tedana_z_statistic_inflation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Investigate inflated component z-statistics in tedana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tsalo/.local/lib/python3.6/site-packages/scikit_learn-0.21.2-py3.6-macosx-10.7-x86_64.egg/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import os.path as op\n",
    "\n",
    "import numpy as np\n",
    "import tedana.stats as tedstats\n",
    "import statsmodels.api as sm\n",
    "from scipy import stats\n",
    "from scipy.special import ndtri\n",
    "\n",
    "import pandas as pd\n",
    "from nilearn.image import math_img\n",
    "from nilearn.masking import apply_mask, unmask\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def p_to_z(p, tail='two'):\n",
    "    \"\"\"Convert p-values to z-values.\n",
    "    \"\"\"\n",
    "    eps = np.spacing(1)\n",
    "    p = np.array(p)\n",
    "    p[p < eps] = eps\n",
    "    if tail == 'two':\n",
    "        z = ndtri(1 - (p / 2))\n",
    "        z = np.array(z)\n",
    "    elif tail == 'one':\n",
    "        z = ndtri(1 - p)\n",
    "        z = np.array(z)\n",
    "        z[z < 0] = 0\n",
    "    else:\n",
    "        raise ValueError('Argument \"tail\" must be one of [\"one\", \"two\"]')\n",
    "\n",
    "    if z.shape == ():\n",
    "        z = z[()]\n",
    "    return z\n",
    "\n",
    "\n",
    "def computefeats_sm(data, mmix):\n",
    "    \"\"\"\n",
    "    Converts `data` to component space using `mmix`\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    data : (S x T) array_like\n",
    "        Input data\n",
    "    mmix : (T [x C]) array_like\n",
    "        Mixing matrix for converting input data to component space, where `C`\n",
    "        is components and `T` is the same as in `data`\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    data_Z : (S x C) :obj:`numpy.ndarray`\n",
    "        Data in component space\n",
    "    \"\"\"\n",
    "    # demean masked data\n",
    "    data2 = data - np.mean(data, axis=1, keepdims=True)\n",
    "    data2 = stats.zscore(data, axis=1)\n",
    "    mmix2 = stats.zscore(mmix, axis=0)\n",
    "\n",
    "    data_Z = np.empty((data.shape[0], mmix.shape[1]))\n",
    "    for i_voxel in range(data.shape[0]):\n",
    "        model = sm.OLS(data2[i_voxel, :], mmix2)\n",
    "        results = model.fit()\n",
    "        zvalues = p_to_z(results.pvalues, tail='two') * np.sign(results.params)\n",
    "        data_Z[i_voxel, :] = zvalues\n",
    "    return data_Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_dir = '/Users/tsalo/Documents/tsalo/tedana-comparison/sandbox/e5-checkerboard-test'\n",
    "\n",
    "adaptive_mask = op.join(in_dir, 'adaptive_mask.nii.gz')\n",
    "mask = math_img('img == 5', img=adaptive_mask)\n",
    "\n",
    "optcom = op.join(in_dir, 'ts_OC.nii.gz')\n",
    "optcom = apply_mask(optcom, mask).T\n",
    "# Reduce for speed\n",
    "#optcom = optcom[1000:2000, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's start with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([12092]), array([53]))\n",
      "34.39577769510263\n"
     ]
    }
   ],
   "source": [
    "mixing = op.join(in_dir, 'ica_mixing.tsv')\n",
    "mixing = pd.read_csv(mixing, sep='\\t').values\n",
    "\n",
    "betas = op.join(in_dir, 'ica_components.nii.gz')\n",
    "betas = apply_mask(betas, mask).T\n",
    "print(np.where(betas == np.max(betas)))\n",
    "print(np.max(betas))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>   0.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td> -78.997</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>4.478e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 02 Dec 2019</td> <th>  Prob (F-statistic):</th>           <td>  1.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:55:49</td>     <th>  Log-Likelihood:    </th>          <td> -1688.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   160</td>      <th>  AIC:               </th>          <td>   3693.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     2</td>      <th>  BIC:               </th>          <td>   4179.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>   158</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>   <td>   -0.5420</td> <td> 1.34e+04</td> <td>-4.05e-05</td> <td> 1.000</td> <td>-5.76e+04</td> <td> 5.76e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>   <td>    1.8812</td> <td> 1.18e+04</td> <td>    0.000</td> <td> 1.000</td> <td>-5.06e+04</td> <td> 5.06e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>   <td>   -2.0726</td> <td> 1.19e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-5.11e+04</td> <td> 5.11e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>   <td>    1.4310</td> <td> 1.36e+04</td> <td>    0.000</td> <td> 1.000</td> <td>-5.83e+04</td> <td> 5.83e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>   <td>    1.3577</td> <td> 1.05e+04</td> <td>    0.000</td> <td> 1.000</td> <td>-4.52e+04</td> <td> 4.52e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>   <td>    3.8046</td> <td> 1.17e+04</td> <td>    0.000</td> <td> 1.000</td> <td>-5.03e+04</td> <td> 5.03e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th>   <td>   -2.9295</td> <td> 1.06e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-4.58e+04</td> <td> 4.58e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x8</th>   <td>    9.9962</td> <td> 1.36e+04</td> <td>    0.001</td> <td> 0.999</td> <td>-5.83e+04</td> <td> 5.83e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x9</th>   <td>    0.1117</td> <td> 1.06e+04</td> <td> 1.05e-05</td> <td> 1.000</td> <td>-4.57e+04</td> <td> 4.57e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x10</th>  <td>   -2.9505</td> <td> 9939.311</td> <td>   -0.000</td> <td> 1.000</td> <td>-4.28e+04</td> <td> 4.28e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x11</th>  <td>    0.0031</td> <td>  1.1e+04</td> <td> 2.83e-07</td> <td> 1.000</td> <td>-4.72e+04</td> <td> 4.72e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x12</th>  <td>    1.4862</td> <td> 1.07e+04</td> <td>    0.000</td> <td> 1.000</td> <td> -4.6e+04</td> <td>  4.6e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x13</th>  <td>   -0.4733</td> <td> 1.04e+04</td> <td>-4.55e-05</td> <td> 1.000</td> <td>-4.47e+04</td> <td> 4.47e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x14</th>  <td>    1.6238</td> <td> 1.07e+04</td> <td>    0.000</td> <td> 1.000</td> <td>-4.61e+04</td> <td> 4.61e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x15</th>  <td>    1.8379</td> <td> 9433.272</td> <td>    0.000</td> <td> 1.000</td> <td>-4.06e+04</td> <td> 4.06e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x16</th>  <td>   -3.3380</td> <td> 1.03e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-4.44e+04</td> <td> 4.44e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x17</th>  <td>    0.1639</td> <td> 9048.697</td> <td> 1.81e-05</td> <td> 1.000</td> <td>-3.89e+04</td> <td> 3.89e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x18</th>  <td>    0.9175</td> <td> 8984.390</td> <td>    0.000</td> <td> 1.000</td> <td>-3.87e+04</td> <td> 3.87e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x19</th>  <td>    0.5843</td> <td> 1.05e+04</td> <td> 5.56e-05</td> <td> 1.000</td> <td>-4.52e+04</td> <td> 4.52e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x20</th>  <td>   -1.0944</td> <td> 1.02e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-4.37e+04</td> <td> 4.37e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x21</th>  <td>   -1.6730</td> <td> 1.05e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-4.54e+04</td> <td> 4.54e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x22</th>  <td>    0.0223</td> <td> 1.11e+04</td> <td> 2.01e-06</td> <td> 1.000</td> <td>-4.78e+04</td> <td> 4.78e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x23</th>  <td>   -0.2333</td> <td> 1.26e+04</td> <td>-1.85e-05</td> <td> 1.000</td> <td>-5.41e+04</td> <td> 5.41e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x24</th>  <td>    2.5306</td> <td> 1.02e+04</td> <td>    0.000</td> <td> 1.000</td> <td> -4.4e+04</td> <td>  4.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x25</th>  <td>    0.2533</td> <td> 9407.355</td> <td> 2.69e-05</td> <td> 1.000</td> <td>-4.05e+04</td> <td> 4.05e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x26</th>  <td>   -0.0673</td> <td> 1.03e+04</td> <td>-6.53e-06</td> <td> 1.000</td> <td>-4.43e+04</td> <td> 4.43e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x27</th>  <td>    6.1446</td> <td> 9329.460</td> <td>    0.001</td> <td> 1.000</td> <td>-4.01e+04</td> <td> 4.01e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x28</th>  <td>    0.7617</td> <td> 1.14e+04</td> <td> 6.67e-05</td> <td> 1.000</td> <td>-4.92e+04</td> <td> 4.92e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x29</th>  <td>    0.8707</td> <td> 9395.816</td> <td> 9.27e-05</td> <td> 1.000</td> <td>-4.04e+04</td> <td> 4.04e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x30</th>  <td>    0.1276</td> <td> 1.08e+04</td> <td> 1.18e-05</td> <td> 1.000</td> <td>-4.63e+04</td> <td> 4.63e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x31</th>  <td>    5.4611</td> <td> 1.29e+04</td> <td>    0.000</td> <td> 1.000</td> <td>-5.56e+04</td> <td> 5.56e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x32</th>  <td>    1.2560</td> <td> 9570.443</td> <td>    0.000</td> <td> 1.000</td> <td>-4.12e+04</td> <td> 4.12e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x33</th>  <td>   -0.5015</td> <td> 9205.312</td> <td>-5.45e-05</td> <td> 1.000</td> <td>-3.96e+04</td> <td> 3.96e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x34</th>  <td>   10.3272</td> <td> 1.66e+04</td> <td>    0.001</td> <td> 1.000</td> <td>-7.13e+04</td> <td> 7.14e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x35</th>  <td>   -0.5904</td> <td> 1.05e+04</td> <td> -5.6e-05</td> <td> 1.000</td> <td>-4.53e+04</td> <td> 4.53e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x36</th>  <td>   -2.0817</td> <td> 9051.976</td> <td>   -0.000</td> <td> 1.000</td> <td>-3.89e+04</td> <td> 3.89e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x37</th>  <td>    2.6786</td> <td> 9054.988</td> <td>    0.000</td> <td> 1.000</td> <td> -3.9e+04</td> <td>  3.9e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x38</th>  <td>    0.0603</td> <td> 1.03e+04</td> <td> 5.87e-06</td> <td> 1.000</td> <td>-4.42e+04</td> <td> 4.42e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x39</th>  <td>   -2.3235</td> <td> 9280.993</td> <td>   -0.000</td> <td> 1.000</td> <td>-3.99e+04</td> <td> 3.99e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x40</th>  <td>   -1.1269</td> <td> 9292.989</td> <td>   -0.000</td> <td> 1.000</td> <td>   -4e+04</td> <td>    4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x41</th>  <td>   -0.5813</td> <td> 9817.499</td> <td>-5.92e-05</td> <td> 1.000</td> <td>-4.22e+04</td> <td> 4.22e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x42</th>  <td>   -0.0028</td> <td> 9701.572</td> <td>-2.91e-07</td> <td> 1.000</td> <td>-4.17e+04</td> <td> 4.17e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x43</th>  <td>    3.4281</td> <td> 9898.488</td> <td>    0.000</td> <td> 1.000</td> <td>-4.26e+04</td> <td> 4.26e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x44</th>  <td>   -0.5426</td> <td> 1.08e+04</td> <td>-5.02e-05</td> <td> 1.000</td> <td>-4.65e+04</td> <td> 4.65e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x45</th>  <td>   -0.0186</td> <td> 8098.882</td> <td> -2.3e-06</td> <td> 1.000</td> <td>-3.48e+04</td> <td> 3.48e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x46</th>  <td>   -2.7940</td> <td> 1.43e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-6.14e+04</td> <td> 6.14e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x47</th>  <td>   -1.2504</td> <td> 1.11e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-4.78e+04</td> <td> 4.78e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x48</th>  <td>   -4.7515</td> <td> 1.23e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-5.28e+04</td> <td> 5.28e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x49</th>  <td>    0.7629</td> <td> 8828.814</td> <td> 8.64e-05</td> <td> 1.000</td> <td> -3.8e+04</td> <td>  3.8e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x50</th>  <td>    1.1357</td> <td> 1.04e+04</td> <td>    0.000</td> <td> 1.000</td> <td>-4.48e+04</td> <td> 4.48e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x51</th>  <td>    1.0909</td> <td> 9195.637</td> <td>    0.000</td> <td> 1.000</td> <td>-3.96e+04</td> <td> 3.96e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x52</th>  <td>   -2.2230</td> <td> 1.02e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-4.37e+04</td> <td> 4.37e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x53</th>  <td>    1.4999</td> <td> 1.23e+04</td> <td>    0.000</td> <td> 1.000</td> <td>-5.29e+04</td> <td> 5.29e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x54</th>  <td>   58.6374</td> <td> 1.33e+04</td> <td>    0.004</td> <td> 0.997</td> <td> -5.7e+04</td> <td> 5.71e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x55</th>  <td>   -0.4922</td> <td> 9839.232</td> <td>   -5e-05</td> <td> 1.000</td> <td>-4.23e+04</td> <td> 4.23e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x56</th>  <td>   -5.9971</td> <td> 1.12e+04</td> <td>   -0.001</td> <td> 1.000</td> <td>-4.82e+04</td> <td> 4.82e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x57</th>  <td>    1.1919</td> <td> 1.25e+04</td> <td> 9.55e-05</td> <td> 1.000</td> <td>-5.37e+04</td> <td> 5.37e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x58</th>  <td>   -0.7422</td> <td> 8967.919</td> <td>-8.28e-05</td> <td> 1.000</td> <td>-3.86e+04</td> <td> 3.86e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x59</th>  <td>   -0.2136</td> <td> 1.12e+04</td> <td>-1.91e-05</td> <td> 1.000</td> <td>-4.81e+04</td> <td> 4.81e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x60</th>  <td>   -0.7838</td> <td> 8200.412</td> <td>-9.56e-05</td> <td> 1.000</td> <td>-3.53e+04</td> <td> 3.53e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x61</th>  <td>   -0.6898</td> <td> 8635.068</td> <td>-7.99e-05</td> <td> 1.000</td> <td>-3.72e+04</td> <td> 3.72e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x62</th>  <td>   -1.2805</td> <td> 1.03e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-4.44e+04</td> <td> 4.44e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x63</th>  <td>   -0.2491</td> <td> 9994.977</td> <td>-2.49e-05</td> <td> 1.000</td> <td> -4.3e+04</td> <td>  4.3e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x64</th>  <td>    2.2029</td> <td> 8051.710</td> <td>    0.000</td> <td> 1.000</td> <td>-3.46e+04</td> <td> 3.46e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x65</th>  <td>   -2.6762</td> <td> 8036.781</td> <td>   -0.000</td> <td> 1.000</td> <td>-3.46e+04</td> <td> 3.46e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x66</th>  <td>    0.2654</td> <td> 8078.768</td> <td> 3.28e-05</td> <td> 1.000</td> <td>-3.48e+04</td> <td> 3.48e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x67</th>  <td>    0.9897</td> <td> 7891.967</td> <td>    0.000</td> <td> 1.000</td> <td> -3.4e+04</td> <td>  3.