def test_extinction(self):
from astroNN.gaia import extinction_correction
self.assertEqual(np.any([extinction_correction(10., -90.) == -9999.]),
False)
self.assertEqual(extinction_correction(10., -90.), 10.)
self.assertEqual(
np.any([extinction_correction(-99.99, -90.) == -9999.]), True)
def load_apogee_rc(dr=None, metric='distance', extinction=True):
"""
Load apogee red clumps (absolute magnitude measurement)
:param dr: Apogee DR
:type dr: int
:param metric: which metric you want to get back
- "absmag" for k-band absolute magnitude
- "fakemag" for k-band fake magnitude
- "distance" for distance in parsec
:type metric: string
:param extinction: Whether to take extinction into account, only affect when metric is NOT 'distance'
:type extinction: bool
:return: numpy array of ra, dec, metrics_array
:rtype: ndarrays
:History:
| 2018-Jan-21 - Written - Henry Leung (University of Toronto)
| 2018-May-12 - Updated - Henry Leung (University of Toronto)
"""
fullfilename = apogee_vac_rc(dr=dr)
with fits.open(fullfilename) as F:
hdulist = F[1].data
ra = hdulist['RA']
dec = hdulist['DEC']
rc_dist = hdulist['RC_DIST']
rc_parallax = (1 / rc_dist) * u.mas # Convert kpc to parallax in mas
k_mag = hdulist['K']
if extinction:
k_mag = extinction_correction(k_mag, hdulist['AK_TARG'])
if metric == 'distance':
output = rc_dist * 1000
elif metric == 'absmag':
absmag = mag_to_absmag(k_mag, rc_parallax)
output = absmag
elif metric == 'fakemag':
# fakemag requires parallax (mas)
fakemag = mag_to_fakemag(k_mag, rc_parallax)
output = fakemag
else:
raise ValueError('Unknown metric')
return ra, dec, output
format="18A"),
fits.Column(name="LOCATION_ID",
array=allstar_data["LOCATION_ID"],
format="J"),
fits.Column(name="RA", array=allstar_data["RA"], format="D"),
fits.Column(name="DEC", array=allstar_data["DEC"], format="D"),
fits.Column(name="astroNN", array=pred, format="22E"),
fits.Column(name="astroNN_error", array=pred_error["total"], format="22E"),
]
t = fits.BinTableHDU.from_columns(columns_list)
t.writeto(astronn_chem_f)
# ====================================== Distance ====================================== #
corrected_K = extinction_correction(allstar_data["K"], allstar_data["AK_TARG"])
# cross matched APOGEE-Gaia
apogeegaia_file = fits.getdata(gaia_rowmatch_f)
# add the offset we found for inv var weighting parallax
try:
parallax = apogeegaia_file["parallax_w_zp"]
except KeyError:
parallax = apogeegaia_file["parallax"]
parallax_error = apogeegaia_file["parallax_error"]
# set negative parallax after constant offset correction to np.nan
parallax_error[(parallax < 0) & (parallax > 1e10)] = np.nan
parallax[(parallax < 0) & (parallax > 1e10)] = np.nan
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
format="18A"),
fits.Column(name='LOCATION_ID',
array=allstar_data['LOCATION_ID'],
format="J"),
fits.Column(name='RA', array=allstar_data['RA'], format='D'),
fits.Column(name='DEC', array=allstar_data['DEC'], format='D'),
fits.Column(name='astroNN', array=pred, format='22E'),
fits.Column(name='astroNN_error', array=pred_error['total'], format='22E')
]
t = fits.BinTableHDU.from_columns(columns_list)
t.writeto(astronn_chem_f)
# ====================================== Distance ====================================== #
corrected_K = extinction_correction(allstar_data['K'], allstar_data['AK_TARG'])
# cross matched APOGEE-Gaia DR2
apogeegaia_file = fits.getdata(gaia_rowmatch_f)
# add the offset we found for inv var weighting parallax
parallax = apogeegaia_file["parallax"] + 0.052
parallax_error = apogeegaia_file["parallax_error"]
