def initialize(self, data_1, data_2, m1, m2, m1_unm, m2_unm, d12, deg2_decam, deg2_bokmos):
self.d12 = d12 # deg separations between matches objects
self.deg2_decam = deg2_decam
self.deg2_bokmos = deg2_bokmos
self.data["m_decam"] = targets.data_extract(data_1, m1)
self.data["m_bokmos"] = targets.data_extract(data_2, m2)
self.data["u_decam"] = targets.data_extract(data_1, m1_unm)
self.data["u_bokmos"] = targets.data_extract(data_2, m2_unm)
def initialize(self, data_1, data_2, m1, m2, m1_unm, m2_unm, d12,
deg2_decam, deg2_bokmos):
self.d12 = d12 #deg separations between matches objects
self.deg2_decam = deg2_decam
self.deg2_bokmos = deg2_bokmos
self.data['m_decam'] = targets.data_extract(data_1, m1)
self.data['m_bokmos'] = targets.data_extract(data_2, m2)
self.data['u_decam'] = targets.data_extract(data_1, m1_unm)
self.data['u_bokmos'] = targets.data_extract(data_2, m2_unm)
# get lists of tractor cats to compare
fns_1 = read_lines(args.fn1)
log.info("Combining tractor catalogues: ", fns_1)
# if fns_1.size == 1: fns_1,fns_2= [fns_1],[fns_2]
# object to store concatenated matched tractor cats
a = Matched_Cats()
for cnt, cat1, cat2 in zip(range(len(fns_1)), fns_1, fns_2):
data_1, data_2, m1, m2, m1_unm, m2_unm, d12, deg2_decam, deg2_bokmos = match_it(cat1, cat2)
if cnt == 0:
a.initialize(data_1, data_2, m1, m2, m1_unm, m2_unm, d12, deg2_decam, deg2_bokmos)
else:
a.add_d12(d12)
a.deg2_decam += deg2_decam
a.deg2_bokmos += deg2_bokmos
a.add_dict("m_decam", targets.data_extract(data_1, m1))
a.add_dict("m_bokmos", targets.data_extract(data_2, m2))
a.add_dict("u_decam", targets.data_extract(data_1, m1_unm))
a.add_dict("u_bokmos", targets.data_extract(data_2, m2_unm))
# each key a.data[key] becomes DECaLS() object with grz mags,i_lrg, etc
b = {}
b["d12"] = a.d12
b["deg2_decam"] = a.deg2_decam
b["deg2_bokmos"] = a.deg2_bokmos
for match_type in a.data.keys():
b[match_type] = targets.DECaLS(a.data[match_type], w1=True)
# store N matched objects not masked before join decam,bokmos masks
m_decam_not_masked, m_bokmos_not_masked = b["m_decam"].count_not_masked(), b["m_bokmos"].count_not_masked()
# update masks for matched objects to be the join of decam and bokmos masks
mask = np.any((b["m_decam"].mask, b["m_bokmos"].mask), axis=0)
b["m_decam"].update_masks_for_everything(
fns_1 = read_lines(args.fn1)
log.info('Combining tractor catalogues: ', fns_1)
#if fns_1.size == 1: fns_1,fns_2= [fns_1],[fns_2]
#object to store concatenated matched tractor cats
a = Matched_Cats()
for cnt, cat1, cat2 in zip(range(len(fns_1)), fns_1, fns_2):
data_1, data_2, m1, m2, m1_unm, m2_unm, d12, deg2_decam, deg2_bokmos = match_it(
cat1, cat2)
if cnt == 0:
a.initialize(data_1, data_2, m1, m2, m1_unm, m2_unm, d12, deg2_decam,
deg2_bokmos)
else:
a.add_d12(d12)
a.deg2_decam += deg2_decam
a.deg2_bokmos += deg2_bokmos
a.add_dict('m_decam', targets.data_extract(data_1, m1))
a.add_dict('m_bokmos', targets.data_extract(data_2, m2))
a.add_dict('u_decam', targets.data_extract(data_1, m1_unm))
a.add_dict('u_bokmos', targets.data_extract(data_2, m2_unm))
#each key a.data[key] becomes DECaLS() object with grz mags,i_lrg, etc
b = {}
b['d12'] = a.d12
b['deg2_decam'] = a.deg2_decam
b['deg2_bokmos'] = a.deg2_bokmos
for match_type in a.data.keys():
b[match_type] = targets.DECaLS(a.data[match_type], w1=True)
#store N matched objects not masked before join decam,bokmos masks
m_decam_not_masked, m_bokmos_not_masked = b['m_decam'].count_not_masked(
), b['m_bokmos'].count_not_masked()
#update masks for matched objects to be the join of decam and bokmos masks
mask = np.any((b['m_decam'].mask, b['m_bokmos'].mask), axis=0)
