dockerHDDM_Quick_View.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='toc1_'></a>[Ten Minutes Testing New Features](#toc0_)\n",
    "\n",
    "> Integrating Multi-Chain MCMC and Arviz Functionality into the HDDM\n",
    "\n",
    "As is well known, the HDDM itself does not support fitting multiple chains in MCMC, so fitting an HDDM model with multiple chains requires users to perform complex customization. In addition, the Bayesian plotting and testing capabilities included in HDDM are relatively rudimentary, and leveraging arviz (based on xarray's InferenceData) can help users perform more flexible post-analysis.\n",
    "\n",
    "Previous work in our laboratory, [dockerHDDM](https://github.com/hcp4715/dockerHDDM), has implemented multiple-chain MCMC fitting and conversion of hddm models to arviz InferenceData. However, these methods have not been well integrated into the modules on which hddm itself depends. This work ported these features to the core dependency library of hddm, `kabuki`, which fundamentally solves the problems that existed before and makes it more convenient for users to use.\n",
    "\n",
    "We hope that this notebook can provides a good example of how to use our advanced update to run multiple chains in parallel and generate an ArViz InferenceData from the hddm class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, ensure that you are operating within the dockerHDDM environment. Should you find yourself in a local Python or Conda environment rather than the dockerHDDM, which comes pre-installed with the official HDDM, please proceed to remove the original Kabuki package and install our development version from github at https://github.com/panwanke/kabuki, which is compatible with hddm >= 0.8.0.\n",
    "To ensure that all functions properly, we recommend using our HDDM development version at https://github.com/panwanke/hddm. \n",
    "\n",
    "\n",
    "```\n",
    "!pip uninstall kabuki -y\n",
    "!pip install git+https://github.com/panwanke/kabuki\n",
    "!pip uninstall hddm -y\n",
    "!pip install git+https://github.com/panwanke/hddm\n",
    "```\n",
    "\n",
    "And you could see the development version is kabuki 0.6.5RC3 and hddm 0.9.8RC. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Table of contents**<a id='toc0_'></a>    \n",
    "- [Ten Minutes Testing New Features](#toc1_)    \n",
    "    - [Basic Bayesian hierarchical model](#toc1_1_1_)    \n",
    "    - [HDDM with regressors](#toc1_1_2_)    \n",
    "    - [model comparison with LOO](#toc1_1_3_)    \n",
    "  - [statistical inference](#toc1_2_)    \n",
    "\n",
    "<!-- vscode-jupyter-toc-config\n",
    "\tnumbering=false\n",
    "\tanchor=true\n",
    "\tflat=false\n",
    "\tminLevel=1\n",
    "\tmaxLevel=6\n",
    "\t/vscode-jupyter-toc-config -->\n",
    "<!-- THIS CELL WILL BE REPLACED ON TOC UPDATE. DO NOT WRITE YOUR TEXT IN THIS CELL -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "1"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current version of kabuki is:  0.6.5RC4\n",
      "The current version of HDDM is:  1.0.1RC\n",
      "The current version of arviz is:  0.15.1\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action=\"ignore\", category=RuntimeWarning)\n",
    "\n",
    "import hddm\n",
    "import kabuki\n",
    "import arviz as az\n",
    "\n",
    "print(\"The current version of kabuki is: \", kabuki.__version__)\n",
    "print(\"The current version of HDDM is: \", hddm.__version__)\n",
    "print(\"The current version of arviz is: \", az.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the data from the hddm package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "1"
    }
   },
   "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></th>\n",
       "      <th>subj_idx</th>\n",
       "      <th>stim</th>\n",
       "      <th>rt</th>\n",
       "      <th>response</th>\n",
       "      <th>theta</th>\n",
       "      <th>dbs</th>\n",
       "      <th>conf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>LL</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.656275</td>\n",
       "      <td>1</td>\n",
       "      <td>HC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>WL</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.327889</td>\n",
       "      <td>1</td>\n",
       "      <td>LC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>WW</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.480285</td>\n",
       "      <td>1</td>\n",
       "      <td>HC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>WL</td>\n",
       "      <td>2.77</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.927427</td>\n",
       "      <td>1</td>\n",
       "      <td>LC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>WW</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.213236</td>\n",
       "      <td>1</td>\n",
       "      <td>HC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subj_idx stim    rt  response     theta  dbs conf\n",
       "0         0   LL  1.21       1.0  0.656275    1   HC\n",
       "1         0   WL  1.63       1.0 -0.327889    1   LC\n",
       "2         0   WW  1.03       1.0 -0.480285    1   HC\n",
       "3         0   WL  2.77       1.0  1.927427    1   LC\n",
       "4         0   WW  1.14       0.0 -0.213236    1   HC"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = hddm.load_csv(hddm.__path__[0] + \"/examples/cavanagh_theta_nn.csv\")\n",
    "data = data[data['subj_idx'].isin([0,1,2,3,4])]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sampling a basic model to ensure that the python enviroment is working properly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n",
      " [-----------------100%-----------------] 100 of 100 complete in 1.6 sechddm sampling elpased time:  1.674 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<pymc.MCMC.MCMC at 0x7e40cc540b80>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = hddm.HDDM(data, include = ['v', 'a', 't', 'z'])\n",
    "model.sample(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_1_1_'></a>[Basic Bayesian hierarchical model](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let we **run the model with 4 chains in parallel**, and **return the ArViz InferenceData**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "1"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2309: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  tmp2 = (x - v) * (fx - fw)\n",
      "/opt/conda/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2309: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  tmp2 = (x - v) * (fx - fw)\n",
      "/opt/conda/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2309: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  tmp2 = (x - v) * (fx - fw)\n",
      "/opt/conda/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2309: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  tmp2 = (x - v) * (fx - fw)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[--                6%                  ] 31 of 500 complete in 0.5 sec\n",
      "[--                6%                  ] 30 of 500 complete in 0.5 sec\n",
      "[--                6%                  ] 30 of 500 complete in 0.5 sec\n",
      "[--                5%                  ] 29 of 500 complete in 0.5 sec\n",
      "[----             12%                  ] 60 of 500 complete in 1.0 sec\n",
      "[----             11%                  ] 59 of 500 complete in 1.0 sec\n",
      "[----             11%                  ] 57 of 500 complete in 1.0 sec\n",
      "[----             11%                  ] 57 of 500 complete in 1.0 sec\n",
      "[------           17%                  ] 87 of 500 complete in 1.5 sec\n",
      "[------           17%                  ] 87 of 500 complete in 1.5 sec\n",
      "[------           16%                  ] 83 of 500 complete in 1.5 sec\n",
      "[------           16%                  ] 84 of 500 complete in 1.5 sec\n",
      "[--------         23%                  ] 116 of 500 complete in 2.0 sec\n",
      "[--------         23%                  ] 115 of 500 complete in 2.0 sec\n",
      "[--------         22%                  ] 111 of 500 complete in 2.0 sec\n",
      "[--------         21%                  ] 109 of 500 complete in 2.0 sec\n",
      "[-----------      29%                  ] 145 of 500 complete in 2.5 sec\n",
      "[-----------      29%                  ] 145 of 500 complete in 2.6 sec\n",
      "[----------       27%                  ] 135 of 500 complete in 2.5 sec\n",
      "[----------       28%                  ] 140 of 500 complete in 2.6 sec\n",
      "[-------------    35%                  ] 175 of 500 complete in 3.0 sec\n",
      "[------------     34%                  ] 171 of 500 complete in 3.1 sec\n",
      "[------------     33%                  ] 168 of 500 complete in 3.1 sec\n",
      "[------------     32%                  ] 162 of 500 complete in 3.0 sec\n",
      "[---------------  40%                  ] 201 of 500 complete in 3.5 sec\n",
      "[--------------   39%                  ] 196 of 500 complete in 3.6 sec\n",
      "[--------------   39%                  ] 195 of 500 complete in 3.6 sec\n",
      "[--------------   38%                  ] 190 of 500 complete in 3.6 sec\n",
      "[-----------------45%                  ] 229 of 500 complete in 4.0 sec\n",
      "[---------------- 44%                  ] 222 of 500 complete in 4.1 sec\n",
      "[---------------- 44%                  ] 223 of 500 complete in 4.1 sec\n",
      "[---------------- 43%                  ] 217 of 500 complete in 4.1 sec\n",
      "[-----------------51%                  ] 259 of 500 complete in 4.6 sec\n",
      "[-----------------50%                  ] 251 of 500 complete in 4.6 sec\n",
      "[-----------------49%                  ] 246 of 500 complete in 4.6 sec\n",
      "[-----------------50%                  ] 253 of 500 complete in 4.6 sec\n",
      "[-----------------57%-                 ] 289 of 500 complete in 5.1 sec\n",
      "[-----------------56%-                 ] 281 of 500 complete in 5.1 sec\n",
      "[-----------------55%                  ] 275 of 500 complete in 5.1 sec\n",
      "[-----------------56%-                 ] 282 of 500 complete in 5.1 sec\n",
      "[-----------------63%----              ] 318 of 500 complete in 5.6 sec\n",
      "[-----------------60%---               ] 304 of 500 complete in 5.6 sec\n",
      "[-----------------62%---               ] 311 of 500 complete in 5.6 sec\n",
      "[-----------------61%---               ] 309 of 500 complete in 5.6 sec\n",
      "[-----------------69%------            ] 345 of 500 complete in 6.1 sec\n",
      "[-----------------66%-----             ] 331 of 500 complete in 6.1 sec\n",
      "[-----------------67%-----             ] 336 of 500 complete in 6.1 sec\n",
      "[-----------------67%-----             ] 336 of 500 complete in 6.1 sec\n",
      "[-----------------74%--------          ] 372 of 500 complete in 6.6 sec\n",
      "[-----------------71%-------           ] 358 of 500 complete in 6.6 sec\n",
      "[-----------------72%-------           ] 362 of 500 complete in 6.6 sec\n",
      "[-----------------72%-------           ] 364 of 500 complete in 6.6 sec\n",
      "[-----------------80%----------        ] 400 of 500 complete in 7.1 sec\n",
      "[-----------------77%---------         ] 386 of 500 complete in 7.1 sec\n",
      "[-----------------77%---------         ] 389 of 500 complete in 7.1 sec\n",
      "[-----------------78%---------         ] 392 of 500 complete in 7.1 sec\n",
      "[-----------------85%------------      ] 427 of 500 complete in 7.6 sec\n",
      "[-----------------82%-----------       ] 414 of 500 complete in 7.6 sec\n",
      "[-----------------83%-----------       ] 417 of 500 complete in 7.6 sec\n",
      "[-----------------83%-----------       ] 417 of 500 complete in 7.7 sec\n",
      "[-----------------90%--------------    ] 454 of 500 complete in 8.1 sec\n",
      "[-----------------88%-------------     ] 441 of 500 complete in 8.1 sec\n",
      "[-----------------89%-------------     ] 445 of 500 complete in 8.2 sec\n",
      "[-----------------88%-------------     ] 444 of 500 complete in 8.2 sec\n",
      "[-----------------96%----------------  ] 480 of 500 complete in 8.6 sec\n",
      "[-----------------93%---------------   ] 466 of 500 complete in 8.6 sec\n",
      "[-----------------93%---------------   ] 468 of 500 complete in 8.7 sec\n",
      "[-----------------93%---------------   ] 468 of 500 complete in 8.7 sec\n",
      "[-----------------100%-----------------] 500 of 500 complete in 9.1 sec\n",
      "hddm sampling elpased time:  9.08 s\n",
      "[-----------------98%----------------- ] 491 of 500 complete in 9.1 sec\n",
      "[-----------------98%----------------- ] 494 of 500 complete in 9.2 sec\n",
      "[-----------------98%----------------- ] 493 of 500 complete in 9.2 sec\n",
      "[-----------------100%-----------------] 500 of 500 complete in 9.4 sec\n",
      "hddm sampling elpased time:  9.389 s\n",
      "[-----------------100%-----------------] 500 of 500 complete in 9.4 sec\n",
      "hddm sampling elpased time:  9.412 s\n",
      "[-----------------100%-----------------] 500 of 500 complete in 9.4 sec\n",
      "hddm sampling elpased time:  9.435 s\n",
      "hddm sampling elpased time:  14.693 s\n",
      "Start converting to InferenceData...\n",
      "CPU times: user 2.92 s, sys: 50.9 ms, total: 2.97 s\n",
      "Wall time: 14.9 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "            <div>\n",
       "              <div class='xr-header'>\n",
       "                <div class=\"xr-obj-type\">arviz.InferenceData</div>\n",
       "              </div>\n",
       "              <ul class=\"xr-sections group-sections\">\n",
       "              \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_posteriorf35fc406-d573-4908-b56f-f62386c7e87f\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_posteriorf35fc406-d573-4908-b56f-f62386c7e87f\" class = \"xr-section-summary\">posterior</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",
       "<path d=\"M23 26h-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 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",
       "}\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; Size: 452kB\n",
       "Dimensions:   (chain: 4, draw: 500)\n",
       "Coordinates:\n",
       "  * chain     (chain) int64 32B 0 1 2 3\n",
       "  * draw      (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
       "Data variables: (12/28)\n",
       "    a         (chain, draw) float64 16kB 1.881 1.921 2.078 ... 2.244 2.232 1.67\n",
       "    a_std     (chain, draw) float64 16kB 0.177 0.2939 0.2839 ... 0.7136 0.5871\n",
       "    a_subj.0  (chain, draw) float64 16kB 2.145 2.184 2.224 ... 2.079 2.204 2.201\n",
       "    a_subj.1  (chain, draw) float64 16kB 2.165 2.169 2.243 ... 2.074 2.161 2.114\n",
       "    a_subj.2  (chain, draw) float64 16kB 1.688 1.639 1.677 ... 1.501 1.602 1.537\n",
       "    a_subj.3  (chain, draw) float64 16kB 2.167 2.205 2.197 ... 2.171 2.206 2.224\n",
       "    ...        ...\n",
       "    z_std     (chain, draw) float64 16kB 0.03188 0.03272 ... 0.02295 0.03829\n",
       "    z_subj.0  (chain, draw) float64 16kB 0.4288 0.4254 0.4757 ... 0.4693 0.4675\n",
       "    z_subj.1  (chain, draw) float64 16kB 0.4476 0.4421 0.4433 ... 0.467 0.4666\n",
       "    z_subj.2  (chain, draw) float64 16kB 0.4417 0.4463 0.4441 ... 0.471 0.4688\n",
       "    z_subj.3  (chain, draw) float64 16kB 0.4482 0.4579 0.4498 ... 0.4608 0.4681\n",
       "    z_subj.4  (chain, draw) float64 16kB 0.4423 0.4512 0.473 ... 0.4771 0.4756</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-06511be0-bc8f-4a1d-ae14-bf57fd229dfd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-06511be0-bc8f-4a1d-ae14-bf57fd229dfd' 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>: 500</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ccd303d7-7ff6-480c-bdf1-8e7b7d15ac39' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ccd303d7-7ff6-480c-bdf1-8e7b7d15ac39' 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-1392e76f-730c-42b2-8e43-00e0c5bd86fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1392e76f-730c-42b2-8e43-00e0c5bd86fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff5ed4f3-dfd0-443d-b71e-f010b6b27000' class='xr-var-data-in' type='checkbox'><label for='data-ff5ed4f3-dfd0-443d-b71e-f010b6b27000' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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 ... 495 496 497 498 499</div><input id='attrs-eeb9b736-c345-4f37-9dfd-5faf9ac3b7f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eeb9b736-c345-4f37-9dfd-5faf9ac3b7f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c24e21e-63e8-48e8-8541-2de47dc2408c' class='xr-var-data-in' type='checkbox'><label for='data-9c24e21e-63e8-48e8-8541-2de47dc2408c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, ..., 497, 498, 499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b5c028b8-85e3-4f1c-87d7-a86a92f84880' class='xr-section-summary-in' type='checkbox'  ><label for='section-b5c028b8-85e3-4f1c-87d7-a86a92f84880' class='xr-section-summary' >Data variables: <span>(28)</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>a</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.881 1.921 2.078 ... 2.232 1.67</div><input id='attrs-89a6e4e8-53c4-4f25-bdd4-2c9e9fa5f8fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-89a6e4e8-53c4-4f25-bdd4-2c9e9fa5f8fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ca9a926-0770-4b07-abe4-b742160cd630' class='xr-var-data-in' type='checkbox'><label for='data-4ca9a926-0770-4b07-abe4-b742160cd630' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.8814123 , 1.92059275, 2.07774751, ..., 1.84723304, 1.84308654,\n",
       "        1.59838587],\n",
       "       [1.90555791, 1.97013446, 2.06497379, ..., 1.83634126, 2.42606134,\n",
       "        1.92681832],\n",
       "       [1.82003863, 1.85171508, 1.9268197 , ..., 1.93387104, 1.87408902,\n",
       "        1.56373998],\n",
       "       [1.9240788 , 2.10319384, 1.84576253, ..., 2.24448626, 2.23211306,\n",
       "        1.66989474]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_std</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.177 0.2939 ... 0.7136 0.5871</div><input id='attrs-099748b6-cf3e-4cab-b748-0b62c7e1fe62' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-099748b6-cf3e-4cab-b748-0b62c7e1fe62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea297e68-a302-4c6d-91ae-810826be5bac' class='xr-var-data-in' type='checkbox'><label for='data-ea297e68-a302-4c6d-91ae-810826be5bac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.1770193 , 0.29388292, 0.28392296, ..., 0.40874485, 0.3910571 ,\n",
       "        0.28912907],\n",
       "       [0.28412658, 0.52202748, 0.50610701, ..., 1.04020498, 1.00017261,\n",
       "        0.78329553],\n",
       "       [0.27574815, 0.28096313, 0.26624222, ..., 0.18565884, 0.341358  ,\n",
       "        0.33297764],\n",
       "       [0.33301361, 0.73894894, 0.31254108, ..., 1.02195111, 0.71363287,\n",
       "        0.58709957]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_subj.0</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.145 2.184 2.224 ... 2.204 2.201</div><input id='attrs-11e0f48b-4343-48c8-ab43-73a8b97b9a65' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-11e0f48b-4343-48c8-ab43-73a8b97b9a65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8647f0f-6d55-4575-89de-1bf41d6e5857' class='xr-var-data-in' type='checkbox'><label for='data-c8647f0f-6d55-4575-89de-1bf41d6e5857' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2.14472658, 2.18382936, 2.22355226, ..., 2.05770893, 2.03274727,\n",
       "        2.13419776],\n",
       "       [2.26311846, 2.28294325, 2.21135936, ..., 2.24822561, 2.21423238,\n",
       "        2.23949527],\n",
       "       [2.16636274, 2.2418136 , 2.18642787, ..., 2.20059127, 2.07689385,\n",
       "        2.26386916],\n",
       "       [2.29477871, 2.13977564, 2.10400175, ..., 2.07928222, 2.20432331,\n",
       "        2.20100073]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_subj.1</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.165 2.169 2.243 ... 2.161 2.114</div><input id='attrs-4420c9fc-d87e-43c3-880b-aca5339354eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4420c9fc-d87e-43c3-880b-aca5339354eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c5b34f7-7f5e-401a-85a3-d10afc1483a1' class='xr-var-data-in' type='checkbox'><label for='data-8c5b34f7-7f5e-401a-85a3-d10afc1483a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2.16481591, 2.1688628 , 2.24296924, ..., 2.04147446, 2.07375225,\n",
       "        2.1534579 ],\n",
       "       [2.01342598, 2.02965262, 2.06757387, ..., 2.17748938, 2.14855139,\n",
       "        2.08751247],\n",
       "       [2.03457078, 2.03721101, 2.05794135, ..., 2.10535224, 2.11979893,\n",
       "        2.12703276],\n",
       "       [2.12304925, 2.05952157, 2.05270119, ..., 2.07407312, 2.16060337,\n",
       "        2.11377127]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_subj.2</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.688 1.639 1.677 ... 1.602 1.537</div><input id='attrs-30ae4d6e-332f-4891-876a-61bb5e8bb6e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30ae4d6e-332f-4891-876a-61bb5e8bb6e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c1eb842-7b5f-4a13-b8f2-7fb99d9a62e1' class='xr-var-data-in' type='checkbox'><label for='data-3c1eb842-7b5f-4a13-b8f2-7fb99d9a62e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.68813061, 1.63923446, 1.67668261, ..., 1.52178247, 1.5626308 ,\n",
       "        1.50330544],\n",
       "       [1.57879556, 1.61667847, 1.49364722, ..., 1.41843347, 1.59815354,\n",
       "        1.53246002],\n",
       "       [1.56370387, 1.61696301, 1.57394788, ..., 1.62688776, 1.50020862,\n",
       "        1.60965348],\n",
       "       [1.51241223, 1.50457786, 1.49337521, ..., 1.50076505, 1.60182242,\n",
       "        1.53669493]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_subj.3</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.167 2.205 2.197 ... 2.206 2.224</div><input id='attrs-7f58fc74-4849-4546-9733-944c2b34d5ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f58fc74-4849-4546-9733-944c2b34d5ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aedee3fa-07be-4c5e-8583-be947582c395' class='xr-var-data-in' type='checkbox'><label for='data-aedee3fa-07be-4c5e-8583-be947582c395' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2.16684039, 2.20538999, 2.19734813, ..., 2.19193283, 2.13557156,\n",
       "        2.09457066],\n",
       "       [2.07791881, 2.21165874, 2.27358105, ..., 2.20418718, 2.24149348,\n",
       "        2.14984204],\n",
       "       [2.04719409, 2.03225218, 1.9971046 , ..., 2.13151217, 2.06502506,\n",
       "        2.1363123 ],\n",
       "       [2.2642814 , 2.31903281, 2.01052539, ..., 2.17073336, 2.2058513 ,\n",
       "        2.22391471]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_subj.4</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.575 1.498 1.471 ... 1.499 1.511</div><input id='attrs-9c85d1fb-cb47-4190-8125-98f12b9b0236' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c85d1fb-cb47-4190-8125-98f12b9b0236' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-361d3341-dbda-407f-afc3-b85e27a2be43' class='xr-var-data-in' type='checkbox'><label for='data-361d3341-dbda-407f-afc3-b85e27a2be43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.57547589, 1.49808879, 1.47082467, ..., 1.42149063, 1.4736453 ,\n",
