def test_check_distance(self):
"""
create_configurations should return:
[[30, 0.01, 1.5, 4, 'models/gauss_2.0_5x5.conv']], 1
prfs_d = extract_settings_elvis()
# How many times the cross search radius will be increased
tolerance_factor = 2
df = df[df[keys[0]] + prfs_d['tolerance'] * tolerance_factor > i_alpha]
df = df[i_alpha > df[keys[0]] - prfs_d['tolerance'] * tolerance_factor]
df = df[df[keys[1]] + prfs_d['tolerance'] * tolerance_factor > i_delta]
df = df[i_delta > df[keys[1]] - prfs_d['tolerance'] * tolerance_factor]
:return:
"""
i_alpha = 1.0
i_delta = 1.0
index = misc.check_distance(self.df_, i_alpha, i_delta)
return self.assertIs(index, 0)
def create_stars_catalog_thread(idx_l, sub_list, stars_df, full_d, scamp_df):
"""
:param idx_l:
:param sub_list:
:param stars_df:
:param full_d:
:return:
"""
keys = ['ALPHA_J2000', 'DELTA_J2000']
cat_d = create_empty_catalog_dict()
total_thread = len(sub_list)
stdout.write('total stars {} of thread {}\n'.format(total_thread, idx_l))
for idx, star in enumerate(sub_list):
source_df = stars_df[stars_df['IDX'].isin([star])]
alpha = source_df['RA2000(Gaia)'].iloc[0]
delta = source_df['DEC2000(Gaia)'].iloc[0]
source_d = create_empty_catalog_dict()
for dither in range(1, 5, 1):
sex_df = check_source(full_d[dither], alpha, delta, keys)
# Should check source in Scamp too!
if sex_df.empty is not True:
# Returns the index of the closest found source
index = check_distance(sex_df, alpha, delta)
sex_df = sex_df.iloc[[index]]
cat_number = sex_df['CATALOG_NUMBER'].iloc[0]
df = scamp_df[scamp_df['CATALOG_NUMBER'].isin([cat_number])]
df = check_source(df, alpha, delta, keys)
if df.empty is not True:
source_d['DITHER'].append(dither)
catalog_number = int(sex_df['CATALOG_NUMBER'].iloc[0])
source_d['CATALOG_NUMBER'].append(catalog_number)
x_world = float(sex_df['X_WORLD'].iloc[0])
source_d['X_WORLD'].append(x_world)
y_world = float(sex_df['Y_WORLD'].iloc[0])
source_d['Y_WORLD'].append(y_world)
mag_auto = float(sex_df['MAG_AUTO'].iloc[0])
source_d['MAG_AUTO'].append(mag_auto)
magerr_auto = float(sex_df['MAGERR_AUTO'].iloc[0])
source_d['MAGERR_AUTO'].append(magerr_auto)
a_image = float(sex_df['A_IMAGE'].iloc[0])
source_d['A_IMAGE'].append(a_image)
b_image = float(sex_df['B_IMAGE'].iloc[0])
source_d['B_IMAGE'].append(b_image)
theta_image = float(sex_df['THETA_IMAGE'].iloc[0])
source_d['THETA_IMAGE'].append(theta_image)
erra_image = float(sex_df['ERRA_IMAGE'].iloc[0])
source_d['ERRA_IMAGE'].append(erra_image)
errb_image = float(sex_df['ERRB_IMAGE'].iloc[0])
source_d['ERRB_IMAGE'].append(errb_image)
erra_world = float(sex_df['ERRA_WORLD'].iloc[0])
source_d['ERRA_WORLD'].append(erra_world)
errb_world = float(sex_df['ERRB_WORLD'].iloc[0])
source_d['ERRB_WORLD'].append(errb_world)
errtheta_world = float(sex_df['ERRTHETA_WORLD'].iloc[0])
source_d['ERRTHETA_WORLD'].append(errtheta_world)
class_star = float(sex_df['CLASS_STAR'].iloc[0])
source_d['CLASS_STAR'].append(class_star)
pm = float(df['PM'].iloc[0])
source_d['PM'].append(pm)
pmerr = float(df['PMERR'].iloc[0])
source_d['PMERR'].append(pmerr)
if len(source_d['DITHER']) != 0:
for key_ in source_d.keys():
for value_ in source_d[key_]:
cat_d[key_].append(value_)
cat_df = DataFrame(cat_d,
columns=[
'DITHER', 'CATALOG_NUMBER', 'X_WORLD', 'Y_WORLD',
'MAG_AUTO', 'MAGERR_AUTO', 'A_IMAGE', 'B_IMAGE',
'THETA_IMAGE', 'ERRA_IMAGE', 'ERRB_IMAGE',
'ERRA_WORLD', 'ERRB_WORLD', 'ERRTHETA_WORLD',
'CLASS_STAR', 'PM', 'PMERR'
])
cat_df.to_csv('tmp_stars/stars_{}.csv'.format(idx_l))
def create_stars_catalog_thread(idx_l, sub_list, inputs_d, full_d):
