"""
Source classes for continuum files
"""
import astropy.units as u
import h5py
import numpy as np
import pathlib
import plasmapy
from fiasco.io.generic import GenericParser
__all__ = ['GffguParser', 'GffintParser', 'KlgfbParser', 'VernerParser', 'ItohParser',
'HSeqParser', 'HeSeqParser']
[docs]
class GffguParser(GenericParser):
"""
Free-free Gaunt factor as a function of scaled frequency and energy
"""
filetype = 'gffgu'
dtypes = [float, float, float]
units = [u.dimensionless_unscaled, u.dimensionless_unscaled, u.dimensionless_unscaled]
headings = ['u', 'gamma_squared', 'gaunt_factor']
descriptions = ['scaled frequency', 'scaled temperature', 'free-free Gaunt factor']
def __init__(self, filename, **kwargs):
super().__init__(filename, **kwargs)
self.full_path = pathlib.Path(kwargs.get('full_path',
self.ascii_dbase_root / 'continuum' / filename))
self.body_index = 5
[docs]
def preprocessor(self, table, line, index):
if index >= self.body_index and '--' not in line:
super().preprocessor(table, line, index)
[docs]
def to_hdf5(self, hf, df, **kwargs):
grp_name = '/'.join(['continuum', self.filetype])
if grp_name not in hf:
grp = hf.create_group(grp_name)
grp.attrs['chianti_version'] = df.meta['chianti_version']
grp.attrs['footer'] = df.meta['footer']
else:
grp = hf[grp_name]
for name in df.colnames:
col = df[name]
if type(col) == u.Quantity:
data = col.value
else:
data = col.data
if '<U' in data.dtype.str:
numchar = data.dtype.str[2:]
data = data.astype(f'|S{numchar}')
if name in grp:
ds = grp[name]
else:
if data.dtype == np.dtype('O'):
ragged_dtype = h5py.special_dtype(vlen=np.dtype('float64'))
ds = grp.create_dataset(name, data=data, dtype=ragged_dtype)
else:
ds = grp.create_dataset(name, data=data, dtype=data.dtype)
if col.unit is None:
ds.attrs['unit'] = 'SKIP'
else:
ds.attrs['unit'] = col.unit.to_string()
ds.attrs['description'] = df.meta['descriptions'][name]
[docs]
class GffintParser(GffguParser):
"""
Total free-free Gaunt factor as a function of scaled temperature.
"""
filetype = 'gffint'
dtypes = [float, float, float, float, float]
units = [u.dimensionless_unscaled, u.dimensionless_unscaled, u.dimensionless_unscaled,
u.dimensionless_unscaled, u.dimensionless_unscaled]
headings = ['log_gamma_squared', 'gaunt_factor', 's1', 's2', 's3']
descriptions = ['log scaled temperature', 'total free-free Gaunt factor', 'spline coefficient',
'spline coefficient', 'spline coefficient']
def __init__(self, filename, **kwargs):
super().__init__(filename, **kwargs)
self.body_index = 4
[docs]
class KlgfbParser(GenericParser):
"""
Free-bound gaunt factor as a function of photon energy for several different energy levels.
"""
filetype = 'klgfb'
dtypes = [int, int, float, float]
units = [None, None, u.dimensionless_unscaled, u.dimensionless_unscaled]
headings = ['n', 'l', 'log_pe', 'log_gaunt_factor']
descriptions = ['principal quantum number', 'orbital angular momentum number',
'log photon energy divided by ionization potential',
'log free-bound Gaunt factor']
def __init__(self, filename, **kwargs):
super().__init__(filename, **kwargs)
self.full_path = pathlib.Path(kwargs.get('full_path',
self.ascii_dbase_root / 'continuum' / filename))
[docs]
def preprocessor(self, table, line, index):
if index == 0:
pass
elif index == 1:
self._photon_energy = np.array(line.strip().split(), dtype=float)
else:
line = line.strip().split()
gf = np.array(line[2:], dtype=float)
table.append(line[:2] + [self._photon_energy, gf])
[docs]
def to_hdf5(self, hf, df, **kwargs):
grp_name = '/'.join(['continuum', self.filetype])
if grp_name not in hf:
grp = hf.create_group(grp_name)
grp.attrs['chianti_version'] = df.meta['chianti_version']
grp.attrs['footer'] = df.meta['footer']
else:
grp = hf[grp_name]
for name in df.colnames:
col = df[name]
if type(col) == u.Quantity:
data = col.value
else:
data = col.data
if '<U' in data.dtype.str:
numchar = data.dtype.str[2:]
data = data.astype(f'|S{numchar}')
if name in grp:
ds = grp[name]
else:
if data.dtype == np.dtype('O'):
ragged_dtype = h5py.special_dtype(vlen=np.dtype('float64'))
ds = grp.create_dataset(name, data=data, dtype=ragged_dtype)
else:
ds = grp.create_dataset(name, data=data, dtype=data.dtype)
if col.unit is None:
ds.attrs['unit'] = 'SKIP'
else:
ds.attrs['unit'] = col.unit.to_string()
ds.attrs['description'] = df.meta['descriptions'][name]
[docs]
class VernerParser(GenericParser):
"""
Fit parameters for calculating partial photoionization cross-sections using
the method of :cite:t:`verner_analytic_1995`.
