delayed-and-profiler.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `dask.delayed` to parallelize code with threads\n",
    "==========================\n",
    "\n",
    "This example shows how to use `dask.delayed` to turn typical Python code into a parallel dask graph and then compute it in parallel.\n",
    "\n",
    "We start with a simple example computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.44 ms, sys: 166 µs, total: 1.61 ms\n",
      "Wall time: 1 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from time import sleep\n",
    "\n",
    "def slowadd(x, y):\n",
    "    \"\"\" Slow version of ``add`` to simulate work \"\"\"\n",
    "    sleep(1)\n",
    "    return x + y\n",
    "\n",
    "def slowinc(x):\n",
    "    \"\"\" Slow version of ``increment`` to simulate work \"\"\"\n",
    "    sleep(1)\n",
    "    return x + 1\n",
    "\n",
    "def slowsum(L):\n",
    "    \"\"\" Slow version of ``sum`` to simulate work \"\"\"\n",
    "    sleep(0.5)\n",
    "    return sum(L)\n",
    "\n",
    "%time slowadd(1, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Real workflows often have a variety of interrelated function calls. \n",
    "\n",
    "Below we call `slowadd` and `slowinc` on input data in a few list comprehensions.  We finish by summing the results.  This takes ten seconds to finish."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "357\n",
      "CPU times: user 161 µs, sys: 3.97 ms, total: 4.13 ms\n",
      "Wall time: 10.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "data = [1, 2, 3]\n",
    "A = [slowinc(i) for i in data]\n",
    "B = [slowadd(a, 10) for a in A]\n",
    "C = [slowadd(a, 100) for a in A]\n",
    "result = slowsum(A) + slowsum(B) + slowsum(C)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parallelize with `dask.delayed`\n",
    "------------------------------\n",
    "\n",
    "We wrap all of our function calls with `dask.delayed`.  This captures the input and, rather than evaluate the function, puts the function and its inputs into a dask graph visualized below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from dask import delayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.38 ms, sys: 174 µs, total: 1.56 ms\n",
      "Wall time: 1.45 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "data = [1, 2, 3]\n",
    "A = [delayed(slowinc)(i) for i in data]\n",
    "B = [delayed(slowadd)(a, 10) for a in A]\n",
    "C = [delayed(slowadd)(a, 100) for a in A]\n",
    "result = delayed(slowsum)(A) + delayed(slowsum)(B) + delayed(slowsum)(C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This returned instantly because we haven't done any actual work yet. Instead this code builds up a graph of computations.  We visualize that graph below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "try:\n",
    "    img = result.visualize()\n",
    "except RuntimeError as e:        # User may not have graphviz.  Load precomputed result from file.\n",
    "    print(e.args[0])\n",
    "    from IPython.display import Image\n",
    "    img = Image('images/do-and-profiler-graph.png')\n",
    "\n",
    "img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute result\n",
    "----------------\n",
    "\n",
    "This work can happen in parallel.  We execute the computation by calling the `compute` method on our result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 32.8 ms, sys: 180 µs, total: 33 ms\n",
      "Wall time: 3.52 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "357"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time result.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the Profiler to gain intuition\n",
    "---------------------------------------\n",
    "\n",
    "We use the dask `Profiler` to gain intuition about how our computation was run.\n",
    "\n",
    "*Note for readers on github, this plot won't show up on github's notebook viewer.  You can see the results by doing one of the following:*\n",
    "\n",
    "1.  Run this notebook yourself\n",
    "2.  See saved results at [this saved plot](https://cdn.rawgit.com/blaze/dask-examples/16e722631e6217cf08598a6f3ad73f1fb9bb3e82/profiles/timeseries.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"8e7bf94e-fa87-4d8a-88ea-47b30285df10\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[0].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[0].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[0]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._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",
