def camera_to_sky(pos_x, pos_y, focal, pointing_alt, pointing_az):
"""
Parameters
----------
pos_x: X coordinate in camera (distance)
pos_y: Y coordinate in camera (distance)
focal: telescope focal (distance)
pointing_alt: pointing altitude in angle unit
pointing_az: pointing altitude in angle unit
Returns
-------
(alt, az)
Example:
--------
import astropy.units as u
import numpy as np
x = np.array([1,0]) * u.m
y = np.array([1,1]) * u.m
"""
pointing_direction = HorizonFrame(alt=pointing_alt, az=pointing_az)
source_pos_in_camera = NominalFrame(
array_direction=pointing_direction,
pointing_direction=pointing_direction,
x=pos_x / focal * u.rad,
y=pos_y / focal * u.rad,
)
return source_pos_in_camera.transform_to(pointing_direction)
def nominal_to_altaz():
t = np.zeros(10)
t[5] = 1
nom = NominalFrame(x=t * u.deg,
y=t * u.deg,
array_direction=[75 * u.deg, 180 * u.deg])
alt_az = nom.transform_to(HorizonFrame)
print("AltAz Coordinate", alt_az)
def get_event_pos_in_sky(hillas, disp, tel, pointing_direction):
side = 1 # TODO: method to guess side
focal = tel.optics.equivalent_focal_length
source_pos_in_camera = NominalFrame(array_direction=pointing_direction,
pointing_direction=pointing_direction,
x=(hillas.x + side * disp * np.cos(hillas.phi)) / focal * u.rad,
y=(hillas.y + side * disp * np.sin(hillas.phi)) / focal * u.rad
)
horizon_frame = HorizonFrame(alt=pointing_direction.alt, az=pointing_direction.az)
return source_pos_in_camera.transform_to(horizon_frame)
def predict(self, shower_seed, energy_seed):
"""
Parameters
----------
shower_seed: ReconstructedShowerContainer
Seed shower geometry to be used in the fit
energy_seed: ReconstructedEnergyContainer
Seed energy to be used in fit
Returns
-------
ReconstructedShowerContainer, ReconstructedEnergyContainer:
Reconstructed ImPACT shower geometry and energy
"""
horizon_seed = HorizonFrame(az=shower_seed.az, alt=shower_seed.alt)
nominal_seed = horizon_seed.transform_to(NominalFrame(
array_direction=self.array_direction))
source_x = nominal_seed.x.to(u.rad).value
source_y = nominal_seed.y.to(u.rad).value
ground = GroundFrame(x=shower_seed.core_x,
y=shower_seed.core_y, z=0 * u.m)
tilted = ground.transform_to(
TiltedGroundFrame(pointing_direction=self.array_direction)
)
tilt_x = tilted.x.to(u.m).value
tilt_y = tilted.y.to(u.m).value
zenith = 90 * u.deg - self.array_direction.alt
if len(self.hillas_parameters) > 3:
shift = [1]
else:
shift = [1.5, 1, 0.5, 0, -0.5, -1, -1.5]
seed_list = spread_line_seed(self.hillas_parameters,
self.tel_pos_x, self.tel_pos_y,
source_x[0], source_y[0], tilt_x, tilt_y,
energy_seed.energy.value,
shift_frac = shift)
chosen_seed = self.choose_seed(seed_list)
# Perform maximum likelihood fit
fit_params, errors, like = self.minimise(params=chosen_seed[0],
step=chosen_seed[1],
limits=chosen_seed[2],
minimiser_name=self.minimiser_name)
# Create a container class for reconstructed shower
shower_result = ReconstructedShowerContainer()
# Convert the best fits direction and core to Horizon and ground systems and
# copy to the shower container
nominal = NominalFrame(x=fit_params[0] * u.rad,
y=fit_params[1] * u.rad,
array_direction=self.array_direction)
horizon = nominal.transform_to(HorizonFrame())
shower_result.alt, shower_result.az = horizon.alt, horizon.az
tilted = TiltedGroundFrame(x=fit_params[2] * u.m,
y=fit_params[3] * u.m,
pointing_direction=self.array_direction)
ground = project_to_ground(tilted)
shower_result.core_x = ground.x
shower_result.core_y = ground.y
shower_result.is_valid = True
# Currently no errors not availible to copy NaN
shower_result.alt_uncert = np.nan
shower_result.az_uncert = np.nan
shower_result.core_uncert = np.nan
# Copy reconstructed Xmax
shower_result.h_max = fit_params[5] * self.get_shower_max(fit_params[0],
fit_params[1],
fit_params[2],
fit_params[3],
zenith.to(u.rad).value)
shower_result.h_max *= np.cos(zenith)
shower_result.h_max_uncert = errors[5] * shower_result.h_max
shower_result.goodness_of_fit = like
# Create a container class for reconstructed energy
energy_result = ReconstructedEnergyContainer()
# Fill with results
energy_result.energy = fit_params[4] * u.TeV
energy_result.energy_uncert = errors[4] * u.TeV
energy_result.is_valid = True
return shower_result, energy_result
def plot_muon_event(event, muonparams, args=None):
if muonparams['MuonRingParams'] is not None:
# Plot the muon event and overlay muon parameters
fig = plt.figure(figsize=(16, 7))
colorbar = None
colorbar2 = None
#for tel_id in event.dl0.tels_with_data:
for tel_id in muonparams['TelIds']:
idx = muonparams['TelIds'].index(tel_id)
if not muonparams['MuonRingParams'][idx]:
continue
#otherwise...
npads = 2
# Only create two pads if there is timing information extracted
# from the calibration
ax1 = fig.add_subplot(1, npads, 1)
plotter = CameraPlotter(event)
image = event.dl1.tel[tel_id].image[0]
geom = event.inst.subarray.tel[tel_id].camera
tailcuts = (5., 7.)
