hbmm_yu18_ast.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code for fitting Astero RC data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "sns.set_palette('colorblind',20)\n",
    "sns.set_context('notebook')\n",
    "matplotlib.rc('xtick', labelsize=15) \n",
    "matplotlib.rc('ytick', labelsize=15)\n",
    "matplotlib.rc('axes',labelsize=15) \n",
    "matplotlib.rc(\"text\", usetex=True)\n",
    "import pandas as pd\n",
    "import pystan\n",
    "\n",
    "import os\n",
    "import pickle\n",
    "\n",
    "import sys\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(data_file=''):\n",
    "    if data_file == '':\n",
    "        sfile = '../data/KepxDR2/rcxyu18.csv'\n",
    "    elif data_file != '':\n",
    "        sfile = data_file\n",
    "    df = pd.read_csv(sfile)\n",
    "    return df\n",
    "\n",
    "def get_bcs(tempdiff):\n",
    "    BCs = pd.read_csv(__datdir__+'BCs/Logg_perturbed/casagrande_bcs_'+str(tempdiff)+'.csv')\n",
    "\n",
    "    return BCs\n"
   ]
  },
  {
   "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": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tempdiff = -50.0\n",
    "band = 'K'\n",
    "BCs = get_bcs(tempdiff)\n",
    "df = pd.merge(df, BCs, how='left', on='KICID')\n",
    "\n",
    "bcerr = 0.02 # We assume an error of 0.02mag on the bolometric correction (roughly 1 to 2 %)\n",
    "Mobs = df.Mbol - df['BC_'+band]\n",
    "Munc = np.sqrt(df.Mbol_err**2 + bcerr**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stan model for asteroseismic values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading in existing Stan Model\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "unsupported pickle protocol: 3",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-c13fc9d84b4a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Loading in existing Stan Model'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\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[1;32m     50\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m         \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Saving Stan Model'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36mload\u001b[0;34m(file)\u001b[0m\n\u001b[1;32m   1382\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1383\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1384\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mUnpickler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\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   1385\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1386\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36mload\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    862\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    863\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 864\u001b[0;31m                 \u001b[0mdispatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\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    865\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0m_Stop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstopinst\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    866\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mstopinst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36mload_proto\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    890\u001b[0m         \u001b[0mproto\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mord\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mproto\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 892\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"unsupported pickle protocol: %d\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    893\u001b[0m     \u001b[0mdispatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mPROTO\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_proto\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: unsupported pickle protocol: 3"
     ]
    }
   ],
   "source": [
    "overwrite = False\n",
    "asterostan = '''\n",
    "data {\n",
    "    int<lower = 0> N;\n",
    "    real Mobs[N];\n",
    "    real Munc[N];\n",
    "}\n",
    "parameters {\n",
    "    //Hyperparameters\n",
    "    real mu;\n",
    "    real <lower=0.> sigma;\n",
    "    real <lower=0.5,upper=1.> Q;\n",
    "    real <lower=1.> sigo;\n",
    "\n",
    "    //Latent Parameters\n",
    "    real Mtrue_std[N];\n",
    "}\n",
    "transformed parameters{\n",
    "    real Mtrue[N];\n",
    "    \n",
    "    for (n in 1:N){\n",
    "        Mtrue[n] = mu + sigma * Mtrue_std[n];\n",
    "    }\n",
    "}\n",
    "model {\n",
    "    mu ~ normal(-1.6, 1.0);  //p(theta)\n",
    "    sigma ~ normal(0.0, 1.0); //''\n",
    "    sigo ~ normal(3.0, 2.0);  //''\n",
    "    Q ~ normal(1., 0.1);    //''  \n",
    "    \n",
    "    Mobs ~ normal(Mtrue, Munc); //p(D | theta, alpha)\n",
    "    \n",
    "    //p(alpha | theta)\n",
    "    for (n in 1:N)\n",
    "        target += log_mix(Q,\n",
    "                    normal_lpdf(Mtrue_std[n] | 0., 1.),\n",
    "                    normal_lpdf(Mtrue_std[n] | 0., sigo));\n",
    "}\n",
    "'''\n",
    "model_path = 'asterostan.pkl'\n",
    "if overwrite:\n",
    "    print('Updating Stan model')\n",
    "    sm = pystan.StanModel(model_code = asterostan, model_name='asterostan')\n",
    "    with open(model_path, 'wb') as f:\n",
    "        pickle.dump(sm, f)\n",
    "else:\n",
    "    if os.path.isfile(model_path):\n",
    "        print('Loading in existing Stan Model') \n",
    "        sm = pickle.load(open(model_path, 'rb'))\n",
    "    else:\n",
    "        print('Saving Stan Model') \n",
    "        sm = pystan.StanModel(model_code = asterostan, model_name='asterostan')\n",
    "        with open(model_path, 'wb') as f:\n",
    "            pickle.dump(sm, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from omnitool.literature_values import hawkvals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:pystan:Chain 1: E-BFMI = 0.152946802818\n",
      "WARNING:pystan:E-BFMI below 0.2 indicates you may need to reparameterize your model\n"
     ]
    }
   ],
   "source": [
    "dat = {'N':len(df), 'Mobs':Mobs, 'Munc':Munc, 'muH':hawkvals[band]}\n",
    "\n",
    "#Set up initial guesses\n",
    "from omnitool.literature_values import hawkvals\n",
    "init = {'mu':hawkvals[band], 'sigma':0.1, 'Q':0.95, 'sigo':4.}\n",
    "\n",
    "fit = sm.sampling(data=dat, iter=1000, chains=4, seed=24601, init=[init,init,init,init])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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 698.4x698.4 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import corner\n",
    "chain = np.array([fit['mu'],fit['sigma'],fit['Q'],fit['sigo']])\n",
    "corner.corner(chain.T,labels=['mu','sigma','Q','sigo'],\n",
    "                  quantiles=[0.16, 0.5, 0.84],\\\n",
    "                  show_titles=True, title_kwargs={\"fontsize\": 12})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "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",
      "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 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit.plot(pars=['mu', 'sigma', 'Q', 'sigo'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pystan import diagnostics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "out = fit.extract()\n",
    "output = open('test.pkl', 'wb')\n",
    "pickle.dump(out, output)\n",
    "output.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.7.15rc1 (default, Apr 15 2018, 21:51:34) \\n[GCC 7.3.0]'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "unsupported pickle protocol: 3",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-288e0a533544>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpkl_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'asterostan.pkl'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0md\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[0mpkl_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mpkl_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36mload\u001b[0;34m(file)\u001b[0m\n\u001b[1;32m   1382\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1383\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1384\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mUnpickler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\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   1385\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1386\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36mload\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    862\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    863\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 864\u001b[0;31m                 \u001b[0mdispatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\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    865\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0m_Stop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstopinst\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    866\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mstopinst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36mload_proto\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    890\u001b[0m         \u001b[0mproto\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mord\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mproto\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 892\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"unsupported pickle protocol: %d\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    893\u001b[0m     \u001b[0mdispatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mPROTO\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_proto\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: unsupported pickle protocol: 3"
     ]
    }
   ],
   "source": [
    "pkl_file = open('asterostan.pkl', 'rb')\n",
    "d = pickle.load(pkl_file)\n",
    "pkl_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "pkl_file = open('astero_-50.0_fullchain_dict.pkl', 'rb')\n",
    "d = pickle.load(pkl_file)\n",
    "pkl_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([(u'mu', array([-1.70529849, -1.70633567, -1.70615563, ..., -1.70326767,\n",
      "       -1.70181372, -1.70636021])), (u'sigma', array([0.0347276 , 0.03536052, 0.025973  , ..., 0.03123221, 0.0272148 ,\n",
      "       0.03923571])), (u'Q', array([0.89152916, 0.87678058, 0.88562291, ..., 0.87616854, 0.88371556,\n",
      "       0.89723052])), (u'sigo', array([ 8.85200898,  7.76463449, 11.18607124, ...,  8.92081837,\n",
      "       10.61970978,  8.21502344])), (u'Mtrue_std', array([[-0.07207641, -5.8413646 , -0.19001396, ...,  0.56704902,\n",
      "         0.11949594,  3.32298241],\n",
      "       [-0.04693643, -4.1275176 , -0.89954936, ...,  0.72182634,\n",
      "         2.67859872, -1.51286993],\n",
      "       [ 0.35524754,  0.6616983 , -1.39968024, ..., -0.56905329,\n",
      "         1.29690645,  1.61713046],\n",
      "       ...,\n",
      "       [-1.58964466, -1.46796502, -0.60348942, ..., -1.42078596,\n",
      "        -0.67052499, -0.11546507],\n",
      "       [ 0.20641794, -0.63215533, -0.75655561, ...,  0.97589951,\n",
      "         0.77180899, -0.89362731],\n",
      "       [ 0.05880646, -1.13200885,  0.63261227, ..., -0.3559341 ,\n",
      "        -1.00956093, -1.72581306]])), (u'Mtrue', array([[-1.70780153, -1.90815507, -1.71189722, ..., -1.68560624,\n",
      "        -1.70114869, -1.58989928],\n",
      "       [-1.70799536, -1.85228684, -1.7381442 , ..., -1.68081151,\n",
      "        -1.61161902, -1.75983154],\n",
      "       [-1.69692879, -1.68896934, -1.74250952, ..., -1.72093565,\n",
      "        -1.67247108, -1.66415391],\n",
      "       ...,\n",
      "       [-1.75291579, -1.74911546, -1.72211598, ..., -1.74764196,\n",
      "        -1.72420965, -1.7068739 ],\n",
      "       [-1.69619609, -1.7190177 , -1.72240323, ..., -1.6752548 ,\n",
      "        -1.68080909, -1.7261336 ],\n",
      "       [-1.7040529 , -1.75077538, -1.68153922, ..., -1.72032554,\n",
      "        -1.74597105, -1.7740737 ]])), (u'lp__', array([-12942.02279003, -13113.37378354, -13455.31611694, ...,\n",
      "       -13238.91661227, -13415.40498535, -12744.49601697]))])\n"
     ]
    }
   ],
   "source": [
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference for Stan model: astrostan_04890be6ddbf993fa0f4c1d001122e81.\n",
      "4 chains, each with iter=1000; warmup=500; thin=1; \n",
      "post-warmup draws per chain=500, total post-warmup draws=2000.\n",
      "\n",
      "                  mean se_mean     sd   2.5%    25%    50%    75%  97.5%  n_eff   Rhat\n",
      "mu               -1.74  1.3e-4 5.0e-3  -1.75  -1.74  -1.74  -1.73  -1.73 1482.0    1.0\n",
      "sigma             0.04  1.0e-3 7.1e-3   0.03   0.04   0.04   0.05   0.06   49.0   1.08\n",
      "Q                 0.93  2.3e-3   0.02   0.89   0.92   0.94   0.95   0.97   75.0   1.05\n",
      "sigo              6.95    0.09   1.11    4.9   6.15   6.89   7.67   9.24  146.0   1.03\n",
      "Mtrue_std[1]     -0.03    0.07   1.47  -2.55  -0.64   0.07   0.74   2.14  397.0   1.01\n",
      "Mtrue_std[2]     -0.31    0.03    1.3  -2.76  -0.95  -0.26    0.4   1.91 1384.0    1.0\n",
      "Mtrue_std[3]     -0.71    0.08   1.78  -6.09  -1.21  -0.47   0.19   1.62  514.0   1.01\n",
      "Mtrue_std[4]       0.2    0.12    1.6  -2.05  -0.67   0.05   0.79   4.17  164.0   1.02\n",
      "Mtrue_std[5]      0.45    0.04   1.15  -1.38  -0.26   0.37   1.01   2.77  898.0    1.0\n",
      "Mtrue_std[6]     -0.13    0.02   1.01  -2.23  -0.72  -0.12   0.51   1.74 2664.0    1.0\n",
      "Mtrue_std[7]      0.37    0.03   0.98  -1.38  -0.28   0.34   0.97    2.3  959.0    1.0\n",
      "Mtrue_std[8]     -0.02    0.03    1.1  -2.12  -0.63 9.7e-3   0.67    2.0 1154.0    1.0\n",
      "Mtrue_std[9]     -0.21    0.03   1.12  -2.55  -0.82  -0.19   0.49   1.79 1411.0    1.0\n",
      "Mtrue_std[10]    -0.19    0.03   1.21  -2.42  -0.86  -0.18   0.53   1.96 2172.0    1.0\n",
      "Mtrue_std[11]     -0.3    0.03   1.12  -2.49  -0.93  -0.28   0.42   1.68 1670.0    1.0\n",
      "Mtrue_std[12]    -0.44    0.06   1.31  -3.84  -1.02  -0.31   0.33   1.64  428.0   1.01\n",
      "Mtrue_std[13]     2.26    0.19   3.09  -0.99   0.37   1.19   2.62  10.58  267.0   1.02\n",
      "Mtrue_std[14]    -0.24    0.03   1.17   -2.4  -0.87   -0.2   0.46   1.77 1680.0    1.0\n",
      "Mtrue_std[15]    -0.11    0.02   1.04  -2.14  -0.73  -0.13   0.52   1.85 1835.0    1.0\n",
      "Mtrue_std[16]     0.31    0.03   1.12  -1.67  -0.37   0.29   0.94   2.39 1537.0    1.0\n",
      "Mtrue_std[17]    -2.42    0.25   3.46 -11.65  -2.77  -1.12  -0.27   1.04  189.0    1.0\n",
      "Mtrue_std[18]    -0.09    0.04   1.24  -2.38  -0.77  -0.06    0.6   2.18  919.0    1.0\n",
      "Mtrue_std[19]     1.25    0.07   1.73  -0.95   0.23   0.93   1.72   6.56  565.0    1.0\n",
      "Mtrue_std[20]      0.4    0.04   1.29  -1.64  -0.34    0.3   0.98   2.97  870.0    1.0\n",
      "Mtrue_std[21]     0.35    0.03   1.04  -1.61  -0.31   0.34   0.99   2.32 1229.0    1.0\n",
      "Mtrue_std[22]     0.03    0.02   0.99  -1.94   -0.6   0.05   0.68   2.05 1936.0    1.0\n",
      "Mtrue_std[23]     0.07    0.02   1.11  -1.92  -0.64   0.08   0.75   2.16 2544.0    1.0\n",
      "Mtrue_std[24]     0.25    0.06   1.29  -1.76  -0.45   0.19   0.88   2.42  417.0   1.01\n",
      "Mtrue_std[25]    -0.05    0.07   1.46  -2.52  -0.76  -0.05   0.67   2.27  389.0   1.01\n",
      "Mtrue_std[26]    -0.16    0.03   1.14  -2.43  -0.86  -0.16   0.59   1.93 1831.0    1.0\n",
      "Mtrue_std[27]    -0.91    0.09    1.6  -5.56  -1.42  -0.72 6.4e-3   1.21  344.0    1.0\n",
      "Mtrue_std[28]    -0.11    0.04   1.11  -2.21  -0.77  -0.05   0.59   1.84  855.0    1.0\n",
      "Mtrue_std[29]  -4.5e-3    0.02   1.08  -2.11  -0.65 6.0e-3   0.65   2.03 2088.0    1.0\n",
      "Mtrue_std[30]    -0.14    0.03   1.09  -2.24  -0.82  -0.14   0.62   1.85 1838.0    1.0\n",
      "Mtrue_std[31]      0.1    0.02    1.1  -2.06  -0.57    0.1   0.73   2.27 2133.0    1.0\n",
      "Mtrue_std[32]     0.17    0.02   1.05  -1.78  -0.55   0.14   0.86    2.3 3137.0    1.0\n",
      "Mtrue_std[33]     0.23    0.04   1.18  -1.88  -0.51   0.18   0.89    2.4  811.0    1.0\n",
      "Mtrue_std[34]     0.07    0.04   1.12  -1.99  -0.61   0.06   0.66   1.96  632.0   1.01\n",
      "Mtrue_std[35]    -0.03    0.03    1.1  -2.08  -0.71  -0.03   0.66   2.02 1082.0    1.0\n",
      "Mtrue_std[36]    -0.25    0.02   0.96  -2.19  -0.82  -0.26   0.37   1.65 1971.0    1.0\n",
      "Mtrue_std[37]     0.58    0.09   1.62  -1.59  -0.27   0.37   1.05   5.64  302.0   1.02\n",
      "Mtrue_std[38]    -0.44    0.03   1.06  -2.56  -1.01  -0.35   0.22   1.39 1551.0    1.0\n",
      "Mtrue_std[39]     0.54    0.14   1.82  -1.52  -0.32    0.3   0.96   6.27  175.0   1.02\n",
      "Mtrue_std[40]     0.05    0.02   0.93   -1.8  -0.57   0.03   0.66   1.93 1739.0    1.0\n",
      "Mtrue_std[41]     0.24    0.07   1.26  -1.81  -0.47   0.17   0.84   2.53  297.0   1.01\n",
      "Mtrue_std[42]     0.34    0.08   1.64  -1.89  -0.48    0.2   0.88   4.49  411.0   1.01\n",
      "Mtrue_std[43]     0.35    0.16   2.06  -2.27  -0.61   0.15   0.86    6.7  166.0   1.01\n",
      "Mtrue_std[44]    -0.71    0.15   1.98  -6.64  -1.12  -0.44   0.23   1.45  169.0   1.02\n",
      "Mtrue_std[45]    -0.05    0.03   1.08  -2.02  -0.68   -0.1   0.59   2.03 1720.0    1.0\n",
      "Mtrue_std[46]     0.25    0.03   1.16  -1.87   -0.5   0.23   0.95   2.45 2043.0    1.0\n",
      "Mtrue_std[47]     0.55     0.1   1.72  -1.68   -0.4   0.32   1.07   5.87  292.0   1.02\n",
      "Mtrue_std[48]     -0.1    0.03   1.08   -2.1  -0.78  -0.06   0.59    1.8 1529.0    1.0\n",
      "Mtrue_std[49]      0.1    0.02   1.05  -1.88  -0.56   0.09   0.77   2.12 2391.0    1.0\n",
      "Mtrue_std[50]    -0.28    0.02    1.0  -2.23   -0.9  -0.27   0.36   1.61 1622.0    1.0\n",
      "Mtrue_std[51]     0.31    0.04   1.03   -1.5  -0.37    0.3    0.9   2.35  759.0    1.0\n",
      "Mtrue_std[52]    -0.29    0.03   1.13  -2.38  -0.91  -0.26   0.44   1.63 1697.0    1.0\n",
      "Mtrue_std[53]    -0.22    0.04   1.17  -2.52  -0.82  -0.18   0.52   1.76 1014.0    1.0\n",
      "Mtrue_std[54]     0.23    0.05    1.2  -1.77  -0.51   0.15    0.9    2.6  618.0    1.0\n",
      "Mtrue_std[55]    -0.09    0.03   1.09  -2.12  -0.77  -0.07   0.58   2.09 1310.0    1.0\n",
      "Mtrue_std[56]    -0.16    0.06   1.32  -2.36  -0.78   -0.1   0.59   1.95  543.0   1.01\n",
      "Mtrue_std[57]      0.1    0.03   1.03  -1.87   -0.5   0.07   0.67   2.05 1045.0    1.0\n",
      "Mtrue_std[58]      0.4    0.02   1.01  -1.53  -0.23   0.38   0.98   2.38 2154.0    1.0\n",
      "Mtrue_std[59]     0.17    0.03    1.1  -1.99  -0.45   0.16    0.8    2.3 1107.0    1.0\n",
      "Mtrue_std[60]    -0.17    0.03   1.16  -2.39  -0.82  -0.17   0.54   1.89 1667.0    1.0\n",
      "Mtrue_std[61]     -0.4    0.06   1.36  -3.25  -0.95  -0.27   0.38   1.68  532.0    1.0\n",
      "Mtrue_std[62]    -0.41    0.04   1.37  -3.29  -1.04  -0.29   0.37   1.72  954.0    1.0\n",
      "Mtrue_std[63]     0.29    0.03   1.04  -1.66  -0.39   0.26   0.91   2.24 1165.0    1.0\n",
      "Mtrue_std[64]      0.3    0.03   1.16  -1.77  -0.42   0.27   0.95   2.55 1183.0    1.0\n",
      "Mtrue_std[65]    -1.79     0.1   2.25  -8.18  -2.14  -1.19  -0.48    0.8  477.0   1.01\n",
      "Mtrue_std[66]   2.2e-3    0.04   1.13   -2.0  -0.62  -0.02   0.61   2.05 1017.0    1.0\n",
      "Mtrue_std[67]    -0.18    0.05    1.4  -2.76  -0.84  -0.17   0.52   2.24  727.0    1.0\n",
      "Mtrue_std[68]     0.23    0.03   1.08  -1.75  -0.47   0.25   0.89   2.34 1750.0    1.0\n",
      "Mtrue_std[69]    -0.12    0.03   1.05  -2.14   -0.8  -0.13   0.57   1.92 1553.0    1.0\n",
      "Mtrue_std[70]     0.22    0.05   1.23   -1.8  -0.51   0.17   0.88   2.42  645.0    1.0\n",
      "Mtrue_std[71]     0.07    0.04   1.02  -1.81   -0.6   0.06    0.7   1.98  752.0    1.0\n",
      "Mtrue_std[72]    -1.48    0.16    2.0   -7.5  -1.81  -1.03  -0.34   0.89  152.0   1.04\n",
      "Mtrue_std[73]     0.14    0.02   1.01  -1.86  -0.57   0.16   0.78   2.16 2100.0    1.0\n",
      "Mtrue_std[74]  -5.1e-3    0.04   1.34  -2.07   -0.7-2.6e-3   0.67   2.14  958.0    1.0\n",
      "Mtrue_std[75]    -0.05    0.02   0.98  -1.91  -0.67  -0.05   0.57   1.88 2025.0    1.0\n",
      "Mtrue_std[76]     0.28    0.11   1.67   -2.0  -0.51   0.18   0.85   3.15  249.0   1.01\n",
      "Mtrue_std[77]    -0.92    0.06   1.54  -5.54  -1.39   -0.7  -0.05   1.19  608.0    1.0\n",
      "Mtrue_std[78]    -0.33    0.06   1.24  -2.46  -0.93  -0.27   0.38    1.7  487.0    1.0\n",
      "Mtrue_std[79]    -0.07    0.02   1.03   -2.1   -0.7  -0.03   0.59    1.9 2499.0    1.0\n",
      "Mtrue_std[80]    -0.63    0.05   1.26  -3.54  -1.18  -0.53   0.14    1.3  688.0    1.0\n",
      "Mtrue_std[81]    -0.04    0.07   1.62  -2.52  -0.74  -0.02   0.65   2.39  483.0   1.01\n",
      "Mtrue_std[82]     0.43    0.06   1.36  -1.56  -0.29   0.34    1.0   2.45  505.0   1.01\n",
      "Mtrue_std[83]    -0.42    0.04   1.19  -2.89  -1.05  -0.35   0.34   1.58 1137.0    1.0\n",
      "Mtrue_std[84]    -0.18    0.03   1.06  -2.27   -0.8  -0.16   0.46   1.81 1541.0    1.0\n",
      "Mtrue_std[85]     7.14    0.31   3.93   0.17    4.3   7.55   9.89  14.28  166.0   1.03\n",
      "Mtrue_std[86]    -0.02    0.02   0.95  -1.89  -0.65  -0.01   0.63   1.81 2115.0    1.0\n",
      "Mtrue_std[87]     0.03    0.02    1.0   -1.9  -0.62 9.6e-3   0.69   1.92 2736.0    1.0\n",
      "Mtrue_std[88]     9.03    0.33   4.17   0.66   6.83   9.49  11.87   16.5  164.0   1.02\n",
      "Mtrue_std[89]    -0.17    0.04   1.13  -2.22  -0.76  -0.11   0.46   1.71  942.0    1.0\n",
      "Mtrue_std[90]    -0.08    0.04   1.14  -2.17  -0.74  -0.08    0.6   1.89  678.0    1.0\n",
      "Mtrue_std[91]     0.02    0.05   1.34  -2.32  -0.73 7.0e-3   0.69   2.52  732.0    1.0\n",
      "Mtrue_std[92]      0.3    0.05    1.3  -1.75  -0.48   0.21   0.95   2.56  574.0   1.01\n",
      "Mtrue_std[93]      0.2    0.03   1.06  -1.67  -0.45   0.16   0.79   2.33 1046.0    1.0\n",
      "Mtrue_std[94]    -0.15    0.05   1.16  -2.29  -0.75  -0.07   0.52   1.82  651.0    1.0\n",
      "Mtrue_std[95]     0.01    0.02   0.97  -1.98  -0.59-6.7e-4   0.64   1.92 2909.0    1.0\n",
      "Mtrue_std[96]     0.45    0.05   1.24  -1.63   -0.3   0.39   1.12   2.66  569.0    1.0\n",
      "Mtrue_std[97]    -0.76     0.1   1.83  -6.82  -1.21  -0.46   0.23   1.46  328.0   1.01\n",
      "Mtrue_std[98]    -0.14    0.02   1.02  -2.07  -0.79  -0.11   0.54   1.76 2359.0    1.0\n",
      "Mtrue_std[99]     0.17    0.05   1.19  -1.86  -0.58   0.13   0.79   2.36  480.0   1.01\n",
      "Mtrue_std[100]    6.69     0.4   5.01  -0.53   1.46   7.19  10.56  16.16  153.0   1.03\n",
      "Mtrue_std[101]    0.72     0.1   1.74  -1.42  -0.25   0.48   1.26   6.24  332.0   1.02\n",
      "Mtrue_std[102]    0.31    0.07   1.37  -1.78  -0.48   0.18   0.92   3.19  440.0   1.01\n",
      "Mtrue_std[103]   -0.24    0.02   0.99  -2.15  -0.82  -0.22   0.41   1.57 2173.0    1.0\n",
      "Mtrue_std[104]   15.97    0.35   4.38   8.02  13.03  15.81  18.79  25.08  157.0   1.02\n",
      "Mtrue_std[105]   -0.16    0.04   1.15  -2.24  -0.81  -0.16   0.51   1.94 1084.0    1.0\n",
      "Mtrue_std[106]   -0.54    0.05   1.21  -3.28  -1.05  -0.45   0.14   1.45  667.0    1.0\n",
      "Mtrue_std[107]    0.19    0.03    1.2  -1.89  -0.57   0.15   0.85   2.51 1266.0    1.0\n",
      "Mtrue_std[108]    0.19    0.08    1.6  -2.15  -0.56   0.12   0.84   2.95  368.0    1.0\n",
      "Mtrue_std[109]   -0.38    0.04   1.13  -2.34  -0.98  -0.32   0.31   1.51  852.0    1.0\n",
      "Mtrue_std[110]    0.71    0.16   2.14  -1.68  -0.36   0.34   1.13   7.89  189.0   1.02\n",
      "Mtrue_std[111]   -0.23    0.02   1.11  -2.38   -0.9   -0.2    0.5   1.83 2503.0    1.0\n",
      "Mtrue_std[112]  -18.78    0.38   4.06 -27.57 -21.23 -18.33 -16.01 -11.91  113.0   1.03\n",
      "Mtrue_std[113]   -0.49    0.05    1.2  -2.65  -1.08   -0.4   0.21   1.47  498.0   1.02\n",
      "Mtrue_std[114]   -0.16    0.02   0.97  -1.99  -0.77  -0.17   0.46   1.71 2834.0    1.0\n",
      "Mtrue_std[115]   -0.31    0.03   1.06  -2.29   -0.9  -0.28   0.36   1.57 1608.0    1.0\n",
      "Mtrue_std[116]   -0.08    0.05   1.21  -2.15  -0.76  -0.04   0.67   1.86  564.0   1.01\n",
      "Mtrue_std[117]    0.13    0.04   1.17  -1.89  -0.52    0.1   0.71   2.27  765.0    1.0\n",
      "Mtrue_std[118]     0.2    0.02   1.02  -1.72  -0.48   0.19   0.83   2.26 2009.0    1.0\n",
      "Mtrue_std[119]   -0.01    0.02   0.98  -1.87  -0.69  -0.03   0.64   1.91 2278.0    1.0\n",
      "Mtrue_std[120]   -0.35    0.03   1.21  -2.91  -0.95  -0.31   0.36   1.61 1344.0    1.0\n",
      "Mtrue_std[121]    0.29    0.05   1.22  -1.69  -0.46   0.21   0.87   2.92  626.0   1.01\n",
      "Mtrue_std[122]   -0.03    0.05   1.21  -2.11  -0.74  -0.07    0.6   2.23  629.0   1.01\n",
      "Mtrue_std[123]   -1.13    0.13   2.48  -9.09  -1.39  -0.59   0.13   1.34  392.0   1.01\n",
      "Mtrue_std[124]    0.56    0.04    1.1  -1.36   -0.1    0.5   1.12   2.93  865.0    1.0\n",
      "Mtrue_std[125]     0.1    0.12   1.95  -3.31  -0.64   0.07    0.8   4.07  284.0   1.01\n",
      "Mtrue_std[126]   -0.36    0.06   1.28  -2.98  -0.95  -0.28   0.39   1.71  414.0   1.01\n",
      "Mtrue_std[127]    1.29    0.15   2.32  -1.29   0.05   0.72   1.57   8.53  232.0   1.01\n",
      "Mtrue_std[128]   -4.53    0.53   5.24 -15.89  -8.73  -1.78  -0.46   1.01   97.0   1.02\n",
      "Mtrue_std[129]    0.23    0.02   0.97  -1.52  -0.43   0.22   0.84   2.08 2498.0    1.0\n",
      "Mtrue_std[130]   -0.14    0.03    1.1  -2.22   -0.8  -0.13   0.56   1.76 1511.0    1.0\n",
      "Mtrue_std[131]   -0.03    0.02   1.05  -2.07  -0.72  -0.01   0.63   2.06 1975.0    1.0\n",
      "Mtrue_std[132]    0.22    0.07   1.45  -2.03  -0.57   0.17   0.88   2.63  419.0   1.01\n",
      "Mtrue_std[133]    0.07    0.06   1.35  -2.23  -0.66   0.04   0.78   2.75  503.0   1.01\n",
      "Mtrue_std[134]    0.08    0.02   1.02  -1.92  -0.56   0.08   0.72   2.17 2576.0    1.0\n",
      "Mtrue_std[135]     0.1    0.03   1.13  -2.01  -0.55   0.12   0.75   2.12 1041.0    1.0\n",
      "Mtrue_std[136]    0.16    0.03   1.09  -1.87  -0.54   0.15    0.8   2.27  984.0    1.0\n",
      "Mtrue_std[137]    0.05    0.02   0.99  -1.83  -0.62   0.03    0.7   1.92 2518.0    1.0\n",
      "Mtrue_std[138]   -0.77    0.05   1.44  -4.84  -1.25  -0.61   0.03   1.35  717.0   1.01\n",
      "Mtrue_std[139]   -0.08    0.02   1.07  -2.07  -0.75  -0.09   0.59   1.91 3001.0    1.0\n",
      "Mtrue_std[140]    -0.1    0.03   0.98  -2.01  -0.72  -0.09   0.54   1.73 1465.0    1.0\n",
      "Mtrue_std[141]   -0.08    0.02   0.98  -2.03  -0.69  -0.06   0.54   1.83 2413.0    1.0\n",
      "Mtrue_std[142]    0.04    0.05   1.23  -2.04  -0.63   0.04   0.69   2.14  696.0    1.0\n",
      "Mtrue_std[143]    0.13    0.03   1.02  -1.83  -0.56   0.12   0.78   2.17 1488.0    1.0\n",
      "Mtrue_std[144]    -0.8    0.13   1.88   -7.2  -1.27  -0.53   0.19   1.55  211.0   1.01\n",
      "Mtrue_std[145]  -13.22    0.25   3.49 -20.75 -15.37 -12.95 -10.78  -7.08  200.0   1.02\n",
