From 75a3d7153537ba80dfdcdc009c28458b70f41004 Mon Sep 17 00:00:00 2001
From: almostmeenal <meenaljhajharia2304@gmail.com>
Date: Fri, 2 Apr 2021 23:46:24 +0530
Subject: [PATCH 1/8] initial commit

---
 .../sampler-stats.ipynb                       | 1901 +++++++++++++++--
 1 file changed, 1723 insertions(+), 178 deletions(-)

diff --git a/examples/diagnostics_and_criticism/sampler-stats.ipynb b/examples/diagnostics_and_criticism/sampler-stats.ipynb
index a9fb9f01a..9ecca3de9 100644
--- a/examples/diagnostics_and_criticism/sampler-stats.ipynb
+++ b/examples/diagnostics_and_criticism/sampler-stats.ipynb
@@ -14,23 +14,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 194,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Runing on PyMC3 v3.11.0\n"
+      "Runing on PyMC3 v3.11.2\n"
      ]
     }
    ],
    "source": [
+    "import arviz as az\n",
     "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 xarray as xr\n",
     "\n",
     "%matplotlib inline\n",
     "\n",
@@ -38,34 +40,30 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 195,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "As a minimal example we sample from a standard normal distribution:"
+    "az.style.use(\"arviz-darkgrid\")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 2,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "model = pm.Model()\n",
-    "with model:\n",
-    "    mu1 = pm.Normal(\"mu1\", mu=0, sigma=1, shape=10)"
+    "As a minimal example we sample from a standard normal distribution:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 196,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/CloudChaoszero/Documents/Projects-Dev/pymc3/pymc3/sampling.py:465: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n",
-      "  warnings.warn(\n",
       "Multiprocess sampling (2 chains in 2 jobs)\n",
       "NUTS: [mu1]\n"
      ]
@@ -88,7 +86,7 @@
        "            }\n",
        "        </style>\n",
        "      <progress value='6000' class='' max='6000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [6000/6000 00:14<00:00 Sampling 2 chains, 0 divergences]\n",
+       "      100.00% [6000/6000 00:02<00:00 Sampling 2 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -103,199 +101,589 @@
      "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 36 seconds.\n"
+      "Sampling 2 chains for 1_000 tune and 2_000 draw iterations (2_000 + 4_000 draws total) took 9 seconds.\n"
      ]
     }
    ],
    "source": [
+    "model = pm.Model()\n",
     "with model:\n",
+    "    mu1 = pm.Normal(\"mu1\", mu=0, sigma=1, shape=10)\n",
     "    step = pm.NUTS()\n",
-    "    trace = pm.sample(2000, tune=1000, init=None, step=step, cores=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "NUTS provides the following statistics:"
+    "    trace = pm.sample(2000, tune=1000, init=None, step=step, cores=2, return_inferencedata=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 197,
    "metadata": {},
    "outputs": [
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:             (chain: 2, draw: 2000)\n",
+       "Coordinates:\n",
+       "  * chain               (chain) int64 0 1\n",
+       "  * draw                (draw) int64 0 1 2 3 4 5 ... 1995 1996 1997 1998 1999\n",
+       "Data variables: (12/13)\n",
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+       "    n_steps             (chain, draw) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0\n",
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+       "    diverging           (chain, draw) bool False False False ... False False\n",
+       "    process_time_diff   (chain, draw) float64 0.000536 0.000379 ... 0.00038\n",
+       "    ...                  ...\n",
+       "    max_energy_error    (chain, draw) float64 0.8227 1.794 ... -0.8218 -0.4617\n",
+       "    energy              (chain, draw) float64 17.63 20.67 21.1 ... 21.72 23.62\n",
+       "    energy_error        (chain, draw) float64 -0.2349 1.462 ... 0.09963 -0.4617\n",
+       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 2 ... 2 2 2 2 2 2 2 2\n",
+       "    perf_counter_start  (chain, draw) float64 4.335e+03 4.335e+03 ... 4.337e+03\n",
+       "    acceptance_rate     (chain, draw) float64 0.7719 0.5954 ... 0.9669 0.9305\n",
+       "Attributes:\n",
+       "    created_at:                 2021-04-02T11:12:40.279166\n",
+       "    arviz_version:              0.11.2\n",
+       "    inference_library:          pymc3\n",
+       "    inference_library_version:  3.11.2\n",
+       "    sampling_time:              8.593060731887817\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-a5c9fac8-9c46-40d8-b8e6-6d7a47681e47' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a5c9fac8-9c46-40d8-b8e6-6d7a47681e47' 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-00ff5c88-cbce-4ef0-9681-d86c6fc19ab9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-00ff5c88-cbce-4ef0-9681-d86c6fc19ab9' 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-320e938e-d03b-4fd5-9adf-b8cee6b7a1f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-320e938e-d03b-4fd5-9adf-b8cee6b7a1f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ff50e71-d5ba-445d-ae6f-d3681a70e881' class='xr-var-data-in' type='checkbox'><label for='data-1ff50e71-d5ba-445d-ae6f-d3681a70e881' 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-f52fbf62-1e48-462d-b7c3-ed70070c1ce5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f52fbf62-1e48-462d-b7c3-ed70070c1ce5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4a4d479-4028-4f3e-92fb-2508bd45f9d6' class='xr-var-data-in' type='checkbox'><label for='data-f4a4d479-4028-4f3e-92fb-2508bd45f9d6' 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-399d3170-ebea-4284-9105-0e53820060b8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-399d3170-ebea-4284-9105-0e53820060b8' 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>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'>1.311 1.311 1.311 ... 1.242 1.242</div><input id='attrs-203a6e41-e04e-4dd7-b9a1-f51916966d5b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-203a6e41-e04e-4dd7-b9a1-f51916966d5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76e4410e-ce2f-4657-bec5-aec761e77698' class='xr-var-data-in' type='checkbox'><label for='data-76e4410e-ce2f-4657-bec5-aec761e77698' 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([[1.31137233, 1.31137233, 1.31137233, ..., 1.31137233, 1.31137233,\n",
+       "        1.31137233],\n",
+       "       [1.24177138, 1.24177138, 1.24177138, ..., 1.24177138, 1.24177138,\n",
+       "        1.24177138]])</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'>0.9645 0.9645 ... 0.9366 0.9366</div><input id='attrs-4b33c73c-af6b-4378-8563-5e1a89cd9a35' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b33c73c-af6b-4378-8563-5e1a89cd9a35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b5efe12-3302-4c84-a7cf-c95d14038ac5' class='xr-var-data-in' type='checkbox'><label for='data-7b5efe12-3302-4c84-a7cf-c95d14038ac5' 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([[0.96451309, 0.96451309, 0.96451309, ..., 0.96451309, 0.96451309,\n",
+       "        0.96451309],\n",
+       "       [0.9365782 , 0.9365782 , 0.9365782 , ..., 0.9365782 , 0.9365782 ,\n",
+       "        0.9365782 ]])</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-00b9c88e-c930-42bb-bd3e-b626c4cacfff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-00b9c88e-c930-42bb-bd3e-b626c4cacfff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-935ea0af-8f67-4aaa-9251-e5157401cba5' class='xr-var-data-in' type='checkbox'><label for='data-935ea0af-8f67-4aaa-9251-e5157401cba5' 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>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'>-11.2 -16.52 ... -17.88 -16.66</div><input id='attrs-a67083a2-47f8-419e-adcc-bbc9031e2f66' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a67083a2-47f8-419e-adcc-bbc9031e2f66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07abd4df-2962-46c0-a1e4-76754644adc0' class='xr-var-data-in' type='checkbox'><label for='data-07abd4df-2962-46c0-a1e4-76754644adc0' 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([[-11.20247984, -16.52469862, -12.73043347, ..., -20.275405  ,\n",
+       "        -18.21896467, -15.32525736],\n",
+       "       [-11.59437266, -11.57830542, -14.7161461 , ..., -16.62284598,\n",
+       "        -17.87732549, -16.660264  ]])</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-844ce0a6-741b-46a2-ad2c-e177f108ae16' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-844ce0a6-741b-46a2-ad2c-e177f108ae16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-163529be-f658-479c-817d-9f37a94c9b6a' class='xr-var-data-in' type='checkbox'><label for='data-163529be-f658-479c-817d-9f37a94c9b6a' 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>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'>0.000536 0.000379 ... 0.00038</div><input id='attrs-287ea176-e3ee-4f14-b28e-bf29f7fa825a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-287ea176-e3ee-4f14-b28e-bf29f7fa825a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-155345dc-9598-4315-b47c-58c2788a4e58' class='xr-var-data-in' type='checkbox'><label for='data-155345dc-9598-4315-b47c-58c2788a4e58' 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([[0.000536, 0.000379, 0.000344, ..., 0.000382, 0.000381, 0.00038 ],\n",
+       "       [0.000333, 0.000327, 0.000329, ..., 0.000368, 0.00036 , 0.00038 ]])</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'>0.0005481 0.0003788 ... 0.000379</div><input id='attrs-d06f871d-8108-476d-8071-184296330abd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d06f871d-8108-476d-8071-184296330abd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5b7e825-0034-47c4-bb3d-0020fc27602d' class='xr-var-data-in' type='checkbox'><label for='data-d5b7e825-0034-47c4-bb3d-0020fc27602d' 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([[0.00054809, 0.0003788 , 0.00034307, ..., 0.00038292, 0.0003815 ,\n",
+       "        0.00037909],\n",
+       "       [0.00033215, 0.00032736, 0.00032822, ..., 0.00036812, 0.00035926,\n",
+       "        0.00037895]])</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'>0.8227 1.794 ... -0.8218 -0.4617</div><input id='attrs-96432d0e-5f27-450e-83a6-4770209a7af1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-96432d0e-5f27-450e-83a6-4770209a7af1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f4c6bfb-d441-45b1-8164-396148fdec92' class='xr-var-data-in' type='checkbox'><label for='data-2f4c6bfb-d441-45b1-8164-396148fdec92' 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([[ 0.82269839,  1.79447705,  1.15714905, ...,  0.86388666,\n",
+       "         1.05197578, -0.53998969],\n",
+       "       [-0.25209082,  1.76383045,  0.92124496, ...,  1.59634003,\n",
+       "        -0.8218098 , -0.46174593]])</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'>17.63 20.67 21.1 ... 21.72 23.62</div><input id='attrs-1e5bd53f-4a81-4ebb-83ed-80db06994e31' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e5bd53f-4a81-4ebb-83ed-80db06994e31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e223a69-620d-4199-bca7-f1357f8e9997' class='xr-var-data-in' type='checkbox'><label for='data-6e223a69-620d-4199-bca7-f1357f8e9997' 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([[17.62978535, 20.66685416, 21.099293  , ..., 26.0617586 ,\n",
+       "        26.93852689, 24.65519466],\n",
+       "       [14.61094838, 18.33611319, 17.73745528, ..., 24.15488273,\n",
+       "        21.72075348, 23.61966299]])</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'>-0.2349 1.462 ... 0.09963 -0.4617</div><input id='attrs-a38afc18-11c5-4f72-956d-1642e91d86b3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a38afc18-11c5-4f72-956d-1642e91d86b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae423b05-36a9-428a-94a5-4a4709ec29be' class='xr-var-data-in' type='checkbox'><label for='data-ae423b05-36a9-428a-94a5-4a4709ec29be' 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([[-0.23490668,  1.46213949, -1.12113861, ...,  0.80407482,\n",
+       "        -0.54147705, -0.53998969],\n",
+       "       [-0.25209082,  0.0499353 ,  0.6903594 , ...,  0.31386244,\n",
+       "         0.09962603, -0.46174593]])</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-aaffd741-01b7-4fce-b27d-dcd4f30b0219' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aaffd741-01b7-4fce-b27d-dcd4f30b0219' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-843907d6-41a1-4165-81d1-2e89c93de01d' class='xr-var-data-in' type='checkbox'><label for='data-843907d6-41a1-4165-81d1-2e89c93de01d' 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>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'>4.335e+03 4.335e+03 ... 4.337e+03</div><input id='attrs-bed21e57-7f5f-4342-bd90-2c50ad8f1c83' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bed21e57-7f5f-4342-bd90-2c50ad8f1c83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-587f3881-b116-4b83-b89d-0c79af191616' class='xr-var-data-in' type='checkbox'><label for='data-587f3881-b116-4b83-b89d-0c79af191616' 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([[4334.91478857, 4334.91550981, 4334.9160145 , ..., 4335.96208391,\n",
+       "        4335.96258439, 4335.96309748],\n",
+       "       [4336.24522579, 4336.24565431, 4336.246082  , ..., 4337.19138069,\n",
+       "        4337.19186217, 4337.1923272 ]])</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'>0.7719 0.5954 ... 0.9669 0.9305</div><input id='attrs-978d1341-1802-4f24-9c6b-a9ec9f4fa88d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-978d1341-1802-4f24-9c6b-a9ec9f4fa88d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87bbef69-e1bf-4dce-9066-39424ec6ca54' class='xr-var-data-in' type='checkbox'><label for='data-87bbef69-e1bf-4dce-9066-39424ec6ca54' 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([[0.77185498, 0.59539075, 0.95573333, ..., 0.49364924, 0.93037344,\n",
+       "        0.90198083],\n",
+       "       [0.92466073, 0.73659794, 0.4590098 , ..., 0.61192868, 0.96693723,\n",
+       "        0.93046804]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-85d84212-8912-4a04-9f52-00c75b4fee5d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-85d84212-8912-4a04-9f52-00c75b4fee5d' 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>2021-04-02T11:12:40.279166</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>8.593060731887817</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
-       "{'depth',\n",
-       " 'diverging',\n",
-       " 'energy',\n",
-       " 'energy_error',\n",
-       " 'max_energy_error',\n",
-       " 'mean_tree_accept',\n",
-       " 'model_logp',\n",
-       " 'perf_counter_diff',\n",
-       " 'perf_counter_start',\n",
-       " 'process_time_diff',\n",
-       " 'step_size',\n",
-       " 'step_size_bar',\n",
-       " 'tree_size',\n",
-       " 'tune'}"
+       "<xarray.Dataset>\n",
+       "Dimensions:             (chain: 2, draw: 2000)\n",
+       "Coordinates:\n",
+       "  * chain               (chain) int64 0 1\n",
+       "  * draw                (draw) int64 0 1 2 3 4 5 ... 1995 1996 1997 1998 1999\n",
+       "Data variables: (12/13)\n",
+       "    step_size           (chain, draw) float64 1.311 1.311 1.311 ... 1.242 1.242\n",
+       "    step_size_bar       (chain, draw) float64 0.9645 0.9645 ... 0.9366 0.9366\n",
+       "    n_steps             (chain, draw) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0\n",
+       "    lp                  (chain, draw) float64 -11.2 -16.52 ... -17.88 -16.66\n",
+       "    diverging           (chain, draw) bool False False False ... False False\n",
+       "    process_time_diff   (chain, draw) float64 0.000536 0.000379 ... 0.00038\n",
+       "    ...                  ...\n",
+       "    max_energy_error    (chain, draw) float64 0.8227 1.794 ... -0.8218 -0.4617\n",
+       "    energy              (chain, draw) float64 17.63 20.67 21.1 ... 21.72 23.62\n",
+       "    energy_error        (chain, draw) float64 -0.2349 1.462 ... 0.09963 -0.4617\n",
+       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 2 ... 2 2 2 2 2 2 2 2\n",
+       "    perf_counter_start  (chain, draw) float64 4.335e+03 4.335e+03 ... 4.337e+03\n",
+       "    acceptance_rate     (chain, draw) float64 0.7719 0.5954 ... 0.9669 0.9305\n",
+       "Attributes:\n",
+       "    created_at:                 2021-04-02T11:12:40.279166\n",
+       "    arviz_version:              0.11.2\n",
+       "    inference_library:          pymc3\n",
+       "    inference_library_version:  3.11.2\n",
+       "    sampling_time:              8.593060731887817\n",
+       "    tuning_steps:               1000"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 197,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "trace.stat_names"
+    "trace.sample_stats"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- `mean_tree_accept`: The mean acceptance probability for the tree that generated this sample. The mean of these values across all samples but the burn-in should be approximately `target_accept` (the default for this is 0.8).\n",
-    "- `diverging`: Whether the trajectory for this sample diverged. If there are many diverging samples, this usually indicates that a region of the posterior has high curvature. Reparametrization can often help, but you can also try to increase `target_accept` to something like 0.9 or 0.95.\n",
-    "- `energy`: The energy at the point in phase-space where the sample was accepted. This can be used to identify posteriors with problematically long tails. See below for an example.\n",
-    "- `energy_error`: The difference in energy between the start and the end of the trajectory. For a perfect integrator this would always be zero.\n",
-    "- `max_energy_error`: The maximum difference in energy along the whole trajectory.\n",
-    "- `depth`: The depth of the tree that was used to generate this sample\n",
-    "- `tree_size`: The number of leafs of the sampling tree, when the sample was accepted. This is usually a bit less than $2 ^ \\text{depth}$. If the tree size is large, the sampler is using a lot of leapfrog steps to find the next sample. This can for example happen if there are strong correlations in the posterior, if the posterior has long tails, if there are regions of high curvature (\"funnels\"), or if the variance estimates in the mass matrix are inaccurate. Reparametrisation of the model or estimating the posterior variances from past samples might help.\n",
-    "- `tune`: This is `True`, if step size adaptation was turned on when this sample was generated.\n",
-    "- `step_size`: The step size used for this sample.\n",
-    "- `step_size_bar`: The current best known step-size. After the tuning samples, the step size is set to this value. This should converge during tuning.\n",
-    "- `model_logp`: The model log-likelihood for this sample."
+    "NUTS provides the following statistics:"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If the name of the statistic does not clash with the name of one of the variables, we can use indexing to get the values. The values for the chains will be concatenated.\n",
+    "- `lp`: The joint log posterior density for the model (up to an additive constant).\n",
     "\n",
-    "We can see that the step sizes converged after the 1000 tuning samples for both chains to about the same value. The first 2000 values are from chain 1, the second 2000 from chain 2."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fb471c0db80>]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(trace[\"step_size_bar\"])"
+    "- `acceptance_rate`: The average acceptance probabilities of all possible samples in the proposed tree.\n",
+    "\n",
+    "- `step_size`: The current integration step size.\n",
+    "\n",
+    "- `step_size_nom`: The nominal integration step size. The `step_size` may differ from this, for example if the step size is jittered. Should only be present if `step_size` is also present and it varies between samples (i.e. step size is jittered).\n",
+    "\n",
+    "- `tree_depth`: The number of tree doublings in the balanced binary tree.\n",
+    "\n",
+    "- `n_steps`: The number of leapfrog steps computed. It is related to `tree_depth` with `n_steps <= 2^tree_dept`.\n",
+    "\n",
+    "- `diverging`: (boolean) Indicates the presence of leapfrog transitions with large energy deviation from starting and subsequent termination of the trajectory. “large” is defined as `max_energy_error` going over a threshold.\n",
+    "\n",
+    "- `energy`: The value of the Hamiltonian energy for the accepted proposal (up to an additive constant).\n",
+    "\n",
+    "- `energy_error`: The difference in the Hamiltonian energy between the initial point and the accepted proposal.\n",
+    "\n",
+    "- `max_energy_error`: The maximum absolute difference in Hamiltonian energy between the initial point and all possible samples in the proposed tree.\n",
+    "\n",
+    "- `int_time`: The total integration time (static HMC sampler)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `get_sampler_stats` method provides more control over which values should be returned, and it also works if the name of the statistic is the same as the name of one of the variables. We can use the `chains` option, to control values from which chain should be returned, or we can set `combine=False` to get the values for the individual chains:"
+    "If the name of the statistic does not clash with the name of one of the variables, we can use indexing to get the values. The values for the chains will be concatenated.\n",
+    "\n",
+    "We can see that the step sizes converged after the 1000 tuning samples for both chains to about the same value. The first 2000 values are from chain 1, the second 2000 from chain 2."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 198,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 720x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "sizes1, sizes2 = trace.get_sampler_stats(\"depth\", combine=False)\n",
-    "fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, sharey=True)\n",
-    "ax1.plot(sizes1)\n",
-    "ax2.plot(sizes2)\n",
-    "\n",
+    "# Note that we use xr.concat to combine chain 1 and chain 2\n",
+    "plt.plot(\n",
+    "    xr.concat(\n",
+    "        (trace.sample_stats[\"step_size_bar\"][0], trace.sample_stats[\"step_size_bar\"][1]), dim=\"draw\"\n",
+    "    )\n",
+    ")\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 199,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/CloudChaoszero/opt/anaconda3/envs/pymc3-dev-py38/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
-      "  warnings.warn(msg, FutureWarning)\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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fAL/aYLBDv1tv3Af8ZJKrknwHgzumPrHMdS6HPmNxmsH/YEiyAfhB4CvLWuXqcAR4T3fVzE3Af1fV2cU8oTP3BapZbrGQ5Fe6xz8M/DbwPcA93Yz1fDV4J7yeY3FF6DMWVfVEks8CjwLfZPBJZTNeInc56/lz8bvAx5I8xmBp4v1V1dytgJN8AngLsC7JFPBB4OXw7XH4DIMrZiaB/2XwP5rFvWZ3GY4kqSEuy0hSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1KD/Bw414tvaFYRfAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 720x480 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "accept = trace.get_sampler_stats(\"mean_tree_accept\", burn=1000)\n",
-    "sns.distplot(accept, kde=False)\n",
-    "\n",
+    "# In this plot we view Chain 1 and Chain 2 separately\n",
+    "sizes1, sizes2 = trace.sample_stats[\"tree_depth\"]\n",
+    "fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, sharey=True)\n",
+    "ax1.plot(sizes1)\n",
+    "ax2.plot(sizes2)\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 200,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "0.8115665358193459"
+       "<Figure size 720x480 with 1 Axes>"
       ]
      },
-     "execution_count": 8,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "accept.mean()"
+    "accept = xr.concat(\n",
+    "    (trace.sample_stats[\"acceptance_rate\"][0], trace.sample_stats[\"acceptance_rate\"][1]), dim=\"draw\"\n",
+    ").values\n",
+    "sns.histplot(accept, kde=False)\n",
+    "plt.show()"
    ]
   },
   {
@@ -307,22 +695,385 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 201,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
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+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;diverging&#x27; (chain: 0, draw: 0)&gt;\n",
+       "array([], shape=(0, 0), dtype=float64)\n",
+       "Coordinates:\n",
+       "  * chain    (chain) int64 \n",
+       "  * draw     (draw) int64 </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><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 0</li><li><span class='xr-has-index'>draw</span>: 0</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9b071612-b66c-4402-9fd3-d154010d29ec' class='xr-array-in' type='checkbox' checked><label for='section-9b071612-b66c-4402-9fd3-d154010d29ec' 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></span></div><div class='xr-array-data'><pre>array([], shape=(0, 0), dtype=float64)</pre></div></div></li><li class='xr-section-item'><input id='section-466af218-6854-4ff2-be04-9d1cd203f124' class='xr-section-summary-in' type='checkbox'  checked><label for='section-466af218-6854-4ff2-be04-9d1cd203f124' 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'></div><input id='attrs-919e3fb3-05d4-4ec7-9fb6-91a074637510' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-919e3fb3-05d4-4ec7-9fb6-91a074637510' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25405ddf-ac54-4373-912c-93cc859abda0' class='xr-var-data-in' type='checkbox'><label for='data-25405ddf-ac54-4373-912c-93cc859abda0' 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([], dtype=int64)</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'></div><input id='attrs-4876a469-ed36-4e23-8fb6-63f6507ff9e8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4876a469-ed36-4e23-8fb6-63f6507ff9e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2936c3e1-41a0-48ea-83d2-a25637935729' class='xr-var-data-in' type='checkbox'><label for='data-2936c3e1-41a0-48ea-83d2-a25637935729' 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([], dtype=int64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-75bf30ab-959b-4cf4-8f53-c8a52af581a2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-75bf30ab-959b-4cf4-8f53-c8a52af581a2' 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": [
-       "(array([], dtype=int64),)"
+       "<xarray.DataArray 'diverging' (chain: 0, draw: 0)>\n",
+       "array([], shape=(0, 0), dtype=float64)\n",
+       "Coordinates:\n",
+       "  * chain    (chain) int64 \n",
+       "  * draw     (draw) int64 "
       ]
      },
-     "execution_count": 9,
+     "execution_count": 201,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "trace[\"diverging\"].nonzero()"
+    "# In this case there are none\n",
+    "trace.sample_stats.where(trace.sample_stats[\"diverging\"] == True, drop=True)[\"diverging\"]"
    ]
   },
   {
@@ -336,28 +1087,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 202,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 600x400 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "energy = trace[\"energy\"]\n",
-    "energy_diff = np.diff(energy)\n",
-    "sns.histplot(energy - energy.mean(), label=\"energy\")\n",
-    "sns.histplot(energy_diff, label=\"energy diff\")\n",
-    "plt.legend()\n",
+    "az.plot_energy(trace, figsize=(6, 4))\n",
     "plt.show()"
    ]
   },
@@ -381,27 +1126,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = pm.Model()\n",
-    "with model:\n",
-    "    mu1 = pm.Bernoulli(\"mu1\", p=0.8)\n",
-    "    mu2 = pm.Normal(\"mu2\", mu=0, sigma=1, shape=10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 203,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/CloudChaoszero/Documents/Projects-Dev/pymc3/pymc3/sampling.py:465: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n",
-      "  warnings.warn(\n",
       "Multiprocess sampling (2 chains in 2 jobs)\n",
       "CompoundStep\n",
       ">BinaryMetropolis: [mu1]\n",
@@ -426,7 +1157,7 @@
        "            }\n",
        "        </style>\n",
        "      <progress value='22000' class='' max='22000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [22000/22000 00:15<00:00 Sampling 2 chains, 0 divergences]\n",
+       "      100.00% [22000/22000 00:05<00:00 Sampling 2 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -441,36 +1172,446 @@
      "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 28 seconds.\n",
+      "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 10% for some parameters.\n"
      ]
     }
    ],
    "source": [
+    "model = pm.Model()\n",
     "with model:\n",
+    "    mu1 = pm.Bernoulli(\"mu1\", p=0.8)\n",
+    "    mu2 = pm.Normal(\"mu2\", mu=0, sigma=1, shape=10)\n",
     "    step1 = pm.BinaryMetropolis([mu1])\n",
     "    step2 = pm.Metropolis([mu2])\n",
-    "    trace = pm.sample(10000, init=None, step=[step1, step2], cores=2, tune=1000)"
+    "    trace = pm.sample(\n",
+    "        10000, init=None, step=[step1, step2], cores=2, tune=1000, return_inferencedata=True\n",
+    "    )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 204,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
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+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
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+       ".xr-array-name {\n",
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+       "  margin-right: 10px;\n",
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+       "\n",
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+       "  color: var(--xr-disabled-color);\n",
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+       "\n",
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+       "  color: var(--xr-font-color0);\n",
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+       ".xr-section-summary {\n",
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+       ".xr-section-summary > span {\n",
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+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
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+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
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+       "  font-size: 11px;\n",
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+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
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+       "\n",
+       ".xr-section-inline-details {\n",
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+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
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+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
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+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
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+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
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+       "  display: none;\n",
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+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
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+       "\n",
+       ".xr-dim-list:before {\n",
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+       "\n",
+       ".xr-dim-list:after {\n",
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+       ".xr-dim-list li:not(:last-child):after {\n",
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+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
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+       "  display: contents;\n",
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+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
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+       "}\n",
+       "\n",
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+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:       (accept_dim_0: 2, chain: 2, draw: 10000)\n",
+       "Coordinates:\n",
+       "  * chain         (chain) int64 0 1\n",
+       "  * draw          (draw) int64 0 1 2 3 4 5 6 ... 9994 9995 9996 9997 9998 9999\n",
+       "  * accept_dim_0  (accept_dim_0) int64 0 1\n",
+       "Data variables:\n",
+       "    accept        (chain, draw, accept_dim_0) float64 0.25 0.002502 ... 0.5614\n",
+       "    p_jump        (chain, draw) float64 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5\n",
+       "    accepted      (chain, draw) bool False False True False ... False True True\n",
+       "    scaling       (chain, draw) float64 0.729 0.729 0.729 ... 0.729 0.729 0.729\n",
+       "Attributes:\n",
+       "    created_at:                 2021-04-02T11:12:53.065081\n",
+       "    arviz_version:              0.11.2\n",
+       "    inference_library:          pymc3\n",
+       "    inference_library_version:  3.11.2\n",
+       "    sampling_time:              11.091216087341309\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-0a5709a1-d298-4382-981e-98e13882d7f7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0a5709a1-d298-4382-981e-98e13882d7f7' 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'>accept_dim_0</span>: 2</li><li><span class='xr-has-index'>chain</span>: 2</li><li><span class='xr-has-index'>draw</span>: 10000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ccf162da-5fa5-4d14-9345-b0bcf9611631' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ccf162da-5fa5-4d14-9345-b0bcf9611631' class='xr-section-summary' >Coordinates: <span>(3)</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-bccf416f-ab15-42cd-80f3-b52945f4948b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bccf416f-ab15-42cd-80f3-b52945f4948b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fe03903-0890-423e-919f-401f5211f3b4' class='xr-var-data-in' type='checkbox'><label for='data-0fe03903-0890-423e-919f-401f5211f3b4' 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 ... 9996 9997 9998 9999</div><input id='attrs-bad5440e-6698-4b6b-bdd4-cb4c5a55ff80' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bad5440e-6698-4b6b-bdd4-cb4c5a55ff80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31b671e2-47bf-4794-b97e-3dc5c55d2bfd' class='xr-var-data-in' type='checkbox'><label for='data-31b671e2-47bf-4794-b97e-3dc5c55d2bfd' 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, ..., 9997, 9998, 9999])</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-beac17a7-a0b0-4e43-ba9b-603299a0ceee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-beac17a7-a0b0-4e43-ba9b-603299a0ceee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7cebc29-df61-40c2-806a-7dd177da01bf' class='xr-var-data-in' type='checkbox'><label for='data-d7cebc29-df61-40c2-806a-7dd177da01bf' 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-9666c4cf-647e-419e-b177-816b06b26379' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9666c4cf-647e-419e-b177-816b06b26379' class='xr-section-summary' >Data variables: <span>(4)</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>accept</span></div><div class='xr-var-dims'>(chain, draw, accept_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.25 0.002502 1.0 ... 1.0 0.5614</div><input id='attrs-eca83bcc-ad36-4dbc-b1e8-4619b61410c5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eca83bcc-ad36-4dbc-b1e8-4619b61410c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37f6e049-7545-471a-85ef-aa9f3fe63605' class='xr-var-data-in' type='checkbox'><label for='data-37f6e049-7545-471a-85ef-aa9f3fe63605' 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([[[2.50000000e-01, 2.50162193e-03],\n",
+       "        [1.00000000e+00, 3.04131487e-01],\n",
+       "        [1.00000000e+00, 2.75917351e-01],\n",
+       "        ...,\n",
+       "        [1.00000000e+00, 5.05600256e-01],\n",
+       "        [1.00000000e+00, 1.20457745e-01],\n",
+       "        [1.00000000e+00, 4.32905427e-01]],\n",
+       "\n",
+       "       [[2.50000000e-01, 9.11142521e-03],\n",
+       "        [1.00000000e+00, 1.46295614e+00],\n",
+       "        [1.00000000e+00, 3.66074682e-03],\n",
+       "        ...,\n",
+       "        [2.50000000e-01, 5.82228319e-02],\n",
+       "        [4.00000000e+00, 1.00805546e+00],\n",
+       "        [1.00000000e+00, 5.61351579e-01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p_jump</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5</div><input id='attrs-65856302-b550-4ed9-af45-421c54f4feb6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65856302-b550-4ed9-af45-421c54f4feb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4a3ba1b-b205-413e-bbdb-3cadc8ddcba8' class='xr-var-data-in' type='checkbox'><label for='data-f4a3ba1b-b205-413e-bbdb-3cadc8ddcba8' 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([[0.5, 0.5, 0.5, ..., 0.5, 0.5, 0.5],\n",
+       "       [0.5, 0.5, 0.5, ..., 0.5, 0.5, 0.5]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accepted</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 True ... True True</div><input id='attrs-b3e542b6-6af9-4a59-b8fb-6ec9e75ce542' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b3e542b6-6af9-4a59-b8fb-6ec9e75ce542' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afc0fbb0-6da3-40f9-b69c-b0187e93c8d4' class='xr-var-data-in' type='checkbox'><label for='data-afc0fbb0-6da3-40f9-b69c-b0187e93c8d4' 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,  True, ..., False, False, False],\n",
+       "       [False,  True, False, ..., False,  True,  True]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scaling</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.729 0.729 0.729 ... 0.729 0.729</div><input id='attrs-af4c4f2e-38a5-4d26-b192-14135aac5554' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af4c4f2e-38a5-4d26-b192-14135aac5554' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d499250-4f83-4f3f-94df-d5057989fe84' class='xr-var-data-in' type='checkbox'><label for='data-7d499250-4f83-4f3f-94df-d5057989fe84' 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([[0.729, 0.729, 0.729, ..., 0.729, 0.729, 0.729],\n",
+       "       [0.729, 0.729, 0.729, ..., 0.729, 0.729, 0.729]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a07c081b-3715-44c0-9c40-0c5768b86847' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a07c081b-3715-44c0-9c40-0c5768b86847' 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>2021-04-02T11:12:53.065081</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>11.091216087341309</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
-       "{'accept', 'accepted', 'p_jump', 'scaling', 'tune'}"
+       "<xarray.Dataset>\n",
+       "Dimensions:       (accept_dim_0: 2, chain: 2, draw: 10000)\n",
+       "Coordinates:\n",
+       "  * chain         (chain) int64 0 1\n",
+       "  * draw          (draw) int64 0 1 2 3 4 5 6 ... 9994 9995 9996 9997 9998 9999\n",
+       "  * accept_dim_0  (accept_dim_0) int64 0 1\n",
+       "Data variables:\n",
+       "    accept        (chain, draw, accept_dim_0) float64 0.25 0.002502 ... 0.5614\n",
+       "    p_jump        (chain, draw) float64 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5\n",
+       "    accepted      (chain, draw) bool False False True False ... False True True\n",
+       "    scaling       (chain, draw) float64 0.729 0.729 0.729 ... 0.729 0.729 0.729\n",
+       "Attributes:\n",
+       "    created_at:                 2021-04-02T11:12:53.065081\n",
+       "    arviz_version:              0.11.2\n",
+       "    inference_library:          pymc3\n",
+       "    inference_library_version:  3.11.2\n",
+       "    sampling_time:              11.091216087341309\n",
+       "    tuning_steps:               1000"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 204,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "trace.stat_names"
+    "trace.sample_stats"
    ]
   },
   {
@@ -482,52 +1623,456 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 205,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
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+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
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+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
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+       "\n",
+       ".xr-section-inline-details {\n",
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+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
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+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
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+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;accept&#x27; (chain: 2, draw: 10000, accept_dim_0: 2)&gt;\n",
+       "array([[[2.50000000e-01, 2.50162193e-03],\n",
+       "        [1.00000000e+00, 3.04131487e-01],\n",
+       "        [1.00000000e+00, 2.75917351e-01],\n",
+       "        ...,\n",
+       "        [1.00000000e+00, 5.05600256e-01],\n",
+       "        [1.00000000e+00, 1.20457745e-01],\n",
+       "        [1.00000000e+00, 4.32905427e-01]],\n",
+       "\n",
+       "       [[2.50000000e-01, 9.11142521e-03],\n",
+       "        [1.00000000e+00, 1.46295614e+00],\n",
+       "        [1.00000000e+00, 3.66074682e-03],\n",
+       "        ...,\n",
+       "        [2.50000000e-01, 5.82228319e-02],\n",
+       "        [4.00000000e+00, 1.00805546e+00],\n",
+       "        [1.00000000e+00, 5.61351579e-01]]])\n",
+       "Coordinates:\n",
+       "  * chain         (chain) int64 0 1\n",
+       "  * draw          (draw) int64 0 1 2 3 4 5 6 ... 9994 9995 9996 9997 9998 9999\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'>draw</span>: 10000</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-9199732f-abff-41bc-9c97-39c90215f033' class='xr-array-in' type='checkbox' checked><label for='section-9199732f-abff-41bc-9c97-39c90215f033' 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>0.25 0.002502 1.0 0.3041 1.0 0.2759 ... 0.05822 4.0 1.008 1.0 0.5614</span></div><div class='xr-array-data'><pre>array([[[2.50000000e-01, 2.50162193e-03],\n",
+       "        [1.00000000e+00, 3.04131487e-01],\n",
+       "        [1.00000000e+00, 2.75917351e-01],\n",
+       "        ...,\n",
+       "        [1.00000000e+00, 5.05600256e-01],\n",
+       "        [1.00000000e+00, 1.20457745e-01],\n",
+       "        [1.00000000e+00, 4.32905427e-01]],\n",
+       "\n",
+       "       [[2.50000000e-01, 9.11142521e-03],\n",
+       "        [1.00000000e+00, 1.46295614e+00],\n",
+       "        [1.00000000e+00, 3.66074682e-03],\n",
+       "        ...,\n",
+       "        [2.50000000e-01, 5.82228319e-02],\n",
+       "        [4.00000000e+00, 1.00805546e+00],\n",
+       "        [1.00000000e+00, 5.61351579e-01]]])</pre></div></div></li><li class='xr-section-item'><input id='section-df20a2e6-cbd3-4635-8a02-f0007973f1ec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-df20a2e6-cbd3-4635-8a02-f0007973f1ec' class='xr-section-summary' >Coordinates: <span>(3)</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-160b2060-d67a-4eac-8c7e-70b08763c0a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-160b2060-d67a-4eac-8c7e-70b08763c0a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a41ac28c-364a-4403-a529-edde39ae8035' class='xr-var-data-in' type='checkbox'><label for='data-a41ac28c-364a-4403-a529-edde39ae8035' 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 ... 9996 9997 9998 9999</div><input id='attrs-415a4d45-8e0e-4d17-b027-985814fa3c93' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-415a4d45-8e0e-4d17-b027-985814fa3c93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b057694f-ab32-4e38-9763-d0b39526e49b' class='xr-var-data-in' type='checkbox'><label for='data-b057694f-ab32-4e38-9763-d0b39526e49b' 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, ..., 9997, 9998, 9999])</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-0117a235-01d3-4574-88c3-89c0fca49db1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0117a235-01d3-4574-88c3-89c0fca49db1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-368ca319-65fb-4be4-ab14-d8686f039400' class='xr-var-data-in' type='checkbox'><label for='data-368ca319-65fb-4be4-ab14-d8686f039400' 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-710b9d61-721a-4edc-9386-fa9925357d7c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-710b9d61-721a-4edc-9386-fa9925357d7c' 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": [
-       "array([[1.        , 0.01449865],\n",
-       "       [1.        , 0.63938711],\n",
-       "       [1.        , 0.28670672],\n",
-       "       ...,\n",
-       "       [4.        , 0.09272909],\n",
-       "       [1.        , 1.1761186 ],\n",
-       "       [1.        , 0.98494351]])"
+       "<xarray.DataArray 'accept' (chain: 2, draw: 10000, accept_dim_0: 2)>\n",
+       "array([[[2.50000000e-01, 2.50162193e-03],\n",
+       "        [1.00000000e+00, 3.04131487e-01],\n",
+       "        [1.00000000e+00, 2.75917351e-01],\n",
+       "        ...,\n",
+       "        [1.00000000e+00, 5.05600256e-01],\n",
+       "        [1.00000000e+00, 1.20457745e-01],\n",
+       "        [1.00000000e+00, 4.32905427e-01]],\n",
+       "\n",
+       "       [[2.50000000e-01, 9.11142521e-03],\n",
+       "        [1.00000000e+00, 1.46295614e+00],\n",
+       "        [1.00000000e+00, 3.66074682e-03],\n",
+       "        ...,\n",
+       "        [2.50000000e-01, 5.82228319e-02],\n",
+       "        [4.00000000e+00, 1.00805546e+00],\n",
+       "        [1.00000000e+00, 5.61351579e-01]]])\n",
+       "Coordinates:\n",
+       "  * chain         (chain) int64 0 1\n",
+       "  * draw          (draw) int64 0 1 2 3 4 5 6 ... 9994 9995 9996 9997 9998 9999\n",
+       "  * accept_dim_0  (accept_dim_0) int64 0 1"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 205,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "trace.get_sampler_stats(\"accept\")"
+    "trace.sample_stats[\"accept\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 206,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: Sun Feb 07 2021\n",
+      "The watermark extension is already loaded. To reload it, use:\n",
+      "  %reload_ext watermark\n",
+      "Last updated: Fri Apr 02 2021\n",
       "\n",
       "Python implementation: CPython\n",
-      "Python version       : 3.8.6\n",
-      "IPython version      : 7.20.0\n",
+      "Python version       : 3.9.2\n",
+      "IPython version      : 7.21.0\n",
       "\n",
-      "numpy     : 1.20.0\n",
       "seaborn   : 0.11.1\n",
-      "pymc3     : 3.11.0\n",
-      "pandas    : 1.2.1\n",
-      "matplotlib: None\n",
+      "matplotlib: 3.3.4\n",
+      "arviz     : 0.11.2\n",
+      "pandas    : 1.2.3\n",
+      "xarray    : 0.17.0\n",
+      "pymc3     : 3.11.2\n",
+      "numpy     : 1.20.1\n",
       "\n",
-      "Watermark: 2.1.0\n",
+      "Watermark: 2.2.0\n",
       "\n"
      ]
     }
@@ -540,9 +2085,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python PyMC3 (Dev)",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "pymc3-dev-py38"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -554,7 +2099,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.9.2"
   }
  },
  "nbformat": 4,

From 9f4584bd97dec8c0050470f41973e3d5ce8d5abf Mon Sep 17 00:00:00 2001
From: almostmeenal <meenaljhajharia2304@gmail.com>
Date: Sat, 3 Apr 2021 00:32:41 +0530
Subject: [PATCH 2/8] added arviz stuff, improved some plots

---
 .../sampler-stats.ipynb                       | 966 +++++-------------
 1 file changed, 283 insertions(+), 683 deletions(-)

