{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \"Hello, world\"  —Stan\n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
       "      }\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",
       "    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",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };var element = document.getElementById(\"1001\");\n",
       "  if (element == null) {\n",
       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n",
       "    return false;\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.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.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    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      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"1001\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n    return false;\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.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.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": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.special\n",
    "import scipy.stats as st\n",
    "\n",
    "import cmdstanpy\n",
    "import arviz as az\n",
    "\n",
    "import bokeh_catplot\n",
    "\n",
    "import bebi103\n",
    "\n",
    "import colorcet\n",
    "\n",
    "import bokeh.io\n",
    "import bokeh.plotting\n",
    "bokeh.io.output_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When getting familiar with a new programming language, we often write a [\"Hello, world\" program](https://en.wikipedia.org/wiki/%22Hello,_World!%22_program). This is a simple, often minimal, to demonstrate some of the basic syntax of the language. Python's Hello, world program is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, world.\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello, world.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we introduce Stan, and write a Hello, world program for it.\n",
    "\n",
    "Before we do, we note that you may run Stan on your own machine if you have managed to get Stan and CmdStanPy installed. Otherwise, you can use AWS using the BE/Bi 103 b 2020 Amazon Machine Image."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basics of Stan programs\n",
    "\n",
    "This is our first introduction to [Stan](http://mc-stan.org/), a **probabilistic programming language** that we will use for much of our statistical modeling. Stan is a separate language. It has a command line interface and interfaces for R, Python, Julia, Matlab, Stata, Scala, and Mathematica.\n",
    "\n",
    "We will be using one of the two Python interfaces, [CmdStanPy](https://cmdstanpy.readthedocs.io/). [PyStan](https://pystan.readthedocs.io) is another popular interface. Remember, though, that Stan is a separate language, and any Stan program you write works across all of these interfaces.\n",
    "\n",
    "Before we dive in and write our first Stan program to draw samples out of the Normal distribution, I want to tell you a few things about Stan. Briefly, Stan works as follows when using the CmdStanPy interface.\n",
    "\n",
    "1. A user writes a model using the Stan language. This is usually stored in a `.stan` file.\n",
    "2. The model is compiled in two steps. First, Stan translates the model in the `.stan` file into [C++ code](https://en.wikipedia.org/wiki/C%2B%2B). Then, that C++ code is compiled into [machine code](https://en.wikipedia.org/wiki/Machine_code).\n",
    "3. Once the machine code is built, the user can, via the CmdStanPy interface, sample out of the distribution defined by the model and perform other calculations (such as optimization, discussed in the next tutorial) with the model.\n",
    "4. The results from the sampling are written to disk as CSV and txt files. As demonstrated below, we conveniently access these files using [ArviZ](https://arviz-devs.github.io/arviz/), so we do not directly interact with them.\n",
    "\n",
    "We will learn the Stan language structure and syntax as we go along. To start with, a Stan program consists of seven sections, called **blocks**. They are, in order\n",
    "\n",
    "- `functions`: Any user-defined functions that can be used in other blocks.\n",
    "- `data`: Any inputs from the user. Most commonly, these are measured data themselves. You can also put user-adjustable parameters in this block as well, but nothing you intend to sample.\n",
    "- `transformed data`: Any transformations that need to be done on the data.\n",
    "- `parameters`: The parameters of the model. Stan will give you samples of the variable described in this block. These are the $\\theta$ of that the posterior $g(\\theta\\mid y)$ describes.\n",
    "- `transformed parameters`: Any transformations that need to be done on the parameters.\n",
    "- `model`: Specification of the generative model. The sampler will sample the parameters $\\theta$ out of this model.\n",
    "- `generated quantities`: Any other quantities you want to generate on each iteration of the sampler.\n",
    "\n",
    "Not all blocks need to be in a Stan program, but they must be in this order. Some other important points to keep in mind as we venture into Stan:\n",
    "\n",
    "1. The [Stan documentation](https://mc-stan.org/users/documentation/) will be a very good friend of yours, both the user's guide and reference manual.\n",
    "2. The index origin of Stan is `1`, not `0` as in Python.\n",
    "3. Stan is strongly statically typed, which means that you need to declare the data type of a variable explicitly before using it.\n",
    "4. All Stan commands must end with a semicolon.\n",
    "5. Blocks of code are separated using curly braces.\n",
    "6. Stan programs are stored outside of your notebook in a `.stan` file. These are text files, which you can prepare with your favorite text editor, including the one included in JupyterLab."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Say hi, Stan\n",
    "\n",
    "With this groundwork laid, let's just go ahead and write our \"Hello, world\" Stan program to generate samples out of a Normal distribution with a specified mean and variance. Here is the code, which I have stored in the file `hello_world.stan`.\n",
    "\n",
    "```stan\n",
    "data {\n",
    "  real mu;\n",
    "  real sigma;\n",
    "}\n",
    "\n",
    "\n",
    "parameters {\n",
    "  real x;\n",
    "}\n",
    "\n",
    "\n",
    "model {\n",
    "  x ~ normal(mu, sigma);\n",
    "}\n",
    "```\n",
    "\n",
    "Note that there are three blocks in this particular Stan code, the `data` block, the `parameters` block, and the `model` block. These are three of the seven possible blocks in a Stan code, and we will explore others in the next part of the lesson when we learn more about Stan after we complete our Hello, world program. The `data` block contains anything that a user wants to input into the program. In our case, we want to input `mu`, the location parameter of the Normal distribution we want to draw from, and `sigma`, its scale parameter. We declare these in the `data` block, and we will pass them in as a Python dictionary when we do the sampling using CmdStanPy.\n",
    "\n",
    "In the `parameters` block, we have the names and types of parameters we want to obtain samples for. In this case, we want to obtain samples of a real number we will call `x`.\n",
    "\n",
    "In the `model` block, we have our statistical model. The syntax is similar to how we would write the model on paper. We specify that `x`, the parameter we want to get samples of, is Normally distributed with location parameter $\\mu$ and scale parameter $\\sigma$.\n",
    "\n",
    "Now that we have our code, we can use CmdStanPy to compile it and get `CmdStanModel`, which is a Python object that provides access to the compiled Stan executable that we can conveniently access using Python syntax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:cmdstanpy:stan to c++ (/Users/bois/Dropbox/git/bebi103_course/2020/b/content/lessons/lesson_04/hello_world.hpp)\n",
      "INFO:cmdstanpy:compiling c++\n",
      "INFO:cmdstanpy:compiled model file: /Users/bois/Dropbox/git/bebi103_course/2020/b/content/lessons/lesson_04/hello_world\n"
     ]
    }
   ],
   "source": [
    "sm = cmdstanpy.CmdStanModel(stan_file='hello_world.stan')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the Stan model, stored as the variable `sm`, we can collect samples from it using the `sm.sample()` method. Stan is expecting inputted data, though, since we specified a `data` block. We pass the expected parameters as a dictionary, where the keys are the name of the variables in the `data` block of our Stan code, and the values are the values we would like to assign those variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'mu': 0.0, 'sigma': 1.0}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can draw samples using HMC. We need to pass in the `data`. We can also pass in the number of chains; that is, the number of Markov chains to use in sampling. We can also pass in the number of sampling iterations to do. We'll do four chains, which each taking 1000 samples. Let's do it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:cmdstanpy:start chain 1\n",
      "INFO:cmdstanpy:start chain 2\n",
