{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dancing statistics\n",
    "\n",
    "[Dataset download](https://s3.amazonaws.com/bebi103.caltech.edu/data/mean_rest_bouts.csv)\n",
    "\n",
    "This lesson was inspired by [Geoff Cumming](http://www.latrobe.edu.au/psychology/staff/profile?uname=GDCumming).\n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Colab setup ------------------\n",
    "import os, sys, subprocess\n",
    "if \"google.colab\" in sys.modules:\n",
    "    cmd = \"pip install --upgrade iqplot bebi103 watermark\"\n",
    "    process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
    "    stdout, stderr = process.communicate()\n",
    "    data_path = \"https://s3.amazonaws.com/bebi103.caltech.edu/data/\"\n",
    "else:\n",
    "    data_path = \"../data/\"\n",
    "# ------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "  const 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",
       "const JS_MIME_TYPE = 'application/javascript';\n",
       "  const HTML_MIME_TYPE = 'text/html';\n",
       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    const 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",
       "    const cell = handle.cell;\n",
       "\n",
       "    const id = cell.output_area._bokeh_element_id;\n",
       "    const 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",
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       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      const cmd_clean = \"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_clean, {\n",
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       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
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       "      return\n",
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       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
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       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
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       "\n",
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       "\n",
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       "      // create a DOM node to render to\n",
       "      const 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",
       "      const 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",
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       "     */\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",
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       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    const events = require('base/js/events');\n",
       "    const 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",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  const 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",
       "    const el = document.getElementById(\"a8a84ec1-eaba-4383-89bb-6b53b861694a\");\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",
       "  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(url) {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (let i = 0; i < css_urls.length; i++) {\n",
       "      const url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\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 (let i = 0; i < js_urls.length; i++) {\n",
       "      const url = js_urls[i];\n",
       "      const element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\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",
       "  };\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",
       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.1.min.js\", \"https://unpkg.com/@holoviz/panel@1.2.3/dist/panel.min.js\"];\n",
       "  const css_urls = [];\n",
       "\n",
       "  const inline_js = [    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "function(Bokeh) {\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "          for (let 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",
       "      const cell = $(document.getElementById(\"a8a84ec1-eaba-4383-89bb-6b53b861694a\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\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));"
      ],
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as st\n",
    "import numba\n",
    "\n",
    "import tqdm\n",
    "\n",
    "import iqplot\n",
    "\n",
    "import bebi103\n",
    "\n",
    "import bokeh.io\n",
    "import bokeh.plotting\n",
    "bokeh.io.output_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "In this fun exercise, we will investigate how **replicable** certain statistical conclusions are.  What I mean by replicability is best understood be working through this notebook.\n",
    "\n",
    "For this lesson we will use the zebrafish embryo sleep data from the [Prober lab](http://www.proberlab.caltech.edu). A description of their work on the genetic regulation of sleep can be found on the [research page of the lab website](http://www.proberlab.caltech.edu/Research). In particular, the [movie](prober_fish.mp4) below comes from their experiments watching moving/sleeping larvae over time.\n",
    "\n",
    "<div style=\"margin: auto; width: 500px;\">\n",
    "\n",
    "<video src=\"prober_fish.mp4\" controls>Prober lab fish</video>\n",
    "    \n",
    "</div>\n",
    "\n",
    "The data we will use are processed from raw data published in [Gandhi et al., 2015](https://doi.org/10.1016/j.neuron.2015.02.016). In their experiment they were studying the effect of a deletion in the gene coding for arylalkylamine N-acetyltransferase (aanat), which is a key enzyme in the rhythmic production of melatonin. Melatonin is a hormone responsible for regulation of circadian rhythms. It is often taken as a drug to treat sleep disorders. The goal of this study is to investigate the effects of aanat deletion on sleep pattern in 5+ day old zebrafish larvae.\n",
    "\n",
    "Among other sleep properties, they measured the mean rest bout length on the sixth night, comparing wild type larvae to the homozygous mutant. A rest bout is defined as a period of time in which the fish does not move. The length of a rest bout is just the amount of time the fish is still. We are primarily interested in the *difference* in the mean bout lengths between the two genotypes. The processed data are found [here](https://s3.amazonaws.com/bebi103.caltech.edu/data/mean_rest_bouts.csv).\n",
    "\n",
    "Let's load the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "df = pd.read_csv(os.path.join(data_path, 'mean_rest_bouts.csv'), comment='#')\n",
    "\n",
    "# Pull out wild type and mutant and drop NAs\n",
    "df = df[df['genotype'].isin(['wt', 'mut'])].dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at these data with an ECDF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "  const render_items = [{\"docid\":\"d087fb8a-05fd-4801-8469-333b88f216b5\",\"roots\":{\"p1002\":\"f84911d1-9bcb-4e6d-b56b-9988c800212b\"},\"root_ids\":[\"p1002\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const 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": "p1002"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = iqplot.ecdf(\n",
    "    df,\n",
    "    cats=\"genotype\",\n",
    "    q=\"mean_rest_bout_length\",\n",
    "    order=[\"wt\", \"mut\"],\n",
    "    x_axis_label=\"mean rest bout length (min)\",\n",
    ")\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution for the mutant appears to be shifted to the left of the wild type, meaning that the mutant has shorted rest bouts. Let's add 95% confidence intervals to the plot to see how the ECDFs might change if we did the experiment again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"ba1aec50-ed07-4741-938d-42ddadbb7aff\" data-root-id=\"p1091\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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      "application/javascript": [
       "(function(root) {\n",
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   "source": [
    "p = iqplot.ecdf(\n",
    "    df,\n",
    "    cats=\"genotype\",\n",
    "    q=\"mean_rest_bout_length\",\n",
    "    order=[\"wt\", \"mut\"],\n",
    "    x_axis_label=\"mean rest bout length (min)\",\n",
    "    conf_int=True,\n",
    ")\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These is strong overlap of the confidence intervals, so it may just be that the differences are due to the finite sample size. We will investigate further with some modeling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cohen's d\n",
    "\n",
    "Cohen's d is a commonly used measure of **effect size** in comparison of two data sets. It is the ratio of the difference of means compared to a pooled standard deviation.\n",
    "\n",
    "\\begin{align}\n",
    "d = \\frac{|\\bar{x} - \\bar{y}|}{\\sqrt{\\left.(n_x \\hat{\\sigma}_x^2 + n_y \\hat{\\sigma}_y^2) \\middle/ (n_x + n_y - 2)\\right.}}.\n",
    "\\end{align}\n",
    "\n",
    "Here, $\\bar{x}$ is the plug-in estimate for the mean of the data from sample $x$, $\\hat{\\sigma}_x^2$ is the plug-in estimate for the variance from sample $x$, and $n_x$ is the number of measurements in sample $x$. The values for sample $y$ are similarly defined.\n",
    "\n",
    "Roughly speaking, Cohen's d tells us how different the means of the data sets are compared to the variability in the data. A large Cohen's d means that the effect is large compared to the variability of the measurement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimates of the difference of means and Cohen's d\n",
    "\n",