# Try a higher threshold for
if geom.cam_id == 'FlashCam':
tailcuts = (10., 12.)
clean_mask = tailcuts_clean(geom,
image,
picture_thresh=tailcuts[0],
boundary_thresh=tailcuts[1])
signals = image * clean_mask
#print("Ring Centre in Nominal Coords:",muonparams[0].ring_center_x,muonparams[0].ring_center_y)
muon_incl = np.sqrt(
muonparams['MuonRingParams'][idx].ring_center_x**2. +
muonparams['MuonRingParams'][idx].ring_center_y**2.)
muon_phi = np.arctan(
muonparams['MuonRingParams'][idx].ring_center_y /
muonparams['MuonRingParams'][idx].ring_center_x)
rotr_angle = geom.pix_rotation
# if event.inst.optical_foclen[tel_id] > 10.*u.m and
# event.dl0.tel[tel_id].num_pixels != 1764:
if geom.cam_id == 'LSTCam' or geom.cam_id == 'NectarCam':
#print("Resetting the rotation angle")
rotr_angle = 0. * u.deg
# Convert to camera frame (centre & radius)
altaz = HorizonFrame(alt=event.mc.alt, az=event.mc.az)
ring_nominal = NominalFrame(
x=muonparams['MuonRingParams'][idx].ring_center_x,
y=muonparams['MuonRingParams'][idx].ring_center_y,
array_direction=altaz,
pointing_direction=altaz)
# embed()
ring_camcoord = ring_nominal.transform_to(
CameraFrame(pointing_direction=altaz,
focal_length=event.inst.optical_foclen[tel_id],
rotation=rotr_angle))
centroid_rad = np.sqrt(ring_camcoord.y**2 + ring_camcoord.x**2)
centroid = (ring_camcoord.x.value, ring_camcoord.y.value)
ringrad_camcoord = muonparams['MuonRingParams'][idx].ring_radius.to(u.rad) \
* event.inst.optical_foclen[tel_id] * 2. # But not FC?
px, py = event.inst.pixel_pos[tel_id]
flen = event.inst.optical_foclen[tel_id]
camera_coord = CameraFrame(x=px,
y=py,
focal_length=flen,
rotation=geom.pix_rotation)
nom_coord = camera_coord.transform_to(
NominalFrame(array_direction=altaz, pointing_direction=altaz))
px = nom_coord.x.to(u.deg)
py = nom_coord.y.to(u.deg)
dist = np.sqrt(
np.power(px -
muonparams['MuonRingParams'][idx].ring_center_x, 2) +
np.power(py -
muonparams['MuonRingParams'][idx].ring_center_y, 2))
ring_dist = np.abs(dist -
muonparams['MuonRingParams'][idx].ring_radius)
pixRmask = ring_dist < muonparams['MuonRingParams'][
idx].ring_radius * 0.4
#if muonparams[1] is not None:
if muonparams['MuonIntensityParams'][idx] is not None:
signals *= muonparams['MuonIntensityParams'][idx].mask
camera1 = plotter.draw_camera(tel_id, signals, ax1)
cmaxmin = (max(signals) - min(signals))
cmin = min(signals)
if not cmin:
cmin = 1.
if not cmaxmin:
cmaxmin = 1.
cmap_charge = colors.LinearSegmentedColormap.from_list(
'cmap_c', [(0 / cmaxmin, 'darkblue'),
(np.abs(cmin) / cmaxmin, 'black'),
(2.0 * np.abs(cmin) / cmaxmin, 'blue'),
(2.5 * np.abs(cmin) / cmaxmin, 'green'),
(1, 'yellow')])
camera1.pixels.set_cmap(cmap_charge)
if not colorbar:
camera1.add_colorbar(ax=ax1, label=" [photo-electrons]")
colorbar = camera1.colorbar
else:
camera1.colorbar = colorbar
camera1.update(True)
camera1.add_ellipse(centroid,
ringrad_camcoord.value,
ringrad_camcoord.value,
0.,
0.,
color="red")
if muonparams['MuonIntensityParams'][idx] is not None:
# continue #Comment this...(should ringwidthfrac also be *0.5?)
ringwidthfrac = muonparams['MuonIntensityParams'][
idx].ring_width / muonparams['MuonRingParams'][
idx].ring_radius
ringrad_inner = ringrad_camcoord * (1. - ringwidthfrac)
ringrad_outer = ringrad_camcoord * (1. + ringwidthfrac)
camera1.add_ellipse(centroid,
ringrad_inner.value,
ringrad_inner.value,
0.,
0.,
color="magenta")
camera1.add_ellipse(centroid,
ringrad_outer.value,
ringrad_outer.value,
0.,
0.,
color="magenta")
npads = 2
ax2 = fig.add_subplot(1, npads, npads)
pred = muonparams['MuonIntensityParams'][idx].prediction
if len(pred) != np.sum(
muonparams['MuonIntensityParams'][idx].mask):
print("Warning! Lengths do not match...len(pred)=",
len(pred), "len(mask)=",
np.sum(muonparams['MuonIntensityParams'][idx].mask))
# Numpy broadcasting - fill in the shape
plotpred = np.zeros(image.shape)
plotpred[muonparams['MuonIntensityParams'][idx].mask ==
True] = pred
camera2 = plotter.draw_camera(tel_id, plotpred, ax2)
if np.isnan(max(plotpred)) or np.isnan(min(plotpred)):
print("nan prediction, skipping...")
continue
c2maxmin = (max(plotpred) - min(plotpred))
if not c2maxmin:
c2maxmin = 1.
c2map_charge = colors.LinearSegmentedColormap.from_list(
'c2map_c',
[(0 / c2maxmin, 'darkblue'),
(np.abs(min(plotpred)) / c2maxmin, 'black'),
(2.0 * np.abs(min(plotpred)) / c2maxmin, 'blue'),
(2.5 * np.abs(min(plotpred)) / c2maxmin, 'green'),
(1, 'yellow')])
camera2.pixels.set_cmap(c2map_charge)
if not colorbar2:
camera2.add_colorbar(ax=ax2, label=" [photo-electrons]")
colorbar2 = camera2.colorbar
else:
camera2.colorbar = colorbar2
camera2.update(True)
plt.pause(1.) # make shorter
# plt.pause(0.1)
# if pp is not None:
# pp.savefig(fig)
#fig.savefig(str(args.output_path) + "_" +
# str(event.dl0.event_id) + '.png')
plt.close()
def plot_muon_event(event, muonparams, geom_dict=None, args=None):
if muonparams[0] is not None:
# Plot the muon event and overlay muon parameters
fig = plt.figure(figsize=(16, 7))
# if args.display:
# plt.show(block=False)
#pp = PdfPages(args.output_path) if args.output_path is not None else None
# pp = None #For now, need to correct this
colorbar = None
colorbar2 = None
for tel_id in event.dl0.tels_with_data:
npads = 2
# Only create two pads if there is timing information extracted
# from the calibration
ax1 = fig.add_subplot(1, npads, 1)
plotter = CameraPlotter(event, geom_dict)
#image = event.dl1.tel[tel_id].calibrated_image
image = event.dl1.tel[tel_id].image[0]
# Get geometry
geom = None
if geom_dict is not None and tel_id in geom_dict:
geom = geom_dict[tel_id]
else:
#log.debug("[calib] Guessing camera geometry")
geom = CameraGeometry.guess(*event.inst.pixel_pos[tel_id],
event.inst.optical_foclen[tel_id])
#log.debug("[calib] Camera geometry found")
if geom_dict is not None:
geom_dict[tel_id] = geom
tailcuts = (5., 7.)