       "        1.47650232],\n",
       "       [1.51661385, 1.54212678, 1.57633581, ..., 1.47500679, 1.54902684,\n",
       "        1.46053406],\n",
       "       [1.50563768, 1.57913194, 1.47587981, ..., 1.51507791, 1.43008969,\n",
       "        1.490811  ],\n",
       "       [1.45032982, 1.44538231, 1.41611486, ..., 1.50908697, 1.49949947,\n",
       "        1.51123461]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.183 0.7882 ... -0.5309 0.1916</div><input id='attrs-d67e4700-250b-4034-9b1b-5f92e5bd3f70' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d67e4700-250b-4034-9b1b-5f92e5bd3f70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82259c9a-69fa-453a-84f3-e6f7ba5ad06d' class='xr-var-data-in' type='checkbox'><label for='data-82259c9a-69fa-453a-84f3-e6f7ba5ad06d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 1.18320081,  0.78820469,  0.65346533, ...,  0.43768071,\n",
       "         0.81336285,  0.64486864],\n",
       "       [ 0.29033818,  0.57571506,  0.74388226, ...,  0.56247008,\n",
       "         0.50459034,  0.57371126],\n",
       "       [ 0.37538971,  0.25090922,  0.56002998, ...,  0.50952462,\n",
       "         0.4284219 ,  0.22167984],\n",
       "       [ 0.41888588,  0.87191131,  0.67169434, ...,  0.93654924,\n",
       "        -0.53092798,  0.19158009]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_std</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.357 0.5399 0.4923 ... 1.028 1.398</div><input id='attrs-9c07d43c-b017-4398-8bac-b47cc32f58b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c07d43c-b017-4398-8bac-b47cc32f58b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ed733d1-cd15-4c25-8595-3a9eac8232c6' class='xr-var-data-in' type='checkbox'><label for='data-0ed733d1-cd15-4c25-8595-3a9eac8232c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.35716396, 0.53989499, 0.49231147, ..., 0.18354427, 0.50641266,\n",
       "        0.54193956],\n",
       "       [0.7909771 , 0.99598991, 0.27391496, ..., 0.30582167, 0.47706451,\n",
       "        0.21536203],\n",
       "       [0.69750015, 0.57996485, 0.40192901, ..., 0.33821022, 0.34986153,\n",
       "        0.5308555 ],\n",
       "       [1.20480855, 0.80348382, 0.56931866, ..., 0.6163974 , 1.02782882,\n",
       "        1.39769931]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_subj.0</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7025 -0.01771 ... 0.225 0.1907</div><input id='attrs-acdb481c-2b90-46ec-8b81-bafd63b6ed7f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-acdb481c-2b90-46ec-8b81-bafd63b6ed7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc648b6c-9fed-4d08-8b59-563b16ec83f9' class='xr-var-data-in' type='checkbox'><label for='data-dc648b6c-9fed-4d08-8b59-563b16ec83f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.70253739, -0.01770599,  0.19798429, ...,  0.09798957,\n",
       "         0.21699252,  0.19680901],\n",
       "       [ 0.01947939,  0.26890843,  0.23801635, ...,  0.10854761,\n",
       "         0.08295095,  0.14122362],\n",
       "       [-0.13976235,  0.39552367,  0.01778187, ...,  0.13926683,\n",
       "         0.10035786,  0.11987207],\n",
       "       [ 0.57239063,  0.31458523,  0.19354013, ...,  0.19522863,\n",
       "         0.22502909,  0.19071675]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_subj.1</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.15 0.846 0.9239 ... 0.682 0.7958</div><input id='attrs-2eeeeb3e-2377-4b17-af7d-693b90b2e58a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2eeeeb3e-2377-4b17-af7d-693b90b2e58a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed3cf0a6-05cd-4584-ae30-77e25293a53c' class='xr-var-data-in' type='checkbox'><label for='data-ed3cf0a6-05cd-4584-ae30-77e25293a53c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.14966354, 0.84601867, 0.92387934, ..., 0.63409569, 0.6186007 ,\n",
       "        0.63490653],\n",
       "       [0.45980541, 0.81150573, 0.73251887, ..., 0.63458119, 0.69331276,\n",
       "        0.65574014],\n",
       "       [0.70680541, 0.78014712, 0.73831225, ..., 0.6466581 , 0.67308524,\n",
       "        0.65630099],\n",
       "       [0.46700472, 0.5944541 , 0.65149915, ..., 0.82468167, 0.68204788,\n",
       "        0.79582839]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_subj.2</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.27 1.161 0.8646 ... 0.5806 0.7772</div><input id='attrs-8b372419-d787-4695-8d7d-c63dde0a3ef0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b372419-d787-4695-8d7d-c63dde0a3ef0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7066ac8d-19d5-4455-82d1-e31a9df27071' class='xr-var-data-in' type='checkbox'><label for='data-7066ac8d-19d5-4455-82d1-e31a9df27071' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.2701927 , 1.16145786, 0.8646054 , ..., 0.59501923, 0.49453108,\n",
       "        0.48287114],\n",
       "       [1.13084342, 0.85969523, 0.76677778, ..., 0.65136752, 0.63295118,\n",
       "        0.590372  ],\n",
       "       [0.65340231, 0.69217144, 0.72007146, ..., 0.53381419, 0.51232688,\n",
       "        0.51161296],\n",
       "       [0.35164328, 0.60328659, 0.64114646, ..., 0.60640981, 0.58062796,\n",
       "        0.77717099]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_subj.3</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.09891 0.0239 ... 0.2741 0.2146</div><input id='attrs-b60524c2-1f02-43d0-a25d-2a1d916dc455' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b60524c2-1f02-43d0-a25d-2a1d916dc455' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1417c796-ab87-4c28-a883-7d193af94493' class='xr-var-data-in' type='checkbox'><label for='data-1417c796-ab87-4c28-a883-7d193af94493' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.09891055,  0.02389873,  0.42152182, ...,  0.10305494,\n",
       "         0.14308687,  0.09699281],\n",
       "       [ 0.50135956,  0.51204951,  0.10558406, ...,  0.19263265,\n",
       "         0.13680948,  0.25953488],\n",
       "       [-0.17247425,  0.09800494,  0.08911169, ...,  0.32741301,\n",
       "         0.06554299,  0.11767111],\n",
       "       [ 0.55632438,  0.3825768 ,  0.30140883, ...,  0.17418451,\n",
       "         0.27406903,  0.21455924]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_subj.4</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.323 1.059 1.033 ... 1.242 0.8878</div><input id='attrs-e8f227ed-ca78-4fc3-a2c3-37263057bbf2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e8f227ed-ca78-4fc3-a2c3-37263057bbf2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e64ba540-48b9-4ec4-b23c-b3d10eb3bddc' class='xr-var-data-in' type='checkbox'><label for='data-e64ba540-48b9-4ec4-b23c-b3d10eb3bddc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.32278184, 1.059443  , 1.03283079, ..., 1.00640741, 0.97972525,\n",
       "        0.99204141],\n",
       "       [1.53541706, 1.60689059, 1.0532    , ..., 0.99011858, 0.98402049,\n",
       "        0.94196297],\n",
       "       [1.16958379, 1.15837177, 1.16016932, ..., 0.97167024, 0.99853627,\n",
       "        1.03129863],\n",
       "       [0.96102618, 1.22149649, 1.22555508, ..., 1.13228935, 1.24153426,\n",
       "        0.88784195]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4956 0.4853 ... 0.5231 0.506</div><input id='attrs-35cbf487-d83d-4008-80e4-d71f468190f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-35cbf487-d83d-4008-80e4-d71f468190f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40afb9f0-ccea-4ac8-9951-1b2f34e349aa' class='xr-var-data-in' type='checkbox'><label for='data-40afb9f0-ccea-4ac8-9951-1b2f34e349aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.49559812, 0.48528   , 0.47407121, ..., 0.44888286, 0.53693752,\n",
       "        0.43872179],\n",
       "       [0.47146567, 0.51825308, 0.43073119, ..., 0.51169744, 0.45069526,\n",
       "        0.48225849],\n",
       "       [0.48234332, 0.47007806, 0.47472128, ..., 0.52450168, 0.38701913,\n",
       "        0.37432043],\n",
       "       [0.47392891, 0.45501531, 0.4856149 , ..., 0.46439425, 0.52314418,\n",
       "        0.50599312]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_std</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.139 0.1369 ... 0.1163 0.1179</div><input id='attrs-d0da1261-6170-49d1-b277-1ecc03923aa8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d0da1261-6170-49d1-b277-1ecc03923aa8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1c37cbc-7112-4ea7-a71a-326853dc5110' class='xr-var-data-in' type='checkbox'><label for='data-a1c37cbc-7112-4ea7-a71a-326853dc5110' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.13895941, 0.13689904, 0.12181974, ..., 0.20251262, 0.14423453,\n",
       "        0.1422906 ],\n",
       "       [0.13350536, 0.12735105, 0.08366518, ..., 0.10469694, 0.09144752,\n",
       "        0.08899444],\n",
       "       [0.0794083 , 0.1451606 , 0.09008498, ..., 0.12233246, 0.09581599,\n",
       "        0.15669056],\n",
       "       [0.09518461, 0.13645661, 0.18223087, ..., 0.12225006, 0.11630176,\n",
       "        0.11791147]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_subj.0</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.637 0.5675 ... 0.6028 0.6004</div><input id='attrs-1188c602-904d-4cdb-9b4b-09b636be8ad7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1188c602-904d-4cdb-9b4b-09b636be8ad7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d33e0b1-8055-468e-a5fc-db23aa9e37c6' class='xr-var-data-in' type='checkbox'><label for='data-0d33e0b1-8055-468e-a5fc-db23aa9e37c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.63703607, 0.56752227, 0.54536947, ..., 0.62405319, 0.6676177 ,\n",
       "        0.59292293],\n",
       "       [0.57691877, 0.53465562, 0.50409515, ..., 0.57437031, 0.52219303,\n",
       "        0.51538818],\n",
       "       [0.54013623, 0.52752703, 0.52797474, ..., 0.53291217, 0.56890387,\n",
       "        0.54207724],\n",
       "       [0.57356906, 0.53344302, 0.61020308, ..., 0.61065043, 0.60284869,\n",
       "        0.6004354 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_subj.1</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4022 0.3705 ... 0.3928 0.3941</div><input id='attrs-dc506f28-ce9c-487e-8880-f49ee676dd81' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc506f28-ce9c-487e-8880-f49ee676dd81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1bf41dc-2077-459d-91ce-a77d072f8c4c' class='xr-var-data-in' type='checkbox'><label for='data-d1bf41dc-2077-459d-91ce-a77d072f8c4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.40216214, 0.3704892 , 0.36246943, ..., 0.41640314, 0.372038  ,\n",
       "        0.38422752],\n",
       "       [0.36962863, 0.39517705, 0.38165273, ..., 0.36515751, 0.38134491,\n",
       "        0.39417915],\n",
       "       [0.39272553, 0.42149033, 0.38343609, ..., 0.38968967, 0.41000842,\n",
       "        0.3815837 ],\n",
       "       [0.36360205, 0.3682754 , 0.39169805, ..., 0.39438143, 0.39284587,\n",
       "        0.39406658]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_subj.2</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4938 0.4752 ... 0.4938 0.4904</div><input id='attrs-a573b28f-301f-48ec-9449-547ca452fd5f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a573b28f-301f-48ec-9449-547ca452fd5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-beb6aa8b-2e91-4c53-acf9-5dcb82d43d4e' class='xr-var-data-in' type='checkbox'><label for='data-beb6aa8b-2e91-4c53-acf9-5dcb82d43d4e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.49383415, 0.47523079, 0.47340343, ..., 0.50901421, 0.50112775,\n",
       "        0.49885396],\n",
       "       [0.50252308, 0.48803705, 0.49685117, ..., 0.50135147, 0.51107227,\n",
       "        0.48932277],\n",
       "       [0.48554483, 0.48785472, 0.48096684, ..., 0.47940329, 0.49561134,\n",
       "        0.49071814],\n",
       "       [0.48473661, 0.49646195, 0.49329136, ..., 0.49451105, 0.49375114,\n",
       "        0.49040501]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_subj.3</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5184 0.5013 ... 0.5475 0.4943</div><input id='attrs-976d3df4-8ee7-4b7e-a693-9959859dc45f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-976d3df4-8ee7-4b7e-a693-9959859dc45f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8d5a6b4-fc9e-4a3e-8dcc-51bb62c993f4' class='xr-var-data-in' type='checkbox'><label for='data-e8d5a6b4-fc9e-4a3e-8dcc-51bb62c993f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.51841127, 0.50128914, 0.50359176, ..., 0.54630098, 0.54993834,\n",
       "        0.57725133],\n",
       "       [0.57310407, 0.54408861, 0.49554324, ..., 0.51515878, 0.50458045,\n",
       "        0.54526156],\n",
       "       [0.54975345, 0.54967082, 0.63311497, ..., 0.54187823, 0.57180841,\n",
       "        0.53891854],\n",
       "       [0.57745131, 0.54745476, 0.55186474, ..., 0.54012391, 0.54746692,\n",
       "        0.49430365]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_subj.4</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3379 0.3297 ... 0.338 0.3313</div><input id='attrs-7a477003-8417-4bc6-8337-995b31ac7f36' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a477003-8417-4bc6-8337-995b31ac7f36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-417c910d-23af-4f07-ba9f-4d4d4a8c0026' class='xr-var-data-in' type='checkbox'><label for='data-417c910d-23af-4f07-ba9f-4d4d4a8c0026' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.33786379, 0.32970765, 0.34909515, ..., 0.3535735 , 0.34250285,\n",
       "        0.34614985],\n",
       "       [0.34082721, 0.33597278, 0.3208345 , ..., 0.34956575, 0.34559674,\n",
       "        0.33146631],\n",
       "       [0.33616219, 0.33935922, 0.32764016, ..., 0.3520898 , 0.34447407,\n",
       "        0.3538631 ],\n",
       "       [0.33999279, 0.35251798, 0.34643194, ..., 0.34261378, 0.33796433,\n",
       "        0.33128296]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4382 0.4443 ... 0.4642 0.4748</div><input id='attrs-49ab909c-30f2-4dd5-a080-568e44eaa607' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-49ab909c-30f2-4dd5-a080-568e44eaa607' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-618013c5-2549-4f48-aaa3-95bf6f5fb3a6' class='xr-var-data-in' type='checkbox'><label for='data-618013c5-2549-4f48-aaa3-95bf6f5fb3a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.43818049, 0.44430512, 0.45448524, ..., 0.49846064, 0.49265334,\n",
       "        0.49226525],\n",
       "       [0.43860137, 0.43405387, 0.43320106, ..., 0.4874923 , 0.49148042,\n",
       "        0.49264133],\n",
       "       [0.44701069, 0.46605219, 0.46230891, ..., 0.49583321, 0.49731662,\n",
       "        0.50106133],\n",
       "       [0.44572629, 0.45149566, 0.45073641, ..., 0.46405145, 0.46415233,\n",
       "        0.47480705]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_std</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.03188 0.03272 ... 0.02295 0.03829</div><input id='attrs-782dd5a4-ec5b-4c1e-aec3-e0303d02a944' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-782dd5a4-ec5b-4c1e-aec3-e0303d02a944' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a006fec-fcba-4b01-83da-9367fd01dd3b' class='xr-var-data-in' type='checkbox'><label for='data-0a006fec-fcba-4b01-83da-9367fd01dd3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.03187658, 0.03271632, 0.05430606, ..., 0.09545624, 0.07017056,\n",
       "        0.05524567],\n",
       "       [0.02635188, 0.02805199, 0.05189616, ..., 0.02854517, 0.03824799,\n",
       "        0.03413102],\n",
       "       [0.03964382, 0.0401346 , 0.06649955, ..., 0.01216253, 0.01248725,\n",
       "        0.01587402],\n",
       "       [0.03053394, 0.01763665, 0.01750898, ..., 0.03167851, 0.02295025,\n",
       "        0.03828615]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_subj.0</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4288 0.4254 ... 0.4693 0.4675</div><input id='attrs-caeb7423-8b12-4e44-87c3-5b84901ce522' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-caeb7423-8b12-4e44-87c3-5b84901ce522' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ff54f2d-fec1-4871-972c-49959ea0cec6' class='xr-var-data-in' type='checkbox'><label for='data-8ff54f2d-fec1-4871-972c-49959ea0cec6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.42875964, 0.42535758, 0.47571709, ..., 0.50727891, 0.51146203,\n",
       "        0.50714039],\n",
       "       [0.4278518 , 0.43053875, 0.42706704, ..., 0.48821777, 0.48454832,\n",
       "        0.48159766],\n",
       "       [0.43802972, 0.45262239, 0.47460303, ..., 0.49905675, 0.50361299,\n",
       "        0.4977687 ],\n",
       "       [0.44389504, 0.45084836, 0.4553216 , ..., 0.47423806, 0.46934933,\n",
       "        0.46746835]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_subj.1</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4476 0.4421 ... 0.467 0.4666</div><input id='attrs-613a83eb-5465-4e02-b022-4c34d5324f80' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-613a83eb-5465-4e02-b022-4c34d5324f80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84b5512a-b6a9-4ac4-9d2a-03d6fe7f39b3' class='xr-var-data-in' type='checkbox'><label for='data-84b5512a-b6a9-4ac4-9d2a-03d6fe7f39b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.44755022, 0.44208666, 0.44332119, ..., 0.50160121, 0.50595114,\n",
       "        0.48155451],\n",
       "       [0.44698005, 0.43611302, 0.43547458, ..., 0.49773171, 0.49876812,\n",
       "        0.49504416],\n",
       "       [0.44903908, 0.46517027, 0.45967621, ..., 0.4958008 , 0.49724331,\n",
       "        0.49955064],\n",
       "       [0.45757593, 0.45242441, 0.44911945, ..., 0.45940725, 0.46703969,\n",
       "        0.46659419]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_subj.2</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4417 0.4463 ... 0.471 0.4688</div><input id='attrs-bcef472e-93f8-4a49-aa42-d80e12e5e91d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bcef472e-93f8-4a49-aa42-d80e12e5e91d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14eb052d-ff0c-49ff-916a-ffc23aadadc5' class='xr-var-data-in' type='checkbox'><label for='data-14eb052d-ff0c-49ff-916a-ffc23aadadc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.44173583, 0.4463399 , 0.44405289, ..., 0.49096075, 0.50561155,\n",
       "        0.488228  ],\n",
       "       [0.4364249 , 0.43199354, 0.44274372, ..., 0.49407442, 0.48528188,\n",
       "        0.48502123],\n",
       "       [0.45608113, 0.45705833, 0.46193732, ..., 0.49910103, 0.49237921,\n",
       "        0.50340631],\n",
       "       [0.45036592, 0.4548539 , 0.45441944, ..., 0.45881428, 0.47098489,\n",
       "        0.46883965]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_subj.3</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4482 0.4579 ... 0.4608 0.4681</div><input id='attrs-46ddfdf9-4149-48ca-847b-90f9ff52bc0b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-46ddfdf9-4149-48ca-847b-90f9ff52bc0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0db53a3a-aa35-45e3-888f-ea8ed9ba271d' class='xr-var-data-in' type='checkbox'><label for='data-0db53a3a-aa35-45e3-888f-ea8ed9ba271d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.44817801, 0.45785573, 0.44976392, ..., 0.53941235, 0.53006122,\n",
       "        0.48255021],\n",
       "       [0.44247487, 0.43951563, 0.43607846, ..., 0.48980974, 0.48458223,\n",
       "        0.48238769],\n",
       "       [0.44539109, 0.47874321, 0.47950377, ..., 0.49746219, 0.49800482,\n",
       "        0.4995549 ],\n",
       "       [0.43703341, 0.4448984 , 0.45092075, ..., 0.46234669, 0.4607909 ,\n",
       "        0.46813852]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_subj.4</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4423 0.4512 ... 0.4771 0.4756</div><input id='attrs-5f1aee5a-8d69-4a08-a225-7a8d3dd60c59' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f1aee5a-8d69-4a08-a225-7a8d3dd60c59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd7817d2-17d0-4df6-b8e7-b186382e3857' class='xr-var-data-in' type='checkbox'><label for='data-bd7817d2-17d0-4df6-b8e7-b186382e3857' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.44229222, 0.45120647, 0.47303436, ..., 0.44791063, 0.49367155,\n",
       "        0.5023091 ],\n",
       "       [0.44053569, 0.42847198, 0.4565725 , ..., 0.48456645, 0.49128435,\n",
       "        0.48826103],\n",
       "       [0.45409793, 0.45871516, 0.46113141, ..., 0.49789844, 0.49644236,\n",
       "        0.49759465],\n",
       "       [0.44196415, 0.44284534, 0.44450657, ..., 0.45274317, 0.47709862,\n",
       "        0.4756009 ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-09847b06-11c0-4f9b-92f5-e91d26f66c79' class='xr-section-summary-in' type='checkbox'  ><label for='section-09847b06-11c0-4f9b-92f5-e91d26f66c79' 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-7fd28f4b-221e-4afb-b069-bea617c1c7c8' class='xr-index-data-in' type='checkbox'/><label for='index-7fd28f4b-221e-4afb-b069-bea617c1c7c8' 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-9636b82b-65a8-4a10-8c34-046f9ea0c81e' class='xr-index-data-in' type='checkbox'/><label for='index-9636b82b-65a8-4a10-8c34-046f9ea0c81e' 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",
       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-546b522f-d860-4856-8916-579758e1afcb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-546b522f-d860-4856-8916-579758e1afcb' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_observed_dataec6aafac-397b-450b-9855-ca5d10aa8bbd\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_observed_dataec6aafac-397b-450b-9855-ca5d10aa8bbd\" class = \"xr-section-summary\">observed_data</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",
       "<path d=\"M23 26h-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 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",
       "}\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; Size: 108kB\n",
       "Dimensions:   (obs_id: 1427)\n",
       "Coordinates:\n",
       "  * obs_id    (obs_id) int64 11kB 0 1 2 3 4 5 ... 1421 1422 1423 1424 1425 1426\n",
       "    subj_idx  (obs_id) int64 11kB 0 0 0 0 0 0 0 0 0 0 0 ... 4 4 4 4 4 4 4 4 4 4\n",
       "    trial     (obs_id) int64 11kB 0 1 2 3 4 5 6 ... 276 277 278 279 280 281 282\n",
       "Data variables:\n",
       "    index     (obs_id) int64 11kB 0 1 2 3 4 5 ... 1421 1422 1423 1424 1425 1426\n",
       "    stim      (obs_id) object 11kB &#x27;LL&#x27; &#x27;WL&#x27; &#x27;WW&#x27; &#x27;WL&#x27; ... &#x27;WW&#x27; &#x27;WL&#x27; &#x27;WL&#x27; &#x27;LL&#x27;\n",
       "    rt        (obs_id) float32 6kB 1.21 1.63 1.03 2.77 ... 0.803 -0.606 0.499\n",
       "    response  (obs_id) int64 11kB 1 1 1 1 0 1 1 0 1 0 1 ... 0 0 1 1 0 1 1 1 0 1\n",
       "    theta     (obs_id) object 11kB 0.656275122261 ... -0.264081867924\n",
       "    dbs       (obs_id) object 11kB 1 1 1 1 1 1 1 1 1 1 1 ... 0 0 0 0 0 0 0 0 0 0\n",
