def add_spex(input_path, database):
"""
Function for adding the SpeX Prism Spectral Library to the database.
Parameters
----------
input_path : str
Path of the data folder.
database : h5py._hl.files.File
The HDF5 database.
Returns
-------
NoneType
None
"""
distance_url = 'https://people.phys.ethz.ch/~stolkert/species/distance.dat'
distance_file = os.path.join(input_path, 'distance.dat')
if not os.path.isfile(distance_file):
urllib.request.urlretrieve(distance_url, distance_file)
distance_data = pd.pandas.read_csv(
distance_file,
usecols=[0, 3, 4],
names=['object', 'distance', 'distance_error'],
delimiter=',',
dtype={
'object': str,
'distance': float,
'distance_error': float
})
database.create_group('spectra/spex')
data_path = os.path.join(input_path, 'spex')
if not os.path.exists(data_path):
os.makedirs(data_path)
url_all = 'http://svo2.cab.inta-csic.es/vocats/v2/spex/' \
'cs.php?RA=180.000000&DEC=0.000000&SR=180.000000&VERB=2'
xml_file_spex = os.path.join(data_path, 'spex.xml')
if not os.path.isfile(xml_file_spex):
urllib.request.urlretrieve(url_all, xml_file_spex)
table = parse_single_table(xml_file_spex)
# name = table.array['name']
twomass = table.array['name2m']
url = table.array['access_url']
unique_id = []
for i, item in enumerate(url):
if twomass[i] not in unique_id:
xml_file_1 = os.path.join(data_path,
twomass[i].decode('utf-8') + '.xml')
if not os.path.isfile(xml_file_1):
urllib.request.urlretrieve(item.decode('utf-8'), xml_file_1)
table = parse_single_table(xml_file_1)
name = table.array['ID']
name = name[0].decode('utf-8')
url = table.array['access_url']
print_message = f'Downloading SpeX Prism Spectral Library... {name}'
print(f'\r{print_message:<72}', end='')
xml_file_2 = os.path.join(data_path, f'spex_{name}.xml')
if not os.path.isfile(xml_file_2):
urllib.request.urlretrieve(url[0].decode('utf-8'), xml_file_2)
unique_id.append(twomass[i])
print_message = 'Downloading SpeX Prism Spectral Library... [DONE]'
print(f'\r{print_message:<72}')
h_twomass = photometry.SyntheticPhotometry('2MASS/2MASS.H')
# 2MASS H band zero point for 0 mag (Cogen et al. 2003)
h_zp = 1.133e-9 # (W m-2 um-1)
for votable in os.listdir(data_path):
if votable.startswith('spex_') and votable.endswith('.xml'):
xml_file = os.path.join(data_path, votable)
table = parse_single_table(xml_file)
wavelength = table.array['wavelength'] # (Angstrom)
flux = table.array['flux'] # Normalized units
wavelength = np.array(wavelength * 1e-4) # (um)
flux = np.array(flux) # (a.u.)
error = np.full(flux.size, np.nan)
# 2MASS magnitudes
j_mag = table.get_field_by_id('jmag').value
h_mag = table.get_field_by_id('hmag').value
ks_mag = table.get_field_by_id('ksmag').value
j_mag = j_mag.decode('utf-8')
h_mag = h_mag.decode('utf-8')
ks_mag = ks_mag.decode('utf-8')
if j_mag == '':
j_mag = np.nan
else:
j_mag = float(j_mag)
if h_mag == '':
h_mag = np.nan
else:
h_mag = float(h_mag)
if ks_mag == '':
ks_mag = np.nan
else:
ks_mag = float(ks_mag)
name = table.get_field_by_id('name').value
name = name.decode('utf-8')
twomass_id = table.get_field_by_id('name2m').value
twomass_id = twomass_id.decode('utf-8')
try:
sptype = table.get_field_by_id('nirspty').value
sptype = sptype.decode('utf-8')
except KeyError:
try:
sptype = table.get_field_by_id('optspty').value
sptype = sptype.decode('utf-8')
except KeyError:
sptype = 'None'
sptype = data_util.update_sptype(np.array([sptype]))[0].strip()
h_flux, _ = h_twomass.magnitude_to_flux(h_mag,
error=None,
zp_flux=h_zp)
phot = h_twomass.spectrum_to_flux(wavelength,
flux) # Normalized units
flux *= h_flux / phot[0] # (W m-2 um-1)
spdata = np.vstack([wavelength, flux, error])
# simbad_id, distance = query_util.get_distance(f'2MASS {twomass_id}')
simbad_id = query_util.get_simbad(f'2MASS {twomass_id}')
if simbad_id is not None:
simbad_id = simbad_id.decode('utf-8')
dist_select = distance_data.loc[distance_data['object'] ==
simbad_id]
if not dist_select.empty:
distance = (dist_select['distance'],
dist_select['distance_error'])
else:
distance = (np.nan, np.nan)
else:
distance = (np.nan, np.nan)
if sptype[0] in ['M', 'L', 'T'] and len(sptype) == 2:
print_message = f'Adding SpeX Prism Spectral Library... {name}'
print(f'\r{print_message:<72}', end='')
dset = database.create_dataset(f'spectra/spex/{name}',
data=spdata)
dset.attrs['name'] = str(name).encode()
dset.attrs['sptype'] = str(sptype).encode()
dset.attrs['simbad'] = str(simbad_id).encode()
dset.attrs['2MASS/2MASS.J'] = j_mag
dset.attrs['2MASS/2MASS.H'] = h_mag
dset.attrs['2MASS/2MASS.Ks'] = ks_mag
dset.attrs['distance'] = distance[0] # (pc)
dset.attrs['distance_error'] = distance[1] # (pc)
print_message = 'Adding SpeX Prism Spectral Library... [DONE]'
print(f'\r{print_message:<72}')
database.close()
def add_vlm_plx(input_path, database):
"""
Function for adding the Database of Ultracool
Parallaxes to the database.
Parameters
----------
input_path : str
Data folder.
database : h5py._hl.files.File
HDF5 database.
