lsst.meas.base.measurementInvestigationLib Namespace Reference

Functions
	rebuildNoiseReplacer (exposure, measCat)
	makeRerunCatalog (schema, oldCatalog, idList, fields=None, resetParents=True, addParents=False)

Function Documentation

makeRerunCatalog()

lsst.meas.base.measurementInvestigationLib.makeRerunCatalog	(		schema,
			oldCatalog,
			idList,
			fields = None,
			resetParents = True,
			addParents = False )

Create a catalog prepopulated with IDs.

This function is used to generate a `~lsst.afw.table.SourceCatalog`
containing blank records with IDs as specified in the ``idList``
parameter.

This function is primarily used when re-running measurements on a
particular footprint. Specifying IDs in the new measurement catalog which
correspond to IDs in the old catalog makes comparing results much easier.

The ``resetParents`` and ``addParents`` options are needed because
`SingleFrameMeasurementTask.runPlugins` will skip child
objects whose parents are not in the catalog.

Parameters
----------
schema : `lsst.afw.table.Schema`
    Schema used to describe the fields in the resulting catalog.

oldCatalog : `lsst.afw.table.SourceCatalog`
    Catalog containing previous measurements.

idList : iterable of `int`
    Iterable whose values should be numbers corresponding to measurement
    IDs which exist in ``oldCatalog``.

fields : iterable of `str`
    Iterable whose entries should be strings corresponding to schema keys
    that exist in both the old catalog and input schema. Fields listed
    will be copied from the old catalog into the new catalog.

resetParents : `bool`
    If `True`, child objects whose parents are not in the ``idList``
    will have their parents reset to zero.

addParents : `bool`
    If `True`, parents of child objects will be added to ``idList`` if
    they are not already present.

Returns
-------
measCat : `lsst.afw.table.SourceCatalog`
    Catalog prepopulated with entries with the IDs specified.

Definition at line 71 of file measurementInvestigationLib.py.

                     resetParents=True, addParents=False):
    """Create a catalog prepopulated with IDs.
 
    This function is used to generate a `~lsst.afw.table.SourceCatalog`
    containing blank records with IDs as specified in the ``idList``
    parameter.
 
    This function is primarily used when re-running measurements on a
    particular footprint. Specifying IDs in the new measurement catalog which
    correspond to IDs in the old catalog makes comparing results much easier.
 
    The ``resetParents`` and ``addParents`` options are needed because
    `SingleFrameMeasurementTask.runPlugins` will skip child
    objects whose parents are not in the catalog.
 
    Parameters
    ----------
    schema : `lsst.afw.table.Schema`
        Schema used to describe the fields in the resulting catalog.
 
    oldCatalog : `lsst.afw.table.SourceCatalog`
        Catalog containing previous measurements.
 
    idList : iterable of `int`
        Iterable whose values should be numbers corresponding to measurement
        IDs which exist in ``oldCatalog``.
 
    fields : iterable of `str`
        Iterable whose entries should be strings corresponding to schema keys
        that exist in both the old catalog and input schema. Fields listed
        will be copied from the old catalog into the new catalog.
 
    resetParents : `bool`
        If `True`, child objects whose parents are not in the ``idList``
        will have their parents reset to zero.
 
    addParents : `bool`
        If `True`, parents of child objects will be added to ``idList`` if
        they are not already present.
 
    Returns
    -------
    measCat : `lsst.afw.table.SourceCatalog`
        Catalog prepopulated with entries with the IDs specified.
    """
 
    if not isinstance(schema, Schema):
        raise RuntimeError("schema must be an instance of "
                           "lsst.afw.table.Schema")
 
    if not isinstance(oldCatalog, SourceCatalog):
        raise RuntimeError("oldCatalog must be an instance of "
                           "lsst.afw.table.SourceCatalogiterable")
 
    if fields is None:
        fields = []
    if not isinstance(fields, Iterable):
        raise RuntimeError("fields list must be an iterable with string"
                           "elements")
    for entry in fields:
        if entry not in schema:
            schema.addField(oldCatalog.schema.find(entry).field)
 
    if addParents:
        newIdList = set()
        for srcId in idList:
            newIdList.add(srcId)
            parentId = oldCatalog.find(srcId).getParent()
            if parentId:
                newIdList.add(parentId)
        idList = newIdList
    idList = sorted(idList)
 
    measCat = SourceCatalog(schema)
    for srcId in idList:
        oldSrc = oldCatalog.find(srcId)
        src = measCat.addNew()
        src.setId(srcId)
        src.setFootprint(oldSrc.getFootprint())
        parent = oldSrc.getParent()
        if resetParents and parent and parent not in idList:
            parent = 0
        src.setParent(parent)
        src.setCoord(oldSrc.getCoord())
        for entry in fields:
            src[entry] = oldSrc[entry]
    return measCat

rebuildNoiseReplacer()

lsst.meas.base.measurementInvestigationLib.rebuildNoiseReplacer	(		exposure,
			measCat )

Recreate the `NoiseReplacer` used in measurement.

Given a measurement catalog and the exposure on which the measurements
were made, reconstruct the `NoiseReplacer` object that was used to mask
out sources during measurement.

Parameters
----------
exposure : `lsst.afw.exposure.Exposure`
    The image on which measurements were made.

measCat : `lsst.afw.table.SourceCatalog`
    Catalog containing the results measurements on each source.

Returns
-------
noiseReplacer : `NoiseReplacer`
    Object used to replace and/or restore sources in the exposure with
    deterministic noise.

Definition at line 29 of file measurementInvestigationLib.py.

def rebuildNoiseReplacer(exposure, measCat):
    """Recreate the `NoiseReplacer` used in measurement.
 
    Given a measurement catalog and the exposure on which the measurements
    were made, reconstruct the `NoiseReplacer` object that was used to mask
    out sources during measurement.
 
    Parameters
    ----------
    exposure : `lsst.afw.exposure.Exposure`
        The image on which measurements were made.
 
    measCat : `lsst.afw.table.SourceCatalog`
        Catalog containing the results measurements on each source.
 
    Returns
    -------
    noiseReplacer : `NoiseReplacer`
        Object used to replace and/or restore sources in the exposure with
        deterministic noise.
    """
 
    algMetadata = measCat.getMetadata()
    noiseReplacerConf = NoiseReplacerConfig()
    noiseReplacerConf.noiseSeedMultiplier = \
        algMetadata.getScalar(SFMT.NOISE_SEED_MULTIPLIER)
    noiseReplacerConf.noiseSource = algMetadata.getScalar(SFMT.NOISE_SOURCE)
    noiseReplacerConf.noiseOffset = algMetadata.getScalar(SFMT.NOISE_OFFSET)
 
    footprints = {src.getId(): (src.getParent(), src.getFootprint())
                  for src in measCat}
 
    try:
        exposureId = algMetadata.getScalar(SFMT.NOISE_EXPOSURE_ID)
    except Exception:
        exposureId = None
 
    noiseReplacer = NoiseReplacer(noiseReplacerConf, exposure, footprints,
                                  exposureId=exposureId)
    return noiseReplacer