def convert_to_fft(window_length, window_step, fft_min_freq, fft_max_freq,
sampling_frequency, file_path):
warnings.filterwarnings("ignore")
type_data = pickle.load(open(file_path, 'rb'))
pipeline = Pipeline(
[FFT(), Slice(fft_min_freq, fft_max_freq),
Magnitude(),
Log10()])
#time_series_data = type_data.data
time_series_data = type_data
start, step = 0, int(np.floor(window_step * sampling_frequency))
stop = start + int(np.floor(window_length * sampling_frequency))
fft_data = []
while stop < time_series_data.shape[1]:
signal_window = time_series_data[:, start:stop]
fft_window = pipeline.apply(signal_window)
fft_dfft_dataata.append(fft_window)
start, stop = start + step, stop + step
fft_data = np.array(fft_data)
#named_data = seizure_type_data(patient_id=type_data.patient_id, seizure_type=type_data.seizure_type, data=fft_data)
#return named_data,os.path.basename(file_path)
return fft_data, os.path.basename(file_path)
def create_s2(window_length, window_step, fft_min_freq, fft_max_freq,
sampling_frequency, file_path):
warnings.filterwarnings("ignore")
type_data = pickle.load(open(file_path, 'rb'))
pipeline = Pipeline([Center_surround_diff()])
time_series_data = type_data.data
start, step = 0, int(np.floor(window_step * sampling_frequency))
stop = start + int(np.floor(window_length * sampling_frequency))
s2_data = []
while stop < time_series_data.shape[1]:
signal_window = time_series_data[:, start:stop]
window = pipeline.apply(signal_window)
s2_data.append(window)
start, stop = start + step, stop + step
s2_data = np.array(s2_data)
named_data = seizure_type_data(patient_id=type_data.patient_id,
seizure_type=type_data.seizure_type,
data=type_data.data,
s1=type_data.s1,
s2=s2_data)
return named_data, os.path.basename(file_path)
def create_s1(window_length, window_step, fft_min_freq, fft_max_freq,
sampling_frequency, file_path):
warnings.filterwarnings("ignore")
type_data = pickle.load(open(file_path, 'rb'))
pipeline = Pipeline(
[Substract_average_plus_P_2(),
IFFT(), Smooth_Gaussian()])
time_series_data = type_data.data
start, step = 0, int(np.floor(window_step * sampling_frequency))
stop = start + int(np.floor(window_length * sampling_frequency))
s1_data = []
while stop < time_series_data.shape[1]:
signal_window = time_series_data[:, start:stop]
window = pipeline.apply(signal_window)
s1_data.append(window)
start, stop = start + step, stop + step
s1_data = np.array(s1_data)
named_data = seizure_type_data(patient_id=type_data.patient_id,
seizure_type=type_data.seizure_type,
data=type_data.data,
s1=s1_data)
return named_data, os.path.basename(file_path)
def create_d(window_length, window_step, fft_min_freq, fft_max_freq,
sampling_frequency, file_path):
warnings.filterwarnings("ignore")
type_data = pickle.load(open(file_path, 'rb'))
#Three of these pipelines are needed, as concatenation takes a different kind of parameter (three maps)
pipeline1 = Pipeline([Normalise()])
pipeline2 = Pipeline([Concatenation()])
pipeline3 = Pipeline([RGB_0_255()])
#The three feature maps
data_ft = type_data.data
data_s1 = type_data.s1
data_s2 = type_data.s2
start, step = 0, int(np.floor(window_step * sampling_frequency))
stop = start + int(np.floor(window_length * sampling_frequency))
d_data = []
while stop < data_ft.shape[1]:
#Window definitions, the maps are of same size & shape so 1 looper can be used for all
window_ft = data_ft[:, start:stop]
window_s1 = data_s1[:, start:stop]
window_s2 = data_s2[:, start:stop]
#Normalise each window value
window_ft_norm = pipeline1.apply(window_ft)
window_s1_norm = pipeline1.apply(window_s1)
window_s2_norm = pipeline1.apply(window_s2)
#Concatenate normalised values
d_norm = pipeline2.apply(window_ft_norm, window_s1_norm,
window_s2_norm)
#RGB 0-255 conversion
d_rgb = pipeline3.apply(d_norm)
d_data.append(d_rgb)
start, stop = start + step, stop + step
d_data = np.array(d_data)
named_data = seizure_type_data(patient_id=type_data.patient_id,
seizure_type=type_data.seizure_type,
data=d_data)
return named_data, os.path.basename(file_path)
