hbmm_yu18_gaia.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code for fitting Astro RC data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/bin/env python\n",
    "# -*- coding: utf-8 -*-\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "sns.set_palette('colorblind')\n",
    "matplotlib.rc('xtick', labelsize=15)\n",
    "matplotlib.rc('ytick', labelsize=15)\n",
    "matplotlib.rc('axes',labelsize=15)\n",
    "\n",
    "import pandas as pd\n",
    "import pystan\n",
    "import corner\n",
    "\n",
    "import pickle\n",
    "import os\n",
    "import sys\n",
    "import glob\n",
    "\n",
    "from omnitool.literature_values import Av_coeffs, hawkvals\n",
    "from omnitool import scalings\n",
    "from omnitool.literature_values import Rsol\n",
    "\n",
    "\n",
    "__outdir__ = os.path.expanduser('~')+'/PhD/Gaia_Project/Output/'\n",
    "__datdir__ = os.path.expanduser('~')+'/PhD/Gaia_Project/data/KepxDR2/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data():\n",
    "    '''Reads in the Yu et al. 2018 data'''\n",
    "    sfile = __datdir__+'rcxyu18.csv'\n",
    "    df = pd.read_csv(sfile)\n",
    "    return df\n",
    "\n",
    "def read_paramdict(majorlabel, minorlabel='', sort='astero'):\n",
    "    '''Reads in results for either:\n",
    "        -A full run series (majorlabel) where the minorlabel is included as a\n",
    "            column in the output.\n",
    "        -A single run (majorlabel and minorlabel).\n",
    "\n",
    "        Returns a pandas dataframe.\n",
    "    '''\n",
    "    loc = __outdir__+majorlabel+'/'\n",
    "\n",
    "    if minorlabel != '':\n",
    "        globlist = glob.glob(loc+sort+'_'+str(float(minorlabel))+'_*pars*.csv')\n",
    "    else:\n",
    "        globlist = glob.glob(loc+sort+'*_*pars*.csv')\n",
    "\n",
    "    minorlabels = [os.path.basename(globloc).split('_')[1] for globloc in globlist]\n",
    "\n",
    "    df = pd.DataFrame()\n",
    "    for n, globloc in enumerate(globlist):\n",
    "        sdf = pd.read_csv(globloc, index_col = 0)\n",
    "        if minorlabels[n] != 'pars.csv':\n",
    "            sdf[majorlabel] = minorlabels[n]\n",
    "        df = df.append(sdf)\n",
    "\n",
    "    return df.sort_values(by=majorlabel)\n",
    "\n",
    "def get_covmatrix(ccd):\n",
    "    #Calculate the sigma for this sample\n",
    "    Draij = np.zeros((len(ccd), len(ccd)))\n",
    "    Ddij = np.zeros_like(Draij)\n",
    "\n",
    "    #There is probably a much faster way to do this... but right now I can't think of one\n",
    "    print('Finding separations, creating cov matrix...')\n",
    "    for i in range(len(ccd)):\n",
    "        for j in range(len(ccd)):\n",
    "            Draij[i, j] = ccd.ra[j] - ccd.ra[i]\n",
    "            Ddij[i, j] = ccd.dec[j] - ccd.dec[j]\n",
    "\n",
    "    thetaij = np.sqrt(Draij**2 + Ddij**2)\n",
    "    Sigmaij = 285*10**-6 * np.exp(-thetaij / 14)\n",
    "    np.fill_diagonal(Sigmaij, np.diag(Sigmaij) + ccd.parallax_error**2)\n",
    "    print('Done.')\n",
    "\n",
    "    return Sigmaij\n",
    "\n",
    "def normal(x, mu, sigma):\n",
    "    return (1/np.sqrt(2*np.pi*sigma**2)) * np.exp(-(x - mu)**2/(2*sigma**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size: 5570\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>KICID</th>\n",
       "      <th>kepmag_x</th>\n",
       "      <th>Length_Quarters</th>\n",
       "      <th>Length_days</th>\n",
       "      <th>numax</th>\n",
       "      <th>numax_err</th>\n",
       "      <th>dnu</th>\n",
       "      <th>dnu_err</th>\n",
       "      <th>amplitude</th>\n",
       "      <th>err.2_x</th>\n",
       "      <th>...</th>\n",
       "      <th>Ebv</th>\n",
       "      <th>Aks</th>\n",
       "      <th>Aj</th>\n",
       "      <th>Ah</th>\n",
       "      <th>H17_Ag</th>\n",
       "      <th>L</th>\n",
       "      <th>L_err</th>\n",
       "      <th>Mbol</th>\n",
       "      <th>Mbol_err</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892760</td>\n",
       "      <td>13.23</td>\n",
       "      <td>6</td>\n",
       "      <td>380.8</td>\n",
       "      <td>29.48</td>\n",
       "      <td>0.48</td>\n",
       "      <td>3.962</td>\n",
       "      <td>0.116</td>\n",
       "      <td>149.7</td>\n",
       "      <td>8.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090092</td>\n",
       "      <td>0.016483</td>\n",
       "      <td>0.066545</td>\n",
       "      <td>0.033477</td>\n",
       "      <td>0.256763</td>\n",
       "      <td>72.126040</td>\n",
       "      <td>13.744428</td>\n",
       "      <td>0.10477</td>\n",
       "      <td>0.206899</td>\n",
       "      <td>0.010827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1026084</td>\n",
       "      <td>12.14</td>\n",
       "      <td>15</td>\n",
       "      <td>1139.0</td>\n",
       "      <td>41.17</td>\n",
       "      <td>0.90</td>\n",
       "      <td>4.414</td>\n",
       "      <td>0.061</td>\n",
       "      <td>63.8</td>\n",
       "      <td>2.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082530</td>\n",
       "      <td>0.015099</td>\n",
       "      <td>0.060960</td>\n",
       "      <td>0.030667</td>\n",
       "      <td>0.235211</td>\n",
       "      <td>81.549973</td>\n",
       "      <td>12.518103</td>\n",
       "      <td>-0.02856</td>\n",
       "      <td>0.166663</td>\n",
       "      <td>0.013321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 165 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     KICID  kepmag_x  Length_Quarters  Length_days  numax  numax_err    dnu  \\\n",
       "0   892760     13.23                6        380.8  29.48       0.48  3.962   \n",
       "1  1026084     12.14               15       1139.0  41.17       0.90  4.414   \n",
       "\n",
       "   dnu_err  amplitude  err.2_x    ...          Ebv       Aks        Aj  \\\n",
       "0    0.116      149.7      8.3    ...     0.090092  0.016483  0.066545   \n",
       "1    0.061       63.8      2.9    ...     0.082530  0.015099  0.060960   \n",
       "\n",
       "         Ah    H17_Ag          L      L_err     Mbol  Mbol_err         Z  \n",
       "0  0.033477  0.256763  72.126040  13.744428  0.10477  0.206899  0.010827  \n",
       "1  0.030667  0.235211  81.549973  12.518103 -0.02856  0.166663  0.013321  \n",
       "\n",
       "[2 rows x 165 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = read_data()\n",
    "print('Size: '+str(len(df)))\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stan model for astrometric values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model works perfectly in the K-band, so I'm going to read in the existing version, and not worry about changes. The issues are in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: 'astrostan.pkl'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-7ad6028c1fb9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodel_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'astrostan.pkl'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: 'astrostan.pkl'"
     ]
    }
   ],
   "source": [
    "model_path = 'astrostan.pkl'\n",
    "sm = pickle.load(open(model_path, 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pystan:COMPILING THE C++ CODE FOR MODEL astrostan_ultcov_3c3fa0b344b0781a701aef1363099834 NOW.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updating Stan model\n"
     ]
    }
   ],
   "source": [
    "overwrite=True\n",
    "astrostan = '''\n",
    "functions {\n",
    "    real bailerjones_lpdf(real r, real L){\n",
    "        return log((1/(2*L^3)) * (r*r) * exp(-r/L));\n",
    "    }\n",
    "    real precalc_multinormal_lpdf(vector oo, vector oo_true, real logdetc, matrix invc, int N, real Nfloat){\n",
    "        vector[N] r;       \n",
    "        r = oo - oo_true;\n",
    "\n",
    "        return -0.5 * ((r' * invc * r) + logdetc + Nfloat * log(2*pi()));\n",
    "    }\n",
    "}\n",
    "data {\n",
    "    int<lower = 0> N;\n",
    "    real<lower= 0> Nfloat;\n",
    "    vector[N] m;\n",
    "    vector<lower=0>[N] m_err;\n",
    "    vector[N] oo;\n",
    "    vector<lower=0>[N] RlEbv;\n",
    "\n",
    "    matrix[N, N] invc;\n",
    "    real logdetc;\n",
    "\n",
    "    real mu_init;\n",
    "    real mu_spread;\n",
    "}\n",
    "parameters {\n",
    "    //Hyperparameters\n",
    "    real mu;\n",
    "    real<lower=0.> sigma;\n",
    "    real<lower=1.> sigo;\n",
    "    real<lower=0.5,upper=1.> Q;\n",
    "    real<lower=.1, upper=4000.> L;\n",
    "    real oo_zp;\n",
    "\n",
    "    //Latent parameters\n",
    "    vector[N] M_infd_std;\n",
    "    vector[N] Ai;\n",
    "    vector<lower = 1.>[N] r_infd;\n",
    "}\n",
    "transformed parameters{\n",
    "    //Inferred and transformed parameters\n",
    "    vector[N] M_infd;\n",
    "\n",
    "    //Operations\n",
    "    for (n in 1:N){\n",
    "        M_infd[n] = mu + sigma * M_infd_std[n]; //Rescale the M fit\n",
    "    }\n",
    "}\n",
    "model {\n",
    "    //Define calculable properties\n",
    "    vector[N] m_true;\n",
    "    vector[N] oo_true;\n",
    "\n",
    "    //Hyperparameters [p(theta_rc, L)]\n",
    "    mu ~ normal(mu_init, mu_spread); // Prior from seismo\n",
    "    sigma ~ normal(0., 1.);\n",
    "    Q ~ normal(1., .25);\n",
    "    sigo ~ normal(3.0, 1.0);\n",
    "    L ~ uniform(0.1, 4000.);   // Prior on the length scale\n",
    "    oo_zp ~ normal(0.0, 500.); // Prior on the offset (in mu as)\n",
    "\n",
    "    //Latent parameters [p(alpha_i | theta_rc, L)]\n",
    "    Ai ~ normal(RlEbv, 0.05);\n",
    "    for (n in 1:N){\n",
    "        r_infd[n] ~ bailerjones(L);\n",
    "        target += log_mix(Q,\n",
    "            normal_lpdf(M_infd_std[n] | 0., 1.),\n",
    "            normal_lpdf(M_infd_std[n] | 0., sigo));\n",
    "    }\n",
    "\n",
    "    //Calculable properties\n",
    "    for (n in 1:N){\n",
    "        m_true[n] = M_infd[n] + 5*log10(r_infd[n]) - 5 + Ai[n];\n",
    "        oo_true[n] = (1000./r_infd[n]) + (oo_zp/1000.);\n",
    "    }\n",
    "\n",
    "    //Observables [p(D | theta_rc, L, alpha)]\n",
    "    oo ~ precalc_multinormal(oo_true, logdetc, invc, N, Nfloat);      \n",
    "    m ~ normal(m_true, m_err); //Measurement uncertainty on magnitude\n",
    "}\n",
    "\n",
    "'''\n",
    "model_path = 'astrostan_ultcov.pkl'\n",
    "if overwrite:\n",
    "    print('Updating Stan model')\n",
    "    sm = pystan.StanModel(model_code = astrostan, model_name='astrostan_ultcov')\n",
    "    with open(model_path, 'wb') as f:\n",
    "        pickle.dump(sm, f)\n",
    "\n",
    "if not os.path.isfile(model_path):\n",
    "    print('Saving Stan Model')\n",
    "    sm = pystan.StanModel(model_code = astrostan, model_name='astrostan_ultcov')\n",
    "    with open(model_path, 'wb') as f:\n",
    "        pickle.dump(sm, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "pkl_file = open('../Output/K_tempscale_noCorrection/astero_50.0_fullchain_dict.pkl', 'rb')\n",
    "d = pickle.load(pkl_file)\n",
    "pkl_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.76064038, -1.75677049, -1.79261007, ..., -1.72146912,\n",
       "        -1.7650943 , -1.75089771],\n",
       "       [-1.79022148, -1.65838613, -1.78469397, ..., -1.73232836,\n",
       "        -1.78531897, -1.8220237 ],\n",
       "       [-1.73593513, -1.77580961, -1.76212419, ..., -1.80497409,\n",
       "        -1.79006531, -1.76663047],\n",
       "       ...,\n",
       "       [-1.76904048, -1.77702415, -1.72140967, ..., -1.80473642,\n",
       "        -1.74840142, -1.74506638],\n",
       "       [-1.78067975, -1.74029811, -2.08600666, ..., -1.8355154 ,\n",
       "        -1.83754664, -1.7759307 ],\n",
       "       [-1.75593498, -1.75596044, -1.79371113, ..., -1.69263176,\n",
       "        -2.01831342, -1.72444511]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['Mtrue']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import corner\n",
    "corner.corner(d[''])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5570,)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:matplotlib.texmanager:No LaTeX-compatible font found for the serif font family in rcParams. Using default.\n",
      "INFO:matplotlib.texmanager:No LaTeX-compatible font found for the serif font family in rcParams. Using default.\n",
      "INFO:matplotlib.texmanager:No LaTeX-compatible font found for the serif font family in rcParams. Using default.\n",
      "INFO:matplotlib.texmanager:No LaTeX-compatible font found for the serif font family in rcParams. Using default.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Mtrueval = np.median(d['Mtrue'],axis=0)\n",
    "sns.distplot(Mtrueval)\n",
    "Mtrueval.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 5570)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['Mtrue'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 5)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2.])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = np.array([[1,1,1,1,1],[2,2,2,2,2]])\n",
    "np.median(test,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finding separations, creating cov matrix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/oliver/.local/lib/python2.7/site-packages/matplotlib/font_manager.py:1331: UserWarning: findfont: Font family [u'serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n",
      "INFO:matplotlib.texmanager:No LaTeX-compatible font found for the serif font family in rcParams. Using default.\n",
      "INFO:matplotlib.texmanager:No LaTeX-compatible font found for the serif font family in rcParams. Using default.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:matplotlib.texmanager:No LaTeX-compatible font found for the serif font family in rcParams. Using default.\n",
      "INFO:matplotlib.texmanager:No LaTeX-compatible font found for the serif font family in rcParams. Using default.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tempdiff = 50.0\n",
    "\n",
    "from sklearn.utils import shuffle\n",
    "ccd = shuffle(df).reset_index(drop=True)[:250]\n",
    "\n",
    "rlebv = ccd.Aks.values\n",
    "mband = ccd.Kmag.values\n",
    "merr = ccd.e_Kmag.values\n",
    "\n",
    "astres = read_paramdict('K_tempscale_noCorrection', str(tempdiff), 'astero')\n",
    "\n",
    "Sigma = get_covmatrix(ccd)\n",
    "logdetc = np.linalg.slogdet(Sigma)[1]\n",
    "invc = np.linalg.inv(Sigma)\n",
    "\n",
    "plt.scatter(ccd.ra, ccd.dec)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = {'N':len(ccd),\n",
    "       'Nfloat':np.float(len(ccd)),\n",
    "        'm': mband,\n",
    "        'm_err': np.ones(len(mband))*10.e-3,\n",
    "        'oo': ccd.parallax.values,\n",
    "        'RlEbv': rlebv,\n",
    "        'logdetc': logdetc,\n",
    "        'invc': invc,\n",
    "        'mu_init': astres['mu'].values[0],\n",
    "        'mu_spread': astres['mu_std'].values[0]}\n",
    "\n",
    "init= {'mu': astres.mu.values[0],\n",
    "        'sigma': astres.sigma.values[0],\n",
    "        'Q': astres.Q.values[0],\n",
    "        'sigo': astres.sigo.values[0],\n",
    "        'L': 1000.,\n",
    "        'oo_zp':-29.}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit = sm.sampling(data=dat, iter=5000, chains=4, seed=24601, \n",
    "                  init=[init,init, init, init])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:matplotlib.texmanager:No LaTeX-compatible font found for the serif font family in rcParams. Using default.\n",
      "INFO:matplotlib.texmanager:No LaTeX-compatible font found for the serif font family in rcParams. Using default.\n",
      "INFO:matplotlib.texmanager:No LaTeX-compatible font found for the serif font family in rcParams. Using default.\n",
      "INFO:matplotlib.texmanager:No LaTeX-compatible font found for the serif font family in rcParams. Using default.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000.8x1000.8 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import corner\n",
    "chain = np.array([fit['mu'],fit['sigma'],fit['Q'],fit['sigo'],fit['L'], fit['oo_zp']])\n",
    "corner.corner(chain.T,\\\n",
    "                labels=[r'$\\mu$(mag)',r'$\\sigma$(mag)','Q',r'$\\sigma_o$(mag)','L(pc)', 'Offset'],\\\n",
    "                quantiles=[0.16, 0.5, 0.84],\\\n",