       "    conf      (obs_id) object 11kB &#x27;HC&#x27; &#x27;LC&#x27; &#x27;HC&#x27; &#x27;LC&#x27; ... &#x27;HC&#x27; &#x27;LC&#x27; &#x27;LC&#x27; &#x27;HC&#x27;</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-17a6521f-a1d4-47ab-9da9-e5643228835c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-17a6521f-a1d4-47ab-9da9-e5643228835c' 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'>obs_id</span>: 1427</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f30c29ae-e342-457a-9aa5-63a6bbf282e1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f30c29ae-e342-457a-9aa5-63a6bbf282e1' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs_id</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1423 1424 1425 1426</div><input id='attrs-90a5d170-655e-4159-a98f-59358fa8537e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-90a5d170-655e-4159-a98f-59358fa8537e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2814e89-ce22-4a58-8fa1-3ed9b66b0b7e' class='xr-var-data-in' type='checkbox'><label for='data-d2814e89-ce22-4a58-8fa1-3ed9b66b0b7e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, ..., 1424, 1425, 1426])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>subj_idx</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 4 4 4 4 4 4 4 4</div><input id='attrs-33101d16-a971-4d3a-be99-c3a8369f1152' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33101d16-a971-4d3a-be99-c3a8369f1152' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ef02165-5a4a-4093-8571-14041216a6f7' class='xr-var-data-in' type='checkbox'><label for='data-3ef02165-5a4a-4093-8571-14041216a6f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, 0, 0, ..., 4, 4, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>trial</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 278 279 280 281 282</div><input id='attrs-19ac2bbf-5d1f-4d2c-b20a-0187861cce76' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-19ac2bbf-5d1f-4d2c-b20a-0187861cce76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f684ea2-61de-4be9-8f6d-71a5901350f7' class='xr-var-data-in' type='checkbox'><label for='data-3f684ea2-61de-4be9-8f6d-71a5901350f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, ..., 280, 281, 282])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8965b854-fcf3-4ca7-bb50-9a9a162cc28a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8965b854-fcf3-4ca7-bb50-9a9a162cc28a' 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>index</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1423 1424 1425 1426</div><input id='attrs-d87c5ad3-343b-463b-8ed4-72c5e955f06f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d87c5ad3-343b-463b-8ed4-72c5e955f06f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8899afd-7d46-486a-9e45-87fecf4f1224' class='xr-var-data-in' type='checkbox'><label for='data-a8899afd-7d46-486a-9e45-87fecf4f1224' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, ..., 1424, 1425, 1426])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stim</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;LL&#x27; &#x27;WL&#x27; &#x27;WW&#x27; ... &#x27;WL&#x27; &#x27;WL&#x27; &#x27;LL&#x27;</div><input id='attrs-5758775b-3709-4f4b-aa7f-61d0dc88358f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5758775b-3709-4f4b-aa7f-61d0dc88358f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b9715eb-1b63-41df-993b-eea5397cd3d4' class='xr-var-data-in' type='checkbox'><label for='data-0b9715eb-1b63-41df-993b-eea5397cd3d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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;LL&#x27;, &#x27;WL&#x27;, &#x27;WW&#x27;, ..., &#x27;WL&#x27;, &#x27;WL&#x27;, &#x27;LL&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rt</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.21 1.63 1.03 ... -0.606 0.499</div><input id='attrs-4c7c511b-eade-4212-a215-14590948ad05' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c7c511b-eade-4212-a215-14590948ad05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0c52588-d427-4b5e-a969-a9c9918bb7b8' class='xr-var-data-in' type='checkbox'><label for='data-d0c52588-d427-4b5e-a969-a9c9918bb7b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.21 ,  1.63 ,  1.03 , ...,  0.803, -0.606,  0.499], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>response</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 1 1 1 0 1 1 0 ... 1 1 0 1 1 1 0 1</div><input id='attrs-bfb4cb74-dbcf-4526-859e-bc381d733408' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bfb4cb74-dbcf-4526-859e-bc381d733408' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35c5588e-f4ac-4c85-ba4d-c1b82c719f90' class='xr-var-data-in' type='checkbox'><label for='data-35c5588e-f4ac-4c85-ba4d-c1b82c719f90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, 1, 1, ..., 1, 0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>theta</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.656275122261 ... -0.264081867924</div><input id='attrs-dbe4ed56-0d68-4545-9d01-0fedff9ae9e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dbe4ed56-0d68-4545-9d01-0fedff9ae9e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-786c81b4-e323-4ad8-b51e-7706e5ed98b1' class='xr-var-data-in' type='checkbox'><label for='data-786c81b4-e323-4ad8-b51e-7706e5ed98b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.656275122261, -0.3278886716619999, -0.480284512399, ...,\n",
       "       1.2524802215, -0.6359418409079999, -0.264081867924], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dbs</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 0 0 0 0 0 0 0 0</div><input id='attrs-563a220a-2e61-4b2c-8980-dbb3d2c36677' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-563a220a-2e61-4b2c-8980-dbb3d2c36677' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e4ce55a-45f3-45e1-a6e7-654d28fcd41f' class='xr-var-data-in' type='checkbox'><label for='data-3e4ce55a-45f3-45e1-a6e7-654d28fcd41f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, 1, 1, ..., 0, 0, 0], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>conf</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;HC&#x27; &#x27;LC&#x27; &#x27;HC&#x27; ... &#x27;LC&#x27; &#x27;LC&#x27; &#x27;HC&#x27;</div><input id='attrs-5ccb9243-bc43-4d03-a3bd-d62a51a718d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ccb9243-bc43-4d03-a3bd-d62a51a718d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-752a4ac4-48d3-4cbb-ab19-f8930e691b42' class='xr-var-data-in' type='checkbox'><label for='data-752a4ac4-48d3-4cbb-ab19-f8930e691b42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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;HC&#x27;, &#x27;LC&#x27;, &#x27;HC&#x27;, ..., &#x27;LC&#x27;, &#x27;LC&#x27;, &#x27;HC&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eb5b4bc0-95ec-4f65-a978-e246431d5deb' class='xr-section-summary-in' type='checkbox'  ><label for='section-eb5b4bc0-95ec-4f65-a978-e246431d5deb' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>obs_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e2326528-536f-4009-9570-72b6b5faa734' class='xr-index-data-in' type='checkbox'/><label for='index-e2326528-536f-4009-9570-72b6b5faa734' 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(RangeIndex(start=0, stop=1427, step=1, name=&#x27;obs_id&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4b921249-b445-456a-853e-eee43ac31c6c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4b921249-b445-456a-853e-eee43ac31c6c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div><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",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       ".xr-wrap{width:700px!important;} </style>"
      ],
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> observed_data"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time\n",
    "model0 = hddm.HDDM(data, include = ['v', 'a', 't', 'z'])\n",
    "# note: when chains more than one, sampling process will be run parallelly, and the tmp_*.db will be saved in the current directory\n",
    "model0_infdata = model0.sample(500, chains = 4, return_infdata = True)\n",
    "model0_infdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we used the arviz to diagnose the model. \n",
    "\n",
    "We can obtain the Rhat, ess, and other diagnostic for each parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "1"
    }
   },
   "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></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>a</th>\n",
       "      <td>1.910</td>\n",
       "      <td>0.240</td>\n",
       "      <td>1.471</td>\n",
       "      <td>2.364</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1584.0</td>\n",
       "      <td>1192.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_std</th>\n",
       "      <td>0.516</td>\n",
       "      <td>0.274</td>\n",
       "      <td>0.197</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.008</td>\n",
       "      <td>751.0</td>\n",
       "      <td>721.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.0</th>\n",
       "      <td>2.199</td>\n",
       "      <td>0.066</td>\n",
       "      <td>2.074</td>\n",
       "      <td>2.316</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1062.0</td>\n",
       "      <td>1490.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.1</th>\n",
       "      <td>2.123</td>\n",
       "      <td>0.067</td>\n",
       "      <td>1.993</td>\n",
       "      <td>2.245</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>783.0</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.2</th>\n",
       "      <td>1.553</td>\n",
       "      <td>0.047</td>\n",
       "      <td>1.461</td>\n",
       "      <td>1.637</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1095.0</td>\n",
       "      <td>1225.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.3</th>\n",
       "      <td>2.158</td>\n",
       "      <td>0.076</td>\n",
       "      <td>2.027</td>\n",
       "      <td>2.309</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>664.0</td>\n",
       "      <td>965.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.4</th>\n",
       "      <td>1.484</td>\n",
       "      <td>0.050</td>\n",
       "      <td>1.387</td>\n",
       "      <td>1.576</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>708.0</td>\n",
       "      <td>1084.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>0.530</td>\n",
       "      <td>0.291</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1.088</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.005</td>\n",
       "      <td>2026.0</td>\n",
       "      <td>1565.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_std</th>\n",
       "      <td>0.576</td>\n",
       "      <td>0.311</td>\n",
       "      <td>0.183</td>\n",
       "      <td>1.138</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.009</td>\n",
       "      <td>622.0</td>\n",
       "      <td>927.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj.0</th>\n",
       "      <td>0.134</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.247</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>475.0</td>\n",
       "      <td>1055.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj.1</th>\n",
       "      <td>0.684</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.003</td>\n",
       "      <td>402.0</td>\n",
       "      <td>913.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj.2</th>\n",
       "      <td>0.589</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.419</td>\n",
       "      <td>0.758</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.002</td>\n",
       "      <td>645.0</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj.3</th>\n",
       "      <td>0.160</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.288</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>589.0</td>\n",
       "      <td>1053.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj.4</th>\n",
       "      <td>1.017</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.815</td>\n",
       "      <td>1.213</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>448.0</td>\n",
       "      <td>951.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>0.479</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1120.0</td>\n",
       "      <td>626.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_std</th>\n",
       "      <td>0.145</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>627.0</td>\n",
       "      <td>569.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.0</th>\n",
       "      <td>0.553</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.498</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1067.0</td>\n",
       "      <td>1381.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.1</th>\n",
       "      <td>0.395</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.426</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>807.0</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.2</th>\n",
       "      <td>0.495</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.513</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>1291.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.3</th>\n",
       "      <td>0.553</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.478</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>453.0</td>\n",
       "      <td>995.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.4</th>\n",
       "      <td>0.345</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.329</td>\n",
       "      <td>0.363</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>688.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.494</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.467</td>\n",
       "      <td>0.519</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>82.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_std</th>\n",
       "      <td>0.036</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>112.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.0</th>\n",
       "      <td>0.493</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>105.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.1</th>\n",
       "      <td>0.496</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>104.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.2</th>\n",
       "      <td>0.492</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.466</td>\n",
       "      <td>0.519</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>97.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.3</th>\n",
       "      <td>0.499</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>120.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z_subj.4</th>\n",
       "      <td>0.489</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.461</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>101.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk   \n",
       "a         1.910  0.240   1.471    2.364      0.006    0.004    1584.0  \\\n",
       "a_std     0.516  0.274   0.197    0.976      0.011    0.008     751.0   \n",
       "a_subj.0  2.199  0.066   2.074    2.316      0.002    0.001    1062.0   \n",
       "a_subj.1  2.123  0.067   1.993    2.245      0.002    0.002     783.0   \n",
       "a_subj.2  1.553  0.047   1.461    1.637      0.001    0.001    1095.0   \n",
       "a_subj.3  2.158  0.076   2.027    2.309      0.003    0.002     664.0   \n",
       "a_subj.4  1.484  0.050   1.387    1.576      0.002    0.001     708.0   \n",
       "v         0.530  0.291   0.005    1.088      0.006    0.005    2026.0   \n",
       "v_std     0.576  0.311   0.183    1.138      0.013    0.009     622.0   \n",
       "v_subj.0  0.134  0.067   0.012    0.247      0.003    0.002     475.0   \n",
       "v_subj.1  0.684  0.076   0.543    0.825      0.004    0.003     402.0   \n",
       "v_subj.2  0.589  0.094   0.419    0.758      0.004    0.002     645.0   \n",
       "v_subj.3  0.160  0.071   0.034    0.288      0.003    0.002     589.0   \n",
       "v_subj.4  1.017  0.110   0.815    1.213      0.005    0.004     448.0   \n",
       "t         0.479  0.068   0.357    0.606      0.002    0.002    1120.0   \n",
       "t_std     0.145  0.077   0.043    0.276      0.003    0.002     627.0   \n",
       "t_subj.0  0.553  0.032   0.498    0.617      0.001    0.001    1067.0   \n",
       "t_subj.1  0.395  0.017   0.362    0.426      0.001    0.000     807.0   \n",
       "t_subj.2  0.495  0.010   0.477    0.513      0.000    0.000    1065.0   \n",
       "t_subj.3  0.553  0.037   0.478    0.619      0.002    0.001     453.0   \n",
       "t_subj.4  0.345  0.009   0.329    0.363      0.000    0.000     688.0   \n",
       "z         0.494  0.014   0.467    0.519      0.002    0.001      82.0   \n",
       "z_std     0.036  0.028   0.002    0.090      0.002    0.002     112.0   \n",
       "z_subj.0  0.493  0.015   0.465    0.520      0.001    0.001     105.0   \n",
       "z_subj.1  0.496  0.015   0.463    0.521      0.002    0.001     104.0   \n",
       "z_subj.2  0.492  0.014   0.466    0.519      0.001    0.001      97.0   \n",
       "z_subj.3  0.499  0.017   0.463    0.529      0.001    0.001     120.0   \n",
       "z_subj.4  0.489  0.016   0.461    0.517      0.002    0.001     101.0   \n",
       "\n",
       "          ess_tail  r_hat  \n",
       "a           1192.0   1.00  \n",
       "a_std        721.0   1.00  \n",
       "a_subj.0    1490.0   1.00  \n",
       "a_subj.1    1040.0   1.00  \n",
       "a_subj.2    1225.0   1.00  \n",
       "a_subj.3     965.0   1.01  \n",
       "a_subj.4    1084.0   1.00  \n",
       "v           1565.0   1.00  \n",
       "v_std        927.0   1.00  \n",
       "v_subj.0    1055.0   1.01  \n",
       "v_subj.1     913.0   1.01  \n",
       "v_subj.2    1248.0   1.00  \n",
       "v_subj.3    1053.0   1.00  \n",
       "v_subj.4     951.0   1.01  \n",
       "t            626.0   1.00  \n",
       "t_std        569.0   1.01  \n",
       "t_subj.0    1381.0   1.00  \n",
       "t_subj.1    1212.0   1.00  \n",
       "t_subj.2    1291.0   1.00  \n",
       "t_subj.3     995.0   1.01  \n",
       "t_subj.4    1027.0   1.00  \n",
       "z            154.0   1.03  \n",
       "z_std        247.0   1.04  \n",
       "z_subj.0     190.0   1.03  \n",
       "z_subj.1     155.0   1.04  \n",
       "z_subj.2     138.0   1.03  \n",
       "z_subj.3     183.0   1.03  \n",
       "z_subj.4     111.0   1.04  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(model0_infdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also plot the traceplot and posterior distribution for each parameter to check the convergence of the model.\n",
    "\n",
    "Note: different chains are plotted in different colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = az.plot_trace(model0_infdata, var_names = ['a', 'v', 't', 'z'], figsize = (6,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_1_2_'></a>[HDDM with regressors](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will try a more complex model, such as hddm with regressors. \n",
    "- In this example, we assume that the decision boundary varies with the condition `conf` and set the `LC` condition as a reference, so that the `v` coefficient represents the difference between LC and HC. \n",
    "- We show how to save a model to an arbitrary directory using the `save_name` argument. \n",
    "- `sample_prior` allow to sample draws from prior distribution, and then used to calculate Bayes Factor (BF). \n",
    "- To enable model comparison, we have added the `loglike = True` argument to calculate the pointwise log likelihood of the model, which can be further used for calculating WAIC and LOOIC. \n",
    "- Additionally, we have added the `ppc = True` argument, which allows us to generate posterior predictive checks for the model.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "notebookRunGroups": {
     "groupValue": "1"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No model attribute --> setting up standard HDDM\n",
      "Set model to ddm\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2309: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  tmp2 = (x - v) * (fx - fw)\n",
      "/opt/conda/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2309: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  tmp2 = (x - v) * (fx - fw)\n",
      "/opt/conda/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2309: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  tmp2 = (x - v) * (fx - fw)\n",
      "/opt/conda/lib/python3.9/site-packages/scipy/optimize/_optimize.py:2309: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  tmp2 = (x - v) * (fx - fw)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[                  1%                  ] 7 of 500 complete in 0.5 sec\n",
      "[                  1%                  ] 7 of 500 complete in 0.5 sec\n",
      "[                  1%                  ] 8 of 500 complete in 0.6 sec\n",
      "[                  1%                  ] 8 of 500 complete in 0.6 sec\n",
      "[-                 3%                  ] 16 of 500 complete in 1.1 sec\n",
      "[-                 3%                  ] 15 of 500 complete in 1.0 sec\n",
      "[-                 3%                  ] 16 of 500 complete in 1.1 sec\n",
      "[-                 3%                  ] 16 of 500 complete in 1.1 sec\n",
      "[-                 4%                  ] 24 of 500 complete in 1.6 sec\n",
      "[-                 4%                  ] 23 of 500 complete in 1.5 sec\n",
      "[-                 4%                  ] 23 of 500 complete in 1.7 sec\n",
      "[-                 4%                  ] 24 of 500 complete in 1.6 sec\n",
      "[--                6%                  ] 31 of 500 complete in 2.2 sec\n",
      "[--                6%                  ] 30 of 500 complete in 2.1 sec\n",
      "[--                5%                  ] 29 of 500 complete in 2.2 sec\n",
      "[--                6%                  ] 31 of 500 complete in 2.2 sec\n",
      "[--                7%                  ] 37 of 500 complete in 2.6 sec\n",
      "[--                7%                  ] 38 of 500 complete in 2.8 sec\n",
      "[--                7%                  ] 36 of 500 complete in 2.8 sec\n",
      "[--                7%                  ] 38 of 500 complete in 2.8 sec\n",
      "[---               8%                  ] 44 of 500 complete in 3.2 sec\n",
      "[---               9%                  ] 45 of 500 complete in 3.3 sec\n",
      "[---               8%                  ] 43 of 500 complete in 3.3 sec\n",
      "[---               9%                  ] 45 of 500 complete in 3.3 sec\n",
      "[---              10%                  ] 51 of 500 complete in 3.7 sec\n",
      "[---              10%                  ] 50 of 500 complete in 3.8 sec\n",
      "[---              10%                  ] 52 of 500 complete in 3.9 sec\n",
      "[---              10%                  ] 52 of 500 complete in 3.8 sec\n",
      "[----             11%                  ] 58 of 500 complete in 4.2 sec\n",
      "[----             11%                  ] 57 of 500 complete in 4.4 sec\n",
      "[----             11%                  ] 59 of 500 complete in 4.4 sec\n",
      "[----             11%                  ] 59 of 500 complete in 4.4 sec\n",
      "[----             13%                  ] 65 of 500 complete in 4.7 sec\n",
      "[----             12%                  ] 64 of 500 complete in 4.9 sec\n",
      "[-----            13%                  ] 66 of 500 complete in 5.0 sec\n",
      "[-----            13%                  ] 66 of 500 complete in 4.9 sec\n",
      "[-----            14%                  ] 73 of 500 complete in 5.3 sec\n",
      "[-----            14%                  ] 71 of 500 complete in 5.4 sec\n",
      "[-----            14%                  ] 73 of 500 complete in 5.5 sec\n",
      "[-----            14%                  ] 73 of 500 complete in 5.4 sec\n",
      "[------           16%                  ] 80 of 500 complete in 5.8 sec\n",
      "[-----            15%                  ] 78 of 500 complete in 5.9 sec\n",
      "[------           16%                  ] 80 of 500 complete in 6.1 sec\n",
