def test_get_last_line_counts(self, line_info_widget, allowed_species,
filter_mode, group_mode):
"""
Test for get_last_line_counts() method.
Since this method depends on get_species_interactions() so we need to
make sure that we select only allowed species i.e. present within the
wavelength range selected by the get_species_interactions(), which is
being done here by allowed_species fixture.
"""
if allowed_species is None:
last_line_counts_df = line_info_widget.get_last_line_counts(
None, filter_mode, group_mode)
# Dataframe contains all falsy values (proxy for empty)
assert last_line_counts_df.all(axis=None) == False
return
for selected_species in allowed_species:
last_line_counts_df = line_info_widget.get_last_line_counts(
selected_species, filter_mode, group_mode)
last_lines_in = (
line_info_widget.line_interaction_analysis[filter_mode].
last_line_in.xs(
key=species_string_to_tuple(selected_species),
level=["atomic_number", "ion_number"],
).reset_index())
last_lines_out = (
line_info_widget.line_interaction_analysis[filter_mode].
last_line_out.xs(
key=species_string_to_tuple(selected_species),
level=["atomic_number", "ion_number"],
).reset_index())
if group_mode == "exc":
expected_df_length = last_lines_in.groupby("line_id").ngroups
elif group_mode == "de-exc":
expected_df_length = last_lines_out.groupby("line_id").ngroups
elif group_mode == "both":
expected_df_length = last_lines_in.groupby(
["line_id", last_lines_out["line_id"]]).ngroups
# Test shape of the dataframe
assert last_line_counts_df.shape == (expected_df_length, 1)
def test_species_string_to_tuple(species_string, species_tuple):
assert species_string_to_tuple(species_string) == species_tuple
with pytest.raises(MalformedSpeciesError):
species_string_to_tuple('II')
with pytest.raises(MalformedSpeciesError):
species_string_to_tuple('He Si')
with pytest.raises(ValueError):
species_string_to_tuple('He IX')
def test_species_string_to_tuple(species_string, species_tuple):
assert species_string_to_tuple(species_string) == species_tuple
with pytest.raises(MalformedSpeciesError):
species_string_to_tuple('II')
with pytest.raises(MalformedSpeciesError):
species_string_to_tuple('He Si')
with pytest.raises(ValueError):
species_string_to_tuple('He IX')
def assemble_plasma(config, model, atom_data=None):
"""
Create a BasePlasma instance from a Configuration object
and a Radial1DModel.
Parameters
----------
config : io.config_reader.Configuration
model : model.Radial1DModel
atom_data : atomic.AtomData
If None, an attempt will be made to read the atomic data
from config.
Returns
-------
: plasma.BasePlasma
"""
# Convert the nlte species list to a proper format.
nlte_species = [
species_string_to_tuple(s) for s in config.plasma.nlte.species
]
# Convert the continuum interaction species list to a proper format.
continuum_interaction_species = [
species_string_to_tuple(s)
for s in config.plasma.continuum_interaction.species
]
continuum_interaction_species = pd.MultiIndex.from_tuples(
continuum_interaction_species, names=["atomic_number", "ion_number"])
if atom_data is None:
if "atom_data" in config:
if os.path.isabs(config.atom_data):
atom_data_fname = config.atom_data
else:
atom_data_fname = os.path.join(config.config_dirname,
config.atom_data)
else:
raise ValueError("No atom_data option found in the configuration.")
logger.info("Reading Atomic Data from %s", atom_data_fname)
try:
atom_data = AtomData.from_hdf(atom_data_fname)
except TypeError as e:
print(
e,
"Error might be from the use of an old-format of the atomic database, \n"
"please see https://github.com/tardis-sn/tardis-refdata/tree/master/atom_data"
" for the most recent version.",
)
raise
atom_data.prepare_atom_data(
model.abundance.index,
line_interaction_type=config.plasma.line_interaction_type,
nlte_species=nlte_species,
)
# Check if continuum interaction species are in selected_atoms
continuum_atoms = continuum_interaction_species.get_level_values(
"atomic_number")
continuum_atoms_in_selected_atoms = np.all(
continuum_atoms.isin(atom_data.selected_atomic_numbers))
if not continuum_atoms_in_selected_atoms:
raise PlasmaConfigError("Not all continuum interaction species "
"belong to atoms that have been specified "
"in the configuration.")
