TemplatePhaseCurveTemporalModel

class gammapy.modeling.models.TemplatePhaseCurveTemporalModel(table, filename=None, **kwargs)

Bases: TemporalModel

Temporal phase curve model.

A timing solution is used to compute the phase corresponding to time and a template phase curve is used to determine the associated norm.

The phasecurve is given as a table with columns phase and norm.

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 (phase, norm) values.

The implementation currently uses scipy.interpolate. InterpolatedUnivariateSpline, using degree k=1 to get linear interpolation. This class also contains an integral method, making the computation of mean fluxes for a given time interval a one-liner.

Parameters:

tableTable

A table with ‘PHASE’ vs ‘NORM’.

filenamestr

The name of the file containing the phase curve.

t_refQuantity

The reference time in mjd. Default is 48442.5 mjd.

phi_refQuantity

The phase at reference time. Default is 0.

f0Quantity

The frequency at t_ref in s-1. Default is 29.946923 s-1.

f1Quantity

The frequency derivative at t_ref in s-2. Default is 0 s-2.

f2Quantity

The frequency second derivative at t_ref in s-3. Default is 0 s-3.

Attributes Summary

covariance
default_parameters
f0	A model parameter.
f1	A model parameter.
f2	A model parameter.
frozen	Frozen status of a model, True if all parameters are frozen.
is_energy_dependent
parameters	Parameters as a Parameters object.
parameters_unique_names
phi_ref	A model parameter.
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, phi_ref, f0, f1, f2)
freeze()	Freeze all parameters.
from_dict(data)	Create a temporal model from a dictionary.
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 the temporal model.
plot_phasogram([ax, n_points])	Plot phasogram of the phase model.
read(path[, t_ref, phi_ref, f0, f1, f2])	Read phasecurve model table from FITS file.
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 dictionary for YAML serialisation.
unfreeze()	Restore parameters frozen status to default.
write([path, overwrite])

Attributes Documentation

covariance

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

f0

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.

f1

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.

f2

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.

frozen

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

is_energy_dependent

parameters

Parameters as a Parameters object.

parameters_unique_names

phi_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.

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 = ['TemplatePhaseCurveTemporalModel', 'template-phase']

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, phi_ref, f0, f1, f2)

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.

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:

norm: The model integrated flux.

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

Plot the temporal model.

Parameters:

time_rangeTime

Times to plot the model.

axAxes, optional

Axis to plot on.

n_pointsint

Number of bins to plot model. Default is 100.

**kwargsdict

Keywords forwarded to errorbar.

Returns:

axAxes, optional

Matplotlib axes.

plot_phasogram(ax=None, n_points=100, **kwargs)

Plot phasogram of the phase model.

Parameters:

axAxes, optional

Axis to plot on. Default is None.

n_pointsint, optional

Number of bins to plot model. Default is 100.

**kwargsdict

Keywords forwarded to errorbar.

Returns:

axAxes, optional

Matplotlib axes.

classmethod read(path, t_ref=<Quantity 48442.5 d>, phi_ref=0, f0=<Quantity 29.946923 1 / s>, f1=<Quantity 0. 1 / s2>, f2=<Quantity 0. 1 / s3>)

Read phasecurve model table from FITS file.

Beware : this does not read parameters. They will be set to defaults.

Parameters:

pathstr or Path

Filename with path.

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.

To fully cover the phase range, t_delta is the minimum between the input and product of the period at 0.5*(t_min + t_max) and the table bin size.

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_maxTime

Lower and upper bound of integration range.

Returns:

time_sumTimeDelta

Summed time in the intervals.

to_dict(full_output=False)

Create dictionary for YAML serialisation.

unfreeze()

Restore parameters frozen status to default.

write(path=None, overwrite=False)