approximateWcs.py

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import numpy
import lsst.afw.image as afwImage
import lsst.afw.table as afwTable
import lsst.afw.geom as afwGeom
from lsst.meas.base import SingleFrameMeasurementTask
from lsst.meas.astrom.sip import makeCreateWcsWithSip
from lsst.afw.image.basicUtils import assertWcsNearlyEqualOverBBox

__all__ = ["approximateWcs"]

class _MockTestCase(object):
    """A fake unit test case class that will enable us to call
    assertWcsNearlyEqualOverBBox from the method approximateWcs"""

    def fail(self, msgStr):
        raise UserWarning("WCS fitting failed " + msgStr)

def approximateWcs(wcs, bbox, order=3, nx=20, ny=20, iterations=3, \
                   skyTolerance=0.001*afwGeom.arcseconds, pixelTolerance=0.02, useTanWcs=False):
    """Approximate an existing WCS as a TAN-SIP WCS

    The fit is performed by evaluating the WCS at a uniform grid of points within a bounding box.

    @param[in] wcs  wcs to approximate
    @param[in] bbox  the region over which the WCS will be fit
    @param[in] order  order of SIP fit
    @param[in] nx  number of grid points along x
    @param[in] ny  number of grid points along y
    @param[in] iterations number of times to iterate over fitting
    @param[in] skyTolerance maximum allowed difference in world coordinates between
               input wcs and approximate wcs (default is 0.001 arcsec)
    @param[in] pixelTolerance maximum allowed difference in pixel coordinates between
               input wcs and approximate wcs (default is 0.02 pixels)
    @param[in] useTanWcs  send a TAN version of wcs to the fitter? It is documented to require that,
        but I don't think the fitter actually cares
    @return the fit TAN-SIP WCS
    """
    if useTanWcs:
        crCoord = wcs.getSkyOrigin()
        crPix = wcs.getPixelOrigin()
        cdMat = wcs.getCDMatrix()
        tanWcs = afwImage.makeWcs(crCoord, crPix, cdMat[0,0], cdMat[0,1], cdMat[1,0], cdMat[1,1])
    else:
        tanWcs = wcs

    # create a matchList consisting of a grid of points covering the bbox
    refSchema = afwTable.SimpleTable.makeMinimalSchema()
    refCoordKey = afwTable.CoordKey(refSchema["coord"])
    refCat = afwTable.SimpleCatalog(refSchema)

    sourceSchema = afwTable.SourceTable.makeMinimalSchema()
    SingleFrameMeasurementTask(schema=sourceSchema) # expand the schema
    sourceCentroidKey = afwTable.Point2DKey(sourceSchema["slot_Centroid"])

    sourceCat = afwTable.SourceCatalog(sourceSchema)

    matchList = afwTable.ReferenceMatchVector()

    bboxd = afwGeom.Box2D(bbox)
    for x in numpy.linspace(bboxd.getMinX(), bboxd.getMaxX(), nx):
        for y in numpy.linspace(bboxd.getMinY(), bboxd.getMaxY(), ny):
            pixelPos = afwGeom.Point2D(x, y)
            skyCoord = wcs.pixelToSky(pixelPos)

            refObj = refCat.addNew()
            refObj.set(refCoordKey, skyCoord)

            source = sourceCat.addNew()
            source.set(sourceCentroidKey, pixelPos)

            matchList.append(afwTable.ReferenceMatch(refObj, source, 0.0))

    # The TAN-SIP fitter is fitting x and y separately, so we have to iterate to make it converge
    for indx in range(iterations) :
        sipObject = makeCreateWcsWithSip(matchList, tanWcs, order, bbox)
        tanWcs = sipObject.getNewWcs()
    fitWcs = sipObject.getNewWcs()

    mockTest = _MockTestCase()
    assertWcsNearlyEqualOverBBox(mockTest, wcs, fitWcs, bbox, maxDiffSky=skyTolerance,
                                 maxDiffPix=pixelTolerance)

    return fitWcs