def test_blocksize_auto(self):
""" test that the blocksize parameter works as intended"""
file = '{}.edf'.format(self.tmp_testfile)
siglen = 256 * 155
signals = np.random.rand(10, siglen)
sheads = highlevel.make_signal_headers([str(x) for x in range(10)],
sample_rate=256,
physical_max=1,
physical_min=-1)
valid_block_sizes = [-1, 1, 5, 31]
for block_size in valid_block_sizes:
highlevel.write_edf(file, signals, sheads, block_size=block_size)
signals2, _, _ = highlevel.read_edf(file)
np.testing.assert_allclose(signals, signals2, atol=0.01)
with self.assertRaises(AssertionError):
highlevel.write_edf(file, signals, sheads, block_size=61)
with self.assertRaises(AssertionError):
highlevel.write_edf(file, signals, sheads, block_size=-2)
# now test non-divisor block_size
siglen = signals.shape[-1]
highlevel.write_edf(file, signals, sheads, block_size=60)
signals2, _, _ = highlevel.read_edf(file)
self.assertEqual(signals2.shape, (10, 256 * 60 * 3))
np.testing.assert_allclose(signals2[:, :siglen], signals, atol=0.01)
np.testing.assert_allclose(signals2[:, siglen:],
np.zeros([10, 25 * 256]),
atol=0.0001)
def test_anonymize(self):
header = highlevel.make_header(technician='tech',
recording_additional='radd',
patientname='name',
patient_additional='padd',
patientcode='42',
equipment='eeg',
admincode='420',
gender='Male',
birthdate='05.09.1980')
annotations = [[0.01, -1, 'begin'], [0.5, -1, 'middle'],
[10, -1, 'end']]
header['annotations'] = annotations
signal_headers = highlevel.make_signal_headers(
['ch' + str(i) for i in range(3)])
signals = np.random.rand(3, 256 * 300) * 200 #5 minutes of eeg
highlevel.write_edf(self.personalized, signals, signal_headers, header)
highlevel.anonymize_edf(self.personalized,
new_file=self.anonymized,
to_remove=[
'patientname', 'birthdate', 'admincode',
'patientcode', 'technician'
],
new_values=['x', '', 'xx', 'xxx', 'xxxx'],
verify=True,
verbose=True)
new_header = highlevel.read_edf_header(self.anonymized)
self.assertEqual(new_header['birthdate'], '')
self.assertEqual(new_header['patientname'], 'x')
self.assertEqual(new_header['admincode'], 'xx')
self.assertEqual(new_header['patientcode'], 'xxx')
self.assertEqual(new_header['technician'], 'xxxx')
highlevel.anonymize_edf(self.personalized,
to_remove=[
'patientname', 'birthdate', 'admincode',
'patientcode', 'technician'
],
new_values=['x', '', 'xx', 'xxx', 'xxxx'],
verify=True)
new_header = highlevel.read_edf_header(self.personalized[:-4] +
'_anonymized.edf')
self.assertEqual(new_header['birthdate'], '')
self.assertEqual(new_header['patientname'], 'x')
self.assertEqual(new_header['admincode'], 'xx')
self.assertEqual(new_header['patientcode'], 'xxx')
self.assertEqual(new_header['technician'], 'xxxx')
with self.assertRaises(AssertionError):
highlevel.anonymize_edf(self.personalized,
new_file=self.anonymized,
to_remove=[
'patientname', 'birthdate',
'admincode', 'patientcode',
'technician'
],
new_values=['x', '', 'xx', 'xxx'],
verify=True)
def test_drop_channel(self):
signal_headers = highlevel.make_signal_headers(
['ch' + str(i) for i in range(5)])
signals = np.random.rand(5, 256 * 300) * 200 #5 minutes of eeg
highlevel.write_edf(self.drop_from, signals, signal_headers)
dropped = highlevel.drop_channels(self.drop_from,
to_keep=['ch1', 'ch2'],
verbose=True)
