fitAffineWcs.py

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# This file is part of meas_astrom.
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__all__ = ["FitAffineWcsTask", "FitAffineWcsConfig", "TransformedSkyWcsMaker"]
 
 
import astshim
import numpy as np
from scipy.optimize import least_squares
 
from lsst.afw.geom import makeSkyWcs, SkyWcs
import lsst.afw.math
from lsst.geom import Point2D, degrees, arcseconds, radians
import lsst.pex.config as pexConfig
import lsst.pipe.base as pipeBase
 
from .makeMatchStatistics import makeMatchStatisticsInRadians
from .setMatchDistance import setMatchDistance
 
 
def _chiFunc(x, refPoints, srcPixels, wcsMaker):
    """Function to minimize to fit the shift and rotation in the WCS.
 
    Parameters
    ----------
    x : `numpy.ndarray`
        Current fit values to test. Float values in array are:
 
        - ``bearingTo``: Direction to move the wcs coord in.
        - ``separation``: Distance along sphere to move wcs coord in.
        - ``affine0,0``: [0, 0] value of the 2x2 affine transform matrix.
        - ``affine0,1``: [0, 1] value of the 2x2 affine transform matrix.
        - ``affine1,0``: [1, 0] value of the 2x2 affine transform matrix.
        - ``affine1,1``: [1, 1] value of the 2x2 affine transform matrix.
    refPoints : `list` of `lsst.afw.geom.SpherePoint`
        Reference object on Sky locations.
    srcPixels : `list` of `lsst.geom.Point2D`
        Source object positions on the pixels.
    wcsMaker : `TransformedSkyWcsMaker`
        Container class for producing the updated Wcs.
 
    Returns
    -------
    outputSeparations : `list` of `float`
        Separation between predicted source location and reference location in
        radians.
    """
    wcs = wcsMaker.makeWcs(x[:2], x[2:].reshape((2, 2)))
 
    outputSeparations = []
    # Fit both sky to pixel and pixel to sky to avoid any non-invertible
    # affine matrices.
    for ref, src in zip(refPoints, srcPixels):
        skySep = ref.getTangentPlaneOffset(wcs.pixelToSky(src))
        outputSeparations.append(skySep[0].asArcseconds())
        outputSeparations.append(skySep[1].asArcseconds())
        xySep = src - wcs.skyToPixel(ref)
        # Convert the pixel separations to units, arcseconds to match units
        # of sky separation.
        outputSeparations.append(
            xySep[0] * wcs.getPixelScale(src).asArcseconds())
        outputSeparations.append(
            xySep[1] * wcs.getPixelScale(src).asArcseconds())
 
    return outputSeparations
 
 
# Keeping this around for now in case any of the fit parameters need to be
# configurable. Likely the maximum allowed shift magnitude (parameter 2 in the
# fit.)
class FitAffineWcsConfig(pexConfig.Config):
    """Config for FitTanSipWcsTask."""
    pass
 
 
class FitAffineWcsTask(pipeBase.Task):
    """Fit a TAN-SIP WCS given a list of reference object/source matches.
 
    This WCS fitter should be used on top of a cameraGeom distortion model as
    the model assumes that only a shift the WCS center position and a small
    affine transform are required.
    """
    ConfigClass = FitAffineWcsConfig
    _DefaultName = "fitAffineWcs"
 
    @pipeBase.timeMethod
    def fitWcs(self,
               matches,
               initWcs,
               bbox=None,
               refCat=None,
               sourceCat=None,
               exposure=None):
        """Fit a simple Affine transform with a shift to the matches and update
        the WCS.
 
        This method assumes that the distortion model of the telescope is
        applied correctly and is accurate with only a slight rotation,
        rotation, and "squish" required to fit to the reference locations.
 
