4.ddm_feature_vis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "# Copyright 2022 by the author(s) of CHI2023 Full Paper \"Short-Form\n",
    "# Videos Degrade Our Capacity to Retain Intentions: Effect of Context\n",
    "# Switching On Prospective Memory\". All rights reserved.\n",
    "#\n",
    "# Use of this source code is governed by a GPLv3 license that\n",
    "# can be found in the LICENSE file.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['text.latex.preamble'] = r'''\\usepackage{libertine}\\usepackage{fontawesome5}\\usepackage{xcolor}'''\n",
    "plt.rcParams['ps.usedistiller'] = 'xpdf'\n",
    "sns.set_theme(style='ticks')\n",
    "\n",
    "\n",
    "colors = sns.color_palette('vlag', as_cmap=False, n_colors=10)\n",
    "c1 = colors[0]\n",
    "c2 = colors[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "      ns: p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "tiktok_pre vs. tiktok_post: Custom statistical test, P_val:4.975e-04\n",
      "youtube_post vs. tiktok_post: Custom statistical test, P_val:1.407e-02\n",
      "twitter_post vs. tiktok_post: Custom statistical test, P_val:2.871e-04\n",
      "rest_post vs. tiktok_post: Custom statistical test, P_val:1.719e-03\n",
      "p-value annotation legend:\n",
      "      ns: p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "twitter_pre vs. twitter_post: Custom statistical test, P_val:4.435e-02\n",
      "youtube_post vs. tiktok_post: Custom statistical test, P_val:1.358e-02\n",
      "twitter_post vs. tiktok_post: Custom statistical test, P_val:2.397e-03\n",
      "rest_post vs. tiktok_post: Custom statistical test, P_val:8.549e-03\n",
      "p-value annotation legend:\n",
      "      ns: p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "rest_post vs. tiktok_post: Custom statistical test, P_val:1.917e-03\n",
      "p-value annotation legend:\n",
      "      ns: p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "youtube_pre vs. youtube_post: Custom statistical test, P_val:2.683e-02\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 936x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_feature = pd.read_csv('data/ddm.csv')\n",
    "fig, axes = plt.subplots(1, 4, figsize=(13, 5))\n",
    "interrupts = ['rest', 'twitter', 'youtube', 'tiktok']\n",
    "measures = ['pre', 'post']\n",
    "features = ['drift', 'noise', 'bound', 'nondectime']\n",
    "features_label = ['Drift ($\\mu$)', 'Variance ($\\sigma$)', 'Bound ($B$)', 'Non-decision Time ($t$)']\n",
    "icons = ['\\\\faSpinner', '\\\\faTwitter', '\\\\faYoutube', '\\\\faTiktok']\n",
    "labels = ['Rest', 'Twitter', 'Youtube', 'Tiktok']\n",
    "sns.boxplot(ax=axes[0], x='interrupt', y=features[0], order=interrupts, hue='measure', hue_order=measures, data=df_feature[(df_feature.task == 'PM')], palette=[c1, c2], showfliers = False)\n",
    "sns.boxplot(ax=axes[1], x='interrupt', y=features[1], order=interrupts, hue='measure', hue_order=measures, data=df_feature[(df_feature.task == 'PM')], palette=[c1, c2], showfliers = False)\n",
    "sns.boxplot(ax=axes[2], x='interrupt', y=features[2], order=interrupts, hue='measure', hue_order=measures, data=df_feature[(df_feature.task == 'PM')], palette=[c1, c2], showfliers = False)\n",
    "sns.boxplot(ax=axes[3], x='interrupt', y=features[3], order=interrupts, hue='measure', hue_order=measures, data=df_feature[(df_feature.task == 'PM')], palette=[c1, c2], showfliers = False)\n",
    "for i in range(0, 4):\n",
    "    axes[i].set_xticklabels(icons, fontsize=16)\n",
    "    axes[i].set_xlabel(None)\n",
    "    axes[i].set_ylabel(None)\n",
    "    axes[i].set_title(features_label[i % 4], fontsize=16)\n",
    "    if i != 3:\n",
    "        g = axes[i].get_legend().remove()\n",
    "    else:\n",
    "        g = axes[i].legend(markerfirst=False, fontsize=12, loc='lower right')\n",
    "        g.set_title(None)\n",
    "        g.set_frame_on(False)\n",
    "\n",
    "from statannotations.Annotator import Annotator\n",
    "pairs = [\n",
