Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Attribute Information:\n",
    "\n",
    "Each data file contains 5 columns, organized as follows.\n",
    "\n",
    "Segment Lower Limb\n",
    "\n",
    "Channel Ch1 Ch2 Ch3 Ch4 Ch5\n",
    "\n",
    "Muscle RF BF VM ST FX\n",
    "\n",
    "Column 0 1 2 3 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Data Directory\n",
    "\n",
    "## Normal sEMG dataset in Txt\n",
    "file_path = \"./SEMG_DB1/N_TXT/\"\n",
    "\n",
    "## Abnormal sEMG dataset in Txt\n",
    "abnormal_path = \"./SEMG_DB1/A_TXT/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## sEMG Plot Function from text file\n",
    "\n",
    "class sEMGData:\n",
    "    \n",
    "    def __init__(self, path):\n",
    "        self.path = path\n",
    "        \n",
    "        # file name\n",
    "        f = open(path, 'r')\n",
    "        basename = os.path.basename(f.name)\n",
    "        \n",
    "        if 'mar' in basename:\n",
    "            self.output = 'mar'\n",
    "        elif 'pie' in basename:\n",
    "            self.output = 'pie'\n",
    "        elif 'sen' in basename:\n",
    "            self.output = 'sen'\n",
    "        \n",
    "        # invalid_raise - sk\n",
    "        # numpy array\n",
    "        self.array = np.genfromtxt(self.path, skip_header=7, invalid_raise=False)\n",
    "        # DataFrame\n",
    "        self.dataframe = pd.DataFrame(self.array, columns=['RF','BF','VM','ST','FX'])\n",
    "        \n",
    "    def return_array(self): \n",
    "        return self.array\n",
    "    \n",
    "    def return_df(self):   \n",
    "        return self.dataframe\n",
    "    \n",
    "    def return_output(self):\n",
    "        return self.output\n",
    "    \n",
    "    def plot_RF(self):    \n",
    "        plt.plot(self.dataframe['RF'])\n",
    "        plt.title('RF - rectus femoris')\n",
    "        plt.show()\n",
    "        \n",
    "    def plot_BF(self):  \n",
    "        plt.plot(self.dataframe['BF'])\n",
    "        plt.title('BF - biceps femoris')\n",
    "        plt.show()\n",
    "        \n",
    "    def plot_VM(self):\n",
    "        plt.plot(self.dataframe['VM'])\n",
    "        plt.title('VM - vastus internus')\n",
    "        plt.show()\n",
    "        \n",
    "    def plot_ST(self):\n",
    "        plt.plot(self.dataframe['ST'])\n",
    "        plt.title('ST - semitendinosus')\n",
    "        plt.show()\n",
    "        \n",
    "    def plot_FX(self):\n",
    "        plt.plot(self.dataframe['FX'])\n",
    "        plt.title('FX - knee flexion ()')\n",
    "        plt.show()  \n",
    "    \n",
    "    def sEMG_plot(self):\n",
    "        # Plot individual sEMG data\n",
    "        \n",
    "        plt.plot(self.dataframe['RF'])\n",
    "        plt.title('RF - rectus femoris')\n",
    "        plt.show()\n",
    "        \n",
    "        plt.plot(self.dataframe['BF'])\n",
    "        plt.title('BF - biceps femoris')\n",
    "        plt.show()\n",
    "        \n",
    "        plt.plot(self.dataframe['VM'])\n",
    "        plt.title('VM - vastus internus')\n",
    "        plt.show()\n",
    "        \n",
    "        plt.plot(self.dataframe['ST'])\n",
    "        plt.title('ST - semitendinosus')\n",
    "        plt.show()\n",
    "        \n",
    "        plt.plot(self.dataframe['FX'])\n",
    "        plt.title('FX - knee flexion ()')\n",
    "        plt.show()       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mar\n",
      "[[ 7.00e-04 -8.30e-03  4.50e-03 -9.10e-03  5.76e+01]\n",
      " [-8.00e-04 -3.80e-03  7.00e-04 -4.60e-03  5.75e+01]\n",
      " [-8.00e-04 -6.80e-03  1.50e-03 -2.30e-03  5.73e+01]\n",
      " ...\n",
      " [-3.00e-03  1.50e-03  4.50e-03  1.50e-03  7.20e+00]\n",
