ConstantTemporalModel

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

Bases: gammapy.modeling.models.temporal.TemporalModel

Constant temporal model.

For more information see Constant temporal model.

Attributes Summary

covariance
default_parameters
frozen	Frozen status of a model, True if all parameters are frozen
is_energy_dependent
parameters	Parameters (Parameters)
reference_time	Reference time in mjd
tag
type

Methods Summary

__call__(time[, energy])	Evaluate model
copy(**kwargs)	A deep copy.
evaluate(time)	Evaluate at given times.
freeze()	Freeze all parameters
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, n_points])	Plot 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 dict 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 (Parameters)

reference_time

Reference time in mjd

tag = ['ConstantTemporalModel', 'const']

type

Methods Documentation

__call__(time, energy=None)

Evaluate model

Parameters

timeTime

Time object

energyQuantity

Energy (optional)

Returns

valuesQuantity

Model values

copy(**kwargs)

A deep copy.

static evaluate(time)

Evaluate at given times.

freeze()

Freeze all parameters

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_minTime

Start times of observation

t_maxTime

Stop times of observation

Returns

normQuantity

Integrated flux norm on the given time intervals

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

Plot Temporal Model.

Parameters

time_rangeTime

times to plot the model

axAxes, optional

Axis to plot on

n_pointsint

Number of bins to plot model

**kwargsdict

Keywords forwarded to errorbar

Returns

axAxes, optional

axis

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

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

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 dict for YAML serilisation

unfreeze()

Restore parameters frozen status to default