04-coin-flips.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Probability - Coin Flips and Experiments "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np \n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)\n",
    "sns.set_style(\"white\")     # See more styling options here: https://seaborn.pydata.org/tutorial/aesthetics.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.uniform(low=0.0, high=1.0)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate a 'flip'\n",
    "def flip(num = 1):\n",
    "    flips = [] \n",
    "    \n",
    "    for i in range(num): \n",
    "        num = np.random.uniform(low=0.0, high=1.0)      \n",
    "        if num > 0.75: \n",
    "            flips.append('H')             # should be doing yield here if you know 'generators'\n",
    "        else: \n",
    "            flips.append('T')\n",
    "    return flips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flips = flip(10) \n",
    "print(flips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "values, counts = np.unique(flips, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "values, counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Repdoducible 'Randomness' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# computers are 'deterministic'. You can not do 'random' in computers! \n",
    "# So, you start with some 'seed' then do deterministic things \n",
    "# this is called pseudo-randomness \n",
    "\n",
    "# sometimes you want to suppress this! \n",
    "\n",
    "np.random.seed(0)   # random numbers and seed "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6027633760716439"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.uniform(low=0.0, high=1.0)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['T', 'T', 'T', 'T', 'T', 'T', 'T', 'H', 'T', 'T']\n",
      "['H' 'T'] [1 9]\n"
     ]
    }
   ],
   "source": [
    "import matplotlib \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np \n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)\n",
    "sns.set_style(\"white\")     # See more styling options here: https://seaborn.pydata.org/tutorial/aesthetics.html\n",
    "\n",
    "\n",
    "np.random.seed(1337)   # random numbers and seed \n",
    "\n",
    "\n",
    "# generate a 'flip'\n",
    "def flip(num = 1):\n",
    "    flips = [] \n",
    "    \n",
    "    for i in range(num): \n",
    "        num = np.random.uniform(low=0.0, high=1.0)      \n",
    "        if num > 0.75: \n",
    "            flips.append('H')             # should be doing yield here if you know 'generators'\n",
    "        else: \n",
    "            flips.append('T')\n",
    "    return flips\n",
    "\n",
    "\n",
    "# Flip \n",
    "flips = flip(10) \n",
    "values, counts = np.unique(flips, return_counts=True)\n",
    "\n",
    "# print values/stats\n",
    "# print(flip())\n",
    "print(flips)\n",
    "print(values, counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Probability of Flips "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter, defaultdict \n",
    "\n",
    "\n",
    "def get_freqs(flips): \n",
    "    keys = Counter(flips).keys()\n",
    "    vals = Counter(flips).values()  \n",
    "    \n",
    "    # print(keys)\n",
    "    # print(vals) \n",
    "    \n",
    "    # return dict(zip(keys, vals))     # bug: what if there are no 'H' or no 'T'\n",
    "\n",
    "    return defaultdict(int, dict(zip(keys, vals)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<class 'int'>, {'T': 9, 'H': 1})\n"
     ]
    }
   ],
   "source": [
    "freqs = get_freqs(flips)\n",
    "print(freqs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1\n"
     ]
    }
   ],
   "source": [
    "prob_h = freqs['H'] / len(flips)\n",
    "print(prob_h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment: Prob calculated based on 1 flip upto N flips "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "maximum_flips = 1000\n",
    "\n",
    "probs = [] \n",
    "for num_flips in range(1, maximum_flips):\n",
    "    flips = flip(num_flips)\n",
    "    freqs = get_freqs(flips)\n",
    "    prob_h = freqs['H'] / len(flips)\n",
    "\n",
    "    probs.append(prob_h)\n",
    "    \n",
    "# print(probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<class 'int'>, {'T': 740, 'H': 259})\n"
     ]
    }
   ],
   "source": [
    "print(freqs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(probs)\n",
    "plt.ylabel('Estimated Probability')\n",
    "plt.xlabel('Number of Flips in Experiment');\n",
    "plt.title(\"Calculating Probability over Number of Flips\")\n",
    "sns.despine(offset=10, trim=True);  # move axes away \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(probs[:10])\n",
    "plt.ylabel('Estimated Probability')\n",
    "plt.xlabel('Number of Flips in Experiment');\n",
    "plt.title(\"Calculating Probability over Number of Flips\")\n",