      " [-4.60e-03 -1.50e-03  6.00e-03  3.00e-03  7.40e+00]\n",
      " [ 1.27e-02  9.70e-03 -5.40e-02 -3.00e-03  7.50e+00]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-13b1858c132e>:21: ConversionWarning: Some errors were detected !\n",
      "    Line #5689 (got 1 columns instead of 5)\n",
      "    Line #5690 (got 1 columns instead of 5)\n",
      "    Line #5691 (got 1 columns instead of 5)\n",
      "    Line #5692 (got 1 columns instead of 5)\n",
      "    Line #5693 (got 1 columns instead of 5)\n",
      "    Line #5694 (got 1 columns instead of 5)\n",
      "    Line #5695 (got 1 columns instead of 5)\n",
      "    Line #5696 (got 1 columns instead of 5)\n",
      "    Line #5697 (got 1 columns instead of 5)\n",
      "    Line #5698 (got 1 columns instead of 5)\n",
      "    Line #5699 (got 1 columns instead of 5)\n",
      "    Line #5700 (got 1 columns instead of 5)\n",
      "    Line #5701 (got 1 columns instead of 5)\n",
      "    Line #5702 (got 1 columns instead of 5)\n",
      "    Line #5703 (got 1 columns instead of 5)\n",
      "    Line #5704 (got 1 columns instead of 5)\n",
      "    Line #5705 (got 1 columns instead of 5)\n",
      "    Line #5706 (got 1 columns instead of 5)\n",
      "    Line #5707 (got 1 columns instead of 5)\n",
      "  self.array = np.genfromtxt(self.path, skip_header=7, invalid_raise=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# instantiate sEMGData\n",
    "\n",
    "# Marching or Walking data\n",
    "nmarching_data = sEMGData(file_path + \"1Nmar.txt\")\n",
    "print(nmarching_data.return_output())\n",
    "print(nmarching_data.return_array())\n",
    "nmarching_data.return_df()\n",
    "nmarching_data.sEMG_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocessing Function\n",
    "\n",
    "def preprocess(folder):\n",
    "    \n",
    "    input_array = []\n",
    "    classes = []\n",
    "    \n",
    "    # iterates through folder\n",
    "    for filename in os.listdir(folder):\n",
    "        if filename.endswith('.txt'):\n",
    "            # instantiates each sEMG data\n",
    "            sEMG = sEMGData(folder + filename)\n",
    "            \n",
    "            input_array.append(sEMG.return_array())\n",
    "            classes.append(sEMG.return_output())\n",
    "    \n",
    "    # finds the max length of a sEMG sequence \n",
    "    max_len = max([len(i) for i in input_array])\n",
    "    \n",
    "    for i in input_array:\n",
    "        if (len(i) == max_len):\n",
    "            largest_array = i\n",
    "    \n",
    "    # list of padded sEMG numpy arrays\n",
    "    padded_array = []\n",
    "    \n",
    "    # loop through input_array and append into new padded array\n",
    "    for i in input_array:\n",
    "        # zero numpy array\n",
    "        zero_array = np.zeros(largest_array.shape)\n",
    "        zero_array[:i.shape[0],:i.shape[1]] = i\n",
    "\n",
    "        padded_array.append(zero_array)\n",
    "    \n",
    "    # convert to 3d numpy array\n",
    "    padded_array = np.dstack(padded_array)\n",
    "    \n",
    "    # transpose array\n",
    "    padded_array = padded_array.transpose(2,0,1)\n",
    "    \n",
    "    return padded_array, classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-13b1858c132e>:21: ConversionWarning: Some errors were detected !\n",
      "    Line #7308 (got 1 columns instead of 5)\n",
      "    Line #7309 (got 1 columns instead of 5)\n",
      "    Line #7310 (got 1 columns instead of 5)\n",
      "    Line #7311 (got 1 columns instead of 5)\n",
      "    Line #7312 (got 1 columns instead of 5)\n",
      "    Line #7313 (got 1 columns instead of 5)\n",
      "    Line #7314 (got 1 columns instead of 5)\n",
      "    Line #7315 (got 1 columns instead of 5)\n",
      "    Line #7316 (got 1 columns instead of 5)\n",
