcsGBJIuAJ6y/UPbZwBPSTq3kTw61pKR9ClSC2Zdku/2x23/rcShLwFvtT0vhzu4VdLvSc3AU2xfIuls0gCks+rmEmFlglpEWJmlKMLXNpfuCpBZYeviSP/c8b9dIxl00oV5Y+DYskHWKuQOq3l5daW8mPoDkYIgCAYG3adkhkla2/YsAEljaFBvdLJPppHOp2XI3g73ApsCPwT+Sf2BSEEQBAOD7lMy3wNul3QZSfoPkqIMlKaTLZkVxvZiYFtJo0meD1vU2q06IY+aPQJg5KprMyzMAUEV4QzSQ3T8N59uM5fZvlDSJJKlSMB7S3ZrLKUrlUwF27Ml3QTsSv2BSMX9JwITAVYfM77LHncQBF1PF9Y6Wak0pFiKdFTJSNoY2Mz2HyWtAoywPbePY9YFFmYFswrwNlJU0cpApEtYdiBSEARB5+lCZyNJo4D3AZtQ0Be2v142j056l32cZLoaA7yK1Po4m+SP3RsbABfkfplhwC9s/1bS34BLJH0T+DM9A5Fq4zCNBDUI77KlhDm5BXRf8bqSFFDzXpJnb8N0siVzJGng0F0Ath+V1Oc4GdsPAMu50NUbiBQEQTBQ6LY+GWCc7b37k0EnB2O+ZHtBZSUHbuu+RxAEQVCW1oaVaQW3S3pdfzLoZEvmZklfAlaR9Hbg08Bv2nXyyujbICgSYWV6iPejyQw8BVKG3YBDJT1BMpeJNFxx67IZdFLJnEAalf8g8Angats/7qA8QRAELUN0pblsn/5m0Ekl8xnbpwFLFYukY3JaEATBoKPblIztKZLWBjYjzV1TYUrZPDqpZA4BqhXKoTXSWoMdppFgecK7bCnhXdYCuqx4SfoYcAzJ+/d+0pjEO0iDM0vRdiUj6UBSjLFXSLqqsGkNUiTmIAiCwUmXKRmSgtkJuNP2HpJeA3ytkQw60ZK5HZgOrEOKi1NhLvBAB+QJgiBoPU2MwixpZeAWYBSpHr/M9lclvYI0IH0McB/wEdsL8qDKC4EdSB/z+9ueXOJU823Pl4SkUbb/LmnzRmRtu5KxPYVkz3t9u8+9rCDhSRTUIMxlS4nYZS2gecWr0SlPDgdm2d5U0gGkKCn7lzjPtBwj8tfAdZJmUSNkV290bJyMpF0l3SNpnqQFkhZL+k+n5AmCIGg1zZq0zIl6U55cltMvAPbLvyfkdfL2PSWpxHneY3u27ROB/yFFUplQ6mIznRyMeQZwIPAosArwMeAHHZQnCIKgpcjlFmAdSZMKyxHL5SUNl3Q/aXbh6+h9ypOxwFSAvH0O8LI+5ZW+U/lt+2bbVwHfbOSaOxog0/Zjkobn0P3nSbq9neePwWZBNWFC7SHMZU2mscGYM23v2Gt2jU15UqvVUkaatwPHV6XtUyOtLp1UMi9IGgncL+m7JGeA1TooTxAEQWtpwTdMySlPpgHjSX0sI4C1gOfq5SnpU6QoLK+SVHTIWoPkvFWaTprLPpLPfxTwPOkGvK+D8gRBELSMyoj/kuay3vOS1s0tGApTnjxMz5QnsOyUJ1fldfL2G/JU9vX4OfCufPy7CssOtg8qf9WdnX65MmJ0Pg36XTeLCPUfLEd4ly1l2KK4F82miXVOo1OenAP8VNJjpBbMAb1lbnsOMEfSAmCO7dkAktaWdK7tj5YVtBODMSeQwkf/MK/fBaybN3/B9mV1Dw6CIOhWmhggs9EpT2zPBz6wAqfauqJgcj6zJC133t7ohLnsC6SmW4VRpBGluwOf6oA8QRAEbaFZ5rI2MizHLgNA0hgabJx0wlw20vbUwvqttp8FnpXUto5/GRTmgKCaJeFyWGFYeJc1n+6rcr5HmlPmMpL0HwROaiSDTiiZtYsrto8qrK5LEATBIGWAtVL6xPaFkiaRBnkKeK/tvzWSRyeUzF2SPl49d4ykTwB3d0CeIAiC9tBlSgYgK5WGFEuRTiiZ44BfS/oQKYAbpKBto+gJgdB6HN5lQQ3CWraUCPXfZNx9A8AlfaVWuu2vl82jEwEynwbeIOmtwJY5+Xe2byhzvKTxpGiiLydVCRNtn5Y7pC4FNgEmAx+0PavJ4gdBEKwQXToz5vOF3ysD7ySNxylNJ8fJ3ACUUixVLAI+Z/s+SWsA90q6jjTh2fW2T5Z0Aml6515CHzhaMsFyKDr+lxIhdlpAl43Dsl2cjgVJ/8ey3sF90skR/yuE7em278u/55K06liWjTJajD4aBEEwIOhCF+ZqVgVe2cgBHQ2Q2V8kbUIakHQXsL7t6ZAUkaT1aux/BHAEwMqj1mqfoEEQBE0cjNkuJD1Ij9TDSR7A32gkj65VMpJWBy4HjrX9nxJTI2B7IjARYM3Vx1qLwjQSVBEm1KUMWxj3otl0W8c/qQ+mwiJgRmEqgVJ0pZLJM8FdDlxk+1c5eYakDXIrZgPSHAtBEAQDhm5RMpI+28s2bH+/bF5d1yeTZ3M7B3i46kKLUUaL0UeDIAg6j0kd/2WWzrNGXnYkhfsam5dPAq9tJKNubMm8kTRNwIN5VjiALwEnA7+QdDjwL0oEgwvvsmA5wrtsKRFWpvkM8E79pdj+GoCka4Hts5MVkk4EftlIXl2nZGzfSu1Z3gD2bKcsQRAEDdElSqbARsCCwvoC0ljE0nSdkgmCIOhGunQw5k+BuyVdkdf3Iw2GL83QVTIGYrBZUM3AsIcPCGLSsibj5g0AbzTySe7LPg3YF3gBOLQy3rB3kX2SpN8DbyLVmofZ/nMjsnZdx38QBEHX4pJL31Qin2wB7AocKem1pEgn19veDLg+rwPsA2yWlyOAs8qcRNIo4DXAasBo4F314pnVI5RMEARBm2jWiP8ViHwyAbjQiTuB0XmoR19cmY9dRIpjVllKM2TNZcJocbjPBFWEx+FSYlK/JmMaKV/r5HlcKkzMg8mXo2Tkk7FAcbLIaTlteh9yjLO9d1mhazFklUwQBEHbKa+3Z9resa+dGoh8UmtDGWlul/Q62w+W2LcmoWSCIAjaRDO9yxqMfDINGF84fBzwZInT7AYcKukJ4CWSsrLtrcvKOXSVjCOUeVCDJWFCrTAsYvs1nSZ6l/UV+eRklo18chVwlKRLgF2AORWzWh/s019Zh66SCYIgaCfNjcLcaOSTq0nuy4+RXJgPKyWyPaW/goaSCYIgaANpMGZztEyjkU9sGzhyRc4laRvSOBmAP9n+SyPHD2El44hTFSxPeJctJbzLWkCXVTmSjgE+DlT6fH4maaLtH5TNYwgrmSAIgvbSrJZMGzkc2MX28wCSvgPcAYSSCYIgGFB04cyYJJNc0RtmMfXNdDUZukrGEDNjBssRJtSlDFsY96K5NC92WRs5D7irKkDmOY1kMHSVTBAEQbvpEnOZpE1J0QO+L+km0ngZAUcD/24kr4hdFgRB0A6cpl8uswwATgXmAti+z/bptk8juT+f2khGQ7clY2DxwHiawQCiS74024Hi/Wg+3VO+NrH9QHWi7Uk5Vlpphq6SCYIgaDddo2NYuZdtqzSSUZjLgiAI2oSWLCm1DADukfTx6sQcSeDeRjIawi2ZGIwZ1CCmf1hKeF82GdNNgzGPBa6QdBA9SmVHYCTwnkYy6jolI+lc4J3A07a3ymk1pxztlIxBEATVCHfNYEzbM4A3SNoD2Con/872DY3m1XVKBjgfOIM0v3WFypSjJ0s6Ia8f32su0fEf1KJLKoF2oIXRqms6XVa+bN8I3NifPLquT8b2LcBzVcn1phwNgiAYONjllkFEN7ZkalFvytFlkHQEcATAysPXaKN4QRAMebqrT6ZpDBYlU4o8R/ZEgLVGrm8WLeqwRMFAw9Hx30Pci6YzQDzH2krXmcvqMCNPNUrVlKNBEAQDhJKmshLmMknnSnpa0kOFtDGSrpP0aP6/dk6XpNMlPSbpAUnbt/Ail2OwKJnKlKOw7JSjQRAEAwPTzD6Z84G9q9IqDlCbAdfndUhTKG+WlyOAs5pxOWXpOnOZpIuB3YF1JE0Dvkr9KUd7Zwg2XYM+6L4ouS1DC8Jc1nSaVOXYvqVGeJcJpLoRkgPUTSQv2wnAhXl2zDsljZa0QaUfu9V0nZKxfWCdTctNORoEQTCQaPE4mXoOUGOBqYX9puW0UDJBEASDivJKZh1JkwrrE7Pj0opQa5KxtjXZh66SscN7JlieQTZGoV/E+9Fc7EYGgM+0vWODZ5hRMYNVOUBNA8YX9hsHPNlg3ivMYOn4D4IgGPi0djBmPQeoq4CDs5fZrsCcdvXHwFBuyQRBELSbJrWUG3SAuhrYF3iMNOnYYU0RoiRDWMkYFoU5IKgiTEQ9LFjYaQkGF6Zp3ouNOEBlr7Ijm3LiFWAIK5kgCIJ2YvDQGzYRSiYIgqAdDNHI70NXyTjiVAXL4xiguxTF+9F8hqD34tBVMkEQBO0mlEwQBEHQGgbfXDFlGLpKJgZjBrUYgjbzuoR3WXMxQzJe4tBVMkEQBO0mWjJBEARBa2gorMygYQgrGeOYGTOoZgiOY6hHvB9NxuAhWL6GsJIJgiBoM0NwvqJQMkEQBO0i+mSGECa8y4LliAG6BRYs6LQEgws7vMuCIAiCFhItmaGD7fhqDZbDQ9BmXpeF0fHfXIZmnTOoJi2TtLekRyQ9JumETssTBEGwlEqo/zLLIGLQKBlJw4EfAvsArwUOlPTazkoVBEFQwEvKLYOIwWQu2xl4zPbjAJIuASYAf6t3wFBsugZBWbxocFV2ncY01xwraW/gNGA48BPbJzct8yYyaFoywFhgamF9Wk5biqQjJE2SNGkhL7VVuCAIhjh201oy3WS5GUwtGdVIW+azwfZEYCLAmhozuAyfQRAMeJpoPWnYctMpBpOSmQaML6yPA56st/NcZs38oy+b0nKp+mYdYGanhRggxL3oIe5FDwPhXmzc3wzmMusPf/Rl65TcfWVJkwrrE/NHcoValptd+itjKxhMSuYeYDNJrwD+DRwAfKjezrbXbZdgvSFpku0dOy3HQCDuRQ9xL3oYLPfC9t5NzK5Py81AYdAoGduLJB0F/IHUEXau7b92WKwgCIJW0JDlppMMGiUDYPtq4OpOyxEEQdBiGrLcdJJBpWS6lIl97zJkiHvRQ9yLHuJeVNFNlht5CMbSCYIgCNrDYBonEwRBEAwwQskEQRAELSOUTB0kWdJPC+sjJD0j6bdNyv8mSV3nlpnvy/cK65+XdGIH5Lha0uh2n7cRJH1Z0l8lPSDpfkm7SBop6VRJ/8yBXH8raaO8/yaSHqrK40RJn8+/z5f0/vz7phwM9gFJf5d0RrvuR63ryuk/qYw6lzRZ0jr59+0tlufVuTw8JulhSb+QtL6kQyWdUeeYAV9+BgvR8V+f54GtJK1i+0Xg7SQvjrYhaYTtgRZv/SXgvZK+bbtjA+Rs71udJkmkfsaOB92S9HrgncD2tl/KFe5I4FvAGsCrbS+WdBhwpaQdVuA0B9meJGkk8G3gSuAtTbqEmvRyXdj+WK1jbL+hhfKsDPwO+Kzt3+S0PYBex8HVKj9Ba4iWTO/8Hviv/PtA4OLKBkmrSTpX0j2S/ixpQk4/VNKvJf1G0hOSjpL02bzPnZLGFPL/sKTbJT0kaed8/ImSJkq6Frgwf93+SdJ9eXlD3m/3/DV7Wf6SvShXskjaQdLNku6V9AdJGzTxniwiefscV71B0rsk3ZWv9Y+S1i9c07lZ3sclHV045rP5+h+SdGxO+0JlH0mnSLoh/95T0s/y78mS1sn352FJZwL3AeMl7SXpjny/filp9SZef1k2AGbafgkgK+TZwGHAcbYX5/TzgHnA21b0RLYXAF8ANpK0TX8F74Plrsv2k1C/dS5pXuH3FyQ9KOkvkk7Oadvmd+MBSVdIWruQ33ck3S3pH5LeVEOeDwF3VBRMlulG25UW4YaSrpH0qKTvFuQolp+/S7ogn/8ySav2/zYFFULJ9M4lwAH5a2lr4K7Cti8DN9jeCdgD+F9Jq+VtW5EK/87AScALtrcD7gAOLuSxWv7K+zRwbiF9B2CC7Q8BTwNvt709sD9wemG/7YBjSQHyXgm8UdJKwA+A99veIed7Uv9uw3L8EDhI0lpV6bcCu+ZrvYRU8VV4DfAO0j35qqSV8tf7YaRwGLsCH5e0HXALUKlQdgRWz9e1G/CnGvJsDlyYz/s88N/A2/I9mwR8tr8XvAJcS1J4/5B0pqS3AJsC/7L9n6p9J5Ge4QqTldZfSPe5ldS6rlJI2gfYD9jF9jZApdK/EDje9tbAg8BXC4eNsL0zqZwX0ytsBdzby2m3Jb03rwP2lzS+xj6bk8K2bA38h/Q+Bk0izGW9YPsBSZuQWjHVgzz3At6tbC8HVgY2yr9vtD0XmCtpDlD5ynqQpKwqXJzPc4ukNdVjI74qm+gAVgLOkLQtsBh4deH4u21PA5B0P7AJ6Wt5K+C63LAZDkxv/OrrY/s/ki4EjgZeLGwaB1yaW04jgScK236Xv35fkvQ0sD5JaVxh+/l8Db8iKZezgB0krUEyz91HUjZvyuesZortO/PvXUkV9m35+keSlHtbsT0vK9E3kT5CLiWZtGqNGaiECKk3nqDsOINaoUaaSq3rknSC7fNLHP424DzbL+S8nssfKqNt35z3uQD4ZeGYX+X/95LKd6Ncb3sOgKS/kWKQTa3aZ6rt2/Lvn5HK2P+twLmCGoSS6ZurSAVud+BlhXQB77P9SHFnpU7Q4jwCSwrrS1j2nldXHpX15wtpxwEzgG1ILc/5hW3F8yzOeQv4q+3X93ZRTeBUUuV/XiHtB8D3bV8laXfgxMK2erIuh+2FkiaTWjm3Aw+QKrRXAQ/XOKR4vwRcZ/vABq6lJeTWxU3ATZIeBD4BbCxpjfwRUmF74DLgWWDtqmzGsKyyrolS6PfXUfv+NJUa13UIcH6JQ0Xj8bUq5aZSZqr5K733Q9Uqd9XUew+DJhDmsr45F/i67Qer0v8AfKbQD7LdCuS9fz52N2BO5YurirWA6bkz+yOklklvPAKsq9RBSzZLbbkCsvWK7eeAXwCHV8lacY44pEQ2twD7SVo1mxrfQ4857Bbg8/n/n4BPAve779HDd5LMhpsC5Lxf3ccxTUfS5pI2KyRtS3o2FwDfz0oBSQeTPhxusz0PmC5pz7xtDLA3yQzZ27lWIrWSptp+oOkXs+y5al1X2Wjm1wIfrfR5SBqTy/ysQn/LR4Cb62VQg58Db5BU6TutTMP+ugby2KjyvpCsFr3e76AxoiXTB9kcdVqNTd8gfc0/kBXNZJLXTSPMUnLvXBP4aJ19zgQul/QB4EaW/WqvJe8CJTfX07MpYkSWsxUhJ74HHFVYPxH4paR/kyr7V/Qh632Szgfuzkk/sf3n/PtPpH6vO2w/L2k+tftjqvN8RtKhwMWSRuXk/wb+UeqKmsfqwA+yCXQR8BhwBDAX+F/gEUmrAM8Ary8oz4OBH6rHTfxrtv9Z5xwXSXoJGAX8kTSfSKupd119YvuabPadJGkByQT9JdIHydlZ+TxOasGWwvaLkt4JnCrpVGAhqeV7TAPX9DBwiKQfAY+SzLVBk4iwMkHQISS9HLgGOLNqrpCgTeQ+19/a3qrDogxaoiUTBB3C9lMkc1MQDFqiJRMEQRC0jOj4D4IgCFpGKJkgCIKgZYSSCYIgCFpGKJlg0KA60YFbdK6ujKIdBO0mvMuCQYF6iQ4cBEHniJZMMFioGR1Y0leUImU/pBTduhKh4SalCM+3KEVx3knSr3K03m/mfUpF6NXAiPocBAOSUDLBYKFedOAzbO+UB9utwrJRGRbYfjNwNmkuliNJwUUPlVSJU9drhN7cYhoIUZ+DYEASSiYYFOS4XzuQQpw8Q4oOfCiwh9IcNw8CbwWKcdyuyv8fJAUVnZ5bQo8DlZDw1RF6d6s6dTHq8/2kECkbN/XigqCLiT6ZYNBQJ+rx1sCOtqcqTRO9cuGQYnTs6sjZlXejrwi9AybqcxAMRKIlEwwKeol6DDAz95O8fwWy7itC74CI+hwEA5VoyQSDhXrRgWeTzGGTgXtWIN9eI/QOoKjPQTAgidhlQVCHiNAbBP0nzGVBEARBy4iWTBAEQdAyoiUTBEEQtIxQMkEQBEHLCCUTBEEQtIxQMkEQBEHLCCUTBEEQtIz/D9EtSD6K8lfSAAAAAElFTkSuQmCC", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax, _ = plot_dataset(dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Categorical data on the y-axis\n", "\n", "This situation is very similar to having categorical data on the x-axis. We reuse the same example." