def test_npix_above_threshold():
len_array = 100
thr = 5.
pix = np.random.uniform(0, 100, len_array)
npix = npix_above_threshold(pix, thr)
assert((npix >= 0) and (npix <= len_array))
def analyze_muon_event(event):
"""
Generic muon event analyzer.
Parameters
----------
event : ctapipe dl1 event container
Returns
-------
muonringparam, muonintensityparam : MuonRingParameter
and MuonIntensityParameter container event
"""
names = [
'LST_LST_LSTCam', 'MST_MST_NectarCam', 'MST_MST_FlashCam',
'MST_SCT_SCTCam', 'SST_1M_DigiCam', 'SST_GCT_CHEC',
'SST_ASTRI_ASTRICam', 'SST_ASTRI_CHEC'
]
tail_cuts = [(5, 7), (5, 7), (10, 12), (5, 7), (5, 7), (5, 7), (5, 7),
(5, 7)] # 10, 12?
impact = [(0.2, 0.9), (0.1, 0.95), (0.2, 0.9), (0.2, 0.9), (0.1, 0.95),
(0.1, 0.95), (0.1, 0.95), (0.1, 0.95)] * u.m
ringwidth = [(0.04, 0.08), (0.02, 0.1), (0.01, 0.1), (0.02, 0.1),
(0.01, 0.5), (0.02, 0.2), (0.02, 0.2), (0.02, 0.2)] * u.deg
total_pix = [1855., 1855., 1764., 11328., 1296., 2048., 2368., 2048]
# 8% (or 6%) as limit
min_pix = [148., 148., 141., 680., 104., 164., 142., 164]
# Need to either convert from the pixel area in m^2 or check the camera specs
ang_pixel_width = [0.1, 0.2, 0.18, 0.067, 0.24, 0.2, 0.17, 0.2, 0.163
] * u.deg
# Found from TDRs (or the pixel area)
hole_rad = [
0.308 * u.m, 0.244 * u.m, 0.244 * u.m, 4.3866 * u.m, 0.160 * u.m,
0.130 * u.m, 0.171 * u.m, 0.171 * u.m
] # Assuming approximately spherical hole
cam_rad = [2.26, 3.96, 3.87, 4., 4.45, 2.86, 5.25, 2.86] * u.deg
# Above found from the field of view calculation
sec_rad = [
0. * u.m, 0. * u.m, 0. * u.m, 2.7 * u.m, 0. * u.m, 1. * u.m, 1.8 * u.m,
1.8 * u.m
]
sct = [False, False, False, True, False, True, True, True]
# Added cleaning here. All these options should go to an input card
cleaning = True
muon_cuts = {
'Name': names,
'tail_cuts': tail_cuts,
'Impact': impact,
'RingWidth': ringwidth,
'total_pix': total_pix,
'min_pix': min_pix,
'CamRad': cam_rad,
'SecRad': sec_rad,
'SCT': sct,
'AngPixW': ang_pixel_width,
'HoleRad': hole_rad
}
logger.debug(muon_cuts)
muonringlist = [] # [None] * len(event.dl0.tels_with_data)
muonintensitylist = [] # [None] * len(event.dl0.tels_with_data)
tellist = []
muon_event_param = {
'TelIds': tellist,
'MuonRingParams': muonringlist,
'MuonIntensityParams': muonintensitylist
}
for telid in event.dl0.tels_with_data:
logger.debug("Analysing muon event for tel %d", telid)
image = event.dl1.tel[telid].image
# Get geometry
teldes = event.inst.subarray.tel[telid]
geom = teldes.camera
x, y = geom.pix_x, geom.pix_y
dict_index = muon_cuts['Name'].index(str(teldes))
logger.debug('found an index of %d for camera %d', dict_index,
geom.cam_id)
tailcuts = muon_cuts['tail_cuts'][dict_index]
logger.debug("Tailcuts are %s", tailcuts)
clean_mask = tailcuts_clean(geom,
image,
picture_thresh=tailcuts[0],
boundary_thresh=tailcuts[1])
# TODO: correct this hack for values over 90
altval = event.mcheader.run_array_direction[1]
if altval > Angle(90, unit=u.deg):
warnings.warn('Altitude over 90 degrees')
altval = Angle(90, unit=u.deg)
telescope_pointing = SkyCoord(alt=altval,
az=event.mcheader.run_array_direction[0],
frame=AltAz())
camera_coord = SkyCoord(
x=x,
y=y,
frame=CameraFrame(
