def stretch_all(sel=0):
for string in ["CFHTLS_*_sci.fits"]:#
for fits in file_seek("../Data/",string):
if sel==0: stretch(fits)
else: stretch_plus(fits)
return
def stretch_all(sel=0):
for string in ["CFHTLS_*_sci.fits"]: #
for fits in file_seek("../Data/", string):
if sel == 0: stretch(fits)
else: stretch_plus(fits)
return
def stretch_plus(fits_original): ## Stretching parameters vmin,vmax=stretch(fits_original) ## Apply to diagnostic files homedir=os.path.dirname(fits_original) for string in ["snap_*_sci.fits","samp_*_sci.fits","resi_*_sci.fits"]:#"CFHTLS_*_sci.fits" for fits in file_seek(homedir,string): stretch(fits, vmin,vmax) return
def stretch_plus(fits_original):
## Stretching parameters
vmin, vmax = stretch(fits_original)
## Apply to diagnostic files
homedir = os.path.dirname(fits_original)
for string in ["snap_*_sci.fits", "samp_*_sci.fits",
"resi_*_sci.fits"]: #"CFHTLS_*_sci.fits"
for fits in file_seek(homedir, string):
stretch(fits, vmin, vmax)
return
def all_kernels(kdim, writefile=True, makeimage=False, vb=False):
## Get files -- path hard-coded
allpngs = numpy.sort(file_seek("../Data/GPSF/",
"snap*d0_CFHTLS_03_g*.png"))
## Collect pairs of files
for i in range(0, len(allpngs), 2):
## Extract image info
image1 = png_pix(allpngs[i])
image2 = png_pix(allpngs[i + 1])
## Cut out extraneous white pixels
image1 = pngcropwhite(image1)
image2 = pngcropwhite(image2)
## Deconvolve
A = get_kernel(image1, image2, kdim)
B = get_kernel(image2, image1, kdim)
##------------------------------------------------------------
## Write kernels to file and make images
## Gaussian to PSF
outfile = os.path.splitext(allpngs[i])[0][:-4] + "_GausstoPSF_" + str(
A.shape[0]) + ".krn"
f = open(outfile, "w")
f.write(
"## Convolution kernel taking 2D Gaussian to PSFEx image\n\n\n")
writearray(f, A, True)
if vb: print "DeconvolveToTargetPSF.py: kernel written to", outfile
## PSF to Gaussian
outfile = os.path.splitext(allpngs[i])[0][:-4] + "_PSFtoGauss_" + str(
B.shape[0]) + ".krn"
f = open(outfile, "w")
f.write(
"## Convolution kernel taking PSFEx image to 2D Gaussian\n\n\n")
writearray(f, B, True)
if vb: print "DeconvolveToTargetPSF.py: kernel written to", outfile
print "\n"
##------------------------------------------------------------
return
def FITStoPNG(args):
i=0
for f_img in file_seek(args[2], args[3]+"*.fits"):
## Prepare destination directory
outdir=os.path.dirname(f_img)+"/Enhanced_Original/" ## Needs to be commented when brightening
if os.path.isdir(outdir)==False: os.mkdir(outdir)
## Outfile name
p_img = outdir+"/"+os.path.basename(f_img)[:-5]+".png"
## File conversion
#os.system("ds9 "+f_img+" -minmax -colorbar no -saveimage png "+p_img+" -exit")
try:
hdulist = pyfits.open(f_img)
scipy.misc.imsave(f_img[:-4]+"png", hdulist[0].data)
i+=1
except:
pass
print "ImageScript.py: FITStoPNG: Converted",i,"files to png format."
