def _detmap(X):
from scipy.ndimage.filters import gaussian_filter
from legacypipe.survey import tim_get_resamp
(tim, targetwcs, H, W) = X
R = tim_get_resamp(tim, targetwcs)
if R is None:
return None,None,None,None,None
ie = tim.getInvvar()
assert(tim.psf_sigma > 0)
psfnorm = 1./(2. * np.sqrt(np.pi) * tim.psf_sigma)
detim = tim.getImage().copy()
detim[ie == 0] = 0.
# Patch SATURATED pixels with the value saturated pixels would have??
#detim[(tim.dq & tim.dq_bits['satur']) > 0] = tim.satval
detim = gaussian_filter(detim, tim.psf_sigma) / psfnorm**2
detsig1 = tim.sig1 / psfnorm
subh,subw = tim.shape
detiv = np.zeros((subh,subw), np.float32) + (1. / detsig1**2)
detiv[ie == 0] = 0.
(Yo,Xo,Yi,Xi) = R
if tim.dq is None:
sat = None
else:
sat = ((tim.dq[Yi,Xi] & tim.dq_saturation_bits) > 0)
return Yo, Xo, detim[Yi,Xi], detiv[Yi,Xi], sat
def _galdetmap(X):
from scipy.ndimage.filters import gaussian_filter
from legacypipe.survey import tim_get_resamp
(tim, galsize, gaussian, targetwcs, H, W) = X
R = tim_get_resamp(tim, targetwcs)
if R is None:
return None,None,None,None
ie = tim.getInvvar()
assert(tim.psf_sigma > 0)
pixscale = tim.subwcs.pixel_scale()
if gaussian:
# convert galsize (in sigmas in arcsec) to pixels
galsigma = galsize / pixscale
sigma = np.hypot(tim.psf_sigma, galsigma)
gnorm = 1./(2. * np.sqrt(np.pi) * sigma)
detim = tim.getImage().copy()
detim[ie == 0] = 0.
detim = gaussian_filter(detim, sigma) / gnorm**2
else:
galsigs = np.sqrt(ExpGalaxy.profile.var[:,0,0]) * galsize / pixscale
galamps = ExpGalaxy.profile.amp
#print('Galaxy sigma: %.2f, PSF sigma: %.2f' % (galsigma, tim.psf_sigma))
print('Galaxy amps', galamps, 'sigmas', galsigs)
sz = 20
xx,yy = np.meshgrid(np.arange(-sz, sz+1), np.arange(-sz, sz+1))
rr = xx**2 + yy**2
normim = 0
detim = 0
img = tim.getImage().copy()
img[ie == 0] = 0.
for amp,sig in zip(galamps, galsigs):
sig = np.hypot(tim.psf_sigma, sig)
detim += amp * gaussian_filter(img, sig)
normim += amp * 1./(2.*np.pi*sig**2) * np.exp(-0.5 * rr / sig**2)
#print('Normimg:', normim.sum())
gnorm = np.sqrt(np.sum(normim**2))
print('Galnorm', gnorm, 'vs psfnorm', 1./(2. * np.sqrt(np.pi) * tim.psf_sigma), 'seeing', tim.psf_fwhm/pixscale)
detim /= gnorm**2
detsig1 = tim.sig1 / gnorm
subh,subw = tim.shape
detiv = np.zeros((subh,subw), np.float32) + (1. / detsig1**2)
detiv[ie == 0] = 0.
(Yo,Xo,Yi,Xi) = R
return Yo, Xo, detim[Yi,Xi], detiv[Yi,Xi]
def _detmap(X):
from scipy.ndimage.filters import gaussian_filter
from legacypipe.survey import tim_get_resamp
(tim, targetwcs, apodize) = X
R = tim_get_resamp(tim, targetwcs)
if R is None:
return None, None, None, None, None
assert (tim.psf_sigma > 0)
psfnorm = 1. / (2. * np.sqrt(np.pi) * tim.psf_sigma)
ie = tim.getInvError()
detim = tim.getImage().copy()
# Zero out all masked pixels
detim[ie == 0] = 0.
tim.getSky().addTo(detim, scale=-1.)
subh, subw = tim.shape
detsig1 = tim.sig1 / psfnorm
detiv = np.zeros((subh, subw), np.float32) + (1. / detsig1**2)
detiv[ie == 0] = 0.