kwargs = dict(
t_rad=model.t_radiative,
abundance=model.abundance,
density=model.density,
atomic_data=atom_data,
time_explosion=model.time_explosion,
w=model.dilution_factor,
link_t_rad_t_electron=0.9,
continuum_interaction_species=continuum_interaction_species,
)
plasma_modules = basic_inputs + basic_properties
property_kwargs = {}
if config.plasma.continuum_interaction.species:
line_interaction_type = config.plasma.line_interaction_type
if line_interaction_type != "macroatom":
raise PlasmaConfigError(
"Continuum interactions require line_interaction_type "
"macroatom (instead of {}).".format(line_interaction_type))
plasma_modules += continuum_interaction_properties
plasma_modules += continuum_interaction_inputs
if config.plasma.continuum_interaction.enable_adiabatic_cooling:
plasma_modules += adiabatic_cooling_properties
if config.plasma.continuum_interaction.enable_two_photon_decay:
plasma_modules += two_photon_properties
transition_probabilities_outputs = [
plasma_property.transition_probabilities_outputs
for plasma_property in plasma_modules
if issubclass(plasma_property, TransitionProbabilitiesProperty)
]
transition_probabilities_outputs = [
item for sublist in transition_probabilities_outputs
for item in sublist
]
property_kwargs[MarkovChainTransProbsCollector] = {
"inputs": transition_probabilities_outputs
}
kwargs.update(
gamma_estimator=None,
bf_heating_coeff_estimator=None,
alpha_stim_estimator=None,
volume=model.volume,
r_inner=model.r_inner,
t_inner=model.t_inner,
)
if config.plasma.radiative_rates_type == "blackbody":
plasma_modules.append(JBluesBlackBody)
elif config.plasma.radiative_rates_type == "dilute-blackbody":
plasma_modules.append(JBluesDiluteBlackBody)
elif config.plasma.radiative_rates_type == "detailed":
plasma_modules += detailed_j_blues_properties + detailed_j_blues_inputs
kwargs.update(
r_inner=model.r_inner,
t_inner=model.t_inner,
volume=model.volume,
j_blue_estimator=None,
)
property_kwargs[JBluesDetailed] = {
"w_epsilon": config.plasma.w_epsilon
}
else:
raise ValueError(
f"radiative_rates_type type unknown - {config.plasma.radiative_rates_type}"
)
if config.plasma.excitation == "lte":
plasma_modules += lte_excitation_properties
elif config.plasma.excitation == "dilute-lte":
plasma_modules += dilute_lte_excitation_properties
if config.plasma.ionization == "lte":
plasma_modules += lte_ionization_properties
elif config.plasma.ionization == "nebular":
plasma_modules += nebular_ionization_properties
if nlte_species:
plasma_modules += nlte_properties
nlte_conf = config.plasma.nlte
plasma_modules.append(LevelBoltzmannFactorNLTE.from_config(nlte_conf))
property_kwargs[StimulatedEmissionFactor] = dict(
nlte_species=nlte_species)
else:
plasma_modules += non_nlte_properties
if config.plasma.line_interaction_type in ("downbranch", "macroatom"):
plasma_modules += macro_atom_properties
if "delta_treatment" in config.plasma:
property_kwargs[RadiationFieldCorrection] = dict(
delta_treatment=config.plasma.delta_treatment)
if config.plasma.helium_treatment == "recomb-nlte":
plasma_modules += helium_nlte_properties
elif config.plasma.helium_treatment == "numerical-nlte":
plasma_modules += helium_numerical_nlte_properties
# TODO: See issue #633
if config.plasma.heating_rate_data_file in ["none", None]:
raise PlasmaConfigError("Heating rate data file not specified")
else:
property_kwargs[HeliumNumericalNLTE] = dict(
heating_rate_data_file=config.plasma.heating_rate_data_file)
else:
plasma_modules += helium_lte_properties
if model._electron_densities:
electron_densities = pd.Series(model._electron_densities.cgs.value)
if config.plasma.helium_treatment == "numerical-nlte":
property_kwargs[IonNumberDensityHeNLTE] = dict(
electron_densities=electron_densities)
else:
property_kwargs[IonNumberDensity] = dict(
electron_densities=electron_densities)
kwargs["helium_treatment"] = config.plasma.helium_treatment
plasma = BasePlasma(
plasma_properties=plasma_modules,
property_kwargs=property_kwargs,
**kwargs,
)
return plasma
def assemble_plasma(config, model, atom_data=None):
"""
Create a BasePlasma instance from a Configuration object
and a Radial1DModel.
