{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started with Kinesis\n",
    "\n",
    "In this notebook, we will generate a mock cluster, observe (add noise) and fit to see if we recover the true parameters of its internal kinematics.\n",
    "\n",
    "Notes on dependences\n",
    "- [gapipes](https://github.com/smoh/gapipes): for custom pandas accessor `g` to get covariance matrices, astropy coordinate objects..\n",
    "- [arviz](https://github.com/arviz-devs/arviz): for visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import astropy.units as u\n",
    "import astropy.coordinates as coords\n",
    "import arviz as az\n",
    "\n",
    "# project dependencies\n",
    "import gapipes as gp\n",
    "import kinesis as kn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(18324)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making and observing a mock cluster\n",
    "\n",
    "First, let's make a cluster with some fiducial parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 150  # number of sources\n",
    "b0 = np.array([17.7, 41.2, 13.3])    # pc\n",
    "v0 = np.array([-6.32, 45.24, 5.30])  # [vx, vy, vz] in km/s\n",
    "sigv = 1.  # dispersion, km/s\n",
    "\n",
    "cl = kn.Cluster(v0, sigv, b0=b0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cluster currently has no members. We need to 'sample' the cluster in order to populate it. There are two methods for sampling a cluster:\n",
    "- sample_sphere: sampling uniformly within a maximum radius\n",
    "- sample_at: sample at exactly the positions you want by giving it astropy coordinates.\n",
    "\n",
    "Let's do uniform sphere for now. All normal methods of `Cluster` will return itself with modified attributes for method chaining.\n",
    "Sampling will modify its `members` from `None` to an instance of `ClusterMembers`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<kinesis.mock.ClusterMembers at 0x7ff1b8b9bc50>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl.sample_sphere(N=N, Rmax=5)\n",
    "cl.members"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two Gaia-like DataFrame associated with `ClusterMembers`:\n",
    "- `truths`: this is the noise-free Gaia data consistent with the `Cluster`'s internal motions\n",
    "- `observed`: this is the noise-added Gaia data. If the `ClusterMembers` has not been `observe`d, it will be `None`.\n",
    "\n",
    "Now let's `observe` the cluster. In order to observe, you need to specify the 3x3 covariance matrix for (parallax, pmra, pmdec) with `cov` keyword. If `cov` is (3,3) array, this will be assumed for all stars. You can also specify different covariance matrix for different stars by specifying `cov` to be (N, 3, 3) array. The noise model is assumed to be Gaussian.\n",
    "\n",
    "It is also possible to specify covariance by giving another Gaia-like data frame using `error_from` keyword. The following columns needed to construct to covariance matrix are expected: `parallax_error`, `pmra_error`, `pmdec_error`, `parallax_pmra_corr`, `parallax_pmdec_corr`, `pmra_pmdec_corr`. This is useful, for example, if you want to simulate data as noisy as the actual Gaia data you want to model.\n",
    "\n",
    "Radial velocity errors should be specified separately with `rv_error` keyword, which should be 1-d array with `N` elements, each specifying the Gaussian standard deviation in km/s. If any element in `rv_error` is `np.nan`, it is assumed that the star does not have RV measurement.\n",
    "\n",
    "This will add noise to the astrometry and radial velocities of the cluster members, and the noise-added Gaia-like data frame is now accessible with `cl.members.observed`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's specify simply 0.01 mas or mas/yr errors for parallax and pmra/pmdec and no available RVs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cluster(b0=[17.7 41.2 13.3], v0=[-6.32 45.24  5.3 ], sigmav=1.0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl.observe(cov=np.eye(3)*0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>parallax</th>\n",
       "      <th>pmra</th>\n",
       "      <th>pmdec</th>\n",
       "      <th>parallax_pmra_corr</th>\n",
       "      <th>parallax_pmdec_corr</th>\n",
       "      <th>pmra_pmdec_corr</th>\n",
       "      <th>parallax_error</th>\n",
       "      <th>pmra_error</th>\n",
       "      <th>pmdec_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69.827908</td>\n",
       "      <td>15.519434</td>\n",
       "      <td>21.658627</td>\n",
       "      <td>100.052890</td>\n",
       "      <td>-26.044631</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63.449065</td>\n",
       "      <td>18.338080</td>\n",
       "      <td>20.766187</td>\n",
       "      <td>116.685278</td>\n",
       "      <td>-26.140222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67.364160</td>\n",
       "      <td>20.868653</td>\n",
       "      <td>21.499381</td>\n",
       "      <td>97.637078</td>\n",
       "      <td>-44.203983</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62.220135</td>\n",
       "      <td>16.061914</td>\n",
       "      <td>20.091140</td>\n",
       "      <td>116.236887</td>\n",
       "      <td>-26.155718</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61.149763</td>\n",
       "      <td>17.653502</td>\n",
       "      <td>22.460105</td>\n",
       "      <td>124.989805</td>\n",
       "      <td>-28.335939</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ra        dec   parallax        pmra      pmdec  parallax_pmra_corr  \\\n",
       "0  69.827908  15.519434  21.658627  100.052890 -26.044631                 0.0   \n",
       "1  63.449065  18.338080  20.766187  116.685278 -26.140222                 0.0   \n",
       "2  67.364160  20.868653  21.499381   97.637078 -44.203983                 0.0   \n",
       "3  62.220135  16.061914  20.091140  116.236887 -26.155718                 0.0   \n",
       "4  61.149763  17.653502  22.460105  124.989805 -28.335939                 0.0   \n",
       "\n",
       "   parallax_pmdec_corr  pmra_pmdec_corr  parallax_error  pmra_error  \\\n",
       "0                  0.0              0.0             0.1         0.1   \n",
       "1                  0.0              0.0             0.1         0.1   \n",
       "2                  0.0              0.0             0.1         0.1   \n",
       "3                  0.0              0.0             0.1         0.1   \n",
       "4                  0.0              0.0             0.1         0.1   \n",
       "\n",
       "   pmdec_error  \n",
       "0          0.1  \n",
       "1          0.1  \n",
       "2          0.1  \n",
       "3          0.1  \n",
       "4          0.1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl.members.observed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting the mock data\n",
    "\n",
    "Now let's try to fit the mock data that we generated.\n",
