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muon analysis r0 to dl1 specific function #556

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muon analysis r0 to dl1 specific function #556

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4375f16
move muon analysis in r0 to dl1 in its own function
vuillaut Nov 5, 2020
b3e19ac
mirror area is already a Quantity
vuillaut Nov 5, 2020
920efe7
docstring
vuillaut Nov 5, 2020
336a6a2
Merge branch 'master' into clean_muon_anaysis_r0_dl1
vuillaut Nov 8, 2020
49a2e1d
Merge branch 'master' into clean_muon_anaysis_r0_dl1
vuillaut Dec 23, 2020
6361784
Merge branch 'master' into clean_muon_anaysis_r0_dl1
rlopezcoto Feb 10, 2021
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185 changes: 106 additions & 79 deletions lstchain/reco/r0_to_dl1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -238,19 +238,13 @@ def r0_to_dl1(

cal_mc = load_calibrator_from_config(config, subarray)

# minimum number of pe in a pixel to include it
# in calculation of muon ring time (peak sample):
min_pe_for_muon_t_calc = 10.

# Dictionary to store muon ring parameters
muon_parameters = create_muon_table()

if not is_simu:

# TODO : add DRS4 calibration config in config file, read it and pass it here
r0_r1_calibrator = LSTR0Corrections(
pedestal_path=pedestal_path, tel_id=1,
)
r0_r1_calibrator = LSTR0Corrections(pedestal_path=pedestal_path, tel_id=1)

# all this will be cleaned up in a next PR related to the configuration files

Expand Down Expand Up @@ -640,9 +634,6 @@ def r0_to_dl1(
dl1_container.trigger_time = event.r0.tel[telescope_id].trigger_time
dl1_container.trigger_type = event.r0.tel[telescope_id].trigger_type

# FIXME: no need to read telescope characteristics like foclen for every event!
foclen = subarray.tel[telescope_id].optics.equivalent_focal_length
mirror_area = u.Quantity(subarray.tel[telescope_id].optics.mirror_area, u.m ** 2)
dl1_container.prefix = tel.prefix

# extra info for the image table
Expand All @@ -661,75 +652,13 @@ def r0_to_dl1(

# Muon ring analysis, for real data only (MC is done starting from DL1 files)
if not is_simu:
bad_pixels = event.mon.tel[telescope_id].calibration.unusable_pixels[0]
# Set to 0 unreliable pixels:
image = tel.image*(~bad_pixels)

# process only promising events, in terms of # of pixels with large signals:
if tag_pix_thr(image):

# re-calibrate r1 to obtain new dl1, using a more adequate pulse integrator for muon rings
numsamples = event.r1.tel[telescope_id].waveform.shape[2] # not necessarily the same as in r0!
bad_pixels_hg = event.mon.tel[telescope_id].calibration.unusable_pixels[0]
bad_pixels_lg = event.mon.tel[telescope_id].calibration.unusable_pixels[1]
# Now set to 0 all samples in unreliable pixels. Important for global peak
# integrator in case of crazy pixels! TBD: can this be done in a simpler
# way?
bad_waveform = np.array(([np.transpose(np.array(numsamples*[bad_pixels_hg])),
np.transpose(np.array(numsamples*[bad_pixels_lg]))]))

# print('hg bad pixels:',np.where(bad_pixels_hg))
# print('lg bad pixels:',np.where(bad_pixels_lg))

event.r1.tel[telescope_id].waveform *= ~bad_waveform
r1_dl1_calibrator_for_muon_rings(event)

tel = event.dl1.tel[telescope_id]
image = tel.image*(~bad_pixels)

# Check again: with the extractor for muon rings (most likely GlobalPeakWindowSum)
# perhaps the event is no longer promising (e.g. if it has a large time evolution)
if not tag_pix_thr(image):
good_ring = False
else:
# read geometry from event.inst. But not needed for every event. FIXME?
geom = subarray.tel[telescope_id].\
camera.geometry

muonintensityparam, dist_mask, \
ring_size, size_outside_ring, muonringparam, \
good_ring, radial_distribution, \
mean_pixel_charge_around_ring,\
muonpars = \
analyze_muon_event(subarray,
event.index.event_id,
image, geom, foclen,
mirror_area, False, '')
# mirror_area, True, './')
# (test) plot muon rings as png files

# Now we want to obtain the waveform sample (in HG and LG) at which the ring light peaks:
bright_pixels_waveforms = event.r1.tel[telescope_id].waveform[:, image > min_pe_for_muon_t_calc, :]
stacked_waveforms = np.sum(bright_pixels_waveforms, axis=-2)
# stacked waveforms from all bright pixels; shape (ngains, nsamples)
hg_peak_sample = np.argmax(stacked_waveforms, axis=-1)[0]
lg_peak_sample = np.argmax(stacked_waveforms, axis=-1)[1]

if good_ring:
fill_muon_event(-1,
muon_parameters,
good_ring,
event.index.event_id,
dragon_time,
muonintensityparam,
dist_mask,
muonringparam,
radial_distribution,
ring_size,
size_outside_ring,
mean_pixel_charge_around_ring,
muonpars,
hg_peak_sample, lg_peak_sample)
r0_dl1_muon_analysis(event,
telescope_id,
r1_dl1_calibrator_for_muon_rings,
subarray,
muon_parameters,
dragon_time,
)