(Yo, Xo, Yi, Xi) = R
if tim.dq is None:
sat = None
else:
sat = ((tim.dq[Yi, Xi] & tim.dq_saturation_bits) > 0)
# Replace saturated pixels by the brightest (non-masked) pixel in the image
if np.any(sat):
I, = np.nonzero(sat)
debug('Filling', len(I), 'saturated detmap pixels with max')
detim[Yi[I], Xi[I]] = np.max(detim)
# detection is based on S/N, so plug in values > 0 for iv
detiv[Yi[I], Xi[I]] = 1. / detsig1**2
detim = gaussian_filter(detim, tim.psf_sigma) / psfnorm**2
detiv = gaussian_filter(detiv, tim.psf_sigma)
if apodize:
apodize = int(apodize)
ramp = np.arctan(np.linspace(-np.pi, np.pi, apodize + 2))
ramp = (ramp - ramp.min()) / (ramp.max() - ramp.min())
# drop first and last (= 0 and 1)
ramp = ramp[1:-1]
detiv[:len(ramp), :] *= ramp[:, np.newaxis]
detiv[:, :len(ramp)] *= ramp[np.newaxis, :]
detiv[-len(ramp):, :] *= ramp[::-1][:, np.newaxis]
detiv[:, -len(ramp):] *= ramp[::-1][np.newaxis, :]
return Yo, Xo, detim[Yi, Xi], detiv[Yi, Xi], sat
def _detmap(X):
from scipy.ndimage.filters import gaussian_filter
from legacypipe.survey import tim_get_resamp
(tim, targetwcs, H, W, apodize) = X
R = tim_get_resamp(tim, targetwcs)
if R is None:
return None, None, None, None, None
assert (tim.psf_sigma > 0)
psfnorm = 1. / (2. * np.sqrt(np.pi) * tim.psf_sigma)
ie = tim.getInvvar()
detim = tim.getImage().copy()
tim.getSky().addTo(detim, scale=-1.)
detim = gaussian_filter(detim, tim.psf_sigma) / psfnorm**2
detsig1 = tim.sig1 / psfnorm
subh, subw = tim.shape
detiv = np.zeros((subh, subw), np.float32) + (1. / detsig1**2)
detiv[ie == 0] = 0.
detiv = gaussian_filter(detiv, tim.psf_sigma)
if apodize:
apodize = int(apodize)
ramp = np.arctan(np.linspace(-np.pi, np.pi, apodize + 2))
ramp = (ramp - ramp.min()) / (ramp.max() - ramp.min())
# drop first and last (= 0 and 1)
ramp = ramp[1:-1]
detiv[:len(ramp), :] *= ramp[:, np.newaxis]
detiv[:, :len(ramp)] *= ramp[np.newaxis, :]
detiv[-len(ramp):, :] *= ramp[::-1][:, np.newaxis]
detiv[:, -len(ramp):] *= ramp[::-1][np.newaxis, :]
(Yo, Xo, Yi, Xi) = R
if tim.dq is None:
sat = None
else:
sat = ((tim.dq[Yi, Xi] & tim.dq_saturation_bits) > 0)
return Yo, Xo, detim[Yi, Xi], detiv[Yi, Xi], sat
def quick_coadds(tims,
bands,
targetwcs,
images=None,
get_cow=False,
get_n2=False,
fill_holes=True):
W = int(targetwcs.get_width())
H = int(targetwcs.get_height())
coimgs = []
cons = []
if get_n2:
cons2 = []
if get_cow:
# moo
cowimgs = []
wimgs = []
for ib, band in enumerate(bands):
coimg = np.zeros((H, W), np.float32)
coimg2 = np.zeros((H, W), np.float32)
con = np.zeros((H, W), np.uint8)
con2 = np.zeros((H, W), np.uint8)
if get_cow:
cowimg = np.zeros((H, W), np.float32)
wimg = np.zeros((H, W), np.float32)
for itim, tim in enumerate(tims):
if tim.band != band:
continue
R = tim_get_resamp(tim, targetwcs)
if R is None:
continue
(Yo, Xo, Yi, Xi) = R
nn = (tim.getInvError()[Yi, Xi] > 0)
if images is None:
coimg[Yo, Xo] += tim.getImage()[Yi, Xi] * nn
coimg2[Yo, Xo] += tim.getImage()[Yi, Xi]
else:
coimg[Yo, Xo] += images[itim][Yi, Xi] * nn
coimg2[Yo, Xo] += images[itim][Yi, Xi]
con[Yo, Xo] += nn
if get_cow:
cowimg[Yo,
Xo] += tim.getInvvar()[Yi, Xi] * tim.getImage()[Yi, Xi]
wimg[Yo, Xo] += tim.getInvvar()[Yi, Xi]
con2[Yo, Xo] += 1
coimg /= np.maximum(con, 1)
if fill_holes:
coimg[con == 0] = coimg2[con == 0] / np.maximum(1, con2[con == 0])
if get_cow:
cowimg /= np.maximum(wimg, 1e-16)
cowimg[wimg == 0] = coimg[wimg == 0]
cowimgs.append(cowimg)
wimgs.append(wimg)
coimgs.append(coimg)
cons.append(con)
if get_n2:
cons2.append(con2)
rtn = [coimgs, cons]
if get_cow:
rtn.extend([cowimgs, wimgs])
if get_n2:
rtn.append(cons2)
return rtn
def quick_coadds(tims, bands, targetwcs, images=None,
get_cow=False, get_n2=False, fill_holes=True, get_max=False,
get_saturated=False,
addnoise=False):
W = int(targetwcs.get_width())
H = int(targetwcs.get_height())
coimgs = []
cons = []
if get_n2:
cons2 = []
if get_cow:
# moo
cowimgs = []
wimgs = []
if get_max:
maximgs = []
if get_saturated:
satur = np.zeros((H,W), np.bool)
if addnoise:
noise = np.zeros((H,W), np.float32)
for band in bands:
coimg = np.zeros((H,W), np.float32)
coimg2 = np.zeros((H,W), np.float32)
con = np.zeros((H,W), np.int16)
con2 = np.zeros((H,W), np.int16)
if get_cow:
cowimg = np.zeros((H,W), np.float32)
wimg = np.zeros((H,W), np.float32)
if get_max:
maximg = np.zeros((H,W), np.float32)
for itim,tim in enumerate(tims):
if tim.band != band:
continue
R = tim_get_resamp(tim, targetwcs)
if R is None:
continue
(Yo,Xo,Yi,Xi) = R
nn = (tim.getInvError()[Yi,Xi] > 0)
if images is None:
coimg [Yo,Xo] += tim.getImage()[Yi,Xi] * nn
coimg2[Yo,Xo] += tim.getImage()[Yi,Xi]
if get_max:
maximg[Yo,Xo] = np.maximum(maximg[Yo,Xo], tim.getImage()[Yi,Xi] * nn)
else:
coimg [Yo,Xo] += images[itim][Yi,Xi] * nn
coimg2[Yo,Xo] += images[itim][Yi,Xi]
if get_max:
maximg[Yo,Xo] = np.maximum(maximg[Yo,Xo], images[itim][Yi,Xi] * nn)
if addnoise:
noise[:,:] = 0.
