clean_fhr.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create CSV files with FHR from the binary files provided in the FHRMA toolbox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: This is **not yet** successfully implemented - still mismatch between files cleaned using MATLAB (directly with FHRMA toolbox) versus cleaning here in Python.\n",
    "\n",
    "## About the CTG files\n",
    "The FHRMA toolbox provides 66 training files and 90 test files with raw CTG data. They are stored as binary .fhr files. The signals were sampled at 4 Hz and stored in the following format. For each sample:\n",
    "* A uint32 serves as the UNIX timestamp for the beginning of the recording\n",
    "* A uint16 corresponds to the FHR from the first sensor (multiplied by 4)\n",
    "* A uint16 for the FHR signal from the second sensor (multiplied by 4) (unused here)\n",
    "* A uint8 for the TOCO signal (multiplied by 2)\n",
    "* A uint8 reserved to store signal quality (unused here).\n",
    "\n",
    "The FHR signal is set to 0 when it is missing.\n",
    "\n",
    "## Background information on binary files\n",
    "Binary files consist of sequential bytes, which are each 8 bits long. Bits are 0s and 1s.\n",
    "\n",
    "Binary files typically have a header indicating the file type.\n",
    "\n",
    "A byte can represent a number from 0 to 255, or -127 to +127 if you use one bit to indicate the minus sign. Groups of bytes can be used to represent larger numbers, for example:\n",
    "| Number of bytes (or bits) | Name | Range (will - this to + this) |\n",
    "| --- | --- | --- |\n",
    "| 1 (8 bits) | Byte | 128 |\n",
    "| 2 (16 bits) | Short | 32,768 |\n",
    "| 4 (32 bits) | Int | 2,147,483,648 |\n",
    "| 8 (64 bits) | Int64 | 9,223,372,036,854,775,808 |\n",
    "\n",
    "## Implementation in MATLAB\n",
    "\n",
    "The .fhr files can be opened with the `fhropen.m` function in MATLAB and then processed with `preprocess.m` - you can see this implementation in the `.m` files in the `fhrma` folder."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation in Python\n",
    "\n",
    "### Set up\n",
    "\n",
    "Load required packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "from scipy import io"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define file paths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "@dataclass(frozen=True)\n",
    "class Paths:\n",
    "    '''Singleton object for storing paths to data and database.'''\n",
    "\n",
    "    raw_fhr = './data/raw_fhr/'\n",
    "    clean_python = './data/clean_fhr_python/'\n",
    "    clean_matlab = './data/clean_fhr_matlab/'\n",
    "    expert = './data/expertAnalyses.mat'\n",
    "\n",
    "\n",
    "paths = Paths()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define functions for import and pre-processing\n",
    "\n",
    "Define function for extracting FHR and saving to csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def import_fhr(file_path):\n",
    "    '''\n",
    "    Imports .fhr files, extracts the data corresponding to FHR1 and FHR2\n",
    "\n",
    "    Parameters:\n",
    "    -----------\n",
    "    file_path : string\n",
    "        Location of the .fhr file\n",
    "\n",
    "    Outputs:\n",
    "    --------\n",
    "    fhr1 : array\n",
    "        FHR signal from the first sensor\n",
    "    fhr2: array\n",
    "        FHR signal from the second sensor\n",
    "    name: string\n",
    "        Name of the record for these FHR signals\n",
    "    '''\n",
    "    # Read file as uint16\n",
    "    content = np.fromfile(file_path, dtype=np.uint16)\n",
    "\n",
    "    # Ignore first two bytes, then reshape so you have a 3 x n array\n",
    "    data = content[2:10000000-1].reshape(-1, 3).T\n",
    "\n",
    "    # Take first array which corresponds to first sensor\n",
    "    fhr1 = data[0]/4\n",
    "    # Take second array which corresponds to second sensor\n",
    "    fhr2 = data[1]/4\n",
    "\n",
    "    # Get file name (remove path, change to csv)\n",
    "    name = file_path.rsplit('/', 1)[1].replace('.fhr', '.csv')\n",
    "\n",
    "    return(fhr1, fhr2, name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define functions used for pre-processing of the FHR signals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_small_part(fhr):\n",
    "    '''\n",
    "    Remove small sections of the signal that are non-physiological or extreme\n",
    "    values (e.g. FHR too high, too low, or too great a change in a small period\n",
    "    of time), and remove really short sections of signal surrounded by missing\n",
    "    signal.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    fhr : array\n",
    "        Fetal heart rate\n",
    "\n",
    "    Outputs:\n",
    "    --------\n",
    "    fhr : array\n",
    "        Cleaned version of FHR\n",
    "    '''\n",
    "    # Remove extreme - set FHR to 0 if it is greater than 220bpm or less than 50bpm\n",
    "    fhr[np.logical_or(fhr>220, fhr<50)] = 0\n",
    "\n",
    "    # Find section of FHR that is preceded and followed by zero values - if that\n",
    "    # section of non-zero values is too short (less than or equal to 5 seconds),\n",
    "    # set all values in that section to zero\n",
    "\n",
    "    # Get indices where FHR transitions from zero to non-zero value\n",
    "    # (e.g. if n has index 396, then 395 was 0 and 396 was not zero)\n",
    "    n = np.where((fhr[:-1] == 0) & (fhr[1:] > 0))[0] + 1\n",
    "\n",
    "    # Loop through each of those transition points\n",
    "    for i in range(len(n)):\n",
    "\n",
    "        # Get array with all indexes of zero after this point\n",
    "        zeros = np.where(fhr[n[i]:] == 0)[0]\n",
    "        # If there were any zeros, get index of the closest (the first)\n",
    "        if len(zeros) > 0:\n",
    "\n",
    "            # f is the length of the non-0 section before the next 0\n",
    "            f = zeros[0]\n",
    "\n",
    "            # If f is less than 5 seconds (5*4), set that whole section to zero\n",
    "            if f+1 < 5*4:\n",
    "                fhr[n[i]:n[i]+f+2] = 0\n",
    "\n",
    "    # Repeat as above - seperately, as some transition points will have changed\n",
    "    n = np.where((fhr[:-1] == 0) & (fhr[1:] > 0))[0] + 1\n",
    "    for i in range(len(n)):\n",
    "        zeros = np.where(fhr[n[i]:] == 0)[0]\n",
    "        if len(zeros) > 0:\n",
    "            f = zeros[0]\n",
    "            # If f is less than 30 seconds (30*4)\n",
    "            if f+1 < 30*4:\n",
    "                # Get array of point before start of zero section before n, and\n",
    "                # after zero section after f, that are non-zero\n",
    "                valid_before = np.where(fhr[:n[i]] > 0)[0]\n",
    "                valid_after = np.where(fhr[n[i]+f:] > 0)[0]\n",
    "                # As long as there were non-zeros both before and after...\n",
    "                if (len(valid_before) > 0) and (len(valid_after) > 0):\n",
    "                    # Get index of last and first non-zero\n",
    "                    last_valid = valid_before[-1]\n",
    "                    first_valid = valid_after[0] + n[i] + f\n",
    "                    # Find difference in FHR between lastvalid and n(i)\n",
    "                    lastvalid_diff = fhr[n[i]] - fhr[last_valid]\n",
    "                    # Find difference in FHR between last non-zero in f section, and nextvalid\n",
    "                    nextvalid_diff = fhr[n[i]+f-1] - fhr[first_valid]\n",
