{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Pseudobatch transformation with uncertainties\n",
    "\n",
    "This notebook describes how to use the Pseudobatch transformation with error propagation of the measurement uncertainties. This utilizes a Bayesian model which is provided as a precompiled model implemented in the programming language [Stan](https://mc-stan.org/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/s143838/.virtualenvs/pseudobatch-dev/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'stan_version_major': '2', 'stan_version_minor': '29', 'stan_version_patch': '2', 'STAN_THREADS': 'false', 'STAN_MPI': 'false', 'STAN_OPENCL': 'false', 'STAN_NO_RANGE_CHECKS': 'false', 'STAN_CPP_OPTIMS': 'false'}\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "\n",
    "from itertools import islice\n",
    "\n",
    "import arviz as az\n",
    "import cmdstanpy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.collections import PolyCollection\n",
    "from scipy.special import logit\n",
    "\n",
    "from pseudobatch.data_correction import pseudobatch_transform\n",
    "from pseudobatch.datasets import load_standard_fedbatch\n",
    "from pseudobatch.error_propagation import run_error_propagation\n",
    "\n",
    "cmdstanpy_logger = logging.getLogger(\"cmdstanpy\")\n",
    "cmdstanpy_logger.disabled = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading data\n",
    "\n",
    "This cell uses the function `load_standard_fedbatch` from pseudobatch's `datasets` module to load a standard dataset. It then adds some columns that will be useful later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Kc_s</th>\n",
       "      <th>mu_max</th>\n",
       "      <th>Yxs</th>\n",
       "      <th>Yxp</th>\n",
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       "      <th>timestamp</th>\n",
       "      <th>...</th>\n",
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       "      <th>c_Glucose</th>\n",
       "      <th>c_Biomass</th>\n",
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       "      <td>170.0</td>\n",
       "      <td>24.285714</td>\n",
       "      <td>...</td>\n",
       "      <td>179.754779</td>\n",
       "      <td>0.075053</td>\n",
       "      <td>5.175767</td>\n",
       "      <td>3.877080</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100047</td>\n",
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       "      <td>5.582507</td>\n",
       "      <td>-7.777595</td>\n",
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       "      <td>8.963843</td>\n",
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       "      <td>0.3</td>\n",
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       "      <td>0.075011</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.15</td>\n",
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       "      <td>0.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>52.857143</td>\n",
       "      <td>...</td>\n",
       "      <td>1178.794116</td>\n",
       "      <td>0.075033</td>\n",
       "      <td>35.029969</td>\n",
       "      <td>28.631766</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100029</td>\n",
       "      <td>153.246173</td>\n",
       "      <td>97.201461</td>\n",
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       "      <td>79.447672</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.85</td>\n",
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       "      <td>0.062881</td>\n",
       "      <td>0.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1662.311084</td>\n",
       "      <td>0.075012</td>\n",
       "      <td>42.688520</td>\n",
       "      <td>34.982129</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100011</td>\n",
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       "      <td>-364.778789</td>\n",
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      "text/plain": [
       "   Kc_s  mu_max   Yxs      Yxp     Yxco2        F0  mu0    s_f  sample_volume   \n",
       "0  0.15     0.3  1.85  0.82151  0.045193  0.062881  0.1  100.0          170.0  \\\n",
       "1  0.15     0.3  1.85  0.82151  0.045193  0.062881  0.1  100.0          170.0   \n",
       "2  0.15     0.3  1.85  0.82151  0.045193  0.062881  0.1  100.0          170.0   \n",
       "3  0.15     0.3  1.85  0.82151  0.045193  0.062881  0.1  100.0          170.0   \n",
       "4  0.15     0.3  1.85  0.82151  0.045193  0.062881  0.1  100.0          170.0   \n",
       "5  0.15     0.3  1.85  0.82151  0.045193  0.062881  0.1  100.0          170.0   \n",
       "6  0.15     0.3  1.85  0.82151  0.045193  0.062881  0.1  100.0          170.0   \n",
       "7  0.15     0.3  1.85  0.82151  0.045193  0.062881  0.1  100.0          170.0   \n",
       "\n",
       "   timestamp  ...    m_CO2_gas  c_Glucose  c_Biomass  c_Product  c_CO2   \n",
       "0  10.000000  ...    38.827037   0.075016   1.337852   0.694735    0.0  \\\n",
       "1  17.142857  ...    92.155995   0.075103   2.664023   1.794378    0.0   \n",
       "2  24.285714  ...   179.754779   0.075053   5.175767   3.877080    0.0   \n",
       "3  31.428571  ...   317.230058   0.075015   9.612284   7.555778    0.0   \n",
       "4  38.571429  ...   521.048177   0.075011  16.561967  13.318358    0.0   \n",
       "5  45.714286  ...   804.712744   0.075009  25.635276  20.841818    0.0   \n",
       "6  52.857143  ...  1178.794116   0.075033  35.029969  28.631766    0.0   \n",
       "7  60.000000  ...  1662.311084   0.075012  42.688520  34.982129    0.0   \n",
       "\n",
       "    mu_true  v_Feed_interval  c_Biomass_pseudobatch  c_Glucose_pseudobatch   \n",
       "0  0.100014        15.906036               1.337852               0.075016  \\\n",
       "1  0.100091        21.847477               2.732828              -2.505689   \n",
       "2  0.100047        35.885358               5.582507              -7.777595   \n",
       "3  0.100013        56.317871              11.403591             -18.546600   \n",
       "4  0.100010        83.496054              23.294434             -40.544661   \n",
       "5  0.100008       116.205937              47.584179             -85.480689   \n",
       "6  0.100029       153.246173              97.201461            -177.272659   \n",
       "7  0.100011       198.077362             198.556125            -364.778789   \n",
       "\n",
       "   c_Product_pseudobatch  \n",
       "0               0.694735  \n",
       "1               1.840722  \n",
       "2               4.181762  \n",
       "3               8.963843  \n",
       "4              18.732292  \n",
       "5              38.686567  \n",
       "6              79.447672  \n",
       "7             162.711567  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SPECIES = [\"Biomass\", \"Glucose\", \"Product\"]\n",
    "samples = load_standard_fedbatch(sampling_points_only=True)\n",
    "samples[\"v_Feed_interval\"] = np.concatenate(\n",
    "    [np.array([samples[\"v_Feed_accum\"].iloc[0]]), np.diff(samples[\"v_Feed_accum\"])]\n",
    ")\n",
    "for species in SPECIES:\n",
    "    samples[f\"c_{species}_pseudobatch\"] = pseudobatch_transform(\n",
    "        measured_concentration=samples[f\"c_{species}\"],\n",
    "        reactor_volume=samples[\"v_Volume\"],\n",
    "        accumulated_feed=samples[\"v_Feed_accum\"],\n",
    "        concentration_in_feed=100 if species == \"Glucose\" else 0,\n",
    "        sample_volume=samples[\"sample_volume\"],\n",
    "    )\n",
    "samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specifying priors\n",
    "\n",
    "The next cell specifies the prior distributions required for pseudobatch's error propagation function. These are set by choosing values for percentiles. Note that it is also possible to set prior distributions using location and scale parameters, for example:\n",
    "\n",
    "```\n",
    "priors = {\n",
    "    ...\n",
    "    prior_v0: {\"mu\": 0, \"sigma\": 1},\n",
    "    ...\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "priors = {\n",
    "    \"prior_apump\": {\"pct1\": np.log(1 - 0.1), \"pct99\": np.log(1 + 0.1)},\n",
    "    \"prior_as\": {\"pct1\": logit(0.05), \"pct99\": logit(0.4)},\n",
    "    \"prior_v0\": {\"pct1\": 1000, \"pct99\": 1030},\n",
    "    \"prior_cfeed\": [{\"loc\": 0, \"scale\": 1}, {\"pct1\": 98, \"pct99\": 102}, {\"loc\": 0, \"scale\": 1}],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the error propagation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "  --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[data-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 !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\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",
       ".xr-index-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",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-no-icon {\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: 4, draw: 1000, sample: 8, species: 3,\n",
       "                          f_nonzero_dim_0: 8, cfeed_nonzero_dim_0: 1)\n",
       "Coordinates:\n",
       "  * chain                (chain) int64 0 1 2 3\n",
       "  * draw                 (draw) int64 0 1 2 3 4 5 6 ... 994 995 996 997 998 999\n",
       "  * sample               (sample) int64 0 1 2 3 4 5 6 7\n",
       "  * species              (species) &lt;U7 &#x27;Biomass&#x27; &#x27;Glucose&#x27; &#x27;Product&#x27;\n",
       "  * f_nonzero_dim_0      (f_nonzero_dim_0) int64 0 1 2 3 4 5 6 7\n",
       "  * cfeed_nonzero_dim_0  (cfeed_nonzero_dim_0) int64 0\n",
       "Data variables: (12/13)\n",
       "    v0                   (chain, draw) float64 1.004e+03 1.026e+03 ... 1.016e+03\n",
       "    m                    (chain, draw, sample, species) float64 1.26e+03 ... ...\n",
       "    as                   (chain, draw, sample) float64 -1.611 -1.375 ... -0.7452\n",
       "    f_nonzero            (chain, draw, f_nonzero_dim_0) float64 14.72 ... 198.5\n",
       "    cfeed_nonzero        (chain, draw, cfeed_nonzero_dim_0) float64 100.3 ......\n",
       "    apump                (chain, draw) float64 0.02635 -0.007463 ... -0.01464\n",
       "    ...                   ...\n",
       "    s                    (chain, draw, sample) float64 169.5 175.9 ... 164.1\n",
       "    v                    (chain, draw, sample) float64 1.019e+03 871.8 ... 509.7\n",
       "    c                    (chain, draw, sample, species) float64 1.237 ... 35.05\n",
       "    pump_bias            (chain, draw) float64 -3.636 nan -6.652 ... nan nan nan\n",
       "    cfeed                (chain, draw, species) float64 0.0 100.3 ... 98.99 0.0\n",
       "    pseudobatch_c        (chain, draw, sample, species) float64 1.237 ... 161.1\n",
       "Attributes:\n",
       "    created_at:                 2023-07-26T11:42:38.192599\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          cmdstanpy\n",
       "    inference_library_version:  1.1.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-15ca34e6-c6d6-44e3-a10d-c4cc605fbd71' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-15ca34e6-c6d6-44e3-a10d-c4cc605fbd71' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>sample</span>: 8</li><li><span class='xr-has-index'>species</span>: 3</li><li><span class='xr-has-index'>f_nonzero_dim_0</span>: 8</li><li><span class='xr-has-index'>cfeed_nonzero_dim_0</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-deb18e3a-bf74-40f4-a451-cbd9beee70f6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-deb18e3a-bf74-40f4-a451-cbd9beee70f6' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-284eb512-c1d4-4acb-90fd-d6771ac0be5c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-284eb512-c1d4-4acb-90fd-d6771ac0be5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62c46ee7-387d-4d0f-80b0-94bfa9cee4ca' class='xr-var-data-in' type='checkbox'><label for='data-62c46ee7-387d-4d0f-80b0-94bfa9cee4ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-8b3e6187-97e2-4575-bbe8-0c710e28b1a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b3e6187-97e2-4575-bbe8-0c710e28b1a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-618e8bed-cc8c-4444-ac89-dad65a36813d' class='xr-var-data-in' type='checkbox'><label for='data-618e8bed-cc8c-4444-ac89-dad65a36813d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-44be0f75-6400-4941-8c0f-a3eff333c112' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44be0f75-6400-4941-8c0f-a3eff333c112' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-918c5795-b9cb-4bb2-b1eb-9e9de00ef718' class='xr-var-data-in' type='checkbox'><label for='data-918c5795-b9cb-4bb2-b1eb-9e9de00ef718' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>species</span></div><div class='xr-var-dims'>(species)</div><div class='xr-var-dtype'>&lt;U7</div><div class='xr-var-preview xr-preview'>&#x27;Biomass&#x27; &#x27;Glucose&#x27; &#x27;Product&#x27;</div><input id='attrs-f90eb78f-e414-4433-82ce-2ff82e97cbf9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f90eb78f-e414-4433-82ce-2ff82e97cbf9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cf48535-60ae-4b7e-9d72-52362c580099' class='xr-var-data-in' type='checkbox'><label for='data-0cf48535-60ae-4b7e-9d72-52362c580099' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Biomass&#x27;, &#x27;Glucose&#x27;, &#x27;Product&#x27;], dtype=&#x27;&lt;U7&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>f_nonzero_dim_0</span></div><div class='xr-var-dims'>(f_nonzero_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</div><input id='attrs-2cf34df2-92f1-436f-83a5-0c62d801d470' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2cf34df2-92f1-436f-83a5-0c62d801d470' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d22274f0-a795-4fc8-89f4-2a85d44d229e' class='xr-var-data-in' type='checkbox'><label for='data-d22274f0-a795-4fc8-89f4-2a85d44d229e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>cfeed_nonzero_dim_0</span></div><div class='xr-var-dims'>(cfeed_nonzero_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-f99469e2-195a-4a9d-9547-162650165f76' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f99469e2-195a-4a9d-9547-162650165f76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f245ec37-2f82-47a3-9022-924241173f0c' class='xr-var-data-in' type='checkbox'><label for='data-f245ec37-2f82-47a3-9022-924241173f0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6812ecbe-6875-4d1b-a239-8a6ccec2db91' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6812ecbe-6875-4d1b-a239-8a6ccec2db91' 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>v0</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.004e+03 1.026e+03 ... 1.016e+03</div><input id='attrs-bf12431e-f9a9-40e5-881e-7bb86f0d717d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bf12431e-f9a9-40e5-881e-7bb86f0d717d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83713f77-37ba-4898-828d-f5b63b2c2b4e' class='xr-var-data-in' type='checkbox'><label for='data-83713f77-37ba-4898-828d-f5b63b2c2b4e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1004.28, 1026.37, 1017.23, ..., 1008.44, 1013.43, 1012.04],\n",
       "       [1004.59, 1007.05, 1003.18, ..., 1021.84, 1019.94, 1009.21],\n",
       "       [1018.23, 1017.56, 1021.04, ..., 1011.12, 1017.02, 1010.47],\n",
       "       [1014.01, 1014.12, 1022.23, ..., 1014.06, 1011.85, 1016.26]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m</span></div><div class='xr-var-dims'>(chain, draw, sample, species)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.26e+03 76.1 ... 37.03 1.787e+04</div><input id='attrs-c93c756b-3e62-4fc8-aa8a-6959eaceba31' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c93c756b-3e62-4fc8-aa8a-6959eaceba31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db776224-9119-4eb7-bdff-adc56cfb9029' class='xr-var-data-in' type='checkbox'><label for='data-db776224-9119-4eb7-bdff-adc56cfb9029' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[ 1260.25  ,    76.1023,   765.462 ],\n",
