plot_user_study.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings; warnings.filterwarnings(action='once')\n",
    "\n",
    "import os\n",
    "import json\n",
    "from src.functions import *\n",
    "import random\n",
    "import datetime\n",
    "\n",
    "from src.simple_TF_continuos import *\n",
    "\n",
    "import absl.logging #prevent checkpoint warnings while training\n",
    "\n",
    "absl.logging.set_verbosity(absl.logging.ERROR)\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "large = 22; med = 16; small = 12\n",
    "params = {'axes.titlesize': large,\n",
    "          'legend.fontsize': med,\n",
    "          'figure.figsize': (16, 10),\n",
    "          'axes.labelsize': med,\n",
    "          'axes.titlesize': med,\n",
    "          'xtick.labelsize': med,\n",
    "          'ytick.labelsize': med,\n",
    "          'figure.titlesize': large}\n",
    "plt.rcParams.update(params)\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "sns.set_style(\"white\")\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "satis  norm score [90. 90. 88. 62.]\n",
      "satis  average: [4.5 4.5 4.4 3.1]\n",
      "easy  norm score [90. 92. 74. 58.]\n",
      "easy  average: [4.5 4.6 3.7 2.9]\n",
      "safe  norm score [ 88.  92. 100.  82.]\n",
      "safe  average: [4.4 4.6 5.  4.1]\n",
      "natural  norm score [78. 96. 80. 62.]\n",
      "natural  average: [3.9 4.8 4.  3.1]\n",
      "predictable  norm score [96. 72. 88. 48.]\n",
      "predictable  average: [4.8 3.6 4.4 2.4]\n",
      "interactions  norm score [34. 26. 40. 78.]\n",
      "interactions  average: [1.7 1.3 2.  3.9]\n",
      "failures  norm score [14.  0. 16.  6.]\n",
      "failures  average: [0.7 0.  0.8 0.3]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1008x576 with 7 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 568,
       "width": 992
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "perf_order  norm score [50. 30. 50. 20.]\n",
      "perf_order  average: [2.  1.2 2.  0.8]\n",
      "easy_order  norm score [52.5 57.5 40.   0. ]\n",
      "easy_order  average: [2.1 2.3 1.6 0. ]\n",
      "time_order  norm score [42.5 50.  52.5  5. ]\n",
      "time_order  average: [1.7 2.  2.1 0.2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 208,
       "width": 392
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "user_data = [\n",
    "             {'name': \"user1\", \"satis\":[2,4,5,4], \"easy\":[3,5,4,3], \"safe\":[1,2,5,5], \"natural\":[4,5,4,3],\"predictable\":[4,3,4,4],\"interactions\":[1,1,1,5], \"failures\":[0,0,0,0], \"perf_order\":\"cadb\",\"easy_order\":\"cbad\",\"time_order\":\"cabd\"},\n",
    "             {'name': \"user2\", \"satis\":[5,4,5,1], \"easy\":[5,3,5,1], \"safe\":[5,4,5,1], \"natural\":[5,5,5,0],\"predictable\":[5,1,5,0],\"interactions\":[1,2,1,7], \"failures\":[0,0,0,1], \"perf_order\":\"acbd\",\"easy_order\":\"acbd\",\"time_order\":\"cabd\"},\n",
    "             {'name': \"user3\", \"satis\":[5,4,2,1], \"easy\":[4,5,1,3], \"safe\":[3,5,5,4], \"natural\":[1,4,2,4],\"predictable\":[5,2,4,1],\"interactions\":[2,2,4,3], \"failures\":[0,0,2,2], \"perf_order\":\"acdb\",\"easy_order\":\"abcd\",\"time_order\":\"abcd\"},\n",