focal_length=teldes.optics.equivalent_focal_length,
rotation=geom.pix_rotation,
telescope_pointing=telescope_pointing,
))
nom_coord = camera_coord.transform_to(
NominalFrame(origin=telescope_pointing))
x = nom_coord.delta_az.to(u.deg)
y = nom_coord.delta_alt.to(u.deg)
if (cleaning):
img = image * clean_mask
else:
img = image
muonring = ChaudhuriKunduRingFitter(None)
logger.debug("img: %s mask: %s, x=%s y= %s", np.sum(image),
np.sum(clean_mask), x, y)
if not sum(img): # Nothing left after tail cuts
continue
muonringparam = muonring.fit(x, y, image * clean_mask)
dist = np.sqrt(
np.power(x - muonringparam.ring_center_x, 2) +
np.power(y - muonringparam.ring_center_y, 2))
ring_dist = np.abs(dist - muonringparam.ring_radius)
muonringparam = muonring.fit(
x, y, img * (ring_dist < muonringparam.ring_radius * 0.4))
dist = np.sqrt(
np.power(x - muonringparam.ring_center_x, 2) +
np.power(y - muonringparam.ring_center_y, 2))
ring_dist = np.abs(dist - muonringparam.ring_radius)
muonringparam = muonring.fit(
x, y, img * (ring_dist < muonringparam.ring_radius * 0.4))
muonringparam.tel_id = telid
muonringparam.obs_id = event.dl0.obs_id
muonringparam.event_id = event.dl0.event_id
dist_mask = np.abs(
dist - muonringparam.ring_radius) < muonringparam.ring_radius * 0.4
pix_im = image * dist_mask
nom_dist = np.sqrt(
np.power(muonringparam.ring_center_x, 2) +
np.power(muonringparam.ring_center_y, 2))
minpix = muon_cuts['min_pix'][dict_index] # 0.06*numpix #or 8%
mir_rad = np.sqrt(teldes.optics.mirror_area.to("m2") / np.pi)
# Camera containment radius - better than nothing - guess pixel
# diameter of 0.11, all cameras are perfectly circular cam_rad =
# np.sqrt(numpix*0.11/(2.*np.pi))
if (npix_above_threshold(pix_im, tailcuts[0]) > 0.1 * minpix
and npix_composing_ring(pix_im) > minpix
and nom_dist < muon_cuts['CamRad'][dict_index]
and muonringparam.ring_radius < 1.5 * u.deg
and muonringparam.ring_radius > 1. * u.deg):
muonringparam.ring_containment = ring_containment(
muonringparam.ring_radius, muon_cuts['CamRad'][dict_index],
muonringparam.ring_center_x, muonringparam.ring_center_y)
# Guess HESS is 0.16
# sec_rad = 0.*u.m
# sct = False
# if numpix == 2048 and mir_rad > 2.*u.m and mir_rad < 2.1*u.m:
# sec_rad = 1.*u.m
# sct = True
#
# Store muon ring parameters (passing cuts stage 1)
# muonringlist[idx] = muonringparam
tellist.append(telid)
muonringlist.append(muonringparam)
muonintensitylist.append(None)
ctel = MuonLineIntegrate(
mir_rad,
hole_radius=muon_cuts['HoleRad'][dict_index],
pixel_width=muon_cuts['AngPixW'][dict_index],
sct_flag=muon_cuts['SCT'][dict_index],
secondary_radius=muon_cuts['SecRad'][dict_index])
if image.shape[0] == muon_cuts['total_pix'][dict_index]:
muonintensityoutput = ctel.fit_muon(
muonringparam.ring_center_x, muonringparam.ring_center_y,
muonringparam.ring_radius, x[dist_mask], y[dist_mask],
image[dist_mask])
muonintensityoutput.tel_id = telid
muonintensityoutput.obs_id = event.dl0.obs_id
muonintensityoutput.event_id = event.dl0.event_id
muonintensityoutput.mask = dist_mask
idx_ring = np.nonzero(pix_im)
muonintensityoutput.ring_completeness = ring_completeness(
x[idx_ring],
y[idx_ring],
pix_im[idx_ring],
muonringparam.ring_radius,
