MicroLIA.simulate

Created on Thu Jun 28 20:30:11 2018

@author: danielgodinez

Module Contents

Classes

RRLyraeTemplateModeler	Template-fitting periods for single-band RR Lyrae
RRLyraeTemplateModelerMultiband	Multiband version of RR Lyrae template-fitting modeler

class MicroLIA.simulate.RRLyraeTemplateModeler(filts='ugriz', optimizer=None, fit_period=False, optimizer_kwds=None)

Bases: BaseTemplateModeler

Template-fitting periods for single-band RR Lyrae

This class contains functionality to evaluate the fit of the Sesar 2010 RR Lyrae templates to single-band data.

Parameters:

• filts (list or iterable of characters (optional)) – The filters of the templates to be used. Items should be among ‘ugriz’. Default is ‘ugriz’; i.e. all available templates.

• optimizer (PeriodicOptimizer instance (optional)) – Optimizer to use to find the best period. If not specified, the LinearScanOptimizer will be used.

• fit_period (bool (optional)) – If True, then fit for the best period when fit() method is called.

• optimizer_kwds (dict (optional) – Dictionary of keyword arguments for constructing the optimizer

See also

RRLyraeTemplateModelerMultiband

multiband version of template model

_raw_templates

_template_ids()

Return the list of template ids

_get_template_by_id(tid)

Get a particular template

Parameters:

template_id (simple type) – Template ID used by base class to define templates

Returns:

phase, y – arrays containing the sorted phase and associated y-values.

Return type:

ndarrays

class MicroLIA.simulate.RRLyraeTemplateModelerMultiband(optimizer=None, fit_period=False, optimizer_kwds=None, *args, **kwargs)

Bases: PeriodicModelerMultiband

Multiband version of RR Lyrae template-fitting modeler

This class contains functionality to evaluate the fit of the Sesar 2010 RR Lyrae templates to multiband data.

Parameters:

optimizer (PeriodicOptimizer instance (optional)) – Optimizer to use to find the best period. If not specified, the LinearScanOptimizer will be used.

See also

RRLyraeTemplateModeler

single band version of template model

_fit(t, y, dy, filts)

Fit the model to the given data

_score(periods)

Compute the score of the model given the periods

_predict(t, filts, period)

Predict the model values at the given times & filters