      "Mtrue_std[146]   -0.19    0.11   1.84  -5.02  -0.81  -0.04   0.76   2.05  269.0   1.02\n",
      "Mtrue_std[147]   -0.42    0.02   1.01  -2.48  -1.01  -0.38   0.24   1.49 1980.0    1.0\n",
      "Mtrue_std[148]    0.18    0.02   0.95   -1.6  -0.43   0.17   0.78    2.0 2447.0    1.0\n",
      "Mtrue_std[149]   -0.07    0.02   0.93  -1.92  -0.65  -0.07   0.51   1.82 3589.0    1.0\n",
      "Mtrue_std[150]    0.57    0.03   1.18  -1.49  -0.14   0.51   1.17   3.09 1160.0    1.0\n",
      "Mtrue_std[151]    0.14    0.02    1.0  -1.89  -0.47   0.15   0.78   2.11 2060.0    1.0\n",
      "Mtrue_std[152]    0.26    0.06   1.27  -1.79  -0.49   0.13   0.88   3.05  384.0   1.02\n",
      "Mtrue_std[153]    0.09    0.03    1.1  -1.91   -0.6   0.06   0.74   2.18 1496.0    1.0\n",
      "Mtrue_std[154]   -0.44    0.08   1.49  -4.26   -1.0  -0.29   0.39   1.61  376.0    1.0\n",
      "Mtrue_std[155]    0.05    0.03   1.07  -1.91  -0.63   0.04   0.72   2.02 1322.0    1.0\n",
      "Mtrue_std[156]   -6.19    0.29   4.31 -14.25  -9.39  -6.53  -1.77    0.1  227.0   1.03\n",
      "Mtrue_std[157]    5.06    0.23    3.6  -0.08   1.62   5.24   7.78  11.83  242.0    1.0\n",
      "Mtrue_std[158]     0.1    0.05   1.23  -2.05   -0.6   0.04   0.74   2.38  553.0    1.0\n",
      "Mtrue_std[159]    0.01    0.06   1.25  -2.13  -0.65   0.03    0.7   2.11  451.0   1.01\n",
      "Mtrue_std[160]   -0.17    0.02   1.03  -2.09   -0.8  -0.16   0.47   1.86 2126.0    1.0\n",
      "Mtrue_std[161]     1.1    0.06   1.44  -0.89   0.23    0.9   1.61   5.47  620.0    1.0\n",
      "Mtrue_std[162] -1.3e-3    0.02   1.08   -2.1  -0.66   0.02   0.71   2.04 2401.0    1.0\n",
      "Mtrue_std[163]    0.38    0.03   1.15  -1.46  -0.31    0.3   0.93   3.02 1298.0    1.0\n",
      "Mtrue_std[164]    0.22    0.06    1.3   -1.8   -0.5   0.15   0.76   2.56  434.0   1.02\n",
      "Mtrue_std[165]   -0.09    0.02    1.0  -2.04  -0.72   -0.1   0.57   1.82 2288.0    1.0\n",
      "Mtrue_std[166]   -0.68    0.08   1.72   -5.5  -1.14  -0.49   0.22   1.64  469.0    1.0\n",
      "Mtrue_std[167]   -0.32    0.06   1.29  -2.66   -0.9  -0.24   0.45   1.74  475.0    1.0\n",
      "Mtrue_std[168]     0.2    0.07   1.27  -1.79  -0.53   0.14   0.78   2.49  291.0   1.01\n",
      "Mtrue_std[169]    0.57    0.04   1.14  -1.28  -0.11   0.49   1.14   2.82  913.0    1.0\n",
      "Mtrue_std[170]   -0.25    0.02   1.06  -2.28  -0.89  -0.23   0.42    1.6 1857.0    1.0\n",
      "Mtrue_std[171]    1.89    0.22    3.4  -1.39-5.0e-3    0.8   1.88  12.28  247.0   1.03\n",
      "Mtrue_std[172]   -0.61    0.13   1.79  -6.03  -1.11  -0.35   0.33   1.62  203.0   1.02\n",
      "Mtrue_std[173]   -0.05    0.03   1.02  -2.09  -0.65  -0.01   0.57   1.85 1646.0    1.0\n",
      "Mtrue_std[174]    0.42    0.07   1.32  -1.64  -0.34   0.31    1.0   3.01  406.0    1.0\n",
      "Mtrue_std[175]   -0.33    0.03   1.14  -2.44   -1.0  -0.29   0.41   1.71 1686.0    1.0\n",
      "Mtrue_std[176]    0.12    0.03   1.19  -1.94  -0.61   0.11   0.77   2.47 1178.0    1.0\n",
      "Mtrue_std[177]   -0.22    0.06   1.28  -2.63  -0.84  -0.15   0.54   1.82  392.0   1.01\n",
      "Mtrue_std[178]   -0.01    0.04    1.2  -2.23  -0.66 6.2e-3   0.67   2.09  787.0    1.0\n",
      "Mtrue_std[179]  6.4e-3     0.1   1.43   -2.6  -0.64   0.06   0.78    2.3  207.0   1.02\n",
      "Mtrue_std[180]   -0.05    0.14   1.64  -2.29  -0.73  -0.05   0.66    2.2  147.0   1.03\n",
      "Mtrue_std[181]    0.28    0.15   1.61  -2.01  -0.61   0.11   0.87    4.3  121.0   1.03\n",
      "Mtrue_std[182]   -0.06    0.02   1.06   -2.2  -0.71  -0.03   0.64   1.87 1955.0    1.0\n",
      "Mtrue_std[183]    -1.3    0.14   2.45  -9.41  -1.54  -0.72 4.1e-3   1.47  318.0    1.0\n",
      "Mtrue_std[184]   -0.38    0.04   1.25  -2.69  -0.97   -0.3   0.35    1.6  966.0    1.0\n",
      "Mtrue_std[185]    0.45    0.03   1.07  -1.41   -0.2   0.39   1.04   2.52 1320.0    1.0\n",
      "Mtrue_std[186]   -0.37    0.09   1.39  -3.33   -0.9  -0.25   0.37   1.64  224.0   1.01\n",
      "Mtrue_std[187]   -0.23    0.07   1.23   -2.4  -0.81  -0.17   0.54   1.74  315.0   1.01\n",
      "Mtrue_std[188]   -0.07    0.03   1.16  -2.11  -0.72  -0.07   0.55   2.05 1117.0    1.0\n",
      "Mtrue_std[189]    0.37    0.01   0.95  -1.46  -0.28   0.38   0.96   2.25 4067.0    1.0\n",
      "Mtrue_std[190]     0.2    0.04   1.25  -1.95  -0.57   0.17   0.89   2.42  955.0    1.0\n",
      "Mtrue_std[191]    0.15    0.03   1.16  -1.86  -0.54   0.09   0.78   2.15 1133.0    1.0\n",
      "Mtrue_std[192]    0.11    0.03   1.13   -2.0  -0.59    0.1   0.77   2.34 1890.0    1.0\n",
      "Mtrue_std[193]     0.2    0.09   1.43  -1.89  -0.55    0.1    0.8   2.59  256.0   1.01\n",
      "Mtrue_std[194]   -0.35    0.06   1.43  -2.81  -0.98  -0.25   0.46   1.72  649.0    1.0\n",
      "Mtrue_std[195]   -0.15    0.02   1.08  -2.32  -0.77  -0.16   0.52   1.87 2659.0    1.0\n",
      "Mtrue_std[196]    0.23    0.03   1.12  -1.76  -0.49   0.18   0.89    2.4 1371.0    1.0\n",
      "Mtrue_std[197]    0.05    0.02   0.96  -1.79  -0.55   0.04   0.69   1.94 1688.0    1.0\n",
      "Mtrue_std[198]   -0.63     0.1    1.8  -5.98  -1.16  -0.39   0.33    1.9  348.0   1.02\n",
      "Mtrue_std[199]    0.32    0.03   1.29  -1.76  -0.42   0.27   0.97   2.64 1515.0    1.0\n",
      "Mtrue_std[200]   -0.39    0.03   1.12   -2.9   -1.0  -0.35   0.32   1.56 1169.0    1.0\n",
      "Mtrue_std[201]    0.64    0.08   1.59  -1.48   -0.2   0.48   1.17   5.29  379.0   1.01\n",
      "Mtrue_std[202]    0.23    0.02   0.97  -1.62  -0.47   0.26   0.93   2.03 2410.0    1.0\n",
      "Mtrue_std[203]   -0.02    0.04   1.34  -2.13  -0.71  -0.06   0.64   2.15 1075.0    1.0\n",
      "Mtrue_std[204]   -0.58    0.09   1.69  -4.85   -1.1  -0.38   0.29   1.51  357.0   1.01\n",
      "Mtrue_std[205]   -0.58    0.07   1.58  -5.12  -1.13  -0.37    0.3   1.65  446.0    1.0\n",
      "Mtrue_std[206]   -0.26    0.04   1.23  -2.64  -0.87  -0.18   0.48   1.79  852.0    1.0\n",
      "Mtrue_std[207]    0.64    0.06    1.3  -1.27   -0.1   0.52   1.19   3.64  440.0   1.01\n",
      "Mtrue_std[208]   -0.28    0.04   1.16  -2.68  -0.88  -0.22   0.44   1.73  692.0   1.01\n",
      "Mtrue_std[209]    0.14    0.03   1.04   -1.8  -0.54   0.12   0.76   2.11 1470.0    1.0\n",
      "Mtrue_std[210]   -0.35    0.07   1.39  -3.89  -0.93  -0.24   0.46   1.75  417.0   1.01\n",
      "Mtrue_std[211]   -0.08    0.06   1.26  -2.31  -0.71  -0.04   0.64    2.0  449.0   1.01\n",
      "Mtrue_std[212]   -0.12    0.03   1.19  -2.49  -0.74  -0.09   0.56   2.05 1314.0    1.0\n",
      "Mtrue_std[213]    0.53    0.09   1.42  -1.49  -0.31   0.37   1.11   4.86  279.0   1.01\n",
      "Mtrue_std[214]   -3.06    0.34   3.96 -12.79  -5.46  -1.33   -0.4   1.12  139.0   1.04\n",
      "Mtrue_std[215]    0.23    0.03   1.13  -1.83  -0.49   0.21   0.91   2.37 1678.0    1.0\n",
      "Mtrue_std[216]    0.19    0.03   1.12  -1.87  -0.49   0.17   0.85   2.34 1984.0    1.0\n",
      "Mtrue_std[217]   -0.09    0.16   2.16  -3.16  -0.74-5.2e-3   0.71   2.87  182.0   1.02\n",
      "Mtrue_std[218]    0.04    0.02   0.97  -1.81   -0.6   0.05   0.65   1.92 3632.0    1.0\n",
      "Mtrue_std[219] -1.1e-3    0.03   1.12  -2.03  -0.69  -0.02   0.72   2.03 1821.0    1.0\n",
      "Mtrue_std[220]   -0.24    0.06   1.46  -2.78  -0.83  -0.14   0.53   1.93  533.0    1.0\n",
      "Mtrue_std[221]    3.97    0.38   4.63  -0.88   0.56   1.56   7.49  14.76  147.0   1.03\n",
      "Mtrue_std[222]    0.09    0.03   1.13  -1.93  -0.55   0.07   0.73   2.15 1557.0    1.0\n",
      "Mtrue_std[223]   -0.37     0.1   1.46   -4.6  -0.95  -0.24   0.42   1.81  208.0   1.01\n",
      "Mtrue_std[224]    0.14    0.06   1.35  -1.93  -0.62   0.04   0.75   2.51  467.0   1.01\n",
      "Mtrue_std[225]   -0.08    0.05   1.26  -2.24  -0.74  -0.05   0.64   2.08  595.0   1.01\n",
      "Mtrue_std[226]    0.06    0.03   1.06  -1.91  -0.64   0.08   0.72   2.12 1668.0    1.0\n",
      "Mtrue_std[227]   -0.08    0.03   1.08  -2.16  -0.76  -0.05   0.61   1.92 1729.0    1.0\n",
      "Mtrue_std[228]   -0.18    0.02   1.06  -2.23  -0.84  -0.16   0.49   1.82 2215.0    1.0\n",
      "Mtrue_std[229]   -0.18    0.02   1.04  -2.14  -0.85  -0.19   0.47   1.84 2248.0    1.0\n",
      "Mtrue_std[230]   -0.19    0.05   1.13  -2.49  -0.78  -0.16   0.49   1.81  498.0    1.0\n",
      "Mtrue_std[231]   -0.18    0.04   1.19  -2.42  -0.84  -0.13   0.55    1.9 1038.0    1.0\n",
      "Mtrue_std[232]    -0.3    0.03   1.09  -2.36  -0.88   -0.3   0.34   1.62 1407.0    1.0\n",
      "Mtrue_std[233]   -0.22    0.02   0.96  -1.99  -0.84  -0.22   0.38   1.61 2068.0    1.0\n",
      "Mtrue_std[234]    0.89    0.09   2.03  -1.35  -0.19   0.54    1.3   7.53  481.0   1.01\n",
      "Mtrue_std[235]   -0.43     0.1   1.53  -4.72  -0.94  -0.25    0.4   1.65  248.0   1.01\n",
      "Mtrue_std[236]   -0.48    0.07   1.46  -4.26  -1.06  -0.34   0.33   1.72  462.0   1.01\n",
      "Mtrue_std[237]  8.0e-3    0.02    0.9  -1.74  -0.58  -0.01   0.59   1.76 2422.0    1.0\n",
      "Mtrue_std[238]   -0.35    0.04   1.22   -3.0  -0.98  -0.29   0.42   1.77  988.0   1.01\n",
      "Mtrue_std[239]   -0.36    0.02    1.0  -2.29  -1.01  -0.32   0.31   1.55 2475.0    1.0\n",
      "Mtrue_std[240]   -0.12    0.05   1.19  -2.32  -0.76  -0.05   0.61   1.89  698.0    1.0\n",
      "Mtrue_std[241]   -0.56     0.1   1.64  -5.74  -1.08  -0.35   0.37   1.65  289.0   1.01\n",
      "Mtrue_std[242]    1.28    0.11   1.92  -1.17   0.17   0.91   1.72   7.19  303.0   1.01\n",
      "Mtrue_std[243]     0.2    0.03   0.98  -1.67  -0.41   0.24   0.83   2.08 1461.0    1.0\n",
      "Mtrue_std[244]   -0.08    0.03   1.04  -2.03   -0.7  -0.08   0.57   1.91 1441.0    1.0\n",
      "Mtrue_std[245]  3.4e-3    0.03   1.11   -2.1  -0.66   0.02    0.7   2.07 1342.0    1.0\n",
      "Mtrue_std[246]   -0.35    0.03   1.12  -2.46   -1.0  -0.31   0.37   1.53 1275.0    1.0\n",
      "Mtrue_std[247]    0.26    0.02   0.92  -1.51  -0.34   0.24   0.84   2.11 3759.0    1.0\n",
      "Mtrue_std[248]    0.87    0.07   1.56  -1.33  -0.07   0.65   1.44   5.67  525.0    1.0\n",
      "Mtrue_std[249]    0.11    0.12   1.74  -2.32  -0.72  -0.03   0.72   4.38  205.0   1.02\n",
      "Mtrue_std[250]     0.3    0.02   1.07  -1.65  -0.39   0.27   0.92   2.33 1947.0    1.0\n",
      "Mtrue_std[251]    0.24    0.04   1.23  -1.79  -0.47   0.15   0.82   3.24  767.0    1.0\n",
      "Mtrue_std[252]     1.3    0.08   1.82  -1.05   0.25   0.93   1.71   6.69  494.0    1.0\n",
      "Mtrue_std[253]     0.7    0.04   1.39  -1.43  -0.11   0.56   1.29    4.4  957.0    1.0\n",
      "Mtrue_std[254] -7.8e-3    0.03   1.11  -2.09  -0.64  -0.02   0.64   2.12 1568.0    1.0\n",
      "Mtrue_std[255]    0.09    0.02   0.99  -1.75  -0.56   0.08   0.67   1.99 2175.0    1.0\n",
      "Mtrue_std[256]  8.8e-3    0.02   1.02  -1.94  -0.65   0.03   0.72   1.82 1712.0    1.0\n",
      "Mtrue_std[257]   -0.09    0.02    1.1  -2.13   -0.8  -0.08   0.64   1.97 3437.0    1.0\n",
      "Mtrue_std[258]    0.13    0.03   1.01  -1.72  -0.51   0.13   0.79   2.16 1537.0    1.0\n",
      "Mtrue_std[259]    0.57    0.12    1.7  -1.68  -0.37   0.35   1.09   5.86  208.0   1.01\n",
      "Mtrue_std[260]   -0.08    0.06   1.25  -2.45  -0.68  -0.05   0.63   2.02  437.0   1.01\n",
      "Mtrue_std[261]   -0.44    0.08   1.54  -3.53  -0.98  -0.33   0.39   1.67  357.0   1.01\n",
      "Mtrue_std[262]    0.58    0.05   1.21  -1.36  -0.14   0.47   1.14   3.35  494.0    1.0\n",
      "Mtrue_std[263]    1.01    0.08   1.54  -1.09   0.11   0.78   1.53   5.69  389.0   1.01\n",
      "Mtrue_std[264]    0.89    0.06   1.48  -1.25   0.04   0.72   1.41   5.19  580.0   1.01\n",
      "Mtrue_std[265]   -0.28    0.03   1.13  -2.43  -0.91  -0.22    0.4   1.78 1164.0    1.0\n",
      "Mtrue_std[266]   -0.34    0.03   1.11  -2.57   -1.0  -0.29   0.37   1.61 1846.0    1.0\n",
      "Mtrue_std[267]   -0.65    0.26   2.21  -8.22  -1.04  -0.29   0.43   1.82   72.0   1.04\n",
      "Mtrue_std[268]   -0.04    0.03    1.1  -2.16  -0.68   0.02   0.65   1.82 1601.0    1.0\n",
      "Mtrue_std[269]   -0.33    0.04   1.06  -2.28  -0.87   -0.3   0.29   1.53  882.0    1.0\n",
      "Mtrue_std[270]   -0.15    0.02   1.05  -2.15  -0.82  -0.12   0.54   1.77 2951.0    1.0\n",
      "Mtrue_std[271]     0.2    0.04    1.1  -1.81  -0.51   0.16    0.8    2.4  931.0    1.0\n",
      "Mtrue_std[272]   -0.04    0.02   1.03  -2.11  -0.68  -0.02   0.62   1.95 2361.0    1.0\n",
      "Mtrue_std[273]   -0.26    0.05   1.37   -2.9  -0.87  -0.21   0.47   1.93  925.0    1.0\n",
      "Mtrue_std[274]    0.51    0.16   1.72  -1.66  -0.41   0.31   1.02   5.34  113.0   1.03\n",
      "Mtrue_std[275]    0.01    0.02   1.02  -2.07  -0.62   0.04   0.69   1.94 2298.0    1.0\n",
      "Mtrue_std[276]    0.28    0.04   1.35  -1.87  -0.45   0.23    0.9   2.99  913.0    1.0\n",
      "Mtrue_std[277]    -5.1    0.74   5.02 -15.38  -9.01  -2.82  -0.84   0.76   46.0   1.07\n",
      "Mtrue_std[278]   -0.07    0.02   1.05  -2.12  -0.74  -0.07   0.61    1.9 1958.0    1.0\n",
      "Mtrue_std[279]    0.26    0.03   1.06  -1.61  -0.43   0.19   0.88   2.22 1141.0    1.0\n",
      "Mtrue_std[280]     0.1    0.05   1.26  -2.05  -0.61   0.05   0.76   2.43  666.0    1.0\n",
      "Mtrue_std[281]   -0.06    0.02   1.04  -2.07  -0.72  -0.04   0.62    2.0 2667.0    1.0\n",
      "Mtrue_std[282]    0.03    0.02   1.02  -1.93   -0.6   0.04   0.68   1.99 2158.0    1.0\n",
      "Mtrue_std[283]    0.16    0.05   1.19  -1.87  -0.55   0.12   0.77   2.53  470.0   1.01\n",
      "Mtrue_std[284]   -0.22    0.03   1.08  -2.32  -0.84  -0.15   0.49   1.68 1246.0    1.0\n",
      "Mtrue_std[285]    0.23    0.05   1.27  -1.91  -0.51   0.24   0.89   2.47  629.0   1.01\n",
      "Mtrue_std[286]    1.03    0.09   1.58  -1.12    0.1    0.8   1.54   5.73  314.0    1.0\n",
      "Mtrue_std[287]   -0.06    0.02   1.11  -1.95  -0.74   -0.1   0.62   1.88 2173.0    1.0\n",
      "Mtrue_std[288]   -0.02    0.04   1.11  -1.98  -0.66   0.03   0.66   1.94  823.0    1.0\n",
      "Mtrue_std[289]    0.08    0.03   1.18  -2.13  -0.63   0.04   0.74   2.41 1448.0    1.0\n",
      "Mtrue_std[290]     0.2    0.02   1.03   -1.6  -0.46   0.19   0.83   2.13 2049.0    1.0\n",
      "Mtrue_std[291]   -0.08    0.03   1.11  -2.16  -0.78  -0.02   0.66   1.95 1718.0    1.0\n",
      "Mtrue_std[292]     1.3    0.18   2.43  -1.26-5.7e-3    0.7   1.54   8.79  187.0   1.02\n",
      "Mtrue_std[293]   -1.54    0.16   2.77  -9.96  -1.66  -0.82  -0.06   1.23  314.0   1.01\n",
      "Mtrue_std[294]    0.11    0.02    1.0  -1.82  -0.58    0.1   0.78   2.09 3234.0    1.0\n",
      "Mtrue_std[295]   -0.21    0.03   1.03  -2.27  -0.84  -0.18   0.43   1.77 1697.0    1.0\n",
      "Mtrue_std[296]    0.11    0.08   1.32   -2.0  -0.62   0.02   0.71   2.34  276.0   1.02\n",
      "Mtrue_std[297]   -0.38    0.04   1.15   -2.5  -0.96  -0.33   0.35   1.56  729.0   1.01\n",
      "Mtrue_std[298]   -0.07    0.03   1.27  -2.29  -0.75  -0.04   0.67   1.94 1349.0    1.0\n",
      "Mtrue_std[299]    -0.2    0.13    1.7  -3.02   -0.8   -0.1    0.6   2.04  169.0   1.02\n",
      "Mtrue_std[300]    -0.2    0.06   1.38  -2.93  -0.87  -0.11   0.59   1.95  456.0    1.0\n",
      "Mtrue_std[301]    -0.2    0.03   1.03  -2.26  -0.83  -0.18   0.49   1.85 1580.0    1.0\n",
      "Mtrue_std[302]    0.03    0.02   1.05  -1.93  -0.58   0.02    0.7   2.07 2304.0    1.0\n",
      "Mtrue_std[303]    0.35    0.02   1.01   -1.5  -0.31   0.33   0.96   2.34 1790.0    1.0\n",
      "Mtrue_std[304]   -1.68    0.14   2.14  -7.95  -1.97  -1.17  -0.46   0.87  242.0   1.01\n",
      "Mtrue_std[305]    0.27    0.03   1.18  -1.73  -0.47   0.19   0.92   2.59 1484.0    1.0\n",
      "Mtrue_std[306]   -0.27    0.05   1.15  -2.34   -0.9  -0.22   0.41   1.67  524.0   1.01\n",
      "Mtrue_std[307]    0.12    0.03    1.1  -1.97  -0.52   0.13    0.8   2.15 1778.0    1.0\n",
      "Mtrue_std[308]   -0.12    0.03    1.1   -2.3  -0.77  -0.13   0.58   2.07 1704.0    1.0\n",
      "Mtrue_std[309]    0.24    0.02   0.93  -1.53  -0.38   0.23   0.82   2.04 2063.0    1.0\n",
      "Mtrue_std[310]    0.05    0.02   0.92  -1.76   -0.5   0.04   0.63   1.91 2211.0    1.0\n",
      "Mtrue_std[311]   -0.12    0.03   1.11  -2.25  -0.78  -0.09   0.53   1.85 1371.0    1.0\n",
      "Mtrue_std[312]   -0.98     0.1   2.06  -7.64  -1.35  -0.62   0.11   1.47  400.0   1.02\n",
      "Mtrue_std[313]    0.13    0.02   1.02  -1.82  -0.57   0.13   0.78   2.08 2146.0    1.0\n",
      "Mtrue_std[314]   -0.12    0.02   1.04  -2.12  -0.79  -0.08   0.56   1.79 2316.0    1.0\n",
      "Mtrue_std[315]   -0.24    0.03   0.98  -2.12  -0.87  -0.24   0.41   1.61 1394.0    1.0\n",
      "Mtrue_std[316]   -0.63    0.06   1.44  -4.35  -1.19  -0.47   0.23    1.4  582.0    1.0\n",
      "Mtrue_std[317]    0.08    0.03   1.04  -1.91  -0.55   0.08   0.72   2.05 1494.0    1.0\n",
      "Mtrue_std[318]   -0.25    0.07   1.42   -3.9  -0.79  -0.16   0.49   1.89  425.0    1.0\n",
      "Mtrue_std[319]    1.87    0.13   2.75  -0.97   0.29   1.07   2.07   9.81  423.0    1.0\n",
      "Mtrue_std[320]    0.25    0.05   1.36  -1.82  -0.52   0.19   0.87   2.48  769.0   1.01\n",
      "Mtrue_std[321]   -0.19    0.02   1.06  -2.25  -0.82  -0.18   0.48   1.79 2553.0    1.0\n",
      "Mtrue_std[322]   -0.03    0.03   1.05  -2.01  -0.73  -0.07   0.62   1.99 1664.0    1.0\n",
      "Mtrue_std[323]   -0.02    0.02   1.07  -1.95  -0.72  -0.04   0.65   2.09 1858.0    1.0\n",
      "Mtrue_std[324]    -0.1    0.03   1.13  -2.04  -0.79  -0.09   0.58   1.92 1313.0    1.0\n",
      "Mtrue_std[325]   -0.14    0.02   1.04  -2.25  -0.76  -0.12   0.53   1.76 1923.0    1.0\n",
      "Mtrue_std[326]   -0.53    0.02   1.05  -2.57  -1.15  -0.52   0.18   1.34 1957.0    1.0\n",
      "Mtrue_std[327]    1.12    0.09   1.56  -1.06   0.21   0.88   1.61   5.82  323.0   1.01\n",
      "Mtrue_std[328]    0.34    0.02   0.92  -1.53  -0.27   0.31   0.97   2.29 3131.0    1.0\n",
      "Mtrue_std[329]    0.55    0.03   1.04  -1.31  -0.12   0.51   1.14   2.64 1617.0    1.0\n",
      "Mtrue_std[330]   -0.02    0.02   1.04  -2.13   -0.7 3.3e-3   0.68   1.96 1873.0    1.0\n",
      "Mtrue_std[331]   -0.18    0.03   1.07  -2.32  -0.84  -0.18   0.51    1.8 1820.0    1.0\n",
      "Mtrue_std[332]    -0.1    0.04   1.27  -2.57  -0.71-4.0e-3   0.63   1.93  852.0    1.0\n",
      "Mtrue_std[333]     0.2    0.02   1.02  -1.75  -0.47   0.22   0.87   2.08 1737.0    1.0\n",
      "Mtrue_std[334]    0.21    0.02   1.04  -1.82  -0.46   0.23   0.87   2.29 2290.0    1.0\n",
      "Mtrue_std[335]   -0.03    0.03   1.34  -2.41   -0.7  -0.04   0.66   2.29 1740.0    1.0\n",
      "Mtrue_std[336]    0.03    0.03   1.24  -2.11  -0.61   0.05   0.74   2.14 1282.0    1.0\n",
      "Mtrue_std[337]    0.05    0.02   1.07  -1.94  -0.65   0.03    0.7   2.18 1930.0    1.0\n",
      "Mtrue_std[338]    0.08    0.04   1.19  -2.01  -0.56   0.09    0.8    2.1 1020.0    1.0\n",
      "Mtrue_std[339]    0.74    0.21   2.04  -1.61  -0.29   0.38   1.16   7.77   90.0   1.04\n",
      "Mtrue_std[340]   -0.42    0.08   1.56  -4.26  -0.98  -0.31    0.4   1.89  348.0   1.01\n",
      "Mtrue_std[341]   -0.19    0.05   1.32  -2.76  -0.82  -0.13   0.63   2.03  682.0    1.0\n",
      "Mtrue_std[342]   -0.02    0.06   1.39  -2.49   -0.7   0.01    0.7   2.14  517.0   1.02\n",
      "Mtrue_std[343]   -0.12    0.04   1.37  -2.57  -0.73  -0.07   0.65   2.07 1067.0    1.0\n",
      "Mtrue_std[344]   -0.37    0.03   1.07  -2.49  -0.99  -0.37   0.32    1.5 1508.0    1.0\n",
      "Mtrue_std[345]   -0.05    0.03   1.08  -1.98  -0.78  -0.06   0.62   1.92 1796.0    1.0\n",
      "Mtrue_std[346]   -0.08    0.02   0.97  -1.95   -0.7  -0.09   0.52   1.84 2026.0    1.0\n",
      "Mtrue_std[347]    0.38    0.02    1.0   -1.5  -0.28   0.32   0.97   2.31 1617.0    1.0\n",
      "Mtrue_std[348]  7.5e-3    0.02   1.04  -1.97  -0.65-3.1e-3   0.71   2.01 2204.0    1.0\n",
      "Mtrue_std[349]   -0.76     0.1   1.72  -5.69  -1.32  -0.52   0.21   1.45  323.0   1.01\n",
      "Mtrue_std[350]   -1.41    0.18   2.64  -9.73  -1.64  -0.77  -0.05   1.27  223.0   1.01\n",
      "Mtrue_std[351]    0.09    0.03   1.22  -2.12  -0.61   0.11   0.77   2.36 2202.0    1.0\n",
      "Mtrue_std[352]   -0.37    0.12   1.43  -3.55  -0.92  -0.26   0.38    1.8  151.0   1.02\n",
      "Mtrue_std[353]    0.23    0.02   0.92  -1.56  -0.39   0.22   0.82   2.07 2101.0    1.0\n",
      "Mtrue_std[354]   -0.06    0.02   0.94  -1.97  -0.64  -0.04   0.53   1.82 3823.0    1.0\n",
      "Mtrue_std[355]   -0.18    0.02   1.05  -2.21   -0.8  -0.15   0.49   1.79 2468.0    1.0\n",
      "Mtrue_std[356]   -0.05    0.02   0.94  -1.99  -0.66  -0.05   0.58    1.8 2021.0    1.0\n",
      "Mtrue_std[357]   -0.14    0.03    1.1  -2.29  -0.78  -0.11   0.57   1.88 1034.0    1.0\n",
      "Mtrue_std[358]   -0.65    0.08    1.6  -5.25  -1.18  -0.47   0.22   1.65  378.0   1.01\n",
      "Mtrue_std[359]   -0.52    0.11    1.4  -3.84  -1.06  -0.39   0.28   1.49  163.0   1.02\n",
      "Mtrue_std[360]   -2.07     0.2   3.43 -12.15  -2.03  -0.92  -0.14   1.25  284.0   1.02\n",
      "Mtrue_std[361]    0.31    0.04   1.15  -1.62  -0.41   0.24   0.92   2.51  876.0    1.0\n",
      "Mtrue_std[362]   -0.18    0.07   1.28  -2.59  -0.74  -0.09   0.55   1.75  337.0   1.01\n",
      "Mtrue_std[363]    0.12    0.03   0.98  -1.78  -0.49   0.14   0.75   1.95 1419.0    1.0\n",
      "Mtrue_std[364]   -0.13    0.03   1.07  -2.24  -0.76  -0.12   0.53   1.84 1565.0    1.0\n",
      "Mtrue_std[365]   -0.02    0.04   1.26  -2.25  -0.75  -0.03   0.67   2.36  959.0    1.0\n",
      "Mtrue_std[366]   -0.27    0.07   1.37  -3.12  -0.87  -0.19    0.5   1.87  429.0   1.01\n",
      "Mtrue_std[367]    0.14    0.02    1.0  -1.76  -0.55   0.13   0.79   2.14 3342.0    1.0\n",
      "Mtrue_std[368]    0.05    0.03   1.15  -2.06  -0.61   0.05   0.72   2.13 1691.0    1.0\n",
      "Mtrue_std[369]   -0.08    0.04   1.14  -2.11   -0.8  -0.13   0.58   2.04 1061.0    1.0\n",
      "Mtrue_std[370]   -0.28    0.12   1.88  -3.27  -0.77  -0.14   0.53   1.95  241.0   1.02\n",
      "Mtrue_std[371]    1.39    0.13   2.14  -1.03    0.2   0.92   1.74   8.04  283.0   1.01\n",
      "Mtrue_std[372]   -0.62    0.09   1.57  -4.46  -1.14  -0.45   0.23   1.52  336.0   1.01\n",
      "Mtrue_std[373]   -0.83    0.13   1.89  -6.81  -1.28  -0.53    0.2   1.51  223.0   1.02\n",
      "Mtrue_std[374]   -0.61    0.12   1.77  -6.31  -1.02  -0.35   0.32   1.55  234.0    1.0\n",
      "Mtrue_std[375]    0.99    0.11   1.98  -1.26   -0.1   0.62   1.45   7.28  331.0   1.02\n",
      "Mtrue_std[376]    0.07    0.02   0.96  -1.83  -0.56   0.05   0.69   1.92 1704.0    1.0\n",
      "Mtrue_std[377]    0.37    0.02    1.0  -1.51  -0.25   0.36   0.95   2.41 2226.0    1.0\n",
      "Mtrue_std[378]    0.04    0.02   1.02  -1.88  -0.59   0.03   0.65   2.03 1687.0    1.0\n",
      "Mtrue_std[379]   -1.19    0.09    1.9  -6.91  -1.68  -0.81  -0.04   1.25  413.0   1.01\n",
      "Mtrue_std[380]    0.32    0.02   1.03  -1.64  -0.34   0.33   0.97   2.32 2675.0    1.0\n",
      "Mtrue_std[381]    0.28    0.05   1.32  -1.79  -0.42   0.21   0.91   2.73  582.0    1.0\n",
      "Mtrue_std[382]   -0.23    0.02    1.1  -2.38  -0.86  -0.21   0.44   1.88 2045.0    1.0\n",
      "Mtrue_std[383]   -0.12    0.02   1.07   -2.3  -0.78  -0.06   0.58   1.83 2115.0    1.0\n",
      "Mtrue_std[384]   -0.26    0.05   1.23  -2.41  -0.85  -0.17   0.48   1.67  532.0   1.01\n",
      "Mtrue_std[385]   -0.02    0.05   1.23  -2.21  -0.67 6.0e-3   0.67   2.12  587.0   1.01\n",
      "Mtrue_std[386]    -0.2    0.02   0.92  -2.07  -0.83  -0.17   0.43   1.58 2110.0    1.0\n",
      "Mtrue_std[387]   -0.16    0.02    1.0  -2.16  -0.81  -0.16   0.47   1.82 2866.0    1.0\n",
      "Mtrue_std[388]   -0.04    0.02   0.95  -1.87  -0.68  -0.04   0.63   1.73 2274.0    1.0\n",
      "Mtrue_std[389]   -0.79    0.09   1.93  -7.33  -1.26  -0.48   0.19   1.53  430.0    1.0\n",
      "Mtrue_std[390]     0.1    0.02   1.06  -1.81  -0.57    0.1   0.76   2.05 2305.0    1.0\n",
      "Mtrue_std[391]   -0.63    0.08   1.82  -5.61  -1.12  -0.41   0.32   1.59  490.0    1.0\n",
      "Mtrue_std[392]    0.41    0.19   2.08  -2.21  -0.59   0.16   0.92   7.51  117.0   1.03\n",
      "Mtrue_std[393]   -0.05    0.03   1.08  -2.03   -0.7  -0.06   0.61   2.08 1459.0    1.0\n",
      "Mtrue_std[394]   -0.17    0.05   1.29  -2.65  -0.76  -0.12   0.53    1.9  581.0   1.01\n",
      "Mtrue_std[395]   -0.07    0.02   0.95  -1.85  -0.74  -0.06   0.59   1.82 3438.0    1.0\n",
      "Mtrue_std[396]   -0.05    0.03   1.16  -2.17  -0.73  -0.04   0.66   2.07 1675.0    1.0\n",
      "Mtrue_std[397]   -0.13    0.02   0.94  -2.04  -0.75  -0.13   0.51   1.67 2387.0    1.0\n",
      "Mtrue_std[398]   -9.14    0.27   4.02 -16.66 -11.71  -9.46  -7.02  -0.42  223.0   1.01\n",
      "Mtrue_std[399]    0.24    0.04   1.19  -1.76  -0.47   0.18   0.85   2.59  960.0    1.0\n",
      "Mtrue_std[400]    1.98     0.3   3.46  -1.22   0.06   0.81    1.9  12.09  132.0   1.01\n",
      "Mtrue_std[401]   -0.18    0.02   0.97  -2.12  -0.83  -0.19   0.46   1.68 2391.0    1.0\n",
      "Mtrue_std[402]   -0.03    0.03   1.18  -2.22  -0.65-7.2e-3   0.62    2.2 1314.0   1.01\n",
      "Mtrue_std[403]    0.67    0.25   2.17  -1.76  -0.42   0.31   1.09   7.67   76.0   1.04\n",
      "Mtrue_std[404]   -0.16    0.04   1.48   -3.1  -0.87   -0.1   0.63   2.13 1108.0    1.0\n",
      "Mtrue_std[405]    0.66    0.15   1.93  -1.84  -0.35   0.38   1.08   6.74  175.0   1.02\n",
      "Mtrue_std[406]   -2.74    0.18   3.19 -10.72  -4.44  -1.46  -0.61   0.73  317.0   1.01\n",
      "Mtrue_std[407]   -6.15    0.33    4.0  -13.4  -9.05  -6.68  -1.95   0.06  146.0   1.01\n",
      "Mtrue_std[408]    0.79    0.05   1.53  -1.31  -0.03   0.62   1.34   4.73  799.0   1.01\n",
      "Mtrue_std[409]    0.37    0.02   1.02  -1.56  -0.28   0.36   0.99   2.37 2139.0    1.0\n",
      "Mtrue_std[410]   -0.11    0.03   1.13  -2.18  -0.73  -0.12   0.57   1.97 1298.0    1.0\n",
      "Mtrue_std[411]    0.35    0.02   1.01  -1.51  -0.28   0.34   0.97   2.23 1925.0    1.0\n",
      "Mtrue_std[412]    0.47    0.14    1.5  -1.38  -0.33   0.35   0.99   2.56  112.0   1.04\n",
      "Mtrue_std[413]   -0.04    0.02    1.1  -2.13  -0.74  -0.03   0.71   1.96 2510.0    1.0\n",
      "Mtrue_std[414]    0.04    0.02   1.06  -1.96  -0.61   0.01   0.71   2.06 1937.0    1.0\n",
      "Mtrue_std[415]    0.13    0.04   1.19  -1.93  -0.62    0.1    0.8   2.24  790.0    1.0\n",
      "Mtrue_std[416]     0.1    0.03    1.1  -1.96  -0.62   0.11   0.76   2.08 1675.0    1.0\n",
      "Mtrue_std[417]   -0.13    0.06   1.13  -2.03  -0.74  -0.09   0.57   1.71  394.0   1.01\n",
      "Mtrue_std[418]   -0.25    0.03    1.1  -2.44  -0.88  -0.21   0.45   1.69 1317.0    1.0\n",
      "Mtrue_std[419]    0.18    0.03   1.09  -1.75  -0.54   0.14   0.84   2.23 1319.0    1.0\n",
      "Mtrue_std[420]   -0.18    0.04   1.41  -2.61  -0.81   -0.1   0.59   1.95 1564.0   1.01\n",
      "Mtrue_std[421]   -0.33    0.03   1.15   -2.7  -0.95  -0.27   0.37   1.68 1168.0    1.0\n",
      "Mtrue_std[422]    0.37    0.07   1.53  -1.87  -0.46   0.19   0.96   4.78  484.0   1.01\n",
      "Mtrue_std[423]    0.25    0.03   1.13  -1.78  -0.45   0.21   0.89   2.41 1334.0    1.0\n",
      "Mtrue_std[424]   -0.02    0.04   1.22  -2.08  -0.71  -0.03   0.63   2.03  842.0    1.0\n",