       "         [ 2311.47  ,    62.1931,  1594.25  ],\n",
       "         [ 3507.8   ,    51.331 ,  3148.67  ],\n",
       "         ...,\n",
       "         [11382.1   ,    35.1027,  9364.01  ],\n",
       "         [14445.9   ,    33.8763, 12084.6   ],\n",
       "         [21867.4   ,    35.0864, 15503.8   ]],\n",
       "\n",
       "        [[ 1469.17  ,    81.1577,   659.826 ],\n",
       "         [ 2450.85  ,    69.4982,  1510.05  ],\n",
       "         [ 4076.87  ,    55.6378,  2879.78  ],\n",
       "         ...,\n",
       "         [12982.6   ,    37.387 , 10686.8   ],\n",
       "         [18350.8   ,    39.6543, 13755.7   ],\n",
       "         [21635.4   ,    38.7365, 18301.8   ]],\n",
       "\n",
       "        [[ 1255.13  ,    76.9101,   709.323 ],\n",
       "         [ 2410.34  ,    62.9537,  1569.29  ],\n",
       "         [ 3692.17  ,    55.6652,  3054.02  ],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [11246.2   ,    34.999 , 10014.5   ],\n",
       "         [17467.6   ,    35.103 , 12819.5   ],\n",
       "         [21576.9   ,    37.2848, 18365.3   ]],\n",
       "\n",
       "        [[ 1459.9   ,    74.9483,   713.075 ],\n",
       "         [ 2216.56  ,    65.5074,  1774.97  ],\n",
       "         [ 4016.81  ,    56.2581,  2631.59  ],\n",
       "         ...,\n",
       "         [11227.6   ,    33.5943,  9978.15  ],\n",
       "         [17285.9   ,    35.4055, 12925.8   ],\n",
       "         [21980.    ,    37.4945, 18377.3   ]],\n",
       "\n",
       "        [[ 1345.59  ,    86.5681,   737.668 ],\n",
       "         [ 2419.95  ,    68.8602,  1652.02  ],\n",
       "         [ 4005.8   ,    52.634 ,  2952.51  ],\n",
       "         ...,\n",
       "         [12914.6   ,    37.4064,  9728.93  ],\n",
       "         [16403.5   ,    35.857 , 15206.1   ],\n",
       "         [20611.5   ,    37.0285, 17867.4   ]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>as</span></div><div class='xr-var-dims'>(chain, draw, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.611 -1.375 ... -0.6284 -0.7452</div><input id='attrs-f1111dd5-532c-4a97-aa7c-c6480e41fbae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f1111dd5-532c-4a97-aa7c-c6480e41fbae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58f73dab-f5dd-4a20-ab33-3b2922cf53b9' class='xr-var-data-in' type='checkbox'><label for='data-58f73dab-f5dd-4a20-ab33-3b2922cf53b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-1.61145 , -1.37504 , -1.13006 , ..., -0.523935, -0.464983,\n",
       "         -0.579321],\n",
       "        [-1.57932 , -1.42477 , -1.29493 , ..., -0.805514, -0.68054 ,\n",
       "         -0.672764],\n",
       "        [-1.58599 , -1.42537 , -1.19552 , ..., -0.526047, -0.581565,\n",
       "         -0.620633],\n",
       "        ...,\n",
       "        [-1.6377  , -1.37162 , -1.20553 , ..., -0.595495, -0.577478,\n",
       "         -0.55281 ],\n",
       "        [-1.55382 , -1.44654 , -1.2579  , ..., -0.511629, -0.58918 ,\n",
       "         -0.783497],\n",
       "        [-1.66794 , -1.44628 , -1.16155 , ..., -0.771301, -0.639267,\n",
       "         -0.556016]],\n",
       "\n",
       "       [[-1.66885 , -1.31531 , -1.09897 , ..., -0.658959, -0.541077,\n",
       "         -0.730251],\n",
       "        [-1.5417  , -1.37375 , -1.17289 , ..., -0.628409, -0.63694 ,\n",
       "         -0.552769],\n",
       "        [-1.58614 , -1.34687 , -1.17039 , ..., -0.59377 , -0.66473 ,\n",
       "         -0.74386 ],\n",
       "...\n",
       "        [-1.5834  , -1.40261 , -1.25027 , ..., -0.594125, -0.599569,\n",
       "         -0.617933],\n",
       "        [-1.61557 , -1.33142 , -1.16926 , ..., -0.695283, -0.607245,\n",
       "         -0.698782],\n",
       "        [-1.59526 , -1.40854 , -1.1514  , ..., -0.613496, -0.532948,\n",
       "         -0.614112]],\n",
       "\n",
       "       [[-1.61888 , -1.41389 , -1.29785 , ..., -0.609123, -0.47581 ,\n",
       "         -0.659437],\n",
       "        [-1.58731 , -1.5102  , -1.22275 , ..., -0.709401, -0.779657,\n",
       "         -0.672479],\n",
       "        [-1.62162 , -1.46535 , -1.28674 , ..., -0.750076, -0.495372,\n",
       "         -0.732939],\n",
       "        ...,\n",
       "        [-1.65852 , -1.4463  , -1.16807 , ..., -0.625564, -0.567387,\n",
       "         -0.700543],\n",
       "        [-1.62591 , -1.43828 , -1.11419 , ..., -0.627168, -0.54059 ,\n",
       "         -0.731704],\n",
       "        [-1.65174 , -1.42194 , -1.20677 , ..., -0.615124, -0.628374,\n",
       "         -0.745247]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>f_nonzero</span></div><div class='xr-var-dims'>(chain, draw, f_nonzero_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>14.72 22.35 33.9 ... 163.0 198.5</div><input id='attrs-50cce01e-0476-4fee-b154-0a96511235a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-50cce01e-0476-4fee-b154-0a96511235a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-031d7a46-b5ab-41ce-a4c9-b5118f2e08e6' class='xr-var-data-in' type='checkbox'><label for='data-031d7a46-b5ab-41ce-a4c9-b5118f2e08e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 14.7223,  22.354 ,  33.9039, ..., 106.614 , 148.04  ,\n",
       "         204.065 ],\n",
       "        [ 16.8728,  22.6609,  38.3539, ..., 113.145 , 148.765 ,\n",
       "         188.272 ],\n",
       "        [ 16.6097,  20.5948,  37.9735, ..., 109.12  , 142.308 ,\n",
       "         186.673 ],\n",
       "        ...,\n",
       "        [ 16.362 ,  22.2266,  36.1202, ..., 111.376 , 151.144 ,\n",
       "         187.373 ],\n",
       "        [ 15.1356,  22.2865,  39.6613, ..., 111.386 , 155.304 ,\n",
       "         200.103 ],\n",
       "        [ 15.8155,  19.4713,  32.7964, ..., 116.148 , 142.927 ,\n",
       "         190.572 ]],\n",
       "\n",
       "       [[ 17.1615,  24.6132,  34.9864, ..., 113.091 , 161.244 ,\n",
       "         199.65  ],\n",
       "        [ 16.6357,  24.0449,  39.7453, ..., 127.192 , 160.674 ,\n",
       "         207.127 ],\n",
       "        [ 16.4602,  24.9019,  42.1566, ..., 124.081 , 166.207 ,\n",
       "         210.954 ],\n",
       "...\n",
       "        [ 14.5347,  24.0051,  33.3133, ..., 123.877 , 148.27  ,\n",
       "         202.581 ],\n",
       "        [ 15.0333,  21.6769,  37.8141, ..., 124.71  , 152.477 ,\n",
       "         194.979 ],\n",
       "        [ 16.1747,  20.4994,  31.8258, ..., 103.776 , 154.249 ,\n",
       "         198.884 ]],\n",
       "\n",
       "       [[ 14.5134,  20.6769,  33.4388, ..., 110.949 , 147.015 ,\n",
       "         191.586 ],\n",
       "        [ 14.5909,  20.671 ,  33.5659, ..., 105.736 , 131.135 ,\n",
       "         185.7   ],\n",
       "        [ 13.203 ,  18.0013,  32.2442, ..., 106.971 , 155.64  ,\n",
       "         183.453 ],\n",
       "        ...,\n",
       "        [ 17.3522,  21.0054,  38.0972, ..., 115.51  , 162.324 ,\n",
       "         206.426 ],\n",
       "        [ 17.6804,  20.9919,  37.0799, ..., 121.973 , 165.976 ,\n",
       "         202.319 ],\n",
       "        [ 17.0594,  23.9935,  37.8593, ..., 116.693 , 163.036 ,\n",
       "         198.473 ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cfeed_nonzero</span></div><div class='xr-var-dims'>(chain, draw, cfeed_nonzero_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>100.3 98.85 99.8 ... 100.9 98.99</div><input id='attrs-2bab2efd-4620-4e62-9077-6bdd54a2d629' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2bab2efd-4620-4e62-9077-6bdd54a2d629' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-449afe8d-8bf0-4ce3-9106-61f477d29b0f' class='xr-var-data-in' type='checkbox'><label for='data-449afe8d-8bf0-4ce3-9106-61f477d29b0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[100.3   ],\n",
       "        [ 98.8484],\n",
       "        [ 99.799 ],\n",
       "        ...,\n",
       "        [ 99.8972],\n",
       "        [100.026 ],\n",
       "        [100.894 ]],\n",
       "\n",
       "       [[ 99.1586],\n",
       "        [ 99.235 ],\n",
       "        [ 99.6794],\n",
       "        ...,\n",
       "        [101.304 ],\n",
       "        [100.717 ],\n",
       "        [ 99.7991]],\n",
       "\n",
       "       [[ 99.9263],\n",
       "        [100.071 ],\n",
       "        [ 99.7721],\n",
       "        ...,\n",
       "        [ 99.8342],\n",
       "        [ 99.8649],\n",
       "        [100.244 ]],\n",
       "\n",
       "       [[100.672 ],\n",
       "        [ 99.8682],\n",
       "        [100.61  ],\n",
       "        ...,\n",
       "        [100.554 ],\n",
       "        [100.92  ],\n",
       "        [ 98.9883]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>apump</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.02635 -0.007463 ... -0.01464</div><input id='attrs-20dc5f49-bd87-4a37-bd3c-a85da878aa52' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20dc5f49-bd87-4a37-bd3c-a85da878aa52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ef417fd-c499-4052-85a6-17f4e64afccc' class='xr-var-data-in' type='checkbox'><label for='data-6ef417fd-c499-4052-85a6-17f4e64afccc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.0263462 , -0.00746303,  0.00129091, ..., -0.0191649 ,\n",
       "        -0.0141706 ,  0.0495135 ],\n",
       "       [-0.0342375 , -0.0700353 , -0.0506324 , ...,  0.0438329 ,\n",
       "        -0.00353409, -0.00361962],\n",
       "       [ 0.00125611,  0.0703719 ,  0.0664022 , ...,  0.00780936,\n",
       "         0.00034441,  0.0337547 ],\n",
       "       [ 0.063847  ,  0.0556138 ,  0.095508  , ..., -0.0109544 ,\n",
       "        -0.0449174 , -0.0146434 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>f</span></div><div class='xr-var-dims'>(chain, draw, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>14.72 22.35 33.9 ... 163.0 198.5</div><input id='attrs-232a4ca4-8ca0-4170-b386-d12a0192b026' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-232a4ca4-8ca0-4170-b386-d12a0192b026' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1db5807-5489-4f96-8e5b-5f15f360ed47' class='xr-var-data-in' type='checkbox'><label for='data-b1db5807-5489-4f96-8e5b-5f15f360ed47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 14.7223,  22.354 ,  33.9039, ..., 106.614 , 148.04  ,\n",
       "         204.065 ],\n",
       "        [ 16.8728,  22.6609,  38.3539, ..., 113.145 , 148.765 ,\n",
       "         188.272 ],\n",
       "        [ 16.6097,  20.5948,  37.9735, ..., 109.12  , 142.308 ,\n",
       "         186.673 ],\n",
       "        ...,\n",
       "        [ 16.362 ,  22.2266,  36.1202, ..., 111.376 , 151.144 ,\n",
       "         187.373 ],\n",
       "        [ 15.1356,  22.2865,  39.6613, ..., 111.386 , 155.304 ,\n",
       "         200.103 ],\n",
       "        [ 15.8155,  19.4713,  32.7964, ..., 116.148 , 142.927 ,\n",
       "         190.572 ]],\n",
       "\n",
       "       [[ 17.1615,  24.6132,  34.9864, ..., 113.091 , 161.244 ,\n",
       "         199.65  ],\n",
       "        [ 16.6357,  24.0449,  39.7453, ..., 127.192 , 160.674 ,\n",
       "         207.127 ],\n",
       "        [ 16.4602,  24.9019,  42.1566, ..., 124.081 , 166.207 ,\n",
       "         210.954 ],\n",
       "...\n",
       "        [ 14.5347,  24.0051,  33.3133, ..., 123.877 , 148.27  ,\n",
       "         202.581 ],\n",
       "        [ 15.0333,  21.6769,  37.8141, ..., 124.71  , 152.477 ,\n",
       "         194.979 ],\n",
       "        [ 16.1747,  20.4994,  31.8258, ..., 103.776 , 154.249 ,\n",
       "         198.884 ]],\n",
       "\n",
       "       [[ 14.5134,  20.6769,  33.4388, ..., 110.949 , 147.015 ,\n",
       "         191.586 ],\n",
       "        [ 14.5909,  20.671 ,  33.5659, ..., 105.736 , 131.135 ,\n",
       "         185.7   ],\n",
       "        [ 13.203 ,  18.0013,  32.2442, ..., 106.971 , 155.64  ,\n",
       "         183.453 ],\n",
       "        ...,\n",
       "        [ 17.3522,  21.0054,  38.0972, ..., 115.51  , 162.324 ,\n",
       "         206.426 ],\n",
       "        [ 17.6804,  20.9919,  37.0799, ..., 121.973 , 165.976 ,\n",
       "         202.319 ],\n",
       "        [ 17.0594,  23.9935,  37.8593, ..., 116.693 , 163.036 ,\n",
       "         198.473 ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>s</span></div><div class='xr-var-dims'>(chain, draw, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>169.5 175.9 178.2 ... 166.0 164.1</div><input id='attrs-b568188c-73cb-4193-a7c4-9f3317380af2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b568188c-73cb-4193-a7c4-9f3317380af2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44a3fbff-b7f9-4895-b806-96dbe523aed3' class='xr-var-data-in' type='checkbox'><label for='data-44a3fbff-b7f9-4895-b806-96dbe523aed3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[169.549, 175.937, 178.175, ..., 169.251, 167.38 , 168.963],\n",
       "        [178.281, 172.123, 162.093, ..., 154.747, 166.411, 174.657],\n",
       "        [175.699, 170.317, 173.367, ..., 178.387, 159.271, 164.883],\n",
       "        ...,\n",
       "        [166.815, 178.119, 170.153, ..., 171.013, 165.861, 176.347],\n",
       "        [179.521, 166.018, 164.889, ..., 186.054, 166.153, 156.656],\n",
       "        [163.119, 168.507, 178.431, ..., 158.748, 167.951, 185.462]],\n",
       "\n",
       "       [[162.027, 187.126, 183.   , ..., 158.596, 172.112, 161.046],\n",
       "        [180.463, 175.201, 172.951, ..., 165.833, 163.127, 188.29 ],\n",
       "        [173.266, 179.817, 173.709, ..., 168.932, 160.365, 168.393],\n",
       "        ...,\n",
       "        [179.175, 168.212, 174.508, ..., 171.7  , 151.895, 176.759],\n",
       "        [165.431, 178.776, 174.863, ..., 154.297, 173.67 , 153.385],\n",
       "        [176.558, 166.629, 160.68 , ..., 182.529, 156.796, 184.868]],\n",
       "\n",
       "       [[148.01 , 173.777, 175.902, ..., 171.356, 168.718, 178.305],\n",
       "        [172.945, 164.008, 164.232, ..., 163.816, 178.72 , 169.799],\n",
       "        [171.359, 162.29 , 164.733, ..., 173.043, 174.54 , 174.348],\n",
       "        ...,\n",
       "        [174.684, 172.723, 163.776, ..., 173.589, 164.006, 175.578],\n",
       "        [171.131, 184.396, 174.428, ..., 161.916, 168.232, 167.286],\n",
       "        [173.139, 171.711, 176.359, ..., 163.542, 168.75 , 170.794]],\n",
       "\n",
       "       [[170.075, 171.976, 158.876, ..., 174.331, 179.195, 163.605],\n",
       "        [174.637, 158.25 , 170.605, ..., 162.326, 144.969, 169.592],\n",
       "        [170.827, 165.622, 162.137, ..., 154.278, 182.601, 156.797],\n",
       "        ...,\n",
       "        [164.985, 169.12 , 179.424, ..., 167.681, 172.153, 169.178],\n",
       "        [169.246, 169.039, 185.15 , ..., 166.847, 176.062, 163.911],\n",
       "        [166.234, 173.188, 174.028, ..., 169.893, 166.049, 164.065]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(chain, draw, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.019e+03 871.8 ... 477.3 509.7</div><input id='attrs-8016ca48-254d-46ee-9413-d0272bc9903d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8016ca48-254d-46ee-9413-d0272bc9903d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f0aa10c-7935-4d26-baf1-2325c74c5ab1' class='xr-var-data-in' type='checkbox'><label for='data-1f0aa10c-7935-4d26-baf1-2325c74c5ab1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[1019.01 ,  871.81 ,  729.777, ...,  455.059,  433.847,\n",
       "          470.532],\n",
       "        [1043.24 ,  887.623,  753.853, ...,  501.045,  495.064,\n",
       "          516.925],\n",
       "        [1033.84 ,  878.734,  746.391, ...,  480.259,  444.18 ,\n",
       "          471.582],\n",
       "        ...,\n",
       "        [1024.8  ,  880.213,  738.214, ...,  481.218,  461.349,\n",
       "          482.861],\n",