    "                    # If both of those differences are more than an absolute value of 25bpm,\n",
    "                    # in the same direction, then set that f section of non-zero all to 0\n",
    "                    if (lastvalid_diff > 25) and (nextvalid_diff > 25):\n",
    "                        fhr[n[i]:n[i]+f+2] = 0\n",
    "                    if (lastvalid_diff < -25) and (nextvalid_diff < -25):\n",
    "                        fhr[n[i]:n[i]+f+2] = 0\n",
    "\n",
    "    return (fhr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def interpol_fhr(fhr):\n",
    "    '''\n",
    "    Interpolation of missing signal, to match interpolfhr() in FHRMA\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    fhr : array\n",
    "        Fetal heart rate\n",
    "\n",
    "    Outputs:\n",
    "    --------\n",
    "    fhr : array\n",
    "        Cleaned version of FHR\n",
    "    '''\n",
    "    # Get the first index where FHR is non-zero and non-NaN\n",
    "    n = np.argmax(np.logical_and(fhr>0, np.isfinite(fhr)))\n",
    "\n",
    "    # Set any values preceding that index to the value of that index\n",
    "    fhr[:n] = fhr[n]\n",
    "\n",
    "    # While you have a value of n, and whilst it's not reached the end of the FHR\n",
    "    while n is not None and n!=-1:\n",
    "\n",
    "        # Get index of first occurence after n that is zero or NaN\n",
    "        n = np.argmax(np.logical_or(fhr==0, ~np.isfinite(fhr)))\n",
    "\n",
    "        # Make boolean array of non-zero or non-NaN after n\n",
    "        nf_mask = np.logical_and(fhr[n:]>0, np.isfinite(fhr[n:]))\n",
    "\n",
    "        # If you have found a subsequent value that is non-zero or non-NaN...\n",
    "        if nf_mask.any() and n!=0:\n",
    "\n",
    "            # Get the index of the first value that is non-zero and non-NaN\n",
    "            nf = np.argmax(nf_mask)+n\n",
    "\n",
    "            # Linear interpolation between nf and the non-zero point before n\n",
    "            # Round to 3dp to match with MATLAB\n",
    "            fhr[(n-1):(nf+1)] = np.linspace(fhr[n-1], fhr[nf], nf-n+2)\n",
    "\n",
    "            # Set n to nf\n",
    "            n = nf\n",
    "\n",
    "        # If there were no subsequent values, exit the while loop\n",
    "        else:\n",
    "            n=-1\n",
    "\n",
    "    # Get last index where FHR is non-zero and non-NaN\n",
    "    n = pd.Series(np.logical_and(fhr>0, np.isfinite(fhr)))[::-1].idxmax()\n",
    "\n",
    "    # Set any values following that index to the value of that index\n",
    "    fhr[n:] = fhr[n]\n",
    "\n",
    "    return (fhr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(fhr1, fhr2, unreliable_signal=[]):\n",
    "    '''\n",
    "    Pre-process the FHR1 and FHR2 signals - merging the signals from each\n",
    "    sensor, removing empty signal at the beginning and end, removing small parts\n",
    "    of the signal that are not trustable, and linear interpolation of missing\n",
    "    parts of the signal.\n",
    "\n",
    "    Parameters:\n",
    "    -----------\n",
    "    fhr1 : array\n",
    "        FHR signal from the first sensor\n",
    "    fhr2: array\n",
    "        FHR signal from the second sensor\n",
    "    unreliable_signal: array\n",
    "        Sections of unreliable signal, as from ExpertAnalyses.mat\n",
    "\n",
    "    Outputs:\n",
    "    --------\n",
    "    new_sig : array\n",
    "        Cleaned FHR signal\n",
    "    '''\n",
    "    # Find the max value element-wise of the two arrays\n",
    "    # This should hopefully fill the occurences where FHR=0 (i.e. no signal)\n",
    "    fhr = np.maximum(fhr1, fhr2)\n",
    "\n",
    "    # If an unreliable signal is provided, set those sections to 0\n",
    "    # It is provided in minutes, so multiply by 60*4 (240) to convert back to quarter seconds\n",
    "    if len(unreliable_signal)>0:\n",
    "        for i in np.arange(0, len(unreliable_signal)):\n",
    "            start = round(unreliable_signal[0, 0]*241)-1\n",
    "            end = round(unreliable_signal[0, 1]*240)\n",
    "            fhr[start:end] = 0\n",
    "            print(fhr[start:end])\n",
    "\n",
    "    # Clean using removesmallpart() - this will remove small sections of the\n",
    "    # signal that are non-physiological or extreme values (e.g. FHR too high,\n",
    "    # too low, or too great a change in a small period of time), and remove\n",
    "    # really short sections of signal surrounded by missing signal.\n",
    "    fhr = remove_small_part(fhr)\n",
    "\n",
    "    # Perform linear interpolation\n",
    "    fhr = interpol_fhr(fhr)\n",
    "\n",
    "    # If any zero remain, set to NaN\n",
    "    fhr[fhr==0] = np.nan\n",
    "\n",
    "    # Round all values to 13dp as in MATLAB\n",
    "    #fhr = np.around(fhr, 13)\n",
    "\n",
    "    # Return the cleaned signal\n",
    "    return (fhr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_fhr_to_csv(fhr_path, csv_path, expert):\n",
    "    '''\n",
    "    Imports all .fhr files in a given directory, extracts the data corresponding\n",
    "    to FHR1 and FHR2, pre-process and save to csv.\n",
    "\n",
    "    Parameters:\n",
    "    -----------\n",
    "    fhr_path : string\n",
    "        Location of .fhr files\n",
    "    csv_path : string\n",
    "        Location to save the .csv files\n",
    "    expert : dataframe\n",
    "        Expert Analysis information which includes unreliable signal identification\n",
    "    '''\n",
    "    # Get list of all the .fhr file names in the directory\n",
    "    files = glob.glob(os.path.join(fhr_path, '*.fhr'))\n",
    "\n",
    "    # Loop through those files...\n",
    "    for file_path in files:\n",
    "\n",
    "        # Import the signals and record name\n",
    "        fhr1, fhr2, name = import_fhr(file_path)\n",
    "\n",
    "        # Find the record of any unreliable signal from the expert analysis\n",
    "        record = name.replace('.csv', '.fhr')\n",
    "        unreliable_signal = expert.loc[\n",
    "            expert.filename == record, 'unreliableSignal'].values[0]\n",
    "        print(record)\n",
    "        print(unreliable_signal)\n",
    "\n",
    "        # Pre-process the signals\n",
    "        fhr = preprocess(fhr1, fhr2, unreliable_signal)\n",
    "\n",
    "        # Save to csv\n",
    "        np.savetxt(os.path.join(csv_path, name), fhr, delimiter=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run functions on an example, to help demonstrate impact of the cleaning process\n",
    "\n",
    "Import ExpertAnalyses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>filename</th>\n",
       "      <th>expertPts</th>\n",
       "      <th>trainingData</th>\n",
       "      <th>baseline</th>\n",
       "      <th>accelerations</th>\n",
       "      <th>decelerations</th>\n",
       "      <th>overshoots</th>\n",
       "      <th>unreliableSignal</th>\n",
       "      <th>notToAnalyse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[test01.fhr]</td>\n",
       "      <td>[[], []]</td>\n",
       "      <td>[[0]]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[[0, 15]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[test02.fhr]</td>\n",
       "      <td>[[], []]</td>\n",
       "      <td>[[0]]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[[0, 15]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[test03.fhr]</td>\n",
       "      <td>[[], []]</td>\n",
