{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(sampler_stats)=\n",
    "# Sampler Statistics\n",
    "\n",
    ":::{post} May 31, 2022\n",
    ":tags: diagnostics\n",
    ":category: beginner\n",
    ":author: Meenal Jhajharia, Christian Luhmann\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on PyMC v4.0.0b6\n"
     ]
    }
   ],
   "source": [
    "import arviz as az\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pymc as pm\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "print(f\"Running on PyMC v{pm.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "az.style.use(\"arviz-darkgrid\")\n",
    "plt.rcParams[\"figure.constrained_layout.use\"] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When checking for convergence or when debugging a badly behaving sampler, it is often helpful to take a closer look at what the sampler is doing. For this purpose some samplers export statistics for each generated sample.\n",
    "\n",
    "As a minimal example we sample from a standard normal distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = pm.Model()\n",
    "with model:\n",
    "    mu1 = pm.Normal(\"mu1\", mu=0, sigma=1, shape=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 2 jobs)\n",
      "NUTS: [mu1]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='12000' class='' max='12000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [12000/12000 00:06<00:00 Sampling 4 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 7 seconds.\n"
     ]
    }
   ],
   "source": [
    "with model:\n",
    "    step = pm.NUTS()\n",
    "    idata = pm.sample(2000, tune=1000, init=None, step=step, chains=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `Note`: NUTS provides the following statistics (these are internal statistics that the sampler uses, you don't need to do anything with them when using PyMC, to learn more about them, {class}`pymc.NUTS`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:             (chain: 4, draw: 2000)\n",
       "Coordinates:\n",
       "  * chain               (chain) int64 0 1 2 3\n",
       "  * draw                (draw) int64 0 1 2 3 4 5 ... 1995 1996 1997 1998 1999\n",
       "Data variables: (12/13)\n",
       "    lp                  (chain, draw) float64 -17.41 -11.12 ... -13.76 -12.35\n",
       "    perf_counter_diff   (chain, draw) float64 0.0009173 0.0009097 ... 0.0006041\n",
       "    acceptance_rate     (chain, draw) float64 0.8478 1.0 ... 0.8888 0.8954\n",
       "    energy_error        (chain, draw) float64 0.3484 -1.357 ... -0.2306 -0.2559\n",
       "    energy              (chain, draw) float64 21.75 18.45 16.03 ... 19.25 16.51\n",
       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 2 ... 2 2 3 2 2 2 2 2\n",
       "    ...                  ...\n",
       "    diverging           (chain, draw) bool False False False ... False False\n",
       "    step_size           (chain, draw) float64 0.8831 0.8831 ... 0.848 0.848\n",
       "    n_steps             (chain, draw) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0\n",
       "    perf_counter_start  (chain, draw) float64 2.591e+05 2.591e+05 ... 2.591e+05\n",
       "    process_time_diff   (chain, draw) float64 0.0009183 0.0009112 ... 0.0006032\n",
       "    max_energy_error    (chain, draw) float64 0.3896 -1.357 ... 0.2427 0.303\n",
       "Attributes:\n",
       "    created_at:                 2022-05-31T19:50:21.571347\n",
       "    arviz_version:              0.12.1\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  4.0.0b6\n",
       "    sampling_time:              6.993547439575195\n",
       "    tuning_steps:               1000</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2f2aea16-aa4d-4a21-ab5a-b638d371f757' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2f2aea16-aa4d-4a21-ab5a-b638d371f757' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 2000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1d827cef-81ba-4ad0-ad63-b4776d87a8b0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1d827cef-81ba-4ad0-ad63-b4776d87a8b0' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-4980e32d-8aa1-4ce3-bcfc-8bd90ffb04b2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4980e32d-8aa1-4ce3-bcfc-8bd90ffb04b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-140b454c-a28e-4e80-9ea4-214f88ad2749' class='xr-var-data-in' type='checkbox'><label for='data-140b454c-a28e-4e80-9ea4-214f88ad2749' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1996 1997 1998 1999</div><input id='attrs-6cd5342b-f60c-415c-b1e6-580793193c4b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6cd5342b-f60c-415c-b1e6-580793193c4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48cb7af1-5062-44e9-a9e9-af2c6e910390' class='xr-var-data-in' type='checkbox'><label for='data-48cb7af1-5062-44e9-a9e9-af2c6e910390' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1997, 1998, 1999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-787549c1-b7e8-4a2d-9b8f-1c44e2f74eba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-787549c1-b7e8-4a2d-9b8f-1c44e2f74eba' class='xr-section-summary' >Data variables: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-17.41 -11.12 ... -13.76 -12.35</div><input id='attrs-499f12fb-2b6b-4b29-a291-7a2da8a98a53' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-499f12fb-2b6b-4b29-a291-7a2da8a98a53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d37f9144-2a78-4a17-821d-6f3b7b90b223' class='xr-var-data-in' type='checkbox'><label for='data-d37f9144-2a78-4a17-821d-6f3b7b90b223' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-17.41481817, -11.11746073, -14.00889121, ..., -14.62579449,\n",
