def get_z_diff(catalog,label='ab'):
"""Returns an array with redshift differences between two objects
observed twice, without considering redshifts failures. It also
returns arrays for redshift confidence and galaxy template"""
unique, repetition = unique_radec(catalog.RA_MAPPING, catalog.DEC_MAPPING, 0.5)
z_diff = np.array([])
z_mean = np.array([])
for r_ind in repetition:
if len(r_ind)>1:
#print r_ind
if (label=='a')|(label=='b'):
good_redshifts = (catalog.ZGAL_FLAG[r_ind] ==label) & (catalog.ZGAL[r_ind]>0)
elif (label =='ab'):
good_redshifts = (catalog.ZGAL_FLAG[r_ind] !='c') & (catalog.ZGAL[r_ind]>0)
if np.sum(good_redshifts)==2:#only two measurements
aux1 = np.diff(catalog.ZGAL[r_ind])
aux2 = np.mean(catalog.ZGAL[r_ind])
z_diff = np.append(z_diff, aux1[0])
z_mean = np.append(z_mean, aux2)
return z_diff
def get_z_diff(catalog, label='ab'):
"""Returns an array with redshift differences between two objects
observed twice, without considering redshifts failures. It also
returns arrays for redshift confidence and galaxy template"""
unique, repetition = unique_radec(catalog.RA_MAPPING, catalog.DEC_MAPPING,
0.5)
z_diff = np.array([])
z_mean = np.array([])
for r_ind in repetition:
if len(r_ind) > 1:
#print r_ind
if (label == 'a') | (label == 'b'):
good_redshifts = (catalog.ZGAL_FLAG[r_ind]
== label) & (catalog.ZGAL[r_ind] > 0)
elif (label == 'ab'):
good_redshifts = (catalog.ZGAL_FLAG[r_ind] !=
'c') & (catalog.ZGAL[r_ind] > 0)
if np.sum(good_redshifts) == 2: #only two measurements
aux1 = np.diff(catalog.ZGAL[r_ind])
aux2 = np.mean(catalog.ZGAL[r_ind])
z_diff = np.append(z_diff, aux1[0])
z_mean = np.append(z_mean, aux2)
return z_diff
def get_z_diff_quad(catalog,label='ab'):
"""Returns an array with redshift differences between two objects
observed twice, without considering redshifts failures. It also
returns arrays for redshift confidence and galaxy template"""
unique, repetition = unique_radec(catalog.RA_MAPPING, catalog.DEC_MAPPING, 2.0)
z_diff = np.array([])
quad = np.array([])
for r_ind in repetition:
if len(r_ind)>1:
#print r_ind
if (label=='a')|(label=='b'):
good_redshifts = (catalog.ZGAL_FLAG[r_ind] ==label) & (catalog.ZGAL[r_ind]>0)
elif (label =='ab'):
good_redshifts = (catalog.ZGAL_FLAG[r_ind] !='c') & (catalog.ZGAL[r_ind]>0)
if np.sum(good_redshifts)>=2:
r_ind2 = np.array(r_ind)
template = catalog.TEMPLATE[r_ind2[good_redshifts]]
labels = catalog.ZGAL_FLAG[r_ind2[good_redshifts]]
z_gal = catalog.ZGAL[r_ind2[good_redshifts]]
for i in range(len(r_ind2[good_redshifts])-1):
diff = catalog.ZGAL[r_ind2[good_redshifts]][i]-catalog.ZGAL[r_ind2[good_redshifts]][i+1:]
quadrant = catalog.OBJECT[r_ind2[good_redshifts]][i].split('_')[0]
for d in diff:
z_diff = np.append(z_diff, d)
quad = np.append(quad,quadrant)
return z_diff,quad
def get_z_diff_quad(catalog, label='ab'):
"""Returns an array with redshift differences between two objects
observed twice, without considering redshifts failures. It also
returns arrays for redshift confidence and galaxy template"""
unique, repetition = unique_radec(catalog.RA_MAPPING, catalog.DEC_MAPPING,
2.0)
z_diff = np.array([])
quad = np.array([])
for r_ind in repetition:
if len(r_ind) > 1:
#print r_ind
if (label == 'a') | (label == 'b'):
good_redshifts = (catalog.ZGAL_FLAG[r_ind]
== label) & (catalog.ZGAL[r_ind] > 0)
elif (label == 'ab'):
good_redshifts = (catalog.ZGAL_FLAG[r_ind] !=
'c') & (catalog.ZGAL[r_ind] > 0)
if np.sum(good_redshifts) >= 2:
r_ind2 = np.array(r_ind)
template = catalog.TEMPLATE[r_ind2[good_redshifts]]
labels = catalog.ZGAL_FLAG[r_ind2[good_redshifts]]
z_gal = catalog.ZGAL[r_ind2[good_redshifts]]
for i in range(len(r_ind2[good_redshifts]) - 1):
diff = catalog.ZGAL[r_ind2[good_redshifts]][
i] - catalog.ZGAL[r_ind2[good_redshifts]][i + 1:]
quadrant = catalog.OBJECT[r_ind2[good_redshifts]][i].split(
'_')[0]
for d in diff:
z_diff = np.append(z_diff, d)
quad = np.append(quad, quadrant)
return z_diff, quad