# set negative parallax after constant offset correction to np.nan
parallax[(parallax < 0)] = np.nan
parallax_error[(parallax < 0)] = np.nan
# inference
net = load_folder(astronn_dist_model)
pred, pred_err = net.test(all_spec)
pred[:, 0][pred[:, 0] == 0.] = np.nan
def compile(self):
h5name_check(self.filename)
hdulist = self.load_allstar()
indices = self.filter_apogeeid_list(hdulist)
info = chips_pix_info(dr=self.apogee_dr)
total_pix = (info[1] - info[0]) + (info[3] - info[2]) + (info[5] -
info[4])
default_length = 900000
spec = np.zeros((default_length, total_pix), dtype=np.float32)
spec_err = np.zeros((default_length, total_pix), dtype=np.float32)
RA = np.zeros(default_length, dtype=np.float32)
DEC = np.zeros(default_length, dtype=np.float32)
SNR = np.zeros(default_length, dtype=np.float32)
individual_flag = np.zeros(default_length, dtype=np.float32)
Kmag = np.zeros(default_length, dtype=np.float32)
AK_TARG = np.zeros(default_length, dtype=np.float32)
# Data array
teff = np.zeros(default_length, dtype=np.float32)
logg = np.zeros(default_length, dtype=np.float32)
MH = np.zeros(default_length, dtype=np.float32)
alpha_M = np.zeros(default_length, dtype=np.float32)
C = np.zeros(default_length, dtype=np.float32)
C1 = np.zeros(default_length, dtype=np.float32)
N = np.zeros(default_length, dtype=np.float32)
O = np.zeros(default_length, dtype=np.float32)
Na = np.zeros(default_length, dtype=np.float32)
Mg = np.zeros(default_length, dtype=np.float32)
Al = np.zeros(default_length, dtype=np.float32)
Si = np.zeros(default_length, dtype=np.float32)
P = np.zeros(default_length, dtype=np.float32)
S = np.zeros(default_length, dtype=np.float32)
K = np.zeros(default_length, dtype=np.float32)
Ca = np.zeros(default_length, dtype=np.float32)
Ti = np.zeros(default_length, dtype=np.float32)
Ti2 = np.zeros(default_length, dtype=np.float32)
V = np.zeros(default_length, dtype=np.float32)
Cr = np.zeros(default_length, dtype=np.float32)
Mn = np.zeros(default_length, dtype=np.float32)
Fe = np.zeros(default_length, dtype=np.float32)
Co = np.zeros(default_length, dtype=np.float32)
Ni = np.zeros(default_length, dtype=np.float32)
Cu = np.zeros(default_length, dtype=np.float32)
Ge = np.zeros(default_length, dtype=np.float32)
Ce = np.zeros(default_length, dtype=np.float32)
Rb = np.zeros(default_length, dtype=np.float32)
Y = np.zeros(default_length, dtype=np.float32)
Nd = np.zeros(default_length, dtype=np.float32)
parallax = np.zeros(default_length, dtype=np.float32)
fakemag = np.zeros(default_length, dtype=np.float32)
# Error array
teff_err = np.zeros(default_length, dtype=np.float32)
logg_err = np.zeros(default_length, dtype=np.float32)
MH_err = np.zeros(default_length, dtype=np.float32)
alpha_M_err = np.zeros(default_length, dtype=np.float32)
C_err = np.zeros(default_length, dtype=np.float32)
C1_err = np.zeros(default_length, dtype=np.float32)
N_err = np.zeros(default_length, dtype=np.float32)
O_err = np.zeros(default_length, dtype=np.float32)
Na_err = np.zeros(default_length, dtype=np.float32)
Mg_err = np.zeros(default_length, dtype=np.float32)
Al_err = np.zeros(default_length, dtype=np.float32)
Si_err = np.zeros(default_length, dtype=np.float32)
P_err = np.zeros(default_length, dtype=np.float32)
S_err = np.zeros(default_length, dtype=np.float32)
K_err = np.zeros(default_length, dtype=np.float32)
Ca_err = np.zeros(default_length, dtype=np.float32)
Ti_err = np.zeros(default_length, dtype=np.float32)
Ti2_err = np.zeros(default_length, dtype=np.float32)
V_err = np.zeros(default_length, dtype=np.float32)
Cr_err = np.zeros(default_length, dtype=np.float32)
Mn_err = np.zeros(default_length, dtype=np.float32)
