def getDataFromFile(self, input_file):
# read the eeg file to the list
data = read(
input_file, delimiter=',', header=1,
to_float=True, transpose=True
)
# choose the channel (in this example its 1st channel - 0)
data = data[0]
return data
# seconds analysed
sec_beg = 15
sec_end = 20
sec_rng = sec_end-sec_beg
# lower and higher range values
rng = [sec_beg*int(fs), sec_end*int(fs)]
############################################
# #
# GET DATA FROM FILE #
# #
############################################
# read the eeg file to the list
data = read(
eeg_file, delimiter=',', header=1, to_float=True, transpose=True
)
# choose the channel (in this example its 1st channel - 0)
data = data[0]
data_rng = data[rng[0]:rng[1]]
# required for futher plotting appropriate x axis (samples)
x_time = [i/fs for i in range(len(data))]
x_time_rng = x_time[rng[0]:rng[1]]
############################################
# #
# FILTERING #
# #
highcut = 50.0
# bandstop values
lowstop = 49.0
highstop = 51.0
# file with eeg data location
eeg_file = args.data_file
############################################
# #
# GET DATA FROM FILE #
# #
############################################
# read the eeg file to the list
data = read(eeg_file, delimiter=',', header=1, to_float=True, transpose=True)
# choose the channel (in this example its 1st channel - 0)
data = data[0]
############################################
# #
# FILTERING #
# #
############################################
# filter data using butt bandstop 49-51 Hz filter (50Hz)
flted_50_stop = flt.butter_bandstop_filter(data,
lowstop,
highstop,
fs,
order=2)
# #
# GET DATA FROM FILE #
# #
############################################
# read the eeg file to the list
# SNIFFER for detecting dialect
#with open(eeg_file, newline='') as csvfile:
# dialect = csv.Sniffer().sniff(csvfile.read(1024))
# csvfile.seek(0)
# reader = csv.reader(csvfile, dialect)
data = read(eeg_file,
delimiter=',',
header=7,
to_float=False,
transpose=False,
comas=False,
mode='rt')
#data = read(
# eeg_file, delimiter=',', header=0, to_float=False, transpose=False,
# comas=False, mode='rt'
# )
# data extraction/ cleanup
data = zip(*data)
data = list(data)
# choose the channel
data = np.asanyarray(data[:12])
data = data.astype(np.float)