diff --git a/examples/diagnostics_and_criticism/sampler-stats.ipynb b/examples/diagnostics_and_criticism/sampler-stats.ipynb
index 9ecca3de9..983635f6b 100644
--- a/examples/diagnostics_and_criticism/sampler-stats.ipynb
+++ b/examples/diagnostics_and_criticism/sampler-stats.ipynb
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,7 +57,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = pm.Model()\n",
+    "with model:\n",
+    "    mu1 = pm.Normal(\"mu1\", mu=0, sigma=1, shape=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -86,7 +97,7 @@
        "            }\n",
        "        </style>\n",
        "      <progress value='6000' class='' max='6000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [6000/6000 00:02<00:00 Sampling 2 chains, 0 divergences]\n",
+       "      100.00% [6000/6000 00:03<00:00 Sampling 2 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -106,479 +117,57 @@
     }
    ],
    "source": [
-    "model = pm.Model()\n",
     "with model:\n",
-    "    mu1 = pm.Normal(\"mu1\", mu=0, sigma=1, shape=10)\n",
     "    step = pm.NUTS()\n",
     "    trace = pm.sample(2000, tune=1000, init=None, step=step, cores=2, return_inferencedata=True)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NUTS provides the following statistics:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
      "data": {
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-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:             (chain: 2, draw: 2000)\n",
-       "Coordinates:\n",
-       "  * chain               (chain) int64 0 1\n",
-       "  * draw                (draw) int64 0 1 2 3 4 5 ... 1995 1996 1997 1998 1999\n",
-       "Data variables: (12/13)\n",
-       "    step_size           (chain, draw) float64 1.311 1.311 1.311 ... 1.242 1.242\n",
-       "    step_size_bar       (chain, draw) float64 0.9645 0.9645 ... 0.9366 0.9366\n",
-       "    n_steps             (chain, draw) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0\n",
-       "    lp                  (chain, draw) float64 -11.2 -16.52 ... -17.88 -16.66\n",
-       "    diverging           (chain, draw) bool False False False ... False False\n",
-       "    process_time_diff   (chain, draw) float64 0.000536 0.000379 ... 0.00038\n",
-       "    ...                  ...\n",
-       "    max_energy_error    (chain, draw) float64 0.8227 1.794 ... -0.8218 -0.4617\n",
-       "    energy              (chain, draw) float64 17.63 20.67 21.1 ... 21.72 23.62\n",
-       "    energy_error        (chain, draw) float64 -0.2349 1.462 ... 0.09963 -0.4617\n",
-       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 2 ... 2 2 2 2 2 2 2 2\n",
-       "    perf_counter_start  (chain, draw) float64 4.335e+03 4.335e+03 ... 4.337e+03\n",
-       "    acceptance_rate     (chain, draw) float64 0.7719 0.5954 ... 0.9669 0.9305\n",
-       "Attributes:\n",
-       "    created_at:                 2021-04-02T11:12:40.279166\n",
-       "    arviz_version:              0.11.2\n",
-       "    inference_library:          pymc3\n",
-       "    inference_library_version:  3.11.2\n",
-       "    sampling_time:              8.593060731887817\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-a5c9fac8-9c46-40d8-b8e6-6d7a47681e47' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a5c9fac8-9c46-40d8-b8e6-6d7a47681e47' 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-00ff5c88-cbce-4ef0-9681-d86c6fc19ab9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-00ff5c88-cbce-4ef0-9681-d86c6fc19ab9' 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-320e938e-d03b-4fd5-9adf-b8cee6b7a1f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-320e938e-d03b-4fd5-9adf-b8cee6b7a1f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ff50e71-d5ba-445d-ae6f-d3681a70e881' class='xr-var-data-in' type='checkbox'><label for='data-1ff50e71-d5ba-445d-ae6f-d3681a70e881' 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-f52fbf62-1e48-462d-b7c3-ed70070c1ce5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f52fbf62-1e48-462d-b7c3-ed70070c1ce5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4a4d479-4028-4f3e-92fb-2508bd45f9d6' class='xr-var-data-in' type='checkbox'><label for='data-f4a4d479-4028-4f3e-92fb-2508bd45f9d6' 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-399d3170-ebea-4284-9105-0e53820060b8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-399d3170-ebea-4284-9105-0e53820060b8' 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>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'>1.311 1.311 1.311 ... 1.242 1.242</div><input id='attrs-203a6e41-e04e-4dd7-b9a1-f51916966d5b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-203a6e41-e04e-4dd7-b9a1-f51916966d5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76e4410e-ce2f-4657-bec5-aec761e77698' class='xr-var-data-in' type='checkbox'><label for='data-76e4410e-ce2f-4657-bec5-aec761e77698' 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([[1.31137233, 1.31137233, 1.31137233, ..., 1.31137233, 1.31137233,\n",
-       "        1.31137233],\n",
-       "       [1.24177138, 1.24177138, 1.24177138, ..., 1.24177138, 1.24177138,\n",
-       "        1.24177138]])</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'>0.9645 0.9645 ... 0.9366 0.9366</div><input id='attrs-4b33c73c-af6b-4378-8563-5e1a89cd9a35' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b33c73c-af6b-4378-8563-5e1a89cd9a35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b5efe12-3302-4c84-a7cf-c95d14038ac5' class='xr-var-data-in' type='checkbox'><label for='data-7b5efe12-3302-4c84-a7cf-c95d14038ac5' 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([[0.96451309, 0.96451309, 0.96451309, ..., 0.96451309, 0.96451309,\n",
-       "        0.96451309],\n",
-       "       [0.9365782 , 0.9365782 , 0.9365782 , ..., 0.9365782 , 0.9365782 ,\n",
-       "        0.9365782 ]])</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-00b9c88e-c930-42bb-bd3e-b626c4cacfff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-00b9c88e-c930-42bb-bd3e-b626c4cacfff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-935ea0af-8f67-4aaa-9251-e5157401cba5' class='xr-var-data-in' type='checkbox'><label for='data-935ea0af-8f67-4aaa-9251-e5157401cba5' 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>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'>-11.2 -16.52 ... -17.88 -16.66</div><input id='attrs-a67083a2-47f8-419e-adcc-bbc9031e2f66' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a67083a2-47f8-419e-adcc-bbc9031e2f66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07abd4df-2962-46c0-a1e4-76754644adc0' class='xr-var-data-in' type='checkbox'><label for='data-07abd4df-2962-46c0-a1e4-76754644adc0' 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([[-11.20247984, -16.52469862, -12.73043347, ..., -20.275405  ,\n",
-       "        -18.21896467, -15.32525736],\n",
-       "       [-11.59437266, -11.57830542, -14.7161461 , ..., -16.62284598,\n",
-       "        -17.87732549, -16.660264  ]])</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-844ce0a6-741b-46a2-ad2c-e177f108ae16' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-844ce0a6-741b-46a2-ad2c-e177f108ae16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-163529be-f658-479c-817d-9f37a94c9b6a' class='xr-var-data-in' type='checkbox'><label for='data-163529be-f658-479c-817d-9f37a94c9b6a' 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>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'>0.000536 0.000379 ... 0.00038</div><input id='attrs-287ea176-e3ee-4f14-b28e-bf29f7fa825a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-287ea176-e3ee-4f14-b28e-bf29f7fa825a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-155345dc-9598-4315-b47c-58c2788a4e58' class='xr-var-data-in' type='checkbox'><label for='data-155345dc-9598-4315-b47c-58c2788a4e58' 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([[0.000536, 0.000379, 0.000344, ..., 0.000382, 0.000381, 0.00038 ],\n",
-       "       [0.000333, 0.000327, 0.000329, ..., 0.000368, 0.00036 , 0.00038 ]])</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'>0.0005481 0.0003788 ... 0.000379</div><input id='attrs-d06f871d-8108-476d-8071-184296330abd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d06f871d-8108-476d-8071-184296330abd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5b7e825-0034-47c4-bb3d-0020fc27602d' class='xr-var-data-in' type='checkbox'><label for='data-d5b7e825-0034-47c4-bb3d-0020fc27602d' 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([[0.00054809, 0.0003788 , 0.00034307, ..., 0.00038292, 0.0003815 ,\n",
-       "        0.00037909],\n",
-       "       [0.00033215, 0.00032736, 0.00032822, ..., 0.00036812, 0.00035926,\n",
-       "        0.00037895]])</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'>0.8227 1.794 ... -0.8218 -0.4617</div><input id='attrs-96432d0e-5f27-450e-83a6-4770209a7af1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-96432d0e-5f27-450e-83a6-4770209a7af1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f4c6bfb-d441-45b1-8164-396148fdec92' class='xr-var-data-in' type='checkbox'><label for='data-2f4c6bfb-d441-45b1-8164-396148fdec92' 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([[ 0.82269839,  1.79447705,  1.15714905, ...,  0.86388666,\n",
-       "         1.05197578, -0.53998969],\n",
-       "       [-0.25209082,  1.76383045,  0.92124496, ...,  1.59634003,\n",
-       "        -0.8218098 , -0.46174593]])</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'>17.63 20.67 21.1 ... 21.72 23.62</div><input id='attrs-1e5bd53f-4a81-4ebb-83ed-80db06994e31' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e5bd53f-4a81-4ebb-83ed-80db06994e31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e223a69-620d-4199-bca7-f1357f8e9997' class='xr-var-data-in' type='checkbox'><label for='data-6e223a69-620d-4199-bca7-f1357f8e9997' 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([[17.62978535, 20.66685416, 21.099293  , ..., 26.0617586 ,\n",
-       "        26.93852689, 24.65519466],\n",
-       "       [14.61094838, 18.33611319, 17.73745528, ..., 24.15488273,\n",
-       "        21.72075348, 23.61966299]])</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'>-0.2349 1.462 ... 0.09963 -0.4617</div><input id='attrs-a38afc18-11c5-4f72-956d-1642e91d86b3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a38afc18-11c5-4f72-956d-1642e91d86b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae423b05-36a9-428a-94a5-4a4709ec29be' class='xr-var-data-in' type='checkbox'><label for='data-ae423b05-36a9-428a-94a5-4a4709ec29be' 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([[-0.23490668,  1.46213949, -1.12113861, ...,  0.80407482,\n",
-       "        -0.54147705, -0.53998969],\n",
-       "       [-0.25209082,  0.0499353 ,  0.6903594 , ...,  0.31386244,\n",
-       "         0.09962603, -0.46174593]])</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-aaffd741-01b7-4fce-b27d-dcd4f30b0219' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aaffd741-01b7-4fce-b27d-dcd4f30b0219' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-843907d6-41a1-4165-81d1-2e89c93de01d' class='xr-var-data-in' type='checkbox'><label for='data-843907d6-41a1-4165-81d1-2e89c93de01d' 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>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'>4.335e+03 4.335e+03 ... 4.337e+03</div><input id='attrs-bed21e57-7f5f-4342-bd90-2c50ad8f1c83' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bed21e57-7f5f-4342-bd90-2c50ad8f1c83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-587f3881-b116-4b83-b89d-0c79af191616' class='xr-var-data-in' type='checkbox'><label for='data-587f3881-b116-4b83-b89d-0c79af191616' 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([[4334.91478857, 4334.91550981, 4334.9160145 , ..., 4335.96208391,\n",
-       "        4335.96258439, 4335.96309748],\n",
-       "       [4336.24522579, 4336.24565431, 4336.246082  , ..., 4337.19138069,\n",
-       "        4337.19186217, 4337.1923272 ]])</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'>0.7719 0.5954 ... 0.9669 0.9305</div><input id='attrs-978d1341-1802-4f24-9c6b-a9ec9f4fa88d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-978d1341-1802-4f24-9c6b-a9ec9f4fa88d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87bbef69-e1bf-4dce-9066-39424ec6ca54' class='xr-var-data-in' type='checkbox'><label for='data-87bbef69-e1bf-4dce-9066-39424ec6ca54' 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([[0.77185498, 0.59539075, 0.95573333, ..., 0.49364924, 0.93037344,\n",
-       "        0.90198083],\n",
-       "       [0.92466073, 0.73659794, 0.4590098 , ..., 0.61192868, 0.96693723,\n",
-       "        0.93046804]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-85d84212-8912-4a04-9f52-00c75b4fee5d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-85d84212-8912-4a04-9f52-00c75b4fee5d' 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>2021-04-02T11:12:40.279166</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>8.593060731887817</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",
-       "Coordinates:\n",
-       "  * chain               (chain) int64 0 1\n",
-       "  * draw                (draw) int64 0 1 2 3 4 5 ... 1995 1996 1997 1998 1999\n",
        "Data variables: (12/13)\n",
-       "    step_size           (chain, draw) float64 1.311 1.311 1.311 ... 1.242 1.242\n",
-       "    step_size_bar       (chain, draw) float64 0.9645 0.9645 ... 0.9366 0.9366\n",
-       "    n_steps             (chain, draw) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0\n",
-       "    lp                  (chain, draw) float64 -11.2 -16.52 ... -17.88 -16.66\n",
+       "    perf_counter_start  (chain, draw) float64 1.594e+03 1.594e+03 ... 1.596e+03\n",
+       "    lp                  (chain, draw) float64 -13.13 -13.67 ... -15.77 -14.19\n",
        "    diverging           (chain, draw) bool False False False ... False False\n",
-       "    process_time_diff   (chain, draw) float64 0.000536 0.000379 ... 0.00038\n",
+       "    max_energy_error    (chain, draw) float64 -0.4791 0.4339 ... 0.8172 -0.4252\n",
+       "    step_size_bar       (chain, draw) float64 0.9529 0.9529 ... 0.9093 0.9093\n",
+       "    acceptance_rate     (chain, draw) float64 0.9819 0.8846 ... 0.6049 1.0\n",
        "    ...                  ...\n",
-       "    max_energy_error    (chain, draw) float64 0.8227 1.794 ... -0.8218 -0.4617\n",
-       "    energy              (chain, draw) float64 17.63 20.67 21.1 ... 21.72 23.62\n",
-       "    energy_error        (chain, draw) float64 -0.2349 1.462 ... 0.09963 -0.4617\n",
-       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 2 ... 2 2 2 2 2 2 2 2\n",
-       "    perf_counter_start  (chain, draw) float64 4.335e+03 4.335e+03 ... 4.337e+03\n",
-       "    acceptance_rate     (chain, draw) float64 0.7719 0.5954 ... 0.9669 0.9305\n",
-       "Attributes:\n",
-       "    created_at:                 2021-04-02T11:12:40.279166\n",
-       "    arviz_version:              0.11.2\n",
-       "    inference_library:          pymc3\n",
-       "    inference_library_version:  3.11.2\n",
-       "    sampling_time:              8.593060731887817\n",
-       "    tuning_steps:               1000"
+       "    process_time_diff   (chain, draw) float64 0.00057 0.00031 ... 0.000412\n",
+       "    perf_counter_diff   (chain, draw) float64 0.00057 0.0003091 ... 0.0004153\n",
+       "    tree_depth          (chain, draw) int64 3 2 2 2 2 2 2 2 ... 2 2 2 2 2 2 2 2\n",
+       "    step_size           (chain, draw) float64 0.8548 0.8548 ... 0.852 0.852\n",
+       "    n_steps             (chain, draw) float64 7.0 3.0 3.0 3.0 ... 3.0 3.0 3.0\n",
+       "    energy_error        (chain, draw) float64 -0.3426 0.125 ... 0.7733 -0.3769"
       ]
      },
-     "execution_count": 197,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "trace.sample_stats"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "NUTS provides the following statistics:"
+    "trace.sample_stats.data_vars"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "[Arviz](https://arviz-devs.github.io/arviz/schema/schema.html#sample-stats) follows the following Name Convention for sample_stats variables:\n",
+    "\n",
     "- `lp`: The joint log posterior density for the model (up to an additive constant).\n",
     "\n",
     "- `acceptance_rate`: The average acceptance probabilities of all possible samples in the proposed tree.\n",
@@ -599,7 +188,8 @@
     "\n",
     "- `max_energy_error`: The maximum absolute difference in Hamiltonian energy between the initial point and all possible samples in the proposed tree.\n",
     "\n",
-    "- `int_time`: The total integration time (static HMC sampler)"
+    "- `int_time`: The total integration time (static HMC sampler)\n",
+    "\n"
    ]
   },
   {
@@ -613,12 +203,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x480 with 1 Axes>"
       ]
@@ -628,25 +218,19 @@
     }
    ],
    "source": [
-    "# Note that we use xr.concat to combine chain 1 and chain 2\n",
-    "plt.plot(\n",
-    "    xr.concat(\n",
-    "        (trace.sample_stats[\"step_size_bar\"][0], trace.sample_stats[\"step_size_bar\"][1]), dim=\"draw\"\n",
-    "    )\n",
-    ")\n",
-    "plt.show()"
+    "az.plot_posterior(trace, group=\"sample_stats\", var_names=\"step_size\", hdi_prob=\"hide\", kind=\"kde\");"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 720x480 with 2 Axes>"
+       "<Figure size 720x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -654,22 +238,17 @@
     }
    ],
    "source": [
-    "# In this plot we view Chain 1 and Chain 2 separately\n",
-    "sizes1, sizes2 = trace.sample_stats[\"tree_depth\"]\n",
-    "fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, sharey=True)\n",
-    "ax1.plot(sizes1)\n",
-    "ax2.plot(sizes2)\n",
-    "plt.show()"
+    "trace.sample_stats[\"tree_depth\"].plot(hue=\"chain\", ls=\"none\", marker=\".\", alpha=0.3);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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mafeM2dD+nn4AAACAr/wybPfr10/SpQOvp728vNy7xvqH67p/yNPuWdNts9kUFRWlwsJC73dCP+RZq+0ZFwAAAPCVX4bt4cOHS5K+/PLLOtcqKyv19ddfy2azqWvXroqNjVX37t2Vm5srl8tVq6/L5VJubq6io6NrnTaZkJDgvXaxvXv3SpLi4+OtvCUAAAAEIL8M2zExMUpKSlJBQUGdPbJffvlllZaWasyYMQoNDVVQUJCmTp0ql8ultLS0Wn3T0tLkcrk0bdq0Wu2eX69atUoVFRXe9t27dys7O1tJSUnq3bu3obsDAABAoPDLFyQl6emnn9bdd9+t5cuX67333lO/fv109OhRHThwQL1799bjjz/u7Tt79mzt2rVL6enpysvL0+DBg3X06FFlZWVp6NChmjlzZq2xExMTNXXqVDkcDk2ePFnJyckqLi7Wzp07dcUVV2j58uXNfbsAAABog/w2bMfExOiNN97Q6tWrtXfvXu3bt09XXnml7rvvPi1YsEDdunXz9rXZbNqyZYtSU1OVmZmpgwcPKioqSikpKVqwYIHCwsLqjP/MM8/Ibrdr+/bt2rRpk2w2m8aOHavFixcrJiamOW8VAAAAbZTfhm1J6tWrl1asWNGgvhEREVq2bJmWLVvWoP7BwcGaMWOGZsyY0ZQSAQAAgEvyyzXbAAAAQFtA2AYAAAAMIWwDAAAAhhC2AQAAAEMI2wAAAIAhhG0AAADAEMI2AAAAYAhhGwAAADCEsA0AAAAYQtgGAAAADPE5bBcVFencuXOX7VdSUqKioiJfPwYAAABotXwO26NHj9bzzz9/2X4vvPCCxowZ4+vHAAAAAK2Wz2Hb7XbL7XY3uC8AAAAQaIyv2T579qzCwsJMfwwAAADgd0Ib0zknJ6fWr//2t7/VafOoqqrSV199paysLA0YMMD3CgEAAIBWqlFh+/7771dQUJD311lZWcrKyrpkf7fbraCgIKWkpPheIQAAANBKNSps33nnnd6wvWPHDsXExCguLq7evu3atVP37t11yy236Lrrrmt6pQAAAEAr06iwvXLlSu9/79ixQ3FxcVqxYoXlRQEAAABtQaPC9g99/vnnVtYBAAAAtDmcIAkAAAAY4vOTbUmqqKjQW2+9pZycHBUXF6uioqLefkFBQfrTn/7UlI8CAAAAWh2fw/apU6c0c+ZMFRQUXPbQmh/uYAIAAAAECp/D9vPPP6/8/HzddNNNeuCBBxQbG6tOnTpZWRsAAADQqvkctrOysnTVVVcpIyNDHTp0sLImAAAAoE3w+QXJiooKXX/99QRtAAAA4BJ8Dtt2u11nz561shYAAACgTfE5bM+ZM0cfffSRjhw5YmU9AAAAQJvh85rtwYMHa9asWZo5c6YeeOABjRgxQj179lRwcP35/aqrrvK5SAAAAKA18jlsjxo1SkFBQXK73Vq7dq3Wrl17yb5BQUE6evSorx8FAAAAtEo+h+34+Hgr6wAAAADaHJ/D9ubNm62sAwAAAGhzfH5BEgAAAMCPI2wDAAAAhvi8jOSll15qcN+goCAtWLDA148CAAAAWqUmhW3PbiT1CQoKkiS53W7CNgAAAAKSz2F7xYoV9bbX1NTo5MmT2r9/v3Jzc3XfffdpyJAhPhcIAAAAtFY+h+1//Md//NHrCxcuVHp6utLS0jRt2jRfPwYAAABotYy+IDlnzhz16NFD//Iv/2LyYwAAAAC/ZHw3ErvdrkOHDpn+GAAAAMDvGA/bx48fV1VVlemPAQAAAPyOz2u2L6ekpERr165VXl6ehg8fbupjAAAAAL/lc9gePXr0Ja+5XC6dO3dObrdbYWFhevTRR339GAAAAKDV8jlsnzhx4tKDhoaqV69eio+P15w5czRgwABfPwYAAABotXwO259//rmVdQAAAABtjvEXJAEAAIBAZWnYLikpUUlJiZVDAgAAAK1Wk3cj2b17tzZt2qTc3FyVl5dLksLCwhQXF6cZM2YoOTm5yUUCAAAArVGTwvbvf/97bd68WW63W5IUERGhoKAglZaWat++fdq/f79mzJihJ5980pJiAQAAgNbE57C9c+dObdq0Sd26ddPDDz+sSZMmKSIiQpJUVlamN998U2vXrtWmTZt0ww03aMKECZYVDQAAALQGPq/ZfvXVV9WhQwdt2bJF06dP9wZtSQoPD9d9992nzZs3q3379nrttdcsKRYAAABoTXwO259//rkSExN19dVXX7LP1VdfrcTEROXl5fn6MQAAAECr5XPYrqysVMeOHS/br2PHjqqsrPT1YwAAAIBWy+ewHRMTo5ycHLlcrkv2+e6775STk6OYmBhfPwYAAABotXwO2+PHj5fT6dSCBQuUn59f5/rXX3+thQsX6syZM/qHf/iHptQIAAAAtEo+70by4IMPateuXfrwww912223afDgwerdu7ckqaioSJ999pmqq6s1ZMgQpaSkWFYwAAAA0Fr4HLbDwsK0efNm/eEPf9Abb7yhTz75RJ988kmt63fffbceeeQRhYWFWVIsAAAA0Jo06VCbTp066Ve/+pWWLFmizz77TKdPn5Ykde/eXdddd12DXqAEAAAA2qpGhe0PP/xQp06d0pAhQzRgwABve8eOHTVs2LBafb/44gt9+umn6tmzpxITE62pFgAAAGhFGhy2T548qXnz5qlXr1564403Ltu/Z8+eWrBggU6dOqX/+q//Uo8ePZpUKAAAANDaNHg3EofDocrKSj322GMKDw+/bP/w8HA9/vjjKi8v17/92781qUgAAACgNWpw2N6/f7+6du2qMWPGNHjw0aNH68orr9TevXt9Kg4AAABozRoctr/88ksNHTq00R8wZMgQffXVV43+OgAAAKC1a3DYdrlcDVo+crHw8HB9++23jf46AAAAoLVrcNiOjIyU0+ls9Ac4nU5FRkY2+usAAACA1q7BYbt///76+OOPVV5e3uDBv/vuO3388cfq37+/T8UBAAAArVmDw/Ytt9yi7777TmvXrm3w4GvXrlV5eblGjRrlU3EAAABAa9bgsH333Xfryiuv1Msvv6y0tDTV1NRcsm9NTY3WrFmjl19+Wd26ddNdd91lSbEAAABAa9LgQ206duyo1atX64EHHlBqaqocDofGjx+vwYMHq2vXrpKkM2fO6OjRo3r33Xf1zTffqEOHDkpNTeXYdgAAAASkRh3XHhcXp9dff12PP/64/vrXvyojI6NOH7fbLUm65ppr9MILL+jaa6+1pFAAAACgtWlU2JakQYMG6T//8z+1Z88e7d69W3l5eTp37pwk6YorrtCgQYOUnJysn//851bXCgAAALQqjQ7bHj//+c8J1AAAAMCPaPALkgAAAAAah7ANAAAAGELYBgAAAAwhbAMAAACGtJqw/fLLL2vgwIEaOHCgPv744zrXy8rKtGLFCt1yyy0aMmSIRo0apeeee07ffvttvePV1NRo8+bNuv3223X99dcrMTFRjzzyiI4fP274TgAAABAoWkXYPnbsmFJTU2Wz2eq97nK5NH36dGVkZKhfv36aNWuWrr76am3cuFEzZ87UhQsX6nzNU089pd/+9rdyu926//77NXLkSGVmZmrKlCnKz883fEcAAAAIBD5v/ddcKisrtXTpUg0aNEh9+/bVf/zHf9Tps379euXl5WnOnDlasmSJt/3FF19Uenq6MjIyNG/ePG/7gQMH5HA4FB8fr40bN6p9+/aSpIkTJ2ru3Ll69tlntWHDBvM3BwAAgDbN759sr1u3Tn/961/1+9//XiEhIXWuu91uORwO2Ww2zZ8/v9a1+fPny2azyeFw1Gr3/HrRokXeoC1JycnJSkhIUFZWloqKigzcDQAAAAKJX4ftzz77TOvWrdPChQs1YMCAevvk5+fr9OnTiouLq7PMxGazKS4uTsePH9fJkye97QcPHvReu9jIkSMlSdnZ2RbeCQAAAAKR34btiooKPfHEE7r22ms1e/bsS/YrKCiQJMXGxtZ73dPuWYftcrlUXFys6Ojoep+U9+3bt9a4AAAAgK/8ds32qlWrlJ+fr3//93+vNxR7nD9/XpIUHh5e73VPe1lZWaP6e/rVJzIyUsHBfvt9CpqoS5cuLV0CmhHzHViY78DCfPunzp07S5JCQkIUGtr0KOrJiZ07d/bLOffLsH348GFt3LhRCxculN1ub+ly6igpKWnpEmBIly5ddPbs2ZYuA82E+Q4szHdgYb79V2lpqSSpurpaVVVVTR6vurraO25zz3lDwr3fhe2qqiotXbpUAwcO1Ny5cy/bPyIiQtLfn1xfzNPueWLd0P6efgAAAIGusLBQTqfTkrGOHTtmyTithd+FbZfL5V1fPWTIkHr73HXXXZKkNWvWqH///pJ0yb2xPe2etds2m01RUVEqLCxUdXV1nSUqnrXanrXbAAAAgaywsFAJCYkqL3dZOm5FRaWl4/krvwvb7du315QpU+q99tFHHyk/P1+jRo1S165d1bt3b8XGxqp79+7Kzc2Vy+WqtSOJy+VSbm6uoqOj1atXL297QkKC3n77beXm5io+Pr7WZ+zdu1eS6rQDAAAEIqfTqfJyl0YkrVVkZNOX95448Z6OfLzCkiUkrYHfhe2wsDD97ne/q/fa0qVLlZ+fr3nz5unGG2/0tk+dOlVr1qxRWlparUNt0tLS5HK59NBDD9UaZ9q0aXr77be1atWqWofa7N69W9nZ2UpKSlLv3r2tvzkAAIBWKjLSrq7dbmjyOCUlLCNpdWbPnq1du3YpPT1deXl5Gjx4sI4ePaqsrCwNHTpUM2fOrNU/MTFRU6dOlcPh0OTJk5WcnKzi4mLt3LlTV1xxhZYvX95CdwIAAIC2pE2EbZvNpi1btig1NVWZmZk6ePCgoqKilJKSogULFigsLKzO1zzzzDOy2+3avn27Nm3aJJvNprFjx2rx4sWKiYlpgbsAAABAW9OqwvbKlSu1cuXKeq9FRERo2bJlWrZsWYPGCg4O1owZMzRjxgwrSwQAAAC8OJkFAAAAMISwDQAAABhC2AYAAAAMIWwDAAAAhhC2AQAAAEMI2wAAAIAhhG0AAADAEMI2AAAAYEirOtQGAAAAl1dYWCin02nJWMeOHbNknEBF2AYAAGhDCgsLlZCQqPJyl6XjVlRUWjpeoCBsAwAAtCFOp1Pl5S6NSFqryEh7k8c7ceI9Hfl4haqqqiyoLvAQtgEAANqgyEi7una7ocnjlJSwjKQpeEESAAAAMISwDQAAABhC2AYAAAAMIWwDAAAAhhC2AQAAAEMI2wAAAIAhhG0AAADAEMI2AAAAYAhhGwAAADCEsA0AAAAYQtgGAAAADCFsAwAAAIYQtgEAAABDCNsAAACAIYRtAAAAwBDCNgAAAGAIYRsAAAAwhLANAAAAGELYBgAAAAwhbAMAAACGELYBAAAAQwjbAAAAgCGEbQAAAMAQwjYAAABgCGEbAAAAMISwDQAAABhC2AYAAAAMIWwDAAAAhhC2AQAAAEMI2wAAAIAhhG0AAADAEMI2AAAAYAhhGwAAADCEsA0AAAAYQtgGAAAADCFsAwAAAIYQtgEAAABDCNsAAACAIYRtAAAAwBDCNgAAAGAIYRsAAAAwhLANAAAAGELYBgAAAAwhbAMAAACGhLZ0AQAAAD9UWFgop9PZpDE6d+6s0tJSSVK3bt0UHR1tRWlAoxG2AQCA3ygsLFRCQqLKy12WjRkWZlN29gECN1oEYRsAAPgNp9Op8nKXRiStVWSk3edxQkJCVF1drZKSY9qf9bCcTidhGy2CsA0AAPxOZKRdXbvd4PPXh4aGqqqqysKKAN/wgiQAAABgCGEbAAAAMISwDQAAABhC2AYAAAAMIWwDAAAAhhC2AQAAAEPY+g8AADSJFSc+ehw7dsyScQB/QdgGAAA+M3HioyRVVFRaOh7QUgjbAADAZ1ad+Ohx4sR7OvLxCg6kQZtB2AYAAE3W1BMfPUpKWEaCtoWwDQAA0AhWrlH36Natm6Kjoy0dE/6BsA0AANBAptaoh4XZlJ19gMDdBhG2AQAAGsjqNerS90tn9mc9LKfTSdhugwjbAAAAjWTVGnW0fRxqAwAAABjil0+2T506pXfeeUd79uzRl19+qb/97W+KjIxUXFycZs+erRtuqPudZFlZmVJTU5WZmani4mJ1795d48aN08KFC9WpU6c6/WtqarR161Zt375dBQUFstlsGjFihBYvXqw+ffo0x20CAAB4WXWgDwcD+Re/DNubN29Wenq6YmJi9LOf/Uxdu3ZVQUGB3nvvPb333nv653/+Z02YMMHb3+Vyafr06crLy1NSUpJuu+025eXlaePGjcrJydHWrVvVoUOHWp/x1FNPyeFw6JprrtH999+v06dP65133tG+ffu0bds2xcbGNvNdAwCAQPTdd6ckBWnevHmWjsvBQP7BL8P29ddfr82bNyshIaFW+0cffaRZs2bp17/+tcaMGaP27dtLktavX6+8vDzNmTNHS5Ys8fZ/8cUXlZ6eroyMjFq/gQ8cOCCHw6H4+Hht3LjRO87EiRM1d+5cPfvss9qwYUMz3CkAAAh0FRUlktyKG/av6tFjaJPH42Ag/+KXYfvWW2+tt33YsGEaPny4srKy9L//+78aOnSo3G63HA6HbDab5s+fX6v//PnztXXrVjkcjlph2+FwSJIWLVrkDdqSlJycrISEBGVlZamoqEhXXXWVgbsDAACoKyJiAAcDtUGt7gXJ0NDQWv/Oz8/X6dOnFRcXJ5vNVquvzWZTXFycjh8/rpMnT3rbDx486L12sZEjR0qSsrOzTd0CAAAAAkSrCttFRUXav3+/oqKiZLd/v7dlQUGBJF1yjbWnPT8/X9L367uLi4sVHR2tkJCQOv379u1ba1wAAADAV365jKQ+lZWVevzxx1VRUaElS5Z4g/L58+clSeHh4fV+nae9rKysUf09/eoTGRmp4OBW9X0KGqFLly4tXQKaEfMdWJhv63Xu3FmSFBIS4v2pc1OEBH////eQ0KaPFxoa6s0LnTt3tmT+rb5fydp7DsjxLJ5jq7WKsF1TU6OlS5cqJydH06ZN05133tmi9ZSUlLTo58OcLl266OzZsy1dBpoJ8x1YmG8zSktLJUnV1dWWvJBXXVP9/b+rmjZeaGioqqqqVF1d7a3Tivm3+n4l6+45YMezeI4boyHh3u8fz9bU1GjZsmV66623dMcdd+g3v/lNresRERGS/v7k+mKeds8T64b29/QDAAAAfOXXYbumpkZPPvmkduzYoYkTJ2rlypV1lm941lh71mRfzNPuWbtts9kUFRWlwsJC73dCP+RZq+0ZFwAAAPCV3y4j8QTtP//5z5owYYKef/75el9ojI2NVffu3ZWbmyuXy1VrRxKXy6Xc3FxFR0erV69e3vaEhAS9/fbbys3NVXx8fK3x9u7dK0l12gEAQOvF6YxoKX4Ztj1LR/785z9r/PjxeuGFF+oN2pIUFBSkqVOnas2aNUpLS6t1qE1aWppcLpceeuihWl8zbdo0vf3221q1alWtQ212796t7OxsJSUlqXfv3uZuEAAANAtOZ0RL88uwvWbNGu3YsUM2m02xsbFau3ZtnT5jxozRoEGDJEmzZ8/Wrl27lJ6erry8PA0ePFhHjx5VVlaWhg4dqpkzZ9b62sTERE2dOlUOh0OTJ09WcnKyiouLtXPnTl1xxRVavnx5s9wnAAAwi9MZ0dL8MmyfOHFC0vfLQNatW1dvn969e3vDts1m05YtW5SamqrMzEwdPHhQUVFRSklJ0YIFCxQWFlbn65955hnZ7XZt375dmzZtks1m09ixY7V48WLFxMSYuzkAANDsOJ0RLcUvw/bKlSu1cuXKRn1NRESEli1bpmXLljWof3BwsGbMmKEZM2b4UiIAAABwWX69GwkAAADQmhG2AQAAAEMI2wAAAIAhhG0AAADAEMI2AAAAYAhhGwAAADCEsA0AAAAYQtgGAAAADCFsAwAAAIYQtgEAAABDCNsAAACAIYRtAAAAwBDCNgAAAGBIaEsXAAAAmldhYaGcTqclYx07dsyScYC2irANAEAAKSwsVEJCosrLXZaOW1FRael4QFtB2AYAIIA4nU6Vl7s0ImmtIiPtTR7vxIn3dOTjFaqqqrKgOqDtIWwDABCAIiPt6trthiaPU1LCMhLgx/CCJAAAAGAIYRsAAAAwhLANAAAAGELYBgAAAAwhbAMAAACGELYBAAAAQ9j6DwAAP2blaY8SJz4CzY2wDQCAnzJ12qPEiY9AcyFsAwDgp6w+7VHixEeguRG2AQDwc1ad9ihx4iPQ3HhBEgAAADCEsA0AAAAYQtgGAAAADCFsAwAAAIYQtgEAAABDCNsAAACAIWz9BwAIaFaf0NitWzdFR0dbNh6A1o2wDQAIWCZOaOzQIUx/+lOGevTo0eSxOFodaP0I2wCAgGX1CY2nTx3QoY+W6+6777agur/jaHWg9SJsAwACnlUnNH5/OqNbccP+VT16DG3yeBytDrR+hG0AACwWETHAwvAOoDVjNxIAAADAEMI2AAAAYAhhGwAAADCEsA0AAAAYwguSAIBWxddDaDp37qzS0tJabexjDcA0wjYAoNUwcQiNxD7WAMwhbAMAjLLyOPRjx475fAhNSEiIqqura7WxjzUA0wjbAAAvK4OxJJ06dUozZz6gCxe+s2xMSerY8epG72MdGhpaJ1SzjzUA0wjbAABJ5pZoSFLC8FR1u3Jwk8fhSTSA1oawDQCQJDmdTp+XaFyKJxx3tDX+SXR9eBINoLUhbAMAaomMtFsSjCXCMQCwzzYAAABgCGEbAAAAMISwDQAAABjCmm0AaMWs3sMaAGAtwjYANCMrw7GpPaw5TREArEPYBoBmYmofa/awBgD/RdgGgB/hL0eN14c9rAHA/xG2ATSY1Ud5d+vWTdHR0ZaNZzVTT6J9OWq8PoRjAPB/hG0ADWIieIaF2ZSdfcBvA7fVJyqyTAMAAg9hG0CDWB08S0qOaX/Ww3I6nX4btj2sOlGRJ9EAEHgI2wAaxcqjvAEAaOsI2wDalMasK+/cubNKS0sveZ19pwEATUXYBtBmmHqhkX2nAQC+ImwDaDMau648JCRE1dXVl7zOC40AgKYibANoUVYu1fCM1dB15aGhoT8apHmhEQDQVIRtAC3iu+9OSQrSvHnzLB+bZR8AAH9B2Ab8SCAdGlNRUSLJrbhh/6oePYZaMibLPgAA/oawDfiJQDw0RpIiIgZYtpUgyz4AAP6GsA34CVOHxnz44Yey25s+HtvgAQDQeIRtBIyGLNG43L7LFzOxTMOqQ2NMrYlmPTQAAA1H2EZAMLX/sj8v07B6TTTroQEAaDzCNgJCQ5doXG7f5R/yLNNwOp1+GbY9rFoTzXpoAAAaj7CNgHK5JRqX23cZAACgMQjb8FtWboNn8uU+q8bmBUQAANoewjb8kqk11la+3McLiAAA4HII27CM1U+irdwGz8TLfbyACAAALiegw/aRI0eUmpqqw4cPq6qqSna7XbNmzdKECRNaurRWx9ST6I4dr/b7l/t4AREAAFxKwIbtAwcOaPbs2Wrfvr1uu+02derUSZmZmVq8eLG++eYbpaSktHSJrYrVB7LwlBcAALQFARm2q6qq9Ktf/UpBQUHaunWrBg0aJElasGCBpkyZoj/84Q8aN26cevfu3cKVmmXiBUSrDmThKS8AAGgLAjJsHzhwQF9//bUmT57sDdqSFBERoYceekhLly7Vjh07tHDhwhassi4rw/GpU6c0c+YDunDhO0vG8+DlPgAAgL8LyLCdnZ0tSUpKSqpzzdOWk5PTrDVdjqk10QnDU9XtysFNHodlHwAAAHUFZNjOz8+XJPXt27fOtaioKNlsNhUUFDRzVT/OsyZ6yNBH1Sm86acV/q34kP7viy2qauBpiQ11/vwXOuPs2ORxysq+bpHxGnOCZEvVyHjWjXm5+fb3e/b38UyM2ZTx6ptvf6qvOcYzMaa/jueZb3+tz+SYgTaevy89DXK73e6WLqK5paSkaN++fcrMzKw3cI8cOVIul0uHDh1qgeoAAADQVgS3dAEAAABAWxWQYTs8PFySdP78+Xqvl5WVKSIiojlLAgAAQBsUkGE7NjZWkupdl11cXCyXy1Xv8hIAAACgMQIybMfHx0uSsrKy6lzztHn6AAAAAL4KyLD905/+VH369NFbb72lvLw8b/v58+e1bt06tWvXTnfeeWfLFQgAAIA2ISB3I5EufVz7iRMn9MQTT3BcOwAAAJosYMO2JB05ckSrV6/W4cOHVVVVJbvdrgceeEATJkxo6dJggSNHjig1NbXW/M6aNatB8+t2u7Vnzx69//77ys3NVVFRkaqqqtS3b19NmDBBDzzwgDp06NAMd4HGaMqc16ekpEQTJ07U6dOnlZSUpA0bNlhcMZrCqvl2Op364x//qA8++EAnT56UzWZTbGysJk2apHvvvddQ9WgsK+b71KlTSk9P1/79+1VUVCSbzaa+ffvqrrvu0u23366QkBCDd4CGevPNN3Xo0CF9+umnOnbsmCorK7VixQpNnjy5UePU1NRo69at2r59uwoKCmSz2TRixAgtXrxYffr0MVR9XQEdttF2NfUnFxcuXND111+v9u3bKyEhQXa7XRUVFcrKylJ+fr6GDh2qzZs3q2NHaw40QNOZ+GnVo48+qvfff18ul4uw7Wesmu+8vDylpKSotLRUycnJ6t+/v1wul/7v//5P7dq1U3p6uuE7QUNYMd/Hjx/X1KlTde7cOSUlJWngwIEqKyvTrl27VFxcrMmTJ2vFihXNcDe4nFGjRunEiRPq0qWLbDabTpw44VPYXr58uRwOh6655holJyfr9OnTeuedd9SpUydt27bNu2GGcW6gjamsrHSPGTPGPWTIEPfRo0e97aWlpe5bb73Vfd1117kLCwt/dIyKigp3Wlqa+9y5c3Xa582b57bb7e709HQj9aPxrJjzi7377rtuu93u3rJli9tut7tTUlKsLhs+smq+z58/77755pvdiYmJ7ry8vHo/By3Pqvl++umn3Xa73Z2RkVGrvaSkxH3zzTe77XZ7o/+egBn79u3zzsUf//hHt91ud7/xxhuNGuPDDz902+1293333ee+cOGCt/2DDz5o9r/TA/IFSbRtBw4c0Ndff62JEydq0KBB3vaIiAg99NBDqqys1I4dO350jHbt2unhhx9WZGRknfZ58+ZJknJycqwvHj6xYs5/6MyZM/r1r3+tSZMmKTk52UTJaAKr5vvVV19VUVGRHn30UV177bV1roeGhlpaN3xj1XwfP35ckur8me7cubPi4uIkSWfPnrWwcvhqxIgR6t27d5PGcDgckqRFixapffv23vbk5GQlJCQoKytLRUVFTfqMhiJso83Jzs6WJCUlJdW55mlrSlD2/A+YtX3+w+o5f/rppxUSEqJf/vKX1hQIS1k13zt37lRQUJDGjRunL7/8Ups3b1Z6erp27dqliooKa4uGz6yab7vdLknavXt3rfbS0lIdPnxYUVFRGjBgQFPLhZ84ePCgbDab9xupHxo5cqSkv//eMo1v29Hm5OfnS1K9BxNFRUXJZrPVe6BRQ73xxhuSpJ/97Gc+jwFrWTnnb775pjIzM7VmzRpFRkZe8qRZtBwr5ruiokLHjh1T165dtXnzZqWmpqqmpsZ7vU+fPlqzZo0GDhxoae1oPKv+fD/44IN6//33tWLFCu3du7fWmu2wsDC99NJLCgsLs7p8tACXy6Xi4mLZ7fZ6H4x5fi81JQs0Bk+20eaUlZVJ+v5HjPUJDw/3OUDt3r1b27ZtU//+/TV16lSfa4S1rJrzU6dO6Xe/+50mTpyoMWPGWFojrGPFfJeUlKi6ulrnzp1TWlqaHnvsMe3fv1979uzR/PnzVVhYqIcfflgXLlywvH40jlV/vq+88kpt27ZNI0eO1N69e7V+/Xq9/vrrOn/+vO688856lxKhdfL8fggPD6/3uqe9uR6mELaBBjpy5IgWL16siIgIrVq1qtYaMLQNy5cvV2hoKMtHAoDnKXZ1dbXuuecepaSkqFu3burRo4cWLVqk8ePH68SJE3r33XdbuFJYpaCgQPfcc4/OnDmjrVu3Kjc3V7t379aCBQuUlpamWbNmqbq6uqXLRBtE2Eabc7nvWMvKyi75hORSPvnkEz344IMKDg7W+vXrdc011zS5TljHijnfsWOH9uzZo6eeekpdu3a1vEZYx4r5/uH1UaNG1bnuafv00099LRMWserv9KVLl6qoqEjr1q3TsGHD1KlTJ/Xs2VNz587V9OnTdfjwYb399tuW1o6W4fn94PmpyMUu99MSqxG20eZ49s2sby1WcXGxXC5XvWv/LuWTTz5RSkqKampqtGHDBl1//fVWlQqLWDHnR48elfT9m+sDBw70/jN69GhJUlZWlgYOHKhJkyZZWzwazYr5ttls6tGjh6Tvd6O4mKeNZSQtz4r5LisrU25urvr376+oqKg614cPHy7p+33X0frZbDZFRUWpsLCw3p9WeH4vNSYLNAVhG21OfHy8pO/D0cU8bZ4+l+MJ2tXV1Vq/fr1uuOEG6wqFZayY85tuuklTpkyp84/ndLqePXtqypQpGjt2rMXVo7Gs+jOemJgoSfriiy/qXPO0NXX7MTSdFfNdWVkp6dJb+505c0aSWB7YhiQkJMjlcik3N7fOtb1790pqeBZosmbb0RtoJpWVle7Ro0f/6AEIx48f97afOnXK/cUXX7hLS0trjfPJJ5+4hw0b5r7xxhvdH330UbPVj8azas7rc/z4cQ618TNWzfehQ4fcdrvdfdttt7lLSkq87adPn3aPHDnSfe2117q//PJL8zeEH2XVfI8bN85tt9vd27dvr9VeUlLiHj9+vNtut7v37dtn9mbQaJc71MbpdLq/+OILt9PprNXuT4facFw72qTGHO27dOlS7dixo9ZRsOfOndOtt96qkpISjRw5st4n2hEREZo1a1Zz3RIuo6lzfimFhYUaPXo0x7X7Gavme+XKlXrllVfUq1cv3XLLLaqqqtKuXbvkdDr1yCOPeA+xQsuyYr53796t+fPnq6qqSj/96U81aNAglZaW6v3339eZM2c0btw4rV69uiVuDxdxOBw6dOiQJOnYsWP67LPPFBcX51328ZOf/MS7I1hqaqpeeuklLVy4UL/4xS9qjXPxce3FxcXauXOnOnXqpNdff11XX311s9wP+2yjTUpMTNSrr76q1atXa+fOnaqqqpLdbteSJUu8ywJ+TFlZmUpKSiR9/+Mmz4+cfqh3796EbT/S1DlH62LVfC9dulR2u11bt27Vjh07FBQUpEGDBuk3v/kNS4b8iBXznZycrNdee00bNmzQoUOHlJOTo/bt26t///5asGCB7rnnHsN3gYY6dOhQnVNBc3Nzay0Jacj2u88884zsdru2b9+uTZs2yWazaezYsVq8eLFiYmIsr/tSeLINAAAAGMILkgAAAIAhhG0AAADAEMI2AAAAYAhhGwAAADCEsA0AAAAYQtgGAAAADCFsAwAAAIYQtgEAAABDCNsAAACAIYRtAAAAwBDCNgAAAGAIYRsAAAAw5P8Bo5w1l9R/Lo8AAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 720x480 with 1 Axes>"
       ]
@@ -679,11 +258,9 @@
     }
    ],
    "source": [
-    "accept = xr.concat(\n",
-    "    (trace.sample_stats[\"acceptance_rate\"][0], trace.sample_stats[\"acceptance_rate\"][1]), dim=\"draw\"\n",
-    ").values\n",
-    "sns.histplot(accept, kde=False)\n",
-    "plt.show()"
+    "az.plot_posterior(\n",
+    "    trace, group=\"sample_stats\", var_names=\"acceptance_rate\", hdi_prob=\"hide\", kind=\"hist\"\n",
+    ");"
    ]
   },
   {
@@ -695,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
@@ -1052,146 +629,26 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;diverging&#x27; (chain: 0, draw: 0)&gt;\n",
-       "array([], shape=(0, 0), dtype=float64)\n",
-       "Coordinates:\n",
-       "  * chain    (chain) int64 \n",
-       "  * draw     (draw) int64 </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><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 0</li><li><span class='xr-has-index'>draw</span>: 0</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9b071612-b66c-4402-9fd3-d154010d29ec' class='xr-array-in' type='checkbox' checked><label for='section-9b071612-b66c-4402-9fd3-d154010d29ec' 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></span></div><div class='xr-array-data'><pre>array([], shape=(0, 0), dtype=float64)</pre></div></div></li><li class='xr-section-item'><input id='section-466af218-6854-4ff2-be04-9d1cd203f124' class='xr-section-summary-in' type='checkbox'  checked><label for='section-466af218-6854-4ff2-be04-9d1cd203f124' 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'></div><input id='attrs-919e3fb3-05d4-4ec7-9fb6-91a074637510' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-919e3fb3-05d4-4ec7-9fb6-91a074637510' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25405ddf-ac54-4373-912c-93cc859abda0' class='xr-var-data-in' type='checkbox'><label for='data-25405ddf-ac54-4373-912c-93cc859abda0' 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([], dtype=int64)</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'></div><input id='attrs-4876a469-ed36-4e23-8fb6-63f6507ff9e8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4876a469-ed36-4e23-8fb6-63f6507ff9e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2936c3e1-41a0-48ea-83d2-a25637935729' class='xr-var-data-in' type='checkbox'><label for='data-2936c3e1-41a0-48ea-83d2-a25637935729' 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([], dtype=int64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-75bf30ab-959b-4cf4-8f53-c8a52af581a2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-75bf30ab-959b-4cf4-8f53-c8a52af581a2' 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>"
+       "</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-8e7368e2-df97-49cf-be41-25e59f9d9c13' class='xr-array-in' type='checkbox' checked><label for='section-8e7368e2-df97-49cf-be41-25e59f9d9c13' 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-240fc396-2ed4-4c0b-a267-0bb1aff5216e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-240fc396-2ed4-4c0b-a267-0bb1aff5216e' 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-957e2a87-00e6-4baa-9840-39e6e1d1dfdb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-957e2a87-00e6-4baa-9840-39e6e1d1dfdb' 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' (chain: 0, draw: 0)>\n",
-       "array([], shape=(0, 0), dtype=float64)\n",
-       "Coordinates:\n",
-       "  * chain    (chain) int64 \n",
-       "  * draw     (draw) int64 "
+       "<xarray.DataArray 'diverging' ()>\n",
+       "array(0)"
       ]
      },
-     "execution_count": 201,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# In this case there are none\n",
-    "trace.sample_stats.where(trace.sample_stats[\"diverging\"] == True, drop=True)[\"diverging\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It is often useful to compare the overall distribution of the\n",
-    "energy levels with the change of energy between successive samples.\n",
-    "Ideally, they should be very similar:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 202,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "az.plot_energy(trace, figsize=(6, 4))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If the overall distribution of energy levels has longer tails, the efficiency of the sampler will deteriorate quickly."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Multiple samplers\n",
-    "\n",
-    "If multiple samplers are used for the same model (e.g. for continuous and discrete variables), the exported values are merged or stacked along a new axis.\n",
-    "\n",
-    "Note that for the `model_logp` sampler statistic, only the last column (i.e. `trace.get_sampler_stat('model_logp')[-1]`) will be the overall model logp."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 203,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "CompoundStep\n",
-      ">BinaryMetropolis: [mu1]\n",
-      ">Metropolis: [mu2]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "                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 00:05<00:00 Sampling 2 chains, 0 divergences]\n",
-       "    </div>\n",
-       "    "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling 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 10% for some parameters.\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = pm.Model()\n",
-    "with model:\n",
-    "    mu1 = pm.Bernoulli(\"mu1\", p=0.8)\n",
-    "    mu2 = pm.Normal(\"mu2\", mu=0, sigma=1, shape=10)\n",
-    "    step1 = pm.BinaryMetropolis([mu1])\n",
-    "    step2 = pm.Metropolis([mu2])\n",
-    "    trace = pm.sample(\n",
-    "        10000, init=None, step=[step1, step2], cores=2, tune=1000, return_inferencedata=True\n",
-    "    )"
+    "trace.sample_stats[\"diverging\"].sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
@@ -1549,69 +1006,205 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:       (accept_dim_0: 2, chain: 2, draw: 10000)\n",
+       "Dimensions:    (chain: 2, draw: 2000, mu1_dim_0: 10)\n",
        "Coordinates:\n",
-       "  * chain         (chain) int64 0 1\n",
-       "  * draw          (draw) int64 0 1 2 3 4 5 6 ... 9994 9995 9996 9997 9998 9999\n",
-       "  * accept_dim_0  (accept_dim_0) int64 0 1\n",
+       "  * chain      (chain) int64 0 1\n",
+       "  * draw       (draw) int64 0 1 2 3 4 5 6 ... 1993 1994 1995 1996 1997 1998 1999\n",
+       "  * mu1_dim_0  (mu1_dim_0) int64 0 1 2 3 4 5 6 7 8 9\n",
        "Data variables:\n",
-       "    accept        (chain, draw, accept_dim_0) float64 0.25 0.002502 ... 0.5614\n",
-       "    p_jump        (chain, draw) float64 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5\n",
-       "    accepted      (chain, draw) bool False False True False ... False True True\n",
-       "    scaling       (chain, draw) float64 0.729 0.729 0.729 ... 0.729 0.729 0.729\n",
+       "    mu1        (chain, draw, mu1_dim_0) float64 nan nan nan nan ... nan nan nan\n",
        "Attributes:\n",
-       "    created_at:                 2021-04-02T11:12:53.065081\n",
+       "    created_at:                 2021-04-02T19:01:16.089527\n",
        "    arviz_version:              0.11.2\n",
        "    inference_library:          pymc3\n",
        "    inference_library_version:  3.11.2\n",
-       "    sampling_time:              11.091216087341309\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-0a5709a1-d298-4382-981e-98e13882d7f7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0a5709a1-d298-4382-981e-98e13882d7f7' 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'>accept_dim_0</span>: 2</li><li><span class='xr-has-index'>chain</span>: 2</li><li><span class='xr-has-index'>draw</span>: 10000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ccf162da-5fa5-4d14-9345-b0bcf9611631' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ccf162da-5fa5-4d14-9345-b0bcf9611631' class='xr-section-summary' >Coordinates: <span>(3)</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-bccf416f-ab15-42cd-80f3-b52945f4948b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bccf416f-ab15-42cd-80f3-b52945f4948b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fe03903-0890-423e-919f-401f5211f3b4' class='xr-var-data-in' type='checkbox'><label for='data-0fe03903-0890-423e-919f-401f5211f3b4' 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 ... 9996 9997 9998 9999</div><input id='attrs-bad5440e-6698-4b6b-bdd4-cb4c5a55ff80' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bad5440e-6698-4b6b-bdd4-cb4c5a55ff80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31b671e2-47bf-4794-b97e-3dc5c55d2bfd' class='xr-var-data-in' type='checkbox'><label for='data-31b671e2-47bf-4794-b97e-3dc5c55d2bfd' 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, ..., 9997, 9998, 9999])</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-beac17a7-a0b0-4e43-ba9b-603299a0ceee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-beac17a7-a0b0-4e43-ba9b-603299a0ceee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7cebc29-df61-40c2-806a-7dd177da01bf' class='xr-var-data-in' type='checkbox'><label for='data-d7cebc29-df61-40c2-806a-7dd177da01bf' 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-9666c4cf-647e-419e-b177-816b06b26379' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9666c4cf-647e-419e-b177-816b06b26379' class='xr-section-summary' >Data variables: <span>(4)</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>accept</span></div><div class='xr-var-dims'>(chain, draw, accept_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.25 0.002502 1.0 ... 1.0 0.5614</div><input id='attrs-eca83bcc-ad36-4dbc-b1e8-4619b61410c5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eca83bcc-ad36-4dbc-b1e8-4619b61410c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37f6e049-7545-471a-85ef-aa9f3fe63605' class='xr-var-data-in' type='checkbox'><label for='data-37f6e049-7545-471a-85ef-aa9f3fe63605' 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([[[2.50000000e-01, 2.50162193e-03],\n",
-       "        [1.00000000e+00, 3.04131487e-01],\n",
-       "        [1.00000000e+00, 2.75917351e-01],\n",
+       "    sampling_time:              8.847715854644775\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-7b9cf52e-3104-4df4-b14f-4dea18b8fad0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7b9cf52e-3104-4df4-b14f-4dea18b8fad0' 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><li><span class='xr-has-index'>mu1_dim_0</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fc22da89-0f16-4944-a292-bf94ada89d08' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fc22da89-0f16-4944-a292-bf94ada89d08' class='xr-section-summary' >Coordinates: <span>(3)</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-61fda56e-590e-4b55-a0e5-e75e027cd8c7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-61fda56e-590e-4b55-a0e5-e75e027cd8c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b6e1b1c-36c2-4965-820c-421dd971b2eb' class='xr-var-data-in' type='checkbox'><label for='data-0b6e1b1c-36c2-4965-820c-421dd971b2eb' 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-3e964c68-e86c-415e-96b5-b873c179dab7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e964c68-e86c-415e-96b5-b873c179dab7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7220a2f8-feb6-4b23-9f96-24662372780b' class='xr-var-data-in' type='checkbox'><label for='data-7220a2f8-feb6-4b23-9f96-24662372780b' 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><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mu1_dim_0</span></div><div class='xr-var-dims'>(mu1_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9</div><input id='attrs-05e01ebf-d2da-4377-9388-f5ca9b843acb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05e01ebf-d2da-4377-9388-f5ca9b843acb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e98c42b-582a-48d4-bd6f-ff7921097ba6' class='xr-var-data-in' type='checkbox'><label for='data-0e98c42b-582a-48d4-bd6f-ff7921097ba6' 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, 4, 5, 6, 7, 8, 9])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-91a95e8e-c755-4aa1-bf00-d8bab8c80981' class='xr-section-summary-in' type='checkbox'  checked><label for='section-91a95e8e-c755-4aa1-bf00-d8bab8c80981' class='xr-section-summary' >Data variables: <span>(1)</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>mu1</span></div><div class='xr-var-dims'>(chain, draw, mu1_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-ac3e4422-6c8a-4ad6-aa0e-51197d1d925f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac3e4422-6c8a-4ad6-aa0e-51197d1d925f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47f40208-96ab-47f7-8f84-43ae16afb787' class='xr-var-data-in' type='checkbox'><label for='data-47f40208-96ab-47f7-8f84-43ae16afb787' 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([[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
        "        ...,\n",
-       "        [1.00000000e+00, 5.05600256e-01],\n",
-       "        [1.00000000e+00, 1.20457745e-01],\n",
-       "        [1.00000000e+00, 4.32905427e-01]],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
        "\n",
-       "       [[2.50000000e-01, 9.11142521e-03],\n",
-       "        [1.00000000e+00, 1.46295614e+00],\n",
-       "        [1.00000000e+00, 3.66074682e-03],\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
        "        ...,\n",
-       "        [2.50000000e-01, 5.82228319e-02],\n",
-       "        [4.00000000e+00, 1.00805546e+00],\n",
-       "        [1.00000000e+00, 5.61351579e-01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p_jump</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5</div><input id='attrs-65856302-b550-4ed9-af45-421c54f4feb6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65856302-b550-4ed9-af45-421c54f4feb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4a3ba1b-b205-413e-bbdb-3cadc8ddcba8' class='xr-var-data-in' type='checkbox'><label for='data-f4a3ba1b-b205-413e-bbdb-3cadc8ddcba8' 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([[0.5, 0.5, 0.5, ..., 0.5, 0.5, 0.5],\n",
-       "       [0.5, 0.5, 0.5, ..., 0.5, 0.5, 0.5]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accepted</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 True ... True True</div><input id='attrs-b3e542b6-6af9-4a59-b8fb-6ec9e75ce542' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b3e542b6-6af9-4a59-b8fb-6ec9e75ce542' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afc0fbb0-6da3-40f9-b69c-b0187e93c8d4' class='xr-var-data-in' type='checkbox'><label for='data-afc0fbb0-6da3-40f9-b69c-b0187e93c8d4' 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,  True, ..., False, False, False],\n",
-       "       [False,  True, False, ..., False,  True,  True]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scaling</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.729 0.729 0.729 ... 0.729 0.729</div><input id='attrs-af4c4f2e-38a5-4d26-b192-14135aac5554' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af4c4f2e-38a5-4d26-b192-14135aac5554' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d499250-4f83-4f3f-94df-d5057989fe84' class='xr-var-data-in' type='checkbox'><label for='data-7d499250-4f83-4f3f-94df-d5057989fe84' 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([[0.729, 0.729, 0.729, ..., 0.729, 0.729, 0.729],\n",
-       "       [0.729, 0.729, 0.729, ..., 0.729, 0.729, 0.729]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a07c081b-3715-44c0-9c40-0c5768b86847' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a07c081b-3715-44c0-9c40-0c5768b86847' 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>2021-04-02T11:12:53.065081</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>11.091216087341309</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-adc3a3fe-d4b5-4440-af51-fe698cad340f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-adc3a3fe-d4b5-4440-af51-fe698cad340f' 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>2021-04-02T19:01:16.089527</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>8.847715854644775</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
-       "Dimensions:       (accept_dim_0: 2, chain: 2, draw: 10000)\n",
+       "Dimensions:    (chain: 2, draw: 2000, mu1_dim_0: 10)\n",
        "Coordinates:\n",
-       "  * chain         (chain) int64 0 1\n",
-       "  * draw          (draw) int64 0 1 2 3 4 5 6 ... 9994 9995 9996 9997 9998 9999\n",
-       "  * accept_dim_0  (accept_dim_0) int64 0 1\n",
+       "  * chain      (chain) int64 0 1\n",
+       "  * draw       (draw) int64 0 1 2 3 4 5 6 ... 1993 1994 1995 1996 1997 1998 1999\n",
+       "  * mu1_dim_0  (mu1_dim_0) int64 0 1 2 3 4 5 6 7 8 9\n",
        "Data variables:\n",
-       "    accept        (chain, draw, accept_dim_0) float64 0.25 0.002502 ... 0.5614\n",
-       "    p_jump        (chain, draw) float64 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5\n",
-       "    accepted      (chain, draw) bool False False True False ... False True True\n",
-       "    scaling       (chain, draw) float64 0.729 0.729 0.729 ... 0.729 0.729 0.729\n",
+       "    mu1        (chain, draw, mu1_dim_0) float64 nan nan nan nan ... nan nan nan\n",
        "Attributes:\n",
-       "    created_at:                 2021-04-02T11:12:53.065081\n",
+       "    created_at:                 2021-04-02T19:01:16.089527\n",
        "    arviz_version:              0.11.2\n",
        "    inference_library:          pymc3\n",
        "    inference_library_version:  3.11.2\n",
-       "    sampling_time:              11.091216087341309\n",
+       "    sampling_time:              8.847715854644775\n",
        "    tuning_steps:               1000"
       ]
      },
-     "execution_count": 204,
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trace.posterior.where(trace.sample_stats[\"diverging\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is often useful to compare the overall distribution of the\n",
+    "energy levels with the change of energy between successive samples.\n",
+    "Ideally, they should be very similar:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_energy(trace, figsize=(6, 4));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If the overall distribution of energy levels has longer tails, the efficiency of the sampler will deteriorate quickly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multiple samplers\n",
+    "\n",
+    "If multiple samplers are used for the same model (e.g. for continuous and discrete variables), the exported values are merged or stacked along a new axis.\n",
+    "\n",
+    "Note that for the `model_logp` sampler statistic, only the last column (i.e. `trace.get_sampler_stat('model_logp')[-1]`) will be the overall model logp."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Multiprocess sampling (2 chains in 2 jobs)\n",
+      "CompoundStep\n",
+      ">BinaryMetropolis: [mu1]\n",
+      ">Metropolis: [mu2]\n"
+     ]
+    },
+    {
+     "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",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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 10% for some parameters.\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = pm.Model()\n",
+    "coords = {\"step\": [\"BinaryMetropolis\", \"Metropolis\"]}\n",
+    "dims = {\"accept\": [\"step\"]}\n",
+    "\n",
+    "with model:\n",
+    "    mu1 = pm.Bernoulli(\"mu1\", p=0.8)\n",
+    "    mu2 = pm.Normal(\"mu2\", mu=0, sigma=1, shape=10)\n",
+    "    step1 = pm.BinaryMetropolis([mu1])\n",
+    "    step2 = pm.Metropolis([mu2])\n",
+    "    trace = pm.sample(\n",
+    "        10000,\n",
+    "        init=None,\n",
+    "        step=[step1, step2],\n",
+    "        cores=2,\n",
+    "        tune=1000,\n",
+    "        return_inferencedata=True,\n",
+    "        idata_kwargs={\"dims\": dims, \"coords\": coords},\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Data variables:\n",
+       "    accepted  (chain, draw) bool False False False False ... False False True\n",
+       "    accept    (chain, draw, accept_dim_0) float64 0.25 0.4891 1.0 ... 1.0 2.984\n",
+       "    p_jump    (chain, draw) float64 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5\n",
+       "    scaling   (chain, draw) float64 0.729 0.729 0.729 ... 0.729 0.729 0.729"
+      ]
+     },
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "trace.sample_stats"
+    "trace.sample_stats.data_vars"
    ]
   },
   {
@@ -1623,7 +1216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
@@ -1981,64 +1574,64 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;accept&#x27; (chain: 2, draw: 10000, accept_dim_0: 2)&gt;\n",
-       "array([[[2.50000000e-01, 2.50162193e-03],\n",
-       "        [1.00000000e+00, 3.04131487e-01],\n",
-       "        [1.00000000e+00, 2.75917351e-01],\n",
+       "array([[[2.50000000e-01, 4.89096541e-01],\n",
+       "        [1.00000000e+00, 5.98009353e-02],\n",
+       "        [4.00000000e+00, 8.32488450e-02],\n",
        "        ...,\n",
-       "        [1.00000000e+00, 5.05600256e-01],\n",
-       "        [1.00000000e+00, 1.20457745e-01],\n",
-       "        [1.00000000e+00, 4.32905427e-01]],\n",
+       "        [2.50000000e-01, 1.19740654e-01],\n",
+       "        [1.00000000e+00, 4.83793600e-02],\n",
+       "        [1.00000000e+00, 1.10393484e-01]],\n",
        "\n",
-       "       [[2.50000000e-01, 9.11142521e-03],\n",
-       "        [1.00000000e+00, 1.46295614e+00],\n",
-       "        [1.00000000e+00, 3.66074682e-03],\n",
+       "       [[2.50000000e-01, 1.09888343e-05],\n",
+       "        [1.00000000e+00, 3.37071637e-01],\n",
+       "        [1.00000000e+00, 8.57329324e+00],\n",
        "        ...,\n",
-       "        [2.50000000e-01, 5.82228319e-02],\n",
-       "        [4.00000000e+00, 1.00805546e+00],\n",
-       "        [1.00000000e+00, 5.61351579e-01]]])\n",
+       "        [1.00000000e+00, 7.96799606e-02],\n",
+       "        [2.50000000e-01, 2.55275918e-04],\n",
+       "        [1.00000000e+00, 2.98391458e+00]]])\n",
        "Coordinates:\n",
        "  * chain         (chain) int64 0 1\n",
        "  * draw          (draw) int64 0 1 2 3 4 5 6 ... 9994 9995 9996 9997 9998 9999\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'>draw</span>: 10000</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-9199732f-abff-41bc-9c97-39c90215f033' class='xr-array-in' type='checkbox' checked><label for='section-9199732f-abff-41bc-9c97-39c90215f033' 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>0.25 0.002502 1.0 0.3041 1.0 0.2759 ... 0.05822 4.0 1.008 1.0 0.5614</span></div><div class='xr-array-data'><pre>array([[[2.50000000e-01, 2.50162193e-03],\n",
-       "        [1.00000000e+00, 3.04131487e-01],\n",
-       "        [1.00000000e+00, 2.75917351e-01],\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'>draw</span>: 10000</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-bb0fb48d-8c96-424f-86b6-f185ae8c7474' class='xr-array-in' type='checkbox' checked><label for='section-bb0fb48d-8c96-424f-86b6-f185ae8c7474' 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>0.25 0.4891 1.0 0.0598 4.0 ... 0.07968 0.25 0.0002553 1.0 2.984</span></div><div class='xr-array-data'><pre>array([[[2.50000000e-01, 4.89096541e-01],\n",
+       "        [1.00000000e+00, 5.98009353e-02],\n",
+       "        [4.00000000e+00, 8.32488450e-02],\n",
        "        ...,\n",
-       "        [1.00000000e+00, 5.05600256e-01],\n",
-       "        [1.00000000e+00, 1.20457745e-01],\n",
-       "        [1.00000000e+00, 4.32905427e-01]],\n",
+       "        [2.50000000e-01, 1.19740654e-01],\n",
+       "        [1.00000000e+00, 4.83793600e-02],\n",
+       "        [1.00000000e+00, 1.10393484e-01]],\n",
        "\n",
-       "       [[2.50000000e-01, 9.11142521e-03],\n",
-       "        [1.00000000e+00, 1.46295614e+00],\n",
-       "        [1.00000000e+00, 3.66074682e-03],\n",
+       "       [[2.50000000e-01, 1.09888343e-05],\n",
+       "        [1.00000000e+00, 3.37071637e-01],\n",
+       "        [1.00000000e+00, 8.57329324e+00],\n",
        "        ...,\n",
-       "        [2.50000000e-01, 5.82228319e-02],\n",
-       "        [4.00000000e+00, 1.00805546e+00],\n",
-       "        [1.00000000e+00, 5.61351579e-01]]])</pre></div></div></li><li class='xr-section-item'><input id='section-df20a2e6-cbd3-4635-8a02-f0007973f1ec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-df20a2e6-cbd3-4635-8a02-f0007973f1ec' class='xr-section-summary' >Coordinates: <span>(3)</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-160b2060-d67a-4eac-8c7e-70b08763c0a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-160b2060-d67a-4eac-8c7e-70b08763c0a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a41ac28c-364a-4403-a529-edde39ae8035' class='xr-var-data-in' type='checkbox'><label for='data-a41ac28c-364a-4403-a529-edde39ae8035' 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 ... 9996 9997 9998 9999</div><input id='attrs-415a4d45-8e0e-4d17-b027-985814fa3c93' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-415a4d45-8e0e-4d17-b027-985814fa3c93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b057694f-ab32-4e38-9763-d0b39526e49b' class='xr-var-data-in' type='checkbox'><label for='data-b057694f-ab32-4e38-9763-d0b39526e49b' 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, ..., 9997, 9998, 9999])</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-0117a235-01d3-4574-88c3-89c0fca49db1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0117a235-01d3-4574-88c3-89c0fca49db1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-368ca319-65fb-4be4-ab14-d8686f039400' class='xr-var-data-in' type='checkbox'><label for='data-368ca319-65fb-4be4-ab14-d8686f039400' 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-710b9d61-721a-4edc-9386-fa9925357d7c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-710b9d61-721a-4edc-9386-fa9925357d7c' 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>"
+       "        [1.00000000e+00, 7.96799606e-02],\n",
+       "        [2.50000000e-01, 2.55275918e-04],\n",
+       "        [1.00000000e+00, 2.98391458e+00]]])</pre></div></div></li><li class='xr-section-item'><input id='section-52735604-3aa4-4aa0-9c0a-d21db4d01c62' class='xr-section-summary-in' type='checkbox'  checked><label for='section-52735604-3aa4-4aa0-9c0a-d21db4d01c62' class='xr-section-summary' >Coordinates: <span>(3)</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-d7fd42d9-37b9-4171-9632-6440628f03b4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7fd42d9-37b9-4171-9632-6440628f03b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98f4f890-226d-4551-9595-15ed72e1d426' class='xr-var-data-in' type='checkbox'><label for='data-98f4f890-226d-4551-9595-15ed72e1d426' 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 ... 9996 9997 9998 9999</div><input id='attrs-0303f4af-92a9-4639-bce8-98f99b8bf917' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0303f4af-92a9-4639-bce8-98f99b8bf917' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e66dbe4-c181-4aac-a58e-72e24a180b28' class='xr-var-data-in' type='checkbox'><label for='data-5e66dbe4-c181-4aac-a58e-72e24a180b28' 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, ..., 9997, 9998, 9999])</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-89e347fe-61cf-4bb9-b49e-b680e58466e4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-89e347fe-61cf-4bb9-b49e-b680e58466e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5539634-6521-4526-b7e9-c587854c81a0' class='xr-var-data-in' type='checkbox'><label for='data-a5539634-6521-4526-b7e9-c587854c81a0' 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-25e2acac-c6f8-4fbc-b624-3691832bd240' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-25e2acac-c6f8-4fbc-b624-3691832bd240' 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, draw: 10000, accept_dim_0: 2)>\n",
-       "array([[[2.50000000e-01, 2.50162193e-03],\n",
-       "        [1.00000000e+00, 3.04131487e-01],\n",
-       "        [1.00000000e+00, 2.75917351e-01],\n",
+       "array([[[2.50000000e-01, 4.89096541e-01],\n",
+       "        [1.00000000e+00, 5.98009353e-02],\n",
+       "        [4.00000000e+00, 8.32488450e-02],\n",
        "        ...,\n",
-       "        [1.00000000e+00, 5.05600256e-01],\n",
-       "        [1.00000000e+00, 1.20457745e-01],\n",
-       "        [1.00000000e+00, 4.32905427e-01]],\n",
+       "        [2.50000000e-01, 1.19740654e-01],\n",
+       "        [1.00000000e+00, 4.83793600e-02],\n",
+       "        [1.00000000e+00, 1.10393484e-01]],\n",
        "\n",
-       "       [[2.50000000e-01, 9.11142521e-03],\n",
-       "        [1.00000000e+00, 1.46295614e+00],\n",
-       "        [1.00000000e+00, 3.66074682e-03],\n",
+       "       [[2.50000000e-01, 1.09888343e-05],\n",
+       "        [1.00000000e+00, 3.37071637e-01],\n",
+       "        [1.00000000e+00, 8.57329324e+00],\n",
        "        ...,\n",
-       "        [2.50000000e-01, 5.82228319e-02],\n",
-       "        [4.00000000e+00, 1.00805546e+00],\n",
-       "        [1.00000000e+00, 5.61351579e-01]]])\n",
+       "        [1.00000000e+00, 7.96799606e-02],\n",
+       "        [2.50000000e-01, 2.55275918e-04],\n",
+       "        [1.00000000e+00, 2.98391458e+00]]])\n",
        "Coordinates:\n",
        "  * chain         (chain) int64 0 1\n",
        "  * draw          (draw) int64 0 1 2 3 4 5 6 ... 9994 9995 9996 9997 9998 9999\n",
        "  * accept_dim_0  (accept_dim_0) int64 0 1"
       ]
      },
-     "execution_count": 205,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2049,7 +1642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [
     {
@@ -2058,19 +1651,19 @@
      "text": [
       "The watermark extension is already loaded. To reload it, use:\n",
       "  %reload_ext watermark\n",
-      "Last updated: Fri Apr 02 2021\n",
+      "Last updated: Sat Apr 03 2021\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.9.2\n",
       "IPython version      : 7.21.0\n",
       "\n",
+      "numpy     : 1.20.1\n",
       "seaborn   : 0.11.1\n",
       "matplotlib: 3.3.4\n",
-      "arviz     : 0.11.2\n",
       "pandas    : 1.2.3\n",
-      "xarray    : 0.17.0\n",
       "pymc3     : 3.11.2\n",
-      "numpy     : 1.20.1\n",
+      "arviz     : 0.11.2\n",
+      "xarray    : 0.17.0\n",
       "\n",
       "Watermark: 2.2.0\n",
       "\n"
@@ -2081,6 +1674,13 @@
     "%load_ext watermark\n",
     "%watermark -n -u -v -iv -w"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 61040ec2525ca99f0684a39fdc21ce55f095e02c Mon Sep 17 00:00:00 2001
From: almostmeenal <meenaljhajharia2304@gmail.com>
Date: Sat, 3 Apr 2021 02:01:58 +0530
Subject: [PATCH 3/8] deleted step size plot, added accept plot

---
 .../sampler-stats.ipynb                       | 532 +++++++++---------
 1 file changed, 265 insertions(+), 267 deletions(-)