      "INFO:cmdstanpy:finish chain 1\n",
      "INFO:cmdstanpy:finish chain 2\n",
      "INFO:cmdstanpy:start chain 3\n",
      "INFO:cmdstanpy:start chain 4\n",
      "INFO:cmdstanpy:finish chain 3\n",
      "INFO:cmdstanpy:finish chain 4\n"
     ]
    }
   ],
   "source": [
    "samples = sm.sample(\n",
    "    data=data,\n",
    "    chains=4,\n",
    "    sampling_iters=1000,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parsing output with ArviZ\n",
    "\n",
    "At this point, Stan did its job and acquired the samples. So, it said \"hello, world.\"\n",
    "Let's take a look at the samples. They are stored as a `CmdStanMCMC` instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CmdStanMCMC: model=hello_world chains=4['method=sample', 'num_samples=1000', 'algorithm=hmc', 'adapt', 'engaged=1']\n",
       " csv_files:\n",
       "\t/var/folders/0y/05rr2ttn5kv_pp6nzsg15mw00000gn/T/tmp23dyrbvy/hello_world-202001241410-1-y02oklnm.csv\n",
       "\t/var/folders/0y/05rr2ttn5kv_pp6nzsg15mw00000gn/T/tmp23dyrbvy/hello_world-202001241410-2-yt1fxv44.csv\n",
       "\t/var/folders/0y/05rr2ttn5kv_pp6nzsg15mw00000gn/T/tmp23dyrbvy/hello_world-202001241410-3-o3xzgsj8.csv\n",
       "\t/var/folders/0y/05rr2ttn5kv_pp6nzsg15mw00000gn/T/tmp23dyrbvy/hello_world-202001241410-4-i0bkf1m9.csv\n",
       " console_files\n",
       "\t/var/folders/0y/05rr2ttn5kv_pp6nzsg15mw00000gn/T/tmp23dyrbvy/hello_world-202001241410-1-y02oklnm.txt\n",
       "\t/var/folders/0y/05rr2ttn5kv_pp6nzsg15mw00000gn/T/tmp23dyrbvy/hello_world-202001241410-2-yt1fxv44.txt\n",
       "\t/var/folders/0y/05rr2ttn5kv_pp6nzsg15mw00000gn/T/tmp23dyrbvy/hello_world-202001241410-3-o3xzgsj8.txt\n",
       "\t/var/folders/0y/05rr2ttn5kv_pp6nzsg15mw00000gn/T/tmp23dyrbvy/hello_world-202001241410-4-i0bkf1m9.txt"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This object that was returned by CmdStanPy points to CSV and text files Stan generated while running. We can load them into a more convenient format using [ArviZ](https://arviz-devs.github.io/arviz/) (pronounced like \"RVs\", the abbreviation for \"recreational vehicles\" or \"random variables\"). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> sample_stats"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples = az.from_cmdstanpy(samples)\n",
    "\n",
    "# Take a look\n",
    "samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We used ArviZ to convert the data type to an ArviZ `InferenceData` data type. This has two groups, `posterior`, which contains the samples, and `sample_stats` which gives information about the sampling. We'll start by looking at the samples themselves. Since the samples were taken using the `model` block, they are assumed to be samples out of a posterior distribution, and are therefore present in the `samples.posterior` group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (chain: 4, draw: 1000)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 0 1 2 3\n",
       "  * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 992 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    x        (chain, draw) float64 -1.28 -1.28 -1.637 ... -0.5305 -0.9143\n",
       "Attributes:\n",
       "    created_at:                 2020-01-24T22:10:54.925322\n",
       "    inference_library:          cmdstanpy\n",
       "    inference_library_version:  0.8.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples.posterior"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a new, interesting data type. This is an xarray Dataset. The [xarray package](http://xarray.pydata.org/) is a very powerful package for data analysis. The two main data type we will use are xarray `DataArray`s and xarray `Dataset`s. You can think of a `DataArray` like a Pandas data frame, except that the data need not be structured in a two-dimensional table like a data frame is. A `Dataset` is a collection of `DataArray`s and associated attributes. Interestingly, if multiple `DataArray`s in a `Dataset` have the same indexes, you can index multiple arrays at the same time.\n",
    "\n",
    "Essentially, you can think of xarray structures as Pandas data frames that can be arbitrarily multidimensional.\n",
    "\n",
    "If we want to access the samples of `x`, we do so like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'x' (chain: 4, draw: 1000)>\n",
       "array([[-1.27992 , -1.27992 , -1.63714 , ...,  0.44351 , -0.20202 ,  0.213056],\n",
       "       [-0.800211, -1.28095 , -0.529018, ...,  0.08819 , -0.038983, -0.017019],\n",
       "       [-0.483601, -1.60441 , -1.03922 , ..., -0.337338,  1.43953 ,  1.58611 ],\n",
       "       [-0.114515, -0.081809, -0.697097, ..., -0.404361, -0.530511, -0.914253]])\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 0 1 2 3\n",
       "  * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 992 993 994 995 996 997 998 999"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples.posterior['x']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that this is a two dimensional array, with the first index (the rows) being the chain and the second index (the columns) being the draw, of which there are 1000 for each chain. We can put all of our draws together by converting the `DataArray` to a Numpy array using the `.values` attribute and then raveling the Numpy array, and then plot an ECDF. The ECDF should look like a Normal distribution with location parameter zero and scale parameter one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"ab6c109b-7139-462a-9514-4aa8196fdf40\" 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 = {\"e30c9fef-5cbf-48bf-8631-d1177668d1c1\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1011\",\"type\":\"LinearAxis\"}],\"center\":[{\"id\":\"1015\",\"type\":\"Grid\"},{\"id\":\"1020\",\"type\":\"Grid\"},{\"id\":\"1047\",\"type\":\"Legend\"}],\"left\":[{\"id\":\"1016\",\"type\":\"LinearAxis\"}],\"plot_height\":300,\"plot_width\":400,\"renderers\":[{\"id\":\"1038\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"1041\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"1027\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"1003\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"1007\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"1005\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"1009\",\"type\":\"LinearScale\"}},\"id\":\"1002\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"source\":{\"id\":\"1034\",\"type\":\"ColumnDataSource\"}},\"id\":\"1039\",\"type\":\"CDSView\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"__ECDF\"}},\"id\":\"1037\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"1024\",\"type\":\"SaveTool\"},{\"attributes\":{\"callback\":null},\"id\":\"1003\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1022\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"1021\",\"type\":\"PanTool\"},{\"attributes\":{\"fill_color\":{\"value\":\"#1f77b3\"},\"line_color\":{\"value\":\"#1f77b3\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"__ECDF\"}},\"id\":\"1036\",\"type\":\"Circle\"},{\"attributes\":{\"text\":\"\"},\"id\":\"1041\",\"type\":\"Title\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"1017\",\"type\":\"BasicTicker\"}},\"id\":\"1020\",\"type\":\"Grid\"},{\"attributes\":{\"data_source\":{\"id\":\"1034\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1036\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1037\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"1039\",\"type\":\"CDSView\"}},\"id\":\"1038\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"data\":{\"__ECDF\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[4000]},\"__dummy_cat\":[\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \"],\"__label\":[\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \",\" \"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999,1000,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050,1051,1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115,1116,1117,1118,1119,1120,1121,1122,1123,1124,1125,1126,1127,1128,1129,1130,1131,1132,1133,1134,1135,1136,1137,1138,1139,1140,1141,1142,1143,1144,1145,1146,1147,1148,1149,1150,1151,1152,1153,1154,1155,1156,1157,1158,1159,1160,1161,1162,1163,1164,1165,1166,1167,1168,1169,1170,1171,1172,1173,1174,1175,1176,1177,1178,1179,1180,1181,1182,1183,1184,1185,1186,1187,1188,1189,1190,1191,1192,1193,1194,1195,1196,1197,1198,1199,1200,1201,1202,1203,1204,1205,1206,1207,1208,1209,1210,1211,1212,1213,1214,1215,1216,1217,1218,1219,1220,1221,1222,1223,1224,1225,1226,1227,1228,1229,1230,1231,1232,1233,1234,1235,1236,1237,1238,1239,1240,1241,1242,1243,1244,1245,1246,1247,1248,1249,1250,1251,1252,1253,1254,1255,1256,1257,1258,1259,1260,1261,1262,1263,1264,1265,1266,1267,1268,1269,1270,1271,1272,1273,1274,1275,1276,1277,1278,1279,1280,1281,1282,1283,1284,1285,1286,1287,1288,1289,1290,1291,1292,1293,1294,1295,1296,1297,1298,1299,1300,1301,1302,1303,1304,1305,1306,1307,1308,1309,1310,1311,1312,1313,1314,1315,1316,1317,1318,1319,1320,1321,1322,1323,1324,1325,1326,1327,1328,1329,1330,1331,1332,1333,1334,1335,1336,1337,1338,1339,1340,1341,1342,1343,1344,1345,1346,1347,1348,1349,1350,1351,1352,1353,1354,1355,1356,1357,1358,1359,1360,1361,1362,1363,1364,1365,1366,1367,1368,1369,1370,1371,1372,1373,1374,1375,1376,1377