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   "source": [
    "# Generation of plots for tutorial on good talks\n",
    "\n",
    "*This document was created from a Jupyter notebook.  You can download the notebook [here](l04_good_plots_for_slides.ipynb).*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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     "metadata": {},
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   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=UserWarning)\n",
    "\n",
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_formats = {'png', 'retina'}\n",
    "\n",
    "# Markov chain Monte Carlo package\n",
    "import emcee\n",
    "\n",
    "import bokeh.plotting\n",
    "import bokeh.io\n",
    "import bokeh.charts\n",
    "import bokeh.models\n",
    "bokeh.io.output_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data set and necessary calculations\n",
    "\n",
    "The data set consists of reversals of worms from three strains, wild type, Channelrhodopsin in the ASH neuron, and Channelrhodopsin in the AVA neuron.  We got, respectively, 0/35, 9/35, and 33/36 reversals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Store data set\n",
    "r = np.array([0, 9, 33])\n",
    "n = np.array([35, 35, 36])\n",
    "strains = ('wild type', 'ASH', 'AVA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The posterior distribution is\n",
    "\n",
    "\\begin{align}\n",
    "P(p_\\mathrm{rev}) = \\frac{(n+1)!}{r!(n-r)!}\\,p_\\mathrm{rev}^r\\,(1-p_\\mathrm{rev})^{n-r}.\n",
    "\\end{align}\n",
    "\n",
    "We can write a function to compute this, since we'll need it for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def posterior(p_rev, r, n):\n",
    "    \"\"\"Log posterior of reversal measurements.\"\"\"\n",
    "    return (n + 1) * st.binom.pmf(r, n, p_rev)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Later, we will plot 95% confidence intervals that contain the highest probability density, or HPD.  This is easily computed analytically for the case where $r = 0$, since, in this case,\n",
    "\n",
    "\\begin{align}\n",
    "P(p_\\mathrm{rev}) = (n+1)(1-p_\\mathrm{rev})^n\n",
    "\\end{align}\n",
    "\n",
    "This decreases monotonically with $p_\\mathrm{rev}$.  Then, the minimum of the HPD interval is $0$.  We can find the maximum by evaluating\n",
    "\n",
    "\\begin{align}\n",
    "\\int_0^{p_\\mathrm{rev}^\\mathrm{max}} \\mathrm{d}p_\\mathrm{rev}\\,P(p_\\mathrm{rev}) = \n",
    "1-(1-p_\\mathrm{rev}^\\mathrm{max})^{n+1} = f_\\mathrm{HPD},\n",
    "\\end{align}\n",
    "\n",
    "where $f_\\mathrm{HPD}$ is the fraction of the total space of values that the HPD covers (in our case, 0.95).  This is solved to give\n",
    "\n",
    "\\begin{align}\n",
    "p_\\mathrm{rev}^\\mathrm{max} = 1 - (1-f_\\mathrm{HPD})^{(n+1)^{-1}}.\n",
    "\\end{align}\n",
    "\n",
    "So, we can compute the HPD for the wild type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Intialize PDs, filling in WT value\n",
    "hpds = [(0, 1 - (1 - 0.95)**(1 / (n[0] + 1))), None, None]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will compute the HPDs using Markov chain Monte Carlo.  First, we need a function for the log posterior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def log_posterior(p_rev, r, n):\n",
    "    \"\"\"Unnormalized log posterior, used for MCMC calculations.\"\"\"\n",
    "    # Get scalar probability (emcee expects array argument)\n",
    "    p = p_rev[0]\n",
    "\n",
    "    if p < 0 or p > 1:\n",
    "        return -np.inf\n",
    "    return r * np.log(p) + (n - r) * np.log(1-p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll write a function to set up and run an MCMC calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sample_posterior_mcmc(r, n, n_walkers=50, n_burn=5000, n_steps=5000):\n",
    "    \"\"\"Get samples of p_rev from the posterior.\"\"\"\n",
    "\n",
    "    # Specify starting points of walkers\n",
    "    p0 = np.empty((n_walkers, 1))\n",
    "    p0[:,0] = np.random.uniform(0, 1, n_walkers)\n",
    "\n",
    "    # Instantiate sampler\n",
    "    sampler = emcee.EnsembleSampler(n_walkers, 1, log_posterior, args=(r, n))\n",
    "    \n",
    "    # Do burn-in and sampling\n",
    "    pos, prob, state = sampler.run_mcmc(p0, n_burn, storechain=False)\n",
    "    _ = sampler.run_mcmc(pos, n_steps)\n",
    "    \n",
    "    return sampler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need a function to compute the HPD from the samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def hpd(sampler, mass_frac):\n",
    "    \"\"\"\n",
    "    Returns highest probability density region given by\n",
    "    a set of samples.\n",
    "    \"\"\"\n",
    "    trace = sampler.flatchain\n",
    "    \n",
