def add_btnextgen(input_path, database, wl_bound, teff_bound, specres):
"""
Function for adding the BT-NextGen atmospheric models to the database.
Parameters
----------
input_path : str
Folder where the data is located.
database : h5py._hl.files.File
Database.
wl_bound : tuple(float, float)
Wavelength range (micron).
teff_bound : tuple(float, float), None
Effective temperature range (K).
specres : float
Spectral resolution.
Returns
-------
NoneType
None
"""
if not wl_bound:
wl_bound = (1e-2, 1e2)
if not os.path.exists(input_path):
os.makedirs(input_path)
data_folder = os.path.join(input_path, 'bt-nextgen/')
files = [
'BT-NextGen_M-0.0_a+0.0_hot.tar', 'BT-NextGen_M+0.3_a+0.0_hot.tar',
'BT-NextGen_M+0.5_a+0.0_hot.tar'
]
urls = [
'https://phoenix.ens-lyon.fr/Grids/BT-NextGen/SPECTRA/BT-NextGen_M-0.0_a+0.0_hot.tar',
'https://phoenix.ens-lyon.fr/Grids/BT-NextGen/SPECTRA/BT-NextGen_M+0.3_a+0.0_hot.tar',
'https://phoenix.ens-lyon.fr/Grids/BT-NextGen/SPECTRA/BT-NextGen_M+0.5_a+0.0_hot.tar'
]
labels = [
'[Fe/H]=0.0 (5.9 GB)', '[Fe/H]=0.3 (6.2 GB)', '[Fe/H]=0.5 (6.4 GB)'
]
for i, item in enumerate(files):
data_file = os.path.join(input_path, item)
if not os.path.isfile(data_file):
sys.stdout.write('Downloading BT-NextGen model spectra ' +
labels[i] + '...')
sys.stdout.flush()
urlretrieve(urls[i], data_file)
sys.stdout.write(' [DONE]\n')
sys.stdout.flush()
sys.stdout.write('Unpacking BT-NextGen model spectra ' + labels[i] +
'...')
sys.stdout.flush()
tar = tarfile.open(data_file)
tar.extractall(data_folder)
tar.close()
sys.stdout.write(' [DONE]\n')
sys.stdout.flush()
teff = []
logg = []
feh = []
flux = []
wavelength = [wl_bound[0]]
while wavelength[-1] <= wl_bound[1]:
wavelength.append(wavelength[-1] + wavelength[-1] / specres)
wavelength = np.asarray(wavelength[:-1])
for _, _, file_list in os.walk(data_folder):
for filename in sorted(file_list):
if filename.startswith('lte') and filename.endswith('.7.bz2'):
sys.stdout.write('\rAdding BT-NextGen model spectra... ' +
filename)
sys.stdout.flush()
teff_val = float(filename[3:6]) * 100.
if teff_bound is not None:
if teff_bound[0] <= teff_val <= teff_bound[1]:
teff.append(teff_val)
logg.append(float(filename[7:9]))
feh.append(float(filename[11:14]))
else:
continue
dataf = pd.pandas.read_csv(data_folder + filename,
usecols=[0, 1],
names=['wavelength', 'flux'],
header=None,
dtype={
'wavelength': str,
'flux': str
},
delim_whitespace=True,
compression='bz2')
dataf['wavelength'] = dataf['wavelength'].str.replace('D', 'E')
dataf['flux'] = dataf['flux'].str.replace('D', 'E')
dataf = dataf.apply(pd.to_numeric)
data = dataf.values
# [Angstrom] -> [micron]
data_wavel = data[:, 0] * 1e-4
# See https://phoenix.ens-lyon.fr/Grids/FORMAT
data_flux = 10.**(data[:, 1] - 8.) # [erg s-1 cm-2 Angstrom-1]
# [erg s-1 cm-2 Angstrom-1] -> [W m-2 micron-1]
data_flux = data_flux * 1e-7 * 1e4 * 1e4
data = np.stack((data_wavel, data_flux), axis=0)
index_sort = np.argsort(data[0, :])
data = data[:, index_sort]
if np.all(np.diff(data[0, :]) < 0):
raise ValueError(
'The wavelengths are not all sorted by increasing value.'
)
indices = np.where((data[0, :] >= wl_bound[0])
& (data[0, :] <= wl_bound[1]))[0]
data = data[:, indices]
flux_interp = interp1d(data[0, :],
data[1, :],
kind='linear',
bounds_error=False,
fill_value=1e-100)
flux.append(flux_interp(wavelength))
data_sorted = data_util.sort_data(np.asarray(teff), np.asarray(logg),
np.asarray(feh), wavelength,
np.asarray(flux))
data_util.write_data('bt-nextgen', ('teff', 'logg', 'feh'), database,
data_sorted)
sys.stdout.write('\rAdding BT-NextGen model spectra... [DONE]'
' \n')
sys.stdout.flush()
def add_btsettl(input_path: str, database: h5py._hl.files.File,
wavel_range: Optional[Tuple[float, float]],
teff_range: Optional[Tuple[float, float]],
spec_res: Optional[float]) -> None:
"""
Function for adding the BT-Settl atmospheric models (solar metallicity) to the database.