       "      <td>[[0]]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[[0, 15]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[test04.fhr]</td>\n",
       "      <td>[[], []]</td>\n",
       "      <td>[[0]]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[[70.09081150708458, 71.21146414770287], [87.6...</td>\n",
       "      <td>[[0, 15]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[test05.fhr]</td>\n",
       "      <td>[[], []]</td>\n",
       "      <td>[[0]]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[[61.57685702018033, 65.04227405247813], [71.3...</td>\n",
       "      <td>[[0, 15]]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       filename expertPts trainingData baseline accelerations decelerations  \\\n",
       "0  [test01.fhr]  [[], []]        [[0]]       []            []            []   \n",
       "1  [test02.fhr]  [[], []]        [[0]]       []            []            []   \n",
       "2  [test03.fhr]  [[], []]        [[0]]       []            []            []   \n",
       "3  [test04.fhr]  [[], []]        [[0]]       []            []            []   \n",
       "4  [test05.fhr]  [[], []]        [[0]]       []            []            []   \n",
       "\n",
       "  overshoots                                   unreliableSignal notToAnalyse  \n",
       "0         []                                                 []    [[0, 15]]  \n",
       "1         []                                                 []    [[0, 15]]  \n",
       "2         []                                                 []    [[0, 15]]  \n",
       "3         []  [[70.09081150708458, 71.21146414770287], [87.6...    [[0, 15]]  \n",
       "4         []  [[61.57685702018033, 65.04227405247813], [71.3...    [[0, 15]]  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load MAT file and convert to dataframe\n",
    "expert = io.loadmat(paths.expert)\n",
    "expert = pd.DataFrame(np.concatenate(expert['data']))\n",
    "expert.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import an example file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[130.75 130.75 129.75 129.75 130.75 131.75 131.75 133.25 133.25 131.5 ]\n",
      "[130.75 130.75 129.75 129.75 130.75 131.75 131.75 133.25 133.25 131.5 ]\n",
      "test04.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filename = 'test04.fhr'\n",
    "\n",
    "# Import an example file\n",
    "file_path = os.path.join(paths.raw_fhr, filename)\n",
    "fhr1, fhr2, name = import_fhr(file_path)\n",
    "\n",
    "# Preview data\n",
    "print(fhr1[:10])\n",
    "print(fhr2[:10])\n",
    "print(name)\n",
    "\n",
    "# Plot one of the data\n",
    "plt.plot(fhr2)\n",
    "plt.ylim([0, 200])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get unreliable signal record from expert analysis, then use to pre-process the raw FHR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[70.09081151 71.21146415]\n",
      " [87.6927866  89.60562473]]\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0.]\n",
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      " 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[130.75 130.75 129.75 129.75 130.75 131.75 131.75 133.25 133.25 131.5 ]\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get unreliable signal measure from ExpertAnalyses\n",
    "unreliable_signal = expert.loc[expert.filename == filename, 'unreliableSignal']\n",
    "if len(unreliable_signal) > 0:\n",
    "    unreliable_signal = unreliable_signal.values[0]\n",
    "print(unreliable_signal)\n",
    "\n",
    "# Pre-process\n",
    "fhr = preprocess(fhr1, fhr2, unreliable_signal)\n",
    "\n",
    "# Preview\n",
    "print(fhr[:10])\n",
    "\n",
    "# Plot\n",
    "plt.plot(fhr)\n",
    "plt.ylim([0, 200])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply functions to the training and test datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test75.fhr\n",
      "[]\n",
      "train64.fhr\n",
      "[]\n",
      "test40.fhr\n",
      "[]\n",
      "test23.fhr\n",
      "[[ 7.875       8.3       ]\n",
      " [22.75       23.225     ]\n",
      " [30.00437318 37.76364015]]\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
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      "test07.fhr\n",
      "[[20.625 21.5  ]\n",
      " [69.325 71.8  ]]\n",
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      "test48.fhr\n",
      "[[49.13374839 50.06118506]\n",
      " [80.86839845 84.36560756]]\n",
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      "test27.fhr\n",
      "[]\n",
      "test43.fhr\n",
      "[]\n",
      "train53.fhr\n",
      "[[25.60139728 26.26416163]]\n",
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      "test62.fhr\n",
      "[]\n",
      "train27.fhr\n",
      "[]\n",
      "test59.fhr\n",
      "[]\n",
      "test17.fhr\n",
      "[[38.375 38.525]\n",
      " [46.95  47.225]]\n",
      "[]\n",
      "[]\n",
      "train63.fhr\n",
      "[]\n",
      "test70.fhr\n",
      "[]\n",
      "test35.fhr\n",
      "[]\n",
      "train25.fhr\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "convert_fhr_to_csv(\n",
    "    fhr_path = paths.raw_fhr,\n",
    "    csv_path = paths.clean_python,\n",
    "    expert = expert\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare MATLAB and Python implementation\n",
    "\n",
    "To validate the implementation in Python, we compare the cleaned signals against those obtained when running the FHRMA package in MATLAB. The csv files compared against were produced using the code in `fhrma/matlab_fhr_preprocess_to_csv.m`. This was based on code from the function `saveMAanalyse.m`. It directly uses functions from the FHRMA package and the raw FHR from the FHRMA package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define function for importing all the csv files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def import_csv(directory, output_dict):\n",
    "    '''\n",
    "    Import csv files from provided directory and save to output_dict\n",
    "\n",
    "    Parameters:\n",
    "    -----------\n",
    "    directory : string\n",
    "        Location of csv files\n",
    "    output_dict : dictionary\n",
    "        Dictionary to save the FHR arrays to, with key as the record name\n",
    "        (manually specified in case you would to append to existing dictionary)\n",
    "\n",
    "    Returns:\n",
    "    --------\n",
    "    output_dict : dictionary\n",
    "        Dictionary of the FHR arrays\n",
    "    '''\n",
    "    # Get list of .csv files in directory\n",
    "    files = glob.glob(os.path.join(directory, '*.csv'))\n",
    "\n",
    "    # Loop through files in the directory\n",
    "    for file in files:\n",
    "        # Get raw name of record (without path or file type)\n",
    "        name = file.replace(directory, '').replace('.csv', '')\n",
    "        # Import and save to dictionary\n",
    "        output_dict[name] = pd.read_csv(file, header=None)[0].values\n",
    "\n",
    "    return output_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import cleaned FHR produced using Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import FHR into dictionary\n",
    "fhr_python = import_csv(\n",
    "    directory=paths.clean_python,\n",
    "    output_dict=dict())\n",
    "\n",
    "# Convert dictionary to dataframe\n",
    "fhr_python_df = pd.DataFrame([[key,value] for key,value in fhr_python.items()],\n",
