def test_encoder_events_corrupt(self):
path = self.main_path.joinpath('data', 'wheel', 'lt5')
for file_events in path.rglob('_iblrig_encoderEvents.raw.*'):
dy = raw._load_encoder_events_file_lt5(file_events)
self.assertTrue(dy.size > 6)
path = self.main_path.joinpath('data', 'wheel', 'ge5')
for file_events in path.rglob('_iblrig_encoderEvents.raw.*'):
dy = raw._load_encoder_events_file_ge5(file_events)
self.assertTrue(dy.size > 6)
def test_wheel_folders(self):
# the wheel folder contains other errors in bpod output that had to be addressed
for wf in self.wheel_lt5_path.glob('_iblrig_encoderPositions*.raw*.ssv'):
df = raw._load_encoder_positions_file_lt5(wf)
self.assertTrue(np.all(np.diff(np.array(df.re_ts)) > 0))
for wf in self.wheel_lt5_path.glob('_iblrig_encoderEvents*.raw*.ssv'):
df = raw._load_encoder_events_file_lt5(wf)
self.assertTrue(np.all(np.diff(np.array(df.re_ts)) > 0))
for wf in self.wheel_ge5_path.glob('_iblrig_encoderPositions*.raw*.ssv'):
df = raw._load_encoder_positions_file_ge5(wf)
self.assertTrue(np.all(np.diff(np.array(df.re_ts)) > 0))
for wf in self.wheel_ge5_path.glob('_iblrig_encoderEvents*.raw*.ssv'):
df = raw._load_encoder_events_file_ge5(wf)
self.assertTrue(np.all(np.diff(np.array(df.re_ts)) > 0))
def test_encoder_events_duds(self):
# TRAINING SESSIONS
path = self.training_lt5['path'] / "raw_behavior_data"
path = next(path.glob("_iblrig_encoderEvents.raw*.ssv"), None)
dy = raw._load_encoder_events_file_lt5(path)
self.assertEqual(dy.bns_ts.dtype.name, 'object')
self.assertTrue(dy.shape[0] == 7)
# -- version >= 5.0.0
path = self.training_ge5['path'] / "raw_behavior_data"
path = next(path.glob("_iblrig_encoderEvents.raw*.ssv"), None)
dy = raw._load_encoder_events_file_ge5(path)
self.assertTrue(dy.shape[0] == 38)
# BIASED SESSIONS
path = self.biased_lt5['path'] / "raw_behavior_data"
path = next(path.glob("_iblrig_encoderEvents.raw*.ssv"), None)
dy = raw._load_encoder_events_file_lt5(path)
self.assertEqual(dy.bns_ts.dtype.name, 'object')
self.assertTrue(dy.shape[0] == 7)
# -- version >= 5.0.0
path = self.biased_ge5['path'] / "raw_behavior_data"
path = next(path.glob("_iblrig_encoderEvents.raw*.ssv"), None)
dy = raw._load_encoder_events_file_ge5(path)
self.assertTrue(dy.shape[0] == 26)
