Bokeh_Mode_Time.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "import os\n",
    "from os.path import join\n",
    "from shapely.geometry import Polygon\n",
    "from pygeotile.tile import Tile\n",
    "%matplotlib inline\n",
    "\n",
    "def qt2geom(qt):\n",
    "    bbox = Tile.from_quad_tree(qt).bounds\n",
    "    # min_lng, min_lat, max_lng, max_lat\n",
    "    poly = Polygon.from_bounds(bbox[0][1], bbox[0][0], bbox[1][1], bbox[1][0]) \n",
    "    return poly\n",
    "\n",
    "def read_cellphone_data(filename):\n",
    "    data = pd.read_csv(filename,\n",
    "                       header = None,\n",
    "                       # a typical dataset has 9 columns with these names\n",
    "                       names = ['qtid_origin',\n",
    "                                'qtid_destination',\n",
    "                                'OD_counts', \n",
    "                                'num_dates',\n",
    "                                'avg_travel_distance (mile)',\n",
    "                                'avg_travel_time (second)',\n",
    "                                'time_period',\n",
    "                                'dominant_mode_1',\n",
    "                                'dominant_mode_2'],\n",
    "                       dtype={'qtid_origin': object, 'qtid_destination': object},                       \n",
    "                      nrows=20000,\n",
    "                      )\n",
    "    return data\n",
    "\n",
    "\n",
    "def get_geo(filename):\n",
    "    data = read_cellphone_data(filename)\n",
    "    # get all qtid in the dataset (origin and destination)\n",
    "    qt_list = pd.concat([data.qtid_origin, data.qtid_destination]).unique()\n",
    "    qt_poly = list(map(qt2geom,qt_list))\n",
    "    qt_dict = gpd.GeoDataFrame(pd.DataFrame(data={'qtid':qt_list,'geometry':qt_poly}))\n",
    "    return qt_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = \"cellphone data 2/fall2017-summer2018/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc_shp = get_geo(data_dir + 'nyc_all.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d6e36c8518>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nyc_shp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>qtid</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>030232213331113120</td>\n",
       "      <td>POLYGON ((-75.9429931640625 42.15220521114355,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>030232213331331113</td>\n",
       "      <td>POLYGON ((-75.93887329101563 42.11248648904184...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>030232213331333330</td>\n",
       "      <td>POLYGON ((-75.94024658203125 42.09924138032294...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>030232233113113333</td>\n",
       "      <td>POLYGON ((-75.93887329101563 41.42625319507269...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>030232233133331321</td>\n",
       "      <td>POLYGON ((-75.94161987304688 41.25406487942275...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 qtid                                           geometry\n",
       "0  030232213331113120  POLYGON ((-75.9429931640625 42.15220521114355,...\n",
       "1  030232213331331113  POLYGON ((-75.93887329101563 42.11248648904184...\n",
       "2  030232213331333330  POLYGON ((-75.94024658203125 42.09924138032294...\n",
       "3  030232233113113333  POLYGON ((-75.93887329101563 41.42625319507269...\n",
       "4  030232233133331321  POLYGON ((-75.94161987304688 41.25406487942275..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_shp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc_df = read_cellphone_data(data_dir + 'nyc_all.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>qtid_origin</th>\n",
       "      <th>qtid_destination</th>\n",
       "      <th>OD_counts</th>\n",
       "      <th>num_dates</th>\n",
       "      <th>avg_travel_distance (mile)</th>\n",
       "      <th>avg_travel_time (second)</th>\n",
       "      <th>time_period</th>\n",
       "      <th>dominant_mode_1</th>\n",
       "      <th>dominant_mode_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>030232213331113120</td>\n",
       "      <td>030232213331113120</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.009944</td>\n",
       "      <td>621.73430</td>\n",
       "      <td>10:00 - 10:30</td>\n",
       "      <td>stay</td>\n",
       "      <td>stay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>030232213331331113</td>\n",
       "      <td>030232213331331112</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009991</td>\n",
       "      <td>422.91666</td>\n",
       "      <td>8:00 - 8:30</td>\n",
       "      <td>stay</td>\n",
       "      <td>stay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>030232213331331113</td>\n",