    "First, we will compute nonparametric estimates from the data. We will estimate the difference in the mean bout length and Cohen's d. For speed, we save the two data sets as NumPy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "wt = df.loc[df.genotype=='wt', 'mean_rest_bout_length'].values\n",
    "mut = df.loc[df.genotype=='mut', 'mean_rest_bout_length'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll write some functions to conveniently generate bootstrap replicates and do our hypothesis tests. These borrow heavily from the lessons on hacker stats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "@numba.jit(nopython=True)\n",
    "def cohen_d(x, y, return_abs=False):\n",
    "    \"\"\"Cohen's d for two data sets.\"\"\"\n",
    "    diff = x.mean() - y.mean()\n",
    "    pooled_variance = (len(x) * np.var(x) + len(y) * np.var(y)) / (len(x) + len(y) - 2)\n",
    "\n",
    "    if return_abs:\n",
    "        return np.abs(diff) / np.sqrt(pooled_variance)\n",
    "    return diff / np.sqrt(pooled_variance)\n",
    "\n",
    "\n",
    "@numba.jit(nopython=True)\n",
    "def t_stat(x, y):\n",
    "    \"\"\"Welch's t-statistic.\"\"\"\n",
    "    return (np.mean(x) - np.mean(y)) / np.sqrt(\n",
    "        np.var(x) / (len(x) - 1) + np.var(y) / (len(y) - 1)\n",
    "    )\n",
    "\n",
    "\n",
    "@numba.jit(nopython=True)\n",
    "def draw_perm_sample(x, y):\n",
    "    \"\"\"Generate a permutation sample.\"\"\"\n",
    "    concat_data = np.concatenate((x, y))\n",
    "    np.random.shuffle(concat_data)\n",
    "    return concat_data[: len(x)], concat_data[len(x) :]\n",
    "\n",
    "\n",
    "@numba.jit(nopython=True)\n",
    "def draw_bs_sample(data):\n",
    "    \"\"\"Draw a single bootstrap sample.\"\"\"\n",
    "    return np.random.choice(data, size=len(data))\n",
    "\n",
    "\n",
    "@numba.jit(nopython=True)\n",
    "def draw_perm_reps_t(x, y, size=10000):\n",
    "    out = np.empty(size)\n",
    "    for i in range(size):\n",
    "        x_perm, y_perm = draw_perm_sample(x, y)\n",
    "        out[i] = t_stat(x_perm, y_perm)\n",
    "    return out\n",
    "\n",
    "\n",
    "@numba.jit(nopython=True)\n",
    "def draw_bs_reps_mean(data, size=10000):\n",
    "    out = np.empty(size)\n",
    "    for i in range(size):\n",
    "        out[i] = np.mean(draw_bs_sample(data))\n",
    "    return out\n",
    "\n",
    "\n",
    "@numba.jit(nopython=True)\n",
    "def draw_bs_reps_diff_mean(x, y, size=10000):\n",
    "    out = np.empty(size)\n",
    "    for i in range(size):\n",
    "        out[i] = np.mean(draw_bs_sample(x)) - np.mean(draw_bs_sample(y))\n",
    "    return out\n",
    "\n",
    "\n",
    "@numba.jit(nopython=True)\n",
    "def draw_bs_reps_cohen_d(x, y, size=10000, return_abs=False):\n",
    "    out = np.empty(size)\n",
    "    for i in range(size):\n",
    "        out[i] = cohen_d(draw_bs_sample(x), draw_bs_sample(y), return_abs)\n",
    "    return out\n",
    "\n",
    "\n",
    "@numba.jit(nopython=True)\n",
    "def draw_bs_reps_t(x, y, size=10000):\n",
    "    \"\"\"\n",
    "    Bootstrap replicates using the Welch's t-statistic.\n",
    "    \"\"\"\n",
    "    out = np.empty(size)\n",
    "    for i in range(size):\n",
    "        out[i] = t_stat(draw_bs_sample(x), draw_bs_sample(y))\n",
    "    return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can compute the replicates. First, let's look at the means and their respective confidence intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "wt_reps = draw_bs_reps_mean(wt)\n",
    "mut_reps = draw_bs_reps_mean(mut)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And from these, compute the 95% confidence interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "wt_mean_conf_int = np.percentile(wt_reps, [2.5, 97.5])\n",
    "mut_mean_conf_int = np.percentile(mut_reps, [2.5, 97.5])\n",
    "\n",
    "summaries = [\n",
    "    dict(estimate=est, conf_int=conf, label=name)\n",
    "    for est, conf, name in zip(\n",
    "        [wt.mean(), mut.mean()], [wt_mean_conf_int, mut_mean_conf_int], [\"WT\", \"mutant\"]\n",
    "    )\n",
    "]\n",
    "\n",
    "p = bebi103.viz.confints(summaries, x_axis_label='mean rest bout lengths (min)')\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is some overlap in the confidence interval, though wild type tend to have longer rest bouts. Just looking at these numbers, we may not be all that certain that there is a discernible difference between wild type and mutant.\n",
    "\n",
    "Now, let's look at the *difference* in the mean rest bout lengths, which we define as $\\delta = \\bar{x}_\\mathrm{wt} - \\bar{x}_\\mathrm{mut}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "δ = WT - MUT:  [-0.06, 0.31, 0.66] minutes\n"
     ]
    }
   ],
   "source": [
    "reps = draw_bs_reps_diff_mean(wt, mut)\n",
    "diff_mean_conf_int = np.percentile(reps, [2.5, 97.5])\n",
    "\n",
    "print(\n",
    "    \"δ = WT - MUT:  [{1:.2f}, {0:.2f}, {2:.2f}] minutes\".format(\n",
    "        np.mean(wt) - np.mean(mut), *tuple(diff_mean_conf_int)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we might expect, on the tail end of the confidence interval for the difference of means, we see that the mutant might actually have longer rest bouts that wild type.\n",
    "\n",
    "Finally, lets compute the Cohen's d to check the effect size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WT - MUT Cohen's d:  [-0.08, 0.54, 1.43]\n"
     ]
    }
   ],
   "source": [
    "reps = draw_bs_reps_cohen_d(wt, mut)\n",
    "cohen_d_conf_int = np.percentile(reps, [2.5, 97.5])\n",
    "\n",
    "print(\n",
    "    \"WT - MUT Cohen's d:  [{1:.2f}, {0:.2f}, {2:.2f}]\".format(\n",
    "        cohen_d(wt, mut), *tuple(cohen_d_conf_int)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the effect size is 0.54, meaning that the mutant tends to have rest bouts 0.5 standard deviations as large as wild type fix. Jacob Cohen would call this a \"medium\" sized effect. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Null hypothesis significance testing\n",
    "\n",
    "We will now perform an NHST on these two data sets. We formulate the hypothesis as follows.\n",
    "\n",
    "- $H_0$: Wild type and mutant fish have the same mean rest about length. \n",
    "- Test statistic: Cohen's d.\n",
    "- At least as extreme as: Cohen's d larger than what was observed.\n",
    "\n",
    "We can then perform a bootstrap hypothesis test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cohen's d p-value: 0.06551\n"
     ]
    }
   ],
   "source": [
    "@numba.jit(nopython=True)\n",
    "def cohen_nhst(wt, mut, size=100000):\n",
    "    \"\"\"\n",
    "    Perform hypothesis test assuming equal means, using\n",
    "    Cohen-d as test statistic.\n",
    "    \"\"\"\n",
    "    # Shift data sets so that they have the same mean.\n",
    "    wt_shifted = wt - np.mean(wt) + np.mean(np.concatenate((wt, mut)))\n",
    "    mut_shifted = mut - np.mean(mut) + np.mean(np.concatenate((wt, mut)))\n",
    "\n",
    "    # Draw replicates of Cohen's d\n",
    "    reps = draw_bs_reps_cohen_d(wt_shifted, mut_shifted, size=size)\n",
    "\n",
    "    # Compute p-value\n",
    "    return np.sum(reps >= cohen_d(wt, mut)) / len(reps)\n",
    "\n",
    "print(\"Cohen's d p-value:\", cohen_nhst(wt, mut))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get a p-value of about 0.07, which, if we use the typical bright line p-value for statistical significance, we would say that this difference is not statistically significant. We could also test what would happen if we used a different test statistic, like the difference of means."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference of means p-value: 0.04049\n"
     ]
    }
   ],
   "source": [
    "# Shift data sets so that they have the same mean.\n",
    "wt_shifted = wt - np.mean(wt) + np.mean(np.concatenate((wt, mut)))\n",
    "mut_shifted = mut - np.mean(mut) + np.mean(np.concatenate((wt, mut)))\n",
    "\n",
    "# Draw replicates of difference of means\n",
    "reps = draw_bs_reps_diff_mean(wt_shifted, mut_shifted, size=100000)\n",
    "\n",
    "# Compute p-value\n",
    "p_val = np.sum(reps >= np.mean(wt) - np.mean(mut)) / len(reps)\n",
    "\n",
    "print('Difference of means p-value:', p_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we get a p-value of about 0.04. We would say that the result is statistically significant if we used a bright line value of 0.05.\n",
    "\n",
    "Finally, let's try a canonical test for this circumstance, the Welch's t-test. As a reminder, the test statistic for the Welch's t-test is\n",
    "\n",
    "\\begin{align}\n",
    "T = \\frac{\\bar{x}_w - \\bar{x}_m}{\\sqrt{\\hat{\\sigma}_w^2/n_w + \\hat{\\sigma}_m^2/n_m}},\n",
    "\\end{align}\n",
    "\n",
    "where $\\hat{\\sigma}_w^2$ and $\\hat{\\sigma}_m^2$ are plug-in estimates for the variances. Importantly, when performing a Welch's t-test, Normality of the two samples is assumed. So, the hypothesis test is defined as follows.\n",
    "\n",
    "- $H_0$: The two samples are both Normally distributed with equal means.\n",
    "- Test statistic: t-statistic.\n",
    "- At least as extreme as: t-statistic (wild type minus mutant) greater than or equal to what was observed.\n",
    "\n",
    "This is implemented as `scipy.stats.ttest_ind()` using the kwarg `equal_var=False`. We divide by two to get the one-tailed test. Note that Welch's t-test is not exact, but is asymptotically exact for large sample sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welch's p-value: 0.05254200490883057\n"
     ]