,1378,1379,1380,1381,1382,1383,1384,1385,1386,1387,1388,1389,1390,1391,1392,1393,1394,1395,1396,1397,1398,1399,1400,1401,1402,1403,1404,1405,1406,1407,1408,1409,1410,1411,1412,1413,1414,1415,1416,1417,1418,1419,1420,1421,1422,1423,1424,1425,1426,1427,1428,1429,1430,1431,1432,1433,1434,1435,1436,1437,1438,1439,1440,1441,1442,1443,1444,1445,1446,1447,1448,1449,1450,1451,1452,1453,1454,1455,1456,1457,1458,1459,1460,1461,1462,1463,1464,1465,1466,1467,1468,1469,1470,1471,1472,1473,1474,1475,1476,1477,1478,1479,1480,1481,1482,1483,1484,1485,1486,1487,1488,1489,1490,1491,1492,1493,1494,1495,1496,1497,1498,1499,1500,1501,1502,1503,1504,1505,1506,1507,1508,1509,1510,1511,1512,1513,1514,1515,1516,1517,1518,1519,1520,1521,1522,1523,1524,1525,1526,1527,1528,1529,1530,1531,1532,1533,1534,1535,1536,1537,1538,1539,1540,1541,1542,1543,1544,1545,1546,1547,1548,1549,1550,1551,1552,1553,1554,1555,1556,1557,1558,1559,1560,1561,1562,1563,1564,1565,1566,1567,1568,1569,1570,1571,1572,1573,1574,1575,1576,1577,1578,1579,1580,1581,1582,1583,1584,1585,1586,1587,1588,1589,1590,1591,1592,1593,1594,1595,1596,1597,1598,1599,1600,1601,1602,1603,1604,1605,1606,1607,1608,1609,1610,1611,1612,1613,1614,1615,1616,1617,1618,1619,1620,1621,1622,1623,1624,1625,1626,1627,1628,1629,1630,1631,1632,1633,1634,1635,1636,1637,1638,1639,1640,1641,1642,1643,1644,1645,1646,1647,1648,1649,1650,1651,1652,1653,1654,1655,1656,1657,1658,1659,1660,1661,1662,1663,1664,1665,1666,1667,1668,1669,1670,1671,1672,1673,1674,1675,1676,1677,1678,1679,1680,1681,1682,1683,1684,1685,1686,1687,1688,1689,1690,1691,1692,1693,1694,1695,1696,1697,1698,1699,1700,1701,1702,1703,1704,1705,1706,1707,1708,1709,1710,1711,1712,1713,1714,1715,1716,1717,1718,1719,1720,1721,1722,1723,1724,1725,1726,1727,1728,1729,1730,1731,1732,1733,1734,1735,1736,1737,1738,1739,1740,1741,1742,1743,1744,1745,1746,1747,1748,1749,1750,1751,1752,1753,1754,1755,1756,1757,1758,1759,1760,1761,1762,1763,1764,1765,1766,1767,1768,1769,1770,1771,1772,1773,1774,1775,1776,1777,1778,1779,1780,1781,1782,1783,1784,1785,1786,1787,1788,1789,1790,1791,1792,1793,1794,1795,1796,1797,1798,1799,1800,1801,1802,1803,1804,1805,1806,1807,1808,1809,1810,1811,1812,1813,1814,1815,1816,1817,1818,1819,1820,1821,1822,1823,1824,1825,1826,1827,1828,1829,1830,1831,1832,1833,1834,1835,1836,1837,1838,1839,1840,1841,1842,1843,1844,1845,1846,1847,1848,1849,1850,1851,1852,1853,1854,1855,1856,1857,1858,1859,1860,1861,1862,1863,1864,1865,1866,1867,1868,1869,1870,1871,1872,1873,1874,1875,1876,1877,1878,1879,1880,1881,1882,1883,1884,1885,1886,1887,1888,1889,1890,1891,1892,1893,1894,1895,1896,1897,1898,1899,1900,1901,1902,1903,1904,1905,1906,1907,1908,1909,1910,1911,1912,1913,1914,1915,1916,1917,1918,1919,1920,1921,1922,1923,1924,1925,1926,1927,1928,1929,1930,1931,1932,1933,1934,1935,1936,1937,1938,1939,1940,1941,1942,1943,1944,1945,1946,1947,1948,1949,1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022,2023,2024,2025,2026,2027,2028,2029,2030,2031,2032,2033,2034,2035,2036,2037,2038,2039,2040,2041,2042,2043,2044,2045,2046,2047,2048,2049,2050,2051,2052,2053,2054,2055,2056,2057,2058,2059,2060,2061,2062,2063,2064,2065,2066,2067,2068,2069,2070,2071,2072,2073,2074,2075,2076,2077,2078,2079,2080,2081,2082,2083,2084,2085,2086,2087,2088,2089,2090,2091,2092,2093,2094,2095,2096,2097,2098,2099,2100,2101,2102,2103,2104,2105,2106,2107,2108,2109,2110,2111,2112,2113,2114,2115,2116,2117,2118,2119,2120,2121,2122,2123,2124,2125,2126,2127,2128,2129,2130,2131,2132,2133,2134,2135,2136,2137,2138,2139,2140,2141,2142,2143,2144,2145,2146,2147,2148,2149,2150,2151,2152,2153,2154,2155,2156,2157,2158,2159,2160,2161,2162,2163,2164,2165,2166,2167,2168,2169,2170,2171,2172,2173,2174,2175,2176,2177,2178,2179,2180,2181,2182,2183,2184,2185,2186,2187,2188,2189,2190,2191,2192,2193,2194,2195,2196,2197,2198,2199,2200,2201,2202,2203,2204,2205,2206,2207,2208,2209,2210,2211,2212,2213,2214,2215,2216,2217,2218,2219,2220,2221,2222,2223,2224,2225,2226,2227,2228,2229,2230,2231,2232,2233,2234,2235,2236,2237,2238,2239,2240,2241,2242,2243,2244,2245,2246,2247,2248,2249,2250,2251,2252,2253,2254,2255,2256,2257,2258,2259,2260,2261,2262,2263,2264,2265,2266,2267,2268,2269,2270,2271,2272,2273,2274,2275,2276,2277,2278,2279,2280,2281,2282,2283,2284,2285,2286,2287,2288,2289,2290,2291,2292,2293,2294,2295,2296,2297,2298,2299,2300,2301,2302,2303,2304,2305,2306,2307,2308,2309,2310,2311,2312,2313,2314,2315,2316,2317,2318,2319,2320,2321,2322,2323,2324,2325,2326,2327,2328,2329,2330,2331,2332,2333,2334,2335,2336,2337,2338,2339,2340,2341,2342,2343,2344,2345,2346,2347,2348,2349,2350,2351,2352,2353,2354,2355,2356,2357,2358,2359,2360,2361,2362,2363,2364,2365,2366,2367,2368,2369,2370,2371,2372,2373,2374,2375,2376,2377,2378,2379,2380,2381,2382,2383,2384,2385,2386,2387,2388,2389,2390,2391,2392,2393,2394,2395,2396,2397,2398,2399,2400,2401,2402,2403,2404,2405,2406,2407,2408,2409,2410,2411,2412,2413,2414,2415,2416,2417,2418,2419,2420,2421,2422,2423,2424,2425,2426,2427,2428,2429,2430,2431,2432,2433,2434,2435,2436,2437,2438,2439,2440,2441,2442,2443,2444,2445,2446,2447,2448,2449,2450,2451,2452,2453,2454,2455,2456,2457,2458,2459,2460,2461,2462,2463,2464,2465,2466,2467,2468,2469,2470,2471,2472,2473,2474,2475,2476,2477,2478,2479,2480,2481,2482,2483,2484,2485,2486,2487,2488,2489,2490,2491,2492,2493,2494,2495,2496,2497,2498,2499,2500,2501,2502,2503,2504,2505,2506,2507,2508,2509,2510,2511,2512,2513,2514,2515,2516,2517,2518,2519,2520,2521,2522,2523,2524,2525,2526,2527,2528,2529,2530,2531,2532,2533,2534,2535,2536,2537,2538,2539,2540,2541,2542,2543,2544,2545,2546,2547,2548,2549,2550,2551,2552,2553,2554,2555,2556,2557,2558,2559,2560,2561,2562,2563,2564,2565,2566,2567,2568,2569,2570,2571,2572,2573,2574,2575,2576,2577,2578,2579,2580,2581,2582,2583,2584,2585,2586,2587,2588,2589,2590,2591,2592,2593,2594,2595,2596,2597,2598,2599,2600,2601,2602,2603,2604,2605,2606,2607,2608,2609,2610,2611,2612,2613,2614,2615,2616,2617,2618,2619,2620,2621,2622,2623,2624,2625,2626,2627,2628,2629,2630,2631,2632,2633,2634,2635,2636,2637,2638,2639,2640,2641,2642,2643,2644,2645,2646,2647,2648,2649,2650,2651,2652,2653,2654,2655,2656,2657,2658,2659,2660,2661,2662,2663,2664,2665,2666,2667,2668,2669,2670,2671,2672,2673,2674,2675,2676,2677,2678,2679,2680,2681,2682,2683,2684,2685,2686,2687,2688,2689,2690,2691,2692,2693,2694,2695,2696,2697,2698,2699,2700,2701,2702,2703,2704,2705,2706,2707,2708,2709,2710,2711,2712,2713,2714,2715,2716,2717,2718,2719,2720,2721,2722,2723,2724,2725,2726,2727,2728,2729,2730,2731,2732,2733,2734,2735,2736,2737,2738,2739,2740,2741,2742,2743,2744,2745,2746,2747,2748,2749,2750,2751,2752,2753,2754,2755,2756,2757,2758,2759,2760,2761,2762,2763,2764,2765,2766,2767,2768,2769,2770,2771,2772,2773,2774,2775,2776,2777,2778,2779,2780,2781,2782,2783,2784,2785,2786,2787,2788,2789,2790,2791,2792,2793,2794,2795,2796,2797,2798,2799,2800,2801,2802,2803,2804,2805,2806,2807,2808,2809,2810,2811,2812,2813,2814,2815,2816,2817,2818,2819,2820,2821,2822,2823,2824,2825,2826,2827,2828,2829,2830,2831,2832,2833,2834,2835,2836,2837,2838,2839,2840,2841,2842,2843,2844,2845,2846,2847,2848,2849,2850,2851,2852,2853,2854,2855,2856,2857,2858,2859,2860,2861,2862,2863,2864,2865,2866,2867,2868,2869,2870,2871,2872,2873,2874,2875,2876,2877,2878,2879,2880,2881,2882,2883,2884,2885,2886,2887,2888,2889,2890,2891,2892,2893,2894,2895,2896,2897,2898,2899,2900,2901,2902,2903,2904,2905,2906,2907,2908,2909,2910,2911,2912,2913,2914,2915,2916,2917,2918,2919,2920,2921,2922,2923,2924,2925,2926,2927,2928,2929,2930,2931,2932,2933,2934,2935,2936,2937,2938,2939,2940,2941,2942,2943,2944,2945,2946,2947,2948,2949,2950,2951,2952,2953,2954,2955,2956,2957,2958,2959,2960,2961,2962,2963,2964,2965,2966,2967,2968,2969,2970,2971,2972,2973,2974,2975,2976,2977,2978,2979,2980,2981,2982,2983,2984,2985,2986,2987,2988,2989,2990,2991,2992,2993,2994,2995,2996,2997,2998,2999,3000,3001,3002,3003,3004,3005,3006,3007,3008,3009,3010,3011,3012,3013,3014,3015,3016,3017,3018,3019,3020,3021,3022,3023,3024,3025,3026,3027,3028,3029,3030,3031,3032,3033,3034,3035,3036,3037,3038,3039,3040,3041,3042,3043,3044,3045,3046,3047,3048,3049,3050,3051,3052,3053,3054,3055,3056,3057,3058,3059,3060,3061,3062,3063,3064,3065,3066,3067,3068,3069,3070,3071,3072,3073,3074,3075,3076,3077,3078,3079,3080,3081,3082,3083,3084,3085,3086,3087,3088,3089,3090,3091,3092,3093,3094,3095,3096,3097,3098,3099,3100,3101,3102,3103,3104,3105,3106,3107,3108,3109,3110,3111,3112,3113,3114,3115,3116,3117,3118,3119,3120,3121,3122,3123,3124,3125,3126,3127,3128,3129,3130,3131,3132,3133,3134,3135,3136,3137,3138,3139,3140,3141,3142,3143,3144,3145,3146,3147,3148,3149,3150,3151,3152,3153,3154,3155,3156,3157,3158,3159,3160,3161,3162,3163,3164,3165,3166,3167,3168,3169,3170,3171,3172,3173,3174,3175,3176,3177,3178,3179,3180,3181,3182,3183,3184,3185,3186,3187,3188,3189,3190,3191,3192,3193,3194,3195,3196,3197,3198,3199,3200,3201,3202,3203,3204,3205,3206,3207,3208,3209,3210,3211,3212,3213,3214,3215,3216,3217,3218,3219,3220,3221,3222,3223,3224,3225,3226,3227,3228,3229,3230,3231,3232,3233,3234,3235,3236,3237,3238,3239,3240,3241,3242,3243,3244,3245,3246,3247,3248,3249,3250,3251,3252,3253,3254,3255,3256,3257,3258,3259,3260,3261,3262,3263,3264,3265,3266,3267,3268,3269,3270,3271,3272,3273,3274,3275,3276,3277,3278,3279,3280,3281,3282,3283,3284,3285,3286,3287,3288,3289,3290,3291,3292,3293,3294,3295,3296,3297,