    "Under the hood, kinesis uses [stan](https://mc-stan.org/) using its python interface [pystan](https://pystan.readthedocs.io/en/latest/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_47c60c044460f8a93fb3bb3ea5a6ba35 NOW.\n",
      "INFO:kinesis.models:Compiling general_model\n"
     ]
    }
   ],
   "source": [
    "fitter = kn.Fitter(include_T=False, recompile=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v0, sigv = [-6.0485426  45.77930913  5.55432163], 0.9072\n",
      "diff from truth: [0.2714574  0.53930913 0.25432163], -0.0928\n"
     ]
    }
   ],
   "source": [
    "df = cl.members.observed.copy()\n",
    "fit = fitter.fit(df, sample=False)\n",
    "print(f\"v0, sigv = {fit['v0']}, {fit['sigv']:.4f}\")\n",
    "print(f\"diff from truth: {fit['v0']-v0}, {fit['sigv']-sigv:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "stanfit = fitter.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> sample_stats\n",
       "\t> observed_data"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stanfit.azfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "azfit=stanfit.azfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hpd_3%</th>\n",
       "      <th>hpd_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_mean</th>\n",
       "      <th>ess_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>v0[0]</th>\n",
       "      <td>-6.060</td>\n",
       "      <td>0.434</td>\n",
       "      <td>-6.856</td>\n",
       "      <td>-5.228</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.008</td>\n",
       "      <td>1615.0</td>\n",
       "      <td>1607.0</td>\n",
       "      <td>1625.0</td>\n",
       "      <td>2122.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v0[1]</th>\n",
       "      <td>45.754</td>\n",
       "      <td>1.000</td>\n",
       "      <td>43.927</td>\n",
       "      <td>47.715</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.018</td>\n",
       "      <td>1632.0</td>\n",
       "      <td>1632.0</td>\n",
       "      <td>1640.0</td>\n",
       "      <td>2098.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v0[2]</th>\n",
       "      <td>5.546</td>\n",
       "      <td>0.328</td>\n",
       "      <td>4.920</td>\n",
       "      <td>6.143</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.006</td>\n",
       "      <td>1649.0</td>\n",
       "      <td>1649.0</td>\n",
       "      <td>1653.0</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigv</th>\n",
       "      <td>0.919</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5188.0</td>\n",
       "      <td>5174.0</td>\n",
       "      <td>5183.0</td>\n",
       "      <td>3203.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean     sd  hpd_3%  hpd_97%  mcse_mean  mcse_sd  ess_mean  ess_sd  \\\n",
       "v0[0]  -6.060  0.434  -6.856   -5.228      0.011    0.008    1615.0  1607.0   \n",
       "v0[1]  45.754  1.000  43.927   47.715      0.025    0.018    1632.0  1632.0   \n",
       "v0[2]   5.546  0.328   4.920    6.143      0.008    0.006    1649.0  1649.0   \n",
       "sigv    0.919  0.037   0.850    0.990      0.001    0.000    5188.0  5174.0   \n",
       "\n",
       "       ess_bulk  ess_tail  r_hat  \n",
       "v0[0]    1625.0    2122.0    1.0  \n",
       "v0[1]    1640.0    2098.0    1.0  \n",
       "v0[2]    1653.0    1991.0    1.0  \n",
       "sigv     5183.0    3203.0    1.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(azfit, var_names=['v0','sigv'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x316.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_forest(azfit, kind='ridgeplot', var_names=['sigv']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 993.6x662.4 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_pair(azfit, var_names=['v0','sigv']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zJQgzmw4sJVpRaoW7Lynbb/H+C4HXgY+5+88Cx1pdabMrk+/3Rz0Uja1JWxOhCufMAE6Lf84Gvk49FM4pTwp9FcatQS9E8pYCGue2oj9pg6gKe7vRXzdpmgZIJEEK58Tbd7q7A2vNrM3M3u7uLwSP+Fj1t+5C2h9/+b8WgZQngpLBJ/6GYaff0Lv9G2Dcquh1+9D2ur2t6E/ylmPad6YdSRantrM13jdpyZpMtxw1TyRZ/+jTEklaC7WACcWiv+l+DjCbCUyP153EzK4Eznb3TyaOuQ9Y4u4/ibf/A1jg7uvLztVbOAd4F/CLUF+kCk4BXs47iCpp5O8Gjf39Qny333f3kVkODFU4J1NxnWThnKIzs/WNujJ3I383aOzvV+vvlmU2Z5bCOZmK64hIfcmSIHoL55jZiUSFc+4tO+ZeYI5FzgF2F+L5g4hUJFThnPuJuji3EHVzfrx6IddMXdwKHadG/m7Q2N+vpt9twIeUItK8tKKUiKRSghCRVEoQKczsJjP7HzPbZGbfM7O2vGMKwcymm9kvzGyLmV2XdzyhmNk7zOw/zexpM3vSzObnHVM1mNkgM3s8HntUdUoQ6X4EnOHu44FngL/POZ6KJYbNzwDGApeb2dh8owrmIPC37n46cA7wiQb6bknzgadrdTEliBTu/kN3PxhvriUa21HveofNu/vvgNKw+brn7i+UJgi6+2tEf0Sj8o0qLDPrAC4CVtTqmkoQ2VwNPJB3EAGMAn6V2O6hwf6IAMysEzgLeCzfSIL7KvA54HCtLtjU60GY2UPA7/Wxa6G7fz8+ZiFR8/WuWsZWJZmGxNczMzsZ+C7wKXf/Td7xhGJmFwMvufsGM5tcq+s2dYJw9w/0t9/MrgIuBi7wxhgw0tBD4s2shSg53OXu9+QdT2CTgEvN7EKgFXizmX3T3a+o5kU1UCpFvEjOV4Dz3X1H3vGEYGaDiR64XgBsIxpGP8vdn8w1sADiRYtWAbvc/VN5x1NNcQvis+5+cbWvpWcQ6ZYBw4AfmdlGM1ued0CVih+6lobNPw38ayMkh9gk4Epgavzfa2P8r61UQC0IEUmlFoSIpFKCEJFUShAikkoJQkRSKUGISColCBFJpQSRAzNbbGafjV9/ycw+EL8+L56qvNHMhsRTzp80s5vKPv8mM3soPu4vqhzr54/zcx9OzqY0s24z63c1ZjObbGa7zez+xHZF05rN7PJ4uHxf+94Z/w73VHKNRtbUQ62LwN0XJTZnAze7+x0AZvaXwEh3/23Zx84CWtx9Qvn5zGyQux8KGOLngRvK34xHLpq7p00c+jBwH0cXWMrikcAjBKcDt/a1w93/D5igBJFOLYgaMbOF8UItDxEVDSq9v9LMZprZXODPgUVmdpeZ3QsMBR5LthLM7K3AN4n+x94Y/yu41cwWmdlPgI+Y2QQzW5tY7OYt8We7zewWM3s4XlhlopndY2b/a2b/2EfMS4Ah8XXuMrPO+HO3AT8D3pH844q/x0ozOxe4FLipFGN8yEfM7L/N7BkzO+8Yf38T44VS/iBuga0ysx/G3/3PzOyfzGyzma2O52SUktgE4Gdmdn5ihOXjZjbsWK7ftNxdP1X+Ad4HbAZOAt5MtPr3Z+N9K4GZ5a/j7T0p55sM3JfY3gp8LrG9iWgOCcCXgK/Gr7uBG+PX84kmar0deBPRRK4RfVxrT+J1J9FU43NS9s8EVqZ8l27gy/HrC4GHMnyvyUStkHOBDcDo+P3FwE+AFuBMopXUZ8T7vgd8OH79XqKSkAD/DkyKX58MDB7o96wfVwuiRs4Dvufur3s0Bbm8rkgI3wIws+FAm7v/OH5/FfAnieNK194MPOnRQiu/BZ7l6JmeaZ5z97XHGWNphuUGomSTxelES71f4u7JMusPuPsBou8xCFgdv785ce7pHFnH41HgK2b2N0S/n4PIgJQgaqfak172Zjyu9DzjcOJ1aTvLM6ny6yS/V2vGax/KeC2AF4D9RM9d3nAuj56BHPC4KcDR3+NDwA/j45YAc4EhRAWm353x+k1NCaI2HgYui3smhgGXVOtC7r4beCVxj38l8ON+PjKQA6V7+hQvmtnpZnYCcFni/deIZsNW6lWiZdZuOJaFUuKW1GB33xlvv9PdN7v7jcB6QAkiAyWIGvBorcRvARuJFjR5pMqXvIroAeEmood0X6rgXLcDm8wsbUWt64ieE6wh+te+5G7g7+IHgu/s85MZufuLREn1a2Z2dsaPfRB4KLH9KTN7wsx+DuyjMZYQrDpN95bCCLkQipmtAFZkeV5iZnvc/eRKr9mI1IKQIvkdcEZpoFQl3H3uQMmhNFAKeLHS6zUqtSBEJJVaECKSSglCRFIpQYhIKiUIEUn1/xOwmf+8xqlkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "v0_diff = stanfit['v0'] - cl.v0[None,:]\n",
    "\n",
    "plt.figure(figsize=(4,3))\n",
    "plt.hist(v0_diff, bins=64, histtype='step', density=True, label=['x','y','z']);\n",
    "plt.xlabel(\"diff from truth [km/s]\");\n",
    "plt.legend(loc='upper left');\n",
    "plt.axvline(0, c='gray', lw=.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_posterior(azfit, var_names=['sigv'], ref_val=cl.sigmav, figsize=(4,3));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.figure(figsize=(4,3))\n",
    "# g = np.einsum('ni,nij,nj->n', a-r['a_model'], np.linalg.inv(C), a-r['a_model'])\n",
    "\n",
    "# plt.hist(g, 32, density=True);\n",
    "# plt.hist(g, np.logspace(0, 4, 32), density=True);\n",
    "# plt.xscale('log');\n",