Fe_err = np.zeros(default_length, dtype=np.float32)
Co_err = np.zeros(default_length, dtype=np.float32)
Ni_err = np.zeros(default_length, dtype=np.float32)
Cu_err = np.zeros(default_length, dtype=np.float32)
Ge_err = np.zeros(default_length, dtype=np.float32)
Ce_err = np.zeros(default_length, dtype=np.float32)
Rb_err = np.zeros(default_length, dtype=np.float32)
Y_err = np.zeros(default_length, dtype=np.float32)
Nd_err = np.zeros(default_length, dtype=np.float32)
parallax_err = np.zeros(default_length, dtype=np.float32)
fakemag_err = np.zeros(default_length, dtype=np.float32)
array_counter = 0
start_time = time.time()
# provide a cont mask so no need to read every loop
if self.cont_mask is None:
maskpath = os.path.join(astroNN.data.datapath(),
f'dr{self.apogee_dr}_contmask.npy')
self.cont_mask = np.load(maskpath)
for counter, index in enumerate(indices):
nvisits = 1
apogee_id = hdulist[1].data['APOGEE_ID'][index]
location_id = hdulist[1].data['LOCATION_ID'][index]
if counter % 100 == 0:
print(
f'Completed {counter + 1} of {indices.shape[0]}, {(time.time() - start_time):.{2}f}s elapsed'
)
if not self.continuum:
path = combined_spectra(dr=self.apogee_dr,
location=location_id,
apogee=apogee_id,
verbose=0)
if path is False:
# if path is not found then we should skip
continue
combined_file = fits.open(path)
_spec = combined_file[
1].data # Pseudo-continuum normalized flux
_spec_err = combined_file[2].data # Spectrum error array
_spec = gap_delete(
_spec, dr=self.apogee_dr) # Delete the gap between sensors
_spec_err = gap_delete(_spec_err, dr=self.apogee_dr)
inSNR = combined_file[0].header['SNR']
combined_file.close()
else:
path = visit_spectra(dr=self.apogee_dr,
location=location_id,
apogee=apogee_id,
verbose=0)
if path is False:
# if path is not found then we should skip
continue
apstar_file = fits.open(path)
nvisits = apstar_file[0].header['NVISITS']
if nvisits == 1:
_spec = apstar_file[1].data
_spec_err = apstar_file[2].data
_spec_mask = apstar_file[3].data
inSNR = np.ones(nvisits)
inSNR[0] = apstar_file[0].header['SNR']
else:
_spec = apstar_file[1].data[1:]
_spec_err = apstar_file[2].data[1:]
_spec_mask = apstar_file[3].data[1:]
inSNR = np.ones(nvisits + 1)
inSNR[0] = apstar_file[0].header['SNR']
for i in range(nvisits):
inSNR[i + 1] = apstar_file[0].header[f'SNRVIS{i + 1}']
# Deal with spectra thats all zeros flux
ii = 0
while ii < _spec.shape[0]:
if np.count_nonzero(_spec[ii]) == 0:
nvisits -= 1
_spec = np.delete(_spec, ii, 0)
_spec_err = np.delete(_spec_err, ii, 0)
_spec_mask = np.delete(_spec_mask, ii, 0)
inSNR = np.delete(inSNR, ii, 0)
ii -= 1
ii += 1
# Just for the sake of program to work, the real nvisits still nvisits
nvisits += 1
# Normalize spectra and Set some bitmask to 0
_spec, _spec_err = self.apstar_normalization(
_spec, _spec_err, _spec_mask)
apstar_file.close()
if nvisits == 1:
individual_flag[array_counter:array_counter + nvisits] = 0
else:
individual_flag[array_counter:array_counter + 1] = 0
individual_flag[array_counter + 1:array_counter + nvisits] = 1
spec[array_counter:array_counter + nvisits, :] = _spec
spec_err[array_counter:array_counter + nvisits, :] = _spec_err
SNR[array_counter:array_counter + nvisits] = inSNR
RA[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['RA'][index], nvisits)
DEC[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['DEC'][index], nvisits)
parallax[array_counter:array_counter + nvisits] = np.tile(
-9999, nvisits)
parallax_err[array_counter:array_counter + nvisits] = np.tile(
-9999, nvisits)
fakemag[array_counter:array_counter + nvisits] = np.tile(
-9999, nvisits)
fakemag_err[array_counter:array_counter + nvisits] = np.tile(