3298,3299,3300,3301,3302,3303,3304,3305,3306,3307,3308,3309,3310,3311,3312,3313,3314,3315,3316,3317,3318,3319,3320,3321,3322,3323,3324,3325,3326,3327,3328,3329,3330,3331,3332,3333,3334,3335,3336,3337,3338,3339,3340,3341,3342,3343,3344,3345,3346,3347,3348,3349,3350,3351,3352,3353,3354,3355,3356,3357,3358,3359,3360,3361,3362,3363,3364,3365,3366,3367,3368,3369,3370,3371,3372,3373,3374,3375,3376,3377,3378,3379,3380,3381,3382,3383,3384,3385,3386,3387,3388,3389,3390,3391,3392,3393,3394,3395,3396,3397,3398,3399,3400,3401,3402,3403,3404,3405,3406,3407,3408,3409,3410,3411,3412,3413,3414,3415,3416,3417,3418,3419,3420,3421,3422,3423,3424,3425,3426,3427,3428,3429,3430,3431,3432,3433,3434,3435,3436,3437,3438,3439,3440,3441,3442,3443,3444,3445,3446,3447,3448,3449,3450,3451,3452,3453,3454,3455,3456,3457,3458,3459,3460,3461,3462,3463,3464,3465,3466,3467,3468,3469,3470,3471,3472,3473,3474,3475,3476,3477,3478,3479,3480,3481,3482,3483,3484,3485,3486,3487,3488,3489,3490,3491,3492,3493,3494,3495,3496,3497,3498,3499,3500,3501,3502,3503,3504,3505,3506,3507,3508,3509,3510,3511,3512,3513,3514,3515,3516,3517,3518,3519,3520,3521,3522,3523,3524,3525,3526,3527,3528,3529,3530,3531,3532,3533,3534,3535,3536,3537,3538,3539,3540,3541,3542,3543,3544,3545,3546,3547,3548,3549,3550,3551,3552,3553,3554,3555,3556,3557,3558,3559,3560,3561,3562,3563,3564,3565,3566,3567,3568,3569,3570,3571,3572,3573,3574,3575,3576,3577,3578,3579,3580,3581,3582,3583,3584,3585,3586,3587,3588,3589,3590,3591,3592,3593,3594,3595,3596,3597,3598,3599,3600,3601,3602,3603,3604,3605,3606,3607,3608,3609,3610,3611,3612,3613,3614,3615,3616,3617,3618,3619,3620,3621,3622,3623,3624,3625,3626,3627,3628,3629,3630,3631,3632,3633,3634,3635,3636,3637,3638,3639,3640,3641,3642,3643,3644,3645,3646,3647,3648,3649,3650,3651,3652,3653,3654,3655,3656,3657,3658,3659,3660,3661,3662,3663,3664,3665,3666,3667,3668,3669,3670,3671,3672,3673,3674,3675,3676,3677,3678,3679,3680,3681,3682,3683,3684,3685,3686,3687,3688,3689,3690,3691,3692,3693,3694,3695,3696,3697,3698,3699,3700,3701,3702,3703,3704,3705,3706,3707,3708,3709,3710,3711,3712,3713,3714,3715,3716,3717,3718,3719,3720,3721,3722,3723,3724,3725,3726,3727,3728,3729,3730,3731,3732,3733,3734,3735,3736,3737,3738,3739,3740,3741,3742,3743,3744,3745,3746,3747,3748,3749,3750,3751,3752,3753,3754,3755,3756,3757,3758,3759,3760,3761,3762,3763,3764,3765,3766,3767,3768,3769,3770,3771,3772,3773,3774,3775,3776,3777,3778,3779,3780,3781,3782,3783,3784,3785,3786,3787,3788,3789,3790,3791,3792,3793,3794,3795,3796,3797,3798,3799,3800,3801,3802,3803,3804,3805,3806,3807,3808,3809,3810,3811,3812,3813,3814,3815,3816,3817,3818,3819,3820,3821,3822,3823,3824,3825,3826,3827,3828,3829,3830,3831,3832,3833,3834,3835,3836,3837,3838,3839,3840,3841,3842,3843,3844,3845,3846,3847,3848,3849,3850,3851,3852,3853,3854,3855,3856,3857,3858,3859,3860,3861,3862,3863,3864,3865,3866,3867,3868,3869,3870,3871,3872,3873,3874,3875,3876,3877,3878,3879,3880,3881,3882,3883,3884,3885,3886,3887,3888,3889,3890,3891,3892,3893,3894,3895,3896,3897,3898,3899,3900,3901,3902,3903,3904,3905,3906,3907,3908,3909,3910,3911,3912,3913,3914,3915,3916,3917,3918,3919,3920,3921,3922,3923,3924,3925,3926,3927,3928,3929,3930,3931,3932,3933,3934,3935,3936,3937,3938,3939,3940,3941,3942,3943,3944,3945,3946,3947,3948,3949,3950,3951,3952,3953,3954,3955,3956,3957,3958,3959,3960,3961,3962,3963,3964,3965,3966,3967,3968,3969,3970,3971,3972,3973,3974,3975,3976,3977,3978,3979,3980,3981,3982,3983,3984,3985,3986,3987,3988,3989,3990,3991,3992,3993,3994,3995,3996,3997,3998,3999],\"x\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[4000]}},\"selected\":{\"id\":\"1056\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1057\",\"type\":\"UnionRenderers\"}},\"id\":\"1034\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"items\":[{\"id\":\"1048\",\"type\":\"LegendItem\"}],\"visible\":false},\"id\":\"1047\",\"type\":\"Legend\"},{\"attributes\":{\"overlay\":{\"id\":\"1046\",\"type\":\"BoxAnnotation\"}},\"id\":\"1023\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"1044\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1056\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1026\",\"type\":\"HelpTool\"},{\"attributes\":{\"label\":{\"value\":\" \"},\"renderers\":[{\"id\":\"1038\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1048\",\"type\":\"LegendItem\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1021\",\"type\":\"PanTool\"},{\"id\":\"1022\",\"type\":\"WheelZoomTool\"},{\"id\":\"1023\",\"type\":\"BoxZoomTool\"},{\"id\":\"1024\",\"type\":\"SaveTool\"},{\"id\":\"1025\",\"type\":\"ResetTool\"},{\"id\":\"1026\",\"type\":\"HelpTool\"}]},\"id\":\"1027\",\"type\":\"Toolbar\"},{\"attributes\":{\"axis_label\":\"x\",\"formatter\":{\"id\":\"1044\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1012\",\"type\":\"BasicTicker\"}},\"id\":\"1011\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1025\",\"type\":\"ResetTool\"},{\"attributes\":{\"axis_label\":\"ECDF\",\"formatter\":{\"id\":\"1042\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1017\",\"type\":\"BasicTicker\"}},\"id\":\"1016\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1017\",\"type\":\"BasicTicker\"},{\"attributes\":{\"ticker\":{\"id\":\"1012\",\"type\":\"BasicTicker\"}},\"id\":\"1015\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1007\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1012\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1009\",\"type\":\"LinearScale\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1046\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"1057\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"callback\":null},\"id\":\"1005\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1042\",\"type\":\"BasicTickFormatter\"}],\"root_ids\":[\"1002\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"e30c9fef-5cbf-48bf-8631-d1177668d1c1\",\"roots\":{\"1002\":\"ab6c109b-7139-462a-9514-4aa8196fdf40\"}}];\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": [
    "bokeh.io.show(\n",
    "    bokeh_catplot.ecdf(\n",
    "        samples.posterior['x'].values.ravel()\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed it does! We have just verified that Stan properly said, \"Hello, world.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Direct sampling\n",
    "\n",
    "Stan can also draw samples out of probability distributions without using MCMC, just as Numpy and Scipy can. For a generic posterior, we must use MCMC, but for many named distributions we can directly sample.\n",
    "\n",
    "Let's draw 300 random numbers from a Normal distribution with location parameter zero and scale parameter one using Numpy and Scipy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"49543e16-8e47-4d54-9377-d7329645ede6\" data-root-id=\"1112\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"0dfb6395-97df-40ee-9690-b36c1119c532\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1121\",\"type\":\"LinearAxis\"}],\"center\":[{\"id\":\"1125\",\"type\":\"Grid\"},{\"id\":\"1130\",\"type\":\"Grid\"},{\"id\":\"1156\",\"type\":\"Legend\"}],\"left\":[{\"id\":\"1126\",\"type\":\"LinearAxis\"}],\"plot_height\":300,\"plot_width\":400,\"renderers\":[{\"id\":\"1147\",\"type\":\"GlyphRenderer\"},{\"id\":\"1161\",\"type\":\"GlyphRenderer\"},{\"id\":\"1175\",\"type\":\"GlyphRenderer\"},{\"id\":\"1191\",\"type\":\"GlyphRenderer\"},{\"id\":\"1209\",\"type\":\"GlyphRenderer\"},{\"id\":\"1229\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"1150\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"1137\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"1113\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"1117\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"1115\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"1119\",\"type\":\"LinearScale\"}},\"id\":\"1112\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{},\"id\":\"1119\",\"type\":\"LinearScale\"},{\"attributes\":{\"data_source\":{\"id\":\"1158\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1159\",\"type\":\"Ray\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1160\",\"type\":\"Ray\"},\"selection_glyph\":null,\"view\":{\"id\":\"1162\",\"type\":\"CDSView\"}},\"id\":\"1161\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"line_color\":\"#1f77b3\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1145\",\"type\":\"Line\"},{\"attributes\":{\"callback\":null,\"data\":{},\"selected\":{\"id\":\"1204\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1205\",\"type\":\"UnionRenderers\"}},\"id\":\"1172\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"source\":{\"id\":\"1158\",\"type\":\"ColumnDataSource\"}},\"id\":\"1162\",\"type\":\"CDSView\"},{\"attributes\":{\"callback\":null,\"data\":{},\"selected\":{\"id\":\"1246\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1247\",\"type\":\"UnionRenderers\"}},\"id\":\"1206\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"angle\":{\"units\":\"rad\",\"value\":0},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"line_width\":{\"value\":2},\"x\":{\"value\":3.1851299178105883},\"y\":{\"value\":1}},\"id\":\"1228\",\"type\":\"Ray\"},{\"attributes\":{\"ticker\":{\"id\":\"1122\",\"type\":\"BasicTicker\"}},\"id\":\"1125\",\"type\":\"Grid\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1190