      "    Line #7317 (got 1 columns instead of 5)\n",
      "    Line #7318 (got 1 columns instead of 5)\n",
      "    Line #7319 (got 1 columns instead of 5)\n",
      "    Line #7320 (got 1 columns instead of 5)\n",
      "    Line #7321 (got 1 columns instead of 5)\n",
      "    Line #7322 (got 1 columns instead of 5)\n",
      "    Line #7323 (got 1 columns instead of 5)\n",
      "    Line #7324 (got 1 columns instead of 5)\n",
      "    Line #7325 (got 1 columns instead of 5)\n",
      "    Line #7326 (got 1 columns instead of 5)\n",
      "    Line #7327 (got 1 columns instead of 5)\n",
      "  self.array = np.genfromtxt(self.path, skip_header=7, invalid_raise=False)\n",
      "<ipython-input-6-13b1858c132e>:21: ConversionWarning: Some errors were detected !\n",
      "    Line #15608 (got 4 columns instead of 5)\n",
      "  self.array = np.genfromtxt(self.path, skip_header=7, invalid_raise=False)\n",
      "<ipython-input-6-13b1858c132e>:21: ConversionWarning: Some errors were detected !\n",
      "    Line #9547 (got 1 columns instead of 5)\n",
      "    Line #9548 (got 1 columns instead of 5)\n",
      "    Line #9549 (got 1 columns instead of 5)\n",
      "    Line #9550 (got 1 columns instead of 5)\n",
      "    Line #9551 (got 1 columns instead of 5)\n",
      "    Line #9552 (got 1 columns instead of 5)\n",
      "    Line #9553 (got 1 columns instead of 5)\n",
      "    Line #9554 (got 1 columns instead of 5)\n",
      "    Line #9555 (got 1 columns instead of 5)\n",
      "    Line #9556 (got 1 columns instead of 5)\n",
      "    Line #9557 (got 1 columns instead of 5)\n",
      "    Line #9558 (got 1 columns instead of 5)\n",
      "    Line #9559 (got 1 columns instead of 5)\n",
      "    Line #9560 (got 1 columns instead of 5)\n",
      "    Line #9561 (got 1 columns instead of 5)\n",
      "    Line #9562 (got 1 columns instead of 5)\n",
      "    Line #9563 (got 1 columns instead of 5)\n",
      "    Line #9564 (got 1 columns instead of 5)\n",
      "    Line #9565 (got 1 columns instead of 5)\n",
      "    Line #9566 (got 1 columns instead of 5)\n",
      "    Line #9567 (got 1 columns instead of 5)\n",
      "  self.array = np.genfromtxt(self.path, skip_header=7, invalid_raise=False)\n",
      "<ipython-input-6-13b1858c132e>:21: ConversionWarning: Some errors were detected !\n",
      "    Line #6571 (got 1 columns instead of 5)\n",
      "    Line #6572 (got 1 columns instead of 5)\n",
      "    Line #6573 (got 1 columns instead of 5)\n",
      "    Line #6574 (got 1 columns instead of 5)\n",
      "    Line #6575 (got 1 columns instead of 5)\n",
      "    Line #6576 (got 1 columns instead of 5)\n",
      "    Line #6577 (got 1 columns instead of 5)\n",
      "    Line #6578 (got 1 columns instead of 5)\n",
      "    Line #6579 (got 1 columns instead of 5)\n",
      "    Line #6580 (got 1 columns instead of 5)\n",
      "    Line #6581 (got 1 columns instead of 5)\n",
      "    Line #6582 (got 1 columns instead of 5)\n",
      "    Line #6583 (got 1 columns instead of 5)\n",
      "    Line #6584 (got 1 columns instead of 5)\n",
      "    Line #6585 (got 1 columns instead of 5)\n",
      "    Line #6586 (got 1 columns instead of 5)\n",
      "    Line #6587 (got 1 columns instead of 5)\n",
      "  self.array = np.genfromtxt(self.path, skip_header=7, invalid_raise=False)\n",
      "<ipython-input-6-13b1858c132e>:21: ConversionWarning: Some errors were detected !\n",
      "    Line #7530 (got 1 columns instead of 5)\n",
      "    Line #7531 (got 1 columns instead of 5)\n",
      "    Line #7532 (got 1 columns instead of 5)\n",
      "    Line #7533 (got 1 columns instead of 5)\n",
      "    Line #7534 (got 1 columns instead of 5)\n",
      "    Line #7535 (got 1 columns instead of 5)\n",
