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, atomic_data, beta_sobolev, j_blues,
stimulated_emission_factor, tau_sobolevs):
if len(j_blues) == 0:
transition_probabilities = None
else:
try:
macro_atom_data = atomic_data.macro_atom_data
except:
macro_atom_data = atomic_data.macro_atom_data_all
transition_probabilities = (
macro_atom_data.transition_probability.values[np.newaxis].T *
beta_sobolev.take(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 = j_blues.take(macro_atom_transition_up_filter,
axis=0, mode='raise')
macro_stimulated_emission = stimulated_emission_factor.take(
macro_atom_transition_up_filter, axis=0, mode='raise')
transition_probabilities[transition_up_filter] *= j_blues * \
macro_stimulated_emission
block_references = np.hstack((
atomic_data.macro_atom_references.block_references,
len(macro_atom_data)))
macro_atom.normalize_transition_probabilities(
transition_probabilities, block_references)
transition_probabilities = pd.DataFrame(transition_probabilities,
index=macro_atom_data.transition_line_id,
columns=tau_sobolevs.columns)
return transition_probabilities