From 590b40f191096182d4504f7ae284cf5b660dec24 Mon Sep 17 00:00:00 2001
From: Christian Luhmann <cluhmann@gmail.com>
Date: Tue, 31 May 2022 10:35:03 -0400
Subject: [PATCH 1/7] updating sampler stats to v4

---
 .../sampler-stats.ipynb                       | 370 +++++++++++-------
 1 file changed, 226 insertions(+), 144 deletions(-)

diff --git a/examples/diagnostics_and_criticism/sampler-stats.ipynb b/examples/diagnostics_and_criticism/sampler-stats.ipynb
index a79389edf..7896c4458 100644
--- a/examples/diagnostics_and_criticism/sampler-stats.ipynb
+++ b/examples/diagnostics_and_criticism/sampler-stats.ipynb
@@ -17,11 +17,18 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING (aesara.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on PyMC3 v3.11.2\n"
+      "Running on PyMC v4.0.0b6\n"
      ]
     }
    ],
@@ -30,12 +37,11 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import pymc3 as pm\n",
-    "import seaborn as sns\n",
+    "import pymc as pm\n",
     "\n",
     "%matplotlib inline\n",
     "\n",
-    "print(f\"Running on PyMC3 v{pm.__version__}\")"
+    "print(f\"Running on PyMC v{pm.__version__}\")"
    ]
   },
   {
@@ -75,29 +81,41 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
+      "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>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
       "text/html": [
        "\n",
        "    <div>\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",
-       "      <progress value='6000' class='' max='6000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [6000/6000 00:03<00:00 Sampling 2 chains, 0 divergences]\n",
+       "      <progress value='12000' class='' max='12000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [12000/12000 00:12<00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -112,14 +130,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 1_000 tune and 2_000 draw iterations (2_000 + 4_000 draws total) took 11 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 14 seconds.\n"
      ]
     }
    ],
    "source": [
     "with model:\n",
     "    step = pm.NUTS()\n",
-    "    trace = pm.sample(2000, tune=1000, init=None, step=step, cores=2, return_inferencedata=True)"
+    "    trace = pm.sample(2000, tune=1000, init=None, step=step, chains=4)"
    ]
   },
   {
@@ -180,7 +198,7 @@
        "}\n",
        "\n",
        ".xr-wrap {\n",
-       "  display: block;\n",
+       "  display: block !important;\n",
        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
        "}\n",
@@ -489,89 +507,137 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:             (chain: 2, draw: 2000)\n",
+       "Dimensions:             (chain: 4, draw: 2000)\n",
        "Coordinates:\n",
-       "  * chain               (chain) int64 0 1\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",
-       "    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 7.234 7.235 7.235 ... 9.779 9.78\n",
-       "    perf_counter_diff   (chain, draw) float64 0.0002823 0.000284 ... 0.0002742\n",
-       "    energy_error        (chain, draw) float64 -0.5087 -0.4354 ... 0.04225 0.1197\n",
-       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 2 ... 2 2 2 3 3 2 2 2\n",
-       "    max_energy_error    (chain, draw) float64 -0.6245 0.5775 ... 0.3449 -0.1726\n",
-       "    ...                  ...\n",
-       "    energy              (chain, draw) float64 20.89 20.49 18.01 ... 17.55 16.76\n",
+       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 3 ... 2 2 2 2 2 2 2 3\n",
+       "    step_size           (chain, draw) float64 1.22 1.22 1.22 ... 1.184 1.184\n",
+       "    perf_counter_diff   (chain, draw) float64 0.001822 0.002302 ... 0.001549\n",
+       "    energy              (chain, draw) float64 14.32 18.67 24.74 ... 14.31 15.77\n",
        "    diverging           (chain, draw) bool False False False ... False False\n",
-       "    step_size_bar       (chain, draw) float64 0.9604 0.9604 ... 0.9419 0.9419\n",
-       "    lp                  (chain, draw) float64 -15.3 -13.86 ... -13.54 -13.95\n",
-       "    acceptance_rate     (chain, draw) float64 1.0 0.8879 0.927 ... 0.9095 0.9688\n",
-       "    step_size           (chain, draw) float64 0.9213 0.9213 ... 0.9988 0.9988\n",
+       "    n_steps             (chain, draw) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 7.0\n",
+       "    ...                  ...\n",
+       "    max_energy_error    (chain, draw) float64 0.3517 0.9423 ... 0.3606 0.8925\n",
+       "    energy_error        (chain, draw) float64 0.08696 0.9423 ... 0.0876 -0.03091\n",
+       "    lp                  (chain, draw) float64 -11.88 -16.3 ... -11.75 -11.46\n",
+       "    acceptance_rate     (chain, draw) float64 0.87 0.4506 ... 0.8245 0.7415\n",
+       "    step_size_bar       (chain, draw) float64 0.9626 0.9626 ... 0.9833 0.9833\n",
+       "    perf_counter_start  (chain, draw) float64 2.399e+05 2.399e+05 ... 2.399e+05\n",
        "Attributes:\n",
-       "    created_at:                 2021-04-06T16:24:24.465349\n",
-       "    arviz_version:              0.11.2\n",
-       "    inference_library:          pymc3\n",
-       "    inference_library_version:  3.11.2\n",
-       "    sampling_time:              10.891047954559326\n",
-       "    tuning_steps:               1000</pre><div class='xr-wrap' hidden><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-cc8d41f3-8ee0-4e2a-9f9f-0e268925301a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cc8d41f3-8ee0-4e2a-9f9f-0e268925301a' 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>: 2</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-45607efa-b00c-42c7-b425-6c29f6380d25' class='xr-section-summary-in' type='checkbox'  checked><label for='section-45607efa-b00c-42c7-b425-6c29f6380d25' 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</div><input id='attrs-e2ed037a-78a5-4626-b47b-64a416959c7b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2ed037a-78a5-4626-b47b-64a416959c7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8733f32e-7818-4da4-bc14-a1f706406f81' class='xr-var-data-in' type='checkbox'><label for='data-8733f32e-7818-4da4-bc14-a1f706406f81' 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><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-bacf9693-9004-4439-b601-31ac943df3e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bacf9693-9004-4439-b601-31ac943df3e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d46070b6-25cc-4c97-8bba-89222051f484' class='xr-var-data-in' type='checkbox'><label for='data-d46070b6-25cc-4c97-8bba-89222051f484' 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-d94dd07c-403e-45c6-852a-875f83235e89' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d94dd07c-403e-45c6-852a-875f83235e89' 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>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 ... 7.0 3.0 3.0 3.0</div><input id='attrs-b326da47-ccf6-45b7-8fcd-d488399f6d65' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b326da47-ccf6-45b7-8fcd-d488399f6d65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26c94d22-4fbc-4542-9e86-b809c6ec77ea' class='xr-var-data-in' type='checkbox'><label for='data-26c94d22-4fbc-4542-9e86-b809c6ec77ea' 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., ..., 3., 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'>7.234 7.235 7.235 ... 9.779 9.78</div><input id='attrs-7e903123-3321-455b-b56e-0c3a1387fec1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e903123-3321-455b-b56e-0c3a1387fec1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-577d2682-d5ac-49b9-b624-4e847f61cbff' class='xr-var-data-in' type='checkbox'><label for='data-577d2682-d5ac-49b9-b624-4e847f61cbff' 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([[7.23425966, 7.23463133, 7.23499786, ..., 8.21941184, 8.21982745,\n",
-       "        8.2202315 ],\n",
-       "       [8.8954175 , 8.89596285, 8.89643539, ..., 9.77893421, 9.77928411,\n",
-       "        9.7796441 ]])</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'>0.0002823 0.000284 ... 0.0002742</div><input id='attrs-5c151dda-6364-4dec-bf8a-2a2a0a781adb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c151dda-6364-4dec-bf8a-2a2a0a781adb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb90fb64-6519-41a2-923b-a83c2b24b424' class='xr-var-data-in' type='checkbox'><label for='data-fb90fb64-6519-41a2-923b-a83c2b24b424' 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([[0.00028233, 0.00028403, 0.00028609, ..., 0.00033475, 0.00032492,\n",
-       "        0.00031708],\n",
-       "       [0.00042187, 0.00031782, 0.00044144, ..., 0.00027279, 0.00027981,\n",
-       "        0.00027424]])</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'>-0.5087 -0.4354 ... 0.04225 0.1197</div><input id='attrs-16455eb6-f992-4b93-99cc-e291520356d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-16455eb6-f992-4b93-99cc-e291520356d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e5ff7b6-e0cc-4082-9b54-4c7112096ddb' class='xr-var-data-in' type='checkbox'><label for='data-2e5ff7b6-e0cc-4082-9b54-4c7112096ddb' 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([[-0.50873119, -0.43537361, -0.2754945 , ..., -0.41472269,\n",
-       "        -0.04624652,  0.58648128],\n",
-       "       [ 0.        ,  0.05162033, -0.51012677, ..., -0.10103067,\n",
-       "         0.04225451,  0.11974125]])</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 2 3 3 2 2 2</div><input id='attrs-9a229c16-906b-4ace-a394-ce07c4e27445' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a229c16-906b-4ace-a394-ce07c4e27445' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a43147a-c497-4f0e-ba24-27d67a9b47e6' class='xr-var-data-in' type='checkbox'><label for='data-4a43147a-c497-4f0e-ba24-27d67a9b47e6' 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, ..., 2, 2, 2],\n",
-       "       [2, 2, 2, ..., 2, 2, 2]])</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'>-0.6245 0.5775 ... 0.3449 -0.1726</div><input id='attrs-fe3f7720-a9af-4a28-b2ed-a070a51b2414' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fe3f7720-a9af-4a28-b2ed-a070a51b2414' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfae479a-ddf6-451f-93a1-964d83a90dc1' class='xr-var-data-in' type='checkbox'><label for='data-dfae479a-ddf6-451f-93a1-964d83a90dc1' 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([[-0.62454923,  0.57749804,  0.68574548, ...,  0.52434551,\n",
-       "         0.23932216,  0.74183316],\n",
-       "       [ 0.61023739, -0.29264149, -0.51012677, ...,  0.78759243,\n",
-       "         0.34488576, -0.17257857]])</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'>0.000282 0.000284 ... 0.000275</div><input id='attrs-6a25c484-f35c-40fa-a546-0551dc660ce4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6a25c484-f35c-40fa-a546-0551dc660ce4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f675bf56-337d-4fc5-b944-b4826a8c4cd9' class='xr-var-data-in' type='checkbox'><label for='data-f675bf56-337d-4fc5-b944-b4826a8c4cd9' 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([[0.000282, 0.000284, 0.000287, ..., 0.000335, 0.000326, 0.000317],\n",
-       "       [0.000422, 0.000319, 0.000432, ..., 0.000273, 0.000279, 0.000275]])</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'>20.89 20.49 18.01 ... 17.55 16.76</div><input id='attrs-165f2820-a189-44dd-a1cb-a53e1f97acc9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-165f2820-a189-44dd-a1cb-a53e1f97acc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6062b5b-9650-420e-835d-10b4d77da559' class='xr-var-data-in' type='checkbox'><label for='data-e6062b5b-9650-420e-835d-10b4d77da559' 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([[20.88764382, 20.4921193 , 18.01116499, ..., 17.49176409,\n",
-       "        14.92873488, 17.43965595],\n",
-       "       [20.20316506, 18.13457635, 17.89032511, ..., 19.00599324,\n",
-       "        17.55328583, 16.76147247]])</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-33b88b64-bdb2-4928-a594-3e54ac5c5e21' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33b88b64-bdb2-4928-a594-3e54ac5c5e21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a672e103-8edc-418e-ace9-97244b43bf67' class='xr-var-data-in' type='checkbox'><label for='data-a672e103-8edc-418e-ace9-97244b43bf67' 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]])</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'>0.9604 0.9604 ... 0.9419 0.9419</div><input id='attrs-e6b1d819-c13e-47e9-b953-0e13c4d48c80' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6b1d819-c13e-47e9-b953-0e13c4d48c80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa047794-eaed-4254-8c4f-ef02d2b6bb6c' class='xr-var-data-in' type='checkbox'><label for='data-fa047794-eaed-4254-8c4f-ef02d2b6bb6c' 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([[0.96041447, 0.96041447, 0.96041447, ..., 0.96041447, 0.96041447,\n",
-       "        0.96041447],\n",
-       "       [0.94194673, 0.94194673, 0.94194673, ..., 0.94194673, 0.94194673,\n",
-       "        0.94194673]])</pre></div></li><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'>-15.3 -13.86 ... -13.54 -13.95</div><input id='attrs-c465f465-d0f6-4584-81cb-a7cdfe6d51ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c465f465-d0f6-4584-81cb-a7cdfe6d51ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-517c3d76-ec67-4532-ac0f-fd8d4a2cca65' class='xr-var-data-in' type='checkbox'><label for='data-517c3d76-ec67-4532-ac0f-fd8d4a2cca65' 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([[-15.3048286 , -13.86066125, -12.92070308, ..., -12.06651172,\n",
-       "        -11.88730918, -14.62170062],\n",
-       "       [-14.30776839, -14.61483991, -12.18578006, ..., -13.37718754,\n",
-       "        -13.53911458, -13.95227994]])</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'>1.0 0.8879 0.927 ... 0.9095 0.9688</div><input id='attrs-2d2ffbde-d619-4a52-8348-4fd8f7782045' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d2ffbde-d619-4a52-8348-4fd8f7782045' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b3bb626-37a9-486a-a67f-da18003d4b4e' class='xr-var-data-in' type='checkbox'><label for='data-7b3bb626-37a9-486a-a67f-da18003d4b4e' 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([[1.        , 0.88790791, 0.9269517 , ..., 0.92792804, 0.91796823,\n",
-       "        0.53323407],\n",
-       "       [0.69160202, 0.96691222, 0.93334257, ..., 0.91483728, 0.90947644,\n",
-       "        0.96880681]])</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'>0.9213 0.9213 ... 0.9988 0.9988</div><input id='attrs-ece8bd7f-873d-44da-b1c3-2fb6a7cba486' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ece8bd7f-873d-44da-b1c3-2fb6a7cba486' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7a20e30-1d16-4c1d-bc62-e13cab832dab' class='xr-var-data-in' type='checkbox'><label for='data-c7a20e30-1d16-4c1d-bc62-e13cab832dab' 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([[0.92128394, 0.92128394, 0.92128394, ..., 0.92128394, 0.92128394,\n",
-       "        0.92128394],\n",
-       "       [0.99880883, 0.99880883, 0.99880883, ..., 0.99880883, 0.99880883,\n",
-       "        0.99880883]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0672ec37-2b1a-4491-b8df-89953eb44f18' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0672ec37-2b1a-4491-b8df-89953eb44f18' 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>2021-04-06T16:24:24.465349</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>pymc3</dd><dt><span>inference_library_version :</span></dt><dd>3.11.2</dd><dt><span>sampling_time :</span></dt><dd>10.891047954559326</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
+       "    created_at:                 2022-05-31T14:30:53.836698\n",
+       "    arviz_version:              0.12.1\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  4.0.0b6\n",
+       "    sampling_time:              13.840092182159424\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-23c65a07-5144-4961-bc0f-81af9097a9e1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-23c65a07-5144-4961-bc0f-81af9097a9e1' 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-52b9ee0b-2036-419a-b405-c1446e15b6de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-52b9ee0b-2036-419a-b405-c1446e15b6de' 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-4ebf7f0d-b3ef-4753-bf4c-6432b5f3a40f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ebf7f0d-b3ef-4753-bf4c-6432b5f3a40f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b7e0044-a098-43a3-ba60-92ef77db687c' class='xr-var-data-in' type='checkbox'><label for='data-6b7e0044-a098-43a3-ba60-92ef77db687c' 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-10fd1a19-e803-407e-849d-ea435789c238' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-10fd1a19-e803-407e-849d-ea435789c238' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3d4aef0-f280-4f24-b0ae-b06d79fb95c0' class='xr-var-data-in' type='checkbox'><label for='data-b3d4aef0-f280-4f24-b0ae-b06d79fb95c0' 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-29515885-219c-43f7-8940-e778432d5f28' class='xr-section-summary-in' type='checkbox'  checked><label for='section-29515885-219c-43f7-8940-e778432d5f28' 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>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 3 ... 2 2 2 2 2 2 2 3</div><input id='attrs-3adf808b-f43a-4399-89df-149cbc7198d7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3adf808b-f43a-4399-89df-149cbc7198d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32b4538f-7065-414c-a94c-a8eab4bd943e' class='xr-var-data-in' type='checkbox'><label for='data-32b4538f-7065-414c-a94c-a8eab4bd943e' 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, ..., 2, 2, 2],\n",
+       "       [2, 2, 2, ..., 2, 2, 2],\n",
+       "       [2, 3, 2, ..., 3, 2, 2],\n",
+       "       [2, 2, 2, ..., 2, 2, 3]])</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'>1.22 1.22 1.22 ... 1.184 1.184</div><input id='attrs-0bddd468-93a0-4d14-957f-7f58011d8cd8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0bddd468-93a0-4d14-957f-7f58011d8cd8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9fb3af5-3a78-4e49-857a-b317f79366b7' class='xr-var-data-in' type='checkbox'><label for='data-b9fb3af5-3a78-4e49-857a-b317f79366b7' 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([[1.21950326, 1.21950326, 1.21950326, ..., 1.21950326, 1.21950326,\n",
+       "        1.21950326],\n",
+       "       [0.72020393, 0.72020393, 0.72020393, ..., 0.72020393, 0.72020393,\n",
+       "        0.72020393],\n",
+       "       [1.05976336, 1.05976336, 1.05976336, ..., 1.05976336, 1.05976336,\n",
+       "        1.05976336],\n",
+       "       [1.18415486, 1.18415486, 1.18415486, ..., 1.18415486, 1.18415486,\n",
+       "        1.18415486]])</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'>0.001822 0.002302 ... 0.001549</div><input id='attrs-7e220570-1bd0-4c92-b2ea-d9fa05f2b33d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e220570-1bd0-4c92-b2ea-d9fa05f2b33d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32e7e8f9-fe16-4e05-bc80-760cf59c5e5e' class='xr-var-data-in' type='checkbox'><label for='data-32e7e8f9-fe16-4e05-bc80-760cf59c5e5e' 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([[0.00182155, 0.00230215, 0.00205968, ..., 0.00063477, 0.00082406,\n",
+       "        0.00248648],\n",
+       "       [0.00145808, 0.00094122, 0.00091943, ..., 0.00491877, 0.00102631,\n",
+       "        0.00074709],\n",
+       "       [0.00089773, 0.00288749, 0.00093999, ..., 0.00268249, 0.00128637,\n",
+       "        0.00096458],\n",
+       "       [0.00116655, 0.00114517, 0.00135329, ..., 0.00066558, 0.00090877,\n",
+       "        0.00154863]])</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'>14.32 18.67 24.74 ... 14.31 15.77</div><input id='attrs-a3bd0545-137a-4998-b849-455d2110a25f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a3bd0545-137a-4998-b849-455d2110a25f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-decb810f-2162-4e6c-aa9d-b7a0fb40ad4e' class='xr-var-data-in' type='checkbox'><label for='data-decb810f-2162-4e6c-aa9d-b7a0fb40ad4e' 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([[14.31547902, 18.67280753, 24.73782083, ..., 19.77783991,\n",
+       "        15.32041915, 14.4975007 ],\n",
+       "       [16.69772368, 21.55805988, 17.54714049, ..., 21.36445455,\n",
+       "        23.88091525, 18.26166119],\n",
+       "       [14.95261594, 15.18133699, 21.29688278, ..., 17.1469732 ,\n",
+       "        16.19667226, 25.32843892],\n",
+       "       [25.84769942, 24.23095369, 24.46081513, ..., 17.34602027,\n",
+       "        14.30789644, 15.76916939]])</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-7c98d638-8845-40b9-9017-cee7aac5bb4a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c98d638-8845-40b9-9017-cee7aac5bb4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99d09b29-4bd5-404f-bc68-ec96d7cd4999' class='xr-var-data-in' type='checkbox'><label for='data-99d09b29-4bd5-404f-bc68-ec96d7cd4999' 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>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 7.0</div><input id='attrs-0bc5c7c8-2d61-427c-9734-0a302c1444f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0bc5c7c8-2d61-427c-9734-0a302c1444f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80098b17-524c-4f0c-a640-e83e07890c8d' class='xr-var-data-in' type='checkbox'><label for='data-80098b17-524c-4f0c-a640-e83e07890c8d' 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., ..., 3., 3., 3.],\n",
+       "       [3., 3., 3., ..., 3., 3., 3.],\n",
+       "       [3., 7., 3., ..., 7., 3., 3.],\n",
+       "       [3., 3., 3., ..., 3., 3., 7.]])</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'>0.001536 0.001848 ... 0.001539</div><input id='attrs-530e1739-66e0-4ab5-8fd5-491416af70a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-530e1739-66e0-4ab5-8fd5-491416af70a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b72dbf6-822b-406d-895a-128012d755f8' class='xr-var-data-in' type='checkbox'><label for='data-4b72dbf6-822b-406d-895a-128012d755f8' 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([[0.00153555, 0.00184848, 0.00168128, ..., 0.00063349, 0.00082557,\n",
+       "        0.00129186],\n",
+       "       [0.00145385, 0.00094207, 0.00091932, ..., 0.00177442, 0.0010048 ,\n",
+       "        0.00074765],\n",
+       "       [0.00089884, 0.00269242, 0.00094059, ..., 0.00268316, 0.00128447,\n",
+       "        0.00096429],\n",
+       "       [0.00116694, 0.00114476, 0.00134822, ..., 0.00066548, 0.00091148,\n",
+       "        0.0015387 ]])</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'>0.3517 0.9423 ... 0.3606 0.8925</div><input id='attrs-a519eff4-db72-4180-8011-8e987990bdff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a519eff4-db72-4180-8011-8e987990bdff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50b3b970-8de1-42cc-977c-014714f71270' class='xr-var-data-in' type='checkbox'><label for='data-50b3b970-8de1-42cc-977c-014714f71270' 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([[ 0.35170638,  0.94233964,  1.7956417 , ...,  1.15748431,\n",
+       "         0.35009928,  0.16891603],\n",
+       "       [-0.88631482,  1.76602009,  0.19630242, ..., -0.81837327,\n",
+       "         1.80535122, -0.82607036],\n",
+       "       [-0.24382593,  0.29918658,  1.60887177, ...,  0.66656511,\n",
+       "        -0.62828708,  2.11279338],\n",
+       "       [ 1.53956924, -1.35463766,  2.44413947, ..., -0.97307343,\n",
+       "         0.36063295,  0.89251209]])</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'>0.08696 0.9423 ... 0.0876 -0.03091</div><input id='attrs-97ce1ff3-a6ef-48e0-8e4c-b330a47e0eef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-97ce1ff3-a6ef-48e0-8e4c-b330a47e0eef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f38d940-e3dc-45c5-9468-25e9541218f1' class='xr-var-data-in' type='checkbox'><label for='data-4f38d940-e3dc-45c5-9468-25e9541218f1' 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([[ 0.08696156,  0.94233964,  0.05662733, ...,  0.        ,\n",
+       "        -0.06831575,  0.04512375],\n",
+       "       [-0.88631482,  0.3569776 , -0.06860921, ..., -0.81837327,\n",
+       "         0.        , -0.82607036],\n",
+       "       [-0.24382593,  0.28926023,  0.10536604, ...,  0.26489312,\n",
+       "        -0.38271845,  0.        ],\n",
+       "       [ 1.2479786 , -1.35463766,  0.        , ..., -0.97307343,\n",
+       "         0.08760141, -0.03090947]])</pre></div></li><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'>-11.88 -16.3 ... -11.75 -11.46</div><input id='attrs-53201e9c-ee80-49a2-bc7a-a8cbb7d82b1e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53201e9c-ee80-49a2-bc7a-a8cbb7d82b1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-708cda65-9ab7-495f-a216-6f029d2c7e49' class='xr-var-data-in' type='checkbox'><label for='data-708cda65-9ab7-495f-a216-6f029d2c7e49' 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([[-11.87745944, -16.29670765, -16.34269384, ..., -12.25893155,\n",
+       "        -12.09451095, -12.21620593],\n",
+       "       [-12.41927296, -13.86371187, -13.47310567, ..., -14.98464477,\n",
+       "        -14.98464477, -11.52710622],\n",
+       "       [-11.63248032, -12.79484614, -13.36618064, ..., -14.878442  ,\n",
+       "        -13.04147932, -13.04147932],\n",
+       "       [-20.10679905, -14.07561821, -14.07561821, ..., -11.36593515,\n",
+       "        -11.7528609 , -11.46147781]])</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'>0.87 0.4506 ... 0.8245 0.7415</div><input id='attrs-356b2429-4043-497f-a110-6a265f6c98d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-356b2429-4043-497f-a110-6a265f6c98d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d3d8ff4-af7f-4d39-b609-f0b2dabb0008' class='xr-var-data-in' type='checkbox'><label for='data-8d3d8ff4-af7f-4d39-b609-f0b2dabb0008' 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([[0.8699512 , 0.45062746, 0.91962988, ..., 0.59122966, 0.90223295,\n",
+       "        0.93578156],\n",
+       "       [1.        , 0.52117974, 0.94964537, ..., 1.        , 0.33325079,\n",
+       "        0.84514585],\n",
+       "       [0.89843666, 0.82261863, 0.68293857, ..., 0.91283551, 1.        ,\n",
+       "        0.52657948],\n",
+       "       [0.59749008, 0.9540548 , 0.49184974, ..., 0.96964818, 0.82453019,\n",
+       "        0.74149041]])</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'>0.9626 0.9626 ... 0.9833 0.9833</div><input id='attrs-791b3887-c19b-46f4-a4d6-810f7f6252d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-791b3887-c19b-46f4-a4d6-810f7f6252d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2f7f1b8-3236-4df1-967b-754bdcc3f397' class='xr-var-data-in' type='checkbox'><label for='data-c2f7f1b8-3236-4df1-967b-754bdcc3f397' 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([[0.96260857, 0.96260857, 0.96260857, ..., 0.96260857, 0.96260857,\n",
+       "        0.96260857],\n",
+       "       [0.98844533, 0.98844533, 0.98844533, ..., 0.98844533, 0.98844533,\n",
+       "        0.98844533],\n",
+       "       [0.94526714, 0.94526714, 0.94526714, ..., 0.94526714, 0.94526714,\n",
+       "        0.94526714],\n",
+       "       [0.98325683, 0.98325683, 0.98325683, ..., 0.98325683, 0.98325683,\n",
+       "        0.98325683]])</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'>2.399e+05 2.399e+05 ... 2.399e+05</div><input id='attrs-6aee8779-33dd-40cd-9e1d-22c5d556ebf0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6aee8779-33dd-40cd-9e1d-22c5d556ebf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db465d38-bb7b-4fc6-b29f-af7c8cf809fb' class='xr-var-data-in' type='checkbox'><label for='data-db465d38-bb7b-4fc6-b29f-af7c8cf809fb' 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([[239907.83232834, 239907.83467303, 239907.83743492, ...,\n",
+       "        239911.83653276, 239911.8374169 , 239911.83845434],\n",
+       "       [239908.11866411, 239908.12046239, 239908.12205767, ...,\n",
+       "        239911.92349805, 239911.92871935, 239911.92997074],\n",
+       "       [239913.82403931, 239913.82883131, 239913.83203039, ...,\n",
+       "        239917.84966245, 239917.85289034, 239917.85446876],\n",
+       "       [239913.93993128, 239913.95082314, 239913.9599893 , ...,\n",
+       "        239917.99035031, 239917.9912292 , 239917.99339407]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-97c5750b-4ecc-4b16-8064-21f7b1e72c1e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-97c5750b-4ecc-4b16-8064-21f7b1e72c1e' 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>2022-05-31T14:30:53.836698</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>13.840092182159424</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
-       "Dimensions:             (chain: 2, draw: 2000)\n",
+       "Dimensions:             (chain: 4, draw: 2000)\n",
        "Coordinates:\n",
-       "  * chain               (chain) int64 0 1\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",
-       "    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 7.234 7.235 7.235 ... 9.779 9.78\n",
-       "    perf_counter_diff   (chain, draw) float64 0.0002823 0.000284 ... 0.0002742\n",
-       "    energy_error        (chain, draw) float64 -0.5087 -0.4354 ... 0.04225 0.1197\n",
-       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 2 ... 2 2 2 3 3 2 2 2\n",
-       "    max_energy_error    (chain, draw) float64 -0.6245 0.5775 ... 0.3449 -0.1726\n",
-       "    ...                  ...\n",
-       "    energy              (chain, draw) float64 20.89 20.49 18.01 ... 17.55 16.76\n",
+       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 3 ... 2 2 2 2 2 2 2 3\n",
+       "    step_size           (chain, draw) float64 1.22 1.22 1.22 ... 1.184 1.184\n",
+       "    perf_counter_diff   (chain, draw) float64 0.001822 0.002302 ... 0.001549\n",
+       "    energy              (chain, draw) float64 14.32 18.67 24.74 ... 14.31 15.77\n",
        "    diverging           (chain, draw) bool False False False ... False False\n",
-       "    step_size_bar       (chain, draw) float64 0.9604 0.9604 ... 0.9419 0.9419\n",
-       "    lp                  (chain, draw) float64 -15.3 -13.86 ... -13.54 -13.95\n",
-       "    acceptance_rate     (chain, draw) float64 1.0 0.8879 0.927 ... 0.9095 0.9688\n",
-       "    step_size           (chain, draw) float64 0.9213 0.9213 ... 0.9988 0.9988\n",
+       "    n_steps             (chain, draw) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 7.0\n",
+       "    ...                  ...\n",
+       "    max_energy_error    (chain, draw) float64 0.3517 0.9423 ... 0.3606 0.8925\n",
+       "    energy_error        (chain, draw) float64 0.08696 0.9423 ... 0.0876 -0.03091\n",
+       "    lp                  (chain, draw) float64 -11.88 -16.3 ... -11.75 -11.46\n",
+       "    acceptance_rate     (chain, draw) float64 0.87 0.4506 ... 0.8245 0.7415\n",
+       "    step_size_bar       (chain, draw) float64 0.9626 0.9626 ... 0.9833 0.9833\n",
+       "    perf_counter_start  (chain, draw) float64 2.399e+05 2.399e+05 ... 2.399e+05\n",
        "Attributes:\n",
-       "    created_at:                 2021-04-06T16:24:24.465349\n",
-       "    arviz_version:              0.11.2\n",
-       "    inference_library:          pymc3\n",
-       "    inference_library_version:  3.11.2\n",
-       "    sampling_time:              10.891047954559326\n",
+       "    created_at:                 2022-05-31T14:30:53.836698\n",
+       "    arviz_version:              0.12.1\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  4.0.0b6\n",
+       "    sampling_time:              13.840092182159424\n",
        "    tuning_steps:               1000"
       ]
      },
@@ -633,9 +699,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 700x300 with 2 Axes>"
+       "<Figure size 1300x300 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -653,7 +719,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 720x480 with 1 Axes>"
       ]
@@ -726,7 +792,7 @@
        "}\n",
        "\n",
        ".xr-wrap {\n",
-       "  display: block;\n",
+       "  display: block !important;\n",
        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
        "}\n",
@@ -1035,7 +1101,7 @@
        "  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' hidden><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-164703c4-22f6-4458-bb49-3ff536f525e3' class='xr-array-in' type='checkbox' checked><label for='section-164703c4-22f6-4458-bb49-3ff536f525e3' 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-e1494d06-c4b0-4518-80f5-42456b3473cb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e1494d06-c4b0-4518-80f5-42456b3473cb' 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-ae119cae-24ce-4829-8773-1c3c20e6d91f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ae119cae-24ce-4829-8773-1c3c20e6d91f' 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>"
+       "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-4c995e9e-11bc-4db8-8eaa-4302ecefb472' class='xr-array-in' type='checkbox' checked><label for='section-4c995e9e-11bc-4db8-8eaa-4302ecefb472' 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-e45e3b0e-4ccb-43b4-b066-60f0915f9c25' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e45e3b0e-4ccb-43b4-b066-60f0915f9c25' 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-7c058cd6-658b-4885-80fb-fb2ad8799d35' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7c058cd6-658b-4885-80fb-fb2ad8799d35' 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",
@@ -1074,7 +1140,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 600x400 with 1 Axes>"
       ]
@@ -1126,31 +1192,43 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
+      "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",
-       "        <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",
-       "      <progress value='22000' class='' max='22000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [22000/22000 00:04<00:00 Sampling 2 chains, 0 divergences]\n",
+       "      <progress value='44000' class='' max='44000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [44000/44000 00:25<00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -1165,8 +1243,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 1_000 tune and 10_000 draw iterations (2_000 + 20_000 draws total) took 11 seconds.\n",
-      "The number of effective samples is smaller than 25% for some parameters.\n"
+      "Sampling 4 chains for 1_000 tune and 10_000 draw iterations (4_000 + 40_000 draws total) took 26 seconds.\n"
      ]
     }
    ],
@@ -1178,9 +1255,8 @@
     "        10000,\n",
     "        init=None,\n",
     "        step=[step1, step2],\n",
-    "        cores=2,\n",
+    "        chains=4,\n",
     "        tune=1000,\n",
-    "        return_inferencedata=True,\n",
     "        idata_kwargs={\"dims\": dims, \"coords\": coords},\n",
     "    )"
    ]
@@ -1193,7 +1269,7 @@
     {
      "data": {
       "text/plain": [
-       "['accept', 'accepted', 'p_jump', 'scaling']"
+       "['p_jump', 'accept', 'accepted', 'scaling']"
       ]
      },
      "execution_count": 12,
@@ -1219,7 +1295,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1656x552 with 2 Axes>"
       ]
@@ -1296,7 +1372,7 @@
        "}\n",
        "\n",
        ".xr-wrap {\n",
-       "  display: block;\n",
+       "  display: block !important;\n",
        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
        "}\n",
@@ -1604,20 +1680,26 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;accept&#x27; (chain: 2, accept_dim_0: 2)&gt;\n",
-       "array([[  3.75      , 269.57489704],\n",
-       "       [  3.75      , 147.77852694]])\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      ,  403.60440702],\n",
+       "       [   3.75      , 1891.65485214],\n",
+       "       [   3.75      ,  873.82009294],\n",
+       "       [   3.75      ,  484.7812048 ]])\n",
        "Coordinates:\n",
-       "  * chain         (chain) int64 0 1\n",
-       "  * accept_dim_0  (accept_dim_0) int64 0 1</pre><div class='xr-wrap' hidden><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>: 2</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-1a56706e-3074-43a7-aad8-07dd5ba50c2e' class='xr-array-in' type='checkbox' checked><label for='section-1a56706e-3074-43a7-aad8-07dd5ba50c2e' 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>3.75 269.6 3.75 147.8</span></div><div class='xr-array-data'><pre>array([[  3.75      , 269.57489704],\n",
-       "       [  3.75      , 147.77852694]])</pre></div></div></li><li class='xr-section-item'><input id='section-46d29b11-57be-4243-843a-2021b92c0355' class='xr-section-summary-in' type='checkbox'  checked><label for='section-46d29b11-57be-4243-843a-2021b92c0355' 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</div><input id='attrs-f95cef0c-4213-48a4-8169-e144d3cea038' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f95cef0c-4213-48a4-8169-e144d3cea038' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c4a1c11-ba15-4a35-b767-98ada6236869' class='xr-var-data-in' type='checkbox'><label for='data-6c4a1c11-ba15-4a35-b767-98ada6236869' 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><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-b16fc994-e1e4-49fd-a6ef-ebc95abeecb2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b16fc994-e1e4-49fd-a6ef-ebc95abeecb2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ab4f398-d064-4e55-8743-40cdc6284d12' class='xr-var-data-in' type='checkbox'><label for='data-5ab4f398-d064-4e55-8743-40cdc6284d12' 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-7f78ebcc-bad2-4881-9300-e1f341f4e01a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7f78ebcc-bad2-4881-9300-e1f341f4e01a' 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>"
+       "  * 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-35730f3b-2f41-40e5-bc48-86531edaa83d' class='xr-array-in' type='checkbox' checked><label for='section-35730f3b-2f41-40e5-bc48-86531edaa83d' 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>3.75 403.6 3.75 1.892e+03 3.75 873.8 3.75 484.8</span></div><div class='xr-array-data'><pre>array([[   3.75      ,  403.60440702],\n",
+       "       [   3.75      , 1891.65485214],\n",
+       "       [   3.75      ,  873.82009294],\n",
+       "       [   3.75      ,  484.7812048 ]])</pre></div></div></li><li class='xr-section-item'><input id='section-70b94b1b-2d9d-4f6a-9c60-a600730d40c5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-70b94b1b-2d9d-4f6a-9c60-a600730d40c5' 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-31eb8755-731c-4513-923b-f242cefe3720' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31eb8755-731c-4513-923b-f242cefe3720' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c56970a-6900-4502-aaac-0e11cf57f516' class='xr-var-data-in' type='checkbox'><label for='data-6c56970a-6900-4502-aaac-0e11cf57f516' 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-49e26221-2b17-4565-99e8-b5d55a41fed1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-49e26221-2b17-4565-99e8-b5d55a41fed1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c03dceb1-7fbc-484e-81cc-b18c1eb1acd7' class='xr-var-data-in' type='checkbox'><label for='data-c03dceb1-7fbc-484e-81cc-b18c1eb1acd7' 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-79d79b5d-45dc-441a-b617-a3ad4c59a968' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-79d79b5d-45dc-441a-b617-a3ad4c59a968' 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: 2, accept_dim_0: 2)>\n",
-       "array([[  3.75      , 269.57489704],\n",
-       "       [  3.75      , 147.77852694]])\n",
+       "<xarray.DataArray 'accept' (chain: 4, accept_dim_0: 2)>\n",
+       "array([[   3.75      ,  403.60440702],\n",
+       "       [   3.75      , 1891.65485214],\n",
+       "       [   3.75      ,  873.82009294],\n",
+       "       [   3.75      ,  484.7812048 ]])\n",
        "Coordinates:\n",
-       "  * chain         (chain) int64 0 1\n",
+       "  * chain         (chain) int64 0 1 2 3\n",
        "  * accept_dim_0  (accept_dim_0) int64 0 1"
       ]
      },
@@ -1638,7 +1720,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1656x552 with 2 Axes>"
       ]
@@ -1666,20 +1748,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: Tue Apr 06 2021\n",
+      "Last updated: Tue May 31 2022\n",
       "\n",
       "Python implementation: CPython\n",
-      "Python version       : 3.9.2\n",
-      "IPython version      : 7.21.0\n",
+      "Python version       : 3.10.4\n",
+      "IPython version      : 8.4.0\n",
       "\n",
-      "seaborn   : 0.11.1\n",
-      "pandas    : 1.2.3\n",
-      "arviz     : 0.11.2\n",
-      "matplotlib: 3.3.4\n",
-      "numpy     : 1.20.1\n",
-      "pymc3     : 3.11.2\n",
+      "arviz     : 0.12.1\n",
+      "pymc      : 4.0.0b6\n",
+      "matplotlib: 3.5.2\n",
+      "pandas    : 1.4.2\n",
+      "numpy     : 1.23.0rc2\n",
       "\n",
-      "Watermark: 2.2.0\n",
+      "Watermark: 2.3.1\n",
       "\n"
      ]
     }
@@ -1693,13 +1774,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Updated by Meenal Jhajharia"
+    "Updated by Meenal Jhajharia\n",
+    "Updated by Christian Luhmann"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1713,7 +1795,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.2"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

