wrist_data_computations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wrist Movement Data Formating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from math import sqrt,acos,degrees\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.signal import resample\n",
    "from tqdm.notebook import tqdm\n",
    "from time import time\n",
    "from Data_preparation_Library import *\n",
    "import os\n",
    "import csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EMG Data Extraction (Cumilative Function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_dataframes(path,file,save=False,duration=None,dtm_theta=None):\n",
    "    file_s = file\n",
    "    file = '/marker_data/' + file\n",
    "    \n",
    "    with open(path+'/'+file, \"r\") as f:\n",
    "        lines = f.readlines()\n",
    "    emg_labels = ['Frame','Sub Frame',\n",
    "                 'IM EMG1',\n",
    "                 'IM EMG2',\n",
    "                 'IM EMG3',\n",
    "                 'IM EMG4',\n",
    "                 'IM EMG5',\n",
    "                 'IM EMG6',\n",
    "                 'IM EMG7',\n",
    "                 'IM EMG8',\n",
    "                 'IM EMG9',\n",
    "                 'IM EMG10',\n",
    "                 'IM EMG11',\n",
    "                 'IM EMG12']\n",
    "    emg_labels_ref = ['Frame','Sub Frame',\n",
    "                 'EMG1',\n",
    "                 'EMG2',\n",
    "                 'EMG3',\n",
    "                 'EMG4',\n",
    "                 'EMG5',\n",
    "                 'EMG6',\n",
    "                 'EMG7',\n",
    "                 'EMG8',\n",
    "                 'EMG9',\n",
    "                 'EMG10',\n",
    "                 'EMG11',\n",
    "                 'EMG12']\n",
    "    marker_labels = ['Frame','Sub Frame',\n",
    "                  'RSHO_X','RSHO_Y','RSHO_Z',\n",
    "                  'RUPA_X','RUPA_Y','RUPA_Z',\n",
    "                  'RELB_X','RELB_Y','RELB_Z',\n",
    "                  'RM1_X','RM1_Y','RM1_Z',\n",
    "                 'RFRM_X','RFRM_Y','RFRM_Z',\n",
    "                 'WRM2_X','WRM2_Y','WRM2_Z',\n",
    "                 'RWRA_X','RWRA_Y','RWRA_Z',\n",
    "                 'RWRB_X','RWRB_Y','RWRB_Z',\n",
    "                 'RFIN_X','RFIN_Y','RFIN_Z']\n",
    "    \n",
    "    pronation_movement = ['No Motion','Supination','Pronation']\n",
    "    flexion_movement = ['No Motion','Extension','Flexion']\n",
    "    radial_movement = ['No Motion','Ulnar','Radial']\n",
    "    dtm_movement = ['No Motion','Forward','Backward']\n",
    "    \n",
    "    #################\n",
    "    # EMG Data Frame#\n",
    "    #################\n",
    "    \n",
    "    emg_lines = []\n",
    "    for line in lines[5:]:\n",
    "        if line=='\\n':\n",
    "            break\n",
    "        emg_lines.append(line.split(','))\n",
    "    emg_df = pd.DataFrame(np.array(emg_lines),columns=lines[3].split(','))\n",
    "    emg_df = emg_df[emg_labels]\n",
    "    emg_df.columns = emg_labels_ref\n",
    "#     print(emg_df)\n",
    "#     print(emg_df.info())\n",
    "#     print(emg_df.describe())\n",
    "#     emg_df = emg_df.interpolate()\n",
    "    if duration != None:\n",
    "        duration = duration*2000\n",
    "        emg_df = emg_df.head(duration)\n",
    "    emg_df = emg_df[emg_df.columns].astype(float)\n",
    "#     duration = emg_df.shape[0]/2000\n",
    "    \n",
    "    # Marker Data Frame\n",
    "    marker_lines = []\n",
    "    marker_line_start = None\n",
    "    if duration == None:\n",
    "        for i in range(len(lines)):\n",
    "            if lines[i]=='Trajectories\\n':\n",
    "                marker_line_start = i\n",
    "                break\n",
    "    else:\n",
    "        marker_line_start = duration+6\n",
    "        \n",
    "    for line in lines[marker_line_start+5:]:\n",
    "        if line=='\\n':\n",
    "            break\n",
    "        marker_lines.append(line.split(','))\n",
    "    marker_df = pd.DataFrame(np.array(marker_lines),columns=marker_labels)\n",
    "    marker_df = marker_df[marker_df.columns].astype(float)\n",
    "    marker_df = marker_df.interpolate()\n",
    "    \n",
    "    # Angles Dataframe\n",
    "    angles_df = compute_wrist_angles(marker_df,degree=True)\n",
    "    \n",
    "    pronations = np.array(angles_df['Pronation_Angle'])\n",
    "    flexions = np.array(angles_df['Flexion_Angle'])\n",
    "    radials = np.array(angles_df['Radial_Angle'])\n",
    "    elbows = np.array(angles_df['Elbow_Joint'])\n",
    "    \n",
    "    # Resampling to EMG SR(2000 Hz) from Vicon SR(100 Hz)\n",
    "    pronations = resample_series(pronations,100,2000)\n",
    "    flexions = resample_series(flexions,100,2000)\n",
    "    radials = resample_series(radials,100,2000)\n",
    "    elbows = resample_series(elbows,100,2000)    \n",
    "    \n",
    "    diff_interval = 1000\n",
    "    \n",
    "    pronation_labels,pronation_movement_labels = direction_labels(pronations,diff_interval,pronation_movement)\n",
    "    flexion_labels,flexion_movement_labels = direction_labels(flexions,diff_interval,flexion_movement)\n",
    "    radial_labels,radial_movement_labels = direction_labels(radials,diff_interval,radial_movement)\n",
    "    \n",
    "    # Dart Throwing Motion Labeling\n",
    "    dtm_labels = []\n",
    "    ln = len(flexion_labels)\n",
