def grd_to_tilt():
grd_coord = GroundFrame(x=1 * u.m, y=2 * u.m, z=0 * u.m)
tilt_coord = grd_coord.transform_to(
TiltedGroundFrame(
pointing_direction=AltAz(alt=90 * u.deg, az=180 * u.deg)))
print(project_to_ground(tilt_coord))
print("Tilted Coordinate", tilt_coord)
def grd_to_tilt():
grd_coord = GroundFrame(x=1 * u.m, y=2 * u.m, z=0 * u.m)
tilt_coord = grd_coord.transform_to(
TiltedGroundFrame(
pointing_direction=HorizonFrame(alt=90 * u.deg, az=180 * u.deg)
)
)
print(project_to_ground(tilt_coord))
print("Tilted Coordinate", tilt_coord)
def plot_hillas_lines(self, hillas_dict, length, frame="ground"):
# xx, yy, zz = np.array(spherical_to_cartesian(
# 1,
# self.array_pointing.alt,
# 0 * u.deg)
# ) # -self.array_pointing.az))
# # plane = Plane(Point3D(0, 0, 0), normal_vector=(xx, yy, zz))
tilted_system = self.tilted_frame
tel_coords_gnd = self.subarray.tel_coords
tilt_tel_pos = tel_coords_gnd.transform_to(tilted_system)
for tel_id in self.tel_ids:
moments = hillas_dict[tel_id]
if frame == "ground":
color = [255, 0, 0]
gnd_pos = project_to_ground(
tilt_tel_pos[self.subarray.tel_indices[tel_id]])
tel_x_pos = gnd_pos.x.to_value(u.m)
tel_y_pos = gnd_pos.y.to_value(u.m)
tel_z_pos = gnd_pos.z.to_value(u.m)
elif frame == "tilted":
color = [0, 255, 0]
# i need those coordinates in GroundFrame to transform to TiltedGroundFrame
# The selection is done afterwards
tel_x_pos = tilt_tel_pos[
self.subarray.tel_indices[tel_id]].x.to_value(u.m)
tel_y_pos = tilt_tel_pos[
self.subarray.tel_indices[tel_id]].y.to_value(u.m)
tel_z_pos = 0
# tilted_tel_pos = tilt_tel_pos[self.subarray.tel_indices[tel_id]]
hillas_line_actor = hillas_lines(
moments=moments,
length=length,
tel_coords=[tel_x_pos, tel_y_pos, tel_z_pos],
frame=frame,
array_pointing=self.array_pointing,
tilted_frame=tilted_system,
# plane=plane,
)
# if frame == "tilted":
# hillas_line_actor.RotateZ(0)#self.array_pointing.az.value)
# hillas_line_actor.RotateY(0)#90 - self.array_pointing.alt.value)
# self.tel_id[tel_id].append(hillas_line_actor)
hillas_line_actor.GetProperty().SetColor(color)
self.ren.AddActor(hillas_line_actor)
def estimate_core_position(self, hillas_dict, array_pointing):
"""
Estimate the core position by intersection the major ellipse lines of each telescope.
Parameters
-----------
hillas_dict: dict[HillasContainer]
dictionary of hillas moments
array_pointing: SkyCoord[HorizonFrame]
Pointing direction of the array
Returns
-----------
core_x: u.Quantity
estimated x position of impact
core_y: u.Quantity
estimated y position of impact
"""
if self.divergent_mode:
psi = u.Quantity(list(self.corrected_angle_dict.values()))
else:
psi = u.Quantity([h.psi for h in hillas_dict.values()])
z = np.zeros(len(psi))
uvw_vectors = np.column_stack([np.cos(psi).value, np.sin(psi).value, z])
tilted_frame = TiltedGroundFrame(pointing_direction=array_pointing)
ground_frame = GroundFrame()
positions = [
(
SkyCoord(*plane.pos, frame=ground_frame)
.transform_to(tilted_frame)
.cartesian.xyz
)
for plane in self.hillas_planes.values()
]
core_position = line_line_intersection_3d(uvw_vectors, positions)
core_pos_tilted = SkyCoord(
x=core_position[0] * u.m,
y=core_position[1] * u.m,
frame=tilted_frame
)
core_pos = project_to_ground(core_pos_tilted)
return core_pos.x, core_pos.y
def estimate_core_position(self, hillas_dict, telescope_pointing):
'''
Estimate the core position by intersection the major ellipse lines of each telescope.