Returns
-------
NoneType
None
"""
data_file = os.path.join(input_path, "vlm-plx-all.fits")
url = ("http://www.as.utexas.edu/~tdupuy/plx/"
"Database_of_Ultracool_Parallaxes_files/vlm-plx-all.fits")
if not os.path.isfile(data_file):
print(
"Downloading Database of Ultracool Parallaxes (307 kB)...",
end="",
flush=True,
)
urllib.request.urlretrieve(url, data_file)
print(" [DONE]")
print("Adding Database of Ultracool Parallaxes...", end="", flush=True)
database.create_group("photometry/vlm-plx")
with fits.open(data_file) as hdu_list:
phot_data = hdu_list[1].data
parallax = phot_data["PLX"] # (mas)
parallax_error = phot_data["EPLX"] # (mas)
name = phot_data["NAME"]
name = np.core.defchararray.strip(name)
sptype = phot_data["OSPTSTR"]
sptype = np.core.defchararray.strip(sptype)
sptype_nir = phot_data["ISPTSTR"]
sptype_nir = np.core.defchararray.strip(sptype_nir)
for i, item in enumerate(sptype):
if item == "null":
sptype[i] = sptype_nir[i]
flag = phot_data["FLAG"]
flag = np.core.defchararray.strip(flag)
sptype = data_util.update_sptype(sptype)
dtype = h5py.special_dtype(vlen=str)
dset = database.create_dataset("photometry/vlm-plx/name",
(np.size(name), ),
dtype=dtype)
dset[...] = name
dset = database.create_dataset("photometry/vlm-plx/sptype",
(np.size(sptype), ),
dtype=dtype)
dset[...] = sptype
dset = database.create_dataset("photometry/vlm-plx/flag",
(np.size(flag), ),
dtype=dtype)
dset[...] = flag
database.create_dataset("photometry/vlm-plx/ra",
data=phot_data["RA"]) # (deg)
database.create_dataset("photometry/vlm-plx/dec",
data=phot_data["DEC"]) # (deg)
database.create_dataset("photometry/vlm-plx/parallax", data=parallax)
database.create_dataset("photometry/vlm-plx/parallax_error",
data=parallax_error)
database.create_dataset("photometry/vlm-plx/MKO/NSFCam.Y",
data=phot_data["YMAG"])
database.create_dataset("photometry/vlm-plx/MKO/NSFCam.J",
data=phot_data["JMAG"])
database.create_dataset("photometry/vlm-plx/MKO/NSFCam.H",
data=phot_data["HMAG"])
database.create_dataset("photometry/vlm-plx/MKO/NSFCam.K",
data=phot_data["KMAG"])
database.create_dataset("photometry/vlm-plx/MKO/NSFCam.Lp",
data=phot_data["LMAG"])
database.create_dataset("photometry/vlm-plx/MKO/NSFCam.Mp",
data=phot_data["MMAG"])
database.create_dataset("photometry/vlm-plx/2MASS/2MASS.J",
data=phot_data["J2MAG"])
database.create_dataset("photometry/vlm-plx/2MASS/2MASS.H",
data=phot_data["H2MAG"])
database.create_dataset("photometry/vlm-plx/2MASS/2MASS.Ks",
data=phot_data["K2MAG"])
print(" [DONE]")
database.close()
def add_spex(input_path: str, database: h5py._hl.files.File) -> None:
"""
Function for adding the SpeX Prism Spectral Library to the database.
Parameters
----------
input_path : str
Path of the data folder.
database : h5py._hl.files.File
The HDF5 database.
Returns
-------
NoneType
None
"""
parallax_url = "https://home.strw.leidenuniv.nl/~stolker/species/parallax.dat"
parallax_file = os.path.join(input_path, "parallax.dat")
if not os.path.isfile(parallax_file):
urllib.request.urlretrieve(parallax_url, parallax_file)
parallax_data = pd.pandas.read_csv(
parallax_file,
usecols=[0, 1, 2],
names=["object", "parallax", "parallax_error"],
delimiter=",",
dtype={"object": str, "parallax": float, "parallax_error": float},
)
database.create_group("spectra/spex")
data_path = os.path.join(input_path, "spex")
if not os.path.exists(data_path):
os.makedirs(data_path)
url_all = "http://svo2.cab.inta-csic.es/vocats/v2/spex/cs.php?" \
"RA=180.000000&DEC=0.000000&SR=180.000000&VERB=2"
xml_file_spex = os.path.join(data_path, "spex.xml")
if not os.path.isfile(xml_file_spex):
urllib.request.urlretrieve(url_all, xml_file_spex)
table = parse_single_table(xml_file_spex)
# name = table.array['name']
twomass = table.array["name2m"]
url = table.array["access_url"]
unique_id = []
print_message = ""
for i, item in enumerate(url):
if twomass[i] not in unique_id:
if isinstance(twomass[i], str):
xml_file_1 = os.path.join(data_path, twomass[i] + ".xml")
else:
# Use decode for backward compatibility
xml_file_1 = os.path.join(
data_path, twomass[i].decode("utf-8") + ".xml"
)
if not os.path.isfile(xml_file_1):
if isinstance(item, str):
urllib.request.urlretrieve(item, xml_file_1)
else:
urllib.request.urlretrieve(item.decode("utf-8"), xml_file_1)
table = parse_single_table(xml_file_1)
name = table.array["ID"]
url = table.array["access_url"]
if isinstance(name[0], str):
name = name[0]
else:
name = name[0].decode("utf-8")
empty_message = len(print_message) * " "
print(f"\r{empty_message}", end="")
print_message = f"Downloading SpeX Prism Spectral Library... {name}"
print(f"\r{print_message}", end="")
xml_file_2 = os.path.join(data_path, f"spex_{name}.xml")
if not os.path.isfile(xml_file_2):
if isinstance(url[0], str):
urllib.request.urlretrieve(url[0], xml_file_2)
else:
urllib.request.urlretrieve(url[0].decode("utf-8"), xml_file_2)
unique_id.append(twomass[i])
empty_message = len(print_message) * " "
print(f"\r{empty_message}", end="")
print_message = "Downloading SpeX Prism Spectral Library... [DONE]"
print(f"\r{print_message}")
h_twomass = photometry.SyntheticPhotometry("2MASS/2MASS.H")
# 2MASS H band zero point for 0 mag (Cogen et al. 2003)
h_zp = 1.133e-9 # (W m-2 um-1)
for votable in os.listdir(data_path):
if votable.startswith("spex_") and votable.endswith(".xml"):
xml_file = os.path.join(data_path, votable)
table = parse_single_table(xml_file)
wavelength = table.array["wavelength"] # (Angstrom)
flux = table.array["flux"] # Normalized units
wavelength = np.array(wavelength * 1e-4) # (um)
flux = np.array(flux) # (a.u.)
error = np.full(flux.size, np.nan)
# 2MASS magnitudes
j_mag = table.get_field_by_id("jmag").value
h_mag = table.get_field_by_id("hmag").value
ks_mag = table.get_field_by_id("ksmag").value
if not isinstance(j_mag, str):
j_mag = j_mag.decode("utf-8")
if not isinstance(h_mag, str):
h_mag = h_mag.decode("utf-8")
if not isinstance(ks_mag, str):
ks_mag = ks_mag.decode("utf-8")
if j_mag == "":
j_mag = np.nan
else:
j_mag = float(j_mag)
if h_mag == "":
h_mag = np.nan
else:
h_mag = float(h_mag)
if ks_mag == "":
ks_mag = np.nan
else:
ks_mag = float(ks_mag)
name = table.get_field_by_id("name").value
if not isinstance(name, str):
name = name.decode("utf-8")
twomass_id = table.get_field_by_id("name2m").value
if not isinstance(twomass_id, str):
twomass_id = twomass_id.decode("utf-8")
# Optical spectral type
try:
sptype_opt = table.get_field_by_id("optspty").value
if not isinstance(sptype_opt, str):
sptype_opt = sptype_opt.decode("utf-8")
sptype_opt = data_util.update_sptype(np.array([sptype_opt]))[0]
except KeyError:
sptype_opt = None
# Near-infrared spectral type
try:
sptype_nir = table.get_field_by_id("nirspty").value
if not isinstance(sptype_nir, str):
sptype_nir = sptype_nir.decode("utf-8")
sptype_nir = data_util.update_sptype(np.array([sptype_nir]))[0]
except KeyError:
sptype_nir = None
h_flux, _ = h_twomass.magnitude_to_flux(h_mag, error=None, zp_flux=h_zp)
phot = h_twomass.spectrum_to_flux(wavelength, flux) # Normalized units
flux *= h_flux / phot[0] # (W m-2 um-1)
spdata = np.column_stack([wavelength, flux, error])
simbad_id = query_util.get_simbad(f"2MASS {twomass_id}")
if simbad_id is not None:
if not isinstance(simbad_id, str):
simbad_id = simbad_id.decode("utf-8")
par_select = parallax_data[parallax_data["object"] == simbad_id]
if not par_select.empty:
parallax = (
par_select["parallax"].values[0],
par_select["parallax_error"].values[0],
)
else:
parallax = (np.nan, np.nan)
else:
parallax = (np.nan, np.nan)
print_message = f"Adding spectra... {name}"
print(f"\r{print_message:<72}", end="")
dset = database.create_dataset(f"spectra/spex/{name}", data=spdata)
dset.attrs["name"] = str(name).encode()
if sptype_opt is not None:
dset.attrs["sptype"] = str(sptype_opt).encode()
elif sptype_nir is not None:
dset.attrs["sptype"] = str(sptype_nir).encode()
else:
dset.attrs["sptype"] = str("None").encode()
dset.attrs["simbad"] = str(simbad_id).encode()
dset.attrs["2MASS/2MASS.J"] = j_mag
dset.attrs["2MASS/2MASS.H"] = h_mag
dset.attrs["2MASS/2MASS.Ks"] = ks_mag
dset.attrs["parallax"] = parallax[0] # (mas)
dset.attrs["parallax_error"] = parallax[1] # (mas)
print_message = "Adding spectra... [DONE]"
print(f"\r{print_message:<72}")
database.close()
def add_vlm_plx(input_path,
database):
"""
Function for adding the Database of Ultracool Parallaxes to the database.
