Frey - Fixed size model.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "from mypy.explib.frey2014 import frey2014\n",
      "import model_fixed.model_fixed as model_f\n",
      "import model_fixed.model_fixed_streak as model_fs\n",
      "import model_fixed.model_fixed_per as model_fp\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": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subjdata = frey2014.sampledata_by_subject()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SUBJ = np.sort(subjdata.keys())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pars = {'data': subjdata[120][0],\n",
      "        't': 20,\n",
      "        's': .5,\n",
      "        'rho': .1}\n",
      "result = model_f.run(pars)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "{'p_sample_A': array([ 0.5       ,  0.73581703,  0.65795272,  0.5602134 ,  0.05      ,\n",
        "         0.03775407,  0.02689414,  0.01824255,  0.01192029,  0.00758582,\n",
        "         0.00474259,  0.02638101,  0.01618759,  0.00988825,  0.00602357,\n",
        "         0.00366312,  0.00222536,  0.00135106]),\n",
        " 'p_sample_B': array([  5.00000000e-01,   8.17574476e-02,   7.31058579e-02,\n",
        "          6.22459331e-02,   4.50000000e-01,   3.39786602e-01,\n",
        "          2.42047279e-01,   1.64182971e-01,   1.07282630e-01,\n",
        "          6.82723620e-02,   4.26832859e-02,   2.93122308e-03,\n",
        "          1.79862100e-03,   1.09869426e-03,   6.69285092e-04,\n",
        "          4.07013772e-04,   2.47262316e-04,   1.50118226e-04]),\n",
        " 'p_stop': array([ 0.        ,  0.18242552,  0.26894142,  0.37754067,  0.5       ,\n",
        "         0.62245933,  0.73105858,  0.81757448,  0.88079708,  0.92414182,\n",
        "         0.95257413,  0.97068777,  0.98201379,  0.98901306,  0.99330715,\n",
        "         0.99592986,  0.99752738,  0.99849882,  0.99908895])}"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Fixed total sample size + switching probability"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fitresults_f = {}\n",
      "fitresults_f_table = pd.DataFrame(index=SUBJ, columns=['T', 's', 'rho', 'nllh_sampling', 'bic', 'success'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for sid in SUBJ[3:]:\n",
      "    print sid\n",
      "    fitresults_f[sid] = model_f.fit_subject_across_gambles(subjdata[sid])\n",
      "    fitresults_f_table.loc[sid] = [fitresults_f[sid]['bf_par']['target'], \n",
      "                                   fitresults_f[sid]['bf_par']['s'],\n",
      "                                   fitresults_f[sid]['bf_par']['rho'],\n",
      "                                   fitresults_f[sid]['nllh'], fitresults_f[sid]['bic'], fitresults_f[sid]['success']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "4\n",
        "best fitting target size:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 24\n",
        "bic: 1316.89619435\n",
        "  status: 0\n",
        "    nfev: 54\n",
        " success: True\n",
        "     fun: 651.29504554039215\n",
        "       x: array([ 0.20426689,  0.09186686])\n",
        " message: 'Optimization terminated successfully.'\n",
        "     nit: 28\n",
        "5\n"
       ]
      },
      {
       "ename": "KeyboardInterrupt",
       "evalue": "",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-74-9a765fd8b4ff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msid\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mSUBJ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;32mprint\u001b[0m \u001b[0msid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mfitresults_f\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msid\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_subject_across_gambles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubjdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msid\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      4\u001b[0m     fitresults_f_table.loc[sid] = [fitresults_f[sid]['bf_par']['target'], \n\u001b[1;32m      5\u001b[0m                                    \u001b[0mfitresults_f\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'bf_par'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m's'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/doug/code/SamplingDynamics/model_fixed/model_fixed.py\u001b[0m in \u001b[0;36mfit_subject_across_gambles\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\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--> 143\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    144\u001b[0m         \u001b[0mnllh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'fun'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m         \u001b[0;31m#print targ, f['fun']\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/doug/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/doug/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    464\u001b[0m         \u001b[0mxbar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msim\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    465\u001b[0m         \u001b[0mxr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mrho\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mxbar\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mrho\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0msim\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 466\u001b[0;31m         \u001b[0mfxr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    467\u001b[0m         \u001b[0mdoshrink\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    468\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/doug/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/doug/code/SamplingDynamics/model_fixed/model_fixed.py\u001b[0m in \u001b[0;36mnloglik_across_gambles\u001b[0;34m(value, args)\u001b[0m\n\u001b[1;32m    107\u001b[0m         \u001b[0mllh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moption\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\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[0;34m'sampledata'\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--> 109\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0moption\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    110\u001b[0m                 \u001b[0mllh\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpfix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'p_sample_A'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fitresults_f_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>T</th>\n",
        "      <th>s</th>\n",
        "      <th>rho</th>\n",
        "      <th>nllh_sampling</th>\n",
        "      <th>bic</th>\n",
        "      <th>success</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1  </th>\n",
        "      <td>  11</td>\n",
        "      <td>   1.214562</td>\n",
        "      <td>    0.10997</td>\n",
        "      <td> 479.8061</td>\n",
        "      <td> 973.3251</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2  </th>\n",
        "      <td> 107</td>\n",
        "      <td> 0.02864035</td>\n",
        "      <td> 0.07303406</td>\n",
        "      <td> 645.6845</td>\n",
        "      <td> 1305.639</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3  </th>\n",
        "      <td>  45</td>\n",
        "      <td>  0.1066994</td>\n",
