def main(file_path):
darklist = functions.find_objname(file_path,functions.read_config_file("BIAS_HEADER"))
master_dark = average_darks(darklist)
try:
functions.save_fits(master_dark,file_path+"temp/master_dark.fits")
except IOError:
os.system("mkdir "+file_path+"temp/")
functions.save_fits(master_dark,file_path+"temp/master_dark.fits")
def main(file_path):
darklist = functions.find_objname(file_path,functions.read_config_file("FLAT_HEADER"))
if len(darklist) == 0:
print "Error: no flats found"
raise IOError
master_dark = average_darks(darklist,file_path+"temp/master_dark.fits")
try:
functions.save_fits(master_dark,file_path+"temp/master_flat.fits")
except IOError:
os.system("mkdir "+file_path+"temp/")
functions.save_fits(master_dark,file_path+"temp/master_flat.fits")
def main(file_path):
### Setup
setup_dir(file_path)
if functions.read_config_file("DELETE_ALL") == "true":
delete_all(file_path)
### Make bias
if not os.path.exists(file_path+"temp/master_dark.fits"):
import master_dark
master_dark.main(file_path)
### Make flatfield
if not os.path.exists(file_path+"temp/master_flat.fits"):
flatfield.main(file_path)
### load sciencelist
sciencelist_temp = functions.find_objname(file_path,functions.read_config_file("OBJECT_HEADER"))
### Check if each one has been fully downloaded
sciencelist = []
for fits in sciencelist_temp:
try:
test = pyfits.getdata(fits)
sciencelist.append(fits)
print "OK",fits
except ValueError:
pass
for fits in sciencelist:
fits_base = string.split(fits,"/")[-1]
fits_base = string.split(fits_base,".")[0]
if not os.path.exists(file_path+"reduced/"+fits_base+".fits"):
print "********************************"
print "Reducing",fits
fits_path = process(fits,file_path+"reduced/")
flatfield.apply_flatfield(fits_path,file_path+"temp/master_flat.fits",file_path+"temp/master_dark.fits")
### Now extract sources in reference image, match, and do photometry
import fistar,transcoord,fiphot
refimage = functions.read_config_file("REFERENCE_IMAGE")
refimage_coords=fistar.fistar(file_path+"reduced/"+refimage)
reflist = loadtxt(functions.read_config_file("OBJECT_LIST"))
###reformat reflist so that it reflects the refimage actual star coordinates
### So that the extraction coordinates are exact
new_reflist = []
for star in reflist:
dist = sqrt((star[1]-refimage_coords[:,1])**2 + (star[2]-refimage_coords[:,2])**2)
indx = argmin(dist)
star[1] = refimage_coords[indx,1]
star[2] = refimage_coords[indx,2]
new_reflist.append(star)
savetxt(file_path+"reduced/object_list",array(new_reflist))
for fits in sciencelist:
fits_base = string.split(fits,"/")[-1]
fits_base = string.split(fits_base,".")[0]
if not os.path.exists(file_path+"/reduced/"+fits_base+".fits.phot"):
### transform the coordinates of extraction
transcoord.transcoord(file_path+"/reduced/"+fits_base+".fits",file_path+"reduced/object_list",file_path+"reduced/"+refimage+".fistar")
### Now apply fiphot and get real photometry out
fiphot.fiphot(file_path+"/reduced/"+fits_base+".fits")