Parameters
----------
input_path : str
Data folder.
database : h5py._hl.files.File
HDF5 database.
Returns
-------
NoneType
None
"""
data_file = os.path.join(input_path, 'vlm-plx-all.fits')
url = 'http://www.as.utexas.edu/~tdupuy/plx/' \
'Database_of_Ultracool_Parallaxes_files/vlm-plx-all.fits'
if not os.path.isfile(data_file):
print('Downloading Database of Ultracool Parallaxes (307 kB)...', end='', flush=True)
urllib.request.urlretrieve(url, data_file)
print(' [DONE]')
print('Adding Database of Ultracool Parallaxes...', end='', flush=True)
database.create_group('photometry/vlm-plx')
with fits.open(data_file) as hdu_list:
phot_data = hdu_list[1].data
parallax = phot_data['PLX'] # (mas)
parallax_error = phot_data['EPLX'] # (mas)
distance = 1./(parallax*1e-3) # (pc)
distance_minus = distance - 1./((parallax+parallax_error)*1e-3) # (pc)
distance_plus = 1./((parallax-parallax_error)*1e-3) - distance # (pc)
distance_error = (distance_plus+distance_minus)/2. # (pc)
name = phot_data['NAME']
name = np.core.defchararray.strip(name)
sptype = phot_data['ISPTSTR']
sptype = np.core.defchararray.strip(sptype)
sptype_op = phot_data['OSPTSTR']
sptype_op = np.core.defchararray.strip(sptype_op)
for i, item in enumerate(sptype):
if item == 'null':
sptype[i] = sptype_op[i]
flag = phot_data['FLAG']
flag = np.core.defchararray.strip(flag)
sptype = data_util.update_sptype(sptype)
dtype = h5py.special_dtype(vlen=str)
dset = database.create_dataset('photometry/vlm-plx/name', (np.size(name), ), dtype=dtype)
dset[...] = name
dset = database.create_dataset('photometry/vlm-plx/sptype', (np.size(sptype), ), dtype=dtype)
dset[...] = sptype
dset = database.create_dataset('photometry/vlm-plx/flag', (np.size(flag), ), dtype=dtype)
dset[...] = flag
database.create_dataset('photometry/vlm-plx/ra', data=phot_data['RA']) # (deg)
database.create_dataset('photometry/vlm-plx/dec', data=phot_data['DEC']) # (deg)
database.create_dataset('photometry/vlm-plx/parallax', data=parallax)
database.create_dataset('photometry/vlm-plx/parallax_error', data=parallax_error)
database.create_dataset('photometry/vlm-plx/distance', data=distance)
database.create_dataset('photometry/vlm-plx/distance_error', data=distance_error)
database.create_dataset('photometry/vlm-plx/MKO/NSFCam.Y', data=phot_data['YMAG'])
database.create_dataset('photometry/vlm-plx/MKO/NSFCam.J', data=phot_data['JMAG'])
database.create_dataset('photometry/vlm-plx/MKO/NSFCam.H', data=phot_data['HMAG'])
database.create_dataset('photometry/vlm-plx/MKO/NSFCam.K', data=phot_data['KMAG'])
database.create_dataset('photometry/vlm-plx/MKO/NSFCam.Lp', data=phot_data['LMAG'])
database.create_dataset('photometry/vlm-plx/MKO/NSFCam.Mp', data=phot_data['MMAG'])
database.create_dataset('photometry/vlm-plx/2MASS/2MASS.J', data=phot_data['J2MAG'])
database.create_dataset('photometry/vlm-plx/2MASS/2MASS.H', data=phot_data['H2MAG'])
database.create_dataset('photometry/vlm-plx/2MASS/2MASS.Ks', data=phot_data['K2MAG'])
print(' [DONE]')
database.close()
def add_irtf(input_path: str,
database: h5py._hl.files.File,
sptypes: Optional[List[str]] = None) -> None:
"""
Function for adding the IRTF Spectral Library to the database.
Parameters
----------
input_path : str
Path of the data folder.
database : h5py._hl.files.File
Database.
sptypes : list(str, ), None
List with the spectral types ('F', 'G', 'K', 'M', 'L', 'T'). All spectral types are
included if set to ``None``.
