LinearTemporalModel

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

Bases: gammapy.modeling.models.TemporalModel

Temporal model with a linear variation.

For more information see Linear temporal model.

Parameters

alphafloat

Constant term of the baseline flux

betaQuantity

Time variation coefficient of the flux

t_ref: `~astropy.units.Quantity`

The reference time in mjd. Frozen per default, at 2000-01-01.

Attributes Summary

alpha	A model parameter.
beta	A model parameter.
covariance
default_parameters
frozen	Frozen status of a model, True if all parameters are frozen
parameters	Parameters (Parameters)
t_ref	A model parameter.
tag
type

Methods Summary

__call__(time)	Evaluate model
copy()	A deep copy.
evaluate(time, alpha, beta, t_ref)	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])	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 serialisation
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 of 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.

beta

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 of 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.

covariance

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

frozen

Frozen status of a model, True if all parameters are frozen

parameters

Parameters (Parameters)

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 of 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.

tag = ['LinearTemporalModel', 'linear']

type

Methods Documentation

__call__(time)

Evaluate model

Parameters

timeTime

Time object

copy()

A deep copy.

static evaluate(time, alpha, beta, t_ref)

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_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, **kwargs)

Plot Temporal Model.

Parameters

time_rangeTime

times to plot the model

axAxes, optional

Axis to plot on

**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_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

unfreeze()

Restore parameters frozen status to default