def residual_fn_BoundMin_default(s, p, tmesh):
# uses L2 norm of error (1-D only)
s.modelArgs[s.parTypeStr][s.freeParNames[0]] = p
s.testModel.compute(**s.modelArgs)
vec = s.goalTraj(tmesh, s.depVarNames) - s.testModel(
s.trajname, tmesh, s.depVarNames)
return math.sqrt(reduce(float.__add__, power(vec.toarray(), 2)))
def residual_fn_BoundMin_default(s, p, tmesh):
# uses L2 norm of error (1-D only)
s.modelArgs[s.parTypeStr][s.freeParNames[0]] = p
s.testModel.compute(**s.modelArgs)
vec = s.goalTraj(tmesh,s.depVarNames) - s.testModel(s.trajname, tmesh,
s.depVarNames)
return math.sqrt(reduce(float.__add__, power(vec.toarray(),2)))
def __call__(self, x):
if (type(x) == type(1) or type(x) == type(1.)):
return self.value(x)
elif (type(x) == type([])):
my_list = []
for xx in x:
my_list.append(self.value(xx))
return my_list
elif (type(x) == numarray.NumArray):
return (numarray.exp(-numarray.power((x-self.mean)/self.sigma, 2)/2.)
/numarray.sqrt(2.*numarray.pi)/self.sigma)
def __call__(self, x):
if (type(x) == type(1) or type(x) == type(1.)):
return self.value(x)
elif (type(x) == type([])):
my_list = []
for xx in x:
my_list.append(self.value(xx))
return my_list
elif (type(x) == numarray.NumArray):
return (numarray.exp(-numarray.power(
(x - self.mean) / self.sigma, 2) / 2.) /
numarray.sqrt(2. * numarray.pi) / self.sigma)
def calcMatrixHelper(builder, traits1, traits2, sc, strainThreshold, verbose):
# intelligently figure out strain count
step0 = time.time()
#localSC = max(findLargestStrain(traits1, sc),
# findLargestStrain(traits2, sc))
commonStrains = findCommonStrains(traits1, traits2)
buildStart = time.time()
matrix1, test1 = builder(traits1, commonStrains)
matrix2, test2 = builder(traits2, commonStrains)
buildTime = time.time() - buildStart
step1 = time.time()
ns = numarray.innerproduct(test1, test2)
# mask all ns less than strainThreshold so the correlation values
# end up masked
# ns is now a MaskedArray and so all ops involving ns will be
# MaskedArrays
ns = MA.masked_less(ns, strainThreshold, copy=0)
# divide-by-zero errors are automatically masked
#ns = -1.0/ns
step2 = time.time()
# see comment above to find out where this ridiculously cool
# matrix algebra comes from
xs = numarray.innerproduct(matrix1, test2)
ys = numarray.innerproduct(test1, matrix2)
xys = numarray.innerproduct(matrix1, matrix2)
# use in-place operations to try to speed things up
numarray.power(matrix1, 2, matrix1)
numarray.power(matrix2, 2, matrix2)
x2s = numarray.innerproduct(matrix1, test2)
y2s = numarray.innerproduct(test1, matrix2)
step3 = time.time()
# parens below are very important
# the instant we touch ns, arrays become masked and
# computation is much, much slower
top = ns*xys - (xs*ys)
bottom1 = ns*x2s - (xs*xs)
bottom2 = ns*y2s - (ys*ys)
bottom = MA.sqrt(bottom1*bottom2)
# mask catches floating point divide-by-zero problems here
corrs = top / bottom
step4 = time.time()
# we define undefined correlations as zero even though there
# is a mathematical distinction
returnValue = MA.filled(corrs, 0.0)
step5 = time.time()
#print ("calcMatrixHelper: %.2f s, %.2f s, %.2f s, %.2f s, %.2f s, %.2f s, total: %.2f s"
# %(buildTime,
# buildStart - step0,
# step2 - step1,
# step3 - step2,
# step4 - step3,
# step5 - step4,
# step5 - step0))
if verbose:
print "Matrix 1:", matrix1
print "Matrix 2:", matrix2
print "Ns:", ns
print "Xs", xs
print "Ys", ys
print "XYs:", xys
print "Top:", top
print "Bottom 1:", bottom1
print "Bottom 2:", bottom2
print "Bottom:", bottom
print "Corrs:", corrsa
return returnValue
def matchum(file1,
file2,
tol=10,
perr=4,
aerr=1.0,
nmax=40,
im_masks1=[],
im_masks2=[],
debug=0,
domags=0,
xrange=None,
yrange=None,
sigma=4,
aoffset=0):
'''Take the output of two sextractor runs and match up the objects with
each other (find out which objects in the first file match up with
objects in the second file. The routine considers a 'match' to be any
two objects that are closer than tol pixels (after applying the shift).
