def _load_header_file(filename, input_directory):
"""Load header file."""
# Load file
file = open(os.path.join(input_directory, '{}.hea'.format(filename)), 'r')
content = file.read().split('\n')
file.close()
return parse_header(header_data=content)
def _load_header_file(self, filename):
"""Load header file."""
# Load file
file = open(
os.path.join(self.raw_path, EXTRACTED_FOLDER_NAMES[self.dataset],
'{}.hea'.format(filename)), 'r')
content = file.read().split('\n')
file.close()
return parse_header(header_data=content)
def _import_meta_data(self):
"""Import meta data JSON files."""
if self.header_data is None:
return json.load(
open(
os.path.join(DATA_PATH, 'formatted',
'{}.json'.format(self.filename))))
else:
meta_data = parse_header(self.header_data)
self.filename = meta_data['filename']
return meta_data
def inference_data_loader(waveforms,
header,
fs_resampled,
p_and_t_waves=False):
"""Convert data and header_data to .npy and dict format."""
# Parse header data
header = parse_header(header_data=header, inference=True)
# Transpose waveforms array
waveforms = waveforms.T
# Resample waveforms
samples = int(waveforms.shape[0] * fs_resampled / header['fs'])
waveforms = signal.resample(x=waveforms, num=samples, axis=0)
# Compute heart rate
hr = _compute_heart_rate(waveforms=waveforms, fs=fs_resampled)
# Get rpeaks
rpeaks = _get_rpeaks(waveforms=waveforms, fs=fs_resampled)
rpeak_array = _get_peak_array(waveforms=waveforms, peaks=rpeaks)
rpeak_times = _get_peak_times(waveforms=waveforms,
peak_array=rpeak_array,
fs=fs_resampled)
# Get P-waves and T-waves
if p_and_t_waves:
p_waves, t_waves = _get_p_and_t_waves(waveforms=waveforms,
rpeaks=rpeaks)
else:
p_waves = None
t_waves = None
p_wave_array = _get_peak_array(waveforms=waveforms, peaks=p_waves)
p_wave_times = _get_peak_times(waveforms=waveforms,
peak_array=p_wave_array,
fs=fs_resampled)
t_wave_array = _get_peak_array(waveforms=waveforms, peaks=t_waves)
t_wave_times = _get_peak_times(waveforms=waveforms,
peak_array=t_wave_array,
fs=fs_resampled)
# Get meta data
meta_data = {
'filename': None,
'dataset': None,
'datetime': header['datetime'],
'channel_order': header['channel_order'],
'age': header['age'],
'sex': header['sex'],
'amp_conversion': header['amp_conversion'],
'fs': header['fs'],
'fs_resampled': fs_resampled,
'length': header['length'],
'num_leads': header['num_leads'],
'labels_SNOMEDCT': None,
'labels_short': None,
'labels_full': None,
'labels_int': None,
'labels_training': None,
'labels_training_merged': None,
'shape': waveforms.shape,
'hr': hr,
'rpeaks': rpeaks,
'rpeak_array': rpeak_array.tolist(),
'rpeak_times': rpeak_times,
'p_waves': p_waves,
'p_wave_array': p_wave_array.tolist(),
'p_wave_times': p_wave_times,
't_waves': t_waves,
't_wave_array': t_wave_array.tolist(),
't_wave_times': t_wave_times,
'labels_unscored_SNOMEDCT': None,
'labels_unscored_short': None,
'labels_unscored_full': None,
'p_and_t_waves': p_and_t_waves
}
return waveforms, meta_data