TemporalModel

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

Bases: gammapy.modeling.models.core.ModelBase

Temporal model base class.

Evaluates on Time objects.

Attributes Summary

covariance
default_parameters
frozen	Frozen status of a model, True if all parameters are frozen.
is_energy_dependent
parameters	Parameters as a Parameters object.
reference_time	Reference time in MJD.
type

Methods Summary

__call__(time[, energy])	Evaluate model.
copy(**kwargs)	Deep copy.
freeze()	Freeze all parameters.
from_dict(data, **kwargs)	Create a temporal model from a dictionary.
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, n_points])	Plot the 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 dictionary for YAML serilisation.
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.

is_energy_dependent

parameters

Parameters as a Parameters object.

reference_time

Reference time in MJD.

type

Methods Documentation

__call__(time, energy=None)

Evaluate model.

Parameters

timeTime

Time object.

energyQuantity, optional

Energy. Default is None.

Returns

valuesQuantity

Model values.

copy(**kwargs)

Deep copy.

freeze()

Freeze all parameters.

classmethod from_dict(data, **kwargs)

Create a temporal model from a dictionary.

Parameters

datadict

Dictionary containing the model parameters.

**kwargsdict

Keyword arguments passed to from_parameters.

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)

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, optional

Oversampling factor to be used for numerical integration. Default is 100.

Returns

normfloat

Integrated flux norm on the given time intervals.

plot(time_range, ax=None, n_points=100, **kwargs)

Plot the temporal model.

Parameters

time_rangeTime

Times to plot the model.

axAxes, optional

Axis to plot on.

n_pointsint

Number of bins to plot model. Default is 100.

**kwargsdict

Keywords forwarded to errorbar.

Returns

axAxes, optional

Matplotlib axes.

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)

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, optional

Time step used for sampling of the temporal model. Default is 1 s.

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

Defines random number generator initialisation. Passed to get_random_state. Default is 0.

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_maxTime

Lower and upper bound of integration range.

Returns

time_sumTimeDelta

Summed time in the intervals.

to_dict(full_output=False)

Create dictionary for YAML serilisation.

unfreeze()

Restore parameters frozen status to default.