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": "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
      "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
}