def sky_to_camera(alt, az, focal, pointing_alt, pointing_az):
"""
Coordinate transform from aky position (alt, az) (in angles) to camera coordinates (x, y) in distance
Parameters
----------
alt: astropy Quantity
az: astropy Quantity
focal: astropy Quantity
pointing_alt: pointing altitude in angle unit
pointing_az: pointing altitude in angle unit
Returns
-------
"""
pointing_direction = HorizonFrame(alt=pointing_alt, az=pointing_az)
event_direction = HorizonFrame(alt=alt, az=az)
nom_frame = NominalFrame(array_direction=pointing_direction,
pointing_direction=pointing_direction)
event_dir_nom = event_direction.transform_to(nom_frame)
camera_pos = NominalFrame(
pointing_direction=pointing_direction,
x=event_dir_nom.x.to(u.rad).value * focal,
y=event_dir_nom.y.to(u.rad).value * focal,
)
# return focal * (event_dir_nom.x.to(u.rad).value, event_dir_nom.y.to(u.rad).value)
return camera_pos
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 test_intersection_nominal_reconstruction():
"""
Testing the reconstruction of the position in the nominal frame with a three-telescopes system.
This is done using a squared configuration, of which the impact point occupies a vertex,
ad the three telescopes the other three vertices.
"""
hill_inter = HillasIntersection()
delta = 1.0 * u.m
horizon_frame = AltAz()
altitude = 70 * u.deg
azimuth = 10 * u.deg
array_direction = SkyCoord(alt=altitude,
az=azimuth,
frame=horizon_frame)
nominal_frame = NominalFrame(origin=array_direction)
focal_length = 28 * u.m
camera_frame = CameraFrame(focal_length=focal_length,
telescope_pointing=array_direction)
cog_coords_camera_1 = SkyCoord(x=delta, y=0 * u.m, frame=camera_frame)
cog_coords_camera_2 = SkyCoord(x=delta / 0.7, y=delta / 0.7, frame=camera_frame)
cog_coords_camera_3 = SkyCoord(x=0 * u.m, y=delta, frame=camera_frame)
cog_coords_nom_1 = cog_coords_camera_1.transform_to(nominal_frame)
cog_coords_nom_2 = cog_coords_camera_2.transform_to(nominal_frame)
cog_coords_nom_3 = cog_coords_camera_3.transform_to(nominal_frame)
# x-axis is along the altitude and y-axis is along the azimuth
hillas_1 = HillasParametersContainer(x=cog_coords_nom_1.delta_alt,
y=cog_coords_nom_1.delta_az,
intensity=100,
psi=0 * u.deg)
hillas_2 = HillasParametersContainer(x=cog_coords_nom_2.delta_alt,
y=cog_coords_nom_2.delta_az,
intensity=100,
psi=45 * u.deg)
hillas_3 = HillasParametersContainer(x=cog_coords_nom_3.delta_alt,
y=cog_coords_nom_3.delta_az,
intensity=100,
psi=90 * u.deg)
hillas_dict = {1: hillas_1, 2: hillas_2, 3: hillas_3}
reco_nominal = hill_inter.reconstruct_nominal(hillas_parameters=hillas_dict)
nominal_pos = SkyCoord(
delta_az=u.Quantity(reco_nominal[0], u.rad),
delta_alt=u.Quantity(reco_nominal[1], u.rad),
frame=nominal_frame
)
np.testing.assert_allclose(nominal_pos.altaz.az.to_value(u.deg), azimuth.to_value(u.deg), atol=1e-8)
np.testing.assert_allclose(nominal_pos.altaz.alt.to_value(u.deg), altitude.to_value(u.deg), atol=1e-8)
def sky_to_camera(alt, az, focal, pointing_alt, pointing_az):
"""
Coordinate transform from aky position (alt, az) (in angles) to camera coordinates (x, y) in distance
Parameters
----------
alt: astropy Quantity
az: astropy Quantity
focal: astropy Quantity
pointing_alt: pointing altitude in angle unit
pointing_az: pointing altitude in angle unit
Returns
-------
"""
pointing_direction = SkyCoord(alt=pointing_alt,
az=pointing_az,
frame=horizon_frame)
camera_frame = CameraFrame(focal_length=focal,
telescope_pointing=pointing_direction)
event_direction = SkyCoord(alt=alt, az=az, frame=horizon_frame)
nom_frame = NominalFrame(origin=pointing_direction, )
camera_pos = event_direction.transform_to(camera_frame)
return camera_pos
def get_muon_center(geom, equivalent_focal_length):
"""
Get the x,y coordinates of the center of the muon ring
