example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of finding the gabor receptive field of a given neuron model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fitgabor import GaborGenerator, trainer_fn\n",
    "from fitgabor.utils import gabor_fn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creat a model neuron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torch.nn import functional as F\n",
    "\n",
    "class Neuron(nn.Module):\n",
    "    def __init__(self, rf):\n",
    "        super().__init__()\n",
    "        h, w = rf.shape\n",
    "        self.rf = torch.tensor(rf.reshape(1, 1, h, w).astype(np.float32))\n",
    "        \n",
    "    def forward(self, x):\n",
    "        return F.elu((x * self.rf).sum()) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta = -np.pi/4\n",
    "rf = gabor_fn(theta, sigma=7, Lambda=14, psi=np.pi/2, gamma=1, center=(15, 15), size=(64, 64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(4, 4), dpi=100)\n",
    "ax.imshow(rf);\n",
    "ax.set(xticks=[], yticks=[]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "neuron = Neuron(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a gabor generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, _, h, w = neuron.rf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.manual_seed(420)\n",
    "gabor_gen = GaborGenerator(image_size=(h, w))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gabor vs. true RF before training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "learned_rf = gabor_gen().squeeze().cpu().data.numpy()\n",
    "true_rf = neuron.rf.squeeze().cpu().data.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3), dpi=100)\n",
    "ax1.imshow(learned_rf);\n",
    "ax1.set(xticks=[], yticks=[], title=\"True RF\")\n",
    "\n",
    "ax2.imshow(true_rf);\n",
    "ax2.set(xticks=[], yticks=[], title=\"Learned gabor\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the gabor generator to maximizes the model output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loss: -6.61:   1% 118/20000 [00:05<14:06, 23.49it/s]\n"
     ]
    }
   ],
   "source": [
    "gabor_gen, evolved_rfs = trainer_fn(gabor_gen, neuron, optimizer=optim.LBFGS, lr=5e-3, save_rf_every_n_epoch=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning evolution of the gabor generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(evolved_rfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AE5KPAAAAAEATko8AAAAAQBOSjwAAAABAE5KPAAAAAEATko8AAAAAQBOSjwAAAABAE5KPAAAAAEATko8AAAAAQBOSjwAAAABAE5KPAAAAAEATko8AAAAAQBOSjwAAAABAE5KPAAAAAEATko8AAAAAQBOSjwAAAABAE5KPAAAAAEATko8AAAAAQBOSjwAAAABAE5KPAAAAAEATko8AAAAAQBOSjwAAAABAE5KPAAAAAEATko8AAAAAQBOSjwAAAABAE5KPAAAAAEATko8AAAAAQBOSjwAAAABAE5KPAAAAAEATko8AAAAAQBOSjwAAAABAE5KPAAAAAEATko8AAAAAQBOSjwAAAABAE5KPAAAAAEATko8AAAAAQBOSjwAAAABAE5KPAAAAAEAT8+t+AO647grnDq/sKQAAAAC4BiofAQAAAIAmJB8BAAAAgCa0XXNz1Vq2tWMDwN0z5fv9Ktu5pJSGS57fiTkAgKuyHZ3KRwAAAACgDclHAAAAAKAJbddcLPYn3ZTeo1iyfEMeCQB4CZf9Hq8df8V27OrtKte9KSERAHBFr+o7/aJYJN7jsjHLHdmOTuUjAAAAANCE5CMAAAAA0IS2a741Zfzj1BGRsRdpSnnxlMtOabO6ZWXHAMAl1eKKi2KABi3ZN3FXGgDgGu3GA5eMP6akW7op+ZUbGpeofAQAAAAAmpB8BAAAAACa0HZ921y1deiSJbgv00o0TKn/bTHhSQs2ANwNU77HX+K7vggV4i+u0Dtdi3u0YwPALVDJI1z5ezzuRneVLejiJSvHV9uxb1AsovIRAAAAAGhC8hEAAAAAaELb9W3wKqc01kpwuwk1wRNLdmslv8OU+t+usr6KG1RqDAB3zZTpjNFlW4OqbU8TJ1zXDitvXRlfPem9jcdQLzURe8rDAgClq3xPXmWrl4uCoPDlP6UT+ipbw1S3gCkPulYqHwEAAACAJiQfAQAAAIAmtF3fBrX2pItctuz4siW4E6dAFS1HsQWqr42UmnDronz5in1IWrIBoL3w3T2pNSgaaut8xoUdSfHCfXiOGKOEf46/0kTKYhubuOyqh1XvUXvfWrABeNNN/C689HToeG7t+Fr8ce67vRtdxlik2CYmlgZeOhYZDyxi/HHu1NecC1H5CAAAAAA0IfkIAAAAADSh7fouuWxpbq21ZzPeGtXVWq039Zrn4ieVVqQUWrCHSh/S0Fderxx/YTv2JadsAgAT1fqJa1MbK63JtYmP9VikfrtCjFliq3V4kBhDDJXWqOq6GksMO7+a0DL+ohZscQsAnFPfPmV865Wijbp20WruZPyQc8fFH1ZikWrr9GzCM1Vjkfq2N5eehH3FLWBUPgIAAAAATUg+AgAAAABNaLu+q+LUpK7SMhTFQ+JY6k3sh6pMp9wt0a2UHhctRrHVaTZevxtf74ZKC1RFrX1q5MDawwIAVzGlTTkaQmtQEUvEoObFt0opFbFIXwlliu7vYTz+KOKSKfFU7X1udg988RY3AEDF7vflhFbreiwSvpMnbC/XDZUkwu4WMOe++8/fumipjkHKrEjQ5EOush3MUH5oQ3h/k1qwx657ibhF5SMAAAAA0ITkIwAAAADQhLbr22ZqeXHtmNq0yXiLKVOMaiW6O5eNpcZd5bqx3HcTS5NjO9T8kuXF8fo75cVFi3kspdaCDcCb6LLfcy8x4fDS28HEfx4v2pbG25vOxSLxZ+tKzBKnXoa2p81mPAgY4rY08dwJ28F0/c4TFm1dE1qwxSUAvCmmtP1OVcsR1NqX4+EhlhgqsUjRWn1B23Ut7zCswzq0WsftYDbxhPmLt4OJr1+4BV0f7/fiZy10O/+dQOUjAAAAANCE5CMAAAAA0ITkIwAAAADQhD0fb5LhZTYxqDThT9nnMaaei/2Kwjr0+w+VVPVwbm+D8f2Y+vX4+xvWYU+Cyp5LlSn1xb4I1f0GLvhY414P9n8E4I1xle+znXPLr8nKvoi1+KP2etwPKQYBle/0YVP+oAh9iv0fx4+KMU4X9pnexNgsbIi0ifs9xeeo7CE17OyzXcQcxf6Wl9j/8WXCRgC4iabEJRNjl+J7OZ7TV9a1vRNn43mRyvbTqd+JRWIIUYtFYtgwhP2hN4twoXDupvIZTNn/cTdu6IbxNzIpR/ISVD4CAAAAAE1IPgIAAAAATWi7vm4TWq27i8pbh9gyFGt20/g6pptnsd0519l2xcj1cP3QNj10+UKbnRx2LM0t3l2l1DiOfx9m8fj4frrRl6OLSorLB6ycf5kWbK1OANx1U9tr4tdvCAliq1Ns7RniCbPxddePxwBFLNLHm+3EIjG+WoXXY/xR2dNlWIU4JrZp13aoWYZrTmx1qsYcl23BBoA7bOp3XvWwIt9SOSp+985DXmR855U0zCfGIvH7/Sy+Hm5dbAcTjonxSoh9uhAbxK1hipAmZPlq28GkVMZjXd++BVvlIwAAAADQhOQjAAAAANCEtusbqqu1Te+qtTTF12N1bFF2G9uu83pWbcHOp64XuT96M4u90imt4y02Ib8d254qpcZDOKYPbU+bcEysTK5UKadhXi8vLlx2QrZWJwDussr33MvEJcU06XiB2eghqQ+tTn3Rgp1fH0JcsV6ELWD6MhaJNylal87yDfvQAlXEIvGZipbt/IP1/tieLGXbU1dreypPeekWbCEJAHdRtdW6iEXK/8Ne/Gr3O/f7U+KXdAwb+vG8SBGXhIcq2p2LWGQ3vZZ/Vky4Phvf3qWLuZDYBR1eX68rveCVWKRowZ5dEItMacEeG/h9ie3oVD4CAAAAAE1IPgIAAAAATWi7vm6xprg2+Xq8U/rcz2qvD0VPUyyhDeW+4eX5Itf+LuZ5PQttT6vQ9nSyWBS3Pp3lX6/CH7Func+plRoXbU8hNV5MpKxMWSpasOMhF5QX116vtkC96BoAcBtMGP5YbbXebXWqxiKVGCcOhoyRaC0WWeR+o1m42UWxyFk1FgmPEadan4bXYywSp1MWk7Iv1/bU7UTctemT1fij+Mi/O8bYawDuukr8cWFepLaFSRRjkVnMi+QLzecxFsnr2IK9LmKRouE5rYpYJPd596F1Om7v0set6Vah/fts/PgYJ6wrMVfxRLvTrmuxSGzBjomiuCXf5vJbwKh8BAAAAACakHwEAAAAAJrQdv2qTWnHvUqXzG4HcSwpDjW1xYTrdTwk55tjB9QmlAjHUuODZe43Oljk9TzUBB/tl61OT/b2tutns4Pt+izl44oW7ND2VEyejOti2mRYDxNasJfF45Vt2FNasONU8O9/oO0agLuo1o4dW53KrqJqe3aMUeKEyXj6MAtxyXo8FtkPbdf3QiwSt4M5PihD2sd7Of54PtvfrotYpBJ/zM+G0ddrk6/LNvQJkyfTCyZhby/7gsmTYhEA7orx/1tfxBhFvDE5Fgkvb8a/l4e+Gz0m5lRiLHJ/eRqOyTfbjUWe7OX44zDmRYba1nThueN2MDEuqcQitbxItBuLhJCoiDmK+KKaL7n8FjAqHwEAAACAJiQfAQAAAIAmtF1fh2qbzJTJ1zuvx1bryjreL5bmbsI46c0sT186W+Z1ytXB6eHyZLt+tDjervt7ZQHvk4N80q+XD7frL2Z5fTrk1uyiXDiUEce2p/qEyXjn8amQRQt2SkUb9qVbsL+74WDCJAC3TAwhuvHu4GooMjZseSvGH8XWKJVrxe/x8O/gmz7HH6u90IYULvSgFovsfC9/s59jkd+EWOSr+f3tuoxFYttTfr55tdWpFrO9uAU7pfok7FoLdqHb+S8A3GRX2Zqucu7uy10lFqkFP9VYJORF1st8oW5CLLLryUFuu/5ViEW+nj/Yrk9DcqK2Nd3lY5Go/uEXWZxKLDK6BV287CViEZWPAAAAAEATko8AAAAAQBParl+FV9X2Uhmc2F1w/aLRJ5QXxzLdWIJcDEsMx69SaLue57FHcZL15l4++e3F0Xb93vJZ8Uzrg5zT/mD/0Xb912FC5afdO9v1ScrlyN1mvNR4dhzKfU/zev4S5cW1SdiTWrAB4BaZuovL96pff9U+7fJHfWUrlaKdKkxwjMeUsUj+gj7cD+Mf7+Xlu8vD7fpcLHIvxxO/2n9ru/758v3t+tM+xiKhBTvEInFrmPlRfhOz8B7q7dW1FuzyZ9UW7O4FbU9iFQDuilq8Ujnm3OHx/8rHWKSyfVs/KRbJuZDdSdbfi7HI24vD4mebEGz9IORFfrb8YLv+5ezt7TrGIilskVdsTZfTMBNjkQte7sZjkSJL+KJ0i7ZrAAAAAOC6ST4CAAAAAE1ou34VJlS4Xro1Jk4YCid3u1MQY0lxLDUOpbmz0K0U62mH01B2HCY7ng65vPjZIrdEx8mRm/v5+PfnZavTu+HXP977fLv+wd6T7foni4+367/qc9nxyRD6qULb014okZ6dhGlPYfLTvBuvwx76nQ8/TsKulXf3lRZsEyYBuAu68bGSRcwRXw//XN3t/tN1MVUyHBe3gwmxSLz17KQSi4QQ9fmEWOSd+fPikT6YP92up8Qif13EIvkeXWx7CjHUPAy3rLY9FXHJbiwyHmds4uc/S6PHAMCNN+F769JbwMQcyaY8qvhV3A4mToeO6/CdPot5kdXlYpGzsM3L+4sce6RUxiK/s//Zdh1jkT9ffLJd/7yLscj41nTdEPIfU1qwQ/wx7MQiQ2VLndiC/Sq3plP5CAAAAAA0IfkIAAAAADSh7fq6FROuwyTDogY5lBfPdkplQ0tTMSE7TrgOrU7FVKdNvu7mMLT5nOWcdJy49Nn8wXb9y4M8OfI/2f+yeKbfm+W263fD9MlPFvm4d0N71DLUQv9F96N879CCHduelk/ilKrwma3CFMrQxjXEtqVUtmEPRatTEKdNzkZa08a71QDg9im2FwkxR9FpE79Xyy/BovUpLmPbU4hFZuG7O36fzo5iC3b+3j+OschiWizy3oRY5P1Ffn0/7FHz0xCLHMfx2vHf7L+JMUc4Im57c3JB/BZ+PRSlACE2ix/m2HYwYhEAbqopW9NFMf4oUiGVkyfGIkWcEXIhRR7hCrHIuyEW+e2DL4pnumws8mfz/IA/7T4M9x7fDiYGai8Vi/TjccZue3Y+/mqxiMpHAAAAAKAJyUcAAAAAoAlt19eh0mpdfT2miHcqYDehV7hbV1qNi1LjUJobJjV2Q77QPLRgz0Kp8WFoPfrr5fvb9Qf75bTrH+/lSU6/vfhqu/4k1AL/VihBfhAeZN7n5/g36aPtutb2tHgaJ1OF0uk4YbOY/JTSLFYqF5Mkw4SnWmn4d+d2tfJvALjN4tdb/L68qB0nxiLzfFwfjivCmvC9PD+O/TphguNh/H4fj0V+tsixyHt75bTrWizyn4aWph+GKZS1WOQnU2KRPLSyiLMuikX6EIHPirancFD40DbzkdYobdcA3DKT/m90EYuE+KP4jtxpIQ6xyCbGIvE7ttKCHVuTl0MlFqnkRX6+eG+7/mDv8nmRSbFIl2ORo3DvoQtb0z0OeZ641d5FeZFiq7m4NV2IP+LHHGKR4SViEZWPAAAAAEATko8AAAAAQBParl+1y051iirTJovLX3DN2IK9WYb1fLzUeH4SS4rzyXvh8MXznJ/uT/P6WXq4Xf9f84+L5/hgmUuHP5p/vV1/uMx1vu+HGt+H/V9t17Ny5vTWT4bQ9jSMlxovnof2rlBe3O1csiw9HikdTil1RVo+HPP9Bzj+mABwo8RwohpDdOPrYqphtPP6Jvxbdhf6czaLcK2wzUlXi0WO8pdrbO1eHL44FvnznVjk/dD6VMQioRfp7y3yA97vciyy7MKYyKDWgl3EIs9ii1aIK3bihjj9O342xTr8ZvRppAW7ukcMANxM1bjksrFIfQeY4vtxKFqLwzExFglbwMyP85WWTyqxyNkVY5EiL3LJWGTI94hTsGMCY5HTMeXWdJvys+zPQpxRacGO5Yqb4kP79pjLbEen8hEAAAAAaELyEQAAAABoQtt1S1Mm/0xpgSpKjcuLDmF60yaU2q5DqXGcarRexgvHyde5F3l+mEt8l9+E8t3DXBLcr/IfncfdO8Uz/eni97brdxZ5+uS7/f+zXf+9Za55/p3Fg/x8936+XZ+FuujTdb7fX2x+tF2fpP184z4/6+wov3yu7bqYEB5OD5XN8XONteHbNjCtTgDcQXHC4ZTtYFJKaQgTGdexBTucsgqtPfPD8evMQtv17CjGIrHtKe8r05/l2OBJeru41v+x+N3t+t15jEX+zXZdi0VS+nfb1el7+R5TYpGhj3HT+HYwKZWfTbFVzCrEb7PQah3iku67k3fjGwC4kS67Nd1LtGOXv87fxavwXRnbjDevIRb503nOi7wVEhTvPvq/t+srxSJDjkWOK7FIMbF7t5M7fGR9iEWGmBeJ2+Z053Mku/HNRVQ+AgAAAABNSD4CAAAAAE1IPgIAAAAATdjz8TYIKeJud9R82PBnCHsDrbo4Uj036s9Ocp/+6lleL8M49T7sbdAfnmzX88f5j8v8KO9H0K3yXpAppfRZ+mC7/l/7/3y7vvdJ3nxyv/t/t+sfh70NfjzPexX8w4O8z8Hjd+9t10fhfn+9yfc6SXvh/eQPrc9v4dvnHSrrsI9jMYZ+HV7/7vO/YOsrALiR4nfXUGycNOFLLf5z9W4sEjYNinsurcJe1N06r89CLLJ4HmKRcI/Z0Vk+N8Yi3+QYYP78fn6kVd5/KaWUPk/vb9f/S/+fbdd7n+TrLrufbtdxn6W/Mz/Yrv/L/b/Zrh+/l18/Dnsu/WzzW9v1aYhFir2od2KRVNmvsbYvdTc7f4w9HwG4dWr7P9ZCkdosjHOxSLYpSuxC/BG+V4u8SIhF9sL9Zoc5f9Ed5fXUWOTL9N52/S9nv79d3wtBwZRYpMiLTIhFTrociwwhLzI7Lh6vGkcU8UfMhczO50jiz19E5SMAAAAA0ITkIwAAAADQhLbr16VSFRxbfYcJvbzdbLPz63DOIv9sMw/lxfH49XgL9uJ5fn35dWiTOg59Ql893i4PnuZR8bOTd8tn2uTW6U+7H2zXf9z/g3zQR3n5397//7brj2e5hPkHs/zkf3Dwt9v1F+/kcuTnZ7m0+Zfrt7fr05Rfnz8vS4Fnp7HVLI2KJcjD5vzrWp0AuFGmbAdS64wptiMJcUka36ekiD1SKlufwk4scTuYIhZZxVgkxxyL5zksXX4Ttk85CrHI10+2y4Nnh+E6ubUppTIW+XXKrUh/0v39fM7H+fn+m/