def subScatLgt(self, inIm, mask, **kwargs):
print "\nSUBTRACTING SCATTERED LIGHT FROM", inIm
pref = kwargs["prefix"]
# allow user to iterate over orders used to model scattered light
while True:
fixScat = bool(
input(
"\nDoes subtraction of scattered light need improvement? (True/False): "
))
if fixScat:
# request orders from user
xOrders = raw_input("X orders (csv): ")
yOrders = raw_input("Y orders (csv): ")
# model and subtract the light, and view the result
iraf.imdelete(pref + inIm)
iraf.gfscatsub(inIm, mask, xorder=xOrders, yorder=yOrders, \
**kwargs)
utils.examIm(pref + inIm + "[SCI]", frame=1)
else:
break
return
# Scattered light
blkmsk = np.loadtxt("blkmask_name.txt", dtype=str).item(0)
blkmsk0 = blkmsk
iraf.imdelete('brg@' + ic.lst_std)
std = np.loadtxt(ic.lst_std, dtype=str)
if (std.size > 1):
raise ValueError(
"Please check if there is only one image for the standard star.")
std0 = std.item(0)
iraf.gfscatsub('rg' + std0,
blkmsk0,
outimage='',
prefix='b',
xorder='3,3,3,3,3,3,3,3,3,3,3,3',
yorder='3,3,3,3,3,3,3,3,3,3,3,3',
cross='yes',
fl_inter='no')
# os.system('ds9 &')
# iraf.sleep(5.0)
# for std in iraf.type(ic.lst_std, Stdout=1):
# std = std.strip()
# for i in range(12):
# iraf.imexamine('brg'+std+'[sci,'+str(i+1)+']', 1)
# Printing the running time
print('--- %.4f seconds ---' % (time.time() - start_time))
def reduceFlats(self, flatList, inPref="", arcList="", arcPath="", \
subscat="no", **kwargs):
print "\nREDUCING FLATS"
# retrieve filenames of individual exposures
flatPaths = utils.getImPaths(flatList)
# process flats one at a time
for flat in flatPaths:
# make additional preparations if QE correction requested
if "fl_qecorr" in kwargs.keys() and kwargs["fl_qecorr"] == "yes":
# move extracted arcs to working directory
refIm = "erg" + utils.matchCentWave(inPref + flat, "./", \
arcList, "arc/")
os.system("mv " + arcPath + refIm + " ./")
# add reference image to kwargs
kwargs["qe_refim"] = refIm
# model and remove the scattered light, if requested;
# otherwise, reduce the image
if subscat == "yes":
# TODO: display all science extensions
# find file holding extracted flat spectra
# TODO: determination of extFlat probably needs improvement
dirList = os.listdir("./")
descendList = [im for im in dirList if flat in im]
extFlat = [im for im in descendList if "erg" in im][0]
print extFlat
# find gaps between fiber bundles on detector
gapFile = "blkMask_" + flat[:flat.find(".")] + ".txt"
iraf.delete(gapFile)
iraf.gffindblocks(extFlat[1:], extFlat, gapFile)
# model and remove scattered light, if necessary
# (and repeat until satisfied)
utils.examIm(extFlat[1:] + "[SCI]", frame=1)
while True:
fixScat = bool(
input(
"Does subtraction of scattered light need improvement? (True/False): "
))
if fixScat:
xOrders = raw_input("X orders (csv): ")
yOrders = raw_input("Y orders (csv): ")
iraf.imdelete("b" + extFlat[1:])
iraf.gfscatsub(extFlat[1:], gapFile, fl_inter="yes", \
prefix="b", xorder=xOrders, yorder=yOrders, \
cross="yes")
utils.examIm("b" + extFlat[1:] + "[SCI]", frame=1)
else:
break
else:
# reduce the image
outPref = self.reduceIm(inPref + flat, **kwargs)
# TODO: for this to work, need to know the proper prefix ...
# (have reduceIm return the prefix?)
self.viewCube(outPref + inPref + flat)
pass
# return extracted arcs to their rightful place
if "fl_qecorr" in kwargs.keys() and kwargs["fl_qecorr"] == "yes":
os.system("mv " + refIm + " " + arcPath)
return