def read_kdwarfs(catalogdir, cps_list):
"""Read in K-Dwarf catalog"""
kdwarfs = pd.read_csv(os.path.join(catalogdir, KDWARF_FILENAME))
stars = pd.DataFrame(columns=Library.LIB_COLS)
nospectra = pd.DataFrame(columns=NOSPECTRA_COLS)
for idx, row in kdwarfs.iterrows():
query_result = cpsutils.find_spectra(row['name'], cps_list)
if not query_result.empty:
new_row = {}
new_row['cps_name'] = str(query_result.iloc[0]['name'])
new_row['lib_obs'] = query_result.iloc[0]['obs']
new_row['snr'] = query_result.iloc[0]['snr']
new_row['Teff'] = row['teff_derived']
new_row['u_Teff'] = row['e_teff_derived']
new_row['feh'] = row['fe']
new_row['u_feh'] = 0.1
new_row['radius'] = row['radius']
new_row['u_radius'] = row['e_radius']
new_row['source'] = 'Gaidos'
new_row['source_name'] = row['name']
stars = stars.append(pd.Series(new_row), ignore_index=True)
else:
new_row = {}
new_row['name'] = row['name']
new_row['source'] = 'Gaidos'
nospectra = nospectra.append(pd.Series(new_row), ignore_index=True)
return stars, nospectra
def read_kdwarfs(catalogdir, cps_list):
"""Read in K-Dwarf catalog"""
kdwarfs = pd.read_csv(os.path.join(catalogdir, KDWARF_FILENAME))
stars = pd.DataFrame(columns=Library.LIB_COLS)
nospectra = pd.DataFrame(columns=NOSPECTRA_COLS)
for idx, row in kdwarfs.iterrows():
query_result = cpsutils.find_spectra(row['name'], cps_list)
if not query_result.empty:
new_row = {}
new_row['cps_name'] = str(query_result.iloc[0]['name'])
new_row['lib_obs'] = query_result.iloc[0]['obs']
new_row['snr'] = query_result.iloc[0]['snr']
new_row['Teff'] = row['teff_derived']
new_row['u_Teff'] = row['e_teff_derived']
new_row['feh'] = row['fe']
new_row['u_feh'] = 0.1
new_row['radius'] = row['radius']
new_row['u_radius'] = row['e_radius']
new_row['source'] = 'Gaidos'
new_row['source_name'] = row['name']
stars = stars.append(pd.Series(new_row), ignore_index=True)
else:
new_row = {}
new_row['name'] = row['name']
new_row['source'] = 'Gaidos'
nospectra = nospectra.append(pd.Series(new_row), ignore_index=True)
return stars, nospectra
def read_mann(catalogdir, cps_list):
"""Read in Mann (2015) catalog
Args:
catalogdir (str): Path of catalog directory.
cps_list (pd.DataFrame): Dataframe containing list of CPS spectra.
Returns:
stars (pd.DataFrame): Stars in source which have CPS spectra
nospec (pd.DataFrame): Stars in source which don't have CPS spectra
"""
mann_data = ascii.read(os.path.join(catalogdir, MANN_FILENAME),
readme=os.path.join(catalogdir, MANN_README))
mann_data = mann_data[~mann_data['CNS3'].mask]
stars = pd.DataFrame(columns=Library.LIB_COLS)
nospectra = pd.DataFrame(columns=NOSPECTRA_COLS)
for row in mann_data:
query_result = cpsutils.find_spectra(row['CNS3'], cps_list)
if not query_result.empty:
new_row = {}
new_row['cps_name'] = str(query_result.iloc[0]['name'])
new_row['lib_obs'] = query_result.iloc[0]['obs']
new_row['snr'] = query_result.iloc[0]['snr']
new_row['Teff'] = row['Teff']
new_row['u_Teff'] = row['e_Teff']
new_row['radius'] = row['R']
new_row['u_radius'] = row['e_R']
new_row['feh'] = row['[Fe/H]']
new_row['u_feh'] = row['e_[Fe/H]']
new_row['mass'] = row['M']
new_row['u_mass'] = row['e_M']
new_row['source'] = 'Mann'
new_row['source_name'] = row['CNS3']
# Calculate logg from radius and mass
logg, u_logg = utils.calc_logg(row['R'], row['e_R'],
row['M'], row['e_M'])
new_row['logg'] = logg
new_row['u_logg'] = u_logg
stars = stars.append(pd.Series(new_row), ignore_index=True)
else:
new_row = {}
new_row['name'] = row['CNS3']
new_row['source'] = 'Mann'
nospectra = nospectra.append(pd.Series(new_row), ignore_index=True)
return stars, nospectra
def read_mann(catalogdir, cps_list):
"""Read in Mann (2015) catalog
Args:
catalogdir (str): Path of catalog directory.
