ConstantTemporalModel

class gammapy.modeling.models.ConstantTemporalModel(norm)

Bases: gammapy.modeling.models.TemporalModel

Constant temporal model.

Parameters:

norm : float

The normalization of the constant temporal model

Attributes Summary

parameters	Parameters (Parameters)
tag

Methods Summary

copy(self)	A deep copy.
create(tag, \*args, \*\*kwargs)	Create a model instance.
evaluate_norm_at_time(self, time)	Evaluate for a given time.
from_dict(data)
sample_time(self, n_events, t_min, t_max[, …])	Sample arrival times of events.
to_dict(self)

Attributes Documentation

parameters

Parameters (Parameters)

tag = 'ConstantTemporalModel'

Methods Documentation

copy(self)

A deep copy.

static create(tag, *args, **kwargs)

Create a model instance.

Examples

>>> from gammapy.modeling import Model
>>> spectral_model = Model.create("PowerLaw2SpectralModel", amplitude="1e-10 cm-2 s-1", index=3)
>>> type(spectral_model)
gammapy.modeling.models.spectral.PowerLaw2SpectralModel

evaluate_norm_at_time(self, time)

Evaluate for a given time.

Parameters:

time : array_like

Time since the reference time.

Returns:

norm : float

Mean norm

classmethod from_dict(data)

sample_time(self, n_events, t_min, t_max, random_state=0)

Sample arrival times of events.

Parameters:

n_events : int

Number of events to sample.

t_min : Time

Start time of the sampling.

t_max : Time

Stop time of the sampling.

random_state : {int, ‘random-seed’, ‘global-rng’, RandomState}

Defines random number generator initialisation. Passed to get_random_state.

Returns:

time : Quantity

Array with times of the sampled events.

to_dict(self)