def _fiducialYPlane(self, root, iy):
assert isinstance(
iy, numbers.Integral) and iy >= 0 and iy <= self.resolution
y = 2.0 * float(iy) / float(self.resolution) - 1.0
c, b = self.center[root], self.y[root]
v = (c[0] + y * b[0], c[1] + y * b[1], c[2] + y * b[2])
return geom.normalize(self.yrot[root](v, 1.0, 0.0))
def _fiducialXPlane(self, root, ix):
assert isinstance(
ix, numbers.Integral) and ix >= 0 and ix <= self.resolution
x = 2.0 * float(ix) / float(self.resolution) - 1.0
c, b = self.center[root], self.x[root]
v = (c[0] + x * b[0], c[1] + x * b[1], c[2] + x * b[2])
return geom.normalize(self.xrot[root](v, 1.0, 0.0))
def getCenter(self, pixelId):
"""Returns the center of a sky-pixel as a unit cartesian 3-vector.
"""
root, ix, iy = self.coords(pixelId)
xc = 2.0 * (float(ix) + 0.5) / float(self.resolution) - 1.0
yc = 2.0 * (float(iy) + 0.5) / float(self.resolution) - 1.0
c, x, y = self.center[root], self.x[root], self.y[root]
return geom.normalize(
(c[0] + xc * x[0] + yc * y[0], c[1] + xc * x[1] + yc * y[1],
c[2] + xc * x[2] + yc * y[2]))
def getCenter(self, pixelId):
"""Returns the center of a sky-pixel as a unit cartesian 3-vector.
"""
root, ix, iy = self.coords(pixelId)
xc = 2.0 * (float(ix) + 0.5) / float(self.resolution) - 1.0
yc = 2.0 * (float(iy) + 0.5) / float(self.resolution) - 1.0
c, x, y = self.center[root], self.x[root], self.y[root]
return geom.normalize((c[0] + xc * x[0] + yc * y[0],
c[1] + xc * x[1] + yc * y[1],
c[2] + xc * x[2] + yc * y[2]))
def buildPoints(refFile, posFile, radius):
"""Builds test data for point within circle matches.
"""
assert radius > 0.0 and radius < 5.0
# Divide unit sphere into latitude angle stripes
phiMin = -90.0
phiMax = 90.0
i = 0
deltaPhi = 4.0 * radius;
phi = phiMin
while phi < phiMax:
centerPhi = geom.clampPhi(max(abs(phi), abs(phi + deltaPhi)))
deltaTheta = geom.maxAlpha(4.0 * radius, centerPhi)
theta = 0.0
refOutput = []
posOutput = []
# Divide latitude angle stripes into boxes (by longitude angle)
while theta < 360.0 - 2.0 * deltaTheta:
# Create a random point inside a sub-region of each box
# such that a circle of the given radius centered on that
# point is guaranteed not to cross the box boundaries
if theta == 0.0:
# make sure longitude angle wrap-around is tested
p = pointInBox(360.0 - 0.125 * deltaTheta,
0.125 * deltaTheta,
geom.clampPhi(phi + deltaPhi * 0.38),
geom.clampPhi(phi + deltaPhi * 0.62))
else:
p = pointInBox(theta + deltaTheta * 0.38,
theta + deltaTheta * 0.62,
geom.clampPhi(phi + deltaPhi * 0.38),
geom.clampPhi(phi + deltaPhi * 0.62))
refId = i
i += 1
# Generate matches
numMatches = random.randint(0, MAX_MATCHES)
pIn = pointsOnCircle(p, radius - MATCH_ACCURACY, numMatches)
pOut = pointsOnCircle(p, radius + MATCH_ACCURACY, numMatches)
if len(pIn) > 1:
# shift first point inwards towards p to make it the
# closest match
v1 = geom.cartesianUnitVector(p)
v2 = geom.cartesianUnitVector(pIn[0])
v3 = geom.normalize((v1[0] + v2[0], v1[1] + v2[1], v1[2] + v2[2]))
pIn[0] = geom.sphericalCoords(v3)
matches = ' '.join(map(str, xrange(i, i + len(pIn))))
# Write out reference position
refOutput.append(
(p[1], "%d,%s,%s,%s\n" % (refId, repr(p[0]), repr(p[1]), matches)))
# Write out matching positions
for p in pIn:
posOutput.append(
(p[1], "%d,%s,%s,%d\n" % (i, repr(p[0]), repr(p[1]), refId)))
