def single_deconvolve(filelist, krn_size, psf_width, vb):
## Deconvolve all FITS images in list
for infile in filelist:
## Run SExtractor to generate source-catalogue
catfile = SD.SEx(infile, vb)
## Run PSFEx to generate model image of PSF
psf_obs = SD.PSFEx(catfile, "snap", vb)
##--------------------------------------------------------------------
## Intermediate step: convert PSF image to an array
psf_obs = IM.pngcropwhite(IM.fits_pix(psf_obs))
##--------------------------------------------------------------------
## Make target psf array
gaussparams = DT.moments(psf_obs)
if type(psf_width) is float:
gaussparams[1]=gaussparams[2]=psf_width#AsecToPix(psf_width,infile)
psf_ref = DT.Gauss_2D(*gaussparams)
#scipy.misc.imsave("./ref_"+str(psf_width)+".png",psf_ref)
##--------------------------------------------------------------------
## Calculate deconvolution-kernel array
## Kernel size = psf width -- assume this is ~ correlation length-scale
krn_size = 3*int(max(gaussparams[1:3])) ### ONLY APPROPRIATE when using simdec
kernarr = DT.get_kernel(psf_obs, psf_ref,[krn_size,krn_size],vb)
## Deconvolve image
DT.deconvolve_image(infile, kernarr, vb)
return
def simultaneous_deconvolve(filelist, krn_size, vb):
s2_max = 0.0
## Find largest PSF in ensemble
for infile in filelist:
## Run SExtractor to generate source-catalogue
catfile = SD.SEx(infile, vb)
## Run PSFEx to generate model image of PSF
psf_obs = SD.PSFEx(catfile, "snap", vb)
### NEED to find FWHM from images without going through the
### following rigmarole:
## Convert PSF image to an array
psf_obs = IM.pngcropwhite(IM.fits_pix(psf_obs))
## Here is how we decide what PSF to use
## Product of the two widths = s2
m = DT.moments(psf_obs)
s2 = m[1] * m[2]
## Which image has largest s2? Call this psf_OBS.
if s2 > s2_max:
s2_max = s2
psf_OBS = psf_obs
##------------------------------------------------------------
## Make target psf array
psf_ref = DT.Gauss_2D(*DT.moments(psf_OBS))
## Calculate kernel array for obs->ref
kernarr = DT.get_kernel(psf_OBS, psf_ref, [krn_size, krn_size], vb)
##------------------------------------------------------------
## Deconvolve to the target PSF
for infile in filelist:
DT.deconvolve_image(infile, kernarr, vb)
return
def simultaneous_deconvolve(filelist, krn_size, vb): s2_max = 0.0 ## Find largest PSF in ensemble for infile in filelist: ## Run SExtractor to generate source-catalogue catfile = SD.SEx(infile, vb) ## Run PSFEx to generate model image of PSF psf_obs = SD.PSFEx(catfile, "snap", vb) ### NEED to find FWHM from images without going through the ### following rigmarole: ## Convert PSF image to an array psf_obs = IM.pngcropwhite(IM.fits_pix(psf_obs)) ## Here is how we decide what PSF to use ## Product of the two widths = s2 m=DT.moments(psf_obs) s2 = m[1]*m[2] ## Which image has largest s2? Call this psf_OBS. if s2 > s2_max: s2_max = s2 psf_OBS = psf_obs ##------------------------------------------------------------ ## Make target psf array psf_ref = DT.Gauss_2D(*DT.moments(psf_OBS)) ## Calculate kernel array for obs->ref kernarr = DT.get_kernel(psf_OBS, psf_ref, [krn_size,krn_size],vb) ##------------------------------------------------------------ ## Deconvolve to the target PSF for infile in filelist: DT.deconvolve_image(infile, kernarr, vb) return
def single_deconvolve(filelist, krn_size, vb):
## Deconvolve all FITS images in list
for infile in filelist:
## Run SExtractor to generate source-catalogue
catfile = SD.SEx(infile, vb)
## Run PSFEx to generate model image of PSF
psf_obs = SD.PSFEx(catfile, "snap", vb)
## Intermediate step: convert PSF image to an array
psf_obs = IM.pngcropwhite(IM.fits_pix(psf_obs))
## Make target psf array
psf_ref = DT.Gauss_2D(*DT.moments(psf_obs))
## Calculate kernel array
kernarr = DT.get_kernel(psf_obs, psf_ref, [krn_size, krn_size], vb)
## Deconvolve image
DT.deconvolve_image(infile, kernarr, vb)
return
def single_deconvolve(filelist, krn_size, vb): ## Deconvolve all FITS images in list for infile in filelist: ## Run SExtractor to generate source-catalogue catfile = SD.SEx(infile, vb) ## Run PSFEx to generate model image of PSF psf_obs = SD.PSFEx(catfile, "snap", vb) ## Intermediate step: convert PSF image to an array psf_obs = IM.pngcropwhite(IM.fits_pix(psf_obs)) ## Make target psf array psf_ref = DT.Gauss_2D(*DT.moments(psf_obs)) ## Calculate kernel array kernarr = DT.get_kernel(psf_obs, psf_ref,[krn_size,krn_size],vb) ## Deconvolve image DT.deconvolve_image(infile, kernarr, vb) return