#!/usr/bin/env python3

# -*- coding: utf-8 -*-

'''

Convert multiple files from Physionet/Computing in Cardiology challenge into 

file single matrix. As input argument 

For more information visit: https://github.com/fernandoandreotti/cinc-challenge2017

 Referencing this work

   Andreotti, F., Carr, O., Pimentel, M.A.F., Mahdi, A., & De Vos, M. (2017). Comparing Feature Based 

   Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG. In 

   Computing in Cardiology. Rennes (France).

--

 cinc-challenge2017, version 1.0, Sept 2017

 Last updated : 27-09-2017

 Released under the GNU General Public License

 Copyright (C) 2017  Fernando Andreotti, Oliver Carr, Marco A.F. Pimentel, Adam Mahdi, Maarten De Vos

 University of Oxford, Department of Engineering Science, Institute of Biomedical Engineering

 fernando.andreotti@eng.ox.ac.uk

 This program is free software: you can redistribute it and/or modify

 it under the terms of the GNU General Public License as published by

 the Free Software Foundation, either version 3 of the License, or

 (at your option) any later version.

 This program is distributed in the hope that it will be useful,

 but WITHOUT ANY WARRANTY; without even the implied warranty of

 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the

 GNU General Public License for more details.

 You should have received a copy of the GNU General Public License

 along with this program.  If not, see <http://www.gnu.org/licenses/>.

'''

import scipy.io

import numpy as np

import glob

# Parameters

dataDir = '/some_path/' # <---- change!!

dataDir = 'training2017/'

FS = 300

WINDOW_SIZE = 60*FS

## Loading time serie signals

files = sorted(glob.glob(dataDir+"*.mat"))

trainset = np.zeros((len(files),WINDOW_SIZE))

count = 0

for f in files:

    record = f[:-4]

    record = record[-6:]

    # Loading

    mat_data = scipy.io.loadmat(f[:-4] + ".mat")

    print('Loading record {}'.format(record))    

    data = mat_data['val'].squeeze()

    # Preprocessing

    print('Preprocessing record {}'.format(record))       

    data = np.nan_to_num(data) # removing NaNs and Infs

    data = data - np.mean(data)

    data = data/np.std(data)

    trainset[count,:min(WINDOW_SIZE,len(data))] = data[:min(WINDOW_SIZE,len(data))].T # padding sequence

    count += 1

## Loading labels    

import csv

csvfile = list(csv.reader(open(dataDir+'REFERENCE.csv')))

traintarget = np.zeros((trainset.shape[0],4))

classes = ['A','N','O','~']

for row in range(len(csvfile)):

    traintarget[row,classes.index(csvfile[row][1])] = 1

# Saving both

scipy.io.savemat('trainingset.mat',mdict={'trainset': trainset,'traintarget': traintarget})