def test_properties(self):
subject_id = "subjectA"
activity_count_collection = ActivityCountCollection(subject_id=subject_id,
data=np.array([[1, 2, 3], [4, 5, 6]]))
self.assertEqual(subject_id, activity_count_collection.subject_id)
self.assertEqual(np.array([1, 4]).tolist(), activity_count_collection.timestamps.tolist())
self.assertEqual(np.array([[2, 3], [5, 6]]).tolist(), activity_count_collection.values.tolist())
def crop(activity_count_collection, interval):
subject_id = activity_count_collection.subject_id
timestamps = activity_count_collection.timestamps
valid_indices = ((timestamps >= interval.start_time)
& (timestamps < interval.end_time)).nonzero()[0]
cropped_data = activity_count_collection.data[valid_indices, :]
return ActivityCountCollection(subject_id=subject_id,
data=cropped_data)
def test_interpolate(self):
subject_id = "subjectA"
data = np.array([[1, 0], [10, 9]])
activity_count_collection = ActivityCountCollection(
subject_id=subject_id, data=data)
interpolated_timestamps, interpolated_counts = ActivityCountFeatureService.interpolate(
activity_count_collection)
self.assertListEqual([0, 1, 2, 3, 4, 5, 6, 7, 8],
interpolated_counts.tolist())
self.assertListEqual([1, 2, 3, 4, 5, 6, 7, 8, 9],
interpolated_timestamps.tolist())
def test_load_cropped(self, mock_cropped_file_path, mock_load):
subject_id = 'subjectA'
mock_cropped_file_path.return_value = path = 'path/to/file'
data = np.array([[1, 2, 3], [4, 5, 6]])
expected_activity_count_collection = ActivityCountCollection(
subject_id=subject_id, data=data)
mock_load.return_value = data
returned_activity_count_collection = ActivityCountService.load_cropped(
subject_id)
mock_cropped_file_path.assert_called_once_with(subject_id)
mock_load.assert_called_once_with(path)
TestHelper.assert_models_equal(self,
expected_activity_count_collection,
returned_activity_count_collection)
def load_raw(file_id):
line_align = -1 # Find alignment line between PSG and actigraphy
project_root = str(utils.get_project_root())
with open(project_root +
'/data/mesa/overlap/mesa-actigraphy-psg-overlap.csv'
) as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
next(csv_file)
for row in csv_reader:
if int(row[0]) == int(file_id):
line_align = int(row[1])
activity = []
elapsed_time_counter = 0
if line_align == -1: # If there was no alignment found
return ActivityCountCollection(subject_id=file_id,
data=np.array([[-1], [-1]]))
with open(project_root + '/data/mesa/actigraphy/mesa-sleep-' +
file_id + '.csv') as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
next(csv_file)
for row in csv_reader:
if int(row[1]) >= line_align:
if row[4] == '':
activity.append([elapsed_time_counter, np.nan])
else:
activity.append([elapsed_time_counter, float(row[4])])
elapsed_time_counter = elapsed_time_counter + 30
data = np.array(activity)
data = utils.remove_nans(data)
return ActivityCountCollection(subject_id=file_id, data=data)
def test_build_feature_array(self, mock_load_cropped, mock_get_feature):
subject_id = "subjectA"
data = np.array([[1, 10], [10, 220], [20, 0], [40, 500], [70, 200],
[90, 0], [100, 0], [120, 4]])
activity_count_collection = ActivityCountCollection(
subject_id=subject_id, data=data)
mock_load_cropped.return_value = activity_count_collection
expected_features = [np.array([0.1]), np.array([0.2])]
mock_get_feature.side_effect = expected_features
expected_feature_array = np.array(expected_features)
valid_epochs = [
Epoch(timestamp=4, index=1),
Epoch(timestamp=50, index=2)
]
returned_feature_array = ActivityCountFeatureService.build(
subject_id, valid_epochs)
self.assertEqual(expected_feature_array.tolist(),
returned_feature_array.tolist())
def test_build(self, mock_psg_service_load_raw, mock_heart_rate_service,
mock_actigraphy_service, mock_time_service):
file_id = 'subjectA'
returned_sleep_data = []
for i in range(30):
returned_sleep_data.append(0)
for i in range(90):
returned_sleep_data.append(1)
for i in range(90):
returned_sleep_data.append(3)
for i in range(30):
returned_sleep_data.append(2)
heart_rate_data = np.array([[0, 20], [4, 60], [25, 95], [35, 333],
[55, 25], [100, 233], [190, 24]])
actigraphy_data = np.array([[0, 1], [15, 2], [30, 3], [45, 4], [60, 5],
[75, 6], [90, 7]])
circadian_data = np.array([[0, 0.3], [5, 0.4], [10, 0.1], [15, 0.6],
[20, 0.7], [25, 0.2], [30, 0.1], [35, 0.3],
[40, 0.4], [45, 0.1], [50, 0.6], [55, 0.7],
[60, 0.2], [65, 0.1]])
mock_psg_service_load_raw.return_value = np.array(returned_sleep_data)
mock_heart_rate_service.load_raw.return_value = HeartRateCollection(
subject_id=file_id, data=heart_rate_data)
mock_actigraphy_service.load_raw.return_value = ActivityCountCollection(
subject_id=file_id, data=actigraphy_data)
mock_time_service.load_circadian_model.return_value = circadian_data
subject = MesaSubjectBuilder.build(file_id)
self.assertEqual([[0], [1], [1], [1], [3], [3], [3], [2]],
subject.labeled_sleep.tolist())
def test_get_interval(self):
activity_count_collection = ActivityCountCollection(subject_id="subjectA",
data=np.array([[1, 2, 3], [4, 5, 6]]))
interval = Interval(start_time=1, end_time=4)
self.assertEqual(interval.start_time, activity_count_collection.get_interval().start_time)
self.assertEqual(interval.end_time, activity_count_collection.get_interval().end_time)
def load_cropped(subject_id):
activity_counts_path = ActivityCountService.get_cropped_file_path(
subject_id)
counts_array = ActivityCountService.load(activity_counts_path)
return ActivityCountCollection(subject_id=subject_id,
data=counts_array)