The spectra had been downloaded from the Theoretical spectra web server
(http://svo2.cab.inta-csic.es/svo/theory/newov2/index.php?models=bt-settl) and resampled
to a spectral resolution of 5000 from 0.1 to 100 um.
Parameters
----------
input_path : str
Folder where the data is located.
database : h5py._hl.files.File
Database.
wavel_range : tuple(float, float), None
Wavelength range (um). The original wavelength points are used if set to ``None``.
teff_range : tuple(float, float), None
Effective temperature range (K). All temperatures are selected if set to ``None``.
spec_res : float, None
Spectral resolution. Not used if ``wavel_range`` is set to ``None``.
Returns
-------
NoneType
None
"""
if not os.path.exists(input_path):
os.makedirs(input_path)
input_file = 'bt-settl.tgz'
data_folder = os.path.join(input_path, 'bt-settl/')
data_file = os.path.join(input_path, input_file)
if not os.path.exists(data_folder):
os.makedirs(data_folder)
url = 'https://people.phys.ethz.ch/~ipa/tstolker/bt-settl.tgz'
if not os.path.isfile(data_file):
print('Downloading Bt-Settl model spectra (130 MB)...',
end='',
flush=True)
urllib.request.urlretrieve(url, data_file)
print(' [DONE]')
print('Unpacking BT-Settl model spectra (130 MB)...', end='', flush=True)
tar = tarfile.open(data_file)
tar.extractall(data_folder)
tar.close()
print(' [DONE]')
teff = []
logg = []
flux = []
if wavel_range is not None and spec_res is not None:
wavelength = read_util.create_wavelengths(wavel_range, spec_res)
else:
wavelength = None
for _, _, file_list in os.walk(data_folder):
for filename in sorted(file_list):
if filename[:9] == 'bt-settl_':
file_split = filename.split('_')
teff_val = float(file_split[2])
logg_val = float(file_split[4])
if teff_range is not None:
if teff_val < teff_range[0] or teff_val > teff_range[1]:
continue
print_message = f'Adding BT-Settl model spectra... {filename}'
print(f'\r{print_message:<69}', end='')
data_wavel, data_flux = np.loadtxt(os.path.join(
data_folder, filename),
unpack=True)
teff.append(teff_val)
logg.append(logg_val)
if wavel_range is None or spec_res is None:
if wavelength is None:
wavelength = np.copy(data_wavel) # (um)
if np.all(np.diff(wavelength) < 0):
raise ValueError(
'The wavelengths are not all sorted by increasing value.'
)
flux.append(data_flux) # (W m-2 um-1)
else:
flux_resample = spectres.spectres(wavelength,
data_wavel,
data_flux,
spec_errs=None,
fill=np.nan,
verbose=False)
if np.isnan(np.sum(flux_resample)):
raise ValueError(
f'Resampling is only possible if the new wavelength '
f'range ({wavelength[0]} - {wavelength[-1]} um) falls '
f'sufficiently far within the wavelength range '
f'({data_wavel[0]} - {data_wavel[-1]} um) of the input '
f'spectra.')
flux.append(flux_resample) # (W m-2 um-1)
print_message = 'Adding BT-Settl model spectra... [DONE]'
print(f'\r{print_message:<69}')
data_sorted = data_util.sort_data(np.asarray(teff), np.asarray(logg), None,
None, None, wavelength, np.asarray(flux))
data_util.write_data('bt-settl', ['teff', 'logg'], database, data_sorted)
def add_drift_phoenix(input_path: str,
database: h5py._hl.files.File,
wavel_range: Optional[Tuple[float, float]] = None,
teff_range: Optional[Tuple[float, float]] = None,
spec_res: float = None) -> None:
"""
Function for adding the DRIFT-PHOENIX atmospheric models to the database. The original spectra
were downloaded from http://svo2.cab.inta-csic.es/theory/newov2/index.php?models=drift and have
been resampled to a spectral resolution of R = 2000 from 0.1 to 50 um.
Parameters
----------
input_path : str
Folder where the data is located.
database : h5py._hl.files.File
Database.
wavel_range : tuple(float, float), None
Wavelength range (um). The full wavelength range (0.1-50 um) is stored if set to ``None``.
Only used in combination with ``spec_res``.
teff_range : tuple(float, float), None
Effective temperature range (K). All available temperatures are stored if set to ``None``.
spec_res : float, None
Spectral resolution. The data is stored with the spectral resolution of the input spectra
(R = 2000) if set to ``None``. Only used in combination with ``wavel_range``.
