def load_apogee_distances(dr=None,
metric='distance',
cuts=True,
extinction=False):
"""
Load apogee distances (absolute magnitude from stellar model)
:param dr: Apogee DR
:type dr: int
:param metric: which metric you want ot get back
- "absmag" for absolute magnitude
- "fakemag" for fake magnitude
- "distance" for distance
:type metric: string
:param cuts: Whether to cut bad data (negative parallax and percentage error more than 20%), or a float to set the threshold
:type cuts: Union[boolean, float]
:param extinction: Whether to take extinction into account
:type extinction: bool
:return: numpy array of ra, dec, metrics_array, metrics_err_array
:rtype: ndarrays
:History: 2018-Jan-25 - Written - Henry Leung (University of Toronto)
"""
fullfilename = apogee_distances(dr=dr)
with fits.open(fullfilename) as F:
hdulist = F[1].data
# Convert kpc to pc
distance = hdulist['BPG_dist50'] * 1000
dist_err = (hdulist['BPG_dist84'] - hdulist['BPG_dist16']) * 1000
allstarfullpath = allstar(dr=dr)
with fits.open(allstarfullpath) as F:
k_mag = F[1].data['K']
if extinction:
k_mag = extinction_correction(k_mag, F[1].data['AK_TARG'])
ra = F[1].data['RA']
dec = F[1].data['DEC']
# Bad index refers to nan index
bad_index = np.argwhere(np.isnan(distance))
if metric == 'distance':
# removed astropy units because of -9999. is dimensionless, will have issues
output = distance
output_err = dist_err
elif metric == 'absmag':
absmag, absmag_err = mag_to_absmag(k_mag, 1 / distance * u.arcsec,
(1 / distance) *
(dist_err / distance))
output = absmag
output_err = absmag_err
elif metric == 'fakemag':
# fakemag requires parallax (mas)
fakemag, fakemag_err = mag_to_fakemag(k_mag, 1000 / distance * u.mas,
(1000 / distance) *
(dist_err / distance))
output = fakemag
output_err = fakemag_err
else:
raise ValueError('Unknown metric')
# Set the nan index to -9999. as they are bad and unknown. Not magic_number as this is an APOGEE dataset
output[bad_index], output_err[bad_index] = -9999., -9999.
if cuts is False:
pass
else:
distance[bad_index], dist_err[bad_index] = -9999., -9999.
good_idx = [(dist_err / distance <
(0.2 if cuts is True else cuts)) & (distance != -9999.)]
ra = ra[good_idx]
dec = dec[good_idx]
output = output[good_idx]
output_err = output_err[good_idx]
return ra, dec, output, output_err
# This script defines global configuration/constant from astroNN.apogee import allstar # milkyway potential parameters _R0 = 8.125 # distance to galactic center in kpc _v0 = 220. # sun tangential velocity in km/x _z0 = 0.0208 # sun above galactic plane in kpc # gaia table to use to cross matching gaia_table_name = "gaiaedr3.gaia_source" # path to the reference allstar (which is dr14 the models are trained on) allstar14_path = allstar(dr=14) # file name for dr14 continuum normalized spectra, can be a path if file is not stored in current dir contspac14_file_name = "contspec_dr14.fits" # base path to dr14apStar files base14_path = "/yngve_data/sdss/apogee/dr14/apogee/spectro/redux/r8/stars" # diff correction (calculate median diff to dr14, and subtract it from current dataset to make it looks more like dr14) # will do north/south separately corr_flag = True # path to your current allstar # allstar_path = "/yngve_data/sdss/apogee/apogeework/apogee/spectro/aspcap/r13/l33/allStar-r13-l33-58932beta.fits" allstar_path = allstar(dr=17) # path on my windows # base path to apStar/asStar files base_path = "/epsen_data/data_overflow/sdss/apogee/dr17/apogee/spectro/redux/dr17/stars/" aspcap_base_path = "/epsen_data/data_overflow/sdss/apogee/dr17/apogee/spectro/aspcap/dr17/synspec/" # base_path = "C:/sdss_local/dr16/apogee/spectro/redux/r12/stars" # path on my windows
def create_data(self): local_path_to_file = allstar(dr=14) self.apogee_data = fits.open(local_path_to_file) self._PICKLE_DIR = "pickles/APOGEE/"