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x68</th>  <td>   -0.9645</td> <td> 8355.631</td> <td>   -0.000</td> <td> 1.000</td> <td> -3.6e+04</td> <td>  3.6e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x69</th>  <td>    0.1178</td> <td> 8058.488</td> <td> 1.46e-05</td> <td> 1.000</td> <td>-3.47e+04</td> <td> 3.47e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x70</th>  <td>    1.2418</td> <td> 7939.399</td> <td>    0.000</td> <td> 1.000</td> <td>-3.42e+04</td> <td> 3.42e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x71</th>  <td>   -0.6118</td> <td> 9601.618</td> <td>-6.37e-05</td> <td> 1.000</td> <td>-4.13e+04</td> <td> 4.13e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x72</th>  <td>    0.9905</td> <td> 8136.714</td> <td>    0.000</td> <td> 1.000</td> <td> -3.5e+04</td> <td>  3.5e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x73</th>  <td>    2.4910</td> <td> 8484.391</td> <td>    0.000</td> <td> 1.000</td> <td>-3.65e+04</td> <td> 3.65e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x74</th>  <td>    1.2731</td> <td> 8547.330</td> <td>    0.000</td> <td> 1.000</td> <td>-3.68e+04</td> <td> 3.68e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x75</th>  <td>   -1.1847</td> <td> 8718.740</td> <td>   -0.000</td> <td> 1.000</td> <td>-3.75e+04</td> <td> 3.75e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x76</th>  <td>   -1.4178</td> <td> 7712.778</td> <td>   -0.000</td> <td> 1.000</td> <td>-3.32e+04</td> <td> 3.32e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x77</th>  <td>   -0.4777</td> <td> 8729.439</td> <td>-5.47e-05</td> <td> 1.000</td> <td>-3.76e+04</td> <td> 3.76e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x78</th>  <td>   -0.9683</td> <td> 8040.813</td> <td>   -0.000</td> <td> 1.000</td> <td>-3.46e+04</td> <td> 3.46e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x79</th>  <td>    1.3869</td> <td> 7725.447</td> <td>    0.000</td> <td> 1.000</td> <td>-3.32e+04</td> <td> 3.32e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x80</th>  <td>   -0.3279</td> <td> 8081.706</td> <td>-4.06e-05</td> <td> 1.000</td> <td>-3.48e+04</td> <td> 3.48e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x81</th>  <td>   -0.5303</td> <td> 8692.802</td> <td> -6.1e-05</td> <td> 1.000</td> <td>-3.74e+04</td> <td> 3.74e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x82</th>  <td>   -1.0013</td> <td> 8055.442</td> <td>   -0.000</td> <td> 1.000</td> <td>-3.47e+04</td> <td> 3.47e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x83</th>  <td>   -0.3217</td> <td> 7896.061</td> <td>-4.07e-05</td> <td> 1.000</td> <td> -3.4e+04</td> <td>  3.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x84</th>  <td>   -0.4157</td> <td> 8069.786</td> <td>-5.15e-05</td> <td> 1.000</td> <td>-3.47e+04</td> <td> 3.47e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x85</th>  <td>    0.5706</td> <td> 8294.639</td> <td> 6.88e-05</td> <td> 1.000</td> <td>-3.57e+04</td> <td> 3.57e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x86</th>  <td>    2.0293</td> <td> 8090.390</td> <td>    0.000</td> <td> 1.000</td> <td>-3.48e+04</td> <td> 3.48e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x87</th>  <td>    0.3780</td> <td> 8054.006</td> <td> 4.69e-05</td> <td> 1.000</td> <td>-3.47e+04</td> <td> 3.47e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x88</th>  <td>   -0.2446</td> <td> 8094.886</td> <td>-3.02e-05</td> <td> 1.000</td> <td>-3.48e+04</td> <td> 3.48e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x89</th>  <td>   -1.2823</td> <td> 8193.641</td> <td>   -0.000</td> <td> 1.000</td> <td>-3.53e+04</td> <td> 3.53e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x90</th>  <td>    0.2062</td> <td> 8287.843</td> <td> 2.49e-05</td> <td> 1.000</td> <td>-3.57e+04</td> <td> 3.57e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x91</th>  <td>   -0.6147</td> <td> 8302.807</td> <td> -7.4e-05</td> <td> 1.000</td> <td>-3.57e+04</td> <td> 3.57e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x92</th>  <td>   -0.2857</td> <td> 8112.705</td> <td>-3.52e-05</td> <td> 1.000</td> <td>-3.49e+04</td> <td> 3.49e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x93</th>  <td>    0.3542</td> <td> 8291.653</td> <td> 4.27e-05</td> <td> 1.000</td> <td>-3.57e+04</td> <td> 3.57e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x94</th>  <td>   -1.6488</td> <td> 7855.776</td> <td>   -0.000</td> <td> 1.000</td> <td>-3.38e+04</td> <td> 3.38e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x95</th>  <td>   -0.8451</td> <td> 7907.270</td> <td>   -0.000</td> <td> 1.000</td> <td> -3.4e+04</td> <td>  3.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x96</th>  <td>    0.7883</td> <td> 8298.381</td> <td>  9.5e-05</td> <td> 1.000</td> <td>-3.57e+04</td> <td> 3.57e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x97</th>  <td>   -0.5490</td> <td> 9112.191</td> <td>-6.02e-05</td> <td> 1.000</td> <td>-3.92e+04</td> <td> 3.92e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x98</th>  <td>   -1.9704</td> <td> 8257.058</td> <td>   -0.000</td> <td> 1.000</td> <td>-3.55e+04</td> <td> 3.55e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x99</th>  <td>    0.3238</td> <td> 8060.614</td> <td> 4.02e-05</td> <td> 1.000</td> <td>-3.47e+04</td> <td> 3.47e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x100</th> <td>    0.3051</td> <td> 7935.208</td> <td> 3.85e-05</td> <td> 1.000</td> <td>-3.41e+04</td> <td> 3.41e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x101</th> <td>    0.7538</td> <td> 9073.396</td> <td> 8.31e-05</td> <td> 1.000</td> <td> -3.9e+04</td> <td>  3.9e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x102</th> <td>   -1.0422</td> <td> 7948.690</td> <td>   -0.000</td> <td> 1.000</td> <td>-3.42e+04</td> <td> 3.42e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x103</th> <td>   -0.1362</td> <td> 7660.509</td> <td>-1.78e-05</td> <td> 1.000</td> <td> -3.3e+04</td> <td>  3.3e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x104</th> <td>   -2.2833</td> <td> 9049.214</td> <td>   -0.000</td> <td> 1.000</td> <td>-3.89e+04</td> <td> 3.89e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x105</th> <td>   -0.2033</td> <td> 8523.089</td> <td>-2.39e-05</td> <td> 1.000</td> <td>-3.67e+04</td> <td> 3.67e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x106</th> <td>    0.2185</td> <td> 8164.256</td> <td> 2.68e-05</td> <td> 1.000</td> <td>-3.51e+04</td> <td> 3.51e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x107</th> <td>   -1.8786</td> <td> 7944.481</td> <td>   -0.000</td> <td> 1.000</td> <td>-3.42e+04</td> <td> 3.42e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x108</th> <td>    0.2151</td> <td> 7807.197</td> <td> 2.76e-05</td> <td> 1.000</td> <td>-3.36e+04</td> <td> 3.36e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x109</th> <td>    1.7077</td> <td> 8406.403</td> <td>    0.000</td> <td> 1.000</td> <td>-3.62e+04</td> <td> 3.62e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x110</th> <td>    1.8302</td> <td> 8174.412</td> <td>    0.000</td> <td> 1.000</td> <td>-3.52e+04</td> <td> 3.52e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x111</th> <td>    0.7323</td> <td> 8884.009</td> <td> 8.24e-05</td> <td> 1.000</td> <td>-3.82e+04</td> <td> 3.82e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x112</th> <td>    0.3466</td> <td> 8214.013</td> <td> 4.22e-05</td> <td> 1.000</td> <td>-3.53e+04</td> <td> 3.53e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x113</th> <td>   -0.0473</td> <td> 8510.320</td> <td>-5.55e-06</td> <td> 1.000</td> <td>-3.66e+04</td> <td> 3.66e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x114</th> <td>    0.3980</td> <td> 8123.444</td> <td>  4.9e-05</td> <td> 1.000</td> <td> -3.5e+04</td> <td>  3.5e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x115</th> <td>    0.9158</td> <td> 8253.087</td> <td>    0.000</td> <td> 1.000</td> <td>-3.55e+04</td> <td> 3.55e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x116</th> <td>   -0.4328</td> <td> 8352.992</td> <td>-5.18e-05</td> <td> 1.000</td> <td>-3.59e+04</td> <td> 3.59e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x117</th> <td>    1.4307</td> <td> 8385.881</td> <td>    0.000</td> <td> 1.000</td> <td>-3.61e+04</td> <td> 3.61e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x118</th> <td>    1.9557</td> <td> 7943.947</td> <td>    0.000</td> <td> 1.000</td> <td>-3.42e+04</td> <td> 3.42e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x119</th> <td>    1.6209</td> <td> 8241.896</td> <td>    0.000</td> <td> 1.000</td> <td>-3.55e+04</td> <td> 3.55e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x120</th> <td>   -0.2560</td> <td> 7803.479</td> <td>-3.28e-05</td> <td> 1.000</td> <td>-3.36e+04</td> <td> 3.36e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x121</th> <td>   -0.3485</td> <td> 7723.460</td> <td>-4.51e-05</td> <td> 1.000</td> <td>-3.32e+04</td> <td> 3.32e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x122</th> <td>    0.6837</td> <td> 7941.329</td> <td> 8.61e-05</td> <td> 1.000</td> <td>-3.42e+04</td> <td> 3.42e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x123</th> <td>    0.3875</td> <td> 8207.426</td> <td> 4.72e-05</td> <td> 1.000</td> <td>-3.53e+04</td> <td> 3.53e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x124</th> <td>    0.3166</td> <td> 8113.045</td> <td>  3.9e-05</td> <td> 1.000</td> <td>-3.49e+04</td> <td> 3.49e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x125</th> <td>   -1.6428</td> <td> 7908.398</td> <td>   -0.000</td> <td> 1.000</td> <td> -3.4e+04</td> <td>  3.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x126</th> <td>    0.5981</td> <td> 8127.998</td> <td> 7.36e-05</td> <td> 1.000</td> <td> -3.5e+04</td> <td>  3.5e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x127</th> <td>    2.1257</td> <td> 8437.622</td> <td>    0.000</td> <td> 1.000</td> <td>-3.63e+04</td> <td> 3.63e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x128</th> <td>   -0.4865</td> <td> 7973.125</td> <td> -6.1e-05</td> <td> 1.000</td> <td>-3.43e+04</td> <td> 3.43e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x129</th> <td>   -0.3751</td> <td> 7998.672</td> <td>-4.69e-05</td> <td> 1.000</td> <td>-3.44e+04</td> <td> 3.44e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x130</th> <td>    0.0225</td> <td> 7703.795</td> <td> 2.93e-06</td> <td> 1.000</td> <td>-3.31e+04</td> <td> 3.31e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x131</th> <td>   -1.4617</td> <td> 8064.715</td> <td>   -0.000</td> <td> 1.000</td> <td>-3.47e+04</td> <td> 3.47e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x132</th> <td>    2.4236</td> <td> 8399.162</td> <td>    0.000</td> <td> 1.000</td> <td>-3.61e+04</td> <td> 3.61e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x133</th> <td>    1.4283</td> <td> 8567.347</td> <td>    0.000</td> <td> 1.000</td> <td>-3.69e+04</td> <td> 3.69e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x134</th> <td>   -0.1005</td> <td> 8141.592</td> <td>-1.23e-05</td> <td> 1.000</td> <td> -3.5e+04</td> <td>  3.5e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x135</th> <td>   -1.0200</td> <td> 8103.775</td> <td>   -0.000</td> <td> 1.000</td> <td>-3.49e+04</td> <td> 3.49e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x136</th> <td>   -0.3307</td> <td> 8162.028</td> <td>-4.05e-05</td> <td> 1.000</td> <td>-3.51e+04</td> <td> 3.51e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x137</th> <td>   -0.7106</td> <td> 7826.816</td> <td>-9.08e-05</td> <td> 1.000</td> <td>-3.37e+04</td> <td> 3.37e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x138</th> <td>   -0.6660</td> <td> 8584.271</td> <td>-7.76e-05</td> <td> 1.000</td> <td>-3.69e+04</td> <td> 3.69e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x139</th> <td>    0.5593</td> <td> 7773.993</td> <td> 7.19e-05</td> <td> 1.000</td> <td>-3.34e+04</td> <td> 3.34e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x140</th> <td>   -0.0387</td> <td> 8041.424</td> <td>-4.81e-06</td> <td> 1.000</td> <td>-3.46e+04</td> <td> 3.46e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x141</th> <td>    0.5320</td> <td> 8348.975</td> <td> 6.37e-05</td> <td> 1.000</td> <td>-3.59e+04</td> <td> 3.59e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x142</th> <td>    0.5712</td> <td> 7667.191</td> <td> 7.45e-05</td> <td> 1.000</td> <td> -3.3e+04</td> <td>  3.3e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x143</th> <td>    1.4825</td> <td> 8007.777</td> <td>    0.000</td> <td> 1.000</td> <td>-3.45e+04</td> <td> 3.45e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x144</th> <td>    0.6619</td> <td> 8165.509</td> <td> 8.11e-05</td> <td> 1.000</td> <td>-3.51e+04</td> <td> 3.51e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x145</th> <td>    2.3904</td> <td> 8004.444</td> <td>    0.000</td> <td> 1.000</td> <td>-3.44e+04</td> <td> 3.44e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x146</th> <td>   -0.8945</td> <td> 7705.158</td> <td>   -0.000</td> <td> 1.000</td> <td>-3.32e+04</td> <td> 3.32e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x147</th> <td>    1.8717</td> <td> 8566.768</td> <td>    0.000</td> <td> 1.000</td> <td>-3.69e+04</td> <td> 3.69e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x148</th> <td>   -0.9307</td> <td> 8613.112</td> <td>   -0.000</td> <td> 1.000</td> <td>-3.71e+04</td> <td> 3.71e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x149</th> <td>    1.2027</td> <td> 9150.540</td> <td>    0.000</td> <td> 1.000</td> <td>-3.94e+04</td> <td> 3.94e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x150</th> <td>    2.4105</td> <td> 7856.191</td> <td>    0.000</td> <td> 1.000</td> <td>-3.38e+04</td> <td> 3.38e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x151</th> <td>    1.3789</td> <td> 8289.178</td> <td>    0.000</td> <td> 1.000</td> <td>-3.57e+04</td> <td> 3.57e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x152</th> <td>    0.6702</td> <td> 7923.680</td> <td> 8.46e-05</td> <td> 1.000</td> <td>-3.41e+04</td> <td> 3.41e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x153</th> <td>   -0.7646</td> <td> 8332.746</td> <td>-9.18e-05</td> <td> 1.000</td> <td>-3.59e+04</td> <td> 3.59e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x154</th> <td>   -1.5148</td> <td> 8011.797</td> <td>   -0.000</td> <td> 1.000</td> <td>-3.45e+04</td> <td> 3.45e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x155</th> <td>   -0.1829</td> <td> 8388.137</td> <td>-2.18e-05</td> <td> 1.000</td> <td>-3.61e+04</td> <td> 3.61e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x156</th> <td>   -1.0890</td> <td> 8119.309</td> <td>   -0.000</td> <td> 1.000</td> <td>-3.49e+04</td> <td> 3.49e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x157</th> <td>    0.2461</td> <td> 8133.985</td> <td> 3.03e-05</td> <td> 1.000</td> <td> -3.5e+04</td> <td>  3.5e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x158</th> <td>   -0.5719</td> <td> 8251.533</td> <td>-6.93e-05</td> <td> 1.000</td> <td>-3.55e+04</td> <td> 3.55e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 3.392</td> <th>  Durbin-Watson:     </th> <td>   0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.183</td> <th>  Jarque-Bera (JB):  </th> <td>   3.737</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.084</td> <th>  Prob(JB):          </th> <td>   0.154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.730</td> <th>  Cond. No.          </th> <td>    9.31</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                      y   R-squared (uncentered):                   0.000\n",
       "Model:                            OLS   Adj. R-squared (uncentered):            -78.997\n",
       "Method:                 Least Squares   F-statistic:                          4.478e-07\n",
       "Date:                Mon, 02 Dec 2019   Prob (F-statistic):                        1.00\n",
       "Time:                        10:55:49   Log-Likelihood:                         -1688.7\n",
       "No. Observations:                 160   AIC:                                      3693.\n",
