_PY06-Distances_Speed-Figure7.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculations of the finger movement speeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 96 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import ast\n",
    "import os\n",
    "import matplotlib.patches as mpatches\n",
    "import itertools\n",
    "from pandarallel import pandarallel\n",
    "pandarallel.initialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Movement speeds in different conditions, for different fingers and phones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "task_length_df = pd.read_csv(\"./out/tasklengths.csv\", sep = \";\")\n",
    "task_length_df[\"Tasks\"] = task_length_df[\"Tasks\"].apply(ast.literal_eval)\n",
    "task_length_df[\"TaskLengths\"] = task_length_df[\"TaskLengths\"].apply(ast.literal_eval)\n",
    "\n",
    "lst = []\n",
    "\n",
    "fingers = [\"Thumb\", \"Index\", \"Middle\", \"Ring\", \"Little\"]\n",
    "tasks = [\"READ\", \"WRITE\", \"Fitts\"]\n",
    "j = \"Fn\"\n",
    "\n",
    "def sqrt_sum(x,y):\n",
    "    x_0 = x[0]\n",
    "    y_0 = y[0]\n",
    "    dist = 0\n",
    "    for x_1, y_1 in zip(x[1:],y[1:]):\n",
    "        a = np.array([x_0,y_0])\n",
    "        b = np.array([x_1,y_1])\n",
    "        dist += np.sqrt(np.sum((a-b)**2, axis=0))\n",
    "        x_0 = x_1\n",
    "        y_0 = y_1\n",
    "        \n",
    "    return dist\n",
    "\n",
    "def readStudyFile(file_path):\n",
    "    lst = []\n",
    "    pid, phone, cond = file_path.split(\"/\")[-1].replace(\".pkl\",\"\")[1:].split(\"_\")\n",
    "    pid = int(pid)\n",
    "    print(\"Reading %s %i %s %s\" % (file_path, pid, phone, cond))\n",
    "        \n",
    "    df = pd.read_pickle(file_path)\n",
    "    df = df[::2]\n",
    "    task_times = None\n",
    "    tasklength_row = task_length_df[(task_length_df[\"Participant\"]==pid)&(task_length_df[\"Phone\"]==phone)&(task_length_df[\"Cond\"]==cond)]\n",
    "    \n",
    "    if len(tasklength_row) == 0:\n",
    "        print(\"None tasklength_row file: %s\" % file_path)\n",
    "        return None\n",
    "    \n",
    "    for index,row in tasklength_row.iterrows():\n",
    "        task_sequence = row[\"Tasks\"]\n",
    "        task_times = row['TaskLengths']\n",
    "    \n",
    "    if task_times is None:\n",
    "        print(\"None task_times file: %s\" % file_path)\n",
    "        return None\n",
    "    \n",
    "    for task in tasks:   \n",
    "        tasktime = task_times[task_sequence.index(task)]\n",
    "        \n",
    "        for f in fingers:\n",
    "            temp_df = df[df[\"Task\"] == task].copy()\n",
    "            \n",
    "            help_dict = {\"Participant\":pid, \"Phone\":phone, \"Cond\":cond}\n",
    "            help_dict[\"Task\"] = task\n",
    "            help_dict[\"TaskLength\"] = tasktime\n",
    "            \n",
    "            a=(temp_df[\"%s_%s_X\"%(f,j)]>temp_df[\"%s_%s_X\"%(f,j)].mean()-temp_df[\"%s_%s_X\"%(f,j)].std()*2)&\\\n",