From e2c58f880e7fdba38458f3e844e6573efc9a9191 Mon Sep 17 00:00:00 2001
From: Christian Luhmann <cluhmann@gmail.com>
Date: Tue, 31 May 2022 10:52:12 -0400
Subject: [PATCH 2/7] fix authorship formatting

---
 examples/diagnostics_and_criticism/sampler-stats.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/diagnostics_and_criticism/sampler-stats.ipynb b/examples/diagnostics_and_criticism/sampler-stats.ipynb
index 7896c4458..77bfefc76 100644
--- a/examples/diagnostics_and_criticism/sampler-stats.ipynb
+++ b/examples/diagnostics_and_criticism/sampler-stats.ipynb
@@ -1774,8 +1774,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Updated by Meenal Jhajharia\n",
-    "Updated by Christian Luhmann"
+    "* Updated by Meenal Jhajharia\n",
+    "* Updated by Christian Luhmann"
    ]
   }
  ],

From c141d1cb563b1c0de2bfe700fe53ad6fe87565a1 Mon Sep 17 00:00:00 2001
From: Christian Luhmann <cluhmann@gmail.com>
Date: Tue, 31 May 2022 11:34:25 -0400
Subject: [PATCH 3/7] update header, footer, and idata var name

---
 .../sampler-stats.ipynb                       | 305 +++++++++---------
 1 file changed, 160 insertions(+), 145 deletions(-)

diff --git a/examples/diagnostics_and_criticism/sampler-stats.ipynb b/examples/diagnostics_and_criticism/sampler-stats.ipynb
index 77bfefc76..c4fff96ee 100644
--- a/examples/diagnostics_and_criticism/sampler-stats.ipynb
+++ b/examples/diagnostics_and_criticism/sampler-stats.ipynb
@@ -4,12 +4,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Sampler statistics\n",
+    "(sampler_stats)=\n",
+    "# Sampler Statistics\n",
     "\n",
     "When checking for convergence or when debugging a badly behaving\n",
     "sampler, it is often helpful to take a closer look at what the\n",
     "sampler is doing. For this purpose some samplers export\n",
-    "statistics for each generated sample."
+    "statistics for each generated sample.\n",
+    "\n",
+    ":::{post} May 31, 2022\n",
+    ":tags: \n",
+    ":category: beginner\n",
+    ":author: Meenal Jhajharia, Christian Luhmann\n",
+    ":::"
    ]
   },
   {
@@ -115,7 +122,7 @@
        "\n",
        "    <div>\n",
        "      <progress value='12000' class='' max='12000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [12000/12000 00:12<00:00 Sampling 4 chains, 0 divergences]\n",
+       "      100.00% [12000/12000 00:09<00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -130,14 +137,14 @@
      "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 14 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 10 seconds.\n"
      ]
     }
    ],
    "source": [
     "with model:\n",
     "    step = pm.NUTS()\n",
-    "    trace = pm.sample(2000, tune=1000, init=None, step=step, chains=4)"
+    "    idata = pm.sample(2000, tune=1000, init=None, step=step, chains=4)"
    ]
   },
   {
@@ -512,105 +519,105 @@
        "  * 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",
-       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 3 ... 2 2 2 2 2 2 2 3\n",
-       "    step_size           (chain, draw) float64 1.22 1.22 1.22 ... 1.184 1.184\n",
-       "    perf_counter_diff   (chain, draw) float64 0.001822 0.002302 ... 0.001549\n",
-       "    energy              (chain, draw) float64 14.32 18.67 24.74 ... 14.31 15.77\n",
-       "    diverging           (chain, draw) bool False False False ... False False\n",
-       "    n_steps             (chain, draw) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 7.0\n",
+       "    perf_counter_diff   (chain, draw) float64 0.001099 0.0008668 ... 0.0005115\n",
+       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 2 ... 2 2 2 2 2 2 2 2\n",
+       "    acceptance_rate     (chain, draw) float64 0.9408 0.4259 ... 0.9382 0.8286\n",
+       "    step_size           (chain, draw) float64 1.143 1.143 1.143 ... 1.422 1.422\n",
+       "    perf_counter_start  (chain, draw) float64 2.436e+05 2.436e+05 ... 2.436e+05\n",
+       "    step_size_bar       (chain, draw) float64 0.965 0.965 ... 0.9295 0.9295\n",
        "    ...                  ...\n",
-       "    max_energy_error    (chain, draw) float64 0.3517 0.9423 ... 0.3606 0.8925\n",
-       "    energy_error        (chain, draw) float64 0.08696 0.9423 ... 0.0876 -0.03091\n",
-       "    lp                  (chain, draw) float64 -11.88 -16.3 ... -11.75 -11.46\n",
-       "    acceptance_rate     (chain, draw) float64 0.87 0.4506 ... 0.8245 0.7415\n",
-       "    step_size_bar       (chain, draw) float64 0.9626 0.9626 ... 0.9833 0.9833\n",
-       "    perf_counter_start  (chain, draw) float64 2.399e+05 2.399e+05 ... 2.399e+05\n",
+       "    energy_error        (chain, draw) float64 -0.2584 0.0 ... -0.4669 0.07765\n",
+       "    diverging           (chain, draw) bool False False False ... False False\n",
+       "    energy              (chain, draw) float64 16.84 20.65 17.17 ... 17.08 15.38\n",
+       "    lp                  (chain, draw) float64 -13.04 -13.04 ... -11.49 -11.91\n",
+       "    n_steps             (chain, draw) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0\n",
+       "    max_energy_error    (chain, draw) float64 -0.2584 1.004 ... -0.4669 0.7978\n",
        "Attributes:\n",
-       "    created_at:                 2022-05-31T14:30:53.836698\n",
+       "    created_at:                 2022-05-31T15:32:48.599814\n",
        "    arviz_version:              0.12.1\n",
        "    inference_library:          pymc\n",
        "    inference_library_version:  4.0.0b6\n",
-       "    sampling_time:              13.840092182159424\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-23c65a07-5144-4961-bc0f-81af9097a9e1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-23c65a07-5144-4961-bc0f-81af9097a9e1' 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-52b9ee0b-2036-419a-b405-c1446e15b6de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-52b9ee0b-2036-419a-b405-c1446e15b6de' 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-4ebf7f0d-b3ef-4753-bf4c-6432b5f3a40f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ebf7f0d-b3ef-4753-bf4c-6432b5f3a40f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b7e0044-a098-43a3-ba60-92ef77db687c' class='xr-var-data-in' type='checkbox'><label for='data-6b7e0044-a098-43a3-ba60-92ef77db687c' 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-10fd1a19-e803-407e-849d-ea435789c238' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-10fd1a19-e803-407e-849d-ea435789c238' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3d4aef0-f280-4f24-b0ae-b06d79fb95c0' class='xr-var-data-in' type='checkbox'><label for='data-b3d4aef0-f280-4f24-b0ae-b06d79fb95c0' 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-29515885-219c-43f7-8940-e778432d5f28' class='xr-section-summary-in' type='checkbox'  checked><label for='section-29515885-219c-43f7-8940-e778432d5f28' 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>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 3 ... 2 2 2 2 2 2 2 3</div><input id='attrs-3adf808b-f43a-4399-89df-149cbc7198d7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3adf808b-f43a-4399-89df-149cbc7198d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32b4538f-7065-414c-a94c-a8eab4bd943e' class='xr-var-data-in' type='checkbox'><label for='data-32b4538f-7065-414c-a94c-a8eab4bd943e' 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, ..., 2, 2, 2],\n",
-       "       [2, 2, 2, ..., 2, 2, 2],\n",
-       "       [2, 3, 2, ..., 3, 2, 2],\n",
-       "       [2, 2, 2, ..., 2, 2, 3]])</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'>1.22 1.22 1.22 ... 1.184 1.184</div><input id='attrs-0bddd468-93a0-4d14-957f-7f58011d8cd8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0bddd468-93a0-4d14-957f-7f58011d8cd8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9fb3af5-3a78-4e49-857a-b317f79366b7' class='xr-var-data-in' type='checkbox'><label for='data-b9fb3af5-3a78-4e49-857a-b317f79366b7' 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([[1.21950326, 1.21950326, 1.21950326, ..., 1.21950326, 1.21950326,\n",
-       "        1.21950326],\n",
-       "       [0.72020393, 0.72020393, 0.72020393, ..., 0.72020393, 0.72020393,\n",
-       "        0.72020393],\n",
-       "       [1.05976336, 1.05976336, 1.05976336, ..., 1.05976336, 1.05976336,\n",
-       "        1.05976336],\n",
-       "       [1.18415486, 1.18415486, 1.18415486, ..., 1.18415486, 1.18415486,\n",
-       "        1.18415486]])</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'>0.001822 0.002302 ... 0.001549</div><input id='attrs-7e220570-1bd0-4c92-b2ea-d9fa05f2b33d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e220570-1bd0-4c92-b2ea-d9fa05f2b33d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32e7e8f9-fe16-4e05-bc80-760cf59c5e5e' class='xr-var-data-in' type='checkbox'><label for='data-32e7e8f9-fe16-4e05-bc80-760cf59c5e5e' 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([[0.00182155, 0.00230215, 0.00205968, ..., 0.00063477, 0.00082406,\n",
-       "        0.00248648],\n",
-       "       [0.00145808, 0.00094122, 0.00091943, ..., 0.00491877, 0.00102631,\n",
-       "        0.00074709],\n",
-       "       [0.00089773, 0.00288749, 0.00093999, ..., 0.00268249, 0.00128637,\n",
-       "        0.00096458],\n",
-       "       [0.00116655, 0.00114517, 0.00135329, ..., 0.00066558, 0.00090877,\n",
-       "        0.00154863]])</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'>14.32 18.67 24.74 ... 14.31 15.77</div><input id='attrs-a3bd0545-137a-4998-b849-455d2110a25f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a3bd0545-137a-4998-b849-455d2110a25f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-decb810f-2162-4e6c-aa9d-b7a0fb40ad4e' class='xr-var-data-in' type='checkbox'><label for='data-decb810f-2162-4e6c-aa9d-b7a0fb40ad4e' 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([[14.31547902, 18.67280753, 24.73782083, ..., 19.77783991,\n",
-       "        15.32041915, 14.4975007 ],\n",
-       "       [16.69772368, 21.55805988, 17.54714049, ..., 21.36445455,\n",
-       "        23.88091525, 18.26166119],\n",
-       "       [14.95261594, 15.18133699, 21.29688278, ..., 17.1469732 ,\n",
-       "        16.19667226, 25.32843892],\n",
-       "       [25.84769942, 24.23095369, 24.46081513, ..., 17.34602027,\n",
-       "        14.30789644, 15.76916939]])</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-7c98d638-8845-40b9-9017-cee7aac5bb4a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c98d638-8845-40b9-9017-cee7aac5bb4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99d09b29-4bd5-404f-bc68-ec96d7cd4999' class='xr-var-data-in' type='checkbox'><label for='data-99d09b29-4bd5-404f-bc68-ec96d7cd4999' 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",
+       "    sampling_time:              10.029414176940918\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-13166d24-4a7e-40c7-ba45-1f02269e400d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-13166d24-4a7e-40c7-ba45-1f02269e400d' 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-11f21958-c7e9-47c7-8678-6779d69a7ffb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-11f21958-c7e9-47c7-8678-6779d69a7ffb' 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-69c4af7e-373c-4491-baf2-912a547542d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-69c4af7e-373c-4491-baf2-912a547542d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1dd6444-dd69-4a61-8753-aae1375565a8' class='xr-var-data-in' type='checkbox'><label for='data-f1dd6444-dd69-4a61-8753-aae1375565a8' 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-27600426-95c8-41e3-a406-ec46809d62cb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27600426-95c8-41e3-a406-ec46809d62cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bce7038b-5969-4f2a-8ece-7cdb8cf8282e' class='xr-var-data-in' type='checkbox'><label for='data-bce7038b-5969-4f2a-8ece-7cdb8cf8282e' 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-fcfe53e5-26b0-4ca2-ad84-2de8a8763969' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fcfe53e5-26b0-4ca2-ad84-2de8a8763969' 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>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'>0.001099 0.0008668 ... 0.0005115</div><input id='attrs-5271cfe6-033a-4910-9170-9b51040bf09b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5271cfe6-033a-4910-9170-9b51040bf09b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad25eb87-c934-454b-84ff-184bd95bec49' class='xr-var-data-in' type='checkbox'><label for='data-ad25eb87-c934-454b-84ff-184bd95bec49' 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([[0.00109886, 0.00086682, 0.00114436, ..., 0.00100552, 0.00163379,\n",
+       "        0.00093155],\n",
+       "       [0.00157082, 0.00149492, 0.00082281, ..., 0.00189045, 0.00103298,\n",
+       "        0.00476531],\n",
+       "       [0.00076913, 0.0011607 , 0.00088153, ..., 0.00187847, 0.00071563,\n",
+       "        0.00106022],\n",
+       "       [0.00070842, 0.00081085, 0.00075074, ..., 0.00048443, 0.00049141,\n",
+       "        0.00051149]])</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 2 2 2 2 2 2</div><input id='attrs-ac63f7f0-9192-42d1-88b9-690bfddb79ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac63f7f0-9192-42d1-88b9-690bfddb79ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb076e81-2927-4705-911e-76ebe896271c' class='xr-var-data-in' type='checkbox'><label for='data-bb076e81-2927-4705-911e-76ebe896271c' 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, ..., 2, 2, 2],\n",
+       "       [3, 3, 2, ..., 3, 2, 2],\n",
+       "       [2, 3, 2, ..., 3, 2, 3],\n",
+       "       [2, 2, 2, ..., 2, 2, 2]])</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'>0.9408 0.4259 ... 0.9382 0.8286</div><input id='attrs-0b0592fc-bdba-470e-9c81-084f26799f22' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b0592fc-bdba-470e-9c81-084f26799f22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1e80971-f7f8-49e7-981d-2ca54f68f19e' class='xr-var-data-in' type='checkbox'><label for='data-c1e80971-f7f8-49e7-981d-2ca54f68f19e' 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([[0.94079511, 0.42592216, 0.91129538, ..., 0.91714132, 1.        ,\n",
+       "        0.77672484],\n",
+       "       [0.80431643, 0.80401649, 0.86704308, ..., 0.82409022, 0.91455319,\n",
+       "        0.78387401],\n",
+       "       [1.        , 0.74758003, 1.        , ..., 0.8942736 , 0.92205389,\n",
+       "        0.90817434],\n",
+       "       [0.59313837, 0.77886422, 0.85724681, ..., 0.84547251, 0.93824725,\n",
+       "        0.82856066]])</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'>1.143 1.143 1.143 ... 1.422 1.422</div><input id='attrs-260ffc89-3f26-4d99-8e89-57bc6d4e7a57' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-260ffc89-3f26-4d99-8e89-57bc6d4e7a57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e988169a-9eeb-494c-85e4-bf1d14e2c576' class='xr-var-data-in' type='checkbox'><label for='data-e988169a-9eeb-494c-85e4-bf1d14e2c576' 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([[1.14312292, 1.14312292, 1.14312292, ..., 1.14312292, 1.14312292,\n",
+       "        1.14312292],\n",
+       "       [0.86458144, 0.86458144, 0.86458144, ..., 0.86458144, 0.86458144,\n",
+       "        0.86458144],\n",
+       "       [0.77502335, 0.77502335, 0.77502335, ..., 0.77502335, 0.77502335,\n",
+       "        0.77502335],\n",
+       "       [1.42201359, 1.42201359, 1.42201359, ..., 1.42201359, 1.42201359,\n",
+       "        1.42201359]])</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'>2.436e+05 2.436e+05 ... 2.436e+05</div><input id='attrs-55a11741-948b-47ff-a0e7-6ea9fb2d5351' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-55a11741-948b-47ff-a0e7-6ea9fb2d5351' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-258fef07-7bb5-4638-b9f6-5b339b59672b' class='xr-var-data-in' type='checkbox'><label for='data-258fef07-7bb5-4638-b9f6-5b339b59672b' 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([[243625.45644577, 243625.45895262, 243625.46007757, ...,\n",
+       "        243628.24075951, 243628.24203385, 243628.24409977],\n",
+       "       [243625.50356802, 243625.50540313, 243625.50715186, ...,\n",
+       "        243628.59140607, 243628.59361562, 243628.59494929],\n",
+       "       [243629.98108714, 243629.9823173 , 243629.98368142, ...,\n",
+       "        243632.79335273, 243632.79550563, 243632.79640507],\n",
+       "       [243630.13241666, 243630.13330628, 243630.13435542, ...,\n",
+       "        243632.8464835 , 243632.84713035, 243632.84777055]])</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'>0.965 0.965 0.965 ... 0.9295 0.9295</div><input id='attrs-a7058ec5-d281-49d5-b39c-3e94abd30033' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a7058ec5-d281-49d5-b39c-3e94abd30033' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93d144c3-ca17-435b-884e-a58292347027' class='xr-var-data-in' type='checkbox'><label for='data-93d144c3-ca17-435b-884e-a58292347027' 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([[0.96504169, 0.96504169, 0.96504169, ..., 0.96504169, 0.96504169,\n",
+       "        0.96504169],\n",
+       "       [0.95309631, 0.95309631, 0.95309631, ..., 0.95309631, 0.95309631,\n",
+       "        0.95309631],\n",
+       "       [0.96003651, 0.96003651, 0.96003651, ..., 0.96003651, 0.96003651,\n",
+       "        0.96003651],\n",
+       "       [0.92949832, 0.92949832, 0.92949832, ..., 0.92949832, 0.92949832,\n",
+       "        0.92949832]])</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'>0.001094 0.0008675 ... 0.0005119</div><input id='attrs-42636bc9-904e-40e9-83b8-3ca040177b36' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42636bc9-904e-40e9-83b8-3ca040177b36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bce343c4-a8ac-4065-9cad-217ca3de9c96' class='xr-var-data-in' type='checkbox'><label for='data-bce343c4-a8ac-4065-9cad-217ca3de9c96' 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([[0.00109381, 0.00086753, 0.00114471, ..., 0.00098246, 0.00099854,\n",
+       "        0.00092242],\n",
+       "       [0.00157136, 0.00149558, 0.00082337, ..., 0.00189196, 0.00103378,\n",
+       "        0.00173109],\n",
+       "       [0.00076914, 0.00116094, 0.00088306, ..., 0.00178604, 0.00068072,\n",
+       "        0.00100309],\n",
+       "       [0.00070886, 0.00081193, 0.00075107, ..., 0.00048467, 0.00049135,\n",
+       "        0.00051189]])</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'>-0.2584 0.0 ... -0.4669 0.07765</div><input id='attrs-af43ca6f-1b35-4dcf-9f1b-b59f85a6f71f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af43ca6f-1b35-4dcf-9f1b-b59f85a6f71f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a5524d0-8f8f-49b7-9432-162a0c2219e9' class='xr-var-data-in' type='checkbox'><label for='data-8a5524d0-8f8f-49b7-9432-162a0c2219e9' 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([[-0.25837625,  0.        , -0.03187653, ...,  0.50823635,\n",
+       "        -0.46877695, -0.25348938],\n",
+       "       [-0.40050094,  0.42751031,  0.00644737, ...,  0.37228705,\n",
+       "        -0.04212365, -0.17705138],\n",
+       "       [-0.74153139,  0.89391277, -0.72225611, ...,  0.03559768,\n",
+       "        -0.30213692, -0.10091159],\n",
+       "       [ 0.2242647 ,  0.63739414,  0.20844137, ...,  0.2114336 ,\n",
+       "        -0.46689975,  0.07765198]])</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-c6f66c51-70ab-4f09-a9e8-db9ddaedaf71' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c6f66c51-70ab-4f09-a9e8-db9ddaedaf71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3f05b98-b4c1-4aca-8260-a913ef0586f1' class='xr-var-data-in' type='checkbox'><label for='data-c3f05b98-b4c1-4aca-8260-a913ef0586f1' 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>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 7.0</div><input id='attrs-0bc5c7c8-2d61-427c-9734-0a302c1444f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0bc5c7c8-2d61-427c-9734-0a302c1444f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80098b17-524c-4f0c-a640-e83e07890c8d' class='xr-var-data-in' type='checkbox'><label for='data-80098b17-524c-4f0c-a640-e83e07890c8d' 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., ..., 3., 3., 3.],\n",
-       "       [3., 3., 3., ..., 3., 3., 3.],\n",
-       "       [3., 7., 3., ..., 7., 3., 3.],\n",
-       "       [3., 3., 3., ..., 3., 3., 7.]])</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'>0.001536 0.001848 ... 0.001539</div><input id='attrs-530e1739-66e0-4ab5-8fd5-491416af70a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-530e1739-66e0-4ab5-8fd5-491416af70a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b72dbf6-822b-406d-895a-128012d755f8' class='xr-var-data-in' type='checkbox'><label for='data-4b72dbf6-822b-406d-895a-128012d755f8' 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([[0.00153555, 0.00184848, 0.00168128, ..., 0.00063349, 0.00082557,\n",
-       "        0.00129186],\n",
-       "       [0.00145385, 0.00094207, 0.00091932, ..., 0.00177442, 0.0010048 ,\n",
-       "        0.00074765],\n",
-       "       [0.00089884, 0.00269242, 0.00094059, ..., 0.00268316, 0.00128447,\n",
-       "        0.00096429],\n",
-       "       [0.00116694, 0.00114476, 0.00134822, ..., 0.00066548, 0.00091148,\n",
-       "        0.0015387 ]])</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'>0.3517 0.9423 ... 0.3606 0.8925</div><input id='attrs-a519eff4-db72-4180-8011-8e987990bdff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a519eff4-db72-4180-8011-8e987990bdff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50b3b970-8de1-42cc-977c-014714f71270' class='xr-var-data-in' type='checkbox'><label for='data-50b3b970-8de1-42cc-977c-014714f71270' 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([[ 0.35170638,  0.94233964,  1.7956417 , ...,  1.15748431,\n",
-       "         0.35009928,  0.16891603],\n",
-       "       [-0.88631482,  1.76602009,  0.19630242, ..., -0.81837327,\n",
-       "         1.80535122, -0.82607036],\n",