diff --git a/examples/diagnostics_and_criticism/sampler-stats.ipynb b/examples/diagnostics_and_criticism/sampler-stats.ipynb
index 983635f6b..72ef90d58 100644
--- a/examples/diagnostics_and_criticism/sampler-stats.ipynb
+++ b/examples/diagnostics_and_criticism/sampler-stats.ipynb
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -41,11 +41,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "az.style.use(\"arviz-darkgrid\")"
+    "az.style.use(\"arviz-darkgrid\")\n",
+    "plt.rcParams[\"figure.constrained_layout.use\"] = False"
    ]
   },
   {
@@ -57,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -112,7 +113,7 @@
      "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 9 seconds.\n"
+      "Sampling 2 chains for 1_000 tune and 2_000 draw iterations (2_000 + 4_000 draws total) took 10 seconds.\n"
      ]
     }
    ],
@@ -131,148 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Data variables: (12/13)\n",
-       "    perf_counter_start  (chain, draw) float64 1.594e+03 1.594e+03 ... 1.596e+03\n",
-       "    lp                  (chain, draw) float64 -13.13 -13.67 ... -15.77 -14.19\n",
-       "    diverging           (chain, draw) bool False False False ... False False\n",
-       "    max_energy_error    (chain, draw) float64 -0.4791 0.4339 ... 0.8172 -0.4252\n",
-       "    step_size_bar       (chain, draw) float64 0.9529 0.9529 ... 0.9093 0.9093\n",
-       "    acceptance_rate     (chain, draw) float64 0.9819 0.8846 ... 0.6049 1.0\n",
-       "    ...                  ...\n",
-       "    process_time_diff   (chain, draw) float64 0.00057 0.00031 ... 0.000412\n",
-       "    perf_counter_diff   (chain, draw) float64 0.00057 0.0003091 ... 0.0004153\n",
-       "    tree_depth          (chain, draw) int64 3 2 2 2 2 2 2 2 ... 2 2 2 2 2 2 2 2\n",
-       "    step_size           (chain, draw) float64 0.8548 0.8548 ... 0.852 0.852\n",
-       "    n_steps             (chain, draw) float64 7.0 3.0 3.0 3.0 ... 3.0 3.0 3.0\n",
-       "    energy_error        (chain, draw) float64 -0.3426 0.125 ... 0.7733 -0.3769"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trace.sample_stats.data_vars"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[Arviz](https://arviz-devs.github.io/arviz/schema/schema.html#sample-stats) follows the following Name Convention for sample_stats variables:\n",
-    "\n",
-    "- `lp`: The joint log posterior density for the model (up to an additive constant).\n",
-    "\n",
-    "- `acceptance_rate`: The average acceptance probabilities of all possible samples in the proposed tree.\n",
-    "\n",
-    "- `step_size`: The current integration step size.\n",
-    "\n",
-    "- `step_size_nom`: The nominal integration step size. The `step_size` may differ from this, for example if the step size is jittered. Should only be present if `step_size` is also present and it varies between samples (i.e. step size is jittered).\n",
-    "\n",
-    "- `tree_depth`: The number of tree doublings in the balanced binary tree.\n",
-    "\n",
-    "- `n_steps`: The number of leapfrog steps computed. It is related to `tree_depth` with `n_steps <= 2^tree_dept`.\n",
-    "\n",
-    "- `diverging`: (boolean) Indicates the presence of leapfrog transitions with large energy deviation from starting and subsequent termination of the trajectory. “large” is defined as `max_energy_error` going over a threshold.\n",
-    "\n",
-    "- `energy`: The value of the Hamiltonian energy for the accepted proposal (up to an additive constant).\n",
-    "\n",
-    "- `energy_error`: The difference in the Hamiltonian energy between the initial point and the accepted proposal.\n",
-    "\n",
-    "- `max_energy_error`: The maximum absolute difference in Hamiltonian energy between the initial point and all possible samples in the proposed tree.\n",
-    "\n",
-    "- `int_time`: The total integration time (static HMC sampler)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If the name of the statistic does not clash with the name of one of the variables, we can use indexing to get the values. The values for the chains will be concatenated.\n",
-    "\n",
-    "We can see that the step sizes converged after the 1000 tuning samples for both chains to about the same value. The first 2000 values are from chain 1, the second 2000 from chain 2."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "az.plot_posterior(trace, group=\"sample_stats\", var_names=\"step_size\", hdi_prob=\"hide\", kind=\"kde\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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N2QeXKR80l2ceeIaO+mO8srQUN78JzwuxaYAflYmTEiNON6Yjo1iq4gcOWXkaWb6C2/94+zqfxdhcJ7bYQzblUJpNh6c3TmPN5hJeMkyf374et/IlsmJAWhumGG8kcwqkMw/DP+X9mPomvN99H1Ntv2daN0eWZiRJGSfKYfGp+z4lZxA38wkjB9eCdR1Cz6OVm85mO4dopMFgMp0Rr48pM4ss6PoNJXcYm+tkU9dpPPFIgzVP16hVE3wf/tidY2olxDgw0vDwO8qks05i/oI+AB580JI+8wDzq//KNG8VmWeJShVGag5xrc5Qq0RKgOs5eD4kNseqwal0+5voD6pk5TpZcSqDxUPp61hPefA+0laLNMkx4h2KDQfI4haB9RiiQmKK1JsQZTlW+QfjT+kjcYv0DCyhbDdQcFs8YecQN1p0soFC7hkKbgMch2ZWwbV5HDfF9WoY15DGXVSbh/JQeBindP8ngVlN4LagNIV617E86LyP+56cRt/Gn3Bc+R6afkI2cy5T3vIBslnH75Mctif2+7AdRRHf+ta3sNYyNDTEf//3f/PUU09x7rnnjvthxUajQX9/P/Pnz8d13Z0eP+iggwC2u0O+dQz31sfGa78vhaHl979r0b15OR3lFvdt6KFs++nILac6+BZWr81RyLd/cXddeOj3cOjMfl5Tu55cuJ5qVqTgNJhT+0/S+l3ExpB3RyBuksaGctbPuTO/x6raIWTVzUQMErgDmNTi4uBhScOQen+OXNi+49cacbFJQsnrxwyvxwLGGIJkiKJbwzFgIyACHwiMoZEVacQ+lZHf0z/g0us8jnGHWDs8ncOLD1Axq4jigGKylpxptp9sLMYa8lSxJoe1kIYZedMELFnm4JDh2hbNLGZmMELFLfFoNY9rG0yb7fIsJ9N84m66zaMYm0JzBGwLAzipQ0CDXudJ6pu6WPGrHG6a8Ar3v4mShFYc0Fd6nGKyif9aeR4nH72Z/IZlNDc3KU0PGBg0lKOHOCL3JE9snEneeZRysI4kznCJyNMkF+c4jPX4DiQ1l6FGgpfbzAx7H1MGf4lJG3hejJNFdNpnKNcHSK0LGPL5KjazhP0uuWgjTvdskr4FuE8twdv8LKEp4RhDR+0xsmU/ZC7zObbjLohDWklGyanSa1oMmVkUG0M0+qdQylzMyEaacS+JmUUf/83q4Vk8kZ3Cwt57GdycEmZFpnsrsFmTRlam4vXTGa/HG3Rwps8hbYYE1WUYG7fvXBACLbIogJEB3OG1WDySNI+XjFBmBHCxMYxUAzrcR+gkYjCZBqZFId6MS4rjZJgsARe8rEk2UscmXWTRMB1phG8i0iQlzlwKzgCHu/9JY7CbzcksuqNnCFohnaZI0W/QyoZ4ZnMnucceZuBZh5mNxymbzdTqCZ0MMsV5lPrgOkbWuXilDjrdfrJag7BZx+30MY6BKGLNhiLZALxj+lLCJEcu2oTvDpA5eZzMZU54J3FcYWV1FgfnVlLxN+CRYlKAlIABpjkjxPFhbKjP4hW5u2iEJR6vH8P84kP0OQ2erB9JOdlMt9sgZ8L2X1GaRfA8+pI76R2p4RMSkWOKtx6PhEbaSUSZOd5jvKZwE5vDQ3jm2U5+9jOYN6tJ97plMLIRNjxKVN2MGyd02nU4RICDJcNLR5jmPcs8P0+HXYfNAh6qn47HJvpG7ufJTX3MKG2mx1/LHB4jnwzjeRaLxcRVTLVOFpTpH5rNQd4jmACGwgppM6LsPktYfYAHf5UjGvDpZDOvKPyeHrOaLARyLqbWj9OoYqMiNiowxR+i5DWoxWVacYke5xGKrWdYY4+ir7yBcvgMqTUkTgkvqTIv/Q3zzTR+572LJ5/MMbQx5LVTllOINzC79Cgd6TrSegZBiEkHwAB2y2eAsow8GQZLkvnE1mGGeYRKuJ6no+OwxuGgjt+QNQyD0RyC3oxg7V2kfpl10dHkRx7mUFZR9wKm+utppnlyJqbXfxaMSxr7BNTwkhAcF7wcTnUdfrYWQ54Eg5Ol9NhBwKNhurGhg7PiTmKvQmjmEUYOU6NHiAfLrPffxh9+D6/p+m9mmkdohC6bN0fMSO6iFRRo2grlbB3O5gTbEbR/qYghZ9sBIMvAMeBGhjQr4roudmQ9UzKHsDgLt9aPqW7C8XuJkjn0Rg8xo/o4pVaDpu3HZlW8NKPiDUMYk7YCsq4OnFo/3vBabH2QrL4ZlxiLJcjqBG6DOMpRp5uS3QStGkk2RMlv0UjyjJgCzvAj2HU5DjU+Ra/OxpFe/KfX4a67HxtO55COR5jqrsZJUmyaEVCny9Rx3C4KWZWO5ghxVsLpmYa34Q+4cQvr5yBqQJZg0hgTNaE5Qi2aTbMKc8wQRdaTRQ6ZF1Gsb4ZaSN7EGC8gTBzS9SsIlt2A11yHaQxiohGImhgaGFz8tEXF+BhSwjBP4LYwBPg2IXAihm0vtDLK8QCpGWag1YmfeVQ8mLdhKSPNMsEr+nCGV+M8eQPDA6eyZmAGx5eW4Dhw/5qTmRbeT0cZnm6ezgxniPzG5fxu2RRSk6M1PMwxwz+mK3mayOQppOvxGqvJ2w6StINp3mY8EzMQ92JSl7xtMS/YQCWo4ZsmzZEyXUE/hYHVBAMbsDmfOLIE2QAzkmXUowKNtEhHIaYrWsOz9dl0ej5BEDFULTEwAgunLmWwVSBzEvLOZnpb/Vjj0p3bTM408ExKah06TRNrDDFFgqSFjcHYHDO9xzjMvRdiC36ETRKc+gBp649MszfRtfZwjun8bzwnoRHlMetWEi//Me6bXwGMP6RjX9jvw3Ycx1xzzTWjPxtj+NM//VP+6q/+atznjoyMAOx2fPXW5VvbAdRqNQA6Ojr2qP2uVCoVHGfyP3s6OJjhpOuYUYlpOr14eY9m7NDJEHOnJfz+qSIzZzhgDN1dLqtWJRzUsZnC8AiZ30mzWSIoFMnHGwnIGHZngR0g9Tpw4oSIElP9DZSLLdLUI8bH4m4J0GCMgzEeBgeXBGshMwWcjqk40TCBN0yWQmTzWJMjM008F7IUMtu+wBvXIc0KGNcntS69/jo6GGIwm42Xg4gyBVMl50dEJk+RBik+Lgng4gDWMaTWwcS2/ZsxDsZ1yFLTvgtpDa4LaVag7NUBD7wCvd0ObDA4WYLrB+A6pInBArgOFpcAi09CvtlPudDCc2FjrRfHTUntZnwnJXASsmAKPcmDNJICaX4WLS+ly1uPSYfxgrnkaZG4JUxWI0nzgMVzwSci5xlqaZ5q1s30XITrZOScKiNxkcyDRlig6IyQEeOajMTNg1/EWIPFwzUJXhDgexmxTbE4mFwXuD62MUin6adUDvCclIbfhRtWSRwf183ImQysh81c0szBuAE2CSh0duLW1uH6LiUyjFPA9ZsU0yZhViCgRd12U3Jb+FkT8EiDHkq5YdiUkOLiOQk2C3CIsLT/+uBmhrT9YpC4AQEhGQ5p6pBZg2sMxiYYv4y1CeDhEJPh4rgGsEA7FFnHxyHDeAYnywjJk+DiuAE5WiSkDNsCaeYSpgVyXkxMAcdAmBVpxZZCayM9wTBNbxZu7VlGmMk0p0rqu1TrLl25FJPvoyN7mCjLU/Y8PM/QzPIQRzieQyU3wjrbg++ktLIivgMm6MC1w+CkREmAX8qw1sWQkuLSPsra+5MLIIt8fDcljAv4gUNCAc8ZwXF94iyPKSTkGCJzSyRJgFucgt9ai+eOMBJXwM+R4YFNSI1LM+nAo86U3GZm5YYh7KLRcDBxStHLyDoqxGlKy5tOBysxkQMYLA4WBweLNQ4dfo1qOpV64tPZ6VBLOimaEVppjnIhI6ODvNsitR4eCZYAQ3PLrjmkmYfjgDEW47gMJd1MNy3yOYfcpn46nSK5Yp6OoIZt/40D33Hbv8inEWHWRWYdjDGAIbU+raxIhRq+iTFeDtf1McaQpR5prkKUxLhOyoxyP1NNyMBwDs828GyDXGeOnNMi8Yq4aR3r5vFs0n4tjENqE1ICfJrt88h1iOMigVfFsyGpW8K6OXxnFca1VN0e3EL7vMz8ElnTJzYdeGaIchDRzEqkGFzAWIvjZu2gQnt/DeAGRdJ0BIjBGDKbw7gJTlIn9VwsARS6cBqDOFmKW5lCM/IpFwcp2WGmdlrWPxMztVDDp4jxu2gMbMZ1U3Bz5ElJssLojQnHTcniFOO0TyXXZfSal5givuPj2gZ+4GGLFdxkgGTYw/UCorSLUmGQINtMMVckC3MkFAmCFIcQ67lk1mBKvXi0oNEgycItx5ZLRrsGWIvBkLpFrJOCC0VqhLaMG+RpZhWsadDt9WPdIlWnD8fkqCUd5O0wvj+Tst8A42Ft1j5uTIo1YBwX3yQYDLEtUMh34rb6IYvBKT1398lmELSHf9TpoeTH5A34WUpkc7RMiYpbbw9L8S1R0IVnDS1bwmv048c1KHdDYxPkiqRhjRQf41oMDhaDn0VE5HHI2q+tcXBchzD1cUxIjiZ15xByXoYt+JTSGlXnFUwv9mABb+19JKaI5zi4gU+aQt6Nibb8Etg71acRz2BmqcrGliXyCszuWk9p3TDkOoiyAvhVSOs4xtK0nRRMDWifU+0hjTkquTqJ9ck5LYaTLrpMTCkYxgljMr8HGzfbv4RnNSJbAccjAxxj2jenPI/Y8Sn6IbXEkLMjhM5MCt5mGqaXsrOSpu3ENSkZHoltX2dcE2KNu2W4q0dqwXFdbOyS8xrUk04ckxBTIkdMZj06GGSa/wy+l1FNe3F9j0biYOojVPwU2LMx1C+2/T5sl0olHn/8cbIsY+PGjfz617/mq1/9Kg888ADf+c53xvyg4r5SrVZflO2EoSVzDes3+Uwr95O0OinbflpuhWc2ejhuyrr1KdOmweAQ5HOwamQKR9BBLn6WgpPghg0SE5AalyAdBtfgxiNkFAmoU4tL1Bp5unN1fGIMKQaLtQ7YtB2+vYyU9vhfx0bYkfVknkeU+FjAMRnGhjg2JUvZsmzLBT7NcG0Tm8a4fkp/PJPAGabLXc1wOJ2gWCO2PlES0BkMYo3BJaL9bEuGg81suy9O+86aQ0aWbvnXuhhj238qtU1qSYkcDUia9A9mHIQlczxsEmPSDLDtnJBmGCDCEGcerVwvzThhTvY7uvx+WnGAaxLi1CXKPJxoMwNhB0WvidtaSz6BehRjcp0kUUzLydPjrCdJLR4hFpckBUuATQAbU3H6ScKAJF8gzio4TgMnCSmbJlEWkDntX0hIMogb2Mxi/BypNdgoIkkcXONiyLDhEMYYTJYxbHvZODKPgzqWEcRDGCfFIyZMfUIciiSYXIrrQJpGGBvRHB4mwJLGKfXMwWZN0rh9Z3uK18RiKZlBbOoTpQGe4+BFAzSiiC7j4doYmxkMUftuJzHWeqTGYrMU8PHSiHaVM1wyHGNJrcXgYeMaxk+AhAwHhxSTtu9sb2WyuB0wE4tjHDxaWFzSFFp4RNYlME1cJyXnhtSjIkWvQcvmyTkN8r7DQH4aA41ByvFa0iSlk37CrJtWkuK5UAtdupI1jMQ5Otw6WeITZwY3icCfQ5ZkVMMO3LRO7Lh0Ow0y8thohNT3iTOXwIuIE4eim2IBl3T02AWXMAKHmDh1yTlN4maG5zVJMpcsjfG9FjaKCIMChbSO51hobCZxAqK0g7zTJEozHC8BA26WUnBHyLKMzeEU1oadDEcZ06dlWD+hkTgwUgXXJZ9soBX7BFvu5BoyDFn7F1abMRKXSVIInJjh4Yy+zmEatoO8G1JrOpSCEVomT4c3vOUXiLB9blrAZO3hRxkYx2CzlC5vEGyOVpgR5noZrvuUa5sZMWWmBBk+ESZzSdMY6wTknIiWcWl/jt/impi808ACsfWxcdhuay2Ok+DGVQIMaVZgfa2XTeSIIkjyRRJTJBweIezJ05Wsb4eiNCTNLBg7emfbJaIdBTNsmuGbBjaFxM21A3rWJM4MWWrIuQOkzYzUOti4juPG+HaE0PrUo4AZ+TU00zwOMdYY0swht6X/hqx9NESNLdt2wFocQkhTMhxMkmK8CNscInN8MsclrW2mkLm0wpR6rpNNwwYnF7CpWabTNLCtkGIQkSYuJCEtm6fT3fLLgw2xadS+SbHlRn6atm+wk2Z4poHJiqTGI4ocokYVr9Ui7yS0kojADtFopYS5KRA26LQhHg2yKCPzXEgScF1svZ/E88H4WCcH1GjfBrGQWnANFoubNjAmhtSlkZUpuS0aERT8KpG1DCa9JJFP0dtIFvVS7hihZTqJo4xaXKTLTzDG4pq0fexasFlKknnkHYtnmmStYdIsA8fHZkn7OpIl7dAdtchynZTSAfrj6QA4qYu1MXlbJ00txoEkM7jREGGWbw+9KB4CzQRTG2z/Ihg2AINLTJp6WDIMKZHNk6dFTNB+bW1GlmT4JBibEZoCpWwTQ8l00mZM3SlTzjaQNDyc4dUkXhnPNkiyTtIoxnGglfoEToxvoH9TzIyOfjbXK7jTDIFpsnqoSDed5MO1BH7c/uwEkFlDwQyTWINnMqy1GFLyXkg1KlEJamTW0ukNgfWpxzk6jY8fj2CyDJO1SPEJTESStm+CGGvbtz+ShMCNaMQ5MJbQdJDLhkiSjIrbTysrgM1IrYtnwvad7S2/kGAz0tQQkOAYaKQpxjiESRHPiclshk8L8HFMwojtZmM8l0OSR6l4/YRxnmLQxJbmUI1dunnxJ6k4oD8guSPHcZgxYwbvf//76e7u5i//8i/5xje+wd/8zd/s9jlb705vvVu9o13dxd727vWOBRzrrve+kMsZjjk+zxPLTmKk/hCvmD5A5FR4OjuJCjmmz4JWE7q7YXovHP8qWLuml99nF3BM9s9U7EaiLM+zhdfQ2DJm27YSjLW4fge1qJfFW8dsT1tO0J1gbYBtNUmj9hty6neS652J3TJmO7/2j7Q2baZupjLQ/dyYbWxKaosY2w79QaF9DWiFkJiAOOil2nEMaeUN9KdHUarewqxsM4Ppwe0x236Dbi/FcQYpmHZQsNanlRVIrI9rwA2g2WriJcMYJyPdMma7lXSyecuYbds5k7S3xEYfOnItCvNOIM2m4a79HSaLIXawSULmQpzm6c8OpTn1BOYd9xqe/COsWDXIQd49FJ2YNenh9LuHM39eixGmMDj9eI7u/j1+61F6ui1PN49tj9me8Qyt6EhqUYjPCNAiokSYzWyP2c7aY7a78gMMZ1NY75/AyIz2mO1c+Ec8J2XYzmW49NyY7ZZNcV3I9U7H9h1Kmq9gSMkOOZ3EeQh38yoyaxkpH4Fz+Pk88+g0Hmpt4OjcXZT8FmHaQ//omO3pTO11cToDkkIPhdChXvNYY08hnNrJXNOi3zmKbCoEtk6t1SKJq7i2znAylU3+ljHb2WrcQp5o1uvJDT6OrW4gsSlbx2xT7iHdMmbba9VJahmtbcZsF4pFqhzWHrNtV4IDTW8OqfEoxBvwnQYOkGwZs20KJRynm+qWMdslsxY3y6glPazabsz2XOobRxgOc9SikMh00DPFo/OIV+LNfSNPPXgUMzfdQrG0AUs3m73tx2wPNy1OucjUKcOYLWO2qeTp65rGH5t9/Pu6D3BiZQmdpZDYT8iMT5aVeDa3kFxHB3O6VjO8eR5JmKdjmzHbkelk0JlLk25K+Rwr0tfSmRtkhltlIH0FtaybcqmKDaYwmM3CCdpjtotei2YasCZ4NZvd9pjtTruRetoNXvuXbCezrEqO4O7medTSTubOgbe9DTq6C6TugvaY7elHEoQZtRoMxz7FbcZsJ26RjVEfK+Nj6A/bY7and45QmjaFas/x9JSeodaf4McJqdfJYd6DGIZwSMAvY7eM2e71DKs2HUWaQFewCTcPNX8OtcqrmLfwNVQfgOEnl7OieQyd/jC9+Y3tv0aVe8mmHo4ZGMBsarG5lWM4bo/ZDjwYyB3FSP4QSmmVOtNJix2UtozZbpkKK93TeMKeTpjmOPRQOPSQHHHjJFi3nNXJkRRzIVNLI+C2sDuM2SbLaKUBbtoEJ8M1HuvtfIZyR9JZaI/ZXuOfNjpm2wsc4oNfi9MaYmajyvr44PaY7aSBF6cU3BYNW2ZzPI/cljHbkZfHbDtmuzKV2Oshqg5johFSZ5sx206GyeXJ5pyIE3SQe3Y1aWZZZ49ioPvVTJ2W49jjYfDpU1i3qc703B/o6fZ5dvNrR8ds1/yZ+FN8zJYx2yQQNiGKIXXANRD4kCsFOF3TMTMOZfOWMdum1IvNTSFrOQShYa09llqlPWZ7un8ffi2h0YC66cbJBXSVI5xdjNlORtbhkBI7OUK6IPAJbELDncpIbg5Ndw42HSSyDXKBoVV5Jab0Gp58HA5OljOtY4DpB0+hcNDxmAee4an6UeSdOr1bxmxHaZmRqEwjLdFyukkLGb2lKq5JSae/cnTMtsnSncZsl5sOBXcaz24+GI9h+oIn6PAGaOV7CIoB9dowuXgjeDncGa/ALng/yZYx26RRO3S6OWyaESdlalvGbJucj+MMkmQ+YeZA1h6z7eU9arnpDNk5JGmDOJnCCH2snP4BFnT9Bj/sx3b2ks09m85HGvRFIzxVPQrfh4P7YrLKUQw7MNUZwZQqtKadxPEL8gA8+GAnK+p/glv9V6a5q0i9bmxpHq2ag63V2diast2Y7ebWMdt2E11BlSkddZreVBqdh5LvWE8weB++bdFKehj0DsU3AxC3GInLNM08KBcZbkIU5TCd0+iZ0sdj7gfoSZeQsxtomSlszrfHbNdbG5iRe4aCaY/ZrmUV4i1jtvFqeK7BxgXWNQ9l1ZYx2wWzGuNm2FIPbtdhbHTOY6g5jd9vHOa48j0Ug5Bg5jz8k/6EbD/+kOR+O/XfWEZGRjjppJM49thjR2cb2Z3TTz+der3O8uXLdxq3/e1vf5urrrqKv//7vx/9wONf/dVfcdttt/GjH/2IV73qVdu1f+CBB3jve9/Ln/zJn/B//s//2e02X+zfqsLQkvfyhLX1WK9AK8nhOJYsM6P/5vPPfWK91YJ80k+u+QyRqdDMzyFf3jIbycg6cALIIjan01m9uZdKOWRWV5XA2fJJ9iQibjZpZQVyxaA9M0Shq/1Yc5CoGRHGAUFXe1lUHcJkIVHUvuHVUTYEfvtCX6u1fzPOFQLSoJt8uT3LRKs6jBcN0DLdZG6OgtskTRKKdgjfTcGm4LVnIwlbkMszus5wuIYbDZDmusl1dBBFlnqrm2ojY9bMDL9YpNWCgtckKBXb/R7ZAOFwe3aCsErSiBihpz0bydTnZk4YGWxhGhvxsiY1M4POnvafpZtJoV3D0dlIDKFToZXkyHshJmnSbMQU7RAWhzAJML5Py3RhgYrfrm+UBTTpatehNUxjw3qscQjK5XZ98mBaQ4T1qL3P29Z+29lI+p8hDMHvnTM6G0l1c0iXs5qyUyUMplNrtn/XLne1ZxCoVLqoNqL2bCQR7X7kafc9KTz3/9DBzRrUhyJaaUDPrK72TDDVIXI5CCrd7dlI+lfRjF3yW2cjcdz2LBVJCCPrCCnRGEmwxmnPRuJlhMF0WkkOt7UBwhZ+pRtDRjhcw4mHsNYh6OgkKGyZQcLLEWZ5onqT3JbZSGpJJ0lxGp0dW2cjMYSRYai/TrVVpLOUtWcjqXSPzkbSqg5TtINkXpEo8QnKhfZsJPUGzdChkMvond5DtX8dYMAvjM5GsnpDDjcapq9nEC/wiOtN8HOkhent8y4bolazDI84lLKNuPEQed+Sdcyk6U/DzRrY1ECxQhZH2x/3tGdgIchDZgm8mFw2RJTlaXrTyJfbs5HUnnkGt1yhMr2LpLqB2gh40+bQyjpphTCtd4zZSOp1GlmRAsPt2UhwiL1uNke9tBKffLlAkkDJb+Lki+1jMwkZ2NCgFTn0TsnwbZ18a5CoMYTfNQXK07bMRtJiczKN/g0ws7SBQskltGWCrq7Rc2rzhhZRo8mUrpiOdE17JovK7OdmI9k8wLr+PJHN05vbSLEEScdc8qVc+1xo0X69sirxSJWWqUC5l+FGbnTfg8DQakEStgiHm3SUYzrcavt8d/3R6xppC0x7NpJG4uE31pM5eWzPK8icPAWG2pe5LefG1uOLQle7ps0BQqebkWZA1GgSt2LccIhcKU/kVpjR49MYWkc+SPFLJWgNQRpDaSrkK0TDQ6OzkVivA1yDaVYJinmCKdO3XE8HCVu0jxcnv/21vRZSYKg9O5BTYWBji2RkkK5pxZ1nI4khbIFxLDYzo9dQvPY1JQztc+d0qUhUb2y/XW+b2Uhi2tf8cmGb2Ui2uTaNbCDevIao1oBihaRjLgW3hWm1X6+gs4IhI5fvZmRwXXvWoa4u8HJs2mQZqYZU8k16phdHZyPZvL5J3o+Zkutvz0ZiOqi1fKII/ELQvq5l1fbsNh0ztpmNZMt13nHbs6VsnY0kTBlu5rHQPu+imFwpeG7fh2vt123KzO1nI9nmPRO/TBwl1Fo5Mi9PZ65JZtzRa0KY5TGtIcqVAjZfIWq1B8xHYUbmFejs3jIbSXOgPVPKltlIaoMNhlsFrIXDDgpoxDGtFrhZg9Rpn5PbzkbSaoETVTG1je1jp9JFVG9Qq4aEaYD12q9TLoDULdJqpAwOOUzvaVLKx6Pv36OzkYRDhMF0Ilsm8GKS2iDDjTzlqRVyTouBAUszLbSvBcViO4NEVZLhQTK/SEclYKRhGFjbpLtYw0vbv7R6lanP1aTsEvhQrycMpTPId3XiRZsotZ7By3lbZiOZQWg6aLWgOdKitm4jvd1NKrOfmyFmf53674AM2ytWrOCss87ihBNO4Ac/+MGYbf/n//yf/OxnP+P73/8+CxYs2O6xCy64gKVLl/LrX/+avr72J3l/+MMf8tnPfpZLL72USy65ZLv211xzDV//+te3C+e7onm29z+qz/hUo7GpPmNTfcanGo1N9RmfajS2/TVs77ffIPnkk0/SbDZ3Wt5sNvniF78ItOfK3mpgYIAVK1YwMDCwXfv3vOc9AHzta18jip77rf7OO+9k6dKlnH766aNBG+Ctb30rHR0dfP/732f9+vWjy9evX8/3v/99uru7WbRo0d7ZSRERERF5Sdtvx2zfcccdXHfddZx44on09fVRLpfZsGEDd911F0NDQ5x00klcdNFFo+1vuOEGrrnmGi655BIuvfTS0eWvfvWrOe+887jppps499xzed3rXkd/fz+33347XV1d/O3f/u12261UKnzmM5/h8ssv55xzzuGss84C4Pbbb2doaIivfvWr++WHMkVERERk/7Pfhu3Xv/71bNy4kd/97nc88MADNBoNyuUyhx9+OG9729v4kz/5Ezxvz7r/v//3/2b+/PnceOON/Mu//AvFYpE3velNfPKTn2Tu3Lk7tX/Xu95Fd3c33/rWt/jJT34CwNFHH83HPvYxTj311L26nyIiIiLy0nVAjtne32nM9v5H9RmfajQ21Wdsqs/4VKOxqT7jU43GpjHbIiIiIiIvMwrbIiIiIiKTRGFbRERERGSSKGyLiIiIiEwShW0RERERkUmisC0iIiIiMkkUtkVEREREJonCtoiIiIjIJFHYFhERERGZJArbIiIiIiKTRGFbRERERGSSKGyLiIiIiEwShW0RERERkUmisC0iIiIiMkkUtkVEREREJonCtoiIiIjIJFHYFhERERGZJArbIiIiIiKTRGFbRERERGSSKGyLiIiIiEwShW0RERERkUmisC0iIiIiMkkUtkVEREREJonCtoiIiIjIJFHYFhERERGZJArbIiIiIiKTRGFbRERERGSSKGyLiIiIiEwShW0RERERkUmisC0iIiIiMkkUtkVEREREJonCtoiIiIjIJFHYFhERERGZJN6+7sDubNiwgTvuuIO77rqLp556ik2bNlGpVDjhhBP48Ic/zHHHHbdH67ngggtYunTpmG3+/u//nrPPPnv05ze+8Y2sWbNml20XLlzI9ddfv8f7ISIiIiIvX/tt2L7++uv5zne+w9y5cznttNPo6elh1apVLF68mMWLF3PVVVdx1llnjbuec845h4ULF+60PEkSvvWtb+E4DqeccspOj3d0dHDhhRfutLyvr29iOyQiIiIiLzv7bdg+9thjuf7663cKysuXL+eiiy7ic5/7HIsWLSIIgjHXc+655+5y+c9//nOstbz2ta9l+vTpOz3e2dnJpZdeOvEdEBEREZGXvf12zPab3/zmXd6RPumkkzj55JOpVqs8/vjjE17/v/3bvwHw7ne/e8LrEBEREREZy357Z3ssnudt9+/ztX79epYsWUJvby+vf/3rd9kmiiJ+8pOfsHHjRsrlMsccc8wejxMXEREREYEDMGyvXbuW3/72t/T29jJ//vwJrePHP/4xWZZxzjnn7Daw9/f3c8UVV2y37JhjjuErX/kKc+fOndB2RUREROTlxVhr7b7uxJ6K45iLL76YZcuW7TSDyJ6y1rJo0SJWr17NL37xCw466KCd2lxzzTWceOKJzJ8/n2KxyMqVK7nuuuu49dZb6evr46c//Snlcnm328iyDMfZb0foiIiIiMiL5IC5s51lGZ/61KdYtmwZ73nPeyYUtAHuueceVq9ezcKFC3cZtAEuueSS7X4+8sgjufLKKwG49dZbuemmm7j44ot3u41qtTqhvr0Q3d3dDA4OvujbPVCoPuNTjcam+oxN9RmfajQ21Wd8qtHY9kV9uru7x21zQNx+zbKMT3/609x22228853v5POf//yE1/VCPhj53ve+F4D7779/wtsXERERkZeP/f7OdpZlXHHFFdxyyy28/e1v50tf+tKEh2hUq1V++ctf0tnZyZlnnvm8n7/1t5dGozGh7YuIiIjIy8t+fWd726B91llnceWVV+K67oTX99Of/pQwDHnHO95BLpd73s9/6KGHAH2xjYiIiIjsmf02bG8dOnLLLbdw5pln8uUvf3nMoD0wMMCKFSsYGBjYbZs9GUKyYsUKms3mLpf/wz/8AwDveMc79nQ3RERERORlbL8dRnLttddy8803UywWmTdvHt/4xjd2arNo0SKOPPJIAG644QauueYaLrnkkl1+8+PDDz/MY489xlFHHcUrX/nK3W739ttv57rrrmPBggXMmjWLQqHAypUrueuuu4jjmI9+9KMsWLBg7+2oiIiIiLxk7bdhe82aNUB7fPQ3v/nNXbbp6+sbDdvj2dMPRp588smsWLGCRx99lOXLl9Nqteju7ua1r30t73//+zn99NOfx16IiIiIyMvZATXP9oFiX0zLo+mAxqb6jE81GpvqMzbVZ3yq0dhUn/GpRmPT1H8iIiIiIi8zCtsiIiIiIpNEYVtEREREZJIobIuIiIiITBKFbRERERGRSaKwLSIiIiIySRS2RUREREQmicK2iIiIiMgkUdgWEREREZkkCtsiIiIiIpNEYVtEREREZJIobIuIiIiITBKFbRERERGRSaKwLSIiIiIySRS2RUREREQmicK2iIiIiMgkUdgWEREREZkkCtsiIiIiIpNEYVtEREREZJIobIuIiIiITBKFbRERERGRSaKwLSIiIiIySRS2RUREREQmicK2iIiIiMgkUdgWEREREZkkCtsiIiIiIpNEYVtEREREZJIobIuIiIiITBKFbRERERGRSaKwLSIiIiIySRS2RUREREQmicK2iIiIiMgkUdgWEREREZkk3r7uwO5s2LCBO+64g7vuuounnnqKTZs2UalUOOGEE/jwhz/Mcccdt0fruffee/nQhz6028e/+MUvcu655+60/Omnn+bqq6/mnnvuodlsMm/ePM4//3ze9773YYyZ8H6JiIiIyMvHfhu2r7/+er7zne8wd+5cTjvtNHp6eli1ahWLFy9m8eLFXHXVVZx11ll7vL6FCxeycOHCnZYfeeSROy178sknOf/882m1Wrz1rW9l2rRp3HnnnXz+859nxYoVfOYzn3lB+yYiIiIiLw/7bdg+9thjuf7663cKyMuXL+eiiy7ic5/7HIsWLSIIgj1a38KFC7n00kv3qO3nPvc5RkZG+Pa3v83rXvc6AC677DIuvvhivv/97/P2t7+d448//vntkIiIiIi87Oy3Y7bf/OY37/JO9EknncTJJ59MtVrl8ccf3+vbffrpp1m2bBknn3zyaNAGCIKAyy67DIAbb7xxr29XRERERF569ts722PxPG+7f/fEypUr+e53v0sYhkyfPp1TTjmF6dOn79Ru6dKlAJx++uk7PXbiiSdSLBZZtmzZBHsuIiIiIi8nB1zYXrt2Lb/97W/p7e1l/vz5e/y82267jdtuu230Z8/z+OAHP8jll1+O67qjy1euXAnAQQcdtNM6XNdl9uzZPPnkkyRJstuwX6lUcJwX/48G3d3dL/o2DySqz/hUo7GpPmNTfcanGo1N9RmfajS2/bE+B1TYjuOYyy+/nCiK+Ou//uvtQvLu9PT08Fd/9Ve84Q1voK+vj2azye9+9zuuuuoqvvvd72KM4VOf+tRo+1qtBkBHR8cu11cqlciyjHq9TqVS2WWbarU6gb17Ybq7uxkcHHzRt3ugUH3GpxqNTfUZm+ozPtVobKrP+FSjse2L+uxJuD9gwnaWZXzqU59i2bJlvOc97+Hss8/eo+cddthhHHbYYaM/F4tFFi1axHHHHcc73/lOrr/+ev7sz/6MKVOmTFLPRUREROTlar/9gOS2sizj05/+NLfddhvvfOc7+fznP/+C19nb28sZZ5xBkiQ8+OCDo8vL5TIAIyMju3xevV7HGEOpVHrBfRARERGRl7b9/s52lmVcccUV3HLLLbz97W/nS1/60l4bD7311n+z2RxdNm/ePABWrVq1U/s0TVm9ejWzZ89+Xh/OFBEREZGXp/36zva2Qfuss87iyiuv3KNx2ntq6x3tvr6+0WULFiwAYMmSJTu1v++++2g0GqNtRERERETGst+G7a1DR2655RbOPPNMvvzlL48ZtAcGBlixYgUDAwPbLX/44Yd32f573/se9957L/PmzeOYY44ZXX7IIYewYMEC7r33Xu68887R5VEU8bWvfQ2A884774XsmoiIiIi8TOy3YyGuvfZabr75ZorFIvPmzeMb3/jGTm0WLVo0+nXrN9xwA9dccw2XXHLJdt8U+YlPfALP8zj66KOZPn06zWaTBx98kD/84Q90dnbuMsR/9rOf5X3vex8f//jHOeuss+jt7eXOO+/kj3/8Ix/84Ac54YQTJnfnRUREROQlYb8N22vWrAGg0WjwzW9+c5dt+vr6RsP27px//vksWbKEZcuWMTQ0hOM4zJo1iwsvvJA//dM/ZcaMGTs957DDDuPGG2/k6quv5s4776TRaDBv3jz+7u/+jve///0vfOdERERE5GXBWGvtvu7ES82+mANTc2+OTfUZn2o0NtVnbKrP+FSjsak+41ONxra/zrO9347ZFhERERE50Clsi4iIiIhMEoVtEREREZFJorAtIiIiIjJJFLZFRERERCaJwraIiIiIyCRR2BYRERERmSQK2yIiIiIik0RhW0RERERkkihsi4iIiIhMEoVtEREREZFJorAtIiIiIjJJFLZFRERERCaJwraIiIiIyCRR2BYRERERmSQK2yIiIiIik0RhW0RERERkkihsi4iIiIhMEoVtEREREZFJorAtIiIiIjJJFLZFRERERCaJwraIiIiIyCRR2BYRERERmSQK2yIiIiIik0RhW0RERERkkihsi4iIiIhMEoVtEREREZFJorAtIiIiIjJJFLZFRERERCaJwraIiIiIyCRR2BYRERERmSQK2yIiIiIik0RhW0RERERkknj7ugO7s2HDBu644w7uuusunnrqKTZt2kSlUuGEE07gwx/+MMcdd9werWf58uUsXryYpUuXsmbNGhqNBn19fZxxxhl89KMfpbOzc6fnvPGNb2TNmjW7XN/ChQu5/vrrX9C+iYiIiMjLwwsK281mk4cffpj+/n6iKNptu7PPPvt5r/v666/nO9/5DnPnzuW0006jp6eHVatWsXjxYhYvXsxVV13FWWedNe56LrvsMgYHBznxxBN517vehTGGpUuX8v/+3//j5z//OT/84Q+ZOnXqTs/r6Ojgwgsv3Gl5X1/f894XEREREXl5mnDY/trXvsZ3v/tdWq3WbttYazHGTChsH3vssVx//fUsXLhwu+XLly/noosu4nOf+xyLFi0iCIIx13PhhRfyrne9i+nTp2/Xr89//vP84Ac/4Nprr+Wzn/3sTs/r7Ozk0ksvfd79FhERERHZakJh+zvf+Q7f+MY3cF2X173udRx88MGUSqW92rE3v/nNu1x+0kkncfLJJ7NkyRIef/xxjjnmmDHX85GPfGSnZcYY/uIv/oIf/OAHLFu2bK/0V0RERERkRxMK2zfddBP5fJ4bbriBo446am/3aVye52337wtZh+u6u3w8iiJ+8pOfsHHjRsrlMsccc8wejxMXEREREYEJhu1169bx6le/ep8E7bVr1/Lb3/6W3t5e5s+fP+H1/PjHPwbgtNNO2+Xj/f39XHHFFdstO+aYY/jKV77C3Llzx1x3pVLBcV78iV66u7tf9G0eSFSf8alGY1N9xqb6jE81GpvqMz7VaGz7Y30mFLZ7e3spFAp7uy/jiuOYyy+/nCiK+Ou//uvd3pUez6OPPsq1117LlClT+PCHP7zT4+eeey4nnngi8+fPp1gssnLlSq677jpuvfVWLrroIn76059SLpd3u/5qtTqhfr0Q3d3dDA4OvujbPVCoPuNTjcam+oxN9RmfajQ21Wd8qtHY9kV99iTcT+j261lnncXSpUtpNBoTefqEZFnGpz71KZYtW8Z73vOeCX3oEuDZZ5/lIx/5CGma8pWvfIWenp6d2lxyySWccsopTJkyhUKhwJFHHsmVV17Ju971LtasWcNNN930AvdGRERERF4OJhS2L730Ul7xilfwsY99jFWrVu3tPu0kyzI+/elPc9ttt/HOd76Tz3/+8xNaz7PPPsuHPvQhBgcH+cd//Ede/epXP6/nv/e97wXg/vvvn9D2RUREROTlZY+GkXzoQx/aaVmWZSxdupSzzjqLWbNmMWPGDIwxO7UzxvC9731vwh3MsowrrriCW265hbe//e186UtfmtB46K1Bu7+/n6uvvpo3vOENz3sdW/9U8GLe0RcRERGRA9cehe2lS5fu9rE0TXn22Wd59tlnd/n4rgL4nto2aJ911llceeWVExqnvW3Q/upXv8qiRYsm1J+HHnoI0BfbiIiIiMie2aOw/atf/Wqy+7GTrUNHbrnlFs4880y+/OUvjxm0BwYGGBwcpLu7e7tx2FuD9saNG/nqV7/Km970pjG3u2LFCmbNmrXTB0BXrFjBP/zDPwDwjne84wXsmYiIiIi8XOxR2N4Xd3KvvfZabr75ZorFIvPmzeMb3/jGTm0WLVrEkUceCcANN9zANddcwyWXXLLdNz9eeOGFrF27lle96lU8/vjjPP744zutZ9v2t99+O9dddx0LFiwYDd0rV67krrvuIo5jPvrRj7JgwYJJ2GMREREReamZ0NR/11xzDUceeSRnnHHGmO1+/etf84c//IFLLrnkeW9jzZo1QHt89De/+c1dtunr6xsN2+Ot54EHHuCBBx7YZZttw/bJJ5/MihUrePTRR1m+fDmtVovu7m5e+9rX8v73v5/TTz/9ee+LiIiIiLw8GWutfb5POuKIIzjnnHP44he/OGa7v/3bv+XHP/4xjz766IQ7eCDaF3Ngau7Nsak+41ONxqb6jE31GZ9qNDbVZ3yq0dheUvNs76k0TffJNymKiIiIiOwPJjUJP/nkk3R2dk7mJkRERERE9lt7PGb7iiuu2O7n+++/f6dlW6VpytNPP83DDz884Wn2REREREQOdHsctm+++ebR/xtjWLVq1bjfHnn44Ydz+eWXT7x3IiIiIiIHsD0O2//yL/8CgLWWCy+8kNe85jX82Z/92S7b+r7PtGnT9OUvIiIiIvKytsdhe+HChaP/P+ecczjxxBO3WyYiIiIiItub0Dzb4035JyIiIiIiEwzbW0VRxM9//nOWL1/Oxo0bAZg2bRonnngib3nLW8jlcnulkyIiIiIiB6IJh+3f/va3fOpTn6K/v58dvxfnxhtv5Mtf/jJf+tKXOO20015wJ0VEREREDkQTCtsPPvggH/3oR4njmOOOO463ve1tox+GXLt2LT/72c944IEH+PM//3O+//3vc9xxx+3VTouIiIiIHAgmFLa/9rWvkSQJn/vc5zj//PN3evyCCy7gRz/6EZ/97Gf5x3/8R/7pn/7pBXdURERERORAM6FvkHzwwQc5+uijdxm0t3rve9/LMcccwwMPPDDRvomIiIiIHNAmFLYdx2Hu3Lnjtps7dy7GmIlsQkRERETkgDehsH3sscfyxBNPjNvuiSee4Nhjj53IJkREREREDngTCtuXXXYZK1eu5B//8R/Jsmynx621/OM//iMrV67ksssue8GdFBERERE5EE3oA5JPPfUU55xzDt/4xje49dZbefOb3zw6G8maNWv45S9/yZo1azjvvPN4+umnefrpp7d7/tlnn/2COy4iIiIisr8zdsdJsvfAEUccgTFmu/m1t47N3tWyray1GGN49NFHJ9rfA8Lg4OCLvs3u7u59st0DheozPtVobKrP2FSf8alGY1N9xqcajW1f1Ke7u3vcNhO6s/3xj39cH3wUERERERnHhML2pZdeurf7ISIiIiLykjOhD0iKiIiIiMj4JnRne1uPPfYYDz30EIODgxx66KGcccYZAERRRBRFlMvlF9xJEREREZED0YTvbD/11FOcf/75nHPOOXz2s5/l6quvZvHixaOP//u//zsLFizgrrvu2isdFRERERE50EwobK9bt44PfvCDPPDAA7zhDW/gb/7mb9hxUpO3vvWt+L7PL37xi73SURERERGRA82EhpFce+21DA4O8oUvfIF3v/vdAFx55ZXbtSkWixx55JE8+OCDL7yXIiIiIiIHoAnd2b777rs5/PDDR4P27vT19bFx48YJdUxERERE5EA3obC9efNmDj744HHbJUlCs9mcyCZERERERA54EwrbXV1drFu3btx2Tz/9NL29vRPZhIiIiIjIAW9CYfuEE07g97///Zhfu7506VL++Mc/snDhwgl3TkRERETkQDahsP0//sf/wFrLX/zFX3DnnXeSpul2j//3f/83l19+OZ7nceGFF+6VjoqIiIiIHGgmNBvJcccdx//3//1//J//83/48z//c/L5PMYYfvGLX7B48WJqtRrGGD772c9yxBFH7O0+i4iIiIgcECb8pTYf+MAHuOGGG3jDG96AMQZrLfV6nSiKOP3007n++ut573vfuzf7KiIiIiJyQHlBX9f+qle9iv/7f/8v1loGBwfJsozu7m5c191b/RMREREROWC9oLC9lTGGnp6evbEqEREREZGXjL0StifDhg0buOOOO7jrrrt46qmn2LRpE5VKhRNOOIEPf/jDHHfccXu8rizLuOGGG7jxxhtZtWoVxWKRU089lU9+8pPMmTNnl8+5++67+da3vsUjjzyCMYajjjqKv/iLv+CUU07ZW7soIiIiIi9xexS2zzjjjAlvwBjD4sWLn/fzrr/+er7zne8wd+5cTjvtNHp6eli1ahWLFy9m8eLFXHXVVZx11ll7tK6/+7u/46abbuKwww7jggsuYOPGjdxxxx385je/4Uc/+hHz5s3brv2tt97K5ZdfTk9PD+eeey4At99+OxdffDFXX301Z5555vPeHxERERF5+THWWjteo4nMKLL1Q5MAjz322PN+/i9+8Qu6urp2mqd7+fLlXHTRRRSLRZYsWUIQBGOu55577uHCCy9kwYIF/PM///No+zvvvJOPfOQjnH766fzTP/3TaPtqtcqiRYtwXZdbbrmFGTNmALB+/XrOPvtsABYvXky5XN7tNgcHB5/3/r4QI+s30Hj6Wdas2ogTVnG9FJuCl/NJrcHEEUkKNgPjgOdCSIVhdxZFZ4Riug5rHSK6qJtOfJsQG48ufzPTCkM0Yp+BeonMLYOBLMnI/AIFp0kap5CEuFkDyIjdTlzHxcs7ENcwUUiW7ySzLq7ngM2II4cszXBchyDIcIMccRTjRZtI4xQHsLkikemg4NQwaUhsCxgDLW8aifXIogjHxuRMiBM4xFFGljn4TobNl2lSoew1SaM6bhYyc7pPWBtgcMTHxA0c3yFOAkwaAxlRMIPY78SN6jTdLhwvR5fbj5MMYpsNbBphHMjlPLLiDCLylOI1NFsJWRbg+jFZGJHhk5gSxsvhOzW8pE6c+WACTM6nkZRxHfCdEGNTclTxvBwjaQdJmOE5KQWvikNKKylg871AAkkLL62SkSP1OoCMNHG2q33qFPCyBqmTI/an4xcLuK210BjC86GQ9xjMZlBLO+mgn5wXY41D0myQOBWCnE9W30TsFPFNhuc1SKMMawr4boMkccAEeAWfhByBkxHkUpqDVdLMgaBEljo4cZXYLRMGsygUPaIohVadFEsp6SdxApruVApOE9938HI+jRYkrZgig3gmJPamkDk+TlQDCzZfJjQlvKAAYRXbrJHh4NkQ14EsV27X2ULBa+Cnm0jSHBEd7Ro5EBRzlIohzTo0W+1+QoprwClVaNgKXjiE41ts2MBaB+MEOAEkEbiBRxQajJfDtSFZEuP7Md3FiDQosnm4A7IM/ByOjfGTTWS0++A5IaU8DKfdtLxewhjK6VoK2SZSUwST4ZsYHJ9mmhs97iM6wWSYLAYgc4o06SSXDhM4VbIwgqCCN2UWWWuEaLhKmp9GIZ/hhIMUTQNjI0aakMaAoV1PxycjIDM+xs2Ry2c0bZnhtBdjoGT76fIH8Z2YOI7xTUY9K+Ak7bpkTkDehyAH1WFIKeJMOYRq3eK3+smlg6TGJw066fI2Uy6EbG70ksQJvtuCVhWydu3zJR/HC6glRap2FmlqKMdrcLIGmfFx8mW6KzG+bTCYTKPZzCiZQdI4Jkt9TOBgwwahqYBXJOc2cKNNlIsOrmcYGYpIs/Z1z/g+nhfQiNvnIoBnQ/ByZI5PkrSvqYET4/sZNdNLQEw+fBrHJIRpCTfIkTM1omZIZDpxbIabNbDGpeV2UbRD5HIJOAG5cpE46CQaHKZgGli3zEjsE8bgJiGRUyZxCpSSfjwTk+XK7X22MW7WPvZTt4xNYrxwE9Y44JZwCjmctIYT1be7trueR2g7sVmGT4NiyYWuXhoDTaJmTGpyOGRktK+VTuBgswzHz5GkkLVCsClBVgXnuXPaJA0yt4jrt49Th5iwlRGZTsDFcR08N4QoJrY+qcnh2RDPtI/bkCJhfhYEHQTxAMVkNWFaIHXbrxetKsYv0lH2GB6JMK7TXmZTykXwCz6NVkDYiLFphFcqUurME9ZjBuJppJlHzqmT90I8zyGJQ7JWTEiRNOikkG6GqEboTSExRQAcGxNkNYxt99F1oNB+iEbkg5cjDUNMGuO4fvsaFEM+q+Gk7feE2KvQdKeOvmfmsibGccgFMa5t0KIbl4QMp7090yB2uknCJl7awGy53em54LvQCmOS2KXh9ZLLmnhpFdfJcAoFCk4D13NoxWXCOML3IIrBddrvPda2z+2t7/GpUyT228N7t15HTbGMcdr7EKaQZQ5u1sDzfUrFmOFWhTD1cbMaJmyQZRlNfwaR037P8GiQpT5ZUKajE8KRGkkIuaJDwWlQbbbfX10vJbUFnI5e3KxJa6RBkINyp89Is0xkShjjQqufwI2JTZnE+qQp+E5MpztI0qoTuRWGmIWTZbhpnXw6iE8dr+Dj+QFeqcjQcAHq/RjbxPULzHrV8XQcPItcKT9pmWtH3d3d47bZozvbuwrLX/jCF/jJT37CBz7wAd72trcxe/ZsANasWcPPfvYzbrjhBs455xz+9m//9nl2u+3Nb37zLpefdNJJnHzyySxZsoTHH3+cY445Zsz13HTTTQBcdtll2wXz173udSxcuJAlS5awdu1aZs2aBcB//Md/MDw8zKWXXjoatAFmzJjBBz/4Qb7+9a+zePHi0eC9rz37q1+Qu/ebTOMp5vghDu330/FkQJo6YCyuYzFAasFalzRzcZ0Yx7Gj68uA1Bqw2y4xGJNizHPT2mw532FLK7PDssyCY7b9t93CgZ3Wk22zjq19zjKH1Ho4WIxJwVgcY7dZn4O1LnHqYcjw3RhMhvvsc33alcSCtT5x6pHhkmHIOy08J8Yxzz3PbmkL7e1tO53Ptv20W37ecX8ya8Cadp/M9vXJLKPLzDbrybL2G8GObZ0tG9xxSiEL7fBrDY6TjvZ/tO/Wbdfcybbbrx33Y3e1yrb+u01/t13H6PGS+oRZgOckmCzD9WJcs3WfDNDuR/uYsmCS0f3MdtjHzLrEiYfF4DspxiQ4znO9zqwDmQtkOE77mNxxv7Zl2OHY6ge7zeu6Yz139f9t17Hd67vN8bzjc9IsoJ4WcMgouvXtXoMda2jYuf+pZcvxY/HMNrV+xmlv0GRbnmRwTLbdsbujrX2Gdv0y69FICwAUnQaeE7dXOcbzn+uXQ/RMQIK/5bxJyLBg25kN4DC2P5ZH19u/9dh0iBMfAN+NcB373LGOafd1m30z5rnzbHR/MoNj7Gi/d9X3nc/FrUVwt6zPgrFYXJK0fbw5bsKWo3R0m9u+Pju+VltX2b6mtl+v0WN/dLtb/rWAyXCcbMtx7IBJR4/v9v4zuk/b9mFX5/6OfbKwXaF2fQ1mp+sS7Hz87bh/lp33e9d1cAiTPK20QNkfwXUSHJONvl7ssM0dz89t+2IAu/m566C1Dpl1cbZ5P9jaLt3SQWeb62eWuVv2tV3j3R0jO+5TtuW12ul9MXNIMw/XSbdutV3XLdeBbMv1bdtl8Nz1Yce6bvua7Xgt2l3/drcPyZbrxXPH0nP7wDbP29V7y9Z+PveekW1zTu5+PVv/zQC7rr3Sba+FmXVJEredO0y6zXG/5f1gy/uAs7W+qUeSOXhuNLp82/3bNjtkQOPZMo+7i5j27kuYekjfrgu4D0xozPa//Mu/8MMf/pAf/OAHO4Xdww8/nMMPP5w3velNvO9972P27NlcdNFFe6OvozzP2+7fsdx7770Ui0VOOOGEnR57zWtew9KlS1m6dOloeF66dCkAp59++k7tTz/9dL7+9a9v135fGlm/gWD59+hmFTk/5PnMAeMAjpttt8wzgEnxnXTX7Y0Fs/Nj29rVSb/tMtfs+O+uL+dbo9i2XMB1Mnyi7d5kt19fhjUZvhOP2acd+QYwMY6Jtwv9Oz7XbG07hl3Np7l1f1zTfiPf8bFt92HH9bjO+G13XJ/jZLtc7m95jV+Ira/Ljn0wO7Rx3RjfjduvlbvD444FttxGZOf+uDus3zUpnv9cux133zUZuDvv855ydrXS3Wxrd21GX9+xtuNEVJxot2+Q423bMex0/Gx7Llu2vrGN3Y9t+wzt+mWm3betj43Xv20fd0yG77ewtEaXubvYAXdXJwdbj80M3w93eszdsj/PrW/3++Y6e77fO52LbjZ6XWkHj5TA2fm5u/t5V/XydvF6bd3uc6/Vto/tfByPdZ7taMfXzez0nzGuwbs4dp/PMTAWx2R4foOS39juuXvyeu1qW1v3s30+ZDz3a+OO2925k+4u3t/2hLvll4Lt1k/7WutvOW+2az/6vGynZWPZ1Wu22zbjrMff4fjb1T7suM4dj7ddvWeMtZ6tRk+dnY7xFN/fcX27vn63r20J/i4Kt6v3YgcoenUOjf+Lx359GB3T3/+i3uEey4TC9o9+9CNOPvnkMe8qH3PMMZx88sncdNNNezVsr127lt/+9rf09vYyf/78Mds2Gg36+/uZP3/+LqcjPOiggwBYtWrV6LKVK1du99h47XelUqngOLt5V9mL6o8/QZANYa1pn097euXbC17kzU24D8+/n1uGP+3rnXsJSe3uf0HJmNhk//vD8fdCTXTfx7Iv67LjXwBeSD9e7P3YcXt7HgEnrw8vNZbn7j5utTf3d1/XbzLO55e6bf9asbdkGDwnJqitI+/6dO3BEI8Xw4TC9rPPPsvhhx8+brtKpcLy5csnsoldiuOYyy+/nCiK+Ou//utx5/MeGRkB2O346q3Lt7YDqNVqAHR0dOxR+12pVqtjPr63ZJUKkdNF2a7HvshXmf3hTWEidwbHZ3f55z2ZuK1/Pt9VWSf65vRSeIkm4415X9Zl6x3hvdGPF3s/dnXH+sUO3C+FY3osW2u645CDvbn+fUlB+/mbjNfMwZJkPlF5Jq00flE+Q7fXxmzvqFKpsGzZMsIwJJfL7bJNGIYsW7aMzs7OiWxiJ1mW8alPfYply5bxnve8Z78YxrGvdcyYztBJFzJ47zfx4qfIacz27sdsm+f6tCsas73zfuyuVi/JMdsc4GO20/1gzHYy9pht2M2YbTRmW2O2t1/njufntn3Z8TqoMdu7P873+ZjtLR15UcdsJyWe9F7PrDe+Zb8ZQgITDNtvetOb+Nd//Vc+8YlP8JnPfGb0w5FbrV69mi984Qts2rSJ973vfS+4k1mW8elPf5rbbruNd77znXz+85/fo+dtvTu99W71jnZ1F3vbu9c7/rYy1l3vfWXOGW9m5Kjj6F+1hjUrN2CiGp6bYNMUL5cns5YsCUlTF5umOK6L46ZE9DDszKDkjJDPNpJZQ0wnDTrxSYjx6PY3M7UwSDPJM1QrkHplMmPIYosJcgSmSZqkEMd4WQ3ISNwKxvUIArBJAxs1MfkKmfXY+oeIMIYsAc8Dzwc355O2EpxkgCxOwKY4uQ5iUyJvGjhZkxZFjHFouVNIrUcWxXjEeCbC89uzRaQZuC6YXJGQCgWvSRY1cGzErGkOzdow1REXkhauZ4gTb8sUDRlxbiaR14GJ6sRuF2yZjcRNqqStGiQhjuvi5xyy4kwSm6MQr6XVSkkzD8+PycKQjDyJKeD4OTyngZsMk2R5LB5OzqeVlHFcg+dEkMXkTA3PC6gnJaIQAjcm59VwbEKU5LD5XqxNsGkLL6uREpC5JYyhPXPCNrXPnCJO1sI6AWHQS75QwGmtxTYG8QKXfM6hms1kJO2g0/S3x1Mbh6wZkrgdBIFHUh8kdfK4jsV3a+3ZSJwintsiTQwWj6Dgk9gcngc5P6Y5XCNLDTYokKYOTlQjdUu0cjMoFjyiMCUN6xgs+WQTmZOj4fZQdJt4noObD2g1IW7FFM0QrmkSe1OxxsfEdcgyTL5MRBkvl8e2qqSt9thPx8Z4bkYalIltABZKbgM/HSDMAmJK7Rq5GX4xRzkf02pm1FvtfhoSjEnxSz00sg7cuIrrWWzYar+hOB5uAGkEbpAjDNszNxgbY+MQP4jpKoZkfgcDtRKkkHk+rk3w0gFS2n3wnZhSLmMk66bl9hLFUErXkrODpCaPMRaPEByfKM2NHvdNU8EYi8naY5m3m43EVMnCEHI9eFNmYJsjxMM1kvwUcoUMv1Ul79RwspDhptueOci4YFOskyfDwxof6+UoBNCkSDXtxXGgaPvp9qr4bkgSxTgmo5kVIX2uLjkPggBG6pDaImbKIVRrFi/sp5BWSXCxuU463c2U8yEDrV7iOCFwWtCqYbN27UulPI7vUY+LDDKLJDV0xGvaMz4YHwplpnQmeLQYiqfQamYUqZIkIWnq4wYONgwJTQeZX6RgGrjxAKWiwfMsw9WQLGtf94yfx3M9mltmI3GMwWQRju+TOAFp3A4arhMTeJaGmYJPTC5aiUtKlBa2XPsaxM0msamAtXhZDWtcIreLPFXyQYpxPLyOIqnfSTQ0TJ4G+J2MhC5RbHDSiNAUydwCubifwIlJgzLWBO3ZSGwdbEbilLFpgtcaAGOwbgEvn8PJGhAPb3dtN55DZCtkqSUwNQpFF1PppTHUJGrEZOa5m2Ou277+2oz2/qeGpNWe5cnLajjuc+e0iUPw8jh++zh1iIlaGbGpkOHhee3PaNgoJMl8MrNlRh4nxAKRLRIXZpEFHQTRAPl0DXFaIHHbrxfhCAR5OkvuNrORtM/PYiHFL+RptTyazRiSkKDcQaEcEDUzNsdTSDKPgtMgcNvvB1kUk4QhkS1ic53k0s0Q1oi8qcSm2A6xWUzO1iEL24HVgdIOs5FkYQxpCK7fvgbFhsDWMWn7PSHxemi4PaPvmTnbBMeh4Ce4tkWDztHZSFyb4DstEqeTtNXEzZ6bjcR12rORhFtmI2l57Vk8grSKcTL8fJGC28J4higp04pbeC4kMThO+73H2va5vfU9PnE6SLwuDGb0OuoWyziOwbd1kiQjzhxcG+J5LuVCykjUQSvx28deWMOmGS1/JqHTfs9waZBumY2ks8MQjtSJw4xC0SHvtK8zJC08NyG1uZ1mIyl1+NSiMiFljHEwrfZxH1MmJiDJIDAxne4QSThM7PQwzAywGU7SoJAN4dhhcoU8vu/hFItURwpk9U04toUT5Jj9quN5xUEz96ugDRMM25dddhn33HMPd955J0uWLOHoo48enc1j7dq1PPLIIyRJwiGHHMJll132gjqYZRlXXHEFt9xyC29/+9v50pe+tMfjoYvFIr29vaxevZo0TXcadrJ17PW247PnzZvHww8/zKpVq3YK27tqvz/omDGduUcewbQXecrBA0l3d/cE/px02KT05cV1/B63nFiNXj7Gq8/Bz3uN+/Px9fz79lx9dv3c53fVPGqXS+c+307tNXt+Ho1F5xi0j4Rd11P1GZ9qNLb9tT4TGmZUqVT44Q9/yHvf+1583+fBBx/kjjvu4I477uDBBx/E8zze85738IMf/IBKpTLhzm0btM866yyuvPLKccdp72jhwoU0Gg3uv//+nR67++67AViwYMHosq3/X7JkyU7tty7bce5vEREREZFd2aMvtRlLs9nkkUceYePGjQD09vZy1FFHUSwWX1DHtg4dufnmmznzzDO56qqrxpzqb2BggMHBQbq7u+np6RldPpEvtTnjjDPwPO+A+VIb2H9/m9tfqD7jU43GpvqMTfUZn2o0NtVnfKrR2PZFfSbtA5LbKhQKnHTSSXvc/qabbuL+++/ni1/84pjtrr32Wm6++WaKxSLz5s3jG9/4xk5tFi1axJFHHgnADTfcwDXXXMMll1zCpZdeOtrm1a9+Needdx433XQT5557Lq973evo7+/n9ttvp6ura6cv3alUKnzmM5/h8ssv55xzzhn9Svjbb7+doaEhvvrVr44ZtEVEREREtnrBYfv5uv/++7nlllvGDdtr1qwB2nNlf/Ob39xlm76+vtGwPZb//b//N/Pnz+fGG2/kX/7lXygWi7zpTW/ik5/8JHPn7jwK8F3vehfd3d1861vf4ic/+QkARx99NB/72Mc49dRTx92eiIiIiAjshWEkz9fWMdiPPvroi7nZF5WGkex/VJ/xqUZjU33GpvqMTzUam+ozPtVobPvrMBLNwy4iIiIiMkkUtkVEREREJonCtoiIiIjIJFHYFhERERGZJArbIiIiIiKTRGFbRERERGSSKGyLiIiIiEySFz1sv8jTeouIiIiI7DMv+jdIfuQjH+Hcc899sTcrIiIiIvKie0Fh+8knn+TGG2/koYceYnBwkDPOOIPLL78caH8t+8MPP8w73/lOurq6Rp9zyCGHcMghh7ygTouIiIiIHAgmHLavu+46rrrqKpIkAcAYs9NXZH7xi18kCALOP//8F9ZLEREREZED0ITGbP/Xf/0Xf//3f8+MGTO45ppr+O1vf7vTWOwTTjiBnp4efvWrX+2VjoqIiIiIHGgmdGf7uuuuo1AocN111zFnzpzdtjviiCN4+umnJ9w5EREREZED2YTubD/yyCO86lWvGjNoA3R3d7Np06YJdUxERERE5EA3obAdxzGlUmncdgMDA7iuO5FNiIiIiIgc8CYUtmfPns1jjz02Zpsoinj88ceZN2/eRDYhIiIiInLAm1DYfuMb38iaNWu47rrrdtvm//2//8fAwABvfvObJ9w5EREREZED2YQ+IPnhD3+Yf//3f+fKK6/kwQcf5E1vehMAmzdv5pe//CW//OUv+fd//3dmz57NBz7wgb3aYRERERGRA8WEwnalUuG6667jE5/4BP/xH//Bz3/+cwDuvvtu7r77bqy1HHrooVx77bWUy+W92mERERERkQPFhL/U5uCDD+bWW2/l17/+Nb/5zW9Ys2YNWZYxY8YMTj31VN7ylrfow5EiIiIi8rL2gr6u3XEcFi1axKJFi/ZWf0REREREXjIm9AFJEREREREZ3wsK20uWLOHjH/84r3nNazj66KO54oorRh+7++67+eIXv8iGDRtecCdFRERERA5EEx5G8oUvfIEbbrgBay3FYpEkSbZ7vLe3l+9973vMnDmTiy666IX2U0RERETkgDOhO9u33HIL3//+9znqqKO4+eabuf/++3dqc8QRRzBz5kx+/etfv+BOioiIiIgciCZ0Z/sHP/gBnZ2dfPvb36anp2e37Q4//HCeeOKJCXdORERERORANqE720888QTHH3/8mEEboFwus2nTpgl1TERERETkQDfhD0gaY8Zts3HjRvL5/EQ3ISIiIiJyQJtQ2J43bx6PPPIIcRzvtk2tVuOxxx7j0EMPnXDnREREREQOZBMK22eeeSb9/f1cddVVu23zla98hZGREd72trdNuHMiIiIiIgeyCX1A8sILL+RnP/sZ3/ve9/jd737HGWecAcCzzz7Ld7/7XX75y19y33338cpXvpLzzjtvr3ZYRERERORAMaGwnc/n+e53v8unPvUp7rrrLh566CEAli9fzvLlywE47bTT+PKXv0wQBHuvtyIiIiIiB5AJf6lNT08P3/72t3nsscdYsmQJa9asIcsyZsyYwWmnncaxxx67N/spIiIiInLAmVDYvuSSS+jt7eWzn/0sRxxxBEccccTe7hcAt956K/fddx8PP/wwTzzxBHEc88UvfpFzzz13j9dxwQUXsHTp0jHb/P3f/z1nn3326M9vfOMbWbNmzS7bLly4kOuvv36Pty8iIiIiL18TCtt33nknixYt2tt92cnXvvY11qxZQ3d3N9OmTdttAB7LOeecw8KFC3daniQJ3/rWt3Ach1NOOWWnxzs6Orjwwgt3Wt7X1/e8+yAiIiIiL08TCtuzZ8+m2Wzu7b7s5Atf+AIHHXQQfX19fPvb3x5z9pPd2d1d8J///OdYa3nta1/L9OnTd3q8s7OTSy+99HlvT0RERERkqwlN/fe2t72NpUuX0t/fv7f7s51TTz110u4k/9u//RsA7373uydl/SIiIiIiE7qz/dGPfpSHHnqID37wg/z1X/81r3/96/F9f2/3bdKsX7+eJUuW0Nvby+tf//pdtomiiJ/85Cds3LiRcrnMMcccw3HHHffidlREREREDmgTCttnnnkm1lrWrVvHJz7xCYwx9PT0kMvldmprjGHx4sUvuKN7049//GOyLOOcc87B83Zdgv7+fq644ortlh1zzDF85StfYe7cuWOuv1Kp4DgT+qPBC9Ld3f2ib/NAovqMTzUam+ozNtVnfKrR2FSf8alGY9sf6zOhsL3jBxWttWzatGmvdGiyWWv5yU9+Aux+CMm5557LiSeeyPz58ykWi6xcuZLrrruOW2+9lYsuuoif/vSnlMvl3W6jWq1OSt/H0t3dzeDg4Iu+3QOF6jM+1Whsqs/YVJ/xqUZjU33GpxqNbV/UZ0/C/YTC9mOPPTaRp+0X7rnnHlavXs3ChQs56KCDdtnmkksu2e7nI488kiuvvBJoT0d40003cfHFF096X0VERETkwDahsQ5XXHEFP/7xj8dtd/PNN/PpT396IpuYNC/kg5Hvfe97Abj//vv3ap9ERERE5KVpQmH75ptvHv1a9rHcd9993HzzzRPZxKSoVqv88pe/pLOzkzPPPPN5P3/rnwoajcbe7pqIiIiIvARN6qf44jjGdd3J3MTz8tOf/pQwDHnHO96xyw9zjuehhx4C9MU2IiIiIrJnJi1sW2v5wx/+8KJ9KnRgYIAVK1YwMDCw2zZ7MoRkxYoVu/zCnhUrVvAP//APALzjHe94gb0VERERkZeDPf6A5Ic+9KHtfr777rt3WrZVmqY888wzbNq0iXe9610T7txNN93EfffdB8ATTzwxumzp0qUAnHjiiZx33nkA3HDDDVxzzTVccsklu/zmx4cffpjHHnuMo446ile+8pW73ebtt9/Oddddx4IFC5g1axaFQoGVK1dy1113EccxH/3oR1mwYMGE90lEREREXj72OGxvDbjQnjt706ZNY07353ker3/967n88ssn3Lldjfm+//77t/uA4tawPZ49/WDkySefzIoVK3j00UdZvnw5rVaL7u5uXvva1/L+97+f008//XnuhYiIiIi8XBlrrd2Thlvn1rbWsmjRIt7ylrfsNkj7vk93d/cB9a2Se9O+mANTc2+OTfUZn2o0NtVnbKrP+FSjsak+41ONxnbAz7O97YcCL7nkEo488kh9UFBEREREZAwT+lKbHb/0RUREREREdjapU/+JiIiIiLycKWyLiIiIiEwShW0RERERkUmisC0iIiIiMkkUtkVEREREJonCtoiIiIjIJFHYFhERERGZJArbIiIiIiKTRGFbRERERGSSKGyLiIiIiEwShW0RERERkUmisC0iIiIiMkkUtkVEREREJonCtoiIiIjIJFHYFhERERGZJArbIiIiIiKTRGFbRERERGSSKGyLiIiIiEwShW0RERERkUmisC0iIiIiMkkUtkVEREREJonCtoiIiIjIJFHYFhERERGZJArbIiIiIiKTRGFbRERERGSSKGyLiIiIiEwShW0RERERkUmisC0iIiIiMkkUtkVEREREJonCtoiIiIjIJFHYFhERERGZJN6+7sBYbr31Vu677z4efvhhnnjiCeI45otf/CLnnnvuHq/j3nvv5UMf+tBuH9/d+p5++mmuvvpq7rnnHprNJvPmzeP888/nfe97H8aYCe2PiIiIiLy87Ndh+2tf+xpr1qyhu7ubadOmsWbNmgmva+HChSxcuHCn5UceeeROy5588knOP/98Wq0Wb33rW5k2bRp33nknn//851mxYgWf+cxnJtwPEREREXn52K/D9he+8AUOOugg+vr6+Pa3v81VV1014XUtXLiQSy+9dI/afu5zn2NkZIRvf/vbvO51rwPgsssu4+KLL+b73/8+b3/72zn++OMn3BcREREReXnYr8dsn3rqqfT19b2o23z66adZtmwZJ5988mjQBgiCgMsuuwyAG2+88UXtk4iIiIgcmPbrO9t708qVK/nud79LGIZMnz6dU045henTp+/UbunSpQCcfvrpOz124oknUiwWWbZs2aT3V0REREQOfC+bsH3bbbdx2223jf7seR4f/OAHufzyy3Fdd3T5ypUrATjooIN2WofrusyePZsnn3ySJEnwvF2Xr1Kp4Dgv/h8Nuru7X/RtHkhUn/GpRmNTfcam+oxPNRqb6jM+1Whs+2N9XvJhu6enh7/6q7/iDW94A319fTSbTX73u99x1VVX8d3vfhdjDJ/61KdG29dqNQA6Ojp2ub5SqUSWZdTrdSqVyi7bVKvVvb8j4+ju7mZwcPBF3+6BQvUZn2o0NtVnbKrP+FSjsak+41ONxrYv6rMn4f4lH7YPO+wwDjvssNGfi8UiixYt4rjjjuOd73wn119/PX/2Z3/GlClT9mEvRUREROSlaL/+gORk6u3t5YwzziBJEh588MHR5eVyGYCRkZFdPq9er2OMoVQqvSj9FBEREZED18s2bMNzt/6bzebosnnz5gGwatWqndqnacrq1auZPXv2bsdri4iIiIhs9bIO21vvaG87veCCBQsAWLJkyU7t77vvPhqNxmgbEREREZGxvGTC9sDAACtWrGBgYGC75Q8//PAu23/ve9/j3nvvZd68eRxzzDGjyw855BAWLFjAvffey5133jm6PIoivva1rwFw3nnnTcIeiIiIiMhLzX49FuKmm27ivvvuA+CJJ54YXbZ1LuwTTzxxNPjecMMNXHPNNVxyySXbfVPkJz7xCTzP4+ijj2b69Ok0m00efPBB/vCHP9DZ2cmXv/zl7ab+A/jsZz/L+973Pj7+8Y9z1lln0dvby5133skf//hHPvjBD3LCCSe8GLsvIiIiIge4/Tps33fffdx8883bLbv//vu5//77R38e7y7z+eefz5IlS1i2bBlDQ0M4jsOsWbO48MIL+dM//VNmzJix03MOO+wwbrzxRq6++mruvPNOGo0G8+bN4+/+7u94//vfv3d2TkRERERe8oy11u7rTrzU7Is5MDX35thUn/GpRmNTfcam+oxPNRqb6jM+1Whs++s82y+ZMdsiIiIiIvsbhW0RERERkUmisC0iIiIiMkkUtkVEREREJonCtoiIiIjIJFHYFhERERGZJArbIiIiIiKTRGFbRERERGSSKGyLiIiIiEwShW0RERERkUmisC0iIiIiMkkUtkVEREREJonCtoiIiIjIJFHYFhERERGZJArbIiIiIiKTRGFbRERERGSSKGyLiIiIiEwShe2XobVr1/LqV7+aJ5544gWt52Mf+xhf/epX91KvRERERF56vH3dATlwfelLX8LzdAiJiIiI7I6SkkxYpVLZ110QERER2a8pbL+EZVnGDTfcwK233sqGDRvo6enh7LPP5i1veQsAa9as4eqrr+aRRx5hzpw5/K//9b845phjAKhWq/zDP/wDDzzwAMPDw8yePZsLL7yQN7/5zaPr/9jHPsb8+fP55Cc/CcDZZ5/N2WefzerVq/n1r39NR0cHF198MWefffaLvu8iIiIi+wOF7Zew//t//y8//elPueyyyzjuuOPYtGkTq1atGn38m9/8Jp/4xCeYM2cO3/zmN/nMZz7Dv/3bv+F5HmEYcsQRR3DBBRdQKpX4zW9+w+c//3n6+vo46qijdrvNf/3Xf+UjH/kIF154If/5n//JlVdeyfHHH89BBx30YuyyiIiIyH5FH5B8iarX69x44418/OMf521vexuzZ8/mVa96Fe9617tG23zgAx/gtNNOY+7cufzZn/0Z69evZ/Xq1QBMmzaND3zgA8yfP5++vj7e85738OpXv5pf/epXY2731FNP5d3vfjdz5szhggsuoFKpcN99903qvoqIiIjsr3Rn+yVq5cqVRFHEggULdtvm0EMPHf3/lClTABgcHGTevHmkacr3vvc9fvWrX9Hf308cx0RRRD6fH3O7267TGMOUKVMYHBx8gXsjIiIicmBS2H6JyuVy47bZdiYRYwzQHucNcMMNN/CjH/2Iv/zLv+TQQw8ln89z9dVXE8fxHq9z63q3rlNERETk5UbDSF6i5syZQy6XY9myZRN6/oMPPshrX/ta3vrWt3LYYYfR19fHM888s5d7KSIiIvLSpjvbL1G5XI4LLriAa6+9Ft/3OfbYYxkaGuKpp57ipJNOGvf5c+bM4T//8z956KGH6Ojo4Ac/+AEDAwMcfPDBL0LvRURERF4aFLZfwv70T/8U13X59re/zaZNm5g6dSrnnHPOHj334osvZu3atfzlX/4luVyOs88+m9e97nXUarVJ7rX8/+3de3hU1bnH8e8ASWASCAFBJATwoIPchBKSQEQplPulQCyKgiKKWilI0XMsWLVFfQ4crVyMF8AerVyskhIMcCJSLHckiUFIFWokJSEEIlHCLWNuzD5/5Jkxw0xukEkyk9/neXhg1l57Z+2Xtfd+Z2ftvURERMR3mAzDMOq7Eb6mPh4IDAkJ0YOIlVB8qqYYVU7xqZziUzXFqHKKT9UUo8rVR3xCQkKqrKMx2yIiIiIiHqJkW0RERETEQ5Rsi4iIiIh4iJJtEREREREPadBvI0lISCA1NZWvvvqK9PR0SkpKWLx4MTExMdXexhdffMGOHTtITk4mJycHq9VKaGgov/jFL3j88cdp1aqVyzrDhg0jJyfH7fYiIyNZu3btNe+TiIiIiDQeDTrZXrFiBTk5OYSEhNC+ffsKE+DKzJs3j/z8fMLDw5k4cSImk4nk5GT+/Oc/8+mnn/Lhhx9yww03uKzXsmVLZsyY4VIeGhp6TfsiIiIiIo1Pg062X375Zbp06UJoaCirV6/mtddeq/E2ZsyYwcSJE7nxxhsdZYZhsGjRIv7617/y5ptv8oc//MFlvVatWjF37tzrar+IiIiING4Nesx2dHT0dd9Jfuyxx5wSbQCTycTs2bMBrnk6cxERERGRqjToO9ue1KxZ2a43bdrU7fLi4mLi4+M5e/YsQUFB9OnTh759+9ZlE0VERETEyzXaZHvjxo0A3HHHHW6X5+XlsXDhQqeyPn36sHTpUjp37lzptoODg2nSpO5/aVCdWYwaM8WnaopR5RSfyik+VVOMKqf4VE0xqlxDjE+jTLaPHTvGm2++Sdu2bZk1a5bL8piYGMLDw7FYLJjNZjIzM3nvvfdISEjgoYceYvPmzQQFBVW4/QsXLniy+W5pCtfKKT5VU4wqp/hUTvGpmmJUOcWnaopR5RrqdO2NLtnOzs7mscce48qVKyxdupQ2bdq41JkzZ47T5x49evDKK68AZa8jjIuLY+bMmXXSXhERERHxXg36Acnalp2dzYMPPkh+fj6vv/46AwcOrNH69957LwCHDh3yRPNERERExMc0mjvb9kQ7Ly+P5cuXM3To0Bpvw/6rAqvVWtvNa7SOHj3KO++8wz//+U9KS0vp1q0b9913H8OHD6/vpomIiIhct0aRbJdPtJctW3bNiVxaWhqgiW1qS2pqKvPmzcPf358RI0ZgNpvZuXMnzz33HN999x3Tpk2r7yaKiIiIXBefSbbPnTtHfn4+ISEhTuOw7Yn22bNnWbZsGSNGjKh0OxkZGXTs2JEWLVq4lP/pT38CYMKECbW/A41MaWkpixcvpkmTJqxcuRKLxQLAI488wsMPP8zKlSsZNmwYN910Uz23VEREROTaNehkOy4ujtTUVADS09MdZcnJyQCEh4czZcoUANavX88bb7zBnDlznGZ+nDFjBqdPn6Zfv3588803fPPNNy4/p3z9xMRE3nvvPSIiIhxJd2ZmJnv27KGkpITHH3+ciIgIj+1zY5GamsqpU6cYP368I9EGCAoKYsaMGbz00kskJibyyCOP1GMrRURERK5Pg062U1NT2bRpk1PZoUOHnB5QtCfbFcnJyQHg8OHDHD582G2d8sl2VFQUGRkZHDt2jC+++ILCwkJCQkK46667uP/++xk8ePA17k39KSoy+PFHaNECAgJM9d0c4KeHTKOiolyW2R9c/fLLL+u0TSIiIiK1rUEn20uWLGHJkiXVqjt37lynpNnO3Z3sykRGRhIZGVmjdRqy3O8MjhwxuFwAQYHQty90uLH+E+7s7GwAwsLCXJa1bdsWs9nsqCMiIiLirRrVq/8am6KiskT7wiVo3RouXIIjRwyKioz6bhqXL18GIDAw0O3ywMBARx0RERERb6Vk24f9+CNcLoC2bcqGj7RtU/a5sLC+WyYiIiLSOCjZ9mEtWpQNHfnhXNld7h/OlX1u3ry+W4ZjuvuCggK3ywsKChx1RERERLyVkm0fFhBgom9fE8Et4fx5CG4JffuaGsRDkvax2u7GZf/www9YrVa347lFREREvImSbR/X4UYTPx9iYviwsr8bwsORAD/72c8ASEpKcll28OBBpzoiIiIi3krJdiMQEGAiOLhh3NG2GzBgAKGhoWzfvt3xDnUoe3Dy/fffx8/PjzFjxtRjC0VERESuX4N+9Z/4rmbNmvHss88yb948fv3rXztN156bm8vcuXPp2LFjfTdTRERE5Loo2ZZ6Ex4ezqpVq3jnnXfYsWMHpaWldOvWjd/85jeMGDGivpsnIiIict2UbEu96tWrF8uXL6/vZoiIiIh4hMZsi4iIiIh4iJJtEREREREPUbItIiIiIuIhSrZFRERERDxEybaIiIiIiIco2RYRERER8RAl2yIiIiIiHqJkW0RERETEQ5Rsi4iIiIh4iJJtEREREREPUbItIiIiIuIhSrZFRERERDxEybaIiIiIiIco2RYRERER8ZBm9d0Aabw++eQTjhw5wr/+9S8yMjIoKSnhueeeY/z48fXdNBEREZFaoWRb6s2qVavIzc2ldevWtG3bltzc3PpukoiIiEit0jASqTfPPvssmzZtYtu2bcTExNR3c0RERERqne5sNwalRVBiBT8zNAuo79Y4REZG1ncTRERERDxKybaPM108TdNTKVBcAP6BXOkUgdGqY303S0RERKRR0DASX1ZaVJZoF17EaBEChRfLPpcW1XfLRERERBoFJdu+rMQKxQUYgTdAs+ZlfxcXlJWLiIiIiMcp2fZlfmbwD8RU8D2UFpb97R9YVi4iIiIiHqdk25c1C+BKpwho3grTj/nQvFXZ5wb0kKSIiIiIL9MDkj7OaNWRUsvoBvk2EhERERFf16CT7YSEBFJTU/nqq69IT0+npKSExYsX1/idzDabjfXr17NhwwaysrIwm81ER0czf/58wsLC3K6zd+9eVq1axddff43JZKJXr17Mnj2bQYMG1cau1a1mAUqyRUREROpBgx5GsmLFCj766CNOnz5N+/btr3k7L7zwAi+//DKGYfDAAw9w5513sn37dn71q1+RmZnpUj8hIYFZs2aRkZFBTEwMkydP5vjx48ycOZNt27Zdxx6JiIiISGPSoO9sv/zyy3Tp0oXQ0FBWr17Na6+9VuNtHDx4kLi4OCIiInj33Xfx9/cHYPz48Tz22GO89NJL/O///q+j/oULF3j55ZcJCQlh06ZNdOjQAYBHH32USZMm8cc//pHBgwcTFBRUOzvZiCUkJHDkyBEAMjIyANi8eTOHDh0CoG/fvkycOLHe2ldfiooMfvwRWrSAgABTfTen4WugkzaJiFwTT53TdK6sNw062Y6Ojr7ubcTFxQEwb948R6INMGTIECIjI9m3bx+nT5+mY8eyiV62bdvGxYsXmTt3riPRBujQoQPTp08nNjaWHTt2MGnSpOtuW2N35MgREhMTncrS0tJIS0tzfG5syXbudwZHjhhcLoCgQOjbFzrcqIS7Ipq0SUR8iafOaTpX1q8GPYykNiQlJWE2m+nfv7/LsjvvvBOA5ORkR5n934MHD3apby8rX1+u3QsvvMDBgwcr/PPCCy/UdxPrVFFRWaJ94RK0bg0XLsGRIwZFRUZ9N61h0qRNIuJLPHVO07my3jXoO9vXy2q1kpeXh8VioWnTpi7Lu3TpAkBWVpajzD6G276sqvruBAcH06RJ3X+PCQkJqfOf6U0aenzy823YjCK6hDUhIMBEUKDBuXwbzZsH0Lp13fSnhh6j8oyCH7A1s0H7rpj8mmMEBoH1e5oE+mMye2Y/vCk+9UHxqZpiVLnGHJ/qntNqGqP6OFfWp4bYh3w62b506RJAheOr7eX2egCXL18GoGXLltWq786FCxdq3tjrFBISQn5+fp3/XG/hDfEpKjJoYjLIyoa2beCHcxDcEgoLfyQ/3/NDSbwhRk5KS2hW2gTOZmIE3lA2aVPzVpQWFENR7e+H18Wnjik+VVOMKtfo41ONc9o1xaiOz5X1qT76UHWSe58fRiLiLQICTPTtayK4JZw/X5Zo9+1r0kOSFdGkTSLiSzx1TtO5st759J1t+91p+93qq7m7i13+7vXV31Yqu+stUhs63GgiZAgUFkLz5nobSVU0aZOI+BJPndN0rqxfPn1n22w2065dO06dOsWVK1dcltvHXpcfn921a1enZVXVF6ltAQEmgoN1R7vamgVAixBdPETEN3jqnKZzZb3x6WQbIDIyEqvV6nh3c3l79+4FICIiwlFm//e+fftc6tvLIiMjPdFUEREREfExPpNsnzt3joyMDM6dO+dUfs899wBls1EWFxc7ynfv3k1ycjKDBw8mNDTUUT5mzBhatmzJunXryM3NdZTn5uaybt06QkJCGD58uIf3RkRERER8QYMesx0XF0dqaioA6enpjjL7e67Dw8OZMmUKAOvXr+eNN95gzpw5zJ0717GNgQMHMmXKFOLi4oiJiWHIkCHk5eWRmJhI69atee6555x+ZnBwMM8//zzPPPMMkydPZuzYsQAkJiZy/vx5li1bptkjRURERKRaGnSynZqayqZNm5zKDh065DQkxJ5sV+bFF1/EYrGwYcMG1qxZg9lsZsSIEcyfP5/OnTu71J84cSIhISGsWrWK+Ph4AHr37s0TTzxRK7NaioiIiEjjYDIMQ9PT1bL6eE9oo38/aRUUn6opRpVTfCqn+FRNMaqc4lM1xahyes+2iIiIiEgjo2RbRERERMRDlGyLiIiIiHiIkm0REREREQ9Rsi0iIiIi4iFKtkVEREREPETJtoiIiIiIhyjZFhERERHxECXbIiIiIiIeomRbRERERMRDNF27iIiIiIiH6M62iIiIiIiHKNkWEREREfEQJdsiIiIiIh6iZFtERERExEOUbIuIiIiIeEiz+m6AXLu0tDRiY2P58ssvKS0txWKx8NBDDzF27Nj6blqt+u677/jkk0/Ys2cP//73v/n+++8JDg6mf//+zJo1i759+zrVj42N5Y033qhwe5999hmdOnVyKd+7dy+rVq3i66+/xmQy0atXL2bPns2gQYNqfZ88YdiwYeTk5LhdFhkZydq1a53KiouLWb16NZs3b+bMmTMEBwczdOhQfvvb39K2bVu329m8eTNr1qzh+PHj+Pn50b9/f5588