    "            (temp_df[\"%s_%s_X\"%(f,j)]<temp_df[\"%s_%s_X\"%(f,j)].mean()+temp_df[\"%s_%s_X\"%(f,j)].std()*2)&\\\n",
    "            (temp_df[\"%s_%s_Y\"%(f,j)]>temp_df[\"%s_%s_Y\"%(f,j)].mean()-temp_df[\"%s_%s_Y\"%(f,j)].std()*2)&\\\n",
    "            (temp_df[\"%s_%s_Y\"%(f,j)]<temp_df[\"%s_%s_Y\"%(f,j)].mean()+temp_df[\"%s_%s_Y\"%(f,j)].std()*2)\n",
    "\n",
    "\n",
    "            t = temp_df[a][\"Time\"]-temp_df.shift()[a][\"Time\"]\n",
    "            val = 20\n",
    "            x = temp_df[a][\"%s_%s_X\"%(f,j)]-temp_df.shift()[a][\"%s_%s_X\"%(f,j)]\n",
    "            x = x[x.index.isin(t[t<=val].index)]\n",
    "\n",
    "            y = temp_df[a][\"%s_%s_Y\"%(f,j)]-temp_df.shift()[a][\"%s_%s_Y\"%(f,j)]\n",
    "            y = y[y.index.isin(t[t<=val].index)]\n",
    "\n",
    "            z = temp_df[a][\"%s_%s_Z\"%(f,j)]-temp_df.shift()[a][\"%s_%s_Z\"%(f,j)]\n",
    "            z = z[z.index.isin(t[t<=val].index)]\n",
    "\n",
    "            t = t[t.index.isin(t[t<=val].index)]\n",
    "            #\n",
    "            l = np.sqrt(np.square(x)+np.square(y))\n",
    "            ql = np.sqrt(np.square(x)+np.square(y)+np.square(z))\n",
    "\n",
    "            cmps = ((l*100)/(t/1000))\n",
    "            len_before = len(cmps)\n",
    "            cmps = cmps[(cmps<cmps.mean()+cmps.std()*2)&(cmps>cmps.mean()-cmps.std()*2)]\n",
    "            len_after = len(cmps)\n",
    "            qcmps = ((ql*100)/(t/1000))\n",
    "            help_dict[\"Joint\"]=\"%s_%s\"%(f,j)\n",
    "            help_dict[\"Speed_2D\"] = cmps.mean()\n",
    "            help_dict[\"2D_dropped\"] = np.round((len_after/len_before),2)\n",
    "            help_dict[\"Speed_3D\"] = qcmps.mean()\n",
    "\n",
    "            lst.append(help_dict)\n",
    "    return lst\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"./TransformedPickles/\"\n",
    "files = []\n",
    "for file in os.listdir(path):\n",
    "    if not file.endswith(\".pkl\"):\n",
    "        continue\n",
    "    files.append(path+file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading ./TransformedPickles/P3_N6_walking.pkl 3 N6 walking\n",
      "Reading ./TransformedPickles/P10_N6_walking.pkl 10 N6 walking\n",
      "Reading ./TransformedPickles/P10_OPO_seated.pkl 10 OPO seated\n",
      "Reading ./TransformedPickles/P5_S4_seated.pkl 5 S4 seated\n",
      "Reading ./TransformedPickles/P16_S4_seated.pkl 16 S4 seated\n",
      "Reading ./TransformedPickles/P21_OPO_walking.pkl 21 OPO walking\n",
      "Reading ./TransformedPickles/P7_OPO_seated.pkl 7 OPO seated\n",
      "Reading ./TransformedPickles/P5_OPO_seated.pkl 5 OPO seated\n",
      "Reading ./TransformedPickles/P10_S3_seated.pkl 10 S3 seated\n",
      "Reading ./TransformedPickles/P12_OPO_seated.pkl 12 OPO seated\n",
      "Reading ./TransformedPickles/P14_S4_seated.pkl 14 S4 seated\n",
      "Reading ./TransformedPickles/P8_OPO_seated.pkl 8 OPO seated\n",
      "Reading ./TransformedPickles/P6_OPO_seated.pkl 6 OPO seated\n",