    "    # Get sorted list\n",
    "    d = np.sort(np.copy(trace))\n",
    "\n",
    "    # Number of total samples taken\n",
    "    n = len(trace)\n",
    "    \n",
    "    # Get number of samples that should be included in HPD\n",
    "    n_samples = np.floor(mass_frac * n).astype(int)\n",
    "    \n",
    "    # Get width (in units of data) of all intervals with n_samples samples\n",
    "    int_width = d[n_samples:] - d[:n-n_samples]\n",
    "    \n",
    "    # Pick out minimal interval\n",
    "    min_int = np.argmin(int_width)\n",
    "    \n",
    "    # Return interval\n",
    "    return np.sort(np.array([d[min_int], d[min_int+n_samples]]).flatten())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will compute the mode and HPD for the ASH and AVA strains."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in (1, 2):\n",
    "    sampler = sample_posterior_mcmc(r[i], n[i])\n",
    "    hpds[i] = hpd(sampler, 0.95)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that all of our calculations are done, we can make our plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ugly plots\n",
    "\n",
    "We'll begin by making the \"ugly\" plots that are more or less defaults, but not really useful for presentations.  We'll start with a bar chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10fba71d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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WkfBgWZYk6brrrjNREgAAAIATGQkO7777rrKzs3X77bfrrrvuUnR0tD755BO9/fbbys/P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\nzjSxDQAAAIAqqkzBYcSIEbr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LL78scZ9vv/1WJ0+eVL9+/eTp6VlsHV9fX919991KTEzU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f1d7b70>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 264,
       "width": 391
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a bad bar plot using Seaborn with its defaults\n",
    "sns.set_style('ticks')\n",
    "sns.barplot(x=strains, y=r/n)\n",
    "plt.ylabel('fraction of reversals')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll use Bokeh to make an ugly plot of the posterior probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"plotdiv\" id=\"708d2776-41f2-445c-8afa-8839582caa12\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"708d2776-41f2-445c-8afa-8839582caa12\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '708d2776-41f2-445c-8afa-8839582caa12' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        Bokeh.$(function() {\n",
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       "            var render_items = [{\"modelid\": \"77b327d2-4ea3-4671-bb2b-24be872112da\", \"notebook_comms_target\": \"d2d12ffa-d705-4907-8ef5-8bfa247c8ca0\", \"docid\": \"c861dd50-1ff2-4bb6-934e-d73d82c50824\", \"elementid\": \"708d2776-41f2-445c-8afa-8839582caa12\"}];\n",
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       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
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       "  }(this));\n",
       "</script>"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "<bokeh.io._CommsHandle at 0x10fc734a8>"
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   ],
   "source": [
    "# p_rev for plotting\n",
    "p_rev = np.linspace(0, 1, 300)\n",
    "\n",
    "# Make plots\n",
    "p = bokeh.plotting.figure(plot_width=650, plot_height=450)\n",
    "p.xaxis.axis_label = 'reversal probability'\n",
    "p.yaxis.axis_label = 'probability'\n",
    "\n",
    "colors = ['blue', 'green', 'red']\n",
    "line_style = ['solid', 'dashed', 'dotted']\n",
    "\n",
    "for i, r_val in enumerate(r):\n",
    "    n_val = n[i]\n",
    "    p.line(p_rev, posterior(p_rev, r_val, n_val), line_width=4, \n",
    "           color=colors[i], line_dash=line_style[i], legend=strains[i])\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pretty plots\n",
    "\n",
    "Now, we'll use Bokeh to make pretty plots for our presentation.  We will not label the axes, since we will label these on the slide with well-placed horizontal text.  We also do not annotate, beyond tick labels.\n",
    "\n",
    "Note that I use the font Concourse T3 for the tick labels.  This font is a font I bought and installed on my system.  You will not have it (unless you bought it), so you will have to change that font selection to make the following code work.\n",
    "\n",
    "We'll start by specifying the colors for our bars and curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colors_bar = ('gray', '#a6cee3', '#fdbf6f')\n",