    "    \n",
    "    for i in range(ln):\n",
    "        if(flexion_labels[i]==0 and radial_labels[i]==0):\n",
    "            dtm_labels.append(0)\n",
    "        elif(flexion_labels[i]==2 and radial_labels[i]==1):\n",
    "            dtm_labels.append(1)\n",
    "        elif(flexion_labels[i]==1 and radial_labels[i]==2):\n",
    "            dtm_labels.append(2)\n",
    "        else:\n",
    "            dtm_labels.append(0)\n",
    "    \n",
    "    emg_df['Pronation_Angle'] = pronations\n",
    "    emg_df['Pronation_Label'] = pronation_labels\n",
    "    \n",
    "    emg_df['Flexion_Angle'] = flexions\n",
    "    emg_df['Flexion_Label'] = flexion_labels\n",
    "    \n",
    "    emg_df['Radial_Angle'] = radials\n",
    "    emg_df['Radial_Label'] = radial_labels\n",
    "    \n",
    "    emg_df['Elbow_Joint_Angle'] = elbows\n",
    "    \n",
    "    emg_df['DTM_Label'] = dtm_labels\n",
    "    \n",
    "    if dtm_theta != None:\n",
    "        dtm_angles = get_DTM_angles(marker_df, dtm_theta)\n",
    "        dtm_angles = resample_series(dtm_angles,100,2000)  \n",
    "        emg_df['DTM_Angle'] = dtm_angles \n",
    "    \n",
    "    if(save==True):\n",
    "        emg_df.to_csv(path+'/computed_'+file_s)\n",
    "        \n",
    "    return emg_df,marker_df, angles_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resample_series(data,sr_origin,sr_new):\n",
    "    \"\"\"\n",
    "    Upsamples Series Vector to required Freq(Hz)\n",
    "    data - Series 1D Array\n",
    "    sr_origin - Origin Sampling Rate\n",
    "    sr_new - New Sampling Rate\n",
    "    Return - Resampled Data to Given Sample Rate\n",
    "    \"\"\"\n",
    "    data = np.array(data)\n",
    "    ln = data.shape[0]\n",
    "    new_ln = int(ln*(sr_new/sr_origin))\n",
    "    resampled_array = resample(data,new_ln)    \n",
    "    return np.array(resampled_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Wrist Angles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_wrist_angles(df,degree=False):\n",
    "    # Wrist Segment\n",
    "    WRM2 = df[['WRM2_X','WRM2_Y','WRM2_Z']]\n",
    "    RWRA = df[['RWRA_X','RWRA_Y','RWRA_Z']]\n",
    "    RWRB = df[['RWRB_X','RWRB_Y','RWRB_Z']]\n",
    "    # Palm Segment\n",
    "    RFIN = df[['RFIN_X','RFIN_Y','RFIN_Z']]\n",
    "\n",
    "    # Elbow Segment\n",
    "    RFRM = df[['RFRM_X','RFRM_Y','RFRM_Z']]\n",
    "    RM1 = df[['RM1_X','RM1_Y','RM1_Z']]\n",
    "    RELB = df[['RELB_X','RELB_Y','RELB_Z']]\n",
    "    # Shoulder Segment\n",
    "    RSHO = df[['RSHO_X','RSHO_Y','RSHO_Z']]\n",
    "    RUPA = df[['RUPA_X','RUPA_Y','RUPA_Z']]\n",
    "    # Bisector Point\n",
    "    MID = (np.array(RWRB) + np.array(RWRA))/2\n",
    "    MIDE = (np.array(RFRM) + np.array(RM1))/2\n",
    "\n",
    "    # Translate Wrist to Elbow Segment Mid\n",
    "    RWRB_E = RWRB - MIDE\n",
    "\n",
    "    flexion_angles = angles_lines(RFIN,WRM2,MID,deg=degree)\n",
    "    radial_angles = angles_lines(RFIN,RWRB,MID,deg=degree)-90\n",
    "    pronation_angles = angles_lines(RFRM,RWRB_E,MIDE,deg=degree)\n",
    "    elbow_angles = angles_lines(RSHO,MID,MIDE,deg=degree)\n",
    "    \n",
    "    df_labels = ['Flexion_Angle','Radial_Angle','Pronation_Angle','Elbow_Joint']\n",
    "#     df_labels = ['Pitch','Yaw','Roll','Elbow_Joint']\n",
    "    ndf = pd.DataFrame(columns=df_labels)\n",
    "    ndf['Flexion_Angle'] = flexion_angles\n",
    "    ndf['Radial_Angle'] = radial_angles\n",
    "    ndf['Pronation_Angle'] = pronation_angles\n",
    "    ndf['Elbow_Joint'] = elbow_angles\n",
    "    return ndf\n",
    "\n",
    "def angles_lines(p1,p2,mid,deg=False):\n",
    "    u = np.array(p1)-np.array(mid)\n",
    "    v = np.array(p2)-np.array(mid)\n",
    "    i1,j1,k1 = u[:,0],u[:,1],u[:,2]\n",
    "    i2,j2,k2 = v[:,0],v[:,1],v[:,2]\n",
    "    angles = []\n",
    "    for t in range(len(i1)):\n",
    "        cos_t = abs(i1[t]*i2[t]+j1[t]*j2[t]+k1[t]*k2[t])\n",
    "        cos_t = cos_t/(sqrt(i1[t]**2+j1[t]**2+k1[t]**2)*sqrt(i2[t]**2+j2[t]**2+k2[t]**2))\n",
    "        if deg==False:\n",
    "            angles.append(acos(cos_t))  \n",
    "        if deg==True:\n",
    "            angles.append(degrees(acos(cos_t)))\n",
    "    return np.array(angles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Movement Labelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def direction_labels(array,interval=50,movements=None,angle_thresh=5):\n",
    "    \"\"\"\n",
    "    0 - No Motion\n",
    "    1 - Positive Direction\n",
    "    2 - Negative Direction\n",
    "    \"\"\"\n",
    "    labels = []\n",
    "    diff_arr = difference(array,interval)\n",
    "    data_len = len(array)\n",
    "    len_diff = int(data_len - len(diff_arr))\n",
    "    \n",
    "    # Hot Labelling\n",
    "    for diff in diff_arr:\n",
    "        if(abs(diff)>angle_thresh):\n",
    "            labels.append(0)\n",
    "        elif(diff>0):\n",
    "            labels.append(1)\n",
    "        elif(diff<0):\n",
    "            labels.append(2)\n",
    "            \n",
    "    # Balencing\n",
    "    if(len_diff%2==0):\n",
    "        nd = int(len_diff/2)\n",
    "        labels = [labels[0]]*nd + labels\n",
    "        labels = labels + [labels[-1]]*nd\n",
    "    else:\n",
    "        nd = int((len_diff+1)/2)\n",
    "        labels = [labels[0]]*nd + labels\n",
    "        labels = labels + [labels[-1]]*nd \n",