    }
   ],
   "source": [
    "print(\"Welch's p-value:\", st.ttest_ind(wt, mut, equal_var=False)[1]/2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we are just above the bright line value of 0.05. We can perform a similar hypothesis test without the Normal assumption using the same test statistic as in the Welch's test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welch's t-test without Normal assumption p-value: 0.06345\n"
     ]
    }
   ],
   "source": [
    "# Draw replicates of t statistic\n",
    "reps = draw_bs_reps_t(wt_shifted, mut_shifted, size=100000)\n",
    "\n",
    "# Compute p-value\n",
    "p_val = np.sum(reps >= t_stat(wt, mut)) / len(reps)\n",
    "\n",
    "print(\"Welch's t-test without Normal assumption p-value:\", p_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we will perform a permutation test. This test is specified as follows.\n",
    "\n",
    "- $H_0$: The sleep bout lengths of mutant and wild type fish are identically distributed.\n",
    "- Test statistic: Welch's t-statistic\n",
    "- At least as extreme as : difference of means is greater than what was observed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Permutation test p-value: 0.05075\n"
     ]
    }
   ],
   "source": [
    "# Draw permutation replicates\n",
    "reps = draw_perm_reps_t(wt, mut, size=100000)\n",
    "\n",
    "# Compute p-value\n",
    "p_val = np.sum(reps >= t_stat(wt, mut)) / len(reps)\n",
    "\n",
    "print(\"Permutation test p-value:\", p_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, all of our tests give p-values that are close to each other, ranging from about 0.04 to 0.07. If we choose bright line p-values to deem something as significant or not, some similar hypothesis/test statistic pairs can give different results. So, my advice is **do not use brightline p-values.** You went through the trouble of computing the p-value, just report it and leave it at that. Don't change a `float` to a `bool`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model comparison\n",
    "\n",
    "As an alternative to NHST, we can ask a similar (but different) question. We can *compare* two generative models. In one model, wild type and mutant sleep mean bout lengths come from the same Normal distribution. In the other, they come from different Normal distributions. We can compute an AIC for each model and then the Akaike weight for the first model (that they come from the same Normal distribution). Again, we can use the convenient feature that for Normal distributions, the MLE is given by the plug-in estimates for the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def akaike_weight(wt, mut):\n",
    "    \"\"\"Compute the Akaike weight for model 1\"\"\"\n",
    "    x_concat = np.concatenate((wt, mut))\n",
    "    mu_1 = np.mean(x_concat)\n",
    "    sigma_1 = np.std(x_concat)\n",
    "    aic_1 = -2 * (st.norm.logpdf(x_concat, mu_1, sigma_1).sum() - 2)\n",
    "    \n",
    "    mu_wt = np.mean(wt)\n",
    "    sigma_wt = np.std(wt)\n",
    "    mu_mut = np.mean(mut)\n",
    "    sigma_mut = np.std(mut)\n",
    "    aic_2 = -2 * (\n",
    "        st.norm.logpdf(wt, mu_wt, sigma_wt).sum()\n",
    "        + st.norm.logpdf(mut, mu_mut, sigma_mut).sum()\n",
    "        - 4\n",
    "    )\n",
    "\n",
    "    aic_max = max(aic_1, aic_2)\n",
    "\n",
    "    return np.exp(-(aic_1 - aic_max) / 2) / (\n",
    "        np.exp(-(aic_1 - aic_max) / 2) + np.exp(-(aic_2 - aic_max) / 2)\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have this function, we can compute the Akaike weight for this data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.598909687738875"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "akaike_weight(wt, mut)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Akaike weight says that we should slightly favor the model where the data points come from the *same* Normal distribution. This runs contrary to our hypothesis tests. The AIC is penalizing the model with more parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dancing\n",
    "\n",
    "We will now do a fun, instructive experiment. We will \"re-acquire\" the data by drawing random samples out of Normal distributions parametrized by the maximum likelihood estimates we obtain from the data. (Recall that the MLE for the mean and variance of a Normally distributed random variable is given by the plug-in estimates.) We will then compute the confidence interval and credible region for $\\delta$ and see how they vary from experiment to experiment. We will later repeat this with p-values and odds ratios.\n",
    "\n",
    "The idea here is that if the data are indeed Gaussian distributed, we are looking at data that could plausibly be generated in an identical experiment.\n",
    "\n",
    "First, we'll write a function to generate new data and use it to generate 500 new data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def new_data(mu, sigma, n):\n",
    "    \"\"\"Generate new data\"\"\"\n",
    "    return np.maximum(np.random.normal(mu, sigma, n), 0.01)\n",
    "\n",
    "# Values from real data\n",
    "mu_wt = np.mean(wt)\n",
    "mu_mut = np.mean(mut)\n",
    "sigma_wt = np.std(wt, ddof=0)\n",
    "sigma_mut = np.std(mut, ddof=0)\n",
    "\n",
    "# How many new data sets to generate\n",
    "n_new_data = 500\n",
    "\n",
    "# Generate new data\n",
    "new_wt = [new_data(mu_wt, sigma_wt, len(wt)) for _ in range(n_new_data)]\n",
    "new_mut = [new_data(mu_mut, sigma_mut, len(mut)) for _ in range(n_new_data)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can do the calculations. First, we'll compute the confidence intervals for $\\delta$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "500it [00:05, 93.89it/s]\n"
     ]
    }
   ],
   "source": [
    "# Set up arrays for storing results\n",
    "conf_int = np.empty((n_new_data, 2))\n",
    "delta = np.empty(n_new_data)\n",
    "\n",
    "# Do calcs!\n",
    "for i, (wt_data, mut_data) in enumerate(tqdm.tqdm(zip(new_wt, new_mut))):\n",
    "    # Compute confidence interval\n",
    "    bs_reps = draw_bs_reps_diff_mean(wt_data, mut_data)\n",
    "    conf_int[i, :] = np.percentile(bs_reps, (2.5, 97.5))\n",
    "\n",
    "    # Sample difference of means\n",
    "    delta[i] = wt_data.mean() - mut_data.mean()\n",
    "\n",
    "# Store the results conveniently\n",
    "df_res = pd.DataFrame(\n",
    "    columns=[\"conf_low\", \"conf_high\", \"delta\"],\n",
    "    data=np.hstack((conf_int, delta.reshape(n_new_data, 1))),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we can do some null hypothesis significance testing. We will compute three p-values, our custom bootstraped p-value with Cohen's d, the p-value from a permutaiton test, and a p-value from Welch's t-test. Remember, with our custom bootstrapped p-value, the hypothesis is that the mutant and wild type sleep bout lengths were drawn out of distributions of the same mean (and no other assumptions). The test statistic is Cohen's d. The hypothesis in the permutation test is that the two data sets are identically distributed. The hypothesis in Welch's t-test is that the mutant and wild type were drawn from Normal distributions with the same mean, but with difference variances. The test statistic is a t-statistic, defined above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "500it [01:50,  4.54it/s]\n"
     ]
    }
   ],
   "source": [
    "# Set up arrays for storing results\n",
    "cohen_p = np.empty(n_new_data)\n",
    "perm_test_p = np.empty(n_new_data)\n",
    "welch_p = np.empty(n_new_data)\n",
    "\n",
    "@numba.jit(nopython=True)\n",
    "def perm_test_t(wt, mut, size=100000):\n",
    "    reps = draw_perm_reps_t(wt, mut, size=size)\n",
    "    return np.sum(reps >= t_stat(wt, mut)) / len(reps)\n",
    "\n",
    "# Do calcs!\n",
    "for i, (wt_data, mut_data) in enumerate(tqdm.tqdm(zip(new_wt, new_mut))):\n",
    "    # Compute p-values\n",
    "    cohen_p[i] = cohen_nhst(wt_data, mut_data)\n",
    "    perm_test_p[i] = perm_test_t(wt_data, mut_data)\n",
    "    welch_p[i] = st.ttest_ind(wt_data, mut_data, equal_var=False)[1] / 2\n",
    "\n",
    "df_res['cohen_p'] = cohen_p\n",
    "df_res['perm_p'] = perm_test_p\n",
    "df_res['welch_p'] = welch_p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can compute the Akaike weights for all of our generated data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_res[\"akaike_weight\"] = [\n",
    "    akaike_weight(wt_data, mut_data) for wt_data, mut_data in zip(new_wt, new_mut)\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dancing confidence intervals(?)\n",
    "\n",
    "To visualize the results, we'll plot the confidence intervals. We'll plot the confidence interval as a bar, and then the bounds of the credible region as dots. For ease of viewing, we will only plot 100 of these and will sort them by the plug-in estimate for δ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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       "  const render_items = [{\"docid\":\"51883a1a-0afd-4439-ba7a-abebc47505a7\",\"roots\":{\"p1265\":\"cf1a47e2-414c-4aa9-808c-7480f7ec53e3\"},\"root_ids\":[\"p1265\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
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       "    let attempts = 0;\n",
       "    const 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);"
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      "application/vnd.bokehjs_exec.v0+json": ""
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     "output_type": "display_data"