kl69epV6/tTm+Lj41m4cGGldQYOHMj7778P+HYfSkhIIDU1la+++or09HRKSkpYvHgxMTExbutfvnyZ2NhYtm/fTl5eHu3bt2fUqFHMmTOHwMBAl/o2m43169ezYcMGsrKyMJvNREdHM3/+fMLCwtz+jIYUt+rGp6SkhH/84x/84x//IC0tjdzcXABuueUWJk+ezL333kvTpk2d1jl16hS/+MUvKvzZc+bMYe7cuS7lZ8+eZfny5ezZs4cLFy7QsWNHJk2axKxZs/Dz86uFva6ZmvShujqWTpw4wfLlyzl48CA//vgjXbt2ZerUqdx3332YTKZr39lrUJP4dO/evcrt7dq1i5tuugnwjT5U02s6eP95SMm2lzp48CCzZs3C39+fcePGERgYyPbt25k/fz65ubk8/PDD9d3EWrN27VreeecdOnfuzB133EGbNm3Iyspix44d7Nixg9dee83tF4zJkycTGhrqUt6qVSuXsoSEBJ555hnatGnjOCEmJiYyc+ZMli9fzujRo2t/xzygZcuWzJgxw6X86jjYbDaeeOIJ9u3bR79+/Rg5ciRZWVnExcXx+eefs2HDBtq0aeO0zttvv83y5csJDQ1l6tSpFBQU8H//939MnTqVv/zlL4SHh3t0365Hjx49mDNnjttln376Kd9++y2DBw92WeaLfWjFihXk5OQQEhJC+/btK/yCBmC1Wpk+fTrHjh1j8ODBjBs3jmPHjvHuu++SkpLC+vXrCQgIcFrnhRdeIC4ujltvvZUHHniAs2fP8sknn7B//34++ugjunbt6lS/ocWtuvE5efIkTz75JGazmUGDBjFs2DAuXbrEzp07WbRoEXv27OHtt992m+jddtttDB8+3KU8MjLSpSwvL4977rmH3NxcRowYQZcuXUhJSWH58uWkpaXx1ltv1XkyWZM+ZOfJY+n48eNMnTqVwsJCxowZQ/v27dm9ezeLFi0iIyOD559//hr39NrUJD4VnZeysrLYsmULt9xyiyPRLs+b+1BNr+k+cR4yxOuUlJQYw4cPN3r37m0cPXrUUX7x4kVj5MiRRq9evYxTp07VYwtr16effmokJSW5lKekpBi9evUyIiIijKKiIkf566+/blgsFuPgwYPV2v758+eNAQMGGFFRUcaZM2cc5WfOnDGioqKMqKgo49KlS9e/Ix42dOhQY+jQodWq+7e//c2wWCzGU089ZdhsNkf5Bx98YFgsFuP55593qn/ixAmjZ8+exsiRI42LFy86yo8ePWr07t3bGDNmjHHlypXa2ZE6VFRUZERGRho9e/Y08vLyHOW+3If279/vOD+sWrXKsFgsxsaNG93WXbFihWGxWIxXX33VqfzVV181LBaLsXLlSqfyzz//3LBYLMa0adOcjsldu3YZFovFePjhh53qN8S4VTc+ubm5xrp164yCggKn8oKCAiMmJsawWCxGYmKi07Ls7GzDYrEYv/vd76rdnmeeecawWCzGBx984Ciz2WzG/PnzDYvFYmzZsqUmu1cratKH6uJYmjZtmmGxWIxdu3Y5yoqKioz777/fsFgsxqFDh2q6i9elJvGpyIsvvmhYLBbj3XffdSr3hT5U02u6L5yHNGbbCx08eJCTJ08yfvx4evTo4Shv2bIlv/71rykpKWHTpk312MLaNXLkSLff1gcMGEBUVBQXLlzgm2++uebtb9u2jYsXLzJ9+nQ6dOjgKO/QoQPTp08nPz+fHTt2XPP2G6K4uDgAnnrqKac7GlOnTiUsLIwtW7ZQWFjoKI+Pj6e0tJQnnniCli1bOsp79OjB+PHjycjIIDU1te52oJbs2LGD8+fP8/Of/5wbbrjhmrfjTX0oOjra7R3GqxmGQVxcHGazmdmzZzstmz17Nmaz2dGP7Oyf582bh7+/v6N8yJAhREZGsm/fPk6fPu0ob4hxq258brzxRqZNm4bZbHYqN5vNzJw5E4CUlJTrasvly5dJTEwkLCyMqVOnOspNJhNPP/00ABs2bLiun3Etqhuja1HTPnHixAlSUlKIiopiyJAhjnJ/f3/mzZsH1H2Mrjc+RUVFbNmyBT8/PyZOnHhdbWmIfagm13RfOQ8p2fZCycnJAG5/7W0vu96TvLdo1qyZ09/lpaSksHr1av785z+zY8cOCgoK3G6jOvG012noiouLiY+PZ+XKlaxbt44jR4641CkqKuLIkSPcfPPNLhcEk8lEdHQ0VquVr776ylFu3/877rjDZXveFqPy/va3vwEwZcoUt8sbYx+yy8zM5OzZs/Tv399tQtm/f3+ys7M5c+aMozwpKcmx7Gp33nkn4BwHX4wb/HQ+unrMtt3Zs2dZv349K1euJC4ujpMnT7qtd/jwYYqLi4mOjnb5NX9oaCg333wzhw4d4sqVK7W7Ax7gqWOpsvrh4eGYzWavux5u376dCxcuMGzYMJfhfHa+2oeuvqb7ynlIY7a9UGZmJgBdunRxWdauXTvMZjNZWVl13Kq6d/r0aQ4cOEC7du2wWCwuy2NjY50+t2rVit///vdMmjTJqbyyeNrLvCWeeXl5Lg8C9unTh6VLl9K5c2egbKypzWZzGbNmZy/PzMxkwIABjn+bzWbatWvnUt/bYmSXk5PD559/TocOHRwn4Ks1xj5kZ29vZf1k3759ZGZmctNNN2G1WsnLy8NisbhNMt3FwRfjBrBx40bA/cUbYP/+/ezfv9/x2WQyMWHCBBYtWuSUUFTn/+DEiROcPn26woe+GgpPHUuV1W/atCmdOnXi+PHjlJaWur0p0xBVdRMAfLMPubum+8p5yDt6nji5fPkygNOv88sLCgri0qVLddmkOldSUsIzzzxDcXEx//mf/+l0UN12223893//N5GRkbRv3568vDx27drF66+/zoIFC2jZsqXT09yVxTMoKAjAK+IZExNDeHg4FosFs9lMZmYm7733HgkJCTz00ENs3rzZqW/Y9+1q9nJ7XOz/rugOizfFqLz4+HhsNhuTJ092OSk31j5UXk37SXXrl4+DL8bto48+Ys+ePQwcONBpWANAixYtmD17NsOHD6dz587YbDaOHj3KsmXL2Lx5M4WFhU5JqX3fKzrX29/C0JBj5OljqarrYWBgIDabjYKCAoKDg2ttvzwlOzubpKQkOnbs6PY3ib7ahyq6pvvKeUjJtngdm83GggULSElJ4Z577nG5MzJixAinz506dWL69Ol069bN8WRxZa9O8lZXP9Xeo0cPXnnlFaDsSeu4uDjHWNLGzmazER8fj8lk4u6773ZZ3lj7kFyfnTt38tJLLxEaGsqrr77qsrxt27aOccR2gwYNol+/fkyePJnt27fz9ddfN/hXadaEjqWa2bhxI4ZhEBMTQ5MmriN9fbEPVXVN9wUas+2Fqvqmdfny5Qq/xXo7m83Gs88+y9atW/nlL3/JokWLqr3uoEGD6Ny5M+np6U53bSuLZ1V3TbzBvffeC8ChQ4eAn/alfAzKs5eXvzNQ2W9LvDFGBw4c4PTp0wwcOLBGvzptTH2opv2kuvXLx8GX4rZ7926efPJJ2rZty/vvv0/79u2rvW6LFi0cD8LZj1P4ad8rOvbs4569JUbl1daxVNX1sKCgAJPJ5PZdzA2NzWZj06ZNNGnSxO1NgMp4ax+q6pruK+chJdteyD52yd0Yory8PKxWq9uxR97OZrOxcOFCNm3axPjx41myZInbb/6VCQkJAeDHH390lFUWT3uZN8fTvs9WqxWAsLAwmjRp4hindjV7efkxcl27dnWMhbuaN8bI/rR6ZWMiK9JY+pC9vdXtJ/Yx/adOnXL7sJW7OPhK3Hbt2sWcOXMICQlhzZo11zT21V2/qs7/gZ+fn9v3MHuD2jiWKqt/5coVTp06RadOnbxivPbevXvJzc0lOjqajh071nh9b+tD1bmm+8p5SMm2F4qIiABg3759LsvsZfY6vsJ+UH788ceMHTuWV155pcIn/StitVr59ttvMZvNjpMSVC+e7l5T5C3S0tKAnya2ad68ObfffjsnTpxwmWzBMAwOHDiA2Wymd+/ejnJ7jMo/kGPnbTHKz8/ns88+o3Xr1i6/4q5KY+pDXbt2pX379hw6dMjxRc3OarVy6NAhOnXq5HSRjoyMdCy72t69ewHnc5MvxG3Xrl3MnTuX4OBg1qxZc80XZfubg8q/Iahfv374+flx4MABDMNwqp+Tk8OJEyfo37+/VySSV6utY6my+qmpqVitVq+5HlbnwcjKeFMfqu413VfOQ0q2vdCgQYMICwtj69atHDt2zFF+6dIlVq5ciZ+fn0+NebL/munjjz9m9OjRvPrqqxUm2pcvX+bEiRMu5YWFhTz//PMUFBQwevRopxPLmDFjaNmyJevWrXNMuQyQm5vLunXrCAkJcTtTV0OSkZHhdDejfPmf/vQnACZMmOAov+eeewBYunSp0wn4ww8/JDs7mwkTJtC8eXNHeUxMDM2aNePtt992+lXbsWPH2Lp1K926dWvQM0iWl5CQQElJCRMmTHB6B6tdY+1DVzOZTEyZMgWr1cpbb73ltOytt97CarU6+pGd/fOKFSsoLi52lO/evZvk5GQGDx7slAh4e9x2797tlGhX9MYEu6NHj7okPFD2qrePP/6Y4OBg7rrrLkd5UFAQ48aNIzs7mw8//NBRbhgGS5cuBXD5P2hI6uJY+o//+A8iIiJISkpi9+7djvLi4mJWrFgBXHvyWpfOnTvHzp07adOmDcOGDauwni/0oZpc033lPGQy3P2vSYNX0XTtOTk5/O53v/Op6dpjY2N54403MJvNPPjgg26/gQ8fPpwePXpw6tQphg8fTp8+fejWrRs33HADP/zwAwcOHCA3NxeLxcKaNWuc7qSA81St9mliExMTyc/PZ9myZYwZM6ZO9vVaxcbG8t577xEREUHHjh1p0aIFmZmZ7Nmzh5KSEh5//HGeeuopR32bzcajjz7qmK49IiKCkydPsn37dkJDQ4mLi6t0uvaRI0c6pmsvKSlp8NO1lzdhwgTS09PZvHkz3bt3d1nu630oLi7OMQFReno6X3/9Nf3793fckQ0PD3ckJ1arlfvuu49//etfDB48mJ49e3L06FH27dtHnz59WLdundOXMoDnnnvOMU3ykCFDyMvLIzExkcDAQD788ENuvvlmp/oNLW7VjU9GRgaTJk2iuLiYcePGuewXlN1htE/9DPDAAw9w8uRJ+vXrR4cOHbhy5QpHjx4lNTUVf39/tw8Lnj171jHV9siRI+ncuTMpKSkcPnyYoUOHVjglvCdVN0Z1dSx9++233HfffRQWFjJ27FjatWvH7t27+fbbb5k+fXqdT9dek2PM7t133+V//ud/mDlzJgsWLKhw277Qh2pyTQffOA8p2fZiaWlpvP7663z55ZeUlpZisViYOXOmo6P4igULFlQ5I+bixYuJiYnh8uXLLF26lLS0NHJycrh48SIBAQF069aNUaNGMX36dJeD0m7Pnj2sWrWKo0ePAtC7d2+eeOIJoqOja32faltycjIffPABx44d4/vvv6ewsJCQkBBuv/127r//frfv+y0uLmb16tUkJCRw5swZWrduzc9//nN++9vfVjib4ubNm3n//fc5fvw4fn5+9O/fn3nz5nnNk+9paWlMmTKF22+/3WXWMTtf70NVHU+TJ09myZIljs+XLl0iNjaW7du38/3339OuXTtGjx7Nb37zG7ev17LZbKxbt44NGzaQlZWF2WwmOjqa+fPnO971frWGFLfqxicpKYkHH3yw0m1FRkaydu1ax+e4uDg+/fRTjh8/Tn5+PjabjRtvvJGBAwcyc+ZMunXr5nY7Z8+eZfny5ezevZsLFy4QGhrKxIkTHTdc6lp1Y1SXx9K///1vli9fTlJSElarla5duzJ16lTuv//+Ov8yUtNjDGDs2LFkZGSQmJhYYT8A3+hDNbmm23n7eUjJtoiIiIiIh2jMtoiIiIiIhyjZFhERERHxECXbIiIiIiIeomRbRERERMRDlGyLiIiIiHiIkm0REREREQ9Rsi0iIiIi4iFKtkVEREREPETJtoiIiIiIhyjZFhERYmNj6d69O/Hx8fXdFBERn6JkW0RERETEQ5Rsi4iIiIh4iJJtEREREREPUbItItKIfPbZZ9x777307duXqKgo5s6dy4kTJ9zWHTZsGN27d8cwDNauXcsvf/lL+vbty8SJEwEwDIOtW7cyf/58Ro0aRb9+/fjZz37Gr371K9avX4/NZnPaXnx8PN27dyc2Ntap/Pz589x22210796duLg4p2Xp6el0796dxx9/vBajICJSd5rVdwNERKRu/PWvf+WPf/wjJpOJAQMG0K5dO44cOcKUKVMYOnRohev94Q9/ID4+noiICLp160ZJSQkAxcXFPP3007Ru3ZpbbrmFnj17cv78eb788ktefPFF/vnPf7JkyRLHdiIiIgBITk522n5KSgqGYQCQlJTElClTHMuSkpIAiIyMrJ0giIjUMSXbIiKNQE5ODosXL8bPz4+3336bO++8E4CSkhIWLlzI5s2bK1x3+/btbNq0iVtvvdWpvGnTprz55psMGTIEPz8/R/m5c+d49NFH2bRpE3fffbcjyQ4LCyM0NJTDhw9TVFREQEAA8FNCfeutt5KSkuL0M+yJuZJtEfFWGkYiItIIbNy4kaKiIsaNG+dItAH8/Pz4/e9/T4sWLSpc99FHH3VJtAGaNWvG8OHDnRJtgDZt2vD0008DZcNWyouIiKC4uJjDhw87ypKTk+nWrRujRo0iNzeXrKwsoGyYSkpKCkFBQfTs2bPG+ywi0hDozraISCPwxRdfADB27FiXZSEhIdxxxx3s2LHD7brDhg2rdNvHjh1j3759nD59msLCQgzDoKCgAIDMzEynuhEREXz88cckJycTFRXF+fPnSU9PZ+rUqY6718nJyXTp0oX09HTy8/MZMmQITZs2rekui4g0CEq2RUQagbNnzwIQGhrqdnlF5QAdO3Z0W15cXMzChQvZunVrhevak267qKgo4KfhIfbx2lFRUfTr1w9/f3/HuG0NIRERX6BkW0REKmUfW321v/zlL2zduhWLxcJ//dd/0atXL1q1aoWfnx8nTpxg9OjRLuuEhYVx0003OcZt28drR0VFERAQQL9+/RzjtpVsi4gv0JhtEZFGoF27dkDZg5LunD59usbb/Pvf/w7A0qVLueuuu2jbtq1j/HZ2dnaF65Uft52cnMwtt9xCmzZtgLLEOjc3l8zMTFJSUggMDKRXr141bpuISEOhZFtEpBEYMGAAANu2bXNZdv78efbv31/jbV68eBGADh06uCz75JNPKlzPfqf673//O+np6Y6hJeWXrVu3jvz8fMLDwzVeW0S8mpJtEZFGICYmBn9/f7Zs2cKBAwcc5SUlJSxevBir1VrjbXbt2hUoe393edu2bSMhIaHC9ezJ9YYNGzAMw2mYiH3c9oYNG4Cf3s0tIuKtlGyLiDQCYWFhLFiwgJKSEh555BEeeOABnnrqKUaNGsVnn33GhAkTarzNWbNm0bRpU1577TViYmJ4+umnufvuu5k3bx4zZsyocL3OnTvToUMHioqKMJlMTsm2fdx2UVERgNNdbxERb6RkW0SkkZg2bRpvvvkmffr0IS0tjX379nHbbbfx0Ucf0aVLlxpvLyIigg8++ICBAwdy6tQpdu7ciZ+fH7GxsUybNq3KdQGn8dp29uTbbDZrvLaIeD2TYZ8jV0REREREapXubIuIiIiIeIiSbRERERERD1GyLSIiIiLiIUq2RUREREQ8RMm2iIiIiIiHKNkWEREREfEQJdsiIiIiIh6iZFtERERExEOUbIuIiIiIeIiSbRERERERD1GyLSIiIiLiIUq2RUREREQ85P8BbLXU1R52NSsAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 720x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "trace.sample_stats[\"tree_depth\"].plot(hue=\"chain\", ls=\"none\", marker=\".\", alpha=0.3);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "az.plot_posterior(\n",
-    "    trace, group=\"sample_stats\", var_names=\"acceptance_rate\", hdi_prob=\"hide\", kind=\"hist\"\n",
-    ");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Find the index of all diverging transitions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -629,26 +489,191 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;diverging&#x27; ()&gt;\n",
-       "array(0)</pre><div class='xr-wrap' 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-8e7368e2-df97-49cf-be41-25e59f9d9c13' class='xr-array-in' type='checkbox' checked><label for='section-8e7368e2-df97-49cf-be41-25e59f9d9c13' 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-240fc396-2ed4-4c0b-a267-0bb1aff5216e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-240fc396-2ed4-4c0b-a267-0bb1aff5216e' 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-957e2a87-00e6-4baa-9840-39e6e1d1dfdb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-957e2a87-00e6-4baa-9840-39e6e1d1dfdb' 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>"
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:             (chain: 2, draw: 2000)\n",
+       "Coordinates:\n",
+       "  * chain               (chain) int64 0 1\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 2 ... 2 2 2 2 3 2 2 2\n",
+       "    max_energy_error    (chain, draw) float64 1.095 -0.8137 ... -1.16 0.9584\n",
+       "    perf_counter_diff   (chain, draw) float64 0.0004437 0.0004427 ... 0.000403\n",
+       "    diverging           (chain, draw) bool False False False ... False False\n",
+       "    perf_counter_start  (chain, draw) float64 6.514 6.515 6.515 ... 9.144 9.145\n",
+       "    lp                  (chain, draw) float64 -17.36 -13.35 ... -11.35 -15.93\n",
+       "    ...                  ...\n",
+       "    acceptance_rate     (chain, draw) float64 0.7963 1.0 0.6071 ... 0.9773 0.41\n",
+       "    step_size_bar       (chain, draw) float64 0.9586 0.9586 ... 0.9527 0.9527\n",
+       "    step_size           (chain, draw) float64 1.21 1.21 1.21 ... 1.327 1.327\n",
+       "    n_steps             (chain, draw) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0\n",
+       "    energy_error        (chain, draw) float64 0.5284 -0.7795 ... -1.16 0.9584\n",
+       "    energy              (chain, draw) float64 23.28 19.36 21.68 ... 18.12 18.48\n",
+       "Attributes:\n",
+       "    created_at:                 2021-04-02T20:30:30.207447\n",
+       "    arviz_version:              0.11.2\n",
+       "    inference_library:          pymc3\n",
+       "    inference_library_version:  3.11.2\n",
+       "    sampling_time:              10.13068675994873\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-d82f557d-357c-400d-bf60-17fd8b974e90' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d82f557d-357c-400d-bf60-17fd8b974e90' 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-57c9b8ef-2d41-4497-8245-4a543785de3f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-57c9b8ef-2d41-4497-8245-4a543785de3f' 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-8f3b2948-ca60-407c-afcc-491d35db59e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f3b2948-ca60-407c-afcc-491d35db59e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f6a4389-fbf8-44dd-aae6-bc01d194c78d' class='xr-var-data-in' type='checkbox'><label for='data-2f6a4389-fbf8-44dd-aae6-bc01d194c78d' 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-a2d68f91-061f-4151-89f9-51bc68c80d4d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2d68f91-061f-4151-89f9-51bc68c80d4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91c417cf-51b7-4d61-b3ce-a8877aa7b3e8' class='xr-var-data-in' type='checkbox'><label for='data-91c417cf-51b7-4d61-b3ce-a8877aa7b3e8' 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-e4edef17-ee6b-4427-b790-4bc3eafc99f3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e4edef17-ee6b-4427-b790-4bc3eafc99f3' 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 2 ... 2 2 2 2 3 2 2 2</div><input id='attrs-371b8947-b67c-4aad-be1d-b38e9d5dac57' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-371b8947-b67c-4aad-be1d-b38e9d5dac57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-175325cd-8bf4-4ee9-abca-b3495ca84817' class='xr-var-data-in' type='checkbox'><label for='data-175325cd-8bf4-4ee9-abca-b3495ca84817' 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'>1.095 -0.8137 ... -1.16 0.9584</div><input id='attrs-36b1b5d2-8f52-4aba-bfb5-713a6ec75d38' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36b1b5d2-8f52-4aba-bfb5-713a6ec75d38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ae99af9-708d-44a0-9eb4-32b733c58764' class='xr-var-data-in' type='checkbox'><label for='data-4ae99af9-708d-44a0-9eb4-32b733c58764' 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([[ 1.09477926, -0.81370792,  1.41056226, ...,  0.23016444,\n",
+       "         0.85341925,  0.39139991],\n",
+       "       [ 1.01417394, -0.76897688,  1.40738998, ...,  0.52951561,\n",
+       "        -1.16016927,  0.95837947]])</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'>0.0004437 0.0004427 ... 0.000403</div><input id='attrs-02d0fd60-6fcf-48c9-be67-4c53ea6e1df4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-02d0fd60-6fcf-48c9-be67-4c53ea6e1df4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-554088a4-1ffa-49c2-8d06-7cc3d01e4015' class='xr-var-data-in' type='checkbox'><label for='data-554088a4-1ffa-49c2-8d06-7cc3d01e4015' 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([[0.00044366, 0.00044272, 0.00045345, ..., 0.00042984, 0.00046012,\n",
+       "        0.00041483],\n",
+       "       [0.0006154 , 0.00055876, 0.00050131, ..., 0.00040885, 0.00039782,\n",
+       "        0.00040304]])</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-5f286670-a3cf-4b17-9f6a-08bd1e70a585' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f286670-a3cf-4b17-9f6a-08bd1e70a585' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae51a3f1-d2a4-4b41-a754-a0ef0f285cdb' class='xr-var-data-in' type='checkbox'><label for='data-ae51a3f1-d2a4-4b41-a754-a0ef0f285cdb' 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>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'>6.514 6.515 6.515 ... 9.144 9.145</div><input id='attrs-1cd99fb2-8079-4f3d-9ac2-d196f414a236' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1cd99fb2-8079-4f3d-9ac2-d196f414a236' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3319218-9403-4090-9317-5231364295dd' class='xr-var-data-in' type='checkbox'><label for='data-c3319218-9403-4090-9317-5231364295dd' 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([[6.51402879, 6.51461453, 6.51519574, ..., 7.80852304, 7.80910764,\n",
+       "        7.80971072],\n",
+       "       [8.0602512 , 8.06105407, 8.06176015, ..., 9.14367871, 9.14420199,\n",
+       "        9.14471271]])</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'>-17.36 -13.35 ... -11.35 -15.93</div><input id='attrs-c45ec7a5-72a1-4f56-bc3f-b7b56081f9d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c45ec7a5-72a1-4f56-bc3f-b7b56081f9d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f4953e2-3fb8-4e85-a6da-0998abb02438' class='xr-var-data-in' type='checkbox'><label for='data-1f4953e2-3fb8-4e85-a6da-0998abb02438' 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([[-17.35887851, -13.35127215, -13.35127215, ..., -12.14420598,\n",
+       "        -12.06093442, -13.31535631],\n",
+       "       [-18.10870175, -14.08871792, -14.86856559, ..., -16.27595529,\n",
+       "        -11.35104998, -15.93251396]])</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'>0.000444 0.000443 ... 0.000403</div><input id='attrs-f355c5c6-54a0-4b71-be84-473d120a8208' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f355c5c6-54a0-4b71-be84-473d120a8208' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8fb9651-1e5b-4906-bf45-0cac343c3653' class='xr-var-data-in' type='checkbox'><label for='data-d8fb9651-1e5b-4906-bf45-0cac343c3653' 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([[0.000444, 0.000443, 0.000454, ..., 0.000431, 0.000461, 0.000415],\n",
+       "       [0.000601, 0.00056 , 0.000501, ..., 0.00041 , 0.000398, 0.000403]])</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'>0.7963 1.0 0.6071 ... 0.9773 0.41</div><input id='attrs-e32852ec-aecd-42c5-a07f-1cdffcf7c153' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e32852ec-aecd-42c5-a07f-1cdffcf7c153' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d0f10bb-90f0-4fab-b7f2-434368990ac6' class='xr-var-data-in' type='checkbox'><label for='data-8d0f10bb-90f0-4fab-b7f2-434368990ac6' 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([[0.79630611, 1.        , 0.60714261, ..., 0.90630591, 0.77602907,\n",
+       "        0.84817974],\n",
+       "       [0.61047955, 1.        , 0.6093098 , ..., 0.66889929, 0.97730698,\n",
+       "        0.41002874]])</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'>0.9586 0.9586 ... 0.9527 0.9527</div><input id='attrs-660e91a3-f1d3-4816-8a28-d47c78784ee7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-660e91a3-f1d3-4816-8a28-d47c78784ee7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70d79569-7aa6-44d8-950b-b3f92a74e64c' class='xr-var-data-in' type='checkbox'><label for='data-70d79569-7aa6-44d8-950b-b3f92a74e64c' 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([[0.9586104 , 0.9586104 , 0.9586104 , ..., 0.9586104 , 0.9586104 ,\n",
+       "        0.9586104 ],\n",
+       "       [0.95267253, 0.95267253, 0.95267253, ..., 0.95267253, 0.95267253,\n",
+       "        0.95267253]])</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'>1.21 1.21 1.21 ... 1.327 1.327</div><input id='attrs-018bcdab-215c-4608-8d6e-f5c79242401b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-018bcdab-215c-4608-8d6e-f5c79242401b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a85fd3b-6262-4f4f-a395-ffa8e1440166' class='xr-var-data-in' type='checkbox'><label for='data-8a85fd3b-6262-4f4f-a395-ffa8e1440166' 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([[1.21038156, 1.21038156, 1.21038156, ..., 1.21038156, 1.21038156,\n",
+       "        1.21038156],\n",
+       "       [1.32699879, 1.32699879, 1.32699879, ..., 1.32699879, 1.32699879,\n",
+       "        1.32699879]])</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 ... 7.0 3.0 3.0 3.0</div><input id='attrs-c202425c-82b0-428e-83b3-8a7a763df338' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c202425c-82b0-428e-83b3-8a7a763df338' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae489e0e-7d22-46db-9212-efecae22e923' class='xr-var-data-in' type='checkbox'><label for='data-ae489e0e-7d22-46db-9212-efecae22e923' 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>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'>0.5284 -0.7795 0.0 ... -1.16 0.9584</div><input id='attrs-e2aba286-129b-4a4d-b4cf-c0666cb6c1b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2aba286-129b-4a4d-b4cf-c0666cb6c1b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb0739d3-f4dc-434d-a80c-caf16cf8269c' class='xr-var-data-in' type='checkbox'><label for='data-bb0739d3-f4dc-434d-a80c-caf16cf8269c' 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([[ 0.52837865, -0.77947853,  0.        , ..., -0.00923995,\n",
+       "        -0.02144183,  0.23291837],\n",
+       "       [ 1.01417394, -0.76897688,  0.18778579, ...,  0.42620014,\n",
+       "        -1.16016927,  0.95837947]])</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'>23.28 19.36 21.68 ... 18.12 18.48</div><input id='attrs-4f09ba22-69c3-49b5-ac6d-5b4742b3692a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f09ba22-69c3-49b5-ac6d-5b4742b3692a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1e96303-3915-41cf-bcce-93bd53041b56' class='xr-var-data-in' type='checkbox'><label for='data-a1e96303-3915-41cf-bcce-93bd53041b56' 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([[23.28269296, 19.35809572, 21.67969152, ..., 15.25618358,\n",
+       "        17.61632251, 15.80788875],\n",
+       "       [20.79914835, 21.11955972, 22.71723033, ..., 21.16314096,\n",
+       "        18.11745063, 18.48340881]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4d86bdb8-8e29-4516-8dcd-d674b92ca9cd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4d86bdb8-8e29-4516-8dcd-d674b92ca9cd' 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>2021-04-02T20:30:30.207447</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>10.13068675994873</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'diverging' ()>\n",
-       "array(0)"
+       "<xarray.Dataset>\n",
+       "Dimensions:             (chain: 2, draw: 2000)\n",
+       "Coordinates:\n",
+       "  * chain               (chain) int64 0 1\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 2 ... 2 2 2 2 3 2 2 2\n",
+       "    max_energy_error    (chain, draw) float64 1.095 -0.8137 ... -1.16 0.9584\n",
+       "    perf_counter_diff   (chain, draw) float64 0.0004437 0.0004427 ... 0.000403\n",
+       "    diverging           (chain, draw) bool False False False ... False False\n",
+       "    perf_counter_start  (chain, draw) float64 6.514 6.515 6.515 ... 9.144 9.145\n",
+       "    lp                  (chain, draw) float64 -17.36 -13.35 ... -11.35 -15.93\n",
+       "    ...                  ...\n",
+       "    acceptance_rate     (chain, draw) float64 0.7963 1.0 0.6071 ... 0.9773 0.41\n",
+       "    step_size_bar       (chain, draw) float64 0.9586 0.9586 ... 0.9527 0.9527\n",
+       "    step_size           (chain, draw) float64 1.21 1.21 1.21 ... 1.327 1.327\n",
+       "    n_steps             (chain, draw) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0\n",
+       "    energy_error        (chain, draw) float64 0.5284 -0.7795 ... -1.16 0.9584\n",
+       "    energy              (chain, draw) float64 23.28 19.36 21.68 ... 18.12 18.48\n",
+       "Attributes:\n",
+       "    created_at:                 2021-04-02T20:30:30.207447\n",
+       "    arviz_version:              0.11.2\n",
+       "    inference_library:          pymc3\n",
+       "    inference_library_version:  3.11.2\n",
+       "    sampling_time:              10.13068675994873\n",
+       "    tuning_steps:               1000"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "trace.sample_stats[\"diverging\"].sum()"
+    "trace.sample_stats"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Arviz](https://arviz-devs.github.io/arviz/schema/schema.html#sample-stats) follows the following Name Convention for sample_stats variables:\n",
+    "\n",
+    "- `lp`: The joint log posterior density for the model (up to an additive constant).\n",
+    "\n",
+    "- `acceptance_rate`: The average acceptance probabilities of all possible samples in the proposed tree.\n",
+    "\n",
+    "- `step_size`: The current integration step size.\n",
+    "\n",
+    "- `step_size_nom`: The nominal integration step size. The `step_size` may differ from this, for example if the step size is jittered. Should only be present if `step_size` is also present and it varies between samples (i.e. step size is jittered).\n",
+    "\n",
+    "- `tree_depth`: The number of tree doublings in the balanced binary tree.\n",
+    "\n",
+    "- `n_steps`: The number of leapfrog steps computed. It is related to `tree_depth` with `n_steps <= 2^tree_dept`.\n",
+    "\n",
+    "- `diverging`: (boolean) Indicates the presence of leapfrog transitions with large energy deviation from starting and subsequent termination of the trajectory. “large” is defined as `max_energy_error` going over a threshold.\n",
+    "\n",
+    "- `energy`: The value of the Hamiltonian energy for the accepted proposal (up to an additive constant).\n",
+    "\n",
+    "- `energy_error`: The difference in the Hamiltonian energy between the initial point and the accepted proposal.\n",
+    "\n",
+    "- `max_energy_error`: The maximum absolute difference in Hamiltonian energy between the initial point and all possible samples in the proposed tree.\n",
+    "\n",
+    "- `int_time`: The total integration time (static HMC sampler)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If the name of the statistic does not clash with the name of one of the variables, we can use indexing to get the values. The values for the chains will be concatenated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x300 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trace.sample_stats[\"tree_depth\"].plot(col=\"chain\", ls=\"none\", marker=\".\", alpha=0.3);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_posterior(\n",
+    "    trace, group=\"sample_stats\", var_names=\"acceptance_rate\", hdi_prob=\"hide\", kind=\"hist\"\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We check if there are any divergences, if yes, how many?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -1005,61 +1030,28 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:    (chain: 2, draw: 2000, mu1_dim_0: 10)\n",
-       "Coordinates:\n",
-       "  * chain      (chain) int64 0 1\n",
-       "  * draw       (draw) int64 0 1 2 3 4 5 6 ... 1993 1994 1995 1996 1997 1998 1999\n",
-       "  * mu1_dim_0  (mu1_dim_0) int64 0 1 2 3 4 5 6 7 8 9\n",
-       "Data variables:\n",
-       "    mu1        (chain, draw, mu1_dim_0) float64 nan nan nan nan ... nan nan nan\n",
-       "Attributes:\n",
-       "    created_at:                 2021-04-02T19:01:16.089527\n",
-       "    arviz_version:              0.11.2\n",
-       "    inference_library:          pymc3\n",
-       "    inference_library_version:  3.11.2\n",
-       "    sampling_time:              8.847715854644775\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-7b9cf52e-3104-4df4-b14f-4dea18b8fad0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7b9cf52e-3104-4df4-b14f-4dea18b8fad0' 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><li><span class='xr-has-index'>mu1_dim_0</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fc22da89-0f16-4944-a292-bf94ada89d08' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fc22da89-0f16-4944-a292-bf94ada89d08' class='xr-section-summary' >Coordinates: <span>(3)</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-61fda56e-590e-4b55-a0e5-e75e027cd8c7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-61fda56e-590e-4b55-a0e5-e75e027cd8c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b6e1b1c-36c2-4965-820c-421dd971b2eb' class='xr-var-data-in' type='checkbox'><label for='data-0b6e1b1c-36c2-4965-820c-421dd971b2eb' 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-3e964c68-e86c-415e-96b5-b873c179dab7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e964c68-e86c-415e-96b5-b873c179dab7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7220a2f8-feb6-4b23-9f96-24662372780b' class='xr-var-data-in' type='checkbox'><label for='data-7220a2f8-feb6-4b23-9f96-24662372780b' 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><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mu1_dim_0</span></div><div class='xr-var-dims'>(mu1_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9</div><input id='attrs-05e01ebf-d2da-4377-9388-f5ca9b843acb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05e01ebf-d2da-4377-9388-f5ca9b843acb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e98c42b-582a-48d4-bd6f-ff7921097ba6' class='xr-var-data-in' type='checkbox'><label for='data-0e98c42b-582a-48d4-bd6f-ff7921097ba6' 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, 4, 5, 6, 7, 8, 9])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-91a95e8e-c755-4aa1-bf00-d8bab8c80981' class='xr-section-summary-in' type='checkbox'  checked><label for='section-91a95e8e-c755-4aa1-bf00-d8bab8c80981' class='xr-section-summary' >Data variables: <span>(1)</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>mu1</span></div><div class='xr-var-dims'>(chain, draw, mu1_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-ac3e4422-6c8a-4ad6-aa0e-51197d1d925f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac3e4422-6c8a-4ad6-aa0e-51197d1d925f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47f40208-96ab-47f7-8f84-43ae16afb787' class='xr-var-data-in' type='checkbox'><label for='data-47f40208-96ab-47f7-8f84-43ae16afb787' 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([[[nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        ...,\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
-       "\n",
-       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        ...,\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-adc3a3fe-d4b5-4440-af51-fe698cad340f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-adc3a3fe-d4b5-4440-af51-fe698cad340f' 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>2021-04-02T19:01:16.089527</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>8.847715854644775</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
+       "</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-078c0216-5ffb-44cb-898a-b7319d5408f9' class='xr-array-in' type='checkbox' checked><label for='section-078c0216-5ffb-44cb-898a-b7319d5408f9' 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-644b4d67-27f9-4d8c-b00c-ffedd5699644' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-644b4d67-27f9-4d8c-b00c-ffedd5699644' 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-e4eeff91-adff-45ec-b18e-0acecb5eed33' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e4eeff91-adff-45ec-b18e-0acecb5eed33' 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.Dataset>\n",
-       "Dimensions:    (chain: 2, draw: 2000, mu1_dim_0: 10)\n",
-       "Coordinates:\n",
-       "  * chain      (chain) int64 0 1\n",
-       "  * draw       (draw) int64 0 1 2 3 4 5 6 ... 1993 1994 1995 1996 1997 1998 1999\n",
-       "  * mu1_dim_0  (mu1_dim_0) int64 0 1 2 3 4 5 6 7 8 9\n",
-       "Data variables:\n",
-       "    mu1        (chain, draw, mu1_dim_0) float64 nan nan nan nan ... nan nan nan\n",
-       "Attributes:\n",
-       "    created_at:                 2021-04-02T19:01:16.089527\n",
-       "    arviz_version:              0.11.2\n",
-       "    inference_library:          pymc3\n",
-       "    inference_library_version:  3.11.2\n",
-       "    sampling_time:              8.847715854644775\n",
-       "    tuning_steps:               1000"
+       "<xarray.DataArray 'diverging' ()>\n",
+       "array(0)"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "trace.posterior.where(trace.sample_stats[\"diverging\"])"
+    "trace.sample_stats[\"diverging\"].sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this case no divergences are found. If there are any, check [this notebook](https://github.com/pymc-devs/pymc-examples/blob/main/examples/diagnostics_and_criticism/Diagnosing_biased_Inference_with_Divergences.ipynb) for  information on handling divergences."
    ]
   },
   {
@@ -1073,12 +1065,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 600x400 with 1 Axes>"
       ]
@@ -1111,7 +1103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -1142,7 +1134,7 @@
        "            }\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",
+       "      100.00% [22000/22000 00:06<00:00 Sampling 2 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -1157,17 +1149,16 @@
      "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",
+      "Sampling 2 chains for 1_000 tune and 10_000 draw iterations (2_000 + 20_000 draws total) took 12 seconds.\n",
       "The number of effective samples is smaller than 10% for some parameters.\n"
      ]
     }
    ],
    "source": [
-    "model = pm.Model()\n",
     "coords = {\"step\": [\"BinaryMetropolis\", \"Metropolis\"]}\n",
     "dims = {\"accept\": [\"step\"]}\n",
     "\n",
-    "with model:\n",
+    "with pm.Model(coords=coords) as model:\n",
     "    mu1 = pm.Bernoulli(\"mu1\", p=0.8)\n",
     "    mu2 = pm.Normal(\"mu2\", mu=0, sigma=1, shape=10)\n",
     "    step1 = pm.BinaryMetropolis([mu1])\n",
@@ -1185,26 +1176,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Data variables:\n",
-       "    accepted  (chain, draw) bool False False False False ... False False True\n",
-       "    accept    (chain, draw, accept_dim_0) float64 0.25 0.4891 1.0 ... 1.0 2.984\n",
-       "    p_jump    (chain, draw) float64 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5\n",
-       "    scaling   (chain, draw) float64 0.729 0.729 0.729 ... 0.729 0.729 0.729"
+       "['accepted', 'accept', 'scaling', 'p_jump']"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "trace.sample_stats.data_vars"
+    "list(trace.sample_stats.data_vars)"
    ]
   },
   {
@@ -1216,7 +1203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1574,64 +1561,64 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;accept&#x27; (chain: 2, draw: 10000, accept_dim_0: 2)&gt;\n",
-       "array([[[2.50000000e-01, 4.89096541e-01],\n",
-       "        [1.00000000e+00, 5.98009353e-02],\n",
-       "        [4.00000000e+00, 8.32488450e-02],\n",
+       "array([[[1.00000000e+00, 4.81947076e-02],\n",
+       "        [2.50000000e-01, 2.56576697e-01],\n",
+       "        [1.00000000e+00, 6.04572017e-02],\n",
        "        ...,\n",
-       "        [2.50000000e-01, 1.19740654e-01],\n",
-       "        [1.00000000e+00, 4.83793600e-02],\n",
-       "        [1.00000000e+00, 1.10393484e-01]],\n",
+       "        [1.00000000e+00, 1.21193314e-02],\n",
+       "        [2.50000000e-01, 7.33432666e-02],\n",
+       "        [2.50000000e-01, 5.00810348e-03]],\n",
        "\n",
-       "       [[2.50000000e-01, 1.09888343e-05],\n",
-       "        [1.00000000e+00, 3.37071637e-01],\n",
-       "        [1.00000000e+00, 8.57329324e+00],\n",
+       "       [[4.00000000e+00, 1.24853200e+00],\n",
+       "        [2.50000000e-01, 3.64256046e-01],\n",
+       "        [1.00000000e+00, 2.34105716e-04],\n",
        "        ...,\n",
-       "        [1.00000000e+00, 7.96799606e-02],\n",
-       "        [2.50000000e-01, 2.55275918e-04],\n",
-       "        [1.00000000e+00, 2.98391458e+00]]])\n",
+       "        [1.00000000e+00, 3.60262141e+00],\n",
+       "        [2.50000000e-01, 3.22903087e-01],\n",
+       "        [2.50000000e-01, 1.45096507e+00]]])\n",
        "Coordinates:\n",
        "  * chain         (chain) int64 0 1\n",
        "  * draw          (draw) int64 0 1 2 3 4 5 6 ... 9994 9995 9996 9997 9998 9999\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'>draw</span>: 10000</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-bb0fb48d-8c96-424f-86b6-f185ae8c7474' class='xr-array-in' type='checkbox' checked><label for='section-bb0fb48d-8c96-424f-86b6-f185ae8c7474' 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>0.25 0.4891 1.0 0.0598 4.0 ... 0.07968 0.25 0.0002553 1.0 2.984</span></div><div class='xr-array-data'><pre>array([[[2.50000000e-01, 4.89096541e-01],\n",
-       "        [1.00000000e+00, 5.98009353e-02],\n",
-       "        [4.00000000e+00, 8.32488450e-02],\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'>draw</span>: 10000</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-39e9fab2-7363-4254-9369-c59fe586701b' class='xr-array-in' type='checkbox' checked><label for='section-39e9fab2-7363-4254-9369-c59fe586701b' 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>1.0 0.04819 0.25 0.2566 1.0 0.06046 ... 3.603 0.25 0.3229 0.25 1.451</span></div><div class='xr-array-data'><pre>array([[[1.00000000e+00, 4.81947076e-02],\n",
+       "        [2.50000000e-01, 2.56576697e-01],\n",
+       "        [1.00000000e+00, 6.04572017e-02],\n",
        "        ...,\n",
-       "        [2.50000000e-01, 1.19740654e-01],\n",
-       "        [1.00000000e+00, 4.83793600e-02],\n",
-       "        [1.00000000e+00, 1.10393484e-01]],\n",
+       "        [1.00000000e+00, 1.21193314e-02],\n",
+       "        [2.50000000e-01, 7.33432666e-02],\n",
+       "        [2.50000000e-01, 5.00810348e-03]],\n",
        "\n",
-       "       [[2.50000000e-01, 1.09888343e-05],\n",
-       "        [1.00000000e+00, 3.37071637e-01],\n",
-       "        [1.00000000e+00, 8.57329324e+00],\n",
+       "       [[4.00000000e+00, 1.24853200e+00],\n",
+       "        [2.50000000e-01, 3.64256046e-01],\n",
+       "        [1.00000000e+00, 2.34105716e-04],\n",
        "        ...,\n",
-       "        [1.00000000e+00, 7.96799606e-02],\n",
-       "        [2.50000000e-01, 2.55275918e-04],\n",
-       "        [1.00000000e+00, 2.98391458e+00]]])</pre></div></div></li><li class='xr-section-item'><input id='section-52735604-3aa4-4aa0-9c0a-d21db4d01c62' class='xr-section-summary-in' type='checkbox'  checked><label for='section-52735604-3aa4-4aa0-9c0a-d21db4d01c62' class='xr-section-summary' >Coordinates: <span>(3)</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-d7fd42d9-37b9-4171-9632-6440628f03b4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7fd42d9-37b9-4171-9632-6440628f03b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98f4f890-226d-4551-9595-15ed72e1d426' class='xr-var-data-in' type='checkbox'><label for='data-98f4f890-226d-4551-9595-15ed72e1d426' 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 ... 9996 9997 9998 9999</div><input id='attrs-0303f4af-92a9-4639-bce8-98f99b8bf917' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0303f4af-92a9-4639-bce8-98f99b8bf917' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e66dbe4-c181-4aac-a58e-72e24a180b28' class='xr-var-data-in' type='checkbox'><label for='data-5e66dbe4-c181-4aac-a58e-72e24a180b28' 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, ..., 9997, 9998, 9999])</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-89e347fe-61cf-4bb9-b49e-b680e58466e4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-89e347fe-61cf-4bb9-b49e-b680e58466e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5539634-6521-4526-b7e9-c587854c81a0' class='xr-var-data-in' type='checkbox'><label for='data-a5539634-6521-4526-b7e9-c587854c81a0' 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-25e2acac-c6f8-4fbc-b624-3691832bd240' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-25e2acac-c6f8-4fbc-b624-3691832bd240' 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>"
+       "        [1.00000000e+00, 3.60262141e+00],\n",
+       "        [2.50000000e-01, 3.22903087e-01],\n",
+       "        [2.50000000e-01, 1.45096507e+00]]])</pre></div></div></li><li class='xr-section-item'><input id='section-aca8f80c-d6be-453e-a8b6-6a28583df2f6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aca8f80c-d6be-453e-a8b6-6a28583df2f6' class='xr-section-summary' >Coordinates: <span>(3)</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-65f0d055-4f99-4910-8951-17460cc2f53a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65f0d055-4f99-4910-8951-17460cc2f53a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47ffae36-dd33-426d-bc04-b990a3fb2329' class='xr-var-data-in' type='checkbox'><label for='data-47ffae36-dd33-426d-bc04-b990a3fb2329' 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 ... 9996 9997 9998 9999</div><input id='attrs-2cd34498-33ba-47dc-a932-1ca0ff0c5fd3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2cd34498-33ba-47dc-a932-1ca0ff0c5fd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b76283df-309b-4ce0-a247-3baad332f7c6' class='xr-var-data-in' type='checkbox'><label for='data-b76283df-309b-4ce0-a247-3baad332f7c6' 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, ..., 9997, 9998, 9999])</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-579a1f25-3c63-498f-ab7f-d276ffeeb00f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-579a1f25-3c63-498f-ab7f-d276ffeeb00f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c746108-383c-4628-bbeb-1c3be1946fcb' class='xr-var-data-in' type='checkbox'><label for='data-7c746108-383c-4628-bbeb-1c3be1946fcb' 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-9ef4a28a-9c27-47ed-9972-55ee3457032b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9ef4a28a-9c27-47ed-9972-55ee3457032b' 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, draw: 10000, accept_dim_0: 2)>\n",
-       "array([[[2.50000000e-01, 4.89096541e-01],\n",
-       "        [1.00000000e+00, 5.98009353e-02],\n",
-       "        [4.00000000e+00, 8.32488450e-02],\n",
+       "array([[[1.00000000e+00, 4.81947076e-02],\n",
+       "        [2.50000000e-01, 2.56576697e-01],\n",
+       "        [1.00000000e+00, 6.04572017e-02],\n",
        "        ...,\n",
-       "        [2.50000000e-01, 1.19740654e-01],\n",
-       "        [1.00000000e+00, 4.83793600e-02],\n",
-       "        [1.00000000e+00, 1.10393484e-01]],\n",
+       "        [1.00000000e+00, 1.21193314e-02],\n",
+       "        [2.50000000e-01, 7.33432666e-02],\n",
+       "        [2.50000000e-01, 5.00810348e-03]],\n",
        "\n",
-       "       [[2.50000000e-01, 1.09888343e-05],\n",
-       "        [1.00000000e+00, 3.37071637e-01],\n",
-       "        [1.00000000e+00, 8.57329324e+00],\n",
+       "       [[4.00000000e+00, 1.24853200e+00],\n",
+       "        [2.50000000e-01, 3.64256046e-01],\n",
+       "        [1.00000000e+00, 2.34105716e-04],\n",
        "        ...,\n",
-       "        [1.00000000e+00, 7.96799606e-02],\n",
-       "        [2.50000000e-01, 2.55275918e-04],\n",
-       "        [1.00000000e+00, 2.98391458e+00]]])\n",
+       "        [1.00000000e+00, 3.60262141e+00],\n",
+       "        [2.50000000e-01, 3.22903087e-01],\n",
+       "        [2.50000000e-01, 1.45096507e+00]]])\n",
        "Coordinates:\n",
        "  * chain         (chain) int64 0 1\n",
        "  * draw          (draw) int64 0 1 2 3 4 5 6 ... 9994 9995 9996 9997 9998 9999\n",
        "  * accept_dim_0  (accept_dim_0) int64 0 1"
       ]
      },
-     "execution_count": 92,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1642,28 +1629,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1656x552 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_posterior(trace, group=\"sample_stats\", var_names=\"accept\", hdi_prob=\"hide\", kind=\"kde\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The watermark extension is already loaded. To reload it, use:\n",
-      "  %reload_ext watermark\n",
       "Last updated: Sat Apr 03 2021\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.9.2\n",
       "IPython version      : 7.21.0\n",
       "\n",
+      "pymc3     : 3.11.2\n",
       "numpy     : 1.20.1\n",
+      "xarray    : 0.17.0\n",
+      "pandas    : 1.2.3\n",
       "seaborn   : 0.11.1\n",
       "matplotlib: 3.3.4\n",
-      "pandas    : 1.2.3\n",
-      "pymc3     : 3.11.2\n",
       "arviz     : 0.11.2\n",
-      "xarray    : 0.17.0\n",
       "\n",
       "Watermark: 2.2.0\n",
       "\n"
@@ -1674,13 +1679,6 @@
     "%load_ext watermark\n",
     "%watermark -n -u -v -iv -w"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From d2a06badabca951d683e13eca2a27cd0e630599c Mon Sep 17 00:00:00 2001
From: almostmeenal <meenaljhajharia2304@gmail.com>
Date: Sat, 3 Apr 2021 15:50:36 +0530
Subject: [PATCH 4/8] plot fixes and inference data description