Parameters
----------
config: ~io.config_reader.Configuration
model: ~model.Radial1DModel
atom_data: ~atomic.AtomData
If None, an attempt will be made to read the atomic data
from config.
Returns
-------
: ~plasma.BasePlasma
"""
# Convert the nlte species list to a proper format.
nlte_species = [species_string_to_tuple(s) for s in
config.plasma.nlte.species]
if atom_data is None:
if 'atom_data' in config:
if os.path.isabs(config.atom_data):
atom_data_fname = config.atom_data
else:
atom_data_fname = os.path.join(config.config_dirname,
config.atom_data)
else:
raise ValueError('No atom_data option found in the configuration.')
logger.info('Reading Atomic Data from %s', atom_data_fname)
try:
atom_data = atomic.AtomData.from_hdf(atom_data_fname)
except TypeError as e:
print (e, 'Error might be from the use of an old-format of the atomic database, \n'
'please see https://github.com/tardis-sn/tardis-refdata/tree/master/atom_data'
',for the most recent version.')
raise
atom_data.prepare_atom_data(
model.abundance.index,
line_interaction_type=config.plasma.line_interaction_type,
nlte_species=nlte_species)
kwargs = dict(t_rad=model.t_radiative, abundance=model.abundance,
density=model.density, atomic_data=atom_data,
time_explosion=model.time_explosion,
w=model.dilution_factor, link_t_rad_t_electron=0.9)
plasma_modules = basic_inputs + basic_properties
property_kwargs = {}
if config.plasma.radiative_rates_type == 'blackbody':
plasma_modules.append(JBluesBlackBody)
elif config.plasma.radiative_rates_type == 'dilute-blackbody':
plasma_modules.append(JBluesDiluteBlackBody)
elif config.plasma.radiative_rates_type == 'detailed':
plasma_modules += detailed_j_blues_properties + detailed_j_blues_inputs
kwargs.update(r_inner=model.r_inner,
t_inner=model.t_inner,
volume=model.volume,
j_blue_estimator=None)
property_kwargs[JBluesDetailed] = {'w_epsilon': config.plasma.w_epsilon}
else:
raise ValueError('radiative_rates_type type unknown - %s',
config.plasma.radiative_rates_type)
if config.plasma.excitation == 'lte':
plasma_modules += lte_excitation_properties
elif config.plasma.excitation == 'dilute-lte':
plasma_modules += dilute_lte_excitation_properties
if config.plasma.ionization == 'lte':
plasma_modules += lte_ionization_properties
elif config.plasma.ionization == 'nebular':
plasma_modules += nebular_ionization_properties
if nlte_species:
plasma_modules += nlte_properties
nlte_conf = config.plasma.nlte
plasma_modules.append(
LevelBoltzmannFactorNLTE.from_config(nlte_conf)
)
property_kwargs[StimulatedEmissionFactor] = dict(
nlte_species=nlte_species)
else:
plasma_modules += non_nlte_properties
if config.plasma.line_interaction_type in ('downbranch', 'macroatom'):
plasma_modules += macro_atom_properties
if 'delta_treatment' in config.plasma:
property_kwargs[RadiationFieldCorrection] = dict(
delta_treatment=config.plasma.delta_treatment)
if config.plasma.helium_treatment == 'recomb-nlte':
plasma_modules += helium_nlte_properties
elif config.plasma.helium_treatment == 'numerical-nlte':
plasma_modules += helium_numerical_nlte_properties
# TODO: See issue #633
if config.plasma.heating_rate_data_file in ['none', None]:
raise PlasmaConfigError('Heating rate data file not specified')
else:
property_kwargs[HeliumNumericalNLTE] = dict(
heating_rate_data_file=config.plasma.heating_rate_data_file)
else:
plasma_modules += helium_lte_properties
if model._electron_densities:
electron_densities = pd.Series(model._electron_densities.cgs.value)
if config.plasma.helium_treatment == 'numerical-nlte':
property_kwargs[IonNumberDensityHeNLTE] = dict(
electron_densities=electron_densities)
else:
property_kwargs[IonNumberDensity] = dict(
electron_densities=electron_densities)
kwargs['helium_treatment'] = config.plasma.helium_treatment
plasma = BasePlasma(plasma_properties=plasma_modules,
property_kwargs=property_kwargs, **kwargs)
return plasma
def generate_plot(
self,
ax=None,
cmap=cm.jet,
bins=None,
xlim=None,
ylim=None,
nelements=None,
twinx=False,
species_list=None,
):
"""Generate the actual "Kromer" plot
Parameters
----------
ax : matplotlib.axes or None
axes object into which the emission part of the Kromer plot should
be plotted; if None, a new one is generated (default None)
cmap : matplotlib.cm.ListedColormap or None
color map object used for the illustration of the different atomic
contributions (default matplotlib.cm.jet)
bins : np.ndarray or None
array of the wavelength bins used for the illustration of the
atomic contributions; if None, the same binning as for the stored
virtual spectrum is used (default None)
xlim : tuple or array-like or None
wavelength limits for the display; if None, the x-axis is
automatically scaled (default None)
ylim : tuple or array-like or None
flux limits for the display; if None, the y-axis is automatically
scaled (default None)
nelements: int or None
number of elements that should be included in the Kromer plots.
