approximateWcs.py

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#
# LSST Data Management System
# Copyright 2008-2017 LSST Corporation.
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__all__ = ["approximateWcs"]
 
import numpy as np
 
import lsst.geom
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.geom.utils import assertWcsAlmostEqualOverBBox
 
 
class _MockTestCase:
    """A fake unit test case class that will enable us to call
    assertWcsAlmostEqualOverBBox 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*lsst.geom.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.
 
    Parameters
    ----------
    wcs : `lsst.afw.geom.SkyWcs`
        wcs to approximate
    bbox : `lsst.geom.Box2I`
        the region over which the WCS will be fit
    order : `int`
        order of SIP fit
    nx : `int`
        number of grid points along x
    ny : `int`
        number of grid points along y
    iterations : `int`
        number of times to iterate over fitting
    skyTolerance : `lsst.geom.Angle`
        maximum allowed difference in world coordinates between
        input wcs and approximate wcs (default is 0.001 arcsec)
    pixelTolerance : `float`
        maximum allowed difference in pixel coordinates between
        input wcs and approximate wcs (default is 0.02 pixels)
    useTanWcs : `bool`
        send a TAN version of wcs to the fitter? It is documented to require that,
        but I don't think the fitter actually cares
 
    Returns
    -------
    fitWcs : `lsst.afw.geom.SkyWcs`
        the fit TAN-SIP WCS
    """
    if useTanWcs:
        crpix = wcs.getPixelOrigin()
        crval = wcs.getSkyOrigin()
        cdMatrix = wcs.getCdMatrix(crpix)
        tanWcs = afwGeom.makeSkyWcs(crpix=crpix, crval=crval, cdMatrix=cdMatrix)
    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 = []
 
    bboxd = lsst.geom.Box2D(bbox)
    for x in np.linspace(bboxd.getMinX(), bboxd.getMaxX(), nx):
        for y in np.linspace(bboxd.getMinY(), bboxd.getMaxY(), ny):
            pixelPos = lsst.geom.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()
    assertWcsAlmostEqualOverBBox(mockTest, wcs, fitWcs, bbox, maxDiffSky=skyTolerance,
                                 maxDiffPix=pixelTolerance)
 
    return fitWcs