    "    (('tiktok', 'pre'), ('tiktok', 'post')),\n",
    "    (('tiktok', 'post'), ('rest', 'post')),\n",
    "    (('tiktok', 'post'), ('twitter', 'post')),\n",
    "    (('tiktok', 'post'), ('youtube', 'post')),\n",
    "]\n",
    "annot = Annotator(axes[0], pairs, data=df_feature[(df_feature.task == 'PM')], x='interrupt', y=features[0], order=interrupts, hue='measure', hue_order=['pre', 'post'])\n",
    "annot.configure(test=None, loc='inside')\n",
    "annot.set_pvalues([.0004975439, .0017185539, .0002871479, .0140690162])\n",
    "annot.annotate()\n",
    "\n",
    "pairs = [\n",
    "    (('twitter', 'pre'), ('twitter', 'post')),\n",
    "    (('tiktok', 'post'), ('rest', 'post')),\n",
    "    (('tiktok', 'post'), ('twitter', 'post')),\n",
    "    (('tiktok', 'post'), ('youtube', 'post')),\n",
    "]\n",
    "annot = Annotator(axes[1], pairs, data=df_feature[(df_feature.task == 'PM')], x='interrupt', y=features[1], order=interrupts, hue='measure', hue_order=['pre', 'post'])\n",
    "annot.configure(test=None, loc='inside')\n",
    "annot.set_pvalues([0.044351243, 0.008548603, 0.002397299, 0.013580022])\n",
    "annot.annotate()\n",
    "\n",
    "\n",
    "pairs = [\n",
    "    (('tiktok', 'post'), ('rest', 'post')),\n",
    "]\n",
    "annot = Annotator(axes[2], pairs, data=df_feature[(df_feature.task == 'PM')], x='interrupt', y=features[2], order=interrupts, hue='measure', hue_order=['pre', 'post'])\n",
    "annot.configure(test=None, loc='inside')\n",
    "annot.set_pvalues([0.001917398])\n",
    "annot.annotate()\n",
    "\n",
    "\n",
    "pairs = [\n",
    "    (('youtube', 'pre'), ('youtube', 'post')),\n",
    "]\n",
    "annot = Annotator(axes[3], pairs, data=df_feature[(df_feature.task == 'PM')], x='interrupt', y=features[3], order=interrupts, hue='measure', hue_order=['pre', 'post'])\n",
    "annot.configure(test=None, loc='inside')\n",
    "annot.set_pvalues([0.02683226])\n",
    "annot.annotate()\n",
    "\n",
    "plt.savefig('figures/fig5_ddm_pm.pdf', bbox_inches='tight',)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 936x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_feature = pd.read_csv('data/ddm.csv')\n",
    "fig, axes = plt.subplots(1, 4, figsize=(13, 5))\n",
    "interrupts = ['rest', 'twitter', 'youtube', 'tiktok']\n",
    "measures = ['pre', 'post']\n",
    "features = ['drift', 'noise', 'bound', 'nondectime']\n",
    "features_label = ['Drift ($\\mu$)', 'Noise ($\\sigma$)', 'Bound ($B$)', 'Non-decision Time ($t$)']\n",
    "icons = ['\\\\faSpinner', '\\\\faTwitter', '\\\\faYoutube', '\\\\faTiktok']\n",
    "labels = ['Rest', 'Twitter', 'Youtube', 'Tiktok']\n",
    "sns.boxplot(ax=axes[0], x='interrupt', y=features[0], order=interrupts, hue='measure', hue_order=measures, data=df_feature[(df_feature.task == 'LD')], palette=[c1, c2], showfliers = False)\n",
    "sns.boxplot(ax=axes[1], x='interrupt', y=features[1], order=interrupts, hue='measure', hue_order=measures, data=df_feature[(df_feature.task == 'LD')], palette=[c1, c2], showfliers = False)\n",
    "sns.boxplot(ax=axes[2], x='interrupt', y=features[2], order=interrupts, hue='measure', hue_order=measures, data=df_feature[(df_feature.task == 'LD')], palette=[c1, c2], showfliers = False)\n",
    "sns.boxplot(ax=axes[3], x='interrupt', y=features[3], order=interrupts, hue='measure', hue_order=measures, data=df_feature[(df_feature.task == 'LD')], palette=[c1, c2], showfliers = False)\n",
    "for i in range(0, 4):\n",
    "    axes[i].set_xticklabels(icons, fontsize=16)\n",
    "    axes[i].set_xlabel(None)\n",
    "    axes[i].set_ylabel(None)\n",
    "    axes[i].set_title(features_label[i % 4], fontsize=16)\n",
    "    if i != 3:\n",
    "        g = axes[i].get_legend().remove()\n",
    "    else:\n",
    "        g = axes[i].legend(markerfirst=False, fontsize=12, loc='lower right')\n",
    "        g.set_title(None)\n",
    "        g.set_frame_on(False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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