      "Mtrue_std[425]    0.15    0.02    1.0   -1.8  -0.48   0.12   0.72   2.17 3553.0    1.0\n",
      "Mtrue_std[426]   -2.09    0.11   2.21  -7.99  -2.74  -1.44  -0.69   0.66  443.0   1.01\n",
      "Mtrue_std[427]    0.65     0.1   1.81  -1.61  -0.29    0.4   1.14   5.92  302.0   1.01\n",
      "Mtrue_std[428]   -0.27    0.04   1.24  -2.53  -0.88  -0.21   0.44   1.75  938.0   1.01\n",
      "Mtrue_std[429]    0.22    0.03   1.18  -1.76  -0.44    0.2   0.82   2.41 1584.0    1.0\n",
      "Mtrue_std[430]   -0.08    0.03   1.12  -2.11  -0.74  -0.06   0.59   1.99 1035.0    1.0\n",
      "Mtrue_std[431]   -0.65    0.15   1.87  -5.75  -1.21   -0.4   0.32   1.81  156.0   1.03\n",
      "Mtrue_std[432]   -0.93    0.15   2.23  -7.89  -1.29  -0.47   0.23   1.51  222.0   1.04\n",
      "Mtrue_std[433]    0.21    0.03   1.07  -1.74  -0.47   0.17   0.86   2.35 1803.0    1.0\n",
      "Mtrue_std[434]    -0.4    0.03   1.08  -2.46   -1.0  -0.36   0.28   1.47 1538.0    1.0\n",
      "Mtrue_std[435]   -0.41    0.07   1.31  -2.79  -0.97  -0.29   0.31   1.53  370.0   1.01\n",
      "Mtrue_std[436]   -0.08    0.02   0.99   -2.0  -0.73  -0.08    0.6   1.75 2027.0    1.0\n",
      "Mtrue_std[437]    0.11    0.03   1.15  -1.94  -0.65   0.09   0.82   2.26 1635.0    1.0\n",
      "Mtrue_std[438]   -0.14    0.02   1.02  -2.19  -0.79  -0.11   0.52   1.76 2253.0    1.0\n",
      "Mtrue_std[439]    0.04    0.03   1.09  -2.02  -0.65   0.03   0.69   2.19 1651.0    1.0\n",
      "Mtrue_std[440]   -0.12    0.03   1.08  -2.27  -0.75   -0.1   0.56   1.89 1799.0    1.0\n",
      "Mtrue_std[441]    0.72    0.07   1.77  -1.58  -0.15    0.5   1.16   6.25  583.0   1.01\n",
      "Mtrue_std[442]    10.9    0.22   3.27   4.63   8.85   10.8  12.86  17.59  224.0   1.02\n",
      "Mtrue_std[443]    0.01    0.04    1.1  -1.94  -0.62   0.02   0.69   2.01  811.0    1.0\n",
      "Mtrue_std[444]    0.27    0.02   1.03  -1.61  -0.44   0.24   0.95   2.19 2331.0    1.0\n",
      "Mtrue_std[445]    0.25    0.04   1.17  -1.74  -0.48   0.18   0.88    2.4  678.0    1.0\n",
      "Mtrue_std[446]    0.26    0.04   1.17  -1.76  -0.48    0.2   0.88   2.41  701.0    1.0\n",
      "Mtrue_std[447]   -0.29    0.09   1.53  -3.55  -0.87  -0.15   0.52    1.9  321.0   1.01\n",
      "Mtrue_std[448]    0.02    0.04   1.25  -2.25  -0.69   0.02   0.73   2.25 1050.0    1.0\n",
      "Mtrue_std[449]    0.14    0.04   1.21  -1.84  -0.55   0.11    0.8   2.36 1044.0   1.01\n",
      "Mtrue_std[450]  4.1e-3    0.02   0.94  -1.81  -0.59   0.02   0.59    1.8 1748.0    1.0\n",
      "Mtrue_std[451]    0.02    0.02   0.99   -1.8  -0.61  -0.02   0.61    1.9 2482.0    1.0\n",
      "Mtrue_std[452]   -0.12    0.02   1.02  -2.21  -0.75  -0.09   0.56    1.8 2379.0    1.0\n",
      "Mtrue_std[453]    0.26    0.02   1.08  -1.79  -0.43   0.28   0.94   2.17 1984.0    1.0\n",
      "Mtrue_std[454]    0.15    0.02   0.94  -1.63  -0.48   0.13   0.78   2.01 2370.0    1.0\n",
      "Mtrue_std[455]   -0.05    0.02   1.13  -2.18  -0.71  -0.04   0.63   1.98 2940.0    1.0\n",
      "Mtrue_std[456]    0.06    0.03   1.21  -2.17  -0.68   0.08   0.78   2.23 1908.0    1.0\n",
      "Mtrue_std[457]    0.21    0.06   1.46  -2.03  -0.56   0.13    0.8   3.51  541.0    1.0\n",
      "Mtrue_std[458]  5.4e-4    0.06   1.34  -2.16  -0.71  -0.03   0.68    2.5  566.0    1.0\n",
      "Mtrue_std[459]   -6.77    0.32    4.4 -14.94  -9.94  -7.32  -2.29   0.15  192.0   1.02\n",
      "Mtrue_std[460]    0.61    0.08    1.7   -1.7  -0.32   0.37   1.15   5.34  501.0   1.01\n",
      "Mtrue_std[461]    0.37    0.07   1.19  -1.63  -0.35   0.29   0.97   2.55  332.0   1.01\n",
      "Mtrue_std[462]    0.43    0.09   1.37  -1.63  -0.38    0.3    1.0   4.29  243.0   1.03\n",
      "Mtrue_std[463]    0.02    0.03   1.09  -1.94  -0.65-6.0e-3   0.66   2.03  978.0    1.0\n",
      "Mtrue_std[464]    0.05    0.02   1.04   -1.9  -0.62   0.03    0.7    2.1 3065.0    1.0\n",
      "Mtrue_std[465]   -0.06    0.04   1.21  -2.37   -0.7  -0.03   0.64   2.04  761.0    1.0\n",
      "Mtrue_std[466]    -0.1    0.03   1.21  -2.27  -0.85  -0.11   0.62   2.11 1794.0    1.0\n",
      "Mtrue_std[467]    0.01    0.02   0.91  -1.82  -0.59   0.02   0.61   1.75 2467.0    1.0\n",
      "Mtrue_std[468]   -0.05    0.02   1.04  -2.01  -0.71  -0.04   0.61   1.88 2188.0    1.0\n",
      "Mtrue_std[469]    -0.2    0.02   1.07  -2.29  -0.86  -0.18   0.48   1.76 2192.0    1.0\n",
      "Mtrue_std[470]    0.04    0.03   1.07  -1.98  -0.62   0.02   0.72   2.07 1360.0    1.0\n",
      "Mtrue_std[471]    0.17    0.07   1.22  -1.95  -0.53   0.12   0.81   2.43  315.0   1.02\n",
      "Mtrue_std[472]   -0.22    0.08   1.44  -2.77  -0.82  -0.12   0.54   1.86  343.0   1.01\n",
      "Mtrue_std[473]    1.73    0.16   2.46  -1.02    0.3   1.04   2.09    9.1  224.0   1.03\n",
      "Mtrue_std[474]    0.14    0.02   0.95  -1.76  -0.49   0.13   0.74   2.03 2684.0    1.0\n",
      "Mtrue_std[475]   -1.45    0.12   2.12   -7.8  -1.84  -0.96  -0.21   1.03  288.0   1.02\n",
      "Mtrue_std[476]    0.13    0.03    1.1  -1.85  -0.53    0.1   0.76   2.18 1732.0    1.0\n",
      "Mtrue_std[477]   -0.24    0.05   1.23  -2.35  -0.87  -0.21    0.5   1.73  735.0    1.0\n",
      "Mtrue_std[478]    0.02    0.02   1.04  -1.96  -0.66-2.4e-3   0.69   2.09 2634.0    1.0\n",
      "Mtrue_std[479]     1.0    0.16   2.17  -1.36  -0.13   0.59   1.34   8.38  189.0   1.01\n",
      "Mtrue_std[480]     0.2    0.07    1.3  -1.92  -0.51   0.15   0.84   2.36  332.0   1.01\n",
      "Mtrue_std[481]    0.18    0.03   1.07  -1.78  -0.49   0.19   0.81   2.21 1791.0    1.0\n",
      "Mtrue_std[482]    0.14    0.03   1.12  -1.85  -0.54   0.11   0.79   2.13 1550.0    1.0\n",
      "Mtrue_std[483]   -3.62    0.33   4.29 -13.97  -6.69  -1.58   -0.5   0.98  169.0   1.02\n",
      "Mtrue_std[484]   -0.31    0.04   1.21  -2.59  -0.92  -0.22   0.41   1.58 1019.0    1.0\n",
      "Mtrue_std[485]   -1.03    0.13   2.14   -8.2  -1.43  -0.59   0.09   1.52  282.0   1.02\n",
      "Mtrue_std[486]    0.12    0.04   1.13  -1.85  -0.54   0.08   0.76   2.25  769.0    1.0\n",
      "Mtrue_std[487]    0.12    0.02   0.95  -1.71   -0.5   0.09   0.73   1.92 2337.0    1.0\n",
      "Mtrue_std[488]   -0.07    0.08   1.38  -2.78  -0.73-9.9e-3   0.66   2.07  281.0   1.02\n",
      "Mtrue_std[489]   -0.22    0.03   1.06  -2.35  -0.83  -0.21   0.44   1.84 1654.0    1.0\n",
      "Mtrue_std[490]    0.13    0.03   1.11  -1.85  -0.53   0.11   0.77   2.16 1300.0    1.0\n",
      "Mtrue_std[491]    0.95    0.11   1.98  -1.31  -0.12   0.59   1.31   7.51  352.0   1.02\n",
      "Mtrue_std[492]   -0.17    0.03   1.02   -2.1   -0.8   -0.2   0.46   1.72 1356.0    1.0\n",
      "Mtrue_std[493]    0.58    0.18   2.02  -1.68  -0.39   0.28   1.01    6.7  122.0   1.02\n",
      "Mtrue_std[494]    0.42    0.19   1.91  -1.76  -0.48   0.19   0.88   5.23   98.0   1.04\n",
      "Mtrue_std[495]   -1.05    0.12   2.11  -7.97  -1.43  -0.63   0.08   1.32  287.0   1.01\n",
      "Mtrue_std[496]    0.02    0.03   1.04  -1.95  -0.62   0.02   0.69   1.96 1657.0    1.0\n",
      "Mtrue_std[497]    0.17    0.04   1.19  -1.95  -0.54   0.14   0.79    2.3 1120.0    1.0\n",
      "Mtrue_std[498]    0.37    0.02   0.98  -1.47  -0.27   0.34   0.97   2.31 1969.0    1.0\n",
      "Mtrue_std[499]    0.75     0.1   1.54  -1.35   -0.1   0.57   1.25   5.38  260.0   1.02\n",
      "Mtrue_std[500]   -0.21    0.08    1.5  -3.03  -0.82  -0.11   0.58   2.09  341.0   1.01\n",
      "Mtrue_std[501]   -0.22    0.02   1.01   -2.2  -0.84   -0.2   0.43   1.69 2509.0    1.0\n",
      "Mtrue_std[502]   -0.56    0.09   1.49  -4.16  -1.11  -0.41   0.25   1.55  249.0   1.01\n",
      "Mtrue_std[503]   -0.27    0.02   1.11  -2.27  -0.93  -0.25   0.42   1.63 3699.0    1.0\n",
      "Mtrue_std[504]   -0.08    0.04   1.23  -2.34  -0.73  -0.08   0.64   2.07  945.0    1.0\n",
      "Mtrue_std[505]    0.11    0.05    1.3  -1.96  -0.63   0.05   0.78   2.23  639.0    1.0\n",
      "Mtrue_std[506]   -0.63    0.06   1.26  -3.39  -1.16  -0.54   0.09   1.45  397.0   1.01\n",
      "Mtrue_std[507]   -0.22    0.16    1.7  -2.72  -0.76  -0.12    0.6    2.0  115.0   1.04\n",
      "Mtrue_std[508]    0.03    0.03   1.12  -1.91  -0.64 9.2e-3   0.64   2.08 1191.0    1.0\n",
      "Mtrue_std[509]    0.07    0.03   1.13  -2.01  -0.62   0.07   0.73   2.29 1725.0    1.0\n",
      "Mtrue_std[510]   -0.14    0.02    1.1  -2.19  -0.78  -0.12   0.53   1.76 2891.0    1.0\n",
      "Mtrue_std[511]   -0.41    0.05   1.23  -2.75  -1.03  -0.33   0.33   1.47  542.0    1.0\n",
      "Mtrue_std[512]   -0.63    0.07   1.53  -4.92  -1.18  -0.44   0.26   1.45  470.0    1.0\n",
      "Mtrue_std[513]    0.04    0.13   1.69  -2.29  -0.69   0.01   0.68   2.05  157.0   1.02\n",
      "Mtrue_std[514]    0.13    0.02   0.93  -1.67  -0.48   0.13   0.69   2.06 2401.0    1.0\n",
      "Mtrue_std[515]    -0.1    0.03   1.04  -2.09  -0.73  -0.08   0.57    1.9 1283.0    1.0\n",
      "Mtrue_std[516]   -0.03    0.03   1.15  -2.07  -0.64  -0.04   0.63   1.87 2112.0    1.0\n",
      "Mtrue_std[517]   -0.04    0.04   1.26  -2.45  -0.73  -0.04   0.63   2.16 1249.0    1.0\n",
      "Mtrue_std[518] -3.7e-3    0.02   1.04  -2.02  -0.67   0.02   0.69   1.89 2089.0    1.0\n",
      "Mtrue_std[519]   -0.19    0.11   1.99  -4.18  -0.78  -0.04   0.67   2.51  334.0   1.01\n",
      "Mtrue_std[520]   -0.41    0.09   1.54  -4.13  -0.91  -0.27   0.37   1.61  268.0   1.01\n",
      "Mtrue_std[521]   -0.22    0.06   1.37  -3.17  -0.82  -0.12   0.58   1.86  513.0    1.0\n",
      "Mtrue_std[522]  2.2e-3    0.02   1.01  -1.93  -0.66-8.3e-3   0.68   2.03 1924.0    1.0\n",
      "Mtrue_std[523]   -0.08    0.02   1.09  -2.16  -0.74  -0.06   0.61   1.93 2774.0    1.0\n",
      "Mtrue_std[524]    0.43    0.04   1.25  -1.63  -0.37   0.34   1.07    2.8  874.0    1.0\n",
      "Mtrue_std[525]    0.05    0.02    1.0  -1.84  -0.59   0.04   0.67   1.91 2142.0    1.0\n",
      "Mtrue_std[526]   -0.28    0.06   1.38  -3.65  -0.86  -0.18   0.52    1.9  601.0    1.0\n",
      "Mtrue_std[527]  6.6e-3    0.03   1.27  -2.29  -0.67-4.9e-3   0.66   2.06 1509.0    1.0\n",
      "Mtrue_std[528]   -0.03    0.02   1.01  -1.97  -0.67  -0.06    0.6   2.02 2389.0    1.0\n",
      "Mtrue_std[529]   -0.54    0.09   1.69  -5.48  -1.04  -0.34   0.31   1.73  389.0   1.01\n",
      "Mtrue_std[530]    0.45    0.09   1.52  -1.71  -0.38   0.34   1.01   3.91  289.0   1.01\n",
      "Mtrue_std[531]    0.28    0.03   1.06  -1.69  -0.36   0.24   0.88   2.37 1478.0    1.0\n",
      "Mtrue_std[532]   -0.28    0.03   1.15  -2.48  -0.93  -0.25   0.43   1.85 1554.0    1.0\n",
      "Mtrue_std[533]   -0.04    0.02    1.0  -2.02  -0.67  -0.04   0.63   1.85 2361.0    1.0\n",
      "Mtrue_std[534]   -0.86    0.09   1.73  -6.29  -1.36  -0.59    0.1   1.42  411.0   1.01\n",
      "Mtrue_std[535]  3.1e-3    0.03   1.09  -2.09  -0.68   0.01   0.72   2.07 1552.0    1.0\n",
      "Mtrue_std[536] -7.5e-3    0.04   1.19  -2.01  -0.71  -0.02   0.66   2.09  727.0    1.0\n",
      "Mtrue_std[537]    -0.3    0.02   0.98  -2.21  -0.93  -0.31   0.33   1.59 3339.0    1.0\n",
      "Mtrue_std[538]   -0.42    0.03    1.0  -2.27  -1.06  -0.38   0.24   1.42 1551.0    1.0\n",
      "Mtrue_std[539]    0.15    0.02   1.04  -1.85  -0.53   0.14   0.82   2.13 1969.0    1.0\n",
      "Mtrue_std[540]   -0.24    0.02   0.97  -2.09  -0.84  -0.22   0.37   1.57 1942.0    1.0\n",
      "Mtrue_std[541]   -0.04    0.02   1.14  -2.18  -0.75  -0.05   0.67   2.06 2131.0    1.0\n",
      "Mtrue_std[542]    0.17    0.03   1.11  -1.78  -0.53   0.13   0.83    2.3 1266.0    1.0\n",
      "Mtrue_std[543]    0.07    0.02   1.03  -1.89  -0.57   0.04   0.69   2.18 2229.0    1.0\n",
      "Mtrue_std[544]    0.23    0.02   0.97  -1.77  -0.37   0.21   0.87   2.21 3301.0    1.0\n",
      "Mtrue_std[545]    -0.1    0.02   1.13  -2.12  -0.77   -0.1   0.54   1.97 2834.0    1.0\n",
      "Mtrue_std[546]   -0.22    0.02   1.05  -2.25  -0.86  -0.19   0.48   1.69 1836.0    1.0\n",
      "Mtrue_std[547]   -0.04    0.05   1.38  -2.35  -0.73  -0.05   0.67    2.2  882.0    1.0\n",
      "Mtrue_std[548]   -0.12    0.04   1.23  -2.32  -0.76  -0.07   0.62   1.98  932.0    1.0\n",
      "Mtrue_std[549]   -0.05    0.02    1.1  -2.16  -0.72  -0.06   0.67   1.97 2201.0    1.0\n",
      "Mtrue_std[550]   -0.55    0.12   1.55  -5.65  -1.07  -0.35   0.32   1.63  163.0   1.02\n",
      "Mtrue_std[551]    0.45    0.03   1.02  -1.51  -0.21   0.44   1.07   2.44 1595.0    1.0\n",
      "Mtrue_std[552]   -0.07    0.04   1.21  -2.46  -0.68  -0.03   0.66   1.94  806.0    1.0\n",
      "Mtrue_std[553]    0.64    0.06   1.24  -1.29  -0.11   0.54    1.2   3.71  440.0   1.01\n",
      "Mtrue_std[554]    0.36    0.03   1.14  -1.53  -0.32   0.31   0.97   2.71 2062.0    1.0\n",
      "Mtrue_std[555]   -0.37    0.04   1.23  -2.62  -0.99  -0.32   0.34   1.67  911.0   1.01\n",
      "Mtrue_std[556]   -0.35    0.03   1.12  -2.48  -0.93  -0.31   0.33   1.64 1399.0    1.0\n",
      "Mtrue_std[557]    1.47    0.19   2.78  -1.25 3.3e-3    0.8   1.65  10.34  219.0   1.01\n",
      "Mtrue_std[558]   -0.31    0.03   1.12  -2.49  -0.94  -0.27   0.39   1.79 1990.0    1.0\n",
      "Mtrue_std[559]   -0.15    0.03   1.06  -2.19  -0.82  -0.15   0.54   1.88 1778.0    1.0\n",
      "Mtrue_std[560]    0.19    0.05   1.32  -1.96  -0.54    0.1   0.83   2.88  775.0    1.0\n",
      "Mtrue_std[561]   -0.45    0.06    1.4  -3.96  -1.04  -0.32   0.39   1.63  505.0   1.01\n",
      "Mtrue_std[562]   -0.66     0.1   1.69  -5.62  -1.16  -0.43   0.24   1.59  262.0   1.02\n",
      "Mtrue_std[563]    0.08    0.02   0.96  -1.81  -0.56   0.06    0.7   2.05 3741.0    1.0\n",
      "Mtrue_std[564]    0.04    0.02   0.95   -1.8  -0.62   0.06   0.65   1.92 1934.0    1.0\n",
      "Mtrue_std[565]    0.18    0.01   0.96  -1.68  -0.48   0.17   0.82   2.11 4133.0    1.0\n",
      "Mtrue_std[566]    0.08    0.03    1.1  -1.99  -0.66   0.05   0.77   2.22 1581.0    1.0\n",
      "Mtrue_std[567]    0.07    0.02   1.05  -1.88  -0.59   0.06   0.75   2.09 2200.0    1.0\n",
      "Mtrue_std[568]    0.23    0.02   0.96  -1.76  -0.36   0.25   0.87   2.07 2028.0    1.0\n",
      "Mtrue_std[569]   -0.54    0.03   1.06  -2.54  -1.09  -0.49   0.09    1.3  982.0    1.0\n",
      "Mtrue_std[570]   -0.62    0.04   1.08  -2.97   -1.2  -0.56   0.06   1.17  898.0    1.0\n",
      "Mtrue_std[571]    0.12    0.06   1.31  -2.17  -0.57   0.13   0.85    2.4  556.0    1.0\n",
      "Mtrue_std[572]   -0.06    0.03   1.17  -2.19  -0.67 4.9e-3   0.62   1.86 1490.0    1.0\n",
      "Mtrue_std[573]   -0.27    0.05   1.32   -2.9  -0.85  -0.19   0.46   1.91  736.0   1.01\n",
      "Mtrue_std[574]   -0.12    0.03   1.11  -2.22  -0.77  -0.06   0.58   1.76 1030.0    1.0\n",
      "Mtrue_std[575]   -0.23    0.03    1.2  -2.49  -0.88  -0.17   0.49   1.82 1252.0    1.0\n",
      "Mtrue_std[576]    -1.9    0.39   3.56  -12.2  -1.91  -0.68   0.14   1.62   83.0   1.02\n",
      "Mtrue_std[577] -6.4e-3    0.02   0.97  -1.82  -0.62  -0.03   0.59   1.91 2575.0    1.0\n",
      "Mtrue_std[578]   -0.04    0.04   1.11  -2.05  -0.67  -0.06   0.58    2.0  908.0    1.0\n",
      "Mtrue_std[579]   -0.23    0.03   1.15  -2.55   -0.9   -0.2   0.53    1.8 1235.0    1.0\n",
      "Mtrue_std[580]   -0.11    0.11   1.33  -3.43  -0.65   0.03   0.63   1.96  159.0   1.04\n",
      "Mtrue_std[581]    -0.3    0.04   1.14  -2.44   -0.9  -0.22   0.42   1.62  967.0    1.0\n",
      "Mtrue_std[582]   -0.23    0.06   1.23   -2.4  -0.81  -0.17   0.51   1.73  451.0   1.01\n",
      "Mtrue_std[583]    0.11    0.04   1.14  -1.79  -0.53   0.06   0.72   2.03  881.0    1.0\n",
      "Mtrue_std[584]   -0.02    0.03   1.15  -2.16   -0.7-9.0e-3   0.67   2.16 2029.0    1.0\n",
      "Mtrue_std[585]   -0.25    0.05   1.26  -2.57  -0.87  -0.18   0.49   1.87  594.0    1.0\n",
      "Mtrue_std[586]   -0.77    0.05   1.31  -3.94  -1.33  -0.66 6.0e-3   1.21  597.0    1.0\n",
      "Mtrue_std[587]   -0.05    0.02    1.0  -1.97  -0.68  -0.06   0.56   1.88 1788.0    1.0\n",
      "Mtrue_std[588]    -0.7    0.19   1.88   -6.4  -1.14  -0.42   0.24   1.54   95.0   1.05\n",
      "Mtrue_std[589]   -0.05    0.02   1.07  -2.05  -0.72  -0.03    0.6   1.93 3340.0    1.0\n",
      "Mtrue_std[590]    0.09    0.05   1.21  -2.05  -0.64   0.04   0.73   2.37  556.0   1.01\n",
      "Mtrue_std[591]    0.38    0.06   1.12  -1.48  -0.33   0.31   0.94    2.4  409.0   1.01\n",
      "Mtrue_std[592]    0.25    0.03   1.12  -1.81  -0.43   0.22   0.88   2.31 1675.0    1.0\n",
      "Mtrue_std[593]    0.22    0.06   1.23  -1.83   -0.5   0.17   0.83   2.48  433.0   1.01\n",
      "Mtrue_std[594]   -0.22    0.03    1.1  -2.48  -0.84  -0.18   0.45   1.82 1027.0   1.01\n",
      "Mtrue_std[595]   -0.31    0.04   1.22  -2.72  -0.95  -0.27   0.42   1.85 1203.0    1.0\n",
      "Mtrue_std[596]    0.11    0.02   1.04  -1.81  -0.59   0.12   0.77   2.08 2433.0    1.0\n",
      "Mtrue_std[597]    0.21    0.03   1.14  -1.88  -0.52   0.19   0.87   2.41 1794.0    1.0\n",
      "Mtrue_std[598]    0.31    0.04   1.19  -1.71  -0.41   0.26   0.98   2.52 1074.0    1.0\n",
      "Mtrue_std[599]    -0.1    0.04   1.05  -2.14   -0.7  -0.06   0.58   1.69  713.0   1.01\n",
      "Mtrue_std[600]    3.27    0.26   3.84  -0.83   0.57   1.55   5.99  12.35  215.0   1.02\n",
      "Mtrue_std[601]    0.19    0.03   1.12  -1.95  -0.45   0.15   0.79   2.41 1806.0    1.0\n",
      "Mtrue_std[602]    0.73    0.04   1.24  -1.25  -0.03   0.66    1.3    3.4  943.0    1.0\n",
      "Mtrue_std[603]    0.66    0.11   1.91  -1.77  -0.28   0.35   1.09   7.15  289.0   1.01\n",
      "Mtrue_std[604]    0.51    0.07   1.37  -1.62  -0.28   0.38   1.06   4.21  388.0   1.01\n",
      "Mtrue_std[605]   -0.08    0.03   1.07  -2.07  -0.76  -0.07   0.56    2.0 1531.0    1.0\n",
      "Mtrue_std[606]   -0.03    0.04   1.11  -1.96  -0.72  -0.08   0.57   1.98  854.0    1.0\n",
      "Mtrue_std[607]    0.85    0.06   1.36  -1.18   0.05   0.69   1.39   4.96  476.0    1.0\n",
      "Mtrue_std[608]   -0.27    0.03   1.17  -2.69  -0.94  -0.24   0.51   1.79 1755.0    1.0\n",
      "Mtrue_std[609]   -0.43    0.02   1.03  -2.39  -1.03  -0.41    0.2    1.5 2659.0    1.0\n",
      "Mtrue_std[610]    -0.2    0.04   1.27  -2.27  -0.84  -0.13   0.53   1.85 1274.0    1.0\n",
      "Mtrue_std[611]   -0.18    0.02   0.96  -1.98  -0.79  -0.17   0.45   1.62 2487.0    1.0\n",
      "Mtrue_std[612]   -0.13    0.03   1.07  -2.07   -0.8  -0.11   0.52   1.79 1176.0    1.0\n",
      "Mtrue_std[613]   -0.07    0.02   0.94  -1.91  -0.65  -0.05   0.51   1.68 1729.0    1.0\n",
      "Mtrue_std[614]    0.05    0.02   0.93  -1.65   -0.6   0.06   0.65    1.8 2179.0    1.0\n",
      "Mtrue_std[615]    7.15    0.52   5.65  -0.57   1.35   7.63  11.37   18.3  117.0   1.02\n",
      "Mtrue_std[616]    0.09    0.04   1.29  -1.99  -0.65   0.08   0.81   2.35 1204.0    1.0\n",
      "Mtrue_std[617]    0.03    0.03   1.05  -2.05  -0.62   0.06   0.69   1.94 1740.0    1.0\n",
      "Mtrue_std[618]     0.1    0.07   1.23  -1.87  -0.65   0.04   0.72   2.32  281.0   1.01\n",
      "Mtrue_std[619]    1.33    0.17   2.59  -1.45  -0.05   0.69    1.6   9.34  232.0   1.02\n",
      "Mtrue_std[620]    0.68    0.03   1.14  -1.24  -0.08   0.62   1.32   3.13 1114.0    1.0\n",
      "Mtrue_std[621]    0.03    0.02   1.06   -2.0  -0.65   0.05    0.7   2.08 3064.0    1.0\n",
      "Mtrue_std[622]    0.05    0.03   1.15  -1.88  -0.66   0.03   0.71   2.08 1265.0    1.0\n",
      "Mtrue_std[623]   -1.56    0.09   1.89  -7.01  -1.92   -1.1  -0.47   0.77  448.0   1.01\n",
      "Mtrue_std[624]    0.58    0.05   1.34  -1.48  -0.17   0.46   1.13    3.4  606.0   1.01\n",
      "Mtrue_std[625]   -0.38    0.05   1.28  -3.38  -0.97  -0.29    0.4   1.65  658.0    1.0\n",
      "Mtrue_std[626]    1.13    0.22   2.77  -1.74  -0.19   0.51   1.34  10.37  157.0   1.03\n",
      "Mtrue_std[627]   -0.11    0.02   1.05  -2.11  -0.75  -0.09   0.57    1.8 2117.0    1.0\n",
      "Mtrue_std[628]    0.12    0.02    1.0  -1.78  -0.54   0.09   0.79   2.08 2750.0    1.0\n",
      "Mtrue_std[629]   -0.34     0.1   1.55  -4.15  -0.93  -0.25   0.48    1.9  253.0   1.01\n",
      "Mtrue_std[630] -5.2e-3    0.04   1.39  -2.29  -0.72  -0.06   0.66   2.55 1065.0    1.0\n",
      "Mtrue_std[631]    0.42    0.09    1.5   -1.7  -0.35   0.31   0.99   2.94  270.0   1.02\n",
      "Mtrue_std[632]    0.09    0.02    1.0  -1.82  -0.57   0.09   0.73   2.01 1927.0    1.0\n",
      "Mtrue_std[633]   -0.15    0.06   1.42  -3.04  -0.79  -0.09   0.62   2.09  590.0    1.0\n",
      "Mtrue_std[634]   -0.05    0.02   1.08  -2.11  -0.75  -0.02   0.64   1.94 2754.0    1.0\n",
      "Mtrue_std[635]   -2.73    0.32   3.95 -13.29  -3.95  -1.15  -0.21    1.2  153.0   1.03\n",
      "Mtrue_std[636]   -0.05    0.02   0.98   -2.0  -0.69  -0.06   0.58   1.85 1841.0    1.0\n",
      "Mtrue_std[637]    0.05    0.02   0.97  -1.77  -0.62   0.03   0.67    2.0 2622.0    1.0\n",
      "Mtrue_std[638]   -0.09    0.03   1.16  -2.17  -0.77  -0.12   0.58   1.93 1336.0    1.0\n",
      "Mtrue_std[639]   -0.25    0.02    1.1  -2.38  -0.94   -0.2   0.46   1.71 3304.0    1.0\n",
      "Mtrue_std[640]    0.08    0.04   1.32  -2.21  -0.54   0.14   0.78   2.28 1104.0    1.0\n",
      "Mtrue_std[641]    0.08    0.02   1.05  -1.86  -0.58   0.06   0.71   2.22 2701.0    1.0\n",
      "Mtrue_std[642]    0.73    0.03   1.15  -1.12   0.03   0.63    1.3   3.24 1111.0    1.0\n",
      "Mtrue_std[643]    0.16    0.03   1.07  -1.79  -0.51   0.14   0.77   2.21 1481.0    1.0\n",
      "Mtrue_std[644]    -0.3    0.05   1.23  -2.32  -0.96  -0.29   0.38   1.68  636.0    1.0\n",
      "Mtrue_std[645]    0.28    0.02   1.09   -1.7  -0.44   0.28   0.94   2.37 1935.0    1.0\n",
      "Mtrue_std[646]    0.04    0.03   1.04  -1.97  -0.63   0.04   0.71   2.09 1571.0    1.0\n",
      "Mtrue_std[647]   -0.18    0.02    1.1  -2.37  -0.81  -0.16    0.5   1.91 2964.0    1.0\n",
      "Mtrue_std[648]   -0.69    0.12   1.84  -6.25  -1.19  -0.43   0.31   1.62  218.0   1.01\n",
      "Mtrue_std[649]   -0.14    0.03   1.13  -2.39  -0.76  -0.08   0.55   1.81 1280.0    1.0\n",
      "Mtrue_std[650]    2.54     0.3   4.08  -1.31   0.13    1.0   2.71  13.95  188.0   1.02\n",
      "Mtrue_std[651]   -0.83    0.11   1.91  -6.83  -1.32  -0.53   0.18   1.64  308.0   1.01\n",
      "Mtrue_std[652]    0.64    0.07   1.68  -1.58  -0.25   0.44   1.18   5.22  547.0    1.0\n",
      "Mtrue_std[653]    0.28    0.07   1.25  -1.87  -0.45    0.2    0.9   2.98  326.0   1.01\n",
      "Mtrue_std[654]    0.33    0.03   1.07  -1.54  -0.36   0.28   0.96   2.37 1158.0    1.0\n",
      "Mtrue_std[655]    0.47     0.1   1.69  -1.85  -0.37   0.26   0.95    5.2  286.0   1.02\n",
      "Mtrue_std[656]     1.0    0.07   1.53  -1.13   0.06   0.81    1.5   5.78  535.0    1.0\n",
      "Mtrue_std[657]    0.58    0.03   1.07  -1.32  -0.08   0.53   1.15   2.68 1100.0    1.0\n",
      "Mtrue_std[658]    0.06    0.11   1.74  -2.24  -0.67  -0.03   0.64   2.85  236.0   1.01\n",
      "Mtrue_std[659]   -0.03    0.03    1.1  -2.03  -0.68  -0.03   0.63   1.93 1898.0    1.0\n",
      "Mtrue_std[660]   -0.15    0.04   1.21  -2.32  -0.72  -0.08   0.58   1.87  720.0    1.0\n",
      "Mtrue_std[661]   -0.55    0.07   1.35  -3.65  -1.14  -0.43   0.22   1.55  432.0    1.0\n",
      "Mtrue_std[662]     0.2    0.05   1.27  -1.85  -0.53   0.12    0.8   2.38  792.0    1.0\n",
      "Mtrue_std[663]    0.06    0.02   1.01  -2.05  -0.57   0.06    0.7   2.01 2768.0    1.0\n",
      "Mtrue_std[664]    0.47    0.08   1.39  -1.56  -0.27   0.35   0.97   3.69  314.0   1.01\n",
      "Mtrue_std[665]     0.8    0.06   1.38  -1.18   0.02   0.65   1.33    4.6  627.0    1.0\n",
      "Mtrue_std[666]   -0.11    0.04   1.21  -2.41  -0.78   -0.1   0.58   2.04  798.0    1.0\n",
      "Mtrue_std[667]    0.07    0.02   1.05  -1.97  -0.62   0.06   0.75   2.06 2821.0    1.0\n",
      "Mtrue_std[668]    -0.1    0.02   0.95  -1.89  -0.75  -0.09   0.53   1.72 2505.0    1.0\n",
      "Mtrue_std[669]     3.2    0.33   4.63  -1.12   0.21   1.15   5.46  15.38  195.0   1.02\n",
      "Mtrue_std[670]   -0.12    0.09   1.54   -2.6  -0.75  -0.01    0.7   2.07  319.0   1.01\n",
      "Mtrue_std[671]    0.01    0.02   1.04  -1.98  -0.64   0.02   0.67   2.02 1786.0    1.0\n",
      "Mtrue_std[672]    0.19    0.09   1.49  -1.94  -0.63   0.08   0.78   3.09  306.0   1.01\n",
      "Mtrue_std[673]    0.27    0.05    1.3  -1.82  -0.48   0.22   0.93   2.58  774.0    1.0\n",
      "Mtrue_std[674]    0.23    0.02    1.0  -1.66  -0.43   0.22   0.86   2.17 1615.0    1.0\n",
      "Mtrue_std[675]   -0.13    0.02   0.95   -1.9  -0.75  -0.15   0.49   1.69 3048.0    1.0\n",
      "Mtrue_std[676]    0.18    0.03   1.23  -2.04   -0.5   0.17   0.86   2.49 1384.0    1.0\n",
      "Mtrue_std[677]    0.17    0.03   1.08  -1.92  -0.49   0.18   0.86   2.25 1778.0    1.0\n",
      "Mtrue_std[678]     0.1    0.02   0.98  -1.78  -0.57    0.1   0.73   1.95 1737.0    1.0\n",
      "Mtrue_std[679]    0.58    0.05   1.19  -1.26  -0.16    0.5   1.15   2.95  519.0    1.0\n",
      "Mtrue_std[680]    0.03    0.03    1.2  -1.92  -0.62   0.03   0.66   2.03 1186.0    1.0\n",
      "Mtrue_std[681]   -0.83    0.05   1.38  -4.32   -1.4  -0.69 7.8e-3   1.18  664.0    1.0\n",
      "Mtrue_std[682]   -0.15    0.03   1.14  -2.45  -0.77  -0.12   0.53   1.86 1846.0    1.0\n",
      "Mtrue_std[683]    0.07    0.03   1.08  -1.96  -0.63   0.06   0.73   2.13 1756.0    1.0\n",
      "Mtrue_std[684]   -0.28    0.08   1.46  -3.18  -0.88  -0.17   0.52   1.85  355.0   1.01\n",
      "Mtrue_std[685]    0.16    0.03   1.17  -1.96  -0.56   0.13   0.82   2.35 1180.0    1.0\n",
      "Mtrue_std[686]   -0.26    0.08   1.57  -4.39  -0.88  -0.12    0.6   2.01  383.0   1.01\n",
      "Mtrue_std[687]    0.88    0.23   1.96  -1.47  -0.17   0.54   1.29   7.59   74.0   1.05\n",
      "Mtrue_std[688]   -0.11    0.07   1.63  -2.99  -0.81  -0.05   0.72   2.37  502.0    1.0\n",
      "Mtrue_std[689]   -0.17    0.02   0.95  -2.02  -0.77  -0.15   0.45   1.71 2567.0    1.0\n",
      "Mtrue_std[690]     0.2    0.05   1.23  -1.88  -0.54   0.15   0.83    2.4  663.0    1.0\n",
      "Mtrue_std[691]   -0.05    0.02   1.05  -2.16  -0.72  -0.02   0.61   1.98 2021.0    1.0\n",
      "Mtrue_std[692]    -0.1    0.06   1.29  -2.56  -0.73  -0.03   0.65   2.12  443.0   1.01\n",
      "Mtrue_std[693]    0.12    0.05   1.17  -1.92   -0.6   0.09   0.76   2.29  539.0   1.01\n",
      "Mtrue_std[694]   -0.06    0.03   1.11   -2.2   -0.7  -0.04   0.65    1.9 1170.0    1.0\n",
      "Mtrue_std[695]   -0.22    0.04   1.26   -2.7  -0.87  -0.18   0.51   1.96  794.0   1.01\n",
      "Mtrue_std[696]   -1.17    0.12   2.28  -8.53  -1.49  -0.63   0.08   1.33  338.0   1.01\n",
      "Mtrue_std[697]   -0.95     0.1    1.9  -6.82   -1.4  -0.64   0.06   1.33  364.0   1.01\n",
      "Mtrue_std[698]   -0.27    0.11   2.09  -3.27  -0.84  -0.12   0.58   2.23  341.0   1.01\n",
      "Mtrue_std[699]   -0.29    0.02   0.93  -2.15  -0.88  -0.28   0.31   1.44 1772.0    1.0\n",
      "Mtrue_std[700]   -0.36    0.08    1.7  -4.65   -0.9  -0.17   0.47   1.86  414.0    1.0\n",
      "Mtrue_std[701]    1.38    0.18    2.7  -1.39  -0.06   0.68   1.58   9.88  228.0   1.01\n",
      "Mtrue_std[702]    0.11    0.03    1.1  -1.92  -0.55   0.11   0.78    2.2 1099.0    1.0\n",
      "Mtrue_std[703]    0.17    0.05   1.34  -2.14  -0.54   0.07   0.78   2.75  795.0    1.0\n",
