"""
Created on Wed Mar 28 13:19:34 2018
@author: mohammad
"""
import os
import numpy as np
import pandas as pd
from pylab import find
import scipy.io
from sklearn.externals import joblib
# -----------------------------------------------------------------------------
# returns a list of the training and testing file locations for easier import
# -----------------------------------------------------------------------------
def get_files():
header_loc, arousal_loc, signal_loc, is_training = [], [], [], []
rootDir = '.'
for dirName, subdirList, fileList in os.walk(rootDir, followlinks=True):
if dirName != '.' and dirName != './test' and dirName != './training':
if dirName.startswith('./training/'):
is_training.append(True)
for fname in fileList:
if '.hea' in fname:
header_loc.append(dirName + '/' + fname)
if '-arousal.mat' in fname:
arousal_loc.append(dirName + '/' + fname)
if 'mat' in fname and 'arousal' not in fname:
signal_loc.append(dirName + '/' + fname)
elif dirName.startswith('./test/'):
is_training.append(False)
arousal_loc.append('')
for fname in fileList:
if '.hea' in fname:
header_loc.append(dirName + '/' + fname)
if 'mat' in fname and 'arousal' not in fname:
signal_loc.append(dirName + '/' + fname)
# combine into a data frame
data_locations = {'header': header_loc,
'arousal': arousal_loc,
'signal': signal_loc,
'is_training': is_training
}
# Convert to a data-frame
df = pd.DataFrame(data=data_locations)
# Split the data frame into training and testing sets.
tr_ind = list(find(df.is_training.values))
te_ind = list(find(df.is_training.values == False))
training_files = df.loc[tr_ind, :]
testing_files = df.loc[te_ind, :]
return training_files, testing_files
# -----------------------------------------------------------------------------
# import the outcome vector, given the file name.
# e.g. /training/tr04-0808/tr04-0808-arousal.mat
# -----------------------------------------------------------------------------
def import_arousals(file_name):
import h5py
import numpy
f = h5py.File(file_name, 'r')
arousals = numpy.array(f['data']['arousals'])
return arousals
def import_signals(file_name):
return np.transpose(scipy.io.loadmat(file_name)['val'])
# -----------------------------------------------------------------------------
# Take a header file as input, and returns the names of the signals
# For the corresponding .mat file containing the signals.
# -----------------------------------------------------------------------------
def import_signal_names(file_name):
with open(file_name, 'r') as myfile:
s = myfile.read()
s = s.split('\n')
s = [x.split() for x in s]
n_signals = int(s[0][1])
n_samples = int(s[0][3])
Fs = int(s[0][2])
s = s[1:-1]
s = [s[i][8] for i in range(0, n_signals)]
return s, Fs, n_samples
# -----------------------------------------------------------------------------
# Get a given subject's data
# -----------------------------------------------------------------------------
def get_subject_data(arousal_file, signal_file, signal_names):
this_arousal = import_arousals(arousal_file)
this_signal = import_signals(signal_file)
this_data = np.append(this_signal, this_arousal, axis=1)
this_data = pd.DataFrame(this_data, index=None, columns=signal_names)
return this_data
def get_subject_data_test(signal_file, signal_names):
this_signal = import_signals(signal_file)
this_data = this_signal
this_data = pd.DataFrame(this_data, index=None, columns=signal_names)
return this_data