      "Reading ./TransformedPickles/P20_S4_seated.pkl 20 S4 seated\n",
      "Reading ./TransformedPickles/P7_N6_seated.pkl 7 N6 seated\n",
      "Reading ./TransformedPickles/P11_N6_seated.pkl 11 N6 seated\n",
      "Reading ./TransformedPickles/P10_S4_seated.pkl 10 S4 seated\n",
      "Reading ./TransformedPickles/P6_S3_walking.pkl 6 S3 walking\n",
      "Reading ./TransformedPickles/P21_OPO_seated.pkl 21 OPO seated\n",
      "Reading ./TransformedPickles/P19_S4_seated.pkl 19 S4 seated\n",
      "Reading ./TransformedPickles/P12_S4_walking.pkl 12 S4 walking\n",
      "Reading ./TransformedPickles/P11_S3_seated.pkl 11 S3 seated\n",
      "Reading ./TransformedPickles/P12_S4_seated.pkl 12 S4 seated\n",
      "Reading ./TransformedPickles/P6_S4_walking.pkl 6 S4 walking\n",
      "Reading ./TransformedPickles/P7_S3_walking.pkl 7 S3 walking\n",
      "Reading ./TransformedPickles/P16_OPO_walking.pkl 16 OPO walking\n",
      "Reading ./TransformedPickles/P14_OPO_walking.pkl 14 OPO walking\n",
      "Reading ./TransformedPickles/P21_S3_seated.pkl 21 S3 seated\n",
      "Reading ./TransformedPickles/P7_S4_walking.pkl 7 S4 walking\n",
      "Reading ./TransformedPickles/P8_OPO_walking.pkl 8 OPO walking\n",
      "Reading ./TransformedPickles/P3_OPO_seated.pkl 3 OPO seated\n",
      "Reading ./TransformedPickles/P3_N6_seated.pkl 3 N6 seated\n",
      "Reading ./TransformedPickles/P4_S4_walking.pkl 4 S4 walking\n",
      "Reading ./TransformedPickles/P11_OPO_walking.pkl 11 OPO walking\n",
      "Reading ./TransformedPickles/P17_OPO_seated.pkl 17 OPO seated\n",
      "Reading ./TransformedPickles/P21_N6_seated.pkl 21 N6 seated\n",
      "Reading ./TransformedPickles/P20_OPO_seated.pkl 20 OPO seated\n",
      "Reading ./TransformedPickles/P5_N6_walking.pkl 5 N6 walking\n",
      "Reading ./TransformedPickles/P3_OPO_walking.pkl 3 OPO walking\n",
      "Reading ./TransformedPickles/P12_N6_seated.pkl 12 N6 seated\n",
      "Reading ./TransformedPickles/P13_OPO_seated.pkl 13 OPO seated\n",
      "Reading ./TransformedPickles/P5_S3_seated.pkl 5 S3 seated\n",
      "Reading ./TransformedPickles/P13_S4_seated.pkl 13 S4 seated\n",
      "Reading ./TransformedPickles/P11_S3_walking.pkl 11 S3 walking\n",
      "Reading ./TransformedPickles/P4_OPO_walking.pkl 4 OPO walking\n",
      "Reading ./TransformedPickles/P13_N6_walking.pkl 13 N6 walking\n",
      "Reading ./TransformedPickles/P14_N6_seated.pkl 14 N6 seated\n",
      "Reading ./TransformedPickles/P13_S3_walking.pkl 13 S3 walking\n",
      "Reading ./TransformedPickles/P19_S3_seated.pkl 19 S3 seated\n",
      "Reading ./TransformedPickles/P12_S3_walking.pkl 12 S3 walking\n",
      "Reading ./TransformedPickles/P16_N6_seated.pkl 16 N6 seated\n",
      "Reading ./TransformedPickles/P20_S4_walking.pkl 20 S4 walking\n",
      "Reading ./TransformedPickles/P14_S3_walking.pkl 14 S3 walking\n",
      "Reading ./TransformedPickles/P7_S3_seated.pkl 7 S3 seated\n",
      "Reading ./TransformedPickles/P10_S3_walking.pkl 10 S3 walking\n",
      "Reading ./TransformedPickles/P10_N6_seated.pkl 10 N6 seated\n",
