docs/example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"76999750-fbc6-4122-8ea2-4106b42f4e7f\">Loading BokehJS ...</span>\n",
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      "application/javascript": [
       "\n",
       "(function(global) {\n",
       "  function now() {\n",
       "    return new Date();\n",
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       "\n",
       "  var force = true;\n",
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       "  if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    window._bokeh_onload_callbacks = [];\n",
       "    window._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "\n",
       "  \n",
       "  if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    window._bokeh_timeout = Date.now() + 5000;\n",
       "    window._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    if (window.Bokeh !== undefined) {\n",
       "      var el = document.getElementById(\"76999750-fbc6-4122-8ea2-4106b42f4e7f\");\n",
       "      el.textContent = \"BokehJS \" + Bokeh.version + \" successfully loaded.\";\n",
       "    } else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete window._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    window._bokeh_onload_callbacks.push(callback);\n",
       "    if (window._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    window._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        window._bokeh_is_loading--;\n",
       "        if (window._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"76999750-fbc6-4122-8ea2-4106b42f4e7f\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '76999750-fbc6-4122-8ea2-4106b42f4e7f' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.6.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.6.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "      document.getElementById(\"76999750-fbc6-4122-8ea2-4106b42f4e7f\").textContent = \"BokehJS is loading...\";\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.6.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.6.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.6.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.6.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < window._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!window._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      window._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"76999750-fbc6-4122-8ea2-4106b42f4e7f\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import perfume\n",
    "import perfume.analyze\n",
    "import pandas as pd\n",
    "import bokeh.io\n",
    "bokeh.io.output_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "To start, set up some functions to benchmark."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "\n",
    "def test_fn_1():\n",
    "    good = np.random.poisson(20)\n",
    "    bad = np.random.poisson(100)\n",
    "    msec = np.random.choice([good, bad], p=[.99, .01])\n",
    "    time.sleep(msec / 3000.)\n",
    "\n",
    "def test_fn_1_no_outliers():\n",
    "    time.sleep(np.random.poisson(20) / 3000.)\n",
    "\n",
    "def test_fn_2():\n",
    "    good = np.random.poisson(5)\n",
    "    bad = np.random.poisson(150)\n",
    "    msec = np.random.choice([good, bad], p=[.95, .05])\n",
    "    time.sleep(msec / 3000.)\n",
    "\n",
    "def test_fn_3():\n",
    "    msec = max(1, np.random.normal(100, 10))\n",
    "    time.sleep(msec / 3000.)\n",
    "\n",
    "numbers = np.arange(0, 1, 1. / (3 * 5000000))\n",
    "\n",
    "def test_fn_4():\n",
    "    return np.sum(numbers)\n",
    "\n",
    "# Create a variable named \"samples\", in this cell.  This way,\n",
    "# if we change these functions, we'll reset the samples so we\n",
    "# don't use old data with changed implementations.\n",
    "samples = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmark\n",
    "\n",
    "Run the benchmark for a while by executing this cell.  Since we capture the output data in `samples`, and pass it back in as an argument, you can interrupt the cell, take a look at the output so far, and then execute this cell again to resume the benchmark."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"bk-plotdiv\" id=\"7eaccc9f-bbf6-41e8-954a-1d996bb8fef6\"></div>\n",
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       "  \n",
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       "  \n",
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       "  \n",
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       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
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       "  \n",
       "  \n",
       "    \n",
