Data exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "af9a58dd-52eb-4909-a973-7db22c15b3f4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import glob\n",
    "import tensorflow as tf\n",
    "import nibabel as nib\n",
    "from nilearn import plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8f8123e4-7524-48cd-992b-3401d1a2cba8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\007sh\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\nilearn\\_utils\\niimg.py:63: UserWarning: Non-finite values detected. These values will be replaced with zeros.\n",
      "  warn(\n",
      "C:\\Users\\007sh\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\nilearn\\plotting\\find_cuts.py:69: UserWarning: Given img is empty. Returning default cut_coords=(0.0, 0.0, 0.0) instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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ntjkzQbX5ZgAAABgN90ug3x2Si391/X/T0LYGRQYVACaZP/zMTzqmvGv9laJsJiHWW+8e0KJwBtVmF+1SpS6b4yY+2QzqO5L+aPZLx7OlbGtW4WEGrinNUtTd3qbKDKo/S9+fxe9nTWutitVi7s+a60IZVPee3f3MqgYSjQAVAABgIqot/+t+y7S/QGFUCFABYJJVq/LkZx/9a+3WXlNOENpxmXaMpZ91zKuYJZXiE4RsBtWVlupTlE3tkwZKmUd3aNB7LzaJKxWrObWVxrimcoqyqTZDGhpvaj/IbftteSqpmJF1QYAdb5oK3GP/MM2f1ZYHBNSX+1myY01tTblQVh81EaACwCSzcyWsajXza/U+p8v/UTiQkyqzNpYNUN3EJxug9qsczA7n4j3/UjF+dbfbOvju5QfNfmvWNMuuRGC7+3Peedvtb4v+hzJU1T703fsPVQkgUAAaAgEqAADARPiVMdy++4XIH/eNERGgAsAk85N2I5VP8ZOFtppT7IGhbGBe0Yeh69ZvVfyD0n1I2q3bH1T5w9bsxhKs7jO4TZXzsTKKD7drdt/YrGbOXByaERaaECXvOp87bjPEoTJVjAEEGgIBKgBMMjuJvlp8ZYNRO+Fciq8Ymq52kx2w6gK00ANCtRmziq21aoeo2iXspXjMN9IQBduUdKl9KTtxxL5ZG+HaYDUUrVuh++14XL8mLJ96qLOuVkW/DNq/n0OK/n7W+qUSQfyoAgAAjFNh0MSdNgC1PRMYMwJUAJhkdiEnKUoQhiab20nAdjhbrDSozUD6D3VFwe2xvOLjCvz7veKr9rBroytnapcXt++r2hwtP/HZJCmdMwf9NVztsZEyqPZaFwj43fr+GARXbgBAohGgAgAAjNNhmYpu5heoQi76XbHaCBVUR4AKAFMglOy0H1q1/jEuKJ4AbbLjOe0D3cX+gFc7cUqB86F7FE9A2gxoqA6qfXStKlc2WZvOB96DHZeaVuUfVk7hklR2kK4dd+pnUEONAibgsKIeBsvWDK71M4EwAlQAmGT+HCZ/3oS/b+8LnSvHXPahtbr4h70XDXWbm4lJdj6Ri/vsxK1a7fbfnz8pzO+1L9fXL92UstG4fZid+BVaKrXa8qi2wL99DoBEI0AFAAAYp2rzoLKqHFterYoHKhGgAsAkyyk8Ccop1DguxbOUaUUfiOWsoy0TZQuRhpb/tH30dtyBWb3JVmkKZXb99vhtDiVmHfu9XdW03GufNyWpsqosGptSPGuaMfvufdgMqn0zUrT0KoBEI0AFgElmg08b0FXr1veDVdsrP6zKkp7N9qDfBy9VzuK3L+ZeINCv35SrXGlU5lJbXrVamUd3X7WedTuc1DU1Fl97EW6TpFTppvSwlPK7+O24BBvAksLCJLEjUfwyqP4vYFlJ/6e0v2Pym9bQCFABAADGyY6rtr//DCs+t09iCPRYEKACwCRZb/btP7a2/qnPznK3Cc5QsnPIPTtf6g6X4v3mjl8GwJdSvKu8tJ/OVfaQ29e3dVBDmWH7Xvzm+GwiNBW41r7/WJnTUkTgtumcwkMA/EwqgEQjQAUAABgnm0G1v//Yrv9QRTfURoAKAJPEfSg1a+Q6p/aDzO8GtBlUKcoguvk+eXMs9gloPy1DY1RDs52aVZ6FlRmU2rxLbbelzaCGFqryy7P650Pvyf/wDk0us0nRiopZea/8lruRzCkmiV8b2Kk29Ds09hyVCFABYArYbmnHTiCyMaVftsYGqDa+jM0PshOD5F1gX8xGs6FxA82KlgPNSW0D8ccOKf5h7A9X8IPSUB1+G4SmvPOWX97Vv99f9dQ/n7ELAfgvCCDRCFABAADGKTRm2j9nt3Txjw4BKgBMMptJbFb0YWW78kMZRrt4kvvHukVR1365pGezopWSmlVZ+6maUP98RlG/vrmmuZTWbc5G7bLvy2aAQ137Teb8SM2yzwwlQO1KVXbCltva/fKHXOlBW/pHeHEAiUCACgCTJNQFnVPlJHupMtCy96QV9bq3mf1MkznY4T3Ib0CoaH+oIba4vd134w6GpEw2OuZ37adVuU6AvGOhZtn3b1c6DRUkCH0f6uK3Y3vToTEEQB1U+4XLFpGw+Ks4OgSoAAAAdeaXRHPbakujIo4AFQAmmV+a035YuWN2RSXbWy8VM6YuQdpu9mN9/TbVOFKKJpQ5Dc3ozyj6lBgwjR6KLk2V0pUuQ5nOx7vYbXf8aNgMqi3fY4WqCFh2YhZzooDGRIAKAAAwTnZBimq/G4aGsKA2AlQAmGTVPpRcdi+j+Adbs3e+RcXMqVQcbppyF7gMalrhAa/2hULnbarTfoLafsnQYuLu2lAh0lw0RjW0rONoCpXbpox0rftzC2VKq5WkAuppSJUTG6V4GV477Ju/i6NDgAoAk8TFkRmF6+TboNVNfLK977Z2vptY39xU5WInNLXeBqBZVQao9lMzrXgfuRswZwvehz5hTYRoLw3VQbU189PeeftnYmNleyxU9B/AzEKACgAAME4Din5pGjTHhxReoINFzUaHABUAJonrgbf1ODOqLFNqu6JtV7W7xy7uFKt5GkrLugdK0SehnW1kl2dqMtfbfXc+a/aHzdb1Y9psaum6Qj66NGsutVv78qGmWCMtFTnaSVBkWoHGQoAKAJOk1ezbYZuhoCoUdNl7yl3ddma9v5UqA0y3Dc3SD0WFOXNfXvFgVCpGn7naL2VKppYn/Nvi/o6dZW/Hitqg1Y48GA1//fNaY1SBifhoaTugyt/jpPgvaPbvr/s7fZ2k+yatdY2PABUAAGCMvjXC+f9/SloxcxGgAsAkCU14Cs3ot1k/22tuM6ixGVf+LP7Qkk2qcswWJ7WrRNlZ+jYtmova5doamvBkM6guazqocOY0NEksVFAg7+1Xu8ey8738xDCAxkE5LgAAACQKGVQAmGR2DlNoLKUda2lLK9mkaXCWVcbcZMeK2jpP/jHbCJveNGNMbbbUf5S73G39SVDDijKoNjEbyqCGFr+yGVqb7M2be2wGdqSxpdRBBRoTASoA1NnN3vfV6uDbCfC2aL8ff8YmRtmozPa1h2bZ263b96Nhd382ut0FmNVmvofiXvf4IYVj5VCVAhs02q780MQmO58rHThebcnT0OsCSD66+AEAAJAoZFABYJLY7J7tlva7+f1MZUUGNW0e1qTKDKnXRV+uFm4KkpazlXkp5c84yofLpIYyqPalbEUq25RQd7odIpDytu4+/35bcsqxtVNTCk+eskITsgAkHwEqANRZrW7nUL1TO/M8trxnaPmZgcBDbV+2LURqZuAHZ9HnK28vqHrg6K51QaHtwvdXV63FlmFNe8dG6ooPVUHw2aC21v8LAMnFL5UAAABIFDKoADDJ/NnmI3VLV1xo6pEG2X7xfGVW08+QOqGsop8B9VYyjc3sr/YMBc7bggO2IIEduSDFZ+mnvOO1zjvVJnbdMUI7ASQLASoA1JkftNlZ+jlVrjDqz1bP+cdz4a5zGzwWqhx321pd8H6gZ6/1x6NW68K378mO+/S72G2Amlblqqs2AE2rMkD1KyL475VyUsDMQBc/AAAAEoUMKgDUmS0vqsB+tW5oqXrWwGYI/ayh/3z/WE7hmfk2E2m72kOvNZrMqf8sy3bRh7r4Q1nXUIbVz/T67fPbWevPGkBykUEFAABAopBBBYBJMpbSRrZKlBPKZPolo6Tq2dlqGVQ/axka6xl6ti9UMstmYN33/nn3wdOsyixJtQyq/bCqNV/Mf23GpAKNiQAVAOos1DVVK1j1u+397njblZ1T7S5+O7GpVlArRcFfTvEPg1pdayN1mfsTskLLllqhSVSh4QB22ESt92r3czVeF0Cy0cUPAACARCGDCgB1Vus3f38SktvmAvu2DGqtSVLVhgDkAuelKKvohhPYyUoj1Um17bc1Ud2z7HAC2xXvnpv27nf77sPI1oy1S5na54y2pBTd+0DjIkAFgDoL/cMammVuA0kblNpgz11Xq4vfD2r917LPl8Iz4u1Yz9DyoKFu9SHTVhuUjhSg2td3+zYQtTVjfSONx7WYwQ80Lrr4AQAAkChkUAGgzlq8721XdlbhDGhoVVObiQytGlUtq5ozx/3n24lD9gPAzrJvDpwPZXirZVBtW1xm1lYpyJhr/WEEGXNttaVYQ5PEQnVc7apcABoLGVQAAAAkChlUAKijrnZVpBXTuXhW0M8AZhWeZGQfEyo5FZpkZTOo1eqghsbDumzFcOlLKn5ApMx9obYOeW0tKJ6NlTnut8+fEOW3xV81ym+zXbUqdL+9D0BjIUAFgHqaK2mgtO+it4KUCkRK1Wbm+5OkcgoHaKHaqaFJSv7Mfn+Wvz/ZKBTsuTYNm7dlg9khc0/a29pn2gDVn9Evb79aF19oklUoKE2pcrgFgMZAFz8AAAAShQwqANTTnPDh9GBxazOkji0D5e/7x0JllWwG1NZMHSmD6uQDx9xrNZt9qZgxzQX2Qxnc0BCCapOcQitO2QlOLhvbpHiG1h8iYLMu9loAjYUAFQDqqUPh6vKlaKspW3uMZrUANTSGVOaYPe8CYDtUoFYVABsw26DPPit0LFQxwHb7N6sy8LRLmWYC520Aa8eYuq1d/tS2NW3usRdsGRSABkQXPwAAABKFDCoA1NMcVaY4TQY1nfWyfYov6enXTJVGXp3J1jn165T694e68jPmuVmFV5BybfYncYWGE4Rqolo2g9rsncurspvevyeUQS0fbFY0M6pJEhlUoCGRQQUAAECikEEFgHpqV+WMpqyigZlZKVPatxN/nGplpkLjSS2bIfXvH1Q88+q4DwA71rNaBtWOU7UZUn+SlD9GNVRGymZFW7xjdgxqqEyVHYMaqzPlUrGtpS938QEBaEAEqABQT7ZQvwtKWxQMUENF5m2AaYvghwLU0GSrahOXQnVKQ7Psc97zct55K/Ra/hAFfylU+6y0aYsNVENd/FWXP/X/ENKKgtW2QKMBNAS6+AEAAJAoZFABoJ5G+lfV1FnKuNJTqpw4JcUnLvnd9lK4xqfNitqspknglrOVdiRCKENps56hJUntfYUqx7LeeSm8+pNrS0bR+2o2r+fab5eKzeSllL8Wa6jRABoOASoA1JOtXh8aLGoGfLoA1Z+Z7ndt2QDVrnVvg9XQuNHQLP8hRd39rid8WPHecBss+j3ofswXKlgQClbdZPqUovdarQ6srRjgvxc7HrdVUrOrjuAi2IzihVgBNCS6+AEAAJAoZFABoJ76Fe9Plyqn3pdSA3aCkMtq2myqzaraDKJjs4u2Cz60upI7llWUzXTn21VZb1SK1yS1GdSx9Jz7GdisovcayhbbDKrNltphCbY33/15dJQuTA0pXhMVQEMigwoAAIBEIYMKAPVk0352KSfLSytm8lGyr1mVCcBm8wg7ScjJK1wnNGuO2Qxr1jtvn2/roNrX8bOy7litbKodb2qfZV9/yFzrrrPn3f22fS5B3aoom+qyLe22ppZLuwJoOASoAFBPdhp6aLaQlYpuccFoi6I683YWvp1s5Me7/iQr/x/2vKJu/VZzvFoAa+/zJyllFR464Ng6pqHg1Q9U/WC6oPhwAH+0RFbxYN3dX574lZcy7huWOQUaFl38AAAASBQyqABQB12XlnYOK94HLUkD3sWBbKqt/Wm79qV41jCveLZUpe/tZCa/29uWmbITsobNefdaNgEsVa4UVVB8dSk/8+rf74QSyPa51eqv2vJYktSh+LABf6nVYUUrdcXWZwXQUAhQAaAeji5tmxRFfi4wfcfs20ittO8vA+qPQc2a22xt0rS3ddzz3D2tkuaW9gdN81wPeLXn23bZMaBu387C98emumOhwNQ+N1TntMlcV3WJ0ypytn3+WAgADYMufgAAACQKGVQAqIejS9ucKjOoc1SsjyoV+6q9ZTn9OVR+ZrRF0YT0UKbSdutXm4/leruHFWVOh8021Ctus5mOXTa1oMpVofyZ/9WO+0JZVTsJymWT/UlY/hKxoeEFABoPGVQAAAAkChlUAKgHN8jTLnVkM6nvlPb7VZHitJOYpCgb2FbadqgyU+hzj7TjNu3z3DGbLR0yW5fstWWsbDLYjgsN1Uy12VxbZSs0PjZUZzWUYc0ommfmMqitiq+01WT23TZdOrjFZa0BNBwCVACoi1KEelRWaiqFey6SG1Rxdr8k9anc3W8DQRugtXnb2D/Utj+/xAa41br4HbuOgOvqtwFqv9nPKQpiZa51QeewKifK2259O+HLdsvb9