    "                show_titles=True, title_kwargs={\"fontsize\": 15})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:pystan:Deprecation warning. In future, use ArviZ library (`pip install arviz`)\n",
      "INFO:matplotlib.texmanager:No LaTeX-compatible font found for the serif font family in rcParams. Using default.\n",
      "INFO:matplotlib.texmanager:No LaTeX-compatible font found for the serif font family in rcParams. Using default.\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "latex was not able to process the following string:\n'oo_zp'\n\nHere is the full report generated by latex:\nThis is pdfTeX, Version 3.14159265-2.6-1.40.18 (TeX Live 2017/Debian) (preloaded format=latex)\n restricted \\write18 enabled.\nentering extended mode\n(/home/oliver/.cache/matplotlib/tex.cache/727c2d578108cf1e362c374cd961d1e3.tex\nLaTeX2e <2017-04-15>\nBabel <3.18> and hyphenation patterns for 84 language(s) loaded.\n(/usr/share/texlive/texmf-dist/tex/latex/base/article.cls\nDocument Class: article 2014/09/29 v1.4h Standard LaTeX document class\n(/usr/share/texlive/texmf-dist/tex/latex/base/size10.clo))\n(/usr/share/texlive/texmf-dist/tex/latex/type1cm/type1cm.sty)\n(/usr/share/texlive/texmf-dist/tex/latex/base/textcomp.sty\n(/usr/share/texlive/texmf-dist/tex/latex/base/ts1enc.def))\n(/usr/share/texlive/texmf-dist/tex/latex/geometry/geometry.sty\n(/usr/share/texlive/texmf-dist/tex/latex/graphics/keyval.sty)\n(/usr/share/texlive/texmf-dist/tex/generic/oberdiek/ifpdf.sty)\n(/usr/share/texlive/texmf-dist/tex/generic/oberdiek/ifvtex.sty)\n(/usr/share/texlive/texmf-dist/tex/generic/ifxetex/ifxetex.sty)\n\nPackage geometry Warning: Over-specification in `h'-direction.\n    `width' (5058.9pt) is ignored.\n\n\nPackage geometry Warning: Over-specification in `v'-direction.\n    `height' (5058.9pt) is ignored.\n\n) (./727c2d578108cf1e362c374cd961d1e3.aux)\n(/usr/share/texlive/texmf-dist/tex/latex/base/ts1cmr.fd)\n*geometry* driver: auto-detecting\n*geometry* detected driver: dvips\n! Missing $ inserted.\n<inserted text> \n                $\nl.13 \\fontsize{12.000000}{15.000000}{\\rmfamily oo_\n                                                  zp}\nNo pages of output.\nTranscript written on 727c2d578108cf1e362c374cd961d1e3.log.\n\n\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/IPython/core/formatters.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    332\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    335\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/oliver/.local/lib/python2.7/site-packages/matplotlib/backend_bases.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2210\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2211\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2212\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2213\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2214\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/oliver/.local/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    516\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 517\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    518\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    519\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/oliver/.local/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    435\u001b[0m             \u001b[0;31m# if toolbar:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m             \u001b[0;31m#     toolbar.set_cursor(cursors.WAIT)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 437\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    438\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/oliver/.local/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/oliver/.local/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1491\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1492\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1493\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1495\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/oliver/.local/lib/python2.7/site-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/oliver/.local/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/oliver/.local/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2633\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2634\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2635\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2637\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/oliver/.local/lib/python2.7/site-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/oliver/.local/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/oliver/.local/lib/python2.7/site-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    704\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    705\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m_wrap_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 706\u001b[0;31m             \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    707\u001b[0m             \u001b[0mtrans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    708\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/oliver/.local/lib/python2.7/site-packages/matplotlib/text.pyc\u001b[0m in \u001b[0;36m_get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    307\u001b[0m                 w, h, d = renderer.get_text_width_height_descent(clean_line,\n\u001b[1;32m    308\u001b[0m                                                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fontproperties\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 309\u001b[0;31m                                                         ismath=ismath)\n\u001b[0m\u001b[1;32m    310\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m                 \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/oliver/.local/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    234\u001b[0m             \u001b[0mfontsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size_in_points\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m             w, h, d = texmanager.get_text_width_height_descent(\n\u001b[0;32m--> 236\u001b[0;31m                 s, fontsize, renderer=self)\n\u001b[0m\u001b[1;32m    237\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/oliver/.local/lib/python2.7/site-packages/matplotlib/texmanager.pyc\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, tex, fontsize, renderer)\u001b[0m\n\u001b[1;32m    499\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    500\u001b[0m             \u001b[0;31m# use dviread. It sometimes returns a wrong descent.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 501\u001b[0;31m             \u001b[0mdvifile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_dvi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    502\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mdviread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDvi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdvifile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m72\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdpi_fraction\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdvi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    503\u001b[0m                 \u001b[0mpage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdvi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/oliver/.local/lib/python2.7/site-packages/matplotlib/texmanager.pyc\u001b[0m in \u001b[0;36mmake_dvi\u001b[0;34m(self, tex, fontsize)\u001b[0m\n\u001b[1;32m    363\u001b[0m                 self._run_checked_subprocess(\n\u001b[1;32m    364\u001b[0m                     [\"latex\", \"-interaction=nonstopmode\", \"--halt-on-error\",\n\u001b[0;32m--> 365\u001b[0;31m                      texfile], tex)\n\u001b[0m\u001b[1;32m    366\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mfname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasefile\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'*'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    367\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dvi'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'tex'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/oliver/.local/lib/python2.7/site-packages/matplotlib/texmanager.pyc\u001b[0m in \u001b[0;36m_run_checked_subprocess\u001b[0;34m(self, command, tex)\u001b[0m\n\u001b[1;32m    342\u001b[0m                     \u001b[0mprog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcommand\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m                     \u001b[0mtex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'unicode_escape'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 344\u001b[0;31m                     exc=exc.output.decode('utf-8')))\n\u001b[0m\u001b[1;32m    345\u001b[0m         \u001b[0m_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreport\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mreport\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: latex was not able to process the following string:\n'oo_zp'\n\nHere is the full report generated by latex:\nThis is pdfTeX, Version 3.14159265-2.6-1.40.18 (TeX Live 2017/Debian) (preloaded format=latex)\n restricted \\write18 enabled.\nentering extended mode\n(/home/oliver/.cache/matplotlib/tex.cache/727c2d578108cf1e362c374cd961d1e3.tex\nLaTeX2e <2017-04-15>\nBabel <3.18> and hyphenation patterns for 84 language(s) loaded.\n(/usr/share/texlive/texmf-dist/tex/latex/base/article.cls\nDocument Class: article 2014/09/29 v1.4h Standard LaTeX document class\n(/usr/share/texlive/texmf-dist/tex/latex/base/size10.clo))\n(/usr/share/texlive/texmf-dist/tex/latex/type1cm/type1cm.sty)\n(/usr/share/texlive/texmf-dist/tex/latex/base/textcomp.sty\n(/usr/share/texlive/texmf-dist/tex/latex/base/ts1enc.def))\n(/usr/share/texlive/texmf-dist/tex/latex/geometry/geometry.sty\n(/usr/share/texlive/texmf-dist/tex/latex/graphics/keyval.sty)\n(/usr/share/texlive/texmf-dist/tex/generic/oberdiek/ifpdf.sty)\n(/usr/share/texlive/texmf-dist/tex/generic/oberdiek/ifvtex.sty)\n(/usr/share/texlive/texmf-dist/tex/generic/ifxetex/ifxetex.sty)\n\nPackage geometry Warning: Over-specification in `h'-direction.\n    `width' (5058.9pt) is ignored.\n\n\nPackage geometry Warning: Over-specification in `v'-direction.\n    `height' (5058.9pt) is ignored.\n\n) (./727c2d578108cf1e362c374cd961d1e3.aux)\n(/usr/share/texlive/texmf-dist/tex/latex/base/ts1cmr.fd)\n*geometry* driver: auto-detecting\n*geometry* detected driver: dvips\n! Missing $ inserted.\n<inserted text> \n                $\nl.13 \\fontsize{12.000000}{15.000000}{\\rmfamily oo_\n                                                  zp}\nNo pages of output.\nTranscript written on 727c2d578108cf1e362c374cd961d1e3.log.\n\n\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.3'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "matplotlib.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference for Stan model: astrostan_ultcov_3c3fa0b344b0781a701aef1363099834.\n",
      "4 chains, each with iter=5000; warmup=2500; thin=1; \n",
      "post-warmup draws per chain=2500, total post-warmup draws=10000.\n",
      "\n",
      "                  mean se_mean     sd   2.5%    25%    50%    75%  97.5%  n_eff   Rhat\n",
      "mu               -1.75  1.4e-5 2.1e-3  -1.76  -1.76  -1.75  -1.75  -1.75  23437    1.0\n",
      "sigma             0.13  4.8e-4   0.01    0.1   0.12   0.13   0.14   0.16  930.0   1.01\n",
      "sigo              4.25  6.6e-3   0.66   3.07   3.79   4.21   4.68   5.64  10046    1.0\n",
      "Q                 0.92  1.1e-3   0.04   0.82    0.9   0.93   0.95   0.98 1231.0    1.0\n",
      "L                885.7    0.24  32.58 822.99 863.17 884.76  907.3 952.64  18272    1.0\n",
      "oo_zp           -28.12    0.11  13.71  -55.5 -37.25 -28.11 -18.77  -1.33  14563    1.0\n",
      "M_infd_std[1]     2.43    0.02    1.2   0.54   1.59   2.21    3.1   5.21 3006.0    1.0\n",
      "M_infd_std[2]    -0.25  7.1e-3   0.72  -1.72  -0.72  -0.25   0.25   1.14  10309    1.0\n",
      "M_infd_std[3]     0.89  5.8e-3   0.54  -0.15   0.53   0.89   1.24   1.99 8947.0    1.0\n",
      "M_infd_std[4]     0.68  7.2e-3   0.71  -0.72    0.2   0.67   1.15   2.07 9778.0    1.0\n",
      "M_infd_std[5]     0.75  5.0e-3   0.48  -0.17   0.43   0.74   1.07    1.7 9134.0    1.0\n",
      "M_infd_std[6]     0.24  7.6e-3   0.75  -1.25  -0.25   0.25   0.74   1.67 9671.0    1.0\n",
      "M_infd_std[7]     0.74  8.8e-3   0.82  -0.81    0.2   0.73   1.27   2.39 8666.0    1.0\n",
      "M_infd_std[8]     0.28  7.8e-3    0.8  -1.28  -0.25   0.28   0.82   1.86  10513    1.0\n",
      "M_infd_std[9]    -0.74  6.9e-3   0.69  -2.15   -1.2  -0.74  -0.27   0.56 9858.0    1.0\n",
      "M_infd_std[10]   -0.94  6.8e-3   0.64  -2.26  -1.36  -0.92   -0.5   0.28 8882.0    1.0\n",
      "M_infd_std[11]    -0.9  8.7e-3   0.83  -2.66   -1.4  -0.86  -0.34   0.61 9070.0    1.0\n",
      "M_infd_std[12]   -0.42  8.3e-3   0.81  -2.05  -0.94   -0.4   0.12    1.1 9539.0    1.0\n",
      "M_infd_std[13]    0.22  5.1e-3   0.49  -0.75  -0.11   0.22   0.54   1.16 9083.0    1.0\n",
      "M_infd_std[14]    1.03  8.1e-3   0.78  -0.47   0.51   1.01   1.53   2.65 9480.0    1.0\n",
      "M_infd_std[15]    0.37  6.4e-3   0.67  -0.95  -0.08   0.37   0.81   1.67  10916    1.0\n",
      "M_infd_std[16]    0.14  7.0e-3   0.69  -1.23   -0.3   0.14   0.59   1.49 9469.0    1.0\n",
      "M_infd_std[17]   -0.84  8.1e-3   0.78  -2.48  -1.31  -0.81  -0.32   0.58 9361.0    1.0\n",
      "M_infd_std[18]    0.97  8.1e-3   0.78  -0.54   0.45   0.95   1.46   2.57 9220.0    1.0\n",
      "M_infd_std[19]    -0.2  7.9e-3   0.79  -1.77  -0.72  -0.18   0.32   1.32 9863.0    1.0\n",
      "M_infd_std[20]    2.65    0.02   0.97   1.06   1.96   2.51   3.23   4.81 2819.0    1.0\n",
      "M_infd_std[21]   -0.28  6.4e-3   0.65   -1.6  -0.71  -0.27   0.15   0.98  10475    1.0\n",
      "M_infd_std[22]    0.15  8.3e-3   0.81  -1.46  -0.37   0.15   0.68   1.75 9550.0    1.0\n",
      "M_infd_std[23]   -1.36    0.01   0.82  -3.21  -1.81   -1.3  -0.82   0.06 5539.0    1.0\n",
      "M_infd_std[24]   -0.19  6.7e-3   0.67  -1.52  -0.62  -0.18   0.26   1.12  10126    1.0\n",
      "M_infd_std[25]   -0.86  6.9e-3   0.71   -2.3  -1.31  -0.84  -0.38   0.46  10557    1.0\n",
      "M_infd_std[26]    1.11  9.3e-3   0.85  -0.44   0.55   1.08    1.6   2.93 8281.0    1.0\n",
      "M_infd_std[27]    0.14  7.2e-3   0.71  -1.27  -0.32   0.15   0.62   1.52 9809.0    1.0\n",
      "M_infd_std[28]   -0.71  8.6e-3   0.78   -2.3  -1.21  -0.69  -0.19   0.75 8166.0    1.0\n",
      "M_infd_std[29]    0.03  6.4e-3   0.65  -1.26  -0.42   0.04   0.47   1.32  10438    1.0\n",
      "M_infd_std[30]    0.66  7.2e-3   0.68  -0.66    0.2   0.65   1.11   2.02 8931.0    1.0\n",
      "M_infd_std[31]    -0.4  7.2e-3    0.7  -1.81  -0.86   -0.4   0.07   0.96 9570.0    1.0\n",
      "M_infd_std[32]   -0.87  7.3e-3   0.72  -2.34  -1.32  -0.86   -0.4    0.5 9763.0    1.0\n",
      "M_infd_std[33]    1.36  8.9e-3   0.73   0.03   0.88   1.33    1.8   2.88 6669.0    1.0\n",
      "M_infd_std[34]    1.23  8.1e-3   0.75  -0.16   0.73    1.2   1.69   2.81 8592.0    1.0\n",
      "M_infd_std[35]   -0.18  6.4e-3   0.66  -1.48   -0.6  -0.18   0.26   1.08  10527    1.0\n",
      "M_infd_std[36]    0.49  6.7e-3   0.65  -0.82   0.06   0.49   0.91   1.74 9475.0    1.0\n",
      "M_infd_std[37]    0.19  5.6e-3   0.53  -0.86  -0.15    0.2   0.54   1.23 8803.0    1.0\n",
      "M_infd_std[38]   -0.32  8.1e-3   0.78  -1.88  -0.81   -0.3   0.19   1.16 9061.0    1.0\n",
      "M_infd_std[39]   -0.27  8.7e-3   0.82   -1.9  -0.81  -0.26   0.27    1.3 8796.0    1.0\n",
      "M_infd_std[40]    0.24  7.2e-3   0.76  -1.25  -0.27   0.25   0.75   1.72  11056    1.0\n",
      "M_infd_std[41]    -1.3    0.05   2.09  -7.56  -1.67  -0.83  -0.12    1.1 1508.0    1.0\n",
      "M_infd_std[42]    0.15  5.1e-3   0.51  -0.85   -0.2   0.15   0.49   1.18 9947.0    1.0\n",
      "M_infd_std[43]    0.85    0.01    1.0  -0.95   0.19    0.8   1.42   3.08 6373.0    1.0\n",
      "M_infd_std[44]    0.45  5.6e-3   0.55  -0.62   0.07   0.44   0.82   1.55 9542.0    1.0\n",
      "M_infd_std[45]    0.91  7.8e-3   0.73  -0.47   0.43   0.91   1.37    2.4 8745.0    1.0\n",
      "M_infd_std[46]   -0.43  7.6e-3   0.78  -1.98  -0.95  -0.42   0.09   1.08  10453    1.0\n",
      "M_infd_std[47]   -0.75  5.9e-3   0.58  -1.91  -1.14  -0.74  -0.36   0.38 9913.0    1.0\n",
