file_name = sys.argv[2]
os.chdir(file_path)
#######################################################################
### Find the Ne Ar arc lamp exposure closest to the object exposure ###
#######################################################################
### Find object JD
hdulist = pyfits.open(file_name)
object_MJD = hdulist[0].header['MJD-OBS']
hdulist.close()
### Compile list of NeAr arcs
object_list = functions.ccdlist_extract(iraf.ccdlist("*.fits",Stdout = 1))
NeAr_match = functions.ccdlist_identify(object_list,"Ne-Ar")
### Open each fine and find its MJD
arc_MJD = []
for i in range(len(NeAr_match)):
hdulist = pyfits.open(object_list[NeAr_match[i]][0])
arc_MJD.append([abs(hdulist[0].header['MJD-OBS'] - object_MJD),object_list[NeAr_match[i]][0]])
hdulist.close()
### Sort arc_MJD according to closest time to object exposure
arc_MJD = functions.sort_array(arc_MJD,0)
### Choose 2 arc images if the 2nd arc frame was taken <30min from object
if len(arc_MJD) > 1:
if arc_MJD[1][0] < 0.02:
arc_names = arc_MJD[0][1] + "\n" + arc_MJD[1][1] + "\n"
### Go through the folders, identify and reduce each smooth image
smooth_files_list = []
stars_to_try = ["ltt4364","eg131"]
for star in stars_to_try:
count = 1
for i in range(len(folders_to_search)):
file_path_current = file_path + folders_to_search[i]
### Find any smooth star exposures
ccdlist_info = iraf.ccdlist(file_path_current + "*.fits", Stdout = 1)
ccdlist_info = functions.ccdlist_extract(ccdlist_info)
ccdlist_match = functions.ccdlist_identify(ccdlist_info,star)
### Reduce any such exposures
if len(ccdlist_match) > 0:
for j in range(len(ccdlist_match)):
file_name = ccdlist_info[ccdlist_match[j]][2] + ".fits"
file_path_temp = file_path_current + "temp/"
file_path_reduced = file_path_current + "reduced/"
print "currently reducing image " + file_path_current + file_name
smooth_files_list,count = reduce_image(file_path_current,file_name,file_path_reduced,smooth_files_list,count)
### If exposures were found for starX, then break the for loop
if count > 1:
break
### Start of program ###
########################
### Make the required folders
print "Making directories temp and reduced"
os.system("mkdir " + file_path + "temp")
os.system("mkdir " + file_path + "reduced")
########################
### Form master bias ###
########################
### Find the bias images
print "Finding the bias frame(s)"
ccdlist_info = iraf.ccdlist(file_path + "*.fits", Stdout=1)
ccdlist_info = functions.ccdlist_extract(ccdlist_info)
ccdlist_match = functions.ccdlist_identify(ccdlist_info,"bias")
os.system("rm " + file_path_temp + "master_bias.fits")
if len(ccdlist_match) < 1:
print "!!!!!!! No Bias frames found, using default bias !!!!!!!"
os.system("cp default_cal_frames/bias.fits " + file_path_temp + "master_bias.fits")
if len(ccdlist_match) >= 1:
input_list = ""
for i in range(len(ccdlist_match)):
bias_file_path = ccdlist_info[ccdlist_match[i]][0]
input_list = input_list + bias_file_path + "\n"
functions.write_string_to_file(input_list,file_path + "bias_list")
print "The bias files found are:"
print input_list
iraf.combine(
