def test_append_signal_too_little_data(self):
apnea_predictor = ApneaDetector()
apnea_predictor.append_signal(self.abdo_signal[0:700])
predictions = apnea_predictor.predictions.get_predictions_as_np_array()
np.testing.assert_almost_equal(self.abdo_signal[0:700],
apnea_predictor.signal,
decimal=5)
class CLI:
def __init__(self, signal_file=None, signal_type="ABDO_RES"):
signal_file = os.getcwd() + os.sep + signal_file
self.edf_signal = self.readEdfFile(signal_file)
self.signal = self.edf_signal[signal_type].to_numpy()
self.apnea_detector = ApneaDetector()
self.apnea_detector.append_signal(self.signal)
predictions = self.apnea_detector.predictions
xml = predictions.get_xml()
def readEdfFile(self, file):
"""Reads EDF file from SHHS dataset. Will need adjustments to work for other signals
Arguments:
file {str} -- Path to edf file
Returns:
DataFrame -- Dataframe of thorax and abdominal signals, with index in deciseconds since start of recording
"""
try:
edf = pyedflib.EdfReader(file)
cols = edf.getSignalLabels()
signal = pd.DataFrame([])
signal['THOR_RES'] = edf.readSignal(cols.index("THOR RES"))
signal['ABDO_RES'] = edf.readSignal(cols.index("ABDO RES"))
signal['SUM'] = signal['ABDO_RES'] + signal['THOR_RES']
finally:
edf._close()
del edf
return signal
def __init__(self, signal_file=None, signal_type="ABDO_RES"):
signal_file = os.getcwd() + os.sep + signal_file
self.edf_signal = self.readEdfFile(signal_file)
self.signal = self.edf_signal[signal_type].to_numpy()
self.apnea_detector = ApneaDetector()
self.apnea_detector.append_signal(self.signal)
predictions = self.apnea_detector.predictions
xml = predictions.get_xml()
def test_append_signal_analyze_whole_recording(self):
apnea_predictor = ApneaDetector()
apnea_predictor.append_signal(self.abdo_signal)
true_signal = self.abdo_signal
appended_signal = apnea_predictor.signal
np.testing.assert_almost_equal(true_signal, appended_signal, decimal=5)
self.assertEqual(len(true_signal), apnea_predictor.signal_length)
df = apnea_predictor.predictions.get_predictions_as_df(
apnea_predictor.predictions.predictions)
print(df)
def test__get_next_unchecked_signal_not_enough_remaining_data_in_signal(
self):
apnea_predictor = ApneaDetector()
data_index = 1200
start_index = 1100
prediction, new_index, signal_start_index = apnea_predictor._get_next_unchecked_signal(
self.abdo_signal[:data_index], start_index)
self.assertEqual(new_index, data_index)
self.assertEqual(len(prediction), self.sliding_window_duration)
self.assertListEqual(
list(prediction),
list(self.abdo_signal[data_index -
self.sliding_window_duration:data_index]))
def test__get_next_unchecked_signal_long_insert(self):
apnea_predictor = ApneaDetector()
large_index = 5000
start_index = 0
prediction, new_index, signal_start_index = apnea_predictor._get_next_unchecked_signal(
self.abdo_signal[:large_index], start_index)
self.assertEqual(new_index, self.sliding_window_overlap)
self.assertEqual(len(prediction), self.sliding_window_duration)
start_index = 400
prediction, new_index, signal_start_index = apnea_predictor._get_next_unchecked_signal(
self.abdo_signal[:large_index], start_index)
self.assertEqual(new_index, self.sliding_window_overlap + start_index)
self.assertEqual(len(prediction), self.sliding_window_duration)
def test_append_signal_long(self):
apnea_predictor = ApneaDetector()
apnea_predictor.append_signal(self.abdo_signal[0:900])
apnea_predictor.append_signal(self.abdo_signal[900:1500])
apnea_predictor.append_signal(self.abdo_signal[1500:10000])
true_signal = self.abdo_signal[0:10000]
appended_signal = apnea_predictor.signal
np.testing.assert_almost_equal(true_signal, appended_signal, decimal=5)
self.assertEqual(10000, apnea_predictor.signal_length)
def test__get_next_unchecked_signal_little_data(self):
apnea_predictor = ApneaDetector()
small_index = 30
medium_index = 50
start_index = 0
prediction, new_index, signal_start_index = apnea_predictor._get_next_unchecked_signal(
self.abdo_signal[:small_index], start_index)
self.assertEqual(new_index, small_index)
self.assertEqual(len(prediction), self.sliding_window_duration)
prediction, new_index, signal_start_index = apnea_predictor._get_next_unchecked_signal(
self.abdo_signal[:small_index + small_index],
start_index + small_index)
self.assertEqual(new_index, small_index + small_index)
self.assertEqual(len(prediction), self.sliding_window_duration)
prediction, new_index, signal_start_index = apnea_predictor._get_next_unchecked_signal(
self.abdo_signal[:small_index + small_index + medium_index],
start_index + small_index + medium_index)
self.assertEqual(new_index, small_index + small_index + medium_index)
self.assertEqual(len(prediction), self.sliding_window_duration)
def test_append_signal_many_small_appends(self):
apnea_predictor = ApneaDetector()
i = 0
while i < 100:
duration = random.randrange(10, 100)
signal = self.abdo_signal[i:i + duration]
apnea_predictor.append_signal(signal)
i += duration
apnea_predictor.append_signal(self.abdo_signal[0:700])
predictions = apnea_predictor.predictions.get_predictions_as_np_array()
def test_append_signal_multiple_detectors(self):
apnea_predictor_1 = ApneaDetector()
apnea_predictor_2 = ApneaDetector()
apnea_predictor_1.append_signal(self.abdo_signal[0:900])
apnea_predictor_1.append_signal(self.abdo_signal[900:1500])
apnea_predictor_2.append_signal(self.abdo_signal[0:900])
apnea_predictor_2.append_signal(self.abdo_signal[900:1500])
apnea_predictor_2.append_signal(self.abdo_signal[1500:10000])
self.assertEqual(apnea_predictor_1.signal_length, 1500)
self.assertEqual(apnea_predictor_2.signal_length, 10000)
self.assertNotEqual(apnea_predictor_1.signal_length,
apnea_predictor_2.signal_length)