      "M_infd_std[48]   -0.12  8.9e-3   0.86  -1.81  -0.68  -0.12   0.45   1.53 9517.0    1.0\n",
      "M_infd_std[49]   -4.93    0.04   2.35  -9.44  -6.59  -5.09  -3.03  -0.84 3800.0    1.0\n",
      "M_infd_std[50]   -0.22  6.9e-3    0.7  -1.63  -0.68  -0.21   0.26   1.11  10221    1.0\n",
      "M_infd_std[51]   -1.25  9.0e-3    0.8  -3.02   -1.7  -1.21  -0.74   0.17 7879.0    1.0\n",
      "M_infd_std[52]    1.83    0.01   0.91   0.27   1.22   1.73   2.32   3.95 4568.0    1.0\n",
      "M_infd_std[53]   -0.49  9.4e-3   0.92  -2.37  -1.06  -0.46   0.12   1.19 9461.0    1.0\n",
      "M_infd_std[54]   -0.58  7.6e-3   0.76  -2.11  -1.07  -0.57  -0.07   0.87 9987.0    1.0\n",
      "M_infd_std[55]    0.13  8.0e-3   0.77  -1.36   -0.4   0.13   0.66   1.62 9251.0    1.0\n",
      "M_infd_std[56]     0.3  7.5e-3    0.7  -1.07  -0.17   0.29   0.76   1.66 8718.0    1.0\n",
      "M_infd_std[57]   -0.31  6.8e-3   0.72  -1.72  -0.77  -0.29   0.17   1.06  11073    1.0\n",
      "M_infd_std[58]   -0.47  7.9e-3   0.77  -2.01  -0.96  -0.46   0.04   1.01 9475.0    1.0\n",
      "M_infd_std[59]    0.05  5.7e-3    0.6  -1.14  -0.36   0.05   0.45   1.23  11213    1.0\n",
      "M_infd_std[60]    0.44  8.4e-3   0.83  -1.21  -0.12   0.44   0.98   2.07 9872.0    1.0\n",
      "M_infd_std[61]    0.66  8.2e-3   0.77  -0.84   0.14   0.65   1.17   2.16 8851.0    1.0\n",
      "M_infd_std[62]    0.14  8.6e-3   0.86  -1.53  -0.42   0.14   0.71   1.81 9949.0    1.0\n",
      "M_infd_std[63]    1.38    0.01   0.98  -0.28   0.76    1.3   1.89   3.76 5933.0    1.0\n",
      "M_infd_std[64]    0.06    0.02   1.25  -2.18  -0.63   0.08   0.78   2.29 4009.0    1.0\n",
      "M_infd_std[65]   -0.18  7.6e-3   0.71  -1.58  -0.65  -0.18   0.29   1.24 8849.0    1.0\n",
      "M_infd_std[66]   -0.53  7.9e-3   0.81  -2.19  -1.04  -0.52   0.02    1.0  10602    1.0\n",
      "M_infd_std[67]     0.9    0.01   0.95  -0.85   0.28   0.86   1.45   2.97 7562.0    1.0\n",
      "M_infd_std[68]   -0.22  7.0e-3   0.71  -1.62  -0.69  -0.21   0.25   1.14  10245    1.0\n",
      "M_infd_std[69]    0.14  7.8e-3    0.8  -1.43   -0.4   0.15   0.66   1.67  10491    1.0\n",
      "M_infd_std[70]    0.04  6.1e-3   0.62  -1.18  -0.37   0.05   0.46   1.28  10213    1.0\n",
      "M_infd_std[71]    0.56  7.0e-3    0.7  -0.84   0.09   0.55   1.03   1.94  10188    1.0\n",
      "M_infd_std[72]    1.51  8.7e-3   0.77   0.09   0.99   1.47   1.96   3.16 7874.0    1.0\n",
      "M_infd_std[73]   -0.41  9.0e-3   0.86   -2.1  -0.96   -0.4   0.17   1.21 9120.0    1.0\n",
      "M_infd_std[74]    0.09  7.5e-3   0.77  -1.42  -0.41    0.1   0.61   1.55  10601    1.0\n",
      "M_infd_std[75]    0.39  5.7e-3   0.55   -0.7   0.02    0.4   0.76   1.48 9453.0    1.0\n",
      "M_infd_std[76]  5.3e-3  4.9e-3   0.51  -0.99  -0.33 8.8e-3   0.34   0.97  10771    1.0\n",
      "M_infd_std[77]   -0.51  4.8e-3   0.52  -1.54  -0.85   -0.5  -0.16   0.51  11771    1.0\n",
      "M_infd_std[78]    1.88    0.01   0.72   0.63    1.4   1.82   2.29   3.56 4028.0    1.0\n",
      "M_infd_std[79]   -0.09  5.3e-3   0.55  -1.18  -0.45  -0.09   0.28   0.98  10790    1.0\n",
      "M_infd_std[80]    0.16  6.0e-3   0.59  -1.02  -0.23   0.17   0.55    1.3 9745.0    1.0\n",
      "M_infd_std[81]   -1.21    0.01   0.97  -3.51  -1.71  -1.13  -0.58   0.41 5099.0    1.0\n",
      "M_infd_std[82]   -0.53  7.8e-3   0.77  -2.09  -1.02  -0.52-9.4e-3   0.94 9669.0    1.0\n",
      "M_infd_std[83]    0.36  5.6e-3   0.57  -0.78  -0.02   0.36   0.74   1.48  10205    1.0\n",
      "M_infd_std[84]     0.6  5.2e-3   0.54  -0.44   0.24    0.6   0.96   1.65  10659    1.0\n",
      "M_infd_std[85]    0.86  6.5e-3   0.63  -0.39   0.43   0.86   1.28   2.08 9405.0    1.0\n",
      "M_infd_std[86]     0.8  9.5e-3   0.88  -0.85   0.22   0.78   1.35   2.59 8719.0    1.0\n",
      "M_infd_std[87]   -0.11  5.3e-3   0.53  -1.17  -0.47   -0.1   0.25   0.91  10259    1.0\n",
      "M_infd_std[88]    0.18  7.4e-3   0.76  -1.32  -0.33   0.18   0.69   1.67  10594    1.0\n",
      "M_infd_std[89]    0.23  6.6e-3   0.69  -1.14  -0.22   0.23   0.69   1.56  10737    1.0\n",
      "M_infd_std[90]     0.4  7.0e-3   0.68  -0.94  -0.05   0.41   0.86    1.7 9321.0    1.0\n",
      "M_infd_std[91]   -0.12  7.5e-3   0.83  -1.75  -0.66  -0.11   0.43   1.49  12061    1.0\n",
      "M_infd_std[92]    0.39  4.8e-3   0.48  -0.55   0.07   0.39   0.71   1.34 9712.0    1.0\n",
      "M_infd_std[93]    0.34  7.0e-3   0.72   -1.1  -0.13   0.35   0.81   1.75  10435    1.0\n",
      "M_infd_std[94]     0.2  6.6e-3   0.69  -1.16  -0.26    0.2   0.65   1.55  10991    1.0\n",
      "M_infd_std[95]    0.88  7.3e-3   0.75  -0.56   0.38   0.88   1.36   2.39  10526    1.0\n",
      "M_infd_std[96]    0.79  7.2e-3   0.75  -0.68    0.3   0.78   1.27    2.3  10798    1.0\n",
      "M_infd_std[97]   -0.35  7.1e-3   0.72  -1.77  -0.83  -0.34   0.12   1.04  10083    1.0\n",
      "M_infd_std[98]    2.15    0.02   1.04   0.45   1.44   2.01   2.69   4.63 3857.0    1.0\n",
      "M_infd_std[99]    -0.4  8.2e-3    0.8  -1.99  -0.91  -0.39   0.13   1.13 9478.0    1.0\n",
      "M_infd_std[100]  -0.47  7.2e-3   0.76  -1.98  -0.96  -0.46   0.04   1.01  11226    1.0\n",
      "M_infd_std[101]   0.03  7.3e-3   0.74  -1.45  -0.46   0.04   0.54   1.44  10216    1.0\n",
      "M_infd_std[102]  -1.07    0.01   0.97   -3.4   -1.6  -0.99  -0.44   0.61 6916.0    1.0\n",
      "M_infd_std[103]   1.08  5.5e-3   0.48   0.15   0.76   1.07    1.4   2.08 7843.0    1.0\n",
      "M_infd_std[104]  -0.52    0.01   0.94  -2.41   -1.1   -0.5   0.11    1.2 7959.0    1.0\n",
      "M_infd_std[105]   0.52  6.9e-3    0.7  -0.89   0.07   0.53   0.99   1.88  10380    1.0\n",
      "M_infd_std[106]   0.56  6.8e-3   0.71  -0.84   0.09   0.55   1.03   1.95  10991    1.0\n",
      "M_infd_std[107]   3.17    0.02    0.9   1.66    2.5   3.09   3.76   5.13 2039.0    1.0\n",
      "M_infd_std[108]  -1.16    0.02   1.16  -4.29  -1.67  -1.02  -0.44   0.62 3966.0    1.0\n",
      "M_infd_std[109]  -0.35  7.2e-3   0.75  -1.84  -0.85  -0.35   0.15   1.08  10925    1.0\n",
      "M_infd_std[110]  -0.87  5.0e-3   0.49  -1.88  -1.19  -0.86  -0.54   0.07 9659.0    1.0\n",
      "M_infd_std[111]  -1.26    0.01    0.9  -3.37  -1.76  -1.19  -0.67   0.29 5103.0    1.0\n",
      "M_infd_std[112]  -1.28  7.0e-3   0.66  -2.63  -1.68  -1.25  -0.84  -0.05 8748.0    1.0\n",
      "M_infd_std[113]   0.13  7.1e-3   0.69  -1.23  -0.33   0.13    0.6   1.45 9397.0    1.0\n",
      "M_infd_std[114]   0.39  7.0e-3   0.74  -1.04  -0.11   0.39   0.89   1.87  11074    1.0\n",
      "M_infd_std[115]   -0.1  7.8e-3   0.78  -1.64  -0.62  -0.09   0.41   1.44  10190    1.0\n",
      "M_infd_std[116]   0.15  6.0e-3   0.63   -1.1  -0.28   0.16   0.58   1.37  11162    1.0\n",
      "M_infd_std[117]   0.55    0.02   1.33  -1.57  -0.25   0.43   1.16   3.88 3482.0    1.0\n",
      "M_infd_std[118]   -0.2  7.5e-3   0.77  -1.73   -0.7  -0.19    0.3   1.29  10723    1.0\n",
      "M_infd_std[119]   0.34  4.2e-3   0.41  -0.48   0.06   0.34   0.62   1.15 9846.0    1.0\n",
      "M_infd_std[120]  -2.29    0.01   0.52  -3.48  -2.59  -2.24  -1.94  -1.43 2019.0    1.0\n",
      "M_infd_std[121]   0.34  6.4e-3   0.65  -0.93   -0.1   0.33   0.78   1.61  10464    1.0\n",
      "M_infd_std[122]   0.57  6.5e-3   0.64  -0.72   0.16   0.57   0.99   1.84 9693.0    1.0\n",
      "M_infd_std[123]   0.02  8.5e-3   0.82  -1.63  -0.53   0.02   0.59    1.6 9373.0    1.0\n",
      "M_infd_std[124]   0.05  6.7e-3   0.67  -1.24   -0.4   0.06    0.5   1.35 9966.0    1.0\n",
      "M_infd_std[125]   0.22  7.3e-3   0.73   -1.2  -0.26   0.22   0.71   1.66  10099    1.0\n",
      "M_infd_std[126]   0.86  8.7e-3   0.85  -0.77    0.3   0.85   1.39    2.6 9554.0    1.0\n",
      "M_infd_std[127]  -0.19  5.4e-3   0.54   -1.3  -0.55  -0.18   0.17   0.85 9978.0    1.0\n",
      "M_infd_std[128]    0.7  6.8e-3    0.7  -0.66   0.22    0.7   1.16   2.09  10585    1.0\n",
      "M_infd_std[129]  -0.62  7.9e-3   0.79  -2.17  -1.13   -0.6  -0.09   0.89  10077    1.0\n",
      "M_infd_std[130]  -0.47  6.9e-3   0.74  -1.91  -0.95  -0.46   0.04   0.97  11652    1.0\n",
      "M_infd_std[131]   0.66  6.3e-3   0.57  -0.45   0.26   0.65   1.04   1.81 8405.0    1.0\n",
      "M_infd_std[132]  -0.29  6.3e-3   0.65   -1.6  -0.72  -0.29   0.15   0.93  10399    1.0\n",
      "M_infd_std[133]  -0.18  6.3e-3   0.63  -1.44   -0.6  -0.16   0.25   1.05  10070    1.0\n",
      "M_infd_std[134]  -0.18  7.8e-3   0.76   -1.7  -0.68  -0.17   0.35   1.25 9645.0    1.0\n",
      "M_infd_std[135]   6.92    0.03   0.99   5.29   6.23   6.82   7.51   9.09 1168.0    1.0\n",
      "M_infd_std[136]   0.32  6.1e-3   0.64  -0.93  -0.12   0.31   0.75   1.58  11154    1.0\n",
      "M_infd_std[137]  -0.12  8.4e-3   0.82  -1.75  -0.67  -0.11   0.42   1.49 9654.0    1.0\n",
      "M_infd_std[138]  -0.29  8.4e-3   0.83  -1.94  -0.83  -0.27   0.28   1.28 9729.0    1.0\n",
      "M_infd_std[139]  -0.55  8.7e-3   0.85  -2.29  -1.08  -0.53   0.02   1.05 9593.0    1.0\n",
      "M_infd_std[140]   0.26  7.9e-3   0.76  -1.23  -0.26   0.26   0.76   1.79 9361.0    1.0\n",
      "M_infd_std[141]   1.08  6.2e-3   0.55   0.05   0.71   1.07   1.43   2.19 7879.0    1.0\n",
      "M_infd_std[142]   0.36  8.0e-3   0.78  -1.15  -0.16   0.35   0.88   1.91 9600.0    1.0\n",
      "M_infd_std[143]   0.03  7.0e-3   0.72  -1.41  -0.45   0.03   0.51   1.41  10437    1.0\n",
      "M_infd_std[144]   0.71  6.9e-3   0.71  -0.68   0.25   0.71   1.16   2.13  10408    1.0\n",
      "M_infd_std[145]   -0.1  7.8e-3   0.81  -1.69  -0.63  -0.09   0.44   1.46  10818    1.0\n",
      "M_infd_std[146]  -1.74    0.02   1.07  -4.47  -2.24  -1.59  -1.04  -0.08 4565.0    1.0\n",
      "M_infd_std[147]   1.65  9.0e-3   0.66   0.48   1.22   1.62   2.03   3.07 5343.0    1.0\n",
      "M_infd_std[148]   0.76  5.5e-3   0.54   -0.3    0.4   0.76   1.11   1.84 9584.0    1.0\n",
      "M_infd_std[149]    1.1  6.7e-3   0.65  -0.14   0.67   1.09   1.51   2.42 9434.0    1.0\n",
      "M_infd_std[150]   0.73  7.3e-3   0.74  -0.72   0.24   0.72   1.23   2.19  10210    1.0\n",
      "M_infd_std[151]   0.42  8.0e-3   0.81  -1.17  -0.13   0.43   0.95   2.03  10181    1.0\n",
      "M_infd_std[152]  -0.09  7.7e-3   0.75  -1.57  -0.59  -0.09   0.41   1.33 9350.0    1.0\n",
      "M_infd_std[153]   1.55  7.2e-3   0.47   0.68   1.24   1.53   1.83   2.53 4290.0    1.0\n",
      "M_infd_std[154]   0.36  6.4e-3   0.65  -0.91  -0.07   0.35   0.79   1.64  10344    1.0\n",
      "M_infd_std[155]    0.6  6.6e-3   0.69  -0.75   0.14    0.6   1.06   1.94  10753    1.0\n",
      "M_infd_std[156]  -0.52  6.6e-3   0.65  -1.83  -0.95  -0.51  -0.09   0.73 9810.0    1.0\n",
      "M_infd_std[157]  -0.13  7.0e-3   0.71  -1.54  -0.58  -0.12   0.35   1.25  10052    1.0\n",
      "M_infd_std[158]  -0.83    0.01   0.88  -2.64  -1.35  -0.79  -0.25   0.74 6458.0    1.0\n",
      "M_infd_std[159]   0.14  6.7e-3   0.69  -1.23  -0.32   0.14   0.61   1.47  10622    1.0\n",
      "M_infd_std[160]   0.23  8.4e-3   0.84  -1.41  -0.34   0.23   0.78   1.87  10063    1.0\n",
      "M_infd_std[161]    0.6  6.7e-3   0.67  -0.72   0.16    0.6   1.05   1.92  10070    1.0\n",
      "M_infd_std[162]  -0.91  9.0e-3   0.83  -2.61  -1.41  -0.88  -0.38   0.62 8622.0    1.0\n",
      "M_infd_std[163]   0.81  6.2e-3    0.6  -0.37   0.41   0.81    1.2    2.0 9367.0    1.0\n",
      "M_infd_std[164]   1.14  9.1e-3   0.85  -0.41   0.59   1.11   1.66    2.9 8645.0    1.0\n",
      "M_infd_std[165]  -0.93  9.4e-3   0.83  -2.68  -1.43   -0.9  -0.39   0.57 7737.0    1.0\n",
      "M_infd_std[166]   0.05  4.9e-3   0.51  -0.97  -0.29   0.05   0.38   1.05  10507    1.0\n",
      "M_infd_std[167]   0.51  7.2e-3   0.74  -0.95   0.02   0.51   1.02   1.99  10577    1.0\n",
      "M_infd_std[168]   0.03  7.9e-3   0.78  -1.55  -0.49   0.04   0.56   1.56 9791.0    1.0\n",
      "M_infd_std[169]  -0.78  6.7e-3   0.64  -2.07  -1.19  -0.77  -0.34   0.47 9180.0    1.0\n",
      "M_infd_std[170]  -0.75  8.8e-3   0.82  -2.43  -1.25  -0.72  -0.21   0.79 8675.0    1.0\n",
      "M_infd_std[171]    2.1    0.01   0.74   0.84   1.61   2.03   2.51   3.83 3630.0    1.0\n",
      "M_infd_std[172]   1.31  7.9e-3   0.72  -0.04   0.84   1.29   1.75   2.79 8209.0    1.0\n",
      "M_infd_std[173]   0.38    0.01   1.03  -1.54  -0.28   0.35   1.01   2.49 7596.0    1.0\n",
      "M_infd_std[174]   0.24  6.4e-3   0.65  -1.04  -0.19   0.24   0.67   1.54  10169    1.0\n",
      "M_infd_std[175]   0.74  6.5e-3   0.64  -0.53   0.32   0.73   1.15   2.02 9704.0    1.0\n",
      "M_infd_std[176]  -0.24  5.7e-3   0.59   -1.4  -0.63  -0.24   0.15   0.92  10733    1.0\n",
      "M_infd_std[177]  -1.49    0.02   1.13  -4.49  -1.98  -1.34  -0.77   0.26 3864.0    1.0\n",
      "M_infd_std[178]   0.44  8.2e-3    0.8  -1.13  -0.09   0.44   0.97   1.97 9522.0    1.0\n",
      "M_infd_std[179]  -0.61  6.2e-3   0.59  -1.79  -0.99   -0.6  -0.21   0.53 8906.0    1.0\n",
      "M_infd_std[180]   -0.3  8.5e-3   0.85  -2.01  -0.84  -0.29   0.26   1.34  10062    1.0\n",
      "M_infd_std[181]   0.32  8.4e-3    0.8  -1.24   -0.2   0.33   0.83   1.87 8931.0    1.0\n",
      "M_infd_std[182]   0.66  4.3e-3   0.43  -0.17   0.38   0.66   0.94   1.52 9752.0    1.0\n",
      "M_infd_std[183]  -0.46  9.1e-3   0.86  -2.18   -1.0  -0.45    0.1   1.12 8873.0    1.0\n",
      "M_infd_std[184]  -0.77  8.9e-3    0.8   -2.4  -1.28  -0.75  -0.24   0.71 8204.0    1.0\n",
      "M_infd_std[185]   0.23  7.3e-3   0.73   -1.2  -0.26   0.24   0.72   1.63 9981.0    1.0\n",
      "M_infd_std[186]  -0.68  6.1e-3   0.64  -1.95  -1.11  -0.68  -0.24   0.55  10953    1.0\n",
      "M_infd_std[187]  -1.13    0.01   0.97  -3.43  -1.65  -1.06  -0.49    0.5 5615.0    1.0\n",
      "M_infd_std[188]   0.24  6.5e-3   0.63  -1.02  -0.18   0.24   0.66   1.48 9605.0    1.0\n",
      "M_infd_std[189] -12.43    0.04   2.36 -17.58  -13.9 -12.28 -10.79  -8.15 4186.0    1.0\n",
      "M_infd_std[190]    0.5  7.9e-3   0.77  -0.99-8.3e-3    0.5   1.01   2.01 9450.0    1.0\n",
      "M_infd_std[191]   0.03 10.0e-3   0.94  -1.73  -0.58   0.04   0.65   1.82 8842.0    1.0\n",
      "M_infd_std[192]  -1.02    0.02   1.11  -3.91  -1.54  -0.91  -0.33   0.74 4893.0    1.0\n",
      "M_infd_std[193]  -0.67  9.7e-3   0.83  -2.34  -1.18  -0.63  -0.13   0.88 7282.0    1.0\n",
      "M_infd_std[194]   6.01    0.03   0.96   4.37   5.34   5.92   6.58   8.11 1455.0    1.0\n",
      "M_infd_std[195]  -0.09  7.3e-3   0.76  -1.57  -0.61  -0.08   0.43   1.35  10692    1.0\n",
      "M_infd_std[196]  -1.66    0.03   1.41  -5.52  -2.16  -1.38  -0.78    0.3 2725.0    1.0\n",
      "M_infd_std[197]   -0.9  6.8e-3   0.68  -2.25  -1.34  -0.89  -0.44   0.38 9854.0    1.0\n",
      "M_infd_std[198]    0.5  4.9e-3   0.49  -0.47   0.17    0.5   0.83   1.47  10118    1.0\n",
      "M_infd_std[199]  -0.06  7.9e-3   0.74  -1.54  -0.56  -0.06   0.44   1.37 8816.0    1.0\n",
      "M_infd_std[200]   0.15  7.1e-3   0.73  -1.28  -0.32   0.16   0.64   1.54  10496    1.0\n",
      "M_infd_std[201]   1.41  7.2e-3   0.53   0.43   1.05   1.39   1.73    2.5 5436.0    1.0\n",
      "M_infd_std[202]   0.29    0.02   1.21  -1.78  -0.44   0.23   0.91   2.78 4180.0    1.0\n",
      "M_infd_std[203]  -0.05  7.4e-3   0.78  -1.59  -0.56  -0.05   0.48   1.46  10987    1.0\n",
      "M_infd_std[204]   1.32  7.5e-3   0.69   0.03   0.87   1.31   1.76   2.73 8372.0    1.0\n",
      "M_infd_std[205]   1.25    0.01   0.86  -0.31   0.69    1.2   1.75   3.16 5371.0    1.0\n",
      "M_infd_std[206]   1.11  6.8e-3   0.69  -0.19   0.65   1.09   1.54   2.54  10314    1.0\n",
      "M_infd_std[207]   0.28  7.9e-3   0.77  -1.21  -0.24   0.29   0.81   1.77 9405.0    1.0\n",
      "M_infd_std[208]   2.12    0.01   0.67   0.97   1.68   2.06   2.49   3.67 2495.0    1.0\n",
      "M_infd_std[209]   0.18  5.7e-3   0.57  -0.94  -0.21   0.18   0.56   1.31  10169    1.0\n",
      "M_infd_std[210]  -0.82  7.9e-3   0.76  -2.37   -1.3  -0.81  -0.32   0.61 9321.0    1.0\n",
      "M_infd_std[211]   0.39  6.9e-3   0.67  -0.93  -0.06   0.39   0.83    1.7 9534.0    1.0\n",
      "M_infd_std[212]   0.27  7.3e-3   0.69  -1.09  -0.18   0.27   0.73   1.62 8968.0    1.0\n",
      "M_infd_std[213]  -0.94  4.5e-3   0.43  -1.81  -1.22  -0.93  -0.65  -0.12 9056.0    1.0\n",
      "M_infd_std[214]   1.07  5.9e-3   0.57  -0.02   0.69   1.06   1.43   2.22 9370.0    1.0\n",
      "M_infd_std[215]  -0.05  5.6e-3   0.57  -1.18  -0.43  -0.05   0.33   1.06  10458    1.0\n",
