def find_crossmatch_failures(survey):
r = pd.read_hdf('/data/jls/GaiaDR2/spectro/%s_input.hdf5' % survey)
# columns = ['parallax', 'parallax_error',
# 'pmra', 'pmdec',
# 'pmra_error', 'pmdec_error',
# 'parallax_pmra_corr', 'parallax_pmdec_corr', 'pmra_pmdec_corr',
# 'source_id', 'G', 'GBP', 'GRP', 'eG', 'eGRP', 'eGBP',
# 'a_g_val', 'a_g_percentile_lower', 'a_g_percentile_upper']
# r = r.drop(columns, axis=1)
if survey == 'GES':
rx = load_ges()
if survey == 'APOGEE':
rx = load_apogee()
if survey == 'RAVE':
rx = load_rave(use_dr5=True)
if survey == 'RAVEON':
rx = load_rave(use_dr5=False)
if survey == 'GALAH':
rx = load_galah()
if survey == 'LAMOST':
rx = load_lamost()
rx = format_columns(rx)
rx = crossmatch_gaia_spectro(rx, no_proper_motion=False, dist_max=5.)
print len(rx), np.count_nonzero(r.source_id > 0), np.count_nonzero(
rx.source_id > 0), np.count_nonzero(r.source_id != rx.source_id)
rx = rx[(rx.source_id > 0)
& (r.source_id != rx.source_id)].reset_index(drop=True)
rx.to_hdf('/data/jls/GaiaDR2/spectro/%s_input_pm.hdf5' % survey, 'data')
def run_apogee():
apogee = load_apogee(output_file=output_folder + 'APOGEE_input.hdf5', use_dr1=DR1)
return
apogee = check_photometry(apogee)
run_distance_pipeline(apogee,
output_folder + 'APOGEE_distances%s.hdf5' % name_str,
'APOGEE_ID', 'APOGEE',
npool=npool,
with_parallax=with_parallax, thin_mag=0.1)
def run_apogee_fillin():
apogee = load_apogee(use_dr1=DR1)
apogee2 = Table.read(output_folder + 'APOGEE_distances.hdf5')
apogee = check_photometry(
apogee[apogee2['flag'] == 3].reset_index(drop=True))
run_distance_pipeline(apogee,
output_folder + 'APOGEE_distances_fillin.hdf5',
'APOGEE_ID',
'APOGEE',
npool=npool,
with_parallax=with_parallax)
def run_apogee():
apogee = load_apogee(use_dr1=DR1)
if TEST:
apogee = apogee.sample(n=TESTNUMBER,
random_state=random_seed).reset_index(drop=True)
apogee = check_photometry(apogee)
run_distance_pipeline(apogee,
output_folder + 'APOGEE_distances%s.hdf5' % name_str,
'APOGEE_ID',
'APOGEE',
npool=npool,
with_parallax=with_parallax)