    "        labels = labels[1:]\n",
    "        \n",
    "    # Movement Labelling\n",
    "    if(movements==None):\n",
    "        return np.array(labels)\n",
    "    else:\n",
    "        movement_labels = []\n",
    "        hot_labels = labels\n",
    "        for lb in labels:\n",
    "            movement_labels.append(movements[lb])\n",
    "        return hot_labels,movement_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def direction_labels(array,interval=50,movements=None,angle_thresh=3):\n",
    "    \"\"\"\n",
    "    0 - No Motion\n",
    "    1 - Positive Direction\n",
    "    2 - Negative Direction\n",
    "    \"\"\"\n",
    "    labels = []\n",
    "    data = np.array(array)\n",
    "    avg = (data.max()-data.min())/2\n",
    "    for x in list(data):\n",
    "        if(abs(avg-x)<angle_thresh):\n",
    "            labels.append(0)\n",
    "        else:\n",
    "            labels.append(3)\n",
    "            \n",
    "    n = len(array)/2000\n",
    "    split_list = split(data,int(n))\n",
    "    group_labels = []\n",
    "    for arr in split_list:\n",
    "        if(difference(arr).mean()>0):\n",
    "            group_labels.append(1)\n",
    "        else:\n",
    "            group_labels.append(2)\n",
    "    direction_labels = []        \n",
    "    for gl in group_labels:\n",
    "        for i in range(2000):\n",
    "            direction_labels.append(gl)\n",
    "    for i in range(len(direction_labels)):\n",
    "        if(labels[i]!=0):\n",
    "            labels[i]=direction_labels[i]\n",
    "            \n",
    "    # Movement Labelling\n",
    "    if(movements==None):\n",
    "        return np.array(labels)\n",
    "    else:\n",
    "        movement_labels = []\n",
    "        hot_labels = labels\n",
    "        for lb in labels:\n",
    "            movement_labels.append(movements[lb])\n",
    "        return hot_labels,movement_labels\n",
    "\n",
    "def split(a, n):\n",
    "    k, m = divmod(len(a), n)\n",
    "    return list((a[i*k+min(i, m):(i+1)*k+min(i+1, m)] for i in range(n)))\n",
    "\n",
    "def difference(dataset, interval=1):\n",
    "\tdiff = list()\n",
    "\tfor i in range(interval, len(dataset)):\n",
    "\t\tvalue = dataset[i] - dataset[i - interval]\n",
    "\t\tdiff.append(value)\n",
    "\treturn np.array(diff)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def direction_labels(array,interval=50,movements=None):\n",
    "    \"\"\"\n",
    "    0 - No Motion\n",
    "    1 - Positive Direction\n",
    "    2 - Negative Direction\n",
    "    \"\"\"\n",
    "    labels = [0]*interval\n",
    "    i=interval\n",
    "    std = np.array(array).std()\n",
    "    while(len(labels)<len(array)):\n",
    "        diff = difference(array[i-interval:i]).mean()\n",
    "        if(abs(diff)<std/1000):\n",
    "            labels.append(0)\n",
    "        elif(diff>0):\n",
    "            labels.append(1)\n",
    "        elif(diff<0):\n",
    "            labels.append(2)\n",
    "        i=i+1\n",
    "    # Movement Labelling\n",
    "    if(movements==None):\n",
    "        return np.array(labels)\n",
    "    else:\n",
    "        movement_labels = []\n",
    "        hot_labels = labels\n",
    "        for lb in labels:\n",
    "            movement_labels.append(movements[lb])\n",
    "        return hot_labels,movement_labels\n",
    "\n",
    "def difference(dataset, interval=1):\n",
    "\tdiff = []\n",
    "\tfor i in range(interval, len(dataset)):\n",
    "\t\tvalue = dataset[i] - dataset[i - interval]\n",
    "\t\tdiff.append(value)\n",
    "\treturn np.array(diff)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DTM Angles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_RDTM(A,B,D,theta):\n",
    "    Ax,Ay,Az = A[0],A[1],A[2]\n",
    "    Bx,By,Bz = B[0],B[1],B[2]\n",
    "    Dx,Dy,Dz = D[0],D[1],D[2]\n",
    "    \n",
    "    pA = G3D.Point(A[0],A[1],A[2])\n",
    "    pB = G3D.Point(B[0],B[1],B[2])\n",
    "    pD = G3D.Point(D[0],D[1],D[2])\n",
    "\n",
    "    a,b,c = plane_normal(A,B,D)\n",
    "\n",
    "    v = normalize_vector([Dx - Bx,Dy - By,Dz - Bz])\n",
    "    lv,mv,nv = v\n",
    "\n",
    "    m = (cos(theta) - sin(theta)*b*lv/nv - (sin(theta)*a*lv**2)/nv*mv - sin(theta)*a*nv/mv)\n",
    "    m = m / ((lv**2 + mv**3 + nv**2)/mv)\n",
    "    l = sin(theta)*b/nv + lv*sin(theta)*a/(nv*mv) + lv*m/mv\n",
    "    n = (sin(theta)*a + nv*m)/mv\n",
    "    \n",
    "    y = normalize_vector([l,m,n])\n",
    "    l,m,n = v\n",
    "    \n",
    "    return intersection_lines([l,m,n],B,[lv,mv,nv],A)\n",
    "\n",
    "def plane_normal(P1,P2,P3):\n",
    "    P12 = np.array(P2) - np.array(P1)\n",
    "    P13 = np.array(P3) - np.array(P1)\n",
    "    normal = np.cross(P12,P13)\n",
    "    normal = normalize_vector(normal)\n",
    "    return normal\n",
    "\n",
    "def intersection_lines(v,B,y,A):\n",
    "    l,m,n = v\n",
    "    lv,mv,lv = y\n",
    "    Bx, By, Bz = [B[0], B[1], B[2]]\n",
    "    Ax, Ay, Az = [A[0], A[1], A[2]]\n",
    "    \n",
    "    lam = lv*(By-Ay) - mv*(Bx+Ax)\n",
    "    lam = lam/(l*mv - m*lv)\n",
    "    mu = (Bx - Ax + lam*l)/lv\n",
    "    \n",
    "    x = Bx + lam*l\n",
    "    y = By + lam*m\n",
    "    z = Bz + lam*n\n",
    "    \n",
    "    return np.array([x,y,z])\n",
    "\n",
    "def normalize_vector(v):\n",
    "    v = np.array(v)\n",