t5O5hPQgz1g1neiuYP9mMs8nC7PgyxyKfrd7brIhZ5Vv57f9GGXfu9C78XKWwH071EqxMAvFYT2qgnbQdTa9PeiUWK7eliXmSWY46zsIdJH75D+xCLLENeZPE4HP/VS8Qi6/FY5I9TyIt8kpf/+P5f5pdDLPJB/2pikcXTMhbZ3Z7uezH86EZyIcUxl8iLqHwEAAAAAJqQfAQAAAAAmtB2/aoNU1pgxmuQyxbseHi9l6rvc53rLJQeD/M8omg1z6/HjuOyBTu0PT3L05Fm34SJTV/ntuv1k1xqvDwqxya9t/pR+FWexvQfuh9u17HU+OzD/Bz/1b2f5euE1Pi7s2fb9e8d/Ga7/s1bodT4NPd6fR2maq668o/5kDvGU6hgLnWjyzR8/3u00eoEwC0Tu6iLl8fbe4t/oi4Dk+KyfYg/YlwyzPN6PbtkLPI8tyrtPw6xyOMcfxSxyHHZO/TuOsccabi3Xf4ibAfzPw9/uF0ff5xjhf/63l9t1x+Etuu3Z7m16nf2P9uuPwuxyPMQi3y1fpQfYTcWmYV2r7MXxxRFa9r36ymt9gDwukz5XqptexYPia28RSxSv2xXxCIhLxJasNchLjkJ+8R0IXcwO8lxyTzkRa4ci6Qci/wqtGD/85AXOfkoP9M/uv9vt+sPZjm3c5VYZDf9N6vEIl2t1X0kjrxMVkTlIwAAAADQhOQjAAAAANCEtuvrUGtdqpUXx3bsC9p9Z2ES9iKU5q6XuWXouI+lxrmMuFvlPwqzk1ymOz/MZbrL57lfeXj2fLte/Tq3QaeU0nyd7/Ful8daD/14C/afbPKbffzDXI789+/9++36fhjFFNcf73+zXX/xME/BPg6lxkfrfN9v5VLqIUz8rk2w1tUEwJ0zPsi6aMEuphquw/qC6LEPscg8tDdtQixyUrQ95TamogU7fI/Pn+dYZHGYt3qZHIukD/M5/Xjb058M/8V2/fUP72/Xf3j/32/XD/scB8VY5Ed7eVua3zzIbU8xFjlc5/umlNIqxXbz/HpX2Q5m0q4+AHCb1P6P9lCJRTbjx+yKsUjMkWzC1nSnoU37uIst2DEWyTHK/CjEImHC9YWxyCp/qb+bPs4/6HJMEKdg/4vNH2zXX/0oxiJ/s10/6nMcVItFPq/kRXZjkSFM/56FnfSqW9MVJ+/8dwKVjwAAAABAE5KPAAAAAEAT2q5fk25CSfFQTH8cPSQN6zJfvNnEX+eT5qG8+N48183uL/L6STjmOEyl7jb5j0V/lluz3zp5b7ueneQS3/UXXxbPtP7ii+16Oc+lvO8uQtvTPE+x/LTLpcb/W3g/X36QS41/++Dz7XoRPpxFHyY/LXM71Fv38/rstPxjvorl2qHUuDt7cQs2ANxaE1pjaq3WxTjDVRmLxKnW0Sxs9bK3CN/doe3pMEykPI7bwWzy93N/lmOGt04nxiKfh7hhlp+3iEX6fN3P0gfb9f++yvf+8gc5Fvnde3mq5H7oj67FIl/fy7HVadjSJqWUVuv4gebn67vKtEkAuOkqu8sVh1TzIrWXw/dizIusyrbrYT7ehh3brvfC5OtFiEuOQgv2UdwOJsQisxCLPDp5P7x+tl2fz4vkXy/mOSfxzmw8Fvk85ev+qxCLfBVikb97/9f5miEv0ocP9tEi91A/upfXU2OR4XRCXuQlxl2rfAQAAAAAmpB8BAAAAACa0Hb9qhUjIyfUoMZpk/HlUAJbvN6V19yEVqLVPK83oaJ2Edqe7i1yWfAyTMT+OpQjH6Zc1tutQwv2Or/+aJ2nVeeC4G+tv/omP8fXeb38j7n96K39XFK8meeH/U2XX/+zs3zvz97L0yN/uP8kjYmlxvuh1Xxv/6w4bn0WPqfwe9THXHxsNRsrAdcLBcBNMqHVqXpqMe06vL4ej2OGnX+63oQ27E2IReK1FiHOOAhbwCxDC/bjEK8cxe1ghtCCHWKRh5uJscg3eQLk8m9DC/d+brXeLHKb1dfdu9v1/7nKscjn7+XpkR8ejMci0fRYJITjXX69W72g7ckEbADuipgX2Yy/XqRX+p2260osEsVtX/b7/L28CHmRpyEoOgzbwaQQi3SbHA88HKbFIuuYF/k0xCIH47HI4/TOdv2va7HIvRzf9JXgb3IssompwbAdTGxvH4lFLhNyqnwEAAAAAJqQfAQAAAAAmtB2/ZoUk6xrtamhjLWY5FRMeNqZMHma16t5vvBpmN50Ftqb9ue51Pbdg8Ptei+U4/4m3Pt5aMEu/rgMuQ36UV8+03yR26iH5/ke3bO83v91Xj+8n0uHN8tcrPxNenu7/mkoNf7yrdByFSY5bXb7wL4Ty6hTSmkWplytwujrTfz8QwlyV56eUjrfcgYAt0ktFilanYqOpvCLsmsnDSE2Wc3y9/hZmOy4Dq1OB2ELmLhehljki3C7w9CCHdueUsqxyMOdbWlqscgQY5FfPduuHx082q43i/zcz1J+/S/PwuTJt8NWNHsxFsnPMYR1nPydUkp9iEXWYT1sKr3UxXYw3x4jFgHgToot2DFeWdX3G4mxyDpsTXc2z9/dMc7Yn+fv3v2D43BM/sL9qo9b041vB3PVWGTv1zkWeViNRfI9/vI0xCLv3NuuYywSTY9F8gc9bPK6CAvj1oDfH3KJWETYAgAAAAA0IfkIAAAAADSh7bqly06+jqfWJjztjjYMpb3r41yCe9LnEt+j0HYcpx3dm+ee7Xf2culv/1a+4a/CrZ4P4y3YQx9fT+lhaJ2efxamQZ7m1qr+ON97/8v8+tm9UC69yNc5TLk1+z+EUuO3HuTy4tg6vg6f92bns+9CuXE3C+XFs/hBh9fT1cqLAeC1qk2+7iovV7eDid9/lQulVExojq3AZ+F79Ti0McW2p71ZXr8VWoa6t/J1vigmT+YWozhXcugfpOjBXo5TYizShfgjnYTnCLHI/RCLbJZ5fRi2ovn0LF//4YOjfJ2w1U3Rgp1KfXhP61osUkTp8TdvOH8sANwklVhk0nZ0ldzJ2HZoY/cb+rAFTPiuPAmt1rG9OuYRylgkn/tluNXzuB3MVWOR4xx/7H2ZX78XY5FFjEVyHPSLVb7345AXOVjma6434To7n2sRi4TPZgjt1am2Hc93r18mFpFCAQAAAACakHwEAAAAAJrQdn0LxBbs3dLkIfQ3xQGJZ33+rX3e723X81m+2KLPpcaPlrlMN7Zgp9D29Mtw76MhlhTHaU8pbea5DPn+QZgu9c1JPmidL9adhfLnp2Hq1FehjSu0YJ+EMuevQqnx3n4uL57N4hTrnbLtWG7chzcVUvFDeL0Lb2/4vry4WiMOAHdPt6n0bJ87MGyfEr5Aj7vldj3vYyyS1w+WOU54ez+3Mkefh+/wOHkytlillNJ68eJYpFuFbVjWlVjky7CNyzy/t+OUY6vHZ/n1xUGOReahpWuzKf+9P06f7IpWsdiCHU/Iy+3vhRICAG6D2nYwU06tHb++YFu7LuZIcl7kOHzH9pVY5N4itz7HFuyYF6m1YO/GIpv5fr5uLRY5y7FCF3IkMRbZ+ypstRdasE9CLPIkxCJHRSxSz4tsJsQiaRa2kBn5vRhMuwYAAAAArpvkIwAAAADQhLbr1yXUCxfTk+MxU6Y97RwTp0/2IZcchxKdhdOfxlLjbnz9YJHLgB8ucqnxBw9zqfBvQvvQUTF5siw3Xi9zm9W9B2EK9rPxUVXdJj/HPHR/L57mdZz2dDbk6x/dz6/PlqGVaqdWe6h9nvG4WHccf5O6kdcA4KZ6VZOv4zGbnRdWtS/F2eghz/v83R2/o+P6fmh7iu3Y60f5Ql+G9qGjIbc2fXuxF8cii6chFonvOw6cPhrC8aEFO8Qip2mR1+GNnoVYpN+ZBjmEz7A6SDyu49Yww/lHBoDbZqj8X+56XiQcfy4vEr6ji5/E7WBy+itu7jKr5EX257l9+VGIRYYiFgmTqHdikSFuRRNikYMJsUjM88RYZBlikaESi5yFFuzVXmjrPheLdKPr8j2EOK0vfvDdi6OnjVL5CAAAAAA0IfkIAAAAADSh7fo61Fqway1QsdT1osnNq0oLdjjkpMvluE8qrU5x6tH+bLVdx7ans4f5+p/vTJo6HsLEp1loe9rLf9z2noQW6ZPxdudYSj0Lg7Lnz0NJ9SyUMoc/zuswwTLNy8Lr4nMupndOKDXuLl9eDAA3wpRpky8xkTK2YXdhr5dNcX6OB87C5OtnlWvGLVL2Qixyb5FboFYPc9PU1zvf4UdhAmQ1FnmcY4j5cYxFxp9pdhpboEKr0jy0WodYZAixyHonFin++X9TWdd8H5dM6ZEHgJtkUixSyZdUtkjZ/XW/qrRgd5XtYLq9NGazH2KReY5FDkIs8vBBjkUeby6KRULeYi9Mvr5fiUUq+hiLHIb3GWKRVYi54oTrYaftOsWp1ptaMmp8/X3YdZlIROUjAAAAANCE5CMAAAAA0IS261et1o47pR61du4FncHFgOZYKhtbsI/jFOz8W57nWJftTeswsenhXu53XvR5UtJ+KDt+eD9eKaXH61zmexKmURdtT/v5Hovn4VlzBXMa8uGFLgyEmuVhmGk4Gp/2Pey0hZ8rNx47qca0awDugsr3WG3bl2rb0+5JE9qe4vTHszTegh3jkvt7+ct+3ucrLUMscu9e2J8lpfTs7XyP4yG2HIVYYS+0OtVikco/03f51qk/ydcPoU7ahJM3+W1+d9L4dQsvao0XiwBwm03o9C1HQFdO2D0nBB39WaUFO24HE159HtbrSiyyCLHI3iLEIvfLWOR5yEMch3hnmIcW7NDaXYtFat/3MRaZhVhkKLJ8+V7DcmfadS0vUhxUef0ltoBR+QgAAAAANCH5CAAAAAA0oe36dalNdaq1NMXDwy+63bLWYbz0uFuP37DWgh0LhIdwndU6H3+wzLW/s/Aci1lZwLx/kEuSDx/l5wgd0mWp8V4oNY4d3PGytban8D770Go+nNZLsod6TXf1HAC4s2ot2OG7t9tU2p5SKuOP+HJse4pjJY/Ht0kpWrDDFjCrsN4P7U2z0Pa0mIU9WVJKewc5ZjkOscJJWuR7z/LrixCLzEIsErd6KVqwY2xWvM9wTIxFdgKZYR5iln486Oh299oBgLtqQgt2mRcpTx8qsUhMn0xqwY4DoMMNNyEW2QvTrmfhOzxuDZNSSssQi5yEvMpp6Iu+bCxSfE79+DFdaNnuu9p7TmU2sFaWeNGE8UtS+QgAAAAANCH5CAAAAAA0IfkIAAAAADRhz8dXbcp+gpfd/7EfP3z3lW5zuf0fu5Mwdj3u/xiuswl7E5yt814Iy3nc1KjUh70O5nv5uLP74Vrh+YZZ2PtpGfZwjOPl4/G1/R8re0ttLtoyKX62lTHx3di+mvaEBOAumhC77O5R2IUfxn0eJ+3/GGOR8H27CscfxdeXORZZhP0fdx87xiKzvbwR0vp+PvIsxD5DvmyaL0IMFsKdGGdM2f+xWJ8Lm8JJlWtViUUAuMtq+z/24/mSlMrv3Nr346T9H0NAsA7xx9EmxiL5i3uxKPecjvrwvLVYZBX2m6zFIrW8SHX/x/Cscc/ublUPMoq4prIXdbn/Y1f+dwKVjwAAAABAE5KPAAAAAEAT2q6v22VbsHdHyhfp4wkt2JtYvpt/sEmx7Skff1YZL3+2yDXB8/m5oe35HqFkt1/mUuPNQSw1Do/a53v0p+G51+PlvLH1a6h8lt3u44VrDfEz2/1wAeBNVm3nKQ+L38WXbsGutT2l8banTfgOX69CzDC7IBYJMdVmkY8b9kMLVOh1iu81tjr1tXalyudUHLKzB8ywqXxm8bDKdjAA8MaotWDvfEcOfTgwxh9TWrBXtVgkbA835NTZaRGL5PjholgkPm8XYpHNfvziDzmZEE/ManmRagt2ZTu53cerPe5QiUuuSOUjAAAAANCE5CMAAAAA0IS265amTL6OprRg9+WFqpMXay3YxbmhBXtVacEOx6/DvYq2p522666PD1VpGQ/nDGHC9TrU/sb3WkybrA1fKlqVxo/59sCwjp9NbarTi64BAG+Q3dbiMhZ5cQt2ebFwnVpbc7hhvM5ZiEX63VhkFi4cA4QiFgmxz148P/Zdh/cwj7FV5VEntifFWCZud1OGgi+42KvshQKAm+6CbU7iVOdaC3aqfN/GY6ot2EUsEraQC0mS7lxeZELPdzhns4wHjcci3erFscik3FNKO9OrX+5Sl4lEVD4CAAAAAE1IPgIAAAAATWi7vqkmtGCntNOaPKEFu9qiE0qHu3U4/nR8CnZRarzYaQWf5XPiVKehdu/QGjUswiPFa8Ypm6HNqtqCfZkW6u1Jldd1NQHwpqvFJak+CbvWgl1t7Sn2eqndPMYl41OwU0pFbNFN+R7vYywSt6IJTxG3gKlNm4ziIbvPcIVWJ93WANxpF8Qco8ek3VhkvAW7iFFq142t2buxxdjhsR175/ghbgHTTcjJhPcQt3rZxMt04++