    "                             columns=['record', 'python'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import cleaned FHR produced using MATLAB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import FHR into dictionary\n",
    "fhr_matlab = import_csv(\n",
    "    directory=paths.clean_matlab,\n",
    "    output_dict=dict())\n",
    "\n",
    "# Convert dictionary to dataframe\n",
    "fhr_matlab_df = pd.DataFrame([[key,value] for key,value in fhr_matlab.items()],\n",
    "                             columns=['record', 'matlab'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine and compare the FHR from Python and MATLAB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>test80</td>\n",
       "      <td>[125.25, 125.25, 125.75, 125.75, 125.25, 125.2...</td>\n",
       "      <td>[125.25, 125.25, 125.75, 125.75, 125.25, 125.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test68</td>\n",
       "      <td>[118.25, 118.5, 118.5, 118.5, 118.5, 118.75, 1...</td>\n",
       "      <td>[118.25, 118.5, 118.5, 118.5, 118.5, 118.75, 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train04</td>\n",
       "      <td>[210.0, 210.0, 210.0, 130.0, 130.0, 130.0, 130...</td>\n",
       "      <td>[210.0, 210.0, 210.0, 130.0, 130.0, 130.0, 130...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train31</td>\n",
       "      <td>[146.25, 146.25, 146.25, 140.5, 140.5, 140.5, ...</td>\n",
       "      <td>[146.25, 146.25, 146.25, 140.5, 140.5, 140.5, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    record                                             python  \\\n",
       "0  train28  [136.5, 136.5, 136.5, 135.5, 135.5, 135.5, 136...   \n",
       "1   test80  [125.25, 125.25, 125.75, 125.75, 125.25, 125.2...   \n",
       "2   test68  [118.25, 118.5, 118.5, 118.5, 118.5, 118.75, 1...   \n",
       "3  train04  [210.0, 210.0, 210.0, 130.0, 130.0, 130.0, 130...   \n",
       "4  train31  [146.25, 146.25, 146.25, 140.5, 140.5, 140.5, ...   \n",
       "\n",
       "                                              matlab  \n",
       "0  [136.5, 136.5, 136.5, 135.5, 135.5, 135.5, 136...  \n",
       "1  [125.25, 125.25, 125.75, 125.75, 125.25, 125.2...  \n",
       "2  [118.25, 118.5, 118.5, 118.5, 118.5, 118.75, 1...  \n",
       "3  [210.0, 210.0, 210.0, 130.0, 130.0, 130.0, 130...  \n",
       "4  [146.25, 146.25, 146.25, 140.5, 140.5, 140.5, ...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge into a single dataframe\n",
    "compare_fhr = pd.merge(fhr_python_df, fhr_matlab_df, on='record')\n",
    "\n",
    "# Preview dataframe\n",
    "compare_fhr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_48334/2148730012.py:11: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'True' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
      "  compare_fhr.loc[compare_fhr.index[index], 'all_match'] = (res_python==res_matlab).all()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>record</th>\n",
       "      <th>python</th>\n",
       "      <th>matlab</th>\n",
       "      <th>n_match</th>\n",
       "      <th>total_possible</th>\n",
       "      <th>all_match</th>\n",
       "      <th>sum_abs_diff</th>\n",
       "      <th>sum_abs_diff_round5dp</th>\n",
       "      <th>n_different</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train28</td>\n",
       "      <td>[136.5, 136.5, 136.5, 135.5, 135.5, 135.5, 136...</td>\n",
       "      <td>[136.5, 136.5, 136.5, 135.5, 135.5, 135.5, 136...</td>\n",
       "      <td>17595.0</td>\n",
       "      <td>17595.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test80</td>\n",
       "      <td>[125.25, 125.25, 125.75, 125.75, 125.25, 125.2...</td>\n",
       "      <td>[125.25, 125.25, 125.75, 125.75, 125.25, 125.2...</td>\n",
       "      <td>23879.0</td>\n",
       "      <td>24253.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1.014087e-10</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>374.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test68</td>\n",
       "      <td>[118.25, 118.5, 118.5, 118.5, 118.5, 118.75, 1...</td>\n",
       "      <td>[118.25, 118.5, 118.5, 118.5, 118.5, 118.75, 1...</td>\n",
       "      <td>23829.0</td>\n",
       "      <td>25274.0</td>\n",
       "      <td>False</td>\n",
       "      <td>9.029112e+02</td>\n",
       "      <td>902.91122</td>\n",
       "      <td>1445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train04</td>\n",
       "      <td>[210.0, 210.0, 210.0, 130.0, 130.0, 130.0, 130...</td>\n",
       "      <td>[210.0, 210.0, 210.0, 130.0, 130.0, 130.0, 130...</td>\n",
       "      <td>17595.0</td>\n",
       "      <td>17595.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train31</td>\n",
       "      <td>[146.25, 146.25, 146.25, 140.5, 140.5, 140.5, ...</td>\n",
       "      <td>[146.25, 146.25, 146.25, 140.5, 140.5, 140.5, ...</td>\n",
       "      <td>11631.0</td>\n",
       "      <td>11631.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    record                                             python  \\\n",
       "0  train28  [136.5, 136.5, 136.5, 135.5, 135.5, 135.5, 136...   \n",
       "1   test80  [125.25, 125.25, 125.75, 125.75, 125.25, 125.2...   \n",
       "2   test68  [118.25, 118.5, 118.5, 118.5, 118.5, 118.75, 1...   \n",
       "3  train04  [210.0, 210.0, 210.0, 130.0, 130.0, 130.0, 130...   \n",
       "4  train31  [146.25, 146.25, 146.25, 140.5, 140.5, 140.5, ...   \n",
       "\n",
       "                                              matlab  n_match  total_possible  \\\n",
       "0  [136.5, 136.5, 136.5, 135.5, 135.5, 135.5, 136...  17595.0         17595.0   \n",
       "1  [125.25, 125.25, 125.75, 125.75, 125.25, 125.2...  23879.0         24253.0   \n",
       "2  [118.25, 118.5, 118.5, 118.5, 118.5, 118.75, 1...  23829.0         25274.0   \n",
       "3  [210.0, 210.0, 210.0, 130.0, 130.0, 130.0, 130...  17595.0         17595.0   \n",
       "4  [146.25, 146.25, 146.25, 140.5, 140.5, 140.5, ...  11631.0         11631.0   \n",
       "\n",
       "  all_match  sum_abs_diff  sum_abs_diff_round5dp  n_different  \n",
       "0      True  0.000000e+00                0.00000          0.0  \n",
       "1     False  1.014087e-10                0.00000        374.0  \n",
       "2     False  9.029112e+02              902.91122       1445.0  \n",
       "3      True  0.000000e+00                0.00000          0.0  \n",
       "4      True  0.000000e+00                0.00000          0.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loop through each record, comparing the python and FHRMA results\n",
    "for index, row in compare_fhr.iterrows():\n",
    "\n",
    "    # Extract the two arrays\n",
    "    res_python = row['python']\n",
    "    res_matlab = row['matlab']\n",
    "\n",
    "    # Save number of matches, total possible matches, and every element matches\n",
    "    compare_fhr.loc[compare_fhr.index[index], 'n_match'] = (res_python==res_matlab).sum()\n",
    "    compare_fhr.loc[compare_fhr.index[index], 'total_possible'] = len(res_python)\n",
    "    compare_fhr.loc[compare_fhr.index[index], 'all_match'] = (res_python==res_matlab).all()\n",
    "\n",
    "    # Find element wise differences\n",
    "    diff = res_python - res_matlab\n",
    "    compare_fhr.loc[compare_fhr.index[index], 'sum_abs_diff'] = abs(diff).sum()\n",
    "\n",
    "# Add column with rounded sum_abs_diff\n",
    "compare_fhr['sum_abs_diff_round5dp'] = round(compare_fhr['sum_abs_diff'], 5)\n",
    "\n",
    "# Add column showing number of non-matching elements\n",
    "compare_fhr['n_different'] = compare_fhr['total_possible'] - compare_fhr['n_match']\n",
