def run(md):
kwds = extractCtorArgs(md)
order = max(md["A_ORDER"], md["B_ORDER"], md["AP_ORDER"], md["BP_ORDER"])
gridShape = Extent2I(5, 5)
a = np.zeros((order + 1, order + 1), dtype=float)
b = np.zeros((order + 1, order + 1), dtype=float)
ap = np.zeros((order + 1, order + 1), dtype=float)
bp = np.zeros((order + 1, order + 1), dtype=float)
for p, q in packedRange(order):
a[p, q] = md.get("A_%d_%d" % (p, q), 0.0)
b[p, q] = md.get("B_%d_%d" % (p, q), 0.0)
ap[p, q] = md.get("AP_%d_%d" % (p, q), 0.0)
bp[p, q] = md.get("BP_%d_%d" % (p, q), 0.0)
approx = SipApproximation(a=a, b=b, ap=ap, bp=bp, gridShape=gridShape, **kwds)
self.compareSolution(md, approx)
diffs = approx.computeMaxDeviation()
self.assertLess(diffs[0], 1E-10)
self.assertLess(diffs[1], 1E-10)
bbox = kwds["bbox"]
pix1 = [Point2D(x, y)
for x in np.linspace(bbox.getMinX(), bbox.getMaxX(), gridShape.getX())
for y in np.linspace(bbox.getMinY(), bbox.getMaxY(), gridShape.getY())]
iwc1a = kwds["pixelToIwc"].applyForward(pix1)
pix2a = kwds["pixelToIwc"].applyInverse(iwc1a)
iwc1b = approx.applyForward(pix1)
assert_allclose(iwc1a, iwc1b, rtol=1E-9, atol=1E-12)
pix2b = approx.applyInverse(iwc1a)
assert_allclose(pix2a, pix2b, rtol=1E-9, atol=1E-12)
def run(md, **kwds2):
kwds = extractCtorArgs(md)
kwds.update(kwds2)
gridShape = Extent2I(20, 20)
approx = SipApproximation(gridShape=gridShape, **kwds)
diffs = approx.computeMaxDeviation()
self.assertLess(diffs[0], 0.1)
self.assertLess(diffs[1], 0.1)
def run(md, **kwds2):
kwds = extractCtorArgs(md)
kwds.update(kwds2)
gridShape = Extent2I(20, 20)
approx = SipApproximation(gridShape=gridShape, **kwds)
diffs = approx.computeMaxDeviation()
self.assertLess(diffs[0], 0.1)
self.assertLess(diffs[1], 0.1)
def run(md, **kwds2):
kwds = extractCtorArgs(md)
kwds['order'] = max(md["A_ORDER"], md["B_ORDER"], md["AP_ORDER"], md["BP_ORDER"])
kwds.update(kwds2)
gridShape = Extent2I(10, 10)
approx = SipApproximation(gridShape=gridShape, **kwds)
diffs = approx.computeMaxDeviation()
self.compareSolution(md, approx)
self.assertLess(diffs[0], 1E-10)
self.assertLess(diffs[1], 1E-10)
def run(md, **kwds2):
kwds = extractCtorArgs(md)
kwds['order'] = max(md["A_ORDER"], md["B_ORDER"], md["AP_ORDER"],
md["BP_ORDER"])
kwds.update(kwds2)
gridShape = Extent2I(10, 10)
approx = SipApproximation(gridShape=gridShape, **kwds)
diffs = approx.computeMaxDeviation()
self.compareSolution(md, approx)
self.assertLess(diffs[0], 1E-10)
self.assertLess(diffs[1], 1E-10)
def run(md):
kwds = extractCtorArgs(md)
order = max(md["A_ORDER"], md["B_ORDER"], md["AP_ORDER"],
md["BP_ORDER"])
gridShape = Extent2I(5, 5)
a = np.zeros((order + 1, order + 1), dtype=float)
b = np.zeros((order + 1, order + 1), dtype=float)
ap = np.zeros((order + 1, order + 1), dtype=float)
bp = np.zeros((order + 1, order + 1), dtype=float)
for p, q in packedRange(order):
a[p, q] = md.get(f"A_{p}_{q}", 0.0)
b[p, q] = md.get(f"B_{p}_{q}", 0.0)
ap[p, q] = md.get(f"AP_{p}_{q}", 0.0)
bp[p, q] = md.get(f"BP_{p}_{q}", 0.0)
approx = SipApproximation(a=a,
b=b,
ap=ap,
bp=bp,
gridShape=gridShape,
**kwds)
self.compareSolution(md, approx)
diffs = approx.computeMaxDeviation()
self.assertLess(diffs[0], 1E-10)
self.assertLess(diffs[1], 1E-10)
bbox = kwds["bbox"]
pix1 = [
Point2D(x, y)
for x in np.linspace(bbox.getMinX(), bbox.getMaxX(),
gridShape.getX())
for y in np.linspace(bbox.getMinY(), bbox.getMaxY(),
gridShape.getY())
]
iwc1a = kwds["pixelToIwc"].applyForward(pix1)
pix2a = kwds["pixelToIwc"].applyInverse(iwc1a)
iwc1b = approx.applyForward(pix1)
assert_allclose(iwc1a, iwc1b, rtol=1E-9, atol=1E-12)
pix2b = approx.applyInverse(iwc1a)
assert_allclose(pix2a, pix2b, rtol=1E-9, atol=1E-12)
def calculateSipWcsHeader(wcs, order, bbox, spacing, header=None):
"""Generate a SIP WCS header approximating a given ``SkyWcs``
Parameters
----------
wcs : `lsst.afw.geom.SkyWcs`
World Coordinate System to approximate as SIP.
order : `int`
SIP order (equal to the maximum sum of the polynomial exponents).
bbox : `lsst.geom.Box2I`
Bounding box over which to approximate the ``wcs``.
spacing : `float`
Spacing between sample points.
header : `lsst.daf.base.PropertyList`, optional
Header to which to add SIP WCS keywords.
Returns
-------
header : `lsst.daf.base.PropertyList`
Header including SIP WCS keywords.
Examples
--------
>>> header = calculateSipWcsHeader(exposure.getWcs(), 3, exposure.getBBox(), 20)
>>> sipWcs = SkyWcs(header)
"""
transform = getPixelToIntermediateWorldCoords(wcs)
crpix = wcs.getPixelOrigin()
cdMatrix = wcs.getCdMatrix()
crval = wcs.getSkyOrigin()
gridNum = Extent2I(int(bbox.getWidth() / spacing + 0.5),
int(bbox.getHeight() / spacing + 0.5))
sip = SipApproximation(transform, crpix, cdMatrix, Box2D(bbox), gridNum,
order)
md = makeTanSipMetadata(sip.getPixelOrigin(), crval, sip.getCdMatrix(),
sip.getA(), sip.getB(), sip.getAP(), sip.getBP())
if header is not None:
header.combine(md)
else:
header = md
return header