def test_global_peak_window_sum(camera_waveforms):
waveforms, _ = camera_waveforms
extractor = GlobalPeakWindowSum()
charge, pulse_time = extractor(waveforms)
assert_allclose(charge[0][0], 232.559, rtol=1e-3)
assert_allclose(charge[1][0], 425.406, rtol=1e-3)
assert_allclose(pulse_time[0][0], 47.823488, rtol=1e-3)
assert_allclose(pulse_time[1][0], 62.931829, rtol=1e-3)
def test_global_peak_window_sum_with_pixel_fraction(subarray):
from ctapipe.image.extractor import GlobalPeakWindowSum
tel_id = 1
camera = subarray.tel[tel_id].camera
sample_rate = camera.readout.sampling_rate.to_value(u.ns**-1)
n_pixels = camera.geometry.n_pixels
selected_gain_channel = np.zeros(n_pixels, dtype=np.uint8)
bright_pixels = np.zeros(n_pixels, dtype=bool)
bright_pixels[np.random.choice(n_pixels, size=int(0.1 * n_pixels))] = True
# signal in dim pixels is in slice 10, signal in bright pixels is in slice 30
waveforms = np.zeros((n_pixels, 50), dtype="float64")
waveforms[~bright_pixels, 9] = 3
waveforms[~bright_pixels, 10] = 5
waveforms[~bright_pixels, 11] = 2
waveforms[bright_pixels, 29] = 5
waveforms[bright_pixels, 30] = 10
waveforms[bright_pixels, 31] = 3
extractor = GlobalPeakWindowSum(
subarray=subarray,
window_width=8,
window_shift=4,
pixel_fraction=0.05,
apply_integration_correction=False,
)
dl1 = extractor(waveforms=waveforms,
telid=tel_id,
selected_gain_channel=selected_gain_channel)
assert np.allclose(dl1.image[bright_pixels], 18)
assert np.allclose(dl1.image[~bright_pixels], 0)
expected = np.average([29, 30, 31], weights=[5, 10, 3])
assert np.allclose(dl1.peak_time[bright_pixels], expected / sample_rate)
def test_dl1_charge_calib(example_subarray):
# copy because we mutate the camera, should not affect other tests
subarray = deepcopy(example_subarray)
camera = subarray.tel[1].camera
# test with a sampling_rate different than 1 to
# test if we handle time vs. slices correctly
sampling_rate = 2
camera.readout.sampling_rate = sampling_rate * u.GHz
n_pixels = camera.geometry.n_pixels
n_samples = 96
mid = n_samples // 2
pulse_sigma = 6
random = np.random.RandomState(1)
x = np.arange(n_samples)
# Randomize times and create pulses
time_offset = random.uniform(-10, +10, n_pixels)
y = norm.pdf(x, mid + time_offset[:, np.newaxis],
pulse_sigma).astype("float32")
camera.readout.reference_pulse_shape = norm.pdf(x, mid,
pulse_sigma)[np.newaxis, :]
camera.readout.reference_pulse_sample_width = 1 / camera.readout.sampling_rate
# Define absolute calibration coefficients
absolute = random.uniform(100, 1000, n_pixels).astype("float32")
y *= absolute[:, np.newaxis]
# Define relative coefficients
relative = random.normal(1, 0.01, n_pixels)
y /= relative[:, np.newaxis]
# Define pedestal
pedestal = random.uniform(-4, 4, n_pixels)
y += pedestal[:, np.newaxis]
event = ArrayEventContainer()
telid = list(subarray.tel.keys())[0]
event.dl0.tel[telid].waveform = y
event.dl0.tel[telid].selected_gain_channel = np.zeros(len(y), dtype=int)
event.r1.tel[telid].selected_gain_channel = np.zeros(len(y), dtype=int)
# Test default
calibrator = CameraCalibrator(
subarray=subarray, image_extractor=FullWaveformSum(subarray=subarray))
calibrator(event)
np.testing.assert_allclose(event.dl1.tel[telid].image, y.sum(1), rtol=1e-4)
event.calibration.tel[telid].dl1.pedestal_offset = pedestal
event.calibration.tel[telid].dl1.absolute_factor = absolute
event.calibration.tel[telid].dl1.relative_factor = relative
# Test without timing corrections
calibrator(event)
dl1 = event.dl1.tel[telid]
np.testing.assert_allclose(dl1.image, 1, rtol=1e-5)
expected_peak_time = (mid + time_offset) / sampling_rate
np.testing.assert_allclose(dl1.peak_time, expected_peak_time, rtol=1e-5)
# test with timing corrections
event.calibration.tel[telid].dl1.time_shift = time_offset / sampling_rate
calibrator(event)
# more rtol since shifting might lead to reduced integral
np.testing.assert_allclose(event.dl1.tel[telid].image, 1, rtol=1e-5)
np.testing.assert_allclose(event.dl1.tel[telid].peak_time,
mid / sampling_rate,
atol=1)
# test not applying time shifts
# now we should be back to the result without setting time shift
calibrator.apply_peak_time_shift = False
calibrator.apply_waveform_time_shift = False
calibrator(event)
np.testing.assert_allclose(event.dl1.tel[telid].image, 1, rtol=1e-4)
np.testing.assert_allclose(event.dl1.tel[telid].peak_time,
expected_peak_time,
atol=1)
# We now use GlobalPeakWindowSum to see the effect of missing charge
# due to not correcting time offsets.
calibrator = CameraCalibrator(
subarray=subarray,
image_extractor=GlobalPeakWindowSum(subarray=subarray))
calibrator(event)
# test with timing corrections, should work
# higher rtol because we cannot shift perfectly
np.testing.assert_allclose(event.dl1.tel[telid].image, 1, rtol=0.01)
np.testing.assert_allclose(event.dl1.tel[telid].peak_time,
mid / sampling_rate,
atol=1)
# test deactivating timing corrections
calibrator.apply_waveform_time_shift = False
calibrator(event)
# make sure we chose an example where the time shifts matter
# charges should be quite off due to summing around global shift
assert not np.allclose(event.dl1.tel[telid].image, 1, rtol=0.1)
assert not np.allclose(
event.dl1.tel[telid].peak_time, mid / sampling_rate, atol=1)