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 757\n" ] } ], "source": [ "sample = Parameter('sample',\n", " label='Sample',\n", " unit='',\n", " set_cmd=None,\n", " get_cmd=None)\n", "gate_voltage = Parameter('gate_v',\n", " label='Gate voltage',\n", " unit='V',\n", " set_cmd=None,\n", " get_cmd=None)\n", "conductance = Parameter('conductance',\n", " label='Conductance',\n", " unit='e^2/hbar',\n", " set_cmd=None,\n", " get_cmd=None)\n", "\n", "meas = Measurement(exp=exp)\n", "meas.register_parameter(sample, paramtype='text')\n", "meas.register_parameter(gate_voltage)\n", "meas.register_parameter(conductance, setpoints=(gate_voltage, sample))\n", "\n", "\n", "with meas.run() as datasaver:\n", "\n", " for samp in ['Nanowire', 'Silicon Chip', 'SQUID', 'Membrane']:\n", "\n", " gate_vs = np.linspace(0, 0.01, 75)\n", "\n", " for gate_v in gate_vs:\n", " datasaver.add_result((sample, samp),\n", " (gate_voltage, gate_v),\n", " (conductance, len(samp)*gate_v))\n", "\n", "dataset = datasaver.dataset" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax, _ = plot_dataset(dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Categorical data on the z-axis\n", "\n", "Categorical data on the z-axis behaves similarly to numerical data on the z-axis; what kind of plot we get depends on the structure of the setpoints (i.e. the x-axis and y-axis data). If the setpoints are on a grid, we get a heatmap. If not, we get a scatter plot." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Gridded setpoints" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 758\n" ] } ], "source": [ "bias_voltage = Parameter('bias_v',\n", " label='Bias voltage',\n", " unit='V',\n", " set_cmd=None,\n", " get_cmd=None)\n", "gate_voltage = Parameter('gate_v',\n", " label='Gate voltage',\n", " unit='V',\n", " set_cmd=None,\n", " get_cmd=None)\n", "useful = Parameter('usefulness',\n", " label='Usefulness of region',\n", " set_cmd=None,\n", " get_cmd=None)\n", "\n", "meas = Measurement(exp=exp)\n", "meas.register_parameter(gate_voltage)\n", "meas.register_parameter(bias_voltage)\n", "meas.register_parameter(useful, setpoints=(bias_voltage, gate_voltage),\n", " paramtype='text')\n", "\n", "\n", "# a function to simulate the usefulness of a region\n", "\n", "def get_usefulness(x, y):\n", " val = np.sin(x)*np.sin(y)\n", " if val < -0.4:\n", " return 'Useless'\n", " if val < 0:\n", " return 'Bad'\n", " if val <0.5:\n", " return 'Possible'\n", " return 'Good'\n", "\n", "\n", "with meas.run() as datasaver:\n", "\n", " for bias_v in np.linspace(0, 3, 100):\n", " for gate_v in np.linspace(-1, 1, 75):\n", " datasaver.add_result((bias_voltage, bias_v),\n", " (gate_voltage, gate_v),\n", " (useful, get_usefulness(bias_v, gate_v)))\n", "\n", "dataset = datasaver.dataset" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax, cax = plot_dataset(dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Scattered setpoints\n", "\n", "The same example as above, but this time with setpoints not on a grid." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 759\n" ] } ], "source": [ "bias_voltage = Parameter('bias_v',\n", " label='Bias voltage',\n", " unit='V',\n", " set_cmd=None,\n", " get_cmd=None)\n", "gate_voltage = Parameter('gate_v',\n", " label='Gate voltage',\n", " unit='V',\n", " set_cmd=None,\n", " get_cmd=None)\n", "useful = Parameter('usefulness',\n", " label='Usefulness of region',\n", " set_cmd=None,\n", " get_cmd=None)\n", "\n", "meas = Measurement(exp=exp)\n", "meas.register_parameter(gate_voltage)\n", "meas.register_parameter(bias_voltage)\n", "meas.register_parameter(useful, setpoints=(bias_voltage, gate_voltage),\n", " paramtype='text')\n", "\n", "\n", "# a function to simulate the usefulness of a region\n", "\n", "def get_usefulness(x, y):\n", " val = np.sin(x)*np.sin(y)\n", " if val < -0.4:\n", " return 'Useless'\n", " if val < 0:\n", " return 'Bad'\n", " if val <0.5:\n", " return 'Possible'\n", " return 'Good'\n", "\n", "\n", "with meas.run() as datasaver:\n", "\n", " for bias_v in 3*(np.random.rand(100)):\n", " for gate_v in 2*(np.random.rand(75)-0.5):\n", " datasaver.add_result((bias_voltage, bias_v),\n", " (gate_voltage, gate_v),\n", " (useful, get_usefulness(bias_v, gate_v)))\n", "\n", "dataset = datasaver.dataset" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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uoHJxcXFxGdD0q3m6iNyLGVJhn1JqurWvFHgc0/t2FfBlpVSD5d35zxwIP36J5QEZEbkYM5w6mNFY789kOd/e9wn3bnmdvR1NTC8exZUTT2P8oGG9n2iDUoqX9qzkkap3aAn6ObF8CpeOm0dpTjIx3w7ktamlhtZQB9OKR5Kr20beYHtbLfdsXsTqxh1U5A3mkvEnM3vweLbV1OH1eBhdXtKV9rUPN/LR5t0cdsgITpk5kReXfcJTi1fRGQyz4OgpXDBvJnk+b0r33l9U1dSzYed+Rgwp4rBDhiPiLBxQW0cn7Z1ByorNd1NT38zrKzejFJw8Yzwjhybv3L6lvYOn3l7DB+u3M2JIERfOO4JJlckvzwkbBkvWbOOt1VspKsjlnOMOZezwUgAaW/28smIjTW1+jpo8ihnjKrrdc2cwxKsfbmT99r2MHVbKgqOnUJiX4+i6HYEQbR2dlA7K7/U5KqVoCXWQo3nI0VOrMx3hIE/teJ/ndi2nxt/IYF8BF449gS+POQZN4vezO8JB3ty7jmp/A4cUlnHC0Cl4dQ9hZeAPByjQcxzXA5f+p19dKFmxcVqBB6IE1S2YEWJvFpGfAIOVUj8WkTMx3eqfiemu/s9KqTmWYFsOzMYMmLYCmKWUakh07dmzZysn66ie3bmMP6z/Lx2G6UlfEHJ1L/ce803GDxpGS9DPi7s/Ylvbfg4truQzIw4n1/oolVKICEop6gOthIwwj1Qt4Zmdy7ry84hGia+Ax064hiJvXtd5HzVU8XL1KjqNAF7Ngz8U4LCS0ZTnFvPI9iXsbKujPdSJQuHRdAxlcO3Uz3F25SwAWkMdvL5nLXs6Gnmkagmd4SCGFU/OU5eHZ2Up4YAZ5nl4aRHnnnAof/vPe3QEDgTP1QU0TSMYNg68M2D0sBLOmjONL500g6KC3F6fYYS65jaefGsVa7fvZdLIMr48dwbDS52EF0qNUNjgp/e8wNtrt6JpGspQVJYVc8vlZ2EoxcihxeR4e/bVauqb+drvHqWuuR0Ar64xpCifmoZWADQBj67zrbOPY+H82Y7L09Dq54LfPEhjq59g2EAEfB6dmy47k3kzJ/R6/sZd+1m9tZqSwnwef2slq7fuIRgyF5B6PTr/89XTKB9cyHfv+DdKKToDIXJ9Xo6ZOppbrjgLXdNoaGnn/N88RENrO2FD4fXo5Od4+ecPL2DMsMFxr+3vDPLNvzzN6q17ALPc3/vCSZx/sr0P5xV1W7lx3b+objc/w7nl0/jF4V+kwONMIALsbq/n0vf+RmOwrdv+HPFwVuUsfnzo5zGUwbK6LaxsqGJIziDmj5jB/o5mLn3vjq5vDMx6O6t0HOsadxFUIYq9+Vw9eQELRh7hqCwiskIp5fxl2zB7Rq5KYh1V2tf7NNHvvv5EZCzw3yhBtQE4WSm1x/Ie/aZSarKI3Gn9/Wh0ushPKXWltb9bung4EVQhI8zpr99ES8jfbb8Ax5dN4erJZ3DRO38hpA405F7RuHLiafxj8+vdPpRE5GgeLhs/j0vHzwPgD+v/y7O7ltERdnb+gXy83DXnCjY2V3PTumdRNoFOlV8jvKgUwun3JgcX5vHwdV9xJGy2721g4e8epTMYIhAK49V1vB6Ne37wZSaPKu/1/FT458vLuPP59+kMhnoc00QwlGJCxRB+c+kCJlWWEQyF+eUDL/PCsg02ufUkx6vzyE+/SjAU5p4XP2DT7loK83KobWqjxd/JYYcM57vnnth1f79/4g0ee2Nlj7cyKM/H67d+E12zHyGEDYPr/vECS9ZuQ2F2ZAKhnh4OPLqQn+Ojub17yCpN4IJ5R/D9L8xl4S2P8vH2vT3OnTFuBPf98EBoLX9nkKfeXs0bKzdTXJDH+h172dfY2uO8319+FqceObHbvqrWfXz1ndsJqO5lLNBzePXUn+HRdNv7jOWqD+7mo4ZttuF6vaJx/pjjeXrnB3SEgygUmgg+zUMgHMSwOSeWXM3LjTMv4MTy3h2Ju4KqfxmInimGWdFEsYRVpBUbiRleOcIua1+8/WlT29lC0OjZyClgyf5PWFq7qZuQAggqg79ufDmp63QaIT6o3cyl4+exqWUPz+xcRqdDIdc9nyAPbl3Ma3vXxE1j7MjJWMDwhlY/P/vni1z7pZOZXFmWUJVy65Nv0trRSaRfFAyHCYbD3PjIIh74cWw09AN0BkMYhiIvJ3nV0ZOLV9sKKQDDKsjm6jouvOkhfvCFufz1uXe6jSh7IxgyeODV5by8fAOBYLgrzwjvr9/Bqq1P8MCPLmB8xVBeWPqJ7aNv9QeoqqmnYmgx+xtbKSsu7Ha/z76zliXrttER514ihMLKtvyGgkde/4i312xj5/5G23PXbKuhIxAi1+fBHwjytd89wu7a5rjPL8JtT73VQ1A9UvVODyEF0Bbu5PhXfk6RJ59Thk/nqkmfYbDPXuXdEQ6yqnF73KoaVAYPVb0dc58qqc5dhxHkzk2vORJULv3LQBRU8bBrBVWC/T0zELkCuAJg9OjeezbF3nyCNh9cBLuPMVUaAmZv9Z19G2yFo1OW129JnKBDB49Cm9qCVATAALU9F+OTAuwfZWI+3LSbr9/6BKPKS7jjO+dRWpRvm27Zhp3YDd7Xba8hFDbw6N1HEw0t7fzyoVd5d10VSikmjyrnhq/NZ8LIodTUt/DKig34A0FOnD6OaWPs5wtb/M5iwikFtz71lqO00RhK8faabQmFW2cgxF3Pv8/vLj+LjoB9I6qAB15dzqsrNiGaYBgGXzxpBtecdyK6pvHkW6scC9CgzUgrQjwhFbmXn9/3Atd8YS7vrNtGdV3vQgqgtrmtx75VDVVx0yugKdTOc7uW89zu5YSVgQCHFJRz8xEXMbYwO6PreOxqr+/T67mkxkC0+ttrqfwigeP2Wft3YYaJj1CJGTo83v4eKKXuUkrNVkrNLivrfQLbq+loKTTeqVDT0dh1TSONIU8wnFh4SlkAfW4DMroT8SkkV8E4fyoyqgt/IMiW6lp+/I/n46bJjWOA4dV1dK37xQ1D8Y0/PMm766oIhQ3ChmL99r1cdtvjPLV4Nefe8E/ueO5d7n7+A77xhye46ZFF2Kmw++Ld1be0JzxuKMXSDTt5+u3VDEpgsPDKik10BEP4O4N0BsM8/fZq7nlxKQCNbf6458WSzmD59ZVb+MpvH+bVFZscC8bYDgaYqsreCGMQtrQRCtjato8LlvyZ2o5mAHJ1Lzla9vvRISNznU2X7DEQBdVzwMXW3xdjBouL7F8oJscATZaK8GVgvogMFpHBwHxrX9rUdrb0maAyrBam0OPcOMEOn57445bhAfAZRBtMSSj9ahA2FGu27aHOpocNcN6Jh/UwXPB5dM6cM7WHyvDDzbvY29BCKMqIQ2GOFn73+Otd81yGMlVdz3+wnuUbd/W4pj/OCCaT2DXUsTS3d3LbU29R39JuW5s0kR6jl45AiIcWrbC2+qYOKsyyNrQmFr7RFOb2tDRtDXXapOwdA8WfNpixLFuDftrDgZTySYaASl174dJ39KugEpFHMcNsTxaRXSLydeBm4DQR2QScZm0DvABsBTYDdwPfAlBK1QO/BpZZv19Z+9Km2JuP6iML1hmDxwDQELBv6J0gmCOyhGk0kFhZ5jMy0hYqoK3DvnG56rPHMGfqaHK8OgW5PnK8HmaMr+CHXzoZMFV9b6zczIpNu9ixr7HHfA9AZ9C+99sRCPLSsk967E9lXisZBMj19d7rV5ZANZT9iCeeQVOrP0DYMMhxcI1M0tjsXFAR08n48ycvUB/saXThlIja8K7Ni1LOw+XTR7/OUSml4s2in2qTVgH/L04+9wL3ZrBoAOR5fCyoOIKXqlemZNxgh2DfWNX4G/mf1U8w1FeURt5CoSeXfZ3NyZ2nAyM6YHcu6UisUCjMsMH2FoA+r4c/ffPz7NjXwJbqOsYMG8y4EUMAuPuFD7jnxQ/wenSUUuT6PHFVT7alE2wNOSZWlrHCZqSVKTy6Rqs/e73+McMGo2saBX28bq2hzdncHphrtiI8u3MZD1ctSevaI3LNNX3L9m9OKx+nuCupDg4OJmOKfuFH084GFC/s/iihYYUT4gkpgB3ttexor00rfwPFYF8BJDkoy9QKBV3T2FPX3LXw1I7R5YMZXX5gvc4H67dz38tLCYTCXSbX7Z3xOwUhw76wpx81uce+LbvTe5694fPq3daYpYqI4PNoBILhrvqR4/Xwoy+byxU278nufcSSTHXQojoId21+Le1rXz1lAQBD8gaxpX1fL6nTR1xRdVAwEOeoBhRezcP108/j5VN+ynmVR+MVHV008uN4gUhEtlesCcKwvBKumbwgufME2O8j3f5lyDAozEvuuTyehEVbPJSC+qae6qp4ashMYcQRmknnoxR/uOpzHHfoWMpLCpkzZTR//+4XOHaaqQ4Ohft3rWMiCqLmqJqDzo0+7MjXfUwtrgTgygmnpZWXU+zWGroMPFxB5ZAHti3mhWpzVBVWRp9M9CZLjubhi6OP4aJDTuTVU37m2BBEKSCQflXQRJJWhbW0O1czJeKZd3quHRMtu71lf5oCNoLPo7O2qob1O/ZR29TGhp37WL9zb9fcVbK3UVzg3PtDupSXHFgHNSpvSFp5dYSDLNlnzjUeNng0J5RNSSs/J7hi6uDAFVQOCBlhHt/+nmNPE/2BYKpNppeYlvo5ujd2njs+HZmpBoZSFBfmJXXOqUdMIsfrzFNBIrbV9PSYVZKEe6dU8Hky89wCoTD3vrSM+pZ2DKVobOvgL88s4cnFqwHIy0lulNrSbm91l2mx7fPqnH3MtK7tn0w/J638DBSv710LmP4pl9X1zTyVy8DHFVQOaA11EMrg4t5sUOjJ5Yujj+naztW9TC2qdHayN/15lggqSXXYCdPHEgylf/3YtVgAJ8/o3X9eOthZJqaKnXn6Xc+/B8CUJF1M2b0CEZg2NjVHyvEIhQzmTB3Ttd0RDqQtDCNzRi9Vf9S1ziqbuDNUBweuoHJAkTePfL3v1CmpEDtn1hbqdOzh3ejMTDUQgRZ//DU0n+zYy9dvfZx51/6Nr938CKu3VPPU26vJhALmqMk9hfL4ivRUUb2SZb1RfYufsGFw+ZlzHKXXNIk7ih5VVsJF85w5YHWO4qHXVnRt/eWTF9N+JLq1wK89FOgTQdVX6yRd0sO1+nOAJhpnVhzBo9vf6e+ixCU/yiv12/s+4acrHyHg1BVTWyJBFc9LlU1KRVw13vvrt/P/bv9Xl4VhU1sHl976OMNLB9mOAJIlYngQTbzFx5kiZCiGlRSy18ZZayYoyPWhaxrvr9+RMJ1Y5qTKUOi61m2hdIQcr4d1Ns5o08FQsLaqpmt7Z3td2nl2Wr76TiqfyjM7l2Zd3R52Z6kOCtwRlQOUUry57+