-       "       [-0.24382593,  0.29918658,  1.60887177, ...,  0.66656511,\n",
-       "        -0.62828708,  2.11279338],\n",
-       "       [ 1.53956924, -1.35463766,  2.44413947, ..., -0.97307343,\n",
-       "         0.36063295,  0.89251209]])</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'>0.08696 0.9423 ... 0.0876 -0.03091</div><input id='attrs-97ce1ff3-a6ef-48e0-8e4c-b330a47e0eef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-97ce1ff3-a6ef-48e0-8e4c-b330a47e0eef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f38d940-e3dc-45c5-9468-25e9541218f1' class='xr-var-data-in' type='checkbox'><label for='data-4f38d940-e3dc-45c5-9468-25e9541218f1' 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([[ 0.08696156,  0.94233964,  0.05662733, ...,  0.        ,\n",
-       "        -0.06831575,  0.04512375],\n",
-       "       [-0.88631482,  0.3569776 , -0.06860921, ..., -0.81837327,\n",
-       "         0.        , -0.82607036],\n",
-       "       [-0.24382593,  0.28926023,  0.10536604, ...,  0.26489312,\n",
-       "        -0.38271845,  0.        ],\n",
-       "       [ 1.2479786 , -1.35463766,  0.        , ..., -0.97307343,\n",
-       "         0.08760141, -0.03090947]])</pre></div></li><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'>-11.88 -16.3 ... -11.75 -11.46</div><input id='attrs-53201e9c-ee80-49a2-bc7a-a8cbb7d82b1e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53201e9c-ee80-49a2-bc7a-a8cbb7d82b1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-708cda65-9ab7-495f-a216-6f029d2c7e49' class='xr-var-data-in' type='checkbox'><label for='data-708cda65-9ab7-495f-a216-6f029d2c7e49' 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([[-11.87745944, -16.29670765, -16.34269384, ..., -12.25893155,\n",
-       "        -12.09451095, -12.21620593],\n",
-       "       [-12.41927296, -13.86371187, -13.47310567, ..., -14.98464477,\n",
-       "        -14.98464477, -11.52710622],\n",
-       "       [-11.63248032, -12.79484614, -13.36618064, ..., -14.878442  ,\n",
-       "        -13.04147932, -13.04147932],\n",
-       "       [-20.10679905, -14.07561821, -14.07561821, ..., -11.36593515,\n",
-       "        -11.7528609 , -11.46147781]])</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'>0.87 0.4506 ... 0.8245 0.7415</div><input id='attrs-356b2429-4043-497f-a110-6a265f6c98d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-356b2429-4043-497f-a110-6a265f6c98d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d3d8ff4-af7f-4d39-b609-f0b2dabb0008' class='xr-var-data-in' type='checkbox'><label for='data-8d3d8ff4-af7f-4d39-b609-f0b2dabb0008' 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([[0.8699512 , 0.45062746, 0.91962988, ..., 0.59122966, 0.90223295,\n",
-       "        0.93578156],\n",
-       "       [1.        , 0.52117974, 0.94964537, ..., 1.        , 0.33325079,\n",
-       "        0.84514585],\n",
-       "       [0.89843666, 0.82261863, 0.68293857, ..., 0.91283551, 1.        ,\n",
-       "        0.52657948],\n",
-       "       [0.59749008, 0.9540548 , 0.49184974, ..., 0.96964818, 0.82453019,\n",
-       "        0.74149041]])</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'>0.9626 0.9626 ... 0.9833 0.9833</div><input id='attrs-791b3887-c19b-46f4-a4d6-810f7f6252d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-791b3887-c19b-46f4-a4d6-810f7f6252d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2f7f1b8-3236-4df1-967b-754bdcc3f397' class='xr-var-data-in' type='checkbox'><label for='data-c2f7f1b8-3236-4df1-967b-754bdcc3f397' 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([[0.96260857, 0.96260857, 0.96260857, ..., 0.96260857, 0.96260857,\n",
-       "        0.96260857],\n",
-       "       [0.98844533, 0.98844533, 0.98844533, ..., 0.98844533, 0.98844533,\n",
-       "        0.98844533],\n",
-       "       [0.94526714, 0.94526714, 0.94526714, ..., 0.94526714, 0.94526714,\n",
-       "        0.94526714],\n",
-       "       [0.98325683, 0.98325683, 0.98325683, ..., 0.98325683, 0.98325683,\n",
-       "        0.98325683]])</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'>2.399e+05 2.399e+05 ... 2.399e+05</div><input id='attrs-6aee8779-33dd-40cd-9e1d-22c5d556ebf0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6aee8779-33dd-40cd-9e1d-22c5d556ebf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db465d38-bb7b-4fc6-b29f-af7c8cf809fb' class='xr-var-data-in' type='checkbox'><label for='data-db465d38-bb7b-4fc6-b29f-af7c8cf809fb' 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([[239907.83232834, 239907.83467303, 239907.83743492, ...,\n",
-       "        239911.83653276, 239911.8374169 , 239911.83845434],\n",
-       "       [239908.11866411, 239908.12046239, 239908.12205767, ...,\n",
-       "        239911.92349805, 239911.92871935, 239911.92997074],\n",
-       "       [239913.82403931, 239913.82883131, 239913.83203039, ...,\n",
-       "        239917.84966245, 239917.85289034, 239917.85446876],\n",
-       "       [239913.93993128, 239913.95082314, 239913.9599893 , ...,\n",
-       "        239917.99035031, 239917.9912292 , 239917.99339407]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-97c5750b-4ecc-4b16-8064-21f7b1e72c1e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-97c5750b-4ecc-4b16-8064-21f7b1e72c1e' 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>2022-05-31T14:30:53.836698</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>13.840092182159424</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
+       "       [False, False, False, ..., False, False, False]])</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'>16.84 20.65 17.17 ... 17.08 15.38</div><input id='attrs-ce37e884-636f-4910-b141-d7694cbef19f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce37e884-636f-4910-b141-d7694cbef19f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aeafcc0b-e30e-4b38-b4bd-d523c5b74282' class='xr-var-data-in' type='checkbox'><label for='data-aeafcc0b-e30e-4b38-b4bd-d523c5b74282' 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([[16.84090827, 20.64906743, 17.1730464 , ..., 18.89371397,\n",
+       "        18.4151088 , 21.19354036],\n",
+       "       [20.40583931, 18.67772106, 21.47325811, ..., 14.97740678,\n",
+       "        17.79418548, 19.48526426],\n",
+       "       [18.27161048, 19.24772916, 20.28605562, ..., 19.68863359,\n",
+       "        20.12653013, 16.88968507],\n",
+       "       [18.4770287 , 16.78315467, 22.72170669, ..., 19.07171212,\n",
+       "        17.07821333, 15.3760757 ]])</pre></div></li><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'>-13.04 -13.04 ... -11.49 -11.91</div><input id='attrs-6ebbd69a-9bae-418b-8d71-e763a14fb243' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ebbd69a-9bae-418b-8d71-e763a14fb243' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fd08e7e-368e-463b-9af5-b1cc5557ef95' class='xr-var-data-in' type='checkbox'><label for='data-7fd08e7e-368e-463b-9af5-b1cc5557ef95' 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([[-13.04008167, -13.04008167, -13.08156212, ..., -16.09968457,\n",
+       "        -14.03780087, -12.75970833],\n",
+       "       [-13.59845508, -15.63749662, -15.357422  , ..., -13.53831179,\n",
+       "        -13.25654472, -12.40317732],\n",
+       "       [-12.98969402, -17.19913149, -13.71364181, ..., -14.70897247,\n",
+       "        -13.25931825, -12.68230831],\n",
+       "       [-11.91448161, -14.61044872, -15.90850345, ..., -13.92129529,\n",
+       "        -11.49066214, -11.90690302]])</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-c433f9a0-4ca3-4646-bbb1-ddb2b5e368fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c433f9a0-4ca3-4646-bbb1-ddb2b5e368fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b6ef69f-7b90-4e15-aa49-3bf5beea5090' class='xr-var-data-in' type='checkbox'><label for='data-4b6ef69f-7b90-4e15-aa49-3bf5beea5090' 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., ..., 3., 3., 3.],\n",
+       "       [7., 7., 3., ..., 7., 3., 3.],\n",
+       "       [3., 7., 3., ..., 7., 3., 7.],\n",
+       "       [3., 3., 3., ..., 3., 3., 3.]])</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'>-0.2584 1.004 ... -0.4669 0.7978</div><input id='attrs-c2d6035c-6f4d-45fb-b654-88397b795c56' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c2d6035c-6f4d-45fb-b654-88397b795c56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8beede08-f4a7-4a5d-a90a-b79eb1446432' class='xr-var-data-in' type='checkbox'><label for='data-8beede08-f4a7-4a5d-a90a-b79eb1446432' 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([[-0.25837625,  1.00377789,  0.58967298, ...,  0.50823635,\n",
+       "        -0.47403301,  1.30851566],\n",
+       "       [ 0.64558332,  0.54525801,  0.28323174, ...,  0.39709167,\n",
+       "         0.55836873,  0.94099552],\n",
+       "       [-0.74153139,  0.9608849 , -0.72225611, ...,  0.25601613,\n",
+       "         1.01757126,  0.1757875 ],\n",
+       "       [ 1.61063735,  0.63739414,  1.53211219, ...,  1.39359771,\n",
+       "        -0.46689975,  0.79781162]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6a32640c-00f6-4251-9b92-99835d0e8901' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6a32640c-00f6-4251-9b92-99835d0e8901' 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>2022-05-31T15:32:48.599814</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>10.029414176940918</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -619,25 +626,25 @@
        "  * 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",
-       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 3 ... 2 2 2 2 2 2 2 3\n",
-       "    step_size           (chain, draw) float64 1.22 1.22 1.22 ... 1.184 1.184\n",
-       "    perf_counter_diff   (chain, draw) float64 0.001822 0.002302 ... 0.001549\n",
-       "    energy              (chain, draw) float64 14.32 18.67 24.74 ... 14.31 15.77\n",
-       "    diverging           (chain, draw) bool False False False ... False False\n",
-       "    n_steps             (chain, draw) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 7.0\n",
+       "    perf_counter_diff   (chain, draw) float64 0.001099 0.0008668 ... 0.0005115\n",
+       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 2 ... 2 2 2 2 2 2 2 2\n",
+       "    acceptance_rate     (chain, draw) float64 0.9408 0.4259 ... 0.9382 0.8286\n",
+       "    step_size           (chain, draw) float64 1.143 1.143 1.143 ... 1.422 1.422\n",
+       "    perf_counter_start  (chain, draw) float64 2.436e+05 2.436e+05 ... 2.436e+05\n",
+       "    step_size_bar       (chain, draw) float64 0.965 0.965 ... 0.9295 0.9295\n",
        "    ...                  ...\n",
-       "    max_energy_error    (chain, draw) float64 0.3517 0.9423 ... 0.3606 0.8925\n",
-       "    energy_error        (chain, draw) float64 0.08696 0.9423 ... 0.0876 -0.03091\n",
-       "    lp                  (chain, draw) float64 -11.88 -16.3 ... -11.75 -11.46\n",
-       "    acceptance_rate     (chain, draw) float64 0.87 0.4506 ... 0.8245 0.7415\n",
-       "    step_size_bar       (chain, draw) float64 0.9626 0.9626 ... 0.9833 0.9833\n",
-       "    perf_counter_start  (chain, draw) float64 2.399e+05 2.399e+05 ... 2.399e+05\n",
+       "    energy_error        (chain, draw) float64 -0.2584 0.0 ... -0.4669 0.07765\n",
+       "    diverging           (chain, draw) bool False False False ... False False\n",
+       "    energy              (chain, draw) float64 16.84 20.65 17.17 ... 17.08 15.38\n",
+       "    lp                  (chain, draw) float64 -13.04 -13.04 ... -11.49 -11.91\n",
+       "    n_steps             (chain, draw) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0\n",
+       "    max_energy_error    (chain, draw) float64 -0.2584 1.004 ... -0.4669 0.7978\n",
        "Attributes:\n",
-       "    created_at:                 2022-05-31T14:30:53.836698\n",
+       "    created_at:                 2022-05-31T15:32:48.599814\n",
        "    arviz_version:              0.12.1\n",
        "    inference_library:          pymc\n",
        "    inference_library_version:  4.0.0b6\n",
-       "    sampling_time:              13.840092182159424\n",
+       "    sampling_time:              10.029414176940918\n",
        "    tuning_steps:               1000"
       ]
      },
@@ -647,7 +654,7 @@
     }
    ],
    "source": [
-    "trace.sample_stats"
+    "idata.sample_stats"
    ]
   },
   {
@@ -699,7 +706,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1300x300 with 4 Axes>"
       ]
@@ -709,7 +716,7 @@
     }
    ],
    "source": [
-    "trace.sample_stats[\"tree_depth\"].plot(col=\"chain\", ls=\"none\", marker=\".\", alpha=0.3);"
+    "idata.sample_stats[\"tree_depth\"].plot(col=\"chain\", ls=\"none\", marker=\".\", alpha=0.3);"
    ]
   },
   {
@@ -719,7 +726,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 720x480 with 1 Axes>"
       ]
@@ -730,7 +737,7 @@
    ],
    "source": [
     "az.plot_posterior(\n",
-    "    trace, group=\"sample_stats\", var_names=\"acceptance_rate\", hdi_prob=\"hide\", kind=\"hist\"\n",
+    "    idata, group=\"sample_stats\", var_names=\"acceptance_rate\", hdi_prob=\"hide\", kind=\"hist\"\n",
     ");"
    ]
   },
@@ -1101,7 +1108,7 @@
        "  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-4c995e9e-11bc-4db8-8eaa-4302ecefb472' class='xr-array-in' type='checkbox' checked><label for='section-4c995e9e-11bc-4db8-8eaa-4302ecefb472' 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-e45e3b0e-4ccb-43b4-b066-60f0915f9c25' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e45e3b0e-4ccb-43b4-b066-60f0915f9c25' 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-7c058cd6-658b-4885-80fb-fb2ad8799d35' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7c058cd6-658b-4885-80fb-fb2ad8799d35' 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>"
+       "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-8e36aa81-ee52-4d72-abf9-2b4f3ab1f282' class='xr-array-in' type='checkbox' checked><label for='section-8e36aa81-ee52-4d72-abf9-2b4f3ab1f282' 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-c922bc47-edb7-43cd-a00c-b5a3e719ee70' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c922bc47-edb7-43cd-a00c-b5a3e719ee70' 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-6aeed993-57cf-4d4a-8f29-d03c07cb37bf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6aeed993-57cf-4d4a-8f29-d03c07cb37bf' 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",
@@ -1114,7 +1121,7 @@
     }
    ],
    "source": [
-    "trace.sample_stats[\"diverging\"].sum()"
+    "idata.sample_stats[\"diverging\"].sum()"
    ]
   },
   {
@@ -1140,7 +1147,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 600x400 with 1 Axes>"
       ]
@@ -1150,7 +1157,7 @@
     }
    ],
    "source": [
-    "az.plot_energy(trace, figsize=(6, 4));"
+    "az.plot_energy(idata, figsize=(6, 4));"
    ]
   },
   {
@@ -1228,7 +1235,7 @@
        "\n",
        "    <div>\n",
        "      <progress value='44000' class='' max='44000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [44000/44000 00:25<00:00 Sampling 4 chains, 0 divergences]\n",
+       "      100.00% [44000/44000 00:20<00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -1243,7 +1250,7 @@
      "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 26 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 10_000 draw iterations (4_000 + 40_000 draws total) took 21 seconds.\n"
      ]
     }
    ],
@@ -1251,7 +1258,7 @@
     "with model:\n",
     "    step1 = pm.BinaryMetropolis([mu1])\n",
     "    step2 = pm.Metropolis([mu2])\n",
-    "    trace = pm.sample(\n",
+    "    idata = pm.sample(\n",
     "        10000,\n",
     "        init=None,\n",
     "        step=[step1, step2],\n",
@@ -1269,7 +1276,7 @@
     {
      "data": {
       "text/plain": [
-       "['p_jump', 'accept', 'accepted', 'scaling']"
+       "['p_jump', 'accept', 'scaling', 'accepted']"
       ]
      },
      "execution_count": 12,
@@ -1278,7 +1285,7 @@
     }
    ],
    "source": [
-    "list(trace.sample_stats.data_vars)"
+    "list(idata.sample_stats.data_vars)"
    ]
   },
   {
@@ -1295,7 +1302,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1656x552 with 2 Axes>"
       ]
@@ -1306,7 +1313,7 @@
    ],
    "source": [
     "az.plot_posterior(\n",
-    "    trace,\n",
+    "    idata,\n",
     "    group=\"sample_stats\",\n",
     "    var_names=\"accept\",\n",
     "    hdi_prob=\"hide\",\n",
@@ -1681,23 +1688,23 @@
        "  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      ,  403.60440702],\n",
-       "       [   3.75      , 1891.65485214],\n",
-       "       [   3.75      ,  873.82009294],\n",
-       "       [   3.75      ,  484.7812048 ]])\n",
+       "array([[  3.75      , 102.36028945],\n",
+       "       [  3.75      , 808.01242909],\n",
+       "       [  3.75      , 193.61490756],\n",
+       "       [  3.75      , 473.08420034]])\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-35730f3b-2f41-40e5-bc48-86531edaa83d' class='xr-array-in' type='checkbox' checked><label for='section-35730f3b-2f41-40e5-bc48-86531edaa83d' 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>3.75 403.6 3.75 1.892e+03 3.75 873.8 3.75 484.8</span></div><div class='xr-array-data'><pre>array([[   3.75      ,  403.60440702],\n",
-       "       [   3.75      , 1891.65485214],\n",
-       "       [   3.75      ,  873.82009294],\n",
-       "       [   3.75      ,  484.7812048 ]])</pre></div></div></li><li class='xr-section-item'><input id='section-70b94b1b-2d9d-4f6a-9c60-a600730d40c5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-70b94b1b-2d9d-4f6a-9c60-a600730d40c5' 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-31eb8755-731c-4513-923b-f242cefe3720' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31eb8755-731c-4513-923b-f242cefe3720' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c56970a-6900-4502-aaac-0e11cf57f516' class='xr-var-data-in' type='checkbox'><label for='data-6c56970a-6900-4502-aaac-0e11cf57f516' 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-49e26221-2b17-4565-99e8-b5d55a41fed1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-49e26221-2b17-4565-99e8-b5d55a41fed1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c03dceb1-7fbc-484e-81cc-b18c1eb1acd7' class='xr-var-data-in' type='checkbox'><label for='data-c03dceb1-7fbc-484e-81cc-b18c1eb1acd7' 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-79d79b5d-45dc-441a-b617-a3ad4c59a968' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-79d79b5d-45dc-441a-b617-a3ad4c59a968' 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>"
+       "  * 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-dc98dd98-85bd-42e5-879d-61d3b1b4c374' class='xr-array-in' type='checkbox' checked><label for='section-dc98dd98-85bd-42e5-879d-61d3b1b4c374' 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>3.75 102.4 3.75 808.0 3.75 193.6 3.75 473.1</span></div><div class='xr-array-data'><pre>array([[  3.75      , 102.36028945],\n",
+       "       [  3.75      , 808.01242909],\n",
+       "       [  3.75      , 193.61490756],\n",
+       "       [  3.75      , 473.08420034]])</pre></div></div></li><li class='xr-section-item'><input id='section-7d0f1914-7b60-403a-948a-74fb4a36742d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7d0f1914-7b60-403a-948a-74fb4a36742d' 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-74b5a8c5-dde2-4b60-94ca-4752ad15b9a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-74b5a8c5-dde2-4b60-94ca-4752ad15b9a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99abaa02-22a3-430d-aedc-fb6fdfef07f1' class='xr-var-data-in' type='checkbox'><label for='data-99abaa02-22a3-430d-aedc-fb6fdfef07f1' 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-86189e95-8ba4-4dd9-800d-d96212aec95e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-86189e95-8ba4-4dd9-800d-d96212aec95e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03c3d334-4519-4ae5-b6cb-4f7c00423b81' class='xr-var-data-in' type='checkbox'><label for='data-03c3d334-4519-4ae5-b6cb-4f7c00423b81' 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-9106714b-91b1-4f4a-9547-a6dd14514589' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9106714b-91b1-4f4a-9547-a6dd14514589' 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      ,  403.60440702],\n",
-       "       [   3.75      , 1891.65485214],\n",
-       "       [   3.75      ,  873.82009294],\n",
-       "       [   3.75      ,  484.7812048 ]])\n",
+       "array([[  3.75      , 102.36028945],\n",
+       "       [  3.75      , 808.01242909],\n",
+       "       [  3.75      , 193.61490756],\n",
+       "       [  3.75      , 473.08420034]])\n",
        "Coordinates:\n",
        "  * chain         (chain) int64 0 1 2 3\n",
        "  * accept_dim_0  (accept_dim_0) int64 0 1"
@@ -1710,7 +1717,7 @@
    ],
    "source": [
     "# Range of accept values\n",
-    "trace.sample_stats[\"accept\"].max(\"draw\") - trace.sample_stats[\"accept\"].min(\"draw\")"
+    "idata.sample_stats[\"accept\"].max(\"draw\") - idata.sample_stats[\"accept\"].min(\"draw\")"
    ]
   },
   {
@@ -1720,7 +1727,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1656x552 with 2 Axes>"
       ]
@@ -1732,7 +1739,7 @@
    "source": [
     "# We can try plotting the density and view the high density intervals to understand the variable better\n",
     "az.plot_density(\n",
-    "    trace,\n",
+    "    idata,\n",
     "    group=\"sample_stats\",\n",
     "    var_names=\"accept\",\n",
     "    point_estimate=\"mean\",\n",
@@ -1754,11 +1761,11 @@
       "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",
+      "arviz     : 0.12.1\n",
       "pandas    : 1.4.2\n",
-      "numpy     : 1.23.0rc2\n",
+      "matplotlib: 3.5.2\n",
       "\n",
       "Watermark: 2.3.1\n",
       "\n"
@@ -1777,6 +1784,14 @@
     "* Updated by Meenal Jhajharia\n",
     "* Updated by Christian Luhmann"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    ":::{include} ../page_footer.md\n",
+    ":::"
+   ]
   }
  ],
  "metadata": {