       "        -15.12640488, -14.92067376],\n",
       "       [-11.79754517, -11.37321931, -12.41967116, ..., -15.0553557 ,\n",
       "        -15.03960873, -11.84774166],\n",
       "       [-14.40032205, -20.68972653, -13.74758915, ..., -15.20451325,\n",
       "        -13.46400824, -14.82413174],\n",
       "       [-13.44881972, -15.9252134 , -11.73013359, ..., -14.77629777,\n",
       "        -13.75935262, -12.34852981]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0009173 0.0009097 ... 0.0006041</div><input id='attrs-e53b0ec6-fef0-4ae5-9965-2dcf329cfbab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e53b0ec6-fef0-4ae5-9965-2dcf329cfbab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52963fb3-6ab2-4617-962b-185f5a1df4ba' class='xr-var-data-in' type='checkbox'><label for='data-52963fb3-6ab2-4617-962b-185f5a1df4ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00091726, 0.00090966, 0.00090066, ..., 0.00086925, 0.00092221,\n",
       "        0.00046417],\n",
       "       [0.00092182, 0.00084332, 0.00081795, ..., 0.00048199, 0.00048006,\n",
       "        0.00048616],\n",
       "       [0.00050058, 0.00061117, 0.0005809 , ..., 0.00170264, 0.00124334,\n",
       "        0.00069834],\n",
       "       [0.00050844, 0.00052396, 0.00054123, ..., 0.00051385, 0.0005149 ,\n",
       "        0.00060409]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8478 1.0 0.5188 ... 0.8888 0.8954</div><input id='attrs-fc8c61cb-a161-4056-aead-f020b2bd6ca8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc8c61cb-a161-4056-aead-f020b2bd6ca8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ab56336-01dd-4ffb-8124-ccf4bbdf5a5e' class='xr-var-data-in' type='checkbox'><label for='data-9ab56336-01dd-4ffb-8124-ccf4bbdf5a5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.84781696, 1.        , 0.5188267 , ..., 0.91934539, 0.85438787,\n",
       "        0.76057673],\n",
       "       [1.        , 0.81181648, 0.93142852, ..., 0.94123492, 0.75399724,\n",
       "        0.93529202],\n",
       "       [1.        , 0.29414685, 1.        , ..., 0.46697774, 0.83249289,\n",
       "        0.65167911],\n",
       "       [0.79107091, 0.56355851, 1.        , ..., 0.92367151, 0.8888214 ,\n",
       "        0.89536079]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3484 -1.357 ... -0.2306 -0.2559</div><input id='attrs-1d5b7d3c-bf6c-41f4-a06c-ac4d19505c83' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d5b7d3c-bf6c-41f4-a06c-ac4d19505c83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf106364-eb0d-4055-a594-cd917c76c217' class='xr-var-data-in' type='checkbox'><label for='data-bf106364-eb0d-4055-a594-cd917c76c217' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.34837379, -1.3573709 ,  0.72431555, ...,  0.2857954 ,\n",
       "         0.1180722 ,  0.03878686],\n",
       "       [-0.15890021, -0.10739294,  0.2229767 , ...,  0.01717362,\n",
       "        -0.13001742, -0.69487895],\n",
       "       [-0.21978325,  1.47962416, -1.65668177, ...,  0.50839378,\n",
       "        -0.41858308,  0.3440592 ],\n",
       "       [-0.14333517,  0.51606888, -0.88905807, ...,  0.0211668 ,\n",
       "        -0.23064281, -0.25594826]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>21.75 18.45 16.03 ... 19.25 16.51</div><input id='attrs-8c090a3d-9fd0-4025-8b4a-9b907e6c97af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8c090a3d-9fd0-4025-8b4a-9b907e6c97af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d2c4d0f-f1b5-47a0-a080-60f092555848' class='xr-var-data-in' type='checkbox'><label for='data-0d2c4d0f-f1b5-47a0-a080-60f092555848' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[21.75076832, 18.45266859, 16.03241029, ..., 17.16983192,\n",
       "        20.13659553, 23.01097321],\n",
       "       [14.09025318, 15.15479489, 13.75490571, ..., 18.64000448,\n",
       "        21.55766921, 19.64569656],\n",
       "       [17.32485111, 25.19494315, 24.5830488 , ..., 21.14581229,\n",
       "        20.19801996, 19.52502693],\n",
       "       [22.87267199, 20.94006237, 16.61780155, ..., 19.3110406 ,\n",