nlvJXZPq6KhDS3mWKvZ9yseNNuL/eoJABoGXfwAAABIFDKoAFAXLhe4SDrit8XdbCltOqBi5lSS3i59ScqWbrHVkFoVZU41p7RtU7xv39ZkkpTKmpJKNoXq169SKcNYOj631H8/qGgOV5ui0QiHzSPsJCSbDbUrQLljtk6pX5PVH6LgD13IKEqANiueOXXts93+rd752B8gGVSgYRGgAkA95EvBaFObpBOK+/NeKW7fUTxAPVTcLZQCVVsov1WS2kvfdJS2Ryi+/qlfvd4WJ82qst9cive7y1wrqXVAau03r18yqPBwAVv71I1RrVZy1K9zapdHtYX+7aIDtrveD1D9Y+X2upvaZCJ8AI2KLn4AAAAkChlUAKiHA6Xt/N9LOr70zUnFTefeaJLU25L+WNxtK/WlD+TNP8a239plAucqqhJQULiQqV2/NDSN36YqXbrSZmJLbWk/EHXx25Wg/NWp3GNbvfOWHW1gM6g2mevXfPWXevUnQdmsaZuklM2cuq07BqBhkUEFAABAopBBBYB6KI0r1dG9UtoNHl1Q3LT9UXrXW8X9PkUpylImtO1NM4bTphVdJjCjKEPYrChzautE2ayqPyA0rfAsJ/c6A4pmO6WlTC56WVv/1G3dsVaFM6tOqGarXQkqrXj9UrcNZVAzgWOptCrrUNlsMYCGRYAKAPVQ6rbXAUmdpVn8OrK0PV7qNAGq6+433fLpg6V9W8k+VJG+XdEkKnf/gKIAbTTFSV1gmjP3mPOuIoANIO0kJlt83x8CYJsvxSdE+S+f8faleADaosoA1o5QiP25EJQCMwpd/AAAAEgUMqgAUA+2jFRHKbXZ4TKpC1Tu7p/32/iyp1K8iz2reApRitdWspOosuY6u25pzjsvxVOY7nioRlRTfDSAa8occ8yWXPW7+PPmWLUu/NiypOYtuK19q6ElXsv79sVco+zMLgANiwAVAOrBFYU/rGg8aIfr9+9QuajpUSoX6i8HqgOKoq4BRV34drp6KEB1UWO74uNRXeDpAlG7lmpOlWNU7XT7VHwIbId3aca8lH2sDVBDRftt8+240tAs/VB3vvvjyZu3lbHvJe9tATQ0uvgBAACQKGRQAWCCut5rvrHT1MvpvAFF0/CPlo46WNw9bC5zp+3yTTZD6rKqdqUku2KSy+D6M/rd1u6H1h9NRfsum9muyt5yWwQgtGiVv7qqe6ztwrdZ1dBKUTZr6u63GVr3VtL54jKvknkoGVRgRiBABYCJOkrSvNL+kYqCyXJ1/RZFYV1bcelSKQpQ3SVSMcB0QZaL2uaaZ7arMkBNt0hzSoNQBxUPVkPHXNSXM8dMpJgp3deeq10+yjbVCsW9dtiADUrdfps5ZtcRyJp9t7XDTjOhlQBsmQEADYkufgAAACQKGVQAmKhjJM0v7c+X1OLqn76rtPWnGpUyq0eVpv7b6vfD5jL3L7TfrW8zp+XnH13cbR2QWkup2Wwp/zigKIM6oMqlQL0Mqhsi0Nofnntkk71DivOzpn550oyixHCLAslg08efHpaaSllem0mNJU1t5tS9KJ9sQMMjgwoAAIBE4fdMAJio+ZI6S/utR0o6vvSNy6A2Kb58VCmj2lbKoHYoygCGMqjNik+SKmdO55qDLkv7rui1MqXt3D6po5RC7VeUQXXpT7/OqlmVqjkbNdG9Ut7c7uZjua3NmqZUOYbVLz3lXirtxo3GloqKntVcyqTa2qtpqXLZ1mZpy68EoMERoALARM2T1OqCxQWSjivtu7DOdoRnzfel8+1mtlSras8sampTfPKV27r9I1Xu7i8HxX1SU6km65zeqLu/2pqj7pOhVcqU+uMzLhAsKFbof7gUOL4THSqrNsHKrhnQ5F9sb7JT+kuvmcqZ+NV25/sFVQE0NLr4AQAAkChkUAFgojpaFPXxH6nK4km2+qcUpSBLqcyWrJQuZVULqsygKqMoG9uheA7SbZvMta6O1VGl7duKrXvqpzabzGs1KZ6YtfWfVGqbS5MOSs2lJO3Rh4rbd7LR6WoZVDtxqtxdH5qNlTZtsbOk7Ns22d7YFkBDI0AFgAl7l4qBqVSM6GywKBWjpjbvmJWR0q473gSS5efYLnypcm593tv3Fxh9W+VhBaGlTqUoQG1TFORlTLPtqALXxH5JB821kub8QerPRi/ls+NS7WgB1+KUH9X6b8UP4P2u/XINWgCNjC5+AAAAJAoZVACYsKMVdcHbbKldCqrJHLNrLUnF9KCd5e/Yvmw7d95fX6nJHBuSdMh7fp/Kwwns+qT2dpd5tJ8KGXPcjRo40tx3SFHm1axF2v5WcXc4V7nSlH0nWW9fkpptMth28dshBrZ9fgaVLn5gRiCDCgAAgEQZdQZ1yZIl6uzsHPnCBOnt7