357 lines
40 KiB
Plaintext
357 lines
40 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"\n",
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"colorDic = {\"blue\" : \"#6599FF\", \"yellow\" : \"#FFAD33\", \"purple\": \"#683b96\", \"green\" : \"#198D6D\", \"red\" : \"#FF523F\"}\n",
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"colors = list(colorDic.values())\n",
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"\n",
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"lables = [\"Finger\", \"Knuckle\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 50,
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"metadata": {},
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"outputs": [],
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"source": [
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"dfTLX = pd.read_csv(\"./DataStudyEvaluation/Knuckle Study - TLX.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 51,
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"metadata": {},
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"outputs": [],
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"source": [
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"dfTLX['Score'] = dfTLX['Mental Demand'] + dfTLX['Physical Demand'] + dfTLX['Temporal Demand'] + dfTLX['Performance'] + dfTLX['Effort'] + dfTLX['Frustration']\n",
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"dfTLX['Score'] = (dfTLX['Score']-6) / 6.0 "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 58,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.axes._subplots.AxesSubplot at 0x7f526f0b9e48>"
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]
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},
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"execution_count": 58,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEnCAYAAAC5ebgKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3X2cT3X+//HHq5lhiKQam6UyWlfDDDPGRSQyQhJRrfpqkSS1adsuRL+VqVXrW2or+q58JTZCErr87tCyKBmDiYmpSU0i6zKTwWB4//6Y8VlXYy4+n/GZOfO8325z63POvM85rzPGq7f3eZ/X25xziIhI+XdBsAMQEZHAUEIXEfEIJXQREY9QQhcR8QgldBERj1BCFxHxCCV0ERGPUEIXEfEIJXQREY8ILayBmU0FegI7nXPN8ve9ANwMHAE2A3c75/YVdq7LLrvM1atXz6+ARUQqmjVr1ux2zkUU1s4Ke/XfzK4DsoG/n5TQuwL/dM7lmtl/AzjnnijsYvHx8S4lJaUo8YuISD4zW+Ociy+sXaFDLs65ZcDe0/YlOedy8ze/AOqWKEoREQmYQIyhDwY+KeibZjbUzFLMLGXXrl0BuJyIiJyNXwndzP4fkAvMLKiNc26ycy7eORcfEVHoEJCIiJRQoQ9FC2Jmg8h7WJrg/KjBe/ToUbZu3UpOTk5JTyHlWHh4OHXr1iUsLCzYoYiUeyVK6GbWHRgBdHTOHfQngK1bt1K9enXq1auHmflzKilnnHPs2bOHrVu3EhkZGexwRMq9QodczGwWsBJoZGZbzeweYCJQHVhkZqlmNqmkAeTk5HDppZcqmVdAZsall16qf52JBEihPXTn3J1n2f1GIINQMq+49GcvEjh6U1RExCNK/FC0tNQb+VFAz5c57qZC21SrVo3s7GwyMzNp0qQJjRs3Jicnh+rVq/PAAw8waNCggMYkIsW3deTyIrWrO65DKUdSdpW5hB5sV199NevWrQPgu+++o2/fvjjnuPvuu4McmYjIuWnI5Rzq16/PSy+9xKuvvhrsUERECqWEXoi4uDjS09ODHYaISKGU0AvhxztTIiLnlRJ6IdatW0eTJk2CHYaISKGU0M8hMzOTxx57jOHDhwc7FBGRQpW5WS5FmWZYmjZv3kxsbKxv2uJDDz2kaYsiUi6UuYQeDNnZ2QDUq1ePQ4cOBTkaEZGS0ZCLiIhHKKGLiHiEErqIiEcooYuIeIQSuoiIRyihi4h4RNmbtphYI8Dnyyq0iZnRv39/ZsyYAUBubi61a9emTZs2fPjhhyW67HPPPceTTz5ZaLt69eqRkpLCZZdddsb+6tWrA3Ds2DH69u3Ln/70J8LDw0sUTyBNmzaNlJQUJk6cGOxQROQk6qEDF154IWlpab456IsWLaJOnTp+nfO5557zO64lS5awYcMGkpOT+e6777jvvvv8PqeIeJcSer4ePXrw0Ud5i2vMmjWLO+/8z8p7Bw4cYPDgwbRu3ZrY2FgWLlwI5PVU+/btS/fu3WnQoAEjRowAYOTIkRw6dIgWLVrQv39/AG655RZatmxJ06ZNmTx5crFiq1atGpMmTWLBggXs3bsXgBdeeIFWrVoRExPDmDFjgLxSBY0bN2bQoEE0bNiQ/v37s3jxYtq3b0+DBg1ITk4GIDk5mWuuuYbY2FjatWvH119/fc77AXjzzTdp2LAhrVu35rPPPiv2z1dESp8Ser477riD2bNnk5OTw/r162nTpo3ve88++yydO3cmOTmZJUuW8Pjjj3PgwAEAUlNTmTNnDhs2bGDOnDn8+OOPjBs3jipVqpCamsrMmTMBmDp1KmvWrCElJYVXX32VPXv2FCu+iy66iMjISDIyMkhKSiIjI4Pk5GRSU1NZs2YNy5YtA+Dbb7/l0UcfJT09nfT0dN5++21WrFjB+PHjff9qaNy4McuXL2fdunU888wzpwwNne1+tm/fzpgxY/jss89YsWIFGzdu9OtnLSKlo+yNoQdJTEwMmZmZzJo1ix49epzyvaSkJN5//33Gjx8PQE5ODlu2bAEgISGBGjXyxv2joqL44YcfuOKKK844/6uvvsr8+fMB+PHHH8nIyODSSy8tVownSvkmJSWRlJREbGwskFe6ICMjgyuvvJLIyEiio6MBaNq0KQkJCZgZ0dHRZGZmApCVlcXAgQPJyMjAzDh69KjvGme7n927d9OpUyciIiIA6NevH998802xYheR0qeEfpJevXrx2GOPsXTp0lN60M455s2bR6NGjU5pv2rVKipXruzbDgkJITc394zzLl26lMWLF7Ny5UqqVq1Kp06dyMnJKVZs+/fvJzMzk4YNG+KcY9SoUWeMqWdmZp4SzwUXXODbvuCCC3yxjR49muuvv5758+eTmZlJp06dfMcU5X5EpGzSkMtJBg8ezJgxY3w93BO6devGhAkTfD3kE2uOnktYWJiv55uVlUXNmjWpWrUq6enpfPHFF8WKKzs7mwceeIBbbrmFmjVr0q1bN6ZOneorKrZt2zZ27txZ5PNlZWX5HvpOmzat0PZt2rThX//6F3v27OHo0aPMnTu3WPGLyPlR9nroRZhmWFrq1q3LQw89dMb+0aNH8/DDDxMTE8Px48eJjIwsdDrj0KFDiYmJIS4ujqlTpzJp0iSaNGlCo0aNaNu2bZHiuf7663HOcfz4cfr06cPo0aMB6Nq1K5s2beKaa64B8h6azpgxg5CQkCKdd8SIEQwcOJCxY8dy002FlyuuXbs2iYmJXHPNNVx88cW0aNGiSNcRkfPLzucSa/Hx8S4lJeWUfZs2bdKKQBWcfgekKLaOXF6kdnXHdSjlSM4/M1vjnIsvrJ2GXEREPKLQhG5mU81sp5mlnbTvEjNbZGYZ+f+tWbphiohIYYrSQ58GdD9t30jgU+dcA+DT/G0REQmiQhO6c24ZsPe03b2B6fmfpwO3BDguEREpppKOof/KObc9//O/gV8FKB4RESkhvx+KurxpMgVOlTGzoWaWYmYpu3bt8vdyIiJSgJLOQ99hZrWdc9vNrDZQ4FstzrnJwGTIm7ZY2Imjp0cX1qRYNgzcUGibkJAQoqOjyc3NpUmTJkyfPp2dO3fSs2dP0tLSCj3+XCZNmkTVqlUZMGBAsY+tVq2a7+Whs8V79OhRQkNDGTBgAH/84x+54ILgT1pKTEykWrVqPPbYY8EORaTCKWkGeB8YmP95ILAwMOEEx4lCWmlpaVSqVIlJkyYF7NzDhg0rUTI/lxPxfvXVVyxatIhPPvmEp59+OqDXEJHypyjTFmcBK4FGZrbVzO4BxgE3mFkG0CV/2xM6dOjAt99+C+QtLHHvvffStGlTunbtyqFDh9i8eTNxcXG+9hkZGb7tkSNHEhUVRUxMjK+HmpiY6Cvq9e2339KlSxeaN29OXFwcmzdvJjs7m4SEBOLi4oiOjvaV5i2qWrVqMXnyZCZOnIhzjmPHjvH444/7Suu+/vrrQF49mY4dO9K7d2/q16/PyJEjmTlzJq1btyY6OprNmzcD8MEHH9CmTRtiY2Pp0qULO3bs8N3H4MGD6dSpE/Xr1+fVV1/1xfDss8/SsGFDrr32Wl8pXhE5/wodcnHO3VnAtxICHEvQ5ebm8sknn9C9e94szYyMDGbNmsX//u//8tvf/pZ58+Zx1113UaNGDVJTU2nRogVvvvkmd999N3v27GH+/Pmkp6djZuzbt++M8/fv35+RI0fSp08fcnJyOH78OJUqVWL+/PlcdNFF7N69m7Zt29KrVy/MrMhx169fn2PHjrFz504WLlxIjRo1WL16NYcPH6Z9+/Z07doVgC+//JJNmzZxySWXUL9+fYYMGUJycjKvvPIKEyZM4OWXX+baa6/liy++wMyYMmUKzz//PC+++CIA6enpLFmyhP3799OoUSPuv/9+1q9fz+zZs0lNTSU3N5e4uDhatmwZgD8NESmuslfLJQhOLEYBeT30e+65h59++onIyEjf/pYtW/rKzw4ZMoQ333yTl156iTlz5pCcnEyNGjUIDw/nnnvuoWfPnvTs2fOUa+zfv59t27bRp08fAN9SckePHuX
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"dfTLX.groupby(\"Task\").mean().plot(kind=\"bar\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 52,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(0, 20)"