      "M_infd_std[216]   1.12  6.4e-3   0.64   -0.1    0.7   1.11   1.53   2.39 9831.0    1.0\n",
      "M_infd_std[217]  -0.34  8.1e-3   0.79  -1.91  -0.86  -0.34   0.19   1.19 9554.0    1.0\n",
      "M_infd_std[218]  -0.28  6.2e-3    0.6  -1.44  -0.68  -0.28   0.13   0.89 9339.0    1.0\n",
      "M_infd_std[219]   1.85    0.01   0.75   0.54   1.35   1.79   2.27   3.54 3779.0    1.0\n",
      "M_infd_std[220]   0.15  6.6e-3   0.67  -1.16  -0.29   0.15    0.6   1.44  10291    1.0\n",
      "M_infd_std[221]  -0.62  6.3e-3   0.64   -1.9  -1.05  -0.61  -0.19   0.62  10265    1.0\n",
      "M_infd_std[222]   0.73  5.9e-3    0.6  -0.45   0.32   0.72   1.12   1.91  10170    1.0\n",
      "M_infd_std[223]   3.24    0.02    1.3   1.03   2.25   3.15   4.16   5.94 2884.0    1.0\n",
      "M_infd_std[224]   0.36  6.9e-3   0.67  -0.98  -0.09   0.37   0.81   1.68 9430.0    1.0\n",
      "M_infd_std[225]  -0.78  7.7e-3   0.77   -2.3  -1.27  -0.76  -0.27   0.68 9919.0    1.0\n",
      "M_infd_std[226]  -0.83    0.01   0.91  -2.76  -1.35  -0.78  -0.23    0.8 7993.0    1.0\n",
      "M_infd_std[227]  -0.13  6.7e-3   0.71  -1.53  -0.59  -0.12   0.35   1.26  11341    1.0\n",
      "M_infd_std[228]    0.3  6.0e-3   0.61   -0.9  -0.11    0.3    0.7    1.5  10453    1.0\n",
      "M_infd_std[229]   0.57  4.8e-3   0.47  -0.35   0.26   0.56   0.88   1.51 9604.0    1.0\n",
      "M_infd_std[230]    1.5  8.1e-3   0.73   0.16   1.02   1.46   1.93   3.06 8087.0    1.0\n",
      "M_infd_std[231]  -0.71  5.0e-3    0.5  -1.73  -1.04   -0.7  -0.36   0.25  10248    1.0\n",
      "M_infd_std[232]  -0.34  9.1e-3   0.89  -2.13  -0.91  -0.33   0.27   1.34 9572.0    1.0\n",
      "M_infd_std[233]    1.3    0.01   0.89  -0.29    0.7   1.24   1.81   3.32 7332.0    1.0\n",
      "M_infd_std[234]   0.15  5.7e-3   0.59   -1.0  -0.24   0.16   0.55    1.3  10446    1.0\n",
      "M_infd_std[235]   0.05  6.1e-3   0.61  -1.16  -0.35   0.06   0.47   1.23  10038    1.0\n",
      "M_infd_std[236]   0.75  8.7e-3   0.81  -0.83   0.21   0.74   1.27   2.37 8822.0    1.0\n",
      "M_infd_std[237]  -0.14  7.5e-3   0.75  -1.63  -0.63  -0.13   0.37   1.34  10009    1.0\n",
      "M_infd_std[238]  -0.35  5.0e-3   0.52  -1.38   -0.7  -0.34 1.7e-3   0.65  10602    1.0\n",
      "M_infd_std[239]   0.02  7.5e-3   0.77  -1.54  -0.48   0.03   0.53   1.53  10688    1.0\n",
      "M_infd_std[240]  -0.96    0.01   1.04  -3.39  -1.51  -0.89  -0.28   0.79 6057.0    1.0\n",
      "M_infd_std[241]  -0.79  7.9e-3   0.81  -2.47  -1.29  -0.75  -0.25   0.69  10570    1.0\n",
      "M_infd_std[242]   0.36  6.1e-3   0.61  -0.87  -0.05   0.36   0.76   1.57  10068    1.0\n",
      "M_infd_std[243]   5.93    0.03   1.63   2.11   5.04   5.98   6.98   8.94 2765.0    1.0\n",
      "M_infd_std[244]   0.41  6.2e-3   0.66  -0.86  -0.03   0.41   0.86    1.7  11136    1.0\n",
      "M_infd_std[245]   -0.5  7.5e-3   0.75   -2.0  -0.99   -0.5 7.4e-3   0.94  10060    1.0\n",
      "M_infd_std[246]    1.6    0.01   0.87   0.07   1.03   1.54   2.08   3.57 4193.0    1.0\n",
      "M_infd_std[247]  -0.16  6.9e-3   0.69  -1.49  -0.62  -0.16   0.32   1.17  10120    1.0\n",
      "M_infd_std[248]  -0.76  7.5e-3    0.7  -2.17  -1.21  -0.75  -0.28   0.56 8810.0    1.0\n",
      "M_infd_std[249]   0.04  6.5e-3    0.7  -1.36  -0.43   0.05   0.51   1.44  11791    1.0\n",
      "M_infd_std[250]  -0.41  6.8e-3    0.7  -1.83  -0.86  -0.39   0.07   0.92  10521    1.0\n",
      "Ai[1]             0.04  4.2e-4   0.05  -0.07 2.0e-3   0.04   0.07   0.14  14819    1.0\n",
      "Ai[2]           5.6e-3  3.5e-4   0.05  -0.09  -0.03 5.9e-3   0.04    0.1  18509    1.0\n",
      "Ai[3]             0.03  3.7e-4   0.05  -0.07-5.9e-3   0.03   0.06   0.12  16409    1.0\n",
      "Ai[4]             0.02  3.4e-4   0.05  -0.08  -0.01   0.02   0.05   0.11  19393    1.0\n",
      "Ai[5]             0.02  4.2e-4   0.05  -0.07-8.7e-3   0.02   0.05   0.11  12319    1.0\n",
      "Ai[6]             0.03  3.4e-4   0.05  -0.07-4.2e-3   0.03   0.06   0.12  20749    1.0\n",
      "Ai[7]             0.02  3.6e-4   0.05  -0.07  -0.01   0.02   0.05   0.12  18081    1.0\n",
      "Ai[8]             0.02  3.7e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  17577    1.0\n",
      "Ai[9]          -1.7e-3  3.8e-4   0.05   -0.1  -0.03-2.2e-3   0.03    0.1  17066    1.0\n",
      "Ai[10]           -0.01  3.6e-4   0.05  -0.11  -0.04  -0.01   0.02   0.08  17507    1.0\n",
      "Ai[11]         -6.2e-3  3.6e-4   0.05   -0.1  -0.04-5.7e-3   0.03   0.09  18947    1.0\n",
      "Ai[12]          3.2e-3  3.7e-4   0.05  -0.09  -0.03 3.2e-3   0.04    0.1  18078    1.0\n",
      "Ai[13]          9.4e-3  4.2e-4   0.05  -0.08  -0.02   0.01   0.04    0.1  12617    1.0\n",
      "Ai[14]            0.03  3.6e-4   0.05  -0.07-6.4e-3   0.03   0.06   0.12  18066    1.0\n",
      "Ai[15]            0.02  3.5e-4   0.05  -0.07  -0.01   0.02   0.05   0.11  18949    1.0\n",
      "Ai[16]          8.6e-3  3.4e-4   0.05  -0.09  -0.02 8.1e-3   0.04    0.1  19482    1.0\n",
      "Ai[17]         -2.1e-3  3.6e-4   0.05   -0.1  -0.04-2.0e-3   0.03    0.1  18345    1.0\n",
      "Ai[18]            0.03  3.5e-4   0.05  -0.07-7.2e-3   0.03   0.06   0.12  18799    1.0\n",
      "Ai[19]          9.4e-3  3.6e-4   0.05  -0.09  -0.02 9.5e-3   0.04    0.1  18283    1.0\n",
      "Ai[20]            0.03  4.3e-4   0.05  -0.06 1.0e-3   0.03   0.07   0.13  14332    1.0\n",
      "Ai[21]          1.3e-3  3.6e-4   0.05  -0.09  -0.03 5.9e-4   0.03    0.1  17286    1.0\n",
      "Ai[22]            0.03  3.4e-4   0.05  -0.07-5.8e-3   0.03   0.06   0.12  19827    1.0\n",
      "Ai[23]           -0.01  3.7e-4   0.05   -0.1  -0.05  -0.01   0.02   0.08  17329    1.0\n",
      "Ai[24]            0.02  3.6e-4   0.05  -0.07-8.5e-3   0.02   0.06   0.12  17455    1.0\n",
      "Ai[25]         -2.7e-3  3.6e-4   0.05   -0.1  -0.04-2.7e-3   0.03   0.09  19063    1.0\n",
      "Ai[26]            0.03  3.6e-4   0.05  -0.07-4.3e-3   0.03   0.06   0.12  18369    1.0\n",
      "Ai[27]            0.02  3.5e-4   0.05  -0.08  -0.01   0.02   0.05   0.11  18857    1.0\n",
      "Ai[28]            0.03  3.5e-4   0.05  -0.07-6.5e-3   0.03   0.06   0.12  19079    1.0\n",
      "Ai[29]            0.02  3.5e-4   0.05  -0.07  -0.01   0.02   0.05   0.11  18184    1.0\n",
      "Ai[30]            0.02  3.8e-4   0.05  -0.07-7.7e-3   0.02   0.06   0.12  16337    1.0\n",
      "Ai[31]         -2.1e-3  3.7e-4   0.05   -0.1  -0.04-1.9e-3   0.03   0.09  16854    1.0\n",
      "Ai[32]         -8.9e-3  3.5e-4   0.05   -0.1  -0.04-9.5e-3   0.02   0.09  18883    1.0\n",
      "Ai[33]            0.04  3.7e-4   0.05  -0.06 3.5e-3   0.04   0.07   0.13  17632    1.0\n",
      "Ai[34]            0.03  3.4e-4   0.05  -0.07-2.5e-3   0.03   0.06   0.12  19359    1.0\n",
      "Ai[35]            0.05  3.5e-4   0.05  -0.04   0.02   0.05   0.09   0.15  18777    1.0\n",
      "Ai[36]            0.02  3.7e-4   0.05  -0.07  -0.01   0.02   0.05   0.12  16934    1.0\n",
      "Ai[37]            0.02  3.9e-4   0.05  -0.07  -0.01   0.02   0.05   0.11  14726    1.0\n",
      "Ai[38]            0.01  3.4e-4   0.05  -0.08  -0.02   0.01   0.04    0.1  19580    1.0\n",
      "Ai[39]            0.01  3.5e-4   0.05  -0.08  -0.02   0.01   0.05   0.11  19300    1.0\n",
      "Ai[40]            0.02  3.7e-4   0.05  -0.07  -0.01   0.02   0.05   0.12  17019    1.0\n",
      "Ai[41]         -3.4e-3  3.6e-4   0.05   -0.1  -0.04-3.3e-3   0.03   0.09  19028    1.0\n",
      "Ai[42]            0.01  3.9e-4   0.05  -0.08  -0.02   0.01   0.04    0.1  14726    1.0\n",
      "Ai[43]            0.03  3.4e-4   0.05  -0.07-4.1e-3   0.03   0.06   0.13  21565    1.0\n",
      "Ai[44]            0.02  4.0e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  14465    1.0\n",
      "Ai[45]            0.04  3.6e-4   0.05  -0.06 6.4e-3   0.04   0.07   0.13  18461    1.0\n",
      "Ai[46]          4.6e-3  3.6e-4   0.05  -0.09  -0.03 4.3e-3   0.04    0.1  18462    1.0\n",
      "Ai[47]          8.5e-3  3.8e-4   0.05  -0.08  -0.02 8.5e-3   0.04    0.1  15762    1.0\n",
      "Ai[48]          9.6e-3  3.4e-4   0.05  -0.08  -0.02 9.5e-3   0.04    0.1  19998    1.0\n",
      "Ai[49]            0.01  3.9e-4   0.05  -0.09  -0.02   0.01   0.05   0.11  16764    1.0\n",
      "Ai[50]          9.3e-3  3.6e-4   0.05  -0.08  -0.02 8.8e-3   0.04    0.1  18150    1.0\n",
      "Ai[51]           -0.01  3.6e-4   0.05  -0.11  -0.04  -0.01   0.02   0.08  18757    1.0\n",
      "Ai[52]            0.05  4.0e-4   0.05  -0.05   0.02   0.05   0.09   0.15  15306    1.0\n",
      "Ai[53]            0.02  3.3e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  21220    1.0\n",
      "Ai[54]          7.7e-4  3.4e-4   0.05   -0.1  -0.03 4.6e-4   0.03    0.1  21404    1.0\n",
      "Ai[55]            0.02  3.5e-4   0.05  -0.08-9.6e-3   0.02   0.06   0.12  19372    1.0\n",
      "Ai[56]            0.01  3.7e-4   0.05  -0.08  -0.02   0.01   0.04    0.1  16862    1.0\n",
      "Ai[57]          1.2e-3  3.5e-4   0.05  -0.09  -0.03 1.7e-3   0.03   0.09  19158    1.0\n",
      "Ai[58]          1.6e-3  3.3e-4   0.05  -0.09  -0.03 1.7e-3   0.03    0.1  21478    1.0\n",
      "Ai[59]            0.02  3.3e-4   0.05  -0.07  -0.01   0.02   0.05   0.11  19945    1.0\n",
      "Ai[60]            0.02  3.5e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  20204    1.0\n",
      "Ai[61]            0.02  3.5e-4   0.05  -0.08  -0.01   0.02   0.05   0.11  18548    1.0\n",
      "Ai[62]            0.02  3.5e-4   0.05  -0.07-9.7e-3   0.02   0.06   0.12  19221    1.0\n",
      "Ai[63]            0.03  3.7e-4   0.05  -0.06 5.7e-4   0.03   0.07   0.13  18528    1.0\n",
      "Ai[64]          7.5e-3  3.4e-4   0.05  -0.09  -0.03 7.4e-3   0.04    0.1  21033    1.0\n",
      "Ai[65]          3.1e-3  3.6e-4   0.05  -0.09  -0.03 3.1e-3   0.04    0.1  18132    1.0\n",
      "Ai[66]          4.7e-3  3.4e-4   0.05  -0.09  -0.03 4.8e-3   0.04    0.1  21093    1.0\n",
      "Ai[67]            0.03  3.6e-4   0.05  -0.06 1.3e-3   0.03   0.07   0.13  19011    1.0\n",
      "Ai[68]          4.9e-3  3.5e-4   0.05  -0.09  -0.03 4.7e-3   0.04    0.1  19302    1.0\n",
      "Ai[69]            0.02  3.4e-4   0.05  -0.07  -0.01   0.02   0.05   0.11  19891    1.0\n",
      "Ai[70]          7.9e-3  3.5e-4   0.05  -0.09  -0.03 8.1e-3   0.04    0.1  18401    1.0\n",
      "Ai[71]            0.02  3.5e-4   0.05  -0.07  -0.01   0.02   0.05   0.11  18462    1.0\n",
      "Ai[72]            0.04  3.5e-4   0.05  -0.05   0.01   0.05   0.08   0.14  20228    1.0\n",
      "Ai[73]          8.2e-3  3.4e-4   0.05  -0.09  -0.02 7.8e-3   0.04   0.11  20783    1.0\n",
      "Ai[74]          9.5e-3  3.4e-4   0.05  -0.08  -0.02   0.01   0.04   0.11  20237    1.0\n",
      "Ai[75]            0.02  3.8e-4   0.05  -0.08  -0.01   0.02   0.05   0.11  15499    1.0\n",
      "Ai[76]            0.02  3.9e-4   0.05  -0.08  -0.01   0.02   0.05   0.11  14765    1.0\n",
      "Ai[77]         -9.9e-4  3.6e-4   0.05  -0.09  -0.03-1.1e-3   0.03   0.09  17869    1.0\n",
      "Ai[78]            0.04  4.1e-4   0.05  -0.06 2.4e-3   0.04   0.07   0.13  14737    1.0\n",
      "Ai[79]            0.03  3.6e-4   0.05  -0.06-5.3e-3   0.03   0.06   0.12  16953    1.0\n",
      "Ai[80]            0.02  3.6e-4   0.05  -0.07  -0.01   0.02   0.05   0.11  16982    1.0\n",
      "Ai[81]          8.1e-4  3.6e-4   0.05  -0.09  -0.03 5.4e-4   0.03    0.1  18463    1.0\n",
      "Ai[82]          9.2e-3  3.5e-4   0.05  -0.08  -0.02 8.5e-3   0.04    0.1  18862    1.0\n",
      "Ai[83]            0.03  3.9e-4   0.05  -0.07-6.4e-3   0.03   0.06   0.12  15049    1.0\n",
      "Ai[84]            0.02  4.0e-4   0.05  -0.07-7.4e-3   0.02   0.06   0.12  14006    1.0\n",
      "Ai[85]            0.02  3.6e-4   0.05  -0.07-7.8e-3   0.02   0.05   0.12  17281    1.0\n",
      "Ai[86]            0.03  3.5e-4   0.05  -0.07-4.1e-3   0.03   0.06   0.13  19078    1.0\n",
      "Ai[87]            0.01  3.9e-4   0.05  -0.08  -0.02   0.01   0.05    0.1  14920    1.0\n",
      "Ai[88]            0.02  3.4e-4   0.05  -0.08  -0.01   0.02   0.05   0.11  20270    1.0\n",
      "Ai[89]            0.03  3.6e-4   0.05  -0.07-6.3e-3   0.03   0.06   0.12  18685    1.0\n",
      "Ai[90]            0.02  3.5e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  18742    1.0\n",
      "Ai[91]            0.03  3.4e-4   0.05  -0.07-7.9e-3   0.03   0.06   0.12  20211    1.0\n",
      "Ai[92]            0.04  4.1e-4   0.05  -0.06 3.8e-3   0.04   0.07   0.13  13119    1.0\n",
      "Ai[93]            0.02  3.3e-4   0.05  -0.08  -0.01   0.02   0.05   0.11  21290    1.0\n",
      "Ai[94]            0.01  3.6e-4   0.05  -0.08  -0.02   0.01   0.04   0.11  17880    1.0\n",
      "Ai[95]            0.03  3.5e-4   0.05  -0.07-3.4e-3   0.03   0.06   0.12  19070    1.0\n",
      "Ai[96]            0.03  3.5e-4   0.05  -0.06 1.2e-3   0.03   0.06   0.13  18987    1.0\n",
      "Ai[97]            0.02  3.5e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  18871    1.0\n",
      "Ai[98]            0.04  3.9e-4   0.05  -0.06 4.8e-3   0.04   0.07   0.13  16688    1.0\n",
      "Ai[99]          6.8e-3  3.5e-4   0.05  -0.09  -0.03 6.4e-3   0.04    0.1  18582    1.0\n",
      "Ai[100]         3.3e-3  3.5e-4   0.05  -0.09  -0.03 3.3e-3   0.04    0.1  19561    1.0\n",
      "Ai[101]           0.02  3.6e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  18175    1.0\n",
      "Ai[102]        -3.9e-3  3.6e-4   0.05   -0.1  -0.04-4.8e-3   0.03   0.09  18093    1.0\n",
      "Ai[103]           0.04  4.1e-4   0.05  -0.05 7.1e-3   0.04   0.07   0.13  13300    1.0\n",
      "Ai[104]           0.01  3.3e-4   0.05  -0.08  -0.02   0.01   0.04   0.11  21079    1.0\n",
      "Ai[105]           0.02  3.2e-4   0.05  -0.07-9.5e-3   0.02   0.06   0.12  22128    1.0\n",
      "Ai[106]           0.02  3.4e-4   0.05  -0.07  -0.01   0.02   0.06   0.11  20047    1.0\n",
      "Ai[107]           0.03  4.8e-4   0.05  -0.08-9.7e-3   0.03   0.06   0.13  12290    1.0\n",
      "Ai[108]           0.02  3.6e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  18610    1.0\n",
      "Ai[109]        -1.6e-5  3.6e-4   0.05   -0.1  -0.03 2.2e-4   0.03   0.09  18445    1.0\n",
      "Ai[110]        -7.1e-3  4.0e-4   0.05   -0.1  -0.04-7.1e-3   0.02   0.09  13514    1.0\n",
      "Ai[111]        -6.8e-3  3.6e-4   0.05   -0.1  -0.04-7.2e-3   0.03   0.09  18802    1.0\n",
      "Ai[112]          -0.02  3.7e-4   0.05  -0.11  -0.05  -0.02   0.02   0.08  16523    1.0\n",
      "Ai[113]           0.02  3.4e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  20334    1.0\n",
      "Ai[114]           0.02  3.4e-4   0.05  -0.08  -0.01   0.02   0.05   0.12  20274    1.0\n",
      "Ai[115]           0.01  3.7e-4   0.05  -0.08  -0.02   0.01   0.05   0.11  17590    1.0\n",
      "Ai[116]           0.04  3.4e-4   0.05  -0.06 3.1e-3   0.03   0.07   0.13  18806    1.0\n",
      "Ai[117]           0.02  3.4e-4   0.05  -0.08  -0.01   0.02   0.05   0.11  20284    1.0\n",
      "Ai[118]           0.01  3.7e-4   0.05  -0.08  -0.02   0.01   0.05   0.11  17505    1.0\n",
      "Ai[119]           0.01  4.4e-4   0.05  -0.08  -0.02   0.01   0.05    0.1  10915    1.0\n",
      "Ai[120]          -0.03  4.8e-4   0.05  -0.13  -0.06  -0.03 6.1e-3   0.07  11524    1.0\n",
      "Ai[121]           0.01  3.5e-4   0.05  -0.08  -0.02   0.01   0.04   0.11  18502    1.0\n",
      "Ai[122]           0.03  3.8e-4   0.05  -0.07-5.5e-3   0.03   0.06   0.12  16191    1.0\n",
      "Ai[123]         7.0e-3  3.5e-4   0.05  -0.09  -0.03 7.4e-3   0.04    0.1  19524    1.0\n",
      "Ai[124]         8.4e-3  3.5e-4   0.05  -0.09  -0.02 8.1e-3   0.04    0.1  19324    1.0\n",
      "Ai[125]           0.02  3.6e-4   0.05  -0.07-9.6e-3   0.02   0.06   0.12  18523    1.0\n",
      "Ai[126]           0.04  3.6e-4   0.05  -0.06 2.7e-3   0.04   0.07   0.13  18871    1.0\n",
      "Ai[127]           0.02  3.8e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  16374    1.0\n",
      "Ai[128]           0.03  3.5e-4   0.05  -0.07-5.9e-3   0.03   0.06   0.12  19423    1.0\n",
      "Ai[129]         5.1e-3  3.5e-4   0.05  -0.09  -0.03 5.4e-3   0.04    0.1  19545    1.0\n",
      "Ai[130]         2.4e-3  3.3e-4   0.05  -0.09  -0.03 2.2e-3   0.03   0.09  20516    1.0\n",
      "Ai[131]           0.03  3.9e-4   0.05  -0.06 5.9e-4   0.03   0.06   0.12  14461    1.0\n",
      "Ai[132]         6.0e-3  3.8e-4   0.05  -0.09  -0.03 6.0e-3   0.04    0.1  16394    1.0\n",
