{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Configuring your machine\n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The instructions below discuss how to set up your machine. After setting it up, make sure you are always operating in the `bebi103` environment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Students who took BE/Bi 103 a last term\n",
    "\n",
    "If you took [BE/Bi 103 a](https://bebi103a.github.io/) last term (and *only* last term, Fall 2023), your computer is mostly configured. You need only to do the following on the command line after activating the `bebi103` environment with conda.\n",
    "\n",
    "```bash\n",
    "pip install cmdstanpy==1.2.0 arviz==0.17.0 bebi103==0.1.18\n",
    "```\n",
    "\n",
    "After applying the above updates, you can skip to the [Stan installation section](#Stan-installation) and continue."
   ]
  },
  {
   "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#Installation-on-your-own-machine), starting with the `Installation on your own machine` section."
   ]
  },
  {
   "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. The instructions below are not guaranteed to work; you may have to do some troubleshooting on your own. Note that you can use Google Colab or AWS for computing as well, so you do not need to worry if you have trouble installing Stan locally."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configuring a C++ toolchain for MacOS\n",
    "\n",
    "On MacOS, you an install Xcode command line tools by running the following on the command line.\n",
    "\n",
    "    xcode-select --install"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configuring a C++ toolchain for Windows\n",
    "\n",
    "*According to the [CmdStanPy documentation](https://mc-stan.org/cmdstanpy/installation.html#cmdstan-installation), you can skip this step, though I did previously verify that the below worked on a Windows machine.*\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",
    "\n",
    "When you do this, make sure you are in the `bebi103` environment."
   ]
  },
  {
   "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 at a Python prompt (either Python, IPython, or in a Jupyter notebook) (again making sure you are in the `bebi103` environment).\n",
    "\n",
    "    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.)\n",
    "\n",
    "If you are using Windows and you skipped configuration of the C++ toolchain, instead run:\n",
    "\n",
    "    import cmdstanpy; cmdstanpy.install_cmdstan(compiler=True)\n"
   ]
  },
  {
   "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. You will see some warning text, and that is expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "      if (view != null) {\n",
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       "\n",
       "    const cell = handle.cell;\n",
       "\n",
       "    const id = cell.output_area._bokeh_element_id;\n",
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       "    if (id != null) {\n",
       "      drop(id)\n",
       "    }\n",
       "\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",
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       "\n",
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       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
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       "\n",
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       "    root._bokeh_timeout = Date.now() + 5000;\n",
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       "\n",
       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
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       "     \"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",
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       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
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       "          callback();\n",
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       "      delete root._bokeh_onload_callbacks\n",
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       "    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-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.0.min.js\", \"https://unpkg.com/@holoviz/panel@1.3.1/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(\"efe143c7-5152-4d6a-93f8-6a447e9e2754\")).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));"
      ],
      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\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\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(\"efe143c7-5152-4d6a-93f8-6a447e9e2754\");\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-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.0.min.js\", \"https://unpkg.com/@holoviz/panel@1.3.1/dist/panel.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(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    }\nif (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(\"efe143c7-5152-4d6a-93f8-6a447e9e2754\")).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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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "21:19:57 - cmdstanpy - INFO - compiling stan file /Users/bois/Dropbox/git/bebi103_course/2024/b/content/lessons/00/schools_code.stan to exe file /Users/bois/Dropbox/git/bebi103_course/2024/b/content/lessons/00/schools_code\n",
      "21:20:10 - cmdstanpy - INFO - compiled model executable: /Users/bois/Dropbox/git/bebi103_course/2024/b/content/lessons/00/schools_code\n",
      "21:20:10 - cmdstanpy - INFO - CmdStan start processing\n",
      "21:20:10 - cmdstanpy - INFO - Chain [1] start processing\n",
      "21:20:10 - cmdstanpy - INFO - Chain [2] start processing\n",
      "21:20:10 - cmdstanpy - INFO - Chain [3] start processing\n",
      "21:20:10 - cmdstanpy - INFO - Chain [4] start processing\n",
      "21:20:10 - cmdstanpy - INFO - Chain [1] done processing\n",
      "21:20:10 - cmdstanpy - INFO - Chain [2] done processing\n",
      "21:20:11 - cmdstanpy - INFO - Chain [3] done processing\n",
      "21:20:11 - cmdstanpy - INFO - Chain [4] done processing\n",
      "21:20:11 - cmdstanpy - WARNING - Some chains may have failed to converge.\n",
      "\tChain 3 had 1 divergent transitions (0.1%)\n",
      "\tChain 4 had 1 divergent transitions (0.1%)\n",
      "\tUse the \"diagnose()\" method on the CmdStanMCMC object to see further information.\n"
     ]
    },
    {
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\"},\"shape\":[4000],\"dtype\":\"float64\",\"order\":\"little\"}],[\"y\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[4000],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1037\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1038\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Circle\",\"id\":\"p1033\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":{\"type\":\"value\",\"value\":\"#1f77b4\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"value\",\"value\":\"#1f77b4\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Circle\",\"id\":\"p1034\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":{\"type\":\"value\",\"value\":\"#1f77b4\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"value\",\"value\":\"#1f77b4\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Circle\",\"id\":\"p1035\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":{\"type\":\"value\",\"value\":\"#1f77b4\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"value\",\"value\":\"#1f77b4\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1010\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p1023\"},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p1024\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p1025\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p1026\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"top_units\":\"canvas\",\"bottom_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p1027\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p1028\"},{\"type\":\"object\",\"name\":\"HelpTool\",\"id\":\"p1029\"}]}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1018\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1019\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1020\"},\"axis_label\":\"\\u03c4\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1021\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1013\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1014\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1015\"},\"axis_label\":\"\\u03bc\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1016\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1017\",\"attributes\":{\"axis\":{\"id\":\"p1013\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1022\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1018\"}}}],\"frame_width\":250,\"frame_height\":250}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"toggle_value1\",\"properties\":[{\"name\":\"active_icons\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"options\",\"kind\":\"Any\",\"default\":{\"type\":\"map\",\"entries\":[[\"favorite\",\"heart\"]]}},{\"name\":\"value\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_reactions\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"_base_url\",\"kind\":\"Any\",\"default\":\"https://tabler-icons.io/static/tabler-icons/icons/\"}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"value\",\"kind\":\"Any\",\"default\":null},{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  const render_items = [{\"docid\":\"db491a9c-ca58-4411-966d-aac8eefc704a\",\"roots\":{\"p1002\":\"b8f6e8ac-e273-4a0d-bed8-8707b7011e3d\"},\"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": [
    "import numpy as np\n",
    "\n",
    "import bebi103\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",
    "bebi103.stan.clean_cmdstan()\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": 2,
   "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     : 1.26.2\n",
      "bokeh     : 3.3.0\n",
      "cmdstanpy : 1.2.0\n",
      "arviz     : 0.17.0\n",
      "jupyterlab: 4.0.10\n",
      "\n",
      "CmdStan : 2.33\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()))"
   ]
  }
 ],
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