noise[Yo[nn],Xo[nn]] = 1./(tim.getInvError()[Yi,Xi][nn])
coimg += noise * np.random.normal(size=noise.shape)
con [Yo,Xo] += nn
if get_cow:
cowimg[Yo,Xo] += tim.getInvvar()[Yi,Xi] * tim.getImage()[Yi,Xi]
wimg [Yo,Xo] += tim.getInvvar()[Yi,Xi]
if get_saturated and tim.dq is not None:
satur[Yo,Xo] |= ((tim.dq[Yi,Xi] & tim.dq_saturation_bits) > 0)
con2 [Yo,Xo] += 1
coimg /= np.maximum(con,1)
if fill_holes:
coimg[con == 0] = coimg2[con == 0] / np.maximum(1, con2[con == 0])
if get_cow:
cowimg /= np.maximum(wimg, 1e-16)
cowimg[wimg == 0] = coimg[wimg == 0]
cowimgs.append(cowimg)
wimgs.append(wimg)
if get_max:
maximgs.append(maximg)
coimgs.append(coimg)
cons.append(con)
if get_n2:
cons2.append(con2)
rtn = [coimgs,cons]
if get_cow:
rtn.extend([cowimgs, wimgs])
if get_n2:
rtn.append(cons2)
if get_max:
rtn.append(maximgs)
if get_saturated:
rtn.append(satur)
return rtn
def quick_coadds(tims, bands, targetwcs, images=None,
get_cow=False, get_n2=False, fill_holes=True):
W = targetwcs.get_width()
H = targetwcs.get_height()
coimgs = []
cons = []
if get_n2:
cons2 = []
if get_cow:
# moo
cowimgs = []
wimgs = []
for ib,band in enumerate(bands):
coimg = np.zeros((H,W), np.float32)
coimg2 = np.zeros((H,W), np.float32)
con = np.zeros((H,W), np.uint8)
con2 = np.zeros((H,W), np.uint8)
if get_cow:
cowimg = np.zeros((H,W), np.float32)
wimg = np.zeros((H,W), np.float32)
for itim,tim in enumerate(tims):
if tim.band != band:
continue
R = tim_get_resamp(tim, targetwcs)
if R is None:
continue
(Yo,Xo,Yi,Xi) = R
nn = (tim.getInvError()[Yi,Xi] > 0)
if images is None:
coimg [Yo,Xo] += tim.getImage()[Yi,Xi] * nn
coimg2[Yo,Xo] += tim.getImage()[Yi,Xi]
else:
coimg [Yo,Xo] += images[itim][Yi,Xi] * nn
coimg2[Yo,Xo] += images[itim][Yi,Xi]
con [Yo,Xo] += nn
if get_cow:
cowimg[Yo,Xo] += tim.getInvvar()[Yi,Xi] * tim.getImage()[Yi,Xi]
wimg [Yo,Xo] += tim.getInvvar()[Yi,Xi]
con2 [Yo,Xo] += 1
coimg /= np.maximum(con,1)
if fill_holes:
coimg[con == 0] = coimg2[con == 0] / np.maximum(1, con2[con == 0])
if get_cow:
cowimg /= np.maximum(wimg, 1e-16)
cowimg[wimg == 0] = coimg[wimg == 0]
cowimgs.append(cowimg)
wimgs.append(wimg)
coimgs.append(coimg)
cons.append(con)
if get_n2:
cons2.append(con2)
rtn = [coimgs,cons]
if get_cow:
rtn.extend([cowimgs, wimgs])
if get_n2:
rtn.append(cons2)
return rtn