        Parameters
        ----------
        matches : `list` of `lsst.afw.table.ReferenceMatch`
            The following fields are read:
 
            - match.first (reference object) coord
            - match.second (source) centroid
 
            The following fields are written:
 
            - match.first (reference object) centroid,
            - match.second (source) centroid
            - match.distance (on sky separation, in radians)
 
        initWcs : `lsst.afw.geom.SkyWcs`
            initial WCS
        bbox : `lsst.geom.Box2I`
            Ignored; present for consistency with FitSipDistortionTask.
        refCat : `lsst.afw.table.SimpleCatalog`
            reference object catalog, or None.
            If provided then all centroids are updated with the new WCS,
            otherwise only the centroids for ref objects in matches are
            updated. Required fields are "centroid_x", "centroid_y",
            "coord_ra", and "coord_dec".
        sourceCat : `lsst.afw.table.SourceCatalog`
            source catalog, or None.
            If provided then coords are updated with the new WCS;
            otherwise only the coords for sources in matches are updated.
            Required fields are "slot_Centroid_x", "slot_Centroid_y", and
            "coord_ra", and "coord_dec".
        exposure : `lsst.afw.image.Exposure`
            Ignored; present for consistency with FitSipDistortionTask.
 
        Returns
        -------
        result : `lsst.pipe.base.Struct`
            with the following fields:
 
            - ``wcs`` :  the fit WCS (`lsst.afw.geom.SkyWcs`)
            - ``scatterOnSky`` :  median on-sky separation between reference
              objects and sources in "matches" (`lsst.afw.geom.Angle`)
        """
        # Create a data-structure that decomposes the input Wcs frames and
        # appends the new transform.
        wcsMaker = TransformedSkyWcsMaker(initWcs)
 
        refPoints = []
        srcPixels = []
        offsetLong = 0
        offsetLat = 0
        # Grab reference coordinates and source centroids. Compute the average
        # direction and separation between the reference and the sources.
        for match in matches:
            refCoord = match.first.getCoord()
            refPoints.append(refCoord)
            srcCentroid = match.second.getCentroid()
            srcPixels.append(srcCentroid)
            srcCoord = initWcs.pixelToSky(srcCentroid)
            deltaLong, deltaLat = srcCoord.getTangentPlaneOffset(refCoord)
            offsetLong += deltaLong.asArcseconds()
            offsetLat += deltaLat.asArcseconds()
        offsetLong /= len(srcPixels)
        offsetLat /= len(srcPixels)
        offsetDist = np.sqrt(offsetLong ** 2 + offsetLat ** 2)
        if offsetDist > 0.:
            offsetDir = np.degrees(np.arccos(offsetLong / offsetDist))
        else:
            offsetDir = 0.
        offsetDir *= np.sign(offsetLat)
        self.log.debug("Initial shift guess: Direction: %.3f, Dist %.3f..." %
                       (offsetDir, offsetDist))
 
        # Best performing fitter in scipy tried so far (vs. default settings in
        # minimize). Exits early because of the xTol value which cannot be
        # disabled in scipy1.2.1. Matrix starting values are non-zero as this
        # results in better fit off-diagonal terms.
        fit = least_squares(
            _chiFunc,
            x0=[offsetDir, offsetDist, 1., 1e-8, 1e-8, 1.],
            args=(refPoints, srcPixels, wcsMaker),
            method='dogbox',
            bounds=[[-360, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf],
                    [360, np.inf, np.inf, np.inf, np.inf, np.inf]],
            ftol=2.3e-16,
            gtol=2.31e-16,
            xtol=2.3e-16)
        self.log.debug("Best fit: Direction: %.3f, Dist: %.3f, "
                       "Affine matrix: [[%.6f, %.6f], [%.6f, %.6f]]..." %
                       (fit.x[0], fit.x[1],
                        fit.x[2], fit.x[3], fit.x[4], fit.x[5]))
 
        wcs = wcsMaker.makeWcs(fit.x[:2], fit.x[2:].reshape((2, 2)))
 
        # Copied from other fit*WcsTasks.
        if refCat is not None:
            self.log.debug("Updating centroids in refCat")
            lsst.afw.table.updateRefCentroids(wcs, refList=refCat)
        else:
            self.log.warn("Updating reference object centroids in match list; "
                          "refCat is None")
            lsst.afw.table.updateRefCentroids(
                wcs,
                refList=[match.first for match in matches])
 
        if sourceCat is not None:
            self.log.debug("Updating coords in sourceCat")
            lsst.afw.table.updateSourceCoords(wcs, sourceList=sourceCat)
        else:
            self.log.warn("Updating source coords in match list; sourceCat is "
                          "None")
            lsst.afw.table.updateSourceCoords(
                wcs,
                sourceList=[match.second for match in matches])
        setMatchDistance(matches)
 
        stats = makeMatchStatisticsInRadians(wcs,
                                             matches,
                                             lsst.afw.math.MEDIAN)
        scatterOnSky = stats.getValue() * radians
 
        self.log.debug("In fitter scatter %.4f" % scatterOnSky.asArcseconds())
 
        return lsst.pipe.base.Struct(
            wcs=wcs,
            scatterOnSky=scatterOnSky,
        )
 
 
class TransformedSkyWcsMaker():
    """Convenience class for appending a shifting an input SkyWcs on sky and
    appending an affine transform.
 
