def setHdus(self, p):
self.hdus = p
self.table = fits_table(self.hdus[1].data)[0]
T = self.table
self.aa = T.aa.astype(float)
self.kk = T.kk.astype(float)
self.airmass = T.airmass
def setHdus(self, p):
self.hdus = p
self.table = fits_table(self.hdus[1].data)[0]
T = self.table
self.aa = T.aa.astype(float)
self.kk = T.kk.astype(float)
self.airmass = T.airmass
def getPsfAtPoints(self, bandnum, x, y):
'''
Reconstruct the SDSS model PSF from KL basis functions.
x,y can be scalars or 1-d numpy arrays.
Return value:
if x,y are scalars: a PSF image
if x,y are arrays: a list of PSF images
'''
rtnscalar = np.isscalar(x) and np.isscalar(y)
x = np.atleast_1d(x)
y = np.atleast_1d(y)
psf = fits_table(self.hdus[bandnum + 1].data)
psfimgs = None
(outh, outw) = (None, None)
# From the IDL docs:
# http://photo.astro.princeton.edu/photoop_doc.html#SDSS_PSF_RECON
# acoeff_k = SUM_i{ SUM_j{ (0.001*ROWC)^i * (0.001*COLC)^j * C_k_ij } }
# psfimage = SUM_k{ acoeff_k * RROWS_k }
for k in range(len(psf)):
nrb = psf.nrow_b[k]
ncb = psf.ncol_b[k]
c = psf.c[k].reshape(5, 5)
c = c[:nrb, :ncb]
(gridi, gridj) = np.meshgrid(range(nrb), range(ncb))
if psfimgs is None:
psfimgs = [
np.zeros_like(psf.rrows[k]) for xy in np.broadcast(x, y)
]
(outh, outw) = (psf.rnrow[k], psf.rncol[k])
else:
assert (psf.rnrow[k] == outh)
assert (psf.rncol[k] == outw)
for i, (xi, yi) in enumerate(np.broadcast(x, y)):
#print 'xi,yi', xi,yi
acoeff_k = np.sum(
((0.001 * xi)**gridi * (0.001 * yi)**gridj * c))
if False: # DEBUG
print 'coeffs:', (0.001 * xi)**gridi * (0.001 * yi)**gridj
print 'c:', c
for (coi, ci) in zip(
((0.001 * xi)**gridi * (0.001 * yi)**gridj).ravel(),
c.ravel()):
print 'co %g, c %g' % (coi, ci)
print 'acoeff_k', acoeff_k
#print 'acoeff_k', acoeff_k.shape, acoeff_k
#print 'rrows[k]', psf.rrows[k].shape, psf.rrows[k]
psfimgs[i] += acoeff_k * psf.rrows[k]
psfimgs = [img.reshape((outh, outw)) for img in psfimgs]
if rtnscalar:
return psfimgs[0]
return psfimgs
def getPsfAtPoints(self, bandnum, x, y):
'''
Reconstruct the SDSS model PSF from KL basis functions.
x,y can be scalars or 1-d numpy arrays.
Return value:
if x,y are scalars: a PSF image
if x,y are arrays: a list of PSF images
'''
rtnscalar = np.isscalar(x) and np.isscalar(y)
x = np.atleast_1d(x)
y = np.atleast_1d(y)
psf = fits_table(self.hdus[bandnum+1].data)
psfimgs = None
(outh, outw) = (None,None)
# From the IDL docs:
# http://photo.astro.princeton.edu/photoop_doc.html#SDSS_PSF_RECON
# acoeff_k = SUM_i{ SUM_j{ (0.001*ROWC)^i * (0.001*COLC)^j * C_k_ij } }
# psfimage = SUM_k{ acoeff_k * RROWS_k }
for k in range(len(psf)):
nrb = psf.nrow_b[k]
ncb = psf.ncol_b[k]
c = psf.c[k].reshape(5, 5)
c = c[:nrb,:ncb]
(gridi,gridj) = np.meshgrid(range(nrb), range(ncb))
if psfimgs is None:
psfimgs = [np.zeros_like(psf.rrows[k]) for xy
in np.broadcast(x,y)]
(outh,outw) = (psf.rnrow[k], psf.rncol[k])
else:
assert(psf.rnrow[k] == outh)
assert(psf.rncol[k] == outw)
for i,(xi,yi) in enumerate(np.broadcast(x,y)):
#print 'xi,yi', xi,yi
acoeff_k = np.sum(((0.001 * xi)**gridi * (0.001 * yi)**gridj * c))
if False: # DEBUG
print 'coeffs:', (0.001 * xi)**gridi * (0.001 * yi)**gridj
print 'c:', c
for (coi,ci) in zip(((0.001 * xi)**gridi * (0.001 * yi)**gridj).ravel(), c.ravel()):
print 'co %g, c %g' % (coi,ci)
print 'acoeff_k', acoeff_k
#print 'acoeff_k', acoeff_k.shape, acoeff_k
#print 'rrows[k]', psf.rrows[k].shape, psf.rrows[k]
psfimgs[i] += acoeff_k * psf.rrows[k]
psfimgs = [img.reshape((outh,outw)) for img in psfimgs]
if rtnscalar:
return psfimgs[0]
return psfimgs
def getMaskPlane(self, name):
# HACK -- this must be described somewhere sensible...
# (yeah, fpM extension 11 !)
maskmap = { 'INTER': 0,
'SATUR': 1,
'CR' : 8,
'GHOST': 9,
}
if not name in maskmap:
raise RuntimeError('Unknown mask plane \"%s\"' % name)
return fits_table(self.hdus[1 + maskmap[name]].data)
def getMaskPlane(self, name):
# HACK -- this must be described somewhere sensible...
# (yeah, fpM extension 11 !)
maskmap = {
'INTER': 0,
'SATUR': 1,
'CR': 8,
'GHOST': 9,
}
if not name in maskmap:
raise RuntimeError('Unknown mask plane \"%s\"' % name)
return fits_table(self.hdus[1 + maskmap[name]].data)
def setHdus(self, p):
self.hdus = p
t = fits_table(p[6].data)
# the table has only one row...
assert(len(t) == 1)
t = t[0]
#self.table = t
self.gain = t.gain
self.dark_variance = t.dark_variance
self.sky = t.sky
self.skyerr = t.skyerr
self.psp_status = t.status
# Double-Gaussian PSF params
self.dgpsf_s1 = t.psf_sigma1_2g
self.dgpsf_s2 = t.psf_sigma2_2g
self.dgpsf_b = t.psf_b_2g
# summary PSF width (sigmas)
self.psf_fwhm = t.psf_width * (2.*np.sqrt(2.*np.log(2.)))
def setHdus(self, p):
self.hdus = p
t = fits_table(p[6].data)
# the table has only one row...
assert (len(t) == 1)
t = t[0]
#self.table = t
self.gain = t.gain
self.dark_variance = t.dark_variance
self.sky = t.sky
self.skyerr = t.skyerr
self.psp_status = t.status
# Double-Gaussian PSF params
self.dgpsf_s1 = t.psf_sigma1_2g
self.dgpsf_s2 = t.psf_sigma2_2g
self.dgpsf_b = t.psf_b_2g
# summary PSF width (sigmas)
self.psf_fwhm = t.psf_width * (2. * np.sqrt(2. * np.log(2.)))