    "\n",
    "# Preview dataframe with the newly added columns\n",
    "compare_fhr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "all_match\n",
       "False    125\n",
       "True      31\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# See how many match\n",
    "display(compare_fhr['all_match'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "        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>record</th>\n",
       "      <th>python</th>\n",
       "      <th>matlab</th>\n",
       "      <th>n_match</th>\n",
       "      <th>total_possible</th>\n",
       "      <th>all_match</th>\n",
       "      <th>sum_abs_diff</th>\n",
       "      <th>sum_abs_diff_round5dp</th>\n",
       "      <th>n_different</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>test80</td>\n",
       "      <td>[125.25, 125.25, 125.75, 125.75, 125.25, 125.2...</td>\n",
       "      <td>[125.25, 125.25, 125.75, 125.75, 125.25, 125.2...</td>\n",
       "      <td>23879.0</td>\n",
       "      <td>24253.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1.014087e-10</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>374.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>test68</td>\n",
       "      <td>[118.25, 118.5, 118.5, 118.5, 118.5, 118.75, 1...</td>\n",
       "      <td>[118.25, 118.5, 118.5, 118.5, 118.5, 118.75, 1...</td>\n",
       "      <td>23829.0</td>\n",
       "      <td>25274.0</td>\n",
       "      <td>False</td>\n",
       "      <td>9.029112e+02</td>\n",
       "      <td>902.91122</td>\n",
       "      <td>1445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>train13</td>\n",
       "      <td>[210.0, 210.0, 210.0, 210.0, 210.0, 210.0, 210...</td>\n",
       "      <td>[210.0, 210.0, 210.0, 210.0, 210.0, 210.0, 210...</td>\n",
       "      <td>29196.0</td>\n",
       "      <td>29226.0</td>\n",
       "      <td>False</td>\n",
       "      <td>7.773338e-12</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>train47</td>\n",
       "      <td>[153.5, 153.0, 153.5, 153.5, 153.5, 153.5, 152...</td>\n",
       "      <td>[153.5, 153.0, 153.5, 153.5, 153.5, 153.5, 152...</td>\n",
       "      <td>25941.0</td>\n",
       "      <td>28022.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2.623860e+03</td>\n",
       "      <td>2623.85984</td>\n",
       "      <td>2081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>test70</td>\n",
       "      <td>[143.25, 143.25, 143.25, 143.0, 142.5, 142.5, ...</td>\n",
       "      <td>[143.25, 143.25, 143.25, 143.0, 142.5, 142.5, ...</td>\n",
       "      <td>25519.0</td>\n",
       "      <td>25925.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1.197265e-10</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>406.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>test04</td>\n",
       "      <td>[130.75, 130.75, 129.75, 129.75, 130.75, 131.7...</td>\n",
       "      <td>[130.75, 130.75, 129.75, 129.75, 130.75, 131.7...</td>\n",
       "      <td>20494.0</td>\n",
       "      <td>21517.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1.738668e+04</td>\n",
       "      <td>17386.67609</td>\n",
       "      <td>1023.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>test76</td>\n",
       "      <td>[144.5, 144.0, 143.0, 143.0, 143.5, 143.5, 143...</td>\n",
       "      <td>[144.5, 144.0, 143.0, 143.0, 143.5, 143.5, 143...</td>\n",
       "      <td>27595.0</td>\n",
       "      <td>29507.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2.320100e+03</td>\n",
       "      <td>2320.09962</td>\n",
       "      <td>1912.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>test22</td>\n",
       "      <td>[124.0, 124.0, 124.0, 124.0, 124.0, 122.25, 12...</td>\n",
       "      <td>[124.0, 124.0, 124.0, 124.0, 124.0, 122.25, 12...</td>\n",
       "      <td>26934.0</td>\n",
       "      <td>27607.0</td>\n",
       "      <td>False</td>\n",
       "      <td>5.819329e+02</td>\n",
       "      <td>581.93294</td>\n",
       "      <td>673.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>train52</td>\n",
       "      <td>[134.75, 135.0, 135.0, 135.75, 136.5, 136.5, 1...</td>\n",
       "      <td>[134.75, 135.0, 135.0, 135.75, 136.5, 136.5, 1...</td>\n",
       "      <td>40801.0</td>\n",
       "      <td>42446.0</td>\n",
       "      <td>False</td>\n",
       "      <td>4.094858e-10</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1645.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>train54</td>\n",
       "      <td>[136.5, 136.5, 135.0, 135.0, 135.75, 135.75, 1...</td>\n",
       "      <td>[136.5, 136.5, 135.0, 135.0, 135.75, 135.75, 1...</td>\n",
       "      <td>23561.0</td>\n",
       "      <td>23798.0</td>\n",
       "      <td>False</td>\n",
       "      <td>5.496048e-11</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>237.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      record                                             python  \\\n",
       "1     test80  [125.25, 125.25, 125.75, 125.75, 125.25, 125.2...   \n",
       "2     test68  [118.25, 118.5, 118.5, 118.5, 118.5, 118.75, 1...   \n",
       "5    train13  [210.0, 210.0, 210.0, 210.0, 210.0, 210.0, 210...   \n",
       "6    train47  [153.5, 153.0, 153.5, 153.5, 153.5, 153.5, 152...   \n",
       "7     test70  [143.25, 143.25, 143.25, 143.0, 142.5, 142.5, ...   \n",
       "..       ...                                                ...   \n",
       "150   test04  [130.75, 130.75, 129.75, 129.75, 130.75, 131.7...   \n",
       "151   test76  [144.5, 144.0, 143.0, 143.0, 143.5, 143.5, 143...   \n",
       "152   test22  [124.0, 124.0, 124.0, 124.0, 124.0, 122.25, 12...   \n",
       "154  train52  [134.75, 135.0, 135.0, 135.75, 136.5, 136.5, 1...   \n",
       "155  train54  [136.5, 136.5, 135.0, 135.0, 135.75, 135.75, 1...   \n",
       "\n",
       "                                                matlab  n_match  \\\n",
       "1    [125.25, 125.25, 125.75, 125.75, 125.25, 125.2...  23879.0   \n",
       "2    [118.25, 118.5, 118.5, 118.5, 118.5, 118.75, 1...  23829.0   \n",
       "5    [210.0, 210.0, 210.0, 210.0, 210.0, 210.0, 210...  29196.0   \n",
       "6    [153.5, 153.0, 153.5, 153.5, 153.5, 153.5, 152...  25941.0   \n",
       "7    [143.25, 143.25, 143.25, 143.0, 142.5, 142.5, ...  25519.0   \n",
       "..                                                 ...      ...   \n",
       "150  [130.75, 130.75, 129.75, 129.75, 130.75, 131.7...  20494.0   \n",
       "151  [144.5, 144.0, 143.0, 143.0, 143.5, 143.5, 143...  27595.0   \n",
       "152  [124.0, 124.0, 124.0, 124.0, 124.0, 122.25, 12...  26934.0   \n",
       "154  [134.75, 135.0, 135.0, 135.75, 136.5, 136.5, 1...  40801.0   \n",
       "155  [136.5, 136.5, 135.0, 135.0, 135.75, 135.75, 1...  23561.0   \n",
       "\n",
       "     total_possible all_match  sum_abs_diff  sum_abs_diff_round5dp  \\\n",
       "1           24253.0     False  1.014087e-10                0.00000   \n",
       "2           25274.0     False  9.029112e+02              902.91122   \n",
       "5           29226.0     False  7.773338e-12                0.00000   \n",
       "6           28022.0     False  2.623860e+03             2623.85984   \n",
       "7           25925.0     False  1.197265e-10                0.00000   \n",
       "..              ...       ...           ...                    ...   \n",
       "150         21517.0     False  1.738668e+04            17386.67609   \n",
       "151         29507.0     False  2.320100e+03             2320.09962   \n",