# Try a higher threshold for
if geom.cam_id == 'FlashCam':
tailcuts = (10., 12.)
#print("Using Tail Cuts:",tailcuts)
clean_mask = tailcuts_clean(geom, image,
picture_thresh=tailcuts[0],
boundary_thresh=tailcuts[1])
signals = image * clean_mask
#print("Ring Centre in Nominal Coords:",muonparams[0].ring_center_x,muonparams[0].ring_center_y)
muon_incl = np.sqrt(muonparams[0].ring_center_x**2. +
muonparams[0].ring_center_y**2.)
muon_phi = np.arctan(muonparams[0].ring_center_y /
muonparams[0].ring_center_x)
rotr_angle = geom.pix_rotation
# if event.inst.optical_foclen[tel_id] > 10.*u.m and
# event.dl0.tel[tel_id].num_pixels != 1764:
if geom.cam_id == 'LSTCam' or geom.cam_id == 'NectarCam':
#print("Resetting the rotation angle")
rotr_angle = 0. * u.deg
# Convert to camera frame (centre & radius)
altaz = HorizonFrame(alt=event.mc.alt, az=event.mc.az)
ring_nominal = NominalFrame(x=muonparams[0].ring_center_x,
y=muonparams[0].ring_center_y,
array_direction=altaz,
pointing_direction=altaz)
# embed()
ring_camcoord = ring_nominal.transform_to(CameraFrame(
pointing_direction=altaz,
focal_length=event.inst.optical_foclen[tel_id],
rotation=rotr_angle))
centroid_rad = np.sqrt(ring_camcoord.y**2 + ring_camcoord.x**2)
centroid = (ring_camcoord.x.value, ring_camcoord.y.value)
ringrad_camcoord = muonparams[0].ring_radius.to(u.rad) \
* event.inst.optical_foclen[tel_id] * 2. # But not FC?
#rot_angle = 0.*u.deg
# if event.inst.optical_foclen[tel_id] > 10.*u.m and event.dl0.tel[tel_id].num_pixels != 1764:
#rot_angle = -100.14*u.deg
px, py = event.inst.pixel_pos[tel_id]
flen = event.inst.optical_foclen[tel_id]
camera_coord = CameraFrame(x=px, y=py,
z=np.zeros(px.shape) * u.m,
focal_length=flen,
rotation=geom.pix_rotation)
nom_coord = camera_coord.transform_to(
NominalFrame(array_direction=altaz,
pointing_direction=altaz)
)
#,focal_length = event.inst.optical_foclen[tel_id])) # tel['TelescopeTable_VersionFeb2016'][tel['TelescopeTable_VersionFeb2016']['TelID']==telid]['FL'][0]*u.m))
px = nom_coord.x.to(u.deg)
py = nom_coord.y.to(u.deg)
dist = np.sqrt(np.power( px - muonparams[0].ring_center_x, 2)
+ np.power(py - muonparams[0].ring_center_y, 2))
ring_dist = np.abs(dist - muonparams[0].ring_radius)
pixRmask = ring_dist < muonparams[0].ring_radius * 0.4
if muonparams[1] is not None:
signals *= muonparams[1].mask
camera1 = plotter.draw_camera(tel_id, signals, ax1)
cmaxmin = (max(signals) - min(signals))
if not cmaxmin:
cmaxmin = 1.
cmap_charge = colors.LinearSegmentedColormap.from_list(
'cmap_c', [(0 / cmaxmin, 'darkblue'),
(np.abs(min(signals)) / cmaxmin, 'black'),
(2.0 * np.abs(min(signals)) / cmaxmin, 'blue'),
(2.5 * np.abs(min(signals)) / cmaxmin, 'green'),
(1, 'yellow')]
)
camera1.pixels.set_cmap(cmap_charge)
if not colorbar:
camera1.add_colorbar(ax=ax1, label=" [photo-electrons]")
colorbar = camera1.colorbar
else:
camera1.colorbar = colorbar
camera1.update(True)
camera1.add_ellipse(centroid, ringrad_camcoord.value,
ringrad_camcoord.value, 0., 0., color="red")
# ax1.set_title("CT {} ({}) - Mean pixel charge"
# .format(tel_id, geom_dict[tel_id].cam_id))
if muonparams[1] is not None:
# continue #Comment this...(should ringwidthfrac also be *0.5?)
ringwidthfrac = muonparams[
1].ring_width / muonparams[0].ring_radius
ringrad_inner = ringrad_camcoord * (1. - ringwidthfrac)
ringrad_outer = ringrad_camcoord * (1. + ringwidthfrac)
camera1.add_ellipse(centroid, ringrad_inner.value,
ringrad_inner.value, 0., 0.,
color="magenta")
camera1.add_ellipse(centroid, ringrad_outer.value,
ringrad_outer.value, 0., 0., color="magenta")
npads = 2
ax2 = fig.add_subplot(1, npads, npads)
pred = muonparams[1].prediction
if len(pred) != np.sum(muonparams[1].mask):
print("Warning! Lengths do not match...len(pred)=",
len(pred), "len(mask)=", np.sum(muonparams[1].mask))
# Numpy broadcasting - fill in the shape
plotpred = np.zeros(image.shape)
plotpred[muonparams[1].mask == True] = pred
camera2 = plotter.draw_camera(tel_id, plotpred, ax2)
c2maxmin = (max(plotpred) - min(plotpred))
if not c2maxmin:
c2maxmin = 1.