nmheJvzgXi8S8SvjMxi9VeolYROUjAAAAANCE5CMAAAAA0IS269flspOvo4vKjiuTsIuq3uLwyg/GL1n2G63G+4eGnWnaQ5xkXUtvTymfnsXDx0dTDZesZD53u+Lj6Go/GH8+AHgTXdBCPKUFu2zzqdxiUwt+xtudh81OwBG3dIk/mhAsFM86iz+ZECfUApDdNzolnqi87e8vJSQB4E6q/f/vi/qBK5Owy3biSvxx2bzIWWXrld28SGy77sfzGbV712OR8WeKOZlqC/Zu4FA7cIph578TqHwEAAAAAJqQfAQAAAAAmtB2fR1eVQv2uWtVSorj1KRY7RtLh2sq055iG3S381BDKuqcwzqNryeUGndD5d6b+PqLr7l770tPjHyJ8mIAuNMq3+lFu3MRf4zHK5NuVd0OZufAGDcUrU6X/OIv2rgqAVwxbXJagGBiNQBMcNUW7Phy+B4vvq4nbAdTHBPHT8ccye4OK7WY6JJ5kVTJ59RvNuGau/eueYVb0Kl8BAAAAACauFTl4+b4uNVzvLmu+i/fUzLzleOnVD4Olaz8RRugFhurXrbycTP+zxSx6rKsMKh8gC/xLyHlMRdXLvi7AHA9/O/vDVeNS/KyiD+mlABWYolYRVBWJaZyaF1tk/eay8YfxesTrp8ueNuXeN3fBYDr4X9/r8nUyseokpqoDYubNKwldpzOKsektDP87pJ5kSJuCgetK8865X3uqqVSGsUi3TChPaTruo9SSp9Oviq8OT4ehuEX1/0QAHedWASqxCIAr4FYBKpeGItMTT52KaUPU0pPX9GDwV3wMKX0y2HKXyIArkQsAqPEIgCviVgERk2KRSYlHwEAAAAALsvAGQAAAACgCclHAAAAAKAJyUcAAAAAoAnJRwAAAACgCclHAAAAAKAJyUcAAAAAoAnJRwAAAACgCclHAAAAAKAJyUcAAAAAoAnJRwAAAACgCclHAAAAAKAJyUcAAAAAoAnJRwAAAACgCclHAAAAAKAJyUcAAAAAoAnJRwAAAACgCclHAAAAAKAJyUcAAAAAoAnJRwAAAACgCclHAAAAAKAJyUcAAAAAoAnJRwAAAACgCclHAAAAAKAJyUcAAAAAoAnJRwAAAACgCclHAAAAAKAJyUcAAAAAoAnJRwAAAACgCclHAAAAAKAJyUcAAAAAoAnJRwAAAACgCclHAAAAAKAJyUcAAAAAoAnJRwAAAACgCclHAAAAAKCJ+ZSDuq7rUkofppSetn0cuFUeppR+OQzDcN0PAnDXiUVglFgE4DURi8CoSbHIpORj+vYv2KdXfiS4ez5OKf3iuh8C4A0gFoFxYhGA10MsAuNeGItMTT4+TSmlj//on6R+f/+qDwW33ub4OH36R/80Jf/qBfC6iEUgEIsAvHZiEQguE4tMTT6mlFLq9/f9JQMAro1YBAC4TmIRuDwDZwAAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAmJB8BAAAAgCYkHwEAAACAJiQfAQAAAIAm5tf9ANwQw4Rjuive47LnT3kmAOBuEIsAANdJLNKMykcAAAAAoAnJRwAAAACgCW3Xb7LLlu/Wjr9q2XFN7bp3pOwYAN54YhEA4DqJRV4LlY8AAAAAQBOSjwAAAABAE9quuVBXKeUdYunvReW+lyw9jtet3TtNvTcAcOuJRQCA6yQWuTqVjwAAAABAE5KPAAAAAEAT2q45p1rWG4/ZxF/Uj6teKpwzVM6vvV65zMQbAwA3nVgEALhOYpFXS+UjAAAAANCE5CMAAAAA0IS269vuklOTirLbKdORqq939WNCffKUWxTHxF9MqHMewtndZd/b+YuNu+xnDABvErHI+OFiEQB4PcQi44ffoFhE5SMAAAAA0ITkIwAAAADQhLbr2+YlSl1jye6U04sq3fiLoVLKe64st3LDuO7zSUNfOaZyyVK4TqVO+dypk+qcK2ttTwC86cQiO8QiAPBaiUV23PxYROUjAAAAANCE5CMAAAAA0IS26ztkqJbmvkQJ7sgPuk14fTN6yHfXHT+/PObFY5eG2YufqT69KbznnYfoxg+r32Os1HjqpCgAeIOIRcYf4qVikRqxCABUiUXGH+K6YxGVjwAAAABAE5KPAAAAAEAT2q5vm92y1gklxdVy3FiCG6+7qVw0HNTFYzblYd3Or8duXZQOx1LjWXzu/PrQT3g/1Vbp8kMbNpWyau1LAPBiYpHXE4u8uBMLAN5MYpFbF4uofAQAAAAAmpB8BAAAAACa0HZ93S5bulodvzTtnGqZbjw8lAcP6/EL1SY87f6sKEMOt47XHWaxzDkfv4mfzSIcXymdjq93F31Q4TPQ9gTAG08sIhYBgOskFrnzsYjKRwAAAACgCclHAAAAAKAJyUcAAAAAoAl7Pl6Hq+zVc8G5RQv+lLHrlX0O4l4DaRZ+sMrLuO9AXxtBn1Lq1uPraOjDfgZhD4O0yDfZVPYqmLLPwe6p3TC+WUGcPF/bk2F7rZfZYwIAbgqxSEEsAgCvmVikcNdjEZWPAAAAAEATko8AAAAAQBParm+obmoJchzTHlPJsYR2qJTaxuNDSXHXj5fcDvN88tDHk8scdizN7UJJcnxP/SqeEJZFOXIcYR9KjePY+Xjj8Kd5qJROp5TSkOJ7DT8MF4sfX3XUPADcYWIRsQgAXCexyN2JRVQ+AgAAAABNSD4CAAAAAE1ou75BqiXF8fWhqxyUUjeMX2CopZjDDbt5rq3t47qrlPUu8kU3/e4fo/yzWC7cn8Vy4fAYodS4qPYNr6/XlXLpNKHUeLbzuRQDmyqlxnHaUyyXHrkGANwVYhGxCABcJ7HI3YxFVD4CAAAAAE1IPgIAAAAATWi7vg7jVaylSknxuRLk4tdd5fUgpJuHWbhuuPB8nmuCF4u8jqXG602+0MmiKOxNq9kir9Msnx9KhONUp1kxySnfI5Yjx+Pj1Kh1MXaqUmq8O9WpMvGpKDXuwg/686XGBk0CcKuJRcQiAHCdxCJvVCyi8hEAAAAAaELyEQAAAABoQtv1LVCUFG8u+Fmsmg3HxalE8fQhTDEqJheFytr9Ra7rvb88Dcfkmx0flH+Mnuztb9eHs4Pt+mzIx3XrnPfuz/K587Duz0KpcSwvXsdy6xeXVG92X4gTn/rKSbWpTd+/7+oILgC4e8QiYhEAuE5ikdsdi6h8BAAAAACakHwEAAAAAJrQdv261CY5Vapjq9Wtu