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