cps_list (pd.DataFrame): Dataframe containing list of CPS spectra.
Returns:
stars (pd.DataFrame): Stars in source which have CPS spectra
nospec (pd.DataFrame): Stars in source which don't have CPS spectra
"""
mann_data = ascii.read(os.path.join(catalogdir, MANN_FILENAME),
readme=os.path.join(catalogdir, MANN_README))
mann_data = mann_data[~mann_data['CNS3'].mask]
stars = pd.DataFrame(columns=Library.LIB_COLS)
nospectra = pd.DataFrame(columns=NOSPECTRA_COLS)
for row in mann_data:
query_result = cpsutils.find_spectra(row['CNS3'], cps_list)
if not query_result.empty:
new_row = {}
new_row['cps_name'] = str(query_result.iloc[0]['name'])
new_row['lib_obs'] = query_result.iloc[0]['obs']
new_row['snr'] = query_result.iloc[0]['snr']
new_row['Teff'] = row['Teff']
new_row['u_Teff'] = row['e_Teff']
new_row['radius'] = row['R']
new_row['u_radius'] = row['e_R']
new_row['feh'] = row['[Fe/H]']
new_row['u_feh'] = row['e_[Fe/H]']
new_row['mass'] = row['M']
new_row['u_mass'] = row['e_M']
new_row['source'] = 'Mann'
new_row['source_name'] = row['CNS3']
# Calculate logg from radius and mass
logg, u_logg = utils.calc_logg(row['R'], row['e_R'], row['M'],
row['e_M'])
new_row['logg'] = logg
new_row['u_logg'] = u_logg
stars = stars.append(pd.Series(new_row), ignore_index=True)
else:
new_row = {}
new_row['name'] = row['CNS3']
new_row['source'] = 'Mann'
nospectra = nospectra.append(pd.Series(new_row), ignore_index=True)
return stars, nospectra
def read_huber(catalogdir, cps_list):
"""Read in Huber (2013) catalog
Args:
catalogdir (str): Path of catalog directory.
cps_list (pd.DataFrame): Dataframe containing list of CPS spectra.
Returns:
stars (pd.DataFrame): Stars in source which have CPS spectra
nospec (pd.DataFrame): Stars in source which don't have CPS spectra
"""
huber_data = ascii.read(os.path.join(catalogdir, HUBER_FILENAME),
readme=os.path.join(catalogdir, HUBER_README))
huber_data = huber_data[huber_data['f_KOI'] != '*']
stars = pd.DataFrame(columns=Library.LIB_COLS)
nospectra = pd.DataFrame(columns=NOSPECTRA_COLS)
for row in huber_data:
query_result = cpsutils.find_spectra('KOI'+str(row['KOI']), cps_list)
if not query_result.empty:
new_row = {}
new_row['cps_name'] = str(query_result.iloc[0]['name'])
new_row['lib_obs'] = query_result.iloc[0]['obs']
new_row['Teff'] = row['Teff']
new_row['u_Teff'] = row['e_Teff']
new_row['radius'] = float(row['Rad'])
new_row['u_radius'] = row['e_Rad']
new_row['feh'] = row['[Fe/H]']
new_row['u_feh'] = row['e_[Fe/H]']
new_row['mass'] = row['Mass']
new_row['u_mass'] = row['e_Mass']
new_row['source'] = 'Huber'
new_row['source_name'] = 'KOI'+str(row['KOI'])
# Calculate logg from radius and mass
logg, u_logg = utils.calc_logg(row['Rad'], row['e_Rad'],
row['Mass'], row['e_Mass'])
new_row['logg'] = logg
new_row['u_logg'] = u_logg
stars = stars.append(pd.Series(new_row), ignore_index=True)
else:
new_row = {}
new_row['name'] = 'KOI'+str(row['KOI'])
new_row['source'] = 'Huber'
nospectra = nospectra.append(pd.Series(new_row), ignore_index=True)
return stars, nospectra
def read_huber(catalogdir, cps_list):
"""Read in Huber (2013) catalog
Args:
catalogdir (str): Path of catalog directory.