-9999, nvisits)
Kmag[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['K'][index], nvisits)
AK_TARG[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['AK_TARG'][index], nvisits)
if self.spectra_only is not True:
teff[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['PARAM'][index, 0], nvisits)
logg[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['PARAM'][index, 1], nvisits)
MH[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['PARAM'][index, 3], nvisits)
alpha_M[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['PARAM'][index, 6], nvisits)
C[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 0], nvisits)
C1[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 1], nvisits)
N[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 2], nvisits)
O[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 3], nvisits)
Na[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 4], nvisits)
Mg[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 5], nvisits)
Al[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 6], nvisits)
Si[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 7], nvisits)
P[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 8], nvisits)
S[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 9], nvisits)
K[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 10], nvisits)
Ca[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 11], nvisits)
Ti[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 12], nvisits)
Ti2[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 13], nvisits)
V[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 14], nvisits)
Cr[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 15], nvisits)
Mn[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 16], nvisits)
Fe[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 17], nvisits)
Co[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 18], nvisits)
Ni[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 19], nvisits)
Cu[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 20], nvisits)
Ge[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 21], nvisits)
Ce[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 22], nvisits)
Rb[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 23], nvisits)
Y[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 24], nvisits)
Nd[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H'][index, 25], nvisits)
if self.use_err is True:
teff_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['TEFF_ERR'][index], nvisits)
logg_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['LOGG_ERR'][index], nvisits)
MH_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['M_H_ERR'][index], nvisits)
alpha_M_err[array_counter:array_counter +
nvisits] = np.tile(
hdulist[1].data['ALPHA_M_ERR'][index],
nvisits)
C_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 0], nvisits)
C1_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 1], nvisits)
N_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 2], nvisits)
O_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 3], nvisits)
Na_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 4], nvisits)
Mg_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 5], nvisits)
Al_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 6], nvisits)
Si_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 7], nvisits)
P_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 8], nvisits)
S_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 9], nvisits)
K_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 10], nvisits)
