{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. Preparing for the course\n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lesson, you will get the necessary computing resources for the class set up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Students who took BE/Bi 103 a\n",
    "\n",
    "If you took [BE/Bi 103 a](https://bebi103a.github.io/) last term, your computer is mostly configured. You should do the following on the command line.\n",
    "\n",
    "```bash\n",
    "conda update --all\n",
    "pip install --upgrade arviz cmdstanpy bebi103 iqplot awscli\n",
    "```\n",
    "\n",
    "After applying the above updates, you can skip to the [AWS setup section](#AWS-setup) and continue.\n",
    "\n",
    "If you do want to upgrade your Anaconda installation for Python 3.9 instead of Python 3.8, you can follow the instructions in [Lesson 0 from BE/Bi 103 a](https://bebi103a.github.io/lessons/00/setting_up_your_computer.html) again, being sure to [uninstall Anaconda](https://bebi103a.github.io/lessons/00/setting_up_your_computer.html#Uninstalling-Anaconda) at the appropriate point as you are doing so."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Students who did not take BE/Bi 103 a\n",
    "\n",
    "If you did not take BE/Bi 103 a last term, complete [Lesson 0 from BE/Bi 103 a](https://bebi103a.github.io/lessons/00/setting_up_your_computer.html), and then proceed. The only exception is that you should install the Anaconda distribution for Python 3.9 and not 3.8. You can also install AWS command line utilities by doing the following on the command line.\n",
    "\n",
    "    pip install --upgrade awscli"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use of Google Colab\n",
    "\n",
    "In order to use Google Colab, you must have a Google account. Caltech students and employees have an account through Caltech’s G Suite. Many of you may have a personal Google account, usually set up for things like GMail, YouTube, etc. For your work in this class, use your Caltech account. This will facilitate collaboration with your teammates in the course, as well as with course staff.\n",
    "\n",
    "Many of you probably use your personal Google account on your machine, so it can get annoying to log in and out of it. A trick that I find useful is to use one browser, e.g., Safari or Microsoft Edge, for your personal use, web browsing, etc., and a different browser for your scientific work, including the work in this class. Google Colab are most tested for Chrome, Firefox, and Safari (in fact JupyterLab, which you will use on your own machine, only supports these three browsers).\n",
    "\n",
    "Once you have either logged out of all of your personal accounts or have a different browser open, you can launch a Colab notebook by simply navigating to https://colab.research.google.com/. Alternatively, you can click the \"Launch in Colab\" badge at the top right of this page, and you will launch this notebook in Colab. That badge will appear in the top right of all pages in the course content generated from notebooks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Watchouts when using Colab\n",
    "\n",
    "If you do run a notebook in Colab, you are doing your computing on one of Google’s computers via a virtual machine. You get two CPU cores and 12 GB of RAM. You can also get GPUs and TPUs (Google’s tensor processing units), but we will not use those in this course. The computing resources should be enough for all of our calculations this term (though you will need more computing power in the sequel of this course). However, there are some limitations you should be aware of.\n",
    "\n",
    "- If your notebook is idle for too long, you will get disconnected from your notebook. \"Idle\" means that cells are not being edited or executed. The idle timeout varies depending on the load on Google's computers; I find that I almost always get disconnected if idle for an hour.\n",
    "- Your virtual machine will disconnect if it is being used for too long. It typically will only available for 12 hours before disconnecting, though times can vary, again based on load.\n",
    "\n",
    "These limitations can result in problems if you are running long-ish Stan calculations. If the calculation takes, say, four hours, you can do it on Colab, but you probably want to go do something else while it is running. If the calculation ends and your Colab sessions sits idle for too long, your virtual machine may disconnect and you may lose your samples. You should therefore have safeguards in place to store your results so you do not lose them. Another obvious limitation is that 12+ hour Stan calculations can result in the virtual machine timing out and disconnecting.\n",