    "    norm = np.linalg.norm(v)\n",
    "    if norm == 0: \n",
    "        return v\n",
    "    else:\n",
    "        return v / norm\n",
    "\n",
    "def get_DTM_angles(marker_df, theta):\n",
    "    # Main Function to Get DTM Angles from Marker DF\n",
    "    all_RDTM = get_RDTM_df(marker_df,theta)\n",
    "    \n",
    "    RFIN = marker_df[['RFIN_X', 'RFIN_Y','RFIN_Z']].values\n",
    "    WRM2 = marker_df[['WRM2_X', 'WRM2_Y','WRM2_Z']].values\n",
    "    RWRA = marker_df[['RWRA_X','RWRA_Y', 'RWRA_Z']].values\n",
    "    RWRB = marker_df[['RWRB_X', 'RWRB_Y','RWRB_Z']].values\n",
    "    MID = (np.array(RWRB) + np.array(RWRA))/2\n",
    "    \n",
    "    dtm_anles = angles_lines(all_RDTM,WRM2,MID,deg=True)\n",
    "    # Angle Filter Parameters\n",
    "    dtm_anles_fl = filter_array(dtm_anles,order=4,cf=5,fs=100)\n",
    "    return dtm_anles_fl\n",
    "\n",
    "def get_RDTM_df(marker_df, theta):\n",
    "    RFIN = marker_df[['RFIN_X', 'RFIN_Y','RFIN_Z']].values\n",
    "    WRM2 = marker_df[['WRM2_X', 'WRM2_Y','WRM2_Z']].values\n",
    "    RWRA = marker_df[['RWRA_X','RWRA_Y', 'RWRA_Z']].values\n",
    "    RWRB = marker_df[['RWRB_X', 'RWRB_Y','RWRB_Z']].values\n",
    "    MID = (np.array(RWRB) + np.array(RWRA))/2\n",
    "    theta = radians(30) # degrees\n",
    "\n",
    "    A_all= RFIN\n",
    "    B_all= MID\n",
    "    D_all= RWRB\n",
    "\n",
    "    RDTM_all = []\n",
    "    for i in range(marker_df.shape[0]):\n",
    "        A = A_all[i]\n",
    "        B = B_all[i]\n",
    "        D = D_all[i]\n",
    "        x,y,z = get_RDTM(A,B,D,theta)\n",
    "        rdtm = [x,y,z]\n",
    "        RDTM_all.append(rdtm)\n",
    "\n",
    "    return np.array(RDTM_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Generation Section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Hammer.csv', 'Hammer01.csv', 'Hammer02.csv']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = []\n",
    "trial_names = ['Hammer']\n",
    "# trial_names = ['Bulb','Hammer','Cup','Screw']\n",
    "for exp in trial_names:\n",
    "    for i in range(3):\n",
    "        if(i==0):\n",
    "            files.append(exp+'.csv')\n",
    "        else:\n",
    "            files.append(exp+'0'+str(i)+'.csv')\n",
    "\n",
    "path = 'Subjects/Varun'\n",
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b2d1d32eaca40d1b5a39a59183f10b3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eapsed- 78.30114150047302 s\n",
      "Eapsed- 76.70172595977783 s\n",
      "Eapsed- 77.84696102142334 s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "start = time()\n",
    "for file in tqdm(files):\n",
    "    extract_dataframes(path,file,save=True,dtm_theta=90-16.1)\n",
    "    print('Eapsed-',time()-start,'s')\n",
    "    start = time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cup.csv', 'Cup01.csv', 'Cup02.csv']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = []\n",
    "trial_names = ['Cup']\n",
    "# trial_names = ['Bulb','Hammer','Cup','Screw']\n",
    "for exp in trial_names:\n",
    "    for i in range(3):\n",
    "        if(i==0):\n",
    "            files.append(exp+'.csv')\n",
    "        else:\n",
    "            files.append(exp+'0'+str(i)+'.csv')\n",
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c46289dc02364991aca98783746fea21",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-e8316a57e7a9>:15: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  m = (cos(theta) - sin(theta)*b*lv/nv - (sin(theta)*a*lv**2)/nv*mv - sin(theta)*a*nv/mv)\n",
      "<ipython-input-8-e8316a57e7a9>:15: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  m = (cos(theta) - sin(theta)*b*lv/nv - (sin(theta)*a*lv**2)/nv*mv - sin(theta)*a*nv/mv)\n",
      "<ipython-input-8-e8316a57e7a9>:17: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  l = sin(theta)*b/nv + lv*sin(theta)*a/(nv*mv) + lv*m/mv\n",
      "<ipython-input-8-e8316a57e7a9>:17: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  l = sin(theta)*b/nv + lv*sin(theta)*a/(nv*mv) + lv*m/mv\n",
      "<ipython-input-8-e8316a57e7a9>:40: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  mu = (Bx - Ax + lam*l)/lv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eapsed- 77.63634276390076 s\n",
      "Eapsed- 75.30815553665161 s\n",
      "Eapsed- 75.46029782295227 s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "start = time()\n",
    "for file in tqdm(files):\n",
    "    extract_dataframes(path,file,save=True,dtm_theta=90-14.52)\n",
    "    print('Eapsed-',time()-start,'s')\n",
    "    start = time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating Fazil Data\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ae790c92a3354747ac81545b0af764a4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=12.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Eapsed- 0.0019953250885009766 s\n",
      "Generating Ingy Data\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dc616188401544a59ef05d8f10d77a09",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=12.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Eapsed- 0.0 s\n",
      "Generating Varun Data\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "07c91269d3dd4699b7cd8398ea877dd7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=12.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Eapsed- 0.002956867218017578 s\n",