def create_data(self,
version=1,
max_number_of_stars=float("inf"),
use_steps=False):
if os.path.exists('{}stars{}.pickle'.format(self._PICKLE_DIR,
version)):
pickle_out = open(
"{}stars{}.pickle".format(self._PICKLE_DIR, version), 'rb')
self._star_dict = pickle.load(pickle_out)
return True
self._star_dict = {
'KIC': [],
'RA': [],
'DEC': [],
'Dnu': [],
'PS': [],
'e_PS': [],
'q': [],
'M': [],
'spectra': [],
'error_spectra': [],
'T_eff': [],
'logg': [],
'RC': [],
'status': [],
'M_H': [],
'C': [],
'N': [],
'O': [],
'Na': [],
'Mg': [],
'Al': [],
'Si': [],
'P': [],
'S': [],
'K': [],
'Ca': [],
'Ti': [],
'V': [],
'Mn': [],
'Fe': [],
'Ni': [],
'Cr': [],
'Co': [],
'Cl': []
}
# First create table of Kepler stars with PS and Δv with their RA, DEC (creates table1.dat)
self._populate_kepler_with_rc_dec()
# Loading in APOGEE star data (RA, DEC)
local_path_to_file = allstar(dr=14)
apogee_data = fits.open(local_path_to_file)
apogee_ra = apogee_data[1].data['RA']
apogee_de = apogee_data[1].data['DEC']
# RA, DEC from KIC table
kic_table = Table.read("tables/KIC_A87/table1.dat", format="ascii")
kic_ra = np.array(kic_table['RA'])
kic_de = np.array(kic_table['DE'])
# Indices overlap between KIC and APOGEE
idx_kic, idx_apogee, sep = xmatch(kic_ra,
apogee_ra,
colRA1=kic_ra,
colDec1=kic_de,
colRA2=apogee_ra,
colDec2=apogee_de)
# Building spectra data for KIC from APOGEE overlaps
for i in range(0, len(idx_apogee)):
index_in_apogee = idx_apogee[i]
index_in_kepler = idx_kic[i]
if version == 1 and not (
apogee_data[1].data['STARFLAG'][index_in_apogee] == 0 and
apogee_data[1].data['ASPCAPFLAG'][index_in_apogee] == 0):
continue
# Version 2 - Subject to the following constraints
# Flag checking, condition is the same as Hawkins et al. 2017, STARFLAG and ASPCAP bitwise OR is 0
# KIC checking, uncertainty in PS should be less than 10s
if version == 2 and not (
apogee_data[1].data['STARFLAG'][index_in_apogee] == 0
and apogee_data[1].data['ASPCAPFLAG'][index_in_apogee] == 0
and kic_table['e_DPi1'][index_in_kepler] < 10):
continue
# Version 3 - Subject to the following constraints
# Flag checking, condition is the same as Hawkins et al. 2017, STARFLAG and ASPCAP bitwise OR is 0
# KIC checking, uncertainty in PS should be less than 10s
if version == 3 and not (
apogee_data[1].data['STARFLAG'][index_in_apogee] == 0
and apogee_data[1].data['ASPCAPFLAG'][index_in_apogee] == 0
and kic_table['e_DPi1'][index_in_kepler] < 10
and apogee_data[1].data['SNR'][index_in_apogee] >= 200):
continue
# Version 5 - DR13 data subject to same connstraints as Hawkings
if version == 5 and not (
apogee_data[1].data['STARFLAG'][index_in_apogee] == 0
and apogee_data[1].data['ASPCAPFLAG'][index_in_apogee] == 0
and kic_table['e_DPi1'][index_in_kepler] < 10):
continue
# Version 6 - DR13 data subject to same connstraints as Hawkings - normalization with bitmask as DR13
if version == 6 and not (
apogee_data[1].data['STARFLAG'][index_in_apogee] == 0
and apogee_data[1].data['ASPCAPFLAG'][index_in_apogee] == 0
and kic_table['e_DPi1'][index_in_kepler] < 10
and apogee_data[1].data['NVISITS'][index_in_apogee] >= 1):
continue
# Version 7 - DR13 data subject to same constraints as Hawkings - no bit mask as DR13
if version == 7 and not (
apogee_data[1].data['STARFLAG'][index_in_apogee] == 0
and kic_table['e_DPi1'][index_in_kepler] < 10
and apogee_data[1].data['NVISITS'][index_in_apogee] >= 1):
continue
# Version 8 - DR13 data subject to same constraints as Hawkings
if version == 8 and not (
apogee_data[1].data['STARFLAG'][index_in_apogee] == 0
and kic_table['e_DPi1'][index_in_kepler] < 10
and apogee_data[1].data['NVISITS'][index_in_apogee] >= 1):
continue
a_apogee_id = apogee_data[1].data['APOGEE_ID'][index_in_apogee]