      "    Line #7536 (got 1 columns instead of 5)\n",
      "    Line #7537 (got 1 columns instead of 5)\n",
      "    Line #7538 (got 1 columns instead of 5)\n",
      "    Line #7539 (got 1 columns instead of 5)\n",
      "    Line #7540 (got 1 columns instead of 5)\n",
      "    Line #7541 (got 1 columns instead of 5)\n",
      "    Line #7542 (got 1 columns instead of 5)\n",
      "    Line #7543 (got 1 columns instead of 5)\n",
      "    Line #7544 (got 1 columns instead of 5)\n",
      "    Line #7545 (got 1 columns instead of 5)\n",
      "    Line #7546 (got 1 columns instead of 5)\n",
      "    Line #7547 (got 1 columns instead of 5)\n",
      "  self.array = np.genfromtxt(self.path, skip_header=7, invalid_raise=False)\n",
      "<ipython-input-6-13b1858c132e>:21: ConversionWarning: Some errors were detected !\n",
      "    Line #9235 (got 1 columns instead of 5)\n",
      "    Line #9236 (got 1 columns instead of 5)\n",
      "    Line #9237 (got 1 columns instead of 5)\n",
      "    Line #9238 (got 1 columns instead of 5)\n",
      "    Line #9239 (got 1 columns instead of 5)\n",
      "    Line #9240 (got 1 columns instead of 5)\n",
      "    Line #9241 (got 1 columns instead of 5)\n",
      "    Line #9242 (got 1 columns instead of 5)\n",
      "    Line #9243 (got 1 columns instead of 5)\n",
      "    Line #9244 (got 1 columns instead of 5)\n",
      "    Line #9245 (got 1 columns instead of 5)\n",
      "    Line #9246 (got 1 columns instead of 5)\n",
      "    Line #9247 (got 1 columns instead of 5)\n",
      "  self.array = np.genfromtxt(self.path, skip_header=7, invalid_raise=False)\n",
      "<ipython-input-6-13b1858c132e>:21: ConversionWarning: Some errors were detected !\n",
      "    Line #15948 (got 4 columns instead of 5)\n",
      "  self.array = np.genfromtxt(self.path, skip_header=7, invalid_raise=False)\n",
      "<ipython-input-6-13b1858c132e>:21: ConversionWarning: Some errors were detected !\n",
      "    Line #5689 (got 1 columns instead of 5)\n",
      "    Line #5690 (got 1 columns instead of 5)\n",
      "    Line #5691 (got 1 columns instead of 5)\n",
      "    Line #5692 (got 1 columns instead of 5)\n",
      "    Line #5693 (got 1 columns instead of 5)\n",
      "    Line #5694 (got 1 columns instead of 5)\n",
      "    Line #5695 (got 1 columns instead of 5)\n",
      "    Line #5696 (got 1 columns instead of 5)\n",
      "    Line #5697 (got 1 columns instead of 5)\n",
      "    Line #5698 (got 1 columns instead of 5)\n",
      "    Line #5699 (got 1 columns instead of 5)\n",
      "    Line #5700 (got 1 columns instead of 5)\n",
      "    Line #5701 (got 1 columns instead of 5)\n",
      "    Line #5702 (got 1 columns instead of 5)\n",
      "    Line #5703 (got 1 columns instead of 5)\n",
      "    Line #5704 (got 1 columns instead of 5)\n",
      "    Line #5705 (got 1 columns instead of 5)\n",
      "    Line #5706 (got 1 columns instead of 5)\n",
      "    Line #5707 (got 1 columns instead of 5)\n",
      "  self.array = np.genfromtxt(self.path, skip_header=7, invalid_raise=False)\n",
      "<ipython-input-6-13b1858c132e>:21: ConversionWarning: Some errors were detected !\n",
      "    Line #24168 (got 4 columns instead of 5)\n",
      "  self.array = np.genfromtxt(self.path, skip_header=7, invalid_raise=False)\n",
      "<ipython-input-6-13b1858c132e>:21: ConversionWarning: Some errors were detected !\n",
      "    Line #5351 (got 1 columns instead of 5)\n",
      "    Line #5352 (got 1 columns instead of 5)\n",
      "    Line #5353 (got 1 columns instead of 5)\n",
      "    Line #5354 (got 1 columns instead of 5)\n",
      "    Line #5355 (got 1 columns instead of 5)\n",
      "    Line #5356 (got 1 columns instead of 5)\n",
      "    Line #5357 (got 1 columns instead of 5)\n",