"""
filetype = 'verner_short'
dtypes = [int, int, int, int, float, float, float, float, float, float]
units = [None, None, None, None, u.eV, u.eV, u.megabarn, u.dimensionless_unscaled,
u.dimensionless_unscaled, u.dimensionless_unscaled]
headings = ['Z', 'n_electrons', 'n', 'l', 'E_thresh', 'E_0_fit', 'sigma_0', 'y_a_fit', 'P_fit',
'y_w_fit']
descriptions = ['atomic number', 'number of electrons', 'principal quantum number',
'orbital angular momentum number',
'threshold energy below which cross-section is 0',
'E_0 fit parameter', 'nominal value of cross-section', 'y_a fit parameter',
'P fit parameter', 'y_w fit parameter']
def __init__(self, filename, **kwargs):
super().__init__(filename, **kwargs)
self.full_path = pathlib.Path(kwargs.get('full_path',
self.ascii_dbase_root / 'continuum' / filename))
[docs]
def to_hdf5(self, hf, df, **kwargs):
for row in df:
el = plasmapy.particles.atomic_symbol(int(row['Z'])).lower()
stage = row['Z'] - row['n_electrons'] + 1
grp_name = f'{el}/{el}_{stage}/continuum/{self.filetype}'
if grp_name not in hf:
grp = hf.create_group(grp_name)
grp.attrs['chianti_version'] = df.meta['chianti_version']
grp.attrs['footer'] = df.meta['footer']
else:
grp = hf[grp_name]
for col in row.colnames:
if col == 'Z' or col == 'n_electrons':
continue
ds = grp.create_dataset(col, data=row[col])
ds.attrs['description'] = df.meta['descriptions'][col]
if not hasattr(row[col], 'unit'):
ds.attrs['unit'] = 'SKIP'
else:
ds.attrs['unit'] = row[col].unit.to_string()
[docs]
class ItohParser(GenericParser):
"""
Fit parameters for calculating relativistic free-free Gaunt factor using the method of
:cite:t:`itoh_relativistic_2000`.
"""
filetype = 'itoh'
dtypes = [int, float]
units = [None, u.dimensionless_unscaled]
headings = ['Z', 'a']
descriptions = ['atomic number', 'fit coefficient']
def __init__(self, filename, **kwargs):
super().__init__(filename, **kwargs)
self.full_path = pathlib.Path(kwargs.get('full_path',
self.ascii_dbase_root / 'continuum' / filename))
[docs]
def preprocessor(self, table, line, index):
a_matrix = np.array(line.strip().split()).reshape((11, 11))
table.append([index+1] + [a_matrix])
[docs]
def to_hdf5(self, hf, df, **kwargs):
for row in df:
el = plasmapy.particles.atomic_symbol(int(row['Z'])).lower()
grp_name = f'{el}/continuum/{self.filetype}'
if grp_name not in hf:
grp = hf.create_group(grp_name)
grp.attrs['chianti_version'] = df.meta['chianti_version']
grp.attrs['footer'] = df.meta['footer']
else:
grp = hf[grp_name]
for col in row.colnames:
if col == 'Z':
continue
ds = grp.create_dataset(col, data=row[col])
ds.attrs['description'] = df.meta['descriptions'][col]
if not hasattr(row[col], 'unit'):
ds.attrs['unit'] = 'SKIP'
else:
ds.attrs['unit'] = row[col].unit.to_string()
[docs]
class HSeqParser(GenericParser):
r"""
Parameters for calculating two-photon continuum for hydrogen-like ions
Notes
-----
* The parameter :math: `\psi_{\text{norm}}` (called :math: `A_{\text{sum}}` in CHIANTI) is a normalization factor of
the integral of the spectral distribution function :math:`\psi(y)` from 0 to 1, such
that :math: `\frac{1}{\psi_{\text{norm}}} \int_{0}^{1} \psi(y) dy = 2`. This normalization is only used For
hydrogenic ions.