Returns
-------
NoneType
None
"""
if not os.path.exists(input_path):
os.makedirs(input_path)
input_file = 'drift-phoenix.tgz'
url = 'https://people.phys.ethz.ch/~ipa/tstolker/drift-phoenix.tgz'
data_folder = os.path.join(input_path, 'drift-phoenix/')
data_file = os.path.join(input_path, input_file)
if not os.path.exists(data_folder):
os.makedirs(data_folder)
if not os.path.isfile(data_file):
print('Downloading DRIFT-PHOENIX model spectra (229 MB)...',
end='',
flush=True)
urllib.request.urlretrieve(url, data_file)
print(' [DONE]')
print('Unpacking DRIFT-PHOENIX model spectra (229 MB)...',
end='',
flush=True)
tar = tarfile.open(data_file)
tar.extractall(data_folder)
tar.close()
print(' [DONE]')
teff = []
logg = []
feh = []
flux = []
if wavel_range is not None and spec_res is not None:
wavelength = read_util.create_wavelengths(wavel_range, spec_res)
else:
wavelength = None
for _, _, files in os.walk(data_folder):
for filename in files:
if filename[:14] == 'drift-phoenix_':
file_split = filename.split('_')
teff_val = float(file_split[2])
logg_val = float(file_split[4])
feh_val = float(file_split[6])
if teff_range is not None:
if teff_val < teff_range[0] or teff_val > teff_range[1]:
continue
print_message = f'Adding DRIFT-PHOENIX model spectra... {filename}'
print(f'\r{print_message:<88}', end='')
data_wavel, data_flux = np.loadtxt(os.path.join(
data_folder, filename),
unpack=True)
teff.append(teff_val)
logg.append(logg_val)
feh.append(feh_val)
if wavel_range is None or spec_res is None:
if wavelength is None:
wavelength = np.copy(data_wavel) # (um)
if np.all(np.diff(wavelength) < 0):
raise ValueError(
'The wavelengths are not all sorted by increasing value.'
)
flux.append(data_flux) # (W m-2 um-1)
else:
flux_resample = spectres.spectres(wavelength,
data_wavel,
data_flux,
spec_errs=None,
fill=np.nan,
verbose=False)
if np.isnan(np.sum(flux_resample)):
raise ValueError(
f'Resampling is only possible if the new wavelength '
f'range ({wavelength[0]} - {wavelength[-1]} um) falls '
f'sufficiently far within the wavelength range '
f'({data_wavel[0]} - {data_wavel[-1]} um) of the input '
f'spectra.')
flux.append(flux_resample) # (W m-2 um-1)
print_message = 'Adding DRIFT-PHOENIX model spectra... [DONE]'
print(f'\r{print_message:<88}')
data_sorted = data_util.sort_data(np.asarray(teff), np.asarray(logg),
np.asarray(feh), None, None, wavelength,
np.asarray(flux))
data_util.write_data('drift-phoenix', ['teff', 'logg', 'feh'], database,
data_sorted)
def add_ames_dusty(input_path: str,
database: h5py._hl.files.File,
wavel_range: Optional[Tuple[float, float]] = None,
teff_range: Optional[Tuple[float, float]] = None,
spec_res: float = None) -> None:
"""
Function for adding the AMES-Dusty atmospheric models to the database. The original spectra
have been resampled to a spectral resolution of R = 2000 from 0.5 to 40 um. Note that a few
of the spectra contain NaNs due to their limited, original wavelength coverage.
Parameters
----------
input_path : str
Folder where the data is located.
database : h5py._hl.files.File
Database.
wavel_range : tuple(float, float), None
Wavelength range (um). The full wavelength range (0.5-40 um) is stored if set to ``None``.
Only used in combination with ``spec_res``.
teff_range : tuple(float, float), None
Effective temperature range (K). All available temperatures are stored if set to ``None``.
spec_res : float, None
Spectral resolution. The data is stored with the spectral resolution of the input spectra
(R = 2000) if set to ``None``. Only used in combination with ``wavel_range``.
Returns
-------
NoneType
None
"""
if not os.path.exists(input_path):
os.makedirs(input_path)
input_file = 'ames-dusty.tgz'
url = 'https://people.phys.ethz.ch/~ipa/tstolker/ames-dusty.tgz'
data_folder = os.path.join(input_path, 'ames-dusty/')
data_file = os.path.join(input_path, input_file)
if not os.path.exists(data_folder):
os.makedirs(data_folder)
if not os.path.isfile(data_file):
print('Downloading AMES-Dusty model spectra (59 MB)...', end='', flush=True)
urllib.request.urlretrieve(url, data_file)
print(' [DONE]')
print('Unpacking AMES-Dusty model spectra (59 MB)...', end='', flush=True)
tar = tarfile.open(data_file)
tar.extractall(data_folder)
tar.close()
print(' [DONE]')
teff = []
logg = []
flux = []
if wavel_range is not None and spec_res is not None:
wavelength = read_util.create_wavelengths(wavel_range, spec_res)
else:
wavelength = None
for _, _, files in os.walk(data_folder):
for filename in files:
if filename[:11] == 'ames-dusty_':
file_split = filename.split('_')
teff_val = float(file_split[2])
logg_val = float(file_split[4])
if teff_range is not None:
if teff_val < teff_range[0] or teff_val > teff_range[1]:
continue
print_message = f'Adding AMES-Dusty model spectra... {filename}'
print(f'\r{print_message:<73}', end='')
data_wavel, data_flux = np.loadtxt(os.path.join(data_folder, filename), unpack=True)
teff.append(teff_val)
logg.append(logg_val)
if wavel_range is None or spec_res is None:
if wavelength is None:
wavelength = np.copy(data_wavel) # (um)
if np.all(np.diff(wavelength) < 0):
raise ValueError('The wavelengths are not all sorted by increasing value.')
