kde-cdf-delta-caf-plots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Original post: [Exploring response time distributions using Python](http://www.marsja.se/response-time-distributions-using-python/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEJCAYAAACzPdE9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQrhCEooQQghXSEIRQgjhCkkoQgghXPE/6imUsxwkh/8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f524fe02e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example data\n",
    "frame = pd.read_csv('flanks.csv', sep=',')\n",
    "\n",
    "# Kernel estimation plot\n",
    "def kde_plot(df, conditions, dv, col_name, save_file=False):\n",
    "    \"\"\"\n",
    "    Creates kernel density estimation plots \n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    conditions : list\n",
    "        Strings of your conditions (e.g., ['congruent', 'incongruent'])\n",
    "    dv : string\n",
    "        Column name for the dependent variable (e.g., response time)\n",
    "    col_name : string\n",
    "        Column name for the column containing condition levels\n",
    "    save_file: boolean\n",
    "        True if image file is to be saved\n",
    "    \n",
    "    \"\"\"\n",
    "    sns.set_style('white')\n",
    "    sns.set_style('ticks')\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    for condition in conditions:\n",
    "        condition_data = df[(df[col_name] == condition)][dv]\n",
    "        sns.kdeplot(condition_data, shade=True, label=condition)\n",
    "        \n",
    "    sns.despine()\n",
    "    \n",
    "    if save_file:\n",
    "        plt.savefig(\"kernel_density_estimate_seaborn_python_response\"\n",
    "                     \"-time.png\")\n",
    "    plt.show()\n",
    "    \n",
    "kde_plot(frame, ['incongruent', 'congruent'], 'RT', 'TrialType',\n",
    "         save_file=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAGrCAYAAADHBT4lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nk/v/InCgAGtO9CYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51fd960310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def cdf(df, conditions=['congruent', 'incongruent']):\n",
    "    \"\"\"\n",
    "    Calculates the cumulative distribution function (CDF)\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    df : pandas dataframe\n",
    "    conditions : list\n",
    "        Containing strings of each condition\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    plot_data : pandas dataframe\n",
    "        CDFs for each condition\n",
    "        \n",
    "    Examples\n",
    "    --------\n",
    "    cdf_dat = cdf(frame, conditions=['incongruent', 'congruent'])\n",
    "    \n",
    "    \"\"\"\n",
    "\n",
    "    data = {i: df[(df.TrialType == conditions[i])] for i in range(len(\n",
    "        conditions))}\n",
    "    plot_data = []\n",
    "\n",
    "    for i, condition in enumerate(conditions):\n",
    "\n",
    "        rt = data[i].RT.sort_values()\n",
    "        yvals = np.arange(len(rt)) / float(len(rt))\n",
    "\n",
    "        # Append it to the data\n",
    "        cond = [condition]*len(yvals)\n",
    "\n",
    "        df = pd.DataFrame(dict(dens=yvals, dv=rt, condition=cond))\n",
    "        plot_data.append(df)\n",
    "\n",
    "    plot_data = pd.concat(plot_data, axis=0)\n",
    "\n",
    "    return plot_data\n",
    "\n",
    "\n",
    "def cdf_plot(cdf_data, save_file=False, legend=True, im_size=6):\n",
    "    \"\"\"\n",
    "    Plot the cumulative distribution functions\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    cdf_data : pandas dataframe\n",
    "        Pre-calculated cumulative distribution functions \n",
    "    save_file : boolean\n",
    "        True if image (png) is going to be saved\n",
    "    legend : boolean\n",
    "        True if legend on plot\n",
    "    im_size : integer\n",
    "        Size of the image (i.e., the plot)\n",
    "    \n",