Returns
-------
NoneType
None
"""
if sptypes is None:
sptypes = ['F', 'G', 'K', 'M', 'L', 'T']
distance_url = 'https://people.phys.ethz.ch/~stolkert/species/distance.dat'
distance_file = os.path.join(input_path, 'distance.dat')
if not os.path.isfile(distance_file):
urllib.request.urlretrieve(distance_url, distance_file)
distance_data = pd.pandas.read_csv(distance_file,
usecols=[0, 3, 4],
names=['object', 'distance', 'distance_error'],
delimiter=',',
dtype={'object': str,
'distance': float,
'distance_error': float})
datadir = os.path.join(input_path, 'irtf')
if not os.path.exists(datadir):
os.makedirs(datadir)
data_file = {'F': os.path.join(input_path, 'irtf/F_fits_091201.tar'),
'G': os.path.join(input_path, 'irtf/G_fits_091201.tar'),
'K': os.path.join(input_path, 'irtf/K_fits_091201.tar'),
'M': os.path.join(input_path, 'irtf/M_fits_091201.tar'),
'L': os.path.join(input_path, 'irtf/L_fits_091201.tar'),
'T': os.path.join(input_path, 'irtf/T_fits_091201.tar')}
data_folder = {'F': os.path.join(input_path, 'irtf/F_fits_091201'),
'G': os.path.join(input_path, 'irtf/G_fits_091201'),
'K': os.path.join(input_path, 'irtf/K_fits_091201'),
'M': os.path.join(input_path, 'irtf/M_fits_091201'),
'L': os.path.join(input_path, 'irtf/L_fits_091201'),
'T': os.path.join(input_path, 'irtf/T_fits_091201')}
data_type = {'F': 'F stars (4.4 MB)',
'G': 'G stars (5.6 MB)',
'K': 'K stars (5.5 MB)',
'M': 'M stars (7.5 MB)',
'L': 'L dwarfs (850 kB)',
'T': 'T dwarfs (100 kB)'}
url_root = 'http://irtfweb.ifa.hawaii.edu/~spex/IRTF_Spectral_Library/Data/'
url = {'F': url_root+'F_fits_091201.tar',
'G': url_root+'G_fits_091201.tar',
'K': url_root+'K_fits_091201.tar',
'M': url_root+'M_fits_091201.tar',
'L': url_root+'L_fits_091201.tar',
'T': url_root+'T_fits_091201.tar'}
for item in sptypes:
if not os.path.isfile(data_file[item]):
print(f'Downloading IRTF Spectral Library - {data_type[item]}...', end='', flush=True)
urllib.request.urlretrieve(url[item], data_file[item])
print(' [DONE]')
print('Unpacking IRTF Spectral Library...', end='', flush=True)
for item in sptypes:
tar = tarfile.open(data_file[item])
tar.extractall(path=datadir)
tar.close()
print(' [DONE]')
database.create_group('spectra/irtf')
for item in sptypes:
for root, _, files in os.walk(data_folder[item]):
for _, filename in enumerate(files):
if filename[-9:] != '_ext.fits':
fitsfile = os.path.join(root, filename)
spdata, header = fits.getdata(fitsfile, header=True)
name = header['OBJECT']
sptype = header['SPTYPE']
if name[-2:] == 'AB':
name = name[:-2]
elif name[-3:] == 'ABC':
name = name[:-3]
spt_split = sptype.split()
if item in ['L', 'T'] or spt_split[1][0] == 'V':
print_message = f'Adding IRTF Spectral Library... {name}'
print(f'\r{print_message:<70}', end='')
simbad_id = query_util.get_simbad(name)
if simbad_id is not None:
simbad_id = simbad_id.decode('utf-8')
dist_select = distance_data.loc[distance_data['object'] == simbad_id]
if not dist_select.empty:
distance = (dist_select['distance'], dist_select['distance_error'])
else:
simbad_id, distance = query_util.get_distance(name)
else:
distance = (np.nan, np.nan)
sptype = data_util.update_sptype(np.array([sptype]))[0]
dset = database.create_dataset(f'spectra/irtf/{name}',
data=spdata)
dset.attrs['name'] = str(name).encode()
dset.attrs['sptype'] = str(sptype).encode()
dset.attrs['simbad'] = str(simbad_id).encode()
dset.attrs['distance'] = distance[0]
dset.attrs['distance_error'] = distance[1]
print_message = 'Adding IRTF Spectral Library... [DONE]'
print(f'\r{print_message:<70}')
database.close()
def add_vlm_plx(input_path,
database):
"""
Function for adding the Database of Ultracool Parallaxes to the database.
Parameters
----------
input_path : str
database : h5py._hl.files.File
Returns
-------
NoneType
None
"""
data_file = os.path.join(input_path, 'vlm-plx-all.fits')
url = 'http://www.as.utexas.edu/~tdupuy/plx/' \
'Database_of_Ultracool_Parallaxes_files/vlm-plx-all.fits'
if not os.path.isfile(data_file):
sys.stdout.write('Downloading Database of Ultracool Parallaxes (307 kB)...')
sys.stdout.flush()
urlretrieve(url, data_file)
sys.stdout.write(' [DONE]\n')
sys.stdout.flush()
sys.stdout.write('Adding Database of Ultracool Parallaxes...')
sys.stdout.flush()
group = 'photometry/vlm-plx'
database.create_group(group)
hdulist = fits.open(data_file)
photdata = hdulist[1].data
plx = photdata['PLX'] # [mas]
distance = 1./(plx*1e-3) # [pc]
name = photdata['NAME']
name = np.core.defchararray.strip(name)
sptype = photdata['ISPTSTR']
sptype = np.core.defchararray.strip(sptype)
sptype_op = photdata['OSPTSTR']
sptype_op = np.core.defchararray.strip(sptype_op)
for i, item in enumerate(sptype):
if item == 'null':
sptype[i] = sptype_op[i]
flag = photdata['FLAG']
flag = np.core.defchararray.strip(flag)
sptype = data_util.update_sptype(sptype)
dtype = h5py.special_dtype(vlen=bytes)
dset = database.create_dataset(group+'/name', (np.size(name), ), dtype=dtype)
dset[...] = name
dset = database.create_dataset(group+'/sptype', (np.size(sptype), ), dtype=dtype)
dset[...] = sptype
dset = database.create_dataset(group+'/flag', (np.size(flag), ), dtype=dtype)
dset[...] = flag
database.create_dataset(group+'/distance', data=distance, dtype='f')
database.create_dataset(group+'/MKO/NSFCam.Y', data=photdata['YMAG'], dtype='f')
database.create_dataset(group+'/MKO/NSFCam.J', data=photdata['JMAG'], dtype='f')
database.create_dataset(group+'/MKO/NSFCam.H', data=photdata['HMAG'], dtype='f')
database.create_dataset(group+'/MKO/NSFCam.K', data=photdata['KMAG'], dtype='f')
database.create_dataset(group+'/MKO/NSFCam.Lp', data=photdata['LMAG'], dtype='f')
database.create_dataset(group+'/MKO/NSFCam.Mp', data=photdata['MMAG'], dtype='f')
database.create_dataset(group+'/2MASS/2MASS.J', data=photdata['J2MAG'], dtype='f')
database.create_dataset(group+'/2MASS/2MASS.H', data=photdata['H2MAG'], dtype='f')
database.create_dataset(group+'/2MASS/2MASS.Ks', data=photdata['K2MAG'], dtype='f')
sys.stdout.write(' [DONE]\n')
sys.stdout.flush()
sys.stdout.write('Querying SIMBAD...')
sys.stdout.flush()
simbad_id = queries.get_simbad(name)
dset = database.create_dataset(group+'/simbad', (np.size(simbad_id), ), dtype=dtype)
dset[...] = simbad_id
sys.stdout.write(' [DONE]\n')
sys.stdout.flush()
database.close()
def add_irtf(input_path, database, sptypes):
"""
Function to add the IRTF Spectral Library to the database.
Parameters
----------
input_path : str
Path of the data folder.
database : h5py._hl.files.File
Database.
sptypes : tuple(str, )
Spectral types ('F', 'G', 'K', 'M', 'L', 'T').
Returns
-------
NoneType
None
"""
datadir = os.path.join(input_path, 'irtf')
if not os.path.exists(datadir):
os.makedirs(datadir)
data_file = {
'F': os.path.join(input_path, 'irtf/F_fits_091201.tar'),
'G': os.path.join(input_path, 'irtf/G_fits_091201.tar'),
'K': os.path.join(input_path, 'irtf/K_fits_091201.tar'),
'M': os.path.join(input_path, 'irtf/M_fits_091201.tar'),
'L': os.path.join(input_path, 'irtf/L_fits_091201.tar'),
'T': os.path.join(input_path, 'irtf/T_fits_091201.tar')
}
data_folder = {
'F': os.path.join(input_path, 'irtf/F_fits_091201'),
'G': os.path.join(input_path, 'irtf/G_fits_091201'),
'K': os.path.join(input_path, 'irtf/K_fits_091201'),
'M': os.path.join(input_path, 'irtf/M_fits_091201'),
'L': os.path.join(input_path, 'irtf/L_fits_091201'),
'T': os.path.join(input_path, 'irtf/T_fits_091201')
}
data_type = {
'F': 'F stars (4.4 MB)',
'G': 'G stars (5.6 MB)',
'K': 'K stars (5.5 MB)',
'M': 'M stars (7.5 MB)',
'L': 'L dwarfs (850 kB)',
'T': 'T dwarfs (100 kB)'
}
url_root = 'http://irtfweb.ifa.hawaii.edu/~spex/IRTF_Spectral_Library/Data/'
url = {
'F': url_root + 'F_fits_091201.tar',
'G': url_root + 'G_fits_091201.tar',
'K': url_root + 'K_fits_091201.tar',
'M': url_root + 'M_fits_091201.tar',
'L': url_root + 'L_fits_091201.tar',
'T': url_root + 'T_fits_091201.tar'
}
for item in sptypes:
if not os.path.isfile(data_file[item]):
sys.stdout.write('Downloading IRTF Spectral Library - ' +
data_type[item] + '...')