Returns a 6-tuple: (x1,y1,x2,y2,o1,o2). o1 and o2 are the ojbects
numbers such that o1[i] in file 1 corresponds to o2[i] in file 2.'''
NA = num.NewAxis
sexdata1 = readsex(file1)
sexdata2 = readsex(file2)
# Use the readsex data to get arrays of the (x,y) positions
x1 = num.asarray(sexdata1[0]['X_IMAGE'])
y1 = num.asarray(sexdata1[0]['Y_IMAGE'])
x2 = num.asarray(sexdata2[0]['X_IMAGE'])
y2 = num.asarray(sexdata2[0]['Y_IMAGE'])
m1 = num.asarray(sexdata1[0]['MAG_BEST'])
m2 = num.asarray(sexdata2[0]['MAG_BEST'])
o1 = num.asarray(sexdata1[0]['NUMBER'])
o2 = num.asarray(sexdata2[0]['NUMBER'])
f1 = num.asarray(sexdata1[0]['FLAGS'])
f2 = num.asarray(sexdata2[0]['FLAGS'])
# First, make a cut on the flags:
gids = num.where(f1 < 4)
x1 = x1[gids]
y1 = y1[gids]
m1 = m1[gids]
o1 = o1[gids]
gids = num.where(f2 < 4)
x2 = x2[gids]
y2 = y2[gids]
m2 = m2[gids]
o2 = o2[gids]
# next, if there is a range to use:
if xrange is not None and yrange is not None:
cond = num.greater(x1, xrange[0])*num.less(x1,xrange[1])*\
num.greater(y1, yrange[0])*num.less(y1,yrange[1])
gids = num.where(cond)
x1 = x1[gids]
y1 = y1[gids]
m1 = m1[gids]
o1 = o1[gids]
cond = num.greater(x2, xrange[0])*num.less(x2,xrange[1])*\
num.greater(y2, yrange[0])*num.less(y2,yrange[1])
gids = num.where(cond)
x2 = x2[gids]
y2 = y2[gids]
m2 = m2[gids]
o2 = o2[gids]
# Use the user masks
for m in im_masks1:
print "applying mask (%d,%d,%d,%d)" % tuple(m)
condx = num.less(x1, m[0]) + num.greater(x1, m[1])
condy = num.less(y1, m[2]) + num.greater(y1, m[3])
gids = num.where(condx + condy)
x1 = x1[gids]
y1 = y1[gids]
m1 = m1[gids]
o1 = o1[gids]
for m in im_masks2:
print "applying mask (%d,%d,%d,%d)" % tuple(m)
condx = num.less(x2, m[0]) + num.greater(x2, m[1])
condy = num.less(y2, m[2]) + num.greater(y2, m[3])
gids = num.where(condx + condy)
x2 = x2[gids]
y2 = y2[gids]
m2 = m2[gids]
o2 = o2[gids]
if nmax:
if len(x1) > nmax:
ids = num.argsort(m1)[0:nmax]
x1 = x1[ids]
y1 = y1[ids]
m1 = m1[ids]
o1 = o1[ids]
if len(x2) > nmax:
ids = num.argsort(m2)[0:nmax]
x2 = x2[ids]
y2 = y2[ids]
m2 = m2[ids]
o2 = o2[ids]
if debug:
print "objects in frame 1:"
print o1
print "objects in frame 2:"
print o2
mp = pygplot.MPlot(2, 1, device='/XWIN')
p = pygplot.Plot()
p.point(x1, y1)
[p.label(x1[i], y1[i], "%d" % o1[i]) for i in range(len(x1))]
mp.add(p)
p = pygplot.Plot()
p.point(x2, y2)
[p.label(x2[i], y2[i], "%d" % o2[i]) for i in range(len(x2))]
mp.add(p)
mp.plot()
mp.close()