\",\"type\":\"Line\"},{\"attributes\":{\"callback\":null,\"data\":{},\"selected\":{\"id\":\"1186\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1187\",\"type\":\"UnionRenderers\"}},\"id\":\"1158\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1246\",\"type\":\"Selection\"},{\"attributes\":{\"data_source\":{\"id\":\"1188\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1189\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1190\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"1192\",\"type\":\"CDSView\"}},\"id\":\"1191\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"data_source\":{\"id\":\"1226\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1227\",\"type\":\"Ray\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1228\",\"type\":\"Ray\"},\"selection_glyph\":null,\"view\":{\"id\":\"1230\",\"type\":\"CDSView\"}},\"id\":\"1229\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null},\"id\":\"1115\",\"type\":\"DataRange1d\"},{\"attributes\":{\"source\":{\"id\":\"1226\",\"type\":\"ColumnDataSource\"}},\"id\":\"1230\",\"type\":\"CDSView\"},{\"attributes\":{\"axis_label\":\"ECDF\",\"formatter\":{\"id\":\"1151\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1127\",\"type\":\"BasicTicker\"}},\"id\":\"1126\",\"type\":\"LinearAxis\"},{\"attributes\":{\"angle\":{\"units\":\"rad\",\"value\":3.141592653589793},\"line_color\":{\"value\":\"#1f77b3\"},\"line_width\":{\"value\":2},\"x\":{\"value\":-4.243948928296987},\"y\":{\"value\":0}},\"id\":\"1159\",\"type\":\"Ray\"},{\"attributes\":{},\"id\":\"1170\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1247\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"1127\",\"type\":\"BasicTicker\"}},\"id\":\"1130\",\"type\":\"Grid\"},{\"attributes\":{\"data_source\":{\"id\":\"1144\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1145\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1146\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"1148\",\"type\":\"CDSView\"}},\"id\":\"1147\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"source\":{\"id\":\"1188\",\"type\":\"ColumnDataSource\"}},\"id\":\"1192\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1171\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"callback\":null},\"id\":\"1113\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1204\",\"type\":\"Selection\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1131\",\"type\":\"PanTool\"},{\"id\":\"1132\",\"type\":\"WheelZoomTool\"},{\"id\":\"1133\",\"type\":\"BoxZoomTool\"},{\"id\":\"1134\",\"type\":\"SaveTool\"},{\"id\":\"1135\",\"type\":\"ResetTool\"},{\"id\":\"1136\",\"type\":\"HelpTool\"}]},\"id\":\"1137\",\"type\":\"Toolbar\"},{\"attributes\":{\"angle\":{\"units\":\"rad\",\"value\":0},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"line_width\":{\"value\":2},\"x\":{\"value\":3.0501534950818634},\"y\":{\"value\":1}},\"id\":\"1174\",\"type\":\"Ray\"},{\"attributes\":{},\"id\":\"1127\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1205\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"source\":{\"id\":\"1144\",\"type\":\"ColumnDataSource\"}},\"id\":\"1148\",\"type\":\"CDSView\"},{\"attributes\":{\"angle\":{\"units\":\"rad\",\"value\":0},\"line_color\":{\"value\":\"#1f77b3\"},\"line_width\":{\"value\":2},\"x\":{\"value\":3.0501534950818634},\"y\":{\"value\":1}},\"id\":\"1173\",\"type\":\"Ray\"},{\"attributes\":{\"text\":\"\"},\"id\":\"1150\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1131\",\"type\":\"PanTool\"},{\"attributes\":{\"angle\":{\"units\":\"rad\",\"value\":3.141592653589793},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"line_width\":{\"value\":2},\"x\":{\"value\":-3.6606670923654656},\"y\":{\"value\":0}},\"id\":\"1208\",\"type\":\"Ray\"},{\"attributes\":{\"source\":{\"id\":\"1172\",\"type\":\"ColumnDataSource\"}},\"id\":\"1176\",\"type\":\"CDSView\"},{\"attributes\":{\"items\":[{\"id\":\"1157\",\"type\":\"LegendItem\"}],\"visible\":false},\"id\":\"1156\",\"type\":\"Legend\"},{\"attributes\":{},\"id\":\"1117\",\"type\":\"LinearScale\"},{\"attributes\":{\"data_source\":{\"id\":\"1172\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1173\",\"type\":\"Ray\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1174\",\"type\":\"Ray\"},\"selection_glyph\":null,\"view\":{\"id\":\"1176\",\"type\":\"CDSView\"}},\"id\":\"1175\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1132\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"1151\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1122\",\"type\":\"BasicTicker\"},{\"attributes\":{\"angle\":{\"units\":\"rad\",\"value\":3.141592653589793},\"line_color\":{\"value\":\"#ff7e0e\"},\"line_width\":{\"value\":2},\"x\":{\"value\":-3.6606670923654656},\"y\":{\"value\":0}},\"id\":\"1207\",\"type\":\"Ray\"},{\"attributes\":{\"angle\":{\"units\":\"rad\",\"value\":0},\"line_color\":{\"value\":\"#ff7e0e\"},\"line_width\":{\"value\":2},\"x\":{\"value\":3.1851299178105883},\"y\":{\"value\":1}},\"id\":\"1227\",\"type\":\"Ray\"},{\"attributes\":{\"overlay\":{\"id\":\"1155\",\"type\":\"BoxAnnotation\"}},\"id\":\"1133\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"source\":{\"id\":\"1206\",\"type\":\"ColumnDataSource\"}},\"id\":\"1210\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1186\",\"type\":\"Selection\"},{\"attributes\":{\"data_source\":{\"id\":\"1206\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1207\",\"type\":\"Ray\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1208\",\"type\":\"Ray\"},\"selection_glyph\":null,\"view\":{\"id\":\"1210\",\"type\":\"CDSView\"}},\"id\":\"1209\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1134\",\"type\":\"SaveTool\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1146\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"1153\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[600]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[600]}},\"selected\":{\"id\":\"1224\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1225\",\"type\":\"UnionRenderers\"}},\"id\":\"1188\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"data\":{},\"selected\":{\"id\":\"1274\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1275\",\"type\":\"UnionRenderers\"}},\"id\":\"1226\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1135\",\"type\":\"ResetTool\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1155\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"1187\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"1224\",\"type\":\"Selection\"},{\"attributes\":{\"label\":{\"value\":\" \"},\"renderers\":[{\"id\":\"1147\",\"type\":\"GlyphRenderer\"},{\"id\":\"1161\",\"type\":\"GlyphRenderer\"},{\"id\":\"1175\",\"type\":\"GlyphRenderer\"},{\"id\":\"1191\",\"type\":\"GlyphRenderer\"},{\"id\":\"1209\",\"type\":\"GlyphRenderer\"},{\"id\":\"1229\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1157\",\"type\":\"LegendItem\"},{\"attributes\":{\"line_color\":\"#ff7e0e\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1189\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"1274\",\"type\":\"Selection\"},{\"attributes\":{\"axis_label\":\"x\",\"formatter\":{\"id\":\"1153\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1122\",\"type\":\"BasicTicker\"}},\"id\":\"1121\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1136\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"1225\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"1275\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[600]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[600]}},\"selected\":{\"id\":\"1170\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1171\",\"type\":\"UnionRenderers\"}},\"id\":\"1144\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"angle\":{\"units\":\"rad\",\"value\":3.141592653589793},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"line_width\":{\"value\":2},\"x\":{\"value\":-4.243948928296987},\"y\":{\"value\":0}},\"id\":\"1160\",\"type\":\"Ray\"}],\"root_ids\":[\"1112\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"0dfb6395-97df-40ee-9690-b36c1119c532\",\"roots\":{\"1112\":\"49543e16-8e47-4d54-9377-d7329645ede6\"}}];\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": "1112"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rg = np.random.default_rng()\n",
    "np_samples = rg.normal(0, 1, size=300)\n",
    "\n",
    "sp_samples = st.norm.rvs(0, 1, size=300)\n",
    "\n",
    "# Plot samples\n",
    "p = bokeh_catplot.ecdf(\n",
    "    np_samples,\n",
    "    style='staircase',\n",
    "    palette=[colorcet.b_glasbey_category10[0]],\n",
    ")\n",
    "\n",
    "p = bokeh_catplot.ecdf(\n",
    "    sp_samples,\n",
    "    style='staircase',\n",
    "    palette=[colorcet.b_glasbey_category10[1]],\n",
    "    p=p,\n",
    ")\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To generate random draws from a Normal distribution without using Markov chain Monte Carlo, we use the following Stan code.\n",
    "\n",
    "```stan\n",
    "data {\n",
    "  real mu;\n",
    "  real sigma;\n",
    "}\n",
    "\n",
    "\n",
    "generated quantities {\n",
    "  real x;\n",
    "\n",
    "  x = normal_rng(mu, sigma);\n",
    "}\n",
    "```\n",
    "\n",
