{
 "cells": [
  {
   "metadata": {
    "collapsed": true,
    "trusted": true
   },
   "cell_type": "code",
   "source": "import numpy as np",
   "execution_count": 1,
   "outputs": []
  },
  {
   "metadata": {
    "collapsed": true,
    "trusted": true
   },
   "cell_type": "code",
   "source": "import json",
   "execution_count": 2,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## PHP code"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "<?php\n\n```\n\n$route = \\Drupal::service('router.route_provider')->getRouteByName('entity.node.canonical');\n$serialized = serialize($router);\n$arr = [$serialized, $serialized, $serialized];\n\n\n$times = [];\nfor ($i = 0; $i < 1000; $i++) {\n  $start = microtime(TRUE);\n  foreach ($arr as $element) {\n    unserialize($element);\n  }\n  $end = microtime(TRUE);\n  $times[] = $end - $start;\n}\n\nfile_put_contents('/tmp/test.txt', json_encode($times));\n\n```"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Before"
  },
  {
   "metadata": {
    "collapsed": true,
    "trusted": true
   },
   "cell_type": "code",
   "source": "data_before = json.load(open('/tmp/before.txt'))\ndata_before = np.array(data_before)",
   "execution_count": 17,
   "outputs": []
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "print np.average(data_before), np.std(data_before)",
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "stream",
     "text": "1.26051902771e-06 6.82836429677e-07\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## After"
  },
  {
   "metadata": {
    "collapsed": true,
    "trusted": true
   },
   "cell_type": "code",
   "source": "data = json.load(open('/tmp/after.txt'))\ndata = np.array(data)",
   "execution_count": 19,
   "outputs": []
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "print np.average(data), np.std(data)",
   "execution_count": 20,
   "outputs": [
    {
     "output_type": "stream",
     "text": "7.99655914307e-07 5.8840063032e-07\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "collapsed": true,
    "trusted": true
   },
   "cell_type": "code",
   "source": "",
   "execution_count": null,
   "outputs": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python2",
   "display_name": "Python 2",
   "language": "python"
  },
  "language_info": {
   "mimetype": "text/x-python",
   "nbconvert_exporter": "python",
   "name": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9",
   "file_extension": ".py",
   "codemirror_mode": {
    "version": 2,
    "name": "ipython"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}