c2map_charge = colors.LinearSegmentedColormap.from_list(
'c2map_c', [(0 / c2maxmin, 'darkblue'),
(np.abs(min(plotpred)) / c2maxmin, 'black'),
(2.0 * np.abs(min(plotpred)) / c2maxmin, 'blue'),
(2.5 * np.abs(min(plotpred)) / c2maxmin, 'green'),
(1, 'yellow')]
)
camera2.pixels.set_cmap(c2map_charge)
if not colorbar2:
camera2.add_colorbar(ax=ax2, label=" [photo-electrons]")
colorbar2 = camera2.colorbar
else:
camera2.colorbar = colorbar2
camera2.update(True)
plt.pause(1.) # make shorter
# plt.pause(0.1)
# if pp is not None:
# pp.savefig(fig)
fig.savefig(str(args.output_path) + "_" +
str(event.dl0.event_id) + '.png')
plt.close()
def nominal_to_altaz():
t = np.zeros(10)
t[5] = 1
nom = NominalFrame(x=t*u.deg,y=t*u.deg,array_direction = [75*u.deg,180*u.deg])
alt_az = nom.transform_to(HorizonFrame)
print("AltAz Coordinate",alt_az)
def predict(self, shower_seed, energy_seed):
"""
Parameters
----------
shower_seed: ReconstructedShowerContainer
Seed shower geometry to be used in the fit
energy_seed: ReconstructedEnergyContainer
Seed energy to be used in fit
Returns
-------
ReconstructedShowerContainer, ReconstructedEnergyContainer:
Reconstructed ImPACT shower geometry and energy
"""
horizon_seed = HorizonFrame(az=shower_seed.az, alt=shower_seed.alt)
nominal_seed = horizon_seed.transform_to(
NominalFrame(array_direction=self.array_direction))
print(nominal_seed)
print(horizon_seed)
print(self.array_direction)
source_x = nominal_seed.x[0].to(u.rad).value
source_y = nominal_seed.y[0].to(u.rad).value
ground = GroundFrame(x=shower_seed.core_x,
y=shower_seed.core_y,
z=0 * u.m)
tilted = ground.transform_to(
TiltedGroundFrame(pointing_direction=self.array_direction))
tilt_x = tilted.x.to(u.m).value
tilt_y = tilted.y.to(u.m).value
lower_en_limit = energy_seed.energy * 0.5
en_seed = energy_seed.energy
if lower_en_limit < 0.04 * u.TeV:
lower_en_limit = 0.04 * u.TeV
en_seed = 0.041 * u.TeV
seed = (source_x, source_y, tilt_x, tilt_y, en_seed.value, 0.8)
step = (0.001, 0.001, 10, 10, en_seed.value * 0.1, 0.1)
limits = ((source_x - 0.01, source_x + 0.01), (source_y - 0.01,
source_y + 0.01),
(tilt_x - 100, tilt_x + 100), (tilt_y - 100, tilt_y + 100),
(lower_en_limit.value, en_seed.value * 2), (0.5, 2))
fit_params, errors = self.minimise(params=seed,
step=step,
limits=limits,
minimiser_name=self.minimiser_name)
# container class for reconstructed showers '''
shower_result = ReconstructedShowerContainer()
nominal = NominalFrame(x=fit_params[0] * u.rad,
y=fit_params[1] * u.rad,
array_direction=self.array_direction)
horizon = nominal.transform_to(HorizonFrame())
shower_result.alt, shower_result.az = horizon.alt, horizon.az
tilted = TiltedGroundFrame(x=fit_params[2] * u.m,
y=fit_params[3] * u.m,
pointing_direction=self.array_direction)
ground = project_to_ground(tilted)
shower_result.core_x = ground.x
shower_result.core_y = ground.y
shower_result.is_valid = True
shower_result.alt_uncert = np.nan
shower_result.az_uncert = np.nan
shower_result.core_uncert = np.nan
zenith = 90 * u.deg - self.array_direction.alt
shower_result.h_max = fit_params[5] * \
self.get_shower_max(fit_params[0],
fit_params[1],
fit_params[2],
fit_params[3],
zenith.to(u.rad).value)
shower_result.h_max_uncert = errors[5] * shower_result.h_max
shower_result.goodness_of_fit = np.nan
shower_result.tel_ids = list(self.image.keys())
energy_result = ReconstructedEnergyContainer()
energy_result.energy = fit_params[4] * u.TeV
energy_result.energy_uncert = errors[4] * u.TeV
energy_result.is_valid = True
energy_result.tel_ids = list(self.image.keys())
# Return interesting stuff
return shower_result, energy_result
def predict(self, shower_seed, energy_seed):
"""
Parameters
----------
source_x: float
Initial guess of source position in the nominal frame
source_y: float
Initial guess of source position in the nominal frame
core_x: float
Initial guess of the core position in the tilted system
core_y: float
Initial guess of the core position in the tilted system
energy: float
Initial guess of energy
Returns
-------
Shower object with fit results
"""
horizon_seed = HorizonFrame(az=shower_seed.az, alt=shower_seed.alt)
nominal_seed = horizon_seed.transform_to(
NominalFrame(array_direction=self.array_direction))
source_x = nominal_seed.x.to(u.rad).value
source_y = nominal_seed.y.to(u.rad).value
ground = GroundFrame(x=shower_seed.core_x,
y=shower_seed.core_y,
z=0 * u.m)
tilted = ground.transform_to(
TiltedGroundFrame(pointing_direction=self.array_direction))
tilt_x = tilted.x.to(u.m).value
tilt_y = tilted.y.to(u.m).value
lower_en_limit = energy_seed.energy * 0.1
if lower_en_limit < 0.04 * u.TeV:
lower_en_limit = 0.04 * u.TeV
# Create Minuit object with first guesses at parameters, strip away the
# units as Minuit doesnt like them
min = Minuit(self.get_likelihood,
print_level=1,
source_x=source_x,
error_source_x=0.01 / 57.3,
fix_source_x=False,
limit_source_x=(source_x - 0.5 / 57.3,
source_x + 0.5 / 57.3),
source_y=source_y,
error_source_y=0.01 / 57.3,
fix_source_y=False,
limit_source_y=(source_y - 0.5 / 57.3,
source_y + 0.5 / 57.3),
core_x=tilt_x,
error_core_x=10,
limit_core_x=(tilt_x - 200, tilt_x + 200),
core_y=tilt_y,
error_core_y=10,
limit_core_y=(tilt_y - 200, tilt_y + 200),