    "    Examples\n",
    "    --------\n",
    "    cdf_dat = cdf(frame, conditions=['incongruent', 'congruent'])\n",
    "    cdf_plot(cdf_dat)\n",
    "    \n",
    "    \"\"\"\n",
    "    sns.set_style('white')\n",
    "    sns.set_style('ticks')\n",
    "\n",
    "    g = sns.FacetGrid(cdf_data, hue=\"condition\", size=im_size)\n",
    "    g.map(plt.plot, \"dv\", \"dens\", alpha=.7, linewidth=1)\n",
    "    if legend:\n",
    "        g.add_legend(title=\"Congruency\")\n",
    "    g.set_axis_labels(\"Response Time (ms.)\", \"Cumulative distribution\")\n",
    "    g.fig.suptitle('Cumulative density functions')\n",
    "\n",
    "    if save_file:\n",
    "        g.savefig(\"cumulative_density_functions_seaborn_python_response\"\n",
    "                  \"-time.png\")\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "# Cumulatitive distribution function and plot\n",
    "cdf_dat = cdf(frame, conditions=['incongruent', 'congruent'])\n",
    "cdf_plot(cdf_dat, legend=True, save_file=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAGrCAYAAACVJgNuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51fd786fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calc_delta_data(df, subid, rt, condition, conditions=['incongruent',\n",
    "                                                   'congruent']):\n",
    "    \"\"\"\n",
    "    Calculating the data for creating delta plots. That is, binning\n",
    "    the response time and accuracy data in deciles. First each subjects\n",
    "    data is binned. Then the binned data is avaraged.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    df : pandas dataframe\n",
    "        Data to bin.\n",
    "    subid : string\n",
    "        Column name for column with the subject identfier\n",
    "    rt : string\n",
    "        Column name for column with the response times\n",
    "    condition : string\n",
    "        Column name for column with the conditions\n",
    "    conditions : list\n",
    "        Containing string of the conditions in the experiment\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    delta_data: Dataframe\n",
    "        Binned avarage response time and response time diff b/w conditions \n",
    "        \n",
    "    Examples\n",
    "    --------\n",
    "    d = calc_delta_data(frame, \"SubID\", \"RT\", \"TrialType\", ['incongruent',\n",
    "                                                       'congruent'])\n",
    "    \n",
    "    \"\"\"\n",
    "\n",
    "\n",
    "    subjects = pd.Series(df[subid].values.ravel()).unique().tolist()\n",
    "    subjects.sort()\n",
    "\n",
    "    deciles = np.arange(0.1, 1., 0.1)\n",
    "\n",
    "    cond_one = conditions[0]\n",
    "    cond_two = conditions[1]\n",
    "\n",
    "    # Frame to store the data (per subject)\n",
    "    arrays = [np.array([cond_one, cond_two]).repeat(len(deciles)),\n",
    "              np.array(deciles).tolist() * 2]\n",
    "    data_delta = pd.DataFrame(columns=subjects, index=arrays)\n",
    "\n",
    "    for subject in subjects:\n",
    "\n",
    "        sub_data_inc = df.loc[(df[subid] == subject) & (df[condition] ==\n",
    "                                                        cond_one)]\n",
    "        sub_data_con = df.loc[(df[subid] == subject) & (df[condition] ==\n",
    "                                                        cond_two)]\n",
    "\n",
    "        inc_q = sub_data_inc[rt].quantile(q=deciles).values\n",
    "        con_q = sub_data_con[rt].quantile(q=deciles).values\n",
    "        for i, dec in enumerate(deciles):\n",
    "            data_delta.loc[(cond_one, dec)][subject] = inc_q[i]\n",
    "            data_delta.loc[(cond_two, dec)][subject] = con_q[i]\n",
    "\n",
    "\n",
    "    # Aggregate deciles\n",
    "    data_delta = data_delta.mean(axis=1).unstack(level=0)\n",
    "    # Calculate difference\n",
    "    data_delta['Diff'] = data_delta[cond_one] - data_delta[cond_two]\n",
