WfAlgorithm

class lsst.ts.wep.estimation.WfAlgorithm

Bases: ABC

Base class for wavefront estimation algorithms

Parameters

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Attributes Summary

history
requiresPairs	Whether the algorithm requires pairs to estimate Zernikes.

Methods Summary

estimateZk(I1[, I2, nollIndices, ...])

Return the wavefront Zernike coefficients in meters.

Attributes Documentation

history

requiresPairs

Whether the algorithm requires pairs to estimate Zernikes.

Methods Documentation

estimateZk(I1: ~lsst.ts.wep.image.Image, I2: ~typing.Optional[~lsst.ts.wep.image.Image] = None, nollIndices: ~typing.Sequence = (4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22), instrument: ~lsst.ts.wep.instrument.Instrument = <lsst.ts.wep.instrument.Instrument object>, startWithIntrinsic: bool = True, returnWfDev: bool = False, units: str = 'm', saveHistory: bool = False) → ndarray

Return the wavefront Zernike coefficients in meters.

Parameters

I1DonutStamp

An Image object containing an intra- or extra-focal donut image.

I2DonutStamp, optional

A second image, on the opposite side of focus from I1. (the default is None)

nollIndicesSequence, optional

List, tuple, or array of Noll indices for which you wish to estimate Zernike coefficients. Note these values must be unique, ascending, and >= 4. (the default is indices 4-22)

instrumentInstrument, optional

The Instrument object associated with the DonutStamps. (the default is the default Instrument)

startWithIntrinsicbool, optional

Whether to start the Zernike estimation process from the intrinsic Zernikes rather than zero. (the default is True)

returnWfDevbool, optional

If False, the full OPD is returned. If True, the wavefront deviation is returned. The wavefront deviation is defined as the OPD - intrinsic Zernikes. (the default is False)

unitsstr, optional

Units in which the Zernike amplitudes are returned. Options are “m”, “nm”, “um”, or “arcsecs”. (the default is “m”)

saveHistorybool, optional

Whether to save the algorithm history in the self.history attribute. If True, then self.history contains information about the most recent time the algorithm was run. (the default is False)

Returns

np.ndarray

Zernike coefficients estimated from the image (or pair of images)