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  {
   "cells": [
    {
     "source": [
      "> This is one of the 100 recipes of the [IPython Cookbook](http://ipython-books.github.io/), the definitive guide to high-performance scientific computing and data science in Python.\n"
     ], 
     "cell_type": "markdown", 
     "metadata": []
    }, 
    {
     "source": [
      "# 4.4. Profiling the memory usage of your code with memory_profiler"
     ], 
     "cell_type": "markdown", 
     "metadata": {}
    }, 
    {
     "source": [
      "Standard imports."
     ], 
     "cell_type": "markdown", 
     "metadata": {}
    }, 
    {
     "cell_type": "code", 
     "language": "python", 
     "outputs": [], 
     "collapsed": false, 
     "input": [
      "import numpy as np"
     ], 
     "metadata": {}
    }, 
    {
     "source": [
      "After installing `memory_profiler`, we can export the IPython extension."
     ], 
     "cell_type": "markdown", 
     "metadata": {}
    }, 
    {
     "cell_type": "code", 
     "language": "python", 
     "outputs": [], 
     "collapsed": false, 
     "input": [
      "%load_ext memory_profiler"
     ], 
     "metadata": {}
    }, 
    {
     "source": [
      "For `%lprun` to work, we need to encapsulate the code in a function, and to save it in a Python script."
     ], 
     "cell_type": "markdown", 
     "metadata": {}
    }, 
    {
     "cell_type": "code", 
     "language": "python", 
     "outputs": [], 
     "collapsed": false, 
     "input": [
      "%%writefile simulation.py\n", 
      "import numpy as np\n", 
      "\n", 
      "def step(*shape):\n", 
      "    # Create a random n-vector with +1 or -1 values.\n", 
      "    return 2 * (np.random.random_sample(shape) < .5) - 1\n", 
      "\n", 
      "def simulate(iterations, n=10000):\n", 
      "    s = step(iterations, n)\n", 
      "    x = np.cumsum(s, axis=0)\n", 
      "    bins = np.arange(-30, 30, 1)\n", 
      "    y = np.vstack([np.histogram(x[i,:], bins)[0] for i in range(iterations)])\n", 
      "    return y"
     ], 
     "metadata": {}
    }, 
    {
     "source": [
      "Now, we need to execute this script to load the function in the interactive namespace."
     ], 
     "cell_type": "markdown", 
     "metadata": {}
    }, 
    {
     "cell_type": "code", 
     "language": "python", 
     "outputs": [], 
     "collapsed": false, 
     "input": [
      "import simulation"
     ], 
     "metadata": {}
    }, 
    {
     "source": [
      "Let's execute the function under the control of the line profiler."
     ], 
     "cell_type": "markdown", 
     "metadata": {}
    }, 
    {
     "cell_type": "code", 
     "language": "python", 
     "outputs": [], 
     "collapsed": false, 
     "input": [
      "%mprun -T mprof0 -f simulation.simulate simulation.simulate(50)"
     ], 
     "metadata": {}
    }, 
    {
     "cell_type": "code", 
     "language": "python", 
     "outputs": [], 
     "collapsed": false, 
     "input": [
      "print(open('mprof0', 'r').read())"
     ], 
     "metadata": {}
    }, 
    {
     "source": [
      "Let's run the simulation with 10 times more iterations."
     ], 
     "cell_type": "markdown", 
     "metadata": {}
    }, 
    {
     "cell_type": "code", 
     "language": "python", 
     "outputs": [], 
     "collapsed": false, 
     "input": [
      "%mprun -T mprof1 -f simulation.simulate simulation.simulate(iterations=500)"
     ], 
     "metadata": {}
    }, 
    {
     "cell_type": "code", 
     "language": "python", 
     "outputs": [], 
     "collapsed": false, 
     "input": [
      "print(open('mprof1', 'r').read())"
     ], 
     "metadata": {}
    }, 
    {
     "source": [
      "> You'll find all the explanations, figures, references, and much more in the book (to be released later this summer).\n\n> [IPython Cookbook](http://ipython-books.github.io/), by [Cyrille Rossant](http://cyrille.rossant.net), Packt Publishing, 2014 (500 pages)."
     ], 
     "cell_type": "markdown", 
     "metadata": {}
    }
   ], 
   "metadata": {}
  }
 ], 
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