    "\n",
    "These limitations are in place so that Google can offer Colab for free. If you want more cores, longer timeouts, etc., you might want to check out Colab Pro. You of course can always run on your own machine or on AWS.\n",
    "\n",
    "There are additional software-specific watchouts when using Colab.\n",
    "\n",
    "- Colab will not render HoloViews plots unless hv.extension('bokeh') is called in each cell that has a HoloViews plot.\n",
    "- Colab does not allow for full functionality [Bokeh](http://bokeh.pydata.org/) apps and some [Panel](http://panel.holoviz.org/) functionality that we will use later in the course when we do dashboarding.\n",
    "- Colab instances have specific software installed, so you will need to install anything else you need in your notebook. This is not a major burden, and is discussed in the next section.\n",
    "\n",
    "I recommend reading the [Colab FAQs](https://research.google.com/colaboratory/faq.html) for more information about Colab."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Software in Colab\n",
    "\n",
    "When you launch a Google Colab notebook, much of the software we will use in class is already installed. It is not always the latest version of the software, however. In fact, as of December 2021, Colab is running Python 3.7, whereas you will run Python 3.9 on your machine and on AWS. Nonetheless, most (but not all) of the analyses we do for this class will work just fine in Colab. We will make every effort to let you know when Colab will not be able to handle activities in class, the most important example being some dashboarding applications.\n",
    "\n",
    "Because the notebooks in Colab have specific software preinstalled, and no more, you will often need to install software before you can run the rest of the code in a notebook. To enable this, when necessary, in the first code cell of each notebook in this class, we will have the following code (or a variant thereof depending on what is needed or if the default installations of Colab change). Running this code will not affect running your notebook on your local machine; the same notebook will work on your local machine or on Colab. Importantly, when using [Stan](http://mc-stan), you will need to install Stan in your Colab session using `cmdstanpy.install_cmdstan()`, which can take some time, usually several minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Colab setup ------------------\n",
    "import os, sys, subprocess\n",
    "if \"google.colab\" in sys.modules:\n",
    "    cmd = \"pip install --upgrade iqplot colorcet datashader bebi103 arviz cmdstanpy watermark\"\n",
    "    process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
    "    stdout, stderr = process.communicate()\n",
    "    import cmdstanpy; cmdstanpy.install_cmdstan()\n",
    "    data_path = \"https://s3.amazonaws.com/bebi103.caltech.edu/data/\"\n",
    "else:\n",
    "    data_path = \"../data/\"\n",
    "# ------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AWS setup\n",
    "\n",
    "We will be doing some involved computations that may tax the computing resources of your own computer. We therefore encourage using Amazon Web Services (AWS) to enable access to more powerful machines for doing calculations. Amazon has the AWS Educate program for students. They give students computing credits on AWS, allowing you to use their machines. We will give you instructions on how to use AWS later in the term, but you should request the credits now because there can be a delay in their approval.\n",
    "\n",
    "Go to the [AWS Educate page](https://www.awseducate.com/), set up an account, and request credits (\\$50 for the term should be enough)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stan installation\n",
    "\n",
    "We will be using [Stan](http://mc-stan) for much of our statistical modeling. Stan has a probabilistic programming language. Programs written in this language, called *Stan programs*, are translated into C++ by the Stan parser, and then the C++ code is compiled. As you will see throughout the class, there are many advantages to this approach.\n",
    "\n",
    "There are many interfaces for Stan, including the two most widely used [RStan](https://cran.r-project.org/web/packages/rstan/vignettes/rstan.html) and [PyStan](https://pystan.readthedocs.io), which are R and Python interfaces, respectively. We will use a newer interface, [CmdStanPy](https://mc-stan.org/cmdstanpy/), which has several advantages that will become apparent when you start using it.\n",