The top nelements are determined based on those with the most packet
interactions
twinx : boolean
determines where the absorption part of the Kromer plot is placed,
if True, the absorption part is attached at the top of the main
axes box, otherwise it is placed below the emission part (default
False)
species_list: list of strings or None
list of strings containing the names of species that should be included in the Kromer plots,
e.g. ['Si II', 'Ca II']
Returns
-------
fig : matplotlib.figure
figure instance containing the plot
"""
self._ax = None
self._pax = None
self._cmap = cmap
self._ax = ax
self._ylim = ylim
self._twinx = twinx
# the species list can contain either a specific element, a specific
# ion, a range of ions, or any combination of these if the list
# contains a range of ions, separate each one into a new entry in the
# species list
full_species_list = []
if species_list is not None:
for species in species_list:
# check if a hyphen is present. If it is, then it indicates a
# range of ions. Add each ion in that range to the list
if "-" in species:
element = species.split(" ")[0]
first_ion_numeral = roman_to_int(
species.split(" ")[-1].split("-")[0])
second_ion_numeral = roman_to_int(
species.split(" ")[-1].split("-")[-1])
for i in np.arange(first_ion_numeral,
second_ion_numeral + 1):
full_species_list.append(element + " " +
int_to_roman(i))
else:
full_species_list.append(species)
self._species_list = full_species_list
else:
self._species_list = None
if xlim is None:
self._xlim = [
np.min(self.mdl.spectrum_wave).value,
np.max(self.mdl.spectrum_wave).value,
]
else:
self._xlim = xlim
if bins is None:
self._bins = self.mdl.spectrum_wave[::-1]
else:
self._bins = bins
# get the elements/species to be included in the plot
self._elements_in_kromer_plot = self.line_info
# if no nelements and no species list is specified, then the number of
# elements to be included in the colourbar is determined from the list
# of unique elements that appear in the model
if nelements is None and species_list is None:
self._nelements = len(
np.unique(self.line_in_and_out_infos_within_xlims.
atomic_number.values))
elif nelements is None and species_list is not None:
# if species_list has been specified, then the number of elements
# to be included is set to the length of that list
self._nelements = len(self._species_list)
else:
# if nelements has been specified, then the number of elements to
# be included is set to the length of that list
self._nelements = nelements
# if the length of self._elements_in_kromer_plot exceeds the requested
# number of elements to be included in the colourbar, then this if
# statement applies
if self._species_list is not None:
# if we have specified a species list then only take those species
# that are requested
mask = np.in1d(self._elements_in_kromer_plot[:, 0],
self.requested_species_ids)
self._elements_in_kromer_plot = self._elements_in_kromer_plot[mask]
elif len(self._elements_in_kromer_plot) > self._nelements:
# if nelements is specified, then sort to find the top contributing
# elements, pick the top nelements, and sort back by atomic number
self._elements_in_kromer_plot = self._elements_in_kromer_plot[
np.argsort(self._elements_in_kromer_plot[:, 1])[::-1]]
self._elements_in_kromer_plot = self._elements_in_kromer_plot[:self
.