Ca_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 11], nvisits)
Ti_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 12], nvisits)
Ti2_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 13], nvisits)
V_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 14], nvisits)
Cr_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 15], nvisits)
Mn_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 16], nvisits)
Fe_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 17], nvisits)
Co_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 18], nvisits)
Ni_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 19], nvisits)
Cu_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 20], nvisits)
Ge_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 21], nvisits)
Ce_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 22], nvisits)
Rb_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 23], nvisits)
Y_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 24], nvisits)
Nd_err[array_counter:array_counter + nvisits] = np.tile(
hdulist[1].data['X_H_ERR'][index, 25], nvisits)
array_counter += nvisits
spec = spec[0:array_counter]
spec_err = spec_err[0:array_counter]
individual_flag = individual_flag[0:array_counter]
RA = RA[0:array_counter]
DEC = DEC[0:array_counter]
SNR = SNR[0:array_counter]
if self.spectra_only is not True:
teff = teff[0:array_counter]
logg = logg[0:array_counter]
Kmag = Kmag[0:array_counter]
AK_TARG = AK_TARG[0:array_counter]
MH = MH[0:array_counter]
alpha_M = alpha_M[0:array_counter]
C = C[0:array_counter]
C1 = C1[0:array_counter]
N = N[0:array_counter]
O = O[0:array_counter]
Na = Na[0:array_counter]
Mg = Mg[0:array_counter]
Al = Al[0:array_counter]
Si = Si[0:array_counter]
P = P[0:array_counter]
S = S[0:array_counter]
K = K[0:array_counter]
Ca = Ca[0:array_counter]
Ti = Ti[0:array_counter]
Ti2 = Ti2[0:array_counter]
V = V[0:array_counter]
Cr = Cr[0:array_counter]
Mn = Mn[0:array_counter]
Fe = Fe[0:array_counter]
Co = Co[0:array_counter]
Ni = Ni[0:array_counter]
Cu = Cu[0:array_counter]
Ge = Ge[0:array_counter]
Ce = Ce[0:array_counter]
Rb = Rb[0:array_counter]
Y = Y[0:array_counter]
Nd = Nd[0:array_counter]
parallax = parallax[0:array_counter]
fakemag = fakemag[0:array_counter]
teff_err = teff_err[0:array_counter]
logg_err = logg_err[0:array_counter]
MH_err = MH_err[0:array_counter]
alpha_M_err = alpha_M_err[0:array_counter]
C_err = C_err[0:array_counter]
C1_err = C1_err[0:array_counter]
N_err = N_err[0:array_counter]
O_err = O_err[0:array_counter]
Na_err = Na_err[0:array_counter]
Mg_err = Mg_err[0:array_counter]
Al_err = Al_err[0:array_counter]
Si_err = Si_err[0:array_counter]
P_err = P_err[0:array_counter]
S_err = S_err[0:array_counter]
K_err = K_err[0:array_counter]
Ca_err = Ca_err[0:array_counter]
Ti_err = Ti_err[0:array_counter]
Ti2_err = Ti2_err[0:array_counter]
V_err = V_err[0:array_counter]
Cr_err = Cr_err[0:array_counter]
Mn_err = Mn_err[0:array_counter]
Fe_err = Fe_err[0:array_counter]
Co_err = Co_err[0:array_counter]
Ni_err = Ni_err[0:array_counter]
Cu_err = Cu_err[0:array_counter]
Ge_err = Ge_err[0:array_counter]
Ce_err = Ce_err[0:array_counter]
Rb_err = Rb_err[0:array_counter]
Y_err = Y_err[0:array_counter]
Nd_err = Nd_err[0:array_counter]
parallax_err = parallax_err[0:array_counter]
fakemag_err = fakemag_err[0:array_counter]
if self.use_esa_gaia is True:
gaia_ra, gaia_dec, gaia_parallax, gaia_err = gaiadr2_parallax(
cuts=True, keepdims=False)
m1, m2, sep = xmatch(RA,
gaia_ra,
maxdist=2,
colRA1=RA,
colDec1=DEC,
colRA2=gaia_ra,
colDec2=gaia_dec,
swap=False)
parallax[m1] = gaia_parallax[m2]