in the NominalFrame
Paramenters
---------
geom: CameraGeometry
equivalent_focal_length: Focal length of the telescope
Returns
---------
x, y: `floats` coordinates in the NominalFrame
"""
x, y = geom.pix_x, geom.pix_y
telescope_pointing = SkyCoord(alt=70 * u.deg, az=0 * u.deg, frame=AltAz())
camera_coord = SkyCoord(x=x,
y=y,
frame=CameraFrame(
focal_length=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)
return x, y
def get_prediction(self, tel_id, shower_reco, energy_reco):
horizon_seed = HorizonFrame(az=shower_reco.az, alt=shower_reco.alt)
nominal_seed = horizon_seed.transform_to(
NominalFrame(array_direction=horizon_seed))
source_x = nominal_seed.x.to(u.rad).value
source_y = nominal_seed.y.to(u.rad).value
ground = GroundFrame(x=shower_reco.core_x,
y=shower_reco.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
x_max = shower_reco.h_max / np.cos(zenith)
# Calculate expected Xmax given this energy
x_max_exp = guess_shower_depth(energy_reco.energy)
# Convert to binning of Xmax, addition of 100 can probably be removed
x_max_bin = x_max - x_max_exp
# Check for range
if x_max_bin > 250 * (u.g * u.cm**-2):
x_max_bin = 250 * (u.g * u.cm**-2)
if x_max_bin < -250 * (u.g * u.cm**-2):
x_max_bin = -250 * (u.g * u.cm**-2)
x_max_bin = x_max_bin.value
impact = np.sqrt(
pow(self.tel_pos_x[tel_id] - tilt_x, 2) +
pow(self.tel_pos_y[tel_id] - tilt_y, 2))
phi = np.arctan2((self.tel_pos_y[tel_id] - tilt_y),
(self.tel_pos_x[tel_id] - tilt_x))
pix_x_rot, pix_y_rot = self.rotate_translate(self.pixel_x[tel_id] * -1,
self.pixel_y[tel_id],
source_x, source_y, phi)
prediction = self.image_prediction(self.type[tel_id],
(90 * u.deg) - shower_reco.alt,
shower_reco.az,
energy_reco.energy.value, impact,
x_max_bin,
pix_x_rot * (180 / math.pi),
pix_y_rot * (180 / math.pi))
prediction *= self.scale[self.type[tel_id]]
# prediction *= self.pixel_area[tel_id]
prediction[prediction < 0] = 0
prediction[np.isnan(prediction)] = 0
return prediction
def test_array_draw():
filename = get_dataset("gamma_test.simtel.gz")
cam_geom = {}
source = hessio_event_source(filename, max_events=2)
r1 = HESSIOR1Calibrator()
dl0 = CameraDL0Reducer()
calibrator = CameraDL1Calibrator()
for event in source:
array_pointing = SkyCoord(
event.mcheader.run_array_direction[1] * u.rad,
event.mcheader.run_array_direction[0] * u.rad,
frame=AltAz)
# array_view = ArrayPlotter(instrument=event.inst,
# system=TiltedGroundFrame(
# pointing_direction=array_pointing))
hillas_dict = {}
r1.calibrate(event)
dl0.reduce(event)
calibrator.calibrate(event) # calibrate the events
# store MC pointing direction for the array
for tel_id in event.dl0.tels_with_data:
pmt_signal = event.dl1.tel[tel_id].image[0]
geom = deepcopy(event.inst.subarray.tel[tel_id].camera)
fl = event.inst.subarray.tel[tel_id].optics.equivalent_focal_length
# Transform the pixels positions into nominal coordinates
camera_coord = CameraFrame(x=geom.pix_x,
y=geom.pix_y,
z=np.zeros(geom.pix_x.shape) * u.m,
focal_length=fl,
rotation=90 * u.deg - geom.cam_rotation)
nom_coord = camera_coord.transform_to(
NominalFrame(array_direction=array_pointing,
pointing_direction=array_pointing))
geom.pix_x = nom_coord.x
geom.pix_y = nom_coord.y
mask = tailcuts_clean(geom,
pmt_signal,
picture_thresh=10.,
boundary_thresh=5.)
try:
moments = hillas_parameters(geom, pmt_signal * mask)
hillas_dict[tel_id] = moments
nom_coord = NominalPlotter(hillas_parameters=hillas_dict,
draw_axes=True)
nom_coord.draw_array()
except HillasParameterizationError as e:
print(e)
continue
def draw_tilted_surface(self, shower_seed, energy_seed,
bins=50, core_range=100 * u.m):
"""
Simple reconstruction for evaluating the likelihood in a grid across the
nominal system, fixing all values but the core position of the gamma rays.