       "        [1028.56 ,  871.33 ,  744.973, ...,  496.394,  465.644,\n",
       "          499.595],\n",
       "        [1027.85 ,  884.205,  748.494, ...,  502.054,  486.233,\n",
       "          508.854]],\n",
       "\n",
       "       [[1021.75 ,  884.337,  732.198, ...,  465.126,  467.774,\n",
       "          495.313],\n",
       "        [1023.68 ,  867.265,  731.81 , ...,  476.709,  471.55 ,\n",
       "          515.55 ],\n",
       "        [1019.64 ,  871.278,  733.617, ...,  474.833,  472.108,\n",
       "          522.698],\n",
       "...\n",
       "        [1025.65 ,  874.976,  735.566, ...,  488.036,  462.717,\n",
       "          501.291],\n",
       "        [1032.05 ,  882.597,  736.015, ...,  486.44 ,  477.001,\n",
       "          503.748],\n",
       "        [1026.64 ,  874.005,  734.119, ...,  465.584,  456.291,\n",
       "          486.425]],\n",
       "\n",
       "       [[1028.52 ,  879.123,  740.586, ...,  494.893,  467.578,\n",
       "          479.968],\n",
       "        [1028.72 ,  874.749,  750.065, ...,  492.297,  461.107,\n",
       "          501.838],\n",
       "        [1035.44 ,  882.609,  749.231, ...,  480.908,  482.27 ,\n",
       "          483.122],\n",
       "        ...,\n",
       "        [1031.41 ,  887.434,  756.411, ...,  481.128,  475.771,\n",
       "          510.044],\n",
       "        [1029.53 ,  881.276,  749.317, ...,  479.236,  478.364,\n",
       "          504.621],\n",
       "        [1033.32 ,  891.075,  755.747, ...,  484.177,  477.319,\n",
       "          509.743]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>c</span></div><div class='xr-var-dims'>(chain, draw, sample, species)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.237 0.07468 ... 0.07264 35.05</div><input id='attrs-e3013ec0-3d65-4a50-88ca-612414c8f32f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e3013ec0-3d65-4a50-88ca-612414c8f32f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc270ac9-9b11-4dba-b1e3-74d72be80f93' class='xr-var-data-in' type='checkbox'><label for='data-cc270ac9-9b11-4dba-b1e3-74d72be80f93' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[ 1.23674  ,  0.0746829,  0.751185 ],\n",
       "         [ 2.65135  ,  0.0713379,  1.82867  ],\n",
       "         [ 4.80667  ,  0.0703379,  4.31457  ],\n",
       "         ...,\n",
       "         [25.0124   ,  0.0771388, 20.5776   ],\n",
       "         [33.2972   ,  0.0780836, 27.8545   ],\n",
       "         [46.4738   ,  0.0745677, 32.9494   ]],\n",
       "\n",
       "        [[ 1.40827  ,  0.0777937,  0.632476 ],\n",
       "         [ 2.76114  ,  0.078297 ,  1.70123  ],\n",
       "         [ 5.40804  ,  0.0738046,  3.82008  ],\n",
       "         ...,\n",
       "         [25.911    ,  0.0746181, 21.329    ],\n",
       "         [37.0675   ,  0.0800994, 27.7857   ],\n",
       "         [41.8541   ,  0.0749365, 35.4052   ]],\n",
       "\n",
       "        [[ 1.21404  ,  0.0743928,  0.686106 ],\n",
       "         [ 2.74297  ,  0.0716414,  1.78585  ],\n",
       "         [ 4.94669  ,  0.0745792,  4.09171  ],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [23.3747   ,  0.0727436, 20.8147   ],\n",
       "         [36.7143   ,  0.0737812, 26.9446   ],\n",
       "         [42.3039   ,  0.0731012, 36.0073   ]],\n",
       "\n",
       "        [[ 1.41803  ,  0.0727985,  0.692621 ],\n",
       "         [ 2.51517  ,  0.0743324,  2.01409  ],\n",
       "         [ 5.36063  ,  0.0750792,  3.51199  ],\n",
       "         ...,\n",
       "         [23.4282   ,  0.0700997, 20.821    ],\n",
       "         [36.1354   ,  0.0740137, 27.0208   ],\n",
       "         [43.5575   ,  0.0743022, 36.418    ]],\n",
       "\n",
       "        [[ 1.3022   ,  0.083777 ,  0.713885 ],\n",
       "         [ 2.71576  ,  0.0772776,  1.85396  ],\n",
       "         [ 5.30046  ,  0.069645 ,  3.90675  ],\n",
       "         ...,\n",
       "         [26.6732   ,  0.0772576, 20.0938   ],\n",
       "         [34.3659   ,  0.0751217, 31.8574   ],\n",
       "         [40.435    ,  0.0726415, 35.0519   ]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pump_bias</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-3.636 nan -6.652 ... nan nan nan</div><input id='attrs-1ae8ca9a-72ba-4c4c-81b3-ebc305a939df' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ae8ca9a-72ba-4c4c-81b3-ebc305a939df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8be1e69e-e52b-4e9f-abdc-817d51eb8a91' class='xr-var-data-in' type='checkbox'><label for='data-8be1e69e-e52b-4e9f-abdc-817d51eb8a91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-3.63643,      nan, -6.65241, ...,      nan,      nan, -3.00551],\n",
       "       [     nan,      nan,      nan, ..., -3.12737,      nan,      nan],\n",
       "       [-6.67974, -2.65396, -2.71202, ..., -4.85243, -7.97369, -3.38863],\n",
       "       [-2.75127, -2.88932, -2.34854, ...,      nan,      nan,      nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cfeed</span></div><div class='xr-var-dims'>(chain, draw, species)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 100.3 0.0 0.0 ... 0.0 98.99 0.0</div><input id='attrs-54f410d5-ae0d-4ce3-be66-400597a924da' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54f410d5-ae0d-4ce3-be66-400597a924da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8fd6eda-7b5c-41d3-a5da-664c6f09ca73' class='xr-var-data-in' type='checkbox'><label for='data-c8fd6eda-7b5c-41d3-a5da-664c6f09ca73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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.    , 100.3   ,   0.    ],\n",
       "        [  0.    ,  98.8484,   0.    ],\n",
       "        [  0.    ,  99.799 ,   0.    ],\n",
       "        ...,\n",
       "        [  0.    ,  99.8972,   0.    ],\n",
       "        [  0.    , 100.026 ,   0.    ],\n",
       "        [  0.    , 100.894 ,   0.    ]],\n",
       "\n",
       "       [[  0.    ,  99.1586,   0.    ],\n",
       "        [  0.    ,  99.235 ,   0.    ],\n",
       "        [  0.    ,  99.6794,   0.    ],\n",
       "        ...,\n",
       "        [  0.    , 101.304 ,   0.    ],\n",
       "        [  0.    , 100.717 ,   0.    ],\n",
       "        [  0.    ,  99.7991,   0.    ]],\n",
       "\n",
       "       [[  0.    ,  99.9263,   0.    ],\n",
       "        [  0.    , 100.071 ,   0.    ],\n",
       "        [  0.    ,  99.7721,   0.    ],\n",
       "        ...,\n",
       "        [  0.    ,  99.8342,   0.    ],\n",
       "        [  0.    ,  99.8649,   0.    ],\n",
       "        [  0.    , 100.244 ,   0.    ]],\n",
       "\n",
       "       [[  0.    , 100.672 ,   0.    ],\n",
       "        [  0.    ,  99.8682,   0.    ],\n",
       "        [  0.    , 100.61  ,   0.    ],\n",
       "        ...,\n",
       "        [  0.    , 100.554 ,   0.    ],\n",
       "        [  0.    , 100.92  ,   0.    ],\n",
       "        [  0.    ,  98.9883,   0.    ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pseudobatch_c</span></div><div class='xr-var-dims'>(chain, draw, sample, species)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.237 -1.374 ... -357.4 161.1</div><input id='attrs-78ca0547-316c-423b-a443-36d6265a8626' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-78ca0547-316c-423b-a443-36d6265a8626' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2aa9c65d-d9c4-4469-8cc7-2ee2243b42a8' class='xr-var-data-in' type='checkbox'><label for='data-2aa9c65d-d9c4-4469-8cc7-2ee2243b42a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[   1.23674 ,   -1.37443 ,    0.751185],\n",
       "         [   2.72112 ,   -4.01536 ,    1.87679 ],\n",
       "         [   5.17352 ,   -9.02823 ,    4.64385 ],\n",
       "         ...,\n",
       "         [  46.05    ,  -85.6679  ,   37.8851  ],\n",
       "         [  93.0559  , -181.241   ,   77.8453  ],\n",
       "         [ 229.346   , -395.756   ,  162.604   ]],\n",
       "\n",
       "        [[   1.40827 ,   -1.52092 ,    0.632476],\n",
       "         [   2.83348 ,   -4.10806 ,    1.7458  ],\n",
       "         [   5.84721 ,   -9.54614 ,    4.1303  ],\n",
       "         ...,\n",
       "         [  46.1833  ,  -78.803   ,   38.0164  ],\n",
       "         [  94.4505  , -154.419   ,   70.7998  ],\n",
       "         [ 167.741   , -298.61    ,  141.896   ]],\n",
       "\n",
       "        [[   1.21404 ,   -1.52899 ,    0.686106],\n",
       "         [   2.8088  ,   -3.92513 ,    1.82871 ],\n",
       "         [   5.33693 ,   -9.39596 ,    4.4145  ],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [  42.821   ,  -85.2131  ,   38.1312  ],\n",
       "         [ 102.089   , -180.537   ,   74.9233  ],\n",
       "         [ 197.609   , -370.501   ,  168.196   ]],\n",
       "\n",
       "        [[   1.41803 ,   -1.66032 ,    0.692621],\n",
       "         [   2.57654 ,   -4.11954 ,    2.06324 ],\n",
       "         [   5.77732 ,   -9.49696 ,    3.78499 ],\n",
       "         ...,\n",
       "         [  43.842   ,  -89.5366  ,   38.9629  ],\n",
       "         [ 103.549   , -189.796   ,   77.4306  ],\n",
       "         [ 208.354   , -383.2     ,  174.203   ]],\n",
       "\n",
       "        [[   1.3022  ,   -1.55046 ,    0.713885],\n",
       "         [   2.79091 ,   -4.29398 ,    1.90527 ],\n",
       "         [   5.7344  ,   -9.66287 ,    4.22659 ],\n",
       "         ...,\n",
       "         [  49.3041  ,  -85.478   ,   37.1423  ],\n",
       "         [  96.4768  , -180.329   ,   89.4345  ],\n",
       "         [ 185.895   , -357.397   ,  161.146   ]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1c7da67b-7c10-4906-8519-490c6c10bf85' class='xr-section-summary-in' type='checkbox'  ><label for='section-1c7da67b-7c10-4906-8519-490c6c10bf85' class='xr-section-summary' >Indexes: <span>(6)</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-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7e1c77d9-b56f-4235-a864-5a90d3ff021a' class='xr-index-data-in' type='checkbox'/><label for='index-7e1c77d9-b56f-4235-a864-5a90d3ff021a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e1878105-30c0-43c2-97b1-49caabaf2dae' class='xr-index-data-in' type='checkbox'/><label for='index-e1878105-30c0-43c2-97b1-49caabaf2dae' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>sample</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d04862ef-6e8b-42d7-ae4d-d6c6f3f47af1' class='xr-index-data-in' type='checkbox'/><label for='index-d04862ef-6e8b-42d7-ae4d-d6c6f3f47af1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7], dtype=&#x27;int64&#x27;, name=&#x27;sample&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>species</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-31d9aa66-f426-4af2-81de-f29daba142ec' class='xr-index-data-in' type='checkbox'/><label for='index-31d9aa66-f426-4af2-81de-f29daba142ec' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Biomass&#x27;, &#x27;Glucose&#x27;, &#x27;Product&#x27;], dtype=&#x27;object&#x27;, name=&#x27;species&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>f_nonzero_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1fe795a7-9c1b-4ee0-9046-c7dbee06bfcf' class='xr-index-data-in' type='checkbox'/><label for='index-1fe795a7-9c1b-4ee0-9046-c7dbee06bfcf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7], dtype=&#x27;int64&#x27;, name=&#x27;f_nonzero_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>cfeed_nonzero_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4c43de40-d376-4a41-837b-9c5178400e03' class='xr-index-data-in' type='checkbox'/><label for='index-4c43de40-d376-4a41-837b-9c5178400e03' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;cfeed_nonzero_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-67f95c35-86a3-4c3a-a7c3-1e75f3ea7a3a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-67f95c35-86a3-4c3a-a7c3-1e75f3ea7a3a' class='xr-section-summary' >Attributes: <span>(4)</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>2023-07-26T11:42:38.192599</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>cmdstanpy</dd><dt><span>inference_library_version :</span></dt><dd>1.1.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_statsa7f34b52-ec56-41e8-b99e-4a6a36ba5a09\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_statsa7f34b52-ec56-41e8-b99e-4a6a36ba5a09\" class = \"xr-section-summary\">sample_stats</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><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",
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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[data-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 !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
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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",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
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       "\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",
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       "\n",
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       "\n",
       ".xr-var-dims {\n",
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       "}\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-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\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",
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       "\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",
       ".xr-index-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",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-no-icon {\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: 4, draw: 1000)\n",
       "Coordinates:\n",
       "  * chain            (chain) int64 0 1 2 3\n",
       "  * draw             (draw) int64 0 1 2 3 4 5 6 ... 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    lp               (chain, draw) float64 -116.9 -115.1 ... -116.7 -113.1\n",
       "    acceptance_rate  (chain, draw) float64 0.9946 0.9119 0.651 ... 0.7792 0.938\n",
       "    step_size        (chain, draw) float64 0.3822 0.3822 ... 0.4056 0.4056\n",
       "    tree_depth       (chain, draw) int64 4 4 4 4 4 4 3 4 4 ... 3 3 3 3 3 3 4 3 3\n",
       "    n_steps          (chain, draw) int64 15 15 15 15 15 15 7 ... 7 7 7 15 15 7\n",
       "    diverging        (chain, draw) bool False False False ... False False False\n",
       "    energy           (chain, draw) float64 148.0 135.9 137.8 ... 141.3 135.3\n",
       "Attributes:\n",
       "    created_at:                 2023-07-26T11:42:38.208145\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          cmdstanpy\n",
       "    inference_library_version:  1.1.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c23cfa7a-76d0-43ee-8563-15ba640f5f54' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c23cfa7a-76d0-43ee-8563-15ba640f5f54' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-42a02a43-2496-4ebf-bf7f-eb5866496410' class='xr-section-summary-in' type='checkbox'  checked><label for='section-42a02a43-2496-4ebf-bf7f-eb5866496410' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-c894c1a4-8b16-4892-a878-2db05fd9d636' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c894c1a4-8b16-4892-a878-2db05fd9d636' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fbd529d-3214-4d72-9099-63e53cfe3489' class='xr-var-data-in' type='checkbox'><label for='data-0fbd529d-3214-4d72-9099-63e53cfe3489' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-12da43e4-9aae-4df4-a556-7d825e088ed6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-12da43e4-9aae-4df4-a556-7d825e088ed6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-599b87a7-8504-4cf4-9f3a-9434afadd83c' class='xr-var-data-in' type='checkbox'><label for='data-599b87a7-8504-4cf4-9f3a-9434afadd83c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-023b9d5b-3f66-4b78-81b4-0621f8bba314' class='xr-section-summary-in' type='checkbox'  checked><label for='section-023b9d5b-3f66-4b78-81b4-0621f8bba314' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-116.9 -115.1 ... -116.7 -113.1</div><input id='attrs-93ba697c-1e0d-4568-8717-1bc78d682760' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93ba697c-1e0d-4568-8717-1bc78d682760' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d63cc0c9-a832-4517-9cfb-484feec9bf3f' class='xr-var-data-in' type='checkbox'><label for='data-d63cc0c9-a832-4517-9cfb-484feec9bf3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-116.876, -115.07 , -114.389, ..., -111.991, -118.54 , -116.482],\n",
