TemporalModel#

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

Bases: gammapy.modeling.models.core.ModelBase

Temporal model base class. evaluates on astropy.time.Time objects

Attributes Summary

covariance

default_parameters

frozen

Frozen status of a model, True if all parameters are frozen

parameters

Parameters (Parameters)

type

Methods Summary

__call__(time)

Evaluate model

copy(**kwargs)

freeze()

Freeze all parameters

from_dict(data)

from_parameters(parameters, **kwargs)

Create model from parameter list

integral(t_min, t_max[, oversampling_factor])

Evaluate the integrated flux within the given time intervals

plot(time_range[, ax])

Plot Temporal Model.

reassign(datasets_names, new_datasets_names)

Reassign a model from one dataset to another

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([full_output])

Create dict for YAML serialisation

unfreeze()

Restore parameters frozen status to default

Attributes Documentation

covariance#
default_parameters = <gammapy.modeling.parameter.Parameters object>#
frozen#

Frozen status of a model, True if all parameters are frozen

parameters#

Parameters (Parameters)

type#

Methods Documentation

__call__(time)[source]#

Evaluate model

Parameters
timeTime

Time object

copy(**kwargs)#
freeze()#

Freeze all parameters

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

Create model from parameter list

Parameters
parametersParameters

Parameters for init

Returns
modelModel

Model instance

integral(t_min, t_max, oversampling_factor=100, **kwargs)[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

oversampling_factorint

Oversampling factor to be used for numerical integration.

Returns
normfloat

Integrated flux norm on the given time intervals

plot(time_range, ax=None, **kwargs)[source]#

Plot Temporal Model.

Parameters
time_rangeTime

times to plot the model

axAxes, optional

Axis to plot on

**kwargsdict

Keywords forwarded to errorbar

Returns
axAxes, optional

axis

reassign(datasets_names, new_datasets_names)#

Reassign a model from one dataset to another

Parameters
datasets_namesstr or list

Name of the datasets where the model is currently defined

new_datasets_namesstr or list

Name of the datasets where the model should be defined instead. If multiple names are given the two list must have the save length, as the reassignment is element-wise.

Returns
modelModel

Reassigned model.

sample_time(n_events, t_min, t_max, t_delta='1 s', 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.

t_deltaQuantity

Time step used for sampling of the temporal model.

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)[source]#

Total time between t_min and t_max

Parameters
t_min, t_maxTime

Lower and upper bound of integration range

Returns
time_sumTimeDelta

Summed time in the intervals.

to_dict(full_output=False)#

Create dict for YAML serialisation

unfreeze()#

Restore parameters frozen status to default