# Now, we make 2-D arrays of all the differences in x and y between each pair
# of objects. e.g., dx1[n,m] is the delta-x between object n and m in file 1 and
# dy2[n,m] is the y-distance between object n and m in file 2.
dx1 = x1[NA, :] - x1[:, NA]
dx2 = x2[NA, :] - x2[:, NA]
dy1 = y1[NA, :] - y1[:, NA]
dy2 = y2[NA, :] - y2[:, NA]
# Same, but with angles
da1 = num.arctan2(dy1, dx1) * 180 / num.pi
da2 = num.arctan2(dy2, dx2) * 180 / num.pi
# Same, but with absolute distances
ds1 = num.sqrt(num.power(dx1, 2) + num.power(dy1, 2))
ds2 = num.sqrt(num.power(dx2, 2) + num.power(dy2, 2))
# Here's the real magic: this is a matrix of matrices (4-D). Consider 4 objects:
# objects i and j in file 1 and objects m and n in file 2. dx[i,j,m,n] is the
# difference between delta-xs for objects i,j in file 1 and m,n in file 2. If object
# i corresponds to object m and object j corresponds to object n, this should be a small
# number, irregardless of an overall shift in coordinate systems between file 1 and 2.
dx = dx1[::, ::, NA, NA] - dx2[NA, NA, ::, ::]
dy = dy1[::, ::, NA, NA] - dy2[NA, NA, ::, ::]
da = da1[::, ::, NA, NA] - da2[NA, NA, ::, ::] + aoffset
ds = ds1[::, ::, NA, NA] - ds2[NA, NA, ::, ::]
# pick out close pairs.
#use = num.less(dy,perr)*num.less(dx,perr)*num.less(num.abs(da),aerr)
use = num.less(ds, perr) * num.less(num.abs(da), aerr)
use = use.astype(num.Int32)
#use = num.less(num.abs(da),perr)
suse = num.add.reduce(num.add.reduce(use, 3), 1)
print suse[0]
guse = num.greater(suse, suse.flat.max() / 2)
i = [j for j in range(x1.shape[0]) if num.sum(guse[j])]
m = [num.argmax(guse[j]) for j in range(x1.shape[0]) if num.sum(guse[j])]
xx0, yy0, oo0, mm0 = num.take([x1, y1, o1, m1], i, 1)
xx1, yy1, oo1, mm1 = num.take([x2, y2, o2, m2], m, 1)
if debug:
mp = pygplot.MPlot(2, 1, device='/XWIN')
p = pygplot.Plot()
p.point(xx0, yy0)
[p.label(xx0[i], yy0[i], "%d" % oo0[i]) for i in range(len(xx0))]
mp.add(p)
p = pygplot.Plot()
p.point(xx1, yy1)
[p.label(xx1[i], yy1[i], "%d" % oo1[i]) for i in range(len(xx1))]
mp.add(p)
mp.plot()
mp.close()
xshift, xscat = stats.bwt(xx0 - xx1)
xscat = max([1.0, xscat])
yshift, yscat = stats.bwt(yy0 - yy1)
yscat = max([1.0, yscat])
mshift, mscat = stats.bwt(mm0 - mm1)
print "xscat = ", xscat
print "yscat = ", yscat
print "xshift = ", xshift
print "yshift = ", yshift
print "mshift = ", mshift
print "mscat = ", mscat
keep = num.less(num.abs(xx0-xx1-xshift),sigma*xscat)*\
num.less(num.abs(yy0-yy1-yshift),sigma*yscat)
