LightCurveTemplateTemporalModel

class gammapy.modeling.models.LightCurveTemplateTemporalModel(map, t_ref=None, filename=None, method=None, values_scale=None)

Bases: TemporalModel

Temporal light curve model.

The lightcurve is given at specific times (and optionally energies) as a norm It can be serialised either as an astropy table or a RegionNDMap

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

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

When the temporal model is energy-dependent, the default interpolation scheme is linear with a log scale for the values. The interpolation method and scale values can be changed with the method and values_scale arguments.

For more information see Light curve temporal model.

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)
LightCurveTemplateTemporalModel model summary:
 Reference time: 59000.49919925926 MJD
 Start time: 58999.99919925926 MJD
 End time: 61862.99919925926 MJD
 Norm min: 0.01551196351647377
Norm max: 1.0

<BLANKLINE>

Compute norm at a given time:

>>> from astropy.time import Time
>>> t = Time(59001.195, format="mjd")
>>> light_curve.evaluate(t)
<Quantity [0.02288737]>

Compute mean norm in a given time interval:

>>> import astropy.units as u
>>> t_r = Time(59000.5, format='mjd')
>>> t_min = t_r + [1, 4, 8] * u.d
>>> t_max = t_r + [1.5, 6, 9] * u.d
>>> light_curve.integral(t_min, t_max)
<Quantity [0.00375698, 0.0143724 , 0.00688029]>

Attributes Summary

covariance
default_parameters
frozen	Frozen status of a model, True if all parameters are frozen.
is_energy_dependent	Whether the model is energy dependent.
parameters	Parameters as a Parameters object.
parameters_unique_names
reference_time	Reference time in MJD.
t_ref	A model parameter.
tag
type

Methods Summary

__call__(time[, energy])	Evaluate model.
copy(**kwargs)	Deep copy.
evaluate(time[, t_ref, energy])	Evaluate the model at given coordinates.
freeze()	Freeze all parameters.
from_dict(data)	Create a temporal model from a dictionary.
from_parameters(parameters, **kwargs)	Create model from parameter list.
from_table(table[, filename])	Create a template model from an astropy table.
integral(t_min, t_max[, oversampling_factor])	Evaluate the integrated flux within the given time intervals.
plot(time_range[, ax, n_points, energy])	Plot the temporal model.
read(filename[, format])	Read a template 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, format])	Create dictionary for YAML serialisation.
to_table()	Convert model to an astropy table.
unfreeze()	Restore parameters frozen status to default.
write(filename[, format, overwrite])	Write a model to disk as per the specified format.

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

Whether the model is energy dependent.

parameters

Parameters as a Parameters object.

parameters_unique_names

reference_time

Reference time in MJD.

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 = ['LightCurveTemplateTemporalModel', 'template']

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.

evaluate(time, t_ref=None, energy=None)

Evaluate the model at given coordinates.

Parameters:

time: `~astropy.time.Time`

Time.

t_ref: `~gammapy.modeling.Parameter`, optional

Reference time for the model. Default is None.

energy: `~astropy.units.Quantity`, optional

Energy. Default is None.

Returns:

valuesQuantity

Model values.

freeze()

Freeze all parameters.

classmethod from_dict(data)

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.

classmethod from_table(table, filename=None)

Create a template model from an astropy table.

Parameters:

tableTable

Table containing the template model.

filenamestr, optional

Name of input file. Default is None.

Returns:

modelLightCurveTemplateTemporalModel

Light curve template model.

integral(t_min, t_max, oversampling_factor=100, **kwargs)

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.

oversampling_factorint, optional

Oversampling factor to be used for numerical integration. Default is 100.

Returns:

normfloat

Integrated flux norm on the given time intervals.

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

Plot the temporal model.

Parameters:

time_rangeTime

Times to plot the model.

axAxes, optional

Axis to plot on. Default is None.

n_pointsint, optional

Number of bins to plot model. Default is 100.

energyquantity, optional

Energies to compute the model at for energy dependent models. Default is None.

**kwargsdict

Keywords forwarded to errorbar.

Returns

——-

axAxes, optional

Matplotlib axes.

classmethod read(filename, format='table')

Read a template model.

Parameters:

filenamestr

Name of file to read.

format{“table”, “map”}

Format of the input file.

Returns:

modelLightCurveTemplateTemporalModel

Light curve template model.

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, format=None)

Create dictionary for YAML serialisation.

to_table()

Convert model to an astropy table.

unfreeze()

Restore parameters frozen status to default.

write(filename, format='table', overwrite=False)

Write a model to disk as per the specified format.

Parameters:

filenamestr

Name of output file.

format{“table” or “map”}

If format is “table”, it is serialised as a Table. If “map”, then it is serialised as a RegionNDMap. Default is “table”.

overwritebool, optional

Overwrite existing file. Default is False.