rr_gauge_oil.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "\n",
    "import pymc3 as pm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import arviz as az\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Archimedes procedure for porous material density determination\n",
    "\n",
    "Experimental procedure is as follow, for further detail refer to standard ASTM B962. \n",
    "\n",
    "1. Measure temperature of liquid to determine its ($\\rho_{\\text{water}}$) and find object theoretical full density ( $\\rho_{\\text{solid}}$ ) from datasheet\n",
    "2. Measure mass of sample(s) in air(A)\n",
    "3. Impregnate the sample(s) with oil\n",
    "4. Measure mass of sample(s) saturated with oil in air (B)\n",
    "5. Measure mass of sample(s) saturated with oil in water (F)\n",
    "\n",
    "\n",
    "\\begin{equation}\n",
    "    \\text{Green Density}, D_g = \\frac{A \\rho_{water}}{B-F}\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "    \\text{true density}, \\pi_d = \\frac{ D_g}{\\rho_{\\text{solid}}} * 100\n",
    "\\end{equation}\n",
    "\n",
    "Parts using two powder type are made. Parts labeled bimodal should have a higher density then parts labelled unimodal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data from excel sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel('./data/green_sample_benchmark.xlsx', sheet_name = 'oil', index_col= [0,1])\n",
    "oil_density = pd.read_excel('./data/green_sample_benchmark.xlsx', sheet_name = 'oil_param', index_col= 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display first few row of excel sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>powder type</th>\n",
       "      <th>dry weight</th>\n",
       "      <th>coated weight</th>\n",
       "      <th>wet coated</th>\n",
       "      <th>exp</th>\n",
       "      <th>operator</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>batch</th>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>1</th>\n",
       "      <td>Bimodal</td>\n",
       "      <td>15.4440</td>\n",
       "      <td>16.6229</td>\n",
       "      <td>13.1582</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bimodal</td>\n",
       "      <td>15.8476</td>\n",
       "      <td>17.0214</td>\n",
       "      <td>13.5416</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>2</th>\n",
       "      <td>Unimodal</td>\n",
       "      <td>15.2508</td>\n",
       "      <td>16.6189</td>\n",
       "      <td>13.0623</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Unimodal</td>\n",
       "      <td>15.3899</td>\n",
       "      <td>16.6853</td>\n",
       "      <td>13.1755</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>1</th>\n",
       "      <td>Unimodal</td>\n",
       "      <td>14.8467</td>\n",
       "      <td>16.4155</td>\n",
       "      <td>12.7117</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         powder type  dry weight  coated weight  wet coated  exp  operator\n",
       "batch ID                                                                  \n",
       "2     1      Bimodal     15.4440        16.6229     13.1582    1         1\n",
       "      5      Bimodal     15.8476        17.0214     13.5416    1         1\n",
       "1     2     Unimodal     15.2508        16.6189     13.0623    1         1\n",
       "      4     Unimodal     15.3899        16.6853     13.1755    1         1\n",
       "4     1     Unimodal     14.8467        16.4155     12.7117    2         1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Carrier fluid density\n",
    "\n",
    "Density of the carrier fluid ($\\rho_{\\text{water}}$) is necessary for density calculation. Since the density of the fluid fluctuate depending on temperature and temperature varies from experiment to experiment, it is recorded for each experiment. For experiment 2 it was recorded at the beginning and at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oil density</th>\n",
       "      <th>water density</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.914</td>\n",
       "      <td>0.99730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.914</td>\n",
       "      <td>0.99780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.914</td>\n",
       "      <td>0.99798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.914</td>\n",
       "      <td>0.99787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     oil density  water density\n",
       "exp                            \n",
       "1          0.914        0.99730\n",
       "2          0.914        0.99780\n",
       "3          0.914        0.99798\n",