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       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "  \n",
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       "  \n",
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       "      try {\n",
       "        window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      }\n",
       "      finally {\n",
       "        delete window._bokeh_onload_callbacks\n",
       "      }\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
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       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
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       "      if (js_urls == null || js_urls.length === 0) {\n",
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       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
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       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
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       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
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       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
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       "      }\n",
       "    };var element = document.getElementById(\"7eaccc9f-bbf6-41e8-954a-1d996bb8fef6\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '7eaccc9f-bbf6-41e8-954a-1d996bb8fef6' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
       "            var docs_json = 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       "      }\n",
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       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
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       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
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       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
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    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table style=\"display:inline\" id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715\" ><caption>Descriptive Timing Statistics</caption> \n",
       "<thead>    <tr> \n",
       "        <th class=\"index_name level0\" >function</th> \n",
       "        <th class=\"col_heading level0 col0\" >test_fn_1</th> \n",
       "        <th class=\"col_heading level0 col1\" >test_fn_2</th> \n",
       "        <th class=\"col_heading level0 col2\" >test_fn_3</th> \n",
       "        <th class=\"col_heading level0 col3\" >test_fn_4</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715level0_row0\" class=\"row_heading level0 row0\" >count</th> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row0_col0\" class=\"data row0 col0\" >158</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row0_col1\" class=\"data row0 col1\" >158</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row0_col2\" class=\"data row0 col2\" >158</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row0_col3\" class=\"data row0 col3\" >158</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715level0_row1\" class=\"row_heading level0 row1\" >mean</th> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row1_col0\" class=\"data row1 col0\" >7.1</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row1_col1\" class=\"data row1 col1\" >3.42</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row1_col2\" class=\"data row1 col2\" >33.5</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row1_col3\" class=\"data row1 col3\" >13.5</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715level0_row2\" class=\"row_heading level0 row2\" >std</th> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row2_col0\" class=\"data row2 col0\" >2.38</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row2_col1\" class=\"data row2 col1\" >8.59</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row2_col2\" class=\"data row2 col2\" >3.04</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row2_col3\" class=\"data row2 col3\" >2.19</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715level0_row3\" class=\"row_heading level0 row3\" >min</th> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row3_col0\" class=\"data row3 col0\" >3.28</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row3_col1\" class=\"data row3 col1\" >0.548</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row3_col2\" class=\"data row3 col2\" >25.7</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row3_col3\" class=\"data row3 col3\" >8.19</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715level0_row4\" class=\"row_heading level0 row4\" >25%</th> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row4_col0\" class=\"data row4 