---
 .../sampler-stats.ipynb                       | 1303 +++--------------
 1 file changed, 232 insertions(+), 1071 deletions(-)

diff --git a/examples/diagnostics_and_criticism/sampler-stats.ipynb b/examples/diagnostics_and_criticism/sampler-stats.ipynb
index 72ef90d58..f9aab578f 100644
--- a/examples/diagnostics_and_criticism/sampler-stats.ipynb
+++ b/examples/diagnostics_and_criticism/sampler-stats.ipynb
@@ -32,7 +32,6 @@
     "import pandas as pd\n",
     "import pymc3 as pm\n",
     "import seaborn as sns\n",
-    "import xarray as xr\n",
     "\n",
     "%matplotlib inline\n",
     "\n",
@@ -137,1073 +136,127 @@
    "outputs": [
     {
      "data": {
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-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:             (chain: 2, draw: 2000)\n",
-       "Coordinates:\n",
-       "  * chain               (chain) int64 0 1\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 2 ... 2 2 2 2 3 2 2 2\n",
-       "    max_energy_error    (chain, draw) float64 1.095 -0.8137 ... -1.16 0.9584\n",
-       "    perf_counter_diff   (chain, draw) float64 0.0004437 0.0004427 ... 0.000403\n",
-       "    diverging           (chain, draw) bool False False False ... False False\n",
-       "    perf_counter_start  (chain, draw) float64 6.514 6.515 6.515 ... 9.144 9.145\n",
-       "    lp                  (chain, draw) float64 -17.36 -13.35 ... -11.35 -15.93\n",
-       "    ...                  ...\n",
-       "    acceptance_rate     (chain, draw) float64 0.7963 1.0 0.6071 ... 0.9773 0.41\n",
-       "    step_size_bar       (chain, draw) float64 0.9586 0.9586 ... 0.9527 0.9527\n",
-       "    step_size           (chain, draw) float64 1.21 1.21 1.21 ... 1.327 1.327\n",
-       "    n_steps             (chain, draw) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0\n",
-       "    energy_error        (chain, draw) float64 0.5284 -0.7795 ... -1.16 0.9584\n",
-       "    energy              (chain, draw) float64 23.28 19.36 21.68 ... 18.12 18.48\n",
-       "Attributes:\n",
-       "    created_at:                 2021-04-02T20:30:30.207447\n",
-       "    arviz_version:              0.11.2\n",
-       "    inference_library:          pymc3\n",
-       "    inference_library_version:  3.11.2\n",
-       "    sampling_time:              10.13068675994873\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-d82f557d-357c-400d-bf60-17fd8b974e90' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d82f557d-357c-400d-bf60-17fd8b974e90' 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-57c9b8ef-2d41-4497-8245-4a543785de3f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-57c9b8ef-2d41-4497-8245-4a543785de3f' 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-8f3b2948-ca60-407c-afcc-491d35db59e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f3b2948-ca60-407c-afcc-491d35db59e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f6a4389-fbf8-44dd-aae6-bc01d194c78d' class='xr-var-data-in' type='checkbox'><label for='data-2f6a4389-fbf8-44dd-aae6-bc01d194c78d' 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-a2d68f91-061f-4151-89f9-51bc68c80d4d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2d68f91-061f-4151-89f9-51bc68c80d4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91c417cf-51b7-4d61-b3ce-a8877aa7b3e8' class='xr-var-data-in' type='checkbox'><label for='data-91c417cf-51b7-4d61-b3ce-a8877aa7b3e8' 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-e4edef17-ee6b-4427-b790-4bc3eafc99f3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e4edef17-ee6b-4427-b790-4bc3eafc99f3' 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 2 ... 2 2 2 2 3 2 2 2</div><input id='attrs-371b8947-b67c-4aad-be1d-b38e9d5dac57' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-371b8947-b67c-4aad-be1d-b38e9d5dac57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-175325cd-8bf4-4ee9-abca-b3495ca84817' class='xr-var-data-in' type='checkbox'><label for='data-175325cd-8bf4-4ee9-abca-b3495ca84817' 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'>1.095 -0.8137 ... -1.16 0.9584</div><input id='attrs-36b1b5d2-8f52-4aba-bfb5-713a6ec75d38' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36b1b5d2-8f52-4aba-bfb5-713a6ec75d38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ae99af9-708d-44a0-9eb4-32b733c58764' class='xr-var-data-in' type='checkbox'><label for='data-4ae99af9-708d-44a0-9eb4-32b733c58764' 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([[ 1.09477926, -0.81370792,  1.41056226, ...,  0.23016444,\n",
-       "         0.85341925,  0.39139991],\n",
-       "       [ 1.01417394, -0.76897688,  1.40738998, ...,  0.52951561,\n",
-       "        -1.16016927,  0.95837947]])</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'>0.0004437 0.0004427 ... 0.000403</div><input id='attrs-02d0fd60-6fcf-48c9-be67-4c53ea6e1df4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-02d0fd60-6fcf-48c9-be67-4c53ea6e1df4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-554088a4-1ffa-49c2-8d06-7cc3d01e4015' class='xr-var-data-in' type='checkbox'><label for='data-554088a4-1ffa-49c2-8d06-7cc3d01e4015' 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([[0.00044366, 0.00044272, 0.00045345, ..., 0.00042984, 0.00046012,\n",
-       "        0.00041483],\n",
-       "       [0.0006154 , 0.00055876, 0.00050131, ..., 0.00040885, 0.00039782,\n",
-       "        0.00040304]])</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-5f286670-a3cf-4b17-9f6a-08bd1e70a585' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f286670-a3cf-4b17-9f6a-08bd1e70a585' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae51a3f1-d2a4-4b41-a754-a0ef0f285cdb' class='xr-var-data-in' type='checkbox'><label for='data-ae51a3f1-d2a4-4b41-a754-a0ef0f285cdb' 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>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'>6.514 6.515 6.515 ... 9.144 9.145</div><input id='attrs-1cd99fb2-8079-4f3d-9ac2-d196f414a236' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1cd99fb2-8079-4f3d-9ac2-d196f414a236' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3319218-9403-4090-9317-5231364295dd' class='xr-var-data-in' type='checkbox'><label for='data-c3319218-9403-4090-9317-5231364295dd' 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([[6.51402879, 6.51461453, 6.51519574, ..., 7.80852304, 7.80910764,\n",
-       "        7.80971072],\n",
-       "       [8.0602512 , 8.06105407, 8.06176015, ..., 9.14367871, 9.14420199,\n",
-       "        9.14471271]])</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'>-17.36 -13.35 ... -11.35 -15.93</div><input id='attrs-c45ec7a5-72a1-4f56-bc3f-b7b56081f9d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c45ec7a5-72a1-4f56-bc3f-b7b56081f9d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f4953e2-3fb8-4e85-a6da-0998abb02438' class='xr-var-data-in' type='checkbox'><label for='data-1f4953e2-3fb8-4e85-a6da-0998abb02438' 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([[-17.35887851, -13.35127215, -13.35127215, ..., -12.14420598,\n",
-       "        -12.06093442, -13.31535631],\n",
-       "       [-18.10870175, -14.08871792, -14.86856559, ..., -16.27595529,\n",
-       "        -11.35104998, -15.93251396]])</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'>0.000444 0.000443 ... 0.000403</div><input id='attrs-f355c5c6-54a0-4b71-be84-473d120a8208' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f355c5c6-54a0-4b71-be84-473d120a8208' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8fb9651-1e5b-4906-bf45-0cac343c3653' class='xr-var-data-in' type='checkbox'><label for='data-d8fb9651-1e5b-4906-bf45-0cac343c3653' 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([[0.000444, 0.000443, 0.000454, ..., 0.000431, 0.000461, 0.000415],\n",
-       "       [0.000601, 0.00056 , 0.000501, ..., 0.00041 , 0.000398, 0.000403]])</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'>0.7963 1.0 0.6071 ... 0.9773 0.41</div><input id='attrs-e32852ec-aecd-42c5-a07f-1cdffcf7c153' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e32852ec-aecd-42c5-a07f-1cdffcf7c153' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d0f10bb-90f0-4fab-b7f2-434368990ac6' class='xr-var-data-in' type='checkbox'><label for='data-8d0f10bb-90f0-4fab-b7f2-434368990ac6' 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([[0.79630611, 1.        , 0.60714261, ..., 0.90630591, 0.77602907,\n",
-       "        0.84817974],\n",
-       "       [0.61047955, 1.        , 0.6093098 , ..., 0.66889929, 0.97730698,\n",
-       "        0.41002874]])</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'>0.9586 0.9586 ... 0.9527 0.9527</div><input id='attrs-660e91a3-f1d3-4816-8a28-d47c78784ee7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-660e91a3-f1d3-4816-8a28-d47c78784ee7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70d79569-7aa6-44d8-950b-b3f92a74e64c' class='xr-var-data-in' type='checkbox'><label for='data-70d79569-7aa6-44d8-950b-b3f92a74e64c' 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([[0.9586104 , 0.9586104 , 0.9586104 , ..., 0.9586104 , 0.9586104 ,\n",
-       "        0.9586104 ],\n",
-       "       [0.95267253, 0.95267253, 0.95267253, ..., 0.95267253, 0.95267253,\n",
-       "        0.95267253]])</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'>1.21 1.21 1.21 ... 1.327 1.327</div><input id='attrs-018bcdab-215c-4608-8d6e-f5c79242401b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-018bcdab-215c-4608-8d6e-f5c79242401b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a85fd3b-6262-4f4f-a395-ffa8e1440166' class='xr-var-data-in' type='checkbox'><label for='data-8a85fd3b-6262-4f4f-a395-ffa8e1440166' 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([[1.21038156, 1.21038156, 1.21038156, ..., 1.21038156, 1.21038156,\n",
-       "        1.21038156],\n",
-       "       [1.32699879, 1.32699879, 1.32699879, ..., 1.32699879, 1.32699879,\n",
-       "        1.32699879]])</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 ... 7.0 3.0 3.0 3.0</div><input id='attrs-c202425c-82b0-428e-83b3-8a7a763df338' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c202425c-82b0-428e-83b3-8a7a763df338' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae489e0e-7d22-46db-9212-efecae22e923' class='xr-var-data-in' type='checkbox'><label for='data-ae489e0e-7d22-46db-9212-efecae22e923' 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>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'>0.5284 -0.7795 0.0 ... -1.16 0.9584</div><input id='attrs-e2aba286-129b-4a4d-b4cf-c0666cb6c1b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2aba286-129b-4a4d-b4cf-c0666cb6c1b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb0739d3-f4dc-434d-a80c-caf16cf8269c' class='xr-var-data-in' type='checkbox'><label for='data-bb0739d3-f4dc-434d-a80c-caf16cf8269c' 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([[ 0.52837865, -0.77947853,  0.        , ..., -0.00923995,\n",
-       "        -0.02144183,  0.23291837],\n",
-       "       [ 1.01417394, -0.76897688,  0.18778579, ...,  0.42620014,\n",
-       "        -1.16016927,  0.95837947]])</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'>23.28 19.36 21.68 ... 18.12 18.48</div><input id='attrs-4f09ba22-69c3-49b5-ac6d-5b4742b3692a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f09ba22-69c3-49b5-ac6d-5b4742b3692a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1e96303-3915-41cf-bcce-93bd53041b56' class='xr-var-data-in' type='checkbox'><label for='data-a1e96303-3915-41cf-bcce-93bd53041b56' 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([[23.28269296, 19.35809572, 21.67969152, ..., 15.25618358,\n",
-       "        17.61632251, 15.80788875],\n",
-       "       [20.79914835, 21.11955972, 22.71723033, ..., 21.16314096,\n",
-       "        18.11745063, 18.48340881]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4d86bdb8-8e29-4516-8dcd-d674b92ca9cd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4d86bdb8-8e29-4516-8dcd-d674b92ca9cd' 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>2021-04-02T20:30:30.207447</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>10.13068675994873</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",
-       "Coordinates:\n",
-       "  * chain               (chain) int64 0 1\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 2 ... 2 2 2 2 3 2 2 2\n",
-       "    max_energy_error    (chain, draw) float64 1.095 -0.8137 ... -1.16 0.9584\n",
-       "    perf_counter_diff   (chain, draw) float64 0.0004437 0.0004427 ... 0.000403\n",
-       "    diverging           (chain, draw) bool False False False ... False False\n",
-       "    perf_counter_start  (chain, draw) float64 6.514 6.515 6.515 ... 9.144 9.145\n",
-       "    lp                  (chain, draw) float64 -17.36 -13.35 ... -11.35 -15.93\n",
-       "    ...                  ...\n",
-       "    acceptance_rate     (chain, draw) float64 0.7963 1.0 0.6071 ... 0.9773 0.41\n",
-       "    step_size_bar       (chain, draw) float64 0.9586 0.9586 ... 0.9527 0.9527\n",
-       "    step_size           (chain, draw) float64 1.21 1.21 1.21 ... 1.327 1.327\n",
-       "    n_steps             (chain, draw) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0\n",
-       "    energy_error        (chain, draw) float64 0.5284 -0.7795 ... -1.16 0.9584\n",
-       "    energy              (chain, draw) float64 23.28 19.36 21.68 ... 18.12 18.48\n",
-       "Attributes:\n",
-       "    created_at:                 2021-04-02T20:30:30.207447\n",
-       "    arviz_version:              0.11.2\n",
-       "    inference_library:          pymc3\n",
-       "    inference_library_version:  3.11.2\n",
-       "    sampling_time:              10.13068675994873\n",
-       "    tuning_steps:               1000"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trace.sample_stats"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[Arviz](https://arviz-devs.github.io/arviz/schema/schema.html#sample-stats) follows the following Name Convention for sample_stats variables:\n",
-    "\n",
-    "- `lp`: The joint log posterior density for the model (up to an additive constant).\n",
-    "\n",
-    "- `acceptance_rate`: The average acceptance probabilities of all possible samples in the proposed tree.\n",
-    "\n",
-    "- `step_size`: The current integration step size.\n",
-    "\n",
-    "- `step_size_nom`: The nominal integration step size. The `step_size` may differ from this, for example if the step size is jittered. Should only be present if `step_size` is also present and it varies between samples (i.e. step size is jittered).\n",
-    "\n",
-    "- `tree_depth`: The number of tree doublings in the balanced binary tree.\n",
-    "\n",
-    "- `n_steps`: The number of leapfrog steps computed. It is related to `tree_depth` with `n_steps <= 2^tree_dept`.\n",
-    "\n",
-    "- `diverging`: (boolean) Indicates the presence of leapfrog transitions with large energy deviation from starting and subsequent termination of the trajectory. “large” is defined as `max_energy_error` going over a threshold.\n",
-    "\n",
-    "- `energy`: The value of the Hamiltonian energy for the accepted proposal (up to an additive constant).\n",
-    "\n",
-    "- `energy_error`: The difference in the Hamiltonian energy between the initial point and the accepted proposal.\n",
-    "\n",
-    "- `max_energy_error`: The maximum absolute difference in Hamiltonian energy between the initial point and all possible samples in the proposed tree.\n",
-    "\n",
-    "- `int_time`: The total integration time (static HMC sampler)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If the name of the statistic does not clash with the name of one of the variables, we can use indexing to get the values. The values for the chains will be concatenated."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 700x300 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "trace.sample_stats[\"tree_depth\"].plot(col=\"chain\", ls=\"none\", marker=\".\", alpha=0.3);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "az.plot_posterior(\n",
-    "    trace, group=\"sample_stats\", var_names=\"acceptance_rate\", hdi_prob=\"hide\", kind=\"hist\"\n",
-    ");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We check if there are any divergences, if yes, how many?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
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-       "\n",
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-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
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-       "</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-078c0216-5ffb-44cb-898a-b7319d5408f9' class='xr-array-in' type='checkbox' checked><label for='section-078c0216-5ffb-44cb-898a-b7319d5408f9' 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-644b4d67-27f9-4d8c-b00c-ffedd5699644' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-644b4d67-27f9-4d8c-b00c-ffedd5699644' 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-e4eeff91-adff-45ec-b18e-0acecb5eed33' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e4eeff91-adff-45ec-b18e-0acecb5eed33' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
       "text/plain": [
-       "<xarray.DataArray 'diverging' ()>\n",
-       "array(0)"
+       "['perf_counter_diff',\n",
+       " 'energy',\n",
+       " 'lp',\n",
+       " 'step_size',\n",
+       " 'step_size_bar',\n",
+       " 'n_steps',\n",
+       " 'diverging',\n",
+       " 'energy_error',\n",
+       " 'tree_depth',\n",
+       " 'perf_counter_start',\n",
+       " 'max_energy_error',\n",
+       " 'process_time_diff',\n",
+       " 'acceptance_rate']"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "trace.sample_stats[\"diverging\"].sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this case no divergences are found. If there are any, check [this notebook](https://github.com/pymc-devs/pymc-examples/blob/main/examples/diagnostics_and_criticism/Diagnosing_biased_Inference_with_Divergences.ipynb) for  information on handling divergences."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It is often useful to compare the overall distribution of the\n",
-    "energy levels with the change of energy between successive samples.\n",
-    "Ideally, they should be very similar:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
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\n",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "az.plot_energy(trace, figsize=(6, 4));"
+    "list(trace.sample_stats.data_vars)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If the overall distribution of energy levels has longer tails, the efficiency of the sampler will deteriorate quickly."
+    "[Arviz](https://arviz-devs.github.io/arviz/schema/schema.html#sample-stats) follows the following Name Convention for sample_stats variables, these may vary depending on the algorithm used by the backend (i.e. an affine invariant sampler has no energy associated). Therefore none of these parameters should be assumed to be present in the sample_stats group. The convention below serves to ensure that if a variable is present with one of these names it will correspond to the definition included here:\n",
+    "\n",
+    "- `mean_tree_accept`: The mean acceptance probability for the tree that generated this sample. The mean of these values across all samples but the burn-in should be approximately target_accept (the default for this is 0.8).\n",
+    "\n",
+    "- `lp`: The joint log posterior density for the model (up to an additive constant).\n",
+    "\n",
+    "- `acceptance_rate`: The average acceptance probabilities of all possible samples in the proposed tree.\n",
+    "\n",
+    "- `step_size`: The current integration step size.\n",
+    "\n",
+    "- `step_size_bar`: The current best known step-size. After the tuning samples, the step size is set to this value. This should converge during tuning.\n",
+    "\n",
+    "- `tree_depth`: The number of tree doublings in the balanced binary tree.\n",
+    "\n",
+    "- `n_steps`: The number of leapfrog steps computed. It is related to `tree_depth` with `n_steps <= 2^tree_dept`.\n",
+    "\n",
+    "- `diverging`: (boolean) Indicates the presence of leapfrog transitions with large energy deviation from starting and subsequent termination of the trajectory. “large” is defined as `max_energy_error` going over a threshold.\n",
+    "\n",
+    "- `energy`: The value of the Hamiltonian energy for the accepted proposal (up to an additive constant).\n",
+    "\n",
+    "- `energy_error`: The difference in the Hamiltonian energy between the initial point and the accepted proposal.\n",
+    "\n",
+    "- `max_energy_error`: The maximum absolute difference in Hamiltonian energy between the initial point and all possible samples in the proposed tree.\n",
+    "\n",
+    "- `int_time`: The total integration time (static HMC sampler)\n",
+    "\n",
+    "- `tree_size`: The number of leafs of the sampling tree, when the sample was accepted. This is usually a bit less than $2 ^ \\text{depth}$. If the tree size is large, the sampler is using a lot of leapfrog steps to find the next sample. This can for example happen if there are strong correlations in the posterior, if the posterior has long tails, if there are regions of high curvature (\"funnels\"), or if the variance estimates in the mass matrix are inaccurate. Reparametrisation of the model or estimating the posterior variances from past samples might help.\n",
+    "\n",
+    "- `tune`: This is True, if step size adaptation was turned on when this sample was generated.\n",
+    "\n",
+    "InferenceData also stores additional info like the date, versions used, sampling time and tuning steps as attributes."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Multiple samplers\n",
-    "\n",
-    "If multiple samplers are used for the same model (e.g. for continuous and discrete variables), the exported values are merged or stacked along a new axis.\n",
-    "\n",
-    "Note that for the `model_logp` sampler statistic, only the last column (i.e. `trace.get_sampler_stat('model_logp')[-1]`) will be the overall model logp."
+    "If the name of the statistic does not clash with the name of one of the variables, we can use indexing to get the values. The values for the chains will be concatenated."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "CompoundStep\n",
-      ">BinaryMetropolis: [mu1]\n",
-      ">Metropolis: [mu2]\n"
-     ]
-    },
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\n",
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "<Figure size 700x300 with 2 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "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 12 seconds.\n",
-      "The number of effective samples is smaller than 10% for some parameters.\n"
-     ]
     }
    ],
    "source": [
-    "coords = {\"step\": [\"BinaryMetropolis\", \"Metropolis\"]}\n",
-    "dims = {\"accept\": [\"step\"]}\n",
-    "\n",
-    "with pm.Model(coords=coords) as model:\n",
-    "    mu1 = pm.Bernoulli(\"mu1\", p=0.8)\n",
-    "    mu2 = pm.Normal(\"mu2\", mu=0, sigma=1, shape=10)\n",
-    "    step1 = pm.BinaryMetropolis([mu1])\n",
-    "    step2 = pm.Metropolis([mu2])\n",
-    "    trace = pm.sample(\n",
-    "        10000,\n",
-    "        init=None,\n",
-    "        step=[step1, step2],\n",
-    "        cores=2,\n",
-    "        tune=1000,\n",
-    "        return_inferencedata=True,\n",
-    "        idata_kwargs={\"dims\": dims, \"coords\": coords},\n",
-    "    )"
+    "trace.sample_stats[\"tree_depth\"].plot(col=\"chain\", ls=\"none\", marker=\".\", alpha=0.3);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "['accepted', 'accept', 'scaling', 'p_jump']"
+       "<Figure size 720x480 with 1 Axes>"
       ]
      },
-     "execution_count": 11,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "list(trace.sample_stats.data_vars)"
+    "az.plot_posterior(\n",
+    "    trace, group=\"sample_stats\", var_names=\"acceptance_rate\", hdi_prob=\"hide\", kind=\"hist\"\n",
+    ");"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Both samplers export `accept`, so we get one acceptance probability for each sampler:"
+    "We check if there are any divergences, if yes, how many?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -1560,81 +613,183 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;accept&#x27; (chain: 2, draw: 10000, accept_dim_0: 2)&gt;\n",
-       "array([[[1.00000000e+00, 4.81947076e-02],\n",
-       "        [2.50000000e-01, 2.56576697e-01],\n",
-       "        [1.00000000e+00, 6.04572017e-02],\n",
-       "        ...,\n",
-       "        [1.00000000e+00, 1.21193314e-02],\n",
-       "        [2.50000000e-01, 7.33432666e-02],\n",
-       "        [2.50000000e-01, 5.00810348e-03]],\n",
-       "\n",
-       "       [[4.00000000e+00, 1.24853200e+00],\n",
-       "        [2.50000000e-01, 3.64256046e-01],\n",
-       "        [1.00000000e+00, 2.34105716e-04],\n",
-       "        ...,\n",
-       "        [1.00000000e+00, 3.60262141e+00],\n",
-       "        [2.50000000e-01, 3.22903087e-01],\n",
-       "        [2.50000000e-01, 1.45096507e+00]]])\n",
-       "Coordinates:\n",
-       "  * chain         (chain) int64 0 1\n",
-       "  * draw          (draw) int64 0 1 2 3 4 5 6 ... 9994 9995 9996 9997 9998 9999\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'>draw</span>: 10000</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-39e9fab2-7363-4254-9369-c59fe586701b' class='xr-array-in' type='checkbox' checked><label for='section-39e9fab2-7363-4254-9369-c59fe586701b' 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>1.0 0.04819 0.25 0.2566 1.0 0.06046 ... 3.603 0.25 0.3229 0.25 1.451</span></div><div class='xr-array-data'><pre>array([[[1.00000000e+00, 4.81947076e-02],\n",
-       "        [2.50000000e-01, 2.56576697e-01],\n",
-       "        [1.00000000e+00, 6.04572017e-02],\n",
-       "        ...,\n",
-       "        [1.00000000e+00, 1.21193314e-02],\n",
-       "        [2.50000000e-01, 7.33432666e-02],\n",
-       "        [2.50000000e-01, 5.00810348e-03]],\n",
-       "\n",
-       "       [[4.00000000e+00, 1.24853200e+00],\n",
-       "        [2.50000000e-01, 3.64256046e-01],\n",
-       "        [1.00000000e+00, 2.34105716e-04],\n",
-       "        ...,\n",
-       "        [1.00000000e+00, 3.60262141e+00],\n",
-       "        [2.50000000e-01, 3.22903087e-01],\n",
-       "        [2.50000000e-01, 1.45096507e+00]]])</pre></div></div></li><li class='xr-section-item'><input id='section-aca8f80c-d6be-453e-a8b6-6a28583df2f6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aca8f80c-d6be-453e-a8b6-6a28583df2f6' class='xr-section-summary' >Coordinates: <span>(3)</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-65f0d055-4f99-4910-8951-17460cc2f53a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65f0d055-4f99-4910-8951-17460cc2f53a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47ffae36-dd33-426d-bc04-b990a3fb2329' class='xr-var-data-in' type='checkbox'><label for='data-47ffae36-dd33-426d-bc04-b990a3fb2329' 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 ... 9996 9997 9998 9999</div><input id='attrs-2cd34498-33ba-47dc-a932-1ca0ff0c5fd3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2cd34498-33ba-47dc-a932-1ca0ff0c5fd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b76283df-309b-4ce0-a247-3baad332f7c6' class='xr-var-data-in' type='checkbox'><label for='data-b76283df-309b-4ce0-a247-3baad332f7c6' 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, ..., 9997, 9998, 9999])</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-579a1f25-3c63-498f-ab7f-d276ffeeb00f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-579a1f25-3c63-498f-ab7f-d276ffeeb00f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c746108-383c-4628-bbeb-1c3be1946fcb' class='xr-var-data-in' type='checkbox'><label for='data-7c746108-383c-4628-bbeb-1c3be1946fcb' 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-9ef4a28a-9c27-47ed-9972-55ee3457032b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9ef4a28a-9c27-47ed-9972-55ee3457032b' 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>"
+       "</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-135004d1-b475-4d70-ba45-41b69ba46b71' class='xr-array-in' type='checkbox' checked><label for='section-135004d1-b475-4d70-ba45-41b69ba46b71' 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-bd90081b-897a-4cb9-8b49-7354162437bb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bd90081b-897a-4cb9-8b49-7354162437bb' 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-580065ab-5344-413f-bf3e-64146b9d473d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-580065ab-5344-413f-bf3e-64146b9d473d' 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, draw: 10000, accept_dim_0: 2)>\n",
-       "array([[[1.00000000e+00, 4.81947076e-02],\n",
-       "        [2.50000000e-01, 2.56576697e-01],\n",
-       "        [1.00000000e+00, 6.04572017e-02],\n",
-       "        ...,\n",
-       "        [1.00000000e+00, 1.21193314e-02],\n",
-       "        [2.50000000e-01, 7.33432666e-02],\n",
-       "        [2.50000000e-01, 5.00810348e-03]],\n",
+       "<xarray.DataArray 'diverging' ()>\n",
+       "array(0)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trace.sample_stats[\"diverging\"].sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this case no divergences are found. If there are any, check [this notebook](https://github.com/pymc-devs/pymc-examples/blob/main/examples/diagnostics_and_criticism/Diagnosing_biased_Inference_with_Divergences.ipynb) for  information on handling divergences."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is often useful to compare the overall distribution of the\n",
+    "energy levels with the change of energy between successive samples.\n",
+    "Ideally, they should be very similar:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_energy(trace, figsize=(6, 4));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If the overall distribution of energy levels has longer tails, the efficiency of the sampler will deteriorate quickly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multiple samplers\n",
+    "\n",
+    "If multiple samplers are used for the same model (e.g. for continuous and discrete variables), the exported values are merged or stacked along a new axis.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Multiprocess sampling (2 chains in 2 jobs)\n",
+      "CompoundStep\n",
+      ">BinaryMetropolis: [mu1]\n",
+      ">Metropolis: [mu2]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
        "\n",
-       "       [[4.00000000e+00, 1.24853200e+00],\n",
-       "        [2.50000000e-01, 3.64256046e-01],\n",
-       "        [1.00000000e+00, 2.34105716e-04],\n",
-       "        ...,\n",
-       "        [1.00000000e+00, 3.60262141e+00],\n",
-       "        [2.50000000e-01, 3.22903087e-01],\n",
-       "        [2.50000000e-01, 1.45096507e+00]]])\n",
-       "Coordinates:\n",
-       "  * chain         (chain) int64 0 1\n",
-       "  * draw          (draw) int64 0 1 2 3 4 5 6 ... 9994 9995 9996 9997 9998 9999\n",
-       "  * accept_dim_0  (accept_dim_0) int64 0 1"
+       "    <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",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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 10 seconds.\n",
+      "The number of effective samples is smaller than 10% for some parameters.\n"
+     ]
+    }
+   ],
+   "source": [
+    "coords = {\"step\": [\"BinaryMetropolis\", \"Metropolis\"]}\n",
+    "dims = {\"accept\": [\"step\"]}\n",
+    "\n",
+    "with pm.Model(coords=coords) as model:\n",
+    "    mu1 = pm.Bernoulli(\"mu1\", p=0.8)\n",
+    "    mu2 = pm.Normal(\"mu2\", mu=0, sigma=1, shape=10)\n",
+    "    step1 = pm.BinaryMetropolis([mu1])\n",
+    "    step2 = pm.Metropolis([mu2])\n",
+    "    trace = pm.sample(\n",
+    "        10000,\n",
+    "        init=None,\n",
+    "        step=[step1, step2],\n",
+    "        cores=2,\n",
+    "        tune=1000,\n",
+    "        return_inferencedata=True,\n",
+    "        idata_kwargs={\"dims\": dims, \"coords\": coords},\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['accept', 'scaling', 'p_jump', 'accepted']"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "trace.sample_stats[\"accept\"]"
+    "list(trace.sample_stats.data_vars)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Both samplers export `accept`, so we get one acceptance probability for each sampler:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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aNbQxDzzwQHznO9+JUaNGxac+9amYPn16jB07tto+461vfWs8++yzm72mdV1yySUxd+7cTXrN2972tth333036TUAAAyuvip51tWf88Ssmmf//fePqVOn9gp9+rK1zl1LEdHVeUnc8Msn42/39ftlvc5dd9hhh1ixYkUsWbIkdtlll17PX7x4cURETJgwod/f45//+Z/j0EMPjT/84Q+xYMGCGDlyZLz2ta+N6dOnx1FHHRUR0atqCwAA2DRphEHDK7fCoPRk1UOHHHJIHHPMMf16zf/93/9FRMTHPvaxeMtb3tLja6tXr662oBsId9xxR8yaNWuTXjN9+nRhEABAgrIA5Igjjojjjz8+li5dutHXbK1z11KpFF1r7ooH7p8VD9zf/9e9+Nx1l112ib///e/x8MMP9znr6JFHHomIiFe84hWbtL6pU6fGhz70oR6PLVy4MJYsWRI77rjjequgAACA/hEGMaTNmDEjLrzwwrj11lv7HQa98MILEdF324zf//731WqjgXD++ecP2HsBAFDbXvva18Y555wTv/vd7+L444/v12u22rlrKaJ++Hfj058qxTv+afPn77zmNa+JG2+8Mf7whz/0+hkfeeSRmD9/fuy888495mxurquuuioiKu3iAACALbPhXgg1olEYlKy99torZsyYEX/729/irLPOilWrVvV6zt///ve44447qv89derUiIi4/vrre/Rdf+KJJ+K73/3u4C8aAIBCeuUrXxmvfe1r4957740vf/nLQ+rcdaBmBr3+9a+PSZMmxd///vf46U9/Wn28tbU1vvGNb0RExPvf//5erzv55JPjve99bzz44IM9Hm9tbY0nnnii1/OvvfbauOKKK+KlL31pvOc979myRQMAAIlUBpkZlLQvfelL8clPfjKuvvrquOmmm2KXXXaJl7zkJbFq1ap47LHHYtGiRfHe9743Xv3qV0dEpa/65ZdfHjNnzoz3vOc9sfvuu8eKFSti1qxZceihh8aDDz4YCxcuzPmn6unrX/96taXG8uXLIyLi9ttvj4985CPV5/zoRz/KZW0AAPTfWWedFSeccEJcfvnl8ctf/nLInbtuaRhUX18fX/rSl+KUU06Jc845J26++eaYMGFC/PWvf41nn302Dj/88F7t7iIinn766Vi4cGGsXr26x+NLly6N97///fHyl788pkyZEvX19fHwww/H/PnzY+LEifGtb32rOj9pXdddd11cf/31ERHVEO25557rcf787//+77Hbbrtt2Q8MAACJSCMMUhmUtDFjxsQPfvCDuO666+J3v/tdPProo3H//ffHmDFjYtKkSfGe97wn3vjGN1afv91228VFF10U5513XsyaNStmzpwZEydOjI9+9KNxzDHHxDvf+c4cf5q+PfHEE70+Jbls2bJYtmxZPgsCAGCzjB07Nq644or42c9+Ftddd92QOXfNKoNiALrO7bPPPnHRRRfFD37wg7j33nvjsccei8mTJ8cxxxwT733ve6NU6n8buu222y7e8Y53xH333Rd33313dHV1xaRJk+IjH/lIHHPMMdHc3Nzn6xYvXtzr/Lmjo6PHY31VZgEAQFGVyv1sQt2f4ad5+dznu+K2P8YW97/emkaPHj2k/02LwB7kzx7kzx7kzx7kzx7kzx7kb6jtwejRo/NewoAYSv+m//Xf9fHbm9rjk/9aincdXRvXTGyaoXYcM7Dsb9rsb/rscdrsb9pqZX/7cw2VxMygESqDAAAA1qtaGDQAlUEAAEDtSSIM0iYOAABg/bLObcIgAAAopsTCIFc2AAAAvQzgzCAAAKD2JBEGNWZhUHu+6wAAABiKqpVB+S4DAADISRJh0PDGyq02cQAAAL1pEwcAAMWWRhg0vHJl0y4MAgAA6KW08acAAAAJSyIMamyo3LZ35LsOAACAoUhlEAAAFFsaYVB3m7h2M4MAAAB6EQYBAECxJREGNXSHQR0qgwAAAAAAAHpIIgxSGQQAALAB3aVBKoMAAKCY0giDsplBwiAAAIBetIkDAIBiSyIMaugOg7SJAwAA6K07CxIGAQBAQSURBmkTBwAAsH5ZZRAAAFBMSYRBWWVQu8ogAACAXrSJAwCAYksiDFIZBAAAsH7CIAAAKLakwiAzgwAAAPogDAIAgEJLIgzK2sR1qAwCAADoxcwgAAAotiTCIG3iAAAA1k8WBAAAxZZGGNRdGdTeEVHW9wAAAKAHM4MAAKDYkgiDsjZxERGdnfmtAwAAYCgSBgEAQLElEQZlbeIitIoDAABYH50UAACgmJIIg9atDGrvyG8dAAAAQ5KhQQAAUGhJhEF1daWor6/cVxkEAADQkzZxAABQbEmEQRFrW8V1CIMAAAB6yAqDhEEAAFBM6YRB3a3itIkDAADoqdRdGiQMAgCAYkonDOquDNImDgAAoKdqm7h8lwEAAOQkmTCoobsyqENlEAAAQA+lap+4XJcBAADkJJ0wKJsZJAwCAADoqbTxpwAAAOlKJgzK2sS1aRMHAADQQ7VNnMogAAAopHTCoKxNnDAIAACgh2qXOGEQAAAUUjphUHdlULswCAAAoIdqZVC+ywAAAHKSTBjUkFUGmRkEAADQN2kQAAAUUjJhUNYmrl0YBAAA0IOZQQAAUGzphEHaxAEAAPRJGAQAAMWWTBjU0B0GaRMHAADwImYGAQBAoaUTBmVt4lQGAQAA9FDK7kiDAACgkJIJg4ZX28S5ugEAAFiXNnEAAFBsyYRBWWWQNnEAAAA9CYMAAKDYkgmDGquVQfmuAwAAYKgplUobfxIAAJCsZMKghobKxU27yiAAAIAeqpVB+S4DAADISTJhUFYZpE0cAADAi2SFQdIgAAAopOTCIG3iAAAAeqpmQcIgAAAopGTCoIaGym2HMAgAAKBPwiAAACimZMKgrDKoTRgEAADQg5lBAABQbOmEQVllkJlBAAAAPVTDIGkQAAAUUjJhUIMwCAAAoE+l6tCgXJcBAADkJJkwKGsT165NHAAAQA8qgwAAoNjSC4NUBgEAAPRJFgQAAMWUTBhUbROnMggAAKAHlUEAAFBsyYRB2sQBAAD0rTozCAAAKKRkwqBqZZA2cQAAAD1UwyCVQQAAUEjJhEHDuyuD2lQGAQAA9KBNHAAAFFsyYZDKIAAAgL6VutMgWRAAABRTOmFQd2VQZ2dEV5dLHAAAgBdTGQQAAMWUTBiUtYmLUB0EAADQJ2EQAAAUUjJhUH392vvt5gYBAABUmRkEAADFlkwYlM0MilAZBAAAsK5qGJTvMgAAgJwkEwaVSqVqINQuDAIAAKhSGQQAAMWWTBgUsbY6qFMYBAAAUJWFQUqDAACgmJIKgxpVBgEAAPRSzYKEQQAAUEhJhUFZZZCZQQAAAOsobfwpAABAutIKgxort8IgAACAtaozg/JdBgAAkJOkwqBqm7j2fNcBAAAwlFTDIGkQAAAUUlJhkDZxAAAAvQmDAACg2JIKg+qFQQAAAL1URwYJgwAAoJCSCoOqbeKEQQAAAL3IggAAoJiSCoOqbeLMDAIAAKjSJg4AAIotqTAoqwzq6Mx3HQAAAEOKMAgAAAotqTCoobFya2YQAADAWqVqaVC+6wAAAPKRVhikTRwAAEAvsiAAACi2pMKgrE1cu8ogAACAKjODAACg2JIKg6qVQcIgAACAqlLeCwAAAHKVVhjUPTOovcPH3QAAADIqgwAAoNjSCoPqK7dmBgEAAKwjKw0SBgEAQCGlFQZpEwcAANBLtTIo32UAAAA5SSoMamysXOG0C4MAAACqtIkDAIBiSyoMyiqDOoVBAAAAvQiDAACgmJIMg1QGAQAArJWNDNInDgAAiimpMKjRzCAAAIBezAwCAIBiSyoMamis3AqDAAAA1jIzCAAAii2tMChrE9ee7zoAAACGFGEQAAAUWlJhkDZxAAAAvakMAgCAYksqDKoXBgEAAPRSytIgAACgkJIKg7LKoHZhEAAAQJUoCAAAii2pMCibGdRhZhAAAECVNnEAAFBsaYZBnfmuAwAAYCgRBgEAQLElFQY1NlZuzQwCAABYhzAIAAAKrT7vBQwkbeLorwULyvHLX7VGa2s5Xn9IxPjxuqgDAAAAAJCmpMKgxu4wqF1lEBvx9W+W4+6/tERExKxZEV/7qjAIAIB0aRMHAADFllSbuGplkDCIjXhibt/3AQAgRdlHn4RBAABQTGmFQd0zgzo7I7q6XOXQt/b2cjz33Nr/XrTY7wsAAGlTGQQAAMWWVhi0TtM71UGsz+LFlYvghoaIurrK78rzz+e9KgAAGDwlXZEBAKDQ0gqDGtbeFwaxPgsWVm6nThkW48f1fAwAAFKkMggAAIpNGEThLOwOfiZProsJEyr3hUEAACRNZRAAABRaUmFQXV0p6rtbxQmDWJ8FCysfh5w0qS4mdodBixbluCAAABhkKoMAAKDY6jf+lNrS0BDR2SkMYv2yyqBJE4dF04jK/UpA5OOSAACkKTvTFQYBAEAxJRcGNTZEtLZGtAuDWI+sJdzkSXUxfHgpIsrVgAgAAFJU8rknAAAotOTCoIbGyq3KINanWhk0qa46Z8rMIAAAUlbqToNUBgEAQDGlFwZ1/3G/vT3fdTA0dXSU49nnKvcnTxoWw4ZV7i9aGNHVVY66Oh+ZBAAgPWYGAQBAsdXlvYCB1tgdBqkMoi+LF1cugIcPjxgzphTjx0XU1VXaCj6/NO/VAQDA4BIGAQBAMSUXBtULg9iA556v3I4dW2mVUV9fitHbVx5b+nxuywIAgMGVVQbluwoAACAnyYVBWWVQuzCIPrzwQuV2223WPrZN9/0XVm799QAAwNZQbYYsDQIAgEJKLgzKZgZ1mBlEH7LAZ5u+wqAXtv56AABgazAzCAAAii3dMKgz33UwNGWBT48waFTPrwEAQGqEQQAAUGzJhUGNjZVbM4PoSzUMGrX2sSwYWiEMAgAgUaXSxp8DAACkK7kwSJs4NuSFFyofheyzTdxKH5MEACBN1cqgfJcBAADkJNkwqF1lEH1YOzNo7UcjzQwCACB52emvNAgAAAopuTCoMasMEgbRh75nBpV6fA0AAFJTzYKEQQAAUEjJhUENZgaxARuaGSQMAgAgVdU2ccIgAAAopPTCoPrKbXu7qxx667MyqDozaOuvBwAAtgYzgwAAoNjSC4O0iWMD1s4MWvuYyiAAAApDGgQAAIWUXhikTRwb0PfMoMrtSmEQAACJ0iYOAACKLbkwqLGhcpUjDOLF2tvL0dZWub++mUFlV8cAACSo1J0GOd0FAIBiSi4M0iaO9cmqgkqliJEj1z6ehUFruiJaW7f+ugAAYGuRBQEAQDElFwY1dodB7cIgXiSbFzRqVERdXan6+PDha0PEFVrFAQCQoKxNnDQIAACKKbkwyMwg1qeveUERlZYZWdu4F4RBAAAkqFTa+HMAAIB0pRcGZW3i2vNdB0NPNQwa1ftr684NAgCA1GRhkJlBAABQTMmFQdrEsT7rqwxa97GslRwAAKREGAQAAMWWXBhUn1UGCYN4kSzo2WAYpDIIAIAEVcOgfJcBAADkJLkwSGUQ67NiReW2zzBoVM/nAABAilQGAQBAMSUXBpkZxPq88ELlyneDM4NWujoGACA9WWWQ0iAAACimZMOgzs5818HQs7K7TdyoUaVeXxs1qudzAAAgRSqDAACgmJILgxobK7faxPFira2V2+bm3l9rbi71eA4AAKTEzCAAACi25MIgbeJYn5YNhkHdz2nZeusBAICtpRoGSYMAAKCQ0g2DVAbxIlnQ02cY1NTzOQAAkBIzgwAAoNiSC4Mau8MgbeJ4sWqbuKbeX2tq6vkcAABISTULEgYBAEAhJRcGNXTPDFIZxItlVT9NfYRB2sQBAJC00safAgAApCu9MKi+ctvREVH2sTfWUW0Tt4HKoBaVQQAAJKjU3SfOFRIAABRTemFQw9r7qoNYV7VNXF8zg5p7PgcAAFKSzQzyeTkAACim5MKgxsa194VBZDo7y9U5UhsKg7SJAwAgRaXq0KBclwEAAOQkuTBIZRB9WTfk6WtmUPZYZ2dEe7srZAAA0qIyCAAAii25MKiurhTDhlXuC4PIZLOAGhoiGhp6T89dd46QVnEAAKRGYRAAABRbcmFQRERjd3VQuzCIblllUHMfVUEREfX1pWqLQa3iAABIjsogAAAotCTDoIbuP+qrDCKTVfv01SIukwVFKoMAAEiWMAgAAAopzTCouzKooz3fdTB0VCuDmtf/nKbur7UIgwAASIyZQQAAUGxJhkHaxPFiWcCzoTAo+5o2cQAApKYaBuW7DAAAICdJhkH1WWWQMIhu2sQBAFBkKoMAAKDYkgyDGoVBvEi/2sQ19XwuAACkopT3AgAAgFwlGQZlM4PazQyiWzUM2lBlkDZxAAAkKqsM0icOAACKKekwqLMz33UwdLS2Vq56+9MmrkWbOAAAUqNNHAAAFFqSYVBjY+W2XZs4uvWnTVy1MqjVFTIAAGmpzgzKdxkAAEBOkgyDssqgDm3i6JZV+zQ3r79belN3GNSqTRwAAIkpdadBKoMAAKCYkg6DVAaRae0OgzbcJq5ygaxNHAAAqSlpEwcAAIWWZBjUmFUGCYPo1q82cU09nwsAAKmo1scLgwAAoJCSDIMaumcGCYPIZJVBzRuqDGru+VwAAEiNLAgAAIopzTCovnLbbmYQ3bJqnw21iWtSGQQAQKK0iQMAgGJLMwyqtolzpUNFv9rEdX/NzCAAAJIjDAIAgEJLMwzSJo4XyVq/9acySJs4AABSk1UG6RMHAADFlGQY1FitDMp3HQwdWbVPvyqDtIkDACAx1TAIAAAopCTDoIaGypWOMIiIiDVryrF6deX+BsMgM4MAAEiUwiAAACi2RMOgym27MIiIahAUsTbw6UvWJq69PaKz02UyAADpKJkZBAAAhZZkGNRoZhDraO0Og0qltb8bfVl3nlBb2+CuCQAAtiZhEAAAFFuSYVBWGdTRnu86GBpWd88LGjEiorSBZumNjWsvkltXr/dpAABQe/SJAwCAQksyDGrUJo51ZMFO04gNP69UKsWI7udkARIAAKSgWhmU7zIAAICcJBkG1WeVQcIgYu3MoBEbmBeUyQIjlUEAAKREmzgAACi2JMOgBmEQ61jdz8qgiLWB0WphEAAACRIGAQBAMSUZBlXbxJkZRES0rjMzaGOywEgYBABASqqTM4VBAABQSEmGQVllUGdnvutgaKi2ietPZVDWJs7MIAAAElLq7hMnCwIAgGJKMgxqbKzctmsTR6yd/9PUj5lBI1QGAQCQIDODAACg2JIMg6ozg7SJIzatMigLjFqFQQAApKS08acAAADpSjoMMjOIiLUt35o2oU2cyiAAAFKiMggAAIotyTAoaxPXoU0cEbF6deWKt1+VQWYGAQCQIIVBAABQbGmGQVllkDCIWKdNXD9mBmVt4rIACQAAUlBaJw0qKw8CAIDCSTMM6q4M0iaOiLXzf5pGbPzzkNrEAQCQop5hUH7rAAAA8pF0GNTZGdHV5Uqn6FZnM4P6VRlUuUpuFQYBAJAQYRAAABRb0mFQhLlBrFsZtPHnjjAzCACAFAmDAACg0JIPg9q0iiu86sygfoRBWWC0WhgEAEBCShvvmAwAACQsyTBo2LC1FzsdwqDCyyqDRvSjTVy1MkibOAAAEqUyCAAAiifJMKhUKlWrg9qFQYVXnRnUnzZx3YHRamEQAAAJWbcwSBgEAADFk2QYFLG2VZyZQWxOmziVQQAApKRkZhAAABRaumFQQ+XWzCCyYKdflUFmBgEAkCAzgwAAoNjSDYO0iaPb6k2YGZQFRtrEAQCQkpI0CAAACi3ZMKihuzJIm7hi6+oqV4OdTZkZpE0cAABJ0SYOAAAKLdkwSGUQERFtbWvvb8rMoLa2SpAEAAApWLcuSBgEAADFk34YpDKo0NZt9zZ8+Mafv25gpFUcAACpKKkMAgCAQks/DFIZVGhZu7cRIyLq6jbeJ33dwEgYBABAKowMAgCAYhMGkbTVrZXb/rSIi6gERtlzzQ0CACAVKoMAAKDYkg2DGhoqtx3CoELLAp2mfoZBEWuDI5VBAACkQhgEAADFlmwYpDKIiLWBTn8rgyLWBketrQO/HgAAyJswCAAAiif5MKhNGFRo1ZlBTf1/jcogAACSs25lUH6rAAAAcpJuGJS1ievIdx3kK5sZtElt4rqDI2EQAACpKK37H9IgAAAonHTDoGqbOFc6RbY5beKau8OgVmEQAACJMDMIAACKLdkwqEFlELE20NmkyqCsTZyZQQAAJKJU2vhzAACAdCUbBq2tDMp3HeRr9RbMDFIZBABAKlQGAQBAsSUcBlWudtqEQYXW2lq50t2UyqDsuWYGAQCQih5hUH7LAAAAcpJwGFS51Sau2LJAp2lTKoOqM4NcJgMAkIaSNAgAAAot+TBIm7hia+2e+zNiRP+bpDeZGQQAQMK0iQMAgOJJNgxqaKjcdgiDCi2b+7MpbeKy4EibOAAAUpIVBwmDAACgeJINg7LKIDODii0LdEZsQpu4LDhqURkEAEBCqmFQvssAAABykHwYpE1csa1tE9f/12TBkcogAACSJA0CAIDCSTcMytrEdeS7DvK1erPaxPV8LQAApCCboqlNHAAAFE+6YZDKIGKdNnGbEAZlwVGrNnEAAKTEzCAAACisZMOgBpVBRERrVhm0CTODVAYBAJAiM4MAAKC4kg2DssqgNpVBhbZ6M2YGZcFRqzAIAICEZGEQAABQPMmGQcO1iSPWqQwyMwgAgIIzMwgAAIor2TCooTsM0iauuMrl8hbNDFptZhAAACmppkG5rgIAAMhBsmFQo8qgwmtvX/upx82aGdRWCZQAACAF1ZlBTnEBAKBw0g2DGiq3HcKgwmpdp7Jn+PD+vy4LjsrliLa2gV0TAADkpRoG5bsMAAAgB+mGQd2VQWu6Ijo7Xe4UUdYirrExYtiw/k/LXTc4ajU3CACARJgZBAAAxZV8GBShVVxRZUFO0ybMC4qoBEfZ74+5QQAAJMPMIAAAKKxkw6CGhrX3OzryWwf5ySqDRmzCvKBMc/drVAYBAJAKM4MAAKC4kg2Dhg0rxbBhlfsqg4opmxm0qZVBEREjul+zWhgEAEBiZEEAAFA8yYZBERGN3dVB7SqDCqlaGbQ5YVBTz/cAAIBapzIIAACKK+0wqHvui8qgYsoqgzYnDMqqiVrNDAIAIBHZyCClQQAAUDzCIJKVzftp2oyZQdrEAQCQnNLGnwIAAKQp6TCooTsM6tAmrpC2pE1ctTJIGAQAQCK0iQMAgOJKOgxSGVRsWYu3JjODAABgbRiU7zIAAIAcpB0GNVRuhUHFtHp15TJ3cyqDRpgZBABAYrIucSqDAACgeNIOg7org9qEQYVUbRO3GTODmqozg1wpAwCQBm3iAACguJIOg4YPr9y2teW7DvKRzftpGrHpk3JHVMOgAVwQAADkqVoalOsqAACAHKQdBmUzg4RBhbQ6mxm0OZVBTZUr5VZhEAAAiTAzCAAAiivpMKhRZVChZUHOlswMWm1mEAAAiTAzCAAAiivpMKjaJs7MoEJaXW0Tt+mvzcKgVmEQAACJEQYBAEDxFCIMahcGFVIWBo3YnDZxWRikTRwAAIkomRkEAACFlXYY1D0zqK3N1U4RZVU9m1UZ1B0grRYGAQCQCjODAACgsNIOg8wMKrTVWzAzSGUQAACpqVYGAQAAhZN4GFS52hEGFVPrAMwMWm1mEAAAiah2iVMaBAAAhZN0GNSYtYkzM6iQBmJmkDZxAACkIqsMEgYBAEDxJB0GaRNXXOVyuVrVsyUzg7SJAwAgGdXSoFxXAQAA5CDtMCirDBIGFU5HR8Sarsr9zZoZ1B0GrV5dCZYAAKDWVSuD8l0GAACQg7TDIJVBhbVue7fNCoO6X9PVFdGuzSAAAAkwMwgAAIqrEGGQP+YXT0t3i7iGhoj6+tKGn9yHdQMkc4MAAEiBmUEAAFBcSYdBjSqDCisLcDanKigiYtiwUjQ2VO6bGwQAQBKEQQAAUFhJh0HZzCCVQcWzursyqGkzw6CIiBHrzA0CAAAAAIBalXYYpDKosFq3sDJo3ddmwRIAANQybeIAAKC4hEEkqdomrmnz3yOrKtImDgCAFGSTNIVBAABQPMUIg7SJK5wswNmiNnFZZZAwCACABGSVQQAAQPGkHQZ1zwxSGVQ8WWu3LWkT19RdVaQyCACAJGgTBwAAhZV0GNTYHQZ1dkasWeOKp0gGtDLIzCAAABKgMggAAIor6TAoaxMXoTqoaAZiZtAIM4MAAEiImUEAAFBcxQmDzA0qlNWrK1e4W9QmzswgAAASUtImDgAACivpMKiurhQNDZX7KoOKpbW7tdsWtYnrrirKgiUAAKhlwiAAACiupMOgiIjh3XOD2oVBhVJtEzcAlUGtZgYBAJACM4MAAKCw0g+DulvFqQwqlmzOT1PT5l/xjhhRea02cQAApMDMIAAAKK7kw6DGLAwyM6hQBqIyKHttqzAIAIAEaBMHAADFlXwYlFUGtQuDCqU6M6hp898je602cQAApEQYBAAAxZN+GNQ9M0ibuGLJKoOaBqAySJs4AABSUDIzCAAACiv9MMjMoEIaiDZxWZCkMggAgCQIgwAAoLCSD4MaVQYVUjbnZ0vaxKkMAgAgJVkWpE0cAAAUT/JhULUyyMygQsmqebaoMiibGSQMAgAgAVmbOGEQAAAUT3HCIJVBhbK6OwzakplBWRikMggAgBQIgwAAoLjSD4O0iSuk1gGYGVRtE2dmEAAAKcjCoHxXAQAA5CD9MKhaGeSSpyg6OsqxZk3l/ogtmBmUVRVpEwcAQAqymUHSIAAAKJ7ihEFmBhXGuuHNlrSJyyqD1qypBEwAAFDLtIkDAIDiSj4MauwOg9q1iSuMrK3bsGERDQ2lDT95A5rWqSpSHQQAQK0TBgEAQHElHwYNb6xc8agMKo7V3cHNllQFRUTU15eivr77Pc0NAgCg1pkZBAAAhZV+GKQyqHCyKp4tmReUyVrFrVYZBABAKqRBAABQOIUJg9qEQYWRBTcjtrAyKGJtdZE2cQAA1LqsgbI2cQAAUDzFCYO0iSuM1u6WblvaJi5ibXWRyiAAAGpdafPHaQIAADUu+TBoRHcY5I/5xVENgwagTVz2Hi1mBgEACXruuXJ89/yumD9/Td5LYSvIwiCVQQAAUDzph0FmvhROFtw0N2/5ezVnYVDLlr8XAMBQc/mV5fjplRHX/VJP5SKohkH5LgMAAMhB8mFQVtlh5ktxZMHNgIRB3e/RKgwCABL04IOV2yk7Dst3IWxd0iAAACic5MOgrDKoVZuvwqiGQQPQJi4Lg1QGAQCp6egox6OPVu7vs3d9vothq9AmDgAAiqswYdBqYVBhtLRUrm4HsjLIzCAAIDVz5kS0d0Rsu23E1KnJXxYQwiAAACiy5K/6mrLKIG3iCmNwZga5YgYA0vLg7Mrt7rtFlLKUgKSZGQQAAMWVfBg0ovuP+e3tEWvWuOwpgrUzg7b8jxrZe2gTBwCkZvZDlXPjPXbPeSFsdSqDAACgeJIPg7LKoIiItrb81sHWY2YQAMDGzX64crvnHqqCiqJaACYMAgCAwkk+DBo+fO1FT6u5L4WQBTdNAxAGZe9hZhAAkJL29nI8/XTl/i6vyHctbH0qgwAAoHiSD4NKpVKMGF65v9rcoEIY0JlBKoMAgAQtWFAJBJqaIsaMyXs1bC1mBgEAQHElHwZFrJ0b1CoMKoTW6sygLX+vrNWcMAgASMnT8yu3O06ufHiKYrDVAABQXIUIg7JWXyqDiqFlIMMglUEAQILmd4dBkyfnuw7yoU0cAAAUTzHCoBGVWzODiqEaBg3AzKBqGOR3BwBIyNPzK2nAjsKgQqlWBgmDAACgcAoRBo3oDoNUBqWvXC5XQz+VQQAAfVvbJk7fsCKpzgwSBgEAQOEUKgwyMyh97e0Ra7oq9wdyZlBrayVoAgBIgTZxxVQNg/JdBgAAkINChEFmBhXHuhU8TQPYJq5c9vsDAKShs7McCxdW7msTV0w+4wQAAMVTiDCo2ibO3JfkZWFQ04iIurotb3syYkREXV3P9wYAqGULF1YqqYcPjxg7Nu/VsDWZGQQAAMVViDCoSZu4wsgCm4FoERcRUSqVqhVGLcJEACAB2bygyZMG5sMz1A4zgwAAoLgKEQZVZwa1uupJXRbYNA1QGBSxdm6QyiAAIAVZi7gJE/JdB1ufmUEAAFBcxQiDzAwqjIGuDFr3vYRBAEAKFi+pRAHjx+e8EHKjMggAAIqnEGFQ04jKR+C0iUtfNQxqGrj3FAYBACl59tnK7fhxWsQVTXXHhUEAAFA4hQiDsjZxq818Sd6gVgb5/QEAErB4SeV23Lh810EO5H8AAFBYhQiDmrKZQSqDkpcFNgMaBpkZBAAkZEl3GDReGFQ41ZlBKoMAAKBwihEGmRlUGGYGAQCsX7lcjsWLK/dVBhVPNQzKdxkAAEAOChEGjegOg1QGpa+lpXJpO5Azg5qqYZDLZgCgtq1atfaceNxL8l0LW1/WJU5lEAAAFE8hwqAmM4MKI6veaRrAMKjaJs7vDwBQ47J5QdtsE9HUZIBM4WgTBwAAhVWIMGhEFgapDEre2plBA/fHjey9tIkDAGqdeUHFVqqWBuW6DAAAIAeFCIOyyiBt4tI3KDODssogYRAAUOOyMMi8oGIyMwgAAIqrEGFQNjNIZVD6Vq2q3I4cyDBoZOVWGAQA1LqsTZx5QcVkZhAAABRXIcKgpnXaxHV1ufJJWRYGjRo1cO+5Tfd7rVw5cO8JAJCHJc9WzoXHjTMvqJDMDAIAgMIqRBiUzQyKiGhry28dDL4ssBnIMGiUMAgASISZQcVmZhAAABRXIcKg4cPX3tcqLm0vdAc22wiDAAB6MTOICFkQAAAUUSHCoLq6UrVVXEtrvmth8HR1latzfUYOZBjUPTNo5aqBe08AgDwsFgYVWkl3QAAAKKxChEEREc3NldssLCA9ra0RXV2V+4NRGdTaGtHZ6XOUAEBtam0txwsvVO5rE1dM1S5xTmkBAKBwChMGjeyu7liluiNZWYu4hoaIxsaBe9/sdyfC7w8AULuWPFu5bWrqeX5DgXSnQcIgAAAoHmEQycj2duTIiNIA9sCor1/bZtDcIACgVmXzgsaPG9hzJWpHdduFQQAAUDjCIJKRBTWjBrBFXCZ7T3ODAIBaZV4QWRgkCwIAgOIpXBjkj/npytrEDeS8oEw1DFIZBADUqGe728SZF1RcZgYBAEBxFS4MUhmUrlXdQc1g9MAXBgEAtW7xkkoC8JKX5LwQclMyMwgAAAqrMGHQqGoY5MonVdrEAQCs39qZQeYFFZYwCAAACqswYZA2cenTJg4AYP3MDKIkBwQAgMIqUBhUufJpEQYlK6v6GtTKoJU+RgkA1Ka1lUH5roP8mBkEAADFVZwwqLlyqzIoXSurM4MG/iOPWZvBF1QGAQA1qL29HEuXVu6rDEIYBAAAxVOcMKi7sqOlJd91MHgGt01cJWBaJQwCAGrQs89VbhsbIrbbLt+1kB9t4gAAoLgKEwZllR1mvqRrVXfV1+C2iRv49wYAGGxL1pkXVJIIFFf31qsMAgCA4ilMGNTc3SZulTZxyaq2iRuMMEibOACghi1eJwyiuOSAAABQXIUJg7LKjlXaxCVrcNvEVW6FiQBALcoqg8YLgwoty4JUBgEAQPEUJgwamVUGrYwou/pJkjZxAAB9W7Kkcv6rMqjYStrEAQBAYRUnDOpu87WmK2L16nzXwsArl8tr28SNHPj31yYOAKhla2cG6RNWaMIgAAAorMKEQU1NEXXdP61Wcelpb4/o6KjcH+w2cV1drp4BgNpiZhARZgYBAECRFSYMKpVK0bxOqzjSklUF1dVVgr+BloVB5XJEa+vAvz8AwGAyM4gIM4MAAKDIChMGRaxt9bVyVb7rYOC9sE6LuLq6gf/I4/DhpWhsqNxfsWLA3x4AYNB0dpbjuecr91UGFZuZQQAAUFyFCoNGdld3tGgTl5zlyyu32203eN8je+/lwiAAoIY8/3xEV1fEsGERo7fPezXkqhoGSYMAAKBoihUGdbeJW6lNXHKWLavcDuYfOLIwKPteAAC1IJsX9JKXRAwbZmgMAABAERUqDMraxK1SGZScZVuhMmj77Su3WRUSAEAtePbZyq15QZRkgQAAUFiFCoOaszDIzKDkbNU2ccIgAKCGZJVB5gWRZUG6xAEAQPEUKgzKKoNWrnT1k5rlyyt7OqiVQVmbuOV+fwCA2rFkSeXcRRhEqTozKN91AAAAW1+hwqCRKoOSlc3x2X67wet9sV33e6sMAgBqyeLFldtxL9EjrOiEQQAAUFyFCoOqf8xfkfNCGHDZzKDttYkDAOhhUXcYNGGHfNfBEJCFQfmuAgAAyEHBwqDKrT/mp2drzAzafvvK7TK/PwBADVm0qHK7gzCo8Kq1YdIgAAAonEKGQVlLMdKxVcKg6sygwfseAAADqbOzHM8+V7m/w/h810L+tIkDAIDiKlQYtL3KoGStnRk0eN9DZRkAUGuWLKn84b+xYW2VM8UlDAIAgOIqZBiksiMtbW3laF1duT+Yf+TIfn9WLI/o6nIFDQAMfdm8oPHjI+rqSht+MukzMwgAAAqrUGFQVtnR2loJEEhDVqkzbFjEyJGD93223bZyu6YrYuWqwfs+AAADxbwg1mVmEAAAFFehwqBRoyqBQYRWXynJ9nL77SJKpcH7xGtjYymamyv3zZ0CAGpBVhlkXhAR2sQBAECRFSoMKpVK5gYlKGv7t90gzgvKZG3o/P4AALVg0aLKX/1VBgEAABRbocKgiLWBgblB6cj2cmsMRd5OmAgA1JC1lUHmBaEyCAAAiqxwYVAWGAiD0rF8a1YGCRMBgBpiZhA9ZGFQvqsAAAByULgwSGVHepYvr1zObo0wyO8PAFAryuVyLMzCIDODiGoWJA0CAIACKmwYtGyZK6BUPP985Xb09oP/vbLKoOeX+v0BAIa2F16IaG2t3B8vDCK0iQMAgCIrXBikzVd6ljxbuX3JSwa/F/64cZXv8eySQf9WAABbZP4zlduxYyNGjDAzCGEQAAAUWfHCoO0rV0DafKVjSXcwM37c4H+vcd3fY7EwCAAY4ubPr9xOnpTvOhhCzAwCAIDCKlwYtLZNXK7LYACtrQwa/O81rvt7PPvs4H8vAIAt8bQwiBcxMwgAAIqrcGFQ1iZOZVAa2tvL1WBv3NYIg7org5Y8G9HV5SoaABi6nnmmcq4yebIWcVRoEwcAAMUlDKKmPftc5baxYW3V12B6ydjKRXRnp98hAGBoy2YGTZ6c7zoYOoRBAABQXMULg7av3C5bFrFmjaugWpfNC3rJuIhSafA/9VpfX4oxoyv3zQ0CAIayahikTRzdSmYGAQBAYRUuDBo9OmJYXcSaroilS/NeDVsqC4PGj9t63zObTbTE3CAAYIhavbpcnXEoDAIAAKBwYVB9fSnGdv8xf9HifNfClssCmZdshXlBmSx4WqIyCAAYop7prgoaNSpi223zXQtDjzZxAABQPIULgyIidhhfuRUG1b4lSypXslu1MqgaBrmKBgCGpnVbxG2NVrrUhpI+cQAAUFjFDIN2qNwuWpTvOthyayuDtt4fOcaPq3wvlUEAwFD19PzK7eTJ+a6DoaWaBQmDAACgcIoZBlUrg1wF1bqsF/64rdgmzswgAGComzuvcp770qk5L4QhZdiwym1HZ77rAAAAtr5ihkE7VD4SpzKo9i3urs4ZtxXbxJkZBAAMdY8/Ubl9+cu0iGOt0dtXbpctzXUZAABADooZBpkZlITOzvLayqCtGAZl32vx4oiyHhsAwBDT1VWOud1h0Mtelu9aGFrGjKncPvt8vusAAAC2vmKGQd0zgxYLg2ragoURa9ZEDB++ddvETZwQMawuonX12jZ1AABDxcJFlfOUhoaIHc0MYh1jx1ZunxcGAQBA4RQzDOquDFq2LKKtTWVHrXryycrtlB0j6uq2XguUhoZSTJzUvYanttq3BQDol6xF3NSpEfX12sSx1tjuyqClz0esWeM6CAAAiqSQYdCoURHNzZX7WsXVrnndYVAeg5GnTum5BgCAoeKJ6rygfNfB0DN6dESpFLGmK2LFirxXAwAAbE2FDINKpVKMz+YGLcp3LWy+p56qfJpxag5hUBZAPfmUT1QCAEPLE09Uzk9e/jJVQfRUX1+K7bar3H/2uXzXAgAAbF2FDIMi1raKW7gw33Ww+bKqnKlTt/4fOqZOqXzPJ1UGAQBDzJzuyqCX7ZTrMhiizA0CAIBiKmwYlFV2zJ2nsqNWPZlnm7isMkgYBAAMIa2t5ZjbHQa94hX5roWhKZsb9JzKIAAAKJTChkE771yp7HhsTs4LYbMsX16OZcsr96fsuPW/fxYGLVocsXq1QBEAGBoeergyD2bcS9ZWwsO6qmGQyiAAACiU4oZBL6/czpkTUS77Y36tefKpyu348RFNTVu/Tdz220Vsu21EuRzx1NNb/dsDAPTpwdmV2732qszJhBcbUw2DXAMBAECRFDYMetlOEXV1EcuW65ddi+bNq9xOnZLP9y+VStXvPXdePmsAAHix+x+o/IF/7z0FQfRt7JjK74ZrIAAAKJbChkHDh5eq7cXmPJ7vWth0DzxY+UPHrtPyW0P2vR+c7VOVAED+yuVyPPBg5f6ee+a7FoausWMrt2YGAQBAsRQ2DIqI2Hnnyq25QbXn/gcqt3vvnd+nXvfeq/K9778/tyUAAFQ99VTEihURjQ0R03bJezUMVWPMDAIAgEIqdhj08sof8+c8rrKjlixbVo55T1bu753jp1732bty+/fHIlpa/A4BAPm6+57K7e67RzQ0aBNH37LKIG3iAACgWIodBnVXBj3ySL7rYNNk7U922iliu+3y+0PH+PGlmLBDRFfX2mHNAAB5ue2PlQ+nvPY1giDW7yVjI0qliJaWiKVLfaAJAACKotBh0D57RdTVRcydF7FwkQuhWvG3+7sHI++V80IiYu/u6qBsTQAAeVjxQjnuu69y/5CDc10KQ1xzcyleOrVy/8GH8l0LAACw9RQ6DNp221LsuUfl/p135rsW+m/WXyu3++Q4LyiTreG+v+a8EACg0G6/I2JNV8TLXxax4475nyMxtO3RfQ304GwfaAIAgKIodBgUEfHqgyoXy7ff6UKoFjz9dDkeeqhS0fWqA/JeTcSBMyq39/01YskSv0MAQD5+/4fKecjrVAXRD3vuUbkGmq3VMQAAFIYw6KDK7T33RrS1+WP+UPebmyp79KoDIl4yNv9PvU6aWIpX7hNRLkf89nd5rwYAKKJ588pxR3eV+5uOyP/8iKEv647w0MMRa9a4BgIAgCIofBj0ip0jxr0koq0t4u578l4NG9LVVY7f/LZy/x/fPHT+0JGt5Te/LUe57GIaANi6rvhZOcrliINfG/HSlw6dcySGrpftFNE0IqKlJWLek3mvBgAA2BoKHwaVSqU47PWV+7/7nT/kD2W3/TFi4aKIkSOHVguUw14fMXx4xNx5EXf9Oe/VAABF8tTT5fjNTZX7H3ifIIj+GTasFLvtVrl/9TXl6OpyHQQAAKkrfBgUsbadxszbI1paXAgNRS0t5Tjn3MrevOvoiOHDh84fO0aOLMXb31a5f/Y5Ze0GAYCtorOzHP/5lXJ0dEQcsH/EPnsPnfMjhr63HVX5fbnulxH/8w2BEAAApE4YFBG77hoxZUqlVdytf8x7NfTlov8tx5JnIyZNijj2g0PvDx0f+XApxo+LeOaZiMsudyENAAyujo5yfO1/yvHQwxHbbBNxxmeG3vkRQ9sb31CK//hsKYbVRfzqxoizzi7HypXOYwEAIFXCoKi0invzGysX0D/5aTna210EDSWLFpfj6msq9z95SmlIVQVlmptLccpJlXVdcWXE0qV+hwCAwbF4cTk+dVo5fvu7iGF1EWecXoodxg+98yOGvn/8h1L8x+dKUSpF/PKGiLe9oxyf+3xXXHlVOX52VTnu/ks5nnuuHGvWOLcFAIBaV5/3AoaKd/xTxNXXRsydG3H2t8ux//6D+/1GjmyLVatcVPXH7/9QjvaOiH1fGfHqg/Jezfq9/tCI3XaNePiRiG9/pxwHD6G5RkOV4yB/9iB/9iB/9iB/9qD//v5YOa7/ZcTKlRFNTRH/9aVSHHSgIIjN96YjSlFfH/Hji8sxd25lTudtf+x5PDY0ROz00nI0NUWUy5XfvaamiBEjKrfNTRHtHRFtqyvPHT48YtiwiI6OyuPDhlWe09xcirrujyN2dVXeKyKirq7yf52dlcfGjK68T1dXxJquiHJXhP8PsXHNzauHfNtz/99q8zWPXB0tQ+B/K0s2cVBs1ePXHuZiZPPqWDVAe+w4HHo2Z39tY+3o63+DR4+OmPGqyizOWlIql8v9+k1dunTpYK8ld/93aznO/GL+J1f07Xvnlga0F/7o0aMH/Pf67r9UPqkLADCYdt8t4gtnlmLKjgNzbjQY50VbYvTo0XkvYUAMtX/TDa2nXC7HnMcjbvl9OeY/E9G1JuKRv0csWlQJZQAAgLW+8T9D64N5/bmGUhm0jtcfWooTjo+Ydd/g/zG/vr4+Ojs7B/37pGL/6QMbBA2WA/aPOO7YiPsfyHsltcFxkD97kD97kD97kD970H/bbhtxxBtK8dpXR9TXD/1zI2pHqVSKV+wc8Yqde/5edXaWY9HiiCeeqFTulOoiVrdGtLZGtLRGtLaWo6U1oqE+YsSIUnR0lKO9PWLNmkp1T0ND5X5LS+X/uroq71GKtZ9s7ipXwqf67qvT55dWXlNXV2mFWDfMp2f7o6GxITraO/Jexnr5yNyWaWhoiI6OfPe3fx8lZnNsrf21h/kZsD22h0PSpu6vbawtfe3v9ttVPqBXa1QG5WSoffqyiOxB/uxB/uxB/uxB/uxB/uxB/obaHqgMGnhDbY8ZePY4bfY3bfY3ffY4bfY3bbWyv/25hqrbCusAAAAAAAAgJ8IgAAAAAACAhAmDAAAAAAAAEiYMAgAAAAAASJgwCAAAAAAAIGHCIAAAAAAAgIQJgwAAAAAAABImDAIAAAAAAEiYMAgAAAAAACBhwiAAAAAAAICECYMAAAAAAAASJgwCAAAAAABImDAIAAAAAAAgYcIgAAAAAACAhAmDAAAAAAAAEiYMAgAAAAAASJgwCAAAAAAAIGHCIAAAAAAAgIQJgwAAAAAAABJWKpfL5bwXUTQvvPBC3HPPPbH//vvHNttsk/dyCske5M8e5M8e5M8e5M8e5M8e5M8epM8ep88ep83+ps3+ps8ep83+pi21/VUZlIOVK1fGrbfeGitXrsx7KYVlD/JnD/JnD/JnD/JnD/JnD/JnD9Jnj9Nnj9Nmf9Nmf9Nnj9Nmf9OW2v4KgwAAAAAAABImDAIAAAAAAEiYMCgHo0aNikMPPTRGjRqV91IKyx7kzx7kzx7kzx7kzx7kzx7kzx6kzx6nzx6nzf6mzf6mzx6nzf6mLbX9LZXL5XLeiwAAAAAAAGBwqAwCAAAAAABImDAIAAAAAAAgYcIgAAAAAACAhAmDAAAAAAAAElaf9wJS8be//S3OPffcmDVrVnR2dsa0adPiuOOOiyOPPLJfr7/mmmvijDPOWO/XL7nkkjjwwAMHarnJue666+Kee+6JBx54IB599NHo6OiIr33ta3H00Udv0vt0dXXFT37yk/jZz34W8+bNi+bm5njNa14Tn/rUp2LKlCmDtPo0DMQe3HXXXXHssceu9+ubs6dFsWjRovj1r38dt912Wzz++OPx7LPPxnbbbRfTp0+PE044IV75ylf2+70cB5tnoPbAcbD52tra4uyzz44HHngg5s2bF8uXL49tt902pkyZEu9+97vjbW97WzQ0NPTrvRwHm2eg9sBxMPAuvPDC+OY3vxkREVdeeWXsu+++/XqdY6H2bel1ClvHpp5Lr1y5Ms4999y46aabYsmSJTF+/Ph485vfHCeffHKMHDmy1/Mdy/nanPNEe1xbNuccyB7Xto2dW9nf2nL44YfH/Pnz+/zajBkz4tJLL+3xWHt7e1x44YVx/fXXx4IFC2K77baLww47LD75yU/G2LFj+3yf66+/Pi655JJ47LHHoqGhIaZPnx7/+q//GnvuueeA/zz07Xe/+11cfvnlMXv27GhpaYlx48bFvvvuG//+7/8eEydOrD4v5eO3VC6Xy3kvotbdeeedccIJJ0RjY2O85S1viZEjR8ZNN90U8+fPj8985jNx/PHHb/Q9sjDoDW94Q+y+++69vv6Od7wjdtxxx8FYfhKy/6c9evToaG5ujvnz52/WH4rOPPPMuOqqq2KXXXaJQw89NBYvXhy//vWvY+TIkXHllVfGTjvtNDg/QAIGYg+yP/7NmDEjZsyY0evrRxxxRJ/HBxHf+MY34gc/+EFMnTo1ZsyYEWPGjIl58+bFzTffHOVyOb75zW/2+48+joPNM1B74DjYfM8//3y8/vWvj3322Sd22mmnGDNmTCxfvjz++Mc/xvz58+Pggw+OH/zgB1FXt/HCaMfB5hmoPXAcDKxHH3003vnOd0Z9fX20tLRsUhjkWKhtA3GdwtaxKefSLS0t8YEPfCAeeuihOPjgg2P33XePhx56KGbOnBl77713/OQnP4nhw4f3eI1jOV+bep5oj2vPpp4D2ePatrFzK/tbew4//PBYsWJFfOhDH+r1tcmTJ/f43+Ourq448cQTY+bMmbHvvvvGq171qpg3b1787ne/ix133DF+9rOfxZgxY3q8x/nnnx/f/va3Y/LkyfGmN70pVq1aFb/61a+io6MjLr744th///0H/WcssnK5HF/84hfjyiuvjKlTp8bBBx8cI0eOjMWLF8fdd98dZ511VhxwwAERUYDjt8wW6ejoKB9xxBHlvfbaqzx79uzq4ytWrCi/6U1vKu+5557lp59+eqPvc/XVV5enTZtWvvrqqwdzucn605/+VP13/v73v79Z/5Z33HFHedq0aeVjjjmm3NbWVn38//7v/8rTpk0rH3/88QO65tQMxB7ceeed5WnTppW/853vDMYSk/bb3/62fNddd/V6/O677y7vueee5Ve96lU9fq/Xx3Gw+QZqDxwHm2/NmjV9/ht3dHSUP/jBD5anTZtW/sMf/rDR93EcbL6B2gPHwcBpb28vv+Md7yi/+93vLp922mnladOmlWfNmtWv1zoWattAXaewdWzKufQ555xTnjZtWvmss87q8fhZZ51VnjZtWvmCCy7o8bhjOX+bep5oj2vPpp4D2ePa1Z9zK/tbew477LDyYYcd1q/n/vznPy9Pmzat/G//9m/lrq6u6uOXX355edq0aeXPf/7zPZ7/xBNPlPfYY4/ym970pvKKFSuqj8+ePbu81157lf/xH/+xvGbNmoH5QejTxRdfXJ42bVr5S1/6Urmzs7PX1zs6Oqr3Uz9+zQzaQnfeeWc8+eST8da3vrXHJ1S32Wab+NjHPhYdHR1x7bXX5rjCYnjNa14TkydP3qL3uOqqqyIi4tRTT43Gxsbq44ceemjMmDEjZs6cGc8888wWfY+UDcQesPne9KY39fnp+QMOOCAOPPDAWL58eTzyyCMbfR/HweYbqD1g89XV1fX4vc3U19fHG9/4xoiImDdv3kbfx3Gw+QZqDxg4F1xwQfz973+P//7v/45hw4Zt0msdC7XNdUpt6e+5dLlcjquuuiqam5vjE5/4RI+vfeITn4jm5ubqsZtxLOdvU84T7XFt2pRzIHtc2zZ2bmV/05ft17/9279FqVSqPv6+970vpkyZEr/85S9j9erV1cevueaa6OzsjI9//OOxzTbbVB/ffffd461vfWvMmTMn7rnnnq33AxTM6tWr47vf/W5MmTIl/uM//qPP47a+vjJJpwjHrzBoC/35z3+OiIiDDz6419eyx+6+++5+v9/s2bPjxz/+cVx44YVx4403xtKlSwdmoWzUXXfdFc3NzTF9+vReX3vd614XEWv3m8E1d+7cuPjii+P73/9+/OIXv4hFixblvaSalv2PWna7IY6DwbEpe5BxHAycrq6u+OMf/xgREdOmTdvo8x0HA29T9yDjONgyDz74YFxwwQVx8sknxyte8YpNfr1jobYN9HUKQ8PcuXNj8eLFMX369Ghubu7xtex4feqpp2LBggXVxx3LQ9uLzxPtcVr6Ogeyx7WrP+dW9rd2tbe3xzXXXBMXXHBBXHbZZfHXv/6113Pa2trir3/9a7zsZS/r9SGOUqkUr3nNa6KlpSUeeOCB6uPZ3r32ta/t9X7ZOZn9HTwzZ86M5cuXxxFHHBFdXV1x0003xYUXXhg//elPe31QsQjHb///KkWf5s6dGxERL33pS3t9bdy4cdHc3LxJn4B98UCyESNGxEknnRQf/ehHt2idbFhLS0ssWbIkpk2b1mdCnO2vTzNvHTfccEPccMMN1f+ur6+PD37wg3H66adv8qeai+6ZZ56J22+/PcaNG7fRP8A6DgbHpuzBuhwHm6+9vT2+//3vR7lcjmXLlsUdd9wRjz/+eBx99NHx6le/eoOvdRwMjC3Zg3U5DjZfe3t7fOYzn4nddtstTjjhhE1+vWOh9g30dQpDQ7Zn6+s9v9NOO8XMmTNj7ty5MXHiRMfyENfXeaI9rm39OQeyx7Wpv+dW9rd2LVmyJM4444wej+29995x9tlnx9SpUyMi4sknn4yurq4N7m9E5Twsm0Ezd+7caG5ujnHjxvV6vv0dfA8++GBEVCo4jzrqqOo5cvbYcccdF5/5zGciohjHrzBoC61cuTIiokeZ37pGjRoVL7zwwkbfZ8cdd4zPf/7zcfDBB8eECRNi+fLlcccdd8TZZ58d3/zmN6OpqSn++Z//eUDXzlrZHo0aNarPr2eP92cv2XxjxoyJT3/603HYYYfF5MmTo7W1NWbNmhXf/OY34+KLL45SqRSf/exn815mzejo6IjTTz892tvb47TTTtvoH04dBwNvU/cgwnEwEDo6OuK8886r/nepVIrjjz8+Pv3pT2/0tY6DgbElexDhOBgI55xzTsydOzeuueaazQrOHAu1b6CuUxha+ntsZvvvWB661neeaI9rW3/OgexxbervuZX9rU1HH3107L///jFt2rRobm6OuXPnxkUXXRTXXXddHHfccXH99df3OHfq7/5m98eMGbPB59vfwfPcc89FRMTFF18ce+yxR1x11VWx8847x0MPPRSf//zn48c//nFMmTIlPvCBDxTi+BUGDREzZszo0UN4xIgR8fa3vz323HPPeOc73xnnnXdevP/979+kFkNQa3bZZZfYZZddqv/d3NwcRxxxRLzyla+Mt73tbXHppZfGiSeeGGPHjs1xlbWhq6srPvvZz8bdd98d73nPe+Ltb3973ksqnM3dA8fBlhs5cmQ88sgj0dXVFYsXL47f//738a1vfSvuu++++MEPfrDeEzUGzpbugeNgy8yaNSt+/OMfx8knn7xJFYkAbB3O1dPlPDRNzq3Sd/LJJ/f479133z2+/vWvR0TEddddF1dddVV8+MMfzmNpbKFyuRwREQ0NDfHd7343dthhh4iozO0755xz4p/+6Z/ioosuig984AN5LnOrMTNoC20s4Vu5cuV6P43XH7vsskvsv//+sWzZspgzZ85mvw8blu3Rusn9ujb2yUoG17hx4+INb3hDdHZ29tmzlZ66urric5/7XNxwww3xtre9Lb785S/363WOg4GzuXuwIY6DTVdXVxcTJkyID3zgA/Gf//mfce+998b555+/wdc4DgbW5uzBhjgONq6zszM++9nPxq677rpFbYYdC7VvsK9TyEd/j81s/x3LQ8/GzhPtcRo2dA5kj2vLpp5b2d+0vPe9742IiHvvvTciNn1/s/sbOh9b930ZeNle7LXXXtUgKDNt2rSYMmVKPPnkk7FixYpCHL/KTLZQ1kNw3rx5sddee/X42pIlS6KlpSX22WefLfoeo0ePjoiI1tbWLXof1i/r3fn000/HmjVrepX8Zr0d++q5ztbhOOifrq6uOOOMM+IXv/hFvPWtb43/9//+X9TV9S/3dxwMjC3Zg41xHGy+/g7mdBwMnoEajuo42LCWlpZqH+wXn5tmsova7373u3HEEUf0+RzHQu3bGtcpbH3ZMbduv/t1ZY9n++9YHlr6c55oj9Pz4nMge1xbNvXcauedd44I+5uK7NqjpaUlIiKmTJkSdXV1/d7f7P6sWbNiyZIlveYG2d/B9/KXvzwi1h/IZI+vXr26EP//WWXQFnrVq14VEREzZ87s9bXssew5m2PNmjXxwAMPRETEpEmTNvt92LgZM2ZES0tLNe1f1x//+MeI2LK9ZMtknwCfPHlyzisZuta9uDzyyCPj61//+ibPiXAcbJmB2IMNcRxsvsWLF0dE9KvdquNgcGzKHmyI42DDGhsb413velef/5ddtBx++OHxrne9a6P/ho6F2jbY1ynkY6eddorx48fHvffeW/3DVCY7XnfccceYOHFi9XHH8tDQ3/NEe5yeF58D2ePasqnnVvY3LX/7298iYu21x4gRI2KfffaJJ554IubPn9/jueVyOW6//fZobm7uERxme/enP/2p1/tn52Trjg5hYB144IEREfH444/3+lpHR0c8+eST0dzcHGPGjCnE8SsM2kKvfvWrY8qUKXHDDTfEQw89VH38hRdeiAsuuCAaGhp69P9dvHhxzJkzp1d5YBb4rGvNmjXxjW98I+bNmxcHHnhgjB8/ftB+jiJ5/vnnY86cOfH888/3ePw973lPRFSGAra3t1cfv/XWW+PPf/5zHHzwwf7wNEDWtwd9HQcREf/7v/8bd911V+y0006x9957b40l1pys3cQvfvGL+Id/+Ic466yzNhhCOA4G3kDtgeNg8z322GN9Vou0trbG1772tYiIOPTQQ6uPOw4G3kDtgeNg840YMSK++tWv9vl/++23X0RE/Mu//Et89atfjd133z0iHAup2tTrFGpDqVSKd7/73dHS0hLf+973enzte9/7XrS0tFSP3YxjOX+bcp5oj2v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\n",
       "text/plain": [
        "<Figure size 1656x552 with 2 Axes>"
       ]
@@ -1644,12 +799,19 @@
     }
    ],
    "source": [
-    "az.plot_posterior(trace, group=\"sample_stats\", var_names=\"accept\", hdi_prob=\"hide\", kind=\"kde\");"
+    "az.plot_posterior(\n",
+    "    trace,\n",
+    "    group=\"sample_stats\",\n",
+    "    var_names=\"accept\",\n",
+    "    hdi_prob=\"hide\",\n",
+    "    kind=\"hist\",\n",
+    "    bins=np.arange(0.0, 4.0, 0.1),\n",
+    ");"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1662,13 +824,12 @@
       "Python version       : 3.9.2\n",
       "IPython version      : 7.21.0\n",
       "\n",
-      "pymc3     : 3.11.2\n",
-      "numpy     : 1.20.1\n",
-      "xarray    : 0.17.0\n",
-      "pandas    : 1.2.3\n",
       "seaborn   : 0.11.1\n",
-      "matplotlib: 3.3.4\n",
       "arviz     : 0.11.2\n",
+      "matplotlib: 3.3.4\n",
+      "pymc3     : 3.11.2\n",
+      "pandas    : 1.2.3\n",
+      "numpy     : 1.20.1\n",
       "\n",
       "Watermark: 2.2.0\n",
       "\n"

From 6ced00b3360b5841e7bb110ee6ee3a9d538b4c59 Mon Sep 17 00:00:00 2001
From: almostmeenal <meenaljhajharia2304@gmail.com>
Date: Sun, 4 Apr 2021 18:47:15 +0530
Subject: [PATCH 5/8] new plot

---
 .../sampler-stats.ipynb                       | 735 ++++++++++++++++--
 1 file changed, 676 insertions(+), 59 deletions(-)

diff --git a/examples/diagnostics_and_criticism/sampler-stats.ipynb b/examples/diagnostics_and_criticism/sampler-stats.ipynb
index f9aab578f..339193b79 100644
--- a/examples/diagnostics_and_criticism/sampler-stats.ipynb
+++ b/examples/diagnostics_and_criticism/sampler-stats.ipynb
@@ -126,7 +126,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "NUTS provides the following statistics:"
+    "NUTS provides the following statistics:\n",
+    "- `Note`: To learn more about NUTS statistics, [check this page](https://docs.pymc.io/api/inference.html#module-pymc3.step_methods.hmc.nuts)"
    ]
   },
   {
@@ -136,20 +137,443 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
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+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:             (chain: 2, draw: 2000)\n",
+       "Coordinates:\n",
+       "  * chain               (chain) int64 0 1\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",
+       "    acceptance_rate     (chain, draw) float64 0.3946 1.0 ... 0.7429 0.9799\n",
+       "    diverging           (chain, draw) bool False False False ... False False\n",
+       "    lp                  (chain, draw) float64 -23.23 -18.3 ... -19.85 -20.25\n",
+       "    perf_counter_diff   (chain, draw) float64 0.0004298 0.0004191 ... 0.0003205\n",
+       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 2 ... 2 2 2 3 2 2 2 2\n",
+       "    ...                  ...\n",
+       "    max_energy_error    (chain, draw) float64 1.515 -1.358 ... 1.001 -0.9029\n",
+       "    perf_counter_start  (chain, draw) float64 6.197 6.198 6.198 ... 8.621 8.621\n",
+       "    step_size           (chain, draw) float64 0.7602 0.7602 ... 1.231 1.231\n",
+       "    step_size_bar       (chain, draw) float64 0.9333 0.9333 ... 0.959 0.959\n",
+       "    energy_error        (chain, draw) float64 1.515 -1.358 ... 1.001 0.1039\n",
+       "    energy              (chain, draw) float64 28.66 27.65 25.17 ... 24.42 26.31\n",
+       "Attributes:\n",
+       "    created_at:                 2021-04-04T13:14:18.324217\n",
+       "    arviz_version:              0.11.2\n",
+       "    inference_library:          pymc3\n",
+       "    inference_library_version:  3.11.2\n",
+       "    sampling_time:              9.566201210021973\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-efd92723-1111-43c6-991e-06b5d335dca8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-efd92723-1111-43c6-991e-06b5d335dca8' 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-5188812d-1800-414a-9184-118368403e6b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5188812d-1800-414a-9184-118368403e6b' 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-92e34213-45f3-470c-af31-b0378f6a454d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-92e34213-45f3-470c-af31-b0378f6a454d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21864d1c-ec42-4d61-830d-db770aeed01a' class='xr-var-data-in' type='checkbox'><label for='data-21864d1c-ec42-4d61-830d-db770aeed01a' 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-ad4fb4d3-259b-4adf-972a-d6eb43060475' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad4fb4d3-259b-4adf-972a-d6eb43060475' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a297dd6-f6e2-459d-92ff-b48df9c6c74a' class='xr-var-data-in' type='checkbox'><label for='data-2a297dd6-f6e2-459d-92ff-b48df9c6c74a' 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-db3e2e0e-a4e4-45df-bca1-d4f07641bc4c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db3e2e0e-a4e4-45df-bca1-d4f07641bc4c' 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 ... 3.0 3.0 3.0 3.0</div><input id='attrs-d6e5bddf-b925-4ebd-add7-7388b6744d7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6e5bddf-b925-4ebd-add7-7388b6744d7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2676846f-48a3-4d2d-ae2d-b70d499b5e4f' class='xr-var-data-in' type='checkbox'><label for='data-2676846f-48a3-4d2d-ae2d-b70d499b5e4f' 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., 3., 3.],\n",
+       "       [3., 3., 3., ..., 3., 3., 3.]])</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'>0.3946 1.0 0.9087 ... 0.7429 0.9799</div><input id='attrs-7285840d-389b-4b82-9af9-1d24c629140e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7285840d-389b-4b82-9af9-1d24c629140e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50462ad1-ac3b-427b-b89c-2d11e3f0cbf8' class='xr-var-data-in' type='checkbox'><label for='data-50462ad1-ac3b-427b-b89c-2d11e3f0cbf8' 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([[0.39459488, 1.        , 0.90874855, ..., 0.79493269, 0.69179347,\n",
+       "        1.        ],\n",
+       "       [1.        , 0.81781807, 0.99202283, ..., 0.67421276, 0.74291879,\n",
+       "        0.97987056]])</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-08d37d71-7fc3-4480-babf-4c3d81b7e68c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08d37d71-7fc3-4480-babf-4c3d81b7e68c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dafe8f39-e20f-4e08-b42f-d7a6c26bd3c9' class='xr-var-data-in' type='checkbox'><label for='data-dafe8f39-e20f-4e08-b42f-d7a6c26bd3c9' 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>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'>-23.23 -18.3 ... -19.85 -20.25</div><input id='attrs-acd28985-c686-4681-883c-61db7e9cb17d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-acd28985-c686-4681-883c-61db7e9cb17d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06926e00-808e-48e6-a0f4-e2e48b7a3490' class='xr-var-data-in' type='checkbox'><label for='data-06926e00-808e-48e6-a0f4-e2e48b7a3490' 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([[-23.22570922, -18.29572925, -16.69194324, ..., -10.7183016 ,\n",
+       "        -14.65828059, -14.31415368],\n",
+       "       [-13.81167101, -20.60126864, -20.65763802, ..., -15.6723819 ,\n",
+       "        -19.84905246, -20.2474132 ]])</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'>0.0004298 0.0004191 ... 0.0003205</div><input id='attrs-0be311b7-d53f-495a-a026-2e8a3c8eca20' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0be311b7-d53f-495a-a026-2e8a3c8eca20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90679a1f-0a7f-4524-88a9-70c713ed19ee' class='xr-var-data-in' type='checkbox'><label for='data-90679a1f-0a7f-4524-88a9-70c713ed19ee' 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([[0.00042982, 0.00041905, 0.00041227, ..., 0.00081057, 0.00045706,\n",
+       "        0.00048381],\n",
+       "       [0.00040327, 0.00040122, 0.00040838, ..., 0.00031657, 0.00031709,\n",
+       "        0.00032051]])</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 2 2 2 2</div><input id='attrs-0e2ed970-7904-4f0b-8d64-bd9bf5636507' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0e2ed970-7904-4f0b-8d64-bd9bf5636507' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81f92518-e1f5-40bb-a2dc-ecbb8bcd558f' class='xr-var-data-in' type='checkbox'><label for='data-81f92518-e1f5-40bb-a2dc-ecbb8bcd558f' 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, 2, 2],\n",
+       "       [2, 2, 2, ..., 2, 2, 2]])</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'>0.00043 0.000419 ... 0.000321</div><input id='attrs-e1a93dc7-21bf-47c3-99a5-f597faa7193e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e1a93dc7-21bf-47c3-99a5-f597faa7193e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64bed859-2e66-4ac9-a638-7d659163276b' class='xr-var-data-in' type='checkbox'><label for='data-64bed859-2e66-4ac9-a638-7d659163276b' 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([[0.00043 , 0.000419, 0.000413, ..., 0.000811, 0.000457, 0.000484],\n",
+       "       [0.000403, 0.000402, 0.000409, ..., 0.000316, 0.000318, 0.000321]])</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'>1.515 -1.358 ... 1.001 -0.9029</div><input id='attrs-1b166f36-0b16-43f5-a817-c7df65210538' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1b166f36-0b16-43f5-a817-c7df65210538' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad79ec69-6a77-4f09-9344-b420c05704fb' class='xr-var-data-in' type='checkbox'><label for='data-ad79ec69-6a77-4f09-9344-b420c05704fb' 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([[ 1.51543035, -1.35821101,  0.35306051, ...,  0.50942943,\n",
+       "         0.78065804, -0.23884907],\n",
+       "       [-0.43578296,  1.74687276, -1.78161327, ...,  1.12062281,\n",
+       "         1.00095637, -0.90290469]])</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'>6.197 6.198 6.198 ... 8.621 8.621</div><input id='attrs-d6d18184-aed2-4f2b-bcd2-036f7a203ac2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6d18184-aed2-4f2b-bcd2-036f7a203ac2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5dd46766-a52f-4143-9974-cd895e76da11' class='xr-var-data-in' type='checkbox'><label for='data-5dd46766-a52f-4143-9974-cd895e76da11' 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([[6.19721368, 6.19777443, 6.19831352, ..., 7.40443384, 7.40539592,\n",
+       "        7.40599166],\n",
+       "       [7.66197288, 7.66250108, 7.66302113, ..., 8.62048104, 8.62088729,\n",
+       "        8.62129346]])</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'>0.7602 0.7602 ... 1.231 1.231</div><input id='attrs-a48f0356-c1f3-4974-b279-e54908eb2306' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a48f0356-c1f3-4974-b279-e54908eb2306' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28957e08-40e2-4b18-a479-31e1b1816755' class='xr-var-data-in' type='checkbox'><label for='data-28957e08-40e2-4b18-a479-31e1b1816755' 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([[0.76016927, 0.76016927, 0.76016927, ..., 0.76016927, 0.76016927,\n",
+       "        0.76016927],\n",
+       "       [1.23071367, 1.23071367, 1.23071367, ..., 1.23071367, 1.23071367,\n",
+       "        1.23071367]])</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'>0.9333 0.9333 ... 0.959 0.959</div><input id='attrs-4b05d649-3773-4ce2-bc16-3ce421f6dd43' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b05d649-3773-4ce2-bc16-3ce421f6dd43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc30659e-75e7-4e63-9218-463105bad1f5' class='xr-var-data-in' type='checkbox'><label for='data-dc30659e-75e7-4e63-9218-463105bad1f5' 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([[0.93329576, 0.93329576, 0.93329576, ..., 0.93329576, 0.93329576,\n",
+       "        0.93329576],\n",
+       "       [0.9590144 , 0.9590144 , 0.9590144 , ..., 0.9590144 , 0.9590144 ,\n",
+       "        0.9590144 ]])</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'>1.515 -1.358 ... 1.001 0.1039</div><input id='attrs-7bd81e4b-e147-4af9-b6b0-d77d7262fbd4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7bd81e4b-e147-4af9-b6b0-d77d7262fbd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b19978c2-3d09-426b-8e6c-5122c45e6609' class='xr-var-data-in' type='checkbox'><label for='data-b19978c2-3d09-426b-8e6c-5122c45e6609' 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([[ 1.51543035, -1.35821101, -0.15216837, ..., -0.08326286,\n",
+       "         0.78065804, -0.04826571],\n",
+       "       [-0.34181219,  1.7057648 ,  0.11232557, ...,  0.90234702,\n",
+       "         1.00095637,  0.10385907]])</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'>28.66 27.65 25.17 ... 24.42 26.31</div><input id='attrs-0138732b-279a-49f3-bd57-163b27260fb1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0138732b-279a-49f3-bd57-163b27260fb1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb287b07-d180-4284-9ca2-d614fa084814' class='xr-var-data-in' type='checkbox'><label for='data-bb287b07-d180-4284-9ca2-d614fa084814' 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([[28.65876424, 27.65179536, 25.16529121, ..., 13.90069897,\n",
+       "        16.44194248, 18.39987281],\n",
+       "       [17.41747035, 25.82823814, 26.25725321, ..., 18.57675378,\n",
+       "        24.42040881, 26.30506902]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9deeadff-c9b4-4ec6-8d60-e5a9812142e3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9deeadff-c9b4-4ec6-8d60-e5a9812142e3' 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>2021-04-04T13:14:18.324217</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>9.566201210021973</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
-       "['perf_counter_diff',\n",
-       " 'energy',\n",
-       " 'lp',\n",
-       " 'step_size',\n",
-       " 'step_size_bar',\n",
-       " 'n_steps',\n",
-       " 'diverging',\n",
-       " 'energy_error',\n",
-       " 'tree_depth',\n",
-       " 'perf_counter_start',\n",
-       " 'max_energy_error',\n",
-       " 'process_time_diff',\n",
-       " 'acceptance_rate']"
+       "<xarray.Dataset>\n",
+       "Dimensions:             (chain: 2, draw: 2000)\n",
+       "Coordinates:\n",
+       "  * chain               (chain) int64 0 1\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",
+       "    acceptance_rate     (chain, draw) float64 0.3946 1.0 ... 0.7429 0.9799\n",
+       "    diverging           (chain, draw) bool False False False ... False False\n",
+       "    lp                  (chain, draw) float64 -23.23 -18.3 ... -19.85 -20.25\n",
+       "    perf_counter_diff   (chain, draw) float64 0.0004298 0.0004191 ... 0.0003205\n",
+       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 2 ... 2 2 2 3 2 2 2 2\n",
+       "    ...                  ...\n",
+       "    max_energy_error    (chain, draw) float64 1.515 -1.358 ... 1.001 -0.9029\n",
+       "    perf_counter_start  (chain, draw) float64 6.197 6.198 6.198 ... 8.621 8.621\n",
+       "    step_size           (chain, draw) float64 0.7602 0.7602 ... 1.231 1.231\n",
+       "    step_size_bar       (chain, draw) float64 0.9333 0.9333 ... 0.959 0.959\n",
+       "    energy_error        (chain, draw) float64 1.515 -1.358 ... 1.001 0.1039\n",
+       "    energy              (chain, draw) float64 28.66 27.65 25.17 ... 24.42 26.31\n",
+       "Attributes:\n",
+       "    created_at:                 2021-04-04T13:14:18.324217\n",
+       "    arviz_version:              0.11.2\n",
+       "    inference_library:          pymc3\n",
+       "    inference_library_version:  3.11.2\n",
+       "    sampling_time:              9.566201210021973\n",
+       "    tuning_steps:               1000"
       ]
      },
      "execution_count": 5,
@@ -158,7 +582,7 @@
     }
    ],
    "source": [
-    "list(trace.sample_stats.data_vars)"
+    "trace.sample_stats"
    ]
   },
   {
@@ -167,44 +591,37 @@
    "source": [
     "[Arviz](https://arviz-devs.github.io/arviz/schema/schema.html#sample-stats) follows the following Name Convention for sample_stats variables, these may vary depending on the algorithm used by the backend (i.e. an affine invariant sampler has no energy associated). Therefore none of these parameters should be assumed to be present in the sample_stats group. The convention below serves to ensure that if a variable is present with one of these names it will correspond to the definition included here:\n",
     "\n",
-    "- `mean_tree_accept`: The mean acceptance probability for the tree that generated this sample. The mean of these values across all samples but the burn-in should be approximately target_accept (the default for this is 0.8).\n",
-    "\n",
-    "- `lp`: The joint log posterior density for the model (up to an additive constant).\n",
-    "\n",
-    "- `acceptance_rate`: The average acceptance probabilities of all possible samples in the proposed tree.\n",
+    "- `process_time_diff`: The time it took to draw the sample, as defined by the python standard library time.process_time. This counts all the CPU time, including worker processes in BLAS and OpenMP.\n",
     "\n",
     "- `step_size`: The current integration step size.\n",
     "\n",
-    "- `step_size_bar`: The current best known step-size. After the tuning samples, the step size is set to this value. This should converge during tuning.\n",
-    "\n",
-    "- `tree_depth`: The number of tree doublings in the balanced binary tree.\n",
-    "\n",
-    "- `n_steps`: The number of leapfrog steps computed. It is related to `tree_depth` with `n_steps <= 2^tree_dept`.\n",
-    "\n",
     "- `diverging`: (boolean) Indicates the presence of leapfrog transitions with large energy deviation from starting and subsequent termination of the trajectory. “large” is defined as `max_energy_error` going over a threshold.\n",
     "\n",
+    "- `lp`: The joint log posterior density for the model (up to an additive constant).\n",
+    "\n",
     "- `energy`: The value of the Hamiltonian energy for the accepted proposal (up to an additive constant).\n",
     "\n",
     "- `energy_error`: The difference in the Hamiltonian energy between the initial point and the accepted proposal.\n",
     "\n",
+    "- `perf_counter_diff`: The time it took to draw the sample, as defined by the python standard library time.perf_counter (wall time).\n",
+    "\n",
+    "- `perf_counter_start`: The value of time.perf_counter at the beginning of the computation of the draw.\n",
+    "\n",
+    "- `n_steps`: The number of leapfrog steps computed. It is related to `tree_depth` with `n_steps <= 2^tree_dept`.\n",
+    "\n",
     "- `max_energy_error`: The maximum absolute difference in Hamiltonian energy between the initial point and all possible samples in the proposed tree.\n",
     "\n",
-    "- `int_time`: The total integration time (static HMC sampler)\n",
+    "- `acceptance_rate`: The average acceptance probabilities of all possible samples in the proposed tree.\n",
     "\n",
-    "- `tree_size`: The number of leafs of the sampling tree, when the sample was accepted. This is usually a bit less than $2 ^ \\text{depth}$. If the tree size is large, the sampler is using a lot of leapfrog steps to find the next sample. This can for example happen if there are strong correlations in the posterior, if the posterior has long tails, if there are regions of high curvature (\"funnels\"), or if the variance estimates in the mass matrix are inaccurate. Reparametrisation of the model or estimating the posterior variances from past samples might help.\n",
+    "- `step_size_bar`: The current best known step-size. After the tuning samples, the step size is set to this value. This should converge during tuning.\n",
+    "\n",
+    "- `tree_depth`: The number of tree doublings in the balanced binary tree.\n",
     "\n",
     "- `tune`: This is True, if step size adaptation was turned on when this sample was generated.\n",
     "\n",
     "InferenceData also stores additional info like the date, versions used, sampling time and tuning steps as attributes."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If the name of the statistic does not clash with the name of one of the variables, we can use indexing to get the values. The values for the chains will be concatenated."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -212,7 +629,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 700x300 with 2 Axes>"
       ]
@@ -232,7 +649,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x480 with 1 Axes>"
       ]
@@ -614,7 +1031,7 @@
        "  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-135004d1-b475-4d70-ba45-41b69ba46b71' class='xr-array-in' type='checkbox' checked><label for='section-135004d1-b475-4d70-ba45-41b69ba46b71' 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-bd90081b-897a-4cb9-8b49-7354162437bb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bd90081b-897a-4cb9-8b49-7354162437bb' 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-580065ab-5344-413f-bf3e-64146b9d473d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-580065ab-5344-413f-bf3e-64146b9d473d' 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' 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-932e9c34-70d8-4bec-afe3-4fd40757ebec' class='xr-array-in' type='checkbox' checked><label for='section-932e9c34-70d8-4bec-afe3-4fd40757ebec' 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-36edaa52-6c91-48db-9343-cae0bf14d801' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-36edaa52-6c91-48db-9343-cae0bf14d801' 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-957837a7-c301-42ba-a0c7-bcdb14f4beb5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-957837a7-c301-42ba-a0c7-bcdb14f4beb5' 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",
@@ -653,7 +1070,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 600x400 with 1 Axes>"
       ]
@@ -686,6 +1103,20 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "coords = {\"step\": [\"BinaryMetropolis\", \"Metropolis\"]}\n",
+    "dims = {\"accept\": [\"step\"]}\n",
+    "\n",
+    "with pm.Model(coords=coords) as model:\n",
+    "    mu1 = pm.Bernoulli(\"mu1\", p=0.8, dims=\"step\")\n",
+    "    mu2 = pm.Normal(\"mu2\", mu=0, sigma=1, dims=\"step\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -715,7 +1146,7 @@
        "            }\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",
+       "      100.00% [22000/22000 00:05<00:00 Sampling 2 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -730,18 +1161,13 @@
      "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 10 seconds.\n",
-      "The number of effective samples is smaller than 10% for some parameters.\n"
+      "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"
      ]
     }
    ],
    "source": [
-    "coords = {\"step\": [\"BinaryMetropolis\", \"Metropolis\"]}\n",
-    "dims = {\"accept\": [\"step\"]}\n",
-    "\n",
-    "with pm.Model(coords=coords) as model:\n",
-    "    mu1 = pm.Bernoulli(\"mu1\", p=0.8)\n",
-    "    mu2 = pm.Normal(\"mu2\", mu=0, sigma=1, shape=10)\n",
+    "with model:\n",
     "    step1 = pm.BinaryMetropolis([mu1])\n",
     "    step2 = pm.Metropolis([mu2])\n",
     "    trace = pm.sample(\n",
@@ -757,16 +1183,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['accept', 'scaling', 'p_jump', 'accepted']"
+       "['accept', 'p_jump', 'scaling', 'accepted']"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -784,12 +1210,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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WWzZCZeVasGBBzj777Nx+++3Zd999c+GFF6Z582U+N2xSZs2aVbPe5OKWvDnkkEOSJH/84x+z++67N1Rpxdlggw2SLP7Dtnr7J598ktVWW63B6irRo48+miTZbrvtFtrXrl27bLDBBhk5cmRmzpyZVq1aNXR5xateu/aza5d/VvX2xa2PzFfzyCOP5JRTTkmbNm1yzTXXpFOnTo1dUlFGjhyZBQsW5OCDD17k/kGDBmXQoEHp06dP/vSnPzVwdeXp3LlzVlhhhcyfP3+Rn5vVn5OzZ89u6NL4CvRRTUtderD6ftZXr4U/bty4zJ8/f6FlyqrXtl/U+vf8e+rb72244YZJzO2yrj69pb+vTUd9+ljz2rQ1xPytt956efnllzN27Ni0adPmS49nyaprf77eeutl+PDhmTx58kL3DVrcvFbva+rn1cv8b/i/8Y1vJEmGDRu20L7qbdXH8O/7bBOy99575ze/+Y172SwFK664Yr73ve8t8r/qf2B22223fO9730vHjh0bt9gmrvqE7o033lho39y5c/P222+nqqpqsWuFUndz585Nknz44YeL3P/hhx+mefPmadmyZUOWtdxYb731stZaa+X555/PrFmzau2bNWtWnn/++ay77rrp0KFDI1VYnuoTzdVXXz3XXHONk/qlYMcdd1zkZ2X1ms8bbLBBvve972XHHXds5ErLsNJKK2XrrbdOkrz++usL7a/e5tykadBHNR117cG+ymd9z549a/Z93mOPPZbEz8HSUN9+z9w2DfXpLc1p01GfPta8Nm0NMX91Of/q2bPnv/9mWEh9+vPqeXr88ccX2reoeSrpvHqZD4O23377dOrUKYMHD86oUaNqtn/00UcZMGBAWrZsaR3lJaR6WYLbb789e+65Zy666CJB0FKy8sor5/zzz1/kf9W/hPnhD3+Y888/P5tttlkjV9u0de7cOTvttFPGjh2bm2++uda+K664ItOnT8/uu+9ec0NIvroePXok+fSGqp+/dPaGG27IpEmT0r17d0s9LSXNmjXLQQcdlFmzZi10hcSf/vSnzJo1a7FXV1B/Q4cOrXWi6YqrpaNv376L/Kw8+uijk3x6wn3++eenb9++jVxpOQ499NAkn66dP2fOnJrtY8aMyW233ZZWrVpl5513bqzyqAd9VNNQnx7sq3zWV//5kksuqfV3eujQoXnmmWey0047CXiXgvr2e+a2aahPb2lOm4769LHmtWlriPnba6+90rp16wwcODCTJk2q2T5p0qQMHDgwbdq0sfrPUlDf/vzAAw9MixYtctlll9X6ez9q1KgMHjw4G264YbbZZpua7SWdVzerVN8Bbxn21FNP5ZhjjsmKK66YffbZJ61atcr999+f8ePH58wzz8xRRx3V2CUWoX///rn00ktTVVWVfv36LfKX47vvvrtwYik766yzctttt2XQoEHp3r17Y5dThLfffjv/8R//kQ8++CDf/OY3ay7zfuqpp9KxY8cMGjRooctCqb/58+fniCOOyLPPPpu2bdtmt912S+vWrWvGeuWVV861117bZC6dXVbcfPPNee6555Iko0ePzogRI9KjR4+ab7lss802Oeigg5J8+m2mQw89NK+88kp22mmndO3aNSNHjsywYcOyxRZbZODAgVl55ZUb7b0s6+o61mPGjMn++++fOXPmZJ999sn666+/0HN17NgxBx54YIPW35TU5+d6UZ5++un069cvhxxySH7xi180SM1NWX3Gu1Kp5LTTTst9992X9ddfPzvttFNmzJiR++67L5988kl+/etf5zvf+U6jvRfqRx+17KtvD/ZVPuvPOeec3Hzzzdl4443Tu3fvTJ48OXfffXdatWqVG2+8cZGfYyw9i+v3zG3TUJ/e0pw2DfXtY83rsmdp98z1nb877rgjZ5xxRtZcc83svffeSZK77747U6ZMye9///vstddeS3tIirC0+/PLLrssF198cTp27Jg99tgjM2fOzF133ZW5c+fm6quvrhUGJeWcVzeJMChJXnzxxfzhD3/I8OHDM2/evHTp0iU/+MEPav5S8e+rPin9IhdccIFfbi1lwqClY+LEifnDH/6Qxx57LFOnTs3Xvva17LbbbjnppJPStm3bxi6vGHPmzMnVV1+de+65J2+++Wbmzp2btm3bZrvttsvxxx9fsx46dfdl/zYfcMABufDCC2v+/NFHH6V///65//778/7776ddu3bZc889c9JJJ9Xc9JBFq+tYVwcRX6Rnz5659tprl3SJxajvz/XnCYPqp77jPW/evAwcODB///vfM3bs2Ky44orp3r17fvjDH1rWognSRy3bvkoPVt/P+gULFmTgwIG56aabMnbs2FRVVWWHHXbIj3/843Tu3HmJvye+2Bf1e+a2aahPb2lOm4b69rHmddmytHvmrzJ/jz76aC6//PKMHDkyyaf3jzvhhBOyww47/JvvdvnREP35nXfemb/97W95/fXX07Jly/To0SOnnXZaNt9880U+Twnn1U0mDAIAAAAAAKD+lvl7BgEAAAAAAPDVCYMAAAAAAAAKJgwCAAAAAAAomDAIAAAAAACgYMIgAAAAAACAggmDAAAAAAAACiYMAgAAAAAAKJgwCAAAAAAAoGDCIAAAAAAAgIIJgwAAAAAAAAomDAIAAAAAACiYMAgAAAAAAKBgwiAAAAAAAICC/X/vglirjDyyLgAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 1656x552 with 2 Axes>"
       ]
@@ -805,31 +1231,222 @@
     "    var_names=\"accept\",\n",
     "    hdi_prob=\"hide\",\n",
     "    kind=\"hist\",\n",
-    "    bins=np.arange(0.0, 4.0, 0.1),\n",
     ");"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We notice that accept variable takes really large values, as outliers in accept 1"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  15.9375    , 1230.85829008],\n",
+       "       [  15.9375    ,  161.88043405]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The outliers are the values reaching upto ~1200\n",
+    "np.ptp(trace.sample_stats[\"accept\"].values, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1656x552 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We can try plotting the density and view the high density intervals to understand the variable better\n",
+    "az.plot_density(\n",
+    "    trace,\n",
+    "    group=\"sample_stats\",\n",
+    "    var_names=\"accept\",\n",
+    "    point_estimate=\"mean\",\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/almostmeenal/anaconda3/envs/p/lib/python3.9/site-packages/arviz/stats/diagnostics.py:561: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples)\n",
+      "/Users/almostmeenal/anaconda3/envs/p/lib/python3.9/site-packages/arviz/stats/diagnostics.py:561: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sd</th>\n",
+       "      <th>hdi_3%</th>\n",
+       "      <th>hdi_97%</th>\n",
+       "      <th>mcse_mean</th>\n",
+       "      <th>mcse_sd</th>\n",
+       "      <th>ess_bulk</th>\n",
+       "      <th>ess_tail</th>\n",
+       "      <th>r_hat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>accept[0]</th>\n",
+       "      <td>1.002</td>\n",
+       "      <td>1.875</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>4.000</td>\n",
+       "      <td>0.012</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>14811.0</td>\n",
+       "      <td>13064.0</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>accept[1]</th>\n",
+       "      <td>0.971</td>\n",
+       "      <td>9.611</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>2.501</td>\n",
+       "      <td>0.068</td>\n",
+       "      <td>0.048</td>\n",
+       "      <td>20212.0</td>\n",
+       "      <td>15849.0</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p_jump</th>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>20000.0</td>\n",
+       "      <td>20000.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>scaling</th>\n",
+       "      <td>1.398</td>\n",
+       "      <td>0.067</td>\n",
+       "      <td>1.331</td>\n",
+       "      <td>1.464</td>\n",
+       "      <td>0.047</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>9.919727e+15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>accepted</th>\n",
+       "      <td>0.428</td>\n",
+       "      <td>0.495</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>19009.0</td>\n",
+       "      <td>19009.0</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
+       "accept[0]  1.002  1.875   0.062    4.000      0.012    0.009   14811.0   \n",
+       "accept[1]  0.971  9.611   0.000    2.501      0.068    0.048   20212.0   \n",
+       "p_jump     0.500  0.000   0.500    0.500      0.000    0.000   20000.0   \n",
+       "scaling    1.398  0.067   1.331    1.464      0.047    0.040       2.0   \n",
+       "accepted   0.428  0.495   0.000    1.000      0.004    0.003   19009.0   \n",
+       "\n",
+       "           ess_tail         r_hat  \n",
+       "accept[0]   13064.0  1.000000e+00  \n",
+       "accept[1]   15849.0  1.000000e+00  \n",
+       "p_jump      20000.0           NaN  \n",
+       "scaling         2.0  9.919727e+15  \n",
+       "accepted    19009.0  1.000000e+00  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pm.summary(trace.sample_stats)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: Sat Apr 03 2021\n",
+      "Last updated: Sun Apr 04 2021\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.9.2\n",
       "IPython version      : 7.21.0\n",
       "\n",
-      "seaborn   : 0.11.1\n",
-      "arviz     : 0.11.2\n",
-      "matplotlib: 3.3.4\n",
       "pymc3     : 3.11.2\n",
       "pandas    : 1.2.3\n",
+      "seaborn   : 0.11.1\n",
       "numpy     : 1.20.1\n",
+      "arviz     : 0.11.2\n",
+      "matplotlib: 3.3.4\n",
       "\n",
       "Watermark: 2.2.0\n",
       "\n"

From 655e6631e9e0da55555316eb84616c3cc77dc171 Mon Sep 17 00:00:00 2001
From: almostmeenal <meenaljhajharia2304@gmail.com>
Date: Tue, 6 Apr 2021 00:40:14 +0530
Subject: [PATCH 6/8] dims changes

---
 .../sampler-stats.ipynb                       | 690 ++++++++++++------
 1 file changed, 466 insertions(+), 224 deletions(-)