# writes mc information per telescope, including photo electron image
if is_simu \
Expand Down Expand Up @@ -838,3 +767,101 @@ def rescale_dl1_charge(event, scaling_factor):

for tel_id, tel in event.dl1.tel.items():
tel.image *= scaling_factor


def r0_dl1_muon_analysis(event, telescope_id, r1_dl1_calibrator_for_muon_rings, subarray, muon_parameters, dragon_time):
"""
Muon analysis in the stage r0 to dl1.
Fills the `muon_parameters` table.

Parameters
----------
event: `ctapipe.containers.DataContainer`
telescope_id: int
r1_dl1_calibrator_for_muon_rings: `LSTCameraCalibrator`
subarray: `ctapipe.instrument.subarray.SubarrayDescription`
muon_parameters: dict `lstchain.image.muon.create_muon_table`
dragon_time: `astropy.Quantity`

"""
# minimum number of pe in a pixel to include it
# in calculation of muon ring time (peak sample):
min_pe_for_muon_t_calc = 10.

# FIXME: no need to read telescope characteristics like foclen for every event!
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We can remove this FIXME comment, it is a pretty old comment in which I misinterpreted that this would imply some re-reading from disk.

foclen = subarray.tel[telescope_id].optics.equivalent_focal_length
mirror_area = subarray.tel[telescope_id].optics.mirror_area

bad_pixels = event.mon.tel[telescope_id].calibration.unusable_pixels[0]
# Set to 0 unreliable pixels:
image = event.dl1.tel[telescope_id].image*(~bad_pixels)
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Is there in ctapipe already some pixel interpolator? Would be interesting to test it. Should be better in case of isolated bad pixels (perhaps a bit dangerous when a whole pixel cluster is unreliable).

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I don't think there is, but yes, I think it is necessary.

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It should be easy enough to implement approaches like a mean of correct neighbors using the neighbor matrix.


# process only promising events, in terms of # of pixels with large signals:
if tag_pix_thr(image):

# re-calibrate r1 to obtain new dl1, using a more adequate pulse integrator for muon rings
numsamples = event.r1.tel[telescope_id].waveform.shape[2] # not necessarily the same as in r0!
bad_pixels_hg = event.mon.tel[telescope_id].calibration.unusable_pixels[0]
bad_pixels_lg = event.mon.tel[telescope_id].calibration.unusable_pixels[1]
# Now set to 0 all samples in unreliable pixels. Important for global peak
# integrator in case of crazy pixels! TBD: can this be done in a simpler
# way?
bad_waveform = np.array(([np.transpose(np.array(numsamples*[bad_pixels_hg])),
np.transpose(np.array(numsamples*[bad_pixels_lg]))]))

# print('hg bad pixels:',np.where(bad_pixels_hg))
# print('lg bad pixels:',np.where(bad_pixels_lg))

event.r1.tel[telescope_id].waveform *= ~bad_waveform
r1_dl1_calibrator_for_muon_rings(event)

tel = event.dl1.tel[telescope_id]
image = tel.image*(~bad_pixels)

# Check again: with the extractor for muon rings (most likely GlobalPeakWindowSum)
# perhaps the event is no longer promising (e.g. if it has a large time evolution)
if not tag_pix_thr(image):
good_ring = False
else:
# read geometry from event.inst. But not needed for every event. FIXME?
camera_geometry = subarray.tel[telescope_id].camera.geometry

muonintensityparam, dist_mask, \
ring_size, size_outside_ring, muonringparam, \
good_ring, radial_distribution, \
mean_pixel_charge_around_ring, \
muonpars = analyze_muon_event(subarray,
event.index.event_id,
image, camera_geometry,
foclen,
mirror_area,
False,
'',
)
# mirror_area, True, './')
# (test) plot muon rings as png files

# Now we want to obtain the waveform sample (in HG and LG) at which the ring light peaks:
bright_pixels_waveforms = event.r1.tel[telescope_id].waveform[:, image > min_pe_for_muon_t_calc, :]
stacked_waveforms = np.sum(bright_pixels_waveforms, axis=-2)
# stacked waveforms from all bright pixels; shape (ngains, nsamples)
hg_peak_sample = np.argmax(stacked_waveforms, axis=-1)[0]
lg_peak_sample = np.argmax(stacked_waveforms, axis=-1)[1]

if good_ring:
fill_muon_event(-1,
muon_parameters,
good_ring,
event.index.event_id,
dragon_time,
muonintensityparam,
dist_mask,
muonringparam,
radial_distribution,
ring_size,
size_outside_ring,
mean_pixel_charge_around_ring,
muonpars,
hg_peak_sample,
lg_peak_sample,
)