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]
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},
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"execution_count": 52,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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|
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"image/png": "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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"M = dfTLX.groupby(\"Task\").mean().Score.values\n",
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"STD = dfTLX.groupby(\"Task\").std().Score.values\n",
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"plt.bar([0], M[0], yerr=STD[0], color=colors[1])\n",
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"plt.bar([1], M[1], yerr=STD[1], color=colors[3])\n",
|
||
|
|
"plt.xticks([0,1], lables)\n",
|
||
|
|
"plt.ylim(0,20)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 147,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"\\ac{RTLX} & 2.0 & 1.6 & 6.6 & 2.5 \\\\\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"s = \"\\\\ac{RTLX} \"\n",
|
||
|
|
"c = \"Score\"\n",
|
||
|
|
"for con in [\"Finger\", \"Knuckle\"]:\n",
|
||
|
|
" s = \"%s & %.1f\" % (s, dfTLX[dfTLX.Task == con].mean()[c])\n",
|
||
|
|
" s = \"%s & %.1f\" % (s, dfTLX[dfTLX.Task == con].std()[c])\n",
|
||
|
|
"print(s + \" \\\\\\\\\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 141,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"dfTLX[[\"ID\", \"Task\", \"Score\"]].to_csv(\"./DataStudyEvaluation/R_TLX.csv\", index=False)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 137,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"dfSUS = pd.read_csv(\"./DataStudyEvaluation/Knuckle Study - SUS.csv\")\n",
|
||
|
|
"dfSUS.Q1 = dfSUS.Q1 - 1\n",
|
||
|
|
"dfSUS.Q3 = dfSUS.Q3 - 1\n",
|
||
|
|
"dfSUS.Q5 = dfSUS.Q5 - 1\n",
|
||
|
|
"dfSUS.Q7 = dfSUS.Q7 - 1\n",
|
||
|
|
"dfSUS.Q9 = dfSUS.Q9 - 1\n",
|
||
|
|
"\n",
|
||
|
|
"dfSUS.Q2 = 5 - dfSUS.Q2\n",
|
||
|
|
"dfSUS.Q4 = 5 - dfSUS.Q4\n",
|
||
|
|
"dfSUS.Q6 = 5 - dfSUS.Q6\n",
|
||
|
|
"dfSUS.Q8 = 5 - dfSUS.Q8\n",
|
||
|
|
"dfSUS.Q10 = 5 - dfSUS.Q10\n",
|
||
|
|
"dfSUS[\"Score\"] = dfSUS.Q1 + dfSUS.Q2 + dfSUS.Q3 + dfSUS.Q4 + dfSUS.Q5 + dfSUS.Q6 + dfSUS.Q7 + dfSUS.Q8 + dfSUS.Q9 + dfSUS.Q10\n",
|
||
|
|
"dfSUS.Score = dfSUS.Score * 2.5"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 138,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"(0, 100)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 138,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"M = dfSUS.groupby(\"Task\").mean().Score.values\n",
|
||
|
|
"STD = dfSUS.groupby(\"Task\").std().Score.values\n",
|
||
|
|
"plt.bar([0], M[0], yerr=STD[0], color=colors[1])\n",
|
||
|
|
"plt.bar([1], M[1], yerr=STD[1], color=colors[3])\n",
|
||
|
|
"plt.xticks([0,1], lables)\n",
|
||
|
|