col0\" >5.91</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row4_col1\" class=\"data row4 col1\" >1.34</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row4_col2\" class=\"data row4 col2\" >31.4</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row4_col3\" class=\"data row4 col3\" >12.9</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715level0_row5\" class=\"row_heading level0 row5\" >50%</th> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row5_col0\" class=\"data row5 col0\" >6.9</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row5_col1\" class=\"data row5 col1\" >1.84</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row5_col2\" class=\"data row5 col2\" >33.6</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row5_col3\" class=\"data row5 col3\" >14.2</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715level0_row6\" class=\"row_heading level0 row6\" >75%</th> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row6_col0\" class=\"data row6 col0\" >7.97</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row6_col1\" class=\"data row6 col1\" >2.35</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row6_col2\" class=\"data row6 col2\" >35.7</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row6_col3\" class=\"data row6 col3\" >14.9</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715level0_row7\" class=\"row_heading level0 row7\" >max</th> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row7_col0\" class=\"data row7 col0\" >30</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row7_col1\" class=\"data row7 col1\" >56.7</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row7_col2\" class=\"data row7 col2\" >41.3</td> \n",
       "        <td id=\"T_121e37b0_8b46_11e7_aac1_5c514fbc4715row7_col3\" class=\"data row7 col3\" >16.9</td> \n",
       "    </tr></tbody> \n",
       "</table style=\"display:inline\"> <style  type=\"text/css\" >\n",
       "    #T_121f36a6_8b46_11e7_aac1_5c514fbc4715row0_col0 {\n",
       "            background-color:  #1a9850;\n",
       "        }    #T_121f36a6_8b46_11e7_aac1_5c514fbc4715row0_col1 {\n",
       "            background-color:  #1a9850;\n",
       "        }    #T_121f36a6_8b46_11e7_aac1_5c514fbc4715row0_col2 {\n",
       "            background-color:  #1a9850;\n",
       "        }    #T_121f36a6_8b46_11e7_aac1_5c514fbc4715row1_col0 {\n",
       "            visibility:  hidden;\n",
       "        }    #T_121f36a6_8b46_11e7_aac1_5c514fbc4715row1_col1 {\n",
       "            background-color:  #1a9850;\n",
       "        }    #T_121f36a6_8b46_11e7_aac1_5c514fbc4715row1_col2 {\n",
       "            background-color:  #1a9850;\n",
       "        }    #T_121f36a6_8b46_11e7_aac1_5c514fbc4715row2_col0 {\n",
       "            visibility:  hidden;\n",
       "        }    #T_121f36a6_8b46_11e7_aac1_5c514fbc4715row2_col1 {\n",
       "            visibility:  hidden;\n",
       "        }    #T_121f36a6_8b46_11e7_aac1_5c514fbc4715row2_col2 {\n",
       "            background-color:  #1a9850;\n",
       "        }</style>  \n",
       "<table style=\"display:inline\" id=\"T_121f36a6_8b46_11e7_aac1_5c514fbc4715\" ><caption>K-S test</caption> \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >test_fn_2</th> \n",
       "        <th class=\"col_heading level0 col1\" >test_fn_3</th> \n",
       "        <th class=\"col_heading level0 col2\" >test_fn_4</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >K-S test Z</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_121f36a6_8b46_11e7_aac1_5c514fbc4715level0_row0\" class=\"row_heading level0 row0\" >test_fn_1</th> \n",
       "        <td id=\"T_121f36a6_8b46_11e7_aac1_5c514fbc4715row0_col0\" class=\"data row0 col0\" >8.49</td> \n",
       "        <td id=\"T_121f36a6_8b46_11e7_aac1_5c514fbc4715row0_col1\" class=\"data row0 col1\" >8.83</td> \n",
       "        <td id=\"T_121f36a6_8b46_11e7_aac1_5c514fbc4715row0_col2\" class=\"data row0 col2\" >7.65</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_121f36a6_8b46_11e7_aac1_5c514fbc4715level0_row1\" class=\"row_heading level0 row1\" >test_fn_2</th> \n",
       "        <td id=\"T_121f36a6_8b46_11e7_aac1_5c514fbc4715row1_col0\" class=\"data row1 col0\" >nan</td> \n",
       "        <td id=\"T_121f36a6_8b46_11e7_aac1_5c514fbc4715row1_col1\" class=\"data row1 col1\" >8.61</td> \n",
       "        <td id=\"T_121f36a6_8b46_11e7_aac1_5c514fbc4715row1_col2\" class=\"data row1 col2\" >8.55</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_121f36a6_8b46_11e7_aac1_5c514fbc4715level0_row2\" class=\"row_heading level0 row2\" >test_fn_3</th> \n",
       "        <td id=\"T_121f36a6_8b46_11e7_aac1_5c514fbc4715row2_col0\" class=\"data row2 col0\" >nan</td> \n",
