def calculate_transition_probabilities(self):
"""
Updating the Macro Atom computations
"""
macro_tau_sobolevs = self.tau_sobolevs[self.atom_data.macro_atom_data['lines_idx'].values.astype(int)]
beta_sobolevs = np.zeros_like(macro_tau_sobolevs)
macro_atom.calculate_beta_sobolev(macro_tau_sobolevs, beta_sobolevs)
transition_probabilities = self.atom_data.macro_atom_data['transition_probability'] * beta_sobolevs
transition_up_filter = self.atom_data.macro_atom_data['transition_type'] == 1
j_blues = self.j_blues[self.atom_data.macro_atom_data['lines_idx'].values[transition_up_filter.__array__()]]
macro_stimulated_emission = self.stimulated_emission_factor[
self.atom_data.macro_atom_data['lines_idx'].values[transition_up_filter.__array__()]]
transition_probabilities[transition_up_filter.__array__()] *= j_blues * macro_stimulated_emission
#reference_levels = np.hstack((0, self.atom_data.macro_atom_references['count_total'].__array__().cumsum()))
#Normalizing the probabilities
#TODO speedup possibility save the new blockreferences with 0 and last block
block_references = np.hstack((self.atom_data.macro_atom_references['block_references'],
len(self.atom_data.macro_atom_data)))
macro_atom.normalize_transition_probabilities(transition_probabilities, block_references)
return transition_probabilities
def calculate_transition_probabilities(self):
"""
Updating the Macro Atom computations
"""
macro_tau_sobolevs = self.tau_sobolevs[
self.atom_data.macro_atom_data['lines_idx'].values.astype(int)]
beta_sobolevs = np.zeros_like(macro_tau_sobolevs)
macro_atom.calculate_beta_sobolev(macro_tau_sobolevs, beta_sobolevs)
transition_probabilities = self.atom_data.macro_atom_data[
'transition_probability'] * beta_sobolevs
transition_up_filter = self.atom_data.macro_atom_data[
'transition_type'] == 1
j_blues = self.j_blues[self.atom_data.macro_atom_data['lines_idx'].
values[transition_up_filter.__array__()]]
macro_stimulated_emission = self.stimulated_emission_factor[
self.atom_data.macro_atom_data['lines_idx'].values[
transition_up_filter.__array__()]]
transition_probabilities[transition_up_filter.__array__(
)] *= j_blues * macro_stimulated_emission
#reference_levels = np.hstack((0, self.atom_data.macro_atom_references['count_total'].__array__().cumsum()))
#Normalizing the probabilities
#TODO speedup possibility save the new blockreferences with 0 and last block
block_references = np.hstack(
(self.atom_data.macro_atom_references['block_references'],
len(self.atom_data.macro_atom_data)))
macro_atom.normalize_transition_probabilities(transition_probabilities,
block_references)
return transition_probabilities
def calculate_nlte_level_populations(self):
"""
Calculating the NLTE level populations for specific ions
"""
if not hasattr(self, 'beta_sobolevs'):
self.beta_sobolevs = np.zeros_like(
self.atom_data.lines['nu'].values)
macro_atom.calculate_beta_sobolev(self.tau_sobolevs,
self.beta_sobolevs)
if self.nlte_options.get('coronal_approximation', False):
beta_sobolevs = np.ones_like(self.beta_sobolevs)
j_blues = np.zeros_like(self.j_blues)
else:
beta_sobolevs = self.beta_sobolevs
j_blues = self.j_blues
if self.nlte_options.get('classical_nebular', False):
print "setting classical nebular = True"
beta_sobolevs[:] = 1.0
for species in self.nlte_species:
logger.info('Calculating rates for species %s', species)
number_of_levels = self.level_populations.ix[species].size
level_populations = self.level_populations.ix[species].values
lnl = self.atom_data.nlte_data.lines_level_number_lower[species]
lnu = self.atom_data.nlte_data.lines_level_number_upper[species]
lines_index = self.atom_data.nlte_data.lines_idx[species]
