Topology in neuroscience
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"# Decoding population activity\n",
"\n",
"_In this notebook we sample population activity using the analytical model, then analyze the topology and successfully decode orientation using cohomological parametrization_"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"**Imports**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import sys\n",
"import seaborn as sns\n",
"import umap\n",
"import numpy as np\n",
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"sys.path.insert(0, './model')\n",
"sys.path.insert(0, './numerical')\n",
"\n",
"from sym_model import Population, sigmoid\n",
"from model import decoding\n",
"from model import persistence\n",
"from utils import get_orientation_phase_grid"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"## Getting the data\n",
"\n",
"**Sampling the activity using the model**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"limit = 500\n",
"# n_theta, n_phi = 18, 24\n",
"n_theta, n_phi = 36, 72\n",
"# n_theta, n_phi = 9, 12\n",
"step_phi, step_theta = 360 // n_phi, 180 // n_theta\n",
"N = 64 # number of cells"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0. 0.] [3.05432619 6.19591884] (2592, 2)\n"
]
}
],
"source": [
"grid = get_orientation_phase_grid(step_phi, step_theta)\n",
"grid = grid.reshape((-1, 2))\n",
"print(grid[0], grid[-1], grid.shape)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"population = Population.sample(8, 8)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle 64$"
],
"text/plain": [
"64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(population.cells)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"phi_deg, theta_deg = grid[:, 1] * 180 / np.pi, grid[:, 0] * 180 / np.pi"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"# res = population.sample_responses(limit, custom_grid=grid, use_sigmoid=False)\n",
"# res, phi_deg, theta_deg = res[:, :, 0], res[:, 0, 1], res[:, 0, 2]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"res = np.abs(population.response_func(grid[:, 1], grid[:, 0]).swapaxes(0, 1)) * 10"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"phi_reorder = sorted(list(range(len(phi_deg[:limit]))), key=lambda x: phi_deg[x])\n",
"theta_reorder = sorted(list(range(len(theta_deg[:limit]))), key=lambda x: theta_deg[x])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left( 2592, \\ 64\\right)$"
],
"text/plain": [
"(2592, 64)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res.shape"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"res_reshaped = res.reshape((n_phi, n_theta, -1))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3.10/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
" warnings.warn(msg, FutureWarning)\n"
]
},
{
"data": {
"text/plain": [
"<AxesSubplot:ylabel='Density'>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(res.ravel())"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"## Plotting joint tunings"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"phase_linspace = np.linspace(0, 360, n_phi)\n",
"orientation_linspace = np.linspace(0, 180, n_theta)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"image/png": 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oXRaxb3IpLhx/L1NdOESz3iCgPMAYQ3M0Rt0UJ1rU6TFttFCPRAu1yzKdnigTevOL8or0TS7Z3I04kqklhXrzyxi0ULss28FqRlMbDtEZs6Y8UiqfIg526aVvftHbyUemhdplzdGYI/14cPwQsUVbHirPnCzU6e1oH/XItFC7qCs+QHc8swk/M6EnXZRXpM+VONkI0a6PkWmhdlF68k4nWx2gI46p/ItEY4QK/IQdGs6gNhzisE7kPCIt1C5y+vBwmj2DhrY8VL5Z516KHLuTMP03ohM5D08LtYuaB292caZQB/w+ZpTrIaLKPydPkoN2641FC7WLItEYBX6hujTo2DZrw0WazCrvmh262SVN704cnRZqF0WiMWZUFOHLcMLPTNSGQ0R0+iKVR/GBJO09fY62qNOTDmihHp4OyuSCxkgXbxyM8mZzZ8YzjmeqNhziiU0tPPjqfurCIS47QwezV7lhjOHJN1tp6jgGOHfuBaCowE9VaSGrdrdTVRbkbQuqmDW12LHtT3RaqF3wuYc2sOtwDwCXLnS2kDbUlJNIGe569E1EYMM/vJNwsY44pty34UCU2372OgAisGhGmaPbX1xTzh93tvPKng6uOms6379xiaPbn8i0UDustz/B7rYebrlsHp9cOZdpZc71TwO897xals+vZM3uI3z2wQ00tnSxYn6Vo/tQajhbmjsBePwzK5k9tdjxBsIPP34xHcf6+erjm9nc/JZpWU9r2kftsG2t3RgDS+ZMcbx/Oq2qNMiyeZWA1c2iVC40tnRRESrgnLoKV47iCgM+ZlQUcf6sKTRHY3T2Dji+j4lKC7XD0oWzoTa7aYmyVV0WZFpZkMYWLdQqNxojXTTUlLs+C0v6b0dz+zgt1A7b2tJFeVHA0UuXRtJQW64tapUTiWSKba3drjdAgMG5F7dqoR6khdphjS1dNNS63+oAK6F3He7R226V6/YdOUZfIpX1BLanorosSLUeLZ5AC7WDkinDtpZuFucgmcFqUSdShp2HenKyP3X62pKjLr20xTV6tDiUFmoH7TtyjNhAMietDjh+iKgtD+W2xpYuCv0+5leX5mR/DTXl7DzcTX8ilZP9eZ0Wagfl6kRi2pzKEooL/dryUK5rjHSxcHophYHclIyG2nIGkmbwfoTTnRZqBzW2dFHgFxZOc/ZGgJH4fcKiGWV60kW5bmtLV86OFEGPFk+mN7w44Cu/3syDr+4nkTKcOb0sZ60OsFoeP31lPwv//kkaasp59NaV+F24dludfqK9/Vz73T9yuLuPRMrk7NwLwNyqEkIFfv7ul2/wpUff5M5rFvHJt83N2f69Rgv1OB3ujvPgq/u5uH4q588KO37L+Fj+8tJ5hEOFRDpjPPp6My/uOMyVi6bnNAY1Of183QEinXE+uXIuZUUBPnBBXc727fcJ3/7QeWxu7uSF7W3c8+Jublw+hwL/6dkJoIV6nB569QADScP//cA5zK0qyfn+51SW8MWrzmQgmWLVrnZ+vLpJC7Uat2TK8NNX9nNJ/VS+8t6GvMRw7Tk1XHtODRfNmcJf/Hgdz245xLvPrclLLPl2en49OWQgmeKBtU1cdkZ1Xor0UAV+Hx+5ZA4v7mhjX/uxvMaiJr4Xdxxmf0cvN62Yk+9QuOLMacyaGuLHa/blO5S8yahQi8jVIrJdRHaJyJ1uB+V1h7vjPLGphf/3/E4OdfXx8eX5T2aAD18yi4BP+Ndnt/PbTRF2HOrOd0iep7l9otf3H+W3myL89x92M60syFVnzch3SPh9wseWzuHVvR389JUmnt7cQnf89BoHZMyuDxHxA98D3gkcBF4TkceNMY1uB+dFq3a189kHN9BxrB+wTnpccea0PEdlmVZexHvPq+VXG5p5YlMLAZ/w5Xcv5uMr6nNyp+REo7l9XCKZ4htPbeMHL+8dfO2L7zrDM33CH1oyi7uf38mXH9sMWH9393zsIs50eKhVr8qkj/oSYJcxZg+AiDwEXAdklcxt3X2s2XMk+whdZoyx/7eep4whPpAavC27O55gS6STAx0xUsaw41A386tLuffGi6gIFTCtvMhTV1l844PncOsV80mkDN9+djv/+JtGfvJKE8GANTD72XUVg0OvBvw+QgV+AkPiF8ETRb2+sphzZ4bd3o0jub2+6agnp0czxgzmNVhddfGBJImU9frBozG2RDrpiifoig3QHI1x0/I5fGzZHPw+ob4yv915Q00pKeTFv3s7R4/1c+BoL3c88ibXfe9l5laV4vfBvKpSFtWUESrwI0CwwE8w4MNn53I6pb2Q25cuqGJKSXajD2ZSqOuAA0OeHwSWnrySiNwC3AIwe/bst2xk56FuPvfghqyC84p5VSXMrSrB5xNWLqjib955BiVBb56HDQb8LJxutTLuvXEJ963ay9q9HRgDLZ0xfvDHPQwkzRhbyb+PLp2di0I9Zm6PldcA96/Zx2MbIy6F6J6iAh+La8qZOSWETAlxxzWLeN95tfkOa0RVpUGqSoMsnF7GE5+t4NvP7qCjt5+BZIr1TUd5/I2J8Rn8+raVWRdqMWb0P1oRuR642hhzs/38RmCpMeYzI71nyZIlZt26dSe81tufIBKNZxVcrviGtCIFCBX6KfT7ELEKX6jQn98AHdSfSNHbn8AYGEiliPenSA4eVRhSHqnh5aEA08qKhl0mIuuNMeOe/iPb3B4urwEOdcXpjifGG44rhuZ2wCcUFfgp8FvPy4oKPHU0OF49fQkSyRQpA32JJH0DKdLp7KXcnjklRFHBW2vKaHmdSbOwGZg1dD/2a1kpLgywYFpuxglQIysM+CgM6NRdNkdye3p5EdNzdy+IGkGpR49ynZBJizoA7ADegZXErwEfMcZsGeU9bUDTMIuqgPZTjtZdXo3Nq3FB/mKbY4wZ951F2eb2KHkN+jmdCo3rRCPm9ZhfQcaYhIh8BngG8AP3jVak7fcMuzMRWefEIasbvBqbV+MCb8eWiWxze7QvBy//Lrwam8aVuYyOFYwxTwJPuhyLUjmnua0mAm9cJKmUUmpEuS7U9+Z4f9nwamxejQu8HVuuefl34dXYNK4MjXkyUSmlVH5p14dSSnmcFmqllPK4nBXqfI9SJiL3ichhEdk85LWpIvKciOy0/59ivy4icrcd6yYRudDFuGaJyAsi0igiW0Tkdi/EJiJFIvKqiLxhx/U1+/W5IrLW3v/DIlJovx60n++yl9e7EZcX5TO3Na9PKbaJl9vWwC3u/sO6RnU3MA8oBN4AGnKx7yExXAZcCGwe8tq3gDvtx3cC37QfXws8hXVH+TJgrYtx1QAX2o/LsG7AaMh3bPb2S+3HBcBae38/B26wX78H+LT9+FbgHvvxDcDDufx88/Uv37mteX165Haukmk58MyQ53cBd+X8h4X6kxJ6O1AzJLG224+/D3x4uPVyEOOvsYbd9ExsQDHwOtaARe1A4OTPFeumkeX244C9nuT6M85DTuU9tzWvxxXXhMjtXHV9DDdKWe4mYBvZdGNMi/24FUjPYZWXeO1DqguwvuHzHpuI+EVkI3AYeA6r5Rg1xqRHIBq678G47OWdQKUbcXmMF3M777kzlNfy2o5pQuW2nky0GevrMm/XKopIKfAI8HljTNfQZfmKzRiTNMacjzVY0SXAolzHoMZH83p4Ey23XbmOuqqqytTX1zu+XaUA1q9f324cGJQpW5rXyk2j5bUr4wLW19cz3Li9SjlBREYawc5VmtfKTaPl9eQdwHWC6Esk+eIvNnGkp4/CgI+vve8s5nhoCiSlTtW6fR38x+92kjKGM2eU8dX3npXvkCYs7aPOs1f3dvCbNyJEewd4cUcbj22YGNMJKTWWH69pYn3TUVq74vzvqn1EPDiv5EShhTrPVu8+QsAn/OJTyzmrtpxVu704jrpS2THGsGZ3O+86azr/+WHr3pXVu703ufVEoYU6z1bvauf8WWFKggFWzq9iw/6j9PZ7c/49pTK141AP7T39rJxfxaIZZUwtKWT1Lm2EnCot1HnUGRvgzeZOViyoAmDFgioGkobX9h3Nc2RKjc8quyivWFCJzycsn1/Jqt3tuHGV2elAC3UevbLnCCkDK+db185fXD+FAr9oy0NNeKt3tzOnspiZU4oBWDm/ikNdfexuO5bnyCYmLdR5tHpXO6ECPxfMngJYM7VfMHuK9lOrCS2RTLF2Twcr5lcNvrZygdUYWa25fUr08rw8aIx00dIZ4w872rh47lQKA8e/L1fOr+I/nt/BU2+2MKWkkKVzpyIieYxWqcz0J1K8sucIe9uP0d2XGCzOALOnFlMXDvHEphbqwiEWTCvVy1CzoIU6x/oSSd7/X6voT6QA+Pjy+hOWv31RNf/+ux18+oHXAXj01hVcaLe4lfKyX29s5m9/uQmAQr/vhBa1iPD2RdX89JX9rN3bwYJppfzuby7PV6gTjhbqHGvtjNOfSPH5P1nIOxums2hG+QnLz50Z5vkvXM7OQz186qfraTpyTAu1mhCajvTi9wmPfHoFVaWFTC0pPGH5l9/dwP9ZMpsfr9nH4xsjpFIGn0+PFjOhfdQ5FonGAVgyZypn1VbgHyZR51eXcvkZ1Sesr5TXRTpjTC8Lcv6s8OBJxKGKCvycM7OCc+oq6E+mOHKsPw9RTkxaqHOspdO6O6smXDTqeqFCP+HiAr2bS00YkWiMmnBozPVqKooG11eZ0UKdY+nkrK3IJKFDtHRqi1pNDC2dcWozKNTpddKNFjU2LdQ5FumMM6W4gFChf8x168JF2upQE4IxxirUFaMfKcLxQq3depnTQp1jLdEYNRm0pkFb1GriOHKsn/5EarBbYzRTigsIBnzaos6CFuocy/TwEKx+7M7YAMf6dOwP5W2DXXoZ5LaIUBcOaYs6C1qoc6w5GqN2jBOJael+bG15KK9LF91sGiERzeuMaaHOoZ6+BN3xRMZdH9qXpyaKwauZMuj6sNYL0aJ5nTEt1DnUMnh4mGky62VMamKIRGMEA7633OQyktqKIg51xxlIplyObHIYs1CLyCwReUFEGkVki4jcnovAJqNIZ3aHhzMqihA5/j7lLM1t50Tscy+ZjktTGw5hDBzq0tzORCYt6gTwBWNMA7AMuE1EGtwNa3JKt4wzPTws8PuoLg0OtsSV4zS3HWJdzZRZXgODN8boVU2ZGbNQG2NajDGv24+7ga1AnduBTUYt0RgiML0884SuDYf0pItLNLedE4nGMz73Agxeb63depnJqo9aROqBC4C1rkQzyUU640wvK6LAn/mvvTZcpCddckBz+9QlkikOd8epy/DcCxxvUeuJ8sxkXDFEpBR4BPi8MaZrmOW3iMg6EVnX1tbmZIyTRktnbMwxPk5WU2G1qHUKI/eMltua12M71N1HypDROB9ppcEA5UUBvfQ0QxkVahEpwErkB4wxjw63jjHmXmPMEmPMkurqaidjnPCaozFe2XOEfe29GY3xMVRNRRHxgRQvbD/M9tZulyI8fY2V25rXIzPG8MaBKL/fdhjI/NxLWm04xLaWbl7Zc4QOHUlvVGOORy3WadwfAluNMd9xP6TJ50P3rKHZ7ov7wAXZdYHOq7Zmwfjkj9YBsPrOKzO+akSNTnN7fNbsPsJHfnC8p2huVXYztsytKuGpza3ccO8rXLqwivv/YqnTIU4amUwcsBK4EXhTRDbar33JGPOka1FNIvGBJM3RGDdcPIv3X1DHeTPDWb3/ijOm8eitK9iwP8rXf9vIviPHtFA7R3N7HHa3WxPV3vOxC5k1tTjrqbW+8afncuPyOdzz4h72tPW4EeKkMWahNsa8DOg0DKcoffnRJXOnsmxe5Rhrv5XPJ1w4ewpTiwv5+m8b9eSLgzS3xycSjVHgF97VMOOUZmqpKC5gxfwqVu1qZ/WudpIpM+xEGkrvTHRdNoPVjGaGXs6kPCYSjTGjomjc02nVhkMkUoa27j6HIpt8tFC7LN03XTfOQl1U4KeqNKiFWnlGJBrL+uT4cNKNmGbN7RFpoXZZ5BRuchlJXbhIk1l5RiQaH3cDBI43YrQRMjIt1C6LRGNMKwtSGBj/r7o2HNJkVp6QSKZo7cp8bPXR6OBjY9NC7TJr/GlnrtKoDYdojurNLyr/DnX3kUwZR3K7rKiA8qKAHi2OQgu1yyJRZ1odYBXq+ECKo70DjmxPqVMVyXLI3rHo0eLotFC7yBhDczTmSD8eMDiWgia0yreIQyfJ0+rCIZr10tMRaaF2UXrCz0xmZs6Enh1XXpHOwWzG9xiNtqhHp4XaRU5dQ51Wq2fHlUdEojEqQgWUBjO5uXlsteEQnbEBenQi52FpoXbR4OHhFGcKdWVJIcGATwu1yjunLs1LS/d16yQZw9NC7aJ0n5tTCS0i1IVDehu5yruIg1czwfG/Ee3WG54WahdFojGKC/1UhAoc22b6Ej2l8sk6Se7MuRcY2q2njZDhaKF2UbrVkemEn5moDRdp14fKq674AN3xhKMt6mllQfw+0dwegTNnAtQJfrn+IC/uaOPVvR2cVVfh6LZrwyEOd/fx2Qc3MHtqiC++60xHvwiUGkl8IMnXf9tIqz0ipJOFOuD3MaO8iMffiNDU0cv1F83k8jN0ooY0bVG74N+e2c6L2w9TESrg6rNmOLrtSxdWc8b0UtbuOcL3XtitszirnHl1bwcPrN3PttZuzqot58I5Uxzd/vvOryXgE37XeIh7X9rt6LYnOm1RO+xITx+tXXG+/O7F3HzpPMe3f9GcKTz715ezvukoH/zv1TRGunQiAZUTjS3WdJJPfO5thIsLHd/+HVcv4o6rF3HXo5t4enMrxhg9WrRpi9phW1useQ0X15S7up9FM8oQOf7Ho5TbGiNd1IVDrhTpoRbXlHO0d4DWLj1aTNNC7bDGlk7A/UJdEgwwt7KExogWapUbjS1druc1QIO9D83t47RQO6wx0kVNRRFTS9xtdYD1ZaAtapUL8YEke9p6aKgpc31fi7RQv4UWaodtbekebBG4raG2nP0dvXTFdTQ95a7trd2kjJVzbisNBqivLGZrqxbqNC3UDooPJNnV1pOTZIbjh4jb7H5xpdySPnJrqHH2ctORNNSWa4t6CC3UDtp5qIdkyuSkHw+Ot24aI5052Z86fTVGuigLBpjp0Lg1Y1k8o5x9R3p1kCabFmoHpU8k5qrrY1pZkMqSQu2nVq5Ln0gc74zjmUo3QrZpbgNaqB3R2TvAun0d/HFnOyWFfmZPLc7JfkWEhtpyNuyPsm5fh44Bohy3u62H1/Z1sK2lK2ddenC8UD/XeIj1TR3EB5I527cX6Q0vDrjl/nWs3dsBwLJ5U3PW6gA4b2aYP+7cxfX3rKEiVMDqO6+kxKExgtXpbU9bD+/695dIpqw5Os+dmZv+aYAZ5UVMKwvy/Zf28P2X9vCRpbP5lw+ck7P9e43+RY/T1pYu1u7t4BMr67ly0TQWzchdqwPgtrcvYPn8SvZ39HLXo2/y2MZmPrp0Tk5jUJPT/a804RO456YllBUFWOLwLeOjERF++akVNHUc42dr9/Po6we546pFVBQ7NxLlRKJdH+P0kzVNBAM+bn/HQi5dWE11WTCn+w8V+lm5oIobLp7FWbXl3L+mSWcpV+PW25/gl+sPcs3ZNbyzYTrL5lUS8Oe2XMyuLObShdXc9vYFxAdS/GL9gZzu30u0UI9DZ2yAxzY08/7z61y/rXYsIsJNy+ewrbWbV+1uGKVO1WMbInTHE9y0PP9HZ2fXVXDRnCn89JUmUqnTsxGSUdeHiFwNfBfwAz8wxnzD1ag87qUdbTyzpZX9Hb3EBpLc6IFkBnjfeXX8y5Pb+OrjW7hwzhTOrq3gw5fM0oFtRqG5fVxfIskP/riX5miMF7e30VBTzkU57O4YzU3L53D7Qxv57EMbqCwp5ENLZnG2w0MIe9mYhVpE/MD3gHcCB4HXRORxY0yj28F5hTGGrlgCg+G+Vfu4+/mdlAUDBAv8vOfcGs8kTKjQz21vn8+9L+3l6c2t/Gztfl7a0cbXrjuLooCfkqA/54evXqa5bRXneH+Kjt5+/vrhjWw8EKWqtBC/T/jslQs88yV/zdk1/Hj2Ptbu6eBYX4KHXjvA1687i6vPqkF8UBYMeCZWN2TSor4E2GWM2QMgIg8B1wFZJfOuwz388OU92UfoMmPsf1iHVClj3WHYl0gB0B0foDHSRVf8+IX31180k39+/9kUFfjzEvNobrlsPrdcNh9jDD98eS//31PbeHpLKwDBgI9FNeVMs/vRC/xCUYGfwJCrVHwieCHfl86t5P0X1Lm9G0dy++HX9rPxQNT56MbJGEgNOV+RSBpiA0kSKYMxcPBoLzsP9wxe1VFS6Oe/P3oh15xTk6+QR1QY8PHorSsBayjhzz20gTseeZM7HnkTgKrSIItryigq8CNAsMBPMOAjndqClddeyO1br1jArCwv4c2kUNcBQ3vxDwJLT15JRG4BbgGYPXv2WzZytLef57ceziq4XBlanAQoKvRT6PchIhQV+HjPebXMqyrBJ8LsqcW8Y/E0z397iwg3XzqPS+ZOZd2+oxisGZ43Rzo5eDSGMYZEyhDrTw7+MZ/8h51PFaGc9PmPmdtj5TXAlkjXhMhtv08IFRw/qppWXsSVi6ZRVRpEBN5+5jTqq0ryGG1mKkuD/OSTS/nNGxE6jvUzkEyx41APOw93097TjzGGvkSKvoEk6Wz2Um5/bFn2XaUy1hUCInI9cLUx5mb7+Y3AUmPMZ0Z6z5IlS8y6deuyDkapTIjIemPMEge2k1Vua14rN42W15l0WDYDs4Y8n2m/ptREp7mtJoRMWtQBYAfwDqwkfg34iDFmyyjvaQOahllUBbSfcrTu8mpsXo0L8hfbHGPMuGc+zTa3R8lr0M/pVGhcJxoxr8fsozbGJETkM8AzWJcw3TdakbbfM+zORGSdE4esbvBqbF6NC7wdWyayze3Rvhy8/LvwamwaV+Yyuo7aGPMk8KTLsSiVc5rbaiLQi2qVUsrjcl2o783x/rLh1di8Ghd4O7Zc8/LvwquxaVwZGvNkolJKqfzSrg+llPI4LdRKKeVxOSvUInK1iGwXkV0icmeu9jtk//eJyGER2Tzktaki8pyI7LT/n2K/LiJytx3rJhG50MW4ZonICyLSKCJbROR2L8QmIkUi8qqIvGHH9TX79bkistbe/8MiUmi/HrSf77KX17sRlxflM7c1r08ptomX28YY1/9hXaO6G5gHFAJvAA252PeQGC4DLgQ2D3ntW8Cd9uM7gW/aj68FnsIa+mMZsNbFuGqAC+3HZVg3YDTkOzZ7+6X24wJgrb2/nwM32K/fA3zafnwrcI/9+Abg4Vx+vvn6l+/c1rw+PXI7V8m0HHhmyPO7gLty/sNC/UkJvR2oGZJY2+3H3wc+PNx6OYjx11jDbnomNqAYeB1rwKJ2IHDy54p108hy+3HAXk9y/RnnIafyntua1+OKa0Lkdq66PoYbpcz1MSwzMN0Y02I/bgWm24/zEq99SHUB1jd83mMTEb+IbAQOA89htRyjxpj0mK9D9z0Yl728E6h0Iy6P8WJu5z13hvJaXtsxTajc1pOJNmN9XebtWkURKQUeAT5vjOkauixfsRljksaY87EGK7oEWJTrGNT4aF4Pb6LltivXUVdVVZn6+nrHt6sUwPr169uNA4MyZUvzWrlptLzOaKyPbNXX16Pj9iq3iMhII9i5SvNauWm0vHalUKvMRXv7+ZPvvEh7Tz+FAR8P3LyUi+un5jsspcbtF+sOcMcjm0gZWDSjjCc/dyk+n7dnRvIq7aPOs5d3tdPe08+Ny+YgwFNvtuY7JKUc8ZtNLUwvL+I959awrbWb7Ye68x3ShKWFOs9W7TpCWTDAV9/bwMX1U1m924vjqCuVnf5Eitf2dvCuhul86drFAKzapbl9qrRQ59nq3e0snTeVgN/HigWVbGvtpr2nL99hKTUuG/YfJTaQZMWCKmrDIeZWlbB695F8hzVhaaHOo4NHe2k60suK+VUArLT/14RWE92q3UfwCSybZ11uvGJ+JWv3HGEgmcpzZBOTFuo8Wr3LKsgrF1gF+uy6CsqLAqzWQ0Q1wa3e1c45dRVUhAoAK8eP9SfZdDCa38AmKC3UebRqdztVpUHOmF4KgN8nLJtXySrtp1YT2LG+BBsPRFlhN0AAls+rRMQ6J6Oyp5fn5cH9rzSxr/0YL+5o47KF1Ygcv2Rp5YIqnm08xD88tpkpJYXcesV8igr8eYxWqcy0dffxw5f30hyNkUiZwa48gCklhTTUlPPI6wfpjA1wcf0Urj67Jo/RTizaos6xnr4E//DYZu5/pQlj4H3n1Z6w/B2LpzG9PMgv1x/k7ud38urejjxFqlR2ntgU4Z4Xd/P7rYdYMK2UJfVTTlj+wQtn0tHTz0/W7OMfH2/MU5QTk7aoc6wlGgPgX68/l+vOf+uYMzOnFLP2S3/CgY5eLv3WC7R0xnIdolKnJNIZJxjwsflrV51wlJj2ybfN5ZNvm8t3nt3Of76wi4FkigK/thUzob+lHGu2C3VtODTqejMqihCB5mg8F2EpNW7N0Ri14dCwRXqo2nCIlIFDXZrbmdJCnWMtnVZy1lQUjbpegd/HtLLgYAtcKa9ricbGzGuAGruRkv5bUGPTQp1jLdEYIjC9PIOErghpMqsJo6UzTk3F6EeKALV2MY9oIyRjWqhzLNIZZ3pZUUZ9c7XhIk1mNSEkkikOdcWpC2feoo5ot17GtFDnWCQaoyaDZAarRR3pjOHGmOFKOelQdx8pc7wIj6Y0GKC8KKAnyrOghTrHWjrj1GZweAjWSZf4QIpo74DLUSk1PulzKZn0UYOV29qizpwW6hwyxlgt6kyT2V6vWbs/lMdlejVTWk2FdutlQwt1Dh3tHaAvkco8mfXsuJogMr2aKa02HNKujyxooc6hyGCrI7sWtSa08rqWaIyyogBlRQUZrV8bDnG0d4BYf9LlyCaHMQu1iMwSkRdEpFFEtojI7bkIbDKKDPbjZdairioNUuAX7ctziea2cyJZnHuB4y1vbYRkJpMWdQL4gjGmAVgG3CYiDe6GNTmlDw8z7frw+YQZ2pfnJs1th0SisYyPFOH434A2QjIzZqE2xrQYY163H3cDW4G3DlKhxhTpjFHo91FZUpjxe6ybXrRQu0Fz2zktnfGMLs1LS7e+I5rbGcmqj1pE6oELgLXDLLtFRNaJyLq2tjaHwptcItE4MyqKspqJubaiSFsdOTBSbmtejy0+kKTjWP/gOZVMTK8IAtCiuZ2RjAu1iJQCjwCfN8Z0nbzcGHOvMWaJMWZJdXW1kzFOGpmOhTBUbThEa1ecZEpvenHLaLmteT22bM+9AAQDfqpKg9qtl6GMhjkVkQKsRH7AGPOouyFNPs9uaWVzpItdbT1ceea0rN5bEw6RTBm++fQ26sIhblo+Z8zRyVTmNLdPXaw/yY9W72N3Ww+Q+bmXtLpwEa/u6+A7z+3gsoVVLKmf6kaYk8KYhVqsqvBDYKsx5jvuhzT5fOEXb9AdT+ATsk7G82ZWUFTg496X9gDWJKELp5e5EeZpR3N7fF7ccZhvPr0NgHBxweCUcplaOq+Se1/aw93P7+SlHW08dttKN8KcFDJpUa8EbgTeFJGN9mtfMsY86VpUk0hXfIDueIK/v3Yxf3nZvKzff+7MMNu+fg3rmzr44H+v4WA0poXaOZrb43DwqNVtsfEr7yRcnPkJ8rQvXbuYL127mDsf2cTz2w47Hd6kMmahNsa8DOix9imKZHlr7UiOX86kfXpO0dwen0g0TnGhf3Cm8VNVGw7R1t1HXyJJMKDzgw5H70x0WbZ3I45kWlkRfp9ooVaeEclwRpexpBshrTpUwoi0ULssPZVW3Thb1H6fMKNcL9VT3hHpjI37SBGON2J08LGRaaF2WSQao8AvVJUGx72tunBIk1l5RiQay2iigLHU6V2KY9JC7TJrWNNQVje5jERnfFFeER9I0t7Tn9X4HiOZoVNzjUkLtcuyHQNhNLXhEK2devOLyr9sx60ZTTDgp7pMb34ZjRZql