      "Mtrue_std[704]    0.72    0.06   1.33  -1.31  -0.05   0.57   1.26   4.18  516.0    1.0\n",
      "Mtrue_std[705] -4.9e-3    0.02   0.94  -1.85  -0.63  -0.01   0.61    1.8 2423.0    1.0\n",
      "Mtrue_std[706]  5.1e-3    0.02   1.01   -2.0  -0.64   0.03   0.64   1.89 2165.0    1.0\n",
      "Mtrue_std[707]    0.03    0.03   1.11  -1.88  -0.65   0.03    0.7   1.98 1910.0    1.0\n",
      "Mtrue_std[708]    -0.4    0.03   1.07  -2.44  -1.04  -0.36   0.28   1.53 1498.0    1.0\n",
      "Mtrue_std[709]    0.12    0.03    1.0  -1.84   -0.5   0.12   0.72   2.03 1472.0    1.0\n",
      "Mtrue_std[710]   -0.39    0.11   1.45  -3.93  -0.99   -0.2   0.47   1.68  186.0   1.02\n",
      "Mtrue_std[711]   -0.66    0.12   1.77  -5.33   -1.2  -0.41   0.27   1.62  219.0   1.02\n",
      "Mtrue_std[712]    0.11    0.04   1.11  -1.96   -0.6   0.06   0.77   2.24  932.0    1.0\n",
      "Mtrue_std[713]    0.25    0.02   1.01   -1.7  -0.41   0.24   0.89   2.22 1872.0    1.0\n",
      "Mtrue_std[714]     0.1    0.03    1.2  -1.91  -0.63   0.07   0.78   2.26 1243.0    1.0\n",
      "Mtrue_std[715]   -0.34    0.02   1.02  -2.47   -0.9  -0.33    0.3   1.66 2359.0    1.0\n",
      "Mtrue_std[716]    0.06    0.04   1.14  -1.97   -0.6   0.04   0.73   2.08  908.0    1.0\n",
      "Mtrue_std[717]   -4.79    0.44   4.96 -15.27  -8.94   -2.1  -0.72   0.78  130.0   1.03\n",
      "Mtrue_std[718]    0.04    0.03   1.11  -1.85  -0.63  -0.01   0.63   2.02 1119.0    1.0\n",
      "Mtrue_std[719]   -0.06    0.05   1.26  -2.12  -0.74  -0.04   0.63    2.1  721.0    1.0\n",
      "Mtrue_std[720] -7.5e-3    0.08   1.39  -2.24  -0.64   0.01   0.69   2.16  309.0   1.01\n",
      "Mtrue_std[721]   -0.98    0.15   2.33  -8.89  -1.33  -0.54    0.2   1.45  227.0   1.01\n",
      "Mtrue_std[722]    0.23    0.03    1.2  -1.88  -0.46   0.18   0.83   2.48 1327.0    1.0\n",
      "Mtrue_std[723]    0.35    0.03   1.08  -1.54  -0.36   0.32   1.01   2.41 1347.0    1.0\n",
      "Mtrue_std[724]   -1.03    0.14   2.18  -7.94  -1.41  -0.61   0.13    1.5  226.0   1.03\n",
      "Mtrue_std[725]    0.69    0.12   1.66  -1.44   -0.2   0.48    1.2   5.63  197.0   1.02\n",
      "Mtrue_std[726]    0.12    0.02   0.95  -1.75  -0.49   0.13   0.77   1.93 2376.0    1.0\n",
      "Mtrue_std[727]     0.1    0.03   1.06  -1.83  -0.55   0.06   0.73    2.0 1334.0    1.0\n",
      "Mtrue_std[728]   -0.16    0.02   1.04  -2.13  -0.82  -0.13    0.5    1.9 2280.0    1.0\n",
      "Mtrue_std[729]   -0.41    0.04   1.26   -2.7  -1.05  -0.35   0.32   1.61 1054.0    1.0\n",
      "Mtrue_std[730]   -0.72    0.18   2.42  -8.04   -1.1  -0.32   0.35   1.85  191.0   1.03\n",
      "Mtrue_std[731]   -0.08    0.02   1.05  -2.12  -0.73  -0.08   0.56   1.93 2107.0    1.0\n",
      "Mtrue_std[732]    0.13    0.02   0.97  -1.77  -0.51   0.13    0.8   1.94 2582.0    1.0\n",
      "Mtrue_std[733]   -0.33    0.04   1.16  -2.47  -0.94   -0.3   0.35   1.63  716.0    1.0\n",
      "Mtrue_std[734]   -0.18    0.03   1.13  -2.34  -0.82  -0.16    0.5    1.8 2039.0    1.0\n",
      "Mtrue_std[735]    0.31    0.03   1.17  -1.73  -0.43   0.27   0.97   2.58 1240.0    1.0\n",
      "Mtrue_std[736]   -0.03    0.04   1.27  -2.28  -0.71  -0.06   0.61   2.14  922.0    1.0\n",
      "Mtrue_std[737]     0.1    0.02   0.98  -1.78  -0.58    0.1   0.73   1.97 1815.0    1.0\n",
      "Mtrue_std[738]   -0.03     0.1   1.84  -2.42  -0.77  -0.05   0.64    2.4  331.0   1.02\n",
      "Mtrue_std[739]  9.7e-3    0.08   1.47  -2.24  -0.69   0.03   0.72   2.13  304.0   1.01\n",
      "Mtrue_std[740]    0.26    0.04   1.25  -1.77  -0.47   0.17   0.87    2.8  938.0    1.0\n",
      "Mtrue_std[741]     0.2    0.04   1.17   -1.8  -0.51   0.13   0.82   2.43  754.0    1.0\n",
      "Mtrue_std[742]    0.07    0.08    1.7  -2.33  -0.71   0.05   0.76   2.95  439.0   1.01\n",
      "Mtrue_std[743]   -0.08    0.02    1.2  -2.17  -0.74  -0.08   0.58   2.06 2350.0    1.0\n",
      "Mtrue_std[744]    0.02    0.04   1.23  -2.06  -0.74  -0.04   0.67   2.42  877.0    1.0\n",
      "Mtrue_std[745]   -0.45    0.06   1.32  -3.04  -1.05  -0.39   0.31   1.58  478.0   1.01\n",
      "Mtrue_std[746]    0.11    0.03   1.07  -1.95  -0.52    0.1   0.72   2.15 1360.0    1.0\n",
      "Mtrue_std[747]    0.02    0.03   1.13  -2.06  -0.67   0.05   0.72   2.16 1495.0    1.0\n",
      "Mtrue_std[748]    0.74    0.06   1.31  -1.18  -0.03    0.6   1.25   4.35  504.0    1.0\n",
      "Mtrue_std[749]   -0.43    0.05   1.22  -2.72  -0.96  -0.36   0.28   1.56  569.0   1.01\n",
      "Mtrue_std[750]    0.46    0.04   1.23  -1.53  -0.27    0.4   1.06   3.25  910.0    1.0\n",
      "Mtrue_std[751]    0.07    0.06   1.22  -2.01  -0.56   0.08    0.7   2.24  430.0   1.01\n",
      "Mtrue_std[752]    0.27    0.02   0.97   -1.5  -0.37   0.25   0.86   2.28 1847.0    1.0\n",
      "Mtrue_std[753] -8.2e-3    0.02   1.07  -2.01  -0.68 9.2e-3   0.67   2.01 2121.0    1.0\n",
      "Mtrue_std[754]    0.07    0.02   1.04  -1.97  -0.59   0.04   0.74   2.13 2325.0    1.0\n",
      "Mtrue_std[755]    1.93    0.16    3.0  -1.08   0.22   0.95   1.98  10.61  339.0   1.01\n",
      "Mtrue_std[756]    1.48    0.26   3.19   -1.6  -0.16   0.59    1.5  11.87  147.0   1.01\n",
      "Mtrue_std[757]   -0.15    0.02   1.01  -2.15  -0.77  -0.13   0.49   1.75 1873.0    1.0\n",
      "Mtrue_std[758]   -0.14    0.05   1.29  -2.43  -0.83  -0.05   0.65   1.98  562.0   1.02\n",
      "Mtrue_std[759]   -0.34    0.08   1.57  -3.25  -0.94  -0.19   0.51   1.78  422.0   1.01\n",
      "Mtrue_std[760]   -0.19    0.03   1.12   -2.5  -0.81  -0.18   0.48   1.81 1252.0    1.0\n",
      "Mtrue_std[761]   10.61    0.44   5.33    0.2   7.76  11.09  14.09  20.68  149.0   1.01\n",
      "Mtrue_std[762]   -0.29    0.06   1.25  -2.84   -0.8  -0.21   0.41   1.61  375.0   1.02\n",
      "Mtrue_std[763]     0.9    0.14   2.21  -1.58  -0.22   0.51    1.3   8.22  263.0   1.02\n",
      "Mtrue_std[764]   -0.25    0.03   1.16  -2.56  -0.89  -0.27   0.45   1.88 1644.0    1.0\n",
      "Mtrue_std[765]   -0.08    0.03   1.03  -2.18  -0.68  -0.03   0.57   1.87 1621.0    1.0\n",
      "Mtrue_std[766]   -0.31    0.02   1.09  -2.41  -1.01  -0.31   0.41   1.77 2400.0    1.0\n",
      "Mtrue_std[767]    0.11    0.04   1.13  -1.84  -0.56   0.09   0.71   2.23  643.0    1.0\n",
      "Mtrue_std[768]   -0.15    0.02   0.98  -2.06  -0.82  -0.12    0.5   1.82 3278.0    1.0\n",
      "Mtrue_std[769] -3.2e-3    0.03   1.06  -2.08  -0.63  -0.01   0.65   1.99 1114.0    1.0\n",
      "Mtrue_std[770]    0.03    0.03    1.1  -2.04  -0.62   0.02    0.7   2.09 1352.0    1.0\n",
      "Mtrue_std[771]    0.33    0.05   1.18  -1.54  -0.39   0.29   0.97   2.35  506.0   1.01\n",
      "Mtrue_std[772]    0.46    0.04   1.14  -1.42  -0.21   0.41   1.05   2.73  950.0    1.0\n",
      "Mtrue_std[773]   -0.11    0.06   1.22  -2.33  -0.73  -0.07   0.59   1.95  490.0   1.01\n",
      "Mtrue_std[774]   -1.95    0.12    2.7  -9.44   -2.3  -1.13  -0.35   0.98  499.0   1.01\n",
      "Mtrue_std[775]    0.04    0.03   1.15  -1.97  -0.67   0.01   0.69   2.23 1134.0    1.0\n",
      "Mtrue_std[776]   -0.37    0.04   1.07   -2.5  -0.94  -0.32   0.31   1.52  705.0    1.0\n",
      "Mtrue_std[777]    0.35    0.05   1.26  -1.65  -0.39   0.28   0.95   3.19  615.0    1.0\n",
      "Mtrue_std[778]    0.46    0.05   1.44  -1.75  -0.31   0.35   0.99   3.83  758.0    1.0\n",
      "Mtrue_std[779]    1.05    0.18   2.37  -1.39  -0.18   0.54   1.36   8.78  178.0   1.02\n",
      "Mtrue_std[780]   -0.44    0.05   1.25  -2.84  -1.03  -0.33   0.32   1.52  678.0    1.0\n",
      "Mtrue_std[781]   -0.46    0.08   1.44  -3.32   -1.0  -0.33   0.32   1.66  318.0   1.01\n",
      "Mtrue_std[782]   -0.24    0.03   1.06  -2.26   -0.9  -0.19   0.45   1.72 1099.0    1.0\n",
      "Mtrue_std[783]   -0.05    0.03   1.14  -2.28  -0.76  -0.04   0.65   2.06 1665.0    1.0\n",
      "Mtrue_std[784]   -0.32    0.05   1.24  -2.85  -0.98  -0.25   0.48    1.7  640.0    1.0\n",
      "Mtrue_std[785]   -0.03    0.04   1.16  -1.95  -0.66  -0.02   0.65   2.02  881.0    1.0\n",
      "Mtrue_std[786]   -0.28    0.04   1.26  -2.67  -0.93  -0.23   0.46   1.85  810.0    1.0\n",
      "Mtrue_std[787]   -0.16    0.06   1.33  -2.65  -0.86  -0.12   0.66   2.08  432.0   1.01\n",
      "Mtrue_std[788]    6.23    0.34   4.37  -0.16   1.75   6.74    9.4  14.36  165.0   1.04\n",
      "Mtrue_std[789]   -0.58    0.07   1.35  -4.33  -1.16  -0.45   0.21    1.5  398.0   1.01\n",
      "Mtrue_std[790]   -0.19    0.04   1.15  -2.39   -0.8  -0.16   0.51   1.84  830.0    1.0\n",
      "Mtrue_std[791]    0.17    0.03   1.08  -1.88  -0.48   0.16   0.81   2.26 1483.0    1.0\n",
      "Mtrue_std[792]  6.0e-3    0.13    1.8  -2.76  -0.69-2.5e-3    0.7   2.43  202.0   1.02\n",
      "Mtrue_std[793]   -0.36    0.04   1.23  -2.79  -0.95  -0.28   0.35   1.71  813.0    1.0\n",
      "Mtrue_std[794]   -0.46    0.03   1.04  -2.66  -1.05  -0.45   0.23   1.43 1648.0    1.0\n",
      "Mtrue_std[795]    0.02    0.02   1.08  -1.99  -0.68   0.02   0.73   1.99 2123.0    1.0\n",
      "Mtrue_std[796]    0.14    0.02   1.02  -1.86  -0.54    0.1   0.82   2.17 1777.0    1.0\n",
      "Mtrue_std[797]    0.47    0.09   1.46  -1.62  -0.39    0.3   1.03   4.68  254.0   1.01\n",
      "Mtrue_std[798]    0.87    0.11   1.91  -1.43  -0.16   0.54   1.32    7.1  278.0   1.02\n",
      "Mtrue_std[799]    0.71    0.04   1.23  -1.37  -0.03   0.59   1.25   3.86  861.0    1.0\n",
      "Mtrue_std[800]  5.1e-3    0.03   1.18  -2.12  -0.69   0.01   0.69   2.12 1312.0    1.0\n",
      "Mtrue_std[801]    0.69    0.03   1.04  -1.16  -0.01   0.66   1.26   2.83 1332.0    1.0\n",
      "Mtrue_std[802]   -0.04    0.02   0.98   -1.9  -0.69  -0.06   0.62    1.8 1582.0    1.0\n",
      "Mtrue_std[803] -1.6e-5    0.02   1.03  -2.08  -0.64   0.02   0.68   1.94 2000.0    1.0\n",
      "Mtrue_std[804]   -6.32    0.36   4.84 -15.84  -9.88  -6.64  -1.54   0.45  180.0   1.01\n",
      "Mtrue_std[805]    0.13    0.08   1.62  -2.56  -0.67   0.03   0.78   3.27  393.0   1.01\n",
      "Mtrue_std[806]    0.03    0.03   0.98  -1.86   -0.6   0.03   0.65   1.85 1455.0    1.0\n",
      "Mtrue_std[807]    0.08    0.02   0.91  -1.71  -0.55   0.09   0.67   1.85 1773.0    1.0\n",
      "Mtrue_std[808]   -0.16    0.02   1.08   -2.4  -0.79  -0.13   0.54   1.89 1881.0    1.0\n",
      "Mtrue_std[809]   -0.18    0.02   1.06   -2.2  -0.84  -0.17   0.49   1.97 2132.0    1.0\n",
      "Mtrue_std[810]    0.18    0.02   1.08  -1.78  -0.51   0.15   0.86   2.31 2558.0    1.0\n",
      "Mtrue_std[811]    -1.2    0.14   2.34  -8.49  -1.45  -0.65  -0.01   1.36  275.0   1.02\n",
      "Mtrue_std[812]    0.06    0.03   1.37  -2.14   -0.7 2.1e-4   0.72   2.56 1537.0    1.0\n",
      "Mtrue_std[813]   -0.06    0.02   0.98   -2.0   -0.7  -0.07   0.62   1.79 2267.0    1.0\n",
      "Mtrue_std[814]     0.1    0.03   1.07  -1.81   -0.6    0.1   0.73   2.07 1072.0    1.0\n",
      "Mtrue_std[815]    0.17    0.07   1.32  -1.86  -0.57   0.06   0.76   2.59  330.0   1.01\n",
      "Mtrue_std[816]    -0.1    0.04   1.43  -2.68  -0.74  -0.05   0.66    2.2 1063.0    1.0\n",
      "Mtrue_std[817]   -0.47    0.14   1.59  -4.03  -1.02  -0.31   0.37   1.75  138.0   1.03\n",
      "Mtrue_std[818]    0.24    0.05    1.1  -1.64  -0.47    0.2   0.88   2.21  531.0   1.01\n",
      "Mtrue_std[819]    0.02    0.02   1.01  -1.94  -0.61 2.1e-3    0.6   2.05 2391.0    1.0\n",
      "Mtrue_std[820]    0.09    0.02   1.05   -1.9   -0.6   0.07   0.76   2.13 2311.0    1.0\n",
      "Mtrue_std[821]   -0.36    0.04   1.11  -2.49  -0.96  -0.32   0.31   1.59  927.0    1.0\n",
      "Mtrue_std[822]    0.34    0.05   1.28  -1.73  -0.42   0.24   0.97   2.67  648.0    1.0\n",
      "Mtrue_std[823]   -0.45    0.04   1.15  -2.82  -1.04  -0.39   0.27   1.49  761.0   1.01\n",
      "Mtrue_std[824]    0.05    0.03    1.1  -2.13  -0.64   0.03   0.72   2.18 1769.0    1.0\n",
      "Mtrue_std[825]    0.61    0.04   1.24  -1.37  -0.13   0.53   1.22   3.39  782.0    1.0\n",
      "Mtrue_std[826]    1.78    0.19   2.68  -0.95   0.29   0.99   1.91   9.89  204.0   1.04\n",
      "Mtrue_std[827]    1.09    0.31   2.53  -1.44  -0.16   0.53   1.35  10.12   69.0   1.07\n",
      "Mtrue_std[828]   -0.68    0.16   2.12  -7.29  -1.12  -0.33    0.4   1.77  169.0   1.04\n",
      "Mtrue_std[829]    0.05    0.02   0.95  -1.84  -0.61   0.05   0.68   1.89 2385.0    1.0\n",
      "Mtrue_std[830]   -0.31    0.04   1.15  -2.57  -0.96  -0.26    0.4   1.76  924.0    1.0\n",
      "Mtrue_std[831]    1.61    0.11   1.95  -0.77   0.43   1.12   2.04   7.25  315.0   1.02\n",
      "Mtrue_std[832]    0.56    0.03   1.05  -1.25  -0.11   0.53    1.2   2.65 1416.0    1.0\n",
      "Mtrue_std[833]    0.34    0.02   0.94  -1.44  -0.29   0.31   0.94   2.23 2226.0    1.0\n",
      "Mtrue_std[834]   -0.25    0.02   1.05  -2.36  -0.85  -0.21    0.4   1.65 2563.0    1.0\n",
      "Mtrue_std[835]   -0.33    0.03   1.05  -2.37  -0.98  -0.31   0.33   1.67 1281.0    1.0\n",
      "Mtrue_std[836]    0.17    0.03    1.1  -1.74  -0.55   0.16    0.8    2.2 1451.0    1.0\n",
      "Mtrue_std[837]   -0.41    0.06   1.22  -2.72  -1.08  -0.35   0.38   1.62  489.0    1.0\n",
      "Mtrue_std[838]   -0.37    0.06   1.42  -3.13  -0.93  -0.26    0.4   1.84  621.0    1.0\n",
      "Mtrue_std[839]    0.46    0.03   0.99  -1.34   -0.2   0.42   1.07   2.43 1530.0    1.0\n",
      "Mtrue_std[840]   -0.03    0.03    1.1  -2.06   -0.7  -0.02    0.6   1.86 1499.0    1.0\n",
      "Mtrue_std[841]    0.08    0.03    1.1  -1.95  -0.59   0.04   0.71   2.15 1092.0    1.0\n",
      "Mtrue_std[842]    0.37    0.04   1.05  -1.54  -0.27   0.33   0.92   2.35  831.0    1.0\n",
      "Mtrue_std[843]    0.15    0.02   1.02  -1.72  -0.55   0.16   0.82    2.1 2717.0    1.0\n",
      "Mtrue_std[844]    -0.1    0.02   0.98   -2.1  -0.74  -0.09   0.56   1.72 2673.0    1.0\n",
      "Mtrue_std[845]    0.28    0.04    1.2  -1.66  -0.44   0.22   0.88   2.44  847.0    1.0\n",
      "Mtrue_std[846]    0.68    0.29   2.26   -1.7  -0.36   0.28   1.03   8.18   59.0   1.07\n",
      "Mtrue_std[847]    0.16    0.03   1.16  -1.89  -0.53   0.18   0.86    2.2 1185.0    1.0\n",
      "Mtrue_std[848]    0.33    0.05   1.31  -1.75  -0.42   0.25   0.94    2.8  578.0    1.0\n",
      "Mtrue_std[849]    0.22    0.03   1.08  -1.86  -0.46   0.21   0.81   2.35 1536.0    1.0\n",
      "Mtrue_std[850]     0.1    0.02   0.98  -1.84  -0.53   0.11   0.68   2.06 2381.0    1.0\n",
      "Mtrue_std[851]   -0.88    0.05   1.38  -4.62  -1.42  -0.71  -0.08   1.18  724.0    1.0\n",
      "Mtrue_std[852]   -0.22    0.03   1.22  -2.61  -0.82  -0.17   0.46   1.87 1447.0    1.0\n",
      "Mtrue_std[853]    0.05    0.02   1.02  -1.86  -0.65   0.04   0.73   2.13 2213.0    1.0\n",
      "Mtrue_std[854]    -0.4    0.08   1.25  -2.68  -0.96  -0.31   0.31   1.55  278.0   1.01\n",
      "Mtrue_std[855]    0.09    0.02   1.09  -1.98  -0.61   0.11    0.8   2.12 2766.0    1.0\n",
      "Mtrue_std[856]    0.11    0.12   1.73  -2.23  -0.67   0.06   0.77   3.46  195.0   1.02\n",
      "Mtrue_std[857]   -0.12    0.05   1.34  -2.75  -0.73  -0.02   0.68   1.99  610.0   1.01\n",
      "Mtrue_std[858]    0.07    0.02   0.89  -1.66  -0.53   0.03   0.66   1.91 2830.0    1.0\n",
      "Mtrue_std[859]   -0.04    0.05   1.42  -2.21  -0.66   0.02   0.66   2.08  856.0    1.0\n",
      "Mtrue_std[860]    0.03    0.08    1.4  -2.14  -0.73  -0.02   0.69    2.4  304.0   1.01\n",
      "Mtrue_std[861]    0.12    0.02   0.98  -1.81  -0.53   0.12   0.76    2.0 1698.0    1.0\n",
      "Mtrue_std[862]   -0.07    0.02   0.97  -1.89  -0.65  -0.09   0.52   1.74 2098.0    1.0\n",
      "Mtrue_std[863]   -0.64    0.04   1.24  -3.85  -1.16  -0.51   0.06   1.33  813.0    1.0\n",
      "Mtrue_std[864]    0.05    0.02   1.06  -1.98   -0.6   0.04   0.71   2.13 1810.0    1.0\n",
      "Mtrue_std[865]  9.1e-3    0.02   0.99  -2.01   -0.6   0.02   0.66   1.91 1862.0    1.0\n",
      "Mtrue_std[866]    0.22    0.08   1.51  -1.94  -0.54   0.13   0.82   2.91  392.0   1.01\n",
      "Mtrue_std[867]    0.03    0.03   1.06  -1.99  -0.65   0.02   0.67   2.06 1356.0    1.0\n",
      "Mtrue_std[868]   -0.63    0.08   1.58  -4.66  -1.16  -0.45    0.2    1.6  428.0   1.02\n",
      "Mtrue_std[869]    0.02    0.04   1.17  -2.19  -0.58   0.04   0.67   2.02  753.0    1.0\n",
      "Mtrue_std[870]    -0.8    0.07   1.47   -5.3  -1.32  -0.59   0.08   1.22  435.0   1.01\n",
      "Mtrue_std[871]    0.17    0.02   1.01  -1.63  -0.51   0.16   0.85   2.12 2743.0    1.0\n",
      "Mtrue_std[872]   -0.01    0.02   1.01  -2.07  -0.65 1.2e-3   0.65   1.91 2799.0    1.0\n",
      "Mtrue_std[873]   -1.01    0.12   2.04  -7.68  -1.43  -0.61   0.11   1.29  308.0   1.03\n",
      "Mtrue_std[874]    0.53     0.1    1.5  -1.62  -0.33   0.41   1.16   3.85  220.0   1.03\n",
      "Mtrue_std[875]    0.57    0.04    1.2  -1.41  -0.15   0.47   1.16   3.22  956.0    1.0\n",
      "Mtrue_std[876]   -0.04    0.02    1.0  -1.92  -0.69  -0.04   0.58   1.98 1640.0    1.0\n",
      "Mtrue_std[877]   -0.27    0.02   0.94   -2.1  -0.83  -0.24   0.34   1.43 2471.0    1.0\n",
      "Mtrue_std[878]    -0.3    0.04   1.08  -2.51   -0.9  -0.28    0.4   1.61  709.0    1.0\n",
      "Mtrue_std[879]   -0.47    0.02   0.99  -2.31  -1.07  -0.46   0.17   1.41 1778.0    1.0\n",
      "Mtrue_std[880]     0.5    0.04   1.16   -1.4   -0.2   0.46   1.09   2.58  722.0    1.0\n",
      "Mtrue_std[881]    0.05    0.06   1.25  -2.29  -0.56   0.05    0.7   2.12  441.0    1.0\n",
      "Mtrue_std[882]   -0.37    0.03   1.18  -2.43  -0.98  -0.32   0.36   1.55 1198.0    1.0\n",
      "Mtrue_std[883]   -0.05    0.03   1.12  -2.18  -0.72  -0.02   0.65   2.04 1115.0    1.0\n",
      "Mtrue_std[884]    0.36    0.04   1.11   -1.6  -0.36   0.33   0.98   2.44  960.0    1.0\n",
      "Mtrue_std[885]   -0.21    0.02   1.03  -2.26  -0.85  -0.17   0.45   1.76 2096.0    1.0\n",
      "Mtrue_std[886]    4.49    0.46   5.09  -1.01   0.55   1.87   8.43  15.76  123.0   1.06\n",
      "Mtrue_std[887]   -0.25    0.03   1.13  -2.35  -0.88  -0.21   0.46   1.76 1060.0    1.0\n",
      "Mtrue_std[888]   -0.05    0.03   1.19  -2.23  -0.71  -0.04    0.7   2.06 1479.0    1.0\n",
      "Mtrue_std[889]   -0.26    0.07   1.33   -2.7  -0.85  -0.19   0.48   1.76  370.0   1.01\n",
      "Mtrue_std[890]    -0.3    0.06   1.42  -3.29  -0.95  -0.18   0.53   1.94  636.0    1.0\n",
      "Mtrue_std[891]    0.09    0.04   1.16  -1.93  -0.62   0.05   0.74   2.27  962.0    1.0\n",
      "Mtrue_std[892]    0.03    0.03   1.27  -2.13  -0.69 7.0e-3   0.74   2.23 1724.0    1.0\n",
      "Mtrue_std[893]    0.25    0.02   0.98  -1.68  -0.39   0.25   0.87   2.18 2660.0    1.0\n",
      "Mtrue_std[894]    0.16    0.03    1.1  -1.85  -0.55   0.14    0.9    2.2 1645.0    1.0\n",
      "Mtrue_std[895]    0.03    0.38    2.8  -5.74   -0.8  -0.01   0.71   7.34   53.0   1.08\n",
      "Mtrue_std[896]    -0.3    0.03   1.17  -2.37  -0.93  -0.26   0.41   1.66 1478.0    1.0\n",
      "Mtrue_std[897]   -0.11    0.03   1.26  -2.37  -0.82  -0.07   0.62   2.07 1576.0    1.0\n",
      "Mtrue_std[898]   -0.34    0.06   1.22  -2.63  -0.88  -0.23   0.37   1.62  366.0   1.01\n",
      "Mtrue_std[899]   -0.01    0.02   0.98  -1.96  -0.66   0.02   0.66   1.83 2152.0    1.0\n",
      "Mtrue_std[900]   -0.31    0.16    2.1  -6.46  -0.72  -0.03   0.64   2.14  173.0   1.02\n",
      "Mtrue_std[901]     0.9    0.14   2.11  -1.55  -0.21   0.52   1.31   7.76  228.0   1.02\n",
      "Mtrue_std[902]   -0.07    0.07    1.4  -2.45  -0.74  -0.03   0.68   2.21  399.0   1.01\n",
      "Mtrue_std[903]   -0.05    0.01   0.98  -1.87   -0.7  -0.05   0.54   1.82 4524.0    1.0\n",
      "Mtrue_std[904]   -0.21    0.04   1.17  -2.42   -0.8  -0.16   0.51   1.83  889.0    1.0\n",
      "Mtrue_std[905]    0.09    0.02   1.07  -1.95  -0.61   0.08   0.71   2.23 2321.0    1.0\n",
      "Mtrue_std[906]   -0.03    0.04   1.18  -2.28   -0.7 9.6e-3   0.62    2.1 1057.0    1.0\n",
      "Mtrue_std[907]   -0.07    0.04   1.18  -2.17  -0.78  -0.08   0.61   2.05  983.0    1.0\n",
      "Mtrue_std[908]   -0.01    0.02    1.2  -2.25  -0.68-8.7e-3   0.66   2.17 2451.0    1.0\n",
      "Mtrue_std[909]    0.02    0.02   1.07  -2.02  -0.66   0.06   0.66   2.14 3299.0    1.0\n",
      "Mtrue_std[910]     0.4     0.1   1.62  -2.01  -0.44   0.26   0.94   5.29  261.0   1.01\n",
      "Mtrue_std[911]   -3.05    0.18   3.43 -11.39  -5.39  -1.66  -0.61   0.83  350.0   1.01\n",
      "Mtrue_std[912]    0.36    0.22   2.28  -2.14  -0.64   0.07   0.83    7.8  111.0   1.03\n",
      "Mtrue_std[913]     0.3    0.05   1.41  -1.82  -0.42   0.22   0.94   2.96  679.0    1.0\n",
      "Mtrue_std[914]   -0.03    0.05   1.21  -2.22  -0.76  -0.06   0.64   2.07  639.0    1.0\n",
      "Mtrue_std[915]    0.38    0.08   1.44   -1.8  -0.42   0.25   0.99   3.85  341.0   1.01\n",
      "Mtrue_std[916]   -1.37    0.09   1.84  -6.87  -1.78  -0.98  -0.32   0.91  423.0   1.02\n",
      "Mtrue_std[917]    0.22    0.03   1.17   -1.7  -0.48   0.18   0.84   2.37 1517.0    1.0\n",
      "Mtrue_std[918]   -0.52    0.08   1.59  -4.73  -1.08  -0.36   0.34   1.76  365.0   1.01\n",
      "Mtrue_std[919]   -0.99    0.06   1.35  -4.73   -1.5  -0.82   -0.2   0.99  522.0    1.0\n",
      "Mtrue_std[920]    0.06    0.02   0.92  -1.74  -0.55   0.08   0.65    1.9 2773.0    1.0\n",
      "Mtrue_std[921]   -1.17    0.15    2.1  -7.92  -1.44  -0.69  -0.05   1.12  193.0   1.01\n",
      "Mtrue_std[922]    0.56    0.11   1.77  -1.66  -0.42   0.36   1.13   5.73  265.0   1.01\n",
      "Mtrue_std[923]    0.19    0.02   0.99  -1.68  -0.46   0.18   0.83   2.16 2333.0    1.0\n",
      "Mtrue_std[924]    0.97    0.11   2.19  -1.42  -0.22   0.59   1.37   8.05  379.0    1.0\n",
      "Mtrue_std[925]   -0.17    0.02   1.03  -2.18  -0.79  -0.18    0.5   1.85 2437.0    1.0\n",
      "Mtrue_std[926]    0.06    0.03   1.04  -1.86   -0.6   0.04    0.7   2.15 1654.0    1.0\n",
      "Mtrue_std[927]    1.47    0.18   2.59   -1.2   0.05   0.77   1.72    9.3  202.0   1.03\n",
      "Mtrue_std[928]    0.63    0.05   1.29  -1.41  -0.15   0.54   1.25   3.24  742.0    1.0\n",
      "Mtrue_std[929]    0.58    0.24   2.66  -2.07  -0.62   0.11   0.94   9.34  121.0   1.03\n",
      "Mtrue_std[930]    -0.6    0.03   1.17  -3.41  -1.18  -0.51   0.12   1.27 1322.0    1.0\n",
      "Mtrue_std[931]    0.23    0.02   0.92  -1.59  -0.38   0.27   0.86   2.02 2815.0    1.0\n",
      "Mtrue_std[932]    0.14    0.02   0.93  -1.63  -0.51   0.15   0.76   1.93 2143.0    1.0\n",
      "Mtrue_std[933]    0.25    0.02   0.89  -1.54  -0.35   0.24   0.85   2.04 2900.0    1.0\n",
      "Mtrue_std[934]     1.0     0.1   2.01  -1.36   -0.1   0.61   1.41   7.54  389.0   1.01\n",
      "Mtrue_std[935]    0.28    0.04   1.11  -1.68  -0.41   0.26   0.88   2.38  941.0    1.0\n",
      "Mtrue_std[936]   -0.25    0.04   1.28  -2.56   -0.9  -0.19   0.49   1.89 1036.0    1.0\n",
      "Mtrue_std[937]    -0.1    0.03   1.09  -2.17  -0.76  -0.07   0.57   1.91 1140.0    1.0\n",
      "Mtrue_std[938]   -0.05    0.03   1.07  -2.06  -0.69  -0.05   0.58    2.0 1790.0    1.0\n",
      "Mtrue_std[939]   -3.11     0.4   4.49 -14.36  -5.52  -1.14  -0.21   1.21  125.0   1.01\n",
      "Mtrue_std[940]   -1.07    0.14   2.19  -8.17  -1.43  -0.63   0.12   1.35  258.0   1.02\n",
      "Mtrue_std[941]    0.18    0.02   1.08   -1.8  -0.45   0.17   0.84   2.24 2129.0    1.0\n",
      "Mtrue_std[942]    0.85    0.05   1.47  -1.23 1.2e-3   0.67   1.41   4.69 1051.0    1.0\n",
      "Mtrue_std[943]    0.41    0.13   1.66  -1.85  -0.44   0.23   0.99   4.35  152.0   1.02\n",
      "Mtrue_std[944]   -0.05    0.03   1.14  -2.15  -0.73  -0.03   0.67   2.08 1376.0    1.0\n",
      "Mtrue_std[945]    0.35    0.07   1.35  -1.69  -0.42   0.27   0.95   2.69  375.0    1.0\n",
      "Mtrue_std[946]    0.12    0.02   1.07   -1.9  -0.52   0.12   0.77   2.18 2196.0    1.0\n",
      "Mtrue_std[947]    1.06     0.1   2.08  -1.32  -0.01   0.72    1.4   7.81  477.0    1.0\n",
      "Mtrue_std[948]    0.26    0.03   1.14   -1.8  -0.49   0.21   0.93   2.54 1398.0    1.0\n",
      "Mtrue_std[949]    0.45    0.06   1.54  -1.67  -0.39   0.34   1.02   3.41  738.0    1.0\n",
      "Mtrue_std[950]   -0.11    0.02   1.09  -2.17  -0.77   -0.1   0.56   1.98 2223.0    1.0\n",
      "Mtrue_std[951]     0.2    0.09   1.56   -2.1  -0.48   0.15   0.81   2.73  322.0   1.01\n",
      "Mtrue_std[952]    0.45    0.04   1.08  -1.43  -0.21    0.4   1.02   2.47  672.0    1.0\n",
      "Mtrue_std[953]    0.33    0.02    1.1  -1.71  -0.39   0.31   0.99    2.5 2175.0    1.0\n",
      "Mtrue_std[954]   -0.16    0.03   1.04  -2.07  -0.74  -0.15    0.5   1.73 1110.0    1.0\n",
      "Mtrue_std[955]    0.06    0.03   1.22  -2.08  -0.67   0.02   0.69   2.51 1372.0    1.0\n",
      "Mtrue_std[956]   -0.73    0.03   1.07  -2.95  -1.28  -0.64  -0.05   1.08 1486.0    1.0\n",
      "Mtrue_std[957]     0.4    0.05   1.31  -1.56  -0.33   0.32   0.98   2.78  725.0    1.0\n",
      "Mtrue_std[958]   -0.03    0.02    1.1  -2.16  -0.69  -0.02   0.62   2.07 2265.0    1.0\n",
      "Mtrue_std[959]  6.3e-3    0.04   1.14  -2.06  -0.66   0.02   0.69   2.11  961.0    1.0\n",
      "Mtrue_std[960]    0.62    0.08   1.71  -1.67  -0.27   0.42   1.12   5.11  443.0   1.01\n",
      "Mtrue_std[961]    0.05    0.03   1.16  -2.04  -0.64   0.07   0.73   2.15 1242.0    1.0\n",
      "Mtrue_std[962]    0.24    0.02   1.03  -1.63  -0.39    0.2   0.81    2.2 1911.0    1.0\n",
      "Mtrue_std[963]   -0.12    0.03   1.14  -2.35  -0.79  -0.08    0.6   1.88 1478.0    1.0\n",
      "Mtrue_std[964]    0.13    0.02   1.03  -1.82  -0.55   0.12   0.77   2.18 2066.0    1.0\n",
      "Mtrue_std[965]   -0.42    0.06   1.28  -2.78  -0.97  -0.34   0.29   1.55  480.0    1.0\n",
      "Mtrue_std[966]    -2.3    0.18    2.8   -9.6  -2.74  -1.29  -0.52    0.8  245.0    1.0\n",
      "Mtrue_std[967]   -0.19    0.06   1.35  -2.33  -0.78  -0.15   0.52   1.95  545.0   1.01\n",
      "Mtrue_std[968]  8.6e-3    0.02   1.07  -1.97  -0.65-1.8e-3   0.67   1.96 1907.0    1.0\n",
      "Mtrue_std[969]    0.07    0.02   1.07  -1.94  -0.61   0.06   0.67   2.15 2277.0    1.0\n",
      "Mtrue_std[970]    2.76     0.3   4.08  -1.22   0.28   1.06   3.32  13.54  179.0   1.01\n",
      "Mtrue_std[971]    0.06    0.03   1.01  -1.88  -0.58   0.07   0.67   2.11 1571.0    1.0\n",
      "Mtrue_std[972]    0.07    0.06   1.19  -2.01  -0.63   0.06   0.71   2.42  438.0   1.01\n",
      "Mtrue_std[973]   -1.06    0.05   1.41  -5.09  -1.59  -0.85  -0.22   1.06  722.0    1.0\n",
      "Mtrue_std[974]   -1.43    0.16   2.57  -9.01  -1.61  -0.76  -0.06   1.23  261.0   1.02\n",
      "Mtrue_std[975]   -0.06    0.04   1.13  -2.28  -0.67  -0.02   0.63   1.93  714.0    1.0\n",
      "Mtrue_std[976]   -0.47    0.05   1.41  -3.87  -0.99  -0.32   0.25   1.57  697.0    1.0\n",
      "Mtrue_std[977]    0.15     0.1   2.01  -2.54  -0.77   0.03   0.79   4.99  373.0   1.02\n",
      "Mtrue_std[978]   -0.05    0.02   1.05  -1.92  -0.71  -0.04    0.6    1.9 2365.0    1.0\n",
      "Mtrue_std[979]    0.23    0.03   1.01  -1.67   -0.4    0.2   0.83   2.12  972.0    1.0\n",
      "Mtrue_std[980]    0.08    0.04   1.24  -1.97  -0.68-2.9e-3   0.74   2.41  939.0    1.0\n",
      "Mtrue_std[981]    0.25    0.06    1.6   -1.9  -0.51   0.14   0.83   2.62  622.0   1.02\n",
      "Mtrue_std[982]   -0.24    0.04   1.25  -2.64  -0.86  -0.19    0.5   1.94 1034.0    1.0\n",
