def plotting_master(self,iteration,file_identifier=''): ''' This function generates plots of the MSI optimization results. Parameters ---------- iteration : int Integer indicating which kinetic model is being plotted. file_identifier : string, optional String which can be used to provide a unique identifier to a saved plot. Here it is used to indicate what yaml file experimental conditions the plot depicts. The default is ''. Returns ------- None. ''' plotting_instance = plotter.Plotting(self.S_matrix, self.s_matrix, self.Y_matrix, self.y, self.z_matrix, self.X, self.sigma, self.covarience, self.original_covariance, self.S_matrix_original, self.exp_dict_list_optimized, self.exp_dict_list_original, self.parsed_yaml_list, self.Ydf, target_value_rate_constant_csv= self.optional_targets[iteration], target_value_rate_constant_csv_extra_values = self.optional_targets[iteration], k_target_value_S_matrix =self.k_target_value_S_matrix, k_target_values=self.run_with_k_target_values, working_directory = self.wdir, sigma_uncertainty_weighted_sensitivity_csv=self.csv_file_sigma, cheby_sensitivity_dict = self.cheb_coeffs[iteration], mapped_to_alpha_full_simulation=self.MSI_instance_two.mapped_to_alpha_full_simulation) #csv_file_sigma = MSI_st_instance_two.data_directory +'/'+'sigma_for_uncertainty_weighted_sensitivity_updated.csv' self.observable_counter_and_absorbance_wl,self.length_of_experimental_data = plotting_instance.lengths_of_experimental_data() self.sigmas_optimized,self.test = plotting_instance.calculating_sigmas(self.S_matrix,self.covarience) self.sigmas_original,self.test2 = plotting_instance.calculating_sigmas(self.S_matrix_original,self.original_covariance) plotting_instance.plotting_observables(sigmas_original = self.sigmas_original,sigmas_optimized= self.sigmas_optimized) self.diag = plotting_instance.getting_matrix_diag(self.covarience) plotting_instance.plotting_rate_constants(optimized_cti_file=self.MSI_instance_two.new_cti_file, original_cti_file=self.original_cti_file, initial_temperature=250, final_temperature=2500, master_equation_reactions = self.master_equation_reactions[iteration]) self.sensitivity, self.top_sensitivity = plotting_instance.sort_top_uncertainty_weighted_sens() self.obs = plotting_instance.plotting_uncertainty_weighted_sens()
########################################################################################################################## #PLOTTING## ########################################################################################################################## plotting_instance = plotter.Plotting( S_matrix, s_matrix, Y_matrix, Y_matrix, z_matrix, X, sigma, covarience, original_covariance, S_matrix_original, exp_dict_list_optimized, exp_dict_list_original, parsed_yaml_list, Ydf, target_value_rate_constant_csv=MSI_st_instance_two.data_directory + '/' + rate_constant_target_value_data_for_plotting, target_value_rate_constant_csv_extra_values=MSI_st_instance_two. data_directory + '/' + rate_constant_target_value_data_extra, k_target_value_S_matrix=k_target_value_S_matrix, k_target_values=run_with_k_target_values, working_directory=working_directory, sigma_uncertainty_weighted_sensitivity_csv='') ##MSI_st_instance_two.data_directory +'/'+'sigma_for_uncertainty_weighted_sensitivity_updated.csv' # observable_counter_and_absorbance_wl, length_of_experimental_data = plotting_instance.lengths_of_experimental_data( )
########################################################################################################################## #PLOTTING## ########################################################################################################################## plotting_instance = plotter.Plotting( S_matrix, s_matrix, Y_matrix, Y_matrix, z_matrix, X, sigma, covarience, original_covariance, S_matrix_original, exp_dict_list_optimized, exp_dict_list_original, parsed_yaml_list, Ydf, target_value_rate_constant_csv=MSI_st_instance_two.data_directory + '/' + 'target_reactions_test.csv', k_target_value_S_matrix=k_target_value_S_matrix, k_target_values=run_with_k_target_values, working_directory=working_directory, shock_tube_instance=MSI_st_instance_two) observable_counter_and_absorbance_wl, length_of_experimental_data = plotting_instance.lengths_of_experimental_data( ) sigmas_optimized, test = plotting_instance.calculating_sigmas( S_matrix, covarience)
k_target_value_S_matrix = None csv_file_sigma = '' plotting_instance = plotter.Plotting( S_matrix, s_matrix, Y_matrix, y, z_matrix, X, sigma, covarience, original_covariance, S_matrix_original, exp_dict_list_optimized, exp_dict_list_original, parsed_yaml_list, Ydf, target_value_rate_constant_csv=os.path.join( MSI_instance_two.data_directory, rate_constant_target_value_data_for_plotting), target_value_rate_constant_csv_extra_values=os.path.join( MSI_instance_two.data_directory, rate_constant_target_value_data_extra), k_target_value_S_matrix=k_target_value_S_matrix, k_target_values=run_with_k_target_values, working_directory=working_directory, sigma_uncertainty_weighted_sensitivity_csv=csv_file_sigma, optimized_cti_file=MSI_instance_two.new_cti_file, original_cti_file=original_cti_file) #csv_file_sigma = MSI_st_instance_two.data_directory +'/'+'sigma_for_uncertainty_weighted_sensitivity_updated.csv' observable_counter_and_absorbance_wl, length_of_experimental_data = plotting_instance.lengths_of_experimental_data(