sys.stdout.flush()
urlretrieve(url[item], data_file[item])
sys.stdout.write(' [DONE]\n')
sys.stdout.flush()
sys.stdout.write('Unpacking IRTF Spectral Library...')
sys.stdout.flush()
for item in sptypes:
tar = tarfile.open(data_file[item])
tar.extractall(path=datadir)
tar.close()
sys.stdout.write(' [DONE]\n')
sys.stdout.flush()
database.create_group('spectra/irtf')
for item in sptypes:
for root, _, files in os.walk(data_folder[item]):
for _, filename in enumerate(files):
if filename[-9:] != '_ext.fits':
fitsfile = os.path.join(root, filename)
spdata, header = fits.getdata(fitsfile, header=True)
name = header['OBJECT']
sptype = header['SPTYPE']
if name[-2:] == 'AB':
name = name[:-2]
elif name[-3:] == 'ABC':
name = name[:-3]
spt_split = sptype.split()
if item in ('L', 'T') or spt_split[1][0] == 'V':
sys.stdout.write('\rAdding IRTF Spectral Library... ' +
'{:<40}'.format(name))
sys.stdout.flush()
simbad_id, distance = queries.get_distance(
name) # [pc]
sptype = data_util.update_sptype(np.array([sptype]))[0]
dset = database.create_dataset('spectra/irtf/' + name,
data=spdata,
dtype='f')
dset.attrs['name'] = str(name).encode()
dset.attrs['sptype'] = str(sptype).encode()
dset.attrs['simbad'] = str(simbad_id).encode()
dset.attrs['distance'] = distance
sys.stdout.write('\rAdding IRTF Spectral Library... ' +
'{:<40}'.format('[DONE]') + '\n')
sys.stdout.flush()
database.close()
def add_spex(input_path, database):
"""
Function for adding the SpeX Prism Spectral Library to the database.
Parameters
----------
input_path : str
Path of the data folder.
database : h5py._hl.files.File
Database.
Returns
-------
NoneType
None
"""
database.create_group('spectra/spex')
data_path = os.path.join(input_path, 'spex')
if not os.path.exists(data_path):
os.makedirs(data_path)
url_all = 'http://svo2.cab.inta-csic.es/vocats/v2/spex/' \
'cs.php?RA=180.000000&DEC=0.000000&SR=180.000000&VERB=2'
xml_file = os.path.join(data_path, 'spex.xml')
urlretrieve(url_all, xml_file)
table = parse_single_table(xml_file)
name = table.array['name']
twomass = table.array['name2m']
url = table.array['access_url']
os.remove(xml_file)
for i, item in enumerate(url):
xml_file = os.path.join(data_path, twomass[i].decode('utf-8') + '.xml')
urlretrieve(item.decode('utf-8'), xml_file)
table = parse_single_table(xml_file)
name = table.array['ID']
name = name[0].decode('utf-8')
url = table.array['access_url']
sys.stdout.write('\rDownloading SpeX Prism Spectral Library... ' +
'{:<40}'.format(name))
sys.stdout.flush()
os.remove(xml_file)
xml_file = os.path.join(data_path, name + '.xml')
urlretrieve(url[0].decode('utf-8'), xml_file)
sys.stdout.write('\rDownloading SpeX Prism Spectral Library... ' +
'{:<40}'.format('[DONE]') + '\n')
sys.stdout.flush()
h_twomass = photometry.SyntheticPhotometry('2MASS/2MASS.H')
transmission = read_filter.ReadFilter('2MASS/2MASS.H')
transmission.get_filter()
# 2MASS H band zero point for 0 mag (Cogen et al. 2003)
h_zp = 1.133e-9 # [W m-2 micron-1]
for votable in os.listdir(data_path):
if votable.endswith('.xml'):
xml_file = os.path.join(data_path, votable)
table = parse_single_table(xml_file)
wavelength = table.array['wavelength'] # [Angstrom]
flux = table.array['flux'] # Normalized units
wavelength = np.array(wavelength * 1e-4) # [micron]
flux = np.array(flux)
# 2MASS magnitudes
j_mag = table.get_field_by_id('jmag').value
h_mag = table.get_field_by_id('hmag').value
ks_mag = table.get_field_by_id('ksmag').value
if j_mag == b'':
j_mag = np.nan
else:
j_mag = float(j_mag)
if h_mag == b'':
h_mag = np.nan
else:
h_mag = float(h_mag)
if ks_mag == b'':
ks_mag = np.nan
else:
ks_mag = float(ks_mag)
name = table.get_field_by_id('name').value
name = name.decode('utf-8')
twomass_id = table.get_field_by_id('name2m').value
sys.stdout.write('\rAdding SpeX Prism Spectral Library... ' +
'{:<40}'.format(name))
sys.stdout.flush()
try:
sptype = table.get_field_by_id('nirspty').value
sptype = sptype.decode('utf-8')
except KeyError:
try:
sptype = table.get_field_by_id('optspty').value
sptype = sptype.decode('utf-8')
except KeyError:
sptype = 'None'
sptype = data_util.update_sptype(np.array([sptype]))[0]
h_flux, _ = h_twomass.magnitude_to_flux(h_mag, None, h_zp)
phot = h_twomass.spectrum_to_photometry(wavelength,
flux) # Normalized units
flux *= h_flux / phot # [W m-2 micron-1]
spdata = np.vstack((wavelength, flux))
simbad_id, distance = queries.get_distance(
'2MASS ' + twomass_id.decode('utf-8')) # [pc]
dset = database.create_dataset('spectra/spex/' + name, data=spdata)
dset.attrs['name'] = str(name)
dset.attrs['sptype'] = str(sptype)
dset.attrs['simbad'] = str(simbad_id)
dset.attrs['2MASS/2MASS.J'] = j_mag
dset.attrs['2MASS/2MASS.H'] = h_mag
dset.attrs['2MASS/2MASS.Ks'] = ks_mag
dset.attrs['distance'] = distance # [pc]
sys.stdout.write('\rAdding SpeX Prism Spectral Library... ' +
'{:<40}'.format('[DONE]') + '\n')
sys.stdout.flush()
database.close()
def add_leggett(input_path, database):
"""
Function for adding the Database of Ultracool Parallaxes to the database.
Parameters
----------
input_path : str
Path of the data folder.
database : h5py._hl.files.File
Database.