      "Mtrue_std[983]     0.2    0.02    1.1  -1.75  -0.46   0.16   0.84   2.19 2040.0    1.0\n",
      "Mtrue_std[984]   -0.04    0.11   1.72  -2.17  -0.81  -0.11   0.58   2.45  229.0   1.02\n",
      "Mtrue_std[985]   -0.12    0.05   1.23  -2.49   -0.8  -0.11    0.6    2.1  628.0    1.0\n",
      "Mtrue_std[986]     0.4    0.04   1.15  -1.49  -0.33   0.32   0.99   2.71  723.0    1.0\n",
      "Mtrue_std[987]    0.69    0.08   1.58  -1.44  -0.16    0.5   1.21   5.45  378.0    1.0\n",
      "Mtrue_std[988]    0.13    0.03   1.11  -1.98  -0.54   0.11   0.79    2.4 1591.0    1.0\n",
      "Mtrue_std[989]   -0.63     0.1   1.63   -4.9  -1.17  -0.43   0.25   1.48  260.0   1.01\n",
      "Mtrue_std[990]    1.57    0.18   2.55  -1.24   0.08   0.86    1.8   8.75  191.0   1.04\n",
      "Mtrue_std[991]   -0.27    0.02   1.03  -2.21  -0.88  -0.24    0.4   1.66 1956.0    1.0\n",
      "Mtrue_std[992]   -0.37    0.07   1.35  -2.77  -0.97  -0.26   0.41   1.62  333.0   1.01\n",
      "Mtrue_std[993]  7.3e-4    0.03   1.06  -1.99  -0.67  -0.04   0.66   2.04 1301.0    1.0\n",
      "Mtrue_std[994]   -1.13    0.37   2.84 -11.31  -1.28   -0.5   0.22   1.45   60.0   1.05\n",
      "Mtrue_std[995]   -0.07    0.03   1.12  -2.17  -0.75  -0.06   0.65   2.01 1781.0    1.0\n",
      "Mtrue_std[996]    0.25    0.02   0.97  -1.65  -0.39   0.22   0.86   2.26 2999.0    1.0\n",
      "Mtrue_std[997]    0.47    0.07   1.57  -1.59  -0.32   0.29   0.96   4.27  489.0   1.01\n",
      "Mtrue_std[998]    1.93    0.23   3.09  -1.24    0.1   0.89   2.14  10.58  182.0   1.02\n",
      "Mtrue_std[999]   -0.13    0.02   0.99  -1.96  -0.77  -0.14   0.46   1.91 1885.0    1.0\n",
      "Mtrue_std[1000]  -0.01    0.02   1.02  -1.91  -0.73  -0.04   0.63   1.97 2085.0    1.0\n",
      "Mtrue[1]         -1.74  2.6e-3   0.06  -1.86  -1.76  -1.73   -1.7  -1.64  562.0   1.01\n",
      "Mtrue[2]         -1.75  1.5e-3   0.06  -1.86  -1.78  -1.75  -1.72  -1.65 1453.0    1.0\n",
      "Mtrue[3]         -1.77  3.0e-3   0.07  -1.98  -1.79  -1.76  -1.73  -1.67  587.0    1.0\n",
      "Mtrue[4]         -1.73  6.4e-3   0.08  -1.83  -1.77  -1.73   -1.7  -1.54  147.0   1.03\n",
      "Mtrue[5]         -1.72  1.5e-3   0.05   -1.8  -1.75  -1.72  -1.69  -1.61 1123.0    1.0\n",
      "Mtrue[6]         -1.74  8.4e-4   0.04  -1.84  -1.77  -1.74  -1.71  -1.66 2810.0    1.0\n",
      "Mtrue[7]         -1.72  1.3e-3   0.04   -1.8  -1.75  -1.72  -1.69  -1.63 1010.0    1.0\n",
      "Mtrue[8]         -1.74  1.5e-3   0.05  -1.83  -1.76  -1.74  -1.71  -1.65 1073.0    1.0\n",
      "Mtrue[9]         -1.75  1.3e-3   0.05  -1.85  -1.77  -1.74  -1.72  -1.66 1441.0    1.0\n",
      "Mtrue[10]        -1.75  1.1e-3   0.05  -1.85  -1.77  -1.74  -1.71  -1.65 2174.0    1.0\n",
      "Mtrue[11]        -1.75  1.2e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.66 1616.0    1.0\n",
      "Mtrue[12]        -1.76  2.9e-3   0.06  -1.91  -1.78  -1.75  -1.72  -1.67  431.0   1.01\n",
      "Mtrue[13]        -1.64  7.9e-3   0.13  -1.78  -1.72  -1.69  -1.61   -1.3  274.0   1.02\n",
      "Mtrue[14]        -1.75  1.3e-3   0.05  -1.84  -1.77  -1.75  -1.72  -1.66 1619.0    1.0\n",
      "Mtrue[15]        -1.74  1.0e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 2077.0    1.0\n",
      "Mtrue[16]        -1.72  1.2e-3   0.05  -1.81  -1.75  -1.72   -1.7  -1.63 1743.0    1.0\n",
      "Mtrue[17]        -1.84    0.01   0.14  -2.21  -1.87  -1.79  -1.75  -1.69  190.0    1.0\n",
      "Mtrue[18]        -1.74  1.7e-3   0.05  -1.85  -1.77  -1.74  -1.71  -1.64  986.0    1.0\n",
      "Mtrue[19]        -1.68  2.8e-3   0.07  -1.78  -1.73   -1.7  -1.66  -1.46  653.0    1.0\n",
      "Mtrue[20]        -1.72  2.0e-3   0.06  -1.81  -1.75  -1.72  -1.69  -1.59  812.0    1.0\n",
      "Mtrue[21]        -1.72  1.2e-3   0.05   -1.8  -1.75  -1.72  -1.69  -1.63 1383.0    1.0\n",
      "Mtrue[22]        -1.73  1.0e-3   0.04  -1.82  -1.76  -1.73  -1.71  -1.65 1919.0    1.0\n",
      "Mtrue[23]        -1.73  9.3e-4   0.05  -1.83  -1.76  -1.73   -1.7  -1.64 2693.0    1.0\n",
      "Mtrue[24]        -1.73  2.3e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63  544.0   1.01\n",
      "Mtrue[25]        -1.74  2.9e-3   0.06  -1.85  -1.77  -1.74  -1.71  -1.63  441.0    1.0\n",
      "Mtrue[26]        -1.74  1.1e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 2190.0    1.0\n",
      "Mtrue[27]        -1.78  3.3e-3   0.07  -1.97   -1.8  -1.77  -1.74  -1.68  422.0    1.0\n",
      "Mtrue[28]        -1.74  1.6e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.66  919.0    1.0\n",
      "Mtrue[29]        -1.74  1.0e-3   0.05  -1.84  -1.76  -1.74  -1.71  -1.65 2180.0    1.0\n",
      "Mtrue[30]        -1.74  1.1e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1886.0    1.0\n",
      "Mtrue[31]        -1.73  1.0e-3   0.05  -1.83  -1.76  -1.73   -1.7  -1.64 2282.0    1.0\n",
      "Mtrue[32]        -1.73  8.4e-4   0.05  -1.82  -1.76  -1.73   -1.7  -1.63 3040.0    1.0\n",
      "Mtrue[33]        -1.73  1.7e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.62  905.0    1.0\n",
      "Mtrue[34]        -1.73  1.7e-3   0.05  -1.82  -1.76  -1.73  -1.71  -1.65  769.0   1.01\n",
      "Mtrue[35]        -1.74  1.4e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 1115.0    1.0\n",
      "Mtrue[36]        -1.75  9.9e-4   0.04  -1.84  -1.77  -1.75  -1.72  -1.66 1894.0    1.0\n",
      "Mtrue[37]        -1.71  3.7e-3   0.07  -1.81  -1.75  -1.72  -1.69  -1.51  337.0   1.01\n",
      "Mtrue[38]        -1.76  1.2e-3   0.05  -1.85  -1.78  -1.75  -1.73  -1.68 1612.0    1.0\n",
      "Mtrue[39]        -1.71  5.9e-3   0.08  -1.81  -1.75  -1.72  -1.69  -1.48  176.0   1.02\n",
      "Mtrue[40]        -1.73  9.7e-4   0.04  -1.82  -1.76  -1.74  -1.71  -1.65 1845.0    1.0\n",
      "Mtrue[41]        -1.73  3.8e-3   0.06  -1.82  -1.76  -1.73   -1.7  -1.62  242.0   1.01\n",
      "Mtrue[42]        -1.72  3.5e-3   0.07  -1.82  -1.76  -1.73   -1.7  -1.54  425.0   1.01\n",
      "Mtrue[43]        -1.72  6.4e-3   0.09  -1.84  -1.76  -1.73   -1.7  -1.46  175.0   1.01\n",
      "Mtrue[44]        -1.77  5.5e-3   0.08   -2.0  -1.79  -1.76  -1.73  -1.67  206.0   1.02\n",
      "Mtrue[45]        -1.74  1.1e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.64 1959.0    1.0\n",
      "Mtrue[46]        -1.73  1.3e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63 1762.0    1.0\n",
      "Mtrue[47]        -1.71  4.1e-3   0.07  -1.81  -1.75  -1.72  -1.69  -1.49  317.0   1.02\n",
      "Mtrue[48]        -1.74  1.2e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.66 1585.0    1.0\n",
      "Mtrue[49]        -1.73  9.9e-4   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 2253.0    1.0\n",
      "Mtrue[50]        -1.75  1.1e-3   0.04  -1.83  -1.78  -1.75  -1.72  -1.67 1654.0    1.0\n",
      "Mtrue[51]        -1.72  1.4e-3   0.04   -1.8  -1.75  -1.72   -1.7  -1.63 1007.0    1.0\n",
      "Mtrue[52]        -1.75  1.2e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.66 1738.0    1.0\n",
      "Mtrue[53]        -1.75  1.6e-3   0.05  -1.85  -1.77  -1.74  -1.71  -1.66 1036.0    1.0\n",
      "Mtrue[54]        -1.73  2.2e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.62  588.0    1.0\n",
      "Mtrue[55]        -1.74  1.3e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 1478.0    1.0\n",
      "Mtrue[56]        -1.74  2.4e-3   0.06  -1.84  -1.77  -1.74  -1.71  -1.65  575.0   1.01\n",
      "Mtrue[57]        -1.73  1.6e-3   0.05  -1.82  -1.76  -1.73  -1.71  -1.64  913.0    1.0\n",
      "Mtrue[58]        -1.72  9.8e-4   0.05   -1.8  -1.75  -1.72  -1.69  -1.63 2098.0    1.0\n",
      "Mtrue[59]        -1.73  1.5e-3   0.05  -1.82  -1.75  -1.73   -1.7  -1.63 1104.0    1.0\n",
      "Mtrue[60]        -1.74  1.3e-3   0.05  -1.85  -1.77  -1.74  -1.71  -1.65 1679.0    1.0\n",
      "Mtrue[61]        -1.75  2.4e-3   0.06  -1.89  -1.78  -1.75  -1.72  -1.66  600.0    1.0\n",
      "Mtrue[62]        -1.75  1.9e-3   0.06  -1.89  -1.78  -1.75  -1.72  -1.66  944.0    1.0\n",
      "Mtrue[63]        -1.72  1.2e-3   0.05  -1.81  -1.75  -1.72   -1.7  -1.64 1388.0    1.0\n",
      "Mtrue[64]        -1.72  1.4e-3   0.05  -1.82  -1.75  -1.73   -1.7  -1.62 1347.0    1.0\n",
      "Mtrue[65]        -1.81  3.9e-3   0.09  -2.07  -1.83  -1.79  -1.76   -1.7  560.0   1.01\n",
      "Mtrue[66]        -1.74  1.4e-3   0.05  -1.83  -1.76  -1.74  -1.71  -1.64 1191.0    1.0\n",
      "Mtrue[67]        -1.74  2.1e-3   0.06  -1.86  -1.77  -1.74  -1.71  -1.64  796.0    1.0\n",
      "Mtrue[68]        -1.73  1.2e-3   0.05  -1.82  -1.76  -1.72   -1.7  -1.63 1684.0    1.0\n",
      "Mtrue[69]        -1.74  1.1e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 1674.0    1.0\n",
      "Mtrue[70]        -1.73  2.1e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63  698.0    1.0\n",
      "Mtrue[71]        -1.73  1.6e-3   0.04  -1.82  -1.76  -1.73   -1.7  -1.65  825.0    1.0\n",
      "Mtrue[72]         -1.8  6.2e-3   0.08  -2.05  -1.82  -1.78  -1.75   -1.7  179.0   1.04\n",
      "Mtrue[73]        -1.73  1.0e-3   0.04  -1.81  -1.76  -1.73   -1.7  -1.64 1875.0    1.0\n",
      "Mtrue[74]        -1.74  1.9e-3   0.06  -1.83  -1.77  -1.74  -1.71  -1.64  944.0    1.0\n",
      "Mtrue[75]        -1.74  9.9e-4   0.04  -1.82  -1.77  -1.74  -1.71  -1.65 1962.0    1.0\n",
      "Mtrue[76]        -1.72  3.8e-3   0.07  -1.83  -1.76  -1.73   -1.7  -1.59  307.0   1.01\n",
      "Mtrue[77]        -1.78  2.8e-3   0.07  -1.98   -1.8  -1.77  -1.74  -1.68  583.0    1.0\n",
      "Mtrue[78]        -1.75  2.4e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.66  518.0    1.0\n",
      "Mtrue[79]        -1.74  8.2e-4   0.04  -1.83  -1.77  -1.74  -1.71  -1.66 2922.0    1.0\n",
      "Mtrue[80]        -1.76  2.1e-3   0.05   -1.9  -1.79  -1.76  -1.73  -1.68  691.0    1.0\n",
      "Mtrue[81]        -1.74  3.5e-3   0.07  -1.85  -1.77  -1.74  -1.71  -1.63  405.0   1.01\n",
      "Mtrue[82]        -1.72  2.6e-3   0.06  -1.81  -1.75  -1.72  -1.69  -1.62  511.0    1.0\n",
      "Mtrue[83]        -1.75  1.5e-3   0.05  -1.87  -1.78  -1.75  -1.72  -1.67 1205.0    1.0\n",
      "Mtrue[84]        -1.74  1.2e-3   0.05  -1.84  -1.77  -1.74  -1.72  -1.66 1540.0    1.0\n",
      "Mtrue[85]        -1.43  8.6e-3   0.16  -1.73  -1.54  -1.41  -1.32  -1.18  335.0   1.01\n",
      "Mtrue[86]        -1.74  9.2e-4   0.04  -1.82  -1.76  -1.74  -1.71  -1.66 2105.0    1.0\n",
      "Mtrue[87]        -1.74  8.0e-4   0.04  -1.82  -1.76  -1.74  -1.71  -1.65 3010.0    1.0\n",
      "Mtrue[88]        -1.35  8.5e-3   0.17  -1.71  -1.43  -1.33  -1.24  -1.08  384.0   1.01\n",
      "Mtrue[89]        -1.74  1.7e-3   0.05  -1.84  -1.77  -1.74  -1.72  -1.66  908.0    1.0\n",
      "Mtrue[90]        -1.74  1.7e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65  847.0    1.0\n",
      "Mtrue[91]        -1.74  2.3e-3   0.06  -1.85  -1.77  -1.74  -1.71  -1.63  667.0    1.0\n",
      "Mtrue[92]        -1.72  2.2e-3   0.06  -1.81  -1.76  -1.73   -1.7  -1.62  654.0   1.01\n",
      "Mtrue[93]        -1.73  1.2e-3   0.05  -1.81  -1.76  -1.73   -1.7  -1.64 1363.0    1.0\n",
      "Mtrue[94]        -1.74  2.1e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65  635.0    1.0\n",
      "Mtrue[95]        -1.74  8.0e-4   0.04  -1.82  -1.76  -1.74  -1.71  -1.65 2948.0    1.0\n",
      "Mtrue[96]        -1.72  2.0e-3   0.05   -1.8  -1.75  -1.72  -1.69  -1.61  750.0    1.0\n",
      "Mtrue[97]        -1.77  4.5e-3   0.08  -2.05  -1.79  -1.76  -1.73  -1.67  327.0   1.01\n",
      "Mtrue[98]        -1.74  9.3e-4   0.05  -1.83  -1.77  -1.74  -1.71  -1.66 2368.0    1.0\n",
      "Mtrue[99]        -1.73  2.3e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63  505.0   1.01\n",
      "Mtrue[100]       -1.45    0.02   0.21  -1.76  -1.67  -1.42  -1.28  -1.07  170.0   1.02\n",
      "Mtrue[101]        -1.7  3.9e-3   0.08   -1.8  -1.75  -1.72  -1.68  -1.46  385.0   1.01\n",
      "Mtrue[102]       -1.72  2.6e-3   0.06  -1.82  -1.76  -1.73   -1.7   -1.6  521.0    1.0\n",
      "Mtrue[103]       -1.75  8.8e-4   0.04  -1.83  -1.77  -1.75  -1.72  -1.66 2358.0    1.0\n",
      "Mtrue[104]       -1.05  5.4e-3   0.16  -1.36  -1.16  -1.04  -0.94  -0.74  937.0   1.01\n",
      "Mtrue[105]       -1.74  1.5e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1120.0    1.0\n",
      "Mtrue[106]       -1.76  2.0e-3   0.05  -1.89  -1.78  -1.76  -1.73  -1.67  683.0    1.0\n",
      "Mtrue[107]       -1.73  1.4e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63 1402.0    1.0\n",
      "Mtrue[108]       -1.73  3.6e-3   0.07  -1.83  -1.76  -1.73   -1.7  -1.59  363.0    1.0\n",
      "Mtrue[109]       -1.75  1.6e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.67  919.0   1.01\n",
      "Mtrue[110]       -1.71  5.7e-3   0.08  -1.81  -1.75  -1.72  -1.69  -1.41  222.0   1.02\n",
      "Mtrue[111]       -1.75  9.3e-4   0.05  -1.85  -1.78  -1.75  -1.72  -1.66 2726.0    1.0\n",
      "Mtrue[112]       -2.54  2.7e-3   0.13  -2.81  -2.63  -2.54  -2.46  -2.31 2230.0    1.0\n",
      "Mtrue[113]       -1.76  2.1e-3   0.05  -1.86  -1.78  -1.75  -1.73  -1.67  584.0   1.01\n",
      "Mtrue[114]       -1.74  8.3e-4   0.04  -1.83  -1.77  -1.74  -1.72  -1.66 2639.0    1.0\n",
      "Mtrue[115]       -1.75  1.1e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.67 1856.0    1.0\n",
      "Mtrue[116]       -1.74  1.9e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65  690.0    1.0\n",
      "Mtrue[117]       -1.73  1.6e-3   0.05  -1.82  -1.76  -1.73  -1.71  -1.63 1036.0    1.0\n",
      "Mtrue[118]       -1.73  9.7e-4   0.04  -1.81  -1.76  -1.73   -1.7  -1.63 2106.0    1.0\n",
      "Mtrue[119]       -1.74  9.4e-4   0.04  -1.82  -1.77  -1.74  -1.71  -1.65 2176.0    1.0\n",
      "Mtrue[120]       -1.75  1.4e-3   0.05  -1.87  -1.78  -1.75  -1.72  -1.66 1498.0    1.0\n",
      "Mtrue[121]       -1.72  2.2e-3   0.06  -1.81  -1.76  -1.73   -1.7  -1.61  623.0   1.01\n",
      "Mtrue[122]       -1.74  2.1e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.63  662.0   1.01\n",
      "Mtrue[123]       -1.79  5.4e-3   0.11  -2.12   -1.8  -1.76  -1.73  -1.68  393.0    1.0\n",
      "Mtrue[124]       -1.71  1.7e-3   0.05   -1.8  -1.74  -1.71  -1.69  -1.61  812.0    1.0\n",
      "Mtrue[125]       -1.73  4.9e-3   0.09  -1.88  -1.76  -1.73   -1.7  -1.58  306.0   1.01\n",
      "Mtrue[126]       -1.75  2.8e-3   0.06  -1.87  -1.78  -1.75  -1.72  -1.66  420.0   1.01\n",
      "Mtrue[127]       -1.68  6.6e-3    0.1  -1.79  -1.74  -1.71  -1.67  -1.37  228.0   1.01\n",
      "Mtrue[128]       -1.93    0.02   0.22   -2.4  -2.11  -1.82  -1.76  -1.69  136.0   1.03\n",
      "Mtrue[129]       -1.73  8.6e-4   0.04   -1.8  -1.76  -1.73   -1.7  -1.64 2433.0    1.0\n",
      "Mtrue[130]       -1.74  1.3e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 1433.0    1.0\n",
      "Mtrue[131]       -1.74  1.0e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.64 2114.0    1.0\n",
      "Mtrue[132]       -1.73  2.4e-3   0.06  -1.83  -1.76  -1.73   -1.7  -1.62  593.0    1.0\n",
      "Mtrue[133]       -1.73  2.7e-3   0.06  -1.84  -1.77  -1.73   -1.7  -1.62  491.0   1.01\n",
      "Mtrue[134]       -1.73  8.9e-4   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 2568.0    1.0\n",
      "Mtrue[135]       -1.73  1.4e-3   0.05  -1.83  -1.76  -1.73   -1.7  -1.64 1313.0    1.0\n",
      "Mtrue[136]       -1.73  1.7e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63  822.0    1.0\n",
      "Mtrue[137]       -1.73  8.8e-4   0.04  -1.82  -1.76  -1.73  -1.71  -1.65 2507.0    1.0\n",
      "Mtrue[138]       -1.77  2.3e-3   0.06  -1.95  -1.79  -1.76  -1.73  -1.68  764.0   1.01\n",
      "Mtrue[139]       -1.74  8.7e-4   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 2867.0    1.0\n",
      "Mtrue[140]       -1.74  1.1e-3   0.04  -1.83  -1.77  -1.74  -1.71  -1.66 1543.0    1.0\n",
      "Mtrue[141]       -1.74  9.0e-4   0.04  -1.83  -1.77  -1.74  -1.71  -1.66 2351.0    1.0\n",
      "Mtrue[142]       -1.73  1.9e-3   0.05  -1.83  -1.76  -1.74  -1.71  -1.64  810.0    1.0\n",
      "Mtrue[143]       -1.73  1.2e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 1383.0    1.0\n",
      "Mtrue[144]       -1.77  5.0e-3   0.08  -2.02  -1.79  -1.76  -1.73  -1.67  248.0   1.01\n",
      "Mtrue[145]       -2.31  2.6e-3   0.12  -2.55  -2.39  -2.31  -2.22  -2.07 2317.0    1.0\n",
      "Mtrue[146]       -1.74  4.9e-3   0.08  -1.94  -1.77  -1.74   -1.7  -1.64  277.0   1.01\n",
      "Mtrue[147]       -1.76  1.1e-3   0.05  -1.85  -1.78  -1.75  -1.73  -1.67 1746.0    1.0\n",
      "Mtrue[148]       -1.73  8.5e-4   0.04  -1.81  -1.76  -1.73   -1.7  -1.64 2489.0    1.0\n",
      "Mtrue[149]       -1.74  7.0e-4   0.04  -1.82  -1.77  -1.74  -1.71  -1.66 3527.0    1.0\n",
      "Mtrue[150]       -1.71  1.6e-3   0.05   -1.8  -1.74  -1.71  -1.68  -1.59 1132.0    1.0\n",
      "Mtrue[151]       -1.73  9.6e-4   0.04  -1.82  -1.76  -1.73   -1.7  -1.64 2025.0    1.0\n",
      "Mtrue[152]       -1.73  2.9e-3   0.06  -1.82  -1.76  -1.73   -1.7   -1.6  377.0   1.02\n",
      "Mtrue[153]       -1.73  1.2e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 1652.0    1.0\n",
      "Mtrue[154]       -1.76  3.8e-3   0.07  -1.95  -1.78  -1.75  -1.72  -1.67  314.0   1.01\n",
      "Mtrue[155]       -1.73  1.3e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 1420.0    1.0\n",
      "Mtrue[156]        -2.0  9.9e-3   0.17   -2.3  -2.14  -2.03  -1.82  -1.73  307.0   1.02\n",
      "Mtrue[157]       -1.52  8.0e-3   0.15  -1.74  -1.66   -1.5   -1.4  -1.26  331.0    1.0\n",
      "Mtrue[158]       -1.73  1.9e-3   0.05  -1.83  -1.76  -1.73   -1.7  -1.62  727.0    1.0\n",
      "Mtrue[159]       -1.74  2.5e-3   0.06  -1.84  -1.76  -1.73  -1.71  -1.64  485.0    1.0\n",
      "Mtrue[160]       -1.74  9.5e-4   0.04  -1.84  -1.77  -1.74  -1.72  -1.65 2242.0    1.0\n",
      "Mtrue[161]       -1.69  2.3e-3   0.06  -1.77  -1.73   -1.7  -1.67  -1.52  707.0    1.0\n",
      "Mtrue[162]       -1.74  9.2e-4   0.05  -1.83  -1.76  -1.74  -1.71  -1.64 2713.0    1.0\n",
      "Mtrue[163]       -1.72  1.4e-3   0.05   -1.8  -1.75  -1.72   -1.7  -1.59 1301.0    1.0\n",
      "Mtrue[164]       -1.73  2.2e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.61  601.0   1.01\n",
      "Mtrue[165]       -1.74  9.7e-4   0.04  -1.83  -1.77  -1.74  -1.71  -1.65 2119.0    1.0\n",
      "Mtrue[166]       -1.77  3.6e-3   0.08  -1.99  -1.79  -1.76  -1.73  -1.66  448.0    1.0\n",
      "Mtrue[167]       -1.75  2.5e-3   0.06  -1.86  -1.78  -1.75  -1.72  -1.66  519.0    1.0\n",
      "Mtrue[168]       -1.73  3.1e-3   0.06  -1.82  -1.76  -1.73   -1.7  -1.63  321.0   1.01\n",
      "Mtrue[169]       -1.71  1.6e-3   0.05  -1.79  -1.74  -1.72  -1.69   -1.6  958.0    1.0\n",
      "Mtrue[170]       -1.75  1.0e-3   0.05  -1.84  -1.77  -1.75  -1.72  -1.66 2112.0    1.0\n",
      "Mtrue[171]       -1.66  8.2e-3   0.14   -1.8  -1.74   -1.7  -1.65  -1.25  291.0   1.02\n",
      "Mtrue[172]       -1.76  4.4e-3   0.07  -1.97  -1.79  -1.75  -1.72  -1.66  264.0   1.01\n",
      "Mtrue[173]       -1.74  1.1e-3   0.04  -1.83  -1.76  -1.74  -1.71  -1.66 1588.0    1.0\n",
      "Mtrue[174]       -1.72  2.7e-3   0.06   -1.8  -1.75  -1.72  -1.69   -1.6  434.0    1.0\n",
      "Mtrue[175]       -1.75  1.1e-3   0.05  -1.86  -1.78  -1.75  -1.72  -1.66 2060.0    1.0\n",
      "Mtrue[176]       -1.73  1.6e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63 1146.0    1.0\n",
      "Mtrue[177]       -1.75  2.2e-3   0.05  -1.86  -1.77  -1.74  -1.71  -1.65  553.0    1.0\n",
      "Mtrue[178]       -1.74  1.7e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.64  885.0    1.0\n",
      "Mtrue[179]       -1.74  4.5e-3   0.06  -1.85  -1.76  -1.73   -1.7  -1.63  206.0   1.02\n",
      "Mtrue[180]       -1.74  5.5e-3   0.07  -1.84  -1.77  -1.74  -1.71  -1.63  156.0   1.03\n",
      "Mtrue[181]       -1.72  5.6e-3   0.07  -1.82  -1.76  -1.73   -1.7  -1.54  147.0   1.03\n",
      "Mtrue[182]       -1.74  1.1e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1929.0    1.0\n",
      "Mtrue[183]       -1.79  5.5e-3    0.1  -2.12  -1.81  -1.77  -1.74  -1.68  357.0    1.0\n",
      "Mtrue[184]       -1.75  1.8e-3   0.06  -1.86  -1.78  -1.75  -1.72  -1.66  939.0   1.01\n",
      "Mtrue[185]       -1.72  1.3e-3   0.05   -1.8  -1.75  -1.72  -1.69  -1.62 1345.0    1.0\n",
      "Mtrue[186]       -1.75  3.8e-3   0.06  -1.87  -1.78  -1.75  -1.72  -1.66  235.0   1.01\n",
      "Mtrue[187]       -1.75  2.9e-3   0.05  -1.85  -1.77  -1.74  -1.71  -1.66  349.0   1.01\n",
      "Mtrue[188]       -1.74  1.5e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 1117.0    1.0\n",
      "Mtrue[189]       -1.72  6.9e-4   0.04   -1.8  -1.75  -1.72  -1.69  -1.64 3695.0    1.0\n",
      "Mtrue[190]       -1.73  1.8e-3   0.06  -1.82  -1.76  -1.73   -1.7  -1.62  981.0    1.0\n",
      "Mtrue[191]       -1.73  1.6e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 1048.0    1.0\n",
      "Mtrue[192]       -1.73  1.2e-3   0.05  -1.83  -1.76  -1.73   -1.7  -1.64 1818.0    1.0\n",
      "Mtrue[193]       -1.73  3.4e-3   0.06  -1.83  -1.76  -1.73   -1.7  -1.61  318.0   1.01\n",
      "Mtrue[194]       -1.75  2.5e-3   0.06  -1.87  -1.78  -1.75  -1.72  -1.66  640.0    1.0\n",
      "Mtrue[195]       -1.74  1.0e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 2149.0    1.0\n",
      "Mtrue[196]       -1.73  1.5e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.62 1159.0    1.0\n",
      "Mtrue[197]       -1.73  1.0e-3   0.04  -1.82  -1.76  -1.74  -1.71  -1.65 1797.0    1.0\n",
      "Mtrue[198]       -1.76  4.3e-3   0.08  -1.99  -1.79  -1.75  -1.72  -1.65  324.0   1.02\n",
      "Mtrue[199]       -1.72  1.3e-3   0.06  -1.81  -1.75  -1.72  -1.69  -1.61 1731.0    1.0\n",
      "Mtrue[200]       -1.75  1.5e-3   0.05  -1.86  -1.78  -1.75  -1.72  -1.67 1108.0    1.0\n",
      "Mtrue[201]       -1.71  3.4e-3   0.07   -1.8  -1.75  -1.72  -1.69  -1.51  415.0   1.01\n",
      "Mtrue[202]       -1.73  9.1e-4   0.04  -1.81  -1.76  -1.73   -1.7  -1.64 2316.0    1.0\n",
      "Mtrue[203]       -1.74  1.8e-3   0.06  -1.84  -1.77  -1.74  -1.71  -1.64 1048.0   1.01\n",
      "Mtrue[204]       -1.76  3.6e-3   0.07  -1.95  -1.78  -1.75  -1.72  -1.67  384.0   1.01\n",
      "Mtrue[205]       -1.76  3.1e-3   0.07  -1.97  -1.79  -1.75  -1.72  -1.66  476.0    1.0\n",
      "Mtrue[206]       -1.75  1.8e-3   0.05  -1.86  -1.77  -1.74  -1.72  -1.65  945.0    1.0\n",
      "Mtrue[207]       -1.71  2.5e-3   0.06  -1.79  -1.74  -1.71  -1.68  -1.58  484.0    1.0\n",
      "Mtrue[208]       -1.75  1.8e-3   0.05  -1.87  -1.77  -1.75  -1.72  -1.66  801.0    1.0\n",
      "Mtrue[209]       -1.73  1.2e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 1594.0    1.0\n",
      "Mtrue[210]       -1.75  2.8e-3   0.06   -1.9  -1.78  -1.75  -1.72  -1.66  447.0   1.01\n",
      "Mtrue[211]       -1.74  2.5e-3   0.06  -1.84  -1.77  -1.74  -1.71  -1.65  494.0   1.01\n",
      "Mtrue[212]       -1.74  1.5e-3   0.05  -1.85  -1.77  -1.74  -1.71  -1.65 1261.0    1.0\n",
      "Mtrue[213]       -1.71  3.2e-3   0.06   -1.8  -1.75  -1.72  -1.69  -1.54  346.0   1.01\n",
      "Mtrue[214]       -1.87    0.01   0.17  -2.27  -1.98   -1.8  -1.75  -1.69  168.0   1.03\n",
      "Mtrue[215]       -1.73  1.1e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.62 1940.0    1.0\n",
      "Mtrue[216]       -1.73  1.1e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63 1995.0    1.0\n",
      "Mtrue[217]       -1.74  5.6e-3   0.08  -1.88  -1.77  -1.74  -1.71   -1.6  219.0   1.02\n",
      "Mtrue[218]       -1.73  7.3e-4   0.04  -1.82  -1.76  -1.74  -1.71  -1.65 3490.0    1.0\n",
      "Mtrue[219]       -1.74  1.2e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.64 1812.0    1.0\n",
      "Mtrue[220]       -1.75  2.3e-3   0.06  -1.87  -1.77  -1.74  -1.71  -1.65  709.0    1.0\n",
      "Mtrue[221]       -1.57    0.01   0.19  -1.77  -1.71  -1.67  -1.41  -1.14  174.0   1.02\n",
      "Mtrue[222]       -1.73  1.2e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 1582.0    1.0\n",
      "Mtrue[223]       -1.75  3.7e-3   0.06  -1.91  -1.78  -1.75  -1.72  -1.66  263.0   1.01\n",
      "Mtrue[224]       -1.73  2.8e-3   0.06  -1.83  -1.76  -1.74   -1.7  -1.61  457.0   1.01\n",
      "Mtrue[225]       -1.74  2.0e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.64  688.0    1.0\n",
      "Mtrue[226]       -1.73  1.1e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 1752.0    1.0\n",
      "Mtrue[227]       -1.74  1.1e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1880.0    1.0\n",
      "Mtrue[228]       -1.74  9.3e-4   0.05  -1.84  -1.77  -1.74  -1.72  -1.65 2487.0    1.0\n",
      "Mtrue[229]       -1.74  9.1e-4   0.05  -1.83  -1.77  -1.74  -1.72  -1.65 2516.0    1.0\n",
      "Mtrue[230]       -1.75  2.3e-3   0.05  -1.85  -1.77  -1.74  -1.72  -1.66  493.0    1.0\n",
      "Mtrue[231]       -1.74  1.6e-3   0.05  -1.85  -1.77  -1.74  -1.71  -1.65 1073.0    1.0\n",
      "Mtrue[232]       -1.75  1.4e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.67 1277.0    1.0\n",
      "Mtrue[233]       -1.75  9.6e-4   0.04  -1.83  -1.77  -1.75  -1.72  -1.66 1967.0    1.0\n",
      "Mtrue[234]        -1.7  3.7e-3   0.09   -1.8  -1.75  -1.71  -1.68  -1.41  526.0   1.01\n",
      "Mtrue[235]       -1.76  4.2e-3   0.07  -1.95  -1.78  -1.75  -1.72  -1.66  257.0   1.01\n",
      "Mtrue[236]       -1.76  3.0e-3   0.06  -1.92  -1.78  -1.75  -1.72  -1.66  469.0   1.01\n",
      "Mtrue[237]       -1.74  8.3e-4   0.04  -1.81  -1.76  -1.74  -1.71  -1.66 2314.0    1.0\n",
      "Mtrue[238]       -1.75  1.7e-3   0.05  -1.88  -1.78  -1.75  -1.72  -1.66 1016.0    1.0\n",
      "Mtrue[239]       -1.75  8.9e-4   0.04  -1.84  -1.78  -1.75  -1.72  -1.67 2480.0    1.0\n",
      "Mtrue[240]       -1.74  1.9e-3   0.05  -1.85  -1.77  -1.74  -1.71  -1.65  766.0    1.0\n",
      "Mtrue[241]       -1.76  4.2e-3   0.07  -1.98  -1.78  -1.75  -1.72  -1.66  294.0   1.01\n",
      "Mtrue[242]       -1.68  4.0e-3   0.08  -1.79  -1.73   -1.7  -1.66  -1.45  380.0    1.0\n",
      "Mtrue[243]       -1.73  1.2e-3   0.04  -1.82  -1.76  -1.73   -1.7  -1.64 1344.0    1.0\n",
      "Mtrue[244]       -1.74  1.1e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 1658.0    1.0\n",
      "Mtrue[245]       -1.74  1.3e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.64 1357.0    1.0\n",
      "Mtrue[246]       -1.75  1.3e-3   0.05  -1.84  -1.78  -1.75  -1.72  -1.67 1380.0    1.0\n",
      "Mtrue[247]       -1.72  6.9e-4   0.04   -1.8  -1.75  -1.73   -1.7  -1.64 3608.0    1.0\n",
      "Mtrue[248]        -1.7  2.9e-3   0.07  -1.79  -1.74  -1.71  -1.67   -1.5  567.0    1.0\n",
      "Mtrue[249]       -1.73  4.8e-3   0.08  -1.84  -1.77  -1.74  -1.71  -1.56  251.0   1.02\n",
      "Mtrue[250]       -1.72  1.1e-3   0.05  -1.81  -1.75  -1.72   -1.7  -1.63 1936.0    1.0\n",
      "Mtrue[251]       -1.73  2.0e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.61  755.0    1.0\n",
      "Mtrue[252]       -1.68  3.1e-3   0.07  -1.78  -1.73   -1.7  -1.66  -1.47  569.0    1.0\n",
      "Mtrue[253]       -1.71  2.0e-3   0.06   -1.8  -1.74  -1.71  -1.68  -1.54  993.0    1.0\n",
      "Mtrue[254]       -1.74  1.1e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.64 1978.0    1.0\n",
      "Mtrue[255]       -1.73  9.1e-4   0.04  -1.82  -1.76  -1.73  -1.71  -1.65 2231.0    1.0\n",
