LightCurveTemplateTemporalModel#
- class gammapy.modeling.models.LightCurveTemplateTemporalModel(map, t_ref=None, filename=None, method=None, values_scale=None)[source]#
Bases:
gammapy.modeling.models.temporal.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 aRegionNDMap
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
andvalues_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: 59000.5 MJD Start time: 59000.5 MJD End time: 61862.5 MJD Norm min: 0.01551196351647377 Norm max: 1.0
Compute
norm
at a given time:>>> t = Time(59001.195, format="mjd") >>> light_curve.evaluate(t) array(0.02287888)
Compute mean
norm
in a given time interval:>>> from astropy.time import Time >>> 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) array([0.0074388942, 0.0071144081, 0.0068115544])
Attributes Summary
Frozen status of a model, True if all parameters are frozen.
Parameters as a
Parameters
object.A model parameter.
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.
- parameters#
Parameters as a
Parameters
object.
- 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
ormin
andmax
properties and consider the fact that there is afactor`
andscale
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
andfactor_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).
- unit
Unit
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
andscale
.- interp{“lin”, “sqrt”, “log”}
Parameter scaling to use for the scan.
- is_normbool
Whether the parameter represents the flux norm of the model.
- prior
Prior
Prior set on the parameter.
- tag = ['LightCurveTemplateTemporalModel', 'template']#
- type#
Methods Documentation
- __call__(time, energy=None)#
Evaluate model.
- copy(**kwargs)#
Deep copy.
- evaluate(time, t_ref=None, energy=None)[source]#
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
- values
Quantity
Model values.
- values
- freeze()#
Freeze all parameters.
- classmethod from_dict(data)[source]#
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
- parameters
Parameters
Parameters for init.
- parameters
- Returns
- model
Model
Model instance.
- model
- classmethod from_table(table, filename=None)[source]#
Create a template model from an astropy table.
- Parameters
- table
Table
Table containing the template model.
- filenamestr, optional
Name of input file. Default is None.
- table
- Returns
- model
LightCurveTemplateTemporalModel
Light curve template model.
- model
- integral(t_min, t_max, oversampling_factor=100, **kwargs)[source]#
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)[source]#
Plot the temporal model.
- Parameters
- time_range
Time
Times to plot the model.
- ax
Axes
, optional Axis to plot on. Default is None.
- n_pointsint, optional
Number of bins to plot model. Default is 100.
- energy
quantity
, optional Energies to compute the model at for energy dependent models. Default is None.
- **kwargsdict
Keywords forwarded to
errorbar
.- Returns
- ——-
- ax
Axes
, optional Matplotlib axes.
- time_range
- classmethod read(filename, format='table')[source]#
Read a template model.
- Parameters
- filenamestr
Name of file to read.
- format{“table”, “map”}
Format of the input file.
- Returns
- model
LightCurveTemplateTemporalModel
Light curve template model.
- 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
- model
Model
Reassigned model.
- 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_min
Time
Start time of the sampling.
- t_max
Time
Stop time of the sampling.
- t_delta
Quantity
, 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
- time
Quantity
Array with times of the sampled events.
- time
- static time_sum(t_min, t_max)#
Total time between t_min and t_max.
- unfreeze()#
Restore parameters frozen status to default.
- write(filename, format='table', overwrite=False)[source]#
Write a model to disk as per the specified format.
- Parameters:
- filenamestr
Name of output file.
- formatstr, either “table” or “map”
If format is “table”, it is serialised as a
Table
. If “map”, then it is serialised as aRegionNDMap
. Default is “table”.- overwritebool, optional
Overwrite existing file. Default is False.