fig-3-scatter-vis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# path = \"correlation npy-20230516T174345Z-001/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# corr_vis = np.load(\"cnn_070921_J553RT.npy\")\n",
    "# corr_vis_sorted = corr_vis[0].argsort()[::-1]\n",
    "# corr_beh = np.load(\"cnn_comb_gru_070921_J553RT_seq_1.npy\")\n",
    "# corr_beh_sorted = corr_beh[0].argsort()[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mx73vX5m9pJ6oY7RL4gynCV2pCdC8v5///E0zj2/txDUGj22xsMJPfU05BwMRfImLnM/vOkBFsRdjzGC6Tv7tsSy8tkXMdYk5hmQy93uEU2or6TwcpC8QIxCJYZdWUrp0FbVvu5yqxcs5EnW0S6LShK7UeD35aidf+OWrHAnFEqs1hXDUobn7CM3dR7AlvqWbm6iNx+vmFpa4OCmdEsMxF8c1eGzB77Xjy/6jDrNLilg+v5zl88vp3d+G8RTTZ0pov+qzRB0Xg1A3p5S7L1upyXyG04Su1Dg07+/nC798Nb65BIaYEy96DyZv4jNUglEXS+LllN5AFBli5orrGqLGEMHFYwlVpV4qS70A9Ha28aPbrqNifi1X3f496qpLAejsC7J2Va0mc6UJXanxWP98KzHHUOKzCadMNYR48k697yaqKA4uPivz8iAX8IhgS7xb4rLqMkqKPOzbu5tf3vVhIqEA77z2psGdhnR3IZVKE7pS4/CbHd2UFtkEIs5RUwuTFzaHWrYfHaofLolNKxBKfR7OqZ/H8pIAl118PdFQgL+/5dvMW3oioahDbyCCz2Np3VwN0oSu1DiEIg6VJV4OBRw8lhB2zFELhIZK28O1Z/HYgt9jU+S1+ePugzzy41vwuhFu/85P2c1cug6HdHchlZEmdKXGwe+ziTrxnuUej0XYcQaPjaVToiUwv9yPz2MTiTm8uq+PB+/7Nk7gMKeddtrEBa4KkjbnUmoc1qyYy5GwgyWCkKx9H7tVXLYEOHgkwkB3G63/ez/iujQP+DSZq6xoQldqHD7wtqV4bInPcjEGr8ca9w9V+FAHL3/rRvZvfpI5po+N2/dPSKyq8GlCV2ocTphfzpfWnoIl8c2cw1H3mB2GbImXUlKlz3FJ3o/0drL3BzfjRIKc/MF/p75+Of2h2CRFrwqN1tCVGqX0Xi0Ac8p8WAI9R6IZnyNp3bUyVWRivZ10/vizmGiIxVfdzVvPbMQS0a3jVNb0O0WpUUjv1TK7xMsLrQdxXIPf52GuWLgYAhGHaMwh6sYXEPltC9d1h531Yo4cRICFV93FohMaqCzxDS4aUiobmtCVylKmzZ13HxhAgHK/l6jj0heKUjOriIri+BZz4ZhDd3+IWKJTIpmmMkaDeIpKKFt2KpUf/x4xPCyuKtFFQ2rUNKErNYzU8srO/QMcPBJh2dxSPHYxAE37+wlFXQ4FoliW4DgufcEotuXQH4oSc83gfPRknTzZ67ykyEP4YAe7f/AvVLz1vVSfcRGWeBDAstBmW2rUNKErNYT08sqRcHwv0L09AVoPHMEYGAg7+Oz4xs6GeAOX3mAMCxArPusg9ZKmEF84JCKEDnaw9wf/gokEKa2tJ+K42JZw/Nwy3nPGYl00pEZNE7pSGWQqr8TceM8Wx4X9h0OAwWPFx9vJjScG+7YAHgSxQBJduGwLYm78dSpjB9n2g3/BjQRZ8o93U7JwOQKcUTebr73nNE3kakw0oSuVwaYd3URi7mAyB/DaFo5rOBJxEr1a4vPPY67BEiHmukf1bnGNwUISLXXj51tiIBLg1e99ChMNsfrDX6N04XKC0RgCfPbCkzSZqzHThK5UBk9s6+BwKMau7gNEHRevbeG1hYFQjEDEGUzQMdfguIZIopd5slZuWyldF3mj1GKLhfGUUfP2f2DuslPwzFuGMYaqEh//+ObjqK8pz9VHVgVAE7pSaZo6+/nz7l68Hosij02x18Y1cCQcoy8UxXWhyBNfGRqKOngtGUzmEL8AGnPjSbzIa1FR4qUvECXau5/okYP4Fp5I7VlrWVM/D4i3wI04DheurMnVR1YFIquELiIXAP8B2MB3jTFfznDOe4EvEv9+3mKMuWoC41RqQmTawPnc+nmcvWIuc8qK3qideyx8Hgs7scTTFigt8iLA/v4wEhNiiW3kYkM0bjFAud9DsddDNNzJlu9/BgOc8JHv4LX92gJXTbgRE7qI2MB9wPlAG/CiiGwwxryWcs5y4LPA24wxh0Rk3mQFrNRYZdrAORxz2bClnV9t6+TG8+t5rb2PSMzluDkl7OkJUOw7ujtGSZGXskiMUMzgOm+Myq20zSySeo5EMP1dbH0gXjNfevW/4vUVUeqzcVxXW+CqCZXNCP1MoNkY0wIgIg8Da4HXUs75EHCfMeYQgDGma6IDVWo8Ms1aAfB7bWoqihkIxbjnqSYMhsoSHwK8fig4WD9PVVFcxEBvACvRWdGS1Fkub2R0AQI9Hex9+HO40RArP/jv+OYv48QFs3Qmi5oU2TTnqgVeT7nflngs1QpghYg8LyIvJEo0xxCR60Vks4hs7u7uHlvESo1BctZK2RB9Ucr8HiIxl709QYo8FkVem1NrK3CNIRiJ4bguBoPjusQcJz7PPCWJRx1zTOnFAP2bf0ksEqT+2q9gzV3KsrllOpNFTZpsRuiZNj9MLxp6gOXAGmAR8JyInGKM6T3qScY8ADwA0NjYOMaO0UqN3samLipLfMOeU1nio2l/P+GYi99rU1Hi48y6Kjr6QrT3BglFHLy2Re3sEoxAz0AEgxlymzmA2ed+kFln/B0rTm7g8tNrufCUBZrM1aTJJqG3AYtT7i8C2jOc84IxJgq0ikgT8QT/4oREqdQ49Qdj1FT4hz3H57Go8HvpDUSoqYgv7S/y2tRVl1JXXTp4XmdfkDctmc1jWzszvk60t5ODv/5Pqi/6JHbZbIqra/nO1Y2ayNWkyyahvwgsF5GlwD5gHZA+g+VR4ErgQRGpJl6CaZnIQJUaj/Jiz+DIeyiRmMuSOSVAfCphmd9DKOrQ0RekvTdE1HHjPVh8Ns4Qw/Jobyf7f/xZTCSEEziEp2w2tgjP7TzAu1dr10Q1uUasoRtjYsDHgCeBvwI/NcZsE5E7ROTSxGlPAj0i8hrwLHCTMaZnsoJWarTOrZ9HbyAy7Dm9gQgXnrKAG8+vJ+I4NHf180JLD3t6AhhjwMTr5AeOROjoDZH+qyE1mc9fdye+ecswgN9r6a5DakpktWORMeZxY8wKY8zxxpi7Eo/daozZkLhtjDE3GmMajDErjTEPT2bQSo3W2Svm4vNYDAyx+09qq9r6mnJuelc9oag7uMrTEqGuuowlVSWUeG2Qoy8kHZPM5x8PxH/AfLaluw6pKaErRdWMMKesiA+8bSl3/u9ricZaQpHHYv6sIoq8NmVFnqMW92zd10d1mY9TaiuOep3nmw/g81h4LIuIuEiiNa7lLcIzax5V511/VDIv8lj4PLbuOqSmhO4pqmaEps5+1j/fyiy/lyVVpfi9dnya4sEgh4NRPvC2pUf1URlqVkw00eK2xGdjixDrP4DlxvCWzabmqi9TNP/4wVF9kdeiyGfHV6OeOH/qPqyasXTYoApe6qKiJXPiI/Dl899I3gOhGOufb+WEeWWDI/ShZsUkOy6W+Gx6Ojrp+OHNlNStZt7Fn8D2xMdHqddLvbYwf5Zfdx1SU0JH6KrgZbuo6LmdBwYfS86KSVdb6ScSc4kc6qTtofiGzpWNl+AmFhi5ie6LxhiKvDbL55XrQiI1ZTShq4KX7aKi1JkoQ82KWVBRTORQB3++/59xw0HO+edvcGbjGVSWxBt3ucRb59bXlPPpd63gm1eu1pa4aspoyUUVvGwXFXUdfiOBn71iLr/a1slAKIbXFjr6guzrDRGJxnjt+18gFg5ywjVfZkXDSsqLvSyuKjmqc6ImcZULmtBVwct2UVHqTJQ5ZUXceH49tz+2jR37+7FF8Hst/D4PS9/9SRzLw+ITTiYQiRGMOpT7Pdo5UeWcJnRV8M6tn8eGLe2Dy/kz6Q1EWLvq6JWc1WU+ijwWi2eXsG/vbl5v2szisy7l1NPPpKbCT8wxRByHuy9bqUlc5QWtoauCN5pFRak27Yh3BK2RwzStv4mOZ/6LVXMt6qrj0x4zXUxVKpc0oauClyyfRByHzr4goagzuH1cZ1+QiONk3DFoY1MX0t/Fj267jkgowLrbHqCkouqoc9IvpiqVS1pyUTNCfU05d1+2kud2HmDj9v10HY6MWPdu37uHp7/60cFkXrPspGPOSb+YqlQuaUJXM8acsiLevbo2666HfbtfIRIODpnM4diLqUrlkn4nKpXGcRxs2+ajH7qOeQ1nUVNbM+S5mS6mKpUrWkNXKkVLSwsrV67kt7/9LWevmEt55exRX0xVKlc0oSuV0NLSwjnnnENnZyezZs0a88VUpXJFSy6qoPUMhNm0o5uNTV30B2P4vEJ1aREHBiLx+nexh3Pr57HE28/ll1xAf38/zzzzDKtXrwbGdjFVqVwRM9wOt5OosbHRbN68OSfvrWaGps5+7nmqiUjMpbLERzASY0tbH+GoS5HXYtWiCvw+D+3t7Tx+13XYTohnN24cTOZK5SMReckY05jpmI7QVUFKbZlbVVpEKOrwavthvLZFic9D1HHZ2n6YM+uqqFu0kKWnn83yt1/KkuUNuQ5dqTHThK7yWnrJJFkiOXvF3GHLHcmWuVWl8XM6+oK4rqHIF+/n4rUtDnW+zi4GaFi+lItvuIXOvqBu5qymNb0oqvJWU2c/n3tkKxu2tOOxLGoq/Hgsiw1b2vncI1tp6uwf8rnpLXPbe0P4PG805wr2tPPadz/Fs/fdTLLsqKs+1XSnCV3lpdSSSU1FMX6vjYjg98bv+2ybe55qomcgnPH5/cEYRZ43vr2jjosl8dvBnnb+fP8nccJBlq79BCLxAz6Pbuaspjctuai8lF4yCUedwZ7kUcfFa1uU+myeeLWD972l7pjnp7fM9doWroHIwWQyD3Dq9V+lbOHywefoqk813ekIXeWl1JJJXyDCn3YfZE9PAEugxGdjCRw8EuH/PtucsfSSvuPQwko/kZjDjke/iRMOsPqGeyiafzwLK99oqdsbiOhmzmpa04Su8lKyZBKOOryyrw9LhGKfB9uyAMG2LEqLPLguGUsv6S1zF1QUY1nC8vd8htP/6ev4a47HsoQFiZ2MdNWnKgSa0FVeSpZMkrNTvPax36qugWKvnbEneeoqz507m3nmu3dx4rxi8JcTrVhM1HFZuXAWBnTVpyoYmtBVXkqWTPb1hvB5Mn+bhmMOCyuLh5ydUl9TzvWrStn09Y+z84WnONi5j1MXzeKiU2s4dVEFoZiL47qsXVXL3Zet1H1A1bSnV4BUXkpu0hyMxpjl9x5zPOq42JZQU+Efsid5S0sLl19yAU4kyO83PasrQFXB0xG6ykvJkoktwkA4huMajDE4riEQieEaw8raCvyJkkv67JRko62BgQGefvppTeZqRtARuhq3sa7mHEl9TTkfXXMCP/rTXgIRh2DMxWdb1M0ppabCPzglMVNP8kOHDuH1ejWZqxlFE7oal/QGWDUVfsIxlw1b2vnVtk5uPL9+XLXpi05dwO92HcBnxzdlTpc+O6Wvr4+KigrOOOMMtm/fjsej3+Jq5tCSixqz8a7mzMZoepK3tLRw6qmncu+99wJoMlczTlYJXUQuEJEmEWkWkZuHOe8KETEikrG1oyosydWcmUbOAGV+T8YphaOV7Em+dlUtjuvSdTh0zOyU1Jr5mjVrxvV+Sk1XIw5hRMQG7gPOB9qAF0VkgzHmtbTzyoFPAH+cjEBV/klvgJVJckrhaDsYDlWXv+3vTj6mLq8XQJWKy+b/pGcCzcaYFgAReRhYC7yWdt6XgH8DPj2hEaq81R+MUZNYaTkUn8ei7VCIR/7clvVF09HU5QOBAO985zs1mStFdiWXWuD1lPtticcGichqYLEx5v8N90Iicr2IbBaRzd3d3aMOVuWX5GrO4RzoD7OreyDrFrijrcuXlJRw++23azJXiuwSumR4bHDfOhGxgHuBT430QsaYB4wxjcaYxrlz52YfpcpL6Q2w0oWjDlvaelkwqzjri6bZ1uV/9uxLbNy4EYCrr75ak7lSZJfQ24DFKfcXAe0p98uBU4DfiMhu4C3ABr0wWvjSG2Claz0wAEBddWnG45kummZTl5f+Lj7zgb/n6quvJhQKjTF6pQpPNgn9RWC5iCwVER+wDtiQPGiM6TPGVBtj6owxdcALwKXGGN0BusCNNKWwoy/EaYsqBxcAZZLehyV9Y4p0vZ1t/PxL1xMJBnjsscfw+4ev4Ss1k4yY0I0xMXr/UW0AAA3xSURBVOBjwJPAX4GfGmO2icgdInLpZAeo8ttwUwqXVZcxt3z4laLpuwQNV5fv7WzjR7ddRyQU4LLP369lFqXSZLXywhjzOPB42mO3DnHumvGHpaaTOWVFvHt17TFTE5/Zvv+oXYMySe/Dcm79PDZsaaemoviYc19++udEQgHO/9Q3+YcL3jFxH0CpAqErRdWkGemiKRy7S9Bwdfl3XPVx3vulH7Lg+JN0IwqlMtCEribNSBdNM+0SlF6X379vLw/d+gG62l9nf38Y/5wa3YhCqSFoQleTZjR9WFIl6/JvmevwP3d8kK7dOwj09+lGFEqNQLsXqUmVTM7P7TzAxu376TocodzvYe2qWt6+vHrIkfbh7nbu/ug6vG6E3z73G70AqlQWNKGrSTfURdOh7N69mzVr1uhyfqVGSRO6mhATtclFz0CY53YPYCoWcta1H2H9dsO5pm3cm2UoNROIMWbksyZBY2Oj2bxZ1x4VgvRmWkUei3DMpTcQweexst7k4jcv/ZUfvHwQ1/KO63WUKmQi8pIxJuNKfL0oqsZloja5+POr2/m7vz2X362/a9I2y1Cq0GlCV+MyEZtctLa28rfnn0c0FOCsy64d8+soNdNpQlfjMppNLjJpbW1lzZo19A8M8Pe3fJuaZSeN6XWUUprQ1TiN1EwLju3XkmSM4YorrmBgYIDzb/wmi5c3jOl1lFJxOstFjUuymdZo+rUkiQjr16/HdV3Wbzdjfh2lVJyO0NW4jKVfS0tLC/feey8Ap512GqtXrx7T6yiljqYJXY3LaPu1JDd0vvPOO+no6Bjz6yiljqUJXY3LaPq1JJN5cgXoggULxvQ6SqnMdGGRmhA9A+HBfi39oRjlfg/nnjh/sF9LejIfajn/SK+j1Ew33MIivcKkJsRI/VpeeuklgsHgiL1ZRtv3RSn1Bi25qEkVjUYBeM973kNzc7M22lJqEmlCV5OmpaWFk08+mSeeeAKAWbNm5TgipQqbllzUpEitmdfU1OQ6HKVmBB2hqwmX7QVQpdTE0oSuJlRXV5cmc6VyREsuatxSN7c4fCRC9Wnn8Il161gyQm8WpdTE0nnoalySm1v0tL9Oud/D/EXH6aYUSk0i3eBCTYrk5hbBng6e+upHefzrN4ExuimFUjmiCV2N2aYd3fS0v84v7/owkdARLvroHYj1xreUbkqh1NTShK7G7JFNf+bXX/0IkdAR1t32nYybU+imFEpNHU3oasw2PfQNoqHAkMkcdFMKpaaSznJRY/auG75A8FA3NcvqhzxHN6VQauroCF2NSktLC9dccw2BQIALVx+PZ86SYc/XTSmUmjpZJXQRuUBEmkSkWURuznD8RhF5TUReEZFnROS4iQ9V5VpyBehjjz3G7t27dVMKpfLMiAldRGzgPuBCoAG4UkTSV4z8BWg0xpwK/A/wbxMdqMqtZDLv7+/nmWeeoaGhQTelUCrPZDNCPxNoNsa0GGMiwMPA2tQTjDHPGmMCibsvAIsmNkyVS+nJPHU5f31NOXdftpK1q2pxXJeuwyEc12XtqlruvmylLipSagplc7WqFng95X4b8OZhzr8OeCLTARG5HrgeYMmS4WuvKn8Eg0FKSkp49NFHM/Zm0U0plMoP2SR0yfBYxn4BIvI+oBF4R6bjxpgHgAcgvvQ/yxhVjvT09FBVVcXJJ5/Mq6++im3buQ5JKTWMbEoubcDilPuLgPb0k0TkPODzwKXGGF3rPc21tLRw+umnc+eddwJoMldqGsgmob8ILBeRpSLiA9YBG1JPEJHVwLeJJ/OuiQ9TTaXUmvkll1yS63CUUlkaMaEbY2LAx4Angb8CPzXGbBORO0Tk0sRp/w6UAT8TkZdFZMMQL6fy3HAXQJVS+S2rJXzGmMeBx9MeuzXl9nkTHJfKgXA4zPnnn6/JXKlpStdkq0FFRUV85Stf4fjjj9dkrtQ0pAld0dLSwvbt27nooou44oorch2OUmqMNKHPcMmaeSQSobm5mdLS0lyHpJQaI03oM1gymSc3dNZkrtT0pt0WZ6j0ZK41c6WmP03oM9RDDz2kyVypAiPG5GYFfmNjo9m8eXNO3nsmM8YgIhhjaGtrY/HixSM/SSmVN0TkJWNMY6ZjOkKfQVpaWjjrrLPYsWMHIqLJXKkCoxdFZ4jUmnkgEBj5CUqpaUdH6DNA+gXQVatW5TokpdQk0IRe4Pbs2aOzWZSaITShF7g5c+awevVqTeZKzQBaQy9Qe/fupaqqirKyMh599NFch6OUmgI6Qi9Ara2tvP3tb+f9739/rkNRSk0hTegFprW1lTVr1jAwMMCtt9468hOUUgVDE3oBSU3mWjNXaubRhF4gjDFcddVVmsyVmsH0omiBEBEefPBBAoGAJnOlZigdoU9zra2t3HnnnRhjqK+v12Su1AymI/RpLLVmfs0112hvFqVmOB2hT1PpF0A1mSulNKFPQzqbRSmViSb0aWjbtm2Ew2FN5kqpo2gNfRoJh8MUFRVxySWXsGvXLt0DVCl1FB2hTxMtLS00NDTw85//HECTuVLqGDpCnwZS+5kvW7Ys1+EopfKUjtDzXPrmFFozV0oNRRN6Huvp6dFkrpTKmib0PFZVVcV1112nyVwplRWtoeehlpYWQqEQDQ0N2gJXKZU1Teh5JlkzLy0tZevWrdi2neuQlFLTRFYlFxG5QESaRKRZRG7OcLxIRH6SOP5HEamb6EBngtQLoA899JAmc6XUqIyY0EXEBu4DLgQagCtFpCHttOuAQ8aYE4B7ga9MdKCFTmezKKXGK5sR+plAszGmxRgTAR4G1qadsxb478Tt/wHeKSIycWEWvttvv12TuVJqXLJJ6LXA6yn32xKPZTzHGBMD+oA56S8kIteLyGYR2dzd3T22iAvU/fffz+9+9ztN5kqpMcsmoWcaaZsxnIMx5gFjTKMxpnHu3LnZxDdjlJSUcNJJJ+U6DKXUNJZNQm8DUpttLwLahzpHRDxABXBwIgJUSimVnWwS+ovAchFZKiI+YB2wIe2cDcA1idtXABuNMceM0JVSSk2eEeehG2NiIvIx4EnABtYbY7aJyB3AZmPMBuB7wA9EpJn4yHzdZAatlFLqWFktLDLGPA48nvbYrSm3Q8B7JjY0pZRSo6G9XJRSqkBoQldKqQKhCV0ppQqEJnSllCoQkqvZhSLSDeyZ4retBg5M8XuORj7Hl8+xQX7Hl8+xQX7Hl8+xQW7iO84Yk3FlZs4Sei6IyGZjTGOu4xhKPseXz7FBfseXz7FBfseXz7FB/sWnJRellCoQmtCVUqpAzLSE/kCuAxhBPseXz7FBfseXz7FBfseXz7FBnsU