       "3          0.914        0.99787"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oil_density.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_hist(data, b):\n",
    "    fig, axes = plt.subplots(1,3, figsize = (15,5))\n",
    "    axes[0].hist(data['dry weight'], bins = b, density = True)\n",
    "    axes[0].set_title('dry weight')\n",
    "    axes[0].set_xlabel('weight (g)')\n",
    "    axes[0].set_ylabel('%')\n",
    "\n",
    "    axes[1].hist(data['wet coated'], bins = b, density = True)\n",
    "    axes[1].set_title('wet weight')\n",
    "    axes[1].set_xlabel('weight (g)')\n",
    "    axes[1].set_ylabel('%')\n",
    "\n",
    "    axes[2].hist(data['coated weight'], bins = b, density = True)\n",
    "    axes[2].set_title('coated weight')\n",
    "    axes[2].set_xlabel('weight (g)')\n",
    "    axes[2].set_ylabel('%')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "plot_hist(data, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute density\n",
    "Each sample was measured 3 times at each stage. Since we are only interested in the uncertainty in the true density ($\\pi_d$), a cartesian product of each measured weights (dry, wet and coated) and liquid density is done for each sample in order to compute true density."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-62aede59eea2>:11: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
      "  sample = data.loc[idx]\n"
     ]
    }
   ],
   "source": [
    "from itertools import product\n",
    "from sklearn import preprocessing\n",
    "\n",
    "density_df = pd.DataFrame(columns = ['batch', 'id', 'density', 'operator', 'powder'])\n",
    "\n",
    "# density of stainless steel 316L (g/cc) taken from data sheet\n",
    "den_ss316 = 8.0\n",
    "\n",
    "unique_id = 0\n",
    "\n",
    "# iterate over each sample to do cartesian product\n",
    "for idx in data.index.unique():\n",
    "    sample = data.loc[idx]\n",
    "\n",
    "    op = int(np.unique(sample['operator']).squeeze())\n",
    "    powder = str(np.unique(sample['powder type']).squeeze())\n",
    "    batch = idx[0]\n",
    "\n",
    "    d_weight = sample['dry weight'].to_numpy()\n",
    "    wc_weight = sample['wet coated'].to_numpy()\n",
    "    c_weight = sample['coated weight'].to_numpy()\n",
    "\n",
    "    exp_num = sample['exp']\n",
    "    # density of liquid taken from datasheet\n",
    "    water_den = np.unique(oil_density.loc[exp_num]['water density'].to_numpy())\n",
    "\n",
    "    prod = product(d_weight, wc_weight, c_weight, water_den)\n",
    "    \n",
    "    # cartesian product\n",
    "    for d_w, wc_w, c_w, water_d in prod:\n",
    "        #compute true density refer to equation on top\n",
    "        den = d_w/(c_w - wc_w)*water_d/den_ss316*100\n",
    "        new_entry = {\n",
    "            'batch' : batch,\n",
    "            'id' : unique_id,\n",
    "            'density' : float(den.squeeze()),\n",
    "            'operator' : op,\n",
    "            'powder' : powder\n",
    "        }\n",
    "\n",
    "        density_df = density_df.append(new_entry, ignore_index = True)\n",
    "    \n",
    "    unique_id += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(density_df['density'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "density_df.to_csv('./data/density_oil.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup Model\n",
    "\n",
    "Fitted a nested model, since samples are not reused across experiment. \n",
    "\n",
    "\\begin{equation}\n",
    "    y_{ijk}  = \\mu_{i} + Part_{j(i)} + \\epsilon_{k(ij)}\n",
    "    \\label{eq:Model}\n",
    "\\end{equation}\n",
    "\n",
    "Each powder type follows a global mean density $\\mu_{i}$. Since the measurement is destructive, therefore the same parts cannot be measured using different methods. Consequently, $Part_{j(i)}$ represent the nesting relationship between the Fixed effect of the powder and the measurement of each part. Finally, $\\epsilon_{k(ij)}$ represents the measurement error ($\\sigma_{gage}$). An operator and a batch random effect were previously included in the model. Including the two effects caused the sampler to diverge. The cause of the divergence was likely due to the low number of replicates for the operator(2). As for the batch effect, the removal did not change the estimated value for $\\sigma_{gage}$. Therefore, it is likely that the variance due to the batch is very low. The model is set up as follows:\n",
    "\n",
    "\\begin{align*}\n",
    "    \\{\\mu_{i} \\sim Uniform(20.0, 60.0)&: i = \\{\\text{Unimodal},\\text{Bimodal}\\}\\} \\\\\n",