cps_list (pd.DataFrame): Dataframe containing list of CPS spectra.
Returns:
stars (pd.DataFrame): Stars in source which have CPS spectra
nospec (pd.DataFrame): Stars in source which don't have CPS spectra
"""
huber_data = ascii.read(os.path.join(catalogdir, HUBER_FILENAME),
readme=os.path.join(catalogdir, HUBER_README))
huber_data = huber_data[huber_data['f_KOI'] != '*']
stars = pd.DataFrame(columns=Library.LIB_COLS)
nospectra = pd.DataFrame(columns=NOSPECTRA_COLS)
for row in huber_data:
query_result = cpsutils.find_spectra('KOI' + str(row['KOI']), cps_list)
if not query_result.empty:
new_row = {}
new_row['cps_name'] = str(query_result.iloc[0]['name'])
new_row['lib_obs'] = query_result.iloc[0]['obs']
new_row['Teff'] = row['Teff']
new_row['u_Teff'] = row['e_Teff']
new_row['radius'] = float(row['Rad'])
new_row['u_radius'] = row['e_Rad']
new_row['feh'] = row['[Fe/H]']
new_row['u_feh'] = row['e_[Fe/H]']
new_row['mass'] = row['Mass']
new_row['u_mass'] = row['e_Mass']
new_row['source'] = 'Huber'
new_row['source_name'] = 'KOI' + str(row['KOI'])
# Calculate logg from radius and mass
logg, u_logg = utils.calc_logg(row['Rad'], row['e_Rad'],
row['Mass'], row['e_Mass'])
new_row['logg'] = logg
new_row['u_logg'] = u_logg
stars = stars.append(pd.Series(new_row), ignore_index=True)
else:
new_row = {}
new_row['name'] = 'KOI' + str(row['KOI'])
new_row['source'] = 'Huber'
nospectra = nospectra.append(pd.Series(new_row), ignore_index=True)
return stars, nospectra
def read_casagrande(catalogdir, cps_list):
"""Read in Casagrande (2006) catalog
Args:
catalogdir (str): Path of catalog directory.
cps_list (pd.DataFrame): Dataframe containing list of CPS spectra.
Returns:
stars (pd.DataFrame): Stars in source which have CPS spectra
nospec (pd.DataFrame): Stars in source which don't have CPS spectra
"""
c_data = pd.read_csv(os.path.join(catalogdir, CASAGRANDE_FILENAME))
stars = pd.DataFrame(columns=Library.LIB_COLS)
nospectra = pd.DataFrame(columns=NOSPECTRA_COLS)
for idx, row in c_data.iterrows():
query_result = cpsutils.find_spectra(row['Name'], cps_list)
if not query_result.empty:
# Calculate stellar radius from angular diameter and parallax
radius, u_radius = utils.calc_radius(row['Plx'], row['e_Plx'],
row['Diam'], row['e_Diam'])
# Exclude stars with u_Teff > 100 or u_R/R > 0.07
if row['e_Teff'] > 100 or u_radius/radius > 0.07:
continue
new_row = {}
new_row['cps_name'] = str(query_result.iloc[0]['name'])
new_row['lib_obs'] = query_result.iloc[0]['obs']
new_row['snr'] = query_result.iloc[0]['snr']
new_row['Teff'] = row['Teff']
new_row['u_Teff'] = row['e_Teff']
new_row['feh'] = row['[Fe/H]']
new_row['u_feh'] = 0.15
new_row['radius'] = radius
new_row['u_radius'] = u_radius
new_row['source'] = 'Casagrande'
new_row['source_name'] = row['Name']
stars = stars.append(pd.Series(new_row), ignore_index=True)
else:
new_row = {}
new_row['name'] = row['Name']
new_row['source'] = 'Casagrande'
nospectra = nospectra.append(pd.Series(new_row), ignore_index=True)
return stars, nospectra
def read_casagrande(catalogdir, cps_list):
"""Read in Casagrande (2006) catalog
Args:
catalogdir (str): Path of catalog directory.
