Frey - seq natmean.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "from mypy.explib.frey2014 import frey2014\n",
      "from model_natmean import model_natmean_pswitch\n",
      "%load_ext autoreload\n",
      "%autoreload 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The autoreload extension is already loaded. To reload it, use:\n",
        "  %reload_ext autoreload\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subjdata = frey2014.sampledata_by_subject()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    Unnamed: 0  partid gamble_lab  samplesize  switchfreq  switchcount  \\\n",
        "40          40       1       S1G1          12    0.100000            1   \n",
        "52          52       1       S2G1          10    0.111111            1   \n",
        "56          56       1       S3G1          10    0.111111            1   \n",
        "60          60       1       S4G1          10    0.111111            1   \n",
        "64          64       1       S5G1          10    0.111111            1   \n",
        "68          68       1       S6G1          10    0.111111            1   \n",
        "72          72       1       S7G1          10    0.100000            1   \n",
        "76          76       1       S8G1          12    0.090909            1   \n",
        "80          80       1       S9G1          10    0.111111            1   \n",
        "0            0       1      S10G1          10    0.111111            1   \n",
        "4            4       1      S11G1          10    0.111111            1   \n",
        "8            8       1      S12G1          12    0.090909            1   \n",
        "12          12       1      S13G1          10    0.111111            1   \n",
        "16          16       1      S14G1          10    0.111111            1   \n",
        "20          20       1      S15G1          11    0.100000            1   \n",
        "24          24       1      S16G1          10    0.111111            1   \n",
        "28          28       1      S17G1          11    0.100000            1   \n",
        "32          32       1      S18G1          10    0.111111            1   \n",
        "36          36       1      S19G1          10    0.111111            1   \n",
        "44          44       1      S20G1          10    0.111111            1   \n",
        "48          48       1      S21G1          12    0.090909            1   \n",
        "41          41       1       S1G2          12    0.090909            1   \n",
        "53          53       1       S2G2          10    0.111111            1   \n",
        "57          57       1       S3G2          10    0.111111            1   \n",
        "61          61       1       S4G2          10    0.111111            1   \n",
        "65          65       1       S5G2          10    0.111111            1   \n",
        "69          69       1       S6G2          10    0.111111            1   \n",
        "73          73       1       S7G2          11    0.111111            1   \n",
        "77          77       1       S8G2          10    0.111111            1   \n",
        "81          81       1       S9G2          11    0.100000            1   \n",
        "..         ...     ...        ...         ...         ...          ...   \n",
        "14          14       1      S13G3          10    0.111111            1   \n",
        "18          18       1      S14G3          10    0.111111            1   \n",
        "22          22       1      S15G3          10    0.111111            1   \n",
        "26          26       1      S16G3          11    0.100000            1   \n",
        "30          30       1      S17G3          11    0.100000            1   \n",
        "34          34       1      S18G3          10    0.111111            1   \n",
        "38          38       1      S19G3          11    0.100000            1   \n",
        "46          46       1      S20G3          10    0.111111            1   \n",
        "50          50       1      S21G3          11    0.100000            1   \n",
        "43          43       1       S1G4          11    0.100000            1   \n",
        "55          55       1       S2G4          10    0.111111            1   \n",
        "59          59       1       S3G4          10    0.111111            1   \n",
        "63          63       1       S4G4          10    0.111111            1   \n",
        "67          67       1       S5G4           9    0.125000            1   \n",
        "71          71       1       S6G4          10    0.111111            1   \n",
        "75          75       1       S7G4          10    0.111111            1   \n",
        "79          79       1       S8G4          11    0.100000            1   \n",
        "83          83       1       S9G4          10    0.111111            1   \n",
        "3            3       1      S10G4          10    0.111111            1   \n",
        "7            7       1      S11G4           8    0.000000            0   \n",
        "11          11       1      S12G4          10    0.111111            1   \n",
        "15          15       1      S13G4          10    0.111111            1   \n",
        "19          19       1      S14G4          10    0.111111            1   \n",
        "23          23       1      S15G4          11    0.100000            1   \n",
        "27          27       1      S16G4          11    0.200000            2   \n",
        "31          31       1      S17G4          10    0.111111            1   \n",
        "35          35       1      S18G4          10    0.111111            1   \n",
        "39          39       1      S19G4          11    0.100000            1   \n",
        "47          47       1      S20G4          10    0.111111            1   \n",
        "51          51       1      S21G4          10    0.111111            1   \n",
        "\n",
        "   decision group domain  session switch_grp pairtype   ev_diff  L_id  \\\n",
        "40        L   old   gain        1       freq    HH-LL  0.832105    30   \n",
        "52        H   old   gain        2       freq    HH-LL  0.902105    31   \n",
        "56        L   old   gain        3       freq    HH-LL  0.927368    28   \n",
        "60        L   old   gain        4       freq    HH-LL  1.021053    30   \n",
        "64        L   old   gain        5       freq    HH-LL  1.133158    37   \n",
        "68        H   old   gain        6       freq    HH-LL  0.987895    31   \n",
        "72        L   old   gain        7       freq    HH-LL  0.843684    38   \n",
        "76        L   old   gain        8       freq    HH-LL  1.088421    37   \n",
        "80        L   old   gain        9       freq    HH-LL  1.182105    33   \n",
        "0         L   old   gain       10       freq    HH-LL  0.925789    28   \n",
        "4         L   old   gain       11       freq    HH-LL  0.960000    37   \n",
        "8         L   old   gain       12       freq    HH-LL  0.964211    30   \n",
        "12        L   old   gain       13       freq    HH-LL  0.815789    40   \n",
        "16        L   old   gain       14       freq    HH-LL  1.056842    43   \n",
        "20        L   old   gain       15       freq    HH-LL  0.921579    30   \n",
        "24        L   old   gain       16       freq    HH-LL  0.709474    45   \n",
        "28        L   old   gain       17       freq    HH-LL  0.874737    25   \n",
        "32        L   old   gain       18       freq    HH-LL  1.156842    30   \n",
        "36        L   old   gain       19       freq    HH-LL  0.837895    31   \n",
        "44        L   old   gain       20       freq    HH-LL  0.907895    31   \n",
        "48        L   old   gain       21       freq    HH-LL  1.084211    28   \n",
        "41        L   old   gain        1       freq    HL-LH  1.026316    24   \n",
        "53        H   old   gain        2       freq    HL-LH  0.778947    24   \n",
        "57        H   old   gain        3       freq    HL-LH  1.021053    27   \n",
        "61        H   old   gain        4       freq    HL-LH  0.823158    27   \n",
        "65        H   old   gain        5       freq    HL-LH  0.737895    24   \n",
        "69        H   old   gain        6       freq    HL-LH  0.908421    24   \n",
        "73        L   old   gain        7       freq    HL-LH  1.035263    24   \n",
        "77        H   old   gain        8       freq    HL-LH  1.207368    24   \n",
        "81        H   old   gain        9       freq    HL-LH  0.825789    23   \n",
        "..      ...   ...    ...      ...        ...      ...       ...   ...   \n",
        "14        H   old   loss       13       freq    HH-LL  0.957895    19   \n",
        "18        L   old   loss       14       freq    HH-LL  1.105263    19   \n",
        "22        H   old   loss       15       freq    HH-LL  0.908421    16   \n",
        "26        H   old   loss       16       freq    HH-LL  1.295789    19   \n",
        "30        L   old   loss       17       freq    HH-LL  0.840526    19   \n",
        "34        L   old   loss       18       freq    HH-LL  1.115789    15   \n",
        "38        L   old   loss       19       freq    HH-LL  1.222105    11   \n",
        "46        L   old   loss       20       freq    HH-LL  0.942105    19   \n",
        "50        H   old   loss       21       freq    HH-LL  1.221053    19   \n",
        "43        L   old   loss        1       freq    HL-LH  1.065789    14   \n",
        "55        H   old   loss        2       freq    HL-LH  0.910000     0   \n",
        "59        L   old   loss        3       freq    HL-LH  1.077895     2   \n",
        "63        L   old   loss        4       freq    HL-LH  1.077895    14   \n",
        "67        L   old   loss        5       freq    HL-LH  0.824737     1   \n",
        "71        H   old   loss        6       freq    HL-LH  0.932632     2   \n",
        "75        H   old   loss        7       freq    HL-LH  0.869474     2   \n",
        "79        H   old   loss        8       freq    HL-LH  0.935263    14   \n",
        "83        L   old   loss        9       freq    HL-LH  1.073158     3   \n",
        "3         L   old   loss       10       freq    HL-LH  1.034737     2   \n",
        "7         L   old   loss       11       freq    HL-LH  0.827368     3   \n",
        "11        H   old   loss       12       freq    HL-LH  1.011053     1   \n",
        "15        L   old   loss       13       freq    HL-LH  1.052105    14   \n",
        "19        L   old   loss       14       freq    HL-LH  1.075263    14   \n",
        "23        L   old   loss       15       freq    HL-LH  0.923684     3   \n",
        "27        H   old   loss       16       freq    HL-LH  0.909474     1   \n",
        "31        L   old   loss       17       freq    HL-LH  0.815789     2   \n",
        "35        L   old   loss       18       freq    HL-LH  1.050526     3   \n",
        "39        H   old   loss       19       freq    HL-LH  1.155789    14   \n",
        "47        L   old   loss       20       freq    HL-LH  0.949474    14   \n",
        "51        L   old   loss       21       freq    HL-LH  1.065263     3   \n",
        "\n",
