ConstantTemporalModel

class gammapy.modeling.models.ConstantTemporalModel(**kwargs)[source]

Bases: gammapy.modeling.models.TemporalModel

Constant temporal model.

Attributes Summary

covariance

default_parameters

parameters

Parameters (Parameters)

tag

Methods Summary

__call__(self, time)

Evaluate model

copy(self)

A deep copy.

create(tag, \*args, \*\*kwargs)

Create a model instance.

evaluate(time)

Evaluate at given times.

from_dict(data)

from_parameters(parameters, \*\*kwargs)

Create model from parameter list

integral(self, t_min, t_max)

Evaluate the integrated flux within the given time intervals

sample_time(n_events, t_min, t_max[, …])

Sample arrival times of events.

time_sum(t_min, t_max)

Total time between t_min and t_max

to_dict(self)

Create dict for YAML serialisation

Attributes Documentation

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

Parameters (Parameters)

tag = 'ConstantTemporalModel'

Methods Documentation

__call__(self, time)

Evaluate model

Parameters
timeTime

Time object

copy(self)

A deep copy.

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

Create a model instance.

Examples

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

Evaluate at given times.

classmethod from_dict(data)
classmethod from_parameters(parameters, **kwargs)

Create model from parameter list

Parameters
parametersParameters

Parameters for init

Returns
modelModel

Model instance

integral(self, t_min, t_max)[source]

Evaluate the integrated flux within the given time intervals

Parameters
t_min: `~astropy.time.Time`

Start times of observation

t_max: `~astropy.time.Time`

Stop times of observation

Returns
normQuantity

Integrated flux norm on the given time intervals

static sample_time(n_events, t_min, t_max, random_state=0)[source]

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.

static time_sum(t_min, t_max)

Total time between t_min and t_max

Parameters
t_min, t_max: `~astropy.time.Time`

Lower and upper bound of integration range

Returns
time_sumTimeDelta

Summed time in the intervals.

to_dict(self)

Create dict for YAML serialisation