return
def all_kernels(kdim, writefile=True,makeimage=False, vb=False):
## Get files -- path hard-coded
allpngs = numpy.sort(file_seek("../Data/GPSF/","snap*d0_CFHTLS_03_g*.png"))
## Collect pairs of files
for i in range (0,len(allpngs),2):
## Extract image info
image1 = png_pix(allpngs[i])
image2 = png_pix(allpngs[i+1])
## Cut out extraneous white pixels
image1 = pngcropwhite(image1)
image2 = pngcropwhite(image2)
## Deconvolve
A=get_kernel(image1,image2, kdim)
B=get_kernel(image2,image1, kdim)
##------------------------------------------------------------
## Write kernels to file and make images
## Gaussian to PSF
outfile=os.path.splitext(allpngs[i])[0][:-4]+"_GausstoPSF_"+str(A.shape[0])+".krn"
f=open(outfile,"w")
f.write("## Convolution kernel taking 2D Gaussian to PSFEx image\n\n\n")
writearray(f,A,True)
if vb: print "DeconvolveToTargetPSF.py: kernel written to",outfile
## PSF to Gaussian
outfile=os.path.splitext(allpngs[i])[0][:-4]+"_PSFtoGauss_"+str(B.shape[0])+".krn"
f=open(outfile,"w")
f.write("## Convolution kernel taking PSFEx image to 2D Gaussian\n\n\n")
writearray(f,B,True)
if vb: print "DeconvolveToTargetPSF.py: kernel written to",outfile
print "\n"
##------------------------------------------------------------
return
def FITStoPNG(args):
i = 0
for f_img in file_seek(args[2], args[3] + "*.fits"):
## Prepare destination directory
outdir = os.path.dirname(
f_img
) + "/Enhanced_Original/" ## Needs to be commented when brightening
if os.path.isdir(outdir) == False: os.mkdir(outdir)
## Outfile name
p_img = outdir + "/" + os.path.basename(f_img)[:-5] + ".png"
## File conversion
#os.system("ds9 "+f_img+" -minmax -colorbar no -saveimage png "+p_img+" -exit")
try:
hdulist = pyfits.open(f_img)
scipy.misc.imsave(f_img[:-4] + "png", hdulist[0].data)
i += 1
except:
pass
print "ImageScript.py: FITStoPNG: Converted", i, "files to png format."
return
def PSFtoPNG_all(args): for psffile in file_seek(args[2], "*.psf"): PSFtoPNG(psffile) return
krn_size = int(a)
elif o in ("-s", "--fstr"):
file_string = a
elif o in ("--sim "):
simul=True
else:
assert False, "Unhandled option."
##--------------------------------------------------------------------
## Is input a collection of files or directory?
## filelist is a list of raw image files.
filelist=[]
for arg in args:
if os.path.isfile(arg): filelist+=[arg]
elif os.path.isdir(arg): filelist+=file_seek(arg,file_string)
if simul is False:
single_deconvolve(filelist, krn_size, psf_width, vb)
else:
simultaneous_deconvolve(filelist, krn_size, vb)
##--------------------------------------------------------------------
if vb: print "deconvolve.py: deconvolution of",len(filelist),\
"file(s) took",round(time.time()-t_init,3),"seconds."
return
##============================================================
def bright_all(factor=8):
for original in file_seek("../Data/", "CFHTLS_*sci.png"):
brig(original, factor, 1)
return
def all_psfs(topdir="~/Dropbox/Hogg_2012/Data/PSFs/",
filestring="snap_*.fits"):
for fitsfile in file_seek(topdir, filestring):
GPSF(fitsfile)
return
def PSFtoPNG_all(args):
for psffile in file_seek(args[2], "*.psf"):
PSFtoPNG(psffile)
return
def all_psfs(topdir="~/Dropbox/Hogg_2012/Data/PSFs/", filestring="snap_*.fits"): for fitsfile in file_seek(topdir, filestring): GPSF(fitsfile) return
def bright_all(factor=8):
for original in file_seek("../Data/","CFHTLS_*sci.png"):
brig(original,factor,1)
return
print main.__doc__
return
elif o in ("-v"):
vb=True
else:
assert False, "Unhandled option"
##--------------------------------------------------------------------
t0=time.time()
## If the input is a single file
if os.path.isfile(args[0]): fits_list = [args[0]]
## If the input is a directory of files
elif os.path.isdir(args[0]): fits_list = file_seek(args[0], "CFHTLS_*sci.fits")
## Run SExtractor and PSFEx on the image
for fitsfile in fits_list:
## Run SExtractor
SEx(fitsfile, vb)
## Run PSFEx on fresh .cat
PSFEx(fitsfile[:-4]+"cat", "snap", vb)
if vb:
print "\nSourceDiagnostics.py: analysed",len(fits_list),"FITS images."
print "\tTook",round(time.time()-t0,2),"seconds."
return
return
elif o in ("-v"):
vb = True
else:
assert False, "Unhandled option"
##--------------------------------------------------------------------
t0 = time.time()
## If the input is a single file
if os.path.isfile(args[0]):
fits_list = [args[0]]
## If the input is a directory of files
elif os.path.isdir(args[0]):
fits_list = file_seek(args[0], "CFHTLS_*sci.fits")
## Run SExtractor and PSFEx on the image
for fitsfile in fits_list:
## Run SExtractor
SEx(fitsfile, vb)
## Run PSFEx on fresh .cat
PSFEx(fitsfile[:-4] + "cat", "snap", vb)
if vb:
print "\nSourceDiagnostics.py: analysed", len(
fits_list), "FITS images."
print "\tTook", round(time.time() - t0, 2), "seconds."
return