      "[------           16%                  ] 80 of 500 complete in 6.0 sec\n",
      "[------           17%                  ] 87 of 500 complete in 6.3 sec\n",
      "[------           17%                  ] 85 of 500 complete in 6.4 sec\n",
      "[------           17%                  ] 87 of 500 complete in 6.6 sec\n",
      "[------           17%                  ] 87 of 500 complete in 6.5 sec\n",
      "[-------          18%                  ] 94 of 500 complete in 6.9 sec\n",
      "[------           18%                  ] 92 of 500 complete in 7.0 sec\n",
      "[-------          18%                  ] 94 of 500 complete in 7.2 sec\n",
      "[-------          19%                  ] 95 of 500 complete in 7.0 sec\n",
      "[-------          20%                  ] 101 of 500 complete in 7.4 sec\n",
      "[-------          19%                  ] 99 of 500 complete in 7.5 sec\n",
      "[-------          20%                  ] 102 of 500 complete in 7.5 sec\n",
      "[-------          20%                  ] 101 of 500 complete in 7.7 sec\n",
      "[--------         21%                  ] 109 of 500 complete in 7.9 sec\n",
      "[--------         21%                  ] 106 of 500 complete in 8.0 sec\n",
      "[--------         21%                  ] 109 of 500 complete in 8.1 sec\n",
      "[--------         21%                  ] 108 of 500 complete in 8.3 sec\n",
      "[--------         23%                  ] 116 of 500 complete in 8.5 sec\n",
      "[--------         22%                  ] 113 of 500 complete in 8.6 sec\n",
      "[--------         23%                  ] 116 of 500 complete in 8.6 sec\n",
      "[--------         23%                  ] 115 of 500 complete in 8.9 sec\n",
      "[---------        24%                  ] 120 of 500 complete in 9.1 sec\n",
      "[---------        24%                  ] 123 of 500 complete in 9.0 sec\n",
      "[---------        24%                  ] 123 of 500 complete in 9.1 sec\n",
      "[---------        24%                  ] 122 of 500 complete in 9.4 sec\n",
      "[---------        25%                  ] 127 of 500 complete in 9.6 sec\n",
      "[---------        26%                  ] 130 of 500 complete in 9.6 sec\n",
      "[---------        26%                  ] 130 of 500 complete in 9.6 sec\n",
      "[---------        25%                  ] 129 of 500 complete in 9.9 sec\n",
      "[----------       26%                  ] 134 of 500 complete in 10.1 sec\n",
      "[----------       27%                  ] 137 of 500 complete in 10.1 sec\n",
      "[----------       27%                  ] 137 of 500 complete in 10.1 sec\n",
      "[----------       27%                  ] 136 of 500 complete in 10.5 sec\n",
      "[----------       28%                  ] 144 of 500 complete in 10.6 sec\n",
      "[----------       28%                  ] 141 of 500 complete in 10.7 sec\n",
      "[----------       28%                  ] 143 of 500 complete in 10.6 sec\n",
      "[----------       28%                  ] 142 of 500 complete in 11.0 sec\n",
      "[-----------      30%                  ] 151 of 500 complete in 11.2 sec\n",
      "[-----------      29%                  ] 147 of 500 complete in 11.2 sec\n",
      "[-----------      30%                  ] 150 of 500 complete in 11.2 sec\n",
      "[-----------      29%                  ] 149 of 500 complete in 11.5 sec\n",
      "[------------     31%                  ] 158 of 500 complete in 11.7 sec\n",
      "[-----------      30%                  ] 154 of 500 complete in 11.7 sec\n",
      "[-----------      31%                  ] 157 of 500 complete in 11.7 sec\n",
      "[-----------      31%                  ] 157 of 500 complete in 12.0 sec\n",
      "[------------     33%                  ] 165 of 500 complete in 12.2 sec\n",
      "[------------     32%                  ] 161 of 500 complete in 12.3 sec\n",
      "[------------     32%                  ] 164 of 500 complete in 12.3 sec\n",
      "[------------     32%                  ] 164 of 500 complete in 12.6 sec\n",
      "[-------------    34%                  ] 172 of 500 complete in 12.7 sec\n",
      "[------------     33%                  ] 168 of 500 complete in 12.8 sec\n",
      "[------------     34%                  ] 171 of 500 complete in 12.8 sec\n",
      "[------------     34%                  ] 171 of 500 complete in 13.1 sec\n",
      "[-------------    35%                  ] 175 of 500 complete in 13.3 sec\n",
      "[-------------    35%                  ] 179 of 500 complete in 13.2 sec\n",
      "[-------------    35%                  ] 178 of 500 complete in 13.3 sec\n",
      "[-------------    35%                  ] 178 of 500 complete in 13.6 sec\n",
      "[--------------   37%                  ] 186 of 500 complete in 13.7 sec\n",
      "[-------------    36%                  ] 182 of 500 complete in 13.8 sec\n",
      "[--------------   37%                  ] 185 of 500 complete in 13.9 sec\n",
      "[--------------   37%                  ] 185 of 500 complete in 14.2 sec\n",
      "[--------------   38%                  ] 193 of 500 complete in 14.3 sec\n",
      "[--------------   38%                  ] 190 of 500 complete in 14.4 sec\n",
      "[--------------   38%                  ] 192 of 500 complete in 14.4 sec\n",
      "[--------------   38%                  ] 192 of 500 complete in 14.7 sec\n",
      "[---------------  40%                  ] 200 of 500 complete in 14.8 sec\n",
      "[---------------  39%                  ] 198 of 500 complete in 14.9 sec\n",
      "[---------------  39%                  ] 199 of 500 complete in 14.9 sec\n",
      "[---------------  39%                  ] 199 of 500 complete in 15.3 sec\n",
      "[---------------  41%                  ] 207 of 500 complete in 15.3 sec\n",
      "[---------------  41%                  ] 205 of 500 complete in 15.5 sec\n",
      "[---------------  41%                  ] 206 of 500 complete in 15.5 sec\n",
      "[---------------  41%                  ] 206 of 500 complete in 15.8 sec\n",
      "[---------------- 42%                  ] 214 of 500 complete in 15.8 sec\n",
      "[---------------- 42%                  ] 212 of 500 complete in 16.0 sec\n",
      "[---------------- 42%                  ] 214 of 500 complete in 16.0 sec\n",
      "[---------------- 42%                  ] 213 of 500 complete in 16.3 sec\n",
      "[---------------- 44%                  ] 222 of 500 complete in 16.4 sec\n",
      "[---------------- 43%                  ] 219 of 500 complete in 16.5 sec\n",
      "[---------------- 44%                  ] 221 of 500 complete in 16.5 sec\n",
      "[---------------- 44%                  ] 220 of 500 complete in 16.9 sec\n",
      "[-----------------45%                  ] 229 of 500 complete in 16.9 sec\n",
      "[-----------------45%                  ] 226 of 500 complete in 17.0 sec\n",
      "[-----------------45%                  ] 228 of 500 complete in 17.1 sec\n",
      "[-----------------45%                  ] 227 of 500 complete in 17.4 sec\n",
      "[-----------------47%                  ] 236 of 500 complete in 17.5 sec\n",
      "[-----------------46%                  ] 233 of 500 complete in 17.5 sec\n",
      "[-----------------47%                  ] 235 of 500 complete in 17.6 sec\n",
      "[-----------------46%                  ] 234 of 500 complete in 17.9 sec\n",
      "[-----------------48%                  ] 243 of 500 complete in 18.0 sec\n",
      "[-----------------48%                  ] 241 of 500 complete in 18.1 sec\n",
      "[-----------------48%                  ] 242 of 500 complete in 18.1 sec\n",
      "[-----------------48%                  ] 242 of 500 complete in 18.5 sec\n",
      "[-----------------50%                  ] 250 of 500 complete in 18.5 sec\n",
      "[-----------------49%                  ] 248 of 500 complete in 18.6 sec\n",
      "[-----------------49%                  ] 249 of 500 complete in 18.7 sec\n",
      "[-----------------49%                  ] 249 of 500 complete in 19.0 sec\n",
      "[-----------------51%                  ] 256 of 500 complete in 19.1 sec\n",
      "[-----------------51%                  ] 255 of 500 complete in 19.1 sec\n",
      "[-----------------51%                  ] 256 of 500 complete in 19.2 sec\n",
      "[-----------------51%                  ] 256 of 500 complete in 19.5 sec\n",
      "[-----------------52%                  ] 263 of 500 complete in 19.6 sec\n",
      "[-----------------52%                  ] 262 of 500 complete in 19.7 sec\n",
      "[-----------------52%                  ] 263 of 500 complete in 19.7 sec\n",
      "[-----------------52%                  ] 263 of 500 complete in 20.0 sec\n",
      "[-----------------54%                  ] 271 of 500 complete in 20.2 sec\n",
      "[-----------------54%                  ] 270 of 500 complete in 20.3 sec\n",
      "[-----------------54%                  ] 271 of 500 complete in 20.3 sec\n",
      "[-----------------54%                  ] 271 of 500 complete in 20.6 sec\n",
      "[-----------------55%-                 ] 278 of 500 complete in 20.7 sec\n",
      "[-----------------55%-                 ] 277 of 500 complete in 20.8 sec\n",
      "[-----------------55%-                 ] 279 of 500 complete in 20.8 sec\n",
      "[-----------------55%-                 ] 279 of 500 complete in 21.1 sec\n",
      "[-----------------57%-                 ] 285 of 500 complete in 21.2 sec\n",
      "[-----------------56%-                 ] 284 of 500 complete in 21.3 sec\n",
      "[-----------------57%-                 ] 287 of 500 complete in 21.4 sec\n",
      "[-----------------57%-                 ] 287 of 500 complete in 21.6 sec\n",
      "[-----------------58%--                ] 293 of 500 complete in 21.8 sec\n",
      "[-----------------58%--                ] 292 of 500 complete in 21.9 sec\n",
      "[-----------------58%--                ] 294 of 500 complete in 21.9 sec\n",
      "[-----------------58%--                ] 294 of 500 complete in 22.2 sec\n",
      "[-----------------60%--                ] 300 of 500 complete in 22.3 sec\n",
      "[-----------------60%--                ] 300 of 500 complete in 22.4 sec\n",
      "[-----------------60%--                ] 302 of 500 complete in 22.4 sec\n",
      "[-----------------60%--                ] 301 of 500 complete in 22.7 sec\n",
      "[-----------------61%---               ] 307 of 500 complete in 22.8 sec\n",
      "[-----------------61%---               ] 307 of 500 complete in 22.9 sec\n",
      "[-----------------62%---               ] 310 of 500 complete in 23.0 sec\n",
      "[-----------------61%---               ] 308 of 500 complete in 23.2 sec\n",
      "[-----------------62%---               ] 314 of 500 complete in 23.3 sec\n",
      "[-----------------63%---               ] 315 of 500 complete in 23.5 sec\n",
      "[-----------------63%----              ] 317 of 500 complete in 23.5 sec\n",
      "[-----------------63%---               ] 315 of 500 complete in 23.7 sec\n",
      "[-----------------64%----              ] 322 of 500 complete in 23.9 sec\n",
      "[-----------------64%----              ] 323 of 500 complete in 24.0 sec\n",
      "[-----------------64%----              ] 322 of 500 complete in 24.2 sec\n",
      "[-----------------65%----              ] 325 of 500 complete in 24.0 sec\n",
      "[-----------------66%-----             ] 330 of 500 complete in 24.4 sec\n",
      "[-----------------66%-----             ] 331 of 500 complete in 24.6 sec\n",
      "[-----------------65%-----             ] 329 of 500 complete in 24.8 sec\n",
      "[-----------------66%-----             ] 333 of 500 complete in 24.6 sec\n",
      "[-----------------67%-----             ] 337 of 500 complete in 24.9 sec\n",
      "[-----------------67%-----             ] 339 of 500 complete in 25.1 sec\n",
      "[-----------------67%-----             ] 336 of 500 complete in 25.3 sec\n",
      "[-----------------68%-----             ] 341 of 500 complete in 25.2 sec\n",
      "[-----------------69%------            ] 345 of 500 complete in 25.5 sec\n",
      "[-----------------69%------            ] 346 of 500 complete in 25.6 sec\n",
      "[-----------------68%------            ] 343 of 500 complete in 25.8 sec\n",
      "[-----------------69%------            ] 349 of 500 complete in 25.7 sec\n",
      "[-----------------70%------            ] 353 of 500 complete in 26.1 sec\n",
      "[-----------------70%------            ] 353 of 500 complete in 26.1 sec\n",
      "[-----------------70%------            ] 350 of 500 complete in 26.3 sec\n",
      "[-----------------71%-------           ] 357 of 500 complete in 26.3 sec\n",
      "[-----------------72%-------           ] 360 of 500 complete in 26.6 sec\n",
      "[-----------------72%-------           ] 360 of 500 complete in 26.6 sec\n",
      "[-----------------71%-------           ] 357 of 500 complete in 26.9 sec\n",
      "[-----------------73%-------           ] 365 of 500 complete in 26.8 sec\n",
      "[-----------------73%-------           ] 367 of 500 complete in 27.2 sec\n",
      "[-----------------73%-------           ] 367 of 500 complete in 27.1 sec\n",
      "[-----------------72%-------           ] 364 of 500 complete in 27.4 sec\n",
      "[-----------------74%--------          ] 373 of 500 complete in 27.3 sec\n",
      "[-----------------74%--------          ] 374 of 500 complete in 27.7 sec\n",
      "[-----------------75%--------          ] 375 of 500 complete in 27.7 sec\n",
      "[-----------------74%--------          ] 371 of 500 complete in 28.0 sec\n",
      "[-----------------76%--------          ] 381 of 500 complete in 27.9 sec\n",
      "[-----------------76%--------          ] 381 of 500 complete in 28.2 sec\n",
      "[-----------------76%---------         ] 383 of 500 complete in 28.3 sec\n",
      "[-----------------75%--------          ] 378 of 500 complete in 28.5 sec\n",
      "[-----------------77%---------         ] 389 of 500 complete in 28.5 sec\n",
      "[-----------------77%---------         ] 388 of 500 complete in 28.7 sec\n",
      "[-----------------78%---------         ] 390 of 500 complete in 28.8 sec\n",
      "[-----------------77%---------         ] 385 of 500 complete in 29.1 sec\n",
      "[-----------------79%----------        ] 397 of 500 complete in 29.0 sec\n",
      "[-----------------79%----------        ] 395 of 500 complete in 29.3 sec\n",
      "[-----------------79%----------        ] 398 of 500 complete in 29.3 sec\n",
      "[-----------------78%---------         ] 392 of 500 complete in 29.6 sec\n",
      "[-----------------80%----------        ] 404 of 500 complete in 29.5 sec\n",
      "[-----------------80%----------        ] 402 of 500 complete in 29.8 sec\n",
      "[-----------------81%----------        ] 406 of 500 complete in 29.9 sec\n",
      "[-----------------79%----------        ] 399 of 500 complete in 30.2 sec\n",
      "[-----------------82%-----------       ] 411 of 500 complete in 30.0 sec\n",
      "[-----------------82%-----------       ] 413 of 500 complete in 30.4 sec\n",
      "[-----------------81%-----------       ] 409 of 500 complete in 30.4 sec\n",
      "[-----------------81%----------        ] 406 of 500 complete in 30.7 sec\n",
      "[-----------------83%-----------       ] 418 of 500 complete in 30.6 sec\n",
      "[-----------------84%-----------       ] 420 of 500 complete in 30.9 sec\n",
      "[-----------------83%-----------       ] 416 of 500 complete in 30.9 sec\n",
      "[-----------------82%-----------       ] 413 of 500 complete in 31.2 sec\n",
      "[-----------------85%------------      ] 426 of 500 complete in 31.1 sec\n",
      "[-----------------84%------------      ] 423 of 500 complete in 31.4 sec\n",
      "[-----------------85%------------      ] 428 of 500 complete in 31.5 sec\n",
      "[-----------------84%-----------       ] 421 of 500 complete in 31.8 sec\n",
      "[-----------------86%------------      ] 434 of 500 complete in 31.7 sec\n",
      "[-----------------86%------------      ] 431 of 500 complete in 32.0 sec\n",
      "[-----------------87%-------------     ] 436 of 500 complete in 32.0 sec\n",
      "[-----------------85%------------      ] 428 of 500 complete in 32.3 sec\n",
      "[-----------------88%-------------     ] 442 of 500 complete in 32.2 sec\n",
      "[-----------------87%-------------     ] 438 of 500 complete in 32.5 sec\n",
      "[-----------------88%-------------     ] 443 of 500 complete in 32.6 sec\n",
      "[-----------------87%-------------     ] 435 of 500 complete in 32.8 sec\n",
      "[-----------------89%--------------    ] 449 of 500 complete in 32.8 sec\n",
      "[-----------------88%-------------     ] 444 of 500 complete in 33.0 sec\n",
      "[-----------------89%--------------    ] 449 of 500 complete in 33.1 sec\n",
      "[-----------------88%-------------     ] 442 of 500 complete in 33.4 sec\n",
      "[-----------------91%--------------    ] 455 of 500 complete in 33.3 sec\n",
      "[-----------------90%--------------    ] 450 of 500 complete in 33.6 sec\n",
      "[-----------------91%--------------    ] 456 of 500 complete in 33.6 sec\n",
      "[-----------------89%--------------    ] 448 of 500 complete in 33.9 sec\n",
      "[-----------------92%---------------   ] 462 of 500 complete in 33.8 sec\n",
      "[-----------------91%--------------    ] 456 of 500 complete in 34.1 sec\n",
      "[-----------------92%---------------   ] 463 of 500 complete in 34.2 sec\n",
      "[-----------------90%--------------    ] 454 of 500 complete in 34.4 sec\n",
      "[-----------------93%---------------   ] 468 of 500 complete in 34.3 sec\n",
      "[-----------------92%---------------   ] 463 of 500 complete in 34.7 sec\n",
      "[-----------------94%---------------   ] 470 of 500 complete in 34.7 sec\n",
      "[-----------------92%---------------   ] 461 of 500 complete in 35.0 sec\n",
      "[-----------------95%----------------  ] 475 of 500 complete in 34.9 sec\n",
      "[-----------------94%---------------   ] 470 of 500 complete in 35.2 sec\n",
      "[-----------------95%----------------  ] 477 of 500 complete in 35.3 sec\n",
      "[-----------------93%---------------   ] 468 of 500 complete in 35.5 sec\n",
      "[-----------------96%----------------  ] 482 of 500 complete in 35.4 sec\n",
      "[-----------------95%----------------  ] 477 of 500 complete in 35.8 sec\n",
      "[-----------------96%----------------  ] 484 of 500 complete in 35.8 sec\n",
      "[-----------------95%----------------  ] 475 of 500 complete in 36.0 sec\n",
      "[-----------------97%----------------- ] 489 of 500 complete in 35.9 sec\n",
      "[-----------------96%----------------  ] 484 of 500 complete in 36.3 sec\n",
      "[-----------------98%----------------- ] 491 of 500 complete in 36.4 sec\n",
      "[-----------------96%----------------  ] 482 of 500 complete in 36.6 sec\n",
      "[-----------------99%----------------- ] 496 of 500 complete in 36.5 sec\n",
      "[-----------------98%----------------- ] 491 of 500 complete in 36.9 sec\n",
      "[-----------------99%----------------- ] 498 of 500 complete in 36.9 sec\n",
      "[-----------------100%-----------------] 500 of 500 complete in 36.9 sec\n",
      "hddm sampling elpased time:  36.883 s\n",
      "[-----------------97%----------------- ] 489 of 500 complete in 37.1 sec\n",
      "[-----------------100%-----------------] 500 of 500 complete in 37.2 sec\n",
      "hddm sampling elpased time:  37.177 s\n",
      "[-----------------99%----------------- ] 499 of 500 complete in 37.4 sec\n",
      "[-----------------99%----------------- ] 496 of 500 complete in 37.7 sec\n",
      "[-----------------100%-----------------] 500 of 500 complete in 37.6 sec\n",
      "hddm sampling elpased time:  37.617 s\n",
      "[-----------------100%-----------------] 500 of 500 complete in 38.0 sec\n",
      "hddm sampling elpased time:  38.036 s\n",
      "hddm sampling elpased time:  43.627 s\n",
      "Start converting to InferenceData...\n",
      "Start to calculate pointwise log likelihood...\n",
      "The time of calculation of loglikelihood took 85.165 seconds\n",
      "Start generating posterior prediction...\n",
      "The time of generating PPC took 35.578 seconds\n",
      "CPU times: user 13.3 s, sys: 897 ms, total: 14.2 s\n",
      "Wall time: 2min 49s\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "model_reg = hddm.HDDMRegressor(data, \"v ~ 1 + C(conf, Treatment('LC'))\",include = ['v', 'a', 't', 'z'])\n",
    "# note: setting save_name argument will delete the _temp*.db file\n",
    "save_name = \"model_fitted/hddmregressor_example\"\n",
    "model_reg_infdata = model_reg.sample(\n",
    "    500, chains = 4, \n",
    "    return_infdata = True, save_name = save_name, \n",
    "    sample_prior = True, loglike = True, ppc = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div>\n",
       "              <div class='xr-header'>\n",
       "                <div class=\"xr-obj-type\">arviz.InferenceData</div>\n",
       "              </div>\n",
       "              <ul class=\"xr-sections group-sections\">\n",
       "              \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_posterior2167d006-f3a1-4962-b506-07017310b38e\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_posterior2167d006-f3a1-4962-b506-07017310b38e\" class = \"xr-section-summary\">posterior</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",
       "<path d=\"M23 26h-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 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",
       "}\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; Size: 468kB\n",
       "Dimensions:                           (chain: 4, draw: 500)\n",
       "Coordinates:\n",
       "  * chain                             (chain) int64 32B 0 1 2 3\n",
       "  * draw                              (draw) int64 4kB 0 1 2 3 ... 497 498 499\n",
       "Data variables: (12/29)\n",
       "    a                                 (chain, draw) float64 16kB 1.868 ... 1.728\n",
       "    a_std                             (chain, draw) float64 16kB 0.3398 ... 0...\n",
       "    a_subj.0                          (chain, draw) float64 16kB 2.082 ... 2.197\n",
       "    a_subj.1                          (chain, draw) float64 16kB 2.169 ... 2.283\n",
       "    a_subj.2                          (chain, draw) float64 16kB 1.592 ... 1.591\n",
       "    a_subj.3                          (chain, draw) float64 16kB 2.12 ... 2.217\n",
       "    ...                                ...\n",
       "    v_Intercept_subj.0                (chain, draw) float64 16kB 0.8891 ... 0...\n",
       "    v_Intercept_subj.1                (chain, draw) float64 16kB 1.562 ... 1.019\n",
       "    v_Intercept_subj.2                (chain, draw) float64 16kB 1.276 ... 0....\n",
       "    v_Intercept_subj.3                (chain, draw) float64 16kB 0.7252 ... 0...\n",
       "    v_Intercept_subj.4                (chain, draw) float64 16kB 1.815 ... 1.088\n",