0WicUeSGaw/ikTK0Nbd58j2lDpVTl3NlFar3Xqj0kLtokQyRWtXfNwnEtPS29GEVvmWzkHnclu79UajhdpFh7v7SKaMo60O0L48lX+RaAyR44MrjVdthTWHok7kPDwt1C5y/vBQT7oob4hEY1SXBh27QaU2HCI2kNSJnEeghdpFxw8PnTmZWFZUQFlRQAu1yjsnz73A8caMdusNTwu1i5pPYfjHsei11MoLmqMxx/qnQc+/jEULtYsi0Rjh4gJKghmNJpsR6+y43hig8scYQ7ODl52CduuNRQu1iyLR7Cb8zITe9KLy7cixfvoTKUe7PqaWFBIM+DS3R+BcU08N+ofHNvPU5laivf1ckeVEAWOpCxfTGRtgyT//jllTQ/zir5YT8Ov3rXJfe08fH/r+Go4e6wecO0kOICLUhUP8ZE0Tv9oQ4VOXz+PmS7MfFniy0r9whxljeGxDM9VlQT508Sw+dbmzyfa+82v5+PI5NNSWs2F/lL3txxzdvlIjeW1vB3vajrFyQRU3v20uKxdUObr9v73qTD540UyCAR+/2dTi6LYnOm1RO+zg0RjdfQluXDaHjyyd7fj268Ihvnbd2Wxr7eKlHW00tnTpRAIqJxpbuvAJ/NufnUdRgfPjRl9zTg3XnFPD13/byANrm0imDH4HxsiZDLRF7bDGFmtu1Ibaclf3M7+6lEK/j8bIW+YZVsoVW1u6mF9d6kqRHqqhppz4QEqPFofQQu2wxojV6jjT5VZugd/HGTNKB78YlHJbY6TL9QYIwOIaax+a28dpoXZYY0sXc6tKCBW6P6VQQ005jZEuve1Wue7osX4inXEaatwv1AumlVLgFz1aHEILtcOsVkdFTvbVUFPOkWP9Opqect3WHHXpARQGfCycVqYt6iG0UDuos3eA5mgsJ60OYPALYYsmtHJZumguzllul2uLeggt1A7a2pq7VgfAohqrH1wTWrmtsaWL6eVBR6aUy0RDTTntPX0c7ta7cEELtaPSBTNXLeryogJmTQ3pIaJyXWOkK2etaTje2Nna0p2zfXqZXkftgDcORHlxRxvPbztMVWmQ6rLctDrA+lJ4dW8Hdz+/kzOml3L12TU527ea3IwxPPTaAQ539bHrcA9XLnL2LtvRLJ5hFeofrdrLpgNR3nteLfVVJTnbv9dooR6nVMrwuYc20HSkF4DrL5qZ0/1ffsY0ntlyiO88twO/T1h1x5TByUKVGo+1ezu469E3AQj4xPE7EUdTUVzAhbPDvLC9jRe2t/H6/qP87ycuydn+vUYL9Ti9uLONpiO9fPeG83nPubXk+kaqjyydzf+5eBYHj/Zyxb/9gZ+tbeJv3nVmboNQk9JP1uwjXFzAqjuuJFTgx5fj5H7k0ytIGfju8zv5f7/fSdORY8ypPD1b1dpHPU73r2miuizINWfX4PcJIrm/5dXvE+ZUlnDlmdP42av76Uskcx6DmlxaO+M8s+UQH1oyi5JgIOdFGqyBmvw+4aNLZ+MX4aevNOU8Bq/QQj0OTUeO8cL2w3z4ktkUBvL/q7xpRT3tPf08vbk136GoCe5na5tIGcPHls7JdyhMLy/iqrNn8PBrB4j1n56NkIy6PkTkauC7gB/4gTHmG65G5WGJZIrvPLeDpze30hVP4BfrG98LLl1QRX1lMV/+1Wb+43c7Oau2nH9+/9mEiwvzHZpnaW4ft/9IL1/61Zs0R2NEojHefuY0ZlcW5zssAG5aNocnNrVw5bf/QEkwwE3L53Djsjl5OYLNhzELtYj4ge8B7wQOAq+JyOPGmEa3g/OCvkSSnYd6ONDRi8Hqt3tlTweXn1FNRaiAJfVTmF7ujZN3Pp/wT9edzS/XHySZMjyzpZWNB6J84V1nUBTwU1kapKG2nFIHZ5yZyE733O441k9jpIvu+AAdvf186+ntGGO4/MxpnD8rzF96aDzoS+ZO5TNvX8D+jl6aozG+8ustvN50lKvOmoGIML+6hHnVpZN2tL1M/mIvAXYZY/YAiMhDwHVAVsm8vukoX/j5xqwDdNrJo2IYAwZDergMYyA+kKQvkQKsx4nU8XcFAz6+/Wfn8cEcX92RqcvOqOayM6oB2LD/KJ/+6ev89cNvDC4XgZJC62MP+IVQgf+E5PaJcHIjJR+p/77zanNxUtSR3P7n3zbyu62HXAgvO8PldmrIODCJpCE2kCSZMhhjOHZSN8LimnK+/7GLPNOKHkpE+OJVVj6kUobvvbCL7/xuB49tjAyuU+j3URjwIUCwwEcw4B/MZREQvJHb//2xi7K+Jj2TQl0HHBjy/CCw9OSVROQW4BaA2bPf2hVQVhTgvFnhrIJzy8kfjs/6FO1lQqjQR6Hf+pCLCnwsrilnblUJfp9QXRqkMkd3Z43XBbOn8PsvXs7+jl6MgZbOGJubu+iMDWAMJFIpYv1JkoPfUif+Ydsv5cXMKTkpFmPm9lh5DTCnsnhC5HbAZ30xp2cEqi4LcnZtBVVlhQjCvOoSCibAbEE+n/DZdyzk+iUz6YwNMJAw7DjUzY7D3SSShpQx9CVS9A2kMHgvt4tPYcA2GWvkNRG5HrjaGHOz/fxGYKkx5jMjvWfJkiVm3bp1WQejVCZEZL0xZokD28kqtzWvlZtGy+tMvj6bgVlDns+0X1NqotPcVhNCJi3qALADeAdWEr8GfMQYs2WU97QBw130WAW0n3K07vJqbF6NC/IX2xxjTPV4N5Jtbo+S16Cf06nQuE40Yl6P2UdtjEmIyGeAZ7AuYbpvtCJtv2fYnYnIOicOWd3g1di8Ghd4O7ZMZJvbo305ePl34dXYNK7MZXSdljHmSeBJl2NRKuc0t9VE4P1TvEopdZrLdaG+N8f7y4ZXY/NqXODt2HLNy78Lr8amcWVozJOJSiml8ku7PpRSyuNyVqhF5GoR2S4iu0Tkzlztd8j+7xORwyKyechrU0XkORHZaf8/xX5dRORuO9ZNInKhi3HNEpEXRKRRRLaIyO1eiE1EikTkVRF5w47ra/brc0Vkrb3/h0Wk0H49aD/fZS+vdyMuL8pnbmten1JsEy+3jTGu/8O69Gk3MA8oBN4AGnKx7yExXAZcCGwe8tq3gDvtx3cC37QfXws8hXXz7TJgrYtx1QAX2o/LsK7rbch3bPb2S+3HBcBae38/B26wX78H+LT9+FbgHvvxDcDDufx88/Uv37mteX165Haukmk58MyQ53cBd+X8h4X6kxJ6O1AzJLG224+/D3x4uPVyEOOvsUZz80xsQDHwOtY4GO1A4OTPFeta5OX244C9nuT6M85DTuU9tzWvxxXXhMjtXHV9DDf4TV2O9j2a6caYFvtxKzDdfpyXeO1DqguwvuHzHpuI+EVkI3AYeA6r5Rg1xiSG2fdgXPbyTqDSjbg8xou5nffcGcpreW3HNKFyW08m2oz1dZm3S2BEpBR4BPi8MaZr6LJ8xWaMSRpjzscaA+MSYFGuY1Djo3k9vImW27kq1F4d/OaQiNQA2P8ftl/PabwiUoCVzA8YYx71UmwAxpgo8ALW4WBYrDEyTt73YFz28grgiJtxeYQXc9sTueP1vIaJk9u5KtSvAQvts6qFWB3yj+do36N5HPi4/fjjWP1o6ddvss9ELwM6hxyuOUpEBPghsNUY8x2vxCYi1SISth+HsPoXt2Il9fUjxJWO93rg93aLabLzYm5rXo8e28TL7Vx1hmOd1d2B1Rf097nsiLf3/yDQAgxg9T/9BVY/0/PATuB3wFR7XcGaomk38CawxMW43oZ1+LcJ2Gj/uzbfsQHnAhvsuDYDX7Ffnwe8CuwCfgEE7deL7Oe77OXzcv0Z5+tfPnNb8/r0yG29M1EppTxOTyYqpZTHaaFWSimP00KtlFIep4VaKaU8Tgu1Ukp5nBZqpZTyOC3USinlcVqolVLK4/5/UA5U29w5KlYAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 432x288 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(3, 2)\n",
"ax = ax.flatten()\n",
"# set title\n",
"fig.suptitle('Marginal Tunings - By Phase')\n",
"for i in range(6):\n",
" ax[i].plot(phase_linspace, res_reshaped.mean(1)[:, i])"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(3, 2)\n",
"ax = ax.flatten()\n",
"# set title\n",
"fig.suptitle('Marginal Tunings - By Orientation')\n",
"for i in range(6):\n",
" ax[i].plot(orientation_linspace, res_reshaped.mean(0)[:, i])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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tPb1ljEF2d6AdGs1dvnVvbN4/ZZoaszTX26XpvjfYaxHdPnW0mJ5itB+a7Jdm+3wymVimoYhwKgb7pIjPomt87uGUT9QEIZ+YhAhdl0/ceQfelwt4SZm0Tb5wX3JglNuKmcPeH0Ic2WwTKy+10DXK3MKBpcRTFx5d+o4Spg8Qq7ftWhrAFBh4cyVKjp3P77dd1gXje48u2E6RoLh59u66adYCN4uYWfbpmqMOmXt0OssaMOylNpsjSbFGMI1FuoQ6QztZ5nnDeHn8XYbbIboTeP1bBw0PQ5N0LCdLpJwsfSDyDieVHR7B+t4srdh57qFiZwnjs09PfLmNmHVZfCHXtLpikm5baBtkPM7dHBtH2mlzTXynQa0QRzkAyQmxzUIbW8CQzeKWhWEck6dqKAdeLx5rJvG7ijXo3m42mreO1BazeSPEcb5Q5X2YzfWLk9fldceM9ma4L08w2sNSdBP0Rvv+Ar44nsLyxO17Nxmf37ddMdp3CQlgZzG/30XMtBxHs3kW4GxCzV1QS7pExiNw7s6mjaxX9l6MxFZKZYNFJSO5Xmz7yka+aznWI2392F8795cxG/RGi4MKVyrneumNZryWVEDKla8ud46QLuReaSHm8z4EmM9RH/KFUrRcPOf5DhgQZxEfUWuxR7mBLfU90m5adEXky8A+uSdzUNUPiMgD4G8C7wW+DHxIVV8/7Xe0Vfx75osEuPaJ8EQ2RWtuhSSBhGyMlliudFEw83xi2bksrnp2nk8kNzXZJN0YTBexVjBScom+QUpuYZFaaJpcux2P0LEj7jYkV0zS5WoXWyE15B4qDuKo9MppyWZxm6dqQRtdXuWNgtVy0Cli9OSdcoNcVVwRQXdGxN2WOLLEkSFODMkJfpJP0DguYtuWGlMDqc37LteeNHsjbV9rKvvP5rNTyr48Jroq6KDWRMpTKUZ7U2pNvdCaLpvqJZTjKGj2dHrFzQTjFTszOGswvhidjMlG+xRhLst2AOfycXQLuYrYSlDGX5+RXEkH9d2B+zsUoQhtuR2XPH/sDm/Tby/uSnR5oUzLeTSLbK6ApZweSlrudhUpd7gSYxbakKeEgHoPMWXBDaHk6EvBnUeSZu23FvEBcRad5VSjdRZ1t2fshT+jqt8YLD8HfEZVf0JEnivLf/m0H3hqZ58fff+vLlo8oxrm6vDJcpRaQrIcxpZpbDgKLYe+ZRYcB7MRIRpm05bkDcwsZm4wM8k9UzqhOcgi3O4b3MzRHDjcgcMeOUzJyfV5OBmP0N0Jem9CeGxE6E3SLXR72UYSJtmvF1uIuxFtFNkJGJcYjz2jxjNpArtNlxsWmhmNiey5GY1EJtZng3gxifc77BZy6biqM3QPJnSPOcI4m8zDbr4zCLtZXMNuyhemccSOI64J7Iw8jYs8Ns777n47ozWBe65jYjqcSeyYDiuJRmLxfC4vYBcx2s/njjB3aGeRac4TuiPBdIbmMOf43NTSHFrcTGkbi5k32DISlkxni0ZX2Zmgza24iTyJS8VWuoD76qsbGz1XRhuD5XS9QfkkFo6R1cbPYcO59A3nwwFvSi5eF4PilEb1kpfXGDc0fiqQ0FBGHfMB6brSzjBoQLdFH/qG30tyHUfGB4HvLfMfB36Vh5ycrQT+aPuNpeiSTxivlqNikn4zTpgnx74fM7YjjkKLFWUWHKpCsBafJDcYq8mGBM01UtHlrWuyxQht84Ehw4PCmLLDhVQ+k78n+fa3KTWyFtJI0TZBm2hGgaaJ3BvPmTSevXbOPTdn13Xcd1NGJvCYmzIynrF4xiYLryWttODecs4d174W1O9D7WuyDcSR5vlRgiZhJ2HRs+n+eM7IBp4YH9GawONNbgnfs7PSEp73YyOB9jTR1YZOHUepZZ4aZqnhrTBmnhpaE5lFhzWJWXDsy4i5QDBKig3JQAo56Rh9riGnoPlYKCkQDWZFcIClCNlHqgPM+WKris673LgMy8bk9fOpf6/nPIK15jhZEeHBEI/H3ErD+eJYYuhYGvqvh8X1jeoqy7F5i6VU+pyCtWz+9vm4rOgq8CuSO1H/tKo+Dzytqi+V978GPL3piyLyUeCjAH/k3Y5/5d5bRE0kFK+RuYYVk/QbacRhGvFavMer8R4HccxL3WMchhEvTe9z6FteO9xhNm/w0wbfOswsW5Ril28h1QgSDSZaJDhM45DO9tuTD5zGkUaOODaEHYPfEeJY8PeKQXpXSTsJmQQmu7lHzjt3D5k4z9OTt3i8mfLAHfKk22fPTnnK7jMWz2NmzlgSe0ayQRzLSG5tbehK4joaP06YGPyOIeyA3xXCPYhjxd+P0CbavY7RyPP4ZMb90YzH2ylPj946ZrTfM1P2TLcw2u8Zi0UYSYNBsINOK/1xNFfPTCNHqsxU2E8Nb6QJh2nE18N99tOYl7rH2Q9jvjHf5fX5Dgddy5uTCV3n8K5F5qakOvLtMynnJu3M4hS0bZDO51RDcWho43KD6+3kQrEdxnXMDumNNzc6R06sCZ7nqRGw+ckR/R84Q2eo5eKG9zbaCWP2+vaNa5s6Rq2tvyiXPeu/W1VfFJGngE+JyO8P31RVFZGNF4cS7OcB/tS3tXqQZkSUpIpHmaniFfZTw0wd++VkOUwjjlLLfhwv/Hu9rSQmQ4p5gAyJLFtA+xGL+rzQ+q0Lg0CWXNLKq3zXRMn5pQQaDTEafLTMS41pGhtGJnBkWvZT7mE3Fk8nlkYingQp9z9vJTHXgLmdRvorieveY8/qypB8ZdQoUm6R1ijEaAjB0kWLj5ZZdExTi4nKvs378K008F4Z6DQBEQt4SRgR7CBR6DWb7Ptj6EgtM7Ucast+miy6lx4NPKCz2OCjpQuW0A+ystKAU7a7zzOWvOMxX3neCZfY9dfOhWI7jOt98yC/v15rNHJcEClCfIqInsSJHVw21VZPE9sN759Q4LI2rmkhsENnyoW6la9xKdFV1RfL9BUR+QTwHcDLIvKMqr4kIs8Arzzsd95KDZ88yhfXpIZOLV4dXi37aYxPbiW9sB9yeuGt+Tjndqe5C2CYOugMZmpyTncuuEOwc3DT7Nmzs5SHfyujDS38epAPjJDtJXbuQKAZCSbm28kYADWYIMS5ofNC1zZ0naNpIq+Nd5g0nt2m434zY2I9jzdHi/RCI5EdMy853eWtMbx4mTBcOVcVV9E81J6blZYV8kFtfF5OjSEmYdo4unnDW6MxoybwyniPifM8NnrA2PpFemHHdOzZ2WI/WkmDfvzLk8yrJWLwalfSC0ep5SCMmKaWN7oJs+h4s5tw5BsO5y3zeUPoLOmwQbzQlMFT3KHk42eq+TVT7DwiXVoY7Vl4dinWxNuZXriS2CpoOLsJeWuXoKu42K3UgpfacAVau+DCoisiu4BR1f0y//3Afwi8AHwY+Iky/cWH/dYbYYcXvvGnABbm6JAMQXMNKKph6otZ2ju6YPHeEjqX82pzg3gpToXc+mxnlNGGFNtBc5hws4Sb5l4ppvRM6VsxF4Z3H5B5xE4Dotne1ItEbPNQcnGec7xhlvN7fuLonHI4GmOahGvyyEvZJO5pTGLkAo2JtCbiTFyYxDO/edEwXDlXGVeSYo8CjcsXLkn5DiSOBBNyjjzMLclZ0tgxHTVMnfLWeIKxSjvyWJsYN4HWRhobGdm8X1uTp/0+NIPKWVIhUY6hZOlSvguaR8c8WkK0TLsm36l0juRN7q/vDdIJTW+0n+ZjyB1lN0wzVZrDlG1Js4iZheLbDcuGHMjrbmHHlyuN7XkE7qJ3c7f7juHCXKam+zTwiZLDccDfUNW/LSKfBX5eRD4CfAX40MN+6GA+4v/7pW85t2WsWVjGKJYxNljGyriaRzFbxqYeM81eXfWlkwRk4S25uf6W33iHJCU5g5sVy9jhJsuYLZYxt7CMTYtl7M1HzzJ2ZXGVmHAHHaIQZxY7Pc0yljuZ5MZKh1qYuwlqlYNrtIxZD+6YZax4vWcUy5gWy1jKx5GPmIMu53I7j3qfPZ6l4UW7bnPa4ea5stiei9u5L26MC4uuqv4h8G0b1r8KfN95fsscCnt/v4wyVuKz7IkGwx5EfX5t8biO0pPIRIpBujdL5x4pZh6Wj+/wIZ8ovnj2prNS081dgrXrcqEpYXyAI4c5alFnaNr82A51+bZYe0eDk0UjS28MT32vKqF4F1n2SBs8viT/wXPv+mvlKuNKSpg3DpBpi3M2i+nIojYPn5eskFpTeqT1++/4FLPslbayH2FzJ5P+GOo9n30+X3Wls83yGGJ53MTzGe11Ns/HTYyoGIgJ6Y+rW8aVxrZyYW5F87kJMPn6YMCQ/sKoQ/G9um7AWroBa4zZvwe5phtjTjH0tp9UxnGwJpdRugHbq+4GfFdJCvNS67MWEyLqczdgc+FuwPnguOluwAuhDWHh+1yMZFd6NlUqm7gVouuOAg/+wWurK9fHNr2ISbofQaz49lL/tNB1k3T53dR5JKZ8e7jhUcwigki2BtnhyFiDR1GvGMThuIezX/d2ICXSm2+tjNQm60bzfoS2sxrtz7sPH2K2PzZqXT/AzdADWoz2vcl+xfsZU75wD3s3zee51lupbOBWiC6qy7EQ1tcXThyOD5Z2lFJrPWlIvmNm6o3bkiCV8T6NKSdY7nWUR48qXQWN5BpcPybvQDj6AZSPDU24GInqbSK6/WhvZeQw6e8qjOacq5hljXDD8I6Sjl+4jg2VedbtoBxD/fLKqGOrF+yHDQ/ai3IeGvCEZu2rbO6u3Cluh+j6gP6Tl0/9yEbP3llG/j9t1P9jJutikCaicdUcrcuFPD3PUyPeLiK7hpa7B/Cb9yGcvB83mekvuR81b9TqynM+PeIsT47QoQ2xUlnjdohuf7t2po9uEt+1dZuEdsN7J26LyPJzYha/IWawPrI0T/dy0gvI4L+8XZ4OcSLFZL5xHwKLpzJQ9uOW9uGljqOHPaqnttZXTuFWiO6yRrT+xhXcol3kBDjNIL0iAA+/UGws/e2U71M9vg9hsB+veR/e1DFUqZzArRBd4Gw10NvARbdjKNY133ex/XgV+/C2HEeVty23SHTv+Mlw1//fNqj7sHIHeBvd51YqlcrNU0W3UqlUtkgV3UqlUtkiVXQrlUpli1TRrVQqlS1SRbdSqVS2SBXdSqVS2SJVdCuVSmWLVNGtVCqVLVJFt1KpVLZIFd1KpVLZIlV0K5VKZYtU0a1UKpUtUkW3UqlUtkgV3UqlUtki1yK6IvIDIvJ5EfmSiDx3HWVUboYa27tJjev2uHLRFREL/BTwLwPfCvyQiHzrVZdT2T41tneTGtftch013e8AvqSqf6iqHfBzwAevoZzK9qmxvZvUuG6R63hcz7uBfzxY/irwnesfEpGPAh8ti/NP6y987hq25aw8CXzjBsv/4zdY9nl4aGxrXFeocb0eHum43tgz0lT1eeB5ABH5e6r6gZvalttQ/k2VfdXUuK6Wf1NlXzU1rqvlX+b715FeeBF4z2D52bKu8uhTY3s3qXHdItchup8F3ici3ywiLfDngReuoZzK9qmxvZvUuG6RK08vqGoQkR8FfhmwwMdU9fce8rXnr3o7zsnbvfwzcYHY3vT/eruXfyZqXLdbvqjqVW1IpVKpVB5C7ZFWqVQqW6SKbqVSqWyRGxfdm+h+KCJfFpHfFZHf6u0fIvJARD4lIl8s0yeusLyPicgrIvK5wbqN5UnmJ8v++B0Ref9Vbcc2qXGtcb3CMu9WXFX1xl7kpP0fAN8CtMBvA9+6hXK/DDy5tu6vAs+V+eeAv3KF5X0P8H7gcw8rD/hB4G8BAnwX8Bs3GaMa1xrXGterjetZNuBjwCtrG/AA+BTwxTJ9oqwX4CeBLwG/A7z/Ib/9p4FfHiz/GPBjNxTEzwPPlPlngM9fcZnvXduHG8sDfhr4oU2fu+LtqXG9g3G9ztjWuF5NXM+SXvgZ4AfW1j0HfEZV3wd8pixDHjDjfeX1UeC/fshvb+p++O4zbNNlUeBXROQ3S/dGgKdV9aUy/zXg6WvehpPK29Y++RlqXK+Dm44rXF9sa1yPl3fuffJQn66q/rqIvHdt9QeB7y3zHwd+FfjLZf1f1yz5f1dEHheRZwYbe1v4blV9UUSeAj4lIr8/fFNVVUS25qXbdnmlzBrXa+Ym4lrKvWuxvVNxvWjniPOq/rEAlivWXwS+yYn7yA732SPnwu/Lgx+54HadiT2e4L48WJQH/FopV9c+c2WBHPw3Xf/9fV5/jXw7CDfbJbPG9QJlDsu4pXGFS8Z2GFfTjj4yesdTjN/1LACTZ96zjOtwz2rOXSzWKYgul/t5Uc3rBvOSyodS4n77FI+1T3HfPgmqaEq/BvCYfVIxAiLcb5/msfEzihFUABEQ0DJFQI3kzSjLSNkU6f/kcnn89LMgMHnmPQowftezi/nZ1756qbheukfaRVVfVZ8XkY8BX9jh/v3vlO+77KY8snxaf2EK/GJZfAH4URH5OfJIT2/eRK2jxvXy3Ma4wsViO4xr+9RT99/9f/+L5Y2BkCpIEiSBRPK0n49gQpl6RSJYDyYoxiu2U0xQ7CxhQsJMA+ITZu6RWQedR6dTtPOkgwMQg723C6MRMmrRyQgaR9ppUWeIE0eyQhoZYmtIDmIrqBViC2ohNUKyoC4vq4FkAVOWBdToUqDL9A+e+zcvFdeLiu7L/S2IiDzDBVVfl90Pf+mC23FXuA/8RJn/JLlF9EvAEfDDW9yOGter5bbEFa4gtn1cJ/dmv/Tt/8IXSCrlZQhqCMkwj46YDF20dMHio8V7SwyW2Fk0CDKzSBDsTDCdYOYGNwPTQXOoGA/tQcLOE81hwB62mKnPFVIROACxNgvu7gTdnRD3RsSxw+85Yit09wyphTAR4hhiA3GiWXx3EuoURglxCdskmjZgbWK3CViTGLs8bUzEmYSThJGEEeUPLhnXi4ruC8CHS8Ef5hKqr6qfvC8PLrgZd4YvqOprkGshwLXehp/C7YyryMM/c1PoqRXG2xJXuKLYquon/8ifvM+/+OALAHi1JDXMUkNC2I9jQjIcxBHT2HAUWg78iFlo2J+3+GiZHo2IweCPHNIJdmbQRjAdIILpFEmCWoNEiwRFQkKcBWtBDBhBnEUbR2odceyIY4vfMcRW8LtCGoHfKWLbQtxNqEuY3YBzkcnYM2oCu23HbtMxtp7HmhkjG9i1cxqJ7NiORuLiZSTxiUvG9aGiKyI/S07APykiXwV+vATu50XkI8BXgA+Vj9/01bxyRm5lXE8TVzl7Px4xFxdpTee469Z08jafLsbXynXH9kk758P3vwiA10REmaniFfbVMVPLa/Eeh2nEG3GH1+IuB3HM1+b3OQwjvj69x6FveeNownzWEKZZMM3coDbXfkUhuT5VoZAa5NAhfiBZzuWUwtgRJpawY+juCXEkdI9BHEPYTcSdBONEu9sxGnke7EzZaTqenuyz52Y80RzxpDtgz055YA/YNXMeN1PGEtmRyFigEaERg0X4Ny4Zn7O4F37ohLeOJetuwdW8cka2Fldjsziti1AvVgMxXYhlv64sy1DYzEB81wXPbBZmOUXMdW27Fp9Maf2Dy/nBe4vv92Ktg+/5E4u9Vq47tgJMpCWhNJLwGoGIRfFEAHbMHICZNoxTwEtgZALBWKxJOJMwJiEmgdHcN9ZobuySnF/lLNfOQUPZ8Zfm3zEgRrE2YUVpbKQ1ESeJkQmMJDAynrEMX7G8YCyGRgyNWMwVdOK9sSdHVN5miJxY+ztVbHsh7YWzLB8T0mHt9jy14vUVvWhasxRS1sS5L1s1fz8lsJI/k8zq79xBBMGKwQJeoZFc400CVhWLYkkYEpZEIwFb8qH9SyTvO5GyOweB0PX5xfGxfOO0C2nf6DX8HUq7oYhi0JKfzdtkJW+nkUQr+eLRSMKQa7hGhEYsDos9x7F1ElV0K9eGGIMZj9AYIcY87d9zTc7L2ZynE2uyWFqTl0Vy/s7k/B3GgOQ83+JMLXYhRLI1yDA4i4tdaMjwfFnTxGxV6murCmmwTjVbmFQhpTyfSu099tOYRTfGsi7B61e/T28DX/U7/Ftf+1MAJBW8WoJafLJMY0NQw1Fo6aLlyLfMgsMHy3TeEIMlzBz43KBm50IzBzeVZUNaB6N9zQ1pBxF36DFHHTLv0M6DJjQm8B6ZzrHO0BjBdNmKEFvBzoXYCmFHiGNDHFm6ScusUV6f3AeXaMYB10RGTWDcekY2stN0tCay4zqciYxMZGQCRlLJ6So5E3NxquhWrpdhGkF0pQYoRSDzdCC4Jk8pAqvOFpE1C6FVY8otqVkV3FLmSg2J4wIsK+kCRVlUhpbpg7j0ii5EOKWc900JiWlZgxdBYlzaVPWUmtgjTlDDa90uCSGuuRdmscnT0OCTYeYdXXCEYPGdQ4OBziCh5G4DGC+YwMpLkhbbmZacLvnOQ1dTO6IKMV8UJYGJisb8u2IVE4QUwBhBHaAQnUAyBGNJKmhxYXQ2EpKhsZEuWZxJtCYs3AuNiSfuk/NQRbdyfTiLub+HdqWGMptnMRKDmYyhcch4DM6ibQNtkz2WI4c6Q2pt8VXmBhZ1uXFFjZBc8VU6QQ1rr0FeEJYeyzWW5nwGPlNd9Zdq9pL2y/287VJe7hImpuwp9QnxEelCru3e5PNqr5GD/Qn/62f+2Q2dHmSxzyTm9aZ4dNsI4wAksF3v09Uisgk7z+JqZ3k/2lnI+7L36fqAHh6hIeQiY4TpDI0R4wMym+MaR/Nmi1pD3GnysTMypFZILtd8kxVia8FAbJrsx3XZn9sZmBW/rloGuWVddqi4AqroVq4PERi1pVYYsximUlu1Nrc+tw3aOHTcoK0jtZY4dqgrJ0x/shTBTc3S2K6GvNyb2QfCi8nG9pWTZXjS9L2iElkslFybTavCkYW2rIuaa2URbJfX2c4Uc3/KAuwtxkqund1RJMDotT6vXdYtLlplGvOdQb8Pcy207MNSk7Vec0036PLi1eXOETL3C9HFB9T7krqJ5c4iC69ECyEg3qCAmRnU5autumw5S52gzmAbKcdQOXackqysHj82X7T7hjwtd1BVdCuPBGoN6f4Ohnxuig+LvKmMRzAeke7vkEaOcK/Nubexwe8Iscn5uNRAmOTaSBxlv2VyShrHnFIYRcQq1iWMTVibaFzEiuJsQkSxJjeYrGtuUiEmg6oQYr49Tknw3qLJEL1B4/J2WLxg5kV0pwaJ4I7AeHBTxc0VO1eawwYJd7chzR0p7/yt+erKhfjqsjtvYpkLV5CYsjiHtEjV5NRBgpDFVELJiYeAppRrtjGiMaFdt+IS0S4LsYaAzDuwBnEOEcG0zTI1ZXLqSq0tvc3sIDWVj9OV7sNm2X14kZa6wmzR7RHdU1q3r7yc28ZdrRQZIe20yDzkk8nafFtoDIxatG2IOy1x4vB72dgexoK/J6QW/D1IrRJ2FG0VHUXsJOJcZGfc0bjIY+NsZr/XzJlYn1+my1YgE7Kp3QRsabHuSWqICD45vFrmyXGUWrrkOAh5+lY3Zh4d+/MRc++Ydw4/d4TOIlOLdII7yo02YSq4qWBnmr2m4cafD3BtmJBoXz5czYvDakMklBzssoFSYtn/fSNk/36pvapqPj76dUmXjbB9Y2ValrFomNUuf37R+CrQdYtlGU5FclsBZNEdNMbmjRw00PZcsWbcHtGFzX/uskJ81h12BVaQM3OH7URDVIQ0spjGLXoT9dHQvjfRyBLHhjApNdyJZLEdgd9LpJGiuwE3iownHffGc3YazzsnB+zajidHB+yYjgfukD07ZcfMedwc0Upkx8xpSYwk13zt4OrWlYTvXC0dhqM04lBbZqnljbjDURrxjXCPo9jyje4e+37Efjfm9dmEmXccHY6JncE3jjgzNFbyranNNWFzQx7drRAC5tU3VtetnafH/MuA9sf9QnDT4jPLz/eCnIoFb/k5XWtIyy4GEJWlGNsixMXtgslpB1mI6cCWuO4VHzS8yp0XXRGkbfP8BpO5rq/bJMRrO/CY9xNO3qnrpvpNO/kE4/1G1o31Pevb3X9uevaffqSQ0qhl5fg+lbxey6sfeCQ1+RVbJbWKNgk7iozGnt1Rx2OjGffbGU+2h+y6Oc+0b7BnZrzDHbBnpuyZGXviGUti1wgNwkgcVmTF2J5IRFXmGvAEDtOcI7UcqmPPTDnSESPjOYhjjOiiFTshWNPivQOxxLklJSV6EJ/TIcnl3PCdJSbSm28tl0863gvrHVDyd9ZryRvO97X1x84fVdCIqkDplKFxQ2cbQNcrVWu9Ftd9vxureufRgFO4FaKbUynFXN7vjIHJXEy5kolh0fVyk1n9DIK72Lkn9Wxa27GnmrBPwtrF7LEDbniAXlEQ7wRDh8Fwalia6QfmemeW/eGtZAN+U4ztrSQagQYZ9CSSFWN7VMGIksie2kaUhkSr2SDvCcXY35eTsqmesg0mIdjS4qYsekYV7rBjLKcBfLjAFzeL88au1+ufPe2Ot1j2Vr4nZu1342r38KH7Swx6lhxfvJo71FshujQO8+Q7VnI7nCW3U3awlEEwpPg8MWbVbN+/1+d8+luNIno6yPHkH1zL65xktt/AIs/V57E25bd6s30vwP/okvvvliJJsYcemWbLz7CDhOk8GIM79LkxRR3GG8IUTCekVvBHltQawsRxOEoctPd4eRIwTWI89jQ2sjvqGLnAxHl2XDa277o5jSQmtsOILoTZDE6s7DE12difDPPUME+OebLZ2J8c+90oG/znLXPv8J0jzLO3yEwt4oXRkWDn4KZgp4qb5RGyTLiriXryORoekj/Z9tgTx2rBxz2158rqXWPbz+0QXRFom2yWi6Vl0xg0lVptTAilBTzGcqsw2KmytCFJn0h3Lott33pZWjAXyfNFzkeWBnu71rup37beMtKzaUCVwVU122cG4rveuyn2F5Y7ntstLdaktLxg9vQdDEJ+mZAtRGohdYAKtsm36WoUSYaUlIQj+sQ0CZ1N+GhpbGTceg7siMZGRnaCk8TYeQy5VmxL989F8cXYH5IlqKGLji5ZYjIc+paowtG8zcMUzh0xGFJnYW4QbzCzbOi3s5y/NR3YLo8V248Re6e5wQF9tsI1/r9bIbqpdcz/6APEp+LjyyZzQkI6n1u+O4+EgM673DIZZHGLI22DNC6Pr1lGHtK2AWdJrcs5wzLVxmRvnhW0N9r3vj0rGw32vUfvYZ7PodF+6FscGu5N1MU647OVhpWHj9wdJCTs64dwNM1x834hvHp0hHiPUcU0DnvY0owd2hrC2KJOCOPcKSKMik+3EVLjik+3+HUdzA3MXPHl2kHMFrHTzZaf3tDfe3T7eMUs9lIG3R5FFvOmDLxtu7zs5uWCMU+Yfv7IL1vqK5U1bofoNsLRU20ePb7UFOw8Ij5hZy6bpKc2CzCwGLWq1C6lbcE5ZDLORvtRi44dqbGksc29UMa2nLRCbFiOIG+K0b6cwOsm+1N7piya4sviMaN9PjFBFqPmL0fQz4M1y10+NzXBvEO9R0PI/eV7fMh3LrMOQiy9uhq0sZi5Ra3BjixqoGlNiZEseqDFZpOpnePGdjh+p9KvLhfG/iK5NPbr0tjf90SLutYjLd+xZFN/norPx6zpQvadViobuCWiC4ffZLCzLER2Dm5msF5xBxbrE9YZzMwV0S235r1Pr22QtkV3xqRRQ9ppCBNLGhn8riU58JPcshzHUgz2EEegTomtorZ4Qa2CVXCK2IRYRUxpOBEwJjfoAIuplv7b2ekiaDHZa5Lc1zwBYWmwl1BuTedybOCVO0VK6OEh6kMR3aUQadct8vULU7tzi/EX1Bpc47JgOpPHWrBlXrIAs5gWsRWyyX3Ds7A2cuwOpfeTahFhXYpwyclLGBj8S3qkN/cvUik+PLRFv/L25XaIbgsHfyThjnKPHzcDd2ixc6UdGWynjIxgWodTzQd36akCuaarkxHx3oi42+B3Hf6eIYwEv5drtGE392iK40QaK9omzE7Ausju2NO6yL3RfNEgs+M6RqVRZmQCY+NXRo8fej5jOavnqcGX0ZZOMtofdi3TrqHzjm7mViwud46k6Gyea7iaVm1BISwvnCb3FlqMLNY3bJZ10jd+Dhs4+0bQobl94UxZbQBdMFw+ydjfN3j264YjjPWv4XKfq06KpnIRWc9fVyoDboXoYpW0GwnY4rYqNUARYpc/EluDBM39qYfD+0E+SZ1FG0tq8gAXYSTEUe5CmhoIu7lGm3YSMo64NrKzM2fUBB5MjhjbwDtG2ft5z855zE3ZMR17dspYfDbaS8xjg5beTZZExCx7N6mjU8tMG47SiFlqeDPuMEsNr/pdprHl9W7Cfjfm0Le81YyId1h0cw+jtFLD7QVNkyImm9uzsKVcmy0inM3soTyddTm62NDILv0IZXCyBfAsrdBDgVwfrLzf3kE6ayW9tW7wH5j7K5VN3ArRFaO4e54g5NtHTDaXS041gGQxbbT0mzYrHlx1toxKlZ/8uRTc8lC6sRJ2EjpK2HuB0bhjZ+R5x84h95o57xrvs+vmPN28xWP2iMftEe+wB4zF88DOaFD2TPF8Yhe+z0wiEUkkZhrwmpipsp8Mc7W8mnaYacPX/OPspzHf8Hu84Xd4w0/4enOPeXR3tR1taS3aaI6PWa8WdrxVU/vKNx5ibIcL+qkfwqVM/ZXKCdwK0V3QN2xsWn8KUsZDXRnxaDBd5u5krVJTnmZKnkaESK61dmppJODVYCWSyKPjWxG8stLDKZEfWZJU8ap56E/K76nBq1vUiIfl9dMKJ5raxciqwVLMceFj0Gh2lZwkomuGz2Ni+zbp5l25GLdCdDUY0itjmml+LLOdQXNIzunuK7ZT3FHETkNuGfYBUlzWoHxA5gE7C6XRxKFicmpCs9HeeJsHTzkyHI0bjtrE6zs7OBf58vgBrYvsDXK6u7ZjYn0x2kd2TEdjlr2eNg6eohafHHN1zIrZft+PmSfHW37MPDgOfM7pzr1jPmtJ8Y6L7sNymw8xtZ+oXzcxcFHN01augFshuhLBHeQRm0wHblZ695Sh8uw8YbqI8XHZMhzjMm9WlmUeMdZgm4SblSeKWkgh9yZLZQzU2OWeTtEbolX8xGGssj8a4WyidZGRCzQ2MnH+2AjyveDaUpVeHz0/JMssOqIapj6PpD/tGkI0uVeTt6g32Wh/10X3uriIAJ7WkFapbIlbIbpmDnv/KHsfrc81WzvLRnN3mEeQt4dd7iAxnaOzWbYhFd+nzudIyg+SM12LmTnsUYM2hnbHkorRPg+k0g9KYogjU4z2eQT52CjBwnTh+VTUFeuR1dJB4mSjvajkVEYii6nmAbBZDOAMNghN8ezajrttGbttVKGt3AJuhehar9x7KSyM5tlgnn2PZpaN5jIvj+yYz9Eum+0XQ77NOxZPZvUB0zWYWcijxR9ufvRLavpHvqxPAZNryPlZXMd7pgHHhXdDT7SVx78sRs/X4vtcGu0rlcrbh0uJroh8GdgnD4QQVPUDIvIA+JvAe4EvAx9S1ddP/Z2gjL8+y4bzGLPhPOT0gfjs51Tv82jynQfviy+ymNm9X7a1hYh0PneYMCZ3D7YGbbLrQV15mfJkWQPJmtKraSiyZVoGDFuOJH/K/xg0BEq/beWxJf0o+r3giioS9FbWvq4qrpXbR43tzXMVNd0/o6rDR/A9B3xGVX9CRJ4ry3/5tB+QLmD/yasrRnPScnSxxahjKS2M9iu9m3qjvfeLwWsWA98UY70MR47vB7sZ+n1l2ZPpRLN9/95JrA/oMlx3mtH+dnLpuFZuLTW2N8h1pBc+CHxvmf848Ks8LICquQbb9+jRdMxsvnw0R1p5ZEdetWa07x8LoyaLnMkjlenQYG9yd9xh7ybZJLRrwzueiU1m+/WeTgPj/SPC+eNaeVSosd0ilxVdBX5F8iAEP62qzwNPq+pL5f2vAU9v+qKIfBT4KMCYHdIbb5ZfvMAI8g8x2g8WVr/3kNHjl5+7gAn0hB5Jj0j30CuLa+XWcaHY1rheHZcV3e9W1RdF5CngUyKy0rlKVVVENqpMCfbzAPfNAz3VcH6a4G788bQQ2EXed2iy78V3WIaREwVRLtCl81Rxvb0phZ6rias8uPV/9G3IhWJb43p1XEp0VfXFMn1FRD4BfAfwsog8o6ovicgzwCsP/6GSl70sK7f1p5jsz2msf7sdYVcW18qto8b25rlw50kR2RWRvX4e+H7gc8ALwIfLxz4M/OJlN/LKGTZkneV13WXcIh7puFZOpcb2dnCZmu7TwCdKHtQBf0NV/7aIfBb4eRH5CPAV4EOX38wb5pYJ4zXz9onr248a21vAhUVXVf8Q+LYN618Fvu8yG1W5OWpc7y41treDuzuYa6VSqdxCquhWKpXKFqmiW6lUKlukim6lUqlskSq6lUqlskWq6FYqlcoWqaJbqVQqW6SKbqVSqWyRKrqVSqWyRaroViqVyhapolupVCpbpIpupVKpbJEqupVKpbJFquhWKpXKFqmiW6lUKlukim6lUqlskSq6lUqlskWq6FYqlcoWqaJbqVQqW6SKbqVSqWyRyzwNuFKpvF3JTxS+Oq77idtXub2X3NQqupVK5XyIIK5ZLms619c1bVAtWby54QtnVLlNwion38yLOacQ97/Vne9r61TRrVQq50KMYCbjE9/XdZFMq0Iq/fuD9Yvv9IJcxFeTZkHuxXj9t3uhLYK4ENJeIMuyDAXZmNXvDtctfvYUQb6NoisiPwD8l4AF/pqq/sR1lFPZPjW2d5NzxVUMjEbH1xdhXMjVUCDXxLRfVlVIKX9HFaSIrQokRWz+jiaTvyuy/N2B4IqRvF1DkV0XV2OWYnqCOK98fvj+FXLloisiFvgp4M8CXwU+KyIvqOo/vOqyKtulxvZuct646qghfvO7lt8fVj5V86toq6S0WJb+vdivS5iUp4t1MaIpITHmdTGiMSKqqA9ZgEPIv20tWLucikDjSvojT7EWrMnzxqDWZDE2kteJoHYwvxBlFusWf62ffeVy+/s6arrfAXxJVf8QQER+DvggUE/MR58a27vJ+eIqkFqbZ3XQrqTLBVHNtVk1WVQTS0E2JotxFNQoJMmCmZY15ZUkQhFjjEDst2G1Ziq9wBqbP2dt/oyzWWxNqfkW0VURsAOhXcwDZiC+sixLhSvhOkT33cA/Hix/FfjO9Q+JyEeBj5bF+af1Fz53DdtyVp4EvnGD5f/xGyz7PDw0tjWuK9zZuP7P/8u/c7NxVb5BAvyNlH+puN5YQ5qqPg88DyAif09VP3BT23Ibyr+psq+aGtfV8m+q7KumxnW1/Mt8/zo6R7wIvGew/GxZV3n0qbG9m9S4bpHrEN3PAu8TkW8WkRb488AL11BOZfvU2N5Naly3yJWnF1Q1iMiPAr9Mtp98TFV/7yFfe/6qt+OcvN3LPxMXiO1N/6+3e/lnosZ1u+XLMSNzpVKpVK6NOuBNpVKpbJEqupVKpbJFblx0ReQHROTzIvIlEXluS2V+WUR+V0R+q7d/iMgDEfmUiHyxTJ+4wvI+JiKviMjnBus2lieZnyz743dE5P1XtR3bpMa1xvUKy7xbcVXVU1/Ax8gd3z43WPcA+BTwxTJ9oqwX4CeBLwG/A7z/Ib9tgT8AvgVogd8GvvVh23TZF/Bl4Mm1dX8VeK7MPwf8lSss73uA96/tw43lAT8I/K2yL78L+I1r2gc1rncwrtcZ2xrXq4nrjW4A8KeBXx4s/xjwYzcUxM8Dz5T5Z4DPX3GZ713bhxvLA34a+KFNn7vi7alxvYNxvc7Y1rheTVwfml5Q1V8HXltb/UHg42X+48CfG6z/65r5u8DjIvLMKT+/qfvhux+2TVeAAr8iIr9ZujcCPK2qL5X5rwFPX/M2nFTeVvZJjeu1caNxhWuNbY3r8fLOvU8u6tM97wa8xO3iu1X1RRF5CviUiPz+8E1VVZGVsZOulW2Xdwo1rlfILYorPNqxvVNxvXTniItuQLli/UXgm0w7+sjoiaeYPPUsKOw89Z4fQcuQcYtRi/J8Hh5udV6SLkcw6l9lnaYEAmLK8G5iuN8+zWOTZ7g/eaYfVejXAPYee1YhD4y0+8Sz3HvwHlUBZDDC0GCqa8unMX7XswBMnnmP9suTd+X52ctffY3lgHG3okvmVcTVutFHJvefYufBsyCw++R7fqTfn/nDa/twff7MG5sn46eeZefp9zB557P9+l8TYPfJ92h/PO0+/iz3nnhWZTH0YH88aTnGlscOZVjCfAxJ3rT+ODIGhMUxdP/euxWBe3vvzseRwMGbL966uMLFYjuMqxP3kR3us0duu7ovD37kGjZzwR5PcF8eLMoDfq2Uq2ufuTLhHfw3Xf/9fV6/VFwvKrovi8gzqvpSuRU59wao6vMi8jHgC+2TT91/7//lLyEJJOaXCXlqO4UEtgMTFOsVU152ljAxYY8C4iMy7ZDOQ+fRoynadaTpLI90f28XmhbZGaNtA21DmjSkxpLGltQYYmtIrZCcEFtIVkgtJAupEdSAOlCb16kBTF5WIQ9TZzYI9OJPDy4kCibmD3zhx//SFPjF8qkXgB+VPLzedwJvDmoo182VxnXn3jvvf/u/8G8Q2zxmabL9vpO8z1b2nSz2nfZJr6FALwpYTkXLmNflwiuJ5TG0mComgImKCfm4kQimy8eOdAkTEtIFpAsQIjLrIAR0OkV9yMeQtYg1yHgEziGTCTiLjlvUWnRk0caSnCG1FjXwa5/6sdsSV7hkbFfiyv373ynfd/1bfEv5tP7CpeJ6UdF9Afgw8BNleqEN0NL90O6GXxp916t0wdF5S4yGOLcQDDIzSBDsTDCdwc7AzrMIu8Mswu1Bg5krzX6LnXrMUVdqJZJF2AgyHsNkTNqbEHdHxInD71lSI3T3DLGFsJNFNo4gjpXkIE4S2iiMEuISron55SIjF3E2MnYBZxKNiRhRnCScyQN/mlKhSCokFYJaQjIkFebRoSp8Ae6XfQnwSXLjxpeAI+CHLxiji3ClcTWz+Eu7X3gNrEGNyQbF4Vim9GK7efxSOD6G6bEBs2F515MGtVVVJJYaa9Ty/nKwbAkxv1emGkIes1UV9QGNMa9LWgbOTmg0iGoeNLscVziHWIsxZjlgdl8bvj1xhSuIrS67C//SFrb3NnOpuD5UdEXkZ4HvBZ4Uka8CP14K/HkR+QjwFeBDF90AVf3kk//0k/zJd77EW92EWXQc+ZaDeYuPlum0JXmDP3JIZ7BTwc2EOAcVIXUg0WCtYrzNNR4fEbcc0FgknxzaOLR1pLEljg1hYggjwd/LNduwC3GkxLESdxI0it0JWBfZGXeMmsBu27HbdIytZ8/NGdnAnpvRSGTHdFhJjIzHopj+HhZIaogISQ2z1ODVcpRaQjL8L/AFVX2t7A8FrvV2DbYT18fap5Gj2TIG/ej8g1H7l49SGcyf9cmtC9Et02GaCZDYPxqmf1LB4P0Yc2tzEVrS2rqUICnav99vU4zLAbZFcnrCBLQfNFsMYheiu/W45s26vtiq6ifvy4Nr2vJHhkvF9aGiq6o/dMJbx+4vLnpgPeEO+eA7fotX4z3ejBNe97u86nfZ92Nenu4xC47XD3boOkc4bIhTg50a1Ap2nhODbiaYYBEF4xPauHzwQ655OAuNI44dYWLxu4bunhDHQnc/12z9PSVNEjKJjHY6Rm3gwe4Ru03H0+N99poZTzYHPGanPGYPeYc7YCyed5gjGknsmUgDNCI0YrCDe+OIklTxKDNVvMJ+augw/Bfn3WFXwDbiSoykN94sz6vqBfUhz6QafuYsDJ8eq7qcT5rFcdMzuWAgxGm5fvAcLy013JXf1kj/7C5ivpPR8lwuGTyHS8960bgmthLbyoW5FU8DFpSx6Rinjrk0jEygkcTIBhoT8WKxNmFEiUZzA4xhdTrI/y1zqqsnti5qVyymfQ4xN6iV3zCKtQlnI62JtCbkbZHIyHhGxjM2nrF4dqVjJJGxJMYiNAgjcTSSH2diykYllCQJrxFDZIbSSaRhcGLfMVQVnc2z+K097lpOEyZzDtFNJ+8/HYowLEUVWH/Ut256b/37i3UJXTw2RiHmVPPKI72v4YGGlbvBLRFdsChW8i25lYSRhEExkl8ifcsJx8V1wLEc4Ckn98pnF8JdyqK/4y3bULbForQS8vbSb69icztQruGKYBDs8MTTBBgMiiXRCrSS6PQOn5xabs8B4qoonamZ+TTh0rNfrFYE9Sy/s0lsN70vsvL9pRAblg/zqlRWuRWi+7XuMf7zr/xZDn1LFy0z75jPm9ygNnXgDWZqMJ0wmkluTOsb0jpo9xO2U9q3AvbQYw87ZO5R79H+KaOdR6zBHpQH6gVFkiW2OUWRWsHvC6k1xLFjPh4xbZVvTO4jTnFjT9NERk1g1ATGLjBxntZEdlyHM5GJ9TSlEc2SFo1okBvSImbRmOaTZRobghpufnjQayQtxeccOpk5rTb8MGHcBidtg1bBrZzMrRDdqMKBb5l2DT7anLv1luQNeIN4wXjJ1h9f7GQhT7MdKNvJZK31ejXHl4qtKBUbkS7saRKzCJuQbWHGZ3uTAaIzaFKitWgyqAoxGUK0xGSwJtElizOJmfUL90LTuxdKnS4hRM0NaV2yJBVmsSGkO1zTvSy3QVgrlSvmdojufsMbv/auLKYJxgufrmL80q9rYm4ks50iQXGziATFTj0S0tKnO5vnXGLXoTEiKuh0CiFifECO5ri2oXnDoY0ljh3JCWlkSI0QGyE1QrJCbG32ljZN9pQWj+7UwpFZek2X+WVd5peHFbWhvzRJvjikNQtUpVK589wK0ZUIo9ezqFIM7lmAB8b23tTuU+4cERNmHrPYzgISYxZcH9AQl1Ygck5PY0IkQLCIN4uBJzRka5BxhhQtyQqmyZ0k1MiiU0RsBmb+YuxPVjY06slKo94KfSowDaZVdCuVtxW3QnTdNPGO35sNuvNmj6UoEFI2ti+m2UMpMRU/pYL3uaW8GNzVh4HZvViCui5/LgRkNgdrkN7Y7my2+rjitbQm+y4NiykmW9RUejM/pTsoxRXBqsG/oLKhNrsQ36q4lcrbjVshuhISzSv7K7nYY+Mp9Mt9r6Ghsb2Ir8a4YnDXNMjrxoiKQfD5c32vor4X0dBvae3SzN8b3ddN/VBE93RTfy/Esp6fXDf2VyqVtwW3QnQJEd54a9WcDms9jZYmdx0MRpLXpaW5fZOxHbLQSlkvJgv1wNC+HMRk2EPKDEz9J5v7Zc0PPGRj+3sV2krlbcvtEN0Y0TffAjYY2uH8pvb8Q6u/MexRxKA3ERv8o+v+0IcY+zdK6EkG/1PM/JVK5e5zK0RXIftpV1ZuFqdjRvfzmtuH/ej7fO8x21Zc7V00tF2KQc/S+rX+fyqVSoVbIrp5ZKduq+WtLh83s5/LyH/Dfe0rlcqjw+0Q3UedmqOtVCpnpHaHqlQqlS1SRbdSqVS2SBXdSqVS2SJVdCuVSmWLVNGtVCqVLVJFt1KpVLZIFd1KpVLZIlV0K5VKZYtU0a1UKpUtUkW3UqlUtsilugGLyJeBffKQMEFVPyAiD4C/CbwX+DLwIVV9/XKbWdkmNa53lxrbm+cqarp/RlW/XVU/UJafAz6jqu8DPlOWK48eNa53lxrbG+Q60gsfBD5e5j8O/LlrKKOyfWpc7y41tlvksqKrwK+IyG+KyEfLuqdV9aUy/zXg6UuWUdk+Na53lxrbG+ayQzt+t6q+KCJPAZ8Skd8fvqmqKrL5IeMl4B8FGLNzyc2oXDE1rneXC8W2xvXquFRNV1VfLNNXgE8A3wG8LCLPAJTpKyd893lV/YCqfqBhdJnNqFwxNa53l4vGtsb16riw6IrIrojs9fPA9wOfA14APlw+9mHgFy+7kZXtUeN6d6mxvR1cJr3wNPCJ8pBGB/wNVf3bIvJZ4OdF5CPAV4APXX4zK1ukxvXuUmN7C7iw6KrqHwLftmH9q8D3XWajKjdHjevdpcb2dlB7pFUqlcoWqaJbqVQqW6Q+DbhSqZyfnBdeclufiL2+nVfBJf9qFd1KpXJ5NonbNoX4OsT1mqiiW6lUzocI0rbL5bRBXDWtLerJ758mzutiKmYwu0FoZUPGdNPnTixOyiadsk2zM//cRqroViqVcyFD0U0niGdZ34uXWJbirAmwAyFOx7+fCypTUyary72YylCYjVn97vr6tf9xEpveWQhxFd1KpbJVrMXc31sKrupSkJJmUe3Ftl9O5TOpvLeYKqgUAV5+D2Pz942ALfNlulgWycIrpkxlRZAXotpPNwnyWdIS/X/pl986y046mSq6lUrlXGhjie96AhKIahalqHk+ZYHtRVViWqyTELJAl6n4kIU4RvAhi2+MQBFYI4hzi3mcAzGIs1lAnc2iaQxqTZ4vUzUGNSzmAbBLEVZhJe2gJ4ivDC4mixEp/vHl9l8V3Uqlci7UGvzjY9Bck5VedFN5RYWQEFU0JCQliAnxAVJCgoMYUREkJhSQlNAIeWx1suBaizQu12ydQ5zLYtu4LLJFfNUKai1YQY3k90w/LyBZVNXk31VhKby91m7MJ6xORbmSxsEqupVK5VyEXeHlf36ERBYvE0CiYjyYCMYrJoD1iukUExQ7j4hP2FlAfESmHeIDMpujszl0HRojYgQzGUPTIuMROm6hccRJk2vZY4daIY4NqRGSE2IrJCukBtRCbAS1oA6SBUyZSn4/13TLVDRPYSm+A6GVJOUCU9b/7cvtvyq6lUrlXKiBMOmFNouR+ixOaiFFxZo8n2uZWegkmdwIFxUDiLdoSsv0gTGrjWWmpAuMQZ1BG0tyhtQakhVim0U3NhDbXF4/TW0uM9kishbUKmo2i26/nT0yqOX2Yptrupfff1V0K29vzuvvvK2dALbIeG/OP/W9f8ihb5kFx8w7Zl2D95Ywa1BvkKnBdIKdCXZqsB00B4rpYLTfYLtE89YId+QxBx1GBGSaa7xGkJ0JOhkRH9sh3msJE0e3Z4it0O0JqRX8LqQRxLESx0pqE0wixiXacaBpAnutZ+wCIxvYbea0NrJrO5yJTKynkUgjESOKlVWbW1SDV0tIeTqNLUENf3DJ/VdFt3K9iGxHqLZljj9POXdUn50k7jczDIqThBVFVRCBlAxJFA1CUsEE0Ka0pTlBkpIsiM05VjWSByMYuBBEZNFAlhvCymdtztGqE5IrqQOnJKdoo+AU2yaMjYxbz6gJ7LYdE+fZcR17bp7F184ZmcDYLEU3C+9SdJMaIoJXi08Or5Yj6wnp8iMnVNGtXB+SW6FPNcYv1j1EoU4xyS9XnSCImwzz18H6/9rwN+8C73AH/OtP/R1ejfd4I+7wetjllW6PwzDipel9jnzLa0cTZrMGf9QSjixmmlMBdp5jZGeCicX6FRJy5BBniw2sNJa1DXHSEHYsftcyvy/EkdA9lmu3fk/RccTsBCY7HTsjzzt2DrnXzHnXeJ9dN+fp5i0euAP2zIx32AN2zJzHTcdYlB0RxmJpxOKw2MFxEjWRULxG5hrwKPtJ6dTwX1xy/1XRrVwbQrb8rJrmFbALgTrVIP8wczwcN8gPTfCb1i3eulzN+FiPpU2dBPyliri1RAxvpTFvxB0O4pj9OOYwjDiMLfPomEdLCJYULQSBKMtGt7R89fnSZf40e3mlzC/saMU1IFGW3485N6xRSMEQgmVuE7PQYEQ5jLnzxr4ZL2qwjQQ6zUI/loiXwEwCrUQaJKc4CkmViOJVmSl4hP3U4sv3L0MV3cr1YQwyHiHrxniAlPIJJtk8r8lkIV5PR0hpXBneepbf7m9Bl+sGRnk4JtpDX+aK6J7DIH9seaWX1dr6w4f/7KNIUMvXw31eD7vsxzFv+An7YcRRaDnyTc7vdo7kDcScYjBFcJeNb9nR0Ht7ZXg31B8n2tvQlt+XmMXXBDA+pxrUG2KweKPMQpa0fTciquBMJJU0QVTD2HgShkYCR9LRSGQsEUPO6dqSE4oIUQWPZaYWr5b9NFmI9mWoolu5PpyFJx8gcenVJMaFQV5UwYdsE4oRjamY47OAiWtWDfLW5tZsaxFjzmSQz/lCk83vxSyff/xsBnlZq6UPa2W5RxWDTgGsdhB47Tp26s3zjW6X//4r35HFNdrcgNZZkrcwN4jPjWeNz2kEOwc7B3eoWA/tQcLOE81bATv1mKMOOo96n3uuGaDziLXYwyxyxjskWWKbG+ViC+5QiCNLGmUbmW+UlycTsMr/PnmAcYmmiTRNoLGRSRNwJjF2Pk+tx0jOSzuT/cG2BDiqkNQQ1BCSISRLlywxGeB3LrX/quhWrg9jYNSiIebaTEwQTTbKU27Rky7nVRGVYpJnaZDvBdc5pIjuwhjf906yJousXYrt0CS/NMNLsQotTfPAijleZXDLu1jZ974qH4/Dbq65RkbpHLDonXVHCdHy6hv3iMGQggFvEG+QkN0KEkvO1rMQXNspbpZF184Spks5l+sjhJgvxml5l6ApITEiIWF8Qm3CzPPdkHMGiTlIEoUUQYKQGkGCoFZJ3hCdEpvE3DUYpxy4iDGKc9mt4GzEGs3OBVMu9OU/KpBUUBViMsQkhGRItSGtcpuJY8ub3/o4ttNill8a5M005JOqGOSZzpB5MckXFgb5nTHaOBi12Rjf5NpNcoY4Kgb5YpJPbmmMT01uHU+OhTleLaXVnCy6ZdqfbSd5NRf5x7Q6v+gcEEFCuRX25XP/YAs7+QYw+4Z7v76zzNH2/z+WlEFSrFckgPEpxz4mzDyLqMxK7GddvtOZd+h8joaw6AasR9Nc2+089rDFNI6mzZ0j0jj3SEutITXZs5va7GxITkgWksvdgJOz+SJrIPW904pPNxjwZ4h97881CUz16VZuM2qyWV2UxYEt0eTHlXiTu3+W3KwYg0pJGfQ52D4/a3LtNluGchohuXLCtaVH0kJ4s9jmE7EIbOmV1BvjswDriuge65EEy1otlNRBmSZZERxJoCHboEwk18DuqHMBAM2112XDWO59tjL1WnqoKcbnuxwJ5RVjTjWlPt2Uljn/npRKrr98V2K+qwmSa8dJ802LAq70dDAKapYxMfli0FvOZD3mgzseYGPsh3c4VxXTKrqVayNM4LV/VrBHOQ9np9AcZYN8zusp7ZstdhYwb02Xo0iVk092d9BxS3x8N1uHdh1+12Tb0L2BQb6BOFHiSEmtouMETaIZB6xLjFtP4yJjF5g4T2MjO67DSWJkA04SjcmNKetezYTgkyWowSfLLDpCskxDQ5csh12LD5a5d/jOEYNBpxbSlnzDN0BzGHnyN9/YkNPu3QZloJt+fT9f8vakLJophIXwLvL5KeZUUKn5SudRW6xkjcs92vqcvrWrOfw+pz9Yl9NKg3x+vwzLh5U9rCF10Th6Nfvv9ojuTYz8fltM+3c1/WcgTEoeVHL/dRMF0Jx/U0iNQYLBLBrEZGnxKvlbdaYY4nMf+9hmv2ZqIY4htUqYKGmcoE3YnYBzkZ1xx6gJ3G/njFzgnpuz6zpGJrDr5jQS2TEdjQk0ErFFdC2JiCFp3o6ZuqVBPrXMk+MwjJjGhoN2xDQ0HPqWo66hC46ZbUnx7oouSZFpt+IwGLpShuJLyrXVPPJYydvGuBxdrLhYNg5qnhQl5py/SK6Imuxu0dwTI4txkNy46lkR275jxcoQjyKrfu6z6s66W+US3A7RLSb6ze+dkLjeZLA/hWMG/VLuib91VkE+KWgnbPdGA/8dvRWVJuKemuIPWsLMYI9yOsDOc6OWmwnGWxAwncNOXR5HtXcjNA4dOeLE4XdzN9DuvhDG2SCfRoq/n9BRwt7z7I49O6OOJxcG+be4Z+c8077Jnp3yuD3igT1gVzoeNx2NwJ4YGjGMpMEgpxrkZ5rYV2GmltfiDjNteCXssZ8mfMPv8arf5Q0/4ZXpHvPo+MoN7vtrJQR45Rt5Pmmx0a5ZvmBViIefSUuR1aHlbvAbObcbQQzqA2Ikp59gxZutsLQP9vOF1cHNh0J7s8/jvRbRFZEfAP5LwAJ/TVV/4vTPG2Qy6b97oTJPNaszsP4M1uv61Wt4IMhy+dpGtO/XPUIm+nPFtrT+9g1Ri1efH11bt0Lxbios8mr9d2TQuEESSIImISUhptzaHJIhFH/lTB0jdXRqmaUGaxIzzSf1jEQstxoGg0UwGBKJqEoi4TUx05RN8mrwavJvacNMW+apYZ4cQS0hZT9ovIJW7m1yrrimtNLgufHRNsNKzqBSoyesP9EHXQ4MLblyACkJ+MU3NnSW6d8/VU82dJo5lXQ1taMrF10RscBPAX8W+CrwWRF5QVX/4YlfMoKM2tV157kaaVrmwI/1fmIZ3LK8eIRIb8KW/kos+dbJcrJhfyC46yMiHTPul/kV8/7wv53j2U23gYvEdim65fZw6Ag4C2vH+bpYS+/i0lyWqmRvpVp8snhT+s4bi1eHp0zVY1AayceAJWFESSr0Y7pGlKSKR/HkXkm+DILisXRF1L1aImaR+00q5XceDc4bV1UldWeoKZx2N3rWO8mVzw0EeIW4PKfW3ttUyondxbfEddR0vwP4kqr+IYCI/BzwQeDkE7NtSO951zHzOqzaOIas+CgHRvX83rIlVGJ5r0/ma7avoJr9gX1uaTCiPTHmWxpV1IcswCHkfFDxjObEfrNI8COly6vI0sTf3yb3ecph0l9kOZL9Ny64p7fPuWKrncH872PaI8F0YGe5Ic3O1wzys4A5msO8y7euxTZE5xEjuH2LhAYJivGWOMopitQI/kBIjSFOLL6Feau8Pr4PjfK/jQPGJsYjT+siIxeYNJ7GRMZ2aZC3orQmLIzxppyqqfRKCsniNRvkZzHXaA99S0yGw66lC5bOO7y3JG/QmYVHK6d77nOWdEz5rp/ThPq4Ep/80f5acEMjzF2H6L6b1QdafBX4zlO/IYKO7KpR/RTRXdRugEXfbHqxLctq8numiK4xCzFeJOH77y9+pwhwb11KKddGN8Wzr8H2z2ZatKTa5Rig1izF1tml0K6L7qPDuWIrCUyXTfL9SwLZyxm1eFz7x7mkhU1ogS6fRNBPTcwjWBkPiGK7cpxYQYs3LRmDJiWIxThhquCdpXOWLloaG5naBmeya8GZhCvD+wGLaV9jDWV4v5gMXbL4aJkHR0zC3DtCsARvSV2xNHXFvP/ocP5z9i5wQx1YbqwhTUQ+Cny0LM4//Xf+vc/d1LYAT3KW+mafe7z6HOwfv/JfvCHW4/rFf+8vXTyuX7305pwtrtfHnY3rp/UXbv/5en1cKq7XIbovAu8ZLD9b1q2gqs8DzwOIyN9T1Q9cw7acidtQ/k2VfU4eGtsa19Xyb6rsc1Ljes7yL/P962hi/SzwPhH5ZhFpgT8PvHAN5VS2T43t3aTGdYtceU1XVYOI/Cjwy2T7ycdU9feuupzK9qmxvZvUuG6Xa8npquongU+e4yvPX8d21PKvnnPG9qb/19u9/DNT47q98mWjsblSqVQq18Kj1W2mUqlUHnFuXHRF5AdE5PMi8iUReW5LZX5ZRH5XRH6rb4kUkQci8ikR+WKZPnGF5X1MRF4Rkc8N1m0sTzI/WfbH74jI+69qO7ZJjWuN6xWWebfiquVhcDfxIift/wD4FqAFfhv41i2U+2XgybV1fxV4rsw/B/yVKyzve4D3A597WHnADwJ/i9xF5LuA37jJGNW41rjWuF5tXG86iH8a+OXB8o8BP3ZDQfw88EyZfwb4/BWX+d61IG4sD/hp4Ic2fe5RedW41rjWuJ782zedXtjU/fDdWyhXgV8Rkd8sPW0AnlbVl8r814Cnr3kbTirvpvbJVVLjery8GteLc6fiejvG090+362qL4rIU8CnROT3h2+qqoocezThtbHt8u4wNa53kzsV15uu6Z6py/BVo6ovlukrwCfIoyy9LCLPAJTpK9e8GSeVdyP75IqpcT1eXo3rBblrcb1p0d1690MR2RWRvX4e+H7gc6XcD5ePfRj4xevcjlPKewH4C6VV9LuANwe3NY8KNa7Hy6txvQB3Mq63IDn/g8AXyK2i/88tlPct5FbX3wZ+ry8TeAfwGeCLwKeBB1dY5s8CL5HHJ/sq8JGTyiO3gv5U2R+/C3zgpmNU41rjWuN6dXGtPdIqlUpli9x0eqFSqVTeVlTRrVQqlS1SRbdSqVS2SBXdSqVS2SJVdCuVSmWLVNGtVCqVLVJFt1KpVLZIFd1KpVLZIv9/A8yNF/B9jgsAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 432x288 with 9 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(3, 3)\n",