    "# plt.yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic cluster model with partial RVs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 150\n",
    "b0 = np.array([17.7, 41.2, 13.3])    # pc\n",
    "v0 = np.array([-6.32, 45.24, 5.30])\n",
    "sigv = 1.\n",
    "\n",
    "cl = kn.Cluster(v0, sigv, b0=b0)\\\n",
    "       .sample_sphere(N=N, Rmax=5)\\\n",
    "       .observe(cov=np.eye(3)*0.01)\n",
    "\n",
    "# Give random half of the stars RV with 0.5 km/s uncertainty\n",
    "Nrv = int(N*0.5)\n",
    "rv_error = 0.5\n",
    "irand = np.random.choice(np.arange(N), size=Nrv, replace=False)\n",
    "\n",
    "df = cl.members.observed.copy()\n",
    "df['radial_velocity'] = np.random.normal(cl.members.truth['radial_velocity'].values, rv_error)\n",
    "df['radial_velocity_error'] = rv_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v0, sigv = [-5.92897309 46.06913061  5.49316135], 0.9835\n",
      "diff from truth: [0.39102691 0.82913061 0.19316135], -0.0165\n"
     ]
    }
   ],
   "source": [
    "df = cl.members.observed.copy()\n",
    "fit = fitter.fit(df, sample=False)\n",
    "print(f\"v0, sigv = {fit['v0']}, {fit['sigv']:.4f}\")\n",
    "print(f\"diff from truth: {fit['v0']-v0}, {fit['sigv']-sigv:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "stanfit_partialrv = fitter.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# workaround for a bug in arviz instantiating InferenceData\n",
    "d = stanfit_partialrv.extract(permuted=False, pars=['v0', 'sigv'])\n",
    "d = {k:np.swapaxes(v, 0, 1) for k,v in d.items()}\n",
    "azfit_partialrv = az.from_dict(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "v0_diff0 = stanfit['v0'] - cl.v0[None,:]\n",
    "v0_diff = stanfit_partialrv['v0'] - cl.v0[None,:]\n",
    "\n",
    "plt.figure(figsize=(4,3))\n",
    "color = ['tab:blue', 'tab:green', 'tab:orange']\n",
    "plt.hist(v0_diff0, bins=64, histtype='step', density=True, color=color, lw=.5);\n",
    "plt.hist(v0_diff, bins=64, histtype='step', density=True, color=color, label=['x','y','z'], lw=2);\n",
    "plt.xlabel(\"diff from truth [km/s]\");\n",
    "plt.legend(loc='upper left');\n",
    "plt.axvline(0, c='gray', lw=.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axs = az.plot_posterior(azfit, var_names=['sigv'], ref_val=cl.sigmav, figsize=(4,3))\n",
    "az.plot_posterior(azfit_partialrv, var_names=['sigv'], ax=axs);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster with rotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 150\n",
    "b0 = np.array([17.7, 41.2, 13.3])    # pc\n",
    "v0 = np.array([-6.32, 45.24, 5.30])\n",
    "sigv = 1.\n",
    "omegas = [40., 20., 50.]\n",
    "\n",
    "cl = kn.Cluster(v0, sigv, b0=b0, omegas=omegas)\\\n",
    "       .sample_sphere(N=N, Rmax=15)\\\n",
    "       .observe(cov=np.eye(3)*0.001)\n",
    "cl0 = kn.Cluster(v0, sigv, b0=b0).sample_at(cl.members.truth.g.icrs)\n",
    "\n",
    "# Give random half of the stars RV with 0.5 km/s uncertainty\n",
    "Nrv = int(N*0.5)\n",
    "rv_error = 0.5\n",
    "irand = np.random.choice(np.arange(N), size=Nrv, replace=False)\n",
    "\n",
    "df = cl.members.observed.copy()\n",
    "df['radial_velocity'] = np.random.normal(cl.members.truth['radial_velocity'].values, rv_error)\n",
    "df['radial_velocity_error'] = rv_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:kinesis.models:Reading model from disk\n"
     ]
    }
   ],
   "source": [
    "m = kn.Fitter(include_T=True)\n",
    "fit = m.fit(df, sample=False, b0=b0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v0, sigv = [-6.382561   45.25883593  5.52632739], 0.9632\n",
      "diff from truth: [-0.062561    0.01883593  0.22632739], -0.0368\n",
      "[[ -5.47942742 -48.81690422  14.15808424]\n",
      " [ 27.02856238   6.34378433 -29.12948134]\n",
      " [ -4.03730063  37.49138498  -3.81971577]]\n",
      "[[  0. -50.  20.]\n",
      " [ 50.   0. -40.]\n",
      " [-20.  40.   0.]]\n"
     ]
    }
   ],
   "source": [
    "print(f\"v0, sigv = {fit['v0']}, {fit['sigv']:.4f}\")\n",
    "print(f\"diff from truth: {fit['v0']-v0}, {fit['sigv']-sigv:.4f}\")\n",
    "print(f\"{fit['T_param']}\")\n",
    "print(f\"{cl.T}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# omegax = 0.5*(r['T_param'][2, 1] - r['T_param'][1, 2])\n",
    "# omegay = 0.5*(r['T_param'][0, 2] - r['T_param'][2, 0])\n",
    "# omegaz = 0.5*(r['T_param'][1, 0] - r['T_param'][0, 1])\n",
    "\n",
    "# w1 = 0.5*(r['T_param'][2, 1] + r['T_param'][1, 2])\n",
    "# w2 = 0.5*(r['T_param'][0, 2] + r['T_param'][2, 0])\n",
    "# w3 = 0.5*(r['T_param'][1, 0] + r['T_param'][0, 1])\n",
    "# w4 = r['T_param'][0, 0]\n",
    "# w5 = r['T_param'][1, 1]\n",
    "# kappa = w4 + w5 + r['T_param'][2, 2]\n",
    "# print(omegax, omegay, omegaz)\n",
    "# print(w1, w2, w3)\n",
    "# print(w4, w5)\n",
    "# print(kappa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "stanfit = m.fit(df, b0=b0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "azfit = az.from_pystan(stanfit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAADTCAYAAAChpK65AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAE6JJREFUeJzt3XuQHNV1x/HvYSVYHsLYK8CEBe8aSIJ4CbJyHBHilcAuLIF4FE4CxiIFlMIfAVGEgMxDViBxiUccTImYULKRKFDkECDlECBAyBqIS0JakLU8YpuHlKwBAQLLCFi8SCd/dK80Gs3O9OxMv6Z/n6otzaNn+uxoz5zbt+/ta+6OiBTLLmkHICLJU+KLFJASX6SAlPgiBaTEFykgJb5IASnxRQpIiS9SQEp8kQIaF8ebTpw40bu6uuJ4axmjDz/8kD322CPtMCRm/f3977j7vrW2iyXxu7q6WL16dRxvLWPU19dHb29v2mFIzMxsfZTt1NQXKSAlvkgBKfFFCiiWY/xKhoeHGRwcZGhoKKld1qW9vZ3Ozk7Gjx+fdigisUss8QcHB5kwYQJdXV2YWVK7jcTd2bhxI4ODg3R3d6cdjpRb8KkKj21KPo4WklhTf2hoiI6OjswlPYCZ0dHRkdnWiEizJXqMn8WkH5Hl2CSkKt80iTX1d1Cp6daU99UfhkgU6tUXKaB0Kv6IZlXouFoQIi2qMBX/9ttvZ/LkyUyePJnu7m6mTZuWdkgiqSlM4l900UWsWbOGVatW0dnZyWWXXZZ2SCKpidTUN7N1wPvAFuATd++JM6g4zZ07l+nTp3PqqaemHcqYdc37950eW7dwZgqRNJnO1yemnmP8ae7+TmyRJGDJkiWsX7+eRYsWpR1K3Solu8hYpdy5l1ynXH9/PzfffDNPPfUUu+zSGkc46xbObM0vhAWb1GEbs6iJ78CjZubAP7r7HeUbmNkcYA7AwQcf3LwIm2TRokW8++672zr1enp6WLx4ccpR1a8lmvSSuqiJf7y7v25m+wGPmdn/uPuTpRuEXwZ3APT09FRfkC+F47Y777wz8X2KZFWkNq+7vx7++xbwAPCFOIMS2UZN/ljUTHwz29PMJozcBr4CPB93YCISnyhN/f2BB8JJLOOAZe7+SKxRieg0XqxqJr67vwock0AsIpKQ1jivJSJ1SeU8/lFLj4rlfQfOG4jlfVvB99Z8j4vXX7zDY/q8iivdATxSVUsOzpFMSDXxm1Vx4mpBJC2JRB84b6BlPi8Zu8JU/GuvvZaJEycyd+5cAK6++mr2339/LrnkkpQjqy3KaL3SL41cje7TefpUFCbxL7jgAs4880zmzp3L1q1bWb58Oc8880zaYVWUq8SVXCpM4nd1ddHR0cFzzz3Hhg0bOPbYY+no6Eg7rIaVfknkuk9A5+0TVZjEB7jwwgtZsmQJb775Jueff37a4cSq/Dh+EpOqbpO7Hv7yQwR9cdQl1cRPupPpjDPOYP78+QwPD7Ns2bJE950kdd5JLYWq+LvuuivTpk1jn332oa2tLe1wEjFSyUuXyS6t7rn7kiiv7OocHJNUEj+tZuXWrVtZsWIF9957byr7T0rumu2SuMIM2X3xxRc59NBDOfHEEznssMPSDkckVYVp6k+aNIlXX3017TBEMqEwiV8kuR3MI4kpTFNfRLZTxW8hEw6fBwSde/UO5sn1OX2pWyqJH9cIszw2a3M92k5ySxW/4HJ9Tr9U6fl8jeKrKdXEb1aFboWqmcfWiuRXYTr3Vq1axdFHH83Q0BAffPABRxxxBM8/r4sF596CTdt/JLLIFd/M2oDVwC/d/ZT4QorHlClTmDVrFtdccw0fffQR5557LkceeWTaYYmkop6m/lzgJWDvmGKJ3fz585kyZQrt7e3ceuutaYcjkpqoy2R3AjOBvwVyu7D8u+++y+bNmxkeHmZoaIg999wz7ZAySaf2Wl/Uin8LcAUwYbQNxrJoZtKdcnPmzOH666/ntdde48orr8zlctkizVAz8c3sFOAtd+83s97Rtqtr0cwU3HXXXYwbN45zzjmHLVu2MHXqVJ544gmmT5+edmiZkdipPU2lTV