    The class assumes that all frames are sequential and are mapped one to the
    next.
 
    Parameters
    ----------
    input_sky_wcs : `lsst.afw.geom.SkyWcs`
        WCS to decompose and append affine matrix and shift in on sky
        location to.
    """
 
    def __init__(self, inputSkyWcs):
        self.frameDict = inputSkyWcs.getFrameDict()
 
        # Grab the order of the frames by index.
        # TODO: DM-20825
        #    Change the frame the transform is appended to to be explicitly
        #    the FIELD_ANGLE->IWC transform. Requires related tickets to be
        #    completed.
        domains = self.frameDict.getAllDomains()
        self.frameIdxs = np.sort([self.frameDict.getIndex(domain)
                                  for domain in domains])
        self.frameMin = np.min(self.frameIdxs)
        self.frameMax = np.max(self.frameIdxs)
 
        # Find frame just before the final mapping to sky and store those
        # indices and mappings for later.
        self.mapFrom = self.frameMax - 2
        if self.mapFrom < self.frameMin:
            self.mapFrom = self.frameMin
        self.mapTo = self.frameMax - 1
        if self.mapTo <= self.mapFrom:
            self.mapTo = self.frameMax
        self.lastMapBeforeSky = self.frameDict.getMapping(
            self.mapFrom, self.mapTo)
 
        # Get the original WCS sky location.
 
        self.origin = inputSkyWcs.getSkyOrigin()
 
    def makeWcs(self, crvalOffset, affMatrix):
        """Apply a shift and affine transform to the WCS internal to this
        class.
 
        A new SkyWcs with these transforms applied is returns.
 
        Parameters
        ----------
        crval_shift : `numpy.ndarray`, (2,)
            Shift in radians to apply to the Wcs origin/crvals.
        aff_matrix : 'numpy.ndarray', (3, 3)
            Affine matrix to apply to the mapping/transform to add to the
            WCS.
 
        Returns
        -------
        outputWcs : `lsst.afw.geom.SkyWcs`
            Wcs with a final shift and affine transform applied.
        """
        # Create a WCS that only maps from IWC to Sky with the shifted
        # Sky origin position. This is simply the final undistorted tangent
        # plane to sky. The PIXELS to SKY map will be become our IWC to SKY
        # map and gives us our final shift position.
        iwcsToSkyWcs = makeSkyWcs(
            Point2D(0., 0.),
            self.origin.offset(crvalOffset[0] * degrees,
                               crvalOffset[1] * arcseconds),
            np.array([[1., 0.], [0., 1.]]))
        iwcToSkyMap = iwcsToSkyWcs.getFrameDict().getMapping("PIXELS", "SKY")
 
        # Append a simple affine Matrix transform to the current to the
        # second to last frame mapping. e.g. the one just before IWC to SKY.
        newMapping = self.lastMapBeforeSky.then(astshim.MatrixMap(affMatrix))
 
        # Create a new frame dict starting from the input_sky_wcs's first
        # frame. Append the correct mapping created above and our new on
        # sky location.
        outputFrameDict = astshim.FrameDict(
            self.frameDict.getFrame(self.frameMin))
        for frameIdx in self.frameIdxs:
            if frameIdx == self.mapFrom:
                outputFrameDict.addFrame(
                    self.mapFrom,
                    newMapping,
                    self.frameDict.getFrame(self.mapTo))
            elif frameIdx >= self.mapTo:
                continue
            else:
                outputFrameDict.addFrame(
                    frameIdx,
                    self.frameDict.getMapping(frameIdx, frameIdx + 1),
                    self.frameDict.getFrame(frameIdx + 1))
        # Append the final sky frame to the frame dict.
        outputFrameDict.addFrame(
            self.frameMax - 1,
            iwcToSkyMap,
            iwcsToSkyWcs.getFrameDict().getFrame("SKY"))
 
        return SkyWcs(outputFrameDict)