      "    Line #5358 (got 1 columns instead of 5)\n",
      "    Line #5359 (got 1 columns instead of 5)\n",
      "    Line #5360 (got 1 columns instead of 5)\n",
      "    Line #5361 (got 1 columns instead of 5)\n",
      "    Line #5362 (got 1 columns instead of 5)\n",
      "    Line #5363 (got 1 columns instead of 5)\n",
      "    Line #5364 (got 1 columns instead of 5)\n",
      "    Line #5365 (got 1 columns instead of 5)\n",
      "    Line #5366 (got 1 columns instead of 5)\n",
      "    Line #5367 (got 1 columns instead of 5)\n",
      "  self.array = np.genfromtxt(self.path, skip_header=7, invalid_raise=False)\n",
      "<ipython-input-6-13b1858c132e>:21: ConversionWarning: Some errors were detected !\n",
      "    Line #7392 (got 1 columns instead of 5)\n",
      "    Line #7393 (got 1 columns instead of 5)\n",
      "    Line #7394 (got 1 columns instead of 5)\n",
      "    Line #7395 (got 1 columns instead of 5)\n",
      "    Line #7396 (got 1 columns instead of 5)\n",
      "    Line #7397 (got 1 columns instead of 5)\n",
      "    Line #7398 (got 1 columns instead of 5)\n",
      "    Line #7399 (got 1 columns instead of 5)\n",
      "    Line #7400 (got 1 columns instead of 5)\n",
      "    Line #7401 (got 1 columns instead of 5)\n",
      "    Line #7402 (got 1 columns instead of 5)\n",
      "    Line #7403 (got 1 columns instead of 5)\n",
      "    Line #7404 (got 1 columns instead of 5)\n",
      "    Line #7405 (got 1 columns instead of 5)\n",
      "    Line #7406 (got 1 columns instead of 5)\n",
      "    Line #7407 (got 1 columns instead of 5)\n",
      "  self.array = np.genfromtxt(self.path, skip_header=7, invalid_raise=False)\n",
      "<ipython-input-6-13b1858c132e>:21: ConversionWarning: Some errors were detected !\n",
      "    Line #6930 (got 1 columns instead of 5)\n",
      "    Line #6931 (got 1 columns instead of 5)\n",
      "    Line #6932 (got 1 columns instead of 5)\n",
      "    Line #6933 (got 1 columns instead of 5)\n",
      "    Line #6934 (got 1 columns instead of 5)\n",
      "    Line #6935 (got 1 columns instead of 5)\n",
      "    Line #6936 (got 1 columns instead of 5)\n",
      "    Line #6937 (got 1 columns instead of 5)\n",
      "    Line #6938 (got 1 columns instead of 5)\n",
      "    Line #6939 (got 1 columns instead of 5)\n",
      "    Line #6940 (got 1 columns instead of 5)\n",
      "    Line #6941 (got 1 columns instead of 5)\n",
      "    Line #6942 (got 1 columns instead of 5)\n",
      "    Line #6943 (got 1 columns instead of 5)\n",
      "    Line #6944 (got 1 columns instead of 5)\n",
      "    Line #6945 (got 1 columns instead of 5)\n",
      "    Line #6946 (got 1 columns instead of 5)\n",
      "    Line #6947 (got 1 columns instead of 5)\n",
      "  self.array = np.genfromtxt(self.path, skip_header=7, invalid_raise=False)\n",
      "<ipython-input-6-13b1858c132e>:21: ConversionWarning: Some errors were detected !\n",
      "    Line #5853 (got 1 columns instead of 5)\n",
      "    Line #5854 (got 1 columns instead of 5)\n",
      "    Line #5855 (got 1 columns instead of 5)\n",
      "    Line #5856 (got 1 columns instead of 5)\n",
      "    Line #5857 (got 1 columns instead of 5)\n",
      "    Line #5858 (got 1 columns instead of 5)\n",
      "    Line #5859 (got 1 columns instead of 5)\n",
      "    Line #5860 (got 1 columns instead of 5)\n",
      "    Line #5861 (got 1 columns instead of 5)\n",
      "    Line #5862 (got 1 columns instead of 5)\n",
      "    Line #5863 (got 1 columns instead of 5)\n",
      "    Line #5864 (got 1 columns instead of 5)\n",
      "    Line #5865 (got 1 columns instead of 5)\n",
      "    Line #5866 (got 1 columns instead of 5)\n",
      "    Line #5867 (got 1 columns instead of 5)\n",
