def test_checm_cleaner_average(example_event):
telid = list(example_event.r0.tel)[0]
data = example_event.r0.tel[telid].waveform
nsamples = data.shape[2]
ped = example_event.mc.tel[telid].pedestal
data_ped = data - np.atleast_3d(ped / nsamples)
data_ped = np.array([data_ped[0], data_ped[0]]) # Test LG functionality
cleaner = CHECMWaveformCleanerAverage()
cleaner.apply(data_ped)
def test_checm_cleaner_average(example_event):
telid = 11
data = example_event.r0.tel[telid].waveform
nsamples = data.shape[2]
ped = example_event.mc.tel[telid].pedestal
data_ped = data - np.atleast_3d(ped / nsamples)
data_ped = np.array([data_ped[0], data_ped[0]]) # Test LG functionality
cleaner = CHECMWaveformCleanerAverage()
cleaned = cleaner.apply(data_ped)
assert_almost_equal(data_ped[0, 0, 0], -2.8, 1)
assert_almost_equal(cleaned[0, 0, 0], -6.4, 1)
def test_checm_cleaner_average():
telid = 11
event = get_test_event()
data = event.r0.tel[telid].adc_samples
nsamples = data.shape[2]
ped = event.mc.tel[telid].pedestal
data_ped = data - np.atleast_3d(ped/nsamples)
data_ped = np.array([data_ped[0], data_ped[0]]) # Test LG functionality
cleaner = CHECMWaveformCleanerAverage(None, None)
cleaned = cleaner.apply(data_ped)
assert_almost_equal(data_ped[0, 0, 0], -2.8, 1)
assert_almost_equal(cleaned[0, 0, 0], -6.4, 1)
def test_checm_cleaner_average(test_event):
telid = 11
event = deepcopy(test_event) # to avoid modifying the test event
data = event.r0.tel[telid].adc_samples
nsamples = data.shape[2]
ped = event.mc.tel[telid].pedestal
data_ped = data - np.atleast_3d(ped / nsamples)
data_ped = np.array([data_ped[0], data_ped[0]]) # Test LG functionality
cleaner = CHECMWaveformCleanerAverage()
cleaned = cleaner.apply(data_ped)
assert_almost_equal(data_ped[0, 0, 0], -2.8, 1)
assert_almost_equal(cleaned[0, 0, 0], -6.4, 1)