Useful for checking the reconstruction performance of the algorithm
Parameters
----------
shower_seed: ReconstructedShowerContainer
Best fit ImPACT shower geometry
energy_seed: ReconstructedEnergyContainer
Best fit ImPACT energy
bins: int
Number of bins in surface evaluation
nominal_range: Quantity
Range over which to create likelihood surface
Returns
-------
ndarray, ndarray, ndarray:
Bin centres in X and Y coordinates and the values of the likelihood at each
position
"""
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[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)
tilt_y = tilted.y.to(u.m)
x_ground_list = np.linspace(tilt_x - core_range, tilt_x + core_range, num=bins)
y_ground_list = np.linspace(tilt_y - core_range, tilt_y + core_range, num=bins)
w = np.zeros([bins, bins])
zenith = 90*u.deg - self.array_direction.alt
for xb in range(bins):
for yb in range(bins):
x_max_scale = shower_seed.h_max / \
self.get_shower_max(source_x,
source_y,
x_ground_list[xb].value,
y_ground_list[yb].value,
zenith.to(u.rad).value)
w[xb][yb] = self.get_likelihood(source_x,
source_y,
x_ground_list[xb].value,
y_ground_list[yb].value,
energy_seed.energy.value, x_max_scale)
return x_ground_list, y_ground_list, w
def nominal_to_altaz():
nom = SkyCoord(
x=0 * u.deg,
y=0 * u.deg,
frame=NominalFrame(origin=AltAz(alt=75 * u.deg, az=180 * u.deg)))
alt_az = nom.transform_to(AltAz())
print("HorizonCoordinate", alt_az)
def test_intersection_xmax_reco():
"""
Test the reconstruction of xmax with two LSTs that are pointing at zenith = 0.
The telescopes are places along the x and y axis at the same distance from the center.
The impact point is hard-coded to be happening in the center of this cartesian system.
"""
hill_inter = HillasIntersection()
horizon_frame = AltAz()
zen_pointing = 10 * u.deg
array_direction = SkyCoord(alt=90 * u.deg - zen_pointing,
az=0 * u.deg,
frame=horizon_frame)
nom_frame = NominalFrame(origin=array_direction)
source_sky_pos_reco = SkyCoord(alt=90 * u.deg - zen_pointing,
az=0 * u.deg,
frame=horizon_frame)
nom_pos_reco = source_sky_pos_reco.transform_to(nom_frame)
delta = 1.0 * u.m
# LST focal length
focal_length = 28 * u.m
hillas_dict = {
1:
HillasParametersContainer(
x=-(delta / focal_length) * u.rad,
y=((0 * u.m) / focal_length) * u.rad,
intensity=1,
),
2:
HillasParametersContainer(
x=((0 * u.m) / focal_length) * u.rad,
y=-(delta / focal_length) * u.rad,
intensity=1,
),
}
x_max = hill_inter.reconstruct_xmax(
source_x=nom_pos_reco.fov_lon,
source_y=nom_pos_reco.fov_lat,
core_x=0 * u.m,
core_y=0 * u.m,
hillas_parameters=hillas_dict,
tel_x={
1: (150 * u.m),
2: (0 * u.m)
},
tel_y={
1: (0 * u.m),
2: (150 * u.m)
},
zen=zen_pointing,
)
print(x_max)
def cam_to_nom():
pix = [np.ones(2048), np.ones(2048), np.zeros(2048)] * u.m
camera_coord = CameraFrame(pix)
# In this case we bypass the telescope system
nom_coord = camera_coord.transform_to(
NominalFrame(array_direction=[75 * u.deg, 180 * u.deg],
pointing_direction=[70 * u.deg, 180 * u.deg],
focal_length=15 * u.m))
print("Nominal Coordinate", nom_coord)
def __init__(self, config, tool, event, **kwargs):
super().__init__(config=config, tool=tool, **kwargs)
self.camera_geom_dict = {}
self.nominal_geom_dict = {}
self.inst = event.inst
array_pointing = HorizonFrame(
alt=event.mcheader.run_array_direction[1] * u.rad,
az=event.mcheader.run_array_direction[0] * u.rad)
self.nom_system = NominalFrame(array_direction=array_pointing,
pointing_direction=array_pointing)
def get_prediction(self, tel_id, shower_reco, energy_reco):
horizon_seed = HorizonFrame(az=shower_reco.az, alt=shower_reco.alt)
nominal_seed = horizon_seed.transform_to(
NominalFrame(array_direction=horizon_seed))
source_x = nominal_seed.x.to(u.rad).value