cps_list (pd.DataFrame): Dataframe containing list of CPS spectra.
Returns:
stars (pd.DataFrame): Stars in source which have CPS spectra
nospec (pd.DataFrame): Stars in source which don't have CPS spectra
"""
c_data = pd.read_csv(os.path.join(catalogdir, CASAGRANDE_FILENAME))
stars = pd.DataFrame(columns=Library.LIB_COLS)
nospectra = pd.DataFrame(columns=NOSPECTRA_COLS)
for idx, row in c_data.iterrows():
query_result = cpsutils.find_spectra(row['Name'], cps_list)
if not query_result.empty:
# Calculate stellar radius from angular diameter and parallax
radius, u_radius = utils.calc_radius(row['Plx'], row['e_Plx'],
row['Diam'], row['e_Diam'])
# Exclude stars with u_Teff > 100 or u_R/R > 0.07
if row['e_Teff'] > 100 or u_radius / radius > 0.07:
continue
new_row = {}
new_row['cps_name'] = str(query_result.iloc[0]['name'])
new_row['lib_obs'] = query_result.iloc[0]['obs']
new_row['snr'] = query_result.iloc[0]['snr']
new_row['Teff'] = row['Teff']
new_row['u_Teff'] = row['e_Teff']
new_row['feh'] = row['[Fe/H]']
new_row['u_feh'] = 0.15
new_row['radius'] = radius
new_row['u_radius'] = u_radius
new_row['source'] = 'Casagrande'
new_row['source_name'] = row['Name']
stars = stars.append(pd.Series(new_row), ignore_index=True)
else:
new_row = {}
new_row['name'] = row['Name']
new_row['source'] = 'Casagrande'
nospectra = nospectra.append(pd.Series(new_row), ignore_index=True)
return stars, nospectra
def read_brewer(catalogdir, cps_list):
"""Read in Brewer (2016) catalog
Args:
catalogdir (str): Path of catalog directory.
cps_list (pd.DataFrame): Dataframe containing list of CPS spectra.
Returns:
stars (pd.DataFrame): Stars in source which have CPS spectra
nospec (pd.DataFrame): Stars in source which don't have CPS spectra
"""
# brewer_data = ascii.read(os.path.join(catalogdir, BREWER_FILENAME))
brewer_data = pd.read_csv(os.path.join(catalogdir, BREWER_FILENAME),
index_col=0)
stars = pd.DataFrame(columns=Library.LIB_COLS)
nospectra = pd.DataFrame(columns=NOSPECTRA_COLS)
for idx, row in brewer_data.iterrows():
query_result = cpsutils.find_spectra(row['NAME'], cps_list)
if not query_result.empty:
new_row = {}
new_row['cps_name'] = str(query_result.iloc[0]['name'])
new_row['lib_obs'] = query_result.iloc[0]['obs']
new_row['snr'] = query_result.iloc[0]['snr']
new_row['Teff'] = row['TEFF']
new_row['u_Teff'] = 25
new_row['logg'] = row['LOGG']
new_row['u_logg'] = 0.028
new_row['feh'] = row['FEH']
new_row['u_feh'] = 0.010
new_row['vsini'] = row['VSINI']
new_row['source'] = 'Brewer'
new_row['source_name'] = row['NAME']
stars = stars.append(pd.Series(new_row), ignore_index=True)
else:
new_row = {}
new_row['name'] = row['NAME']
new_row['source'] = 'Brewer'
nospectra = nospectra.append(pd.Series(new_row), ignore_index=True)
return stars, nospectra
def read_brewer(catalogdir, cps_list):
"""Read in Brewer (2016) catalog
Args:
catalogdir (str): Path of catalog directory.