From 98a16467c4fe40931843877f9854be8122d90388 Mon Sep 17 00:00:00 2001
From: Christian Luhmann <cluhmann@gmail.com>
Date: Tue, 31 May 2022 12:27:58 -0400
Subject: [PATCH 4/7] added diagnostic tag

---
 examples/diagnostics_and_criticism/sampler-stats.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/diagnostics_and_criticism/sampler-stats.ipynb b/examples/diagnostics_and_criticism/sampler-stats.ipynb
index c4fff96ee..de53c465c 100644
--- a/examples/diagnostics_and_criticism/sampler-stats.ipynb
+++ b/examples/diagnostics_and_criticism/sampler-stats.ipynb
@@ -13,7 +13,7 @@
     "statistics for each generated sample.\n",
     "\n",
     ":::{post} May 31, 2022\n",
-    ":tags: \n",
+    ":tags: diagnostics\n",
     ":category: beginner\n",
     ":author: Meenal Jhajharia, Christian Luhmann\n",
     ":::"

From 0bea41d667ce5b58e1d1b1305d978a4efce7ed5f Mon Sep 17 00:00:00 2001
From: Christian Luhmann <cluhmann@gmail.com>
Date: Tue, 31 May 2022 15:30:19 -0400
Subject: [PATCH 5/7] fix paired myst.md file

---
 examples/diagnostics_and_criticism/sampler-stats.ipynb   | 4 ++--
 myst_nbs/diagnostics_and_criticism/sampler-stats.myst.md | 6 +++---
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/examples/diagnostics_and_criticism/sampler-stats.ipynb b/examples/diagnostics_and_criticism/sampler-stats.ipynb
index a79389edf..0913f2cb4 100644
--- a/examples/diagnostics_and_criticism/sampler-stats.ipynb
+++ b/examples/diagnostics_and_criticism/sampler-stats.ipynb
@@ -1699,7 +1699,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1713,7 +1713,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.2"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,
diff --git a/myst_nbs/diagnostics_and_criticism/sampler-stats.myst.md b/myst_nbs/diagnostics_and_criticism/sampler-stats.myst.md
index e0325c4a6..750b4f315 100644
--- a/myst_nbs/diagnostics_and_criticism/sampler-stats.myst.md
+++ b/myst_nbs/diagnostics_and_criticism/sampler-stats.myst.md
@@ -4,9 +4,9 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.13.7
+    jupytext_version: 1.13.8
 kernelspec:
-  display_name: Python 3
+  display_name: Python 3 (ipykernel)
   language: python
   name: python3
 ---
@@ -28,7 +28,7 @@ import seaborn as sns
 
 %matplotlib inline
 
-print(f"Running on PyMC3 v{pm.__version__}")
+print(f"Running on PyMC3 v{pm.__version__}")
 ```
 
 ```{code-cell} ipython3

From 0f73d0b0cd698f8f86ac999cd2f9bf1a692cc988 Mon Sep 17 00:00:00 2001
From: Christian Luhmann <cluhmann@gmail.com>
Date: Tue, 31 May 2022 15:51:22 -0400
Subject: [PATCH 6/7] update myst.md file

---
 .../sampler-stats.ipynb                       | 257 +++++++++---------
 .../sampler-stats.myst.md                     |  48 ++--
 2 files changed, 160 insertions(+), 145 deletions(-)