source_y = nominal_seed.y.to(u.rad).value
print(self.array_direction[0])
ground = GroundFrame(x=shower_reco.core_x,
y=shower_reco.core_y,
z=0 * u.m)
tilted = ground.transform_to(
TiltedGroundFrame(pointing_direction=HorizonFrame(
alt=self.array_direction[0], az=self.array_direction[1])))
tilt_x = tilted.x.to(u.m).value
tilt_y = tilted.y.to(u.m).value
zenith = 90 * u.deg - self.array_direction[0]
azimuth = self.array_direction[1]
x_max_exp = 300 + 93 * np.log10(energy_reco.energy.value)
x_max = shower_reco.h_max / np.cos(zenith)
# Convert to binning of Xmax, addition of 100 can probably be removed
x_max_bin = x_max.value - x_max_exp
if x_max_bin > 100:
x_max_bin = 100
if x_max_bin < -100:
x_max_bin = -100
impact = np.sqrt(
pow(self.tel_pos_x[tel_id] - tilt_x, 2) +
pow(self.tel_pos_y[tel_id] - tilt_y, 2))
phi = np.arctan2((self.tel_pos_y[tel_id] - tilt_y),
(self.tel_pos_x[tel_id] - tilt_x))
pix_x_rot, pix_y_rot = self.rotate_translate(self.pixel_x[tel_id] * -1,
self.pixel_y[tel_id],
source_x, source_y, phi)
prediction = self.image_prediction(self.type[tel_id], 20 * u.deg,
0 * u.deg, energy_reco.energy.value,
impact, x_max_bin,
pix_x_rot * (180 / math.pi),
pix_y_rot * (180 / math.pi))
prediction *= self.scale[self.type[tel_id]]
prediction[prediction < 0] = 0
prediction[np.isnan(prediction)] = 0
return prediction
def test_cam_to_nominal():
from ctapipe.coordinates import CameraFrame, NominalFrame
telescope_pointing = SkyCoord(alt=70 * u.deg, az=0 * u.deg, frame=AltAz())
array_pointing = SkyCoord(alt=72 * u.deg, az=0 * u.deg, frame=AltAz())
cam_frame = CameraFrame(focal_length=28 * u.m,
telescope_pointing=telescope_pointing)
cam = SkyCoord(x=0.5 * u.m, y=0.1 * u.m, frame=cam_frame)
nom_frame = NominalFrame(origin=array_pointing)
cam.transform_to(nom_frame)
def to_nominal_frame(self, alt, az):
alt_LST = 70 # deg
if self.plike:
alt_LST = 69.6 # deg
az_LST = 180 # deg
point = AltAz(alt=alt_LST * u.deg, az=az_LST * u.deg)
# alt = np.array(indexes['alt']) # altitude
# az = np.array(indexes['az']) # azimuth
# print("\nalt shape: {}, az shape: {}".format(indexes['alt'].shape, indexes['az'].shape))
src = AltAz(alt=alt * u.deg, az=az * u.deg)
source_direction = src.transform_to(NominalFrame(origin=point))
delta_alt = source_direction.delta_alt.deg
delta_az = source_direction.delta_az.deg
return delta_alt, delta_az
def cam_to_nom():
pix = [np.ones(2048), np.ones(2048), np.zeros(2048)] * u.m
camera_coord = CameraFrame(pix, focal_length=15 * u.m)
# In this case we bypass the telescope system
nom_coord = camera_coord.transform_to(
NominalFrame(
pointing_direction=HorizonFrame(alt=70 * u.deg, az=180 * u.deg),
array_direction=HorizonFrame(alt=75 * u.deg, az=180 * u.deg)
)
)
alt_az = camera_coord.transform_to(
HorizonFrame(
pointing_direction=HorizonFrame(alt=70 * u.deg, az=180 * u.deg),
array_direction=HorizonFrame(alt=75 * u.deg, az=180 * u.deg)
)
)
print("Nominal Coordinate", nom_coord)
print("AltAz coordinate", alt_az)
def cam_to_nom():
pix_x = np.ones(2048) * u.m
pix_y = np.ones(2048) * u.m
pointing_direction = SkyCoord(alt=70 * u.deg,
az=180 * u.deg,
frame=AltAz())
camera_frame = CameraFrame(focal_length=15 * u.m,
telescope_pointing=pointing_direction)
camera_coord = SkyCoord(pix_x, pix_y, frame=camera_frame)
# In this case we bypass the telescope system
nominal_frame = NominalFrame(origin=AltAz(alt=75 * u.deg, az=180 * u.deg))
nom_coord = camera_coord.transform_to(nominal_frame)
horizon = camera_coord.transform_to(AltAz())
print("Nominal Coordinate", nom_coord)
print("Horizon coordinate", horizon)
def get_event_pos_in_camera(event, tel):
"""
Return the position of the source in the camera frame
Parameters
----------
event: `ctapipe.io.containers.DataContainer`