P+LkZC9ne2ANAYaOenKx+l0wg5/gRlsCnQZGgAOaoJbU4jMqMZCpMzFvB6dAJx1Hg/u+/FHmbwCqhpaHGcv2Cv4gP44JOdPfbtqU9uPVmyeHQtq81cRyCAUoonF69KmE5ZC4kVZmgTzWZYtX1vA699uCmj5ROB0VGhQfKTCOFhh0c0ZpWOA+CIwWMZkkKMtv8riMhYEVkbs+8GEbk2yXxOFpH/ZrZ0mccVVA5oCvq7fJANVIJGGEMZvLl3nXMjCgvNCzLKjzanCb0igDY8iDa6E/2kBhjkXFiVDspjVJl9MMH6FnvT5WSneTw2oTA0Ebx6z5Hcy8s2Jpd5khiGwf6m7IymAMKGOQ/mxDlsNHk5PRUlgVCYpiQW8sZFDLMDIwqvrvP1M47qOnTeqKPTyjpH87JgpBnfak3TTuoC2Xu2LgcXrqByQJ7u7bNooDoa+Vrya7Q6jSDfX/Eg/nAgpUl+7fC2bq6VRAM8oE13rj67+pwTU3pOTs/weHTbdS9ej85ZUdZnEexiNmWS8pJCWwGZSQxDcUiCBdR2tHXYq8uSFXg9UWhHNaOf1Ih+Rh3GyDY8Ufc/b9ihKS+g1dH42fTzuqJUv7F3bZca0CU5RORqEflYRFaLyGPWvgIRuVdElonIRyLyeZvzbNOIyKEislREVlp5TrTSPi8iq0RkrYicn817cgWVA3J0L/NHHI4vi96cI4uIy3OLaTdSW6P1UcM2BAsJHxsAACAASURBVDBSmYS2aW9FQMqCOLEaEIEHoybWY5kQx7BhaFGBbcBAO/VVMBQmx8adkACHDI8fnTZbVA4tSSkcfTLomjB/1sTeEzogI12tgjDiVYhPoaa3cNsbr3Ydemz7uylfo9iXz7zhh3Zte0R3Z49S5yfAEUqpw4GrrH3XA68rpY4C5gG/F5GCmPPipbkKM6L6TMxI6ruAM4BqpdQMK+jtS9m8IVdQOeRH0z7H1KKRiPVfphmRW8ytR36NoWno5f3hAO/s35DSiCrhQGhw7z1xpWBrTR1748w5xQtxIQK6jQfyePfQFseq8Ln3es4hxvM7mCkmVJSyu7Ypa/lXlhWjaVrXeqp08XnT72iphqiOgg7rvdu6Ntc37U45RE19oJXXaw5MuQzJye67i+AZuE3gUBFZHvW7IuZ4vAetgNXAwyLyVSDy8c4HfiIiK4E3gVwgNihfvDTvAT8VkR8DY5RSfmAN8BkR+Z2InKiUyt6HgCuoHPN+7SY2NFejrP8yTa7Hx+wh49N2Q1MfaM24ya3kOVOhaUjcwH0rt9iGCGN/U1tSKim70VdHMMS67TU99geC2VUdPbF4dcJAhU7Jz/FQGCPI83K83HXNl4D483vJMrq8BK+DsCSJUC0Hht4ioBcc+BbGDSpPqxP31w0HOuXbW7Pv5w/MpScDlNpI7Dzrd1fM8TogVo1QCtQCnwX+F5gFrBARD+aA+gtKqZnWb7RSan3M+bZplFKPAJ8D/MDLInKKUmqjlf8a4Lci8otM3nwsrqBygFKK33/8n6yayooVT+T4sp7OVZNBQyNEhtefDHZ236VF+XFVYZkS7XYjLZ9HZ+LInoEwG1oz454pHgoYXpp+zz9sKB657ivcdNkCvnjS4fxy4XwW/+FbXXkXZ8jDxraaesJpLlLW8qPqlgEnDD9QX88ccURanbjqjkaqLAG1sy19U3cn1Aezu4QhWyilWoE9InIqgIiUYqrjlgCjlFJvAD8CSoBCzBh93xFrEllE7BbV2aYRkXHAVqXUXzDjAh4uIhVAu1LqIeBW4Mis3SyuoHJEU9BPYxpxopxQb1k4pavyaA+nFrQuIYPCHDCA7onPo5Of4+XGyxakZEzhS6KXb5d7IBTmtCN7zuNk2/xFKZg6OhPeHgRDGRx2yAhOOmwcM8aP7DZy/ObZx2XgGqbpetqOdEdY9UtBgSeX7xw2v+vQy3sSm9E74dmdywDY3tq3HuMPUhYCP7NUda8DvwR2AA+JyBrgI+CPSqlGzJh9XmC1Zdb+a5v84qU5H1hrXWcK8ABwGLDU2nc98Jss3SPgLvh1RL7HZzbAWZzdrQu0srphOy9Vr0w5Dw2hLKeIPf4GQhlc1S95QbSzzYZDNeoYa/OhIZei/BxOmzWJkUOKOeuYaQwtjp2bPUBJQS6NNubROV4dr+4h4FDAxgvz8eaqLXz1M7O67dM0IZwhD+d2eHSNnfsbE4ZvcUIoHOb2Z5ewZO02vLpOMBxm9qRR/P6Ks8n1eTjvhMN4eNEKW3+GfYmmCeOMCoJ5fo4eMoHLJsyjPPfACPqd2g1pX+OTZlNF3BbK7mj404BS6mNMo4dYTrBJ6weutNn/JuZ8VKI0vwV+G7P7ZTIUSd0JA3JEJSKTLVPIyK9ZRK6xFrTtjtp/ZtQ514nIZhHZICKnZ7I8Ps3DcUMnZTJLW76z/D62taWnm798Qo+Yk2kjg6zIwBrI4DD6iS1oJ9Rx0alHcP1Fn+GS049KKKQAJowcaru/MC+H1o70R4EvLI1Vt0NelgMOhsIGm6tr0+6/GIbi7TXb6AyGae0I0BkMs2zjTv749FsAvL5yM3vqnS+MzhaGZnD1tAU8M/darpt+DsNyu6t5w0b6naOIMUZJbuL65PJ/iwEpqJRSGyITepgTdu3AM9bhP0ZN9r0AICLTgAuAQzH1tHeISEYXuGxvy74qwh8OpDUS8mg6e/wNZFrpFa3NEzF/WqlB51jnhj4fx3HfU5dM2PME7KrtuSB75oSRGck7EZnwS6voueYrEAzz3Hsfo5TiwVeXpx2zK/kS2dRDDfzFrVS3N9j64ZtSnP7zHltgzjWeMuywtPNywug0Q5O49A0Hg+rvVGCLUmp7gvmPzwOPKaU6gW0ishk4GtOsMm1agv6M+DHLNgEjxGPb38Wr6YTCmVnsGveRCzxYtZiHVr9PbiiHr447gcuPOzFuPpkKMhiPsM395vkOhuodn0AwhKEUuxyawGti7zk9JQrD0C4gqqvfo89p4hdrnsArOjm6lx9OO5v5I2Z0nfKN8aewon5rWpf9XOVsAM4fcywPVi1OKy8n7PAP/O/aZYCOqGK4AHg0avvb1uroe0UkYp45Eoh29rbL2tcNEbkisi5h//79jgvg1fSuIHYHA33hdRpACaiSAO2lLdxd/yI/e+mZuGmL4liuxfPdlyzFBT3NjFNa+NwHRO7Y59HJ83kZHCeGV1FBLrqmOa57Ge0L+BT6KfVoh7WhzWxBP6MOKQ0RVgYdRpCmYDu/WfMvVjVs7zplUU3P4JXJEomnVuzLTzsvl08PA1pQiYgP037/SWvX34DxwExgD3BbJKnN6T0+W6XUXZF1CWVlPc2Z7egMB7lp7TOEM23ynSWOGTKxTwIXKHVgtBVxt/RKYAXtAXuvGo2t9iq+sKHQM+Ceys4zxSc7nHdGUmVE6aCkhW3FkCIqhhQxbcww/vfqc+Oq9Vr9nSilMuq+y3FZRSEFBtqYDrSRAewU6R1GkPu3vtW1/dzu+J5JnLK+aTcAW/toHZXLwcGAFlTAAuBDpdReAKXUXqVUWCllAHdjqvfAHEGNijqvErBfYZokv1rzVEbMbvuKGn8jXx5zHF4HrzadhcG2badmsLLaPiRFbhzDBq9H59arzqZ0UD5eXcOja0waOTTpklXYrN/aXZd9R8JHTR7NsMGDyE1CzVjb3EZ1XTNrq2r41l/+RTCOmlYp82fnTioVNBEqHbp8knxnHbNqf33X347DyiTgkaq3AXhr77q083L59DDQBdWFRKn9RGRE1LFzgYjPleeAC0QkR0QOASYCS9O9eHuok9dq1hxUPsd2+ev5zuQzuHXWwl7TTnAYWNEpSoMhRfbWWrMmjbLdf+jocuYePp5Xbr6CZ391KYtuuZK7vvcl256/SHwzkY5A/zgwHTeilGd+eQlXfvaY3hNjziN1Bk3BFAobdARCpnFKjDDSRJg1qRJNE/xJGlLcduVZzJsxnrwc05myrgkFuT5uueIsB2FVzPhjMqF3IxcBZpaMTapsvbGxeQ8AftchrUsUA3a2WUTygdPobtd/i4jMxPyaqiLHlFLrROQJ4GNM31b/T6n0Y8fvaW84qIQU0OUZ4Kgh43tNm456JVr1F0EU+Lz2fZ8PN+6y3f/JTlM9p2nCiNIiAFraOyx1V/enLwher9bV0HftF8jzJe9xPlN4dZ1JcczvY7GbR1IKivJz6AyG8AdC5Po85Hg9XH+RudQg2fHUD+96vpsHj9mTR3H7t8/Fo+s9nl1UKaCiA9p0pDjMXadexs9XP8H+zmY0y79lrPpbAWMKnanQnVLiMzs6J5VP5dldy/CHU3PQ7BRvZo2DXbLEgBVUSql2YEjMvq8lSH8jcGMmy5DvzYzrmr4kZJgNUcAI9boQNR1TeFttlG4aWNjR1G6/gLMjGKIzGCInymHqO+uqbMttKEXApqHVRPjsnKkOSp15Fq/exqot1SxZV5VyHqGwwXfOPYF7XlxKsLGV4vxcfvCluYwuN+fdKoYUsWGX8/m2WDdTH3yyk2eWrOVLc2fgiRMBGECOaEX3AAZU5Jfy/LyfALC9dT9fe/evtuukFtWs4cKxx5vnI2n7wTy53AzXcmTpIZxQNoU3964jmH6fMy7ZjIjgkjkGuuqvXxmeW0yx9+CyPoo0E1tb9nbF9unT66dgehar5qtrbovbmNrlLiIZiLWUGnvqm3lnXVXazmlvfvR1quuaCYUN9ja28vP7XmLRR2ZE3kvmH9XL2b1z78vLMJRB7rgQDAqgndCAvqAWfW49DOmEoQFTSAFo8Oqe1WxsruahbW/z0p743lI6olR0hWlG+AVoiPK9p4sQyqKQAsjz9N9I3MU5rqBKgIhw88yLDqpelwfhrk2vcdXSu7OuNrGjNpC8B4WI8OkMhggbBprEr5Z2A7ZQ2OClZem770mF/U2tGQnQGIwRzJ2hML9/4k0A5s+eRGFueg1qq7+DVQ3b6Zxahz6vCSkNIT4FxWH045vRj+luePLMzqV8/f07uWPjKzxctSSuQ+YZg8d0/b2gws7PaXJUtZojx48aqlhUszbrqvfOcP90cFyS4+BpgfuJWUPG8djx3+X2DS/xYcM2moPmJLMgKcfeSYRP86RlPVUXbOPeLW9kpWxOyE+hV71uWw03PrqIbXvq8egaU0aVx00b764SyLasMXZYCVV7G7OW/77GVsKGYa6lSvN9ThlVzva2WlSMQUqXCjdmqqba39BVhxItHt/bcWAxcm1n5tw8LapZk1WVX4RwH1zDJX3cEVUv1PgbWfjeX3lj3zqagu0oYELhcI4bkpmoq9F4RefQYnvrOKe0hjr6TUgBvahKY8tlbl/156fYUl1nzkGFwqyt2pP0dYeV9PQ6X96L/8F0yXYYeogWKqmbqGsiXHfRqWxtsXdjZYfTOrQrymPLyoaqZIvWg8iSiXWN9sY3mSZouILqYMAVVL2w8N3/pTXU3WnqptYa3q/bnPFreTSdDc2708ojV8uuI9beyNPtry8+hYxvB92wfgoO8YPPIBjqrvZKxcPCzn09PYu3+LPrgXtTdR2eNAMR9kbEfmHKKKfWdQrvsKD5jFF4Sgyuv2ou44YPwadnXoEyIvfAQuvWNIN+Aky2/AVmIi8n9MWozSV9XNVfAvb6G2mME1gt48EJIe05JR2NstyibhPSfU1doJWy3J6LSsvmhajLaYep7dCpQY4BGmhjAgTfLEn7ukU2LpT8gew3QpMqy9i0uzYjkX7t8Hg0Ptq8O26E5B7MakaNDOARy2GuwM3VT3H4ISP48uhjeWBbZv3n7cpwgMPIiHxE3uA+8cPXF15cXNLHHVElYFvLweXG5dxRRzEyr6crob7EJ/Z9n4bcJnPBrm56PRDdnB9RRUE8evrNhc/TP1W5YsggCnOzN4oNhsL8/T/vOYyrpdB0ulrfyPyTQvG7dc9RnufMK0Uy7Ok8MEc3cVBF2vl91LANgC+PdraAOl087jqqgwJXUCVgXIY9N2Sb/+xewaSi7Ie2iIdXdCry7QVlXGMAAa9PR3QFpUEoDKXkqPadNNYxpcPm3XW0dTjzopCKONY1jU92xuswKRjfhhzaAkMDICCF9iO7jS0Z8SjWg7yoJRA/O+w8JI0xio4w1IpwPb5oeNplc4JHcwXVwYArqBJQllvU30VIik4jlPYcV6r4xMOl409Oae3Wwkum4T2zHs8xTXhObsRbkbz7nP4yH9mxr9GRebrXo3P89DG2fvtK43hPH11egqZJjwW80WgjO9EndKAf04TMaEEG2ZdlkDePpoBzlbBTP5DHDTkQUHRcYTnzR6QeRyqM6lrwO7SPvr2OfljC4ZI8rqBKwL7O7Ds1zTTL6rb06fUEoSJvMNdOO4uvjz8lbrpEFe2hPW9haAZ4FeiKUEW0nzmFEzG04KgpPfYV5GR/MWciIRJhROkgPrj9aspLimw9evgDQQrzupc1L8fL3777BQBGDi0GTSGjOtCObEab2gp5YXMElWteX3TQRsePlBw0wtyx6VXn9+VQ9K9pOuCEeNHetby1r2ek5WR4asf7gLlMY0Jh9kdV6YwAXfoO15giAYHQwecYMxMerJNBoSjLLaIstzhhOIo5QyfyXu2mHvsHeXJpDcVY543ohMIg2kQ/MrITBFStF21tEbNGjmHphp3dIusOKcrnwnk9F5sGs7yYszcXVRHqmts57cd3EgiG7eeavIqhZ3YS2tZOoE7HV2owbHwuuYWmeL9o/gx+U/UEKi+MeEAZoI/zY6wvQPIOGPUkcrJeH2jlv7vSD8MRixlR2uSp7e9181SRCoujBF26a8ec4HVVfwcF7ogqAc2hvjGRzSTDbCzuss2qhu1c99Ej/GvHB3HTfH/KWRR6cvFYK3N1NPJ1HyPyBts2R55TG9EqO02jCw20siCFp7Vx05UL+N4XTmLkkCJKB+Vz4ckzefLnC8nL6WnQ0ENw5oUhN0SmFIWJTdMPXCMQClPX3E6rvxNKg2gnNaCfvR/99FpkfDvBSY1UB+oIjWxHO7yFUGUb1Z313Lzu3wDsKa6BQlNIgfk8xAPa9OSsO5NZX+dLwcigJbbDkQIBa11Te6iTra3O132liuuU9uDAHVElYFzhwWVMAXDpuJP59bp/9fl1O4wgt298ibMrZ+G1cTk1prCMJ064hid3vM+6pl1MHDSc88ccx6s1q9m+qZZOOxc90XJAoC3cwQt7PuSrp57IWSdOodMIUZ5TFHck17VfU2izmpFhAcJ7vLAiM8L88HEVKBQfboqdF1RQHIIWDxgHyqaKQujHNnYJHHIV2pQ20Omx2MFA8dbej1FK8eyu5baWGMmGqUom8vNRQ8bzQd0WIgI3ngPjYbkHlhYM9qa/wLrA8r23t6OxT+YdZ5Ye0gdXcUkXV1AlwKvpeERLy8t4X+IRnd3+BqYVVfJxc9+s7I8mbBhUtzfEDf0wNLeIb06a323fuaOO5l87lrK/s7lLbamj2UZUVsCiPWtZvG89axt3IiKU5RTxy8O/xOFRPuci5Od66QiE0Ka3IsPMKLVS70ujAex+5opNu/jiSYfZCCrMBc0GSFkAGes3t3ONHq6K4ljzA6awUqieqtEsI0C1vxGPaHQYQbQ4ihcN4aqJp3Vt1wda0772FMtq9YVdH6adlxMyEV3aKev9gzlm5Rcdpv5tVstysOGq/hJQ29mCfhCpBkIqzKrG7X2iMrGj0wh2xRNySqEnlweP+zaXjZ/HtOJKTiibzKnDp8dNv6GlmlUN2wmqMAEjxG5/Pd9Zfl83n3MRiscJSBgZ3dEVSl3yE1noKRAD8oKgh6GsExneae4Ta/Vs9E8Uz75jF4lWoMWDNqMV7ZgmtIoA2rAgUhROehSkMJ9RX6Iwff1FHNEaVqdBgBzx4NM8eDWdc0YdxekVM7rOS/bd2/HZyiMBaOojtXu6xh8ufcOAHVGJSBXQAoSBkFJqtoiUAo8DYzEDJ35ZKdUgpo7nz8CZQDtwiVIq7S5ZsTc/62EGMolXdLyantCoIbsIHUaQZBVrhd5cLhs/j8vGzwNgyb5PeLVmte3IJ6TCPfZ3hAM8u3MpV0b17gH2jaqG7fndu2OHdMDHhTbTVAoKg+jzmkw9nE6XwWF4STE0e21dFZrhSBQMCSKFYVSzBxo84AsjYzq6CaZkX8vQnEHoonHuqNn8Y8sbyZ0cB6cGIHaq2Hw9h4vHzWVYXjGHl4xhZH5pt+OHloxief3WtMo33FIlnjJ8uqnydHFh4I+o5imlZiqlZlvbPwEWKaUmAousbYAFmOHnJwJXAH/LxMU1ifihOTjwajqHlYzuN0ebCtUVuDGp85Riad1m/rrhJR7a9jaVeaVxG1O7/Qr4sH5bj/1hPYx+YveRlqaDdmwjaBGzd/M3aswg9BOazZbc8pohmuVJozSOAYYoGBREn9uAPqcZbXor+nGNaCc2ok3uPZR7b1w+wTT3P2bo5LTzilCg9+7dPpHJ9vSSUZwxYmYPIQWweG/6o5OXq1cBMGvwuLTzcvn0MGBHVHH4PHCy9ff9wJvAj639DyilFPC+iJSIyAilVPJuuKNoCLRZH+3AF1bTiiv5ybTPIwh3b17Ub+VINnZXyAhz7YcP8VHDNvzhAD7RuWPjK3hFT8phqJ1w9moeQqrngk6tLIScVYuxz4va6WPwLBhakkd1g8KubyLj/agqm0W5AvqcZsg16KYhLg7BoORGULoI4agL6wgf1G7h3FFz+P3H/3aeUS84mW9VKNu52aAR4toPH6Q9HGDSoBFcO+1sZg4e23V8R5vzKMTx2G+tXVxUszbtvFw+PQzkEZUCXhGRFSJyhbVvWET4WP9GAheNBHZGnbvL2tcNEblCRJaLyPL9+3v/qAb7CrLifDYbXD35DCYXVbCpZU+XCXh/sK5xZ9xjTYF2Hti6mOs+eoR7Nr9OXWcLr+xZ3SWkAAIqTEiFk/ZqPaWop5+5ytzEfg+18iCe2W14fbCmaWdcH0davoEc3mKOwjTD+lfhmeZHK4gRUlijsCTd/4VjpGMYxVv7TKu/zRmcc4wXADEWu4XMQRWm3XpPG1v2cPWy+9jcUtN1vKdSNnVe2vNRxvJyGRiISI6IXCQiPxWRX0R+Ts4dyCOq45VS1SJSDrwqIp8kSGvXxPSchVDqLuAugNmzZ/f6VWXCiqmv+N6K+zl26CRmlozt19X28Vwo7W6v55L37sAf6iSgwizeu56Hq5YwtqDMsdd4DUFHI0h3Iaajcd7oOT3S13T2NLCIED3aqbe8zUf22Y2E9DGdqPIgxh4fKEEb0cnl00/mH1vfINURt2C6ngqqsO0ap7Ay7f6ceL/IJNL1/+7X7TE3aAS5Z/Pr/PaIiwDwoKXdscu3VJNbDjKH0C6O+DfQBKwA4rtRsWHACiqlVLX17z4ReQY4GtgbUemJyAggUpt3AdERByuBtL1wtgQOngW//nCQ92o3cXjJmH6LsSPA7CH2cwu3ffxfmoIH5m2ChAmGwmxrdd4gCdbaKLvpIhvh3B5K6lvo/fp5Bvq4A6biT+x8n1mDD2FFw9aURJUmEvd+uq6JoCOE+lD9bM7aORM4H0UFSyz05tIYTG9ubt7wQ828PDnszezrs8WpT0OXjFCplDojlRMHpOpPRApEZFDkb2A+sBZ4DrjYSnYxpoTG2r9QTI4BmtKdnwLI82TfV1wm8YcDvFazpt9Uf9+edEZcb9Tv1NoPiNvCzlujMCqOFabiORsLsWwHkWwKtuPT9JRFSFgpOoxgQo8RBgpfnGCUA4HoucFUHBLHsqPVVMmfVTkr7bycUJZzcDmePsh5V0RS8lo8IAUVMAxYIiKrgKXA80qpl4CbgdNEZBNwmrUN8AKwFdgM3A18KxOF6Oxjv3mZoCnY1uemH1OLRnLHUd/ga+NOipsmc2WyUZGhuo3WIuR5erdwS5el9dl3AjxhAIebKY/ycj4qf0ja+T1ctQSAs0b2jaAanoUYXS5xOQFYISIbRGS1iKwRkdVOThyQqj+l1FZghs3+OuBUm/0K+H+ZLsfwvPQjz/Y5SpJylZMJ9nY0saF5N7NKD8n6Gi67O8vTfcwtn9pjf0MfzDFm22uJKPjG+FO5esV9Wb1OqpxZccAZ8JfHHMeK+q1J+RSMJWCNmAsyMDpzwuoExj8uGWdBqiemNKISkUoRuVZE/i0iy0RksYjcISKfFelHk7MMU+jJ5dghE/u7GElR29nS51r3+kArd256jTs2vpL1a+VqXnKjVGF5uo9pxZWcaCOo+oJsz3EogdpAS1avkSqmq6UD3tNbQ37beFvJcNSQ8QAsTXPhsFP6wkO7i4lSajtQApxt/Uqsfb2StFARkfuAe4EA8DvgQkxV22vAGZgqu/g6oIOMG2deyJQMhNjuK8LKQO+HvkKHEeSx7e/iD2U3EF1pTiG3HvE15gyZwPTiUfxg6lncPvtS27mx6cXZj3aczuihN6YXj0IXjUctdVgmcCJGPA6bBcWB0PEAz+xclvYI85eHfQmAdzKweNgJrilF3yEi3wUexlxWVA48JCLfcXJuKqq/25RSdqvx1gL/EhEfMDqFfAckaxp3JGWZlg45mhePaEkZGMRSnlvEbn99BkvlHF2EvR2NjC0s7z1xFMksqT6xfCq/Xvs0LUE/IsIf1v+XfD2Hz9hElr1g7ImsWvVoUmXpD3ziwaPpBI0QQRXGIxo+zct1088BoMYf38w+WZw852RMzKM7JsE053QF2N5ey7Tiyj5zA5ab7II3l3T4OjBHKdUGICK/A94Dbu/txFS63meISGW8g0qpgFJqcwr5DjiUUtyw+kk6VfaNKgQ4e+QsJqYZ1fTE8qn91ksMK4PyFOJhORVSumi8tPsj9nY00R4O0BbqpD0c4JdrnmJ7a88F3FOL41bTAUVAhbj/2G9xyfiTOb5sMgsPOYknT/weEweNAPq+168n0Szs6zgQBXtBxcy0LC0V8B8ruOOsOMscMo2dl36XrCHQbRFkGIfVO5UR1UjgPRHZBjwKPKmUqk0hnwHPvs5mGoPJBadLFQU8X72iK3BcKpTnFDEkp7BfPr1czcu5o44mPwVLOx1x5NVAQ/DbRJANqTD/3rWMq6ec2W2/nUf1gYgGIMLlE3rYCQEwqmAI65qchW3REHyi05FG52pU/hCq2p25Q4pu6M8ddTSv1qxhTeOOBGckJrL4e2Nz2qtLHHGwhPD5lHAf8IG1LhbgHOAeJycmPaJSSn0PU7X3c+BwYLWIvCgiCyNrnz4tBMJ9uczSXLSbjsVec8jP0rr+GcwWenMTmqcnMjpw6nonaLlX6nG+MqizsfD7sI8m5NPFAHIS+EhcOG6u41GVgUp7wff2duf9ziLPAR+IObqXO4++nCMHpxaMME/3cepwU4W7o60upTxcBi5KqT8AlwL1QANwqVLqT07OTWnWXZm8pZT6JqZHiD8B3wP6JxBSlijw5PSrO6Jk6QgHWdtP5raNgTZ+seqJuMfTtQYDMzBkPOOFQ4tH9dhXlpv9xZx9UTvmDTs0bvBCO9L1uZeMJVwkNAuYDoZ/8OED3QwsnKKLxszBYzi+bBIA/mDfBIsckZfYH6RL+ohIkfVvKWZ4poeAB4Ht1r5eScs8zFpl/CvgfzGtAH+aTn4DjdKcQsYUDLU95tQyqq9R/RSWJKQMVjdup67T3pS62Juf9jXCKhx3ZLY/aq4kwkNb3077mr2RKQvLLS01bGmp4dU9q9nYdpZDXQAAIABJREFU3NP7VybmUrJhDfpi9QHnsc/tWs5H9VUpicmwMtjYvKfL6W1fBU70JQqx7JIpHrH+XQEsj/pFtnsl6bckIhOBCzDN0sPAY8B8a5Hup46hOYOosglfMBC9qufpPqYUVXTzv9aX6CK0hjoZktNTA9ycAb+JmmhxVaMbbBp3p/Ms6ZCpOY77ty3mk6bdaCIYSjGleCR/mnUx+Z6cjHU+srEQfFvbAYvY/+xe4dg7ux0toQ6e3/Uh5489rttauWxS1e46v802SqmzrH9T0wmT2ojqZSAXOF8pdZhS6sZPq5BqDXawqtHRerR+RxBmDh5DsSf9kUuq5Oo+Km0C6gEoSb+xTdTQ7jtIDCfsGOTJZX3TLjqMIO3hAB1GkHWNO7lt/X8BeK1mjeO8MjFmSkadGT23lm7AzoAR6jLEKPDmppWXU9zlvn2HiBxp8xsv0vuwNhVjinFKqeuVUmusixeJSGnkl8oNDFTawp0HjXflw0tG88dZF5Pr6ft1IRpCrubl+unnxVUvZXuuLxJwry/REQrT9CdYllOEoVQPv5JBFealPStRSvFo1TuO8srVvEzKyOJ05+9qVP4B1fggT/rCJSL4DNca79PIHcD7mKGW7rb+fgzYKCLzE52YcgdMRK4Ukb3Aakxdo2N948FCeU4RRRmYW8k2GvDdKWeiicY5lUf12XULxMuo/CHMKh3HnXMu56QEboyyvYAz2cjCmSDfk0N7Gp44NISWoJ/OOOqykGFgoGgOJlabCkKhnstvj7iQTS3pm3UnY0wRrepLN8QHwLI608lvPC/8mabUW9gn13EBTEOKI5RSs5VSs4CZmI4iPgPckujEdDQF1wKHKqXGKqUOsX59s0qvjxARLhk3t7+L0Ss+zUtNRyMhI8yRQ8ZxRFR48GzSpoLs62hiXdMuvr/iAXYmMCk+dujEHqNTASYPqsjIqPWzFUf22Jdtc5erJnwmLRdKBspq6O3vf8bg0eiicVL5lIT5KBRBI8QbNesyGmW3NwSojPKYXpk/JO03WWstMzikIDnvJqlSmlPQJ9dxAWCKUmpdZEMp9TGm4Op16iidb3kLkH4XaoDz942v9ncReqXDCPLrNU/z7WX3EjLCfHvyGeRL33if7jRCtIc7qQ+0cd3KR+Km+/7Usyjy5nd5LsjRPBR6cvnsyCMyIqhOH9nD2T4FWnbnOTLl2sg+xhZ8buRswFxH1RudKsQL1SszUh5wpvzL0bxcMu7kru2F404iJ80YYBEjitMrer7PbLC59eBcUSMiYRFZKSJrReRJEcmI6kdErhKRhdbfb4rIbJs0l4jIX1PIfoOI/E1E5lq/OzDVfjlAQiucdATVdZiBsO4Ukb9EfmnkN+Cobq+nJdw36znSxR8OsL55N8/v/pDvLv8n7Sq7zmFjUSiq2vbH9QYxIm8wT5/0fa6adBrzhx/ON8afylMnfZ9X96xOyoLSrsLqaLyw+6Me+/1Gdp9BOh4YnPDXjS8DUOIrQHcgOryanrE5VZ948MoB9ZudarXEl8/UKMe/h5WM5obDv5TWdecMmQBkZt3dpxy/UmqmUmo65tKgqzKRqVLq70qpBzKRlw2XYMYMvAZz3e1Wa18QmBf3LNITVHcCr2NOiK2I+n1q2NXWP85dU8UfDvDkjvf7bSJaQwglsPwa5M3jorEn8JuZF3Dx+LkM9hWm4CqnZwMWxqDexjNFNj2bO8n/7Ioj0xIc9YHWruc5uah3T/AhI8xQm6UBqXBYySjCykAQNISAjcPZmo4m/rT++a5tpVRaro80pMu7yaI9dn6vXeLwNjABQES+b42y1orINda+AhF5XkRWWfvPt/bfLCIfW0EMb7X23SAi10bl/VURedc67+jYC4tImYg8bYV7WiYix8crpFLKj2lQ8ROl1DlKqVuVUu1KKUMplTB4XDqCKqSU+r5S6j6l1P2RXxr5dSEio0TkDRFZLyLrLPfwkYe42xryrhSRM6POuU5ENlvRI0/PRDkG4lqpRAjmvFp/RSYuzSmkIsmV/sk6/LUTDj7NYxuPangKDnKT4YjSsfjEftK/QM/BHw7giXM8WQ4pLOs1TVCF8WkehvrSF1arGndgoFCohAL5v7s/7Pp78b71PLI99ZAkJ5RNZprlSLimozHlfJJBH7gjt6Eisjzqd4VdIsu0ewGwRkRmYboomgMcA1wuIkdghl+qVkrNsEZgL1kW2ufy/9k77/A2qqwPv2dG1S29hxRCCAQIJITQO0uvu/TOEmDpfWnLAgvsArsLLL13WHovgaWFTgJJCKSHdMdpTuJutTnfHyPbsjWyZY2kOHx+n0ePpauZO6OxNOfec8/5HTvOYBRwS4rzKFTVXbBLOT3h8P5/gLtUdQfgD8BjqT6QiBwOTAMmxF9vJyJvt3EdAHeG6jMROVtE+uUgPD0KXK6qW2Jf8PNFZGT8vbviU97tVPV9gPh7xwNbYf9THhBxf4cYsJHJq/gNL4f2H5P3Cr8+w0OB6ePmbY/LaXRfqp7DVpSBgeSv3vXbHJ2zcwGojYY5dODYZi4yAJ+Y7N1nK75YPZuwS+X9hnD/r9fMTWv7ZXVrGT9sb7r7koMEWp5na6SrF5i43UuLv6HeQTQ4XYYVN1UOGBd3AeaaQjM/+VoZsCYeHdfweKTF+0ERmYYdab0EW9x1N+ANVa2Jz1BeB3YHfgb2E5HbRWR3Va0AKoF64DER+T2p4w3+C6CqXwAlItKy7Pl+wH3xc3k7vk2qkdINwDhgfbzPacCQdC6GG0N1IvF1KrIcnq6qZao6Jf68CpiFrdqeiiOAF1U1pKoLsf2gSdPU9jKosKfrxeF80i/YtVXXWy4Y3W0Ifxr+O17f4wq26dr+MmSbBHu0vVEcs5Ub7QPzk6sLj+k+FE8Oi0i+tvR7ftdna0p8wWbt3fxFDCvu4+guay+KoqpUhdOPW3qr9EceGncWfQJdKDB9FHr8+A0PF404MK3901kPa2BUgsai26TrteGmSgU79sx9ZW2BRhHcjZC6hAH7haoaJsVYTlXnAttjG6x/iMhfVTWKfY98DVvFfEKK47ScTrd8bQA7J5zLgPg924lo3Ei2m4x/xQkh6YmPrIeni8gQYDTwfbzpgrhP9QkRaZjyDAAS1ViX4WDY4jPAH0Tkh9Wr25bXERHu3P7UDVIxNxNW1Fc0017LBz19xZw8dHe6+9vOR6mO1HP9tBc5auK/uHLKs6wP1fCHwTul3L7Q46fQtB8+w8PWDsKzDSyuSVb8nlQ+P6dlHCyUf856l+oWAqor6yv4z5wPsnIMBT4s+6ld621V0Tqe+PUzBhX04IiBO3DD1kfzwd7XctyQXdNaM/OmmZPmEYO/JgRPuJnJB00fO/VsmkWl0ozMJh4Mxg9rdQ1/Y+ML4EgRKRCRQmzX3pci0h+oVdXngH8BY0SkCOgS90pdgp3T5ETDetZuQIWDofkIuKDhhYik6gfgFxE5ETBFZLiI3Is90WmTTLT+dlPVlI7ouFLuoBRVgNt7rCJsi3+JqlaKyIPAzdi/35uBfwN/xHkkkfTLjk+fHwEYO3ZsWr/8HXoM48VdL+b4r+7Oa45KJtTFwpTWrcvrMT9dNaPtjYDZ60s59bv7G1+X1q1l4qpbGVnsPFEOiIc7x5xGTbSesBVlbI9hfL1qDtPWL3LcfvN4kcFE8qEkv6hmVc6CNgwEUwyeXjCxXUcorV3Lslo7EOjHtQv5ctUsntvVrvi9WVFf5la3HvCQSgrJIwY9fMWErShjug/l8i0Po2egycuzrh2zvkQChpdNi3qzZ++RjW19grldXwSIYPHA3P9xw7a5dRHnC1WdIiJPAZPiTY+p6tT4mv0/RcTCjrA7FygG3hKRAPb989IU3a4TkW+AEux7bUsuAu4XkenY9uQLUkcgXghcB4SwhWo/JPXaWDMySef/g4jcgT1V/BFYja39txl2iOFg4PIM+m2GiHixjdTzqvo6gKquTHj/UeDd+Mtl2OVGGhgIJKuUZkgXXwGIwAZSJm8PAdPrqpR9e0l3FD1+0sOO7TOrSh3bDcPAUovdEpJdD+g/ilt+ec1x/eRCB7fWdnlIfM5lZKGFElOLsnYOPhLPyEJZVreWf8x4kxu2+QPzq1e0uX8qpXaf4eWGUUcztscwx/czKUUvwIiS/py+6Z7NPBfV0fx8h98vm7JRGipVdXRhxGs+3dmi7UNso9CSpOURVb0x4fleKY7xFPBU/Pka4rOu1ojHDNykqldiG6t2kWnhxEOAMuAY7JnNZcBw4GFV3UNVJ7e330TEXpF/HJgVv/AN7YnD5qOw5TfAXsQ7XkT8IjI0fi6TyBKFHn/eAxQyZac8+PYTSbd8R3vXa+piYbbu2tzVty7sXG3ZK6aj629El/4dXqmxLbeyqmbF9fxh2U+c8e1DaRtWv4NOqMcwWjX+7Slh34ACP61fzNXTXuC6n15sTK146tfP2t1XJnT8oedvA1WNYa+TZURGAmmqug5bVPDRTA/cBrsCp2CHXDak218LnBD3gSq2btQ58fOZISIvAzOxIwbPj18Y19RFw1w19flsdJUXopbFoIKeLGlHlVY3jO3uPLp2i4nBstq19PAXE7Wi9PAXM2nNfEcVh4jGeL90Cru3kBp6ZN7HHf5GZCAo4mhAPGLgMUy26TqIb9KM+muNOVXpOxk2Le7Ngmq7BIZtKIV/jTmlVQ0+0zDswj8ZELKifL5yBt+umceuvUYwufw3WZDh/ztT4+HorwCNo84Gj1lrdMiqYfE1MKfB8Put7HMrcGu2z+WWX15jyrqN50ezsr6C53a9kPvmTODlJd/m/HhfrZ6Vk36jWFz641ONShddfYXs3muLlIZneW2ye+ztpR1fI7n1MHBBVRlW1Dcrhqo9FHsLGFTQg1+rV2GJctiAsWzbdXCr+7iNcoyqxSuLv2XXXiPoF+jSrNZVJ78JugPlwD4JbYodRt8qHdJQdRRqoiE+XzXTdZ2dfOIxTAKml4tGHJgXQ5XL5OIVCeHO68I1vFOaWvhEHOpd1eZYQinXRDWGhfJOaf4N7qTy+Y3PLY3x+rLvmV1ZyhM7/4moWo6SSkHTR8RlZd4GYeOLtjiYb77+j6u+OulYqOoZme7baahaoTpSt8FKu2eKKcLKuvVcPiVXcl3N8WAStWJ5KcvQ2n8i3ZDqjZG2ynzki5mVy9jjoxuIqMXgwp78eeThzQIreviLqHRpqBpKjLRX4aST3zZu6lEViMj18eg74nHxh2bv1DY8XX0FKfNwOuIivSkGY3sM4/iv/8Pcqraju7JBlBh7f/w37pz1bqvJxu1JIs2EHlmQDepoFHkCmGJktXinB3cDirDGGgWIL/vxGeZWNq17rcyCmnzvuOxVvl2dnXRs3IQTPYkdD79z/PUy0oyJ31hYF65NqWzQEedZMbVYWbeemjyF9jYQsiK8sXQyd8xMLdt1dIrE3kCWlD+qoxuHyn0qWmoC+sTkT8N/Zz9vxzVqbUBgikE002gHB0JWhCd//bzxdTgL8Uu/iytF/LDmV9d9ddIxSNBqTSlY2xZuDNUwVb2DeB2RuDJuR5xoZExXX2Grsj0dkc9WpJeAm21CVoT3l09NUmlo4ILND6RvoLlMWHdfIWcP2ycrZepnV2Ytba5dZGu2c/qme9LNV4gpBiXeIOePOJBjBtnG3WjHIVpLSvdm2T2rwC8VTUnVsSwYquEldgZKZyDFb4qGtal7M+3AjWM/LCJB4pMLERmGPcP6zRAwvRw7eCdeXvxdynLhHY3qDVg/S1HKQ1UUeZOFPp9eMDEpD6omGmJZ/Tq2KOnPnMrljSHaJoLSvmTa2jzPIhvIRsKviXDIgDEc0G9b5laWsVlxX4YWN1W4zUYwT6HpZ5++W7cakJIJiQOTbHgZPiz7iVHdBrM+Q5WLTjoks0RkEdArrmDRgAAaV29vFTeG6gZsdYpNROR57Nyn01301yE5etBOvL5k0kZjgTekLmHEiqXU/Ht1SbKxD1lR3i2dQlC8zW74il04z2pHIItTMIeJkVJloSPRL9CNO2e9y6Ty+XgMk6hlsW23QfxrzCkEzPQrNRsp8rEAamOhditcpEN9rCmyssQTdB1M0eDCLXEY7HSycaKqJ4hIX2x1jMMz6cONKO3/gN9jG6f/AmNV9fNM++uo3Dj9FWpj+QtzdutKSlcpIhcYSEox0VTXMGxFqYg1v7lZqGMQS2tXZnT3IUlt/fKgF5fJf8uMa/gFTR/FniDb9RjCpPL5hKwoNdEQISvCtHWLuTNelDDdiMrWZncK/Lg2+/mAiUe8cmRG96BmbNt1CAAHDxjjuq908P+Go0U7Eqq6QlW3xVY0Ko4/lqvq4nT2dxP1NwZb168MW1dvkIgMixfy+k1QEa5l+voljSGzuabA9NE7UOIoX5MuPfzFBI30R+HZxEIp9Pgd39u222DHm3p7ynC0VoTw+EHJ67QjUgjeZosi088h/bdvt7E6sP92