def calculate_transition_probabilities(self):
"""
Updating the Macro Atom computations
"""
macro_atom_data = self.atom_data.macro_atom_data
if not hasattr(self, 'beta_sobolevs'):
self.beta_sobolevs = np.zeros_like(self.tau_sobolevs.values)
if not self.beta_sobolevs_precalculated:
macro_atom.calculate_beta_sobolev(self.tau_sobolevs.values.ravel(order='F'),
self.beta_sobolevs.ravel(order='F'))
transition_probabilities = (macro_atom_data.transition_probability.values[np.newaxis].T *
self.beta_sobolevs.take(self.atom_data.macro_atom_data.lines_idx.values.astype(int),
axis=0, mode='raise')).copy('F')
transition_up_filter = (macro_atom_data.transition_type == 1).values
macro_atom_transition_up_filter = macro_atom_data.lines_idx.values[transition_up_filter]
j_blues = self.j_blues.values.take(macro_atom_transition_up_filter, axis=0, mode='raise')
macro_stimulated_emission = self.stimulated_emission_factor.take(macro_atom_transition_up_filter, axis=0, mode='raise')
transition_probabilities[transition_up_filter] *= j_blues * macro_stimulated_emission
#Normalizing the probabilities
block_references = np.hstack((self.atom_data.macro_atom_references.block_references,
len(macro_atom_data)))
macro_atom.normalize_transition_probabilities(transition_probabilities, block_references)
return pd.DataFrame(transition_probabilities, index=macro_atom_data.transition_line_id,
columns=self.tau_sobolevs.columns)
def calculate_transition_probabilities(self):
"""
Updating the Macro Atom computations
"""
macro_atom_data = self.atom_data.macro_atom_data
if not hasattr(self, 'beta_sobolevs'):
self.beta_sobolevs = np.zeros_like(self.tau_sobolevs.values)
if not self.beta_sobolevs_precalculated:
macro_atom.calculate_beta_sobolev(self.tau_sobolevs.values.ravel(order='F'),
self.beta_sobolevs.ravel(order='F'))
transition_probabilities = (macro_atom_data.transition_probability.values[np.newaxis].T *
self.beta_sobolevs.take(self.atom_data.macro_atom_data.lines_idx.values.astype(int),
axis=0, mode='raise')).copy('F')
transition_up_filter = (macro_atom_data.transition_type == 1).values
macro_atom_transition_up_filter = macro_atom_data.lines_idx.values[transition_up_filter]
j_blues = self.j_blues.values.take(macro_atom_transition_up_filter, axis=0, mode='raise')
macro_stimulated_emission = self.stimulated_emission_factor.take(macro_atom_transition_up_filter, axis=0, mode='raise')
transition_probabilities[transition_up_filter] *= j_blues * macro_stimulated_emission
#Normalizing the probabilities
block_references = np.hstack((self.atom_data.macro_atom_references.block_references,
len(macro_atom_data)))
macro_atom.normalize_transition_probabilities(transition_probabilities, block_references)
return pd.DataFrame(transition_probabilities, index=macro_atom_data.transition_line_id,
columns=self.tau_sobolevs.columns)
def calculate(self, tau_sobolevs):
if getattr(self, 'beta_sobolev', None) is None:
beta_sobolev = np.zeros_like(tau_sobolevs.values)
else:
beta_sobolev = self.beta_sobolev
macro_atom.calculate_beta_sobolev(
tau_sobolevs.values.ravel(order='F'),
beta_sobolev.ravel(order='F'))
return beta_sobolev
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.tau_sobolevs.values)
macro_atom.calculate_beta_sobolev(self.tau_sobolevs.values.ravel(order='F'),
self.beta_sobolevs.ravel(order='F'))
self.beta_sobolevs_precalculated = True
if self.nlte_config.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.values
if self.nlte_config.get('classical_nebular', False):