Returns
-------
NoneType
None
"""
data_file1 = os.path.join(input_path, '2010_phot.xls')
url1 = 'http://staff.gemini.edu/~sleggett/2010_phot.xls'
data_file2 = os.path.join(input_path, 'datafile8.txt')
url2 = 'http://staff.gemini.edu/~sleggett/datafile8.txt'
if not os.path.isfile(data_file1):
print('Downloading Leggett L and T Dwarf Data (88 kB)...',
end='',
flush=True)
urllib.request.urlretrieve(url1, data_file1)
print(' [DONE]')
if not os.path.isfile(data_file2):
print('Downloading Leggett T6+ and Y Dwarf Data (44 kB)...',
end='',
flush=True)
urllib.request.urlretrieve(url2, data_file2)
print(' [DONE]')
print('Adding Leggett L and T Dwarf Data...', end='', flush=True)
group = 'photometry/leggett'
database.create_group(group)
dataframe = pd.pandas.read_excel(data_file1)
dataframe.columns = dataframe.columns.str.replace('\'', '')
modulus = np.asarray(dataframe['M-m']) # M-m (mag)
modulus_error = np.asarray(dataframe['Mmerr']) # M-m (mag)
distance = 10.**(-modulus / 5. + 1.) # (pc)
distance_lower = distance - 10.**(-(modulus + modulus_error) / 5. + 1.
) # (pc)
distance_upper = 10.**(-(modulus - modulus_error) / 5. +
1.) - distance # (pc)
distance_error = (distance_lower + distance_upper) / 2.
name = np.asarray(dataframe['Name'])
sptype = np.asarray(dataframe['Type'])
sptype = data_util.update_sptype(sptype)
mag_y = np.asarray(dataframe['Y'])
mag_j = np.asarray(dataframe['J'])
mag_h = np.asarray(dataframe['H'])
mag_k = np.asarray(dataframe['K'])
mag_lp = np.asarray(dataframe['L'])
mag_mp = np.asarray(dataframe['M'])
mag_ch1 = np.asarray(dataframe['Ch1'])
mag_ch2 = np.asarray(dataframe['Ch2'])
mag_ch3 = np.asarray(dataframe['Ch3'])
mag_ch4 = np.asarray(dataframe['Ch4'])
mag_w1 = np.repeat(np.nan, np.size(name))
mag_w2 = np.repeat(np.nan, np.size(name))
mag_w3 = np.repeat(np.nan, np.size(name))
mag_w4 = np.repeat(np.nan, np.size(name))
print(' [DONE]')
print('Adding Leggett T6+ and Y Dwarf Data...', end='', flush=True)
file_io = open(data_file2, 'r')
lines = file_io.readlines()[69:]
for item in lines:
name = np.append(name, item[0:16])
spt_tmp = item[62:66]
if spt_tmp[0] == '2':
spt_tmp = 'T' + spt_tmp[1]
elif spt_tmp[0] == '3':
spt_tmp = 'Y' + spt_tmp[1]
sptype = np.append(sptype, spt_tmp)
modulus = float(item[67:73]) # M-m (mag)
if modulus == 999.:
modulus = np.nan
distance = np.append(distance, 10.**(-modulus / 5. + 1.)) # (pc)
mag = np.zeros(14)
mag[0] = float(item[95:101]) # MKO Y
mag[1] = float(item[102:107]) # MKO J
mag[2] = float(item[108:114]) # MKO H
mag[3] = float(item[115:121]) # MKO K
mag[4] = float(item[122:128]) # MKO L'
mag[5] = float(item[129:135]) # MKO M'
mag[6] = float(item[136:142]) # Spitzer/IRAC 3.6 um
mag[7] = float(item[143:149]) # Spitzer/IRAC 4.5 um
mag[8] = float(item[150:156]) # Spitzer/IRAC 5.8 um
mag[9] = float(item[157:163]) # Spitzer/IRAC 8.0 um
mag[10] = float(item[164:170]) # WISE W1
mag[11] = float(item[171:176]) # WISE W2
mag[12] = float(item[177:183]) # WISE W3
mag[13] = float(item[184:190]) # WISE W4
for j, mag_item in enumerate(mag):
if mag_item == 999.:
mag[j] = np.nan
mag_y = np.append(mag_y, mag[0])
mag_j = np.append(mag_j, mag[1])
mag_h = np.append(mag_h, mag[2])
mag_k = np.append(mag_k, mag[3])
mag_lp = np.append(mag_lp, mag[4])
mag_mp = np.append(mag_mp, mag[5])
mag_ch1 = np.append(mag_ch1, mag[6])
mag_ch2 = np.append(mag_ch2, mag[7])
mag_ch3 = np.append(mag_ch3, mag[8])
mag_ch4 = np.append(mag_ch4, mag[9])
mag_w1 = np.append(mag_w1, mag[10])
mag_w2 = np.append(mag_w2, mag[11])
mag_w3 = np.append(mag_w3, mag[12])
mag_w4 = np.append(mag_w4, mag[13])
file_io.close()
dtype = h5py.special_dtype(vlen=str)
dset = database.create_dataset(group + '/name', (np.size(name), ),
dtype=dtype)
dset[...] = name
dset = database.create_dataset(group + '/sptype', (np.size(sptype), ),
dtype=dtype)
dset[...] = sptype
flag = np.repeat('null', np.size(name))
dset = database.create_dataset(group + '/flag', (np.size(flag), ),
dtype=dtype)
dset[...] = flag
database.create_dataset(group + '/distance', data=distance)
database.create_dataset(group + '/distance_error', data=distance_error)
database.create_dataset(group + '/MKO/NSFCam.Y', data=mag_y)
database.create_dataset(group + '/MKO/NSFCam.J', data=mag_j)
database.create_dataset(group + '/MKO/NSFCam.H', data=mag_h)
database.create_dataset(group + '/MKO/NSFCam.K', data=mag_k)
database.create_dataset(group + '/MKO/NSFCam.Lp', data=mag_lp)
database.create_dataset(group + '/MKO/NSFCam.Mp', data=mag_mp)
database.create_dataset(group + '/Spitzer/IRAC.I1', data=mag_ch1)
database.create_dataset(group + '/Spitzer/IRAC.I2', data=mag_ch2)
database.create_dataset(group + '/Spitzer/IRAC.I3', data=mag_ch3)
database.create_dataset(group + '/Spitzer/IRAC.I4', data=mag_ch4)
database.create_dataset(group + '/WISE/WISE.W1', data=mag_w1)
database.create_dataset(group + '/WISE/WISE.W2', data=mag_w2)
database.create_dataset(group + '/WISE/WISE.W3', data=mag_w3)
database.create_dataset(group + '/WISE/WISE.W4', data=mag_w4)
print(' [DONE]')
database.close()
def add_leggett(input_path, database):
"""
Function for adding the Database of Ultracool Parallaxes to the database.
Parameters
----------
input_path : str
Path of the data folder.
database : h5py._hl.files.File
Database.
