{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: MKL_THREADING_LAYER=GNU\n"
     ]
    }
   ],
   "source": [
    "%env MKL_THREADING_LAYER GNU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import arviz as az\n",
    "from arviz.tests.helpers import load_cached_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = load_cached_models(500, 2)\n",
    "stan_model, stan_fit = models['pystan']\n",
    "\n",
    "# this is the OrderedDict\n",
    "sample_dict = stan_fit.extract(stan_fit.model_pars, permuted=False)\n",
    "\n",
    "# this is an example of the API being used carefully\n",
    "stan_xarray = az.convert_to_xarray(stan_fit, {'school': np.arange(8)}, dims={'theta': ['school'], 'theta_tilde': ['school']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_xarray = az.convert_to_xarray(sample_dict, {'chains': 2, 'school': np.arange(8)}, dims={'theta': ['school'], 'theta_tilde': ['school']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (chain: 2, draw: 500, school: 8)\n",
       "Coordinates:\n",
       "  * school       (school) int64 0 1 2 3 4 5 6 7\n",
       "  * draw         (draw) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 ...\n",
       "  * chain        (chain) int64 0 1\n",
       "Data variables:\n",
       "    mu           (chain, draw) float64 7.671 7.671 8.908 8.525 8.152 5.315 ...\n",
       "    tau          (chain, draw) float64 6.842 6.842 0.8769 1.08 0.1644 0.5318 ...\n",
       "    theta_tilde  (chain, draw, school) float64 0.5065 0.9721 0.7126 1.597 ...\n",
       "    theta        (chain, draw, school) float64 11.14 14.32 12.55 18.6 3.667 ..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stan_xarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (chain: 2, draw: 500, school: 8)\n",
       "Coordinates:\n",
       "  * school       (school) int64 0 1 2 3 4 5 6 7\n",
       "  * draw         (draw) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 ...\n",
       "  * chain        (chain) int64 0 1\n",
       "Data variables:\n",
       "    mu           (chain, draw) float64 7.671 7.671 8.908 8.525 8.152 5.315 ...\n",
       "    tau          (chain, draw) float64 6.842 6.842 0.8769 1.08 0.1644 0.5318 ...\n",
       "    theta_tilde  (chain, draw, school) float64 0.5065 0.9721 0.7126 1.597 ...\n",
       "    theta        (chain, draw, school) float64 11.14 14.32 12.55 18.6 3.667 ..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_xarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: iteration over an xarray.Dataset will change in xarray v0.11 to only include data variables, not coordinates. Iterate over the Dataset.variables property instead to preserve existing behavior in a forwards compatible manner.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all((stan_xarray == dict_xarray).all())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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",
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