       "152         27607.0     False  5.819329e+02              581.93294   \n",
       "154         42446.0     False  4.094858e-10                0.00000   \n",
       "155         23798.0     False  5.496048e-11                0.00000   \n",
       "\n",
       "     n_different  \n",
       "1          374.0  \n",
       "2         1445.0  \n",
       "5           30.0  \n",
       "6         2081.0  \n",
       "7          406.0  \n",
       "..           ...  \n",
       "150       1023.0  \n",
       "151       1912.0  \n",
       "152        673.0  \n",
       "154       1645.0  \n",
       "155        237.0  \n",
       "\n",
       "[125 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# See the false entries\n",
    "display(compare_fhr[compare_fhr['all_match'] == False])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at an example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train62_python = fhr_python_df.loc[fhr_python_df['record']=='train62', 'python'].values[0]\n",
    "train62_matlab = fhr_matlab_df.loc[fhr_matlab_df['record']=='train62', 'matlab'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43767\n",
      "43767\n"
     ]
    }
   ],
   "source": [
    "print(len(train62_python))\n",
    "print(len(train62_matlab))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([166.5       , 166.5       , 169.5       , 170.        ,\n",
       "       171.        , 171.        , 172.25      , 172.        ,\n",
       "       172.        , 172.        , 172.        , 172.        ,\n",
       "       172.        , 172.        , 172.        , 172.        ,\n",
       "       172.        , 172.        , 172.        , 172.        ,\n",
       "       172.        , 172.        , 172.        , 171.27302632,\n",
       "       170.54605263, 169.81907895, 169.09210526, 168.36513158,\n",
       "       167.63815789, 166.91118421, 166.18421053, 165.45723684,\n",
       "       164.73026316, 164.00328947, 163.27631579, 162.54934211,\n",
       "       161.82236842, 161.09539474, 160.36842105, 159.64144737,\n",
       "       158.91447368, 158.1875    , 157.46052632, 156.73355263,\n",
       "       156.00657895, 155.27960526, 154.55263158, 153.82565789,\n",
       "       153.09868421, 152.37171053, 151.64473684, 150.91776316,\n",
       "       150.19078947, 149.46381579, 148.73684211, 148.00986842,\n",
       "       147.28289474, 146.55592105, 145.82894737, 145.10197368,\n",
       "       144.375     , 143.64802632, 142.92105263, 142.19407895,\n",
       "       141.46710526, 140.74013158, 140.01315789, 139.28618421,\n",
       "       138.55921053, 137.83223684, 137.10526316, 136.37828947,\n",
       "       135.65131579, 134.92434211, 134.19736842, 133.47039474,\n",
       "       132.74342105, 132.01644737, 131.28947368, 130.5625    ,\n",
       "       129.83552632, 129.10855263, 128.38157895, 127.65460526,\n",
       "       126.92763158, 126.20065789, 125.47368421, 124.74671053,\n",
       "       124.01973684, 123.29276316, 122.56578947, 121.83881579,\n",
       "       121.11184211, 120.38486842, 119.65789474, 118.93092105,\n",
       "       118.20394737, 117.47697368, 116.75      , 116.75      ,\n",
       "       114.        , 114.        , 112.        , 112.        ,\n",
       "       114.75      , 114.75      , 118.        , 118.        ,\n",
       "       120.        , 120.        , 121.        , 121.        ,\n",
       "       121.25      , 121.25      , 121.75      , 121.75      ,\n",
       "       123.5       , 125.25      , 125.25      , 125.25      ,\n",
       "       125.25      , 124.        , 124.        , 122.5       ,\n",
       "       122.5       , 120.        , 120.        , 120.        ,\n",
       "       116.        , 116.        , 114.75      , 114.75      ,\n",
       "       116.5       , 116.5       , 118.        , 118.        ,\n",
       "       120.25      , 120.25      , 121.25      , 121.25      ,\n",
       "       121.5       , 121.5       , 122.25      , 122.75      ,\n",
       "       122.75      , 122.75      , 122.75      , 122.75      ,\n",
       "       122.75      , 122.75      , 122.75      , 123.        ,\n",
       "       123.        , 120.25      , 120.25      , 119.25      ,\n",
       "       119.25      , 117.25      , 117.25      , 117.25      ,\n",
       "       114.        , 114.        , 110.75      , 110.75      ,\n",
       "       111.25      , 111.25      , 111.25      , 111.25      ,\n",
       "       111.25      , 111.25      , 111.25      , 111.5       ,\n",
       "       111.5       , 111.5       , 111.5       , 111.5       ,\n",
       "       111.5       , 111.5       , 100.25      , 100.25      ,\n",
       "       103.75      , 103.75      , 103.75      , 106.25      ,\n",
       "       106.25      , 107.5       , 107.5       , 107.75      ,\n",
       "       107.75      , 107.75      , 107.75      , 108.5       ,\n",
       "       108.5       , 109.25      , 109.25      , 109.25      ,\n",
       "       109.5       , 109.5       , 110.        , 110.        ,\n",
       "       111.        , 111.        , 112.5       , 112.5       ,\n",
       "       112.75      , 112.75      , 112.5       , 112.5       ,\n",
       "       112.5       , 111.        , 111.        , 111.        ,\n",
       "       111.        , 111.25      , 111.25      , 111.25      ,\n",
       "       111.25      , 111.        , 111.        , 110.25      ,\n",
       "       110.25      , 110.25      , 109.75      , 109.75      ,\n",
       "       108.25      , 108.25      , 108.25      , 108.25      ,\n",
       "       110.        , 110.        , 112.        , 112.        ,\n",
       "       112.75      , 112.75      , 112.75      , 112.75      ,\n",
       "       112.75      , 112.25      , 112.25      , 112.75      ,\n",
       "       112.75      , 114.5       , 114.5       , 117.25      ,\n",
       "       117.25      , 118.75      , 118.75      , 119.25      ,\n",
       "       119.25      , 118.75      , 118.75      , 118.        ,\n",
       "       118.        , 116.25      , 116.25      , 116.25      ,\n",
       "       116.25      , 116.        , 116.        , 116.75      ,\n",
       "       116.75      , 118.        , 118.        , 119.25      ,\n",
       "       119.25      , 120.25      , 120.25      , 121.5       ,\n",
       "       121.5       , 122.25      , 122.25      , 122.75      ,\n",
       "       122.75      , 123.        , 123.        , 122.25      ,\n",
       "       122.25      , 121.        , 121.        , 120.75      ,\n",
       "       120.75      , 118.        , 118.        , 117.75      ,\n",
       "       117.75      , 117.        , 117.        , 117.25      ,\n",
       "       117.25      , 117.25      , 117.25      , 117.        ,\n",
       "       117.        , 116.25      , 116.25      , 116.25      ,\n",
       "       114.25      , 114.25      , 113.        , 113.        ,\n",
       "       112.25      , 112.25      , 112.25      , 112.25      ,\n",
       "       114.75      , 114.75      , 116.75      , 116.75      ,\n",
       "       120.25      , 120.25      , 120.        , 120.        ,\n",
       "       117.75      , 117.75      , 115.5       , 115.5       ,\n",
       "       114.        , 114.        , 115.        , 115.        ,\n",
       "       115.        , 116.25      , 116.25      , 117.5       ,\n",
       "       117.5       , 117.5       , 117.5       , 116.5       ,\n",
       "       116.5       , 116.5       , 116.5       , 116.5       ,\n",