"""
:param idx_l:
:param sub_list:
:param inputs_d:
:param full_d:
:return:
"""
save = True
keys = ['ALPHA_J2000', 'DELTA_J2000']
cat_d = create_empty_catalog_dict()
total_thread = len(sub_list)
stdout.write('total stars {} of thread {}\n'.format(total_thread, idx_l))
for idx, star in enumerate(sub_list):
stars_df = inputs_d['stars']
source_df = stars_df[stars_df['IDX'].isin([star])]
alpha = source_df['RA2000(Gaia)'].iloc[0]
delta = source_df['DEC2000(Gaia)'].iloc[0]
source_d = create_empty_catalog_dict()
for dither in range(1, 5, 1):
o_df = check_source(full_d[dither], alpha, delta, keys)
if o_df.empty is not True:
# Returns the index of the closest found source
index = check_distance(o_df, alpha, delta)
o_df = o_df.iloc[[index]]
source_d['DITHER'].append(dither)
catalog_number = int(o_df['CATALOG_NUMBER'].iloc[0])
source_d['CATALOG_NUMBER'].append(catalog_number)
x_world = float(o_df['X_WORLD'].iloc[0])
source_d['X_WORLD'].append(x_world)
y_world = float(o_df['Y_WORLD'].iloc[0])
source_d['Y_WORLD'].append(y_world)
mag_auto = float(o_df['MAG_AUTO'].iloc[0])
source_d['MAG_AUTO'].append(mag_auto)
magerr_auto = float(o_df['MAGERR_AUTO'].iloc[0])
source_d['MAGERR_AUTO'].append(magerr_auto)
erra_world = float(o_df['ERRA_WORLD'].iloc[0])
source_d['ERRA_WORLD'].append(erra_world)
errb_world = float(o_df['ERRB_WORLD'].iloc[0])
source_d['ERRB_WORLD'].append(errb_world)
errtheta_world = float(o_df['ERRTHETA_WORLD'].iloc[0])
source_d['ERRTHETA_WORLD'].append(errtheta_world)
if len(source_d['DITHER']) != 0:
for key_ in source_d.keys():
for value_ in source_d[key_]:
cat_d[key_].append(value_)
cat_df = DataFrame(cat_d,
columns=[
'DITHER', 'CATALOG_NUMBER', 'X_WORLD', 'Y_WORLD',
'MAG_AUTO', 'MAGERR_AUTO', 'ERRA_WORLD',
'ERRB_WORLD', 'ERRTHETA_WORLD'
])
if save:
cat_df.to_csv('tmp_stars/stars_{}.csv'.format(idx_l))
def create_galaxies_catalog_thread(idx_l, sub_list, galaxies_df, full_d):
"""
:param idx_l:
:param sub_list:
:param galaxies_df:
:param full_d:
:return:
"""
save = True
keys = ['ALPHA_J2000', 'DELTA_J2000']
cat_d = create_empty_catalog_dict()
total_thread = len(sub_list)
stdout.write('total galaxies {} of thread {}\n'.format(
total_thread, idx_l))
for idx, galaxy in enumerate(sub_list):
source_df = galaxies_df[galaxies_df['IDX'].isin([galaxy])]
alpha = source_df['ra'].iloc[0]
delta = source_df['dec'].iloc[0]
source_d = create_empty_catalog_dict()
for dither in range(1, 5, 1):
o_df = check_source(full_d[dither], alpha, delta, keys)
if o_df.empty is not True:
# Returns the index of the closest found source
index = check_distance(o_df, alpha, delta)
o_df = o_df.iloc[[index]]
source_d['DITHER'].append(dither)
catalog_number = int(o_df['CATALOG_NUMBER'].iloc[0])
source_d['CATALOG_NUMBER'].append(catalog_number)
x_world = float(o_df['X_WORLD'].iloc[0])
source_d['X_WORLD'].append(x_world)
y_world = float(o_df['Y_WORLD'].iloc[0])
source_d['Y_WORLD'].append(y_world)
mag_auto = float(o_df['MAG_AUTO'].iloc[0])
source_d['MAG_AUTO'].append(mag_auto)
magerr_auto = float(o_df['MAGERR_AUTO'].iloc[0])
source_d['MAGERR_AUTO'].append(magerr_auto)
a_image = float(o_df['A_IMAGE'].iloc[0])
source_d['A_IMAGE'].append(a_image)
b_image = float(o_df['B_IMAGE'].iloc[0])
source_d['B_IMAGE'].append(b_image)
theta_image = float(o_df['THETA_IMAGE'].iloc[0])
source_d['THETA_IMAGE'].append(theta_image)
erra_image = float(o_df['ERRA_IMAGE'].iloc[0])
source_d['ERRA_IMAGE'].append(erra_image)
errb_image = float(o_df['ERRB_IMAGE'].iloc[0])
source_d['ERRB_IMAGE'].append(errb_image)