    "    \\text{Part}_{j(i)} &\\sim N(0, \\sigma_{\\text{part}}) \\\\\n",
    "    \\mu &= \\mu_{i} + \\text{Part}_{j(i)} \\\\\n",
    "    y_{ijk} &\\sim  N(\\mu, \\sigma_{\\text{gage}}) \n",
    "\\end{align*}\n",
    "\\begin{equation}\n",
    "    \\sigma_{\\text{part}} \\sim \\text{HalfCauchy}(25)\n",
    "    \\label{eq:model_priors_p}\n",
    "\\end{equation}\n",
    "\\begin{equation}\n",
    "    \\sigma_{\\text{gage}} \\sim \\text{HalfCauchy}(25) \n",
    "    \\label{eq:model_priors_g}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "basic_model = pm.Model()\n",
    "\n",
    "n_part = unique_id\n",
    "\n",
    "powder_mean = density_df.groupby('powder').mean()['density']\n",
    "\n",
    "part_id = density_df['id'].to_numpy().astype(np.int32)\n",
    "obs_pores = density_df['density'].to_numpy().astype(np.int32)\n",
    "\n",
    "# Relabel data to integers\n",
    "powder_enc = preprocessing.LabelEncoder()\n",
    "powder_enc.fit(['Unimodal','Bimodal'])\n",
    "powder_data = powder_enc.transform(density_df['powder'])\n",
    "\n",
    "#compute starting point for mean inference\n",
    "test_val_mu = [powder_mean[c] for c in powder_enc.classes_]\n",
    "\n",
    "A = 10000\n",
    "with basic_model:\n",
    "    \n",
    "    # setup independent mu_i\n",
    "    mu_p = pm.Uniform('muP', 20.0, 60.0, shape = 2, testval = test_val_mu)\n",
    "    mu_real = pm.math.switch(powder_data, mu_p[1], mu_p[0])\n",
    "    \n",
    "    # setup prior for part variance and error variance\n",
    "    sig_repeat = pm.HalfCauchy('sig_repeat', 25)\n",
    "    sig_part = pm.HalfCauchy('sig_part', 25)\n",
    "    \n",
    "    # each part will vary independently (nesting relationship)\n",
    "    P_t = pm.Normal('P_t', mu = 0, sigma = 1, shape = n_part)\n",
    "    \n",
    "    # setup mu\n",
    "    mu = pm.Deterministic('mu', mu_real + P_t[part_id]*sig_part)\n",
    "    \n",
    "    # add error and data to model\n",
    "    porosity = pm.Normal('density', mu = mu, sigma = sig_repeat, observed = obs_pores)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [P_t, sig_part, sig_repeat, muP]\n",
      "Sampling 4 chains, 0 divergences: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 44000/44000 [02:04<00:00, 354.41draws/s]\n",
      "The number of effective samples is smaller than 25% for some parameters.\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40000/40000 [00:35<00:00, 1135.83it/s]\n"
     ]
    }
   ],
   "source": [
    "with basic_model:\n",
    "    trace = pm.sample(A, chains = 4, tune = 1000, target_accept=0.9)\n",
    "    prior = pm.sample_prior_predictive()\n",
    "    posterior_predictive = pm.sample_posterior_predictive(trace)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert trace to arviz data format for diagnostic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "trace_data_az = az.from_pymc3(\n",
    "    trace=trace,\n",
    "    prior=prior,\n",
    "    posterior_predictive=posterior_predictive,\n",
    "    model=basic_model\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./data/oil_trace.nc'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace_data_az.to_netcdf('./data/oil_trace.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trace plot\n",
    "The sampler is exploring the probability space by sampling different values of the model input parameters ($\\mu_i, \\sigma_{\\text{part}}, \\sigma_{\\text{gage}}$). The left graph shows the probability density of the sampled values. The right graph shows the sampled with respect to sample number. The left graph is computed by summing up the right graph. When the left graph looks skewed towards zero or the right graph does not resemble a thick caterpillar, this indicates a biased sampling and a lack of convergence. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\m384wang\\Anaconda3\\envs\\pymc3_dep\\lib\\site-packages\\arviz\\data\\io_pymc3.py:87: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.traceplot(trace, var_names=['muP','sig_repeat','sig_part'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictive check plot\n",
    "\n",
    "Predictive posterior plot overlays data generated by the model's posterior over the real data. This is useful to check the fitness of the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_ppc(trace_data_az, data_pairs={\"density\":\"density\"}, kind = 'scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forest plot\n",
    "\n",