diff --git a/examples/diagnostics_and_criticism/sampler-stats.ipynb b/examples/diagnostics_and_criticism/sampler-stats.ipynb
index 339193b79..589901f60 100644
--- a/examples/diagnostics_and_criticism/sampler-stats.ipynb
+++ b/examples/diagnostics_and_criticism/sampler-stats.ipynb
@@ -126,8 +126,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "NUTS provides the following statistics:\n",
-    "- `Note`: To learn more about NUTS statistics, [check this page](https://docs.pymc.io/api/inference.html#module-pymc3.step_methods.hmc.nuts)"
+    "- `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)."
    ]
   },
   {
@@ -495,57 +494,57 @@
        "  * chain               (chain) int64 0 1\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",
-       "    acceptance_rate     (chain, draw) float64 0.3946 1.0 ... 0.7429 0.9799\n",
-       "    diverging           (chain, draw) bool False False False ... False False\n",
-       "    lp                  (chain, draw) float64 -23.23 -18.3 ... -19.85 -20.25\n",
-       "    perf_counter_diff   (chain, draw) float64 0.0004298 0.0004191 ... 0.0003205\n",
-       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 2 ... 2 2 2 3 2 2 2 2\n",
+       "    perf_counter_diff   (chain, draw) float64 0.0003295 0.0005849 ... 0.0003195\n",
+       "    max_energy_error    (chain, draw) float64 0.6713 0.2162 ... -0.5191 0.3634\n",
+       "    step_size_bar       (chain, draw) float64 0.9636 0.9636 ... 0.9695 0.9695\n",
+       "    process_time_diff   (chain, draw) float64 0.00033 0.000585 ... 0.00032\n",
+       "    tree_depth          (chain, draw) int64 2 3 2 3 2 2 2 2 ... 2 2 2 2 2 2 2 2\n",
+       "    n_steps             (chain, draw) float64 3.0 7.0 3.0 7.0 ... 3.0 3.0 3.0\n",
        "    ...                  ...\n",
-       "    max_energy_error    (chain, draw) float64 1.515 -1.358 ... 1.001 -0.9029\n",
-       "    perf_counter_start  (chain, draw) float64 6.197 6.198 6.198 ... 8.621 8.621\n",
-       "    step_size           (chain, draw) float64 0.7602 0.7602 ... 1.231 1.231\n",
-       "    step_size_bar       (chain, draw) float64 0.9333 0.9333 ... 0.959 0.959\n",
-       "    energy_error        (chain, draw) float64 1.515 -1.358 ... 1.001 0.1039\n",
-       "    energy              (chain, draw) float64 28.66 27.65 25.17 ... 24.42 26.31\n",
+       "    lp                  (chain, draw) float64 -12.46 -12.5 ... -12.19 -13.3\n",
+       "    energy              (chain, draw) float64 17.48 15.32 14.85 ... 17.72 15.27\n",
+       "    perf_counter_start  (chain, draw) float64 6.43 6.43 6.431 ... 9.019 9.019\n",
+       "    energy_error        (chain, draw) float64 -0.381 -0.0468 ... -0.4983 0.3634\n",
+       "    diverging           (chain, draw) bool False False False ... False False\n",
+       "    step_size           (chain, draw) float64 0.6989 0.6989 ... 0.6632 0.6632\n",
        "Attributes:\n",
-       "    created_at:                 2021-04-04T13:14:18.324217\n",
+       "    created_at:                 2021-04-05T19:08:29.364867\n",
        "    arviz_version:              0.11.2\n",
        "    inference_library:          pymc3\n",
        "    inference_library_version:  3.11.2\n",
-       "    sampling_time:              9.566201210021973\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-efd92723-1111-43c6-991e-06b5d335dca8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-efd92723-1111-43c6-991e-06b5d335dca8' 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-5188812d-1800-414a-9184-118368403e6b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5188812d-1800-414a-9184-118368403e6b' 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-92e34213-45f3-470c-af31-b0378f6a454d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-92e34213-45f3-470c-af31-b0378f6a454d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21864d1c-ec42-4d61-830d-db770aeed01a' class='xr-var-data-in' type='checkbox'><label for='data-21864d1c-ec42-4d61-830d-db770aeed01a' 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-ad4fb4d3-259b-4adf-972a-d6eb43060475' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad4fb4d3-259b-4adf-972a-d6eb43060475' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a297dd6-f6e2-459d-92ff-b48df9c6c74a' class='xr-var-data-in' type='checkbox'><label for='data-2a297dd6-f6e2-459d-92ff-b48df9c6c74a' 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-db3e2e0e-a4e4-45df-bca1-d4f07641bc4c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db3e2e0e-a4e4-45df-bca1-d4f07641bc4c' 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 ... 3.0 3.0 3.0 3.0</div><input id='attrs-d6e5bddf-b925-4ebd-add7-7388b6744d7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6e5bddf-b925-4ebd-add7-7388b6744d7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2676846f-48a3-4d2d-ae2d-b70d499b5e4f' class='xr-var-data-in' type='checkbox'><label for='data-2676846f-48a3-4d2d-ae2d-b70d499b5e4f' 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., 3., 3.],\n",
-       "       [3., 3., 3., ..., 3., 3., 3.]])</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'>0.3946 1.0 0.9087 ... 0.7429 0.9799</div><input id='attrs-7285840d-389b-4b82-9af9-1d24c629140e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7285840d-389b-4b82-9af9-1d24c629140e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50462ad1-ac3b-427b-b89c-2d11e3f0cbf8' class='xr-var-data-in' type='checkbox'><label for='data-50462ad1-ac3b-427b-b89c-2d11e3f0cbf8' 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([[0.39459488, 1.        , 0.90874855, ..., 0.79493269, 0.69179347,\n",
-       "        1.        ],\n",
-       "       [1.        , 0.81781807, 0.99202283, ..., 0.67421276, 0.74291879,\n",
-       "        0.97987056]])</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-08d37d71-7fc3-4480-babf-4c3d81b7e68c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08d37d71-7fc3-4480-babf-4c3d81b7e68c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dafe8f39-e20f-4e08-b42f-d7a6c26bd3c9' class='xr-var-data-in' type='checkbox'><label for='data-dafe8f39-e20f-4e08-b42f-d7a6c26bd3c9' 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>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'>-23.23 -18.3 ... -19.85 -20.25</div><input id='attrs-acd28985-c686-4681-883c-61db7e9cb17d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-acd28985-c686-4681-883c-61db7e9cb17d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06926e00-808e-48e6-a0f4-e2e48b7a3490' class='xr-var-data-in' type='checkbox'><label for='data-06926e00-808e-48e6-a0f4-e2e48b7a3490' 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([[-23.22570922, -18.29572925, -16.69194324, ..., -10.7183016 ,\n",
-       "        -14.65828059, -14.31415368],\n",
-       "       [-13.81167101, -20.60126864, -20.65763802, ..., -15.6723819 ,\n",
-       "        -19.84905246, -20.2474132 ]])</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'>0.0004298 0.0004191 ... 0.0003205</div><input id='attrs-0be311b7-d53f-495a-a026-2e8a3c8eca20' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0be311b7-d53f-495a-a026-2e8a3c8eca20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90679a1f-0a7f-4524-88a9-70c713ed19ee' class='xr-var-data-in' type='checkbox'><label for='data-90679a1f-0a7f-4524-88a9-70c713ed19ee' 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([[0.00042982, 0.00041905, 0.00041227, ..., 0.00081057, 0.00045706,\n",
-       "        0.00048381],\n",
-       "       [0.00040327, 0.00040122, 0.00040838, ..., 0.00031657, 0.00031709,\n",
-       "        0.00032051]])</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 2 2 2 2</div><input id='attrs-0e2ed970-7904-4f0b-8d64-bd9bf5636507' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0e2ed970-7904-4f0b-8d64-bd9bf5636507' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81f92518-e1f5-40bb-a2dc-ecbb8bcd558f' class='xr-var-data-in' type='checkbox'><label for='data-81f92518-e1f5-40bb-a2dc-ecbb8bcd558f' 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, 2, 2],\n",
-       "       [2, 2, 2, ..., 2, 2, 2]])</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'>0.00043 0.000419 ... 0.000321</div><input id='attrs-e1a93dc7-21bf-47c3-99a5-f597faa7193e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e1a93dc7-21bf-47c3-99a5-f597faa7193e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64bed859-2e66-4ac9-a638-7d659163276b' class='xr-var-data-in' type='checkbox'><label for='data-64bed859-2e66-4ac9-a638-7d659163276b' 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([[0.00043 , 0.000419, 0.000413, ..., 0.000811, 0.000457, 0.000484],\n",
-       "       [0.000403, 0.000402, 0.000409, ..., 0.000316, 0.000318, 0.000321]])</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'>1.515 -1.358 ... 1.001 -0.9029</div><input id='attrs-1b166f36-0b16-43f5-a817-c7df65210538' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1b166f36-0b16-43f5-a817-c7df65210538' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad79ec69-6a77-4f09-9344-b420c05704fb' class='xr-var-data-in' type='checkbox'><label for='data-ad79ec69-6a77-4f09-9344-b420c05704fb' 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([[ 1.51543035, -1.35821101,  0.35306051, ...,  0.50942943,\n",
-       "         0.78065804, -0.23884907],\n",
-       "       [-0.43578296,  1.74687276, -1.78161327, ...,  1.12062281,\n",
-       "         1.00095637, -0.90290469]])</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'>6.197 6.198 6.198 ... 8.621 8.621</div><input id='attrs-d6d18184-aed2-4f2b-bcd2-036f7a203ac2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6d18184-aed2-4f2b-bcd2-036f7a203ac2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5dd46766-a52f-4143-9974-cd895e76da11' class='xr-var-data-in' type='checkbox'><label for='data-5dd46766-a52f-4143-9974-cd895e76da11' 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([[6.19721368, 6.19777443, 6.19831352, ..., 7.40443384, 7.40539592,\n",
-       "        7.40599166],\n",
-       "       [7.66197288, 7.66250108, 7.66302113, ..., 8.62048104, 8.62088729,\n",
-       "        8.62129346]])</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'>0.7602 0.7602 ... 1.231 1.231</div><input id='attrs-a48f0356-c1f3-4974-b279-e54908eb2306' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a48f0356-c1f3-4974-b279-e54908eb2306' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28957e08-40e2-4b18-a479-31e1b1816755' class='xr-var-data-in' type='checkbox'><label for='data-28957e08-40e2-4b18-a479-31e1b1816755' 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([[0.76016927, 0.76016927, 0.76016927, ..., 0.76016927, 0.76016927,\n",
-       "        0.76016927],\n",
-       "       [1.23071367, 1.23071367, 1.23071367, ..., 1.23071367, 1.23071367,\n",
-       "        1.23071367]])</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'>0.9333 0.9333 ... 0.959 0.959</div><input id='attrs-4b05d649-3773-4ce2-bc16-3ce421f6dd43' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b05d649-3773-4ce2-bc16-3ce421f6dd43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc30659e-75e7-4e63-9218-463105bad1f5' class='xr-var-data-in' type='checkbox'><label for='data-dc30659e-75e7-4e63-9218-463105bad1f5' 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([[0.93329576, 0.93329576, 0.93329576, ..., 0.93329576, 0.93329576,\n",
-       "        0.93329576],\n",
-       "       [0.9590144 , 0.9590144 , 0.9590144 , ..., 0.9590144 , 0.9590144 ,\n",
-       "        0.9590144 ]])</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'>1.515 -1.358 ... 1.001 0.1039</div><input id='attrs-7bd81e4b-e147-4af9-b6b0-d77d7262fbd4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7bd81e4b-e147-4af9-b6b0-d77d7262fbd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b19978c2-3d09-426b-8e6c-5122c45e6609' class='xr-var-data-in' type='checkbox'><label for='data-b19978c2-3d09-426b-8e6c-5122c45e6609' 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([[ 1.51543035, -1.35821101, -0.15216837, ..., -0.08326286,\n",
-       "         0.78065804, -0.04826571],\n",
-       "       [-0.34181219,  1.7057648 ,  0.11232557, ...,  0.90234702,\n",
-       "         1.00095637,  0.10385907]])</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'>28.66 27.65 25.17 ... 24.42 26.31</div><input id='attrs-0138732b-279a-49f3-bd57-163b27260fb1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0138732b-279a-49f3-bd57-163b27260fb1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb287b07-d180-4284-9ca2-d614fa084814' class='xr-var-data-in' type='checkbox'><label for='data-bb287b07-d180-4284-9ca2-d614fa084814' 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([[28.65876424, 27.65179536, 25.16529121, ..., 13.90069897,\n",
-       "        16.44194248, 18.39987281],\n",
-       "       [17.41747035, 25.82823814, 26.25725321, ..., 18.57675378,\n",
-       "        24.42040881, 26.30506902]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9deeadff-c9b4-4ec6-8d60-e5a9812142e3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9deeadff-c9b4-4ec6-8d60-e5a9812142e3' 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>2021-04-04T13:14:18.324217</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>9.566201210021973</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
+       "    sampling_time:              10.044129133224487\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-e9faae16-6b4c-42da-a536-6b1f2a507272' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e9faae16-6b4c-42da-a536-6b1f2a507272' 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-5455956f-5d74-48fc-9511-271f15f6fdfd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5455956f-5d74-48fc-9511-271f15f6fdfd' 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-fed4bec5-6d92-4cd3-b7aa-15325401504f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fed4bec5-6d92-4cd3-b7aa-15325401504f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b78f5d4f-13c7-4a28-ac01-1da431116ffb' class='xr-var-data-in' type='checkbox'><label for='data-b78f5d4f-13c7-4a28-ac01-1da431116ffb' 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-63bd9520-c5f2-45b7-84ae-ad311fcbe209' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-63bd9520-c5f2-45b7-84ae-ad311fcbe209' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5375b2fb-d246-471f-9053-79f725d46833' class='xr-var-data-in' type='checkbox'><label for='data-5375b2fb-d246-471f-9053-79f725d46833' 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-525aa893-adcb-41f9-a40c-cccf82ea146e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-525aa893-adcb-41f9-a40c-cccf82ea146e' 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'>0.0003295 0.0005849 ... 0.0003195</div><input id='attrs-9ac163b3-e4b8-4d3a-a803-e1af0e0c9219' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ac163b3-e4b8-4d3a-a803-e1af0e0c9219' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f43f788-4014-43c4-ad52-7502a8858eab' class='xr-var-data-in' type='checkbox'><label for='data-2f43f788-4014-43c4-ad52-7502a8858eab' 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([[0.00032947, 0.00058491, 0.00032905, ..., 0.0006286 , 0.00051097,\n",
+       "        0.00058371],\n",
+       "       [0.0003442 , 0.00035389, 0.00036091, ..., 0.00034443, 0.00032249,\n",
+       "        0.00031953]])</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'>0.6713 0.2162 ... -0.5191 0.3634</div><input id='attrs-d1c0d802-ae07-452f-a516-b024dddea8ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d1c0d802-ae07-452f-a516-b024dddea8ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d13d26d6-ab5d-4159-ab1a-144487ede1c1' class='xr-var-data-in' type='checkbox'><label for='data-d13d26d6-ab5d-4159-ab1a-144487ede1c1' 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([[ 0.67131179,  0.21617272, -0.20972849, ..., -0.64167087,\n",
+       "         2.60917638,  1.03776088],\n",
+       "       [-1.25523578,  1.13951164, -0.59184443, ..., -0.74449021,\n",
+       "        -0.5191076 ,  0.3633549 ]])</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'>0.9636 0.9636 ... 0.9695 0.9695</div><input id='attrs-df279000-502a-4d86-a14e-b2fe71bd305f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df279000-502a-4d86-a14e-b2fe71bd305f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9c59f5e-965c-4636-afff-eeca4fae2225' class='xr-var-data-in' type='checkbox'><label for='data-c9c59f5e-965c-4636-afff-eeca4fae2225' 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([[0.96357598, 0.96357598, 0.96357598, ..., 0.96357598, 0.96357598,\n",
+       "        0.96357598],\n",
+       "       [0.9695334 , 0.9695334 , 0.9695334 , ..., 0.9695334 , 0.9695334 ,\n",
+       "        0.9695334 ]])</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'>0.00033 0.000585 ... 0.00032</div><input id='attrs-1007968d-cb97-47a8-8940-1ec4702eb5af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1007968d-cb97-47a8-8940-1ec4702eb5af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-414b9e75-642b-468f-b616-79f6607772e6' class='xr-var-data-in' type='checkbox'><label for='data-414b9e75-642b-468f-b616-79f6607772e6' 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([[0.00033 , 0.000585, 0.00033 , ..., 0.000604, 0.000511, 0.000564],\n",
+       "       [0.000344, 0.000355, 0.000362, ..., 0.000346, 0.000322, 0.00032 ]])</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 3 2 3 2 2 2 2 ... 2 2 2 2 2 2 2 2</div><input id='attrs-01a31584-6f23-4b00-b06a-5d28ec982a33' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01a31584-6f23-4b00-b06a-5d28ec982a33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc32a5ad-aba7-4a0d-b539-5e66cebda7b9' class='xr-var-data-in' type='checkbox'><label for='data-cc32a5ad-aba7-4a0d-b539-5e66cebda7b9' 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, 3, 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>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 7.0 3.0 7.0 ... 3.0 3.0 3.0 3.0</div><input id='attrs-7fb9148d-f3fe-4eff-b429-b2f24ef468d3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7fb9148d-f3fe-4eff-b429-b2f24ef468d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf2576df-2233-4809-be56-5d8db307f284' class='xr-var-data-in' type='checkbox'><label for='data-bf2576df-2233-4809-be56-5d8db307f284' 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., 7., 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>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'>0.9151 0.9221 ... 0.9509 0.828</div><input id='attrs-5489e7c9-122b-4f08-8a87-58b0dc6f6b89' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5489e7c9-122b-4f08-8a87-58b0dc6f6b89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c632b64-973d-4de1-9ed0-76562ec6c848' class='xr-var-data-in' type='checkbox'><label for='data-2c632b64-973d-4de1-9ed0-76562ec6c848' 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([[0.91511546, 0.92205993, 0.96748532, ..., 1.        , 0.12458339,\n",
+       "        0.7968372 ],\n",
+       "       [0.98645798, 0.9263687 , 0.85228559, ..., 1.        , 0.95089605,\n",
+       "        0.82799622]])</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'>-12.46 -12.5 ... -12.19 -13.3</div><input id='attrs-66265707-8b32-44a7-a86d-b0c6be3d62d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66265707-8b32-44a7-a86d-b0c6be3d62d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3363c32-c077-4226-9474-b32bb776dddd' class='xr-var-data-in' type='checkbox'><label for='data-a3363c32-c077-4226-9474-b32bb776dddd' 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([[-12.46354345, -12.49950721, -13.11316909, ..., -10.37706339,\n",
+       "        -10.37706339, -10.37706339],\n",
+       "       [-15.11546609, -13.39506368, -15.2192683 , ..., -15.07686574,\n",
+       "        -12.18672473, -13.29738336]])</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'>17.48 15.32 14.85 ... 17.72 15.27</div><input id='attrs-99862ba6-2a88-4dc7-ab48-c3e4f26f162c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99862ba6-2a88-4dc7-ab48-c3e4f26f162c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f03075f-2cbe-468f-971b-c2011a8d20d2' class='xr-var-data-in' type='checkbox'><label for='data-0f03075f-2cbe-468f-971b-c2011a8d20d2' 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([[17.47700101, 15.32435336, 14.85443441, ..., 14.09352881,\n",
+       "        22.56869736, 14.20870246],\n",
+       "       [25.98916838, 20.33923926, 17.80078921, ..., 17.61550446,\n",
+       "        17.71500947, 15.26897105]])</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'>6.43 6.43 6.431 ... 9.019 9.019</div><input id='attrs-e3fa67e7-0bda-46c3-8cc7-197f1faaccc8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e3fa67e7-0bda-46c3-8cc7-197f1faaccc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80308890-8348-4649-a950-94d582be2691' class='xr-var-data-in' type='checkbox'><label for='data-80308890-8348-4649-a950-94d582be2691' 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([[6.4298268 , 6.43025576, 6.43094001, ..., 7.60678498, 7.60802156,\n",
+       "        7.60869476],\n",
+       "       [8.062209  , 8.06265274, 8.0631078 , ..., 9.0181419 , 9.01860942,\n",
+       "        9.01902456]])</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'>-0.381 -0.0468 ... -0.4983 0.3634</div><input id='attrs-db864825-f0a9-47e6-9614-e14b48873b05' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-db864825-f0a9-47e6-9614-e14b48873b05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdabdc1b-0441-489a-8197-bbe0d24bd10b' class='xr-var-data-in' type='checkbox'><label for='data-fdabdc1b-0441-489a-8197-bbe0d24bd10b' 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([[-0.38100043, -0.04679905,  0.12205405, ..., -0.64167087,\n",
+       "         0.        ,  0.        ],\n",
+       "       [-1.25523578, -0.20823156,  0.37421834, ..., -0.08588938,\n",
+       "        -0.49834727,  0.3633549 ]])</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-69b4a0cd-488a-43ca-9b86-671fec531184' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-69b4a0cd-488a-43ca-9b86-671fec531184' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed6ea44f-dd51-4c72-beac-0d9074b8a5c5' class='xr-var-data-in' type='checkbox'><label for='data-ed6ea44f-dd51-4c72-beac-0d9074b8a5c5' 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</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6989 0.6989 ... 0.6632 0.6632</div><input id='attrs-5a61100e-267e-4d35-811e-72c8837d4c04' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a61100e-267e-4d35-811e-72c8837d4c04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5649936c-1911-47e8-850f-84b53ce231aa' class='xr-var-data-in' type='checkbox'><label for='data-5649936c-1911-47e8-850f-84b53ce231aa' 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([[0.69891308, 0.69891308, 0.69891308, ..., 0.69891308, 0.69891308,\n",
+       "        0.69891308],\n",
+       "       [0.66322222, 0.66322222, 0.66322222, ..., 0.66322222, 0.66322222,\n",
+       "        0.66322222]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b732c2f1-0662-42fa-bbf2-5edeb2d6b795' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b732c2f1-0662-42fa-bbf2-5edeb2d6b795' 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>2021-04-05T19:08:29.364867</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>10.044129133224487</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -554,25 +553,25 @@
        "  * chain               (chain) int64 0 1\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",
-       "    acceptance_rate     (chain, draw) float64 0.3946 1.0 ... 0.7429 0.9799\n",
-       "    diverging           (chain, draw) bool False False False ... False False\n",
-       "    lp                  (chain, draw) float64 -23.23 -18.3 ... -19.85 -20.25\n",
-       "    perf_counter_diff   (chain, draw) float64 0.0004298 0.0004191 ... 0.0003205\n",
-       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 2 ... 2 2 2 3 2 2 2 2\n",
+       "    perf_counter_diff   (chain, draw) float64 0.0003295 0.0005849 ... 0.0003195\n",
+       "    max_energy_error    (chain, draw) float64 0.6713 0.2162 ... -0.5191 0.3634\n",
+       "    step_size_bar       (chain, draw) float64 0.9636 0.9636 ... 0.9695 0.9695\n",
+       "    process_time_diff   (chain, draw) float64 0.00033 0.000585 ... 0.00032\n",
+       "    tree_depth          (chain, draw) int64 2 3 2 3 2 2 2 2 ... 2 2 2 2 2 2 2 2\n",
+       "    n_steps             (chain, draw) float64 3.0 7.0 3.0 7.0 ... 3.0 3.0 3.0\n",
        "    ...                  ...\n",
-       "    max_energy_error    (chain, draw) float64 1.515 -1.358 ... 1.001 -0.9029\n",
-       "    perf_counter_start  (chain, draw) float64 6.197 6.198 6.198 ... 8.621 8.621\n",
-       "    step_size           (chain, draw) float64 0.7602 0.7602 ... 1.231 1.231\n",
-       "    step_size_bar       (chain, draw) float64 0.9333 0.9333 ... 0.959 0.959\n",
-       "    energy_error        (chain, draw) float64 1.515 -1.358 ... 1.001 0.1039\n",
-       "    energy              (chain, draw) float64 28.66 27.65 25.17 ... 24.42 26.31\n",
+       "    lp                  (chain, draw) float64 -12.46 -12.5 ... -12.19 -13.3\n",
+       "    energy              (chain, draw) float64 17.48 15.32 14.85 ... 17.72 15.27\n",
+       "    perf_counter_start  (chain, draw) float64 6.43 6.43 6.431 ... 9.019 9.019\n",
+       "    energy_error        (chain, draw) float64 -0.381 -0.0468 ... -0.4983 0.3634\n",
+       "    diverging           (chain, draw) bool False False False ... False False\n",
+       "    step_size           (chain, draw) float64 0.6989 0.6989 ... 0.6632 0.6632\n",
        "Attributes:\n",
-       "    created_at:                 2021-04-04T13:14:18.324217\n",
+       "    created_at:                 2021-04-05T19:08:29.364867\n",
        "    arviz_version:              0.11.2\n",
        "    inference_library:          pymc3\n",
        "    inference_library_version:  3.11.2\n",
-       "    sampling_time:              9.566201210021973\n",
+       "    sampling_time:              10.044129133224487\n",
        "    tuning_steps:               1000"
       ]
      },
@@ -589,7 +588,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[Arviz](https://arviz-devs.github.io/arviz/schema/schema.html#sample-stats) follows the following Name Convention for sample_stats variables, these may vary depending on the algorithm used by the backend (i.e. an affine invariant sampler has no energy associated). Therefore none of these parameters should be assumed to be present in the sample_stats group. The convention below serves to ensure that if a variable is present with one of these names it will correspond to the definition included here:\n",
+    "The sample statistics variables are defined as follows:\n",
     "\n",
     "- `process_time_diff`: The time it took to draw the sample, as defined by the python standard library time.process_time. This counts all the CPU time, including worker processes in BLAS and OpenMP.\n",
     "\n",
@@ -617,9 +616,16 @@
     "\n",
     "- `tree_depth`: The number of tree doublings in the balanced binary tree.\n",
     "\n",
-    "- `tune`: This is True, if step size adaptation was turned on when this sample was generated.\n",
-    "\n",
-    "InferenceData also stores additional info like the date, versions used, sampling time and tuning steps as attributes."
+    "- `tune`: This is True, if step size adaptation was turned on when this sample was generated."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some points to `Note`:\n",
+    "- Some of the sample statistics used by NUTS are renamed when converting to `InferenceData` to follow [ArviZ's naming convention](https://arviz-devs.github.io/arviz/schema/schema.html#sample-stats), while some are specific to PyMC3 and keep their internal PyMC3 name in the resulting InferenceData object.\n",
+    "- `InferenceData` also stores additional info like the date, versions used, sampling time and tuning steps as attributes."
    ]
   },
   {
@@ -629,7 +635,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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XioALP4ok12DwfVDQaEXSpkAsj48Tda6sFOVEhIu+cblEn/brp8BeK8opKBD97XKLbfv1BXolKfjxIlBZIcaYwyHGstEozo+UFAVaje+YFIg+cbs8ZV4CCi+JN3WbJ1kUfSDeuAxGoHdv37bnzou+kmXxX7jnzd9kBMLMYryVVwCSBHTvJsZyqVUcj8EgxluYSYyDygpRZkQEMH6cgrl3i/1nZyvILwB+/FEk/VAC+1cdA5NukTF0cIedqdVslwoVnDptR1GRgohwYPhwIDFBwqVCBdnZIplyu0Sb2WxiXLplX5tEhAPR0Qoqq8Q4T0xUEBnpW7d7nIKeiaJcAA2W+eOPIpZeKgRkN2CJFTHl9pmiLv513bFDwZHvRDyoqRH9GxEBDBmiIOV6EZvz8+BNAocPB4pLFPzpbeD0aRE/LBZg8iQZxUVAzikRIyVJnEtRkSJuREWJMdgzETCFKcjKAoqKxfiNCAcsMeLcHTSo4f199plYt+QyUFQk9qvRiETSbBbLZc851a27iEk1NUDOadGOCoCU/uKY1D75/gcZn30m6lFrF68nxAGnzwCHjvhiuDqu1VhT5blAEhMt6nGpULS/wynW7969Asm9fOdsz0TRjmo8V/sVqB/P1P6pO17U81yNk717K5BdwKmzwKUCUf+knkDaaFEOAHzyiYLde0VcioryjYGGxo1OJ877i/niPNdpAZdLwfGToh2MRqBvH2DYMF8c8R+T4WG+OvqPW50OAefBlo9lZHwoYqD6XmuxiDaePCnwPa+wWMRDnQ7olQQMHqTAYvG1x7HjIr6rH7RvGC4jJ8fXn0k9fXG3rEyMyYQEYNjQwLaue/7u2KF4+0ntl+ISBR99JGJnVbUYe4pc/3xRj7Oj6PAJrMPhwJ///GcoioKysjJ8++23OHv2LGbPno0xY8ZccVubzYbi4mKkpKRAq16u8dOnTx8AaNKV3KaorJTxySfAufMu6HVA8WUR7CoqgIoqMcjU+85ycoAzpwBFEiegogB2h1hHpxMDx24Xb6B6g3jd4fB9mpckkQTZ7YDJIE76MLO4ulVyWfwXbhZB55BfgiPLvkRRrxcByVYj6qjXi5Pb5RInkVUD7NkDxMQAPXoCJaWiHL3Ot6+iYrF+ba3nE64OyNoPXLgA5BUA5WXiJFXLLNMC+/YB+fmAWwagiKBSUQHoNIDTLcqSPOeIRiOSqZxq4OxpICZW1L2kRAQIox7QGYCLeWJf0IgySy6LcrRaQOsAzteIejtdYr92u6iTBPFJuapK1CE8XCTGOp1I5E6dAgoLRN9U20SgLCwUb4hJPQCX7GsHt1vsL647UFTkwsa/A7Exol1Ly0RAdDrVsSneUI0GkWx+uxeIjgJ6JQOSFjiZAyiygl17RLKl1Yj6ZWcD118HQAJyrMCZ0yJJVceG0yGOG4pYR5LE35UVYoyYzeKlnFO+vrbbRX20WvHhSX28X//+QG4ucOYMEB0JaHTiQ1B+vvhbbxR1q6wEtDqxn8uXRZkajSj3xEngfK5IFMsrxDEAon2rq4E8p2iHXsmivuerRD1G3Ajs/160tSUWOHte9JE6LqyehDOpp/iQYzSIhNPhFHWuqhJ1qK4WYyUqUoz9Hy+I/0dEiL44c1aUqX5DoF7NMJl841CvF+Xt+RbokaAgPFyczyUlwI8XfR8wa2pFmeoYuHzZjc8+A/r2lhEZGfpJrN0ukg6nS0FMDHC5VCQK4WMUZGeLq5VRUUBWlhiL4eHiPMzPF+3YzdOP9lpgwAAx1g4c9CWAZeViHBmNwMGDChQFsNUGlmkyiTIuXxb9pdGImLl/P9C9m4LbpvuuBh48qOCH40BJsair2y36stoGGHNE4vHd90BSL/FNwOVSYN8+BfsPiPNeUcQ4LS0FNm0WV9IqK8QYkWXRz/ZakXSZI8SHd0UBDh8RH4CcDpGMVniSgGPHgXPngN59Avd3/IQYT+Fhvv0mJIixX14uzvdLlwCtBPRIEol1caE4l8sqfAnsmdMi6czOVqAfKZLiy1ZPQlQBnMoRcfnYMXHOaDTivN/zLWCJVrD/oIgllmiRZBUUiMTbahXJs8bzDZXDIaPMCvRMEv2l1YpjrqkVFwJcsngPgCQSMzWehZsVTJ0ixlJ2tuIdL99+K441IUHESYdDJJjncsW+9XqxLC8f+OEoYDQocLmAfftFf0ie2Lh/PxATrcBkChw3ADBsKLD/oIj/qanA99+LxFiN85WVoo6xFvHhGxDjt6xcHMeZcnG+9+wJyBDjNiEBGHOTGPfZ2QrsqQr+tlGMF5fL915bWQns3iPGqaKIzrqQJ9Yzeb5JPJkj1vvZ7WJM5uWJmONwinPn9Bng5EnxPqy4RVmll3194naL9bQ6Mc7Utq77DcnBgwpO5Ij3E0US+9VqFJzMAc7/KMZzlecimdEgcg/1fFHHlmVi28aZ5ujwCazT6cSaNWu8f0uShAcffBBPP/30VbetrKwEAERERDT4urpcXa8x0dHRTXruWVmZC9W2aoSHKzAZdaiplWGzyaiplWAyKpAAWGIkuNzi05/TKYKIXi+CkMstgo0kiUCgXkHSeJIyjUacABpJnLRaz9+SBtBoJFhiJDgdCsxhgMulQY9EDUouy3A63dB4BrfT5UuCtVpRlk4n9ilJvq9ltTr/rxE0iI7WoqxMhk7nDtiXyaig1CaSZaNRQvdYDaxlMsrKJSiyApNJfBJWyzSZxDFVVGrRrZsGRqMEjeSEXqcAEmCQALskApjb8wlQ8hyvyw0YdBrxxlgsQ68TSZUlWoKtRoHLLUELcTw6nQK9XhyfwSiu6EIS7WU0eBJqt2hDSQvoNb43JY3nyrRBL94anC7RDt27S5DdQJhJXN3WaDXoHishP0+GyaigrFxCz0QJWp0G0VFAfr6EhDgtunfXoqLSDUV2wWaTEN9dQWWFSPglCYiO0qCqSoYsa9C9mw4mkwbWMjdKLkuw2VyIjwcqyiVYLG5cLgEsFh0URYJGkuF0uaHTApJRJE8aSRwHABh0IjlWv/LUagFzmCSubroV0d+eK8rl5b43XQkicBn0WoSFub0foiIiJNTUKHA5xN9GgwY6rYyaWqBbhASbTYFGI9rPoPe8wTt8yUxZmW8MG40iSGq1ot4JcXrIiqfdDYBBr4fR6IRG64bJKEF2u73JpARRruwGtFoNIsIAl1sLkwlwOt1wOhVYLAoqKjzfLFSJq+/eD3/wXJHWiwQXEP92u30fMABfYmv0fE0su4GSy3roDTokxGtw8aIDUVEyZLcCCRIAN2w23xjoFiuholIPRTHDYumYobapsQ0ArFYZsmJHXHcNjMZIRIQrKLXKkGU9ZMWJPskaOJ0KwsOdsNtlSNCge6yC3FwXkpMkyIoEc5iMGpuC6Cg9jEYNCgud0Os1kKBBn2QJtXYFifE6WMvE+OyTrA0o0+2SEBbmAhQFJqMYP2Fh4k3aZjPCZApDTIwGVquMWnstjEYX9AY39HoZBs+FgIhwER8jwg24mOdGYrwesbFaRIQrOHXahdLSWkiSSBDVJM/hEOeWRivGtppkabUAJCAiXANIOoSbNaitcSA5SYPCQgWRkbK4Shyjh61Ghs0GJMYbAvZns7nQJ1mL4hIZkuSETuf7tsTlElfb1DgYEa5FdbUbLjdgd2gQHw8UFwEJcYDdISHWYoCsSKi2aVFrr0WPBAkFBTJ69FBQXqZA61Tgcov3CFOYBIddgewG8gu0sNkU9OghQZElGAwuOOwigRJT0HzTGPQ63zmr1UqwxGhx6pQTZrOEhHgtamsV5OU5IEEKiGcOpw4mk0lcSFHs3vFiNDqg0boRHa1FdbUCk1GG3aGBIruh0ykwGiRERoh963RaVNt0qKlRAMmJiAg1logrvuXlBmi0moBxAyiAooNG60BUmIIwow5Go0PEJc/2siza11ajRVSUBoqieMdkeYUMvV6BRiujezcdAAmFhU6YTFpERhhgiZFQapVRUAjU1tpgMon3GzXWGfSeC0JWDRITtSKpVBzQagCjCYgMl2Cv9VxxUEwwGl2QFSc0koRusYAsS9BoZJRUArExOlRWAvHxMnJzZcRaJLicCoxhkjgXTBqYTBpvW8fEaALO31p7LcLD3bB4llvL3Cgr16Cy0oXwcAWyrMBY65sap9H6zpdYixGyIsFkMgJonx9eqatjRlU/4eHhOHnyJGRZRlFREb7++mv88Y9/xJEjR/Duu+82mpy2pqb+4oRWKyPcDBQWaWCvcaOqWpwYYSYFFVXiU7K1TBFvhrIIBIokgoEizj8o8FxtdQYmJOrVU1kWnwAlGVA0gFEnLvXLsgJrmQKXW1xRjYpyo+CSG5GRIhDKCuB2+pIbwJPYKCJIKor4z+32XZlUg7RGI6O8XEatetVS59uXGlyrbYCsKCgpdUOrEZ+Eq23iKpZ/meo0g6hIN2pr3aitEXVwunxXYBXF9ylYrYPi+arf4ZJRZQMg+drHWi6SVZ1WATS+/alXwxS7KAOKCFJqUqUer84t9qvT+pK92lrA4PmkbzYBsgEoKVEQFSWuNGh1gOyWUVICbzvo9QoKixXEdZdRXqGFOVyB0+lCSYkL1TaxnV6vwOq50qRegS2vkL3tXHLZAY1WJEtJPcSbaFGR5zjLPFMDrC4kJIp203uufKqfwrUakbDD04Zu2ddObjdgq1Gg93zqd6lXo2tEu9TUArGxYvw4HIDD6UZNjQhiTgdQXqGIdvNc/Yckw25Xv25XvFcxZVmMX43fB6XqalFfdfzZ7b721+uBwmIn4uPEsTidgMPpFNNDPG2r0Yr11QRTUcQyt1tGRSWQEC8SaQWiPKvVdxVY0oipIwaj7xyrqQlMVp11zg11H7IsxovLLa7odO/mhEHvRGGRGM8VFYFXYP3HwOVSHSIjXJCkclitV08S2+MNoTm/pmO3K9BICopLzAgLs+FyqefKvEaMr9wLoo2q1fmSOvGtjehfBd08Y0ujAcornJA0on+dThlKmNi+ezfgUpED0ZGi/euWaTKJvoPkmyMvKwrCTIDZXIvaWjusVnEF1mRUYLeLsep0+saamIsro6q6FiYjcKnICQXiimhkhDgHlNNin+oHLDWGym4xtmXP+aV1A1CAqmoZUByotgGmMKDksgy3W1xVMxkAa5mYpGg2A5eKXAH7M5uB3Asu8dW1Z4w7Xb7xaTKK81ErAVXVbjgcQJgRMBpkFHnGYWEx0M0ClFprEB8nLiiYjEBBodj2stXzrYlexLlaO+BwKt5vI3r2cCMv33PVNVpso0AkXxWeq7zqV8tOl4jhhcVO9EgUcSncLMZ/QYETLs83a4qkBMQzg96J2tpaAIBGUrx9q57n5eUyqj0fFrp3k8VFCxdgl8SUE4NB3MQdbnaJ+aWKSKyrbaLdYmOA6Gg7DPrAcQMAkJyQ3eLr9hq7w/vh2e6JN06nKN8cJt6XADF+cy+Iq9BOp/oB1gEFnrhUK6OyyomKStG2PRLE+KysFG2nxhOHU4wJi0VGba2MWs/4dcti/5UaRXwbCQWQamC3e973FTGP32hUoNOJDxKlZS4obvHtrvjWSYGsiLguSYDBKOqvtrXVGngF1mRUUF0tvjVQJHEcfZLFlMLCYsBR64vNDs+FCvV8UcdWbW0NgNhm/4pfW8S3Dp/AqjQaDRITE/GrX/0KFosF//7v/4633noLzzzzTKPbREZGAgCq1Ek9dajL1fVaKjJSg5kzZWz9RIezZx2IilTnwKLeHNi+fYHEePGVptvd8BxYk6n5c2BjLeocWHjmwAIR5qvPge2RWH8OrCUWGDoY3rmZ3WPFfJ66+zIa4Z0Da9ADI0cA6enwzoF1uX1lxljEa+np8M4H6tUTKDX7pjZERQXOgY2KVufAwjsHtm+fhubAinY6e85zM9EV5sAa9IFzYCP95sCaw8UxhpnUObDwzoFVv97r0xveObCWGF875OWLwBgfr8OtU93eObCxMaIO6jpRkb45sOYw4MbhvnaGDKSmABN/KqFHT98c2IgI8bV+hFnUtU+yuPJytTmwRmP9ObBDB8M3Bxa+ObDxceKGtopyEYgTE8UydQ5sXFzgHNjoKDE/S52Lpdc1NAdWfNWu3gRSfw6sGIvqMSkQ/Tp4oLi5oeAScF3fK8+BTUiEd+5adJRvDqzBEDgHNjXVNwdWpwN+cl3T58COHQNMnCjeELKzxfyz3ra6c2B9Y6BbNy0m3eLqFNMHAPENy/DhwKnTEoqKxJv28OESIiMlDB/umdNYBQxI8c0hlCDiRF6+GHvX9RU3AapzYEeNhHcOrATR9t0solwADZbpsIvET50DGxMDjB4N3HST74YUo1HCyJFAdbX4pqvWHjgHtn8K0LMHMGoUkJ8nvh1Qj+e6nyi4bBVf1btcIpZOngTvHFhZaXgObPc4oGeiGFPqHNj4eHG+6HXi3B00qOH9qXNg+/cXH1htNtFO6hzYRJc4VijA9deLmFR3DuxPrhfn0/DhErp1kzB9um8ObFSUKDshTnwV7D8HduwYYNo0CX37iVhT7jnv1TmwbrnuHFgNknvJ0GpEfyXEATfe4IvnWglIGy3GjH88GzXK1z/Dh/v61v88j40R51HPJBEv1TmwgIhrg4eIfgbETU7+c2BHjwbGj2943LjdwOiRYrpaZYVov7694Z0DGxkp3lN6JSNgDqwEX1xS58Bq/MZtdbWvHxMTJMy92zcHVlF877XjxooxpLZRcpLnwk+1GEupKcDgQWK8qe2hzoE1GtQ5sPDOgY2JEe1Rdw5s927AoIGBbe1//qrnxMlTgKSI/Y4fLyF1gG8ObITnw7r/HNj+Kb6x1ZFu5JIURb32FzoqKysxatQoDBs2zPuUgcaMHz8e1dXVOHDgQL15sO+88w5WrlyJ3//+91e8iau5nzS02mjk5FhhMomBIL4CEHdhqzfVxMeJuw4rKsSny6pq8ZVvnOcqVEWV+OSuNwSWrd7lLSliYIVHiDfb2lpxpchgEH+r+5RlCSaTeApBTg7gcKl3s4vEyWaDt57qXF3x6Q+IihBfmwNARYUCBY3vy+VSUFYmyure3XfHZUmJ0mCZ6utquVqN7ytmvWfqgt3hm+Lg317qvv23CY8AoqN8dbU7fGWYDIHtVFnlW253iiAQHyf2Y3f42tm/n2prAZtNgdUqEsCIiMC6+LeDzQb07WuB210ecIx119HpfO1ft52jowLvDC4sFG8WZnNgvwLiyvBla/0xofZn3XFkMIjyHQ4FRUUi2dR5EmmXSwRHh8M3Ly0iQvL2Y1SEb6zU2sWbnE7nG992z/i0lop69Osj2q+kREGtXeyjrFw8+cFsFuNd3d7/mNQPcP5t4nYB58+LKwf9+og6+7er/7ZqX1ksYj3/887hEHMRDSZRjsMB77mRmCASXlVVtfjQ060b0LNH4F3OtbXwHndD/auOgaZqjyuwzY1tAGA2x6CgwOq5+TPwCo9/v6nt498mMTG+81gd53XX9S+3sTL9z2P/mFKXev6p8UBRfGNW3Y//PvzvyM45Jfo+JcX3FAL1fJEV33nlf/6rZVRWyigrE2NclhFwTje2v7Iy0T5VVWKuanKyOJ/VcmprxTnnH5PUdjWbA49J5V8P9XUAyM/3PKWjzrhWY4140oHvngybzRcn+vSOQXV1eb3+8o91/rG4bjxrbLzUjZMmE7wxyj/G+48N/7jkPwYaGjdqeWo7GwxSQDt071Y/jtSNSw2N27ptXlAg4+xZz7QPTWAb133PKyoWSXJSkq9P/fflH7fUpxD496d/3AV8sf1qTyFoqF/U8e3/HtzQ+QKIWNURrsCGZAJ75swZzJgxAyNGjMCHH354xXV/85vf4JNPPsH69esxevTogNfmzZuHrKwsfP3110hKSmq0jGA6Kpg3Buo8OAaouWMgVBJYju2ujf1PHSWB7bDfbZ0+fRo16gMW/dTU1GD58uUAxLNcVaWlpThz5gxKS0sD1r/77rsBAKtWrYJDfVgagG+++QZZWVkYP378FZNXIiIiIupYOuwc2G3btuG9997DyJEjkZSUhIiICBQWFmLHjh0oKyvDqFGjcP/993vXz8jIwJo1a7BgwQIsXLjQu/ymm27CnDlzkJmZidmzZ2PixIkoLi7Gp59+ipiYGDz//PPtcHREREREFKwOm8DefPPNKCoqwuHDh3HkyBHYbDZEREQgNTUVM2fOxC9+8QvodE2r/ssvv4yUlBRs3LgRH3zwAcxmM6ZOnYrFixejd+/ebXwkRERERNSaQnIO7LXGObDUXBwDxDmw1Bmx/4lzYImIiIiIgsAEloiIiIhCChNYIiIiIgopTGCJiIiIKKQwgSUiIiKikMIEloiIiIhCChNYIiIiIgopTGCJiIiIKKQwgSUiIiKikMIEloiIiIhCChNYIiIiIgopTGCJiIiIKKQwgSUiIiKikMIEloiIiIhCChNYIiIiIgopTGCJiIiIKKQwgSUiIiKikMIEloiIiIhCChNYIiIiIgopTGCJiIiIKKQwgSUiIiKikMIEloiIiIhCChNYIiIiIgopTGCJiIiIKKQwgSUiIiKikMIEloiIiIhCChNYIiIiIgopupZsXFNTg6NHj6K4uBgOh6PR9e64446W7IaIiIiIyCvoBHbVqlVYu3YtamtrG11HURRIksQEloiIiIhaTVAJ7Lvvvou33noLWq0WEydORL9+/RAeHt6qFSssLMS2bduwY8cOnD17FiUlJYiOjsaIESPw8MMPY/jw4U0qZ9++fbjvvvsafX358uWYPXt2a1WbiIiIiNpYUAlsZmYmTCYTMjIyMHjw4NauEwBg3bp1ePfdd9G7d2+MGzcOsbGxyM3Nxfbt27F9+3asXLkSM2bMaHJ5aWlpSEtLq7d84MCBrVltIiIiImpjQSWwBQUFuOmmm9oseQWAYcOGYd26dfWSzgMHDuD+++/H7373O0yZMgUGg6FJ5aWlpWHhwoVtUVUiIiIiuoaCegpBXFwcwsLCWrsuAW699dYGr5iOGjUK6enpKC8vx8mTJ9u0DkRERETU8QR1BXbGjBn4+9//DpvNBrPZ3Np1uiqdThfw/6Y4f/481q5dC7vdjoSEBIwZMwYJCQltVUUiIiIiaiNBJbALFy7E4cOH8cQTT+Dll19Gnz59WrtejcrPz8eePXsQFxeHlJSUJm+3detWbN261fu3TqfDvffeiyVLlkCr1bZFVYmIiIioDTQpgW3oLn5ZlpGVlYUZM2agZ8+eSExMhCRJ9daTJAnvv/9+y2sKwOl0YsmSJXA4HPjtb3/bpMQzNjYWTz/9NG655RYkJSWhpqYGhw8fxsqVK7F27VpIkoSlS5e2Sv2IiIiIqO1JiqIoV1tpwIABwe9AknD8+PGgt1fJsoxnnnkGW7duxd13341XXnmlReUVFxdj1qxZqKiowI4dO9CtW7cr7luj4Y+WEVHnwthGRKGqSVdgv/rqq7auxxXJsoxnn30WW7duxaxZs/DSSy+1uMy4uDhMnjwZmZmZyM7OxqRJkxpdt7y8vFllWywWWK3WllaRQhjHADV3DFgsljasTcOaG9sAju2ujv1PwYyBtohvTUpgk5KSWn3HTSXLMpYtW4bNmzfj9ttvx2uvvdZqVwzUBq2pqWmV8oiIiIio7QWVCa5Zs6ZJV2W//vprrFmzJphdAAhMXmfMmIHXX3+9VW+4ys7OBtC+CToRERERNU/QCez27duvut7XX3+NN998M5hdeKcNbN68GdOnT8eKFSuumLyWlpbizJkzKC0tDVh+9OjRBtd///33sW/fPvTt2xdDhw4Nqo5EREREdO0F9RitpnK73UF/3f/mm29i06ZNMJvN6Nu3L956661660yZMsX7U7AZGRlYs2YNFixYEPCLW0899RR0Oh2GDBmChIQE1NTUIDs7G8eOHUNUVNRVE2MiIiIi6ljaNIE9ffo0oqKigto2Ly8PAGCz2fD22283uE5SUpI3gW3M3LlzsWvXLuzfvx9lZWXQaDTo2bMn5s+fjwcffBCJiYlB1Y+IiIiI2keTHqMFAMuWLfP+e9OmTejTpw9GjBjR4Lputxvnzp3D0aNHMWXKFKxevbp1attOgrnbjndpdm0cAxQKTyEIZoxybHdt7H8KqacQACJpVUmShNzcXOTm5l5xm9TUVCxZsiT42hERERER1dHkBPaDDz4AACiKgvnz52PChAl45JFHGlxXr9cjPj6ed/cTERERUatrcgKblpbm/fedd96JkSNHBiwjIiIiIroWgrqJa/ny5a1dDyIiIiKiJmnRUwgcDgc+//xzHDhwAEVFRQCA+Ph4jBw5EtOmTYPRaGyVShIRERERqYJOYPfs2YOlS5eiuLgYdR9ksHHjRqxYsQKvvfYaxo0b1+JKEhERERGpgkpgs7Oz8dhjj8HpdGL48OGYOXOm94at/Px8fPLJJzhy5Agef/xxrF+/HsOHD2/VShMRERFR1xVUArtq1Sq4XC787ne/w9y5c+u9Pm/ePGzYsAEvvvgi3njjDfzlL39pcUWJiIiIiAAgqN95zc7OxpAhQxpMXlW//OUvMXToUBw5ciTYuhERERER1RNUAqvRaNC7d++rrte7d29IkhTMLoiIiIiIGhRUAjts2DDk5ORcdb2cnBwMGzYsmF0QERERETUoqAR20aJFOH/+PN544w3IslzvdUVR8MYbb+D8+fNYtGhRiytJRERERKQK6iaus2fP4s4778Rbb72FLVu24NZbb/U+hSAvLw9ffvkl8vLyMGfOHJw7dw7nzp0L2P6OO+5occWJiIiIqGuSlLoPcW2CAQMGQJKkgOe/qnNdG1qmUhQFkiTh+PHjwda3XVit1matb7FYmr0NdS4cA9TcMWCxWNqwNg0LZoxybHdt7H8KZgy0RXwL6grsr3/9a96cRURERETtIqgEduHCha1dDyIiIiKiJgnqJi4iIiIiovYS1BVYfydOnMB3330Hq9WK66+/HpMnTwYAOBwOOBwOREREtLiSRERERESqoK/Anj17FnPnzsWdd96JF198Ef/93/+N7du3e1//+OOPMXr0aOzYsaNVKkpEREREBASZwBYUFODee+/FkSNHcMstt+CZZ55B3YcZ3HbbbdDr9fjiiy9apaJERERERECQUwjefPNNWK1WvPrqq7jrrrsAAK+//nrAOmazGQMHDkR2dnbLa0lERERE5BHUFdidO3ciNTXVm7w2JikpCUVFRUFVjIiIiIioIUElsJcvX0a/fv2uup7L5UJNTU0wuyAiIiIialBQCWxMTAwKCgquut65c+cQFxcXzC6IiIiIiBoUVAI7YsQIfP/991f8SdisrCycOnUKaWlpQVeOiIiIiKiuoBLYhx56CIqi4Mknn8Q333wDt9sd8Pq3336LJUuWQKfTYf78+a1SUSIiIiIiIMinEAwfPhzPPfcc/vM//xOPP/44TCYTJEnCF198ge3bt6OqqgqSJOHFF1/EgAEDWrvORERERNSFBf1DBvfccw8yMjJwyy23QJIkKIqC6upqOBwOjB8/HuvWrcMvf/nL1qwrEREREVHLfkr2hhtuwJ/+9CcoigKr1QpZlmGxWKDValurfkREREREAVqUwKokSUJsbGxrFEVEREREdEWtksC2hcLCQmzbtg07duzA2bNnUVJSgujoaIwYMQIPP/wwhg8f3uSyZFlGRkYGNm7ciNzcXJjNZowdOxaLFy9GcnJyGx4FEREREbW2JiWwkydPDnoHkiRh+/btzd5u3bp1ePfdd9G7d2+MGzcOsbGxyM3Nxfbt27F9+3asXLkSM2bMaFJZL7zwAjIzM9G/f3/MmzcPRUVF2LZtG3bv3o0NGzagb9++za4fEREREbWPJiWweXl5zS5YvbErWMOGDcO6devqPUf2wIEDuP/++/G73/0OU6ZMgcFguGI5e/fuRWZmJkaPHo2//vWv3vVvv/12PProo3jllVfwl7/8Jeh6EhEREdG11aQE9sSJE/WWvfrqq/joo49wzz33YObMmejVqxcAkex+8sknyMjIwJ133onnn38+qIrdeuutDS4fNWoU0tPTsWvXLpw8eRJDhw69YjmZmZkAgEWLFgUkuxMnTkRaWhp27dqF/Px89OzZM6h61mW3K8jKqsXnX8gouAS4ZMCgBZwuQJIAnR7Q6wF7LeBwAToNYDQBERGAoxaoqAR0WiAsHJAUoLZWlKHViu0UN6AziNecTkCGKEstW6MRfysKAI3Yt90htu/eXZThsAM1NUBNLRBmEvWuton9Gkzi0RR6I2CrAqpqRB3DTOIY1DrX3ZdGB0RFALU1YhvIgE4HRISLfTudot6yC6isAtxusb3RAGj1gNMBGPTiOCStOE7///u3l7pvdRtZFm0ky4Bb9q1jMvna0OECFABhhsB9GMPEOuWVgCL7+kdxB7atAkDS+I7RZhf7kfye4yHLvn1odZdRViHaUqfzree/jloPey1Q6whs51qH2FajEW0bHgbYakQdJY0ox+lpj4hwwGAQbaqOFyBwzEhawOUIbCejTvS3f1vLAGpsYtuoKN+xGnViTMou31hR+02rCRzf4eGAUS/GWFm5WB4RLupUUyv+HRYmXq+o9LSD3zGpbabA15+SRhxjdDTgdgGlVrGeVu8bgzLEumpfhYcBdruonyT5+tNoACKjxflWVi7aqEcPQHYDZWWA0y2WuRyi7cPCAHsNYKsV9VPHtkHvO24F4j+DVtQ5qSdw110OJMS3SljpMI4dk/HNzlLk/qh4+0qjEeNK/bd/P/ifjxqNGFuK4jvv1b/Vc0QdA5DF+uo4UMe6f5sbTaJv1DilbqPWR+2nMJPnvJN8cUOrEXHR7fb1pfo3IMZaQoJ4LT9fxC+jof75Ujc21a2rW/bVWT2n/Y9JrbN/e5iMIlaXlIg6qeWosbqyShy/utz//Gus3LrniNp2dce1yQTExABWq2gLg17s0xwuzg+HC9BIl+Fyim0Uxdf3/n3of976n9d1+0Ztd3VMGEzivHO4xDYGnYhDToevnv7jzD8uqe8rQP2y1TZXz1G7Q7RXdJTo68pKoNbpe01RxD7830MbGrdqPTTwHVe4WYwdu0PU17+N/dtJqxXtrcZU//7TQOzL/9j89+//Hh9m8L0HAoHnEND4OaGOSf+xora/IgXWTR07GgDhEcCkm20YPkxGZGTQD7JqFUHNgf3ggw/wt7/9DR9++GG9BDI1NRWpqamYOnUq/u3f/g29evXC/fff3xp19dLpdAH/v5J9+/bBbDZjxIgR9V6bMGECsrKykJWVhTvuuKPF9bpUqOAPf1CwZ191i8siotCW+VEl7p4DPPlY+wb51rLiDzK2/BMQb7NE1FV98WUNknoCzz8nY+jg9otvQe15w4YNSE9Pv+LVz6FDhyI9Pd17BbS15OfnY8+ePYiLi0NKSsoV17XZbCguLkavXr0afLRXnz59AAC5ubktrpfdruDzzxXs2dfiooioE3C5gI82AadOye1dlRY7dkxNXomIgLx8YH0GUFnZfvEtqCuwFy5cQGpq6lXXi46OxoEDB4LZRYOcTieWLFkCh8OB3/72t1d93mxlZSUAICIiosHX1eXqeo2Jjo6GRnPlXN9qlXExvxKA64rrEVHX4XQCeXlmpKWFtXdVGtSU2AYAp89WA6ht+woRUcgoKtJAUSJhsbTPA62C2mt0dDT2798Pu90Oo9HY4Dp2ux379+9HVFRUiyqokmUZS5cuxf79+3H33Xe3ylf+TVVeXn7Vdex2Bb168qs1IvLR64GkJBus1qsnfxaL5RrUKFBTYhsAXH9d6F9FJqLWFR8vQ5LKYbVe/UNwW8S3oKYQTJ06FcXFxXjqqadw8eLFeq9fvHgRixYtQklJCaZOndriSsqyjGeffRZbt27FrFmz8NJLLzVpu8jISABAVVVVg6+ry9X1WsJolDBtmoSx6S0uiog6AZ0OmH0n0L9/6M+BHTRIg5/Pau9aEFFHkdQTuPcetOuNXEFdgV20aBH27t2Lb775Brt27cKQIUO8d/Hn5+fjhx9+gMvlwnXXXYdFixa1qIKyLGPZsmXYvHkzbr/9drz22mtN+soLAMxmM+Li4nDx4kW43e56Uw7Uua/qXNiWSkyQ8MrLwIULZny+vRqFBeKOZoNefJUoSeKKjF4n7o62O8Xd3CaTuHPb7gAqK8QbnzkMgCTuNnS5PXeU63x3VEICXE5xx2Gt3Ve2RhJ/K4pYx6AX+9JogTjPUwjsDnF3eW0NYPJ8s1ldLco1Gjx3/+oBmw2oqhb7NoeJ/al1rrsvrRaIjBR3wVdV++oZGSFecznF325Z3PHp8jyFwGQU5TmdgN4gypQksb3///3bS923uo0ii/LcsueuYs86JqOvDe1OUVaYKXAfRs86FRWiLdX+UZTAtlUgtlOP0VYj9uM/FN2ybx9anbijXZJEeep6/uuo9bDbgRp7YDvXePar9dytajaLu/Zlzx2jblkcv1Yr2lh9CoE6XoDAMSNJYk6mfzsZDeI//7aWZXFsGq24Q1c9VqNB1M0t+8aK2m8abeD4DjcDBqPYrrxMtG+kZxZPbY24izXM81SFygqxnf8xqW2mwNefGs9TCKKixDKr1fPUA61vDMqK+FvtK7MZcDg8d+FKvv40GkU/2h2ifno9kJgoyigrE+2k14n/azznp90u6ut0+sa20eCrrwKx3KAXd2v3TAJm3xmJhPjOc0PnM7/RYOZ0Gbt2a3D+R9l77FqNGFfqv/37wf981EiiDWXFd96rf6vniDoG1Du81XGgjnX/NjeZxH9qnFK3Ueuj9pM5TMQIdVyoY9ZuF+upfan+DYixlpggyrp0ScQPk7H++VI3NtWtq+wWMcr/nPY/JrXO/u1hMgGx3YDLl0XZajlqrK6sBCD5lvuff42VW/ccUduu7rgOM4knfZSXifcuowGQAISZPXe3e57M4XT4tlH73r8P/c9b//O6bt+o7a6OCaNBnHd2pye+60Uccjp99fQfZ/5xSX1fAeqXrba5eo7a7aINY6L9nkJg970mK2If/u+hDY1btR4ayXdcEeFAXLyIPS5nYBv7t5NWK8pTY6p//0mSeM3/2Pz37/8eH2byvQcCgecQ0Pg5oY5J/7Gitr+CwLqpY0cjiScm3XxzGIYNqQnNpxBER0fjb3/7G1auXIktW7YgOzsb2dnZ3tdNJhNmz56Np59+GtHR0UFXzj95nTFjBl5//fWrznutKy0tDZ988gkOHTqE0aNHB7y2c+dOAKi3vCWMRgmjR5tw/fU1rVYmhR6LxQKr1dre1aB2ZLEYYLV2ngQWEFdix43j2O7KGNvIYjHDarW3dzWC/ynZqKgovPTSS1i6dCl++OEHFBUVAQDi4uIwePBgmM3mFlVMnTawefNmTJ8+HStWrLhi8lpaWgqr1QqLxYLY2Fjv8rvvvhuffPIJVq1aFfBDBt988w2ysrIwfvx4JCUltaiuRERERHTttPjWsbCwMIwaNarJ62dmZuLQoUNYvnz5Fdd78803sWnTJpjNZvTt2xdvvfVWvXWmTJmCgQMHAgAyMjKwZs0aLFiwAAsXLvSuc9NNN2HOnDnIzMzE7NmzMXHiRBQXF+PTTz9FTExM0D+0QERERETt45o/++DQoUPYvHnzVRNY9edrbTYb3n777QbXSUpK8iawV/Lyyy8jJSUFGzduxAcffACz2YypU6di8eLF6N27d/MPgoiIiIjajaQo6lTfa0Od03r8+PFrudsWae58H84RIo4Bau4YaI/HaAUzRjm2uzb2PwUzBjrMY7SIiIiIiNoLE1giIiIiCilMYImIiIgopDCBJSIiIqKQwgSWiIiIiEIKE1giIiIiCilMYImIiIgopFzzBPYaP3aWiIiIiDqZa/5LXI8++ihmz559rXdLRERERJ1EixLY06dPY+PGjfjuu+9gtVoxefJkLFmyBID4ydijR49i1qxZiImJ8W5z3XXX4brrrmtRpYmIiIio6wo6gX3vvfewcuVKuFwuAIAkSfV+Wmz58uUwGAyYO3duy2pJREREROQR1BzY//u//8Pvf/97JCYmYs2aNdizZ0+9ua0jRoxAbGwsvvrqq1apKBEREREREOQV2Pfeew9hYWF47733kJyc3Oh6AwYMwLlz54KuHBERERFRXUFdgf3hhx9www03XDF5BQCLxYKSkpKgKkZERERE1JCgElin04nw8PCrrldaWgqtVhvMLoiIiIiIGhRUAturVy+cOHHiius4HA6cPHkSffv2DWYXREREREQNCiqBnTRpEvLy8vDee+81us7//M//oLS0FLfeemvQlSMiIiIiqiuom7gefvhhfPzxx3j99deRnZ2NqVOnAgAuX76ML7/8El9++SU+/vhj9OrVC/fcc0+rVpiIiIiIujZJCfK3Xc+dO4ennnoKp06dgiRJUBQFkiQBED8Xe/311+PNN99Enz59WrXC7aHu822vxmKxNHsb6lw4Bqi5Y8BisbRhbRoWzBjl2O7a2P8UzBhoi/gW9A8Z9OvXD1u2bMHXX3+N3bt3Iy8vD7IsIzExEWPHjsW0adN4AxcRERERtboW/ZSsRqPBlClTMGXKlNaqDxERERHRFQV1ExcRERERUXtpUQK7a9cu/PrXv8aECRMwZMgQLFu2zPvazp07sXz5chQWFra4kkREREREqqCnELz66qvIyMiAoigwm81wuVwBr8fFxeH9999Hjx49cP/997e0nkREREREAIK8Art582asX78egwcPxqZNm3Do0KF66wwYMAA9evTA119/3eJKEhERERGpgroC++GHHyIqKgrvvPMOYmNjG10vNTUVOTk5QVeOiIiIiKiuoK7A5uTk4MYbb7xi8goAERERKCkpCapiREREREQNCfomLvVHC66kqKgIJpMp2F0QEREREdUTVALbt29f/PDDD3A6nY2uU1VVhRMnTuD6668PunJERERERHUFlcBOnz4dxcXFWLlyZaPr/OEPf0BlZSVmzpwZdOWIiIiIiOoK6iau+fPn45NPPsH777+Pw4cPY/LkyQCACxcuYO3atfjyyy9x8OBBDBo0CHPmzAm6clu2bMHBgwdx9OhR5OTkwOl0Yvny5Zg9e3aTy9i3bx/uu+++Rl9vbnlERERE1L6CSmBNJhPWrl2LpUuXYseOHfjuu+8AAAcOHMCBAwcAAOPGjcOKFStgMBiCrtyqVauQl5cHi8WC+Ph45OXlBV1WWloa0tLS6i0fOHBg0GUSERER0bUX9A8ZxMbG4p133sGJEyewa9cu5OXlQZZlJCYmYty4cRg2bFiLK/fqq6+iT58+SEpKwjvvvHPFKQtXk5aWhoULF7a4TkRERETUvoJKYBcsWIC4uDi8+OKLGDBgAAYMGNDa9QIAjB07tk3KJSIiIqLQFVQC+80332DKlCmtXZc2df78eaxduxZ2ux0JCQkYM2YMEhIS2rtaRERERNRMQSWwvXr1Qk1NTWvXpU1t3boVW7du9f6t0+lw7733YsmSJdBqte1YMyIiIiJqjqAS2JkzZ+Kvf/0riouLERcX19p1alWxsbF4+umnccsttyApKQk1NTU4fPgwVq5cibVr10KSJCxduvSKZURHR0Ojad4TxywWS0uqTZ0AxwB19DEQTGwDOv5xUdti/1NHGANBJbCPPfYYvvvuO9x777347W9/i5tvvhl6vb6169Yq+vfvj/79+3v/NpvNmDJlCoYPH45Zs2Zh3bp1eOSRR9CtW7dGyygvL2/WPi0WC6xWa9B1ptDHMUDNHQPt8YbQ3NgGcGx3dex/CmYMtEV8CyqBnT59OhRFQUFBAZ566ilIkoTY2FgYjcZ660qShO3bt7e4oq0tLi4OkydPRmZmJrKzszFp0qT2rhIRERERNUFQCWzd57EqioKSkpJWqdC1pH4iCLX5vERERERdWVAJ7IkTJ1q7Hu0iOzsbAJCUlNTONSEiIiKipmr+7H0Ay5Ytwz/+8Y+rrrdp0yY8++yzweyi2UpLS3HmzBmUlpYGLD969GiD67///vvYt28f+vbti6FDh16LKhIRERFRKwjqCuymTZsAAL/4xS+uuN7BgwexadMm/Od//mcwu0FmZiYOHjwIAMjJyfEuy8rKAgCMHDkSc+bMAQBkZGRgzZo1WLBgQcAvbj311FPQ6XQYMmQIEhISUFNTg+zsbBw7dgxRUVFYsWIFH6NFREREFEKC/inZpnA6nS1KDtUE2N+hQ4dw6NAh799qAtuYuXPnYteuXdi/fz/Kysqg0WjQs2dPzJ8/Hw8++CASExODrh8RERERXXuSoihKczcaMGAA7rzzTixfvrzRdRRFwaxZs1BWVoadO3e2qJLtLZjHRfAxI10bxwCFwmO0ghmjHNtdG/ufQu4xWvfdd1/A3zt37qy3TOV2u/Hjjz+ipKQEP//5z1tWQyIiIiIiP01OYNV5p4B4tmtJSckVH52l0+lw8803Y8mSJS2rIRERERGRnyYnsF999RUAMTVgypQpmDZtWqPJqV6vh8Vi6bC/zkVEREREoavJCaz/s1IXLFiAgQMH8vmpRERERHTNBfUUggULFrR2PYiIiIiImiSoHzIgIiIiImovTGCJiIiIKKQwgSUiIiKikMIEloiIiIhCChNYIiIiIgopTGCJiIiIKKQwgSUiIiKikMIEloiIiIhCChNYIiIiIgopTGCJiIiIKKQwgSUiIiKikMIEloiIiIhCChNYIiIiIgopTGCJiIiIKKQwgSUiIiKikMIEloiIiIhCChNYIiIiIgopTGCJiIiIKKQwgSUiIiKikMIEloiIiIhCChNYIiIiIgopTGCJiIiIKKQwgSUiIiKikMIEloiIiIhCiq69K3AlW7ZswcGDB3H06FHk5OTA6XRi+fLlmD17drPKkWUZGRkZ2LhxI3Jzc2E2mzF27FgsXrwYycnJbVR7IiIiImoLHTqBXbVqFfLy8mCxWBAfH4+8vLygynnhhReQmZmJ/v37Y968eSgqKsK2bduwe/dubNiwAX379m3dihMRERFRm+nQUwheffVVfP3119i7dy/mzp0bVBl79+5FZmYmRo8ejY8++gjPPPMMVqxYgTfffBNlZWV45ZVXWrnWRERERNSWOvQV2LFjx7a4jMzMTADAokWLYDAYvMsnTpyItLQ07Nq1C/n5+ejZs2eL90VEREREba9DX4FtDfv27YPZbMaIESPqvTZhwgQAQFZW1rWuFhEREXnY7QrKyhTY7Up7V4VCRIe+AttSNpsNxcXFSElJgVarrfd6nz59AAC5ubnXumpEREQE4FKhguxsBVXVQEQ4MHw4kJggtXe1qIPr1FdgKysrAQARERENvq4uV9cjIiKia8duF8lreSUQEwOUVwLZ2bwSS1fXqa/Atpbo6GhoNM3L9S0WSxvVhkIFxwB19DEQTGwDOv5xUdtqzf63WmXIih19kjUwGiVEhCsotcowmYyIienU19hCWkeIAZ06gY2MjAQAVFVVNfi6ulxdrzHl5eXN2q/FYoHVam3WNtS5cAxQc8dAe7whNDe2ARzbXV1r97/drkAjKci9AHSLBS6XAtGRQG1tDaxWTiPoiIIZA20R3zr1xxuz2Yy4uDhcvHgRbre73uvq3Fd1LiwRERFdO0ajhOHDJURHAmVlInkdPlyC0cjkla6sUyewAJCWlgabzYZDhw7Ve23nzp0AgNGjR1/rahERERHEDVs3T5QwZZL4P2/goqboNAlsaWkpzpw5g9LS0oDld999NwDxq14Oh8O7/JtvvkFWVhbGjx+PpKSka1pXIiIi8jEaJURH88orNV2HngObmZmJgwcPAgBycnK8y9Tnto4cORJz5swBAGRkZGDNmjVYsGABFi5c6C3jpptuwpw5c5CZmYnZs2dj4sSJKC4uxqeffoqYmBg8//zz1/ioiIiIiKglOnQCe/DgQWzatClg2aFDhwKmA6gJ7JW8/PLLSElJwcaNG/HBBx/AbDZj6tSpWLx4MXr37t3q9SYiIiKitiMpisKHrV1FMHfb8S7dro1jgELhKQTBjFGO7a6N/U98CgERERERURB4BZaIiIiIQgqvwBIRERFRSGECS0REREQhhQksEREREYUUJrBEREREFFI69HNgQ813332H1atX4/Dhw3C5XEhJScH999+PGTNmtHfVqBFbtmzBwYMHcfToUeTk5MDpdGL58uWYPXt2g+tXVVVh9erV+OKLL1BcXIz4+HhMmzYNCxYsQHh4eL31ZVlGRkYGNm7ciNzcXJjNZowdOxaLFy9GcnJyg/vYuXMn/vznP+OHH36AJEkYPHgwnnzySYwZM6ZVj72rKywsxLZt27Bjxw6cPXsWJSUliI6OxogRI/Dwww9j+PDh9bbpqv3P2BZ6GNu6tq4Q3/gUglayd+9ePPzwwzAYDJg5cybCw8PxxRdfIC8vD//v//0/PPjgg+1dRWrApEmTkJeXB4vFArPZjLy8vEaDvM1mw69+9SscP34c48ePx8CBA3H8+HHs2rULQ4cORUZGBoxGY8A2zz//PDIzM9G/f39MnDgRRUVF2LZtG8LDw7Fhwwb07ds3YP0tW7ZgyZIliI2N9SYHn376KaxWK/77v/8b06dPb7O26Gr+67/+C++++y569+6NtLQ0xMbGIjc3F9u3b4eiKFi5cmVAgtZV+5+xLTQxtnVtXSK+KdRiTqdTmTJlijJkyBDl2LFj3uUVFRXKrbfeqgwePFi5ePFiO9aQGrN7925v3/z5z39WUlJSlH/84x8Nrrtq1SolJSVFWbFiRcDyFStWKCkpKcrbb78dsPzbb79VUlJSlHvuuUex2+3e5f/3f/+npKSkKA8++GDA+mVlZcqoUaOU9PR0paCgwLu8oKBASU9PV9LT05XKysoWHS/5fP7558q+ffvqLd+/f78yePBgZfTo0QH91hX7n7EtdDG2dW1dIb5xDmwr2Lt3L3788UfcfvvtGDhwoHd5ZGQkHn/8cTidzno/iUsdw9ixY5GUlHTV9RRFQWZmJsxmM5588smA15588kmYzWZkZmYGLFf/XrRoEQwGg3f5xIkTkZaWhl27diE/P9+7/LPPPkNFRQXuvfdeJCYmepcnJibi3nvvhdVqxfbt24M6Tqrv1ltvRVpaWr3lo0aNQnp6OsrLy3Hy5EkAXbf/GdtCF2Nb19YV4hsT2FaQlZUFABg/fny919Rl+/fvv6Z1otZ1/vx5FBUVYcSIETCbzQGvmc1mjBgxAhcuXEBBQYF3+b59+7yv1TVhwgQAvrHj/+8rjSP/9ant6HS6gP931f5nbOv8uurY7so6S3xjAtsKzp8/DwDo06dPvdfi4uJgNpuRm5t7jWtFrUntv7pzelTqcnUs2Gw2FBcXo1evXtBqtfXWV8eK/7i40jhqaH1qG/n5+dizZw/i4uKQkpICoOv2P2Nb59dVx3ZX1ZniGxPYVlBVVQVAfK3WkIiICFRWVl7LKlErU/svIiKiwdfV5epYaOr6/uPiSuOoofWp9TmdTixZsgQOhwO//e1vvcG5q/Y/Y1vn11XHdlfU2eIbE1giIohHwixduhT79+/H3XffjTvuuKO9q0RE1Co6Y3xjAtsKrvbpoaqqqtErGBQa1P5TP0XWpS5Xx0JT1/cfF1caR1e7EkYtI8synn32WWzduhWzZs3CSy+9FPB6V+1/xrbOr6uO7a6ks8Y3JrCtQJ0f0tD8jeLiYthstgbnfVDoUPtPncdTl7pcHQtmsxlxcXG4ePEi3G53vfXVseI/Lq40jhpan1qHLMtYtmwZNm3ahNtvvx2vvfYaNJrA0NhV+5+xrfPrqmO7q+jM8Y0JbCsYPXo0AGDXrl31XlOXqetQaOrbty/i4+Nx6NAh2Gy2gNdsNhsOHTqEXr16oUePHt7laWlp3tfq2rlzJ4DAcdGUcdTQY1EoeGpw37x5M2bMmIHXX3+9wZsSumr/M7Z1fl11bHcFnT2+MYFtBWPGjEFycjK2bt2K48ePe5dXVlbi7bffhl6v7xTzTboySZIwZ84c2Gw2/OlPfwp47U9/+hNsNhvuvvvugOXq36tWrYLD4fAu/+abb5CVlYXx48cHPKfxtttuQ2RkJNavX49Lly55l1+6dAnr16+HxWLBlClT2uLwuiT1a7XNmzdj+vTpWLFiRYPBHei6/c/Y1vl11bHd2XWF+Mafkm0l/LnF0JSZmYmDBw8CAHJycvDDDz9gxIgR3q8yRo4ciTlz5gAQn0T/7d/+DSdOnMD48eMxaNAgHDt2zPtTe+vXr4fJZAoov+5P7RUXF+PTTz9FeHg4/va3v6Ffv34B61/pp/b++Mc/4rbbbmvrJukyVq9ejTVr1sBsNuO+++7zPhPR35QpU7wP8O+q/c/YFpoY27q2rhDfmMC2ou+++w5vvPEGDh8+DJfLhZSUFDzwwAMBvzdMHcvSpUuv+EtCd955J1577TXv35WVlVi9ejW++OILlJSUIC4uDtOnT8evf/3rBh8pIssy1q9fj40bNyI3Nxdmsxljx47F4sWL0bt37wb3uWPHDvz5z3/GsWPHAABDhgzBE088gbFjx7bwaMnf1foeQL3fju+q/c/YFnoY27q2rhDfmMASERERUUjhHFgiIiIiCilMYImIiIgopDCBJSIiIqKQwgSWiIiIiEIKE1giIiIiCilMYImIiIgopDCBJSIiIqKQwgSWiIiIiEIKE1giIiIiCilMYInqWL16NVJTU/HRRx+1d1WIiFoNYxt1JkxgiYiIiCikMIElIiIiopDCBJaIiIiIQgoTWOqyvvrqK/zyl7/E8OHDkZ6ejoULF+LcuXMNrjtp0iSkpqZCURSsW7cOs2bNwvDhw/Hzn/8cAKAoCrZu3YrFixdj2rRpuOGGG3DjjTfirrvuQkZGBmRZDijvo48+QmpqKlavXh2wvKysDAMGDEBqaioyMzMDXsvJyUFqaioee+yxVmwFIupsGNuoK9C1dwWI2sOHH36I3/3ud5AkCaNGjUJcXByys7MxZ84c3HLLLY1u9+KLL+Kjjz7C6NGj8ZOf/AROpxMA4HA48PTTTyMmJgbXX389Bg0ahLKyMhw+fBgvv/wyvv/+e7z22mveckaPHg0AyMrKCih///79UBQFALBv3z7MmTPH+9q+ffsAAGlpaa3TCETU6TC2UVfBBJa6nLy8PCxfvhx6vR5vvfUWJkyYAABwOp1YtmwZ/vnPfza67RdffIFNmzahf//+Acu1Wi3efPNNTJw4EXq93ru8tLQUjzzyCDZt2oRf/OIX3uCenJyMpKQkHDlyBHa7HUajEYAvkPfv3x/79+8P2If6hsAgT0QNYWyjroRTCKjL+cc//gG73Y6ZM2d6AzwA6PV6PPfccwgLC2t020ceeaRegAcAnU6HKVOmBAR4AIiNjcXTTz8NQHyt52/06NFwOBw4cuSId1lWVhZ+8pOfYNq0abh06RJyc3MBiK/x9u/fj4iICAwaNKjZx0xEnR9jG3UlvAJLXc6BAwcAADNmzKj3msViwbhx47B9+/YGt500adIVyz5+/Dh27dqF/Px81NbWQlEUVFdXAwDOnz8fsO7o0aOxefNmZGVlIT09HWVlZcjJycHcuXO9VyKysrLQp08f5OTkwGq1YuLEidBqtc09ZCLqAhjbqCthAktdTlFREQAgKSmpwdcbWw4APXv2bHC5w+HAsmXLsHXr1ka3VYO9Kj09HYDv6zN1jlh6ejpuuOEGGAwG71wxfsVGRFfD2EZdCRNYomZQ53PVtXbtWmzduhUpKSl45plnMHjwYERFRUGv1+PcuXOYPn16vW2Sk5PRo0cP71wxdY5Yeno6jEYjbrjhBu9cMQZ5ImpLjG0UajgHlrqcuLg4AOKGh4bk5+c3u8wvv/wSAPCHP/wBP/3pT9GtWzfvnLELFy40up3/XLGsrCxcf/31iI2NBSAC+qVLl3D+/Hns378f4eHhGDx4cLPrRkRdA2MbdSVMYKnLGTVqFADgs88+q/daWVkZdu/e3ewyKyoqAACJiYn1Xtu2bVuj26lXHb788kvk5OR4v3rzf239+vWwWq0YOXIk54gRUaMY26grYQJLXc7s2bNhMBjw8ccfY8+ePd7lTqcTy5cvh81ma3aZffv2BSCewejvs88+w5YtWxrdTg3qGzduhKIoAV+jqXPFNm7cCMD3fEUiooYwtlFXwgSWupzk5GQsXboUTqcTDz30EObNm4ff/OY3mDZtGr766iv87Gc/a3aZDz/8MLRaLVauXInZs2fj6aefxi9+8QssWrQI8+fPb3S73r17IzExEXa7HZIkBQR5da6Y3W4HgIArGEREdTG2UVfCBJa6pHvuuQdvvvkmhg4diu+++w67du3CgAEDsGHDBvTp06fZ5Y0ePRr/+7//i5tuugkXL17Ev/71L+j1eqxevRr33HPPVbcFEDBHTKUGfbPZzDliRHRVjG3UVUiK+ttuREREREQhgFdgiYiIiCikMIElIiIiopDCBJaIiIiIQgoTWCIiIiIKKUxgiYiIiCikMIElIiIiopDCBJaIiIiIQgoTWCIiIiIKKUxgiYiIiCikMIElIiIiopDCBJaIiIiIQgoTWCIiIiIKKUxgiYiIiCik/H/dZzeF++a6RwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 700x300 with 2 Axes>"
       ]
@@ -649,7 +655,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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E+fv7KyQkRLVq1VJ2draOHDmigwcPqnr16vrxxx+v6vwA3L5Ysw3AGH9/fy1btkzTpk3Tpk2btGHDBlWtWlUPPPCARo4cWeJdJR5//HE1a9ZMc+bM0datW/XDDz+oYsWKqlWrlgYMGFDkFm1NmzbVihUrNHfuXEVGRmrbtm0qLCxUYGCgOnfu7PRIbuniXTr++c9/auXKlVqxYoV2796tuLg4+fr6qmbNmnr00UfVpUsX1a9fv8jYPD099dlnn2n69On6/vvvdfLkSVWpUkW9e/fWH/7wh2LvDDJmzBhVqFBBa9as0dq1a5WXlydJevnllxUQEKDu3btr/vz5mj59uuLi4pSQkKB69epp5MiRGjx4sIYMGXK1fwXFcnNz01tvvaUuXbpo0aJF+vXXX7V//375+fmpbt26evzxxxUSEuL0nocffljNmzfXvHnztHnzZsXExKigoEA1atRQ/fr1NWDAAOPrnQcNGqS6detq9uzZOnjwoPbv36+GDRtq4sSJ6t+/f7G3ZJSkBx98UFOmTNHChQu1detWXbhwQZLUqlUrR2w3btxYK1as0KJFi7R27Vrt27dP27dvV9WqVRUUFKTBgweX6laJAHA5rmwDQCnYr2yHh4cXWe4BAEBJWLMNAAAAGEJsAwAAAIawZhsA4FLjx48v9b5du3ZV165dDY4GAMoXa7YBAC51+Ycrf8vld0oBgFsNsQ0AAAAYwpptAAAAwBBiGwAAADDE5R+QPHv2bJFtVapUUXp6uqu/FG4BzA2UhLmBkjA3UBLmBkpiam4EBARccZ/rcmW7QgUuoKN4zA2UhLmBkjA3UBLmBkpSnnODWQkAAAAYQmwDAAAAhhDbAAAAgCHENgAAAGAIsQ0AAAAYQmwDAAAAhhDbAAAAgCHENgAAAGAIsQ0AAAAYQmwDAAAAhhDbAAAAgCHENgAAAGAIsQ0AAAAYQmwDAAAAhhDbAAAAgCHENgAAAGAIsQ0AAAAYQmwDAAAAhniU9wAAAABwe1gTabn0eN26uLn0eCZwZRsAAAAwhNgGAAAADCG2AQAAAEOIbQAAAMAQYhsAAAAwhNgGAAAADCG2AQAAAEOIbQAAAMAQYhsAAAAwhNgGAAAADCG2AQAAAEOIbQAAAMAQYhsAAAAwhNgGAAAADCG2AQAAAEOIbQAAAMAQYhsAAAAwhNgGAAAADCG2AQAAAEOIbQAAAMAQYhsAAAAwhNgGAAAADCG2AQAAAEOIbQAAAMAQYhsAAAAwhNgGAAAADCG2AQAAAEOIbQAAAMAQYhsAAAAwhNgGAAAADCG2AQAAAEOIbQAAAMAQYhsAAAAwhNgGAAAADCG2AQAAAEOIbQAAAMAQYhsAAAAwhNgGAAAADCG2AQAAAEOIbQAAAMAQYhsAAAAwhNgGAAAADCG2AQAAAEOIbQAAAMAQYhsAAAAwhNgGAAAADCG2AQAAAEM8ynsAAAAAN6NDhw5p9uzZiomJ0YULF1S3bl09+uij6tu3rypUKNv1zNzcXC1atEhr165VQkKC8vPzVaNGDd133316/vnnFRwc7LR/QUGBfvjhB+3Zs0d79uxRXFycsrOz1bNnT02cONGVp4lrRGwDAACU0c6dOzVy5Ejl5OSoadOmql27trZv365//OMf2rlzp6ZMmSI3N7dSHSsnJ0cjRozQrl275OfnpxYtWsjLy0v79u3TihUrtHbtWs2YMUOhoaGO92RlZWnChAmmTg8uRGwDAACUQX5+viZNmqScnByNHj1a/fv3l3QxgEePHq3IyEi1bdtWvXr1KtXxvv76a+3atUtNmzbVRx99pMqVK0u6ePX6gw8+0LJly/Thhx/qk08+cbzHw8NDDz/8sEJDQ9W0aVMdPXpUU6ZMcf3J4pqxZhsAAKAM1q9fr6SkJDVu3NgR2pLk4+OjV199VZK0cOHCUh9v+/btkqT+/fs7QluS3N3dNWzYMEnSnj17nN7j7e2tSZMmqV+/fmrevLkqVqx4tacDw7iyDQDAbS4pKUm9e/dWixYt9P777+uzzz7TunXrlJ6ergYNGmjo0KHq0KGDJCkyMlILFizQoUOH5O3tra5du2rEiBGqVKmS0zGzs7O1ePFiRUZGKiEhQZLUsGFD9e7dW4888kiRMWzfvl1r165VbGysTp06pdzcXAUFBaljx44aOHCg/Pz8nPaPjo7WiBEj1LNnT40ePVqffvqpNm3apLS0NNWrV0/9+/fXo48+auT7tXnzZklSp06dirwWGhqq4OBgHTx4UElJSapTp84Vj+fp6XnFfapUqVL2geKGwJVtAAAg6eLyiJEjR+r7779Xs2bNdPfdd+vAgQMaP368tm3bpoULF2rixIny8fHR/fffr8LCQi1dulTvvPOO03FSU1P14osv6pNPPtGZM2fUokUL3XvvvTp69Kj+/Oc/629/+1uRrz1t2jR9++23qlixolq3bq3WrVvr/PnzmjdvnoYPH66srKxix5yZmamhQ4dq06ZNatWqlcLCwnT06FG9/fbb+uabb4x8n/bv3y9JTmuoLxUSEiJJOnDgQKmOd//990u6eDU8MzPTsb2goEAzZ86UJGO/OMA8rmwDAABJFz/017p1a/373/+Wt7e3JOk///mPpkyZonfffVcZGRmaPXu2mjRpIklKSUnRwIEDtXr1ag0fPtxxx4wpU6bowIED6tevn0aMGCEvLy9J0pkzZ/Taa69p2bJleuCBB9S2bVvH1x4yZIjCwsKcllHk5ubq/fff19dff62FCxdqyJAhRca8YcMGdevWTW+++aZq1aqls2fPKioqSq+//rrmzJmjxx57zGn/l19+WbGxsWX6vkyYMMFp/fXJkyclSYGBgcXuX7NmTUnSiRMnSnX8hx56SFu3btWaNWv0xBNPKCwsTF5eXoqLi1NqaqoGDBigwYMHl2nMuHEQ2wAAQJJUoUIFjRs3zhHaktSzZ09Nnz5dx48f1wsvvOAIbelibPbo0UOLFi1SbGysgoODFR8fr82bN6tp06YaPXq00y3wqlevrvHjx+v555/Xv//9b6fYbteuXZHxeHl5acyYMfr222+1YcOGYmPb19dXr732miPoJSkiIkKNGjUqdilH27ZtVbt27TJ9X+rWrev0Z/tV9suXztjZt5d0Nf5y7u7umjx5smrVqqX58+frxx9/dLwWEhKi++67T+7u7mUaM24cxDYAAJAk1a5dW/Xr13faVqFCBQUFBSktLc2x3OFS9qvZZ86ckST99NNPkqSOHTsWe6/pkJAQ+fj4FPnAnySdOnVKmzZt0tGjR3X+/HkVFhZKurim2b7u+3KhoaHFrmeuV6+eDh48qDNnzjjF9sCBA4s9TnnKyMjQ66+/rr179+qVV15Rp06dVKlSJW3fvl1///vfNXbsWL311lvq1q1beQ8VV4HYBgAAkkpeFmG/0l3c6/bXcnNzJUnJycmSpE8//VSffvppiV8rJyfH6c9ffvmlZsyYofz8/DKN2b5k43I+Pj5O43IlHx8fZWRkKDs7u9jX7dvtY7iSf/zjH4qNjdWYMWPUr18/x/aOHTsqMDBQgwcP1kcffaROnTrJw4N0u9nwNwYAACTpig9hKc1DWizLkiTdc889RZ56WJJdu3Y57i/9yiuvqGXLlqpevbpjaUivXr10+vTpqx7TpebOnasjR46U6T2/+93vdO+99zr+XKtWLWVkZCglJUWNGzcusv+pU6ckSUFBQVc8dkFBgdasWSNJ6ty5c5HXmzRpojp16igxMVGJiYlq0KBBmcaO8kdsAwAAl7Ff/e7YsaMGDBhQqvesX79ekvTSSy8VuS1gdna2Y4mKK2zZsqXMH5Bs2bKlU2w3btxY+/fvV1xcXLFrzfft2ydJuuuuu6547LNnzyovL0/SxfXnxbFvP3fuXJnGjRsDsQ0AAFwmPDxcM2fOVFRUVKlj2x6RxS0JWbduneNquStc+hTGq9WuXTutXLlSP/zwQ5G7hOzbt0+JiYlq1KhRqe6x7e/vL09PT+Xl5SkuLk6tWrVyev38+fM6duyYpNJdKceNh/tsAwAAl2nWrJnCw8O1Y8cOvffeezp//nyRffbv368tW7Y4/mz/UOaKFSuc1mwfPnxYH3/8sflBl9GDDz6oOnXqaP/+/U5Pirxw4YLjHuKXPlnSbuTIkerXr592797t2Obl5aU2bdpIkj788EOn5TI5OTl69913lZ2drbCwMNWoUcPUKcEgrmwDAACXmjx5ssaMGaOvvvpKq1evVuPGjVWjRg2dP39eBw4c0MmTJ9WvXz/Hrf969eqlL7/8Ups2bVLfvn3VpEkTZWRkKDY2VhEREdq9e3ep71l9PXh4eGjy5MkaNWqUPvzwQ61du1ZBQUH69ddfdfr0aXXu3LnYp2QeP35cJ06cKPLBytGjR2v37t2Kj49X3759HY9f37t3r1JSUuTv76/XX3+9yPHeffddx5KV9PR0SRefbnnpLRI///xzV546rgKxDQAAXKpatWqaNWuWvvnmG61Zs0bx8fHauXOnqlWrpjp16qhv375Ot7GrUqWK5syZo+nTpys2NlabNm1S7dq1NWzYMA0YMEBPPvlkOZ5N8cLCwjRnzhzNmjVLMTExOnDggIKDgzVgwAD169evTB/crFu3rubNm6d58+Zpy5Yt2r59uyzLUs2aNdWnTx8NHDiw2CU2hw8fdrpKLklpaWlKS0u71tODC7lZrlwIpYsL/S8XEBBQ7HaAuYGSMDdQEuYGSsLcuPGtiXRpdqpbl9L9UmNqbgQEBFxxH9ZsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgiEd5DwAAAAA3pjWRVnkP4abHlW0AAADAEGIbAAAAMIRlJAAAALcIln3ceLiyDQAAABhCbAMAAACGENsAAACAIcQ2AAAAYAixDQAAABhCbAMAAACGENsAAACAIcQ2AAAAYAixDQAAABhCbAMAAACGENsAAACAIcQ2AAAAYAixDQAAABhCbAMAAACGENsAAACAIcQ2AAAAYAixDQAAABhCbAMAAACGENsAAACAIcQ2AAAAYAixDQAAABhCbAMAAACGENsAAACAIcQ2AAAAYAixDQAAABhCbAMAAACGENsAAACAIcQ2AAAAYAixDQAAABhCbAMAAACGeJT3AAAAAG5XayKt8h4CDOPKNgAAAGAIsQ0AAAAYQmwDAAAAhhDbAAAAgCHENgAAAGAIsQ0AAAAYQmwDAAAAhhDbAAAAgCHENgAAAGAIsQ0AAAAYQmwDAAAAhniU9wAAAMDNa02k5dLjdevi5tLjAeWNK9sAAACAIcQ2AAAAYAixDQAAABhCbAMAAACG8AFJAABwy+IDnChvXNkGAAAADOHKNgAAuGFcy5VoX98cnT/v2ivZwLUitgEAAErJ1ctScOtjGQkAAABgCLENAAAAGEJsAwAAAIawZhsAgNsIa46B64sr2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhxDYAAABgCLENAAAAGEJsAwAAAIYQ2wAAAIAhHuU9AAAAULI1kVZ5DwHANeDKNgAAAGAIsQ0AAAAYQmwDAAAAhhDbAAAAgCHENgAAAGAIsQ0AAAAYQmwDAAAAhhDbAAAAgCE81AYAABfjQTQA7IhtAMBtjzgGYArLSAAAAABDiG0AAADAEGIbAAAAMITYBgAAAAwhtgEAAABDiG0AAADAEGIbAAAAMITYBgAAAAwhtgEAAABDeIIkAOCmwxMfAdwsuLINAAAAGMKVbQCAca68Eu3rm+OyYwGAaVzZBgAAAAwhtgEAAABDWEYCAOXA1R/w69bFzaXHAwC4Ble2AQAAAEO4sg0AKIJb6wGAa3BlGwAAADCEK9sAcAvgSjQA3Ji4sg0AAAAYQmwDAAAAhrCMBMAtiWUVAIAbAVe2AQAAAEO4sg3cJniICgAA1x9XtgEAAABDuLIN4IZw+ZV3X98cnT/PumsAwM2NK9sAAACAIVzZBnBVuNsHAABXxpVtAAAAwBBiGwAAADCEZSTADYplGgAA3Py4sg0AAAAYQmwDAAAAhhDbAAAAgCGs2cZtjXXRAADAJGIbRl0pZsv6lMBuXdyudUgAAADXDbGNmwpXogEAwM2E2L7JEZ8AAAA3Lj4gCQAAABjCle0r4MoxAAAArtYtF9vEMQAAAG4UbpZlGa3Tc+fOKTo6Wq1atZKfn5/JL4WbDHMDJWFuoCTMDZSEuYGSlPfcML5mOzMzU1FRUcrMzDT9pXCTYW6gJMwNlIS5gZIwN1CS8p4bfEASAAAAMITYBgAAAAwxHtuVK1dWRESEKleubPpL4SbD3EBJmBsoCXMDJWFuoCTlPTeMf0ASAAAAuF2xjAQAAAAwhNgGAAAADCG2AQAAAEOIbQAAAMAQYhsAAAAwxONq3rRjxw5NmzZNsbGxys/Pl81m06BBg9SzZ89SHyM3N1czZ87UihUrlJycrCpVqqhTp04aM2aMqlevfjXDwg3gWuaGZVnasGGD1q1bp5iYGCUlJSk/P18NGjRQz5499cILL6hixYrX4Sxggit+blwqPT1dvXr10qlTp9S+fXt9/vnnLh4xrhdXzY0zZ87os88+0/r165WcnCwfHx/dcccdeuyxx/TMM88YGj1McsXcOHnypGbNmqXNmzcrKSlJPj4+atCggfr166dHH31U7u7uBs8ArvbNN98oOjpau3btUnx8vPLy8jR16lT17t27TMcpLCzUggULtGTJEh09elQ+Pj5q166dXnnlFdWrV8+lYy7zrf+2bt2qF198UV5eXnrkkUfk6+ur1atXKzExUa+//roGDx58xWMUFhZq6NCh2rRpk+69917dd999Onr0qNasWaO6detqyZIlqlat2lWfFMrHtc6NnJwchYWFycvLS+Hh4bLZbMrNzdWmTZt05MgRNW/eXPPmzZO3t/d1OiO4iit+blzu1Vdf1bp165SVlUVs38RcNTf27t2rwYMHKyMjQxEREWrUqJGysrJ08OBBeXp6atasWYbPBK7mirmRkJCgp556SmlpaWrfvr1CQkKUmZmpyMhIpaSkqHfv3po6dep1OBu4SufOnZWYmKiAgAD5+PgoMTHxqmJ7woQJWrp0qRo3bqyIiAidOnVK3333nXx9fbV48WLdcccdrhu0VQZ5eXlW165drWbNmll79uxxbM/IyLC6d+9u3X333dbx48eveJxly5ZZNpvNGjt2rFVYWOjY/uWXX1o2m8168803yzIs3ABcMTdyc3OtGTNmWGlpaUW2Dx8+3LLZbNasWbOMjB/muOrnxqVWrVpl2Ww2a/78+ZbNZrMGDx7s6mHjOnDV3Dh37pz14IMPWm3atLH27t1b7NfBzcVVc2PSpEmWzWazvvjiC6ft6enp1oMPPmjZbLYy//xB+frxxx8df2efffaZZbPZrK+++qpMx9iyZYtls9msAQMGWDk5OY7t69evN/JvSpnWbG/dulXHjh1Tr1691KRJE8d2Pz8/vfTSS8rLy9Py5cuveJylS5dKksaOHSs3NzfH9qefflr16tXTt99+q+zs7LIMDeXMFXPD09NTL7/8sqpUqVJk+/DhwyVJP//8s+sHD6Nc9XPDLjU1VZMnT9Zjjz2miIgIE0PGdeKqufHll18qKSlJr776qkJDQ4u87uFxVSsmUY5cNTcSEhIkqcjPCn9/f7Vs2VKSdPbsWReOHKa1a9dOwcHB13QMe4eOHj1aXl5eju0REREKDw/Xpk2blJSUdE1f41Jliu1t27ZJktq3b1/kNfu2K8VQTk6Ofv31V915551Fvllubm5q166dsrKytGvXrrIMDeXMFXPjt9j/sWRt3c3H1XNj0qRJcnd31xtvvOGaAaLcuGpurFy5Um5uburRo4cOHTqkefPmadasWYqMjFRubq5rB43rwlVzw2azSZKioqKctmdkZCg2NlaBgYG66667rnW4uMn89NNP8vHxcfzCdakOHTpI+r856Apl+nX/yJEjkqQGDRoUeS0wMFA+Pj46evTobx7j2LFjKiwsLHEtjH37kSNH1Lp167IMD+XIFXPjt3z11VeSpAceeOCqj4Hy4cq58c0332j16tX6+OOPVaVKFZ07d86VQ8V15oq5kZubq/j4eFWrVk3z5s3TtGnTVFhY6Hi9Xr16+vjjjxUSEuLSscMsV/3cGDJkiNatW6epU6dq48aNTmu2K1WqpOnTp6tSpUquHj5uYFlZWUpJSZHNZiv2Ap59zl1Ls1yuTFe2MzMzJV383zjFqVy58hX/8bO/Xrly5RKPcenXws3BFXOjJFFRUVq8eLEaNWqkp5566qrHiPLhqrlx8uRJvf322+rVq5e6du3q0jGifLhibqSnp6ugoEBpaWmaMWOG/ud//kebN2/Whg0b9Pvf/17Hjx/Xyy+/rJycHJePH+a46udGjRo1tHjxYnXo0EEbN27U7NmztWjRIp07d06PP/54scuOcGsrbYe68mIO99nGDW3Hjh165ZVX5Ofnpw8//NBpbRVuLxMmTJCHhwfLR+DEfhW7oKBA/fv31+DBg1W9enXVqlVLo0eP1kMPPaTExEStWrWqnEeK8nD06FH1799fqampWrBggWJiYhQVFaURI0ZoxowZGjRokAoKCsp7mLjFlSm2r1T7mZmZJf4Wamd/vaQr1/btJf3GgRuTK+bG5Xbu3KkhQ4aoQoUKmj17tho3bnzN48T154q5sXz5cm3YsEETJ07ktqC3EFf+myJdvCXY5ezb+BzQzcVV/6aMHz9eSUlJ+vTTT9W6dWv5+voqKChIw4YN07PPPqvY2Fj997//denYcWMrbYeWtVl+S5li276eurh1LCkpKcrKyip2fdWl6tWrpwoVKjjWY13Ovt2l9zeEca6YG5fauXOnBg8erMLCQn3++ecKCwtz1VBxnblibuzZs0fSxU+Oh4SEOP7r0qWLJGnTpk0KCQnRY4895trBwyhXzA0fHx/VqlVL0sU7TFzOvo1lJDcXV8yNzMxMxcTEqFGjRgoMDCzy+v333y/p4j3acfvw8fFRYGCgjh8/Xuz/1bDPubI0y5WUKbbvu+8+SRf/YbucfZt9n5JUqlRJYWFhOnz4sBITE51esyxLmzdvlo+Pj5o1a1aWoaGcuWJu2NlDu6CgQLNnz9Y999zjuoHiunPF3GjRooX69OlT5D/7U+SCgoLUp08fdevWzcWjh0mu+rnRpk0bSdKBAweKvGbfdq23CsP15Yq5kZeXJ6nkW/ulpqZKEssTb0Ph4eHKyspSTExMkdc2btwoqfTNUipluSl3Xl6e1aVLl9+8yXxCQoJj+8mTJ60DBw5YGRkZTsfhoTa3HlfNjZ07d1qtW7e27r33XuuXX365buOHOa6aG8VJSEjgoTY3MVfNjejoaMtms1mPPPKIlZ6e7th+6tQpq0OHDlZoaKh16NAh8ycEl3HV3OjRo4dls9msJUuWOG1PT0+3HnroIctms1k//vij2ZOBMVd6qM2ZM2esAwcOWGfOnHHafr0famP0ce3jx4/X8uXLizxGs7jHtR87dkyrV69WcHCwli5dyrrMm9C1zo20tDR1795d6enp6tChQ7FXtP38/DRo0KDrdUpwEVf83CjO8ePH1aVLFx7XfhNz1dz4y1/+ojlz5qh27drq1KmT8vPzFRkZqTNnzmjs2LGOB2Ph5uGKuREVFaXf//73ys/PV9u2bdWkSRNlZGRo3bp1Sk1NVY8ePfTRRx+Vx+nhKi1dulTR0dGSpPj4eO3evVstW7Z0LPto1aqV485l06ZN0/Tp0zVy5EiNGjXK6TiXP649JSVFK1eulK+vrxYtWqQ777zTZWMu82O12rRpoy+//FIfffSRVq5cqfz8fNlsNr322muO/6V7JRUqVNAnn3yimTNn6ptvvtEXX3yhqlWrqk+fPhozZgyhfZO61rmRmZmp9PR0SRf/N479f+VcKjg4mNi+Cbni5wZuTa6aG+PHj5fNZtOCBQu0fPlyubm5qUmTJnrrrbdYXnSTcsXciIiI0MKFC/X5558rOjpaP//8s7y8vNSoUSONGDFC/fv3N3wWcLXo6OgiTw+NiYlxWhJSmtsE/+lPf5LNZtOSJUs0d+5c+fj4qFu3bnrllVdUv359l465zFe2AQAAAJQO99kGAAAADCG2AQAAAEOIbQAAAMAQYhsAAAAwhNgGAAAADCG2AQAAAEOIbQAAAMAQYhsAAAAwhNgGAAAADCG2AQAAAEOIbQAAAMAQYhsAAAAw5P8B7RGQo4v5ds0AAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 720x480 with 1 Axes>"
       ]
@@ -1031,7 +1037,7 @@
        "  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-932e9c34-70d8-4bec-afe3-4fd40757ebec' class='xr-array-in' type='checkbox' checked><label for='section-932e9c34-70d8-4bec-afe3-4fd40757ebec' 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-36edaa52-6c91-48db-9343-cae0bf14d801' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-36edaa52-6c91-48db-9343-cae0bf14d801' 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-957837a7-c301-42ba-a0c7-bcdb14f4beb5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-957837a7-c301-42ba-a0c7-bcdb14f4beb5' 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' 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-a59930f5-ab57-45b8-8d34-0f4b62ec7a87' class='xr-array-in' type='checkbox' checked><label for='section-a59930f5-ab57-45b8-8d34-0f4b62ec7a87' 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-6b418a36-25ff-4bd2-9112-ba718faedbbd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6b418a36-25ff-4bd2-9112-ba718faedbbd' 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-ac660851-ff62-44f2-9bc0-a60bcd9bf68e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ac660851-ff62-44f2-9bc0-a60bcd9bf68e' 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",
@@ -1070,7 +1076,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 600x400 with 1 Axes>"
       ]
@@ -1105,12 +1111,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "coords = {\"step\": [\"BinaryMetropolis\", \"Metropolis\"]}\n",
+    "coords = {\"step\": [\"BinaryMetropolis\", \"Metropolis\"], \"obs\": [\"mu1\"]}\n",
     "dims = {\"accept\": [\"step\"]}\n",
     "\n",
     "with pm.Model(coords=coords) as model:\n",
-    "    mu1 = pm.Bernoulli(\"mu1\", p=0.8, dims=\"step\")\n",
-    "    mu2 = pm.Normal(\"mu2\", mu=0, sigma=1, dims=\"step\")"
+    "    mu1 = pm.Bernoulli(\"mu1\", p=0.8)\n",
+    "    mu2 = pm.Normal(\"mu2\", mu=0, sigma=1, dims=\"obs\")"
    ]
   },
   {
@@ -1146,7 +1152,7 @@
        "            }\n",
        "        </style>\n",
        "      <progress value='22000' class='' max='22000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [22000/22000 00:05<00:00 Sampling 2 chains, 0 divergences]\n",
+       "      100.00% [22000/22000 00:06<00:00 Sampling 2 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -1161,7 +1167,7 @@
      "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",
+      "Sampling 2 chains for 1_000 tune and 10_000 draw iterations (2_000 + 20_000 draws total) took 13 seconds.\n",
       "The number of effective samples is smaller than 25% for some parameters.\n"
      ]
     }
@@ -1189,7 +1195,7 @@
     {
      "data": {
       "text/plain": [
-       "['accept', 'p_jump', 'scaling', 'accepted']"
+       "['p_jump', 'accepted', 'scaling', 'accept']"
       ]
      },
      "execution_count": 12,
@@ -1215,7 +1221,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1656x552 with 2 Axes>"
       ]
@@ -1238,7 +1244,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We notice that accept variable takes really large values, as outliers in accept 1"
+    "We notice that `accept` sometimes takes really high values (jumps from regions of low probability to regions of much higher probability). "
    ]
   },
   {
@@ -1248,9 +1254,373 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
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+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;accept&#x27; (chain: 2, accept_dim_0: 2)&gt;\n",
+       "array([[   3.75      , 1601.21960006],\n",
+       "       [   3.75      ,  182.75018508]])\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-e20481b6-6a33-4b94-ade8-fd3ccc0e80e9' class='xr-array-in' type='checkbox' checked><label for='section-e20481b6-6a33-4b94-ade8-fd3ccc0e80e9' 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 1.601e+03 3.75 182.8</span></div><div class='xr-array-data'><pre>array([[   3.75      , 1601.21960006],\n",
+       "       [   3.75      ,  182.75018508]])</pre></div></div></li><li class='xr-section-item'><input id='section-2862812f-e976-4ade-a97d-109576d7c785' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2862812f-e976-4ade-a97d-109576d7c785' 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-fc9ef482-5f51-4d41-bdd4-f3fd2fa6a90a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc9ef482-5f51-4d41-bdd4-f3fd2fa6a90a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f3240a1-3621-4245-96ad-4ddb036f75e9' class='xr-var-data-in' type='checkbox'><label for='data-4f3240a1-3621-4245-96ad-4ddb036f75e9' 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-edb02fd1-aace-4c5a-9267-b125cd4f45fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-edb02fd1-aace-4c5a-9267-b125cd4f45fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a27318a-2393-4a22-af70-b2caa9047c9d' class='xr-var-data-in' type='checkbox'><label for='data-6a27318a-2393-4a22-af70-b2caa9047c9d' 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-a716de26-deb6-416d-baa2-7b7586f41732' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a716de26-deb6-416d-baa2-7b7586f41732' 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": [
-       "array([[  15.9375    , 1230.85829008],\n",
-       "       [  15.9375    ,  161.88043405]])"
+       "<xarray.DataArray 'accept' (chain: 2, accept_dim_0: 2)>\n",
+       "array([[   3.75      , 1601.21960006],\n",
+       "       [   3.75      ,  182.75018508]])\n",
+       "Coordinates:\n",
+       "  * chain         (chain) int64 0 1\n",
+       "  * accept_dim_0  (accept_dim_0) int64 0 1"
       ]
      },
      "execution_count": 14,
@@ -1259,8 +1629,8 @@
     }
    ],
    "source": [
-    "# The outliers are the values reaching upto ~1200\n",
-    "np.ptp(trace.sample_stats[\"accept\"].values, axis=1)"
+    "# Range of accept values\n",
+    "trace.sample_stats[\"accept\"].max(\"draw\") - trace.sample_stats[\"accept\"].min(\"draw\")"
    ]
   },
   {
@@ -1270,7 +1640,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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       "text/plain": [
        "<Figure size 1656x552 with 2 Axes>"
       ]
@@ -1289,143 +1659,6 @@
     ");"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/almostmeenal/anaconda3/envs/p/lib/python3.9/site-packages/arviz/stats/diagnostics.py:561: RuntimeWarning: invalid value encountered in double_scalars\n",
-      "  (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples)\n",
-      "/Users/almostmeenal/anaconda3/envs/p/lib/python3.9/site-packages/arviz/stats/diagnostics.py:561: RuntimeWarning: invalid value encountered in double_scalars\n",
-      "  (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>mean</th>\n",
-       "      <th>sd</th>\n",
-       "      <th>hdi_3%</th>\n",
-       "      <th>hdi_97%</th>\n",
-       "      <th>mcse_mean</th>\n",
-       "      <th>mcse_sd</th>\n",
-       "      <th>ess_bulk</th>\n",
-       "      <th>ess_tail</th>\n",
-       "      <th>r_hat</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>accept[0]</th>\n",
-       "      <td>1.002</td>\n",
-       "      <td>1.875</td>\n",
-       "      <td>0.062</td>\n",
-       "      <td>4.000</td>\n",
-       "      <td>0.012</td>\n",
-       "      <td>0.009</td>\n",
-       "      <td>14811.0</td>\n",
-       "      <td>13064.0</td>\n",
-       "      <td>1.000000e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>accept[1]</th>\n",
-       "      <td>0.971</td>\n",
-       "      <td>9.611</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>2.501</td>\n",
-       "      <td>0.068</td>\n",
-       "      <td>0.048</td>\n",
-       "      <td>20212.0</td>\n",
-       "      <td>15849.0</td>\n",
-       "      <td>1.000000e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p_jump</th>\n",
-       "      <td>0.500</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>0.500</td>\n",
-       "      <td>0.500</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>20000.0</td>\n",
-       "      <td>20000.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>scaling</th>\n",
-       "      <td>1.398</td>\n",
-       "      <td>0.067</td>\n",
-       "      <td>1.331</td>\n",
-       "      <td>1.464</td>\n",
-       "      <td>0.047</td>\n",
-       "      <td>0.040</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>9.919727e+15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>accepted</th>\n",
-       "      <td>0.428</td>\n",
-       "      <td>0.495</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.003</td>\n",
-       "      <td>19009.0</td>\n",
-       "      <td>19009.0</td>\n",
-       "      <td>1.000000e+00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
-       "accept[0]  1.002  1.875   0.062    4.000      0.012    0.009   14811.0   \n",
-       "accept[1]  0.971  9.611   0.000    2.501      0.068    0.048   20212.0   \n",
-       "p_jump     0.500  0.000   0.500    0.500      0.000    0.000   20000.0   \n",
-       "scaling    1.398  0.067   1.331    1.464      0.047    0.040       2.0   \n",
-       "accepted   0.428  0.495   0.000    1.000      0.004    0.003   19009.0   \n",
-       "\n",
-       "           ess_tail         r_hat  \n",
-       "accept[0]   13064.0  1.000000e+00  \n",
-       "accept[1]   15849.0  1.000000e+00  \n",
-       "p_jump      20000.0           NaN  \n",
-       "scaling         2.0  9.919727e+15  \n",
-       "accepted    19009.0  1.000000e+00  "
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pm.summary(trace.sample_stats)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 17,
@@ -1435,18 +1668,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: Sun Apr 04 2021\n",
+      "The watermark extension is already loaded. To reload it, use:\n",
+      "  %reload_ext watermark\n",
+      "Last updated: Tue Apr 06 2021\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.9.2\n",
       "IPython version      : 7.21.0\n",
       "\n",
-      "pymc3     : 3.11.2\n",
+      "matplotlib: 3.3.4\n",
       "pandas    : 1.2.3\n",
+      "arviz     : 0.11.2\n",
+      "pymc3     : 3.11.2\n",
       "seaborn   : 0.11.1\n",
       "numpy     : 1.20.1\n",
-      "arviz     : 0.11.2\n",
-      "matplotlib: 3.3.4\n",
       "\n",
       "Watermark: 2.2.0\n",
       "\n"
@@ -1457,6 +1692,13 @@
     "%load_ext watermark\n",
     "%watermark -n -u -v -iv -w"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Updated by Meenal Jhajharia"
+   ]
   }
  ],
  "metadata": {

From 2c1bfbccd380cff6a9e2a6181204e1c56a616010 Mon Sep 17 00:00:00 2001
From: almostmeenal <meenaljhajharia2304@gmail.com>
Date: Tue, 6 Apr 2021 01:02:35 +0530
Subject: [PATCH 7/8] remove tune

---
 examples/diagnostics_and_criticism/sampler-stats.ipynb | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/examples/diagnostics_and_criticism/sampler-stats.ipynb b/examples/diagnostics_and_criticism/sampler-stats.ipynb
index 589901f60..e0bcf5e38 100644
--- a/examples/diagnostics_and_criticism/sampler-stats.ipynb
+++ b/examples/diagnostics_and_criticism/sampler-stats.ipynb
@@ -614,9 +614,7 @@
     "\n",
     "- `step_size_bar`: The current best known step-size. After the tuning samples, the step size is set to this value. This should converge during tuning.\n",
     "\n",
-    "- `tree_depth`: The number of tree doublings in the balanced binary tree.\n",
-    "\n",
-    "- `tune`: This is True, if step size adaptation was turned on when this sample was generated."
+    "- `tree_depth`: The number of tree doublings in the balanced binary tree."
    ]
   },
   {

From 3c787860476ad74449472e817369ca864d59580b Mon Sep 17 00:00:00 2001
From: almostmeenal <meenaljhajharia2304@gmail.com>
Date: Tue, 6 Apr 2021 21:55:08 +0530
Subject: [PATCH 8/8] rerun cells

---
 .../sampler-stats.ipynb                       | 158 +++++++++---------
 1 file changed, 78 insertions(+), 80 deletions(-)