       "    v_C(conf, Treatment(&#x27;LC&#x27;))[T.HC]  (chain, draw) float64 16kB -1.004 ... -...</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-f2bfb6c3-b27c-44d4-a90d-fb8c7f499ec5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f2bfb6c3-b27c-44d4-a90d-fb8c7f499ec5' 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>: 500</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f8abbcfe-8285-4c55-bae9-f65948a1cc78' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f8abbcfe-8285-4c55-bae9-f65948a1cc78' 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-b8d978c7-6ead-4967-858a-e99ae9d503f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b8d978c7-6ead-4967-858a-e99ae9d503f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1a9f905-5f10-4ec1-9f39-529867dd2666' class='xr-var-data-in' type='checkbox'><label for='data-a1a9f905-5f10-4ec1-9f39-529867dd2666' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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 ... 495 496 497 498 499</div><input id='attrs-2673ead9-b7ea-45a3-877b-0daf1389f5dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2673ead9-b7ea-45a3-877b-0daf1389f5dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74703a89-f0a6-490c-a9d8-5eaff3ee98da' class='xr-var-data-in' type='checkbox'><label for='data-74703a89-f0a6-490c-a9d8-5eaff3ee98da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, ..., 497, 498, 499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d6574654-05ad-469f-baa7-01667de60a87' class='xr-section-summary-in' type='checkbox'  ><label for='section-d6574654-05ad-469f-baa7-01667de60a87' class='xr-section-summary' >Data variables: <span>(29)</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>a</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.868 2.016 1.899 ... 1.777 1.728</div><input id='attrs-7efff327-48f2-4def-a5a0-9b157970046a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7efff327-48f2-4def-a5a0-9b157970046a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-402940c7-efd0-4d0d-aa01-b7d73cf63f59' class='xr-var-data-in' type='checkbox'><label for='data-402940c7-efd0-4d0d-aa01-b7d73cf63f59' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.86795196, 2.01609111, 1.89851426, ..., 1.87241443, 2.03191997,\n",
       "        2.1244501 ],\n",
       "       [1.86458034, 1.64549978, 2.1907907 , ..., 1.75709115, 1.80500005,\n",
       "        1.83260948],\n",
       "       [2.02204396, 2.05509072, 1.81347365, ..., 2.10836358, 1.67104748,\n",
       "        2.15711343],\n",
       "       [1.93434755, 2.0382587 , 1.9772613 , ..., 1.99967088, 1.77713153,\n",
       "        1.72777176]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_std</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3398 0.428 ... 0.4778 0.5067</div><input id='attrs-eaf4b21a-730a-49e0-8a85-0ba2e139a7c7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eaf4b21a-730a-49e0-8a85-0ba2e139a7c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eacc6caf-b377-4f43-8986-7c0b54ba1701' class='xr-var-data-in' type='checkbox'><label for='data-eacc6caf-b377-4f43-8986-7c0b54ba1701' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.33983203, 0.42795788, 0.61358287, ..., 0.43920427, 0.19077672,\n",
       "        0.66699139],\n",
       "       [0.31082935, 0.48477673, 0.41112982, ..., 0.18982009, 0.3319538 ,\n",
       "        0.18817437],\n",
       "       [0.32920267, 0.46345545, 0.39651744, ..., 0.28503479, 0.36793389,\n",
       "        0.47093925],\n",
       "       [0.59208798, 0.57325487, 0.46196283, ..., 0.37326551, 0.47782659,\n",
       "        0.50665252]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_subj.0</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.082 2.169 2.177 ... 2.252 2.197</div><input id='attrs-515031f6-1ce6-4879-b127-b17b93e83de7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-515031f6-1ce6-4879-b127-b17b93e83de7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d2fd8b5-1006-4c37-916b-58bfc20f3a29' class='xr-var-data-in' type='checkbox'><label for='data-6d2fd8b5-1006-4c37-916b-58bfc20f3a29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2.08166864, 2.16863066, 2.17746915, ..., 2.26597601, 2.21064149,\n",
       "        2.21255054],\n",
       "       [2.0749464 , 2.10136352, 2.10999959, ..., 2.11026945, 2.03188127,\n",
       "        2.04003961],\n",
       "       [2.24250073, 2.3232926 , 2.23896363, ..., 2.20610826, 2.16687612,\n",
       "        2.10025513],\n",
       "       [2.10421415, 2.10321296, 2.12237479, ..., 2.26439322, 2.25178562,\n",
       "        2.19695755]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_subj.1</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.169 2.169 2.191 ... 2.075 2.283</div><input id='attrs-6c9d9095-924e-432d-a645-4fafff9f7597' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6c9d9095-924e-432d-a645-4fafff9f7597' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb0051c8-7385-46aa-b864-b057fa7aad43' class='xr-var-data-in' type='checkbox'><label for='data-bb0051c8-7385-46aa-b864-b057fa7aad43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2.16885205, 2.16942038, 2.19115962, ..., 2.01569014, 2.10019854,\n",
       "        2.1855088 ],\n",
       "       [2.1117763 , 2.02689251, 2.36123227, ..., 2.03049693, 2.07848182,\n",
       "        2.10208392],\n",
       "       [2.26129248, 2.26499095, 2.23176756, ..., 2.17830347, 2.21384451,\n",
       "        2.227846  ],\n",
       "       [2.24667963, 2.10607337, 2.08770846, ..., 2.0818384 , 2.07513399,\n",
       "        2.28280757]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_subj.2</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.592 1.618 1.594 ... 1.602 1.591</div><input id='attrs-3285d558-e6eb-49c0-92a6-2f135f3f57b3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3285d558-e6eb-49c0-92a6-2f135f3f57b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fd5b06d-7ad5-41d0-a2b5-183ce26524e8' class='xr-var-data-in' type='checkbox'><label for='data-4fd5b06d-7ad5-41d0-a2b5-183ce26524e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.59183601, 1.61844912, 1.59402242, ..., 1.58495808, 1.58732486,\n",
       "        1.59160877],\n",
       "       [1.59710565, 1.59003924, 1.59909663, ..., 1.55776966, 1.58950926,\n",
       "        1.56478306],\n",
       "       [1.5480169 , 1.57925427, 1.61641814, ..., 1.56293462, 1.62741862,\n",
       "        1.57524895],\n",
       "       [1.61188115, 1.55163523, 1.51008161, ..., 1.52304746, 1.60208561,\n",
       "        1.59068662]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_subj.3</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.12 2.147 2.125 ... 2.127 2.217</div><input id='attrs-5c03402e-ebbe-41e8-80b3-944706525ba1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c03402e-ebbe-41e8-80b3-944706525ba1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcdfa00f-24ac-4da7-8f38-79e5ef1ad696' class='xr-var-data-in' type='checkbox'><label for='data-bcdfa00f-24ac-4da7-8f38-79e5ef1ad696' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2.11951723, 2.14679863, 2.12546378, ..., 2.2727453 , 2.17485292,\n",
       "        2.23813161],\n",
       "       [2.12537649, 2.12095212, 2.15451404, ..., 2.14927772, 2.2360609 ,\n",
       "        2.20037662],\n",
       "       [2.06800195, 2.11356003, 2.14222158, ..., 2.13578179, 2.09237914,\n",
       "        2.01506409],\n",
       "       [2.2574656 , 2.32214812, 2.22502246, ..., 2.11687438, 2.12651557,\n",
       "        2.21705575]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_subj.4</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.515 1.5 1.491 ... 1.499 1.463</div><input id='attrs-1d00843d-f589-436c-aa2d-863338df9952' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d00843d-f589-436c-aa2d-863338df9952' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4def53df-e461-41b7-92f8-2edd9c990124' class='xr-var-data-in' type='checkbox'><label for='data-4def53df-e461-41b7-92f8-2edd9c990124' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.51513422, 1.50034869, 1.49129039, ..., 1.51349118, 1.45379746,\n",
       "        1.44883022],\n",
       "       [1.52893015, 1.50108858, 1.47954226, ..., 1.43974648, 1.48961951,\n",
       "        1.5115747 ],\n",
       "       [1.53336654, 1.41012526, 1.51659528, ..., 1.48969401, 1.44083729,\n",
       "        1.48913105],\n",
       "       [1.49074188, 1.46226508, 1.51339864, ..., 1.54555104, 1.49939193,\n",
       "        1.46336537]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.492 0.4795 ... 0.4381 0.4857</div><input id='attrs-4dbe1a4b-ad60-4cfb-8a5f-96a2831746b2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4dbe1a4b-ad60-4cfb-8a5f-96a2831746b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e8a420f-fb30-48e3-bec9-d8d62dc92c04' class='xr-var-data-in' type='checkbox'><label for='data-4e8a420f-fb30-48e3-bec9-d8d62dc92c04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.49196409, 0.47949861, 0.46702203, ..., 0.47097682, 0.43220401,\n",
       "        0.35364168],\n",
       "       [0.45514527, 0.40309064, 0.50099074, ..., 0.55373227, 0.51532078,\n",
       "        0.46888238],\n",
       "       [0.46605748, 0.41249187, 0.43279199, ..., 0.47918465, 0.48561037,\n",
       "        0.48406892],\n",
       "       [0.43019842, 0.39678249, 0.55907605, ..., 0.44322314, 0.43809595,\n",
       "        0.48570965]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_std</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1112 0.08678 ... 0.1444 0.1696</div><input id='attrs-5e894168-995b-4304-a793-07d870c8bc7d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5e894168-995b-4304-a793-07d870c8bc7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e52c9657-23c2-45c3-8b09-02b58bba4a9c' class='xr-var-data-in' type='checkbox'><label for='data-e52c9657-23c2-45c3-8b09-02b58bba4a9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.1111552 , 0.08678322, 0.21856331, ..., 0.10424134, 0.1311699 ,\n",
       "        0.31506402],\n",
       "       [0.15289114, 0.07450862, 0.09754519, ..., 0.10109454, 0.10667835,\n",
       "        0.12807837],\n",
       "       [0.11391165, 0.10228474, 0.26800105, ..., 0.08810636, 0.11732726,\n",
       "        0.09442798],\n",
       "       [0.11593605, 0.1200436 , 0.21603932, ..., 0.16602098, 0.14442261,\n",
       "        0.16960426]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_subj.0</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5878 0.5525 ... 0.5385 0.5693</div><input id='attrs-df879f32-26e9-43f1-a9db-d31a662d86d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df879f32-26e9-43f1-a9db-d31a662d86d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-505219a0-7ab5-4e25-9191-749571b8d635' class='xr-var-data-in' type='checkbox'><label for='data-505219a0-7ab5-4e25-9191-749571b8d635' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.58775429, 0.55247955, 0.58641797, ..., 0.51456827, 0.53412358,\n",
       "        0.57682848],\n",
       "       [0.60835656, 0.57553889, 0.55056535, ..., 0.66258591, 0.63778079,\n",
       "        0.64017533],\n",
       "       [0.48983037, 0.48792696, 0.48125827, ..., 0.55595075, 0.55269708,\n",
       "        0.59635201],\n",
       "       [0.6251222 , 0.61620138, 0.61527854, ..., 0.52560092, 0.53846085,\n",
       "        0.56934809]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_subj.1</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.404 0.3629 ... 0.3854 0.3561</div><input id='attrs-c564eb19-e4ba-4754-bd34-93ebec61c52d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c564eb19-e4ba-4754-bd34-93ebec61c52d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f3573fa-b7e6-4ddd-9d95-4f4c3a5fe5f7' class='xr-var-data-in' type='checkbox'><label for='data-1f3573fa-b7e6-4ddd-9d95-4f4c3a5fe5f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.40398134, 0.36292156, 0.37558211, ..., 0.41461756, 0.39134546,\n",
       "        0.39784776],\n",
       "       [0.36483692, 0.36153836, 0.38037329, ..., 0.39961507, 0.41156535,\n",
       "        0.40102198],\n",
       "       [0.35930275, 0.35484711, 0.36217296, ..., 0.40983038, 0.38506651,\n",
       "        0.39622227],\n",
       "       [0.39142613, 0.41857093, 0.37963263, ..., 0.38440031, 0.38544636,\n",
       "        0.35607629]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_subj.2</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4886 0.4875 ... 0.4908 0.481</div><input id='attrs-33385693-f933-47ad-9fec-f0957b2a7b05' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33385693-f933-47ad-9fec-f0957b2a7b05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7d2c70d-49c3-4522-880d-83bf577c3c2a' class='xr-var-data-in' type='checkbox'><label for='data-b7d2c70d-49c3-4522-880d-83bf577c3c2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.48857236, 0.48748681, 0.48243574, ..., 0.49530866, 0.49210816,\n",
       "        0.49472033],\n",
       "       [0.49313771, 0.48138384, 0.4839734 , ..., 0.50314447, 0.48972043,\n",
       "        0.49734283],\n",
       "       [0.48911096, 0.49986672, 0.47686127, ..., 0.49505632, 0.47919147,\n",
       "        0.48024607],\n",
       "       [0.48953175, 0.47663648, 0.50460747, ..., 0.50430749, 0.49075221,\n",
       "        0.4810079 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_subj.3</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5658 0.543 ... 0.5519 0.5902</div><input id='attrs-94a813ad-28ff-4a0d-8334-851ca8509dba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-94a813ad-28ff-4a0d-8334-851ca8509dba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d322f3d-63a6-4a20-9251-45a39a6aae26' class='xr-var-data-in' type='checkbox'><label for='data-8d322f3d-63a6-4a20-9251-45a39a6aae26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.56576568, 0.5430207 , 0.55420856, ..., 0.51283289, 0.51552136,\n",
       "        0.51609813],\n",
       "       [0.58263398, 0.52755226, 0.50503834, ..., 0.56996652, 0.51726888,\n",
       "        0.51078782],\n",
       "       [0.60340891, 0.57159701, 0.56342839, ..., 0.56512101, 0.63340367,\n",
       "        0.61160282],\n",
       "       [0.49620149, 0.46333331, 0.55773601, ..., 0.53791076, 0.55188483,\n",
       "        0.59017639]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_subj.4</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3423 0.3277 ... 0.3306 0.3558</div><input id='attrs-95fc0d20-39a7-41ba-904d-bc9f576408e6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95fc0d20-39a7-41ba-904d-bc9f576408e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-247aa56b-a1c5-460b-b4be-2dd0745246dd' class='xr-var-data-in' type='checkbox'><label for='data-247aa56b-a1c5-460b-b4be-2dd0745246dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.34233853, 0.32767894, 0.3489294 , ..., 0.34897426, 0.34619847,\n",
       "        0.35154731],\n",
       "       [0.33877312, 0.34271922, 0.3523759 , ..., 0.35846212, 0.34792829,\n",
       "        0.33770611],\n",
       "       [0.34236584, 0.34793934, 0.34524897, ..., 0.35304093, 0.35100201,\n",
       "        0.34952381],\n",
       "       [0.34275282, 0.34653564, 0.34206312, ..., 0.34798122, 0.33063837,\n",
       "        0.35580684]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4345 0.4423 ... 0.4859 0.4863</div><input id='attrs-5726407e-065a-4b31-8817-c11726bda3e6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5726407e-065a-4b31-8817-c11726bda3e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3f3c569-5b71-4a39-8e62-eae10c4d7e0b' class='xr-var-data-in' type='checkbox'><label for='data-c3f3c569-5b71-4a39-8e62-eae10c4d7e0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.43448694, 0.44233536, 0.44350523, ..., 0.48508567, 0.5025915 ,\n",
       "        0.49712056],\n",
       "       [0.43705617, 0.45434111, 0.46699853, ..., 0.50620699, 0.50632386,\n",
       "        0.50648883],\n",
       "       [0.44494172, 0.43760573, 0.45092172, ..., 0.50612188, 0.49518002,\n",
       "        0.50799937],\n",
       "       [0.44549566, 0.45121112, 0.45748955, ..., 0.48574106, 0.4859146 ,\n",
       "        0.48633289]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_std</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.03411 0.04762 ... 0.01031</div><input id='attrs-8b8d57c7-5fa2-4b2d-9ad0-70a8d953bec3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b8d57c7-5fa2-4b2d-9ad0-70a8d953bec3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-188aecff-0ef8-4e4b-8f82-2d7f01b29a1c' class='xr-var-data-in' type='checkbox'><label for='data-188aecff-0ef8-4e4b-8f82-2d7f01b29a1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.03410708, 0.04762052, 0.04381631, ..., 0.07705665, 0.06852292,\n",
       "        0.03519499],\n",
       "       [0.06141998, 0.05583856, 0.07459408, ..., 0.00447014, 0.00558516,\n",
       "        0.00934588],\n",
       "       [0.03501658, 0.05908013, 0.03108277, ..., 0.08078618, 0.0441438 ,\n",
       "        0.04789131],\n",
       "       [0.06534703, 0.10425386, 0.13240705, ..., 0.00756643, 0.00879082,\n",
       "        0.01031472]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_subj.0</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4284 0.4595 ... 0.4857 0.482</div><input id='attrs-5cf40f8d-902b-4b76-81d3-f23429529a6e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5cf40f8d-902b-4b76-81d3-f23429529a6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64b427dc-2d38-4313-8cfd-9a96a11ba20a' class='xr-var-data-in' type='checkbox'><label for='data-64b427dc-2d38-4313-8cfd-9a96a11ba20a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.42841895, 0.45947387, 0.46881272, ..., 0.47766345, 0.47788847,\n",
       "        0.47964227],\n",
       "       [0.44521561, 0.44670928, 0.49286949, ..., 0.50575857, 0.50447006,\n",
       "        0.50707927],\n",
       "       [0.42914523, 0.43099005, 0.47102083, ..., 0.50133686, 0.50346044,\n",
       "        0.5044126 ],\n",
       "       [0.50238027, 0.51080503, 0.49169528, ..., 0.48580738, 0.48573738,\n",
       "        0.48199764]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_subj.1</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4441 0.4629 ... 0.4879 0.4883</div><input id='attrs-ee502bbe-80bc-4673-a091-b4ef4961f73b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee502bbe-80bc-4673-a091-b4ef4961f73b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-187486b9-df04-4ab1-8d69-c2d173f4ede0' class='xr-var-data-in' type='checkbox'><label for='data-187486b9-df04-4ab1-8d69-c2d173f4ede0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.44407892, 0.46294721, 0.45208231, ..., 0.50662159, 0.48867947,\n",
       "        0.49120514],\n",
       "       [0.44632543, 0.46159169, 0.48560369, ..., 0.5071311 , 0.50643342,\n",
       "        0.50444932],\n",
       "       [0.46249874, 0.45795907, 0.45354478, ..., 0.5264877 , 0.52989311,\n",
       "        0.50533939],\n",
       "       [0.46189277, 0.48478253, 0.47018791, ..., 0.48282357, 0.48792445,\n",
       "        0.48827801]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_subj.2</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4337 0.4439 ... 0.4843 0.481</div><input id='attrs-621c13b2-8c77-4f6c-856b-18cecf52eefd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-621c13b2-8c77-4f6c-856b-18cecf52eefd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eba36e23-3499-413a-99b4-b65e530761d7' class='xr-var-data-in' type='checkbox'><label for='data-eba36e23-3499-413a-99b4-b65e530761d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.43370571, 0.44389517, 0.44058858, ..., 0.50064084, 0.50150904,\n",
       "        0.49253562],\n",
       "       [0.42519113, 0.4910896 , 0.47962177, ..., 0.50713522, 0.50607784,\n",
       "        0.50614858],\n",
       "       [0.42913013, 0.44893223, 0.4498319 , ..., 0.4966122 , 0.48608638,\n",
       "        0.48600183],\n",
       "       [0.42214962, 0.42896274, 0.43725212, ..., 0.484766  , 0.48430722,\n",
       "        0.48096359]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_subj.3</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4511 0.4598 ... 0.4905 0.4847</div><input id='attrs-980313c1-faa6-4876-a0f0-fb5417362465' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-980313c1-faa6-4876-a0f0-fb5417362465' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4819804b-3a27-42e5-b31a-ca0a079cde0f' class='xr-var-data-in' type='checkbox'><label for='data-4819804b-3a27-42e5-b31a-ca0a079cde0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.4511062 , 0.4598212 , 0.45445921, ..., 0.51644205, 0.47845636,\n",
       "        0.47964418],\n",
       "       [0.47138064, 0.4517527 , 0.47688195, ..., 0.50371746, 0.50875413,\n",
       "        0.50917804],\n",
       "       [0.44585789, 0.44769155, 0.44646905, ..., 0.5121372 , 0.51563288,\n",
       "        0.50515984],\n",
       "       [0.42680831, 0.48227075, 0.49704474, ..., 0.48471973, 0.49049819,\n",
       "        0.48474045]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_subj.4</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.43 0.4295 ... 0.4846 0.4888</div><input id='attrs-1d48e2bc-2d6b-4692-bedd-d7f0d7ef3916' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d48e2bc-2d6b-4692-bedd-d7f0d7ef3916' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90c91c2a-ca51-41f1-a866-b2c2093f95b5' class='xr-var-data-in' type='checkbox'><label for='data-90c91c2a-ca51-41f1-a866-b2c2093f95b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.43001218, 0.42948212, 0.42629961, ..., 0.49263046, 0.48983354,\n",
       "        0.48566462],\n",
       "       [0.4425415 , 0.43195493, 0.45773295, ..., 0.50673424, 0.50615939,\n",
       "        0.50682544],\n",
       "       [0.43649585, 0.4441881 , 0.47183103, ..., 0.45149853, 0.51579474,\n",
       "        0.48275167],\n",
       "       [0.44341352, 0.44704606, 0.50158395, ..., 0.48749398, 0.4846492 ,\n",
       "        0.48883265]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_Intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.169 0.8435 ... 1.048 0.8761</div><input id='attrs-6d8931d2-3331-4589-9fd6-c9b2feb1cf75' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6d8931d2-3331-4589-9fd6-c9b2feb1cf75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb985b5c-8628-432e-98f5-db373e262658' class='xr-var-data-in' type='checkbox'><label for='data-bb985b5c-8628-432e-98f5-db373e262658' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.16879337, 0.84353258, 0.48661904, ..., 0.7377904 , 0.77450359,\n",
       "        0.89134312],\n",
       "       [0.97409267, 0.77389269, 0.62319015, ..., 0.80713531, 0.84351926,\n",
       "        0.66933603],\n",
       "       [0.97394367, 0.8642123 , 1.18944807, ..., 1.08853351, 1.05530372,\n",
       "        0.78965763],\n",
       "       [1.01955545, 0.43312221, 0.59671324, ..., 0.87915822, 1.04830915,\n",
       "        0.87610369]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_Intercept_std</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5637 0.7251 ... 0.6267 0.3995</div><input id='attrs-73a14c8d-f855-401f-a4af-dc30e6f560e4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-73a14c8d-f855-401f-a4af-dc30e6f560e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58187b3a-3adf-407b-8166-c3e865697cd4' class='xr-var-data-in' type='checkbox'><label for='data-58187b3a-3adf-407b-8166-c3e865697cd4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.56373046, 0.72507371, 0.9084431 , ..., 0.29282141, 0.26377892,\n",
       "        0.43811668],\n",
       "       [0.31069692, 0.49118274, 0.76252967, ..., 0.346719  , 0.33574332,\n",
       "        0.72669514],\n",
       "       [0.39255904, 0.4531049 , 0.32108143, ..., 0.8513787 , 0.42123644,\n",
       "        0.49546036],\n",
       "       [0.59821042, 0.3023846 , 0.89546912, ..., 0.3460406 , 0.62672104,\n",
