LocalDipoleDiffFluxErr

class lsst.pipe.tasks.functors.LocalDipoleDiffFluxErr(instFluxPosCol, instFluxNegCol, instFluxPosErrCol, instFluxNegErrCol, photoCalibCol, photoCalibErrCol, **kwargs)

Bases: LocalDipoleMeanFlux

Compute the error on the absolute difference of dipole fluxes.

See also

LocalNanojansky

LocalNanojanskyErr

LocalMagnitude

LocalMagnitudeErr

LocalDipoleMeanFlux

LocalDipoleMeanFluxErr

LocalDipoleDiffFlux

LocalDipoleDiffFluxErr

Attributes Summary

columns	Columns required to perform calculation
logNJanskyToAB
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)
instFluxErrToMagnitudeErr(instFlux, ...)	Convert instrument flux err to nanojanskys.
instFluxErrToNanojanskyErr(instFlux, ...)	Convert instrument flux to nanojanskys.
instFluxToMagnitude(instFlux, localCalib)	Convert instrument flux to nanojanskys.
instFluxToNanojansky(instFlux, localCalib)	Convert instrument flux to nanojanskys.
multilevelColumns(data[, columnIndex, ...])	Returns columns needed by functor from multilevel dataset

Attributes Documentation

columns

logNJanskyToAB = 31.4

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)

instFluxErrToMagnitudeErr(instFlux, instFluxErr, localCalib, localCalibErr)

Convert instrument flux err to nanojanskys.

Parameters:

instFluxnumpy.ndarray or pandas.Series

Array of instrument flux measurements

instFluxErrnumpy.ndarray or pandas.Series

Errors on associated instFlux values

localCalibnumpy.ndarray or pandas.Series

Array of local photometric calibration estimates.

localCalibErrnumpy.ndarray or pandas.Series

Errors on associated localCalib values

Returns:

calibMagErr: numpy.ndarray or pandas.Series

Error on calibrated AB magnitudes.

instFluxErrToNanojanskyErr(instFlux, instFluxErr, localCalib, localCalibErr)

Convert instrument flux to nanojanskys.

Parameters:

instFluxnumpy.ndarray or pandas.Series

Array of instrument flux measurements

instFluxErrnumpy.ndarray or pandas.Series

Errors on associated instFlux values

localCalibnumpy.ndarray or pandas.Series

Array of local photometric calibration estimates.

localCalibErrnumpy.ndarray or pandas.Series

Errors on associated localCalib values

Returns:

calibFluxErrnumpy.ndarray or pandas.Series

Errors on calibrated flux measurements.

instFluxToMagnitude(instFlux, localCalib)

Convert instrument flux to nanojanskys.

Parameters:

instFluxnumpy.ndarray or pandas.Series

Array of instrument flux measurements

localCalibnumpy.ndarray or pandas.Series

Array of local photometric calibration estimates.

Returns:

calibMagnumpy.ndarray or pandas.Series

Array of calibrated AB magnitudes.

instFluxToNanojansky(instFlux, localCalib)

Convert instrument flux to nanojanskys.

Parameters:

instFluxnumpy.ndarray or pandas.Series

Array of instrument flux measurements

localCalibnumpy.ndarray or pandas.Series

Array of local photometric calibration estimates.

Returns:

calibFluxnumpy.ndarray or pandas.Series

Array of calibrated flux measurements.

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.