research-rainfallradar/aimodel/src/Matplotlib heatmap test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d8e56915-9b53-45bb-b8b1-f492e0f47cdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8b766567-aeb7-4e7c-b163-9a8b7d46ed03",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = tf.random.uniform([5,5,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "95175ce2-0f61-4f63-9ef3-6983d0ca3f60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgMAAAGdCAYAAACPX3D5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAew0lEQVR4nO3df3CU5d3v8c8SyIYiCQdSYhQIeUrVaARkYzWBoKLEw/jQ0mmHVCo/FBwjUZumYLvljAqjs9Y+YhwxKaEtyBQl4w8szzkRXY/KD6MzEGEEpFqL5wnQhJj0NPwobmT3Pn845sxegSQbNrmzXu+Xc/+RazfX/d39w3z5fq/ruj2O4zgCAADWGuR2AAAAwF0kAwAAWI5kAAAAy5EMAABgOZIBAAAsRzIAAIDlSAYAALAcyQAAAJYjGQAAwHKD3Q7ga8GMYrdDSFjXTGlyO4SENe/AULdDSGgbxobcDiFh/enYpW6HkNAe/K8/9en8X7YcjttcQ9L/LW5z9ZUBkwwAADBgRMJuR9CvaBMAAGA5KgMAAJiciNsR9CuSAQAATBGSAQAArOZYVhlgzQAAAJajMgAAgIk2AQAAlqNNAAAAbEJlAAAAk2WHDpEMAABgok0AAABsQmUAAAATuwkAALAbhw4BAACrUBkAAMBEmwAAAMtZ1iYgGQAAwGTZOQOsGQAAwHJUBgAAMNEmAADAcpYtIKRNAACA5agMAABgok0AAIDlaBMAAACbUBkAAMDgOHadM0AyAACAybI1A7QJAACwHJUBAABMli0gJBkAAMBkWZuAZAAAABMPKgIAADahMgAAgIk2QdeOHj2qqqoq1dXVqampSR6PRxkZGSooKFBJSYnGjh3bF3ECANB/LFtAGFObYNeuXcrJydGWLVs0adIkLViwQHfccYcmTZqkV199VVdddZXefffdvooVAAD0gZgqAz//+c+1ZMkSPfXUU+d9vaysTLt37+5ynlAopFAoFDXW7oSV7EmKJRwAAPqGZW2CmCoDBw4cUElJyXlfv+eee3TgwIFu5wkEAkpLS4u6Np8+FEsoAAD0nUgkflcCiCkZyMzMVF1d3Xlff++995SZmdntPH6/X21tbVHXT4blxBIKAACIk5jaBMuWLVNJSYnq6+s1c+ZMZWRkyOPxqKmpScFgUL///e9VUVHR7Txer1derzdqjBYBAGDASJB/0cdLTMnA0qVLNWrUKD311FNau3atwuGvDmVISkqSz+fTxo0bNXfu3D4JFACA/sJTC7tRXFys4uJiffnll2ppaZEkpaena8iQIXEPDgAA9L1eHzo0ZMiQHq0PAAAg4dAmAADAcpZtLSQZAADAZFllgAcVAQBgOSoDAACYaBMAAGA52gQAAMAmVAYAADDRJgAAwHK0CQAAgE2oDAAAYLKsMkAyAACAybI1A7QJAACwHJUBAABMtAkAALCcZW0CkgEAAEyWVQZYMwAAgOWoDAAAYKJNAACA5WgTAAAAm1AZAADAZFllgGQAAACT47gdQb+iTQAAwABSWVmp7OxspaSkyOfzaefOnV2+f9OmTZo0aZK+9a1