LightCurveTemplateTemporalModel

class gammapy.modeling.models.LightCurveTemplateTemporalModel

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

default_parameters
is_energy_dependent	Whether the model is energy dependent.
t_ref	A model parameter.
tag

Methods Summary

evaluate(time[, t_ref, energy])	Evaluate the model at given coordinates.
from_dict(data)	Create a temporal model from a dictionary.
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.
to_dict([full_output, format])	Create dictionary for YAML serialisation.
to_table()	Convert model to an astropy table.
write([filename, format, overwrite])	Write a model to disk as per the specified format.

Attributes Documentation

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

is_energy_dependent

Whether the model is energy dependent.

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. Default is “”.

minfloat, str or quantity, optional

Minimum (sometimes used in fitting). If None, set to numpy.nan. Default is None.

maxfloat, str or quantity, optional

Maximum (sometimes used in fitting). Default is numpy.nan.

frozenbool, optional

Frozen (used in fitting). Default is False.

errorfloat, optional

Parameter error. Default is 0.

scan_minfloat, optional

Minimum value for the parameter scan. Overwrites scan_n_sigma. Default is None.

scan_maxfloat, optional

Maximum value for the parameter scan. Overwrites scan_n_sigma. Default is None.

scan_n_values: int, optional

Number of values to be used for the parameter scan. Default is 11.

scan_n_sigmaint, optional

Number of sigmas to scan. Default is 2.

scan_values: `numpy.array`, optional

Scan values. Overwrites all the scan keywords before. Default is None.

scale_method{‘scale10’, ‘factor1’, None}, optional

Method used to set factor and scale. Default is “scale10”.

interp{“lin”, “sqrt”, “log”}, optional

Parameter scaling to use for the scan. Default is “lin”.

priorPrior, optional

Prior set on the parameter. Default is None.

tag = ['LightCurveTemplateTemporalModel', 'template']

Methods Documentation

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.

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_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.

to_dict(full_output=False, format=None)

Create dictionary for YAML serialisation.

to_table()

Convert model to an astropy table.

write(filename=None, format='table', overwrite=False)

Write a model to disk as per the specified format.

Parameters:

filenamestr, optional

Name of output file. By default, the template model will be saved with the LightCurveTemplateTemporalModel.filename attribute. If filename is provided, this attribute will be used.

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.

__init__(map, t_ref=None, filename=None, method=None, values_scale=None)

classmethod __new__(*args, **kwargs)