muonringparam.ring_center_x,
muonringparam.ring_center_y,
threshold=30,
bins=30)
muonintensityoutput.ring_size = np.sum(pix_im)
dist_ringwidth_mask = np.abs(
dist - muonringparam.ring_radius) < (
muonintensityoutput.ring_width)
pix_ringwidth_im = image * dist_ringwidth_mask
idx_ringwidth = np.nonzero(pix_ringwidth_im)
muonintensityoutput.ring_pix_completeness = npix_above_threshold(
pix_ringwidth_im[idx_ringwidth], tailcuts[0]) / len(
pix_im[idx_ringwidth])
logger.debug(
"Tel %d Impact parameter = %s mir_rad=%s "
"ring_width=%s", telid,
muonintensityoutput.impact_parameter, mir_rad,
muonintensityoutput.ring_width)
conditions = [
muonintensityoutput.impact_parameter * u.m <
muon_cuts['Impact'][dict_index][1] * mir_rad,
muonintensityoutput.impact_parameter >
muon_cuts['Impact'][dict_index][0],
muonintensityoutput.ring_width <
muon_cuts['RingWidth'][dict_index][1],
muonintensityoutput.ring_width >
muon_cuts['RingWidth'][dict_index][0]
]
if all(conditions):
muonintensityparam = muonintensityoutput
idx = tellist.index(telid)
muonintensitylist[idx] = muonintensityparam
logger.debug("Muon found in tel %d, tels in event=%d",
telid, len(event.dl0.tels_with_data))
else:
continue
return muon_event_param
def analyze_muon_event(event_id, image, geom, equivalent_focal_length,
mirror_area, plot_rings, plots_path):
"""
Analyze an event to fit a muon ring
Paramenters
---------
event_id: `int` id of the analyzed event
image: `np.ndarray` number of photoelectrons in each pixel
geom: CameraGeometry
equivalent_focal_length: `float` focal length of the telescope
mirror_area: `float` mirror area of the telescope
plot_rings: `bool` plot the muon ring
plots_path: `string` path to store the figures
Returns
---------
muonintensityoutput ``
muonringparam ``
good_ring `bool` it determines whether the ring can be used for analysis or not
TODO: several hard-coded quantities that can go into a configuration file
"""
tailcuts = [10, 5]
cam_rad = 2.26 * u.deg
# some cuts for good ring selection:
min_pix = 148 # (8%) minimum number of pixels in the ring with >0 signal
min_pix_fraction_after_cleaning = 0.1 # minimum fraction of the ring pixels that must be above tailcuts[0]
min_ring_radius = 0.8*u.deg # minimum ring radius
max_ring_radius = 1.5*u.deg # maximum ring radius
max_radial_stdev = 0.1*u.deg # maximum standard deviation of the light distribution along ring radius
max_radial_excess_kurtosis = 1. # maximum excess kurtosis
min_impact_parameter = 0.2 # in fraction of mirror radius
max_impact_parameter = 0.9 # in fraction of mirror radius
ring_integration_width = 0.25 # +/- integration range along ring radius, in fraction of ring radius (was 0.4 until 20200326)
outer_ring_width = 0.2 # in fraction of ring radius, width of ring just outside the integrated muon ring, used to check pedestal bias
x, y = pixel_coords_to_telescope(geom, equivalent_focal_length)
muonringparam, clean_mask, dist, image_clean = fit_muon(x, y, image, geom, tailcuts)
mirror_radius = np.sqrt(mirror_area / np.pi)
dist_mask = np.abs(dist - muonringparam.ring_radius
) < muonringparam.ring_radius * ring_integration_width
pix_ring = image * dist_mask
pix_outside_ring = image * ~dist_mask