VVPT890NwMYM37eGs3RilKNcxUN2Owwx9w/t02qHJ1pJ0ENqDLvaFellyomWcVmDknFjKk77q4rZWiHvUdJxeyjLSNlm+VnUG0yuEmxzKmv+YB5rabK69KKJlTFMqh2CG+ojFSoJJa/BdDQRv2jvGfPHrW3N9bo8/7+fi1fvnwWf2iiUfHz1mjmKerWP0LRhCm/Xqk9JsVnJtlJUn4X/rCirn+/u9/dM2D25Z0fkHJD0aNcfFyugi8zS0nx+qutZl/e/tGmKTawdftNigqk2vOl188MS2lvKdPYzHw7Ccp29btjtn6rPy8NQEMbdRf/dHxYrl27VqlUSp/4xCeC53/3u9/pxhtv1OLFi9XW1qaTTjpJf/mXf1k+397e3nBZKECa/J+3b3/72/qLv/gLvfvd71ZHR4eWL1+uW265RQcPHoxd94c//EFf/OIXdd555+mYY47R0UcfrTPPPFMPP/xwsM38vAEA6iGxv2t++9vf1o9//OOq53t6enT22WdLkm666SYtXrxY+/fv109+8pOpaiLQsP7qr/5KixYt0tVXX60TTjhBv/jFL7R9+3Y98cQTev7558sB8o9//GNt3rxZF110kb7whS8ok8noW9/6ltavX6///u//Vnd39zS/k2TYsuU7E7q/q2ujapeRGlKUYW1VfLiAJP1RUQ7STj0qZVXzA+EEa0W91dIjW83xjHeNnaTU4X0vFbOjrt8+ryjb6V7X1pbKSqnSfrMdImCzpn7XvV1VK9S+xH6qARiLRP4oDwwM6JZbbtHf/u3fqqurK3jNjTfeqEwmo5/+9KeaP3/+FLcQaGyPPPKIVq9eHTv2/ve/X9dee60efPBBXX/99ZKkU089Va+++qpOPPHE8nUf//jHtWbNGm3btk2bNm3SnDlzprLpM9KWLTsn/TW6PlPaySneBS/Fa4vamfEtigI/u7VFCNyIBRcA90lyfyWyij5lbMxtRzDkzLXuWGgMqg1E7fnyErClLUudAjPCmGbxP/3000qlUnr00Ucrzj300ENKpVI1s56jdccddyifz+uzn/1s8Pwrr7yi73znO7r11ls1f/58DQwMaHh4OHgt0Ij6+/t1yimn6JRTTlF/f3/5+FtvvaWFCxfqrLPOUi4XKoM+On5wKkmXXHKJJOl//ud/yseWLl0aC04lKZVKad26dRocHNTrr78+7jYAAFDNmDKoq1ev1pIlS/Tggw+WP8ycBx98UCeffLI+8IEPaHBwUG+//faonumPWdu3b59uv/127dixo+o4vO9///uSpOOOO04f/OAH9V//9V9Kp9Nau3at7r33Xp100kljeVtA4rS3t+trX/uazj77bG3evFlf+tKXJEk333yzDh06pJ07dyqdTk/oZ83329/+dlTXjfVaJMOW/zs1r9N1uaIsZpuizKvLoGYUZXDtfLBhs3Vzu3KKj1xw99sRDjbzK1UOOQDQkMYUoKZSKV199dX60pe+pEOHDumoo4rrPL/55pv67ne/q82bN0uSvvGNb+i6664b1TMLhfi6drfccotWrFih9evXV73n1VdflVQcR7dq1So9/PDD2rdvn7q7u7VmzRq99NJL6ujoqHo/0AjOOOMMbdq0Sdu2bdMll1yi3/3ud/r3f/933XXXXVq2bJmkif2s+bZt26Z0Oq2PfexjNa9766239JWvfEXnnnuuFi5cOLo3AwDAGIx5DOqGDRu0detWPfLII+UZ8w8//LCy2ayuvvpqSdKHPvQhfe973xtzY55++ml961vf0nPPPVfzur6+Yo2/BQsW6PHHH1dTU3GkwvHHH68rrrhCDz30UHkMHdDI/uEf/kH/+Z//qWuvvVZ9fX06//zz9Td/8zfl8+P9WfM99NBD+upXv6pNmzbpPe95T9Xr8vm8rrrqKh08eFBf/vKXJ/y6mHm2fHNyn9/1AUUZ2JwqF+UK1GMF0IAK47Bq1arCBRdcUP7+zDPPLJx55pnjeVTZ8PBw4b3vfW9hw4YNseOSCjfffHPs2M0331yQVOju7o4dz2azhUwmU7juuuvKx1asWFFQ8Z8zvmbI12zz05/+tCCp0NbWVnj99dfr/vxnnnmm0NbWVvjQhz5UGB4ernntxz/+8YKkwv333x88z88bX3zxxdf4vxAZ1yz+DRs26FOf+pR+85vfaHBwUM8++6y2b99ePt/f369Dhw7VeEJkwYIFkqT7779fe/bs0b/8y79o7969sWvefvtt7d27V8cee6w6Ojq0aNEiScUxqFY6ndb8+fN14MCB8bwtIJGeeuopScXqFq+++qqWLl1aPjeenzVr9+7d+vM//3O9973v1SOPPKJMpvo/Cd3d3brnnnt0++2365prrhnjuwAAYAzGE9W++eabhebm5sIdd