        "        L_ev   L_ev_var  H_id      H_ev   H_ev_var  \n",
        "40  5.157895  19.347763    23  5.990000  64.809112  \n",
        "52  5.040000  23.957980    26  5.942105  62.387961  \n",
        "56  5.018421  29.693878    23  5.945789  67.270460  \n",
        "60  4.816842  12.728097    27  5.837895  79.530818  \n",
        "64  4.896842  24.510410    23  6.030000  67.491651  \n",
        "68  5.096316  11.519481    23  6.084211  66.489796  \n",
        "72  5.171053  29.250464    27  6.014737  79.530818  \n",
        "76  4.870526  23.801484    29  5.958947  60.046382  \n",
        "80  4.980000  20.356215    23  6.162105  65.585240  \n",
        "0   5.052105  24.244486    41  5.977895  71.864564  \n",
        "4   5.067000  24.030000    26  6.027000  60.560000  \n",
        "8   5.000000  14.479489    27  5.964211  79.530818  \n",
        "12  5.094737  10.899814    27  5.910526  79.585946  \n",
        "16  5.137895  19.619872    26  6.194737  62.259328  \n",
        "20  4.917895  16.923521    23  5.839474  65.571016  \n",
        "24  5.157895  18.882705    41  5.867368  67.356215  \n",
        "28  5.105263  27.849928    34  5.980000  79.110707  \n",
        "32  4.968421  18.882705    41  6.125263  76.326531  \n",
        "36  5.150526  10.721707    23  5.988421  67.159555  \n",
        "44  5.021053  11.519481    29  5.928947  66.046707  \n",
        "48  4.905263  26.556586    23  5.989474  63.925995  \n",
        "41  5.068421  77.873869    33  6.094737  17.996289  \n",
        "53  5.176842  76.686663    37  5.955789  23.193774  \n",
        "57  4.969474  61.633478    33  5.990526  14.693878  \n",
        "61  5.136842  60.193774    37  5.960000  18.947879  \n",
        "65  5.195789  77.528963    33  5.933684  16.883117  \n",
        "69  5.123158  77.528963    33  6.031579  13.588126  \n",
        "73  4.977895  74.442383    37  6.013158  21.681097  \n",
        "77  4.910526  73.642754    35  6.117895  23.686663  \n",
        "81  5.064737  60.795506    28  5.890526  28.882705  \n",
        "..       ...        ...   ...       ...        ...  \n",
        "14 -6.047368  12.618557     2 -5.089474  66.659373  \n",
        "18 -6.034737  10.285714     7 -4.929474  74.442383  \n",
        "22 -6.057895  22.086786     2 -5.149474  65.581530  \n",
        "26 -6.126316  28.987013     2 -4.830526  61.633478  \n",
        "30 -5.933684  16.883117    14 -5.093158  61.071325  \n",
        "34 -6.117895  23.686663     7 -5.002105  77.528963  \n",
        "38 -6.156842  29.640074    14 -4.934737  61.617398  \n",
        "46 -5.810526  15.762523     8 -4.868421  79.547310  \n",
        "50 -6.047368  14.693878     7 -4.826316  74.788975  \n",
        "43 -6.162105  65.585240    16 -5.096316  11.519481  \n",
        "55 -6.020000  64.830000    22 -5.110000  24.740000  \n",
        "59 -5.926316  74.143063    20 -4.848421  15.666873  \n",
        "63 -5.907368  68.150278    20 -4.829474  10.768914  \n",
        "67 -5.818421  60.842301    20 -4.993684  16.923521  \n",
        "71 -5.837895  79.530818    17 -4.905263  26.556586  \n",
        "75 -5.964211  79.530818    10 -5.094737  10.899814  \n",
        "79 -6.030000  67.491651    20 -5.094737  18.150484  \n",
        "83 -6.125263  76.326531    17 -5.052105  24.244486  \n",
        "3  -6.081053  77.712637    10 -5.046316  11.397032  \n",
        "7  -5.977895  71.864564    16 -5.150526  10.721707  \n",
        "11 -5.928947  66.046707    20 -4.917895  16.923521  \n",
        "15 -5.990000  64.809112    21 -4.937895  29.334158  \n",
        "19 -5.945789  67.270460    22 -4.870526  23.801484  \n",
        "23 -5.820526  78.502989    22 -4.896842  24.510410  \n",
        "27 -5.930526  64.649351    16 -5.021053  11.519481  \n",
        "31 -5.847368  76.536082    15 -5.031579  26.754690  \n",
        "35 -5.867368  67.356215    20 -4.816842  12.728097  \n",
        "39 -6.010526  64.479489    20 -4.854737  15.666873  \n",
        "47 -5.989474  63.925995    16 -5.040000  23.957980  \n",
        "51 -6.198947  77.803546    21 -5.133684  29.576582  \n",
        "\n",
        "[84 rows x 19 columns]\n"
       ]
      },
      {
       "ename": "NameError",
       "evalue": "global name 'dummy' is not defined",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-20-08241b2f6546>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msubjdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfrey2014\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msampledata_by_subject\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/markant/code/py/mypy/explib/frey2014/frey2014.py\u001b[0m in \u001b[0;36msampledata_by_subject\u001b[0;34m()\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgambles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;32mprint\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m     \u001b[0;32mprint\u001b[0m \u001b[0mdummy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mNameError\u001b[0m: global name 'dummy' is not defined"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SUBJ = np.sort(subjdata.keys())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sid = 120"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sdata = subjdata[sid]\n",
      "#sdata = filter(lambda x: np.max(x['outcomes']) > 0, subjdata[sid])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gdata = sdata[0]\n",
      "pars = {'data': gdata,\n",
      "        'theta': 10,\n",
      "        'rho': .1,\n",
      "        'eval_pow': .4,\n",
      "        'eval_crit': 0.,\n",
      "        'z_sd': 5.}\n",
      "result = model_natmean.run(pars)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[  2.27501319e-02   8.63916702e-03   1.64797946e-03   2.35559381e-04\n",
        "   2.11068652e-03   2.11068652e-03   1.49098649e-02   6.21537157e-02\n",
        "   6.21537157e-02   6.21537157e-02   1.98137821e-01   7.98239114e-02\n",
        "   7.98239114e-02   2.47521479e-02   5.83136831e-03   1.84851260e-03\n",
        "   1.84851260e-03   5.12361296e-04   1.23969060e-04]\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pts = np.transpose((range(len(gdata['sampledata'])), gdata['sampledata'], gdata['outcomes']))\n",
      "print pts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[  0   0  -5]\n",
        " [  1   0 -13]\n",
        " [  2   0 -13]\n",
        " [  3   1 -18]\n",
        " [  4   1   0]\n",
        " [  5   1 -22]\n",
        " [  6   1 -18]\n",
        " [  7   1   0]\n",
        " [  8   1   0]\n",
        " [  9   1 -22]\n",
        " [ 10   0 -13]\n",
        " [ 11   0   0]\n",
        " [ 12   0 -13]\n",
        " [ 13   0 -13]\n",
        " [ 14   0  -5]\n",
        " [ 15   0   0]\n",
        " [ 16   0  -5]\n",
        " [ 17   0  -5]]\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(3, 1, figsize=(20,10))\n",
      "for pt in pts:\n",
      "    ax[0].plot(pt[0], pt[2], 'x', color=['blue', 'red'][pt[1]])\n",
      "ax[0].set_ylim(-30, 30)\n",
      "\n",
      "ax[1].plot(result['pref'])\n",
      "ax[1].set_ylim(-10, 10)\n",
      "\n",
      "ax[2].plot(result['p_sample_A'], label='p(sample A)', color='blue')\n",
      "ax[2].plot(result['p_sample_B'], label='p(sample B)', color='red')\n",
      "ax[2].plot(result['p_stop_choose_A'], '--', label='p(stop+choose A)', color='blue')\n",
      "ax[2].plot(result['p_stop_choose_B'], '--', label='p(stop+choose B)', color='red')\n",
      "ax[2].legend()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rZ1sC+sGLx9mmBsSZ51OyrCSa/trODOnZ5oB2N3fp0Ok+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\nEEJ8tv8Hgo2ykGG0WB0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a4b6810>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fitresults_nm = {}\n",
      "cols = ['mu', 'sd', 'eval_pow', 'eval_crit', 'rho', 'p_guess', 'nllh_sampling', 'nllh_choice', 'bic', 'success']\n",
      "fitresults_nm_table = pd.DataFrame(index=SUBJ, columns=cols)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subjdata[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "[{'choice': 0,\n",
        "  'gid': 'S1G1',\n",
        "  'outcomes': array([ 1, 18,  0,  0, 18, 18,  4,  0,  4,  0,  4, 12]),\n",
        "  'samples': array([1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]),\n",
        "  'sid': 1},\n",
        " {'choice': 0,\n",
        "  'gid': 'S1G2',\n",
        "  'outcomes': array([0, 1, 1, 1, 1, 0, 6, 0, 0, 6, 0, 6]),\n",
        "  'samples': array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]),\n",
        "  'sid': 1},\n",
        " {'choice': 0,\n",
        "  'gid': 'S1G3',\n",
        "  'outcomes': array([ -5,   0,   0, -13,  -5,   0, -22,   0,   0,   0]),\n",
        "  'samples': array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1]),\n",
        "  'sid': 1},\n",
        " {'choice': 0,\n",
        "  'gid': 'S1G4',\n",
        "  'outcomes': array([ -5,  -5, -13, -13,   0,  -1,  -1, -18,  -1,  -1, -18]),\n",
        "  'samples': array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]),\n",
        "  'sid': 1},\n",
        " {'choice': 0,\n",
        "  'gid': 'S2G3',\n",
        "  'outcomes': array([ -1,  -1, -23, -23,  -1,  -6,  -6,  -6, -12, -12]),\n",
        "  'samples': array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0]),\n",
        "  'sid': 1},\n",
        " {'choice': 1,\n",
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        "  'outcomes': array([-12, -12,  -4, -12, -12, -18,   0,   0, -18,   0, -18]),\n",
        "  'samples': array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]),\n",
        "  'sid': 1},\n",
        " {'choice': 0,\n",
        "  'gid': 'S19G3',\n",
        "  'outcomes': array([-18,   0,   0,   0, -18,   0,  -4,  -4,  -4,  -4,  -4]),\n",
        "  'samples': array([1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]),\n",
        "  'sid': 1},\n",
        " {'choice': 0,\n",
        "  'gid': 'S20G3',\n",
        "  'outcomes': array([  0,   0,   0, -18,   0, -12,  -6,  -6,  -6,  -6]),\n",
        "  'samples': array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0]),\n",
        "  'sid': 1},\n",
        " {'choice': 0,\n",
        "  'gid': 'S20G2',\n",
        "  'outcomes': array([ 1,  1, 18,  1,  1,  4,  0, 12,  4,  0]),\n",
        "  'samples': array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1]),\n",
        "  'sid': 1},\n",
        " {'choice': 0,\n",
        "  'gid': 'S20G1',\n",
        "  'outcomes': array([ 5, 13,  5,  5,  5,  3,  3,  3,  3, 24]),\n",
        "  'samples': array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1]),\n",
        "  'sid': 1},\n",
        " {'choice': 0,\n",
        "  'gid': 'S20G4',\n",
        "  'outcomes': array([ -1,  -1,  -1,  -1,  -1, -13,  -5,   0,  -5,   0]),\n",
        "  'samples': array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1]),\n",
        "  'sid': 1},\n",
        " {'choice': 1,\n",
        "  'gid': 'S21G3',\n",
        "  'outcomes': array([ -6,   0,  -6,  -6, -12,   0,   0,  -1,   0,  -1,   0]),\n",
        "  'samples': array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]),\n",
        "  'sid': 1},\n",
        " {'choice': 0,\n",
        "  'gid': 'S21G4',\n",
        "  'outcomes': array([-12, -12, -12,  -2,   0,  -1, -23,  -1,  -1,  -1]),\n",