       "Df Residuals:                       2   BIC:                                      4179.\n",
       "Df Model:                         158                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1            -0.5420   1.34e+04  -4.05e-05      1.000   -5.76e+04    5.76e+04\n",
       "x2             1.8812   1.18e+04      0.000      1.000   -5.06e+04    5.06e+04\n",
       "x3            -2.0726   1.19e+04     -0.000      1.000   -5.11e+04    5.11e+04\n",
       "x4             1.4310   1.36e+04      0.000      1.000   -5.83e+04    5.83e+04\n",
       "x5             1.3577   1.05e+04      0.000      1.000   -4.52e+04    4.52e+04\n",
       "x6             3.8046   1.17e+04      0.000      1.000   -5.03e+04    5.03e+04\n",
       "x7            -2.9295   1.06e+04     -0.000      1.000   -4.58e+04    4.58e+04\n",
       "x8             9.9962   1.36e+04      0.001      0.999   -5.83e+04    5.83e+04\n",
       "x9             0.1117   1.06e+04   1.05e-05      1.000   -4.57e+04    4.57e+04\n",
       "x10           -2.9505   9939.311     -0.000      1.000   -4.28e+04    4.28e+04\n",
       "x11            0.0031    1.1e+04   2.83e-07      1.000   -4.72e+04    4.72e+04\n",
       "x12            1.4862   1.07e+04      0.000      1.000    -4.6e+04     4.6e+04\n",
       "x13           -0.4733   1.04e+04  -4.55e-05      1.000   -4.47e+04    4.47e+04\n",
       "x14            1.6238   1.07e+04      0.000      1.000   -4.61e+04    4.61e+04\n",
       "x15            1.8379   9433.272      0.000      1.000   -4.06e+04    4.06e+04\n",
       "x16           -3.3380   1.03e+04     -0.000      1.000   -4.44e+04    4.44e+04\n",
       "x17            0.1639   9048.697   1.81e-05      1.000   -3.89e+04    3.89e+04\n",
       "x18            0.9175   8984.390      0.000      1.000   -3.87e+04    3.87e+04\n",
       "x19            0.5843   1.05e+04   5.56e-05      1.000   -4.52e+04    4.52e+04\n",
       "x20           -1.0944   1.02e+04     -0.000      1.000   -4.37e+04    4.37e+04\n",
       "x21           -1.6730   1.05e+04     -0.000      1.000   -4.54e+04    4.54e+04\n",
       "x22            0.0223   1.11e+04   2.01e-06      1.000   -4.78e+04    4.78e+04\n",
       "x23           -0.2333   1.26e+04  -1.85e-05      1.000   -5.41e+04    5.41e+04\n",
       "x24            2.5306   1.02e+04      0.000      1.000    -4.4e+04     4.4e+04\n",
       "x25            0.2533   9407.355   2.69e-05      1.000   -4.05e+04    4.05e+04\n",
       "x26           -0.0673   1.03e+04  -6.53e-06      1.000   -4.43e+04    4.43e+04\n",
       "x27            6.1446   9329.460      0.001      1.000   -4.01e+04    4.01e+04\n",
       "x28            0.7617   1.14e+04   6.67e-05      1.000   -4.92e+04    4.92e+04\n",
       "x29            0.8707   9395.816   9.27e-05      1.000   -4.04e+04    4.04e+04\n",
       "x30            0.1276   1.08e+04   1.18e-05      1.000   -4.63e+04    4.63e+04\n",
       "x31            5.4611   1.29e+04      0.000      1.000   -5.56e+04    5.56e+04\n",
       "x32            1.2560   9570.443      0.000      1.000   -4.12e+04    4.12e+04\n",
       "x33           -0.5015   9205.312  -5.45e-05      1.000   -3.96e+04    3.96e+04\n",
       "x34           10.3272   1.66e+04      0.001      1.000   -7.13e+04    7.14e+04\n",
       "x35           -0.5904   1.05e+04   -5.6e-05      1.000   -4.53e+04    4.53e+04\n",
       "x36           -2.0817   9051.976     -0.000      1.000   -3.89e+04    3.89e+04\n",
       "x37            2.6786   9054.988      0.000      1.000    -3.9e+04     3.9e+04\n",
       "x38            0.0603   1.03e+04   5.87e-06      1.000   -4.42e+04    4.42e+04\n",
       "x39           -2.3235   9280.993     -0.000      1.000   -3.99e+04    3.99e+04\n",
       "x40           -1.1269   9292.989     -0.000      1.000      -4e+04       4e+04\n",
       "x41           -0.5813   9817.499  -5.92e-05      1.000   -4.22e+04    4.22e+04\n",
       "x42           -0.0028   9701.572  -2.91e-07      1.000   -4.17e+04    4.17e+04\n",
       "x43            3.4281   9898.488      0.000      1.000   -4.26e+04    4.26e+04\n",
       "x44           -0.5426   1.08e+04  -5.02e-05      1.000   -4.65e+04    4.65e+04\n",
       "x45           -0.0186   8098.882   -2.3e-06      1.000   -3.48e+04    3.48e+04\n",
       "x46           -2.7940   1.43e+04     -0.000      1.000   -6.14e+04    6.14e+04\n",
       "x47           -1.2504   1.11e+04     -0.000      1.000   -4.78e+04    4.78e+04\n",
       "x48           -4.7515   1.23e+04     -0.000      1.000   -5.28e+04    5.28e+04\n",
       "x49            0.7629   8828.814   8.64e-05      1.000    -3.8e+04     3.8e+04\n",
       "x50            1.1357   1.04e+04      0.000      1.000   -4.48e+04    4.48e+04\n",
       "x51            1.0909   9195.637      0.000      1.000   -3.96e+04    3.96e+04\n",
       "x52           -2.2230   1.02e+04     -0.000      1.000   -4.37e+04    4.37e+04\n",
       "x53            1.4999   1.23e+04      0.000      1.000   -5.29e+04    5.29e+04\n",
       "x54           58.6374   1.33e+04      0.004      0.997    -5.7e+04    5.71e+04\n",
       "x55           -0.4922   9839.232     -5e-05      1.000   -4.23e+04    4.23e+04\n",
       "x56           -5.9971   1.12e+04     -0.001      1.000   -4.82e+04    4.82e+04\n",
       "x57            1.1919   1.25e+04   9.55e-05      1.000   -5.37e+04    5.37e+04\n",
       "x58           -0.7422   8967.919  -8.28e-05      1.000   -3.86e+04    3.86e+04\n",
       "x59           -0.2136   1.12e+04  -1.91e-05      1.000   -4.81e+04    4.81e+04\n",
       "x60           -0.7838   8200.412  -9.56e-05      1.000   -3.53e+04    3.53e+04\n",
       "x61           -0.6898   8635.068  -7.99e-05      1.000   -3.72e+04    3.72e+04\n",
       "x62           -1.2805   1.03e+04     -0.000      1.000   -4.44e+04    4.44e+04\n",
       "x63           -0.2491   9994.977  -2.49e-05      1.000    -4.3e+04     4.3e+04\n",
       "x64            2.2029   8051.710      0.000      1.000   -3.46e+04    3.46e+04\n",
       "x65           -2.6762   8036.781     -0.000      1.000   -3.46e+04    3.46e+04\n",
       "x66            0.2654   8078.768   3.28e-05      1.000   -3.48e+04    3.48e+04\n",
       "x67            0.9897   7891.967      0.000      1.000    -3.4e+04     3.4e+04\n",
       "x68           -0.9645   8355.631     -0.000      1.000    -3.6e+04     3.6e+04\n",
       "x69            0.1178   8058.488   1.46e-05      1.000   -3.47e+04    3.47e+04\n",
       "x70            1.2418   7939.399      0.000      1.000   -3.42e+04    3.42e+04\n",
       "x71           -0.6118   9601.618  -6.37e-05      1.000   -4.13e+04    4.13e+04\n",
       "x72            0.9905   8136.714      0.000      1.000    -3.5e+04     3.5e+04\n",
       "x73            2.4910   8484.391      0.000      1.000   -3.65e+04    3.65e+04\n",
       "x74            1.2731   8547.330      0.000      1.000   -3.68e+04    3.68e+04\n",
       "x75           -1.1847   8718.740     -0.000      1.000   -3.75e+04    3.75e+04\n",
       "x76           -1.4178   7712.778     -0.000      1.000   -3.32e+04    3.32e+04\n",
       "x77           -0.4777   8729.439  -5.47e-05      1.000   -3.76e+04    3.76e+04\n",
       "x78           -0.9683   8040.813     -0.000      1.000   -3.46e+04    3.46e+04\n",
       "x79            1.3869   7725.447      0.000      1.000   -3.32e+04    3.32e+04\n",
       "x80           -0.3279   8081.706  -4.06e-05      1.000   -3.48e+04    3.48e+04\n",
       "x81           -0.5303   8692.802   -6.1e-05      1.000   -3.74e+04    3.74e+04\n",
       "x82           -1.0013   8055.442     -0.000      1.000   -3.47e+04    3.47e+04\n",
       "x83           -0.3217   7896.061  -4.07e-05      1.000    -3.4e+04     3.4e+04\n",
       "x84           -0.4157   8069.786  -5.15e-05      1.000   -3.47e+04    3.47e+04\n",
       "x85            0.5706   8294.639   6.88e-05      1.000   -3.57e+04    3.57e+04\n",
       "x86            2.0293   8090.390      0.000      1.000   -3.48e+04    3.48e+04\n",
       "x87            0.3780   8054.006   4.69e-05      1.000   -3.47e+04    3.47e+04\n",
       "x88           -0.2446   8094.886  -3.02e-05      1.000   -3.48e+04    3.48e+04\n",
       "x89           -1.2823   8193.641     -0.000      1.000   -3.53e+04    3.53e+04\n",
       "x90            0.2062   8287.843   2.49e-05      1.000   -3.57e+04    3.57e+04\n",
       "x91           -0.6147   8302.807   -7.4e-05      1.000   -3.57e+04    3.57e+04\n",
       "x92           -0.2857   8112.705  -3.52e-05      1.000   -3.49e+04    3.49e+04\n",
       "x93            0.3542   8291.653   4.27e-05      1.000   -3.57e+04    3.57e+04\n",
       "x94           -1.6488   7855.776     -0.000      1.000   -3.38e+04    3.38e+04\n",
       "x95           -0.8451   7907.270     -0.000      1.000    -3.4e+04     3.4e+04\n",
       "x96            0.7883   8298.381    9.5e-05      1.000   -3.57e+04    3.57e+04\n",
       "x97           -0.5490   9112.191  -6.02e-05      1.000   -3.92e+04    3.92e+04\n",
       "x98           -1.9704   8257.058     -0.000      1.000   -3.55e+04    3.55e+04\n",
       "x99            0.3238   8060.614   4.02e-05      1.000   -3.47e+04    3.47e+04\n",
       "x100           0.3051   7935.208   3.85e-05      1.000   -3.41e+04    3.41e+04\n",
       "x101           0.7538   9073.396   8.31e-05      1.000    -3.9e+04     3.9e+04\n",
       "x102          -1.0422   7948.690     -0.000      1.000   -3.42e+04    3.42e+04\n",
       "x103          -0.1362   7660.509  -1.78e-05      1.000    -3.3e+04     3.3e+04\n",
       "x104          -2.2833   9049.214     -0.000      1.000   -3.89e+04    3.89e+04\n",
       "x105          -0.2033   8523.089  -2.39e-05      1.000   -3.67e+04    3.67e+04\n",
       "x106           0.2185   8164.256   2.68e-05      1.000   -3.51e+04    3.51e+04\n",
       "x107          -1.8786   7944.481     -0.000      1.000   -3.42e+04    3.42e+04\n",
       "x108           0.2151   7807.197   2.76e-05      1.000   -3.36e+04    3.36e+04\n",
       "x109           1.7077   8406.403      0.000      1.000   -3.62e+04    3.62e+04\n",
       "x110           1.8302   8174.412      0.000      1.000   -3.52e+04    3.52e+04\n",
       "x111           0.7323   8884.009   8.24e-05      1.000   -3.82e+04    3.82e+04\n",
       "x112           0.3466   8214.013   4.22e-05      1.000   -3.53e+04    3.53e+04\n",
       "x113          -0.0473   8510.320  -5.55e-06      1.000   -3.66e+04    3.66e+04\n",
       "x114           0.3980   8123.444    4.9e-05      1.000    -3.5e+04     3.5e+04\n",
       "x115           0.9158   8253.087      0.000      1.000   -3.55e+04    3.55e+04\n",
       "x116          -0.4328   8352.992  -5.18e-05      1.000   -3.59e+04    3.59e+04\n",
       "x117           1.4307   8385.881      0.000      1.000   -3.61e+04    3.61e+04\n",
       "x118           1.9557   7943.947      0.000      1.000   -3.42e+04    3.42e+04\n",
       "x119           1.6209   8241.896      0.000      1.000   -3.55e+04    3.55e+04\n",
       "x120          -0.2560   7803.479  -3.28e-05      1.000   -3.36e+04    3.36e+04\n",
       "x121          -0.3485   7723.460  -4.51e-05      1.000   -3.32e+04    3.32e+04\n",
       "x122           0.6837   7941.329   8.61e-05      1.000   -3.42e+04    3.42e+04\n",
       "x123           0.3875   8207.426   4.72e-05      1.000   -3.53e+04    3.53e+04\n",
       "x124           0.3166   8113.045    3.9e-05      1.000   -3.49e+04    3.49e+04\n",
       "x125          -1.6428   7908.398     -0.000      1.000    -3.4e+04     3.4e+04\n",
       "x126           0.5981   8127.998   7.36e-05      1.000    -3.5e+04     3.5e+04\n",
       "x127           2.1257   8437.622      0.000      1.000   -3.63e+04    3.63e+04\n",
       "x128          -0.4865   7973.125   -6.1e-05      1.000   -3.43e+04    3.43e+04\n",
       "x129          -0.3751   7998.672  -4.69e-05      1.000   -3.44e+04    3.44e+04\n",
       "x130           0.0225   7703.795   2.93e-06      1.000   -3.31e+04    3.31e+04\n",
       "x131          -1.4617   8064.715     -0.000      1.000   -3.47e+04    3.47e+04\n",
       "x132           2.4236   8399.162      0.000      1.000   -3.61e+04    3.61e+04\n",
       "x133           1.4283   8567.347      0.000      1.000   -3.69e+04    3.69e+04\n",
       "x134          -0.1005   8141.592  -1.23e-05      1.000    -3.5e+04     3.5e+04\n",
       "x135          -1.0200   8103.775     -0.000      1.000   -3.49e+04    3.49e+04\n",
       "x136          -0.3307   8162.028  -4.05e-05      1.000   -3.51e+04    3.51e+04\n",
       "x137          -0.7106   7826.816  -9.08e-05      1.000   -3.37e+04    3.37e+04\n",
       "x138          -0.6660   8584.271  -7.76e-05      1.000   -3.69e+04    3.69e+04\n",
       "x139           0.5593   7773.993   7.19e-05      1.000   -3.34e+04    3.34e+04\n",
       "x140          -0.0387   8041.424  -4.81e-06      1.000   -3.46e+04    3.46e+04\n",
       "x141           0.5320   8348.975   6.37e-05      1.000   -3.59e+04    3.59e+04\n",
       "x142           0.5712   7667.191   7.45e-05      1.000    -3.3e+04     3.3e+04\n",
       "x143           1.4825   8007.777      0.000      1.000   -3.45e+04    3.45e+04\n",
       "x144           0.6619   8165.509   8.11e-05      1.000   -3.51e+04    3.51e+04\n",
       "x145           2.3904   8004.444      0.000      1.000   -3.44e+04    3.44e+04\n",
       "x146          -0.8945   7705.158     -0.000      1.000   -3.32e+04    3.32e+04\n",
       "x147           1.8717   8566.768      0.000      1.000   -3.69e+04    3.69e+04\n",
       "x148          -0.9307   8613.112     -0.000      1.000   -3.71e+04    3.71e+04\n",
       "x149           1.2027   9150.540      0.000      1.000   -3.94e+04    3.94e+04\n",
       "x150           2.4105   7856.191      0.000      1.000   -3.38e+04    3.38e+04\n",
       "x151           1.3789   8289.178      0.000      1.000   -3.57e+04    3.57e+04\n",
       "x152           0.6702   7923.680   8.46e-05      1.000   -3.41e+04    3.41e+04\n",
       "x153          -0.7646   8332.746  -9.18e-05      1.000   -3.59e+04    3.59e+04\n",
       "x154          -1.5148   8011.797     -0.000      1.000   -3.45e+04    3.45e+04\n",
       "x155          -0.1829   8388.137  -2.18e-05      1.000   -3.61e+04    3.61e+04\n",
       "x156          -1.0890   8119.309     -0.000      1.000   -3.49e+04    3.49e+04\n",
       "x157           0.2461   8133.985   3.03e-05      1.000    -3.5e+04     3.5e+04\n",
       "x158          -0.5719   8251.533  -6.93e-05      1.000   -3.55e+04    3.55e+04\n",
       "==============================================================================\n",
       "Omnibus:                        3.392   Durbin-Watson:                   0.000\n",
       "Prob(Omnibus):                  0.183   Jarque-Bera (JB):                3.737\n",
       "Skew:                           0.084   Prob(JB):                        0.154\n",
       "Kurtosis:                       3.730   Cond. No.                         9.31\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mixing_z = stats.zscore(mixing, axis=0)\n",