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   "source": [
    "# Sort by delta for easy display\n",
    "df_res_sorted = df_res.sort_values(by=\"delta\").iloc[::5]\n",
    "\n",
    "# Set up figure\n",
    "p = bokeh.plotting.figure(frame_height=250, frame_width=700, y_axis_label=\"δ (min)\")\n",
    "\n",
    "# Populate glyphs\n",
    "p.circle(np.arange(len(df_res_sorted)), df_res_sorted['delta'])\n",
    "x_conf = [[i, i] for i in range(len(df_res_sorted))]\n",
    "y_conf = [[r[\"conf_low\"], r[\"conf_high\"]] for _, r in df_res_sorted.iterrows()]\n",
    "p.multi_line(x_conf, y_conf, line_width=2, color=\"#1f77b4\")\n",
    "\n",
    "# Turn off axis ticks for x\n",
    "p.xaxis.visible = False\n",
    "p.xgrid.visible = False\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The confidence interval can vary from experiment to experiment, but not that much. That is, the confidence intervals \"dance\" around, but all within about a factor of 3 of the original observed values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dancing: p-values\n",
    "\n",
    "Now, let's look at the p-values and the Akaike weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  function embed_document(root) {\n",
       "  const docs_json = {\"5f77d488-8c01-4b88-a267-42989aae75f6\":{\"version\":\"3.2.1\",\"title\":\"Bokeh 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    "p = bokeh.plotting.figure(\n",
    "    x_axis_type=\"log\",\n",
    "    y_axis_type=\"log\",\n",
    "    x_axis_label=\"Welch's p-value\",\n",
    "    frame_width=500,\n",
    "    frame_height=500,\n",
    "    x_range=[1e-6, 1],\n",
    "    y_range=[1e-6, 1],\n",
    ")\n",
    "p.circle(\n",
    "    df_res[\"welch_p\"],\n",
    "    df_res[\"cohen_p\"],\n",
    "    color=\"#1f77b4\",\n",
    "    alpha=0.5,\n",
    "    legend_label=\"Cohen's d p-value\",\n",
    ")\n",
    "p.circle(\n",
    "    df_res[\"welch_p\"],\n",
    "    df_res[\"perm_p\"],\n",
    "    color=\"#ffbb78\",\n",
    "    alpha=0.5,\n",
    "    legend_label=\"permuation test p-value\",\n",
    ")\n",
    "p.circle(\n",
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    "    df_res[\"akaike_weight\"],\n",
    "    color=\"#2ca02c\",\n",
    "    alpha=0.5,\n",
    "    legend_label=\"Akaike weight\",\n",
    ")\n",
    "\n",
    "p.legend.location = \"bottom_right\"\n",
    "bokeh.io.show(p)"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The custom p-value computed with a Cohen's d test statistic and the permutation test p-value are nearly equal to the Welch's p-value.\n",
    "\n",
    "But what is really striking here is the scale! Wow! In 500 repeats, we get p-values ranging over four or five orders of magnitude! That's three exclamations in a row! Four, now. Those exclamation points are there to highlight that the p-value is not a reproducible statistic at all.\n",
    "\n",
    "The Akaike weights are less variable, and more conservative. Not many of them dip below 0.1, and we would be unlikely to select one model against another for most of the values of the Akaike weights we calculated. However, they are still rather variable, and in 500 repeats the can var over many orders of magnitude as well. Though better than the p-values, they are still not terribly reproducible.\n",
    "\n",
    "Conversely, both confidence intervals don't really dance much, and p-values and odds ratios dance like [this](https://www.youtube.com/watch?v=XQ7z57qrZU8)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The effect of sample size on dancing\n",
    "\n",
    "The zebrafish sleep experiment had only about 20 samples, so maybe larger sample sizes will result in less extreme dancing of p-values. Let's do a numerical experiment to look at that. We will take 15, 20, 50, and 100 samples for our experimental \"repeats\" and investigate how the p-value varies. For speed, we will only compute the p-value from Welch's t-test, which we showed previously to track closely with our custom p-values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:05<00:00, 175.42it/s]\n"
     ]
    }
   ],
   "source": [
    "n_new_data = 1000\n",
    "n_samples = [15, 20, 50, 100]\n",
    "p_vals = np.empty((n_new_data, len(n_samples)))\n",
    "akaike_weights = np.empty((n_new_data, len(n_samples)))\n",
    "\n",
    "# Do calcs!\n",
    "for i in tqdm.tqdm(range(n_new_data)):\n",
    "    for j, n in enumerate(n_samples):\n",
    "        # Generate new data\n",
    "        new_wt = new_data(mu_wt, sigma_wt, n)\n",
    "        new_mut = new_data(mu_mut, sigma_mut, n)\n",
    "\n",
    "        # Compute p-values and Akaikie weights\n",
    "        p_vals[i,j] = st.ttest_ind(new_wt, new_mut, equal_var=False)[1]/2\n",
    "        akaike_weights[i, j] = akaike_weight(new_wt, new_mut)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the ECDFs of p-values and Akaike weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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S39FPwjk5kssqkU/COTmSyyqRT9xrjfMU8VFP3GuN8xTxUU/f48EHYUgRj9/jwQdhSBGPwijKny1KUY/CKMqfLUpRj+6SJeG3jdGP7pIl4beN0Y/AJ5FLGVkRj8AnkUsZWRGPy4xMGuCmkY/LjEwa4KaRj+HlxqqpqRGP4eXGqqmpEY/H0OLF5mqRj8fQ4sXmapGP8+xpausrkY/z7Glq6yuRj/Th94nH2RHP9OH3icfZEc/GMmmQVP9Rz8YyaZBU/1HP0ynK3mkDEg/TKcreaQMSD+At/qdECZIP4C3+p0QJkg/CPUzpBOVSD8I9TOkE5VIP6xGl9T96Eg/rEaX1P3oSD+D33tyK/pIP4Pfe3Ir+kg/x4eNpokNST/Hh42miQ1JP3hbEp2KDUk/eFsSnYoNST+disvmf1JJP52Ky+Z/Ukk/iZSG0alUST+JlIbRqVRJP5Sige7Ie0k/lKKB7sh7ST/KW6pYU9tJP8pbqlhT20k/GOnZSz/jST8Y6dlLP+NJP2O2sQU3I0o/Y7axBTcjSj/u62kPcTZKP+7raQ9xNko/5JncOcSKSj/kmdw5xIpKP+9pEwK2y0o/72kTArbLSj8/TQUOnNpKPz9NBQ6c2ko/bFOT7akUSz9sU5PtqRRLP0GFQddYI0s/QYVB11gjSz/L6Le7anJLP8vot7tqcks/IdcCO87CSz8h1wI7zsJLP3QGwcZf50s/dAbBxl/nSz/ASH4G1g9MP8BIfgbWD0w/yVdnT6RGTD/JV2dPpEZMP27S2L0PU0w/btLYvQ9TTD8yuXkASYBMPzK5eQBJgEw/7rTgsmBSTT/utOCyYFJNP3HNPtIrmE0/cc0+0iuYTT/YzuI8jbZNP9jO4jyNtk0/lD8Jqv8dTj+UPwmq/x1OP6eouYvdLU4/p6i5i90tTj8LEfvUaC5OPwsR+9RoLk4/ffFU5e6BTj998VTl7oFOPyqy2eGTqE4/KrLZ4ZOoTj8PUkOW6fNOPw9SQ5bp804/hS0IAs02Tz+FLQgCzTZPP9bV+BXLVk8/1tX4FctWTz9fKv8taXZPP18q/y1pdk8/nKOr/YCZTz+co6v9gJlPP+DezufwsE8/4N7O5/CwTz9Df1KSP95PP0N/UpI/3k8/LRRrCaVPUD8tFGsJpU9QP65Jb/6lblA/rklv/qVuUD/N5nFBZaBQP83mcUFloFA/usTChuKrUD+6xMKG4qtQP0TxK/20vVA/RPEr/bS9UD+3neiCO8FQP7ed6II7wVA/HU9OYI7NUD8dT05gjs1QP5b6Y51/8FA/lvpjnX/wUD+RzSTLQ/tQP5HNJMtD+1A/zlHWD3AzUT/OUdYPcDNRPz09WEyxNVE/PT1YTLE1UT88eYDGEEVRPzx5gMYQRVE/jsevgJplUT+Ox6+AmmVRPy8+fBIciVE/Lz58EhyJUT+HZUGehI5RP4dlQZ6EjlE/tSkIdFKjUT+1KQh0UqNRP9GWxvLVylE/0ZbG8tXKUT+7fhXg/eNRP7t+FeD941E/toprTfI2Uj+2imtN8jZSPzRCIsOdhFI/NEIiw52EUj8ZWPf7wqdSPxlY9/vCp1I/A4tfXHC0Uj8Di19ccLRSPycNAuulIlM/Jw0C66UiUz+gKSUE60FTP6ApJQTrQVM/3DuakJVTUz/cO5qQlVNTP2CELGRpZlM/YIQsZGlmUz+OagPr1HFTP45qA+vUcVM/elVkf4SYUz96VWR/hJhTP9YU0jpw1VM/1hTSOnDVUz8fHZ8/0ulTPx8dnz/S6VM/GmHEwB84VD8aYcTAHzhUP76OVwt2U1Q/vo5XC3ZTVD9pMCc25G5UP2kwJzbkblQ/nGjHmZCKVD+caMeZkIpUP5egmtfzlFQ/l6Ca1/OUVD/t1GLktBNVP+3UYuS0E1U/nkgXc4EZVT+eSBdzgRlVP7I+qk5ZlVU/sj6qTlmVVT+WrwYGvJ9VP5avBga8n1U/mSaPUii6VT+ZJo9SKLpVPzb14NZsD1Y/NvXg1mwPVj++nbhqfG1WP76duGp8bVY/GRm5a3iIVj8ZGblreIhWPwRTrPgg0FY/BFOs+CDQVj/lWp+xnhhXP+Van7GeGFc/cVCu+xZiVz9xUK77FmJXP+7osDY/hFc/7uiwNj+EVz9NoxgY84dXP02jGBjzh1c/d9v9293pVz932/3b3elXP2r67O4GSVg/avrs7gZJWD/MXzBoOaJYP8xfMGg5olg/fGc2n5LMWD98ZzafksxYP8CITqhZFFk/wIhOqFkUWT+o6XJtIiJZP6jpcm0iIlk/3XLupxWTWT/dcu6nFZNZPxWYjIj4w1k/FZiMiPjDWT9bwqra59tZP1vCqtrn21k/ZqIngmPsWT9moieCY+xZP7WBlr4aAVo/tYGWvhoBWj85sZlMKRtaPzmxmUwpG1o/KuTR1xxHWj8q5NHXHEdaPwC2yUrTTlo/ALbJStNOWj++UWKhZ1laP75RYqFnWVo/mCCCdYiGWj+YIIJ1iIZaP95ABfkRslo/3kAF+RGyWj/X2sEwDbtaP9fawTANu1o/6F42TUe7Wj/oXjZNR7taP5pecoaYKls/ml5yhpgqWz9o0p/+pW5bP2jSn/6lbls/Cgv6rMWDWz8KC/qsxYNbPx5cygylpVs/HlzKDKWlWz+bnjc+PbtbP5ueNz49u1s/PdntkckwXD892e2RyTBcP/OzrAtROVw/87OsC1E5XD+PFf4h9TddP48V/iH1N10/v4XIW/VyXT+/hchb9XJdPyLcANM+UV4/ItwA0z5RXj8dgMWZSqxePx2AxZlKrF4/s9Rdi1pQXz+z1F2LWlBfP5fk1A3xV18/l+TUDfFXXz/JjmFE5XNfP8mOYUTlc18/jACCma7gXz+MAIKZruBfP0A8HQfxEWA/QDwdB/ERYD96mahApR1gP3qZqEClHWA/ZM/L6y1vYD9kz8vrLW9gP8nQcNyLIGE/ydBw3IsgYT/fwgrb1zVhP9/CCtvXNWE/HorJLfE1YT8eiskt8TVhP9k8qgAkQWE/2TyqACRBYT+VXh99v1ZhP5VeH32/VmE/1DTmiDpzYT/UNOaIOnNhP9Wwiz6dnmE/1bCLPp2eYT8k1x7xgaNhPyTXHvGBo2E/udJS0ZilYj+50lLRmKViP3kXj7hLqmI/eRePuEuqYj/Ak5T6zMtiP8CTlPrMy2I/ZOYHQzvuYj9k5gdDO+5iP6y7vDMrAGM/rLu8MysAYz9boVnIgStjP1uhWciBK2M/isMjQTg8Yz+KwyNBODxjPxUTl5TcPWM/FROXlNw9Yz8rPHmRxVBjPys8eZHFUGM/YDEZ40CKYz9gMRnjQIpjP/cToUZ+tWM/9xOhRn61Yz8Gj5u7PdBjPwaPm7s90GM/VtbzXSsKZD9W1vNdKwpkP9B36rZOl2Q/0Hfqtk6XZD85yO2ytb9kPznI7bK1v2Q/FIqikiLAZD8UiqKSIsBkP/tv4PYS1GQ/+2/g9hLUZD9hY+lqyu1kP2Fj6WrK7WQ/MDWaAEkPZT8wNZoASQ9lP6Q2EV1ufGU/pDYRXW58ZT8y3ISDFX1lPzLchIMVfWU/ajdwkIWiZT9qN3CQhaJlP+AURd9Pw2U/4BRF30/DZT87T1c22d1lPztPVzbZ3WU/rrNPXCElZj+us09cISVmP9coa+LHJmY/1yhr4scmZj9OS8GNFTBmP05LwY0VMGY/leuuXL9lZj+V665cv2VmP/n0KkxdcGY/+fQqTF1wZj9w6vwUZL9mP3Dq/BRkv2Y/FZ/3WhvDZj8Vn/daG8NmP4YkiPmQ/mY/hiSI+ZD+Zj/SGo3YWGpnP9IajdhYamc/Le0yL+hxZz8t7TIv6HFnP/XsAtshp2c/9ewC2yGnZz8UkzRGN8BnPxSTNEY3wGc/VY7rmaTBZz9VjuuZpMFnP8gdYgumy2c/yB1iC6bLZz9jEKats85nP2MQpq2zzmc/GtrVM43UZz8a2tUzjdRnP8ji5e+o3Wc/yOLl76jdZz+gz64kyd5nP6DPriTJ3mc/gd3it2ntZz+B3eK3ae1nP56iByqz92c/nqIHKrP3Zz9QzzsYVRdoP1DPOxhVF2g/sq7aJSw/aD+yrtolLD9oPyhdWqnxfWg/KF1aqfF9aD/PAXhIw7VoP88BeEjDtWg/sAEPfSbaaD+wAQ99JtpoP2hdP3lg7Gg/aF0/eWDsaD/pxvZp5WJpP+nG9mnlYmk/xFbzT8FoaT/EVvNPwWhpPzi06TKmxmk/OLTpMqbGaT8vdn5y+zJqPy92fnL7Mmo/KQgFax2Qaj8pCAVrHZBqP0rfpECFmmo/St+kQIWaaj9e5O966q5qP17k73rqrmo/XExv3DPkaj9cTG/cM+RqP13/dETrDms/Xf90ROsOaz8/fk9l7VBrPz9+T2XtUGs/T1AZRApfaz9PUBlECl9rPz5aXFQeuWs/PlpcVB65az98LmyEifhrP3wubISJ+Gs/xMJLt58RbD/Ewku3nxFsP+mI8FCxGmw/6YjwULEabD8Jm4+aeJdsPwmbj5p4l2w/FvN8o2PJbD8W83yjY8lsP5wXMlOX5Gw/nBcyU5fkbD/3jkNe2BZtP/eOQ17YFm0/fDuWnNQsbT98O5ac1CxtP6hYyW6TVW0/qFjJbpNVbT9PgZ+TbFZtP0+Bn