# This is a list of x,y,object# in each file.
xx0, yy0, oo0, xx1, yy1, oo1 = num.compress(keep,
[xx0, yy0, oo0, xx1, yy1, oo1],
1)
if debug:
print file1, oo0
print file2, oo1
mp = pygplot.MPlot(2, 1, device='temp.ps/CPS')
p1 = pygplot.Plot()
p1.point(xx0, yy0, symbol=25, color='red')
for i in range(len(xx0)):
p1.label(xx0[i], yy0[i], " %d" % oo0[i], color='red')
mp.add(p1)
p2 = pygplot.Plot()
p2.point(xx1, yy1, symbol=25, color='green')
for i in range(len(xx1)):
p2.label(xx1[i], yy1[i], " %d" % oo1[i], color='green')
mp.add(p2)
mp.plot()
mp.close()
if domags:
return (xx0, yy0, mm0, xx1, yy1, mm1, mshift, mscat, oo0, oo1)
else:
return (xx0, yy0, xx1, yy1, oo0, oo1)
def combine_images(images,
output,
reference=None,
method='average',
zscale=1,
fitgeometry='general',
function='polynomial',
xxorder=3,
yyorder=3,
xyorder=3,
yxorder=3,
objects=None,
trim=0,
interactive=1,
maxiter=5,
reject=3,
sigma=None,
xrange=None,
yrange=None,
rectify=1,
thresh=2.0,
scale=0.125,
fwhm=4.0,
debug=0,
aoffset=0,
creject=None):
'''This Routine will take a list of images and use rectify_image to rectify
each with respect to referece. It will then use imcombine to combine the
images into one and output to "output". Default method is 'average', but
you can use any available to imcombine. The rest of the arguments are sent
to rectify_images. If zscale=1, all the images are scaled to a common
zmag (30). Also, if a file with _sigma.fits is found for all input files,
we combine them too. If you specify a sigma, that file will hold the
standard deviations from the files.'''
names = get_files(images)
if reference is None:
reference = names[0]
names = names[1:]
pclip = -0.5
lsigma = 3.
hsigma = 3.
nkeep = -2
if creject:
creject = "pclip"
else:
creject = "none"
# Check to see if we have _sigma files for each input file
if os.path.isfile(reference.replace('.fits', '_sigma.fits')):
do_sigmas = 1
else:
do_sigmas = 0
sigmas = []
for name in names:
if os.path.isfile(name.replace('.fits', '_sigma.fits')):
sigmas.append(name.replace('.fits', '_sigma.fits'))
else:
do_sigmas = 0
break
# Put the reference image first, to ensure that all positional header keywords are
# valid.
if do_sigmas:
ref_sig = reference.replace('.fits', '_sigma.fits')
update = 0
if zscale:
zmag = get_header(reference, "ZMAG", float)
if zmag is not None and zmag != 26:
update = 1
zmagfac = num.power(10, (zmag - 30) / 2.5)
if zmagfac != 1:
print 'zmagfac = ', zmagfac
iraf.imarith(reference, '/', zmagfac, reference)
iraf.hedit(reference, "ZMAG", 30.0, add=1, verify=0)
if do_sigmas:
iraf.imarith(ref_sig, '/', zmagfac, ref_sig)
iraf.hedit(ref_sig, "ZMAG", 30.0, add=1, verify=0)
print "Using %s as reference" % (reference)
if rectify:
nuke(reference.replace('.fits', '_rec.fits'))
iraf.imcopy(reference, reference.replace('.fits', '_rec.fits'))
if do_sigmas:
nuke(reference.replace('.fits', '_rec_sigma.fits'))
iraf.imcopy(reference.replace('.fits', '_sigma.fits'),
reference.replace('.fits', '_rec_sigma.fits'))
temps = [reference.replace('.fits', '_rec.fits')]
if do_sigmas:
sig_temps = [reference.replace('.fits', '_rec_sigma.fits')]
else:
temps = [reference]
if do_sigmas: sig_temps = [ref_sig]
i = 0
for name in names:
print name
if rectify:
temp = name.replace('.fits', '_rec.fits')
nuke(temp)
if do_sigmas:
(x0, x1, y0, y1) = rectify_image(name,
reference,
output=temp,
fitgeometry=fitgeometry,
function=function,