"ax = ax.flatten()\n",
"# set title\n",
"fig.suptitle('Joint Tunings')\n",
"for i in range(9):\n",
" # 100 is for scaling\n",
" ax[i].imshow(res_reshaped[:, :, i * 5], cmap='viridis', extent=[0, 100, 0, 100])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fefe239a140>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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TjneOV4Gk9cBjwD0RcTJ/7kKnoFxOehmco8DO7PGOqm+gSBqlHZqHIuLcQeGBOwWll8HZD1wv6brqpLA7aZ+aMTAkifadBA9FxFezpwbuFJReHx3/PPA1oAk8GBFf6dnCe0DSp4F/B34MtKruL9Pez3kE+BmqO+1ExIlLspIrxN8cWxHvHFsRB8eKODhWxMGxIg6OFXFwrIiDY0UcHCvy/7pF32Rs4eijAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(res_reshaped[:, :, 0], cmap='viridis')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"## PCA"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"res_df = pd.DataFrame(res, columns=['x' + str(i) for i in range(N)])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(res_df.corr())"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"Let's take 6 components"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PC1</th>\n",
" <th>PC2</th>\n",
" <th>PC3</th>\n",
" <th>PC4</th>\n",
" <th>PC5</th>\n",
" <th>PC6</th>\n",
" <th>PC7</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-2.554005</td>\n",
" <td>0.175027</td>\n",
" <td>4.007938</td>\n",
" <td>0.260290</td>\n",
" <td>-1.340241</td>\n",
" <td>0.224762</td>\n",
" <td>-0.057796</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-2.550704</td>\n",
" <td>0.167788</td>\n",
" <td>3.999983</td>\n",
" <td>0.247652</td>\n",
" <td>-1.335254</td>\n",
" <td>0.209454</td>\n",
" <td>-0.059524</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-2.533912</td>\n",
" <td>0.159211</td>\n",
" <td>3.960137</td>\n",
" <td>0.232972</td>\n",
" <td>-1.321489</td>\n",
" <td>0.192545</td>\n",
" <td>-0.059221</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-2.526949</td>\n",
" <td>0.149337</td>\n",
" <td>3.942689</td>\n",
" <td>0.215383</td>\n",
" <td>-1.314273</td>\n",
" <td>0.170773</td>\n",
" <td>-0.058336</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-2.537594</td>\n",
" <td>0.138229</td>\n",
" <td>3.965756</td>\n",
" <td>0.194695</td>\n",
" <td>-1.318734</td>\n",
" <td>0.143174</td>\n",
" <td>-0.057358</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PC1 PC2 PC3 PC4 PC5 PC6 PC7\n",
"0 -2.554005 0.175027 4.007938 0.260290 -1.340241 0.224762 -0.057796\n",
"1 -2.550704 0.167788 3.999983 0.247652 -1.335254 0.209454 -0.059524\n",
"2 -2.533912 0.159211 3.960137 0.232972 -1.321489 0.192545 -0.059221\n",
"3 -2.526949 0.149337 3.942689 0.215383 -1.314273 0.170773 -0.058336\n",
"4 -2.537594 0.138229 3.965756 0.194695 -1.318734 0.143174 -0.057358"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"N_COMP = 7\n",
"pca = PCA(n_components=N_COMP)\n",
"pca.fit(res_df)\n",
"data_pca = pca.transform(res_df)\n",
"data_pca = pd.DataFrame(data_pca, columns=[f'PC{i+1}' for i in range(N_COMP)])\n",
"data_pca.head()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"image/png": "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",
"text/latex": [
"$\\displaystyle \\left( 2592, \\ 7\\right)$"
],
"text/plain": [
"(2592, 7)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_pca.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"## UMAP decoding"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"# reducer = umap.UMAP()\n",
"# embedding = reducer.fit_transform(data_pca.to_numpy())"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"# %matplotlib notebook\n",
"# plt.scatter(\n",
"# embedding[:, 0],\n",
"# embedding[:, 1])\n",
"# plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"# embedding.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"## Cohomological decoding"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"step = 5"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Decoding... done\n"
]
}
],
"source": [
"param_1 = decoding.cohomological_parameterization(data_pca[::step]).to_numpy()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fefe07366e0>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"plt.title(\"Parameter 1 of CP vs. phase\")\n",
"plt.scatter(phi_deg[::step], param_1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Orientation is decoded:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fefe07c8e80>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.title(\"Parameter 1 of CP vs. orientation\")\n",
"plt.scatter(theta_deg[::step], param_1)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"data_without_features = decoding.remove_feature(data_pca[::step].to_numpy(), pd.DataFrame(param_1))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": true,
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"image/png": 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81t1GcE8fYsbOsbskVQPVfcYzspzvEn1ZSF0cP51T6bKqpfwc+GU7nKq6qWjjxo0kTp1NXHw3Vq9JJuaBjdBUnw41JJWGgYhMF5GdQA8R2VHiz0/AzvopsWoTB8UVf45sEsRVffTxVZ1ln4J/jIAFV8C83rDzo0o379atG+PGjWN10hpieg12d0FWDYpU9rRQRKKAFsBc4MESq04bY2o/9G0FEhISzObNm2u176rdv/Jzxlku79aaTq0ifFyZgxUWQEBg2e+/eQVWlvgrj2wPs3eX2Wz79u2cf/75hIaGWlik8hUR2WKMKbePUFVtBieNMQeMMdcZYw4CZ3E/XWgqIn7VXXD4BW2ZMriTBkF1/bAC5naEx9vAl/9Vq0O4XC6GDBnC7NmzfVycskN1eyBeLSL7gJ+AtcAB4HML61JWys+FJbdAzkkozId18+DAOu9t+t3g7jgEEBAMox7zWl00eGlsbCwPP/xw/dStLFXdTkePA5cAXxlj+ovIbwHtQNRQ5Z+FvCzv77LSvJdDm8Etq+DYbmjaBpqdG+y0ZBAkJSVpF2OHqO7ThDxjTBoQICIBxpgkqng3Qfmx0CjoPeHccstu0Hlo2e2CmkD7fl5BkJuby+TJkzUIHKi6VwYZItIUcAGLROQYUL3xr5V/+sN86DkOck5DjzHuK4FqCAkJYenSpbRu3VqDwGEqfZpQvJFIBO7GwwDgBiAKWOS5WvCZujxNUNZyuVwkJyczZ452JGrIKnuaUK0rA2NMpoicB3QzxrwpIuFAOc+jlGMc+sbdqNi+H64jIcUzHc2cOZOmTZvaXZ2yQLXCQERuxT0ASTTQBegA/B0Ybl1pNZOdV0BGVh5tIpsQoO8l1M3ef8HiiYBxv2vwXh5xnbqwevVqDQIHq26bwR3ARcAGAGPMPhHxmxkxv/4xldve3sLpnHwGdWrBm/9xEeEhOgp8rX3zCsVBUDQwyRcrtY3A4ar7NCHHGJNbtCAiQbg7H/mFRz79jtM5+QBsOpDO4g2HbK6ogQtx//Y/eLKQ85p7BiZpFWVzUcpq1Q2DtSLyEBAmIiOBD4Fl1pVVM9l5haWWC2yqxBlO/+ZRCGnKjX1C2HZbBDEX/R7CmttclbJadcPgQeA47peTbgM+A/ym29ldw7tS1EzQoXkYExLiKt9BVcjlchHf9xJW9XsZfv8KIde9A3980+6yVD2o7tOEQhH5BPjEGHPc2pJqbuKgjvTv2IJfMs7SP64FUeH6xlxtFPUsjIuLo2f/i0DbCBqVql5hFhF5VERSgT3AHhE5LiL/WdWBRSRURDaKyLci8r2I/MVXRZene9tIhvZoo0FQSyWDQOc+bJyquk24B/cox4OMMdHGmGjgYuAyEbmnin1zgGHGmL5AP2C0iFxS14IrsvvIKVbt/pWMrNyqN1Ze9u7dq0GgqrxNmAyMNMakFn1hjNkvIjcCXwDPV7SjZ1j1M57FYM8fS55ALN5wiP/3yU6MgfZRofzzjsto20zfr6+url278sADD3DLLbdoEDRiVV0ZBJcMgiKedoMqr8c9k65uB44BXxpjNpSzzTQR2Swim48fr11zxN+SfqSoV/UvJ7P5cLOOgVgd69atY//+/QQEBPDII49oEDRyVYVBZdfcVV6PG2MKjDH9gFjgIhG5sJxt6jyjUn6h96PFQr/pAeGHftkGu5bi+mIZV155JdOnT7e7IuUnqrpN6Csip8r5XnDPklQtxpgMEUkCRuOekMWnCkr96y+s0cRPjcjGV+GzP+M6mOcexbhTFxYuXGh3VcpPVBoGxphav4wkIq1xj4OQISJhuEdYfqq2x6tMWLB3meEh+g5VuZLnuYOgqIvx3El6a6CKWdmBPwZ4U0QCcd+OfGCMWW7FiU5l53stZ+ZoD8TymJAI/rI259zch+3bV72TajQsCwNjzA6gv1XHL+nk2Tyv5aXf/sw9I7vXx6kbFLl6HkvSJnH2dDrt+gyDgX+yuyTlRxzxal8AULIJsXVTndarJJfLxbPPPsvixYuJeng/UbmnIayF3WUpP1PddxP82k2Xnlf8OTAA/vvaPjZW41+Kehbu2bOH06dPQ2CQBoEqlyOuDP5r3IVceWEMKSeyuKxbK2KiwuwuyS+U7mLctm1bu0tSfswRVwbGGH7JOMsvJ7NJz8yreodGIDk5WbsYqxpxRBg8tnw3937wLc99uZff/S2ZXb+U1zWicWnRogUDBw7UIFDV5ogwWLzxYPHnvALDwq9/srEae/30008YY+jVqxdr1qzRIFDV5ogwyC/w7nHYWG8VkpOT6d27N/PmzQNARAeGVdXniDDoG9vca/l3/RpfZ5rk5GRGjx5NXFwckyZNsrsc1QA5IgxemTyAAR2bExMVym2Xd2Zs38YVBiWDQNsIVG05IgwWfXOIrYcyOHIym8UbDvHjsTNV7+QQ6enpjB07VoNA1ZkjwuC9TeeGRj+dk8+KHUdsrKZ+tWjRgrfeekuDQNWZIzod5eZ7j2eQlZtfwZbO4XK5SE9PZ9y4cYwbN87ucpQDOCIMSreZhwQ54oKnQkU9C7t