    "Forest plot on estimated real mean $\\mu_{i}$ to check if the values make sense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\m384wang\\Anaconda3\\envs\\pymc3_dep\\lib\\site-packages\\arviz\\data\\io_pymc3.py:87: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x345.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.plots.forestplot(trace, var_names=['muP'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute split $\\hat{R}$ \n",
    "\n",
    "In the Markov-Chain Monte Carlo (MCMC) sampler, the sampling is done simultaneously in multiple processes (chain). The rank normalized $\\hat{R}$ checks for convergence by comparing the variance between chain with the variance within chain. If convergence has been achieved then the variance should be the same ($\\hat{R}=1$) (Vehtari et al). [Vehtari et al.](https://arxiv.org/pdf/1903.08008v1.pdf \"vehtari\") recommends a $\\hat{R} < 1.01$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (P_t_dim_0: 35, muP_dim_0: 2, mu_dim_0: 372)\n",
       "Coordinates:\n",
       "  * P_t_dim_0   (P_t_dim_0) int32 0 1 2 3 4 5 6 7 8 ... 27 28 29 30 31 32 33 34\n",
       "  * muP_dim_0   (muP_dim_0) int32 0 1\n",
       "  * mu_dim_0    (mu_dim_0) int32 0 1 2 3 4 5 6 7 ... 365 366 367 368 369 370 371\n",
       "Data variables:\n",
       "    P_t         (P_t_dim_0) float64 1.0 1.0 1.001 1.001 ... 1.001 1.001 1.001\n",
       "    muP         (muP_dim_0) float64 1.001 1.001\n",
       "    sig_repeat  float64 1.0\n",
       "    sig_part    float64 1.001\n",
       "    mu          (mu_dim_0) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5353a7e8-1e1b-446b-ae6f-584264554b2d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5353a7e8-1e1b-446b-ae6f-584264554b2d' 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'>P_t_dim_0</span>: 35</li><li><span class='xr-has-index'>muP_dim_0</span>: 2</li><li><span class='xr-has-index'>mu_dim_0</span>: 372</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-98590606-ba90-43c9-882b-a4191a94b4d8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-98590606-ba90-43c9-882b-a4191a94b4d8' 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'>P_t_dim_0</span></div><div class='xr-var-dims'>(P_t_dim_0)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 29 30 31 32 33 34</div><input id='attrs-b15a0352-c5cf-4ef8-a5c1-efc13f1e0dad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b15a0352-c5cf-4ef8-a5c1-efc13f1e0dad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4111e386-6ff3-4b30-ba67-3eb9144e5dbc' class='xr-var-data-in' type='checkbox'><label for='data-4111e386-6ff3-4b30-ba67-3eb9144e5dbc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>muP_dim_0</span></div><div class='xr-var-dims'>(muP_dim_0)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-e6db880f-476e-4216-a7a4-a3834efb9c93' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6db880f-476e-4216-a7a4-a3834efb9c93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f51d16a-f4d5-4954-93e6-e7195f04ab60' class='xr-var-data-in' type='checkbox'><label for='data-8f51d16a-f4d5-4954-93e6-e7195f04ab60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mu_dim_0</span></div><div class='xr-var-dims'>(mu_dim_0)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 367 368 369 370 371</div><input id='attrs-a1dbf18f-71ef-4247-a3d8-7d934da77364' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a1dbf18f-71ef-4247-a3d8-7d934da77364' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91c45034-9c0a-48d0-b329-fe87415677ae' class='xr-var-data-in' type='checkbox'><label for='data-91c45034-9c0a-48d0-b329-fe87415677ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#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, ..., 369, 370, 371])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0c29a465-dfdf-4d6a-a877-9016a5cb0c4c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c29a465-dfdf-4d6a-a877-9016a5cb0c4c' class='xr-section-summary' >Data variables: <span>(5)</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>P_t</span></div><div class='xr-var-dims'>(P_t_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.001 ... 1.001 1.001 1.001</div><input id='attrs-a954184d-14f3-4c78-b8dd-2202b627b67b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a954184d-14f3-4c78-b8dd-2202b627b67b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c068a09f-8244-41e5-8ab3-45c651abdad7' class='xr-var-data-in' type='checkbox'><label for='data-c068a09f-8244-41e5-8ab3-45c651abdad7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.0002049 , 1.0004686 , 1.00095466, 1.00101573, 1.00072378,\n",
       "       1.00092097, 1.00135362, 1.00121031, 1.00065131, 1.00083459,\n",
       "       1.00067765, 1.00062369, 1.00074333, 1.00070743, 1.00064432,\n",