# if np.isnan(np.sum(data_flux)):
# Three of the files contain partially NaNs due to a more limited
# wavelength coverage in the original spectra (before using spectres)
# data_flux = np.full(data_wavel.shape[0], np.nan)
flux.append(data_flux) # (W m-2 um-1)
else:
flux_resample = spectres.spectres(wavelength,
data_wavel,
data_flux,
spec_errs=None,
fill=np.nan,
verbose=False)
if np.isnan(np.sum(flux_resample)):
raise ValueError(f'Resampling is only possible if the new wavelength '
f'range ({wavelength[0]} - {wavelength[-1]} um) falls '
f'sufficiently far within the wavelength range '
f'({data_wavel[0]} - {data_wavel[-1]} um) of the input '
f'spectra.')
flux.append(flux_resample) # (W m-2 um-1)
print_message = 'Adding AMES-Dusty model spectra... [DONE]'
print(f'\r{print_message:<73}')
data_sorted = data_util.sort_data(np.asarray(teff),
np.asarray(logg),
None,
None,
None,
wavelength,
np.asarray(flux))
data_util.write_data('ames-dusty',
['teff', 'logg'],
database,
data_sorted)
def add_exo_rem(input_path: str,
database: h5py._hl.files.File,
wavel_range: Optional[Tuple[float, float]] = None,
teff_range: Optional[Tuple[float, float]] = None,
spec_res: Optional[float] = None) -> None:
"""
Function for adding the Exo-REM atmospheric models to the database.
Parameters
----------
input_path : str
Folder where the data is located.
database : h5py._hl.files.File
Database.
wavel_range : tuple(float, float), None
Wavelength range (um). The original wavelength points with a spectral resolution of 5000
are used if set to ``None``.
teff_range : tuple(float, float), None
Effective temperature range (K). All temperatures are selected if set to ``None``.
spec_res : float, None
Spectral resolution. Not used if ``wavel_range`` is set to ``None``.
Returns
-------
NoneType
None
"""
if not os.path.exists(input_path):
os.makedirs(input_path)
input_file = 'exo-rem.tgz'
url = 'https://people.phys.ethz.ch/~ipa/tstolker/exo-rem.tgz'
data_folder = os.path.join(input_path, 'exo-rem/')
data_file = os.path.join(data_folder, input_file)
if not os.path.exists(data_folder):
os.makedirs(data_folder)
if not os.path.isfile(data_file):
print('Downloading Exo-REM model spectra (790 MB)...',
end='',
flush=True)
urllib.request.urlretrieve(url, data_file)
print(' [DONE]')
print('Unpacking Exo-REM model spectra (790 MB)...', end='', flush=True)
tar = tarfile.open(data_file)
tar.extractall(data_folder)
tar.close()
print(' [DONE]')
teff = []
logg = []
feh = []
co_ratio = []
flux = []
if wavel_range is not None and spec_res is not None:
wavelength = read_util.create_wavelengths(wavel_range, spec_res)
else:
wavelength = None
for _, _, files in os.walk(data_folder):
for filename in files:
if filename[:8] == 'exo-rem_':
file_split = filename.split('_')
teff_val = float(file_split[2])
logg_val = float(file_split[4])
feh_val = float(file_split[6])
co_val = float(file_split[8])
if logg_val == 5.:
continue
if co_val in [0.8, 0.85]:
continue
if teff_range is not None:
if teff_val < teff_range[0] or teff_val > teff_range[1]:
continue
print_message = f'Adding Exo-REM model spectra... {filename}'
print(f'\r{print_message:<84}', end='')
data_wavel, data_flux = np.loadtxt(os.path.join(
data_folder, filename),
unpack=True)
teff.append(teff_val)
logg.append(logg_val)
feh.append(feh_val)
co_ratio.append(co_val)
if wavel_range is None or spec_res is None:
if wavelength is None:
wavelength = np.copy(data_wavel) # (um)
if np.all(np.diff(wavelength) < 0):
raise ValueError(
'The wavelengths are not all sorted by increasing value.'
)
flux.append(data_flux) # (W m-2 um-1)
else:
flux_resample = spectres.spectres(wavelength,
data_wavel,
data_flux,
spec_errs=None,
fill=np.nan,
verbose=False)
if np.isnan(np.sum(flux_resample)):
raise ValueError(
f'Resampling is only possible if the new wavelength '
f'range ({wavelength[0]} - {wavelength[-1]} um) falls '
f'sufficiently far within the wavelength range '
f'({data_wavel[0]} - {data_wavel[-1]} um) of the input '
f'spectra.')
flux.append(flux_resample) # (W m-2 um-1)
print_message = 'Adding Exo-REM model spectra... [DONE]'
print(f'\r{print_message:<84}')
print('Grid points with the following parameters have been excluded:')
print(' - log(g) = 5')
print(' - C/O = 0.8')
print(' - C/O = 0.85')
data_sorted = data_util.sort_data(np.asarray(teff), np.asarray(logg),
np.asarray(feh), np.asarray(co_ratio),
None, wavelength, np.asarray(flux))
data_util.write_data('exo-rem', ['teff', 'logg', 'feh', 'co'], database,
data_sorted)
def add_ames_dusty(input_path, database, wl_bound, teff_bound, specres):
"""
Function for adding the AMES-Dusty atmospheric models to the database.