       "        19.24706179, 16.51436075]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 2 2 2 2 2 2 2 ... 2 2 3 2 2 2 2 2</div><input id='attrs-62a59235-b160-4fd0-9712-4fff12aa234f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-62a59235-b160-4fd0-9712-4fff12aa234f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac579db0-bac0-4cef-9461-7a77fd847dc9' class='xr-var-data-in' type='checkbox'><label for='data-ac579db0-bac0-4cef-9461-7a77fd847dc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2, 2, 2, ..., 3, 3, 2],\n",
       "       [2, 2, 2, ..., 2, 2, 2],\n",
       "       [2, 2, 2, ..., 3, 2, 2],\n",
       "       [2, 2, 2, ..., 2, 2, 2]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size_bar</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9358 0.9358 ... 0.9276 0.9276</div><input id='attrs-1d86a912-d656-45e7-8455-cbc7cd7de6da' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d86a912-d656-45e7-8455-cbc7cd7de6da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-056ea606-d48e-4266-a472-45b83d72203c' class='xr-var-data-in' type='checkbox'><label for='data-056ea606-d48e-4266-a472-45b83d72203c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.93583778, 0.93583778, 0.93583778, ..., 0.93583778, 0.93583778,\n",
       "        0.93583778],\n",
       "       [0.96472688, 0.96472688, 0.96472688, ..., 0.96472688, 0.96472688,\n",
       "        0.96472688],\n",
       "       [0.97413339, 0.97413339, 0.97413339, ..., 0.97413339, 0.97413339,\n",
       "        0.97413339],\n",
       "       [0.92757304, 0.92757304, 0.92757304, ..., 0.92757304, 0.92757304,\n",
       "        0.92757304]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-cdc177dd-9191-4f92-8f85-ce9f1f3e76cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cdc177dd-9191-4f92-8f85-ce9f1f3e76cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7287367-b59a-4d75-9834-649815578bf1' class='xr-var-data-in' type='checkbox'><label for='data-f7287367-b59a-4d75-9834-649815578bf1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8831 0.8831 ... 0.848 0.848</div><input id='attrs-5b20ee53-11a9-4778-aea7-7046f55407af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5b20ee53-11a9-4778-aea7-7046f55407af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-006ebe06-87e8-4e2b-b1bc-e7be065e96b8' class='xr-var-data-in' type='checkbox'><label for='data-006ebe06-87e8-4e2b-b1bc-e7be065e96b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.88314133, 0.88314133, 0.88314133, ..., 0.88314133, 0.88314133,\n",
       "        0.88314133],\n",
       "       [0.98491061, 0.98491061, 0.98491061, ..., 0.98491061, 0.98491061,\n",
       "        0.98491061],\n",
       "       [0.79717973, 0.79717973, 0.79717973, ..., 0.79717973, 0.79717973,\n",
       "        0.79717973],\n",
       "       [0.84803742, 0.84803742, 0.84803742, ..., 0.84803742, 0.84803742,\n",
       "        0.84803742]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0 3.0</div><input id='attrs-95c8dd92-df9e-4777-8355-fab750b6a460' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95c8dd92-df9e-4777-8355-fab750b6a460' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecb329b2-3812-4bde-b765-859d43bd1d5b' class='xr-var-data-in' type='checkbox'><label for='data-ecb329b2-3812-4bde-b765-859d43bd1d5b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[3., 3., 3., ..., 7., 7., 3.],\n",
       "       [3., 3., 3., ..., 3., 3., 3.],\n",
       "       [3., 3., 3., ..., 7., 3., 3.],\n",
       "       [3., 3., 3., ..., 3., 3., 3.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_start</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.591e+05 2.591e+05 ... 2.591e+05</div><input id='attrs-4016ed88-4db6-4334-ad47-9ea1f72ab4dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4016ed88-4db6-4334-ad47-9ea1f72ab4dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39d16478-4754-4ebc-95ed-489f5029a657' class='xr-var-data-in' type='checkbox'><label for='data-39d16478-4754-4ebc-95ed-489f5029a657' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[259080.94794072, 259080.9491462 , 259080.95033726, ...,\n",
       "        259082.86608642, 259082.86711713, 259082.86821631],\n",
       "       [259080.90127503, 259080.90247671, 259080.90355453, ...,\n",
       "        259082.70122436, 259082.70185994, 259082.70249527],\n",
       "       [259083.94110883, 259083.94177995, 259083.94255876, ...,\n",
       "        259085.68260603, 259085.68468606, 259085.68622814],\n",
       "       [259084.07774306, 259084.07843519, 259084.07920939, ...,\n",
       "        259085.84236159, 259085.8430168 , 259085.84369495]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>process_time_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0009183 0.0009112 ... 0.0006032</div><input id='attrs-48953b79-f512-4253-b58d-37c4967d2616' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-48953b79-f512-4253-b58d-37c4967d2616' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecffd8f6-d179-45d3-a542-bfe93b48fdad' class='xr-var-data-in' type='checkbox'><label for='data-ecffd8f6-d179-45d3-a542-bfe93b48fdad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00091832, 0.00091119, 0.00090133, ..., 0.00086943, 0.00092247,\n",