       "       [-116.884, -121.594, -113.139, ..., -120.336, -116.775, -114.016],\n",
       "       [-120.565, -111.369, -114.961, ..., -115.606, -114.973, -112.762],\n",
       "       [-113.324, -124.247, -135.066, ..., -114.074, -116.736, -113.119]])</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'>0.9946 0.9119 ... 0.7792 0.938</div><input id='attrs-cb5a97c1-3776-45de-9deb-273c4a53e105' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cb5a97c1-3776-45de-9deb-273c4a53e105' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d88cbe3b-ab45-4511-9b3c-1b6af7ca8b01' class='xr-var-data-in' type='checkbox'><label for='data-d88cbe3b-ab45-4511-9b3c-1b6af7ca8b01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.994602, 0.911914, 0.650962, ..., 0.954583, 0.677699, 0.690599],\n",
       "       [0.994596, 0.832143, 1.      , ..., 0.678364, 0.918352, 0.963192],\n",
       "       [0.993588, 0.937408, 0.714282, ..., 0.881652, 0.681962, 0.872412],\n",
       "       [0.465148, 0.698281, 0.775669, ..., 0.815171, 0.779162, 0.937983]])</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'>0.3822 0.3822 ... 0.4056 0.4056</div><input id='attrs-a62299c2-ae9f-4706-af81-3060b301ac28' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a62299c2-ae9f-4706-af81-3060b301ac28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-585e52ba-a3e2-4f17-8df4-d1e85001cae9' class='xr-var-data-in' type='checkbox'><label for='data-585e52ba-a3e2-4f17-8df4-d1e85001cae9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.382163, 0.382163, 0.382163, ..., 0.382163, 0.382163, 0.382163],\n",
       "       [0.375341, 0.375341, 0.375341, ..., 0.375341, 0.375341, 0.375341],\n",
       "       [0.400517, 0.400517, 0.400517, ..., 0.400517, 0.400517, 0.400517],\n",
       "       [0.405606, 0.405606, 0.405606, ..., 0.405606, 0.405606, 0.405606]])</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'>4 4 4 4 4 4 3 4 ... 3 3 3 3 3 4 3 3</div><input id='attrs-134ed8e5-0141-4a55-a92f-8f6d2810ac13' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-134ed8e5-0141-4a55-a92f-8f6d2810ac13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cada90b5-8f85-49ca-8e1b-a7db843aea2b' class='xr-var-data-in' type='checkbox'><label for='data-cada90b5-8f85-49ca-8e1b-a7db843aea2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[4, 4, 4, ..., 3, 3, 4],\n",
       "       [4, 4, 4, ..., 3, 3, 3],\n",
       "       [4, 3, 3, ..., 3, 3, 3],\n",
       "       [3, 3, 4, ..., 4, 3, 3]])</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'>int64</div><div class='xr-var-preview xr-preview'>15 15 15 15 15 15 ... 7 7 7 15 15 7</div><input id='attrs-66f2ea6f-1910-4d91-b598-77f22caa3468' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66f2ea6f-1910-4d91-b598-77f22caa3468' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f2df8fc-7139-4b30-81ac-5a346ec5a943' class='xr-var-data-in' type='checkbox'><label for='data-6f2df8fc-7139-4b30-81ac-5a346ec5a943' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, 15, 15, ..., 15, 15, 15],\n",
       "       [15, 15, 15, ...,  7, 15,  7],\n",
       "       [15,  7,  7, ...,  7,  7,  7],\n",
       "       [ 7, 15, 15, ..., 15, 15,  7]])</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-e2673d79-0053-4cfe-a0a1-187a2fa6c952' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2673d79-0053-4cfe-a0a1-187a2fa6c952' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df2f6f58-5ce5-4ec0-93b6-cc848d6265a5' class='xr-var-data-in' type='checkbox'><label for='data-df2f6f58-5ce5-4ec0-93b6-cc848d6265a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>148.0 135.9 137.8 ... 141.3 135.3</div><input id='attrs-0d627d2c-1129-40c0-9f1e-8e09de57291f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0d627d2c-1129-40c0-9f1e-8e09de57291f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-005eded7-278c-4fde-9adc-e219bdedbdd3' class='xr-var-data-in' type='checkbox'><label for='data-005eded7-278c-4fde-9adc-e219bdedbdd3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[147.991, 135.919, 137.792, ..., 132.822, 138.581, 147.163],\n",
       "       [136.179, 140.415, 135.727, ..., 133.117, 141.27 , 130.038],\n",
       "       [145.494, 136.356, 134.797, ..., 138.449, 135.42 , 141.897],\n",
       "       [137.41 , 139.092, 158.273, ..., 136.753, 141.295, 135.299]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-15d606ee-8531-4707-bd7f-0c8474b2094b' class='xr-section-summary-in' type='checkbox'  ><label for='section-15d606ee-8531-4707-bd7f-0c8474b2094b' class='xr-section-summary' >Indexes: <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-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0376f781-f838-4de1-a9ff-8ebbe28930c6' class='xr-index-data-in' type='checkbox'/><label for='index-0376f781-f838-4de1-a9ff-8ebbe28930c6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-48fdd39a-06bd-4282-a82c-d6b00065f01c' class='xr-index-data-in' type='checkbox'/><label for='index-48fdd39a-06bd-4282-a82c-d6b00065f01c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9712fe1c-7268-4eb8-b419-17c30f2316de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9712fe1c-7268-4eb8-b419-17c30f2316de' class='xr-section-summary' >Attributes: <span>(4)</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>2023-07-26T11:42:38.208145</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>cmdstanpy</dd><dt><span>inference_library_version :</span></dt><dd>1.1.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
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       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_prior0a48bc3a-26b4-4a14-b898-08dc8da5cdf4\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_prior0a48bc3a-26b4-4a14-b898-08dc8da5cdf4\" class = \"xr-section-summary\">prior</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><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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       "  --xr-background-color-row-even: #111111;\n",
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       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
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       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
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       "\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",
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       "\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-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\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",
       ".xr-index-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",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-no-icon {\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: 4, draw: 1000, sample: 8, species: 3,\n",
       "                          f_nonzero_dim_0: 8, cfeed_nonzero_dim_0: 1)\n",
       "Coordinates:\n",
       "  * chain                (chain) int64 0 1 2 3\n",
       "  * draw                 (draw) int64 0 1 2 3 4 5 6 ... 994 995 996 997 998 999\n",
       "  * sample               (sample) int64 0 1 2 3 4 5 6 7\n",
       "  * species              (species) &lt;U7 &#x27;Biomass&#x27; &#x27;Glucose&#x27; &#x27;Product&#x27;\n",
       "  * f_nonzero_dim_0      (f_nonzero_dim_0) int64 0 1 2 3 4 5 6 7\n",
       "  * cfeed_nonzero_dim_0  (cfeed_nonzero_dim_0) int64 0\n",
       "Data variables: (12/13)\n",
       "    v0                   (chain, draw) float64 1e+03 1.03e+03 ... 1.013e+03\n",
       "    m                    (chain, draw, sample, species) float64 64.23 ... 0.0...\n",
       "    as                   (chain, draw, sample) float64 -0.5429 -1.458 ... -1.604\n",
       "    f_nonzero            (chain, draw, f_nonzero_dim_0) float64 0.0001213 ......\n",
       "    cfeed_nonzero        (chain, draw, cfeed_nonzero_dim_0) float64 100.2 ......\n",
       "    apump                (chain, draw) float64 0.01743 -0.04791 ... 0.01483\n",
       "    ...                   ...\n",
       "    s                    (chain, draw, sample) float64 367.5 119.4 ... 96.31\n",
       "    v                    (chain, draw, sample) float64 1e+03 632.5 ... 575.4\n",
       "    c                    (chain, draw, sample, species) float64 0.06423 ... 4...\n",
       "    pump_bias            (chain, draw) float64 -4.05 nan ... -5.669 -4.211\n",
       "    cfeed                (chain, draw, species) float64 0.0 100.2 ... 99.16 0.0\n",
       "    pseudobatch_c        (chain, draw, sample, species) float64 0.06423 ... 6...\n",
       "Attributes:\n",
       "    created_at:                 2023-07-26T11:42:38.416724\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          cmdstanpy\n",
       "    inference_library_version:  1.1.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ec0b4a93-4a73-4611-b3f0-c6dbd61c7581' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ec0b4a93-4a73-4611-b3f0-c6dbd61c7581' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>sample</span>: 8</li><li><span class='xr-has-index'>species</span>: 3</li><li><span class='xr-has-index'>f_nonzero_dim_0</span>: 8</li><li><span class='xr-has-index'>cfeed_nonzero_dim_0</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a8ac8260-75c5-471a-a094-57a018b10555' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a8ac8260-75c5-471a-a094-57a018b10555' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-2faef195-048d-4f7a-8601-f7c4d3946b4f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2faef195-048d-4f7a-8601-f7c4d3946b4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3fdcfbe-1851-476c-8a18-b3e9aa1ce7cb' class='xr-var-data-in' type='checkbox'><label for='data-c3fdcfbe-1851-476c-8a18-b3e9aa1ce7cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-83b0d8ef-8db9-49c4-a591-18012930230e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-83b0d8ef-8db9-49c4-a591-18012930230e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3847722-1b4b-4a97-806d-6e58c2c9c58b' class='xr-var-data-in' type='checkbox'><label for='data-b3847722-1b4b-4a97-806d-6e58c2c9c58b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-3bf994bc-b2db-4782-a57c-abe29a7bd972' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3bf994bc-b2db-4782-a57c-abe29a7bd972' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cabe1ab6-cc75-4750-be8a-66cb0737a99a' class='xr-var-data-in' type='checkbox'><label for='data-cabe1ab6-cc75-4750-be8a-66cb0737a99a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>species</span></div><div class='xr-var-dims'>(species)</div><div class='xr-var-dtype'>&lt;U7</div><div class='xr-var-preview xr-preview'>&#x27;Biomass&#x27; &#x27;Glucose&#x27; &#x27;Product&#x27;</div><input id='attrs-4838d42a-3555-408d-8040-1e63f5bf1140' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4838d42a-3555-408d-8040-1e63f5bf1140' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e76a71b5-12b0-4f33-8e3d-659e408a9a7f' class='xr-var-data-in' type='checkbox'><label for='data-e76a71b5-12b0-4f33-8e3d-659e408a9a7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Biomass&#x27;, &#x27;Glucose&#x27;, &#x27;Product&#x27;], dtype=&#x27;&lt;U7&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>f_nonzero_dim_0</span></div><div class='xr-var-dims'>(f_nonzero_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</div><input id='attrs-11ce4149-09da-4fb5-8532-af4d7e3d3638' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-11ce4149-09da-4fb5-8532-af4d7e3d3638' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b8be000-f981-45af-8e5e-5cd0c9b1b4c8' class='xr-var-data-in' type='checkbox'><label for='data-0b8be000-f981-45af-8e5e-5cd0c9b1b4c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>cfeed_nonzero_dim_0</span></div><div class='xr-var-dims'>(cfeed_nonzero_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-dcd87fd1-62a6-4a9c-95d8-14bcdadfdff8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dcd87fd1-62a6-4a9c-95d8-14bcdadfdff8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9fe1724-d86f-4ca9-bc08-07d0849f5e6e' class='xr-var-data-in' type='checkbox'><label for='data-d9fe1724-d86f-4ca9-bc08-07d0849f5e6e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d5ca5770-e1bc-4cd9-890e-4c1acf33a7c5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d5ca5770-e1bc-4cd9-890e-4c1acf33a7c5' 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>v0</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 1.03e+03 ... 1.013e+03</div><input id='attrs-e3dc77fd-06cd-423e-9872-e69cc622a3a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e3dc77fd-06cd-423e-9872-e69cc622a3a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-954cdd47-0aec-44f7-a1d9-0c4a840cc2d3' class='xr-var-data-in' type='checkbox'><label for='data-954cdd47-0aec-44f7-a1d9-0c4a840cc2d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1000.01, 1029.51,  998.31, ..., 1012.8 , 1018.92, 1016.28],\n",
       "       [1017.95, 1016.67, 1022.83, ..., 1017.61, 1015.25, 1005.08],\n",
       "       [1024.83, 1004.56, 1027.32, ..., 1015.66, 1017.  , 1021.06],\n",
       "       [1014.89, 1011.57, 1009.04, ..., 1011.48, 1018.12, 1012.96]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>m</span></div><div class='xr-var-dims'>(chain, draw, sample, species)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>64.23 2.832 ... 9.135e+03 0.02464</div><input id='attrs-0650b6b6-ea53-47de-8313-82b00e14f664' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0650b6b6-ea53-47de-8313-82b00e14f664' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5702977c-2648-4e00-90a0-cc408d7599bf' class='xr-var-data-in' type='checkbox'><label for='data-5702977c-2648-4e00-90a0-cc408d7599bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[6.42338e+01, 2.83231e+00, 4.50369e-07],\n",
       "         [3.00299e+01, 7.21765e+01, 4.95520e+01],\n",
       "         [1.01573e+00, 2.44152e-01, 1.15692e+05],\n",
       "         ...,\n",
       "         [4.69170e+02, 2.41317e+06, 3.43002e-05],\n",
       "         [9.25024e+02, 1.08882e+00, 1.05365e-03],\n",
       "         [1.10870e-05, 1.63778e-08, 3.85569e+00]],\n",
       "\n",
       "        [[6.77890e-02, 3.53510e-04, 1.61740e+06],\n",
       "         [1.70402e+00, 2.10164e+00, 4.58934e-01],\n",
       "         [2.96449e+00, 2.01231e+00, 3.83524e-05],\n",
       "         ...,\n",
       "         [1.04303e-04, 2.03996e-08, 2.12224e+02],\n",
       "         [1.67946e-03, 6.35328e-01, 6.84100e+02],\n",
       "         [1.54526e+07, 8.53593e+06, 3.65695e-02]],\n",
       "\n",
       "        [[1.05210e+03, 1.25976e+01, 4.13069e-05],\n",
       "         [7.51364e+02, 3.71345e+03, 6.07804e-01],\n",
       "         [3.78939e-04, 8.02288e-06, 3.74993e+07],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [2.55659e+00, 1.44897e+03, 1.60561e+01],\n",
       "         [1.82798e+01, 1.27726e+00, 5.26247e-02],\n",
       "         [2.25316e-02, 1.99703e-01, 5.96437e+00]],\n",
       "\n",
       "        [[2.10966e+06, 6.98334e-03, 1.96589e+04],\n",
       "         [8.98142e-03, 2.93811e-05, 6.26642e-04],\n",
       "         [1.43374e-07, 2.65631e+02, 5.76502e-01],\n",
       "         ...,\n",
       "         [7.21660e+02, 7.02706e-02, 4.15819e-05],\n",
       "         [9.67124e-04, 3.97935e-01, 1.34320e+01],\n",
       "         [6.42678e+00, 2.83542e-04, 2.14261e+00]],\n",
       "\n",