diff --git a/examples/diagnostics_and_criticism/sampler-stats.ipynb b/examples/diagnostics_and_criticism/sampler-stats.ipynb
index de53c465c..ae6bd214a 100644
--- a/examples/diagnostics_and_criticism/sampler-stats.ipynb
+++ b/examples/diagnostics_and_criticism/sampler-stats.ipynb
@@ -122,7 +122,7 @@
        "\n",
        "    <div>\n",
        "      <progress value='12000' class='' max='12000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [12000/12000 00:09<00:00 Sampling 4 chains, 0 divergences]\n",
+       "      100.00% [12000/12000 00:06<00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -137,7 +137,7 @@
      "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 10 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 7 seconds.\n"
      ]
     }
    ],
@@ -519,105 +519,105 @@
        "  * 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",
-       "    perf_counter_diff   (chain, draw) float64 0.001099 0.0008668 ... 0.0005115\n",
-       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 2 ... 2 2 2 2 2 2 2 2\n",
-       "    acceptance_rate     (chain, draw) float64 0.9408 0.4259 ... 0.9382 0.8286\n",
-       "    step_size           (chain, draw) float64 1.143 1.143 1.143 ... 1.422 1.422\n",
-       "    perf_counter_start  (chain, draw) float64 2.436e+05 2.436e+05 ... 2.436e+05\n",
-       "    step_size_bar       (chain, draw) float64 0.965 0.965 ... 0.9295 0.9295\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",
-       "    energy_error        (chain, draw) float64 -0.2584 0.0 ... -0.4669 0.07765\n",
        "    diverging           (chain, draw) bool False False False ... False False\n",
-       "    energy              (chain, draw) float64 16.84 20.65 17.17 ... 17.08 15.38\n",
-       "    lp                  (chain, draw) float64 -13.04 -13.04 ... -11.49 -11.91\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",
-       "    max_energy_error    (chain, draw) float64 -0.2584 1.004 ... -0.4669 0.7978\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:                 2022-05-31T15:32:48.599814\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:              10.029414176940918\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-13166d24-4a7e-40c7-ba45-1f02269e400d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-13166d24-4a7e-40c7-ba45-1f02269e400d' 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-11f21958-c7e9-47c7-8678-6779d69a7ffb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-11f21958-c7e9-47c7-8678-6779d69a7ffb' 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-69c4af7e-373c-4491-baf2-912a547542d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-69c4af7e-373c-4491-baf2-912a547542d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1dd6444-dd69-4a61-8753-aae1375565a8' class='xr-var-data-in' type='checkbox'><label for='data-f1dd6444-dd69-4a61-8753-aae1375565a8' 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-27600426-95c8-41e3-a406-ec46809d62cb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27600426-95c8-41e3-a406-ec46809d62cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bce7038b-5969-4f2a-8ece-7cdb8cf8282e' class='xr-var-data-in' type='checkbox'><label for='data-bce7038b-5969-4f2a-8ece-7cdb8cf8282e' 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-fcfe53e5-26b0-4ca2-ad84-2de8a8763969' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fcfe53e5-26b0-4ca2-ad84-2de8a8763969' 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>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'>0.001099 0.0008668 ... 0.0005115</div><input id='attrs-5271cfe6-033a-4910-9170-9b51040bf09b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5271cfe6-033a-4910-9170-9b51040bf09b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad25eb87-c934-454b-84ff-184bd95bec49' class='xr-var-data-in' type='checkbox'><label for='data-ad25eb87-c934-454b-84ff-184bd95bec49' 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([[0.00109886, 0.00086682, 0.00114436, ..., 0.00100552, 0.00163379,\n",
-       "        0.00093155],\n",
-       "       [0.00157082, 0.00149492, 0.00082281, ..., 0.00189045, 0.00103298,\n",
-       "        0.00476531],\n",
-       "       [0.00076913, 0.0011607 , 0.00088153, ..., 0.00187847, 0.00071563,\n",
-       "        0.00106022],\n",
-       "       [0.00070842, 0.00081085, 0.00075074, ..., 0.00048443, 0.00049141,\n",
-       "        0.00051149]])</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 2 2 2 2 2 2</div><input id='attrs-ac63f7f0-9192-42d1-88b9-690bfddb79ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac63f7f0-9192-42d1-88b9-690bfddb79ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb076e81-2927-4705-911e-76ebe896271c' class='xr-var-data-in' type='checkbox'><label for='data-bb076e81-2927-4705-911e-76ebe896271c' 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, ..., 2, 2, 2],\n",
-       "       [3, 3, 2, ..., 3, 2, 2],\n",
-       "       [2, 3, 2, ..., 3, 2, 3],\n",
-       "       [2, 2, 2, ..., 2, 2, 2]])</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'>0.9408 0.4259 ... 0.9382 0.8286</div><input id='attrs-0b0592fc-bdba-470e-9c81-084f26799f22' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b0592fc-bdba-470e-9c81-084f26799f22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1e80971-f7f8-49e7-981d-2ca54f68f19e' class='xr-var-data-in' type='checkbox'><label for='data-c1e80971-f7f8-49e7-981d-2ca54f68f19e' 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([[0.94079511, 0.42592216, 0.91129538, ..., 0.91714132, 1.        ,\n",
-       "        0.77672484],\n",
-       "       [0.80431643, 0.80401649, 0.86704308, ..., 0.82409022, 0.91455319,\n",
-       "        0.78387401],\n",
-       "       [1.        , 0.74758003, 1.        , ..., 0.8942736 , 0.92205389,\n",
-       "        0.90817434],\n",
-       "       [0.59313837, 0.77886422, 0.85724681, ..., 0.84547251, 0.93824725,\n",
-       "        0.82856066]])</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'>1.143 1.143 1.143 ... 1.422 1.422</div><input id='attrs-260ffc89-3f26-4d99-8e89-57bc6d4e7a57' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-260ffc89-3f26-4d99-8e89-57bc6d4e7a57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e988169a-9eeb-494c-85e4-bf1d14e2c576' class='xr-var-data-in' type='checkbox'><label for='data-e988169a-9eeb-494c-85e4-bf1d14e2c576' 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([[1.14312292, 1.14312292, 1.14312292, ..., 1.14312292, 1.14312292,\n",
-       "        1.14312292],\n",
-       "       [0.86458144, 0.86458144, 0.86458144, ..., 0.86458144, 0.86458144,\n",
-       "        0.86458144],\n",
-       "       [0.77502335, 0.77502335, 0.77502335, ..., 0.77502335, 0.77502335,\n",
-       "        0.77502335],\n",
-       "       [1.42201359, 1.42201359, 1.42201359, ..., 1.42201359, 1.42201359,\n",
-       "        1.42201359]])</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'>2.436e+05 2.436e+05 ... 2.436e+05</div><input id='attrs-55a11741-948b-47ff-a0e7-6ea9fb2d5351' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-55a11741-948b-47ff-a0e7-6ea9fb2d5351' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-258fef07-7bb5-4638-b9f6-5b339b59672b' class='xr-var-data-in' type='checkbox'><label for='data-258fef07-7bb5-4638-b9f6-5b339b59672b' 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([[243625.45644577, 243625.45895262, 243625.46007757, ...,\n",
-       "        243628.24075951, 243628.24203385, 243628.24409977],\n",
-       "       [243625.50356802, 243625.50540313, 243625.50715186, ...,\n",
-       "        243628.59140607, 243628.59361562, 243628.59494929],\n",
-       "       [243629.98108714, 243629.9823173 , 243629.98368142, ...,\n",
-       "        243632.79335273, 243632.79550563, 243632.79640507],\n",
-       "       [243630.13241666, 243630.13330628, 243630.13435542, ...,\n",
-       "        243632.8464835 , 243632.84713035, 243632.84777055]])</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'>0.965 0.965 0.965 ... 0.9295 0.9295</div><input id='attrs-a7058ec5-d281-49d5-b39c-3e94abd30033' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a7058ec5-d281-49d5-b39c-3e94abd30033' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93d144c3-ca17-435b-884e-a58292347027' class='xr-var-data-in' type='checkbox'><label for='data-93d144c3-ca17-435b-884e-a58292347027' 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([[0.96504169, 0.96504169, 0.96504169, ..., 0.96504169, 0.96504169,\n",
-       "        0.96504169],\n",
-       "       [0.95309631, 0.95309631, 0.95309631, ..., 0.95309631, 0.95309631,\n",
-       "        0.95309631],\n",
-       "       [0.96003651, 0.96003651, 0.96003651, ..., 0.96003651, 0.96003651,\n",
-       "        0.96003651],\n",
-       "       [0.92949832, 0.92949832, 0.92949832, ..., 0.92949832, 0.92949832,\n",
-       "        0.92949832]])</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'>0.001094 0.0008675 ... 0.0005119</div><input id='attrs-42636bc9-904e-40e9-83b8-3ca040177b36' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42636bc9-904e-40e9-83b8-3ca040177b36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bce343c4-a8ac-4065-9cad-217ca3de9c96' class='xr-var-data-in' type='checkbox'><label for='data-bce343c4-a8ac-4065-9cad-217ca3de9c96' 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([[0.00109381, 0.00086753, 0.00114471, ..., 0.00098246, 0.00099854,\n",
-       "        0.00092242],\n",
-       "       [0.00157136, 0.00149558, 0.00082337, ..., 0.00189196, 0.00103378,\n",
-       "        0.00173109],\n",
-       "       [0.00076914, 0.00116094, 0.00088306, ..., 0.00178604, 0.00068072,\n",
-       "        0.00100309],\n",
-       "       [0.00070886, 0.00081193, 0.00075107, ..., 0.00048467, 0.00049135,\n",
-       "        0.00051189]])</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'>-0.2584 0.0 ... -0.4669 0.07765</div><input id='attrs-af43ca6f-1b35-4dcf-9f1b-b59f85a6f71f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af43ca6f-1b35-4dcf-9f1b-b59f85a6f71f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a5524d0-8f8f-49b7-9432-162a0c2219e9' class='xr-var-data-in' type='checkbox'><label for='data-8a5524d0-8f8f-49b7-9432-162a0c2219e9' 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([[-0.25837625,  0.        , -0.03187653, ...,  0.50823635,\n",
-       "        -0.46877695, -0.25348938],\n",
-       "       [-0.40050094,  0.42751031,  0.00644737, ...,  0.37228705,\n",
-       "        -0.04212365, -0.17705138],\n",
-       "       [-0.74153139,  0.89391277, -0.72225611, ...,  0.03559768,\n",
-       "        -0.30213692, -0.10091159],\n",
-       "       [ 0.2242647 ,  0.63739414,  0.20844137, ...,  0.2114336 ,\n",
-       "        -0.46689975,  0.07765198]])</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-c6f66c51-70ab-4f09-a9e8-db9ddaedaf71' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c6f66c51-70ab-4f09-a9e8-db9ddaedaf71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3f05b98-b4c1-4aca-8260-a913ef0586f1' class='xr-var-data-in' type='checkbox'><label for='data-c3f05b98-b4c1-4aca-8260-a913ef0586f1' 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",
+       "    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>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'>16.84 20.65 17.17 ... 17.08 15.38</div><input id='attrs-ce37e884-636f-4910-b141-d7694cbef19f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce37e884-636f-4910-b141-d7694cbef19f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aeafcc0b-e30e-4b38-b4bd-d523c5b74282' class='xr-var-data-in' type='checkbox'><label for='data-aeafcc0b-e30e-4b38-b4bd-d523c5b74282' 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([[16.84090827, 20.64906743, 17.1730464 , ..., 18.89371397,\n",
-       "        18.4151088 , 21.19354036],\n",
-       "       [20.40583931, 18.67772106, 21.47325811, ..., 14.97740678,\n",
-       "        17.79418548, 19.48526426],\n",
-       "       [18.27161048, 19.24772916, 20.28605562, ..., 19.68863359,\n",
-       "        20.12653013, 16.88968507],\n",
-       "       [18.4770287 , 16.78315467, 22.72170669, ..., 19.07171212,\n",
-       "        17.07821333, 15.3760757 ]])</pre></div></li><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'>-13.04 -13.04 ... -11.49 -11.91</div><input id='attrs-6ebbd69a-9bae-418b-8d71-e763a14fb243' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ebbd69a-9bae-418b-8d71-e763a14fb243' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fd08e7e-368e-463b-9af5-b1cc5557ef95' class='xr-var-data-in' type='checkbox'><label for='data-7fd08e7e-368e-463b-9af5-b1cc5557ef95' 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([[-13.04008167, -13.04008167, -13.08156212, ..., -16.09968457,\n",
-       "        -14.03780087, -12.75970833],\n",
-       "       [-13.59845508, -15.63749662, -15.357422  , ..., -13.53831179,\n",
-       "        -13.25654472, -12.40317732],\n",
-       "       [-12.98969402, -17.19913149, -13.71364181, ..., -14.70897247,\n",
-       "        -13.25931825, -12.68230831],\n",
-       "       [-11.91448161, -14.61044872, -15.90850345, ..., -13.92129529,\n",
-       "        -11.49066214, -11.90690302]])</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-c433f9a0-4ca3-4646-bbb1-ddb2b5e368fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c433f9a0-4ca3-4646-bbb1-ddb2b5e368fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b6ef69f-7b90-4e15-aa49-3bf5beea5090' class='xr-var-data-in' type='checkbox'><label for='data-4b6ef69f-7b90-4e15-aa49-3bf5beea5090' 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., ..., 3., 3., 3.],\n",
-       "       [7., 7., 3., ..., 7., 3., 3.],\n",
-       "       [3., 7., 3., ..., 7., 3., 7.],\n",
-       "       [3., 3., 3., ..., 3., 3., 3.]])</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'>-0.2584 1.004 ... -0.4669 0.7978</div><input id='attrs-c2d6035c-6f4d-45fb-b654-88397b795c56' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c2d6035c-6f4d-45fb-b654-88397b795c56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8beede08-f4a7-4a5d-a90a-b79eb1446432' class='xr-var-data-in' type='checkbox'><label for='data-8beede08-f4a7-4a5d-a90a-b79eb1446432' 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([[-0.25837625,  1.00377789,  0.58967298, ...,  0.50823635,\n",
-       "        -0.47403301,  1.30851566],\n",
-       "       [ 0.64558332,  0.54525801,  0.28323174, ...,  0.39709167,\n",
-       "         0.55836873,  0.94099552],\n",
-       "       [-0.74153139,  0.9608849 , -0.72225611, ...,  0.25601613,\n",
-       "         1.01757126,  0.1757875 ],\n",
-       "       [ 1.61063735,  0.63739414,  1.53211219, ...,  1.39359771,\n",
-       "        -0.46689975,  0.79781162]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6a32640c-00f6-4251-9b92-99835d0e8901' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6a32640c-00f6-4251-9b92-99835d0e8901' 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>2022-05-31T15:32:48.599814</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>10.029414176940918</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
+       "       [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",
@@ -626,25 +626,25 @@
        "  * 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",
-       "    perf_counter_diff   (chain, draw) float64 0.001099 0.0008668 ... 0.0005115\n",
-       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 2 ... 2 2 2 2 2 2 2 2\n",
-       "    acceptance_rate     (chain, draw) float64 0.9408 0.4259 ... 0.9382 0.8286\n",
-       "    step_size           (chain, draw) float64 1.143 1.143 1.143 ... 1.422 1.422\n",
-       "    perf_counter_start  (chain, draw) float64 2.436e+05 2.436e+05 ... 2.436e+05\n",
-       "    step_size_bar       (chain, draw) float64 0.965 0.965 ... 0.9295 0.9295\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",
-       "    energy_error        (chain, draw) float64 -0.2584 0.0 ... -0.4669 0.07765\n",
