coroutine_spin_test.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:246de87d1823dcfd28158f479ebeba5932bd42b37de3a5679728fa970c2fcc5c"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"http://mirageforum.com/forum/attachment.php?attachmentid=159&d=1370617142\">"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "back to [Index](index.ipynb)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "coroutine_spin_test"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "2014-07-19"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "testing performance of coroutines"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Cross-notebook include shim\n",
      "with open(\"nbinclude.ipynb\") as nbinclude_f: # don't rename nbinclude_f\n",
      "    import IPython.nbformat.current\n",
      "    get_ipython().run_cell(IPython.nbformat.current.read(nbinclude_f, 'json').worksheets[0].cells[0].input)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class CoroutineSpinner:\n",
      "    def __init__(self, coroutine):\n",
      "        self.coroutine=coroutine\n",
      "        self.running=False\n",
      "    def run(self):\n",
      "        self.running=True\n",
      "        while(self.running):\n",
      "            delay=next(self.coroutine)\n",
      "            if delay < 0:\n",
      "                return\n",
      "            time.sleep(delay)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time\n",
      "import numpy as np\n",
      "times=np.zeros(shape=[100], dtype=np.float32)\n",
      "def time_test():\n",
      "    i=0\n",
      "    last_time=time.time()\n",
      "    while True:\n",
      "        now=time.time()\n",
      "        times[i]=now-last_time\n",
      "#         print i, times[i]\n",
      "        last_time=now\n",
      "        i+=1\n",
      "        if i == 100:\n",
      "            yield -1\n",
      "        yield .01"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cs=CoroutineSpinner(time_test())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cs.run()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(times)\n",
      "plt.xlabel"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "<function matplotlib.pyplot.xlabel>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9kpKSF27eU7w14IDvgH3RAfuiA/bFjRE2FGprayfq9fpGrVbbpFKpfHl5eZsq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\nAAj6/zD7RJx/7WdrAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x181e7b0>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}