NanoJansky

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

Bases: Photometry

Convert instrumental flux to nanojanskys.

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	Do not explode by band if used on object table.
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 DataFrame/Handle objects.
dn2MagErr(dn, dnErr, fluxMag0, fluxMag0Err)	Convert instrumental flux error to AB magnitude error.
dn2flux(dn, fluxMag0)	Convert instrumental flux to nanojanskys.
dn2fluxErr(dn, dnErr, fluxMag0, fluxMag0Err)	Convert instrumental flux error to nanojanskys.
dn2mag(dn, fluxMag0)	Convert instrumental flux to AB magnitude.
fail(df)
hypot(a, b)	Compute sqrt(a^2 + b^2) without under/overflow.
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

Do not explode by band if used on object table.

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 DataFrame/Handle objects.

dn2MagErr(dn, dnErr, fluxMag0, fluxMag0Err)

Convert instrumental flux error to AB magnitude error.

dn2flux(dn, fluxMag0)

Convert instrumental flux to nanojanskys.

dn2fluxErr(dn, dnErr, fluxMag0, fluxMag0Err)

Convert instrumental flux error to nanojanskys.

dn2mag(dn, fluxMag0)

Convert instrumental flux to AB magnitude.

fail(df)

classmethod hypot(a, b)

Compute sqrt(a^2 + b^2) without under/overflow.

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

Returns columns needed by functor from multilevel dataset.

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

Parameters:

datavarious

The data as either DeferredDatasetHandle, or InMemoryDatasetHandle.

columnIndex (optional): pandas `~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.