    "colors_plot = ('gray', '#1f78b4', '#ff7f00')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll start with our horizontal bar chart.  We use Bokeh's `quad()` function to get the bars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "            var render_items = [{\"modelid\": \"26960f5e-fa46-4837-9a15-97e35f8a1308\", \"notebook_comms_target\": \"0eff76f4-09b7-4ac5-94b3-06dbdc6a3491\", \"docid\": \"2e2b3908-4618-4b96-817b-02100dd48473\", \"elementid\": \"1ad11400-30d8-412d-aa68-31a4d4869fc6\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x10fc966a0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set up canvas on which to paint plot\n",
    "p = bokeh.plotting.figure(plot_width=550, plot_height=290, x_range=[0, 1],\n",
    "                          y_range=[0.5, 3.25], \n",
    "                          border_fill_alpha=0, background_fill_alpha=0, )\n",
    "p.axis.major_tick_in = 0\n",
    "p.axis.minor_tick_line_color = None\n",
    "p.axis.major_label_text_font = 'Concourse T3'\n",
    "p.grid.grid_line_color = None\n",
    "p.axis.axis_line_color = '#a6aaa9'\n",
    "p.axis.major_tick_line_color = '#a6aaa9'\n",
    "p.axis.major_label_text_color = '#53585f'\n",
    "p.axis.axis_line_width = 2\n",
    "p.axis.major_tick_line_width = 2\n",
    "p.yaxis.visible = None\n",
    "p.outline_line_alpha = 0\n",
    "p.xaxis[0].ticker = bokeh.models.FixedTicker(ticks=[0, 1])\n",
    "\n",
    "# Grid lines\n",
    "for i in range(1, 4):\n",
    "    p.line([0, 1], [i]*2, color='#A6AAA9', line_width=2)\n",
    "\n",
    "# Populate bars\n",
    "for i in range(3):\n",
    "    p.quad(0, r[i]/n[i], 3-i+0.25, 3-i-0.25, color=colors_bar[i])\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll make a plot of the posterior probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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       "      run_inline_js();\n",
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    {
     "data": {
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x10fccd278>"
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     "execution_count": 13,
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   "source": [
    "# Values of p for plot\n",
    "p_rev = np.linspace(0, 1, 300)\n",
    "\n",
    "# Set up plot\n",
    "p = bokeh.plotting.figure(plot_width=650, plot_height=450, x_range=[-0.01,1.01],\n",
    "                          border_fill_alpha=0, background_fill_alpha=0, )\n",
    "p.axis.major_tick_in = 0\n",
    "p.axis.minor_tick_line_color = None\n",
    "p.axis.major_label_text_font = 'Concourse T3'\n",
    "p.grid.grid_line_color = None\n",
    "p.axis.axis_line_color = '#a6aaa9'\n",
    "p.axis.major_tick_line_color = '#a6aaa9'\n",
    "p.axis.major_label_text_color = '#53585f'\n",
    "p.axis.axis_line_width = 2\n",
    "p.axis.major_tick_line_width = 2\n",
    "p.outline_line_alpha = 0\n",
    "\n",
    "# Populate glyphs\n",
    "for i, r_val in enumerate(r):\n",
    "    n_val = n[i]\n",
    "    p.line(p_rev, posterior(p_rev, r_val, n_val), line_width=4, \n",
    "           color=colors_plot[i])\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we will make a plot of the confidence intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"5de4c5c7-4570-48da-8342-4ea5263c8c58\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '5de4c5c7-4570-48da-8342-4ea5263c8c58' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        Bokeh.$(function() {\n",
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       "            var render_items = [{\"modelid\": \"27d351c1-11e2-44e2-8053-99af29c63127\", \"notebook_comms_target\": \"7623333b-f71e-41e6-934f-794597680d26\", \"docid\": \"c02ef7c8-e39c-42b7-99ae-17cfc9937daa\", \"elementid\": \"5de4c5c7-4570-48da-8342-4ea5263c8c58\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x10fd00a58>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set up plot\n",
    "p = bokeh.plotting.figure(plot_width=550, plot_height=290, x_range=[-0.02,1.02],\n",
    "                          y_range=[0.7, 3.3], \n",
    "                          border_fill_alpha=0, background_fill_alpha=0, )\n",
    "p.axis.major_tick_in = 0\n",
    "p.axis.minor_tick_line_color = None\n",
    "p.axis.major_label_text_font = 'Concourse T3'\n",
    "p.grid.grid_line_color = None\n",
    "p.axis.axis_line_color = '#a6aaa9'\n",
    "p.axis.major_tick_line_color = '#a6aaa9'\n",
    "p.axis.major_label_text_color = '#53585f'\n",
    "p.axis.axis_line_width = 2\n",
    "p.axis.major_tick_line_width = 2\n",
    "p.yaxis.visible = None\n",
    "p.outline_line_alpha = 0\n",
    "\n",
    "# Grid lines\n",
    "for i in range(1, 4):\n",
    "    p.line([0, 1], [i]*2, color='#A6AAA9', line_width=2)\n",
    "\n",
    "# Plot data\n",
    "for i in range(3):\n",
    "    p.circle(r[i]/n[i], 3-i, color=colors_plot[i], size=10)\n",
    "    p.line(hpds[i], [3-i]*2, color=colors_plot[i], line_width=6, line_cap='round')\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  }
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