signals2, signal_headers, header = highlevel.read_edf(dropped)
np.testing.assert_allclose(signals[1:3, :], signals2, atol=0.01)
highlevel.drop_channels(self.drop_from,
self.drop_from[:-4] + '2.edf',
to_drop=['ch0', 'ch1', 'ch2'])
signals2, signal_headers, header = highlevel.read_edf(
self.drop_from[:-4] + '2.edf')
np.testing.assert_allclose(signals[3:, :], signals2, atol=0.01)
with self.assertRaises(AssertionError):
highlevel.drop_channels(self.drop_from,
to_keep=['ch1'],
to_drop=['ch3'])
def test_read_write_edf(self):
startdate = datetime.now()
t = startdate
startdate = datetime(t.year, t.month, t.day, t.hour, t.minute,
t.second)
header = highlevel.make_header(technician='tech',
recording_additional='radd',
patientname='name',
patient_additional='padd',
patientcode='42',
equipment='eeg',
admincode='420',
gender='Male',
startdate=startdate,
birthdate='05.09.1980')
annotations = [[0.01, -1, 'begin'], [0.5, -1, 'middle'],
[10, -1, 'end']]
header['annotations'] = annotations
signal_headers1 = highlevel.make_signal_headers(
['ch' + str(i) for i in range(5)])
signals = np.random.rand(5, 256 * 300) * 200 #5 minutes of eeg
success = highlevel.write_edf(self.edfplus_data_file, signals,
signal_headers1, header)
self.assertTrue(os.path.isfile(self.edfplus_data_file))
self.assertGreater(os.path.getsize(self.edfplus_data_file), 0)
self.assertTrue(success)
signals2, signal_headers2, header2 = highlevel.read_edf(
self.edfplus_data_file)
self.assertEqual(len(signals2), 5)
self.assertEqual(len(signals2), len(signal_headers2))
for shead1, shead2 in zip(signal_headers1, signal_headers2):
self.assertDictEqual(shead1, shead2)
self.assertDictEqual(header, header2)
np.testing.assert_allclose(signals, signals2, atol=0.01)
signals = (signals * 100).astype(np.int8)
success = highlevel.write_edf(self.edfplus_data_file,
signals,
signal_headers1,
header,
digital=True)
self.assertTrue(os.path.isfile(self.edfplus_data_file))
self.assertGreater(os.path.getsize(self.edfplus_data_file), 0)
self.assertTrue(success)
signals2, signal_headers2, header2 = highlevel.read_edf(
self.edfplus_data_file, digital=True)
self.assertEqual(len(signals2), 5)
self.assertEqual(len(signals2), len(signal_headers2))
for shead1, shead2 in zip(signal_headers1, signal_headers2):
self.assertDictEqual(shead1, shead2)
self.assertDictEqual(header, header2)
np.testing.assert_array_equal(signals, signals2)
def write_data_edf(raw, dst_file, uid, task):
sfreq = 250
data = raw.get_data() * 1e6
# Create writer
writer = pyedflib.EdfWriter(str(dst_file), len(EEG_channels), file_type=1)
# Create header
writer.setPatientName(uid)
# assert task == 'pegNormal' or 'pegInversion'
# if task == 'pegNormal' or 'Low':
# label = 'low workload'
# elif task == 'pegInversion' or 'High':
# label = 'high workload'
# writer.setPatientAdditional(label)
# Signals
signal_headers = highlevel.make_signal_headers(EEG_channels,
sample_rate=sfreq)
writer.setSignalHeaders(signal_headers)
writer.writeSamples(data)
# close
writer.close()
def test_read_write_edf(self):
startdate = datetime.now()
t = startdate
startdate = datetime(t.year,t.month,t.day,t.hour, t.minute,t.second)
header = highlevel.make_header(technician='tech', recording_additional='r_add',
patientname='name', patient_additional='p_add',
