def get_energies(method):
'''
Small helper method to query all energies for the selected levels
that match a specific method.
'''
q = (self.session.query(
LevelEnergy.level_id.label('level_id'),
LevelEnergy.quantity.to('eV').value.label('energy')).join(
subq, LevelEnergy.level_id == subq.c.level_id).filter(
LevelEnergy.method == method))
return pd.DataFrame(q.all(),
columns=query_columns(q)).set_index('level_id')
def _get_all_lines_data(self):
"""
This function returns line data about the selected atoms from the selected
DataSources. The data is returned in a pandas DataFrame. The index is
'line_id' and the following columns exist:
lower_level_id, upper_level_id, wavelength [angstrom], gf, loggf
Note that the wavelength is given as vacuum wavelength
"""
levels_subq = self._build_levels_q()
wavelength = aliased(LineWavelength)
gf = aliased(LineGFValue)
lines_q = (
self.session.
query(
Line.line_id.label('line_id'),
Line.lower_level_id.label('lower_level_id'),
Line.upper_level_id.label('upper_level_id'),
wavelength.quantity.to('angstrom').value.label('wavelength'),
gf.quantity.value.label('gf'),
wavelength.medium.label('wl_medium')
).
join(wavelength).
join(gf).
join(
levels_subq,
Line.lower_level_id == levels_subq.c.level_id)
)
lines = pd.DataFrame(
lines_q.all(),
columns=query_columns(lines_q)
).set_index('line_id')
air_mask = lines['wl_medium'] == MEDIUM_AIR
lines.loc[air_mask, 'wavelength'] = convert_wavelength_air2vacuum(
lines.loc[air_mask, 'wavelength'])
lines.pop('wl_medium')
lines['loggf'] = np.log10(lines['gf'])
if lines.index.duplicated().any():
raise ValueError(
'There are duplicated line_ids, something went wrong!')
return lines
def get_energies(method):
'''
Small helper method to query all energies for the selected levels
that match a specific method.
'''
q = (
self.session.
query(
LevelEnergy.level_id.label('level_id'),
LevelEnergy.quantity.to('eV').value.label('energy')
).
join(
subq,
LevelEnergy.level_id == subq.c.level_id
).
filter(
LevelEnergy.method == method
)
)
return pd.DataFrame(
q.all(),
columns=query_columns(q)
).set_index('level_id')
def _get_all_levels_data(self):
"""
This function returns level data about the selected atoms from the selected
DataSources. The data is returned in a pandas DataFrame. The index is
'level_id' and the following columns exist:
atomic_number, ion_number, g, energy [eV]
Note about the energy: The database has three different values for the
method of determining the energy: meas(ured), theor(etical) and None
(for ground states from NIST afaik) A level can have multiple energies
associated, that is a measured and a theoretical energy. In that case,
we pick the energy in order of availability according to the list above
(1st measured etc.)
"""
subq = self._build_levels_q()
levels_data_q = (self.session.query(
Level.level_id.label('level_id'),
Level.atomic_number.label('atomic_number'),
Level.ion_charge.label('ion_number'),
Level.g.label('g'),
).join(subq, Level.level_id == subq.c.level_id))
def get_energies(method):
'''
Small helper method to query all energies for the selected levels
that match a specific method.
'''
q = (self.session.query(
LevelEnergy.level_id.label('level_id'),
LevelEnergy.quantity.to('eV').value.label('energy')).join(
subq, LevelEnergy.level_id == subq.c.level_id).filter(
LevelEnergy.method == method))
return pd.DataFrame(q.all(),
columns=query_columns(q)).set_index('level_id')
levels = pd.DataFrame(
levels_data_q.all(),
columns=query_columns(levels_data_q)).set_index('level_id')
energies = [get_energies(k) for k in ['meas', 'theor', None]]
energy = pd.DataFrame(index=levels.index)
energy['energy'] = np.nan
for df in energies: # Go backwards and skip the last
# update data based on index
energy.update(df, overwrite=False)
levels['energy'] = energy.energy
if levels.g.isnull().any():
print("Some of the ground state g-values are not available."
" This is likely because very heavy elements are included"
"and is not unusual. They will be omitted from these"
"calculations")
levels = levels.loc[~levels.g.isnull()]
if levels.isnull().any().any():
raise ValueError('Inconsistent database, some values are None.' +
str(levels[levels.isnull()]))
return levels
def _get_all_levels_data(self):
"""
This function returns level data about the selected atoms from the selected
DataSources. The data is returned in a pandas DataFrame. The index is
'level_id' and the following columns exist:
atomic_number, ion_number, g, energy [eV]
Note about the energy: The database has three different values for the
method of determining the energy: meas(ured), theor(etical) and None
(for ground states from NIST afaik) A level can have multiple energies
associated, that is a measured and a theoretical energy. In that case,
we pick the energy in order of availability according to the list above
(1st measured etc.)
"""
subq = self._build_levels_q()
levels_data_q = (
self.session.
query(
Level.level_id.label('level_id'),
Level.atomic_number.label('atomic_number'),
Level.ion_charge.label('ion_number'),
Level.g.label('g'),
).
join(
subq,
Level.level_id == subq.c.level_id
)
)
def get_energies(method):
'''
Small helper method to query all energies for the selected levels
that match a specific method.
'''
q = (
self.session.
query(
LevelEnergy.level_id.label('level_id'),
LevelEnergy.quantity.to('eV').value.label('energy')
).
join(
subq,
LevelEnergy.level_id == subq.c.level_id
).
filter(
LevelEnergy.method == method
)
)
return pd.DataFrame(
q.all(),
columns=query_columns(q)
).set_index('level_id')
levels = pd.DataFrame(
levels_data_q.all(),
columns=query_columns(levels_data_q)
).set_index('level_id')
energies = [get_energies(k) for k in ['meas', 'theor', None]]
energy = pd.DataFrame(index=levels.index)
energy['energy'] = np.nan
for df in energies: # Go backwards and skip the last
# update data based on index
energy.update(df, overwrite=False)
levels['energy'] = energy.energy
if levels.g.isnull().any():
print ("Some of the ground state g-values are not available."
" This is likely because very heavy elements are included"
"and is not unusual. They will be omitted from these"
"calculations")
levels = levels.loc[~levels.g.isnull()]
if levels.isnull().any().any():
raise ValueError(
'Inconsistent database, some values are None.' +
str(levels[levels.isnull()]))
return levels