        "      <td> 0.04900662</td>\n",
        "      <td>  682.748</td>\n",
        "      <td> 1380.531</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4  </th>\n",
        "      <td>  24</td>\n",
        "      <td>  0.2042669</td>\n",
        "      <td> 0.09186686</td>\n",
        "      <td>  651.295</td>\n",
        "      <td> 1316.896</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5  </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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6  </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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7  </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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8  </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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9  </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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12 </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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13 </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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15 </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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16 </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>70 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 75,
       "text": [
        "       T           s         rho nllh_sampling       bic success\n",
        "1     11    1.214562     0.10997      479.8061  973.3251    True\n",
        "2    107  0.02864035  0.07303406      645.6845  1305.639    True\n",
        "3     45   0.1066994  0.04900662       682.748  1380.531    True\n",
        "4     24   0.2042669  0.09186686       651.295  1316.896    True\n",
        "5    NaN         NaN         NaN           NaN       NaN     NaN\n",
        "6    NaN         NaN         NaN           NaN       NaN     NaN\n",
        "7    NaN         NaN         NaN           NaN       NaN     NaN\n",
        "8    NaN         NaN         NaN           NaN       NaN     NaN\n",
        "9    NaN         NaN         NaN           NaN       NaN     NaN\n",
        "12   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "13   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "15   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "16   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "17   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "18   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "20   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "21   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "22   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "23   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "24   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "25   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "26   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "27   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "28   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "29   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "30   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "31   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "32   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "33   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "34   NaN         NaN         NaN           NaN       NaN     NaN\n",
        "..   ...         ...         ...           ...       ...     ...\n",
        "106  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "109  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "110  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "111  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "112  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "113  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "114  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "115  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "116  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "117  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "118  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "119  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "120  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "121  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "122  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "123  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "124  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "125  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "126  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "127  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "128  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "129  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "130  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "131  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "132  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "133  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "134  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "135  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "136  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "137  NaN         NaN         NaN           NaN       NaN     NaN\n",
        "\n",
        "[70 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Total sample size + streak size"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Fixed sample size per option"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fitresults_fp = {}\n",
      "fitresults_fp_table = pd.DataFrame(index=SUBJ, columns=['T', 's', 'nllh_sampling', 'bic', 'success'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for sid in SUBJ[6:8]:\n",
      "    print sid\n",
      "    fitresults_fp[sid] = model_fp.fit_subject_across_gambles(subjdata[sid])\n",
      "    fitresults_fp_table.loc[sid] = [fitresults_fp[sid]['bf_par']['target'], \n",
      "                                    fitresults_fp[sid]['bf_par']['s'],\n",
      "                                    fitresults_fp[sid]['nllh'], fitresults_fp[sid]['bic'], fitresults_fp[sid]['success']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "7\n",
        "(grid) target size: 0, nllh: 4849.95096319"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 1, nllh: 4849.95082238"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 2, nllh: 4849.95082238"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 3, nllh: 4849.95082238"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 4, nllh: 4849.95098693"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 5, nllh: 4849.95097399"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 6, nllh: 4849.95097209"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 7, nllh: 4849.95101329"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 8, nllh: 4849.95099362"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 9, nllh: 4849.95082238"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 10, nllh: 4849.95098531"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 11, nllh: 4849.95099844"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 12, nllh: 4849.9509853"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 13, nllh: 4849.95095291"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 14, nllh: 