EstimateZernikesTieConfig#

class lsst.ts.wep.task.EstimateZernikesTieConfig(*args, **kw)#

Bases: EstimateZernikesBaseConfig

TIE-specific configuration parameters for Zernike estimation.

Attributes Summary

`binning`	Binning factor to apply to the donut stamps before estimating Zernike coefficients.
`centerBinary`	Whether to use a binary template when centering the image.
`centerTol`	Maximum absolute change in any Zernike coefficient (in meters) for which the images will be recentered.
`compGain`	Gain used to update Zernikes for image compensation.
`compSequence`	Max Noll index to compensate during each iteration of TIE.
`convergeTol`	The maximum absolute change in any Zernike amplitude (in meters) between subsequent TIE iterations below which convergence is declared.
`instConfigFile`	' the path will be understood as relative to the ts_wep policy directory.
`maskKwargs`	Dictionary of mask keyword arguments to pass to mask creation.
`maxIter`	Maximum number of iterations for the TIE loop.
`modelPupilKernelSize`	The size of the Gaussian kernel to convolve with the model pupil when estimating Zernikes with a single donut.
`nollIndices`	Noll indices for which you wish to estimate Zernike coefficients.
`opticalModel`	The optical model to use for mapping between the image and pupilplanes.
`requireConverge`	Whether to require that the TIE converges.
`returnWfDev`	If True, returns wavefront deviation.
`saveHistory`	Whether to save the algorithm history in the task metadata.
`startWithIntrinsic`	Whether to start Zernike estimation from the intrinsic Zernikes.
`timeout`	Timeout in seconds for the multiprocessing pool.

Attributes Documentation

binning: Field#: Binning factor to apply to the donut stamps before estimating Zernike coefficients. A value of 1 means no binning. (int, default 1)

centerBinary: Field#: Whether to use a binary template when centering the image. (the default is True) (bool, default True)

centerTol: Field#: Maximum absolute change in any Zernike coefficient (in meters) for which the images will be recentered. If 0, the images are recentered on every iteration. (the default is 1e-9) (float, default 1e-09)

compGain: Field#: Gain used to update Zernikes for image compensation. (the default is 0.6) (float, default 0.6)

compSequence: ListField#: Max Noll index to compensate during each iteration of TIE. Once the end of the sequence is reached, all Zernike coefficients are used during compensation. (the default is [4, 4, 6, 6, 13, 13, 13, 13, 22, 22, 22, 22]) (List, default [4, 4, 6, 6, 13, 13, 13, 13, 22, 22, 22, 22])

convergeTol: Field#: The maximum absolute change in any Zernike amplitude (in meters) between subsequent TIE iterations below which convergence is declared. (the default is 1e-9) (float, default 1e-09)

instConfigFile: pexConfig.Field#

‘ the path will be understood as relative to the ts_wep policy directory. If not provided, the default instrument for the camera will be loaded. (str, default None)

Type:: Path to a instrument configuration file to override the instrument configuration. If begins with ‘policy

maskKwargs: DictField#: Dictionary of mask keyword arguments to pass to mask creation. To see possibilities, see docstring for lsst.ts.wep.imageMapper.ImageMapper.createPupilMasks(). (the default is an emtpy dictionary) (Dict, default {})

maxIter: Field#: Maximum number of iterations for the TIE loop. (the default is 30) (int, default 30)

modelPupilKernelSize: Field#: The size of the Gaussian kernel to convolve with the model pupil when estimating Zernikes with a single donut. (the default is 2) (float, default 2)

nollIndices: pexConfig.Field#: Noll indices for which you wish to estimate Zernike coefficients. Note these values must be unique, ascending, >= 4, and azimuthal pairs must be complete. For example, if nollIndices contains 5, it must also contain 6 (because 5 and 6 are the azimuthal pairs for astigmatism). (List, default (4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28))

opticalModel: ChoiceField#

The optical model to use for mapping between the image and pupilplanes. Can be ‘offAxis’, ‘onAxis’, or ‘paraxial’. offAxis is anumerical model that is valid for all optical systems, but requiresan accurate Batoid model. onAxis is an analytic model that is validfor all optical systems near the optical axis. paraxial is ananalytic model that is valid for slow optical systems near theoptical axis. offAxis is recommended when you have a Batoid modeland onAxis is recommended when you do not. paraxial is primarilymeant for testing (the default is ‘offAxis’) (str, default 'offAxis')

Allowed values:

'offAxis': Numerical model fit by Batoid telescope model.
'onAxis': Analytic model only suitable for small field angles.
'paraxial': Analytic model only suitable for slow optical systems.
'None': Field is optional

requireConverge: Field#: Whether to require that the TIE converges. If True, and the TIE did not converge, the TIE returns NaNs. (bool, default False)

returnWfDev: pexConfig.Field#: If True, returns wavefront deviation. If False, returns full OPD. (bool, default False)

saveHistory: pexConfig.Field#: Whether to save the algorithm history in the task metadata. Depending on the algorithm, saving the history might slow down estimation, but doing so will provide intermediate products from the estimation process. (bool, default False)

startWithIntrinsic: pexConfig.Field#: Whether to start Zernike estimation from the intrinsic Zernikes. (bool, default True)

timeout: pexConfig.Field#: Timeout in seconds for the multiprocessing pool. If the pool does not complete within this time, an empty results list is returned. (int, default 600)