EstimateZernikesDanishConfig#

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

Bases: EstimateZernikesBaseConfig

Danish-specific configuration parameters for Zernike estimation.

Attributes Summary

`binning`	Binning factor to apply to the donut stamps before estimating Zernike coefficients.
`bkgOrder`	Order of the background polynomial to fit.
`doAoiThroughput`	Whether to apply angle-of-incidence throughput correction in the danish forward model.
`instConfigFile`	' the path will be understood as relative to the ts_wep policy directory.
`jointFitPair`	Whether to jointly fit intra/extra pairs, when a pair is provided.
`lstsqKwargs`	A dictionary containing any of the keyword arguments for scipy.optimize.least_squares, except `fun`, `x0`, `jac`, or `args`.
`modelSpiderShadows`	Whether to include the spider shadows or not in the danish forward model.
`nollIndices`	Noll indices for which you wish to estimate Zernike coefficients.
`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.
`systematicLossAlpha`	Fractional systematic uncertainty for the danish loss function.
`timeout`	Timeout in seconds for the multiprocessing pool.
`triangleMode`	Whether to use the triangle mode in the danish forward model.

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)

bkgOrder: Field#: Order of the background polynomial to fit. A value of -1 means no background fitting. (int, default -1)

doAoiThroughput: Field#: Whether to apply angle-of-incidence throughput correction in the danish forward model. (bool, default False)

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

jointFitPair: Field#: Whether to jointly fit intra/extra pairs, when a pair is provided. If False, Zernikes are estimated for each individually, then averaged. (bool, default True)

lstsqKwargs: DictField#: A dictionary containing any of the keyword arguments for scipy.optimize.least_squares, except fun, x0, jac, or args. (Dict, default {})

modelSpiderShadows: Field#: Whether to include the spider shadows or not in the danish forward model. (bool, default False)

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))

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)

systematicLossAlpha: Field#: Fractional systematic uncertainty for the danish loss function. The effective per-pixel variance becomes var + model + (alpha * model)**2, which caps per-pixel SNR and down-weights pixels dominated by unmodeled correlated residuals. A value of 0 (the default) recovers the standard chi-squared loss. (float, default 0.0)

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)

triangleMode: Field#: Whether to use the triangle mode in the danish forward model. (bool, default False)