    "    # Calculate average\n",
    "    data_delta['Average'] = (data_delta[cond_one] + data_delta[cond_two]) / 2\n",
    "\n",
    "    return data_delta\n",
    "\n",
    "\n",
    "def delta_plot(delta_data, save_file=False, im_size=6):\n",
    "    \"\"\"\n",
    "    Delta plot\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    delta_data : pandas dataframe\n",
    "        Binned avarage response time and response time diff b/w conditions \n",
    "    save_file : boolean\n",
    "        True if file (.png) of plot is going to be saved\n",
    "    im_size : integer\n",
    "        Size of the image (i.e., the plot)\n",
    "        \n",
    "    Examples\n",
    "    -------\n",
    "    d = calc_delta_data(frame, \"SubID\", \"RT\", \"TrialType\", ['incongruent',\n",
    "                                                       'congruent'])\n",
    "    delta_plot(d, save_file=True)\n",
    "    \"\"\"\n",
    "\n",
    "    ymax = delta_data['Diff'].max() + 10\n",
    "    ymin = -10\n",
    "    xmin = delta_data['Average'].min() - 20\n",
    "    xmax = delta_data['Average'].max() + 20\n",
    "\n",
    "    g = sns.FacetGrid(delta_data, ylim=(ymin, ymax), xlim=(xmin, xmax),\n",
    "                      size=im_size)\n",
    "    g.map(plt.scatter, \"Average\", \"Diff\", s=50, alpha=.7, linewidth=1,\n",
    "          edgecolor=\"white\")\n",
    "    g.map(plt.plot, \"Average\", \"Diff\", alpha=.7, linewidth=1)\n",
    "    g.set_axis_labels(\"Avarage RTs (ms.)\", \"Effect (ms.)\")\n",
    "    g.fig.suptitle('Delta Plot')\n",
    "    if save_file:\n",
    "        g.savefig(\"delta_plot_seaborn_python_response-time.png\")\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "    sns.plt.show()\n",
    "    \n",
    "\n",
    "d = calc_delta_data(frame, \"SubID\", \"RT\", \"TrialType\", ['incongruent',\n",
    "                                                       'congruent'])\n",
    "\n",
    "delta_plot(d, save_file=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAGrCAYAAADHBT4lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHtRySooAAAALSURBVADixB4A4h5BU1sNPdRWqgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51fd935c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calc_caf(df, subid, rt, acc, condition, quantiles=[0.25, 0.50, 0.75, 1]):\n",
    "    \"\"\"\n",
    "    Calculates conditional accuracy functions (CAF) per subject and agregates into \n",
    "    one CAF. \n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    df : pandas dataframe\n",
    "        A subset containing the data from one of the conditions\n",
    "    subid : string\n",
    "        Contains the colum name with the subject identifier\n",
    "    rt : string \n",
    "        column name for column containing the response/reaction times\n",
    "    acc : string  \n",
    "        column name for column containing the accuracy data\n",
    "    condition :string \n",
    "        column name for column containing the conditions identfiers\n",
    "    quantiles: list \n",
    "        contains the quantiles (e.g., [0.25, 0.50, 0.75, 1])\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "        a multi-indexed pandas data frame\n",
    "\n",
    "    Examples:\n",
    "     incongr = frame[(frame.TrialType == 'incongruent)]\n",
    "     incongr_caf = calc_caf(incongr, \"SubID\", \"RT\", \"ACC\", \"incongruent\")\n",
    "    \"\"\"\n",
    "\n",
    "    # Subjects\n",
    "    subjects = pd.Series(df[subid].values.ravel()).unique().tolist()\n",
    "    subjects.sort()\n",
    "\n",
    "    # Multi-index frame for data:\n",
    "    arrays = [np.array(['rt'] * len(quantiles) + ['acc'] * len(quantiles)),\n",
    "              np.array(quantiles * 2)]\n",
    "\n",
    "    data_caf = pd.DataFrame(columns=subjects, index=arrays)\n",
    "\n",
    "    # Calculate CAF for each subject\n",
    "    for subject in subjects:\n",
    "\n",
    "        sub_data = df.loc[(df[subid] == subject)]\n",