    "Let's compile it, and then comment on the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:cmdstanpy:stan to c++ (/Users/bois/Dropbox/git/bebi103_course/2020/b/content/lessons/lesson_04/norm_rng.hpp)\n",
      "INFO:cmdstanpy:compiling c++\n",
      "INFO:cmdstanpy:compiled model file: /Users/bois/Dropbox/git/bebi103_course/2020/b/content/lessons/lesson_04/norm_rng\n"
     ]
    }
   ],
   "source": [
    "sm_rng = cmdstanpy.CmdStanModel(stan_file='norm_rng.stan')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two blocks in this particular Stan code, the `data` block we have seen before and the `generated quantities` block. In the `generated quantities` block, we have code for that tells Stan what to generate for each set of parameters it encountered while doing Markov chain Mote Carlo. Here, we are not performing Markov chain Monte Carlo, so we do the \"sampling\" in **fixed parameter mode** when we call `sm_rng.sample()` by setting the `fixed_param` kwarg to `True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:cmdstanpy:start chain 1\n",
      "INFO:cmdstanpy:finish chain 1\n"
     ]
    }
   ],
   "source": [
    "# Draw samples\n",
    "stan_samples = sm_rng.sample(\n",
    "    data=data,\n",
    "    chains=1,\n",
    "    sampling_iters=300,\n",
    "    fixed_param=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To convert this sampling object to a Numpy array, we can first convert it to an ArviZ `InferenceData` instance and then extract the Numpy array. Note that we will define the samples as coming from a \"posterior,\" even though it is not a posterior, since that's the default for ArviZ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to ArviZ InferenceData\n",
    "stan_samples = az.from_cmdstanpy(\n",
    "    posterior=stan_samples\n",
    ")\n",
    "\n",
    "# Extract Numpy array\n",
    "stan_samples = stan_samples.posterior['x'].values.flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can add the ECDF of these samples to the plot of Numpy and Scipy samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"0521d6f3-9118-4025-b152-a36308c032e0\" data-root-id=\"1112\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"be89cb8c-8fcb-4258-84e2-758ff1961652\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1121\",\"type\":\"LinearAxis\"}],\"center\":[{\"id\":\"1125\",\"type\":\"Grid\"},{\"id\":\"1130\",\"type\":\"Grid\"},{\"id\":\"1156\",\"type\":\"Legend\"}],\"left\":[{\"id\":\"1126\",\"type\":\"LinearAxis\"}],\"plot_height\":300,\"plot_width\":400,\"renderers\":[{\"id\":\"1147\",\"type\":\"GlyphRenderer\"},{\"id\":\"1161\",\"type\":\"GlyphRenderer\"},{\"id\":\"1175\",\"type\":\"GlyphRenderer\"},{\"id\":\"1191\",\"type\":\"GlyphRenderer\"},{\"id\":\"1209\",\"type\":\"GlyphRenderer\"},{\"id\":\"1229\",\"type\":\"GlyphRenderer\"},{\"id\":\"1393\",\"type\":\"GlyphRenderer\"},{\"id\":\"1477\",\"type\":\"GlyphRenderer\"},{\"id\":\"1567\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"1150\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"1137\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"1113\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"1117\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"1115\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"1119\",\"type\":\"LinearScale\"}},\"id\":\"1112\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"angle\":{\"units\":\"rad\",\"value\":0},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"line_width\":{\"value\":2},\"x\":{\"value\":3.1851299178105883},\"y\":{\"value\":1}},\"id\":\"1228\",\"type\":\"Ray\"},{\"attributes\":{\"angle\":{\"units\":\"rad\",\"value\":0},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"line_width\":{\"value\":2},\"x\":{\"value\":2.45297},\"y\":{\"value\":1}},\"id\":\"1566\",\"type\":\"Ray\"},{\"attributes\":{},\"id\":\"1246\",\"type\":\"Selection\"},{\"attributes\":{\"data_source\":{\"id\":\"1226\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1227\",\"type\":\"Ray\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1228\",\"type\":\"Ray\"},\"selection_glyph\":null,\"view\":{\"id\":\"1230\",\"type\":\"CDSView\"}},\"id\":\"1229\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null},\"id\":\"1115\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1247\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"callback\":null,\"data\":{},\"selected\":{\"id\":\"1624\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1625\",\"type\":\"UnionRenderers\"}},\"id\":\"1564\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1442\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1117\",\"type\":\"LinearScale\"},{\"attributes\":{\"data_source\":{\"id\":\"1474\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1475\",\"type\":\"Ray\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1476\",\"type\":\"Ray\"},\"selection_glyph\":null,\"view\":{\"id\":\"1478\",\"type\":\"CDSView\"}},\"id\":\"1477\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"source\":{\"id\":\"1474\",\"type\":\"ColumnDataSource\"}},\"id\":\"1478\",\"type\":\"CDSView\"},{\"attributes\":{\"source\":{\"id\":\"1226\",\"type\":\"ColumnDataSource\"}},\"id\":\"1230\",\"type\":\"CDSView\"},{\"attributes\":{\"angle\":{\"units\":\"rad\",\"value\":3.141592653589793},\"line_color\":{\"value\":\"#1f77b3\"},\"line_width\":{\"value\":2},\"x\":{\"value\":-4.243948928296987},\"y\":{\"value\":0}},\"id\":\"1159\",\"type\":\"Ray\"},{\"attributes\":{\"dimension\":1,\"ticker\":{\"id\":\"1127\",\"type\":\"BasicTicker\"}},\"id\":\"1130\",\"type\":\"Grid\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1146\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"1443\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"angle\":{\"units\":\"rad\",\"value\":3.141592653589793},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"line_width\":{\"value\":2},\"x\":{\"value\":-3.03442},\"y\":{\"value\":0}},\"id\":\"1476\",\"type\":\"Ray\"},{\"attributes\":{},\"id\":\"1625\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"data_source\":{\"id\":\"1158\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1159\",\"type\":\"Ray\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1160\",\"type\":\"Ray\"},\"selection_glyph\":null,\"view\":{\"id\":\"1162\",\"type\":\"CDSView\"}},\"id\":\"1161\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"data\":{},\"selected\":{\"id\":\"1204\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1205\",\"type\":\"UnionRenderers\"}},\"id\":\"1172\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"source\":{\"id\":\"1158\",\"type\":\"ColumnDataSource\"}},\"id\":\"1162\",\"type\":\"CDSView\"},{\"attributes\":{\"data_source\":{\"id\":\"1564\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1565\",\"type\":\"Ray\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1566\",\"type\":\"Ray\"},\"selection_glyph\":null,\"view\":{\"id\":\"1568\",\"type\":\"CDSView\"}},\"id\":\"1567\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1170\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1171\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"callback\":null},\"id\":\"1113\",\"type\":\"DataRange1d\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1131\",\"type\":\"PanTool\"},{\"id\":\"1132\",\"type\":\"WheelZoomTool\"},{\"id\":\"1133\",\"type\":\"BoxZoomTool\"},{\"id\":\"1134\",\"type\":\"SaveTool\"},{\"id\":\"1135\",\"type\":\"ResetTool\"},{\"id\":\"1136\",\"type\":\"HelpTool\"}]},\"id\":\"1137\",\"type\":\"Toolbar\"},{\"attributes\":{\"angle\":{\"units\":\"rad\",\"value\":0},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"line_width\":{\"value\":2},\"x\":{\"value\":3.0501534950818634},\"y\":{\"value\":1}},\"id\":\"1174\",\"type\":\"Ray\"},{\"attributes\":{},\"id\":\"1127\",\"type\":\"BasicTicker\"},{\"attributes\":{\"line_color\":\"#2ba02b\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1391\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"1624\",\"type\":\"Selection\"},{\"attributes\":{\"angle\":{\"units\":\"rad\",\"value\":0},\"line_color\":{\"value\":\"#1f77b3\"},\"line_width\":{\"value\":2},\"x\":{\"value\":3.0501534950818634},\"y\":{\"value\":1}},\"id\":\"1173\",\"type\":\"Ray\"},{\"attributes\":{},\"id\":\"1131\",\"type\":\"PanTool\"},{\"attributes\":{\"source\":{\"id\":\"1390\",\"type\":\"ColumnDataSource\"}},\"id\":\"1394\",\"type\":\"CDSView\"},{\"attributes\":{\"source\":{\"id\":\"1172\",\"type\":\"ColumnDataSource\"}},\"id\":\"1176\",\"type\":\"CDSView\"},{\"attributes\":{\"data_source\":{\"id\":\"1172\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1173\",\"type\":\"Ray\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1174\",\"type\":\"Ray\"},\"selection_glyph\":null,\"view\":{\"id\":\"1176\",\"type\":\"CDSView\"}},\"id\":\"1175\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1132\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1392\",\"type\":\"Line\"},{\"attributes\":{\"overlay\":{\"id\":\"1155\",\"type\":\"BoxAnnotation\"}},\"id\":\"1133\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"1186\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1134\",\"type\":\"SaveTool\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[600]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[600]}},\"selected\":{\"id\":\"1224\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1225\",\"type\":\"UnionRenderers\"}},\"id\":\"