# mask to select pixels just outside the ring that will be integrated to obtain the ring's intensity:
dist_mask_2 = np.logical_and(~dist_mask, np.abs(dist-muonringparam.ring_radius) < muonringparam.ring_radius*(ring_integration_width+outer_ring_width))
pix_ring_2 = image[dist_mask_2]
# nom_dist = np.sqrt(np.power(muonringparam.ring_center_x,2)
# + np.power(muonringparam.ring_center_y, 2))
muonringparam.ring_containment = ring_containment(
muonringparam.ring_radius,
cam_rad,
muonringparam.ring_center_x,
muonringparam.ring_center_y)
radial_distribution = radial_light_distribution(
muonringparam.ring_center_x,
muonringparam.ring_center_y,
x[clean_mask], y[clean_mask],
image[clean_mask])
# Do complicated calculations (minuit-based max likelihood ring fit) only for selected rings:
candidate_clean_ring = all(
[radial_distribution['standard_dev'] < max_radial_stdev,
radial_distribution['excess_kurtosis'] < max_radial_excess_kurtosis,
npix_above_threshold(pix_ring, tailcuts[0]) > min_pix_fraction_after_cleaning * min_pix,
npix_composing_ring(pix_ring) > min_pix,
muonringparam.ring_radius < max_ring_radius,
muonringparam.ring_radius > min_ring_radius
])
if candidate_clean_ring:
ctel = MuonLineIntegrate(
mirror_radius, hole_radius = 0.308 * u.m,
pixel_width=0.1 * u.deg,
sct_flag=False,
secondary_radius = 0. * u.m
)
muonintensityoutput = ctel.fit_muon(
muonringparam.ring_center_x,
muonringparam.ring_center_y,
muonringparam.ring_radius,
x[dist_mask], y[dist_mask],
image[dist_mask])
dist_ringwidth_mask = np.abs(dist - muonringparam.ring_radius) < (muonintensityoutput.ring_width)
# We do the calculation of the ring completeness (i.e. fraction of whole circle) using the pixels
# within the "width" fitted using MuonLineIntegrate.
muonintensityoutput.ring_completeness = ring_completeness(
x[dist_ringwidth_mask], y[dist_ringwidth_mask], image[dist_ringwidth_mask],
muonringparam.ring_radius,
muonringparam.ring_center_x,
muonringparam.ring_center_y,
threshold=30,
bins=30)
pix_ringwidth_im = image[dist_ringwidth_mask]
muonintensityoutput.ring_pix_completeness = npix_above_threshold(pix_ringwidth_im, tailcuts[0]) / len(pix_ringwidth_im)
else:
muonintensityoutput = MuonIntensityParameter()
# Set default values for cases in which the muon intensity fit (with MuonLineIntegrate) is not done:
muonintensityoutput.ring_width = np.nan*u.deg
muonintensityoutput.impact_parameter_pos_x = np.nan*u.m
muonintensityoutput.impact_parameter_pos_y = np.nan*u.m
muonintensityoutput.impact_parameter = np.nan*u.m
muonintensityoutput.ring_pix_completeness = np.nan
muonintensityoutput.ring_completeness = np.nan
muonintensityoutput.mask = dist_mask
muonintensityoutput.ring_size = np.sum(pix_ring)
size_outside_ring = np.sum(pix_outside_ring * clean_mask)
# This is just mean charge per pixel in pixels just around the ring (on the outer side):
mean_pixel_charge_around_ring = np.sum(pix_ring_2)/len(pix_ring_2)
if candidate_clean_ring:
print("Impact parameter={:.3f}, ring_width={:.3f}, ring radius={:.3f}, ring completeness={:.3f}".format(
muonintensityoutput.impact_parameter, muonintensityoutput.ring_width,
muonringparam.ring_radius, muonintensityoutput.ring_completeness,
))
