SineTemporalModel#
- class gammapy.modeling.models.SineTemporalModel(**kwargs)[source]#
Bases:
gammapy.modeling.models.temporal.TemporalModel
Temporal model with a sinusoidal modulation.
For more information see Sine temporal model.
- Parameters
- ampfloat
Amplitude of the sinusoidal function
- t_ref: `~astropy.units.Quantity`
The reference time in mjd.
- omega: `~astropy.units.Quantity`
Pulsation of the signal.
Attributes Summary
A model parameter.
Frozen status of a model, True if all parameters are frozen
A model parameter.
Parameters (
Parameters
)A model parameter.
Methods Summary
__call__
(time)Evaluate model
copy
(**kwargs)evaluate
(time, amp, omega, 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, 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 serialisation
unfreeze
()Restore parameters frozen status to default
Attributes Documentation
- amp#
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 of 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.
- covariance#
- default_parameters = <gammapy.modeling.parameter.Parameters object>#
- frozen#
Frozen status of a model, True if all parameters are frozen
- omega#
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 of 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.
- 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
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 of 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.
- tag = ['SineTemporalModel', 'sinus']#
- type#
Methods Documentation
- copy(**kwargs)#
- freeze()#
Freeze all parameters
- classmethod from_dict(data)#
- classmethod from_parameters(parameters, **kwargs)#
Create model from parameter list
- Parameters
- parameters
Parameters
Parameters for init
- parameters
- Returns
- model
Model
Model instance
- model
- integral(t_min, t_max)[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
- Returns
- normfloat
Integrated flux norm on the given time intervals
- plot(time_range, ax=None, n_points=100, **kwargs)#
Plot Temporal 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
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
- 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
- to_dict(full_output=False)#
Create dict for YAML serialisation
- unfreeze()#
Restore parameters frozen status to default