1188\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1135\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"1187\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"line_color\":\"#ff7e0e\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1189\",\"type\":\"Line\"},{\"attributes\":{\"source\":{\"id\":\"1564\",\"type\":\"ColumnDataSource\"}},\"id\":\"1568\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1136\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"1275\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":{\"__ndarray__\":\"ILhzv835EMAguHO/zfkQwDTe1s7PigzANN7Wzs+KDMARMk+U9wEGwBEyT5T3AQbA4ix2sIDvBMDiLHawgO8EwLH+IHi23AHAsf4geLbcAcDnC93g6/YAwOcL3eDr9gDAP3iadKeCAMA/eJp0p4IAwABgYFhZcADAAGBgWFlwAMBUfxGDNzEAwFR/EYM3MQDAz9vMjlQrAMDP28yOVCsAwGpzVpJwiv6/anNWknCK/r+ZRdsf98n9v5lF2x/3yf2/HlcYLfa1/b8eVxgt9rX9vx26pX1QsP2/HbqlfVCw/b+U5m6Vl5P9v5TmbpWXk/2/Q5a2PYQ//b9DlrY9hD/9v4U/qDvSt/y/hT+oO9K3/L+ADVFQ0876v4ANUVDTzvq/70nb/70N+r/vSdv/vQ36vytHjvs3Dfq/K0eO+zcN+r8A6BPz1vH5vwDoE/PW8fm/Xp8O1gC3979enw7WALf3v/w1JLhvmfe//DUkuG+Z97+Ftndr60H3v4W2d2vrQfe/1WqO398E97/Vao7f3wT3v5XHSWc8e/a/lcdJZzx79r/uBQrkfzz2v+4FCuR/PPa/B6eSCQza9b8Hp5IJDNr1v62zMibPi/W/rbMyJs+L9b9s7aPBp8D0v2zto8GnwPS/LJaJhaW/9L8slomFpb/0v/i7x0eAp/S/+LvHR4Cn9L8IbX+1HKb0vwhtf7UcpvS/osRlmU6h9L+ixGWZTqH0vxElHfHLPvS/ESUd8cs+9L8C/+WDJjz0vwL/5YMmPPS/QhclMSso9L9CFyUxKyj0v69cpBoSKPS/r1ykGhIo9L9VuRkv2yL0v1W5GS/bIvS/wiVbhZj487/CJVuFmPjzv91KFfN8uvK/3UoV83y68r/azBguArTyv9rMGC4CtPK/mz5Wzzuy8r+bPlbPO7Lyvy2FCzhOyvG/LYULOE7K8b9+kim/J0Hxv36SKb8nQfG/StpkqFzA8L9K2mSoXMDwvwbbKd0TrPC/Btsp3ROs8L/kKUZM/5Twv+QpRkz/lPC/HbECW0yA8L8dsQJbTIDwv7BFeCwa4u+/sEV4LBri77+Rmk0eCNXvv5GaTR4I1e+/NCn13YzT7r80KfXdjNPuvy46DG+1pe2/LjoMb7Wl7b+6mH9ytXTtv7qYf3K1dO2/+5Gq59tH7b/7karn20ftv1sMuIiq3+y/Wwy4iKrf7L90lsUsX/7rv3SWxSxf/uu/EvSEgwOa678S9ISDA5rrvx9+YcPxkeu/H35hw/GR67/0i70xe4frv/SLvTF7h+u/D2ZRGKGC678PZlEYoYLrv4jDgB9bFeu/iMOAH1sV67/By8xQjOXqv8HLzFCM5eq/kCDArwKn6b+QIMCvAqfpv8RaOiKIZ+i/xFo6Iohn6L811WksDnXnvzXVaSwOdee/qoYkP6kl57+qhiQ/qSXnv+zVsKme6ea/7NWwqZ7p5r8AgvSdTd3mvwCC9J1N3ea/oVoVaFK05b+hWhVoUrTlv6vip1x7oOW/q+KnXHug5b+cGPW7AoHlv5wY9bsCgeW/1Bt6iNs25b/UG3qI2zblvxAuEDSqS+S/EC4QNKpL5L+L5tQRjYDjv4vm1BGNgOO/8VuUYcpF47/xW5RhykXjv6UiLsOM1uK/pSIuw4zW4r/o8TskxU/iv+jxOyTFT+K/84GZgqhK4r/zgZmCqErivyWINrVrKOK/JYg2tWso4r+TXb+CoB/iv5Ndv4KgH+K/A6F1Ntrf4b8DoXU22t/hvzkw11nTyeG/OTDXWdPJ4b82zJ6ALLrhvzbMnoAsuuG/cAY5JXiq4b9wBjkleKrhv9BAtdstPuG/0EC12y0+4b8ODakOsePgvw4NqQ6x4+C/tgZyPgvT4L+2BnI+C9Pgv4oV9JkszeC/ihX0mSzN4L+Ekmvz4Yjgv4SSa/PhiOC/tCo+wMdi4L+0Kj7Ax2LgvxqfYyYaXeC/Gp9jJhpd4L/SPJK29Bbgv9I8krb0FuC/wykTbVxp37/DKRNtXGnfv9xZIzsPlt6/3FkjOw+W3r8FngL1lmPevwWeAvWWY96/B+brB3Fi3r8H5usHcWLevykfjgny9N2/KR+OCfL03b8EwGn4ksjdvwTAafiSyN2/00GpFxgJ3b/TQakXGAndv7H07wXUjdy/sfTvBdSN3L83tnLhgGfcvze2cuGAZ9y/OzAp01od2r87MCnTWh3av/aivBv4Etq/9qK8G/gS2r9wshChP7TYv3CyEKE/tNi/fcATrlx/2L99wBOuXH/Yv1rRQmhuZti/WtFCaG5m2L8IPxC/PPTXvwg/EL889Ne/dXAsV9RV1791cCxX1FXXvxsjLZ4fB9e/GyMtnh8H178VSLi43/fVvxVIuLjf99W/8XAZb+7a1b/xcBlv7trVv4PROJXxYdW/g9E4lfFh1b+th4gd3rPTv62HiB3es9O/vwekPqif07+/B6Q+qJ/Tv8HEH1YZitO/wcQfVhmK078wn7cQf5nSvzCftxB/mdK/dMUD41oH0r90xQPjWgfSv27hBDU5bNC/buEENTls0L9hxHVdDmrQv2HEdV0OatC/o8FWQa/gz7+jwVZBr+DPv3eMkQIJS8+/d4yRAglLz7/IItZXBcfOv8gi1lcFx86/UDDbhuFVzr9QMNuG4VXOv47FcFYQLM6/jsVwVhAszr+2VzwMUYfNv7ZXPAxRh82/N5oxPWBMy783mjE9YEzLv1ESacmOl8q/URJpyY6Xyr/y1/Usk8/Jv/LX9SyTz8m/Fufs+U1wx78W5+z5TXDHv/2gfuFWcMW//aB+4VZwxb9IuLlF+T/Fv0i4uUX5P8W/jVLBk8+yw7+NUsGTz7LDv9LMsCbJGsK/0sywJskawr91B+Q/YfPBv3UH5D9h88G/EEkr7zzzwb8QSSvvPPPBvxOGtKQ8eMG/E4a0pDx4wb/5mZome17Bv/mZmiZ7XsG/7jMeKRwAvr/uMx4pHAC+vxLlTX+CX7y/EuVNf4JfvL9798oIHXq7v3v3yggderu/STKZsMHdub9JMpmwwd25v+MANmD8irm/4wA2YPyKub83n3yNFF64vzeffI0UXri/P5TcL1r+t78/lNwvWv63v6cLoflTu7e/pwuh+VO7t7+qqibyZrC2v6qqJvJmsLa/R9tc7C5Usr9H21zsLlSyv3fkbGRklbC/d+RsZGSVsL+XWmEtZi+wv5daYS1mL7C/KSX3w2Wypr8pJffDZbKmv0yTAUKUGKS/TJMBQpQYpL+OjFU6m4yhv46MVTqbjKG/lYf40isrob+Vh/jSKyuhv4PDitpVd5u/g8OK2lV3m7+533I9UHaVv7nfcj1QdpW/SvKpkTOPhr9K8qmRM4+Gv9SLdG+wZXk/1It0b7BleT9Q6+VwXQZ6P1Dr5XBdBno/58/K9jztkD/nz8r2PO2QPyZjBo6fbJw/JmMGjp9snD8p4e0ScpSdPynh7RJylJ0/N0J+1fCwnT83Qn7V8LCdP6uHJHtrzaI/q4cke2vNoj8eYhhHi92oPx5iGEeL3ag/ZDNxYoDWrD9kM3FigNasP1LkPgQBXq8/UuQ+BAFerz9RznwJXHqyP1HOfAlcerI/C7FhfkOvsj8LsWF+Q6+yPyDNlGMe/rQ/IM2UYx7+tD9dIWfiCV25P10hZ+IJXbk/wg123ynkuT/CDXbfKeS5PzMytlkaJ7s/MzK2WRonuz8NrRDFYZLAPw2tEMVhksA/c5uALT3awD9zm4AtPdrAPywXUTewZcI/LBdRN7Blwj/A9k2es5bCP8D2TZ6zlsI/CdZWBwSTwz8J1lYHBJPDPyQcs5KWAsQ/JByzkpYCxD/YLdRcXgjEP9gt1FxeCMQ/HFFvzuggxD8cUW/O6CDEP8C6nD8jYcQ/wLqcPyNhxD+kuK2dI67FP6S4rZ0jrsU/VefI1cmyxT9V58jVybLFPxy57LU2LcY/HLnstTYtxj8w5P2R18zHPzDk/ZHXzMc/olLe6bOpyD+iUt7ps6nIPwJ9LFReq8g/An0sVF6ryD/OEztpP+bIP84TO2k/5sg/kquE0ZZbyT+Sq4TRllvJP55aXB24nMk/nlpcHbicyT/3qb1vhG3LP/epvW+Ebcs/PjXy5H1tzD8+NfLkfW3MPwKnakdTIdE/AqdqR1Mh0T+1ZK1UjIjRP7VkrVSMiNE/SRZUrcL50T9JFlStwvnRP2R4MhVRKNI/ZHgyFVEo0j/Oq5jq/pnVP86rmOr+mdU/Is0C4g6M1j8izQLiDozWP7bL4Wkq4NY/tsvhaSrg1j/MrDSxan3XP8ysNLFqfdc/HlHa3nwQ2D8eUdrefBDYP0nD5ANrVtg/ScPkA2tW2D9omvKbZlvaP2ia8ptmW9o/lXppuu9p2j+Vemm672naP2Le33mIldo/Yt7feYiV2j88RZQU70PbPzxFlBTvQ9s/CYFJVOrl3T8JgUlU6uXdP3fyL5lJjd4/d/IvmUmN3j9Nj+SskKPeP02P5KyQo94/MuaXkUDu3z8y5peRQO7fP8nXPvVEX+A/ydc+9URf4D+63OMacrfgP7rc4xpyt+A/CdOuheQc4T8J066F5BzhP4mtnj2NteE/ia2ePY214T+OAy/26LjhP44DL/bouOE/L0zC+JLD4T8vTML4ksPhP/jXbMDhG+I/+NdswOEb4j+upqmayWjiP66mqZrJaOI/4+Nr3RB24j/j42vdEHbiPwfP2/i0bOQ/B8/b+LRs5D/bPiEO5YfkP9s+IQ7lh+Q/B0DQq7mL5D8HQNCruYvkPxEx1lgC+OQ/ETHWWAL45D/MicmhlJTlP8yJyaGUlOU/RjcE7wqY5T9GNwTvCpjlPxUBhFutxeU/FQGEW63F5T8wefLclublPzB58tyW5uU/q/hP7KL25T+r+E/sovblP971aiw4puY/3vVqLDim5j+A4Jcd+8XmP4Dglx37xeY/juC24tTa5j+O4Lbi1NrmPypjXbgQ3uY/KmNduBDe5j9dt/qsdjvnP123+qx2O+c/mqx6XP7I5z+arHpc/sjnP7HD2I556uc/scPYjnnq5z8iOr8wUEfoPyI6vzBQR+g/jmen7c456T+OZ6ftzjnpP6IEkhQ90uk/ogSSFD3S6T89VYxhQ8jqPz1VjGFDyOo/Rceh1yUr6z9Fx6HXJSvrP7LgKEUkZOs/suAoRSRk6z9wfnEa77brP3B+cRrvtus/xQT7u7rI6z/FBPu7usjrPw5HBU1ezus/DkcFTV7O6z8Ym9BgTc/rPxib0GBNz+s/eNIiNWr36z940iI1avfrP740pfCLB+w/vjSl8IsH7D+hMdAfT5PsP6Ex0B9Pk+w/dZd4PAyf7T91l3g8DJ/tP+VWv+rqAe4/5Va/6uoB7j8ScEK9dA/vPxJwQr10D+8/L8wd1QZh7z8vzB3VBmHvPyMHO42Wee8/Iwc7jZZ57z8k0QhSAhHwPyTRCFICEfA/vVyVJNQ58D+9XJUk1DnwP5C80wgSWfA/kLzTCBJZ8D9cudk5s2fwP1y52TmzZ/A/ZcnAyGgF8T9lycDIaAXxP235mnWdSfE/bfmadZ1J8T8fGb/AIFvxPx8Zv8AgW/E/39KR7puD8T/f0pHum4PxP0NFivd2AvI/Q0WK93YC8j+zuNM7qMXyP7O40zuoxfI/6BgNVbN58z/oGA1Vs3nzP7QAbww6sPM/tABvDDqw8z9KpfNJ3LPzP0ql80ncs/M/4bhOvkDT8z/huE6+QNPzPwlbOWNg+vM/CVs5Y2D68z9Qe9c8lnf0P1B71zyWd/Q/CqwrTruh9D8KrCtOu6H0P7J2zGSY0fQ/snbMZJjR9D+aCq+on1/1P5oKr6ifX/U/jTSO87iY9T+NNI7zuJj1P5zEhQQMy/U/nMSFBAzL9T8hL5xe9ND1PyEvnF700PU/ARJCc30i9j8BEkJzfSL2P80VeMnBLPY/zRV4ycEs9j/9i8qmc+f2P/2LyqZz5/Y/8KRShxPR9z/wpFKHE9H3P/gVDdfzbPk/+BUN1/Ns+T9wqHSv2Z75P3CodK/Znvk/n8ftStOu+T+fx+1K0675P0e0YwdUM/o/R7RjB1Qz+j9GFXE0YOn6P0YVcTRg6fo/JlhwFLwg+z8mWHAUvCD7P9qZPgFeQ/s/2pk+AV5D+z8czft3LKb7PxzN+3cspvs/IiOeP0VT/D8iI54/RVP8Pz2Ghenegfw/PYaF6d6B/D/Z7Vo/dIr8P9ntWj90ivw/T19VEK+p/D9PX1UQr6n8P0ofgVKREf0/Sh+BUpER/T9o5lV2F7z+P2jmVXYXvP4/XHPRR9jF/z9cc9FH2MX/P0DikRUTFgBAQOKRFRMWAEDShaF7QjYCQNKFoXtCNgJATQIpZlbVAkBNAilmVtUCQLG+tbPbUAhAsb61s9tQCEDsQCngtmYIQOxAKeC2ZghA\",\"dtype\":\"float64\",\"shape\":[600]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[600]}},\"selected\":{\"id\":\"1170\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1171\",\"type\":\"UnionRenderers\"}},\"id\":\"1144\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1531\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"1119\",\"type\":\"LinearScale\"},{\"attributes\":{\"data_source\":{\"id\":\"1390\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1391\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1392\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"1394\",\"type\":\"CDSView\"}},\"id\":\"1393\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"line_color\":\"#1f77b3\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1145\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"1530\",\"type\":\"Selection\"},{\"attributes\":{\"callback\":null,\"data\":{},\"selected\":{\"id\":\"1246\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1247\",\"type\":\"UnionRenderers\"}},\"id\":\"1206\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"angle\":{\"units\":\"rad\",\"value\":3.141592653589793},\"line_color\":{\"value\":\"#2ba02b\"},\"line_width\":{\"value\":2},\"x\":{\"value\":-3.03442},\"y\":{\"value\":0}},\"id\":\"1475\",\"type\":\"Ray\"},{\"attributes\":{\"ticker\":{\"id\":\"1122\",\"type\":\"BasicTicker\"}},\"id\":\"1125\",\"type\":\"Grid\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1190\",\"type\":\"Line\"},{\"attributes\":{\"callback\":null,\"data\":{},\"selected\":{\"id\":\"1186\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1187\",\"type\":\"UnionRenderers\"}},\"id\":\"1158\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"1188\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1189\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1190\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"1192\",\"type\":\"CDSView\"}},\"id\":\"1191\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"axis_label\":\"ECDF\",\"formatter\":{\"id\":\"1151\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1127\",\"type\":\"BasicTicker\"}},\"id\":\"1126\",\"type\":\"LinearAxis\"},{\"attributes\":{\"data_source\":{\"id\":\"1144\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1145\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1146\