       "        [[3.96551e-04, 2.29341e-03, 3.40343e-02],\n",
       "         [1.40233e-05, 4.32872e+04, 9.53240e-01],\n",
       "         [6.45956e+02, 1.93294e-02, 4.48403e-03],\n",
       "         ...,\n",
       "         [7.16330e-01, 5.60274e+01, 2.37559e+05],\n",
       "         [1.32057e-01, 1.25808e+00, 4.33180e-03],\n",
       "         [2.50374e+00, 9.13477e+03, 2.46380e-02]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>as</span></div><div class='xr-var-dims'>(chain, draw, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.5429 -1.458 ... -3.14 -1.604</div><input id='attrs-2cae035c-b70d-4dee-b9fd-32aecc23890a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2cae035c-b70d-4dee-b9fd-32aecc23890a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4408fd3c-02f4-44a8-8e4c-23a111872d4b' class='xr-var-data-in' type='checkbox'><label for='data-4408fd3c-02f4-44a8-8e4c-23a111872d4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-0.542877, -1.4576  , -2.5526  , ..., -1.69709 , -1.13109 ,\n",
       "         -1.86492 ],\n",
       "        [-2.47984 , -1.90079 , -1.457   , ..., -1.75737 , -2.1248  ,\n",
       "         -1.3452  ],\n",
       "        [-0.55962 , -1.56682 , -1.49974 , ..., -1.51519 , -1.75603 ,\n",
       "         -1.14184 ],\n",
       "        ...,\n",
       "        [-1.22542 , -1.53176 , -1.55441 , ..., -1.24641 , -0.833327,\n",
       "         -1.85412 ],\n",
       "        [-2.27596 , -1.4459  , -1.89711 , ..., -2.1018  , -2.10566 ,\n",
       "         -1.5493  ],\n",
       "        [-0.992743, -2.35741 , -1.84232 , ..., -2.06689 , -1.79535 ,\n",
       "         -1.45693 ]],\n",
       "\n",
       "       [[-1.46975 , -2.07006 , -1.68816 , ..., -1.64944 , -0.860841,\n",
       "         -1.83518 ],\n",
       "        [-2.06836 , -1.34418 , -1.93706 , ..., -0.84366 , -2.93158 ,\n",
       "         -1.20463 ],\n",
       "        [-1.26011 , -1.83348 , -1.46642 , ..., -2.61368 , -0.442435,\n",
       "         -2.34645 ],\n",
       "...\n",
       "        [-1.37535 , -1.83928 , -0.984905, ..., -2.14932 , -2.22709 ,\n",
       "         -1.18259 ],\n",
       "        [-1.97526 , -1.43216 , -2.66976 , ..., -1.19693 , -1.13574 ,\n",
       "         -2.19904 ],\n",
       "        [-1.36122 , -1.92877 , -0.565397, ..., -2.14539 , -2.24186 ,\n",
       "         -0.957739]],\n",
       "\n",
       "       [[-2.13337 , -1.98461 , -1.49845 , ..., -1.97516 , -2.93709 ,\n",
       "         -1.11558 ],\n",
       "        [-1.29762 , -1.50387 , -2.68405 , ..., -1.43594 , -1.76061 ,\n",
       "         -2.11108 ],\n",
       "        [-1.32954 , -1.67364 , -2.26787 , ..., -1.62895 , -1.9326  ,\n",
       "         -1.96192 ],\n",
       "        ...,\n",
       "        [-2.25449 , -1.88143 , -1.18077 , ..., -1.72973 , -2.82624 ,\n",
       "         -2.30495 ],\n",
       "        [-1.35201 , -1.69243 , -2.31912 , ..., -1.71742 , -0.236966,\n",
       "         -1.39635 ],\n",
       "        [-1.55135 , -2.20097 , -1.10804 , ..., -1.99062 , -3.13959 ,\n",
       "         -1.60429 ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>f_nonzero</span></div><div class='xr-var-dims'>(chain, draw, f_nonzero_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0001213 2.336e-05 ... 0.09431</div><input id='attrs-071b500b-e187-4646-8b25-6543d128672b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-071b500b-e187-4646-8b25-6543d128672b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-169f8f51-0314-4ac7-9d7d-cc470520faac' class='xr-var-data-in' type='checkbox'><label for='data-169f8f51-0314-4ac7-9d7d-cc470520faac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[1.21299e-04, 2.33607e-05, 4.92723e+00, ..., 3.18292e+00,\n",
       "         4.00032e-01, 6.23146e-04],\n",
       "        [2.34555e+02, 3.66887e+06, 2.13852e-01, ..., 1.82399e-01,\n",
       "         1.71784e+01, 2.25517e+01],\n",
       "        [8.84941e-05, 4.21956e-04, 5.77890e+03, ..., 5.77135e-01,\n",
       "         1.05590e-02, 1.72958e-01],\n",
       "        ...,\n",
       "        [3.48706e+00, 7.82453e-06, 9.53168e+01, ..., 1.40515e-02,\n",
       "         3.49266e+03, 2.25721e+00],\n",
       "        [9.15542e-03, 4.53906e+01, 2.41390e+01, ..., 1.81142e+00,\n",
       "         7.76906e-06, 3.36838e-01],\n",
       "        [2.30997e+04, 1.05072e-01, 2.52806e-02, ..., 1.80508e+04,\n",
       "         1.05008e+03, 1.38580e-03]],\n",
       "\n",
       "       [[2.85795e-06, 1.55175e+02, 4.03791e-04, ..., 1.18527e+02,\n",
       "         2.63141e+00, 9.33969e+01],\n",
       "        [9.01011e+02, 2.62614e+00, 4.43566e+00, ..., 1.74140e-04,\n",
       "         2.80322e-03, 4.88730e-03],\n",
       "        [1.18409e-03, 4.11321e-01, 2.29134e-01, ..., 4.37431e+03,\n",
       "         2.07527e+02, 1.39193e+02],\n",
       "...\n",
       "        [6.36673e+00, 1.89482e+02, 3.01707e+00, ..., 5.11626e+00,\n",
       "         2.49539e-02, 3.69775e-03],\n",
       "        [1.66930e-01, 3.75900e-03, 2.63737e-01, ..., 1.84671e-01,\n",
       "         1.40364e+01, 1.39062e+02],\n",
       "        [4.48296e+00, 3.90948e+02, 2.56323e+00, ..., 6.00433e+00,\n",
       "         4.74720e+00, 3.26977e-03]],\n",
       "\n",
       "       [[1.09931e+02, 3.74887e-02, 1.07991e+01, ..., 2.23173e+00,\n",
       "         9.03481e-01, 2.80277e+06],\n",
       "        [7.65596e+01, 2.60105e+00, 9.66204e-02, ..., 1.46780e+01,\n",
       "         1.49747e-01, 3.22552e-05],\n",
       "        [4.68810e-02, 1.54881e+01, 9.93957e-03, ..., 1.09936e+00,\n",
       "         1.30652e-04, 1.83484e-04],\n",
       "        ...,\n",
       "        [3.26629e-03, 1.66889e-04, 5.19119e-01, ..., 3.66670e-02,\n",
       "         4.50971e+00, 1.72431e+00],\n",
       "        [2.00908e+01, 1.85488e+00, 1.02106e+00, ..., 2.85183e+01,\n",
       "         5.24988e-01, 2.67498e+01],\n",
       "        [2.94461e-03, 2.59572e+00, 6.86566e-01, ..., 4.96756e-01,\n",
       "         8.86819e-01, 9.43056e-02]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cfeed_nonzero</span></div><div class='xr-var-dims'>(chain, draw, cfeed_nonzero_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>100.2 99.76 100.4 ... 100.4 99.16</div><input id='attrs-d73ee399-d8e6-4fa5-b66c-a38098a1cb20' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d73ee399-d8e6-4fa5-b66c-a38098a1cb20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7fac9cf-f097-4f00-8063-3ff72e333154' class='xr-var-data-in' type='checkbox'><label for='data-c7fac9cf-f097-4f00-8063-3ff72e333154' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[100.228 ],\n",
       "        [ 99.7626],\n",
       "        [100.357 ],\n",
       "        ...,\n",
       "        [100.541 ],\n",
       "        [100.216 ],\n",
       "        [ 98.8992]],\n",
       "\n",
       "       [[ 99.8223],\n",
       "        [ 99.842 ],\n",
       "        [100.535 ],\n",
       "        ...,\n",
       "        [ 99.8127],\n",
       "        [100.524 ],\n",
       "        [ 99.7742]],\n",
       "\n",
       "       [[ 99.3149],\n",
       "        [ 99.8001],\n",
       "        [ 98.54  ],\n",
       "        ...,\n",
       "        [100.782 ],\n",
       "        [ 99.296 ],\n",
       "        [100.621 ]],\n",
       "\n",
       "       [[ 99.8644],\n",
       "        [ 99.6745],\n",
       "        [ 99.6554],\n",
       "        ...,\n",
       "        [ 99.1771],\n",
       "        [100.375 ],\n",
       "        [ 99.1556]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>apump</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.01743 -0.04791 ... 0.01483</div><input id='attrs-6c19ab3f-eb75-4ae5-b723-d4765cab4b5c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6c19ab3f-eb75-4ae5-b723-d4765cab4b5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f254d5df-d447-4a60-abfe-ec64a7901e28' class='xr-var-data-in' type='checkbox'><label for='data-f254d5df-d447-4a60-abfe-ec64a7901e28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.0174305 , -0.0479107 ,  0.0403345 , ...,  0.0176288 ,\n",
       "        -0.0131928 , -0.0315209 ],\n",
       "       [ 0.00994115, -0.0584738 ,  0.048532  , ..., -0.0030137 ,\n",
       "         0.012801  ,  0.0156245 ],\n",
       "       [ 0.0127417 , -0.0163274 , -0.0478062 , ..., -0.013462  ,\n",
       "        -0.00109005, -0.015151  ],\n",
       "       [ 0.0234188 ,  0.017933  ,  0.0511075 , ..., -0.00727002,\n",
       "         0.00345047,  0.0148317 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>f</span></div><div class='xr-var-dims'>(chain, draw, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0001213 2.336e-05 ... 0.09431</div><input id='attrs-950fcb88-5b81-437f-8b04-4f3fde69de4c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-950fcb88-5b81-437f-8b04-4f3fde69de4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6bcc4ca-9327-4f8d-9678-ecb79a75e888' class='xr-var-data-in' type='checkbox'><label for='data-e6bcc4ca-9327-4f8d-9678-ecb79a75e888' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[1.21299e-04, 2.33607e-05, 4.92723e+00, ..., 3.18292e+00,\n",
       "         4.00032e-01, 6.23146e-04],\n",
       "        [2.34555e+02, 3.66887e+06, 2.13852e-01, ..., 1.82399e-01,\n",
       "         1.71784e+01, 2.25517e+01],\n",
       "        [8.84941e-05, 4.21956e-04, 5.77890e+03, ..., 5.77135e-01,\n",
       "         1.05590e-02, 1.72958e-01],\n",
       "        ...,\n",
       "        [3.48706e+00, 7.82453e-06, 9.53168e+01, ..., 1.40515e-02,\n",
       "         3.49266e+03, 2.25721e+00],\n",
       "        [9.15542e-03, 4.53906e+01, 2.41390e+01, ..., 1.81142e+00,\n",
       "         7.76906e-06, 3.36838e-01],\n",
       "        [2.30997e+04, 1.05072e-01, 2.52806e-02, ..., 1.80508e+04,\n",
       "         1.05008e+03, 1.38580e-03]],\n",
       "\n",
       "       [[2.85795e-06, 1.55175e+02, 4.03791e-04, ..., 1.18527e+02,\n",
       "         2.63141e+00, 9.33969e+01],\n",
       "        [9.01011e+02, 2.62614e+00, 4.43566e+00, ..., 1.74140e-04,\n",
       "         2.80322e-03, 4.88730e-03],\n",
       "        [1.18409e-03, 4.11321e-01, 2.29134e-01, ..., 4.37431e+03,\n",
       "         2.07527e+02, 1.39193e+02],\n",
       "...\n",
       "        [6.36673e+00, 1.89482e+02, 3.01707e+00, ..., 5.11626e+00,\n",
       "         2.49539e-02, 3.69775e-03],\n",
       "        [1.66930e-01, 3.75900e-03, 2.63737e-01, ..., 1.84671e-01,\n",
       "         1.40364e+01, 1.39062e+02],\n",
       "        [4.48296e+00, 3.90948e+02, 2.56323e+00, ..., 6.00433e+00,\n",
       "         4.74720e+00, 3.26977e-03]],\n",
       "\n",
       "       [[1.09931e+02, 3.74887e-02, 1.07991e+01, ..., 2.23173e+00,\n",
       "         9.03481e-01, 2.80277e+06],\n",
       "        [7.65596e+01, 2.60105e+00, 9.66204e-02, ..., 1.46780e+01,\n",
       "         1.49747e-01, 3.22552e-05],\n",
       "        [4.68810e-02, 1.54881e+01, 9.93957e-03, ..., 1.09936e+00,\n",
       "         1.30652e-04, 1.83484e-04],\n",
       "        ...,\n",
       "        [3.26629e-03, 1.66889e-04, 5.19119e-01, ..., 3.66670e-02,\n",
       "         4.50971e+00, 1.72431e+00],\n",
       "        [2.00908e+01, 1.85488e+00, 1.02106e+00, ..., 2.85183e+01,\n",
       "         5.24988e-01, 2.67498e+01],\n",
       "        [2.94461e-03, 2.59572e+00, 6.86566e-01, ..., 4.96756e-01,\n",
       "         8.86819e-01, 9.43056e-02]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>s</span></div><div class='xr-var-dims'>(chain, draw, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>367.5 119.4 37.42 ... 24.91 96.31</div><input id='attrs-d049bd60-ad5a-4c4e-b13c-933f40d14bd4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d049bd60-ad5a-4c4e-b13c-933f40d14bd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-129e2d9a-301b-451b-b439-c5c96b59b73f' class='xr-var-data-in' type='checkbox'><label for='data-129e2d9a-301b-451b-b439-c5c96b59b73f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[3.67521e+02, 1.19435e+02, 3.74250e+01, ..., 4.08605e+01,\n",
       "         5.45050e+01, 2.26564e+01],\n",
       "        [9.76912e+01, 4.77176e+05, 6.03217e+05, ..., 3.04843e+05,\n",
       "         1.88581e+05, 3.26243e+05],\n",
       "        [3.63021e+02, 1.09696e+02, 1.15034e+03, ..., 5.64697e+02,\n",
       "         3.78462e+02, 5.30242e+02],\n",
       "        ...,\n",
       "        [2.30680e+02, 1.39630e+02, 1.29318e+02, ..., 8.09604e+01,\n",
       "         1.14337e+03, 3.56496e+02],\n",
       "        [9.48967e+01, 1.84804e+02, 1.05491e+02, ..., 5.42630e+01,\n",
       "         4.81864e+01, 6.93863e+01],\n",
       "        [6.52024e+03, 1.52166e+03, 2.19857e+03, ..., 3.10879e+03,\n",
       "         3.64737e+03, 4.14963e+03]],\n",
       "\n",
       "       [[1.90337e+02, 1.10113e+02, 1.36153e+02, ..., 1.21551e+02,\n",
       "         1.88756e+02, 7.42929e+01],\n",
       "        [2.15183e+02, 3.52659e+02, 1.70930e+02, ..., 3.09732e+02,\n",
       "         3.64466e+01, 1.57685e+02],\n",
       "        [2.25998e+02, 1.09879e+02, 1.28914e+02, ..., 3.17341e+02,\n",
       "         1.77548e+03, 2.53547e+02],\n",
       "...\n",
       "        [2.06201e+02, 1.37865e+02, 2.36696e+02, ..., 9.60957e+01,\n",
       "         8.02530e+01, 1.74575e+02],\n",
       "        [1.23916e+02, 1.72185e+02, 4.67290e+01, ..., 1.23356e+02,\n",
       "         1.02672e+02, 4.57979e+01],\n",
       "        [2.09255e+02, 1.53183e+02, 3.82812e+02, ..., 7.07594e+01,\n",
       "         5.85367e+01, 1.52776e+02]],\n",
       "\n",
       "       [[1.19114e+02, 1.21523e+02, 1.63482e+02, ..., 5.98464e+01,\n",
       "         2.17646e+01, 6.91916e+05],\n",
       "        [2.33476e+02, 1.55891e+02, 4.48384e+01, ..., 1.23174e+02,\n",
       "         7.59866e+01, 4.77402e+01],\n",
       "        [2.11136e+02, 1.28474e+02, 6.42633e+01, ..., 9.71707e+01,\n",
       "         6.26536e+01, 5.33435e+01],\n",
       "        ...,\n",
       "        [9.60533e+01, 1.21042e+02, 1.86735e+02, ..., 7.07772e+01,\n",
       "         2.25721e+01, 3.47237e+01],\n",
       "        [2.13396e+02, 1.28511e+02, 6.26126e+01, ..., 6.11047e+01,\n",
       "         1.50342e+02, 4.31100e+01],\n",
       "        [1.77158e+02, 8.35582e+01, 1.87550e+02, ..., 8.18750e+01,\n",
       "         2.49112e+01, 9.63125e+01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(chain, draw, sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 632.5 518.0 ... 600.2 575.4</div><input id='attrs-9c29b3db-36ec-4bd4-a111-f9ebfd6c6b89' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c29b3db-36ec-4bd4-a111-f9ebfd6c6b89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20c7837e-65f5-430a-bbe2-da6ca85c465b' class='xr-var-data-in' type='checkbox'><label for='data-20c7837e-65f5-430a-bbe2-da6ca85c465b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[1.00001e+03, 6.32486e+02, 5.17978e+02, ..., 2.63878e+02,\n",
       "         2.23418e+02, 1.68913e+02],\n",
       "        [1.26406e+03, 3.67004e+06, 3.19287e+06, ..., 2.07206e+06,\n",
       "         1.76724e+06, 1.57868e+06],\n",
       "        [9.98310e+02, 6.35289e+02, 6.30449e+03, ..., 3.13422e+03,\n",
       "         2.56954e+03, 2.19125e+03],\n",
       "        ...,\n",
       "        [1.01628e+03, 7.85603e+02, 7.41290e+02, ..., 3.62528e+02,\n",
       "         3.77423e+03, 2.63311e+03],\n",
       "        [1.01893e+03, 9.69421e+02, 8.08756e+02, ..., 4.98181e+02,\n",
       "         4.43918e+02, 3.96068e+02],\n",
       "        [2.41159e+04, 1.75958e+04, 1.60742e+04, ..., 2.76690e+04,\n",
       "         2.56103e+04, 2.19629e+04]],\n",
       "\n",
       "       [[1.01795e+03, 9.82791e+02, 8.72679e+02, ..., 7.54111e+02,\n",
       "         6.35192e+02, 5.39833e+02],\n",
       "        [1.91768e+03, 1.70512e+03, 1.35690e+03, ..., 1.02982e+03,\n",
       "         7.20086e+02, 6.83645e+02],\n",
       "        [1.02283e+03, 7.97240e+02, 6.87590e+02, ..., 4.64881e+03,\n",