       "       116.5       , 116.5       , 116.5       , 116.5       ,\n",
       "       116.25      , 116.25      , 105.        , 105.        ,\n",
       "       104.25      , 104.25      , 104.25      , 104.25      ,\n",
       "       104.25      , 104.5       , 104.5       , 101.5       ,\n",
       "       101.5       , 101.5       , 101.5       ,  93.75      ,\n",
       "        93.75      ,  93.75      ,  94.        ,  94.        ,\n",
       "        95.        ,  95.        ,  95.        , 104.        ,\n",
       "       104.        , 106.        , 106.        , 106.        ,\n",
       "       106.        , 106.        , 106.        , 106.        ,\n",
       "       106.        , 106.        , 106.        , 105.5       ,\n",
       "       105.5       , 105.5       , 105.25      , 105.25      ,\n",
       "       105.25      , 105.25      , 105.25      , 106.        ,\n",
       "       106.        , 106.        , 105.75      , 105.75      ,\n",
       "       106.        , 106.        , 106.        , 106.        ,\n",
       "       108.5       , 108.5       , 107.25      , 107.25      ,\n",
       "       107.        , 107.        , 107.        , 106.5       ,\n",
       "       106.5       , 105.5       , 105.5       , 105.        ,\n",
       "       105.        , 107.25      , 107.25      , 108.5       ,\n",
       "       108.5       , 108.5       , 108.        , 108.        ,\n",
       "       106.5       , 106.5       , 106.25      , 106.25      ,\n",
       "       106.25      , 104.        , 104.        , 103.25      ,\n",
       "       103.25      , 103.75      , 103.75      , 103.75      ,\n",
       "       103.75      , 103.75      , 104.25      , 104.25      ,\n",
       "       103.5       , 103.5       , 103.5       , 101.25      ,\n",
       "       101.25      , 101.25      , 101.25      , 101.        ,\n",
       "       101.        , 101.        ,  99.        ,  99.        ,\n",
       "        99.        ,  99.        ,  99.        , 101.5       ,\n",
       "       101.5       , 102.        , 102.        , 102.5       ,\n",
       "       102.5       , 102.5       , 102.75      , 102.75      ,\n",
       "       103.75      , 103.75      , 105.25      , 105.25      ,\n",
       "       105.25      , 105.25      , 104.75      , 104.75      ,\n",
       "       104.75      , 104.75      , 104.75      , 104.75      ,\n",
       "       104.75      , 105.        , 105.        , 105.        ,\n",
       "       105.        , 105.        , 105.        , 105.        ,\n",
       "       105.        , 105.        , 105.        , 103.71428571,\n",
       "       102.42857143, 101.14285714,  99.85714286,  98.57142857,\n",
       "        97.28571429,  96.        ,  96.        ,  96.        ,\n",
       "        96.        ,  96.        ,  96.        ,  94.25      ,\n",
       "        94.25      ,  97.25      ,  97.25      ,  96.75      ,\n",
       "        96.75      ,  96.75      ,  96.75      ,  96.75      ,\n",
       "        96.75      ,  96.75      ,  96.75      ,  96.75      ,\n",
       "        96.75      ,  96.75      ,  96.75      ,  96.75      ,\n",
       "        98.09756098,  99.44512195, 100.79268293, 102.1402439 ,\n",
       "       103.48780488, 104.83536585, 106.18292683, 107.5304878 ,\n",
       "       108.87804878, 110.22560976, 111.57317073, 112.92073171,\n",
       "       114.26829268, 115.61585366, 116.96341463, 118.31097561,\n",
       "       119.65853659, 121.00609756, 122.35365854, 123.70121951,\n",
       "       125.04878049, 126.39634146, 127.74390244, 129.09146341,\n",
       "       130.43902439, 131.78658537, 133.13414634, 134.48170732,\n",
       "       135.82926829, 137.17682927, 138.52439024, 139.87195122,\n",
       "       141.2195122 , 142.56707317, 143.91463415, 145.26219512,\n",
       "       146.6097561 , 147.95731707, 149.30487805, 150.65243902,\n",
       "       152.        , 152.        , 153.25      , 154.75      ,\n",
       "       154.75      , 155.5       , 156.25      , 156.25      ,\n",
       "       157.5       , 157.25      , 157.25      , 158.        ,\n",
       "       158.25      , 158.25      , 159.        , 160.        ,\n",
       "       160.        , 160.        , 160.        , 160.        ,\n",
       "       160.        , 160.25      , 160.25      ])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train62_python[37391:37954]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([167.48714539, 167.47429078, 167.46143617, 167.44858156,\n",
       "       167.43572695, 167.42287234, 167.41001773, 167.39716312,\n",
       "       167.38430851, 167.3714539 , 167.35859929, 167.34574468,\n",
       "       167.33289007, 167.32003546, 167.30718085, 167.29432624,\n",
       "       167.28147163, 167.26861702, 167.25576241, 167.2429078 ,\n",
       "       167.23005319, 167.21719858, 167.20434397, 167.19148936,\n",
       "       167.17863475, 167.16578014, 167.15292553, 167.14007092,\n",
       "       167.12721631, 167.1143617 , 167.10150709, 167.08865248,\n",
       "       167.07579787, 167.06294326, 167.05008865, 167.03723404,\n",
       "       167.02437943, 167.01152482, 166.99867021, 166.9858156 ,\n",
       "       166.97296099, 166.96010638, 166.94725177, 166.93439716,\n",
       "       166.92154255, 166.90868794, 166.89583333, 166.88297872,\n",
       "       166.87012411, 166.8572695 , 166.84441489, 166.83156028,\n",
       "       166.81870567, 166.80585106, 166.79299645, 166.78014184,\n",
       "       166.76728723, 166.75443262, 166.74157801, 166.7287234 ,\n",
       "       166.71586879, 166.70301418, 166.69015957, 166.67730496,\n",
       "       166.66445035, 166.65159574, 166.63874113, 166.62588652,\n",
       "       166.61303191, 166.6001773 , 166.5873227 , 166.57446809,\n",
       "       166.56161348, 166.54875887, 166.53590426, 166.52304965,\n",
       "       166.51019504, 166.49734043, 166.48448582, 166.47163121,\n",
       "       166.4587766 , 166.44592199, 166.43306738, 166.42021277,\n",
       "       166.40735816, 166.39450355, 166.38164894, 166.36879433,\n",
       "       166.35593972, 166.34308511, 166.3302305 , 166.31737589,\n",
       "       166.30452128, 166.29166667, 166.27881206, 166.26595745,\n",
       "       166.25310284, 166.24024823, 166.22739362, 166.21453901,\n",
       "       166.2016844 , 166.18882979, 166.17597518, 166.16312057,\n",
       "       166.15026596, 166.13741135, 166.12455674, 166.11170213,\n",
       "       166.09884752, 166.08599291, 166.0731383 , 166.06028369,\n",
       "       166.04742908, 166.03457447, 166.02171986, 166.00886525,\n",
       "       165.99601064, 165.98315603, 165.97030142, 165.95744681,\n",
       "       165.9445922 , 165.93173759, 165.91888298, 165.90602837,\n",
       "       165.89317376, 165.88031915, 165.86746454, 165.85460993,\n",
       "       165.84175532, 165.82890071, 165.8160461 , 165.80319149,\n",
       "       165.79033688, 165.77748227, 165.76462766, 165.75177305,\n",
       "       165.73891844, 165.72606383, 165.71320922, 165.70035461,\n",
       "       165.6875    , 165.67464539, 165.66179078, 165.64893617,\n",
       "       165.63608156, 165.62322695, 165.61037234, 165.59751773,\n",
       "       165.58466312, 165.57180851, 165.5589539 , 165.54609929,\n",
       "       165.53324468, 165.52039007, 165.50753546, 165.49468085,\n",
       "       165.48182624, 165.46897163, 165.45611702, 165.44326241,\n",
       "       165.4304078 , 165.41755319, 165.40469858, 165.39184397,\n",
       "       165.37898936, 165.36613475, 165.35328014, 165.34042553,\n",