5NsVm0/UcGvvhtZbT9Rwa++G1ltP3QCuxfReW0/dAK7F9F5bT/UtL0Km5ltP9S0vQqbmW0/IZ9hi0TebT8hn2GLRN5tP7gtzzQLLW4/uC3PNAstbj/MmeMwzGxuP8yZ4zDMbG4/8QLT4/Ftbj/xAtPj8W1uP+sdVdsLfG4/6x1V2wt8bj8q7BpU441uPyrsGlTjjW4/hF8oef+dbj+EXyh5/51uP4Fznsqynm4/gXOeyrKebj+ckUKPj/JuP5yRQo+P8m4/JPZiN5V7bz8k9mI3lXtvPw5oKaP9228/Dmgpo/3bbz8+ezNT9v9vPz57M1P2/28/CP3UPMwzcD8I/dQ8zDNwPx2xmofpXHA/HbGah+lccD/J9cTYWF9wP8n1xNhYX3A/rMNSSS56cD+sw1JJLnpwP9jQ7805mHA/2NDvzTmYcD9gRme4qcNwP2BGZ7ipw3A/ywoxDbPbcD/LCjENs9twP6p5lbC7A3E/qnmVsLsDcT9WdADIICxxP1Z0AMggLHE/cHi4aJQscT9weLholCxxPyQ873n4LnE/JDzvefgucT/0LMwAtIFxP/QszAC0gXE/ndYCrRDscT+d1gKtEOxxP2xEvi5x83E/bES+LnHzcT//JPTqT/lxP/8k9OpP+XE/f9uyesldcj9/27J6yV1yPzzI/fVQ7nI/PMj99VDucj/a190xB1NzP9rX3TEHU3M/IVxfr9jMcz8hXF+v2MxzPybQgSAw1HM/JtCBIDDUcz+M1x7uuQJ0P4zXHu65AnQ/EVphbcsJdD8RWmFtywl0P5WsfgkZGnQ/lax+CRkadD9EDubxUEx0P0QO5vFQTHQ/0bjlwHp0dD/RuOXAenR0P+ShMwSBinQ/5KEzBIGKdD+XxHktXJp0P5fEeS1cmnQ/QY8skEy+dD9BjyyQTL50PzrCV9z30HQ/OsJX3PfQdD9Ry5gXDO10P1HLmBcM7XQ/HTYuEwbydD8dNi4TBvJ0PyVWRdkNQHU/JVZF2Q1AdT/idMeV/Fp1P+J0x5X8WnU/FUb/WSl1dT8VRv9ZKXV1P7AdmUSco3U/sB2ZRJyjdT/+tTBhSq91P/61MGFKr3U/tGWSZhIIdj+0ZZJmEgh2PxrZ8xFDEnY/GtnzEUMSdj9iG4foxhp2P2Ibh+jGGnY/4faowqUldj/h9qjCpSV2P/1ZO7HArXY//Vk7scCtdj/6EDzbWNd2P/oQPNtY13Y/9nr7yFVhdz/2evvIVWF3P62J3GbqfXc/rYncZup9dz/ZINJFlqh3P9kg0kWWqHc/KOBnkKesdz8o4GeQp6x3Px/GFeBaunc/H8YV4Fq6dz+Q3PhYd/l3P5Dc+Fh3+Xc/FRN+obEPeD8VE36hsQ94P0RArwK4Gng/RECvArgaeD8RPpD60R14PxE+kPrRHXg/DTQzDF4zeD8NNDMMXjN4Py0Z26baRHg/LRnbptpEeD8H1Fr3Y0Z4PwfUWvdjRng/TxWqfNdieD9PFap812J4P0MueOhngXg/Qy546GeBeD/VsNVhSpN4P9Ww1WFKk3g/yj7pb+2YeD/KPulv7Zh4P6G0kzMjsXg/obSTMyOxeD/v3fdEwu14P+/d90TC7Xg//w5YS/PxeD//DlhL8/F4PwJW8IhBknk/AlbwiEGSeT+2FWGRz7Z5P7YVYZHPtnk/3jWGu/q6eT/eNYa7+rp5P2VN2mtVw3k/ZU3aa1XDeT+HrNIdnxV6P4es0h2fFXo/6zhw0vAVej/rOHDS8BV6P0mE4S2LbXo/SYThLYttej/XRFq/0316P9dEWr/TfXo/XRYLpmKJej9dFgumYol6P8Pe57aHmXo/w97ntoeZej9vhgcYByB7P2+GBxgHIHs/z+GUdvZpez/P4ZR29ml7Pz32LyFBOnw/PfYvIUE6fD/8btvF9p58P/xu28X2nnw/QPylFfivfD9A/KUV+K98PzjI64N00Xw/OMjrg3TRfD9GzdkqveB8P0bN2Sq94Hw/Q+TrsO4FfT9D5Ouw7gV9P3Rm9mVXEH0/dGb2ZVcQfT9nTJC69FB9P2dMkLr0UH0/XwnKt/IRfj9fCcq38hF+P9B2B2sSEn4/0HYHaxISfj8ADv32HBh+PwAO/fYcGH4/xYnyhJZCfj/FifKElkJ+P4xuVitbwn4/jG5WK1vCfj+SklkV6NF+P5KSWRXo0X4/h7HkT/Lpfj+HseRP8ul+PwzIK+NUEX8/DMgr41QRfz+CFLN1wEB/P4IUs3XAQH8/lwnpUTekfz+XCelRN6R/P/NIc9vL8n8/80hz28vyfz8Dh88fFVmAPwOHzx8VWYA/hEpdvsl+gD+ESl2+yX6AP05Xld54jIA/TleV3niMgD+oFk3xmqaAP6gWTfGapoA/UHruvy+3gD9Qeu6/L7eAP0ALci/buoA/QAtyL9u6gD/rjpBBzb+AP+uOkEHNv4A/I3gREl7lgD8jeBESXuWAP0RyJauA5oA/RHIlq4DmgD9lGyXrl/qAP2UbJeuX+oA/nh0lShhIgT+eHSVKGEiBP+i6e6uiXoE/6Lp7q6JegT9+FJIwBnuBP34UkjAGe4E/1aWre5eqgT/Vpat7l6qBP+AiQYRetoE/4CJBhF62gT+S5hJK8tCBP5LmEkry0IE/9A7o897mgT/0Dujz3uaBP/4u6fXWAoI//i7p9dYCgj8MhoFzhBGCPwyGgXOEEYI/mhp3WZyWgj+aGndZnJaCPw7Tksf9pYI/DtOSx/2lgj/0c95Lp7KCP/Rz3kunsoI/j0ZOKV3rgj+PRk4pXeuCPwiGGbkw7oI/CIYZuTDugj/ASuvi7/OCP8BK6+Lv84I/B0/BxFIfgz8HT8HEUh+DP/1/B3UdVIM//X8HdR1Ugz8G3Ie/416DPwbch7/jXoM/zxT8VSNwgz/PFPxVI3CDP4mwLmkelIM/ibAuaR6Ugz+2EcQq6rWDP7YRxCrqtYM/pPZfjT4ghD+k9l+NPiCEPwBTyvIqMYQ/AFPK8ioxhD+4V1qjt2+EP7hXWqO3b4Q/XWHDQuN3hD9dYcNC43eEP34mQRMUyIQ/fiZBExTIhD9noWsZZPmEP2ehaxlk+YQ/5PTuWOwRhT/k9O5Y7BGFP66nFx94FIU/rqcXH3gUhT/4CQdBjIeFP/gJB0GMh4U/u5Efcxe+hT+7kR9zF76FP7vVa5NoUYY/u9Vrk2hRhj8PINY6RXSGPw8g1jpFdIY/4Fza3vOFhj/gXNre84WGP7beV1+GG4c/tt5XX4Ybhz9QC23AK2qHP1ALbcAraoc/5XId5Pdqhz/lch3k92qHP+JOBsPqgIc/4k4Gw+qAhz8y15xKDIOHPzLXnEoMg4c/n0jw6WqRhz+fSPDpapGHP2h90BHlkoc/aH3QEeWShz8cvkPVqimIPxy+Q9WqKYg/K4K3EoFKiD8rgrcSgUqIPzpC0iE8S4g/OkLSITxLiD8onPb3W6+IPyic9vdbr4g/GMH5JBO6iD8YwfkkE7qIP84Nds/bYIk/zg12z9tgiT8wPjFZ52iJPzA+MVnnaIk/ov2Kr0Qkij+i/YqvRCSKPxy7lTs6QIo/HLuVOzpAij9RpEcax0GKP1GkRxrHQYo/2T/nVKVEij/ZP+dUpUSKPwjuIVcYY4o/CO4hVxhjij84gq9Tzm6KPziCr1POboo/l8d+CFWEij+Xx34IVYSKP9Mn9wUio4o/0yf3BSKjij/jfj56o9yKP+N+Pnqj3Io/vbobLnzeij+9uhsufN6KPzaQBcm7LYs/NpAFybstiz/i4DQyuzaLP+LgNDK7Nos/zNWtDRFLiz/M1a0NEUuLP3e9mdIT24s/d72Z0hPbiz9p/8YHRUGMP2n/xgdFQYw/fV4PfwVJjD99Xg9/BUmMP44oztUjTYw/jijO1SNNjD8qhZp6A1OMPyqFmnoDU4w/DcUnB6SQjD8NxScHpJCMPxdWxihLk4w/F1bGKEuTjD+KDRdNBc6MP4oNF00Fzow/s0NPf6/ojD+zQ09/r+iMP1qOnp66QI4/Wo6enrpAjj9xqy33QlSOP3GrLfdCVI4/NoeJ5kBdjj82h4nmQF2OP5f3n2++3Y4/l/efb77djj/pFugZwt2OP+kW6BnC3Y4/w8lVD+nfjj/DyVUP6d+OP5yyEAYE4o4/nLIQBgTijj8jvTXQa3SPPyO9NdBrdI8/nUbzo1XAjz+dRvOjVcCPP5gBroUm/I8/mAGuhSb8jz/BArcrH1GQP8ECtysfUZA/ziGCGKhVkD/OIYIYqFWQPx+yzwxybJA/H7LPDHJskD9fSyKTp32QP19LIpOnfZA/EqsIf2PPkD8Sqwh/Y8+QP9YRup241ZA/1hG6nbjVkD+N7jrw8+qQP43uOvDz6pA/wFSMtAZskT/AVIy0BmyRP9vBIgyxeZE/28EiDLF5kT8Bl8x1GnqRPwGXzHUaepE/wOd+5CmHkT/A537kKYeRP02TDo3Vm5E/TZMOjdWbkT+/6UDjrKKRP7/pQOOsopE/LnM9GLHOkT8ucz0Ysc6RP79v8W734pE/v2/xbvfikT8wlBjhePyRPzCUGOF4/JE/HyULZjNWkj8fJQtmM1aSPwrn5qnDWJI/CufmqcNYkj/SIpfpsGaSP9Iil+mwZpI/w/z4eNy8kj/D/Ph43LySP3o3oGgOypI/ejegaA7Kkj/dl2pJaNKSP92Xaklo0pI/RTnpPxfZkj9FOek/F9mSPyfrtN8D9ZI/J+u03wP1kj9aqvKzPBGTP1qq8rM8EZM/XTszgMcVkz9dOzOAxxWTPzWRm2iGQpM/NZGbaIZCkz8Qw3jad0uTPxDDeNp3S5M/H4Qngf5Skz8fhCeB/lKTP76mbYUQipM/vqZthRCKkz8lZpW7Av2TPyVmlbsC/ZM/CoKZ9VU1lD8Kgpn1VTWUPzbWc+iHVpQ/NtZz6IdWlD++5oL+zl+UP77mgv7OX5Q/TvNjkkdglD9O82OSR2CUPzuekTN2wJQ/O56RM3bAlD/WIe+Xb/aUP9Yh75dv9pQ/XXHIXDX3lD9dcchcNfeUPzqszqPKQJU/OqzOo8pAlT/Vc46+jliVP9Vzjr6OWJU/qL4rMMtnlT+oviswy2eVP6FlFoObkZU/oWUWg5uRlT8kcoeYNCKWPyRyh5g0IpY/kMzxTxZDlj+QzPFPFkOWP++aJotuwpY/75omi27Clj9dPUW2G/mWP109RbYb+ZY/chEVa6wElz9yERVrrASXPzLBZbI/Bpc/MsFlsj8Glz8jA9/M3YmXPyMD38zdiZc/HMUrabSulz8cxStptK6XP5Kkth/9tpc/kqS2H/22lz9FfsIh7LqXP0V+wiHsupc/T+RcZo/Tlz9P5Fxmj9OXP11IeoeULJg/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JQcA//Knx0k1iwD/8qfHSTWLAP1CNl24Sg8A/UI2XbhKDwD+kcD0K16PAP6RwPQrXo8A/+FPjpZvEwD/4U+Olm8TAP0w3iUFg5cA/TDeJQWDlwD+gGi/dJAbBP6AaL90kBsE/9P3UeOkmwT/0/dR46SbBP0jhehSuR8E/SOF6FK5HwT+cxCCwcmjBP5zEILByaME/8KfGSzeJwT/wp8ZLN4nBP0SLbOf7qcE/RIts5/upwT+YbhKDwMrBP5huEoPAysE/7FG4HoXrwT/sUbgehevBPz81XrpJDMI/PzVeukkMwj+TGARWDi3CP5MYBFYOLcI/5/up8dJNwj/n+6nx0k3CPzvfT42XbsI/O99PjZduwj+PwvUoXI/CP4/C9Shcj8I/46WbxCCwwj/jpZvEILDCPzeJQWDl0MI/N4lBYOXQwj+LbOf7qfHCP4ts5/up8cI/30+Nl24Swz/fT42XbhLDPzMzMzMzM8M/MzMzMzMzwz+HFtnO91PDP4cW2c73U8M/2/l+arx0wz/b+X5qvHTDPy/dJAaBlcM/L90kBoGVwz+DwMqhRbbDP4PAyqFFtsM/16NwPQrXwz/Xo3A9CtfDPyuHFtnO98M/K4cW2c73wz9/arx0kxjEP39qvHSTGMQ/001iEFg5xD/TTWIQWDnEPycxCKwcWsQ/JzEIrBxaxD97FK5H4XrEP3sUrkfhesQ/z/dT46WbxD/P91PjpZvEPyPb+X5qvMQ/I9v5fmq8xD93vp8aL93EP3e+nxov3cQ/y6FFtvP9xD/LoUW28/3EPx+F61G4HsU/H4XrUbgexT9zaJHtfD/FP3Noke18P8U/x0s3iUFgxT/HSzeJQWDFPxsv3SQGgcU/Gy/dJAaBxT9vEoPAyqHFP28Sg8DKocU/w/UoXI/CxT/D9Shcj8LFPxfZzvdT48U/F9nO91PjxT9qvHSTGATGP2q8dJMYBMY/vp8aL90kxj++nxov3STGPxKDwMqhRcY/EoPAyqFFxj9mZmZmZmbGP2ZmZmZmZsY/ukkMAiuHxj+6SQwCK4fGPw4tsp3vp8Y/Di2yne+nxj9iEFg5tMjGP2IQWDm0yMY/tvP91Hjpxj+28/3UeOnGPwrXo3A9Csc/CtejcD0Kxz9eukkMAivHP166SQwCK8c/sp3vp8ZLxz+yne+nxkvHPwaBlUOLbMc/BoGVQ4tsxz9aZDvfT43HP1pkO99Pjcc/rkfhehSuxz+uR+F6FK7HPwIrhxbZzsc/AiuHFtnOxz9WDi2yne/HP1YOLbKd78c/qvHSTWIQyD+q8dJNYhDIP/7UeOkmMcg//tR46SYxyD9SuB6F61HIP1K4HoXrUcg/ppvEILByyD+mm8QgsHLIP/p+arx0k8g/+n5qvHSTyD9OYhBYObTIP05iEFg5tMg/okW28/3UyD+iRbbz/dTIP/YoXI/C9cg/9ihcj8L1yD9KDAIrhxbJP0oMAiuHFsk/nu+nxks3yT+e76fGSzfJP/LSTWIQWMk/8tJNYhBYyT9GtvP91HjJP0a28/3UeMk/mpmZmZmZyT+amZmZmZnJP+58PzVeusk/7nw/NV66yT9CYOXQItvJP0Jg5dAi28k/lkOLbOf7yT+WQ4ts5/vJP+kmMQisHMo/6SYxCKwcyj89CtejcD3KPz0K16NwPco/ke18PzVeyj+R7Xw/NV7KP+XQItv5fso/5dAi2/l+yj85tMh2vp/KPzm0yHa+n8o/jZduEoPAyj+Nl24Sg8DKP+F6FK5H4co/4XoUrkfhyj81XrpJDALLPzVeukkMAss/iUFg5dAiyz+JQWDl0CLLP90kBoGVQ8s/3SQGgZVDyz8xCKwcWmTLPzEIrBxaZMs/hetRuB6Fyz+F61G4HoXLP9nO91Pjpcs/2c73U+Olyz8tsp3vp8bLPy2yne+nxss/gZVDi2znyz+BlUOLbOfLP9V46SYxCMw/1XjpJjEIzD8pXI/C9SjMPylcj8L1KMw/fT81XrpJzD99PzVeuknMP9Ei2/l+asw/0SLb+X5qzD8lBoGVQ4vMPyUGgZVDi8w/eekmMQiszD956SYxCKzMP83MzMzMzMw/zczMzMzMzD8hsHJoke3MPyGwcmiR7cw/dZMYBFYOzT91kxgEVg7NP8l2vp8aL80/yXa+nxovzT8dWmQ730/NPx1aZDvfT80/cT0K16NwzT9xPQrXo3DNP8UgsHJokc0/xSCwcmiRzT8ZBFYOLbLNPxkEVg4tss0/bef7qfHSzT9t5/up8dLNP8HKoUW2880/wcqhRbbzzT8UrkfhehTOPxSuR+F6FM4/aJHtfD81zj9oke18PzXOP7x0kxgEVs4/vHSTGARWzj8QWDm0yHbOPxBYObTIds4/ZDvfT42Xzj9kO99PjZfOP7gehetRuM4/uB6F61G4zj8MAiuHFtnOPwwCK4cW2c4/YOXQItv5zj9g5dAi2/nOP7TIdr6fGs8/tMh2vp8azz8IrBxaZDvPPwisHFpkO88/XI/C9Shczz9cj8L1KFzPP7ByaJHtfM8/sHJoke18zz8EVg4tsp3PPwRWDi2ync8/WDm0yHa+zz9YObTIdr7PP6wcWmQ7388/rBxaZDvfzz8AAAAAAADQPwAAAAAAANA/qvHSTWIQ0D+q8dJNYhDQP1TjpZvEINA/VOOlm8Qg0D/+1HjpJjHQP/7UeOkmMdA/qMZLN4lB0D+oxks3iUHQP1K4HoXrUdA/UrgehetR0D/8qfHSTWLQP/yp8dJNYtA/ppvEILBy0D+mm8QgsHLQP1CNl24Sg9A/UI2XbhKD0D/6fmq8dJPQP/p+arx0k9A/pHA9Ctej0D+kcD0K16PQP05iEFg5tNA/TmIQWDm00D/4U+Olm8TQP/hT46WbxNA/okW28/3U0D+iRbbz/dTQP0w3iUFg5dA/TDeJQWDl0D/2KFyPwvXQP/YoXI/C9dA/oBov3SQG0T+gGi/dJAbRP0oMAiuHFtE/SgwCK4cW0T/0/dR46SbRP/T91HjpJtE/nu+nxks30T+e76fGSzfRP0jhehSuR9E/SOF6FK5H0T/y0k1iEFjRP/LSTWIQWNE/nMQgsHJo0T+cxCCwcmjRP0a28/3UeNE/Rrbz/dR40T/wp8ZLN4nRP/Cnxks3idE/mpmZmZmZ0T+amZmZmZnRP0SLbOf7qdE/RIts5/up0T/ufD81XrrRP+58PzVeutE/mG4Sg8DK0T+YbhKDwMrRP0Jg5dAi29E/QmDl0CLb0T/sUbgehevRP+xRuB6F69E/lkOLbOf70T+WQ4ts5/vRPz81XrpJDNI/PzVeukkM0j/pJjEIrBzSP+kmMQisHNI/kxgEVg4t0j+TGARWDi3SPz0K16NwPdI/PQrXo3A90j/n+6nx0k3SP+f7qfHSTdI/ke18PzVe0j+R7Xw/NV7SPzvfT42XbtI/O99PjZdu0j/l0CLb+X7SP+XQItv5ftI/j8L1KFyP0j+PwvUoXI/SPzm0yHa+n9I/ObTIdr6f0j/jpZvEILDSP+Olm8QgsNI/jZduEoPA0j+Nl24Sg8DSPzeJQWDl0NI/N4lBYOXQ0j/hehSuR+HSP+F6FK5H4dI/i2zn+6nx0j+LbOf7qfHSPzVeukkMAtM/NV66SQwC0z/fT42XbhLTP99PjZduEtM/iUFg5dAi0z+JQWDl0CLTPzMzMzMzM9M/MzMzMzMz0z/dJAaBlUPTP90kBoGVQ9M/hxbZzvdT0z+HFtnO91PTPzEIrBxaZNM/MQisHFpk0z/b+X5qvHTTP9v5fmq8dNM/hetRuB6F0z+F61G4HoXTPy/dJAaBldM/L90kBoGV0z/ZzvdT46XTP9nO91PjpdM/g8DKoUW20z+DwMqhRbbTPy2yne+nxtM/LbKd76fG0z/Xo3A9CtfTP9ejcD0K19M/gZVDi2zn0z+BlUOLbOfTPyuHFtnO99M/K4cW2c730z/VeOkmMQjUP9V46SYxCNQ/f2q8dJMY1D9/arx0kxjUPylcj8L1KNQ/KVyPwvUo1D/TTWIQWDnUP9NNYhBYOdQ/fT81XrpJ1D99PzVeuknUPycxCKwcWtQ/JzEIrBxa1D/RItv5fmrUP9Ei2/l+atQ/exSuR+F61D97FK5H4XrUPyUGgZVDi9Q/JQaBlUOL1D/P91PjpZvUP8/3U+Olm9Q/eekmMQis1D956SYxCKzUPyPb+X5qvNQ/I9v5fmq81D/NzMzMzMzUP83MzMzMzNQ/d76fGi/d