      "Mtrue[256]       -1.74  1.1e-3   0.04  -1.82  -1.76  -1.74  -1.71  -1.66 1760.0    1.0\n",
      "Mtrue[257]       -1.74  8.2e-4   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 3293.0    1.0\n",
      "Mtrue[258]       -1.73  1.1e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 1546.0    1.0\n",
      "Mtrue[259]       -1.71  4.7e-3   0.07  -1.81  -1.75  -1.72  -1.69  -1.51  224.0   1.01\n",
      "Mtrue[260]       -1.74  2.7e-3   0.06  -1.85  -1.77  -1.74  -1.71  -1.64  439.0    1.0\n",
      "Mtrue[261]       -1.76  3.1e-3   0.07  -1.89  -1.78  -1.75  -1.72  -1.66  429.0   1.01\n",
      "Mtrue[262]       -1.71  2.3e-3   0.05   -1.8  -1.74  -1.72  -1.69  -1.59  531.0    1.0\n",
      "Mtrue[263]       -1.69  3.1e-3   0.06  -1.78  -1.73   -1.7  -1.67  -1.51  437.0   1.01\n",
      "Mtrue[264]        -1.7  2.8e-3   0.07  -1.79  -1.74   -1.7  -1.67  -1.49  562.0   1.01\n",
      "Mtrue[265]       -1.75  1.4e-3   0.05  -1.84  -1.78  -1.75  -1.72  -1.66 1193.0    1.0\n",
      "Mtrue[266]       -1.75  1.2e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.67 1824.0    1.0\n",
      "Mtrue[267]       -1.76    0.01   0.09  -2.09  -1.78  -1.75  -1.72  -1.66   73.0   1.04\n",
      "Mtrue[268]       -1.74  1.2e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 1704.0    1.0\n",
      "Mtrue[269]       -1.75  1.4e-3   0.05  -1.84  -1.78  -1.75  -1.72  -1.67 1108.0    1.0\n",
      "Mtrue[270]       -1.74  8.2e-4   0.05  -1.83  -1.77  -1.74  -1.71  -1.66 3126.0    1.0\n",
      "Mtrue[271]       -1.73  1.6e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.62 1000.0    1.0\n",
      "Mtrue[272]       -1.74  9.9e-4   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 2213.0    1.0\n",
      "Mtrue[273]       -1.75  1.7e-3   0.06  -1.85  -1.77  -1.75  -1.72  -1.65 1145.0    1.0\n",
      "Mtrue[274]       -1.71  7.0e-3   0.07  -1.81  -1.75  -1.72  -1.69  -1.52  114.0   1.03\n",
      "Mtrue[275]       -1.74  9.3e-4   0.05  -1.83  -1.76  -1.73  -1.71  -1.65 2331.0    1.0\n",
      "Mtrue[276]       -1.72  1.9e-3   0.06  -1.82  -1.76  -1.73   -1.7   -1.6  955.0    1.0\n",
      "Mtrue[277]       -1.95    0.03   0.21  -2.36  -2.13  -1.87  -1.77   -1.7   53.0   1.06\n",
      "Mtrue[278]       -1.74  1.0e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 2055.0    1.0\n",
      "Mtrue[279]       -1.73  1.3e-3   0.05  -1.81  -1.76  -1.73   -1.7  -1.63 1277.0    1.0\n",
      "Mtrue[280]       -1.73  2.3e-3   0.06  -1.83  -1.76  -1.73   -1.7  -1.62  599.0    1.0\n",
      "Mtrue[281]       -1.74  8.8e-4   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 2710.0    1.0\n",
      "Mtrue[282]       -1.73  9.9e-4   0.05  -1.82  -1.76  -1.73  -1.71  -1.65 2067.0    1.0\n",
      "Mtrue[283]       -1.73  2.1e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.62  602.0    1.0\n",
      "Mtrue[284]       -1.75  1.4e-3   0.05  -1.84  -1.77  -1.74  -1.72  -1.66 1202.0    1.0\n",
      "Mtrue[285]       -1.73  2.1e-3   0.06  -1.82  -1.76  -1.73   -1.7  -1.63  686.0   1.01\n",
      "Mtrue[286]       -1.69  3.8e-3   0.07  -1.78  -1.73   -1.7  -1.67   -1.5  322.0    1.0\n",
      "Mtrue[287]       -1.74  9.8e-4   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 2500.0    1.0\n",
      "Mtrue[288]       -1.74  1.6e-3   0.05  -1.83  -1.76  -1.74  -1.71  -1.65  915.0    1.0\n",
      "Mtrue[289]       -1.73  1.4e-3   0.05  -1.83  -1.76  -1.74   -1.7  -1.63 1426.0    1.0\n",
      "Mtrue[290]       -1.73  1.1e-3   0.04   -1.8  -1.76  -1.73   -1.7  -1.64 1829.0    1.0\n",
      "Mtrue[291]       -1.74  1.3e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1567.0    1.0\n",
      "Mtrue[292]       -1.68  7.2e-3    0.1  -1.79  -1.74  -1.71  -1.67  -1.36  201.0   1.02\n",
      "Mtrue[293]        -1.8  6.8e-3   0.12  -2.17  -1.81  -1.77  -1.74  -1.68  305.0   1.01\n",
      "Mtrue[294]       -1.73  8.0e-4   0.04  -1.82  -1.76  -1.73   -1.7  -1.64 3142.0    1.0\n",
      "Mtrue[295]       -1.75  1.1e-3   0.05  -1.84  -1.77  -1.74  -1.72  -1.66 1779.0    1.0\n",
      "Mtrue[296]       -1.73  3.0e-3   0.05  -1.83  -1.76  -1.74   -1.7  -1.63  341.0   1.01\n",
      "Mtrue[297]       -1.75  1.7e-3   0.05  -1.86  -1.78  -1.75  -1.72  -1.67  873.0    1.0\n",
      "Mtrue[298]       -1.74  1.4e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1355.0    1.0\n",
      "Mtrue[299]       -1.75  5.8e-3   0.07  -1.87  -1.77  -1.74  -1.71  -1.65  166.0   1.02\n",
      "Mtrue[300]       -1.74  2.6e-3   0.06  -1.87  -1.77  -1.74  -1.71  -1.65  515.0    1.0\n",
      "Mtrue[301]       -1.75  1.2e-3   0.05  -1.84  -1.77  -1.74  -1.72  -1.66 1469.0    1.0\n",
      "Mtrue[302]       -1.74  9.5e-4   0.05  -1.82  -1.76  -1.74  -1.71  -1.65 2390.0    1.0\n",
      "Mtrue[303]       -1.72  1.1e-3   0.05   -1.8  -1.75  -1.72   -1.7  -1.63 1821.0    1.0\n",
      "Mtrue[304]       -1.81  5.1e-3   0.09  -2.07  -1.83  -1.79  -1.76   -1.7  315.0   1.01\n",
      "Mtrue[305]       -1.72  1.3e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.61 1567.0    1.0\n",
      "Mtrue[306]       -1.75  2.1e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.66  549.0   1.01\n",
      "Mtrue[307]       -1.73  1.2e-3   0.05  -1.83  -1.76  -1.73   -1.7  -1.64 1696.0    1.0\n",
      "Mtrue[308]       -1.74  1.2e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.64 1634.0    1.0\n",
      "Mtrue[309]       -1.73  8.8e-4   0.04  -1.81  -1.75  -1.73   -1.7  -1.65 2110.0    1.0\n",
      "Mtrue[310]       -1.73  8.7e-4   0.04  -1.81  -1.76  -1.74  -1.71  -1.65 2175.0    1.0\n",
      "Mtrue[311]       -1.74  1.3e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1418.0    1.0\n",
      "Mtrue[312]       -1.78  4.2e-3   0.09  -2.05   -1.8  -1.76  -1.73  -1.67  429.0   1.01\n",
      "Mtrue[313]       -1.73  1.0e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 2078.0    1.0\n",
      "Mtrue[314]       -1.74  9.7e-4   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 2356.0    1.0\n",
      "Mtrue[315]       -1.75  1.2e-3   0.04  -1.83  -1.77  -1.75  -1.72  -1.67 1281.0    1.0\n",
      "Mtrue[316]       -1.76  2.8e-3   0.06  -1.94  -1.79  -1.76  -1.73  -1.67  543.0    1.0\n",
      "Mtrue[317]       -1.73  1.1e-3   0.05  -1.82  -1.76  -1.73  -1.71  -1.64 1565.0    1.0\n",
      "Mtrue[318]       -1.75  2.9e-3   0.06   -1.9  -1.77  -1.74  -1.71  -1.65  442.0    1.0\n",
      "Mtrue[319]       -1.65  5.6e-3   0.12  -1.78  -1.72  -1.69  -1.64  -1.31  451.0    1.0\n",
      "Mtrue[320]       -1.73  2.2e-3   0.06  -1.82  -1.76  -1.73   -1.7  -1.62  750.0   1.01\n",
      "Mtrue[321]       -1.74  9.2e-4   0.05  -1.84  -1.77  -1.74  -1.72  -1.66 2418.0    1.0\n",
      "Mtrue[322]       -1.74  1.1e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 1862.0    1.0\n",
      "Mtrue[323]       -1.74  1.1e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 1826.0    1.0\n",
      "Mtrue[324]       -1.74  1.3e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 1449.0    1.0\n",
      "Mtrue[325]       -1.74  1.1e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.66 1907.0    1.0\n",
      "Mtrue[326]       -1.76  1.1e-3   0.05  -1.85  -1.79  -1.76  -1.73  -1.68 1758.0    1.0\n",
      "Mtrue[327]       -1.69  3.4e-3   0.07  -1.78  -1.73   -1.7  -1.66   -1.5  371.0   1.01\n",
      "Mtrue[328]       -1.72  7.6e-4   0.04   -1.8  -1.75  -1.72   -1.7  -1.64 2865.0    1.0\n",
      "Mtrue[329]       -1.71  1.2e-3   0.05  -1.79  -1.74  -1.71  -1.69  -1.61 1587.0    1.0\n",
      "Mtrue[330]       -1.74  1.0e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 2030.0    1.0\n",
      "Mtrue[331]       -1.74  1.1e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.66 1804.0    1.0\n",
      "Mtrue[332]       -1.74  1.8e-3   0.06  -1.85  -1.77  -1.74  -1.71  -1.65  937.0    1.0\n",
      "Mtrue[333]       -1.73  1.1e-3   0.04  -1.81  -1.76  -1.73   -1.7  -1.64 1814.0    1.0\n",
      "Mtrue[334]       -1.73 10.0e-4   0.05  -1.81  -1.76  -1.73   -1.7  -1.63 2182.0    1.0\n",
      "Mtrue[335]       -1.74  1.7e-3   0.06  -1.85  -1.77  -1.74  -1.71  -1.63 1368.0    1.0\n",
      "Mtrue[336]       -1.74  1.6e-3   0.05  -1.83  -1.76  -1.73   -1.7  -1.64 1167.0    1.0\n",
      "Mtrue[337]       -1.73  1.1e-3   0.05  -1.82  -1.76  -1.74  -1.71  -1.64 1875.0    1.0\n",
      "Mtrue[338]       -1.73  1.7e-3   0.05  -1.83  -1.76  -1.73   -1.7  -1.64 1041.0    1.0\n",
      "Mtrue[339]        -1.7  8.8e-3   0.08  -1.81  -1.75  -1.72  -1.69  -1.42   93.0   1.04\n",
      "Mtrue[340]       -1.75  3.6e-3   0.07  -1.93  -1.78  -1.75  -1.72  -1.65  354.0   1.01\n",
      "Mtrue[341]       -1.74  2.0e-3   0.06  -1.86  -1.77  -1.74  -1.71  -1.64  781.0    1.0\n",
      "Mtrue[342]       -1.74  2.8e-3   0.06  -1.85  -1.77  -1.74  -1.71  -1.64  481.0   1.02\n",
      "Mtrue[343]       -1.74  1.8e-3   0.06  -1.85  -1.77  -1.74  -1.71  -1.64 1078.0    1.0\n",
      "Mtrue[344]       -1.75  1.2e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.67 1525.0    1.0\n",
      "Mtrue[345]       -1.74  1.1e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 2081.0    1.0\n",
      "Mtrue[346]       -1.74  9.7e-4   0.04  -1.83  -1.77  -1.74  -1.71  -1.65 1954.0    1.0\n",
      "Mtrue[347]       -1.72  1.1e-3   0.04   -1.8  -1.75  -1.72  -1.69  -1.63 1534.0    1.0\n",
      "Mtrue[348]       -1.74 10.0e-4   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 2207.0    1.0\n",
      "Mtrue[349]       -1.77  4.0e-3   0.07  -1.99  -1.79  -1.76  -1.73  -1.67  344.0   1.01\n",
      "Mtrue[350]        -1.8  7.7e-3   0.11  -2.15  -1.81  -1.77  -1.74  -1.68  213.0   1.01\n",
      "Mtrue[351]       -1.73  1.2e-3   0.05  -1.83  -1.76  -1.73   -1.7  -1.63 2040.0    1.0\n",
      "Mtrue[352]       -1.75  4.5e-3   0.06  -1.88  -1.78  -1.75  -1.72  -1.66  181.0   1.02\n",
      "Mtrue[353]       -1.73  9.2e-4   0.04   -1.8  -1.75  -1.73   -1.7  -1.64 1983.0    1.0\n",
      "Mtrue[354]       -1.74  6.8e-4   0.04  -1.83  -1.76  -1.74  -1.71  -1.65 3706.0    1.0\n",
      "Mtrue[355]       -1.74  9.4e-4   0.05  -1.84  -1.77  -1.74  -1.72  -1.66 2472.0    1.0\n",
      "Mtrue[356]       -1.74  9.3e-4   0.04  -1.82  -1.76  -1.74  -1.71  -1.65 1995.0    1.0\n",
      "Mtrue[357]       -1.74  1.6e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1020.0    1.0\n",
      "Mtrue[358]       -1.76  3.3e-3   0.07  -1.97  -1.79  -1.76  -1.73  -1.66  430.0   1.01\n",
      "Mtrue[359]       -1.76  4.5e-3   0.06  -1.91  -1.78  -1.75  -1.72  -1.67  177.0   1.02\n",
      "Mtrue[360]       -1.82  8.2e-3   0.14  -2.25  -1.83  -1.78  -1.74  -1.68  306.0   1.01\n",
      "Mtrue[361]       -1.72  1.8e-3   0.05  -1.81  -1.75  -1.73   -1.7  -1.62  847.0    1.0\n",
      "Mtrue[362]       -1.74  2.6e-3   0.05  -1.85  -1.77  -1.74  -1.71  -1.65  437.0   1.01\n",
      "Mtrue[363]       -1.73  1.1e-3   0.04  -1.81  -1.76  -1.73   -1.7  -1.65 1565.0    1.0\n",
      "Mtrue[364]       -1.74  1.2e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.66 1487.0    1.0\n",
      "Mtrue[365]       -1.74  1.9e-3   0.06  -1.84  -1.77  -1.74  -1.71  -1.63  910.0    1.0\n",
      "Mtrue[366]       -1.75  3.0e-3   0.06  -1.88  -1.77  -1.74  -1.72  -1.65  423.0   1.01\n",
      "Mtrue[367]       -1.73  7.6e-4   0.04  -1.81  -1.76  -1.73   -1.7  -1.64 3483.0    1.0\n",
      "Mtrue[368]       -1.73  1.3e-3   0.05  -1.83  -1.76  -1.73  -1.71  -1.64 1666.0    1.0\n",
      "Mtrue[369]       -1.74  1.6e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.64 1028.0    1.0\n",
      "Mtrue[370]       -1.75  4.5e-3   0.08  -1.88  -1.77  -1.74  -1.71  -1.64  279.0   1.01\n",
      "Mtrue[371]       -1.68  4.8e-3   0.09  -1.78  -1.73   -1.7  -1.66  -1.41  328.0   1.01\n",
      "Mtrue[372]       -1.76  3.6e-3   0.07  -1.91  -1.79  -1.76  -1.73  -1.67  340.0   1.01\n",
      "Mtrue[373]       -1.77  5.4e-3   0.08  -2.03  -1.79  -1.76  -1.73  -1.67  236.0   1.01\n",
      "Mtrue[374]       -1.76  4.4e-3   0.07  -1.98  -1.78  -1.75  -1.72  -1.67  272.0    1.0\n",
      "Mtrue[375]       -1.69  4.4e-3   0.08   -1.8  -1.74  -1.71  -1.67  -1.42  371.0   1.01\n",
      "Mtrue[376]       -1.73  9.8e-4   0.04  -1.81  -1.76  -1.73  -1.71  -1.65 1833.0    1.0\n",
      "Mtrue[377]       -1.72  9.1e-4   0.04   -1.8  -1.75  -1.72  -1.69  -1.63 2355.0    1.0\n",
      "Mtrue[378]       -1.73  1.1e-3   0.05  -1.82  -1.76  -1.73  -1.71  -1.64 1632.0    1.0\n",
      "Mtrue[379]       -1.79  3.7e-3   0.08  -2.01  -1.81  -1.77  -1.74  -1.68  451.0   1.01\n",
      "Mtrue[380]       -1.72  8.6e-4   0.05  -1.81  -1.75  -1.72  -1.69  -1.63 2780.0    1.0\n",
      "Mtrue[381]       -1.72  2.2e-3   0.06  -1.82  -1.76  -1.73   -1.7  -1.61  676.0    1.0\n",
      "Mtrue[382]       -1.75  1.0e-3   0.05  -1.85  -1.77  -1.75  -1.72  -1.66 2177.0    1.0\n",
      "Mtrue[383]       -1.74  1.0e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 2050.0    1.0\n",
      "Mtrue[384]       -1.75  2.5e-3   0.06  -1.85  -1.77  -1.74  -1.72  -1.66  487.0   1.01\n",
      "Mtrue[385]       -1.74  1.9e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.64  746.0   1.01\n",
      "Mtrue[386]       -1.75  8.8e-4   0.04  -1.83  -1.77  -1.74  -1.72  -1.67 2143.0    1.0\n",
      "Mtrue[387]       -1.74  8.4e-4   0.04  -1.83  -1.77  -1.74  -1.72  -1.65 2860.0    1.0\n",
      "Mtrue[388]       -1.74  9.1e-4   0.04  -1.82  -1.77  -1.74  -1.71  -1.66 2150.0    1.0\n",
      "Mtrue[389]       -1.77  3.7e-3   0.08  -2.02  -1.79  -1.76  -1.73  -1.67  460.0    1.0\n",
      "Mtrue[390]       -1.73  9.1e-4   0.05  -1.82  -1.76  -1.73   -1.7  -1.65 2485.0    1.0\n",
      "Mtrue[391]       -1.76  3.4e-3   0.07  -1.97  -1.79  -1.75  -1.72  -1.67  482.0    1.0\n",
      "Mtrue[392]       -1.72  7.6e-3   0.09  -1.84  -1.76  -1.73   -1.7  -1.44  127.0   1.02\n",
      "Mtrue[393]       -1.74  1.2e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.64 1551.0    1.0\n",
      "Mtrue[394]       -1.74  2.2e-3   0.06  -1.85  -1.77  -1.74  -1.71  -1.65  662.0    1.0\n",
      "Mtrue[395]       -1.74  7.2e-4   0.04  -1.82  -1.77  -1.74  -1.71  -1.66 3416.0    1.0\n",
      "Mtrue[396]       -1.74  1.2e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.64 1738.0    1.0\n",
      "Mtrue[397]       -1.74  8.4e-4   0.04  -1.83  -1.77  -1.74  -1.72  -1.66 2504.0    1.0\n",
      "Mtrue[398]       -2.13  9.5e-3   0.16   -2.4  -2.24  -2.15  -2.06  -1.75  297.0   1.01\n",
      "Mtrue[399]       -1.73  1.7e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.61  962.0    1.0\n",
      "Mtrue[400]       -1.65    0.01   0.15  -1.79  -1.73   -1.7  -1.65  -1.22  150.0   1.01\n",
      "Mtrue[401]       -1.74  8.8e-4   0.04  -1.83  -1.77  -1.74  -1.72  -1.66 2319.0    1.0\n",
      "Mtrue[402]       -1.74  1.3e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.64 1437.0   1.01\n",
      "Mtrue[403]       -1.71  8.7e-3   0.08  -1.82  -1.76  -1.72  -1.69  -1.45   93.0   1.03\n",
      "Mtrue[404]       -1.74  2.0e-3   0.07  -1.89  -1.77  -1.74  -1.71  -1.64 1131.0    1.0\n",
      "Mtrue[405]       -1.71  5.7e-3   0.08  -1.82  -1.75  -1.72  -1.69  -1.46  200.0   1.02\n",
      "Mtrue[406]       -1.86  7.3e-3   0.13  -2.18  -1.94   -1.8  -1.76   -1.7  345.0    1.0\n",
      "Mtrue[407]        -2.0    0.01   0.16  -2.27  -2.12  -2.03  -1.83  -1.73  210.0    1.0\n",
      "Mtrue[408]        -1.7  2.2e-3   0.07  -1.79  -1.74  -1.71  -1.68  -1.52  850.0   1.01\n",
      "Mtrue[409]       -1.72  9.5e-4   0.04  -1.81  -1.75  -1.72  -1.69  -1.63 2233.0    1.0\n",
      "Mtrue[410]       -1.74  1.5e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1164.0    1.0\n",
      "Mtrue[411]       -1.72  1.0e-3   0.04   -1.8  -1.75  -1.72  -1.69  -1.63 1961.0    1.0\n",
      "Mtrue[412]       -1.72  4.8e-3   0.06   -1.8  -1.75  -1.72  -1.69  -1.62  148.0   1.02\n",
      "Mtrue[413]       -1.74  1.0e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 2354.0    1.0\n",
      "Mtrue[414]       -1.73  1.1e-3   0.05  -1.82  -1.76  -1.74  -1.71  -1.64 1914.0    1.0\n",
      "Mtrue[415]       -1.73  1.8e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64  841.0    1.0\n",
      "Mtrue[416]       -1.73  1.1e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 1769.0    1.0\n",
      "Mtrue[417]       -1.74  2.1e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.66  515.0   1.01\n",
      "Mtrue[418]       -1.75  1.3e-3   0.05  -1.85  -1.77  -1.75  -1.72  -1.66 1373.0    1.0\n",
      "Mtrue[419]       -1.73  1.3e-3   0.05  -1.81  -1.76  -1.73   -1.7  -1.63 1402.0    1.0\n",
      "Mtrue[420]       -1.74  1.6e-3   0.06  -1.86  -1.77  -1.74  -1.71  -1.65 1393.0   1.01\n",
      "Mtrue[421]       -1.75  1.5e-3   0.05  -1.86  -1.78  -1.75  -1.72  -1.66 1135.0    1.0\n",
      "Mtrue[422]       -1.72  2.8e-3   0.06  -1.82  -1.76  -1.73  -1.69  -1.55  517.0    1.0\n",
      "Mtrue[423]       -1.73  1.3e-3   0.05  -1.81  -1.76  -1.73   -1.7  -1.63 1406.0    1.0\n",
      "Mtrue[424]       -1.74  1.6e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65  985.0    1.0\n",
      "Mtrue[425]       -1.73  7.4e-4   0.04  -1.81  -1.76  -1.73   -1.7  -1.64 3530.0    1.0\n",
      "Mtrue[426]       -1.83  4.0e-3   0.09  -2.05  -1.86   -1.8  -1.77  -1.71  509.0    1.0\n",
      "Mtrue[427]       -1.71  4.2e-3   0.08  -1.81  -1.75  -1.72  -1.69  -1.47  338.0   1.01\n",
      "Mtrue[428]       -1.75  1.9e-3   0.06  -1.86  -1.77  -1.74  -1.72  -1.66  895.0   1.01\n",
      "Mtrue[429]       -1.73  1.2e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.62 1805.0    1.0\n",
      "Mtrue[430]       -1.74  1.5e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1095.0    1.0\n",
      "Mtrue[431]       -1.77  6.0e-3   0.08   -2.0  -1.79  -1.75  -1.72  -1.66  176.0   1.02\n",
      "Mtrue[432]       -1.78  6.5e-3   0.09  -2.07  -1.79  -1.76  -1.73  -1.67  209.0   1.04\n",
      "Mtrue[433]       -1.73  1.1e-3   0.05  -1.81  -1.76  -1.73   -1.7  -1.63 1930.0    1.0\n",
      "Mtrue[434]       -1.75  1.2e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.67 1418.0    1.0\n",
      "Mtrue[435]       -1.75  2.4e-3   0.05  -1.86  -1.78  -1.75  -1.72  -1.67  481.0    1.0\n",
      "Mtrue[436]       -1.74  9.6e-4   0.04  -1.83  -1.77  -1.74  -1.71  -1.66 2046.0    1.0\n",
      "Mtrue[437]       -1.73  1.3e-3   0.05  -1.82  -1.77  -1.73   -1.7  -1.63 1476.0    1.0\n",
      "Mtrue[438]       -1.74  1.1e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1885.0    1.0\n",
      "Mtrue[439]       -1.74  1.1e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.64 1759.0    1.0\n",
      "Mtrue[440]       -1.74  1.2e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1671.0    1.0\n",
      "Mtrue[441]        -1.7  3.1e-3   0.08  -1.81  -1.74  -1.72  -1.69  -1.46  620.0   1.01\n",
      "Mtrue[442]       -1.27  3.3e-3   0.12  -1.52  -1.34  -1.26  -1.19  -1.05 1347.0    1.0\n",
      "Mtrue[443]       -1.74  1.4e-3   0.05  -1.83  -1.76  -1.74  -1.71  -1.65 1141.0    1.0\n",
      "Mtrue[444]       -1.72  9.3e-4   0.05  -1.81  -1.76  -1.73  -1.69  -1.64 2338.0    1.0\n",
      "Mtrue[445]       -1.73  2.1e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.62  635.0    1.0\n",
      "Mtrue[446]       -1.73  2.0e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.62  716.0    1.0\n",
      "Mtrue[447]       -1.75  3.3e-3   0.06  -1.89  -1.77  -1.74  -1.71  -1.65  366.0   1.01\n",
      "Mtrue[448]       -1.74  1.7e-3   0.06  -1.83  -1.76  -1.74  -1.71  -1.63 1015.0    1.0\n",
      "Mtrue[449]       -1.73  1.6e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.62 1141.0   1.01\n",
      "Mtrue[450]       -1.74 10.0e-4   0.04  -1.82  -1.76  -1.74  -1.71  -1.65 1808.0    1.0\n",
      "Mtrue[451]       -1.74  8.9e-4   0.04  -1.82  -1.76  -1.74  -1.71  -1.65 2495.0    1.0\n",
      "Mtrue[452]       -1.74  9.5e-4   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 2369.0    1.0\n",
      "Mtrue[453]       -1.72  1.1e-3   0.05  -1.82  -1.75  -1.72  -1.69  -1.63 1968.0    1.0\n",
      "Mtrue[454]       -1.73  8.9e-4   0.04  -1.81  -1.76  -1.73   -1.7  -1.65 2215.0    1.0\n",
      "Mtrue[455]       -1.74  9.5e-4   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 2702.0    1.0\n",
      "Mtrue[456]       -1.73  1.2e-3   0.05  -1.84  -1.77  -1.73   -1.7  -1.63 1905.0    1.0\n",
      "Mtrue[457]       -1.73  2.4e-3   0.06  -1.83  -1.76  -1.73   -1.7  -1.59  649.0    1.0\n",
      "Mtrue[458]       -1.74  2.5e-3   0.06  -1.84  -1.77  -1.74  -1.71  -1.62  575.0    1.0\n",
      "Mtrue[459]       -2.02    0.01   0.18  -2.32  -2.16  -2.05  -1.85  -1.73  247.0   1.02\n",
      "Mtrue[460]       -1.71  3.4e-3   0.07  -1.81  -1.75  -1.72  -1.69   -1.5  484.0   1.01\n",
      "Mtrue[461]       -1.72  2.3e-3   0.05  -1.81  -1.75  -1.72  -1.69  -1.62  475.0    1.0\n",
      "Mtrue[462]       -1.72  3.6e-3   0.06  -1.81  -1.75  -1.72  -1.69  -1.56  272.0   1.03\n",
      "Mtrue[463]       -1.74  1.3e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.64 1398.0    1.0\n",
      "Mtrue[464]       -1.73  8.2e-4   0.05  -1.83  -1.76  -1.74  -1.71  -1.64 3236.0    1.0\n",
      "Mtrue[465]       -1.74  1.8e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.64  850.0    1.0\n",
      "Mtrue[466]       -1.74  1.2e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.64 1994.0    1.0\n",
      "Mtrue[467]       -1.74  8.4e-4   0.04  -1.82  -1.76  -1.74  -1.71  -1.66 2361.0    1.0\n",
      "Mtrue[468]       -1.74  9.2e-4   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 2424.0    1.0\n",
      "Mtrue[469]       -1.75  1.0e-3   0.05  -1.84  -1.77  -1.74  -1.72  -1.66 2185.0    1.0\n",
      "Mtrue[470]       -1.73  1.2e-3   0.05  -1.82  -1.76  -1.74  -1.71  -1.64 1521.0    1.0\n",
      "Mtrue[471]       -1.73  2.8e-3   0.05  -1.83  -1.76  -1.73   -1.7  -1.62  353.0   1.02\n",
      "Mtrue[472]       -1.75  3.2e-3   0.06  -1.86  -1.77  -1.74  -1.71  -1.65  369.0   1.01\n",
      "Mtrue[473]       -1.66  6.7e-3    0.1  -1.78  -1.72  -1.69  -1.65  -1.36  238.0   1.02\n",
      "Mtrue[474]       -1.73  8.6e-4   0.04  -1.81  -1.76  -1.73   -1.7  -1.64 2427.0    1.0\n",
      "Mtrue[475]        -1.8  4.8e-3   0.09  -2.06  -1.82  -1.78  -1.74  -1.69  342.0   1.01\n",
      "Mtrue[476]       -1.73  1.2e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 1640.0    1.0\n",
      "Mtrue[477]       -1.75  1.8e-3   0.05  -1.85  -1.78  -1.74  -1.72  -1.66  834.0    1.0\n",
      "Mtrue[478]       -1.74  9.2e-4   0.05  -1.83  -1.76  -1.74  -1.71  -1.64 2522.0    1.0\n",
      "Mtrue[479]       -1.69  6.5e-3   0.09   -1.8  -1.74  -1.71  -1.68  -1.39  197.0   1.01\n",
      "Mtrue[480]       -1.73  3.0e-3   0.06  -1.82  -1.76  -1.73   -1.7  -1.63  349.0   1.01\n",
      "Mtrue[481]       -1.73  1.0e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 1962.0    1.0\n",
      "Mtrue[482]       -1.73  1.2e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 1548.0    1.0\n",
      "Mtrue[483]       -1.89    0.01   0.18   -2.3  -2.02  -1.81  -1.76  -1.69  175.0   1.01\n",
      "Mtrue[484]       -1.75  1.8e-3   0.05  -1.86  -1.78  -1.75  -1.72  -1.66  921.0    1.0\n",
      "Mtrue[485]       -1.78  5.0e-3   0.09  -2.07   -1.8  -1.76  -1.73  -1.67  323.0   1.01\n",
      "Mtrue[486]       -1.73  1.8e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64  765.0    1.0\n",
      "Mtrue[487]       -1.73  8.3e-4   0.04  -1.81  -1.76  -1.73  -1.71  -1.65 2589.0    1.0\n",
      "Mtrue[488]       -1.74  3.5e-3   0.06  -1.86  -1.77  -1.74  -1.71  -1.64  301.0   1.02\n",
      "Mtrue[489]       -1.75  1.1e-3   0.05  -1.84  -1.77  -1.74  -1.72  -1.66 1734.0    1.0\n",
      "Mtrue[490]       -1.73  1.3e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 1388.0    1.0\n",
      "Mtrue[491]       -1.69  5.2e-3   0.09  -1.79  -1.74  -1.71  -1.68  -1.41  276.0   1.03\n",
      "Mtrue[492]       -1.74  1.2e-3   0.04  -1.83  -1.77  -1.74  -1.72  -1.66 1491.0    1.0\n",
      "Mtrue[493]       -1.71  6.8e-3   0.08  -1.81  -1.75  -1.72  -1.69  -1.47  138.0   1.02\n",
      "Mtrue[494]       -1.72  8.3e-3   0.08  -1.82  -1.76  -1.73   -1.7  -1.49  103.0   1.04\n",
      "Mtrue[495]       -1.78  5.3e-3   0.09  -2.09   -1.8  -1.76  -1.73  -1.68  297.0   1.01\n",
      "Mtrue[496]       -1.74  1.0e-3   0.05  -1.82  -1.76  -1.74  -1.71  -1.65 1890.0    1.0\n",
      "Mtrue[497]       -1.73  1.4e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63 1259.0    1.0\n",
      "Mtrue[498]       -1.72  1.1e-3   0.04   -1.8  -1.75  -1.72  -1.69  -1.63 1612.0    1.0\n",
      "Mtrue[499]        -1.7  3.7e-3   0.06   -1.8  -1.74  -1.71  -1.68  -1.52  302.0   1.02\n",
      "Mtrue[500]       -1.75  3.3e-3   0.06  -1.86  -1.77  -1.74  -1.71  -1.65  364.0   1.01\n",
      "Mtrue[501]       -1.75  9.3e-4   0.04  -1.84  -1.77  -1.75  -1.72  -1.66 2297.0    1.0\n",
      "Mtrue[502]       -1.76  4.0e-3   0.06  -1.91  -1.79  -1.75  -1.73  -1.67  261.0   1.01\n",
      "Mtrue[503]       -1.75  9.1e-4   0.05  -1.84  -1.78  -1.75  -1.72  -1.66 3000.0    1.0\n",
      "Mtrue[504]       -1.74  1.9e-3   0.05  -1.85  -1.77  -1.74  -1.71  -1.64  825.0    1.0\n",
      "Mtrue[505]       -1.73  2.0e-3   0.05  -1.83  -1.76  -1.73   -1.7  -1.63  747.0    1.0\n",
      "Mtrue[506]       -1.76  2.7e-3   0.05  -1.89  -1.79  -1.76  -1.73  -1.68  415.0    1.0\n",
      "Mtrue[507]       -1.75  5.9e-3   0.07  -1.86  -1.77  -1.74  -1.71  -1.64  133.0   1.03\n",
      "Mtrue[508]       -1.74  1.4e-3   0.05  -1.82  -1.76  -1.74  -1.71  -1.65 1220.0    1.0\n",
      "Mtrue[509]       -1.73  1.2e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63 1592.0    1.0\n",
      "Mtrue[510]       -1.74  8.9e-4   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 2915.0    1.0\n",
      "Mtrue[511]       -1.75  2.1e-3   0.05  -1.86  -1.78  -1.75  -1.72  -1.67  661.0    1.0\n",
      "Mtrue[512]       -1.76  3.0e-3   0.07  -1.96  -1.79  -1.76  -1.72  -1.67  508.0    1.0\n",
      "Mtrue[513]       -1.74  4.9e-3   0.07  -1.84  -1.77  -1.74  -1.71  -1.64  184.0   1.02\n",
      "Mtrue[514]       -1.73  8.7e-4   0.04  -1.81  -1.76  -1.73  -1.71  -1.64 2288.0    1.0\n",
      "Mtrue[515]       -1.74  1.2e-3   0.04  -1.83  -1.77  -1.74  -1.71  -1.66 1473.0    1.0\n",
      "Mtrue[516]       -1.74  1.0e-3   0.05  -1.83  -1.76  -1.74  -1.71  -1.65 2109.0    1.0\n",
      "Mtrue[517]       -1.74  1.5e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.64 1357.0    1.0\n",
      "Mtrue[518]       -1.74  1.0e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 2039.0    1.0\n",
      "Mtrue[519]       -1.75  4.8e-3   0.09  -1.95  -1.77  -1.74  -1.71  -1.62  354.0   1.01\n",
      "Mtrue[520]       -1.75  3.8e-3   0.06  -1.91  -1.78  -1.75  -1.72  -1.67  299.0   1.01\n",
      "Mtrue[521]       -1.75  2.6e-3   0.06  -1.88  -1.77  -1.74  -1.71  -1.65  542.0    1.0\n",
      "Mtrue[522]       -1.74  1.0e-3   0.04  -1.82  -1.76  -1.74  -1.71  -1.64 1843.0    1.0\n",
      "Mtrue[523]       -1.74  9.0e-4   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 2984.0    1.0\n",
      "Mtrue[524]       -1.72  1.9e-3   0.06  -1.81  -1.75  -1.72  -1.69   -1.6  879.0    1.0\n",
      "Mtrue[525]       -1.73  9.3e-4   0.04  -1.82  -1.76  -1.73  -1.71  -1.65 2263.0    1.0\n",
      "Mtrue[526]       -1.75  2.5e-3   0.06  -1.89  -1.77  -1.74  -1.71  -1.66  603.0    1.0\n",
      "Mtrue[527]       -1.74  1.5e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.64 1387.0    1.0\n",
