karlafej/pacman.ipynb

## pacman.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": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.rc_context at 0x1106e04a8>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import animation\n",
    "import matplotlib.patches\n",
    "\n",
    "plt.xkcd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PIKNGpoPmkJ6J0Sf9V8BJgI9qtcrKlSt54onrWbJkCc8++yyVinyvon1thXa9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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1169b7b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set up the figure\n",
    "fig = plt.figure()\n",
    "ax = plt.axes(xlim=(-2, 2), ylim=(-2, 2))\n",
    "ax.set_facecolor('black')\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "eye = matplotlib.patches.Circle((0, 0.5), 0.07, color = 'blue')\n",
    "pacman = matplotlib.patches.Wedge((0,0), 1, 25.5, -25.5, color = 'yellow')\n",
    "\n",
    "# initialization function: plot the background of each frame\n",
    "def init():\n",
    "    pacman.radius = 1\n",
    "    ax.add_patch(pacman)\n",
    "    ax.add_patch(eye)\n",
    "    return pacman, eye,\n",
    "\n",
    "# animation function\n",
    "def animate(i):\n",
    "    x = (np.cos(2*pi/10*i))\n",
    "    y = x*22\n",
    "    z = x/70\n",
    "    eye.center = (0+z, 0.5-z)\n",
    "    pacman.theta1 = 25.5-y\n",
    "    pacman.theta2 = -25.5+y\n",
    "    pacman._recompute_path()\n",
    "    return pacman, eye,\n",
    "\n",
    "# call the animator.  blit=True means only re-draw the parts that have changed.\n",
    "anim = animation.FuncAnimation(fig, animate, init_func=init,\n",
    "                               frames=10, interval=60, blit=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# display the animation as html5 in Jupyter notebook\n",
    "rc('animation', html='html5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<matplotlib.animation.FuncAnimation at 0x116a378d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "anim.save('./pics/animation.mp4', fps=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "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": "3.6.1"
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 },
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