      "Ai[133]           0.02  3.7e-4   0.05  -0.07  -0.01   0.02   0.05   0.11  16559    1.0\n",
      "Ai[134]         4.1e-3  3.6e-4   0.05  -0.09  -0.03 4.6e-3   0.04   0.09  17090    1.0\n",
      "Ai[135]           0.01  3.7e-4   0.05  -0.08  -0.02   0.01   0.05   0.11  16993    1.0\n",
      "Ai[136]           0.01  3.7e-4   0.05  -0.08  -0.02   0.01   0.05   0.11  17389    1.0\n",
      "Ai[137]           0.02  3.5e-4   0.05  -0.08  -0.01   0.02   0.05   0.12  19680    1.0\n",
      "Ai[138]         1.6e-3  3.7e-4   0.05  -0.09  -0.03 9.6e-4   0.03    0.1  17260    1.0\n",
      "Ai[139]         7.0e-3  3.3e-4   0.05  -0.09  -0.03 7.0e-3   0.04    0.1  21333    1.0\n",
      "Ai[140]           0.01  3.6e-4   0.05  -0.08  -0.02   0.01   0.04   0.11  18604    1.0\n",
      "Ai[141]           0.04  4.0e-4   0.05  -0.05 7.8e-3   0.04   0.07   0.13  14191    1.0\n",
      "Ai[142]           0.02  3.6e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  17896    1.0\n",
      "Ai[143]         7.2e-3  3.6e-4   0.05  -0.09  -0.03 7.2e-3   0.04    0.1  18236    1.0\n",
      "Ai[144]           0.03  3.6e-4   0.05  -0.07-7.4e-3   0.03   0.06   0.12  17617    1.0\n",
      "Ai[145]         6.3e-3  3.5e-4   0.05  -0.09  -0.03 6.2e-3   0.04    0.1  20240    1.0\n",
      "Ai[146]          -0.01  3.7e-4   0.05  -0.11  -0.05  -0.02   0.02   0.08  18268    1.0\n",
      "Ai[147]           0.06  4.0e-4   0.05  -0.03   0.03   0.06   0.09   0.16  14730    1.0\n",
      "Ai[148]           0.02  3.7e-4   0.05  -0.07  -0.01   0.02   0.05   0.11  16566    1.0\n",
      "Ai[149]           0.03  3.7e-4   0.05  -0.07-5.2e-3   0.03   0.06   0.12  16776    1.0\n",
      "Ai[150]           0.03  3.6e-4   0.05  -0.07-6.8e-3   0.03   0.06   0.12  17776    1.0\n",
      "Ai[151]           0.02  3.4e-4   0.05  -0.07-9.4e-3   0.02   0.06   0.12  20214    1.0\n",
      "Ai[152]           0.01  3.5e-4   0.05  -0.08  -0.02   0.01   0.05   0.11  19584    1.0\n",
      "Ai[153]           0.04  4.3e-4   0.05  -0.06 7.8e-3   0.04   0.07   0.14  12922    1.0\n",
      "Ai[154]           0.01  3.3e-4   0.05  -0.08  -0.02   0.01   0.05    0.1  19446    1.0\n",
      "Ai[155]           0.02  3.6e-4   0.05  -0.07-8.9e-3   0.02   0.06   0.12  18354    1.0\n",
      "Ai[156]        -3.6e-3  3.6e-4   0.05   -0.1  -0.04-4.1e-3   0.03   0.09  18190    1.0\n",
      "Ai[157]         8.6e-3  3.5e-4   0.05  -0.08  -0.02 8.0e-3   0.04    0.1  18996    1.0\n",
      "Ai[158]         5.3e-3  3.5e-4   0.05  -0.09  -0.03 5.2e-3   0.04    0.1  19532    1.0\n",
      "Ai[159]           0.02  3.5e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  18314    1.0\n",
      "Ai[160]           0.02  3.5e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  19199    1.0\n",
      "Ai[161]           0.02  3.8e-4   0.05  -0.07  -0.01   0.02   0.06   0.12  16778    1.0\n",
      "Ai[162]         1.5e-3  3.6e-4   0.05  -0.09  -0.03 1.8e-3   0.03    0.1  18456    1.0\n",
      "Ai[163]           0.04  3.8e-4   0.05  -0.05 6.1e-3   0.04   0.07   0.13  14980    1.0\n",
      "Ai[164]           0.04  3.4e-4   0.05  -0.06 7.3e-3   0.04   0.07   0.14  20518    1.0\n",
      "Ai[165]        -9.2e-3  3.6e-4   0.05  -0.11  -0.04-9.4e-3   0.02   0.09  19518    1.0\n",
      "Ai[166]           0.02  4.1e-4   0.05  -0.07  -0.01   0.02   0.05   0.11  14143    1.0\n",
      "Ai[167]           0.02  3.4e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  19647    1.0\n",
      "Ai[168]           0.01  3.4e-4   0.05  -0.08  -0.02   0.01   0.05   0.11  19826    1.0\n",
      "Ai[169]        -2.9e-3  3.6e-4   0.05   -0.1  -0.04-2.9e-3   0.03   0.09  18215    1.0\n",
      "Ai[170]        -4.8e-4  3.6e-4   0.05  -0.09  -0.03-4.8e-4   0.03   0.09  17879    1.0\n",
      "Ai[171]           0.04  4.1e-4   0.05  -0.05 9.3e-3   0.04   0.08   0.14  14149    1.0\n",
      "Ai[172]           0.04  3.7e-4   0.05  -0.06 5.2e-3   0.04   0.07   0.13  16992    1.0\n",
      "Ai[173]           0.01  3.5e-4   0.05  -0.09  -0.02   0.01   0.04   0.11  19237    1.0\n",
      "Ai[174]           0.02  3.3e-4   0.05  -0.07-7.1e-3   0.02   0.06   0.12  20856    1.0\n",
      "Ai[175]           0.02  3.5e-4   0.05  -0.07-8.3e-3   0.02   0.05   0.12  18487    1.0\n",
      "Ai[176]           0.03  3.6e-4   0.05  -0.06-1.9e-3   0.03   0.06   0.12  16781    1.0\n",
      "Ai[177]        -2.3e-3  3.6e-4   0.05   -0.1  -0.04-2.3e-3   0.03    0.1  18728    1.0\n",
      "Ai[178]           0.02  3.5e-4   0.05  -0.07-8.1e-3   0.02   0.06   0.12  19081    1.0\n",
      "Ai[179]         1.3e-3  3.8e-4   0.05  -0.09  -0.03 1.4e-3   0.03    0.1  15194    1.0\n",
      "Ai[180]         5.1e-3  3.4e-4   0.05  -0.09  -0.03 5.4e-3   0.04    0.1  19830    1.0\n",
      "Ai[181]           0.02  3.4e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  20418    1.0\n",
      "Ai[182]           0.02  4.3e-4   0.05  -0.07  -0.01   0.02   0.05   0.11  11750    1.0\n",
      "Ai[183]           0.02  3.4e-4   0.05  -0.07  -0.01   0.02   0.05   0.12  19774    1.0\n",
      "Ai[184]        -4.3e-3  3.6e-4   0.05   -0.1  -0.04-4.6e-3   0.03   0.09  17449    1.0\n",
      "Ai[185]           0.02  3.5e-4   0.05  -0.08  -0.01   0.02   0.05   0.12  19715    1.0\n",
      "Ai[186]        -6.9e-3  3.5e-4   0.05   -0.1  -0.04-6.9e-3   0.03   0.08  17945    1.0\n",
      "Ai[187]         8.0e-4  3.6e-4   0.05  -0.09  -0.03 7.6e-4   0.03    0.1  18310    1.0\n",
      "Ai[188]           0.02  3.7e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  15901    1.0\n",
      "Ai[189]           0.01  3.6e-4   0.05  -0.08  -0.02   0.01   0.05   0.11  18595    1.0\n",
      "Ai[190]           0.02  3.6e-4   0.05  -0.07-9.8e-3   0.02   0.06   0.12  18633    1.0\n",
      "Ai[191]           0.01  3.5e-4   0.05  -0.08  -0.02   0.01   0.05   0.11  19258    1.0\n",
      "Ai[192]        -4.8e-3  3.6e-4   0.05   -0.1  -0.04-4.6e-3   0.03   0.09  18493    1.0\n",
      "Ai[193]         2.5e-3  3.5e-4   0.05  -0.09  -0.03 2.4e-3   0.04    0.1  18504    1.0\n",
      "Ai[194]           0.03  3.7e-4   0.05  -0.07-5.7e-3   0.03   0.06   0.13  17587    1.0\n",
      "Ai[195]           0.02  3.6e-4   0.05  -0.08  -0.01   0.02   0.05   0.11  18520    1.0\n",
      "Ai[196]          -0.01  3.6e-4   0.05  -0.11  -0.05  -0.01   0.02   0.08  18949    1.0\n",
      "Ai[197]           0.04  3.7e-4   0.05  -0.05 8.8e-3   0.04   0.07   0.13  16945    1.0\n",
      "Ai[198]           0.02  4.1e-4   0.05  -0.07  -0.01   0.02   0.05   0.11  13663    1.0\n",
      "Ai[199]         8.6e-3  3.5e-4   0.05  -0.09  -0.02 8.5e-3   0.04    0.1  18738    1.0\n",
      "Ai[200]           0.02  3.5e-4   0.05  -0.08  -0.01   0.02   0.05   0.11  19435    1.0\n",
      "Ai[201]           0.03  4.2e-4   0.05  -0.06-1.6e-3   0.03   0.06   0.12  12736    1.0\n",
      "Ai[202]           0.02  3.6e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  18896    1.0\n",
      "Ai[203]           0.02  3.4e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  20688    1.0\n",
      "Ai[204]           0.04  3.8e-4   0.05  -0.05   0.01   0.05   0.08   0.14  16878    1.0\n",
      "Ai[205]           0.03  3.7e-4   0.05  -0.06-2.9e-3   0.03   0.06   0.13  17337    1.0\n",
      "Ai[206]           0.03  3.8e-4   0.05  -0.07-5.9e-3   0.03   0.06   0.12  16206    1.0\n",
      "Ai[207]           0.03  3.6e-4   0.05  -0.07-5.8e-3   0.03   0.06   0.12  18442    1.0\n",
      "Ai[208]           0.05  4.1e-4   0.05  -0.05   0.02   0.05   0.08   0.14  14865    1.0\n",
      "Ai[209]           0.02  3.4e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  18923    1.0\n",
      "Ai[210]        -5.4e-3  3.6e-4   0.05   -0.1  -0.04-5.0e-3   0.03   0.09  18384    1.0\n",
      "Ai[211]           0.02  3.5e-4   0.05  -0.07  -0.02   0.02   0.05   0.11  17801    1.0\n",
      "Ai[212]           0.02  3.5e-4   0.05  -0.07  -0.01   0.02   0.05   0.11  18576    1.0\n",
      "Ai[213]          -0.01  4.2e-4   0.05  -0.11  -0.05  -0.01   0.02   0.08  12103    1.0\n",
      "Ai[214]           0.03  3.7e-4   0.05  -0.06 1.8e-3   0.03   0.06   0.13  15767    1.0\n",
      "Ai[215]           0.01  3.7e-4   0.05  -0.08  -0.02   0.01   0.05   0.11  16210    1.0\n",
      "Ai[216]           0.03  3.6e-4   0.05  -0.07-5.7e-3   0.03   0.06   0.12  17764    1.0\n",
      "Ai[217]         7.3e-3  3.5e-4   0.05  -0.09  -0.02 7.4e-3   0.04    0.1  18616    1.0\n",
      "Ai[218]        -2.3e-4  3.8e-4   0.05  -0.09  -0.03-6.9e-5   0.03   0.09  15058    1.0\n",
      "Ai[219]           0.04  4.0e-4   0.05  -0.06 4.1e-3   0.04   0.07   0.13  15128    1.0\n",
      "Ai[220]           0.03  3.6e-4   0.05  -0.07-6.5e-3   0.03   0.06   0.12  17827    1.0\n",
      "Ai[221]        -5.2e-4  3.6e-4   0.05   -0.1  -0.03-4.4e-4   0.03    0.1  17651    1.0\n",
      "Ai[222]           0.03  3.6e-4   0.05  -0.06 6.8e-4   0.03   0.07   0.12  17424    1.0\n",
      "Ai[223]           0.04  4.4e-4   0.05  -0.06 4.4e-3   0.04   0.07   0.14  14030    1.0\n",
      "Ai[224]           0.02  3.6e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  17274    1.0\n",
      "Ai[225]         8.8e-4  3.4e-4   0.05   -0.1  -0.03 5.7e-4   0.03    0.1  20862    1.0\n",
      "Ai[226]        -3.5e-3  3.4e-4   0.05   -0.1  -0.04-3.7e-3   0.03   0.09  19936    1.0\n",
      "Ai[227]           0.01  3.5e-4   0.05  -0.08  -0.02   0.01   0.05   0.11  19410    1.0\n",
      "Ai[228]           0.01  3.6e-4   0.05  -0.08  -0.02   0.01   0.05   0.11  17575    1.0\n",
      "Ai[229]           0.02  4.2e-4   0.05  -0.08  -0.01   0.02   0.05   0.11  13081    1.0\n",
      "Ai[230]           0.04  3.5e-4   0.05  -0.06 5.1e-3   0.04   0.07   0.13  19272    1.0\n",
      "Ai[231]        -7.5e-3  3.9e-4   0.05   -0.1  -0.04-8.3e-3   0.02   0.09  14667    1.0\n",
      "Ai[232]         6.2e-3  3.4e-4   0.05  -0.09  -0.03 6.2e-3   0.04    0.1  20228    1.0\n",
      "Ai[233]           0.04  3.6e-4   0.05  -0.06 4.4e-3   0.04   0.07   0.13  18955    1.0\n",
      "Ai[234]           0.02  3.5e-4   0.05  -0.07  -0.01   0.02   0.05   0.11  17438    1.0\n",
      "Ai[235]           0.02  3.8e-4   0.05  -0.07  -0.01   0.02   0.05   0.11  16346    1.0\n",
      "Ai[236]           0.02  3.5e-4   0.05  -0.08  -0.01   0.02   0.05   0.12  20373    1.0\n",
      "Ai[237]           0.01  3.7e-4   0.05  -0.08  -0.02   0.01   0.05   0.11  17618    1.0\n",
      "Ai[238]         4.2e-3  3.7e-4   0.05  -0.09  -0.03 4.3e-3   0.04    0.1  15706    1.0\n",
      "Ai[239]           0.01  3.4e-4   0.05  -0.08  -0.02   0.01   0.05   0.11  20745    1.0\n",
      "Ai[240]        -4.1e-3  3.4e-4   0.05   -0.1  -0.04-4.3e-3   0.03   0.09  20061    1.0\n",
      "Ai[241]        -1.5e-3  3.3e-4   0.05   -0.1  -0.03-1.9e-3   0.03   0.09  21728    1.0\n",
      "Ai[242]           0.02  3.6e-4   0.05  -0.08  -0.02   0.02   0.05   0.11  17391    1.0\n",
      "Ai[243]           0.04  3.7e-4   0.05  -0.06 2.3e-3   0.04   0.07   0.14  18668    1.0\n",
      "Ai[244]           0.02  3.6e-4   0.05  -0.08  -0.01   0.02   0.05   0.11  18149    1.0\n",
      "Ai[245]         3.3e-3  3.4e-4   0.05  -0.09  -0.03 3.6e-3   0.04    0.1  19702    1.0\n",
      "Ai[246]           0.04  3.8e-4   0.05  -0.05 9.0e-3   0.04   0.07   0.14  16750    1.0\n",
      "Ai[247]         4.2e-3  3.6e-4   0.05  -0.09  -0.03 4.6e-3   0.04    0.1  17387    1.0\n",
      "Ai[248]         1.3e-3  3.6e-4   0.05  -0.09  -0.03 1.5e-3   0.03    0.1  17650    1.0\n",
      "Ai[249]         8.1e-3  3.4e-4   0.05  -0.09  -0.02 8.1e-3   0.04    0.1  19352    1.0\n",
      "Ai[250]         2.6e-3  3.7e-4   0.05  -0.09  -0.03 3.4e-3   0.04   0.09  17091    1.0\n",
      "r_infd[1]       2454.8    1.67 142.46 2168.9 2358.6 2460.3 2554.4 2720.2 7271.0    1.0\n",
      "r_infd[2]       2589.3     1.1 108.86 2384.9 2514.7 2585.7 2660.1 2813.7 9793.0    1.0\n",
      "r_infd[3]       1255.2     0.3  33.32 1191.6 1232.4 1254.7 1277.2 1323.5  12429    1.0\n",
      "r_infd[4]       1789.5    0.73  73.27 1653.5 1739.0 1787.8 1837.6 1940.9  10072    1.0\n",
      "r_infd[5]       951.58    0.16  18.42 916.12 939.04 951.16  964.0  988.7  13553    1.0\n",
      "r_infd[6]       2481.6    1.09  108.4 2281.7 2405.4 2477.2 2552.9 2704.1 9906.0    1.0\n",
      "r_infd[7]       3969.6    2.07 190.73 3602.6 3840.8 3964.2 4094.8 4351.4 8487.0    1.0\n",
      "r_infd[8]       3610.9    1.81 173.93 3285.0 3491.2 3604.0 3725.9 3964.4 9192.0    1.0\n",
      "r_infd[9]       1850.4    0.71  71.03 1719.0 1801.5 1846.5 1896.0 1996.5  10130    1.0\n",
      "r_infd[10]      1781.6    0.61  61.43 1667.8 1739.1 1779.5 1821.6 1907.5  10190    1.0\n",
      "r_infd[11]      2868.1    1.47 139.96 2620.9 2771.5 2858.2 2954.7 3169.9 9126.0    1.0\n",
      "r_infd[12]      4379.6    2.14 213.46 3988.5 4234.8 4372.6 4515.0 4823.0 9914.0    1.0\n",
      "r_infd[13]      1152.7    0.22  24.97 1104.9 1136.0 1152.1 1169.0 1202.4  13371    1.0\n",
      "r_infd[14]      1721.6    0.76  76.43 1573.8 1669.4 1719.9 1772.7 1874.1  10246    1.0\n",
      "r_infd[15]      1823.1    0.65  67.79 1694.1 1777.1 1821.0 1866.7 1964.0  10815    1.0\n",
      "r_infd[16]      3472.3    1.38 137.12 3211.2 3379.1 3468.6 3559.9 3757.0 9932.0    1.0\n",
      "r_infd[17]      2925.6     1.4 132.88 2686.4 2834.9 2918.3 3005.5 3207.6 8991.0    1.0\n",
      "r_infd[18]      1946.5    0.89  87.08 1779.7 1889.1 1945.4 2003.3 2120.7 9608.0    1.0\n",
      "r_infd[19]      4645.7    2.28 220.07 4231.3 4495.9 4638.7 4787.0 5101.4 9318.0    1.0\n",
      "r_infd[20]      2788.6    1.32 117.69 2560.5 2708.0 2790.4 2868.5 3019.5 7986.0    1.0\n",
      "r_infd[21]      1814.8    0.62   65.5 1692.3 1770.3 1812.5 1857.3 1946.3  11250    1.0\n",
      "r_infd[22]      3924.4    1.99 191.34 3568.1 3793.8 3917.6 4047.0 4316.0 9217.0    1.0\n",
      "r_infd[23]      3512.7     1.8 155.73 3241.1 3406.8 3501.7 3604.4 3860.6 7462.0    1.0\n",
      "r_infd[24]      3143.1    1.16 118.23 2916.2 3065.1 3139.6 3218.5 3387.2  10390    1.0\n",
      "r_infd[25]      3286.6    1.31 129.87 3050.0 3195.8 3279.7 3371.4 3553.3 9831.0    1.0\n",
      "r_infd[26]      2555.7     1.3 122.47 2310.5 2476.5 2555.2 2636.7 2797.3 8863.0    1.0\n",
      "r_infd[27]      3449.2    1.46 141.59 3190.0 3350.7 3446.1 3540.2 3742.1 9444.0    1.0\n",
      "r_infd[28]      2076.4    1.01  93.86 1907.0 2010.7 2073.5 2136.4 2271.8 8566.0    1.0\n",
      "r_infd[29]      2800.4    1.01 101.29 2611.3 2731.3 2796.4 2865.5 3006.6  10041    1.0\n",
      "r_infd[30]      2203.4    0.86  84.13 2042.1 2146.8 2201.0 2258.0 2374.9 9577.0    1.0\n",
      "r_infd[31]      3682.9    1.56 147.54 3411.1 3581.6 3676.5 3777.7 3989.2 8960.0    1.0\n",
      "r_infd[32]      2362.5     1.0  93.74 2191.7 2298.6 2358.0 2421.9 2554.0 8823.0    1.0\n",
      "r_infd[33]      2796.8    1.14 107.12 2591.1 2724.3 2796.9 2867.4 3013.4 8772.0    1.0\n",
      "r_infd[34]      2117.2    0.88  86.72 1950.5 2058.1 2115.5 2174.6 2291.7 9790.0    1.0\n",
      "r_infd[35]      1641.0    0.57  58.93 1528.9 1600.8 1638.9 1679.6 1762.5  10700    1.0\n",
      "r_infd[36]      1986.5    0.77  71.53 1853.8 1938.5 1984.0 2031.9 2134.4 8605.0    1.0\n",
      "r_infd[37]      1111.5    0.26  28.16 1058.6 1092.2 1111.0 1129.5 1168.7  12082    1.0\n",
      "r_infd[38]      3817.3    1.86 176.69 3493.4 3695.8 3810.1 3928.5 4187.7 9023.0    1.0\n",
      "r_infd[39]      4508.8    2.44 222.43 4095.7 4357.9 4499.6 4651.7 4959.0 8325.0    1.0\n",
      "r_infd[40]      3895.7    1.67 175.11 3562.7 3775.0 3889.3 4010.5 4251.7  10987    1.0\n",
      "r_infd[41]      1577.9     5.7 221.14 1349.0 1458.6 1523.6 1609.2 2249.4 1504.0    1.0\n",
      "r_infd[42]      1194.3    0.25   28.9 1139.4 1174.2 1194.1 1213.6 1251.5  12931    1.0\n",
      "r_infd[43]      4445.8    2.93 257.71 3911.7 4283.8 4450.7 4612.9 4944.0 7732.0    1.0\n",
      "r_infd[44]      1231.5    0.31  32.76 1170.2 1208.8 1230.6 1253.5 1297.6  11527    1.0\n",
      "r_infd[45]      2998.4    1.28 124.05 2761.8 2914.6 2995.9 3079.5 3253.0 9408.0    1.0\n",
      "r_infd[46]      3965.7    1.81 184.45 3625.3 3837.9 3957.1 4085.6 4352.5  10441    1.0\n",
      "r_infd[47]      1415.8     0.4  41.51 1336.8 1387.6 1414.8 1443.2 1500.9  11030    1.0\n",
      "r_infd[48]      4167.2    2.25 217.94 3766.3 4021.7 4157.8 4304.3 4619.4 9380.0    1.0\n",