       "    var el = document.getElementById(\"8e7bf94e-fa87-4d8a-88ea-47b30285df10\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._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",
       "    root._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",
       "        root._bokeh_is_loading--;\n",
       "        if (root._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(\"8e7bf94e-fa87-4d8a-88ea-47b30285df10\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '8e7bf94e-fa87-4d8a-88ea-47b30285df10' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"8e7bf94e-fa87-4d8a-88ea-47b30285df10\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._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",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._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    var el = document.getElementById(\"8e7bf94e-fa87-4d8a-88ea-47b30285df10\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._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    root._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        root._bokeh_is_loading--;\n        if (root._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(\"8e7bf94e-fa87-4d8a-88ea-47b30285df10\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '8e7bf94e-fa87-4d8a-88ea-47b30285df10' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"8e7bf94e-fa87-4d8a-88ea-47b30285df10\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._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}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.plotting import output_notebook\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"e9a4d9e7-f0a0-408b-8c64-a6a427e7d6a4\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"66e24c54-fbc3-40c9-9338-efe722e8c623\":{\"roots\":{\"references\":[{\"attributes\":{},\"id\":\"a4fc96ba-1372-4f99-8779-b53f6df54cb3\",\"type\":\"CategoricalScale\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"height\":{\"units\":\"data\",\"value\":1},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"358507db-c64e-4873-855e-d7a355b020e1\",\"type\":\"Rect\"},{\"attributes\":{\"source\":{\"id\":\"e420e7ca-57fd-41fe-83dc-1cee03ab3db4\",\"type\":\"ColumnDataSource\"}},\"id\":\"0b3bbdd1-d35a-4800-86f3-8ecc48588aa4\",\"type\":\"CDSView\"},{\"attributes\":{\"plot\":null,\"text\":\"Profile Results\"},\"id\":\"84dc5d2b-70df-46dc-92d3-c107f91a4368\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"d993863d-3a4d-4f29-85d5-0163cf9f544f\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"callback\":null,\"end\":3.5154351689998293},\"id\":\"f435c9df-2356-42f2-a145-9c8eea0660bc\",\"type\":\"Range1d\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"f824f604-bb0f-451b-bf33-9dd35f66662f\",\"type\":\"HoverTool\"},{\"id\":\"398053dd-bf4f-4981-94b4-851c1429251b\",\"type\":\"SaveTool\"},{\"id\":\"9c6e1bd6-9156-4e91-81b2-d0ab7da771a8\",\"type\":\"ResetTool\"},{\"id\":\"6ecb862a-68a0-450d-a252-009c778a8153\",\"type\":\"WheelZoomTool\"},{\"id\":\"e39dcd13-381b-4fa5-b753-b72e89774f7e\",\"type\":\"PanTool\"}]},\"id\":\"c607c68d-5743-4d1d-842a-f432bf599521\",\"type\":\"Toolbar\"},{\"attributes\":{\"below\":[{\"id\":\"6158bc0e-2b56-417f-ae8e-25842e05a8f1\",\"type\":\"LinearAxis\"}],\"left\":[{\"id\":\"4a89c430-6683-4217-8d17-d2a272b089c2\",\"type\":\"CategoricalAxis\"}],\"plot_height\":300,\"plot_width\":800,\"renderers\":[{\"id\":\"6158bc0e-2b56-417f-ae8e-25842e05a8f1\",\"type\":\"LinearAxis\"},{\"id\":\"e148d502-013e-4c27-8bd9-c57670ab8851\",\"type\":\"Grid\"},{\"id\":\"4a89c430-6683-4217-8d17-d2a272b089c2\",\"type\":\"CategoricalAxis\"},{\"id\":\"7709f4a6-88e4-4c67-8dc3-79387f85892b\",\"type\":\"Grid\"},{\"id\":\"6183344d-12b0-4dc0-b2d1-4780bf06f078\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"84dc5d2b-70df-46dc-92d3-c107f91a4368\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"c607c68d-5743-4d1d-842a-f432bf599521\",\"type\":\"Toolbar\"},\"toolbar_location\":\"above\",\"x_range\":{\"id\":\"f435c9df-2356-42f2-a145-9c8eea0660bc\",\"type\":\"Range1d\"},\"x_scale\":{\"id\":\"b9d0b152-bac7-41fd-a346-d10076c30a6e\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"f954b7a6-6745-498e-b325-4d83f7854adc\",\"type\":\"FactorRange\"},\"y_scale\":{\"id\":\"a4fc96ba-1372-4f99-8779-b53f6df54cb3\",\"type\":\"CategoricalScale\"}},\"id\":\"f48b70ec-3bb8-42d5-8f1e-9b2e386cbc9e\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"callback\":null,\"factors\":[\"0\",\"1\",\"2\",\"3\"]},\"id\":\"f954b7a6-6745-498e-b325-4d83f7854adc\",\"type\":\"FactorRange\"},{\"attributes\":{\"axis_label\":\"Time (s)\",\"axis_line_color\":{\"value\":null},\"formatter\":{\"id\":\"d993863d-3a4d-4f29-85d5-0163cf9f544f\",\"type\":\"BasicTickFormatter\"},\"major_tick_line_color\":{\"value\":null},\"plot\":{\"id\":\"f48b70ec-3bb8-42d5-8f1e-9b2e386cbc9e\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"4e26d9b6-1ab1-44db-b723-74b237f0ae02\",\"type\":\"BasicTicker\"}},\"id\":\"6158bc0e-2b56-417f-ae8e-25842e05a8f1\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"4e26d9b6-1ab1-44db-b723-74b237f0ae02\",\"type\":\"BasicTicker\"},{\"attributes\":{\"grid_line_color\":{\"value\":null},\"plot\":{\"id\":\"f48b70ec-3bb8-42d5-8f1e-9b2e386cbc9e\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"4e26d9b6-1ab1-44db-b723-74b237f0ae02\",\"type\":\"BasicTicker\"}},\"id\":\"e148d502-013e-4c27-8bd9-c57670ab8851\",\"type\":\"Grid\"},{\"attributes\":{\"axis_label\":\"Worker ID\",\"axis_line_color\":{\"value\":null},\"formatter\":{\"id\":\"ce4b2328-ef04-4c36-9765-93c6fff43625\",\"type\":\"CategoricalTickFormatter\"},\"major_tick_line_color\":{\"value\":null},\"plot\":{\"id\":\"f48b70ec-3bb8-42d5-8f1e-9b2e386cbc9e\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"a34925ff-16c2-45aa-bc85-568a28113dae\",\"type\":\"CategoricalTicker\"}},\"id\":\"4a89c430-6683-4217-8d17-d2a272b089c2\",\"type\":\"CategoricalAxis\"},{\"attributes\":{},\"id\":\"a34925ff-16c2-45aa-bc85-568a28113dae\",\"type\":\"CategoricalTicker\"},{\"attributes\":{\"dimension\":1,\"grid_line_color\":{\"value\":null},\"plot\":{\"id\":\"f48b70ec-3bb8-42d5-8f1e-9b2e386cbc9e\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"a34925ff-16c2-45aa-bc85-568a28113dae\",\"type\":\"CategoricalTicker\"}},\"id\":\"7709f4a6-88e4-4c67-8dc3-79387f85892b\",\"type\":\"Grid\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"x\",\"y\",\"function\",\"color\",\"key\"],\"data\":{\"color\":[\"#35B778\",\"#35B778\",\"#35B778\",\"#30678D\",\"#30678D\",\"#30678D\",\"#30678D\",\"#30678D\",\"#FDE724\",\"#30678D\",\"#FDE724\",\"#FDE724\",\"#440154\",\"#440154\"],\"function\":[\"slowinc(*)\",\"slowinc(*)\",\"slowinc(*)\",\"slowadd(_, *)\",\"slowadd(_, *)\",\"slowadd(_, *)\",\"slowadd(_, *)\",\"slowadd(_, *)\",\"slowsum([_, _, _])\",\"slowadd(_, *)\",\"slowsum([_, _, _])\",\"slowsum([_, _, _])\",\"add(_, _)\",\"add(_, _)\"],\"key\":[\"slowinc-68796ac2-f229-4867-b3b4-b4aa3ba45de3\",\"slowinc-21acee32-2af8-4651-bfef-639734e61579\",\"slowinc-1f5df4e8-8064-4b40-a9c7-e5ea14029585\",\"slowadd-755f5db3-07fa-4a4d-a474-5ae0c0bee8e9\",\"slowadd-0be9698f-fe74-45ab-ade3-6cb4e65e8f0d\",\"slowadd-3107d63d-676e-451a-b262-21890c4d60a2\",\"slowadd-52fab0f5-e1f5-40ee-9acc-e5f3fb4532c5\",\"slowadd-89b8a0cb-46ec-413c-8b4a-271b9abd0ccc\",\"slowsum-8be52b34-0713-4161-ab02-5c28ba47d1cd\",\"slowadd-8a345129-067a-40f3-ade2-f5b6fc4ddad2\",\"slowsum-840bf3e7-995c-4234-a6bd-933b08cfd1aa\",\"slowsum-5250359a-6c1f-4f96-ab0f-249aed9f3d07\",\"add-97a4df338503ceef4b96de741ddbb722\",\"add-c3ac9e8889e0cfaeac8ca6a050565e85\"],\"width\":[1.003401219000807,1.0029532309999922,1.003505974993459,1.004726628001663,1.0041916470072465,1.0046788150066277,1.0043597240000963,1.0038440429925686,0.5031396200065501,1.003422366004088,0.5027239550108789,0.5018457300029695,0.0010675919911591336,0.0010946679976768792],\"x\":[0.5017006095004035,0.5015192624850897,0.50181545248779,1.505381668001064,1.5053269695054041,1.5057541215064703,1.505758428000263,2.509370340485475,2.2593310579977697,2.509660506984801,2.259464468501392,3.2623113059962634,3.5137925039889524,3.514887835000991],\"y\":[3,4,2,1,4,3,2,3,1,2,4,4,1,3]}},\"id\":\"e420e7ca-57fd-41fe-83dc-1cee03ab3db4\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_color\":{\"field\":\"color\"},\"height\":{\"units\":\"data\",\"value\":1},\"line_color\":{\"value\":\"gray\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"69a563f9-5469-4fde-86e3-846d7541e2fc\",\"type\":\"Rect\"},{\"attributes\":{\"callback\":null,\"point_policy\":\"follow_mouse\",\"tooltips\":\"\\n    <div>\\n        <span style=\\\"font-size: 14px; font-weight: bold;\\\">Key:</span>&nbsp;\\n        <span style=\\\"font-size: 10px; font-family: Monaco, monospace;\\\">@key</span>\\n    </div>\\n    <div>\\n        <span style=\\\"font-size: 14px; font-weight: bold;\\\">Task:</span>&nbsp;\\n        <span style=\\\"font-size: 10px; font-family: Monaco, monospace;\\\">@function</span>\\n    </div>\\n    \"},\"id\":\"f824f604-bb0f-451b-bf33-9dd35f66662f\",\"type\":\"HoverTool\"},{\"attributes\":{},\"id\":\"398053dd-bf4f-4981-94b4-851c1429251b\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"9c6e1bd6-9156-4e91-81b2-d0ab7da771a8\",\"type\":\"ResetTool\"},{\"attributes\":{\"dimensions\":\"width\"},\"id\":\"6ecb862a-68a0-450d-a252-009c778a8153\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"dimensions\":\"width\"},\"id\":\"e39dcd13-381b-4fa5-b753-b72e89774f7e\",\"type\":\"PanTool\"},{\"attributes\":{\"data_source\":{\"id\":\"e420e7ca-57fd-41fe-83dc-1cee03ab3db4\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"69a563f9-5469-4fde-86e3-846d7541e2fc\",\"type\":\"Rect\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"358507db-c64e-4873-855e-d7a355b020e1\",\"type\":\"Rect\"},\"selection_glyph\":null,\"view\":{\"id\":\"0b3bbdd1-d35a-4800-86f3-8ecc48588aa4\",\"type\":\"CDSView\"}},\"id\":\"6183344d-12b0-4dc0-b2d1-4780bf06f078\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"ce4b2328-ef04-4c36-9765-93c6fff43625\",\"type\":\"CategoricalTickFormatter\"},{\"attributes\":{},\"id\":\"b9d0b152-bac7-41fd-a346-d10076c30a6e\",\"type\":\"LinearScale\"}],\"root_ids\":[\"f48b70ec-3bb8-42d5-8f1e-9b2e386cbc9e\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.13\"}};\n",
       "  var render_items = [{\"docid\":\"66e24c54-fbc3-40c9-9338-efe722e8c623\",\"elementid\":\"e9a4d9e7-f0a0-408b-8c64-a6a427e7d6a4\",\"modelid\":\"f48b70ec-3bb8-42d5-8f1e-9b2e386cbc9e\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "f48b70ec-3bb8-42d5-8f1e-9b2e386cbc9e"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style=\"display: table;\"><div style=\"display: table-row;\"><div style=\"display: table-cell;\"><b title=\"bokeh.plotting.figure.Figure\">Figure</b>(</div><div style=\"display: table-cell;\">id&nbsp;=&nbsp;'f48b70ec-3bb8-42d5-8f1e-9b2e386cbc9e', <span id=\"a9c50f8d-283c-49ac-b1b5-f8e482a2a865\" style=\"cursor: pointer;\">&hellip;)</span></div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">above&nbsp;=&nbsp;[],</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">aspect_scale&nbsp;=&nbsp;1,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">background_fill_alpha&nbsp;=&nbsp;{'value': 1.0},</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">background_fill_color&nbsp;=&nbsp;{'value': '#ffffff'},</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">below&nbsp;=&nbsp;[LinearAxis(id='6158bc0e-2b56-417f-ae8e-25842e05a8f1', ...)],</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">border_fill_alpha&nbsp;=&nbsp;{'value': 1.0},</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">border_fill_color&nbsp;=&nbsp;{'value': '#ffffff'},</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">css_classes&nbsp;=&nbsp;[],</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">disabled&nbsp;=&nbsp;False,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">extra_x_ranges&nbsp;=&nbsp;{},</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">extra_y_ranges&nbsp;=&nbsp;{},</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">h_symmetry&nbsp;=&nbsp;True,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">height&nbsp;=&nbsp;None,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">hidpi&nbsp;=&nbsp;True,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">js_event_callbacks&nbsp;=&nbsp;{},</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">js_property_callbacks&nbsp;=&nbsp;{},</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">left&nbsp;=&nbsp;[CategoricalAxis(id='4a89c430-6683-4217-8d17-d2a272b089c2', ...)],</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">lod_factor&nbsp;=&nbsp;10,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">lod_interval&nbsp;=&nbsp;300,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">lod_threshold&nbsp;=&nbsp;2000,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">lod_timeout&nbsp;=&nbsp;500,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">match_aspect&nbsp;=&nbsp;False,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">min_border&nbsp;=&nbsp;5,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">min_border_bottom&nbsp;=&nbsp;None,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">min_border_left&nbsp;=&nbsp;None,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">min_border_right&nbsp;=&nbsp;None,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">min_border_top&nbsp;=&nbsp;None,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">name&nbsp;=&nbsp;None,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_alpha&nbsp;=&nbsp;{'value': 