      "  self.array = np.genfromtxt(self.path, skip_header=7, invalid_raise=False)\n"
     ]
    }
   ],
   "source": [
    "# Returns two arrays of the folders sEMG data\n",
    "# the input and the class\n",
    "\n",
    "input_array, classes = preprocess(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[-8.00e-04 -4.60e-03  3.00e-03 -9.80e-03 -5.91e+01]\n",
      "  [ 7.00e-04 -5.30e-03  1.50e-03 -6.00e-03 -5.92e+01]\n",
      "  [ 0.00e+00 -4.60e-03  3.00e-03 -1.50e-03 -5.93e+01]\n",
      "  ...\n",
      "  [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]\n",
      "  [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]\n",
      "  [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]]\n",
      "\n",
      " [[-3.00e-03 -3.00e-03 -1.95e-02  4.50e-03 -5.26e+01]\n",
      "  [-8.00e-04 -1.50e-03 -2.03e-02  5.20e-03 -5.26e+01]\n",
      "  [-1.50e-03 -1.50e-03 -1.65e-02  3.70e-03 -5.26e+01]\n",
      "  ...\n",
      "  [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]\n",
      "  [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]\n",
      "  [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]]\n",
      "\n",
      " [[-3.00e-03  7.00e-04 -8.00e-04  6.70e-03 -6.00e-01]\n",
      "  [-1.50e-03  3.30e-02  7.00e-04  1.27e-02 -6.00e-01]\n",
      "  [-8.00e-04  2.92e-02  0.00e+00  5.20e-03 -6.00e-01]\n",
      "  ...\n",
      "  [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]\n",
      "  [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]\n",
      "  [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[-8.00e-04  1.50e-03  7.00e-04 -2.30e-03  4.90e+00]\n",
      "  [-8.00e-04 -8.00e-04  3.00e-03 -2.30e-03  4.90e+00]\n",
      "  [ 0.00e+00  7.00e-04  1.50e-03  0.00e+00  4.90e+00]\n",
      "  ...\n",
      "  [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]\n",
      "  [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]\n",
      "  [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]]\n",
      "\n",
      " [[-1.50e-03  0.00e+00  7.00e-04  0.00e+00 -1.00e+00]\n",
      "  [-8.00e-04  1.50e-03  3.00e-03 -1.50e-03 -1.00e+00]\n",
      "  [ 7.00e-04  1.50e-03  3.70e-03 -2.30e-03 -1.00e+00]\n",
      "  ...\n",
      "  [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]\n",
      "  [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]\n",
      "  [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]]\n",
      "\n",
      " [[ 4.50e-03  2.20e-03  7.00e-04  3.70e-03  2.60e+00]\n",
      "  [ 1.42e-02 -6.00e-03  7.00e-04  0.00e+00  2.60e+00]\n",
      "  [ 1.65e-02 -6.00e-03  0.00e+00  0.00e+00  2.60e+00]\n",
      "  ...\n",
      "  [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]\n",
      "  [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]\n",
      "  [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]]]\n"
     ]
    }
   ],
   "source": [
    "print(input_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(33, 36900, 5)\n"
     ]
    }
   ],
   "source": [
    "print(input_array.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(36900, 5)\n"
     ]
    }
   ],
   "source": [
    "print(input_array[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-8.00e-04 -4.60e-03  3.00e-03 -9.80e-03 -5.91e+01]\n",
      " [ 7.00e-04 -5.30e-03  1.50e-03 -6.00e-03 -5.92e+01]\n",
      " [ 0.00e+00 -4.60e-03  3.00e-03 -1.50e-03 -5.93e+01]\n",
      " ...\n",
      " [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]\n",
      " [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]\n",
      " [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00]]\n"
     ]
    }
   ],
   "source": [
    "# print individual sEMG data\n",
    "print(input_array[0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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