      "Generating Abhishek Data\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e629bcbaacc41858f038ef6a6e64412",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=12.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Eapsed- 0.001995086669921875 s\n",
      "Generating Nikhil Data\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a06068a51c9344b4a00e80b9ac3dc8a5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=12.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Eapsed- 0.001995086669921875 s\n",
      "Generating Jai Data\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5af2fe33ea804afc96153bfcf1209e53",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=12.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: ''",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-fed565c84f1d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mstart\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mfile\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiles\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m         \u001b[0mextract_dataframes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m         \u001b[0mstart\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Eapsed-'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m's'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-2-5a38056e5713>\u001b[0m in \u001b[0;36mextract_dataframes\u001b[1;34m(path, file, save)\u001b[0m\n\u001b[0;32m     74\u001b[0m         \u001b[0mmarker_lines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m','\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m     \u001b[0mmarker_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmarker_lines\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmarker_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 76\u001b[1;33m     \u001b[0mmarker_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmarker_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmarker_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     77\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     78\u001b[0m     \u001b[1;31m# Angles Dataframe\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m   5535\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5536\u001b[0m             \u001b[1;31m# else, only a single dtype is given\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5537\u001b[1;33m             \u001b[0mnew_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5538\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"astype\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5539\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    565\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"raise\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    566\u001b[0m     ) -> \"BlockManager\":\n\u001b[1;32m--> 567\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"astype\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    568\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    569\u001b[0m     def convert(\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[0;32m    394\u001b[0m                 \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    395\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 396\u001b[1;33m                 \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    397\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    398\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\pandas\\core\\internals\\blocks.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    588\u001b[0m             \u001b[0mvals1d\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    589\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 590\u001b[1;33m                 \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvals1d\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    591\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    592\u001b[0m                 \u001b[1;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[1;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[0;32m    987\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    988\u001b[0m         \u001b[1;31m# Explicit copy, or required since NumPy can't view from / to object.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 989\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    990\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    991\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: ''"
     ]
    }
   ],
   "source": [
    "subjects = ['Fazil','Ingy','Varun','Abhishek','Nikhil','Jai']\n",
    "for subject in subjects:\n",
    "    print('Generating '+subject+' Data')\n",
    "    path = 'Subjects/' + subject\n",
    "    start = time()\n",
    "    for file in tqdm(files):\n",
    "        extract_dataframes(path,file,save=True)\n",
    "        start = time()\n",
