SetSelector#

class lsst.analysis.tools.actions.vector.SetSelector(*args, **kw)#

Bases: SelectorBase

Selects rows with any number of column values within a given set.

For example, given a set of patches (1, 2, 3), and a set of columns (index_1, index_2), return all rows with either index_1 or index_2

in the set (1, 2, 3).

Notes#

The values are given as floats for flexibility. Integers above the floating point limit (2^53 + 1 = 9,007,199,254,740,993 for 64 bits) will not compare exactly with their float representations.

Attributes Summary

`plotLabelKey`	Key to use when populating plot info, ignored if empty string (`str`, default `''`)
`values`	The set of acceptable values (`List`, default `[]`)
`vectorKeys`	Keys to select from data (`List`, default `[]`)

Methods Summary

`__call__`(data, **kwargs)	Return a mask of rows with values in the specified set.
`getInputSchema`()	Return the schema an `AnalysisAction` expects to be present in the arguments supplied to the __call__ method.

Attributes Documentation

plotLabelKey#: Key to use when populating plot info, ignored if empty string (str, default '')

values#: The set of acceptable values (List, default [])

vectorKeys#: Keys to select from data (List, default [])

Methods Documentation

__call__(data: MutableMapping[str, ndarray[tuple[Any, ...], dtype[_ScalarT]] | Scalar | HealSparseMap | Tensor | Mapping], **kwargs) → ndarray[tuple[Any, ...], dtype[_ScalarT]]#

Return a mask of rows with values in the specified set.

Parameters#

data : KeyedData

Returns#

resultVector: A mask of the rows with values in the specified set.

getInputSchema() → Iterable[tuple[str, type[ndarray[tuple[Any, ...], dtype[_ScalarT]]] | type[Scalar] | type[HealSparseMap] | type[Tensor] | type[Mapping]]]#

Return the schema an AnalysisAction expects to be present in the arguments supplied to the __call__ method.

Returns#

resultKeyedDataSchema: The schema this action requires to be present when calling this action, keys are unformatted.