Inheritance diagram for lsst.meas.astrom.fitTanSipWcs.FitTanSipWcsTask:

Public Member Functions
	fitWcs (self, matches, initWcs, bbox=None, refCat=None, sourceCat=None, exposure=None)

	initialWcs (self, matches, wcs)

	rejectMatches (self, matches, wcs, rejected)

	plotFit (self, matches, wcs, rejected)

Static Public Attributes
	ConfigClass = FitTanSipWcsConfig

Protected Member Functions
	_fitWcs (self, matches, wcs)

Static Protected Attributes
str	_DefaultName = "fitTanSipWcs"

Detailed Description

Fit a TAN-SIP WCS given a list of reference object/source matches.

Definition at line 75 of file fitTanSipWcs.py.

Member Function Documentation

◆ _fitWcs()

lsst.meas.astrom.fitTanSipWcs.FitTanSipWcsTask._fitWcs	(	self,
		matches,
		wcs )

protected

Fit a Wcs based on the matches and a guess Wcs.

Parameters
----------
matches : `list` of `lsst.afw.table.ReferenceMatch`
    List of sources matched to references.
wcs : `lsst.afw.geom.SkyWcs`
    Current WCS.

Returns
-------
sipObject : `lsst.meas.astrom.sip.CreateWcsWithSip`
    Fitted SIP object.

Definition at line 216 of file fitTanSipWcs.py.

    def _fitWcs(self, matches, wcs):
        """Fit a Wcs based on the matches and a guess Wcs.
 
        Parameters
        ----------
        matches : `list` of `lsst.afw.table.ReferenceMatch`
            List of sources matched to references.
        wcs : `lsst.afw.geom.SkyWcs`
            Current WCS.
 
        Returns
        -------
        sipObject : `lsst.meas.astrom.sip.CreateWcsWithSip`
            Fitted SIP object.
        """
        for i in range(self.config.numIter):
            sipObject = makeCreateWcsWithSip(matches, wcs, self.config.order)
            wcs = sipObject.getNewWcs()
        return sipObject
 

◆ fitWcs()

lsst.meas.astrom.fitTanSipWcs.FitTanSipWcsTask.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

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`
    the region over which the WCS will be valid (an lsst:afw::geom::Box2I);
    if None or an empty box then computed from matches
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`)

Definition at line 82 of file fitTanSipWcs.py.

    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
 
        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`
            the region over which the WCS will be valid (an lsst:afw::geom::Box2I);
            if None or an empty box then computed from matches
        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`)
        """
        if bbox is None:
            bbox = lsst.geom.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 exceptions.AstrometryFitFailure(f"All matches rejected in fitter iteration {rej+1}")
            self.log.debug(
                "Iteration %d of astrometry fitting: rejected %d outliers, out of %d total matches.",
                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.warning("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.warning("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 exceptions.AstrometryFitFailure(
                "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,
        )
 

◆ initialWcs()

lsst.meas.astrom.fitTanSipWcs.FitTanSipWcsTask.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.

Parameters
----------
matches : `list` of `lsst.afw.table.ReferenceMatch`
    List of sources matched to references.
wcs : `lsst.afw.geom.SkyWcs`
    Current WCS.

Returns
-------
newWcs : `lsst.afw.geom.SkyWcs`
    Initial WCS guess from estimated crpix and crval.

Definition at line 183 of file fitTanSipWcs.py.

    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.
 
        Parameters
        ----------
        matches : `list` of `lsst.afw.table.ReferenceMatch`
            List of sources matched to references.
        wcs : `lsst.afw.geom.SkyWcs`
            Current WCS.
 
        Returns
        -------
        newWcs : `lsst.afw.geom.SkyWcs`
            Initial WCS guess from estimated crpix and crval.
        """
        crpix = lsst.geom.Extent2D(0, 0)
        crval = lsst.sphgeom.Vector3d(0, 0, 0)
        for mm in matches:
            crpix += lsst.geom.Extent2D(mm.second.getCentroid())
            crval += mm.first.getCoord().getVector()
        crpix /= len(matches)
        crval /= len(matches)
        newWcs = afwGeom.makeSkyWcs(crpix=lsst.geom.Point2D(crpix),
                                    crval=lsst.geom.SpherePoint(crval),
                                    cdMatrix=wcs.getCdMatrix())
        return newWcs
 

◆ plotFit()

lsst.meas.astrom.fitTanSipWcs.FitTanSipWcsTask.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.

Parameters
----------
matches : `list` of `lsst.afw.table.ReferenceMatch`
    List of sources matched to references.
wcs : `lsst.afw.geom.SkyWcs`
    Fitted WCS.
rejected : array-like of `bool`
    Array of matches rejected from the fit.

Definition at line 263 of file fitTanSipWcs.py.

    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.
 
        Parameters
        ----------
        matches : `list` of `lsst.afw.table.ReferenceMatch`
            List of sources matched to references.
        wcs : `lsst.afw.geom.SkyWcs`
            Fitted WCS.
        rejected : array-like of `bool`
            Array of matches rejected from the fit.
        """
        try:
            import matplotlib.pyplot as plt
        except ImportError as e:
            self.log.warning("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()

◆ rejectMatches()

lsst.meas.astrom.fitTanSipWcs.FitTanSipWcsTask.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.

Parameters
----------
matches : `list` of `lsst.afw.table.ReferenceMatch`
    List of sources matched to references.
wcs : `lsst.afw.geom.SkyWcs`
    Fitted WCS.
rejected : array-like of `bool`
    Array of matches rejected from the fit. Unused.

Returns
-------
rejectedMatches : `ndarray` of type `bool`
    Matched objects found to be outside of tolerance.

Definition at line 236 of file fitTanSipWcs.py.

    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.
 
        Parameters
        ----------
        matches : `list` of `lsst.afw.table.ReferenceMatch`
            List of sources matched to references.
        wcs : `lsst.afw.geom.SkyWcs`
            Fitted WCS.
        rejected : array-like of `bool`
            Array of matches rejected from the fit. Unused.
 
        Returns
        -------
        rejectedMatches : `ndarray` of type `bool`
            Matched objects found to be outside of tolerance.
        """
        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())
 

Member Data Documentation

◆ _DefaultName

str lsst.meas.astrom.fitTanSipWcs.FitTanSipWcsTask._DefaultName = "fitTanSipWcs"

staticprotected

Definition at line 79 of file fitTanSipWcs.py.

◆ ConfigClass

lsst.meas.astrom.fitTanSipWcs.FitTanSipWcsTask.ConfigClass = FitTanSipWcsConfig

static

Definition at line 78 of file fitTanSipWcs.py.

The documentation for this class was generated from the following file:

/j/snowflake/release/lsstsw/stack/lsst-scipipe-8.0.0/Linux64/meas_astrom/gff1a9f87cc+0dd8ce4237/python/lsst/meas/astrom/fitTanSipWcs.py

Public Member Functions

Static Public Attributes

Protected Member Functions

Static Protected Attributes