tel: `ctapipe.instruement.telescope.TelescopeDescription`
Returns
-------
(x, y) (float, float): position in the camera
"""
array_pointing = HorizonFrame(alt=event.mcheader.run_array_direction[1],
az=event.mcheader.run_array_direction[0])
event_direction = HorizonFrame(alt=event.mc.alt.to(u.rad),
az=event.mc.az.to(u.rad))
nom_frame = NominalFrame(array_direction=array_pointing,
pointing_direction=array_pointing)
event_dir_nom = event_direction.transform_to(nom_frame)
focal = tel.optics.equivalent_focal_length
return focal * (event_dir_nom.x.to(u.rad).value, event_dir_nom.y.to(u.rad).value)
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 predict(self,
hillas_dict,
inst,
array_pointing,
telescopes_pointings=None):
"""
Parameters
----------
hillas_dict: dict
Dictionary containing Hillas parameters for all telescopes
in reconstruction
inst : ctapipe.io.InstrumentContainer
instrumental description
array_pointing: SkyCoord[AltAz]
pointing direction of the array
telescopes_pointings: dict[SkyCoord[AltAz]]
dictionary of pointing direction per each telescope
Returns
-------
ReconstructedShowerContainer:
"""
# filter warnings for missing obs time. this is needed because MC data has no obs time
warnings.filterwarnings(action='ignore',
category=MissingFrameAttributeWarning)
# stereoscopy needs at least two telescopes
if len(hillas_dict) < 2:
raise TooFewTelescopesException(
"need at least two telescopes, have {}".format(
len(hillas_dict)))
# check for np.nan or 0 width's as these screw up weights
if any(
[np.isnan(hillas_dict[tel]['width'].value)
for tel in hillas_dict]):
raise InvalidWidthException(
"A HillasContainer contains an ellipse of width==np.nan")
if any([hillas_dict[tel]['width'].value == 0 for tel in hillas_dict]):
raise InvalidWidthException(
"A HillasContainer contains an ellipse of width==0")
if telescopes_pointings is None:
telescopes_pointings = {
tel_id: array_pointing
for tel_id in hillas_dict.keys()
}
tilted_frame = TiltedGroundFrame(pointing_direction=array_pointing)
ground_positions = inst.subarray.tel_coords
grd_coord = GroundFrame(x=ground_positions.x,
y=ground_positions.y,
z=ground_positions.z)
tilt_coord = grd_coord.transform_to(tilted_frame)
tel_x = {
tel_id: tilt_coord.x[tel_id - 1]
for tel_id in list(hillas_dict.keys())
}
tel_y = {
tel_id: tilt_coord.y[tel_id - 1]
for tel_id in list(hillas_dict.keys())
}
nom_frame = NominalFrame(origin=array_pointing)
hillas_dict_mod = copy.deepcopy(hillas_dict)
for tel_id, hillas in hillas_dict_mod.items():
# prevent from using rads instead of meters as inputs
assert hillas.x.to(u.m).unit == u.Unit('m')
focal_length = inst.subarray.tel[
tel_id].optics.equivalent_focal_length
camera_frame = CameraFrame(
telescope_pointing=telescopes_pointings[tel_id],
focal_length=focal_length,
)
cog_coords = SkyCoord(x=hillas.x, y=hillas.y, frame=camera_frame)
cog_coords_nom = cog_coords.transform_to(nom_frame)
hillas.x = cog_coords_nom.delta_alt
hillas.y = cog_coords_nom.delta_az
src_x, src_y, err_x, err_y = self.reconstruct_nominal(hillas_dict_mod)
core_x, core_y, core_err_x, core_err_y = self.reconstruct_tilted(
hillas_dict_mod, tel_x, tel_y)
err_x *= u.rad
err_y *= u.rad
nom = SkyCoord(delta_az=src_x * u.rad,
delta_alt=src_y * u.rad,
frame=nom_frame)
# nom = sky_pos.transform_to(nom_frame)
sky_pos = nom.transform_to(array_pointing.frame)
result = ReconstructedShowerContainer()
result.alt = sky_pos.altaz.alt.to(u.rad)
result.az = sky_pos.altaz.az.to(u.rad)
tilt = SkyCoord(
x=core_x * u.m,
y=core_y * u.m,
frame=tilted_frame,
)
grd = project_to_ground(tilt)
result.core_x = grd.x
result.core_y = grd.y
x_max = self.reconstruct_xmax(
nom.delta_az,
nom.delta_alt,
tilt.x,
tilt.y,
hillas_dict_mod,
tel_x,
tel_y,
90 * u.deg - array_pointing.alt,
)