    "    print('Eapsed-',time()-start,'s')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing Section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +: 'NoneType' and 'int'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-65-77804555b245>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mfile\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Hammer01.csv'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0memg_df\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmarker_df\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mangles_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mextract_dataframes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-64-a79c704b8fba>\u001b[0m in \u001b[0;36mextract_dataframes\u001b[1;34m(path, file, save)\u001b[0m\n\u001b[0;32m     73\u001b[0m             \u001b[0mmarker_line_start\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m             \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 75\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmarker_line_start\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     76\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;34m'\\n'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     77\u001b[0m             \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'NoneType' and 'int'"
     ]
    }
   ],
   "source": [
    "file = 'Hammer01.csv'\n",
    "emg_df,marker_df, angles_df = extract_dataframes(path,file,save=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = np.array(emg_df['Flexion_Angle'])\n",
    "labels = direction_labels(array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pronation_Angle</th>\n",
       "      <th>Flexion_Angle</th>\n",
       "      <th>Radial_Angle</th>\n",
       "      <th>Elbow_Joint_Angle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>54000.000000</td>\n",
       "      <td>54000.000000</td>\n",
       "      <td>54000.000000</td>\n",
       "      <td>54000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>83.002648</td>\n",
       "      <td>33.569402</td>\n",
       "      <td>-6.259551</td>\n",
       "      <td>31.998065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.273903</td>\n",
       "      <td>9.799764</td>\n",
       "      <td>3.508042</td>\n",
       "      <td>3.917907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>58.969157</td>\n",
       "      <td>5.614780</td>\n",
       "      <td>-12.989439</td>\n",
       "      <td>28.056503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>85.873560</td>\n",
       "      <td>29.136508</td>\n",
       "      <td>-8.866880</td>\n",
       "      <td>29.494273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>86.748187</td>\n",
       "      <td>35.007697</td>\n",
       "      <td>-6.247308</td>\n",
       "      <td>30.587601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>87.093753</td>\n",
       "      <td>40.804670</td>\n",
       "      <td>-3.353273</td>\n",
       "      <td>32.635749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000591</td>\n",
       "      <td>54.398740</td>\n",
       "      <td>0.003850</td>\n",
       "      <td>53.363444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pronation_Angle  Flexion_Angle  Radial_Angle  Elbow_Joint_Angle\n",
       "count     54000.000000   54000.000000  54000.000000       54000.000000\n",
       "mean         83.002648      33.569402     -6.259551          31.998065\n",
       "std           8.273903       9.799764      3.508042           3.917907\n",
       "min          58.969157       5.614780    -12.989439          28.056503\n",
       "25%          85.873560      29.136508     -8.866880          29.494273\n",
       "50%          86.748187      35.007697     -6.247308          30.587601\n",
       "75%          87.093753      40.804670     -3.353273          32.635749\n",
       "max          90.000591      54.398740      0.003850          53.363444"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emg_df[['Pronation_Angle','Flexion_Angle','Radial_Angle','Elbow_Joint_Angle']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 54000 entries, 0 to 53999\n",
      "Data columns (total 22 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Frame              54000 non-null  float64\n",
      " 1   Sub Frame          54000 non-null  float64\n",
      " 2   EMG1               54000 non-null  float64\n",
      " 3   EMG2               54000 non-null  float64\n",
      " 4   EMG3               54000 non-null  float64\n",
      " 5   EMG4               54000 non-null  float64\n",
      " 6   EMG5               54000 non-null  float64\n",
      " 7   EMG6               54000 non-null  float64\n",
      " 8   EMG7               54000 non-null  float64\n",
      " 9   EMG8               54000 non-null  float64\n",
      " 10  EMG9               54000 non-null  float64\n",
      " 11  EMG10              54000 non-null  float64\n",
      " 12  EMG11              54000 non-null  float64\n",
      " 13  EMG12              54000 non-null  float64\n",
      " 14  Pronation_Angle    54000 non-null  float64\n",
      " 15  Pronation_Label    54000 non-null  int64  \n",
      " 16  Flexion_Angle      54000 non-null  float64\n",
      " 17  Flexion_Label      54000 non-null  int64  \n",
      " 18  Radial_Angle       54000 non-null  float64\n",
      " 19  Radial_Label       54000 non-null  int64  \n",
      " 20  Elbow_Joint_Angle  54000 non-null  float64\n",
      " 21  DTM_Label          54000 non-null  int64  \n",
      "dtypes: float64(18), int64(4)\n",
      "memory usage: 9.1 MB\n"
     ]
    }
   ],
   "source": [
    "emg_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "      <td>2.0</td>\n",
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       "      <td>1388.94</td>\n",
       "      <td>145.160</td>\n",
       "      <td>-1438.94</td>\n",
       "      <td>1367.98</td>\n",
       "      <td>227.434</td>\n",
       "      <td>-1428.36</td>\n",
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       "      <td>1432.410</td>\n",