Parameters
----------
input_path : str
Folder where the data is located.
database : h5py._hl.files.File
Database.
wl_bound : tuple(float, float)
Wavelength range (micron).
teff_bound : tuple(float, float), None
Effective temperature range (K).
specres : float
Spectral resolution.
Returns
-------
NoneType
None
"""
if not wl_bound:
wl_bound = (1e-2, 1e2)
if not os.path.exists(input_path):
os.makedirs(input_path)
data_folder = os.path.join(input_path, 'ames-dusty/')
if not os.path.exists(data_folder):
os.makedirs(data_folder)
input_file = 'SPECTRA.tar'
label = '[Fe/H]=0.0 (106 MB)'
url = 'https://phoenix.ens-lyon.fr/Grids/AMES-Dusty/SPECTRA.tar'
data_file = os.path.join(data_folder, input_file)
if not os.path.isfile(data_file):
sys.stdout.write(f'Downloading AMES-Dusty model spectra {label}...')
sys.stdout.flush()
urlretrieve(url, data_file)
sys.stdout.write(' [DONE]\n')
sys.stdout.flush()
sys.stdout.write(f'Unpacking AMES-Dusty model spectra {label}...')
sys.stdout.flush()
tar = tarfile.open(data_file)
tar.extractall(data_folder)
tar.close()
sys.stdout.write(' [DONE]\n')
sys.stdout.flush()
data_folder += 'SPECTRA/'
teff = []
logg = []
flux = []
wavelength = [wl_bound[0]]
while wavelength[-1] <= wl_bound[1]:
wavelength.append(wavelength[-1] + wavelength[-1] / specres)
wavelength = np.asarray(wavelength[:-1])
for _, _, file_list in os.walk(data_folder):
for filename in sorted(file_list):
if filename.startswith('lte') and filename.endswith('.7.gz'):
sys.stdout.write('\rAdding AMES-Dusty model spectra... ' +
filename + ' ')
sys.stdout.flush()
teff_val = float(filename[3:5]) * 100.
logg_val = float(filename[6:9])
feh_val = float(filename[10:13])
if feh_val != 0.:
continue
if teff_bound is not None:
if teff_val < teff_bound[0] or teff_val > teff_bound[1]:
continue
data_wavel = []
data_flux = []
with gzip.open(data_folder + filename, 'rt') as gz_file:
for line in gz_file:
line_split = line.split()
if len(line_split) > 1:
tmp_wavel = line_split[0].strip()
tmp_flux = line_split[1].strip()
if len(tmp_wavel) == 21 and tmp_wavel[-4] == 'D' \
and tmp_flux[-4] == 'D':
data_wavel.append(
float(line[1:23].replace('D', 'E')))
data_flux.append(
float(line[25:35].replace('D', 'E')))
# See https://phoenix.ens-lyon.fr/Grids/FORMAT
data_wavel = np.asarray(
data_wavel) * 1e-4 # [Angstrom] -> [micron]
data_flux = 10.**(np.asarray(data_flux) - 8.
) # [erg s-1 cm-2 Angstrom-1]
# [erg s-1 cm-2 Angstrom-1] -> [W m-2 micron-1]
data_flux = data_flux * 1e-7 * 1e4 * 1e4
data = np.stack((data_wavel, data_flux), axis=1)
index_sort = np.argsort(data[:, 0])
data = data[index_sort, :]
if np.all(np.diff(data[:, 0]) < 0):
raise ValueError(
'The wavelengths are not all sorted by increasing value.'
)
indices = np.where((data[:, 0] >= wl_bound[0])
& (data[:, 0] <= wl_bound[1]))[0]
if indices.size > 0:
teff.append(teff_val)
logg.append(logg_val)
data = data[indices, :]
flux_interp = interp1d(data[:, 0],
data[:, 1],
kind='linear',
bounds_error=False,
fill_value=1e-100)
flux.append(flux_interp(wavelength))
data_sorted = data_util.sort_data(np.asarray(teff), np.asarray(logg), None,
wavelength, np.asarray(flux))
data_util.write_data('ames-dusty', ('teff', 'logg'), database, data_sorted)
sys.stdout.write('\rAdding AMES-Dusty model spectra... [DONE]'
' \n')
sys.stdout.flush()
def add_drift_phoenix(input_path, database):
"""
Function for adding the DRIFT-PHOENIX atmospheric models to the database.
Parameters
----------
input_path : str
database : h5py._hl.files.File
Returns
-------
NoneType
None
"""
if not os.path.exists(input_path):
os.makedirs(input_path)
data_file = os.path.join(input_path, 'drift-phoenix.tgz')
data_folder = os.path.join(input_path, 'drift-phoenix/')
url = 'https://people.phys.ethz.ch/~stolkert/species/drift-phoenix.tgz'
if not os.path.isfile(data_file):
sys.stdout.write('Downloading DRIFT-PHOENIX model spectra (151 MB)...')
sys.stdout.flush()
urlretrieve(url, data_file)
sys.stdout.write(' [DONE]\n')
sys.stdout.flush()
sys.stdout.write('Unpacking DRIFT-PHOENIX model spectra...')
sys.stdout.flush()
tar = tarfile.open(data_file)
tar.extractall(input_path)
tar.close()
sys.stdout.write(' [DONE]\n')
sys.stdout.flush()
teff = []
logg = []
feh = []
wavelength = None
flux = []
for _, _, file_list in os.walk(data_folder):
for filename in sorted(file_list):
if filename.startswith('lte_'):
sys.stdout.write('\rAdding DRIFT-PHOENIX model spectra... ' +
filename)
sys.stdout.flush()
teff.append(float(filename[4:8]))
logg.append(float(filename[9:12]))
feh.append(float(filename[12:16]))
data = np.loadtxt(data_folder + filename)
if wavelength is None:
# [Angstrom] -> [micron]
wavelength = data[:, 0] * 1e-4
if np.all(np.diff(wavelength) < 0):
raise ValueError(
'The wavelengths are not all sorted by increasing value.'