        "  'samples': array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0]),\n",
        "  'sid': 1},\n",
        " {'choice': 1,\n",
        "  'gid': 'S21G2',\n",
        "  'outcomes': array([ 7,  7,  7,  0,  7,  1, 23,  1,  1,  1]),\n",
        "  'samples': array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0]),\n",
        "  'sid': 1},\n",
        " {'choice': 0,\n",
        "  'gid': 'S21G1',\n",
        "  'outcomes': array([ 0,  1, 18,  1,  1, 18,  0,  0,  3,  3, 13, 13]),\n",
        "  'samples': array([1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]),\n",
        "  'sid': 1}]"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for sid in SUBJ[:1]:\n",
      "    print sid\n",
      "    fitresults_nm[sid] = model_natmean_pswitch.fit_subject_across_gambles(subjdata[sid], \n",
      "                                                   {}, \n",
      "                                                   ['mu', 'sd', 'eval_pow', 'rho', 'p_guess'])\n",
      "    \n",
      "    pars = {'data': subjdata[sid],\n",
      "            'fitting': []}\n",
      "    for p in ret['bf_par']:\n",
      "        pars[p] = ret['bf_par'][p]\n",
      "    fitresult = model_natmean.nloglik_across_gambles([], pars)\n",
      "\n",
      "    fitresults_nm_table.loc[sid] = [fitresults_nm[sid]['bf_par']['mu'], \n",
      "                                    fitresults_nm[sid]['bf_par']['sd'], \n",
      "                                    fitresults_nm[sid]['bf_par']['eval_pow'],\n",
      "                                    0, #fitresults_nm[sid]['bf_par']['eval_crit'],\n",
      "                                    fitresults_nm[sid]['bf_par']['rho'],\n",
      "                                    fitresults_nm[sid]['bf_par']['p_guess'],\n",
      "                                    fitresult['llh_sampling'], fitresult['llh_choice'], \n",
      "                                    fitresults_nm[sid]['bic'], fitresults_nm[sid]['success']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1\n"
       ]
      },
      {
       "ename": "KeyError",
       "evalue": "'options'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-13-4ef5d22a0180>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m     fitresults_nm[sid] = model_natmean_pswitch.fit_subject_across_gambles(subjdata[sid], \n\u001b[1;32m      4\u001b[0m                                                    \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m                                                    ['mu', 'sd', 'eval_pow', 'rho', 'p_guess'])\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     pars = {'data': subjdata[sid],\n",
        "\u001b[0;32m/Users/markant/code/SamplingDynamics/model_natmean/model_natmean_pswitch.pyc\u001b[0m in \u001b[0;36mfit_subject_across_gambles\u001b[0;34m(data, fixed, fitting, obj)\u001b[0m\n\u001b[1;32m    172\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msucceeded\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0miter\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m         \u001b[0minit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mrandstart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpar\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpar\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpars\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'fitting'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mminimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnloglik_across_gambles\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpars\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Nelder-Mead'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    175\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'success'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m             \u001b[0msucceeded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/markant/anaconda/lib/python2.7/site-packages/scipy/optimize/_minimize.pyc\u001b[0m in \u001b[0;36mminimize\u001b[0;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[1;32m    411\u001b[0m                       callback=callback, **options)\n\u001b[1;32m    412\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'nelder-mead'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 413\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_minimize_neldermead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    414\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'powell'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    415\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_minimize_powell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/markant/anaconda/lib/python2.7/site-packages/scipy/optimize/optimize.pyc\u001b[0m in \u001b[0;36m_minimize_neldermead\u001b[0;34m(func, x0, args, callback, xtol, ftol, maxiter, maxfev, disp, return_all, **unknown_options)\u001b[0m\n\u001b[1;32m    436\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mretall\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m         \u001b[0mallvecs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0msim\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 438\u001b[0;31m     \u001b[0mfsim\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    439\u001b[0m     \u001b[0mnonzdelt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.05\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m     \u001b[0mzdelt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.00025\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/markant/anaconda/lib/python2.7/site-packages/scipy/optimize/optimize.pyc\u001b[0m in \u001b[0;36mfunction_wrapper\u001b[0;34m(*wrapper_args)\u001b[0m\n\u001b[1;32m    279\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mwrapper_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    280\u001b[0m         \u001b[0mncalls\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 281\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapper_args\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    282\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    283\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mncalls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/markant/code/SamplingDynamics/model_natmean/model_natmean_pswitch.pyc\u001b[0m in \u001b[0;36mnloglik_across_gambles\u001b[0;34m(value, args)\u001b[0m\n\u001b[1;32m    126\u001b[0m         \u001b[0m_pars\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 128\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_pars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0msampledata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'samples'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/markant/code/SamplingDynamics/model_natmean/model_natmean_pswitch.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(pars)\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0;31m# get preference trajectory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0mobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'samples'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'outcomes'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m     pref = get_state_trajectory(data['options'],\n\u001b[0m\u001b[1;32m     36\u001b[0m                                 \u001b[0mobs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m                                 \u001b[0meval_crit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mKeyError\u001b[0m: 'options'"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fitresults_nm_table"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>theta</th>\n",
        "      <th>eval_pow</th>\n",
        "      <th>eval_crit</th>\n",
        "      <th>z_sd</th>\n",
        "      <th>rho</th>\n",
        "      <th>nllh_sampling</th>\n",
        "      <th>nllh_choice</th>\n",
        "      <th>bic</th>\n",
        "      <th>success</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1  </th>\n",
        "      <td> 0.01492069</td>\n",
        "      <td> 3.833862e-07</td>\n",
        "      <td> 0.005349505</td>\n",
        "      <td>  2.364548</td>\n",
        "      <td> 4.004898e-06</td>\n",
        "      <td> 1316.451</td>\n",
        "      <td> 296.2213</td>\n",
        "      <td> 11322.63</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2  </th>\n",
        "      <td>   46.27923</td>\n",
        "      <td> 1.007557e-11</td>\n",
        "      <td>   0.1899089</td>\n",
        "      <td>  23.37353</td>\n",
        "      <td>   0.06658327</td>\n",
        "      <td>  1561.26</td>\n",
        "      <td> 246.8558</td>\n",
        "      <td> 1461.131</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3  </th>\n",
        "      <td>   22.06032</td>\n",
        "      <td> 9.379131e-11</td>\n",
        "      <td>   0.1387516</td>\n",
        "      <td>  10.02313</td>\n",
        "      <td>   0.04704259</td>\n",
        "      <td>   1502.3</td>\n",
        "      <td> 284.8483</td>\n",
        "      <td>  1755.95</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4  </th>\n",
        "      <td>   17.36685</td>\n",
        "      <td> 7.444339e-09</td>\n",
        "      <td> -0.03907936</td>\n",
        "      <td>  9.264579</td>\n",
        "      <td>   0.08929572</td>\n",
        "      <td>  1409.98</td>\n",
        "      <td> 423.2529</td>\n",
        "      <td>  1649.19</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5  </th>\n",
        "      <td>   23.01949</td>\n",
        "      <td> 9.935018e-11</td>\n",
        "      <td>    0.163112</td>\n",
        "      <td>  10.62392</td>\n",
        "      <td>   0.04961759</td>\n",
        "      <td> 1550.585</td>\n",
        "      <td> 333.8479</td>\n",
        "      <td>  1830.48</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6  </th>\n",
        "      <td>      3.457</td>\n",
        "      <td> 3.328196e-11</td>\n",
        "      <td> -0.01947863</td>\n",
        "      <td>  7.382892</td>\n",
        "      <td>    0.1100487</td>\n",
        "      <td> 1953.534</td>\n",
        "      <td> 347.9005</td>\n",
        "      <td> 9830.553</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7  </th>\n",
        "      <td>         14</td>\n",
        "      <td>    0.4329125</td>\n",
        "      <td>          19</td>\n",
        "      <td> 0.1711956</td>\n",
        "      <td>    0.0645698</td>\n",
        "      <td> 16865.35</td>\n",
        "      <td> 712.3304</td>\n",
        "      <td> 149801.6</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8  </th>\n",
        "      <td>   33.37748</td>\n",
        "      <td> 1.418399e-12</td>\n",
        "      <td> -0.02208184</td>\n",
        "      <td>  12.79463</td>\n",
        "      <td>    0.1261295</td>\n",
        "      <td>  6033.97</td>\n",
        "      <td> 400.2277</td>\n",
        "      <td> 9058.702</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9  </th>\n",
        "      <td>   2119.959</td>\n",
        "      <td>      1.34602</td>\n",
        "      <td>    1.567558</td>\n",
        "      <td>  1078.714</td>\n",
        "      <td>    0.0798576</td>\n",
        "      <td> 1453.063</td>\n",
        "      <td> 287.9511</td>\n",
        "      <td>  1691.08</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12 </th>\n",