       "       1.00077924, 1.00112069, 1.00069368, 1.00125097, 1.00119211,\n",
       "       1.00058562, 1.00050884, 1.00077031, 1.00073721, 1.00049027,\n",
       "       1.00038773, 1.00038226, 1.0009259 , 1.00094602, 1.00049631,\n",
       "       1.00138614, 1.00124608, 1.00141291, 1.00135585, 1.00112458])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>muP</span></div><div class='xr-var-dims'>(muP_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.001 1.001</div><input id='attrs-b48e01b2-05ae-4e35-a6b6-ec1be9407229' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b48e01b2-05ae-4e35-a6b6-ec1be9407229' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bb5196d-9bcb-4aa5-8c7f-94a00dcf4fa6' class='xr-var-data-in' type='checkbox'><label for='data-5bb5196d-9bcb-4aa5-8c7f-94a00dcf4fa6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.00082541, 1.00129751])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sig_repeat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0</div><input id='attrs-bc4071f1-3908-475e-897e-3829c55cfc10' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc4071f1-3908-475e-897e-3829c55cfc10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1973cf13-fd79-4abe-bbbf-025627cd8649' class='xr-var-data-in' type='checkbox'><label for='data-1973cf13-fd79-4abe-bbbf-025627cd8649' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1.0000968)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sig_part</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.001</div><input id='attrs-65f912a8-cfc5-4c33-90e2-c9cc8f4d8db7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65f912a8-cfc5-4c33-90e2-c9cc8f4d8db7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33ae0cad-7c94-4a44-a63c-56bd8464b1e5' class='xr-var-data-in' type='checkbox'><label for='data-33ae0cad-7c94-4a44-a63c-56bd8464b1e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1.00056165)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mu</span></div><div class='xr-var-dims'>(mu_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0</div><input id='attrs-073ce6d5-92f4-4e7f-9491-00e7eed37ea6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-073ce6d5-92f4-4e7f-9491-00e7eed37ea6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b23e3ac1-0368-4587-8c96-232595bc1c92' class='xr-var-data-in' type='checkbox'><label for='data-b23e3ac1-0368-4587-8c96-232595bc1c92' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.00008943, 1.00011422, 1.00014336, 1.00008789, 0.99998241,\n",
       "       0.99998241, 0.99998241, 0.99998241, 0.99998241, 0.99998241,\n",
       "       0.99998241, 0.99998241, 1.00015302, 1.00015302, 1.00015302,\n",
       "       1.00015302, 1.00015302, 1.00015302, 1.00015302, 1.00015302,\n",
       "       1.00012718, 1.00012718, 1.00012718, 1.00012718, 1.00012718,\n",
       "       1.00012718, 1.00012718, 1.00012718, 0.99996314, 0.99996314,\n",
       "       0.99996314, 0.99996314, 0.99996314, 0.99996314, 0.99996314,\n",
       "       0.99996314, 0.9999983 , 0.9999983 , 0.9999983 , 0.9999983 ,\n",
       "       0.9999983 , 0.9999983 , 0.9999983 , 0.9999983 , 1.0000605 ,\n",
       "       1.0000605 , 1.0000605 , 1.0000605 , 1.0000605 , 1.0000605 ,\n",
       "       1.0000605 , 1.0000605 , 1.00017523, 1.00017523, 1.00017523,\n",
       "       1.00017523, 1.00017523, 1.00017523, 1.00017523, 1.00017523,\n",
       "       1.000311  , 1.000311  , 1.000311  , 1.000311  , 1.000311  ,\n",
       "       1.000311  , 1.000311  , 1.000311  , 1.00010119, 1.00010119,\n",
       "       1.00010119, 1.00010119, 1.00010119, 1.00010119, 1.00010119,\n",
       "       1.00010119, 1.0000028 , 1.0000028 , 1.0000028 , 1.0000028 ,\n",
       "       1.0000028 , 1.0000028 , 1.0000028 , 1.0000028 , 1.00014934,\n",
       "       1.00014934, 1.00014934, 1.00014934, 1.00014934, 1.00014934,\n",
       "       1.00014934, 1.00014934, 0.99997986, 0.99997986, 0.99997986,\n",
       "       0.99997986, 0.99997986, 0.99997986, 0.99997986, 0.99997986,\n",
       "...\n",
       "       1.00012857, 0.99998139, 0.99998139, 0.99998139, 0.99998139,\n",
       "       0.99998139, 0.99998139, 0.99998139, 0.99998139, 0.99998139,\n",
       "       0.99998139, 0.99998139, 0.99998139, 0.99998139, 0.99998139,\n",
       "       0.99998139, 0.99998139, 0.99995942, 0.99995942, 0.99995942,\n",
       "       0.99995942, 0.99995942, 0.99995942, 0.99995942, 0.99995942,\n",
       "       0.99995942, 0.99995942, 0.99995942, 0.99995942, 0.99995942,\n",
       "       0.99995942, 0.99995942, 0.99995942, 1.00034156, 1.00034156,\n",