      "Mtrue[528]       -1.74  9.1e-4   0.04  -1.82  -1.77  -1.74  -1.71  -1.65 2364.0    1.0\n",
      "Mtrue[529]       -1.76  3.5e-3   0.07  -1.98  -1.78  -1.75  -1.72  -1.66  413.0    1.0\n",
      "Mtrue[530]       -1.72  4.2e-3   0.07  -1.81  -1.75  -1.72  -1.69  -1.56  262.0   1.01\n",
      "Mtrue[531]       -1.72  1.1e-3   0.05  -1.81  -1.75  -1.73   -1.7  -1.63 1702.0    1.0\n",
      "Mtrue[532]       -1.75  1.4e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.65 1465.0    1.0\n",
      "Mtrue[533]       -1.74  9.1e-4   0.04  -1.83  -1.77  -1.74  -1.71  -1.65 2394.0    1.0\n",
      "Mtrue[534]       -1.77  3.7e-3   0.08  -2.01   -1.8  -1.76  -1.73  -1.68  408.0   1.01\n",
      "Mtrue[535]       -1.74  1.2e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.64 1568.0    1.0\n",
      "Mtrue[536]       -1.74  1.8e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.64  848.0    1.0\n",
      "Mtrue[537]       -1.75  8.0e-4   0.04  -1.84  -1.78  -1.75  -1.72  -1.67 2936.0    1.0\n",
      "Mtrue[538]       -1.76  1.0e-3   0.04  -1.84  -1.78  -1.75  -1.73  -1.67 1969.0    1.0\n",
      "Mtrue[539]       -1.73  9.9e-4   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 2102.0    1.0\n",
      "Mtrue[540]       -1.75  9.3e-4   0.04  -1.83  -1.77  -1.75  -1.72  -1.66 2095.0    1.0\n",
      "Mtrue[541]       -1.74  1.0e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 2277.0    1.0\n",
      "Mtrue[542]       -1.73  1.4e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63 1260.0    1.0\n",
      "Mtrue[543]       -1.73  9.9e-4   0.05  -1.82  -1.76  -1.73  -1.71  -1.64 2159.0    1.0\n",
      "Mtrue[544]       -1.73  7.8e-4   0.04  -1.81  -1.75  -1.73   -1.7  -1.64 3122.0    1.0\n",
      "Mtrue[545]       -1.74  8.8e-4   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 3099.0    1.0\n",
      "Mtrue[546]       -1.75  1.0e-3   0.05  -1.84  -1.77  -1.74  -1.72  -1.66 2061.0    1.0\n",
      "Mtrue[547]       -1.74  1.9e-3   0.06  -1.84  -1.77  -1.74  -1.71  -1.63  968.0    1.0\n",
      "Mtrue[548]       -1.74  1.8e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65  952.0    1.0\n",
      "Mtrue[549]       -1.74  1.1e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 2143.0    1.0\n",
      "Mtrue[550]       -1.76  4.7e-3   0.07  -1.97  -1.78  -1.75  -1.72  -1.67  198.0   1.01\n",
      "Mtrue[551]       -1.72  1.1e-3   0.05   -1.8  -1.74  -1.72  -1.69  -1.63 1647.0    1.0\n",
      "Mtrue[552]       -1.74  1.9e-3   0.05  -1.85  -1.77  -1.74  -1.71  -1.65  800.0    1.0\n",
      "Mtrue[553]       -1.71  2.4e-3   0.05  -1.79  -1.74  -1.71  -1.68  -1.58  500.0   1.01\n",
      "Mtrue[554]       -1.72  1.2e-3   0.05  -1.81  -1.75  -1.72  -1.69  -1.62 1839.0    1.0\n",
      "Mtrue[555]       -1.75  1.5e-3   0.05  -1.86  -1.78  -1.75  -1.72  -1.67 1122.0    1.0\n",
      "Mtrue[556]       -1.75  1.4e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.66 1358.0    1.0\n",
      "Mtrue[557]       -1.67  8.4e-3   0.12  -1.79  -1.74   -1.7  -1.66  -1.28  205.0   1.01\n",
      "Mtrue[558]       -1.75  1.2e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.66 1835.0    1.0\n",
      "Mtrue[559]       -1.74  1.2e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 1536.0    1.0\n",
      "Mtrue[560]       -1.73  2.0e-3   0.06  -1.82  -1.76  -1.73   -1.7  -1.61  842.0    1.0\n",
      "Mtrue[561]       -1.76  2.8e-3   0.06   -1.9  -1.78  -1.75  -1.72  -1.66  471.0   1.01\n",
      "Mtrue[562]       -1.77  4.4e-3   0.07  -1.99  -1.79  -1.76  -1.73  -1.67  269.0   1.02\n",
      "Mtrue[563]       -1.73  6.8e-4   0.04  -1.82  -1.76  -1.73  -1.71  -1.65 4089.0    1.0\n",
      "Mtrue[564]       -1.73  9.6e-4   0.04  -1.82  -1.76  -1.73  -1.71  -1.65 1952.0    1.0\n",
      "Mtrue[565]       -1.73  6.8e-4   0.04  -1.81  -1.76  -1.73   -1.7  -1.64 4120.0    1.0\n",
      "Mtrue[566]       -1.73  1.2e-3   0.05  -1.83  -1.77  -1.73   -1.7  -1.63 1617.0    1.0\n",
      "Mtrue[567]       -1.73  1.0e-3   0.05  -1.83  -1.76  -1.73   -1.7  -1.64 2158.0    1.0\n",
      "Mtrue[568]       -1.73  9.3e-4   0.04  -1.81  -1.75  -1.73   -1.7  -1.64 2045.0    1.0\n",
      "Mtrue[569]       -1.76  1.2e-3   0.05  -1.85  -1.79  -1.76  -1.73  -1.68 1336.0    1.0\n",
      "Mtrue[570]       -1.76  1.6e-3   0.05  -1.87  -1.79  -1.76  -1.73  -1.69  852.0   1.01\n",
      "Mtrue[571]       -1.73  2.4e-3   0.06  -1.84  -1.76  -1.73   -1.7  -1.63  568.0    1.0\n",
      "Mtrue[572]       -1.74  1.3e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1562.0    1.0\n",
      "Mtrue[573]       -1.75  2.1e-3   0.06  -1.87  -1.77  -1.74  -1.72  -1.65  785.0   1.01\n",
      "Mtrue[574]       -1.74  1.4e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1152.0    1.0\n",
      "Mtrue[575]       -1.75  1.3e-3   0.05  -1.85  -1.78  -1.74  -1.72  -1.65 1540.0    1.0\n",
      "Mtrue[576]       -1.82    0.02   0.16  -2.27  -1.82  -1.77  -1.73  -1.67   91.0   1.02\n",
      "Mtrue[577]       -1.74  8.0e-4   0.04  -1.82  -1.76  -1.74  -1.71  -1.65 2910.0    1.0\n",
      "Mtrue[578]       -1.74  1.8e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.64  813.0    1.0\n",
      "Mtrue[579]       -1.75  1.4e-3   0.05  -1.85  -1.78  -1.75  -1.71  -1.66 1271.0    1.0\n",
      "Mtrue[580]       -1.74  4.0e-3   0.06  -1.87  -1.77  -1.74  -1.71  -1.65  194.0   1.03\n",
      "Mtrue[581]       -1.75  1.7e-3   0.05  -1.86  -1.78  -1.75  -1.72  -1.66  960.0    1.0\n",
      "Mtrue[582]       -1.75  2.4e-3   0.05  -1.85  -1.77  -1.74  -1.71  -1.66  496.0   1.01\n",
      "Mtrue[583]       -1.73  1.6e-3   0.05  -1.82  -1.76  -1.73  -1.71  -1.65  930.0    1.0\n",
      "Mtrue[584]       -1.74  1.2e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.64 1999.0    1.0\n",
      "Mtrue[585]       -1.75  2.1e-3   0.06  -1.86  -1.77  -1.74  -1.71  -1.65  712.0    1.0\n",
      "Mtrue[586]       -1.77  2.2e-3   0.06  -1.92   -1.8  -1.76  -1.74  -1.68  653.0    1.0\n",
      "Mtrue[587]       -1.74  1.1e-3   0.04  -1.83  -1.77  -1.74  -1.71  -1.66 1733.0    1.0\n",
      "Mtrue[588]       -1.77  7.3e-3   0.08   -2.0  -1.79  -1.75  -1.73  -1.67  110.0   1.04\n",
      "Mtrue[589]       -1.74  8.1e-4   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 3490.0    1.0\n",
      "Mtrue[590]       -1.73  2.3e-3   0.05  -1.83  -1.76  -1.73   -1.7  -1.62  551.0   1.01\n",
      "Mtrue[591]       -1.72  2.4e-3   0.05   -1.8  -1.75  -1.72  -1.69  -1.63  428.0   1.01\n",
      "Mtrue[592]       -1.73  1.2e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63 1685.0    1.0\n",
      "Mtrue[593]       -1.73  2.1e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.62  602.0    1.0\n",
      "Mtrue[594]       -1.75  1.5e-3   0.05  -1.85  -1.77  -1.74  -1.72  -1.66 1069.0   1.01\n",
      "Mtrue[595]       -1.75  1.4e-3   0.05  -1.86  -1.78  -1.75  -1.72  -1.65 1450.0    1.0\n",
      "Mtrue[596]       -1.73  9.7e-4   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 2221.0    1.0\n",
      "Mtrue[597]       -1.73  1.1e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63 2122.0    1.0\n",
      "Mtrue[598]       -1.72  1.7e-3   0.05  -1.82  -1.75  -1.72  -1.69  -1.62 1027.0    1.0\n",
      "Mtrue[599]       -1.74  1.6e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.66  859.0    1.0\n",
      "Mtrue[600]        -1.6    0.01   0.16  -1.77  -1.71  -1.67  -1.48  -1.23  225.0   1.02\n",
      "Mtrue[601]       -1.73  1.1e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.62 1937.0    1.0\n",
      "Mtrue[602]        -1.7  1.6e-3   0.05  -1.79  -1.74  -1.71  -1.68  -1.58 1068.0    1.0\n",
      "Mtrue[603]       -1.71  4.5e-3   0.08  -1.81  -1.75  -1.72  -1.69  -1.45  318.0   1.01\n",
      "Mtrue[604]       -1.71  2.9e-3   0.06  -1.81  -1.75  -1.72  -1.69  -1.55  430.0   1.01\n",
      "Mtrue[605]       -1.74  1.2e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 1501.0    1.0\n",
      "Mtrue[606]       -1.74  1.5e-3   0.05  -1.82  -1.77  -1.74  -1.71  -1.65 1031.0    1.0\n",
      "Mtrue[607]        -1.7  2.4e-3   0.06  -1.79  -1.73  -1.71  -1.68  -1.54  566.0    1.0\n",
      "Mtrue[608]       -1.75  1.2e-3   0.05  -1.86  -1.78  -1.75  -1.72  -1.66 1812.0    1.0\n",
      "Mtrue[609]       -1.76  8.9e-4   0.05  -1.85  -1.78  -1.75  -1.73  -1.67 2690.0    1.0\n",
      "Mtrue[610]       -1.75  1.4e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1400.0    1.0\n",
      "Mtrue[611]       -1.74  9.2e-4   0.04  -1.83  -1.77  -1.74  -1.72  -1.66 2110.0    1.0\n",
      "Mtrue[612]       -1.74  1.3e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 1326.0    1.0\n",
      "Mtrue[613]       -1.74  8.9e-4   0.04  -1.82  -1.76  -1.74  -1.71  -1.66 2126.0    1.0\n",
      "Mtrue[614]       -1.73  9.0e-4   0.04  -1.81  -1.76  -1.73  -1.71  -1.66 2135.0    1.0\n",
      "Mtrue[615]       -1.43    0.02   0.23  -1.76  -1.67  -1.39  -1.25  -1.01  135.0   1.01\n",
      "Mtrue[616]       -1.73  1.6e-3   0.06  -1.83  -1.76  -1.73   -1.7  -1.63 1258.0    1.0\n",
      "Mtrue[617]       -1.74  1.1e-3   0.05  -1.83  -1.76  -1.73  -1.71  -1.65 1706.0    1.0\n",
      "Mtrue[618]       -1.73  3.0e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63  313.0   1.01\n",
      "Mtrue[619]       -1.68  7.0e-3   0.11   -1.8  -1.74  -1.71  -1.67  -1.34  242.0   1.01\n",
      "Mtrue[620]       -1.71  1.5e-3   0.05  -1.79  -1.74  -1.71  -1.68  -1.59 1109.0    1.0\n",
      "Mtrue[621]       -1.73  8.5e-4   0.05  -1.83  -1.77  -1.73   -1.7  -1.64 3024.0    1.0\n",
      "Mtrue[622]       -1.73  1.5e-3   0.05  -1.82  -1.77  -1.73  -1.71  -1.64 1235.0    1.0\n",
      "Mtrue[623]        -1.8  3.2e-3   0.08  -2.03  -1.82  -1.79  -1.76  -1.71  601.0    1.0\n",
      "Mtrue[624]       -1.71  2.1e-3   0.06   -1.8  -1.74  -1.72  -1.69  -1.57  701.0    1.0\n",
      "Mtrue[625]       -1.75  2.3e-3   0.06  -1.89  -1.78  -1.75  -1.72  -1.66  641.0    1.0\n",
      "Mtrue[626]       -1.69  8.9e-3   0.11  -1.81  -1.75  -1.71  -1.68  -1.33  165.0   1.03\n",
      "Mtrue[627]       -1.74  1.0e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.66 1973.0    1.0\n",
      "Mtrue[628]       -1.73  8.3e-4   0.04  -1.82  -1.76  -1.73   -1.7  -1.64 2815.0    1.0\n",
      "Mtrue[629]       -1.75  4.4e-3   0.07  -1.93  -1.78  -1.75  -1.72  -1.65  249.0   1.01\n",
      "Mtrue[630]       -1.74  2.0e-3   0.06  -1.84  -1.77  -1.74  -1.71  -1.62 1067.0    1.0\n",
      "Mtrue[631]       -1.72  4.4e-3   0.07  -1.81  -1.75  -1.72  -1.69  -1.58  246.0   1.02\n",
      "Mtrue[632]       -1.73  1.0e-3   0.04  -1.82  -1.76  -1.73   -1.7  -1.64 1856.0    1.0\n",
      "Mtrue[633]       -1.74  2.6e-3   0.06  -1.87  -1.77  -1.74  -1.71  -1.64  611.0    1.0\n",
      "Mtrue[634]       -1.74  9.4e-4   0.05  -1.84  -1.77  -1.74  -1.71  -1.64 2610.0    1.0\n",
      "Mtrue[635]       -1.85    0.01   0.16  -2.27  -1.91  -1.79  -1.75  -1.68  195.0   1.02\n",
      "Mtrue[636]       -1.74  1.0e-3   0.04  -1.82  -1.77  -1.74  -1.71  -1.66 1756.0    1.0\n",
      "Mtrue[637]       -1.73  8.6e-4   0.04  -1.82  -1.76  -1.74  -1.71  -1.65 2546.0    1.0\n",
      "Mtrue[638]       -1.74  1.3e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 1412.0    1.0\n",
      "Mtrue[639]       -1.75  8.4e-4   0.05  -1.85  -1.78  -1.75  -1.72  -1.66 3312.0    1.0\n",
      "Mtrue[640]       -1.73  1.6e-3   0.06  -1.84  -1.76  -1.73   -1.7  -1.63 1253.0    1.0\n",
      "Mtrue[641]       -1.73  9.1e-4   0.05  -1.82  -1.76  -1.73  -1.71  -1.64 2550.0    1.0\n",
      "Mtrue[642]        -1.7  1.6e-3   0.05  -1.79  -1.74  -1.71  -1.68  -1.59 1045.0    1.0\n",
      "Mtrue[643]       -1.73  1.2e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63 1520.0    1.0\n",
      "Mtrue[644]       -1.75  2.4e-3   0.06  -1.84  -1.78  -1.75  -1.72  -1.66  567.0    1.0\n",
      "Mtrue[645]       -1.72  1.1e-3   0.05  -1.81  -1.76  -1.73   -1.7  -1.63 1972.0    1.0\n",
      "Mtrue[646]       -1.74  1.1e-3   0.05  -1.83  -1.76  -1.73  -1.71  -1.64 1669.0    1.0\n",
      "Mtrue[647]       -1.74  9.0e-4   0.05  -1.85  -1.77  -1.74  -1.71  -1.65 2996.0    1.0\n",
      "Mtrue[648]       -1.77  5.1e-3   0.08  -1.99  -1.79  -1.75  -1.72  -1.67  231.0   1.01\n",
      "Mtrue[649]       -1.74  1.3e-3   0.05  -1.85  -1.77  -1.74  -1.71  -1.65 1403.0    1.0\n",
      "Mtrue[650]       -1.63    0.01   0.17   -1.8  -1.73  -1.69  -1.61  -1.16  205.0   1.02\n",
      "Mtrue[651]       -1.77  4.6e-3   0.08  -2.01  -1.79  -1.76  -1.73  -1.67  319.0   1.01\n",
      "Mtrue[652]       -1.71  3.1e-3   0.07   -1.8  -1.75  -1.72  -1.68   -1.5  556.0    1.0\n",
      "Mtrue[653]       -1.72  2.7e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.61  382.0   1.01\n",
      "Mtrue[654]       -1.72  1.3e-3   0.05  -1.81  -1.75  -1.72   -1.7  -1.63 1336.0    1.0\n",
      "Mtrue[655]       -1.72  4.1e-3   0.07  -1.82  -1.75  -1.72  -1.69  -1.51  313.0   1.02\n",
      "Mtrue[656]       -1.69  2.5e-3   0.06  -1.78  -1.73   -1.7  -1.67   -1.5  655.0    1.0\n",
      "Mtrue[657]       -1.71  1.4e-3   0.05  -1.79  -1.74  -1.71  -1.69  -1.61 1148.0    1.0\n",
      "Mtrue[658]       -1.73  5.0e-3   0.08  -1.85  -1.77  -1.74  -1.71   -1.6  244.0   1.01\n",
      "Mtrue[659]       -1.74  1.2e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 1755.0    1.0\n",
      "Mtrue[660]       -1.74  1.7e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65  871.0    1.0\n",
      "Mtrue[661]       -1.76  3.0e-3   0.06   -1.9  -1.79  -1.75  -1.73  -1.67  406.0    1.0\n",
      "Mtrue[662]       -1.73  1.8e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.62  886.0    1.0\n",
      "Mtrue[663]       -1.73  8.5e-4   0.04  -1.83  -1.76  -1.73  -1.71  -1.65 2710.0    1.0\n",
      "Mtrue[664]       -1.72  3.5e-3   0.06  -1.81  -1.75  -1.72  -1.69  -1.57  327.0   1.01\n",
      "Mtrue[665]        -1.7  2.2e-3   0.06  -1.79  -1.74  -1.71  -1.68  -1.55  728.0    1.0\n",
      "Mtrue[666]       -1.74  1.7e-3   0.05  -1.85  -1.77  -1.74  -1.71  -1.64  949.0    1.0\n",
      "Mtrue[667]       -1.73  8.8e-4   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 2855.0    1.0\n",
      "Mtrue[668]       -1.74  8.4e-4   0.04  -1.83  -1.77  -1.74  -1.71  -1.66 2555.0    1.0\n",
      "Mtrue[669]        -1.6    0.01    0.2  -1.79  -1.73  -1.68  -1.49  -1.11  210.0   1.02\n",
      "Mtrue[670]       -1.74  3.3e-3   0.06  -1.85  -1.77  -1.74  -1.71  -1.64  370.0   1.01\n",
      "Mtrue[671]       -1.74  1.1e-3   0.05  -1.82  -1.76  -1.74  -1.71  -1.64 1720.0    1.0\n",
      "Mtrue[672]       -1.73  3.5e-3   0.06  -1.83  -1.76  -1.73   -1.7   -1.6  336.0   1.01\n",
      "Mtrue[673]       -1.72  1.8e-3   0.06  -1.82  -1.76  -1.73   -1.7  -1.61  922.0    1.0\n",
      "Mtrue[674]       -1.73  1.1e-3   0.04  -1.81  -1.76  -1.73   -1.7  -1.64 1679.0    1.0\n",
      "Mtrue[675]       -1.74  7.5e-4   0.04  -1.82  -1.77  -1.74  -1.72  -1.66 3034.0    1.0\n",
      "Mtrue[676]       -1.73  1.4e-3   0.05  -1.83  -1.76  -1.73   -1.7  -1.62 1426.0    1.0\n",
      "Mtrue[677]       -1.73  1.1e-3   0.05  -1.83  -1.76  -1.73   -1.7  -1.63 1849.0    1.0\n",
      "Mtrue[678]       -1.73  1.1e-3   0.04  -1.81  -1.76  -1.73   -1.7  -1.65 1689.0    1.0\n",
      "Mtrue[679]       -1.71  2.2e-3   0.05  -1.79  -1.74  -1.72  -1.69   -1.6  542.0    1.0\n",
      "Mtrue[680]       -1.74  1.4e-3   0.05  -1.82  -1.76  -1.74  -1.71  -1.65 1330.0    1.0\n",
      "Mtrue[681]       -1.77  2.1e-3   0.06  -1.92   -1.8  -1.77  -1.74  -1.68  769.0    1.0\n",
      "Mtrue[682]       -1.74  1.2e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1814.0    1.0\n",
      "Mtrue[683]       -1.73  1.1e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 1901.0    1.0\n",
      "Mtrue[684]       -1.75  3.5e-3   0.06  -1.88  -1.78  -1.74  -1.71  -1.65  335.0   1.01\n",
      "Mtrue[685]       -1.73  1.4e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63 1248.0    1.0\n",
      "Mtrue[686]       -1.75  3.6e-3   0.07  -1.93  -1.78  -1.74  -1.71  -1.65  375.0   1.01\n",
      "Mtrue[687]        -1.7  9.0e-3   0.08   -1.8  -1.74  -1.71  -1.68  -1.44   81.0   1.05\n",
      "Mtrue[688]       -1.74  2.8e-3   0.07  -1.88  -1.77  -1.74   -1.7  -1.63  575.0    1.0\n",
      "Mtrue[689]       -1.74  8.6e-4   0.04  -1.83  -1.77  -1.74  -1.72  -1.66 2433.0    1.0\n",
      "Mtrue[690]       -1.73  2.1e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.62  692.0    1.0\n",
      "Mtrue[691]       -1.74  1.0e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 2086.0    1.0\n",
      "Mtrue[692]       -1.74  2.5e-3   0.06  -1.85  -1.77  -1.74  -1.71  -1.64  505.0   1.01\n",
      "Mtrue[693]       -1.73  1.8e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63  760.0    1.0\n",
      "Mtrue[694]       -1.74  1.3e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.64 1439.0    1.0\n",
      "Mtrue[695]       -1.75  1.9e-3   0.06  -1.86  -1.77  -1.74  -1.71  -1.65  850.0   1.01\n",
      "Mtrue[696]       -1.79  5.2e-3    0.1  -2.09   -1.8  -1.76  -1.73  -1.67  346.0   1.01\n",
      "Mtrue[697]       -1.78  4.1e-3   0.08  -2.04   -1.8  -1.76  -1.73  -1.68  391.0   1.01\n",
      "Mtrue[698]       -1.75  4.4e-3   0.08  -1.89  -1.77  -1.74  -1.71  -1.63  362.0   1.01\n",
      "Mtrue[699]       -1.75 10.0e-4   0.04  -1.83  -1.77  -1.75  -1.72  -1.67 1729.0    1.0\n",
      "Mtrue[700]       -1.75  3.7e-3   0.07  -1.94  -1.78  -1.74  -1.72  -1.66  399.0    1.0\n",
      "Mtrue[701]       -1.68  6.7e-3   0.11   -1.8  -1.74  -1.71  -1.67  -1.34  267.0   1.01\n",
      "Mtrue[702]       -1.73  1.5e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 1092.0    1.0\n",
      "Mtrue[703]       -1.73  2.1e-3   0.06  -1.83  -1.76  -1.73   -1.7  -1.61  768.0    1.0\n",
      "Mtrue[704]        -1.7  2.7e-3   0.06  -1.79  -1.74  -1.71  -1.68  -1.56  492.0    1.0\n",
      "Mtrue[705]       -1.74  8.7e-4   0.04  -1.82  -1.76  -1.74  -1.71  -1.65 2431.0    1.0\n",
      "Mtrue[706]       -1.74  9.4e-4   0.04  -1.82  -1.76  -1.74  -1.71  -1.65 2197.0    1.0\n",
      "Mtrue[707]       -1.74  1.1e-3   0.05  -1.82  -1.76  -1.73  -1.71  -1.65 2039.0    1.0\n",
      "Mtrue[708]       -1.75  1.3e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.67 1340.0    1.0\n",
      "Mtrue[709]       -1.73  1.2e-3   0.04  -1.82  -1.76  -1.73   -1.7  -1.64 1488.0    1.0\n",
      "Mtrue[710]       -1.75  4.3e-3   0.06  -1.91  -1.78  -1.75  -1.72  -1.66  219.0   1.02\n",
      "Mtrue[711]       -1.76  4.6e-3   0.07  -1.98  -1.79  -1.76  -1.72  -1.66  249.0   1.01\n",
      "Mtrue[712]       -1.73  1.4e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63 1121.0    1.0\n",
      "Mtrue[713]       -1.73  1.0e-3   0.05  -1.81  -1.75  -1.73   -1.7  -1.63 1895.0    1.0\n",
      "Mtrue[714]       -1.73  1.5e-3   0.05  -1.83  -1.76  -1.73   -1.7  -1.63 1210.0    1.0\n",
      "Mtrue[715]       -1.75  9.2e-4   0.05  -1.85  -1.78  -1.75  -1.72  -1.67 2419.0    1.0\n",
      "Mtrue[716]       -1.73  1.6e-3   0.05  -1.83  -1.76  -1.73   -1.7  -1.64  961.0    1.0\n",
      "Mtrue[717]       -1.94    0.02   0.21  -2.36  -2.11  -1.84  -1.77   -1.7  161.0   1.02\n",
      "Mtrue[718]       -1.73  1.5e-3   0.05  -1.82  -1.76  -1.74  -1.71  -1.64 1080.0    1.0\n",
      "Mtrue[719]       -1.74  1.7e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65  871.0    1.0\n",
      "Mtrue[720]       -1.74  3.0e-3   0.06  -1.84  -1.77  -1.74  -1.71  -1.63  384.0   1.01\n",
      "Mtrue[721]       -1.78  6.1e-3    0.1  -2.12   -1.8  -1.76  -1.73  -1.67  258.0   1.01\n",
      "Mtrue[722]       -1.73  1.3e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.62 1489.0    1.0\n",
      "Mtrue[723]       -1.72  1.2e-3   0.05   -1.8  -1.75  -1.72  -1.69  -1.62 1458.0    1.0\n",
      "Mtrue[724]       -1.78  5.9e-3   0.09  -2.06   -1.8  -1.76  -1.73  -1.67  238.0   1.02\n",
      "Mtrue[725]       -1.71  4.5e-3   0.07   -1.8  -1.74  -1.72  -1.68  -1.49  229.0   1.02\n",
      "Mtrue[726]       -1.73  8.4e-4   0.04  -1.81  -1.76  -1.73   -1.7  -1.65 2511.0    1.0\n",
      "Mtrue[727]       -1.73  1.2e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.65 1447.0    1.0\n",
      "Mtrue[728]       -1.74  9.5e-4   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 2366.0    1.0\n",
      "Mtrue[729]       -1.75  1.9e-3   0.06  -1.87  -1.78  -1.75  -1.72  -1.66  923.0    1.0\n",
      "Mtrue[730]       -1.77  7.3e-3    0.1  -2.08  -1.79  -1.75  -1.72  -1.65  188.0   1.03\n",
      "Mtrue[731]       -1.74  1.1e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1976.0    1.0\n",
      "Mtrue[732]       -1.73  8.9e-4   0.04  -1.81  -1.76  -1.73   -1.7  -1.65 2394.0    1.0\n",
      "Mtrue[733]       -1.75  1.9e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.66  737.0    1.0\n",
      "Mtrue[734]       -1.74  1.0e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 2275.0    1.0\n",
      "Mtrue[735]       -1.72  1.5e-3   0.05  -1.82  -1.75  -1.72  -1.69  -1.61 1224.0    1.0\n",
      "Mtrue[736]       -1.74  1.7e-3   0.06  -1.84  -1.77  -1.74  -1.71  -1.64 1016.0    1.0\n",
      "Mtrue[737]       -1.73  9.8e-4   0.04  -1.82  -1.76  -1.73  -1.71  -1.65 1993.0    1.0\n",
      "Mtrue[738]       -1.74  4.4e-3   0.08  -1.86  -1.77  -1.74  -1.71  -1.62  305.0   1.02\n",
      "Mtrue[739]       -1.74  3.7e-3   0.07  -1.84  -1.77  -1.74  -1.71  -1.64  312.0   1.01\n",
      "Mtrue[740]       -1.73  1.6e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.61 1116.0    1.0\n",
      "Mtrue[741]       -1.73  1.7e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.62  874.0    1.0\n",
      "Mtrue[742]       -1.73  3.2e-3   0.07  -1.84  -1.77  -1.73   -1.7  -1.61  478.0   1.01\n",
      "Mtrue[743]       -1.74  1.1e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.64 2298.0    1.0\n",
      "Mtrue[744]       -1.74  1.8e-3   0.06  -1.83  -1.77  -1.74  -1.71  -1.63  908.0    1.0\n",
      "Mtrue[745]       -1.76  2.8e-3   0.06  -1.88  -1.78  -1.75  -1.72  -1.67  468.0   1.01\n",
      "Mtrue[746]       -1.73  1.3e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 1383.0    1.0\n",
      "Mtrue[747]       -1.74  1.3e-3   0.05  -1.83  -1.76  -1.73  -1.71  -1.64 1458.0    1.0\n",
      "Mtrue[748]        -1.7  2.4e-3   0.06  -1.79  -1.74  -1.71  -1.68  -1.56  578.0    1.0\n",
      "Mtrue[749]       -1.76  1.9e-3   0.05  -1.87  -1.78  -1.75  -1.72  -1.67  781.0    1.0\n",
      "Mtrue[750]       -1.72  2.0e-3   0.06   -1.8  -1.75  -1.72  -1.69  -1.59  786.0    1.0\n",
      "Mtrue[751]       -1.73  2.6e-3   0.05  -1.82  -1.76  -1.73  -1.71  -1.63  438.0   1.01\n",
      "Mtrue[752]       -1.72  9.7e-4   0.04   -1.8  -1.75  -1.73   -1.7  -1.64 1942.0    1.0\n",
      "Mtrue[753]       -1.74  1.0e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 2146.0    1.0\n",
      "Mtrue[754]       -1.73  9.4e-4   0.05  -1.83  -1.76  -1.74   -1.7  -1.64 2494.0    1.0\n",
      "Mtrue[755]       -1.65  7.5e-3   0.13  -1.78  -1.73   -1.7  -1.65  -1.27  315.0   1.02\n",
      "Mtrue[756]       -1.67    0.01   0.13  -1.81  -1.74  -1.71  -1.67  -1.25  154.0   1.01\n",
      "Mtrue[757]       -1.74  1.0e-3   0.04  -1.83  -1.77  -1.74  -1.71  -1.66 1968.0    1.0\n",
      "Mtrue[758]       -1.74  2.1e-3   0.06  -1.85  -1.77  -1.74  -1.71  -1.65  722.0   1.02\n",
      "Mtrue[759]       -1.75  3.2e-3   0.07  -1.88  -1.78  -1.74  -1.71  -1.65  449.0   1.02\n",
      "Mtrue[760]       -1.74  1.3e-3   0.05  -1.85  -1.77  -1.74  -1.72  -1.66 1390.0    1.0\n",
      "Mtrue[761]       -1.28    0.01   0.21  -1.73  -1.39  -1.25  -1.14  -0.92  218.0   1.01\n",
      "Mtrue[762]       -1.75  2.6e-3   0.05  -1.87  -1.77  -1.75  -1.72  -1.66  442.0   1.01\n",
      "Mtrue[763]        -1.7  5.6e-3   0.09  -1.81  -1.75  -1.71  -1.68  -1.39  289.0   1.02\n",
      "Mtrue[764]       -1.75  1.2e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.65 1692.0    1.0\n",
      "Mtrue[765]       -1.74  1.1e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1836.0    1.0\n",
      "Mtrue[766]       -1.75  1.0e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.66 2339.0    1.0\n",
      "Mtrue[767]       -1.73  1.8e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64  755.0    1.0\n",
      "Mtrue[768]       -1.74  8.0e-4   0.04  -1.83  -1.77  -1.74  -1.72  -1.66 2982.0    1.0\n",
      "Mtrue[769]       -1.74  1.4e-3   0.05  -1.83  -1.76  -1.74  -1.71  -1.64 1139.0    1.0\n",
      "Mtrue[770]       -1.74  1.4e-3   0.05  -1.83  -1.76  -1.74  -1.71  -1.64 1362.0    1.0\n",
      "Mtrue[771]       -1.72  1.8e-3   0.05  -1.81  -1.75  -1.73   -1.7  -1.63  718.0   1.01\n",
      "Mtrue[772]       -1.72  1.4e-3   0.05   -1.8  -1.75  -1.72  -1.69  -1.62 1206.0    1.0\n",
      "Mtrue[773]       -1.74  2.1e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65  625.0   1.01\n",
      "Mtrue[774]       -1.82  4.8e-3   0.11  -2.13  -1.84  -1.79  -1.75   -1.7  530.0    1.0\n",
      "Mtrue[775]       -1.73  1.4e-3   0.05  -1.82  -1.77  -1.74  -1.71  -1.64 1236.0    1.0\n",
      "Mtrue[776]       -1.75  1.6e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.67  779.0    1.0\n",
      "Mtrue[777]       -1.72  2.2e-3   0.06  -1.81  -1.75  -1.72   -1.7  -1.59  640.0    1.0\n",
      "Mtrue[778]       -1.72  2.1e-3   0.06  -1.82  -1.75  -1.72  -1.69  -1.57  874.0    1.0\n",
      "Mtrue[779]       -1.69  6.6e-3    0.1   -1.8  -1.75  -1.71  -1.68  -1.39  220.0   1.02\n",
      "Mtrue[780]       -1.76  2.1e-3   0.06  -1.87  -1.78  -1.75  -1.72  -1.67  686.0    1.0\n",
      "Mtrue[781]       -1.76  3.6e-3   0.06  -1.88  -1.78  -1.75  -1.72  -1.67  312.0   1.01\n",
      "Mtrue[782]       -1.75  1.4e-3   0.05  -1.84  -1.78  -1.74  -1.72  -1.66 1105.0    1.0\n",
      "Mtrue[783]       -1.74  1.2e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1730.0    1.0\n",
      "Mtrue[784]       -1.75  1.9e-3   0.05  -1.87  -1.78  -1.75  -1.72  -1.66  767.0    1.0\n",
      "Mtrue[785]       -1.74  1.6e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65  992.0    1.0\n",
      "Mtrue[786]       -1.75  1.8e-3   0.05  -1.85  -1.78  -1.75  -1.71  -1.66  938.0    1.0\n",
      "Mtrue[787]       -1.74  2.8e-3   0.06  -1.86  -1.77  -1.74  -1.71  -1.65  451.0   1.01\n",
      "Mtrue[788]       -1.47    0.01   0.18  -1.74  -1.66  -1.44  -1.34  -1.15  209.0   1.03\n",
      "Mtrue[789]       -1.76  2.9e-3   0.06  -1.93  -1.79  -1.76  -1.73  -1.67  405.0   1.01\n",
      "Mtrue[790]       -1.74  1.8e-3   0.05  -1.85  -1.77  -1.74  -1.71  -1.65  819.0    1.0\n",
      "Mtrue[791]       -1.73  1.3e-3   0.05  -1.81  -1.76  -1.73   -1.7  -1.64 1402.0    1.0\n",
      "Mtrue[792]       -1.74  5.4e-3   0.08  -1.85  -1.77  -1.74  -1.71  -1.62  212.0   1.01\n",
      "Mtrue[793]       -1.75  1.8e-3   0.05  -1.87  -1.78  -1.75  -1.72  -1.66  861.0    1.0\n",
      "Mtrue[794]       -1.76  1.2e-3   0.05  -1.86  -1.78  -1.76  -1.73  -1.67 1457.0    1.0\n",
      "Mtrue[795]       -1.74  1.0e-3   0.05  -1.83  -1.77  -1.74   -1.7  -1.65 2223.0    1.0\n",
      "Mtrue[796]       -1.73  1.1e-3   0.04  -1.81  -1.76  -1.73   -1.7  -1.64 1655.0    1.0\n",
      "Mtrue[797]       -1.72  3.9e-3   0.06  -1.81  -1.75  -1.72  -1.69  -1.54  261.0   1.01\n",
      "Mtrue[798]        -1.7  4.8e-3   0.08   -1.8  -1.74  -1.71  -1.68  -1.44  284.0   1.02\n",