i += 1
# Write out positions with no matches
for p in pOut:
posOutput.append(
(p[1], "%d,%s,%s,\n" % (i, repr(p[0]), repr(p[1]))))
i += 1
theta += deltaTheta
posOutput.sort()
refOutput.sort()
for r in refOutput:
refFile.write(r[1])
for p in posOutput:
posFile.write(p[1])
phi += deltaPhi
refFile.flush()
posFile.flush()
def getAllSipWcs(cursor, qsp, kind):
"""Constructs a Wcs object from each entry in the Science_Ccd_Exposure
table. SIP distortion parameters are read in from
Science_Ccd_Exposure_Metadata. Returns a 3-tuple with the following
contents:
- a mapping from sky-tiles to a list of (WCS, filter) tuples for
overlapping science CCDs
- a list of all (WCS, filter) tuples read in
- a bounding circle (lsst.geom.SphericalCircle) for all science CCDs.
"""
wcsMap = {}
wcsList = []
offset = 0
centers = []
radius = 0.0
n = 0
blocksize = 1000
# An alternative would be to create WCSes from FITS metadata or FITS
# files themselves, but these aren't always available.
while True:
cursor.execute("""SELECT scienceCcdExposureId, filterId,
raDeSys, equinox, ctype1, ctype2,
crpix1, crpix2, crval1, crval2,
cd1_1, cd1_2, cd2_1, cd2_2
FROM Science_Ccd_Exposure LIMIT %d, %d
""" % (offset, blocksize))
rows = cursor.fetchall()
if len(rows) == 0:
break
# For each CCD in the results, build up a metadata PropertySet
# including SIP distortion terms and use it to obtain a WCS.
for row in rows:
ps = dafBase.PropertySet()
ps.setString("RADESYS", row[2])
ps.setDouble("EQUINOX", row[3])
ps.setString("CTYPE1", row[4])
ps.setString("CTYPE2", row[5])
ps.setString("CUNIT1", "deg")
ps.setString("CUNIT2", "deg")
ps.setDouble("CRPIX1", row[6])
ps.setDouble("CRPIX2", row[7])
ps.setDouble("CRVAL1", row[8])
ps.setDouble("CRVAL2", row[9])
ps.setDouble("CD1_1", row[10])
ps.setDouble("CD1_2", row[11])
ps.setDouble("CD2_1", row[12])
ps.setDouble("CD2_2", row[13])
if row[4].endswith("-SIP") and row[5].endswith("-SIP"):
cursor.execute("""
SELECT metadataKey, intValue
FROM Science_Ccd_Exposure_Metadata
WHERE scienceCcdExposureId = %d AND
intValue IS NOT NULL AND
metadataKey RLIKE '^[AB]P?_ORDER$'
""" % row[0])
for k, v in cursor.fetchall():
ps.setInt(k, v)
cursor.execute("""
SELECT metadataKey, doubleValue
FROM Science_Ccd_Exposure_Metadata
WHERE scienceCcdExposureId = %d AND
doubleValue IS NOT NULL AND
metadataKey RLIKE '^[AB]P?_[0-9]+_[0-9]+$'
""" % row[0])
for k, v in cursor.fetchall():
ps.setDouble(k, v)
wcs = afwImage.makeWcs(ps)
wcsList.append([wcs, row[1]])
if kind == 'imsim':
poly = qs.imageToPolygon(wcs, 4072.0, 4000.0, 0.0)
else:
poly = qs.imageToPolygon(wcs, 2048.0, 4610.0, 0.0)
bc = poly.getBoundingCircle()
radius = max(radius, bc.getRadius())
centers.append(geom.cartesianUnitVector(bc.getCenter()))
pix = qsp.intersect(poly)
for skyTile in pix:
if skyTile in wcsMap:
wcsMap[skyTile].append([wcs, row[1]])
else:
wcsMap[skyTile] = [[wcs, row[1]]]
n = offset + len(rows)
print "... %d" % n
offset += blocksize
x, y, z = 0.0, 0.0, 0.0
for v in centers:
x += v[0]
y += v[1]
z += v[2]
cen = geom.normalize((x, y, z))
r = max(geom.cartesianAngularSep(cen, v) for v in centers) + 2.0 * radius
return wcsMap, wcsList, geom.SphericalCircle(cen, r)
def buildPoints(refFile, posFile, radius):
"""Builds test data for point within circle matches.