4849.95094797"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 15, nllh: 4849.95082238"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 16, nllh: 4849.9509241"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 17, nllh: 4849.95095779"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 18, nllh: 4849.95093537"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 19, nllh: 4849.95082238"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 20, nllh: 4849.9509412"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 21, nllh: 4849.95099623"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 22, nllh: 4849.95100966"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 23, nllh: 4849.95082238"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 24, nllh: 4849.95097883"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 25, nllh: 4849.9509637"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 26, nllh: 4849.95082238"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 27, nllh: 4849.95082238"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 28, nllh: 4849.95095207"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 29, nllh: 4849.95096143"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 30, nllh: 4849.95082238"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 31, nllh: 4849.95095634"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 32, nllh: 4849.9509448"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 33, nllh: 4849.06698416"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 34, nllh: 4844.3375916"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 35, nllh: 4835.54532679"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 36, nllh: 4822.72734002"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 37, nllh: 4805.94080881"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 38, nllh: 4785.27061291"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 39, nllh: 4760.83000371"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 40, nllh: 4732.77377066"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 41, nllh: 4701.28856803"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 42, nllh: 4666.59707852"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 43, nllh: 4628.95282311"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 44, nllh: 4588.63188229"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 45, nllh: 4545.92536064"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 46, nllh: 4501.13147179"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 47, nllh: 4454.54721979"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 48, nllh: 4406.46262311"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 49, nllh: 4357.15448062"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 50, nllh: 4306.88323373"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 51, nllh: 4255.89016273"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 52, nllh: 4204.3963899"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 53, nllh: 4152.60227774"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 54, nllh: 4100.68773684"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 55, nllh: 4048.81319116"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 56, nllh: 3997.12018861"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 57, nllh: 3945.73346963"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 58, nllh: 3894.76145883"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 59, nllh: 3844.29831876"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 60, nllh: 3794.42488253"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 61, nllh: 3745.2100379"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 62, nllh: 3696.71246679"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 63, nllh: 3648.9810631"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 64, nllh: 3602.05502558"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 65, nllh: 3555.96858718"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 66, nllh: 3510.74752926"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 67, nllh: 3466.41203282"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 68, nllh: 3422.97732369"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 69, nllh: 3380.45457563"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 70, nllh: 3338.84839064"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 71, nllh: 3298.16385667"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 72, nllh: 3258.4004472"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 73, nllh: 3219.55523717"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 74, nllh: 3181.62343513"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 75, nllh: 3144.59825162"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 76, nllh: 3108.4708446"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 77, nllh: 3073.23125031"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 78, nllh: 3038.86805568"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 79, nllh: 3005.36880166"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 80, nllh: 2972.72017009"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 81, nllh: 2940.90809531"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 82, nllh: 2909.9180386"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 83, nllh: 2879.73452694"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 84, nllh: 2850.34233193"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 85, nllh: 2821.72522761"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 86, nllh: 2793.8675003"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 87, nllh: 2766.75277076"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 88, nllh: 2740.36464855"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 89, nllh: 2714.68690021"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 90, nllh: 2689.70316624"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 91, nllh: 2665.39719614"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 92, nllh: 2641.75271845"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 93, nllh: 2618.75378438"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 94, nllh: 2596.38448948"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 95, nllh: 2574.62881879"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 96, nllh: 2553.47168323"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 97, nllh: 2532.89760228"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 98, nllh: 2512.89111286"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 99, nllh: 2493.43785175"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(grid) target size: 100, nllh: 2474.52305515"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
