def estimateBackground(fine, infile, flex=None, outname=None):
if outname is None:
print "Need an output name"
return
dX = 0
dY = 0
infile[0].data = infile[0].data.astype(np.float64)
data = infile[0].data.copy()
for ff in fine:
if not ff.ok: continue
if ff.xrange is None: continue
if ff.poly is None: continue
xs = np.arange(*ff.xrange)
ys = np.round(np.poly1d(ff.poly)(xs)).astype(np.int)
for dY in xrange(-5,6):
ty = ys.copy() - dY
try: data[ty,xs] = np.nan
except: pass
from astropy.convolution import convolve, convolve_fft, Box2DKernel
print "Traditional convolve (pass 1)"
k = Box2DKernel(17)
flt = data.copy()
NaNs = ~np.isfinite(data)
OKs = np.isfinite(data)
for i in xrange(5):
flt = convolve(flt, k)
flt[OKs] = data[OKs]
#IO.writefits(pf.PrimaryHDU(flt), "test_%i.fits.gz" % i, clobber=True)
data[NaNs] = flt[NaNs]
fname = os.path.join(os.path.dirname(outname),
"lf_" + os.path.basename(outname))
#IO.writefits(data, fname, clobber=True)
print "FFT convolve (pass 2)"
#k = Box2DKernel(70)
#flt = convolve_fft(data, k)
flt = gaussian_filter(data, 100)
fname = os.path.join(os.path.dirname(outname),
"bgd_" + os.path.basename(outname))
HDU = pf.PrimaryHDU(flt)
IO.writefits(HDU, fname, clobber=True)
fname = os.path.join(os.path.dirname(outname),
"bs_" + os.path.basename(outname))
infile[0].header["BGDSUB"] = "Background subtracted with %s" % fname
infile[0].data -= flt
IO.writefits(infile, fname, clobber=True)
return flt
description="""Divides a science frame by the flat frame, handles flexure
""", formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('flatfits', type=str, help='Flat field fits file')
parser.add_argument('toflattenfits', type=str, help='To flatten fits file')
parser.add_argument('--flexnpy', type=str, help='Flexure .npy file', default=None)
parser.add_argument('--outfile', type=str, help='Output filename', default=None)
args = parser.parse_args()
flat = pf.open(args.flatfits)[0].data
toflat = pf.open(args.toflattenfits)
if args.flexnpy is not None:
flex = np.load(args.flexnpy)[0]
nmToPix = flex['skyline']/240
dX = np.int(np.round(flex['dXnm'] * nmToPix))
dY = np.int(np.round(flex['dYpix']))
print("Rolling by %i/%i" % (dX, dY))
flat = np.roll(flat, dY, 0)
flat = np.roll(flat, dX, 1)
toflat[0].header["FLATROLL"] = ("%s/%s" % (dX, dY),
"Flat field flexure correction")
toflat[0].data /= flat
toflat[0].header["FLATBY"] = (args.toflattenfits, "Flat field applied")
toflat[0].data = toflat[0].data.astype(np.float32)
IO.writefits( toflat, args.outfile, clobber=True, no_lossy_compress=True)
def estimateBackground(fine, infile, flex=None, outname=None):
if outname is None:
print "Need an output name"
return
dX = 0
dY = 0
infile[0].data = infile[0].data.astype(np.float64)
data = infile[0].data.copy()
for ff in fine:
if not ff.ok: continue
if ff.xrange is None: continue
if ff.poly is None: continue
xs = np.arange(*ff.xrange)
ys = np.round(np.poly1d(ff.poly)(xs)).astype(np.int)
for dY in xrange(-5, 6):
ty = ys.copy() - dY
try:
data[ty, xs] = np.nan
except:
pass
from astropy.convolution import convolve, convolve_fft, Box2DKernel
print "Traditional convolve (pass 1)"
k = Box2DKernel(17)
flt = data.copy()
NaNs = ~np.isfinite(data)
OKs = np.isfinite(data)
for i in xrange(5):
flt = convolve(flt, k)
flt[OKs] = data[OKs]
#IO.writefits(pf.PrimaryHDU(flt), "test_%i.fits.gz" % i, clobber=True)
data[NaNs] = flt[NaNs]
fname = os.path.join(os.path.dirname(outname),
"lf_" + os.path.basename(outname))
#IO.writefits(data, fname, clobber=True)
print "FFT convolve (pass 2)"
#k = Box2DKernel(70)
#flt = convolve_fft(data, k)
flt = gaussian_filter(data, 100)
fname = os.path.join(os.path.dirname(outname),
"bgd_" + os.path.basename(outname))
HDU = pf.PrimaryHDU(flt)
IO.writefits(HDU, fname, clobber=True)
fname = os.path.join(os.path.dirname(outname),
"bs_" + os.path.basename(outname))