1D93vp8aL93UPyGwcmiR7dQ/IbByaJHt1D/LoUW28/3UP8uhRbbz/dQ/dZMYBFYO1T91kxgEVg7VPx+F61G4HtU/H4XrUbge1T/Jdr6fGi/VP8l2vp8aL9U/c2iR7Xw/1T9zaJHtfD/VPx1aZDvfT9U/HVpkO99P1T/HSzeJQWDVP8dLN4lBYNU/cT0K16Nw1T9xPQrXo3DVPxsv3SQGgdU/Gy/dJAaB1T/FILByaJHVP8UgsHJokdU/bxKDwMqh1T9vEoPAyqHVPxkEVg4tstU/GQRWDi2y1T/D9Shcj8LVP8P1KFyPwtU/bef7qfHS1T9t5/up8dLVPxfZzvdT49U/F9nO91Pj1T/ByqFFtvPVP8HKoUW289U/arx0kxgE1j9qvHSTGATWPxSuR+F6FNY/FK5H4XoU1j++nxov3STWP76fGi/dJNY/aJHtfD811j9oke18PzXWPxKDwMqhRdY/EoPAyqFF1j+8dJMYBFbWP7x0kxgEVtY/ZmZmZmZm1j9mZmZmZmbWPxBYObTIdtY/EFg5tMh21j+6SQwCK4fWP7pJDAIrh9Y/ZDvfT42X1j9kO99PjZfWPw4tsp3vp9Y/Di2yne+n1j+4HoXrUbjWP7gehetRuNY/YhBYObTI1j9iEFg5tMjWPwwCK4cW2dY/DAIrhxbZ1j+28/3UeOnWP7bz/dR46dY/YOXQItv51j9g5dAi2/nWPwrXo3A9Ctc/CtejcD0K1z+0yHa+nxrXP7TIdr6fGtc/XrpJDAIr1z9eukkMAivXPwisHFpkO9c/CKwcWmQ71z+yne+nxkvXP7Kd76fGS9c/XI/C9Shc1z9cj8L1KFzXPwaBlUOLbNc/BoGVQ4ts1z+wcmiR7XzXP7ByaJHtfNc/WmQ730+N1z9aZDvfT43XPwRWDi2yndc/BFYOLbKd1z+uR+F6FK7XP65H4XoUrtc/WDm0yHa+1z9YObTIdr7XPwIrhxbZztc/AiuHFtnO1z+sHFpkO9/XP6wcWmQ739c/Vg4tsp3v1z9WDi2yne/XPwAAAAAAANg/AAAAAAAA2D+q8dJNYhDYP6rx0k1iENg/VOOlm8Qg2D9U46WbxCDYP/7UeOkmMdg//tR46SYx2D+oxks3iUHYP6jGSzeJQdg/UrgehetR2D9SuB6F61HYP/yp8dJNYtg//Knx0k1i2D+mm8QgsHLYP6abxCCwctg/UI2XbhKD2D9QjZduEoPYP/p+arx0k9g/+n5qvHST2D+kcD0K16PYP6RwPQrXo9g/TmIQWDm02D9OYhBYObTYP/hT46WbxNg/+FPjpZvE2D+iRbbz/dTYP6JFtvP91Ng/TDeJQWDl2D9MN4lBYOXYP/YoXI/C9dg/9ihcj8L12D+gGi/dJAbZP6AaL90kBtk/SgwCK4cW2T9KDAIrhxbZP/T91HjpJtk/9P3UeOkm2T+e76fGSzfZP57vp8ZLN9k/SOF6FK5H2T9I4XoUrkfZP/LSTWIQWNk/8tJNYhBY2T+cxCCwcmjZP5zEILByaNk/Rrbz/dR42T9GtvP91HjZP/Cnxks3idk/8KfGSzeJ2T+amZmZmZnZP5qZmZmZmdk/RIts5/up2T9Ei2zn+6nZP+58PzVeutk/7nw/NV662T+YbhKDwMrZP5huEoPAytk/QmDl0CLb2T9CYOXQItvZP+xRuB6F69k/7FG4HoXr2T+WQ4ts5/vZP5ZDi2zn+9k/PzVeukkM2j8/NV66SQzaP+kmMQisHNo/6SYxCKwc2j+TGARWDi3aP5MYBFYOLdo/PQrXo3A92j89CtejcD3aP+f7qfHSTdo/5/up8dJN2j+R7Xw/NV7aP5HtfD81Xto/O99PjZdu2j8730+Nl27aP+XQItv5fto/5dAi2/l+2j+PwvUoXI/aP4/C9Shcj9o/ObTIdr6f2j85tMh2vp/aP+Olm8QgsNo/46WbxCCw2j+Nl24Sg8DaP42XbhKDwNo/N4lBYOXQ2j83iUFg5dDaP+F6FK5H4do/4XoUrkfh2j+LbOf7qfHaP4ts5/up8do/NV66SQwC2z81XrpJDALbP99PjZduEts/30+Nl24S2z+JQWDl0CLbP4lBYOXQIts/MzMzMzMz2z8zMzMzMzPbP90kBoGVQ9s/3SQGgZVD2z+HFtnO91PbP4cW2c73U9s/MQisHFpk2z8xCKwcWmTbP9v5fmq8dNs/2/l+arx02z+F61G4HoXbP4XrUbgehds/L90kBoGV2z8v3SQGgZXbP9nO91Pjpds/2c73U+Ol2z+DwMqhRbbbP4PAyqFFtts/LbKd76fG2z8tsp3vp8bbP9ejcD0K19s/16NwPQrX2z+BlUOLbOfbP4GVQ4ts59s/K4cW2c732z8rhxbZzvfbP9V46SYxCNw/1XjpJjEI3D9/arx0kxjcP39qvHSTGNw/KVyPwvUo3D8pXI/C9SjcP9NNYhBYOdw/001iEFg53D99PzVeukncP30/NV66Sdw/JzEIrBxa3D8nMQisHFrcP9Ei2/l+atw/0SLb+X5q3D97FK5H4XrcP3sUrkfhetw/JQaBlUOL3D8lBoGVQ4vcP8/3U+Olm9w/z/dT46Wb3D956SYxCKzcP3npJjEIrNw/I9v5fmq83D8j2/l+arzcP83MzMzMzNw/zczMzMzM3D93vp8aL93cP3e+nxov3dw/IbByaJHt3D8hsHJoke3cP8uhRbbz/dw/y6FFtvP93D91kxgEVg7dP3WTGARWDt0/H4XrUbge3T8fhetRuB7dP8l2vp8aL90/yXa+nxov3T9zaJHtfD/dP3Noke18P90/HVpkO99P3T8dWmQ730/dP8dLN4lBYN0/x0s3iUFg3T9xPQrXo3DdP3E9CtejcN0/Gy/dJAaB3T8bL90kBoHdP8UgsHJokd0/xSCwcmiR3T9vEoPAyqHdP28Sg8DKod0/GQRWDi2y3T8ZBFYOLbLdP8P1KFyPwt0/w/UoXI/C3T9t5/up8dLdP23n+6nx0t0/F9nO91Pj3T8X2c73U+PdP8HKoUW2890/wcqhRbbz3T9qvHSTGATeP2q8dJMYBN4/FK5H4XoU3j8UrkfhehTeP76fGi/dJN4/vp8aL90k3j9oke18PzXeP2iR7Xw/Nd4/EoPAyqFF3j8Sg8DKoUXeP7x0kxgEVt4/vHSTGARW3j9mZmZmZmbeP2ZmZmZmZt4/EFg5tMh23j8QWDm0yHbeP7pJDAIrh94/ukkMAiuH3j9kO99PjZfeP2Q730+Nl94/Di2yne+n3j8OLbKd76feP7gehetRuN4/uB6F61G43j9iEFg5tMjeP2IQWDm0yN4/DAIrhxbZ3j8MAiuHFtneP7bz/dR46d4/tvP91Hjp3j9g5dAi2/neP2Dl0CLb+d4/CtejcD0K3z8K16NwPQrfP7TIdr6fGt8/tMh2vp8a3z9eukkMAivfP166SQwCK98/CKwcWmQ73z8IrBxaZDvfP7Kd76fGS98/sp3vp8ZL3z9cj8L1KFzfP1yPwvUoXN8/BoGVQ4ts3z8GgZVDi2zfP7ByaJHtfN8/sHJoke183z9aZDvfT43fP1pkO99Pjd8/BFYOLbKd3z8EVg4tsp3fP65H4XoUrt8/rkfhehSu3z9YObTIdr7fP1g5tMh2vt8/AiuHFtnO3z8CK4cW2c7fP6wcWmQ7398/rBxaZDvf3z9WDi2yne/fP1YOLbKd798/AAAAAAAA4D8AAAAAAADgP9V46SYxCOA/1XjpJjEI4D+q8dJNYhDgP6rx0k1iEOA/f2q8dJMY4D9/arx0kxjgP1TjpZvEIOA/VOOlm8Qg4D8pXI/C9SjgPylcj8L1KOA//tR46SYx4D/+1HjpJjHgP9NNYhBYOeA/001iEFg54D+oxks3iUHgP6jGSzeJQeA/fT81XrpJ4D99PzVeukngP1K4HoXrUeA/UrgehetR4D8nMQisHFrgPycxCKwcWuA//Knx0k1i4D/8qfHSTWLgP9Ei2/l+auA/0SLb+X5q4D+mm8QgsHLgP6abxCCwcuA/exSuR+F64D97FK5H4XrgP1CNl24Sg+A/UI2XbhKD4D8lBoGVQ4vgPyUGgZVDi+A/+n5qvHST4D/6fmq8dJPgP8/3U+Olm+A/z/dT46Wb4D+kcD0K16PgP6RwPQrXo+A/eekmMQis4D956SYxCKzgP05iEFg5tOA/TmIQWDm04D8j2/l+arzgPyPb+X5qvOA/+FPjpZvE4D/4U+Olm8TgP83MzMzMzOA/zczMzMzM4D+iRbbz/dTgP6JFtvP91OA/d76fGi/d4D93vp8aL93gP0w3iUFg5eA/TDeJQWDl4D8hsHJoke3gPyGwcmiR7eA/9ihcj8L14D/2KFyPwvXgP8uhRbbz/eA/y6FFtvP94D+gGi/dJAbhP6AaL90kBuE/dZMYBFYO4T91kxgEVg7hP0oMAiuHFuE/SgwCK4cW4T8fhetRuB7hPx+F61G4HuE/9P3UeOkm4T/0/dR46SbhP8l2vp8aL+E/yXa+nxov4T+e76fGSzfhP57vp8ZLN+E/c2iR7Xw/4T9zaJHtfD/hP0jhehSuR+E/SOF6FK5H4T8dWmQ730/hPx1aZDvfT+E/8tJNYhBY4T/y0k1iEFjhP8dLN4lBYOE/x0s3iUFg4T+cxCCwcmjhP5zEILByaOE/cT0K16Nw4T9xPQrXo3DhP0a28/3UeOE/Rrbz/dR44T8bL90kBoHhPxsv3SQGgeE/8KfGSzeJ4T/wp8ZLN4nhP8UgsHJokeE/xSCwcmiR4T+amZmZmZnhP5qZmZmZmeE/bxKDwMqh4T9vEoPAyqHhP0SLbOf7qeE/RIts5/up4T8ZBFYOLbLhPxkEVg4tsuE/7nw/NV664T/ufD81XrrhP8P1KFyPwuE/w/UoXI/C4T+YbhKDwMrhP5huEoPAyuE/bef7qfHS4T9t5/up8dLhP0Jg5dAi2+E/QmDl0CLb4T8X2c73U+PhPxfZzvdT4+E/7FG4HoXr4T/sUbgehevhP8HKoUW28+E/wcqhRbbz4T+WQ4ts5/vhP5ZDi2zn++E/arx0kxgE4j9qvHSTGATiPz81XrpJDOI/PzVeukkM4j8UrkfhehTiPxSuR+F6FOI/6SYxCKwc4j/pJjEIrBziP76fGi/dJOI/vp8aL90k4j+TGARWDi3iP5MYBFYOLeI/aJHtfD814j9oke18PzXiPz0K16NwPeI/PQrXo3A94j8Sg8DKoUXiPxKDwMqhReI/5/up8dJN4j/n+6nx0k3iP7x0kxgEVuI/vHSTGARW4j+R7Xw/NV7iP5HtfD81XuI/ZmZmZmZm4j9mZmZmZmbiPzvfT42XbuI/O99PjZdu4j8QWDm0yHbiPxBYObTIduI/5dAi2/l+4j/l0CLb+X7iP7pJDAIrh+I/ukkMAiuH4j+PwvUoXI/iP4/C9Shcj+I/ZDvfT42X4j9kO99PjZfiPzm0yHa+n+I/ObTIdr6f4j8OLbKd76fiPw4tsp3vp+I/46WbxCCw4j/jpZvEILDiP7gehetRuOI/uB6F61G44j+Nl24Sg8DiP42XbhKDwOI/YhBYObTI4j9iEFg5tMjiPzeJQWDl0OI/N4lBYOXQ4j8MAiuHFtniPwwCK4cW2eI/4XoUrkfh4j/hehSuR+HiP7bz/dR46eI/tvP91Hjp4j+LbOf7qfHiP4ts5/up8eI/YOXQItv54j9g5dAi2/niPzVeukkMAuM/NV66SQwC4z8K16NwPQrjPwrXo3A9CuM/30+Nl24S4z/fT42XbhLjP7TIdr6fGuM/tMh2vp8a4z+JQWDl0CLjP4lBYOXQIuM/XrpJDAIr4z9eukkMAivjPzMzMzMzM+M/MzMzMzMz4z8IrBxaZDvjPwisHFpkO+M/3SQGgZVD4z/dJAaBlUPjP7Kd76fGS+M/sp3vp8ZL4z+HFtnO91PjP4cW2c73U+M/XI/C9Shc4z9cj8L1KFzjPzEIrBxaZOM/MQisHFpk4z8GgZVDi2zjPwaBlUOLbOM/2/l+arx04z/b+X5qvHTjP7ByaJHtfOM/sHJoke184z+F61G4HoXjP4XrUbgeheM/WmQ730+N4z9aZDvfT43jPy/dJAaBleM/L90kBoGV4z8EVg4tsp3jPwRWDi2yneM/2c73U+Ol4z/ZzvdT46XjP65H4XoUruM/rkfhehSu4z+DwMqhRbbjP4PAyqFFtuM/WDm0yHa+4z9YObTIdr7jPy2yne+nxuM/LbKd76fG4z8CK4cW2c7jPwIrhxbZzuM/16NwPQrX4z/Xo3A9CtfjP6wcWmQ73+M/rBxaZDvf4z+BlUOLbOfjP4GVQ4ts5+M/Vg4tsp3v4z9WDi2yne/jPyuHFtnO9+M/K4cW2c734z8AAAAAAADkPwAAAAAAAOQ/1XjpJjEI5D/VeOkmMQjkP6rx0k1iEOQ/qvHSTWIQ5D9/arx0kxjkP39qvHSTGOQ/VOOlm8Qg5D9U46WbxCDkPylcj8L1KOQ/KVyPwvUo5D/+1HjpJjHkP/7UeOkmMeQ/001iEFg55D/TTWIQWDnkP6jGSzeJQeQ/qMZLN4lB5D99PzVeuknkP30/NV66SeQ/UrgehetR5D9SuB6F61HkPycxCKwcWuQ/JzEIrBxa5D/8qfHSTWLkP/yp8dJNYuQ/0SLb+X5q5D/RItv5fmrkP6abxCCwcuQ/ppvEILBy5D97FK5H4XrkP3sUrkfheuQ/UI2XbhKD5D9QjZduEoPkPyUGgZVDi+Q/JQaBlUOL5D/6fmq8dJPkP/p+arx0k+Q/z/dT46Wb5D/P91PjpZvkP6RwPQrXo+Q/pHA9Ctej5D956SYxCKzkP3npJjEIrOQ/TmIQWDm05D9OYhBYObTkPyPb+X5qvOQ/I9v5fmq85D/4U+Olm8TkP/hT46WbxOQ/zczMzMzM5D/NzMzMzMzkP6JFtvP91OQ/okW28/3U5D93vp8aL93kP3e+nxov3eQ/TDeJQWDl5D9MN4lBYOXkPyGwcmiR7eQ/IbByaJHt5D/2KFyPwvXkP/YoXI/C9eQ/y6FFtvP95D/LoUW28/3kP6AaL90kBuU/oBov3SQG5T91kxgEVg7lP3WTGARWDuU/SgwCK4cW5T9KDAIrhxblPx+F61G4HuU/H4XrUbge5T/0/dR46SblP/T91HjpJuU/yXa+nxov5T/Jdr6fGi/lP57vp8ZLN+U/nu+nxks35T9zaJHtfD/lP3Noke18P+U/SOF6FK5H5T9I4XoUrkflPx1aZDvfT+U/HVpkO99P5T/y0k1iEFjlP/LSTWIQWOU/x0s3iUFg5T/HSzeJQWDlP5zEILByaOU/nMQgsHJo5T9xPQrXo3DlP3E9CtejcOU/Rrbz/dR45T9GtvP91HjlPxsv3SQGgeU/Gy/dJAaB5T/wp8ZLN4nlP/Cnxks3ieU/xSCwcmiR5T/FILByaJHlP5qZmZmZmeU/mpmZmZmZ5T9vEoPAyqHlP28Sg8DKoeU/RIts5/up5T9Ei2zn+6nlPxkEVg4tsuU/GQRWDi2y5T/ufD81XrrlP+58PzVeuuU/w/UoXI/C5T/D9Shcj8LlP5huEoPAyuU/mG4Sg8DK5T9t5/up8dLlP23n+6nx0uU/QmDl0CLb5T9CYOXQItvlPxfZzvdT4+U/F9nO91Pj5T/sUbgehevlP+xRuB6F6+U/wcqhRbbz5T/ByqFFtvPlP5ZDi2zn++U/lkOLbOf75T9qvHSTGATmP2q8dJMYBOY/PzVeukkM5j8/NV66SQzmPxSuR+F6FOY/FK5H4XoU5j/pJjEIrBzmP+kmMQisHOY/vp8aL90k5j++nxov3STmP5MYBFYOLeY/kxgEVg4t5j9oke18PzXmP2iR7Xw/NeY/PQrXo3A95j89CtejcD3mPxKDwMqhReY/EoPAyqFF5j/n+6nx0k3mP+f7qfHSTeY/vHSTGARW5j+8dJMYBFbmP5HtfD81XuY/ke18PzVe5j9mZmZmZmbmP2ZmZmZmZuY/O99PjZdu5j8730+Nl27mPxBYObTIduY/EFg5tMh25j/l0CLb+X7mP+XQItv5fuY/ukkMAiuH5j+6SQwCK4fmP4/C9Shcj+Y/j8L1KFyP5j9kO99PjZfmP2Q730+Nl+Y/ObTIdr6f5j85tMh2vp/mPw4tsp3vp+Y/Di2yne+n5j/jpZvEILDmP+Olm8QgsOY/uB6F61G45j+4HoXrUbjmP42XbhKDwOY/jZduEoPA5j9iEFg5tMjmP2IQWDm0yOY/N4lBYOXQ5j83iUFg5dDmPwwCK4cW2eY/DAIrhxbZ5j/hehSuR+HmP+F6FK5H4eY/tvP91Hjp5j+28/3UeOnmP4ts5/up8eY/i2zn+6nx5j9g5dAi2/nmP2Dl0CLb+eY/NV66SQwC5z81XrpJDALnPwrXo3A9Cuc/CtejcD0K5z/fT42XbhLnP99PjZduEuc/tMh2vp8a5z+0yHa+nxrnP4lBYOXQIuc/iUFg5dAi5z9eukkMAivnP166SQwCK+c/MzMzMzMz5z8zMzMzMzPnPwisHFpkO+c/CKwcWmQ75z/dJAaBlUPnP90kBoGVQ+c/sp3vp8ZL5z+yne+nxkvnP4cW2c73U+c/hxbZzvdT5z9cj8L1KFznP1yPwvUoXOc/MQisHFpk5z8xCKwcWmTnPwaBlUOLbOc/BoGVQ4ts5z/b+X5qvHTnP9v5fmq8dOc/sHJoke185z+wcmiR7XznP4XrUbgehec/hetRuB6F5z9aZDvfT43nP1pkO99Pjec/L90kBoGV5z8v3SQGgZXnPwRWDi2ynec/BFYOLbKd5z/ZzvdT46XnP9nO91Pjpec/rkfhehSu5z+uR+F6FK7nP4PAyqFFtuc/g8DKoUW25z9YObTIdr7nP1g5tMh2vuc/LbKd76fG5z8tsp3vp8bnPwIrhxbZzuc/AiuHFtnO5z/Xo3A9CtfnP9ejcD0K1+c/rBxaZDvf5z+sHFpkO9/nP4GVQ4ts5+c/gZVDi2zn5z9WDi2yne/nP1YOLbKd7+c/K4cW2c735z8rhxbZzvfnPwAAAAAAAOg/AAAAAAAA6D/VeOkmMQjoP9V46SYxCOg/qvHSTWIQ6D+q8dJNYhDoP39qvHSTGOg/f2q8dJMY6D9U46WbxCDoP1TjpZvEIOg/KVyPwvUo6D8pXI/C9SjoP/7UeOkmMeg//tR46SYx6D/TTWIQWDnoP9NNYhBYOeg/qMZLN4lB6D+oxks3iUHoP30/NV66Seg/fT81XrpJ6D9SuB6F61HoP1K4HoXrUeg/JzEIrBxa6D8nMQisHFroP/yp8dJNYug//Knx0k1i6D/RItv5fmroP9Ei2/l+aug/ppvEILBy6D+mm8QgsHLoP3sUrkfheug/exSuR+F66D9QjZduEoPoP1CNl24Sg+g/JQaBlUOL6D8lBoGVQ4voP/p+arx0k+g/+n5qvHST6D/P91PjpZvoP8/3U+Olm+g/pHA9Ctej6D+kcD0K16PoP3npJjEIrOg/eekmMQis6D9OYhBYObToP05iEFg5tOg/I9v5fmq86D8j2/l+arzoP/hT46WbxOg/+FPjpZvE6D/NzMzMzMzoP83MzMzMzOg/okW28/3U6D+iRbbz/dToP3e+nxov3eg/d76fGi/d6D9MN4lBYOXoP0w3iUFg5eg/IbByaJHt6D8hsHJoke3oP/YoXI/C9eg/9ihcj8L16D/LoUW28/3oP8uhRbbz/eg/oBov3SQG6T+gGi/dJAbpP3WTGARWDuk/dZMYBFYO6T9KDAIrhxbpP0oMAiuHFuk/H4XrUbge6T8fhetRuB7pP/T91HjpJuk/9P3UeOkm6T/Jdr6fGi/pP8l2vp8aL+k/nu+nxks36T+e76fGSzfpP3Noke18P+k/c2iR7Xw/6T9I4XoUrkfpP0jhehSuR+k/HVpkO99P6T8dWmQ730/pP/LSTWIQWOk/8tJNYhBY6T/HSzeJQWDpP8dLN4lBYOk/nMQgsHJo6T+cxCCwcmjpP3E9CtejcOk/cT0K16Nw6T9GtvP91HjpP0a28/3UeOk/Gy/dJAaB6T8bL90kBoHpP/Cnxks3iek/8KfGSzeJ6T/FILByaJHpP8UgsHJokek/mpmZmZmZ6T+amZmZmZnpP28Sg8DKoek/bxKDwMqh6T9Ei2zn+6npP0SLbOf7qek/GQRWDi2y6T8ZBFYOLbLpP+58PzVeuuk/7nw/NV666T/D9Shcj8LpP8P1KFyPwuk/mG4Sg8DK6T+YbhKDwMrpP23n+6nx0uk/bef7qfHS6T9CYOXQItvpP0Jg5dAi2+k/F9nO91Pj6T8X2c73U+PpP+xRuB6F6+k/7FG4HoXr6T/ByqFFtvPpP8HKoUW28+k/lkOLbOf76T+WQ4ts5/vpP2q8dJMYBOo/arx0kxgE6j8/NV66SQzqPz81XrpJDOo/FK5H4XoU6j8UrkfhehTqP+kmMQisHOo/6SYxCKwc6j++nxov3STqP76fGi/dJOo/kxgEVg4t6j+TGARWDi3qP2iR7Xw/Neo/aJHtfD816j89CtejcD3qPz0K16NwPeo/EoPAyqFF6j8Sg8DKoUXqP+f7qfHSTeo/5/up8dJN6j+8dJMYBFbqP7x0kxgEVuo/ke18PzVe6j+R7Xw/NV7qP2ZmZmZmZuo/ZmZmZmZm6j8730+Nl27qPzvfT42Xbuo/EFg5tMh26j8QWDm0yHbqP+XQItv5fuo/5dAi2/l+6j+6SQwCK4fqP7pJDAIrh+o/j8L1KFyP6j+PwvUoXI/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xN7AP3URdL7E3sA/j2Gv8FoRwT+PYa/wWhHBP5wu/TemGsE/nC79N6YawT+iJ4akmaDBP6InhqSZoME/YkaXbPemwT9iRpds96bBP5wySPJ6xcE/nDJI8nrFwT+8hqDSfs3BP7yGoNJ+zcE/uoT+BP/2wT+6hP4E//bBP0rpkO2z+sE/SumQ7bP6wT9XUfnVjRbCP1dR+dWNFsI/LLjPOXwXwj8suM85fBfCP4AJhhdsRsI/gAmGF2xGwj8gbztpEFPCPyBvO2kQU8I/2v3NByJYwj/a/c0HIljCP0zLKHe6cMI/TMsod7pwwj+1+QF+q4DCP7X5AX6rgMI/t21qtT6Fwj+3bWq1PoXCP7U7Gt3ejsI/tTsa3d6Owj8pbMTRILDCPylsxNEgsMI/+R0GvtKwwj/5HQa+0rDCP664Sj/Ks8I/rrhKP8qzwj/jWnivu9nCP+NaeK+72cI/14ppYMbhwj/XimlgxuHCP6oJozlp48I/qgmjOWnjwj/jXZkTrQXDP+NdmROtBcM/JtCXZ1A6wz8m0JdnUDrDPyuNC5XLUcM/K40LlctRwz9AkmARC1XDP0CSYBELVcM/1dHnVc1xwz/V0edVzXHDP5T7zs3qpcM/lPvOzeqlwz8s0P9SR9LDPyzQ/1JH0sM/8ovVHPPmwz/yi9Uc8+bDP9xJYmA37sM/3EliYDfuwz9jHzG3QvzDP2MfMbdC/MM/R0DP11MOxD9HQM/XUw7EP/TbIrQKFMQ/9NsitAoUxD/kAI9Y/xnEP+QAj1j/GcQ/VlDSHTllxD9WUNIdOWXEPxkxWUJWtsQ/GTFZQla2xD8xyuw6jbbEPzHK7DqNtsQ/NB0xMNEOxT80HTEw0Q7FP/UZRmZHgsU/9RlGZkeCxT+p0JrN3IPFP6nQms3cg8U/p/14Ro2fxT+n/XhGjZ/FP6qNrMRQvsU/qo2sxFC+xT9/toh6ljzGP3+2iHqWPMY/pZoiJvVlxj+lmiIm9WXGP57AoEppb8Y/nsCgSmlvxj8gH59x63XGPyAfn3HrdcY/53GyCxWHxj/ncbILFYfGP0X2m2tJl8Y/Rfaba0mXxj/xY4XfVZ3GP/Fjhd9VncY/fzNJHiW7xj9/M0keJbvGPysDqzDvu8Y/KwOrMO+7xj+7zADyQAXHP7vMAPJABcc/zN/4G80Zxz/M3/gbzRnHP2dgIdg8Nsc/Z2Ah2Dw2xz8kGtvuDWLHPyQa2+4NYsc/vgA19BZ7xz++ADX0FnvHP0nTlpxAjsc/SdOWnECOxz+SBV30OqjHP5IFXfQ6qMc/pLJnRDnDxz+ksmdEOcPHP6/BdG3Y2Mc/r8F0bdjYxz/zg5f6YB7IP/ODl/pgHsg/PGTI020zyD88ZMjTbTPIP6LmUAHKVMg/ouZQAcpUyD9XS2a1qn7IP1dLZrWqfsg/YougA0yNyD9ii6ADTI3IPzRgjb8Wocg/NGCNvxahyD/KadZixOPIP8pp1mLE48g/qGHpE0TtyD+oYekTRO3IP++7Ip4n7sg/77sinifuyD9kvDPTN/7IP2S8M9M3/sg/BwJr1ncGyT8HAmvWdwbJP5SR27BNOck/lJHbsE05yT/TUeK89TrJP9NR4rz1Osk/Ex+phfVryT8TH6mF9WvJP9kcaQEBhsk/2RxpAQGGyT/LnFddKY3JP8ucV10pjck/gdO7iw+XyT+B07uLD5fJP+lvc45yrsk/6W9zjnKuyT8vVB1+IsjJPy9UHX4iyMk/NFitviTZyT80WK2+JNnJP7UvOWgy+8k/tS85aDL7yT/ITSUdov7JP8hNJR2i/sk/thc0v1AKyj+2FzS/UArKP/UbKVrdFMo/9RspWt0Uyj+jmSI3bi7KP6OZIjduLso/djlG2hhDyj92OUbaGEPKP5gCoEFrSco/mAKgQWtJyj/eU/FyV/bKP95T8XJX9so/W8f5+aUYyz9bx/n5pRjLP8mq6CYSb8s/yaroJhJvyz9XzkXxX3fLP1fORfFfd8s/GiaLZMmXyz8aJotkyZfLP5J4PLYAnss/kng8tgCeyz8tj2ZBh8zLPy2PZkGHzMs/lAswZGQezD+UCzBkZB7MP+ru5AY1I8w/6u7kBjUjzD8GekXlzjLMPwZ6ReXOMsw/9EbRGg+FzD/0RtEaD4XMP2duIT2ehcw/Z24hPZ6FzD+wMauC2JfMP7Axq4LYl8w/TkU0E7uzzD9ORTQTu7PMP2btvP3aUc0/Zu28/dpRzT9ACCYx7FnNP0AIJjHsWc0/dRhQQBBfzT91GFBAEF/NP0wXhL5CY80/TBeEvkJjzT9PrgbzOqHNP0+uBvM6oc0/T/koIJ68zT9P+SggnrzNPyUZi/zYAM4/JRmL/NgAzj+Gbo6GUi7OP4ZujoZSLs4/oteOouVHzj+i146i5UfOP2dXz5/mSc4/Z1fPn+ZJzj/Mczz61F7OP8xzPPrUXs4/Zx16G999zj9nHXob333OP67R8f7YgM4/rtHx/tiAzj8UKRewu4TOPxQpF7C7hM4/DAHzcTqazj8MAfNxOprOP4JSWjckyM4/glJaNyTIzj/SGDuhbPXOP9IYO6Fs9c4/dZEbtIcLzz91kRu0hwvPP9CaReeGhs8/0JpF54aGzz//SQRgeJHPP/9JBGB4kc8/B4K2YH2dzz8HgrZgfZ3PPynzUbtHBdA/KfNRu0cF0D9ZwZnxxA/QP1nBmfHED9A/uxO3f/If0D+7E7d/8h/QP2UszxBjKtA/ZSzPEGMq0D96VdAJnDvQP3pV0AmcO9A/IszBZSpF0D8izMFlKkXQP95GDwe/RdA/3kYPB79F0D9zYBrjnkfQP3NgGuOeR9A/CV4i2/RQ0D8JXiLb9FDQP7IY666abtA/shjrrppu0D8EGGyRMW/QPwQYbJExb9A/Me+SONFv0D8x75I40W/QP1DkvAL8sNA/UOS8Avyw0D80xlO7pMDQPzTGU7ukwNA/BU7FmhzH0D8FTsWaHMfQP5ggOOg27NA/mCA46Dbs0D8deQUBi/jQPx15BQGL+NA/DL19u2EO0T8MvX27YQ7RP48YwpAmKtE/jxjCkCYq0T+8RyNTrjnRP7xHI1OuOdE/6eFFgvZU0T/p4UWC9lTRP3nQFTe1jNE/edAVN7WM0T9gAW1A54zRP2ABbUDnjNE/kyGPcS610T+TIY9xLrXRPxV0KnyRxtE/FXQqfJHG0T+lH84tz/TRP6Ufzi3P9NE/NaZhSf700T81pmFJ/vTRP3wr5lzpA9I/fCvmXOkD0j9p3WLkeQrSP2ndYuR5CtI/0XWD7kkW0j/RdYPuSRbSP61jm/qgMNI/rWOb+qAw0j9jjd3zrzLSP2ON3fOvMtI/BFaxyvQ20j8EVrHK9DbSP74lxG7VS9I/viXEbtVL0j/fLtDof4bSP98u0Oh/htI/2fnHKI6K0j/Z+ccojorSP7Chi8XrktI/sKGLxeuS0j/atrEcx5fSP9q2sRzHl9I/YTcSodGh0j9hNxKh0aHSP+fuQxWSqtI/5+5DFZKq0j8rY1+hwq7SPytjX6HCrtI/ZCcrDWS00j9kJysNZLTSP+vJPWaVuNI/68k9ZpW40j8jxip3+dDSPyPGKnf50NI/q+dPoXzf0j+r50+hfN/SP4MUoTDQ39I/gxShMNDf0j86Mb0AkejSPzoxvQCR6NI/HPAzuubp0j8c8DO65unSPycWQAx89tI/JxZADHz20j+6Ax69sQLTP7oDHr2xAtM/Wxj+K4wH0z9bGP4rjAfTPzuH4ouOEdM/O4fii44R0z9TsAAUHxrTP1OwABQfGtM/LEVFoUcl0z8sRUWhRyXTPxSfyXAZTNM/FJ/JcBlM0z+Ubpa4BnDTP5RulrgGcNM/eguZO6F50z96C5k7oXnTP029XW2TntM/Tb1dbZOe0z+nIOMunKbTP6cg4y6cptM/nFOX5sLV0z+cU5fmwtXTPyBVWV+8BtQ/IFVZX7wG1D8rkEPsgBDUPyuQQ+yAENQ/rPCeV30b1D+s8J5XfRvUP0Q5+J1citQ/RDn4nVyK1D+HzAPi/YrUP4fMA+L9itQ/wkf4sM6U1D/CR/iwzpTUP+G/kY2bvNQ/4b+RjZu81D+ZkZoBmd3UP5mRmgGZ3dQ/YP3hkW7t1D9g/eGRbu3UP26H778E/dQ/bofvvwT91D99CX7SLgjVP30JftIuCNU/D/oxc3Eb1T8P+jFzcRvVP49HffPbNtU/j0d989s21T9PazN1sl/VP09rM3WyX9U/ob2GBFqH1T+hvYYEWofVPymj0OogCNY/KaPQ6iAI1j86jZtwLBzWPzqNm3AsHNY/tEoe7EAr1j+0Sh7sQCvWP1HUi7JGMNY/UdSLskYw1j/fLYPdDEfWP98tg90MR9Y/GIyv0X5H1j8YjK/RfkfWPxD5QSfdcdY/EPlBJ91x1j8izCfFUHrWPyLMJ8VQetY/k2kmTtuJ1j+TaSZO24nWPzKZU+OXrtY/MplT45eu1j8qR3Wln7LWPypHdaWfstY//hhO10Wz1j/+GE7XRbPWP6RZFGTFttY/pFkUZMW21j8ZDsL2B8vWPxkOwvYHy9Y/vy2SBbrw1j+/LZIFuvDWP/mn9wm59tY/+af3Cbn21j/xQG5O+PvWP/FAbk74+9Y/rczmcqP81j+tzOZyo/zWP3qFiZEELNc/eoWJkQQs1z/9AMspRC7XP/0AyylELtc/+qL+kn8u1z/6ov6Sfy7XP7zjqm4GPdc/vOOqbgY91z+J7wZCg3HXP4nvBkKDcdc/4t3aD/x21z/i3doP/HbXP8dvYetCfdc/x29h60J91z9/IzaziobXP38jNrOKhtc/tnJLzMCQ1z+2ckvMwJDXP19TK3L/v9c/X1Mrcv+/1z+LlJ0KVOnXP4uUnQpU6dc/On8GRl3q1z86fwZGXerXP7h7JutnCtg/uHsm62cK2D91vo43cAzYP3W+jjdwDNg/nMsEXrwP2D+cywRevA/YP9HXWMcJENg/0ddYxwkQ2D/IVR2//xLYP8hVHb//Etg/oT70Xw4f2D+hPvRfDh/YP0KVkSIgPNg/QpWRIiA82D9qiX+3IULYP2qJf7chQtg/f3TB4VBV2D9/dMHhUFXYP7HToWjwWNg/sdOhaPBY2D/B9xn0CF7YP8H3GfQIXtg/bc/uJ7Bl2D9tz+4nsGXYP8C98UGweNg/wL3xQbB42D8a+ZL1gILYPxr5kvWAgtg/A+EBeCyM2D8D4QF4LIzYP7BzoQCSmdg/sHOhAJKZ2D9ts05pk6/YP22zTmmTr9g/DIh3GbXe2D8MiHcZtd7YP13mvYui39g/Xea9i6Lf2D8MAwNBUwHZPwwDA0FTAdk/DFoOtKoC2T8MWg60qgLZP31evV3aGdk/fV69XdoZ2T+2FsrceCTZP7YWytx4JNk/6sHxydYr2T/qwfHJ1ivZP0sTNLxmMNk/SxM0vGYw2T9ADHCYCjjZP0AMcJgKONk/hEshpMJT2T+ESyGkwlPZP1k6H+uWXdk/WTof65Zd2T9xEor941/ZP3ESiv3jX9k/ZLuFSjqQ2T9ku4VKOpDZP7+ShvQnvNk/v5KG9Ce82T/5zsbnsr7ZP/nOxueyvtk/pAcjhIwC2j+kByOEjALaP09GJkMhEdo/T0YmQyER2j+GCsvg4ifaP4YKy+DiJ9o/LhxRZa8s2j8uHFFlryzaP83vhG+lUto/ze+Eb6VS2j8/9R/NgF3aPz/1H82AXdo/yd1N8qNv2j/J3U3yo2/aPxYCLDGld9o/FgIsMaV32j9mO/AAt4LaP2Y78AC3gto/cemEDamD2j9x6YQNqYPaP11khfPajNo/XWSF89qM2j/eh/pjmaLaP96H+mOZoto/GjqDTXLq2j8aOoNNcuraPye3vwP/+do/J7e/A//52j8DosdcDAPbPwOix1wMA9s/42IuNDIa2z/jYi40MhrbPy/e7SvCJts/L97tK8Im2z/IGxkXWF/bP8gbGRdYX9s/6GLAlL1m2z/oYsCUvWbbP5a+xS5ga9s/lr7FLmBr2z+2McmAwXTbP7YxyYDBdNs/MNqxnRh82z8w2rGdGHzbPxLVC1NMfNs/EtULU0x82z/iqhmwpI/bP+KqGbCkj9s/6cIPy2mZ2z/pwg/LaZnbP3CzjdRqnds/cLON1Gqd2z/g1rGJRLjbP+DWsYlEuNs/s9vJvfQS3D+z28m99BLcPy63fuJZL9w/Lrd+4lkv3D+JFMUCTUXcP4kUxQJNRdw/w05qcKRU3D/DTmpwpFTcP1AAkYEPVdw/UACRgQ9V3D/QM2PZ2GfcP9AzY9nYZ9w/apImZVaC3D9qkiZlVoLcP4xDvjeAhdw/jEO+N4CF3D9yAMsTnY7cP3IAyxOdjtw/NnmPFe7J3D82eY8V7sncP5soEVps4Nw/mygRWmzg3D/vXpML+QHdP+9ekwv5Ad0/bY0BzBJg3T9tjQHMEmDdPxopcXd2bN0/Gilxd3Zs3T+B2c1XcI7dP4HZzVdwjt0/IU2o9ayQ3T8hTaj1rJDdP+FuNtcppt0/4W421ymm3T9UDlUx7q7dP1QOVTHurt0//1IjsIjM3T//UiOwiMzdPx8zW7Y9zd0/HzNbtj3N3T/Bt+ZONN3dP8G35k403d0/H/NVjdvq3T8f81WN2+rdP76kZBuZ9d0/vqRkG5n13T/YZ2cwkPzdP9hnZzCQ/N0/Q0aVyTUC3j9DRpXJNQLeP4p6i0d3CN4/inqLR3cI3j+DAIXiuQ3eP4MAheK5Dd4/KZfG/WAS3j8pl8b9YBLePwC0lQOpG94/ALSVA6kb3j9MsSbWOiXeP0yxJtY6Jd4/ujX5ESIs3j+6NfkRIizeP3mRBXMQL94/eZEFcxAv3j9ihfwiFkfeP2KF/CIWR94/ig576o1M3j+KDnvqjUzeP5bZVrqDVd4/ltlWuoNV3j834QikLF7ePzfhCKQsXt4/cUkIj9hs3j9xSQiP2GzeP+F5Lxq8ct4/4XkvGrxy3j/nJekztYTeP+cl6TO1hN4//LoOXSqS3j/8ug5dKpLeP7zBuszd4N4/vMG6zN3g3j/u4L2Q3/7eP+7gvZDf/t4/J6ErO0wH3z8noSs7TAffP9hZd9ssHt8/2Fl32ywe3z8HNvkzlyDfPwc2+TOXIN8/zGSpzrRa3z/MZKnOtFrfPya2KQGAaN8/JrYpAYBo3z+cNKW5WYDfP5w0pblZgN8/Xn+DCqWa3z9ef4MKpZrfP5alvz4nod8/lqW/Pieh3z/veRmTQ67fP+95GZNDrt8/WDJ6alu33z9YMnpqW7ffPyPE61Qbxd8/I8TrVBvF3z/NLbBUHsffP80tsFQex98/njEnYpnL3z+eMSdimcvfP07Im9b2098/Tsib1vbT3z8QufFpzdffPxC58WnN198/x1wNdKoC4D/HXA10qgLgPzMUFft7COA/MxQV+3sI4D8c3yFD3hzgPxzfIUPeHOA/UCtOj4Ug4D9QK06PhSDgP6esJrP1OeA/p6wms/U54D+tg7cRA0LgP62DtxEDQuA/AeGPKERD4D8B4Y8oREPgPzDF8bDSROA/MMXxsNJE4D+/Gak6DUrgP78ZqToNSuA/2a8l4F5k4D/ZryXgXmTgP8sL0OnzZOA/ywvQ6fNk4D/ocZU0rG3gP+hxlTSsbeA/cSD8uqpx4D9xIPy6qnHgPy29awF/dOA/Lb1rAX904D9nxaTf5HXgP2fFpN/kdeA/87M7SN944D/zsztI33jgP2P0DgOjgeA/Y/QOA6OB4D/56anAG5bgP/npqcAbluA/GsCYboGW4D8awJhugZbgP6q9tHSamuA/qr20dJqa4D94L1+SjqPgP3gvX5KOo+A/b+AKQD2r4D9v4ApAPavgP15lwYeUteA/XmXBh5S14D8ifOVT/dTgPyJ85VP91OA/Mqpq4rLa4D8yqmristrgP6QkAraTFOE/pCQCtpMU4T+/t7tmgBrhP7+3u2aAGuE/1KfpeO0y4T/Up+l47TLhP4Xm+wssNuE/heb7Cyw24T8RK8CvTDbhPxErwK9MNuE/RWdH6r9G4T9FZ0fqv0bhP1K074VoR+E/UrTvhWhH4T+cpq4oIFLhP5ymriggUuE/4lsizdZm4T/iWyLN1mbhP83Jw04tb+E/zcnDTi1v4T/mUoIVwHPhP+ZSghXAc+E/cfAZXzWf4T9x8BlfNZ/hP95uVj04tuE/3m5WPTi24T/0ifq42dXhP/SJ+rjZ1eE/5DnpnZ/W4T/kOemdn9bhP8bs+7+Y7OE/xuz7v5js4T9p/47xGvLhP2n/jvEa8uE/ez+NOHfy4T97P404d/LhPxs2bizr/eE/GzZuLOv94T8zTqgULQ/iPzNOqBQtD+I/2mrPNPYX4j/aas809hfiP7+ZFRpsK+I/v5kVGmwr4j88M/SqeCziPzwz9Kp4LOI/Rqlo9DE84j9GqWj0MTziP54+2nDXR+I/nj7acNdH4j8xzrrh6l/iPzHOuuHqX+I/