result.core_uncert = np.sqrt(core_err_x**2 + core_err_y**2) * u.m
result.tel_ids = [h for h in hillas_dict_mod.keys()]
result.average_intensity = np.mean(
[h.intensity for h in hillas_dict_mod.values()])
result.is_valid = True
src_error = np.sqrt(err_x**2 + err_y**2)
result.alt_uncert = src_error.to(u.rad)
result.az_uncert = src_error.to(u.rad)
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, 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 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 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 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: AltAz
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 = SkyCoord(x=src_x * u.rad,
y=src_y * u.rad,
frame=NominalFrame(array_direction=array_direction))
horiz = nom.transform_to(AltAz())
result = ReconstructedShowerContainer()
result.alt, result.az = horiz.alt, horiz.az
tilt = SkyCoord(
x=core_x * u.m,
y=core_y * u.m,
frame=TiltedGroundFrame(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**2 + core_err_y**2) * u.m
result.tel_ids = [h for h in hillas_parameters.keys()]
result.average_intensity = np.mean(
[h.intensity for h in hillas_parameters.values()])
result.is_valid = True
src_error = np.sqrt(err_x**2 + err_y**2)
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 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)
plt.pause(0.01)
if args.write:
plt.savefig('CT{:03d}_EV{:010d}_S{:02d}.png'
.format(args.tel, event.dl0.event_id, ii))
else:
# display integrated event:
im = event.dl0.tel[args.tel].adc_sums[args.channel]
im = apply_mc_calibration(im, args.tel)
disp.image = im
if args.hillas:
clean_mask = reco.cleaning.tailcuts_clean(geom,im,1,picture_thresh=10,boundary_thresh=5)
camera_coord = CameraFrame(x=x,y=y,z=np.zeros(x.shape)*u.m)
nom_coord = camera_coord.transform_to(NominalFrame(array_direction=[70*u.deg,0*u.deg],
pointing_direction=[70*u.deg,0*u.deg],
focal_length=tel['TelescopeTable_VersionFeb2016'][tel['TelescopeTable_VersionFeb2016']['TelID']==args.tel]['FL'][0]*u.m))
image = np.asanyarray(im * clean_mask, dtype=np.float64)
nom_x = nom_coord.x
nom_y = nom_coord.y
hillas = reco.hillas_parameters(x,y,im * clean_mask)
hillas_nom = reco.hillas_parameters(nom_x,nom_y,im * clean_mask)
print (hillas)
print (hillas_nom)
disp.image = im * clean_mask
disp.overlay_moments(hillas, color='seagreen', linewidth=3)
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
cleaning_level = {
'LSTCam': (3.5, 7.5, 2), # ?? (3, 6) for Abelardo...
'FlashCam': (4, 8, 2), # there is some scaling missing?
'ASTRICam': (5, 7, 2),
}
input_url = get_dataset_path('gamma_test_large.simtel.gz')
with event_source(input_url=input_url) as source:
calibrator = CameraCalibrator(eventsource=source, )
for event in source:
calibrator.calibrate(event)
nominal_frame = NominalFrame(
origin=SkyCoord(alt=70 * u.deg, az=0 * u.deg, frame=HorizonFrame))
nom_delta_az = []
nom_delta_alt = []
photons = []
for tel_id, dl1 in event.dl1.tel.items():
camera = event.inst.subarray.tels[tel_id].camera
focal_length = event.inst.subarray.tels[
tel_id].optics.equivalent_focal_length
image = dl1.image[0]
# telescope mc info
mc_tel = event.mc.tel[tel_id]
telescope_pointing = SkyCoord(
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, hillas_dict, subarray, array_pointing, telescopes_pointings=None):
"""
Parameters
----------
hillas_dict: dict
Dictionary containing Hillas parameters for all telescopes
in reconstruction
inst : ctapipe.io.InstrumentContainer
instrumental description
array_pointing: SkyCoord[AltAz]
pointing direction of the array
telescopes_pointings: dict[SkyCoord[AltAz]]
dictionary of pointing direction per each telescope
Returns
-------
ReconstructedShowerContainer:
"""