infile[0].header["BGDSUB"] = "Background subtracted with %s" % fname
infile[0].data -= flt
IO.writefits(infile, fname, clobber=True)
return flt
def estimateBackground(fine, infile, gausswidth=100, outname=None):
if outname is None:
print "Need an output name"
return
infile[0].data = infile[0].data.astype(np.float64)
data = infile[0].data.copy()
for ff in fine:
if not ff.ok:
continue
if ff.xrange is None:
continue
if ff.poly is None:
continue
xs = np.arange(*ff.xrange)
ys = np.round(np.poly1d(ff.poly)(xs)).astype(np.int)
for dY in xrange(-5, 6):
ty = ys.copy() - dY
try:
data[ty, xs] = np.nan
except:
pass
from astropy.convolution import convolve, convolve_fft, Box2DKernel
print "Traditional convolve (pass 1)"
k = Box2DKernel(17)
flt = data.copy()
nans = ~np.isfinite(data)
oks = np.isfinite(data)
for i in xrange(5):
flt = convolve(flt, k)
flt[oks] = data[oks]
print "\tIteration %d of 5" % (i+1)
# IO.writefits(pf.PrimaryHDU(flt), "test_%i.fits.gz" % i, clobber=True)
data[nans] = flt[nans]
# fname = os.path.join(os.path.dirname(outname),
# "lf_" + os.path.basename(outname))
# IO.writefits(data, fname, clobber=True)
# print "FFT convolve (pass 2)"
print "Gaussian filter with width = %d (pass 2)" % gausswidth
# k = Box2DKernel(70)
# flt = convolve_fft(data, k)
flt = gaussian_filter(data, gausswidth)
ofname = os.path.join(os.path.dirname(outname),
"bgd_" + os.path.basename(outname))
HDU = pf.PrimaryHDU(flt)
HDU.header["GAUFWID"] = (gausswidth, 'Gaussian filter width in pixels')
IO.writefits(HDU, ofname, clobber=True)
print "Background image in %s" % ofname + ".gz"
infile[0].header["BGDSUB"] = "Background subtracted using %s" % ofname
infile[0].header["GAUFWID"] = (gausswidth,
'Gaussian filter width in pixels')
ofname = os.path.join(os.path.dirname(outname),
"bs_" + os.path.basename(outname))
infile[0].data -= flt
IO.writefits(infile, ofname, clobber=True)
print "Subtracted image in %s" % ofname + ".gz"
return flt
def estimateBackground(fine, infile, gausswidth=100, outname=None):
if outname is None:
print "Need an output name"
return
infile[0].data = infile[0].data.astype(np.float64)
data = infile[0].data.copy()
for ff in fine:
if not ff.ok:
continue
if ff.xrange is None:
continue
if ff.poly is None:
continue
xs = np.arange(*ff.xrange)
ys = np.round(np.poly1d(ff.poly)(xs)).astype(np.int)
for dY in xrange(-5, 6):
ty = ys.copy() - dY
try:
data[ty, xs] = np.nan
except:
pass
from astropy.convolution import convolve, convolve_fft, Box2DKernel
print "Traditional convolve (pass 1)"
k = Box2DKernel(17)
flt = data.copy()
nans = ~np.isfinite(data)
oks = np.isfinite(data)
for i in xrange(5):
flt = convolve(flt, k)
flt[oks] = data[oks]
print "\tIteration %d of 5" % (i + 1)
# IO.writefits(pf.PrimaryHDU(flt), "test_%i.fits.gz" % i, clobber=True)
data[nans] = flt[nans]
# fname = os.path.join(os.path.dirname(outname),
# "lf_" + os.path.basename(outname))
# IO.writefits(data, fname, clobber=True)
# print "FFT convolve (pass 2)"
print "Gaussian filter with width = %d (pass 2)" % gausswidth
# k = Box2DKernel(70)
# flt = convolve_fft(data, k)
flt = gaussian_filter(data, gausswidth)
ofname = os.path.join(os.path.dirname(outname),
"bgd_" + os.path.basename(outname))
HDU = pf.PrimaryHDU(flt)
HDU.header["GAUFWID"] = (gausswidth, 'Gaussian filter width in pixels')
IO.writefits(HDU, ofname, clobber=True)
print "Background image in %s" % ofname + ".gz"
infile[0].header["BGDSUB"] = "Background subtracted using %s" % ofname
infile[0].header["GAUFWID"] = (gausswidth,
'Gaussian filter width in pixels')
ofname = os.path.join(os.path.dirname(outname),
"bs_" + os.path.basename(outname))
infile[0].data -= flt
IO.writefits(infile, ofname, clobber=True)
print "Subtracted image in %s" % ofname + ".gz"
return flt
''', formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('flatfits', type=str, help='Flat field fits file')