       "        0.39950002]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_Intercept_subj.0</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8891 0.8076 ... 0.3761 0.3417</div><input id='attrs-946969c5-4259-42a7-9158-7cdc6e5c7ed1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-946969c5-4259-42a7-9158-7cdc6e5c7ed1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85df904c-142f-43f7-9bad-40458a7c95b9' class='xr-var-data-in' type='checkbox'><label for='data-85df904c-142f-43f7-9bad-40458a7c95b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.88906575, 0.80760755, 0.40844506, ..., 0.51682124, 0.42431411,\n",
       "        0.53948235],\n",
       "       [0.14433822, 0.44682114, 0.44042649, ..., 0.3319243 , 0.33356908,\n",
       "        0.3690915 ],\n",
       "       [0.63202979, 0.56943365, 0.53415394, ..., 0.34345279, 0.33139508,\n",
       "        0.25853887],\n",
       "       [0.05642016, 0.16669835, 0.25845176, ..., 0.34577658, 0.37607453,\n",
       "        0.3416734 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_Intercept_subj.1</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.562 1.132 1.114 ... 0.9582 1.019</div><input id='attrs-916b7711-e8f6-4cd0-bf7a-b6af0a48043d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-916b7711-e8f6-4cd0-bf7a-b6af0a48043d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b54fce9-a613-4203-92a7-479be15c3087' class='xr-var-data-in' type='checkbox'><label for='data-3b54fce9-a613-4203-92a7-479be15c3087' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.56182047, 1.13230444, 1.11363808, ..., 0.87279341, 1.07123017,\n",
       "        1.03810083],\n",
       "       [0.81871629, 0.8629514 , 1.12856316, ..., 0.8549642 , 0.84600541,\n",
       "        0.9957263 ],\n",
       "       [1.1517621 , 1.0929747 , 1.15072549, ..., 0.93345566, 0.91334782,\n",
       "        0.86511039],\n",
       "       [0.66583286, 0.95962208, 1.08563346, ..., 0.8792256 , 0.95823194,\n",
       "        1.01915402]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_Intercept_subj.2</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.276 1.166 ... 0.7455 0.7835</div><input id='attrs-ca6cc011-8362-4dd1-bd98-fc0d453ea30f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca6cc011-8362-4dd1-bd98-fc0d453ea30f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e56b6e25-cc39-4609-b13f-3e1197d0b51f' class='xr-var-data-in' type='checkbox'><label for='data-e56b6e25-cc39-4609-b13f-3e1197d0b51f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.27614211, 1.16603499, 0.97465999, ..., 0.86532779, 0.90357077,\n",
       "        0.93451601],\n",
       "       [0.85978876, 0.8132229 , 0.79240825, ..., 0.74210794, 0.69178167,\n",
       "        0.86473715],\n",
       "       [1.2831822 , 1.21434839, 0.99497873, ..., 0.88934985, 0.8610321 ,\n",
       "        0.85105974],\n",
       "       [1.05348258, 0.85447107, 1.1472289 , ..., 0.83533809, 0.74554294,\n",
       "        0.78345407]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_Intercept_subj.3</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7252 0.7738 ... 0.3442 0.3843</div><input id='attrs-72cd2628-236d-4e23-a617-6589f29bd5eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72cd2628-236d-4e23-a617-6589f29bd5eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e00e9be-407c-4cd4-be52-524cc84490bf' class='xr-var-data-in' type='checkbox'><label for='data-2e00e9be-407c-4cd4-be52-524cc84490bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.72516045, 0.77381482, 0.45101347, ..., 0.44938082, 0.4823722 ,\n",
       "        0.57018236],\n",
       "       [0.26865686, 0.36655992, 0.57035889, ..., 0.40973428, 0.41600608,\n",
       "        0.32685822],\n",
       "       [0.65952245, 0.67763756, 0.60136406, ..., 0.42011972, 0.36935963,\n",
       "        0.3794509 ],\n",
       "       [0.45734457, 0.44262186, 0.44738263, ..., 0.39109554, 0.34416566,\n",
       "        0.38431843]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_Intercept_subj.4</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.815 1.563 1.56 ... 1.097 1.088</div><input id='attrs-0715e469-596b-4d11-8d40-f5e737ec4c0b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0715e469-596b-4d11-8d40-f5e737ec4c0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4b72f98-877f-4085-9a4b-9a936c8263c5' class='xr-var-data-in' type='checkbox'><label for='data-e4b72f98-877f-4085-9a4b-9a936c8263c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.8146939 , 1.5630474 , 1.55955052, ..., 1.42228706, 1.15159345,\n",
       "        1.40191605],\n",
       "       [1.51354925, 1.5378198 , 1.48495699, ..., 1.35714752, 1.12159098,\n",
       "        1.19268571],\n",
       "       [1.55348412, 1.53980956, 1.39494038, ..., 1.51127889, 1.28498867,\n",
       "        1.44340199],\n",
       "       [0.99908327, 1.15353571, 1.33542201, ..., 1.34939043, 1.09685976,\n",
       "        1.08809736]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_C(conf, Treatment(&#x27;LC&#x27;))[T.HC]</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.004 -0.8982 ... -0.3739 -0.4013</div><input id='attrs-4d3e5868-6787-438b-b104-97d8bc837639' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d3e5868-6787-438b-b104-97d8bc837639' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2de90c7d-6802-4985-9ee9-2843600edd15' class='xr-var-data-in' type='checkbox'><label for='data-2de90c7d-6802-4985-9ee9-2843600edd15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-1.00376485, -0.89822199, -0.68753924, ..., -0.56298029,\n",
       "        -0.54857853, -0.56121333],\n",
       "       [-0.27230759, -0.27050897, -0.5456006 , ..., -0.48357478,\n",
       "        -0.43979016, -0.44770615],\n",
       "       [-0.66545514, -0.60921186, -0.63621705, ..., -0.51898975,\n",
       "        -0.5079039 , -0.49648014],\n",
       "       [-0.18854233, -0.53149342, -0.56065472, ..., -0.36859904,\n",
       "        -0.37385271, -0.40134672]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8d77ad60-da82-4f8d-9940-1082a3cf858c' class='xr-section-summary-in' type='checkbox'  ><label for='section-8d77ad60-da82-4f8d-9940-1082a3cf858c' 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-da8d1c97-0b9b-4e4f-a55c-9c53a527ce73' class='xr-index-data-in' type='checkbox'/><label for='index-da8d1c97-0b9b-4e4f-a55c-9c53a527ce73' 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-a2363ac7-3956-48fa-aa03-5659a63b92bf' class='xr-index-data-in' type='checkbox'/><label for='index-a2363ac7-3956-48fa-aa03-5659a63b92bf' 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",
       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d7a26fc3-4b83-45aa-a042-8be718d6b59a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d7a26fc3-4b83-45aa-a042-8be718d6b59a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_posterior_predictiveca4d87b0-0d5b-43b1-9fe9-2c1bb4d3a8b6\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_posterior_predictiveca4d87b0-0d5b-43b1-9fe9-2c1bb4d3a8b6\" class = \"xr-section-summary\">posterior_predictive</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",
       "<path d=\"M23 26h-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 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",
       "}\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; Size: 114MB\n",
       "Dimensions:   (chain: 4, draw: 500, obs_id: 1427)\n",
       "Coordinates:\n",
       "  * chain     (chain) int64 32B 0 1 2 3\n",
       "  * draw      (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
       "  * obs_id    (obs_id) int64 11kB 0 1 2 3 4 5 ... 1421 1422 1423 1424 1425 1426\n",
       "    node      (chain, draw, obs_id) object 23MB &#x27;wfpt.0&#x27; &#x27;wfpt.0&#x27; ... &#x27;wfpt.4&#x27;\n",
       "    trial     (chain, draw, obs_id) int64 23MB 0 1 2 3 4 ... 278 279 280 281 282\n",
       "    subj_idx  (chain, draw, obs_id) int64 23MB 0 0 0 0 0 0 0 0 ... 4 4 4 4 4 4 4\n",
       "Data variables:\n",
       "    rt        (chain, draw, obs_id) float64 23MB -2.84 0.9768 ... 0.6258 0.7068\n",
       "    response  (chain, draw, obs_id) int64 23MB 0 1 0 1 0 0 1 1 ... 0 1 1 1 1 1 1</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-b59df279-7123-479a-a9ba-00eaf0c9283b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b59df279-7123-479a-a9ba-00eaf0c9283b' 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>: 500</li><li><span class='xr-has-index'>obs_id</span>: 1427</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7ad64dc4-6ca2-4ab3-b1e6-7f38c1303da5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7ad64dc4-6ca2-4ab3-b1e6-7f38c1303da5' 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-2ee6aee6-687e-45dc-88b4-f4b9102f9646' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2ee6aee6-687e-45dc-88b4-f4b9102f9646' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a4b8021-697a-493f-a418-6f1865329e11' class='xr-var-data-in' type='checkbox'><label for='data-9a4b8021-697a-493f-a418-6f1865329e11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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 ... 495 496 497 498 499</div><input id='attrs-75f3d262-2d6c-4adc-ad36-425e8a024816' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-75f3d262-2d6c-4adc-ad36-425e8a024816' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc3e82a5-f0d3-42e8-a1e6-3656e169a2c5' class='xr-var-data-in' type='checkbox'><label for='data-fc3e82a5-f0d3-42e8-a1e6-3656e169a2c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, ..., 497, 498, 499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs_id</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1423 1424 1425 1426</div><input id='attrs-db26153b-b729-4e09-9fc0-082b0acb3bfb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-db26153b-b729-4e09-9fc0-082b0acb3bfb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c76db100-e1cc-4b4b-adbc-1724ba3e60ac' class='xr-var-data-in' type='checkbox'><label for='data-c76db100-e1cc-4b4b-adbc-1724ba3e60ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, ..., 1424, 1425, 1426])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>node</span></div><div class='xr-var-dims'>(chain, draw, obs_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;wfpt.0&#x27; &#x27;wfpt.0&#x27; ... &#x27;wfpt.4&#x27;</div><input id='attrs-546714cc-97a9-4829-9e6e-fe2ecb687989' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-546714cc-97a9-4829-9e6e-fe2ecb687989' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42d4d754-6f91-4b0d-b4b6-63f26447e6eb' class='xr-var-data-in' type='checkbox'><label for='data-42d4d754-6f91-4b0d-b4b6-63f26447e6eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        ...,\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;]],\n",
       "\n",
       "       [[&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "...\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;]],\n",
       "\n",
       "       [[&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        ...,\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;]]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>trial</span></div><div class='xr-var-dims'>(chain, draw, obs_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 278 279 280 281 282</div><input id='attrs-a7afd9bb-a95f-46bf-8d03-535d566300e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a7afd9bb-a95f-46bf-8d03-535d566300e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f4b2e19-1b75-4136-9ccc-84ef058c2ead' class='xr-var-data-in' type='checkbox'><label for='data-1f4b2e19-1b75-4136-9ccc-84ef058c2ead' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        ...,\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282]],\n",
       "\n",
       "       [[  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        ...,\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282]],\n",
       "\n",
       "       [[  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        ...,\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282]],\n",
       "\n",
       "       [[  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        ...,\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>subj_idx</span></div><div class='xr-var-dims'>(chain, draw, obs_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 4 4 4 4 4 4 4 4</div><input id='attrs-1bc7bdb4-bb79-472f-9cde-0202b916dd69' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1bc7bdb4-bb79-472f-9cde-0202b916dd69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-696c4733-03ac-4b7e-a2cf-e79132b5abce' class='xr-var-data-in' type='checkbox'><label for='data-696c4733-03ac-4b7e-a2cf-e79132b5abce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4]],\n",
       "\n",
       "       [[0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4]],\n",
       "\n",
       "       [[0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4]],\n",
       "\n",
       "       [[0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5f3254cb-f883-4662-8435-c16320913a82' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5f3254cb-f883-4662-8435-c16320913a82' class='xr-section-summary' >Data variables: <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>rt</span></div><div class='xr-var-dims'>(chain, draw, obs_id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.84 0.9768 ... 0.6258 0.7068</div><input id='attrs-de01e8c6-418d-428b-adbc-8799b682c4cb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de01e8c6-418d-428b-adbc-8799b682c4cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcd46438-1d36-449e-8568-d4dab3724cae' class='xr-var-data-in' type='checkbox'><label for='data-dcd46438-1d36-449e-8568-d4dab3724cae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-2.83977342,  0.97675288, -1.64474773, ...,  0.49733865,\n",
       "          0.80933607,  1.08933246],\n",
       "        [-2.86949396,  0.87547898, -1.64147449, ...,  0.61667877,\n",
       "          0.9846741 ,  0.71067756],\n",
       "        [-2.79144049,  1.04141569, -0.89541757, ...,  0.64792907,\n",
       "          0.50092953, -0.52492958],\n",
       "        ...,\n",
       "        [-2.0445838 ,  1.46655965,  1.81257296, ...,  0.50497437,\n",
       "          0.70797318,  0.44097427],\n",
       "        [-2.02013707,  1.61211801,  1.82212782, ...,  0.58419877,\n",
       "          0.63119835,  0.46319854],\n",
       "        [-2.10684395,  1.52781987,  1.87183297, ...,  0.62054741,\n",
       "          0.47254735,  0.73854589]],\n",
       "\n",
       "       [[-2.08736968,  2.60639381,  1.80235624, ...,  1.0847671 ,\n",
       "          0.62877297, -0.93376905],\n",
       "        [-2.18755817,  1.43953133,  1.79854   , ..., -0.51071942,\n",
       "          0.54471946,  0.82571656],\n",
       "        [-2.03657866,  1.56255662,  1.83156919, ...,  0.42437586,\n",
       "          0.55737615, -1.08537006],\n",
       "...\n",
       "        [-4.10987568, -1.09494734, -2.66498041, ...,  1.07203531,\n",
       "          0.62304103,  0.99903619],\n",
       "        [-4.10562181, -1.09269369, -2.65872669, ...,  0.82799941,\n",
       "          0.70900095,  0.4870021 ],\n",
       "        [-3.93529248, -1.3323462 , -2.58338881, ...,  0.6025241 ,\n",
       "          0.79852158, -2.22355533]],\n",
       "\n",
       "       [[-4.2150445 , -1.12511933, -2.73315191, ...,  1.231745  ,\n",
       "         -1.10474658, -0.89174932],\n",
       "        [-3.88914657, -0.96520042, -1.33919573, ...,  1.19352829,\n",
       "         -0.67053503,  0.64853531],\n",
       "        [-3.89922285, -0.9612776 , -1.33127284, ...,  0.48606324,\n",
       "         -1.29005456,  1.09105706],\n",
       "        ...,\n",
       "        [-1.13359666,  0.9435991 , -2.24162507, ...,  0.92097741,\n",
       "          0.45898128,  0.55298144],\n",
       "        [-1.14845657,  0.95345902, -2.29648685, ...,  0.56463867,\n",
       "          0.63463801,  0.45063844],\n",
       "        [-1.12434459,  1.04034555, -2.19836807, ...,  0.4318068 ,\n",
       "          0.62580693,  0.70680583]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>response</span></div><div class='xr-var-dims'>(chain, draw, obs_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 0 1 0 0 1 1 ... 1 0 1 1 1 1 1 1</div><input id='attrs-f9d7f7c7-c7b5-48cf-828c-5582732b4ffc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f9d7f7c7-c7b5-48cf-828c-5582732b4ffc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c035fe8-128e-43bf-ab5e-e0650d59c2a3' class='xr-var-data-in' type='checkbox'><label for='data-6c035fe8-128e-43bf-ab5e-e0650d59c2a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, 0, ..., 1, 1, 1],\n",
       "        [0, 1, 0, ..., 1, 1, 1],\n",
       "        [0, 1, 0, ..., 1, 1, 0],\n",
       "        ...,\n",
       "        [0, 1, 1, ..., 1, 1, 1],\n",
       "        [0, 1, 1, ..., 1, 1, 1],\n",
       "        [0, 1, 1, ..., 1, 1, 1]],\n",
       "\n",
       "       [[0, 1, 1, ..., 1, 1, 0],\n",
       "        [0, 1, 1, ..., 0, 1, 1],\n",
       "        [0, 1, 1, ..., 1, 1, 0],\n",
       "        ...,\n",
       "        [1, 1, 0, ..., 0, 1, 1],\n",
       "        [1, 1, 0, ..., 1, 1, 1],\n",
       "        [1, 1, 0, ..., 1, 1, 1]],\n",
       "\n",
       "       [[1, 1, 0, ..., 1, 1, 0],\n",
       "        [1, 1, 0, ..., 0, 0, 1],\n",
       "        [1, 1, 0, ..., 1, 1, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 1, 1, 1],\n",
       "        [0, 0, 0, ..., 1, 1, 1],\n",
       "        [0, 0, 0, ..., 1, 1, 0]],\n",
       "\n",
       "       [[0, 0, 0, ..., 1, 0, 0],\n",
       "        [0, 0, 0, ..., 1, 0, 1],\n",
       "        [0, 0, 0, ..., 1, 0, 1],\n",
       "        ...,\n",
       "        [0, 1, 0, ..., 1, 1, 1],\n",
       "        [0, 1, 0, ..., 1, 1, 1],\n",
       "        [0, 1, 0, ..., 1, 1, 1]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2fb81b08-54b3-476f-8d6a-4576507b34fb' class='xr-section-summary-in' type='checkbox'  ><label for='section-2fb81b08-54b3-476f-8d6a-4576507b34fb' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ba5b21b9-6b65-4433-a583-ea374aa2ef5d' class='xr-index-data-in' type='checkbox'/><label for='index-ba5b21b9-6b65-4433-a583-ea374aa2ef5d' 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-efb0a602-7e36-48b6-9552-c6bb3c15d858' class='xr-index-data-in' type='checkbox'/><label for='index-efb0a602-7e36-48b6-9552-c6bb3c15d858' 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",
       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>obs_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f1e54f6d-c7fa-4782-8ab9-1dfc7460aa54' class='xr-index-data-in' type='checkbox'/><label for='index-f1e54f6d-c7fa-4782-8ab9-1dfc7460aa54' 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",
       "       1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;obs_id&#x27;, length=1427))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-501dd078-6285-41de-a635-1ca46c1c0af2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-501dd078-6285-41de-a635-1ca46c1c0af2' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_log_likelihood0a514fb7-d1bc-496f-87c2-7db8200acda9\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_log_likelihood0a514fb7-d1bc-496f-87c2-7db8200acda9\" class = \"xr-section-summary\">log_likelihood</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",
       "<path d=\"M23 26h-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 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",
       "}\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; Size: 91MB\n",
       "Dimensions:   (chain: 4, draw: 500, obs_id: 1427)\n",
       "Coordinates:\n",
       "  * chain     (chain) int64 32B 0 1 2 3\n",
       "  * draw      (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n",
       "  * obs_id    (obs_id) int64 11kB 0 1 2 3 4 5 ... 1421 1422 1423 1424 1425 1426\n",
       "    node      (chain, draw, obs_id) object 23MB &#x27;wfpt.0&#x27; &#x27;wfpt.0&#x27; ... &#x27;wfpt.4&#x27;\n",
       "    trial     (chain, draw, obs_id) int64 23MB 0 1 2 3 4 ... 278 279 280 281 282\n",
       "    subj_idx  (chain, draw, obs_id) int64 23MB 0 0 0 0 0 0 0 0 ... 4 4 4 4 4 4 4\n",
       "Data variables:\n",
       "    log_lik   (chain, draw, obs_id) float64 23MB -1.369 -0.9652 ... 0.1809</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-cd14ee51-19bf-44c4-a470-7816552d91aa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cd14ee51-19bf-44c4-a470-7816552d91aa' 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>: 500</li><li><span class='xr-has-index'>obs_id</span>: 1427</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-195fd917-0e83-4415-aa36-c6609598a2c0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-195fd917-0e83-4415-aa36-c6609598a2c0' 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-af29793e-0f06-40ef-936c-720617978191' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af29793e-0f06-40ef-936c-720617978191' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-031a5d5d-5dd2-47a5-ac7d-1f4448116d8f' class='xr-var-data-in' type='checkbox'><label for='data-031a5d5d-5dd2-47a5-ac7d-1f4448116d8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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 ... 495 496 497 498 499</div><input id='attrs-b14cb7d3-262b-434f-a309-7b5015348d02' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b14cb7d3-262b-434f-a309-7b5015348d02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db838ee5-6910-45e0-b442-ad2d8dd4f6fc' class='xr-var-data-in' type='checkbox'><label for='data-db838ee5-6910-45e0-b442-ad2d8dd4f6fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, ..., 497, 498, 499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs_id</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1423 1424 1425 1426</div><input id='attrs-064e018b-8fce-4055-b92a-2db9947475ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-064e018b-8fce-4055-b92a-2db9947475ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-562ee8c3-d05a-4c0c-9c68-52a21cf1251e' class='xr-var-data-in' type='checkbox'><label for='data-562ee8c3-d05a-4c0c-9c68-52a21cf1251e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, ..., 1424, 1425, 1426])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>node</span></div><div class='xr-var-dims'>(chain, draw, obs_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;wfpt.0&#x27; &#x27;wfpt.0&#x27; ... &#x27;wfpt.4&#x27;</div><input id='attrs-56d679b8-d708-40e1-9a96-c2947023c2f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56d679b8-d708-40e1-9a96-c2947023c2f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e38bb3a3-f2ea-4aab-8161-703524818a96' class='xr-var-data-in' type='checkbox'><label for='data-e38bb3a3-f2ea-4aab-8161-703524818a96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        ...,\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;]],\n",
       "\n",
       "       [[&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "...\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;]],\n",
       "\n",
       "       [[&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        ...,\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;],\n",
       "        [&#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, &#x27;wfpt.0&#x27;, ..., &#x27;wfpt.4&#x27;, &#x27;wfpt.4&#x27;,\n",
       "         &#x27;wfpt.4&#x27;]]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>trial</span></div><div class='xr-var-dims'>(chain, draw, obs_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 278 279 280 281 282</div><input id='attrs-32f6d7ff-9e7c-4b68-990c-0e8d8de5dfa2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-32f6d7ff-9e7c-4b68-990c-0e8d8de5dfa2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dbb075e-5e06-48ff-8187-3c7b7e72b283' class='xr-var-data-in' type='checkbox'><label for='data-4dbb075e-5e06-48ff-8187-3c7b7e72b283' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        ...,\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282]],\n",