# Now add the conditions based on the detailed muon ring fit made by MuonLineIntegrate:
conditions = [
candidate_clean_ring,
muonintensityoutput.impact_parameter <
max_impact_parameter * mirror_radius,
muonintensityoutput.impact_parameter >
min_impact_parameter * mirror_radius,
# TODO: To be applied when we have decent optics.
# muonintensityoutput.ring_width
# < 0.08,
# NOTE: inside "candidate_clean_ring" cuts there is already a cut in the st dev of light distribution along ring radius,
# which is also a measure of the ring width
# muonintensityoutput.ring_width
# > 0.04
]
muonintensityparam = muonintensityoutput
if all(conditions):
good_ring = True
else:
good_ring = False
if(plot_rings and plots_path and good_ring):
focal_length = equivalent_focal_length
ring_telescope = SkyCoord(
delta_az=muonringparam.ring_center_x,
delta_alt=muonringparam.ring_center_y,
frame=TelescopeFrame()
)
ring_camcoord = ring_telescope.transform_to(CameraFrame(
focal_length=focal_length,
rotation=geom.cam_rotation,
))
centroid = (ring_camcoord.x.value, ring_camcoord.y.value)
radius = muonringparam.ring_radius
width = muonintensityoutput.ring_width
ringrad_camcoord = 2 * radius.to(u.rad) * focal_length
ringwidthfrac = width / radius
ringrad_inner = ringrad_camcoord * (1. - ringwidthfrac)
ringrad_outer = ringrad_camcoord * (1. + ringwidthfrac)
fig, ax = plt.subplots(figsize=(10, 10))
plot_muon_event(ax, geom, image * clean_mask, centroid, ringrad_camcoord,
ringrad_inner, ringrad_outer, event_id)
plt.figtext(0.15, 0.20, 'radial std dev: {0:.3f}'.format(radial_distribution['standard_dev']))
plt.figtext(0.15, 0.18, 'radial excess kurtosis: {0:.3f}'.format(radial_distribution['excess_kurtosis']))
plt.figtext(0.15, 0.16, 'fitted ring width: {0:.3f}'.format(width))
plt.figtext(0.15, 0.14, 'ring completeness: {0:.3f}'.format(muonintensityoutput.ring_completeness))
fig.savefig('{}/Event_{}_fitted.png'.format(plots_path, event_id))
if(plot_rings and not plots_path):
print("You are trying to plot without giving a path!")
return muonintensityparam, size_outside_ring, muonringparam, good_ring, radial_distribution, mean_pixel_charge_around_ring
def analyze_muon_event(event):
"""
Generic muon event analyzer.
Parameters
----------
event : ctapipe dl1 event container
Returns
-------
muonringparam, muonintensityparam : MuonRingParameter
and MuonIntensityParameter container event
"""
names = ['LST:LSTCam', 'MST:NectarCam', 'MST:FlashCam', 'MST-SCT:SCTCam',
'SST-1M:DigiCam', 'SST-GCT:CHEC', 'SST-ASTRI:ASTRICam', 'SST-ASTRI:CHEC']
tail_cuts = [(5, 7), (5, 7), (10, 12), (5, 7),
(5, 7), (5, 7), (5, 7), (5, 7)] # 10, 12?
impact = [(0.2, 0.9), (0.1, 0.95), (0.2, 0.9), (0.2, 0.9),
(0.1, 0.95), (0.1, 0.95), (0.1, 0.95), (0.1, 0.95)] * u.m
ringwidth = [(0.04, 0.08), (0.02, 0.1), (0.01, 0.1), (0.02, 0.1),
(0.01, 0.5), (0.02, 0.2), (0.02, 0.2), (0.02, 0.2)] * u.deg
total_pix = [1855., 1855., 1764., 11328., 1296., 2048., 2368., 2048]
# 8% (or 6%) as limit
min_pix = [148., 148., 141., 680., 104., 164., 142., 164]
# Need to either convert from the pixel area in m^2 or check the camera specs
ang_pixel_width = [0.1, 0.2, 0.18, 0.067, 0.24, 0.2, 0.17, 0.2, 0.163] * u.deg
