Week7_lecture.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lecture notes from week 7\n",
    "Programming for the Behavioral Sciences\n",
    "\n",
    "This lectures begins with some basics in reading and writing data to a file.\n",
    "\n",
    "Also, data from the visual search experiment will be visualized."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic reading and writing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Go through all files in a folder and combine them \n",
    "import glob, os\n",
    "files = glob.glob('img' + os.sep + 'vs_results' + os.sep + '*.csv')\n",
    "\n",
    "f_out = open('out.csv', 'wb')\n",
    "\n",
    "for i, f in enumerate(files):\n",
    "    \n",
    "    # Get the participant number from the file name\n",
    "    p_id = f[-6:-4]\n",
    "    \n",
    "    # open the file and read it's content\n",
    "    f_content = open(f, 'rb')\n",
    "    \n",
    "    # Skip first line (only write it once!)\n",
    "    if i == 0:\n",
    "        line = f_content.readline() \n",
    "        outline = 'p_id\\t' + line\n",
    "        f_out.write(outline)\n",
    "        continue        \n",
    "    else:\n",
    "        f_content.readline()         \n",
    "\n",
    "    \n",
    "    # read the file line by line and write to new file\n",
    "    for line in f_content:\n",
    "        outline = p_id + '\\t' +  line\n",
    "        f_out.write(outline)\n",
    "        \n",
    "    # Close the file\n",
    "    f_content.close()\n",
    "    \n",
    "# Close the files\n",
    "f_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0  trial           rt condition  n_distractors  p_id\n",
      "0           0      1  1386.393100        BB              5     1\n",
      "1           1      1  1334.045001        BB             10     1\n",
      "2           2      1  1901.347063        BB             20     1\n",
      "3           3      1   599.287600        RB              5     1\n",
      "4           4      1   856.740035        RB             10     1\n"
     ]
    }
   ],
   "source": [
    "# The same using pandas\n",
    "import pandas\n",
    "files = glob.glob('img' + os.sep + 'vs_results' + os.sep + '*.csv')\n",
    "\n",
    "df = pd.DataFrame()\n",
    "out = []\n",
    "for f in files:\n",
    "    p_id = f[-6:-4]\n",
    "    df = pd.read_csv(f, index_col=None, header=0, sep='\\t')\n",
    "    df['p_id'] = int(p_id)\n",
    "    out.append(df)\n",
    "    \n",
    "combined = pd.concat(out)\n",
    "\n",
    "print(combined.head())\n",
    "\n",
    "# Save as csv\n",
    "combined.to_csv('out_pandas.csv', sep = '\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the results from the visual search experiment\n",
    "\n",
    "The results (fabricated data) are stored in a csv file where each row corresponds to one trial (observation). Each trial contains information about the participant ID (p_id), the trial number (trial), the reaction time in ms (rt), the condition (BB or RB; they indicate the colors of the target and distractors), and the number of distractors (n_distractors).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Read data into pandas dataframe\n",
    "df = pd.read_csv('img\\\\results_visual_search.csv', sep = '\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0  p_id  trial           rt condition  n_distractors\n",
      "0           0     1      1  1392.217102        BB              5\n",
      "1           1     1      1  1719.767411        BB             10\n",
      "2           2     1      1  2356.294151        BB             20\n",
      "3           3     1      1   583.532726        RB              5\n",
      "4           4     1      1   539.555994        RB             10\n"
     ]
    }
   ],
   "source": [
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAFXCAYAAABOYlxEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcb88da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot reaction times across participants\n",
    "sns.barplot(x = 'p_id', y = 'rt', data = df)\n",
    "plt.xlabel('Participant ID')\n",
    "plt.ylabel('Reaction time (ms)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUQAAAEUCAYAAABTZE8xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUUICUQghSkggCiFEif8PaLAUzWvaKFsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcecb9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot reaction times across \n",
    "sns.factorplot(x=\"n_distractors\", y=\"rt\",hue='condition',data = df)\n",
    "plt.xlabel('Number of distractors')\n",
    "plt.ylabel('Search time (ms)')   \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perhaps it would make more sense construct the plot based on the average value of each participant from each trial and condition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUQAAAEUCAYAAABTZE8xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nESaezi8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xddff438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Take the mean over different trials\n",
    "df_avg = df.groupby(['p_id', 'n_distractors', 'condition']).mean()\n",
    "df_avg.reset_index(inplace=True)  \n",
    "\n",
    "# Plot reaction times across \n",
    "sns.factorplot(x=\"n_distractors\", y=\"rt\",hue='condition',data = df_avg)\n",
    "plt.xlabel('Number of distractors')\n",
    "plt.ylabel('Search time (ms)')   \n",
    "plt.show()"
   ]
  },
  {
   "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": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}