"""
filetype = 'hseq_2photon'
dtypes = [int, float, int, float, float, float]
units = [None, u.dimensionless_unscaled, None, 1/u.s, u.dimensionless_unscaled, u.dimensionless_unscaled]
headings = ['Z', 'y', 'Z_0', 'A', 'psi_norm', 'psi']
descriptions = ['atomic number', 'fraction of energy carried by one of the two photons',
'nominal atomic number', 'radiative decay rate', 'normalization of the integral of psi from 0 to 1',
'spectral distribution function']
def __init__(self, filename, **kwargs):
super().__init__(filename, **kwargs)
self.full_path = pathlib.Path(kwargs.get('full_path',
self.ascii_dbase_root / 'continuum' / filename))
[docs]
def preprocessor(self, table, line, index):
if index == 0:
self._y0 = np.array(line.strip().split(), dtype=float)
elif index == 1:
self._z0 = np.array(line.strip().split(), dtype=float)
else:
line = line.strip().split()
table.append([line[0]] + [self._y0] + [self._z0] + line[1:3] + [np.array(line[3:])])
[docs]
def to_hdf5(self, hf, df, **kwargs):
for row in df:
el = plasmapy.particles.atomic_symbol(int(row['Z'])).lower()
grp_name = f'{el}/continuum/{self.filetype}'
if grp_name not in hf:
grp = hf.create_group(grp_name)
grp.attrs['chianti_version'] = df.meta['chianti_version']
grp.attrs['footer'] = df.meta['footer']
else:
grp = hf[grp_name]
for col in row.colnames:
if col == 'Z':
continue
ds = grp.create_dataset(col, data=row[col])
ds.attrs['description'] = df.meta['descriptions'][col]
if not hasattr(row[col], 'unit'):
ds.attrs['unit'] = 'SKIP'
else:
ds.attrs['unit'] = row[col].unit.to_string()
[docs]
class HeSeqParser(GenericParser):
"""
Parameters for calculating two-photon continuum for helium-like ions.
"""
filetype = 'heseq_2photon'
dtypes = [int, float, float, float]
units = [None, u.dimensionless_unscaled, 1/u.s, u.dimensionless_unscaled]
headings = ['Z', 'y', 'A', 'psi']
descriptions = ['atomic number', 'fraction of energy carried by one of the two photons',
'radiative decay rate', 'spectral distribution function']
def __init__(self, filename, **kwargs):
super().__init__(filename, **kwargs)
self.full_path = pathlib.Path(kwargs.get('full_path',
self.ascii_dbase_root / 'continuum' / filename))
[docs]
def preprocessor(self, table, line, index):
if index == 0:
self._y0 = np.array(line.strip().split(), dtype=float)
else:
line = line.strip().split()
table.append([line[0]] + [self._y0] + line[1:2] + [np.array(line[2:])])
[docs]
def to_hdf5(self, hf, df, **kwargs):
for row in df:
el = plasmapy.particles.atomic_symbol(int(row['Z'])).lower()
grp_name = f'{el}/continuum/{self.filetype}'
if grp_name not in hf:
grp = hf.create_group(grp_name)
grp.attrs['chianti_version'] = df.meta['chianti_version']
grp.attrs['footer'] = df.meta['footer']
else:
grp = hf[grp_name]
for col in row.colnames:
if col == 'Z':
continue
ds = grp.create_dataset(col, data=row[col])
ds.attrs['description'] = df.meta['descriptions'][col]
if not hasattr(row[col], 'unit'):
ds.attrs['unit'] = 'SKIP'
else:
ds.attrs['unit'] = row[col].unit.to_string()