)
# [erg s-1 cm-2 Angstrom-1] -> [W m-2 micron-1]
flux.append(data[:, 1] * 1e-7 * 1e4 * 1e4)
data_sorted = data_util.sort_data(np.asarray(teff), np.asarray(logg),
np.asarray(feh), wavelength,
np.asarray(flux))
data_util.write_data('drift-phoenix', ('teff', 'logg', 'feh'), database,
data_sorted)
sys.stdout.write(
'\rAdding DRIFT-PHOENIX model spectra... [DONE] \n')
sys.stdout.flush()
def add_petitcode_hot_cloudy(input_path: str,
database: h5py._hl.files.File,
wavel_range: Optional[Tuple[float, float]] = None,
teff_range: Optional[Tuple[float, float]] = None,
spec_res: Optional[float] = 1000.) -> None:
"""
Function for adding the petitCODE hot cloudy atmospheric models to the database.
Parameters
----------
input_path : str
Folder where the data is located.
database : h5py._hl.files.File
Database.
wavel_range : tuple(float, float), None
Wavelength range (um). The original wavelength points are used if set to None.
teff_range : tuple(float, float), None
Effective temperature range (K). All temperatures are selected if set to None.
spec_res : float, None
Spectral resolution. Not used if ``wavel_range`` is set to None.
Returns
-------
NoneType
None
"""
if not os.path.exists(input_path):
os.makedirs(input_path)
data_folder = os.path.join(input_path, 'petitcode-hot-cloudy/')
url = 'https://people.phys.ethz.ch/~ipa/tstolker/petitcode-hot-cloudy.tgz'
data_file = os.path.join(input_path, 'petitcode-hot-cloudy.tgz')
if not os.path.isfile(data_file):
print('Downloading petitCODE hot cloudy model spectra (276 MB)...',
end='',
flush=True)
urllib.request.urlretrieve(url, data_file)
print(' [DONE]')
print('Unpacking petitCODE hot cloudy model spectra (276 MB)...',
end='',
flush=True)
tar = tarfile.open(data_file)
tar.extractall(data_folder)
tar.close()
print(' [DONE]')
teff = []
logg = []
feh = []
co_ratio = []
fsed = []
flux = []
if wavel_range is not None:
wavelength = read_util.create_wavelengths(wavel_range, spec_res)
else:
wavelength = None
for _, _, files in os.walk(data_folder):
for filename in files:
file_split = filename.split('_')
teff_val = float(file_split[2])
logg_val = float(file_split[4])
feh_val = float(file_split[6])
co_ratio_val = float(file_split[8])
fsed_val = float(file_split[10])
if teff_range is not None:
if teff_val < teff_range[0] or teff_val > teff_range[1]:
continue
print_message = f'Adding petitCODE hot cloudy model spectra... {filename}'
print(f'\r{print_message:<111}', end='')
data = np.loadtxt(os.path.join(data_folder, filename))
teff.append(teff_val)
logg.append(logg_val)
feh.append(feh_val)
co_ratio.append(co_ratio_val)
fsed.append(fsed_val)
if wavel_range is None:
if wavelength is None:
# (cm) -> (um)
wavelength = data[:, 0] * 1e4
if np.all(np.diff(wavelength) < 0):
raise ValueError(
'The wavelengths are not all sorted by increasing value.'
)
# (erg s-1 cm-2 Hz-1) -> (W m-2 um-1)
flux.append(data[:, 1] * 1e-9 * constants.LIGHT /
(wavelength * 1e-6)**2)
else:
# (cm) -> (um)
data_wavel = data[:, 0] * 1e4
# (erg s-1 cm-2 Hz-1) -> (W m-2 um-1)
data_flux = data[:, 1] * 1e-9 * constants.LIGHT / (data_wavel *
1e-6)**2
try:
flux.append(
spectres.spectres(wavelength, data_wavel, data_flux))
except ValueError:
flux.append(np.zeros(wavelength.shape[0]))
warnings.warn(
'The wavelength range should fall within the range of the '
'original wavelength sampling. Storing zeros instead.')
print_message = 'Adding petitCODE hot cloudy model spectra... [DONE]'
print(f'\r{print_message:<111}')
data_sorted = data_util.sort_data(np.asarray(teff), np.asarray(logg),
np.asarray(feh), np.asarray(co_ratio),
np.asarray(fsed), wavelength,
np.asarray(flux))
data_util.write_data('petitcode-hot-cloudy',
['teff', 'logg', 'feh', 'co', 'fsed'], database,
data_sorted)
def add_petitcode_hot_clear(input_path: str,
database: h5py._hl.files.File,
data_folder: str,
wavel_range: Optional[Tuple[float, float]] = None,
teff_range: Optional[Tuple[float, float]] = None,
spec_res: Optional[float] = 1000.) -> None:
"""
Function for adding the petitCODE hot clear atmospheric models to the database.