      "Reading ./TransformedPickles/P3_S3_walking.pkl 3 S3 walking\n",
      "Reading ./TransformedPickles/P13_OPO_walking.pkl 13 OPO walking\n",
      "Reading ./TransformedPickles/P6_S4_seated.pkl 6 S4 seated\n",
      "Reading ./TransformedPickles/P20_S3_seated.pkl 20 S3 seated\n",
      "Reading ./TransformedPickles/P14_OPO_seated.pkl 14 OPO seated\n",
      "Reading ./TransformedPickles/P10_OPO_walking.pkl 10 OPO walking\n",
      "Reading ./TransformedPickles/P20_N6_walking.pkl 20 N6 walking\n",
      "Reading ./TransformedPickles/P8_N6_seated.pkl 8 N6 seated\n",
      "Reading ./TransformedPickles/P13_N6_seated.pkl 13 N6 seated\n",
      "Reading ./TransformedPickles/P4_N6_seated.pkl 4 N6 seated\n",
      "Reading ./TransformedPickles/P6_N6_walking.pkl 6 N6 walking\n",
      "Reading ./TransformedPickles/P4_S3_walking.pkl 4 S3 walking\n",
      "Reading ./TransformedPickles/P14_N6_walking.pkl 14 N6 walking\n",
      "Reading ./TransformedPickles/P4_OPO_seated.pkl 4 OPO seated\n",
      "Reading ./TransformedPickles/P19_OPO_walking.pkl 19 OPO walking\n",
      "Reading ./TransformedPickles/P6_S3_seated.pkl 6 S3 seated\n",
      "Reading ./TransformedPickles/P17_S3_seated.pkl 17 S3 seated\n",
      "Reading ./TransformedPickles/P12_S3_seated.pkl 12 S3 seated\n",
      "Reading ./TransformedPickles/P14_S4_walking.pkl 14 S4 walking\n",
      "Reading ./TransformedPickles/P16_S3_seated.pkl 16 S3 seated\n",
      "Reading ./TransformedPickles/P5_N6_seated.pkl 5 N6 seated\n",
      "Reading ./TransformedPickles/P4_S4_seated.pkl 4 S4 seated\n",
      "Reading ./TransformedPickles/P19_S3_walking.pkl 19 S3 walking\n",
      "Reading ./TransformedPickles/P11_S4_walking.pkl 11 S4 walking\n",
      "Reading ./TransformedPickles/P16_S3_walking.pkl 16 S3 walking\n",
      "Reading ./TransformedPickles/P17_N6_seated.pkl 17 N6 seated\n",
      "Reading ./TransformedPickles/P16_N6_walking.pkl 16 N6 walking\n",
      "Reading ./TransformedPickles/P4_N6_walking.pkl 4 N6 walking\n",
      "Reading ./TransformedPickles/P8_S3_walking.pkl 8 S3 walking\n",
      "Reading ./TransformedPickles/P17_OPO_walking.pkl 17 OPO walking\n",
      "Reading ./TransformedPickles/P5_S3_walking.pkl 5 S3 walking\n",
      "Reading ./TransformedPickles/P13_S3_seated.pkl 13 S3 seated\n",
      "Reading ./TransformedPickles/P5_S4_walking.pkl 5 S4 walking\n",
      "Reading ./TransformedPickles/P21_N6_walking.pkl 21 N6 walking\n",
      "Reading ./TransformedPickles/P12_OPO_walking.pkl 12 OPO walking\n",
      "Reading ./TransformedPickles/P10_S4_walking.pkl 10 S4 walking\n",
      "Reading ./TransformedPickles/P17_N6_walking.pkl 17 N6 walking\n",
      "Reading ./TransformedPickles/P8_N6_walking.pkl 8 N6 walking\n",
      "Reading ./TransformedPickles/P20_S3_walking.pkl 20 S3 walking\n",
      "Reading ./TransformedPickles/P8_S3_seated.pkl 8 S3 seated\n",
      "Reading ./TransformedPickles/P19_S4_walking.pkl 19 S4 walking\n",
      "Reading ./TransformedPickles/P6_OPO_walking.pkl 6 OPO walking\n",