       "        0.00046421],\n",
       "       [0.00092232, 0.00084386, 0.00081837, ..., 0.00048233, 0.0004802 ,\n",
       "        0.00048617],\n",
       "       [0.00050046, 0.00052388, 0.0005813 , ..., 0.00170327, 0.001244  ,\n",
       "        0.00069857],\n",
       "       [0.00050863, 0.00052436, 0.00054133, ..., 0.0005133 , 0.00051457,\n",
       "        0.00060323]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3896 -1.357 ... 0.2427 0.303</div><input id='attrs-5ca37b8a-0510-4335-bdb8-d9bcf8d1bef9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ca37b8a-0510-4335-bdb8-d9bcf8d1bef9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01c3a78f-cd90-4458-aa08-596877449d0f' class='xr-var-data-in' type='checkbox'><label for='data-01c3a78f-cd90-4458-aa08-596877449d0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.3896375 , -1.3573709 ,  0.72431555, ...,  0.34345   ,\n",
       "         0.42071847,  1.28485975],\n",
       "       [-0.21887002,  0.50399625,  0.2229767 , ..., -0.78518544,\n",
       "         1.03001369, -0.69487895],\n",
       "       [-1.00930297,  1.47962416, -1.65668177, ...,  1.11431688,\n",
       "         0.47088174,  0.88911589],\n",
       "       [ 1.87193377,  1.14125086, -0.88905807, ...,  0.31397184,\n",
       "         0.24269705,  0.30297649]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-330600d7-fabe-4df8-94fa-dd5cb311b25b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-330600d7-fabe-4df8-94fa-dd5cb311b25b' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2022-05-31T19:50:21.571347</dd><dt><span>arviz_version :</span></dt><dd>0.12.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>4.0.0b6</dd><dt><span>sampling_time :</span></dt><dd>6.993547439575195</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:             (chain: 4, draw: 2000)\n",
       "Coordinates:\n",
       "  * chain               (chain) int64 0 1 2 3\n",
       "  * draw                (draw) int64 0 1 2 3 4 5 ... 1995 1996 1997 1998 1999\n",
       "Data variables: (12/13)\n",
       "    lp                  (chain, draw) float64 -17.41 -11.12 ... -13.76 -12.35\n",
       "    perf_counter_diff   (chain, draw) float64 0.0009173 0.0009097 ... 0.0006041\n",
       "    acceptance_rate     (chain, draw) float64 0.8478 1.0 ... 0.8888 0.8954\n",
       "    energy_error        (chain, draw) float64 0.3484 -1.357 ... -0.2306 -0.2559\n",
       "    energy              (chain, draw) float64 21.75 18.45 16.03 ... 19.25 16.51\n",
       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 2 ... 2 2 3 2 2 2 2 2\n",
       "    ...                  ...\n",
       "    diverging           (chain, draw) bool False False False ... False False\n",
       "    step_size           (chain, draw) float64 0.8831 0.8831 ... 0.848 0.848\n",
       "    n_steps             (chain, draw) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0\n",
       "    perf_counter_start  (chain, draw) float64 2.591e+05 2.591e+05 ... 2.591e+05\n",
       "    process_time_diff   (chain, draw) float64 0.0009183 0.0009112 ... 0.0006032\n",
       "    max_energy_error    (chain, draw) float64 0.3896 -1.357 ... 0.2427 0.303\n",
       "Attributes:\n",
       "    created_at:                 2022-05-31T19:50:21.571347\n",
       "    arviz_version:              0.12.1\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  4.0.0b6\n",
       "    sampling_time:              6.993547439575195\n",
       "    tuning_steps:               1000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata.sample_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sample statistics variables are defined as follows:\n",
    "\n",
    "- `process_time_diff`: The time it took to draw the sample, as defined by the python standard library time.process_time. This counts all the CPU time, including worker processes in BLAS and OpenMP.\n",
    "\n",
    "- `step_size`: The current integration step size.\n",
    "\n",
    "- `diverging`: (boolean) Indicates the presence of leapfrog transitions with large energy deviation from starting and subsequent termination of the trajectory. “large” is defined as `max_energy_error` going over a threshold.\n",
    "\n",
    "- `lp`: The joint log posterior density for the model (up to an additive constant).\n",
    "\n",
    "- `energy`: The value of the Hamiltonian energy for the accepted proposal (up to an additive constant).\n",
    "\n",
    "- `energy_error`: The difference in the Hamiltonian energy between the initial point and the accepted proposal.\n",
    "\n",
    "- `perf_counter_diff`: The time it took to draw the sample, as defined by the python standard library time.perf_counter (wall time).\n",
    "\n",
    "- `perf_counter_start`: The value of time.perf_counter at the beginning of the computation of the draw.\n",
    "\n",
    "- `n_steps`: The number of leapfrog steps computed. It is related to `tree_depth` with `n_steps <= 2^tree_dept`.\n",
    "\n",