xxorder=xxorder,
yyorder=yyorder,
xyorder=xyorder,
yxorder=yxorder,
objects=objects,
interactive=interactive,
maxiter=maxiter,
reject=reject,
xrange=xrange,
yrange=yrange,
thresh=thresh,
scale=scale,
fwhm=fwhm,
image_sigma=sigmas[i],
reference_sigma=ref_sig,
debug=debug,
aoffset=aoffset)
else:
(x0, x1, y0, y1) = rectify_image(name,
reference,
output=temp,
fitgeometry=fitgeometry,
function=function,
xxorder=xxorder,
yyorder=yyorder,
xyorder=xyorder,
yxorder=yxorder,
objects=objects,
interactive=interactive,
maxiter=maxiter,
reject=reject,
xrange=xrange,
yrange=yrange,
thresh=thresh,
scale=scale,
fwhm=fwhm,
debug=debug,
aoffset=aoffset)
else:
temp = name
bpm = get_header(name, 'BPM', str)
if bpm and rectify:
bpm_fits = bpm.replace('.pl', '.fits')
temp_bpm = bpm.replace('.pl', '_rec.fits')
temp_bpm_pl = bpm.replace('.pl', '_rec.pl')
nuke(bpm_fits)
nuke(temp_bpm)
nuke(temp_bpm_pl)
iraf.imcopy(bpm, bpm_fits)
iraf.imarith(bpm_fits, "*", 10.0, bpm_fits)
if objects is None: sigmaobjects = 'geomap.objects'
iraf.geotran(input=bpm_fits,
output=temp_bpm,
database='geomap.db',
transforms=sigmaobjects,
geometry='geometric',
interpolant="linear")
iraf.imreplace(temp_bpm, lower=0.02, upper="INDEF", value=1.)
iraf.imcopy(temp_bpm, temp_bpm_pl)
iraf.hedit(temp, 'BPM', temp_bpm_pl, update=1, verify=0)
if do_sigmas:
if rectify:
temp_sig = sigmas[i].replace('_sigma.fits', '_rec_sigma.fits')
nuke(temp_sig)
if objects is None:
sigmaobjects = 'geomap.objects'
else:
sigmaobjects = objects
iraf.geotran(input=sigmas[i],
output=temp_sig,
database='geomap.db',
transforms=sigmaobjects,
geometry='geometric',
interpolant="linear")
else:
temp_sig = sigmas[i]
if rectify:
if i == 0:
xmin = x0
ymin = y0
xmax = x1
ymax = y1
else:
xmin = max(xmin, x0)
ymin = max(ymin, y0)
xmax = min(xmax, x1)
ymax = min(ymax, y1)
if zscale:
zmag = get_header(name, "ZMAG", float)
if zmag is not None and zmag != 26:
zmagfac = num.power(10, (zmag - 30) / 2.5)
if zmagfac != 1:
print 'zmagfac = ', zmagfac
iraf.imarith(temp, '/', zmagfac, temp)
iraf.hedit(temp, 'ZMAG', 30.0, add=1, verify=0)
if do_sigmas:
iraf.imarith(temp_sig, '/', zmagfac, temp_sig)
iraf.hedit(temp_sig, 'ZMAG', 30.0, add=1, verify=0)
temps.append(temp)
if do_sigmas: sig_temps.append(temp_sig)
i = i + 1
Nimages = len(temps)
temps = string.join(temps, ',')
print "Combining ", temps
nuke(output)
if sigma is not None:
iraf.imcombine(temps,
output,
combine=method,
sigma=sigma,
masktyp='badvalue',
maskval=1.,
reject=creject,
lsigma=lsigma,
hsigma=hsigma,
nkeep=nkeep)
else:
iraf.imcombine(temps,
output,
combine=method,
masktyp='badvalue',
maskval=1.,
reject=creject,
lsigma=lsigma,
hsigma=hsigma,
nkeep=nkeep)
if do_sigmas:
Ns = []
for this in sig_temps:
N = this.replace('.fits', '_N.fits')
nuke(N)
iraf.imcopy(this, N)
iraf.imreplace(N, lower=0.01, upper="INDEF", value=1)
Ns.append(N)
sig_temps = string.join(sig_temps, ',')
Ns = string.join(Ns, ',')
iraf.imfunction(sig_temps, sig_temps, 'square')
file = output.replace('.fits', '_sigma.fits')
nuke(file)
iraf.imcombine(sig_temps, file, combine='sum')
if method == 'average':
nuke("N.fits")
iraf.imcombine(Ns, 'N.fits', combine='sum')
iraf.imfunction('N.fits', 'N.fits', 'square')
iraf.imarith(file, '/', 'N.fits', file)
iraf.imfunction(file, file, 'sqrt')
iraf.imfunction(sig_temps, sig_temps, 'sqrt')
if update:
iraf.hedit(output, "ZMAG", 30.0, verify=0, add=1)
#nuke('temp?.fits')