       "       1.00034156, 1.00034156, 1.00034156, 1.00034156, 1.00034156,\n",
       "       1.00034156, 1.00034156, 1.00034156, 1.00034156, 1.00034156,\n",
       "       1.00034156, 1.00034156, 1.00034156, 1.00034156, 1.00029664,\n",
       "       1.00029664, 1.00029664, 1.00029664, 1.00029664, 1.00029664,\n",
       "       1.00029664, 1.00029664, 1.00029664, 1.00029664, 1.00029664,\n",
       "       1.00029664, 1.00029664, 1.00029664, 1.00029664, 1.00029664,\n",
       "       0.99997716, 0.99997716, 0.99997716, 0.99997716, 0.99997716,\n",
       "       0.99997716, 0.99997716, 0.99997716, 0.99997716, 0.99997716,\n",
       "       0.99997716, 0.99997716, 0.99997716, 0.99997716, 0.99997716,\n",
       "       0.99997716, 1.00003034, 1.00003034, 1.00003034, 1.00003034,\n",
       "       1.00003034, 1.00003034, 1.00003034, 1.00003034, 1.00003034,\n",
       "       1.00003034, 1.00003034, 1.00003034, 1.00003034, 1.00003034,\n",
       "       1.00003034, 1.00003034])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eee6565f-6cba-4c09-a0de-640e4274a6a6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-eee6565f-6cba-4c09-a0de-640e4274a6a6' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (P_t_dim_0: 35, muP_dim_0: 2, mu_dim_0: 372)\n",
       "Coordinates:\n",
       "  * P_t_dim_0   (P_t_dim_0) int32 0 1 2 3 4 5 6 7 8 ... 27 28 29 30 31 32 33 34\n",
       "  * muP_dim_0   (muP_dim_0) int32 0 1\n",
       "  * mu_dim_0    (mu_dim_0) int32 0 1 2 3 4 5 6 7 ... 365 366 367 368 369 370 371\n",
       "Data variables:\n",
       "    P_t         (P_t_dim_0) float64 1.0 1.0 1.001 1.001 ... 1.001 1.001 1.001\n",
       "    muP         (muP_dim_0) float64 1.001 1.001\n",
       "    sig_repeat  float64 1.0\n",
       "    sig_part    float64 1.001\n",
       "    mu          (mu_dim_0) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.rhat(trace_data_az)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate highest density interval (HDI)\n",
    "\n",
    "Taken from [arviz doc](https://arviz-devs.github.io/arviz/api/generated/arviz.hdi.html#arviz.hdi \"arviz\") : The HDI is the minimum width Bayesian credible interval (BCI)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (P_t_dim_0: 35, hdi: 2, muP_dim_0: 2, mu_dim_0: 372)\n",
       "Coordinates:\n",
       "  * P_t_dim_0   (P_t_dim_0) int32 0 1 2 3 4 5 6 7 8 ... 27 28 29 30 31 32 33 34\n",
       "  * muP_dim_0   (muP_dim_0) int32 0 1\n",
       "  * mu_dim_0    (mu_dim_0) int32 0 1 2 3 4 5 6 7 ... 365 366 367 368 369 370 371\n",
       "  * hdi         (hdi) &lt;U6 &#x27;lower&#x27; &#x27;higher&#x27;\n",
       "Data variables:\n",
       "    P_t         (P_t_dim_0, hdi) float64 0.0504 1.327 0.5333 ... -0.5963 0.3443\n",
       "    muP         (muP_dim_0, hdi) float64 52.89 54.52 50.89 52.58\n",
       "    sig_repeat  (hdi) float64 0.4083 0.4722\n",
       "    sig_part    (hdi) float64 1.392 2.258\n",
       "    mu          (mu_dim_0, hdi) float64 54.11 55.73 55.05 ... 51.71 51.3 51.71</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e4f3ae9d-e80e-4203-855d-39b5767a3268' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e4f3ae9d-e80e-4203-855d-39b5767a3268' 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'>P_t_dim_0</span>: 35</li><li><span class='xr-has-index'>hdi</span>: 2</li><li><span class='xr-has-index'>muP_dim_0</span>: 2</li><li><span class='xr-has-index'>mu_dim_0</span>: 372</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f3cc88dc-3c42-488e-87ed-afc4abdfbe7d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f3cc88dc-3c42-488e-87ed-afc4abdfbe7d' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>P_t_dim_0</span></div><div class='xr-var-dims'>(P_t_dim_0)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 29 30 31 32 33 34</div><input id='attrs-0328c5ab-2a9a-4de7-ad82-925774023f7b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0328c5ab-2a9a-4de7-ad82-925774023f7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6d1cf2f-80fe-4118-aac9-28494ea09c8c' class='xr-var-data-in' type='checkbox'><label for='data-b6d1cf2f-80fe-4118-aac9-28494ea09c8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>muP_dim_0</span></div><div class='xr-var-dims'>(muP_dim_0)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-d9e652f5-dd45-46d9-a266-ebf859e32930' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d9e652f5-dd45-46d9-a266-ebf859e32930' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d2d6a85-0b18-4d47-8f43-9e65d064520c' class='xr-var-data-in' type='checkbox'><label for='data-2d2d6a85-0b18-4d47-8f43-9e65d064520c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mu_dim_0</span></div><div