/Gn477hq5BG8u9dVTFw5KykfLWxFeX/5VFSVAQXZKfOWi29x4ozvgP7bcv5m+7vqr6zOFtM9aMBoSjzBNrZ2T2wjSz/ZmBGRPYF5wP3YlX7nisge6ezrxk/0AHYZ+kewpZS+BV6MH9zdt7WDELIiedP484jBHWNO5oVdL3JVbM9veJI08vKFgaSMOLxsi0MJmv5GY2OKQcD0EmiHUW1NxeHlpcnJzWN6DE2770zYvKQ/V448lHE9hrerku+Qwl6ctumeHDxgNEGPj7oUIsIRK4qFsnlJ/7T63RBO35YDk9M224sCFwOlsjq7sm9Z7bpmbsVc4STJ1UnOuBPYX1X3VNU9gAOAu9LZ0c13exEwOl59cnvsmlG/YFd8vMNFvx2G9qyRZINxPTbj1SXfuxr5rg3X8HPFkqydU3uwUHoGShzfG1bchxd3u5gjB+7A8OJ+HNJ/NM/vchGju2fHmCysTo4Sm7gyN/JODcysXMa8qpXcu8MZPLnzn/Ck+XOqbBFKn6o4p9fwYIrB7IplafW7IVbjqh2SkesyVAQRYId4TaoPlk9Lu8pwJxsNXlWd0/AiXtAxrdwLN266LVS1MRZaVWeKyGhVXZDLcuT5JNiOhWy3RNXihukvs7i67YKOrVFWt26D/cAbZlRFnuSFcFXlthlvNiZyzqsqY2lNOZdveShfr57l+iY7rKhPUtuPOdZoDMUifLN6NqO6DWJEyQD267sNE1b81OZ+5aEq/j3rHfoEunJw/9EpZw5hK0pMrYzrPOWDOodoWEEycpcrcEA/OwCsJhrKi8O9u7ftgp+dZI0fRORx4Nn465OwS0W1iRtDNSdeyPDF+OvjsN1+fuK5VRs7dbFwq5FU2ebD5T+5PlI47i7aEHgNT0p1ihumv5KkNjB1/SLumPmWayMlCBc41KPKtdvWwKDQG2DC8mk8NO9/LE8zqu6D5VNR7BnEQ/P+hyGGYw5SkyrFxrGOYqnFJT887er7N7eyjFHdBrNTz+G8tCSt4q8ZYwCXbnlwTo/RSTPOBc7HLrYo2EUWH0hnRzeuv9OB+dhljC8FFsTbItgFFDd6uvoK8qJh14CFug7caE/U4GZFfbIqz9PdX5hSo+2jMueZxvSK9lXh3af3VvT0F2MgGAgBw8s/tjuBoUW9k7YtzPGMOIaFgcHff3kjbSMFTWZHsQcWqWbAMbW/EUHTOUClI5AYNffswi/5rnyeq/7eK50CwM49h+PNcbK9AoMLe+X0GJ00oaohVb1TVX+vqkep6l2qmlbmT8YzqrgSxb/jj5ZUZ9pvRyJg+jhswPa8tWxySs2/DkNMwRRKvEHWRpwLDCYiwPmbH8hfpr9oB0Co2lWMXbBX761I5fbNxizPQPjrNkcT9PhYWLOK+liEzYv7pRxM7NJrBP9b8XPT/gtDUB7B2r7Qfr0sAvUW1lA/eNr/2QV4asFn1De4v7JwDZ3o6itgWV05sjaKObsOq5cHa3juI+LS4dShezY+f2HhV677WxNPb6iK1ud8IqnAn6c+x9t7XZXbA3XimowNlYgMB/4BjMQuRQ+Aqm6ahfPK9JwOBP4DmMBjqnqb2z57BUqSFdSjFua31cR2K244cNN7qg0Zq24P3Zw2boKyOoL/vQr2vGEH3ljWosCyFT8nUxqfqyl2NdV1UQKPrqD+0uQ1njaPWx8jeMdKtK+X8CFdeN2YxPkjDnAsAZENPIZJWf16ulR5mfbKD4RqwwQOGcOmo5zrJI3qOsg2VLUWhTeWYs4LUXdmD1gepuBvZRiromDY16Lusj5Ed2rfeoUCFZE6vP+rIPBsObImhvb2UH96DyJ7lThfu5ji+aKKwEvrsHp5CB3djdioIEQV79c1GItCWJv4iOxWBH4DBdaGqgneVIr3+6a1Ki0yqL5nE7RPfC26zoKAYVvPTIylKkTswc6YbkOZNnMugYdX4/m5Dg0I4YO7EDqhB3ib+jYRDh4wuvF1bYroxdaOKdNqCL5eQWSPYiJ7FTd6FN5cOplIprqE7RgwJJaS6aTj4uaO8iS2OsVd2K6+M9iAWn8iYmLH5/8OWyB3soi8raozM+0zYkV5ZsHEZP00Q6DKajhwyxOxfyghy/5Ri7T/qjQsYCS1p/gBqkIU/K+tZ6tjSni7JK7IoIoxt56Cf6wgdEw3YlsFMcoi+F5ZR3THAjxjTMxrFkG4jXD4lseNG25jXgjfdzWoCb4PKqi9vh+lu5Qz1CGwod0qESEL/M090+FIhIc+fYeZJ34GqsQiMZ6/5VUOGr8v5919RtJsbmTXTTARfA+txpxdD1GI7FFM8QVLkfIoDSWsBKXg9hVU3zsIa2AKd2FUk2ddUSXwaTW+B1cjIbszWRUl+J9V9ift7SU2zN/0OSKKrI0SvG8VRp1iLgnj+aWO6tsGUPiPFUhVDKlTNCAEnlwDf98UUwx8z63B+31t869EtUXRxUuJDfRirowiNRaRPYupuzjZBZou/sfW4PummiWbrqZwVhVSayEKElb8r6/HWBqh7rp+jdsLwmtLvuOiLex1nq7eQlaGKqAqCkVm6u9qQ7sInuVRvD/W4plRh+9/FRQ+aQdTfLNmTvK+LfdvD639djrJOyJiAEWqWpnO9m7WqIKq+gkgqrpYVW+keeXGfDMOmK+qC1Q1jB3kcYSbDstD1c4uK0PwzKlL7ZqIKUWXLMU7oQKiFqyN4nt7Pb5X1mIsDoFlj6oJxW/cIQujNAz1DTdyh44tbfxrzqzD82ONPYoGiCieuXbI8zu3vEePCSG8/y2HsFJw83KMVVEK7l9N8XlLKLy5DO/segJPr6VufgW+shjSxkDYWOgQlRZVzKV2u8RAQkrwzpXUhJ07M1q5uSStk5VHmj5vIqbw7WfTCNeFCddHiMUsQnVhJjzxKT9/mRyKvnWXTTAXhvF+VolEARPMX0NQEyOpzmJU8X4QH12HLdswJZyP1MSSzylkUfDM2kYj1YCElOJHyym4phTfG+uRtVGkKob3k0oKL16KUafNti28YbltOOPtUq/I+hjG1QtRVWpfLU0atwggVRbeWSGMtTEkpHg/q0LKUw86vK2V+KhX/O9WYKyNEfmhAqm3ml0jCSveyTVIWVOcVBSrWYBMAV77uz2xqvn1a3bN1HbBRhXqLLzf1zR+ZnN+iAFT7bGzxL/a5sw6Cq4vpeiPCwneVoaxNNxkXFRtl3dLnA7dMIBsSQf36P+WEJEXRKRERAqBmdgBeVems6+bGVV93CrOE5ELgFIg8+GcewYAiSvzy4AdEzcQkbOBswEGDRrUZofdfIUpjZFnbojo3Hpimweau/miiue7GszFYYIPr0G9QsH98ZDzmMLzawkdVIJ3Wh3VY4L4X1mH/7V19p3HgtBBJYTO7Jk0epeptUgPD4V/KUUaZ3NQd24vrELD7gNYuWg1kftDBGOKFJkYa62UE7oZL/xEpC6CYRj2D9bpPhZRjNURrE2bL+jL+hjer5uvhUmVhZaF7YLTLUgVtCHABSMO5KEfP8CcF0ILDALf17L+eIf6UnUWnq+TJZpCtSE+feFLRu0xsln75A+nEbhyMY0epBgYK6PJRgrb2BprohgL6wnevpL6c3sR2zIAFgQeWY25KEzdNf2w+tmuNmNNlOBtZcTWOoeW63r7OMFnygk+U+64TeOxK5P/R2IB66NEo1EiNekF0apXMOeFiHb3QI2F56daotsVgN8gMC/CaZvtyaNzP8HaMmgPURtu3grBO1c2Owdx8LqpKuaSENF+TakvvfxNeXPWe+UEPy3FWBMlNq4I7SnNfxt1FsF7V+L9vhY1IHxkVzw/NrkzpV5ZN3E5jIcBpUGmz6im4LYVEFIEMFZW451UQ/VtA7E2D+B9fz3R3UvQoNHkkqy38L9YTuh0hyAJxf4NSvyvCZ4pdXBIWpe3E/eMVNVKETkJeB+4Cjs8/Z9t7ejGUF0CFGCHGt6M7f471UV/bnF0ljV7ofoItpIGY8eObXPO7ze9HDN4Z15Z/G3TgjlAvYXUWATuWknNvzaxb/CGvf4j1RaBh2zDJBGl4O5VzW+MMcX/TgViQfDeVXgn1TYbkfsnVCI1FvV/6t00AjSEgodXYa6KQaT5Bw3es5roZj7M+KxHUayY/cMOPLqm1Yv19gMf2s9rLIyf67BGBZvfWGKK7/m1RA5JTuL1fFiBZ2rz/B6JKH26dE3aFmD3Plsy8fMf8D28EmNpBO3rpf6snozeZxu8r66l5K+LMHy2wRQEvxUhdHx38MVvdnUWnmm1eKakn1P02YtfI7VNcZQCGFNqoD75X69ArJtJ0aVLIQxF15RidTEhKMgKe5ZSfN4SrF4eMEBW2m2pBupFXQupXtd2UEvDeaXCNFNXmEoybjHFMpRuc4TII8sxfq23Z5JxXgv8l8ISRf0GdX/sgTU8gLE8TOCJcjyzmychO3mfJQJW16bRTMD0ctLQ3Rtf+z6pxPeTnQBceONyai/pg/fzSoz1MaJjCiCkeD+vtmeDgP/bXB7MAAAgAElEQVT5tc2PIdClp/1dK5hYS/Dl1c1+G6Kg9UrwnpVIyMJYHkWfXYd6wFgXA59gdTcwymKE9ypBhyQMrlQxJ9VQcGsZBA0IW1j9fIzeacsUVzf76HoP0bf+X0cZekXECxwJ3KeqERGnYWMybgzVEFWdjB3hdwaAiBwDfO+iTzcsAxK1gwZiaxC64vzN9ydgennil0+IeRVZEyXw8GrMNTGUGCUnx5NKo9ijupg2G406jt7jEyLvlzXJN4OQ4vu4Ct/EKqLb2gKznum1EB+4O+rlzW8a1ZuephuJpHK/OFD4zxXUndsL78QqjLIIsZFBYpv68b+5DmubANFeTaNoz0cVBF9Yl3zuhhCudZ5h7LN0EJMvf91eKgBYEKLw2lJGXLstz939CpFQpFmRmMDL9Xh+qiO8fwkEBO+X1Xi+r3G8nv4CP/ucuHtS++zvbLdU4nkGvqpJ6bENvF8B0abtjYoYtFhrN1Y3d635gj5ACNU2nby/wMeZfz+R/5z7aIojNae4eyFVa5ON2ja7b4lhGMRibfun1APqEQrvWMmYPfvy81KLUAsvYCwSxVhlf/qim8pa7c/peyYGFNy9GiOqWGMKOeXao9mp5/DG97v5CmnQBzEXhSm6ZGljX96J1Un/O6djHHTmvgBULFuPrE52YwpgLgg37iuVCT+2kGKW2SvK5sI6ooN9TSOJ8iiF/1xh/zar7etpLA7Tf5/Okvd55GFsRaOfgC9EZDCQ1hqVG0N1DfBKGm35YjIwXESGYrshjwdOdNupIQbjh+3Dezs+S3VVbbObvwCaOMMJt29httWRcgS8P7Q9e2jZR1GXQmoratu9Rmysi1Hw9xWNfSbeDAL3rCL0h25gge/9Cjylzq4o02NS2MVZvf2h8U+Atjhfhfdum4CV4kbsmVOPZ07zkX6Pfl0pL1vfrG3EuM3YZvfkkfGyuc43Y8ebMNiDjXYycucR7H/anjx1/UusWrqGvkN6c+Y/TqJidfrRZMHiAkdDtd8pdui3GII6rNk1a4mCEbWvo+kxMD0eGkc3cSyn9Zz2YIG52DbIvlUx3v3mRQ6fvgM9+tk3+96DejFnclNl3mauxHQOrbBsznKGbj2IFQtXpT2TdHo/eNdqrJfWIxE7CMYojTQOEBOZ+Mp3XPH4+WmcXCduUdV7gHsSmhaLSFo5t+0OphCRg0TkXmCAiNyT8HiKjH7q2UFVo8AF2DVPZgEvJ0o8uWH96gpq1tU4zlA6mlhUcffCjPdtcMlA889lrolR8PAaCh5dk9JIAYzcaXO69XF2/VWsdh44pTJSqShfsT6p7eeJs1j484bRNzzwjH1Yv6qSyrVVqCoV5VVUrK5kwpOfpd3HqsXOslkPXfYUAFvtMsLxfWnxAHuGd8hZ+xENJ/+fPN70E2g9vtbHsLFIjNrKWl66483GtrIFK9PuPxX3Xvg4AL5AWhJwKTGi4FkSwSyLYi51NlIA4bqNpdLcxo+IXBwPphAReVxEppBmAF4mUX/LsRfA6uN/Gx5vY6vhbjBU9X1V3VxVh6nqrdnq99dpaZVM6RDUVtVheDZM8cRQfR7qdjkFblkWj1/zXFK7ZDuXrQWGabBwxhIevuIZ6qrsBNXailruveAxFv7i3nDWVdcTiUT421tX4Q+mp7Kx53E72/llRc31Fg1TKChJv1bZzoePxR/0UVASxB/0OSZyRyMxpn7clFBdUOy+2GHlGntAs9tR41z3lQ7+YMdV/fgN8sd4OPr+QC/sJaO0cl3bfUdT1Z9U9SlgmKo+nfB4XVWzX0J0A/P9+1O48aiNRwxeEGKRDRNzO2fSfNa3w+WVTWZ+myzds+VOm+f0mFbM4qXb33R8L9pygShDTNOkuFsRr6x6nNP/djybjR7S6kBk+fyV/PvMB6lc0zw60oopo/bcMm0XwCFn7cfLKx7jhlev4C8vX4bH5zwb6zmwR+Pzw887kEChuxt/sNg2phXl+RG32e/UtMohdZIdGr59BwNPqupPpPmNzMT193O87v0UEZne8tHe/joyqspdZz9EqC5/FX7d4C/wscmWuS0W2BbzfnRWLC8oya3kj2UlG+e9j08upphtnNaOckF9TYj62npEDAIFqY3B7O/nUV/r7M766vVJackSmR6T0ftuQ0FxkDH7jWKnQ7Zn6922xGzhOvQX+Dj2yqaqvnscszMHnO5O5vPUG48FYN4PuVW+b2DlQnfVCjppFz+KyEfYhupDESkmzUy2TIIpDs1gn42SNaVrqVy78cgWbrvXVuxw4GgmvT+13Ws/2WLI1s75aeEcuwV7DkxO3vqwHetEHRURYe2KdZyz3ZVUr6smGkktK+TxefD6PNS1sk06nHLD0RiGYa+5ranEH/Qxao+RTP3k52bbRSNRhm03pNm5XnDvmSyZU9rMJdge9jlxNwAChfkpsTPpg6l5OU4nAJwJbAcsUNVaEelBPGK8LTJx/S1ueGCvU20Tf9TF234zFBQHiNRvHBVLxBDKFqxihwNHt7kQnisGjuhPrwRXUCKJYfO5YJcjktc0ls5xnZ2wwbEsi//+4w0qy6taNVIAGPa6VqaIwJY7Def4q45i8odTObr3mRzbdzxHdDmNp294KWn7WMTijlPva9YWjUSpqUgvfyz5+NI4uBh3yNiM+uikQ6PY2rAXxV8XkqAT2xoZr7qLyLHAJOAY4FjgexE5OtP+OiLh0MZhpMB2Qa1asprJH0zliifOwxf0uV4vSBcxhIGb9+PuL29OuU0kS2s2qRiz7zZJbV16OqhbZBExcx/zqQrfvDU5rRlytD7za2x4DG5971ru/uoWFs9axnUH/4PK8ipbuKKVXIfJHzbNSEJ1IS7a5TrmTs7MbaeqTHrfLvOx/6l7YpgbJiiok5zxALAzcEL8dRW2PmubuPkmXAfsoKqnqeqp2Fp717vor8NROm/Fhj6FdhGqDfPduz+w93G78vyiB9jXIQk2Fzw6/d88OfueRlUBJ1q70Up7voUOtsEwhe/jN7hEIg4h2tlEYy0Tw7KL6TUxTYOKNWnlRLqiz6Be7HDgaAzD4L6LnmjVOCWSmJv1xn0TmD91oavzqK22lS3WlJbnxX1t5GGw0UkjO6rq+dieOOLBd2n5eN0YKkNVVyW8LnfZX4ejpOfGV6a6R397raZLzxK+ey+tKs+ueeCSp5j6aetrEmYrkWrtKvXlFJ4eU9avSo42XL8q9zd4tzWTDNOgx4DuSbMHwzQ48oIDEZG8BPMkRvUtakdOWmIKwNv3feA6uGTQiP4APHtTfnQD/IHO8PQ8EolXuVAAEelFmsEUbgzLBBH5UEROF5HTgfewhQZ/M5R0z63ryAlvwIvXRbJjgzBrfU294807F0z5eDrXH347Hz6VOnjhoPH7Orb3HtQzK/lOfQYna6gZRsccN5keg2BxgGBxgL5DenH3lzdz0Ph98Pq9BAr9eP1e9jlhN8bfdjJgpxzkmv6bNZXv8BekH8iw2eihjc/T1TZsjb2OsyM1Z3yTosxHlgmFNo6I3t8I9wBvAL1F5FbgK+Dv6eyY8S9ZVa/E1m4aBWwLPKKqv6lSmeG6cKszgWwjhuDzezn3rtMzDohoWDPwF/jjOnT5IVQb4sHLniIacV4nOe7PRyZ9JsNjsP8Ze2UlxPuXr2YntY3ZL3ndqiPQe1BPLnvkT9z67rU8Nfde+g7pzSUPnsNzi+7nhlev4Nlf7+OqZy7E47WvV5feqV2q2aJ73yZFka4p1EVaIoZwxePnNb4u6eVuYCciLJtX1th3PrCinXU+8oWqPg/8GbvgbhlwpKqmNXV2E0xxKTBJVS9T1UtV9Y1M++qo9OjfHW8gfzd7tZTaqjoeuOgJom0VM0zBvCkLqSyv4vZT77XVEvJILGqllNG5+08PJ30mK2rxxt3ZmYSXl65NarviifNyHvCQyYCiuqKWvY7blW123xIRQVV54bbXOWnQuVxz0K2cOORcnvjLC43rRMdecXgbPbrng8c+4bThF/DtOz+wyeb9295B7IHAkK2bdKD7b9rX1TmoKnMmzQfAyHGUaCcbjHnYs6q3gRoRabveEu5cfyXYSVtfisj5IpKilvnGi2EaGHka2TWglrYdhtwK1etquGT36/n8pa+zeFbpEamPpAyoSJVXU1ORftmO1hgxbrOktmg4agc85AjxSEYDipbX6NmbXubJa//b+H+3ohb//fsb3H/xEwCsXtZ6PatssfzXlfz1iNsxTAN/K0nFACjM+HoOHz/7RWOTW60/ERi81UAAata7dyN20rEQkQuBlcD/gHexl4veTWdfN66/m1R1K+B8oD8wUUQ+zrS/jsia0rXUVdVt6NNoF7FojNVL1rhXys4AMaCwq7OenJUlBQcnzTvDNBoVDRJ54bbcTvI1qhkpbvTbtDe3HH8Xj139HGULVvL8La87bvfW/RMAmJznpNTP/vsVYw/Yts3t6mtCvPPQRwmv3c3gVWnMw6ur3Lh+d52kxcXACFXdSlVHqeo2qjoqnR2zsQCzCliBHfW3ISv8Zp1URfE6Mt36dEkpodOMhIlittbhDNOg7FfnUXW2kpA32XJAUkj4yF02p/+wZLfT5//N/axyx0PGtDtEffrEWUx8+Rteu+s9zhp1uaP8EwAKoVCIklbC/nOBFbP4/t30IkYjCbmGBcXuZbK+fmOy6z466bAsJanCW3q4WaM6V0Q+Bz4BegJnpWsdNxZqNsJRXUs9tpQkGOBYlhaUI/VRiro5lxnZ67hdHNt7DnCoW58C02Oy4KdFSSHh86cs5IePfkraviEnJ5f8Om2Ro7J4S7x+D4YpmF6zschiNBJtVnDRCY/HwzGXH5aVc91ki/R1IKPRtt3P/qCPfU9qytXb/eidXCuQNKjOt1R/zxWdScV5ZQHwuYhcIyKXNTzS2dHNf2kwcEl8GneDqs500VeHRFONdDsw2QgRzhhJndhbX+N8Qw63Q6JKDMHrTw7dr68J8cXL3yS153J9qoGls5e3GbXoD/q45vlL6Du0D7F2rD8GCv2YpsluR+3o9jQpKAny0NR/pr9DG5fOMA36b9aXw89rquxz9GWHUeJSDaRBAmroNmmtsbtmQ2li/j9lCfb6lA8oTni0Scb+GFW9OtN9W0NETgIawtyrgXPjcvCIyCJs2Y0YEFXVsfH27sBLwBDsUsfHZqPkSL5GddmkqFshq5fmZ/G9JYYp1FbV090h+GvyhGmO+1SWVzm2AxR2KbCNgNizvoPP2o8Pn/g0aTsxxDEUP1Vl3GySjoJDqD7Mw5c/3W6lDH+RHdCQjdpWtVV1LEqznxHjhjFn0q+tbmNLdq2hYnUlvQfZOWwFxUF6DujGOofiluniD9oDkW5981QivlOYIm+o6k2Z7rth1EtbZyGwp6quE5GDgEeAxCHl3qq6psU+VwOfqOptInJ1/LXrnK5eA3sQKPJTX52/KqBub67SRpKr4REsh0rF2SAWteg5wPkGk8k62PWvXIbX5yVUF2br3bYAYMLjnyRt5wt42f+0vRzbQ7UdIKFTYeXi1U2leBMuv2EaKUf1FSsricViPHjpk1k5hwt2vCatTZfNLWu7O1Xqa0O8ePubXHT/WQC8+/D/WDKz1NVpDtrSjvpbPCM/FZtNszMMPteIyDu0MkdX1TbzLzqcg1ZVv0mYDX0HDExjtyOAp+PPnwaOzMa5mKbJSX/Jj86uiFBQHGTUniNd9VM6t3XF8FwZKQCvz5tyNrfPCbvh9TcfF3m8JsXdnWWqTI/Bgp8W8/aDH/L2AxP49q3J+Pxebnz9SgKFfoLFAQKFfnwBLydd9wdG7JAcnr5rFlxmWUXth+k1G5Up+gzu1eoal2EYjsnMGR3e0rR0FWvWp5cyEIvEmP5Fk8f/4+e+cC33NGzbIYBzXlwuSBnI0kk2+Rfw71YebeJqRiUig4HhqvqxiAQBj6qm9uW0nzOBDxJeK/CRiCjwsKo+Em/vo6plAKpaJiKO0YcicjZwNsCgQen5wF/559sZnnr7OHD8Plx8/1l88eq3/PTZjIz7ac+aT7aJhqPNFA4SGX/7ycz54VeWzVlOLBrD9Jr0HNCDodsM5otXkteXYlGLp294qXFGNO3TX/jomYnc+t41vLT8USa+8i21lXXscfROKUuLFHV1DuzIJh6v2e68NxGoq6rH4/Ow46FjePPeD1Jua1mWq7y6lrRLVzENGtQzAKqzkPu0ptQe6DitReaCfBW+zDbxvNW7gJ2AdUAYuMOt8EI8QO4KVf3B9UnGUdWJbvtwE/V3FvAqtowS2DMf57rcmfW/N7ahSnTh7aqqY4CDgPNFpF11pFX1EVUdq6pje/VK1oZryYpFq1pdQ8kqake1zU1RITddDNNIP/Ivy6gqdSmCJgpLCrjl3WvY/eid6DesLzseMobbPvwLleWphWMT3Xb1NSFmfDOHz/77FTcf+2/uOe9RHrv6Oa458Bbm/OC8nlK9LvdFL9sTSddANByL/43y5j0ftB24kEZU4Yaiam3C7yMLuRxr4+tbm44a7Lqv3ypiT8HfBL5Q1U1VdXvgeNLzPm0wRGShiCxo+UhnXzeuv/OBXYFKAFWdR4Z5VHFli2nxR38RGQU8Bhyhqo2+JFVdHv+7CluGo6Fa3koR6Rfvqx92bpdrKvNQXqGBz1/6mvnTFjLDpZun1yY92hVZlm3Klzu7bJbNK+OPW1zMx89+weIZS/ni5W85c+QlhNvhKqqvrue+i55g6ie/EA1HiYajLJ65jCv3uZHysuTYmcPO3T/jz5EuRd0Kuezx8/DnoPaXmIJhGB06hLqyvGkwkI18r4ao1V6DnGfJ2aYDjwFaYx8grKoPNTTEi9neKyIBEXlSRH4WkanxAT+ttAdF5EURmS4iLwHuk+FSMxbYIf7YHVuk9rl0dnTzCwipauNdRkQ8ZFj0QFXvV9XtVHU7bHfk68Apqjo3of9CESlueA7sD/wSf/tt4LT489OAtzI5j5ZsNmbTvN0k6qrquWS361k8a5m7jjakJ0MgWOT8Pb9z/IPUVNQ2Bg5YllJfE2LFotVpd2+YBvU1IWItcnyikRgfPJYsivLLV7lX4N5028HcddaDhFLMJN3Q4JaKdeAQ6mBCZOz+p+3l+sa/Ou76694nP1F/4w4Zk5fjZEBPEfkh4XF2wntbAckF2GzOB1DVbbALFD4tIoFW2s8FauM5sLcC2+fm44Cqlic8SlX1bmyj2yZu1qgmisi1QFBEfgecB7zjor8G/gr0AB6ILzI3hKH3Ad6It3mAF1R1Qnyf24CXReRM7Fj9Y7JwHhiGwVm3n8zDVzyTje7aJFQbcq0aXbE6f7PAlviDfvpt6jyp/uVr55niWoeZUCpS3QQjoQhLHMrOv/3ABIets4fpMfj4mS9yt86hG26xP93o08Roy3B9xLX3ryEKLy11lSzg8+VnLSwD1jSk37SFiNwP7Ia9TrUMuBdAVWeLyGJg8/j7Tu17YM9sUNXpIjI92x8k4TwTRwUG9gwrt3lU2CHgZwI/A+cA76vqoy76A0BVxwPjHdoXYJcTcdqnHHAueOSSrr27JIUU5xK3N718liVJxB/0cc6/TsGb4oefjZt5SY9i1q10VmDp6uB2qliT2/XFG964kr8edntOjyEi9B7UkxUL0/dm9xrYndXL3EXNaTpfeGlej+r1u9yPU8cdbN/LUiWIZ5tv38lazEA+mQH8oeGFqp4vIj2BH4BU+QGtjYDz5YdJjPCLYqciJYt0OuDmrnahqj6qqseo6tGq+qiIXOyivw6HZVn8e/yDG9ad1k58wfxXLPX6vRw0fl8OPSf1mlDL0PRMqG4lbHpNWfKNuaDEWSA3Gxx9+WHscMB2Oeu/gUgkyvn3nNGufdwaKSC9uqsK7z3a5HKtXOs+6u/sf54CwFa7jnDdVzpkSz4sz3wKBETk3IS2hi/7F8BJACKyOTAImJNm+9bY9QWzSoJduF5V944/fqeqZ6tqWv55N4bqNIe201301+FYPHNZxnWhNgRiCNFQ/sPTI6EIHzz+Kc/f+lrKbVKpfLTH1Rlp5bPN/Dr5+17SwzlHyw2Hnbs/zy16gHP+eSoeT+7z5T0ek50OGZsVQ58LEqtIt0e30QkxBW9cvHjbvdzlE6ZLn8E983KcbKK2HMqRwJ7xSLpJ2PmjVwEPAKaI/Iyt1nO6qoZaaX8QKIq7/P4MTMrBKTeMtO7JtIN2f/tF5ATgRGCoiCQmGRVjK6j/Zsi3G83r96KqGRtHtRRjA7n+QrUhXrr9TY65/DD8DrO6qnLnUPFsre84rc2likB0w4QnPuPnL2Zx3YuXMnBEv7Z3cEGP/t0a12wiIfcDpmxLSnl8Jjsd2rT2vu9Ju/PMjS9n3J/GlGdufJmzbj+FxTNcBhWlydgDcz8rzgXxvNHjU7x9usP29Sna61rpJ1vMisvf9WqxBib2KbQtZp7JXe0bbF/jbJpnF18OHJhBfx2WTUYMwF+Qvwq/x111BLscntb6aUr6DN1wlVbEEFYtaaluteHIRZnxSCjCohlLuXjX61zXX2qLv39wLQCPXZ1WBG+blKRQAckUK6YcNL5paXjeFHc5gABfvPodAK//JzuVn9vi0xe+ystx/j+jqidgJybPBw5LeBwa/9sm7TZU8Xj9z1V1Z1WdmPCYoqobj58sDUSE61++LK0yDm7x+E1Ou/G4VrXpvIG2I5S69izhsD/lPn/IiWg4Svd+zmHF3fs5K1aYHiMrwqBBhwKGvhwOMmor67j6wFuz0leqr9f9Fz0FwGv/ec/1MTw+D9e/cjn7ndKuHPlWMT0Gnzz3ZePrhdPd6/M1XAs3wrbtoXYjLOWzMaKqK1R1W+wc10DcjixW1cXp7O9GmWInEZksItUiEhaRmIhsuNjoHLHjwdsz/o4Tc36caCjGcQPOpraVisKRNOSRIqEIPQd2dx3mngm+oI/CFAEM1zx3MUYLwVwxhN9fcmjawSqeVhQ3nMLic13OfM7381334fGaKUO6p0+cYbuCs+D2i4ajXH/4bfzx1hO5/pXLGku+uyESivLla981vq6tSk8jsDV6bWKvGW2391au++qkYyEihwHTgAnx19u1WD5KiZsFjfuwE8fmYWczjycep/9b4oePfuKZG17Jy7HWlq1j5rdz294wBR6fh76b9uGp61/aIBpm4foIKxY5h1Fvt/fW/PW1y+netyuGaVDSs5g/P3UBC6YvSt1hC1s7YtzwlJv2GpC8KO621pDptfD6s+8+3GLcZhx7xeFc/OBZvLHu6Va3DYXCWXM/11XV8+qd77DHH3bmvLv/mJWZrD+hvEo2Co3238yuEXP05YfjcVmEMR18aXgpOskaN2KrCa0HUNVp2KWZ2sTVyruqzgdMVY2p6pPA3m7662ioKneOfzCvpSJaqi60C1VCdaG0aiTlAoGUbtJff1rE7afcy/rVlVgxi+p1Ndz9p0daVwZv8THm/firo1ip6TEYvv3QpPZEwdT0sA/YpUeEhz+bxXuLfuadBT+z0+/WJ59MnIbP6/FaFHeLYnraNmxLZpfy5n0f8NwtrzkqaiTi8ZjscczO7fsYrfD9e7aggUjrEZcN0XdtUb6iKWE7G4VGe8dnVN37dmWLnVIPTLKF2e7vSCcuiKpqfkvRA7Ui4gOmicgdInIpkHu56jyypnQtFXnU+3OLKsz8JvMZmVv6Du1Dn8HOYr8PXPwkddX1TRJKMYtQbahRoDUd1FLH4oOxqMV2+2yT1L7VbqlycZyMTlPbYxNnM3jzsH0zFzjq7NQBIgecsScnX17GqzNn8NwPM3l15gyOvWBlimPYFJWs445XZnL2X6bw5Ytt5cgLP38xq41tbEyP0abqeNXaaq7c9yb+vN/fWq2AvOl26YnCrkko6+IvcJ/D99Ub3wMw4+vZWStv0hp1rbjaO8k6v4jIidhh8sNF5F7s4Lw2cWOoTonvfwFQA2xCQrb0b4FgUYDIRpRHFYvGqKvO/w/P4/dQ2LWAv7x0acptUkkotWcGGQlHU87Yvn1rclLb8NGb0tJgHHraKgKFySN/w7C3G7fveoq7Wc0CHLbbtYZtd6mmsaBUvM/+m/Xh0ru9nHjJeoKFFoGgUlBkcdIlK9lkeOqIwECBsuX2texxWAW3PDcL0+s8E/EFfXg8JmvSrM3k9XsJFLbuJqwsr2LaZ7+0ug3AvCkL0zpm4tUt7uY+qrAhuOGtBz503VcnHY4LsXUKQ8B/sQXNL0lnx4wNVTxio15VK1X1JlW9LO4K/M0geZROyhaxSP4z7QVsXbpW1oSy4Rby+r0Oa2/26/lTFyVtX18bxh+0mrYT5dyby7jjlV8p7hrF67fw+ux1qHNvLqVbrwhbjXMOCLjj1QXc8tyvHHD8WvY7Zi1n37CMJ6buDzUPYZrNXcOBAmXzUbWkmrl5ffY5GYa97fm3lCaJHxumwfjbTgLsEu/pEA1H8QWys56Vbmh/YiLykK03cX3c3eLFLkvnt11lOBt0ZGX63xqqWquq16nqDthV22+P53e1SSYJv0cAA1X1/vjr74EGf8+fVfXV9vbZUamtqs+rzl82SCcyEJonf7pNBI2EokRCUW45/i6emHm346wnG8tmpscgElKaRwEIYiirliarsG+/t5ePnoJe/cNcff9ivv+4ENOEEdvV8eJPM/jpmyJCtQbb7FxDUZcYBcUW336YulTF2L1r2GGfmsbPI1XXYQ8Ok9nt0Aq+ntCV+prmAQFev7LT/s3d9AefvBbxjeWpW0qpXGtS1CXGqddtyuHn22mJB521Ly/d1napt2gkRvny9EV+s4G/oElxpEdfd4rnHp+H0285DoAhWw9i3g/u87LaZOMs87FRIiIvAH8CYsCPQBcRuVNV/9nWvpmsJP6Z5pnMfuz6IoXAk9jFFH8TFHUrzGv0nL/ATyQUcRWt5vF7oBXlacMQ9v/j3nz39o+N8jclPYqzorq+cvFqVi5eTd8hyaHi2VBFCNdHKCiOcfJlKxR6d+cAACAASURBVNn/uHV4fRY/fFbMQzcOAE2eCe249y/03STM2TeUMnKHWrbesWkbjxe237NJLUMV9jy8gory1D+JRPtrP099nXfcr4pbnl3ILecMJlRnIGJX1734jqXstH/ytT74uI846FiIhASvXxGZAdW9oOTqVkqIKGP3rqKuxmDm5EJU83/XTUwB+PZddwKv+5ywG/6Avc51xLkH8L+nPnfVXzq0tk7XSdYZqaqVInIS8D625NOPQE4MlU9Vlya8/iquXl4erxP1m6GqvAqPz5M3vb9gcYCibkWUl2aoRCUweORAZjjo3jVgWcqExz9tNkvMVmmQWCT1GpIv4HWMnhSRtKMUVS3G7FHFoaeV4w/Y++x8QCVb71jD608kj+YlVsbtr8ynSzcLacPDIwJen/L7s9dkpZieYcBW42p4YcpMZv9YQCRsMHJsDb6Aw2eVpnNo9n7t01ByNZMnTHM8RrDQ4qRLVzB4RIjaKpNrjt+UpfOdNRVzReLgo64qc6WOYFGAcQeNbnwdi1mIKTk3JIVdcydc3EkSXhHxYusU3qeqERFJ6x+ciYO22R1BVS9IeNl2ffeNiO79uhGL5a9a7vqVFZkbKeyQ4kFbppHImavySVZqIdg9j90lSYdQDKGoW6pIMYeTVCjuGms0UgCmBwIFFsdd6ODyis6ma4+2jVSzc2q3kXLeQcQ2VqYJW42rZbvdqp2NVMoeAGJYVoRuvbs4vhuNCn02iVBYbNGjb4S///dXRJQuPSJsCH/1JlsMyGg/X9DHwBH92fWocY1tX772XV5mO2N+l3Wx8E5S8zCwCNv79oWIDCZeIb4tMjFU34vIWS0bReQccqO8u8GIRWMblWvAilmscWHo3OIL+lKWmNj99zsluTRVlT4DnW+qpsNc3x+02OvIZGmdQIFSWOJk8DJK2WgnAdyVdWsLk6MvGY0/2HzA5PFabD2uhh597Nm+YUC3XlEO/+Maxl+fn0CElpx8/dFpb+vxmhR2LWDYdoM57abjuOuLvzXLe/N4zbysH035X87qBHbSAlW9R1UHqOrBcQX4JaSZe5uJoboUOENEPhORf8cfn2Mr86YVatgaIrKXiFSIyLT4468J7x0oInNEZL6IXJ3QPlREvheReSLyUjy/yzWzvp+XjW7yRixqsbZs3QbLto9FY/TapIfje8/+7ZVke6SwblUsITLPpu+gei78x1J8AQvTYyGi+IMx9v3DWrbdxSkqT8C3l3N7zglCz4ngyU0FbxFl530+48SLV+ILWBQUx/AFLLYYXcu1DzaXSfN44YyrylhT5t5w3vreNYzac6Qd8l6UOj+qsEtTROJb7aiofMI1v+e1VU/w0JR/cewVhycp7u917C55mRRWr8utzFYnICKXtXhcKiKnAEPS1Ydt9zdaVVcBu4jIPtgx8QDvqeqn7e2rFb5U1UMTG0TEBO4HfoddbnmyiLytqjOB24G7VPVFEXkIu/Lwg25PorbSvXZZvqlcU80BZ+zNR09/nldFDQDTYzaT1ElkwXRn7cm1K+Hky1bx0n198PgUVTjugtUceOI6tt21hs/e7Eqo1mCXAyvZYkyq/4dCwEGI1xgKVq4jx7yw5mDsVMIsY4xAxESjszj+otUcdkY5i2YF6NY7Sv8hzv9bMWC7XatpXZipbbr16Uq3Pl3xF/gIFgWwohZhh4jSU25oKtDa2tpo85OEY686gqmf/Ex9TYhxB49OCqvPl2JEPgSnO3EsNz8EuE5EblTVF9vqIONvQ9wwZdM4tcU4YH68JD0i8iJwhIjMAvbBrpEFdgGxG8mCodpu763ddpF3aqpqufC+8exw4Ghu+sO/3EkytRMRWP7rSgYOT67T1LVXCVZkJYf/cQ0jtq1lwcwAbz3Ri+pKDyddtopDTy9n5qRCirvH2GqHGkSg/5AwJ12SZgn2+o/Au1nztuCxUHNbFj5Za1STEyMFNLrvPSMgtoTCYitlnheAZdnBGFtuX0fvgWFWLfOROKs0TMUfVGJRZY9DKxg+qpalv/r59PXu1FY1hdGLCJft+VfCdWEsS6leV4Mv4MXjNYlGY3ZKmiHsdtQ4jrro4IT+07/pH9nl1GbVdU+/+XhOuq5JL+C1u92XtU8HX7BT6y/XqOpNTu0i0h34GGjTUHXUbLedReQnEflARBpmbQOAxGjDZfG2HsD6hClkQ3sSInK2iPwgIj+sXp2cd9OSguICRu6yecYfYkPg9XsREXY8ZAwHnpna/evxmgzacgBde6fOG2ovsahFoUO5DYDxt+7CY1/M4Q9nr2bMntUcceYaHvl8DmddX4oIdO0RY5eDKtlmxxqMTL6VYYe6Qr58LJTn0HVklWFZFlJ0flqbm6a9ViUCd701nxFjathsmxq2GFND38H1XPvQEkq6xXhs4hwu+HspR44vZ/xfVvD0d7MYOKx5xF4kFMFKiOgL10fsSDwRDNOgsCTI3sfv1kwRv3u6eVSaXAL+qetf5Lmbm8Sf5/6YnjKGW3r2d1eVuJPMUdW1pOmf74iGagowOF675F6gIdPR6QO1zP5MbE9uVH1EVceq6thevdILUNx+/23zmr3u9lgl3QqZPWkeJww8hw8eTz3h3e/kPfj35zdltR5PQUmQbn2c607tfegXBItijZFvXp8dXn3QSVmqOxRzCB4yNlwRyewRQ7wjae9PtWe/KPe8+yv3fjCfu96az1PfzGH3