    "\n",
    "Whichever interface you use needs to have Stan installed and functional, which means you have to have an installed C++ toolchain. Installation and compilation can be tricky and varies from operating system to operating system. To facilitate configuration of Stan and also to allow you to tackle more involved calculations, we will use [AWS](https://aws.amazon.com) for computing with Stan. Within the first few weeks of class, you will receive instructions on how to use AWS. We have a pre-built Amazon Machine Image (AMI) that has all of the installations you need, and you can run your calculations on those machines. Bear in mind that because of the difficulties involved with local installations, we may not be able to provide support for all local installations of Stan, CmdStanPy, or other Stan interfaces. AWS provides a viable, if not free, alternative with more computing power.\n",
    "\n",
    "Note, however, that you can also use Stan and CmdStanPy on Google Colab, but you are limited to only two cores for their free service.\n",
    "\n",
    "That said, if you would like to install Stan and CmdStanPy locally, you may do so. Read on for instructions, though we offer no guarantees that they will work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configuring a C++ toolchain for MacOS\n",
    "\n",
    "If you are using MacOS and you installed XCode as was required for the BE/Bi 103 a installations, you should already have a C++ toolchain. You can skip ahead to [install Stan with CmdStanPy](#Installing-Stan-with-CmdStanPy)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configuring a C++ toolchain for Windows\n",
    "\n",
    "You need to install a C++ toolchain for Windows. One possibility is to install a [MinGW](http://www.mingw.org) toolchain, and one way to do that is using `conda`.\n",
    "\n",
    "    conda install libpython m2w64-toolchain -c msys2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configuring a C++ toolchain for Linux\n",
    "\n",
    "If you are using Linux, we assume you already have the C++ utilities installed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installing Stan with CmdStanPy\n",
    "\n",
    "If you have a functioning C++ toolchain, you can use CmdStanPy to install Stan/CmdStan. You can do this by running the following on the command line.\n",
    "\n",
    "    python -c \"import cmdstanpy; cmdstanpy.install_cmdstan()\"\n",
    "    \n",
    "This may take several minutes to run. (I did it on my Raspberry Pi, and it took hours.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking your Stan installation\n",
    "\n",
    "To check your Stan installation, you can run the following code. It will take several seconds for the model to compile and then sample. In the end, you should see a scatter plot of samples. You might not appreciate it yet, but this is a nifty demonstration of Stan's power to sample hierarchical models, which is no trivial feat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.3.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1002\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(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    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.3.min.js\": \"dM3QQsP+wXdHg42wTqW85BjZQdLNNIXqlPw/BgKoExPmTG7ZLML4EGqLMfqHT6ON\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.3.min.js\": \"8x57I4YuIfu8XyZfFo0XVr2WAT8EK4rh/uDe3wF7YuW2FNUSNEpJbsPaB1nJ2fz2\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.3.min.js\": \"3QTqdz9LyAm2i0sG5XTePsHec3UHWwVsrOL68SYRoAXsafvfAyqtQ+h440+qIBhS\"};\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      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.3.3.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
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     "output_type": "stream",
     "text": [
      "INFO:cmdstanpy:compiling stan file /Users/bois/Dropbox/git/bebi103_course/2022/b/content/lessons/00/schools_code.stan to exe file /Users/bois/Dropbox/git/bebi103_course/2022/b/content/lessons/00/schools_code\n",
      "INFO:cmdstanpy:compiled model executable: /Users/bois/Dropbox/git/bebi103_course/2022/b/content/lessons/00/schools_code\n",
      "INFO:cmdstanpy:CmdStan start procesing\n",
      "INFO:cmdstanpy:Chain [1] start processing\n",
      "INFO:cmdstanpy:Chain [2] start processing\n",
      "INFO:cmdstanpy:Chain [3] start processing\n",
      "INFO:cmdstanpy:Chain [4] start processing\n",
      "INFO:cmdstanpy:Chain [1] done processing\n",
      "INFO:cmdstanpy:Chain [2] done processing\n",
      "INFO:cmdstanpy:Chain [3] done processing\n",