       "      <td>483.7750</td>\n",
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       "      <td>-1464.21</td>\n",
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       "      <td>571.353</td>\n",
       "      <td>-1441.550</td>\n",
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       "      <th>2</th>\n",
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       "      <td>-22.3958</td>\n",
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       "      <td>145.209</td>\n",
       "      <td>-1438.93</td>\n",
       "      <td>1367.91</td>\n",
       "      <td>228.247</td>\n",
       "      <td>-1427.60</td>\n",
       "      <td>...</td>\n",
       "      <td>1432.230</td>\n",
       "      <td>483.8070</td>\n",
       "      <td>-1416.38</td>\n",
       "      <td>1440.900</td>\n",
       "      <td>492.399</td>\n",
       "      <td>-1464.09</td>\n",
       "      <td>1428.080</td>\n",
       "      <td>571.415</td>\n",
       "      <td>-1441.400</td>\n",
       "      <td>1453.900</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-22.3791</td>\n",
       "      <td>-1415.53</td>\n",
       "      <td>1388.92</td>\n",
       "      <td>145.180</td>\n",
       "      <td>-1438.95</td>\n",
       "      <td>1367.82</td>\n",
       "      <td>227.387</td>\n",
       "      <td>-1428.43</td>\n",
       "      <td>...</td>\n",
       "      <td>1432.020</td>\n",
       "      <td>483.8580</td>\n",
       "      <td>-1416.28</td>\n",
       "      <td>1440.690</td>\n",
       "      <td>492.432</td>\n",
       "      <td>-1464.01</td>\n",
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       "      <td>571.517</td>\n",
       "      <td>-1441.230</td>\n",
       "      <td>1453.670</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-22.3759</td>\n",
       "      <td>-1415.55</td>\n",
       "      <td>1388.90</td>\n",
       "      <td>145.252</td>\n",
       "      <td>-1438.94</td>\n",
       "      <td>1367.74</td>\n",
       "      <td>227.368</td>\n",
       "      <td>-1428.40</td>\n",
       "      <td>...</td>\n",
       "      <td>1431.810</td>\n",
       "      <td>483.8690</td>\n",
       "      <td>-1416.20</td>\n",
       "      <td>1440.460</td>\n",
       "      <td>492.465</td>\n",
       "      <td>-1463.92</td>\n",
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       "      <td>571.530</td>\n",
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       "      <td>-1019.34</td>\n",
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       "      <td>143.980</td>\n",
       "      <td>-1026.62</td>\n",
       "      <td>893.527</td>\n",
       "      <td>141.161</td>\n",
       "      <td>-953.965</td>\n",
       "      <td>914.613</td>\n",
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       "    <tr>\n",
       "      <th>2696</th>\n",
       "      <td>2697.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.4269</td>\n",
       "      <td>-1302.31</td>\n",
       "      <td>1330.91</td>\n",
       "      <td>136.034</td>\n",
       "      <td>-1261.03</td>\n",
       "      <td>1141.05</td>\n",
       "      <td>127.628</td>\n",
       "      <td>-1284.33</td>\n",
       "      <td>...</td>\n",
       "      <td>929.395</td>\n",
       "      <td>104.2320</td>\n",
       "      <td>-1022.38</td>\n",
       "      <td>925.282</td>\n",
       "      <td>141.667</td>\n",
       "      <td>-1029.24</td>\n",
       "      <td>894.583</td>\n",
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       "      <td>915.196</td>\n",
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       "      <th>2697</th>\n",
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       "      <td>1332.02</td>\n",
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       "      <td>-1263.29</td>\n",
       "      <td>1142.14</td>\n",
       "      <td>127.131</td>\n",
       "      <td>-1287.11</td>\n",
       "      <td>...</td>\n",
       "      <td>930.182</td>\n",
       "      <td>101.9910</td>\n",
       "      <td>-1025.37</td>\n",
       "      <td>926.239</td>\n",
       "      <td>139.457</td>\n",
       "      <td>-1031.88</td>\n",
       "      <td>895.549</td>\n",
       "      <td>134.743</td>\n",
       "      <td>-958.960</td>\n",
       "      <td>916.230</td>\n",
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       "      <th>2698</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>58.8699</td>\n",
       "      <td>-1305.83</td>\n",
       "      <td>1333.07</td>\n",
       "      <td>134.527</td>\n",
       "      <td>-1265.44</td>\n",
       "      <td>1143.18</td>\n",
       "      <td>126.575</td>\n",
       "      <td>-1289.73</td>\n",
       "      <td>...</td>\n",
       "      <td>931.101</td>\n",
       "      <td>99.8715</td>\n",
       "      <td>-1028.28</td>\n",
       "      <td>927.177</td>\n",
       "      <td>137.130</td>\n",
       "      <td>-1034.06</td>\n",
       "      <td>896.338</td>\n",
       "      <td>131.741</td>\n",
       "      <td>-961.574</td>\n",
       "      <td>917.140</td>\n",
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       "    <tr>\n",
       "      <th>2699</th>\n",
       "      <td>2700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.5846</td>\n",
       "      <td>-1307.58</td>\n",
       "      <td>1334.05</td>\n",
       "      <td>133.805</td>\n",