energy=energy_seed.energy.value,
error_energy=energy_seed.energy.value * 0.05,
limit_energy=(lower_en_limit.value,
energy_seed.energy.value * 10.),
x_max_scale=1,
error_x_max_scale=0.1,
limit_x_max_scale=(0.5, 2),
fix_x_max_scale=False,
errordef=1)
min.tol *= 1000
min.strategy = 0
# Perform minimisation
migrad = min.migrad()
fit_params = min.values
errors = min.errors
# print(migrad)
# print(min.minos())
# container class for reconstructed showers '''
shower_result = ReconstructedShowerContainer()
nominal = NominalFrame(x=fit_params["source_x"] * u.rad,
y=fit_params["source_y"] * u.rad,
array_direction=self.array_direction)
horizon = nominal.transform_to(HorizonFrame())
shower_result.alt, shower_result.az = horizon.alt, horizon.az
tilted = TiltedGroundFrame(x=fit_params["core_x"] * u.m,
y=fit_params["core_y"] * u.m,
pointing_direction=self.array_direction)
ground = project_to_ground(tilted)
shower_result.core_x = ground.x
shower_result.core_y = ground.y
shower_result.is_valid = True
shower_result.alt_uncert = np.nan
shower_result.az_uncert = np.nan
shower_result.core_uncert = np.nan
zenith = 90 * u.deg - self.array_direction[0]
shower_result.h_max = fit_params["x_max_scale"] * \
self.get_shower_max(fit_params["source_x"],
fit_params["source_y"],
fit_params["core_x"],
fit_params["core_y"],
zenith.to(u.rad).value)
shower_result.h_max_uncert = errors["x_max_scale"] * shower_result.h_max
shower_result.goodness_of_fit = np.nan
shower_result.tel_ids = list(self.image.keys())
energy_result = ReconstructedEnergyContainer()
energy_result.energy = fit_params["energy"] * u.TeV
energy_result.energy_uncert = errors["energy"] * u.TeV
energy_result.is_valid = True
energy_result.tel_ids = list(self.image.keys())
# Return interesting stuff
return shower_result, energy_result
def predict(self, shower_seed, energy_seed):
"""
Parameters
----------
shower_seed: ReconstructedShowerContainer
Seed shower geometry to be used in the fit
energy_seed: ReconstructedEnergyContainer
Seed energy to be used in fit
Returns
-------
ReconstructedShowerContainer, ReconstructedEnergyContainer:
Reconstructed ImPACT shower geometry and energy
"""
horizon_seed = HorizonFrame(az=shower_seed.az, alt=shower_seed.alt)
nominal_seed = horizon_seed.transform_to(NominalFrame(array_direction=self.array_direction))
print(nominal_seed)
print(horizon_seed)
print(self.array_direction)
source_x = nominal_seed.x[0].to(u.rad).value
source_y = nominal_seed.y[0].to(u.rad).value
ground = GroundFrame(x=shower_seed.core_x,
y=shower_seed.core_y, z=0 * u.m)
tilted = ground.transform_to(
TiltedGroundFrame(pointing_direction=self.array_direction)
)
tilt_x = tilted.x.to(u.m).value
tilt_y = tilted.y.to(u.m).value
lower_en_limit = energy_seed.energy * 0.5
en_seed = energy_seed.energy
if lower_en_limit < 0.04 * u.TeV:
lower_en_limit = 0.04 * u.TeV
en_seed = 0.041 * u.TeV
seed = (source_x, source_y, tilt_x,
tilt_y, en_seed.value, 0.8)
step = (0.001, 0.001, 10, 10, en_seed.value*0.1, 0.1)
limits = ((source_x-0.01, source_x+0.01),
(source_y-0.01, source_y+0.01),
(tilt_x-100, tilt_x+100),
(tilt_y-100, tilt_y+100),
(lower_en_limit.value, en_seed.value*2),
(0.5,2))
fit_params, errors = self.minimise(params=seed, step=step, limits=limits,
minimiser_name=self.minimiser_name)
# container class for reconstructed showers '''
shower_result = ReconstructedShowerContainer()
nominal = NominalFrame(x=fit_params[0] * u.rad,
y=fit_params[1] * u.rad,
array_direction=self.array_direction)
horizon = nominal.transform_to(HorizonFrame())
shower_result.alt, shower_result.az = horizon.alt, horizon.az
tilted = TiltedGroundFrame(x=fit_params[2] * u.m,
y=fit_params[3] * u.m,
pointing_direction=self.array_direction)
ground = project_to_ground(tilted)
shower_result.core_x = ground.x
shower_result.core_y = ground.y
shower_result.is_valid = True
shower_result.alt_uncert = np.nan
shower_result.az_uncert = np.nan
shower_result.core_uncert = np.nan
zenith = 90*u.deg - self.array_direction.alt
shower_result.h_max = fit_params[5] * \
self.get_shower_max(fit_params[0],
fit_params[1],
fit_params[2],
fit_params[3],
zenith.to(u.rad).value)
shower_result.h_max_uncert = errors[5] * shower_result.h_max
shower_result.goodness_of_fit = np.nan
shower_result.tel_ids = list(self.image.keys())
energy_result = ReconstructedEnergyContainer()
energy_result.energy = fit_params[4] * u.TeV
energy_result.energy_uncert = errors[4] * u.TeV
energy_result.is_valid = True
energy_result.tel_ids = list(self.image.keys())
# Return interesting stuff
return shower_result, energy_result
def plot_muon_event(event, muonparams, args=None):
if muonparams['MuonRingParams'] is not None:
# Plot the muon event and overlay muon parameters
fig = plt.figure(figsize=(16, 7))
colorbar = None
colorbar2 = None
#for tel_id in event.dl0.tels_with_data:
for tel_id in muonparams['TelIds']:
idx = muonparams['TelIds'].index(tel_id)
if not muonparams['MuonRingParams'][idx]:
continue
#otherwise...
npads = 2
# Only create two pads if there is timing information extracted
# from the calibration
ax1 = fig.add_subplot(1, npads, 1)
plotter = CameraPlotter(event)
image = event.dl1.tel[tel_id].image[0]
geom = event.inst.subarray.tel[tel_id].camera
tailcuts = (5., 7.)
