LightCurveTemplateTemporalModel

class gammapy.modeling.models.LightCurveTemplateTemporalModel(table)

Bases: gammapy.modeling.models.TemporalModel

Temporal light curve model.

The lightcurve is given as a table with columns time and norm.

The norm is supposed to be a unite-less multiplicative factor in the model, to be multiplied with a spectral model.

The model does linear interpolation for times between the given (time, norm) values.

The implementation currently uses scipy.interpolate.InterpolatedUnivariateSpline, using degree k=1 to get linear interpolation. This class also contains an integral method, making the computation of mean fluxes for a given time interval a one-liner.

Parameters:

table : Table

A table with ‘TIME’ vs ‘NORM’

Examples

Read an example light curve object:

>>> from gammapy.modeling.models import LightCurveTemplateTemporalModel
>>> path = '$GAMMAPY_DATA/tests/models/light_curve/lightcrv_PKSB1222+216.fits'
>>> light_curve = LightCurveTemplateTemporalModel.read(path)

Show basic information about the lightcurve:

>>> print(light_curve)
LightCurve model summary:
Start time: 59000.5 MJD
End time: 61862.5 MJD
Norm min: 0.01551196351647377
Norm max: 1.0

Compute norm at a given time:

>>> light_curve.evaluate_norm_at_time(46300)
0.49059393580053845

Compute mean norm in a given time interval:

>>> light_curve.mean_norm_in_time_interval(46300, 46301)

Attributes Summary

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[, ext_mode])	Evaluate for a given time.
from_dict(data)
mean_norm_in_time_interval(self, time_min, …)	Compute mean norm in a given time interval.
read(path)	Read lightcurve model table from FITS file.
sample_time(self, n_events, t_min, t_max[, …])	Sample arrival times of events.
to_dict(self)

Attributes Documentation

parameters

Parameters (Parameters)

tag = 'LightCurveTemplateTemporalModel'

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, ext_mode=3)

Evaluate for a given time.

Parameters:

time : array_like

Time since the reference time.

ext_mode : int or str, optional

Controls the extrapolation mode for elements not in the interval defined by the knot sequence. if ext=0 or ‘extrapolate’, return the extrapolated value. if ext=1 or ‘zeros’, return 0 if ext=2 or ‘raise’, raise a ValueError if ext=3 of ‘const’, return the boundary value. The default value is 0.

Returns:

norm : array_like

classmethod from_dict(data)

mean_norm_in_time_interval(self, time_min, time_max)

Compute mean norm in a given time interval.

TODO: vectorise, i.e. allow arrays of time intervals in a single call.

Parameters:

time_min, time_max : float

Time interval

Returns:

norm : float

Mean norm

classmethod read(path)

Read lightcurve model table from FITS file.

TODO: This doesn’t read the XML part of the model yet.

sample_time(self, n_events, t_min, t_max, t_delta='1 s', random_state=0)

Sample arrival times of events.

Parameters:

n_events : int

Number of events to sample.

t_min : Time

Start time of the sampling.

t_max : Time

Stop time of the sampling.

t_delta : Quantity

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:

time : Quantity

Array with times of the sampled events.

to_dict(self)