cps_list (pd.DataFrame): Dataframe containing list of CPS spectra.
Returns:
stars (pd.DataFrame): Stars in source which have CPS spectra
nospec (pd.DataFrame): Stars in source which don't have CPS spectra
"""
# brewer_data = ascii.read(os.path.join(catalogdir, BREWER_FILENAME))
brewer_data = pd.read_csv(os.path.join(catalogdir, BREWER_FILENAME),
index_col=0)
stars = pd.DataFrame(columns=Library.LIB_COLS)
nospectra = pd.DataFrame(columns=NOSPECTRA_COLS)
for idx, row in brewer_data.iterrows():
query_result = cpsutils.find_spectra(row['NAME'], cps_list)
if not query_result.empty:
new_row = {}
new_row['cps_name'] = str(query_result.iloc[0]['name'])
new_row['lib_obs'] = query_result.iloc[0]['obs']
new_row['snr'] = query_result.iloc[0]['snr']
new_row['Teff'] = row['TEFF']
new_row['u_Teff'] = 25
new_row['logg'] = row['LOGG']
new_row['u_logg'] = 0.028
new_row['feh'] = row['FEH']
new_row['u_feh'] = 0.010
new_row['vsini'] = row['VSINI']
new_row['source'] = 'Brewer'
new_row['source_name'] = row['NAME']
stars = stars.append(pd.Series(new_row), ignore_index=True)
else:
new_row = {}
new_row['name'] = row['NAME']
new_row['source'] = 'Brewer'
nospectra = nospectra.append(pd.Series(new_row), ignore_index=True)
return stars, nospectra
def read_bruntt(catalogdir, cps_list):
"""Read in Bruntt (2012) catalog
Args:
catalogdir (str): Path of catalog directory.
cps_list (pd.DataFrame): Dataframe containing list of CPS spectra.
Returns:
stars (pd.DataFrame): Stars in source which have CPS spectra
nospec (pd.DataFrame): Stars in source which don't have CPS spectra
"""
b_data = ascii.read(os.path.join(catalogdir, BRUNTT_FILENAME),
readme=os.path.join(catalogdir, BRUNTT_README))
stars = pd.DataFrame(columns=Library.LIB_COLS)
nospectra = pd.DataFrame(columns=NOSPECTRA_COLS)
for row in b_data:
query_result = cpsutils.find_spectra('KIC'+str(row['KIC']), cps_list)
if not query_result.empty:
new_row = {}
new_row['cps_name'] = str(query_result.iloc[0]['name'])
new_row['lib_obs'] = query_result.iloc[0]['obs']
new_row['snr'] = query_result.iloc[0]['snr']
new_row['Teff'] = row['Teff']
new_row['u_Teff'] = 60
new_row['feh'] = row['[Fe/H]']
new_row['u_feh'] = 0.06
new_row['logg'] = row['logg']
new_row['u_logg'] = 0.03
new_row['vsini'] = row['vsini']
new_row['source'] = 'Bruntt'
new_row['source_name'] = 'KIC'+str(row['KIC'])
stars = stars.append(pd.Series(new_row), ignore_index=True)
else:
new_row = {}
new_row['name'] = 'KIC'+str(row['KIC'])
new_row['source'] = 'Bruntt'
nospectra = nospectra.append(pd.Series(new_row), ignore_index=True)
return stars, nospectra
def read_bruntt(catalogdir, cps_list):
"""Read in Bruntt (2012) catalog
Args:
catalogdir (str): Path of catalog directory.
cps_list (pd.DataFrame): Dataframe containing list of CPS spectra.