class='xr-var-dims'>(mu_dim_0)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 367 368 369 370 371</div><input id='attrs-267f65b1-c91f-47d2-8b55-51563386fea9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-267f65b1-c91f-47d2-8b55-51563386fea9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce53b1d4-2030-46ec-addb-2e750bce9210' class='xr-var-data-in' type='checkbox'><label for='data-ce53b1d4-2030-46ec-addb-2e750bce9210' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#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, ..., 369, 370, 371])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>hdi</span></div><div class='xr-var-dims'>(hdi)</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;lower&#x27; &#x27;higher&#x27;</div><input id='attrs-1f6c53ac-585e-4a22-b7fe-bd8a6ba25e53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f6c53ac-585e-4a22-b7fe-bd8a6ba25e53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2be6e831-1c61-4a68-bf97-88caf6cdc0d2' class='xr-var-data-in' type='checkbox'><label for='data-2be6e831-1c61-4a68-bf97-88caf6cdc0d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>hdi_prob :</span></dt><dd>0.94</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;lower&#x27;, &#x27;higher&#x27;], dtype=&#x27;&lt;U6&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c31dd39e-c220-4997-a2f7-196c6a2d2b0c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c31dd39e-c220-4997-a2f7-196c6a2d2b0c' class='xr-section-summary' >Data variables: <span>(5)</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>P_t</span></div><div class='xr-var-dims'>(P_t_dim_0, hdi)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0504 1.327 ... -0.5963 0.3443</div><input id='attrs-df5d3a0b-48fb-49e0-86c1-ef7dbe42df39' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df5d3a0b-48fb-49e0-86c1-ef7dbe42df39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a244070c-a58a-42fd-a0b5-c4766d40a246' class='xr-var-data-in' type='checkbox'><label for='data-a244070c-a58a-42fd-a0b5-c4766d40a246' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.05040454,  1.3265142 ],\n",
       "       [ 0.53327834,  1.86816929],\n",
       "       [ 0.01482925,  1.29521847],\n",
       "       [ 0.51843802,  1.8618607 ],\n",
       "       [-1.79185197, -0.67161134],\n",
       "       [-0.90661369,  0.07089492],\n",
       "       [ 0.1833449 ,  1.20775401],\n",
       "       [-0.6175607 ,  0.34273097],\n",
       "       [-1.45708913, -0.42550164],\n",
       "       [-1.32303253, -0.31471766],\n",
       "       [-0.86721787,  0.09263776],\n",
       "       [-1.30992114, -0.29903838],\n",
       "       [-1.46028691, -0.42457789],\n",
       "       [-1.17548948, -0.18484207],\n",
       "       [-0.6560623 ,  0.28705155],\n",
       "       [-1.16186996, -0.18106307],\n",
       "       [-0.6042446 ,  0.35938508],\n",
       "       [-1.56164202, -0.49317852],\n",
       "       [-0.32584639,  0.65041043],\n",
       "       [-0.06482608,  0.92331093],\n",
       "       [ 0.24711599,  1.22131828],\n",
       "       [ 0.49664023,  1.52624722],\n",
       "       [-1.45458396, -0.43668998],\n",
       "       [-0.85846451,  0.07204972],\n",
       "       [ 0.74856018,  1.8451485 ],\n",
       "       [ 1.20998091,  2.47500504],\n",
       "       [ 0.7347091 ,  1.82408721],\n",
       "       [-1.77238031, -0.69448916],\n",
       "       [-0.87922244,  0.07871655],\n",
       "       [-3.24595933, -1.75259856],\n",
       "       [ 0.45738823,  1.50938758],\n",
       "       [-0.31946214,  0.62731909],\n",
       "       [ 0.20718706,  1.20856983],\n",
       "       [ 0.45394116,  1.50414406],\n",
       "       [-0.59634912,  0.34431323]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>muP</span></div><div class='xr-var-dims'>(muP_dim_0, hdi)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>52.89 54.52 50.89 52.58</div><input id='attrs-761c2265-2f7f-41ea-a913-7be38572fd34' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-761c2265-2f7f-41ea-a913-7be38572fd34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac7fc6b7-8896-4476-82f3-d6856cd80027' class='xr-var-data-in' type='checkbox'><label for='data-ac7fc6b7-8896-4476-82f3-d6856cd80027' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[52.89194285, 54.5183393 ],\n",