9xR6O7uLjQ3NxfefPPN8vn77rtvzL8t/P3f//2I1z766KOFQqFQePLJJwuSCn/3d38Xa9fg4GAhnU4XbrjhhvIxMjoz72s22b17d6GlpaVw3XXXFVasWFFYsmRJ4eDBg+Xz4/lZc371q18VFixYUFi2bFnh97//fc12bN++vSCp8OlPf7rmdfy88cUXX3yN/wuRVKEwwsyJKj7ykY9o7969GhgY0PLly/XYY4+Vz/3v//6vfvnLX47qOWvWrJFULB31yiuvVJy/5JJLdNFFF+mGG27QGWecoYULF2pwcFAnnHCC5s6dq1/+8pdqaysuMfiv//qvuvHGG/XNb35Tl112mSRp5cqVeuGFF8bzFpFQ4/wr23CGh4d1xhln6MCBA3rppZf061//WqtWrdJVV12lHTt2SBrfz5pUnIV/9tlna2BgQD/60Y9qVr54+OGHdeWVV+qKK67Q17/+daVS1adJ8/MGAOM3Wz7fRmPchfo3bNhQnu172223xc4tXLhwzLN7Xc3HkKVLl2rdunXl71tbW/XFL35R1157rc477zxdc8012rdvn/75n/9Z5557ri699NKxvRkggf7xH/9RL774on7wgx/oiCOO0J/+6Z+qq6tLX/jCF/Sxj31MF1100bh+1iTpwgsv1Ouvv65NmzZp165d2rVrV/nccccdp7Vr10qSfvKTn2jDhg2aP3++PvjBD+rBBx+MPeess87Su9/97om9UQAAfONNvQ4ODhbmzZtXOOqoowr9/f31yuhWkConSTnf+MY3Cu973/sKra2theOOO67wiU98ovDHP/4xdg1djjPvazb4+c9/XshkMoVPfvKTsePZbLawatWqwqJFiwoHDhwY9/Nr/fmef/755etGGkJw3333xZ7LzxtffPHF1/i/EBl3F382m9WiRYv0Z3/2Z/rqV786nkdMCbocZ55x/pXFFODnDQDGj8+3yJiWOrX+4z/+Q2+++aY2bNhQz/YAAABglhvzGNTnnntOL730km677TatWLFC559//mS0CwAAALPUmDOo9957r/76r/9axx57rO6///7JaBMAAABmsXGPQQUAYCaoVToNmEqEZJFxj0EFAAAAJgMBKgAAABKFABUAAACJQoAKAACARCFABQAAQKIQoAIAACBRCFABAACQKGNeSQoAgJlkyZIl6uzsnO5mjFpvb696enqmuxnApKJQPwBgVuvv71d7e/t0N2PU+vv7tXz5coLUGYiQLEIXPwBgVpvs4HTjxo1KpVIVX6ecckrw+tdee01XXnmljj32WLW3t+s973mPNm/eHGtvI2V8gfGgix8AgEnW2tqqr3zlK7FjRx11VMV1L774olavXq3Fixfrlltu0fz587Vv3z6ypZh1CFABAJhkmUxGV199dc1r8vm8rrnmGp1yyil6+umnG2rYAVBvBKgAgFlv7969Wrp0adXz9RgbmMvl9M477+jII48Mnv/ud7+rl19+WU888YTa29t1+PBhtba2Kp1OT/i1gUZDgAoAmPWOOeYYff3rX48dGx4e1mc+8xm1tLRIkg4fPqzDhw+P+Kx0Oq158+bFjh0+fFhHHnmkDh8+rHnz5umKK67Qtm3bNHfu3PI13//+9yUVhwOcdtpp+vnPf66WlhZdcskluueee/Sud71rom8TaBgEqACAWW/OnDkVXfA333yz+vr69L3vfU+SdMcdd6i7u3vEZ5144onau3dv+fuFCxdq06ZNWrlypfL5vJ588kndc8892r17t374wx8qkyl+FL/66quSpMsvv1wXXnihPv/5z2v37t3aunWrenp6tGvXLqVSqTq9YyDZCFABAPDcf//9uueee/RP//RPuuCCCyRJGzZs0DnnnDPivf7Y0a1bt8a+X79+vZYtW6bNmzfrkUce0fr16yVJfX19kqRVq1bpgQcekCR99KMfVUdHhz7/+c/rBz/4gdasWTPh9wY0AuqgAgBgvPjiizrrrLO0bt06PfTQQ5PyGv39/Zo7d66uu+668uz+iy++WI8//ri+9rWvacOGDeVr9+3bpxNPPFHd3d3q6uqSJK1cuVIvvPDCpLQN04eQLEIGFQCAkgMHDuijH/2oli1bVlEWqq+vr5zlrCWdTuuYY46peU17e7vmz5+vt956q3xs0aJFkqTjjjsudu2xxx5bbhswW1CoHwAAFcs8XXXVVTp48KAeffRRdXR0xM7feeedWrhw4Yhfq1atGvG13n77bfX29sYC2fe///2SpDfeeCN27f79+yVpxKAXmEnIoAIAIKm7u1tPPfWUvvOd7wRLTo1nDOrAwICGh4d1xBFHxK657bbbVCgUdOGFF5aPfeQjH9GnPvUp3Xfffdq4caOamoo5JJfJXbt27bjeF9CIGIMKAJj1fvGLX+h973ufzjvvPF1//fUV50cqsl/N3r17tWLFCl1xxRXlpU2feuopPfHEE7rwwgv1+OOPlwNRqRi4dnV1ae3atVq3bp12796tf/u3f9P69etj42EZgzozEZJFCFABALPeD3/4w/Js/ZDxflQePHhQn/zkJ/Xss89q//79yuVy+pM/+RNdddVV+uxnP6vm5uaK17n77rv15S9/Wb/+9a+1YMECXXvtterq6opdS4A6MxGSRQhQAQBoMASoMxMhWYRJUgAAAEgUAlQAAAAkCgEqAAAAEoUxqAAAAEgUMqgAAABIFAJUAAAAJAoBKgAAABKFABUAAACJQoAKAACARCFABQAAQKIQoAIAACBRCFABAACQKASoAAAASBQCVAAAACQKASoAAAAShQAVAAAAiUKACgAAgEQhQAUAAECiEKACAAAgUQhQAQAAkCgEqAAAAEgUAlQAAAAkCgEqAAAAEoUAFQAAAIlCgAoAAIBEIUAFAABAohCgAgAAIFEIUAEAAJAoBKgAAABIFAJUAAAAJAoBKgAAABKFABUAAACJQoAKAACARPl/nbnMh8S6L5YAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 660x260 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the first nii image\n",
    "nii_image1 = nib.load('Output/2.nii')\n",
    "# Load the second nii image\n",
    "nii_image2 = nib.load('Test/2.nii')\n",
    "# Plot the first image\n",
    "display = plotting.plot_img(nii_image1, cmap='gray')\n",
    "# Add the second image as an overlay\n",
    "display.add_overlay(nii_image2, cmap='hot', alpha=0.5, colorbar=False)\n",
    "\n",
    "# Show the plot\n",
    "plotting.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1a10cb61-2c3c-4175-a595-8b669647160a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x = nii_image2.get_fdata()\n",
    "y = nii_image1.get_fdata()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9cf2cbb7-e246-4f70-9128-8722ace6a856",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from skimage.metrics import structural_similarity as ssim\n",
    "\n",
    "def calculate_ssim(image1, image2):\n",
    "    \"\"\"\n",
    "    Calculates the Structural Similarity Index (SSIM) between two 3D images.\n",
    "\n",
    "    Args:\n",
    "        image1 (ndarray): 3D array representing the first image.\n",
    "        image2 (ndarray): 3D array representing the second image.\n",
    "\n",
    "    Returns:\n",
    "        ssim_score (float): The SSIM score between the two images.\n",
    "    \"\"\"\n",
    "\n",
    "    # Ensure that the input arrays are of the same shape\n",
    "    if image1.shape != image2.shape:\n",
    "        raise ValueError(\"Input images must have the same dimensions.\")\n",
    "\n",
    "    # Initialize the SSIM score\n",
    "    ssim_score = 0\n",
    "\n",
    "    # Loop through each slice of the 3D images and calculate the SSIM\n",