patientcode='42', equipment='eeg', admincode='120',
gender='Male', startdate=startdate,birthdate='05.09.1980')
annotations = [[0.01, -1, 'begin'],[0.5, -1, 'middle'],[10, -1, 'end']]
signal_headers1 = highlevel.make_signal_headers(['ch'+str(i) for i in range(5)])
for file_type in [-1,0,1,2,3]:
if file_type in [0, 2]:
header['annotations'] = []
else:
header['annotations'] = annotations
file = '{}_{}_phys.edf'.format(self.tmp_testfile, file_type)
signals = np.random.rand(5, 256*300)*200 #5 minutes of eeg
success = highlevel.write_edf(file, signals, signal_headers1, header, file_type=file_type)
self.assertTrue(os.path.isfile(file))
self.assertGreater(os.path.getsize(file), 0)
self.assertTrue(success)
signals2, signal_headers2, header2 = highlevel.read_edf(file)
self.assertEqual(len(signals2), 5)
self.assertEqual(len(signals2), len(signal_headers2))
for shead1, shead2 in zip(signal_headers1, signal_headers2):
self.assertDictEqual(shead1, shead2)
np.testing.assert_allclose(signals, signals2, atol=0.01)
if file_type in [-1, 1, 3]:
self.assertDictEqual(header, header2)
file = '{}_{}_dig.edf'.format(self.tmp_testfile, file_type)
signals = (signals*100).astype(np.int8)
success = highlevel.write_edf(file, signals, signal_headers1, header, digital=True)
self.assertTrue(os.path.isfile(file))
self.assertGreater(os.path.getsize(file), 0)
self.assertTrue(success)
signals2, signal_headers2, header2 = highlevel.read_edf(file, digital=True)
self.assertEqual(len(signals2), 5)
self.assertEqual(len(signals2), len(signal_headers2))
np.testing.assert_array_equal(signals, signals2)
for shead1, shead2 in zip(signal_headers1, signal_headers2):
self.assertDictEqual(shead1, shead2)
# EDF/BDF header writing does not correctly work yet
if file_type in [-1, 1, 3]:
self.assertDictEqual(header, header2)
def test_annotation_bytestring(self):
header = highlevel.make_header(technician='tech',
recording_additional='radd',
patientname='name',
patient_additional='padd',
patientcode='42',
equipment='eeg',
admincode='420',
gender='Male',
birthdate='05.09.1980')
annotations = [[0.01, b'-1', 'begin'], [0.5, b'-1', 'middle'],
[10, -1, 'end']]
header['annotations'] = annotations
signal_headers = highlevel.make_signal_headers(
['ch' + str(i) for i in range(3)])
signals = np.random.rand(3, 256 * 300) * 200 #5 minutes of eeg
highlevel.write_edf(self.edfplus_data_file, signals, signal_headers,
header)
_, _, header2 = highlevel.read_edf(self.edfplus_data_file)
highlevel.write_edf(self.edfplus_data_file, signals, signal_headers,
header)
_, _, header3 = highlevel.read_edf(self.edfplus_data_file)
self.assertEqual(header2['annotations'], header3['annotations'])
destinationFile = dst / file.with_suffix(".edf").name
# if not p2.exists():
# p2.mkdir(parents=True)
sfreq = 250
df = pd.read_csv(file)
data = df[EEG_channels].values.transpose()
# Create writer
writer = pyedflib.EdfWriter(str(destinationFile),
len(EEG_channels),
file_type=1)
# Create header
writer.setPatientName(user)
#Set label
# label = df['label'].values.mean()
# label = "low workload" if label < 7.5 else "high workload"
# writer.setPatientAdditional(label)
# Signals
signal_headers = highlevel.make_signal_headers(EEG_channels,
sample_rate=250)
writer.setSignalHeaders(signal_headers)
writer.writeSamples(data)
#close
writer.close()