k_target_value_S_matrix = None ########################################################################################################################## #PLOTTING## ########################################################################################################################## plotting_instance = plotter.Plotting( S_matrix, s_matrix, Y_matrix, Y_matrix, z_matrix, X, sigma, covarience, original_covariance, S_matrix_original, exp_dict_list_optimized, exp_dict_list_original, parsed_yaml_list, Ydf, target_value_rate_constant_csv=MSI_st_instance_two.data_directory + '/' + rate_constant_target_value_data, k_target_value_S_matrix=k_target_value_S_matrix, k_target_values=run_with_k_target_values) observable_counter_and_absorbance_wl, length_of_experimental_data = plotting_instance.lengths_of_experimental_data( ) sigmas_optimized, test = plotting_instance.calculating_sigmas( S_matrix, covarience) sigmas_original, test2 = plotting_instance.calculating_sigmas(
def plotting_no_master(self, iteration,file_identifier=''): ''' This function generates plots of the MSI optimization results when no master-equation material is contained in the MSI simulations. Parameters ---------- iteration : int Integer indicating which kinetic model is being plotted. file_identifier : string, optional String which can be used to provide a unique identifier to a saved plot. Here it is used to indicate what yaml file experimental conditions the plot depicts. The default is ''. Returns ------- None. ''' plotting_instance = plotter.Plotting(self.S_matrix, self.s_matrix, self.Y_matrix, self.Y_matrix, self.z_matrix, self.X, self.sigma, self.covarience, self.original_covariance, self.S_matrix_original, self.exp_dict_list_optimized, self.exp_dict_list_original, self.parsed_yaml_list, self.Ydf, target_value_rate_constant_csv= self.optional_targets[iteration], k_target_value_S_matrix =self.k_target_value_S_matrix, k_target_values=self.run_with_k_target_values, working_directory=self.wdir, shock_tube_instance = self.MSI_instance_two, optimized_cti_file=self.MSI_instance_two.new_cti_file, original_cti_file=self.original_cti_file) self.observable_counter_and_absorbance_wl,self.length_of_experimental_data = plotting_instance.lengths_of_experimental_data() self.sigmas_optimized,test = plotting_instance.calculating_sigmas(self.S_matrix,self.covarience) self.sigmas_original,self.test2 = plotting_instance.calculating_sigmas(self.S_matrix_original,self.original_covariance) plotting_instance.plotting_observables(sigmas_original = self.sigmas_original,sigmas_optimized= self.sigmas_optimized, file_identifier=self.models[iteration].rstrip('.cti')+'_'+file_identifier, filetype='.pdf') self.diag = plotting_instance.getting_matrix_diag(self.covarience) #plotting_instance.Y_matrix_plotter(Y_matrix,exp_dict_list_optimized,y,sigma) plotting_instance.plotting_rate_constants(optimized_cti_file=self.MSI_instance_two.new_cti_file, original_cti_file=self.original_cti_file, initial_temperature=250, final_temperature=2500) self.sensitivity, self.top_sensitivity = plotting_instance.sort_top_uncertainty_weighted_sens() self.obs = plotting_instance.plotting_uncertainty_weighted_sens()
#csv_file_sigma = MSI_st_instance_two.data_directory +'/'+'sigma_for_uncertainty_weighted_sensitivity_glarborg.csv' csv_file_sigma = '' plotting_instance = plotter.Plotting(S_matrix, s_matrix, Y_matrix, y, z_matrix, X, sigma, covarience, original_covariance, S_matrix_original, exp_dict_list_optimized, exp_dict_list_original, parsed_yaml_list, Ydf, target_value_rate_constant_csv= MSI_st_instance_two.data_directory +'/'+ rate_constant_target_value_data_for_plotting , target_value_rate_constant_csv_extra_values = MSI_st_instance_two.data_directory +'/'+rate_constant_target_value_data_extra, k_target_value_S_matrix =k_target_value_S_matrix, k_target_values=run_with_k_target_values, working_directory = working_directory, sigma_uncertainty_weighted_sensitivity_csv=csv_file_sigma, cheby_sensitivity_dict = cheb_sensitivity_dict, mapped_to_alpha_full_simulation=MSI_st_instance_two.mapped_to_alpha_full_simulation, T_min=T_min, T_max=T_max, P_min=P_min, P_max=P_max) #csv_file_sigma = MSI_st_instance_two.data_directory +'/'+'sigma_for_uncertainty_weighted_sensitivity_updated.csv' observable_counter_and_absorbance_wl,length_of_experimental_data = plotting_instance.lengths_of_experimental_data()