SI_1_parameter_estimates.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from os.path import join\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import linregress\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from plotting_functions import cm2inch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load individual estimates (in sample fits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "multiplicative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "parameters = ['v', 'gamma', 's', 'tau']\n",
    "\n",
    "estimates_list = []\n",
    "\n",
    "estimate_files = [file for file in os.listdir(join('results', 'estimates', 'in_sample', 'GLAM'))\n",
    "                  if file.endswith('.csv')]\n",
    "\n",
    "for file in estimate_files:\n",
    "    _, subject, _, _ = file.split('_')\n",
    "    subject = int(subject)\n",
    "    estimates = pd.read_csv(join('results', 'estimates', 'in_sample', 'GLAM', file), index_col=0)\n",
    "    subject_estimates = {parameter: estimates.loc[parameter + '__0_0', 'MAP']\n",
    "                         for parameter in parameters}\n",
    "    subject_estimates['subject'] = subject\n",
    "    estimates_list.append(pd.DataFrame(subject_estimates, index=np.zeros(1)))\n",
    "\n",
    "individual_estimates = pd.concat(estimates_list).sort_values('subject').reset_index(drop=True)\n",
    "\n",
    "individual_estimates['dataset'] = np.array(39 * ['krajbich2010'] + 30 * ['krajbich2011'] + 24 * ['folke2016'] + 25 * ['tavares2017'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gamma estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    118.000000\n",
       "mean       0.145932\n",
       "std        0.394087\n",
       "min       -1.030000\n",
       "25%       -0.122500\n",
       "50%        0.125000\n",
       "75%        0.407500\n",
       "max        0.970000\n",
       "Name: gamma, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "individual_estimates['gamma'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>folke2016</th>\n",
       "      <td>24.0</td>\n",
       "      <td>-0.017083</td>\n",
       "      <td>0.282804</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.025</td>\n",
       "      <td>0.2075</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>krajbich2010</th>\n",
       "      <td>39.0</td>\n",
       "      <td>0.262821</td>\n",
       "      <td>0.484197</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.230</td>\n",
       "      <td>0.6750</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>krajbich2011</th>\n",
       "      <td>30.0</td>\n",
       "      <td>0.179000</td>\n",
       "      <td>0.408347</td>\n",
       "      <td>-1.03</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.4625</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tavares2017</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0.080400</td>\n",
       "      <td>0.230552</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.1900</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count      mean       std   min   25%    50%     75%   max\n",
       "dataset                                                                 \n",
       "folke2016      24.0 -0.017083  0.282804 -0.50 -0.25 -0.025  0.2075  0.46\n",
       "krajbich2010   39.0  0.262821  0.484197 -0.89 -0.07  0.230  0.6750  0.97\n",
       "krajbich2011   30.0  0.179000  0.408347 -1.03 -0.07  0.305  0.4625  0.81\n",
       "tavares2017    25.0  0.080400  0.230552 -0.36  0.00  0.080  0.1900  0.56"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "individual_estimates.groupby('dataset')['gamma'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "multiplicative_estimates = individual_estimates.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure S1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "\n",
    "def figure_si1(estimates,\n",
    "               parameters=['v', 'gamma', 's', 'tau'],\n",
    "               labels=dict(v=r'$v\\ [ms^{-1}]$',\n",
    "                           gamma=r'$\\gamma$',\n",
    "                           s=r'$\\sigma\\ [ms^{-1}]$',\n",
    "                           tau=r'$\\tau$'),\n",
    "               bins=dict(v=np.linspace(0, 0.0002, 21),\n",
    "                         gamma=np.linspace(-1.2, 1, 21),\n",
    "                         s=np.linspace(0, 0.02, 21),\n",
    "                         tau=np.linspace(0, 5, 21)),\n",
    "               ticks=dict(v=[0, 0.0001, 0.0002],\n",
    "                          gamma=[-1, 0, 1],\n",
    "                          s=[0, 0.01, 0.02],\n",
    "                          tau=[0, 1, 2, 3, 4, 5]),\n",
    "               limits=dict(v=[0, 0.000225],\n",
    "                           gamma=[-1.5, 1.25],\n",
    "                           s=[0, 0.0225],\n",
    "                           tau=[0, 5.5]),\n",
    "               figsize=cm2inch(18, 20),\n",
    "               fontsize=8):\n",
    "    \"\"\"\n",
    "    Figure SI 1 - Parameter estimates\n",
    "    Plots pairwise scatterplots of parameter\n",
    "    estimates, color-coded by data set.\n",
    "    \n",
    "    Reference:\n",
    "    Ratcliff & Tuerlinckx, 2002; Figure 6\n",
    "\n",
    "    parameters:\n",
    "    -----------\n",
    "    estimates <pandas.DataFrame>\n",
    "    \n",
    "    Returns:\n",
    "    --------\n",
    "    matplotlib.figure, matplotlib.axes\n",