      "r_infd[49]      5721.8    9.71 732.28 4502.8 5140.0 5716.4 6209.4 7224.8 5683.0    1.0\n",
      "r_infd[50]      3315.2    1.29 131.98 3070.4 3224.3 3309.9 3402.8 3580.1  10537    1.0\n",
      "r_infd[51]      2416.8    1.18 107.26 2230.0 2343.0 2409.2 2481.5 2649.9 8205.0    1.0\n",
      "r_infd[52]      2749.1    1.49 128.34 2486.7 2667.2 2750.3 2835.6 2998.6 7458.0    1.0\n",
      "r_infd[53]      4711.3    2.87 268.12 4239.3 4529.6 4694.8 4867.6 5290.3 8698.0    1.0\n",
      "r_infd[54]      3668.7    1.61 158.39 3378.0 3560.2 3661.0 3769.7 3992.0 9694.0    1.0\n",
      "r_infd[55]      3921.5    1.92 180.48 3585.5 3795.1 3917.2 4042.4 4289.9 8826.0    1.0\n",
      "r_infd[56]      1780.1    0.71  70.02 1648.2 1732.6 1777.2 1826.0 1923.4 9773.0    1.0\n",
      "r_infd[57]      2468.7    1.01 103.16 2277.6 2398.1 2464.4 2534.7 2678.0  10391    1.0\n",
      "r_infd[58]      3628.2    1.72 166.95 3319.9 3513.5 3621.0 3733.4 3984.5 9417.0    1.0\n",
      "r_infd[59]      1310.0    0.38  41.57 1230.5 1281.4 1309.2 1337.2 1394.6  11918    1.0\n",
      "r_infd[60]      2705.3    1.37 135.16 2453.1 2614.6 2701.1 2792.3 2981.9 9683.0    1.0\n",
      "r_infd[61]      2145.5    1.01  95.92 1962.6 2080.2 2142.1 2208.4 2339.9 8987.0    1.0\n",
      "r_infd[62]      2862.0    1.51 147.54 2583.9 2761.9 2858.4 2958.4 3165.5 9529.0    1.0\n",
      "r_infd[63]      2081.1    1.27 113.59 1843.2 2009.7 2083.6 2157.7 2293.0 8023.0    1.0\n",
      "r_infd[64]      2721.2    3.53 215.99 2357.6 2594.9 2709.9 2835.4 3131.3 3748.0    1.0\n",
      "r_infd[65]      3445.6    1.51 141.83 3176.5 3349.5 3443.6 3537.8 3738.4 8877.0    1.0\n",
      "r_infd[66]      2800.0    1.35 135.22 2558.8 2705.5 2793.2 2884.2 3089.7  10090    1.0\n",
      "r_infd[67]      4117.2    2.43  227.8 3651.9 3968.1 4116.4 4266.6 4562.6 8800.0    1.0\n",
      "r_infd[68]      2189.3    0.87  89.12 2025.7 2128.1 2186.4 2247.0 2370.6  10570    1.0\n",
      "r_infd[69]      2593.3    1.21 123.75 2363.7 2507.6 2588.8 2674.6 2847.3  10395    1.0\n",
      "r_infd[70]      2980.3    0.96  99.55 2792.9 2912.2 2976.1 3046.2 3186.4  10802    1.0\n",
      "r_infd[71]      2317.5    0.91  93.87 2142.8 2251.3 2315.1 2379.1 2507.3  10712    1.0\n",
      "r_infd[72]      2765.8    1.16 113.95 2543.4 2689.6 2766.1 2842.0 2988.0 9608.0    1.0\n",
      "r_infd[73]      4701.3    2.51 241.09 4269.1 4537.1 4689.8 4852.3 5197.9 9219.0    1.0\n",
      "r_infd[74]      3731.6    1.76 171.85 3412.6 3613.6 3727.4 3841.8 4085.9 9520.0    1.0\n",
      "r_infd[75]      1511.5    0.41  40.63 1432.4 1483.8 1511.0 1538.7 1591.1 9706.0    1.0\n",
      "r_infd[76]      937.48    0.19  21.71 896.92 922.49 936.94 951.73 981.51  12470    1.0\n",
      "r_infd[77]      974.41    0.21  23.84 929.76  957.9  973.7 990.25 1022.4  12901    1.0\n",
      "r_infd[78]      1732.9    0.59  58.01 1620.4 1694.5 1732.5 1771.9 1848.4 9577.0    1.0\n",
      "r_infd[79]      1158.4    0.29  31.56 1099.5 1136.1 1157.5 1179.1 1222.9  11830    1.0\n",
      "r_infd[80]      1645.5     0.5  51.55 1550.0 1610.2 1643.2 1679.3 1752.1  10684    1.0\n",
      "r_infd[81]      2914.8    2.09 160.65 2651.5 2805.6 2898.6 3001.5 3284.3 5892.0    1.0\n",
      "r_infd[82]      2267.1    1.05 102.62 2078.1 2198.1 2263.1 2330.5 2482.6 9606.0    1.0\n",
      "r_infd[83]      1402.6    0.38  41.12 1325.1 1374.1 1401.7 1429.3 1486.9  11409    1.0\n",
      "r_infd[84]      1313.6    0.29  34.68 1248.5 1289.5 1312.6 1336.8 1384.2  14227    1.0\n",
      "r_infd[85]      1724.5    0.62  60.12 1611.4 1682.4 1723.3 1764.9 1846.4 9274.0    1.0\n",
      "r_infd[86]      2613.0    1.41 136.15 2344.0 2523.8 2611.1 2702.3 2877.2 9310.0    1.0\n",
      "r_infd[87]      1179.2    0.27  29.09 1124.6 1159.2 1178.1 1198.1 1238.1  12042    1.0\n",
      "r_infd[88]      3351.9    1.49  149.4 3075.5 3248.8 3346.9 3449.4 3657.5  10036    1.0\n",
      "r_infd[89]      2900.3    1.11 113.45 2687.3 2822.1 2897.4 2974.4 3135.2  10410    1.0\n",
      "r_infd[90]      2019.8    0.78  77.25 1875.9 1966.9 2017.7 2069.5 2178.8 9751.0    1.0\n",
      "r_infd[91]      4394.0    2.05 214.98 4000.1 4247.4 4383.4 4531.2 4839.1  10995    1.0\n",
      "r_infd[92]      770.25    0.15  15.91 739.81 759.43 769.97 780.82 802.31  11985    1.0\n",
      "r_infd[93]      1758.9    0.72  73.13 1621.3 1708.6 1756.5 1805.7 1909.4  10428    1.0\n",
      "r_infd[94]      3684.5    1.42 147.56 3403.8 3584.2 3679.6 3780.4 3984.7  10744    1.0\n",
      "r_infd[95]      2855.0     1.2 122.84 2618.5 2772.5 2852.2 2936.7 3103.7  10511    1.0\n",
      "r_infd[96]      3203.4    1.38 137.56 2942.4 3111.0 3201.3 3293.3 3479.9 9957.0    1.0\n",
      "r_infd[97]      1948.3    0.79  80.83 1797.4 1892.5 1945.5 2000.8 2114.1  10542    1.0\n",
      "r_infd[98]      2244.6    1.23 115.21 2008.2 2169.2 2248.8 2322.8 2460.4 8742.0    1.0\n",
      "r_infd[99]      3600.9    1.75 170.33 3284.9 3485.1 3593.6 3707.6 3956.2 9428.0    1.0\n",
      "r_infd[100]     2233.5    0.98  99.35 2050.1 2165.1 2229.6 2297.3 2441.1  10275    1.0\n",
      "r_infd[101]     2911.1    1.27 125.91 2678.0 2823.6 2907.1 2995.1 3170.3 9807.0    1.0\n",
      "r_infd[102]     4584.2    3.17 263.96 4139.4 4404.8 4560.2 4732.1 5181.6 6919.0    1.0\n",
      "r_infd[103]     903.61    0.15  17.93 869.11 891.35 903.29 915.14 939.67  14022    1.0\n",
      "r_infd[104]     4689.2    2.96 267.44 4224.3 4510.8 4672.9 4850.8 5247.9 8156.0    1.0\n",
      "r_infd[105]     1652.6    0.69  67.23 1528.9 1606.7 1650.3 1696.4 1788.9 9474.0    1.0\n",
      "r_infd[106]     2107.5    0.83  86.68 1941.0 2048.4 2105.2 2165.1 2281.9  11010    1.0\n",
      "r_infd[107]     1190.9    0.41  39.34 1117.7 1163.2 1189.8 1216.8 1271.2 9016.0    1.0\n",
      "r_infd[108]     4668.3     4.7 317.71 4187.5 4462.1 4627.5 4816.7 5490.1 4565.0    1.0\n",
      "r_infd[109]     2483.0    1.08 109.01 2281.3 2408.2 2478.9 2553.0 2709.8  10197    1.0\n",
      "r_infd[110]     1010.9    0.17  20.67  971.7 996.66 1010.6 1024.4 1052.8  13963    1.0\n",
      "r_infd[111]     1669.4    0.98  84.86 1524.4 1612.2 1662.0 1717.5 1860.1 7435.0    1.0\n",
      "r_infd[112]     1773.7    0.57  59.44 1662.9 1733.3 1771.9 1810.8 1897.8  10986    1.0\n",
      "r_infd[113]     3590.8    1.46 141.64 3323.9 3493.2 3586.5 3683.6 3882.6 9383.0    1.0\n",
      "r_infd[114]     2956.6    1.24 128.08 2714.6 2869.2 2953.7 3042.1 3216.7  10750    1.0\n",
      "r_infd[115]     4332.8    2.05 201.83 3952.8 4197.1 4328.2 4462.9 4751.2 9692.0    1.0\n",
      "r_infd[116]     1235.5     0.4  43.56 1153.4 1205.3 1234.4 1264.7 1324.0  11787    1.0\n",
      "r_infd[117]     9265.2   10.12 699.49 7682.2 8877.1 9291.5 9707.1  1.1e4 4774.0    1.0\n",
      "r_infd[118]     3749.5    1.71 171.86 3431.2 3630.7 3742.5 3859.3 4107.1  10092    1.0\n",
      "r_infd[119]     504.07    0.05   6.45 491.87 499.66 503.92 508.37 517.17  16186    1.0\n",
      "r_infd[120]     238.45    0.01   1.46 235.63 237.47 238.45 239.42 241.31  18511    1.0\n",
      "r_infd[121]     1646.7    0.55  59.38 1533.8 1605.4 1645.8 1686.1 1765.0  11473    1.0\n",
      "r_infd[122]     2028.4    0.71  72.04 1891.6 1979.4 2027.2 2075.2 2177.1  10322    1.0\n",
      "r_infd[123]     4338.6    2.19 212.59 3945.6 4191.6 4332.4 4475.1 4782.1 9416.0    1.0\n",
      "r_infd[124]     3481.5    1.29 130.54 3233.1 3391.6 3477.3 3566.2 3749.4  10182    1.0\n",
      "r_infd[125]     2346.6    0.99  99.58 2156.0 2279.6 2344.5 2411.3 2545.7  10086    1.0\n",
      "r_infd[126]     2717.3    1.39 135.57 2454.2 2624.8 2716.1 2807.6 2987.5 9537.0    1.0\n",
      "r_infd[127]     1133.5    0.28  30.12 1076.7 1112.7 1132.5 1153.2 1195.3  11735    1.0\n",
      "r_infd[128]     2834.6    1.13 114.06 2619.4 2754.3 2831.0 2911.6 3062.6  10107    1.0\n",
      "r_infd[129]     2409.2    1.11 110.82 2203.3 2334.0 2404.3 2479.9 2638.7 9979.0    1.0\n",
      "r_infd[130]     2659.0    1.08 116.41 2444.7 2579.2 2654.4 2734.6 2899.8  11513    1.0\n",
      "r_infd[131]     1272.2    0.37  37.96 1200.9 1246.6 1271.2 1297.0 1350.8  10476    1.0\n",
      "r_infd[132]     1180.4    0.38  41.74 1101.5 1151.5 1179.2 1208.1 1266.2  11844    1.0\n",
      "r_infd[133]     1473.9    0.51  50.88 1379.0 1438.9 1472.4 1507.7 1577.0  10074    1.0\n",
      "r_infd[134]     4086.1    1.88 183.67 3749.4 3960.0 4078.0 4206.4 4461.1 9572.0    1.0\n",
      "r_infd[135]     1152.5     0.2  26.31 1102.3 1134.7 1152.1 1170.1 1205.3  16806    1.0\n",
      "r_infd[136]     2038.2    0.68   72.7 1902.5 1987.9 2035.9 2087.3 2186.8  11557    1.0\n",
      "r_infd[137]     4602.9    2.44 229.92 4179.0 4446.2 4592.9 4748.5 5077.7 8891.0    1.0\n",
      "r_infd[138]     4638.6    2.42 228.62 4207.4 4482.8 4630.2 4783.4 5112.7 8911.0    1.0\n",
      "r_infd[139]     4179.1    2.22 214.73 3790.7 4032.5 4169.1 4309.3 4625.8 9343.0    1.0\n",
      "r_infd[140]     2867.2    1.31 128.89 2622.9 2779.3 2865.1 2950.7 3130.9 9749.0    1.0\n",
      "r_infd[141]     1061.8    0.25  26.83 1010.3 1043.1 1061.4 1079.8 1115.6  11806    1.0\n",
      "r_infd[142]     3891.6    1.83 180.34 3550.5 3770.5 3888.6 4008.5 4252.0 9680.0    1.0\n",
      "r_infd[143]     2378.6    0.99  99.18 2191.5 2310.6 2376.7 2442.9 2579.0  10008    1.0\n",
      "r_infd[144]     3372.6    1.37 137.66 3111.8 3279.4 3369.1 3462.8 3648.2  10060    1.0\n",
      "r_infd[145]     4276.5    2.08 207.36 3891.8 4133.9 4269.1 4410.1 4707.0 9977.0    1.0\n",
      "r_infd[146]     3501.0    2.67 204.58 3176.5 3362.6 3476.8 3604.8 4007.2 5855.0    1.0\n",
      "r_infd[147]     1272.5     0.4  39.65 1196.3 1245.8 1271.9 1298.8 1352.3 9639.0    1.0\n",
      "r_infd[148]     1303.5    0.34  34.03 1238.1 1280.1 1302.9 1325.8 1371.7  10189    1.0\n",
      "r_infd[149]     1415.6    0.45  48.02 1324.8 1382.6 1415.0 1447.2 1511.1  11576    1.0\n",
      "r_infd[150]     2323.4    1.02 101.02 2133.3 2253.4 2320.4 2388.6 2529.7 9726.0    1.0\n",
      "r_infd[151]     2170.2    1.06 105.82 1971.8 2099.3 2166.1 2237.9 2390.5 9924.0    1.0\n",
      "r_infd[152]     1965.6    0.87  86.69 1805.5 1904.1 1963.0 2021.5 2142.2 9982.0    1.0\n",
      "r_infd[153]     550.43    0.06   7.52 536.27 545.24  550.3 555.57  565.1  13530    1.0\n",
      "r_infd[154]     1708.8     0.6  61.85 1593.6 1666.0 1706.9 1749.8 1833.6  10671    1.0\n",
      "r_infd[155]     3063.7    1.17 119.06 2838.1 2982.8 3059.6 3141.4 3309.2  10366    1.0\n",
      "r_infd[156]     1959.5    0.66  69.26 1830.5 1911.7 1957.2 2003.9 2101.2  11062    1.0\n",
      "r_infd[157]     3033.1    1.23 125.51 2798.3 2947.1 3028.2 3114.6 3295.8  10383    1.0\n",
      "r_infd[158]     2760.1    1.68 142.81 2511.8 2662.7 2750.3 2846.5 3061.4 7243.0    1.0\n",
      "r_infd[159]     2282.7     0.9  91.37 2110.9 2220.8 2280.2 2342.7 2468.0  10388    1.0\n",
      "r_infd[160]     4512.1    2.33 229.54 4075.9 4352.7 4508.4 4662.6 4977.1 9730.0    1.0\n",
      "r_infd[161]     3218.0     1.2  121.0 2987.0 3136.4 3215.7 3296.2 3465.7  10184    1.0\n",
      "r_infd[162]     2343.5    1.12  113.2 2140.2 2267.5 2337.8 2410.4 2581.4  10192    1.0\n",
      "r_infd[163]     1342.9    0.41  41.95 1263.9 1314.1 1341.9 1371.1 1427.9  10365    1.0\n",
      "r_infd[164]     3454.1    1.65 165.14 3128.2 3342.2 3454.4 3562.7 3780.2  10014    1.0\n",
      "r_infd[165]     2384.3    1.27 113.87 2183.7 2307.4 2378.1 2451.9 2623.4 8041.0    1.0\n",
      "r_infd[166]     989.05     0.2  22.88 945.61 973.45 988.34 1003.9 1035.9  13134    1.0\n",
      "r_infd[167]     2425.5    1.02 104.94 2229.1 2352.4 2423.7 2495.2 2639.5  10612    1.0\n",
      "r_infd[168]     2388.8    1.13 111.11 2183.0 2312.2 2385.4 2461.1 2620.3 9698.0    1.0\n",
      "r_infd[169]     3056.6    1.12 107.33 2859.0 2982.4 3052.0 3128.2 3275.3 9174.0    1.0\n",
      "r_infd[170]     3434.5    1.73 166.27 3133.1 3320.9 3425.4 3537.6 3791.0 9248.0    1.0\n",
      "r_infd[171]     1212.7    0.38  38.99 1135.9 1187.2 1212.1 1238.8 1290.4  10277    1.0\n",
      "r_infd[172]     3036.2    1.15 116.67 2812.3 2957.2 3033.7 3112.2 3271.4  10208    1.0\n",
      "r_infd[173]     7699.0    5.51 486.63 6711.0 7392.3 7705.2 8013.0 8657.4 7795.0    1.0\n",
      "r_infd[174]     1601.2    0.56  57.86 1491.4 1561.9 1599.6 1638.6 1719.5  10766    1.0\n",
      "r_infd[175]     1616.6    0.57  56.91 1508.3 1577.8 1615.3 1653.6 1733.9  10101    1.0\n",
      "r_infd[176]     1274.6    0.37  39.97 1199.2 1247.0 1273.5 1300.4 1355.9  11654    1.0\n",
      "r_infd[177]     4409.6    4.25 283.07 3972.6 4218.3 4372.4 4542.1 5120.2 4432.0    1.0\n",
      "r_infd[178]     2055.4    0.99  97.91 1873.0 1988.8 2051.2 2117.5 2258.1 9700.0    1.0\n",
      "r_infd[179]     1494.0    0.43  45.04 1409.4 1462.9 1492.2 1523.7 1586.2  10774    1.0\n",
      "r_infd[180]     4892.5    2.66 254.97 4423.5 4714.2 4882.4 5054.2 5421.4 9194.0    1.0\n",
      "r_infd[181]     3810.5    1.97 181.47 3473.5 3688.1 3806.7 3929.1 4179.8 8525.0    1.0\n",
      "r_infd[182]     471.04    0.05   5.96 459.71 466.87 470.97  475.1 482.88  17399    1.0\n",
      "r_infd[183]     2543.5     1.4 131.12 2309.4 2453.5 2535.9 2626.7 2819.5 8810.0    1.0\n",
      "r_infd[184]     3304.2    1.68 152.72 3031.6 3198.4 3297.0 3400.5 3619.2 8231.0    1.0\n",
      "r_infd[185]     1772.6    0.76  74.43 1631.2 1721.3 1770.1 1820.6 1925.6 9688.0    1.0\n",
      "r_infd[186]     1753.0    0.61  63.19 1635.2 1709.0 1750.7 1795.8 1879.8  10832    1.0\n",
      "r_infd[187]     4283.0    3.09 239.61 3878.9 4122.0 4259.6 4416.2 4842.2 6024.0    1.0\n",
      "r_infd[188]     3016.3    1.07 103.38 2822.9 2945.7 3011.7 3085.1 3227.2 9419.0    1.0\n",
      "r_infd[189]     9202.7    18.0 1229.8 7172.6 8320.8 9081.1 9931.8  1.2e4 4666.0    1.0\n",
      "r_infd[190]     2928.4    1.38 133.27 2677.6 2837.7 2927.4 3015.6 3197.1 9332.0    1.0\n",
      "r_infd[191]     1501.9    0.95  85.24 1344.6 1444.4 1498.8 1555.6 1671.0 8102.0    1.0\n",
      "r_infd[192]     3054.6    2.91 203.96 2743.7 2923.0 3029.3 3151.0 3559.1 4913.0    1.0\n",
      "r_infd[193]     4049.2    2.32 199.07 3689.0 3916.8 4038.7 4169.0 4468.5 7335.0    1.0\n",
      "r_infd[194]     1369.7    0.34  42.55 1289.6 1340.5 1368.0 1397.7 1456.5  15360    1.0\n",
      "r_infd[195]     3340.1    1.49 150.72 3059.1 3235.5 3334.9 3439.6 3650.4  10273    1.0\n",
      "r_infd[196]     5453.3    7.03 442.04 4838.7 5169.6 5374.7 5622.7 6631.6 3955.0    1.0\n",
      "r_infd[197]     2331.1    0.89  88.54 2167.8 2270.4 2327.5 2388.2 2516.6 9976.0    1.0\n",
      "r_infd[198]     754.66    0.15   15.8 725.21 743.75 754.31 765.08 786.66  11767    1.0\n",
      "r_infd[199]     4179.2     2.0 180.31 3840.9 4055.5 4172.4 4297.5 4553.0 8102.0    1.0\n",
      "r_infd[200]     2374.8    1.01 101.94 2185.5 2304.8 2371.1 2440.8 2583.5  10241    1.0\n",
      "r_infd[201]     887.19    0.17  18.81 851.47 874.24 886.78 899.74 925.25  12397    1.0\n",
      "r_infd[202]     9935.5    9.69 712.95 8520.0 9516.6 9940.4  1.0e4  1.1e4 5415.0    1.0\n",
      "r_infd[203]     3941.8    1.78 185.39 3596.4 3814.4 3933.0 4061.5 4325.5  10880    1.0\n",
      "r_infd[204]     1668.0    0.62   61.0 1549.4 1627.3 1666.3 1708.3 1790.7 9732.0    1.0\n",
      "r_infd[205]     2360.9    1.22 114.25 2131.3 2284.8 2360.6 2436.4 2587.1 8739.0    1.0\n",
      "r_infd[206]     1998.9    0.71  74.39 1854.1 1948.9 1998.8 2047.1 2146.7  11000    1.0\n",
      "r_infd[207]     3423.6    1.67 156.59 3128.0 3317.2 3420.0 3526.1 3746.2 8806.0    1.0\n",
      "r_infd[208]     1142.8    0.28  30.72 1083.1 1122.2 1142.4 1163.0 1204.0  12479    1.0\n",
      "r_infd[209]     1362.5    0.38  39.58 1288.0 1335.0 1361.7 1389.0 1442.5  10708    1.0\n",
      "r_infd[210]     3547.1    1.62 154.97 3262.2 3443.1 3539.9 3643.3 3874.7 9203.0    1.0\n",
      "r_infd[211]     3396.7     1.3 127.48 3152.5 3308.1 3394.8 3482.0 3651.5 9575.0    1.0\n",
      "r_infd[212]     3225.8    1.33 126.56 2987.8 3140.2 3220.4 3309.0 3486.1 9037.0    1.0\n",
      "r_infd[213]     504.26    0.05   6.59 491.42 499.76 504.21 508.71 517.41  15601    1.0\n",
      "r_infd[214]     1252.1    0.35  35.87 1184.6 1227.6 1251.1 1276.0 1324.2  10543    1.0\n",
      "r_infd[215]     1400.3    0.39  41.45 1321.8 1371.8 1399.4 1427.3 1485.9  11166    1.0\n",
      "r_infd[216]     1508.8    0.49  50.64 1412.4 1473.9 1507.9 1542.2 1611.8  10836    1.0\n",