    "model = sm.OLS(optcom[12092, :], mixing[:, :-1])\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>   <td>   160</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>   <td>     2</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>        <td>Gaussian</td>     <th>  Df Model:          </th>   <td>   157</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>       <td>identity</td>     <th>  Scale:             </th> <td>6.8905e+09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th>  <td> -1688.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Mon, 02 Dec 2019</td> <th>  Deviance:          </th> <td>1.3781e+10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>10:55:59</td>     <th>  Pearson chi2:      </th>  <td>1.38e+10</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>          <td>3</td>        <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>      <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>   <td>   -0.5420</td> <td> 1.34e+04</td> <td>-4.05e-05</td> <td> 1.000</td> <td>-2.62e+04</td> <td> 2.62e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>   <td>    1.8812</td> <td> 1.18e+04</td> <td>    0.000</td> <td> 1.000</td> <td> -2.3e+04</td> <td>  2.3e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>   <td>   -2.0726</td> <td> 1.19e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-2.33e+04</td> <td> 2.33e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>   <td>    1.4310</td> <td> 1.36e+04</td> <td>    0.000</td> <td> 1.000</td> <td>-2.66e+04</td> <td> 2.66e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>   <td>    1.3577</td> <td> 1.05e+04</td> <td>    0.000</td> <td> 1.000</td> <td>-2.06e+04</td> <td> 2.06e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>   <td>    3.8046</td> <td> 1.17e+04</td> <td>    0.000</td> <td> 1.000</td> <td>-2.29e+04</td> <td> 2.29e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th>   <td>   -2.9295</td> <td> 1.06e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-2.09e+04</td> <td> 2.08e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x8</th>   <td>    9.9962</td> <td> 1.36e+04</td> <td>    0.001</td> <td> 0.999</td> <td>-2.66e+04</td> <td> 2.66e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x9</th>   <td>    0.1117</td> <td> 1.06e+04</td> <td> 1.05e-05</td> <td> 1.000</td> <td>-2.08e+04</td> <td> 2.08e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x10</th>  <td>   -2.9505</td> <td> 9939.311</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.95e+04</td> <td> 1.95e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x11</th>  <td>    0.0031</td> <td>  1.1e+04</td> <td> 2.83e-07</td> <td> 1.000</td> <td>-2.15e+04</td> <td> 2.15e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x12</th>  <td>    1.4862</td> <td> 1.07e+04</td> <td>    0.000</td> <td> 1.000</td> <td>-2.09e+04</td> <td>  2.1e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x13</th>  <td>   -0.4733</td> <td> 1.04e+04</td> <td>-4.55e-05</td> <td> 1.000</td> <td>-2.04e+04</td> <td> 2.04e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x14</th>  <td>    1.6238</td> <td> 1.07e+04</td> <td>    0.000</td> <td> 1.000</td> <td> -2.1e+04</td> <td>  2.1e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x15</th>  <td>    1.8379</td> <td> 9433.272</td> <td>    0.000</td> <td> 1.000</td> <td>-1.85e+04</td> <td> 1.85e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x16</th>  <td>   -3.3380</td> <td> 1.03e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-2.02e+04</td> <td> 2.02e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x17</th>  <td>    0.1639</td> <td> 9048.697</td> <td> 1.81e-05</td> <td> 1.000</td> <td>-1.77e+04</td> <td> 1.77e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x18</th>  <td>    0.9175</td> <td> 8984.390</td> <td>    0.000</td> <td> 1.000</td> <td>-1.76e+04</td> <td> 1.76e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x19</th>  <td>    0.5843</td> <td> 1.05e+04</td> <td> 5.56e-05</td> <td> 1.000</td> <td>-2.06e+04</td> <td> 2.06e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x20</th>  <td>   -1.0944</td> <td> 1.02e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.99e+04</td> <td> 1.99e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x21</th>  <td>   -1.6730</td> <td> 1.05e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-2.07e+04</td> <td> 2.07e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x22</th>  <td>    0.0223</td> <td> 1.11e+04</td> <td> 2.01e-06</td> <td> 1.000</td> <td>-2.18e+04</td> <td> 2.18e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x23</th>  <td>   -0.2333</td> <td> 1.26e+04</td> <td>-1.85e-05</td> <td> 1.000</td> <td>-2.47e+04</td> <td> 2.47e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x24</th>  <td>    2.5306</td> <td> 1.02e+04</td> <td>    0.000</td> <td> 1.000</td> <td>   -2e+04</td> <td> 2.01e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x25</th>  <td>    0.2533</td> <td> 9407.355</td> <td> 2.69e-05</td> <td> 1.000</td> <td>-1.84e+04</td> <td> 1.84e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x26</th>  <td>   -0.0673</td> <td> 1.03e+04</td> <td>-6.53e-06</td> <td> 1.000</td> <td>-2.02e+04</td> <td> 2.02e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x27</th>  <td>    6.1446</td> <td> 9329.460</td> <td>    0.001</td> <td> 0.999</td> <td>-1.83e+04</td> <td> 1.83e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x28</th>  <td>    0.7617</td> <td> 1.14e+04</td> <td> 6.67e-05</td> <td> 1.000</td> <td>-2.24e+04</td> <td> 2.24e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x29</th>  <td>    0.8707</td> <td> 9395.816</td> <td> 9.27e-05</td> <td> 1.000</td> <td>-1.84e+04</td> <td> 1.84e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x30</th>  <td>    0.1276</td> <td> 1.08e+04</td> <td> 1.18e-05</td> <td> 1.000</td> <td>-2.11e+04</td> <td> 2.11e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x31</th>  <td>    5.4611</td> <td> 1.29e+04</td> <td>    0.000</td> <td> 1.000</td> <td>-2.53e+04</td> <td> 2.53e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x32</th>  <td>    1.2560</td> <td> 9570.443</td> <td>    0.000</td> <td> 1.000</td> <td>-1.88e+04</td> <td> 1.88e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x33</th>  <td>   -0.5015</td> <td> 9205.312</td> <td>-5.45e-05</td> <td> 1.000</td> <td> -1.8e+04</td> <td>  1.8e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x34</th>  <td>   10.3272</td> <td> 1.66e+04</td> <td>    0.001</td> <td> 1.000</td> <td>-3.25e+04</td> <td> 3.25e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x35</th>  <td>   -0.5904</td> <td> 1.05e+04</td> <td> -5.6e-05</td> <td> 1.000</td> <td>-2.07e+04</td> <td> 2.07e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x36</th>  <td>   -2.0817</td> <td> 9051.976</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.77e+04</td> <td> 1.77e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x37</th>  <td>    2.6786</td> <td> 9054.988</td> <td>    0.000</td> <td> 1.000</td> <td>-1.77e+04</td> <td> 1.78e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x38</th>  <td>    0.0603</td> <td> 1.03e+04</td> <td> 5.87e-06</td> <td> 1.000</td> <td>-2.01e+04</td> <td> 2.01e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x39</th>  <td>   -2.3235</td> <td> 9280.993</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.82e+04</td> <td> 1.82e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x40</th>  <td>   -1.1269</td> <td> 9292.989</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.82e+04</td> <td> 1.82e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x41</th>  <td>   -0.5813</td> <td> 9817.499</td> <td>-5.92e-05</td> <td> 1.000</td> <td>-1.92e+04</td> <td> 1.92e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x42</th>  <td>   -0.0028</td> <td> 9701.572</td> <td>-2.91e-07</td> <td> 1.000</td> <td> -1.9e+04</td> <td>  1.9e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x43</th>  <td>    3.4281</td> <td> 9898.488</td> <td>    0.000</td> <td> 1.000</td> <td>-1.94e+04</td> <td> 1.94e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x44</th>  <td>   -0.5426</td> <td> 1.08e+04</td> <td>-5.02e-05</td> <td> 1.000</td> <td>-2.12e+04</td> <td> 2.12e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x45</th>  <td>   -0.0186</td> <td> 8098.882</td> <td> -2.3e-06</td> <td> 1.000</td> <td>-1.59e+04</td> <td> 1.59e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x46</th>  <td>   -2.7940</td> <td> 1.43e+04</td> <td>   -0.000</td> <td> 1.000</td> <td> -2.8e+04</td> <td> 2.79e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x47</th>  <td>   -1.2504</td> <td> 1.11e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-2.18e+04</td> <td> 2.18e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x48</th>  <td>   -4.7515</td> <td> 1.23e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-2.41e+04</td> <td>  2.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x49</th>  <td>    0.7629</td> <td> 8828.814</td> <td> 8.64e-05</td> <td> 1.000</td> <td>-1.73e+04</td> <td> 1.73e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x50</th>  <td>    1.1357</td> <td> 1.04e+04</td> <td>    0.000</td> <td> 1.000</td> <td>-2.04e+04</td> <td> 2.04e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x51</th>  <td>    1.0909</td> <td> 9195.637</td> <td>    0.000</td> <td> 1.000</td> <td> -1.8e+04</td> <td>  1.8e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x52</th>  <td>   -2.2230</td> <td> 1.02e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.99e+04</td> <td> 1.99e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x53</th>  <td>    1.4999</td> <td> 1.23e+04</td> <td>    0.000</td> <td> 1.000</td> <td>-2.41e+04</td> <td> 2.41e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x54</th>  <td>   58.6374</td> <td> 1.33e+04</td> <td>    0.004</td> <td> 0.996</td> <td>-2.59e+04</td> <td> 2.61e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x55</th>  <td>   -0.4922</td> <td> 9839.232</td> <td>   -5e-05</td> <td> 1.000</td> <td>-1.93e+04</td> <td> 1.93e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x56</th>  <td>   -5.9971</td> <td> 1.12e+04</td> <td>   -0.001</td> <td> 1.000</td> <td> -2.2e+04</td> <td>  2.2e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x57</th>  <td>    1.1919</td> <td> 1.25e+04</td> <td> 9.55e-05</td> <td> 1.000</td> <td>-2.45e+04</td> <td> 2.45e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x58</th>  <td>   -0.7422</td> <td> 8967.919</td> <td>-8.28e-05</td> <td> 1.000</td> <td>-1.76e+04</td> <td> 1.76e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x59</th>  <td>   -0.2136</td> <td> 1.12e+04</td> <td>-1.91e-05</td> <td> 1.000</td> <td>-2.19e+04</td> <td> 2.19e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x60</th>  <td>   -0.7838</td> <td> 8200.412</td> <td>-9.56e-05</td> <td> 1.000</td> <td>-1.61e+04</td> <td> 1.61e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x61</th>  <td>   -0.6898</td> <td> 8635.068</td> <td>-7.99e-05</td> <td> 1.000</td> <td>-1.69e+04</td> <td> 1.69e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x62</th>  <td>   -1.2805</td> <td> 1.03e+04</td> <td>   -0.000</td> <td> 1.000</td> <td>-2.02e+04</td> <td> 2.02e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x63</th>  <td>   -0.2491</td> <td> 9994.977</td> <td>-2.49e-05</td> <td> 1.000</td> <td>-1.96e+04</td> <td> 1.96e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x64</th>  <td>    2.2029</td> <td> 8051.710</td> <td>    0.000</td> <td> 1.000</td> <td>-1.58e+04</td> <td> 1.58e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x65</th>  <td>   -2.6762</td> <td> 8036.781</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.58e+04</td> <td> 1.57e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x66</th>  <td>    0.2654</td> <td> 8078.768</td> <td> 3.28e-05</td> <td> 1.000</td> <td>-1.58e+04</td> <td> 1.58e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x67</th>  <td>    0.9897</td> <td> 7891.967</td> <td>    0.000</td> <td> 1.000</td> <td>-1.55e+04</td> <td> 1.55e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x68</th>  <td>   -0.9645</td> <td> 8355.631</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.64e+04</td> <td> 1.64e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x69</th>  <td>    0.1178</td> <td> 8058.488</td> <td> 1.46e-05</td> <td> 1.000</td> <td>-1.58e+04</td> <td> 1.58e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x70</th>  <td>    1.2418</td> <td> 7939.399</td> <td>    0.000</td> <td> 1.000</td> <td>-1.56e+04</td> <td> 1.56e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x71</th>  <td>   -0.6118</td> <td> 9601.618</td> <td>-6.37e-05</td> <td> 1.000</td> <td>-1.88e+04</td> <td> 1.88e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x72</th>  <td>    0.9905</td> <td> 8136.714</td> <td>    0.000</td> <td> 1.000</td> <td>-1.59e+04</td> <td> 1.59e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x73</th>  <td>    2.4910</td> <td> 8484.391</td> <td>    0.000</td> <td> 1.000</td> <td>-1.66e+04</td> <td> 1.66e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x74</th>  <td>    1.2731</td> <td> 8547.330</td> <td>    0.000</td> <td> 1.000</td> <td>-1.68e+04</td> <td> 1.68e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x75</th>  <td>   -1.1847</td> <td> 8718.740</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.71e+04</td> <td> 1.71e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x76</th>  <td>   -1.4178</td> <td> 7712.778</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.51e+04</td> <td> 1.51e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x77</th>  <td>   -0.4777</td> <td> 8729.439</td> <td>-5.47e-05</td> <td> 1.000</td> <td>-1.71e+04</td> <td> 1.71e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x78</th>  <td>   -0.9683</td> <td> 8040.813</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.58e+04</td> <td> 1.58e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x79</th>  <td>    1.3869</td> <td> 7725.447</td> <td>    0.000</td> <td> 1.000</td> <td>-1.51e+04</td> <td> 1.51e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x80</th>  <td>   -0.3279</td> <td> 8081.706</td> <td>-4.06e-05</td> <td> 1.000</td> <td>-1.58e+04</td> <td> 1.58e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x81</th>  <td>   -0.5303</td> <td> 8692.802</td> <td> -6.1e-05</td> <td> 1.000</td> <td> -1.7e+04</td> <td>  1.7e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x82</th>  <td>   -1.0013</td> <td> 8055.442</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.58e+04</td> <td> 1.58e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x83</th>  <td>   -0.3217</td> <td> 7896.061</td> <td>-4.07e-05</td> <td> 1.000</td> <td>-1.55e+04</td> <td> 1.55e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x84</th>  <td>   -0.4157</td> <td> 8069.786</td> <td>-5.15e-05</td> <td> 1.000</td> <td>-1.58e+04</td> <td> 1.58e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x85</th>  <td>    0.5706</td> <td> 8294.639</td> <td> 6.88e-05</td> <td> 1.000</td> <td>-1.63e+04</td> <td> 1.63e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x86</th>  <td>    2.0293</td> <td> 8090.390</td> <td>    0.000</td> <td> 1.000</td> <td>-1.59e+04</td> <td> 1.59e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x87</th>  <td>    0.3780</td> <td> 8054.006</td> <td> 4.69e-05</td> <td> 1.000</td> <td>-1.58e+04</td> <td> 1.58e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x88</th>  <td>   -0.2446</td> <td> 8094.886</td> <td>-3.02e-05</td> <td> 1.000</td> <td>-1.59e+04</td> <td> 1.59e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x89</th>  <td>   -1.2823</td> <td> 8193.641</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.61e+04</td> <td> 1.61e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x90</th>  <td>    0.2062</td> <td> 8287.843</td> <td> 2.49e-05</td> <td> 1.000</td> <td>-1.62e+04</td> <td> 1.62e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x91</th>  <td>   -0.6147</td> <td> 8302.807</td> <td> -7.4e-05</td> <td> 1.000</td> <td>-1.63e+04</td> <td> 1.63e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x92</th>  <td>   -0.2857</td> <td> 8112.705</td> <td>-3.52e-05</td> <td> 1.000</td> <td>-1.59e+04</td> <td> 1.59e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x93</th>  <td>    0.3542</td> <td> 8291.653</td> <td> 