Parameters
-----------
hillas_dict: dict[HillasContainer]
dictionary of hillas moments
telescope_pointing: SkyCoord[HorizonFrame]
Pointing direction of the array
Returns
-----------
core_x: u.Quantity
estimated x position of impact
core_y: u.Quantity
estimated y position of impact
'''
psi = u.Quantity([h.psi for h in hillas_dict.values()])
z = np.zeros(len(psi))
uvw_vectors = np.column_stack([np.cos(psi).value, np.sin(psi).value, z])
tilted_frame = TiltedGroundFrame(pointing_direction=telescope_pointing)
ground_frame = GroundFrame()
positions = [
(
SkyCoord(*plane.pos, frame=ground_frame)
.transform_to(tilted_frame)
.cartesian.xyz
)
for plane in self.hillas_planes.values()
]
core_position = line_line_intersection_3d(uvw_vectors, positions)
core_pos_tilted = SkyCoord(
x=core_position[0] * u.m,
y=core_position[1] * u.m,
frame=tilted_frame
)
core_pos = project_to_ground(core_pos_tilted)
return core_pos.x, core_pos.y
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 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_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 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 = SkyCoord(
az=shower_seed.az, alt=shower_seed.alt, frame=HorizonFrame()
)
nominal_seed = horizon_seed.transform_to(
NominalFrame(origin=self.array_direction)
)
source_x = nominal_seed.delta_az.to_value(u.rad)
source_y = nominal_seed.delta_alt.to_value(u.rad)
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 = SkyCoord(
x=fit_params[0] * u.rad,
y=fit_params[1] * u.rad,
frame=NominalFrame(origin=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 hillas_lines(moments,
length,
tel_coords,
frame,
array_pointing,
tilted_frame,
plane=None):
angle_in = moments.psi.to_value(u.rad)
# a = Point3D(1, 0, 0)
# b = Point3D(np.cos(angle), np.sin(angle), 0)
if frame == "tilted":
angle = angle_in + np.pi / 2.
# origin = Point3D(0, 0, 0)
# line_a = Ray3D(origin, a)
# line_b = Ray3D(origin, b)
# angle_tilt = line_b.angle_between(line_a)
# angle_tilted = (float(angle_tilt.evalf()) * u.rad).to_value(u.deg)
a = np.array([1, 0, 0])
b = np.array([np.cos(angle), np.sin(angle), 0])
angle_tilt_np = u.Quantity(angle_util(a, b), u.rad).to_value(u.deg)
psi = angle_tilt_np
elif frame == "ground":
# b_in_plane = plane.projection(b)
# a_in_plane = plane.projection(a)
# line_a_plane = Ray3D(origin, a_in_plane)
# line_b_plane = Ray3D(origin, b_in_plane)
# angle_gnd = line_b_plane.angle_between(line_a_plane)
# angle_ground = (float(angle_gnd.evalf()) * u.rad).to_value(u.deg)
# psi = angle_ground - array_pointing.az.to_value(u.deg)
if angle_in < 0:
angle_in = angle_in + np.pi
angle = angle_in
fix_a_in_plane = SkyCoord(x=1 * u.m, y=0 * u.m, frame=tilted_frame)
fix_b_in_plane = SkyCoord(x=np.cos(angle) * u.m,
y=np.sin(angle) * u.m,
frame=tilted_frame)
angle_fix_np = u.Quantity(
angle_util(
project_to_ground(fix_a_in_plane).cartesian.xyz.to_value(u.m),
project_to_ground(fix_b_in_plane).cartesian.xyz.to_value(u.m)),
u.rad).to_value(u.deg)
psi = angle_fix_np - array_pointing.az.to_value(u.deg) + 90
cylinder = vtk.vtkCylinderSource()
cylinder.SetResolution(4)
cylinder.SetRadius(1)
cylinder.SetHeight(length)
cylinder.Update()
rotate_cylinder = rotate_object(cylinder, 'z', psi)
mapper = vtk.vtkPolyDataMapper()
mapper.SetInputConnection(rotate_cylinder.GetOutputPort())
actor = vtk.vtkActor()
actor.SetMapper(mapper)
transform = vtk.vtkTransform()
transform.PostMultiply()
transform.Translate(*tel_coords)
if frame == "tilted":
transform.RotateY(90 - array_pointing.alt.value)
transform.RotateZ(-array_pointing.az.value)
actor.SetUserTransform(transform)
return actor
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):
"""Predict method for the ImPACT reconstructor.