_nelements]
self._elements_in_kromer_plot = self._elements_in_kromer_plot[
np.argsort(self._elements_in_kromer_plot[:, 0])]
else:
# if the length of self._elements_in_kromer_plot is less than the
# requested number of elements in the model, then this requested
# length is updated to be the length of length of
# self._elements_in_kromer_plot
self._nelements = len(self._elements_in_kromer_plot)
# this will reset nelements if species_list is turned on
# it's possible to request a species that doesn't appear in the plot
# this will ensure that species isn't counted when determining labels
# and colours
if self._species_list is not None:
labels = []
for species in self._species_list:
if " " in species:
atomic_number = species_string_to_tuple(species)[0]
ion_number = species_string_to_tuple(species)[1]
species_id = atomic_number * 100 + ion_number
if species_id in self._elements_in_kromer_plot:
labels.append(species)
else:
labels.append(species)
self._nelements = len(labels)
self._axes_handling_preparation()
self._generate_emission_part()
self._generate_photosphere_part()
self._generate_and_add_colormap()
self._generate_and_add_legend()
self._paxes_handling_preparation()
self._generate_absorption_part()
self._axis_handling_label_rescale()
return plt.gcf()
def line_info(self):
"""produces list of elements to be included in the kromer plot"""
# gets list of elements and number of emitted packets
self.last_line_interaction_out_id = self.line_out_infos
self.last_line_interaction_out_angstrom = self.line_out_nu.to(
units.Angstrom, equivalencies=units.spectral())
self.last_line_interaction_out_id[
"emitted_wavelength"] = self.last_line_interaction_out_angstrom
self.line_out_infos_within_xlims = self.last_line_interaction_out_id.loc[
(self.last_line_interaction_out_id.emitted_wavelength >=
self._xlim[0])
& (self.last_line_interaction_out_id.emitted_wavelength <=
self._xlim[1])]
# gets list of elements and number of absorbed packets
self.last_line_interaction_in_id = self.line_in_infos
self.last_line_interaction_in_angstrom = self.line_in_nu.to(
units.Angstrom, equivalencies=units.spectral())
self.last_line_interaction_in_id[
"emitted_wavelength"] = self.last_line_interaction_in_angstrom
self.line_in_infos_within_xlims = self.last_line_interaction_in_id.loc[
(self.last_line_interaction_in_id.emitted_wavelength >=
self._xlim[0])
& (self.last_line_interaction_in_id.emitted_wavelength <=
self._xlim[1])]
self.line_in_and_out_infos_within_xlims = pd.concat([
self.line_in_infos_within_xlims, self.line_out_infos_within_xlims
])
# this generates the 4-digit ID for all transitions in the model
# (e.g. Fe III line --> 2602)
self.line_in_and_out_infos_within_xlims["ion_id"] = (
self.line_in_and_out_infos_within_xlims["atomic_number"] * 100 +
self.line_in_and_out_infos_within_xlims["ion_number"])
# this is a list that will hold which elements should all be in the
# same colour. This is used if the user requests a mix of ions and
# elements.
self.keep_colour = []
# this reads in the species specified by user and generates the 4-digit
# ID keys for them
if self._species_list is not None:
# create a list of the ions ids requested by species_list
requested_species_ids = []
# check if there are any digits in the species list. If there are
# then exit
# species_list should only contain species in the Roman numeral
# format, e.g. Si II, and each ion must contain a space
if any(char.isdigit()
for char in " ".join(self._species_list)) == True:
raise ValueError(
"All species must be in Roman numeral form, e.g. Si II")
else:
# go through each of the request species. Check whether it is
# an element or ion (ions have spaces). If it is an element,
# add all possible ions to the ions list. Otherwise just add
# the requested ion
for species in self._species_list:
if " " in species:
requested_species_ids.append([
species_string_to_tuple(species)[0] * 100 +
species_string_to_tuple(species)[1]
])
else:
atomic_number = elements.loc[elements['chem_symbol'] ==
species.lower(),
'atomic_no'].values[0]
requested_species_ids.append([
atomic_number * 100 + i
for i in np.arange(atomic_number)
])
self.keep_colour.append(atomic_number)
self.requested_species_ids = [
species_id for list in requested_species_ids
for species_id in list
]
# now we are getting the list of unique values for 'ion_id' if we would
# like to use species. Otherwise we get unique atomic numbers
if self._species_list is not None:
self._elements_in_kromer_plot = np.c_[np.unique(
self.line_in_and_out_infos_within_xlims.ion_id.values,
return_counts=True,
)]
else:
self._elements_in_kromer_plot = np.c_[np.unique(
self.line_in_and_out_infos_within_xlims.atomic_number.values,
return_counts=True,
)]
return self._elements_in_kromer_plot
def assemble_plasma(config, model, atom_data=None):
"""
Create a BasePlasma instance from a Configuration object
and a Radial1DModel.