    "\n",
    "        subject_cdf = sub_data[rt].quantile(q=quantiles).values\n",
    "\n",
    "        # calculate mean response time and proportion of error for each bin\n",
    "        for i, q in enumerate(subject_cdf):\n",
    "\n",
    "            quantile = quantiles[i]\n",
    "\n",
    "            # First quantile\n",
    "            if i < 1:\n",
    "                # Subset\n",
    "                temp_df = sub_data[(sub_data[rt] < subject_cdf[i])]\n",
    "                # RT\n",
    "                data_caf.loc[('rt', quantile)][subject] = temp_df[rt].mean()\n",
    "                # Accuracy\n",
    "                data_caf.loc[('acc', quantile)][subject] = temp_df[acc].mean()\n",
    "\n",
    "            # Second  & third quantile\n",
    "            elif i == 1 or i < len(quantiles):\n",
    "\n",
    "                # Subset\n",
    "                temp_df = sub_data[(sub_data[rt] > subject_cdf[i - 1]) & (\n",
    "                    sub_data[rt] < q)]\n",
    "                # RT\n",
    "                data_caf.loc[('rt', quantile)][subject] = temp_df[rt].mean()\n",
    "\n",
    "                # Accuracy\n",
    "                data_caf.loc[('acc', quantile)][subject] = temp_df[acc].mean()\n",
    "\n",
    "            # Last quantile MAY WANT TO CHANGE THIS\n",
    "            elif i == len(quantiles):\n",
    "                # Subset\n",
    "                temp_df = sub_data[(sub_data[rt] > subject_cdf[i])]\n",
    "\n",
    "                # RT\n",
    "                data_caf.loc[('rt', quantile)][subject] = temp_df[rt].mean()\n",
    "\n",
    "                # Accuracy\n",
    "                data_caf.loc[('acc', quantile)][subject] = temp_df[acc].mean()\n",
    "\n",
    "    # Aggregate subjects CAFs\n",
    "    data_caf = data_caf.mean(axis=1).unstack(level=0)\n",
    "\n",
    "    # Add trialtype\n",
    "    data_caf['trialtype'] = [condition for _ in range(len(quantiles))]\n",
    "\n",
    "    return data_caf\n",
    "\n",
    "\n",
    "def caf_plot(df, save_file=False, im_size=6):\n",
    "    \"\"\"\n",
    "    Plotting the conditional accuracy functions (CAF)\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    df : pandas dataframe \n",
    "        containing the CAF(s) calculated with calc_caf\n",
    "    save_file : boolean\n",
    "        True if planning to save file\n",
    "    im_size : integer\n",
    "        Size of the image (i.e., the plot)\n",
    "        \n",
    "    Examples\n",
    "    --------\n",
    "    incongr = frame[(frame.TrialType == 'incongruent)]\n",
    "    incongr_caf = calc_caf(incongr, \"SubID\", \"RT\", \"ACC\", \"incongruent\")\n",
    "    caf_plot(incongr_caf)\n",
    "    \"\"\"\n",
    "    sns.set_style('white')\n",
    "    sns.set_style('ticks')\n",
    "\n",
    "    g = sns.FacetGrid(df, hue=\"trialtype\", size=im_size, ylim=(0, 1.1))\n",
    "    g.map(plt.scatter, \"rt\", \"acc\", s=50, alpha=.7, linewidth=1,\n",
    "          edgecolor=\"white\")\n",
    "    g.map(plt.plot, \"rt\", \"acc\", alpha=.7, linewidth=1)\n",
    "    g.add_legend(title=\"Congruency\")\n",
    "    g.set_axis_labels(\"Response Time (ms.)\", \"Accuracy\")\n",
    "    g.fig.suptitle('Conditional Accuracy Functions')\n",
    "\n",
    "    if save_file:\n",
    "        g.savefig(\"conditional_accuracy_function_seaborn_python_response\"\n",
    "                   \"-time.png\")\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "inc = calc_caf(frame[(frame.TrialType == \"incongruent\")], \"SubID\", \"RT\",\n",
    "               \"ACC\",\n",
    "               \"incongruent\", quantiles=[.25, .50, .75, 1.])\n",
    "con = calc_caf(frame[(frame.TrialType == \"congruent\")], \"SubID\", \"RT\", \"ACC\",\n",
    "               \"congruent\")\n",
    "df_caf = pd.concat([inc, con])\n",
    "\n",
    "caf_plot(df_caf, save_file=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}