parser.add_argument('toflattenfits', type=str, help='To flatten fits file')
parser.add_argument('--flexnpy', type=str, help='Flexure .npy file', default=None)
parser.add_argument('--outfile', type=str, help='Output filename', default=None)
args = parser.parse_args()
flat = pf.open(args.flatfits)[0].data
toflat = pf.open(args.toflattenfits)
if args.flexnpy is not None:
flex = np.load(args.flexnpy)[0]
nmToPix = flex['skyline']/240
dX = np.int(np.round(flex['dXnm'] * nmToPix))
dY = np.int(np.round(flex['dYpix']))
print "Rolling by %i/%i" % (dX, dY)
flat = np.roll(flat, dY, 0)
flat = np.roll(flat, dX, 1)
toflat[0].header["FLATROLL"] = ("%s/%s" % (dX, dY),
"Flat field flexure correction")
toflat[0].data /= flat
toflat[0].header["FLATBY"] = (args.toflattenfits, "Flat field applied")
toflat[0].data = toflat[0].data.astype(np.float64)
IO.writefits( toflat, args.outfile, clobber=True, no_lossy_compress=True)
def estimate_background(fine,
infile,
gausswidth=100,
outname=None,
outint=False,
fft_filt=False):
if outname is None:
print("Need an output name")
return
infile[0].data = infile[0].data.astype(np.float32)
data = infile[0].data.copy()
# loop over each trace
for ff in fine:
if not ff.ok and not ff.bkg_ok:
continue
if ff.xrange is None:
continue
if ff.poly is None:
continue
# get trace spatial ranges (xs, ys)
xs = np.arange(*ff.xrange)
ys = np.round(np.poly1d(ff.poly)(xs)).astype(np.int)
# mask above and below each trace with nan's
for dY in range(-4, 5):
ty = ys.copy() - dY
try:
data[ty, xs] = np.nan
except:
pass
from astropy.convolution import convolve, convolve_fft, Box2DKernel
print("Traditional convolve (pass 1)")
# get convolution kernel
k = Box2DKernel(17)
# start with original masked image for background
bkg = data.copy()
# write out starting image if requested
if outint:
IO.writefits(pf.PrimaryHDU(bkg), "test_0.fits", clobber=True)
print("Wrote test_0.fits.gz")
# keep track of nans
nans = ~np.isfinite(data)
# good data
oks = np.isfinite(data)
# iterate five times to remove object light from bkg
for i in range(5):
# convolve entire image
bkg = convolve(bkg, k)
# replace background light
bkg[oks] = data[oks]
print("\tIteration %d of 5" % (i + 1))
# write out each iteration if requested
if outint:
IO.writefits(pf.PrimaryHDU(bkg),
"test_%i.fits" % (i + 1),
clobber=True)
print("Wrote test_%i.fits.gz" % i)
# insert iterative smoothed object pixels
data[nans] = bkg[nans]
# any remaining nans?
nans = ~np.isfinite(data)
# fill them in with median value
if np.count_nonzero(nans) > 0:
data[nans] = np.nanmedian(data[oks])
# write out intermediate result if requested
if outint:
fname = os.path.join(os.path.dirname(outname),
"lf_" + os.path.basename(outname))
IO.writefits(data, fname, clobber=True)
print("Wrote %s" % fname + ".gz")
# use FFT filter if requested
if fft_filt:
print("FFT convolve (pass 2)")
k = Box2DKernel(70)
bkg = convolve_fft(data, k)
# else, use gaussian filter of requested width in pixels
else:
print("Gaussian filter with width = %d (pass 2)" % gausswidth)
bkg = gaussian_filter(data, gausswidth)
# write resulting background to a gzipped fits file
ofname = os.path.join(os.path.dirname(outname),
"bgd_" + os.path.basename(outname))
HDU = pf.PrimaryHDU(bkg)
HDU.header["GAUFWID"] = (gausswidth, 'Gaussian filter width in pixels')
IO.writefits(HDU, ofname, clobber=True)
print("Background image in %s" % ofname + ".gz")
# record which file in output header
infile[0].header["BGDSUB"] = "Background subtracted using %s" % ofname
infile[0].header["GAUFWID"] = (gausswidth,
'Gaussian filter width in pixels')
ofname = os.path.join(os.path.dirname(outname),
"bs_" + os.path.basename(outname))
# subtract background
infile[0].data -= bkg
# write out the resulting fits file
IO.writefits(infile, ofname, clobber=True)
print("Subtracted image in %s" % ofname + ".gz")
return bkg
