2-bokeh.ipynb

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    "Introduction to Bokeh\n",
    "=====================\n",
    "\n",
    "In this tutorial we'll learn how to use Bokeh to build interactive visualizations viewable in a browser.  Generally this tutorial will have the following format\n",
    "\n",
    "`plotting` - Intermediate interface allowing control to all parts of a plot\n",
    "    - Vectorized attributes\n",
    "    - Toolbars\n",
    "    - Linked Brushing\n",
    "    - Interactivity\n",
    "\n",
    "Install\n",
    "-------\n",
    "\n",
    "This tutorial uses many different libraries that are all available with the [Anaconda Distribution](http://continuum.io/downloads). Once you have Anaconda install, please run these commands from a terminal:\n",
    "\n",
    "```\n",
    "$ conda install -y bokeh\n",
    "```\n",
    "\n",
    "\n",
    "<hr/>\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to [Bokeh](http://bokeh.pydata.org/)\n",
    "\n",
    "Provide a first-class visualization library for web-aware applications, without\n",
    "requiring web-level programming.\n",
    "\n",
    "Write a visualization python. Bokeh creates data descripors and a scenegraph consumed by BokehJS.\n",
    "This works in ipython notebook, creating static files and interacting with dynamic data sources.\n",
    "\n",
    "Bokeh includes pre-built schemas in bokeh.charts, a low-level composition interface (similar to matplotlib),\n",
    "a server for large and/or dynamic datasources and widgets for providing client-side realtime interaction.\n",
    "\n",
    "The non-JS framework also has prototypes in other languages (Scala, Julia...maybe R).\n",
    "\n",
    "**Note:** There are examples notebooks in bokeh/examples/plotting/notebooks. Start an ipython notebook server there to get more examples.\n",
    "\n",
    "[Gallery](http://bokeh.pydata.org/docs/gallery.html) --\n",
    "[tutorial](http://bokeh.pydata.org/tutorial/index.html) -- \n",
    "[Documentation](http://bokeh.pydata.org/docs/user_guide.html) --\n",
    "[Repo](https://github.com/bokeh/bokeh)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output\n",
    "\n",
    "Bokeh can output to html, a notebook, or just fragments for embedding in a web application. \n",
    "\n",
    "To start playing, we'll use the notebook. Later we will see the other types of output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "outputs": [
    {
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       "\n",
       "    <div class=\"bk-banner\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"6b415dca-d10e-4e2a-9118-14fb63a94c21\">Loading BokehJS ...</span>\n",
       "    </div>"
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       "    window._bokeh_onload_callbacks = [];\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    delete window._bokeh_onload_callbacks\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",
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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",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.11.1.min.js'];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      Bokeh.$(\"#6b415dca-d10e-4e2a-9118-14fb63a94c21\").text(\"BokehJS successfully loaded\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i](window.Bokeh);\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": [
    "from bokeh.plotting import output_notebook  \n",
    "output_notebook() # Tell Bokeh to output in an ipython notebook (other options later)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr/>\n",
    "\n",
    "## Plotting.py\n",
    "\n",
    "This is a mid-level interface, used by the charting library (and other parts of bokeh).   It can also be used directly.\n",
    "The basic idea: there is an active plot, and you are modifying it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from bokeh.plotting import *\n",
    "N = 102\n",
    "lin_arr = np.linspace(0, 4*np.pi, N)\n",
    "sin_arr = np.sin(lin_arr)\n",
    "cos_arr = np.cos(lin_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr />\n",
    "\n",
    "## Scatter Plots\n",
    "\n",
    "To begin with let's make a scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      function(Bokeh) {\n",
       "        Bokeh.$(function() {\n",