a_location_id = apogee_data[1].data['LOCATION_ID'][index_in_apogee]
local_path_to_file_for_star = None
if version == 6 or version == 7 or version == 8:
local_path_to_file_for_star = visit_spectra(
dr=14, location=a_location_id, apogee=a_apogee_id)
elif version == 4 or version == 5:
local_path_to_file_for_star = combined_spectra(
dr=13, location=a_location_id, apogee=a_apogee_id)
else:
local_path_to_file_for_star = combined_spectra(
dr=14, location=a_location_id, apogee=a_apogee_id)
if (local_path_to_file_for_star):
# Filter out the dr=14 gaps, value to be appended to the array
spectra_no_gap = None
error_spectra = None
if version in (6, 7, 8):
# Read from visit spectra of the star
spectra_data = fits.open(local_path_to_file_for_star)
spectra, mask_spectra = None, None
# Best fit spectra data - use for spectra data
if apogee_data[1].data['NVISITS'][index_in_apogee] == 1:
spectra = spectra_data[1].data
error_spectra = spectra_data[2].data
mask_spectra = spectra_data[3].data
elif apogee_data[1].data['NVISITS'][index_in_apogee] > 1:
spectra = spectra_data[1].data[1]
error_spectra = spectra_data[2].data[1]
mask_spectra = spectra_data[3].data[1]
if version == 6:
norm_spec, norm_spec_err = apogee_continuum(
spectra,
error_spectra,
cont_mask=None,
deg=2,
dr=13,
bitmask=mask_spectra,
target_bit=None)
spectra_no_gap = norm_spec
error_spectra = norm_spec_err
elif version == 7:
norm_spec, norm_spec_err = apogee_continuum(
spectra,
error_spectra,
cont_mask=None,
deg=2,
dr=13,
bitmask=None,
target_bit=None)
spectra_no_gap = norm_spec
error_spectra = norm_spec_err
elif version == 8:
# Bit mask value is now 1
norm_spec, norm_spec_err = apogee_continuum(
spectra,
error_spectra,
cont_mask=None,
deg=2,
dr=13,
bitmask=mask_spectra,
target_bit=None,
mask_value=1)
spectra_no_gap = norm_spec
error_spectra = norm_spec_err
del mask_spectra
elif version == 4 or version == 5:
spectra_data = fits.open(local_path_to_file_for_star)
# Best fit spectra data - use for spectra data
spectra = spectra_data[3].data
spectra_no_gap = gap_delete(spectra, dr=13)
else:
spectra_data = fits.open(local_path_to_file_for_star)
# Best fit spectra data - use for spectra data
spectra = spectra_data[3].data
spectra_no_gap = gap_delete(spectra, dr=14)
spectra_no_gap = spectra_no_gap.flatten()
# APOGEE data
self._star_dict['T_eff'].append(
apogee_data[1].data['Teff'][index_in_apogee].copy())
self._star_dict['logg'].append(
apogee_data[1].data['logg'][index_in_apogee].copy())
# Metals/H
self._star_dict['M_H'].append(
apogee_data[1].data['M_H'][index_in_apogee])
self._star_dict['C'].append(
apogee_data[1].data['X_H'][index_in_apogee][0])
self._star_dict['N'].append(
apogee_data[1].data['X_H'][index_in_apogee][2])
self._star_dict['O'].append(
apogee_data[1].data['X_H'][index_in_apogee][3])
self._star_dict['Na'].append(
apogee_data[1].data['X_H'][index_in_apogee][4])
self._star_dict['Mg'].append(
apogee_data[1].data['X_H'][index_in_apogee][5])
self._star_dict['Al'].append(
apogee_data[1].data['X_H'][index_in_apogee][6])
self._star_dict['Si'].append(
apogee_data[1].data['X_H'][index_in_apogee][7])
self._star_dict['P'].append(
apogee_data[1].data['X_H'][index_in_apogee][8])
self._star_dict['S'].append(
apogee_data[1].data['X_H'][index_in_apogee][9])
self._star_dict['K'].append(
apogee_data[1].data['X_H'][index_in_apogee][10])
self._star_dict['Ca'].append(
apogee_data[1].data['X_H'][index_in_apogee][11])
self._star_dict['Ti'].append(
apogee_data[1].data['X_H'][index_in_apogee][12])
self._star_dict['V'].append(
apogee_data[1].data['X_H'][index_in_apogee][14])
self._star_dict['Mn'].append(
apogee_data[1].data['X_H'][index_in_apogee][16])
self._star_dict['Fe'].append(
apogee_data[1].data['X_H'][index_in_apogee][17])