parallax_err[m1] = gaia_err[m2]
fakemag[m1], fakemag_err[m1] = mag_to_fakemag(
extinction_correction(Kmag[m1], AK_TARG[m1]), parallax[m1],
parallax_err[m1])
elif self.use_anderson_2017 is True:
gaia_ra, gaia_dec, gaia_parallax, gaia_err = anderson_2017_parallax(
)
m1, m2, sep = xmatch(RA,
gaia_ra,
maxdist=2,
colRA1=RA,
colDec1=DEC,
epoch1=2000.,
colRA2=gaia_ra,
colDec2=gaia_dec,
epoch2=2000.,
swap=False)
parallax[m1] = gaia_parallax[m2]
parallax_err[m1] = gaia_err[m2]
fakemag[m1], fakemag_err[m1] = mag_to_fakemag(
extinction_correction(Kmag[m1], AK_TARG[m1]), parallax[m1],
parallax_err[m1])
print(f'Creating {self.filename}.h5')
h5f = h5py.File(f'{self.filename}.h5', 'w')
h5f.create_dataset('spectra', data=spec)
h5f.create_dataset('spectra_err', data=spec_err)
h5f.create_dataset('in_flag', data=individual_flag)
h5f.create_dataset('index', data=indices)
if self.spectra_only is not True:
h5f.create_dataset('SNR', data=SNR)
h5f.create_dataset('RA', data=RA)
h5f.create_dataset('DEC', data=DEC)
h5f.create_dataset('Kmag', data=Kmag)
h5f.create_dataset('AK_TARG', data=AK_TARG)
h5f.create_dataset('teff', data=teff)
h5f.create_dataset('logg', data=logg)
h5f.create_dataset('M', data=MH)
h5f.create_dataset('alpha', data=alpha_M)
h5f.create_dataset('C', data=C)
h5f.create_dataset('C1', data=C1)
h5f.create_dataset('N', data=N)
h5f.create_dataset('O', data=O)
h5f.create_dataset('Na', data=Na)
h5f.create_dataset('Mg', data=Mg)
h5f.create_dataset('Al', data=Al)
h5f.create_dataset('Si', data=Si)
h5f.create_dataset('P', data=P)
h5f.create_dataset('S', data=S)
h5f.create_dataset('K', data=K)
h5f.create_dataset('Ca', data=Ca)
h5f.create_dataset('Ti', data=Ti)
h5f.create_dataset('Ti2', data=Ti2)
h5f.create_dataset('V', data=V)
h5f.create_dataset('Cr', data=Cr)
h5f.create_dataset('Mn', data=Mn)
h5f.create_dataset('Fe', data=Fe)
h5f.create_dataset('Co', data=Co)
h5f.create_dataset('Ni', data=Ni)
h5f.create_dataset('Cu', data=Cu)
h5f.create_dataset('Ge', data=Ge)
h5f.create_dataset('Ce', data=Ce)
h5f.create_dataset('Rb', data=Rb)
h5f.create_dataset('Y', data=Y)
h5f.create_dataset('Nd', data=Nd)
h5f.create_dataset('parallax', data=parallax)
h5f.create_dataset('fakemag', data=fakemag)
if self.use_err is True:
h5f.create_dataset('AK_TARG_err', data=np.zeros_like(AK_TARG))
h5f.create_dataset('teff_err', data=teff_err)
h5f.create_dataset('logg_err', data=logg_err)
h5f.create_dataset('M_err', data=MH_err)
h5f.create_dataset('alpha_err', data=alpha_M_err)
h5f.create_dataset('C_err', data=C_err)
h5f.create_dataset('C1_err', data=C1_err)
h5f.create_dataset('N_err', data=N_err)
h5f.create_dataset('O_err', data=O_err)
h5f.create_dataset('Na_err', data=Na_err)
h5f.create_dataset('Mg_err', data=Mg_err)
h5f.create_dataset('Al_err', data=Al_err)
h5f.create_dataset('Si_err', data=Si_err)
h5f.create_dataset('P_err', data=P_err)
h5f.create_dataset('S_err', data=S_err)
h5f.create_dataset('K_err', data=K_err)
h5f.create_dataset('Ca_err', data=Ca_err)
h5f.create_dataset('Ti_err', data=Ti_err)
h5f.create_dataset('Ti2_err', data=Ti2_err)
h5f.create_dataset('V_err', data=V_err)
h5f.create_dataset('Cr_err', data=Cr_err)
h5f.create_dataset('Mn_err', data=Mn_err)
h5f.create_dataset('Fe_err', data=Fe_err)
h5f.create_dataset('Co_err', data=Co_err)
h5f.create_dataset('Ni_err', data=Ni_err)
h5f.create_dataset('Cu_err', data=Cu_err)
h5f.create_dataset('Ge_err', data=Ge_err)
h5f.create_dataset('Ce_err', data=Ce_err)
h5f.create_dataset('Rb_err', data=Rb_err)
h5f.create_dataset('Y_err', data=Y_err)
h5f.create_dataset('Nd_err', data=Nd_err)
h5f.create_dataset('parallax_err', data=parallax_err)