# Try a higher threshold for
if geom.cam_id == 'FlashCam':
tailcuts = (10., 12.)
clean_mask = tailcuts_clean(geom, image,
picture_thresh=tailcuts[0],
boundary_thresh=tailcuts[1])
signals = image * clean_mask
#print("Ring Centre in Nominal Coords:",muonparams[0].ring_center_x,muonparams[0].ring_center_y)
muon_incl = np.sqrt(muonparams['MuonRingParams'][idx].ring_center_x**2. +
muonparams['MuonRingParams'][idx].ring_center_y**2.)
muon_phi = np.arctan(muonparams['MuonRingParams'][idx].ring_center_y /
muonparams['MuonRingParams'][idx].ring_center_x)
rotr_angle = geom.pix_rotation
# if event.inst.optical_foclen[tel_id] > 10.*u.m and
# event.dl0.tel[tel_id].num_pixels != 1764:
if geom.cam_id == 'LSTCam' or geom.cam_id == 'NectarCam':
#print("Resetting the rotation angle")
rotr_angle = 0. * u.deg
# Convert to camera frame (centre & radius)
altaz = HorizonFrame(alt=event.mc.alt, az=event.mc.az)
ring_nominal = NominalFrame(x=muonparams['MuonRingParams'][idx].ring_center_x,
y=muonparams['MuonRingParams'][idx].ring_center_y,
array_direction=altaz,
pointing_direction=altaz)
# embed()
ring_camcoord = ring_nominal.transform_to(CameraFrame(
pointing_direction=altaz,
focal_length=event.inst.optical_foclen[tel_id],
rotation=rotr_angle))
centroid_rad = np.sqrt(ring_camcoord.y**2 + ring_camcoord.x**2)
centroid = (ring_camcoord.x.value, ring_camcoord.y.value)
ringrad_camcoord = muonparams['MuonRingParams'][idx].ring_radius.to(u.rad) \
* event.inst.optical_foclen[tel_id] * 2. # But not FC?
px, py = event.inst.pixel_pos[tel_id]
flen = event.inst.optical_foclen[tel_id]
camera_coord = CameraFrame(x=px, y=py,
z=np.zeros(px.shape) * u.m,
focal_length=flen,
rotation=geom.pix_rotation)
nom_coord = camera_coord.transform_to(
NominalFrame(array_direction=altaz,
pointing_direction=altaz)
)
px = nom_coord.x.to(u.deg)
py = nom_coord.y.to(u.deg)
dist = np.sqrt(np.power( px - muonparams['MuonRingParams'][idx].ring_center_x, 2)
+ np.power(py - muonparams['MuonRingParams'][idx].ring_center_y, 2))
ring_dist = np.abs(dist - muonparams['MuonRingParams'][idx].ring_radius)
pixRmask = ring_dist < muonparams['MuonRingParams'][idx].ring_radius * 0.4
#if muonparams[1] is not None:
if muonparams['MuonIntensityParams'][idx] is not None:
signals *= muonparams['MuonIntensityParams'][idx].mask
camera1 = plotter.draw_camera(tel_id, signals, ax1)
cmaxmin = (max(signals) - min(signals))
cmin = min(signals)
if not cmin:
cmin = 1.
if not cmaxmin:
cmaxmin = 1.
cmap_charge = colors.LinearSegmentedColormap.from_list(
'cmap_c', [(0 / cmaxmin, 'darkblue'),
(np.abs(cmin) / cmaxmin, 'black'),
(2.0 * np.abs(cmin) / cmaxmin, 'blue'),
(2.5 * np.abs(cmin) / cmaxmin, 'green'),
(1, 'yellow')]
)
camera1.pixels.set_cmap(cmap_charge)
if not colorbar:
camera1.add_colorbar(ax=ax1, label=" [photo-electrons]")
colorbar = camera1.colorbar
else:
camera1.colorbar = colorbar
camera1.update(True)
camera1.add_ellipse(centroid, ringrad_camcoord.value,
ringrad_camcoord.value, 0., 0., color="red")
if muonparams['MuonIntensityParams'][idx] is not None:
# continue #Comment this...(should ringwidthfrac also be *0.5?)
ringwidthfrac = muonparams['MuonIntensityParams'][idx].ring_width / muonparams['MuonRingParams'][idx].ring_radius
ringrad_inner = ringrad_camcoord * (1. - ringwidthfrac)
ringrad_outer = ringrad_camcoord * (1. + ringwidthfrac)
camera1.add_ellipse(centroid, ringrad_inner.value,
ringrad_inner.value, 0., 0.,
color="magenta")
camera1.add_ellipse(centroid, ringrad_outer.value,
ringrad_outer.value, 0., 0., color="magenta")
npads = 2
ax2 = fig.add_subplot(1, npads, npads)
pred = muonparams['MuonIntensityParams'][idx].prediction
if len(pred) != np.sum(muonparams['MuonIntensityParams'][idx].mask):
print("Warning! Lengths do not match...len(pred)=",
len(pred), "len(mask)=", np.sum(muonparams['MuonIntensityParams'][idx].mask))
# Numpy broadcasting - fill in the shape
plotpred = np.zeros(image.shape)
plotpred[muonparams['MuonIntensityParams'][idx].mask == True] = pred
camera2 = plotter.draw_camera(tel_id, plotpred, ax2)
if np.isnan(max(plotpred)) or np.isnan(min(plotpred)):
print("nan prediction, skipping...")
continue
c2maxmin = (max(plotpred) - min(plotpred))
if not c2maxmin:
c2maxmin = 1.