Returns:
stars (pd.DataFrame): Stars in source which have CPS spectra
nospec (pd.DataFrame): Stars in source which don't have CPS spectra
"""
b_data = ascii.read(os.path.join(catalogdir, BRUNTT_FILENAME),
readme=os.path.join(catalogdir, BRUNTT_README))
stars = pd.DataFrame(columns=Library.LIB_COLS)
nospectra = pd.DataFrame(columns=NOSPECTRA_COLS)
for row in b_data:
query_result = cpsutils.find_spectra('KIC' + str(row['KIC']), cps_list)
if not query_result.empty:
new_row = {}
new_row['cps_name'] = str(query_result.iloc[0]['name'])
new_row['lib_obs'] = query_result.iloc[0]['obs']
new_row['snr'] = query_result.iloc[0]['snr']
new_row['Teff'] = row['Teff']
new_row['u_Teff'] = 60
new_row['feh'] = row['[Fe/H]']
new_row['u_feh'] = 0.06
new_row['logg'] = row['logg']
new_row['u_logg'] = 0.03
new_row['vsini'] = row['vsini']
new_row['source'] = 'Bruntt'
new_row['source_name'] = 'KIC' + str(row['KIC'])
stars = stars.append(pd.Series(new_row), ignore_index=True)
else:
new_row = {}
new_row['name'] = 'KIC' + str(row['KIC'])
new_row['source'] = 'Bruntt'
nospectra = nospectra.append(pd.Series(new_row), ignore_index=True)
return stars, nospectra
def read_vonbraun(catalogdir, cps_list):
"""Read in Von Braun (2013) catalog
Args:
catalogdir (str): Path of catalog directory.
cps_list (pd.DataFrame): Dataframe containing list of CPS spectra.
Returns:
stars (pd.DataFrame): Stars in source which have CPS spectra
nospec (pd.DataFrame): Stars in source which don't have CPS spectra
"""
vb_data = ascii.read(os.path.join(catalogdir, VONBRAUN_FILENAME))
stars = pd.DataFrame(columns=Library.LIB_COLS)
nospectra = pd.DataFrame(columns=NOSPECTRA_COLS)
for row in vb_data:
query_result = cpsutils.find_spectra(row['Star'], cps_list)
if not query_result.empty:
new_row = {}
new_row['cps_name'] = str(query_result.iloc[0]['name'])
new_row['lib_obs'] = query_result.iloc[0]['obs']
new_row['snr'] = query_result.iloc[0]['snr']
new_row['Teff'] = row['Teff']
new_row['u_Teff'] = row['eTeff']
new_row['radius'] = row['Radius']
new_row['u_radius'] = row['eRadius']
new_row['feh'] = row['FeH']
new_row['u_feh'] = 0.10
new_row['source'] = 'Von Braun'
new_row['source_name'] = row['Star']
stars = stars.append(pd.Series(new_row), ignore_index=True)
else:
new_row = {}
new_row['name'] = row['Star']
new_row['source'] = 'Von Braun'
nospectra = nospectra.append(pd.Series(new_row), ignore_index=True)
return stars, nospectra
def read_vonbraun(catalogdir, cps_list):
"""Read in Von Braun (2013) catalog
Args:
catalogdir (str): Path of catalog directory.
cps_list (pd.DataFrame): Dataframe containing list of CPS spectra.
