def test_image_prediction(self):
pixel_x = np.array([0]) * u.deg
pixel_y = np.array([0]) * u.deg
image = np.array([1])
pixel_area = np.array([1]) * u.deg * u.deg
self.impact_reco.set_event_properties(
{1: image},
{1: pixel_x},
{1: pixel_y},
{1: pixel_area},
{1: "CHEC"},
{1: 0 * u.m},
{1: 0 * u.m},
array_direction=[0 * u.deg, 0 * u.deg],
)
"""First check image prediction by directly accessing the function"""
pred = self.impact_reco.image_prediction(
"CHEC",
zenith=0,
azimuth=0,
energy=1,
impact=50,
x_max=0,
pix_x=pixel_x,
pix_y=pixel_y,
)
assert np.sum(pred) != 0
"""Then check helper function gives the same answer"""
shower = ReconstructedGeometryContainer()
shower.is_valid = True
shower.alt = 0 * u.deg
shower.az = 0 * u.deg
shower.core_x = 0 * u.m
shower.core_y = 100 * u.m
shower.h_max = 300 + 93 * np.log10(1)
energy = ReconstructedEnergyContainer()
energy.is_valid = True
energy.energy = 1 * u.TeV
pred2 = self.impact_reco.get_prediction(1,
shower_reco=shower,
energy_reco=energy)
print(pred, pred2)
assert pred.all() == pred2.all()
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