    "    \"\"\"\n",
    "    datasets = ['krajbich2010', 'krajbich2011', 'folke2016', 'tavares2017']\n",
    "    n_datasets = len(datasets)\n",
    "    n_parameters = len(parameters)\n",
    "    \n",
    "    fig = plt.figure(figsize=figsize)\n",
    "\n",
    "    hist_ax_00 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (0, 0))\n",
    "    hist_ax_10 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (1, 0))\n",
    "    hist_ax_20 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (2, 0))\n",
    "    hist_ax_30 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (3, 0))\n",
    "\n",
    "    hist_ax_01 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (4, 1))\n",
    "    hist_ax_11 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (5, 1))\n",
    "    hist_ax_21 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (6, 1))\n",
    "    hist_ax_31 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (7, 1))\n",
    "\n",
    "    hist_ax_02 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (8, 2))\n",
    "    hist_ax_12 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (9, 2))\n",
    "    hist_ax_22 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (10, 2))\n",
    "    hist_ax_32 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (11, 2))\n",
    "\n",
    "    hist_ax_03 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (12, 3))\n",
    "    hist_ax_13 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (13, 3))\n",
    "    hist_ax_23 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (14, 3))\n",
    "    hist_ax_33 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (15, 3))\n",
    "\n",
    "    ax01 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (4, 0), rowspan=n_datasets)\n",
    "    ax02 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (8, 0), rowspan=n_datasets)\n",
    "    ax03 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (12, 0), rowspan=n_datasets)\n",
    "\n",
    "    ax12 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (8, 1), rowspan=n_datasets)\n",
    "    ax13 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (12, 1), rowspan=n_datasets)\n",
    "\n",
    "    ax23 = plt.subplot2grid((n_datasets*n_parameters, n_parameters), (12, 2), rowspan=n_datasets)\n",
    "    \n",
    "    hist_axs = np.array([[hist_ax_00, hist_ax_10, hist_ax_20, hist_ax_30],\n",
    "                         [hist_ax_01, hist_ax_11, hist_ax_21, hist_ax_31],\n",
    "                         [hist_ax_02, hist_ax_12, hist_ax_22, hist_ax_32],\n",
    "                         [hist_ax_03, hist_ax_13, hist_ax_23, hist_ax_33]])\n",
    "    scatter_axs = np.array([[np.nan, np.nan, np.nan, np.nan],\n",
    "                            [ax01,   np.nan, np.nan, np.nan],\n",
    "                            [ax02,   ax12,   np.nan, np.nan],\n",
    "                            [ax03,   ax13,   ax23,   np.nan]])\n",
    "\n",
    "    colors = np.array(['C{}'.format(d) for d, dataset in enumerate(datasets)])\n",
    "    dataset_index = pd.Categorical(estimates['dataset'], categories=datasets).codes\n",
    "    \n",
    "    for p, (pary, parylabel) in enumerate(zip(parameters, labels)):\n",
    "        for q, (parx, parxlabel) in enumerate(zip(parameters, labels)):\n",
    "            if q > p:\n",
    "                continue\n",
    "            elif p == q:\n",
    "                for d, dataset in enumerate(datasets):\n",
    "                    if d == 0:\n",
    "                        hist_axs[p, d].set_title(labels[parx], fontsize=fontsize, fontweight='black')\n",
    "                    hist_axs[p, d].hist(estimates.loc[estimates['dataset'] == dataset, parx],\n",
    "                                        bins=bins[pary],\n",
    "                                        color=colors[d],\n",
    "                                        edgecolor='white',\n",
    "                                        linewidth=1)\n",
    "                    hist_axs[p, d].set_xticks(ticks[parx])\n",
    "                    hist_axs[p, d].set_xticklabels([])\n",
    "                    hist_axs[p, d].set_yticks([0,10])\n",
    "                    hist_axs[p, d].set_yticklabels([0,10], fontsize=5)\n",
    "                    hist_axs[p, d].set_xlim(limits[parx])\n",
    "                    hist_axs[p, d].set_ylim([0,10])\n",
    "                    hist_axs[p, d].spines['top'].set_visible(False)\n",
    "                    hist_axs[p, d].spines['right'].set_visible(False)\n",
    "                    \n",
    "\n",
    "            else:\n",
    "                scatter_axs[p, q].scatter(estimates[parx],\n",
    "                                          estimates[pary],\n",
    "                                          marker='o',\n",
    "                                          color='none',\n",
    "                                          edgecolor=colors[dataset_index],\n",
    "                                          linewidth=0.5,\n",
    "                                          s=30)\n",
    "                scatter_axs[p, q].scatter(estimates[parx],\n",
    "                                          estimates[pary],\n",
    "                                          marker='o',\n",
    "                                          color=colors[dataset_index],\n",
    "                                          alpha=0.5,\n",