      "Reading ./TransformedPickles/P14_S3_seated.pkl 14 S3 seated\n",
      "Reading ./TransformedPickles/P21_S3_walking.pkl 21 S3 walking\n",
      "Reading ./TransformedPickles/P4_S3_seated.pkl 4 S3 seated\n",
      "Reading ./TransformedPickles/P7_N6_walking.pkl 7 N6 walking\n",
      "Reading ./TransformedPickles/P20_N6_seated.pkl 20 N6 seated\n",
      "Reading ./TransformedPickles/P17_S4_seated.pkl 17 S4 seated\n",
      "Reading ./TransformedPickles/P19_OPO_seated.pkl 19 OPO seated\n",
      "Reading ./TransformedPickles/P13_S4_walking.pkl 13 S4 walking\n",
      "Reading ./TransformedPickles/P21_S4_walking.pkl 21 S4 walking\n",
      "Reading ./TransformedPickles/P19_N6_seated.pkl 19 N6 seated\n",
      "Reading ./TransformedPickles/P7_OPO_walking.pkl 7 OPO walking\n",
      "Reading ./TransformedPickles/P11_N6_walking.pkl 11 N6 walking\n",
      "Reading ./TransformedPickles/P11_OPO_seated.pkl 11 OPO seated\n",
      "Reading ./TransformedPickles/P17_S3_walking.pkl 17 S3 walking\n",
      "Reading ./TransformedPickles/P3_S4_walking.pkl 3 S4 walking\n",
      "Reading ./TransformedPickles/P12_N6_walking.pkl 12 N6 walking\n",
      "Reading ./TransformedPickles/P11_S4_seated.pkl 11 S4 seated\n",
      "Reading ./TransformedPickles/P6_N6_seated.pkl 6 N6 seated\n",
      "Reading ./TransformedPickles/P17_S4_walking.pkl 17 S4 walking\n",
      "Reading ./TransformedPickles/P16_OPO_seated.pkl 16 OPO seated\n",
      "Reading ./TransformedPickles/P8_S4_walking.pkl 8 S4 walking\n",
      "Reading ./TransformedPickles/P8_S4_seated.pkl 8 S4 seated\n",
      "Reading ./TransformedPickles/P3_S3_seated.pkl 3 S3 seated\n",
      "Reading ./TransformedPickles/P3_S4_seated.pkl 3 S4 seated\n",
      "Reading ./TransformedPickles/P7_S4_seated.pkl 7 S4 seated\n",
      "Reading ./TransformedPickles/P21_S4_seated.pkl 21 S4 seated\n",
      "Reading ./TransformedPickles/P20_OPO_walking.pkl 20 OPO walking\n",
      "Reading ./TransformedPickles/P16_S4_walking.pkl 16 S4 walking\n",
      "Reading ./TransformedPickles/P19_N6_walking.pkl 19 N6 walking\n",
      "Reading ./TransformedPickles/P5_OPO_walking.pkl 5 OPO walking\n",
      "CPU times: user 431 ms, sys: 1.09 s, total: 1.53 s\n",
      "Wall time: 27.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "dfFiles = pd.DataFrame(files)\n",
    "dfFiles.columns=[\"File\"]\n",
    "ret = dfFiles.File.parallel_apply(lambda x: readStudyFile(x))\n",
    "ret2 = list(itertools.chain.from_iterable(ret))\n",
    "df = pd.DataFrame(ret2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_list = ['Index_Fn', 'Middle_Fn', 'Ring_Fn', 'Little_Fn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x126 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x126 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "phones = [\"S3\",\"S4\",\"OPO\",\"N6\"]\n",
    "phone_labels = [\"S3\",\"S4\",\"OPO\",\"N6\"]\n",
    "conditions = ['seated','walking']\n",
    "colors = ['#FF523E','#188D6C','#6499FF','#FFAC33']\n",
    "fingers = [\"Index_Fn\",\"Middle_Fn\",\"Ring_Fn\",\"Little_Fn\", \"Mean\"]\n",