# Found from TDRs (or the pixel area)
hole_rad = [0.308 * u.m, 0.244 * u.m, 0.244 * u.m,
4.3866 * u.m, 0.160 * u.m, 0.130 * u.m,
0.171 * u.m, 0.171 * u.m] # Assuming approximately spherical hole
cam_rad = [2.26, 3.96, 3.87, 4., 4.45, 2.86, 5.25, 2.86] * u.deg
# Above found from the field of view calculation
sec_rad = [0. * u.m, 0. * u.m, 0. * u.m, 2.7 * u.m,
0. * u.m, 1. * u.m, 1.8 * u.m, 1.8 * u.m]
sct = [False, False, False, True, False, True, True, True]
# Added cleaning here. All these options should go to an input card
cleaning = True
muon_cuts = {'Name': names, 'tail_cuts': tail_cuts, 'Impact': impact,
'RingWidth': ringwidth, 'total_pix': total_pix,
'min_pix': min_pix, 'CamRad': cam_rad, 'SecRad': sec_rad,
'SCT': sct, 'AngPixW': ang_pixel_width, 'HoleRad': hole_rad}
logger.debug(muon_cuts)
muonringlist = [] # [None] * len(event.dl0.tels_with_data)
muonintensitylist = [] # [None] * len(event.dl0.tels_with_data)
tellist = []
muon_event_param = {'TelIds': tellist,
'MuonRingParams': muonringlist,
'MuonIntensityParams': muonintensitylist}
for telid in event.dl0.tels_with_data:
logger.debug("Analysing muon event for tel %d", telid)
image = event.dl1.tel[telid].image[0]
# Get geometry
teldes = event.inst.subarray.tel[telid]
geom = teldes.camera
x, y = geom.pix_x, geom.pix_y
dict_index = muon_cuts['Name'].index(str(teldes))
logger.debug('found an index of %d for camera %d',
dict_index, geom.cam_id)
tailcuts = muon_cuts['tail_cuts'][dict_index]
logger.debug("Tailcuts are %s", tailcuts)
clean_mask = tailcuts_clean(geom, image, picture_thresh=tailcuts[0],
boundary_thresh=tailcuts[1])
# TODO: correct this hack for values over 90
altval = event.mcheader.run_array_direction[1]
if altval > Angle(90, unit=u.deg):
warnings.warn('Altitude over 90 degrees')
altval = Angle(90, unit=u.deg)
telescope_pointing = SkyCoord(
alt=altval,
az=event.mcheader.run_array_direction[0],
frame=HorizonFrame()
)
camera_coord = SkyCoord(
x=x, y=y,
frame=CameraFrame(
focal_length=teldes.optics.equivalent_focal_length,
rotation=geom.pix_rotation,
telescope_pointing=telescope_pointing,
)
)
nom_coord = camera_coord.transform_to(
NominalFrame(origin=telescope_pointing)
)
x = nom_coord.delta_az.to(u.deg)
y = nom_coord.delta_alt.to(u.deg)
if(cleaning):
img = image * clean_mask
else:
img = image
muonring = ChaudhuriKunduRingFitter(None)
logger.debug("img: %s mask: %s, x=%s y= %s", np.sum(image),
np.sum(clean_mask), x, y)
if not sum(img): # Nothing left after tail cuts
continue
muonringparam = muonring.fit(x, y, image * clean_mask)
dist = np.sqrt(np.power(x - muonringparam. ring_center_x, 2)
+ np.power(y - muonringparam.ring_center_y, 2))
ring_dist = np.abs(dist - muonringparam.ring_radius)
muonringparam = muonring.fit(
x, y, img * (ring_dist < muonringparam.ring_radius * 0.4)
)
dist = np.sqrt(np.power(x - muonringparam.ring_center_x, 2) +
np.power(y - muonringparam.ring_center_y, 2))
ring_dist = np.abs(dist - muonringparam.ring_radius)
muonringparam = muonring.fit(
x, y, img * (ring_dist < muonringparam.ring_radius * 0.4)
)
muonringparam.tel_id = telid
muonringparam.obs_id = event.dl0.obs_id
muonringparam.event_id = event.dl0.event_id
dist_mask = np.abs(dist - muonringparam.