self._star_dict['Ni'].append(
apogee_data[1].data['X_H'][index_in_apogee][19])
self._star_dict['Cr'].append(
apogee_data[1].data['X_H'][index_in_apogee][15])
self._star_dict['Co'].append(
apogee_data[1].data['X_H'][index_in_apogee][18])
self._star_dict['Cl'].append(
apogee_data[1].data['X_H'][index_in_apogee][1])
# KIC data
self._star_dict['KIC'].append(
kic_table['KIC'][index_in_kepler])
self._star_dict['RA'].append(kic_table['RA'][index_in_kepler])
self._star_dict['DEC'].append(kic_table['DE'][index_in_kepler])
self._star_dict['Dnu'].append(
kic_table['Dnu'][index_in_kepler])
self._star_dict['PS'].append(
kic_table['DPi1'][index_in_kepler])
self._star_dict['e_PS'].append(
kic_table['e_DPi1'][index_in_kepler])
self._star_dict['q'].append(kic_table['q'][index_in_kepler])
self._star_dict['M'].append(kic_table['M'][index_in_kepler])
self._star_dict['status'].append(
kic_table['Status'][index_in_kepler])
self._star_dict['RC'].append(
self.is_red_clump(kic_table['DPi1'][index_in_kepler],
kic_table['Dnu'][index_in_kepler]))
# Gap delete doesn't return row vector, need to manually reshape
self._star_dict['spectra'].append(spectra_no_gap)
if version in (6, 7, 8):
self._star_dict['error_spectra'].append(
error_spectra.flatten())
del error_spectra
# Close file handler
del spectra_data
del spectra
# Check max condition
if i > max_number_of_stars - 1:
break
# Convert to numpy arrays
self._star_dict['KIC'] = np.array(self._star_dict['KIC'])
self._star_dict['RA'] = np.array(self._star_dict['RA'])
self._star_dict['DEC'] = np.array(self._star_dict['DEC'])
self._star_dict['Dnu'] = np.array(self._star_dict['Dnu'])
self._star_dict['PS'] = np.array(self._star_dict['PS'])
self._star_dict['e_PS'] = np.array(self._star_dict['e_PS'])
self._star_dict['spectra'] = np.array(self._star_dict['spectra'])
self._star_dict['logg'] = np.array(self._star_dict['logg'])
self._star_dict['T_eff'] = np.array(self._star_dict['T_eff'])
self._star_dict['RC'] = np.array(self._star_dict['RC'])
self._star_dict['status'] = np.array(self._star_dict['status'])
self._star_dict['M'] = np.array(self._star_dict['M'])
self._star_dict['M_H'] = np.array(self._star_dict['M_H'])
self._star_dict['C'] = np.array(self._star_dict['C'])
self._star_dict['N'] = np.array(self._star_dict['N'])
self._star_dict['O'] = np.array(self._star_dict['O'])
self._star_dict['Na'] = np.array(self._star_dict['Na'])
self._star_dict['Mg'] = np.array(self._star_dict['Mg'])
self._star_dict['Al'] = np.array(self._star_dict['Al'])
self._star_dict['Si'] = np.array(self._star_dict['Si'])
self._star_dict['P'] = np.array(self._star_dict['P'])
self._star_dict['S'] = np.array(self._star_dict['S'])
self._star_dict['K'] = np.array(self._star_dict['K'])
self._star_dict['Ca'] = np.array(self._star_dict['Ca'])
self._star_dict['Ti'] = np.array(self._star_dict['Ti'])
self._star_dict['V'] = np.array(self._star_dict['V'])
self._star_dict['Mn'] = np.array(self._star_dict['Mn'])
self._star_dict['Fe'] = np.array(self._star_dict['Fe'])
self._star_dict['Ni'] = np.array(self._star_dict['Ni'])
self._star_dict['Cl'] = np.array(self._star_dict['Cl'])
self._star_dict['Cr'] = np.array(self._star_dict['Cr'])
self._star_dict['Co'] = np.array(self._star_dict['Co'])
self._star_dict['q'] = np.array(self._star_dict['q'])
if version == 6:
self._star_dict['error_spectra'] = np.array(
self._star_dict['error_spectra'])
# Pickle for caching
pickle_out = open("{}stars{}.pickle".format(self._PICKLE_DIR, version),
'wb')
pickle.dump(self._star_dict, pickle_out)
pickle_out.close()
return True
def load_allstar(self):
self.apogee_dr = apogee_default_dr(dr=self.apogee_dr)
allstarpath = allstar(dr=self.apogee_dr)
hdulist = fits.open(allstarpath)
print(f'Loading allStar DR{self.apogee_dr} catalog')
return hdulist