       "         4.53900e+03, 2.90271e+03],\n",
       "...\n",
       "        [1.02203e+03, 1.00531e+03, 8.70463e+02, ..., 9.20500e+02,\n",
       "         8.24429e+02, 7.44180e+02],\n",
       "        [1.01716e+03, 8.93252e+02, 7.21330e+02, ..., 5.31658e+02,\n",
       "         4.22338e+02, 4.58729e+02],\n",
       "        [1.02554e+03, 1.20724e+03, 1.05662e+03, ..., 6.75426e+02,\n",
       "         6.09414e+02, 5.50880e+02]],\n",
       "\n",
       "       [[1.12482e+03, 1.00575e+03, 8.95024e+02, ..., 4.91206e+02,\n",
       "         4.32263e+02, 2.80318e+06],\n",
       "        [1.08813e+03, 8.57258e+02, 7.01464e+02, ..., 6.40948e+02,\n",
       "         5.17924e+02, 4.41938e+02],\n",
       "        [1.00908e+03, 8.13437e+02, 6.84973e+02, ..., 5.92599e+02,\n",
       "         4.95428e+02, 4.32775e+02],\n",
       "        ...,\n",
       "        [1.01148e+03, 9.15428e+02, 7.94905e+02, ..., 4.69898e+02,\n",
       "         4.03631e+02, 3.82783e+02],\n",
       "        [1.03821e+03, 8.26670e+02, 6.99180e+02, ..., 4.01465e+02,\n",
       "         3.40885e+02, 2.17293e+02],\n",
       "        [1.01296e+03, 8.38400e+02, 7.55529e+02, ..., 6.81208e+02,\n",
       "         6.00219e+02, 5.75402e+02]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>c</span></div><div class='xr-var-dims'>(chain, draw, sample, species)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.06423 0.002832 ... 4.282e-05</div><input id='attrs-e6c05aae-8953-4d5f-92a3-448d21926ba1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6c05aae-8953-4d5f-92a3-448d21926ba1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-144ea5de-c3be-4e10-96ab-f2eac3d5a118' class='xr-var-data-in' type='checkbox'><label for='data-144ea5de-c3be-4e10-96ab-f2eac3d5a118' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[6.42333e-02, 2.83229e-03, 4.50366e-10],\n",
       "         [4.74792e-02, 1.14116e-01, 7.83448e-02],\n",
       "         [1.96095e-03, 4.71356e-04, 2.23352e+02],\n",
       "         ...,\n",
       "         [1.77798e+00, 9.14503e+03, 1.29985e-07],\n",
       "         [4.14034e+00, 4.87346e-03, 4.71607e-06],\n",
       "         [6.56370e-08, 9.69596e-11, 2.28264e-02]],\n",
       "\n",
       "        [[5.36278e-05, 2.79662e-07, 1.27952e+03],\n",
       "         [4.64306e-07, 5.72647e-07, 1.25049e-07],\n",
       "         [9.28472e-07, 6.30253e-07, 1.20119e-11],\n",
       "         ...,\n",
       "         [5.03380e-11, 9.84506e-15, 1.02422e-04],\n",
       "         [9.50331e-10, 3.59504e-07, 3.87102e-04],\n",
       "         [9.78831e+00, 5.40702e+00, 2.31646e-08]],\n",
       "\n",
       "        [[1.05388e+00, 1.26189e-02, 4.13768e-08],\n",
       "         [1.18271e+00, 5.84529e+00, 9.56736e-04],\n",
       "         [6.01062e-08, 1.27257e-09, 5.94803e+03],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [5.44074e-03, 3.08358e+00, 3.41692e-02],\n",
       "         [4.52883e-02, 3.16444e-03, 1.30378e-04],\n",
       "         [5.88626e-05, 5.21713e-04, 1.55816e-02]],\n",
       "\n",
       "        [[2.03201e+03, 6.72632e-06, 1.89354e+01],\n",
       "         [1.08646e-05, 3.55414e-08, 7.58031e-07],\n",
       "         [2.05060e-10, 3.79918e-01, 8.24540e-04],\n",
       "         ...,\n",
       "         [1.79757e+00, 1.75035e-04, 1.03575e-07],\n",
       "         [2.83709e-06, 1.16736e-03, 3.94034e-02],\n",
       "         [2.95766e-02, 1.30488e-06, 9.86048e-03]],\n",
       "\n",
       "        [[3.91477e-07, 2.26406e-06, 3.35988e-05],\n",
       "         [1.67262e-08, 5.16307e+01, 1.13698e-03],\n",
       "         [8.54972e-01, 2.55839e-05, 5.93496e-06],\n",
       "         ...,\n",
       "         [1.05156e-03, 8.22472e-02, 3.48733e+02],\n",
       "         [2.20015e-04, 2.09603e-03, 7.21703e-06],\n",
       "         [4.35129e-03, 1.58754e+01, 4.28187e-05]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pump_bias</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-4.05 nan -3.211 ... -5.669 -4.211</div><input id='attrs-5fbe474c-2af5-4265-828e-59a5c027a121' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5fbe474c-2af5-4265-828e-59a5c027a121' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62687dd1-de3e-4407-8a9e-c4457f5ec0c2' class='xr-var-data-in' type='checkbox'><label for='data-62687dd1-de3e-4407-8a9e-c4457f5ec0c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-4.04954,      nan, -3.21055, ..., -4.03822,      nan,      nan],\n",
       "       [-4.61107,      nan, -3.02553, ...,      nan, -4.35823, -4.15891],\n",
       "       [-4.36288,      nan,      nan, ...,      nan,      nan,      nan],\n",
       "       [-3.75422, -4.02111, -2.97382, ...,      nan, -5.66925, -4.21099]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cfeed</span></div><div class='xr-var-dims'>(chain, draw, species)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 100.2 0.0 0.0 ... 0.0 99.16 0.0</div><input id='attrs-de965f81-d690-448a-9d45-7ca27c59a78e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de965f81-d690-448a-9d45-7ca27c59a78e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84857440-6f9e-4907-ad8b-b666b2ea1d7d' class='xr-var-data-in' type='checkbox'><label for='data-84857440-6f9e-4907-ad8b-b666b2ea1d7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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.    , 100.228 ,   0.    ],\n",
       "        [  0.    ,  99.7626,   0.    ],\n",
       "        [  0.    , 100.357 ,   0.    ],\n",
       "        ...,\n",
       "        [  0.    , 100.541 ,   0.    ],\n",
       "        [  0.    , 100.216 ,   0.    ],\n",
       "        [  0.    ,  98.8992,   0.    ]],\n",
       "\n",
       "       [[  0.    ,  99.8223,   0.    ],\n",
       "        [  0.    ,  99.842 ,   0.    ],\n",
       "        [  0.    , 100.535 ,   0.    ],\n",
       "        ...,\n",
       "        [  0.    ,  99.8127,   0.    ],\n",
       "        [  0.    , 100.524 ,   0.    ],\n",
       "        [  0.    ,  99.7742,   0.    ]],\n",
       "\n",
       "       [[  0.    ,  99.3149,   0.    ],\n",
       "        [  0.    ,  99.8001,   0.    ],\n",
       "        [  0.    ,  98.54  ,   0.    ],\n",
       "        ...,\n",
       "        [  0.    , 100.782 ,   0.    ],\n",
       "        [  0.    ,  99.296 ,   0.    ],\n",
       "        [  0.    , 100.621 ,   0.    ]],\n",
       "\n",
       "       [[  0.    ,  99.8644,   0.    ],\n",
       "        [  0.    ,  99.6745,   0.    ],\n",
       "        [  0.    ,  99.6554,   0.    ],\n",
       "        ...,\n",
       "        [  0.    ,  99.1771,   0.    ],\n",
       "        [  0.    , 100.375 ,   0.    ],\n",
       "        [  0.    ,  99.1556,   0.    ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pseudobatch_c</span></div><div class='xr-var-dims'>(chain, draw, sample, species)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.06423 0.00282 ... 6.549e-05</div><input id='attrs-593896a4-f5ac-49d0-8631-95e0db6b4a64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-593896a4-f5ac-49d0-8631-95e0db6b4a64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d76e1bf2-1076-47ab-80b0-e14b0c917196' class='xr-var-data-in' type='checkbox'><label for='data-d76e1bf2-1076-47ab-80b0-e14b0c917196' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[ 6.42333e-02,  2.82013e-03,  4.50366e-10],\n",
       "         [ 4.74792e-02,  1.14100e-01,  7.83448e-02],\n",
       "         [ 1.97978e-03, -9.62108e-01,  2.25497e+02],\n",
       "         ...,\n",
       "         [ 1.82268e+00,  9.37246e+03,  1.33253e-07],\n",
       "         [ 4.25205e+00, -2.69947e+00,  4.84332e-06],\n",
       "         [ 6.74083e-08, -2.70485e+00,  2.34425e-02]],\n",
       "\n",
       "        [[ 5.36278e-05, -1.85116e+01,  1.27952e+03],\n",
       "         [ 1.46096e-03, -3.13826e+05,  3.93472e-04],\n",
       "         [ 2.92148e-03, -3.13826e+05,  3.77960e-08],\n",
       "         ...,\n",
       "         [ 1.58392e-07, -3.13828e+05,  3.22277e-01],\n",
       "         [ 2.99030e-06, -3.13831e+05,  1.21805e+00],\n",
       "         [ 3.08003e+04, -2.96822e+05,  7.28906e-05]],\n",
       "\n",
       "        [[ 1.05388e+00,  1.26100e-02,  4.13768e-08],\n",
       "         [ 1.18271e+00,  5.84521e+00,  9.56736e-04],\n",
       "         [ 7.20974e-07, -1.10343e+03,  7.13466e+04],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [ 5.74932e-03, -2.36694e+00,  3.61072e-02],\n",
       "         [ 4.83977e-02, -6.80620e+00,  1.39330e-04],\n",
       "         [ 6.31886e-05, -7.28861e+00,  1.67267e-02]],\n",
       "\n",
       "        [[ 2.03201e+03, -1.94238e+00,  1.89354e+01],\n",
       "         [ 1.08890e-05, -2.16811e+00,  7.59736e-07],\n",
       "         [ 2.05822e-10, -1.93391e+00,  8.27603e-04],\n",
       "         ...,\n",
       "         [ 1.96549e+00, -1.13187e+01,  1.13251e-07],\n",
       "         [ 3.10691e-06, -1.14869e+01,  4.31507e-02],\n",
       "         [ 3.69364e-02, -2.69196e+01,  1.23142e-02]],\n",
       "\n",
       "        [[ 3.91477e-07, -2.85974e-04,  3.35988e-05],\n",
       "         [ 1.67782e-08,  5.14828e+01,  1.14051e-03],\n",
       "         [ 8.58407e-01, -3.98672e-01,  5.95880e-06],\n",
       "         ...,\n",
       "         [ 1.60576e-03, -5.21328e+01,  5.32526e+02],\n",
       "         [ 3.36466e-04, -5.24792e+01,  1.10369e-05],\n",
       "         [ 6.65548e-03, -2.82252e+01,  6.54930e-05]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-12b81b48-f3e5-49ea-a537-e7955b55fccc' class='xr-section-summary-in' type='checkbox'  ><label for='section-12b81b48-f3e5-49ea-a537-e7955b55fccc' class='xr-section-summary' >Indexes: <span>(6)</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-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a46154ed-0eb3-4f26-bce8-60b568e5b1c6' class='xr-index-data-in' type='checkbox'/><label for='index-a46154ed-0eb3-4f26-bce8-60b568e5b1c6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ed8f42e3-7d90-42df-95d4-c7303c58b0cb' class='xr-index-data-in' type='checkbox'/><label for='index-ed8f42e3-7d90-42df-95d4-c7303c58b0cb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>sample</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fe24a5b5-5525-4c4d-8723-cffce5a4c896' class='xr-index-data-in' type='checkbox'/><label for='index-fe24a5b5-5525-4c4d-8723-cffce5a4c896' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7], dtype=&#x27;int64&#x27;, name=&#x27;sample&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>species</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-99bf089e-22fc-4e57-93f0-a5a4a152258a' class='xr-index-data-in' type='checkbox'/><label for='index-99bf089e-22fc-4e57-93f0-a5a4a152258a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Biomass&#x27;, &#x27;Glucose&#x27;, &#x27;Product&#x27;], dtype=&#x27;object&#x27;, name=&#x27;species&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>f_nonzero_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-222a9630-af46-4fa9-998a-2762662e40f9' class='xr-index-data-in' type='checkbox'/><label for='index-222a9630-af46-4fa9-998a-2762662e40f9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7], dtype=&#x27;int64&#x27;, name=&#x27;f_nonzero_dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>cfeed_nonzero_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d05c3bc4-ddf7-4df4-a305-03d4063a8db5' class='xr-index-data-in' type='checkbox'/><label for='index-d05c3bc4-ddf7-4df4-a305-03d4063a8db5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;cfeed_nonzero_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-816c85cf-3f2a-4f2a-9642-7b909fa8cf70' class='xr-section-summary-in' type='checkbox'  checked><label for='section-816c85cf-3f2a-4f2a-9642-7b909fa8cf70' class='xr-section-summary' >Attributes: <span>(4)</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>2023-07-26T11:42:38.416724</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>cmdstanpy</dd><dt><span>inference_library_version :</span></dt><dd>1.1.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_stats_priorc70309a6-54e4-4394-94af-485887d91673\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_stats_priorc70309a6-54e4-4394-94af-485887d91673\" class = \"xr-section-summary\">sample_stats_prior</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><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 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[data-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 !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
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       "\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",
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       "  grid-column: 1;\n",
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       "\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",
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       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\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",
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       "\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",
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-no-icon {\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: 4, draw: 1000)\n",
       "Coordinates:\n",
       "  * chain            (chain) int64 0 1 2 3\n",
       "  * draw             (draw) int64 0 1 2 3 4 5 6 ... 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    lp               (chain, draw) float64 -55.79 -52.93 ... -47.96 -45.09\n",
       "    acceptance_rate  (chain, draw) float64 0.5194 0.9292 0.801 ... 0.7753 0.7589\n",
       "    step_size        (chain, draw) float64 0.5615 0.5615 ... 0.5039 0.5039\n",
       "    tree_depth       (chain, draw) int64 3 3 3 3 3 3 3 3 3 ... 3 3 3 3 3 3 3 3 3\n",
       "    n_steps          (chain, draw) int64 7 7 7 7 7 7 7 7 7 ... 7 7 7 7 7 7 7 7 7\n",
       "    diverging        (chain, draw) bool False False False ... False False False\n",
       "    energy           (chain, draw) float64 83.66 79.28 76.56 ... 67.48 71.66\n",
       "Attributes:\n",
       "    created_at:                 2023-07-26T11:42:38.429657\n",
       "    arviz_version:              0.15.1\n",
       "    inference_library:          cmdstanpy\n",
       "    inference_library_version:  1.1.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-92db4694-586e-4f36-9335-c1f510242a04' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-92db4694-586e-4f36-9335-c1f510242a04' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fc841ae0-52aa-461b-8216-244f285d0177' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fc841ae0-52aa-461b-8216-244f285d0177' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-055eebc6-6520-4113-8740-6b4a07d1e8e4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-055eebc6-6520-4113-8740-6b4a07d1e8e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17232281-4d82-4877-add0-c81e4dfe010d' class='xr-var-data-in' type='checkbox'><label for='data-17232281-4d82-4877-add0-c81e4dfe010d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-35f62062-71f2-4744-9652-2b77cfa156fe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-35f62062-71f2-4744-9652-2b77cfa156fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f0e5869-633a-4688-8613-82157d675e6e' class='xr-var-data-in' type='checkbox'><label for='data-5f0e5869-633a-4688-8613-82157d675e6e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c6788f06-d9d9-43ce-96ed-24f04b6aefa6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c6788f06-d9d9-43ce-96ed-24f04b6aefa6' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-55.79 -52.93 ... -47.96 -45.09</div><input id='attrs-7f15f3bf-806e-4122-9532-d79b1bca3ea6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f15f3bf-806e-4122-9532-d79b1bca3ea6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f7e810c-af5b-4bc9-a855-2a8fcac7a048' class='xr-var-data-in' type='checkbox'><label for='data-9f7e810c-af5b-4bc9-a855-2a8fcac7a048' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-55.7856, -52.9261, -61.0515, ..., -45.3104, -40.1642, -59.1841],\n",