    "sns.despine(offset=10, trim=True);  # move axes away \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(probs[maximum_flips-10:])\n",
    "plt.ylabel('Estimated Probability')\n",
    "plt.xlabel('Number of Flips in Experiment');\n",
    "plt.title(\"Calculating Probability over Number of Flips\")\n",
    "sns.despine(offset=10, trim=True);  # move axes away \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bokeh For Interactive Plots "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install bokeh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.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",
       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\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) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"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.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.2.min.js\": \"ufR9RFnRs6lniiaFvtJziE0YeidtAgBRH6ux2oUItHw5WTvE1zuk9uzhUU/FJXDp\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.2.min.js\": \"8QM/PGWBT+IssZuRcDcjzwIh1mkOmJSoNMmyYDZbCfXJg3Ap1lEvdVgFuSAwhb/J\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.2.min.js\": \"Jm8cH3Rg0P6UeZhVY5cLy1WzKajUT9KImCY+76hEqrcJt59/d8GPvFHjCkYgnSIn\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.2.min.js\": \"Ozhzj+SI7ywm74aOI/UajcWz+C0NjsPunEVyVIrxzYkB+jA+2tUw8x5xJCbVtK5I\"};\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      if (url in hashes) {\n",
       "        element.crossOrigin = \"anonymous\";\n",
       "        element.integrity = \"sha384-\" + hashes[url];\n",
       "      }\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.2.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\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.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"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) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"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.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.2.min.js\": \"ufR9RFnRs6lniiaFvtJziE0YeidtAgBRH6ux2oUItHw5WTvE1zuk9uzhUU/FJXDp\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.2.min.js\": \"8QM/PGWBT+IssZuRcDcjzwIh1mkOmJSoNMmyYDZbCfXJg3Ap1lEvdVgFuSAwhb/J\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.2.min.js\": \"Jm8cH3Rg0P6UeZhVY5cLy1WzKajUT9KImCY+76hEqrcJt59/d8GPvFHjCkYgnSIn\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.2.min.js\": \"Ozhzj+SI7ywm74aOI/UajcWz+C0NjsPunEVyVIrxzYkB+jA+2tUw8x5xJCbVtK5I\"};\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.0.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.0.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.0.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.0.2.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\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.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.io import show, output_notebook\n",
    "from bokeh.plotting import figure\n",
    "\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = figure(title=\"Simple Line Plot in Bokeh\", \n",
    "           x_axis_label='Number of Flips in Experiment', \n",
    "           y_axis_label='Estimated Probability', \n",
    "           plot_width=580, plot_height=380)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"9b08c14a-8594-44e3-9458-1d6723d88936\" data-root-id=\"1002\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"608ce49d-c0f2-4ca9-a2f8-22b5a1372bbe\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1013\"}],\"center\":[{\"id\":\"1016\"},{\"id\":\"1020\"}],\"left\":[{\"id\":\"1017\"}],\"plot_height\":380,\"plot_width\":580,\"renderers\":[{\"id\":\"1038\"},{\"id\":\"1099\"}],\"title\":{\"id\":\"1003\"},\"toolbar\":{\"id\":\"1028\"},\"x_range\":{\"id\":\"1005\"},\"x_scale\":{\"id\":\"1009\"},\"y_range\":{\"id\":\"1007\"},\"y_scale\":{\"id\":\"1011\"}},\"id\":\"1002\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"text\":\"Simple Line Plot in Bokeh\"},\"id\":\"1003\",\"type\":\"Title\"},{\"attributes\":{\"axis_label\":\"Estimated 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of Flips in Experiment\",\"formatter\":{\"id\":\"1041\"},\"ticker\":{\"id\":\"1014\"}},\"id\":\"1013\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1041\",\"type\":\"BasicTickFormatter\"}],\"root_ids\":[\"1002\"]},\"title\":\"Bokeh Application\",\"version\":\"2.0.2\"}};\n",
       "  var render_items = [{\"docid\":\"608ce49d-c0f2-4ca9-a2f8-22b5a1372bbe\",\"root_ids\":[\"1002\"],\"roots\":{\"1002\":\"9b08c14a-8594-44e3-9458-1d6723d88936\"}}];\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",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1002"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add a line renderer with legend and line thickness\n",
    "x = range(1, maximum_flips)\n",
    "p.line(x=x, y=probs)\n",
    "\n",
    "# Show the results\n",
    "show(p)"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}