ring_radius) < muonringparam.ring_radius * 0.4
pix_im = image * dist_mask
nom_dist = np.sqrt(np.power(muonringparam.ring_center_x,
2) + np.power(muonringparam.ring_center_y, 2))
minpix = muon_cuts['min_pix'][dict_index] # 0.06*numpix #or 8%
mir_rad = np.sqrt(teldes.optics.mirror_area.to("m2") / np.pi)
# Camera containment radius - better than nothing - guess pixel
# diameter of 0.11, all cameras are perfectly circular cam_rad =
# np.sqrt(numpix*0.11/(2.*np.pi))
if(npix_above_threshold(pix_im, tailcuts[0]) > 0.1 * minpix
and npix_composing_ring(pix_im) > minpix
and nom_dist < muon_cuts['CamRad'][dict_index]
and muonringparam.ring_radius < 1.5 * u.deg
and muonringparam.ring_radius > 1. * u.deg):
muonringparam.ring_containment = ring_containment(
muonringparam.ring_radius,
muon_cuts['CamRad'][dict_index],
muonringparam.ring_center_x,
muonringparam.ring_center_y)
# Guess HESS is 0.16
# sec_rad = 0.*u.m
# sct = False
# if numpix == 2048 and mir_rad > 2.*u.m and mir_rad < 2.1*u.m:
# sec_rad = 1.*u.m
# sct = True
#
# Store muon ring parameters (passing cuts stage 1)
# muonringlist[idx] = muonringparam
tellist.append(telid)
muonringlist.append(muonringparam)
muonintensitylist.append(None)
ctel = MuonLineIntegrate(
mir_rad, hole_radius=muon_cuts['HoleRad'][dict_index],
pixel_width=muon_cuts['AngPixW'][dict_index],
sct_flag=muon_cuts['SCT'][dict_index],
secondary_radius=muon_cuts['SecRad'][dict_index]
)
if image.shape[0] == muon_cuts['total_pix'][dict_index]:
muonintensityoutput = ctel.fit_muon(muonringparam.ring_center_x,
muonringparam.ring_center_y,
muonringparam.ring_radius,
x[dist_mask], y[dist_mask],
image[dist_mask])
muonintensityoutput.tel_id = telid
muonintensityoutput.obs_id = event.dl0.obs_id
muonintensityoutput.event_id = event.dl0.event_id
muonintensityoutput.mask = dist_mask
idx_ring = np.nonzero(pix_im)
muonintensityoutput.ring_completeness = ring_completeness(
x[idx_ring], y[idx_ring], pix_im[idx_ring],
muonringparam.ring_radius,
muonringparam.ring_center_x,
muonringparam.ring_center_y,
threshold=30,
bins=30)
muonintensityoutput.ring_size = np.sum(pix_im)
dist_ringwidth_mask = np.abs(dist - muonringparam.ring_radius
) < (muonintensityoutput.ring_width)
pix_ringwidth_im = image * dist_ringwidth_mask
idx_ringwidth = np.nonzero(pix_ringwidth_im)
muonintensityoutput.ring_pix_completeness = npix_above_threshold(
pix_ringwidth_im[idx_ringwidth], tailcuts[0]) / len(
pix_im[idx_ringwidth])
logger.debug("Tel %d Impact parameter = %s mir_rad=%s "
"ring_width=%s", telid,
muonintensityoutput.impact_parameter, mir_rad,
muonintensityoutput.ring_width)
conditions = [
muonintensityoutput.impact_parameter * u.m <
muon_cuts['Impact'][dict_index][1] * mir_rad,
muonintensityoutput.impact_parameter
> muon_cuts['Impact'][dict_index][0],
muonintensityoutput.ring_width
< muon_cuts['RingWidth'][dict_index][1],
muonintensityoutput.ring_width
> muon_cuts['RingWidth'][dict_index][0]
]
if all(conditions):
muonintensityparam = muonintensityoutput
idx = tellist.index(telid)
muonintensitylist[idx] = muonintensityparam
logger.debug("Muon found in tel %d, tels in event=%d",
telid, len(event.dl0.tels_with_data))
else:
continue
return muon_event_param
def analyze_muon_event(event_id, image, geom, equivalent_focal_length,
mirror_area, plot_rings, plots_path):
"""
Analyze an event to fit a muon ring
Paramenters
---------
event_id: `int` id of the analyzed event
image: `np.ndarray` number of photoelectrons in each pixel
geom: CameraGeometry
equivalent_focal_length: `float` focal length of the telescope
mirror_area: `float` mirror area of the telescope
plot_rings: `bool` plot the muon ring
plots_path: `string` path to store the figures
Returns
---------
muonintensityoutput ``
muonringparam ``