    "- `max_energy_error`: The maximum absolute difference in Hamiltonian energy between the initial point and all possible samples in the proposed tree.\n",
    "\n",
    "- `acceptance_rate`: The average acceptance probabilities of all possible samples in the proposed tree.\n",
    "\n",
    "- `step_size_bar`: The current best known step-size. After the tuning samples, the step size is set to this value. This should converge during tuning.\n",
    "\n",
    "- `tree_depth`: The number of tree doublings in the balanced binary tree."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some points to `Note`:\n",
    "- Some of the sample statistics used by NUTS are renamed when converting to `InferenceData` to follow {ref}`ArviZ's naming convention <arviz:schema>`, while some are specific to PyMC3 and keep their internal PyMC3 name in the resulting InferenceData object.\n",
    "- `InferenceData` also stores additional info like the date, versions used, sampling time and tuning steps as attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1300x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idata.sample_stats[\"tree_depth\"].plot(col=\"chain\", ls=\"none\", marker=\".\", alpha=0.3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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T6ben6L766iutWrVKK1as0L59+xQdHa3y5curevXqevjhh9WtWzfVrVvXpW5eXl6aMWOGpk2bpp9++kmnT59WpUqV1K9fP7344otun9wbN26cSpUqpbVr1yo0NFTZ2dmSpFGjRsnX11c9e/bUvHnzNG3aNEVHR+v48eOqU6eOxowZo5CQED3zzDPX+itwy8PDQ2+//ba6deumhQsXateuXYqJiVHFihVVu3ZtPfroowoICHDap1evXmrevLnmzp2rLVu2KDIyUrm5uapatarq1q2rQYMGFft4paefflq1a9fWrFmzFBsbq5iYGDVo0ECTJk3SwIED3U6RIUn333+/3n33XS1YsEDbtm3TpUuXJElt2rRxhKtGjRppxYoVWrhwoUJDQ3Xw4EHt3LlTd955p/z8/BQSElKoqSsAmEPPFXCbu/zdglfevgMAmMeYKwAAAIMIVwAAAAYx5goA/s+rr75a6G27d++u7t27F2NtANyqGHMFAP/nysHwBbnySUYAsCNcAQAAGMSYKwAAAIMIVwAAAAYVekC7yXeE3UwqVarkeD8aXNE+BaN9Ckb7FIz2KRjtUzDap2C+vr4lduzffc9VqVK/+yYoEO1TMNqnYLRPwWifgtE+BaN9bl78ZgAAAAwiXAEAABhEuAIAADCIcAUAAGAQ4QoAAMAgwhUAAIBBhCsAAACDCFcAAAAGEa4AAAAMIlwBAAAYRLgCAAAwiHAFAABgEOEKAADAIMIVAACAQYQrAAAAgzxLugIAgFvD2nWWsbJ6dPMwVhZws6HnCgAAwCDCFQAAgEGEKwAAAIMIVwAAAAYRrgAAAAwiXAEAABhEuAIAADCIcAUAAGAQ4QoAAMAgwhUAAIBBhCsAAACDCFcAAAAGEa4AAAAMIlwBAAAYRLgCAAAwiHAFAABgEOEKAADAIMIVAACAQYQrAAAAgwhXAAAABhGuAAAADCJcAQAAGES4AgAAMIhwBQAAYBDhCgAAwCDCFQAAgEGEKwAAAIMIVwAAAAYRrgAAAAwiXAEAABhEuAIAADCIcAUAAGAQ4QoAAMAgwhUAAIBBhCsAAACDCFcAAAAGEa4AAAAMIlwBAAAYRLgCAAAwyLOkKwAAuLkcOXJEs2bNUmRkpC5duqTatWvr4Ycflm+Vx1WqVNH+Js/JydKWLYu0d3eozp07rtzcHFX0qaqft9+roUOHyt/f32n7hIQEbdq0SVu2bNHhw4eVlJSkChUqqEmTJurXr586d+5s8lSBYuFhWZZVmA2TkpKKuy4lwtfX97Y9NxNon4LRPgWjfQp2M7bPnj17NGbMGGVmZqpp06aqWbOmdu7cqXPnzqlZ4AN6YsC78vDwKFRZ2dmZmv3lGB0/vldly1ZU3brN5elVRidPHlRyUoK8vb312WefqXHjxo59RowYod27d6tMmTJq2bKlfHx8dPLkSe3fv1+SNGDAAI0bN644Tv2WczNePzcTX1/fEjs2PVcAAElSTk6O3nzzTWVmZmrs2LEaOHCgJCk9PV1jx47Vnj3rFRW1Sq1bP1So8n75ebmOH98rf/8mejrkE5UtW0GSlJeXqz27PtbSpUs1ZcoUTZ8+3bFP9erV9fLLL6t3796qXbu2Izxs3rxZEyZM0MKFC9WhQwe1a9fO8NkD5jDmCgAgSdqwYYNOnjypRo0aOYKVJHl7e+vll1+WJG0O/7bQ5cXF7ZQkdQwe6AhWklSqVGmNGDFCkhw9UnbvvvuuHn/8cZUvX95p+X333aeHH35YkrRmzZrCnxRQAui5AnDbOnnypPr166dWrVpp8uTJmjFjhtavX68LFy6oXr16Gj58uDp16iRJWrdunebPn68jR46oXLly6t69u0aPHq2yZcs6lZmRkaFFixZp3bp1On78uCSpQYMG6tevnx56yLVHZ+fOnQoNDVVUVJTOnDmjrKws+fn5qXPnznrxxRddtt+xY4dGjx6t3r17a+zYsfr888+1ceNGpaSkqE6dOho4cKAjZJi2ZcsWSVLXrl1d1jVu3Fi+lf115vQRJSUlyNe35lXL8/T0uuo2lSpVKnT97r77bknS2bNnC70PUBLouQJw28vJydGYMWP0008/KTAwUM2aNdPhw4f16quvKiIiQgsWLNCkSZPk7e2tdu3aKS8vT0uWLNHf//53p3LOnz+vZ599VtOnT9e5c+fUqlUrtWzZUseOHdM777yjf/7zny7Hnjp1qv7973/rjjvuUNu2bdW2bVulpaVp7ty5evLJJ5Wenu62zqmpqRo+fLjCw8PVsmVLtWjRQseOHdP/+3//T8uXLy+WdoqJiZEkpzFQl6tVyyZJOnXqcKHKu/vuIEnSlvAFyshIdSzPy8vVzJkzJalIQfHkyZOSpCpVqhR6H6Ak0HMF4La3Z88etW3bVsuWLVO5cuUkSStXrtS7776rf/zjH0pJSdGsWbPUpEkTSVJiYqKGDBmiNWvWaOTIkY4n2t59910dPnxYTzzxhEaPHq0yZcpIks6dO6dXXnlFS5cu1X333acOHTo4jv3MM8+oRYsWqlDhf7fFsrKyNHnyZP3www9asGCBnnnmGZc6b9y4UT169NAbb7zhOE5YWJgmTpyo2bNn65FHHnHaftSoUYqKiipSu7z++uvq06eP4+fTp09LkqpVq+Z2ex+f6pKkC8mnClX+PS3/oJiY7dqze63+9WE/1a3XXJ6ed+jkyWhdSj+vQYMGKSQkpFBlXbx4UatXr5YkR28jcLMiXAG47ZUqVUoTJkxwBCtJ6t27t6ZNm6YTJ05o2LBhjmAl/RYuHnzwQS1cuFBRUVHy9/fXoUOHtGXLFjVt2lRjx451mpKgSpUqevXVVzV06FAtW7bMKVx17NjRpT5lypTRuHHjtHLlSm3cuNFtuCpfvrxeeeUVR7CSpC5duqhhw4aKjY3VyZMnVatWLce6Dh06qGbNq9+qu1zt2rWdfrb3ol15K/R/9f6t/TIz0wpVfqlSpfXY42+qUqUaCt80T4cObnGsCwgI0L333qvSpUsXqqwPPvhASUlJCgwM1P3331+ofYCSQrgCcNurWbOm6tat67SsVKlS8vPzU3Jystsnz+y9VefOnZMkbd++XZLUuXNnt3M9BQQEyNvb22WAtiSdOXNG4eHhOnbsmNLS0pSXlydJ8vLycozbulLjxo3djkeqU6eOYmNjde7cOadwNWTIELfllKRLl1L07fxXFX/igHo/NF7Nmt0vrzJlFXd0p9aFTtZLL72kt99+Wz169CiwnDlz5ig0NFQ+Pj56++23Cz0VBFBSCFcAbnv53eay92S5W29fl5WVJem3yS0l6fPPP9fnn3+e77EyMzOdfv7222/12WefKScnp0h1rl69utvl3t7eTvUyydvbWykpKcrIyHC7PivrkiTpjjvKu11/pdX/maK4o1Hq1XusOnTs71jepGln9eheXSEhIfrkk0/UtWtXeXq6/zpavXq1pk+frnLlymny5Mkuk44CNyPCFYDb3tV6OgrTE2Kfb/mee+4p9Bf83r179cknn6hChQoaP368WrdurSpVqjhu9T388MNKTEy85jpdbs6cOYqLiyvSPn/84x/VsmVLx881atRQSkqKEhMT1ahRI5ftU1LOSJIq3el31bLz8nK1e/daSVKzwAdc1jdp0kS1atVSfHy84uPjVa9ePZdt/vvf/+rdd9+Vp6en3n//fQUGBhb21IASRbgCgEKw92517txZgwYNKtQ+GzZskCQ999xzLtM0ZGRkGJ1SYOvWrUUe0N66dWuncNWoUSPFxMQoOjra7VixkycPSZL8/O6+atlpqUnKzc2WJJUt676nyz6X1cWLF13WRUZGavz48ZKkt99+m0lDcUshXAFAIQQFBWnmzJkKCwsrdLiyhwZ3t/jWr1+vQr59rFAun+X8WnXs2FGrVq3Sf//7X5en+A4ePKik8/GqXqNBoea4Kufto9KlvZSbm634+Gg1aNDGaX1aWpp+/fVXSZKfn3NPWHR0tP785z8rKytLr732mh54wLXnC7iZMc8VABRCYGCggoKCtHv3bn344YdKS3N9Yi4mJkZbt251/GwfRL9ixQqnMVdHjx7Vp59+WvyVLqL7779ftWrVUkxMjBYsWOBYfunSJcccXvcFP+my3+wvx2jKR0/oxPF9jmWenmXUqFF7SdLqVVN0MeV/vXTZ2Zn6xz/+oYyMDLVo0UJVq1Z1rDt27JjGjx+vtLQ0vfbaa05TRQC3CnquAKCQ3nrrLY0bN07fffed1qxZo0aNGqlq1apKS0vT4cOHdfr0aT3xxBOOqRj69Omjb7/9VuHh4erfv7+aNGmilJQURUVFqUuXLoqOjlZ8fHwJn9X/eHp66q233tILL7ygKVOmKDQ0VH5+ftq1a5fOnj2rZoFd1apVb5f9zp+PV3LyKWVnOw/m79X7RZ04sU+nEmI05eMnVKdOc3l63aH4E/t18eJZ+fj4aOLEiU77vPHGG0pKSpKvr6/27dunyMhIl+PVr1//pnw6ErAjXAFAIVWuXFlffPGFli9frrVr1+rQoUPas2ePKleurFq1aql///5O0wpUqlRJs2fP1rRp0xQVFaXw8HDVrFlTI0aM0KBBg9S/f/8CjlYyWrRoodmzZ+uLL75QZGSkDh8+LH9/fw0aNEiVq/Yv0kD7ylVqa/QLc7Vp41wdOrRVcXG/jQnzqVRdjz32mIYMGeJyyzQlJUWSlJSUpO+//95tua1atSJc4abmYRXypr/9zeS3G19f39v23EygfQpG+xSM9inYrdY+a9eZGyPWo9vVQ9qt1j43Gu1TMF9f3xI7NmOuAAAADCJcAQAAGES4AgAAMIhwBQAAYBDhCgAAwCDCFQAAgEGEKwAAAIMIVwAAAAYRrgAAAAwiXAEAABhEuAIAADCIcAUAAGAQ4QoAAMAgwhUAAIBBhCsAAACDCFcAAAAGEa4AAAAMIlwBAAAYRLgCAAAwiHAFAABgEOEKAADAIMIVAACAQYQrAAAAgwhXAAAABhGuAAAADCJcAQAAGES4AgAAMIhwBQAAYBDhCgAAwCDCFQAAgEGEKwAAAIMIVwAAAAYRrgAAAAwiXAEAABhEuAIAADCIcAUAAGAQ4QoAAMAgwhUAAIBBhCsAAACDCFcAAAAGEa4AAAAMIlwBAAAYRLgCAAAwiHAFAABgEOEKAADAIMIVAACAQYQrAAAAgwhXAAAABhGuAAAADCJcAQAAGES4AgAAMIhwBQAAYBDhCgAAwCDCFQAAgEGEKwAAAIMIVwAAAAYRrgAAAAwiXAEAABhEuAIAADCIcAUAAGAQ4QoAAMAgwhUAAIBBhCsAAACDCFcAAAAGEa4AAAAMIlwBAAAYRLgCAAAwiHAFAABgEOEKAADAIMIVAACAQYQrAAAAgwhXAAAABhGuAAAADCJcAQAAGES4AgAAMIhwBQAAYBDhCgAAwCDCFQAAgEGEKwAAAIMIVwAAAAYRrgAAAAwiXAEAABhEuAIAADCIcAUAAGAQ4QoAAMAgwhUAAIBBhCsAAACDCFcAAAAGEa4AAAAMIlwBAAAYRLgCAAAwiHAFAABgEOEKAADAIMIVAACAQYQrAAAAgwhXAAAABhGuAAAADCJcAQAAGES4AgAAMIhwBQAAYBDhCgAAwCDCFQAAgEGEKwAAAIMIVwAAAAYRrgAAAAwiXAEAABhEuAIAADCIcAUAAGAQ4QoAAMAgwhUAAIBBhCsAAACDCFcAAAAGEa4AAAAMIlwBAAAYRLgCAAAwiHAFAABgEOEKAADAIMIVAACAQYQrAAAAgwhXAAAABhGuAAAADCJcAQAAGES4AgAAMMizpCsAACgea9dZJV0F4HeJnisAAACDCFcAAAAGEa4AAAAMIlwBAAAYRLgCAAAwiHAFAABgEOEKAADAIMIVAACAQYQrAAAAgwhXAAAABhGuAAAADCJcAQAAGES4AgAAMIhwBQAAYBDhCgAAwCDCFQAAgEGEKwAAAIMIVwAAAAYRrgAAAAwiXAEAABhEuAIAADCIcAUAAGAQ4QoAAMAgwhUAAIBBhCsAAACDCFcAAAAGEa4AAAAMIlwBAAAYRLgCAAAwiHAFAABgEOEKAADAIMIVAACAQYQrAAAAgwhXAAAABhGuAAAADCJcAQAAGES4AgAAMIhwBQAAYBDhCgAAwCDCFQAAgEGEKwAAAIMIVwAAAAYRrgAAAAwiXAEAABjkWdIVAAD8z9p1VklXAcB1oucKAADAIHquAOA6XWtvU/nymUpLo6cKuN3QcwUAAGAQ4QoAAMAgwhUAAIBBhCsAAACDCFcAAAAGEa4AAAAMIlwBAAAYRLgCAAAwiHAFAABgEOEKAADAIMIVAACAQYQrAAAAgwhXAAAABhGuAAAADCJcAQAAGES4AgAAMIhwBQAAYBDhCgAAwCDCFQAAgEGEKwAAAIMIVwAAAAYRrgAAAAwiXAEAABhEuAIAADCIcAUAAGAQ4QoAAMAgwhUAAIBBhCsAAACDCFcAAAAGEa4AAAAMIlwBAAAYRLgCAAAwiHAFAABgEOEKAADAIMIVAACAQYQrAAAAgwhXAAAABhGuAAAADCJcAQAAGES4AgAAMIhwBQAAYBDhCgAAwCDCFQAAgEGEKwAAAIMIVwAAAAYRrgAAAAwiXAEAABhEuAIAADCIcAUAAGAQ4QoAAMAgwhUAAIBBniVdAQC40daus0q6CgBuY/RcAQAAGES4AgAAMIhwBQAAYBDhCgAAwCDCFQAAgEGEKwAAAIMIVwAAAAYRrgAAAAwiXAEAABhEuAIAADCIcAUAAGAQ4QoAAMAgwhUAAIBBhCsAAACDPEu6AgBQGGvXWSVdBQAoFHquAAAADCJcAQAAGES4AgAAMIhwBQAAYBDhCgAAwCDCFQAAgEGEKwAAAIMIVwAAAAYRrgAAAAwiXAEAABhEuAIAADCIcAUAAGAQ4QoAAMAgwhUAAIBBniVdAQC3r/+szlRamlXS1QCAG4qeKwAAAIMIVwAAAAYRrgAAAAwiXAEAABhEuAIAADCIcAUAAGAQ4QoAAMAgwhUAAIBBTCIKwGHtOrMTfpYvb7Q4ALgl0HMFAABgEOEKAADAIMIVAACAQYQrAAAAgxjQDtziTA9CBwBcH3quAAAADKLnCigB9DYBwO2LnisAAACDCFcAAAAGEa4AAAAMIlwBAAAYRLgCAAAwiHAFAABgEFMxAIWQ39QJ5ctnKi2NaRUAAP9DzxUAAIBBhCsAAACDuC2I2xazoAMASgI9VwAAAAYRrgAAAAwiXAEAABhEuAIAADCIcAUAAGAQTwvipsITfgCAWx09VwAAAAbRc4XrQk8TAADO6LkCAAAwyMOyrN9t18PFixe1Y8cOtWnTRhUrVizp6tx0aJ+C0T4Fo30KRvsUjPYpGO1TsJJun991z1VqaqrCwsKUmppa0lW5KdE+BaN9Ckb7FIz2KRjtUzDap2Al3T6/63AFAABgGuEKAADAoN91uKpQoYK6dOmiChUqlHRVbkq0T8Fon4LRPgWjfQpG+xSM9ilYSbfP73pAOwAAgGm/654rAAAA0whXAAAABhGuAAAADCJcAQAAGES4AgAAMOi2e3Fzamqqpk6dqjVr1igxMVHVq1fXgw8+qDFjxqh8+fKFKiMuLk4//vijNm3apGPHjik5OVlVqlRRu3btNHLkSDVs2NBln1dffVXff/99vmUePHjwms/pWuzevVtTp05VVFSUcnJyZLPZ9PTTT6t3796FLiMrK0szZ87UihUrlJCQoEqVKqlr164aN26cqlSp4nafFStWaM6cOTp8+LC8vLzUunVrvfjii2rWrJmpUzPietrHsixt3LhR69evV2RkpE6ePKmcnBzVq1dPvXv31rBhw3THHXe47BcQEJBvmX379tX7779/Xedk0vVeP8uWLdNf/vKXfNfPmTNH7dq1M37cG+V66/nAAw8oPj6+wG3mz5+vtm3bOn6+Va6f5cuXa8eOHdq7d68OHTqk7Oxsvffee+rXr1+RysnLy9P8+fO1ePFiHTt2TN7e3urYsaPGjx+vOnXquN1n06ZNmjFjhvbt2ycPDw81a9ZMzz//vDp06GDi1K6bibb55ZdfFBoaqoiICMXHxys9PV3+/v7q1q2bRo4cKR8fH5d9CrregoKCNHfu3Gs+J5NMtM/27ds1ZMiQfNfnV97Ro0f18ccfa9u2bbp06ZLq16+vAQMGaODAgfLw8CjyudxW4So9PV2DBw/WgQMHFBwcrIceekgHDhzQV199pZ9//lnz5893+6V3pSlTpmjVqlWy2Wzq1q2bKlSooEOHDmn58uX66aefNGvWLN17771u9x0yZIjbi/tG2rZtm5599lmVKVNGDz30kMqXL681a9Zo/PjxOnXqlEJCQq5aRl5enkaNGqXw8HC1bNlSPXv21LFjx7RkyRJt3bpVixcvVuXKlZ32mT59uj7++GP5+/trwIABSktL03/+8x8NGDBAX3/9tdq0aVNcp1wk19s+WVlZGjFihMqUKaOgoCAFBwcrKytL4eHh+uijjxQaGqq5c+eqXLlyLvv6+/urb9++LsubNGli7Pyul4nrx65bt25uz83f379Yj1ucTNRzyJAhunjxosvypKQkzZ8/X5UqVVLz5s1d1t8K18+UKVMUHx8vX19fVa9e/aohMj+TJk3SkiVL1KhRIz311FM6c+aMVq9erc2bN2vRokWqX7++0/bLly/XhAkTVLlyZceX56pVqzRs2DB9/PHH+sMf/nC9p3bdTLTN2LFjlZSUpDZt2uiRRx6Rh4eHIiIiNGvWLP30009auHChqlat6rJfxYoVNXToUJfl7v4vlhRT1470W2gMCgpyWe7u/8rhw4c1YMAAZWRkqFevXqpevbrCwsL09ttvKzY2Vm+88UbRK2DdRqZMmWLZbDbrww8/dFr+4YcfWjabzfr8888LVc53331n7du3z2X5ypUrLZvNZvXu3dtl3cSJEy2bzWYdP3782ipvSHZ2ttW9e3crMDDQ2r9/v2N5SkqK1bNnT6tZs2bWiRMnrlrO0qVLLZvNZr300ktWXl6eY/m3335r2Ww264033nDa/ujRo1bTpk2tnj17WikpKY7l+/fvtwIDA61evXpZubm5Bs7w+phon6ysLOuzzz6zkpOTXZaPHDnSstls1hdffOGyn81mswYPHmzmRIqJqevnu+++s2w2m/Xdd9/d0OMWt+Ku55dffmnZbDbrnXfecVl3K1w/lmVZmzdvdrTBjBkzinQd2G3dutWy2WzWoEGDrMzMTMfyDRs2WDabzQoJCXHaPjk52Wrbtq3Vrl07KyEhwbE8ISHBateundWuXTvr4sWL13FWZphomxkzZlinTp1yWpaXl2e9+eabls1ms9566y2Xfbp27Wp17dr12it+g5hon23btlk2m8365JNPCr3PoEGDLJvNZm3YsMGxLDMz03ryySctm81mRUZGFqkOlmVZt82YK8uytGTJEnl7e+v55593Wvf888/L29tbS5YsKVRZ/fr1U9OmTV2WP/TQQ6pfv74OHz6s8+fPG6m3adu2bdOvv/6qPn36OCX0ihUr6rnnnlN2dnaBty/t7G310ksvOXWJDhgwQHXq1NG///1vZWRkOJYvW7ZMOTk5GjVqlNMbyJs0aaI+ffooNjZWO3bsMHGK18VE+3h5eWnUqFGqVKmSy/KRI0dKkn7++Wfzlb8BTF0/t8pxi6q467l06VJJ0mOPPXbddS0pHTt2vO7eEPvnz9ixY1WmTBnH8i5duigoKEjh4eE6efKkY/mPP/6olJQUDR48WH5+fo7lfn5+Gjx4sJKSkhQaGnpddTLBRNuMGDFCNWrUcFrm4eHh+N67VT97JDPtU1RHjx7Vzz//rHbt2qlLly6O5WXKlNHYsWMlSYsXLy5yubfNbcG4uDidOXNGwcHB8vb2dlrn7e2t1q1bKzw8XAkJCapZs+Y1H8fLy0uS5Onpvuk2bNigtLQ0lSlTRg0aNFCHDh2cPhyKW0REhCQpODjYZZ192dX+82VmZmrXrl266667XC50Dw8Pdez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      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_posterior(\n",
    "    idata, group=\"sample_stats\", var_names=\"acceptance_rate\", hdi_prob=\"hide\", kind=\"hist\"\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We check if there are any divergences, if yes, how many?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;diverging&#x27; ()&gt;\n",
       "array(0)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'diverging'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-47aa5a24-473e-4def-b6ab-a657a70a43b3' class='xr-array-in' type='checkbox' checked><label for='section-47aa5a24-473e-4def-b6ab-a657a70a43b3' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0</span></div><div class='xr-array-data'><pre>array(0)</pre></div></div></li><li class='xr-section-item'><input id='section-67cd149c-22f5-4420-b05b-7b0987ff081b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-67cd149c-22f5-4420-b05b-7b0987ff081b' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-0d9138e0-fa99-4138-8331-97793dc4d913' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0d9138e0-fa99-4138-8331-97793dc4d913' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'diverging' ()>\n",
       "array(0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata.sample_stats[\"diverging\"].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case no divergences are found. If there are any, check [this notebook](https://github.com/pymc-devs/pymc-examples/blob/main/examples/diagnostics_and_criticism/Diagnosing_biased_Inference_with_Divergences.ipynb) for  information on handling divergences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is often useful to compare the overall distribution of the\n",
    "energy levels with the change of energy between successive samples.\n",
    "Ideally, they should be very similar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_energy(idata, figsize=(6, 4));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the overall distribution of energy levels has longer tails, the efficiency of the sampler will deteriorate quickly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple samplers\n",
    "\n",
    "If multiple samplers are used for the same model (e.g. for continuous and discrete variables), the exported values are merged or stacked along a new axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "coords = {\"step\": [\"BinaryMetropolis\", \"Metropolis\"], \"obs\": [\"mu1\"]}\n",
    "dims = {\"accept\": [\"step\"]}\n",
    "\n",
    "with pm.Model(coords=coords) as model:\n",
    "    mu1 = pm.Bernoulli(\"mu1\", p=0.8)\n",
    "    mu2 = pm.Normal(\"mu2\", mu=0, sigma=1, dims=\"obs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 2 jobs)\n",
      "CompoundStep\n",
      ">BinaryMetropolis: [mu1]\n",
      ">Metropolis: [mu2]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='44000' class='' max='44000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [44000/44000 00:14<00:00 Sampling 4 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 10_000 draw iterations (4_000 + 40_000 draws total) took 15 seconds.\n"
     ]
    }
   ],
   "source": [
    "with model:\n",
    "    step1 = pm.BinaryMetropolis([mu1])\n",
    "    step2 = pm.Metropolis([mu2])\n",
    "    idata = pm.sample(\n",
    "        10000,\n",
    "        init=None,\n",
    "        step=[step1, step2],\n",
    "        chains=4,\n",
    "        tune=1000,\n",