       "      <td>030232213331331113</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0.002881</td>\n",
       "      <td>678.78125</td>\n",
       "      <td>10:00 - 10:30</td>\n",
       "      <td>stay</td>\n",
       "      <td>stay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>030232213331333330</td>\n",
       "      <td>030232213331333330</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.006721</td>\n",
       "      <td>581.08330</td>\n",
       "      <td>6:00 - 6:30</td>\n",
       "      <td>pedestrian</td>\n",
       "      <td>pedestrian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>030232213331333330</td>\n",
       "      <td>030232213331333330</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001947</td>\n",
       "      <td>410.19696</td>\n",
       "      <td>8:30 - 9:00</td>\n",
       "      <td>stay</td>\n",
       "      <td>stay</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          qtid_origin    qtid_destination  OD_counts  num_dates  \\\n",
       "0  030232213331113120  030232213331113120          4          2   \n",
       "1  030232213331331113  030232213331331112          2          1   \n",
       "2  030232213331331113  030232213331331113          8          4   \n",
       "3  030232213331333330  030232213331333330          2          2   \n",
       "4  030232213331333330  030232213331333330          1          1   \n",
       "\n",
       "   avg_travel_distance (mile)  avg_travel_time (second)    time_period  \\\n",
       "0                    0.009944                 621.73430  10:00 - 10:30   \n",
       "1                    0.009991                 422.91666    8:00 - 8:30   \n",
       "2                    0.002881                 678.78125  10:00 - 10:30   \n",
       "3                    0.006721                 581.08330    6:00 - 6:30   \n",
       "4                    0.001947                 410.19696    8:30 - 9:00   \n",
       "\n",
       "  dominant_mode_1 dominant_mode_2  \n",
       "0            stay            stay  \n",
       "1            stay            stay  \n",
       "2            stay            stay  \n",
       "3      pedestrian      pedestrian  \n",
       "4            stay            stay  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19987, 6)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_df.groupby(['qtid_origin','qtid_destination','time_period']).count().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000, 9)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.plotting import figure, show, ColumnDataSource, save\n",
    "from bokeh.embed import components\n",
    "from bokeh.io import output_notebook\n",
    "from bokeh.models import FactorRange, CategoricalColorMapper, HoverTool, ColumnDataSource, Panel\n",
    "from bokeh.models.widgets import CheckboxGroup, Slider, RangeSlider, Tabs\n",
    "\n",
    "from bokeh.layouts import column, row, WidgetBox\n",
    "from bokeh.palettes import Category20_16\n",
    "\n",
    "from bokeh.application.handlers import FunctionHandler\n",
    "from bokeh.application import Application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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=\"1001\">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 != null && id in Bokeh.index) {\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 id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\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[toinsert.length - 1].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[toinsert.length - 1].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[toinsert.length - 1]);\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(\"1001\");\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(\"1001\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.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-1.0.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.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(\"1001\")).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(\"1001\");\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(\"1001\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.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-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.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(\"1001\")).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": [
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getPolyCoords(row, geom, coord_type):\n",
    "    \"\"\"Returns the coordinates ('x' or 'y') of edges of a Polygon exterior\"\"\"\n",
    "    \n",
    "    # Parse the exterior of the coordinate\n",
    "    exterior = row[geom].exterior\n",
    "\n",