2Uin88MMvMZgDtwN5mdre7nzvWnaZx6mr27NnMnj0bgLa2NlauXJl4DCJZEWUJrW8C3wQIK/7ljSS9yDY6BZeawpzHF5Ht6hq55+59QN9Yd+buhKvuZo575rolRGKTWMVvb29n48aNmUwwd2fjxo20t7enHYpIIhIbq9/Z2cng4CBvv/12UrusS3t7O52dnWmHIZKIxBJ//PjxdHd3J7U7EalC03Kl9WiKbk3q1RcpIFV8aR2l1V2jA6tSxRcpICW+SAGpqd/iRmZATh73S/7skQ90iS8BlPi5VGnmnObNSz2U+C2i/MugvLKffs0dgFbZkYCO8XNMVV7GShU/56Im/6Un/Ta9vb1Aa1yOXBqjxE9BnhKv/BBCrYzWoKa+SAGp4qcoy51r5ZU918tryU6U+BI/DZ/NHDX1RQpIFV+Sk8YUWU3RrUiJnxM6xpZmirKgRjvwJLBbuP2/uPu34g5MpCGaoltVlIr/MTDd3Teb2XjgaTN72N1XxBybVKDz6NIMURbUcGBzeHd8+JO9S+VKtqjKZlrU1XLbgH7gUOA2d99p/amxLJop+aOVdFtDpMR39y3AZDPbB3jAzI509+fLtsn0opmSEvWkZ1K9K+n8ysz6gJOB52tsLi0ksZV0JRFRevX3BYbDpN8dOAm4IfbIJHaFm5uvc/rbRKn4BwBLw+P8XYB/dvcH4w2r9eRpRp60vii9+muBYxOIRRJSWt0b+ULKTUefzunvRCP3ElZPk1rH0hIXJb7UTR19+afEz4FMN6NLqRmdG5qWK1JAqvjSfAU/VZYHqvgiBaSKnyHqKJOkqOKLFJAqfgblphdfcksVX6SAVPGlKY5aehR0B9dhUHsl+1TxRQpIFV8aouG7+aSKL1JAqvgx0hx8ySpVfJECUsVPQB4ua1W4y3AVnCq+SAGp4jdZ3o7rG74MV+kc/O4cradQ8AtvquKLFFCUy2sfBNwFfBbYCtzh7t+NO7C8K9xx8oJNkIfz+LrwJhCtqf8J8Jfu/qyZTQD6zewxd38x5thEJCY1m/ru/oa7Pxvefh94CTgw7sBEJD51HeObWRfBNfYrLpppZqvNbPXbb7/dnOhEJBaRe/XNbC/gPuBSd/91+fNaNFNyq/xYvwC9/JEqvpmNJ0j6e9z9/nhDEpG4RenVN+D7wEvu/p34QxJJSHllL1Avf5Sm/vHAN4ABM1sTPnaVuz8UX1iSJg3fbX1RFs18GrAEYhGRhGjIrmzTrFV0JfuU+CnTVWskDRqrL1JAqvgZoWvpS5JU8UUKSIkvUkBq6ktV5b37Oq/fGlTxRQpIFV8qKq/s9ZzXLz1FqU7LbFLiN0gDXSSPlPjSNFpOKz+U+E2iTi/JEyW+1K9A01dblRI/BUVqBqujL5uU+DJ2BbhEVatS4qeolSugOvqyTYkvUq4Ay2tp5J5IAanii4wo0PJaSnypSzBScRkA6+p8bfmxfiv3cWSdEl+kmhY93o9yXf0fAKcAb7n7kfGHJFm0rv2cbbe7hpbV9dryyq5e/vRFqfhLgEUES2WLFEOLH+9Hua7+k+FimYXW6Cy8lqlyCzZBk2YkalRfepp2Ok+r5YrkR9M694qyWm6js/BU2TSqLws0gEekgHQ6L0aqZi2mhU7tRTmd909ALzDRzAaBb7n79+MOTFLWgj3Zsl2UXv2zkwikVVSq8jquz7kWPLWnpr5Ul/MmrVSmxI+JqrxkmXr1RQpIFb8KXTO/utLPR1cZzhdVfJECUsWPQNVsR6WfR2FbRTk/p6+KL1JAqviSCbmZqdci5/SV+LJdjv+QpT5KfEmVZuqlQ4kvO8thZ5XUR4lfdBls3ufmeD/HlPhl6j09peZpvHLxJZDDU3tKfAk04Q+20pdmPWMgdLyfHCX+KKr9wWrq7c6aPZAnF18COT61V8jEL+xos4SsWzgzls84d81+yGzTv5CJ3yyZ/eOrpYnVqVZTXhN5sqnQia8/xHyo1OzP3Dp85ZU9403/wiR+4Zv3fQuh77QdH4uxGVppIk/5/4G+eNNTmMQvnJ0qzh+kEkY1YzkMqLYOXyY7XTN6qi9S4pvZycB3gTZgsbsvjDWqGNVTZTLbm9yIFP74yj/zKK2vRk8NZlL5l0Clw4GE/n/MvfqiN2bWBvwc+DIwCKwCznb3F0d7TU9Pj69evbrq+zbzP7aeZvy6hTObltCpVxOofSwZ/iH19fXR29sbfzx1qvf/brTtJxw+b9vtgfMGslX9m3W8H+FLwcz63b2n1nZRKv4XgJfd/dXwjZcDpwGjJv5YJXEcPpakH3jtfys/kfEOnFZQmuzV/j7ef2l7IzTYbudGaXr9PPUtK15J6TLlzRAl8Q8E/q/k/iDw++UbmdkcYE54d7OZ/azB2CYC7zT4Hk1h1Z/OTJwV/fW26LMd53Y7xGk3pBhJbYl9pgal/5fVfC7KRlESv9Ledjo+KF00sxnMbHWUJkvaFGdz5SVOyFes5aJcemsQOKjkfifwejzhiEgSoiT+KuAwM+s2s12BPwV+FG9YIhKnKGvnfWJmfwH8B8HpvB+4+wuxR9bEw4aYKc7mykuckK9Yd1DzdJ6ItB5dXlukgJT4IgWUmcQ3s8+Y2WNm9ovw309X2bbNzJ4zsweTjDHcd804zewgM/svM3vJzF4ws7kJxneymf3MzF42s3kVnjczuzV8fq2ZHZdUbHXG+fUwvrVm9hMzOyaLcZZsN8XMtpjZWUnGN2bunokf4EZgXnh7HnBDlW0vIxgO9WAW4wQOAI4Lb08gGPI8KYHY2oBXgM8DuwI/Ld8vMAN4mGB8xheBlSl8hlHinAp8Orz91azGWbLdE8BDwFlJxzmWn8xUfIJhwEvD20uB0yttZGadwExgcUJxlasZp7u/4e7PhrffB14iGAEZt23Dq939N8DI8OpSpwF3eWAFsI+ZHZBAbHXF6e4/cff3wrsrCMaPJC3K5wlwMXAf8FaSwTUiS4m/v7u/AUHiAPuNst0twBXA1qQCKxM1TgDMrAs4FlgZe2SVh1eXf+FE2SZu9cZwAUErJWk14zSzA4EzgNsTjKthic7HN7PHgc9WeOrqiK8/BXjL3fvNrLeZsZXtp6E4S95nL4JKcKm7/7oZsdXaZYXHys/XRhqCHbPIMZjZNILE/8NYI6osSpy3AFe6+xazSGPpMyHRxHf3k0Z7zsw2mNkB7v5G2PSs1Gw6HphlZjOAdmBvM7vb3c/NWJyY2XiCpL/H3e9vZnxVRBlenYUh2JFiMLOjCQ7pvuruGxOKrVSUOHuA5WHSTwRmmNkn7v6vyYQ4Rml3MpR0kNzEjp1mN9bYvpd0OvdqxklQKe4Cbkk4tnHAq0A32zujjijbZiY7du49k8JnGCXOg4GXgalJx1dPnGXbLyEnnXupB1DyoXUA/wn8Ivz3M+HjvwU8VGH7tBK/ZpwEzVIH1gJrwp8ZCcU3g+AswivA1eFjFwEXhbcNuC18fgDoSen/u1aci4H3Sj6/1VmMs2zb3CS+huyKFFCWevVFJCFKfJECUuKLFJASX6SAlPgiBaTEFykgJX4TmdkCM7s8vH2dmZ0U3j4hnJ67xsx2N7Obwvs3lb1+NzN7PNzuT2KO9aoxvu50M5tUcr/PzKpeadbMes1sk5k9VHK/oSnVZna2mVUcQm1mh4Sf4eZG9tHKtHZeTNx9fsndrwM3u/udAGb258C+7v5x2cuOBca7++Ty9zOzNnff0sQQrwK+XWE/RnBJttEmQZ0OPEj9C6o85e6n1Pmaak4Gbq30hLu/AkxW4o9OFb9BZnZ1eKGGx4HfKXl8iZmdZWYXAn8MzDeze8zsR8CewMrSqm5m+wF3E/zBrgmr1jozm29mTwNfM7PJZrYivDjFAyMXAQmr7t+b2ZPhxT+mmNn94cVC/qZCzAuB3cP93GNmXeHr/gF4FjioNGnC32OJmU0FZgE3jcQYbvI1M3vGzH5uZifU+flNseCiKp8PW0xLzezR8Hc/08xuNLMBM3sknP8w8uU0GXjWzL4UxrImfJ8J9ey/sNIeOpjnH+D3CIa97gHsTTC2/HIvG75J2VBOYPMo79dLyTBkYB1wRcn9tcCXwtvXEc4FAPoILwgCzCWYSHIAsBvBRJOOCvvaXHK7i2Ca8xdHef4sYMkov0sf8Hfh7RnA4xF+r16CVsNUoB84OHx8AfA0MB44BviQYIIOwAPA6eHt4wiuKQDwb8Dx4e29gHG1Pmf9ZOtCHHl0AvCAu3/owbTbONYb+CGAmX0K2Mfdfxw+vhT4o5LtRvY9ALzgwcVAPiaYZFI6w2w06z24MMdYjMw+7Cf4EonicILLU5/q7qWLEz7s7sMEv0cb8Ej4+EDJe5/M9vn5/w18x8wuIfh8PhnLL1A0SvzGxT3Z4YOI2430F2wtuT1yP0pfTvl+Sn+v9oj73hJxXwBvAEME/Ro7vZcHfQzDHpZudvw9vgI8Gm63ELgQ2B1YYWa/G3H/habEb8yTwBlhT/0E4NS4duTum4D3So6hvwH8uMpLahkeOWYexQYzO9zMdiG4wsyI9wmuI9ioXxFMEf52PRdVCVs+4zycn29mh7j7gLvfAKwGlPgRKPEb4MF19X5IMG30PuCpmHd5HkHH2lqCzq3rGnivO4C1ZnbPKM/PIzgOf4KgOo9YDvxV2JF2SMVXRuTuGwi+LG8zs51WYB7Fl4HHS+5fambPm9lPgY9I5xJduaNpuRK7sKJf7k04nWdmi4HFUfojzGyzu+/V6D5bkSq+JOE3wJEjA3ga4e4X1kr6kQE8wIZG99eqVPFFCkgVX6SAlPgiBaTEFykgJb5IAf0/6Qeb0hNwLzgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "v0_diff = stanfit['v0'] - cl.v0[None,:]\n",
    "\n",
    "plt.figure(figsize=(4,3))\n",
    "color = ['tab:blue', 'tab:green', 'tab:orange']\n",
    "plt.hist(v0_diff, bins=64, histtype='step', density=True, color=color, label=['x','y','z'], lw=2);\n",
    "plt.xlabel(\"diff from truth [km/s]\");\n",
    "plt.legend(loc='upper left');\n",
    "plt.axvline(0, c='gray', lw=.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 3, figsize=(8, 8), sharex=True, sharey=True)\n",
    "fig.subplots_adjust(bottom=0.1, top=0.95, right=0.95, left=0.15)\n",
    "ax = ax.ravel()\n",
    "for cax, cT, truth in zip(ax, stanfit[\"T_param\"].reshape((-1, 9)).T, cl.T.ravel()):\n",
    "    cax.hist(cT, bins=32, density=True, histtype=\"step\")\n",
    "    cax.axvline(truth, c=\"k\")\n",
    "    cax.axvline(0, c=\"gray\", lw=0.5)\n",
    "fig.text(0.55, 0.05, \"m/s/pc\", ha=\"center\", va=\"center\", size=20)\n",
    "fig.text(0.05, 0.55, \"Density\", ha=\"center\", va=\"center\", rotation=90, size=20)\n",
    "fig.savefig(\"mock_posterior_T.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster with rotation - ideal case\n",
    "\n",
    "Test the most ideal case when the cluster has small dispersion and all velocities are measured to high precision."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 150\n",
    "b0 = np.array([17.7, 41.2, 13.3])    # pc\n",
    "v0 = np.array([-6.32, 45.24, 5.30])\n",
    "sigv = 0.1 # small dispersion\n",
    "omegas = [40., 20., 50.]\n",
    "\n",
    "cl = kn.Cluster(v0, sigv, b0=b0, omegas=omegas)\\\n",
    "       .sample_sphere(N=N, Rmax=15)\\\n",
    "       .observe(cov=np.eye(3)*0.001)\n",
    "cl0 = kn.Cluster(v0, sigv, b0=b0).sample_at(cl.members.truth.g.icrs)\n",
    "\n",
    "# Give all stars observed RV with small uncertainty\n",
    "Nrv = int(N)\n",
    "rv_error = 0.1\n",
    "irand = np.random.choice(np.arange(N), size=Nrv, replace=False)\n",
    "\n",
    "df = cl.members.observed.copy()\n",
    "df['radial_velocity'] = np.random.normal(cl.members.truth['radial_velocity'].values, rv_error)\n",
    "df['radial_velocity_error'] = rv_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:kinesis.models:Reading model from disk\n"