        "    diverging           (chain, draw) bool False False False ... False False\n",
-       "    energy              (chain, draw) float64 16.84 20.65 17.17 ... 17.08 15.38\n",
-       "    lp                  (chain, draw) float64 -13.04 -13.04 ... -11.49 -11.91\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",
-       "    max_energy_error    (chain, draw) float64 -0.2584 1.004 ... -0.4669 0.7978\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:                 2022-05-31T15:32:48.599814\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:              10.029414176940918\n",
+       "    sampling_time:              6.993547439575195\n",
        "    tuning_steps:               1000"
       ]
      },
@@ -706,7 +706,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1300x300 with 4 Axes>"
       ]
@@ -726,7 +726,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlcAAAGxCAYAAABPzpOGAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA4kElEQVR4nO3dfXzP9eL/8efYxDDN5ZiryGcuRq6ai4bkokM6xSkRkRUSherQOZXq1O9Up3OUKJFSLnKZ4jgU4xhztbK5NmZMzDBsZptdv39/9P18jo/PZ7PxmqHH/Xbzx94Xr/fr/drb5/Pc6/16v94elmVZAgAAgBGlSroCAAAAtxPCFQAAgEGEKwAAAIMIVwAAAAYRrgAAAAwiXAEAABhEuAIAADCIcAUAAGAQ4QoAAMAgwhWAfAUEBCggIKCkq/G79dRTTykgIEDbt28v6aoAKALCFQAAgEGEKwAAAIM8eHEz4N7u3bv1448/KiIiQgkJCbpw4YJ8fHzUokULDRkyRB07dnS739GjR/XNN99o69atOnXqlEqXLi0/Pz8FBQXpySeflM1mc9r+woULmjt3rtavX69jx44pOztb1apVU2BgoPr166cuXbo4bZ+Tk6Pvv/9eK1as0MGDB5Wenq7q1aurU6dOeu6551SzZk2n7bdv364hQ4YoKChIM2fO1PTp07V69WqdOnVKlSpVUufOnTV27FjVqFHDsc/UqVM1bdq0fNtm3bp1ql27trKzs7Vq1Spt3LhR+/bt05kzZ5STk6OaNWuqU6dOGj58uFO5dk899ZQiIiI0Z84c+fj46NNPP9XPP/+stLQ01a1bV4899piGDRsmDw8Pt8ffunWrFixYoJ07d+r8+fOqUKGC/P391aVLFz311FPy9fV1+Z3Mnj1bW7Zs0enTp1WmTBk1btxY/fv31yOPPJLveRbG5edSunRpzZo1Szt37lRycrL+/ve/q1+/fkpNTXW006FDh3TmzBlJUp06dfTAAw/omWeekY+Pj6NM++8sP++995769evn+PnChQv65ptvtG7dOv3666/Ky8tT3bp11atXLw0bNkzlypW7rnMEUDSEKyAfTz/9tLZv3667775bNWvWVLly5XT8+HHt27dPkvTXv/5VQ4cOddrn3//+t/76178qKytLtWrVUvPmzZWXl6fjx4/r4MGDGj16tF544QXH9tHR0RoxYoROnz6tihUrqnXr1ipfvrwSEhJ04MABtWjRQnPnznVsn5qaqlGjRikiIkLe3t4KDAyUr6+vDh06pKNHj+rOO+/U7Nmz1bRpU8c+9i/qVq1aybIsHTx4UEFBQSpXrpx27NihxMREVatWTfPmzVP9+vUlSaGhoQoNDdX3338vSerbt6/TeU6YMEGVK1fWqVOn1KVLF1WsWFENGzaUn5+fLl26pAMHDujMmTOqXLmyFi5cqHr16jntbw8kI0aM0OzZs1W3bl01btxYiYmJ2rFjh3JzczVkyBC99tprLr+Xd99919EmTZo0UYMGDXTx4kUdPXpUx48f15w5c9SuXTvH9qtXr9bEiROVmZmpBg0aqGHDhrp48aJ2796t9PR09evXT++9916hr4sr2c/lySef1MKFC9WgQQMFBATowoUL6tu3r/r06aNffvlFgwYNUuXKlXXXXXepRo0aSklJ0d69e5WcnKx69epp0aJFjlAYGxurL774Qps2bdLZs2cVHBysatWqOY752GOPqW3btpKkw4cP69lnn1VCQoKqVaumxo0by9PTU3v27NHZs2fVpEkTzZ07VxUrVrzmcwRQRBYAtzZs2GCdPn3aZXlkZKTVunVrq1mzZtapU6ccy/fs2WM1a9bMCggIsObMmWPl5uY67XfixAlrz549jp/T0tKsLl26WDabzZowYYKVmprqtH1KSoq1efNmp2UvvfSSZbPZrJEjR1pnz551Wjd79mzLZrNZPXv2tHJychzLt23bZtlsNstms1k9evSw4uPjHesyMjKsF154wbLZbFb//v1dztW+X34uXrxohYaGWpmZmU7Ls7KyrH/961+WzWazhg8f7rLf4MGDHWUvWLDAad2WLVusgIAAq0mTJlZCQoLTujlz5lg2m80KCgqytm7d6lLurl27rJMnTzp+jo6OtgIDA63mzZtbP/30k9O2J06csPr06WPZbDbr+++/z/ccr+byc5k3b57bbRISEqwtW7a4XBPp6enWhAkTLJvNZr311lv5lr1t2za35V66dMnq3r27ZbPZrI8++sjp95Cenu64Xl599dVrPj8ARUe4Aq6BPThc/mX6/PPPWzabzXrnnXcKVcbXX39t2Ww265FHHnEKQ/k5fPiwFRAQYAUHB1sXL150u83w4cMtm81mrV+/3rHs8nC1du1al33Onj1r3XPPPZbNZrN27NjhtO5q4epqgoODrcaNG7vU1x4axowZ43a/Z555xiX0ZGdnW+3bt7dsNptLUMrPuHHjLJvNZn355Zdu1+/atcuy2WxW3759C3dCbtjPZciQIde0f3p6utW0aVOrffv2+ZadX7j69ttvHWHbndTUVKtDhw5W06ZNreTk5GuqH4Ci8yzpnjPgZpaUlKSwsDAdOnRIKSkpysnJkSTFxcVJ+m0sjyTl5uZqy5YtkqT+/fsXquxNmzZJ+u0WT+nSpa+6fVhYmCzLUufOnVWhQgW32wQFBSksLExRUVHq2rWr0zofHx9169bNZZ8qVaqoU6dOWrNmjSIiItS6detC1f9y0dHR2rp1q06cOKH09HRZ/zfaIDc3V3l5efr111+dblXaXVlHu4YNG2rTpk2OsUmStG/fPp0/f16+vr7q0aPHVeuUl5enjRs3SpJ69+7tdpvmzZvL29tbBw4cUGZmpu64446rlpufBx988KrbREZG6pdfflFCQoIyMjIc7eTl5aXz58/rwoULqlSpUqGPGRYWJknq1auX2/Xly5dXYGCgwsLCtGfPHgUHBxe6bADXjnAF5GPx4sV67733lJ6enu82aWlpkqTk5GTHdnfddVehyj958qQkqUGDBoXa/vjx45KkpUuXaunSpQVue/78eZdl/v7++Q4Qr127tiTp1KlThaqLXXp6uiZMmKC1a9cWuF1qaqrb5VcOvrezh8fMzEzHsvj4eEm/tW9+53G55ORkx3GvfCggv+3dDb4vLH9//3zXnTt3Ti+88IJ27NhRYBmpqalFClf2a2LChAmaMGFCgdu6uyYAFA/CFeDG3r17NWnSJJUuXVqvvPKKHnjgAcegdg8PDy1atEiTJk1y9DzcCHl5eZJ+G8TduHHjAre95557rukYRT2fyZMna+3atWrQoIFefvllNW/eXL6+vipTpowkacCAAYqKisq33FKlim82GHt7Sa4D8t3x8vK6ruOVLVs233WvvfaaduzYoVatWumFF15Q48aN5ePj4zhmcHCwEhMTi9z+9nPs1KmTqlatWuC2tWrVKlLZAK4d4Qpw48cff5RlWRo8eLCGDx/ust5+W9DuzjvvVLly5XTp0iUdPXrUZboFd2rWrKnY2FgdOXIk32kdrtxeklq3bq1JkyYV7kQuY+/5KWidn59fkcpcvXq1JOmjjz5yG/iubKfrYQ8HcXFxsizrqr1Xvr6+Klu2rDIyMhxPN5aE9PR0bdy4UaVKldLMmTOdplywrz979uw1lV2zZk0dOXJEjz32mP7whz+YqC4AA5hEFHDjwoULktz/tZ+Zmak1a9Y4LStdurQjIC1evLhQx+jUqZMk6bvvvlNubu5Vt+/cubMkaf369U63yworJSVF69evd1l+/vx5x/ivoKAgp3X2nhX7WLMr2dvJ3S2xTZs2KSkpqcj1zI992onz588rNDT0qttf/juxh8CScPHiReXm5qpChQouwUqSVqxYkW+Plb3987s+7NdESZ4fAFeEK8CNhg0bSpJ++OEHp/FCmZmZeuutt3TixAmXfZ577jl5enpq/vz5mj9/vssXZnx8vPbu3ev4+fHHH5efn5/279+v119/3WVsV2pqqmOQvCQ1bdpUDz74oBISEjRmzBi3dUhPT9eKFSvy7Qn54IMPnMZVZWVl6e2331Z6erpatGihNm3aOG1vH4N0+PBht+XZx4tdPheXJB05ckRvvvmm232ulaenp5577jlJ0htvvKGff/7ZZZvdu3c7nd+YMWPk5eWlDz/8UN9//73TrUK7Q4cOuYRlk6pWrapKlSopJSVFP/zwg9O6nTt3avLkyfnua2//mJgYt+v79+8vf39//fjjj/rwww/djm1LTEwsdOAHYAaTiAJupKSk6NFHH1V8fLzuvPNOtW3bVqVLl9Yvv/yijIwM/elPf9KcOXPUt29fvf/++479fvjhB73++uvKzs6Wv7+/AgMDZVmWjh8/rujoaJdJRPfv368RI0YoMTFRPj4+at26tby9vQucRHTMmDHaunWrvLy81LhxY9WuXVuWZSk+Pl7R0dGOWdPtAfHySUTz8vJ06NAhtW/fXmXLltWOHTt05swZValSRfPmzXMZXP/BBx/oq6++kq+vr9q3b6/y5ctLkl555RX5+vpqzZo1evHFF2VZlmw2mxo1aqRz585px44datOmjTIzMxUVFeUysefls5pfvtzOPkP8mDFjnNrLsiy99dZbWrhwoaTfAuddd92l1NRUHTlyJN9JRP/yl7/o0qVL8vPz09133y1fX19duHBBhw4d0qlTp9S7d2999NFH13StXO1cJOnrr792TFR6zz33qE6dOjp58qSioqL0xz/+Ub/88ovi4+MdM9/bbdiwQSNHjpSXl5eCg4NVuXJleXh46E9/+pPjqc6YmBiNHDlS8fHx8vHxUUBAgGrUqKGMjAzFxcUpNjZWVapU0ebNm6/p/AAUHWOuADd8fHy0dOlSTZ06VeHh4dq4caPuvPNO3XfffRozZky+T309+uijCgwM1OzZs7Vt2zb997//1R133KEaNWpo0KBBLo/MN23aVCtWrNCcOXO0bt06RUREKC8vT9WqVdMDDzzg9IoT6ben6L766iutWrVKK1as0L59+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+82VmZmrXrl266667XC50Dw8PdezYUYsWLdLevXsdY0Lsx73vvvvcHnfZsmWKiIjI93bqjWKifQpivy5Kly7tdn1KSooWLVqkpKQkVapUSa1bty5wLM2NZrp99u/fr+TkZOXk5Kh27drq0KGDfH19i/24xaU46xkZGanY2FgFBgaqcePGbre52a8fU7Zv3+743L5Sp06dFBERoYiICD366KOSrv57mTp1qtP2t6OrffZkZWVp2bJlOnPmjCpUqKDmzZvrnnvuuZFVvKHi4uL09ddfKzMzUzVq1FCHDh1cQqlU8LXTpk0beXt7X9P/6dsmXB07dkySXO7D29WvX1/h4eGKi4u75nC1e/duxcTEqHnz5vmOq3rnnXecfq5WrZree+89derU6ZqOWVRxcXGSpHr16rmsq1atmry9vR1tlZ9ff/1VeXl5Bbal/Vj2cBUXFydvb29Vq1bNZXt7Xa523BvBRPsU5LvvvpPkPmRKUnR0tCZNmuS0rFOnTvrggw/yfUjgRjLdPlcOlC1btqxGjx6tESNGFOtxi0tx1tPea/X444/nu83Nfv2YkJ6ersTERNlsNrdBwd3nSUG/l5vp86c4Xe2zJzEx0eUhk+bNm2vy5MmqW7dusdfvRlu5cqVWrlzp+NnT01ODBw/WhAkTnK6rgq6d0qVLq3bt2jp8+LBycnLy7VRx57YJV/bBofm9pNG+PDU19ZrLnzhxokqVKqU///nPLuvbtm2rLl26qGXLlqpcubJOnTqllStXaubMmRo1apQWLFjgdoCqafbzu/zW3OUqVKjgdiDt5a6lLVNTU10GuF+5/dWOeyOYaJ/8hIWFadGiRWrYsKHbL8iQkBD17NlT9evXl5eXl2JiYvTZZ59p48aNGjlypBYtWpTvX503iqn2qV27tt544w0FBwfLz89PFy5c0NatWzV58mT961//Urly5fTUU08ZP25xK656pqWlafXq1SpXrpz69Onjdptb4foxobCfP5e3c0G/l5vp86e4HDhwQJ9++qmqVKmiZ5991mV9v3791KZNG9lsNnl7eysuLk6zZ8/W8uXL9fTTT2vFihW3zQugK1eurJdfflldu3aVv7+/Ll26pKioKP3rX//S119/LQ8PD7366quO7a/2f7p8+fLKy8tTWlqay1CQgtx04er9999XVlZWobcfMmRIvj0spmRkZGj06NE6cuSIxo8f7/YR8ivHSNSrV0+jR49WjRo19Nprr+nTTz/V559/Xqz1RMnZvXu3xo8fr4oVK2rKlClubwVPnDjR6edWrVppxowZGjp0qCIiIrRu3Tr17NnzRlW5WF35pE7ZsmX16KOPqlmzZvrTn/6kadOmaeDAgUX6S/B2tmrVKqWnp6tv3775fsn9nq4fFN7x48c1YsQI5ebmavLkyW7/yB0zZozTz02aNNE//vEPSb89ZblkyRINGzbshtS3uDVq1EiNGjVy/Ozt7a3u3bvrnnvu0R//+EfNnTtXw4cPL/ae3pvuk23RokVKT08v9PYPPvig6tev70id+fVM2ZcXNZ1nZmbq+eef1/bt2zVy5Eg999xzRdq/b9+++tvf/qaoqKgi7XetrvZXWmpq6lXT97W0ZUF/sV/tL4MbyUT7XGnPnj165plnVKpUKc2aNcvpP/bVlCpVSo8//rgiIiIUGRlZ4l+OxdE+l2vUqJHatGmjLVu2KDY21jFeqLiPa0px1dN+S6eoA9lvtuvHhMJ+/lz+eXL57+XKMX030+ePacePH9eQIUOUlJSkqVOnqn379kXa/4knntDy5csVGRl524Sr/FSrVk3dunXTkiVLtGvXLj3wwAOSrv5/Oi0tTR4eHoWeJ9PupgtX1xpC7PdL7fdPr2RfXpReroyMDD3//PPavHmznn32Wb300ktFrlfp0qXl4+OjCxcuFHnfa2E/v2PHjikwMNBpXWJiotLT09WiRYsCy6hTp45KlSpVpLasX7++oqKilJiY6DLuyj7Wwd097RvNRPtcbs+ePQoJCVFeXp6++uqrIu1rZ/8yKMofFcXFdPu4Yz/fS5cu3dDjmlAc9Tx8+LCioqLUoEEDp0lDC+tmun5MsI/dPHHihHJzc11udbr7PKlfv7727t2rY8eOuYSrm+nzxyR7sEpMTNTHH3+srl27FrmM2+3auZqrffZcKTc3VydOnFDt2rWL3Mt+20zFUL9+fVWvXl2RkZEuF0p6eroiIyNVu3btQg9mvzxYhYSEuB1nVRgnT55UYmLiDXu81P40Xnh4uMs6+7KrPbFXtmxZtWjRQkePHnWZxM2yLG3ZskXe3t5OXy72Mjdv3pzvcd1N6HajmWgfO3uwys3N1axZs675yZtdu3ZJ+m2cUkkz2T7u5Obmau/evZKkWrVq3bDjmlIc9bze6RdupuvHlKCgIMfn9pU2bdokybmdC/N7uRk+f0y5PFh99NFH6t69+zWVs3v3bkk310Sixcn+f+Xy8y3o2tmxY4fS09Ov6bPntglXHh4eevzxx5Wenq7PPvvMad1nn32m9PR09e/f32n5pUuXFBsb6zRfivS/W4GbN2/WsGHDXMY6XCkxMVGnT592WZ6SkuIYOJffIFXTOnTooDp16mjlypU6cOCAY/nFixf1+eefy8vLy+lx5DNnzig2NtalS9TeVpMnT5ZlWY7lCxcu1PHjx/Xwww+rbNmyjuX9+vWTp6enpk+f7lTWgQMHtHLlSjVs2PCmmKHdVPvs3btXISEhysnJ0RdffKFWrVoVeNyDBw8qOzvbZXlkZKRmzZolLy+vm2IGaZPtc6Xc3Fz985//1LFjx9SuXTtVr179mo9bUky1j112draWL19+1fO7Va6fojp//rxiY2Nd5g20f/5MmTLFaQxuWFiYIiIiFBwc7PQF2atXL1WsWFHz5s3TqVOnHMtPnTqlefPmydfX95oDSEnJr23swerMmTOaPHmyevToUWA5sbGxTj01ly//5z//KUl6+OGHzVX8Bsmvfdx99kjSN998o+3bt6t+/fpOD5c1aNBA9957r7Zv366wsDDH8qysLE2ZMkVSwU/w5sfDuvyb8xaXnp6ugQMHKjo6WsHBwWratKn279+v8PBwNW/eXPPmzXMKBPZ3EF35biX7ewKrVaumJ554wu2x+vbt6/hLcfv27QoJCVGrVq1Ur149Va5cWQkJCdq0aZOSk5PVvn17zZw5s1Cv3jEhv9dzxMfHa+LEiU6v57Cf65XvW8rLy9Pw4cMdr7+599579euvv2rNmjXy9/fXkiVLCnz9Tc+ePR2vv8nOzr4lXn9T2PZJTk5Wz549deHCBXXq1Mltj1XFihX19NNPO5WzYcMGtWnTRjVr1pSnp6diYmK0efNmeXh4aNKkSRo4cGCxn3thmLh+AgICHP9q1KihCxcuKCIiQnFxcfLz89O8efNc3g9XlOOWJBPtY/fTTz/pxRdfVM+ePTV16tR8j3krXT9LlixxTBh86NAh7du3T61bt3bclmvTpo3jy2rq1KmaNm2axowZoxdeeMGpnNdff93x+psuXbooMTFRq1atUvny5bVw4ULdddddTttf/vob+zseV61apaSkJH300Ufq1atXcZ/6VZloG/t7Alu2bOl2biZJTttPnTpVs2fP1r333qtatWqpXLlyiouL08aNG5Wdna2RI0de05CX4mCqfTw9PRUYGKgaNWro0qVL2rVrl/bv3y8fHx99+eWXLrfuY2JiNHDgQGVkZKh3796qVq2awsLCFBMTo8GDB1/T629uujFX18Pb21vz5s1zvLh5+/btqlatmkJCQjR69GinYFUQ+62wxMRETZs2ze02QUFBjnBVt25d9e3bV3v27FFoaKhSU1Pl7e2tgIAA9enTR48//vgNfUS6ffv2+vbbb/XJJ59o1apVjhfLvvLKK4V+sWypUqU0ffp0zZw5U8uXL9fXX3+tO++8U4899pjGjRvn9omUUaNGyd/fX998840WLFggLy8vtW3bVmPHjr2pXtx8ve2TmprqGEO3adMmx22Ky/n7+zuFq27duiklJUXR0dHasmWLsrOzVbVqVT300EMaOnToTTGeyM7E9RMSEqKdO3dqy5YtunDhgry8vFS3bl2NGjVKw4YNczvo28RxbwST9SzsLcFb6frZsWOHyyz1kZGRTrf4CtMT8Le//U02m02LFy/WnDlz5O3trR49emj8+PFu52V65JFH5OvrqxkzZmjZsmWSpMDAQI0aNUodO3a8zrMyw0Tb2L+fdu7cqZ07d7rd5vKw0a5dO8XGxurAgQP65ZdflJGRIV9fX3Xu3FlPPvlkvgGtJJhonwEDBig8PFw///yzkpOTVapUKdWqVUtDhw5VSEiI0wz+do0aNdLixYv18ccfKywsTOnp6apfv74mTZqkJ5988prO5bbquQIAAChpt82YKwAAgJsB4QoAAMAgwhUAAIBBhCsAAACDCFcAAAAGEa4AAAAMIlwBAAAYRLgCAAAwiHAFAABgEOEKAADAIMIVAACAQYQrAAAAg/4/6vnGIXe5l44AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 720x480 with 1 Axes>"
       ]
@@ -1108,7 +1108,7 @@
        "  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-8e36aa81-ee52-4d72-abf9-2b4f3ab1f282' class='xr-array-in' type='checkbox' checked><label for='section-8e36aa81-ee52-4d72-abf9-2b4f3ab1f282' 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-c922bc47-edb7-43cd-a00c-b5a3e719ee70' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c922bc47-edb7-43cd-a00c-b5a3e719ee70' 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-6aeed993-57cf-4d4a-8f29-d03c07cb37bf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6aeed993-57cf-4d4a-8f29-d03c07cb37bf' 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>"
+       "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",
@@ -1147,7 +1147,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 600x400 with 1 Axes>"
       ]
@@ -1235,7 +1235,7 @@
        "\n",
        "    <div>\n",
        "      <progress value='44000' class='' max='44000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [44000/44000 00:20<00:00 Sampling 4 chains, 0 divergences]\n",
+       "      100.00% [44000/44000 00:14<00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -1250,7 +1250,7 @@
      "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 21 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 10_000 draw iterations (4_000 + 40_000 draws total) took 15 seconds.\n"
      ]
     }
    ],
@@ -1276,7 +1276,7 @@
     {
      "data": {
       "text/plain": [
-       "['p_jump', 'accept', 'scaling', 'accepted']"
+       "['p_jump', 'scaling', 'accepted', 'accept']"
       ]
      },
      "execution_count": 12,
@@ -1302,7 +1302,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABRcAAAH9CAYAAACA1UuyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA6z0lEQVR4nO3dfbzX8+H/8WeX6lQIpQuRbxxKNRUNQ3M5c7Gfmouv2ZoZdqGNbb7DZsb3y5flasZcjnzJtRlmWIpFrnWBromiVDJdyFGddH5/uJ0zxylO71XnpPv9dnPTeb/fn8/n9fm8qvPqcd6f96dBRUVFRQAAAAAAVlPDuh4AAAAAALB+EhcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFgFraYYcdssMOO9T1MAAAYK2w3gWKEBcBAAAAgELERQAAAACgkAYVFRUVdT0IoP57+eWX88gjj+T555/P7Nmzs3Dhwmy88cbp2bNnBg4cmD322GOlt3vjjTfyf//3f3nmmWcyZ86cNGrUKO3atUvfvn3zrW99K6WlpdWOX7hwYW655ZY89thjmTFjRsrLy9OmTZt07949AwYMSL9+/aodv3z58vzlL3/JAw88kClTpqSsrCxt27bNXnvtlR/+8Idp3759teOfe+65DBw4MH379s11112Xq6++Og8//HDmzJmTTTbZJHvvvXdOOeWUbLnlllW3ueKKK3LllVeu8rUZMWJEttpqq9V9SQEAqEesd613gWLERaBWjjvuuDz33HPZbrvt0r59+zRv3jxvvfVWJkyYkCT51a9+le9+97vVbvPXv/41v/rVr7Js2bJ06NAhPXr0yIoVK/LWW29lypQpOfnkk/OTn/yk6vjJkyfnpJNOyty5c9OqVav07t07LVq0yOzZszNp0qT07Nkzt9xyS9Xxixcvzo9+9KM8//zzKSkpSffu3dO6detMnTo1b7zxRjbddNMMGTIk3bp1q7pN5WKrV69eqaioyJQpU9K3b980b948o0ePzrx589KmTZsMHTo0nTt3TpIMHz48w4cPz1/+8pckSf/+/as9z1/+8pfZbLPN1ujrDQDAumW9a70LFCMuArUycuTIdO3aNW3btq22fezYsTnhhBOydOnSjBgxouonoOPHj89//ud/Zvny5fn1r3+dY489Ng0b/utKDLNmzcr8+fPTvXv3JElZWVkOPvjgzJ49O4cffnjOPvvstGjRour4999/P6+88kq1nxj/4he/yIMPPph99tkn559/fjbffPOqfTfddFMuuOCCdO7cOQ899FAaNWqU5F+LrSTZZpttctNNN6VDhw5JkqVLl+a//uu/8ve//z0777xz7rzzzmrPtfLi1lOmTPn3XkwAAOod613rXaAY11wEaqVfv341FlpJ0qtXrxx77LEpLy/P8OHDq7ZfffXVKS8vz7e//e185zvfqbbQSpKOHTtWLbSS5O67787s2bPTtWvX/O///m+1hVaStGrVqtpCa9q0afnb3/6Wtm3b5uKLL6620Eo+/slzv379Mn369DzxxBMrfU6//OUvqxZaSbLRRhvlt7/9bZo3b55x48ZlzJgxtXhlAAD4IrDeBSimcV0PAFh/zJ8/PyNHjszUqVOzaNGiLF++PEkyffr0JB9fbyZJPvroozz99NNJkqOOOqpW9/3kk08mSY444oiqn7p+lpEjR6aioiJ77713WrZsudJj+vbtm5EjR2bs2LHZZ599qu3beOONs99++9W4zeabb5699torw4YNy/PPP5/evXvXavwAAKz/rHcBVp+4CNTKXXfdlQsuuCBlZWWrPOaDDz5IkixYsKDquG233bZW9//2228nSf7jP/6jVse/9dZbSZJ77rkn99xzz2ce+95779XY1rFjxzRo0GClx1derHrOnDm1GgsAAOs/612AYsRF4HONHz8+Z599dho1apTTTjst++67b9VFrhs0aJA777wzZ599dtblJVxXrFiRJOnatWt23HHHzzz2S1/6UqHHcElaAIANg/UuQHHiIvC5HnnkkVRUVOTb3/52TjzxxBr7K98mUmnTTTdN8+bN8+GHH+aNN95IaWnp5z5G+/btM23atLz++uvVrjXzWccnSe/evXP22WfX7ol8wqxZsz53X7t27Vb7fgEAWP9Y7wIU5wNdgM+1cOHCJKl2MehKS5cuzbBhw6pta9SoUdWC6a677qrVY+y1115Jkj//+c/56KOPPvf4vffeO0ny2GOPZenSpbV6jE9atGhRHnvssRrb33vvvarr4fTt27faviZNmiRJ1bV3AAD4YrDe/Zj1LlCEuAh8ri5duiRJ7rvvvixevLhq+9KlS3POOedk5syZNW7zwx/+MI0bN86tt96aW2+9tcZbLmbNmpXx48dXfX3kkUemXbt2mThxYs4666wa17pZvHhx1UWzk6Rbt2752te+ltmzZ2fQoEErHUNZWVkeeOCBvPvuuyt9Xr/73e+qXWdm2bJlOffcc1NWVpaePXumT58+1Y7fcsstkySvvfbaSu8PAID1k/Xux6x3gSIaVLjIAvA5Fi1alMMPPzyzZs3Kpptuml122SWNGjXKiy++mCVLluSb3/xmbr755vTv3z8XXnhh1e3uu+++nHXWWSkvL0/Hjh3TvXv3VFRU5K233srkyZNz8skn5yc/+UnV8RMnTsxJJ52UefPmZeONN07v3r1TUlKS2bNnZ9KkSenZs2duueWWquMXL16cQYMG5ZlnnkmTJk2y4447ZquttkpFRUVmzZqVyZMnp7y8PA899FDVgvG5557LwIED06tXr6xYsSJTp07NbrvtlmbNmmX06NF55513svnmm2fo0KE1Lrb9u9/9LjfeeGNat26d3XbbLS1atEiSnHbaaWnduvXanAIAANYi692PWe8CRYiLQK289957ueKKKzJq1KjMmTMnm266aXbbbbcMGjQoo0ePzplnnlljsZV8/FPPIUOG5Nlnn80777yTjTbaKFtuuWV22223HHPMMdluu+1qPM7NN9+cESNGZObMmVmxYkXatGmTHj16ZMCAAVVvJ6m0YsWKPPTQQ3nggQcyYcKELFy4MC1atEjbtm3TvXv37LfffunXr1/VWzwqF1t9+/bNtddemyuvvDJ///vfM3fu3GyyySbZe++989Of/rTqGjeftHTp0vzhD3/Io48+mrfffjvl5eVJkhEjRlR94h4AAOsn613rXaAYcRHYoHxysfXJnwoDAMAXgfUusK655iIAAAAAUIi4CAAAAAAUIi4CAAAAAIW45iIAAAAAUIgzFwEAAACAQsRFAAAAAKCQxnU9AIDaePjhh3Pbbbdl8uTJKS8vz9Zbb53DDjssxx13XJo0aVLXwwMAgEJef/31PPXUU5kwYUImTJiQadOm5aOPPsopp5ySH//4x3U9PIDPJS4C9d7555+fm2++OY0bN85uu+2WkpKSPPvss7n44ovz+OOP58Ybb0yzZs3qepgAALDabr/99tx88811PQyAwsRFoF4bPnx4br755pSUlGTo0KHZaaedkiTvvfdevvvd72b06NG5/PLLc/rpp9fxSAEAYPWVlpbm+OOPT7du3dKtW7dce+21uf/+++t6WAC1Ji4C9do111yTJDnppJOqwmKSbLbZZvntb3+bY489NkOHDs2Pf/zjtGrVqq6GCQAAhRx55JHVvm7Y0EcjAOsXf2sB9dbcuXPzyiuvJEkOPfTQGvt32WWXtG/fPsuWLcvIkSPX9fAAAABggycuAvXWxIkTkySbbrppOnXqtNJjunfvXu1YAAAAYN0RF4F6a+bMmUmS9u3br/KYdu3aVTsWAAAAWHfERaDe+uCDD5IkzZs3X+UxLVq0qHYsAAAAsO6IiwAAAABAIeIiUG9VnpX44YcfrvKYyjMWK48FAAAA1h1