happy_holidays.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "from IPython.display import HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adapted from https://matplotlib.org/gallery/animation/rain.html (Author: Nicolas P. Rougier)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.animation import FuncAnimation\n",
    "\n",
    "\n",
    "plt.xkcd() \n",
    "\n",
    "# Create new Figure and an Axes which fills it.\n",
    "fig = plt.figure(figsize=(7, 7))\n",
    "ax = fig.add_axes([0, 0, 1, 1], frameon=False)\n",
    "ax.set_xlim(0, 1), ax.set_xticks([])\n",
    "ax.set_ylim(0, 1), ax.set_yticks([])\n",
    "\n",
    "# Create rain data\n",
    "n_drops = 100\n",
    "rain_drops = np.zeros(n_drops, dtype=[('position', float, 2),\n",
    "                                      ('size',     float, 1),\n",
    "                                      ('growth',   float, 1),\n",
    "                                      ('color',    float, 4)])\n",
    "\n",
    "# Initialize the raindrops in random positions and with\n",
    "# random growth rates.\n",
    "rain_drops['position'] = np.random.uniform(0, 1, (n_drops, 2))\n",
    "rain_drops['growth'] = np.random.uniform(2, 20, n_drops)\n",
    "rain_drops['color'] = np.random.uniform(0, 1, (n_drops, 4))\n",
    "\n",
    "# Construct the scatter which we will update during animation\n",
    "# as the raindrops develop.\n",
    "scat = ax.scatter(rain_drops['position'][:, 0], rain_drops['position'][:, 1],\n",
    "                  s=rain_drops['size'], lw=0.5, edgecolors=rain_drops['color'],\n",
    "                  facecolors='none', marker=(6,1), linewidth=3)\n",
    "\n",
    "ax.text(.6, .2, 'Happy Holidays!', style='italic', fontsize=20)\n",
    "\n",
    "\n",
    "def update(frame_number):\n",
    "    # Get an index which we can use to re-spawn the oldest raindrop.\n",
    "    current_index = frame_number % n_drops\n",
    "\n",
    "    # Make all colors more transparent as time progresses.\n",
    "    rain_drops['color'][:, 3] -= 1/len(rain_drops)\n",
    "    rain_drops['color'][:, 3] = np.clip(rain_drops['color'][:, 3], 0, 1)\n",
    "\n",
    "    # Make all circles bigger.\n",
    "    rain_drops['size'] += rain_drops['growth']\n",
    "\n",
    "    # Pick a new position for oldest rain drop, resetting its size,\n",
    "    # color and growth factor.\n",
    "    rain_drops['position'][current_index] = np.random.uniform(0, 1, 2)\n",
    "    rain_drops['size'][current_index] = 5\n",
    "    rain_drops['color'][current_index] = np.random.uniform(0, 1, 4)\n",
    "    rain_drops['growth'][current_index] = np.random.uniform(2, 20)\n",
    "\n",
    "    # Update the scatter collection, with the new colors, sizes and positions.\n",
    "    scat.set_edgecolors(rain_drops['color'])\n",
    "    scat.set_sizes(rain_drops['size'])\n",
    "    scat.set_offsets(rain_drops['position'])\n",
    "\n",
    "\n",
    "# Construct the animation, using the update function as the animation\n",
    "# director.\n",
    "animation = FuncAnimation(fig, update, interval=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "rc('animation', html='html5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<matplotlib.animation.FuncAnimation at 0x116edde10>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "animation.save('anim2.mp4', fps=20, \n",
    "          extra_args=['-vcodec', 'h264', \n",
    "                      '-pix_fmt', 'yuv420p'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "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_minor": 2
}