# Now, let's clean up the gain and rdnoise headers, as they don't seem to
# be handled in the PANIC pipeline. NOTE: we really need a noise map, but
# for now, we'll deal with just global values.
gain = get_header(output, "gain", float)
rdnoise = get_header(output, "rdnoise", float)
if gain is None:
# have to compute it from scratch
egain = get_header(output, "egain", float)
enoise = get_header(output, "enoise", float)
nloop = get_header(output, "nloops", int)
i = 1
key = ""
# This seems to be the only safe way to compute this. Can't trust
# NDITHERS
while key is not None:
key = get_header(output, "imcmb%03d" % (i), str)
i = i + 1
ndither = i - 1
print "N, ndither, nloop, egain, enoise = ", Nimages, ndither, nloop, egain, enoise
gain = egain * Nimages * nloop * ndither
rdnoise = enoise * num.sqrt(Nimages * nloop * ndither)
else:
gain = gain * Nimages
rdnoise = rdnoise * num.sqrt(Nimages)
iraf.hedit(output, "gain", gain, verify=0, add=1)
iraf.hedit(output, "rdnoise", rdnoise, verify=0, add=1)
# If requested, trim to the common regions of the combined images
if rectify and trim and Nimages > 1:
iraf.imcopy(output + "[%d:%d,%d:%d]" % (xmin, xmax, ymin, ymax),
output.replace('.fits', '_trim.fits'))
def recenter(self):
"""
Reset the reference position values to correspond to the center
of the reference frame.
Algorithm used here developed by Colin Cox - 27-Jan-2004.
"""
if self.ctype1.find('TAN') < 0 or self.ctype2.find('TAN') < 0:
print 'WCS.recenter() only supported for TAN projections.'
raise TypeError
# Check to see if WCS is already centered...
if self.crpix1 == self.naxis1 / 2. and self.crpix2 == self.naxis2 / 2.:
# No recentering necessary... return without changing WCS.
return
# This offset aligns the WCS to the center of the pixel, in accordance
# with the 'align=center' option used by 'drizzle'.
#_drz_off = -0.5
_drz_off = 0.
_cen = (self.naxis1 / 2. + _drz_off, self.naxis2 / 2. + _drz_off)
# Compute the RA and Dec for center pixel
_cenrd = self.xy2rd(_cen)
_cd = N.array([[self.cd11, self.cd12], [self.cd21, self.cd22]],
type=N.Float64)
_ra0 = DEGTORAD(self.crval1)
_dec0 = DEGTORAD(self.crval2)
_ra = DEGTORAD(_cenrd[0])
_dec = DEGTORAD(_cenrd[1])
# Set up some terms for use in the final result
_dx = self.naxis1 / 2. - self.crpix1
_dy = self.naxis2 / 2. - self.crpix2
_dE, _dN = DEGTORAD(N.dot(_cd, (_dx, _dy)))
_dE_dN = 1 + N.power(_dE, 2) + N.power(_dN, 2)
_cosdec = N.cos(_dec)
_sindec = N.sin(_dec)
_cosdec0 = N.cos(_dec0)
_sindec0 = N.sin(_dec0)
_n1 = N.power(_cosdec, 2) + _dE * _dE + _dN * _dN * N.power(_sindec, 2)
_dra_dE = (_cosdec0 - _dN * _sindec0) / _n1
_dra_dN = _dE * _sindec0 / _n1
_ddec_dE = -_dE * N.tan(_dec) / _dE_dN
_ddec_dN = (1 / _cosdec) * ((_cosdec0 / N.sqrt(_dE_dN)) -
(_dN * N.sin(_dec) / _dE_dN))