       "        <td id=\"T_121f36a6_8b46_11e7_aac1_5c514fbc4715row2_col1\" class=\"data row2 col1\" >nan</td> \n",
       "        <td id=\"T_121f36a6_8b46_11e7_aac1_5c514fbc4715row2_col2\" class=\"data row2 col2\" >8.89</td> \n",
       "    </tr></tbody> \n",
       "</table style=\"display:inline\"> <style  type=\"text/css\" >\n",
       "    #T_121ff12c_8b46_11e7_aac1_5c514fbc4715row0_col0 {\n",
       "            background-color:  #1a9850;\n",
       "        }    #T_121ff12c_8b46_11e7_aac1_5c514fbc4715row0_col1 {\n",
       "            background-color:  #1a9850;\n",
       "        }    #T_121ff12c_8b46_11e7_aac1_5c514fbc4715row0_col2 {\n",
       "            background-color:  #1a9850;\n",
       "        }    #T_121ff12c_8b46_11e7_aac1_5c514fbc4715row1_col0 {\n",
       "            visibility:  hidden;\n",
       "        }    #T_121ff12c_8b46_11e7_aac1_5c514fbc4715row1_col1 {\n",
       "            background-color:  #1a9850;\n",
       "        }    #T_121ff12c_8b46_11e7_aac1_5c514fbc4715row1_col2 {\n",
       "            background-color:  #1a9850;\n",
       "        }    #T_121ff12c_8b46_11e7_aac1_5c514fbc4715row2_col0 {\n",
       "            visibility:  hidden;\n",
       "        }    #T_121ff12c_8b46_11e7_aac1_5c514fbc4715row2_col1 {\n",
       "            visibility:  hidden;\n",
       "        }    #T_121ff12c_8b46_11e7_aac1_5c514fbc4715row2_col2 {\n",
       "            background-color:  #1a9850;\n",
       "        }</style>  \n",
       "<table style=\"display:inline\" id=\"T_121ff12c_8b46_11e7_aac1_5c514fbc4715\" ><caption>Bucketed K-S test</caption> \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >test_fn_2</th> \n",
       "        <th class=\"col_heading level0 col1\" >test_fn_3</th> \n",
       "        <th class=\"col_heading level0 col2\" >test_fn_4</th> \n",
       "    </tr>    <tr> \n",
       "        <th class=\"index_name level0\" >K-S test Z</th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "        <th class=\"blank\" ></th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_121ff12c_8b46_11e7_aac1_5c514fbc4715level0_row0\" class=\"row_heading level0 row0\" >test_fn_1</th> \n",
       "        <td id=\"T_121ff12c_8b46_11e7_aac1_5c514fbc4715row0_col0\" class=\"data row0 col0\" >16</td> \n",
       "        <td id=\"T_121ff12c_8b46_11e7_aac1_5c514fbc4715row0_col1\" class=\"data row0 col1\" >22</td> \n",
       "        <td id=\"T_121ff12c_8b46_11e7_aac1_5c514fbc4715row0_col2\" class=\"data row0 col2\" >22</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_121ff12c_8b46_11e7_aac1_5c514fbc4715level0_row1\" class=\"row_heading level0 row1\" >test_fn_2</th> \n",
       "        <td id=\"T_121ff12c_8b46_11e7_aac1_5c514fbc4715row1_col0\" class=\"data row1 col0\" >nan</td> \n",
       "        <td id=\"T_121ff12c_8b46_11e7_aac1_5c514fbc4715row1_col1\" class=\"data row1 col1\" >22</td> \n",
       "        <td id=\"T_121ff12c_8b46_11e7_aac1_5c514fbc4715row1_col2\" class=\"data row1 col2\" >22</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_121ff12c_8b46_11e7_aac1_5c514fbc4715level0_row2\" class=\"row_heading level0 row2\" >test_fn_3</th> \n",
       "        <td id=\"T_121ff12c_8b46_11e7_aac1_5c514fbc4715row2_col0\" class=\"data row2 col0\" >nan</td> \n",
       "        <td id=\"T_121ff12c_8b46_11e7_aac1_5c514fbc4715row2_col1\" class=\"data row2 col1\" >nan</td> \n",
       "        <td id=\"T_121ff12c_8b46_11e7_aac1_5c514fbc4715row2_col2\" class=\"data row2 col2\" >22</td> \n",
       "    </tr></tbody> \n",
       "</table style=\"display:inline\"> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples = perfume.bench(test_fn_1, test_fn_2, test_fn_3, test_fn_4,\n",
    "                        samples=samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing the samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the format of the output, each function execution gets its begin and end time recorded:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>function</th>\n",
       "      <th colspan=\"2\" halign=\"left\">test_fn_1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">test_fn_2</th>\n",
       "      <th colspan=\"2\" halign=\"left\">test_fn_3</th>\n",
       "      <th colspan=\"2\" halign=\"left\">test_fn_4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timing</th>\n",
       "      <th>begin</th>\n",
       "      <th>end</th>\n",
       "      <th>begin</th>\n",
       "      <th>end</th>\n",
       "      <th>begin</th>\n",
       "      <th>end</th>\n",
       "      <th>begin</th>\n",
       "      <th>end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.668990e+07</td>\n",
       "      <td>6.668991e+07</td>\n",
       "      <td>6.668991e+07</td>\n",
       "      <td>6.668991e+07</td>\n",