    "finger_labels = [r\"Index ($F_1$)\", r\"Middle ($F_2$)\", r\"Ring ($F_3$)\", r\"Little ($F_4$)\",  r\"Mean\"]\n",
    "\n",
    "width = 0.2\n",
    "\n",
    "\n",
    "fig = []\n",
    "ax = []\n",
    "for i in range(len(conditions)):\n",
    "    fig1, ax1 = plt.subplots(1, figsize=(6, 1.75))\n",
    "    fig.append(fig1)\n",
    "    ax.append(ax1)\n",
    "\n",
    "    \n",
    "for idx_condition, condition in enumerate(conditions): \n",
    "    xticks = []\n",
    "    for idx_finger, finger in enumerate(fingers):\n",
    "        xticks.append(float(idx_finger) + 1.5 * width)\n",
    "        \n",
    "        for idx_phone, phone in enumerate(phones):\n",
    "            if (finger != \"Mean\"):\n",
    "                mean = df[(df.Joint == finger) & (df.Cond == condition) & (df.Phone == phone)].Speed_2D.mean()\n",
    "                sd = df[(df.Joint == finger) & (df.Cond == condition) & (df.Phone == phone)].Speed_2D.std()\n",
    "            else:\n",
    "                mean = df[(df.Cond == condition) & (df.Phone == phone) & (df[\"Joint\"].isin(f_list))].Speed_2D.mean()\n",
    "                sd = df[(df.Cond == condition) & (df.Phone == phone) & (df[\"Joint\"].isin(f_list))].Speed_2D.std()\n",
    "\n",
    "            \n",
    "            pos = idx_finger + (idx_phone * width) \n",
    "            ax[idx_condition].bar(pos, mean, yerr = sd/2, width = width - 0.02, capsize = 3, color = colors[idx_phone])\n",
    "\n",
    "            \n",
    "    fontsize = 8\n",
    "    if (idx_condition == 0):\n",
    "        patches = []\n",
    "        for i in range(len(colors)):\n",
    "            patch = mpatches.Patch(color=colors[i], label=phone_labels[i])\n",
    "            patches.append(patch)\n",
    "        ax[idx_condition].legend(handles= patches, ncol = 4, loc='upper center', bbox_to_anchor = (0.5, 0.97), borderaxespad=0., fontsize=fontsize) \n",
    "    \n",
    "    condLbl = [\"Sitting\", \"Walking\"]\n",
    "    ax[idx_condition].tick_params(axis='both', which='major', labelsize=fontsize)\n",
    "    ax[idx_condition].tick_params(axis='both', which='minor', labelsize=fontsize)\n",
    "          \n",
    "    ax[idx_condition].set_ylabel(\"Finger Activity in $cm/s$\", fontsize=fontsize)\n",
    "    ax[idx_condition].set_xticklabels(finger_labels, fontsize=fontsize)\n",
    "    ax[idx_condition].set_xticks(xticks)\n",
    "    ax[idx_condition].set_xlim(-(width + 0.1), len(xticks) - (width-0.1))\n",
    "    ax[idx_condition].set_ylim(0, 5.2)\n",
    "    \n",
    "    fig[idx_condition].savefig(\"./figures/finger_activity_\" + condition + \".pdf\", bbox_inches=\"tight\", pad_inches=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby([\"Cond\", \"Joint\", \"Participant\", \"Phone\"]).mean().Speed_2D.reset_index().rename(columns={\"Joint\": \"Finger\", \"Participant\": \"PId\"}).to_csv(\"./out/speeds.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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}