def create_catalog():
"""
:return:
"""
cats_d = extract_cats_d() # extracts dataframes from catalogues
full_d = create_full_cats(cats_d) # creates dataframe from CCDs catalogues
inputs_d = extract_inputs_d()
save = True
unique_sources = inputs_d['stars']['IDX']
total_stars = inputs_d['stars']['IDX'].size
sub_list_size = total_stars / 18
sub_list_l = []
for idx_sub_list in range(0, 18, 1):
if idx_sub_list != (18 - 1):
idx_down = sub_list_size * idx_sub_list
idx_up = sub_list_size * (idx_sub_list + 1)
sub_list_l.append(unique_sources[idx_down:idx_up])
else:
idx_down = sub_list_size * idx_sub_list
sub_list_l.append(unique_sources[idx_down:])
areas_j = []
for idx_l in range(0, 18, 1):
areas_p = Process(target=create_stars_catalog_thread,
args=(idx_l, sub_list_l[idx_l], inputs_d, full_d))
areas_j.append(areas_p)
areas_p.start()
active_areas = list([job.is_alive() for job in areas_j])
while True in active_areas:
active_areas = list([job.is_alive() for job in areas_j])
pass
# Merges areas
# Merges catalogs
stars_list = []
for idx_csv in range(0, 18, 1):
stars_ = read_csv('tmp_stars/stars_{}.csv'.format(idx_csv),
index_col=0)
stars_list.append(stars_)
stars_df = concat(stars_list)
positions_table = concat([stars_df['X_WORLD'], stars_df['Y_WORLD']],
axis=1)
if save:
positions_table.to_csv('regions_detected/stars.reg',
index=False,
header=False,
sep=" ")
return stars_df
def create_catalog():
"""
:return:
"""
stars_df = extract_stars_df()
cats_d = extract_cats_d() # extracts dataframes from catalogues
full_d = create_full_cats(cats_d) # creates dataframe from CCDs catalogues
scamp_df = create_scamp_df()
unique_sources = stars_df['IDX']
total_stars = stars_df['IDX'].size
sub_list_size = total_stars / 18
sub_list_l = []
for idx_sub_list in range(0, 18, 1):
if idx_sub_list != (18 - 1):
idx_down = sub_list_size * idx_sub_list
idx_up = sub_list_size * (idx_sub_list + 1)
sub_list_l.append(unique_sources[idx_down:idx_up])
else:
idx_down = sub_list_size * idx_sub_list
sub_list_l.append(unique_sources[idx_down:])
extract_j = []
for idx_l in range(0, 18, 1):
extract_p = Process(target=create_stars_catalog_thread,
args=(idx_l, sub_list_l[idx_l], stars_df, full_d,
scamp_df))
extract_j.append(extract_p)
extract_p.start()
active_extract = list([job.is_alive() for job in extract_j])
while True in active_extract:
active_extract = list([job.is_alive() for job in extract_j])
pass
# Merges areas
# Merges catalogs
stars_list = []
for idx_csv in range(0, 18, 1):
stars_ = read_csv('tmp_stars/stars_{}.csv'.format(idx_csv),
index_col=0)
stars_list.append(stars_)
stars_df = concat(stars_list)
stars_df.to_csv('catalogues_detected/scamp_stars.csv')
return stars_df
def create_catalog():
"""
:return:
"""
cats_d = extract_cats_d() # extracts dataframes from catalogues
full_d = create_full_cats(cats_d) # creates dataframe from CCDs catalogues
galaxies_df = extract_galaxies_df()
save = True
unique_sources = galaxies_df['IDX']
total_galaxies = galaxies_df['IDX'].size
sub_list_size = total_galaxies / 18
sub_list_l = []
for idx_sub_list in range(0, 18, 1):
if idx_sub_list != (18 - 1):
idx_down = sub_list_size * idx_sub_list
idx_up = sub_list_size * (idx_sub_list + 1)
sub_list_l.append(unique_sources[idx_down:idx_up])
else:
idx_down = sub_list_size * idx_sub_list
sub_list_l.append(unique_sources[idx_down:])
areas_j = []
for idx_l in range(0, 18, 1):
areas_p = Process(target=create_galaxies_catalog_thread,
args=(idx_l, sub_list_l[idx_l], galaxies_df, full_d))
areas_j.append(areas_p)
areas_p.start()
active_areas = list([job.is_alive() for job in areas_j])
while True in active_areas:
active_areas = list([job.is_alive() for job in areas_j])
pass
# Merges areas
# Merges catalogs
galaxies_list = []
for idx_csv in range(0, 18, 1):
galaxies_ = read_csv('tmp_galaxies/galaxies_{}.csv'.format(idx_csv),
index_col=0)
galaxies_list.append(galaxies_)
galaxies_df = concat(galaxies_list)
if save:
galaxies_df.to_csv('catalogues_detected/galaxies.csv')
return galaxies_df
def create_catalog():
"""
:return:
"""
cats_d = extract_cats_d() # extracts dataframes from catalogues
full_d = create_full_cats(cats_d) # creates dataframe from CCDs catalogues
ssos_df = propagate_dithers()
ssos_clean_df = filter_by_position(ssos_df)
unique_sources = list(set(ssos_clean_df['SOURCE'].tolist()))
total_ssos = len(list(set(ssos_clean_df['SOURCE'].tolist())))
sub_list_size = total_ssos / 10
sub_list_l = []
for idx_sub_list in range(0, 10, 1):
if idx_sub_list != (10 - 1):
idx_down = sub_list_size * idx_sub_list
idx_up = sub_list_size * (idx_sub_list + 1)
sub_list_l.append(unique_sources[idx_down:idx_up])
else:
idx_down = sub_list_size * idx_sub_list
sub_list_l.append(unique_sources[idx_down:])
areas_j = []
for idx_l in range(0, 10, 1):
areas_p = Process(target=create_ssos_catalog_thread,
args=(idx_l, sub_list_l[idx_l], ssos_clean_df,
full_d))
areas_j.append(areas_p)
areas_p.start()
active_areas = list([job.is_alive() for job in areas_j])
while True in active_areas:
active_areas = list([job.is_alive() for job in areas_j])
pass
# Merges areas
# Merges catalogs
ssos_list = []
for idx_csv in range(0, 10, 1):
ssos_ = read_csv('tmp_ssos/ssos_{}.csv'.format(idx_csv),
index_col=0)
ssos_list.append(ssos_)
ssos_df = concat(ssos_list)
ssos_df.to_csv('catalogues_detected/ssos.csv')
return ssos_df