PqXucJZi4j8+pe5wlmLiP2+Iw9EFZeI/b4jD0QVl4j+Z/ovO3mXiP5n+i87eZeI/vA1rOt9m4j+8DWs632biP0ib83sSaeI/SJvzexJp4j9J/jG0hILiP0n+MbSEguI/gCKwc+2C4j+AIrBz7YLiPwVkFPISheI/BWQU8hKF4j9s8/y4qYziP2zz/LipjOI/A3ZakLOQ4j8DdlqQs5DiP/AX+WZEkeI/8Bf5ZkSR4j8z6O8MVZLiPzPo7wxVkuI/QO3Ge7uU4j9A7cZ7u5TiPw1sAebhnOI/DWwB5uGc4j+TuN4aOsziP5O43ho6zOI/KK/FDx3T4j8or8UPHdPiP0CZgU0y2OI/QJmBTTLY4j/z327DIOniP/PfbsMg6eI/g9c55Gfs4j+D1znkZ+ziP76NlHNq8eI/vo2Uc2rx4j/hdBk/W/3iP+F0GT9b/eI//PcNbVwB4z/89w1tXAHjPycJ+rRDFeM/Jwn6tEMV4z8yRkG7XRXjPzJGQbtdFeM/LHK+2JAc4z8scr7YkBzjPyD31NkoPeM/IPfU2Sg94z9k/rwtFkzjP2T+vC0WTOM/nKGTa4NN4z+coZNrg03jP0Z6z8zWYuM/RnrPzNZi4z86tmgPkGbjPzq2aA+QZuM/URaMpbFm4z9RFoylsWbjP71MQs4mcOM/vUxCziZw4z9TuL+bLXrjP1O4v5steuM/FcYOkW174z8Vxg6RbXvjP01MnVyygeM/TUydXLKB4z/9L0mGkZrjP/0vSYaRmuM/VNyE2JKb4z9U3ITYkpvjPzxEAlV/quM/PEQCVX+q4z9koTaetMHjP2ShNp60weM/xsaPrgDj4z/Gxo+uAOPjP5p044UU4+M/mnTjhRTj4z+ReApMJOXjP5F4Ckwk5eM/1sKM3tjm4z/Wwoze2ObjP9WaLGCc7OM/1ZosYJzs4z9DMECP3vvjP0MwQI/e++M/Gj7EB7Mh5D8aPsQHsyHkPw49FkdrIuQ/Dj0WR2si5D8IUCIc8TPkPwhQIhzxM+Q/OyIMvFE05D87Igy8UTTkPwObeeRBOuQ/A5t55EE65D+73B6rgT7kP7vcHquBPuQ/w4M4jtE/5D/DgziO0T/kP79QFCWoXuQ/v1AUJahe5D8X7OgnnV/kPxfs6CedX+Q/ZCUWUGFj5D9kJRZQYWPkP8k7eKjVaeQ/yTt4qNVp5D/nXxyiOW3kP+dfHKI5beQ/3xS7RP6L5D/fFLtE/ovkP2vK0/rAqOQ/a8rT+sCo5D8OUHOeuq3kPw5Qc566reQ/1uLjz1605D/W4uPPXrTkP40iZ/M4vuQ/jSJn8zi+5D9xLVIFi9nkP3EtUgWL2eQ/4rQk403c5D/itCTjTdzkP7U9Rukg5OQ/tT1G6SDk5D9Ycb42S+fkP1hxvjZL5+Q/ychgx0Dw5D/JyGDHQPDkP9zWt3Al+OQ/3Na3cCX45D+FfqRFcwTlP4V+pEVzBOU/valkvfMM5T+9qWS98wzlP9pnQx4SGeU/2mdDHhIZ5T8yr6Rp1BnlPzKvpGnUGeU/XiOBHvAZ5T9eI4Ee8BnlP+PqY4SzI+U/4+pjhLMj5T/vL/Bthj3lP+8v8G2GPeU/wO/K7pdS5T/A78rul1LlP0j0sAGoU+U/SPSwAahT5T8hxCyHA1zlPyHELIcDXOU/P5g6a75e5T8/mDprvl7lP9rlWowUZ+U/2uVajBRn5T8+35eHDXHlPz7fl4cNceU/gBFKWyBz5T+AEUpbIHPlP5YHlBFnfeU/lgeUEWd95T89xvRTOn/lPz3G9FM6f+U/Igl0L4mD5T8iCXQviYPlP8YJFnuXtuU/xgkWe5e25T+eQHnPSdrlP55Aec9J2uU/lsxMe9ni5T+WzEx72eLlP4b4v2Wj+OU/hvi/ZaP45T/fGFqBxfzlP98YWoHF/OU/D5mdqFYB5j8PmZ2oVgHmPxXA6EI/DOY/FcDoQj8M5j9uawsrdxjmP25rCyt3GOY/6bnQENYh5j/pudAQ1iHmP7O8yypzJ+Y/s7zLKnMn5j+dKCHv3TLmP50oIe/dMuY/wt0TPSBK5j/C3RM9IErmPw4VrLnRVuY/DhWsudFW5j9KWXkN/mPmP0pZeQ3+Y+Y/E0/pOHZl5j8TT+k4dmXmP2ppxFaUaeY/amnEVpRp5j+Wh2T4XH3mP5aHZPhcfeY/QZPu8WiB5j9Bk+7xaIHmP2ICXzSegeY/YgJfNJ6B5j/zTJw5dITmP/NMnDl0hOY/8YwdhxyJ5j/xjB2HHInmP7Q2/biSlOY/tDb9uJKU5j/6IHdcJpfmP/ogd1wml+Y/HQSUPIWh5j8dBJQ8haHmP63JGiODteY/rckaI4O15j+5xoCpirXmP7nGgKmKteY/tVaFD9a15j+1VoUP1rXmP+uIGpqq0+Y/64gamqrT5j8hfwTAFd/mPyF/BMAV3+Y/uYU4S2rf5j+5hThLat/mP/CcCVMV6eY/8JwJUxXp5j/SJySncOrmP9InJKdw6uY/RHEiGRP85j9EcSIZE/zmPyQuQGV3/eY/JC5AZXf95j9PRGqXIAbnP09EapcgBuc/qxq/urAb5z+rGr+6sBvnP/m0OYQiHOc/+bQ5hCIc5z/xwgtNgSLnP/HCC02BIuc/3LYkaNIi5z/ctiRo0iLnP8boQeKwJ+c/xuhB4rAn5z8q6ODtKy/nPyro4O0rL+c/x5HQmZow5z/HkdCZmjDnP3lCaU85Mec/eUJpTzkx5z/pskZWrDPnP+myRlasM+c/fQVEnB825z99BUScHzbnP4M7tS5vN+c/gzu1Lm835z/C21Yn9zrnP8LbVif3Ouc/rcGCdeBE5z+twYJ14ETnPyU/EfMsRec/JT8R8yxF5z+jdJGLR0jnP6N0kYtHSOc/Pawx81BN5z89rDHzUE3nP7lHsnvoU+c/uUeye+hT5z+3bRo4NVfnP7dtGjg1V+c/ZHELUaxl5z9kcQtRrGXnP/p7m+PabOc/+nub49ps5z9+h9gKsXLnP36H2Aqxcuc/9nrcbteE5z/2etxu14TnP3u6oLmLhuc/e7qguYuG5z9zelXiw5HnP3N6VeLDkec/GH3pL9ib5z8Yfekv2JvnP+6WtvV5nOc/7pa29Xmc5z85jaos/Z3nPzmNqiz9nec/+/JMxwes5z/78kzHB6znP6Fp99p+t+c/oWn32n635z8IQUKihrfnPwhBQqKGt+c/rrBsvs275z+usGy+zbvnP7wB1PLYvec/vAHU8ti95z/Wxy47673nP9bHLjvrvec/5TyTMOC/5z/lPJMw4L/nPyXVydaMwec/JdXJ1ozB5z/qi7K74crnP+qLsrvhyuc/Dbr0+h3P5z8NuvT6Hc/nP3qK9cHLz+c/eor1wcvP5z+p2HeIf9HnP6nYd4h/0ec/iufHTh7W5z+K58dOHtbnPwPzGLkE2ec/A/MYuQTZ5z/0ptJFH9rnP/Sm0kUf2uc/QycABPfb5z9DJwAE99vnP5PNCVvi3Oc/k80JW+Lc5z9USQ3gE+DnP1RJDeAT4Oc/VoTXtuDu5z9WhNe24O7nPxgwG9Hi8uc/GDAb0eLy5z9Q41OxgvrnP1DjU7GC+uc/fT+N28D65z99P43bwPrnP9vXGHW+++c/29cYdb775z/CTWfZpP3nP8JNZ9mk/ec/hZEPHOsT6D+FkQ8c6xPoP2ZrAxQjH+g/ZmsDFCMf6D/Wl22q0CPoP9aXbarQI+g/w5ey6bcp6D/Dl7LptynoPzCckW/QL+g/MJyRb9Av6D/0xHaxPzPoP/TEdrE/M+g/dWmz99s06D91abP32zToP1bVG+y2Neg/VtUb7LY16D+4/t6/kjfoP7j+3r+SN+g/LoPZ0SI56D8ug9nRIjnoP7qPyEY1ROg/uo/IRjVE6D/krelA+0voP+St6UD7S+g/N3w5QvZd6D83fDlC9l3oP3+P3+uEY+g/f4/f64Rj6D8UWG/mlXboPxRYb+aVdug/1WYT+KZ56D/VZhP4pnnoP9wPqfbUeug/3A+p9tR66D86jAhAznzoPzqMCEDOfOg/Wk8g3/aF6D9aTyDf9oXoP3MjWKxJhug/cyNYrEmG6D/tk2Gce4joP+2TYZx7iOg/2NHuZF6O6D/Y0e5kXo7oP74XV3nMlOg/vhdXecyU6D8kVckrAqvoPyRVySsCq+g/pItkSgmv6D+ki2RKCa/oP3yfDprGu+g/fJ8Omsa76D8G/oVAUcvoPwb+hUBRy+g/urqu9j7R6D+6uq72PtHoP3WJyZbm1Og/dYnJlubU6D8UHFYviNvoPxQcVi+I2+g/0H5exJvi6D/Qfl7Em+LoP0mwqweQ5ug/SbCrB5Dm6D8XCOhbVuzoPxcI6FtW7Og/aIQW+8Hy6D9ohBb7wfLoPyOAr8E1Bek/I4CvwTUF6T+brDconQbpP5usNyidBuk/5r1g6YoU6T/mvWDpihTpPwr2ouA7Fuk/Cvai4DsW6T+xHffCjhbpP7Ed98KOFuk/SRg3C7AY6T9JGDcLsBjpPz2Iwko1Hek/PYjCSjUd6T99v+N00x3pP32/43TTHek/WCfX16446T9YJ9fXrjjpP2+i66cNTek/b6Lrpw1N6T+uTZI3S1XpP65NkjdLVek/MO6bmPNV6T8w7puY81XpP1L5JBByaek/UvkkEHJp6T+pxGWn+HnpP6nEZaf4eek/so932M2G6T+yj3fYzYbpP1Y+ePhvh+k/Vj54+G+H6T9ks154O6DpP2SzXng7oOk/YtGiyAOz6T9i0aLIA7PpP3RKl0z8t+k/dEqXTPy36T9VocZ997vpP1Whxn33u+k/hufZe5a/6T+G59l7lr/pP0m4yRbfwek/SbjJFt/B6T+jaob0usbpP6NqhvS6xuk/d+0adAPH6T937Rp0A8fpP3hkcymvyOk/eGRzKa/I6T8ybgy7VdHpPzJuDLtV0ek/5qMjUsLS6T/moyNSwtLpP/20q20c0+k//bSrbRzT6T8FnfJrM9npPwWd8msz2ek/qHgeMqTs6T+oeB4ypOzpPz/GLG1o9+k/P8YsbWj36T94KnLIkwPqP3gqcsiTA+o/isf+2isK6j+Kx/7aKwrqPzbQN2yLDeo/NtA3bIsN6j+B6yB8HiTqP4HrIHweJOo/DsjSyZEq6j8OyNLJkSrqP0Vi9/MRLeo/RWL38xEt6j+C3m0R3DDqP4LebRHcMOo/k54xgVw16j+TnjGBXDXqP9VuipENROo/1W6KkQ1E6j8kLJ9bh0XqPyQsn1uHReo/KFI9fWZM6j8oUj19ZkzqPyzwbFR2T+o/LPBsVHZP6j/5GT6/hlHqP/kZPr+GUeo/h3Gc8xRU6j+HcZzzFFTqPzK8d0JNXuo/Mrx3Qk1e6j/Y5a76bmDqP9jlrvpuYOo/vtMNYbtq6j++0w1hu2rqPwoAxSzMeuo/CgDFLMx66j/uwf4Oj4bqP+7B/g6Phuo/2jFxXNmG6j/aMXFc2YbqP7HlFO3hieo/seUU7eGJ6j/4cJTmgorqP/hwlOaCiuo/bWFSNZKQ6j9tYVI1kpDqP7EQPeyPlOo/sRA97I+U6j8HzpIhGZzqPwfOkiEZnOo/LkLSS+yh6j8uQtJL7KHqP2ZJ4PHfpuo/Zkng8d+m6j9uUEW/zKfqP25QRb/Mp+o/mpyXHbWs6j+anJcdtazqP8S1anfetuo/xLVqd9626j+plEjfXLjqP6mUSN9cuOo/LQBX2WK86j8tAFfZYrzqPwzgGkDLvOo/DOAaQMu86j/yCSYyFcjqP/IJJjIVyOo/WW7tKV3K6j9Zbu0pXcrqPyqDf5I70uo/KoN/kjvS6j9Tan+tIN/qP1Nqf60g3+o/ChX4yGTf6j8KFfjIZN/qPxd5c8Ey4Oo/F3lzwTLg6j//OwZdU+PqP/87Bl1T4+o/ftwa8J3k6j9+3BrwneTqP4JKdfJi6Oo/gkp18mLo6j+zzHns+OrqP7PMeez46uo/w1pKIQvr6j/DWkohC+vqP0AChjw38eo/QAKGPDfx6j8mMVXoF/LqPyYxVegX8uo/GHY2XG3y6j8YdjZcbfLqP+9baYxg9eo/71tpjGD16j8SoR4G8/3qPxKhHgbz/eo/9cOvlC4E6z/1w6+ULgTrP/UshDmtDOs/9SyEOa0M6z/qcmmy4hnrP+pyabLiGes/21rIs1Ee6z/bWsizUR7rP+jvhgXTHus/6O+GBdMe6z/wPvHXFyHrP/A+8dcXIes/vYjqB7wp6z+9iOoHvCnrP6aD6h0rKus/poPqHSsq6z8jw0zqZC7rPyPDTOpkLus/MQQUih816z8xBBSKHzXrPz3ofCAkNes/Peh8ICQ16z8pHDgG2DbrPykcOAbYNus/zF2QGARG6z/MXZAYBEbrP+KKlwGnR+s/4oqXAadH6z9r39CNBEnrP2vf0I0ESes/XZEEAF9J6z9dkQQAX0nrP03BdSNgS+s/TcF1I2BL6z9kMQphkUvrP2QxCmGRS+s/thjTIERc6z+2GNMgRFzrP+X5KuYsX+s/5fkq5ixf6z+oXkF9Wl/rP6heQX1aX+s/rVlngsFj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      "application/vnd.bokehjs_exec.v0+json": ""
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     "output_type": "display_data"
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   ],
   "source": [
    "# Make tidy data frames for convenient plotting\n",
    "df_p = pd.DataFrame(data=p_vals, columns=[\"n = \" + str(n) for n in n_samples])\n",
    "df_p = df_p.melt(var_name=\"n\", value_name=\"p\")\n",
    "df_akaike = pd.DataFrame(\n",
    "    data=akaike_weights, columns=[\"n = \" + str(n) for n in n_samples]\n",
    ")\n",
    "df_akaike = df_akaike.melt(var_name=\"n\", value_name=\"akaike_weight\")\n",
    "\n",
    "# Make plots\n",
    "p1 = iqplot.ecdf(\n",
    "    df_p,\n",
    "    cats=[\"n\"],\n",
    "    q=\"p\",\n",
    "    x_axis_label=\"p-value\",\n",
    "    x_axis_type=\"log\",\n",
    "    order=[\"n = 15\", \"n = 20\", \"n = 50\", \"n = 100\"],\n",
    "    frame_width=500,\n",
    "    frame_height=150,\n",
    "    palette=bokeh.palettes.d3[\"Category20c\"][4],\n",
    ")\n",
    "p2 = iqplot.ecdf(\n",
    "    df_akaike,\n",
    "    cats=[\"n\"],\n",
    "    q=\"akaike_weight\",\n",
    "    order=[\"n = 15\", \"n = 20\", \"n = 50\", \"n = 100\"],\n",
    "    x_axis_label=\"Akaike weight\",\n",
    "    x_axis_type=\"log\",\n",
    "    frame_width=500,\n",
    "    frame_height=150,\n",
    "    palette=bokeh.palettes.d3[\"Category20c\"][8][4:],\n",
    ")\n",
    "p1.legend.location = \"top_left\"\n",
    "p2.legend.location = \"top_left\"\n",
    "p1.x_range = p2.x_range\n",
    "\n",
    "bokeh.io.show(bokeh.layouts.gridplot([p1, p2], ncols=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that even though the p-value and Akaike weight have large spreads as the number of samples increases, they also shift leftward. This is because small differences in samples can be discerned with large sample sizes. But notice that the p-value and the Akaike weight varies over **orders of magnitude** for similar data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "This little exercise in reproducibility tells use that because the p-values \"dance\", and to a lesser extent so do the Akaike weights, we had better be sure the dancefloor is far to the left. This suggests large $n$ is needed.\n",
    "\n",
    "I would argue that you should do a similar \"dancing\" analysis of your data sets when you have a reasonable generative model in mind so that you can decide what constitutes a small p-value of Akaike weight."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.11.5\n",
      "IPython version      : 8.15.0\n",
      "\n",
      "numpy     : 1.24.3\n",
      "scipy     : 1.11.1\n",
      "pandas    : 2.0.3\n",
      "numba     : 0.57.0\n",
      "tqdm      : 4.65.0\n",
      "bokeh     : 3.2.1\n",
      "iqplot    : 0.3.5\n",
      "bebi103   : 0.1.17\n",
      "jupyterlab: 4.0.6\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -p numpy,scipy,pandas,numba,tqdm,bokeh,iqplot,bebi103,jupyterlab"
   ]
  }
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