      "INFO:cmdstanpy:Chain [4] done processing\n"
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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[4000]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[4000]}},\"selected\":{\"id\":\"1048\"},\"selection_policy\":{\"id\":\"1047\"}},\"id\":\"1034\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1023\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"1048\",\"type\":\"Selection\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"right_units\":\"screen\",\"syncable\":false,\"top_units\":\"screen\"},\"id\":\"1026\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"1045\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1041\",\"type\":\"AllLabels\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1036\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"1042\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1010\",\"type\":\"LinearScale\"},{\"attributes\":{\"source\":{\"id\":\"1034\"}},\"id\":\"1038\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1024\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"1004\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1044\",\"type\":\"AllLabels\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1035\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"1008\",\"type\":\"LinearScale\"},{\"attributes\":{\"data_source\":{\"id\":\"1034\"},\"glyph\":{\"id\":\"1035\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1036\"},\"view\":{\"id\":\"1038\"}},\"id\":\"1037\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1025\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"1020\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"1021\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"overlay\":{\"id\":\"1026\"}},\"id\":\"1022\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"1039\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1017\",\"type\":\"BasicTicker\"},{\"attributes\":{\"active_multi\":null,\"tools\":[{\"id\":\"1020\"},{\"id\":\"1021\"},{\"id\":\"1022\"},{\"id\":\"1023\"},{\"id\":\"1024\"},{\"id\":\"1025\"}]},\"id\":\"1027\",\"type\":\"Toolbar\"}],\"root_ids\":[\"1003\"]},\"title\":\"Bokeh Application\",\"version\":\"2.3.3\"}};\n",
       "  var render_items = [{\"docid\":\"f2d022c9-ffa4-469a-9590-12b6911b28e3\",\"root_ids\":[\"1003\"],\"roots\":{\"1003\":\"62a23c0b-a5f5-4e2b-a22a-0107fdb7cd6d\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1003"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "import cmdstanpy\n",
    "import arviz as az\n",
    "\n",
    "import bokeh.plotting\n",
    "import bokeh.io\n",
    "bokeh.io.output_notebook()\n",
    "\n",
    "schools_data = {\n",
    "    \"J\": 8,\n",
    "    \"y\": [28, 8, -3, 7, -1, 1, 18, 12],\n",
    "    \"sigma\": [15, 10, 16, 11, 9, 11, 10, 18],\n",
    "}\n",
    "\n",
    "schools_code = \"\"\"\n",
    "data {\n",
    "  int<lower=0> J; // number of schools\n",
    "  vector[J] y; // estimated treatment effects\n",
    "  vector<lower=0>[J] sigma; // s.e. of effect estimates\n",
    "}\n",
    "\n",
    "parameters {\n",
    "  real mu;\n",
    "  real<lower=0> tau;\n",
    "  vector[J] eta;\n",
    "}\n",
    "\n",
    "transformed parameters {\n",
    "  vector[J] theta = mu + tau * eta;\n",
    "}\n",
    "\n",
    "model {\n",
    "  eta ~ normal(0, 1);\n",
    "  y ~ normal(theta, sigma);\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "with open(\"schools_code.stan\", \"w\") as f:\n",
    "    f.write(schools_code)\n",
    "\n",
    "sm = cmdstanpy.CmdStanModel(stan_file=\"schools_code.stan\")\n",
    "samples = sm.sample(data=schools_data, output_dir=\"./\", show_progress=False)\n",
    "samples = az.from_cmdstanpy(samples)\n",
    "\n",
    "# Make a plot of samples\n",
    "p = bokeh.plotting.figure(\n",
    "    frame_height=250, frame_width=250, x_axis_label=\"μ\", y_axis_label=\"τ\"\n",
    ")\n",
    "p.circle(\n",
    "    np.ravel(samples.posterior[\"mu\"]), \n",
    "    np.ravel(samples.posterior[\"tau\"]), \n",
    "    alpha=0.1\n",
    ")\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.9.7\n",
      "IPython version      : 7.29.0\n",
      "\n",
      "numpy     : 1.20.3\n",
      "bokeh     : 2.3.3\n",
      "cmdstanpy : 1.0.0\n",
      "arviz     : 0.11.4\n",
      "jupyterlab: 3.2.1\n",
      "\n",
      "CmdStan : 2.28\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -p numpy,bokeh,cmdstanpy,arviz,jupyterlab\n",
    "print(\"CmdStan : {0:d}.{1:d}\".format(*cmdstanpy.cmdstan_version()))"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "jupytext": {
   "target_format": "ipynb,auto:percent"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}