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        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-81-3616880f4c68>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msid\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mSUBJ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;32mprint\u001b[0m \u001b[0msid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mfitresults_fp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msid\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_fp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_subject_across_gambles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubjdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msid\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      4\u001b[0m     fitresults_fp_table.loc[sid] = [fitresults_fp[sid]['bf_par']['target'], \n\u001b[1;32m      5\u001b[0m                                     \u001b[0mfitresults_fp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'bf_par'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m's'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/doug/code/SamplingDynamics/model_fixed/model_fixed_per.py\u001b[0m in \u001b[0;36mfit_subject_across_gambles\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\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--> 142\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    143\u001b[0m         \u001b[0mnllh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'fun'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m         \u001b[0;32mprint\u001b[0m \u001b[0;34m'(grid) target size: %i, nllh: %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtarg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'fun'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/doug/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/doug/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    495\u001b[0m                     \u001b[0;31m# Perform an inside contraction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    496\u001b[0m                     \u001b[0mxcc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mpsi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mxbar\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mpsi\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0msim\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 497\u001b[0;31m                     \u001b[0mfxcc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxcc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    498\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    499\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mfxcc\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mfsim\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/doug/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/doug/code/SamplingDynamics/model_fixed/model_fixed_per.py\u001b[0m in \u001b[0;36mnloglik_across_gambles\u001b[0;34m(value, args)\u001b[0m\n\u001b[1;32m    103\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moption\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\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[0;34m'sampledata'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0moption\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m                 \u001b[0mllh\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpfix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'p_sample_A'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrial\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[0m\u001b[1;32m    106\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m                 \u001b[0mllh\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpfix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'p_sample_B'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrial\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/Users/doug/code/SamplingDynamics/model_fixed/fitting.py\u001b[0m in \u001b[0;36mpfix\u001b[0;34m(p)\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpfix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1e-5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e-5\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[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/doug/anaconda/lib/python2.7/site-packages/numpy/core/fromnumeric.pyc\u001b[0m in \u001b[0;36mamax\u001b[0;34m(a, axis, out, keepdims)\u001b[0m\n\u001b[1;32m   2120\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2121\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2122\u001b[0;31m             \u001b[0mamax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2123\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2124\u001b[0m             return _methods._amax(a, axis=axis,\n",
        "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fitresults_fp_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>T</th>\n",
        "      <th>s</th>\n",
        "      <th>nllh_sampling</th>\n",
        "      <th>bic</th>\n",
        "      <th>success</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1  </th>\n",
        "      <td>   6</td>\n",
        "      <td>    2.216468</td>\n",
        "      <td> 299.3968</td>\n",
        "      <td> 973.3251</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2  </th>\n",
        "      <td> 165</td>\n",
        "      <td> 0.009711953</td>\n",
        "      <td> 691.0724</td>\n",
        "      <td> 1305.639</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3  </th>\n",
        "      <td>  21</td>\n",
        "      <td>    0.158508</td>\n",
        "      <td> 724.0696</td>\n",
        "      <td> 1380.531</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4  </th>\n",
        "      <td>   9</td>\n",
        "      <td>   0.4968509</td>\n",
        "      <td> 583.3686</td>\n",
        "      <td>  1173.89</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5  </th>\n",
        "      <td>  35</td>\n",
        "      <td>  0.07308094</td>\n",
        "      <td> 777.3804</td>\n",
        "      <td> 1562.286</td>\n",
        "      <td> True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6  </th>\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>7  </th>\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>8  </th>\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>9  </th>\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>12 </th>\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>13 </th>\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>15 </th>\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>16 </th>\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>17 </th>\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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </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",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>70 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "       T            s nllh_sampling       bic success\n",
        "1      6     2.216468      299.3968  973.3251    True\n",
        "2    165  0.009711953      691.0724  1305.639    True\n",
        "3     21     0.158508      724.0696  1380.531    True\n",
        "4      9    0.4968509      583.3686   1173.89    True\n",
        "5     35   0.07308094      777.3804  1562.286    True\n",
        "6    NaN          NaN           NaN       NaN     NaN\n",
        "7    NaN          NaN           NaN       NaN     NaN\n",
        "8    NaN          NaN           NaN       NaN     NaN\n",
        "9    NaN          NaN           NaN       NaN     NaN\n",
        "12   NaN          NaN           NaN       NaN     NaN\n",
        "13   NaN          NaN           NaN       NaN     NaN\n",
        "15   NaN          NaN           NaN       NaN     NaN\n",
        "16   NaN          NaN           NaN       NaN     NaN\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",
        "[70 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}