"""
assert radius > 0.0 and radius < 5.0
# Divide unit sphere into latitude angle stripes
phiMin = -90.0
phiMax = 90.0
i = 0
deltaPhi = 4.0 * radius
phi = phiMin
while phi < phiMax:
centerPhi = geom.clampPhi(max(abs(phi), abs(phi + deltaPhi)))
deltaTheta = geom.maxAlpha(4.0 * radius, centerPhi)
theta = 0.0
refOutput = []
posOutput = []
# Divide latitude angle stripes into boxes (by longitude angle)
while theta < 360.0 - 2.0 * deltaTheta:
# Create a random point inside a sub-region of each box
# such that a circle of the given radius centered on that
# point is guaranteed not to cross the box boundaries
if theta == 0.0:
# make sure longitude angle wrap-around is tested
p = pointInBox(360.0 - 0.125 * deltaTheta, 0.125 * deltaTheta,
geom.clampPhi(phi + deltaPhi * 0.38),
geom.clampPhi(phi + deltaPhi * 0.62))
else:
p = pointInBox(theta + deltaTheta * 0.38,
theta + deltaTheta * 0.62,
geom.clampPhi(phi + deltaPhi * 0.38),
geom.clampPhi(phi + deltaPhi * 0.62))
refId = i
i += 1
# Generate matches
numMatches = random.randint(0, MAX_MATCHES)
pIn = pointsOnCircle(p, radius - MATCH_ACCURACY, numMatches)
pOut = pointsOnCircle(p, radius + MATCH_ACCURACY, numMatches)
if len(pIn) > 1:
# shift first point inwards towards p to make it the
# closest match
v1 = geom.cartesianUnitVector(p)
v2 = geom.cartesianUnitVector(pIn[0])
v3 = geom.normalize(
(v1[0] + v2[0], v1[1] + v2[1], v1[2] + v2[2]))
pIn[0] = geom.sphericalCoords(v3)
matches = ' '.join(map(str, xrange(i, i + len(pIn))))
# Write out reference position
refOutput.append(
(p[1],
"%d,%s,%s,%s\n" % (refId, repr(p[0]), repr(p[1]), matches)))
# Write out matching positions
for p in pIn:
posOutput.append(
(p[1],
"%d,%s,%s,%d\n" % (i, repr(p[0]), repr(p[1]), refId)))
i += 1
# Write out positions with no matches
for p in pOut:
posOutput.append(
(p[1], "%d,%s,%s,\n" % (i, repr(p[0]), repr(p[1]))))
i += 1
theta += deltaTheta
posOutput.sort()
refOutput.sort()
for r in refOutput:
refFile.write(r[1])
for p in posOutput:
posFile.write(p[1])
phi += deltaPhi
refFile.flush()
posFile.flush()
def _fiducialYPlane(self, root, iy):
assert isinstance(iy, (int,long)) and iy >= 0 and iy <= self.resolution
y = 2.0 * float(iy) / float(self.resolution) - 1.0
c, b = self.center[root], self.y[root]
v = (c[0] + y * b[0], c[1] + y * b[1], c[2] + y * b[2])
return geom.normalize(self.yrot[root](v, 1.0, 0.0))
def _fiducialXPlane(self, root, ix):
assert isinstance(ix, (int,long)) and ix >= 0 and ix <= self.resolution
x = 2.0 * float(ix) / float(self.resolution) - 1.0
c, b = self.center[root], self.x[root]
v = (c[0] + x * b[0], c[1] + x * b[1], c[2] + x * b[2])
return geom.normalize(self.xrot[root](v, 1.0, 0.0))
def getAllSipWcs(cursor, qsp, kind):
"""Constructs a Wcs object from each entry in the Science_Ccd_Exposure
table. SIP distortion parameters are read in from
Science_Ccd_Exposure_Metadata. Returns a 3-tuple with the following
contents:
- a mapping from sky-tiles to a list of (WCS, filter) tuples for
overlapping science CCDs
- a list of all (WCS, filter) tuples read in
- a bounding circle (lsst.geom.SphericalCircle) for all science CCDs.