3o2roSilVyGbaCF0ppQqWJnSllCoQBZ3QRaRKRJ4SkZ2Jv2dnOGeViPxBRLaJyCsi8g+THFNeb7idRXw3ishria/VMyJyXL7ElnLeFSJiRGRKp5NlE5+IvDfx9dsmIj/Kp/hEZImIPCsif0n8+140RXGtF5EuEXl1iOMiIt9IxP2KiJw+FXGNIr5/TMT1ioj8XkROm8r4jmKMKdg/wL8BNydu3wx8JcM5K4DlidsLgQ6gcpLisYFdwDLAB2wBGtLO+QjwrcTtdcBPpvDrlU185wAlidv/NFXxZRNb4rxyYBPwAtCYZ1+75cBfgNmJ+/PyLL4HgH9K3G4Adk9RbGcDpwOvDnH8IuAJ4jujvQX441R93bKM76yUf9MLpzq+1D8FPULn6M2r/xt4d/oJxpgdxpididvtQBcwWfvj5fuG2yPGZ4x51hgTSNx9gfgOVnkRW8KXiP8iD01RXEnZxPch4D5jzCEAY0xXnsVngFmJ2xUcuzPZpDDGbGL4Hc7WAt83cS8AlSKyYCpig5HjM8b8PvlvytT+TByj0BP6fGNMB0Di73nDnSwiZxIfveyapHgmbMPtSZJNfKmuIz5ymgojxiYiq4HFxpj/N0Uxpcrma7cCWCEiz4vICyJywZRFl118XwTeJyJtxPc/+PjUhDai0X5f5tJU/kwcI6sNLvKZiDwN1GQ49PlRvs4C4AfANcYYdyJiy/Q2GR4b04bbkyTr9xaR9wGNwDsmNaKUt8zw2GBsImIB9wLXTlE86bL52nmIl13WEB/FPScipxhjeic5NsguviuBB40xXxORtxLfheyUSfx5yFYufyayJiLnEE/of5OrGKZ9QjfGnDfUMRHZLyILjDEdiYSd8b+4IjIL+F/glsR/6SbLaDbcbsvBhtvZxIeInEf8F+Y7jDHhPImtHDgF+E2iQlUDbBCRS40xm/MgvuQ5LxhjokCriDQRT/Av5kl81wEXABhj/iAifuLNp6ayNJRJVt+XuSQipwLfBS40xvTkKo5CL7mkbl59DfDL9BMSG18/QrxG97NJjiffN9weMb5EWePbwKVTXAMeNjZjTJ8xptoYU2eMqSNey5yqZD5ifAmPEr+ojIhUEy/BtORRfHuBdybiOwnwA91TFN9wNgBXJ2a7vAXoS5ZS84GILAF+AbzfGLMjp8Hk6mrsVPwhXnt+BtiZ+Lsq8Xgj8N3E7fcBUeDllD+rJjGmi4AdxOv0n088dgfx5APxH6KfAc3An4BlU/w1Gym+p4H9KV+rDfkSW9q5v2EKZ7lk+bUT4B7gNWArsC7P4msAnic+A+Zl4F1TFNePic8uixIfjV8H3ADckPJ1uy8R99Yc/LuOFN93gUMpPxObpzK+1D+69F8ppQpEoZdclFJqxtCErpRSBUITulJKFQhN6EopVSA0oSulVIHQhK6UUgVCE7pSShWI/w86rRDMnCLOugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mouse = \"070921_J553RT\"\n",
    "corr_vis = np.load(\"corr_cnn_shifter/\" + f\"{mouse}_shifter_True_seq_1.npy\")\n",
    "corr_beh = np.load(\"corr_cnn_gru_shifter/\" + f\"{mouse}_seq_1.npy\")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(corr_vis[0], corr_beh[0], alpha=.6, s=100)\n",
    "ax.plot([0, 1], [0, 1], 'k--')\n",
    "ax.axis('equal')\n",
    "fig.savefig(f'scatter-{mouse}.pdf', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(corr_beh[0] - corr_vis[0], range=(-0.2, 0.2), ec='k', bins=21)\n",
