def combine_catalogs(header, cats, columns, data_start, destination, conversion_factor=1):
# Combines a list of catalogs (cats) row by row and adds the given header
# at the top of the file, writes the resulting catalog to (destination)
# RA and DEC MUST be the first two columns, flux and fluxerr MUST be the
# next two columns. (There should be 4 columns)
# Change conversion factor to multiple flux and fluxerr by that value. Useful when
# converting between Jy and uJy
jtools.check_paths(cats)
num_entries = 0 # default value in case something goes wrong.
table = []
ra_col = columns[0]
dec_col = columns[1]
flux_col = columns[2]
fluxerr_col = columns[3]
num_params = len(columns) - 2 # We subtract 2 because of the RA/DEC columns we've already accounted for
for (n, c) in enumerate(cats):
if n == 0:
data = param_get(c, [ra_col, dec_col], data_start)
num_entries = len(data[0])
for i in range(num_entries):
table.append([i+1, data[0][i], data[1][i]])
data = param_get(c, columns[2:], data_start)
for i in range(num_entries):
temp = []
for j in range(num_params):
temp.append(data[j][i])
for t in temp:
table[i].append(t)
#table[i].append(float(data[0][i])*conversion_factor)
#table[i].append(float(data[1][i])*conversion_factor)
jtools.write_table(table, header, destination)
return
def fits_add(file_paths, destination, header_index=0, conversion_factor=1.0):
# Adds a list of fits files together, uses the header of file_paths[header_index]
# and multiplies by the conversion factor if you are converting units
jtools.check_paths(file_paths)
d_list = []
for f in file_paths:
hdu = pyfits.open(f)
d_list.append(hdu[0].data)
hdu.close()
hfits = pyfits.open(file_paths[header_index])
h = hfits[0].header
hfits.close()
data_sum = sum(d_list) * conversion_factor / len(file_paths)
pyfits.writeto(destination, data_sum, header=h, clobber=1)
return