       "      <td>6.668991e+07</td>\n",
       "      <td>6.668994e+07</td>\n",
       "      <td>6.668994e+07</td>\n",
       "      <td>6.668995e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.668995e+07</td>\n",
       "      <td>6.668995e+07</td>\n",
       "      <td>6.668995e+07</td>\n",
       "      <td>6.668995e+07</td>\n",
       "      <td>6.668995e+07</td>\n",
       "      <td>6.668998e+07</td>\n",
       "      <td>6.668998e+07</td>\n",
       "      <td>6.668999e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.668999e+07</td>\n",
       "      <td>6.669000e+07</td>\n",
       "      <td>6.669000e+07</td>\n",
       "      <td>6.669000e+07</td>\n",
       "      <td>6.669000e+07</td>\n",
       "      <td>6.669004e+07</td>\n",
       "      <td>6.669004e+07</td>\n",
       "      <td>6.669004e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.669004e+07</td>\n",
       "      <td>6.669005e+07</td>\n",
       "      <td>6.669005e+07</td>\n",
       "      <td>6.669006e+07</td>\n",
       "      <td>6.669006e+07</td>\n",
       "      <td>6.669009e+07</td>\n",
       "      <td>6.669009e+07</td>\n",
       "      <td>6.669010e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.669010e+07</td>\n",
       "      <td>6.669011e+07</td>\n",
       "      <td>6.669011e+07</td>\n",
       "      <td>6.669011e+07</td>\n",
       "      <td>6.669011e+07</td>\n",
       "      <td>6.669014e+07</td>\n",
       "      <td>6.669014e+07</td>\n",
       "      <td>6.669015e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "function     test_fn_1                   test_fn_2                \\\n",
       "timing           begin           end         begin           end   \n",
       "0         6.668990e+07  6.668991e+07  6.668991e+07  6.668991e+07   \n",
       "1         6.668995e+07  6.668995e+07  6.668995e+07  6.668995e+07   \n",
       "2         6.668999e+07  6.669000e+07  6.669000e+07  6.669000e+07   \n",
       "3         6.669004e+07  6.669005e+07  6.669005e+07  6.669006e+07   \n",
       "4         6.669010e+07  6.669011e+07  6.669011e+07  6.669011e+07   \n",
       "\n",
       "function     test_fn_3                   test_fn_4                \n",
       "timing           begin           end         begin           end  \n",
       "0         6.668991e+07  6.668994e+07  6.668994e+07  6.668995e+07  \n",
       "1         6.668995e+07  6.668998e+07  6.668998e+07  6.668999e+07  \n",
       "2         6.669000e+07  6.669004e+07  6.669004e+07  6.669004e+07  \n",
       "3         6.669006e+07  6.669009e+07  6.669009e+07  6.669010e+07  \n",
       "4         6.669011e+07  6.669014e+07  6.669014e+07  6.669015e+07  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One thing we can do is plot each function's distribution as it develops over simulated time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  \n",
       "  \n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "  \n",
       "    function display_loaded() {\n",
       "      if (window.Bokeh !== undefined) {\n",
       "        var el = document.getElementById(\"6d754101-4b83-455a-918a-dc63bf4167a3\");\n",
       "        el.textContent = \"BokehJS \" + Bokeh.version + \" successfully loaded.\";\n",
       "      } else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(display_loaded, 100)\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      try {\n",
       "        window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      }\n",
       "      finally {\n",
       "        delete window._bokeh_onload_callbacks\n",
       "      }\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"6d754101-4b83-455a-918a-dc63bf4167a3\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '6d754101-4b83-455a-918a-dc63bf4167a3' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
       "            var docs_json = 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       "            var render_items = [{\"docid\":\"f6b87480-077f-4d66-9735-829211ed327a\",\"elementid\":\"6d754101-4b83-455a-918a-dc63bf4167a3\",\"modelid\":\"cfb2bb74-9072-4361-b80d-31afedb321e1\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"6d754101-4b83-455a-918a-dc63bf4167a3\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "perfume.analyze.cumulative_quantiles_plot(samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can run a K-S test and see whether our functions are significantly different:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_fn_2</th>\n",
       "      <th>test_fn_3</th>\n",
       "      <th>test_fn_4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K-S test Z</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>test_fn_1</th>\n",
       "      <td>8.494414</td>\n",
       "      <td>8.831940</td>\n",