    "    if coord_type == 'x':\n",
    "        # Get the x coordinates of the exterior\n",
    "        return list( exterior.coords.xy[0] )\n",
    "    elif coord_type == 'y':\n",
    "        # Get the y coordinates of the exterior\n",
    "        return list( exterior.coords.xy[1] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getVectorCoords(row, geom, coord_type):\n",
    "    \"\"\"Returns the coordinates ('x' or 'y') of edges of a Polygon exterior\"\"\"\n",
    "    join_row = nyc_shp[nyc_shp['qtid']==row[geom]]\n",
    "    join_row = list(join_row['geometry'])[0]\n",
    "    \n",
    "    # Parse the exterior of the coordinate\n",
    "    centroid = join_row.centroid\n",
    "\n",
    "    if coord_type == 'x':\n",
    "        # Get the x coordinates of the exterior\n",
    "        return centroid.coords.xy[0][0]\n",
    "    elif coord_type == 'y':\n",
    "        # Get the y coordinates of the exterior\n",
    "        return centroid.coords.xy[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the Polygon Centroid x and y coordinates\n",
    "nyc_df['x0'] = nyc_df.apply(getVectorCoords, geom='qtid_origin', coord_type='x', axis=1)\n",
    "nyc_df['y0'] = nyc_df.apply(getVectorCoords, geom='qtid_origin', coord_type='y', axis=1)\n",
    "nyc_df['x1'] = nyc_df.apply(getVectorCoords, geom='qtid_destination', coord_type='x', axis=1)\n",
    "nyc_df['y1'] = nyc_df.apply(getVectorCoords, geom='qtid_destination', coord_type='y', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the Polygon x and y coordinates\n",
    "nyc_shp['x'] = nyc_shp.apply(getPolyCoords, geom='geometry', coord_type='x', axis=1)\n",
    "nyc_shp['y'] = nyc_shp.apply(getPolyCoords, geom='geometry', coord_type='y', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a copy, drop the geometry column and create ColumnDataSource\n",
    "nyc_shp_source = nyc_shp.drop('geometry', axis=1).copy()\n",
    "nyc_shp_source = ColumnDataSource(nyc_shp_source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a copy, drop the geometry column and create ColumnDataSource\n",
    "nyc_df_source = nyc_df.copy()\n",
    "nyc_df_source = ColumnDataSource(nyc_df_source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "boro_shp = gpd.read_file(\"Borough Boundaries/geo_export_dab0de5c-dadd-4bea-9105-37b2d151df7e.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d6e5cc1128>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boro_shp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getAllBoro(geoms,boros):\n",
    "    x = []\n",
    "    y = []\n",
    "    boro_list = []\n",
    "    for geom, boro in zip(geoms,boros):\n",
    "        for sub_geom in geom:\n",
    "            x.append(list(sub_geom.exterior.coords.xy[0]))\n",
    "            y.append(list(sub_geom.exterior.coords.xy[1]))\n",
    "            boro_list.append(boro)\n",
    "    return x,y,boro_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y,boro_list = getAllBoro(boro_shp['geometry'],boro_shp['boro_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "boro_source = pd.DataFrame(data = {'x':x,'y':y,'boro_name':boro_list})\n",
    "boro_source = boro_source.copy()\n",
    "boro_source = ColumnDataSource(boro_source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d6e5d345c0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "qt = gpd.read_file('cellphone data 2/quadtree_dictionary/nyc_quadtree/nyc_quadtree.shp')\n",
    "qt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>qtid</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>030232213331113120</td>\n",
       "      <td>POLYGON ((-75.9429931640625 42.15220521114355,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>030232213331331113</td>\n",
       "      <td>POLYGON ((-75.93887329101563 42.11248648904184...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>030232213331333330</td>\n",
       "      <td>POLYGON ((-75.94024658203125 42.09924138032294...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>030232233113113333</td>\n",
       "      <td>POLYGON ((-75.93887329101563 41.42625319507269...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>030232233133331321</td>\n",
       "      <td>POLYGON ((-75.94161987304688 41.25406487942275...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 qtid                                           geometry\n",
       "0  030232213331113120  POLYGON ((-75.9429931640625 42.15220521114355,...\n",
       "1  030232213331331113  POLYGON ((-75.93887329101563 42.11248648904184...\n",
       "2  030232213331333330  POLYGON ((-75.94024658203125 42.09924138032294...\n",
       "3  030232233113113333  POLYGON ((-75.93887329101563 41.42625319507269...\n",
       "4  030232233133331321  POLYGON ((-75.94161987304688 41.25406487942275..."