# filter warnings for missing obs time. this is needed because MC data has no obs time
warnings.filterwarnings(action="ignore", category=MissingFrameAttributeWarning)
# stereoscopy needs at least two telescopes
if len(hillas_dict) < 2:
raise TooFewTelescopesException(
"need at least two telescopes, have {}".format(len(hillas_dict))
)
# check for np.nan or 0 width's as these screw up weights
if any([np.isnan(hillas_dict[tel]["width"].value) for tel in hillas_dict]):
raise InvalidWidthException(
"A HillasContainer contains an ellipse of width==np.nan"
)
if any([hillas_dict[tel]["width"].value == 0 for tel in hillas_dict]):
raise InvalidWidthException(
"A HillasContainer contains an ellipse of width==0"
)
if telescopes_pointings is None:
telescopes_pointings = {
tel_id: array_pointing for tel_id in hillas_dict.keys()
}
tilted_frame = TiltedGroundFrame(pointing_direction=array_pointing)
grd_coord = subarray.tel_coords
tilt_coord = grd_coord.transform_to(tilted_frame)
tel_ids = list(hillas_dict.keys())
tel_indices = subarray.tel_ids_to_indices(tel_ids)
tel_x = {
tel_id: tilt_coord.x[tel_index]
for tel_id, tel_index in zip(tel_ids, tel_indices)
}
tel_y = {
tel_id: tilt_coord.y[tel_index]
for tel_id, tel_index in zip(tel_ids, tel_indices)
}
nom_frame = NominalFrame(origin=array_pointing)
hillas_dict_mod = {}
for tel_id, hillas in hillas_dict.items():
if isinstance(hillas, CameraHillasParametersContainer):
focal_length = subarray.tel[tel_id].optics.equivalent_focal_length
camera_frame = CameraFrame(
telescope_pointing=telescopes_pointings[tel_id],
focal_length=focal_length,
)
cog_coords = SkyCoord(x=hillas.x, y=hillas.y, frame=camera_frame)
cog_coords_nom = cog_coords.transform_to(nom_frame)
else:
telescope_frame = TelescopeFrame(
telescope_pointing=telescopes_pointings[tel_id]
)
cog_coords = SkyCoord(
fov_lon=hillas.fov_lon,
fov_lat=hillas.fov_lat,
frame=telescope_frame,
)
cog_coords_nom = cog_coords.transform_to(nom_frame)
hillas_dict_mod[tel_id] = HillasParametersContainer(
fov_lon=cog_coords_nom.fov_lon,
fov_lat=cog_coords_nom.fov_lat,
psi=hillas.psi,
width=hillas.width,
length=hillas.length,
intensity=hillas.intensity,
)
src_fov_lon, src_fov_lat, err_fov_lon, err_fov_lat = self.reconstruct_nominal(
hillas_dict_mod
)
core_x, core_y, core_err_x, core_err_y = self.reconstruct_tilted(
hillas_dict_mod, tel_x, tel_y
)
err_fov_lon *= u.rad
err_fov_lat *= u.rad
nom = SkyCoord(
fov_lon=src_fov_lon * u.rad, fov_lat=src_fov_lat * u.rad, frame=nom_frame
)
sky_pos = nom.transform_to(array_pointing.frame)
tilt = SkyCoord(x=core_x * u.m, y=core_y * u.m, frame=tilted_frame)
grd = project_to_ground(tilt)
x_max = self.reconstruct_xmax(
nom.fov_lon,
nom.fov_lat,
tilt.x,
tilt.y,
hillas_dict_mod,
tel_x,
tel_y,
90 * u.deg - array_pointing.alt,
)
src_error = np.sqrt(err_fov_lon ** 2 + err_fov_lat ** 2)
result = ReconstructedGeometryContainer(
alt=sky_pos.altaz.alt.to(u.rad),
az=sky_pos.altaz.az.to(u.rad),
core_x=grd.x,
core_y=grd.y,
core_uncert=u.Quantity(np.sqrt(core_err_x ** 2 + core_err_y ** 2), u.m),
tel_ids=[h for h in hillas_dict_mod.keys()],
average_intensity=np.mean([h.intensity for h in hillas_dict_mod.values()]),
is_valid=True,
alt_uncert=src_error.to(u.rad),
az_uncert=src_error.to(u.rad),
h_max=x_max,
h_max_uncert=u.Quantity(np.nan * x_max.unit),
goodness_of_fit=np.nan,
)
return 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
table = "CameraTable_VersionFeb2016_TelID"
for tel_id in container.dl0.tels_with_data:
x, y = event.meta.pixel_pos[tel_id]
if geom == 0:
geom = io.CameraGeometry.guess(x, y,event.meta.optical_foclen[tel_id])
image = apply_mc_calibration(event.dl0.tel[tel_id].adc_sums[0], tel_id)
if image.shape[0] >1000:
continue
clean_mask = tailcuts_clean(geom,image,1,picture_thresh=5,boundary_thresh=7)