       "      <td>7.650598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_fn_2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.606922</td>\n",
       "      <td>8.550668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_fn_3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.888194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            test_fn_2  test_fn_3  test_fn_4\n",
       "K-S test Z                                 \n",
       "test_fn_1    8.494414   8.831940   7.650598\n",
       "test_fn_2         NaN   8.606922   8.550668\n",
       "test_fn_3         NaN        NaN   8.888194"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perfume.analyze.ks_test(perfume.analyze.timings(samples))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can convert them to elapsed timings instead of begin/end time points, get resampled timings to see outliers show a stronger presence, or isolate samples to be as if they ran by themselves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>function</th>\n",
       "      <th colspan=\"2\" halign=\"left\">test_fn_1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">test_fn_2</th>\n",
       "      <th colspan=\"2\" halign=\"left\">test_fn_3</th>\n",
       "      <th colspan=\"2\" halign=\"left\">test_fn_4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timing</th>\n",
       "      <th>begin</th>\n",
       "      <th>end</th>\n",
       "      <th>begin</th>\n",
       "      <th>end</th>\n",
       "      <th>begin</th>\n",
       "      <th>end</th>\n",
       "      <th>begin</th>\n",
       "      <th>end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.879083</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.532343</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.188473</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.879083</td>\n",
       "      <td>13.432623</td>\n",
       "      <td>1.532343</td>\n",
       "      <td>2.720641</td>\n",
       "      <td>29.188473</td>\n",
       "      <td>59.487223</td>\n",
       "      <td>9.990919</td>\n",
       "      <td>19.318506</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.432623</td>\n",
       "      <td>21.638016</td>\n",
       "      <td>2.720641</td>\n",
       "      <td>3.269045</td>\n",
       "      <td>59.487223</td>\n",
       "      <td>93.088024</td>\n",
       "      <td>19.318506</td>\n",
       "      <td>28.697242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.638016</td>\n",
       "      <td>29.194047</td>\n",
       "      <td>3.269045</td>\n",
       "      <td>6.157080</td>\n",
       "      <td>93.088024</td>\n",
       "      <td>128.720489</td>\n",
       "      <td>28.697242</td>\n",
       "      <td>39.406731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29.194047</td>\n",
       "      <td>34.441334</td>\n",
       "      <td>6.157080</td>\n",
       "      <td>7.378798</td>\n",
       "      <td>128.720489</td>\n",
       "      <td>160.600259</td>\n",
       "      <td>39.406731</td>\n",
       "      <td>48.569794</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "function  test_fn_1            test_fn_2             test_fn_3              \\\n",
       "timing        begin        end     begin       end       begin         end   \n",
       "0          0.000000   7.879083  0.000000  1.532343    0.000000   29.188473   \n",
       "1          7.879083  13.432623  1.532343  2.720641   29.188473   59.487223   \n",
       "2         13.432623  21.638016  2.720641  3.269045   59.487223   93.088024   \n",
       "3         21.638016  29.194047  3.269045  6.157080   93.088024  128.720489   \n",
       "4         29.194047  34.441334  6.157080  7.378798  128.720489  160.600259   \n",
       "\n",
       "function  test_fn_4             \n",
       "timing        begin        end  \n",
       "0          0.000000   9.990919  \n",
       "1          9.990919  19.318506  \n",
       "2         19.318506  28.697242  \n",
       "3         28.697242  39.406731  \n",
       "4         39.406731  48.569794  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timings = perfume.analyze.timings(samples)\n",
    "bt = perfume.analyze.bucket_resample_timings(samples)\n",
    "isolated = perfume.analyze.isolate(samples)\n",
    "isolated.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With these, and other charting libraries, you can do whatever you want with the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>function</th>\n",
       "      <th>test_fn_1</th>\n",
       "      <th>test_fn_2</th>\n",
       "      <th>test_fn_3</th>\n",
       "      <th>test_fn_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>158.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.099615</td>\n",
