fitTanSipWcs.py

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from __future__ import absolute_import, division, print_function
from builtins import zip
from builtins import range

import numpy as np

import lsst.afw.image as afwImage
import lsst.afw.coord as afwCoord
import lsst.afw.geom as afwGeom
import lsst.afw.table as afwTable
import lsst.pex.config as pexConfig
import lsst.pipe.base as pipeBase
from .setMatchDistance import setMatchDistance
from .sip import makeCreateWcsWithSip

__all__ = ["FitTanSipWcsTask", "FitTanSipWcsConfig"]


class FitTanSipWcsConfig(pexConfig.Config):
    order = pexConfig.RangeField(
        doc="order of SIP polynomial",
        dtype=int,
        default=4,
        min=0,
    )
    numIter = pexConfig.RangeField(
        doc="number of iterations of fitter (which fits X and Y separately, and so benefits from " +
        "a few iterations",
        dtype=int,
        default=3,
        min=1,
    )
    numRejIter = pexConfig.RangeField(
        doc="number of rejection iterations",
        dtype=int,
        default=1,
        min=0,
    )
    rejSigma = pexConfig.RangeField(
        doc="Number of standard deviations for clipping level",
        dtype=float,
        default=3.0,
        min=0.0,
    )
    maxScatterArcsec = pexConfig.RangeField(
        doc="maximum median scatter of a WCS fit beyond which the fit fails (arcsec); " +
        "be generous, as this is only intended to catch catastrophic failures",
        dtype=float,
        default=10,
        min=0,
    )

# The following block adds links to this task from the Task Documentation page.
## \addtogroup LSST_task_documentation
## \{
## \page measAstrom_fitTanSipWcsTask
## \ref FitTanSipWcsTask "FitTanSipWcsTask"
##      Fit a TAN-SIP WCS given a list of reference object/source matches
## \}


class FitTanSipWcsTask(pipeBase.Task):
    """!Fit a TAN-SIP WCS given a list of reference object/source matches

    @anchor FitTanSipWcsTask_

    @section meas_astrom_fitTanSipWcs_Contents Contents

     - @ref meas_astrom_fitTanSipWcs_Purpose
     - @ref meas_astrom_fitTanSipWcs_Initialize
     - @ref meas_astrom_fitTanSipWcs_IO
     - @ref meas_astrom_fitTanSipWcs_Schema
     - @ref meas_astrom_fitTanSipWcs_Config
     - @ref meas_astrom_fitTanSipWcs_Example
     - @ref meas_astrom_fitTanSipWcs_Debug

    @section meas_astrom_fitTanSipWcs_Purpose  Description

    Fit a TAN-SIP WCS given a list of reference object/source matches.
    See CreateWithSip.h for information about the fitting algorithm.

    @section meas_astrom_fitTanSipWcs_Initialize   Task initialisation

    @copydoc \_\_init\_\_

    @section meas_astrom_fitTanSipWcs_IO       Invoking the Task

    @copydoc fitWcs

    @section meas_astrom_fitTanSipWcs_Config       Configuration parameters

    See @ref FitTanSipWcsConfig

    @section meas_astrom_fitTanSipWcs_Example  A complete example of using FitTanSipWcsTask

    FitTanSipWcsTask is a subtask of AstrometryTask, which is called by PhotoCalTask.
    See \ref meas_photocal_photocal_Example.

    @section meas_astrom_fitTanSipWcs_Debug        Debug variables

    FitTanSipWcsTask does not support any debug variables.
    """
    ConfigClass = FitTanSipWcsConfig
    _DefaultName = "fitWcs"

    @pipeBase.timeMethod
    def fitWcs(self, matches, initWcs, bbox=None, refCat=None, sourceCat=None, exposure=None):
        """!Fit a TAN-SIP WCS from a list of reference object/source matches

        @param[in,out] matches  a list of reference object/source matches
            (an lsst::afw::table::ReferenceMatchVector)
            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)
        @param[in] initWcs  initial WCS
        @param[in] bbox  the region over which the WCS will be valid (an lsst:afw::geom::Box2I);
            if None or an empty box then computed from matches
        @param[in,out] refCat  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".
        @param[in,out] sourceCat  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".
        @param[in] exposure  Ignored; present for consistency with FitSipDistortionTask.

        @return an lsst.pipe.base.Struct with the following fields:
        - wcs  the fit WCS as an lsst.afw.image.Wcs
        - scatterOnSky  median on-sky separation between reference objects and sources in "matches",
            as an lsst.afw.geom.Angle
        """
        if bbox is None:
            bbox = afwGeom.Box2I()

        import lsstDebug
        debug = lsstDebug.Info(__name__)

        wcs = self.initialWcs(matches, initWcs)
        rejected = np.zeros(len(matches), dtype=bool)
        for rej in range(self.config.numRejIter):
            sipObject = self._fitWcs([mm for i, mm in enumerate(matches) if not rejected[i]], wcs)
            wcs = sipObject.getNewWcs()
            rejected = self.rejectMatches(matches, wcs, rejected)
            if rejected.sum() == len(rejected):
                raise RuntimeError("All matches rejected in iteration %d" % (rej + 1,))
            self.log.debug(
                "Iteration {0} of astrometry fitting: rejected {1} outliers, "
                "out of {2} total matches.".format(
                    rej, rejected.sum(), len(rejected)
                )
            )
            if debug.plot:
                print("Plotting fit after rejection iteration %d/%d" % (rej + 1, self.config.numRejIter))
                self.plotFit(matches, wcs, rejected)
        # Final fit after rejection
        sipObject = self._fitWcs([mm for i, mm in enumerate(matches) if not rejected[i]], wcs)
        wcs = sipObject.getNewWcs()
        if debug.plot:
            print("Plotting final fit")
            self.plotFit(matches, wcs, rejected)

