TemplatePhaseCurveTemporalModel

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

Bases: gammapy.modeling.models.temporal.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

phi_refQuantity

The phase at reference time. Default is 0.

f0Quantity

The frequency at t_ref in 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
parameters	Parameters (Parameters)
phi_ref	A model parameter.
t_ref	A model parameter.
tag
type

Methods Summary

__call__(time)	Evaluate model
copy(**kwargs)
evaluate(time, t_ref, phi_ref, f0, f1, f2)
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.
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 dict 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 of 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.

is_normbool

Whether the parameter represents the flux norm of the model.

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 of 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.

is_normbool

Whether the parameter represents the flux norm of the model.

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 of 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.

is_normbool

Whether the parameter represents the flux norm of the model.

frozen

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

parameters

Parameters (Parameters)

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 of 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.

is_normbool

Whether the parameter represents the flux norm of the model.

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 of 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.

is_normbool

Whether the parameter represents the flux norm of the model.

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

type

Methods Documentation

__call__(time)

Evaluate model

Parameters

timeTime

Time object

copy(**kwargs)

evaluate(time, t_ref, phi_ref, f0, f1, f2)

freeze()

Freeze all parameters

classmethod from_dict(data)

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 Temporal Model.

Parameters

time_rangeTime

times to plot the model

axAxes, optional

Axis to plot on

n_pointsint

Number of bins to plot model

**kwargsdict

Keywords forwarded to errorbar

Returns

axAxes, optional

axis

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

Plot phasogram of the phase model.

Parameters

axAxes, optional

Axis to plot on

n_pointsint

Number of bins to plot model

**kwargsdict

Keywords forwarded to errorbar

Returns

axAxes, optional

axis

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.

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 dict for YAML serialisation

unfreeze()

Restore parameters frozen status to default

write(path=None, overwrite=False)