good_ring `bool` it determines whether the ring can be used for analysis or not
TODO: several hard-coded quantities that can go into a configuration file
"""
tailcuts = [5, 10]
cam_rad = 2.26 * u.deg
min_pix = 148 # 8%
x, y = get_muon_center(geom, equivalent_focal_length)
muonringparam, clean_mask, dist, image_clean = fit_muon(
x, y, image, geom, tailcuts)
mirror_radius = np.sqrt(mirror_area / np.pi)
dist_mask = np.abs(
dist - muonringparam.ring_radius) < muonringparam.ring_radius * 0.4
pix_ring = image * dist_mask
pix_out_ring = image * ~dist_mask
nom_dist = np.sqrt(
np.power(muonringparam.ring_center_x, 2) +
np.power(muonringparam.ring_center_y, 2))
muonringparam.ring_containment = ring_containment(
muonringparam.ring_radius, cam_rad, muonringparam.ring_center_x,
muonringparam.ring_center_y)
ctel = MuonLineIntegrate(mirror_radius,
hole_radius=0.308 * u.m,
pixel_width=0.1 * u.deg,
sct_flag=False,
secondary_radius=0. * u.m)
muonintensityoutput = ctel.fit_muon(muonringparam.ring_center_x,
muonringparam.ring_center_y,
muonringparam.ring_radius,
x[dist_mask], y[dist_mask],
image[dist_mask])
muonintensityoutput.mask = dist_mask
idx_ring = np.nonzero(pix_ring)
muonintensityoutput.ring_completeness = ring_completeness(
x[idx_ring],
y[idx_ring],
pix_ring[idx_ring],
muonringparam.ring_radius,
muonringparam.ring_center_x,
muonringparam.ring_center_y,
threshold=30,
bins=30)
muonintensityoutput.ring_size = np.sum(pix_ring)
size_outside_ring = np.sum(pix_out_ring * clean_mask)
dist_ringwidth_mask = np.abs(dist - muonringparam.ring_radius) < (
muonintensityoutput.ring_width)
pix_ringwidth_im = image * dist_ringwidth_mask
idx_ringwidth = np.nonzero(pix_ringwidth_im)
muonintensityoutput.ring_pix_completeness = npix_above_threshold(
pix_ringwidth_im[idx_ringwidth], tailcuts[0]) / len(
pix_ring[idx_ringwidth])
print(
"Impact parameter = %s"
"ring_width=%s, ring radius=%s, ring completeness=%s" %
(muonintensityoutput.impact_parameter, muonintensityoutput.ring_width,
muonringparam.ring_radius, muonintensityoutput.ring_completeness))
conditions = [
muonintensityoutput.impact_parameter <
0.9 * mirror_radius, # 90% inside the mirror
muonintensityoutput.impact_parameter >
0.2 * mirror_radius, # 20% inside the mirror
npix_above_threshold(pix_ring, tailcuts[0]) > 0.1 * min_pix,
npix_composing_ring(pix_ring) > min_pix,
muonringparam.ring_radius < 1.5 * u.deg,
muonringparam.ring_radius > 1. * u.deg
# TODO: To be applied when we have decent optics
# muonintensityoutput.ring_width
# < 0.08,
# muonintensityoutput.ring_width
# > 0.04
]
muonintensityparam = muonintensityoutput
if all(conditions):
good_ring = True
else:
good_ring = False
if (plot_rings and plots_path and good_ring):
altaz = AltAz(alt=70 * u.deg, az=0 * u.deg)
focal_length = equivalent_focal_length
ring_nominal = SkyCoord(delta_az=muonringparam.ring_center_x,
delta_alt=muonringparam.ring_center_y,
frame=NominalFrame(origin=altaz))
ring_camcoord = ring_nominal.transform_to(
CameraFrame(focal_length=focal_length,
rotation=geom.pix_rotation,
telescope_pointing=altaz))
centroid = (ring_camcoord.x.value, ring_camcoord.y.value)
radius = muonringparam.ring_radius
width = muonintensityoutput.ring_width
ringrad_camcoord = 2 * radius.to(u.rad) * focal_length
ringwidthfrac = width / radius
ringrad_inner = ringrad_camcoord * (1. - ringwidthfrac)
ringrad_outer = ringrad_camcoord * (1. + ringwidthfrac)
fig, ax = plt.subplots(figsize=(10, 10))
plot_muon_event(ax, geom, image * clean_mask, centroid,
ringrad_camcoord, ringrad_inner, ringrad_outer,
event_id)
fig.savefig('{}/Event_{}_fitted.png'.format(plots_path, event_id))
if (plot_rings and not plots_path):
print("You are trying to plot without giving a path!")
return muonintensityparam, size_outside_ring, muonringparam, good_ring