print "setting classical nebular = True"
beta_sobolevs = np.ones_like(self.beta_sobolevs)
for species in self.nlte_config.species:
logger.info('Calculating rates for species %s', species)
number_of_levels = self.atom_data.levels.energy.ix[species].count()
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, len(self.t_rads)), dtype=np.float64)
r_ul_matrix_reshaped = r_ul_matrix.reshape((number_of_levels**2, len(self.t_rads)))
r_ul_matrix_reshaped[r_ul_index] = A_uls[np.newaxis].T
r_ul_matrix_reshaped[r_ul_index] *= beta_sobolevs[lines_index]
stimulated_emission_matrix = np.zeros_like(r_ul_matrix)
stimulated_emission_matrix.reshape((number_of_levels**2, len(self.t_rads)))[r_lu_index] = self.stimulated_emission_factor[lines_index]
r_lu_matrix = np.zeros_like(r_ul_matrix)
r_lu_matrix_reshaped = r_lu_matrix.reshape((number_of_levels**2, len(self.t_rads)))
r_lu_matrix_reshaped[r_lu_index] = B_lus[np.newaxis].T * 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_electrons) * \
self.electron_densities.values
rates_matrix = r_lu_matrix + r_ul_matrix + collision_matrix
for i in xrange(number_of_levels):
rates_matrix[i, i] = -rates_matrix[:, i].sum(axis=0)
rates_matrix[0, :, :] = 1.0
x = np.zeros(rates_matrix.shape[0])
x[0] = 1.0
for i in xrange(len(self.t_rads)):
relative_level_populations = np.linalg.solve(rates_matrix[:, :, i], x)
self.level_populations[i].ix[species] = relative_level_populations * self.ion_populations[i].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.tau_sobolevs.values)
macro_atom.calculate_beta_sobolev(self.tau_sobolevs.values.ravel(order='F'),
self.beta_sobolevs.ravel(order='F'))
self.beta_sobolevs_precalculated = True
if self.nlte_config.get('coronal_approximation', False):
beta_sobolevs = np.ones_like(self.beta_sobolevs)
j_blues = np.zeros_like(self.j_blues)
logger.info('using coronal approximation = setting beta_sobolevs to 1 AND j_blues to 0')
else:
beta_sobolevs = self.beta_sobolevs
j_blues = self.j_blues.values
if self.nlte_config.get('classical_nebular', False):
logger.info('using Classical Nebular = setting beta_sobolevs to 1')
beta_sobolevs = np.ones_like(self.beta_sobolevs)
for species in self.nlte_config.species:
logger.info('Calculating rates for species %s', species)
number_of_levels = self.atom_data.levels.energy.ix[species].count()
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, len(self.t_rads)), dtype=np.float64)
r_ul_matrix_reshaped = r_ul_matrix.reshape((number_of_levels**2, len(self.t_rads)))
r_ul_matrix_reshaped[r_ul_index] = A_uls[np.newaxis].T + B_uls[np.newaxis].T * j_blues[lines_index]
r_ul_matrix_reshaped[r_ul_index] *= beta_sobolevs[lines_index]
r_lu_matrix = np.zeros_like(r_ul_matrix)
r_lu_matrix_reshaped = r_lu_matrix.reshape((number_of_levels**2, len(self.t_rads)))
r_lu_matrix_reshaped[r_lu_index] = B_lus[np.newaxis].T * j_blues[lines_index] * beta_sobolevs[lines_index]
collision_matrix = self.atom_data.nlte_data.get_collision_matrix(species, self.t_electrons) * \
self.electron_densities.values
rates_matrix = r_lu_matrix + r_ul_matrix + collision_matrix
for i in xrange(number_of_levels):
rates_matrix[i, i] = -rates_matrix[:, i].sum(axis=0)
rates_matrix[0, :, :] = 1.0
x = np.zeros(rates_matrix.shape[0])
x[0] = 1.0
for i in xrange(len(self.t_rads)):
relative_level_populations = np.linalg.solve(rates_matrix[:, :, i], x)
self.level_populations[i].ix[species] = relative_level_populations * self.ion_populations[i].ix[species]
return