camera_coord = CameraFrame(x=x,y=y,z=np.zeros(x.shape)*u.m)
nom_coord = camera_coord.transform_to(NominalFrame(array_direction=[container.mc.alt,container.mc.az],
pointing_direction=[container.mc.alt,container.mc.az],
focal_length=tel['TelescopeTable_VersionFeb2016'][tel['TelescopeTable_VersionFeb2016']['TelID']==tel_id]['FL'][0]*u.m))
x = nom_coord.x.to(u.deg)
y = nom_coord.y.to(u.deg)
img = image*clean_mask
noise = 5
weight = img / (img+noise)
centre_x,centre_y,radius = chaudhuri_kundu_circle_fit(x,y,image*clean_mask)
dist = np.sqrt(np.power(x-centre_x,2) + np.power(y-centre_y,2))
ring_dist = np.abs(dist-radius)
centre_x,centre_y,radius = chaudhuri_kundu_circle_fit(x,y,image*(ring_dist<radius*0.3))
dist = np.sqrt(np.power(x-centre_x,2) + np.power(y-centre_y,2))
def set_event_properties(
self,
image,
time,
pixel_x,
pixel_y,
type_tel,
tel_x,
tel_y,
array_direction,
hillas,
):
"""The setter class is used to set the event properties within this
class before minimisation can take place. This simply copies a
bunch of useful properties to class members, so that we can
use them later without passing all this information around.
Parameters
----------
image: dict
Amplitude of pixels in camera images
time: dict
Time information per each pixel in camera images
pixel_x: dict
X position of pixels in nominal system
pixel_y: dict
Y position of pixels in nominal system
type_tel: dict
Type of telescope
tel_x: dict
X position of telescope in TiltedGroundFrame
tel_y: dict
Y position of telescope in TiltedGroundFrame
array_direction: SkyCoord[AltAz]
Array pointing direction in the AltAz Frame
hillas: dict
dictionary with telescope IDs as key and
HillasParametersContainer instances as values
Returns
-------
None
"""
# First store these parameters in the class so we can use them
# in minimisation For most values this is simply copying
self.image = image
self.tel_pos_x = np.zeros(len(tel_x))
self.tel_pos_y = np.zeros(len(tel_x))
self.ped = np.zeros(len(tel_x))
self.tel_types, self.tel_id = list(), list()
max_pix_x = 0
px, py, pa, pt = list(), list(), list(), list()
self.hillas_parameters = list()
# So here we must loop over the telescopes
for x, i in zip(tel_x, range(len(tel_x))):
px.append(pixel_x[x].to(u.rad).value)
if len(px[i]) > max_pix_x:
max_pix_x = len(px[i])
py.append(pixel_y[x].to(u.rad).value)
pa.append(image[x])
pt.append(time[x])
self.tel_pos_x[i] = tel_x[x].to(u.m).value
self.tel_pos_y[i] = tel_y[x].to(u.m).value
self.ped[i] = self.ped_table[type_tel[x]]
self.tel_types.append(type_tel[x])
self.tel_id.append(x)
self.hillas_parameters.append(hillas[x])
# Most interesting stuff is now copied to the class, but to remove our requirement
# for loops we must copy the pixel positions to an array with the length of the
# largest image
# First allocate everything
shape = (len(tel_x), max_pix_x)
self.pixel_x, self.pixel_y = ma.zeros(shape), ma.zeros(shape)
self.image, self.time, self.ped = (
ma.zeros(shape),
ma.zeros(shape),
ma.zeros(shape),
)
self.tel_types = np.array(self.tel_types)
# Copy everything into our masked arrays
for i in range(len(tel_x)):
array_len = len(px[i])
self.pixel_x[i][:array_len] = px[i]
self.pixel_y[i][:array_len] = py[i]
self.image[i][:array_len] = pa[i]
self.time[i][:array_len] = pt[i]
self.ped[i][:array_len] = self.ped_table[self.tel_types[i]]
# Set the image mask
mask = self.image == 0.0
self.pixel_x[mask], self.pixel_y[mask] = ma.masked, ma.masked
self.image[mask] = ma.masked
self.time[mask] = ma.masked
self.array_direction = array_direction
self.nominal_frame = NominalFrame(origin=self.array_direction)
# Finally run some functions to get ready for the event
self.get_hillas_mean()
self.initialise_templates(type_tel)