      "r_infd[217]     4113.1    2.01 192.17 3761.1 3979.0 4106.7 4237.7 4512.5 9154.0    1.0\n",
      "r_infd[218]     1687.4    0.49  53.22 1586.9 1650.5 1685.9 1722.8 1795.5  11850    1.0\n",
      "r_infd[219]     1301.7    0.46  45.78 1213.8 1271.3 1300.9 1331.9 1392.8 9815.0    1.0\n",
      "r_infd[220]     2513.5    0.93  94.11 2340.1 2449.2 2510.9 2574.4 2706.5  10207    1.0\n",
      "r_infd[221]     3090.7     1.1 110.75 2887.7 3014.2 3085.1 3162.1 3319.5  10124    1.0\n",
      "r_infd[222]     1423.0    0.43  44.64 1338.8 1392.1 1422.0 1452.4 1513.3  10733    1.0\n",
      "r_infd[223]     1784.4    1.26 107.23 1591.5 1705.4 1779.5 1860.1 1995.4 7226.0    1.0\n",
      "r_infd[224]     3406.3    1.34 130.91 3156.2 3316.4 3404.4 3491.3 3670.8 9516.0    1.0\n",
      "r_infd[225]     3578.6     1.6 161.05 3283.3 3467.9 3572.1 3682.1 3913.2  10128    1.0\n",
      "r_infd[226]     5069.9    3.09  274.6 4596.2 4884.0 5050.2 5230.3 5672.0 7905.0    1.0\n",
      "r_infd[227]     2466.0    0.98 102.64 2274.3 2395.7 2462.2 2531.5 2676.5  11068    1.0\n",
      "r_infd[228]     1202.2    0.36  38.84 1128.6 1176.1 1201.5 1227.3 1281.2  11376    1.0\n",
      "r_infd[229]     612.11     0.1  11.61 590.19 604.04 611.86 619.82  635.5  13225    1.0\n",
      "r_infd[230]     1193.7    0.42   45.4 1106.7 1163.2 1193.4 1223.4 1285.0  11548    1.0\n",
      "r_infd[231]     884.72    0.18   19.6 847.11 871.42 884.36 897.32 924.65  12128    1.0\n",
      "r_infd[232]     4551.3    2.61 247.36 4106.0 4380.7 4540.1 4703.3 5071.7 8985.0    1.0\n",
      "r_infd[233]     2369.0    1.22 118.08 2134.3 2290.4 2368.9 2448.7 2597.2 9313.0    1.0\n",
      "r_infd[234]     1722.8     0.5  53.78 1621.7 1685.9 1720.7 1758.8 1830.6  11494    1.0\n",
      "r_infd[235]     1825.0    0.58  58.68 1714.7 1784.4 1823.5 1863.6 1945.4  10301    1.0\n",
      "r_infd[236]     2554.6    1.28  122.0 2322.2 2472.0 2550.2 2632.7 2804.0 9137.0    1.0\n",
      "r_infd[237]     2208.1     1.0  97.99 2029.4 2140.5 2204.7 2271.1 2409.8 9540.0    1.0\n",
      "r_infd[238]     935.01     0.2  22.56  891.7 919.67 934.76 949.88 980.18  12820    1.0\n",
      "r_infd[239]     1187.1    0.54  53.64 1087.9 1150.4 1184.9 1221.1 1299.5 9894.0    1.0\n",
      "r_infd[240]     2744.9    2.29 174.24 2460.4 2629.6 2728.8 2836.3 3143.8 5797.0    1.0\n",
      "r_infd[241]     3917.6    1.92 187.43 3581.1 3791.7 3906.3 4032.2 4317.7 9564.0    1.0\n",
      "r_infd[242]     1675.8    0.56   55.1 1572.2 1638.7 1674.1 1712.2 1789.1 9831.0    1.0\n",
      "r_infd[243]     3286.3    3.46  274.4 2856.0 3101.1 3248.3 3421.8 4001.9 6277.0    1.0\n",
      "r_infd[244]     1654.0    0.59  61.26 1538.5 1611.8 1652.8 1694.4 1778.8  10897    1.0\n",
      "r_infd[245]     3971.9    1.77 172.94 3654.4 3852.3 3964.7 4081.8 4335.4 9500.0    1.0\n",
      "r_infd[246]     3143.0    1.54 140.74 2861.7 3050.1 3143.5 3236.6 3419.8 8303.0    1.0\n",
      "r_infd[247]     2404.9    0.94  94.58 2227.8 2340.4 2401.8 2467.6 2597.0  10206    1.0\n",
      "r_infd[248]     3213.0    1.42 128.23 2978.1 3123.3 3208.7 3296.8 3476.8 8177.0    1.0\n",
      "r_infd[249]     2425.4    0.94   96.7 2244.4 2359.3 2422.9 2488.1 2621.2  10670    1.0\n",
      "r_infd[250]     3351.9    1.31 134.19 3101.8 3261.1 3346.4 3437.8 3626.5  10520    1.0\n",
      "M_infd[1]        -1.45  1.8e-3   0.14  -1.68  -1.55  -1.46  -1.36  -1.14 6283.0    1.0\n",
      "M_infd[2]        -1.79  9.2e-4   0.09  -1.98  -1.85  -1.79  -1.72  -1.61  10109    1.0\n",
      "M_infd[3]        -1.64  6.6e-4   0.07  -1.77  -1.69  -1.64  -1.59   -1.5  10871    1.0\n",
      "M_infd[4]        -1.67  9.2e-4   0.09  -1.84  -1.73  -1.67  -1.61  -1.49 9700.0    1.0\n",
      "M_infd[5]        -1.66  6.2e-4   0.06  -1.78   -1.7  -1.66  -1.62  -1.54 9490.0    1.0\n",
      "M_infd[6]        -1.72  9.7e-4    0.1  -1.91  -1.79  -1.72  -1.66  -1.54 9556.0    1.0\n",
      "M_infd[7]        -1.66  1.1e-3    0.1  -1.86  -1.73  -1.66  -1.59  -1.45 8492.0    1.0\n",
      "M_infd[8]        -1.72  1.0e-3    0.1  -1.92  -1.79  -1.72  -1.65  -1.52 9856.0    1.0\n",
      "M_infd[9]        -1.85  9.2e-4   0.09  -2.03  -1.91  -1.85  -1.79  -1.68 9317.0    1.0\n",
      "M_infd[10]       -1.87  8.3e-4   0.08  -2.04  -1.93  -1.87  -1.82  -1.72 9531.0    1.0\n",
      "M_infd[11]       -1.87  1.1e-3   0.11  -2.09  -1.93  -1.86   -1.8  -1.68 8987.0    1.0\n",
      "M_infd[12]       -1.81  1.1e-3    0.1  -2.01  -1.87  -1.81  -1.74  -1.61 9557.0    1.0\n",
      "M_infd[13]       -1.73  6.4e-4   0.06  -1.85  -1.77  -1.73  -1.68   -1.6 9214.0    1.0\n",
      "M_infd[14]       -1.62  9.9e-4    0.1  -1.81  -1.69  -1.62  -1.56  -1.42  10044    1.0\n",
      "M_infd[15]       -1.71  8.3e-4   0.09  -1.87  -1.76  -1.71  -1.65  -1.54  10518    1.0\n",
      "M_infd[16]       -1.74  8.9e-4   0.09  -1.91  -1.79  -1.74  -1.68  -1.57 9524.0    1.0\n",
      "M_infd[17]       -1.86  1.0e-3    0.1  -2.07  -1.92  -1.86   -1.8  -1.68 9239.0    1.0\n",
      "M_infd[18]       -1.63  1.0e-3    0.1  -1.82   -1.7  -1.63  -1.57  -1.43 9346.0    1.0\n",
      "M_infd[19]       -1.78  1.0e-3    0.1  -1.98  -1.85  -1.78  -1.71  -1.58 9806.0    1.0\n",
      "M_infd[20]       -1.42  1.3e-3   0.11  -1.62   -1.5  -1.43  -1.34   -1.2 7063.0    1.0\n",
      "M_infd[21]       -1.79  8.1e-4   0.08  -1.96  -1.84  -1.79  -1.73  -1.63  10476    1.0\n",
      "M_infd[22]       -1.73  1.1e-3    0.1  -1.94   -1.8  -1.74  -1.67  -1.53 9356.0    1.0\n",
      "M_infd[23]       -1.93  1.2e-3    0.1  -2.15  -1.99  -1.92  -1.86  -1.75 7462.0    1.0\n",
      "M_infd[24]       -1.78  8.5e-4   0.09  -1.95  -1.83  -1.78  -1.72  -1.61  10094    1.0\n",
      "M_infd[25]       -1.86  9.1e-4   0.09  -2.05  -1.92  -1.86   -1.8  -1.69 9834.0    1.0\n",
      "M_infd[26]       -1.61  1.2e-3   0.11  -1.81  -1.68  -1.62  -1.55  -1.38 8588.0    1.0\n",
      "M_infd[27]       -1.74  9.2e-4   0.09  -1.92   -1.8  -1.73  -1.68  -1.56 9717.0    1.0\n",
      "M_infd[28]       -1.85  1.1e-3    0.1  -2.05  -1.91  -1.84  -1.78  -1.66 8537.0    1.0\n",
      "M_infd[29]       -1.75  8.2e-4   0.08  -1.92  -1.81  -1.75  -1.69  -1.59  10299    1.0\n",
      "M_infd[30]       -1.67  9.1e-4   0.09  -1.84  -1.73  -1.67  -1.61   -1.5 9076.0    1.0\n",
      "M_infd[31]       -1.81  9.3e-4   0.09  -1.99  -1.86   -1.8  -1.75  -1.63 9211.0    1.0\n",
      "M_infd[32]       -1.87  9.5e-4   0.09  -2.05  -1.92  -1.86   -1.8  -1.69 9266.0    1.0\n",
      "M_infd[33]       -1.58  9.8e-4   0.09  -1.75  -1.64  -1.58  -1.52  -1.39 8431.0    1.0\n",
      "M_infd[34]        -1.6  9.9e-4   0.09  -1.78  -1.66   -1.6  -1.54   -1.4 9071.0    1.0\n",
      "M_infd[35]       -1.78  8.1e-4   0.08  -1.94  -1.83  -1.78  -1.72  -1.62  10593    1.0\n",
      "M_infd[36]       -1.69  9.0e-4   0.08  -1.86  -1.75  -1.69  -1.64  -1.53 8622.0    1.0\n",
      "M_infd[37]       -1.73  7.1e-4   0.07  -1.86  -1.77  -1.73  -1.68   -1.6 9006.0    1.0\n",
      "M_infd[38]        -1.8  1.0e-3    0.1   -2.0  -1.86  -1.79  -1.73  -1.61 8972.0    1.0\n",
      "M_infd[39]       -1.79  1.1e-3    0.1   -2.0  -1.86  -1.79  -1.72  -1.59 8710.0    1.0\n",
      "M_infd[40]       -1.72  9.2e-4    0.1  -1.91  -1.79  -1.72  -1.66  -1.53  11038    1.0\n",
      "M_infd[41]       -1.92  6.7e-3   0.26  -2.72  -1.97  -1.86  -1.77  -1.61 1558.0    1.0\n",
      "M_infd[42]       -1.73  6.5e-4   0.06  -1.86  -1.78  -1.73  -1.69  -1.61 9866.0    1.0\n",
      "M_infd[43]       -1.65  1.5e-3   0.13  -1.87  -1.73  -1.65  -1.57  -1.37 7133.0    1.0\n",
      "M_infd[44]        -1.7  7.1e-4   0.07  -1.83  -1.74   -1.7  -1.65  -1.56 9733.0    1.0\n",
      "M_infd[45]       -1.64  9.7e-4   0.09  -1.81   -1.7  -1.64  -1.58  -1.45 9115.0    1.0\n",
      "M_infd[46]       -1.81  1.0e-3    0.1  -2.01  -1.88  -1.81  -1.74  -1.62 9278.0    1.0\n",
      "M_infd[47]       -1.85  7.5e-4   0.07   -2.0   -1.9  -1.85   -1.8  -1.71 9789.0    1.0\n",
      "M_infd[48]       -1.77  1.1e-3   0.11  -1.98  -1.84  -1.77   -1.7  -1.56 9640.0    1.0\n",
      "M_infd[49]       -2.38  4.0e-3   0.29  -2.91  -2.58   -2.4  -2.15  -1.86 5194.0    1.0\n",
      "M_infd[50]       -1.78  9.0e-4   0.09  -1.96  -1.84  -1.78  -1.72  -1.62 9845.0    1.0\n",
      "M_infd[51]       -1.91  1.1e-3    0.1  -2.13  -1.97  -1.91  -1.85  -1.73 7972.0    1.0\n",
      "M_infd[52]       -1.52  1.4e-3   0.11  -1.72   -1.6  -1.53  -1.45  -1.28 6540.0    1.0\n",
      "M_infd[53]       -1.82  1.2e-3   0.12  -2.06  -1.89  -1.81  -1.74   -1.6 9250.0    1.0\n",
      "M_infd[54]       -1.83  9.7e-4    0.1  -2.02  -1.89  -1.83  -1.76  -1.64  10022    1.0\n",
      "M_infd[55]       -1.74  1.0e-3    0.1  -1.93  -1.81  -1.74  -1.67  -1.54 9091.0    1.0\n",
      "M_infd[56]       -1.72  9.1e-4   0.09  -1.89  -1.78  -1.72  -1.66  -1.54 9359.0    1.0\n",
      "M_infd[57]       -1.79  8.9e-4   0.09  -1.98  -1.85  -1.79  -1.73  -1.62  10711    1.0\n",
      "M_infd[58]       -1.81  1.0e-3    0.1  -2.01  -1.88  -1.81  -1.75  -1.63 9414.0    1.0\n",
      "M_infd[59]       -1.75  7.3e-4   0.08   -1.9   -1.8  -1.75   -1.7   -1.6  11131    1.0\n",
      "M_infd[60]        -1.7  1.1e-3   0.11  -1.91  -1.77   -1.7  -1.63  -1.49  10080    1.0\n",
      "M_infd[61]       -1.67  1.0e-3    0.1  -1.86  -1.74  -1.67   -1.6  -1.47 9398.0    1.0\n",
      "M_infd[62]       -1.74  1.1e-3   0.11  -1.95  -1.81  -1.74  -1.66  -1.52  10077    1.0\n",
      "M_infd[63]       -1.58  1.4e-3   0.12  -1.79  -1.66  -1.59  -1.51   -1.3 7102.0    1.0\n",
      "M_infd[64]       -1.75  2.5e-3   0.16  -2.04  -1.83  -1.74  -1.65  -1.45 4010.0    1.0\n",
      "M_infd[65]       -1.78  9.6e-4   0.09  -1.96  -1.84  -1.78  -1.72   -1.6 9072.0    1.0\n",
      "M_infd[66]       -1.82  1.0e-3    0.1  -2.04  -1.89  -1.82  -1.75  -1.63 9934.0    1.0\n",
      "M_infd[67]       -1.64  1.3e-3   0.12  -1.86  -1.72  -1.64  -1.57  -1.39 8289.0    1.0\n",
      "M_infd[68]       -1.78  9.0e-4   0.09  -1.97  -1.84  -1.78  -1.72  -1.61  10151    1.0\n",
      "M_infd[69]       -1.74 10.0e-4    0.1  -1.94  -1.81  -1.73  -1.67  -1.54  10343    1.0\n",
      "M_infd[70]       -1.75  7.9e-4   0.08   -1.9   -1.8  -1.75  -1.69  -1.59  10109    1.0\n",
      "M_infd[71]       -1.68  9.0e-4   0.09  -1.86  -1.74  -1.68  -1.62  -1.51 9968.0    1.0\n",
      "M_infd[72]       -1.56  1.0e-3    0.1  -1.74  -1.63  -1.56   -1.5  -1.36 8610.0    1.0\n",
      "M_infd[73]       -1.81  1.1e-3   0.11  -2.03  -1.88   -1.8  -1.73   -1.6 9539.0    1.0\n",
      "M_infd[74]       -1.74  9.7e-4    0.1  -1.94  -1.81  -1.74  -1.67  -1.55  10351    1.0\n",
      "M_infd[75]        -1.7  7.3e-4   0.07  -1.84  -1.75   -1.7  -1.66  -1.57 9186.0    1.0\n",
      "M_infd[76]       -1.75  6.2e-4   0.06  -1.88   -1.8  -1.75  -1.71  -1.63  10751    1.0\n",
      "M_infd[77]       -1.82  6.1e-4   0.07  -1.95  -1.86  -1.82  -1.77  -1.69  11837    1.0\n",
      "M_infd[78]       -1.51  9.6e-4   0.09  -1.67  -1.57  -1.52  -1.46  -1.33 8042.0    1.0\n",
      "M_infd[79]       -1.77  6.7e-4   0.07   -1.9  -1.81  -1.76  -1.72  -1.63  10840    1.0\n",
      "M_infd[80]       -1.73  7.5e-4   0.08  -1.88  -1.78  -1.73  -1.68  -1.59 9943.0    1.0\n",
      "M_infd[81]       -1.91  1.6e-3   0.12  -2.19  -1.98   -1.9  -1.83   -1.7 5965.0    1.0\n",
      "M_infd[82]       -1.82  1.0e-3    0.1  -2.02  -1.89  -1.82  -1.75  -1.64 9621.0    1.0\n",
      "M_infd[83]       -1.71  7.2e-4   0.07  -1.85  -1.76  -1.71  -1.66  -1.56  10204    1.0\n",
      "M_infd[84]       -1.68  6.5e-4   0.07  -1.81  -1.72  -1.68  -1.63  -1.54  10870    1.0\n",
      "M_infd[85]       -1.64  8.9e-4   0.08   -1.8   -1.7  -1.64  -1.59  -1.49 8272.0    1.0\n",
      "M_infd[86]       -1.65  1.2e-3   0.11  -1.86  -1.73  -1.65  -1.58  -1.41 8984.0    1.0\n",
      "M_infd[87]       -1.77  6.7e-4   0.07   -1.9  -1.81  -1.77  -1.72  -1.64  10170    1.0\n",
      "M_infd[88]       -1.73  9.4e-4    0.1  -1.92   -1.8  -1.73  -1.67  -1.54  10808    1.0\n",
      "M_infd[89]       -1.72  8.5e-4   0.09   -1.9  -1.78  -1.72  -1.67  -1.55  10584    1.0\n",
      "M_infd[90]        -1.7  8.9e-4   0.09  -1.87  -1.76   -1.7  -1.64  -1.54 9438.0    1.0\n",
      "M_infd[91]       -1.77  9.8e-4   0.11  -1.98  -1.84  -1.77   -1.7  -1.56  11778    1.0\n",
      "M_infd[92]        -1.7  6.3e-4   0.06  -1.82  -1.74   -1.7  -1.66  -1.58 9394.0    1.0\n",
      "M_infd[93]       -1.71  9.2e-4   0.09   -1.9  -1.77  -1.71  -1.65  -1.53 9981.0    1.0\n",
      "M_infd[94]       -1.73  8.5e-4   0.09   -1.9  -1.79  -1.73  -1.67  -1.55  10744    1.0\n",
      "M_infd[95]       -1.64  9.5e-4    0.1  -1.82  -1.71  -1.64  -1.58  -1.45  10216    1.0\n",
      "M_infd[96]       -1.65  9.6e-4    0.1  -1.84  -1.72  -1.65  -1.59  -1.46  10113    1.0\n",
      "M_infd[97]        -1.8  9.2e-4   0.09  -1.98  -1.86   -1.8  -1.74  -1.62 9869.0    1.0\n",
      "M_infd[98]       -1.48  1.4e-3   0.12   -1.7  -1.57  -1.49  -1.41   -1.2 7572.0    1.0\n",
      "M_infd[99]       -1.81  1.0e-3    0.1  -2.01  -1.87   -1.8  -1.74  -1.61 9503.0    1.0\n",
      "M_infd[100]      -1.81  9.4e-4    0.1  -2.01  -1.88  -1.81  -1.75  -1.63  10821    1.0\n",
      "M_infd[101]      -1.75  9.3e-4   0.09  -1.94  -1.81  -1.75  -1.68  -1.57  10151    1.0\n",
      "M_infd[102]      -1.89  1.5e-3   0.12  -2.18  -1.96  -1.88  -1.81  -1.68 7229.0    1.0\n",
      "M_infd[103]      -1.62  6.1e-4   0.06  -1.73  -1.66  -1.62  -1.58   -1.5 9861.0    1.0\n",
      "M_infd[104]      -1.82  1.3e-3   0.12  -2.07  -1.89  -1.82  -1.74   -1.6 8016.0    1.0\n",
      "M_infd[105]      -1.69  9.1e-4   0.09  -1.87  -1.75  -1.69  -1.63  -1.51 9700.0    1.0\n",
      "M_infd[106]      -1.68  8.9e-4   0.09  -1.86  -1.74  -1.68  -1.62   -1.5  10537    1.0\n",
      "M_infd[107]      -1.35  1.1e-3    0.1  -1.53  -1.42  -1.35  -1.29  -1.17 7170.0    1.0\n",
      "M_infd[108]       -1.9  2.1e-3   0.14  -2.28  -1.97  -1.89  -1.81  -1.67 4687.0    1.0\n",
      "M_infd[109]       -1.8  9.3e-4    0.1  -1.99  -1.86   -1.8  -1.74  -1.62  10660    1.0\n",
      "M_infd[110]      -1.87  6.2e-4   0.06  -1.99  -1.91  -1.86  -1.82  -1.74 9907.0    1.0\n",
      "M_infd[111]      -1.92  1.3e-3   0.11  -2.17  -1.98  -1.91  -1.84  -1.72 7444.0    1.0\n",
      "M_infd[112]      -1.92  8.3e-4   0.08  -2.08  -1.97  -1.92  -1.86  -1.76 9926.0    1.0\n",
      "M_infd[113]      -1.74  9.1e-4   0.09  -1.91   -1.8  -1.74  -1.68  -1.57 9277.0    1.0\n",
      "M_infd[114]       -1.7  9.1e-4   0.09  -1.89  -1.77   -1.7  -1.64  -1.52  10720    1.0\n",
      "M_infd[115]      -1.77  1.0e-3    0.1  -1.97  -1.83  -1.77   -1.7  -1.57 9910.0    1.0\n",
      "M_infd[116]      -1.73  7.6e-4   0.08  -1.89  -1.79  -1.73  -1.68  -1.58  11208    1.0\n",
      "M_infd[117]      -1.68  2.6e-3   0.16  -1.95  -1.79   -1.7   -1.6  -1.28 3995.0    1.0\n",
      "M_infd[118]      -1.78  9.6e-4    0.1  -1.98  -1.84  -1.78  -1.72  -1.59  10602    1.0\n",
      "M_infd[119]      -1.71  5.2e-4   0.05  -1.81  -1.75  -1.71  -1.67  -1.61  10153    1.0\n",
      "M_infd[120]      -2.05  5.2e-4   0.05  -2.15  -2.08  -2.04  -2.01  -1.94  11133    1.0\n",
      "M_infd[121]      -1.71  8.1e-4   0.08  -1.87  -1.77  -1.71  -1.65  -1.55  10646    1.0\n",
      "M_infd[122]      -1.68  8.2e-4   0.08  -1.84  -1.73  -1.68  -1.63  -1.52 9815.0    1.0\n",
      "M_infd[123]      -1.75  1.1e-3   0.11  -1.96  -1.82  -1.75  -1.68  -1.55 9473.0    1.0\n",
      "M_infd[124]      -1.75  8.6e-4   0.09  -1.91  -1.81  -1.75  -1.69  -1.58 9818.0    1.0\n",
      "M_infd[125]      -1.73  9.2e-4   0.09  -1.91  -1.79  -1.73  -1.66  -1.54  10225    1.0\n",
      "M_infd[126]      -1.64  1.1e-3   0.11  -1.85  -1.72  -1.65  -1.57  -1.42 9333.0    1.0\n",
      "M_infd[127]      -1.78  6.9e-4   0.07  -1.92  -1.82  -1.78  -1.73  -1.65 9836.0    1.0\n",
      "M_infd[128]      -1.66  8.9e-4   0.09  -1.84  -1.73  -1.66   -1.6  -1.49  10181    1.0\n",
      "M_infd[129]      -1.83  1.0e-3    0.1  -2.03   -1.9  -1.83  -1.77  -1.64  10031    1.0\n",
      "M_infd[130]      -1.81  8.9e-4    0.1   -2.0  -1.88  -1.81  -1.75  -1.63  11530    1.0\n",
      "M_infd[131]      -1.67  7.9e-4   0.07  -1.81  -1.72  -1.67  -1.62  -1.52 8686.0    1.0\n",
      "M_infd[132]      -1.79  8.1e-4   0.08  -1.96  -1.85  -1.79  -1.73  -1.63  10345    1.0\n",
      "M_infd[133]      -1.78  8.2e-4   0.08  -1.94  -1.83  -1.77  -1.72  -1.62 9592.0    1.0\n",
      "M_infd[134]      -1.78  9.8e-4    0.1  -1.97  -1.84  -1.78  -1.71  -1.59 9769.0    1.0\n",