xEai3OnbsmCSZPXv2Ko+ZM2dOtWMBAACAdUdcBOqtbt26JUkWLFiQt956a6XHjB8/Pkmy0047rbNxAQAAAB8TF4F6q127dunRo0eS5MEHH6yx/8UXX8zs2bPTtGnT9OvXb10PDwAAADZ44iJQr/3whz9Mklx33XWZMGFC1fb58+fn3HPPTZJ8+9vfTqtWrepkfAAAALAha1BRUVFR14MA+CznnXdebrnlljRp0iS77bZbSkpK8swzz2TRokXp3bt3hgwZkmbNmtX1MAEAYLVNmDCh6ofmSfLmm29m/vz5adeuXbbccsuq7VdeeWXatm1bF0ME+EyN63oAAJ/nrLPOSu/evXPbbbdl7NixWb58ebbeeuuceOKJOe6449K0adO6HiIAABSyePHivPTSSzW2z5kzp+rDC5Nk2bJl63JYALXmzEUAAAAAoBDXXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAopNafFj1//vy1OY7PtMkmm2ThwoV19visO+Z6w2CeNxzmesOxIc5169at63oIrGHz58/fIH8v13fmpH4yL/WPOamfzEv9Y05qr7br3fXizMWGDdeLYbIGmOsNg3necJjrDYe55ovC7+X6x5zUT+al/jEn9ZN5qX/MyZrnFQUAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQhrX9QBYPz06omKt3G+LFkvzwQc17/uA/RqslccDAKj06IiKVa5FVod1CwCwIXHmIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFNK7rAUB989FHH+Xxxx/PxIkTM3HixEyePDlLlizJwQcfnLPPPruuhwcArCdef/31/OlPf8qYMWPy4YcfZquttsphhx2Wo446Kg0brt7P+JctW5Y77rgjw4cPz1tvvZXly5dniy22yK677prvfve76dix4ypvO2bMmNx11115+eWX8/7772eTTTbJdtttlwEDBmTvvff+d58mALCBExfhU8rKynLWWWfV9TAAgPXYK6+8kkGDBmXp0qXp1q1b2rdvn3HjxuX3v/99XnnllZx33nlp0KBBre5r6dKlOfnkkzN+/Pi0atUqvXr1StOmTTNlypQ88MADGT58eK666qrsuOOONW57/fXX54YbbkjTpk3Ts2fPtG7dOvPmzctLL72UNm3aiIsAwL9NXIRPady4cb7+9a9nxx13TLdu3TJjxoycd955dT0sAGA9sXz58vz2t7/N0qVLc8opp+SYY45J8vEPME855ZSMGDEiu+++ew499NBa3d99992X8ePHp1u3bvnDH/6Qli1bJvn43RaXXXZZ7rnnnlx++eW5+uqrq93uwQcfzA033JCddtopF1xwQdq2bVu1b8mSJZk1a9YaesYAwIbMNRfhU5o3b57f/va3Ofroo9OjR49stNFGdT0kAGA98o9//CNvv/12tt9++6qwmCQlJSX5xS9+kSS5/fbba31/48aNS5Icc8wxVWExSRo1apSTTjopSTJx4sRqt1myZEmuuOKKlJSUZPDgwdXCYpI0a9YsXbp0Wa3nBQCwMs5c3AC8/fbbGTBgQHr16pVLL7001157bR577LEsXLgw22yzTU488cTstddeSZIRI0bk1ltvzeuvv57mzZtn//33z8knn5xmzZpVu89ly5bkmWfuzPhXRuS9f85MkrTd8j/St2//9Op9SI0xTJ8+LuNfHp7p08dl4cJ3snz50my6abt07dYve+39nTRv3qra8W+8PiY33nByevU6OAcd/NP87nfX5oknnsiiRYvSqVOnHHPMMTnssMPW0isGAHzSzJkzs99++6Vv37659tprc/nll+eRRx7J/Pnz06VLl/zkJz/JvvvumyR5+OGHc+ONN+bVV19NSUlJDj744Jx22mk11hIffvhhbr755jz88MOZMWNGklTFuP79+9cYw4svvpiHHnooL7zwQubMmZOlS5emQ4cO2X///XPSSSdl4403rnb8c889l4EDB6Z///4544wzctlll2XEiBFZsGBBOnfunOOOOy5HHHHEWnm9nn766STJPvvsU2PfjjvumI4dO2batGl5++2306FDh8+9vyZNmnzuMZtsskm1r//xj39k4cKFOeyww7L55pvXcuQAAKtPXNyALF++PIMGDcrbb7+dXr16ZcGCBRk3blzVgnvatGm58sor06tXr3z5y1/OuHHjcvfdd2fhwoX57//+76r7ee+993LdtT/N3DmvpWXLzdO5c69UpCJvvflK7v3zeZk1a3IOPewX1R77749cmTmzX027dtvlP7rskuXLl2b221Pz5BO3ZMrkp3LSD6/PRhuV1Bjzh0sW57prT0rDBh9m5513rhrz+eefnxUrVuT//b//t9ZfNwDgY+Xl5TnuuOMyc+bM7LLLLpk/f35efPHFDBo0KH/6058yderUXHTRRdl1112z55575oUXXsgtt9yS+fPn55JLLqm6n3/+85/53ve+lylTpqRNmzbZddddU1FRkbFjx+aMM87I+PHj85vf/KbaYw8ePDiTJ0/ODjvskN133z1Lly7NhAkTcv311+cf//hH7rzzzrRo0aLGmBctWpSjjz46ZWVl6dOnT9WYf/3rX6eioiJHHnnkGn+dXn311SRZ6TUQk2SHHXbIrFmz8tprr9UqLn75y1/OsGHDcvvtt2f33Xev9rbo6667Lklq/NB19OjRSZIePXrk/fffz9///vdMmzYtG220UXr27Jm99947jRv7pwAA8O+zotiAvPLKK9lll11y7733pnnz5kk+vhbPeeedl8GDB2fRokX505/+lK5duyZJ5s2bl4EDB2bYsGH5wQ9+UPUphOedd17mznktu+9xVA782slp3LhpkmTx4vcy9ObT8tyz92SHHfbI9qW7Vz32Pvt8P1tv0yPNmv3rrTzLly/L3x68NC++cH+efur27LPv92uMefKkJ9Kj5wH545W/SdOmHz/OyJEjc/rpp2fIkCE14uKPfvSjjB07drVel7POOqvW1zwCgA3Z2LFjs9tuu2X48OEpKfn4h4L33ntvzjzzzJxzzjlZsGBB7rjjjvTo0SNJMnfu3PTv3z8PPvhgTj311HTq1ClJcuaZZ2bKlCkZOHBg/uu//qvqe/y7776bH/zgBxk6dGj69etX7cNGTj755PTu3TutWv3r3Q7Lli3LeeedlzvvvDNDhgzJoEGDaox5xIgROeSQQ3LhhRdWPc7w4cNz8skn56qrrqoRF/94xfGZNu3F1Xpd+n/zrPT+xDs35s6dmyRp06bNSo+vfIvynDlzanX/Bx10UJ599tk8+uij6d+/f3r27JmmTZtm8uTJee+993Lsscfm+OOPr3abN954I0myYMGCHHPMMXn33Xer9t1xxx3p0qVLLr300my55Za1f6IAACshLm5AGjZsmF/+8pdVYTFJDj744Fx55ZWZOXNmvve971WFxeTjBfHXvva13HHHHRk7dmw6duyYqVOn5umnn07Hjl1z0NdPScOG/7psZ8uWm+Ubh5+eq/94XJ5/7i/V4mLpDv/6daXGjZvm4ENOzZjRD2bypCdXGhc32qhFDj3sF1X/GEiSfv36pUuXLit9O9Huu++e9u3br9brstVWW63W8QCwoWrYsGHOOeecqrCYJIcffngGDx6cGTNm5Ec/+lFVWEySLbfcMocddlhuuummvPDCC+nUqVMmTZqUkSNHpkePHjnzzDOrrSW22GKL/M///E/69++f22+/vVpc7NevX43xNG3aNL/61a/y5z//OY899thK42LLli3zm9/8ptpaYv/9909paWmmTp2amTNnVlsL7Nj1K9l447Y17uezbL5Z9bVEWVlZktR4K3ilyu2Vx32eRo0a5ZxzzsmWW26ZoUOH5qmnnqrat8MOO2TXXXdNo0aNqt3m/fffT5Jce+212WabbXLeeedl++23z/Tp0zN48OBMmTIlZ555Zm644YZaf2o1AMDKiIsbkPbt22frrbeutq1hw4Zp165dFixYkC9/+cs1blN5tuI///nPJB9fvyhJunbbu9o/Bip16LBDmjYtycyZE2vsW7TwnUyePCrvvjsjS5d8kIqKiiRJo0ZN8s9/vrXSMXfouGNKSjapsb1Tp06ZNm1a/vnPf1aLiwMHDlzp/QAA/76OHTtm2223rbatYcOG6dChQ+bPn58999yzxm0qz1acN29ekmTUqFFJPg58K1tLdOvWLSUlJXnllVdq7Js7d24ee+yxvP7661m8eHHVWqJJkyaZPn36Sse80047pXXr1jW2d+7cOVOnTs28efOqxcX99v9+Pvjgg5XeV11ZtGhRTj/99EyaNCk/+9nPss8++6RZs2YZN25cLrnkkvz85z/PueeemwMOOKDqNitWrEjycZi87LLLqs5Q3GmnnXLZZZflm9/8ZiZOnJjnn39+pWtAAIDaEhc3IKt6a07lmYwr21+5b9myZUmS2bNnJ0mGP3pthj967Sofa/nypdW+fmrU7Xl02FX56KPlqzXmjTde+Zgrz5ioHBcAsPat6i20ldc6XNn+T3/PnjVrVpLksssuy2WXXbbKx/r09/ghQ4bkkksuSXl5+WqNuV27dp855rWxligpKcmiRYuyZMmSle6v3P7JM0A/y+9///uMHTs2p556ao4++uiq7XvvvXfatGmT448/Pn/4wx+yzz77VF1HsXINt8suu9SYl8022yx77LFHRowYkbFjx4qLAMC/RVzcgHzeW15q85aYyjMEttnmS9lss461ety33hyfRx7+Q5o1a5n/d/ip2Xbb3mnZavOqazUOvvCwvP/+uyu9bcMGNc9o+Cw333zzKs9cWJVvfOMb2XnnnVfrNgCwIVrZmYafVJu1ROUZdX369KnxjopVGTduXC688MK0atUq//M//5O+ffumTZs2VW913nPPPavOjFzdMX/aiOE35O1Zr67Wbfrs8o1s0/lLVV9vueWWWbRoUebNm5ftt9++xvHvvPNOklWHz0/66KOP8uijjyZJ1Sdyf1LXrl3ToUOHzJo1K7Nmzco222yT5ON3rEydOnWVl4up3D5//vzPHQMAwGcRF1ktlWc3du22d76y57dqdZuJE0cmSfY/4Afp9YmLnSdJefmSLF78zzU2vmeeeWa1P9Cld+/e4iIArCOVQW3//fev8SEkq1IZ1372s5+lf//+1fYtWbKk2oeV/LsmT3pqtT/QpfN/9K4WF7fffvu8+uqrmTx5cvbYY48ax0+ZMiVJst12233ufc+fP7/qbM2VfRr2J7dXXmcxSUpLSzNy5Mhq2z5p0aJFSVLtWtwAAEWIi6yWvn375rrrrsvEiSNrHReXfPjxonZlF0cfP/6xqrMh14Srr756jd0XALDmfeUrX8nll1+eRx99tNZxsTKErext14888sgaXUuc/JMb/+1rLu6xxx556KGH8vjjj9d4jlOmTMmsWbPSpUuXateNXpWNN944TZo0SXl5eSZPnpw+ffpU2//BBx/kzTffTFL9TMi99tor119/fV5++eUsX7686u3Sycdnj7700ktJPv5AGACAf8fqvU+EDV737t3Tt2/fvDnj5fz1gYuyZEnNxffs2a/m1anPVH29+RYfX8h99Oi/Vrvm4jvvvJFhj1y19gcNANQbX/rSl/KVr3wlY8aMybnnnpvFixfXOGby5Ml54oknqr7u3LlzkuSee+6pds3F1157LRdffPFaH/Pq+upXv5oOHTrk1Vdfze233161/cMPP6wa7zHHHFPjdoMGDcrRRx+dCRMmVG1r2rRpdttttyTJ5ZdfXu0szaVLl2bw4MFZsmRJevbsmS222KJqX2lpafr27Zs5c+bkmmuuqRZghwwZkhkzZqR169b56le/usaeNwCwYXLmIqvtnHPOyfe/f2qef+7evPzSo2nffvu02niLLFmyOHPnTMvChXOz+x5HZfvS3ZMkvfscmqdG3Z4pk0fl8suOTsetuubDskWZPn1sduy6d2bNnJgFC+bU8bOqbvDgwVVvWVq4cGGS5Omnn873v//9qmNuuOGGOhkbAKzvLrroopxwwgm57bbb8uCDD2bHHXdM27Zts3jx4kyZMiWzZ8/OwIEDs/feeydJBgwYkCFDhuTxxx/PQQcdlB49emThwoV54YUXst9+++WVV16p+qCY+qBx48Y555xz8pOf/CSXX355hg8fnnbt2uWll17Ku+++m3333TeHHHJIjdvNnDkzc+bMqfFBMKecckomTJiQqVOn5qijjkqPHj2y0UYbZdKkSZk3b1423njjnH766TXu79e//nVOOOGEDB06NE888US6dOmS6dOn54033shGG22Uc88919uiAYB/m7jIattss81y4g+uy+gXH8grLz+a2bOn5s03X0nLlpul9WYdstvuR6ZHzwOqji8p2SQ//PGNGfb3P2b6G2MzedKTad26Q/bd/6Tsuee3ctklR9Ths1m5N954o9pZA0myYMGCLFiwoG4GBABfIJtvvnnuuOOO3HXXXfnb3/6WSZMmZezYsdliiy3SqVOnfOc736kW31q3bp177rknF110UV544YU89thj2WqrrfLTn/403//+93PAAQd8xqPVjZ49e2bIkCG5/vrrM2bMmLz22mvp2LFjjj322Bx99NG1+vCbSltttVVuueWW3HLLLXnmmWcybty4VFRUpG3btjniiCMycODAtG1b8/IzW265ZW6++ebccMMNGTVqVJ588sm0atUqBxxwQI477rh06dJlTT5lAGAD1aCilhepqctPkmvdurVPsqtnHh2x5q5t9EktWrRY6XWODtiv9gtw6j9/pjcc5nrDsSHOdevWret6CKxhd93z3irXIqvDumXN2hD/flkfmJf6x5zUT+al/jEntVfb9a5rLgIAAAAAhYiLAAAAAEAh4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAhjet6AOvCoyMq1vljHrBfg3X+mAAAAACwLjlzEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgkMZ1PQAAYN15dETFWrnfFi2W5oMPat73Afs1WCuPBwAA1A/OXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKERcBAAAAAAKERcBAAAAgELERQAAAACgEHERAAAAAChEXAQAAAAAChEXAQAAAIBCxEUAAAAAoBBxEQAAAAAoRFwEAAAAAAoRFwEAAACAQsRFAAAAAKAQcREAAAAAKKRBRUVFRV0P4rO8//77GT16dPr06ZNWrVrV9XBYi8z1hsE8bzjM9YbDXPNF4fdy/WNO6ifzUv+Yk/rJvNQ/5mTtqPdnLi5evDgjR47M4sWL63oorGXmesNgnjcc5nrDYa75ovB7uf4xJ/WTeal/zEn9ZF7qH3OydtT7uAgAAAAA1E/iIgAAAABQSL2Piy1btky/fv3SsmXLuh4Ka5m53jCY5w2Hud5wmGu+KPxern/MSf1kXuofc1I/mZf6x5ysHfX+A10AAAAAgPqp3p+5CAAAAADUT+IiAAAAAFCIuAgAAAAAFCIuAgAAAACFNK7rAazKyy+/nCuuuCJjx47N8uXLU1pamuOOOy4HH3xwXQ+NNeT+++/P6NGjM378+EydOjXl5eW54IILMmDAgLoeGmvY3Llz8/DDD+eJJ57I66+/nnfffTebbLJJevfunRNOOCFf+tKX6nqIrAFLly7NpZdemvHjx2fGjBlZuHBhNt5443Tq1ClHHnlkvvGNb6RJkyZ1PUzWouuuuy6XXHJJkuTOO+/MzjvvXLcDglqy7ly7VnfNt3jx4lxxxRUZNmxY5s2bl7Zt2+ZrX/taBg0alBYtWtQ4fsWKFbn11ltz1113ZcaMGSkpKckee+yRn/3sZ+nUqdPafnrrpSJrM/OydhVZR5mTuvF56x3zsvbtu+++mTVr1kr39e3bN7fccku1bcuWLct1112XBx54ILNnz84mm2ySffbZJ6eeemo233zzld7PAw88kJtvvjmvvfZamjRpkt69e+enP/1pdtpppzX+fL4I6uWnRT/77LM54YQT0rRp0xxyyCFp0aJFhg0bllmzZuX000/P8ccfX9dDZA2o/AuhdevWKSkpyaxZs8TFL6iLL744119/fbbeeuv07ds3m222WWbMmJHhw4enoqIil1xyiX/AfQG89957+epXv5qePXumc+fO2WyzzbJw4cI8+eSTmTVrVvbcc89cf/31adjQSfNfRFOnTs03v/nNNG7cOGVlZeIi6w3rzrVvddZ8ZWVl+da3vpVJkyZlzz33TNeuXTNp0qSMGjUqPXr0yK233pqNNtqo2m3OOuus3H333dl+++3Tr1+/vPPOO3n44YfTokWL3HnnnencufM6eqbrj9Vdm5mXtW9111HmpG583nrHvKwb++67bxYtWpTvfve7NfZ17Nix2veXFStW5MQTT8yoUaOy8847Z9ddd82MGTPy6KOPZquttspdd92VzTbbrNp9XH311fn973+fjh075sADD8wHH3yQv/3tbykvL89NN92UPn36rPXnuN6pqGfKy8sr9t9//4ru3btXTJw4sWr7okWLKg488MCKnXbaqWLmzJl1OELWlKeeeqpqLq+99tqK0tLSij//+c91PCrWhr///e8Vzz33XI3tL7zwQsVOO+1Useuuu1YsXbq0DkbGmvTRRx+tdB7Ly8srvv3tb1eUlpZWPP744+t+YKx1y5Ytq+jfv3/FkUceWXHaaadVlJaWVowdO7auhwWfy7pz3VidNd/ll19eUVpaWnHRRRdV237RRRdVlJaWVlxzzTXVtj/zzDMVpaWlFccee2y170H/+Mc/KkpLSyuOP/74NfxsvhhWd21mXta+1V1HmZN1rzbrHfOybuyzzz4V++yzT62OveeeeypKS0srfv7zn1esWLGiavttt91WUVpaWvGb3/ym2vFvvPFGRbdu3SoOPPDAikWLFlVtnzhxYkX37t0rvv71r1d89NFHa+aJfIHUu9NHnn322bz55ps59NBD07Vr16rtrVq1yg9/+MOUl5fnL3/5Sx2OkDVljz32SMeOHet6GKwDBx54YPr27Vtj+y677JIvf/nLWbhwYaZMmVIHI2NNatiwYZo2bVpje+PGjXPAAQckSWbMmLGuh8U6cM011+TVV1/N//7v/6ZRo0Z1PRyoNevOdaO2a76KiorcfffdKSkpyY9//ONq+3784x+npKQkd999d7XtlV+fcsop1b4H9evXL3379s2oUaPy9ttvr4Fn8cWyOmsz87JurM46ypzUjc9b75iX+qnyNf75z3+eBg0aVG3/z//8z3Tq1Cl//etfs2TJkqrt9957b5YvX54f/ehHadWqVdX2rl275tBDD820adMyevTodfcE1hP1Li4+//zzSZI999yzxr7KbS+88MI6HROw9jRu3Lja//niWbFiRZ588skkSWlpaR2PhjVtwoQJueaaazJo0KBst912dT0cWC3WnfXL9OnT884776R3794pKSmptq+kpCS9e/fOW2+9ldmzZ1dtf+6556r2fdpee+2V5F/zTO18em1mXurWytZR5mTdq816x7ysW8uWLcu9996ba665JkOHDs1LL71U45ilS5fmpZdeyrbbblvjh1wNGjTIHnvskbKysowfP75qe+Xr/ZWvfKXG/VWuDcxJTfXuX/PTp09PkmyzzTY19rVp0yYlJSXOfIEviLfffjtPP/102rRpIzp9gSxbtizXXnttKioqsmDBgjzzzDN5/fXXM2DAgOy+++51PTzWoGXLluX000/PjjvumBNOOKGuhwOrzbqzfql8rVd1fbHOnTtn1KhRmT59etq3b5+ysrLMmzcvpaWlKz2LqHJezWHtrWxtZl7Wrdqso8zJulXb9Y55WbfmzZuXM888s9q2Hj165NJLL83WW2+dJHnzzTezYsWKz5yT5OP1wC677FL165KSkrRp06bG8eZk1epdXFy8eHGSVDv99JNatmyZ999/f10OCVgLysvL88tf/jLLli3Laaed5q2UXyDl5eW58sorq75u0KBBjj/++PziF7+ow1GxNlx++eWZPn167r33Xn+GWS9Zd9Yvla91y5YtV7q/cnvlvNX2eHNYO6tam5mXdas26yhzsm7Vdr1jXtadAQMGpE+fPiktLU1JSUmmT5+eIUOG5P77789xxx2XBx54oNr38NrOSeWvP/0BL58+3pzUVO/iIvDFt2LFipxxxhl54YUXctRRR+Xwww+v6yGxBrVo0SJTpkzJihUr8s477+Sxxx7LZZddlnHjxuX6669f5Td31i9jx47NjTfemEGDBjnzGGA9Z21Wf1hH1S/WO/XToEGDqn3dtWvXDB48OEly//335+677873vve9uhjaBqveXXPx80rw4sWLV/nTZaD+W7FiRX71q1/lwQcfzDe+8Y2ce+65dT0k1pKGDRumXbt2+da3vpX//u//zpgxY3L11VfX9bBYA5YvX54zzjgjO+ywQ0466aS6Hg4UZt1Zv1S+1p88g+STKrdXzlttjzeHn+3z1mbmpW581jrKnKwbq7veMS917+ijj06SjBkzJsnqz0nlrz9rXfDJ++Vf6t2Zi5XveZ8xY0a6d+9ebd+8efNSVlaWnj171sHIgH/XihUrcuaZZ+a+++7LoYcemgsvvDANG9a7n3GwFrj48RdLWVlZ1bXqPv29ulLl4u6Pf/xj9t9//3U1NFgt1p31S+W1rCr/fvm0yu2V81Z5TayZM2fmo48+qvF2xcprYq3smpp8rDZrM/NS9z69jjIn68bqrne6dOmSxLzUpdatWyf5eO6SpFOnTmnYsGGt56Ty12PHjs28efNqXHfRnKxavftX/a677pokGTVqVI19ldsqjwHWH59cvB588MEZPHiwa7RtQN55550kPhX8i6Jp06Y54ogjVvpf5eJs3333zRFHHFHjk/mgPrHurF86d+6ctm3bZsyYMVX/MKxUVlaWMWPGZKuttkr79u2rtvft27dq36dVfsKuOVy52q7NzEvd+/Q6ypysG6u73jEvde/ll19Okqr1Z7NmzdKzZ8+88cYbmTVrVrVjKyoq8vTTT6ekpKRaPK58vZ966qka91+5Nujbt+9aGf/6rN7Fxd133z2dOnXKgw8+mEmTJlVtf//993PNNdekSZMmrgEC65nKt9vcd999Oeigg3LRRRcJi19Ar732Wj788MMa2z/88MNccMEFSZJ+/fqt62GxFjRr1iznn3/+Sv/r1atXkuQHP/hBzj///HTt2rWORwurZt1ZvzRo0CBHHnlkysrKctVVV1Xbd9VVV6WsrCxHHXVUte2VX19++eVZtmxZ1faRI0fm+eefz5577umHHCuxOmsz87JurM46ypysG6u73jEv68a0adNW+mdl2rRpufjii5Mkhx12WNX2ytf40ksvTUVFRdX2O+64I2+99VYOO+ywNGvWrGr7gAED0rhx41x99dXV3h49adKkPPjgg+nSpUv69Omzxp/X+q5BxSdf3Xri2WefzQknnJCmTZvmkEMOSYsWLTJs2LDMmjUrp59+eo4//vi6HiJrwN13353Ro0cnSaZOnZoJEyakd+/eVacY9+nTJ0ceeWRdDpE15IorrsiVV16ZkpKSDBw4cKVnr+2///4ixHruiiuuyJAhQ9KnT5907NgxLVu2zNy5c/PEE09kwYIF2WWXXXLDDTdU++bNF88ZZ5yRv/zlL7nzzjuz88471/Vw4HNZd659q7PmKysryzHHHJPJkydnzz33TLdu3TJx4sSMGjUqPXr0yNChQ2t8HznrrLNy9913Z/vtt0+/fv0yb968PPTQQ2nRokXuuOOObLvttuv2Ca8HVndtZl7WvtVdR5mTurWq9Y55Wfsq/6zsuuuu6dChQ5o3b57p06fniSeeSHl5eX7wgx/k5z//edXxK1asyIknnphRo0Zl5513zq677po333wzw4YNS8eOHXP33XfX+HToq6++Or///e/TsWPHHHjggfnggw/yt7/9LeXl5bnpppvExZWol3Ex+fh01j/84Q8ZO3Zsli9fntLS0nzve9/LwQcfXNdDYw2p/At5Vfr3758LL7xwHY6IteXz5jpJLrjgggwYMGAdjYi14ZVXXsldd92VsWPHZu7cuSkrK0vLli2zww475JBDDsk3v/lNb4veAIiLrI+sO9eu1V3zvf/++7niiisybNiwvPvuu2nTpk0OOuignHzyySv9pNwVK1Zk6NChueuuuzJjxoyUlJRkjz32yM9+9rNsvfXWa+U5re+KrM3My9pVZB1lTurOZ613zMva9fzzz+e2227LpEmT8u6772bJkiVp3bp1evbsmW9961tV1yj9pGXLluW6667L/fffn9mzZ2fTTTfNV7/61Zx66qnZYostVvo4DzzwQP7v//4vr732Wpo0aZLevXvnlFNOyU477bS2n+J6qd7GRQAAAACgfqt311wEAAAAANYP4iIAAAAAUIi4CAAAAAAUIi4CAAAAAIWIiwAAAABAIeIiAAAAAFCIuAgAAAAAFCIuAgAAAACFiIsAAAAAQCHiIgAAAABQiLgIAAAAABQiLgIAAAAAhYiLAAAAAEAh/x9Vqxh3vdzeEgAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 1656x552 with 2 Axes>"
       ]
@@ -1688,23 +1688,23 @@
        "  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      , 102.36028945],\n",
-       "       [  3.75      , 808.01242909],\n",
-       "       [  3.75      , 193.61490756],\n",
-       "       [  3.75      , 473.08420034]])\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-dc98dd98-85bd-42e5-879d-61d3b1b4c374' class='xr-array-in' type='checkbox' checked><label for='section-dc98dd98-85bd-42e5-879d-61d3b1b4c374' 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>3.75 102.4 3.75 808.0 3.75 193.6 3.75 473.1</span></div><div class='xr-array-data'><pre>array([[  3.75      , 102.36028945],\n",
-       "       [  3.75      , 808.01242909],\n",
-       "       [  3.75      , 193.61490756],\n",
-       "       [  3.75      , 473.08420034]])</pre></div></div></li><li class='xr-section-item'><input id='section-7d0f1914-7b60-403a-948a-74fb4a36742d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7d0f1914-7b60-403a-948a-74fb4a36742d' 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-74b5a8c5-dde2-4b60-94ca-4752ad15b9a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-74b5a8c5-dde2-4b60-94ca-4752ad15b9a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99abaa02-22a3-430d-aedc-fb6fdfef07f1' class='xr-var-data-in' type='checkbox'><label for='data-99abaa02-22a3-430d-aedc-fb6fdfef07f1' 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-86189e95-8ba4-4dd9-800d-d96212aec95e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-86189e95-8ba4-4dd9-800d-d96212aec95e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03c3d334-4519-4ae5-b6cb-4f7c00423b81' class='xr-var-data-in' type='checkbox'><label for='data-03c3d334-4519-4ae5-b6cb-4f7c00423b81' 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-9106714b-91b1-4f4a-9547-a6dd14514589' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9106714b-91b1-4f4a-9547-a6dd14514589' 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>"
+       "  * 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      , 102.36028945],\n",
-       "       [  3.75      , 808.01242909],\n",
-       "       [  3.75      , 193.61490756],\n",
-       "       [  3.75      , 473.08420034]])\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"
@@ -1727,7 +1727,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1656x552 with 2 Axes>"
       ]
@@ -1761,11 +1761,11 @@
       "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",
-      "arviz     : 0.12.1\n",
-      "pandas    : 1.4.2\n",
       "matplotlib: 3.5.2\n",
+      "pandas    : 1.4.2\n",
       "\n",
       "Watermark: 2.3.1\n",
       "\n"
@@ -1795,6 +1795,9 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "notebook_metadata_filter": "substitutions"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
diff --git a/myst_nbs/diagnostics_and_criticism/sampler-stats.myst.md b/myst_nbs/diagnostics_and_criticism/sampler-stats.myst.md
index 750b4f315..28cb9b344 100644
--- a/myst_nbs/diagnostics_and_criticism/sampler-stats.myst.md
+++ b/myst_nbs/diagnostics_and_criticism/sampler-stats.myst.md
@@ -1,5 +1,6 @@
 ---
 jupytext:
+  notebook_metadata_filter: substitutions
   text_representation:
     extension: .md
     format_name: myst
@@ -11,24 +12,30 @@ kernelspec:
   name: python3
 ---
 