Parameters
----------
config: ~io.config_reader.Configuration
model: ~model.Radial1DModel
atom_data: ~atomic.AtomData
If None, an attempt will be made to read the atomic data
from config.
Returns
-------
: ~plasma.BasePlasma
"""
# Convert the nlte species list to a proper format.
nlte_species = [
species_string_to_tuple(s) for s in config.plasma.nlte.species
]
if atom_data is None:
if 'atom_data' in config:
if os.path.isabs(config.atom_data):
atom_data_fname = config.atom_data
else:
atom_data_fname = os.path.join(config.config_dirname,
config.atom_data)
else:
raise ValueError('No atom_data option found in the configuration.')
logger.info('Reading Atomic Data from %s', atom_data_fname)
try:
atom_data = atomic.AtomData.from_hdf(atom_data_fname)
except TypeError as e:
print(
e,
'Error might be from the use of an old-format of the atomic database, \n'
'please see https://github.com/tardis-sn/tardis-refdata/tree/master/atom_data'
',for the most recent version.')
raise
atom_data.prepare_atom_data(
model.abundance.index,
line_interaction_type=config.plasma.line_interaction_type,
nlte_species=nlte_species)
kwargs = dict(t_rad=model.t_radiative,
abundance=model.abundance,
density=model.density,
atomic_data=atom_data,
time_explosion=model.time_explosion,
w=model.dilution_factor,
link_t_rad_t_electron=0.9)
plasma_modules = basic_inputs + basic_properties
property_kwargs = {}
if config.plasma.radiative_rates_type == 'blackbody':
plasma_modules.append(JBluesBlackBody)
elif config.plasma.radiative_rates_type == 'dilute-blackbody':
plasma_modules.append(JBluesDiluteBlackBody)
elif config.plasma.radiative_rates_type == 'detailed':
plasma_modules += detailed_j_blues_properties + detailed_j_blues_inputs
kwargs.update(r_inner=model.r_inner,
t_inner=model.t_inner,
volume=model.volume,
j_blue_estimator=None)
property_kwargs[JBluesDetailed] = {
'w_epsilon': config.plasma.w_epsilon
}
else:
raise ValueError('radiative_rates_type type unknown - %s',
config.plasma.radiative_rates_type)
if config.plasma.excitation == 'lte':
plasma_modules += lte_excitation_properties
elif config.plasma.excitation == 'dilute-lte':
plasma_modules += dilute_lte_excitation_properties
if config.plasma.ionization == 'lte':
plasma_modules += lte_ionization_properties
elif config.plasma.ionization == 'nebular':
plasma_modules += nebular_ionization_properties
if nlte_species:
plasma_modules += nlte_properties
nlte_conf = config.plasma.nlte
plasma_modules.append(LevelBoltzmannFactorNLTE.from_config(nlte_conf))
property_kwargs[StimulatedEmissionFactor] = dict(
nlte_species=nlte_species)
else:
plasma_modules += non_nlte_properties
if config.plasma.line_interaction_type in ('downbranch', 'macroatom'):
plasma_modules += macro_atom_properties
if 'delta_treatment' in config.plasma:
property_kwargs[RadiationFieldCorrection] = dict(
delta_treatment=config.plasma.delta_treatment)
if config.plasma.helium_treatment == 'recomb-nlte':
plasma_modules += helium_nlte_properties
elif config.plasma.helium_treatment == 'numerical-nlte':
plasma_modules += helium_numerical_nlte_properties
# TODO: See issue #633
if config.plasma.heating_rate_data_file in ['none', None]:
raise PlasmaConfigError('Heating rate data file not specified')
else:
property_kwargs[HeliumNumericalNLTE] = dict(
heating_rate_data_file=config.plasma.heating_rate_data_file)
else:
plasma_modules += helium_lte_properties
if model._electron_densities:
electron_densities = pd.Series(model._electron_densities.cgs.value)
if config.plasma.helium_treatment == 'numerical-nlte':
property_kwargs[IonNumberDensityHeNLTE] = dict(
electron_densities=electron_densities)
else:
property_kwargs[IonNumberDensity] = dict(
electron_densities=electron_densities)
kwargs['helium_treatment'] = config.plasma.helium_treatment
plasma = BasePlasma(plasma_properties=plasma_modules,
property_kwargs=property_kwargs,
**kwargs)
return plasma
