LSSTApplications  18.0.0+46,18.0.0+93,19.0.0,19.0.0+1,19.0.0+2,19.0.0+3,19.0.0+4,19.0.0-1-g20d9b18+2,19.0.0-1-g3dc8cbe+2,19.0.0-1-g425ff20,19.0.0-1-g5549ca4,19.0.0-1-g580fafe+2,19.0.0-1-g5db401e+3,19.0.0-1-g6fe20d0+1,19.0.0-1-g7011481+2,19.0.0-1-g8c57eb9+2,19.0.0-1-g9828021+2,19.0.0-1-gb5175dc+2,19.0.0-1-gd7f3e1b+2,19.0.0-1-gdc0e4a7+2,19.0.0-1-ge272bc4+2,19.0.0-2-g0d9f9cd+2,19.0.0-2-g1c703f9ef+1,19.0.0-2-g3d9e4fb2+2,19.0.0-2-gd955cfd+2,19.0.0-3-g2d13df8,19.0.0-3-g63079e6+2,19.0.0-7-g8a434f2+1,19.0.0-7-gf796fef9+3,w.2019.49
LSSTDataManagementBasePackage
Classes | Functions
lsst.meas.astrom.approximateWcs Namespace Reference

Classes

class  _MockTestCase
 

Functions

def approximateWcs (wcs, bbox, order=3, nx=20, ny=20, iterations=3, skyTolerance=0.001 *lsst.geom.arcseconds, pixelTolerance=0.02, useTanWcs=False)
 

Function Documentation

◆ approximateWcs()

def lsst.meas.astrom.approximateWcs.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

Definition at line 44 of file approximateWcs.py.

44  skyTolerance=0.001*lsst.geom.arcseconds, pixelTolerance=0.02, useTanWcs=False):
45  """Approximate an existing WCS as a TAN-SIP WCS
46 
47  The fit is performed by evaluating the WCS at a uniform grid of points
48  within a bounding box.
49 
50  Parameters
51  ----------
52  wcs : `lsst.afw.geom.SkyWcs`
53  wcs to approximate
54  bbox : `lsst.geom.Box2I`
55  the region over which the WCS will be fit
56  order : `int`
57  order of SIP fit
58  nx : `int`
59  number of grid points along x
60  ny : `int`
61  number of grid points along y
62  iterations : `int`
63  number of times to iterate over fitting
64  skyTolerance : `lsst.geom.Angle`
65  maximum allowed difference in world coordinates between
66  input wcs and approximate wcs (default is 0.001 arcsec)
67  pixelTolerance : `float`
68  maximum allowed difference in pixel coordinates between
69  input wcs and approximate wcs (default is 0.02 pixels)
70  useTanWcs : `bool`
71  send a TAN version of wcs to the fitter? It is documented to require that,
72  but I don't think the fitter actually cares
73 
74  Returns
75  -------
76  fitWcs : `lsst.afw.geom.SkyWcs`
77  the fit TAN-SIP WCS
78  """
79  if useTanWcs:
80  crpix = wcs.getPixelOrigin()
81  crval = wcs.getSkyOrigin()
82  cdMatrix = wcs.getCdMatrix(crpix)
83  tanWcs = afwGeom.makeSkyWcs(crpix=crpix, crval=crval, cdMatrix=cdMatrix)
84  else:
85  tanWcs = wcs
86 
87  # create a matchList consisting of a grid of points covering the bbox
88  refSchema = afwTable.SimpleTable.makeMinimalSchema()
89  refCoordKey = afwTable.CoordKey(refSchema["coord"])
90  refCat = afwTable.SimpleCatalog(refSchema)
91 
92  sourceSchema = afwTable.SourceTable.makeMinimalSchema()
93  SingleFrameMeasurementTask(schema=sourceSchema) # expand the schema
94  sourceCentroidKey = afwTable.Point2DKey(sourceSchema["slot_Centroid"])
95 
96  sourceCat = afwTable.SourceCatalog(sourceSchema)
97 
98  matchList = []
99 
100  bboxd = lsst.geom.Box2D(bbox)
101  for x in np.linspace(bboxd.getMinX(), bboxd.getMaxX(), nx):
102  for y in np.linspace(bboxd.getMinY(), bboxd.getMaxY(), ny):
103  pixelPos = lsst.geom.Point2D(x, y)
104  skyCoord = wcs.pixelToSky(pixelPos)
105 
106  refObj = refCat.addNew()
107  refObj.set(refCoordKey, skyCoord)
108 
109  source = sourceCat.addNew()
110  source.set(sourceCentroidKey, pixelPos)
111 
112  matchList.append(afwTable.ReferenceMatch(refObj, source, 0.0))
113 
114  # The TAN-SIP fitter is fitting x and y separately, so we have to iterate to make it converge
115  for indx in range(iterations):
116  sipObject = makeCreateWcsWithSip(matchList, tanWcs, order, bbox)
117  tanWcs = sipObject.getNewWcs()
118  fitWcs = sipObject.getNewWcs()
119 
120  mockTest = _MockTestCase()
121  assertWcsAlmostEqualOverBBox(mockTest, wcs, fitWcs, bbox, maxDiffSky=skyTolerance,
122  maxDiffPix=pixelTolerance)
123 
124  return fitWcs
125 
A floating-point coordinate rectangle geometry.
Definition: Box.h:413
def assertWcsAlmostEqualOverBBox(testCase, wcs0, wcs1, bbox, maxDiffSky=0.01 *lsst.geom.arcseconds, maxDiffPix=0.01, nx=5, ny=5, msg="WCSs differ")
Definition: utils.py:155
Custom catalog class for record/table subclasses that are guaranteed to have an ID, and should generally be sorted by that ID.
Definition: fwd.h:63
Lightweight representation of a geometric match between two records.
Definition: fwd.h:101
std::shared_ptr< SkyWcs > makeSkyWcs(TransformPoint2ToPoint2 const &pixelsToFieldAngle, lsst::geom::Angle const &orientation, bool flipX, lsst::geom::SpherePoint const &boresight, std::string const &projection="TAN")
Construct a FITS SkyWcs from camera geometry.
Definition: SkyWcs.cc:516
CreateWcsWithSip< MatchT > makeCreateWcsWithSip(std::vector< MatchT > const &matches, afw::geom::SkyWcs const &linearWcs, int const order, geom::Box2I const &bbox=geom::Box2I(), int const ngrid=0)
Factory function for CreateWcsWithSip.
A FunctorKey used to get or set celestial coordinates from a pair of lsst::geom::Angle keys...
Definition: aggregates.h:210