Q9Zz5nWl9OgbIVhkG4pgoUVRSYxL/900BlTVJEMCdii6WmqLCq+v5Zbj72omjWW4LC3zzI2vsHimfR69U6x1Zpvq9Rufe/+3Qnz5KK0M9Q5hqETk/IbgCaBIVasBVPV97Nj7ntgzpUSNloHAcmAN0FVEPC3aXRMORXjlX++4LhfRHqyY1UyWpr14fB6u3v8W1q5Y36oMzmcvfc1pwy8iHEpPycIZpaA4RiAYo1f/MBWrK6hJta4X/s5xpiSSpWvrtNZQ93x2+k590Bz3D01f68yuk2GAYTZdnp0PqMTTwttlmLDlmFoChXagRmEXMFup/dWAFbO4/6LHEzpydz1Ulfcf+wSAQ87+nau+0qVziSr3iMjPIjK9xWMZcBtwXjp9dAiN+7gcU4MkU18REVVVERmHbUzLsWuYDBeRoUAptjrGifHtPgOOxvZ1nga8lY3zWr9y/Qap6xRxUSivviaMlYaunh1o4SbYQuk/pJ4//a2Mnn0jDB1Zz5SJhcz9YQGj93Fa28uxsTf7JrfFUqueZwcfeEdBJFkQN30CQGuJsqmcBpnh9Tl/nw0Tnps8g3DIYP4vRfztj8PS6m/JrNLG59k4y8Uz7BnV6H3zsz487uAxeTnO/3MObfFagXJVTdtv3iEMVQuOBs4VkShQBxwfj7mPisgFwIeACTyhqjPi+1wFvCgitwBTgccd+m03XXt3yWswQjZQVUJ1+Yj2E0yPLRXUwPZ71fDL3FT5ezm+jqbTsqT7shOtUvhH8GwOFS4MlTkEYq2Vs1C0PnsxS6lmECJQ3FWBGN17VVBQEqMynZS8hA4HjujP0tnunBn1cbmo0rn5yQWLRDae6ggbK+mWm2+NDmeoVPU+7OrBTu+9j60R1bJ9AXZUYFbZGJWVi7oWsmJhmpFyLhEjeXQ+cGipw5aQc3Vfy8FAxnIs5l/7LBjJlYXbRatGyh54UPOYu2M04gHSuDELHHVmGU/fkRy92ZJAYdNgYO5k96kAlWtt/cVPXnAIjskBP370U16O04k7Opyh6kisLcuvEnU2aClTlB0S3U8Nz5XyFcmhvd16p5Jwyoah8mJPplu6yrzgcSj0F81x5JiGILYohwcwMQwPltV2hGp63Q2FWHpJ7FuOTc8rMyhBNql8eXp1s1ojFBdUnp2nZPtUeX+5wLO6hh4Pp1UnsJMWbHxThjxS3KM4b4K0WUFg6exUM5pMcS6rAbbGXhLeFOH8ZiZh/p74wwCCEDwapCfJqyEmUnBs0t45dzcSwTaeucKyZ1S+PbLTXWxp29vEqalwCqZoPtDwF/g4+rLDXJ5Uc6JxV1xBSe7FYg1DOPis/XJ+nE7c02moWiGxhMFGgdr5L/nA67MYumVT+QlVEDEQTRFuXnw5yV83cWhLoOBUKBwPwVOg+zMYXW4Cr0NxPgmCOCW/52GQYWQmxJoe8Vmod2SW+kt/7XLV8mRni2Eq3fsKBSVBfAEvf7jsMHY5YofG97v1dU5NaA9detp5fbseuUMbW7rHspSais7w9I2BTtdfK2ysa1TrV2WnbEcqAkGLrXes5uBTmqLq7DV1C4w+zjtFpmC77RJnYSZQhB3Q6UD4G4j+CgiEP8cqvBDCU0lyIWo9WvcWUnhSiw4yLzuRNpZ7d5eNg2vUOxYRA63LVom39CMv16/x0q13GI/HNlA1lSZbjq3l/LtPo3TJlozYYVijUWng8kfP4S+H3e7qDPc+YTcAtt5tC1f9pMtb903gnH+empdjdZI5nYaqFarXVbe9UQeje99uaRuqXY7cgVjU4sf/TSMaas1N1uT+M0ylqEuM0/68nGFbtxyhC1jrwXAo9VH7HLarLJEotgSREwZE59HovosthsprsY1bS+rihrCloco1HtKsUpAGDut3hWfaf6PZcueapOMOFYFjz1vFyZetslUkYg1q9oqvdwEDthrtuN+kCT8hhl3uJRN8fi+Hn3cAkB/XH+TPA9GJOza+KUMeCbvIZ9pQrFu5Pu2E4XP+dRrTJ850MFLNb5r9BocwTMUwlbF7V3LX2/PYbJuwQwKvoqmq3moqgxQF0ymXxUvyTVVxdl95wJNe3k92ydb3I0XMePX98SfZmhmmv55W1EXx+RVfQAkWxp/7AVIbkJ+/mJmxkQK48c0/Uxg3UAXF+S0A2UnHptNQtULRRlj90+v38uenL8AX9LVpsGZ+PYv66oaboHLUWat4afovTCidzqMTZzN2b7ue0ymXr+TdRdN5d9H0/2vvzMOkKK/9/znV++zAsAyg4gKixB1XJKAkiqLiLkajeI3+EjUxGr1xTXK9RhON1xiNa/Sqidc9bgSNYlzjBhLcd4OKIjvM3tPL+f3x1sz0zPQ+3dM9+H6ep5+urnqr6nR1dZ13Oe/38N93LmX46Gjq+Tgpz5Ym4i/2bpKVyRxehOQtgij4Cz47YQBJcW2ib6IaS+Pkc8AZCVlqBqal/THiG85D2+ah2rPSMHqrUXkrPXi8Ht564b2uzwPV0vH6MytwWEqPdVRpGLHpcJPAbRBx4CnfYcIuWzJqXH1GhYtH/vhkVxr40y79klMuXk5dfQwR2HR8mP/+81L2m7OGXaY34/EY0dNMKKlqwunCgLNtMQRSHMcD7U9neYxCU9zec/PzFEDzwT89uXpHUtKcL/x3aPsruuEcdPUhqHZrRR7zn4fiC+YX7h2Lxmhc3d2NOlC9GfFBlBj1m4x1VGnonNORlCJqhEmemmm19TUc+uMD+NHO5/L5e5kVAt5faOaqeH1xDp67pk9WXceBs678krr6HB4a2pS5TNYkPvR87thXMm8Zg3gyuaRiC7n5GJDIwkI4w/YHofGyHHbI1PUWh9inaNO1XWvGbDWKYEV+jipUFewhZ/TqvMU5HyNYkbsSyUDqeFryxzqqNDSly/5ZxIpYvvqCrc1t3HXpA7Q2ZdlCcU8zdstw6q68XO+QeCpHlYesk38GUAkEwL8X1N1K8si1CiQwPcn6IteWZQjFnUfVmdivEN1gUdAN2Rf3jCMrB9nWnZrjwavn0dacpnKXgmBlgAmTt2T3Wd2O6pM3cp+s3Z6uYmkZ1NiovzTUjaglGhk8Wn+R9ghP3/VCzvttWJO6Ty/3MYcC1n06/k6XY+p4AZpaIfQ9aLsHIwMJEATf+OQZfouNbxI4FSZpY0GcSW9CJjy9YBOXs209KMSyjDTUbufw6t8W5zy2VDeylh9cfhwzjpuKJ6FvecjILHNbWb4R2BZVGpZ/8nWpTciZlg2555dat8rvjoUUgkLeUvGey5GF4BkPvu3o6tZzRkDNJYiUIlNrGKrPAymW+G2bCaagFMn92smqWzMh2nLIqNqczuALeDnm3NnsP3cfvL1Sz8fjg6eCaCk+1lGlQQbhhN+cu+oAxxMvXF4eLfJM/+ZfQiRh0m/8C1h7PJp04m0/BWMz4Z8GLbeB9qc15SPleJBUAA5Ufq8fx8+XLL9TxRldi4f/9CACOY0TCfdc8TCHDZ3L5d//A6sTtAI7EyhaLGAdVVrGjm/AHyxFTT1/ghWBrJLeAfgCDoFQjB32KmAARMoUM6kG2XP1kBF6PkQVNIy2JlNvKPKYhXa43ZD9OY+CZxv6XgeB4BFmjCr6TrIdy4Pm33Ut7rr/jsy95Bg8WQojRzoibFjZSPP6Fp6950VOn/zzrsSbbY0DoCpiGTRYR5UGx3E48AczSm1GTkyaMpGTL/tehjlUyra7tnDlw1GuevgTLrx5aeFaVJJElQKg8ocpdqgswEk7IJLkYS5FnlqgEdDcu1p7EoXYGyR1VJ0RlJEP+nmOXsctJPFPenw88uyD2X5altqECd3N8ZjStK6ZJ+941qywmXctCZSdoxKRczvT0ovI2yISE5Gh7ralblrjJSKyKGGfoSLylIh85L4XZCQ2Fosx/9ZSzc/Jj/E7b8FRPzuE6xel0lxTzrxiGZfd/SkTd3ib8du1UZ3b0EJ6vClU0gX6hpY7pJZQypUkYxqFUh1PhZOm2y4n4vQNdIhD+3wzz62gDrf4HmDZh/klT4yEo135ofx5zsfKleohhagoWYpN2TkqVb1SVXdU1R2B84HnVDVxAGIfd/vkhHXnAU+r6njgafdzv1m1bA0dbYNLC+zJ258BYNTmycVhv7VbC/sctp5QRRwpRvh2qnxHLX+hrzMp5ByWJA/z2v8q4PGTEG+juMK3EUzXZiHvwWyuuYfsA4L7Xnfth47Suq+NQPHWk7fK+xi5cNo1/zEg57H0j7JzVL04Frg7i3KzgTvc5TuAQwtx8g8WFjlDbBFodIV04/HkD4u9D1pPIFTESY5J021Q4InASfCO62uKVADFDHOO0f8ZHkFSjt9JBSIOeFIloywWuUTc9QxYia87DZ8n/2jZ1ibTlfrto/fI+xjZIo4MujHobypl66jEPGVmAg8mrFbgSRF5XUROTVg/UlWXA7jvI1Ic81QRWSQii1atypw1dbDJJwFUVIcA6GhLPsE2Enb6JRyanhCO/1vJNzmp+hdDBTp3kgdO7FMgh0muuaJe8kvO6AUEpBIq55KylaMtqMaQqlMY+EGbbBQ3BCr27/oUb74JwgvYc78N5DvZ2hswv2PD5iO77uVioXHlxnPuyFzQUnLK1lEBBwP/7NXtN0VVdwYOAE4XkZwGIVT1ZlWdrKqThw8fnrH8HgdNzlim3Ghza6TVQ7uDGkS6HxrPPDSkgHOmelF9TuptKRUr2imIukOsr5KBxlZS2O7FXkTeIK8HsjMWGfk2MmIxTvXZpHbWfsAxqhuBWTmcIPO93X8ckFqk8pTuVS23AnDMj1dTXRel57XRhFdqaoaY+9ZxHI67+IiCWpyMVV+sKfo5LP2nLByViJyeEEAx2l09h17dfqr6lfu+EngI6JTMXiEiDe6xGoCVhbArEo4intxrsv6Qj5HjBuJh0ZeYK7LZ2aLy+uKoJnwHoXiOquVPXSK3fUlVQ1dycybJfo9g8iCOWJHHF6ML89svvhRdMQldMZH4urPBSdE9KbWICBpbBZGXybqb0akEzxb52ZY1Cr5dwUm4z90IyLr6KKf+4ivGTWzl8FNXctxZX3PISau57on3OfQH6XsyhjR0Zwle8OfnimJ5D6wm7aCgLCSUVPWPQGfyHUSkFpgGHJ+wrhJwVLXJXd4PuMTd/ChwIvAb9/2RQtjVuLoRn89LR44PvEhHlBVLM3ctFoP6MUbF4KN/mRZGRXWcxrXd9ZF9D1+XJI9UgYivg9gX4N00xx1z6D5zRrkCtJ2/iYD4kIqj+paN5C5smhtho/en6/LcXyE8L83mVajG0ZabIL6BrAVw1YGaX0HTVRB7y5zHGWNUNDackWnvLFHoeAptfQCpPNqs8m3rTsaG4WM7uPFpM8abOPVhk62W8+Eb1Xy4pJpopOf3CVYGmHbUXgCsW7GOf79lJ/1aDGXRokrCYcCTqj1mj44EXhSRN4DXgL+p6hPutt8A3xWRj4Dvup/7zbDRQ4l25K6OrSVKHSAC+8yZwh2/upfzZ16KiDLlgPV4vN0tFq+vgCoUfVCQVIEB6Sb85lBf8ow3skldu1dC7ZWIk0RmKPLP7I+bL76dMV13xbmoqgrh58lJpV2/hPWnuPOz4sa2+GroeJaC102b/6d7ueZyOh8pO05pRaSvVqQ/CCeeF+fwn87qmSVAYJOtR7PXIaa7ff6t/yisnZZBTVk6KlW9XVXn9Fr3qaru4L4mqeqvE7atUdUZqjrefU+mp5MzjWua8lFlLQkiUFNfw24zd+K+Kx4hFulg0m4tHH/2CoYMjxIImVbLy0/U0hEuxndywDsBSZXzKLA/fR+Sjkl42EcrL81tGXkJ4gnzdLQZ1v8suYRSdFkWdvcTGQKBYkWoiVGmcIbluF+YnmoZcaAd2ueTdZBEtiS0Jh3fFjDs8bTFHQei4TU8/qcFPbuJFb744CvWLDfHW/Nl7mNHex8+mJNn5oY7v3SJiLwhIotFZK8c9/+ViKQZVC4vytJRlQuOx8EzSPT+fAEfv3jgZ7z7yoco4PEqZ135Bapw87MfcPKFy9l71nq22aWF7sj1AjksqQRnFDLkD6mL1JwHngZXvw7z7tQjtVdA6ORetqSr9fcepAdoQZtvT1K2IPWV9ESWQPiZJDYVAP80RByk8mTSJ57Mkqx1GHPJbN0zMlbcycmp6neq8MxDVSaFTq9LFglHmX/LAgAm7jYBJ4f/Xu3wal6d/6/sze6yN+ddyoU2dz7pDpj5ppeX2qBiMjiewiWitr6GLXfaHCfPRIYDSUd7hF/OvoKO9g6iHVE62h2GjooybFSUyuo4s/9jDRff8hknnb+Ciir3CRE6CpM1t39I3XXI8KcRz5jUZZyhUHuN6bqTGnDGQe3VaHQZtF5Hz6dWHrmrws8kWTkAXbDxAs21c8Zh5lQ5QMBEBtaaRIcS/A65OY/+koOwsK+7Iq+xr9DVh6QtHovBsw/XJd8WjfHBIiPJNO3oPQlWZX9vbljVRKQ99+AZj7cshun7Sw2wDkBEqkTkabeV9ZaIzO4sJCIXisgHIrIA2LpUxubDRvErFZML7jqTM6dcyLoVRZyPUyDCbR28v/BjNK54fXFClRnGo9r+SiEy1EpgSsYy2rEE1p6ICUdXiL0H6042wRFZR/0JKZ2PDPJbuf5RJPIKRD8Gz+YQmIb0+E7rC3ASD9kFrmTr4INQd0X3Xo1Xk8nJffJ2gGgkdf141ecmU/OXH39Ne3PxhWlj0YFJeV8EQiKyBFO7aQD2dde3A4epaqOI1AOviMijwM6YSOqdMM/9xcDrA292ftgWVQZGbT6Co/5zduaCZUAkHOHDhaZGmp36RGH+pPFVhxLveDNtGW26DJPssPMhqEA7xD9Ls1c9xjkJyDCouYKUt2zwoCQrB9GE7cjrJm1IxQkQ2LeHk4o33VigkxQix9NQ40hDc2HEKziehCCWjmfT7qkKzz2SPrdWpzLF3Zf9lXiJgpLKhPpOcQL3dWqv7Z1dfxMxwgh3ikkHLcBlIvImsAAYgwlEmwo8pKqtqtqIiZQeNAzyamjxue93j3LHL+4ttRlZ4/Wbn7S9dQAf0rF3Ye2RxCt/ilN9WvIykbdS7JzuYbQBU2GMm6CJ+Ffg3xs6/kn3Q1fMWFfFMUn2dyjMw3kAaFuAbjgX4mtBatGqHyMVx5lgitY/ldq6bkL7I/4dwDcZcXp3R2a+1uF2GDI8wrpVySd5j9jESDK9+/KH/bU0O8q3W391Lz3TlKjqy27raThwoPu+i6pGRGQp3crJg9bz2xZVGiIdEW6/+O6c02uXkrrhNW5YsBZRKikFLb9PqTGYn0JEBNMKc6PYmm+AqtOh6gzTZSh1EDoSGfYI4iRLL1JcCR7AzSVVANofcOeHxU0kXfOVaKs7373YyShzoe1htPESdPUs4hsu6hm559su4+6nX7qcO197j1uee5/x2/f9XjX1RivSFxyYOvRgCZZKh4hMxHQfrAFqgZWuk9oH2Mwt9jxwmIiERKQao/wzaBj8v1IRWfPVWqIdg6RG7uLxehjW4LDVdq3FU6BIR/iJFBsKYUwHtD0EkffNQ12b3OUUocy+AjmRlDgQnAmhozHPic6/Uz6t2V7JF7UNWq51T5NUurK4OJul2NDmOs4wtD0K7Y91bZHqn5NODksEHA/4A8qm48P89v5PqB3WsxK44jMzUX7KoQMTat7ZAzEICXWq+QD3Aieqagy4C5jspkE6DngfQFUXu+WWYPRTXyiN2fkxaH+lgaBpXapstcVDHEHj+T/UK2qC3PbS13hkOeKYcYGBDcEt5i2l0PY40ERXCy36FrpmDgxfgHh6zzcqdvenIKFZiHdTtPoCdPXBEP+CgnU3xtegGjMSS/H8cjzlhyCVx6P+KdD2IKDQejt9W8XtaMudSMhE+olvWxhyM7rupKzO4vUq+x29jvtv6HbEwUoT6Xfwj/bjviuKP4yy076ZW4HliKomvblVdTWwZ4ptvwZ+nWxbuWNbVGloWluopH7Z0x8nBRBuXkkg0IjXZyZXDvQ8EQl+N8WWQtxqfqCRvg/MFrT1//oWjxQ7qMkPjpvmQtdBvMCyWTLSzEuKfV7Y42bEB/5dcXxb4dT8HIIHkrLrNtbTgUpgCsjmWZ0lEFJGb9GzJRl37//hY+uT7VIwHI8Qqg7xg98cn7mwpeRYR5WGCbsUW9izsIgjjBgLaB7zkApFyvGUfBxwgK6UGITAOz71ccLPJllZ7OugrtoDoDEKPlbdKfia02Bj/+fFIZ5eIr/Z13biLbeDLs2qx4+qxgAAEwhJREFUbFuzwzuv9syw+6WbHTjSUZxxYY+buscf9HPyr49l04mp5/5ZygfrqNIQjQ50NEL/8HgcDvnRVIqa2iID2rEk1ZbcD1Z3LVT+P6g4CRl6W/rU8vFkrd8CPLTT0o7GPkNjq9G2Ryi4Y4y9Y4IVkiSFTE26rtcs/+4KhLuHMMSpJWU3qmd016KGX4Smq8nmt+4IC2tXennusZ6TfztD0lf8uziizrGI6ZZtbwlzy3l/4bXHc1ezsAw81lGl4cuPlpfahJxQINzup5SOitiKAh3IC7FlEF8PusEETiTT8+vETTHRA19O6cryQEDq0NX7Q8tNFD76V1GNQOVpZN+qSTeumu190d6zu9EzipTfzdPQtagtt2GiNNMTj8Fjtw/jJ7PGEwn3fAQNddN81I2sydLW/Am3dnD7xfcU/TyW/mODKdLg8Q4uPx6LxJh301NMutpLdxqMNHi3heh7FPQBmzRMPB+i0PRbzPeIo+3zzaTfVCQbjAtMhsiTBbInGWqi3rSZYk1RcRw/KlEUD4WaoJ0ZBe/E7o/RjzCt0yROKPpp93J8deYjK9x7fT23X568y23MeOP4/vr7+TnYmz9ffDiQQSqWfBlcT+IBpqIq/3k4uQhqFpLP329Pk1IjEZ9RQSj0A9a3bYoN+YZsu60AbUsfrODfve86J7mmXEGJvkvx5lE6qCra8r9k5aSkIXOZrPAZVfuu49aQ8jsmplcJTCebbM0HHreGk87/ioqqntGR/iDsfZj5HZ+6cwCSJsKg0PG0WEeVlqohlZkLpSAeK033W9MGh6wayr4dIZZvwr/USCrnUDE