       "\n",
       "       [[  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        ...,\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282]],\n",
       "\n",
       "       [[  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        ...,\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282]],\n",
       "\n",
       "       [[  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        ...,\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282],\n",
       "        [  0,   1,   2, ..., 280, 281, 282]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>subj_idx</span></div><div class='xr-var-dims'>(chain, draw, obs_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 4 4 4 4 4 4 4 4</div><input id='attrs-74359305-a4cb-4db7-a3de-211592e9dca9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-74359305-a4cb-4db7-a3de-211592e9dca9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3af64be6-82cc-4b4a-acc1-bdef9576c0ca' class='xr-var-data-in' type='checkbox'><label for='data-3af64be6-82cc-4b4a-acc1-bdef9576c0ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4]],\n",
       "\n",
       "       [[0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4]],\n",
       "\n",
       "       [[0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4]],\n",
       "\n",
       "       [[0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4],\n",
       "        [0, 0, 0, ..., 4, 4, 4]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-62668fb6-7b58-454f-a5a3-6ebf591c12b8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-62668fb6-7b58-454f-a5a3-6ebf591c12b8' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>log_lik</span></div><div class='xr-var-dims'>(chain, draw, obs_id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.369 -0.9652 ... -1.177 0.1809</div><input id='attrs-b77629b9-f87d-41ad-a8a2-f553f46eb192' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b77629b9-f87d-41ad-a8a2-f553f46eb192' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d3fbfe9-02f9-4598-b9d3-e4ede495fb13' class='xr-var-data-in' type='checkbox'><label for='data-6d3fbfe9-02f9-4598-b9d3-e4ede495fb13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-1.36936331, -0.96523836, -1.31313285, ...,  0.00980195,\n",
       "         -1.82065202, -0.06791229],\n",
       "        [-1.34000566, -1.0158102 , -1.24986242, ..., -0.06735888,\n",
       "         -1.58470035, -0.08660615],\n",
       "        [-1.53830804, -1.18412872, -1.45359132, ..., -0.00485062,\n",
       "         -1.47595236, -0.03103679],\n",
       "        ...,\n",
       "        [-1.32188478, -1.16618915, -1.22190645, ..., -0.08697495,\n",
       "         -1.62813944,  0.2503369 ],\n",
       "        [-1.37619723, -1.22157687, -1.26542374, ..., -0.17660084,\n",
       "         -1.25705655,  0.16830439],\n",
       "        [-1.2174238 , -1.10194318, -1.12982038, ..., -0.1174647 ,\n",
       "         -1.47880006,  0.29840696]],\n",
       "\n",
       "       [[-1.32530409, -1.4176641 , -1.25653719, ..., -0.03356315,\n",
       "         -1.56836967,  0.2842955 ],\n",
       "        [-1.01244832, -1.18982972, -0.9263132 , ..., -0.02591583,\n",
       "         -1.50876923,  0.28348812],\n",
       "        [-1.26612306, -1.23276069, -1.10942501, ..., -0.04647322,\n",
       "         -1.49189114,  0.18323108],\n",
       "...\n",
       "        [-1.36806905, -1.26342992, -1.23215672, ..., -0.026489  ,\n",
       "         -1.50481584,  0.16623625],\n",
       "        [-1.35065687, -1.28692692, -1.1978263 , ..., -0.18202672,\n",
       "         -1.4668591 ,  0.38865273],\n",
       "        [-1.34761804, -1.31115603, -1.18610439, ..., -0.08566464,\n",
       "         -1.57190877,  0.29745673]],\n",
       "\n",
       "       [[-1.20193542, -1.45630551, -1.06389121, ..., -0.17802934,\n",
       "         -1.00420663,  0.04570829],\n",
       "        [-1.46768539, -1.36342261, -1.3002363 , ..., -0.13399391,\n",
       "         -1.1221836 , -0.03206839],\n",
       "        [-1.44823833, -1.28284116, -1.32232955, ..., -0.13216415,\n",
       "         -1.58897464,  0.25023807],\n",
       "        ...,\n",
       "        [-1.26841946, -1.27647836, -1.1622047 , ..., -0.07455104,\n",
       "         -1.57315934,  0.2591631 ],\n",
       "        [-1.22401134, -1.24306299, -1.11983333, ..., -0.2006653 ,\n",
       "         -1.27725168,  0.18582521],\n",
       "        [-1.25486464, -1.24909826, -1.1528491 , ..., -0.15717257,\n",
       "         -1.17653883,  0.18093093]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5d994dca-17ad-404e-a154-b7c65d7272de' class='xr-section-summary-in' type='checkbox'  ><label for='section-5d994dca-17ad-404e-a154-b7c65d7272de' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-541ed1ff-1d87-4990-aa01-d2cb6887e55e' class='xr-index-data-in' type='checkbox'/><label for='index-541ed1ff-1d87-4990-aa01-d2cb6887e55e' 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-a18d1831-ca73-42a8-8e4c-c612c3fbd2e5' class='xr-index-data-in' type='checkbox'/><label for='index-a18d1831-ca73-42a8-8e4c-c612c3fbd2e5' 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",
       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>obs_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3bdfdb29-0da9-4047-9936-c9abfac111c2' class='xr-index-data-in' type='checkbox'/><label for='index-3bdfdb29-0da9-4047-9936-c9abfac111c2' 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",
       "       1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;obs_id&#x27;, length=1427))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c448bca7-fac3-4d58-9834-226ecb19e11f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c448bca7-fac3-4d58-9834-226ecb19e11f' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_prior41d7137f-5a7c-4969-bf70-d0fa4fa7df59\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_prior41d7137f-5a7c-4969-bf70-d0fa4fa7df59\" 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",
       "<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",
       "<path d=\"M23 26h-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 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",
       "}\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; Size: 480kB\n",
       "Dimensions:                           (chain: 1, draw: 2000)\n",
       "Coordinates:\n",
       "  * chain                             (chain) int64 8B 0\n",
       "  * draw                              (draw) int64 16kB 0 1 2 ... 1997 1998 1999\n",
       "Data variables: (12/29)\n",
       "    a                                 (chain, draw) float64 16kB 1.346 ... 1.975\n",
       "    a_std                             (chain, draw) float64 16kB 1.631 ... 0....\n",
       "    a_subj.0                          (chain, draw) float64 16kB 0.7649 ... 1...\n",
       "    a_subj.1                          (chain, draw) float64 16kB 2.266 ... 2.597\n",
       "    a_subj.2                          (chain, draw) float64 16kB 2.98 ... 3.015\n",
       "    a_subj.3                          (chain, draw) float64 16kB 2.611 ... 1.78\n",
       "    ...                                ...\n",
       "    v_Intercept_subj.0                (chain, draw) float64 16kB -1.459 ... -...\n",
       "    v_Intercept_subj.1                (chain, draw) float64 16kB -1.343 ... -...\n",
       "    v_Intercept_subj.2                (chain, draw) float64 16kB -2.784 ... -...\n",
       "    v_Intercept_subj.3                (chain, draw) float64 16kB -0.7562 ... ...\n",
       "    v_Intercept_subj.4                (chain, draw) float64 16kB -4.453 ... -...\n",
       "    v_C(conf, Treatment(&#x27;LC&#x27;))[T.HC]  (chain, draw) float64 16kB 1.045 ... -1...\n",
       "Attributes:\n",
       "    created_at:     2024-06-20T14:27:49.731033\n",
       "    arviz_version:  0.15.1</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-54032dca-1832-4d1a-9ef3-5a347625642f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-54032dca-1832-4d1a-9ef3-5a347625642f' 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>: 1</li><li><span class='xr-has-index'>draw</span>: 2000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-71b79cb8-9453-49c6-aa93-66785dbc9605' class='xr-section-summary-in' type='checkbox'  checked><label for='section-71b79cb8-9453-49c6-aa93-66785dbc9605' 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</div><input id='attrs-e71d0cea-840c-49ab-921b-ca8d59e2823f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e71d0cea-840c-49ab-921b-ca8d59e2823f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5171bc58-750f-477d-b076-472a588d5040' class='xr-var-data-in' type='checkbox'><label for='data-5171bc58-750f-477d-b076-472a588d5040' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1996 1997 1998 1999</div><input id='attrs-140d3ddc-9d62-4edf-8af2-23a925e663db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-140d3ddc-9d62-4edf-8af2-23a925e663db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8f7fb7a-968c-49e8-8219-7b7fd6133d16' class='xr-var-data-in' type='checkbox'><label for='data-b8f7fb7a-968c-49e8-8219-7b7fd6133d16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1997, 1998, 1999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8a4f7001-c885-4e56-a745-94da8f550296' class='xr-section-summary-in' type='checkbox'  ><label for='section-8a4f7001-c885-4e56-a745-94da8f550296' class='xr-section-summary' >Data variables: <span>(29)</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>a</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.346 1.377 1.506 ... 1.056 1.975</div><input id='attrs-e8d34c59-2871-4dd7-8efb-3bda36c28251' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e8d34c59-2871-4dd7-8efb-3bda36c28251' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a34aa22-2e10-45f0-9009-0d32d99a35d6' class='xr-var-data-in' type='checkbox'><label for='data-7a34aa22-2e10-45f0-9009-0d32d99a35d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.34628394, 1.37664985, 1.50627311, ..., 2.63671138, 1.05627249,\n",
       "        1.97491323]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_std</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.631 3.304 0.9919 ... 2.443 0.4472</div><input id='attrs-a2f0ad75-0069-4222-92d8-266a98aaa72b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2f0ad75-0069-4222-92d8-266a98aaa72b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce3ab3cf-e62f-4bdd-9e22-7d76f3cdd05a' class='xr-var-data-in' type='checkbox'><label for='data-ce3ab3cf-e62f-4bdd-9e22-7d76f3cdd05a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.63102378, 3.30440699, 0.99194573, ..., 1.20624497, 2.44308513,\n",
       "        0.44719066]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_subj.0</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7649 1.824 1.833 ... 2.028 1.892</div><input id='attrs-cb8fc249-4c1e-4e7e-8ea5-263041a0116f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cb8fc249-4c1e-4e7e-8ea5-263041a0116f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54b7cf4f-9feb-421e-a63e-f89705111015' class='xr-var-data-in' type='checkbox'><label for='data-54b7cf4f-9feb-421e-a63e-f89705111015' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.76492791, 1.82407726, 1.83345928, ..., 1.99501223, 2.02813209,\n",
       "        1.89193753]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_subj.1</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.266 1.469 2.241 ... 2.791 2.597</div><input id='attrs-b7ab480a-7e5f-4d5d-8da5-1007d0ee2a4a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b7ab480a-7e5f-4d5d-8da5-1007d0ee2a4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cb53246-db33-455c-a082-2706e4eb7c88' class='xr-var-data-in' type='checkbox'><label for='data-2cb53246-db33-455c-a082-2706e4eb7c88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2.26586859, 1.46934458, 2.2408885 , ..., 1.18491594, 2.79057334,\n",
       "        2.59684615]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_subj.2</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.98 2.079 1.389 ... 1.675 3.015</div><input id='attrs-b769c41a-660b-46ed-9dce-acc8753fa5b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b769c41a-660b-46ed-9dce-acc8753fa5b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f7978f1-d68b-4985-80af-a4fd26d88137' class='xr-var-data-in' type='checkbox'><label for='data-0f7978f1-d68b-4985-80af-a4fd26d88137' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2.97968   , 2.0791172 , 1.38924323, ..., 2.30359387, 1.6746453 ,\n",
       "        3.01530853]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_subj.3</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.611 1.628 1.366 ... 1.787 1.78</div><input id='attrs-972b7ab0-cd75-48cb-92c3-7c8b725aa08e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-972b7ab0-cd75-48cb-92c3-7c8b725aa08e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7e23461-4cbc-4183-a865-6ff90fd08b69' class='xr-var-data-in' type='checkbox'><label for='data-d7e23461-4cbc-4183-a865-6ff90fd08b69' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2.61075001, 1.62840575, 1.3661333 , ..., 2.05932357, 1.7866715 ,\n",
       "        1.78013656]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a_subj.4</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.703 1.763 1.649 ... 1.708 2.48</div><input id='attrs-bcbcfbbb-6e86-4154-adb6-fb5208b1acc8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bcbcfbbb-6e86-4154-adb6-fb5208b1acc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2112395-3a08-40f5-af1e-279307ef5bc6' class='xr-var-data-in' type='checkbox'><label for='data-f2112395-3a08-40f5-af1e-279307ef5bc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.70261683, 1.76292702, 1.64934561, ..., 1.56292685, 1.70816001,\n",
       "        2.47998081]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1087 0.4532 ... 0.2005 0.2797</div><input id='attrs-6ff43182-b777-41d7-af47-51a3a846c384' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ff43182-b777-41d7-af47-51a3a846c384' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77b08992-f14c-4a1b-ba5f-44c1eaffdd7c' class='xr-var-data-in' type='checkbox'><label for='data-77b08992-f14c-4a1b-ba5f-44c1eaffdd7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.10866028, 0.45324719, 0.0929621 , ..., 0.17630289, 0.20048703,\n",
       "        0.27966667]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_std</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8154 0.437 ... 0.1329 0.3133</div><input id='attrs-be6d012a-975c-46bb-8e4d-6c2c4b01b665' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be6d012a-975c-46bb-8e4d-6c2c4b01b665' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fc1b416-771a-4b50-9085-70c4f3721ce4' class='xr-var-data-in' type='checkbox'><label for='data-5fc1b416-771a-4b50-9085-70c4f3721ce4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.81542203, 0.43702374, 1.70922421, ..., 0.39127977, 0.1329159 ,\n",
       "        0.31333184]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_subj.0</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.101 0.02219 ... 0.8476 0.6623</div><input id='attrs-c95b15de-a058-40c3-ac68-4301bf6a275b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c95b15de-a058-40c3-ac68-4301bf6a275b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1b1d8d5-f89d-43ac-8eb7-0b6b5c1da337' class='xr-var-data-in' type='checkbox'><label for='data-b1b1d8d5-f89d-43ac-8eb7-0b6b5c1da337' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.10098465, 0.02219031, 0.06723426, ..., 0.20132505, 0.84757841,\n",
       "        0.66232225]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_subj.1</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5021 0.2237 ... 0.09215 0.5037</div><input id='attrs-8a8bcc53-547c-4cec-8c7e-297122c1bd96' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8a8bcc53-547c-4cec-8c7e-297122c1bd96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65aad161-9612-45be-9891-26fb9aaab69d' class='xr-var-data-in' type='checkbox'><label for='data-65aad161-9612-45be-9891-26fb9aaab69d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.50211052, 0.22373535, 0.5691789 , ..., 0.45978697, 0.09215172,\n",
       "        0.50367633]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_subj.2</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.02756 0.2519 ... 0.1782 0.16</div><input id='attrs-e5863aa2-7291-4634-a019-383e0264e9d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e5863aa2-7291-4634-a019-383e0264e9d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7fa0ef5-a542-4fa6-9794-1d060a9e075b' class='xr-var-data-in' type='checkbox'><label for='data-a7fa0ef5-a542-4fa6-9794-1d060a9e075b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.02756342, 0.25189147, 0.22633592, ..., 0.10555578, 0.17819636,\n",
       "        0.16003982]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_subj.3</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.07664 0.2042 ... 0.003139 0.3335</div><input id='attrs-9e44488a-94d9-48f1-b923-733953fc8390' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e44488a-94d9-48f1-b923-733953fc8390' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bab5d022-df0e-4d16-af1d-fe155f7cd4a9' class='xr-var-data-in' type='checkbox'><label for='data-bab5d022-df0e-4d16-af1d-fe155f7cd4a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[7.66400412e-02, 2.04221539e-01, 2.28853945e-04, ...,\n",
       "        7.54680485e-01, 3.13879475e-03, 3.33494723e-01]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_subj.4</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.127 0.3876 ... 0.07385 0.004272</div><input id='attrs-bff92896-62ac-43fa-8db9-3a9deec20bf9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bff92896-62ac-43fa-8db9-3a9deec20bf9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4edd624-b122-4c73-876c-3ccd70e665c8' class='xr-var-data-in' type='checkbox'><label for='data-b4edd624-b122-4c73-876c-3ccd70e665c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.12702064, 0.3875975 , 0.35127955, ..., 0.31339672, 0.07384954,\n",
       "        0.0042715 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3163 0.6124 ... 0.5251 0.5947</div><input id='attrs-c59c4210-fe62-4cc1-bbf0-10f392d0528f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c59c4210-fe62-4cc1-bbf0-10f392d0528f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ed2072b-ed40-4e94-8460-771b101ce6b2' class='xr-var-data-in' type='checkbox'><label for='data-5ed2072b-ed40-4e94-8460-771b101ce6b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.31625203, 0.61241978, 0.81510862, ..., 0.68449007, 0.52509097,\n",
       "        0.59473677]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_std</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.04033 0.03069 ... 0.018 0.004044</div><input id='attrs-ad91ce0d-3c9c-418a-bedf-71f1d14681b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad91ce0d-3c9c-418a-bedf-71f1d14681b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-709b097c-9df4-4054-8eb6-d9e2a68dcc24' class='xr-var-data-in' type='checkbox'><label for='data-709b097c-9df4-4054-8eb6-d9e2a68dcc24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.04032743, 0.03068524, 0.00758432, ..., 0.02307575, 0.01799804,\n",
       "        0.00404432]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_subj.0</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5944 0.5942 ... 0.5948 0.5938</div><input id='attrs-fbac50c8-e4c1-4c33-ad67-b3aae23f4456' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fbac50c8-e4c1-4c33-ad67-b3aae23f4456' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b3c70ec-27eb-4816-9de3-bb975c42167b' class='xr-var-data-in' type='checkbox'><label for='data-9b3c70ec-27eb-4816-9de3-bb975c42167b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.59441061, 0.59418556, 0.59462706, ..., 0.59589916, 0.59484098,\n",
       "        0.5937923 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_subj.1</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5942 0.5951 ... 0.5939 0.5948</div><input id='attrs-9d1eaef4-3972-4bf2-b9c7-e08b4cbf16ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d1eaef4-3972-4bf2-b9c7-e08b4cbf16ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd329da0-86e4-46c9-9612-d6f46d30fbea' class='xr-var-data-in' type='checkbox'><label for='data-bd329da0-86e4-46c9-9612-d6f46d30fbea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.59415363, 0.59510102, 0.59547369, ..., 0.5947107 , 0.59387383,\n",
       "        0.59481547]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_subj.2</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5938 0.595 ... 0.5941 0.5959</div><input id='attrs-ab97e655-eef4-4e4e-88b5-b1ef073737b2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ab97e655-eef4-4e4e-88b5-b1ef073737b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d971254f-1840-4875-b6a3-4487aae9757d' class='xr-var-data-in' type='checkbox'><label for='data-d971254f-1840-4875-b6a3-4487aae9757d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.59378729, 0.5950071 , 0.59496599, ..., 0.59558427, 0.59408553,\n",
       "        0.59590256]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_subj.3</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5957 0.5938 ... 0.5942 0.5949</div><input id='attrs-b159f866-ba90-44fc-a99a-93081232a9a4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b159f866-ba90-44fc-a99a-93081232a9a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e60330b-e363-4f62-8af5-17ed30663e76' class='xr-var-data-in' type='checkbox'><label for='data-0e60330b-e363-4f62-8af5-17ed30663e76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.59574097, 0.59380064, 0.59648006, ..., 0.59528372, 0.59423404,\n",
       "        0.59490618]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_subj.4</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5938 0.5948 ... 0.5949 0.5943</div><input id='attrs-69a28dd5-fa90-4553-a481-ee25c4a19c5e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-69a28dd5-fa90-4553-a481-ee25c4a19c5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad81bbac-b6f7-4ca1-b069-57ba755a8523' class='xr-var-data-in' type='checkbox'><label for='data-ad81bbac-b6f7-4ca1-b069-57ba755a8523' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.59384147, 0.59484197, 0.59557078, ..., 0.59396031, 0.59493261,\n",
       "        0.59427525]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_Intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-3.779 -0.07333 ... 5.202 -2.115</div><input id='attrs-3d308421-f0bc-45cd-86d2-33e93f3fc650' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d308421-f0bc-45cd-86d2-33e93f3fc650' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1819f59e-3ff3-426c-a47b-fe7c81ea8684' class='xr-var-data-in' type='checkbox'><label for='data-1819f59e-3ff3-426c-a47b-fe7c81ea8684' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-3.7794223 , -0.07332657, -0.52052452, ..., -2.09095923,\n",
       "         5.20158027, -2.11492987]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_Intercept_std</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.345 1.581 1.852 ... 0.4436 1.201</div><input id='attrs-1465343c-0813-4524-ace0-0ef38545d1fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1465343c-0813-4524-ace0-0ef38545d1fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00edff40-e2d7-4fe9-a451-ecae269b9f05' class='xr-var-data-in' type='checkbox'><label for='data-00edff40-e2d7-4fe9-a451-ecae269b9f05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2.34494005, 1.58095889, 1.85235126, ..., 2.03224597, 0.44360188,\n",