Used to calculate the reconstructed ImPACT shower geometry and energy.
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:
"""
self.reset_interpolator()
horizon_seed = SkyCoord(az=shower_seed.az,
alt=shower_seed.alt,
frame=AltAz())
nominal_seed = horizon_seed.transform_to(self.nominal_frame)
source_x = nominal_seed.fov_lon.to_value(u.rad)
source_y = nominal_seed.fov_lat.to_value(u.rad)
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
seeds = spread_line_seed(
self.hillas_parameters,
self.tel_pos_x,
self.tel_pos_y,
source_x,
source_y,
tilt_x,
tilt_y,
energy_seed.energy.value,
shift_frac=[1],
)[0]
# Perform maximum likelihood fit
fit_params, errors, like = self.minimise(
params=seeds[0],
step=seeds[1],
limits=seeds[2],
minimiser_name=self.minimiser_name,
)
# Create a container class for reconstructed shower
shower_result = ReconstructedGeometryContainer()
# Convert the best fits direction and core to Horizon and ground systems and
# copy to the shower container
nominal = SkyCoord(
fov_lon=fit_params[0] * u.rad,
fov_lat=fit_params[1] * u.rad,
frame=self.nominal_frame,
)
horizon = nominal.transform_to(AltAz())
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 available 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, 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
----------
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 estimate_core_position(self, event, hillas_dict, array_pointing,
corrected_angle_dict, hillas_planes):
"""
Estimate the core position by intersection the major ellipse lines of each telescope.
Parameters
-----------
hillas_dict: dict[HillasContainer]
dictionary of hillas moments
array_pointing: SkyCoord[HorizonFrame]
Pointing direction of the array
Returns
-----------
core_x: u.Quantity
estimated x position of impact
core_y: u.Quantity
estimated y position of impact
Notes
-----
The part of the algorithm taking into account divergent pointing
mode and the usage of a corrected psi angle is explained in [gasparetto]_
section 7.1.4.
"""
# Since psi has been recalculated in the fake CameraFrame
# it doesn't need any further corrections because it is now independent
# of both pointing and cleaning/parametrization frame.
# This angle will be used to visualize the telescope-wise directions of
# the shower core the ground.
psi_core = corrected_angle_dict
# Record these values
for tel_id in hillas_dict.keys():
event.dl1.tel[tel_id].parameters.core.psi = psi_core[tel_id]
# Transform them for numpy
psi = u.Quantity(list(psi_core.values()))
# Estimate the position of the shower's core
# from the TiltedFram to the GroundFrame
z = np.zeros(len(psi))
uvw_vectors = np.column_stack(
[np.cos(psi).value, np.sin(psi).value, z])
tilted_frame = TiltedGroundFrame(pointing_direction=array_pointing)
ground_frame = GroundFrame()
positions = [(SkyCoord(
*plane.pos,
frame=ground_frame).transform_to(tilted_frame).cartesian.xyz)
for plane in hillas_planes.values()]
core_position = line_line_intersection_3d(uvw_vectors, positions)
core_pos_tilted = SkyCoord(x=core_position[0] * u.m,
y=core_position[1] * u.m,
frame=tilted_frame)
core_pos = project_to_ground(core_pos_tilted)
return core_pos.x, core_pos.y
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 = SkyCoord(
x=src_x * u.rad,
y=src_y * u.rad,
frame=NominalFrame(array_direction=array_direction)
)
horiz = nom.transform_to(HorizonFrame())
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