3xQ5jyf42DJN8Eq8Hqs/tu7ro8486FWuKRdy84tnoTHqg+meFOa34SJzXJd5N3OCK3pWNEFI5N+Hj4WQKzReB2qHKkT9cxR/mf4jPb/4n/kCcUZu2M/0I85v1N/o1W+KDbBz6m0rJHJWITBSRl0Uk3DsviojMdFV+PxaR8xLWby4ir4rIRyJyr4hpOohIwP38sbt9XCFs9Hg9KVtGgYoCpF0oAi0b2pChfwbPJqQMJnBGI3VXQSTPVOpp0BRjVE7NzyF0At0POweCh0PtpWQ/duKB4MEgiaHLAai9BsczvG/x6NKs7c4Ph6Kq0kiNCU/3ZiN0HYOmX4I/meZhzieG9qd7rhlyPXi3AkIgVUAAqk5BgjO6C7U9nPUZvD5o2KyDo09fyWZbtzHnJyu4Zt5H+NQcY68BmvAbiQxaUdpvFKUco1oL/AQ4NHGlmIQ2f8Rk6l0GLBSRR1X1XeC3wNWqeo+I3AicDNzgvq9T1a1EZI5bLpn4W07U1tcwYfKWfPDaRz1UHnxBH7X11az8PPfkbsXGcQTxTYT6BRD7FI23mQyxHa+ZrrDQYUhgb0Q8qJPk4d5f0sj9OLUXoTUXYgSeg4gI8abfk72qt884V01sYYSh8TzUv0vfCb8dL+Vsfm4UWUtQW1CN5zA/S5DKI1BxINyPybIa6TPeJJ4RMOxRiH5glEB8k/q2ntseIpdAHq8PTjh3BSecm1C5iZlkl+Mmjc3X+pyIRQeX8sw3lZK1qFR1paoupO+o/27Ax25G3w7gHmC2qwy8L/CAW+4Oup3cbPcz7vYZbvl+c8FdZzJk1BBC1UF8AS/BygDb7jGBLbYfV4jDF5zOwWgRQbxb4vi/hVN9Gs6w23GG/B4nOK0ruR2VJxTeAE0/SVpEEAnR9fN0vELyh33QvKTSvPBDxbEQX0GfW0YjaNv9SY5R7NryAP19sghS6IFvQuYyaXHA31cPVcRUgiQwJXkXb4bfPheevP3Zgh0rHYFgefaMWHpSjlF/Y4AvEj4vA3YHhgHrVTWasH5M731UNSoiG9zyOf7D+9KwxUj+8u8/8sq8xaz6YjUTJm/JpL225vWn3uTV+YsHrC89Gzxeh6POyT4lieMbT9y7E0QLN5dE/DvltoNnM5MlN1lNfNg9SGyZmcAc2AvCzyWNQoewG5nWC+920PGPbKwmvy68cOYiWdO7VemAf29EHNS/B7SlS4eSgH9X8GwKzb/L044QBKYivkm57ypDzTSCfmEcx0D9q444qxBdpZZiU47BFMlaQppmfbp9eh5Y5NTO/C6rVmU/odDn9zH18N05/MxZfGvKRESEyfvtwOwfH5D1MQC8Pg91I2qYesQeBCoCiAhVQyu7Io8cR5g+ZwqSRySSP+Tn20ftyZRDd81pPxl2N1T8kIIEBYS+j0gwc7nE81fOpW+adR/4JuH4tkWC+yGhg0yGYO/WJFfaDYFvh76rK7L8fZyROdmccILUIfPO5mT99/Jub2wQN22GVIAzDKm9xHys+iGpJy+7qh0SQuquRcSP490E/PskKRuCwKwUxwmCdzuk5kKk7prs7O5N5XFkju4U+v7e3fZJYBoAh/34wLxMSDWmXDeilrFbd09OFoH95k7npEuPzes8loFlQFtUInI6cIr78UBVTTaJYRmwScLnscBXmNZRnYh43VZV5/rEfZaJyThXixkD64Gq3gzcDDB58uR+V9pOv/okdt53O66Yex3NvQRsRQRFqaytYKd9t+OQ0/anbkQtm207FscxYcexaAyvz0ssFqNlfSuVtRV4vB5mnz6Tiw66nNbGVhTw+bxM2ntrPn3zcxpX96yxiiMc9pMDmXbUnmyzxwRy7fEUcZCas9HQ/uja40HbMTV7P91jMKnmZLnyRlIDVWcgFd/L6dwA4tsG6q5BGy901SVi4N8LqevbIhDftqh/J+h4ne7WjANOJRI6rE95x7sFce92EE2VCwsgCNUXQ+MFoI101288bpejB/xTzNhMbCnd18ILnmFQ97+w5hB6ZLaVahj2f9D8R2i7j27FCi/IcNDVJrJO4+DdChlyEzjV0P4kGv0A8W4JwZldTl88Y9D6ebD2ZIh/br4zjnHOganGiQdn9uiOkyHXo823QettJrQ/MBWpOR+cBnRtM0QSwr8928Kwe3Gc/il5SMWxaPvjRtG+z5yrAFT9FAkdDNqBNt8A7fMSygXAMwZCxpHOOG4q8256krdffL/PefwhP2O2HMmXH3+Nx+sBgeohVVz+94v4+tMV3Hj2Haz+ci2xaAyPz8Ow0UO5+vlLqBteS/OGFlZ9sYaxExrw+TOH0lvKA9GS5IJIMEDkV0Czqv7O/ewFPgRmAF8CC4Hvqeo7InI/8GBCMMWbqnq96wC3U9UfusEUh6vq0enOO3nyZF20aFFBv8tLjy7k4Wsfp2ldM1MP353ZZxxAZU3v5HLZoap89u4yVJVxkzZBRGhe38IVc6/j1b+ZLscxWzdwwV/OZPzOhdEk1PhatPVBiC1F/Lugge9C673Qerc7ThLBtHZ2hppf4vjGFeS8gAkaiH0FTlXqEHdANYw2/wFaHwA6wD8NqTkP8YxKUb4DXX8ehB/HON2Q+x4FpwFqLsIJzkBjX6FN10DHCyC1Rrap4qgux6/xZrTpKjetRdw4hupzEGeoOUfbfIi+A77tkeBMRHxG/ij8DNp6L2i7URkPHeKmJ3kPnOFIjuNJGvsaop+AZ1MTNp4n8VgjxD4CzzicPqrz+aMaM985/E/wjITADESME+oaG8Xc37Q/YsSE460QOgCpOLFPXrFX/vY691/5CF9+soL2lnaGjKzjyLMO4sBTvsPXS1fy7ksfMrShjh2mT8JxnK5jv/ncu/z7rc8Zu/Vodv7Odl3b8kVEXs82kWEqamSo7i4zMhcEFugD/T7fxkTJHJWIjAIWATWYAYpmYFtVbRSRA4HOcLDbXHl6RGQLTHDFUOBfwPGqGhZT9fwzsBOmJTVHVT/tfc5EiuGoBopIR4RYNE6wothadhsPqlHTWpRKTKupAwjk3AK1fDOxjqq0lCyYQlW/xnTfJds2H+ijoeI6nz4TLFS1HTiq0DaWKz6/D58NVsoJEa87/wfMOEluY2kWi6V0lGMwhcVisVgsXVhHZbFYLJayxjoqi8VisZQ11lFZLBaLpayxjspisVgsZY11VBaLxWIpa0o+4bdUiMgqIEsBtS7qKYB+YJEpdxvL3T4ofxvL3T7Y+GzcTFX7lW5ARJ5wz5kNq1V1Zn/OtzHxjXVU+SAii8p9El6521ju9kH521ju9oG10VJYbNefxWKxWMoa66gsFovFUtZYR5UbN5fagCwodxvL3T4ofxvL3T6wNloKiB2jslgsFktZY1tUFovFYilrrKOyWCwWS1ljHVUWiMhMEflARD4WkfPK3R4RmSsiq0Rkifv6QSns7GXTbSKyUkTeLrUtkNkeEZkuIhsSruEvBtrGJDZtIiLPiMh7IvKOiJxZ7vaU23UUkaCIvCYib7g2/1cp7bFkhx2jyoCYtKQfAt/FpLxfCByrqu+Wqz0iMheYrKpnlMLGZIjItzHJMe9U1W+Vuz0iMh04R1UPGmjbUiEiDUCDqi4WkWrgdeDQEt6LGe0pt+soJlNmpao2i4gPeBE4U1VfKbFpljTYFlVmdgM+VtVPVbUDk2F4trUnN1T1eUz25bKg3OzJBlVdrqqL3eUm4D1gjLUne9TQ7H70uS9bWy9zrKPKzBjgi4TPyyjtnzFbe44QkTdF5AER2WRgTNvo2NPtInpcRCaV2phERGQcsBPwamktMWSwp6yuo4h4RGQJsBJ4SlXL4hpaUmMdVWYkybpS1sCysecxYJyqbg8sAO4oulUbH4sx+m47ANcCD5fYni5EpAp4EPipqjaWuT1ldx1VNaaqOwJjgd1EpORd0Zb0WEeVmWVAYotkLPBViWyBLOxR1TWqGnY/3gLsMkC2bTSoamNnF5Gqzgd8IpKtoGjRcMdVHgTuUtW/lrs95XodAVR1PfAsYMVfyxzrqDKzEBgvIpuLiB+YAzxazva4g9ydHIIZO7DkgIiMcgfeEZHdMP+VNSW2SYBbgfdU9X9KaUu29pTbdRSR4SJS5y6HgO8A75fKHkt2eEttQLmjqlEROQP4O+ABblPVd8rNHhG5BFikqo8CPxGRQ4AoJmBgbqns7URE7gamA/Uisgz4pareWk72YAbWUdUbgSOBH4lIFGgD5mjpQ2SnAN8H3nLHWAAucFsqZWMPsCmU7XVsAO5wo2cd4D5VnVdCeyxZYMPTLRaLxVLW2K4/i8VisZQ11lFZLBaLpayxjspisVgsZY11VBaLxWIpa6yjslgsFktZYx2VpSSISMxV035DRBaLyF7u+tEi8kAJ7FkqIvUiUicipw3QOQ8VkV+4CuMv99rmFZEVItIgIr8TkX0HwiaLpRyx4emWkiAizapa5S7vj5kPNK2E9iwFJgNVwLyBUHgXkZcwE7LXAp8BU1V1qbttJnCuqs4Qkc2AW1R1v2LbZLGUI7ZFZSkHaoB1YMRNO3NEucsvuC2uxFZXg4g877bI3haRqYkHE5EDROS+hM/TReQxd/lYEXnL3e+3SWz5DbCle+wrRaRKRJ52z/+WiMxOOO7FIvK+iDwlIneLyDnu+i1F5AkRed21f2Lvk4jIBCCsqqtVNQ7cDxyTUGQOcDeAqn4GDBORUTlfWYtlY0BV7cu+BvwFxIAlGPmaDcAu7vpxwNvucgUQdJfHY5Q3AH4GXOgue4DqXsf2Ap9j8g4B3AAcD4x21w93y/wDkz8JYClQn3j+hGPVuMv1wMcYYeDJrv0hoBr4CJN3CeBpYLy7vDvwjyTf/yTgqoTPuwL/cpcDGGXvIQnbbwGOKPXvZl/2VYqXlVCylIo2NQrWiMiewJ1JVKx9wHUisiPGsU1w1y8EbnMFUR9W1SWJO6mRmXoCONgd75oF/CewL/Csqq5yz3sX8G3SK3oLcJmYRItxTEqVkcDewCOq2uYeq7PFVgXsBdzvStyBcTy9aQBWJdi80G29bQ1sA7yiqusSyq/EOFqL5RuHdVSWkqOqL7uK2sN7bToLWAHsgOmmbnfLP+86jlnAn0XkSlW9s9e+9wKnY8Z/FqpqkyR4jhw4zrVrF1WNuGNZQZKnW8G1c32nE05DG1Dba909mC6/bXC7/RIIuvtYLN847BiVpeS4Yzge+qpq1wLL1YzhfN8tgxtcsFJVb8God++c5LDPuutPwTgtMEn9prnRfR7gWOC5Xvs1YbryEm1Y6TqpfYDN3PUvYlpsQbcVNQtMWgvg3yJylGuriMgOSex7D9iq17q7MV2U+9JXoX8C8HaS41gsGz22RWUpFaEExW0BTlTVWK9Gz/XAg+5D/xmgxV0/HThXRCJAM3BC74O7x5qHUY4/0V23XETOd48lwHxVfaTXfmtE5J9uQMfjwG+Bx0RkEd1jap1ddY8Cb2Ai9hZhxtrAtMJuEJGLMN2X97jlEnkeuEpERFXVPea7ItIKvK6qnd+1M+fTVu45LJZvHDY83WLJExGpUtVmEanAOJ5TVXVxDvtfAzymqgsylDsM2FlVL+6fxRbL4MR2/Vks+XOz2ypcDDyYi5NyuQwT2ZgJL3BVrsZZLBsLtkVlsVgslrLGtqgsFovFUtZYR2WxWCyWssY6KovFYrGUNdZRWSwWi6WssY7KYrFYLGXN/wfBDCmcsh1j6AAAAABJRU5ErkJggg==", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax, cax = plot_dataset(dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Categorical data on x-axis and z-axis\n", "\n", "For completeness, we include two examples of this situation. One resulting in a grid and one resulting in a scatter plot. We reuse the example with the x- and y-axes having numerical data with just a slight modification." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 760\n" ] } ], "source": [ "sample = Parameter('sample',\n", " label='Sample',\n", " set_cmd=None,\n", " get_cmd=None)\n", "gate_voltage = Parameter('gate_v',\n", " label='Gate voltage',\n", " unit='V',\n", " set_cmd=None,\n", " get_cmd=None)\n", "useful = Parameter('usefulness',\n", " label='Usefulness of region',\n", " set_cmd=None,\n", " get_cmd=None)\n", "\n", "meas = Measurement(exp=exp)\n", "meas.register_parameter(sample, paramtype='text')\n", "meas.register_parameter(gate_voltage)\n", "meas.register_parameter(useful, setpoints=(sample, gate_voltage),\n", " paramtype='text')\n", "\n", "samples = ['nanowire', '2DEG', 'spin qubit', 'nanowire_alt']\n", "\n", "# a function to simulate the usefulness of a region\n", "\n", "\n", "def get_usefulness(x, y):\n", " x_num = samples.index(x)*4/len(samples)\n", " val = np.sin(x_num)*np.sin(y)\n", " if val < -0.4:\n", " return 'Useless'\n", " if val < 0:\n", " return 'Bad'\n", " if val <0.5:\n", " return 'Possible'\n", " return 'Good'\n", "\n", "\n", "with meas.run() as datasaver:\n", "\n", " for samp in samples:\n", " for gate_v in np.linspace(-1, 1, 75):\n", " datasaver.add_result((sample, samp),\n", " (gate_voltage, gate_v),\n", " (useful, get_usefulness(samp, gate_v)))\n", "\n", "dataset = datasaver.dataset" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax, cax = plot_dataset(dataset)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting experimental run with id: 761\n" ] } ], "source": [ "sample = Parameter('sample',\n", " label='Sample',\n", " set_cmd=None,\n", " get_cmd=None)\n", "gate_voltage = Parameter('gate_v',\n", " label='Gate voltage',\n", " unit='V',\n", " set_cmd=None,\n", " get_cmd=None)\n", "useful = Parameter('usefulness',\n", " label='Usefulness of region',\n", " set_cmd=None,\n", " get_cmd=None)\n", "\n", "meas = Measurement(exp=exp)\n", "meas.register_parameter(sample, paramtype='text')\n", "meas.register_parameter(gate_voltage)\n", "meas.register_parameter(useful, setpoints=(sample, gate_voltage),\n", " paramtype='text')\n", "\n", "samples = ['nanowire', '2DEG', 'spin qubit', 'nanowire_alt']\n", "\n", "# a function to simulate the usefulness of a region\n", "\n", "\n", "def get_usefulness(x, y):\n", " x_num = samples.index(x)*4/len(samples)\n", " val = np.sin(x_num)*np.sin(y)\n", " if val < -0.4:\n", " return 'Useless'\n", " if val < 0:\n", " return 'Bad'\n", " if val <0.5:\n", " return 'Possible'\n", " return 'Good'\n", "\n", "\n", "with meas.run() as datasaver:\n", "\n", " for samp in samples:\n", " for gate_v in 2*(np.random.rand(75)-0.5):\n", " datasaver.add_result((sample, samp),\n", " (gate_voltage, gate_v),\n", " (useful, get_usefulness(samp, gate_v)))\n", "\n", "dataset = datasaver.dataset" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax, cax = plot_dataset(dataset)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.5" } }, "nbformat": 4, "nbformat_minor": 2 }