Parameters
----------
input_path : str
Folder where the data is located.
database : h5py._hl.files.File
Database.
data_folder : str
Path with input data.
wavel_range : tuple(float, float), None
Wavelength range (um). The original wavelength points are used if set to None.
teff_range : tuple(float, float), None
Effective temperature range (K). All temperatures are selected if set to None.
spec_res : float, None
Spectral resolution. Not used if ``wavel_range`` is set to None.
Returns
-------
NoneType
None
"""
if not os.path.exists(input_path):
os.makedirs(input_path)
teff = []
logg = []
feh = []
co_ratio = []
flux = []
if wavel_range is not None:
wavelength = read_util.create_wavelengths(wavel_range, spec_res)
else:
wavelength = None
for _, _, files in os.walk(data_folder):
for filename in files:
teff_val = float(filename[9:13])
logg_val = float(filename[19:23])
feh_val = float(filename[28:32])
co_ratio_val = float(filename[36:40])
if teff_range is not None:
if teff_val < teff_range[0] or teff_val > teff_range[1]:
continue
print_message = f'Adding petitCODE hot clear model spectra... {filename}'
print(f'\r{print_message:<100}', end='')
data = np.loadtxt(os.path.join(data_folder, filename))
teff.append(teff_val)
logg.append(logg_val)
feh.append(feh_val)
co_ratio.append(co_ratio_val)
if wavel_range is None:
if wavelength is None:
# (cm) -> (um)
wavelength = data[:, 0] * 1e4
if np.all(np.diff(wavelength) < 0):
raise ValueError(
'The wavelengths are not all sorted by increasing value.'
)
# (erg s-1 cm-2 Hz-1) -> (W m-2 um-1)
flux.append(data[:, 1] * 1e-9 * constants.LIGHT /
(wavelength * 1e-6)**2)
else:
# (cm) -> (um)
data_wavel = data[:, 0] * 1e4
# (erg s-1 cm-2 Hz-1) -> (W m-2 um-1)
data_flux = data[:, 1] * 1e-9 * constants.LIGHT / (data_wavel *
1e-6)**2
try:
flux.append(
spectres.spectres(wavelength, data_wavel, data_flux))
except ValueError:
flux.append(np.zeros(wavelength.shape[0]))
warnings.warn(
'The wavelength range should fall within the range of the '
'original wavelength sampling. Storing zeros instead.')
print_message = 'Adding petitCODE hot clear model spectra... [DONE]'
print(f'\r{print_message:<100}')
data_sorted = data_util.sort_data(np.asarray(teff), np.asarray(logg),
np.asarray(feh), np.asarray(co_ratio),
None, wavelength, np.asarray(flux))
data_util.write_data('petitcode-hot-clear', ['teff', 'logg', 'feh', 'co'],
database, data_sorted)
def add_model_grid(
model_name: str,
input_path: str,
database: h5py._hl.files.File,
wavel_range: Optional[Tuple[float, float]],
teff_range: Optional[Tuple[float, float]],
spec_res: Optional[float],
) -> None:
"""
Function for adding a grid of model spectra to the database.
The original spectra had been resampled to logarithmically-
spaced wavelengths, so at a constant resolution,
:math:`\\lambda/\\Delta\\lambda`. This function downloads
the model grid, unpacks the tar file, and adds the spectra
and parameters to the database.
Parameters
----------
model_name : str
Name of the model grid.
input_path : str
Folder where the data is located.
database : h5py._hl.files.File
Database.
wavel_range : tuple(float, float), None
Wavelength range (um). The original wavelength
points are used if set to ``None``.
teff_range : tuple(float, float), None
Effective temperature range (K). All temperatures
are selected if set to ``None``.
spec_res : float, None
Spectral resolution for resampling. Not used if
``wavel_range`` is set to ``None`` and/or
``spec_res`` is set to ``None``
Returns
-------
NoneType
None
"""
data_file = pathlib.Path(__file__).parent.resolve() / "model_data.json"
with open(data_file, "r", encoding="utf-8") as json_file:
model_data = json.load(json_file)
if model_name in model_data.keys():
model_info = model_data[model_name]
else:
raise ValueError(
f"The {model_name} atmospheric model is not available. "
f"Please choose one of the following models: "
f"'ames-cond', 'ames-dusty', 'atmo', 'bt-settl', "
f"'bt-nextgen', 'drift-phoexnix', 'petitcode-cool-clear', "
f"'petitcode-cool-cloudy', 'petitcode-hot-clear', "
f"'petitcode-hot-cloudy', 'exo-rem', 'bt-settl-cifist', "
f"'bt-cond', 'bt-cond-feh', 'blackbody', 'sonora-cholla', "
f"'sonora-bobcat', 'sonora-bobcat-co', 'koester-wd'")
if model_name == "bt-settl":
warnings.warn("It is recommended to use the CIFIST "
"grid of the BT-Settl, because it is "
"a newer version. In that case, set "
"model='bt-settl-cifist' when using "
"add_model of Database.")