       "      <td>3.423204</td>\n",
       "      <td>33.526883</td>\n",
       "      <td>13.455989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.382655</td>\n",
       "      <td>8.588543</td>\n",
       "      <td>3.039051</td>\n",
       "      <td>2.193190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.280499</td>\n",
       "      <td>0.548404</td>\n",
       "      <td>25.672770</td>\n",
       "      <td>8.190510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.908776</td>\n",
       "      <td>1.335742</td>\n",
       "      <td>31.432720</td>\n",
       "      <td>12.909524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.904165</td>\n",
       "      <td>1.840656</td>\n",
       "      <td>33.613588</td>\n",
       "      <td>14.226354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.966824</td>\n",
       "      <td>2.353760</td>\n",
       "      <td>35.680228</td>\n",
       "      <td>14.863801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29.985672</td>\n",
       "      <td>56.681542</td>\n",
       "      <td>41.308623</td>\n",
       "      <td>16.934982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "function   test_fn_1   test_fn_2   test_fn_3   test_fn_4\n",
       "count     158.000000  158.000000  158.000000  158.000000\n",
       "mean        7.099615    3.423204   33.526883   13.455989\n",
       "std         2.382655    8.588543    3.039051    2.193190\n",
       "min         3.280499    0.548404   25.672770    8.190510\n",
       "25%         5.908776    1.335742   31.432720   12.909524\n",
       "50%         6.904165    1.840656   33.613588   14.226354\n",
       "75%         7.966824    2.353760   35.680228   14.863801\n",
       "max        29.985672   56.681542   41.308623   16.934982"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
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queee02233aaXXnpJvb29837G2rVr1dvbq1wupxUrVuj9998/5eefffZZ7d+/X4VCQc8/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Mg/Po4/LYOjN9/V4920HSm4PwoEDcWHIGc4JCTE58FqrbFYb1zMEw/ZEmKBeNhU1UR5i//EOt7L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\nKaXi0v8HtCByyApl/GEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f94cd0fda90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh import palettes\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16, 9))\n",
    "for col, color in zip(timings.columns, palettes.Set1[len(timings.columns)]):\n",
    "    sns.distplot(timings[col], label=col, color=color, ax=ax,\n",
    "#                  hist_kws=dict(cumulative=True),\n",
    "#                  kde_kws=dict(cumulative=True)\n",
    "                )\n",
    "ax.set_xlabel('millis')\n",
    "ax.legend()\n",
    "timings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f94c9fa8f28>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T39c5tNzbWU1CuLd+a4Opgc8E/A8wNc5izleSTcAvAP9BA731ly72AEeA+6qqib6AdwJ/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f94cbb131d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "timings['test_fn_1'].hist(cumulative=True, normed=1, alpha=0.3)\n",
    "timings['test_fn_2'].hist(cumulative=True, normed=1, alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f94c9d21a58>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iW9W/z+2y7rIfozGkbB+IiG9Ur9lnIuLCLusu+/oPKdt7IuJoy+t2TZd1x3HcPtmS63BE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\nJalAlrskFchyl6QCWe6SVCDLXZIK9H9wDitCogGE6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f94c9da7e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "bt['test_fn_1'].hist(cumulative=True, normed=1, alpha=0.3)\n",
    "bt['test_fn_2'].hist(cumulative=True, normed=1, alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f94c9ad46a0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NLDx1cka5jvRH72BXj5IJdYvnHJ9K5Hx8xadoaQ/zTqi3X/nqj5rAwlMnp7ol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\nIqLz/gtIox478k+JeQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f94c9ad46d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(timings#, diag_kws={'cumulative': True}\n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Ks_2sampResult(statistic=0.95569620253164556, pvalue=5.5872505324246181e-65),\n",
       " Ks_2sampResult(statistic=0.71199999999999997, pvalue=4.691029271698989e-223))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats\n",
    "bt = perfume.analyze.bucket_resample_timings(samples)\n",
    "(scipy.stats.ks_2samp(timings['test_fn_1'], timings['test_fn_2']),\n",
    " scipy.stats.ks_2samp(bt['test_fn_1'], bt['test_fn_2']))"
   ]
  }
 ],
 "metadata": {
  "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": "3.6.2"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 1,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}