    "        idata_kwargs={\"dims\": dims, \"coords\": coords},\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['p_jump', 'scaling', 'accepted', 'accept']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(idata.sample_stats.data_vars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both samplers export `accept`, so we get one acceptance probability for each sampler:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LluW6667L/fffn9mzZ2fTTTfNV7/61Zx66qnZYostVvo4DzzwQP7v//4vr732Wpo0aZLevXvnlFNOyU477bS2n+J6qd7GRQAAAACgfqt311wEAAAAANYP4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh/x9Vqxh3vdzeEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1656x552 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_posterior(\n",
    "    idata,\n",
    "    group=\"sample_stats\",\n",
    "    var_names=\"accept\",\n",
    "    hdi_prob=\"hide\",\n",
    "    kind=\"hist\",\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice that `accept` sometimes takes really high values (jumps from regions of low probability to regions of much higher probability)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;accept&#x27; (chain: 4, accept_dim_0: 2)&gt;\n",
       "array([[  3.75      , 573.3089824 ],\n",
       "       [  3.75      , 184.17692429],\n",
       "       [  3.75      , 194.61242919],\n",
       "       [  3.75      ,  88.51883672]])\n",
       "Coordinates:\n",
       "  * chain         (chain) int64 0 1 2 3\n",
       "  * accept_dim_0  (accept_dim_0) int64 0 1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'accept'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>accept_dim_0</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a685c51e-1795-4ac9-a743-109935a04949' class='xr-array-in' type='checkbox' checked><label for='section-a685c51e-1795-4ac9-a743-109935a04949' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>3.75 573.3 3.75 184.2 3.75 194.6 3.75 88.52</span></div><div class='xr-array-data'><pre>array([[  3.75      , 573.3089824 ],\n",
       "       [  3.75      , 184.17692429],\n",
       "       [  3.75      , 194.61242919],\n",
       "       [  3.75      ,  88.51883672]])</pre></div></div></li><li class='xr-section-item'><input id='section-d644dff3-1273-44b8-951e-992f447e513f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d644dff3-1273-44b8-951e-992f447e513f' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-2e13b43d-ddd7-4e87-8c7f-b27ee4536c9c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e13b43d-ddd7-4e87-8c7f-b27ee4536c9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-651271fa-b825-4960-bafd-a8c18d311eaf' class='xr-var-data-in' type='checkbox'><label for='data-651271fa-b825-4960-bafd-a8c18d311eaf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>accept_dim_0</span></div><div class='xr-var-dims'>(accept_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-31b98dbe-115b-444f-97c9-31208261cd8f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31b98dbe-115b-444f-97c9-31208261cd8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-905289ae-f52b-45fc-8f61-b49536d480a8' class='xr-var-data-in' type='checkbox'><label for='data-905289ae-f52b-45fc-8f61-b49536d480a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9dc78cbd-392f-4691-b992-6a8152a8fd7c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9dc78cbd-392f-4691-b992-6a8152a8fd7c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'accept' (chain: 4, accept_dim_0: 2)>\n",
       "array([[  3.75      , 573.3089824 ],\n",
       "       [  3.75      , 184.17692429],\n",
       "       [  3.75      , 194.61242919],\n",
       "       [  3.75      ,  88.51883672]])\n",
       "Coordinates:\n",
       "  * chain         (chain) int64 0 1 2 3\n",
       "  * accept_dim_0  (accept_dim_0) int64 0 1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Range of accept values\n",
    "idata.sample_stats[\"accept\"].max(\"draw\") - idata.sample_stats[\"accept\"].min(\"draw\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1656x552 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can try plotting the density and view the high density intervals to understand the variable better\n",
    "az.plot_density(\n",
    "    idata,\n",
    "    group=\"sample_stats\",\n",
    "    var_names=\"accept\",\n",
    "    point_estimate=\"mean\",\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n",
    "* Updated by Meenal Jhajharia in April 2021 ([pymc-examples#95](https://github.com/pymc-devs/pymc-examples/pull/95))\n",
    "* Updated to v4 by Christian Luhmann in May 2022 ([pymc-examples#338](https://github.com/pymc-devs/pymc-examples/pull/338))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Tue May 31 2022\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.10.4\n",
      "IPython version      : 8.4.0\n",
      "\n",
      "arviz     : 0.12.1\n",
      "numpy     : 1.23.0rc2\n",
      "pymc      : 4.0.0b6\n",
      "matplotlib: 3.5.2\n",
      "pandas    : 1.4.2\n",
      "\n",
      "Watermark: 2.3.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{include} ../page_footer.md\n",
    ":::"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "notebook_metadata_filter": "substitutions"
  },
  "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}