        "      <td>   4.550896</td>\n",
        "      <td> 2.939154e-10</td>\n",
        "      <td> -0.09771406</td>\n",
        "      <td>  8.483801</td>\n",
        "      <td>   0.02393645</td>\n",
        "      <td> 4526.721</td>\n",
        "      <td>   419.08</td>\n",
        "      <td>  19326.8</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13 </th>\n",
        "      <td>   6.097284</td>\n",
        "      <td> 1.960546e-07</td>\n",
        "      <td> -0.06940977</td>\n",
        "      <td>  3.369713</td>\n",
        "      <td>    0.9419728</td>\n",
        "      <td> 2408.736</td>\n",
        "      <td> 273.0477</td>\n",
        "      <td> 1182.344</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15 </th>\n",
        "      <td>   3.896724</td>\n",
        "      <td> 8.257474e-09</td>\n",
        "      <td> 0.003955272</td>\n",
        "      <td>  2.279738</td>\n",
        "      <td>    0.9963976</td>\n",
        "      <td> 2364.092</td>\n",
        "      <td> 182.2185</td>\n",
        "      <td> 1053.876</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16 </th>\n",
        "      <td>   37.68418</td>\n",
        "      <td>    0.2719868</td>\n",
        "      <td>  -0.0110391</td>\n",
        "      <td>  15.85828</td>\n",
        "      <td>    0.9985917</td>\n",
        "      <td> 5947.856</td>\n",
        "      <td> 240.0858</td>\n",
        "      <td> 1102.482</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17 </th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18 </th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20 </th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21 </th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22 </th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23 </th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24 </th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25 </th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26 </th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27 </th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28 </th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29 </th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30 </th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31 </th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32 </th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33 </th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34 </th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>106</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>109</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>110</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>111</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>112</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>113</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>114</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>115</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>116</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>117</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>118</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>119</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>120</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>121</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>122</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>123</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>124</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>125</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>126</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>127</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>128</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>129</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>130</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>131</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>132</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>133</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>134</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>135</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>136</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>137</th>\n",
        "      <td>        NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>         NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td>          NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>70 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 127,
       "text": [
        "          theta      eval_pow    eval_crit       z_sd           rho  \\\n",
        "1    0.01492069  3.833862e-07  0.005349505   2.364548  4.004898e-06   \n",
        "2      46.27923  1.007557e-11    0.1899089   23.37353    0.06658327   \n",
        "3      22.06032  9.379131e-11    0.1387516   10.02313    0.04704259   \n",
        "4      17.36685  7.444339e-09  -0.03907936   9.264579    0.08929572   \n",
        "5      23.01949  9.935018e-11     0.163112   10.62392    0.04961759   \n",
        "6         3.457  3.328196e-11  -0.01947863   7.382892     0.1100487   \n",
        "7            14     0.4329125           19  0.1711956     0.0645698   \n",
        "8      33.37748  1.418399e-12  -0.02208184   12.79463     0.1261295   \n",
        "9      2119.959       1.34602     1.567558   1078.714     0.0798576   \n",
        "12     4.550896  2.939154e-10  -0.09771406   8.483801    0.02393645   \n",
        "13     6.097284  1.960546e-07  -0.06940977   3.369713     0.9419728   \n",
        "15     3.896724  8.257474e-09  0.003955272   2.279738     0.9963976   \n",
        "16     37.68418     0.2719868   -0.0110391   15.85828     0.9985917   \n",
        "17          NaN           NaN          NaN        NaN           NaN   \n",
        "18          NaN           NaN          NaN        NaN           NaN   \n",
        "20          NaN           NaN          NaN        NaN           NaN   \n",
        "21          NaN           NaN          NaN        NaN           NaN   \n",
        "22          NaN           NaN          NaN        NaN           NaN   \n",
        "23          NaN           NaN          NaN        NaN           NaN   \n",
        "24          NaN           NaN          NaN        NaN           NaN   \n",
        "25          NaN           NaN          NaN        NaN           NaN   \n",
        "26          NaN           NaN          NaN        NaN           NaN   \n",
        "27          NaN           NaN          NaN        NaN           NaN   \n",
        "28          NaN           NaN          NaN        NaN           NaN   \n",
        "29          NaN           NaN          NaN        NaN           NaN   \n",
        "30          NaN           NaN          NaN        NaN           NaN   \n",
        "31          NaN           NaN          NaN        NaN           NaN   \n",
        "32          NaN           NaN          NaN        NaN           NaN   \n",
        "33          NaN           NaN          NaN        NaN           NaN   \n",
        "34          NaN           NaN          NaN        NaN           NaN   \n",
        "..          ...           ...          ...        ...           ...   \n",
        "106         NaN           NaN          NaN        NaN           NaN   \n",
        "109         NaN           NaN          NaN        NaN           NaN   \n",
        "110         NaN           NaN          NaN        NaN           NaN   \n",
        "111         NaN           NaN          NaN        NaN           NaN   \n",
        "112         NaN           NaN          NaN        NaN           NaN   \n",
        "113         NaN           NaN          NaN        NaN           NaN   \n",
        "114         NaN           NaN          NaN        NaN           NaN   \n",
        "115         NaN           NaN          NaN        NaN           NaN   \n",
        "116         NaN           NaN          NaN        NaN           NaN   \n",
        "117         NaN           NaN          NaN        NaN           NaN   \n",
        "118         NaN           NaN          NaN        NaN           NaN   \n",
        "119         NaN           NaN          NaN        NaN           NaN   \n",
        "120         NaN           NaN          NaN        NaN           NaN   \n",
        "121         NaN           NaN          NaN        NaN           NaN   \n",
        "122         NaN           NaN          NaN        NaN           NaN   \n",
        "123         NaN           NaN          NaN        NaN           NaN   \n",
        "124         NaN           NaN          NaN        NaN           NaN   \n",
        "125         NaN           NaN          NaN        NaN           NaN   \n",
        "126         NaN           NaN          NaN        NaN           NaN   \n",
        "127         NaN           NaN          NaN        NaN           NaN   \n",
        "128         NaN           NaN          NaN        NaN           NaN   \n",
        "129         NaN           NaN          NaN        NaN           NaN   \n",
        "130         NaN           NaN          NaN        NaN           NaN   \n",
        "131         NaN           NaN          NaN        NaN           NaN   \n",
        "132         NaN           NaN          NaN        NaN           NaN   \n",
        "133         NaN           NaN          NaN        NaN           NaN   \n",
        "134         NaN           NaN          NaN        NaN           NaN   \n",
        "135         NaN           NaN          NaN        NaN           NaN   \n",
        "136         NaN           NaN          NaN        NaN           NaN   \n",
        "137         NaN           NaN          NaN        NaN           NaN   \n",
        "\n",
        "    nllh_sampling nllh_choice       bic success  \n",
        "1        1316.451    296.2213  11322.63   False  \n",
        "2         1561.26    246.8558  1461.131    True  \n",
        "3          1502.3    284.8483   1755.95    True  \n",
        "4         1409.98    423.2529   1649.19    True  \n",
        "5        1550.585    333.8479   1830.48    True  \n",
        "6        1953.534    347.9005  9830.553    True  \n",
        "7        16865.35    712.3304  149801.6    True  \n",
        "8         6033.97    400.2277  9058.702    True  \n",
        "9        1453.063    287.9511   1691.08   False  \n",
        "12       4526.721      419.08   19326.8    True  \n",
        "13       2408.736    273.0477  1182.344   False  \n",
        "15       2364.092    182.2185  1053.876    True  \n",
        "16       5947.856    240.0858  1102.482   False  \n",
        "17            NaN         NaN       NaN     NaN  \n",
        "18            NaN         NaN       NaN     NaN  \n",
        "20            NaN         NaN       NaN     NaN  \n",
        "21            NaN         NaN       NaN     NaN  \n",
        "22            NaN         NaN       NaN     NaN  \n",
        "23            NaN         NaN       NaN     NaN  \n",
        "24            NaN         NaN       NaN     NaN  \n",
        "25            NaN         NaN       NaN     NaN  \n",
        "26            NaN         NaN       NaN     NaN  \n",
        "27            NaN         NaN       NaN     NaN  \n",
