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()