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       "<p><code>&lt;Bokeh Notebook handle for <strong>In[4]</strong>&gt;</code></p>"
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    "\n",
    "p1 = figure()\n",
    "p1.scatter(lin_arr, sin_arr, color=\"#FF00FF\")\n",
    "p1.scatter(lin_arr, cos_arr, color=\"green\")\n",
    "show(p1)"
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   "source": [
    "<hr />\n",
    "\n",
    "## Exercise\n",
    "\n",
    "Play with plotting arrays, try editting\n",
    "- color,\n",
    "- markers,\n",
    "- alpha (value between 0-1), and \n",
    "- size (int of pixels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "            var render_items = [{\"docid\":\"6684ca8f-2b25-48e7-8393-77a7c6a07b01\",\"elementid\":\"a8bb9a22-8660-42d0-a791-b08e30308422\",\"modelid\":\"db642b68-2771-483c-a962-72cd90696295\",\"notebook_comms_target\":\"99db3c63-1b4e-4fa1-ae7e-6879556f9e38\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
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       "      function(Bokeh) {\n",
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       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\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",
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       "  }(this));\n",
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     "data": {
      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[5]</strong>&gt;</code></p>"
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      "text/plain": [
       "<bokeh.io._CommsHandle at 0x10da01470>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p2 = figure()\n",
    "p2.scatter(x=lin_arr, y=sin_arr, color=\"red\")\n",
    "show(p2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "            var render_items = [{\"docid\":\"917854f2-6382-4af2-ae34-53d7c5cdf0e8\",\"elementid\":\"f58de980-c71d-411e-871d-9b776c76c12b\",\"modelid\":\"cebe2cbf-8d74-42e7-a36d-fb543fec63c0\",\"notebook_comms_target\":\"49db1958-4cd2-406f-98aa-fabacd5301b8\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\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",
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       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
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       "<p><code>&lt;Bokeh Notebook handle for <strong>In[6]</strong>&gt;</code></p>"
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   "source": [
    "p3 = figure()\n",
    "p3.scatter(x=lin_arr, y=cos_arr , marker=\"square\", color=\"green\")\n",
    "show(p3)"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other plotting things...\n",
    "There are lots of [glyph types](http://bokeh.pydata.org/docs/reference/plotting.html) and lots of [properties](http://bokeh.pydata.org/docs/user_guide/objects.html#userguide-objects-line-properties)...here is just a sample"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr />\n",
    "## Everything is vectorized\n",
    "\n",
    "While we have only been passing vectors for locations, we can do so for almost any parameter.\n",
    "\n",
    "Let's use the `cos_arr` to size the circles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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       "            var render_items = [{\"docid\":\"60522847-5e42-494b-ac63-423d94cee7f8\",\"elementid\":\"411e3ab2-b40b-48e5-9567-238a452d5885\",\"modelid\":\"1cae7daf-6e55-4e30-bde3-c7033803fc21\",\"notebook_comms_target\":\"ae4a0b76-37ac-4860-9faa-5fd5a4e32d57\"}];\n",
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       "      function(Bokeh) {\n",
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       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
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       "  \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",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[7]</strong>&gt;</code></p>"
      ],
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x10da21ba8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p4 = figure()\n",
    "p4.scatter(x=lin_arr, y=sin_arr, size=cos_arr**2*10)\n",
    "show(p4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr/>\n",
    "\n",
