{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams['figure.figsize'] = 10, 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f15b874af60>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AwBgSGACYloXsAACGkcAAwMzMgQEAGEMCAwCzsg4MAMA4EhgAmJkEBgBgDAkMAEyqUinr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\nwOIoYACAxVHAAACL8/8B1ZRyZxq97jsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15b83707f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.array(\n",
    "    ((1, 2, 1),\n",
    "     (3, 3, 0),\n",
    "     (0, 0, 4))\n",
    ")\n",
    "    \n",
    "plt.imshow(\n",
    "    data,\n",
    "    interpolation='nearest', \n",
    "    aspect='auto',\n",
    "    cmap='Reds',  # other colors: https://matplotlib.org/examples/color/colormaps_reference.html\n",
    "    vmin=0,\n",
    "    vmax=5,\n",
    ")\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.2+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}