vKTMzU3feeadaW1tjuifJAAAApkgkflcMampqVFZWphUrVmjv3r0qLCzUrFmz1NDQcM7379q1SwsWLNDixYt18OBBvfjii9q9e7eWLFkS031JBgAAMLmUDKxevVqLFy/WkiVLlJOTo4qKCo0dO1ZVVVXnfP/777+v8ePH64EHHlB2dramTZume+65R3v27InpviQDAAD0oVAopBMnTkRdoVCo0/va29tVX1+voqKiqPGioiLV1dWdc+6CggIdPXpUtbW1chxHx48f10svvaTbbrstphhJBgAAMDmRuF2BQEBpaWlRVyAQ6HTLlpYWhcNhZWRkRI1nZGSoqanpnGEWFBRo06ZNKi4uVnJysi6++GKNGDFCzzzzTEwfl2QAAABTHNsEfr9fbW1tUZff7z/vrT0eT9TPjuN0GvvaRx99pAceeEAPPfSQ6uvrtW3bNn322WcqKSmJ6eOytRAAAFMctxZ6vV55vd5u35eenq6kpKROVYDm5uZO1YKvBQIBTZ06VcuXL5ckTZw4UcOGDVNhYaEeffRRZWZm9ihGKgMAAAwAycnJ8vl8CgaDUePBYFAFBQXn/J1//etfGjQo+k95UlKSpK8qCj1FZQAAAJNLJxCWl5dr/vz5ysvLU35+vqqrq9XQ0NBR9vf7/Tp27Jg2btwoSZo9e7buvvtuVVVV6dZbb1VjY6PKysr0ve99T5dcckmP70syAACAyaVkoLi4WK2trVq1apUaGxuVm5ur2tpaZWVlSZIaGxujzhxYtGiRTp48qTVr1ugXv/iFRowYoRkzZug3v/lNTPf1OLHUEfrQXeN/7HYICevZJya6HULCGn7HWrdDSGhn/t71yWg4v1P33uV2CAntv734Tp/Of+YPy+I219DF/xG3ufoKlQEAAEwODyoCAMBqTmRAFM37DbsJAACwHJUBAABMLi0gdAvJAAAAJsvWDNAmAADAclQGAAAwWbaAkGQAAAATawYAALCcZckAawYAALAclQEAAEwD46T+fkMyAACAiTYBAACwCZUBAABMbC0EAMBynEAIAABsQmUAAAATbQIAAOzmsJsAAADYhMoAAAAm2gQAAFjOst0EJAMAAJgsqwywZgAAAMtRGQAAwGTZbgKSAQAATLQJAACATagMAABgYjcBAACWo00AAABsQmUAAACDbc8mIBkAAMBEmwAAANiEygAAACbLKgMkAwAAmCzbWhj3NsGRI0d01113xXtaAAD6T8SJ35UA4p4M/OMf/9Bzzz3X5XtCoZBOnDgRdYWdcLxDAQAAPRBzm2Dr1q1dvn748OFu5wgEAlq5cmXU2OS0HF0z4spYwwEAIO6cBPkXfbzEnAzMmTNHHo9HjnP+L8rj8XQ5h9/vV3l5edTY/VcvjDUUAAD6hmXJQMxtgszMTL388suKRCLnvD744INu5/B6vUpNTY26kjxJvfoAAADgwsScDPh8vi7/4HdXNQAAYMCLROJ3JYCY2wTLly/X6dOnz/v6hAkT9Pbbb19QUAAAuMqyNkHMyUBhYWGXrw8bNkw33HBDrwMCAAD9i0OHAAAwURkAAMButq1940FFAABYjsoAAAAm2gQAAFiOZAAAALvZdhwxawYAALAclQEAAEyWVQZIBgAAMCXGKcJxQ5sAAADLURkAAMBg2wJCkgEAAEyWJQO0CQAAsByVAQAATJYtICQZAADAYNuaAdoEAABYjsoAAAAm2gQAANjNtjYByQAAACbLKgOsGQAAwHJUBgAAMDiWVQZIBgAAMFmWDNAmAADAciQDAAAYnEj8rlhVVlYqOztbKSkp8vl82rlzZ5fvD4VCWrFihbKysuT1evWd73xHf/zjH2O6J20CAABMLrUJampqVFZWpsrKSk2dOlVr167VrFmz9NFHH2ncuHHn/J25c+fq+PHj+sMf/qAJEyaoublZZ8+ejem