Returns
-------
NoneType
None
"""
data_file1 = os.path.join(input_path, "2010_phot.xls")
url1 = "http://staff.gemini.edu/~sleggett/2010_phot.xls"
data_file2 = os.path.join(input_path, "datafile8.txt")
url2 = "http://staff.gemini.edu/~sleggett/datafile8.txt"
if not os.path.isfile(data_file1):
print("Downloading Leggett L and T Dwarf Data (88 kB)...",
end="",
flush=True)
urllib.request.urlretrieve(url1, data_file1)
print(" [DONE]")
if not os.path.isfile(data_file2):
print("Downloading Leggett T6+ and Y Dwarf Data (44 kB)...",
end="",
flush=True)
urllib.request.urlretrieve(url2, data_file2)
print(" [DONE]")
print("Adding Leggett L and T Dwarf Data...", end="", flush=True)
group = "photometry/leggett"
database.create_group(group)
dataframe = pd.pandas.read_excel(data_file1)
dataframe.columns = dataframe.columns.str.replace("'", "")
modulus = np.asarray(dataframe["M-m"]) # M-m (mag)
modulus_error = np.asarray(dataframe["Mmerr"]) # M-m (mag)
distance = 10.0**(-modulus / 5.0 + 1.0) # (pc)
distance_lower = distance - 10.0**(-(modulus + modulus_error) / 5.0 + 1.0
) # (pc)
distance_upper = 10.0**(-(modulus - modulus_error) / 5.0 +
1.0) - distance # (pc)
distance_error = (distance_lower + distance_upper) / 2.0
name = np.asarray(dataframe["Name"])
# Near-infrared spectral type
sptype = np.asarray(dataframe["Type"])
sptype = data_util.update_sptype(sptype)
sptype = np.asarray(sptype)
mag_y = np.asarray(dataframe["Y"])
mag_j = np.asarray(dataframe["J"])
mag_h = np.asarray(dataframe["H"])
mag_k = np.asarray(dataframe["K"])
mag_lp = np.asarray(dataframe["L"])
mag_mp = np.asarray(dataframe["M"])
mag_ch1 = np.asarray(dataframe["Ch1"])
mag_ch2 = np.asarray(dataframe["Ch2"])
mag_ch3 = np.asarray(dataframe["Ch3"])
mag_ch4 = np.asarray(dataframe["Ch4"])
mag_w1 = np.repeat(np.nan, np.size(name))
mag_w2 = np.repeat(np.nan, np.size(name))
mag_w3 = np.repeat(np.nan, np.size(name))
mag_w4 = np.repeat(np.nan, np.size(name))
print(" [DONE]")
print("Adding Leggett T6+ and Y Dwarf Data...", end="", flush=True)
file_io = open(data_file2, "r")
lines = file_io.readlines()[69:]
for item in lines:
name = np.append(name, item[0:16])
spt_tmp = item[62:66]
if spt_tmp[0] == "2":
spt_tmp = "T" + spt_tmp[1]
elif spt_tmp[0] == "3":
spt_tmp = "Y" + spt_tmp[1]
sptype = np.append(sptype, spt_tmp)
modulus = float(item[67:73]) # M-m (mag)
if modulus == 999.0:
modulus = np.nan
distance = np.append(distance, 10.0**(-modulus / 5.0 + 1.0)) # (pc)
mag = np.zeros(14)
mag[0] = float(item[95:101]) # MKO Y
mag[1] = float(item[102:107]) # MKO J
mag[2] = float(item[108:114]) # MKO H
mag[3] = float(item[115:121]) # MKO K
mag[4] = float(item[122:128]) # MKO L'
mag[5] = float(item[129:135]) # MKO M'
mag[6] = float(item[136:142]) # Spitzer/IRAC 3.6 um
mag[7] = float(item[143:149]) # Spitzer/IRAC 4.5 um
mag[8] = float(item[150:156]) # Spitzer/IRAC 5.8 um
mag[9] = float(item[157:163]) # Spitzer/IRAC 8.0 um
mag[10] = float(item[164:170]) # WISE W1
mag[11] = float(item[171:176]) # WISE W2
mag[12] = float(item[177:183]) # WISE W3
mag[13] = float(item[184:190]) # WISE W4
for j, mag_item in enumerate(mag):
if mag_item == 999.0:
mag[j] = np.nan
mag_y = np.append(mag_y, mag[0])
mag_j = np.append(mag_j, mag[1])
mag_h = np.append(mag_h, mag[2])
mag_k = np.append(mag_k, mag[3])
mag_lp = np.append(mag_lp, mag[4])
mag_mp = np.append(mag_mp, mag[5])
mag_ch1 = np.append(mag_ch1, mag[6])
mag_ch2 = np.append(mag_ch2, mag[7])
mag_ch3 = np.append(mag_ch3, mag[8])
mag_ch4 = np.append(mag_ch4, mag[9])
mag_w1 = np.append(mag_w1, mag[10])
mag_w2 = np.append(mag_w2, mag[11])
mag_w3 = np.append(mag_w3, mag[12])
mag_w4 = np.append(mag_w4, mag[13])
file_io.close()
dtype = h5py.special_dtype(vlen=str)
dset = database.create_dataset(group + "/name", (np.size(name), ),
dtype=dtype)
dset[...] = name
dset = database.create_dataset(group + "/sptype", (np.size(sptype), ),
dtype=dtype)
dset[...] = sptype
flag = np.repeat("null", np.size(name))
dset = database.create_dataset(group + "/flag", (np.size(flag), ),
dtype=dtype)
dset[...] = flag
database.create_dataset(group + "/distance", data=distance)
database.create_dataset(group + "/distance_error", data=distance_error)
database.create_dataset(group + "/MKO/NSFCam.Y", data=mag_y)
database.create_dataset(group + "/MKO/NSFCam.J", data=mag_j)
database.create_dataset(group + "/MKO/NSFCam.H", data=mag_h)
database.create_dataset(group + "/MKO/NSFCam.K", data=mag_k)
database.create_dataset(group + "/MKO/NSFCam.Lp", data=mag_lp)
database.create_dataset(group + "/MKO/NSFCam.Mp", data=mag_mp)
database.create_dataset(group + "/Spitzer/IRAC.I1", data=mag_ch1)
database.create_dataset(group + "/Spitzer/IRAC.I2", data=mag_ch2)
database.create_dataset(group + "/Spitzer/IRAC.I3", data=mag_ch3)
database.create_dataset(group + "/Spitzer/IRAC.I4", data=mag_ch4)
database.create_dataset(group + "/WISE/WISE.W1", data=mag_w1)
database.create_dataset(group + "/WISE/WISE.W2", data=mag_w2)
database.create_dataset(group + "/WISE/WISE.W3", data=mag_w3)
database.create_dataset(group + "/WISE/WISE.W4", data=mag_w4)
print(" [DONE]")
database.close()
def add_mamajek(input_path, database):
"""
Function for adding "A Modern Mean Dwarf Stellar Color and Effective Temperature Sequence".
http://www.pas.rochester.edu/~emamajek/EEM_dwarf_UBVIJHK_colors_Teff.txt
Parameters
----------
input_path : str
Path of the data folder.
database : h5py._hl.files.File
Database.
Returns
-------
NoneType
None
"""
data_file = os.path.join(input_path, 'EEM_dwarf_UBVIJHK_colors_Teff.txt')
url = 'http://www.pas.rochester.edu/~emamajek/EEM_dwarf_UBVIJHK_colors_Teff.txt'
if not os.path.isfile(data_file):
sys.stdout.write(
'Downloading Stellar Colors and Effective Temperatures (53 kB)...')
sys.stdout.flush()
urlretrieve(url, data_file)
sys.stdout.write(' [DONE]\n')
sys.stdout.flush()
sys.stdout.write('Adding Stellar Colors and Effective Temperatures...')