       "       165.32757092, 165.31471631, 165.3018617 , 165.28900709,\n",
       "       165.27615248, 165.26329787, 165.25044326, 165.23758865,\n",
       "       165.22473404, 165.21187943, 165.19902482, 165.18617021,\n",
       "       165.1733156 , 165.16046099, 165.14760638, 165.13475177,\n",
       "       165.12189716, 165.10904255, 165.09618794, 165.08333333,\n",
       "       165.07047872, 165.05762411, 165.0447695 , 165.03191489,\n",
       "       165.01906028, 165.00620567, 164.99335106, 164.98049645,\n",
       "       164.96764184, 164.95478723, 164.94193262, 164.92907801,\n",
       "       164.9162234 , 164.90336879, 164.89051418, 164.87765957,\n",
       "       164.86480496, 164.85195035, 164.83909574, 164.82624113,\n",
       "       164.81338652, 164.80053191, 164.7876773 , 164.7748227 ,\n",
       "       164.76196809, 164.74911348, 164.73625887, 164.72340426,\n",
       "       164.71054965, 164.69769504, 164.68484043, 164.67198582,\n",
       "       164.65913121, 164.6462766 , 164.63342199, 164.62056738,\n",
       "       164.60771277, 164.59485816, 164.58200355, 164.56914894,\n",
       "       164.55629433, 164.54343972, 164.53058511, 164.5177305 ,\n",
       "       164.50487589, 164.49202128, 164.47916667, 164.46631206,\n",
       "       164.45345745, 164.44060284, 164.42774823, 164.41489362,\n",
       "       164.40203901, 164.3891844 , 164.37632979, 164.36347518,\n",
       "       164.35062057, 164.33776596, 164.32491135, 164.31205674,\n",
       "       164.29920213, 164.28634752, 164.27349291, 164.2606383 ,\n",
       "       164.24778369, 164.23492908, 164.22207447, 164.20921986,\n",
       "       164.19636525, 164.18351064, 164.17065603, 164.15780142,\n",
       "       164.14494681, 164.1320922 , 164.11923759, 164.10638298,\n",
       "       164.09352837, 164.08067376, 164.06781915, 164.05496454,\n",
       "       164.04210993, 164.02925532, 164.01640071, 164.0035461 ,\n",
       "       163.99069149, 163.97783688, 163.96498227, 163.95212766,\n",
       "       163.93927305, 163.92641844, 163.91356383, 163.90070922,\n",
       "       163.88785461, 163.875     , 163.86214539, 163.84929078,\n",
       "       163.83643617, 163.82358156, 163.81072695, 163.79787234,\n",
       "       163.78501773, 163.77216312, 163.75930851, 163.7464539 ,\n",
       "       163.73359929, 163.72074468, 163.70789007, 163.69503546,\n",
       "       163.68218085, 163.66932624, 163.65647163, 163.64361702,\n",
       "       163.63076241, 163.6179078 , 163.60505319, 163.59219858,\n",
       "       163.57934397, 163.56648936, 163.55363475, 163.54078014,\n",
       "       163.52792553, 163.51507092, 163.50221631, 163.4893617 ,\n",
       "       163.47650709, 163.46365248, 163.45079787, 163.43794326,\n",
       "       163.42508865, 163.41223404, 163.39937943, 163.38652482,\n",
       "       163.37367021, 163.3608156 , 163.34796099, 163.33510638,\n",
       "       163.32225177, 163.30939716, 163.29654255, 163.28368794,\n",
       "       163.27083333, 163.25797872, 163.24512411, 163.2322695 ,\n",
       "       163.21941489, 163.20656028, 163.19370567, 163.18085106,\n",
       "       163.16799645, 163.15514184, 163.14228723, 163.12943262,\n",
       "       163.11657801, 163.1037234 , 163.09086879, 163.07801418,\n",
       "       163.06515957, 163.05230496, 163.03945035, 163.02659574,\n",
       "       163.01374113, 163.00088652, 162.98803191, 162.9751773 ,\n",
       "       162.9623227 , 162.94946809, 162.93661348, 162.92375887,\n",
       "       162.91090426, 162.89804965, 162.88519504, 162.87234043,\n",
       "       162.85948582, 162.84663121, 162.8337766 , 162.82092199,\n",
       "       162.80806738, 162.79521277, 162.78235816, 162.76950355,\n",
       "       162.75664894, 162.74379433, 162.73093972, 162.71808511,\n",
       "       162.7052305 , 162.69237589, 162.67952128, 162.66666667,\n",
       "       162.65381206, 162.64095745, 162.62810284, 162.61524823,\n",
       "       162.60239362, 162.58953901, 162.5766844 , 162.56382979,\n",
       "       162.55097518, 162.53812057, 162.52526596, 162.51241135,\n",
       "       162.49955674, 162.48670213, 162.47384752, 162.46099291,\n",
       "       162.4481383 , 162.43528369, 162.42242908, 162.40957447,\n",
       "       162.39671986, 162.38386525, 162.37101064, 162.35815603,\n",
       "       162.34530142, 162.33244681, 162.3195922 , 162.30673759,\n",
       "       162.29388298, 162.28102837, 162.26817376, 162.25531915,\n",
       "       162.24246454, 162.22960993, 162.21675532, 162.20390071,\n",
       "       162.1910461 , 162.17819149, 162.16533688, 162.15248227,\n",
       "       162.13962766, 162.12677305, 162.11391844, 162.10106383,\n",
       "       162.08820922, 162.07535461, 162.0625    , 162.04964539,\n",
       "       162.03679078, 162.02393617, 162.01108156, 161.99822695,\n",
       "       161.98537234, 161.97251773, 161.95966312, 161.94680851,\n",
       "       161.9339539 , 161.92109929, 161.90824468, 161.89539007,\n",
       "       161.88253546, 161.86968085, 161.85682624, 161.84397163,\n",
       "       161.83111702, 161.81826241, 161.8054078 , 161.79255319,\n",
       "       161.77969858, 161.76684397, 161.75398936, 161.74113475,\n",
       "       161.72828014, 161.71542553, 161.70257092, 161.68971631,\n",
       "       161.6768617 , 161.66400709, 161.65115248, 161.63829787,\n",
       "       161.62544326, 161.61258865, 161.59973404, 161.58687943,\n",
       "       161.57402482, 161.56117021, 161.5483156 , 161.53546099,\n",
       "       161.52260638, 161.50975177, 161.49689716, 161.48404255,\n",
       "       161.47118794, 161.45833333, 161.44547872, 161.43262411,\n",
       "       161.4197695 , 161.40691489, 161.39406028, 161.38120567,\n",
       "       161.36835106, 161.35549645, 161.34264184, 161.32978723,\n",
       "       161.31693262, 161.30407801, 161.2912234 , 161.27836879,\n",
       "       161.26551418, 161.25265957, 161.23980496, 161.22695035,\n",
       "       161.21409574, 161.20124113, 161.18838652, 161.17553191,\n",
       "       161.1626773 , 161.1498227 , 161.13696809, 161.12411348,\n",
       "       161.11125887, 161.09840426, 161.08554965, 161.07269504,\n",
       "       161.05984043, 161.04698582, 161.03413121, 161.0212766 ,\n",
       "       161.00842199, 160.99556738, 160.98271277, 160.96985816,\n",
       "       160.95700355, 160.94414894, 160.93129433, 160.91843972,\n",
       "       160.90558511, 160.8927305 , 160.87987589, 160.86702128,\n",
       "       160.85416667, 160.84131206, 160.82845745, 160.81560284,\n",
       "       160.80274823, 160.78989362, 160.77703901, 160.7641844 ,\n",
       "       160.75132979, 160.73847518, 160.72562057, 160.71276596,\n",
       "       160.69991135, 160.68705674, 160.67420213, 160.66134752,\n",
       "       160.64849291, 160.6356383 , 160.62278369, 160.60992908,\n",
       "       160.59707447, 160.58421986, 160.57136525, 160.55851064,\n",
       "       160.54565603, 160.53280142, 160.51994681, 160.5070922 ,\n",
       "       160.49423759, 160.48138298, 160.46852837, 160.45567376,\n",
       "       160.44281915, 160.42996454, 160.41710993, 160.40425532,\n",
       "       160.39140071, 160.3785461 , 160.36569149, 160.35283688,\n",
       "       160.33998227, 160.32712766, 160.31427305, 160.30141844,\n",
       "       160.28856383, 160.27570922, 160.26285461])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train62_matlab[37391:37954]"
   ]
  }
 ],
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