4.27e-05</td> <td> 1.000</td> <td>-1.63e+04</td> <td> 1.63e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x94</th>  <td>   -1.6488</td> <td> 7855.776</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.54e+04</td> <td> 1.54e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x95</th>  <td>   -0.8451</td> <td> 7907.270</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.55e+04</td> <td> 1.55e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x96</th>  <td>    0.7883</td> <td> 8298.381</td> <td>  9.5e-05</td> <td> 1.000</td> <td>-1.63e+04</td> <td> 1.63e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x97</th>  <td>   -0.5490</td> <td> 9112.191</td> <td>-6.02e-05</td> <td> 1.000</td> <td>-1.79e+04</td> <td> 1.79e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x98</th>  <td>   -1.9704</td> <td> 8257.058</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.62e+04</td> <td> 1.62e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x99</th>  <td>    0.3238</td> <td> 8060.614</td> <td> 4.02e-05</td> <td> 1.000</td> <td>-1.58e+04</td> <td> 1.58e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x100</th> <td>    0.3051</td> <td> 7935.208</td> <td> 3.85e-05</td> <td> 1.000</td> <td>-1.56e+04</td> <td> 1.56e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x101</th> <td>    0.7538</td> <td> 9073.396</td> <td> 8.31e-05</td> <td> 1.000</td> <td>-1.78e+04</td> <td> 1.78e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x102</th> <td>   -1.0422</td> <td> 7948.690</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.56e+04</td> <td> 1.56e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x103</th> <td>   -0.1362</td> <td> 7660.509</td> <td>-1.78e-05</td> <td> 1.000</td> <td> -1.5e+04</td> <td>  1.5e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x104</th> <td>   -2.2833</td> <td> 9049.214</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.77e+04</td> <td> 1.77e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x105</th> <td>   -0.2033</td> <td> 8523.089</td> <td>-2.39e-05</td> <td> 1.000</td> <td>-1.67e+04</td> <td> 1.67e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x106</th> <td>    0.2185</td> <td> 8164.256</td> <td> 2.68e-05</td> <td> 1.000</td> <td> -1.6e+04</td> <td>  1.6e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x107</th> <td>   -1.8786</td> <td> 7944.481</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.56e+04</td> <td> 1.56e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x108</th> <td>    0.2151</td> <td> 7807.197</td> <td> 2.76e-05</td> <td> 1.000</td> <td>-1.53e+04</td> <td> 1.53e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x109</th> <td>    1.7077</td> <td> 8406.403</td> <td>    0.000</td> <td> 1.000</td> <td>-1.65e+04</td> <td> 1.65e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x110</th> <td>    1.8302</td> <td> 8174.412</td> <td>    0.000</td> <td> 1.000</td> <td> -1.6e+04</td> <td>  1.6e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x111</th> <td>    0.7323</td> <td> 8884.009</td> <td> 8.24e-05</td> <td> 1.000</td> <td>-1.74e+04</td> <td> 1.74e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x112</th> <td>    0.3466</td> <td> 8214.013</td> <td> 4.22e-05</td> <td> 1.000</td> <td>-1.61e+04</td> <td> 1.61e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x113</th> <td>   -0.0473</td> <td> 8510.320</td> <td>-5.55e-06</td> <td> 1.000</td> <td>-1.67e+04</td> <td> 1.67e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x114</th> <td>    0.3980</td> <td> 8123.444</td> <td>  4.9e-05</td> <td> 1.000</td> <td>-1.59e+04</td> <td> 1.59e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x115</th> <td>    0.9158</td> <td> 8253.087</td> <td>    0.000</td> <td> 1.000</td> <td>-1.62e+04</td> <td> 1.62e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x116</th> <td>   -0.4328</td> <td> 8352.992</td> <td>-5.18e-05</td> <td> 1.000</td> <td>-1.64e+04</td> <td> 1.64e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x117</th> <td>    1.4307</td> <td> 8385.881</td> <td>    0.000</td> <td> 1.000</td> <td>-1.64e+04</td> <td> 1.64e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x118</th> <td>    1.9557</td> <td> 7943.947</td> <td>    0.000</td> <td> 1.000</td> <td>-1.56e+04</td> <td> 1.56e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x119</th> <td>    1.6209</td> <td> 8241.896</td> <td>    0.000</td> <td> 1.000</td> <td>-1.62e+04</td> <td> 1.62e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x120</th> <td>   -0.2560</td> <td> 7803.479</td> <td>-3.28e-05</td> <td> 1.000</td> <td>-1.53e+04</td> <td> 1.53e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x121</th> <td>   -0.3485</td> <td> 7723.460</td> <td>-4.51e-05</td> <td> 1.000</td> <td>-1.51e+04</td> <td> 1.51e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x122</th> <td>    0.6837</td> <td> 7941.329</td> <td> 8.61e-05</td> <td> 1.000</td> <td>-1.56e+04</td> <td> 1.56e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x123</th> <td>    0.3875</td> <td> 8207.426</td> <td> 4.72e-05</td> <td> 1.000</td> <td>-1.61e+04</td> <td> 1.61e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x124</th> <td>    0.3166</td> <td> 8113.045</td> <td>  3.9e-05</td> <td> 1.000</td> <td>-1.59e+04</td> <td> 1.59e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x125</th> <td>   -1.6428</td> <td> 7908.398</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.55e+04</td> <td> 1.55e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x126</th> <td>    0.5981</td> <td> 8127.998</td> <td> 7.36e-05</td> <td> 1.000</td> <td>-1.59e+04</td> <td> 1.59e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x127</th> <td>    2.1257</td> <td> 8437.622</td> <td>    0.000</td> <td> 1.000</td> <td>-1.65e+04</td> <td> 1.65e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x128</th> <td>   -0.4865</td> <td> 7973.125</td> <td> -6.1e-05</td> <td> 1.000</td> <td>-1.56e+04</td> <td> 1.56e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x129</th> <td>   -0.3751</td> <td> 7998.672</td> <td>-4.69e-05</td> <td> 1.000</td> <td>-1.57e+04</td> <td> 1.57e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x130</th> <td>    0.0225</td> <td> 7703.795</td> <td> 2.93e-06</td> <td> 1.000</td> <td>-1.51e+04</td> <td> 1.51e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x131</th> <td>   -1.4617</td> <td> 8064.715</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.58e+04</td> <td> 1.58e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x132</th> <td>    2.4236</td> <td> 8399.162</td> <td>    0.000</td> <td> 1.000</td> <td>-1.65e+04</td> <td> 1.65e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x133</th> <td>    1.4283</td> <td> 8567.347</td> <td>    0.000</td> <td> 1.000</td> <td>-1.68e+04</td> <td> 1.68e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x134</th> <td>   -0.1005</td> <td> 8141.592</td> <td>-1.23e-05</td> <td> 1.000</td> <td> -1.6e+04</td> <td>  1.6e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x135</th> <td>   -1.0200</td> <td> 8103.775</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.59e+04</td> <td> 1.59e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x136</th> <td>   -0.3307</td> <td> 8162.028</td> <td>-4.05e-05</td> <td> 1.000</td> <td> -1.6e+04</td> <td>  1.6e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x137</th> <td>   -0.7106</td> <td> 7826.816</td> <td>-9.08e-05</td> <td> 1.000</td> <td>-1.53e+04</td> <td> 1.53e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x138</th> <td>   -0.6660</td> <td> 8584.271</td> <td>-7.76e-05</td> <td> 1.000</td> <td>-1.68e+04</td> <td> 1.68e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x139</th> <td>    0.5593</td> <td> 7773.993</td> <td> 7.19e-05</td> <td> 1.000</td> <td>-1.52e+04</td> <td> 1.52e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x140</th> <td>   -0.0387</td> <td> 8041.424</td> <td>-4.81e-06</td> <td> 1.000</td> <td>-1.58e+04</td> <td> 1.58e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x141</th> <td>    0.5320</td> <td> 8348.975</td> <td> 6.37e-05</td> <td> 1.000</td> <td>-1.64e+04</td> <td> 1.64e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x142</th> <td>    0.5712</td> <td> 7667.191</td> <td> 7.45e-05</td> <td> 1.000</td> <td> -1.5e+04</td> <td>  1.5e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x143</th> <td>    1.4825</td> <td> 8007.777</td> <td>    0.000</td> <td> 1.000</td> <td>-1.57e+04</td> <td> 1.57e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x144</th> <td>    0.6619</td> <td> 8165.509</td> <td> 8.11e-05</td> <td> 1.000</td> <td> -1.6e+04</td> <td>  1.6e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x145</th> <td>    2.3904</td> <td> 8004.444</td> <td>    0.000</td> <td> 1.000</td> <td>-1.57e+04</td> <td> 1.57e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x146</th> <td>   -0.8945</td> <td> 7705.158</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.51e+04</td> <td> 1.51e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x147</th> <td>    1.8717</td> <td> 8566.768</td> <td>    0.000</td> <td> 1.000</td> <td>-1.68e+04</td> <td> 1.68e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x148</th> <td>   -0.9307</td> <td> 8613.112</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.69e+04</td> <td> 1.69e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x149</th> <td>    1.2027</td> <td> 9150.540</td> <td>    0.000</td> <td> 1.000</td> <td>-1.79e+04</td> <td> 1.79e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x150</th> <td>    2.4105</td> <td> 7856.191</td> <td>    0.000</td> <td> 1.000</td> <td>-1.54e+04</td> <td> 1.54e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x151</th> <td>    1.3789</td> <td> 8289.178</td> <td>    0.000</td> <td> 1.000</td> <td>-1.62e+04</td> <td> 1.62e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x152</th> <td>    0.6702</td> <td> 7923.680</td> <td> 8.46e-05</td> <td> 1.000</td> <td>-1.55e+04</td> <td> 1.55e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x153</th> <td>   -0.7646</td> <td> 8332.746</td> <td>-9.18e-05</td> <td> 1.000</td> <td>-1.63e+04</td> <td> 1.63e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x154</th> <td>   -1.5148</td> <td> 8011.797</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.57e+04</td> <td> 1.57e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x155</th> <td>   -0.1829</td> <td> 8388.137</td> <td>-2.18e-05</td> <td> 1.000</td> <td>-1.64e+04</td> <td> 1.64e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x156</th> <td>   -1.0890</td> <td> 8119.309</td> <td>   -0.000</td> <td> 1.000</td> <td>-1.59e+04</td> <td> 1.59e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x157</th> <td>    0.2461</td> <td> 8133.985</td> <td> 3.03e-05</td> <td> 1.000</td> <td>-1.59e+04</td> <td> 1.59e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x158</th> <td>   -0.5719</td> <td> 8251.533</td> <td>-6.93e-05</td> <td> 1.000</td> <td>-1.62e+04</td> <td> 1.62e+04</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  160\n",
       "Model:                            GLM   Df Residuals:                        2\n",
       "Model Family:                Gaussian   Df Model:                          157\n",
       "Link Function:               identity   Scale:                      6.8905e+09\n",
       "Method:                          IRLS   Log-Likelihood:                -1688.7\n",
       "Date:                Mon, 02 Dec 2019   Deviance:                   1.3781e+10\n",
       "Time:                        10:55:59   Pearson chi2:                 1.38e+10\n",
       "No. Iterations:                     3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1            -0.5420   1.34e+04  -4.05e-05      1.000   -2.62e+04    2.62e+04\n",
       "x2             1.8812   1.18e+04      0.000      1.000    -2.3e+04     2.3e+04\n",
       "x3            -2.0726   1.19e+04     -0.000      1.000   -2.33e+04    2.33e+04\n",
       "x4             1.4310   1.36e+04      0.000      1.000   -2.66e+04    2.66e+04\n",
       "x5             1.3577   1.05e+04      0.000      1.000   -2.06e+04    2.06e+04\n",
       "x6             3.8046   1.17e+04      0.000      1.000   -2.29e+04    2.29e+04\n",
       "x7            -2.9295   1.06e+04     -0.000      1.000   -2.09e+04    2.08e+04\n",
       "x8             9.9962   1.36e+04      0.001      0.999   -2.66e+04    2.66e+04\n",
       "x9             0.1117   1.06e+04   1.05e-05      1.000   -2.08e+04    2.08e+04\n",
       "x10           -2.9505   9939.311     -0.000      1.000   -1.95e+04    1.95e+04\n",
       "x11            0.0031    1.1e+04   2.83e-07      1.000   -2.15e+04    2.15e+04\n",
       "x12            1.4862   1.07e+04      0.000      1.000   -2.09e+04     2.1e+04\n",
       "x13           -0.4733   1.04e+04  -4.55e-05      1.000   -2.04e+04    2.04e+04\n",
       "x14            1.6238   1.07e+04      0.000      1.000    -2.1e+04     2.1e+04\n",
       "x15            1.8379   9433.272      0.000      1.000   -1.85e+04    1.85e+04\n",
       "x16           -3.3380   1.03e+04     -0.000      1.000   -2.02e+04    2.02e+04\n",
       "x17            0.1639   9048.697   1.81e-05      1.000   -1.77e+04    1.77e+04\n",
       "x18            0.9175   8984.390      0.000      1.000   -1.76e+04    1.76e+04\n",
       "x19            0.5843   1.05e+04   5.56e-05      1.000   -2.06e+04    2.06e+04\n",
       "x20           -1.0944   1.02e+04     -0.000      1.000   -1.99e+04    1.99e+04\n",
       "x21           -1.6730   1.05e+04     -0.000      1.000   -2.07e+04    2.07e+04\n",
       "x22            0.0223   1.11e+04   2.01e-06      1.000   -2.18e+04    2.18e+04\n",
       "x23           -0.2333   1.26e+04  -1.85e-05      1.000   -2.47e+04    2.47e+04\n",
       "x24            2.5306   1.02e+04      0.000      1.000      -2e+04    2.01e+04\n",
       "x25            0.2533   9407.355   2.69e-05      1.000   -1.84e+04    1.84e+04\n",
       "x26           -0.0673   1.03e+04  -6.53e-06      1.000   -2.02e+04    2.02e+04\n",
       "x27            6.1446   9329.460      0.001      0.999   -1.83e+04    1.83e+04\n",
       "x28            0.7617   1.14e+04   6.67e-05      1.000   -2.24e+04    2.24e+04\n",
       "x29            0.8707   9395.816   9.27e-05      1.000   -1.84e+04    1.84e+04\n",
       "x30            0.1276   1.08e+04   1.18e-05      1.000   -2.11e+04    2.11e+04\n",
       "x31            5.4611   1.29e+04      0.000      1.000   -2.53e+04    2.53e+04\n",
       "x32            1.2560   9570.443      0.000      1.000   -1.88e+04    1.88e+04\n",
       "x33           -0.5015   9205.312  -5.45e-05      1.000    -1.8e+04     1.8e+04\n",
       "x34           10.3272   1.66e+04      0.001      1.000   -3.25e+04    3.25e+04\n",
       "x35           -0.5904   1.05e+04   -5.6e-05      1.000   -2.07e+04    2.07e+04\n",
       "x36           -2.0817   9051.976     -0.000      1.000   -1.77e+04    1.77e+04\n",
       "x37            2.6786   9054.988      0.000      1.000   -1.77e+04    1.78e+04\n",
       "x38            0.0603   1.03e+04   5.87e-06      1.000   -2.01e+04    2.01e+04\n",
       "x39           -2.3235   9280.993     -0.000      1.000   -1.82e+04    1.82e+04\n",
       "x40           -1.1269   9292.989     -0.000      1.000   -1.82e+04    1.82e+04\n",
       "x41           -0.5813   9817.499  -5.92e-05      1.000   -1.92e+04    1.92e+04\n",
       "x42           -0.0028   9701.572  -2.91e-07      1.000    -1.9e+04     1.9e+04\n",
       "x43            3.4281   9898.488      0.000      1.000   -1.94e+04    1.94e+04\n",
       "x44           -0.5426   1.08e+04  -5.02e-05      1.000   -2.12e+04    2.12e+04\n",
       "x45           -0.0186   8098.882   -2.3e-06      1.000   -1.59e+04    1.59e+04\n",