       "       [50.88789518, 52.57556468]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sig_repeat</span></div><div class='xr-var-dims'>(hdi)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4083 0.4722</div><input id='attrs-3693b959-60bf-4b56-a95e-e1cd6e95db16' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3693b959-60bf-4b56-a95e-e1cd6e95db16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c104bd0f-02b3-413e-a629-b58cc6f5bb5c' class='xr-var-data-in' type='checkbox'><label for='data-c104bd0f-02b3-413e-a629-b58cc6f5bb5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.40831727, 0.47223401])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sig_part</span></div><div class='xr-var-dims'>(hdi)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.392 2.258</div><input id='attrs-30612f66-63a0-45a5-b7be-89801bce9a18' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30612f66-63a0-45a5-b7be-89801bce9a18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52fbb7b4-8131-42af-890c-268a8776a094' class='xr-var-data-in' type='checkbox'><label for='data-52fbb7b4-8131-42af-890c-268a8776a094' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.3924772 , 2.25828961])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mu</span></div><div class='xr-var-dims'>(mu_dim_0, hdi)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>54.11 55.73 55.05 ... 51.3 51.71</div><input id='attrs-9d039004-7c0c-493b-bdc6-18ea07de2d9b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d039004-7c0c-493b-bdc6-18ea07de2d9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4aab98ab-e19e-4230-8e10-b93983ecb695' class='xr-var-data-in' type='checkbox'><label for='data-4aab98ab-e19e-4230-8e10-b93983ecb695' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[54.11365302, 55.72556442],\n",
       "       [55.05415815, 56.68806652],\n",
       "       [52.1290023 , 53.73764389],\n",
       "       [53.0614181 , 54.6725131 ],\n",
       "       [49.2242649 , 49.80800232],\n",
       "       [49.2242649 , 49.80800232],\n",
       "       [49.2242649 , 49.80800232],\n",
       "       [49.2242649 , 49.80800232],\n",
       "       [49.2242649 , 49.80800232],\n",
       "       [49.2242649 , 49.80800232],\n",
       "       [49.2242649 , 49.80800232],\n",
       "       [49.2242649 , 49.80800232],\n",
       "       [50.71126332, 51.29733727],\n",
       "       [50.71126332, 51.29733727],\n",
       "       [50.71126332, 51.29733727],\n",
       "       [50.71126332, 51.29733727],\n",
       "       [50.71126332, 51.29733727],\n",
       "       [50.71126332, 51.29733727],\n",
       "       [50.71126332, 51.29733727],\n",
       "       [50.71126332, 51.29733727],\n",
       "...\n",
       "       [53.29068453, 53.70281272],\n",
       "       [53.29068453, 53.70281272],\n",
       "       [53.29068453, 53.70281272],\n",
       "       [53.29068453, 53.70281272],\n",
       "       [51.29913971, 51.71047639],\n",
       "       [51.29913971, 51.71047639],\n",
       "       [51.29913971, 51.71047639],\n",
       "       [51.29913971, 51.71047639],\n",
       "       [51.29913971, 51.71047639],\n",
       "       [51.29913971, 51.71047639],\n",
       "       [51.29913971, 51.71047639],\n",
       "       [51.29913971, 51.71047639],\n",
       "       [51.29913971, 51.71047639],\n",
       "       [51.29913971, 51.71047639],\n",
       "       [51.29913971, 51.71047639],\n",
       "       [51.29913971, 51.71047639],\n",
       "       [51.29913971, 51.71047639],\n",
       "       [51.29913971, 51.71047639],\n",
       "       [51.29913971, 51.71047639],\n",
       "       [51.29913971, 51.71047639]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d37c91b6-c365-4339-8a84-af4cdce88d49' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d37c91b6-c365-4339-8a84-af4cdce88d49' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (P_t_dim_0: 35, hdi: 2, muP_dim_0: 2, mu_dim_0: 372)\n",
       "Coordinates:\n",
       "  * P_t_dim_0   (P_t_dim_0) int32 0 1 2 3 4 5 6 7 8 ... 27 28 29 30 31 32 33 34\n",
       "  * muP_dim_0   (muP_dim_0) int32 0 1\n",
       "  * mu_dim_0    (mu_dim_0) int32 0 1 2 3 4 5 6 7 ... 365 366 367 368 369 370 371\n",
       "  * hdi         (hdi) <U6 'lower' 'higher'\n",
       "Data variables:\n",
       "    P_t         (P_t_dim_0, hdi) float64 0.0504 1.327 0.5333 ... -0.5963 0.3443\n",
       "    muP         (muP_dim_0, hdi) float64 52.89 54.52 50.89 52.58\n",
       "    sig_repeat  (hdi) float64 0.4083 0.4722\n",
       "    sig_part    (hdi) float64 1.392 2.258\n",
       "    mu          (mu_dim_0, hdi) float64 54.11 55.73 55.05 ... 51.71 51.3 51.71"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdi_data = az.hdi(trace_data_az)\n",
    "hdi_data.to_netcdf('./data/oil_inference.nc')\n",
    "hdi_data"
   ]
  }
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