       "       [-48.587 , -52.4947, -53.8964, ..., -56.2946, -49.5678, -43.754 ],\n",
       "       [-53.5976, -63.2841, -60.94  , ..., -44.4558, -44.6492, -46.5779],\n",
       "       [-57.6424, -49.7302, -53.2738, ..., -45.6268, -47.9561, -45.0866]])</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'>0.5194 0.9292 ... 0.7753 0.7589</div><input id='attrs-6eeb971c-c489-4c4f-91a6-feeb905136eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6eeb971c-c489-4c4f-91a6-feeb905136eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e480dd7-0c4c-4822-b7b6-9162f9f5a403' class='xr-var-data-in' type='checkbox'><label for='data-6e480dd7-0c4c-4822-b7b6-9162f9f5a403' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.519365, 0.929226, 0.800956, ..., 0.887268, 0.88844 , 0.368372],\n",
       "       [0.986649, 0.840211, 0.723908, ..., 0.719321, 0.981445, 0.976452],\n",
       "       [0.932185, 0.682316, 0.984736, ..., 0.744799, 0.887542, 0.586365],\n",
       "       [0.847668, 0.973235, 0.894665, ..., 0.867236, 0.775253, 0.75894 ]])</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'>0.5615 0.5615 ... 0.5039 0.5039</div><input id='attrs-06f37758-a6bb-4e72-9de6-1e88326c895e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-06f37758-a6bb-4e72-9de6-1e88326c895e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac5a5f26-f39d-4de1-a550-3fbf23d0ea96' class='xr-var-data-in' type='checkbox'><label for='data-ac5a5f26-f39d-4de1-a550-3fbf23d0ea96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.561533, 0.561533, 0.561533, ..., 0.561533, 0.561533, 0.561533],\n",
       "       [0.553654, 0.553654, 0.553654, ..., 0.553654, 0.553654, 0.553654],\n",
       "       [0.538248, 0.538248, 0.538248, ..., 0.538248, 0.538248, 0.538248],\n",
       "       [0.503856, 0.503856, 0.503856, ..., 0.503856, 0.503856, 0.503856]])</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'>3 3 3 3 3 3 3 3 ... 3 3 3 3 3 3 3 3</div><input id='attrs-b7a2fe0b-817c-4cfc-b692-1a2db4af7f5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b7a2fe0b-817c-4cfc-b692-1a2db4af7f5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19792480-3973-4b79-bd55-875d6551c61b' class='xr-var-data-in' type='checkbox'><label for='data-19792480-3973-4b79-bd55-875d6551c61b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[3, 3, 3, ..., 3, 3, 3],\n",
       "       [3, 3, 3, ..., 3, 3, 3],\n",
       "       [3, 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>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>7 7 7 7 7 7 7 7 ... 7 7 7 7 7 7 7 7</div><input id='attrs-45eddeaa-1237-4373-83ab-4c4f23fa6654' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45eddeaa-1237-4373-83ab-4c4f23fa6654' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65e1f198-f301-4a30-b4f7-13e86423d9c8' class='xr-var-data-in' type='checkbox'><label for='data-65e1f198-f301-4a30-b4f7-13e86423d9c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, 7, 7, ..., 7, 7, 7],\n",
       "       [7, 7, 7, ..., 7, 7, 7],\n",
       "       [7, 7, 7, ..., 7, 7, 7],\n",
       "       [7, 7, 7, ..., 7, 7, 7]])</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-90390d54-7b89-4346-8a15-86ccf9de2307' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-90390d54-7b89-4346-8a15-86ccf9de2307' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f816da3-3fcc-407e-8161-482ac7a011bd' class='xr-var-data-in' type='checkbox'><label for='data-3f816da3-3fcc-407e-8161-482ac7a011bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>83.66 79.28 76.56 ... 67.48 71.66</div><input id='attrs-e14ee00d-ab19-4747-bd09-44d24b613860' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e14ee00d-ab19-4747-bd09-44d24b613860' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a73cd6dd-39d5-437e-992d-b1b60d299338' class='xr-var-data-in' type='checkbox'><label for='data-a73cd6dd-39d5-437e-992d-b1b60d299338' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[83.6626, 79.2827, 76.5604, ..., 67.4224, 59.1145, 68.1582],\n",
       "       [71.9445, 64.4977, 80.6689, ..., 74.38  , 73.6346, 61.7268],\n",
       "       [81.7597, 83.2746, 85.4647, ..., 65.7008, 60.0708, 70.9257],\n",
       "       [73.2203, 74.5063, 68.4407, ..., 62.5682, 67.48  , 71.6636]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b70e0092-0a0a-45e5-a8c6-80afe6541f2f' class='xr-section-summary-in' type='checkbox'  ><label for='section-b70e0092-0a0a-45e5-a8c6-80afe6541f2f' class='xr-section-summary' >Indexes: <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-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2babff5b-6d30-436f-bd47-8616ecdcc3df' class='xr-index-data-in' type='checkbox'/><label for='index-2babff5b-6d30-436f-bd47-8616ecdcc3df' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4b13f8b6-78aa-42b4-b32e-fc6135aa26cc' class='xr-index-data-in' type='checkbox'/><label for='index-4b13f8b6-78aa-42b4-b32e-fc6135aa26cc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ab877662-a761-497c-b7d8-d6e67ae03e3a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ab877662-a761-497c-b7d8-d6e67ae03e3a' class='xr-section-summary' >Attributes: <span>(4)</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>2023-07-26T11:42:38.429657</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</dd><dt><span>inference_library :</span></dt><dd>cmdstanpy</dd><dt><span>inference_library_version :</span></dt><dd>1.1.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "              </ul>\n",
       "            </div>\n",
       "            <style> /* CSS stylesheet for displaying InferenceData 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-sections.group-sections {\n",
       "  grid-template-columns: auto;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
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       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
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       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
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       "\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",
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       "\n",
       ".xr-section-summary-in + label:before {\n",
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       "  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",
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      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> sample_stats\n",
       "\t> prior\n",
       "\t> sample_stats_prior"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata = run_error_propagation(\n",
    "    y_concentration=samples[[f\"c_{species}\" for species in SPECIES]],\n",
    "    y_reactor_volume=samples[\"v_Volume\"],\n",
    "    y_feed_in_interval=samples[\"v_Feed_interval\"],\n",
    "    y_sample_volume=samples[\"sample_volume\"],\n",
    "    y_concentration_in_feed=[0, 100, 0],\n",
    "    sd_reactor_volume=0.05,\n",
    "    sd_concentration=[0.05] * 3,\n",
    "    sd_feed_in_interval=0.05,\n",
    "    sd_sample_volume=0.05,\n",
    "    sd_concentration_in_feed=0.05,\n",
    "    prior_input=priors,\n",
    "    species_names=SPECIES\n",
    ")\n",
    "idata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "The next cell prints some diagnostic information. By inspecting it we can see that:\n",
    "\n",
    "* There were no post warmup divergent transitions, indicating that the sampler was able to explore the posterior without significant approximation errors.\n",
    "* There were no parameters with r_hat values more than 0.01 away from 1, indicating that the chains converged.\n",
    "* The posterior `mcse_sd` parameters are fairly small."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "/Users/s143838/.virtualenvs/pseudobatch-dev/lib/python3.10/site-packages/arviz/stats/diagnostics.py:592: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples)\n",
      "/Users/s143838/.virtualenvs/pseudobatch-dev/lib/python3.10/site-packages/arviz/stats/diagnostics.py:592: RuntimeWarning: invalid value encountered in scalar divide\n",
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       "                    mean     sd   hdi_3%  hdi_97%  mcse_mean  mcse_sd   \n",
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       "\n",
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      "arviz - WARNING - Array contains NaN-value.\n",
      "/Users/s143838/.virtualenvs/pseudobatch-dev/lib/python3.10/site-packages/arviz/stats/diagnostics.py:592: RuntimeWarning: invalid value encountered in scalar divide\n",
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       "      <td>0.070</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>1.000000e-03</td>\n",
       "      <td>7492.0</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>2727.340</td>\n",
       "      <td>6.164198e+09</td>\n",
       "      <td>4.359074e+09</td>\n",
       "      <td>8877.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4000.0</td>\n",
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       "      <td>4000.0</td>\n",
       "      <td>4000.0</td>\n",
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       "<p>119 rows × 9 columns</p>\n",
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       "                                   mean            sd    hdi_3%   hdi_97%   \n",
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       "...                                 ...           ...       ...       ...   \n",
       "apump                     -6.000000e-03  4.400000e-02    -0.092     0.070   \n",
       "pseudobatch_c[7, Product]  6.206539e+09  3.906427e+11     0.000  2727.340   \n",
       "pump_bias                           NaN           NaN   -11.833       NaN   \n",
       "cfeed[Biomass]             0.000000e+00  0.000000e+00     0.000     0.000   \n",
       "cfeed[Product]             0.000000e+00  0.000000e+00     0.000     0.000   \n",
       "\n",
       "                              mcse_mean       mcse_sd  ess_bulk  ess_tail   \n",
       "v0                         5.800000e-02  4.100000e-02   11762.0    2675.0  \\\n",
       "c[5, Product]              2.519860e+09  1.781939e+09    6104.0    3090.0   \n",
       "c[5, Glucose]              2.215507e+10  1.566714e+10    6059.0    3041.0   \n",
       "c[5, Biomass]              2.103310e+06  1.487494e+06    7532.0    3018.0   \n",
       "c[4, Product]              5.951675e+08  4.209284e+08    6743.0    2939.0   \n",
       "...                                 ...           ...       ...       ...   \n",
       "apump                      1.000000e-03  1.000000e-03    7492.0    2746.0   \n",
       "pseudobatch_c[7, Product]  6.164198e+09  4.359074e+09    8877.0    2982.0   \n",
       "pump_bias                           NaN           NaN       NaN       NaN   \n",
       "cfeed[Biomass]             0.000000e+00  0.000000e+00    4000.0    4000.0   \n",
       "cfeed[Product]             0.000000e+00  0.000000e+00    4000.0    4000.0   \n",
       "\n",
       "                           r_hat  \n",
       "v0                           1.0  \n",
       "c[5, Product]                1.0  \n",
       "c[5, Glucose]                1.0  \n",
       "c[5, Biomass]                1.0  \n",
       "c[4, Product]                1.0  \n",
       "...                          ...  \n",
       "apump                        1.0  \n",
       "pseudobatch_c[7, Product]    1.0  \n",
       "pump_bias                    NaN  \n",
       "cfeed[Biomass]               NaN  \n",
       "cfeed[Product]               NaN  \n",
       "\n",
       "[119 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "arviz - WARNING - Array contains NaN-value.\n",
      "/Users/s143838/.virtualenvs/pseudobatch-dev/lib/python3.10/site-packages/arviz/stats/diagnostics.py:592: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples)\n",
      "/Users/s143838/.virtualenvs/pseudobatch-dev/lib/python3.10/site-packages/arviz/stats/diagnostics.py:592: RuntimeWarning: invalid value encountered in scalar divide\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>v0</th>\n",
       "      <td>1013.674</td>\n",
       "      <td>6.047</td>\n",
       "      <td>1002.120</td>\n",
       "      <td>1024.430</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.060</td>\n",
       "      <td>5069.0</td>\n",
       "      <td>3144.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c[5, Product]</th>\n",
       "      <td>20.841</td>\n",
       "      <td>1.030</td>\n",
       "      <td>18.947</td>\n",
       "      <td>22.801</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.010</td>\n",
       "      <td>5480.0</td>\n",
       "      <td>3012.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c[5, Glucose]</th>\n",
       "      <td>0.075</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5702.0</td>\n",
       "      <td>3056.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c[5, Biomass]</th>\n",
       "      <td>25.649</td>\n",
       "      <td>1.276</td>\n",
       "      <td>23.197</td>\n",
       "      <td>27.988</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.011</td>\n",
       "      <td>6228.0</td>\n",
       "      <td>3694.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c[4, Product]</th>\n",
       "      <td>13.328</td>\n",
       "      <td>0.676</td>\n",
       "      <td>12.090</td>\n",
       "      <td>14.615</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.006</td>\n",
       "      <td>5446.0</td>\n",
       "      <td>3471.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apump</th>\n",
       "      <td>0.009</td>\n",
       "      <td>0.033</td>\n",
       "      <td>-0.054</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2136.0</td>\n",
       "      <td>2454.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pseudobatch_c[7, Product]</th>\n",
       "      <td>155.983</td>\n",
       "      <td>12.100</td>\n",
       "      <td>133.595</td>\n",
       "      <td>178.295</td>\n",
       "      <td>0.230</td>\n",
       "      <td>0.163</td>\n",
       "      <td>2737.0</td>\n",
       "      <td>3220.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pump_bias</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cfeed[Biomass]</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cfeed[Product]</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               mean      sd    hdi_3%   hdi_97%  mcse_mean   \n",
       "v0                         1013.674   6.047  1002.120  1024.430      0.085  \\\n",
       "c[5, Product]                20.841   1.030    18.947    22.801      0.014   \n",
       "c[5, Glucose]                 0.075   0.004     0.068     0.082      0.000   \n",
       "c[5, Biomass]                25.649   1.276    23.197    27.988      0.016   \n",
       "c[4, Product]                13.328   0.676    12.090    14.615      0.009   \n",
       "...                             ...     ...       ...       ...        ...   \n",
       "apump                         0.009   0.033    -0.054     0.070      0.001   \n",
       "pseudobatch_c[7, Product]   155.983  12.100   133.595   178.295      0.230   \n",
       "pump_bias                       NaN     NaN   -11.956       NaN        NaN   \n",
       "cfeed[Biomass]                0.000   0.000     0.000     0.000      0.000   \n",
       "cfeed[Product]                0.000   0.000     0.000     0.000      0.000   \n",
       "\n",
       "                           mcse_sd  ess_bulk  ess_tail  r_hat  \n",
       "v0                           0.060    5069.0    3144.0    1.0  \n",
       "c[5, Product]                0.010    5480.0    3012.0    1.0  \n",
       "c[5, Glucose]                0.000    5702.0    3056.0    1.0  \n",