06cKYMWMICnLEX6OymSP+1XRoEe613KNtpE2VWK9kF+OVK1dqECifcUQY7Dnq3ePwq92/2lSJtXQ4c2UlR4RBbqlORxt/OmFTJdZasWKFBoGyjCPCoLRmYc6aSCU/390gOnfuXNavX69BoCxhWRiISJyIJInILs+MSndbda52zbwHM3FSD0SXy0WvXr3Yt28fIkLz5s3tLkk5lJVXBvnAbGNMT+AS4A4R6WnFiZ4e35fwYPeP0i8uiimDO1lxmnpX1EYQEBBA06ZN7S5HOZyVYyAeAY54Pp8Wkd1AB2CXr891effWbHx4JOmZubRvHkZgQMN/QUcbC1V9q5fnUiLSCffgqOXOqARMA+jYsWOtjn8iM5fHlu/i8IksxvaJYepl8XWotp7tXg77voDWPeDi2yEgkM2bN2sQqHpneRiISFNgCTDLGFNm1BFjzAJgAUBCQkKtZj+594PtrNnjnppt88F0YpqHcWWvdrUvuj7s/AjW/Dek7Tv33alf4Mon6NGjBxMmTOCJJ57QIFD1xtKnCSISjDsIFhljPrbqPN8ezvBa3ply0qpT+caRb+HjW72DANiStIwzZ84QGRnJ66+/rkGg6pWVTxMEeA3YbYx5zqrzQNnp1TL9/d2EYz+A8X6fwnUwnyvm7ebuuy176KJUpay8MrgM95Tuw0Rku+fPGCtOFFFqOrXo8BArTuM7MX0h6NwIzq7DQuJ7ucR16szjjz9uY2GqMbPyaUIyZd8hskS3tpEcOZVTvDy4S8v6OG3N7FoKK2ZDzinIzwHcVzOug/nuuQ/P68TqJB2zUNnHET0Qk39M9Vr+31X7KtjSJtkn3W0EmccgP5uiIMgvNNy8NNs99+HfZmsQKFs54pW3Uk0G7D162vqTbn8XDn8DHQbCgCmVb3s2wxMC3oIChOXXhxEZEU7MwERr6lSqmhwRBqVZPijw5jdg+Sz35y0LIec0DL6j4u2j4qDzUNi/BnDfGnz+Yz5PjL+A7gMuhwE3QcsuFhetqisvL4+UlBSys8sGeEMRGhpKbGwswcHVf0/HkWEQZfWLSj9+VWp5VeVhEBAA138AOz7AtXE7iU//jdh2bbh/8iqaR/th+0Yjl5KSQmRkJJ06dWqQw80bY0hLSyMlJYX4+Op3wHNEm0FUqHemjbK6w1GbUq9YNG1d9T5BTXBldiLxvvnEduxE0rqNGgR+Kjs7m5YtWzbIIAD3fBktW7as8ZWNI8Igu9QYiGlncq094eV/hrhLzi1/9084uB4KKu7fUPSuQWxsLElJSdpY6OcaahAUqU39jgiDnFJhsHbvMWtPKAFwpsRoSgU58MZoeDIGtr5d7i6pqanEx8drECi/5YgwKB2CHaPDy9/QF3KzYOFVkF7OfI4Fue6GxbPpxV9lZGQA8Ic//IFt27ZpECi/5YgwCA30ToMLYppZd7Id77kfKVakMB9yMwH3rUF8fDzLly8H0MFLlV9zRBjked8lkJJ+1rqTFRZ4L4e1gA4J55Z7/g6iYovbCNq2bcvAgQOtq0cpH3HIryrvXkeFplZvQpfv0Dew7G7IPgWXzoT+N8LWN+HoTpBAGPlX6P1H2LsSAptA99HaWOgwQ4cOLfPdhAkTmDFjBllZWYwZU/aVm6lTpzJ16lRSU1MZP36817o1a9ZYVGndOCIMosJCSMs89wShp69uEwoL4d3r4KxntOV/zYG4i+Dmr+DoDohoDdGe57i9/gDA/v37NQiUT3z++ec8/fTTDBkyhGHDhvHoo4+SkJDAs88+a8n5HBEGl3ZtybJv3fMrCjDsgra+OXDumXNBUCTjELTq7u6F+Ot30GWY+9bhYDLE9CV+1JP85S9/4frrr9cgcIjKfpOHh4dXur5Vq1a1vhJYtWoVSUlJPP300zz//PN89dVXvPvuu+zatYuePX0/nKgjwiB537kXlQywds8x+sU1r/0BUzbDhvkQEgGdfgMHXO7vm7Z1Xwm8lHDu0eLRHe4aDuUTHbaZngHBzJ79TO3PrZSH8dzuRkdHc+bMGQoKCjDGFH/va44Ig/SsPK/lJVtTuHtE99odLOMQvPU791UBuK8CEp92v3/QZyJ8ONW7jwHnXkMe1CGQpASfj/eqGqkRI0YwfPhw+vXrx6OPPsqoUaPo27cvN910kyXnc0QYlHb0ZA1fMEn7P/jsz5CV6n4yUBQEAKl7odc17i7H+blwZLvXrsXjEUQFsPiaMOjy27r/AEoBiYmJJCaee5v13//+t6Xnc2QYBNd0qPT3rofjP7g/H/kWAoKh0HO10bwjHN4EG18BY9z9CDyKg6B5MKufvpGYnoNh0C0++imUql+WhYGIvA6MBY4ZYy606jzl6dKmBrMwFxa6f/uXNOgW93e5Z4BAeP8GoLDMrv/zda57YJI74omZ+nqdalbKblZeGSwEXgLesvAc5Tqdk1f1RkUCAtxPBIpeSw4MgVM/u7sWH95I6T4M4G7YERHevTaMM7mGtpeML7ONUg2NlWMg/tszeUq9O5CaVbMdrnzS3VB46Bv4ZSvsXlrhpq6D+Tz27xw+mhBOs37jiOgxBvpeV8eKlbKf7W0GvphRqbQaPXj58r9g3Tz354jKxyUo2ViYlSc0+8190L5fLatUyr/Y/m6CMWaBMSbBGJPQunU1BgnxpV1LzwUBQObxCjc911gYxOo/D6Ld5PkaBMpRbL8ysNXye8p+12W4e6ajU78UPzlYd+jcFcHqu3oSc38lby0q1UDZfmVghWo9WMzPhaw07+9adYfBMyGirdcjxLZNAxgcF8jqKeHEDBzt01qV8hdWTq/2LrAe6CEiKSJys1XnKq1abQZBIXDB1eeWA4LdjxPf+T38vAmAfWnu7p9dowP4cnpnYq550t3YqFQ9mD9/PtOnT/f67sILL2T37t2WnM/Kpwn+38Q+/nXY9g4c2wUbF3itKmojmDOkCf9v2rUw4S0ICKzgQEr53s6dOxkwYEDxcnZ2NgcOHKB791p2ta+CI28Tqi0wGAZOhTzvwVBKPjX4j/7BcOE1GgSqUu98c5DZH3zLog0HfXbMHTt2eIXBzp076d69O4GB1vy/2LgbEAG2ve3+41EyCFbfGkPM5VPc7yYoVYF/uPbz+Ar3pfuSrSnk5hfyp8uqP19BRb7//nuuueaa4pGOz5w5w9ixY+t83Io03jA4mw4r53hNiHIqx/D798+6g2BKBDGPfAdhze2rUTUI60rN9bnux9Q6h8Hhw4dp3bo1P/zwQ/F3d955J/Hx8WRmZjJjxgxCQkIYOnQoN9xwQ53OVaTx3iZ8eid8+65X34JmTYT3rg1zPzW4YqoGgaqWnu29R9byxYC8O3fupFevXl7f7dq1iz59+vDxxx8zfvx4Xn31VZYurbi3bE013iuDI98Wf0w+lM/hk4bregczsksQhDaHK5+wrzbVoNw9vDvZeYVsPZTOwI4tmDmsW52PuWPHjjKjGX3//ff07t2bzZs307t3bwCfth80zjDISnfPjIw7CEa/k0Wn5gGM7xlEcOuucPMX0KQGbz6qRi0kKIBHxvp2GLKdO3d6tQ+cOHECYwzt2rUjNjaWlJQU+vXrR2Fh2bdpa6vxhcGJn2DhGMg9XRwEcVEBfDk5nODf3g/DHra7QqVYtGiR13J0dDTHjrlnCrvmmmu48847WbFiBVdffXV5u9dK4wqDfV/B4j+CKfQKgtVTwokZcCUMudfuCpWqUkREBG+88YbPj9u4wuDjW8C4L6uSfipwB8FNke6BSS7Ux4eqcWscYWCMu6fh2ZPkFhhCAoWHLw9h1iUhRE5527tbslKNVON4tLhyDiy9k+RDufR46QzfHStARIhs302DQCkPZ18Z7FoGK++HU0e82ghahglEd3bPjKSUApwcBvu+hA9uBCjbWHjDKzDgRpsLVMq/ODcMPAOXbD9acC4IprUn5oqboL9vum8q5SSODIO/Br4OJw8D0KNlAFP6BvPI5U2IeWADNGtvc3VK+SfHNSDeFPgvJgd9xcafC8jINoQFCy9fFUbMHx7TIFCqEo4LgzuDPiH5UD7D3szkjs884xQkPg1DZtlal1L+ztIwEJHRIrJHRH4UkQetPFeRNQcLi8cjeGZkKLTrDQn1NuKaUj5T38OeWTkGYiDwNyAR6AlcJyK+n1S+hOzD33H94uN0iPIMXtp7KNyyCgId2TSiHM5Jw55dBPxojNlvjMkF3gN+Z9XJTGEBJ754BSLbkDdxATF/2Q9Tl0FQE6tOqZRb3llYOhNeHgxL74K8Gs4CXgEnDXvWAThcYjkFuLj0Rr6aUUkCAmkz/j8hMJjCptHuKdSVqg9JT8BWz5Six3a5B8UZ+dc6H7a+hz2zvQGxrjMquVwuora9hTGFBEW1JahpNHv+OsqCSpWqwPFSs3in7qvzIYuGPTt48CAHDhzgwIEDTJo0id69e7N//35uvvlmxo/37YS/VobBz0BcieVYz3c+43K5SExMJPfQDrY9cBn7nxzDgf++iiYhwb48jVKV617ql0+3kXU+ZGXDnnXu3JnXXnutzucozcrbhE1ANxGJxx0Ck4DrfXXwoiCIi4tj9erVREdH++rQStXMoFugSTNI2QRxF0Pvuv/GrmzYM6tYOYlKvojcCfwLCAReN8Z874tjlw6CmJgYXxxWqdrrM8H9x0cqG/bMKpa2GRhjPjPGdDfGdDHG+GyE0bNnz9KtWzcNAuVYixYt4rrrzk1KVnLYs7S0NG6//Xa2bdvG3LlzfXbOBvUAPjU1lVatWjFq1ChGjBhBQIDt7Z9K1buWLVvy97//3efHbTD/mlwuF507d+bDDz8E0CBQyscaxL+oojaCDh06MGTIELvLUcqR/D4MtLFQqfrh12Fw+PBhDQJlC2OM3SXUSW3q9+swiIuL45lnntEgUPUqNDSUtLS0BhsIxhjS0tIIDQ2t0X5++TTB5XIRERHBgAEDuP322+0uRzUyRdOXHT9+vOqN/VRoaCixsbE12sfvwqCojaBPnz6sW7eu+CUNpepLcHAw8fF1m1K9IfKr24QzZ86QmJhIbGwsS5Ys0SBQqh75VRjs27eP2NhYkpKStI1AqXrmV2EQHBysQaCUTcSfWkxF5DhwsA6HaAWk+qgcf6A/j/9raD/TecaYcgcO8aswqCsR2WyMSbC7Dl/Rn8f/Oeln8qvbBKWUfTQMlFKA88Jggd0F+Jj+PP7PMT+To9oMlFK157QrA6VULWkYKKUAB4WBHfM6WkVE4kQkSUR2icj3InK33TX5gogEisg2EVludy11JSLNReQjEflBRHaLyGC7a6orR7QZeOZ13AuMxD1z0ybgOmPMLlsLqyURiQFijDFbRSQS2AL8vqH+PEVE5F4gAWhmjLFuaqB6ICJvAi5jzD9EJAQIN8Zk2FxWnTjlyqBe53W0mjHmiDFmq+fzaWA37unqGiwRiQWuAv5hdy11JSJRwOXAawDGmNyGHgTgnDAob17HBv2Pp4iIdAL6AxtsLqWu5gH3A4U21+EL8cBx4A3Pbc8/RCTC7qLqyilh4Egi0hRYAswyxpyyu57aEpGxwDFjzBa7a/GRIGAA8Ioxpj+QCTTodipwThhYPq9jfRORYNxBsMgY87Hd9dTRZcA4ETmA+xZumIi8Y29JdZICpBhjiq7WPsIdDg2aU8KgeF5HT2POJGCpzTXVmrhHdXkN2G2Mec7ueurKGDPHGBNrjOmE++9mtTHmRpvLqjVjzFHgsIj08Hw1HGjQjbvgh8Oe1YaV8zra5DJgMrBTRLZ7vnvIGPOZfSWpUmYCizy/fPYDf7K5njpzxKNFpVTdOeU2QSlVRxoGSilAw0Ap5aFhoJQCNAyUUh4aBsqLiBSIyHYR+VZEtorIpZ7v24vIRxXs00lEri+xPFVEXqqvmpVvaBio0s4aY/oZY/oCc4C5AMaYX4wx40tvLCJBQCfg+tLrVMPiiE5HyjLNgHQofmFquTHmQhGZClwDNMXdyasJcIGng9Sbnn3ai8hKoAvwT2PM/fVevaoRDQNVWpjnH3UoEAMMq2C7AUAfY8wJERkK3Fc0RoEnLPrhftsyB9gjIi8aYw6XfyjlD/Q2QZVWdJtwPjAaeEvKnwH3S2PMiUqOs8oYc9IYk4273/55VhSrfEfDQFXIGLMe9/Rh5U3HlVnF7jklPhegV6F+T8NAVUhEzsfdJpBWxaangUjrK1JW0rRWpYWVeFNSgJuMMQXl3ykU2wEUiMi3wEI8jY6qYdG3FpVSgN4mKKU8NAyUUoCGgVLKQ8NAKQVoGCilPDQMlFKAhoFSyuP/A9GkLWU40TvtAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Decoding... done\n"
]
}
],
"source": [
"param_2 = decoding.cohomological_parameterization(\n",
" pd.DataFrame(data_without_features)).to_numpy()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fefe01e0760>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.title(\"Parameter 2 of CP vs. orientation\")\n",
"plt.scatter(theta_deg[::step], param_2)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fefdfd443d0>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.title(\"Parameter 2 of CP vs. phase\")\n",
"plt.scatter(phi_deg[::step], param_2)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"smooth_par_2 = []\n",
"step_deg = 30\n",
"for d in range(0, 360, step_deg):\n",
" delta = [param_2[i] for i in range(len(param_2)) if d < phi_deg[i] < d + step_deg]\n",
" if not len(delta):\n",
" smooth_par_2.append(0)\n",
" continue\n",
" smooth_par_2.append(sum(delta) / len(delta))"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fefdfda1090>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(range(0, 360, step_deg), smooth_par_2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.10.5 64-bit",
"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": "3.10.5"
},
"vscode": {
"interpreter": {
"hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
}
}
},
"nbformat": 4,
"nbformat_minor": 0
}