sys.stdout.flush()
group = 'photometry/mamajek'
database.create_group(group)
dataframe = pd.read_csv(
data_file,
delimiter=r"\s+",
nrows=124,
header=22,
na_values=['...', '....', '.....', ',..', '19.52:'],
dtype={
'SpT': str,
'Teff': float,
'logT': float,
'BCv': float,
'U-B': float,
'Mv': float,
'logL': float,
'B-V': float,
'Bt-Vt': float,
'V-Rc': float,
'V-Ic': float,
'V-Ks': float,
'J-H': float,
'H-Ks': float,
'Ks-W1': float,
'W1-W2': float,
'W1-W3': float,
'W1-W4': float,
'Msun': float,
'logAge': str,
'b-y': float,
'M_J': float,
'M_Ks': float,
'Mbol': float,
'i-z': float,
'z-Y': float,
'R_Rsun': float,
'SpT': str,
'G-V': float,
'Bp-Rp': float,
'M_G': float,
'G-Rp': float
})
dataframe.columns = dataframe.columns.str.replace('#', '')
sptype = np.asarray(dataframe['SpT'])
sptype = data_util.update_sptype(sptype)
flag = np.repeat('star', sptype.size)
distance = np.repeat(10., sptype.size) # [pc]
v_mag = dataframe['Mv'] # [mag]
b_mag = dataframe['B-V'] + dataframe['Mv'] # [mag]
u_mag = dataframe['U-B'] + b_mag # [mag]
j_mag = dataframe['M_J'] # [mag]
ks_mag = dataframe['M_Ks'] # [mag]
h_mag = dataframe['H-Ks'] + ks_mag # [mag]
w1_mag = -dataframe['Ks-W1'] + ks_mag # [mag]
w2_mag = -dataframe['W1-W2'] + w1_mag # [mag]
w3_mag = -dataframe['W1-W3'] + w1_mag # [mag]
w4_mag = -dataframe['W1-W4'] + w1_mag # [mag]
dtype = h5py.special_dtype(vlen=bytes)
dset = database.create_dataset(group + '/sptype', (np.size(sptype), ),
dtype=dtype)
dset[...] = sptype
dset = database.create_dataset(group + '/flag', (np.size(flag), ),
dtype=dtype)
dset[...] = flag
database.create_dataset(group + '/distance', data=distance, dtype='f')
database.create_dataset(group + '/Teff', data=dataframe['Teff'], dtype='f')
database.create_dataset(group + '/U', data=u_mag, dtype='f')
database.create_dataset(group + '/B', data=b_mag, dtype='f')
database.create_dataset(group + '/V', data=v_mag, dtype='f')
database.create_dataset(group + '/J', data=j_mag, dtype='f')
database.create_dataset(group + '/H', data=h_mag, dtype='f')
database.create_dataset(group + '/Ks', data=ks_mag, dtype='f')
database.create_dataset(group + '/W1', data=w1_mag, dtype='f')
database.create_dataset(group + '/W2', data=w2_mag, dtype='f')
database.create_dataset(group + '/W3', data=w3_mag, dtype='f')
database.create_dataset(group + '/W4', data=w4_mag, dtype='f')
sys.stdout.write(' [DONE]\n')
sys.stdout.flush()
database.close()
def add_irtf(input_path: str,
database: h5py._hl.files.File,
sptypes: Optional[List[str]] = None) -> None:
"""
Function for adding the IRTF Spectral Library to the database.
Parameters
----------
input_path : str
Path of the data folder.
database : h5py._hl.files.File
Database.
sptypes : list(str), None
List with the spectral types ('F', 'G', 'K', 'M', 'L', 'T'). All spectral types are
included if set to ``None``.
Returns
-------
NoneType
None
"""
if sptypes is None:
sptypes = ["F", "G", "K", "M", "L", "T"]
parallax_url = "https://home.strw.leidenuniv.nl/~stolker/species/parallax.dat"
parallax_file = os.path.join(input_path, "parallax.dat")
if not os.path.isfile(parallax_file):
urllib.request.urlretrieve(parallax_url, parallax_file)
parallax_data = pd.pandas.read_csv(
parallax_file,
usecols=[0, 1, 2],
names=["object", "parallax", "parallax_error"],
delimiter=",",
dtype={
"object": str,
"parallax": float,
"parallax_error": float
},
)
datadir = os.path.join(input_path, "irtf")
if not os.path.exists(datadir):
os.makedirs(datadir)
data_file = {
"F": os.path.join(input_path, "irtf/F_fits_091201.tar"),
"G": os.path.join(input_path, "irtf/G_fits_091201.tar"),
"K": os.path.join(input_path, "irtf/K_fits_091201.tar"),
"M": os.path.join(input_path, "irtf/M_fits_091201.tar"),
"L": os.path.join(input_path, "irtf/L_fits_091201.tar"),
"T": os.path.join(input_path, "irtf/T_fits_091201.tar"),
}
data_folder = {
"F": os.path.join(input_path, "irtf/F_fits_091201"),
"G": os.path.join(input_path, "irtf/G_fits_091201"),
"K": os.path.join(input_path, "irtf/K_fits_091201"),
"M": os.path.join(input_path, "irtf/M_fits_091201"),
"L": os.path.join(input_path, "irtf/L_fits_091201"),
"T": os.path.join(input_path, "irtf/T_fits_091201"),
}
data_type = {
"F": "F stars (4.4 MB)",
"G": "G stars (5.6 MB)",
"K": "K stars (5.5 MB)",
"M": "M stars (7.5 MB)",
"L": "L dwarfs (850 kB)",
"T": "T dwarfs (100 kB)",
}
url_root = "http://irtfweb.ifa.hawaii.edu/~spex/IRTF_Spectral_Library/Data/"
url = {
"F": url_root + "F_fits_091201.tar",
"G": url_root + "G_fits_091201.tar",
"K": url_root + "K_fits_091201.tar",
"M": url_root + "M_fits_091201.tar",
"L": url_root + "L_fits_091201.tar",
"T": url_root + "T_fits_091201.tar",
}
for item in sptypes:
if not os.path.isfile(data_file[item]):
print(
f"Downloading IRTF Spectral Library - {data_type[item]}...",
end="",
flush=True,
)
urllib.request.urlretrieve(url[item], data_file[item])
print(" [DONE]")
print("Unpacking IRTF Spectral Library...", end="", flush=True)
for item in sptypes:
tar = tarfile.open(data_file[item])
tar.extractall(path=datadir)
tar.close()
print(" [DONE]")
database.create_group("spectra/irtf")
print_message = ""
for item in sptypes:
for root, _, files in os.walk(data_folder[item]):
for _, filename in enumerate(files):
if filename[-9:] != "_ext.fits":
fitsfile = os.path.join(root, filename)
spdata, header = fits.getdata(fitsfile, header=True)
spdata = np.transpose(spdata)
name = header["OBJECT"]
sptype = header["SPTYPE"]
if name[-2:] == "AB":
name = name[:-2]
elif name[-3:] == "ABC":
name = name[:-3]
spt_split = sptype.split()
if item in ["L", "T"] or spt_split[1][0] == "V":
empty_message = len(print_message) * " "
print(f"\r{empty_message}", end="")
print_message = f"Adding spectra... {name}"
print(f"\r{print_message}", end="")
simbad_id = query_util.get_simbad(name)
if simbad_id is not None:
# For backward compatibility
if not isinstance(simbad_id, str):
simbad_id = simbad_id.decode("utf-8")
par_select = parallax_data[parallax_data["object"]
== simbad_id]
if not par_select.empty:
parallax = (
par_select["parallax"],
par_select["parallax_error"],
)
else:
simbad_id, parallax = query_util.get_parallax(
name)
else:
parallax = (np.nan, np.nan)
sptype = data_util.update_sptype(np.array([sptype]))[0]
dset = database.create_dataset(f"spectra/irtf/{name}",
data=spdata)
dset.attrs["name"] = str(name).encode()
dset.attrs["sptype"] = str(sptype).encode()
dset.attrs["simbad"] = str(simbad_id).encode()
dset.attrs["parallax"] = parallax[0]
dset.attrs["parallax_error"] = parallax[1]
empty_message = len(print_message) * " "
print(f"\r{empty_message}", end="")
print_message = "Adding spectra... [DONE]"
print(f"\r{print_message}")
database.close()