erra_world = float(o_df['ERRA_WORLD'].iloc[0])
source_d['ERRA_WORLD'].append(erra_world)
errb_world = float(o_df['ERRB_WORLD'].iloc[0])
source_d['ERRB_WORLD'].append(errb_world)
errtheta_world = float(o_df['ERRTHETA_WORLD'].iloc[0])
source_d['ERRTHETA_WORLD'].append(errtheta_world)
class_star = float(o_df['CLASS_STAR'].iloc[0])
source_d['CLASS_STAR'].append(class_star)
if len(source_d['DITHER']) != 0:
for key_ in source_d.keys():
for value_ in source_d[key_]:
cat_d[key_].append(value_)
# for key_ in cat_d.keys():
# print(key_, len(cat_d[key_]))
cat_df = DataFrame(cat_d,
columns=[
'DITHER', 'CATALOG_NUMBER', 'X_WORLD', 'Y_WORLD',
'MAG_AUTO', 'MAGERR_AUTO', 'A_IMAGE', 'B_IMAGE',
'THETA_IMAGE', 'ERRA_IMAGE', 'ERRB_IMAGE',
'ERRA_WORLD', 'ERRB_WORLD', 'ERRTHETA_WORLD',
'CLASS_STAR'
])
if save:
cat_df.to_csv('tmp_galaxies/galaxies_{}.csv'.format(idx_l))
def create_ssos_catalog_thread(idx_l, sub_list, ssos_df, full_d):
"""
:param idx_l:
:param sub_list:
:param ssos_df:
:param full_d:
:return:
"""
keys = ['ALPHA_J2000', 'DELTA_J2000']
# Creates an empty catalog for all detected sources
cat_d = create_output_catalog_dict()
total_thread = len(sub_list)
stdout.write('total SSOs {} of thread {}\n'.format(total_thread, idx_l))
for idx, sso in enumerate(sub_list):
source_df = ssos_df[ssos_df['SOURCE'].isin([sso])]
for dither_ in source_df['DITHER'].tolist():
dither_df = source_df[source_df['DITHER'].isin([dither_])]
# Gets alpha/delta of actual source
alpha = float(dither_df['RA'].iloc[0])
delta = float(dither_df['DEC'].iloc[0])
o_df = check_source(full_d[dither_], alpha, delta, keys)
if o_df.empty is not True:
# Returns the index of the closest found source
index = check_distance(o_df, alpha, delta)
o_df = o_df.iloc[[index]]
idx_ = int(dither_df['IDX'].iloc[0])
cat_d['IDX'].append(idx_)
source = int(dither_df['SOURCE'].iloc[0])
cat_d['SOURCE'].append(source)
cat_d['DITHER'].append(dither_)
alpha_j2000 = float(o_df['ALPHA_J2000'].iloc[0])
cat_d['RA'].append(alpha_j2000)
delta_j2000 = float(o_df['DELTA_J2000'].iloc[0])
cat_d['DEC'].append(delta_j2000)
vel = float(dither_df['VEL'].iloc[0])
cat_d['VEL'].append(vel)
abmag = float(dither_df['ABMAG'].iloc[0])
cat_d['ABMAG'].append(abmag)
theta = float(dither_df['THETA'].iloc[0])
cat_d['THETA'].append(theta)
mag_auto = float(o_df['MAG_AUTO'].iloc[0])
cat_d['MAG_AUTO'].append(mag_auto)
a_image = float(o_df['A_IMAGE'].iloc[0])
cat_d['A_IMAGE'].append(a_image)
b_image = float(o_df['B_IMAGE'].iloc[0])
cat_d['B_IMAGE'].append(b_image)
theta_image = float(o_df['THETA_IMAGE'].iloc[0])
cat_d['THETA_IMAGE'].append(theta_image)
erra_image = float(o_df['ERRA_IMAGE'].iloc[0])
cat_d['ERRA_IMAGE'].append(erra_image)
errb_image = float(o_df['ERRB_IMAGE'].iloc[0])
cat_d['ERRB_IMAGE'].append(errb_image)
magerr_auto = float(o_df['MAGERR_AUTO'].iloc[0])
cat_d['MAGERR_AUTO'].append(magerr_auto)
erra_world = float(o_df['ERRA_WORLD'].iloc[0])
cat_d['ERRA_WORLD'].append(erra_world)
errb_world = float(o_df['ERRB_WORLD'].iloc[0])
cat_d['ERRB_WORLD'].append(errb_world)
errtheta_world = float(o_df['ERRTHETA_WORLD'].iloc[0])
cat_d['ERRTHETA_WORLD'].append(errtheta_world)
class_star = float(o_df['CLASS_STAR'].iloc[0])
cat_d['CLASS_STAR'].append(class_star)
cat_df = DataFrame(cat_d,
columns=[
'IDX', 'SOURCE', 'DITHER', 'RA', 'DEC', 'VEL',
'ABMAG', 'THETA', 'MAG_AUTO', 'A_IMAGE', 'B_IMAGE',
'THETA_IMAGE', 'ERRA_IMAGE', 'ERRB_IMAGE',
'MAGERR_AUTO', 'ERRA_WORLD', 'ERRB_WORLD',
'ERRTHETA_WORLD', 'CLASS_STAR'
])
cat_df.to_csv('tmp_ssos/ssos_{}.csv'.format(idx_l))