c2map_charge = colors.LinearSegmentedColormap.from_list(
'c2map_c', [(0 / c2maxmin, 'darkblue'),
(np.abs(min(plotpred)) / c2maxmin, 'black'),
(2.0 * np.abs(min(plotpred)) / c2maxmin, 'blue'),
(2.5 * np.abs(min(plotpred)) / c2maxmin, 'green'),
(1, 'yellow')]
)
camera2.pixels.set_cmap(c2map_charge)
if not colorbar2:
camera2.add_colorbar(ax=ax2, label=" [photo-electrons]")
colorbar2 = camera2.colorbar
else:
camera2.colorbar = colorbar2
camera2.update(True)
plt.pause(1.) # make shorter
# plt.pause(0.1)
# if pp is not None:
# pp.savefig(fig)
#fig.savefig(str(args.output_path) + "_" +
# str(event.dl0.event_id) + '.png')
plt.close()
def predict(self, shower_seed, energy_seed):
"""
Parameters
----------
source_x: float
Initial guess of source position in the nominal frame
source_y: float
Initial guess of source position in the nominal frame
core_x: float
Initial guess of the core position in the tilted system
core_y: float
Initial guess of the core position in the tilted system
energy: float
Initial guess of energy
Returns
-------
Shower object with fit results
"""
horizon_seed = HorizonFrame(az=shower_seed.az, alt=shower_seed.alt)
nominal_seed = horizon_seed.transform_to(
NominalFrame(array_direction=self.array_direction)
)
source_x = nominal_seed.x.to(u.rad).value
source_y = nominal_seed.y.to(u.rad).value
ground = GroundFrame(x=shower_seed.core_x,
y=shower_seed.core_y, z=0 * u.m)
tilted = ground.transform_to(
TiltedGroundFrame(pointing_direction=self.array_direction)
)
tilt_x = tilted.x.to(u.m).value
tilt_y = tilted.y.to(u.m).value
lower_en_limit = energy_seed.energy * 0.1
if lower_en_limit < 0.04 * u.TeV:
lower_en_limit = 0.04 * u.TeV
# Create Minuit object with first guesses at parameters, strip away the
# units as Minuit doesnt like them
min = Minuit(self.get_likelihood,
print_level=1,
source_x=source_x,
error_source_x=0.01 / 57.3,
fix_source_x=False,
limit_source_x=(source_x - 0.5 / 57.3,
source_x + 0.5 / 57.3),
source_y=source_y,
error_source_y=0.01 / 57.3,
fix_source_y=False,
limit_source_y=(source_y - 0.5 / 57.3,
source_y + 0.5 / 57.3),
core_x=tilt_x,
error_core_x=10,
limit_core_x=(tilt_x - 200, tilt_x + 200),
core_y=tilt_y,
error_core_y=10,
limit_core_y=(tilt_y - 200, tilt_y + 200),
energy=energy_seed.energy.value,
error_energy=energy_seed.energy.value * 0.05,
limit_energy=(lower_en_limit.value,
energy_seed.energy.value * 10.),
x_max_scale=1, error_x_max_scale=0.1,
limit_x_max_scale=(0.5, 2),
fix_x_max_scale=False,
errordef=1)
min.tol *= 1000
min.strategy = 0
# Perform minimisation
migrad = min.migrad()
fit_params = min.values
errors = min.errors
# print(migrad)
# print(min.minos())
# container class for reconstructed showers '''
shower_result = ReconstructedShowerContainer()
nominal = NominalFrame(x=fit_params["source_x"] * u.rad,
y=fit_params["source_y"] * u.rad,
array_direction=self.array_direction)
horizon = nominal.transform_to(HorizonFrame())
shower_result.alt, shower_result.az = horizon.alt, horizon.az
tilted = TiltedGroundFrame(x=fit_params["core_x"] * u.m,
y=fit_params["core_y"] * u.m,
pointing_direction=self.array_direction)
ground = project_to_ground(tilted)
shower_result.core_x = ground.x
shower_result.core_y = ground.y
shower_result.is_valid = True
shower_result.alt_uncert = np.nan
shower_result.az_uncert = np.nan
shower_result.core_uncert = np.nan
zenith = 90 * u.deg - self.array_direction[0]
shower_result.h_max = fit_params["x_max_scale"] * \
self.get_shower_max(fit_params["source_x"],
fit_params["source_y"],
fit_params["core_x"],
fit_params["core_y"],
zenith.to(u.rad).value)
shower_result.h_max_uncert = errors["x_max_scale"] * shower_result.h_max
shower_result.goodness_of_fit = np.nan
shower_result.tel_ids = list(self.image.keys())
energy_result = ReconstructedEnergyContainer()
energy_result.energy = fit_params["energy"] * u.TeV
energy_result.energy_uncert = errors["energy"] * u.TeV
energy_result.is_valid = True
energy_result.tel_ids = list(self.image.keys())
# Return interesting stuff
return shower_result, energy_result
def predict(self, hillas_parameters, tel_x, tel_y, array_direction):
"""
Parameters
----------
hillas_parameters: dict
Dictionary containing Hillas parameters for all telescopes
in reconstruction
tel_x: dict
Dictionary containing telescope position on ground for all
telescopes in reconstruction
tel_y: dict
Dictionary containing telescope position on ground for all
telescopes in reconstruction
array_direction: HorizonFrame
Pointing direction of the array
Returns
-------
ReconstructedShowerContainer:
"""
src_x, src_y, err_x, err_y = self.reconstruct_nominal(hillas_parameters)
core_x, core_y, core_err_x, core_err_y = self.reconstruct_tilted(
hillas_parameters, tel_x, tel_y)
err_x *= u.rad
err_y *= u.rad
nom = NominalFrame(x=src_x * u.rad, y=src_y * u.rad,
array_direction=array_direction)
horiz = nom.transform_to(HorizonFrame())
result = ReconstructedShowerContainer()
result.alt, result.az = horiz.alt, horiz.az
tilt = TiltedGroundFrame(x=core_x * u.m, y=core_y * u.m,
pointing_direction=array_direction)
grd = project_to_ground(tilt)
result.core_x = grd.x
result.core_y = grd.y
x_max = self.reconstruct_xmax(nom.x, nom.y,
tilt.x, tilt.y,
hillas_parameters,
tel_x, tel_y,
90 * u.deg - array_direction.alt)
result.core_uncert = np.sqrt(core_err_x * core_err_x
+ core_err_y * core_err_y) * u.m
result.tel_ids = [h for h in hillas_parameters.keys()]
result.average_size = np.mean([h.size for h in hillas_parameters.values()])
result.is_valid = True
src_error = np.sqrt(err_x * err_x + err_y * err_y)
result.alt_uncert = src_error.to(u.deg)
result.az_uncert = src_error.to(u.deg)
result.h_max = x_max
result.h_max_uncert = np.nan
result.goodness_of_fit = np.nan
return result
def plot_muon_event(event, muonparams, geom_dict=None, args=None):
if muonparams[0] is not None:
# Plot the muon event and overlay muon parameters
fig = plt.figure(figsize=(16, 7))
# if args.display:
# plt.show(block=False)
#pp = PdfPages(args.output_path) if args.output_path is not None else None
# pp = None #For now, need to correct this
colorbar = None
colorbar2 = None
for tel_id in event.dl0.tels_with_data:
npads = 2
# Only create two pads if there is timing information extracted
# from the calibration
ax1 = fig.add_subplot(1, npads, 1)
plotter = CameraPlotter(event, geom_dict)
#image = event.dl1.tel[tel_id].calibrated_image
image = event.dl1.tel[tel_id].image[0]
# Get geometry
geom = None
if geom_dict is not None and tel_id in geom_dict:
geom = geom_dict[tel_id]
else:
#log.debug("[calib] Guessing camera geometry")
geom = CameraGeometry.guess(*event.inst.pixel_pos[tel_id],
event.inst.optical_foclen[tel_id])
#log.debug("[calib] Camera geometry found")
if geom_dict is not None:
geom_dict[tel_id] = geom
tailcuts = (5., 7.)
