TemporalModel

class gammapy.modeling.models.TemporalModel

Bases: ModelBase

Temporal model base class.

Evaluates on Time objects.

Attributes Summary

default_parameters
is_energy_dependent
reference_time	Reference time in MJD.
type

Methods Summary

__call__(time[, energy])	Evaluate model.
from_dict(data, **kwargs)	Create a temporal model from a dictionary.
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.
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.

Attributes Documentation

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

is_energy_dependent

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.

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.

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.

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.

__init__(**kwargs)

classmethod __new__(*args, **kwargs)