     ]
    }
   ],
   "source": [
    "m = kn.Fitter(include_T=True)\n",
    "fit = m.fit(df, sample=False, b0=b0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v0, sigv = [-6.31158325 45.23302118  5.30911615], 0.0974\n",
      "diff from truth: [ 0.00841675 -0.00697882  0.00911615], -0.0026\n",
      "[[ -0.53514835 -49.6406874   18.48196561]\n",
      " [ 52.87740998   3.07364283 -38.98217046]\n",
      " [-19.87667794  42.07821215   2.22755235]]\n",
      "[[  0. -50.  20.]\n",
      " [ 50.   0. -40.]\n",
      " [-20.  40.   0.]]\n"
     ]
    }
   ],
   "source": [
    "print(f\"v0, sigv = {fit['v0']}, {fit['sigv']:.4f}\")\n",
    "print(f\"diff from truth: {fit['v0']-v0}, {fit['sigv']-sigv:.4f}\")\n",
    "print(f\"{fit['T_param']}\")\n",
    "print(f\"{cl.T}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:pystan:8 of 4000 iterations saturated the maximum tree depth of 10 (0.2%)\n",
      "WARNING:pystan:Run again with max_treedepth larger than 10 to avoid saturation\n"
     ]
    }
   ],
   "source": [
    "stanfit = m.fit(df, b0=b0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (T_param_dim_0: 3, T_param_dim_1: 3, a_model_dim_0: 150, a_model_dim_1: 3, chain: 4, draw: 1000, rv_model_dim_0: 150, x: 3)\n",
       "Coordinates:\n",
       "  * chain           (chain) int64 0 1 2 3\n",
       "  * draw            (draw) int64 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
       "  * x               (x) <U2 'v1' 'v2' 'v3'\n",
       "  * a_model_dim_0   (a_model_dim_0) int64 0 1 2 3 4 5 ... 145 146 147 148 149\n",
       "  * a_model_dim_1   (a_model_dim_1) int64 0 1 2\n",
       "  * rv_model_dim_0  (rv_model_dim_0) int64 0 1 2 3 4 5 ... 145 146 147 148 149\n",
       "  * T_param_dim_0   (T_param_dim_0) int64 0 1 2\n",
       "  * T_param_dim_1   (T_param_dim_1) int64 0 1 2\n",
       "Data variables:\n",
       "    v0              (chain, draw, x) float64 -6.303 45.23 5.299 ... 45.23 5.306\n",
       "    sigv            (chain, draw) float64 0.1037 0.0987 0.104 ... 0.1028 0.1002\n",
       "    a_model         (chain, draw, a_model_dim_0, a_model_dim_1) float64 23.24 ... 18.95\n",
       "    rv_model        (chain, draw, rv_model_dim_0) float64 42.07 41.79 ... 35.94\n",
       "    T_param         (chain, draw, T_param_dim_0, T_param_dim_1) float64 0.1346 ... 2.193\n",
       "Attributes:\n",
       "    created_at:                 2019-05-22T21:57:36.938397\n",
       "    inference_library:          pystan\n",
       "    inference_library_version:  2.18.0.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azfit = az.from_pystan(stanfit, coords={'x':['v1','v2','v3']}, dims={'v0':['x']})\n",
    "azfit.posterior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (chain: 4, draw: 1000)\n",
       "Coordinates:\n",
       "  * chain        (chain) int64 0 1 2 3\n",
       "  * draw         (draw) int64 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    accept_stat  (chain, draw) float64 0.8102 0.9105 0.9356 ... 0.9653 0.813\n",
       "    stepsize     (chain, draw) float64 0.1635 0.1635 0.1635 ... 0.171 0.171\n",
       "    treedepth    (chain, draw) int64 5 6 5 5 5 5 5 5 5 5 ... 6 7 6 10 6 5 6 5 6\n",
       "    n_leapfrog   (chain, draw) int64 31 63 31 63 31 31 ... 1023 127 63 127 31 63\n",
       "    diverging    (chain, draw) bool False False False ... False False False\n",
       "    energy       (chain, draw) float64 -1.252e+03 -1.248e+03 ... -1.273e+03\n",
       "    lp           (chain, draw) float64 1.326e+03 1.335e+03 ... 1.345e+03\n",
       "Attributes:\n",
       "    created_at:                 2019-05-22T21:57:36.944296\n",
       "    inference_library:          pystan\n",
       "    inference_library_version:  2.18.0.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azfit.sample_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "v0_diff = stanfit['v0'] - cl.v0[None,:]\n",
    "\n",
    "plt.figure(figsize=(4,3))\n",
    "color = ['tab:blue', 'tab:green', 'tab:orange']\n",
    "plt.hist(v0_diff, bins=64, histtype='step', density=True, color=color, label=['x','y','z'], lw=2);\n",
    "plt.xlabel(\"diff from truth [km/s]\");\n",
    "plt.legend(loc='upper left');\n",
    "plt.axvline(0, c='gray', lw=.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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_posterior(azfit, var_names=['sigv']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/YD3v/naEV0d0uOR86enp3H333XTq1Innn3/+ipmEcCRScMLmNGxY8SoALi4uFaYv2BpHXklpKRQUFJCamorFYsHHx+eSy0tKSiIoKIjhw4eTnZ3Nq6++SmRkJB4eHrz88sukpKRUmN/b2xuT6Y+t9+dev6CgoHxa8+bNCQ4OvuL7OL+4IiIiOHnyZPn9mJiY8k2JGRkZFZ6Xnp5e/n7bBXkzrmcoX24+yb3dm9M20LvCvAUFBYwYMYLCwkJ+/PHH8qxCCCk4Uc8UW6zM3RBD5+CGJJZN8/X1xcnJiejo6ArFdI6/vz/Hjh1j165dLFu2jFtuuaX8sfz8/GrlmDhx4kWDWC7Uv39/1qxZA8DSpUspLCwsfywoKAgXFxdCQkI4dOhQheedu39u39xfb27N97tP8dbyQ3z+QPfy+SwWC2PHjmX//v1s3LiRgICAar0XIeyVFJyoV375XxKnMgt4YlAIv5RNGzhwIBaLhczMTG6++eZLPu9ckZ0/fP7kyZNER0fTqVPVDzGo6ibKjh07XnKeoUOHsmTJEl5//XXM5tKLsy5cuJCQkBA6dCjdJOnj7sykARFMX3aIzSfO0rNFY6D0cILly5ezcuVKWrduXeX3IIS9k4IT9YbWmk/WniCiiQe9WvwxTL5169Y89thjjBkzhilTphAVFUVBQQH79+/nyJEjzJkzhzZt2hAcHMzkyZN57bXXyM7OZurUqTRr1qxaWcLCwggLC7vm9/Tss8/y1VdfMW7cOB5++GG2bdvGJ598wscff1xhE+cTN7UmcMD9vBHSkCWTejN9+nRmz57Nc889h8lkYvPmzeXztmvXDm9v70u9nBAORQpO1Bubjp/lQFIWb97dEZOp4mmsZs6cSatWrfj00095+eWX8fb2pl27djz44INA6Zrbd999xxNPPMHIkSMJDg7mhRdeYM2aNezbt8+ItwOUDqBZvnw5f/3rXxk6dChNmzblnXfe4aGHHqown8VioUd4I7bHZ7Bif3L58XvTp09n+vTpFeZdvXp1hUEyQjgqOZOJqDfGfbaFg0nZbPj7jbg5m42OU+dKLFaGvLcOgBVP34CTWQ5jFQ5LzmQi7MfBpCzWH03lgT5hDlluAE5mE88OacPxM7ks3plw9ScI4eCk4ES9MHdDDA2czdzXo7nRUQw1pH0AXUIaMuO3oxQUy5UDhLgSKThh89Jyi/hhzynuvK4ZDd0d+zgvpRRTbmnN6awCFm6r3Cm8hHBUUnDC5i3YFkdRiZXxvcKMjmITerVoTFRoIz5Ze/yK14wTwtFJwQmbVmKx8tXmOHq1aFxvL2ha05RSPDEwklOZBXy/K/HqTxDCQUnBCZu28mAKiRn5jO8dZnQUmzKgVRM6NPPm47XHsVzicjpCCCk4YeO+2BhLkI8bg9r6Gx3FpiileGJAJDGpufz8vySj4whhk6TghM06kpzNphNnub9XqBzzdQlD2jcl0t+Tj1Yfu+RFUYVwdPJXQ9isLzbG4uJkYsz1jn1owOWYTIpJAyI4dDqbVYdSrv4EIRyMFJywSZn5xXy3M5HhnYPw9XDsQwOuZHjnIEJ8G/Dh6mNU4axEQjgEKThhkxbtSCC/2MIEGVxyRU5mE4/eEMGe+Ay2xqQZHUcImyIFJ2yO1ar5clMs3UIb0aHZpS9iKv4wslswvh4ufLo+xugoQtgUKThhc9YeOUPs2Tw5NKCS3JzN3N8zlFWHkjlxJsfoOELYDCk4YXPmbYyliZcrt7RvanSUemNcz1CczSY+2yBrcUKcIwUnbEpMai5rj5zhvh7NcXGSX8/KauLlyp1dmrF4ZwJpuUVGxxHCJshfEGFT5m+Nw2xSjO0uhwZU1YP9wikotvLV5pNGRxHCJkjBCZtRUGzh2+3xDG4XgL+3m9Fx6p1WAV70b9WELzadlEvpCIEUnLAhK/afJj2vmLEOfs23a/Fwvxak5hTy455TRkcRwnBScMJmfLU5jtDG7vSJ8DM6Sr3VJ7IxbZp68dn6GDnwWzg8KThhE44mZ7M1No17uzfHZFJGx6m3lFI83K8Fh5OzWXc01eg4QhhKCk7YhK+2xOFsVozqFmx0lHpvWOcg/L1cmbP+hNFRhDCUFJwwXH6Rhe92JnBLh0Aae7oaHafec3EyMb53GOuPpnLodJbRcYQwjBScMNxPe0+RVVDCfTK4pMbc16M5DZzNzJHTdwkHJgUnDPf11jgimnjQI9zX6Ch2o6G7C6Oigvlx9ynOZBcaHUcIQ0jBCUMdOJXFrrgMxvYIRSkZXFKTJvQOo8hi5b9y4LdwUFJwwlBfbz2Ji5OJu69rZnQUu9OiiSc3tfHnqy1y4LdwTFJwwjC5hSV8v+sUt3cKpKG7XNS0NkzsG05qThFL5cBv4YCk4IRhftxzipxCGVxSm3pHNKZ1gBdzo2PlwG/hcKTghGG+3hJHm6ZeXNe8kdFR7JZSiol9wziYlMWmE2eNjiNEnZKCE4bYm5DB/xIzGdujuQwuqWUjujTD18OFuRtijY4iRJ2SghOG+HpLHA2czdzRVQaX1DY3ZzP392jOqkPJxKbmGh1HiDojBSfqXFZBMT/sPsXwzkF4uzkbHcch3N8zFCeTYt7GWKOjCFFnpOBEnfthVyL5xRa5LE4d8vd2Y1inIL7ZHk9mfrHRcYSoE1Jwok5prflqSxwdmnnTKdjH6DgOZWLfcPKKSi8qK4QjkIITdWpnXAaHTmcztrucuaSudWjmQ/dwXz6PjqXEYjU6jhC1TgpO1KmvtpzE09WJ4V2CjI7ikCb2CScxI5/fDiQbHUWIWicFJ+pMRl4RP+9NYkSXIDxdnYyO45BubhdAcKMGzI2WqwwI+ycFJ+rM4p2JFJZYua9HqNFRHJbZpJjQO4xtsensTcgwOo4QtUoKTtQJrTXzt8bRJaQh7YK8jY7j0EZfH4KnqxOfR8caHUWIWiUFJ+rEjpPpHEvJYWx3OTTAaF5uzoyKCmbpnlMkZxUYHUeIWiMFJ+rEgm3xeLiYua1ToNFRBKXXirNozZeb5Fpxwn5JwYlal1VQzM97kxjeJQgPGVxiE0IbezCobYBcK07YNSk4UeuW7jlFfrGF0dfL5klb8mDfcNLzivl+V6LRUYSoFVJwotYt3BZPm6ZedJYzl9iUHuG+tAv0Zm50jFwrTtglKThRq/afymRvQiajrw+RM5fYmNJrxYVzJDmHDcdSjY4jRI2TghO16ptt8bg4mbhTLotjk4Z1DsTP05W5G+TAb2F/pOBErSkotrBkVyJDOzSlobuL0XHEJbg6mRnXM5TVh89wLCXH6DhC1CgpOFFrlu87TVZBCaOvDzE6iriC+3o2x8VsYt5GWYsT9kUKTtSaBdviCG3sTs/wxkZHEVfg5+nKiC5BLN6RSEZekdFxhKgxUnCiVsSk5rL5RBr3RIVgMsngEls3sW84+cUWvt4aZ3QUIWqMFJyoFQu3xWM2KUZ2CzY6iqiEtoHe9Gvpx7zoWApL5MBvYR+k4ESNK7ZYWbQjgRtb+xPg7WZ0HFFJD/drQUp2IT/uPmV0FCFqhBScqHG/H0ohNaeQMTK4pF7p19KPNk29+HT9CTnwW9gFKThR4xZui8ffy5UBrZsYHUVUgVKKh/u14EhyDmuPnDE6jhDXTApO1KikzHzWHE5hVFQwTmb59apvhnUOoqm3G7PXnTA6ihDXTP4CiRq1aHsCVg33RMnmyfrIxcnEA33C2Hj8LPsSM42OI8Q1kYITNcZq1SzcHk/viMaENvYwOo6opnt7NMfT1YlP18tanKjfpOBEjdl4/CwJ6fly5pJ6ztvNmTHXh/DT3iQSM/KNjiNEtUnBiRqzYFscPg2cGdK+qdFRxDV6oG84AJ/LSZhFPSYFJ2pEWm4Rv+5P5s6uzXBzNhsdR1yjZg0bcHunQOZvjSMzv9joOEJUixScqBFLdiVSZLEyprtsnrQXD/drQW6RhQVy+i5RT0nBiWumtWbB1ji6hDSkTVNvo+OIGtKhmQ+9IxrzeXQsRSVWo+MIUWVScOKa7YzL4GhKjpy5xA49fEMLTmcV8NNeOX2XqH+k4MQ1W7gtDncXM7d3DjI6iqhhA1o1oaW/J7PXyem7RP0jBSeuSXZBMUv3JDGsUxCerk5GxxE1TCnFwze04NDpbDYcSzU6jhBVIgUnrslPe5PIL7YwWgaX2K0RXYJo4uUqp+8S9Y4UnLgmC7bF0yrAk64hDY2OImqJq5OZCb3DWH80lf2n5PRdov6QghPVdjApiz3xGYy+vjlKyVW77dn9PUPxdHXik7WyFifqDyk4UW0Lt8XjYjZxZ9dmRkcRtcyngTNjezTnp72niE/LMzqOEJUiBSeqpaDYwpJdiQxuH4Cvh4vRcUQdmNgnHLNJMUdOwizqCSk4US0r9p8mM7+YMdc3NzqKqCNNfdy4o0szFm6P52xOodFxhLgqKThRLQu3xRPcqAG9IxobHUXUoUf7t6Cg2MoXm04aHUWIq5KCE1V28mwuG4+fZXRUCCaTDC5xJJH+XgxqG8B/NsWSV1RidBwhrkgKTlTZwm3xmBSMkqt2O6THB7QgI6+YhdvijY4ixBVJwYkqKbFY+XZHAje29qepj5vRcYQBuoX6cn1YI+asj6HYIidhFrZLCk5UyerDZziTXShX7XZwj/WPIDEjX07CLGyaFJyokoXb4mji5cqNbfyNjiIMdGNrf1r6e/LJWjkJs7BdUnCi0pKzCvj9UAojuwXjbJZfHUdmMike7R/BodPZrDlyxug4QlyS/JUSlfbNtnisGu6RwSUCGN45iEAfN2atOW50FCEuSQpOVIrVqlmwLZ7eEY0J9/MwOo6wAS5OJh7sG86WmDR2xaUbHUeIi0jBiUpZfyyVxIx87u0uZy4RfxjTvTnebk7MWitrccL2SMGJSpm/JQ5fDxcGtw8wOoqwIZ6uTvypVxi/Hkjm+Jkco+MIUYEUnLiqlOwCVh5MZmS3YFydzEbHETZmQp8wXMwmPpULogobIwUnrmrRjgRKrJoxcuybuAQ/T1dGRQXz3c5EUrIKjI4jRDkpOHFFVqtmwdZ4eoT70qKJp9FxhI16uF8LSqxWPouOMTqKEOWk4MQVbTx+lri0PMb2kMEl4vJCG3swtGMgX2+OI6ug2Og4QgBScOIq5m+No6G7M0PaNzU6irBxj/ePILuwhK+3xBkdRQhACk5cQWpOIb8eOM3d1wXj5iyDS8SVdWjmQ99IP+ZuiKGwxGJ0HCGk4MTlLd6RQLFFc293GVwiKufR/i1IyS5kyc5Eo6MIIQUnLk1rzfytcXQP8yXS38voOKKe6BvpR/sgb2avO4HVKidhFsaSghOXtOnEWWLP5jFG1t5EFSileKx/BCdSc/n1wGmj4wgHJwUnLmn+1ni83Zy4tWOg0VFEPTO0Q1NCG7vz4epjcikdYSgpOHGRM9mFrNh3mrtkcImoBieziUkDItiXmMWaw3IpHWEcKThxkQVb4yiyWBnXK9ToKKKeurNrMM0aNuDfvx+VtThhGCk4UUGxxcp/t5ykX0s/IurpmUu+//57OnXqhKurK+Hh4bz77rtXnP/pp59GKcXf/va3CtMPHTpEjx498PHxYcyYMeTkVDyZ8Lp162jWrNlF0y9l3rx5KKUuOe+0adPw8/Mrvx8bG4tSqvzm5eVFVFQU33zzzWXn8fDwICIigvvuu4/169dfNU9tc3Ey8diACHbFZRB97KzRcYSDkoITFazYf5rkrEIm9A4zOkq1REdHc9ddd9G9e3eWLl3KxIkT+fvf/8577713yfkPHDjA3Llz8fb2vuixCRMmEBkZyTfffMOBAwf45z//Wf6Y1Wrl6aefZvr06Xh61s4XgbfffptNmzaxePFiWrZsyejRo/npp58uOc8vv/zCSy+9xNmzZ7nhhht45ZVXaiVTVYzqFkyAtysf/H7U6CjCUWmtK3sTDmDkx9G635u/6xKL1ego1TJ48GDdr1+/CtOeeeYZ3ahRI11YWHjR/DfddJN+8cUXdWhoqJ48eXL59OzsbA3olJQUrbXWCxYs0FFRUeWPz549W3fv3l1brZX7nD7//HMN6Ozs7Isemzp1qm7cuHH5/ZiYGA3opUuXlk+zWCy6VatW+tZbb73sPOe89NJLGtCrV6+uVLba9NlxwNJXAAAbh0lEQVT6Ezr07z/pLSfOGh1F2JdK9ZaswYly+09lsi02nT/1CsVsUkbHqZbdu3czaNCgCtMGDx5Meno6mzZtqjB90aJFHDx4kH/84x8XLaeoqAiABg0aAODu7l4+LSsri5deeon3338fpermczKZTHTp0oXY2Nirzjt16lSCgoKYNWtW7Qe7inu7N8fP00XW4oQhpOBEuf9sPEkDZzOjutXfY98KCgpwcXGpMM3V1RWAgwcPlk/Lz89n8uTJvPHGG3h4eFy0HF9fX8LDw/nggw9IS0tj9uzZREVFAfDaa68xaNAgevbsWeV8FouFkpKSCjer1Vqp58bGxtK06dXPCWo2mxk4cCCbN2+ucr6a1sDFzMP9WrD+aCq74tKNjiMcjJPRAYRtSM8t4vvdidx1XTA+7s5Gx6m2yMhItm3bVmHa1q1bAUhLSyufNn36dAIDA7n//vsvu6yZM2cyatQonn/+eVq2bMnMmTM5duwYn332GXv37q1WvoYNG15yeuPGjS+aZrVaKSkpISsrizlz5rB161Y++OCDSr1OcHAwycnJ1cpY0+7vGcqstcf54PdjzJ1wvdFxhAORghMALNweT2GJlfG96/ehAY899hiPP/44n376KSNHjmTr1q288847QOmaDUBMTAxvv/02v//++xU3MQ4dOpSUlBQSEhKIiIjAbDYzfPhwnnnmGYKDg5k5cyZvvvkmAP/4xz+YNGnSVfOtW7eufLPnObNnz+a77767aN4RI0aU/9vZ2Zm//vWvPP7441f/EMCmhuZ7uDrxYN9w3v71CPsSM+nQzMfoSMJBSMEJii1W/rMxlp4tfGnT9OLRhPXJxIkT2bNnD48//jiPPPII7u7uvPnmmzz55JMEBAQApWU0dOhQ2rRpQ0ZGBlC6tlRYWEhGRgY+Pj7lxefu7k6rVq0AWLlyJXv27GHhwoXs2bOHl156iY0bNwLQq1cv+vbtS6dOna6Yr2vXrheNurxwZOQ5M2bMoG/fvnh5eREeHn7RptcrSUxMLH+/tuBPvcP4ZN0JPvj9KJ+MizI6jnAQsg9OsHTPKU5lFvDoDRFGR7lmZrOZDz/8kDNnzrB3716Sk5PL95Wd+3n48GG+++47GjVqVH6Lj4/nww8/pFGjRiQmXnwm/JKSEp5++mneeustGjRowJo1axg4cCBt2rShTZs23HTTTaxdu7ZG30tkZCRRUVG0bt26SuVWUlLC77//Tq9evWo0z7XwdnPmgd5hrNifzMGkLKPjCAcha3AOTmvNJ2tP0DrAiwGtmxgdp8acKy6Ajz76iN69e9OmTRsA5syZc9EB12PGjKF///48/vjjNGly8ecwa9YsGjVqxOjRo8un5eXllf87NzfXZjYLvvrqq5w6dYrHHnvM6CgVPNi3BZ9vjOWdX48wZ7ysxYnaJwXn4NYcOcPh5GzeGdW5zoa816bNmzezYcMGunTpQlZWFvPnz2fFihVs2LChfJ5zoyHP5+bmRkhICAMGDLjosfT0dF555RVWrFhRPu2GG25gypQpzJ07F4Dff/+dN954o+bf0FUcPnwYPz8/ioqKiImJYcGCBSxfvpxp06bRv3//Os9zJT7uzjx6Qwve/vUIO+PSua55I6MjCTsnBefgPll7nEAfN4Z1DjI6So1wdnZm4cKFTJs2DZPJRL9+/YiOjqZjx47VXubUqVMZPnw41113Xfm0rl278tZbb/HCCy8ApWcU6dy58zXnr6pzpxdzc3MjMDCQXr16sW7dOvr161fnWSrjgT7hfB4dyzu/Huarh6p+mIUQVaGqsFnFNra/iBqzOz6DO2ZG8+JtbXmoXwuj4wgH8dmGGF776QBfPdSDPpF+V3+CEBer1OYmGWTiwGavO463mxNjujc3OopwIPf1aE6gjxv/WnHYZvZbCvskBeegYlJzWbbvNON6heLpKluqRd1xczbz1E0t2R2fwcqDKUbHEXZMCs5BzV53HGezifH19KoBon67u1sw4X4evL3iMBarrMWJ2iEF54Di0/L4dnsCo6NC8PdyMzqOcEDOZhN/vbkVh5OzWbwzweg4wk5JwTmgmauPYTIpnrgx0ugowoHd3imQrs0b8q8Vh8ktLDE6jrBDUnAOJj4tj0U7EhjbvTlNfWTtTRhHKcWLt7XjTHYhn6w9bnQcYYek4BzMjN+OYDIpHh9Q/0/LJeq/bqGNGNY5iNnrT3AqI9/oOMLOSME5kH2JmSzZncjEPuEEeMvam7ANU4a0xqrhXysOGx1F2BkpOAehtWb6soM0bODMpBtl7U3YjhBfdx7sG86SXYnsic8wOo6wI1JwDmLNkTNEHzvLX25qibdb/b2gqbBPkwZE4Ofpwms/HZCDv0WNkYJzABar5o1fDhHW2J37etTvC5oK++Tl5syzQ1qz/WQ6i3bIYQOiZkjBOYBFO+I5nJzNlFva4OIk/8mFbRrVLYTrmjdk+rJDpOcWGR1H2AH5a2fn8opKeOfXI1zXvCFDOzQ1Oo4Ql2UyKf7vzo5k5hfz1opDRscRdkAKzs59tPo4KdmFPH9rW7u43puwb20DvXmgdxjzt8az42Sa0XFEPScFZ8eOpWTzybrj3NW1GVFhvkbHEaJSnr65FYE+brywZB8lFqvRcUQ9JgVnp7TWvPj9Pho4m3n+trZGxxGi0jxdnZg6rB2HTmczb2Os0XFEPSYFZ6cW7Uhg84k0/j60DX6erkbHEaJKhrRvysA2/rz962FiUnONjiPqKSk4O5ScVcBrPx2ge5gv914vFzMV9Y9Sin/e2REXs4nJ3+yWS+qIapGCszNaa57/7n8Ullh5c2QnTCYZWCLqp6Y+brwyoj074zKYs/6E0XFEPSQFZ2e+3ZHAqkMpPDukNeF+HkbHEeKa3NGlGUPaB/DOb0c4mpxtdBxRz0jB2ZHY1Fxe+XE/PVv48kCfcKPjCHHNlCo9Ns7T1YnJ3+6hWEZViiqQgrMTxRYrTy/cjdmkePeeLphl06SwE36errx+Rwf2JmTy4e/HjI4j6hEpODvx5rJD7I7PYPpdnQhq2MDoOELUqFs7BnJX12Z88PtRNh0/a3QcUU9IwdmB5fuSmLMhhvG9QrmtU6DRcYSoFa/d0YEwPw+eWrCL1JxCo+OIekAKrp47lpLN377dS+eQhnJAt7BrHq5OzBx7HRn5xTyzcDdWOXRAXIUUXD2WkVfEg19sx83ZxEf3XYerk9noSELUqraB3kwd1o71R1P5eO1xo+MIGycFV08VlViZ9NVOkjIK+GRcN5rJfjfhIMZ2b87tnQJ597cjbDkh++PE5UnB1UNWq2bKoj1sPH6W6Xd1pFuonEhZOA6lFNPv6kiorzuTvtpJYka+0ZGEjZKCq2e01kxfdpDvd5/i2SGtubtbsNGRhKhzXm7OzP5TFEUlVh75z3byiyxGRxI2SAqunpmx8iifri8dMTlpQITRcYQwTKS/J+/f24UDSVlMWbwXrWXQiahICq4e+fD3o/x71VHuiQpm6rD2cgFT4fAGtgng2SGtWbrnlAw6ERdxMjqAuDqtNW8uP8ystce5s2szpt8lJ1EW4pzH+0dwKCmbf604TAs/T27p0NToSMJGqCqs1sv6vwEsVs3LP+zjqy1x3NejOa+O6CCn4RLiAvlFFsbO2cyBU1l89VAPuYK9/avUH0EpOBtWbLEy+Zs9/LjnFI8PiGDKkNayWVKIy0jLLWLkxxs5m1vE4sd7EenvZXQkUXuk4OqzzPxi/vz1TtYfTWXKLa2ZNCDS6EhC2Lz4tDzu/Ggjrk4mvpvUmwBvN6MjidohBVdfnTiTw0P/2U58Wh6v39GB0XJVbiEqbV9iJqM/2USIrzsLH+mFj7uz0ZFEzatUwckoShuz4Wgqd8yMJiOvmP8+2OOy5ZaUlMSzzz5L586d8fT0JCQkhPHjx3Pq1KmrvoZS6pI3V1fXmn47QtSIb7/9luHDh9OsWTM8PT3p1q0b8+fPv+S8HZr5MGtcN06cyWX4tC8YOOhmGjdujK+vL4MGDWLLli11nF4YRUZR2girVfPZhhjeWH6IyCaezBkfRYiv+2Xn37FjB0uWLOGhhx6iR48eJCcnM23aNHr37s2+ffvw9PS87HM3bdp00bRhw4bRp0+fGnkvQtS0d999l/DwcGbMmIGfnx+//PILY8eOJTU1lSeffPKi+fu1bMLUgQH86bZRNA5tzSeffU4DZzP/+te/GDx4MHv37iU0NNSAdyLqlNa6sjdRS87mFOoJc7fo0L//pB/5zzadXVB81eekp6fr4uKK8x0+fFgDet68eVV6/a1bt2pAL1iwoErPE6KunDlz5qJp9957rw4LC7vscz7++GNtMpl06DML9T2zNuq8whKdlpamTSaT/uijj2ozrqh9leot2URpsM0nzjL0/XVEHzvLqyPaM+v+bni6Xn3FumHDhjg5VZyvVatWuLu7k5KSUqUM8+fPx8PDg2HDhlXpeULUFT8/v4umde3a9Yq/68XFxTg5OfHu2B5si03jwS+2oZzdcHJykrOeOAgpOIMUFFt4Y9khxn66GQ8XJ5Y80Zs/9Qq7psMA9u7dS15eHu3atav0c7TWfPvtt4wYMQJ398tvEhXC1mzcuPGKv+t333037u7urP3yXV66qRnR/ztO19vup2HDRowaNaoOkwqjyD44A+yKS+fZRXs5lpLDmOtDeOn2dnhUYq3tSqxWK0899RQtW7Zk8ODBlX7e+vXrSUhIYMyYMdf0+kLUpVWrVvHDDz8wd+7cy84TFBTE6tWruf322/n3v/8NgNnTl26PvkWJy+X3UQv7IQVXh/KLLLy36gifrjtBgLcbX0zsTv9WTa76PK01FssfZ0tXSmE2V7y46XPPPcemTZtYu3Ytzs6VHxY9f/58GjVqxJAhQyr/RoQwUGxsLGPHjmXEiBFMmDDhsvMlJSUxcuRIunXrxpw5cwB4/a0ZbJn9D4Y5ubNw8jAimkjR2bXK7qyry72H9mjVwdO6zxurdOjff9JTvt2jM/OLKv3c1atXa0qPQ9SA7t+/f4XHZ86cqZVSVR4kUlxcrJs0aaIfeuihKj1PCKOcPXtWt2nTRkdFRemcnJwrzvvMM8/o0NBQXVT0x/9rhYWFOrBZsG7SY4TuNG2FXnckpbYji9pRqd6SNbhadiojn1eW7mfF/mQi/T2Z/3BPekU0rtIyunXrxrZt28rve3n9cQqixYsX8+STT/LWW28xevToKi131apVnDlzhnvvvbdKzxPCCHl5edx+++0UFRXx888/4+HhccX5Dx06RPv27Sts0XBxcaFLp44UFBfg7O3G+Llbef7WtjzYN1xOg2eHpOBqSUGxhU/XneCjNcfRaJ4d0pqH+7XAxanq43q8vLyIioq6aPqaNWu47777+POf/8zf/va3Ki93/vz5NG3alAEDBlT5uULUpZKSEkaNGsXRo0eJjo7G39//qs8JDQ3ll19+oaioCBcXFwAKCwvZt28fw4YN461JvZn8zR5e//kg+09lMf2ujrg5m6+yVFGfyKm6apjWmp//l8T0Xw6RmJHP0A5Nef7Wtlc8aLs6Dh48SK9evQgLC+Ojjz7CZPqjOJs0aUJEROnFUP/zn/8wceJEjh8/XuHA1sLCQgICApgwYQLvvfdejWYToqY98sgjfPrpp7z//vt07969wmNdu3bF1dWVyMhI+vfvz2effQaUngyhZ8+eDB48mEmTJqG1ZubMmaxcuZLt27fTuXNnrFbNzNXHeOe3I7QO8OK9MV1oG+htxFsUVVOp1W1Zg6tB22LTeHPZIbafTKdtoDdvj+pc5c2RlbVlyxYyMzPZs2fPRWcgGT9+PPPmzQNKR1daLJaLjvtZtmwZmZmZMnpS1Au//vorAE899dRFj8XExBAWFkZJSUmFwVjdunVj+fLlvPLKK4wbNw6Ajh078ttvv9G5c2cATCbFkze1pGOwD88u2suID6OZPLgVD/VrIZelsgOyBlcDDiZl8faKw6w6lEITL1eeGdSK0deHyP8gQtQjablFPPfdXlbsT6ZHuC9vjexEaOMr7+cThpGrCdS2uLN5zFh5hO93J+Ll6sRjAyJ4oHc4DVxkO74Q9ZHWmsU7E5n2436KSqw8fEM4kwZEXvNxqqLGScHVlvi0PGatPc432+MxmxQP9AnnsRsi5LIcQtiJ5KwC3lx2iO92JdLU243nbm3DsE5BmGSrjK2QgqtpJ87k8NGa4yzZlYhZKUZFBfOXm1rKRRWFsFM7TqYx9cf97EvMok1TL54c2JKhHZpK0RlPCq4maK3ZdOIs86Jj+e1gMq5OJsZ2D+WRG1rQ1EeKTQh7Z7FqftidyIerj3HiTC4t/T3588BIhnYIrNZhP6JGSMFdi/wiC9/vTmRedCyHk7Np5O7M2B7NeaBPOH6ecmFQIRyNxVp6CNAHq45yNCWHJl6u3Ht9CPf2aE6gTwOj4zkaKbjqOJqczbc7Eli4LZ7M/GLaBXozoU8YwzsHyUGgQgisVs2aIyn8d3Mcqw+noICBbfwZ0aUZg9oGyCCzuiEFV1lnsgv5cc8pluxKYF9iFmaT4pb2TZnQJ4yo0EZyCh8hxCXFp+Xx9dY4vtuZQHJWIe4uZm5uF8CtHQPpE+lXqWs7imqRgruSE2dy+O1AMr8dSGZHXDpaQ8dmPtzZtRnDOgfRxEs2QwohKsdi1WyJOcvSPUks25dERl4xTiZFVFgjBrT2p3+rJrRp6iVflmuOFNz5zmQXsunEWTYdP8vmE2eJSc0FoH2QNze3C+D2ToFE+ntdZSlCCHFlxRYr22PTWXvkDGuPnOFgUhYAAd6u9AhvzHXNG3JdaCPaBnrjbJZBKtXkuAWXkVfEkeQc9iZksDchk70JGcSezQPA09WJ7uG+3NDSj0HtAghuZJ9XsX7nnXeYNm0aOTk5RkcRos54enoybdo0Jk+ebHSUcslZBeVltz02jeSsQgDcnE10Cm7Idc0b0TbQi9ZNvWjh5ykjMyvHPgtOa01WQQmpOYWkZheSkl3IybO5nEjNJSY1l9jUXNLzisvnD/Jxo2OwD11CGtErojEdgrxxcoBvTUFBQSQlJRkdQ4g6FxgYyKlTp4yOcUlaa05lFrDzZDo749LZGZfB/sRMSqylf16dTIoWTTxoFeBFS38vQhu707yxO8193Wns4SKbOP9gmwW3Oz6DLSfOYtEaq1VjsfLHv7XGYtUUlVjJLSwhr8hCXlEJuWU/03KKSM0toqjEetFyA33cCGvsQXgTD1r4eRDRxJMOzXwcdl+arMEJR2SLa3BXU1hiISY1l8Onszl8OpsjydkcTs4mPi2/wnzuLmaa+7qX34IaNsDf25Umnq74e7vh7+XqSKcUs82Cm7X2OG8sO3TRdJMCs0lhUgpXJxMerk40cDHj4eKEu4sZdxczjTxcaOLpip+nK35eLvh5utLEy5Xmvu64uzjMf1ghhAMoKLaQkJ5HXFoeJ8+W/oxPK/0Zl5ZHQfHFX/TdXcz4e7ni7+WGj7szPg2c8XYr/enTwAkvN2fcnM24OplwdTbh6lTx307nnaHlwmrQaKwaLFYrJVZNiUVj1ZoSa+mKScX71tKVl7J5LVZNA2czg9s3ramPxzYLrrDEQolFl5dZ6U9k1VsIISpJa01GXjEp2YWkZBdwpmx3TUrWH/cz84vJyi8mM7+Y3CLL1Rday0Ibu7P22RtranG2WXBCCCHqVrHFSnZBCdkFxRSWWCkstlJYYin9d4ml7L6VYou1wsrGhS1iNpWulDiZFKayn+bzbk4mU4V5zp/X1clUk4P6pOCEEELYpUoVnP0PJxRCCOGQpOCEEELYJSk4IYQQdkkKTgghhF2SghNCCGGXpOCEEELYJSk4IYQQdkkKTgghhF2q9IHer7zyynLAr3bj2LwgwDZPU24M+TwuJp9JRfJ5XEw+k4qq83mkTp069ZarzqW1llslb9OmTdNGZ7Clm3we8pnI5yGfiS1/HrKJUgghhF2SgquaV4wOYGPk87iYfCYVyedxMflMKqq1z6MqJ1sWQggh6g1ZgxNCCGGXpOCEEELYJSk4IYQQdkkK7hqoUsuVUlopNdLoPEZRSvkqpT5QSh1SSuUrpeKVUh8rpRobna2uKKUmKaVilFIFSqkdSql+RmcyilLqOaXUNqVUllLqjFJqqVKqg9G5bIVS6vmyvxkfGp3FSEqpQKXUF2W/IwVKqQNKqf41+RpScNdmMmAxOoQNCAKaAVOAjsD9wA3AfCND1RWl1GjgfeCfQFdgI7BMKdXc0GDGGQB8BPQGBgIlwEqllK+RoWyBUqon8DCw1+gsRlJKNQSiKb0y921AW+BJIKVGX0dGUVaPUioKWAJ0A5KBUVrrRcamsh1KqVuBn4CGWusso/PUJqXUFmCv1vrh86YdBRZprZ8zLpltUEp5ApnAHVrrpUbnMYpSygfYSWnBvQzs01r/2dhUxlBK/RPor7XuU5uvI2tw1aCU8qJ07eRRrXWNfuOwI95AIZBndJDapJRyofRLzq8XPPQrpWswArwo/VuTbnQQg82m9EvP70YHsQF3AFuUUguVUilKqd1KqT8rpVRNvogUXPXMApZrrX8xOogtKtv88Brwqda6xOg8tcwPMFO6Fn++ZKBp3cexSe8Du4FNRgcxilLqYSASeMnoLDaiBTAJOAEMofR35A3giZp8EaeaXFh9ppR6HXjhKrPdCIQAnYGoWg9lsMp+JlrrNec9xwNYCiRSuk/OUVy4rV9dYprDUUq9C/QF+mqtHXJ/tVKqNaX7Z/tprYuMzmMjTMD28zbh71JKtaS04Gps8I0U3B/eA/57lXnigAlAOyDngrXphUqpTVrrvrUTzxCV/UyA8n0t59Zqb9daF9RWMBuSSulAowvX1vy5eK3OoSilZgBjKP0SdMLoPAbqRema/r7z/maYgRuUUo8BHlrrQqPCGSQJOHDBtIPAUzX5IlJwZbTWqZT+sboipdQLwNsXTP4f8Dfgh1qIZpjKfiZQvl9yGaVrLrdorXNqM5ut0FoXKaV2ADcD35730M3AYmNSGU8p9T6l5TZAa33I6DwG+x7YfsG0z4GjlK7ZOeJaXTTQ+oJprYCTNfkiUnBVpLVOpHTzW7myb2XxjvottazcfqV0YMkdgEfZpkqANAfYLPMu8KVSaiul/+M+RumhE7MMTWUQpdRMYBylvwvpSqlza7c5jvLF53xa6wwg4/xpSqlcSv/f2GdMKsPNADaWrTAspPTwmr8Az9fki0jBiZrQDehZ9u8jFzx2I7CmTtPUMa31wrKD2l8EAoF9wK1a6xr9NlqPTCr7ueqC6a8A0+o2irBFWuttSqk7KF2DfYnSXR0vUXr8ZI2R4+CEEELYJTlMQAghhF2SghNCCGGXpOCEEELYJSk4IYQQdkkKTgghhF2SghNCCGGXpOCEEELYJSk4IYQQdkkKTgghhF36f4Gu4s+0sbmeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_posterior(azfit, var_names=['T_param'], coords={'T_param_dim_0':[0], 'T_param_dim_1':[0]});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 3, figsize=(8, 8), sharex=True, sharey=True)\n",
    "fig.subplots_adjust(bottom=0.1, top=0.95, right=0.95, left=0.15)\n",
    "ax = ax.ravel()\n",
    "for cax, cT, truth in zip(ax, stanfit[\"T_param\"].reshape((-1, 9)).T, cl.T.ravel()):\n",
    "    cax.hist(cT, bins=32, density=True, histtype=\"step\")\n",
    "    cax.axvline(truth, c=\"k\")\n",
    "    cax.axvline(0, c=\"gray\", lw=0.5)\n",
    "fig.text(0.55, 0.05, \"m/s/pc\", ha=\"center\", va=\"center\", size=20)\n",
    "fig.text(0.05, 0.55, \"Density\", ha=\"center\", va=\"center\", rotation=90, size=20)\n",
    "fig.savefig(\"mock_posterior_T.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>hpd_3%</th>\n",
       "      <th>hpd_97%</th>\n",
       "      <th>ess_mean</th>\n",
       "      <th>ess_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>v0[0]</th>\n",