+JAMAAAwQq1ev1uLFi7VkyRJJUkVFhV5//XVVVVUpEAh0ev+2bdu0fft2HT58WCNHjpQkjR8/Pub70iYAAMAQzzZBKBTSiRMnoq5QKNTpnu3t7aqvr1dRUVHUeFFRkerq6s4Z59atW5WXl6cnnnhCl156qS677DItW7ZMZ86cienzkgwAAGCIZzIQCASUlpYWdZ3rX/ktLS0Kh8PKyMiIGs/IyFBTU9M54zx8+LB27dqlAwcOaMuWLaqoqNBLL72k0tLSmD4vbQIAAAzxPGfA7/ervLw8aszr9Z73/R6PJzoWx+k09rVIJCKPx6NNmzYpLS1N0lethh//+Md69tlnNXTo0B7FSDIAAEAf8nq9Xf7x/1p6erqSkpI6VQGam5s7VQu+lpmZqUsvvbQjEZCknJwcOY6jo0eP6rvf/W6PYqRNAACAyfHE7+qh5ORk+Xw+BYPBqPFgMKiCgoJz/s7UqVP197//XadOneoY++STTzRo0CCNGTOmx/f2OI4zIB7N1P5fH7gdQsLaVLDa7RASVlrYsmPG4ix/QqPbISSsURt/43YICS35377Xp/M3Tb8xbnNdvOOdHr+3pqZG8+fP1+9+9zvl5+erurpa69at08GDB5WVlSW/369jx45p48aNkqRTp04pJydH119/vVauXKmWlhYtWbJEN9xwg9atW9fj+9ImAABggCguLlZra6tWrVqlxsZG5ebmqra2VllZWZKkxsZGNTQ0dLz/oosuUjAY1P3336+8vDyNGjVKc+fO1aOPPhrTfakMfANQGeg9KgMXhspA71EZuDB9XRlonHZT3ObK3PV23ObqK1QGAAAw2PbUQhYQAgBgOSoDAAAYnBh2AXwTkAwAAGCgTQAAAKxCZQAAAIMToU0AAIDVBsam+/5DMgAAgMG2ygBrBgAAsByVAQAADLZVBkgGAAAw2LZmgDYBAACWozIAAICBNgEAAJaz7Thi2gQAAFiOygAAAAbbnk1AMgAAgCFCmwAAANiEygAAAAbbFhCSDAAAYGBrIQAAluMEQgAAYBUqAwAAGGgTAABgObYWAgAAq1AZAADAwNZCAAAsx24CAABgFSoDAAAYbFtASDIAAIDBtjUDtAkAALAclQEAAAwsIOzGmTNntGvXLn300UedXvviiy+0cePGbucIhUI6ceJE1BUKtccaCgAAfSLieOJ2JYKYkoFPPvlEOTk5mj59uq6++mrdeOONamxs7Hi9ra1Nd955Z7fzBAIBpaWlRV1PVK6PPXoAAPqA43jidiWCmJKBX/7yl7r66qvV3Nysjz/+WKmpqZo6daoaGhpiuqnf71dbW1vU9eDS7pMIAAAQfzGtGairq9Obb76p9PR0paena+vWrSotLVVhYaHefvttDRs2rEfzeL1eeb3eqLH2/5scSygAAPSZRCnvx0tMycCZM2c0eHD0rzz77LMaNGiQbrjhBj3//PNxDQ4AADdYtn4wtmTgiiuu0J49e5STkxM1/swzz8hxHH3/+9+Pa3AAAKDvxbRm4Ic//KFeeOGFc762Zs0a3X777XJs248BAPjGYTdBF/x+v2pra8/7emVlpSKRyAUHBQCAm9hNAAAArMIJhAAAGGyrcZMMAABgcJQY5f14oU0AAIDlqAwAAGCIWLYxjmQAAABDxLI2AckAAAAG1gwAAACrUBkAAMDA1kIAACxHmwAAAFiFygAAAAbaBAAAWM62ZIA2AQAAlqMyAACAwbYFhCQDAAAYInblArQJAACwHZUBAAAMPJsAAADLWfbQQpIBAABMbC0EAABWoTIAAIAh4mHNAAAAVrNtzQBtAgAALEdlAAAAg20LCEkGAAAwcAIhAABwTWVlpbKzs5WSkiKfz6edO3f26PfeffddDR48WJMnT475niQDAAAYIvLE7YpFTU2NysrKtGLFCu3du1eFhYWaNWuWGhoauvy9trY2LViwQDfffHOvPi/JAAAABieOVyxWr16txYsXa8mSJcrJyVFFRYXGjh2rqqqqLn/vnnvu0bx585Sfnx/jHb9CMgAAQB8KhUI6ceJE1BUKhTq9r729XfX19SoqKooaLyoqUl1d3XnnX79+vf72t7/p4Ycf7nWMA2YB4S0Fy90OIWG1O2G3Q0hYQ5KS3A4hoe07eonbISSsFY5t69UTSzwXEAYCAa1cuTJq7OGHH9YjjzwSNdbS0qJwOKyMjIyo8YyMDDU1NZ1z7r/+9a/61a9+pZ07d2rw4N7/SR8wyQAAAANFPFM1v9+v8vLyqDGv13ve93uM0w8dx+k0JknhcFjz5s3TypUrddlll11QjCQDAAAY4nkCodfr7fKP/9fS09OVlJTUqQrQ3NzcqVogSSdPntSePXu0d+9e3XfffZKkSCQix3E0ePBgvfHGG5oxY0aPYmTNAAAAA0BycrJ8Pp+CwWDUeDAYVEFBQaf3p6amav/+/dq3b1/HVVJSossvv1z79u3Tdddd1+N7UxkAAMDg1qFD5eXlmj9/vvLy8pSfn6/q6mo1NDSopKRE0lcth2PHjmnjxo0aNGiQcnNzo35/9OjRSklJ6TTeHZIBAAAMbi3vLC4uVmtrq1atWqXGxkbl5uaqtrZWWVlZkqTGxsZuzxzoDY/jOAPi4UzTL+3dQQlgN8GFGOJhN8GFuGnIxW6HkLBW/O/73Q4hoXm/c32fzr9uzB1xm+vuo3+K21x9hcoAAAAG2zZ+kgwAAGBweFARAACwCZUBAAAMtAkAALCcbckAbQIAACxHZQAAAMOA2HPfj0gGAAAwuHUCoVtIBgAAMLBmAAAAWIXKAAAABtsqAyQDAAAYbFtASJsAAADLURkAAMDAbgIAACxn25oB2gQAAFiOygAAAAbbFhCSDAAAYIhYlg7QJgAAwHJUBgAAMNi2gJBkAAAAg11NApIBAAA6sa0ywJoBAAAsR2UAAAADJxACAGA5thYCAACrUBkAAMBgV12AZAAAgE7YTQAAAKwSc2Xg0KFDev/995Wfn68rrrhCf/nLX/T0008rFArpjjvu0IwZM7qdIxQKKRQKRY1FnIgGechNAADuYwFhF7Zt26bJkydr2bJluuaaa7Rt2zZNnz5dn376qRoaGnTrrbfqrbfe6naeQCCgtLS0qOvIyf/T288AAEBcOXG8EkFMycCqVau0fPlytba2av369Zo3b57uvvtuBYNBvfnmm3rwwQf1+OOPdzuP3+9XW1tb1DV2+PjefgYAAHABYkoGDh48qEWLFkmS5s6dq5MnT+pHP/pRx+u33367Pvzww27n8Xq9Sk1NjbpoEQAABopIHK9E0OvdBIMGDVJKSopGjBjRMTZ8+HC1tbXFIy4AAFzDmoEujB8/Xp9++mnHz++9957GjRvX8fORI0eUmZkZv+gAAHCBbWsGYqoM3HvvvQqHwx0/5+bmRr3+2muv9Wg3AQAAGDhiSgZKSkq6fP2xxx67oGAAABgIEqXXHy+cQAgAgMFJmAJ/fLCEHwAAy1EZAADAQJsAAADLsbUQAABYhcoAAAAGu+oCJAMAAHRCmwAAAFiFygAAAAZ2EwAAYDnbDh0iGQAAwGBbZYA1AwAAWI7KAAAABtoEAABYjjYBAACwCpUBAAAMEYc2AQAAVrMrFaBNAACA9agMAABgsO3ZBCQDAAAYbNtaSJsAAADLURkAAMBg2zkDJAMAABhYMwAAgOVYMwAAAKxCZQAAAANrBgAAsJxj2XHEtAkAALAcyQAAAIaInLhdsaqsrFR2drZSUlLk8/m0c+fO8773lVde0cyZM/Xtb39bqampys/P1+uvvx7zPUkGAAAwROJ4xaKmpkZlZWVasWKF9u7dq8LCQs2aNUsNDQ3nfP+OHTs0c+ZM1dbWqr6+XjfddJNmz56tvXv3xnRfjzNAGiNn/leF2yEkrI+XvuV2CAkr9CXLZi7Ezf+M7X84+P+O/zzP7RAS2rDHXuzT+WeP+/e4zfWfDf+zx++97rrrNGXKFFVVVXWM5eTkaM6cOQoEAj2a46qrrlJxcbEeeuihHt+XygAAAAYnjv/1VHt7u+rr61VUVBQ1XlRUpLq6uh7NEYlEdPLkSY0cOTKmz8s/iwAAMMTzBMJQKKRQKBQ15vV65fV6o8ZaWloUDoeVkZERNZ6RkaGmpqYe3evJJ5/U6dOnNXfu3JhipDIAAEAfCgQCSktLi7q6Kvl7PJ6onx3H6TR2Li+88IIeeeQR1dTUaPTo0THFSGUAAABDPJfT+f1+lZeXR42ZVQFJSk9PV1JSUqcqQHNzc6dqgammpkaLFy/Wiy++qFtuuSXmGKkMAABgiOduAq/Xq9TU1KjrXMlAcnKyfD6fgsFg1HgwGFRBQcF5Y33hhRe0aNEiPf/887rtttt69XmpDAAAYHDrQUXl5eWaP3++8vLylJ+fr+rqajU0NKikpETSV1WGY8eOaePGjZK+SgQWLFigp59+Wtdff31HVWHo0KFKS0vr8X1JBgAAGCCKi4vV2tqqVatWqbGxUbm5uaqtrVVWVpYkqbGxMerMgbVr1+rs2bMqLS1VaWlpx/jChQu1YcOGHt+XZAAAAEM8dxPEaunSpVq6dOk5XzP/wL/zzjtxuSfJAAAAhgFyHl+/YQEhAACWozIAAIDBzTaBG0gGAAAwuLWbwC20CQAAsByVAQAADBHLFhCSDAAAYLArFaBNAACA9agMAABgYDcBAACWIxkAAMBynEAIAACsQmUAAAADbQIAACzHCYQAAMAqVAYAADDYtoCQZAAAAINtawZoEwAAYDkqAwAAGGgTAABgOdoEAADAKlQGAAAw2HbOAMkAAACGCGsGAACwm22VAdYMAABgubhUBhzHkcfjicdUAAC4zrY2QVwqA16vV4cOHYrHVAAAuM6J43+JIKbKQHl5+TnHw+GwHn/8cY0aNUqStHr16i7nCYVCCoVCUWORL8/KO4QlDAAA9LeY/vpWVFRo0qRJGjFiRNS44zg6dOiQhg0b1qN2QSAQ0MqVK6PGfn37rfofP/3vsYQDAECfsK1NEFMy8Nhjj2ndunV68sknNWPGjI7xIUOGaMOGDbryyit7NI/f7+9UZYi8VR1LKAAA9JlEKe/HS0xrBvx+v2pqanTvvfdq2bJl+vLLL3t1U6/Xq9TU1KiLFgEAAO6IeQHhtddeq/r6en3++efKy8vT/v372UkAAPhGiThO3K5E0Kt/jl900UV67rnntHnzZs2cOVPhcDjecQEA4Brb2gQXVJv/yU9+omnTpqm+vl5ZWVnxigkAAPSjC27UjxkzRmPGjIlHLAAADAiOE3E7hH7Fqj0AAAwR2gQAANjNSZCFf/HCg4oAALAclQEAAAy0CQAAsBxtAgAAYBUqAwAAGBLl5MB4IRkAAMBg2wmEtAkAALAclQEAAAy2LSAkGQAAwGDb1kLaBAAAWI7KAAAABtoEAABYjq2FAABYzrbKAGsGAACwHJUBAAAMtu0mIBkAAMBAmwAAAFiFygAAAAZ2EwAAYDkeVAQAAKxCZQAAAANtAgAALMduAgAAYBUqAwAAGGxbQEgyAACAwbY2AckAAAAG25IB1gwAAGA5KgMAABjsqgtIHse2WkiMQqGQAoGA/H6/vF6v2+EkHL6/3uO76z2+uwvD92cfkoFunDhxQmlpaWpra1Nqaqrb4SQcvr/e47vrPb67C8P3Zx/WDAAAYDmSAQAALEcyAACA5UgGuuH1evXwww+ziKaX+P56j++u9/juLgzfn31YQAgAgOWoDAAAYDmSAQAALEcyAACA5UgGAACwHMlANyorK5Wdna2UlBT5fD7t3LnT7ZASwo4dOzR79mxdcskl8ng8evXVV90OKWEEAgFde+21Gj58uEaPHq05c+bo448/djushFBVVaWJEycqNTVVqampys/P12uvveZ2WAkpEAjI4/GorKzM7VDQD0gGulBTU6OysjKtWLFCe/fuVWFhoWbNmqWGhga3QxvwTp8+rUmTJmnNmjVuh5Jwtm/frtLSUr3//vsKBoM6e/asioqKdPr0abdDG/DGjBmjxx9/XHv27NGePXs0Y8YM/eAHP9DBgwfdDi2h7N69W9XV1Zo4caLboaCfsLWwC9ddd52mTJmiqqqqjrGcnBzNmTNHgUDAxcgSi8fj0ZYtWzRnzhy3Q0lIn3/+uUaPHq3t27dr+vTpboeTcEaOHKnf/va3Wrx4sduhJIRTp05pypQpqqys1KOPPqrJkyeroqLC7bDQx6gMnEd7e7vq6+tVVFQUNV5UVKS6ujqXooKN2traJH31Rw09Fw6HtXnzZp0+fVr5+fluh5MwSktLddttt+mWW25xOxT0o8FuBzBQtbS0KBwOKyMjI2o8IyNDTU1NLkUF2ziOo/Lyck2bNk25ubluh5MQ9u/fr/z8fH3xxRe66KKLtGXLFl155ZVuh5UQNm/erA8++EC7d+92OxT0M5KBbng8nqifHcfpNAb0lfvuu08ffvihdu3a5XYoCePyyy/Xvn379M9//lMvv/yyFi5cqO3bt5MQdOPIkSP62c9+pjfeeEMpKSluh4N+RjJwHunp6UpKSupUBWhubu5ULQD6wv3336+tW7dqx44dGjNmjNvhJIzk5GRNmDBBkpSXl6fdu3fr6aef1tq1a12ObGCrr69Xc3OzfD5fx1g4HNaOHTu0Zs0ahUIhJSUluRgh+hJrBs4jOTlZPp9PwWAwajwYDKqgoMClqGADx3F033336ZVXXtFbb72l7Oxst0NKaI7jKBQKuR3GgHfzzTdr//792rdvX8eVl5enn/70p9q3bx+JwDcclYEulJeXa/78+crLy1N+fr6qq6vV0NCgkpISt0Mb8E6dOqVPP/204+fPPvtM+/bt08iRIzVu3DgXIxv4SktL9fzzz+vPf/6zhg8f3lGdSktL09ChQ12ObmD79a9/rVmzZmns2LE6efKkNm/erHfeeUfbtm1zO7QBb/jw4Z3WpQwbNkyjRo1ivYoFSAa6UFxcrNbWVq1atUqNjY3Kzc1VbW2tsrKy3A5twNuzZ49uuummjp/Ly8slSQsXLtSGDRtciioxfL2V9cYbb4waX79+vRYtWtT/ASWQ48ePa/78+WpsbFRaWpomTpyobdu2aebMmW6HBgxonDMAAIDlWDMAAIDlSAYAALAcyQAAAJYjGQAAwHIkAwAAWI5kAAAAy5EMAABgOZIBAAAsRzIAAIDlSAYAALAcyQAAAJYjGQAAwHL/D8UFD+Am/N5gAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_data_heatmap_positive = test_data[:,:,1] # extract the 1st channel, ref ChatGPT and tested manually myself\n",
    "\n",
    "sns.heatmap(test_data_heatmap_positive)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c2b28d45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fa8a40df730>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_data_heatmap_positive)\n",
    "plt.colorbar()"
   ]
  }
 ],
 "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}