        if refCat is not None:
            self.log.debug("Updating centroids in refCat")
            afwTable.updateRefCentroids(wcs, refList=refCat)
        else:
            self.log.warn("Updating reference object centroids in match list; refCat is None")
            afwTable.updateRefCentroids(wcs, refList=[match.first for match in matches])

        if sourceCat is not None:
            self.log.debug("Updating coords in sourceCat")
            afwTable.updateSourceCoords(wcs, sourceList=sourceCat)
        else:
            self.log.warn("Updating source coords in match list; sourceCat is None")
            afwTable.updateSourceCoords(wcs, sourceList=[match.second for match in matches])

        self.log.debug("Updating distance in match list")
        setMatchDistance(matches)

        scatterOnSky = sipObject.getScatterOnSky()

        if scatterOnSky.asArcseconds() > self.config.maxScatterArcsec:
            raise pipeBase.TaskError(
                "Fit failed: median scatter on sky = %0.3f arcsec > %0.3f config.maxScatterArcsec" %
                (scatterOnSky.asArcseconds(), self.config.maxScatterArcsec))

        return pipeBase.Struct(
            wcs=wcs,
            scatterOnSky=scatterOnSky,
        )

    def initialWcs(self, matches, wcs):
        """Generate a guess Wcs from the astrometric matches

        We create a Wcs anchored at the center of the matches, with the scale
        of the input Wcs.  This is necessary because matching returns only
        matches with no estimated Wcs, and the input Wcs is a wild guess.
        We're using the best of each: positions from the matches, and scale
        from the input Wcs.
        """
        crpix = afwGeom.Extent2D(0, 0)
        crval = afwGeom.Extent3D(0, 0, 0)
        for mm in matches:
            crpix += afwGeom.Extent2D(mm.second.getCentroid())
            crval += afwGeom.Extent3D(mm.first.getCoord().toIcrs().getVector())
        crpix /= len(matches)
        crval /= len(matches)
        newWcs = afwImage.Wcs(afwCoord.IcrsCoord(afwGeom.Point3D(crval)).getPosition(),
                              afwGeom.Point2D(crpix), wcs.getCDMatrix())
        return newWcs

    def _fitWcs(self, matches, wcs):
        """Fit a Wcs based on the matches and a guess Wcs"""
        for i in range(self.config.numIter):
            sipObject = makeCreateWcsWithSip(matches, wcs, self.config.order)
            wcs = sipObject.getNewWcs()
        return sipObject

    def rejectMatches(self, matches, wcs, rejected):
        """Flag deviant matches

        We return a boolean numpy array indicating whether the corresponding
        match should be rejected.  The previous list of rejections is used
        so we can calculate uncontaminated statistics.
        """
        fit = [wcs.skyToPixel(m.first.getCoord()) for m in matches]
        dx = np.array([ff.getX() - mm.second.getCentroid().getX() for ff, mm in zip(fit, matches)])
        dy = np.array([ff.getY() - mm.second.getCentroid().getY() for ff, mm in zip(fit, matches)])
        good = np.logical_not(rejected)
        return (dx > self.config.rejSigma*dx[good].std()) | (dy > self.config.rejSigma*dy[good].std())

    def plotFit(self, matches, wcs, rejected):
        """Plot the fit

        We create four plots, for all combinations of (dx, dy) against
        (x, y).  Good points are black, while rejected points are red.
        """
        try:
            import matplotlib.pyplot as plt
        except ImportError as e:
            self.log.warn("Unable to import matplotlib: %s", e)
            return

        fit = [wcs.skyToPixel(m.first.getCoord()) for m in matches]
        x1 = np.array([ff.getX() for ff in fit])
        y1 = np.array([ff.getY() for ff in fit])
        x2 = np.array([m.second.getCentroid().getX() for m in matches])
        y2 = np.array([m.second.getCentroid().getY() for m in matches])

        dx = x1 - x2
        dy = y1 - y2

        good = np.logical_not(rejected)

        figure = plt.figure()
        axes = figure.add_subplot(2, 2, 1)
        axes.plot(x2[good], dx[good], 'ko')
        axes.plot(x2[rejected], dx[rejected], 'ro')
        axes.set_xlabel("x")
        axes.set_ylabel("dx")

        axes = figure.add_subplot(2, 2, 2)
        axes.plot(x2[good], dy[good], 'ko')
        axes.plot(x2[rejected], dy[rejected], 'ro')
        axes.set_xlabel("x")
        axes.set_ylabel("dy")

        axes = figure.add_subplot(2, 2, 3)
        axes.plot(y2[good], dx[good], 'ko')
        axes.plot(y2[rejected], dx[rejected], 'ro')
        axes.set_xlabel("y")
        axes.set_ylabel("dx")

        axes = figure.add_subplot(2, 2, 4)
        axes.plot(y2[good], dy[good], 'ko')
        axes.plot(y2[rejected], dy[rejected], 'ro')
        axes.set_xlabel("y")
        axes.set_ylabel("dy")

        plt.show()