    "Let's play with colors now. Brewer is a popular set of palletes. Here we pick one and then build a vector of colors for the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Brewer Palettes: dict_keys(['PuRd', 'RdPu', 'Blues', 'Greens', 'PiYG', 'GnBu', 'YlGn', 'YlOrRd', 'Oranges', 'Reds', 'RdBu', 'Spectral', 'Purples', 'PuOr', 'RdYlBu', 'PRGn', 'BrBG', 'BuPu', 'Greys', 'PuBuGn', 'RdYlGn', 'YlOrBr', 'OrRd', 'BuGn', 'PuBu', 'RdGy', 'YlGnBu'])\n",
      "Brewer Grey Palettes: dict_keys([3, 4, 5, 6, 7, 8, 9])\n"
     ]
    }
   ],
   "source": [
    "from bokeh.palettes import brewer\n",
    "print(\"Brewer Palettes:\", brewer.keys())\n",
    "print(\"Brewer Grey Palettes:\", brewer[\"Greys\"].keys())\n",
    "palette = brewer[\"Greys\"][9] + list(reversed(brewer[\"Greys\"][9]))\n",
    "colors = palette * int(len(lin_arr) / len(palette)) + palette[0:len(lin_arr) % len(palette)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "\n",
       "    <div class=\"plotdiv\" id=\"76225bac-1f45-487a-873f-a16bbbb7b651\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\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(\"76225bac-1f45-487a-873f-a16bbbb7b651\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '76225bac-1f45-487a-873f-a16bbbb7b651' 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",
       "        Bokeh.$(function() {\n",
       "            var docs_json = 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       "<p><code>&lt;Bokeh Notebook handle for <strong>In[9]</strong>&gt;</code></p>"
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       "<bokeh.io._CommsHandle at 0x10da400b8>"
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   "source": [
    "p5 = figure()\n",
    "p5.scatter(x=lin_arr, y=sin_arr, size=cos_arr**2*10 + 5, fill_color=colors)\n",
    "show(p5)"
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   "cell_type": "markdown",
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   "source": [
    "<hr/>\n",
    "\n",
    "## Tools\n",
    "\n",
    "If you notice the bar at the top of the you see several places to interact with the plot.\n",
    "\n",
    "These are tools and there a many different tools built into the Bokeh.\n",
    "\n",
    "Let's take a look at HoverTools, but first we use Bokeh's data source which watches changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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   "outputs": [],
   "source": [
    "source = ColumnDataSource(\n",
    "    data=dict(\n",
    "        x=lin_arr,\n",
    "        y=sin_arr,\n",
    "        size=cos_arr**2*10 + 5,\n",
    "        colors=colors\n",
    "    )\n",
    ")"
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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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   "outputs": [
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      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[11]</strong>&gt;</code></p>"
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       "<bokeh.io._CommsHandle at 0x10da60080>"
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     "metadata": {},
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   ],
   "source": [
    "from bokeh.models import HoverTool\n",
    "from collections import OrderedDict\n",
    "\n",
    "TOOLS=\"crosshair,pan,wheel_zoom,box_zoom,reset,hover,previewsave\"\n",
    "p6 = figure(title=\"Hoverful Scatter\", tools=TOOLS)\n",
    "p6.circle(x=\"x\", y=\"y\", size=\"size\", source=source,\n",
    "    fill_color=\"colors\", fill_alpha=0.6, line_color=None)\n",
    "\n",
    "hover = p6.select(dict(type=HoverTool))\n",
    "hover.tooltips = OrderedDict([\n",
    "    (\"index\", \"$index\"),\n",
    "    (\"(x,y)\", \"(@x, @y)\"),\n",
    "    (\"size\", \"@size\"),\n",
    "    (\"fill color\", \"$color[hex, swatch]:fill_color\"),\n",
    "])\n",
    "show(p6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr />\n",
    "\n",
    "## Linking two plots\n",
    "\n",
    "One of the best aspects of Bokeh is linking plots. We can link the brushing. This will allow you to select and pan with the plots both reacting to each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "        Bokeh.$(function() {\n",
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       "            var render_items = [{\"docid\":\"b2600863-05d8-48ed-affc-6cafe4c887b4\",\"elementid\":\"73217f8b-85f7-473a-831d-56ba98c1c222\",\"modelid\":\"d6415756-6edb-4d44-9064-2b457e251481\",\"notebook_comms_target\":\"a99f8391-e43a-42b1-83c0-4df96d8287a2\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\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"