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "available_modes = nyc_df['dominant_mode_2'].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def modify_doc(doc):\n",
    "    \n",
    "    def make_dataset(modes_list,range_start,range_end):\n",
    "        times_list = []\n",
    "        for i in [x/10.0 for x in range(range_start*10,range_end*10+5,5)]:\n",
    "            if(i%1==0):\n",
    "                times_list.append(str(int(i)) + ':00'+ ' - ' + str(int(i)) + ':30')\n",
    "            else:\n",
    "                times_list.append(str(int(i)) + ':30'+ ' - ' + str(int(i+1)) + ':00')\n",
    "\n",
    "        subset = nyc_df[(nyc_df['time_period'].isin(times_list))&(nyc_df['dominant_mode_2'].isin(modes_list))]\n",
    "\n",
    "        return ColumnDataSource(subset)\n",
    "    \n",
    "    def make_plot(src):\n",
    "        # Blank plot with correct labels\n",
    "        p = figure(plot_width = 700, plot_height = 600, \n",
    "                  title = 'Greater NYC Commute by Time Period and Modes')\n",
    "\n",
    "        # Plot grid\n",
    "        p.patches('x', 'y', source=boro_source, fill_alpha=0.25, line_color=\"blue\", line_width=0.5)\n",
    "\n",
    "        p.segment(x0='x0', y0='y0', x1='x1', y1='y1', source=src, color=\"red\", line_width=1.0)\n",
    "\n",
    "        #Add Hover\n",
    "        #hover = HoverTool(tooltips = [('(x,y)', '($x,$y)'),('dominant_mode_2', '@dominant_mode_2')],mode = 'vline')\n",
    "        #p.add_tools(hover)\n",
    "\n",
    "        return p\n",
    "    \n",
    "    # Update function takes three default parameters\n",
    "    def update(attr, old, new):\n",
    "        # Get the list of carriers for the graph\n",
    "        modes_to_plot = [modes_selection.labels[i] for i in \n",
    "                            modes_selection.active]\n",
    "        \n",
    "        # Value for the range slider is a tuple (start, end)\n",
    "\n",
    "        range_start = range_select.value[0]\n",
    "\n",
    "        range_end = range_select.value[1]\n",
    "\n",
    "        # Make a new dataset based on the selected carriers and the \n",
    "        # make_dataset function defined earlier\n",
    "        new_src = make_dataset(modes_to_plot,range_start,range_end)\n",
    "\n",
    "        # Update the source used the quad glpyhs\n",
    "        src.data.update(new_src.data)\n",
    "\n",
    "        \n",
    "    modes_selection = CheckboxGroup(labels=available_modes, active = [2])\n",
    "    modes_selection.on_change('active', update)\n",
    "    \n",
    "    \n",
    "    # RangeSlider to change the maximum and minimum values on time_period\n",
    "\n",
    "    range_select = RangeSlider(start = 6, end = 10, value = (6, 8),\n",
    "\n",
    "                               step = 1, title = 'Time Range')\n",
    "\n",
    "    # Update the plot when the value is changed\n",
    "\n",
    "    range_select.on_change('value', update)\n",
    "\n",
    "    \n",
    "    controls = WidgetBox(modes_selection,range_select)\n",
    "    \n",
    "    initial_modes = [modes_selection.labels[i] for i in modes_selection.active]\n",
    "    \n",
    "    src = make_dataset(initial_modes,6,8)\n",
    "    \n",
    "    p = make_plot(src)\n",
    "    \n",
    "    layout = row(controls, p)\n",
    "    doc.add_root(layout)\n",
    "    \n",
    "# Set up an application\n",
    "handler = FunctionHandler(modify_doc)\n",
    "app = Application(handler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.bokehjs_exec.v0+json": "",
      "text/html": [
       "\n",
       "<script src=\"http://localhost:59545/autoload.js?bokeh-autoload-element=1005&bokeh-absolute-url=http://localhost:59545&resources=none\" id=\"1005\"></script>"
      ]
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "server_id": "5c5688c83297402f8add5a721d0e5c74"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(app)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Static Screenshot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='Bokeh_Mode_Time.png'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}