{
 "metadata": {
  "name": "9932_06_01"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Chapter 6, example 1\n",
      "====================\n",
      "\n",
      "**Note**: the profilers uses here display their results in an external window in the notebook, so that these results are not shown here. You will need to execute this notebook yourself if you want to see the actual outputs.",
      "\n",
      "Here we illustrate how to use some line-by-line profilers on a function defined in `myscript.py`:\n",
      "\n",
      "    import numpy as np\n",
      "    import matplotlib.pyplot as plt\n",
      "    def myfun():\n",
      "        dx = np.random.randn(1000, 10000)\n",
      "        x = np.sum(dx, axis=0)\n",
      "        plt.hist(x, bins=np.linspace(-100, 100, 20))\n",
      "        "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We first need to load the extensions provided by the `line_profiler` and `memory_profiler` modules."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext line_profiler"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext memory_profiler"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We import and execute the function once."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from myscript import myfun"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "myfun()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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EAMBghAAAGIwQAACDEQIAYDBCAAAMRggAgMEIAQAwGCEAAAYjBADAYIQAABiMEAAAgxUU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\n+z//2KZJFdb2lQAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, let's profile it line by line with `%lprun`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%lprun -f myfun myfun()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's find out the amount of memory used by this function with `%memit`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%memit -i myfun()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "maximum of 1: 125.195312 MB per loop\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can also do line-by-line profiling with memory consumption, using `%mprun`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%mprun -f myfun myfun()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "('',)\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 7
    }
   ],
   "metadata": {}
  }
 ]
}