# Compute new CD matrix values now...
_cd11n = _cosdec * (self.cd11 * _dra_dE + self.cd21 * _dra_dN)
_cd12n = _cosdec * (self.cd12 * _dra_dE + self.cd22 * _dra_dN)
_cd21n = self.cd11 * _ddec_dE + self.cd21 * _ddec_dN
_cd22n = self.cd12 * _ddec_dE + self.cd22 * _ddec_dN
_new_orient = RADTODEG(N.arctan2(_cd12n, _cd22n))
# Update the values now...
self.crpix1 = _cen[0]
self.crpix2 = _cen[1]
self.crval1 = RADTODEG(_ra)
self.crval2 = RADTODEG(_dec)
# Keep the same plate scale, only change the orientation
self.rotateCD(_new_orient)
# These would update the CD matrix with the new rotation
# ALONG with the new plate scale which we do not want.
self.cd11 = _cd11n
self.cd12 = _cd12n
self.cd21 = _cd21n
self.cd22 = _cd22n
def recenter(self):
"""
Reset the reference position values to correspond to the center
of the reference frame.
Algorithm used here developed by Colin Cox - 27-Jan-2004.
"""
if self.ctype1.find('TAN') < 0 or self.ctype2.find('TAN') < 0:
print 'WCS.recenter() only supported for TAN projections.'
raise TypeError
# Check to see if WCS is already centered...
if self.crpix1 == self.naxis1/2. and self.crpix2 == self.naxis2/2.:
# No recentering necessary... return without changing WCS.
return
# This offset aligns the WCS to the center of the pixel, in accordance
# with the 'align=center' option used by 'drizzle'.
#_drz_off = -0.5
_drz_off = 0.
_cen = (self.naxis1/2.+ _drz_off,self.naxis2/2. + _drz_off)
# Compute the RA and Dec for center pixel
_cenrd = self.xy2rd(_cen)
_cd = N.array([[self.cd11,self.cd12],[self.cd21,self.cd22]],type=N.Float64)
_ra0 = DEGTORAD(self.crval1)
_dec0 = DEGTORAD(self.crval2)
_ra = DEGTORAD(_cenrd[0])
_dec = DEGTORAD(_cenrd[1])
# Set up some terms for use in the final result
_dx = self.naxis1/2. - self.crpix1
_dy = self.naxis2/2. - self.crpix2
_dE,_dN = DEGTORAD(N.dot(_cd,(_dx,_dy)))
_dE_dN = 1 + N.power(_dE,2) + N.power(_dN,2)
_cosdec = N.cos(_dec)
_sindec = N.sin(_dec)
_cosdec0 = N.cos(_dec0)
_sindec0 = N.sin(_dec0)
_n1 = N.power(_cosdec,2) + _dE*_dE + _dN*_dN*N.power(_sindec,2)
_dra_dE = (_cosdec0 - _dN*_sindec0)/_n1
_dra_dN = _dE*_sindec0 /_n1
_ddec_dE = -_dE*N.tan(_dec) / _dE_dN
_ddec_dN = (1/_cosdec) * ((_cosdec0 / N.sqrt(_dE_dN)) - (_dN*N.sin(_dec) / _dE_dN))
# Compute new CD matrix values now...
_cd11n = _cosdec * (self.cd11*_dra_dE + self.cd21 * _dra_dN)
_cd12n = _cosdec * (self.cd12*_dra_dE + self.cd22 * _dra_dN)
_cd21n = self.cd11 * _ddec_dE + self.cd21 * _ddec_dN
_cd22n = self.cd12 * _ddec_dE + self.cd22 * _ddec_dN
_new_orient = RADTODEG(N.arctan2(_cd12n,_cd22n))
# Update the values now...
self.crpix1 = _cen[0]
self.crpix2 = _cen[1]
self.crval1 = RADTODEG(_ra)
self.crval2 = RADTODEG(_dec)
# Keep the same plate scale, only change the orientation
self.rotateCD(_new_orient)
# These would update the CD matrix with the new rotation
# ALONG with the new plate scale which we do not want.
self.cd11 = _cd11n
self.cd12 = _cd12n
self.cd21 = _cd21n
self.cd22 = _cd22n