diff --git a/examples/diagnostics_and_criticism/sampler-stats.ipynb b/examples/diagnostics_and_criticism/sampler-stats.ipynb
index e0bcf5e38..db25574a2 100644
--- a/examples/diagnostics_and_criticism/sampler-stats.ipynb
+++ b/examples/diagnostics_and_criticism/sampler-stats.ipynb
@@ -112,7 +112,7 @@
      "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 10 seconds.\n"
+      "Sampling 2 chains for 1_000 tune and 2_000 draw iterations (2_000 + 4_000 draws total) took 11 seconds.\n"
      ]
     }
    ],
@@ -494,57 +494,57 @@
        "  * chain               (chain) int64 0 1\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 0.0003295 0.0005849 ... 0.0003195\n",
-       "    max_energy_error    (chain, draw) float64 0.6713 0.2162 ... -0.5191 0.3634\n",
-       "    step_size_bar       (chain, draw) float64 0.9636 0.9636 ... 0.9695 0.9695\n",
-       "    process_time_diff   (chain, draw) float64 0.00033 0.000585 ... 0.00032\n",
-       "    tree_depth          (chain, draw) int64 2 3 2 3 2 2 2 2 ... 2 2 2 2 2 2 2 2\n",
-       "    n_steps             (chain, draw) float64 3.0 7.0 3.0 7.0 ... 3.0 3.0 3.0\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",
-       "    lp                  (chain, draw) float64 -12.46 -12.5 ... -12.19 -13.3\n",
-       "    energy              (chain, draw) float64 17.48 15.32 14.85 ... 17.72 15.27\n",
-       "    perf_counter_start  (chain, draw) float64 6.43 6.43 6.431 ... 9.019 9.019\n",
-       "    energy_error        (chain, draw) float64 -0.381 -0.0468 ... -0.4983 0.3634\n",
+       "    energy              (chain, draw) float64 20.89 20.49 18.01 ... 17.55 16.76\n",
        "    diverging           (chain, draw) bool False False False ... False False\n",
-       "    step_size           (chain, draw) float64 0.6989 0.6989 ... 0.6632 0.6632\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",
        "Attributes:\n",
-       "    created_at:                 2021-04-05T19:08:29.364867\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:              10.044129133224487\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-e9faae16-6b4c-42da-a536-6b1f2a507272' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e9faae16-6b4c-42da-a536-6b1f2a507272' 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-5455956f-5d74-48fc-9511-271f15f6fdfd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5455956f-5d74-48fc-9511-271f15f6fdfd' 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-fed4bec5-6d92-4cd3-b7aa-15325401504f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fed4bec5-6d92-4cd3-b7aa-15325401504f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b78f5d4f-13c7-4a28-ac01-1da431116ffb' class='xr-var-data-in' type='checkbox'><label for='data-b78f5d4f-13c7-4a28-ac01-1da431116ffb' 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-63bd9520-c5f2-45b7-84ae-ad311fcbe209' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-63bd9520-c5f2-45b7-84ae-ad311fcbe209' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5375b2fb-d246-471f-9053-79f725d46833' class='xr-var-data-in' type='checkbox'><label for='data-5375b2fb-d246-471f-9053-79f725d46833' 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-525aa893-adcb-41f9-a40c-cccf82ea146e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-525aa893-adcb-41f9-a40c-cccf82ea146e' 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'>0.0003295 0.0005849 ... 0.0003195</div><input id='attrs-9ac163b3-e4b8-4d3a-a803-e1af0e0c9219' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ac163b3-e4b8-4d3a-a803-e1af0e0c9219' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f43f788-4014-43c4-ad52-7502a8858eab' class='xr-var-data-in' type='checkbox'><label for='data-2f43f788-4014-43c4-ad52-7502a8858eab' 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([[0.00032947, 0.00058491, 0.00032905, ..., 0.0006286 , 0.00051097,\n",
-       "        0.00058371],\n",
-       "       [0.0003442 , 0.00035389, 0.00036091, ..., 0.00034443, 0.00032249,\n",
-       "        0.00031953]])</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'>0.6713 0.2162 ... -0.5191 0.3634</div><input id='attrs-d1c0d802-ae07-452f-a516-b024dddea8ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d1c0d802-ae07-452f-a516-b024dddea8ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d13d26d6-ab5d-4159-ab1a-144487ede1c1' class='xr-var-data-in' type='checkbox'><label for='data-d13d26d6-ab5d-4159-ab1a-144487ede1c1' 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([[ 0.67131179,  0.21617272, -0.20972849, ..., -0.64167087,\n",
-       "         2.60917638,  1.03776088],\n",
-       "       [-1.25523578,  1.13951164, -0.59184443, ..., -0.74449021,\n",
-       "        -0.5191076 ,  0.3633549 ]])</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'>0.9636 0.9636 ... 0.9695 0.9695</div><input id='attrs-df279000-502a-4d86-a14e-b2fe71bd305f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df279000-502a-4d86-a14e-b2fe71bd305f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9c59f5e-965c-4636-afff-eeca4fae2225' class='xr-var-data-in' type='checkbox'><label for='data-c9c59f5e-965c-4636-afff-eeca4fae2225' 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([[0.96357598, 0.96357598, 0.96357598, ..., 0.96357598, 0.96357598,\n",
-       "        0.96357598],\n",
-       "       [0.9695334 , 0.9695334 , 0.9695334 , ..., 0.9695334 , 0.9695334 ,\n",
-       "        0.9695334 ]])</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'>0.00033 0.000585 ... 0.00032</div><input id='attrs-1007968d-cb97-47a8-8940-1ec4702eb5af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1007968d-cb97-47a8-8940-1ec4702eb5af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-414b9e75-642b-468f-b616-79f6607772e6' class='xr-var-data-in' type='checkbox'><label for='data-414b9e75-642b-468f-b616-79f6607772e6' 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([[0.00033 , 0.000585, 0.00033 , ..., 0.000604, 0.000511, 0.000564],\n",
-       "       [0.000344, 0.000355, 0.000362, ..., 0.000346, 0.000322, 0.00032 ]])</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 3 2 3 2 2 2 2 ... 2 2 2 2 2 2 2 2</div><input id='attrs-01a31584-6f23-4b00-b06a-5d28ec982a33' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01a31584-6f23-4b00-b06a-5d28ec982a33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc32a5ad-aba7-4a0d-b539-5e66cebda7b9' class='xr-var-data-in' type='checkbox'><label for='data-cc32a5ad-aba7-4a0d-b539-5e66cebda7b9' 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, 3, 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>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 7.0 3.0 7.0 ... 3.0 3.0 3.0 3.0</div><input id='attrs-7fb9148d-f3fe-4eff-b429-b2f24ef468d3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7fb9148d-f3fe-4eff-b429-b2f24ef468d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf2576df-2233-4809-be56-5d8db307f284' class='xr-var-data-in' type='checkbox'><label for='data-bf2576df-2233-4809-be56-5d8db307f284' 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., 7., 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>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'>0.9151 0.9221 ... 0.9509 0.828</div><input id='attrs-5489e7c9-122b-4f08-8a87-58b0dc6f6b89' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5489e7c9-122b-4f08-8a87-58b0dc6f6b89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c632b64-973d-4de1-9ed0-76562ec6c848' class='xr-var-data-in' type='checkbox'><label for='data-2c632b64-973d-4de1-9ed0-76562ec6c848' 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([[0.91511546, 0.92205993, 0.96748532, ..., 1.        , 0.12458339,\n",
-       "        0.7968372 ],\n",
-       "       [0.98645798, 0.9263687 , 0.85228559, ..., 1.        , 0.95089605,\n",
-       "        0.82799622]])</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'>-12.46 -12.5 ... -12.19 -13.3</div><input id='attrs-66265707-8b32-44a7-a86d-b0c6be3d62d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66265707-8b32-44a7-a86d-b0c6be3d62d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3363c32-c077-4226-9474-b32bb776dddd' class='xr-var-data-in' type='checkbox'><label for='data-a3363c32-c077-4226-9474-b32bb776dddd' 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([[-12.46354345, -12.49950721, -13.11316909, ..., -10.37706339,\n",
-       "        -10.37706339, -10.37706339],\n",
-       "       [-15.11546609, -13.39506368, -15.2192683 , ..., -15.07686574,\n",
-       "        -12.18672473, -13.29738336]])</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'>17.48 15.32 14.85 ... 17.72 15.27</div><input id='attrs-99862ba6-2a88-4dc7-ab48-c3e4f26f162c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99862ba6-2a88-4dc7-ab48-c3e4f26f162c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f03075f-2cbe-468f-971b-c2011a8d20d2' class='xr-var-data-in' type='checkbox'><label for='data-0f03075f-2cbe-468f-971b-c2011a8d20d2' 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([[17.47700101, 15.32435336, 14.85443441, ..., 14.09352881,\n",
-       "        22.56869736, 14.20870246],\n",
-       "       [25.98916838, 20.33923926, 17.80078921, ..., 17.61550446,\n",
-       "        17.71500947, 15.26897105]])</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'>6.43 6.43 6.431 ... 9.019 9.019</div><input id='attrs-e3fa67e7-0bda-46c3-8cc7-197f1faaccc8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e3fa67e7-0bda-46c3-8cc7-197f1faaccc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80308890-8348-4649-a950-94d582be2691' class='xr-var-data-in' type='checkbox'><label for='data-80308890-8348-4649-a950-94d582be2691' 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([[6.4298268 , 6.43025576, 6.43094001, ..., 7.60678498, 7.60802156,\n",
-       "        7.60869476],\n",
-       "       [8.062209  , 8.06265274, 8.0631078 , ..., 9.0181419 , 9.01860942,\n",
-       "        9.01902456]])</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'>-0.381 -0.0468 ... -0.4983 0.3634</div><input id='attrs-db864825-f0a9-47e6-9614-e14b48873b05' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-db864825-f0a9-47e6-9614-e14b48873b05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdabdc1b-0441-489a-8197-bbe0d24bd10b' class='xr-var-data-in' type='checkbox'><label for='data-fdabdc1b-0441-489a-8197-bbe0d24bd10b' 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([[-0.38100043, -0.04679905,  0.12205405, ..., -0.64167087,\n",
-       "         0.        ,  0.        ],\n",
-       "       [-1.25523578, -0.20823156,  0.37421834, ..., -0.08588938,\n",
-       "        -0.49834727,  0.3633549 ]])</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-69b4a0cd-488a-43ca-9b86-671fec531184' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-69b4a0cd-488a-43ca-9b86-671fec531184' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed6ea44f-dd51-4c72-beac-0d9074b8a5c5' class='xr-var-data-in' type='checkbox'><label for='data-ed6ea44f-dd51-4c72-beac-0d9074b8a5c5' 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</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6989 0.6989 ... 0.6632 0.6632</div><input id='attrs-5a61100e-267e-4d35-811e-72c8837d4c04' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a61100e-267e-4d35-811e-72c8837d4c04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5649936c-1911-47e8-850f-84b53ce231aa' class='xr-var-data-in' type='checkbox'><label for='data-5649936c-1911-47e8-850f-84b53ce231aa' 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([[0.69891308, 0.69891308, 0.69891308, ..., 0.69891308, 0.69891308,\n",
-       "        0.69891308],\n",
-       "       [0.66322222, 0.66322222, 0.66322222, ..., 0.66322222, 0.66322222,\n",
-       "        0.66322222]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b732c2f1-0662-42fa-bbf2-5edeb2d6b795' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b732c2f1-0662-42fa-bbf2-5edeb2d6b795' 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>2021-04-05T19:08:29.364867</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>10.044129133224487</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
+       "    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>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -553,25 +553,25 @@
        "  * chain               (chain) int64 0 1\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 0.0003295 0.0005849 ... 0.0003195\n",
-       "    max_energy_error    (chain, draw) float64 0.6713 0.2162 ... -0.5191 0.3634\n",
-       "    step_size_bar       (chain, draw) float64 0.9636 0.9636 ... 0.9695 0.9695\n",
-       "    process_time_diff   (chain, draw) float64 0.00033 0.000585 ... 0.00032\n",
-       "    tree_depth          (chain, draw) int64 2 3 2 3 2 2 2 2 ... 2 2 2 2 2 2 2 2\n",
-       "    n_steps             (chain, draw) float64 3.0 7.0 3.0 7.0 ... 3.0 3.0 3.0\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",
-       "    lp                  (chain, draw) float64 -12.46 -12.5 ... -12.19 -13.3\n",
-       "    energy              (chain, draw) float64 17.48 15.32 14.85 ... 17.72 15.27\n",
-       "    perf_counter_start  (chain, draw) float64 6.43 6.43 6.431 ... 9.019 9.019\n",
-       "    energy_error        (chain, draw) float64 -0.381 -0.0468 ... -0.4983 0.3634\n",
+       "    energy              (chain, draw) float64 20.89 20.49 18.01 ... 17.55 16.76\n",
        "    diverging           (chain, draw) bool False False False ... False False\n",
-       "    step_size           (chain, draw) float64 0.6989 0.6989 ... 0.6632 0.6632\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",
        "Attributes:\n",
-       "    created_at:                 2021-04-05T19:08:29.364867\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:              10.044129133224487\n",
+       "    sampling_time:              10.891047954559326\n",
        "    tuning_steps:               1000"
       ]
      },
@@ -633,7 +633,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 700x300 with 2 Axes>"
       ]
@@ -653,7 +653,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x480 with 1 Axes>"
       ]
@@ -1035,7 +1035,7 @@
        "  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-a59930f5-ab57-45b8-8d34-0f4b62ec7a87' class='xr-array-in' type='checkbox' checked><label for='section-a59930f5-ab57-45b8-8d34-0f4b62ec7a87' 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-6b418a36-25ff-4bd2-9112-ba718faedbbd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6b418a36-25ff-4bd2-9112-ba718faedbbd' 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-ac660851-ff62-44f2-9bc0-a60bcd9bf68e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ac660851-ff62-44f2-9bc0-a60bcd9bf68e' 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' 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>"
       ],
       "text/plain": [
        "<xarray.DataArray 'diverging' ()>\n",
@@ -1074,7 +1074,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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s+zzDQr+PegUyEYkRCmMi0mkrVgZrt7qr73hD+kkGf2kn79hQP1Z4zBKKSQayjWW7a3jb7+ApkIlIDFAYE5FO+8erlnDUIg+80IdxTKdGxZrbtw/qaltuSzaQaSwbPIfPAnqLE5Hop3cqEemUw4ctS7/ofpPX9JMM2WcYAhVdH76ywK49rbenGkjFssw1bHBVzS8i0U1hTEQ6ZdGS8CzW3X+qg5MGbnU3DmKhqhKKi1vflGGC7cre8zscDl/jcxGRsFMYE5GQua7ltTe6vw6lkxoMY15322I02LsPmq1V3SQHqLKGt1TQLyJRTGFMREL25Yq2R6E6K2ecIbkfBCq6fywAN9D2BQWm2RWWn6p+TESilN6dRCRki9+0+HzdP06/cxyMMe132++CwkKob+NCgCQDacayyjVsU/2YiEQhhTERCUl5heWTT9ueDuyMpGzIGefghrkzqwUOHGj7tnQgALwXcNQQVkSijsKYiITk/Q8gEIaRrJyzHJIyIVDV/WO1YINTqMe2uoDgdGUfoEjTlSIShfSuJCIhWfKmDctVlH0nOkDDpY7hZuBAO81oHQPpxrLONez2NF0pItFDYUxEjmvfPsvGTXR7+aOkLMg+04R9irKJhZISqG1jdAwgDai3ho/8DgFNV4pIlFAYE5Hj+t93CUvH/ewxPTRF2Zxpf6km07Bc0n5rWK9i/pA8/fTTTJ06lZUrV/bI8VeuXMnUqVN5+umne+T4IrEgKdInICLRzVrLm291v7cYQM5ZhuAUZQ8OSzWMjg0dCqmprW9ONmCsZanrcIbPJUOZLGF98sknLFu2jPz8fAoKCqitrWXevHncdtttnTrOokWLeOihh1psM8aQmZnJiBEjuPLKK7niiita3W/q1KkdHve5557jjDPOAOCOO+4gLy8PgF//+tdMmzatzfvccsstbNy4EYAnnniCSZMmtXi8U045hRdffDH0J9cDamtr+cc//kF+fj75+fns3r0bay2vvvoqw4YN69SxtmzZwnvvvcfmzZvJz8+ntLSUCRMm8Pvf/77N/R988EGWLFnS4TFvv/12brnllk6dR3cpjIlIh/K3wP52rlLsDCcF+oxzcGt7YX7QwOHDcNJJbd+cDZR6hpUBhwuS1Z4/ksaOHcvf//53+vbt2+uP/cILL5CXl0dmZiYDBw5k79693Tre5MmTOfvsswFwXZfDhw/z8ccf89BDD7Fjxw7uueeeVvfp06cP1113XZvHGzBgQKttPp+PhQsXthnGtm/fzsaNG/H5fLjdvey5BxUXF/P4448DkJubS3Z2NuXl5V061kcffcSzzz5LcnIyJ598MqWlpR3uP2PGDIYOHdrmbS+88AI1NTWce+65XTqX7lAYE5EOvftesLdYd9/bM0cakrLBXxae8+qQhaKi4OhYUhvvco6BFCx5ruHsJMjR6FjEpKWlceqpp0bksefPn0///v056aSTePfdd/l//+//det4U6ZM4eabb26xbf/+/dx00028/PLL3HbbbaSlpbW4vW/fvp0aifvqV7/KZ599RklJCf369Wtx2xtvvIHjOEydOpXPPvus60+kh/Xt25ff/va3nHnmmfTp04f77ruPL774okvHmjlzJhdccAEjR46krKyMyy+/vMP9Z8yYwYwZM1pt37x5MwsWLGDEiBGMHTu2S+fSHQpjItIuz7O88273gxhA9pkOJgmsv/vHCoUFjhyB3Ny2b88ESqxhRcDhogQdHcvLy+Nvf/sb69ato7Kykn79+jF69Gi++c1vMn78+Fb7v/322zz//PPs3r2brKwsZs6cyZ133tkiYPj9fv75z3/y2WefsWPHDkpKSsjKyuIrX/kKt9xyC6NGjWpxzJUrV3LXXXe1mh6cM2cOEByt+MMf/sD7779PWVkZJ598MvPmzeOiiy7q9vNv6zmG27Bhwzj55JPJz8+nurq6VRjrrCuvvJJPP/2UN998kxtvvLFpeyAQ4O2332bKlCkMHjy4u6fdozIyMsI2+jR8+PCwHOeNN94A4KqrrgrL8TpLBfwi0q6Nm6AoDMsfAfQZb3otiAFg4XBh+1eAGgOpxrLWNZQkYBZ78cUXufPOO1m+fDlTpkzhxhtvZPLkyWzdupUPPvig1f6vvPIKv/jFLzjttNOYO3cu2dnZvPTSSzz88MMt9isvL+c3v/kN9fX1nHfeeXzjG99g4sSJfP7559x+++1N9UyhcF2Xe++9l2XLlnHhhRdy6aWXsm/fPh544AGWLVvW7a9Bbzhw4AC7d+9m8ODB9O/fv9vHGzduHKeddhqLFy9usf2TTz6hpKSEK6+8stuPkWhqa2v53//9X1JSUrj00ksjcg4aGRORdr3/QXimKFNzIW2wwa0Oz3mFyg1AaSkcM5vTJAMowbDKdZjpJE4iKygo4Le//S0DBw7kqaeealE0ba3lyJEjre7z5Zdf8swzz3DKKacAwQ+wm2++mXfffZd77rmHQYMGAZCdnc1rr73WanRm+/bt3Hrrrfz+979vqhc6nsLCQkaPHs2TTz5JcnIyABdffDH33HMPf/vb3yJS29OR5cuXU1dXBxytGfv0009JS0trdwq0tLS0zStJx40bx1e/+tU273PFFVfw+OOPs3HjRsaMGQPAwoUL6dOnD9OnT2fFihVheT5///vfqagIfQHZGTNmNF1wEEs++OADKisrmTVrFn369InIOSiMiUibPM/y7vthmqIc5eCk9VK9WHMGCo+0H8aMgVQbbAQ7OQn6JEjt2D//+U88z2P+/Pmtrl4zxjQFq+ZuuOGGpiAGwVqvWbNmsWDBAjZv3tx0n5SUlDanyYYPH87EiRNZtmwZgUCApLaK+dpw3333NQUxgHPOOYfc3NxOjbD1lhUrVrQKQj6fj7lz5zJixIg271NWVsaCBQtabb/hhhvaDWOzZ8/m97//PQsXLmTMmDEUFhaybNkyrrvuuhZfq+76+9//zsH2+sS0YejQoTEZxiI9RQkKYyLSjo2bgssLhUP2aAPWdL9rbGdZqKoMNoFtr1Qng2Dt2OqAw4wEqR1rDDJTpkwJ+T7H1noBTaGrsrKyxfYtW7bw/PPPs2bNGoqKiggcs45WaWkpAwcOPO5jZmdnt9nqYPDgwaxfvz7kc+8td955Z1MBv+d5HDlyhI8//pjHHnuMzz//nGeffZasrKwW9+lKq4n+/ftz/vnn8+6773LfffexZMkSXNdts31Gd7z22mthPV402rNnD6tXr2bYsGFMnjw5YuehmjERadPHnwanKLvLJAeL93ulpUWbJxAs5G/3ZgMpDbVjVQnSlb+yshJjTEiBqFFmZmarbb6GH5DmbRTWrl3Lrbfeyocffsjpp5/O9ddfz7x585g3bx6nn346APX19V1+zMbH9cLR+K4HOY7D4MGDue6667jxxhvZt28fL7/8ctiOf8UVV1BRUcEHH3zAokWLOPPMM5u+vhK6RYsWYa3liiuuwIRjvbcu0siYiLRireWDD8IzRZl5miEpKwJTlI0a2lwMG9b+KgIZQKk1bHANU5LiP5FlZ2c31YaF+8q7Z555hvr6ev7whz+0ulpxw4YNFBQUhPXxYkFjq4RNmzaF7ZjnnXceAwcO5IknnqCwsJAf/OAHYTt2o3ivGXNdlyVLluDz+cI+qthZCmMi0srOXe0vuN1ZWacbTJLB+iMXcjwv2JW/jR6aQLDvmA9LnuswweeSHOe1Y2PGjGHTpk0sX7487B9C+/btIycnp1UQq62tJT8/P6yPFSsaA004R/N8Ph+zZ8/mL3/5C6mpqVx88cVhO3ajeK8ZW7p0KYWFhZx//vkRbweiMCYirXzyaXAUKRyfHdljnJ5d/ihER4raD2NwtO/YFs8w1hf58+1Jc+fO5bXXXuOpp55i0qRJLTqSN46YtVXEH4rc3Fx2797N9u3bm3pAua7LY489RklJSVjOP5bU1dXxj3/8A4AJEyaE9djf/OY3GTduHDk5OWRnZ4f12BBdNWMHDx6ktraW3Nzcbvdqa7Rw4UKAqGgHojAmIq188FF41qL0ZULmqb3f0qIt1VUdF/InGcCDvIDDGMclguUjPW7kyJHcd999PProo9x4441Mnz6d3NxciouLycvL4/zzz+ff//3fu3Ts66+/nmXLljF//nxmzpxJSkoKq1atorCwkIkTJ7Jq1aowP5uu++ijj/joo4+AYD8wgI8//rjp36eeemqrjvodad7awlpLUVERn3/+OYcPH+aMM87g2muvDev59+/fv81u8rHgsccea1q6aNu2bQA8/vjjpKenA8ErG5uPrv7nf/4neXl5rdbb3LlzJ8899xxA09d+165dPPjgg037/Md//Eerxy8qKuKzzz6jf//+7a7z2ZsUxkSkhcJCS7jKerJGGJx08BeF53jdYoK1Yyec0P4uGcZy0MJeazjJxPfo2PXXX8/w4cN54YUX+Pzzz6mpqaFfv36MHTuWmTNndvm406ZN4+GHH+bZZ5/lrbfeIi0tjUmTJvHLX/6yzRYOkbRly5ZWi0YXFBQ01bVNmDChU2Hs2NYW6enpnHTSScydO5dvfvObYRvRiQfvv/9+qynQ5s2GJ06cGNIKCUVFRa2+h8XFxS22tRXGGq9Aveyyy0Jus9KTjLWhXWueiMPLIonotTcsv340PEFk2DU+hl7pUHcoLIfrNp8PzjqLdke9rA1OVZ7t87gsJbqv1hOR2HDsGqJtUWsLEWnh088sTpim6HLGGGwYrsgMF9eF8vL2bzcG0oxli2eoiO+BMRGJIgpjItKkttayYmV46u2T+0DaMINb0/1jhU3DVGVH0oAaa9joxnHRmIhEFYUxEWmyKg+OaZbeZZkjDL50oiuMWSgr7/g5OgZ8xrLWdXA1OiYivUBhTESaLP08PF33ATJHNLy9RFvplQ32HOtI4xJJOz2NjolIz1MYExEgeCn+p0vD03UfgutR2jCNsoVb0XHW3Ew24FpYr6lKEekFCmMiAsCu3R2v4dgZyX0hLdfg1obneOFWUx3sOdaRNGPZ7hnKNVUpIj1MYUxEAFi2vP2WD52VOdzgS4uyerHmzPGnKtOBWmvYrNExEelhCmMiAgTrxcIlc4QDDR3to5I9/lSlaSjkX+860bCak4jEMYUxEaG21rJmbbDpaThknxld/cXa4q8PLpHUkXSgyBr2Wo2OiUjPURgTEdasDV9Li+Q+kDY0yvqLtcVA8XGmKpOBgIVNAYUxEek5CmMiwvIV4WtpkRHt9WKNGlpcdDQaaAykNHTkr9NUpYj0EIUxEeHzz8PX0iJzeJTXizUTCEBlZcf7pANVGLaq55iI9BCFMZEEd+SIZfee8B0ve1T014s1CeGqSp8BLFoeSUR6TFKkT0BEImvFqvAdKykL0k+IgXqxRhZKSuGkk1q39ag6bKktC/67zlrWAqcmW3Ii/Ctsnz6QO0TBcNGiRTz00EP8+Mc/5oorrui1x73jjjvIy8vjiy++6LXHlPinMCaS4L78MlgvFo5pyozTDE4a+I/TNiKaeC5UVEBOztFtVYctr90SwPO33PeD3j21NqWkwAt/6V4g279/P9dccw0A/fv354033iApqfXHwY4dO/jmN78JQG5uLq+99lqXHzNRTZ069bj7KNiJwphIArPWsuzLMNaLneZgHGJnmhKapiqbh7HaMloFsWhRXw9lZZA7pPvH8vl8FBcXs3TpUqZPn97q9oULF+I40VnNcuGFFzJu3DgGDhwY6VM5rj59+nDddddF+jQkiimMiSSwXbuhtDR8x8saZbAxULjfgg1+DU4+OXwrEMSKr3zlKxQUFLBo0aJWYSwQCPDWW29xzjnnkJeXF6EzbF9WVhZZWVmRPo2Q9O3bl9tuuy3SpyFRLDp/5RGRXrFyZfgCiC8dMk4xeFG6HmVHPC84VZloUlNTmTVrFp999hnFxS3nlhu3tVePVVhYyNNPP828efOYPXs2F1xwAXPmzOFXv/pVq2MBPPjgg0ydOpV9+/bx17/+lW984xtccMEFPPjgg037rFq1iv/7f/8vF154IRdffDEPPPAAhw4d4o477mg13bdo0SKmTp3KokWLWmyfOnUqd9xxB0VFRTz44INccsklzJgxg3nz5rFy5cpW57V582Z+/etfc+ONNzJz5kxmzJjBTTfdxHPPPUcgXM33OmHOnDnMmTOH6upqHn30Ua644gouuOACbrrpJt5///027+P3+3nhhRe4+eabufDCC7nooouYP38+H3/8cat9w/l9+MMf/sDUqVN599132zyvhQsXMnXqVJ599tluflXin8KYSAJbscqGLYxlnBoj/cXaEsJVlfHqiiuuwHVd3nzzzRbbFy5cSE5ODjNmzGjzfqtXr+aFF16gf//+zJo1i+uvv54TTzyRV199ldtuu43KdnqGPPLIIzz77LOMHj2aG264gREjRgCwbNky7rnnHjZu3MhFF13EnDlzOHjwIPPnz2/3WO2prKxk/vz5bN++ndmzZzNjxgw2b97Mfffdx7Zt21rs+/rrr/PRRx8xYsQI5syZw5VXXom1lieffJIf//jHnXrccHFdl3vvvZdly5Zx4YUXcumll7Jv3z4eeOABli1b1mLf+vp67r33Xh577DEArrzySi655BIOHjzID3/4Q15++eU2HyMc34err74ax3F444032nyM119/HZ/P16sXWMQqTVOKJCjXtaxcFRwVCofM4QbjA9v7gwndl8BTlWPHjmXEiBEsXryYm266CYCioiI+//xzrrnmGlJSUtq836RJk1i8eDEZGRktti9ZsoQHH3yQl19+mW9/+9ut7rd161aee+45cnNzm7a5rsvPf/5zPM/jiSeeYPz48U23PfjggyxZsqRTz6mgoIBrr72W733ve001b5MnT+bhhx/mlVde4f7772/a91vf+hbf//738TXremyt5eGHH2bhwoWsWbOGs88+u1OPf6zS0lKefvrpNm879dRTmTVrVotthYWFjB49mieffJLk5GQALr74Yu655x7+9re/ce655zbtu2DBAlatWsUtt9zCbbfdhmn4Aa6qquLuu+/mscce48ILL2TQoEEtHiMc34ehQ4dy7rnn8sUXX7B//36GDRvWdNv27dtZv349M2bMYMCAAZ34aiUmjYyJJKht26G6OnzHyzrDgRjuUp+oU5UQHB1r/PAEWLx4Ma7rcuWVV7Z7n/79+7cKYgCzZ88mMzOTL7/8ss373XTTTS0CAMCaNWs4ePAg06ZNaxEAAObPn98iKIUiPT2du+66q8XFB5dddhk+n4+NGze22Dc3N7fV8Y0xXHvttQDtPo/OKCsrY8GCBW3+eeedd9q8z3333dcUxADOOecccnNzW5y/53n885//5MQTT2wRxAAyMzO55ZZb8Pv9fPjhh62OH67vw9y5c7HWsnDhwhbbG0fLrr766ra/KNKCRsZEElTe6uAoUDgWB3dSgyNjMTlF2aiNqyoTxaWXXsoTTzzBokWLGDduHIsXL+aMM87gjDPO6PB+H3zwAa+99hr5+flUVFTgNrss98iRI23eZ+zYsa22FRQUALQ5AjVkyBCGDBnC/v37Q34+J510UqugmJSURP/+/VtNtfn9fl5++WXeffdddu7cSU1NDbbZi6K959EZp5xyCi+++GLI+2dnZ7cYZWo0ePDgpsAMsGvXLsrLyxk4cCB/+tOfWu1f2nB1zq5du1rdFq7vw/nnn8+gQYNYtGgRt956Kz6fD7/fz5tvvsmQIUNCau0hCmMiCWtVQ71YOMJYxqkGXzr4y7p/rIixUFoGJ8fw6F5X9evXj2nTpvHOO+9w0UUXsWvXLr73ve91eJ+//vWvPP744/Tr148pU6YwePBgUlNTAXjxxRepr69v8379+/dvta2qqqrpPNq7T2fCWGZmZpvbfT5fi8AI8KMf/YhPP/2Uk08+ma9//ev069ePpKQkKisrO3wePamj8/ea1RWUl5cDwSnB7du3t3u8mprWvyWF6/vg8/m46qqrWLBgAZ9//jnTpk3jww8/pKysjOuuuy5qW6NEG4UxkQTkeZa81WGsFzvNYHwG64/tJOO5x1+rMl5dddVVfPjhh/z0pz8lNTWVSy65pN19A4EAf/7znxk4cCDPPfdciw92ay3PP/98px67MXyUtHMVRVtXZ4bDxo0b+fTTT5k6dSqPPPJIi2m49evXd2o0KxIav25f+9rX+PnPfx6243X2+3D11VfzzDPP8PrrrzNt2jTeeOMNHMfpcJpbWlJkFUlA27ZBdRinFLNOd1pM7cQsE96+a7Hk3HPPZdCgQRQWFjJ9+nRyOpivLSsro7KyknHjxrUaYdm0aRN1dXWdeuzTTz8dgLVr17a67fDhwxw6dKhTxwvVvn37ADjvvPNa1UOtXr26Rx4znE499VQyMzPZtGlTWNpwdPX7MHjwYM477zyWLl3K2rVrWbFiBeeee26rmjRpn8KYSALKWxO+qwZNMmSOiM3+Yq00rFUZD7mys3w+H7/61a/45S9/yR133NHhvv369SM1NZX8/Hxqa49+48vLy3nkkUc6/dhnn302ubm5fPrpp6xbt67FbU899VSrqcVwaQwLa9asabF9+/btPPfccz3ymOGUlJTENddcw8GDB3nsscfaDGTbtm0LeWSxO9+HuXPn4rouDzzwANZaFe53kqYpRRJQ3uow1oudbEjKBH+cXInoBqA2AcMYwOjRoxk9evRx93Mch2uvvZYXXniBf/3Xf2XatGlUVVXxxRdfkJub26qNwvH4fD5++MMf8oMf/IC7776bmTNnMnDgQPLy8igsLOT0009n69atXX1a7RozZgxjxozhvffeo6ioiLFjx3Lo0CE++eQTzj///HabrHZWR60tAK655pout3+47bbbyM/P56WXXmLp0qWMHz+efv36UVhYyLZt2ygoKOBPf/pTmzVix+rO92Hq1Knk5uZy8OBBBgwYwLRp07r0fBKVwphIgrHWsiovzPViyQZbHycJxkAd4CRH5/qUKSnQp0+kzwLuvPNOcnJyWLx4Ma+++mpT89dbb72VG2+8sdPHO++883jsscf44x//yHvvvUdqairnnHMODz30EN/97nfbLWrvDp/PxyOPPMKTTz7JF198waZNmzjxxBO55557OO+888IWxhpbW7SnO724UlJS+O///m8WLlzIkiVL+OCDD/D7/fTv359TTz2VuXPnNjV0DUVXvw+O4zB79mz+/Oc/c/nll7e58Ly0z9gQCz3aK+gTkdiyfYfl5m+HLziddoeP/lN81B2KkzAGJCfDaYMttR1cHWotVACTkyxn+XpvQc4+fSB3SOJ0pq2qquKyyy5jxIgR/M///E+kTydhhfJ9+N73vsfSpUt56aWXOOmkk3r5DKNXe1enNqfoKpJg1qwNX38x44Ps0x3cOJvX8/vByTYMGNzxfkkWKo3hjJTwLSuVqGpqavA8r8XIi+u6/O53v6Ourq7dZZkkvLr6fdixYwdLly5lypQpCmJdoDAmkmDWrAlfvVj6SQZfFrjx1g7CQFkZpKd3vFs6cMTCIQu5CmPdsmfPHubPn8+5557LCSecQHV1NatXr2bHjh0MHz6cf/mXf4n0KSaEzn4f3n77bXbv3t20VNK8efMicdoxT2FMJIH0RL2Yk2Lwx0u9WKOGqyqPd2V+ClCFocB1yHV6b6oyHg0aNIiLLrqIvLw8vvjiC1zXZciQIdx0003827/9G+nHS8YSFp39Prz22musWbOG3NxcHnjgAb7yla9E6Mxjm2rGRBLIwYOW674RvuB06m0+BpwXX/VizY0dGyyY70i5hb4G5qW4OBodE5FjhFIzpj5jIglk7brj7xMyB7JHOXh18RnEIDhVeTzpQKk17LNKYiLSNQpjIglk7XrLMY3Guyx9mCEpm9heHPw4SkMIY0lAANjqKoyJSNcojIkkkLw8CFcz88wRBicVvM6tfBNTKiuP//UyBpKxbPYMgfgdJBSRHqQwJpIgKiosu3eH73iZIwzE+9SchfIQVhZIByqsYY8X518PEekRCmMiCWL9RgjbwI0D2WfGd70YAAbKQ5mqNOBaKFAYE5EuUBgTSRDr1oWxXmyoIbkPuPGwOHhHbLBuLJRrzlOMpcA1xFuXDxHpeQpjIglizdrw1YtlDDc4KeDFexgDPBeqqo6/XxpQiWGXRsdEpJMUxkQSQCBg2bQ5fMfLOt0ACRI6DJSXH3+3JAOe1VWVItJ56sAvkgC2bYf6+jAdLEHqxUwSmIZfV8uqYFAIT9eHZZNrONuFlB7KZGkGspT3ROKKwphIAli/PnyLg6cPa6gXq+7+saKVSYKMUw2mWY3dAfdoOGuPBTzgz/VJpJqeCasZwM2prgKZdModd9zRtMRRqB588EGWLFnCq6++yrBhw3rw7ERhTCQBrNsQvsXBG/uL+eN4hTTjEAxiFmzDkpOeC0nHCWMmeBdcgjVk4eYHqoFa273Rsf3793PNNdd0uE9ubi6vvfZa1x8kTsyZMwcgLr8WixYt4qGHHuLHP/4xV1xxRaRPJ6EpjIkkgDVrw7c4eNbIxv5i8T1NCQ1BrOFpugFISQ7hPgRDk8/0QFGuDQa9cDnxxBO55JJL2rwtOzs7jI8kkfaTn/yE2trOXXFz5513cvPNNzN48OAeOitppDAmEueKiy2FhWE6mAPZo+O/XqwtoV6J6iO4PFKNhcwon0o88cQTue222yJ9GtILcnNzO32fgQMHMnDgwB44GzmWwphInFu/IXzHSj/RkJQDbgitHuJRIABJIb5r1lhDZg/VjUXC1KlTmTBhAg899BBPPPEEn332GbW1tYwcOZI777yTSZMmtbpPVVUVL7zwAu+//z779+8nOTmZsWPH8u1vf5vx48e32Lexpumjjz7iz3/+M++88w4HDx7k3/7t35oC4wcffMCzzz7Ljh07yMjI4IILLuDuu+/m5ptvBo5OJf7kJz/h7bffZsGCBYwdO7bVef3xj3/kf/7nf3jwwQe5+OKL23y+x07lTp06tenf8+bN47bbbmPlypXcddddzJs3j3PPPZc//elPbNy4kcrKyqbarIULF/Lxxx9TUFBAcXExaWlpjB49mptvvrnV16z58aZNm8aTTz7Jhg0bMMYwefJk7r333la1W5s3b+bZZ59l48aNFBcXk5mZydChQ5k+fTrf/va3W319G8+rsR4M4KGHHuKhhx5q2vfYfdqqGVu0aBGvvvoqO3bsAOC0007jmmuuaTXd2ZXnlIgUxkTi3IaNwWav4egxljXC4CSDP47Xo+yIG2IYc4BaglOKYeqzGxUqKyuZP38+WVlZzJ49m+LiYt577z3uu+8+nnnmGUaMGNG0b1lZGXfccQfbt2/nK1/5CnPnzqWqqoqPP/6Yu+66i4cffpgZM2a0eowf/ehHFBQUMHXqVLKzs5s+qBcuXMjPfvYzMjMzmT17NpmZmXz++ed85zvfwXVdfM06Gs+dO5e3336bN954o1UYc12XRYsW0adPHy688MJ2n2t2djbz5s3jxRdfBOCGG25oum3ixIkt9l23bh3PPvsskyZN4uqrr+bQoUNNt/36179m5MiRnHPOOfTr14/CwkI++ugj7rnnHn7xi18wffr0Vo+9adMmnn/+eSZNmsScOXPYsmULH330Edu2beOvf/0rqampAGzZsoXbb78dx3GYPn06ubm5VFRUsHPnTl5//fUWYexYM2bMoLKyko8//pjp06dz+umnt7vvsR555BFefvllBg0axJVXXgkEg/JDDz3Eli1b+O53v9vl55SoFMZE4ty69WFcHHxkY3+x+Bnx6YyAC6F8ZDgEpypro3yqcu/evTz99NNt3jZu3Di++tWvtthWUFDAtddey/e+9z0cJ1gRN3nyZB5++GFeeeUV7r///qZ9H3nkEbZv386PfvQjrr766qbtd9xxB9/+9rf5xS9+wdSpU1t9CB85coTnn3+ePn36NG2rqKjgv//7v0lPT+fPf/4zJ598ctOx7rvvPjZv3txiGm78+PGcdtppvPPOO9x3332kp6c33fbFF19w+PBhvvGNb5CSktLu1yY7O5vbbruNxYsXA3Q4nbt8+fJ2i+D/9re/tRr5OXLkCN/+9rd5/PHH2wxjS5cu5ac//SmzZs1q2vaf//mfvPnmm3z88cdN2998803q6+v51a9+1eo4ZWUdr+M1Y8YMKioqmsJYqAX8eXl5vPzyy5x66qn86U9/IisrC4Bbb72VW2+9lZdeeomLLrqo1chnqM8pUanpq0gcCwQsm/PDcyzja6gXq03MIAbBq1G9TgTb6ihfSH3v3r0sWLCgzT9ttUBIT0/nrrvuagpiAJdddhk+n4+NGzc2bSstLeW9995j8uTJLYIYQP/+/bnpppsoKSnhyy+/bPUYt956a4sgBvDxxx9TXV3NlVde2RTEAJKSkpg/f36bz23OnDlUV1fzzjvvtNj+xhtvALQ6r+4YNWpUu2GmrSm4gQMHcuGFF7Jnzx4OHDjQ6vYJEya0CieNx2/+dW7U1qjSsV/DcGmc2rz11lubghhATk4O8+bNA2gKsM119jklGo2MicSxHTvC1+w1/WRDUha4leE5XqwKuJASwtyjA9QR3VOVU6dO5Te/+U3I+5900klkZGS02JaUlET//v2prDz6g7Fx40Zc16W+vr7Nkbc9e/YAsGvXLqZNm9bitrZqvAoKCgA4++yzW902duzYFlOUjS677DKefPJJXn/9da666ioAioqK+PTTTznrrLM47bTTjvd0QzZmzJh2b9u3bx/PPvssK1eupLCwkPpjXpBHjhxh6NChLbaNGjWq1XEar2hs/nWeOXMmL774Ivfffz9f//rXmTJlCuPHj+/Rqx/z84O/3R07VQs01cBt2bKl1W2hPqdEpTAmEsc2hPEXzqyRBifF4E/wlbADAehgdqtJ41RlTTd7gkWTzMzMNrf7fD7cZnPh5Q3rR61du5a1a9e2e7yamppW2/r3799qW1XD4qD9+vVrdZvjOPTt27fV9uzsbGbOnMmSJUvYtm0bI0aMYPHixbiuG9ZRsfbOGYKhc968eVRVVTFx4kSmTZtGZmYmxhhWrVpFXl5eq3AGbX+dGwNn86/zuHHjePLJJ3nmmWf43//9XxYtWgQEw+Fdd93V5kUV3VVdXY3jOG1+L/r3748xpun71VyozylRKYyJxLENm8JYvH+GSdRSsRY8LzhdaUIMWNXWkBVHV1WGovGD98Ybb+Q73/lOp+5r2vjCNh6vpKR1p2HP8ygtLWXQoEGtbrvmmmtYsmQJr7/+Ot/97ndZuHAhmZmZfP3rX+/UOXXV3//+d8rLy/nJT37C7NmzW9z2y1/+kry8vG4/xvjx4/nNb35DbW0tGzZs4NNPP+XVV1/lu9/9Li+88AInnHBCtx+juYyMDDzPo6SkpFUILSkpwVrbbmiX9qlmTCSOrV0bniDmpEDWGQ5uTWKFiva4gdD2c4B6giNkiWTMmDEYY1i3bl1Yjtd4pV9bo2yNU6JtGTduHCNHjuStt95i2bJl7Nmzh0suuYS0tNDXR/D5fHhd7Ji8b98+gFbF9dbaDkcMuyItLY1JkyZx77338q1vfYu6ujqWL1/e4X0aR6Y68/wapxtXrVrV6rbGbWeccUbIx5MghTGROFVZadm3PzzHyjjNkJQJbutZpYQUCDHgOgTXqky0ax4GDBjAzJkzWbduHc8//zy2jXW41q9fH3JH+OnTp5ORkcHChQvZu3dv0/ZAIMBTTz3V4X3nzJlDeXl5Ux+tzk5R5uTkUFZWRl1d5/u5NF7huWbNmhbbn3vuObZt29bp4x1r3bp1bZ5XcXExQIdXi0LwuQEtWnEcz2WXXQbAggULWkxHVlZW8qc//anFPhI6TVOKxKlNm8N3rKyRBuMzWH9ipQrjHF2bsrmAGwxZobBApTWkhmGO19/tI7TUUWsLgJtvvrnL/Z9+8IMfsHv3bn73u9/x5ptvctZZZ5GVlcXhw4fZtGkTe/bsYfHixSGNUmVnZ/Od73yHX/ziF/zbv/0bs2bNauozlpyczKBBg1pc4dnc7NmzeeKJJygsLOTMM89ss5C8I5MmTWLTpk38+7//O+PHjycpKYkJEyYwYcKE49537ty5LFq0iB/96EfMnDmTPn36sH79erZs2cL555/PZ5991qlzOdZf/vIXVq5cyfjx4xk2bBgpKSnk5+ezYsUKTjjhhA77qEFw5DA1NZUXX3yRioqKptq7W265pd37TJgwgeuvv56XX36ZG2+8ka997WtYa/nggw84fPgw//Iv/xLS10ZaUhgTiVObNoPjhGdNyuwznfCsMh4jrAfWDbbzMO1cCul6wa9vKOqAKsJzVWUGkBamCwIaW1u05xvf+EaXw1ifPn344x//yCuvvMK7777L22+/jed5DBgwgNNPP51bbrmlU+0X5syZQ05ODs888wyLFy8mMzOTCy64gLvuuou5c+e2WxuVmZnJjBkzeOutt7pUuH/LLbdQUVHBZ599xpo1a3Bdl3nz5oUUOEaNGsVvf/tbnnrqKT788EN8Ph9nnXUWTz31FJ988km3w9g111xDVlYWGzZsaKo/GzJkCN/61rf45je/edzarT59+vDzn/+cP/3pT7z++utNo2wdhTGA733ve4waNYpXX321adWD4cOHc/vtt2vB8S4ytq3x4za0VTgpItHr/v/PY+nn3c9QvgwY96tkDBBIoCvQTVJwZKztG6F/f8gdcvzjWAvlGM5Pchnn636gTTPxc3VmOOzZs4frr7+emTNn8rOf/azNfW666Sb279/PokWLVFwuva6tK0+PpZExkThkrWX9hvAMZmWOMPjSwZ9gv4/ZQMcXj5YXw0mhrL1sIMlaDliHCx1dwt9V5eXlpKWltaiDqq2tbeqT1tbSShDs/L5t2zbmzJmjICZRS2FMJA4VFsJxVkMJWeYIJ1g7lWiXBB5HfR3U1UEos3ipwAEPSj3oq8umuiQvL4+f/exnnHvuuQwZMoSysjJWrFjBgQMHmDx5cqt2Ff/4xz84fPgwb7zxBqmpqU2LiYtEI4UxkTi0KUxLIAHkjDFYDei0qbwc2mhv1UoaUGoN2z3DRCdxau/C6bTTTmPKlCmsXbuW0tJSAE488URuv/12brrpplYF/H/5y18oLCzk5JNP5oEHHmhzWSKRaKGaMZE49Ic/evztxe73GEvqA+N+nox1wW3dVDvhZWXD6SND27fUwskOfDNFyVYkkYRSM6YBc5E4tGFjmLruN9SLqb9Y2yorQ184vGmqUgNjInIMhTGROON5Nmw9xjJHOmAIvalWorFQXhHarmlAvTXscHUppIi0pDAmEmf27oUQG5sfV844gw13p9F4YkK/UMIYwFi2eHrbFZGW9K4gEmfCNSqWMhBSBxlNUXbEBsNYqC1E0oD9HpRrqlJEmlEYE4kzm/ItSWFo9Z410sGXpnqx43FdqK4Obd9UoA5NVYpISwpjInFmw8bQF7LuSOZIE6wX0yhOxwyUhjhV6TR8Pbd6CmMicpTCmEgcCQQsW7eG51g5Yx28uvAcK65ZKCsNffdUY9njGaoUckWkgcKYSBzZsRP8YSi4TxsKKf01RRmquoZu/KFIA2ox7NDomIg0UBgTiSP5Yeq8nznSwUkBL0xXZSaCUK+qbJyqLFDdmIg0UBgTiSObNlt84SjeP8MQLBiTUJWUhr5vqrHs8gw1mqoUERTGROJKWDrvO5Az2sGrU1LojOoqCIQ4RZxKcKpyp6YqRQSFMZG4UV9v2bGz+8fJOMmQlKN6sa4I9apKnwn2JtumqUoRQWFMJG5s2x6e9SgzRxqcZKMrKbugtDT0fVONZYdnqNcApEjCUxgTiROb8xuW3Omm7FEaremqikpwA6HtmwZUY9itqUqRhKcwJhInNm+2ON18RZtkyDrDwa3VcE2XWCgrD23XxqnK7QpjIglPYUwkToSjeD/zVENSJrghLu8jxzBQXBL67knGstU1BJR9RRKawphIHKirs+ze0/3jZI40mCSDDUPj2IRkoaKic1OVlRj2anRMJKEpjInEgYKt4HndP072KCc4dyZd14mpymQDrkUtLkQSnMKYSBzI39L94n0nDTJHGLW06K4uTFXmewZPGVgkYSmMicSB/PzuF+9nDjf40tVfrNsapioDnZiqrLBwUGFMJGEpjInEgQ2bul+8nzXSYHxgQwwR0gEbes+xZMBvDTs8vR2LJCq9+kViXF2dZU8YivezznSwYag7k6Di4tD2MwZ8xlLgGpXriSQohTGRGLdte/eL931ZkHmK6sXCqaoK6utD2zcNKLLBPyKSeBTGRGJcOIr3s4YbnHTwFMbCx0BJiIX8KTROVeqqSpFEpDAmEuO2bAlD8f5IB2PAhmFtS2lg4UhRaLsaAxjLVtWNiSQkvfJFYlw4Ou/njDYKYj2gvg6qq0LbNw046EG5pipFEo7CmEgMq6+37NrdvWMk94G0E1Qv1iMMFIVYyJ8K1GHUAFYkASmMicSwHTvDsB7lCIMvTf3FeoQNXlUZygUWTsPC4dtchTGRRKMwJhLDtmzp/jEyRzjBdwK1tegRnhd6z7E0Y9ntGWo0VSmSUBTGRGJYfoHF5+veMXLGamHwHmVCL+RPBWow7NZUpUhCURgTiWEbu1m8n9IfUoeoXqxHWaiqhLra4+/qa5iq3K6pSpGEojAmEqMCAcv2Hd07RuYIJ1gvFkJQkG7oxOhYirFs9wx+TVWKJAyFMZEYtXNX6ItRtydzRMMIjOrFepaFoqLQCvnTgCoMezVVKZIwFMZEYlQ4ivezx6herLe4bmiF/EkGXFCLC5EEojAmEqO2FFiSkrp+/5SBkDrIaIqyFx0uDG2/JCxbPYOnqUqRhKAwJhKjNm7q3jRlU72Yivd7TU11cAHx40kDyiwcUhgTSQgKYyIxyHUtW7d17xhZjfVi+sDvPSa00bFkgguH79JalSIJQa90kRi0dy/U13fvGNljHDzVi/UuC6Ulx//eGQOOsRSoxYVIQlAYE4lBW7Z27/4pgyBlAHiaoux9BgpDGB1LAwotlOhKV5G4pzAmEoO2bOle5/0s9ReLHAtHjhy/WW8qUI9hl66qFIl7CmMiMWhzfvc672eqXiyiPC8YyDpiDGBhu8KYSNxTGBOJMdZathR07xiqF4u8w4eP3wQ2VQuHiyQEhTGRGHPoUGjtEdqTOji4JqXqxSIrEIDi4o73SQVqtXC4SNxTGBOJMfndHBXLHK56sWhx8FBwYfD2+BqmKnfoqkqRuKYwJhJjulu8r3qx6OGvh5KSjvdJalg4PKDvl0jcUhgTiTGb80NbcLo9qheLLgcOdjw61rhw+H6r0TGReKUwJhJjNm/u+MO7I6mD1V8s2tTXdbyAeBIQAHZpqlIkbimMicSQI0WWsvKu3z9zuIMvVfVi0ebAQdqdNjYGfA0Lh3c1hItIdFMYE4khBd0t3h9poKEoXKJHXS2UlLZ/expQbKFI3zeRuKQwJhJD8reArxuv2pwxDl4317SUnnHgQPvTzylAvVU3fpF4pTAmEkPyt1i8rtaLDQn2F3NVLxaV6urav7LSGDANV1WKSPxRGBOJIRs3db14P3OEg5MKnurFotb+DkbHUoF9nqFKU5UicUdhTCRGlJZaioq6fv+skeovFu389bT7PU4j2I1fU5Ui8UdhTCRGFGzt3v2zR6teLBYcONB2HznHBEfN1I1fJP4ojInEiPwt4HTxFZs2FFL6qaVFLAgE4MiRtm9LMZYdnsGv0U2RuKIwJhIj8rd0/RNY9WKx5eBB8NzW29OAagz7NFUpElcUxkRixKZNXV8GKbgepVG9WIxwXTh0uPX2JAMusFNhTCSuKIyJxICqKsvBQ128s2nsL6YkFksOHw5OWR4rCUuBuvGLxBWFMZEYsKUbnffThhqS+6q/WKzxLBxqI4CnAWUWDiuMicQNhTGRGLClINj4sysyhxvVi8UiC4WFwXYXzSUDfms0VSkSRxTGRGJA/hbb5SspM0casPrgjkWWhkXEm2nsxr/N09u3SLzQq1kkBmzcGCzq7jSnoV6sTnNaMckGm8DW1bXcnAYc8qBc31aRuKAwJhLlamos+/Z37b7pJxiS+6heLKaZYCPY5lKBOnXjF4kbCmMiUW7rtq6vR5k10uCkgFd3/H0lStngAuI11Uc3OSa4fbu68YvEBYUxkSi3ZUs3ivdPb+gvJrHNwL5jRsdSjGWXZ9AMtEjsUxgTiXJdLd43SZBzpoNbq0/rmGehohyqqo5uSgNqMOzRVKVIzFMYE4ly67tYvJ9xssGXDZ7qxeKDgf3NRsd8JtiLbLvCmEjMUxgTiWJ1dZa9e7t238yRBifJ4NUff1+JARYqK6Cq8uimZGPZ5hlcDX6KxDSFMZEoVrC16+tRZo/SyzvuGNjfrO9YGlBpDQfUR04kpundWiSK5XexeN9JhazTDW6NhkziSuPoWEPtWBIQAHboqkqRmKYwJhLFNud3rXg/8zSDLwPc6uPvKzGmWd8xY8CHZasWDheJaQpjIlFsQxeL9zNHGowPbCD85yQRZqGiAqobgnYaUGyhSGFMJGYpjIlEqe4U72ePcbBdrDWTGGDgYEPtWApQr4XDRWKawphIlOpq8X5SFmSeYrQEUjyzUFYGtbXBqUrHWLZq4XCRmKVXr0iUys/vWvF+5kiDL13rUcY9A4cOBf+ZChzwoEJTlSIxSWFMJEpt6mLxftbpTnAFpC7UmkkMsVBcAn6/Fg4XiXUKYyJRasOGrhXv55xlVLifQAoLgwuHWwvb1OJCJCYpjIlEoepqy959nb9f6mBIHWTU0iJRWCg8EqwtTNXC4SIxS2FMJAoVbKVLfaOyznDwpYFbG/5zkujkuVBcfHTh8N2aqhSJOQpjIlGoq8X7WaMMYECjIwnlcOHRhcPVjV8k9iiMiUShTZttp8OYSYKcMQ6e5qkSTl1tcJmkZBPsxh/Qj4BITFEYE4lC6zZ0vsdYxqmG5BwIqF4s8Zhg7VgaUIlhnxYOF4kpCmMiUaaiwjZ1V++M7DMMJslg68N/ThLlLJSWgvUHO5rs1FSlSExRGBOJMpvzu3a/7LFO16r+JT4YKCqCJCxbPIOnHwWRmKEwJhJlNufT6WavSTmQearRFGUis3DkCKRaKLNwWGFMJGYojIlEmfUbbKcHuLJHOfgyUH+xBBcIQE0F+K1hh9aqFIkZerWKRBFrLes3dH62MXt0Q41QFxYWlzhioOhIcOHwAtdo1lokRiiMiUSRw4VQVtbJOzmQM87BU+G+WCgvh6QAHLFQrDAmEhMUxkSiyKZNnb9P5mmG5L7gVoX9dCQWGagshjpr2Klu/CIxQWFMJIps3Gzx+Tp3n+wzDU6K0ciYBNngVKUxlgLVjYnEBL1SRaLI+vXgup27T5+zHKyr+Sg5qr4eqIUDHpTrR0Mk6imMiUQJ17Wd7jGW0h/STza6ilJaMlB+BOrQVKVILFAYE4kSO3Y2jGh0QvYYB1+6WlrIMSyUFgevyt2mbvwiUU9hTCRKbOxC8X7OWAMY0FSUHMPzIFBt2eUZqvXzIRLVFMZEosSGDZ0r3nfSgiNjbo0+aaUNBiqPQC2GXZqqFIlqCmMiUWLNus4V72efaUjK1hSltMNCeWlwhGy7pipFoprCmEgUqKiw7N3bufvkjHMwDthAz5yTxD5rwV9t2e4Z6jWAKhK1FMZEosCmzZ3b3yRD3/EOXm3PnI/EiYapymoMuzVVKRK1FMZEosD6DeB04tWYNTLYdT+grvvSEQsVZRDwYLvCmEjUUhgTiQLr1tlOLeqcc5aDSQLr77lzkjjRMFW51TUENFUpEpUUxkQizPMs6zYQchgzPug3ycGr69nzkjjRMFVZiWGvRsdEopLCmEiE7dwJtZ2o/co83ZDSX1OUEiILlWXg11SlSNRSGBOJsHUbOrd/n7McTDJYLQwuoWqYqtziGbSMqUj0URgTibB160Nv9mp80G+ypiil86qKocIa9luNjolEG4UxkQhbvTr0Zq9ZZxhSBmiKUjqvogT8nlUDWJEopDAmEkElJZaDh0Lfv8+EhqsoNUUpnWWhvgbyPYOnqUqRqKIwJhJBa9eHvq+T0jBFqUav0hUGakqgzBr2K4yJRBWFMZEIWrcu9HqxnHENjV4re/SUJF5ZKC8Cv7Vsd/XWLxJN9IoUiaC81aHXi/Wd5GCM1qKUrrMWArWaqhSJNgpjIhFSW2sp2Bravkk50OdsB7e6Z89J4lzTVCUcUBgTiRoKYyIRsnETeF5o+/ad6JCUqasopZuapirRVKVIFNGrUSRC1q4LfXHwAec5WA8IMbyJtMd1IVCvqUqRaKIwJhIheatDWxw841RDxilGhfsSHgZqS6HUwkGFMZGooDAmEgGBgGXd+tAWB+93joOTCl5Nz5+XJAAL5UeCU5XbNFUpEhX0ShSJgIKtUB9C41YnDfp/Vb3FJLz8fvD8mqoUiRYKYyIRsHoNmBBWpek7wSGlHwQqev6cJIEYqCsLTlXqqkqRyFMYE4mA1attSGFs4HQHrMGG2ItMJCQWyovBj9FUpUgU0KtQpJd5niVvzfHbWmScZsgcbvBXaOhCwq+2BghYTVWKRAGFMZFetn0HVIfQvHXgBSrcl57lL9dUpUg0UBgT6WWh1Isl94N+U9RxX3pWeQkEMGzVVKVIROkVKNLLVq06fr3YgGkOSVlGhfvSoyorwfEsmz2Dq9ExkYhRGBPpRZ5nWZnXcb2YLx0GzfDh1VvQB6T0JBu8UrfcGvbaEK4oEZEeoTAm0ou2bYeq46wv2W+KQ8oACJT1zjlJAjNQUQoBC1tdhTGRSFEYE+lFeXkd14uZZBj89eA6lGpnIT3OQnkZJGHJdw0BjcSKRITCmEgvWpln6Wj8od8kh/QTDP6SXjslSXCeB141VGDY5Wl0TCQSFMZEeonrWlbl0W5PJ+ODIZc4WMAGevXUJJEZqCwN/lxu0VSlSEQojIn0kq3boKaDnmH9znFIP0WjYtLLLJSWQoqxbPUMdZqqFOl1CmMivWRVB/ViJhmGzA6+HK2/F09KhODC4aYWqjBs11SlSK9TGBPpJV+uaH/IYcBXHTJOMviLe/GERBoZqCgDNFUpEhEKYyK9wO+3rF4Dto085qTBkNm+4BWUqhWTSLBQUgqpxrLDM1RqqlKkVymMifSCTZuhvr7t2wZ9zSEtF9WKSUTV1oDjh1qMeo6J9DKFMZFesHIVOG282pL7wZBLfHj16ismkVdRBmDZ5DltjuKKSM9QGBPpBcu/tG0ugZR7mY/kvgZ/aa+fkkgrpaWQDuz3oEhhTKTXKIyJ9LDaWsvGTa23Z5xmGDDNwa3SGpQSHSqrIMmFemso8PTxINJb9GoT6WFr14F77BSkAydc58OXHlyoWSQqWCgvB5+xbHANrn5JEOkVCmMiPWzFKovP13LbgPMcskerlYVEGROcqswASqxhr3qOifQKhTGRHrb8y5YjY0k5MPRqH3jgtXOFpUhENIyMORYCFjbrqkqRXqEwJtKDyiss27a23Db0ah+pgwz1GhWTKGQtVJQHl0faouWRRHqFwphID8rLa1mbn32mYeA0h0ClhTaurhSJOHP0qsoqDAWaqhTpcQpjIj3oyxVH68WcFDjxBh9OisGtjOx5ibTLQllZcKoSCxtcfUyI9DS9ykR60BfLjtaLDb7YIeNUQ70aOEmU8zwor4B0Y9nnwRGN4or0KIUxkR5y4IDl4KHgv9NPNAyZ7cOr0/qTEgMapipTgTpr2KTRMZEepVeYSA9ZsTL4t/HBSTf6SMpEnfYlNthgGLMWkoxlg2cIaEBXpMcojIn0kC9XWBwHBs10yD5TV09KbPE8qKgI9hwrs4btKuQX6TEKYyI9wPMsy1dAyhAYeqUPzw9WPcUklhgoKYEkExwhW6+eYyI9RmFMpAds2waV1XDijUkkZYO/JNJnJNJJFkrLgiNkacay0zMUqZBfpEcojIn0gC9XwqAZDn3GGgUxiVnWCwayNIKF/BtVyC/SI/TKEukBy7dYhl7twwbAq4v02Yh0kYHiYjAGko1lnWeoVyG/SNgpjImEWW2tpXSsj+R+UK9RMYllDUsj+f3BQv5ya8hXIb9I2CmMiYTZ/+425JztBNtYaBRB4kBJCfgMGCx5AQdPP9ciYaUwJhJGlRZWpPjAgFcT6bMRCY8jRcG/M4BD1rBbo2MiYaUwJhJGn/odqpMM9UWRPhOR8KmrhaoqSDEQsLBGbS5EwkphTCRMdrqGtX5DTYkFtQCQeGKgqOEXjHRj2eYZrVcpEkYKYyJh4LfwYcChqt7gVkX6bETCzEJxCXhuQ5sLDKvV5kIkbPRqEgmDVQHDQWvwF1vQDI7EIesFA5kxkIplg2soVyG/SFgojIl0U5mF5a5DEpaKUnQFpcStwsLg3xlAtTWsCegjRCQc9EoS6abP/A6VGJLrgv2YROJVbS1UVh5tArvaNVTplw+RblMYE+mGPZ5hk2dIx1JREemzEelhBg4fDv4zE6jU6JhIWOhVJNJFnoVP/A711pAGwTCmejGJZxbKyqCuDpyG0bE811Ct0TGRblEYE+mifM+wxxqyTfCTqKIC1YtJ/DtmdKzcGlZrdEykW/QKEukCv4WlAQewJBuorgJPfZckEdhgz7GA/+jo2ErXUKlfRES6TGFMpAvWuYZCa8hq+H+5piglgVjgcMOVlY21Y19qdEyky/TqEemkGgvLXAcHS1JDACsvR1OUkjhsMIw1jo6lGssa11Cs0WGRLlEYE+mkvIBDqWfIbvi/G4Dq6oiekkivsxYOHgr+OwOoxvBZwMHqlxKRTlMYE+mESgsrXUOysTgNo2IVlZE9J5GIsFB4JHhlpTGQiSXfM+y2mq8X6SyFMZFOWBVwqLSGzGbb1NJCEtm+fcG/UwG/NXzsd3A1OibSKQpjIiGqsLDaNaQ0GxWDYN8l1YtJQmroO1ZeHhwdyzaWfdaw1tVvJyKdoTAmEqK8gEOVNWQ021anJZAk0RnYvSfY2iXZgA/L0oCjRcRFOkFhTCQEle2MipWXR+6cRKKCBX897N8f/G8WUI7hI7+K+UVCpTAmEoI1bYyKgerFRBoVFgYXEXcMZGDZ7Dnke3pxiIRCYUzkOGoaRsWSjxkVs1ZLIIk0MbBjZ7DVS5oBD8sHAYcKvT5EjkthTOQ41rmGimOuoASo0hJIIkdZCARgx67gv3OAUmt41+/gKZCJdEhhTKQD9RbyXAfnmFEx0BSlSCsWKsrhwMHgdGUmli2eQ56urhTpkMKYSAc2e4aSZmtQNlemJZBE2nTwIJSWQmrD1ZWfBBz2aRRZpF0KYyLtcC2sDDgYLL5jfrEPBKBGSyCJtGvnzuBUfhZQaw1v+n1U6pcXkTYpjIm0Y5tnONxGrRg0TFGKSLsssG0b1NdDjrEUWsNbfoeAAplIKwpjIm2wFla5wT5JyW2Uu5SXo3oxkY5YcD0o2Ao2AFlYtnoOn2gxcZFWFMZE2rDPGvZ6kGHa/tQoV72YyPHZ4AoVBdsgyYNULCtch1Uq6BdpQWFMpA15AYPfGlLauK2mJlgzJiIhsFBbC9u2Q7oFg+WjgMMmBTKRJgpjIsco8qDAM6Qbi9EUpUj32WB3/p27IMtCAHjb77BdgUwEUBgTaWWN61CHIa2d28vK0BSlSBeUlsL+A8GGsHXAIr/DLi2ZJKIwJtJclYUNriGFtkfFXBeq1NJCpMsOH4aiIugDVFvDG/UOexTIJMEpjIk0s8E1bS4I3khrUYp03549wWnLvsZSZQ2v1zvsVSCTBKYwJtIgYGF1O0sfNSorQ/ViImGwfQf464OBrMIaXtMImSQwhTGRBls8Q3E7TV4BsFoCSSRcPA+27Qj29OtnLJUNgUw1ZJKIFMZEaGjyGnDAQlI7nwXVNeCqpYVIeFiorYG9+8CYllOWO3WVpSQYhTERYLdnOGANme00eQW1tBDpCUVHoKTkaCCrtoY3/A47FMgkgSiMiQB5riFgIaWD9/9StbQQ6RG7dwfXsGwKZMBCBTJJIApjkvCOeLC9oclre/x+qFFLC5Ee4dlgQ1hsQyCDpkCmKUtJBApjkvBWH6fJKzRMUYpIz7BQVQmHC4P/bQpkDVOWu1XUL3FOYUwSWuVxmrw2Ki1F9WIiPWz//uA6lnBMDVm9wz4vsucm0pMUxiShrQs4VHfQ5BWCl+Cr2atIz7PArl3Bq5vhaCALtr3wcVCBTOKUwpgkrDoLqz1DUgdNXiE4RWkVxER6noXqaigsPLqpMZCVW8Nrfh9HFMgkDimMScLa5BrKOmry2kBd90V61/79UFd39P+NgazECwayMv1yJHFGYUwSUsDCCtfBYPF1ELSsVUsLkd5mgd17Wm5zDPQxlkLP8Hq9j2q9JiWOKIxJQsr3DEesIes4+1VWguf2yimJSCMLlRVQXNxys89AjrHs8wwL/T7qFcgkTiiMScJxLXwZcADb7tJHjUo1RSkSMXv3QuCYJciSDGQby3bX8LbfwVUgkzigMCYJp8AzHAphVAwLpSVoilIkQlwvGMiOlWwg01g2eA6fBBxdYCMxT2FMEopnYXnDm3fycUa8qqpa/1YuIr3IBtetbKvpcqqBVCxfug6r1aVfYpzCmCSULQ0Lgmd1sPRRo5JSNEUpEgV27267djPDgMHyQcBhuwKZxDCFMUkYbidGxRp/I9cUpUjk+f2w/0Dbt2UBfmCJ3+GwepBJjFIYk4SxuROjYpWaohSJKoWFwdKBYxkDfYAKG7zCslK/QEkMUhiThOC38EXDFZTHHRWjYVRMsx4i0cPAzl3B5cla3dTQg+yQVcsLiU0KY5IQ1rmGwlCuoCTY6LVYU5Qi0cVCfR0caGe60mcgB8tO1/CO38HT61diiMKYxL1qC1+4Dk4IfcUgeOWWGr2KRKfDh4MNYdvS2PJiveewNKCPN4kd+mmVuLc84FDmGbJD3L+oCE1RikSxHbvAbaemM9VAMpYvXMMGXWEpMUJhTOJaoQd5riHVWJwQ3pcDASgrR1OUIlEsEIBdu9u/PdOAB7zrd9jjKZBJ9FMYk7jlWfgo4FBrDRkh3kftLERigIWysuCUZXtygGprWOR3KFLLC4lyCmMStzZ5hm2eQ5axmBB/OT5S1LPnJCLhs28/VFa2fZsx0NdYSj3DG34fVfolS6KYwpjEpUoLHze0skgJMYhVV0NtTY+eloiE2fbtUFfX9m1OQ8uLg2p5IVFOYUzijrXwod+h1DPkdOJ+R46gwn2RWGKDi4lv3dZ+k2afgWwsO1zDm36HgAKZRCGFMYk7Gz3DJs8hI8SifQhemVVcjOrFRGKNhfr6YCBz22lJk2wgy1g2eQ7vqQeZRCGFMYkrJV5wVMxiSevEKFdRcXBETURikIWamo4DWYqBdCx5nsMnDWvUikQLhTGJG34LbwZ8lNvOTU9aC4cLe+y0RKQ32GDdZ8HW9qcs0wykYlnmOixTIJMoojAmccE2tLHY5RqyO3H1JAQvkffX99y5iUgvaRghy89vv6g/w0ASlk9cw0o1hZUooTAmcWG1a8hzHdJNaAuBN3foECrcF4kXFur9sDkfKtpZNinTBF/yHwUc1gT04pfIUxiTmLfVNXwQcDBY0jv5vlpZGZzaUOG+SByxwfVlt26F/fvbrgfNbuzSH3BYqxEyiTCFMYlpez3DEr+DH8jqwv0PHESjYiJx7NCh4ChZTXXr27IJBrJ3/A6rNUImEaQwJjFrnwev1ztUWUMf6FSdGARHxSor0KiYSJyrrQ0Gsr17W15taUwwkFmCI2Rf+o2K+iUiFMYkJu314LX64JWT/TpZsN9o/wE0KiaSCBoCVuER2LABCguPTl0aE5yyNMCHro9PdZWlRIDCmMScba7h1XofFd0IYuXlUFWJRsVEEokNjozt3QsbN0JJCU3vAVkGfFiWug7vBhxcvTdIL0qK9AmIhMpayHMNHwaCNWJ9uxjErIW9+wj+Kqw3XJGEVF8PO3fCgTQYmgt9+wavsqyxlpWuQ7WFS5M9UjV6Lr1AYUxiQr2FD/wOazwHB9ulGrFGR45AXW1YT09EYlRdbTCUpaRC7hDo3w8cY9noOlRaw5UpLjkKZNLDjLWhzY6XlJT09LmItKnQg7f9PvZ4hnTT+fYVzfn9wekJzwvf+YlI/PAlweBB0HcgVDuGgY7lsmSXE1TUI13Ur1+/4+6jMCZRy7WwxjV8GgheMZljLEnd/A11+45gx31NT4pIR4yBvv0hY4ghJ8XytWSPs5yulUZIYgsljGmaUqJSoQfvB3zs9AwOtsuF+s2VlEBZaVhOT0TinLVQUgwlRZbSXHg122FvhsfX0ywpCmQSZhoZk6hSa+HLgMNK11BjDVkmPG989XWwabOmJ0Wka3yZkJxjyC7zuCLdZcpIMBomkxBomlJihmthk2dYGnAo8gwpxpJJ14v0m/M8yN8SbPyo6UkR6SqTDKkDob4Iqv/XZcYwyyWzYNhQhTJpn8KYRD1rYVtDCDtgg70msgFfmN7brIUdO1UnJiJhYiBlIFgXij722PcPlzNOgZkXGWZcAEMVzOQYCmMStayFXZ7hC9dhj2dwLWQZS3KY38d274aiovAeU0TElwlJ2YbqXR57/+ZSmW+xFoYPhxkXwNRzDWeOAl+4frOUmKUwJlHHWthtDV8GDDs9gz+MdWHHPs6ePQpiItJzjA9SBhrcWkvhex4HFrl4teA4wfKIrEyYOhXOmWyYNBFyhyiYJSKFMYkanoWdnuFL12GPB35ryDSWFMJTF9acG4Cdu4JLHomI9LSkbPBlGmp2B6cty9Yc/Vj1+Y4uTj54MEyeCBPGG84+W7VmiUJhTCLOb2GLZ1gVcDhog9ORGT0UwiAYwHbthkAA1YiJSK8xPkgZYPBcS+lKj/2vutQdbr1f83A2aCBMngyTJhomT4KBAxTO4pHCmERMpYUNrmG161DSUJifCT3Sn8daqKiAQ4ehsiL8xxcRCZUvHZL6QKACCt/3OPyuS6CD96Xm4ey0U+GCaTD9AsOoM9Q6I14ojEmvshYOWFjvOmxyDdXW4GtoUdHdzvnHqq+HwkKoqYWqyob+YVr4W0SiRFIf8KUb6osth99xOfKxh1t1/Pv5HHA9GDIYLr4YLp1lOOUUhbJYpjAmvaLKQoFrWO86HLTBerBUY0kHnB56DzlcCPv29syxRUTCwoHkvuAkQ30JHPnQ5cgnHv4QP04bLwQYMxrmXm246GuQmqpgFmsUxqTH1DQU5Be4hu2eoRaDIRjAeqoerLnDh2HffjQSJiLRz4HkPuCkGgKVltI8j+KlHhX5FkJYFcSY4MxDdjZcMwfmzjGqL4shCmMSNtZCiYXdnmGHZ9jtGWowWAtpxpJGz42CtUVhTERijoGkrGCPMuuH2oOW4s89SlZ61B0K7RCOEwxns74ON37DMPw0hbJopzAmXeZZKLZwyBoOeIZdnqHUgr8hgKU2BLBI9TNUGBORWOakQFIOmCSDW2Op2m4pXeFRujq0aczGwv9zp8BN3zRMGK+C/2ilMCYh8VsotVBkDUXWcNiD/Z6hlqPhK8lYUumdKchQKIyJSLzwZYAvC4wDbjVUbrGUrPAoW+N1eCUmHK0rGzE8GMq+diEkh3spE+kWhTFpIdAw1VhsTfCPB4etocxCAAjQ+AK2JAPJRE/4OpbCmIjEHdOwzFJm8NLwQBWUrfUoWe5RvsFi/R3ctaGurE+fYF3ZlZcbBg+OwjfvBKQwlsDqLRRaKPQMhdZw2DMcseAnONploGnEKwma/vRm3Vd3KIyJSFxzICmzob7MhfoiKFnuUbLCo3pnx298jhN8f596LlxxmeGrUyGlJ5o8SkgUxhKEtVABHPAMBz3D3obgVU+w470FfA2hK5nYCl3tURgTkURhkoJLLjmp4NVDzW5L0RcepSs7ri9rnMLMyICLvgYXTg+ukalpzN6lMBanrIUyC/utYX/DlY2lDeELC6ZhuaHG4BWN04zdpTAmIonISQsGs8b6svKNHqWrLOXrO64vayz4T0uFKefAlCnBYHbiCSr872kKY3HCs1Bk4aA1HHANu62hvCF8NU41phCs74r1Ea9QKYyJSEIzjdOYwfoytxoqCyzlGzyqtlmq97RfY9Y4YgbQtw985SwYPTq4BNOI4TBAPczCSmEsBlkLZUBRQ63XQS84AlbTbOQrEcPXsRTGREQaOJCUAU4GGMfg+YPhrGaPpXq3pb7QUl9iCVRAoMri1QSnO70A2MDRz5HGgJaVBaeeElwr88QTDcOGQm4u5A6Bvn01ktZZCmNRKmChmuAyQhXWUGGhzAbrvAo9Qx0NLSUALCQ3m3bUayBIYUxEpG0mBXxpwRoz40Djwr3WDV4MYD3AC/7yj234u/E2Fzw/eH6LrQO3BvyVwXDnVlm8asgA+qZZBqZbBmdBbj9D7pDgepq5QyAjQx9UzYUSxpJ64Tw6pdTC234fAcAHJBPsb5VmINVAGpYMA+lAhjn673DXI1oLLsGrD+sJ9uKqJxiSAk2tIIL7eA37uoCHCd6vcR8LdUAtwdGtGqCuYQUMt2HfxkBhmhXZp2GDDVX1My0iIp1g6yFQ32ILGDC+hj8O4BDc1tDRn+Tgnqbxc8eY4H4GjOMEj2JtcFTMBIPdkQAUurCuDuqLLfW7LPXF4NRYMn0wIN0yMAsGZ8OQPjC0P+QOgv79ISlJH27NRV8Y8ww7vWCKDzJ4NvhNMw2brQn+HDmAD4tDcMou3QQDWjoEG5SaYKDzNezfqOGXAtyGwFTfEJjqMNRZgs1OG25r+MUBD7CYhr+bnV3zkZnmP1u22T7GYjh6zg7BL3wKFh/B+XsREZEeY4NTkjbQxTsf82/jC17laZKCFxVknGTIPM0c/Rw0wTWMd7uwq9mInLcHvAIwriXJgxRfcEWXZF/wMzvZB0kO+Jzg9KnT8FlqveA0asCFQKDhbxf8LvgD4PeC/3YNeI7BNvRrMj4gyXDKadBv4NHBk1zH8i8pISwM2kuiLow5JjgaVtc82TT80zb7d2OgamxUWkPwCkPs0fs5ze5+bAa3x/zpKtteuG+23TZMObb7bddUW6fVZ0DqEH3hRESiljka2JpkNbuRYDCqPvYTunH6tPn/2zh2018NH/I+gn/aUpUCptkH9uHoyWFAFIaxYcZyRbIXnL4TaUdNqmX91obaBxERkXYYA6NGQU6zeqYcE12/zKuAX0RERKSHhFLAr2olERERkQhSGBMRERGJIIUxERERkQhSGBMRERGJIIUxERERkQhSGBMRERGJIIUxERERkQhSGBMRERGJIIUxERERkQhSGBMRERGJIIUxERERkQhSGBMRERGJIIUxERERkQhSGBMRERGJIIUxERERkQgy1lob6ZMQERERSVQaGRMRERGJIIUxERERkQhSGBMRERGJIIUxERERkQhSGBMRERGJIIUxERERkQhSGBMRERGJIIUxERERkQhSGBMRERGJoP8fAJmhNb8b268AAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 600x400 with 1 Axes>"
       ]
@@ -1150,7 +1150,7 @@
        "            }\n",
        "        </style>\n",
        "      <progress value='22000' class='' max='22000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [22000/22000 00:06<00:00 Sampling 2 chains, 0 divergences]\n",
+       "      100.00% [22000/22000 00:04<00:00 Sampling 2 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -1165,7 +1165,7 @@
      "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 13 seconds.\n",
+      "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"
      ]
     }
@@ -1193,7 +1193,7 @@
     {
      "data": {
       "text/plain": [
-       "['p_jump', 'accepted', 'scaling', 'accept']"
+       "['accept', 'accepted', 'p_jump', 'scaling']"
       ]
      },
      "execution_count": 12,
@@ -1219,7 +1219,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1656x552 with 2 Axes>"
       ]
@@ -1605,17 +1605,17 @@
        "  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      , 1601.21960006],\n",
-       "       [   3.75      ,  182.75018508]])\n",
+       "array([[  3.75      , 269.57489704],\n",
+       "       [  3.75      , 147.77852694]])\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-e20481b6-6a33-4b94-ade8-fd3ccc0e80e9' class='xr-array-in' type='checkbox' checked><label for='section-e20481b6-6a33-4b94-ade8-fd3ccc0e80e9' 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 1.601e+03 3.75 182.8</span></div><div class='xr-array-data'><pre>array([[   3.75      , 1601.21960006],\n",
-       "       [   3.75      ,  182.75018508]])</pre></div></div></li><li class='xr-section-item'><input id='section-2862812f-e976-4ade-a97d-109576d7c785' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2862812f-e976-4ade-a97d-109576d7c785' 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-fc9ef482-5f51-4d41-bdd4-f3fd2fa6a90a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc9ef482-5f51-4d41-bdd4-f3fd2fa6a90a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f3240a1-3621-4245-96ad-4ddb036f75e9' class='xr-var-data-in' type='checkbox'><label for='data-4f3240a1-3621-4245-96ad-4ddb036f75e9' 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-edb02fd1-aace-4c5a-9267-b125cd4f45fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-edb02fd1-aace-4c5a-9267-b125cd4f45fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a27318a-2393-4a22-af70-b2caa9047c9d' class='xr-var-data-in' type='checkbox'><label for='data-6a27318a-2393-4a22-af70-b2caa9047c9d' 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-a716de26-deb6-416d-baa2-7b7586f41732' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a716de26-deb6-416d-baa2-7b7586f41732' 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' 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>"
       ],
       "text/plain": [
        "<xarray.DataArray 'accept' (chain: 2, accept_dim_0: 2)>\n",
-       "array([[   3.75      , 1601.21960006],\n",
-       "       [   3.75      ,  182.75018508]])\n",
+       "array([[  3.75      , 269.57489704],\n",
+       "       [  3.75      , 147.77852694]])\n",
        "Coordinates:\n",
        "  * chain         (chain) int64 0 1\n",
        "  * accept_dim_0  (accept_dim_0) int64 0 1"
@@ -1638,7 +1638,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1656x552 with 2 Axes>"
       ]
@@ -1659,27 +1659,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The watermark extension is already loaded. To reload it, use:\n",
-      "  %reload_ext watermark\n",
       "Last updated: Tue Apr 06 2021\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.9.2\n",
       "IPython version      : 7.21.0\n",
       "\n",
-      "matplotlib: 3.3.4\n",
+      "seaborn   : 0.11.1\n",
       "pandas    : 1.2.3\n",
       "arviz     : 0.11.2\n",
-      "pymc3     : 3.11.2\n",
-      "seaborn   : 0.11.1\n",
+      "matplotlib: 3.3.4\n",
       "numpy     : 1.20.1\n",
+      "pymc3     : 3.11.2\n",
       "\n",
       "Watermark: 2.2.0\n",
       "\n"