    "plt.savefig(f'hist-{mouse}.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mouse = \"110421_J569LT\"\n",
    "corr_vis = np.load(\"corr_cnn_shifter/\" + f\"{mouse}_shifter_True_seq_1.npy\")\n",
    "corr_beh = np.load(\"corr_cnn_gru_shifter/\" + f\"{mouse}_seq_1.npy\")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "# ax.scatter(0.2, 0.8)\n",
    "ax.scatter(corr_vis[0], corr_beh[0], alpha=.6, s=100, color=\"orange\")\n",
    "ax.plot([0, 1], [0, 1], 'k--')\n",
    "ax.axis('equal')\n",
    "fig.savefig(f'scatter-{mouse}.pdf', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.hist([0,0], range=(-0.2, 0.2), ec='k', bins=21)\n",
    "plt.hist(corr_beh[0] - corr_vis[0], range=(-0.2, 0.2), ec='k', bins=21, color=\"orange\")\n",
    "plt.savefig(f'hist-{mouse}.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mouse = \"101521_J559NC\"\n",
    "corr_vis = np.load(\"corr_cnn_shifter/\" + f\"{mouse}_shifter_True_seq_1.npy\")\n",
    "corr_beh = np.load(\"corr_cnn_gru_shifter/\" + f\"{mouse}_seq_1.npy\")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "# ax.scatter(0.2, 0.8)\n",
    "# ax.scatter(0.8, 0.2)\n",
    "ax.scatter(corr_vis[0], corr_beh[0], alpha=.6, s=100, color=\"green\")\n",
    "ax.plot([0, 1], [0, 1], 'k--')\n",
    "ax.axis('equal')\n",
    "fig.savefig(f'scatter-{mouse}.pdf', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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twC3AKzc51491v0ffAr4C3LnaYzrrXMBzu7+/27vr121yro8Di/xfX1031vbyo/SS1KitdghFkjQlC1ySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ16n8B0rntFelJ2GEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.hist([0,0], range=(-0.2, 0.2), ec='k', bins=21)\n",
    "# plt.hist([0,0], range=(-0.2, 0.2), ec='k', bins=21)\n",
    "plt.hist(corr_beh[0] - corr_vis[0], range=(-0.2, 0.2), ec='k', bins=21, color=\"green\")\n",
    "plt.savefig(f'hist-{mouse}.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}