-# Sampler statistics
+(sampler_stats)=
+# Sampler Statistics
 
 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.
 
+:::{post} May 31, 2022
+:tags: diagnostics
+:category: beginner
+:author: Meenal Jhajharia, Christian Luhmann
+:::
+
 ```{code-cell} ipython3
 import arviz as az
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import pymc3 as pm
-import seaborn as sns
+import pymc as pm
 
 %matplotlib inline
 
-print(f"Running on PyMC3 v{pm.__version__}")
+print(f"Running on PyMC v{pm.__version__}")
 ```
 
 ```{code-cell} ipython3
@@ -47,13 +54,13 @@ with model:
 ```{code-cell} ipython3
 with model:
     step = pm.NUTS()
-    trace = pm.sample(2000, tune=1000, init=None, step=step, cores=2, return_inferencedata=True)
+    idata = pm.sample(2000, tune=1000, init=None, step=step, chains=4)
 ```
 
 - `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 PyMC3, to learn more about them, [check this page](https://docs.pymc.io/api/inference.html#module-pymc3.step_methods.hmc.nuts).
 
 ```{code-cell} ipython3
-trace.sample_stats
+idata.sample_stats
 ```
 
 The sample statistics variables are defined as follows:
@@ -91,19 +98,19 @@ Some points to `Note`:
 - `InferenceData` also stores additional info like the date, versions used, sampling time and tuning steps as attributes.
 
 ```{code-cell} ipython3
-trace.sample_stats["tree_depth"].plot(col="chain", ls="none", marker=".", alpha=0.3);
+idata.sample_stats["tree_depth"].plot(col="chain", ls="none", marker=".", alpha=0.3);
 ```
 
 ```{code-cell} ipython3
 az.plot_posterior(
-    trace, group="sample_stats", var_names="acceptance_rate", hdi_prob="hide", kind="hist"
+    idata, group="sample_stats", var_names="acceptance_rate", hdi_prob="hide", kind="hist"
 );
 ```
 
 We check if there are any divergences, if yes, how many?
 
 ```{code-cell} ipython3
-trace.sample_stats["diverging"].sum()
+idata.sample_stats["diverging"].sum()
 ```
 
 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.
@@ -115,7 +122,7 @@ energy levels with the change of energy between successive samples.
 Ideally, they should be very similar:
 
 ```{code-cell} ipython3
-az.plot_energy(trace, figsize=(6, 4));
+az.plot_energy(idata, figsize=(6, 4));
 ```
 
 If the overall distribution of energy levels has longer tails, the efficiency of the sampler will deteriorate quickly.
@@ -139,26 +146,25 @@ with pm.Model(coords=coords) as model:
 with model:
     step1 = pm.BinaryMetropolis([mu1])
     step2 = pm.Metropolis([mu2])
-    trace = pm.sample(
+    idata = pm.sample(
         10000,
         init=None,
         step=[step1, step2],
-        cores=2,
+        chains=4,
         tune=1000,
-        return_inferencedata=True,
         idata_kwargs={"dims": dims, "coords": coords},
     )
 ```
 
 ```{code-cell} ipython3
-list(trace.sample_stats.data_vars)
+list(idata.sample_stats.data_vars)
 ```
 
 Both samplers export `accept`, so we get one acceptance probability for each sampler:
 
 ```{code-cell} ipython3
 az.plot_posterior(
-    trace,
+    idata,
     group="sample_stats",
     var_names="accept",
     hdi_prob="hide",
@@ -170,13 +176,13 @@ We notice that `accept` sometimes takes really high values (jumps from regions o
 
 ```{code-cell} ipython3
 # Range of accept values
-trace.sample_stats["accept"].max("draw") - trace.sample_stats["accept"].min("draw")
+idata.sample_stats["accept"].max("draw") - idata.sample_stats["accept"].min("draw")
 ```
 
 ```{code-cell} ipython3
 # We can try plotting the density and view the high density intervals to understand the variable better
 az.plot_density(
-    trace,
+    idata,
     group="sample_stats",
     var_names="accept",
     point_estimate="mean",
@@ -188,4 +194,10 @@ az.plot_density(
 %watermark -n -u -v -iv -w
 ```
 
-Updated by Meenal Jhajharia
+* Updated by Meenal Jhajharia
+* Updated by Christian Luhmann
+
++++
+
+:::{include} ../page_footer.md
+:::

From e42d7bd5634586148e3e8bd545bb86cfee0bc552 Mon Sep 17 00:00:00 2001
From: Christian Luhmann <cluhmann@gmail.com>
Date: Tue, 31 May 2022 15:57:56 -0400
Subject: [PATCH 7/7] tried downgrading jupytext

---
 myst_nbs/diagnostics_and_criticism/sampler-stats.myst.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/myst_nbs/diagnostics_and_criticism/sampler-stats.myst.md b/myst_nbs/diagnostics_and_criticism/sampler-stats.myst.md
index 28cb9b344..ef18c9d38 100644
--- a/myst_nbs/diagnostics_and_criticism/sampler-stats.myst.md
+++ b/myst_nbs/diagnostics_and_criticism/sampler-stats.myst.md
@@ -5,7 +5,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.13.8
+    jupytext_version: 1.13.7
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python