NumStarLabeller

class lsst.pipe.tasks.functors.NumStarLabeller(filt=None, dataset=None, noDup=None)

Bases: lsst.pipe.tasks.functors.Labeller

Attributes Summary

columns	Columns required to perform calculation
labels
name
noDup
shortname	Short name of functor (suitable for column name/dict key)

Methods Summary

__call__(parq[, dropna])	Call self as a function.
difference(data1, data2, **kwargs)	Computes difference between functor called on two different ParquetTable objects
fail(df)
multilevelColumns(data[, columnIndex, …])	Returns columns needed by functor from multilevel dataset

Attributes Documentation

columns

Columns required to perform calculation

labels = {'maybe': 1, 'notStar': 2, 'star': 0}

name = 'label'

noDup

shortname

Short name of functor (suitable for column name/dict key)

Methods Documentation

__call__(parq, dropna=False, **kwargs)

Call self as a function.

difference(data1, data2, **kwargs)

Computes difference between functor called on two different ParquetTable objects

fail(df)

multilevelColumns(data, columnIndex=None, returnTuple=False)

Returns columns needed by functor from multilevel dataset

To access tables with multilevel column structure, the MultilevelParquetTable or DeferredDatasetHandle need to be passed either a list of tuples or a dictionary.

Parameters:

data : MultilevelParquetTable or DeferredDatasetHandle

columnIndex (optional): pandas `Index` object

either passed or read in from DeferredDatasetHandle.

`returnTuple` : bool

If true, then return a list of tuples rather than the column dictionary specification. This is set to True by CompositeFunctor in order to be able to combine columns from the various component functors.