PowerLawTemporalModel#

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

Bases: TemporalModel

Temporal model with a Power Law decay.

For more information see PowerLaw temporal model.

Parameters:

alphafloat: Decay time power. Default is 1.
t_ref: `~astropy.units.Quantity`: The reference time in mjd. Frozen by default, at 2000-01-01.
t0: `~astropy.units.Quantity`: The scaling time in mjd. Fixed by default, at 1 day.

Attributes Summary

`alpha`	A model parameter.
`covariance`
`default_parameters`
`frozen`	Frozen status of a model, True if all parameters are frozen.
`is_energy_dependent`
`parameters`	Parameters as a `Parameters` object.
`parameters_unique_names`
`reference_time`	Reference time in MJD.
`t0`	A model parameter.
`t_ref`	A model parameter.
`tag`
`type`

Methods Summary

`__call__`(time[, energy])	Evaluate model.
`copy`(**kwargs)	Deep copy.
`evaluate`(time, alpha, t_ref[, t0])	Evaluate at given times.
`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)	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

alpha#

A model parameter.

Note that the parameter value has been split into a factor and scale like this:

value = factor x scale

Users should interact with the value, quantity or min and max properties and consider the fact that there is a factor` and scale an implementation detail.

That was introduced for numerical stability in parameter and error estimation methods, only in the Gammapy optimiser interface do we interact with the factor, factor_min and factor_max properties, i.e. the optimiser “sees” the well-scaled problem.

Parameters:

namestr: Name.
valuefloat or Quantity: Value.
scalefloat, optional: Scale (sometimes used in fitting).
unitUnit or str, optional: Unit.
minfloat, optional: Minimum (sometimes used in fitting).
maxfloat, optional: Maximum (sometimes used in fitting).
frozenbool, optional: Frozen (used in fitting).
errorfloat: Parameter error.
scan_minfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_maxfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_n_values: int: Number of values to be used for the parameter scan.
scan_n_sigmaint: Number of sigmas to scan.
scan_values: `numpy.array`: Scan values. Overwrites all the scan keywords before.
scale_method{‘scale10’, ‘factor1’, None}: Method used to set factor and scale.
interp{“lin”, “sqrt”, “log”}: Parameter scaling to use for the scan.
priorPrior: Prior set on the parameter.

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.

parameters_unique_names#

reference_time#: Reference time in MJD.

t0#

A model parameter.

Note that the parameter value has been split into a factor and scale like this:

value = factor x scale

Users should interact with the value, quantity or min and max properties and consider the fact that there is a factor` and scale an implementation detail.

That was introduced for numerical stability in parameter and error estimation methods, only in the Gammapy optimiser interface do we interact with the factor, factor_min and factor_max properties, i.e. the optimiser “sees” the well-scaled problem.

Parameters:

namestr: Name.
valuefloat or Quantity: Value.
scalefloat, optional: Scale (sometimes used in fitting).
unitUnit or str, optional: Unit.
minfloat, optional: Minimum (sometimes used in fitting).
maxfloat, optional: Maximum (sometimes used in fitting).
frozenbool, optional: Frozen (used in fitting).
errorfloat: Parameter error.
scan_minfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_maxfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_n_values: int: Number of values to be used for the parameter scan.
scan_n_sigmaint: Number of sigmas to scan.
scan_values: `numpy.array`: Scan values. Overwrites all the scan keywords before.
scale_method{‘scale10’, ‘factor1’, None}: Method used to set factor and scale.
interp{“lin”, “sqrt”, “log”}: Parameter scaling to use for the scan.
priorPrior: Prior set on the parameter.

t_ref#

A model parameter.

Note that the parameter value has been split into a factor and scale like this:

value = factor x scale

Users should interact with the value, quantity or min and max properties and consider the fact that there is a factor` and scale an implementation detail.

That was introduced for numerical stability in parameter and error estimation methods, only in the Gammapy optimiser interface do we interact with the factor, factor_min and factor_max properties, i.e. the optimiser “sees” the well-scaled problem.

Parameters:

namestr: Name.
valuefloat or Quantity: Value.
scalefloat, optional: Scale (sometimes used in fitting).
unitUnit or str, optional: Unit.
minfloat, optional: Minimum (sometimes used in fitting).
maxfloat, optional: Maximum (sometimes used in fitting).
frozenbool, optional: Frozen (used in fitting).
errorfloat: Parameter error.
scan_minfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_maxfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_n_values: int: Number of values to be used for the parameter scan.
scan_n_sigmaint: Number of sigmas to scan.
scan_values: `numpy.array`: Scan values. Overwrites all the scan keywords before.
scale_method{‘scale10’, ‘factor1’, None}: Method used to set factor and scale.
interp{“lin”, “sqrt”, “log”}: Parameter scaling to use for the scan.
priorPrior: Prior set on the parameter.

tag = ['PowerLawTemporalModel', 'powerlaw']#

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.

static evaluate(time, alpha, t_ref, t0=<Quantity 1. d>)[source]#: Evaluate at given times.

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)[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:

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.