def test_fortran_write(self):
# Create Fortran contiguous array
signals = np.random.randint(-2048, 2048, [4, 5000000])
signals = np.asfortranarray(signals)
# Write
highlevel.write_edf_quick(self.edfplus_data_file,
signals.astype(np.int32),
sfreq=250,
digital=True)
# Read and check
signals2, _, _ = highlevel.read_edf(self.edfplus_data_file,
digital=True,
verbose=True)
np.testing.assert_allclose(signals, signals2)
# Create Fortran contiguous list
signals = [np.random.randint(-2048, 2048, (5000000, ))] * 4
# Write
highlevel.write_edf_quick(self.edfplus_data_file,
signals,
sfreq=250,
digital=True)
# Read and check
signals2, _, _ = highlevel.read_edf(self.edfplus_data_file,
digital=True,
verbose=True)
np.testing.assert_allclose(signals, signals2)
def test_read_write_accented(self):
signals = np.random.rand(3, 256*60)
highlevel.write_edf_quick(self.test_accented, signals, sfreq=256)
signals2, _, _ = highlevel.read_edf(self.test_accented)
np.testing.assert_allclose(signals, signals2, atol=0.00002)
# if os.name!='nt':
self.assertTrue(os.path.isfile(self.test_accented), 'File does not exist')
def test_quick_write(self):
signals = np.random.randint(-2048, 2048, [3, 256*60])
highlevel.write_edf_quick(self.edfplus_data_file, signals.astype(np.int32), sfreq=256, digital=True)
signals2, _, _ = highlevel.read_edf(self.edfplus_data_file, digital=True)
np.testing.assert_allclose(signals, signals2)
signals = np.random.rand(3, 256*60)
highlevel.write_edf_quick(self.edfplus_data_file, signals, sfreq=256)
signals2, _, _ = highlevel.read_edf(self.edfplus_data_file)
np.testing.assert_allclose(signals, signals2, atol=0.00002)
def test_read_unicode(self):
signals = np.random.rand(3, 256*60)
success = highlevel.write_edf_quick(self.edfplus_data_file, signals, sfreq=256)
self.assertTrue(success)
shutil.copy(self.edfplus_data_file, self.test_unicode)
signals2, _, _ = highlevel.read_edf(self.test_unicode)
self.assertTrue(os.path.isfile(self.test_unicode), 'File does not exist')
def test_read_write_decimal_sample_rates(self):
signals = np.random.randint(-2048, 2048, [3, 256 * 60])
highlevel.write_edf_quick(self.edfplus_data_file,
signals.astype(np.int32),
sfreq=8.5,
digital=True)
signals2, _, _ = highlevel.read_edf(self.edfplus_data_file,
digital=True,
verbose=True)
np.testing.assert_allclose(signals, signals2)
signals = np.random.rand(3, 256 * 60)
highlevel.write_edf_quick(self.edfplus_data_file,
signals,
sfreq=8.5,
digital=False)
signals2, _, _ = highlevel.read_edf(self.edfplus_data_file,
digital=False,
verbose=True)
np.testing.assert_allclose(signals, signals2, atol=0.0001)
def test_read_write_accented(self):
signals = np.random.rand(3, 256 * 60)
highlevel.write_edf_quick(self.test_accented, signals, sfreq=256)
signals2, _, _ = highlevel.read_edf(self.test_accented)
np.testing.assert_allclose(signals, signals2, atol=0.00002)
def ECG_writer(signal_sqi, file_name, file_type, info = None):
"""
Parameters
----------
signal_sqi : SignalSQI object containing signals, sampling rate and sqi
file_name : name of file to write, with extension. For edf file_type,
possible extensions are edf, edf+, bdf, bdf+. For mit file_type, :
possible extensions are... :
file_type : edf or mit or csv
info : list
In case of writing edf file: A list containing signal_headers and
header (in
order). signal_headers is a list of dict with one signal header for
each signal channel. header (dict) contain ecg file information.
In case of writing wfdb record (mit file): A dict containing header
as defined in .hea file.
(Default value = None)
Returns
-------
"""
signals = signal_sqi.signals
sampling_rate = signal_sqi.sampling_rate
start_datetime = signal_sqi.start_datetime
assert isinstance(sampling_rate, int) or isinstance(sampling_rate,
float), \
'sampling rate must be either int or float'
if file_type == 'edf':
signals = signals.transpose()
if info is not None:
signal_headers = info[1]
header = info[0]
annotations = header['annotations']
# issue https://github.com/holgern/pyedflib/issues/119
for i in range(len(annotations)):
if isinstance(header['annotations'][i][1], bytes):
header['annotations'][i][1] = \
float(str(header['annotations'][i][1], 'utf-8'))
highlevel.write_edf(file_name, signals, signal_headers,
header, file_type = -1, digital = False,
block_size = -1)
else:
highlevel.write_edf_quick(file_name, signals, sampling_rate)
return os.path.isfile(file_name)
if file_type == 'mit':
if info is None:
raise Exception("Header dict needed")
else:
wrsamp(record_name = file_name.split('/')[-1],
fs = sampling_rate,
units = info['units'],
sig_name = info['sig_name'],
p_signal = signals,
base_date = info['base_date'],
base_time = info['base_time'],
comments = info['comments'],
write_dir = '/'.join(file_name.split('/')[:-1]))
return glob.glob(file_name + '.*')
if file_type == 'csv':
timestamps = generate_timestamp(start_datetime, sampling_rate,
signals.shape[0])
signals = pd.DataFrame(np.hstack((np.array(timestamps).reshape(-1, 1),
signals)))
signals.to_csv(path_or_buf = file_name, index = False, header = True)
return os.path.isfile(file_name)