        "28            NaN         NaN       NaN     NaN  \n",
        "29            NaN         NaN       NaN     NaN  \n",
        "30            NaN         NaN       NaN     NaN  \n",
        "31            NaN         NaN       NaN     NaN  \n",
        "32            NaN         NaN       NaN     NaN  \n",
        "33            NaN         NaN       NaN     NaN  \n",
        "34            NaN         NaN       NaN     NaN  \n",
        "..            ...         ...       ...     ...  \n",
        "106           NaN         NaN       NaN     NaN  \n",
        "109           NaN         NaN       NaN     NaN  \n",
        "110           NaN         NaN       NaN     NaN  \n",
        "111           NaN         NaN       NaN     NaN  \n",
        "112           NaN         NaN       NaN     NaN  \n",
        "113           NaN         NaN       NaN     NaN  \n",
        "114           NaN         NaN       NaN     NaN  \n",
        "115           NaN         NaN       NaN     NaN  \n",
        "116           NaN         NaN       NaN     NaN  \n",
        "117           NaN         NaN       NaN     NaN  \n",
        "118           NaN         NaN       NaN     NaN  \n",
        "119           NaN         NaN       NaN     NaN  \n",
        "120           NaN         NaN       NaN     NaN  \n",
        "121           NaN         NaN       NaN     NaN  \n",
        "122           NaN         NaN       NaN     NaN  \n",
        "123           NaN         NaN       NaN     NaN  \n",
        "124           NaN         NaN       NaN     NaN  \n",
        "125           NaN         NaN       NaN     NaN  \n",
        "126           NaN         NaN       NaN     NaN  \n",
        "127           NaN         NaN       NaN     NaN  \n",
        "128           NaN         NaN       NaN     NaN  \n",
        "129           NaN         NaN       NaN     NaN  \n",
        "130           NaN         NaN       NaN     NaN  \n",
        "131           NaN         NaN       NaN     NaN  \n",
        "132           NaN         NaN       NaN     NaN  \n",
        "133           NaN         NaN       NaN     NaN  \n",
        "134           NaN         NaN       NaN     NaN  \n",
        "135           NaN         NaN       NaN     NaN  \n",
        "136           NaN         NaN       NaN     NaN  \n",
        "137           NaN         NaN       NaN     NaN  \n",
        "\n",
        "[70 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sid = 4\n",
      "fr = fitresults_nm[sid]\n",
      "states = []\n",
      "for sd in subjdata[sid]:\n",
      "    pars = {'data': sd}\n",
      "    for p in fr['bf_par']:\n",
      "        pars[p] = fr['bf_par'][p]\n",
      "    result = model_natmean.run(pars)\n",
      "    states.append([sd['choice'], result['pref']])\n",
      "states_A = [st[1] for st in filter(lambda s: s[0]==0, states)]\n",
      "states_B = [st[1] for st in filter(lambda s: s[0]==1, states)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "max_t = max([sd['sampledata'].size for sd in sdata])\n",
      "fig, ax = plt.subplots(1, 2, figsize=(20,5))\n",
      "for i, gstate in enumerate(states_A):\n",
      "    ax[0].plot(gstate, color='blue', alpha=.2)\n",
      "ax[0].plot([0, max_t+1], [ ret['bf_par']['theta'], ret['bf_par']['theta']], color='blue')\n",
      "ax[0].plot([0, max_t+1], [-ret['bf_par']['theta'], -ret['bf_par']['theta']], color='red')\n",
      "for i, gstate in enumerate(states_B):\n",
      "    ax[1].plot(gstate, color='red', alpha=.2)\n",
      "ax[1].plot([0, max_t+1], [ ret['bf_par']['theta'], ret['bf_par']['theta']], color='blue')\n",
      "ax[1].plot([0, max_t+1], [-ret['bf_par']['theta'], -ret['bf_par']['theta']], color='red')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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IxeQDjaIiqYpItqEDkP8vL0uiZGlJ1hoej4UHDxTu3PFgdFRHUZGFjg4Dx4/H\ncfiw8/qpafJfIgHMz2uYmNAwN6dSu4g1NZmoqJCfk/76ar/Jk2N78g5ihgEMDkoV0fCwDtN02sw6\nOzNvM1trbYtcSYlTRbQdH2C50dKSkwgCZPc6u8JsJ4jFnHNgaUnOATtR6PVu99HlJztpFwwqWBZS\nw8W38j0D7SxMCu1MbluDYXgYBe+9B+3mTWhzczDa22E0NwN79jhVLHaVz9AQCm7dgvbgARCLwWpp\nQaK5Gaq5WdqvKiqkUkbTgKkp6AMDshOZZcGqrYWladAWF2F5vTDb2mDs3Ss/x54RZC+S4nGolRWo\naFSSMnZVUFkZVCwGtbAgSQmvF1ZlJSxdl2RFOAwVi0mFTmWlJGXsahZ7EWYYQH8/9AcPoA8PA6bp\ntJlttKiKxVJtZtr0NKziYhmm3di48a5qdlWQ3SJXUuJUNG20qMqkRS5T9s5t9uNVXCyPS0VFdqp8\nki1yKhR64S1yRERbiUmhF2R0FPjBDwpw+bIHk5MaWlpMvPZaDF/6kgxcfv/9Hhw50o1QSOHmTYXL\nl2WW0NKSwoEDBk6fjuPnf95MvUGKRoHxcQ0PHyo8fCjDp6uqTHR2Gmhr2/gTdftN18LC09+oX7sG\nfPRRAe7d0xEKKXR2Gjh6NIHTp52kjy252QUmJjRMTmrQNKzakn7trI9MdxCbmgL6+nQMDmp49EhD\nQ4OJvXtlePbaY8mU3SJnH4/dImcfy5N2WUv/3ocffoDPfOb15zuYHe5pvbSLi/abemf3usrKnb8j\nmH0OvPPOR+jqOgVNc5KEpaXuqUzZib3Q4bAkD0MhBaWcv0mbbfHciTFmk9vjY1JoZ3LDGgx9fSh4\n/33ot29DhcNIHDiA+IkTwJkz6Ll0afXz6to1FFy6BP3OHUmgdHUh/vrrwLFjznWiUakeuXkTnnv3\noKanYVVUwDh0CMahQ1hX4jw0BL2/H/rQEFQ4DGPPHpgdHTA7O9fvIBaPS0LGrvIpKZGkTFOTlGWv\nfVFKJJxExeKiDIK2q1mS973qb8fUFPS+vlR1lNnQAHPvXhj792PDRdXUFLTxcWjj41CJBMzdu2E1\nNjpzkNJlWhVkV1clK6ys4uLVrVqPW4St+frSu+/i7MGDch+lpZII8vnWH182xWJy3MFgKhlmNjRk\nb8i2XekUDOLqBx/g6C//cnbud4dy++sb48t9bo+Ru49tobt3gTffLMTVqzpmZjTs3Wvg3LkovvIV\nEz7f6uuuBLqZAAAgAElEQVQODpbh+vVCfPKJjnBYYe9eA1/60goOHVIwTYWyMrsdzEoNW11ZAerr\nLXR0xBGNWlhY0DAwoOPePdlBrKHBQEkJsLQkiSBNk4G8lZUmiork9WZlBYhEnJ1Sm5rkeI4dA44d\niwOIY2QEuHy5AB98UIC//EupbDp82MTLLyfQ2Cj3U1QEtLVZaG01sLAgCav33ivA8rJCVZWJ+Xkf\njh6VNcHaHcQWFyVJ9LgdxOrrgfp6A93dBpaXpc1sYEDh4kUvysos7N0rVUTNzZn/jjTNqUgAJHEW\nCimMjWmIxdSaamKn93xtlfHYWBnu3NGe2CLnNvbvLhjUEAwqFBbK49jZaeRUtY19DtTVLePQIQOR\niJwD4+NyDpSXO4nC5/0QM9+lnzOhkILHI4nDvXvNTW2EQ0T0WL29KPjwQ+g3bwLxOIwDB7Dyz/4Z\ncOrU+utevizXvXsXKCiAcfgwVr7+deDll1dfz96JrK8P+sOHsMrKYBw4APNzn4PSdajFRZkbZBiw\nioudHb6qqmCcPAnjxAlgbk6qdi5dQsEPfwizqkqGUzc3Q1VWSqVQWRnMri4Yp05J8iEYlNausbHU\nTKK1c3+s0lL5meGwJJXu3ZN/Li9H0eysfAKiaUB9PYz6ehjd3TJH6d49qIEBeC9elHj27pUqIntR\nVV8v84aOHQNCIUkQ9fdDv3JF2syamqSKKNmWlZqTBKSqgrSxsY2rggoKYNXUwKqpkeOzh1UPDECZ\nptyHUk5FlW3NIqwgEoFVWQlzz57nrzZ6VoWFUg1WW5tKhj13EsqudAoGJamWrBhb2r07O8dMRJQl\nrBR6ivfeA/7H//Di9m0PlpYUdu82sW9fHEeOOK9ThiHtEdPTwNCQVNcYBtDebuLEiRhefVWuZ1kq\nuW08MDICDA3pGB+XSpzKSknu+HzOa6P9+riyIkmjhQUN0ai86aqpkTfsHs/qCtflZUkaRaMy72Vl\nRWHPHgMdHSY6O811H2BFIsDly8AnnxRgcFCHxyOtXQcOJLB//+rdVpPt5pic1DAy8myzguwdxEIh\n2V3oaTuIDQwA/f06Bgd1xOPOse/bZ77wVrDlZefYV1ZWJxPcsk22aUqMditgcbGVSqq5sfUuvSUz\nHJaqNruKiEmMZ5M+sD4UUvB6rVRFkBvPGdo8VgrtTLm0BsNbb8H77rvQ+/oAXYfZ0oLESy8BbW2r\n5wPFYsD9+yi4dw9qfBzwemHs2QPjwAGgocFJSFiWlDfPzUGbnYUKBmU+T3OzzOdJr9yxZ+yMjEAb\nGoI2MyN3UV0Ns7JSEh/2IizZh69CIeDhQ+hTU1Bzc7BKSmAcOoT4q68Cr766vuXJrgpaWpL7Tk+W\nbDQjJ1nNok1Py7HX1kplUFPT+ooWw3AqmgYHoeJxp6Jp3771rWCJBLSJCajxcWiTk7AKC53dzOrr\n1/9u7Kqg5LDnVFVQRQVy6pOkbDMMeUyCQafSae0AciKiLcb2sSz4278Fvv99L3p7PYhGFfbvN/Dp\nT0fxcz8niQk7GaRpQH8/cPeuBw8f6tA0oLPTwMsvJ3DixOrEjmVJS9b4uIbJSZnx09Qks4c8Hqfl\nyTRVcnCvmb7ZRUr6DkzSomGlBk2bpmzZXVnpJC7m5pxWrclJDTU1ZjLJIm1uaw0MANeve3D/vrSZ\ntbYaOHjQxCuvGKisXH8sAwNy//asoLY2Ax0dG88KMozVbWZP20FsZsY59pkZDbt3m2hvl2PfzG6r\nzyPTFrmdzP49LCxIYqS01EkE5dN6Jb2qLRSSJ+raqjYS9jkTDMr5b8/tqqjYOLlLBDAptFPt6DVY\nNAr85CfwvvsuPHfvwiothfHyy4h2dwP798t17CRMJALt44/huXkT+sOHMCsrYR46hPhrrwEtLc51\ngVTCQxsdlWRNba1UxOzZs3rwMiD3Ozoq1TDz8zDTt5mPxaSyZmIC6tGjVJWPVVQEVVgoCaaKCqRe\nRHp7UXDlCvQ7d6CCQZl39MorME6fBmprn++xynRW0MyM02Y2M5MahG3s27fxFvYzMxLr5CTU8jLM\n+nqnzWxtQimtKkiFQlINlN5mlosvqKYpi79nnDugolGZfRSJwCovd+YwsSSZiLYBk0Kb9L3vAW++\nWYQ7d+Rd8b59CXz5yyv45/989fUSCeDWLUmc9PV5UFQkO4YdPZrA3r3O9Xp6enDmTDcmJjSMjsoW\n8GVlFhobTezZYz522OraocmlpU7ywX4NjsedAa6PHjm/Qq8XqKpyduHaYNYgHjwABgZ0DA3p0HVn\n4HN7+/rXrYUF4No1D+7eVRga0lFTIxU7L7+cwMTE+j7Mx80K2rtX2t7Wvpau3UGstNRCQYHs1rY2\nORGLAX19Gvr7NYyM6PB6pbXt0CEj1SKXDfYW6xcuXME//sevbnid5E6omJqStrRwWOHwYQNVVTs3\nmZBIOPOnIhGp2Lp79yI+//nTrl2vZNorbP/uc6UybKt7oe1zJhiU4d32MPmKihfXduf2fm+3x8ek\n0M6049Zg0Sjwwx+i6Px56A8ewPL5kDhxArEvfnF9u1cwCL2nB/r169AHByWxceQI4t3dQEMDgLTn\n1coKtJERSQTNz0siqKVFqmrWJjXCYWjDwzJnJxyWBEhz88ZzdpaXpQrk0SOoqSlgeVmGT5eVSdWO\nXVmz9lO92Vl4LlyA1tsrx15bC/PgQcTPnnUSXs/CMNBz8eL6vx0bzQqqr4dZVbV65zXLkoRSfz+0\ngQHoo6OwCgthtLTA2LsXqrXVuZ59m0gklSDC3BxQXg6zrg5mW9v6+07OMlg1F8k0pTqptFS2ibev\nu/bnJL++eO8eTn/xi8/+mGRLIuFU+SwtwbJf7B63o1n618mk4LMMqXb7337A/TEyvtzn9hg5UygD\nf/qnwE9/Wpxsm7Kwf7+Bf/Nvwvj85+UDAsMAxsclIXHzJnDrVgGGhnSUl8tOXp///Arq65Fq07p1\nS+YCzc4qvPdeMy5dKkR5uYXqaqC+XqqMpA1MT90/APh8Jurq5DXEfl0sKpIESTisMDOjYX5e7lvX\npdWstdVCba2F9nYz9dpj78A0N6cwMqKt24GpsBA4eBA4eNAAYGB0VFq13n+/EG++qVBTY2LXLhM1\nNc56qabGwmuvWTh1ysT9+8Dt2zp+8pMiTE29gqtXC3DsWBxnzkhF9ONmBV2+XIiiIqCuzkRdnYmq\nKud1VCq65Y3n4KCGRALo7QVqa2X3s8ZGSaAVFgKHDpk4dMgEkMDQkBz7tWseXLmiUF9vorFRbpNp\nC0sk4iRMLAvJGULxDa+7dpDu/v0mvF4LiYTC9LSGoaGnt8i9KLGYE9fKilSg1dZaKC+Xc2ZsLO7a\nhNBmFBXJ866+3kIi4VTkjY9rKCx0fqfZmjm5E9kJ54UFaff0+WRWWFubyY1YiCh7Fhag/fVfo/DC\nBehDQzBrapA4cQIr//Jfym5egCwQ5uaA+XkUXL4MrbcX2vg4zOZmGIcPI/a1r8Ee6KgsCwgGgbk5\n1H78MQoePpRkRG2tzM85cECSO9EotP5+uf/lZZkZNDkJtbICY/duGHV1slACZPvyyUnn/u1PhACp\nCGprAw4dAgAYAPDoEbTxcXh6e6EuXZIKo4YGSSwVFQG1tUh8+cvAl78sf2w//BAF166h6D/9JyjL\ngtHcDKOjA9i3z/mUzv65hiGVOOEw1PIyyiMRqM5OqUSxX5TsWUGHD0ONjkLv74d26xb0khKZlVNW\n5uxmJjsBwDxxAuarrwJjY9CGh+F96y2oSARGYyPM1lZYbW2p4dbW7t1INDTIJwazs9BmZ+Wx1DS5\nb3tbd6WcxaxSzryhkhKguBhW8vv2v6d/bbfPGUND2TvXniYelwqfYBBqeVkqnKqr5ffLFz4iyhN5\nVSm0uAj88R8Db79dnNymHWhoMPDKKyYOHLBSf/uVktfrsTFgclLH7KwGn89EQ4O0L9lDpTVNXufk\nzTcwP68QDst1a2rkDbjdVq3rVup1L30reaWAUEgG+1ZVyXb0u3ebKC2V+7VndxiGQkGBiXgcWFzU\nsLLy5ERIspI32fKhQdOcN7WP28lsZETD1JQkoXw+OZbm5tVJHFtfH3DpUgFu3ZLWrj17ZFj12bNx\n+8O6VaampHVufFxDNCqJk5IS2Q2qqsppI9N1WW9MTGgYH1eYnJQ35Lt3m2hqkuTZWpEIMDYm9/3o\nkUJFhTwmzc3rB4Cnx/ssW6yntxgFgxo8HmcXro1m0NjtfXabTUGBlWwHfDHJhGjUiSsWU8nKDnPH\nVjDlAvt9gF3VZicO7edSrq8Zo1EnERSLSSKospLnDD0/VgrtTNtWKTQ2Bs93v4vCS5egjY/DqqlB\noqMDxsmT0kqVnihYWpJKlqEhaKEQzMZGxA8fhnX8OFZtI6kUMDsLfXAQ2sgI1NKSJDTq6+U+k8kV\ny15wraxALS1BRSKAaUpyp74e2LVr4xk+a762ioqebTeqSERazOzWLp9Pqpqam7GuFx8ABgbguXED\n2r17soNYczPMffukcsfjkZak9G3q7UqcYBDKNGUgdXJeUmoAtF21Amy8g1hFBTbcrjYYhH7/PrQH\nD6BNTsKsqZE2s64ubDh3YHlZkiqTk9Dm5gDDkIHVjY2wqqvX78a2E9i7zS0sQMVi8vuprARf+IjI\nLdg+tsb4OPDNb2o4f74I09MaSkstvPJKHL/2azF8/vOrrxsOA1eueHDrlobRUQ2NjSZeesnEq68m\n1r2GRyLA6KiGkRFJ6NTVmWhpkTlBm2k1icUkETI4aFfMKOzaZaK93UBHh7Wu3SwcliTLxISTCGlq\nksqajRIh9g5MoZBatQNTefn61hjTBCYmnC3pAdmSvqHBREPD+jfBwSDQ06Pjxg0ZEF1RYeHgQQMn\nT8ZTld/pVSuPHkk7yvIyEI2qdVVBa83M2LOYNCwvPzkZJgklOfaJCS25MYcce1mZnTBxtli3Bwyv\nrWJOJFYP0rUTQZlWIa1tkXNm1lhPTCbYb9R1Xaq1nmR52UkEmSZSSajkWpCyzG7xDAalmmajFs+d\nbnnZeT4ahkJFhYmKivzYZY9eHCaFdqYXmhS6dQsF//N/ovDKFajZWZgtLYiePQvjq1/Fut7vsTF4\nrl6FfusWVCgEY+9exI8cke1T1y5Uxseh37kDfWBAdiJra5Mt2Ds71x9DOJxqCYJSkiyprMzeVuNP\nk1xUaRMT0n4FSIKooQHrFlWxGNT4ODwXL0K7e1cSRLW1MA4fRuLIEakisq2sSFzT01ALC/LH2+OB\ntWuXM89moxel5A5iKhTaeAexdMm5A/rAAPShIUDXYbS2ym5mbW2S/FpYkGRTYWFqFzUVjUqlVlGR\nc9/bXWZrt/0tLEAZBkx7+PNjPilVCwuwqqq2/7iJiDaBSSEA9+4B3/qWjosXvZib01BRYeH48Rh+\n67fiOHFi9XVnZ4GPP/bg9m0NU1Nack6NiePHjXUJilBIEkGjoxoiEUlOtLTIrltr3+Bn0qcYDjuz\nO5SS7bQTCQvz805lzdMSIZOTdnWNtMLV1ZmoqTHR2rr+NT7Tocn2lvTj47LtdG2thYmJK/jSl46t\ne62Mx4Fr14CrVwvQ26tjaUmhocHE/v0JvP66VPykfxBjVwWNjcncpYICaYmzWWn5EMuSRNLQkMLw\nsI6ZGYXmZkkm1dU5lTjpH+6FQsDMjMLsrOwIV1Mjx2APVX7cB4LXr3+Ez372RFYH6a6dF2UP67WT\nCfYb9WBQIZGQN+pVVeurl4Bnr3R6Erf30r6I+NYOfrcrw3y+zH4Xm5FpfJGIc85YFlKJzq0+zufB\nczS3MSm0M215UujqVRR+73souHYNKhiE2d6OlTfegPkv/sX6gcYDA/Bcvy6DmJeXkdi3D4mjR6Ut\na20iaGhIEkEPHwKAbLXe1QXYM3CSet5/H91HjsguXcEgLI/HSQTthG0mFxac2T+hkBxbaSmskhKo\n5BbwqWHVhgHcvQvPjRvQ+/uhlpZg7t6Nu4uL2Pf6607yp6REWtwSCUloLC5ChcOSxGluhtXUtHFV\nUKY7iI2OQr9+HZ67d6Hm5pD43OecOU1rF5DxuFRKjY5KtZSuO7uZbfQJY5qs/W1cWnISgpblnAcb\nlYUnk4daMCjVVJWVUum0BZ/2uP1vP+D+GBlf7nN7jHk7U+inPwV+//cL8PBhASIRDaWlJjo7Dfzq\nr8bQ2Civq/fve3D3rrz5npuTJEcwqKGx0UB7u4FjxwwUFsqHOtev6zBNhWhUkjaLi1K10dEhu4tt\nNENwackZVDs0VI7qarmCvRW9fL1+4LK9pXN7++ot4/fsMXHkiJmqCnr4UMPHH+uoqnIqa3w+WTc1\nNwMNDSY6Oy0MDio8fKjhxg0PlpYUmpsN7N1robNTEl0eD1BdLbNC0tujBgfleNfuwFRZCVRWStVU\nLCatWrdvF+InPylASYlzLDU1sh5paVHw+Qy88YaBYNDC3bsePHjgwQcfaGhqkjaz06fj2LNHjqWl\nxUxuFGJgYUHWIelJGsBOnEkF1UsvmTh71kBpqYWFBWBqSqqIdF0qmhobzQ1/P2uTYelv4NeuZaan\nI6itfXKFTqa8XmDXLgu7dlmpbb2nphRu3tQRiQClpTKIvKlp44qpxUU7aeRUOq09Z+jFsmd8VVbK\nuWL/DRgZ0WAYUpFXUGBtuGGJLf3vg8fjJJSyUbGzdgaWzCMz+cEnEWXX3/wNvIEAPPfvQ0WjMBob\nsXLiBPDGG0BREZRhQL98Wa4bDEKbmZHkjmnCaG+XHcOSu3VolgWMjsofysFB6A8fQhselkqU1lYk\nXn4ZqKsDLAvayop8Epi2uPI9fAitvh5WZaVUtOy07dErK2F6vbCqq6FmZqTNbHIS2vIyrOpqWJYl\n1SnJyh+0tcGoqoJ59CgwPQ19bExavEZGYC4twTIMuT+74qWy0pnbE4kA8bgM296oKqigAFZNDaya\nmlU7iGkPHjg7iJWVQSUSkjwKh2G99BJir70Gy+NJfV/v75eqoOSuImplRebzlJfDfOklGGfOAKGQ\nzFy6exfq0iWZ99TQIAmlbL4ohcNSDWTvglZZCbO1df3PSC6AtWTSyEoOit6R5wwR0RbL6UqhH/wA\n+KM/KsTDhwWIRhXKy020tZno7jbR0CBtQXZ7VCQCzM9rePQIqYqRlhYDBw8iVQliJxFWVuxEEFKV\nO6WlQFGRJBPSh84ahpMI0jSs29r7WTYsyIRdFTQ2pmNyUoNSVmo+T3GxPQ/Emc8TDMpA5v5+aaeq\nrjbR2irbujc2rr//lRWnNWZl5clDk01TWrsePNDw4IGerCKSZFxnp4WKitXXj0SACxc0XL3qwcCA\njuJiC11dBk6fjuPYsfX3vbgo7S2LiyqVOKuoeHyLztycJKwmJqSSq7bWRFOTJK3Wfjhoz4mxf3fA\ni9uOfKM36oWFFmIx+V48rlItZoCFcFhaFAsLnTY2rld2PnvweyLx9Oe//T27miy9xdN+Lj8LO8kb\nDEpVn8fjPG92wgfklF9YKbQzZa1S6M//HEV/8zfw3L8PGAaM1lbETp2SRJCuO2XKSgHz89AePYKa\nn5fBxHV1SOzfD3R0rP5DaJpOu9LwMKzycmkNO3Bg/dwh+/9rv9b19VVGO4FdtbKwAADrq1aSiypt\nZATaw4fQQiFYJSUwGhpgdXaun88Ti0GbmoKyK3EKC50t6Tea/ROLOW1jdlWQXWm0dlERj0NNTUEb\nG4M2MyMJk/p6mE1Ncszpj6+9Ffv4uMwUUgpmWRmsxka5740WVbEYtLExZ+ZScbFz7DU1mT2u6cmd\nUEgqwyorpdpp7QufaTqVUaGQ8xhUVGxJRRAR0XZxffvYd74DfOc7XoyOehCPK1RVGTh9ehnf+pZT\nlRyPI1mtI9Uv8/MKRUXA3r0GOjqkimSttS1c9huptR8smKYkZcbHdUxMSMVGQ4NUd9TWvpihs+nz\neSYnpaVqaUnBslRy9o9UnKx9jTcMoL8fePBAx/CwLNb27DHQ0WGgs3N9m5lhrK+sKS+3kxWrq1Yq\nKy0oJS1vExPymNfUWKivN1Fba67bkt6ygBs3pM2sr0/H4qLCnj0GmpoMvPGGhXhc2qvs30Om67uV\nFTtBJLuC+Xzye/L5Nt4FdWVFklDhsDyeVVUW9u83s7KD2EbDqh/3Rt00gUePZKv7yUlJynV1mU9M\nhpH7yEB5+XsUDkuLZ/oMrHR2AjV9BtbTEqhELwKTQjvTcyWF/vRPUfTWW/AMDgJKIdHRgZV/+k+B\nr31t9fWSc3T00VFok5OwiopgNjVtPGg5FpMBy3190IeHZbBxR4ckgta2m+WaxcVUAgLJ1jCrsnJ9\nq5Wd2Eifz6PrUEtLqWSaVVPjbHn/mAGM9rbxankZZn09rMZGmV30mN1I7CQRlILl8UjiqKAgNXw5\nNazaTqbYW8zbO4yZJpTHs34+T/qW9OPjcpv6eklseTzry2anp6FNT0ObmYGKx2G0tMDcvVuqe+yt\neR+zlb0yDKlSshNBa2M1DDmOhQWpdCotdRJBOzF5SESUBa5MCv3hHwJ/+ZdejI97YJoKNTUGPvOZ\nZfze761fL8zMAMPDkpgAZMhwTY0JpRTm5yVcezCsYdgVG1JxY38yb79GpG8bbxhSORMOOxUb5eUW\nTFMlZ4oAy8sKxcVAWZmFO3c+xOuvnwaw/rXsMa9r6762pbeXrKzgsbtKRSL2fB4Ns7OSUCgvt1Ba\nKpUla3/O7Czw8KGOsTFnVlBTk1QSlZauP6a5OWB6WsPsrIbh4Qf47GfbsH+/XD/9ddU0gdlZlawg\nklk+9fVOQqm4eHVlxOSkhnv3FHp7dQSD0h7W3Gxi3z6paNroA69M2Ik801Sr5g7Z/3e+loANAzh/\n/iMcO3YqlQyz35A/a4XzRm/UHzes2k6+LSzI+fU8ybBMuL2X1i3xpScV06vabty4hJdeOoPFRWc+\nVTZnYO0EbvkdPo7b42NSaGfKaA0WDgN/9mcofust6A8fwvJ6kejqQvSrXwV+6ZdWXzeRkFao0VFo\nU1OwysqcRNDaRUU0Cr2vT5JBExMya6ajA8bBgxsnPDKwrc+rtS1JBQWZV61UVmLdH/JYTJI9Y2PQ\npqfxcTCII+fOPX4HsXBYEkQTE4DHIwmijaqCFhZk5s/gINTcnFynoUESOHZCyF6IRCKSWFlYkAWn\nvXiy5xClVwVNTEiV0cSEJMN8PkmERaPSsmYfi70gBFLb0SMcxrXz53Hi8GHZda201Ll/+9jTF3Eb\nVYYlEs5Q6fSd23y+9Z+AbgO3/+0H3B8j48t9bo/RNTOFvvEN4J13ihAMyiEWF1vYvz+Bz3zG/nCi\nCN/8pryhjkYlUbKyIq8LdvKhpAS4d09ftdW8aQIrKwrRqAWl5M1USYkzdNi+bnrVs67LDJjSUkky\neb2SJLLbf3QdqKqS1rGlJYXZWYXBwQr4fNqqY3lStbO9W+pG/w5Yqe9VVT1+V6mSEqCjw0RHh5lq\n7bKH4BrG+vaozk7g7FkZ6ry0JBVEg4MKly97UFZmoa3NQEODidJSuY/WVguvvJJARYWFd98dxZ49\nezA8rOGTT3RUVtoJHwuWJTsydXWZOHnSgMcj92+/qZXt2pFqlWpsNPEP/6GJb3wjjuQHVXjwABgY\n0BEIFELXgdZWA52dBtrbM3891zQkW+WefUZQbe0KWlvlsbF3EBse1p64g5id3LG3o7ffqDc0mOvW\nd4mEU+0ViUgCr7LSwp495k5Yr9AOohTg88nzF7CwsiLnTiSiw+ez0NLCc4aIsmhxEfjDP0TJO++k\nqnwS+/cj+u/+HZBcQOuWBdy8KS/YdpXHo0eytfeuXVLlUVwMFYtBT1YVIRqVlqTpaahQSBJBXV2I\nf/nLO3Pb8mdlmjLYeWFBKmK8XpljU1+/cdVKMmGUXrViNjU9uWqlsBDmnj3Anj0wACy9/Tag1ONn\nBZWVwdy/H+b+/etnBSV/tgqFgIICmI2NSJw6hVQZvT0/KBiEdv++/I4BWOXl0kLW1rZ6Ps/KCtTC\nArQbN6APD0tc1dUwOjuR+NSnsG5LXLtCaWFBElF2wqesTBZsPh+WWlpgvvyy89iGQtDGxmQAtD0I\ne20yLBZzEkHRqBxvbS3M8vIXU8ZPRJTDsvoJnt/v1wD83wBeARAF8PVAIPBg7fUe9ynVL/8ycPFi\nMcJhHUpZqKw08IUvrOB3fsdJBNht6uEwsLSkYXFRobjYQk2NvFG3P2DaijdJmW5FbSdCgkGVGjpr\nVxi96Ncn+41kKOTMCrKPJT1hYRjA3JzCnTsKt2/rmJuTAc5dXQa6uhLo6HDWOPG43OfsrCRMwmFJ\nuFVUSPtac7OZqvIJhaSCy97BTJJlknAqLXUqcTaalTM+Dty/L9vdLy6uH5z9LJJVzs90XpimrIE2\nmsGydgcxXbcQiUg8sZjzuFZUrJ8DY7f92eePPf9pO84HIqKtxkqhF+dZ11/AY9Zg4+PQ/uN/RNH7\n70ObnYVVXo7Y0aOIf+MbwOnTqz+pikalumR8XNqaamulIqilxdkpwhYOy3VHR1OJIKulRVqacrl1\nx25JCgYlEVRS4lQErY0rHncSQZGIJCvsSplsLFafZVbQ1JT8HiYmgHhcjsHngyopSX2dSijZSRh7\nF67kIlFFowDgJHGKi+UcGBuT5KHPJ79fn08GUC8uShtcebkMoK6qWj9bKBp1jt2uCvJ45FiqqtbH\nurTkXN9OhhUWyuVYbPXObVs5HJKIaIfb7kqhXwRQGAgEXvP7/acB/EnyexuangZ+/deBK1eKEYno\n0DQLtbUJ/NqvLeM//IfV1zVNJ8ESCkkiqL5eqlFeVLtE+u5R6VtRT05qG25FXVoqrWoNDVZq6Oyj\nR5JAKSlxrv8ihgYXFQFFRfKYJRLOsY+Pa9A0qUQyDGlVKyuz8PLLFl5/PQ5dl4qjvj4dV68W4M03\npfrJ3tq9pUXi278/kUpszMzIbmnnz3swPi7tY+XlFg4eXL9zW/IDLIRC0m4mVRFOgk8pqfJpbDTw\n6cwgZL4AACAASURBVE8bCIel+qu/X+Hdd73o7DTQ1iY7jq1tJVw7rLquTrau34g9MNye4aLrUvG0\ntirI65Xf6fy87Bw2OamjpETmVFVVWfB6ZWi0vc6LRp1twO22v7o6k+sVIiLKpozWXwCAe/fg+eM/\nRuFHH0Gbm4NZVYX4yZOI/8ZvACdOrL5uJCJJhZER2WZ+926YnZ0bV7iEQtCGhyVhEInAbGhA4sAB\neTHfiZ+A2D3yz9Dbr+xh0UtLqZYks6VlfXJnbdWKz7d1VSuFhbBqa2HV1jpVQXNz0Hp6ZDc42d5U\nfn51NeDzQVmWtIE9eiSVOMlB4GZpKczTp2E2NsLYt29VpZMFAKEQ9Pv3ob3/vpMI2rVLWgS9XijT\nlPtNzhzSRkdlTtD8PMy6Oph79jhzjjwewOuFtWsXLK8X2vAw9Bs3oGZnYbzyCoxTp9bHWloqiaOG\nBkm2hUJANCr397gyeiIieqpsJ4VeB/AWAAQCgUt+v//Vx12xrq4EiYQGwEJtbRxf+9oy/sk/cWb4\nfPihvGgaBlBYKG+oi4vlDXpdndMusbCgVs3gKSra+t2jgKdvRX337mWcO/dqKplQWAjU1lqorbVS\niZBgUMP0tCRlfD4rtcvZVh+7xyOJjUTCSVpYlszUKS6Wf9d1K3Uc5eXA3r0mamoshMMm5uaACxf6\nMTZ2ALdvW9i7V1q7WlslITQ6KnONioqAY8cSKCgAolENU1M6VlY0BIOSxPH5UpXCqdaYSEQSRBMT\n2qrKG3sXubIy4MQJAydOAIaRwOSkzFG6cMEDy0Iy4WLB65VKHns+T1OTudGHd6lEkF3tY7dwXbzY\ng5MnuxEKSSXU7dtaKpEGAE1NJg4cSOCzn3XWd3Zl2MCAhkeP5HrFxZI0a2gwd9x6xe29tIwv97k9\nRrfHRy/UM6+/AKC0rU2GEZeVIdrcDOMf/SOpzEgkgLffhvbmmzIjqKAAWmUlkEhIAmTXLlhNTQAA\nNTYm26ObZmoHKm1mBkgkZFh0XR3Q2gqYJrSpKRnul1ywKcuShEBZmZNUymTo4hO+d+3qVRy3tzTd\n6DbJKhaEw7JtelERUFYmx2KXCK+dc2N/r6QEVnW1tFCtTe6srDiJoERCEib19VtStbLh345EArh2\nDQWffAJ9YACWzwdj3z7Z5a262jleTZNKp3BYklxlZTA7O5EoL4fl9UJpmlTe9PdLMsvrhTY/L7uF\nJSvDjDNnEG9qWt8eF4tJ/MEgVDQKs6sLxqlTUvljzznq74d+5QqswkL5nSQSUJoGo7ER8dOngfp6\n9Fy4gKf+ZSwogJXpTmU7RD787Xd7jIwv9+VDjJnIdlLIByCUdtnw+/1aIBAw114xkZAqjKNH42hp\nGcLSEnD+fCt0HRgaGgAAdHa2wzQV+voGUVERwxtv7IPXa6G39yMAwMmTsub56KMrUAp49dWTCAY1\nfP/711FSksBnP3sCPp+FDz/sAYDUL76nZ+suNzZa+NnPPsDgYD8ePTqJ4WENd+58hJKSOD73uVPw\neoELF1bf/p13LqKvz4MDB04jFpOEUmlpAp///GnoevaO7+TJbiwsKPzsZ9cQj2v49KePYvduE1NT\nPVBKrh+NAn//95exsOBFU9MJJBLAgwc3UFu7jC984QTKyuT+6up68a/+VTuGhoD/9t/68Rd/UYnC\nwmbs3m3B47mFw4dn8Yu/eCb183Ud+PKXuzE5Cbz11k3Mzhbh5ZcPYPduE6OjH6KqKoY33uhGSQlw\n9er7AIAzZyQp8847HyMS8eDs2aPw+SzcvPkBvF4T3d3daGoC+vvfh6YVoLHxDIaGNLz5Zj9ME/js\nZzvR3Gzh2rUPUFQk14/FgB//+DKWlgpw+PCr8Pks9PVdQElJAi+/7Dxevb29eOmlbkxOKrz99l1E\noxpOnepCe7uJmzevYXDQQmvrMSwvW7h2rQcrKzpeeuk1LCwo9PZeQ1FRAqdOvYpEQuH8+U/g9Rr4\nzGeOo6LCwpUrz/b7eu21bhgGcOnS1pyvtq18PmznZcbHy7y8/ZfphXnm9RcAIBSCBUCZJsz5eagf\n/QglZWWwACxFIjAAlFZUQJkmIvPzWK6pQfmJE1BHjuDO7dsAgAMdHVDLyxjo7QU0DXuPHEHiwAFc\nGx4G4nEcS5YFX716FdA0HD9+HNA0fHztGmBZePWll6AmJ/HJnTtIlJbi2Kc+BZSW4uKHHwIAzpw9\nCwByWSmcPXsWUCr172dfew0AcOHiRUApvPbaa4BS+PCnP0V4Zgav2+fkBx9AGQa6Dx+GCoVw/YMP\nkCgtxYlz52CVl+PDd96BZ34ep+rqAACX791DorQUZz73OUCpJ5/jkQiu/P3fo2BpCcePHoXl8+GD\n4WEYxcXoPnRo/fWzdLm3t1cur6zg1n/9r/ANDGCfrsPctQu9SmH+tddw5hd+wbl9MIjuM2eggkF8\n/M478Kys4OjP/Rys1la819sLRCLoTrYUvt/TAy0SQXd9PTyXLmHwww+RKC5G5xe+gMTrr6Pnzh1Y\nY2Pobm+Xx/+nP0XB0hJO798PlUjg8v37iD/m8TOjUXzy4YfwTk3hUH09oBT6HjxArKICB/bsATwe\n9Fy44MS3RY/fdl92e3zpdsrxMD7Gl4+XM5HtmUJ/AuDDQCAQSF4eCQQCLWuv9/bbb1sff3wcP/iB\nF319HqysSGvPqVNR/Ot/baCra/X1kzubYnxcw+SktBjt3i3bwKe3Itns9ih74K/X61TivOhZhulV\nQaGQgqY583M2qgrKZCvqZ7G87LQvmSZSj8NGVStynHJdpZCqzonF5HHUdbltWZmFUAgYHdUxMaGh\nuFgqccrKTExN6RgclN/Trl0m2ttN7NljwOuVx8I0ncdlfh6YmpIKoqUlqbwqKwPa2szUh0+m6XzQ\nZ7eZLS0pLC3Jbm+VlSbKy1Wy3Uz+0zSpgJL7Vmk70Uk1U13d+p3bbKOjwO3bOsbGdBQWWqn2NHv+\nom1pCZiYkPa7+XkN+/YZqKqSSqO151h6i5w9nHxti5xt7bDqXbtM7N797AOyiYh2Cs4UenGedf0F\nJGcK/cEfoPjKFejhMADAKC/H8uuvA/9/e3ceHMd933n/3T0DgMRNEAQIELzA+5JIibIuypItWam1\ns0k2ftqO7SRrVzaJXY83x7NPJRXn2X2S1JPU400cJ6nax5tynDjxOptK246TyBVbkbySDJ0UL/GQ\neIIEQQAEQBD3NdPdzx+/6ZnBwQMgjpmez6tKJQ44GPYXwwF+/M73+PKXmbHys6uL2MsvE3/7bWLn\nz0NZGd6GDSQefpjg4YdnX7E+F2FL1uCgWS8ezty511araVUr6RXrlZW3rtwJq30GB7HGxzPzdior\nMxVNIyPpxwUys4Rm2wK2GIaHiR8+jH36NHZ7O35jI/7u3SQPHoTq6qn3HRjA6urCunnTxBNu8sra\nfmKHQxfb2rDa2rBXroTRUdPu1dBgBlCHA59TFVZ0dRG7fp3kM89AEJjHrK6efXPcpUvE3nsPa3wc\nSkrwm5pMu1l2lU9Pj6ki6uoyFWybN+Nv3JiZcyQiInOy3DOFXgX+LeA6jvMI8M6t7viLvwi/+IsT\nwASvvgpf/WoRr71WwnPPmZk1O3cm+NjHJvnYx8yZYN0607IDpn2po8Pm9Ok4r79uEkoNDSYxsWKF\n+bkdDvs1/8jOtCQFAenkQW3tzMrXhZZpjzLZkLA9qqPDtEdVVGRmC8ViZgtpTU1ATU0wZRX1pUvm\nYDR9g9hsRkYyiSAwyZb16/0Z55Xsxx8YsInHzdesudmfloAKUhVDNm+/HaOtzaakBLZs8Th4MElD\nQ2aGzqZNHg8/7E3ZIHb6dDHxuEmWlJdnNrHZdvj1CVLnETOA+cSJGGVlZnZTba1PVZVpZRsZgRs3\nLHp7bQYHzWN5nmnVqq01iZuVK03yLNy2BrBmjUcyGaQ2zYUtYDZBYNrSLl+G996LcflyjFgMtm71\neP/7ExQXm/vevGnjeUH6OgcGbAYGLIqLzUyrsrLbD7ye3iI3Npb5+zgxYdoiIazAz7TMaauUiIjc\npbs+fwHguoyFv/7t36bku9+l7PnnsXbswF+xgsnNm0l+4Qvw4Q+blqnHHiO5Zw8JyyJ4912K3n2X\nkm9/G/7+7/F27GDy4EF48slMUiD7HaDZfh0E5sATBBCLEdTUEKxaZdrQhoaw2tuxw6HJ5eUmOZB1\nYLNu1T6WamOzBgZMy1tFhfkvTHSNj2ONjZn7jo5iDQ+bdxLDz0/93/J98DwzmHlkxMwQisVg5Uoz\nTHnTJvzNm+8tGRYOq+7vN3Nxdu689X17e4kfOYJ95gz29et4Gzfi3Xcfk5/+9MxETE8PsbNniZ0/\njzU0hF9TY5JaZWVmBk8qEYhtQ1sb8VOnsNrasCYn8XbvJvGTPwl1dZlVuOH2sfZ2YqdPm21yvm+S\nO+vWzdwu5nlw4QLxs2eJtbURlJbibdliNpzV1s4e35o1ZjbR/fdnhkn39WFfvWr+DoQbx5ZiCKeI\nSAFa6Eohi8z2C4DPuK57bvr9brV9DEyS4o/+CF5+eSVXr8awLDOLZ+vWBA8+CEVF5pLDs0UyCf39\nMDRkMzRkkgJr1pjBxOGbENmr5pNJUz0zNmbmyKxda1a4b9/uUVW1cF+LlpY79ykmEpnqkbupCgqT\nCeEGseyE0thYWOVjE4uZipXZKqPCRFB/v6lcKinJbMua/rN2chKuXbO5ds2iu9tm1SqTeGtq8nnz\nzVfZt+9QunLnToOzw2RYGCuY5y8eN0kwU/FjnquwMqyz0+bCBZsbN2x83ySB9uzxWL9+aoVYeJ0X\nL5oh3kFgsXq1z7ZtHps2BTOSYaOjcOKExZkzcS5dilFV5bNrl8/+/UnWr5/6/Pk+XL9u0d5uqp9i\nsSCdgFy9euaGsbsVzjO6ccOip8fGskzia/Vq89xVVi5+Vdvd/B3NZ4ov/0U9xqjHp0qhpXO35y+4\n/RmM732P2B/+ISXnzmGPj0Mshl9dTeLgQYKPfQzLtjPJE9+Hjg5ip06ZVeSDg/g1NSQ3biTYu9ck\nAMJ3r8If2JZFkPUYQTjLJ1xHHiYiwCRnwsTNyAjYdmZL1sqVmcccH89UsQDHW1vZ98QTmbXp2Y85\nOpq5byxmHisenzpvJ/yc6R8zBy3svj6TFFm71lTSrF1795vUksnMJrKsYdWzbiLr6iJ+9KipCLp5\nE6+5GW/vXvz9+2l5++2p3zuuXyf27rvELl3CGh0luXkz3vbt0Nw883GPHaPozTeJnT4NgLdnj5nl\nE85hyrpWTp8mfvw48fPnCcrK8HbsILl/v3ncbJOT2OfPY58/T6ytDb+6Gn/rVrydO5mxBeQupL83\nhoOzUxvHsKypK+zzdGNH1L/3Q/RjVHz5L+oxLmulkOu6AfC5e3mMigr43d8FUu9h/d3fwb/8Swln\nzxZx7JhJ4jz44CQf/3iS3bunrqoPArh8GS5cMFUf16/Dxo0ezc3elFXqobExuHDB5uJFm9dfL6G8\nPGDjxszQ5MVWVGSSAKtX311V0MqVpLeuJRKmfensWZvr1802s4YGn6Ymb8YWz3BzW7iFq7TUJIEa\nGmZubhsfh6tXzep4U4FjEiDve19iytevqCiYMTh7cNDiwoVYenB2dntUeKZKvSlILBZWx1hMTJgk\nXVGRSYTcuAHd3SYJU14ODQ1JYjEYH7dpbzfDqoeHzbBq3zdVUSMjFo2NATt2JEkkAnp7bS5ejPHe\nexb19T41NQFDQ9DWZnP1aozKStPa9uyzCeLxcItcjJ6egLKygPb2UlpbbYaHzderudnnwAGPIMi0\nd12/bqeHn8+WVJtuYsIkgvr7zeD0ykrzHGzf7oeLOtItfK2t5gsWJobuZtambZNOFIZnYRERib6F\nOH8B8JGP4H3kI4yC2Rzxm7/Jitdfp+SHP4QXX8SrrmbswQfhP/9n2LoVbJsEmB9A/f3YL71E0dtv\nE3/xRTPoeNcuJh99FFLzf6aYvkq9pCTzD/5bvLuUvn9vr/mY7xNUVOCvW2fal0pL6W9pIdi+3fx+\n6nBlpxIxQVGR2X5VVcW8evIBH8yGtY4O4mfPYr31FsHq1fgNDfiNjTMrd8JNZAMDZrB1ZeWth1W3\ntRE/epTYu+9ijYzgNTcz+fTTcN99MxNP7e3EzpwxlTueZ+771FMzEzYAr79O0eHDxN57D0pK8Hbv\nZvxzn4Pdu6feb7Zh1Xv2mPumBoynjYyYaqQLF4i1t5tKn61bmfjAB1iwd1lTpdZBZaXZfjY2hjUw\ngN3ZaVbQx+OwYgX+2rXzfj5FRGSBK4Xu1m3fpbqN996Dv/u7OG+8UUxHh015ecB99yX58R+fIDVP\nb4qeHrO+vLXVVJvcqSro8mU4dy7GlSsxJiYsNmzw2LLFZ9s2f9HbzKa7VVWQbZukweCgaTsKK4vC\nlfeDg2aTWGlpgO+b26OjmfuWl2eqW8LkwtiY2Rg2PGyqeMy8ptk3dt2NsCqou9ts7goC02rf0GCq\na7LPS2Ey7MIFi4sXY3R3xygqMhvm6usDKiunJkHGx6GtDa5ejdHVZbN7t6nuqagIKC6e8YYgR49a\nnDpVRFubTXm5z44dPnv2eDQ3Z9rpwuqkoSFzvX19FrEYbNhg2hJXrTJtZtPfbLubWUFhBVd/v4Xn\nWVRV+ann4c5vcI2Pm3Yyy8rMFAo/J/v/4a89j/SGNM+zspKKQU5uARaRaFOlUG6a7xmMr36V4r/6\nK4ra2sxmqZUrSWzbRuJXfgV+8idn3v/llyl+4w0zT2ZsDG/bNhIHD+I//fTMxEk4syaV9AlsOzP/\np7w8fViwBgexBwZMgiBVWWQlEgQrVmTuX1xsHqu/H2toyCSbwrk/i3GYm5w0SYqODuzr1wlWrjSr\n30tKsBIJmJxMt7Gl485+R+fSJeJnzmC3tgKYjWEHDsDevTP/rNR8ntjlyxCL4W3dirdjBzQ1Tb1f\nIgGvvkrx228TO3eOoKoKb+9eEu9/P6QGRKeNj2MfPUrsxAliV66Y5M7evSQffHBmu1dvL0WvvIJ9\n4gR2ZyfJD34Qf9s2vN27762Vbi7CGVS9vVgjI5BIYPf2mm1va9eaxNw8qpNERKJkrmewvEoKZRsd\nhW9+E374wxWcPx/H96G52eOJJ8b41Kdm/hzLrgpqa4tRXm6SHmvW+OmfHdnJh/5+uHw5xtWrZoZN\ndbVPdTXU1PiUlk5tkc/+2e77Zpj06tWZNqN7PYNkD872/cy8pFutWO/pMS1JYcVOebmpzslOIAwP\nB3R2mmqc8XEz0HjfviQbNtzbXMfpw6rDwc+Tk5kWuTBRcfNmZlh1aWlAY6NPfb2fTtiF1xsmbMLk\nS2mpSdRUVgYzvrb9/XDsWJyTJ021U1OTz549PgcOJCktNcOn29tNO5xtmyRZWVnAypVWuuInTL6F\n7X0DA6YSKTsJN9ubmGECyCTD7FQyzMS1evXMNrbFNDGRSVaNjlqUlWWqt5Y6wSkihUlJody0EGcw\n3noL+4tfZMXx49iDgwTxON7atSQOHgTHMffJPih1dBA7c4b4pUtYfX1QVUVyxw4STz2VSX5kH8LC\nNq/ubuz+foJYjGDdOrzt2wlqa6dWEgWBqVLq6MDq6MAaHSWorTXVQ42Niz88MjQ2ZhJRly9jX7tm\nDm+lpWaeUUWFaZNLvbsUXLxI/ORJk9yxLLzmZvxNmwg2b87MzykrM1+7CxeIXbhAvLXVzOdpbsbb\ntYsZGzBGR7Ffe4344cPELl40g6L37SPx+OOwYcPU+85lWHVnJ0UtLdjHjmF3d+M1N5M8eBD/0CGW\nrCx5eNh8bQcHTRthVZX5GoV/vu9DV1d6WDVBYCq3GhqgoeHeDrYiInmoYJJC0730EnzrW8W8804R\nN2+aJMdDDyX45CcT3H//zPu3tppqk+vXLZJJM6x67VrTzhMmW7Jn7LW12XR1mX/sFxUFrF1r2pfW\nrMkMTAbzf9+H733vGA0ND9Hba5INjY3m/tN/1t6rycnMUOmJiUxiY7bqkL4+M3unvd0MuW5s9Ghq\nmn2D252EfZi3GlZdXT1zLlIiAefP25w/b1q4SkszlVtr187cwhUml4aHTVLjVsmw3l44ciTOqVM2\nPT02Gzd63H+/xwMPTB+YbZIl4dfr+nWTMBkdBbBSz6n5O/DWW1P7TO9UFRQO9w5/r6LCzAhKJEzL\nXpgMW4pZQdNNT6rFYuY6Tp9+jQ996NEF+3NGRkgPN29sXP6NaVHvFY56fBD9GKMen5JCuWkxzmD8\n5m+y4vvfJ3bjBlYyiVdVRXL3bvxPfQoaG00yJGs2j3X0KEVHjhBrbSUoKcHbsYPxQ4fgwx82yaCB\nAazhYYLSUoKyMqzJSeyeHqzeXoKqKvMP/ro6LMuaOp+nqopX336bx++///YbxBZKWLXS3w9gkhXV\n1ZlNZGGLXF8f1smTxC5dwu7sNJU7+/bNnM8zOop14waxkyexL1wws4Tq6vB378bbty9dAZP+3jEw\nQKylxQyhbmszlTIPPGAqgmap8pk+rNrfswfvgQdmVm61tlL06qvETpzAGhjA27qVyfe9Dx5/nBlz\nBxZBy49+xKH77zdtf6mkY7ra627axFJJQruz07QN1taavzPr1i1dIus2ov69H6Ifo+LLf1GPcbm3\njy2bp56Cp56aBCZpa4NvftPmtddW8HM/V0JZWcDOnR7PPjvOJz5h7r95M2ze7AHmH/sdHTadnTZn\nz8ZYtcokBhob/fTq9pqa1DteeNy4Ye7f2hrn9Omp28/CN6TWrRvh0KHklKHJP/pRHMuCtWt9Ghr8\neb95MT6eSWwkk6Ylqb5+9hXrPT2mNayjw1SuNDT4HDyYnLFxdi48D/r7i7h40WzhKikxSYb16730\nMhHfN0kCz4MbN0x1Tk+PGVa9e7fPj/2Y2fZhWuFsTp0yiRMwX5PRUdMyV10dsGHDzC1c167B0aNx\n3nvPrIRvbvZ4//snOXBg5pmvr8/MSZqYgLIy8/Uys3wy9wn/Dly+bHPkSIyenlVkf5+YvkFsdBQ6\nOiwuXoxz86bFtm0eq1fPvrktO3EWzgq6my1yC8W2M9VlYJ6XwUGL3t6VnDoVm9JmNtfB2UNDmXbG\n8M+prl7+hJCIiCyhL36R8S9+0fz6n/6Joq99jaIzZ7B/5VcIyspIbN/O5C//MvzET5j7vP/9JMPP\nff55Sr73Pcr+4i+wvvxl/PXrmXzkEZKf/GS6LSoA/F27zHaxs2eJnT1L/Ic/JCgtxd+8GW/rVjPz\nxrbxS0sJ6usJ6uvNYOdUksm+di0zt6iqav7tTmHSKqtqZdZNZMkknDhB/J13iF28aObzbN5M8qmn\nsEpLCWwbq6SEYHgYbJvYuXOZ+Ty1tXgHDpBoaMAKAtM219FBMDREMDlJ44svUvLP/4zd1YW/bh3J\nAwfwfv7nSffcgzkAXr9O/J13sM+exe7rw9u4keT27fg/8RPYxcXg+9g3bpj7dneb7WKnT2ONjJDc\nuZPxn/5pOHhwZiJoZMQM6w6ru6YPOuQ2G+JmG4o47deVly9jr11LUF2NX18/92qv6moz7Hr3btPe\nd+0aVmcnRadPk/jwh5euekxEJE9EplLodr7+dXjxxRWcO2dmBW3a5PH44xN86lOmdSz7Z5EZ4GzT\n2WkGCRcVBdTXmyTCmjUQBFb6vqmNpnR22ly/bqqIKivN/evr/RnzcFIVznR12XR32wwNmS1Za9aY\nyqOwGjoIrFv+rBwfN/8P/5E/2yr069dNEqSz0yYeh8ZGszEs3MY2H8mkecxwE1lpqUkiVFQE6bNC\n9mybkRFTITM+bs4oTU3+rK10k5MmqdDba3Hzpkks7Nrlz1rp1NYGb78d5+zZGCMjFtu2Jdm/P8me\nPTMTQWEy7No18yCNjT4bNvi33IY6Pdbh4ZkV1GHFTZgECTe3lZfPbbDz+Him4mhszKygDxMzS/AG\n3BTTN+CFg7NvtQEvTHANDJjtdUVFmQouzXgUkWyqFMpNS3oGa2vD/v3fp+Stt4h1d5s2s6Ymxp95\nBuuXfmlGlU/Q2UnRD35A0bFj2B0dZpvZnj0kDhzAqqrCnpgw9y0txVqxwgyR7u7G6ukxlUVVVfj1\n9WbGTdiLnr0tLZXMsIaGwPPMHJr6+ttvEJs+rLq4ONO+NP0H3/Aw9vPPm+1eV64QVFaSbGoi2LIF\nKivNqvvwMVOtZvGrV2FggKCmBq+xkWDTJlNplL0JracnPVTaGhjAX73aJHf27MnMzwnL1nt6zBay\nq1exxsfxGxvxmpthyxamH9iC3l5iV6+mW668DRtIPProzE1kkE6G2QMDpp0v3Jh2u2GHt/hYMP3d\nsOmfU1y8JBVJIiJRVbDtY3frzTfBdYs4cqSYGzcsamoCdu5M8KEPJdm7d+bPpYEBswmru9sMfK6t\n9amrC1i7NjObJXv2TU9PQHe32Qhm26Ta0nzq6mZuW52cNAml7m6zmrysDOrrTYXSqlWz/ywtKpqZ\ngAirkdrbzfDlFSsyq+PvpV1tfDzTbnbjhsXq1UE6uTP9HBQmdwYGTKJj+nye6Y8bDl9OJsP7zl7p\ndP68aQ07dy6G78O2bR7335+cdf5iZ6e53uxk2Pr1/j3NG/S8zFyh7M1tVVULk8AJH39w0Dx+mGRZ\njg1i01vkgPS1+H5me11JiakGqqrSfCIRuTUlhXLTcp7B+PKXKfnHfyR++TJWMom/ahWTBw7g/8zP\nmG1mkDkITEzACy9QcvgwdmcnFBfjNzSQ2LbNtFwVF0/t3TeDFbGvXzctWhUV+M3NJPfsIb1SNvtA\nlUiYJEdXl0n2ZG8QKy29+2HVfX3Ev/994m+/bVq41q3DC1u4Vq2aOmNgaIjY+fOmNayvD2/dOvyd\nO/Gbm7ESCfNnDg+bwdn9/cRPnCB29qxp4dq+ncmDB00Ll+elq5Ws8XGT3Ll8GfvKFfD9hRlWfatk\nWHX17NvhZPlNTJi/a0qoiRQ8JYXmoK8P/vZvbV59tYRLl2KsWBGwY4fHBz84zs/8zMz7j45mh7rq\nhQAAIABJREFUkg69vVZ6rftss4JaWlrYvfsQHR1mmPPg4NQ2s+lJFd83FT7h/X0/02Y22xtYyaRJ\nKF29aip3ystNwqapyZ+1euhujY6aCpurV01rWF2dz/r1MzeRtbS08NBDh9JtbJOTVipZMntyZ3Q0\nkwgKAtM+VV09e6XTqVNmWPT58zHicdi1y2P//iTbts38GnR1wbVrU4dVr1+fGVY9H8kkPP/8W+zc\n+QgjI6aSJ0wEzbW9ai6CINPaZYaKL+4GsTv10mZvwLNtqK5enmqm+Yp6r3DU44Poxxj1+JQUyk25\ncgbj1Vcp+upXKTpxAruvj6CigsTOnUx+7GPwsY/NvP+ZMxQdPoz97rvYvb14mzfj79tH8rHHpszP\naWlp4dAjj8Dp08RPnSJ28SIkk3hbtpDcvRv27ZtZ4TM5id3ebtq3Ll82g4qbmvC2bjVVPtMPYW1t\nFL3wArGjR7G7ukxb2MGDeB/60MzNVz09xM6eNevbh4bwNmwguWMHbN/OjEPFmTMUtbSYWT5DQ/jr\n15PYu5fgiSdMZU7qgNXS0sKh6mqTNHrvPSzPM8mozZsJtm7NzFAqLTXvPl24QPzsWWJtbWZY9ZYt\neDt3zhxW7fsm0TQwYJJh4Va3xdrcdgtR/964oPGNjaXnWlmeh9/UZJ6vZabnML9FPT6IfowFO1No\nPmpq4POf9/n858cA+N734PvfL+HP/7yML33JYsMGn8cfn+DjH/cIZ9Nt22ZW1N9qVtC6dX76Z2xN\njZlFtHevz/i4ue+1axbvvFNEaWmQvn+YUKqthdpan337fAYGzP1PnozT0mIqmtas8VOVwSYRVF1t\nklKPPppIt7L7vkm+JBLmH/WlpZkEza1a0oaGMsmo4WEzpHvtWp8dO4L053Z1mV+Y1iebY8dqGR+P\np1vIysoCkkkzP6i3d+rjT06aX1dXB2zc6M+ogEkk4I034MiRIs6ejbNiRcCWLT5PPJFk7Vpzn7Gx\nGO+8Y+5r4re4ccNm+3aPLVt89uxJ3FMyLNzc1t9vKp1GR+PU1ARs2uQv2dIKs63NbGxrbAzSG8R6\ney3a2uwl3yC2ciWsXBlQX68ZQSIissAef5zE44+TAHMQ+aM/YuVLL1H+W78Fv/3beOvWMfbss/Af\n/6NJhuzeTWL3bvO5vb3E3nyT2PHjrPynfyJYtQpv1y4Sjzxifj8eh/vvJ3n//WZ20bVrxE+coPiV\nV7C/9S2zbWvnTpK7dpmZPWEbW10diS1bCCYmsDs7iR09ivXaa/i1tQQlJcTfew/7nXfMAOjmZiYP\nHcJ/8sn0YGkrCEyJ+dWrxE6fxr5yBWtsDL+pieSGDaYaJxbD9n1TCu37cOwYxcePY7W2Yvl+OhHE\nli0mSTM+jv3KK6aKqLMTu6+PA+fOsWLTJvxNm0ju2weNjVi+jwVYFy+awdddXdh9fVjDw/irVxNs\n3Uri4YfTc4fsmzfNu6OQPrRZnkdQVmba8NatW/jB3LIwRkdNEmhgAILAPF9NTTMHhouI3KWCrhS6\nndOnTZvZ0aNFdHTYqeHTSbZsCdKJiqlt6hYDA2bWysSEaQPbuNFj165MNUwQmH/4B4H5Odzba3Pj\nho3nwZo1PmvW+NTWBlO2nyWTcPOmxdWrFp2dNr5vUV8fsHmzx6pVZpNVWA2dSMDwsKnsSCRMde/E\nhPnHfZi8KSkxFz04GM5CMte7dq1JBIWbyLIrrMfGMturws1a1dWZFetT29yyhgymW95mvik3Ogqv\nvWZz9GicCxdi6eTZE08kaGyc+piJhKnQunbNVGitWmWqotavn1lxNRcTE5lE0OSklapemr3Sabll\nzzMaGspsEKuqWtpV9yIid0uVQrkpH85g/M3fUPKd7xA/dw5rZMTM0Nm6Ff8DH8isVw8PYYkE1oUL\nxC9dMsOYPQ9/0yYSu3aZ2ThlZZn7pubz2EeOmARPV5cZVr1mDUFjo1l3nz3LZ2QEq70d++pV7KEh\n/JoaEvv3Ezz2GEFNTaaNyrLg8mUzqPnyZUgm8TduxNuwIT0AGyAID1hvvUXx6dPYra2mLWvLFpMI\n2rw5cwCJxUyVT0cH8fZ2rOvXCVauJFizBq+pCausDIqKMlVBsRh0d2N1dWHfuEGwahV+TQ1Bba3Z\n0jY6auYxpbaxTbn28M+Mx7W+PVcND5tE0OCgmcsUtjPmwDY1Eck9ah9bBGNj8N3vwrFjxVy8GMO2\nYcsWj4cfnuTRR01FbSyWqQIeG4NTp2KcOWPT1ma2mW3fblqgsjePhgYHTaXOtWumZSscXlxWFhAE\nM+fzhKu/BwfNSnUA3w8oLjYJk+wWrnBOzMCATVubqbAZHjabvjZvNomV6dXDcOutUveyTn1gAFpa\nYhw/HqO1NcbatT733+9z6FCChoap9x0fDwdb2/T1WdTWBqxfP/uw6rkYG8skgjzPbCILB3bnWiLo\ndsI2M5MAtO5pg5iIyGJQUig35dsZjCNHiH3jG5QcOYLd00OwciXJXbuY+PCH4ad+aupcIYALFyh+\n+WXsU6ewe3pMW9Xu3XhbtmD7PtboKH5DA966ddDYaKp6zp4lduUK1sQEXmkpARAbHQXbxtu0ybRa\nNTebyp2hIdNiNThIcOmSmeVz7RqUluLt2mXWzE/vd5+chH/9V0reeIPY2bNmJf199zH5gQ/MnPsz\nOWm2hZ09azaRrV6Nv327uYbstqAggBs3iL33HnZrq6leWrcOf+tW/B07Zr4b53lTrz0eN61hlZXo\n3aUclD3TKXy+wkSQNnuIyB0oKbQEjh2DlpZiTp+26e212bzZ48ABjw9+MNPuFPYpJpOm6ujUKVMR\n43kmobR7d3JKW3tYtdLdbdHRYVqYRkZM21g4tyhcIz82lllJPzFhkhm2bbaWlZZmWoxKSsyconAl\nvW2bdrbq6oBYzFTHVFRkkglm7o9JTBUVZYYJz/az5277MLu64KWX4pw4EaOjw6apyWfvXo+HHvKo\nrZ3aZjYxYeLq6DDXYNrrZs4zmqswiTYwYN1xc9tc48sFk5OZKqKRkcwGsaqq4LazIPMpxvlQfPkv\n6jFGPT4lhXJTXp/BRkbgr/6KFS++SPziRTNUedMmxp54Aj7zGairy7yu+vqwz5wh/vzzZlhzfz9B\natX75GOPwWOPZR738mUzfPnMGazublMxE4/jb9tmVt5v2wZr1pjy7XfeIX7yJPHz5wmKi/E3bMDb\nvBlr9ep0FU5QWQljY9gvvkjRa68Ra23Fr6nBf+ABJp9+2rSGZRsbI3bmDPb589idnfj19fjh3J8w\nYRP2/k9McOy553iors5sIqurI1i/Hr+2Fmt01LTCjY2ZNrDKSoJp76RZ4eOMjKQTRFYymakiyt58\nNpe18tMf/3aff4fHfOPCBd73kY/M5W9GXrnl9/4w6RgmgkpKMsO982yzR9R/vim+/Bf1GDVTaAkc\nOAAHDphBOV1d8PLLMd5+O853v1tMVVXAvn0e8Xg127aZ56G+HurqPD74QY/OTpMgeu65Ev76r23q\n6/3UhrKAhgaTnNmxIwCCdJtZe7vNG2/EGRy0KCvzaWryuO8+UzmT/eaO75uqo/PnbS5dinHjRoxV\nq3yamz0OHUqyalV2FGZmTUeHzenTNj09NhUVJgGzYYNHZeX8vz5tbfDCC0W8/XaM7m6bjRs97rvP\n42d/NpGucrUsi/5+0xpmWtMgmTRVUjt2JGlsvLcK5uFhk2AKW95uNc8oCoqLYfXqgNWrgykbxC5e\ntLEs0knCfKuGEhERmaKsDD7/ecY//3lz+8UXKf7bv6XsO9/B+su/xK+tZV9dHUVvvAFr1+LX1ZH4\n+MdJhMOfDx+m+M03Wflnf4b1X/8r/rp1BPE41Nfjb91K8oknoK7O3HdiwiSLTp4k/u1vY5teeoLy\ncvz160nef7/ZLub72OPjcOUK9s2bpmrn6lWsvj78tWvxHnqIiV//ddLvGoZ6e4kfOYJ95gx2V1c6\nuZM8dMi0dlkWsba27PkAJmGQTFI8OGiuddphKSgrI1izZmpV0MDArVfEl5aazwkfv6sLe3TUtKlV\nVhKUl6evZcrnhW1wszxmkH2/ab8/ZRX9bdbWJzo75/53I1953tTh3qWlZkZQQ4O2iInIklGl0AKa\nnIQf/QjefNO0mfk+bN7ssWdPggceICshkm5V5913w1k5ZpDw+vUmidPUZObuhC1C8XhAUVHAwICd\nHgq9Zo3P5s0+mzd7JJM27e0W16/bqUHFPqtX+wSB+fywKqiszLwTMzycWbFeXW2SUWaIdGYdeaYl\n6c7JhLNn4cUXi3nnHZu+PputWz0ef3yCD31o5psb2ZVO2S1cFRXz/9qHA7MHBuz01+t2lU6FImyX\nC/8OhJvUKioCzY8UkUWlSqHcFNUzGK2t2F//OiWHDxPr7SWorMTbvp2Jxx+Hp5+emXy4fJmiw4cJ\nJiaIjY3h19aaOUBbtkBVFXZHh0mS9PTgr1qFb9v4GzZktouFq+ZHRohduIB95Qr29etmrf2WLXh7\n987cRNbVRfzwYez33ksPq/b27sXfv39mS1C4VWpgACuZxA+3gC32Ozypd5fSbUu2bRJEVVUmKad3\nl+5dMmkSQf39ZsB5eXlmy5tmAIjIAlD7WA5pbYVXXy3izBmb7m6bdevMDJ1HH505Qwfg8mU4cSLG\nuXNxBgYsmpo89uzx2L/fn5EwGR2FV1+1OXYszsWLMSoqglRbVoI9e6beN5mE3l7TltXdbZ7y2lpT\nFbRq1exJk/HxTDJhfDzTZlZZmUkmnDoFL7xQzKlTMYaHLXbtSvLII5N84AMzE0FhC1d/v/nzwxlF\n82ljzx7wPTxspa+zpCRIP+69VNkmEjA6at11tbNlQVlZkPNVSMlkJuk3NGTmSoXP6b3MihIRmY2S\nQrmpUM5gfPe7rHjpJbOyfXwcb8sWJh57DP+jHyW99jU0OYl96hSxI0dMC9nEBN62bSQPHMB/9FFm\n9GL39ZlZPhcvYt+4gbdhg2k127Fj5gGorY340aPE3n0Xa2SE5LZtZmPYfffN3O41y1apdCJouYyO\nZtrMJiczLXIVFdpONheJRGZ1/NiY+TpWV5tWQw33FpEFpqRQjpjepzgwAG++aXP8uJktVFkZsGOH\nx8GDCZqaMomNFStMYsPzAi5fjnH5cqYqaONGU1HT02OGL69aZSqCGhp8enstLl2KceWKWSvf2Gjm\nEJWWmrascPtYeblZMz80lNlUFgRmo5htm0QLZP4fBKTW25tEwoULFu3tMU6d6qemZhX33edx6NAk\nTz4582twt8Oqwyqf/n6b4WHwfeuWSZiZlc9BekbQvVTZTk5mz2myePfdwzz00MFbVEBP3bBmrt/C\n98Oh4D7l5bn9Mz4I4F//9XV2735sXpVh+SDqvcJRjw+iH2PU41NSKDdF/Qw26+vq5EmKnnuOoqNH\nTavWmjUkDxxg8tlnsVeswG5vN/N5amsJ1q7Ftyxira3E2tqwhofx6usJNmyAiQkzkHp4GG/DBpJb\ntsCWLVjTZ/Bcu0bszBli774LyWQ6wTRjqDTMa6vUsn3vSCQyM2+GhwlWrMhUES1gWXZkvjdOTGQS\nQZOTBJWV+NXVtJw4waEnnljuq1tUkXkOb0Hx5b+ox6iZQjmqqgqefdbn2WfNLKK33oKjR4v4xjdW\nMDkJGzf6bNvmsXkzqTk4FnV1AfX1HomER1ubzYULpiKnvj5gwwYvXWkzPGyzciXs3WvWug8PQ2tr\njIsXbSYmzOau+nrTfha2hq9YAStXBqxZY2YLjYyYREwsNusiD86di9HaahOLwYYNPuvWXeEjHykl\nCKCy0mJw0FyLqQgyg6KLi02yprl55up4M/8okwxbudIkjOrrzfas6cmYxUhSjI9nEkFmnpFPfb2p\nyurvH2XTJn8Oj2a+joODFj09NleuWJSVZSpxcm0+oEmoeTQ1mflVYetgd7e59vLyzLWrpV1ERPLa\nvn0k9u0jAdDfj/3tb1Py2muUfeELBCtX4m/cSHLXLqzKSqyrV7FtG4qK8Ldtg9FRs13slVcgFsPf\nvJngwQcJysuxV6yA/n7zZ3R0YF+4QOzatfTWsolPfGLmJrLUO2HW4CD2wEB6q5Tf3Jz7W6WKighq\naghqaqbGcekSQGbIdlTeXZqP8fF0tVfY9uc3NExt+yvUr42I5CxVCuWAvj44f94kXbJnBW3b5qU3\njmULZw0NDJh15NmtXdNbkX0fOjuhs9Oms9MMHl671lQXNTTMXs2SWq7ByZNxzp+PU1YWVjUl2bBh\n6n3Hxsyw6mvXbG7cMIOi160zq+OnVzt7XmYL2PCwSZqEVT5LVYFsNqyZNjaT0DLJqIWuzPa8cIB2\nZiZUVZV5jnJ986vnTW0zKyrKXHuut8iJSO5QpVBu0hlsmvPniZ88aTaUDQ3hbdyIv2sX3gMPzGzb\nykqEWDdvmkTQ9evEenoIqqvxtmzB277dDIDONttWqbAiKNfeNZqv8fF0fNb4eGZOTkVF9Acm52Lb\nn4gUNLWP5bnJSbM97OJFm7a2GEVFAZs3e2zd6tHcPPP+iUTmH/DDw5l15JWVs88K6usjPah6cNCi\nrs6noSGgpsbnvfdsTp2KcemS2Vq2fbvPwYNJ6uvN52a3co2OmsTK8HBmWHV5ecDYWCYRUlSUqTQZ\nGLAYHTUJrDDBsFSz9IaHM8koy8q0sS1lgmNkJJPISyatrFatpfs6zFe4zWxw0AwGz772XG6RE5Hl\npaRQbtIZ7DZ6e4kfP24GQbe3469Zg799u9k0tmGDedfk3DniZ88Sa2sjKCnBb2zEr6vDKi2dupLe\nsmbdKhVUVUU/SZJMZrafDQ0RFBdn5g/dxXr7YPVqs0Utl82j7U9EZKkoKZQjFqpPsb0dLlwwiZrh\nYTN8urnZVO7cajZP+A/4ILCwrIAgsGb7mcvICJw9a3HhQoyuLpvVq4PUtjSf6urpLVxT5+icOvU2\nzzzz4G2TO2EiJEyCLFUSIQiyE0F2ukqnquruByovZp/p5GTmORoZMYm8MFE2fZblYppPjGGL3OCg\nSfLlcotc1HuFox4fRD/GqMenpFBuivoZbMFeV6my6fjJk8QuXgTbxrJtvIYG/H378HbtMrMBsu6f\nrgYaGgJYtK1SefW9IzxwhsMqb7OKPvz1q2++yeOzDatcTrdo+5vPPKW8ev7mKeoxKr78F/UYNVMo\nYpqaoKnJ46mnPAYGzGyfs2dtXnopzurVmTaz+nrzs7Sy0rREQUAiYR4j+2ft+PjUdq/m5oAnnkiw\nbt3MuT+3c+PGKKtWBbe9T1mZ2coFt7/fQsgeVj04aOYZVVcHbN3qLWmi5W4UF5vtb7W1Qbj5lcFB\nk5yz7UySZbG3zs5HSQmsWWNmUfl+pkXu+nWbWGzqtYuIiOSteBweeIDkAw+QBLhyBSuZxPJ9rPFx\n7Js3CTzPVAXF4xCPE6xaRbBqVaYCRuW05iAzx0NBkCsl1LO1/VVV4W3bFp22PxERVCmUtzzPDIC+\ncCHGlSsxbBs2bPDYvt0Mq87+eTo6Cu3tNlevmgHQdXU+69eb2T/5tE10erWxqQgybWxDQ5lh1fe6\niWw5hfOiBgctJifNwOeqKlNllevPVVgZNjhoZl3lU4uciCw8VQrlJp3BFsC0qqCgpCSzhetuS5Il\nN3le4bb9iUhkqFKoQMRisGMH7NjhAR4dHWZY9WuvFfHcczZNTR51dQG9vTYjIxZr1vg0Nnrs2ZOp\nPuntNb+4VXt3aHr72WytaHf62Gy/n/34d3qcW1Ubh/OMmpryK8F1K6WlJqa1a02l19CQmUPU3m6z\nYkWmEicXz5xhZVhDQ5CeddXXZ3H1qp2edVVVtbQtciIiIgtuelXQ8LBpK2ptBbK2cJWXq1ooHyST\nJhHU3481MpJu+/ObmojE4VJE5A70nW6RLHWfYmMjNDaaBNHwsKkg6uyEDRuS6dYyyzIVRrOte7ft\nW7d3QzDjY2+88TqPPvrojFlDs3/+/D+2XO1TudBnWlQENTUBNTXBlHlRra3mgJmpxJnf12kxYywq\ngtWrA1avntoid/GinWpzXPwWuVx4DhdT1OOD6McY9fhElsOSv64sCyoqzJDpdetgYgJrYAC7uxvr\nyhWCsjJTaVJZuWCVJlH/3rEk8SUSphqovx9rbMw8fzU1+Js2LXoiL+rPH0Q/RsWX/wohxrlQUiiC\nysth/36P/fsX889IUlW1+LOCxJg+L2p8nPQsnytXTJtZmGjJtepm25567WNj5to7O+28a5ETERG5\nrZISgro6gro604oUDifu7CQoKspUEZWVLfeVFp5Uws7q78eanCSorMSvq2Pe766JiESEZgqJ5DnP\ny8zyGRoyQ7bDBFGub0ZNJqdee663yInI3dNModykM9gySrWZWYODWKkh1UFlJUFFhdrMFsv4uEkC\nDQxgJZP4YdWWEkEiEmGaKSRSYGIxWLUqYNWqIL35dWDA4soVG9+3UrN8/JwcbRCPT22RGx42175Q\nLXIiIiI5o7zczKtpbITJSZMcunEDu63NDDQOq4g0fO/ejI6mE0EEgZkPtG7dnLegiYgUihz7J2J0\ntLS0LPclLCrFl5vCza/r1gXs2uWzdavHihUBPT02p0/HuHTJprfXYnIy92JMjWWgqSlg926f5maf\n4mLo7rY5dSpGa6vNjRsWicTdPV6uxbfQoh4fRD/GqMcnshzy5nVVXExQW4vf3Iy3Zw9BbS3W+Dix\nCxew330Xq6PDvFMyi7yJcZ7mFd/wMFZ7O/aZM9hXroBl4W/ciL97t5n1lEMJoag/fxD9GBVf/iuE\nGOdi3pVCjuP8O+B/c133U6nbjwB/AiSB513X/b2FuUQRma+SElizJmDNmgDPM9vMBgcturps2trK\n6eiwqKoKcnK0wYoVsGJFQF1dMKVFrrPTpqjIzCHKhxY5EZGFpjNYxNh2Zu05mEqXwUHsa9ewEgkz\nBDmsIorFlvdac0VqA4c1OIg9MEAQjxNUV+M3N5sDhIiI3LV5NWQ4jvOnwLPAMdd1P5n62DHgp13X\nbXUc53vAb7uue3y2z1c/u8jyGxnJJFoSCSurVSvI+TNnuM1sYMBKt8iF155rLXIihUozhRaHzmAF\nJpHIzCEaHiZYuTKTICq05Ifvm8HdAwNYg4MEJSUmmVZdDcXFy311IiI5Y6lmCr0K/APwywCO41QC\nJa7rtqZ+/wfAM8CsBxIRWX5lZVBWFtDQEDA5aZIsfX0WV6/arFyZqcTJxdEG5eVQXh7Q2BgwMWGu\nvbfXoq3NpqwsSM1Ryr1NbCIiC0BnsEJSVESwejXB6tVTq2MuXQLIJIiiOnzP80xCbGAAa2jIzF6q\nqsJvaEA/5EVEFsZtk0KO4/wC8GvTPvxp13X/3nGcp7I+VgkMZt0eAppv99gtLS0cOnQo/WsgUrdP\nnjzJ5z73uZy5HsWn+KbfDj+W/fu1tQGvvNJCR0ecPXse5eJFm+PHj7J69Tg/9mOP5NT1h7cPH87c\n9n14/vk3GB0toqTE4yMfed+yX99SPn+5dH0LcXt6rMt9PYpv7rdl/nQGm9/tr3zlK+zbty9nrmdB\nb1sWLe+8kzmjjI1x+IUXiI+McHDPHoKKCl4/c4ZkWRmPP/nk8l/vPG+fOn6cz33yk1j9/RxvaSG5\nciUPPv00/vr1tLz+OnR25tT1zvV2oZ4xc+n6FF9hx6cz2EzzfkshdSD5Zdd1P5F6l+p113X3pH7v\nV4G467pfmu1zC6F0OfvAFUWKL//dbYxjY2aEQb5VZkf9OYx6fBD9GKMen9rHFo/OYLcW9dcV3CLG\nZDLTZjY0ZFqrKivNnKKVK5fnQucikTDVQP39HH/tNfa///0E1dWmCipifeEF+3c0QhRf/ot6jHM9\ngy1IUih1+xjwUaAVeA74Hdd1D8/2uVE/kIiIiBQ6JYUWj85gcltBYLZxpdquIKvNrLw8d5IsExPp\nRJA1OUlQWYlfXR3dVjgRkSWyVDOFAILUf6HPAt8EYsAPbnUYEREREZF7ojOY3JplQUWF2Vq2bl06\n+WJ3d2NduUJQVmYGNFdWLv1cnvFxkwQaGMBKJvGrqvDXrlUiSERkGS3Ld99CeJcq6iVpii//RT1G\nxZf/oh5j1ONTpVBuivoZLOqvK7jHGD3PbPBKtZoFRUWZKqKysoW90NDoaDoRRBCYhFRVlalamkXU\nn8OoxwfRj1Hx5b+ox7iUlUIiIiIiIpIvYjEzq6e62tweGTFVRFevYnmeqS6qqiKoqLi3NrPhYVMN\nNDAAlkVQXY2/cSOUli5MHCIismBUKSQiIiILTpVCuUlnMLmlycnMsOqREbP+PawiKim5/ecGAaQq\nkOyBAYJ43CSXqqthxYqluX4REQFUKSQiIiIiInNVXExQW0tQWwu+n24zs7u7CWw7M4cobPsK7zMw\nYFrRSkoIqqrwtm3Lv5WlIiIFLEfWD0RPS0vLcl/ColJ8+S/qMSq+/Bf1GKMen8hyKITX1ZLEmEoC\n+evX4+3ZY1q/bBu7o4PYqVPYFy8SO30aq7eXoLQUb8cO/G3bCOrq7jkhFPXnMOrxQfRjVHz5rxBi\nnAtVComIiIiIyK2Vlpp2srVrIZGAsTEzHyiuf0qIiOQ7zRQSERGRBaeZQrlJZzAREZFom+sZTO1j\nIiIiIiIiIiIFSEmhRRL1PkXFl/+iHqPiy39RjzHq8Yksh0J4XUU9RsWX/6Ieo+LLf4UQ41woKSQi\nIiIiIiIiUoA0U0hEREQWnGYK5SadwURERKJNM4VEREREREREROSOlBRaJFHvU1R8+S/qMSq+/Bf1\nGKMen8hyKITXVdRjVHz5L+oxKr78VwgxzoWSQiIiIiIiIiIiBUgzhURERGTBaaZQbtIZTEREJNo0\nU0hERERERERERO5ISaFFEvU+RcWX/6Ieo+LLf1GPMerxiSyHQnhdRT1GxZf/oh6j4st/hRDjXCgp\nJCIiIiIiIiJSgDRTSERERBacZgrlJp3BREREok0zhURERERERERE5I6UFFokUe9TVHwThhISAAAP\nr0lEQVT5L+oxKr78F/UYox6fyHIohNdV1GNUfPkv6jEqvvxXCDHOhZJCIiIiIiIiIiIFSDOFRERE\nZMFpplBu0hlMREQk2jRTSERERERERERE7khJoUUS9T5FxZf/oh6j4st/UY8x6vGJLIdCeF1FPUbF\nl/+iHqPiy3+FEONcKCkkIiIiIiIiIlKANFNIREREFpxmCuUmncFERESiba5nsPhc/wDHcaqA/wFU\nAMXA/+G67huO4zwC/AmQBJ53Xff35vrYIiIiIjI7ncFERERkoc2nfezXgX91Xfcp4NPAf0t9/L8D\nn3Bd9xDwsOM4+xfkCvNU1PsUFV/+i3qMii//RT3GqMcni0JnsDsohNdV1GNUfPkv6jEqvvxXCDHO\nxZwrhYAvAxOpXxcBY47jVADFruu2pj7+A+AZ4Pi9X6KIiIiIoDOYiIiILLDbJoUcx/kF4NemffjT\nrusecRxnLfAN4FeBKmAw6z5DQPNCXmi+OXTo0HJfwqJSfPkv6jEqvvwX9RijHp/cG53B5qcQXldR\nj1Hx5b+ox6j48l8hxDgXt00Kua77NeBr0z/uOM4+4H8C/8l13R85jlOJ6W8PVQL9t3vslpaW9JMR\nlm/ptm7rtm7rtm7rdnRuy/zpDKbbuq3buq3buq3bS3EGm/NWEMdxdgPfARzXdU9mffwY8FGgFXgO\n+B3XdQ/P9hiFsPki+8AVRYov/0U9RsWX/6IeY9Tj0/axhacz2J1F/XUF0Y9R8eW/qMeo+PJf1GNc\n9O1jwB9gNl78meM4AP2u6/474LPAN4EY8INbHUZEREREZF50BhMREZEFtSzv4EX9XSoREZFCp0qh\n3KQzmIiISLTN9Qw2n5X0IiIiIiIiIiKS55QUWiThoKeoUnz5L+oxKr78F/UYox6fyHIohNdV1GNU\nfPkv6jEqvvxXCDHOhZJCIiIiIiIiIiIFSDOFREREZMFpplBu0hlMREQk2jRTSERERERERERE7khJ\noUUS9T5FxZf/oh6j4st/UY8x6vGJLIdCeF1FPUbFl/+iHqPiy3+FEONcKCkkIiIiIiIiIlKANFNI\nREREFpxmCuUmncFERESiTTOFRERERERERETkjpQUWiRR71NUfPkv6jEqvvwX9RijHp/IciiE11XU\nY1R8+S/qMSq+/FcIMc6FkkIiIiIiIiIiIgVIM4VERERkwWmmUG7SGUxERCTaNFNIRERERERERETu\nSEmhRRL1PkXFl/+iHqPiy39RjzHq8Yksh0J4XUU9RsWX/6Ieo+LLf4UQ41woKSQiIiIiIiIiUoA0\nU0hEREQWnGYK5SadwURERKJNM4VEREREREREROSOlBRaJFHvU1R8+S/qMSq+/Bf1GKMen8hyKITX\nVdRjVHz5L+oxKr78VwgxzoWSQiIiIiIiIiIiBUgzhURERGTBaaZQbtIZTEREJNo0U0hERERERERE\nRO5ISaFFEvU+RcWX/6Ieo+LLf1GPMerxiSyHQnhdRT1GxZf/oh6j4st/hRDjXCgpJCIiIiIiIiJS\ngDRTSERERBacZgrlJp3BREREok0zhURERERERERE5I6UFFokUe9TVHz5L+oxKr78F/UYox6fyHIo\nhNdV1GNUfPkv6jEqvvxXCDHOhZJCIiIiIiIiIiIFaM69/o7jlAF/C1QDk8C/d123w3GcR4A/AZLA\n867r/t6tHkP97CIiItGmmUILT2cwERERuZOlmCn0H4DDrus+CfwP4DdSH//vwCdc1z0EPOw4zv55\nPLaIiIiIzE5nMBEREVlQc04Kua77p8AfpG5uBG46jlMBFLuu25r6+A+AZxbmEvNT1PsUFV/+i3qM\nii//RT3GqMcnC09nsDsrhNdV1GNUfPkv6jEqvvxXCDHORfx2v+k4zi8Avzbtw592XfeI4zgvAnuB\nZ4EqYDDrPkNA80JeqIiIiEih0BlMRERElsJtk0Ku634N+Notfu9px3F2AN8DDgAVWb9dCfTf7rFb\nWlo4dOhQ+tdA5G5nx5oL16P4FJ9u63Y+3T506FBOXY/im/ttmT+dweb/dy7K8RXKGUXx6bZu67bO\nYEt3BpvPoOnfAtpd1/2G4zhNwAuu6+50HOcY8FGgFXgO+B3XdQ/P9hgacigiIhJtGjS98HQGExER\nkTtZikHTXwM+6TjO/8JswPhM6uOfBb4JvAkcvdVhpFBMfycgahRf/ot6jIov/0U9xqjHJ4tCZ7A7\nKITXVdRjVHz5L+oxKr78VwgxzkV8rp/gum438G9m+fibwKMLcVEiIiIiMpXOYCIiIrLQlqWsW6XL\nIiIi0ab2sdykM5iIiEi0LUX7mIiIiIiIiIiI5DklhRZJ1PsUFV/+i3qMii//RT3GqMcnshwK4XUV\n9RgVX/6LeoyKL/8VQoxzoaSQiIiIiIiIiEgB0kwhERERWXCaKZSbdAYTERGJNs0UEhERERERERGR\nO1JSaJFEvU9R8eW/qMeo+PJf1GOMenwiy6EQXldRj1Hx5b+ox6j48l8hxDgXSgqJiIiIiIiIiBQg\nzRQSERGRBaeZQrlJZzAREZFo00whERERERERERG5IyWFFknU+xQVX/6LeoyKL/9FPcaoxyeyHArh\ndRX1GBVf/ot6jIov/xVCjHOhpJCIiIiIiIiISAHSTCERERFZcJoplJt0BhMREYk2zRQSERERERER\nEZE7UlJokUS9T1Hx5b+ox6j48l/UY4x6fCLLoRBeV1GPUfHlv6jHqPjyXyHEOBdKComIiIiIiIiI\nFCDNFBIREZEFp5lCuUlnMBERkWjTTCEREREREREREbkjJYUWSdT7FBVf/ot6jIov/0U9xqjHJ7Ic\nCuF1FfUYFV/+i3qMii//FUKMc6GkkIiIiIiIiIhIAdJMIREREVlwmimUm3QGExERiTbNFBIRERER\nERERkTuKL9cfvKqmZrn+aBEREclys69vuS9BlpDOYCIiIrkhF85gy5YUyoXgF1NLSwuHDh1a7stY\nNIov/0U9RsWX/6IeY9Tjk9wV5TNYIbyuoh6j4st/UY9R8eW/QohxLjRTSERERBacZgrlJp3BRERE\nok0zhURERERERERE5I6UFFokLS0ty30Ji0rx5b+ox6j48l/UY4x6fCLLoRBeV1GPUfHlv6jHqPjy\nXyHEOBfzninkOM5O4A2gznXdScdxHgH+BEgCz7uu+3sLdI0iIiIikqIzmIiIiCyUeVUKOY5TCXwJ\nGM/68FeAT7iuewh42HGc/QtwfXkr6oOrFF/+i3qMii//RT3GqMcni0NnsNsrhNdV1GNUfPkv6jEq\nvvxXCDHOxZyTQo7jWMCfA78FjKU+VgmUuK7bmrrbD4BnFuoiRURERAqdzmAiIiKy0G6bFHIc5xcc\nxzmZ/R/wz8D3XNd9J3U3C6gEBrM+dQioWpQrzhNR71NUfPkv6jEqvvwX9RijHp/cG53B5qcQXldR\nj1Hx5b+ox6j48l8hxDgXc14V6zjOeaA9dfMR4E3g3wJvuK67J3WfXwXirut+abbHeOGFF14CnpzP\nBYuIiEheePmZZ555arkvIkp0BhMREZG7MKcz2JyTQtkcx2kFdqSGHB4DPgq0As8Bv+O67uF7eXwR\nERERmUlnMBEREVkI894+lhJk/fqzwDeBGPADHUZEREREFo3OYCIiIiIiIiIiIiIiIiIiIiIiIiIi\nIiIiIiIiIiIiIiIiIiIiIiIiIgLc4/axuXIcxwb+P+A+YAL4D67rXlzKa1hsjuMcBQZSNy+5rvsL\ny3k9C8VxnIeB/9d13Q84jrMV+DrgA6eA/9113eB2n5/rpsV3APhn4Hzqt7/iuu7fL9/V3RvHcYqA\nvwQ2AiXA/wO8S4Sew1vE2I7ZwnMudbe8fR4dx4kBXwW2Y4bLfhbzPfTrROc5nC3GYiLyHIYcx6kD\njgBPY567rxOR5xBmxFdGxJ6/fKYzWP7SGSx/v2/oDJb/3/+jfgYrlPMX6Ax2u+fwXrePzdVPAcWu\n6z6W+gHwpdTHIsFxnBUArut+YLmvZSE5jvMbwM8Cw6kP/THwBdd1X3Ec5yvATwLfXa7ru1ezxPcg\n8Meu6/7x8l3VgvoU0OO67s85jrMKOAEcI0LPIbPH+LvAlyLyPP444Luue8hxnCeBP0h9PErP4fQY\nfx/zD4OoPIfhwfnPgRHMmzJR+146Pb4HidDzFwE6g+UhncHyns5g+S/qZ7DIn79AZ7A7sRfx2mbz\nOPB9ANd13wQOLvGfv9juB0odx/mB4zgvpg5dUXAB+GkylWUPuK77SurX/wI8syxXtXCmx/cg8BHH\ncV52HOcvHMcpX75LWxAu8F9Sv7aBBNF7DmeLMTLPo+u6/wj8curmJuAm8GCUnsNZYuwnQs9hyh8C\nXwE6U7ej9jqcER/Rev7ync5g+UlnsPymM1ieP49RP4MVyPkLdAa77XO41EmhSmAw67aXKmeOihHg\nD13X/TFM6d03oxCf67rfAZJZH8puOxwGqpb2ihbWLPG9Cfyfrus+CVwC/u9lubAF4rruiOu6w47j\nVGB+cP9fTH3tR+E5nB7jbwNvEa3n0XMc5+vAnwLfJGKvQ5g1xsg8h47jfBrzTurzqQ9ZROg5nCU+\niNDzFxE6g+UhncHy+/uGzmCReR4jfQaL8vkLdAbjLp7Dpf5hOQhUZP/5ruv6S3wNi+kc5oWE67rn\ngRtAw7Je0eLIfs4qMBnlKPkH13WPpX79XeDAcl7MQnAcZz3wQ+BvXNf9n0TwOZwW498RwefRdd1P\nAzuAvwBWZP1WJJ5DmBLjV4HnI/Qcfgb4kOM4/wvYD/w1sCbr9/P9OZwtvn+J0PMXBTqDRUPkfn5P\nE7mf3TqDReN5jPoZLMLnL9AZ7I7P4VInhV4FPgzgOM4jwDtL/Ocvts9gevRxHKcR865c520/Iz8d\nS/WcAvwb4JXb3TkPfd9xnIdSv34aeHs5L+ZeOY5TDzwP/Ibrul9PfThSz+EtYozM8+g4zs85jvNb\nqZtjgAe8HbHncHqMPvCdqDyHrus+6bruU6l5J8eBn8f8HY3EczhLfP8e+G5Unr+I0BksGiL183sW\nkfnZDTqDpX6d189j1M9gUT9/gc5g3MVzuNSDpv8Bk8V6NXX7M0v85y+2rwF/5ThO+JfqMxF7Fy6c\nyP6fgK86jlMMnAG+tXyXtKDC+D4L/DfHcRKYA+UvLd8lLYgvYEoi/4vjOGHP968Cfxah53C2GH8N\n+HJEnsdvAV93HOdloAjz/L1HtF6Hs8XYRrRei9kCovu9FDIbTKL6/OUjncHym85g+UlnsPx/HqN+\nBiu08xfoDCYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi\nIiIiIiIiIiIiIiIiIiIiIiIiIiIiIjns/wcoR/xqND79NAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108cee9d0>"
       ]
      }
     ],
     "prompt_number": 133
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For all subjects, plot the natural mean over time for each gamble, grouped by which option was chosen. This will give a pretty good idea of those subjects that are likely to be fit well with this model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.transpose((subjdata[115][0]['sampledata'], subjdata[115][0]['outcomes']))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 128,
       "text": [
        "array([[  0,   0],\n",
        "       [  0,  -1],\n",
        "       [  0,  -1],\n",
        "       [  0,   0],\n",
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        "       [  1,  -5],\n",
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     ],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}