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"1148\",\"type\":\"CDSView\"}},\"id\":\"1147\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"source\":{\"id\":\"1188\",\"type\":\"ColumnDataSource\"}},\"id\":\"1192\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1204\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1205\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"source\":{\"id\":\"1144\",\"type\":\"ColumnDataSource\"}},\"id\":\"1148\",\"type\":\"CDSView\"},{\"attributes\":{\"text\":\"\"},\"id\":\"1150\",\"type\":\"Title\"},{\"attributes\":{\"angle\":{\"units\":\"rad\",\"value\":3.141592653589793},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"line_width\":{\"value\":2},\"x\":{\"value\":-3.6606670923654656},\"y\":{\"value\":0}},\"id\":\"1208\",\"type\":\"Ray\"},{\"attributes\":{\"items\":[{\"id\":\"1157\",\"type\":\"LegendItem\"}],\"visible\":false},\"id\":\"1156\",\"type\":\"Legend\"},{\"attributes\":{},\"id\":\"1151\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1122\",\"type\":\"BasicTicker\"},{\"attributes\":{\"angle\":{\"units\":\"rad\",\"value\":3.141592653589793},\"line_color\":{\"value\":\"#ff7e0e\"},\"line_width\":{\"value\":2},\"x\":{\"value\":-3.6606670923654656},\"y\":{\"value\":0}},\"id\":\"1207\",\"type\":\"Ray\"},{\"attributes\":{\"source\":{\"id\":\"1206\",\"type\":\"ColumnDataSource\"}},\"id\":\"1210\",\"type\":\"CDSView\"},{\"attributes\":{\"callback\":null,\"data\":{},\"selected\":{\"id\":\"1530\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1531\",\"type\":\"UnionRenderers\"}},\"id\":\"1474\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"1206\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1207\",\"type\":\"Ray\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1208\",\"type\":\"Ray\"},\"selection_glyph\":null,\"view\":{\"id\":\"1210\",\"type\":\"CDSView\"}},\"id\":\"1209\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"angle\":{\"units\":\"rad\",\"value\":0},\"line_color\":{\"value\":\"#2ba02b\"},\"line_width\":{\"value\":2},\"x\":{\"value\":2.45297},\"y\":{\"value\":1}},\"id\":\"1565\",\"type\":\"Ray\"},{\"attributes\":{},\"id\":\"1153\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"callback\":null,\"data\":{},\"selected\":{\"id\":\"1274\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1275\",\"type\":\"UnionRenderers\"}},\"id\":\"1226\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"1155\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[600]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[600]}},\"selected\":{\"id\":\"1442\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1443\",\"type\":\"UnionRenderers\"}},\"id\":\"1390\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1224\",\"type\":\"Selection\"},{\"attributes\":{\"label\":{\"value\":\" \"},\"renderers\":[{\"id\":\"1147\",\"type\":\"GlyphRenderer\"},{\"id\":\"1161\",\"type\":\"GlyphRenderer\"},{\"id\":\"1175\",\"type\":\"GlyphRenderer\"},{\"id\":\"1191\",\"type\":\"GlyphRenderer\"},{\"id\":\"1209\",\"type\":\"GlyphRenderer\"},{\"id\":\"1229\",\"type\":\"GlyphRenderer\"},{\"id\":\"1393\",\"type\":\"GlyphRenderer\"},{\"id\":\"1477\",\"type\":\"GlyphRenderer\"},{\"id\":\"1567\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1157\",\"type\":\"LegendItem\"},{\"attributes\":{},\"id\":\"1274\",\"type\":\"Selection\"},{\"attributes\":{\"axis_label\":\"x\",\"formatter\":{\"id\":\"1153\",\"type\":\"BasicTickFormatter\"},\"ticker\":{\"id\":\"1122\",\"type\":\"BasicTicker\"}},\"id\":\"1121\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"1225\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"angle\":{\"units\":\"rad\",\"value\":0},\"line_color\":{\"value\":\"#ff7e0e\"},\"line_width\":{\"value\":2},\"x\":{\"value\":3.1851299178105883},\"y\":{\"value\":1}},\"id\":\"1227\",\"type\":\"Ray\"},{\"attributes\":{\"angle\":{\"units\":\"rad\",\"value\":3.141592653589793},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"line_width\":{\"value\":2},\"x\":{\"value\":-4.243948928296987},\"y\":{\"value\":0}},\"id\":\"1160\",\"type\":\"Ray\"}],\"root_ids\":[\"1112\"]},\"title\":\"Bokeh Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"be89cb8c-8fcb-4258-84e2-758ff1961652\",\"roots\":{\"1112\":\"0521d6f3-9118-4025-b152-a36308c032e0\"}}];\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": "1112"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = bokeh_catplot.ecdf(\n",
    "    stan_samples,\n",
    "    style='staircase',\n",
    "    palette=[colorcet.b_glasbey_category10[2]],\n",
    "    p=p,\n",
    ")\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why are we using *that*?\n",
    "\n",
    "Yes, sampling using MCMC with Stan is a novel feature, and we used it to sample out of a trivial distribution (a unit Normal), but we can use it to sample out of very complex distributions. But with respect to the direct sampling we just did, you might be thinking, \"Sampling using Stan was **so** much harder than with Numpy! Why are we doing that?\" The answer is that for more complicated models, and doing things like prior predictive checks and posterior predictive checks, using Stan for all modeling is much more convenient.\n",
    "\n",
    "Recalling also last term's course, here is a breakdown of when we will use the respective samplers.\n",
    "\n",
    "- We will use Numpy for sampling techniques in frequentist-based inference, that is for things like computing confidence intervals and p-values using resampling methods.\n",
    "- We will use `scipy.stats` when plotting distributions and using optimization methods in Bayesian inference.\n",
    "- We will occasionally use Numpy for prior predictive checks and posterior predictive checks (defined in coming lessons).\n",
    "- We will use Stan for everything else. This includes all Bayesian modeling that does not use optimization (and even some that does)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Displaying your Stan code\n",
    "\n",
    "When you are working on assignments, your Stan models are written as separate files. They should of course be committed to your repository. It is also instructive to display the Stan code in the Jupyter notebook. This is easily accomplished for any CmdStanPy model using the `code()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data {\n",
      "  real mu;\n",
      "  real sigma;\n",
      "}\n",
      "\n",
      "\n",
      "parameters {\n",
      "  real x;\n",
      "}\n",
      "\n",
      "\n",
      "model {\n",
      "  x ~ normal(mu, sigma);\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(sm.code())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should do this in your notebooks so the code is visible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving samples\n",
    "\n",
    "While your samples are saved in CSV and text files by Stan, is is convenient to save the sampling information in a format the can immediately be read into an ArviZ InferenceData object. The [NetCDF format](https://en.wikipedia.org/wiki/NetCDF) is useful for this. ArviZ enables saving as NetCDF as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'stan_hello_world.nc'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples.to_netcdf('stan_hello_world.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When calling the function, it returns the string of the filename to which the NetCDF file is written. The samples can be read from the NetCDF file using `az.from_netcdf()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = az.from_netcdf('stan_hello_world.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning up the shrapnel\n",
    "\n",
    "When using Stan, CmdStanPy leaves a lot of files on your file system.\n",
    "\n",
    "1. Your stan model is translated into C++, and the result is stored in a `.hpp` file.\n",
    "2. The `.hpp` file is compiled into an object file (`.o` file).\n",
    "3. The `.o` file is used to build an executable.\n",
    "\n",
    "All of these files are deposited in your present working directory, and can get annoying for version control purposes and can add clutter. To clean them up after you are finished running your models, you can run the function below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "bebi103.stan.clean_cmdstan()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When doing sampling the results are stored in a `/var/` directory in various CSV and text files. We never work with these directly, but rather read them into RAM in a convenience `az.InferenceData` object using ArviZ. When exiting your session, CmdStanPy deletes all of these CSV files, etc., unless you specifically say which directory to store the results in your call to `sm.sample()` using the `outpur_dir` kwarg."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPython 3.7.6\n",
      "IPython 7.11.1\n",
      "\n",
      "numpy 1.18.1\n",
      "pandas 0.24.2\n",
      "scipy 1.3.1\n",
      "cmdstanpy 0.8.0\n",
      "arviz 0.6.1\n",
      "bokeh_catplot 0.1.7\n",
      "bokeh 1.4.0\n",
      "colorcet 2.0.2\n",
      "jupyterlab 1.2.5\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -p numpy,pandas,scipy,cmdstanpy,arviz,bokeh_catplot,bokeh,colorcet,jupyterlab"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}