Returns:
stars (pd.DataFrame): Stars in source which have CPS spectra
nospec (pd.DataFrame): Stars in source which don't have CPS spectra
"""
vb_data = ascii.read(os.path.join(catalogdir, VONBRAUN_FILENAME))
stars = pd.DataFrame(columns=Library.LIB_COLS)
nospectra = pd.DataFrame(columns=NOSPECTRA_COLS)
for row in vb_data:
query_result = cpsutils.find_spectra(row['Star'], cps_list)
if not query_result.empty:
new_row = {}
new_row['cps_name'] = str(query_result.iloc[0]['name'])
new_row['lib_obs'] = query_result.iloc[0]['obs']
new_row['snr'] = query_result.iloc[0]['snr']
new_row['Teff'] = row['Teff']
new_row['u_Teff'] = row['eTeff']
new_row['radius'] = row['Radius']
new_row['u_radius'] = row['eRadius']
new_row['feh'] = row['FeH']
new_row['u_feh'] = 0.10
new_row['source'] = 'Von Braun'
new_row['source_name'] = row['Star']
stars = stars.append(pd.Series(new_row), ignore_index=True)
else:
new_row = {}
new_row['name'] = row['Star']
new_row['source'] = 'Von Braun'
nospectra = nospectra.append(pd.Series(new_row), ignore_index=True)
return stars, nospectra
def read_ramirez(catalogdir, cps_list):
"""Read in Ramirez (2005) catalog
Args:
catalogdir (str): Path of catalog directory.
cps_list (pd.DataFrame): Dataframe containing list of CPS spectra.
Returns:
stars (pd.DataFrame): Stars in source which have CPS spectra
nospec (pd.DataFrame): Stars in source which don't have CPS spectra
"""
ramirez_data = pd.read_csv(os.path.join(catalogdir, RAMIREZ_FILENAME))
stars = pd.DataFrame(columns=Library.LIB_COLS)
nospectra = pd.DataFrame(columns=NOSPECTRA_COLS)
for idx, row in ramirez_data.iterrows():
query_result = cpsutils.find_spectra(row['name'], cps_list)
if not query_result.empty:
new_row = {}
new_row['cps_name'] = str(query_result.iloc[0]['name'])
new_row['lib_obs'] = query_result.iloc[0]['obs']
new_row['Teff'] = row['Teff']
new_row['u_Teff'] = row['u_Teff']
new_row['logg'] = row['logg']
new_row['u_logg'] = row['u_logg']
new_row['feh'] = row['feh']
new_row['u_feh'] = row['u_feh']
new_row['source'] = 'Ramirez'
new_row['source_name'] = row['name']
stars = stars.append(pd.Series(new_row), ignore_index=True)
else:
new_row = {}
new_row['name'] = row['name']
new_row['source'] = 'Ramirez'
nospectra = nospectra.append(pd.Series(new_row), ignore_index=True)
return stars, nospectra
def read_ramirez(catalogdir, cps_list):
"""Read in Ramirez (2005) catalog
Args:
catalogdir (str): Path of catalog directory.
cps_list (pd.DataFrame): Dataframe containing list of CPS spectra.
Returns:
stars (pd.DataFrame): Stars in source which have CPS spectra
nospec (pd.DataFrame): Stars in source which don't have CPS spectra
"""
ramirez_data = pd.read_csv(os.path.join(catalogdir, RAMIREZ_FILENAME))
stars = pd.DataFrame(columns=Library.LIB_COLS)
nospectra = pd.DataFrame(columns=NOSPECTRA_COLS)
for idx, row in ramirez_data.iterrows():
query_result = cpsutils.find_spectra(row['name'], cps_list)
if not query_result.empty:
new_row = {}
new_row['cps_name'] = str(query_result.iloc[0]['name'])
new_row['lib_obs'] = query_result.iloc[0]['obs']
new_row['Teff'] = row['Teff']
new_row['u_Teff'] = row['u_Teff']
new_row['logg'] = row['logg']
new_row['u_logg'] = row['u_logg']
new_row['feh'] = row['feh']
new_row['u_feh'] = row['u_feh']
new_row['source'] = 'Ramirez'
new_row['source_name'] = row['name']
stars = stars.append(pd.Series(new_row), ignore_index=True)
else:
new_row = {}
new_row['name'] = row['name']
new_row['source'] = 'Ramirez'
nospectra = nospectra.append(pd.Series(new_row), ignore_index=True)
return stars, nospectra