colormapping with imageio.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11b2a9518>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "arr = np.repeat(np.arange(100), 100, axis=0).reshape(100,100)\n",
    "\n",
    "plt.imshow(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "colmap_arr = plt.cm.gist_earth(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 100, 4)\n"
     ]
    }
   ],
   "source": [
    "import imageio\n",
    "\n",
    "print(colmap_arr.shape)\n",
    "imageio.imsave(\"test.png\", colmap_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.7"
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 },
 "nbformat": 4,
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}