if not os.path.exists(input_path):
os.makedirs(input_path)
input_file = f"{model_name}.tgz"
data_folder = os.path.join(input_path, model_name)
data_file = os.path.join(input_path, input_file)
if not os.path.exists(data_folder):
os.makedirs(data_folder)
url = f"https://home.strw.leidenuniv.nl/~stolker/species/{model_name}.tgz"
if not os.path.isfile(data_file):
print(
f"Downloading {model_info['name']} model "
f"spectra ({model_info['file size']})...",
end="",
flush=True,
)
urllib.request.urlretrieve(url, data_file)
print(" [DONE]")
print(
f"Unpacking {model_info['name']} model "
f"spectra ({model_info['file size']})...",
end="",
flush=True,
)
tar = tarfile.open(data_file)
tar.extractall(data_folder)
tar.close()
print(" [DONE]")
if "information" in model_info:
print(f"Model information: {model_info['information']}")
if "reference" in model_info:
print(f"Please cite {model_info['reference']} when "
f"using {model_info['name']} in a publication")
if "url" in model_info:
print(f"Reference URL: {model_info['url']}")
teff = []
if "logg" in model_info["parameters"]:
logg = []
else:
logg = None
if "feh" in model_info["parameters"]:
feh = []
else:
feh = None
if "c_o_ratio" in model_info["parameters"]:
c_o_ratio = []
else:
c_o_ratio = None
if "fsed" in model_info["parameters"]:
fsed = []
else:
fsed = None
if "log_kzz" in model_info["parameters"]:
log_kzz = []
else:
log_kzz = None
flux = []
if wavel_range is not None and spec_res is not None:
wavelength = read_util.create_wavelengths(wavel_range, spec_res)
print(f"Wavelength range (um) = {wavel_range[0]} - {wavel_range[1]}")
print(f"Spectral resolution = {spec_res}")
else:
wavelength = None
print(f"Wavelength range (um) = "
f"{model_info['wavelength range'][0]} - "
f"{model_info['wavelength range'][1]}")
print(f"Spectral resolution = {model_info['resolution']}")
if teff_range is None:
print(
f"Teff range (K) = {model_info['teff range'][0]} - {model_info['teff range'][1]}"
)
else:
print(f"Teff range (K) = {teff_range[0]} - {teff_range[1]}")
print_message = ""
for _, _, file_list in os.walk(data_folder):
for filename in sorted(file_list):
if filename[:len(model_name)] == model_name:
file_split = filename.split("_")
param_index = file_split.index("teff") + 1
teff_val = float(file_split[param_index])
if teff_range is not None:
if teff_val < teff_range[0] or teff_val > teff_range[1]:
continue
teff.append(teff_val)
if logg is not None:
param_index = file_split.index("logg") + 1
logg.append(float(file_split[param_index]))
if feh is not None:
param_index = file_split.index("feh") + 1
feh.append(float(file_split[param_index]))
if c_o_ratio is not None:
param_index = file_split.index("co") + 1
c_o_ratio.append(float(file_split[param_index]))
if fsed is not None:
param_index = file_split.index("fsed") + 1
fsed.append(float(file_split[param_index]))
if log_kzz is not None:
param_index = file_split.index("logkzz") + 1
log_kzz.append(float(file_split[param_index]))
empty_message = len(print_message) * " "
print(f"\r{empty_message}", end="")
print_message = (
f"Adding {model_info['name']} model spectra... {filename}")
print(f"\r{print_message}", end="")
data_wavel, data_flux = np.loadtxt(os.path.join(
data_folder, filename),
unpack=True)
if wavel_range is None or spec_res is None:
if wavelength is None:
wavelength = np.copy(data_wavel) # (um)
if np.all(np.diff(wavelength) < 0):
raise ValueError(
"The wavelengths are not all sorted by increasing value."
)
flux.append(data_flux) # (W m-2 um-1)
else:
flux_resample = spectres.spectres(
wavelength,
data_wavel,
data_flux,
spec_errs=None,
fill=np.nan,
verbose=False,
)
if np.isnan(np.sum(flux_resample)):
raise ValueError(
f"Resampling is only possible if the new wavelength "
f"range ({wavelength[0]} - {wavelength[-1]} um) falls "
f"sufficiently far within the wavelength range "
f"({data_wavel[0]} - {data_wavel[-1]} um) of the input "
f"spectra.")
flux.append(flux_resample) # (W m-2 um-1)
empty_message = len(print_message) * " "
print(f"\r{empty_message}", end="")
print_message = f"Adding {model_info['name']} model spectra... [DONE]"
print(f"\r{print_message}")
if logg is not None:
logg = np.asarray(logg)
if feh is not None:
feh = np.asarray(feh)
if c_o_ratio is not None:
c_o_ratio = np.asarray(c_o_ratio)
if fsed is not None:
fsed = np.asarray(fsed)
if log_kzz is not None:
log_kzz = np.asarray(log_kzz)
data_sorted = data_util.sort_data(
np.asarray(teff),
logg,
feh,
c_o_ratio,
fsed,
log_kzz,
wavelength,
np.asarray(flux),
)
data_util.write_data(model_name, model_info["parameters"], database,
data_sorted)