A_uls = self.atom_data.nlte_data.A_uls[species]
B_uls = self.atom_data.nlte_data.B_uls[species]
B_lus = self.atom_data.nlte_data.B_lus[species]
r_lu_index = lnu * number_of_levels + lnl
r_ul_index = lnl * number_of_levels + lnu
r_ul_matrix = np.zeros((number_of_levels, number_of_levels),
dtype=np.float64)
r_ul_matrix.ravel()[r_ul_index] = A_uls
r_ul_matrix.ravel()[r_ul_index] *= beta_sobolevs[lines_index]
stimulated_emission_matrix = np.zeros_like(r_ul_matrix)
stimulated_emission_matrix.ravel()[r_lu_index] = 1 - (
(level_populations[lnu] * B_uls) /
(level_populations[lnl] * B_lus))
stimulated_emission_matrix[stimulated_emission_matrix < 0.] = 0.0
r_lu_matrix = np.zeros_like(r_ul_matrix)
r_lu_matrix.ravel()[r_lu_index] = B_lus * j_blues[
lines_index] * beta_sobolevs[lines_index]
r_lu_matrix *= stimulated_emission_matrix
collision_matrix = self.atom_data.nlte_data.get_collision_matrix(
species, self.t_electron) * self.electron_density
rates_matrix = r_lu_matrix + r_ul_matrix + collision_matrix
for i in xrange(number_of_levels):
rates_matrix[i, i] = -np.sum(rates_matrix[:, i])
rates_matrix[0] = 1.0
x = np.zeros(rates_matrix.shape[0])
x[0] = 1.0
relative_level_populations = np.linalg.solve(rates_matrix, x)
self.level_populations.ix[
species] = relative_level_populations * self.ion_populations.ix[
species]
return
def calculate_nlte_level_populations(self):
"""
Calculating the NLTE level populations for specific ions
"""
if not hasattr(self, 'beta_sobolevs'):
self.beta_sobolevs = np.zeros_like(self.atom_data.lines['nu'].values)
macro_atom.calculate_beta_sobolev(self.tau_sobolevs, self.beta_sobolevs)
if self.nlte_options.get('coronal_approximation', False):
beta_sobolevs = np.ones_like(self.beta_sobolevs)
j_blues = np.zeros_like(self.j_blues)
else:
beta_sobolevs = self.beta_sobolevs
j_blues = self.j_blues
if self.nlte_options.get('classical_nebular', False):
print "setting classical nebular = True"
beta_sobolevs[:] = 1.0
for species in self.nlte_species:
logger.info('Calculating rates for species %s', species)
number_of_levels = self.level_populations.ix[species].size
level_populations = self.level_populations.ix[species].values
lnl = self.atom_data.nlte_data.lines_level_number_lower[species]
lnu = self.atom_data.nlte_data.lines_level_number_upper[species]
lines_index = self.atom_data.nlte_data.lines_idx[species]
A_uls = self.atom_data.nlte_data.A_uls[species]
B_uls = self.atom_data.nlte_data.B_uls[species]
B_lus = self.atom_data.nlte_data.B_lus[species]
r_lu_index = lnu * number_of_levels + lnl
r_ul_index = lnl * number_of_levels + lnu
r_ul_matrix = np.zeros((number_of_levels, number_of_levels), dtype=np.float64)
r_ul_matrix.ravel()[r_ul_index] = A_uls
r_ul_matrix.ravel()[r_ul_index] *= beta_sobolevs[lines_index]
stimulated_emission_matrix = np.zeros_like(r_ul_matrix)
stimulated_emission_matrix.ravel()[r_lu_index] = 1 - ((level_populations[lnu] * B_uls) / (
level_populations[lnl] * B_lus))
stimulated_emission_matrix[stimulated_emission_matrix < 0.] = 0.0
r_lu_matrix = np.zeros_like(r_ul_matrix)
r_lu_matrix.ravel()[r_lu_index] = B_lus * j_blues[lines_index] * beta_sobolevs[lines_index]
r_lu_matrix *= stimulated_emission_matrix
collision_matrix = self.atom_data.nlte_data.get_collision_matrix(species,
self.t_electron) * self.electron_density
rates_matrix = r_lu_matrix + r_ul_matrix + collision_matrix
for i in xrange(number_of_levels):
rates_matrix[i, i] = -np.sum(rates_matrix[:, i])
rates_matrix[0] = 1.0
x = np.zeros(rates_matrix.shape[0])
x[0] = 1.0
relative_level_populations = np.linalg.solve(rates_matrix, x)
self.level_populations.ix[species] = relative_level_populations * self.ion_populations.ix[species]
return