78OtbNdmI4UJxzFUtg4ESnmnjezPH1pvcwXimXED5Yn2/WjxXHlCVN6cpDLi3+1fLhdfz1/sF2fpmV47lhqnJ9vXi0vHp/kVJ1wtaMoN66UGg/xM4vX6nb+CwC3xWVjkYkdOGIRsQgATCIWeWNjEZWPAAAAAEATko8AAAAAQBParlua2js9olYpe+7UOMkpTjsKpcaxpLg/Da+Hkt1VyuXFZ/PFdr07sel77y4Pt+u3F4fFzzah5PcH+4+2658tP9iuf9G/vV2fpL3wrDkfHt/D/CivZ+E9pKFSq92Nv/ztr8enXBV/G170e6fVCYDb4CqxSPwn6hhvnDsw/EwsMroUiwDwxhKLiEWSykcAAAAAoBHJRwAAAACgCW3X12ColKZWK1bjtKFNeVTxq1BpW0xBiutQTzs7DWXHq7w+DX8sni/yhKZv9g+267N7OW/9/uJp8UwfzPOvf2f/s+36B3tPtus/X3yyXf+8z2XHx0O+X6yx7jb5zc3DQKnZWbjxUay1zu9n6MrPbOjGS5JjqfEwu6imGwBuN7GIWAQArpNY5M2KRVQ+AgAAAABNSD4CAAAAAE1ou75uodS1KDuu1SDvpIuHWfhFcX5exilI/Wn+QayynR2FUuOzfJPjMHHps8WD7frdg7e2698++KJ4pvdmz/Jxy7z+ZPHldv3+Ir/+Z/P8gD/tPgz3zuXMXRxz9U1+8NlJfjmWVM9Owuc62ykv7uP0p/j5j3/mQz9SajyMHgoAt49YRCwCANdJLHLnYxGVjwAAAABAE5KPAAAAAEAT2q5vkljdGic5XZAiHkIZcpxEVJTQVkqNYwnucghTkw7HS40P073t+ueL97brD/ZyqXBKKf14L09y+u3FV9v1J6EW+Idh8tODWR7TNO/zbKWfdB9t10fh3kOXn2n5OJQah/fWrfO6D6+nlNIs/KmPpcexjLgYnjUfOUarEwB3kVhELAIA16kWi1wwbVkscvNjEZWPAAAAAEATko8AAAAAQBParq9bN74uJgnVjk87ZbCbWBYcrhUnP4XLzo9DSfFxLutdPsnXWRzm/HQsNX6WHm7Xfz7/uHim90O58Ufzr7frD5e5zvf92WK7vt/9Vb53F0YzBT8Z8j3itKcUSo0XuWI59aswvWpTfpb9WficKqXGMS2/Keqzu++ueUHNNwDcJmIRsQgAXKcpsUgsndspoxOL3PxYROUjAAAAANCE5CMAAAAA0IS265ZiVeuUatRKqfFFLVDlr3MuebUJr4Zy2s3h+K1nR5uwziW+y29iqfEyXDP/0XmS3i6u9afz39uu35odbddvP/rJdv37y1zz/DuLB+Hsf7ddnb6X73G6zuu/GH60XR+n/e166PP7LyZT7VYsx8rtMP1pqJRkd134Dei+PbkLny8A3FhiEbEIAFwnsYhYJKl8BAAAAAAakXwEAAAAAJqQfAQAAAAAmrDn4+sS+unjtgXDcO7I8wcV4+VrJ6S0KVLJ+RdnoX9/dpIvvHqe13vhfrPDPPq9O8rr+Td5DPz8+f18p1Xe8yCllL5M723X/3L2+9v1vdnJdr3f/XS7jnsb/J15Hhf/Dw/yPgeP38uvH4d9Dn62+a3t+qTb266HWX7/s+Pi8VIXPo/i92UTj8kfSDcLB333evw5ANwKYhGxCABcJ7HIGxuLqHwEAAAAAJqQfAQAAAAAmtB2/SrUK36zWjXqEOfFhwvVrrmTLu5moSZ2nk/ahJLYsy7PR+9DWWx/ki+2fJ6PWTwOx3+VS4LT10+2y4NneTb97CSXE6eUUrfOY95/nXL57x+nf5AP+iQv//H9v8wvz/MzfRBmwf/B/t9u11+883C7PjzLpc2frt/Zrk9Tfn3xtPzQ+pP0QmWpcV5vf7suMVIeAJoTi5TPJBYBgNdLLFI+k1ikvN/0QwEAAAAAppN8BAAAAACa0Hb9ulTKhYsJT7FktZYW7soLxYlDfZ8vMCzyeh2OOQm/5d06TD46ySXF82d5OtL+4zCx6XEuL14/yevlcVmv++76h+FX97arX4VS438eSo1PPsrTov7R/X+7XX8wy3W9b89yOfPf3f/1dv3ZW7nU+Plpvs5X60fhGco/5rNZKLE+C9Ob4kdbKQfvLv4xANxcYhGxCABcJ7HIGxuLqHwEAAAAAJqQfAQAAAAAmtB2fR1qpcabULQaSo2LsuOhXtjahwlPs7DeLPJ0pNN5fv04TD4qSo1P8+vzo1ymuwiTnIZnz7fr1a9/UzzHfJXv9276OP+gy6XGcdrTv9j8wXb91Y/ub9d/eP9vtutH/fF2vd+fbdc/2nu8XX/+8MF2fRxKjQ/X+b4ppTSEKVezfNkUhkjVDTv/BYDbSCwiFgGA6yQWeaNiEZWPAAAAAEATko8AAAAAQBParl+TrlaOGl4vKofXYdpQeH1YleXFw3y83DiWF++FCU+LRZ6UdBSmPR11odR4E8pvz/a360cn74fXc4nv+osvi3vHXy/m+Y/YO7MP83P3+bqfp3zdf7XK9/7qB7nU+HfvfZbfTygv7sMH+2iRa4Uf3cvr05NcapxSSqt1/Mxy/n04DZ95LOmOjJgE4JYSi4hFAOA6iUXe3FhE5SMAAAAA0ITkIwAAAADQhLbrVyGWml528mA4vqhYXYdDimrYnfLis5w/Xs9DqWw4aR5KjfeXuTR3Mcs3eRrKdA/TXn6mUGrcbfLUpIfDD7frfMR3z/HVN3n9dV4v/2O+7lsHearTZp5Lmx+nd7brf73Kfzw/fy/f+8N7eZJTX/nA9+d5RNPe/lnxs/VpfuJNUUacP78+lnGPlBobMAnAjSIWKYhFAOA1E4sUxCIllY8AAAAAQBOSjwAAAABAE9qub5JYahzrV1f1EUJDl/PHm1BevArrTRhqtOjDtKf9k+16Oc+lxl+FY56ng3C3WEj8MK+68vnmi3zD4flhWB/le//6WT7/4FF+1mX+I/ks3OMvQ0nwV+/c264f7eXpTVEsr571ZX1wv8zvdR3Ks4dN/tDjGV2csPX9IdL2ANxFYhGxCABcJ7HInYxFhC0AAAAAQBOSjwAAAABAE9quX7XKhKc4mamrjQQaxsuIu/Xoy+fuN/S5BPdslm9yMs/FsrGM+MHydLuOE566t/K5X4Zb1UqNh/5Bih7s5T9W88+e5Ose5/ul4zxpae/L/Pq9e7EsOq8PUy4p/sUq3/vxg1xefBAmVq034To7n2sffgPW4bMZ1pVJTvH0714fZmZMAnBDiUXEIgBwncQiYpEdKh8BAAAAgCYkHwEAAACAJrRd31DVEuR1fcJTihOeuvxbe9zni/VhwtEs3OTBMk94ehTW6a28/DKU6T5P+9t1LGtOKaXNPP/s3kH+2fKbfN3uLJQzr/NzLJ+GqVNf5futQ6nxSdrbrp+EqUxHB7m8eB7Khjeb8jOL5cZxINUQP/RZ/sEw8nsxSNsDcMeJRcpYJE7PPBaLAEBzYpG7E4sIWwAAAACAJiQfAQAAAIAmtF23NGXCUxo/JlVe3y077sLFNsVPcl55HUqNj8IRsbw4Tjq6t8hTluLkp/Wjw+3663DfwyGXE6eU0hDKnNfL5XZ98CCXGi+ehlFVxfvLv5gfhbLjJ6HcN7Rgn6bFdn0WSo1Xe6F8eWcC0xDKjeM6VUqNu5ii//59X1DlDQA3hljklcUii6fhfS7FIgAwiVhELJJUPgIAAAAAjUg+AgAAAABNaLt+XSqlxuUxQzikG3v5/Lnh1/2qUmrc5bLecEh63u2lMXHq0cEiT0q6F9arB7lQ+fHO1KSjMHVpmIVS470w4el+fn1+XPtAstlpKDs+jNOe8nqV8vXjJKdhp7w4hSlXKT57PKwbX3//0bz4iQHghhGLiEUA4DqJRd7YWETlIwAAAADQhOQjAAAAANCEtuvrMKGitRx1VDlh95xQU9yf1aY95RLcs/Dqs1BSvK6sF32+0t5itV3fu39S3OH5Op9znPJUp6LUeD9c91mYLhUeagip8WISVr51mp3EMuL4FPnkYbkz1Wm33Hj7g8o6+r7We3e8FgDcJmIRsQgAXCexyBsVi6h8BAAAAACakHwEAAAAAJrQdn3dJpQaF6W1ZQ1yGmIVcjw/TnuaUGq8CscfhiM2m5yf3gtTnfpws3lfXnV5kI87WefzT4f8xy1OY1rv5fXsOLyHdbho/Jz68WNi2XF/WnvPqfxTX0u/Tyk1BoC7QCwiFgGA6yQWufOxiMpHAAAAAKAJyUcAAAAAoAnJRwAAAACgCXs+3iS1fQ7CRgVDX25u0MXG/Uo/fjyjus9BmMcex8gfbfJ6tcy56vk8n93tjFfv+/zr2V7efGB9P1wr7KsQR8HPF/mYOF6+tvFDsc9BeNZuk0/oVjsbQsTLxvR7V/nQis+1K/8LAHeJWEQsAgDXSSxyJ2MRlY8AAAAAQBOSjwAAAABAE9qub6pqCW1ZyluUG4d64VrxazFqflUpNQ456U0YA3+6DuXBi3zGbHZuaHu+X7hhF87Z7BdFz+PL03Cdda32Oq6H0WO63cerPW6XL6aTCYA3nlhELAIA10kscmdiEZWPAAAAAEATko8AAAAAQBParm+ZYSddHMuNa6XGtQlP8ZhqqXG44RCmJq3XoVR4Xtbrdn1ZAp1/EF4P52yWlfuF99OtQplytTy4st4V3kfqxp/1RdXFOqEAeFOJRcQiAHCdxCK3LxZR+QgAAAAANCH5CAAAAAA0oe36Noi1rDvVsLWJT0VpbijHHSqVv0UJ8rpSPBuHJsUy4FV5/DCPF4snVa4b3sOwyMfHKuKuG38/lerg8mO6oBa4MgjqghOmHAQAd4xYRCwCANdJLHKrYxGVjwAAAABAE5KPAAAAAEAT2q5vm93y1lgeO6XUONbQThi+lGqlxtFmp7w43DvVJjxVDPH4We1+8V6V9xavufsWagcWJ8XjR16/3NsCgLtDLCIWAYDrJBa5dbGIykcAAAAAoAnJRwAAAACgCW3Xt11tAFMsNQ6jnIqpTsOLS3OLW8UxS2GS0+6kqK5S8lw86wWTqsaOGSpp8i5V3ttFTIkEgFdHLFJ9jiqxCAC8OmKR6nNUveZYROUjAAAAANDEpSofN8fHrZ6DVy1ku6ubqU7I8BebkoaDYsb9XPZ9Fo+LD5LG15Xn6+KmrpUNVIt/dbhihv/cBqwXHO/vAsD18L+/t4hYpE4sAnBr+d/fW0QsUveaY5FumFCT2XXdRymlTydfFd4cHw/D8IvrfgiAu04sAlViEYDXQCwCVS+MRaYmH7uU0ocppaev6MHgLniYUvrlMOUvEQBXIhaBUWIRgNdELAKjJsUik5KPAAAAAACXZeAMAAAAANCE5CMAAAAA0ITkIwAAAADQhOQjAAAAANCE5CMAAAAA0ITkIwAAAADQhOQjAAAAANDE/w/Tx+HUJ5NA2gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2000x1200 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_rows = 4\n",
    "n_cols = (len(evolved_rfs) + n_rows - 1) // n_rows\n",
    "\n",
    "fig, axes = plt.subplots(n_rows, n_cols, dpi=100, figsize=(20, 12))\n",
    "\n",
    "for ind, ax in enumerate(axes.flat):\n",
    "    if ind < len(evolved_rfs):\n",
    "        ax.imshow(evolved_rfs[ind])\n",
    "        ax.set(xticks=[], yticks=[])\n",
    "    else:\n",
    "        ax.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gabor vs. true RF after training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "learned_rf = gabor_gen().squeeze().cpu().data.numpy()\n",
    "true_rf = neuron.rf.squeeze().cpu().data.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3), dpi=100)\n",
    "\n",
    "ax2.imshow(true_rf);\n",
    "ax1.set(xticks=[], yticks=[], title=\"True RF\")\n",
    "\n",
    "ax1.imshow(learned_rf);\n",
    "ax2.set(xticks=[], yticks=[], title=\"Learned gabor\");"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}