CustomFunctor

class lsst.pipe.tasks.functors.CustomFunctor(expr, **kwargs)

Bases: Functor

Arbitrary computation on a catalog

Column names (and thus the columns to be loaded from catalog) are found by finding all words and trying to ignore all “math-y” words.

Parameters:

exprstr

Expression to evaluate, to be parsed and executed by mag_aware_eval.

Attributes Summary

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

Methods Summary

__call__(data[, 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

name

noDup

shortname

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

Methods Documentation

__call__(data, dropna=False)

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:

dataMultilevelParquetTable 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.