    "    for i in range(image1.shape[0]):\n",
    "        # Calculate SSIM for each slice using the skimage.metrics module\n",
    "        slice_score = ssim(image1[i], image2[i], data_range=image1.max() - image1.min())\n",
    "        # Add the SSIM score for each slice to the overall score\n",
    "        ssim_score += slice_score\n",
    "\n",
    "    # Average the SSIM scores for each slice to get the final SSIM score\n",
    "    ssim_score /= image1.shape[0]\n",
    "\n",
    "    return ssim_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df0d9947-fa5b-4f33-8eda-8b096cf55f9f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input images must have the same dimensions.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mcalculate_ssim\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m)\n",
      "Cell \u001b[1;32mIn[4], line 18\u001b[0m, in \u001b[0;36mcalculate_ssim\u001b[1;34m(image1, image2)\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[38;5;66;03m# Ensure that the input arrays are of the same shape\u001b[39;00m\n\u001b[0;32m     17\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m image1\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m!=\u001b[39m image2\u001b[38;5;241m.\u001b[39mshape:\n\u001b[1;32m---> 18\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInput images must have the same dimensions.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     20\u001b[0m \u001b[38;5;66;03m# Initialize the SSIM score\u001b[39;00m\n\u001b[0;32m     21\u001b[0m ssim_score \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
      "\u001b[1;31mValueError\u001b[0m: Input images must have the same dimensions."
     ]
    }
   ],
   "source": [
    "print(calculate_ssim(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d1a4d94-a7f8-4fa9-9ace-24f99dbcfe93",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_mri_images = [os.path.abspath(path) for path in glob.glob(f'Test/*.nii')]\n",
    "predicted_mri_images = [os.path.abspath(path) for path in glob.glob(f'Output/*.nii')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6645196c-9ac1-4933-811e-bab0b0f0dd7e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_mri_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "108cda07-dd58-4246-b803-cdcb3b751448",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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