lsst.meas.astrom.verifyWcs Namespace Reference

Functions
def	checkMatches (srcMatchSet, exposure, log=None)

Function Documentation

checkMatches()

def lsst.meas.astrom.verifyWcs.checkMatches	(		srcMatchSet,
			exposure,
			log = None
	)

Check astrometric matches and assess Wcs quality by computing statics
over spacial cells in the image.

Parameters
----------
srcMatchSet : `list` of `lsst.afw.table.ReferenceMatch`
    List of matched sources to a reference catalog.
exposure : `lsst.afw.image.Exposure`
    Image the sources in srcMatchSet were detected/measured in.
log : `lsst.log.Log`
    Logger object.

Returns
-------
values : `dict`
    Result dictionary with fields:

    - ``minObjectsPerCell`` : (`int`)
    - ``maxObjectsPerCell`` : (`int`)
    - ``meanObjectsPerCell`` : (`float`)
    - ``stdObjectsPerCell`` : (`float`)

Definition at line 35 of file verifyWcs.py.

def checkMatches(srcMatchSet, exposure, log=None):
    """Check astrometric matches and assess Wcs quality by computing statics
    over spacial cells in the image.

    Parameters
    ----------
    srcMatchSet : `list` of `lsst.afw.table.ReferenceMatch`
        List of matched sources to a reference catalog.
    exposure : `lsst.afw.image.Exposure`
        Image the sources in srcMatchSet were detected/measured in.
    log : `lsst.log.Log`
        Logger object.

    Returns
    -------
    values : `dict`
        Result dictionary with fields:

        - ``minObjectsPerCell`` : (`int`)
        - ``maxObjectsPerCell`` : (`int`)
        - ``meanObjectsPerCell`` : (`float`)
        - ``stdObjectsPerCell`` : (`float`)
    """
    if not exposure:
        return {}

    if log is None:
        log = Log.getLogger("meas.astrom.verifyWcs.checkMatches")

    im = exposure.getMaskedImage().getImage()
    width, height = im.getWidth(), im.getHeight()
    nx, ny = 3, 3
    w, h = width//nx, height//ny

    if w == 0:
        w = 1
    while nx*w < width:
        w += 1

    if h == 0:
        h = 1
    while ny*h < height:
        h += 1

    cellSet = afwMath.SpatialCellSet(
        lsst.geom.Box2I(lsst.geom.Point2I(0, 0), lsst.geom.Extent2I(width, height)), w, h)
    #
    # Populate cellSet
    #
    i = -1
    for m in srcMatchSet:
        i += 1

        src = m.second
        csrc = afwDetection.Source()
        csrc.setId(i)
        csrc.setXAstrom(src.getXAstrom())
        csrc.setYAstrom(src.getYAstrom())

        try:
            cellSet.insertCandidate(measAlg.PsfCandidateF(csrc, exposure.getMaskedImage()))
        except Exception as e:
            log.warn(str(e))

    ncell = len(cellSet.getCellList())
    nobj = np.ndarray(ncell, dtype='i')

    for i in range(ncell):
        cell = cellSet.getCellList()[i]

        nobj[i] = cell.size()

        dx = np.ndarray(cell.size())
        dy = np.ndarray(cell.size())

        j = 0
        for cand in cell:
            #
            # Swig doesn't know that we're a SpatialCellImageCandidate;  all it knows is that we have
            # a SpatialCellCandidate so we need an explicit (dynamic) cast
            #
            mid = cand.getSource().getId()
            dx[j] = srcMatchSet[mid].first.getXAstrom() - srcMatchSet[mid].second.getXAstrom()
            dy[j] = srcMatchSet[mid].first.getYAstrom() - srcMatchSet[mid].second.getYAstrom()

            j += 1

        log.debug("%s %-30s  %8s  dx,dy = %5.2f,%5.2f  rms_x,y = %5.2f,%5.2f",
                  cell.getLabel(), cell.getBBox(), ("nobj=%d" % cell.size()),
                  dx.mean(), dy.mean(), dx.std(), dy.std())

    nobj.sort()

    values = {}
    values["minObjectsPerCell"] = int(nobj[0])  # otherwise it's a numpy integral type
    values["maxObjectsPerCell"] = int(nobj[-1])
    values["meanObjectsPerCell"] = nobj.mean()
    values["stdObjectsPerCell"] = nobj.std()

    return values

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