       "x46           -2.7940   1.43e+04     -0.000      1.000    -2.8e+04    2.79e+04\n",
       "x47           -1.2504   1.11e+04     -0.000      1.000   -2.18e+04    2.18e+04\n",
       "x48           -4.7515   1.23e+04     -0.000      1.000   -2.41e+04     2.4e+04\n",
       "x49            0.7629   8828.814   8.64e-05      1.000   -1.73e+04    1.73e+04\n",
       "x50            1.1357   1.04e+04      0.000      1.000   -2.04e+04    2.04e+04\n",
       "x51            1.0909   9195.637      0.000      1.000    -1.8e+04     1.8e+04\n",
       "x52           -2.2230   1.02e+04     -0.000      1.000   -1.99e+04    1.99e+04\n",
       "x53            1.4999   1.23e+04      0.000      1.000   -2.41e+04    2.41e+04\n",
       "x54           58.6374   1.33e+04      0.004      0.996   -2.59e+04    2.61e+04\n",
       "x55           -0.4922   9839.232     -5e-05      1.000   -1.93e+04    1.93e+04\n",
       "x56           -5.9971   1.12e+04     -0.001      1.000    -2.2e+04     2.2e+04\n",
       "x57            1.1919   1.25e+04   9.55e-05      1.000   -2.45e+04    2.45e+04\n",
       "x58           -0.7422   8967.919  -8.28e-05      1.000   -1.76e+04    1.76e+04\n",
       "x59           -0.2136   1.12e+04  -1.91e-05      1.000   -2.19e+04    2.19e+04\n",
       "x60           -0.7838   8200.412  -9.56e-05      1.000   -1.61e+04    1.61e+04\n",
       "x61           -0.6898   8635.068  -7.99e-05      1.000   -1.69e+04    1.69e+04\n",
       "x62           -1.2805   1.03e+04     -0.000      1.000   -2.02e+04    2.02e+04\n",
       "x63           -0.2491   9994.977  -2.49e-05      1.000   -1.96e+04    1.96e+04\n",
       "x64            2.2029   8051.710      0.000      1.000   -1.58e+04    1.58e+04\n",
       "x65           -2.6762   8036.781     -0.000      1.000   -1.58e+04    1.57e+04\n",
       "x66            0.2654   8078.768   3.28e-05      1.000   -1.58e+04    1.58e+04\n",
       "x67            0.9897   7891.967      0.000      1.000   -1.55e+04    1.55e+04\n",
       "x68           -0.9645   8355.631     -0.000      1.000   -1.64e+04    1.64e+04\n",
       "x69            0.1178   8058.488   1.46e-05      1.000   -1.58e+04    1.58e+04\n",
       "x70            1.2418   7939.399      0.000      1.000   -1.56e+04    1.56e+04\n",
       "x71           -0.6118   9601.618  -6.37e-05      1.000   -1.88e+04    1.88e+04\n",
       "x72            0.9905   8136.714      0.000      1.000   -1.59e+04    1.59e+04\n",
       "x73            2.4910   8484.391      0.000      1.000   -1.66e+04    1.66e+04\n",
       "x74            1.2731   8547.330      0.000      1.000   -1.68e+04    1.68e+04\n",
       "x75           -1.1847   8718.740     -0.000      1.000   -1.71e+04    1.71e+04\n",
       "x76           -1.4178   7712.778     -0.000      1.000   -1.51e+04    1.51e+04\n",
       "x77           -0.4777   8729.439  -5.47e-05      1.000   -1.71e+04    1.71e+04\n",
       "x78           -0.9683   8040.813     -0.000      1.000   -1.58e+04    1.58e+04\n",
       "x79            1.3869   7725.447      0.000      1.000   -1.51e+04    1.51e+04\n",
       "x80           -0.3279   8081.706  -4.06e-05      1.000   -1.58e+04    1.58e+04\n",
       "x81           -0.5303   8692.802   -6.1e-05      1.000    -1.7e+04     1.7e+04\n",
       "x82           -1.0013   8055.442     -0.000      1.000   -1.58e+04    1.58e+04\n",
       "x83           -0.3217   7896.061  -4.07e-05      1.000   -1.55e+04    1.55e+04\n",
       "x84           -0.4157   8069.786  -5.15e-05      1.000   -1.58e+04    1.58e+04\n",
       "x85            0.5706   8294.639   6.88e-05      1.000   -1.63e+04    1.63e+04\n",
       "x86            2.0293   8090.390      0.000      1.000   -1.59e+04    1.59e+04\n",
       "x87            0.3780   8054.006   4.69e-05      1.000   -1.58e+04    1.58e+04\n",
       "x88           -0.2446   8094.886  -3.02e-05      1.000   -1.59e+04    1.59e+04\n",
       "x89           -1.2823   8193.641     -0.000      1.000   -1.61e+04    1.61e+04\n",
       "x90            0.2062   8287.843   2.49e-05      1.000   -1.62e+04    1.62e+04\n",
       "x91           -0.6147   8302.807   -7.4e-05      1.000   -1.63e+04    1.63e+04\n",
       "x92           -0.2857   8112.705  -3.52e-05      1.000   -1.59e+04    1.59e+04\n",
       "x93            0.3542   8291.653   4.27e-05      1.000   -1.63e+04    1.63e+04\n",
       "x94           -1.6488   7855.776     -0.000      1.000   -1.54e+04    1.54e+04\n",
       "x95           -0.8451   7907.270     -0.000      1.000   -1.55e+04    1.55e+04\n",
       "x96            0.7883   8298.381    9.5e-05      1.000   -1.63e+04    1.63e+04\n",
       "x97           -0.5490   9112.191  -6.02e-05      1.000   -1.79e+04    1.79e+04\n",
       "x98           -1.9704   8257.058     -0.000      1.000   -1.62e+04    1.62e+04\n",
       "x99            0.3238   8060.614   4.02e-05      1.000   -1.58e+04    1.58e+04\n",
       "x100           0.3051   7935.208   3.85e-05      1.000   -1.56e+04    1.56e+04\n",
       "x101           0.7538   9073.396   8.31e-05      1.000   -1.78e+04    1.78e+04\n",
       "x102          -1.0422   7948.690     -0.000      1.000   -1.56e+04    1.56e+04\n",
       "x103          -0.1362   7660.509  -1.78e-05      1.000    -1.5e+04     1.5e+04\n",
       "x104          -2.2833   9049.214     -0.000      1.000   -1.77e+04    1.77e+04\n",
       "x105          -0.2033   8523.089  -2.39e-05      1.000   -1.67e+04    1.67e+04\n",
       "x106           0.2185   8164.256   2.68e-05      1.000    -1.6e+04     1.6e+04\n",
       "x107          -1.8786   7944.481     -0.000      1.000   -1.56e+04    1.56e+04\n",
       "x108           0.2151   7807.197   2.76e-05      1.000   -1.53e+04    1.53e+04\n",
       "x109           1.7077   8406.403      0.000      1.000   -1.65e+04    1.65e+04\n",
       "x110           1.8302   8174.412      0.000      1.000    -1.6e+04     1.6e+04\n",
       "x111           0.7323   8884.009   8.24e-05      1.000   -1.74e+04    1.74e+04\n",
       "x112           0.3466   8214.013   4.22e-05      1.000   -1.61e+04    1.61e+04\n",
       "x113          -0.0473   8510.320  -5.55e-06      1.000   -1.67e+04    1.67e+04\n",
       "x114           0.3980   8123.444    4.9e-05      1.000   -1.59e+04    1.59e+04\n",
       "x115           0.9158   8253.087      0.000      1.000   -1.62e+04    1.62e+04\n",
       "x116          -0.4328   8352.992  -5.18e-05      1.000   -1.64e+04    1.64e+04\n",
       "x117           1.4307   8385.881      0.000      1.000   -1.64e+04    1.64e+04\n",
       "x118           1.9557   7943.947      0.000      1.000   -1.56e+04    1.56e+04\n",
       "x119           1.6209   8241.896      0.000      1.000   -1.62e+04    1.62e+04\n",
       "x120          -0.2560   7803.479  -3.28e-05      1.000   -1.53e+04    1.53e+04\n",
       "x121          -0.3485   7723.460  -4.51e-05      1.000   -1.51e+04    1.51e+04\n",
       "x122           0.6837   7941.329   8.61e-05      1.000   -1.56e+04    1.56e+04\n",
       "x123           0.3875   8207.426   4.72e-05      1.000   -1.61e+04    1.61e+04\n",
       "x124           0.3166   8113.045    3.9e-05      1.000   -1.59e+04    1.59e+04\n",
       "x125          -1.6428   7908.398     -0.000      1.000   -1.55e+04    1.55e+04\n",
       "x126           0.5981   8127.998   7.36e-05      1.000   -1.59e+04    1.59e+04\n",
       "x127           2.1257   8437.622      0.000      1.000   -1.65e+04    1.65e+04\n",
       "x128          -0.4865   7973.125   -6.1e-05      1.000   -1.56e+04    1.56e+04\n",
       "x129          -0.3751   7998.672  -4.69e-05      1.000   -1.57e+04    1.57e+04\n",
       "x130           0.0225   7703.795   2.93e-06      1.000   -1.51e+04    1.51e+04\n",
       "x131          -1.4617   8064.715     -0.000      1.000   -1.58e+04    1.58e+04\n",
       "x132           2.4236   8399.162      0.000      1.000   -1.65e+04    1.65e+04\n",
       "x133           1.4283   8567.347      0.000      1.000   -1.68e+04    1.68e+04\n",
       "x134          -0.1005   8141.592  -1.23e-05      1.000    -1.6e+04     1.6e+04\n",
       "x135          -1.0200   8103.775     -0.000      1.000   -1.59e+04    1.59e+04\n",
       "x136          -0.3307   8162.028  -4.05e-05      1.000    -1.6e+04     1.6e+04\n",
       "x137          -0.7106   7826.816  -9.08e-05      1.000   -1.53e+04    1.53e+04\n",
       "x138          -0.6660   8584.271  -7.76e-05      1.000   -1.68e+04    1.68e+04\n",
       "x139           0.5593   7773.993   7.19e-05      1.000   -1.52e+04    1.52e+04\n",
       "x140          -0.0387   8041.424  -4.81e-06      1.000   -1.58e+04    1.58e+04\n",
       "x141           0.5320   8348.975   6.37e-05      1.000   -1.64e+04    1.64e+04\n",
       "x142           0.5712   7667.191   7.45e-05      1.000    -1.5e+04     1.5e+04\n",
       "x143           1.4825   8007.777      0.000      1.000   -1.57e+04    1.57e+04\n",
       "x144           0.6619   8165.509   8.11e-05      1.000    -1.6e+04     1.6e+04\n",
       "x145           2.3904   8004.444      0.000      1.000   -1.57e+04    1.57e+04\n",
       "x146          -0.8945   7705.158     -0.000      1.000   -1.51e+04    1.51e+04\n",
       "x147           1.8717   8566.768      0.000      1.000   -1.68e+04    1.68e+04\n",
       "x148          -0.9307   8613.112     -0.000      1.000   -1.69e+04    1.69e+04\n",
       "x149           1.2027   9150.540      0.000      1.000   -1.79e+04    1.79e+04\n",
       "x150           2.4105   7856.191      0.000      1.000   -1.54e+04    1.54e+04\n",
       "x151           1.3789   8289.178      0.000      1.000   -1.62e+04    1.62e+04\n",
       "x152           0.6702   7923.680   8.46e-05      1.000   -1.55e+04    1.55e+04\n",
       "x153          -0.7646   8332.746  -9.18e-05      1.000   -1.63e+04    1.63e+04\n",
       "x154          -1.5148   8011.797     -0.000      1.000   -1.57e+04    1.57e+04\n",
       "x155          -0.1829   8388.137  -2.18e-05      1.000   -1.64e+04    1.64e+04\n",
       "x156          -1.0890   8119.309     -0.000      1.000   -1.59e+04    1.59e+04\n",
       "x157           0.2461   8133.985   3.03e-05      1.000   -1.59e+04    1.59e+04\n",
       "x158          -0.5719   8251.533  -6.93e-05      1.000   -1.62e+04    1.62e+04\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = sm.GLM(optcom[12092, :], mixing[:, :-1])\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4min 31s, sys: 28 s, total: 4min 59s\n",
      "Wall time: 2min 56s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "dofs = []\n",
    "things_to_run = list(range(0, mixing.shape[1] - 2, 5))\n",
    "feats1 = np.empty((optcom.shape[0], len(things_to_run)))\n",
    "feats2 = np.empty((optcom.shape[0], len(things_to_run)))\n",
    "\n",
    "for i, n_comps in enumerate(things_to_run):\n",
    "    temp_mixing = mixing[:, :mixing.shape[1] - n_comps]\n",
    "    dof = mixing.shape[0] - (mixing.shape[1] - n_comps)\n",
    "    dofs.append(dof)\n",
    "    temp_feats1 = tedstats.computefeats2(optcom, temp_mixing)\n",
    "    temp_feats1 = stats.zscore(temp_feats1, axis=0)  # what we do for Z_maps\n",
    "\n",
    "    temp_feats2 = computefeats_sm(optcom, temp_mixing)\n",
    "    feats1[:, i] = temp_feats1[:, 0]\n",
    "    feats2[:, i] = temp_feats2[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/ipykernel/__main__.py:7: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_palette(sns.color_palette(\"RdBu_r\", feats2.shape[1]))\n",
    "fig, ax = plt.subplots(figsize=(16, 8))\n",
    "for i in range(feats2.shape[1]):\n",
    "    sns.distplot(feats2[:, i], label='DOF={0:03d}'.format(dofs[i]), ax=ax, bins=100, norm_hist=True)\n",
    "ax.legend(ncol=2)\n",
    "ax.set_title('PCA: statsmodels')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/ipykernel/__main__.py:6: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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iIiIifqXEU0RERERERPxKiaeIiIiIiIj4lRJPERERERER8SslniIiIiIiIuJXSjxFRERERETEr5R4ioiIiIiMAYGBgViWhdvtJiEhgY0bN9LT0+OrP3DgAMnJyURHRxMdHU1ZWZmvbu3atbhcLizLwrIsioqKLmtM27ZZtmwZUVFRxMfHc/DgQV/dPffcw6RJk7j33nv73FNaWkpUVBTGGNrb2we56tFltMWgsLCQuLg44uLieOWVV3zl/ohB0JD0IiIiIiIil+3lt/46pP3lzI7qt8348eOprq4GoLW1lZycHDo7OykuLqalpYWcnBzKy8tJTEykvb2dtLQ0XC4XGRkZAKxYsYKCgoIBzWvXrl3U1dVRV1eH1+tlyZIleL1eAL73ve/xySef8Pzzz/e552tf+xr33nsvs2fPHtBYA9Vz4tiQ9hcQGtZvm9EUg9/+9rccPHiQ6upqTp06xezZs5k3bx4TJkzwSwy04ykiIiIiMsY4HA7KysooLS3Ftm22bNnCggULSExMBCA8PJz169ezbt26QY1TUVFBXl4exhhSU1Pp6OigubkZgG984xtcd911F9zz1a9+lZtvvnlQ434RjHQMDh8+zJ133klQUBBf+tKXiI+PZ/fu3YB/YqDEU0RERERkDJo6dSrd3d20trZSU1NDUlJSn3qPx0NNTY3vuqSkxHfMc8+ePUDvDtzZsnNfZ5OlpqYmIiMjfX1ERETQ1NQ0DKv7YhjJGCQkJLB7924++eQT2tvb2bt3Lw0NDX5bq47ailxFnr8prs/14y3vjdBMRERE5GpzsWOeJSUlIzSbsWkoY3D33XfzzjvvcNttt3HDDTcwa9YsAgMDh2KaF6UdTxERERGRMai+vp7AwEAcDgexsbFUVVX1qa+qqsLtdl+yj/5221wuV59dtMbGRlwu19Av5gtqpGOwevVqqqureeONN7Btm+nTpw/xCv9JO54iIiIiImNMW1sbixcvJj8/H2MMS5cuJSUlhezsbCzL4ujRoxQWFrJmzZpL9tPfbltWVhalpaXMnz8fr9fLxIkTcTqdQ7mUL6yRjkF3dzcdHR1MnjyZQ4cOcejQIe6+++6hXGIfSjxFRERERMaAkydPYlkWXV1dBAUFkZuby8qVKwFwOp3s2LGDRYsWcfz4cWzbZvny5WRmZg5qzPT0dHbu3ElUVBQhISFs3brVV3fHHXdw5MgRTpw4QUREBD/72c9IS0tj8+bNrF+/npaWFuLj40lPT+eFF14Y1DxGi9EUg66uLu644w4AJkyYwI4dOwgK6k0P/REDY9v2oDoYCI/HY1dWVg7beCJjjZ7xFBER+afzPxdh5D4ba2triYmJGZGxRfzlYu9rY0yVbdue89vqGU8RERERERHxKyWeIiIiIiIi4ldKPEVERERERMSvlHiKiIiIiIiIXynxFBEREREREb9S4ikiIiIiIiJ+pcRTRERERGQMCAwMxLIs3G43CQkJbNy4kZ6eHl/9gQMHSE5OJjo6mujoaMrKynx1a9euxeVyYVkWlmVRVFR0WWPats2yZcuIiooiPj6egwcP+uruueceJk2axL333nvBPatXr2b69OnExMSwefPmQa589BhtMSgsLCQuLo64uDheeeWVPvcMdQyCBt2DiIiIiIgMyK/++I8h7e/h227ut8348eOprq4GoLW1lZycHDo7OykuLqalpYWcnBzKy8tJTEykvb2dtLQ0XC4XGRkZAKxYsYKCgoIBzWvXrl3U1dVRV1eH1+tlyZIleL1eAL73ve/xySef8Pzzz/e5Z9u2bTQ0NHDkyBECAgJobW0d0JiXq6ejeUj7C5jk7LfNaIrBb3/7Ww4ePEh1dTWnTp1i9uzZzJs3jwkTJvglBtrxFBEREREZYxwOB2VlZZSWlmLbNlu2bGHBggUkJiYCEB4ezvr161m3bt2gxqmoqCAvLw9jDKmpqXR0dNDc3JvwfeMb3+C666674J7nnnuONWvWEBAQ4Jvr1WikY3D48GHuvPNOgoKC+NKXvkR8fDy7d+8G/BMDJZ4iIiIiImPQ1KlT6e7uprW1lZqaGpKSkvrUezweampqfNclJSW+Y5579uwBenfgzpad+zqbLDU1NREZGenrIyIigqampkvO629/+xuvvPIKHo+HefPmUVdXN1RLHnVGMgYJCQns3r2bTz75hPb2dvbu3UtDQwPgnxjoqK2IiIiIiPTrYsc8S0pKhnycU6dOce2111JZWcmrr77KwoUL2b9//5CP80U0lDG4++67eeedd7jtttu44YYbmDVrFoGBgYB/YqAdTxERERGRMai+vp7AwEAcDgexsbFUVVX1qa+qqsLtdl+yj/5221wul28XDaCxsRGXy3XJPiMiIsjOzgbg/vvv59ChQ1eyvC+EkY7B6tWrqa6u5o033sC2baZPnw74Jwba8RQRERERGWPa2tpYvHgx+fn5GGNYunQpKSkpZGdnY1kWR48epbCwkDVr1lyyn/5227KysigtLWX+/Pl4vV4mTpyI03npL+G577772Lt3L1/5ylfYt2+fLxm62ox0DLq7u+no6GDy5MkcOnSIQ4cOcffddwP+iYESTxERERGRMeDkyZNYlkVXVxdBQUHk5uaycuVKAJxOJzt27GDRokUcP34c27ZZvnw5mZmZgxozPT2dnTt3EhUVRUhICFu3bvXV3XHHHRw5coQTJ04QERHBz372M9LS0igqKuKRRx6hpKSE0NBQXnjhhUHNYTQZTTHo6urijjvuAGDChAns2LGDoKDe9NAfMTC2bQ+6k8vl8XjsysrKYRtPZKx5/qa4PtePt7w3QjMREREZeed/LsLIfTbW1tYSExMzImOL+MvF3tfGmCrbtj3nt9UzniIiIiIiIuJXSjxFRERERETEr5R4ioiIiIiIiF8p8RQRERERERG/UuIpIiIiIiIiftVv4mmMiTTG7DXGHDbG1Bhj/uuZ8jBjzBvGmLoz/73e/9MVERERERGRL5rL2fH8DHjCtu1YIBVYaoyJBYqA39m2PQ343ZlrEREREREZhQIDA7EsC7fbTUJCAhs3bqSnp8dXf+DAAZKTk4mOjiY6OpqysjJf3dq1a3G5XFiWhWVZFBVd3v/0t22bZcuWERUVRXx8PAcPHgSgurqaWbNm4Xa7iY+P55VXXvHdU1paSlRUFMYY2tvbh2j1o8NIxODIkSPMmjWL4OBgNmzY0Kdu9+7d3HrrrURFRbFu3TpfuT9iENRfA9u2m4HmM/8+boypBVzAN4HZZ5ptB94CCodkViIiIiIiV7FX32kY0v6yZ0b222b8+PFUV1cD0NraSk5ODp2dnRQXF9PS0kJOTg7l5eUkJibS3t5OWloaLpeLjIwMAFasWEFBQcGA5rVr1y7q6uqoq6vD6/WyZMkSvF4vISEhvPjii0ybNo0PP/yQpKQk0tLSmDRpEl/72te49957mT179oD/DgPR0/qPIe0vwHFzv21GIgZhYWFs3ryZ8vLyPuXd3d0sXbqUN954g4iICGbOnElWVhaxsbF+icGAnvE0xtwMfBXwAjeeSUoBWoAbP+ee7xhjKo0xlW1tbYOYqoiIiIiIDAWHw0FZWRmlpaXYts2WLVtYsGABiYmJAISHh7N+/fo+u2BXoqKigry8PIwxpKam0tHRQXNzM9OnT2fatGkATJkyBYfDwdlc4atf/So333zzoMb9IhiuGDgcDmbOnMm4ceP6lP/5z38mKiqKqVOncs011zB//nwqKioA/8TgshNPY0wo8BtguW3bnefW2bZtA/bF7rNtu8y2bY9t254bbrhhUJMVEREREZGhMXXqVLq7u2ltbaWmpoakpKQ+9R6Ph5qaGt91SUmJ75jnnj17gN4duLNl577OJktNTU1ERv5zNzYiIoKmpqY+4/z5z3/m9OnT3HLLLf5a6qg1HDH4PJcTm6HU71FbAGPMOHqTzl/Ytv3qmeKPjDFO27abjTFOoNVfkxQRERERkZF1sWOeJSUlg+qzubmZ3Nxctm/fTkCAfnCjP/6IwXC5nG+1NcDPgFrbtp85p+rfgUfP/PtRoGLopyciIiIiIv5QX19PYGAgDoeD2NhYqqqq+tRXVVXhdrsv2Ud/u20ul4uGhn8+z9rY2IjL5QKgs7OTjIwMnn76aVJTU4d4dV8MwxGDz3Op2PjD5ex4fg3IBf6HMab6TNm/AuuAXxljHgM+AB72zxRFRERERGQotbW1sXjxYvLz8zHGsHTpUlJSUsjOzsayLI4ePUphYSFr1qy5ZD/97bZlZWVRWlrK/Pnz8Xq9TJw4EafTyenTp7n//vvJy8vjwQcfHMqlfWEMVww+z8yZM6mrq+Pvf/87LpeLX/7yl7z88stX1NfluJxvtT0AmM+p/sbQTkdERERERPzh5MmTWJZFV1cXQUFB5ObmsnLlSgCcTic7duxg0aJFHD9+HNu2Wb58OZmZmYMaMz09nZ07dxIVFUVISAhbt24F4Fe/+hW///3vOXr0KNu2bQNg27ZtWJbF5s2bWb9+PS0tLcTHx5Oens4LL7wwqHmMFiMRg5aWFjweD52dnQQEBLBp0yYOHz7MhAkTKC0tJS0tje7ubhYuXOjbXfVHDEzv9wIND4/HY1dWVg7beCJjzfM3xfW5frzlvRGaiYiIyMg7/3MRRu6zsba2lpiYmBEZW8RfLva+NsZU2bbtOb+tnuAVERERERERv1LiKSIiIiIiIn6lxFNERERERET8SomniIiIiIiI+JUSTxEREREREfErJZ4iIiIiIiLiV0o8RURERETGgMDAQCzLwu12k5CQwMaNG+np6fHVHzhwgOTkZKKjo4mOjqasrMxXt3btWlwuF5ZlYVkWRUVFlzWmbdssW7aMqKgo4uPjOXjwIADV1dXMmjULt9tNfHw8r7zySp97Vq9ezfTp04mJiWHz5s1D9BcYeSMRgyNHjjBr1iyCg4PZsGFDn7rdu3dz6623EhUVxbp163zl/ohB0KB7EBERERGRAfn3v3w4pP1lJUzpt8348eOprq4GoLW1lZycHDo7OykuLqalpYWcnBzKy8tJTEykvb2dtLQ0XC4XGRkZAKxYsYKCgoIBzWvXrl3U1dVRV1eH1+tlyZIleL1eQkJCePHFF5k2bRoffvghSUlJpKWlMWnSJLZt20ZDQwNHjhwhICCA1tbWgf9BLsNnDbVD2l9QZP+/0zoSMQgLC2Pz5s2Ul5f3Ke/u7mbp0qW88cYbREREMHPmTLKysoiNjfVLDLTjKSIiIiIyxjgcDsrKyigtLcW2bbZs2cKCBQtITEwEIDw8nPXr1/fZBbsSFRUV5OXlYYwhNTWVjo4OmpubmT59OtOmTQNgypQpOBwO2traAHjuuedYs2YNAQEBvrlejYYrBg6Hg5kzZzJu3Lg+5X/+85+Jiopi6tSpXHPNNcyfP5+KigrAPzFQ4ikiIiIiMgZNnTqV7u5uWltbqampISkpqU+9x+OhpqbGd11SUuI75rlnzx6gdwfubNm5r7PJUlNTE5GRkb4+IiIiaGpq6jPOn//8Z06fPs0tt9wCwN/+9jdeeeUVPB4P8+bNo66uzi/rHw2GIwaf51Kx8UcMdNRWRERERET6dbFjniUlJYPqs7m5mdzcXLZv3+7bXTt16hTXXnstlZWVvPrqqyxcuJD9+/cPapyrhT9icDH+iIF2PEVERERExqD6+noCAwNxOBzExsZSVVXVp76qqgq3233JPvrbbXO5XDQ0NPjaNzY24nK5AOjs7CQjI4Onn36a1NRUX5uIiAiys7MBuP/++zl06NCQrHc0Go4YfJ5LxcYfMdCOp4iIiIjIGNPW1sbixYvJz8/HGMPSpUtJSUkhOzsby7I4evQohYWFrFmz5pL99LfblpWVRWlpKfPnz8fr9TJx4kScTienT5/m/vvvJy8vjwcffLDPPffddx979+7lK1/5Cvv27WP69OmDXu9oNFwx+DwzZ86krq6Ov//977hcLn75y1/y8ssvA/6JgRJPEREREZEx4OTJk1iWRVdXF0FBQeTm5rJy5UoAnE4nO3bsYNGiRRw/fhzbtlm+fDmZmZmDGjM9PZ2dO3cSFRVFSEgIW7duBeBXv/oVv//97zl69Cjbtm0DYNu2bb6fCXnkkUcoKSkhNGqtlsQAACAASURBVDSUF154YVBzGE1GIgYtLS14PB46OzsJCAhg06ZNHD58mAkTJlBaWkpaWhrd3d0sXLjQt7vqjxgY27YH3cnl8ng8dmVl5bCNJzLWPH9TXJ/rx1veG6GZiIiIjLzzPxdh5D4ba2triYnp/+c2RL5ILva+NsZU2bbtOb+tnvEUERERERERv1LiKSIiIiIiIn6lxFNERERERET8SomniIiIiIiI+JUSTxEREREREfErJZ4iIiIiIiLiV0o8RURERETGgMDAQCzLwu12k5CQwMaNG+np6fHVHzhwgOTkZKKjo4mOjqasrMxXt3btWlwuF5Zl+X5r83LYts2yZcuIiooiPj6egwcPAvDBBx+QmJjom89Pf/pT3z2rV68mMjKS0NDQIVr56DESMThy5AizZs0iODiYDRs29KlbuHAhDoeDuLi+Pz3061//GrfbTUBAAEP1c5hBQ9KLiIiIiIhctt21Hw1pf/fE3Nhvm/Hjx1NdXQ1Aa2srOTk5dHZ2UlxcTEtLCzk5OZSXl5OYmEh7eztpaWm4XC4yMjIAWLFiBQUFBQOa165du6irq6Ourg6v18uSJUvwer04nU7efvttgoODOXHiBHFxcWRlZTFlyhQyMzPJz89n2rRpA/9DDEDXXw8OaX/johL7bTMSMQgLC2Pz5s2Ul5dfULdgwQLy8/PJy8vrUx4XF8err77K448/PqCxLkU7niIiIiIiY4zD4aCsrIzS0lJs22bLli0sWLCAxMTe5Ck8PJz169ezbt26QY1TUVFBXl4exhhSU1Pp6OigubmZa665huDgYABOnTrVZ9cvNTUVp9M5qHG/CIYrBg6Hg5kzZzJu3LgL6u68807CwsIuKI+JieHWW28d1LjnU+IpIiIiIjIGTZ06le7ublpbW6mpqSEpKalPvcfjoaamxnddUlLiO+a5Z88eoHcH7mzZua+zyVJTUxORkZG+PiIiImhqagKgoaGB+Ph4IiMjKSwsZMqUKf5e8qgzHDEYLXTUVkRERERE+nWxY54lJSVX3F9kZCSHDh3iww8/5L777uPBBx/kxhv7PzI8lg11DIaTdjxFRERERMag+vp6AgMDcTgcxMbGUlVV1ae+qqoKt9t9yT76221zuVw0NDT42jc2NuJyufr0MWXKFOLi4ti/f/8QreyLYzhiMFpox1NEREREZIxpa2tj8eLF5OfnY4xh6dKlpKSkkJ2djWVZHD16lMLCQtasWXPJfvrbbcvKyqK0tJT58+fj9XqZOHEiTqeTxsZGJk+ezPjx4/n44485cOAAK1asGMoljnrDFYPRQomniIiIiMgYcPLkSSzLoquri6CgIHJzc1m5ciUATqeTHTt2sGjRIo4fP45t2yxfvpzMzMxBjZmens7OnTuJiooiJCSErVu3AlBbW8sTTzyBMQbbtikoKGDGjBkArFq1ipdffplPPvmEiIgIvv3tb7N27dpBzWO0GIkYtLS04PF46OzsJCAggE2bNnH48GEmTJjAt771Ld566y3a29uJiIiguLiYxx57jNdee43vfve7tLW1kZGR0eeZ0itlbNseVAcD4fF47KH6HRgRudDzN/X9DabHW94boZmIiIiMvPM/F2HkPhtra2uJiYkZkbFF/OVi72tjTJVt257z2+oZTxEREREREfErJZ4iIiIiIiLiV0o8RURERERExK+UeIqIiIiIiIhfKfEUERERERERv1LiKSIiIiIiIn6lxFNEREREZAwIDAzEsizcbjcJCQls3LiRnp4eX/2BAwdITk4mOjqa6OhoysrKfHVr167F5XJhWRaWZVFUVHRZY9q2zbJly4iKiiI+Pp6DBw8C8MEHH5CYmOibz09/+lPfPadPn+Y73/kO06dPJzo6mt/85jdD9BcYeSMRgyNHjjBr1iyCg4PZsGFDn7qFCxficDiIi7vwp4d+8pOfEB0djdvtZtWqVVe44n8KGnQPIiIiIiIyIL//W/uQ9nfnLeH9thk/fjzV1dUAtLa2kpOTQ2dnJ8XFxbS0tJCTk0N5eTmJiYm0t7eTlpaGy+UiIyMDgBUrVlBQUDCgee3atYu6ujrq6urwer0sWbIEr9eL0+nk7bffJjg4mBMnThAXF0dWVhZTpkzh6aefxuFw8P7779PT08OxY8cG/ge5DJ++9/aQ9ndt3Kx+24xEDMLCwti8eTPl5eUX1C1YsID8/Hzy8vL6lO/du5eKigr+8pe/EBwcTGtr64DGvBjteIqIiIiIjDEOh4OysjJKS0uxbZstW7awYMECEhMTAQgPD2f9+vWsW7duUONUVFSQl5eHMYbU1FQ6Ojpobm7mmmuuITg4GIBTp0712fX7+c9/zpNPPglAQEAA4eH9J9VfRMMVA4fDwcyZMxk3btwFdXfeeSdhYWEXlD/33HMUFRX5YuRwOAY1B1DiKSIiIiIyJk2dOpXu7m5aW1upqakhKSmpT73H46GmpsZ3XVJS4jvmuWfPHqB3B+5s2bmvs8lSU1MTkZGRvj4iIiJoamoCoKGhgfj4eCIjIyksLGTKlCl0dHQA8NRTT5GYmMhDDz3ERx995Ne/w0gajhhciffff5/9+/eTkpLC17/+dd55550r7ussHbUVEREREZF+XeyYZ0lJyRX3FxkZyaFDh/jwww+57777ePDBBwkMDKSxsZHbbruNZ555hmeeeYaCggJeeumlwU7/qjDUMfg8n332GceOHeNPf/oT77zzDg8//DD19fUYY664T+14ioiIiIiMQfX19QQGBuJwOIiNjaWqqqpPfVVVFW63+5J99Lfb5nK5aGho8LVvbGzE5XL16WPKlCnExcWxf/9+Jk+eTEhICNnZ2QA89NBDvi8kuhoNRwyuREREBNnZ2RhjSE5OJiAggPb2wT2XrB1PEREREZExpq2tjcWLF5Ofn48xhqVLl5KSkkJ2djaWZXH06FEKCwtZs2bNJfvpb7ctKyuL0tJS5s+fj9frZeLEiTidThobG5k8eTLjx4/n448/5sCBA6xYsQJjDJmZmbz11lvcdddd/O53vyM2NnYolz5qDFcMrsR9993H3r17mTNnDu+//z6nT58e9LO2SjxFRERERMaAkydPYlkWXV1dBAUFkZuby8qVKwFwOp3s2LGDRYsWcfz4cWzbZvny5WRmZg5qzPT0dHbu3ElUVBQhISFs3boVgNraWp544gmMMdi2TUFBATNmzADgRz/6Ebm5uSxfvpwbbrjBd8/VYCRi0NLSgsfjobOzk4CAADZt2sThw4eZMGEC3/rWt3jrrbdob28nIiKC4uJiHnvsMRYuXMjChQuJi4vjmmuuYfv27YM6ZgtgbNseVAcD4fF47MrKymEbT2Ssef6mvr/B9HjLeyM0ExERkZF3/ucijNxnY21tLTExMSMytoi/XOx9bYypsm3bc35bPeMpIiIiIiIifqXEU0RERERERPxKiaeIiIiIiIj4lRJPERERERER8SslniIiIiIiIuJXSjxFRERERETEr5R4ioiIiIiMAYGBgViWhdvtJiEhgY0bN9LT0+OrP3DgAMnJyURHRxMdHU1ZWZmvbu3atbhcLizLwrIsioqKLmtM27ZZtmwZUVFRxMfHc/DgwT71nZ2dREREkJ+f7ytbvXo1kZGRhIaGDnLFo89IxODIkSPMmjWL4OBgNmzY4CtvaGhgzpw5xMbG4na7+fGPf+yr+/Wvf43b7SYgIICh+jnMoCHpRURERERELts7/+/HQ9rfzC9f32+b8ePHU11dDUBrays5OTl0dnZSXFxMS0sLOTk5lJeXk5iYSHt7O2lpabhcLjIyMgBYsWIFBQUFA5rXrl27qKuro66uDq/Xy5IlS/B6vb76p556ijvvvLPPPZmZmeTn5zNt2rQBjTVQJyp/P6T9hXru7LfNSMQgLCyMzZs3U15e3qc8KCiIjRs3kpiYyPHjx0lKSmLu3LnExsYSFxfHq6++yuOPPz6gsS5FO54iIiIiImOMw+GgrKyM0tJSbNtmy5YtLFiwgMTERADCw8NZv34969atG9Q4FRUV5OXlYYwhNTWVjo4OmpubAaiqquKjjz7i7rvv7nNPamoqTqdzUON+EQxXDBwOBzNnzmTcuHF9yp1Op2+s6667jpiYGJqamgCIiYnh1ltvHdS451PiKSIiIiIyBk2dOpXu7m5aW1upqakhKSmpT73H46GmpsZ3XVJS4jvmuWfPHqB3B+5s2bmvs8lSU1MTkZGRvj4iIiJoamqip6eHJ554os/Rz7FoOGJwOf7xj3/w7rvvkpKSMjQLuwgdtRURERERkX5d7JhnSUnJFfX17LPPkp6eTkRExFBMbcwYyhicdeLECR544AE2bdrEhAkTBtXXpSjxFBEREREZg+rr6wkMDMThcBAbG0tVVRXf/OY3ffVVVVW43e5L9rFixQr27t17Qfn8+fMpKirC5XLR0NDgK29sbMTlcvH222+zf/9+nn32WU6cOMHp06cJDQ0d9LHSL5rhiMGldHV18cADD/DII4+QnZ19ZYu4TEo8RURERETGmLa2NhYvXkx+fj7GGJYuXUpKSgrZ2dlYlsXRo0cpLCxkzZo1l+ynv922rKwsSktLmT9/Pl6vl4kTJ+J0OvnFL37ha7Nt2zYqKyvHXNI5XDH4PLZt89hjjxETE8PKlSuvqI+BUOIpIiIiIjIGnDx5Esuy6OrqIigoiNzcXF/C4XQ62bFjB4sWLeL48ePYts3y5cvJzMwc1Jjp6ens3LmTqKgoQkJC2Lp1a7/3rFq1ipdffplPPvmEiIgIvv3tb7N27dpBzWO0GIkYtLS04PF46OzsJCAggE2bNnH48GEOHTrESy+9xIwZM7AsC4Af/OAHpKen89prr/Hd736XtrY2MjIy+jxTeqWMbduD6mAgPB6PPVS/AyMiF3r+prg+14+3vDdCMxERERl5538uwsh9NtbW1hITEzMiY4v4y8Xe18aYKtu2Pee31bfaioiIiIiIiF8p8RQRERERERG/UuIpIiIiIiIifqXEU0RERERERPxKiaeIiIiIiIj4lRJPERERERER8SslniIiIiIiY0BgYCCWZeF2u0lISGDjxo309PT46g8cOEBycjLR0dFER0dTVlbmq1u7di0ulwvLsrAsi6Kiossa07Ztli1bRlRUFPHx8Rw8eLBPfWdnJxEREeTn5/vKTp8+zXe+8x2mT59OdHQ0v/nNbwa58tFjJGJw5MgRZs2aRXBwMBs2bPCVNzQ0MGfOHGJjY3G73fz4xz/uc99PfvIToqOjcbvdrFq1apArh6BB9yAiIiIiIgPy3kedQ9pf3I0T+m0zfvx4qqurAWhtbSUnJ4fOzk6Ki4tpaWkhJyeH8vJyEhMTaW9vJy0tDZfLRUZGBgArVqygoKBgQPPatWsXdXV11NXV4fV6WbJkCV6v11f/1FNPceedd/a55+mnn8bhcPD+++/T09PDsWPHBjTm5fqf+98Y0v4m3jG33zYjEYOwsDA2b95MeXl5n/KgoCA2btxIYmIix48fJykpiblz5xIbG8vevXupqKjgL3/5C8HBwbS2tg5ozIvRjqeIiIiIyBjjcDgoKyujtLQU27bZsmULCxYsIDExEYDw8HDWr1/PunXrBjVORUUFeXl5GGNITU2lo6OD5uZmAKqqqvjoo4+4++67+9zz85//nCeffBKAgIAAwsPDBzWH0Wq4YuBwOJg5cybjxo3rU+50On1jXXfddcTExNDU1ATAc889R1FREcHBwb4+BkuJp4iIiIjIGDR16lS6u7tpbW2lpqaGpKSkPvUej4eamhrfdUlJie+Y5549e4DeHbizZee+ziZLTU1NREZG+vqIiIigqamJnp4ennjiiT5HPwE6OjqA3p3QxMREHnroIT766CO/rH80GI4YXI5//OMfvPvuu6SkpADw/vvvs3//flJSUvj617/OO++8M+i16qitiIiIiIj062LHPEtKSq6or2effZb09HQiIiL6lH/22Wc0NjZy22238cwzz/DMM89QUFDASy+9dMXzvpoMZQzOOnHiBA888ACbNm1iwoTeI9ufffYZx44d409/+hPvvPMODz/8MPX19RhjrngcJZ4iIiIiImNQfX09gYGBOBwOYmNjqaqq4pvf/KavvqqqCrfbfck+VqxYwd69ey8onz9/PkVFRbhcLhoaGnzljY2NuFwu3n77bfbv38+zzz7LiRMnOH36NKGhofzwhz8kJCSE7OxsAB566CF+9rOfDdGKR5/hiMGldHV18cADD/DII4/4/ubQuzOdnZ2NMYbk5GQCAgJob2/nhhtuGOAK/0mJp4iIiIjIGNPW1sbixYvJz8/HGMPSpUtJSUkhOzsby7I4evQohYWFrFmz5pL99LfblpWVRWlpKfPnz8fr9TJx4kScTie/+MUvfG22bdtGZWWl72hoZmYmb731FnfddRe/+93viI2NHfyCR6HhisHnsW2bxx57jJiYGFauXNmn7r777mPv3r3MmTOH999/n9OnTw/6WVslniIiIiIiY8DJkyexLIuuri6CgoLIzc31JRxOp5MdO3awaNEijh8/jm3bLF++nMzMzEGNmZ6ezs6dO4mKiiIkJIStW7f2e8+PfvQjcnNzWb58OTfccMNl3fNFMRIxaGlpwePx0NnZSUBAAJs2beLw4cMcOnSIl156iRkzZmBZFgA/+MEPSE9PZ+HChSxcuJC4uDiuueYatm/fPqhjtgDGtu1BdTAQHo/HrqysHLbxRMaa52+K63P9eMt7IzQTERGRkXf+5yKM3GdjbW0tMTExIzK2iL9c7H1tjKmybdtzflt9q62IiIiIiIj4lRJPERERERER8SslniIiIiIiIuJXSjxFRERERETEr5R4ioiIiIiIiF8p8RQRERERERG/6jfxNMb83BjTaox575yytcaYJmNM9ZlXun+nKSIiIiIigxEYGIhlWbjdbhISEti4cSM9PT2++gMHDpCcnEx0dDTR0dGUlZX56tauXYvL5cKyLCzLoqio6LLGtG2bZcuWERUVRXx8PAcPHrxgPpZlkZWV5SsvLS0lKioKYwzt7e1DsPLRYyRicOTIEWbNmkVwcDAbNmzwlX/66ackJyeTkJCA2+3m+9//vq/OHzEIuow224BS4MXzykts295wYXMREREREbmUvx79/4a0v6jJX+q3zfjx46murgagtbWVnJwcOjs7KS4upqWlhZycHMrLy0lMTKS9vZ20tDRcLhcZGRkArFixgoKCggHNa9euXdTV1VFXV4fX62XJkiV4vd4L5nOur33ta9x7773Mnj17QGMNVPvrO4e0v/C7+9+LG4kYhIWFsXnzZsrLy/uUBwcH8+abbxIaGkpXVxe333478+bNIzU11S8x6HfH07bt3wPHhmxEEREREREZUQ6Hg7KyMkpLS7Ftmy1btrBgwQISExMBCA8PZ/369axbt25Q41RUVJCXl4cxhtTUVDo6Omhubr7kPV/96le5+eabBzXuF8FwxcDhcDBz5kzGjRvXp9wYQ2hoKABdXV10dXVhjAH8E4PBPOOZb4w5dOYo7vWf18gY8x1jTKUxprKtrW0Qw4mIiIiIyFCZOnUq3d3dtLa2UlNTQ1JSUp96j8dDTU2N77qkpMR3zHPPnj1A7w7c2bJzX2eTpaamJiIjI319RERE0NTUBPQe9fR4PKSmpl6wGzdWDEcMLqW7uxvLsnA4HMydO5eUlJShXeA5Lueo7cU8B/w3wD7z343Awos1tG27DCgD8Hg89hWOJyIiIiIiI+hixzxLSkquuL8PPvgAl8tFfX09d911FzNmzOCWW24Z7DSvakMdg8DAQKqrq+no6OD+++/nvffeIy4ubrDTvKgr2vG0bfsj27a7bdvuAf4NSB7aaYmIiIiIiD/V19cTGBiIw+EgNjaWqqqqPvVVVVW43e5L9tHfbpvL5aKhocHXvrGxEZfL5auD3l2/2bNn8+677w7l8r4QhiMGl2PSpEnMmTOH3bt3X9E6LscV7XgaY5y2bZ89nH0/8N6l2ouIiIiIyOjR1tbG4sWLyc/PxxjD0qVLSUlJITs7G8uyOHr0KIWFhaxZs+aS/fS325aVlUVpaSnz58/H6/UyceJEnE4nH3/8MSEhIQQHB9Pe3s4f/vAHVq1aNZRLHPWGKwaXGn/cuHFMmjSJkydP8sYbb1BYWHhFfV2OfhNPY8z/BcwGwo0xjcD3gdnGGIveo7b/AB732wxFRERERGTQTp48iWVZdHV1ERQURG5uLitXrgTA6XSyY8cOFi1axPHjx7Ftm+XLl5OZmTmoMdPT09m5cydRUVGEhISwdetWAGpra3n88ccJCAigp6eHoqIiYmNjAdi8eTPr16+npaWF+Ph40tPTeeGFFwa3+FFiJGLQ0tKCx+Ohs7OTgIAANm3axOHDh2lububRRx+lu7ubnp4eHn74Ye69917APzEwtj18j116PB67srJy2MYTGWuev6nvmfzHW3QYQURExq7zPxdh5D4ba2triYmJGZGxRfzlYu9rY0yVbdue89sO5lttRURERERERPqlxFNERERERET8SomniIiIiIiI+JUSTxEREREREfErJZ4iIiIiIiLiV0o8RURERERExK+UeIqIiIiIjAGBgYFYloXb7SYhIYGNGzfS09Pjqz9w4ADJyclER0cTHR1NWVmZr27t2rW4XC4sy8KyLIqKii5rTNu2WbZsGVFRUcTHx3Pw4MEL5mNZFllZWX3uWb16NdOnTycmJobNmzcPwepHh5GIwZEjR5g1axbBwcFs2LDBV/7pp5+SnJxMQkICbreb73//+746f8QgaNA9iIiIiIjIgDT+z5ND2l/ExPH9thk/fjzV1dUAtLa2kpOTQ2dnJ8XFxbS0tJCTk0N5eTmJiYm0t7eTlpaGy+UiIyMDgBUrVlBQUDCgee3atYu6ujrq6urwer0sWbIEr9d7wXzOtW3bNhoaGjhy5AgBAQG0trYOaMzL1VLx70Pa303fzOq3zUjEICwsjM2bN1NeXt6nPDg4mDfffJPQ0FC6urq4/fbbmTdvHqmpqX6JgXY8RURERETGGIfDQVlZGaWlpdi2zZYtW1iwYAGJiYkAhIeHs379etatWzeocSoqKsjLy8MYQ2pqKh0dHTQ3N1/ynueee441a9YQEBDgm+vVaLhi4HA4mDlzJuPGjetTbowhNDQUgK6uLrq6ujDGAP6JgRJPEREREZExaOrUqXR3d9Pa2kpNTQ1JSUl96j0eDzU1Nb7rkpIS3zHPPXv2AL07cGfLzn2dTZaampqIjIz09REREUFTUxPQe9TT4/GQmpraZzfub3/7G6+88goej4d58+ZRV1fnt7/BSBuOGFxKd3c3lmXhcDiYO3cuKSkpgH9ioKO2IiIiIiLSr4sd8ywpKbni/j744ANcLhf19fXcddddzJgxg1tuuYVTp05x7bXXUllZyauvvsrChQvZv3//YKd/VRjqGAQGBlJdXU1HRwf3338/7733HnFxcX6JgXY8RURERETGoPr6egIDA3E4HMTGxlJVVdWnvqqqCrfbfck++tttc7lcNDQ0+No3Njbicrl8ddC76zd79mzeffddoHdXNDs7G4D777+fQ4cODc2CR6HhiMHlmDRpEnPmzPn/27v76CirQ9/jvz2TySRDeC0E0gSKnIgJKA4w8nK8p+r1AApCgZ6riBW9KBWP1IUuW7jXtVh61l0LDxcXlMZjy+VU7aW1eroUPIpib0ULVqNE8YUXiVJKkpImAUJCCMm87PvHvJAE8kby5IV8P2tlZWaeZ55nT3ae2fPbe8/z6K233pLkTB0w4gkAAAD0MeXl5Vq+fLlWrFghY4weeughTZ06VQsXLpTf79eJEye0atUqrVmzpsXttDbaNm/ePOXl5WnRokXKz8/XwIEDlZGRoVOnTsnn88nr9aqiokLvv/++fvKTn0iS5s+fr127dumKK67Qe++9p7Fjx3ba6+5JuqoOWtq/x+PRoEGDVFtbq9///vdatWqVJGfqgOAJAAAA9AG1tbXy+/0KBoNKSkrS3XffrUcffVSSlJGRoa1bt2rZsmWqrq6WtVYrV67U3LlzO7TP2bNna8eOHcrOzpbP59Nzzz0nSTp48KAeeOABuVwuRSIRrV69WuPGjZMkrV69WnfddZc2bNigtLQ0bdmypWMvvAfpjjooLS1VIBBQVVWVXC6XNm7cqAMHDuj48eO65557FA6HFYlEdPvtt+u2226T5EwdGGtthzfSVoFAwO7du7fL9gf0Nb8YcXWj+w+UftlNJQEAoPs1bRel7msbDx48qNzc3G7ZN+CUi/1fG2MKrLWBpuvyHU8AAAAAgKMIngAAAAAARxE8AQAAAACOIngCAAAAABxF8AQAAAAAOIrgCQAAAABwFMETAAAA6APcbrf8fr/Gjx+va6+9Vk8//bQikUhi+Z49ezRlyhTl5OQoJydHmzdvTix74oknlJmZKb/fL7/fr9WrV7dpn9ZaPfzww8rOztaECRP0ySefJJYdO3ZMM2fOVG5ursaNG6ejR49KkvLy8pSdnS1jjCoqKjrnxfcQ3VEHhw4d0vTp0+X1erV+/foLlofDYU2cODFxDU/JmTpI6pStAAAAAGiz8jN1nbq9YWneVtdJTU3Vvn37JEllZWVavHixqqqq9OSTT6q0tFSLFy/Wtm3bNGnSJFVUVGjWrFnKzMzUnDlzJEmPPPKIHnvssXaV680331RhYaEKCwuVn5+vBx98UPn5+ZKkJUuW6PHHH9eMGTN05swZuVzRMbHrr79et912m2688cZ27au9il9+pVO3l3X7wlbX6Y46GDJkiDZt2qRt27ZddPlPf/pT5ebmqqqqKvGYE3XAiCcAAADQx6Snp2vz5s3Ky8uTtVbPPPOM7r33Xk2aNEmSNHToUK1bt05PPfVUh/azfft2LVmyRMYYTZs2TZWVlTp+/LgOHDigUCikGTNmSJLS0tLk8/kkSRMnuiiTYAAAIABJREFUTtTo0aM7tN/eoKvqID09Xdddd508Hs8Fy4qLi/XGG2/o/vvvb/S4E3VA8AQAAAD6oDFjxigcDqusrEz79+/X5MmTGy0PBALav39/4v6GDRsS0zx37twpKToCF3+s4U88LJWUlGjkyJGJbWRlZamkpESHDx/WoEGDtHDhQk2cOFE//vGPFQ6Hu+BV9yxdUQctWblypdatW5cYbXYSU20BAAAAtOpi0zw3bNhwSdsKhULavXu3Pv30U40aNUp33HGHnn/+ed13332dUdTLVmfWweuvv6709HRNnjxZ7777bieUrmWMeAIAAAB90JEjR+R2u5Wenq5x48apoKCg0fKCggKNHz++xW20NtqWmZmpoqKixPrFxcXKzMxUVlaW/H6/xowZo6SkJM2fP7/RiYf6iq6og+a8//77eu211zR69GgtWrRI77zzjn7wgx90+DU1hxFPAAAAoI8pLy/X8uXLtWLFChlj9NBDD2nq1KlauHCh/H6/Tpw4oVWrVmnNmjUtbqe10bZ58+YpLy9PixYtUn5+vgYOHKiMjAylp6ersrJS5eXlGjZsmN555x0FAoHOfIk9XlfVQXPWrl2rtWvXSpLeffddrV+/Xlu3br2kbbUFwRMAAADoA2pra+X3+xUMBpWUlKS7775bjz76qCQpIyNDW7du1bJly1RdXS1rrVauXKm5c+d2aJ+zZ8/Wjh07lJ2dLZ/Pp+eee05S9LIi69ev18033yxrrSZPnqxly5ZJkjZt2qR169aptLRUEyZM0OzZs7Vly5aOvfgeojvqoLS0VIFAQFVVVXK5XNq4caMOHDigAQMGNPscJ+rAWGs7tIH2CAQCdu/evV22P6Cv+cWIqxvdf6D0y24qCQAA3a9puyh1X9t48OBB5ebmdsu+Aadc7P/aGFNgrb1g+JrveAIAAAAAHEXwBAAAAAA4iuAJAAAAAHAUwRMAAAAA4CiCJwAAAADAUQRPAAAAAICjCJ4AAABAH+B2u+X3+zV+/Hhde+21evrppxWJRBLL9+zZoylTpignJ0c5OTnavHlzYtkTTzyhzMxM+f1++f1+rV69uk37tNbq4YcfVnZ2tiZMmKBPPvkksezYsWOaOXOmcnNzNW7cOB09ejTxnMcff1xjx45Vbm6uNm3a1Dl/gB6gO+rg0KFDmj59urxer9avX3/B8nA4rIkTJ+q2225LPOZEHSR1eAsAAAAA2uV0bX2nbm9ganKr66Smpmrfvn2SpLKyMi1evFhVVVV68sknVVpaqsWLF2vbtm2aNGmSKioqNGvWLGVmZmrOnDmSpEceeUSPPfZYu8r15ptvqrCwUIWFhcrPz9eDDz6o/Px8SdKSJUv0+OOPa8aMGTpz5oxcruiY2PPPP6+ioiIdOnRILpdLZWVl7dpnWx351cudur0xS25vdZ3uqIMhQ4Zo06ZN2rZt20WX//SnP1Vubq6qqqoSjzlRB4x4AgAAAH1Menq6Nm/erLy8PFlr9cwzz+jee+/VpEmTJElDhw7VunXr9NRTT3VoP9u3b9eSJUtkjNG0adNUWVmp48eP68CBAwqFQpoxY4YkKS0tTT6fT5L07LPPas2aNYkgmp6e3qEy9FRdVQfp6em67rrr5PF4LlhWXFysN954Q/fff3+jx52oA4InAAAA0AeNGTNG4XBYZWVl2r9/vyZPntxoeSAQ0P79+xP3N2zYkJjmuXPnTknREbj4Yw1/4mGppKREI0eOTGwjKytLJSUlOnz4sAYNGqSFCxdq4sSJ+vGPf6xwOCxJ+uabb/TSSy8pEAjo1ltvVWFhodN/im7TFXXQkpUrV2rdunWJgBnnRB0w1RYAAABAqy42zXPDhg2XtK1QKKTdu3fr008/1ahRo3THHXfo+eef13333ae6ujqlpKRo7969euWVV7R06VLt3r27M15Cr9eZdfD6668rPT1dkydP1rvvvttomRN1wIgnAAAA0AcdOXJEbrdb6enpGjdunAoKChotLygo0Pjx41vcRmujbZmZmSoqKkqsX1xcrMzMTGVlZcnv92vMmDFKSkrS/PnzEyceysrK0sKFCyVJCxYs0Oeff96ZL7tH6Yo6aM7777+v1157TaNHj9aiRYv0zjvv6Ac/+IEkZ+qAEU8AAACgjykvL9fy5cu1YsUKGWP00EMPaerUqVq4cKH8fr9OnDihVatWac2aNS1up7XRtnnz5ikvL0+LFi1Sfn6+Bg4cqIyMDKWnp6uyslLl5eUaNmyY3nnnHQUCAUnS/PnztWvXLl1xxRV67733NHbs2E573T1JV9VBc9auXau1a9dKkt59912tX79eW7duleRMHRA8AQAAgD6gtrZWfr9fwWBQSUlJuvvuu/Xoo49KkjIyMrR161YtW7ZM1dXVstZq5cqVmjt3bof2OXv2bO3YsUPZ2dny+Xx67rnnJEUvK7J+/XrdfPPNstZq8uTJWrZsmSRp9erVuuuuu7RhwwalpaVpy5YtHXvhPUh31EFpaakCgYCqqqrkcrm0ceNGHThwQAMGDGj2OU7UgbHWdngjbRUIBOzevXu7bH9AX/OLEVc3uv9A6ZfdVBIAALpf03ZR6r628eDBg8rNze2WfQNOudj/tTGmwFobaLou3/EEAAAAADiK4AkAAAAAcBTBEwAAAADgKIInAAAAAMBRBE8AAAAAgKMIngAAAAAARxE8AQAAgD7A7XbL7/dr/Pjxuvbaa/X0008rEokklu/Zs0dTpkxRTk6OcnJytHnz5sSyJ554QpmZmfL7/fL7/Vq9enWb9mmt1cMPP6zs7GxNmDBBn3zyiSRp165diW35/X6lpKRo27ZtkqS8vDxlZ2fLGKOKiopO/At0v+6og0OHDmn69Onyer1av359o2WjR4/WNddcI7/fr0Dg/BVQ/uM//kPjx4+Xy+VSZ10OM6lTtgIAAACgzc7WBTt1ez6vp9V1UlNTtW/fPklSWVmZFi9erKqqKj355JMqLS3V4sWLtW3bNk2aNEkVFRWaNWuWMjMzNWfOHEnSI488oscee6xd5XrzzTdVWFiowsJC5efn68EHH1R+fr5uuummRFlOnjyp7OxszZw5U5J0/fXX67bbbtONN97Yrn2115fPvdSp27v6v9/R6jrdUQdDhgzRpk2bEsG+qV27dmno0KGNX8vVV+uVV17RAw880K59tYQRTwAAAKCPSU9P1+bNm5WXlydrrZ555hnde++9mjRpkiRp6NChWrdunZ566qkO7Wf79u1asmSJjDGaNm2aKisrdfz48Ubr/O53v9Ott94qn88nSZo4caJGjx7dof32Bl1VB+np6bruuuvk8bTeORGXm5urq666qkP7bYrgCQAAAPRBY8aMUTgcVllZmfbv36/Jkyc3Wh4IBLR///7E/Q0bNiSmee7cuVNSdASu4ZTZ+E88LJWUlGjkyJGJbWRlZamkpKTRfn7729/qzjvvdOpl9mhdUQctMcZo5syZmjx5cqNpvU5gqi0AAACAVl1smueGDRs6tM3jx4/riy++0KxZszq0nb6is+tgz549yszMVFlZmWbMmKGcnBx997vf7WgxL4oRTwAAAKAPOnLkiNxut9LT0zVu3DgVFBQ0Wl5QUKDx48e3uI3WRtsyMzNVVFSUWL+4uFiZmZmJ+y+//LIWLFjQrmmgl5OuqIOWxOsiPT1dCxYs0EcffXTpL6YVjHgCAAAAfUx5ebmWL1+uFStWyBijhx56SFOnTtXChQvl9/t14sQJrVq1SmvWrGlxO62Nts2bN095eXlatGiR8vPzNXDgQGVkZCSWv/jii1q7dm2nvKbepqvqoDk1NTWKRCLq37+/ampq9Pbbb7e6r44geAIAAAB9QG1trfx+v4LBoJKSknT33Xfr0UcflSRlZGRo69atWrZsmaqrq2Wt1cqVKzV37twO7XP27NnasWOHsrOz5fP59NxzzyWWHT16VEVFRbrhhhsaPWfTpk1at26dSktLNWHCBM2ePVtbtmzpUDl6iu6og9LSUgUCAVVVVcnlcmnjxo06cOCAKioqtGDBAklSKBTS4sWLdcstt0iSXn31Vf3oRz9SeXm55syZ0+g7pZfKWGs7tIH2CAQCtrOuAwPgQr8YcXWj+w+UftlNJQEAoPs1bRel7msbDx48qNzc3G7ZN+CUi/1fG2MKrLWBpuvyHU8AAAAAgKMIngAAAAAARxE8AQAAAACOIngCAAAAABxF8AQAAAAAOIrgCQAAAABwFMETAAAA6APcbrf8fr/Gjx+va6+9Vk8//bQikUhi+Z49ezRlyhTl5OQoJydHmzdvTix74oknlJmZKb/fL7/fr9WrV7dpn9ZaPfzww8rOztaECRP0ySefSJJ27dqV2Jbf71dKSoq2bduWeM7jjz+usWPHKjc3V5s2berEv0L36o46OHTokKZPny6v16v169c3WjZ69Ghdc8018vv9CgQaXwHlZz/7mXJycjR+/Hj95Cc/6cCrjkrq8BYAAAAAtEt9KNSp20tOav1jfWpqqvbt2ydJKisr0+LFi1VVVaUnn3xSpaWlWrx4sbZt26ZJkyapoqJCs2bNUmZmpubMmSNJeuSRR/TYY4+1q1xvvvmmCgsLVVhYqPz8fD344IPKz8/XTTfdlCjLyZMnlZ2drZkzZ0qSnn/+eRUVFenQoUNyuVwqKytr1z7b6uMtL3bq9q67/85W1+mOOhgyZIg2bdqUCPZN7dq1S0OHDr3gse3bt+uzzz6T1+vtlDpgxBMAAADoY9LT07V582bl5eXJWqtnnnlG9957ryZNmiRJGjp0qNatW6ennnqqQ/vZvn27lixZImOMpk2bpsrKSh0/frzROr/73e906623yufzSZKeffZZrVmzRi6XK1HWy1FX1UF6erquu+46eTyeNj/n2Wef1erVq+X1ehPb6CiCJwAAANAHjRkzRuFwWGVlZdq/f78mT57caHkgEND+/fsT9zds2JCY5rlz505J0RG4hlNm4z/xsFRSUqKRI0cmtpGVlaWSkpJG+/ntb3+rO+88P1r4zTff6KWXXlIgENCtt96qwsLCTn/tPUVX1EFLjDGaOXOmJk+e3Gha7+HDh7V7925NnTpVN9xwgz7++OMOv1am2gIAAABo1cWmeW7YsKFD2zx+/Li++OILzZo1K/FYXV2dUlJStHfvXr3yyitaunSpdu/e3aH9XC46uw727NmjzMxMlZWVacaMGcrJydF3v/tdhUIhnTx5Uh9++KE+/vhj3X777Tpy5IiMMZe8L0Y8AQAAgD7oyJEjcrvdSk9P17hx41RQUNBoeUFBgcaPH9/iNlobbcvMzFRRUVFi/eLiYmVmZibuv/zyy1qwYEGjaaBZWVlauHChJGnBggX6/PPPO/xae6quqIOWxOsiPT1dCxYs0EcffSTpfB0YYzRlyhS5XC5VVFRc4quMYsQTAAAA6GPKy8u1fPlyrVixQsYYPfTQQ5o6daoWLlwov9+vEydOaNWqVVqzZk2L22lttG3evHnKy8vTokWLlJ+fr4EDByojIyOx/MUXX9TatWsbPWf+/PnatWuXrrjiCr333nsaO3bspb/QHqyr6qA5NTU1ikQi6t+/v2pqavT2228n9hWvg5tuukmHDx9WfX39BScgai+CJwAAANAH1NbWyu/3KxgMKikpSXfffbceffRRSVJGRoa2bt2qZcuWqbq6WtZarVy5UnPnzu3QPmfPnq0dO3YoOztbPp9Pzz33XGLZ0aNHVVRUpBtuuKHRc1avXq277rpLGzZsUFpamrZs2dKhMvQk3VEHpaWlCgQCqqqqksvl0saNG3XgwAFVVFRowYIFkqRQKKTFixfrlltukSQtXbpUS5cu1dVXX63k5GS98MILHZpmK0nGWtuhDbRHIBCwe/fu7bL9AX3NL0Zc3ej+A6VfdlNJAADofk3bRan72saDBw8qNze3W/YNOOVi/9fGmAJrbaDpunzHEwAAAADgKIInAAAAAMBRBE8AAAAAgKMIngAAAEAX6MpzqwBOa+//M8ETAAAAcFhKSopOnDhB+MRlwVqrEydOKCUlpc3P4XIqAAAAgMOysrJUXFys8vLy7i4K0ClSUlKUlZXV5vVbDZ7GmF9Kuk1SmbX26thjQyS9JGm0pKOSbrfWnrqE8gIAAACXPY/HoyuuuKK7iwF0m7ZMtX1e0i1NHlst6Q/W2isl/SF2HwAAAACAC7QaPK21f5R0ssnD35P0Quz2C5Lmd3K5AAAAAACXiUs9udBwa+3x2O1SScM7qTwAAAAAgMtMh89qa6On5mr29FzGmB8aY/YaY/byZWoAAAAA6HsuNXj+zRiTIUmx32XNrWit3WytDVhrA8OGDbvE3QEAAAAAeqtLDZ6vSbondvseSds7pzgAAAAAgMtNq8HTGPOipA8kXWWMKTbG3CfpKUkzjDGFkv4xdh8AAAAAgAu0eh1Pa+2dzSy6uZPLAgAAAAC4DHX45EIAAAAAALSE4AkAAAAAcBTBEwAAAADgKIInAAAAAMBRBE8AAAAAgKMIngAAAAAARxE8AQAAAACOIngCAAAAABxF8AQAAAAAOIrgCQAAAABwFMETAAAAAOAogicAAAAAwFEETwAAAACAowieAAAAAABHETwBAAAAAI4ieAIAAAAAHEXwBAAAAAA4iuAJAAAAAHAUwRMAAAAA4CiCJwAAAADAUQRPAAAAAICjCJ4AAAAAAEcRPAEAAAAAjiJ4AgAAAAAcRfAEAAAAADiK4AkAAAAAcBTBEwAAAADgKIInAAAAAMBRBE8AAAAAgKMIngAAAAAARxE8AQAAAACOIngCAAAAABxF8AQAAAAAOIrgCQAAAABwFMETAAAAAOAogicAAAAAwFEETwAAAACAowieAAAAAABHETwBAAAAAI4ieAIAAAAAHEXwBAAAAAA4iuAJAAAAAHAUwRMAAAAA4CiCJwAAAADAUQRPAAAAAICjCJ4AAAAAAEcRPAEAAAAAjiJ4AgAAAAAcRfAEAAAAADiK4AkAAAAAcBTBEwAAAADgKIInAAAAAMBRBE8AAAAAgKMIngAAAAAARxE8AQAAAACOIngCAAAAABxF8AQAAAAAOIrgCQAAAABwFMETAAAAAOAogicAAAAAwFEETwAAAACAowieAAAAAABHETwBAAAAAI4ieAIAAAAAHEXwBAAAAAA4iuAJAAAAAHAUwRMAAAAA4CiCJwAAAADAUQRPAAAAAICjCJ4AAAAAAEcRPAEAAAAAjiJ4AgAAAAAcRfAEAAAAADiK4AkAAAAAcBTBEwAAAADgKIInAAAAAMBRBE8AAAAAgKMIngAAAAAARxE8AQAAAACOIngCAAAAABxF8AQAAAAAOIrgCQAAAABwVFJHnmyMOSqpWlJYUshaG+iMQgEAAAAALh8dCp4xN1lrKzphOwAAAACAyxBTbQEAAAAAjupo8LSS3jbGFBhjftgZBQIAAAAAXF46OtX2v1hrS4wx6ZJ+b4w5ZK39Y8MVYoH0h5I0atSoDu4OAAAAANDbdGjE01pbEvtdJulVSVMuss5ma23AWhsYNmxYR3YHAAAAAOiFLjl4GmP6GWP6x29Lminpy84qGAAAAADg8tCRqbbDJb1qjIlv5zfW2rc6pVQAAAAAgMvGJQdPa+0RSdd2YlkAAAAAAJchLqcCAAAAAHAUwRMAAAAA4CiCJwAAAADAUQRPAAAAAICjCJ4AAAAAAEcRPAEAAAAAjiJ4AgAAAAAcRfAEAAAAADiK4AkAAAAAcBTBEwAAAADgKIInAAAAAMBRBE8AAAAAgKMIngAAAAAARxE8AQAAAACOIngCAAAAABxF8AQAAAAAOIrgCQAAAABwFMETAAAAAOAogicAAAAAwFEETwAAAACAowieAAAAAABHETwBAAAAAI4ieAIAAAAAHEXwBAAAAAA4iuAJAAAAAHAUwRMAAAAA4CiCJwAAAADAUQRPAAAAAICjCJ4AAAAAAEcRPAEAAAAAjiJ4AgAAAAAcRfAEAAAAADiK4AkAAAAAcBTBEwAAAADgKIInAAAAAMBRBE8AAAAAgKMIngAAAAAARxE8AQAAAACOIngCAAAAABxF8AQAAAAAOIrgCQAAAABwFMETAAAAAOAogicAAAAAwFEETwAAAACAowieAAAAAABHETwBAAAAAI4ieAIAAAAAHEXwBAAAAAA4iuAJAAAAAHAUwRMAAAAA4CiCJwAAAADAUQRPAAAAAICjCJ4AAAAAAEcRPAEAAAAAjiJ4AgAAAAAcRfAEAAAAADiK4AkAAAAAcBTBEwAAAADgKIInAAAAAMBRBE8AAAAAgKMIngAAAAAARxE8AQAAAACOIngCAAAAABxF8AQAAAAAOIrgCQAAAABwFMETAAAAAOAogicAAAAAwFEETwAAAACAowieAAAAAABHETwBAAAAAI4ieAIAAAAAHEXwBAAAAAA4Kqm7CwCgfUqPFan0z0dV9fWfFT55UoqEpHBYNhhU1vf/UUZWklQfcWv3C79WyvBva+S4bI0YNbKbSw4AQNuVHitS4fsfqu5P7yv456M6W3ZS506fVV1tvYLBiEKhsMJhq3DEKhKxClsrayUryVob/S3J2sbb/V++7Eb3jZGMJJcxMvH7xshtJJfLKMntUlKSS0ket5KTk5TsS5ZvyACljsxQ0jXXaMz8efrOVWO75o8C9GLGNj0aHRQIBOzevXu7bH9AT/GXrw7rm9+8qLqCT3X2+AmdrTqrc7X1CtaHFQxFFApHog2nVaLhjFiriJRoRKXzvzvCxH/iDa2MjJFcRnIbI7fLyO028rhd8iS5lex1K3VAqvp/e6hScq5S+qLFumb65E4oCQCgrync94WKfvNr1X72papLylVzulbnzgVVF4xE20MbDZJhK0Xasd2mbVv8McXaOHOR5yTaVhu91zSoWrW93Y0G12g7mmSkJLdLyUkupXiT5EvzasDwgeqXO1YDv3+Hrrvlpna8MqD3McYUWGsDFzxO8AQuzV++OqxvfrFZtV8cVPVfK3SmKtp41l9C4xlvMF1GMjJyxYKgy5jE73gPbOJxV/SxxDZM42bVWquItbKRaIhN9ALHeoUj9vzjkfj6kiK29YbWSHLHg6qRkmJBNTnZrdQUj/oN8mnglaPkmztP/3DPXe38ywIAeqPSY0Xa//RGVX20V5V/rdSZmjrV1UdUH44oGOtcbak9dCkW3FxSUmyk0eM28njc8nqT5PV5lTrYp9Rh35JnRLpc3xmttElTWgxyvxhx9QWPPVD6ZZtf01++Oqy/7PlQwc8/U6j0rwqWV6r2VJVqq2tVV1uv+kQHslUoEv0J22jb31xb2rAN9biMkpNcSk1JUv+BqRp8RYb6zZmjG360vM1lBHoagifQTrtf+LXOvvqqThUW6fTJGtXGe2QjNhoqWzh0mjaeHrdLyR6XUlM98vVPVergNKVkpsuTk6Nht9zWaSOITRvY9jSuDZUeK9KhN3YqvK9Adcf+qprj5aqpPKva2nrV1YcVClsFwxGFYyO0LX2YiDewSa6GDaxHaQNSNGjUcKX+/RSNu28pU4EBoIf74oMC/e3n/6ZTnx/S6fIqnT0b1LlgWMGwVbCZdtFI0RFAl1FSrA1ITnbLl5qsfoP7acB3MpQ8fZpy77zDkXago8GzIz56bYeq/vM11Rw8rKq/ndbZM3U6VxdSMBRRMBxRKNaGtvZ3S3a7lJLsVr9+yRr07UHqP/laXfmjFUzvRY9F8AQaKD1WpIP/9gtV7flAlX89qTPV53SuPqz6UETBSCxMXeR5Lp0PUN4kl7zeaOOZNiRN/UeNcLTxbIvOCp6XIj596twXB1VV/DedOVWj2tpoWK8PRxRq4e8qxcJprPfX64mG0/6DUjX47zLlm3ULvb8A4LCPXtuhU796QacOHVXVyRqdrW3Q4RqxCl/kOfGO1mR3NCClprgTI3e+W2/VTSsf6uqX0Uh3Bs+2Ktz3hYp+/qwqP92vyuOVqjnbeKS4udFTt1G0Q9dtlOJxKy3NqyFZQzTgH65X7j8/QIcuug3BE33Kx2/tUuWvntOp/YU6feKMzp4Nqi7WKxuyVqFmehfdid5FI2+SW75+yRo4tL8GT8jR0KX3yn/DP3T5a2mP7gyebbXvvd2qeOFXqtof7QGuaTIVKxS5eDiN148nNoKc4nHJ50vWwGEDNHD8lRp2/9IeXz8A0J3efXaLzr72nzp1pERVlWdVWxfrcI21jS11uCa7jJI9LvlSPBow2KchY0fJd8cd+oc7/1tXv4x26Q3Bsy12bXxGtW+9pZN/Pq7qWN3VBcMKRqRQ7ERKDTVsM5PdLqV63Urrn6Iho9KVNvO/6qb/uao7Xgb6CIInLiu7/nW9zv6/XTp5tFRnqmrPN54tBJd4r6zHJXljX/hPG5CiwVnD1O+Gv78spnv2huDZFl98UKC//Z+f6/Rnh3S6/LRqauobjJxGOw4u9s7lUmzU1B3rAfYmqV+aV4Ozhipt2mSNW/FQr69jALiY0mNFOvDvv1TNe3/SyWNlTWbyNP++GZ9tkuw28nrc8vk8GvitNA2+ZqyGLbu/13foXS7BszXv/eznOvP66zp55K+qPn1OtXWhFus+PpU38RUYb/Q7pt/6u2/L973v6cYH7++Ol4HLBMETvcafXt6umldf1qkDR1R1ska1Z+t1Lhj9PkRrU07ijWdykks+n0cDhqRp8Lgr1P/7i/T3t3+vy19LV7tcgmdb/PHff6Wzr23XqcIiVVee1dnaoOpDEdWHW/4ObmJKrzs6XTo6pdenQaMzlDLtOo377/cQTgH0KKXHinRw62909r3dqjxaqurTtdFg0cp5B5rO5EnxRL8nOHDEIA2c4u8T3xPsK8GzJfH/n+qdf9CponJVV5/Tubpwi1N5G02hjgXTgUOiI939Fi3q8SPd6F4ET/QIf/z3X6n2jddV+fWxWKgMqi4UmwLbwhlgG37J3uuOfrcyrZ9XgzMHKy0wSdn//MBl33iSplG6AAAOMElEQVS2RV8Knq2Jf4+3+oOPVFlcEe39b9DQNjcyLkluNTg5VINT4vfr79WAEUPkm5Cr4Xfdw2VlLmN8WEVXiAeCug/yVXXseKPvxgdD8RkezXe4Np3l4U2Ofs9v8MhhSvvudI374bI+35HGsdy60mNFOpD3jKrfz9fJ4pM6c6ZO54Jh1YfadtIoj9soOckdPTPvIJ8Gjfm2Um64Ubl3fL/P///1VQRPOKatZ21r7jIdTd+8vElupfo8GjCknwZlj1LqnNv03fuWdPXL6pUInu2z773dqvjl8zr95WGdLq+KnrW3jR/4pOiHvvjlbeKjCvGgmux1K9WXLN/gNKVljZDnmvH6u8V30kHSS/BhFZfiiw8KVP7GdgUPHFJNyd90trJG52rqVF8fVn0wOu01esmNlk+2JjX+ekhykkveZLf6+aKjlQMm5Gr40mV0frUBx3LH/eWrwyr8WZ5Of/ipKv8W+/pL/DNeK4MGDS8b43EbJXvc8qYkqV//FPUfPkT9cv5OQ+68s9dP6UZjjgRPY8wtkn6q6ADBFmvtUy2tT/Ds+Xa/8GvV7dmtc9/8RWfKT+lc9bnotSnrw9E3mHDs+lQRKazmw6TU+A0n0SMWO1vpgG+laWDOaPnmL2S6RicieDrjo9d2qGrbq6r56mudbti5Eo5eqzV+3bbWrlEnxa7XquhFzuPXaXUbI7dLchuX3G4jT5JLHo9LyV6PvL5keXxeedP6yTOgn9zDhso1PF2eK6/SqGnX9ekg6+QHyott26l99QR9/b2j9FiRDv9+l0L7P1ewpETB8kqdq6xWXc051Z8LKhgMKxgMKxyfndPwx0qRVtrDuGiYlNwuEx2pTHJFz+Kdmqy0wT4NGJUh7/Spyv3BYkaKOgnBs2t89NoOnX75JVUdPBK91E7s8mv1odgARDNnRY5r+pkx2pEbDarJyW6l+JKVOqif+g0fquQrx6j/zDktXr8V3avTg6cxxi3psKQZkoolfSzpTmvtgeaeQ/DsXKXHilR04Gud+eIzhY8dla0oV/3J06qvPqNgTZ3qztap7lxIwfqQQrGLG59vKGMfkmMNpm3HB+b4CE/8jcETm4boS/NqwPCB6pc7VgPmztOUebO74K+Ahvr6h8eeIH4N1OCf3lftX4p1pvy0as+cU11dSPUNPrhGYqOp7TkGm2PiPyZ+25y/bRouN+cfM9H1XCb2uIltK3bDxLcde3J8m43WMdE149uSOb9+/BN4oomJ34/diD5uzy9q0hQ1bJuaLrdq/Am/aTN2sVatpbbOXnCj5W1d7AkXrGcvelNS7G9rGtyO37rgMSX+5ufvm+bXiddD480lntN4exfW58X213A7TRc2fNxcfJVG+2n6Ghpu1lopErGyNnps2Ej0mIjfV+y2bfhbjW8rdmZPaxXdRvy3lAiF0XXPryfFHm9a7FbEO4/cincaxdrEWKdRijdJKf286jckTSlZI5R8rV9jvr+gT3cUdReCZ89ReqxIX21/Q+f2vKczfy7RmYpq1Z6NBtT4LLn49cFbmm0UF29u4h245ztyjVyxz6hut5HH447+pHjk9XnlHdBP3mGD5c7IkGdsjoYErmP2QCdrLngmdWCbUyR9ba09EtvBbyV9T1KzwbMne/fZLTr9698oEonIhq1sOBJr+KxsJBL7bRs8FvttrWwk9qGmUaMYDXiRsD3fmEZiP4n1Yo2qLtKQqmEjeWFjeSkNpXT+AI0GyOh32FzGFW04Xa7oqGRyklJSPfINSJVv+BB5rxyjATNvIUgCbTBi1EiNePB+6RLPCPjxW7tU/cEfFf7miOr+WqbaU1UKngsqWBdUOBRWMBhROBz7icQ6kGLvK5FG7ymx94lIg/eORqHv/HtI133h4vJxQb7qIOqg40yT2xfrdHG74p0o5ztdXLERSHciOLrlSU6S15cs74BUeQcOVHJGutzZ2Rp8/d8zJRC4RCNGjdSIHy2X2nhd7i8+KFD5W68reOiQzhWXquZkjc7VRDtyg8GIQvGpvrGZB8GIVb2sbKg9Hbn/KUl6WeffL6TGHbmuWKdqtMOpYQeuSXyuljnf4euK3Xa5TKxjNxqEXS7J5XJFb7tdcrmNXC6XTIP15DLnt22MjMsVfdxt5HJHC5KU7NXcd3e2+RX2JB0JnpmSihrcL5Y0tWPF6T6nfvV/9cmnxV26T9P0d4Pe7vg/e7y3Od5YukzsHzf2T+w20d4ct9uVaDCTvB55vElKGdRf3hHfkuc731FKYLrGTJvE1B3gEl1qr3lbRqE7a1pnW7bT2rZKjxWp+mSl3nnsX6JTe71ueVNSNGLKRNm6OqmuVgqHZevOydQFZevPKRIMyRWsj3aaBUOywZCMyxV943K7E41qJCna5ETq6mVdRlYuWZdLUrSxPR+ETTREu40iIRObxhhRJBx9k7SRoGxECgUjCofrFa4LK1xfp8i5kCQpWFsrW3tW9dW1kiRXZaUkyed2t+nv01OdDYcVGTRIyf1TlTzkW3KlJMmd7JXb65bbnSyP1yXj8sjltnIZl1wycptI9EOTbLStiVh5U5OjHayRsGQjMsGgwvX1ko01QpHohDgTjij+LUQbsXK53dGhQ0my0XUikYgUjn3Ei8S6RMPnn5O4umDseTb+/EhsHWujZXC75fJ4ZLweuZK9kjGyyUmyycmKuDyKuFzyjR4t95AhSuo/UL7hQzR4xPA2jSAyEwTona6ZPlnqwEjkvvd269T7f1L4z39W8Hip6k+dVm1Vrepr61VfF1IoGFYoFOvEbdCB23TGhI29rYVi3bgNJ9B0RweuS9LcLtxfZ+rIVNt/knSLtfb+2P27JU211q5ost4PJf0wdvcqSV+1YfNDJVVcUsHQ3ai73ol6652ot96LuuudqLfei7rrnai33uk71tphTR/syIhniaSGw2dZsccasdZulrS5PRs2xuy92Lxg9HzUXe9EvfVO1FvvRd31TtRb70Xd9U7U2+XF1YHnfizpSmPMFcaYZEmLJL3WOcUCAAAAAFwuLnnE01obMsaskLRT0cup/NJau7/TSgYAAAAAuCx0ZKqtrLU7JO3opLI01K6puehRqLveiXrrnai33ou6652ot96LuuudqLfLyCWfXAgAAAAAgLboyHc8AQAAAABoVY8InsaY/22MOWSM+dwY86oxZlAz691ijPnKGPO1MWZ1V5cTjRlj/psxZr8xJmKMafaMY8aYo8aYL4wx+4wxe7uyjLi4dtQdx1wPYowZYoz5vTGmMPZ7cDPrhWPH2z5jDCd960atHUPGGK8x5qXY8nxjzOiuLyWaakO93WuMKW9wnN3fHeVEY8aYXxpjyowxF71Yq4naFKvXz40xk7q6jLhQG+rtRmPM6QbH25quLiM6R48InpJ+L+lqa+0ESYcl/Y+mKxhj3JKekXSrpHGS7jTGjOvSUqKpLyUtlPTHNqx7k7XWzymxe4xW645jrkdaLekP1torJf0hdv9iamPHm99aO6/rioeG2ngM3SfplLU2W9IGSf/ataVEU+1473upwXG2pUsLieY8L+mWFpbfKunK2M8PJT3bBWVC655Xy/UmSbsbHG//0gVlggN6RPC01r5trQ3F7n6o6DVBm5oi6Wtr7RFrbb2k30r6XleVERey1h601n7V3eVA+7Wx7jjmep7vSXohdvsFSfO7sSxoXVuOoYZ1+jtJNxtjTBeWERfiva+Xstb+UdLJFlb5nqRf2agPJQ0yxmR0TenQnDbUGy4TPSJ4NrFU0psXeTxTUlGD+8Wxx9DzWUlvG2MKjDE/7O7CoM045nqe4dba47HbpZKGN7NeijFmrzHmQ2MM4bT7tOUYSqwT64A9LelbXVI6NKet733fj03X/J0xZmTXFA0dRLvWe003xnxmjHnTGDO+uwuDS9Ohy6m0hzHm/0kacZFFj1trt8fWeVxSSNKvu6pcaFlb6q0N/ou1tsQYky7p98aYQ7HeLTiok+oOXaylemt4x1prjTHNnZb8O7Fjboykd4wxX1hrv+nssgJ92H9KetFaW2eMeUDRUev/2s1lAi5Xnyjarp0xxsyWtE3R6dLoZboseFpr/7Gl5caYeyXdJulme/FrvJRIatijmBV7DA5qrd7auI2S2O8yY8yrik5jIng6rBPqjmOuG7RUb8aYvxljMqy1x2PTw8qa2Ub8mDtijHlX0kRJBM+u15ZjKL5OsTEmSdJASSe6pnhoRqv1Zq1tWEdbJK3rgnKh42jXeiFrbVWD2zuMMf9mjBlqra3oznKh/XrEVFtjzC2SfiJpnrX2bDOrfSzpSmPMFcaYZEmLJHG2xh7OGNPPGNM/flvSTEVPbIOej2Ou53lN0j2x2/dIumDk2hgz2Bjjjd0eKul6SQe6rIRoqC3HUMM6/SdJ7zTT+Yqu02q9Nfle4DxJB7uwfLh0r0laEju77TRJpxt8fQE9lDFmRPy778aYKYrmFzroeqEeETwl5Unqr+g0zH3GmJ9LkjHm28aYHVLiuy8rJO1U9A3+ZWvt/u4qMCRjzAJjTLGk6ZLeMMbsjD2eqDdFv4O2xxjzmaSPJL1hrX2re0qMuLbUHcdcj/SUpBnGmEJJ/xi7L2NMwBgTP6tmrqS9sWNul6SnrLUEz27Q3DFkjPkXY0z8bMP/LulbxpivJT2q5s9UjC7Sxnp72EQvSfWZpIcl3ds9pUVDxpgXJX0g6SpjTLEx5j5jzHJjzPLYKjskHZH0taT/I+mfu6moaKAN9fZPkr6MHW+bJC2ig653MtQbAAAAAMBJPWXEEwAAAABwmSJ4AgAAAAAcRfAEAAAAADiK4AkAAAAAcBTBEwAAAADgKIInAAAAAMBRBE8AAAAAgKMIngAAAAAAR/1/xrpmCz504/oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 8))\n",
    "for i in range(feats1.shape[1]):\n",
    "    sns.distplot(feats1[:, i], label='DOF={0:03d}'.format(dofs[i]), ax=ax, bins=100, norm_hist=True)\n",
    "ax.legend(ncol=2)\n",
    "ax.set_title('PCA: TEDANA')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now ICA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixing = op.join(in_dir, 'ica_mixing.tsv')\n",
    "mixing = pd.read_csv(mixing, sep='\\t').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5min 29s, sys: 30.2 s, total: 5min 59s\n",
      "Wall time: 3min 25s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "dofs = []\n",
    "things_to_run = list(range(0, mixing.shape[1] - 2, 5))\n",
    "feats1 = np.empty((optcom.shape[0], len(things_to_run)))\n",
    "feats2 = np.empty((optcom.shape[0], len(things_to_run)))\n",
    "\n",
    "for i, n_comps in enumerate(things_to_run):\n",
    "    temp_mixing = mixing[:, :mixing.shape[1] - n_comps]\n",
    "    dof = mixing.shape[0] - (mixing.shape[1] - n_comps)\n",
    "    dofs.append(dof)\n",
    "    temp_feats1 = tedstats.computefeats2(optcom, temp_mixing)\n",
    "    temp_feats1 = stats.zscore(temp_feats1, axis=0)  # what we do for Z_maps\n",
    "\n",
    "    temp_feats2 = computefeats_sm(optcom, temp_mixing)\n",
    "    feats1[:, i] = temp_feats1[:, 0]\n",
    "    feats2[:, i] = temp_feats2[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/ipykernel/__main__.py:7: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_palette(sns.color_palette(\"RdBu_r\", feats2.shape[1]))\n",
    "fig, ax = plt.subplots(figsize=(16, 8))\n",
    "for i in range(feats2.shape[1]):\n",
    "    sns.distplot(feats2[:, i], label='DOF={0:03d}'.format(dofs[i]), ax=ax, bins=100, norm_hist=True)\n",
    "ax.legend(ncol=2)\n",
    "ax.set_title('ICA: statsmodels')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tsalo/anaconda/envs/python3/lib/python3.6/site-packages/ipykernel/__main__.py:6: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 8))\n",
    "for i in range(feats1.shape[1]):\n",
    "    sns.distplot(feats1[:, i], label='DOF={0:03d}'.format(dofs[i]), ax=ax, bins=100, norm_hist=True)\n",
    "ax.legend(ncol=2)\n",
    "ax.set_title('ICA: TEDANA')\n",
    "fig.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:python3]",
   "language": "python",
   "name": "conda-env-python3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}