      "M_infd[135]      -0.87  5.8e-4   0.07  -1.01  -0.92  -0.87  -0.83  -0.74  14821    1.0\n",
      "M_infd[136]      -1.71  7.8e-4   0.08  -1.87  -1.77  -1.71  -1.66  -1.55  11011    1.0\n",
      "M_infd[137]      -1.77  1.1e-3   0.11  -1.98  -1.84  -1.77   -1.7  -1.56 9478.0    1.0\n",
      "M_infd[138]      -1.79  1.1e-3   0.11   -2.0  -1.86  -1.79  -1.72  -1.59 9733.0    1.0\n",
      "M_infd[139]      -1.82  1.1e-3   0.11  -2.04  -1.89  -1.82  -1.75  -1.62 9708.0    1.0\n",
      "M_infd[140]      -1.72 10.0e-4    0.1  -1.91  -1.79  -1.72  -1.66  -1.53 9461.0    1.0\n",
      "M_infd[141]      -1.62  7.0e-4   0.07  -1.75  -1.66  -1.62  -1.57  -1.48 9578.0    1.0\n",
      "M_infd[142]      -1.71  1.0e-3    0.1   -1.9  -1.78  -1.71  -1.64  -1.51 9593.0    1.0\n",
      "M_infd[143]      -1.75  9.0e-4   0.09  -1.93  -1.81  -1.75  -1.69  -1.57  10368    1.0\n",
      "M_infd[144]      -1.66  9.1e-4   0.09  -1.84  -1.72  -1.66   -1.6  -1.48 9990.0    1.0\n",
      "M_infd[145]      -1.77  9.9e-4    0.1  -1.97  -1.83  -1.77   -1.7  -1.57  10896    1.0\n",
      "M_infd[146]      -1.98  1.7e-3   0.13  -2.29  -2.04  -1.96  -1.89  -1.76 5753.0    1.0\n",
      "M_infd[147]      -1.54  8.8e-4   0.08  -1.69   -1.6  -1.55  -1.49  -1.37 8381.0    1.0\n",
      "M_infd[148]      -1.66  7.2e-4   0.07  -1.79   -1.7  -1.66  -1.61  -1.52 9190.0    1.0\n",
      "M_infd[149]      -1.61  8.2e-4   0.08  -1.77  -1.67  -1.61  -1.56  -1.45 9909.0    1.0\n",
      "M_infd[150]      -1.66  9.8e-4   0.09  -1.84  -1.72  -1.66   -1.6  -1.47 9417.0    1.0\n",
      "M_infd[151]       -1.7  1.0e-3    0.1   -1.9  -1.77   -1.7  -1.63  -1.49 9954.0    1.0\n",
      "M_infd[152]      -1.77  1.0e-3    0.1  -1.96  -1.83  -1.77   -1.7  -1.58 9127.0    1.0\n",
      "M_infd[153]      -1.56  5.4e-4   0.06  -1.67  -1.59  -1.56  -1.52  -1.44  10679    1.0\n",
      "M_infd[154]      -1.71  8.3e-4   0.08  -1.87  -1.76  -1.71  -1.65  -1.54 9912.0    1.0\n",
      "M_infd[155]      -1.68  8.8e-4   0.09  -1.85  -1.74  -1.68  -1.62  -1.51 9950.0    1.0\n",
      "M_infd[156]      -1.82  8.0e-4   0.08  -1.99  -1.88  -1.82  -1.77  -1.66  10585    1.0\n",
      "M_infd[157]      -1.77  9.1e-4   0.09  -1.95  -1.83  -1.77  -1.71  -1.59 9848.0    1.0\n",
      "M_infd[158]      -1.86  1.3e-3   0.11  -2.09  -1.93  -1.86  -1.79  -1.66 7022.0    1.0\n",
      "M_infd[159]      -1.74  8.7e-4   0.09  -1.91  -1.79  -1.74  -1.68  -1.56  10372    1.0\n",
      "M_infd[160]      -1.72  1.1e-3   0.11  -1.94   -1.8  -1.72  -1.65  -1.52 9915.0    1.0\n",
      "M_infd[161]      -1.68  9.0e-4   0.09  -1.84  -1.73  -1.68  -1.62  -1.51 9161.0    1.0\n",
      "M_infd[162]      -1.87  1.1e-3   0.11  -2.09  -1.94  -1.87   -1.8  -1.67 9737.0    1.0\n",
      "M_infd[163]      -1.65  7.9e-4   0.08   -1.8   -1.7  -1.65   -1.6   -1.5 9209.0    1.0\n",
      "M_infd[164]      -1.61  1.1e-3   0.11  -1.81  -1.68  -1.61  -1.54  -1.38 9009.0    1.0\n",
      "M_infd[165]      -1.87  1.2e-3    0.1  -2.09  -1.94  -1.87   -1.8  -1.68 8088.0    1.0\n",
      "M_infd[166]      -1.75  6.3e-4   0.06  -1.88  -1.79  -1.75   -1.7  -1.62  10512    1.0\n",
      "M_infd[167]      -1.69  9.2e-4   0.09  -1.87  -1.75  -1.69  -1.62   -1.5  10528    1.0\n",
      "M_infd[168]      -1.75  1.0e-3    0.1  -1.95  -1.82  -1.75  -1.68  -1.56 9739.0    1.0\n",
      "M_infd[169]      -1.85  8.7e-4   0.08  -2.02  -1.91  -1.85   -1.8  -1.69 8884.0    1.0\n",
      "M_infd[170]      -1.85  1.1e-3    0.1  -2.07  -1.92  -1.85  -1.78  -1.65 9173.0    1.0\n",
      "M_infd[171]      -1.49  9.3e-4   0.09  -1.65  -1.55  -1.49  -1.43   -1.3 8650.0    1.0\n",
      "M_infd[172]      -1.59  9.5e-4   0.09  -1.76  -1.65  -1.59  -1.53   -1.4 8974.0    1.0\n",
      "M_infd[173]       -1.7  1.5e-3   0.13  -1.95  -1.79  -1.71  -1.62  -1.43 7688.0    1.0\n",
      "M_infd[174]      -1.72  8.2e-4   0.08  -1.89  -1.78  -1.72  -1.67  -1.56  10240    1.0\n",
      "M_infd[175]      -1.66  8.3e-4   0.08  -1.82  -1.71  -1.66  -1.61  -1.49 9585.0    1.0\n",
      "M_infd[176]      -1.78  7.1e-4   0.07  -1.93  -1.83  -1.78  -1.73  -1.64  11114    1.0\n",
      "M_infd[177]      -1.94  2.1e-3   0.14  -2.29  -2.01  -1.93  -1.85  -1.72 4430.0    1.0\n",
      "M_infd[178]       -1.7  1.0e-3    0.1   -1.9  -1.77   -1.7  -1.63   -1.5 9748.0    1.0\n",
      "M_infd[179]      -1.83  7.6e-4   0.07  -1.98  -1.88  -1.83  -1.78  -1.69 9548.0    1.0\n",
      "M_infd[180]      -1.79  1.1e-3   0.11  -2.01  -1.86  -1.79  -1.72  -1.58 9972.0    1.0\n",
      "M_infd[181]      -1.71  1.1e-3    0.1  -1.91  -1.78  -1.71  -1.65  -1.52 8779.0    1.0\n",
      "M_infd[182]      -1.67  5.2e-4   0.05  -1.78  -1.71  -1.67  -1.63  -1.56  10766    1.0\n",
      "M_infd[183]      -1.81  1.1e-3   0.11  -2.03  -1.88  -1.81  -1.74  -1.61 9204.0    1.0\n",
      "M_infd[184]      -1.85  1.1e-3    0.1  -2.06  -1.92  -1.85  -1.78  -1.66 8209.0    1.0\n",
      "M_infd[185]      -1.72  9.4e-4   0.09  -1.91  -1.79  -1.72  -1.66  -1.55 9708.0    1.0\n",
      "M_infd[186]      -1.84  8.1e-4   0.08   -2.0   -1.9  -1.84  -1.79  -1.68  10355    1.0\n",
      "M_infd[187]       -1.9  1.5e-3   0.12  -2.18  -1.97  -1.89  -1.82  -1.69 6145.0    1.0\n",
      "M_infd[188]      -1.72  8.3e-4   0.08  -1.88  -1.78  -1.72  -1.67  -1.56 9497.0    1.0\n",
      "M_infd[189]      -3.34  4.3e-3   0.29  -3.94  -3.53  -3.33  -3.14   -2.8 4516.0    1.0\n",
      "M_infd[190]      -1.69  1.0e-3    0.1  -1.88  -1.76  -1.69  -1.62   -1.5 8902.0    1.0\n",
      "M_infd[191]      -1.75  1.3e-3   0.12  -1.98  -1.83  -1.75  -1.67  -1.52 8818.0    1.0\n",
      "M_infd[192]      -1.88  1.9e-3   0.14  -2.24  -1.95  -1.87   -1.8  -1.66 5226.0    1.0\n",
      "M_infd[193]      -1.84  1.2e-3   0.11  -2.05  -1.91  -1.83  -1.77  -1.64 7431.0    1.0\n",
      "M_infd[194]      -0.99  7.0e-4   0.08  -1.16  -1.05  -0.99  -0.93  -0.83  14321    1.0\n",
      "M_infd[195]      -1.77  9.5e-4    0.1  -1.96  -1.83  -1.76   -1.7  -1.58  10477    1.0\n",
      "M_infd[196]      -1.96  2.7e-3   0.17  -2.42  -2.03  -1.93  -1.85  -1.72 3877.0    1.0\n",
      "M_infd[197]      -1.87  8.8e-4   0.09  -2.04  -1.93  -1.87  -1.81   -1.7 9741.0    1.0\n",
      "M_infd[198]      -1.69  6.1e-4   0.06  -1.81  -1.73  -1.69  -1.65  -1.57  10355    1.0\n",
      "M_infd[199]      -1.76  1.0e-3    0.1  -1.95  -1.83  -1.76   -1.7  -1.58 8221.0    1.0\n",
      "M_infd[200]      -1.73  9.1e-4   0.09  -1.92  -1.79  -1.73  -1.67  -1.56  10408    1.0\n",
      "M_infd[201]      -1.57  6.7e-4   0.06   -1.7  -1.62  -1.58  -1.53  -1.45 9315.0    1.0\n",
      "M_infd[202]      -1.72  2.2e-3   0.15  -1.98  -1.81  -1.72  -1.64   -1.4 4656.0    1.0\n",
      "M_infd[203]      -1.76  9.6e-4    0.1  -1.96  -1.83  -1.76  -1.69  -1.57  10868    1.0\n",
      "M_infd[204]      -1.58  9.4e-4   0.09  -1.75  -1.64  -1.59  -1.53  -1.41 8596.0    1.0\n",
      "M_infd[205]      -1.59  1.2e-3   0.11  -1.79  -1.67   -1.6  -1.53  -1.36 7954.0    1.0\n",
      "M_infd[206]      -1.61  8.6e-4   0.09  -1.78  -1.67  -1.61  -1.56  -1.44  10233    1.0\n",
      "M_infd[207]      -1.72  1.0e-3    0.1  -1.91  -1.78  -1.72  -1.65  -1.52 9073.0    1.0\n",
      "M_infd[208]      -1.48  7.8e-4   0.08  -1.63  -1.54  -1.49  -1.43  -1.32 9674.0    1.0\n",
      "M_infd[209]      -1.73  7.2e-4   0.07  -1.87  -1.78  -1.73  -1.68  -1.59  10146    1.0\n",
      "M_infd[210]      -1.86  1.0e-3    0.1  -2.06  -1.92  -1.86  -1.79  -1.68 9027.0    1.0\n",
      "M_infd[211]       -1.7  9.0e-4   0.09  -1.87  -1.76   -1.7  -1.65  -1.53 9130.0    1.0\n",
      "M_infd[212]      -1.72  9.2e-4   0.09   -1.9  -1.78  -1.72  -1.66  -1.55 9039.0    1.0\n",
      "M_infd[213]      -1.87  5.2e-4   0.05  -1.98  -1.91  -1.87  -1.84  -1.77  10687    1.0\n",
      "M_infd[214]      -1.62  7.2e-4   0.07  -1.76  -1.67  -1.62  -1.57  -1.47  10095    1.0\n",
      "M_infd[215]      -1.76  7.1e-4   0.07   -1.9  -1.81  -1.76  -1.71  -1.62  10527    1.0\n",
      "M_infd[216]      -1.61  8.0e-4   0.08  -1.77  -1.66  -1.61  -1.56  -1.45  10029    1.0\n",
      "M_infd[217]       -1.8  1.1e-3    0.1   -2.0  -1.86   -1.8  -1.73   -1.6 9310.0    1.0\n",
      "M_infd[218]      -1.79  7.8e-4   0.08  -1.94  -1.84  -1.79  -1.74  -1.64 9500.0    1.0\n",
      "M_infd[219]      -1.52  9.8e-4   0.09  -1.69  -1.58  -1.52  -1.46  -1.33 8236.0    1.0\n",
      "M_infd[220]      -1.73  8.5e-4   0.09   -1.9  -1.79  -1.73  -1.68  -1.57  10096    1.0\n",
      "M_infd[221]      -1.83  8.2e-4   0.08   -2.0  -1.89  -1.83  -1.78  -1.68  10072    1.0\n",
      "M_infd[222]      -1.66  7.7e-4   0.08  -1.81  -1.71  -1.66  -1.61  -1.51 9847.0    1.0\n",
      "M_infd[223]      -1.34  1.8e-3   0.15  -1.62  -1.46  -1.34  -1.23  -1.06 6588.0    1.0\n",
      "M_infd[224]      -1.71  8.9e-4   0.09  -1.88  -1.77  -1.71  -1.65  -1.54 9310.0    1.0\n",
      "M_infd[225]      -1.85  9.8e-4    0.1  -2.05  -1.92  -1.85  -1.79  -1.67  10010    1.0\n",
      "M_infd[226]      -1.86  1.3e-3   0.12  -2.11  -1.93  -1.85  -1.78  -1.65 8292.0    1.0\n",
      "M_infd[227]      -1.77  8.6e-4   0.09  -1.95  -1.83  -1.77  -1.71  -1.59  11178    1.0\n",
      "M_infd[228]      -1.72  7.6e-4   0.08  -1.87  -1.77  -1.72  -1.66  -1.56  10472    1.0\n",
      "M_infd[229]      -1.68  6.2e-4   0.06   -1.8  -1.72  -1.68  -1.64  -1.56 9362.0    1.0\n",
      "M_infd[230]      -1.56  9.2e-4   0.09  -1.73  -1.62  -1.57   -1.5  -1.37 9875.0    1.0\n",
      "M_infd[231]      -1.84  6.0e-4   0.06  -1.97  -1.89  -1.84   -1.8  -1.72  11452    1.0\n",
      "M_infd[232]       -1.8  1.2e-3   0.11  -2.02  -1.87   -1.8  -1.72  -1.58 9453.0    1.0\n",
      "M_infd[233]      -1.59  1.2e-3   0.11  -1.79  -1.66  -1.59  -1.52  -1.35 8428.0    1.0\n",
      "M_infd[234]      -1.73  7.3e-4   0.07  -1.88  -1.78  -1.73  -1.68  -1.59  10506    1.0\n",
      "M_infd[235]      -1.75  7.7e-4   0.08   -1.9   -1.8  -1.75  -1.69   -1.6  10197    1.0\n",
      "M_infd[236]      -1.66  1.1e-3    0.1  -1.86  -1.73  -1.66  -1.59  -1.45 8780.0    1.0\n",
      "M_infd[237]      -1.77  9.7e-4    0.1  -1.96  -1.84  -1.77  -1.71  -1.58 9805.0    1.0\n",
      "M_infd[238]       -1.8  6.4e-4   0.07  -1.93  -1.84   -1.8  -1.75  -1.67  10682    1.0\n",
      "M_infd[239]      -1.75  9.5e-4    0.1  -1.95  -1.81  -1.75  -1.69  -1.56  10656    1.0\n",
      "M_infd[240]      -1.88  1.7e-3   0.13  -2.19  -1.95  -1.87  -1.79  -1.65 6122.0    1.0\n",
      "M_infd[241]      -1.86  1.0e-3    0.1  -2.07  -1.92  -1.85  -1.79  -1.67  10093    1.0\n",
      "M_infd[242]      -1.71  8.0e-4   0.08  -1.86  -1.76  -1.71  -1.66  -1.55 9622.0    1.0\n",
      "M_infd[243]       -1.0  2.3e-3   0.19  -1.47   -1.1  -0.98  -0.88  -0.68 6505.0    1.0\n",
      "M_infd[244]       -1.7  8.0e-4   0.08  -1.87  -1.76   -1.7  -1.64  -1.54  10964    1.0\n",
      "M_infd[245]      -1.82  9.8e-4    0.1  -2.01  -1.88  -1.82  -1.75  -1.63 9568.0    1.0\n",
      "M_infd[246]      -1.55  1.3e-3   0.11  -1.75  -1.62  -1.56  -1.48  -1.32 7109.0    1.0\n",
      "M_infd[247]      -1.77  8.9e-4   0.09  -1.95  -1.83  -1.77  -1.71   -1.6 9924.0    1.0\n",
      "M_infd[248]      -1.85  9.7e-4   0.09  -2.03  -1.91  -1.85  -1.79  -1.68 8454.0    1.0\n",
      "M_infd[249]      -1.75  8.3e-4   0.09  -1.93  -1.81  -1.75  -1.69  -1.57  11631    1.0\n",
      "M_infd[250]      -1.81  8.7e-4   0.09  -1.99  -1.86   -1.8  -1.75  -1.64  10415    1.0\n",
      "lp__            -265.6    1.04  30.12 -332.1 -283.6 -263.6 -244.9 -212.3  833.0   1.01\n",
      "\n",
      "Samples were drawn using NUTS at Wed Oct 31 10:31:14 2018.\n",
      "For each parameter, n_eff is a crude measure of effective sample size,\n",
      "and Rhat is the potential scale reduction factor on split chains (at \n",
      "convergence, Rhat=1).\n"
     ]
    }
   ],
   "source": [
    "print(fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:matplotlib.texmanager:No LaTeX-compatible font found for the serif font family in rcParams. Using default.\n",
      "INFO:matplotlib.texmanager:No LaTeX-compatible font found for the serif font family in rcParams. Using default.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of Rhats: 1007\n",
      "Total number of Rhats with the NaN values removed: 1007\n",
      "Total number of Rhats over 1.01: 0\n",
      "Total number of Rhats over 1.1: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:matplotlib.texmanager:No LaTeX-compatible font found for the serif font family in rcParams. Using default.\n",
      "INFO:matplotlib.texmanager:No LaTeX-compatible font found for the serif font family in rcParams. Using default.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = fit.summary()\n",
    "rhat = s['summary'][:,-1]\n",
    "rhatfin = rhat[np.isfinite(rhat)]\n",
    "print('Total number of Rhats: '+str(len(rhat)))\n",
    "print('Total number of Rhats with the NaN values removed: '+str(len(rhatfin)))\n",
    "print('Total number of Rhats over 1.01: '+str(len(rhat[rhat > 1.01])))\n",
    "print('Total number of Rhats over 1.1: '+str(len(rhat[rhat > 1.1])))\n",
    "sns.distplot(rhatfin)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some further diagnostic plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu = np.median(fit['mu'])\n",
    "sigma = np.median(fit['sigma'])\n",
    "sigo = (np.median(fit['sigo']))* sigma\n",
    "Q = np.median(fit['Q'])\n",
    "Minfd = np.median(fit['M_infd'],axis=0)\n",
    "rinfd = np.median(fit['r_infd'],axis=0)\n",
    "Linfd = np.median(fit['L'],axis=0)\n",
    "\n",
    "x = np.linspace(Minfd.min(), Minfd.max(), 1000)\n",
    "fg = normal(x, mu, sigma)\n",
    "bg = normal(x, mu, sigo)\n",
    "L = Q*fg + (1-Q)*bg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "left, bottom, width, height = 0.1, 0.47, 1., 0.60\n",
    "fig = plt.figure(1, figsize=(8,8))\n",
    "sax = fig.add_axes([left, bottom, width, height])\n",
    "xax = fig.add_axes([left, 0.1, width, 0.3],sharex=sax)\n",
    "\n",
    "sax.scatter(Minfd,mband,s=5,zorder=1000)\n",
    "\n",
    "sns.distplot(Minfd, ax=xax, label='M obs')\n",
    "xax.plot(x,fg,label='Foreground',zorder=999)\n",
    "xax.plot(x,bg,label='Background',zorder=998)\n",
    "xax.plot(x, L,label='True likelihood',zorder=997)\n",
    "xax.legend()\n",
    "\n",
    "sax.grid()\n",
    "xax.grid()\n",
    "\n",
    "sax.set_xlabel('Absmag in K')\n",
    "sax.set_ylabel('App mag in K')\n",
    "xax.set_ylabel('Units arbitrary')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot compares the inferred absolute Kband magnitude to the observed absolute Kband magnitude--- there are more data points at the red clump overdensity which is expected, as the points will have been placed there within the constraints of the observational uncertainties. \n",
    "\n",
    "There appears to be some kind of offset still, as there appears to be some correlation around the bisector at the inferred absolute magnitude positions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "f = np.abs(ccd.parallax_error/ccd.parallax)\n",
    "c = plt.scatter(ccd.r_est,rinfd,c=f, vmin=0., vmax=1.)\n",
    "fig.colorbar(c, label='Fractional parallax uncertainty',extend='max')\n",
    "ax.plot(ccd.r_est, ccd.r_est,c='k',linestyle='-.')\n",
    "ax.set_xlabel('Inferred distance')\n",
    "ax.set_ylabel('Bailer-Jones catalogue distance')\n",
    "plt.show()\n",
    "print('Our L: '+str(Linfd))\n",
    "print('BJ+18 median L: '+str(np.median(ccd.r_length_prior)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given that our L is smaller, we expect the mode of the prior distribution to be at a **lower distance**, and therefore we expect stars with high fractional uncertainties to be have a **lower inferred distance**. This means we would expect high fractional uncertainty stars to be *below* the bisector. That appears to be the case, although we'll need to run on more stars to confirm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = ccd.r_est\n",
    "prior = (1/(2*Linfd**3)) * (r*r) * np.exp(-r/Linfd)\n",
    "\n",
    "sns.distplot(ccd.r_est,label='r est')\n",
    "sns.distplot(rinfd, label='r infd')\n",
    "plt.scatter(r,prior,s=5,label='Prior')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this it looks like the best fit value of L does not comply well for the full dataset--- could this be because L varies as a function of galactic position?"
   ]
  }
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