def reduce_raw_data(self):
"""
Use the raw data in self.raw_data to populate the A and K_dex matrices
"""
# find the maximum v and j from the Einstein and collisional rates data
# sets and adjust the labels of the energy levels according to that
v_max_data, j_max_data = population.find_v_max_j_max_from_data(
self.raw_data.a_info_nnz, self.raw_data.collision_rates_info_nnz)
self.energy_levels = self.raw_data.energy_levels
self.energy_levels.set_labels(v_max=v_max_data + 1)
#
# reduce the Einstein coefficients to a 2D matrix (construct the A
# matrix) [n_levels, n_levels]
# A_reduced_slow = population.reduce_einstein_coefficients_slow(
# self.raw_data.A_info_nnz,
# self.energy_levels)
a_matrix = population.reduce_einstein_coefficients(
self.raw_data.a, self.energy_levels)
self.a_matrix = a_matrix
# getting the collisional de-excitation matrix (K_dex) (for all
# tabulated values) [n_level, n_level, n_T_kin_values]
k_dex_matrix = population.reduce_collisional_coefficients_slow(
self.raw_data.collision_rates_info_nnz,
self.energy_levels,
reduced_data_is_upper_to_lower_only=True)
# compute the interpolator that produces K_dex at a certain temperature
k_dex_matrix_interpolator = population.compute_k_dex_matrix_interpolator(
k_dex_matrix, self.raw_data.collision_rates_t_range)
self.k_dex_matrix_interpolator = k_dex_matrix_interpolator
def reduce_raw_data(self):
"""
Use the raw data in self.raw_data to populate the A and K_dex matrices
"""
self.energy_levels = self.raw_data.energy_levels
# find the maximum v and j from the Einstein and collisional rates
# datasets and adjust the labels of the energy levels accordingly
v_max_data, j_max_data = population.find_v_max_j_max_from_data(
self.raw_data.a_info_nnz, self.raw_data.collision_rates_info_nnz,
self.energy_levels)
self.energy_levels.set_labels(v_max=v_max_data + 1)
# reduce the Einstein coefficients to a 2D matrix (construct the A
# matrix) [n_levels, n_levels]
# A_reduced_slow = population.reduce_einstein_coefficients_slow(
# self.raw_data.A_info_nnz,
# self.energy_levels)
a_matrix = population.reduce_einstein_coefficients(
self.raw_data.a, self.energy_levels)
# check that the upper triangular elements are zero. If not, issue a
# warning and set them to zero. This check has been introduced because
# for H2 the data by Simbotin has non-zero values in the uppter tri
# part that are identified with the - sign in the paper
if (a_matrix[numpy.triu_indices(a_matrix.shape[0])] != 0.0).any():
print('warning: non-zero elements found in the reduced upper ')
print(
'warning: triangular part of the A matrix, set them to zero.')
a_matrix[numpy.triu_indices(a_matrix.shape[0])] = 0.0
self.a_matrix = a_matrix
# DEBUG
# utils.display_matrix(a_matrix.value, None)
# get the collisional de-excitation matrix (K_dex) (for all
# tabulated values) [n_level, n_level, n_T_kin_values]
k_dex_matrix = population.reduce_collisional_coefficients_slow(
self.raw_data.collision_rates_info_nnz,
self.energy_levels,
reduced_data_is_upper_to_lower_only=True)
# compute the interpolator that produces K_dex at a certain temperature
k_dex_matrix_interpolator = population.compute_k_dex_matrix_interpolator(
k_dex_matrix, self.raw_data.collision_rates_t_range)
self.k_dex_matrix_interpolator = k_dex_matrix_interpolator