       "      <td>-6.312</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-6.329</td>\n",
       "      <td>-6.295</td>\n",
       "      <td>4194.0</td>\n",
       "      <td>4193.0</td>\n",
       "      <td>4182.0</td>\n",
       "      <td>3104.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v0[1]</th>\n",
       "      <td>45.233</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>45.213</td>\n",
       "      <td>45.255</td>\n",
       "      <td>3715.0</td>\n",
       "      <td>3715.0</td>\n",
       "      <td>3726.0</td>\n",
       "      <td>2801.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v0[2]</th>\n",
       "      <td>5.309</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.292</td>\n",
       "      <td>5.325</td>\n",
       "      <td>3736.0</td>\n",
       "      <td>3736.0</td>\n",
       "      <td>3744.0</td>\n",
       "      <td>2761.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigv</th>\n",
       "      <td>0.102</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.110</td>\n",
       "      <td>4079.0</td>\n",
       "      <td>4065.0</td>\n",
       "      <td>4091.0</td>\n",
       "      <td>2782.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_param[0,0]</th>\n",
       "      <td>-0.193</td>\n",
       "      <td>1.470</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.026</td>\n",
       "      <td>-2.713</td>\n",
       "      <td>2.825</td>\n",
       "      <td>4264.0</td>\n",
       "      <td>1603.0</td>\n",
       "      <td>4248.0</td>\n",
       "      <td>2994.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_param[0,1]</th>\n",
       "      <td>-49.671</td>\n",
       "      <td>1.497</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.017</td>\n",
       "      <td>-52.673</td>\n",
       "      <td>-47.076</td>\n",
       "      <td>4041.0</td>\n",
       "      <td>4041.0</td>\n",
       "      <td>4047.0</td>\n",
       "      <td>2859.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_param[0,2]</th>\n",
       "      <td>18.266</td>\n",
       "      <td>1.330</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.015</td>\n",
       "      <td>15.793</td>\n",
       "      <td>20.782</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>4002.0</td>\n",
       "      <td>2856.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_param[1,0]</th>\n",
       "      <td>53.288</td>\n",
       "      <td>1.709</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.019</td>\n",
       "      <td>49.914</td>\n",
       "      <td>56.317</td>\n",
       "      <td>4033.0</td>\n",
       "      <td>4033.0</td>\n",
       "      <td>4037.0</td>\n",
       "      <td>2718.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_param[1,1]</th>\n",
       "      <td>3.204</td>\n",
       "      <td>1.761</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.158</td>\n",
       "      <td>6.763</td>\n",
       "      <td>3929.0</td>\n",
       "      <td>3461.0</td>\n",
       "      <td>3938.0</td>\n",
       "      <td>2981.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_param[1,2]</th>\n",
       "      <td>-38.332</td>\n",
       "      <td>1.551</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.017</td>\n",
       "      <td>-41.152</td>\n",
       "      <td>-35.436</td>\n",
       "      <td>4335.0</td>\n",
       "      <td>4335.0</td>\n",
       "      <td>4326.0</td>\n",
       "      <td>3082.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_param[2,0]</th>\n",
       "      <td>-19.730</td>\n",
       "      <td>1.366</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.015</td>\n",
       "      <td>-22.275</td>\n",
       "      <td>-17.212</td>\n",
       "      <td>4171.0</td>\n",
       "      <td>4171.0</td>\n",
       "      <td>4169.0</td>\n",
       "      <td>2701.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_param[2,1]</th>\n",
       "      <td>42.125</td>\n",
       "      <td>1.415</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.016</td>\n",
       "      <td>39.570</td>\n",
       "      <td>44.876</td>\n",
       "      <td>4161.0</td>\n",
       "      <td>4161.0</td>\n",
       "      <td>4166.0</td>\n",
       "      <td>3082.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_param[2,2]</th>\n",
       "      <td>2.086</td>\n",
       "      <td>1.235</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.015</td>\n",
       "      <td>-0.092</td>\n",
       "      <td>4.417</td>\n",
       "      <td>4306.0</td>\n",
       "      <td>3546.0</td>\n",
       "      <td>4309.0</td>\n",
       "      <td>2835.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                mean     sd  mcse_mean  mcse_sd  hpd_3%  hpd_97%  ess_mean  \\\n",
       "v0[0]         -6.312  0.009      0.000    0.000  -6.329   -6.295    4194.0   \n",
       "v0[1]         45.233  0.011      0.000    0.000  45.213   45.255    3715.0   \n",
       "v0[2]          5.309  0.009      0.000    0.000   5.292    5.325    3736.0   \n",
       "sigv           0.102  0.004      0.000    0.000   0.094    0.110    4079.0   \n",
       "T_param[0,0]  -0.193  1.470      0.023    0.026  -2.713    2.825    4264.0   \n",
       "T_param[0,1] -49.671  1.497      0.024    0.017 -52.673  -47.076    4041.0   \n",
       "T_param[0,2]  18.266  1.330      0.021    0.015  15.793   20.782    4000.0   \n",
       "T_param[1,0]  53.288  1.709      0.027    0.019  49.914   56.317    4033.0   \n",
       "T_param[1,1]   3.204  1.761      0.028    0.021   0.158    6.763    3929.0   \n",
       "T_param[1,2] -38.332  1.551      0.024    0.017 -41.152  -35.436    4335.0   \n",
       "T_param[2,0] -19.730  1.366      0.021    0.015 -22.275  -17.212    4171.0   \n",
       "T_param[2,1]  42.125  1.415      0.022    0.016  39.570   44.876    4161.0   \n",
       "T_param[2,2]   2.086  1.235      0.019    0.015  -0.092    4.417    4306.0   \n",
       "\n",
       "              ess_sd  ess_bulk  ess_tail  r_hat  \n",
       "v0[0]         4193.0    4182.0    3104.0    1.0  \n",
       "v0[1]         3715.0    3726.0    2801.0    1.0  \n",
       "v0[2]         3736.0    3744.0    2761.0    1.0  \n",
       "sigv          4065.0    4091.0    2782.0    1.0  \n",
       "T_param[0,0]  1603.0    4248.0    2994.0    1.0  \n",
       "T_param[0,1]  4041.0    4047.0    2859.0    1.0  \n",
       "T_param[0,2]  4000.0    4002.0    2856.0    1.0  \n",
       "T_param[1,0]  4033.0    4037.0    2718.0    1.0  \n",
       "T_param[1,1]  3461.0    3938.0    2981.0    1.0  \n",
       "T_param[1,2]  4335.0    4326.0    3082.0    1.0  \n",
       "T_param[2,0]  4171.0    4169.0    2701.0    1.0  \n",
       "T_param[2,1]  4161.0    4166.0    3082.0    1.0  \n",
       "T_param[2,2]  3546.0    4309.0    2835.0    1.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(azfit, var_names=['v0', 'sigv', 'T_param'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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