    },
    {
     "data": {
      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[12]</strong>&gt;</code></p>"
      ],
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x10da78630>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "N = 300\n",
    "x = np.linspace(0, 4*np.pi, N)\n",
    "y1 = np.sin(x)\n",
    "y2 = np.cos(x)\n",
    "\n",
    "source = ColumnDataSource()\n",
    "source.add(data=x, name='x')\n",
    "source.add(data=y1, name='y1')\n",
    "source.add(data=y2, name='y2')\n",
    "\n",
    "TOOLS = \"pan,wheel_zoom,box_zoom,reset,save,box_select,lasso_select\"\n",
    "\n",
    "s1 = figure(tools=TOOLS, plot_width=350, plot_height=350)\n",
    "s1.scatter('x', 'y1', source=source)\n",
    "\n",
    "# Linked brushing in Bokeh is expressed by sharing data sources between\n",
    "# renderers. Note below that s2.scatter is called with the `source`\n",
    "# keyword argument, and supplied with the same data source from s1.scatter\n",
    "s2 = figure(tools=TOOLS, plot_width=350, plot_height=350, x_range=s1.x_range)\n",
    "s2.scatter('x', 'y2', source=source, )\n",
    "\n",
    "p = gridplot([[s1,s2]])\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr />\n",
    "\n",
    "## Basic interactivity\n",
    "\n",
    "Bokeh lets you use a Python update function to update your plots.\n",
    "\n",
    "In IPython notebook we can use the interactive widgets provided by the notebook. One can also use Flask or Bokeh-server to embed in outside the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bokeh.models.renderers.GlyphRenderer at 0x10d8f2198>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(0, 2*np.pi, 2000)\n",
    "y = np.sin(x)\n",
    "\n",
    "source = ColumnDataSource(data=dict(x=x, y=y))\n",
    "\n",
    "p = figure(title=\"simple line example\", plot_height=300, plot_width=600)\n",
    "p.line(x, y, color=\"#2222aa\", line_width=3, source=source, name=\"foo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/cdoig/anaconda/envs/py101/lib/python3.4/site-packages/ipykernel/__main__.py:8: BokehDeprecationWarning: bokeh.models.sources.push_notebook was deprecated in Bokeh 0.11.0; please use bokeh.io.push_notebook instead\n"
     ]
    }
   ],
   "source": [
    "from IPython.html.widgets import interact\n",
    "@interact(f=[\"sin\", \"cos\", \"tan\"], w=(0,100), A=(1,10), phi=(0, 10, 0.1))\n",
    "def update(f, w=1, A=1, phi=0):\n",
    "    if   f == \"sin\": func = np.sin\n",
    "    elif f == \"cos\": func = np.cos\n",
    "    elif f == \"tan\": func = np.tan\n",
    "    source.data['y'] = A * func(w * x + phi)\n",
    "    source.push_notebook()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "            var render_items = [{\"docid\":\"1b2ecf20-dbc0-4d09-a525-81b873cc6ccb\",\"elementid\":\"cd5e95cb-002d-44a1-95ce-9dac783b0afd\",\"modelid\":\"78f24f2a-fecc-4e51-aa09-04d50f7a7081\",\"notebook_comms_target\":\"779aedec-5fbe-4e96-b328-4ec7338fe2b9\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\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"
    },
    {
     "data": {
      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[15]</strong>&gt;</code></p>"
      ],
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x10dae4438>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr/>\n",
    "\n",
    "## [bokeh.charts](http://bokeh.pydata.org/docs/user_guide/charts.html)\n",
    "Common Schemas for common tasks (and parameters).\n",
    "\n",
    "Expects data to be formatted as either an OrderedDict or a pandas dataframe.\n",
    "\n",
    "Supported Schemas: Bar, Boxplot, Categorical Heatmap, Histogram, Scatter, Timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "from bokeh.charts import Histogram\n",
    "from bokeh.io import show\n",
    "\n",
    "mu, sigma = 0, 0.5\n",
    "normal_dist = OrderedDict(normal=np.random.normal(mu, sigma, size=1000))\n",
    "hist = Histogram(normal_dist, bins=50,\n",
    "                 title=\"kwargs, dict_input\", \n",
    "                 ylabel=\"frequency\", \n",
    "                 width=400, \n",
    "                 height=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
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