def building_matrices(self, loop_counter=0):
matrix_builder_instance = ml.OptMatrix()
self.matrix_builder_instance = matrix_builder_instance
S_matrix = matrix_builder_instance.load_S(self.experiment_dictonaries,
self.list_of_parsed_yamls,
dk=self.perturbment)
self.S_matrix = S_matrix
if loop_counter == 0:
Y_matrix, Y_data_frame = matrix_builder_instance.load_Y(
self.experiment_dictonaries,
self.list_of_parsed_yamls,
loop_counter=loop_counter)
else:
Y_matrix, Y_data_frame = matrix_builder_instance.load_Y(
self.experiment_dictonaries,
self.list_of_parsed_yamls,
loop_counter=loop_counter,
X=self.X_to_subtract_from_Y)
self.Y_matrix = Y_matrix
self.Y_data_frame = Y_data_frame
z_matrix, z_data_frame, sigma = matrix_builder_instance.build_Z(
self.experiment_dictonaries,
self.list_of_parsed_yamls,
loop_counter=loop_counter,
reaction_uncertainty=self.data_directory + '/' +
self.reaction_uncertainty_csv)
self.z_matrix = z_matrix
self.z_data_frame = z_data_frame
self.sigma = sigma
return
def building_matrices(self, loop_counter=0):
matrix_builder_instance = ml.OptMatrix()
self.matrix_builder_instance = matrix_builder_instance
S_matrix = matrix_builder_instance.load_S(
self.experiment_dictonaries,
self.list_of_parsed_yamls,
dk=self.perturbment,
master_equation_reactions=self.master_equation_reactions,
mapped_master_equation_sensitivites=self.MP_for_S_matrix,
master_equation_flag=self.master_equation_flag)
self.S_matrix = S_matrix
if loop_counter == 0:
Y_matrix, Y_data_frame = matrix_builder_instance.load_Y(
self.experiment_dictonaries,
self.list_of_parsed_yamls,
loop_counter=loop_counter,
master_equation_flag=self.master_equation_flag,
master_equation_uncertainty_df=self.
master_equation_uncertainty_df,
master_equation_reactions=self.master_equation_reactions)
else:
Y_matrix, Y_data_frame = matrix_builder_instance.load_Y(
self.experiment_dictonaries,
self.list_of_parsed_yamls,
loop_counter=loop_counter,
X=self.X_to_subtract_from_Y,
master_equation_flag=self.master_equation_flag,
master_equation_uncertainty_df=self.
master_equation_uncertainty_df,
master_equation_reactions=self.master_equation_reactions)
self.Y_matrix = Y_matrix
self.Y_data_frame = Y_data_frame
z_matrix, z_data_frame, sigma, active_parameters = matrix_builder_instance.build_Z(
self.experiment_dictonaries,
self.list_of_parsed_yamls,
loop_counter=loop_counter,
reaction_uncertainty=self.data_directory + '/' +
self.reaction_uncertainty_csv,
master_equation_uncertainty_df=self.master_equation_uncertainty_df,
master_equation_flag=self.master_equation_flag,
master_equation_reaction_list=self.master_equation_reactions)
self.z_matrix = z_matrix
self.z_data_frame = z_data_frame
self.sigma = sigma
self.active_parameters = active_parameters
return
]
yaml_instance = yp.Parser()
list_of_yaml_objects = yaml_instance.load_yaml_list(yaml_list=yaml_file_list)
list_of_experiment_dicts = yaml_instance.parsing_multiple_dictonaries(
list_of_yaml_objects=list_of_yaml_objects)
optimization_instance = opt.Optimization_Utility()
test = optimization_instance.looping_over_parsed_yaml_files(
list_of_experiment_dicts,
yaml_file_list,
processor=test_p,
kineticSens=1,
physicalSens=1,
dk=.01)
matix_instance = ml.OptMatrix()
Y_matrix, Y1 = matix_instance.load_Y(test,
list_of_experiment_dicts,
loop_counter=0)
z_matrix, z1, sigma = matix_instance.build_Z(
test,
list_of_experiment_dicts,
loop_counter=0,
reaction_uncertainty='MSI/data/test_data/uncertainty_test.csv')
x1, x2, x3, x4 = matix_instance.breakup_delta_x(z_matrix[257:],
test,
loop_counter=0)
# simulation:sim.instruments.shock_tube.shockTube,
# interpolated_kinetic_sens:dict,
# interpolated_tp_sens:list,
# interpolated_species_sens:list,
# interpolated_absorbance:list=[]):
optimization_instance = opt.Optimization_Utility()
exp_1 = optimization_instance.build_single_exp_dict(
1, test_tube, int_ksens_exp_mapped, int_tp_psen_against_experimental,
int_spec_psen_against_experimental) #no absorbance in experiment 1
exp_2 = optimization_instance.build_single_exp_dict(
2,
test_tube2,
int_ksens_exp_mapped2,
int_tp_psen_against_experimental2,
int_spec_psen_against_experimental2,
interpolated_absorbance=interp_abs2_exp) #absorbance in experiment 2
print("Experiments built successfully")
#print(exp_2['ksens']['A'][0].shape)
#print(exp_2['species'][0].shape)
#print(exp_2['species'][1].shape)
#print(exp_2['species'][2].shape)
####################
# Build the S matrix #
####################
mloader = ml.OptMatrix()
S = mloader.load_S([exp_1, exp_2])
print(S)