ConstantTemporalModel

class gammapy.modeling.models.ConstantTemporalModel(**kwargs)

Bases: gammapy.modeling.models.TemporalModel

Constant temporal model.

Parameters

normfloat

The normalization of the constant temporal model

Attributes Summary

default_parameters
norm	A model parameter.
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)	Create dict for YAML serialisation

Attributes Documentation

default_parameters = <gammapy.modeling.parameter.Parameters object>

norm

A model parameter.

Note that the parameter value has been split into a factor and scale like this:

value = factor x scale

Users should interact with the value, quantity or min and max properties and consider the fact that there is a factor` and scale an implementation detail.

That was introduced for numerical stability in parameter and error estimation methods, only in the Gammapy optimiser interface do we interact with the factor, factor_min and factor_max properties, i.e. the optimiser “sees” the well-scaled problem.

Parameters

namestr

Name

factorfloat or Quantity

Factor

scalefloat, optional

Scale (sometimes used in fitting)

unitUnit or str, optional

Unit

minfloat, optional

Minimum (sometimes used in fitting)

maxfloat, optional

Maximum (sometimes used in fitting)

frozenbool, optional

Frozen? (used in fitting)

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

timearray_like

Time since the reference time.

Returns

normfloat

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_eventsint

Number of events to sample.

t_minTime

Start time of the sampling.

t_maxTime

Stop time of the sampling.

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

Defines random number generator initialisation. Passed to get_random_state.

Returns

timeQuantity

Array with times of the sampled events.

to_dict(self)

Create dict for YAML serialisation