ImPACTReconstructor

class ctapipe.reco.impact.ImPACTReconstructor(**kwargs: Any)

Bases: ctapipe.core.component.Component

This class is an implementation if the impact_reco Monte Carlo Template based image fitting method from parsons14. This method uses a comparision of the predicted image from a library of image templates to perform a maximum likelihood fit for the shower axis, energy and height of maximum.

Because this application is computationally intensive the usual advice to use astropy units for all quantities is ignored (as these slow down some computations), instead units within the class are fixed:

• Angular units in radians

• Distance units in metres

• Energy units in TeV

References

parsons14

Parsons & Hinton, Astroparticle Physics 56 (2014), pp. 26-34

Attributes Summary

ped_table
spe

Methods Summary

get_hillas_mean()	This is a simple function to find the peak position of each image in an event which will be used later in the Xmax calculation.
get_likelihood(source_x, source_y, core_x, …)	Get the likelihood that the image predicted at the given test position matches the camera image.
get_likelihood_min(x)	Wrapper class around likelihood function for use with scipy minimisers
get_likelihood_nlopt(x, grad)	Wrapper class around likelihood function for use with scipy minimisers
get_shower_max(source_x, source_y, core_x, …)	Function to calculate the depth of shower maximum geometrically under the assumption that the shower maximum lies at the brightest point of the camera image.
image_prediction(tel_type, energy, impact, …)	Creates predicted image for the specified pixels, interpolated from the template library.
initialise_templates(tel_type)	Check if templates for a given telescope type has been initialised and if not do it and add to the dictionary
minimise(params, step, limits[, …])	Parameters
predict(shower_seed, energy_seed)	Predict method for the ImPACT reconstructor.
predict_time(tel_type, energy, impact, x_max)	Creates predicted image for the specified pixels, interpolated from the template library.
reset_interpolator()	This function is needed in order to reset some variables in the interpolator at each new event.
rotate_translate(pixel_pos_x, pixel_pos_y, …)	Function to perform rotation and translation of pixel lists
set_event_properties(image, time, pixel_x, …)	The setter class is used to set the event properties within this class before minimisation can take place.

Attributes Documentation

ped_table = {'CHEC': 0.5, 'DUMMY': 0, 'FlashCam': 2.3, 'LSTCam': 2.8, 'NectarCam': 2.3}

spe = 0.5

Methods Documentation

get_hillas_mean()

This is a simple function to find the peak position of each image in an event which will be used later in the Xmax calculation. Peak is found by taking the average position of the n hottest pixels in the image.

get_likelihood(source_x, source_y, core_x, core_y, energy, x_max_scale, goodness_of_fit=False)

Get the likelihood that the image predicted at the given test position matches the camera image.

Parameters

source_x: float

Source position of shower in the nominal system (in deg)

source_y: float

Source position of shower in the nominal system (in deg)

core_x: float

Core position of shower in tilted telescope system (in m)

core_y: float

Core position of shower in tilted telescope system (in m)

energy: float

Shower energy (in TeV)

x_max_scale: float

Scaling factor applied to geometrically calculated Xmax

goodness_of_fit: boolean

Determines whether expected likelihood should be subtracted from result

Returns

——-

float: Likelihood the model represents the camera image at this position

get_likelihood_min(x)

Wrapper class around likelihood function for use with scipy minimisers

Parameters

x: ndarray

Array of minimisation parameters

Returns

float: Likelihood value of test position

get_likelihood_nlopt(x, grad)

Wrapper class around likelihood function for use with scipy minimisers

Parameters

x: ndarray

Array of minimisation parameters

Returns

float: Likelihood value of test position

get_shower_max(source_x, source_y, core_x, core_y, zen)

Function to calculate the depth of shower maximum geometrically under the assumption that the shower maximum lies at the brightest point of the camera image.

Parameters

source_x: float

Event source position in nominal frame

source_y: float

Event source position in nominal frame

core_x: float

Event core position in telescope tilted frame

core_y: float

Event core position in telescope tilted frame

zen: float

Zenith angle of event

Returns

float: Depth of maximum of air shower

image_prediction(tel_type, energy, impact, x_max, pix_x, pix_y)

Creates predicted image for the specified pixels, interpolated from the template library.

Parameters

tel_type: string

Telescope type specifier

energy: float

Event energy (TeV)

impact: float

Impact diance of shower (metres)

x_max: float

Depth of shower maximum (num bins from expectation)

pix_x: ndarray

X coordinate of pixels

pix_y: ndarray

Y coordinate of pixels

Returns

ndarray: predicted amplitude for all pixels

initialise_templates(tel_type)

Check if templates for a given telescope type has been initialised and if not do it and add to the dictionary

Parameters

tel_type: dictionary

Dictionary of telescope types in event

Returns

boolean: Confirm initialisation

minimise(params, step, limits, minimiser_name='minuit', max_calls=0)

Parameters

params: ndarray

Seed parameters for fit

step: ndarray

Initial step size in the fit

limits: ndarray

Fit bounds

minimiser_name: str

Name of minimisation method

max_calls: int

Maximum number of calls to minimiser

Returns

——-

tuple: best fit parameters and errors

predict(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:

predict_time(tel_type, energy, impact, x_max)

Creates predicted image for the specified pixels, interpolated from the template library.

Parameters

tel_type: string

Telescope type specifier

energy: float

Event energy (TeV)

impact: float

Impact diance of shower (metres)

x_max: float

Depth of shower maximum (num bins from expectation)

Returns

ndarray: predicted amplitude for all pixels

reset_interpolator()

This function is needed in order to reset some variables in the interpolator at each new event. Without this reset, a new event starts with information from the previous event.

static rotate_translate(pixel_pos_x, pixel_pos_y, x_trans, y_trans, phi)

Function to perform rotation and translation of pixel lists

Parameters

pixel_pos_x: ndarray

Array of pixel x positions

pixel_pos_y: ndarray

Array of pixel x positions

x_trans: float

Translation of position in x coordinates

y_trans: float

Translation of position in y coordinates

phi: float

Rotation angle of pixels

Returns

ndarray,ndarray: Transformed pixel x and y coordinates

set_event_properties(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