os.system("rm smooth_dir/master_smooth.fits")
os.system("rm smooth_dir/smooth_*.fits")
### 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:
########################
### 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:"
########################
### Start of program ###
########################
### Set file_path
file_path = sys.argv[1]
#file_path = "/priv/miner3/hat-south/george/Honours/data/wifes/2010/"
file_path_temp = file_path + "temp/"
file_path_reduced = file_path + "reduced/"
############################################
### Open file_path and find all SpecPhot ###
############################################
### Get a list of fits files with objects begining in "HD"
object_list = functions.ccdlist_extract(iraf.ccdlist(file_path +"*.fits",Stdout = 1))
HD_match = functions.ccdlist_identify_HD(object_list)
HD_match = []
for i in range(len(object_list)):
HD_match.append(i)
### Open those images and check if NOTES says "RV Standard"
SP_match = check_head(HD_match,"NOTES","SpecPhot")
### Write the file_names to temp textfile
SP_list = open(file_path_temp + "SpecPhot_list","w")
for i in range(len(SP_match)):
file_name = object_list[SP_match[i]][2] + "\n"
SP_list.write(file_name)
zero='Zero',
flat='nFlat')
iraf.ccdproc(images=cal,
ccdtype='',
fixpix=False,
overscan=False,
darkcor=False,
trim=True,
zerocor=True,
flatcor=True,
trimsec='[*,20:4600]',
zero='Zero',
flat='niFlat')
iraf.ccdlist('Zero, nFlat, niFlat, {0}, {1}'.format(science, cal))
iraf.imstat('Zero')
iraf.imstat('nFlat')
iraf.imstat(science)
iraf.imstat(cal)
ref = str(raw_input('Imagen de referencia (eg. hd161103_0001.fits):'))
iraf.imexamine(ref)
iraf.apall.unlearn()
iraf.apall(input=science, format='onedspec', readnoise='rdnoise', gain='gain')
iraf.apall(input=cal,
format='onedspec',