    "                                          linewidth=0,\n",
    "                                          s=30)\n",
    "                scatter_axs[p, q].set_xticks(ticks[parx])\n",
    "                scatter_axs[p, q].set_yticks(ticks[pary])\n",
    "                scatter_axs[p, q].set_xticklabels([])\n",
    "                scatter_axs[p, q].set_yticklabels([])\n",
    "                scatter_axs[p, q].set_xlim(limits[parx])\n",
    "                scatter_axs[p, q].set_ylim(limits[pary])\n",
    "                scatter_axs[p, q].spines['right'].set_visible(False)\n",
    "                scatter_axs[p, q].spines['top'].set_visible(False)\n",
    "\n",
    "                \n",
    "            if (q == 0) and (p != 0):\n",
    "                scatter_axs[p, q].set_ylabel(labels[pary], fontsize=fontsize)\n",
    "                scatter_axs[p, q].set_yticks(ticks[pary])\n",
    "                scatter_axs[p, q].set_yticklabels(ticks[pary], fontsize=fontsize)\n",
    "            if (p == (n_parameters - 1)) and (q != p):\n",
    "                scatter_axs[p, q].set_xticklabels(ticks[parx], fontsize=fontsize)\n",
    "                scatter_axs[p, q].set_xlabel(labels[parx], fontsize=fontsize)\n",
    "    \n",
    "    hist_axs[-1, -1].set_xlabel(labels[parameters[-1]], fontsize=fontsize)\n",
    "    hist_axs[-1, -1].set_xticks(ticks[parameters[-1]])\n",
    "    hist_axs[-1, -1].set_xticklabels(ticks[parameters[-1]], fontsize=fontsize)\n",
    "    hist_axs[-1, -1].set_xlim(limits[parameters[-1]])\n",
    "    \n",
    "    # add panel labeling\n",
    "    # histograms\n",
    "    for label, ax in zip(list('acfj'),\n",
    "                         list((hist_ax_00, hist_ax_01, hist_ax_02, hist_ax_03))):\n",
    "        ax.text(-0.25, 2.5, label, transform=ax.transAxes,\n",
    "                fontsize=fontsize, fontweight='bold', va='top') \n",
    "    # add frequency labeling\n",
    "    for ax in [hist_ax_20, hist_ax_21, hist_ax_22, hist_ax_23]:\n",
    "        ax.set_ylabel('                 Frequency', fontsize=6)\n",
    "    # scatterplots\n",
    "    i = 0\n",
    "    scatter_labels = list('bdeghi')\n",
    "    for ax in scatter_axs.ravel():\n",
    "        if ax is not np.nan:\n",
    "            ax.text(-0.25, 1.1, scatter_labels[i], transform=ax.transAxes,\n",
    "                    fontsize=fontsize, fontweight='bold', va='top')\n",
    "            ax.spines['top'].set_visible(False)\n",
    "            ax.spines['right'].set_visible(False)\n",
    "            # Font sizes\n",
    "            ax.tick_params(axis='both', labelsize=fontsize)\n",
    "            i += 1\n",
    "    \n",
    "    fig.tight_layout(h_pad=0., w_pad=1)\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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4VZJHdmynt6+CYHSAYCLB/vL5XDCtl6HyFob6OqkJxtiZmETl89vp3RXg0KFt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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1258e2400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure_si1(individual_estimates);\n",
    "plt.savefig(join('results', 'figures', 'si_figure_1_parameter_estimates_correlations.png'), dpi=330)\n",
    "plt.savefig(join('results', 'figures', 'si_figure_1_parameter_estimates_correlations.pdf'), dpi=330)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "parameters = ['v', 'gamma', 's', 'tau']\n",
    "corrs = {}\n",
    "corrs_table = {}\n",
    "ps = {}\n",
    "ps_table = {}\n",
    "for p1 in parameters:\n",
    "    corrs_table[p1] = []\n",
    "    ps_table[p1] = []\n",
    "    for p2 in parameters:\n",
    "        _,_,r,p,_ = linregress(multiplicative_estimates[p1].values,\n",
    "                               multiplicative_estimates[p2].values)\n",
    "        corrs[(p1, p2)] = r\n",
    "        corrs_table[p1].append(r)\n",
    "        ps[(p1, p2)] = p\n",
    "        ps_table[p1].append(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "          gamma         s       tau         v\n",
       "v      0.063014  0.261000 -0.588664  1.000000\n",
       "gamma  1.000000  0.097936 -0.098303  0.063014\n",
       "s      0.097936  1.000000 -0.267730  0.261000\n",
       "tau   -0.098303 -0.267730  1.000000 -0.588664"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_table = pd.DataFrame(corrs_table, index=parameters)\n",
    "r_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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   "outputs": [
    {
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       "      <td>2.373948e-12</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "      <td>0.291392</td>\n",
       "      <td>2.895794e-01</td>\n",
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       "      <td>4.308266e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tau</th>\n",
       "      <td>0.289579</td>\n",
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       "          gamma         s           tau             v\n",
       "v      0.497841  0.004308  2.373948e-12  0.000000e+00\n",
       "gamma  0.000000  0.291392  2.895794e-01  4.978410e-01\n",
       "s      0.291392  0.000000  3.378296e-03  4.308266e-03\n",
       "tau    0.289579  0.003378  0.000000e+00  2.373948e-12"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "ps_table = pd.DataFrame(ps_table, index=parameters)\n",
    "ps_table"
   ]
  }
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