       "        1.20065387]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_Intercept_subj.0</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.459 -1.972 ... -0.3999 -1.917</div><input id='attrs-6cade20e-c67c-429a-aea9-ca8fc8c38be4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6cade20e-c67c-429a-aea9-ca8fc8c38be4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a33c8586-5a80-438f-87b6-e982b46a3db6' class='xr-var-data-in' type='checkbox'><label for='data-a33c8586-5a80-438f-87b6-e982b46a3db6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-1.45942262, -1.97178267, -1.83519571, ..., -1.28837481,\n",
       "        -0.39989603, -1.91698242]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_Intercept_subj.1</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.343 -3.849 ... -1.73 -0.3641</div><input id='attrs-6252a2cb-639a-4093-af0c-0678dd65b966' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6252a2cb-639a-4093-af0c-0678dd65b966' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d1de869-38b3-4b7e-98ae-8f15390def2d' class='xr-var-data-in' type='checkbox'><label for='data-3d1de869-38b3-4b7e-98ae-8f15390def2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-1.34309787, -3.84915676, -2.99584917, ..., -0.4980547 ,\n",
       "        -1.72978467, -0.36408823]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_Intercept_subj.2</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.784 -1.704 ... -2.731 -3.188</div><input id='attrs-7799468c-e1cf-4a28-88c4-0fb1b6d28209' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7799468c-e1cf-4a28-88c4-0fb1b6d28209' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-913883a9-5860-42fb-a5ab-b48d0f8e5631' class='xr-var-data-in' type='checkbox'><label for='data-913883a9-5860-42fb-a5ab-b48d0f8e5631' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-2.78367796, -1.70383549, -3.30666153, ..., -1.47388816,\n",
       "        -2.73104536, -3.18820094]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_Intercept_subj.3</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.7562 -3.448 ... -0.7604 -2.475</div><input id='attrs-4abbfb15-2435-4a56-91b7-fa1b6b2ae5e7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4abbfb15-2435-4a56-91b7-fa1b6b2ae5e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37a0df6e-a270-4df9-b30f-7ec10a2e5a08' class='xr-var-data-in' type='checkbox'><label for='data-37a0df6e-a270-4df9-b30f-7ec10a2e5a08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.75622734, -3.44800426, -3.71397026, ..., -1.52659188,\n",
       "        -0.76043102, -2.47460785]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_Intercept_subj.4</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-4.453 -2.281 ... -2.623 -1.344</div><input id='attrs-c526fcfc-8366-46f5-ba96-ccf2e8ef834d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c526fcfc-8366-46f5-ba96-ccf2e8ef834d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e23fb773-80f8-4263-b428-64d7a7c59108' class='xr-var-data-in' type='checkbox'><label for='data-e23fb773-80f8-4263-b428-64d7a7c59108' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-4.45315536, -2.28089253, -0.00581267, ..., -1.93220479,\n",
       "        -2.62278436, -1.34406864]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_C(conf, Treatment(&#x27;LC&#x27;))[T.HC]</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.045 11.33 9.563 ... -3.901 -18.91</div><input id='attrs-7ebfd0f4-756f-4279-ba46-0df90f4a887e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7ebfd0f4-756f-4279-ba46-0df90f4a887e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47552faa-f5c6-4504-b2e7-8e0209bf5de5' class='xr-var-data-in' type='checkbox'><label for='data-47552faa-f5c6-4504-b2e7-8e0209bf5de5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[  1.0451014 ,  11.32526426,   9.56286015, ...,   1.67352264,\n",
       "         -3.90138754, -18.90785442]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b7267352-fb66-45f0-ae5a-fcc0c72a4fda' class='xr-section-summary-in' type='checkbox'  ><label for='section-b7267352-fb66-45f0-ae5a-fcc0c72a4fda' 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-0bfe1ea3-cfdd-429e-b775-f68fadfbdd8f' class='xr-index-data-in' type='checkbox'/><label for='index-0bfe1ea3-cfdd-429e-b775-f68fadfbdd8f' 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;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-d16788f8-88b9-44d5-8c54-e60437543ed4' class='xr-index-data-in' type='checkbox'/><label for='index-d16788f8-88b9-44d5-8c54-e60437543ed4' 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",
       "       1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=2000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b7b881cc-83ca-4dbe-987d-08b362d3da4f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b7b881cc-83ca-4dbe-987d-08b362d3da4f' class='xr-section-summary' >Attributes: <span>(2)</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>2024-06-20T14:27:49.731033</dd><dt><span>arviz_version :</span></dt><dd>0.15.1</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_observed_data7f4001f6-5ea5-4f3b-bfd8-f2acd3653d15\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_observed_data7f4001f6-5ea5-4f3b-bfd8-f2acd3653d15\" class = \"xr-section-summary\">observed_data</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",
       "<path d=\"M23 26h-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 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",
       "}\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; Size: 108kB\n",
       "Dimensions:   (obs_id: 1427)\n",
       "Coordinates:\n",
       "  * obs_id    (obs_id) int64 11kB 0 1 2 3 4 5 ... 1421 1422 1423 1424 1425 1426\n",
       "    subj_idx  (obs_id) int64 11kB 0 0 0 0 0 0 0 0 0 0 0 ... 4 4 4 4 4 4 4 4 4 4\n",
       "    trial     (obs_id) int64 11kB 0 1 2 3 4 5 6 ... 276 277 278 279 280 281 282\n",
       "Data variables:\n",
       "    index     (obs_id) int64 11kB 0 1 2 3 4 5 ... 1421 1422 1423 1424 1425 1426\n",
       "    stim      (obs_id) object 11kB &#x27;LL&#x27; &#x27;WL&#x27; &#x27;WW&#x27; &#x27;WL&#x27; ... &#x27;WW&#x27; &#x27;WL&#x27; &#x27;WL&#x27; &#x27;LL&#x27;\n",
       "    rt        (obs_id) float32 6kB 1.21 1.63 1.03 2.77 ... 0.803 -0.606 0.499\n",
       "    response  (obs_id) int64 11kB 1 1 1 1 0 1 1 0 1 0 1 ... 0 0 1 1 0 1 1 1 0 1\n",
       "    theta     (obs_id) object 11kB 0.656275122261 ... -0.264081867924\n",
       "    dbs       (obs_id) object 11kB 1 1 1 1 1 1 1 1 1 1 1 ... 0 0 0 0 0 0 0 0 0 0\n",
       "    conf      (obs_id) object 11kB &#x27;HC&#x27; &#x27;LC&#x27; &#x27;HC&#x27; &#x27;LC&#x27; ... &#x27;HC&#x27; &#x27;LC&#x27; &#x27;LC&#x27; &#x27;HC&#x27;</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-ae8baf6c-cc36-4fb6-952b-75ca0274f02a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ae8baf6c-cc36-4fb6-952b-75ca0274f02a' 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'>obs_id</span>: 1427</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b0da65a3-0777-4d3b-a986-6108625db086' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b0da65a3-0777-4d3b-a986-6108625db086' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>obs_id</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1423 1424 1425 1426</div><input id='attrs-bba8e573-d6e9-4849-bef5-22dc737c0bac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bba8e573-d6e9-4849-bef5-22dc737c0bac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a284eaa-e71e-44dc-a6ac-a2c1f0d92d72' class='xr-var-data-in' type='checkbox'><label for='data-5a284eaa-e71e-44dc-a6ac-a2c1f0d92d72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, ..., 1424, 1425, 1426])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>subj_idx</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 4 4 4 4 4 4 4 4</div><input id='attrs-719fcd69-6359-4794-ba7b-d1cdfaaa703c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-719fcd69-6359-4794-ba7b-d1cdfaaa703c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adae9130-c905-4c6d-adae-4dd83cfd9947' class='xr-var-data-in' type='checkbox'><label for='data-adae9130-c905-4c6d-adae-4dd83cfd9947' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, 0, 0, ..., 4, 4, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>trial</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 278 279 280 281 282</div><input id='attrs-226cc9be-b7e3-41d0-a1a2-51ea93e01453' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-226cc9be-b7e3-41d0-a1a2-51ea93e01453' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7964b722-1de0-415b-8507-21fc277b8a55' class='xr-var-data-in' type='checkbox'><label for='data-7964b722-1de0-415b-8507-21fc277b8a55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, ..., 280, 281, 282])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1c85baed-43eb-4f6a-9717-fbaa3ffcbf58' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1c85baed-43eb-4f6a-9717-fbaa3ffcbf58' 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>index</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1423 1424 1425 1426</div><input id='attrs-909d4139-387f-4c61-8cf0-57c2373b3eb7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-909d4139-387f-4c61-8cf0-57c2373b3eb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c374ed2e-e8a9-4e2a-af40-9dcba7027cb4' class='xr-var-data-in' type='checkbox'><label for='data-c374ed2e-e8a9-4e2a-af40-9dcba7027cb4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, ..., 1424, 1425, 1426])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stim</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;LL&#x27; &#x27;WL&#x27; &#x27;WW&#x27; ... &#x27;WL&#x27; &#x27;WL&#x27; &#x27;LL&#x27;</div><input id='attrs-d15de1ea-bd2d-4ddc-a99c-9d7405322e5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d15de1ea-bd2d-4ddc-a99c-9d7405322e5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3808d7e0-27e0-49b8-a83f-2efdae52b53e' class='xr-var-data-in' type='checkbox'><label for='data-3808d7e0-27e0-49b8-a83f-2efdae52b53e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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;LL&#x27;, &#x27;WL&#x27;, &#x27;WW&#x27;, ..., &#x27;WL&#x27;, &#x27;WL&#x27;, &#x27;LL&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rt</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.21 1.63 1.03 ... -0.606 0.499</div><input id='attrs-72595cba-d82b-40d7-bf9b-d47208efec62' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72595cba-d82b-40d7-bf9b-d47208efec62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac143bca-041e-4b3b-92a2-49711abcd525' class='xr-var-data-in' type='checkbox'><label for='data-ac143bca-041e-4b3b-92a2-49711abcd525' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.21 ,  1.63 ,  1.03 , ...,  0.803, -0.606,  0.499], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>response</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 1 1 1 0 1 1 0 ... 1 1 0 1 1 1 0 1</div><input id='attrs-00f903d0-26a2-4011-a68c-4b606e562f17' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-00f903d0-26a2-4011-a68c-4b606e562f17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2398717-0465-4d1f-bbbf-860b44cec14f' class='xr-var-data-in' type='checkbox'><label for='data-b2398717-0465-4d1f-bbbf-860b44cec14f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, 1, 1, ..., 1, 0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>theta</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.656275122261 ... -0.264081867924</div><input id='attrs-502e83e7-3dc0-4d9a-bd08-d77ca3b4ab1c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-502e83e7-3dc0-4d9a-bd08-d77ca3b4ab1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15c92493-1a89-4072-9c97-9caf9d08a14c' class='xr-var-data-in' type='checkbox'><label for='data-15c92493-1a89-4072-9c97-9caf9d08a14c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.656275122261, -0.3278886716619999, -0.480284512399, ...,\n",
       "       1.2524802215, -0.6359418409079999, -0.264081867924], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dbs</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 0 0 0 0 0 0 0 0</div><input id='attrs-f53bf794-2c88-4723-8ddc-26755165ecab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f53bf794-2c88-4723-8ddc-26755165ecab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f12ed7b-13bd-4d27-96c1-8e58f718e17f' class='xr-var-data-in' type='checkbox'><label for='data-4f12ed7b-13bd-4d27-96c1-8e58f718e17f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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, 1, 1, ..., 0, 0, 0], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>conf</span></div><div class='xr-var-dims'>(obs_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;HC&#x27; &#x27;LC&#x27; &#x27;HC&#x27; ... &#x27;LC&#x27; &#x27;LC&#x27; &#x27;HC&#x27;</div><input id='attrs-bcd3a64f-2eb9-4e49-bccf-62776805fcd6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bcd3a64f-2eb9-4e49-bccf-62776805fcd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05645e63-81f6-41a6-bf8c-3c5d386a8c49' class='xr-var-data-in' type='checkbox'><label for='data-05645e63-81f6-41a6-bf8c-3c5d386a8c49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink: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;HC&#x27;, &#x27;LC&#x27;, &#x27;HC&#x27;, ..., &#x27;LC&#x27;, &#x27;LC&#x27;, &#x27;HC&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0628f8f5-e802-4e9f-bf18-dee4ea95d667' class='xr-section-summary-in' type='checkbox'  ><label for='section-0628f8f5-e802-4e9f-bf18-dee4ea95d667' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>obs_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3f8c2ccd-e289-489c-81a2-6e797f5d6726' class='xr-index-data-in' type='checkbox'/><label for='index-3f8c2ccd-e289-489c-81a2-6e797f5d6726' 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(RangeIndex(start=0, stop=1427, step=1, name=&#x27;obs_id&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8398f212-0a0f-4734-8b63-86b190230e70' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8398f212-0a0f-4734-8b63-86b190230e70' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div><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",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       ".xr-wrap{width:700px!important;} </style>"
      ],
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> posterior_predictive\n",
       "\t> log_likelihood\n",
       "\t> prior\n",
       "\t> observed_data"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_reg_infdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the InfData consist of five parts, including the prior, posterior, posterior_predictive, log_likelhood, and observed_data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you set the 'save_name' argument to a `str` of the desired file path, hddm will save the model to two files in that location: the model itself (e.g. `hddmregressor_example.hddm`) and the trace of the model (`hddmregressor_example.db`). \n",
    "\n",
    "Additionally, if you use the argument `return_infdata = True`, it convert the hddm model to ArViz InferenceData, the InferenceData will also be saved to the same path (`hddmregressor_example.nc`).\n",
    "\n",
    "As a result, we can easily load the model and InferenceData using the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": ""
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.03 s, sys: 256 ms, total: 3.28 s\n",
      "Wall time: 3.28 s\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "save_name = \"model_fitted/hddmregressor_example\"\n",
    "# loading the inference data is faster than loading the origin hddm class\n",
    "# model_reg = hddm.load(save_name + \".hddm\")\n",
    "model_reg_infdata = az.from_netcdf(save_name + \".nc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can use the InferenceData to calculate WAIC and LOOIC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 952 ms, sys: 3.47 ms, total: 955 ms\n",
      "Wall time: 954 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Computed from 2000 posterior samples and 1427 observations log-likelihood matrix.\n",
       "\n",
       "         Estimate       SE\n",
       "elpd_loo -3818.90   884.58\n",
       "p_loo     2012.18        -\n",
       "\n",
       "There has been a warning during the calculation. Please check the results.\n",
       "------\n",
       "\n",
       "Pareto k diagnostic values:\n",
       "                         Count   Pct.\n",
       "(-Inf, 0.5]   (good)     1408   98.7%\n",
       " (0.5, 0.7]   (ok)          3    0.2%\n",
       "   (0.7, 1]   (bad)         1    0.1%\n",
       "   (1, Inf)   (very bad)   15    1.1%"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time\n",
    "hddm_reg_LOO = az.loo(model_reg_infdata)\n",
    "hddm_reg_LOO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id='toc1_1_3_'></a>[model comparison with LOO](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To determine if the task condition `conf` affects the drift rate. We can compare `model0` and `model_reg` to test the effect of conf on the drift rate.\n",
    "\n",
    "We can use the `az.compare` function to compare the two models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "1"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start converting to InferenceData...\n",
      "Start to calculate pointwise log likelihood...\n",
      "The time of calculation of loglikelihood took 5.997 seconds\n",
      "CPU times: user 3.16 s, sys: 72.6 ms, total: 3.23 s\n",
      "Wall time: 6.88 s\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>rank</th>\n",
       "      <th>elpd_waic</th>\n",
       "      <th>p_waic</th>\n",
       "      <th>elpd_diff</th>\n",
       "      <th>weight</th>\n",
       "      <th>se</th>\n",
       "      <th>dse</th>\n",
       "      <th>warning</th>\n",
       "      <th>scale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model0(baseline)</th>\n",
       "      <td>0</td>\n",
       "      <td>-12366.847830</td>\n",
       "      <td>10529.831055</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5628.680090</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_reg(v ~ conf)</th>\n",
       "      <td>1</td>\n",
       "      <td>-13118.118858</td>\n",
       "      <td>11311.400172</td>\n",
       "      <td>751.271028</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5976.425112</td>\n",
       "      <td>1131.441842</td>\n",
       "      <td>True</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     rank     elpd_waic        p_waic   elpd_diff  weight   \n",
       "model0(baseline)        0 -12366.847830  10529.831055    0.000000     0.5  \\\n",
       "model_reg(v ~ conf)     1 -13118.118858  11311.400172  751.271028     0.5   \n",
       "\n",
       "                              se          dse  warning scale  \n",
       "model0(baseline)     5628.680090     0.000000     True   log  \n",
       "model_reg(v ~ conf)  5976.425112  1131.441842     True   log  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time\n",
    "# To compare models by loo or waic, we should make sure \n",
    "# that the pointwise log-likelihood is calculated. \n",
    "# Another way is to use to_infdata methods with loglike=True,\n",
    "# but ensure that the 'tmp_*.db' file for each chain is in the current directory \n",
    "model0_infdata = model0.to_infdata(loglike=True)\n",
    "\n",
    "compare_dict = {\n",
    "    \"model0(baseline)\": model0_infdata, \n",
    "    \"model_reg(v ~ conf)\": model_reg_infdata}\n",
    "az.compare(compare_dict, ic=\"waic\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the model with varing of drift rate get higher elpd value that indicate it is better than the baseline model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Futhermore, we can plot the posterior predictive checks to check the best model.\n",
    "\n",
    "The below code show how to plot the ppc for each subject like the hddm's `plot_posterior_predictive` (e.g. `model_reg.plot_posterior_predictive(figsize=(14, 10))`)\n",
    "\n",
    "More examples see [dockerHDDM_Workflow](./dockerHDDM_Workflow.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot: xlabel='rt / rt\\n0'>,\n",
       "       <AxesSubplot: xlabel='rt / rt\\n1'>,\n",
       "       <AxesSubplot: xlabel='rt / rt\\n2'>,\n",
       "       <AxesSubplot: xlabel='rt / rt\\n3'>], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2944x552 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_reg_infdata = model_reg_infdata.assign_coords(\n",
    "  obs_id=model_reg_infdata.observed_data.coords['subj_idx'].values, \n",
    "  groups=\"observed_vars\")\n",
    "\n",
    "az.plot_ppc(model_reg_infdata, var_names= 'rt', coords={'obs_id': [0, 1, 2, 3]}, flatten=[])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='toc1_2_'></a>[statistical inference](#toc0_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, statistical inferences are made from the parameter posterior distributions of the optimal model, i.e., the effect of conf on drift rate `v`.\n",
    "\n",
    "Using `az.plot_posterior()`, we can plot the parameter posterior distributions as well as display statistics related to the posterior distributions.\n",
    "\n",
    "Note that by setting the RPOE (region of practical equivalence) we assume that the difference in `v` between the two conditions (LC vs. HC) ranges from -0.2 to 0.2. If the 95% HDI of the posterior distribution of the regression coefficients (LC - HC) exceeds this range, we make a plausible inference of difference.\n",
    "\n",
    "In this example, the 95% HDI of the posterior distribution of the regression coefficients for the regression of `v` less than 0 and the lower ROPE boundary -0.2. \n",
    "Therefore, we make the inference that there is sufficient evidence of an effect of the conflict level on the rate of evidence accumulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = az.plot_posterior(\n",
    "    model_reg_infdata, var_names = \"v_C(conf, Treatment('LC'))[T.HC]\", \n",
    "    hdi_prob=0.95, rope = [-0.2,0.2], rope_color=\"r\",\n",
    "    figsize = (4,4)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The reference value is outside of the posterior. This translate into infinite support for H1, which is most likely an overstatement.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-1.0, 0.5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "ax = az.plot_bf(\n",
    "    model_reg_infdata, \n",
    "    var_name=[\"v_C(conf, Treatment('LC'))[T.HC]\"], \n",
    "    ref_val=0,\n",
    "    figsize = (4,4)\n",
    ")\n",
    "plt.xlim(-1,0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In conclusion, our contribution has three key benefits:\n",
    "1. You now have the freedom to save the model to any desired path, even if it is not available in the original hddm.\n",
    "2. Rather than generating separate db files for each chain (which could not be moved to other paths in the previous version), we now save all chains' db files in a single location of your choosing.\n",
    "3. We provide a simple way to convert the hddm model to ArViz InferenceData, which allows you to calculate pointwise loglikelihood for model comparison (e.g. WAIC and LOOIC), and generate predictions for model checking (e.g. posterior predictive checks).\"\n",
    "\n",
    "Lastly, I hope you enjoy using this version as it will be beneficial to your modeling work.\n",
    "\n",
    "If you have any questions, please contact me at [panwanke@163.com](panwanke@163.com) or commit an issue at [epool/kabuki](https://gitee.com/epool/kabuki/issues) ↗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}