       "      <td>-1267.47</td>\n",
       "      <td>1144.25</td>\n",
       "      <td>126.096</td>\n",
       "      <td>-1292.25</td>\n",
       "      <td>...</td>\n",
       "      <td>931.899</td>\n",
       "      <td>97.8777</td>\n",
       "      <td>-1031.10</td>\n",
       "      <td>927.958</td>\n",
       "      <td>135.177</td>\n",
       "      <td>-1036.65</td>\n",
       "      <td>897.180</td>\n",
       "      <td>129.112</td>\n",
       "      <td>-964.233</td>\n",
       "      <td>918.146</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2700 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Frame  Sub Frame   RSHO_X   RSHO_Y   RSHO_Z   RUPA_X   RUPA_Y   RUPA_Z  \\\n",
       "0        1.0        0.0 -22.3984 -1415.42  1388.94  145.151 -1438.92  1368.05   \n",
       "1        2.0        0.0 -22.4007 -1415.48  1388.94  145.160 -1438.94  1367.98   \n",
       "2        3.0        0.0 -22.3958 -1415.50  1388.92  145.209 -1438.93  1367.91   \n",
       "3        4.0        0.0 -22.3791 -1415.53  1388.92  145.180 -1438.95  1367.82   \n",
       "4        5.0        0.0 -22.3759 -1415.55  1388.90  145.252 -1438.94  1367.74   \n",
       "...      ...        ...      ...      ...      ...      ...      ...      ...   \n",
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       "2699  2700.0        0.0  57.5846 -1307.58  1334.05  133.805 -1267.47  1144.25   \n",
       "\n",
       "       RELB_X   RELB_Y  ...    WRM2_Z    RWRA_X   RWRA_Y    RWRA_Z   RWRB_X  \\\n",
       "0     227.470 -1428.33  ...  1432.580  483.7280 -1416.61  1441.340  492.324   \n",
       "1     227.434 -1428.36  ...  1432.410  483.7750 -1416.51  1441.140  492.356   \n",
       "2     228.247 -1427.60  ...  1432.230  483.8070 -1416.38  1440.900  492.399   \n",
       "3     227.387 -1428.43  ...  1432.020  483.8580 -1416.28  1440.690  492.432   \n",
       "4     227.368 -1428.40  ...  1431.810  483.8690 -1416.20  1440.460  492.465   \n",
       "...       ...      ...  ...       ...       ...      ...       ...      ...   \n",
       "2695  128.029 -1281.46  ...   928.286  106.6080 -1019.34   924.160  143.980   \n",
       "2696  127.628 -1284.33  ...   929.395  104.2320 -1022.38   925.282  141.667   \n",
       "2697  127.131 -1287.11  ...   930.182  101.9910 -1025.37   926.239  139.457   \n",
       "2698  126.575 -1289.73  ...   931.101   99.8715 -1028.28   927.177  137.130   \n",
       "2699  126.096 -1292.25  ...   931.899   97.8777 -1031.10   927.958  135.177   \n",
       "\n",
       "       RWRB_Y    RWRB_Z   RFIN_X    RFIN_Y    RFIN_Z  \n",
       "0    -1464.31  1428.460  571.371 -1441.660  1454.360  \n",
       "1    -1464.21  1428.290  571.353 -1441.550  1454.130  \n",
       "2    -1464.09  1428.080  571.415 -1441.400  1453.900  \n",
       "3    -1464.01  1427.880  571.517 -1441.230  1453.670  \n",
       "4    -1463.92  1427.640  571.530 -1441.140  1453.410  \n",
       "...       ...       ...      ...       ...       ...  \n",
       "2695 -1026.62   893.527  141.161  -953.965   914.613  \n",
       "2696 -1029.24   894.583  137.634  -956.372   915.196  \n",
       "2697 -1031.88   895.549  134.743  -958.960   916.230  \n",
       "2698 -1034.06   896.338  131.741  -961.574   917.140  \n",
       "2699 -1036.65   897.180  129.112  -964.233   918.146  \n",
       "\n",
       "[2700 rows x 29 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marker_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2696</th>\n",
       "      <td>49.595587</td>\n",
       "      <td>-1.452591</td>\n",
       "      <td>84.975974</td>\n",
       "      <td>50.146337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2697</th>\n",
       "      <td>48.983187</td>\n",
       "      <td>-1.514625</td>\n",
       "      <td>85.167659</td>\n",
       "      <td>50.434258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2698</th>\n",
       "      <td>48.950820</td>\n",
       "      <td>-1.240858</td>\n",
       "      <td>85.256950</td>\n",
       "      <td>50.671959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2699</th>\n",
       "      <td>49.036380</td>\n",
       "      <td>-1.471969</td>\n",
       "      <td>85.339841</td>\n",
       "      <td>50.835199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2700 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Flexion_Angle  Radial_Angle  Pronation_Angle  Elbow_Joint\n",
       "0         10.969856     -6.998489        66.088010    32.607633\n",
       "1         10.981128     -6.991139        66.072454    32.608413\n",
       "2         11.041238     -6.996751        66.034850    32.612226\n",
       "3         10.987252     -6.933277        65.996566    32.617912\n",
       "4         11.001650     -6.956871        66.023879    32.637077\n",
       "...             ...           ...              ...          ...\n",
       "2695      50.098737     -1.422367        84.785396    49.798187\n",
       "2696      49.595587     -1.452591        84.975974    50.146337\n",
       "2697      48.983187     -1.514625        85.167659    50.434258\n",
       "2698      48.950820     -1.240858        85.256950    50.671959\n",
       "2699      49.036380     -1.471969        85.339841    50.835199\n",
       "\n",
       "[2700 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "angles_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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