       "c[5, Biomass]                0.011    6228.0    3694.0    1.0  \n",
       "c[4, Product]                0.006    5446.0    3471.0    1.0  \n",
       "...                            ...       ...       ...    ...  \n",
       "apump                        0.001    2136.0    2454.0    1.0  \n",
       "pseudobatch_c[7, Product]    0.163    2737.0    3220.0    1.0  \n",
       "pump_bias                      NaN       NaN       NaN    NaN  \n",
       "cfeed[Biomass]               0.000    4000.0    4000.0    NaN  \n",
       "cfeed[Product]               0.000    4000.0    4000.0    NaN  \n",
       "\n",
       "[119 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(az.summary(idata.sample_stats))\n",
    "display(az.summary(idata.prior).sort_values(\"r_hat\"))\n",
    "display(az.summary(idata.posterior).sort_values(\"r_hat\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting some modelled quantities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_timecourse_qs(\n",
    "    ax: plt.Axes,\n",
    "    varname: str, \n",
    "    idata_group: xr.Dataset, \n",
    "    timepoints: pd.Series,\n",
    "    coords: dict,\n",
    "    quantiles: list = [0.025, 0.975],\n",
    "    **fill_between_kwargs\n",
    ") -> PolyCollection:\n",
    "    var_draws = idata_group[varname]\n",
    "    for k, v in coords.items():\n",
    "        if k in var_draws.coords:\n",
    "            var_draws = var_draws.sel({k:v})\n",
    "    qs = var_draws.quantile(quantiles, dim=[\"chain\", \"draw\"]).to_dataframe()[varname].unstack(\"quantile\")\n",
    "    low = qs[0.025].values\n",
    "    high = qs[0.975].values\n",
    "    x = timepoints.values\n",
    "    return ax.fill_between(x, low, high, **fill_between_kwargs)\n",
    "\n",
    "\n",
    "f, axes = plt.subplots(1, 3, figsize=[14, 5])\n",
    "for ax, species in zip(axes, SPECIES):\n",
    "    pcs = []\n",
    "    line_patches = []\n",
    "    for var, color in zip([\"c\", \"pseudobatch_c\"], [\"tab:blue\", \"tab:orange\"]):\n",
    "        true_value_colname = \"c_\" + species if var == \"c\" else f\"c_{species}_pseudobatch\"\n",
    "        pc = plot_timecourse_qs(\n",
    "            ax,\n",
    "            var,\n",
    "            idata.posterior,\n",
    "            samples[\"timestamp\"],\n",
    "            {\"species\": [species]},\n",
    "            color=color,\n",
    "            alpha=0.5,\n",
    "        )\n",
    "        pcs += [pc]\n",
    "        line = ax.plot(samples[\"timestamp\"], samples[true_value_colname], color=color)\n",
    "        line_patches += [line[-1]]\n",
    "    txt = ax.set(xlabel=\"Time\", title=species)\n",
    "    if all(samples[true_value_colname] > 0):\n",
    "        ax.semilogy()\n",
    "\n",
    "f.suptitle(\"Species concentrations\")\n",
    "legend = f.legend(\n",
    "    [*pcs, *line_patches],\n",
    "    [\"Untransformed\", \"Pseudobatch transformed\", \"True untransformed\", \"Truth transformed externally\"],\n",
    "    ncol=1, \n",
    "    loc=\"right\", \n",
    "    frameon=False,\n",
    "    bbox_to_anchor = [1.11, 0.5]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimating the growth rate\n",
    "\n",
    "One thing that you might want to do with pseudobatch transformation is to estimate the measured cells' growth rate.\n",
    "\n",
    "This can be done for each sample from our posterior distribution, giving us an idea of the range of growth rate estimates that are consistent with our model. Even better, since we used a simulated dataset we know that the true growth rate is 0.1, so we can compare our estimated growth rates with the truth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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>draw</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3990</th>\n",
       "      <th>3991</th>\n",
       "      <th>3992</th>\n",
       "      <th>3993</th>\n",
       "      <th>3994</th>\n",
       "      <th>3995</th>\n",
       "      <th>3996</th>\n",
       "      <th>3997</th>\n",
       "      <th>3998</th>\n",
       "      <th>3999</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>0.102516</td>\n",
       "      <td>0.096615</td>\n",
       "      <td>0.101327</td>\n",
       "      <td>0.096538</td>\n",
       "      <td>0.101856</td>\n",
       "      <td>0.100044</td>\n",
       "      <td>0.099655</td>\n",
       "      <td>0.097026</td>\n",
       "      <td>0.099064</td>\n",
       "      <td>0.097570</td>\n",
       "      <td>...</td>\n",
       "      <td>0.101227</td>\n",
       "      <td>0.100904</td>\n",
       "      <td>0.099847</td>\n",
       "      <td>0.099308</td>\n",
       "      <td>0.099897</td>\n",
       "      <td>0.098900</td>\n",
       "      <td>0.099894</td>\n",
       "      <td>0.099580</td>\n",
       "      <td>0.10023</td>\n",
       "      <td>0.099364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intercept</th>\n",
       "      <td>-0.807900</td>\n",
       "      <td>-0.614030</td>\n",
       "      <td>-0.776267</td>\n",
       "      <td>-0.619113</td>\n",
       "      <td>-0.797399</td>\n",
       "      <td>-0.721281</td>\n",
       "      <td>-0.691591</td>\n",
       "      <td>-0.667151</td>\n",
       "      <td>-0.676970</td>\n",
       "      <td>-0.619378</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.724136</td>\n",
       "      <td>-0.735155</td>\n",
       "      <td>-0.708653</td>\n",
       "      <td>-0.679298</td>\n",
       "      <td>-0.656992</td>\n",
       "      <td>-0.661648</td>\n",
       "      <td>-0.707765</td>\n",
       "      <td>-0.695189</td>\n",
       "      <td>-0.70449</td>\n",
       "      <td>-0.685411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 4000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "draw           0         1         2         3         4         5      \n",
       "slope      0.102516  0.096615  0.101327  0.096538  0.101856  0.100044  \\\n",
       "intercept -0.807900 -0.614030 -0.776267 -0.619113 -0.797399 -0.721281   \n",
       "\n",
       "draw           6         7         8         9     ...      3990      3991   \n",
       "slope      0.099655  0.097026  0.099064  0.097570  ...  0.101227  0.100904  \\\n",
       "intercept -0.691591 -0.667151 -0.676970 -0.619378  ... -0.724136 -0.735155   \n",
       "\n",
       "draw           3992      3993      3994      3995      3996      3997   \n",
       "slope      0.099847  0.099308  0.099897  0.098900  0.099894  0.099580  \\\n",
       "intercept -0.708653 -0.679298 -0.656992 -0.661648 -0.707765 -0.695189   \n",
       "\n",
       "draw          3998      3999  \n",
       "slope      0.10023  0.099364  \n",
       "intercept -0.70449 -0.685411  \n",
       "\n",
       "[2 rows x 4000 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fit_log_linear_model(y, x):\n",
    "    logy = np.log(y.values)\n",
    "    slope, intercept = np.polyfit(x, logy, deg=1)\n",
    "    return xr.DataArray([slope, intercept])\n",
    "\n",
    "growth_coeffs = pd.DataFrame(\n",
    "    idata.posterior[\"pseudobatch_c\"]\n",
    "    .sel(species=\"Biomass\")\n",
    "    .stack(chaindraw=(\"chain\", \"draw\"))\n",
    "    .groupby(\"chaindraw\")\n",
    "    .map(fit_log_linear_model, x=samples[\"timestamp\"], shortcut=True)\n",
    "    .values, \n",
    "    columns=[\"slope\", \"intercept\"]\n",
    ")\n",
    "growth_coeffs.index.name = \"draw\"\n",
    "\n",
    "display(growth_coeffs.T)\n",
    "\n",
    "f, ax = plt.subplots()\n",
    "hist = ax.hist(growth_coeffs[\"slope\"], bins=50, alpha=0.5)\n",
    "vline = ax.axvline(samples[\"mu_true\"].iloc[0], c=\"red\", label=\"True growth rate\")\n",
    "txt = ax.set(\n",
    "    xlabel=\"Growth rate (1/h)\", \n",
    "    ylabel=\"Frequency\", \n",
    "    title=\"Distribution of growth rate estimates\"\n",
    ")\n",
    "leg = ax.legend(frameon=False)\n",
    "\n",
    "\n",
    "# the 0.025, 0.5 and 0.975 quantiles of the fitted slopes\n",
    "# print(fitted_growth_rates.slope.quantile([0.025, 0.5, 0.975]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimating yield coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Glucose yield coefficients:\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3990</th>\n",
       "      <th>3991</th>\n",
       "      <th>3992</th>\n",
       "      <th>3993</th>\n",
       "      <th>3994</th>\n",
       "      <th>3995</th>\n",
       "      <th>3996</th>\n",
       "      <th>3997</th>\n",
       "      <th>3998</th>\n",
       "      <th>3999</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>-1.744210</td>\n",
       "      <td>-1.757008</td>\n",
       "      <td>-1.741915</td>\n",
       "      <td>-1.723624</td>\n",
       "      <td>-1.871941</td>\n",
       "      <td>-1.838772</td>\n",
       "      <td>-1.823377</td>\n",
       "      <td>-1.836021</td>\n",
       "      <td>-1.699397</td>\n",
       "      <td>-2.036570</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.845025</td>\n",
       "      <td>-1.872864</td>\n",
       "      <td>-1.875026</td>\n",
       "      <td>-1.919493</td>\n",
       "      <td>-1.854989</td>\n",
       "      <td>-1.886462</td>\n",
       "      <td>-1.896649</td>\n",
       "      <td>-1.864343</td>\n",
       "      <td>-1.842283</td>\n",
       "      <td>-1.924881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intercept</th>\n",
       "      <td>-2.587623</td>\n",
       "      <td>1.628419</td>\n",
       "      <td>-0.189456</td>\n",
       "      <td>-0.116613</td>\n",
       "      <td>2.007992</td>\n",
       "      <td>2.594703</td>\n",
       "      <td>2.372684</td>\n",
       "      <td>0.084090</td>\n",
       "      <td>0.129974</td>\n",
       "      <td>4.094919</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.764112</td>\n",
       "      <td>-1.661884</td>\n",
       "      <td>3.475286</td>\n",
       "      <td>4.223898</td>\n",
       "      <td>-0.092433</td>\n",
       "      <td>-1.238829</td>\n",
       "      <td>1.127363</td>\n",
       "      <td>1.162724</td>\n",
       "      <td>-0.245896</td>\n",
       "      <td>3.203416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 4000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0         1         2         3         4         5      \n",
       "slope     -1.744210 -1.757008 -1.741915 -1.723624 -1.871941 -1.838772  \\\n",
       "intercept -2.587623  1.628419 -0.189456 -0.116613  2.007992  2.594703   \n",
       "\n",
       "               6         7         8         9     ...      3990      3991   \n",
       "slope     -1.823377 -1.836021 -1.699397 -2.036570  ... -1.845025 -1.872864  \\\n",
       "intercept  2.372684  0.084090  0.129974  4.094919  ... -1.764112 -1.661884   \n",
       "\n",
       "               3992      3993      3994      3995      3996      3997   \n",
       "slope     -1.875026 -1.919493 -1.854989 -1.886462 -1.896649 -1.864343  \\\n",
       "intercept  3.475286  4.223898 -0.092433 -1.238829  1.127363  1.162724   \n",
       "\n",
       "               3998      3999  \n",
       "slope     -1.842283 -1.924881  \n",
       "intercept -0.245896  3.203416  \n",
       "\n",
       "[2 rows x 4000 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product yield coefficients:\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3990</th>\n",
       "      <th>3991</th>\n",
       "      <th>3992</th>\n",
       "      <th>3993</th>\n",
       "      <th>3994</th>\n",
       "      <th>3995</th>\n",
       "      <th>3996</th>\n",
       "      <th>3997</th>\n",
       "      <th>3998</th>\n",
       "      <th>3999</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>0.717567</td>\n",
       "      <td>0.830149</td>\n",
       "      <td>0.799137</td>\n",
       "      <td>0.811471</td>\n",
       "      <td>0.820391</td>\n",
       "      <td>0.820898</td>\n",
       "      <td>0.781038</td>\n",
       "      <td>0.815743</td>\n",
       "      <td>0.803372</td>\n",
       "      <td>0.897239</td>\n",
       "      <td>...</td>\n",
       "      <td>0.833248</td>\n",
       "      <td>0.790404</td>\n",
       "      <td>0.889563</td>\n",
       "      <td>0.877858</td>\n",
       "      <td>0.808814</td>\n",
       "      <td>0.822912</td>\n",
       "      <td>0.862858</td>\n",
       "      <td>0.836267</td>\n",
       "      <td>0.825557</td>\n",
       "      <td>0.881137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intercept</th>\n",
       "      <td>2.035210</td>\n",
       "      <td>-0.845795</td>\n",
       "      <td>-0.920503</td>\n",
       "      <td>0.366229</td>\n",
       "      <td>-1.117119</td>\n",
       "      <td>-1.476975</td>\n",
       "      <td>-0.584068</td>\n",
       "      <td>0.767538</td>\n",
       "      <td>-0.163943</td>\n",
       "      <td>-1.097187</td>\n",
       "      <td>...</td>\n",
       "      <td>0.044753</td>\n",
       "      <td>1.346261</td>\n",
       "      <td>-2.497430</td>\n",
       "      <td>-2.395350</td>\n",
       "      <td>0.753787</td>\n",
       "      <td>0.738677</td>\n",
       "      <td>-0.983234</td>\n",
       "      <td>-1.065378</td>\n",
       "      <td>-0.643284</td>\n",
       "      <td>-1.157595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 4000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0         1         2         3         4         5      \n",
       "slope      0.717567  0.830149  0.799137  0.811471  0.820391  0.820898  \\\n",
       "intercept  2.035210 -0.845795 -0.920503  0.366229 -1.117119 -1.476975   \n",
       "\n",
       "               6         7         8         9     ...      3990      3991   \n",
       "slope      0.781038  0.815743  0.803372  0.897239  ...  0.833248  0.790404  \\\n",
       "intercept -0.584068  0.767538 -0.163943 -1.097187  ...  0.044753  1.346261   \n",
       "\n",
       "               3992      3993      3994      3995      3996      3997   \n",
       "slope      0.889563  0.877858  0.808814  0.822912  0.862858  0.836267  \\\n",
       "intercept -2.497430 -2.395350  0.753787  0.738677 -0.983234 -1.065378   \n",
       "\n",
       "               3998      3999  \n",
       "slope      0.825557  0.881137  \n",
       "intercept -0.643284 -1.157595  \n",
       "\n",
       "[2 rows x 4000 columns]"
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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fit_linear_model(y, x):\n",
    "    slope, intercept = np.polyfit(x, y, deg=1)\n",
    "    return xr.DataArray([slope, intercept])\n",
    "\n",
    "yield_dfs = {}\n",
    "\n",
    "for species in [\"Glucose\", \"Product\"]:\n",
    "    yield_dfs[species] = pd.DataFrame(\n",
    "        idata.posterior[\"pseudobatch_c\"]\n",
    "        .sel(species=[\"Biomass\", species])\n",
    "        .stack(chaindraw=[\"chain\", \"draw\"])\n",
    "        .groupby(\"chaindraw\")\n",
    "        .map(lambda arr: fit_linear_model(arr.sel(species=species), arr.sel(species=\"Biomass\")))\n",
    "        .values,\n",
    "        columns=[\"slope\", \"intercept\"]\n",
    "    )\n",
    "print(\"Glucose yield coefficients:\")\n",
    "display(yield_dfs[\"Glucose\"].T)\n",
    "print(\"Product yield coefficients:\")\n",
    "display(yield_dfs[\"Product\"].T)\n",
    "\n",
    "f, axes = plt.subplots(1, 2, figsize=[15, 5])\n",
    "for ax, species, true_value_colname in zip(axes, [\"Glucose\", \"Product\"], [\"Yxs\", \"Yxp\"]):\n",
    "    hist = ax.hist(np.abs(yield_dfs[species][\"slope\"]), bins=50, alpha=0.5)\n",
    "    vline = ax.axvline(samples[true_value_colname].iloc[0], color=\"red\", label=\"True value\")\n",
    "    title = ax.set(title=f\"Distribution of {species} yield coefficient estimates\")\n",
    "    leg = ax.legend(frameon=False)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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