Photometry

class lsst.pipe.tasks.functors.Photometry(colFlux, colFluxErr=None, calib=None, **kwargs)

Bases: Functor

Attributes Summary

AB_FLUX_SCALE
COADD_ZP
FIVE_OVER_2LOG10
LOG_AB_FLUX_SCALE
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
dn2MagErr(dn, dnErr, fluxMag0, fluxMag0Err)
dn2flux(dn, fluxMag0)
dn2fluxErr(dn, dnErr, fluxMag0, fluxMag0Err)
dn2mag(dn, fluxMag0)
fail(df)
hypot(a, b)
multilevelColumns(data[, columnIndex, ...])	Returns columns needed by functor from multilevel dataset

Attributes Documentation

AB_FLUX_SCALE = 3630780547701.003

COADD_ZP = 27

FIVE_OVER_2LOG10 = 1.0857362047581296

LOG_AB_FLUX_SCALE = 12.56

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

dn2MagErr(dn, dnErr, fluxMag0, fluxMag0Err)

dn2flux(dn, fluxMag0)

dn2fluxErr(dn, dnErr, fluxMag0, fluxMag0Err)

dn2mag(dn, fluxMag0)

fail(df)

classmethod hypot(a, b)

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:

datavarious

The data as either MultilevelParquetTable, DeferredDatasetHandle, or InMemoryDatasetHandle.

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.