lsst.meas.modelfit.psf.psfContinued.GeneralShapeletPsfApproxMixin Class Reference

Inheritance diagram for lsst.meas.modelfit.psf.psfContinued.GeneralShapeletPsfApproxMixin:

Public Member Functions
	__init__ (self, config, name, schema)
	measure (self, measRecord, exposure)
	fail (self, measRecord, error=None)

Public Attributes
	sequence

Detailed Description

Mixin base class for fitting shapelet approximations to the PSF model

This class does almost all of the work for its two derived classes,
GeneralShapeletPsfApproxSingleFramePlugin and
GeneralShapeletPsfApproxForcedPlugin, which simply adapt it to the
slightly different interfaces for single-frame and forced measurement.  It
in turn delegates its work to the C++ GeneralPsfFitter class; it holds
sequence of these corresponding to different models (generally with
increasing complexity). Each GeneralPsfFitter starts with the result of
the previous one as an input, using GeneralPsfFitter::adapt to hopefully
allow these previous fits to reduce the time spent on the next one.

At present, this plugin does not define any failure flags, which will
almost certainly have to be changed in the future.  So far, however, I
haven't actually seen it fail on any PSFs I've given it, so I'll wait
until we can run on large enough data volumes to see what the actual
failure modes are, instead of trying to guess them in advance.

Definition at line 107 of file psfContinued.py.

Constructor & Destructor Documentation

__init__()

lsst.meas.modelfit.psf.psfContinued.GeneralShapeletPsfApproxMixin.__init__	(		self,
			config,
			name,
			schema )

Initialize the plugin, creating a sequence of GeneralPsfFitter
instances to do the fitting and MultiShapeletFunctionKey instances to
save the results to a record.

Reimplemented in lsst.meas.modelfit.psf.psfContinued.GeneralShapeletPsfApproxSingleFramePlugin, and lsst.meas.modelfit.psf.psfContinued.GeneralShapeletPsfApproxForcedPlugin.

Definition at line 127 of file psfContinued.py.

    def __init__(self, config, name, schema):
        """Initialize the plugin, creating a sequence of GeneralPsfFitter
        instances to do the fitting and MultiShapeletFunctionKey instances to
        save the results to a record.
        """
        self.sequence = []
        for m in config.sequence:
            fitter = GeneralPsfFitterAlgorithm(
                config.models[m].makeControl(),
                schema,
                schema[name][m].getPrefix()
            )
            self.sequence.append((fitter, schema[name][m].getPrefix()))
 

Member Function Documentation

fail()

lsst.meas.modelfit.psf.psfContinued.GeneralShapeletPsfApproxMixin.fail	(		self,
			measRecord,
			error = None )

Reimplemented in lsst.meas.modelfit.psf.psfContinued.GeneralShapeletPsfApproxSingleFramePlugin, and lsst.meas.modelfit.psf.psfContinued.GeneralShapeletPsfApproxForcedPlugin.

Definition at line 183 of file psfContinued.py.

    def fail(self, measRecord, error=None):
        pass
 
 

measure()

lsst.meas.modelfit.psf.psfContinued.GeneralShapeletPsfApproxMixin.measure	(		self,
			measRecord,
			exposure )

Fit the configured sequence of models the given Exposure's Psf, as
evaluated at measRecord.getCentroid(), then save the results to
measRecord.

Reimplemented in lsst.meas.modelfit.psf.psfContinued.GeneralShapeletPsfApproxSingleFramePlugin, and lsst.meas.modelfit.psf.psfContinued.GeneralShapeletPsfApproxForcedPlugin.

Definition at line 141 of file psfContinued.py.

    def measure(self, measRecord, exposure):
        """Fit the configured sequence of models the given Exposure's Psf, as
        evaluated at measRecord.getCentroid(), then save the results to
        measRecord.
        """
        if not exposure.hasPsf():
            raise lsst.meas.base.FatalAlgorithmError(
                "GeneralShapeletPsfApprox requires Exposure to have a Psf")
        psf = exposure.getPsf()
        psfImage = psf.computeKernelImage(measRecord.getCentroid())
        psfShape = psf.computeShape(measRecord.getCentroid())
        lastError = None
        lastModel = None
        # Fit the first element in the sequence, using the PSFs moments to
        # initialize the parameters For every other element in the fitting
        # sequence, use the previous fit to initialize the parameters
        lastResult = None
        for fitter, name in self.sequence:
            try:
                if lastModel is None:
                    fitter.measure(measRecord, psfImage, psfShape)
                else:
                    fitter.measure(measRecord, psfImage,
                                   fitter.adapt(lastResult, lastModel))
                lastResult = measRecord.get(fitter.getKey())
                lastModel = fitter.getModel()
            except lsst.meas.base.baseMeasurement.FATAL_EXCEPTIONS:
                raise
            except lsst.meas.base.MeasurementError as error:
                fitter.fail(measRecord, error.cpp)
                lastError = error
            except Exception as error:
                fitter.fail(measRecord)
                lastError = error
        # When we are done with all the fitters, raise the last error if there
        # was one. This gives the calling task a chance to do whatever it
        # wants
        if lastError is not None:
            raise lastError
 

Member Data Documentation

sequence

lsst.meas.modelfit.psf.psfContinued.GeneralShapeletPsfApproxMixin.sequence

Definition at line 132 of file psfContinued.py.

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The documentation for this class was generated from the following file:

• /j/snowflake/release/lsstsw/stack/lsst-scipipe-8.0.0/Linux64/meas_modelfit/g591dd9f2cf+b2918d57ae/python/lsst/meas/modelfit/psf/psfContinued.py