    "             {'name': \"user4\", \"satis\":[5,5,5,5], \"easy\":[4,4,4,4], \"safe\":[5,5,5,5], \"natural\":[4,5,5,5],\"predictable\":[5,5,5,5],\"interactions\":[2,1,4,4], \"failures\":[1,0,2,0], \"perf_order\":\"dcab\",\"easy_order\":\"abcd\",\"time_order\":\"bacd\"},\n",
    "             {'name': \"user5\", \"satis\":[4,5,4,3], \"easy\":[5,5,3,1], \"safe\":[5,5,5,3], \"natural\":[4,5,4,3],\"predictable\":[5,4,5,1],\"interactions\":[1,1,3,3], \"failures\":[0,0,2,0], \"perf_order\":\"bacd\",\"easy_order\":\"bacd\",\"time_order\":\"bacd\"},\n",
    "             {'name': \"user6\", \"satis\":[4,5,4,3], \"easy\":[5,5,3,1], \"safe\":[5,5,5,3], \"natural\":[4,5,4,3],\"predictable\":[5,4,5,1],\"interactions\":[1,1,3,3], \"failures\":[0,0,2,0], \"perf_order\":\"bacd\",\"easy_order\":\"bacd\",\"time_order\":\"bacd\"},\n",
    "             {'name': \"user7\", \"satis\":[5,3,5,2], \"easy\":[5,5,5,4], \"safe\":[5,5,5,5], \"natural\":[3,5,3,3],\"predictable\":[5,5,5,5],\"interactions\":[1,1,1,3], \"failures\":[0,0,0,0], \"perf_order\":\"cabd\",\"easy_order\":\"cbad\",\"time_order\":\"cbad\"},\n",
    "             {'name': \"user8\", \"satis\":[5,5,5,5], \"easy\":[5,5,5,4], \"safe\":[5,5,5,5], \"natural\":[4,5,5,3],\"predictable\":[5,5,5,4],\"interactions\":[2,1,1,5], \"failures\":[1,0,0,0], \"perf_order\":\"cabd\",\"easy_order\":\"bcad\",\"time_order\":\"bcad\"},\n",
    "             {'name': \"user9\", \"satis\":[5,5,5,4], \"easy\":[4,5,5,4], \"safe\":[5,5,5,5], \"natural\":[5,5,5,4],\"predictable\":[5,4,5,3],\"interactions\":[5,1,1,5], \"failures\":[5,0,0,0], \"perf_order\":\"cdba\",\"easy_order\":\"abcd\",\"time_order\":\"cbad\"},\n",
    "             {'name': \"user10\", \"satis\":[5,5,4,3], \"easy\":[5,4,2,4], \"safe\":[5,5,5,5], \"natural\":[5,4,3,3],\"predictable\":[4,3,1,0],\"interactions\":[1,2,1,1], \"failures\":[0,0,0,0], \"perf_order\":\"abdc\",\"easy_order\":\"bacd\",\"time_order\":\"cdab\"}            \n",
    "             ]\n",
    "interaction_name = [\"Kinestetic\", \"NL\", \"Drawing\", \"hand-tunning\"]       \n",
    "questions = [\"satis\",\"easy\",\"safe\",\"natural\",\"predictable\",\"interactions\",\"failures\"]     \n",
    "criteria  = [\"perf_order\", \"easy_order\", \"time_order\"]\n",
    "plot_questions(user_data,questions,interaction_name, file=\"/home/gari/ros_ws/src/motion_refiner/media/users_study_questions.svg\")\n",
    "plot_criteria(user_data,criteria,interaction_name,file=\"/home/gari/ros_ws/src/motion_refiner/media/users_study_criteria.svg\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 56.24999993, 100.        ,  64.7058822 ,  91.66666675])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interactions_average = np.array([1.77777778, 1.33333333, 1.88888889, 4.])\n",
    "failures_average =  np.array([0.77777778, 0.0, 0.66666667, 0.33333333])\n",
    "100-(failures_average/interactions_average)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([139.        ,  81.66666667, 120.        , 284.33333333])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t=np.array([[2*60+22, 3*60+10, 4*60, 5*60+0],\n",
    "[4*60+25, 0*60+30, 2*30, 5*60+10],\n",
    "[0*37+10, 0*60+25, 2*30, 4*60+3]])\n",
    "np.mean(t,axis=0)\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "0956b21fe8d22d01112fda653bcc9e2805cf6be5cf9d5c4618a087e664cfcb09"
  },
  "kernelspec": {
   "display_name": "Python 3.8.2 ('py_ros')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}