h5f.create_dataset('fakemag_err', data=fakemag_err)
h5f.close()
print(f'Successfully created {self.filename}.h5 in {currentdir}')
def test_arXiv_1902_08634 (self):
"""
astroNN spectrophotometric distance
"""
from astroNN.apogee import visit_spectra, apogee_continuum
from astroNN.gaia import extinction_correction, fakemag_to_pc
from astropy.io import fits
# first model
models_url = ["https://github.com/henrysky/astroNN_gaia_dr2_paper/trunk/astroNN_no_offset_model",
"https://github.com/henrysky/astroNN_gaia_dr2_paper/trunk/astroNN_constant_model",
"https://github.com/henrysky/astroNN_gaia_dr2_paper/trunk/astroNN_multivariate_model"]
for model_url in models_url:
download_args = ["svn", "export", model_url]
res = subprocess.Popen(download_args, stdout=subprocess.PIPE)
output, _error = res.communicate()
if not _error:
pass
else:
raise ConnectionError(f"Error downloading the models {model_url}")
opened_fits = fits.open(visit_spectra(dr=14, location=4405, apogee='2M19060637+4717296'))
spectrum = opened_fits[1].data
spectrum_err = opened_fits[2].data
spectrum_bitmask = opened_fits[3].data
# using default continuum and bitmask values to continuum normalize
norm_spec, norm_spec_err = apogee_continuum(spectrum, spectrum_err,
bitmask=spectrum_bitmask, dr=14)
# correct for extinction
K = extinction_correction(opened_fits[0].header['K'], opened_fits[0].header['AKTARG'])
# ===========================================================================================#
# load neural net
neuralnet = load_folder('astroNN_no_offset_model')
# inference, if there are multiple visits, then you should use the globally
# weighted combined spectra (i.e. the second row)
pred, pred_err = neuralnet.test(norm_spec)
# convert prediction in fakemag to distance
pc, pc_error = fakemag_to_pc(pred[:, 0], K, pred_err['total'][:, 0])
# assert distance is close enough
# http://simbad.u-strasbg.fr/simbad/sim-id?mescat.distance=on&Ident=%406876647&Name=KIC+10196240&submit=display+selected+measurements#lab_meas
# no offset correction so further away
self.assertEqual(pc.value < 1250, True)
self.assertEqual(pc.value > 1100, True)
# ===========================================================================================#
# load neural net
neuralnet = load_folder('astroNN_constant_model')
# inference, if there are multiple visits, then you should use the globally
# weighted combined spectra (i.e. the second row)
pred, pred_err = neuralnet.test(np.hstack([norm_spec, np.zeros((norm_spec.shape[0], 4))]))
# convert prediction in fakemag to distance
pc, pc_error = fakemag_to_pc(pred[:, 0], K, pred_err['total'][:, 0])
# assert distance is close enough
# http://simbad.u-strasbg.fr/simbad/sim-id?mescat.distance=on&Ident=%406876647&Name=KIC+10196240&submit=display+selected+measurements#lab_meas
self.assertEqual(pc.value < 1150, True)
self.assertEqual(pc.value > 1000, True)
# ===========================================================================================#
# load neural net
neuralnet = load_folder('astroNN_multivariate_model')
# inference, if there are multiple visits, then you should use the globally
# weighted combined spectra (i.e. the second row)
pred, pred_err = neuralnet.test(np.hstack([norm_spec, np.zeros((norm_spec.shape[0], 4))]))
# convert prediction in fakemag to distance
pc, pc_error = fakemag_to_pc(pred[:, 0], K, pred_err['total'][:, 0])
# assert distance is close enough
# http://simbad.u-strasbg.fr/simbad/sim-id?mescat.distance=on&Ident=%406876647&Name=KIC+10196240&submit=display+selected+measurements#lab_meas
self.assertEqual(pc.value < 1150, True)
self.assertEqual(pc.value > 1000, True)