HTML5 video embedded in Jupyter Notebooks

HTML5 video.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rc, animation\n",
    "%matplotlib inline\n",
    "rc('animation', html='html5')                ### <<<<------THE MAGIC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://louistiao.me/posts/notebooks/embedding-matplotlib-animations-in-jupyter-notebooks/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<matplotlib.animation.FuncAnimation at 0x7f6c69035358>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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AwMMP299MySnpBosNGmSTxF14ofUOSnULF9o8SFWq2GjiOnVKfg15Y8MG4JRTbFxAZqZt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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c69bcbdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes(xlim=(0, 2), ylim=(-2, 2))\n",
    "line, = ax.plot([], [], lw=2)\n",
    "\n",
    "def init():\n",
    "    line.set_data([], [])\n",
    "    return line,\n",
    "\n",
    "def animate(i):\n",
    "    x = np.linspace(0, 2, 1000)\n",
    "    y = np.sin(2 * np.pi * (x - 0.01 * i))\n",
    "    line.set_data(x, y)\n",
    "    return line,\n",
    "\n",
    "anim = animation.FuncAnimation(fig, animate, init_func=init,\n",
    "                               frames=100, interval=20, blit=True)\n",
    "anim"
   ]
  },
  {
   "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}