"plt.ylim(0,100)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 146,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"SUS & 89.6 & 8.8 & 57.9 & 16.1 \\\\\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"s = \"SUS \"\n",
|
||
|
|
"c = \"Score\"\n",
|
||
|
|
"for con in [\"Finger\", \"Knuckle\"]:\n",
|
||
|
|
" s = \"%s & %.1f\" % (s, dfSUS[dfSUS.Task == con].mean()[c])\n",
|
||
|
|
" s = \"%s & %.1f\" % (s, dfSUS[dfSUS.Task == con].std()[c])\n",
|
||
|
|
"print(s + \" \\\\\\\\\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 140,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"dfSUS[[\"ID\", \"Task\", \"Score\"]].to_csv(\"./DataStudyEvaluation/R_SUS.csv\", index=False)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 107,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"dfQ = pd.read_csv(\"./DataStudyEvaluation/Knuckle Study - Quest..csv\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 108,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"cols = ['Difficult-Easy', 'Slow-Fast', 'Frustrated-Successful', 'Inaccurate-Accurate', 'Straining-Comfortable']\n",
|
||
|
|
"lablesQ = ['Easiness', 'Speed', 'Success', 'Accuracy', 'Comfort']"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 123,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Easiness & 6.7 & 0.8 & 4.8 & 1.6 \\\\\n",
|
||
|
|
"Speed & 6.7 & 0.5 & 2.9 & 1.2 \\\\\n",
|
||
|
|
"Success & 6.1 & 1.6 & 3.6 & 1.7 \\\\\n",
|
||
|
|
"Accuracy & 6.2 & 1.3 & 3.4 & 1.4 \\\\\n",
|
||
|
|
"Comfort & 6.7 & 0.7 & 3.1 & 1.4 \\\\\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"for i, c in enumerate(cols):\n",
|
||
|
|
" s = lablesQ[i] + \" \"\n",
|
||
|
|
" for con in [\"Finger\", \"Knuckle\"]:\n",
|
||
|
|
" s = \"%s & %.1f\" % (s, dfQ[dfQ.Task == con].mean()[c])\n",
|
||
|
|
" s = \"%s & %.1f\" % (s, dfQ[dfQ.Task == con].std()[c])\n",
|
||
|
|
" print(s + \" \\\\\\\\\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 109,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"(1, 7)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 109,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"M = dfQ[dfQ.Task == \"Knuckle\"].mean()[cols]\n",
|
||
|
|
"STD = dfQ[dfQ.Task == \"Knuckle\"].std()[cols]\n",
|
||
|
|
"plt.bar(np.array(np.arange(len(M))) -0.2, M, yerr=STD, width=.4, color=colors[1])\n",
|
||
|
|
"\n",
|
||
|
|
"M = dfQ[dfQ.Task == \"Finger\"].mean()[cols]\n",
|
||
|
|
"STD = dfQ[dfQ.Task == \"Finger\"].std()[cols]\n",
|
||
|
|
"plt.bar(np.array(np.arange(len(M))) +0.2, M, yerr=STD, width=.4, color=colors[3])\n",
|
||
|
|
"plt.xticks(list(range(len(M))), lablesQ)\n",
|
||
|
|
"plt.ylim(1,7)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 136,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"dfX = dfQ.copy(deep=True)\n",
|
||
|
|
"lst = dfX.columns[:2].to_list()\n",
|
||
|
|
"lst.extend(lablesQ)\n",
|
||
|
|
"dfX.columns = lst\n",
|
||
|
|
"dfX.to_csv(\"./DataStudyEvaluation/R_Quest.csv\", index=False)"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"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": "3.6.7"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|