# Try a higher threshold for
if geom.cam_id == 'FlashCam':
tailcuts = (10., 12.)
#print("Using Tail Cuts:",tailcuts)
clean_mask = tailcuts_clean(geom,
image,
picture_thresh=tailcuts[0],
boundary_thresh=tailcuts[1])
signals = image * clean_mask
#print("Ring Centre in Nominal Coords:",muonparams[0].ring_center_x,muonparams[0].ring_center_y)
muon_incl = np.sqrt(muonparams[0].ring_center_x**2. +
muonparams[0].ring_center_y**2.)
muon_phi = np.arctan(muonparams[0].ring_center_y /
muonparams[0].ring_center_x)
rotr_angle = geom.pix_rotation
# if event.inst.optical_foclen[tel_id] > 10.*u.m and
# event.dl0.tel[tel_id].num_pixels != 1764:
if geom.cam_id == 'LSTCam' or geom.cam_id == 'NectarCam':
#print("Resetting the rotation angle")
rotr_angle = 0. * u.deg
# Convert to camera frame (centre & radius)
altaz = HorizonFrame(alt=event.mc.alt, az=event.mc.az)
ring_nominal = NominalFrame(x=muonparams[0].ring_center_x,
y=muonparams[0].ring_center_y,
array_direction=altaz,
pointing_direction=altaz)
# embed()
ring_camcoord = ring_nominal.transform_to(
CameraFrame(pointing_direction=altaz,
focal_length=event.inst.optical_foclen[tel_id],
rotation=rotr_angle))
centroid_rad = np.sqrt(ring_camcoord.y**2 + ring_camcoord.x**2)
centroid = (ring_camcoord.x.value, ring_camcoord.y.value)
ringrad_camcoord = muonparams[0].ring_radius.to(u.rad) \
* event.inst.optical_foclen[tel_id] * 2. # But not FC?
#rot_angle = 0.*u.deg
# if event.inst.optical_foclen[tel_id] > 10.*u.m and event.dl0.tel[tel_id].num_pixels != 1764:
#rot_angle = -100.14*u.deg
px, py = event.inst.pixel_pos[tel_id]
flen = event.inst.optical_foclen[tel_id]
camera_coord = CameraFrame(x=px,
y=py,
z=np.zeros(px.shape) * u.m,
focal_length=flen,
rotation=geom.pix_rotation)
nom_coord = camera_coord.transform_to(
NominalFrame(array_direction=altaz, pointing_direction=altaz))
#,focal_length = event.inst.optical_foclen[tel_id])) # tel['TelescopeTable_VersionFeb2016'][tel['TelescopeTable_VersionFeb2016']['TelID']==telid]['FL'][0]*u.m))
px = nom_coord.x.to(u.deg)
py = nom_coord.y.to(u.deg)
dist = np.sqrt(
np.power(px - muonparams[0].ring_center_x, 2) +
np.power(py - muonparams[0].ring_center_y, 2))
ring_dist = np.abs(dist - muonparams[0].ring_radius)
pixRmask = ring_dist < muonparams[0].ring_radius * 0.4
if muonparams[1] is not None:
signals *= muonparams[1].mask
camera1 = plotter.draw_camera(tel_id, signals, ax1)
cmaxmin = (max(signals) - min(signals))
if not cmaxmin:
cmaxmin = 1.
cmap_charge = colors.LinearSegmentedColormap.from_list(
'cmap_c', [(0 / cmaxmin, 'darkblue'),
(np.abs(min(signals)) / cmaxmin, 'black'),
(2.0 * np.abs(min(signals)) / cmaxmin, 'blue'),
(2.5 * np.abs(min(signals)) / cmaxmin, 'green'),
(1, 'yellow')])
camera1.pixels.set_cmap(cmap_charge)
if not colorbar:
camera1.add_colorbar(ax=ax1, label=" [photo-electrons]")
colorbar = camera1.colorbar
else:
camera1.colorbar = colorbar
camera1.update(True)
camera1.add_ellipse(centroid,
ringrad_camcoord.value,
ringrad_camcoord.value,
0.,
0.,
color="red")
# ax1.set_title("CT {} ({}) - Mean pixel charge"
# .format(tel_id, geom_dict[tel_id].cam_id))
if muonparams[1] is not None:
# continue #Comment this...(should ringwidthfrac also be *0.5?)
ringwidthfrac = muonparams[1].ring_width / muonparams[
0].ring_radius
ringrad_inner = ringrad_camcoord * (1. - ringwidthfrac)
ringrad_outer = ringrad_camcoord * (1. + ringwidthfrac)
camera1.add_ellipse(centroid,
ringrad_inner.value,
ringrad_inner.value,
0.,
0.,
color="magenta")
camera1.add_ellipse(centroid,
ringrad_outer.value,
ringrad_outer.value,
0.,
0.,
color="magenta")
npads = 2
ax2 = fig.add_subplot(1, npads, npads)
pred = muonparams[1].prediction
if len(pred) != np.sum(muonparams[1].mask):
print("Warning! Lengths do not match...len(pred)=",
len(pred), "len(mask)=", np.sum(muonparams[1].mask))
# Numpy broadcasting - fill in the shape
plotpred = np.zeros(image.shape)
plotpred[muonparams[1].mask == True] = pred
camera2 = plotter.draw_camera(tel_id, plotpred, ax2)
c2maxmin = (max(plotpred) - min(plotpred))
if not c2maxmin:
c2maxmin = 1.
c2map_charge = colors.LinearSegmentedColormap.from_list(
'c2map_c',
[(0 / c2maxmin, 'darkblue'),
(np.abs(min(plotpred)) / c2maxmin, 'black'),
(2.0 * np.abs(min(plotpred)) / c2maxmin, 'blue'),
(2.5 * np.abs(min(plotpred)) / c2maxmin, 'green'),
(1, 'yellow')])
camera2.pixels.set_cmap(c2map_charge)
if not colorbar2:
camera2.add_colorbar(ax=ax2, label=" [photo-electrons]")
colorbar2 = camera2.colorbar
else:
camera2.colorbar = colorbar2
camera2.update(True)
plt.pause(1.) # make shorter
# plt.pause(0.1)
# if pp is not None:
# pp.savefig(fig)
fig.savefig(
str(args.output_path) + "_" + str(event.dl0.event_id) + '.png')
plt.close()