"""
wcsMap = {}
wcsList = []
offset = 0
centers = []
radius = 0.0
n = 0
blocksize = 1000
# An alternative would be to create WCSes from FITS metadata or FITS
# files themselves, but these aren't always available.
while True:
cursor.execute(
"""SELECT scienceCcdExposureId, filterId,
raDeSys, equinox, ctype1, ctype2,
crpix1, crpix2, crval1, crval2,
cd1_1, cd1_2, cd2_1, cd2_2
FROM Science_Ccd_Exposure LIMIT %d, %d
""" % (offset, blocksize))
rows = cursor.fetchall()
if len(rows) == 0:
break
# For each CCD in the results, build up a metadata PropertySet
# including SIP distortion terms and use it to obtain a WCS.
for row in rows:
ps = dafBase.PropertySet()
ps.setString("RADESYS", row[2])
ps.setDouble("EQUINOX", row[3])
ps.setString("CTYPE1", row[4])
ps.setString("CTYPE2", row[5])
ps.setString("CUNIT1", "deg")
ps.setString("CUNIT2", "deg")
ps.setDouble("CRPIX1", row[6])
ps.setDouble("CRPIX2", row[7])
ps.setDouble("CRVAL1", row[8])
ps.setDouble("CRVAL2", row[9])
ps.setDouble("CD1_1", row[10])
ps.setDouble("CD1_2", row[11])
ps.setDouble("CD2_1", row[12])
ps.setDouble("CD2_2", row[13])
if row[4].endswith("-SIP") and row[5].endswith("-SIP"):
cursor.execute("""
SELECT metadataKey, intValue
FROM Science_Ccd_Exposure_Metadata
WHERE scienceCcdExposureId = %d AND
intValue IS NOT NULL AND
metadataKey RLIKE '^[AB]P?_ORDER$'
""" % row[0])
for k, v in cursor.fetchall():
ps.setInt(k, v)
cursor.execute("""
SELECT metadataKey, doubleValue
FROM Science_Ccd_Exposure_Metadata
WHERE scienceCcdExposureId = %d AND
doubleValue IS NOT NULL AND
metadataKey RLIKE '^[AB]P?_[0-9]+_[0-9]+$'
""" % row[0])
for k, v in cursor.fetchall():
ps.setDouble(k, v)
wcs = afwImage.makeWcs(ps)
wcsList.append([wcs, row[1]])
if kind == 'imsim':
poly = qs.imageToPolygon(wcs, 4072.0, 4000.0, 0.0)
else:
poly = qs.imageToPolygon(wcs, 2048.0, 4610.0, 0.0)
bc = poly.getBoundingCircle()
radius = max(radius, bc.getRadius())
centers.append(geom.cartesianUnitVector(bc.getCenter()))
pix = qsp.intersect(poly)
for skyTile in pix:
if skyTile in wcsMap:
wcsMap[skyTile].append([wcs, row[1]])
else:
wcsMap[skyTile] = [[wcs, row[1]]]
n = offset + len(rows)
print "... %d" % n
offset += blocksize
x, y, z = 0.0, 0.0, 0.0
for v in centers:
x += v[0]; y += v[1]; z += v[2]
cen = geom.normalize((x, y, z))
r = max(geom.cartesianAngularSep(cen, v) for v in centers) + 2.0*radius
return wcsMap, wcsList, geom.SphericalCircle(cen, r)