      "Mtrue[799]       -1.71  1.9e-3   0.05   -1.8  -1.74  -1.71  -1.68  -1.56  856.0    1.0\n",
      "Mtrue[800]       -1.74  1.4e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.64 1385.0    1.0\n",
      "Mtrue[801]       -1.71  1.3e-3   0.05  -1.79  -1.74  -1.71  -1.68   -1.6 1244.0   1.01\n",
      "Mtrue[802]       -1.74  1.1e-3   0.04  -1.82  -1.77  -1.74  -1.71  -1.66 1578.0    1.0\n",
      "Mtrue[803]       -1.74  9.8e-4   0.05  -1.83  -1.76  -1.74  -1.71  -1.65 2131.0    1.0\n",
      "Mtrue[804]       -2.01    0.01    0.2  -2.36  -2.17  -2.03   -1.8  -1.72  286.0   1.01\n",
      "Mtrue[805]       -1.73  3.3e-3   0.07  -1.86  -1.77  -1.74   -1.7  -1.59  420.0   1.01\n",
      "Mtrue[806]       -1.74 10.0e-4   0.04  -1.82  -1.76  -1.73  -1.71  -1.65 1824.0    1.0\n",
      "Mtrue[807]       -1.73  9.9e-4   0.04  -1.82  -1.76  -1.73  -1.71  -1.66 1692.0    1.0\n",
      "Mtrue[808]       -1.74  1.2e-3   0.05  -1.85  -1.77  -1.74  -1.71  -1.66 1781.0    1.0\n",
      "Mtrue[809]       -1.74  1.0e-3   0.05  -1.84  -1.77  -1.74  -1.72  -1.65 2156.0    1.0\n",
      "Mtrue[810]       -1.73  9.2e-4   0.05  -1.82  -1.76  -1.73   -1.7  -1.63 2702.0    1.0\n",
      "Mtrue[811]       -1.79  6.1e-3    0.1   -2.1   -1.8  -1.77  -1.74  -1.67  269.0   1.03\n",
      "Mtrue[812]       -1.73  1.5e-3   0.06  -1.84  -1.77  -1.74  -1.71  -1.62 1626.0    1.0\n",
      "Mtrue[813]       -1.74  9.1e-4   0.04  -1.82  -1.77  -1.74  -1.71  -1.65 2303.0    1.0\n",
      "Mtrue[814]       -1.73  1.4e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 1155.0    1.0\n",
      "Mtrue[815]       -1.73  2.8e-3   0.06  -1.82  -1.76  -1.73   -1.7  -1.62  397.0   1.01\n",
      "Mtrue[816]       -1.74  1.9e-3   0.06  -1.85  -1.77  -1.74  -1.71  -1.64 1087.0    1.0\n",
      "Mtrue[817]       -1.76  5.7e-3   0.07  -1.91  -1.78  -1.75  -1.72  -1.66  145.0   1.03\n",
      "Mtrue[818]       -1.73  1.9e-3   0.05  -1.81  -1.76  -1.73   -1.7  -1.64  654.0   1.01\n",
      "Mtrue[819]       -1.74  9.6e-4   0.05  -1.83  -1.76  -1.74  -1.71  -1.64 2199.0    1.0\n",
      "Mtrue[820]       -1.73  9.9e-4   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 2297.0    1.0\n",
      "Mtrue[821]       -1.75  1.7e-3   0.05  -1.86  -1.78  -1.75  -1.72  -1.67  899.0    1.0\n",
      "Mtrue[822]       -1.72  2.1e-3   0.06  -1.81  -1.75  -1.73   -1.7  -1.62  717.0    1.0\n",
      "Mtrue[823]       -1.76  1.9e-3   0.05  -1.87  -1.78  -1.75  -1.73  -1.67  701.0   1.01\n",
      "Mtrue[824]       -1.73  1.2e-3   0.05  -1.83  -1.76  -1.73   -1.7  -1.64 1737.0    1.0\n",
      "Mtrue[825]       -1.71  1.9e-3   0.05   -1.8  -1.74  -1.72  -1.68   -1.6  793.0   1.01\n",
      "Mtrue[826]       -1.66  6.5e-3   0.11  -1.78  -1.72  -1.69  -1.65  -1.35  275.0   1.03\n",
      "Mtrue[827]       -1.69    0.01    0.1   -1.8  -1.74  -1.71  -1.68  -1.35   83.0   1.06\n",
      "Mtrue[828]       -1.77  7.4e-3   0.09  -2.08  -1.79  -1.75  -1.72  -1.66  156.0   1.04\n",
      "Mtrue[829]       -1.73  8.6e-4   0.04  -1.82  -1.76  -1.73  -1.71  -1.65 2411.0    1.0\n",
      "Mtrue[830]       -1.75  1.6e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.66  972.0    1.0\n",
      "Mtrue[831]       -1.67  4.2e-3   0.08  -1.77  -1.72  -1.69  -1.64  -1.44  358.0   1.02\n",
      "Mtrue[832]       -1.71  1.3e-3   0.05  -1.79  -1.74  -1.71  -1.68  -1.62 1298.0    1.0\n",
      "Mtrue[833]       -1.72  9.2e-4   0.04   -1.8  -1.75  -1.72   -1.7  -1.63 2022.0    1.0\n",
      "Mtrue[834]       -1.75  9.9e-4   0.05  -1.85  -1.77  -1.75  -1.72  -1.66 2339.0    1.0\n",
      "Mtrue[835]       -1.75  1.3e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.66 1250.0    1.0\n",
      "Mtrue[836]       -1.73  1.2e-3   0.05  -1.81  -1.76  -1.73   -1.7  -1.63 1588.0    1.0\n",
      "Mtrue[837]       -1.76  2.5e-3   0.06  -1.86  -1.78  -1.75  -1.72  -1.67  502.0    1.0\n",
      "Mtrue[838]       -1.75  2.4e-3   0.06  -1.88  -1.78  -1.75  -1.72  -1.65  638.0    1.0\n",
      "Mtrue[839]       -1.72  1.2e-3   0.04   -1.8  -1.74  -1.72  -1.69  -1.62 1354.0    1.0\n",
      "Mtrue[840]       -1.74  1.2e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 1780.0    1.0\n",
      "Mtrue[841]       -1.73  1.4e-3   0.05  -1.82  -1.76  -1.73  -1.71  -1.64 1281.0    1.0\n",
      "Mtrue[842]       -1.72  1.4e-3   0.05   -1.8  -1.75  -1.72   -1.7  -1.63 1035.0    1.0\n",
      "Mtrue[843]       -1.73  9.1e-4   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 2605.0    1.0\n",
      "Mtrue[844]       -1.74  8.3e-4   0.04  -1.83  -1.77  -1.74  -1.71  -1.66 2691.0    1.0\n",
      "Mtrue[845]       -1.72  1.7e-3   0.05  -1.81  -1.76  -1.73   -1.7  -1.62  968.0    1.0\n",
      "Mtrue[846]       -1.71    0.01    0.1  -1.81  -1.75  -1.72  -1.69  -1.39   63.0   1.06\n",
      "Mtrue[847]       -1.73  1.3e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63 1376.0    1.0\n",
      "Mtrue[848]       -1.72  2.3e-3   0.06  -1.81  -1.75  -1.73   -1.7  -1.61  622.0    1.0\n",
      "Mtrue[849]       -1.73  1.1e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63 1677.0    1.0\n",
      "Mtrue[850]       -1.73  9.2e-4   0.04  -1.82  -1.76  -1.73  -1.71  -1.64 2303.0    1.0\n",
      "Mtrue[851]       -1.78  2.2e-3   0.06  -1.92   -1.8  -1.77  -1.74  -1.69  724.0    1.0\n",
      "Mtrue[852]       -1.75  1.3e-3   0.05  -1.85  -1.77  -1.74  -1.72  -1.66 1523.0    1.0\n",
      "Mtrue[853]       -1.73  9.8e-4   0.04  -1.82  -1.76  -1.73  -1.71  -1.64 2075.0    1.0\n",
      "Mtrue[854]       -1.75  2.9e-3   0.05  -1.86  -1.78  -1.75  -1.72  -1.67  335.0   1.01\n",
      "Mtrue[855]       -1.73  9.3e-4   0.05  -1.83  -1.76  -1.73   -1.7  -1.64 2720.0    1.0\n",
      "Mtrue[856]       -1.73  5.6e-3   0.08  -1.84  -1.77  -1.73   -1.7  -1.56  191.0   1.02\n",
      "Mtrue[857]       -1.74  2.4e-3   0.06  -1.87  -1.77  -1.74  -1.71  -1.64  651.0    1.0\n",
      "Mtrue[858]       -1.73  7.2e-4   0.04  -1.81  -1.76  -1.73  -1.71  -1.65 2994.0    1.0\n",
      "Mtrue[859]       -1.74  2.1e-3   0.06  -1.84  -1.77  -1.74  -1.71  -1.64  764.0    1.0\n",
      "Mtrue[860]       -1.74  3.1e-3   0.06  -1.83  -1.77  -1.74  -1.71  -1.63  361.0   1.01\n",
      "Mtrue[861]       -1.73  1.1e-3   0.04  -1.82  -1.76  -1.73   -1.7  -1.64 1611.0    1.0\n",
      "Mtrue[862]       -1.74  9.2e-4   0.04  -1.82  -1.77  -1.74  -1.71  -1.66 2160.0    1.0\n",
      "Mtrue[863]       -1.76  1.9e-3   0.05   -1.9  -1.79  -1.76  -1.73  -1.68  838.0    1.0\n",
      "Mtrue[864]       -1.73  1.1e-3   0.05  -1.83  -1.76  -1.73  -1.71  -1.64 1763.0    1.0\n",
      "Mtrue[865]       -1.74 10.0e-4   0.05  -1.83  -1.76  -1.74  -1.71  -1.65 2051.0    1.0\n",
      "Mtrue[866]       -1.73  3.1e-3   0.06  -1.82  -1.76  -1.73   -1.7  -1.61  406.0   1.01\n",
      "Mtrue[867]       -1.74  1.3e-3   0.05  -1.82  -1.76  -1.74  -1.71  -1.65 1285.0    1.0\n",
      "Mtrue[868]       -1.76  2.7e-3   0.06  -1.93  -1.79  -1.76  -1.73  -1.67  555.0   1.02\n",
      "Mtrue[869]       -1.74  1.7e-3   0.05  -1.83  -1.76  -1.74  -1.71  -1.65  896.0    1.0\n",
      "Mtrue[870]       -1.77  3.2e-3   0.07  -1.97   -1.8  -1.76  -1.73  -1.68  416.0   1.01\n",
      "Mtrue[871]       -1.73  8.8e-4   0.05  -1.81  -1.76  -1.73   -1.7  -1.64 2631.0    1.0\n",
      "Mtrue[872]       -1.74  8.8e-4   0.05  -1.83  -1.76  -1.74  -1.71  -1.65 2677.0    1.0\n",
      "Mtrue[873]       -1.78  4.8e-3   0.08  -2.05   -1.8  -1.76  -1.73  -1.68  313.0   1.02\n",
      "Mtrue[874]       -1.71  4.5e-3   0.07   -1.8  -1.75  -1.72  -1.69  -1.57  223.0   1.02\n",
      "Mtrue[875]       -1.71  1.7e-3   0.05   -1.8  -1.74  -1.72  -1.69  -1.59 1020.0    1.0\n",
      "Mtrue[876]       -1.74  1.1e-3   0.04  -1.82  -1.77  -1.74  -1.71  -1.65 1558.0    1.0\n",
      "Mtrue[877]       -1.75  8.7e-4   0.04  -1.83  -1.77  -1.75  -1.72  -1.67 2279.0    1.0\n",
      "Mtrue[878]       -1.75  1.9e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.67  631.0    1.0\n",
      "Mtrue[879]       -1.76  1.1e-3   0.04  -1.84  -1.78  -1.76  -1.73  -1.68 1623.0    1.0\n",
      "Mtrue[880]       -1.71  1.9e-3   0.05   -1.8  -1.74  -1.72  -1.69  -1.62  694.0   1.01\n",
      "Mtrue[881]       -1.73  2.5e-3   0.05  -1.84  -1.76  -1.73  -1.71  -1.64  477.0    1.0\n",
      "Mtrue[882]       -1.75  1.5e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.67 1155.0    1.0\n",
      "Mtrue[883]       -1.74  1.4e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 1229.0    1.0\n",
      "Mtrue[884]       -1.72  1.4e-3   0.05  -1.81  -1.75  -1.72  -1.69  -1.62 1191.0    1.0\n",
      "Mtrue[885]       -1.75  1.0e-3   0.05  -1.84  -1.77  -1.74  -1.72  -1.65 2009.0    1.0\n",
      "Mtrue[886]       -1.54    0.02   0.21  -1.78  -1.71  -1.65  -1.37  -1.08  126.0   1.06\n",
      "Mtrue[887]       -1.75  1.4e-3   0.05  -1.84  -1.78  -1.75  -1.72  -1.66 1178.0    1.0\n",
      "Mtrue[888]       -1.74  1.4e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.64 1374.0    1.0\n",
      "Mtrue[889]       -1.75  2.6e-3   0.06  -1.85  -1.77  -1.74  -1.72  -1.66  453.0   1.01\n",
      "Mtrue[890]       -1.75  2.4e-3   0.06  -1.89  -1.78  -1.74  -1.71  -1.65  681.0    1.0\n",
      "Mtrue[891]       -1.73  1.6e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63 1054.0    1.0\n",
      "Mtrue[892]       -1.74  1.3e-3   0.05  -1.83  -1.77  -1.74   -1.7  -1.64 1699.0    1.0\n",
      "Mtrue[893]       -1.73  8.2e-4   0.04  -1.81  -1.75  -1.73   -1.7  -1.64 2885.0    1.0\n",
      "Mtrue[894]       -1.73  1.1e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 1728.0    1.0\n",
      "Mtrue[895]       -1.74    0.02   0.12  -1.96  -1.77  -1.74  -1.71  -1.44   55.0   1.08\n",
      "Mtrue[896]       -1.75  1.3e-3   0.05  -1.85  -1.78  -1.75  -1.72  -1.66 1607.0    1.0\n",
      "Mtrue[897]       -1.74  1.5e-3   0.06  -1.85  -1.77  -1.74  -1.71  -1.64 1383.0    1.0\n",
      "Mtrue[898]       -1.75  2.7e-3   0.05  -1.86  -1.78  -1.75  -1.72  -1.66  403.0   1.01\n",
      "Mtrue[899]       -1.74  9.0e-4   0.04  -1.82  -1.76  -1.74  -1.71  -1.65 2246.0    1.0\n",
      "Mtrue[900]       -1.75  6.9e-3   0.09  -2.02  -1.77  -1.74  -1.71  -1.64  182.0   1.02\n",
      "Mtrue[901]        -1.7  5.2e-3   0.09   -1.8  -1.75  -1.71  -1.68  -1.43  271.0   1.01\n",
      "Mtrue[902]       -1.74  2.9e-3   0.06  -1.85  -1.77  -1.74  -1.71  -1.63  445.0   1.01\n",
      "Mtrue[903]       -1.74  6.3e-4   0.04  -1.82  -1.77  -1.74  -1.71  -1.65 4787.0    1.0\n",
      "Mtrue[904]       -1.75  1.9e-3   0.05  -1.85  -1.77  -1.74  -1.71  -1.66  756.0   1.01\n",
      "Mtrue[905]       -1.73  9.7e-4   0.05  -1.82  -1.76  -1.73  -1.71  -1.64 2394.0    1.0\n",
      "Mtrue[906]       -1.74  1.6e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.64 1048.0    1.0\n",
      "Mtrue[907]       -1.74  1.6e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.64 1032.0    1.0\n",
      "Mtrue[908]       -1.74  1.1e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.63 2541.0    1.0\n",
      "Mtrue[909]       -1.74  8.3e-4   0.05  -1.83  -1.77  -1.73  -1.71  -1.64 3278.0    1.0\n",
      "Mtrue[910]       -1.72  4.4e-3   0.07  -1.83  -1.76  -1.73  -1.69  -1.51  270.0   1.01\n",
      "Mtrue[911]       -1.87  7.1e-3   0.14   -2.2  -1.97  -1.81  -1.76   -1.7  393.0   1.01\n",
      "Mtrue[912]       -1.72  9.2e-3    0.1  -1.84  -1.76  -1.73   -1.7  -1.42  110.0   1.03\n",
      "Mtrue[913]       -1.72  2.5e-3   0.06  -1.82  -1.75  -1.73   -1.7  -1.61  653.0    1.0\n",
      "Mtrue[914]       -1.74  2.0e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.64  675.0    1.0\n",
      "Mtrue[915]       -1.72  3.5e-3   0.06  -1.82  -1.75  -1.73  -1.69  -1.57  331.0   1.01\n",
      "Mtrue[916]        -1.8  3.4e-3   0.07  -2.02  -1.82  -1.78  -1.75   -1.7  490.0   1.01\n",
      "Mtrue[917]       -1.73  1.2e-3   0.05  -1.81  -1.76  -1.73   -1.7  -1.63 1692.0    1.0\n",
      "Mtrue[918]       -1.76  3.4e-3   0.07  -1.93  -1.78  -1.75  -1.72  -1.66  389.0   1.01\n",
      "Mtrue[919]       -1.78  2.6e-3   0.06  -1.94   -1.8  -1.77  -1.74   -1.7  506.0    1.0\n",
      "Mtrue[920]       -1.73  7.7e-4   0.04  -1.81  -1.76  -1.73  -1.71  -1.65 2787.0    1.0\n",
      "Mtrue[921]       -1.79  6.2e-3   0.09  -2.08   -1.8  -1.77  -1.74  -1.69  211.0   1.01\n",
      "Mtrue[922]       -1.71  4.5e-3   0.08  -1.81  -1.75  -1.72  -1.69  -1.51  280.0   1.01\n",
      "Mtrue[923]       -1.73  9.3e-4   0.04  -1.81  -1.76  -1.73   -1.7  -1.64 2274.0    1.0\n",
      "Mtrue[924]       -1.69  4.5e-3   0.09   -1.8  -1.75  -1.71  -1.68  -1.41  397.0    1.0\n",
      "Mtrue[925]       -1.74  9.3e-4   0.05  -1.84  -1.77  -1.74  -1.71  -1.66 2436.0    1.0\n",
      "Mtrue[926]       -1.73  1.1e-3   0.05  -1.82  -1.76  -1.73  -1.71  -1.65 1680.0    1.0\n",
      "Mtrue[927]       -1.67  6.6e-3    0.1  -1.79  -1.73   -1.7  -1.66  -1.37  245.0   1.03\n",
      "Mtrue[928]       -1.71  2.0e-3   0.06   -1.8  -1.74  -1.71  -1.68  -1.59  768.0   1.01\n",
      "Mtrue[929]       -1.71  9.7e-3   0.11  -1.83  -1.76  -1.73   -1.7  -1.38  130.0   1.03\n",
      "Mtrue[930]       -1.76  1.4e-3   0.05  -1.88  -1.79  -1.76  -1.73  -1.68 1268.0    1.0\n",
      "Mtrue[931]       -1.73  7.7e-4   0.04  -1.81  -1.75  -1.72   -1.7  -1.65 2858.0    1.0\n",
      "Mtrue[932]       -1.73  8.7e-4   0.04  -1.81  -1.76  -1.73   -1.7  -1.65 2311.0    1.0\n",
      "Mtrue[933]       -1.73  7.6e-4   0.04   -1.8  -1.75  -1.73   -1.7  -1.65 2738.0    1.0\n",
      "Mtrue[934]       -1.69  4.4e-3   0.09   -1.8  -1.74  -1.71  -1.67  -1.42  400.0   1.01\n",
      "Mtrue[935]       -1.72  1.5e-3   0.05  -1.81  -1.75  -1.73   -1.7  -1.63 1052.0    1.0\n",
      "Mtrue[936]       -1.75  1.6e-3   0.06  -1.86  -1.78  -1.75  -1.72  -1.66 1121.0    1.0\n",
      "Mtrue[937]       -1.74  1.4e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1195.0    1.0\n",
      "Mtrue[938]       -1.74  1.1e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 1943.0    1.0\n",
      "Mtrue[939]       -1.87    0.02   0.19  -2.34  -1.97  -1.79  -1.75  -1.68  139.0   1.01\n",
      "Mtrue[940]       -1.78  5.7e-3   0.09  -2.08   -1.8  -1.76  -1.73  -1.67  279.0   1.02\n",
      "Mtrue[941]       -1.73  1.0e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63 2122.0    1.0\n",
      "Mtrue[942]        -1.7  1.9e-3   0.06  -1.79  -1.74  -1.71  -1.67  -1.52 1090.0    1.0\n",
      "Mtrue[943]       -1.72  5.5e-3   0.07  -1.81  -1.76  -1.73  -1.69  -1.55  164.0   1.02\n",
      "Mtrue[944]       -1.74  1.4e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.65 1331.0    1.0\n",
      "Mtrue[945]       -1.72  2.9e-3   0.06  -1.81  -1.75  -1.72  -1.69  -1.61  393.0    1.0\n",
      "Mtrue[946]       -1.73 10.0e-4   0.05  -1.82  -1.76  -1.73   -1.7  -1.64 2233.0    1.0\n",
      "Mtrue[947]       -1.69  4.0e-3   0.09   -1.8  -1.74  -1.71  -1.67   -1.4  486.0    1.0\n",
      "Mtrue[948]       -1.73  1.4e-3   0.05  -1.81  -1.76  -1.73   -1.7  -1.62 1385.0    1.0\n",
      "Mtrue[949]       -1.72  2.2e-3   0.06  -1.81  -1.75  -1.72  -1.69  -1.59  853.0    1.0\n",
      "Mtrue[950]       -1.74  1.1e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 2150.0    1.0\n",
      "Mtrue[951]       -1.73  3.6e-3   0.07  -1.83  -1.76  -1.73   -1.7  -1.62  344.0   1.01\n",
      "Mtrue[952]       -1.72  1.7e-3   0.05   -1.8  -1.75  -1.72  -1.69  -1.62  778.0    1.0\n",
      "Mtrue[953]       -1.72  1.1e-3   0.05  -1.81  -1.75  -1.72  -1.69  -1.62 1967.0    1.0\n",
      "Mtrue[954]       -1.74  1.2e-3   0.05  -1.84  -1.77  -1.74  -1.72  -1.66 1357.0    1.0\n",
      "Mtrue[955]       -1.73  1.4e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.62 1434.0    1.0\n",
      "Mtrue[956]       -1.77  1.2e-3   0.05  -1.87  -1.79  -1.77  -1.74  -1.69 1478.0    1.0\n",
      "Mtrue[957]       -1.72  2.0e-3   0.05   -1.8  -1.75  -1.72  -1.69   -1.6  771.0    1.0\n",
      "Mtrue[958]       -1.74  1.1e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.64 2223.0    1.0\n",
      "Mtrue[959]       -1.74  1.7e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.64  949.0    1.0\n",
      "Mtrue[960]       -1.71  3.3e-3   0.07  -1.81  -1.75  -1.72  -1.69  -1.52  465.0   1.01\n",
      "Mtrue[961]       -1.73  1.3e-3   0.05  -1.83  -1.76  -1.73  -1.71  -1.64 1390.0    1.0\n",
      "Mtrue[962]       -1.73  1.0e-3   0.04  -1.81  -1.75  -1.73   -1.7  -1.64 1927.0    1.0\n",
      "Mtrue[963]       -1.74  1.4e-3   0.05  -1.85  -1.77  -1.74  -1.71  -1.65 1355.0    1.0\n",
      "Mtrue[964]       -1.73  1.1e-3   0.05  -1.81  -1.76  -1.73   -1.7  -1.64 1919.0    1.0\n",
      "Mtrue[965]       -1.75  2.4e-3   0.05  -1.87  -1.78  -1.75  -1.72  -1.67  519.0    1.0\n",
      "Mtrue[966]       -1.84  7.4e-3   0.12  -2.14  -1.87  -1.79  -1.76   -1.7  256.0    1.0\n",
      "Mtrue[967]       -1.74  2.3e-3   0.06  -1.84  -1.77  -1.74  -1.71  -1.65  583.0   1.01\n",
      "Mtrue[968]       -1.74  1.0e-3   0.05  -1.83  -1.77  -1.74  -1.71  -1.64 2096.0    1.0\n",
      "Mtrue[969]       -1.73  9.4e-4   0.05  -1.82  -1.76  -1.73  -1.71  -1.64 2446.0    1.0\n",
      "Mtrue[970]       -1.62    0.01   0.17  -1.79  -1.72  -1.69  -1.58  -1.18  214.0   1.02\n",
      "Mtrue[971]       -1.73  1.2e-3   0.04  -1.82  -1.76  -1.73  -1.71  -1.64 1475.0    1.0\n",
      "Mtrue[972]       -1.73  2.1e-3   0.05  -1.83  -1.76  -1.73   -1.7  -1.63  563.0   1.01\n",
      "Mtrue[973]       -1.78  2.0e-3   0.06  -1.95  -1.81  -1.77  -1.75  -1.69  881.0    1.0\n",
      "Mtrue[974]        -1.8  6.4e-3   0.11  -2.13  -1.81  -1.77  -1.74  -1.68  279.0   1.02\n",
      "Mtrue[975]       -1.74  1.9e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65  703.0    1.0\n",
      "Mtrue[976]       -1.76  2.2e-3   0.06  -1.89  -1.78  -1.75  -1.72  -1.67  752.0    1.0\n",
      "Mtrue[977]       -1.73  4.4e-3   0.09  -1.85  -1.77  -1.74   -1.7  -1.53  396.0   1.01\n",
      "Mtrue[978]       -1.74  9.3e-4   0.05  -1.82  -1.77  -1.74  -1.71  -1.65 2414.0    1.0\n",
      "Mtrue[979]       -1.73  1.4e-3   0.04  -1.81  -1.75  -1.73   -1.7  -1.64 1015.0    1.0\n",
      "Mtrue[980]       -1.73  1.6e-3   0.05  -1.83  -1.77  -1.74   -1.7  -1.62 1103.0    1.0\n",
      "Mtrue[981]       -1.73  2.6e-3   0.07  -1.83  -1.76  -1.73   -1.7  -1.61  652.0   1.02\n",
      "Mtrue[982]       -1.75  1.6e-3   0.05  -1.86  -1.78  -1.74  -1.72  -1.65 1091.0    1.0\n",
      "Mtrue[983]       -1.73  1.0e-3   0.05  -1.82  -1.76  -1.73   -1.7  -1.63 2272.0    1.0\n",
      "Mtrue[984]       -1.74  4.4e-3   0.07  -1.84  -1.77  -1.74  -1.71  -1.63  260.0   1.01\n",
      "Mtrue[985]       -1.74  2.0e-3   0.05  -1.85  -1.77  -1.74  -1.71  -1.64  742.0    1.0\n",
      "Mtrue[986]       -1.72  1.9e-3   0.05   -1.8  -1.75  -1.72  -1.69  -1.61  758.0   1.01\n",
      "Mtrue[987]       -1.71  3.5e-3   0.07   -1.8  -1.74  -1.72  -1.68   -1.5  378.0    1.0\n",
      "Mtrue[988]       -1.73  1.2e-3   0.05  -1.83  -1.76  -1.73   -1.7  -1.62 1716.0    1.0\n",
      "Mtrue[989]       -1.76  4.2e-3   0.07  -1.96  -1.79  -1.76  -1.73  -1.67  266.0   1.01\n",
      "Mtrue[990]       -1.67  6.8e-3    0.1  -1.79  -1.73   -1.7  -1.66  -1.38  238.0   1.03\n",
      "Mtrue[991]       -1.75  9.9e-4   0.05  -1.84  -1.78  -1.75  -1.72  -1.66 2069.0    1.0\n",
      "Mtrue[992]       -1.75  2.8e-3   0.06  -1.86  -1.78  -1.75  -1.72  -1.66  425.0    1.0\n",
      "Mtrue[993]       -1.74  1.2e-3   0.05  -1.82  -1.77  -1.74  -1.71  -1.64 1470.0    1.0\n",
      "Mtrue[994]       -1.79    0.02   0.12  -2.22  -1.79  -1.76  -1.73  -1.67   65.0   1.05\n",
      "Mtrue[995]       -1.74  1.2e-3   0.05  -1.84  -1.77  -1.74  -1.71  -1.65 1718.0    1.0\n",
      "Mtrue[996]       -1.73  8.3e-4   0.04  -1.81  -1.75  -1.73   -1.7  -1.63 2805.0    1.0\n",
      "Mtrue[997]       -1.72  3.2e-3   0.07  -1.81  -1.75  -1.72  -1.69  -1.53  478.0   1.01\n",
      "Mtrue[998]       -1.65  9.6e-3   0.13  -1.79  -1.73   -1.7  -1.64  -1.29  185.0   1.02\n",
      "Mtrue[999]       -1.74  9.8e-4   0.04  -1.83  -1.77  -1.74  -1.72  -1.66 1991.0    1.0\n",
      "Mtrue[1000]      -1.74  9.6e-4   0.05  -1.82  -1.77  -1.74  -1.71  -1.65 2226.0    1.0\n",
      "lp__             -2126   10.28  63.65  -2250  -2169  -2126  -2083  -2002   38.0    1.1\n",
      "\n",
      "Samples were drawn using NUTS at Mon Oct 29 14:40:01 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": 41,
   "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": [
      "[1.00008738 1.00695752 1.00753221 ... 0.99985355 1.00002808 1.00975362]\n",
      "Total number of Rhats: 2005\n",
      "Total number of Rhats with the NaN values removed: 2005\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",
    "print(s['summary'][:,-1])\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",
    "sns.distplot(rhatfin)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some diagnostic plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "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",
    "Mtrue = np.median(fit['Mtrue'],axis=0)\n",
    "\n",
    "x = np.linspace(Mobs.min(), Mobs.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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'Units arbitrary')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e9f357050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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(Mobs,m,s=5,zorder=1000)\n",
    "sax.errorbar(Mobs,m,xerr=Munc,c='grey',fmt='o',alpha=.1,zorder=999)\n",
    "\n",
    "sns.distplot(Mobs, 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": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'Units arbitrary')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e9f3b7910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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(Mtrue,m,s=10,zorder=1000)\n",
    "sax.errorbar(Mobs,m,xerr=Munc,c='grey',fmt='o',alpha=.3,zorder=999)\n",
    "\n",
    "sns.distplot(Mtrue, ax=xax, label='M infd')\n",
    "sns.distplot(Mobs, 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": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'Units arbitrary')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e6fcabc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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",
    "c = sax.scatter(Mtrue,m,s=10,c=sel.M_noCorrection,cmap='viridis_r',zorder=1000)\n",
    "sax.errorbar(Mobs,m,xerr=Munc,c='grey',fmt='o',alpha=.3,zorder=999)\n",
    "\n",
    "sns.distplot(Mtrue, ax=xax, label='M infd')\n",
    "sns.distplot(Mobs, 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",
    "fig.colorbar(c)\n",
    "\n",
    "sax.set_xlabel('Absmag in K')\n",
    "sax.set_ylabel('App mag in K')\n",
    "xax.set_ylabel('Units arbitrary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e6de87d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,10))\n",
    "sns.distplot(Mobs, label='Observed data')\n",
    "sns.distplot(Mtrue, label='Inferred truth')\n",
    "plt.plot(x, L, label='Fit distribution')\n",
    "plt.legend(fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "SystemExit",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[0;31mSystemExit\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/IPython/core/interactiveshell.py:2886: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n",
      "  warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n"
     ]
    }
   ],
   "source": [
    "sys.exit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other useful code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "generated quantities {\n",
    "    vector[N] post_prob;\n",
    "    vector[N] bg_pp;\n",
    "    real ll_fg;\n",
    "    real ll_bg;\n",
    "    \n",
    "    for (n in 1:N){\n",
    "        ll_fg = log(Q) + normal_lpdf(M_true_std[n] | 0, 1);\n",
    "        ll_bg = log(1-Q) + normal_lpdf(M_true_std[n] | 0, tsigo);\n",
    "        post_prob[n] = exp(ll_fg - log_sum_exp(ll_fg, ll_bg));\n",
    "        bg_pp[n] = exp(ll_bg - log_sum_exp(ll_fg, ll_bg));\n",
    "    }\n",
    "}\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Calculate recall vs precision for various Kass+Raftery94 cut off scales\n",
    "'''Calculate the Kass+Raftery94 lnK values'''\n",
    "lnK = np.log(post_prob) - np.log(bg_pp)\n",
    "\n",
    "recall, precision = [], []\n",
    "for lim in np.linspace(lnK.min(),lnK.max(),1000):\n",
    "    mask = lnK > lim\n",
    "    cheb_correct = len(Ks[mask][stage[mask]==2])\n",
    "    cheb_total = len(Ks[stage==2])\n",
    "    identified_total = len(Ks[mask])\n",
    "    recall.append(float(cheb_correct)/float(cheb_total))\n",
    "    try:\n",
    "        precision.append(float(cheb_correct)/float(identified_total))\n",
    "    except:\n",
    "        precision.append(0.)\n",
    "        \n",
    "fig, ax = plt.subplots()\n",
    "col = ax.scatter(recall, precision, c=np.linspace(lnK.min(),lnK.max(),1000),vmin=0.)\n",
    "ax.axhline(0.9,c='r',label='0.9 precision')\n",
    "ax.set_xlabel('Recall')\n",
    "ax.set_ylabel('Precision')\n",
    "ax.grid()\n",
    "ax.set_axisbelow(True)\n",
    "fig.colorbar(col, label='lnK cut-off point',extend='min')\n",
    "plt.show()        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "left, bottom, width, height = 0.1, 0.47, 0.85, 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])\n",
    "\n",
    "# cax = fig.add_axes([left+width+0.01, bottom, 0.05, height])\n",
    "\n",
    "c = sax.scatter(M,m,s=5,c=post_prob,zorder=1000)\n",
    "# fig.colorbar(c, cax=cax, label='Posterior Probability')\n",
    "sax.errorbar(M,m,xerr=M_err,c='grey',fmt='none',alpha=.3,zorder=999)\n",
    "\n",
    "sns.distplot(M,ax=xax)\n",
    "\n",
    "xax.scatter(M,fg,s=3,label='Foreground', alpha=.5,c='orange',zorder=999)\n",
    "xax.scatter(M,bg,s=3,label='Background', alpha=.5,c='red',zorder=998)\n",
    "xax.scatter(M, L,s=3,label='True likelihood', alpha=.5,c='green',zorder=997)\n",
    "\n",
    "sax.set_xlabel('Absmag in K')\n",
    "sax.set_ylabel('App mag in K')\n",
    "xax.set_ylabel('Units arbitrary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig, (ax,ax2)  = plt.subplots(2,figsize=(10,10))\n",
    "ax.scatter(M[~mask],m[~mask],s=5,c=post_prob[~mask],zorder=1000, label='Outliers')\n",
    "fig.colorbar(c,ax=ax, label='Posterior Probability')\n",
    "ax.errorbar(M[~mask],m[~mask],xerr=M_err[~mask],c='grey',fmt='none',alpha=.3,zorder=999,label='Unc')\n",
    "ax.scatter(M[mask],m[mask],s=1,alpha=.1,c='r', label='Inliers')\n",
    "ax.legend()\n",
    "\n",
    "ax2.scatter(M[mask],m[mask],s=5,c=post_prob[mask],zorder=1000, label='Inliers')\n",
    "fig.colorbar(c, ax = ax2, label='Posterior Probability')\n",
    "ax2.errorbar(M[mask],m[mask],xerr=M_err[mask],c='grey',fmt='none',alpha=.3,zorder=999,label='Unc')\n",
    "ax2.scatter(M[~mask],m[~mask],s=10,alpha=.5,c='r',label='Outliers')\n",
    "ax2.legend()\n",
    "\n",
    "ax.set_xlabel('Absmag in K')\n",
    "ax.set_ylabel('App mag in K')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What the above plot indicates is that inlier stars are defined by uncertainty more than anything else, and that allt he outlier stars have uncertainties that end near a similar limit, whereas the inlier stars can be more spread out, so long as their uncertainties have a chance at placing them at the overdensity."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}