1.0},</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_cap&nbsp;=&nbsp;'butt',</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_color&nbsp;=&nbsp;{'value': '#e5e5e5'},</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_dash&nbsp;=&nbsp;[],</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_dash_offset&nbsp;=&nbsp;0,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_join&nbsp;=&nbsp;'miter',</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">outline_line_width&nbsp;=&nbsp;{'value': 1},</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">output_backend&nbsp;=&nbsp;'canvas',</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">plot_height&nbsp;=&nbsp;300,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">plot_width&nbsp;=&nbsp;800,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">renderers&nbsp;=&nbsp;[LinearAxis(id='6158bc0e-2b56-417f-ae8e-25842e05a8f1', ...), Grid(id='e148d502-013e-4c27-8bd9-c57670ab8851', ...), CategoricalAxis(id='4a89c430-6683-4217-8d17-d2a272b089c2', ...), Grid(id='7709f4a6-88e4-4c67-8dc3-79387f85892b', ...), GlyphRenderer(id='6183344d-12b0-4dc0-b2d1-4780bf06f078', ...)],</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">right&nbsp;=&nbsp;[],</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">sizing_mode&nbsp;=&nbsp;'fixed',</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">subscribed_events&nbsp;=&nbsp;[],</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">tags&nbsp;=&nbsp;[],</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">title&nbsp;=&nbsp;Title(id='84dc5d2b-70df-46dc-92d3-c107f91a4368', ...),</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">title_location&nbsp;=&nbsp;'above',</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">toolbar&nbsp;=&nbsp;Toolbar(id='c607c68d-5743-4d1d-842a-f432bf599521', ...),</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">toolbar_location&nbsp;=&nbsp;'above',</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">toolbar_sticky&nbsp;=&nbsp;True,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">v_symmetry&nbsp;=&nbsp;False,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">width&nbsp;=&nbsp;None,</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">x_range&nbsp;=&nbsp;Range1d(id='f435c9df-2356-42f2-a145-9c8eea0660bc', ...),</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">x_scale&nbsp;=&nbsp;LinearScale(id='b9d0b152-bac7-41fd-a346-d10076c30a6e', ...),</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">y_range&nbsp;=&nbsp;FactorRange(id='f954b7a6-6745-498e-b325-4d83f7854adc', ...),</div></div><div class=\"f2923475-2302-47df-8b8a-964a835931f7\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">y_scale&nbsp;=&nbsp;CategoricalScale(id='a4fc96ba-1372-4f99-8779-b53f6df54cb3', ...))</div></div></div>\n",
       "<script>\n",
       "(function() {\n",
       "  var expanded = false;\n",
       "  var ellipsis = document.getElementById(\"a9c50f8d-283c-49ac-b1b5-f8e482a2a865\");\n",
       "  ellipsis.addEventListener(\"click\", function() {\n",
       "    var rows = document.getElementsByClassName(\"f2923475-2302-47df-8b8a-964a835931f7\");\n",
       "    for (var i = 0; i < rows.length; i++) {\n",
       "      var el = rows[i];\n",
       "      el.style.display = expanded ? \"none\" : \"table-row\";\n",
       "    }\n",
       "    ellipsis.innerHTML = expanded ? \"&hellip;)\" : \"&lsaquo;&lsaquo;&lsaquo;\";\n",
       "    expanded = !expanded;\n",
       "  });\n",
       "})();\n",
       "</script>\n"
      ],
      "text/plain": [
       "Figure(id='f48b70ec-3bb8-42d5-8f1e-9b2e386cbc9e', ...)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask.diagnostics import Profiler\n",
    "prof = Profiler()\n",
    "\n",
    "with prof:\n",
    "    result.compute()\n",
    "\n",
    "prof.visualize()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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 },
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}