ExpDecayTemporalModel

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

Bases: gammapy.modeling.models.TemporalModel

Temporal model with an exponential decay.

\[F(t) = exp(t - t_ref)/t0\]

Parameters

t0Quantity

Decay time scale

t_ref: `~astropy.units.Quantity`

The reference time in mjd

Attributes Summary

covariance
default_parameters
parameters	Parameters (Parameters)
t0	A model parameter.
t_ref	A model parameter.
tag
type

Methods Summary

__call__(time)	Evaluate model
copy()	A deep copy.
create(tag[, model_type])	Create a model instance.
evaluate(time, t0, t_ref)	Evaluate at given times
from_dict(data)
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])	Plot Temporal Model.
time_sum(t_min, t_max)	Total time between t_min and t_max
to_dict([full_output])	Create dict for YAML serialisation

Attributes Documentation

covariance

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

parameters

Parameters (Parameters)

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)

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)

tag = ['ExpDecayTemporalModel', 'exp-decay']

type

Methods Documentation

__call__(time)

Evaluate model

Parameters

timeTime

Time object

copy()

A deep copy.

static create(tag, model_type=None, *args, **kwargs)

Create a model instance.

Examples

>>> from gammapy.modeling.models import Model
>>> spectral_model = Model.create("pl-2", model_type="spectral", amplitude="1e-10 cm-2 s-1", index=3)
>>> type(spectral_model)
gammapy.modeling.models.spectral.PowerLaw2SpectralModel

static evaluate(time, t0, t_ref)

Evaluate at given times

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)

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)

Plot Temporal Model.

Parameters

time_rangeTime

times to plot the model

axAxes, optional

axis

Returns

axAxes, optional

axis

static time_sum(t_min, t_max)

Total time between t_min and t_max

Parameters

t_min, t_max: `~astropy.time.Time`

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