abs - some error handling improvements

modules/performFEM/OpenSeesPy/createOpenSeesPyDriver.cpp

@@ -310,7 +310,7 @@ int createDriver(int argc, const char **argv) {
   std::ifstream modelFile(mainScript);
   std::string line;
   while (std::getline(modelFile, line)) {
-      std::cout << line << std::endl; // Print line to console
+      // std::cout << line << std::endl; // Print line to console
       templateFile << line << std::endl; // Write line to template file
   }
   templateFile.close();

modules/performUQ/UCSD_UQ/UCSD_UQ.py

@@ -60,10 +60,13 @@ def main(args):  # noqa: D103
                 text=True,
                 check=False,
             )
-            result.check_returncode()
+            result.check_returncode()  # Raises CalledProcessError if return code is non-zero
         except subprocess.CalledProcessError:
             with open(err_file, 'a') as f:  # noqa: PTH123
                 f.write(f'ERROR: {result.stderr}')
+        else:
+            # Print success if no error occurs
+            print('SUCCESS')  # noqa: T201
 
 
 if __name__ == '__main__':

modules/performUQ/UCSD_UQ/mainscript.py

@@ -7,6 +7,7 @@
 import json
 import shlex
 import sys
+import traceback
 from pathlib import Path
 
 path_to_common_uq = Path(__file__).parent.parent / 'common'
@@ -64,10 +65,11 @@ def main(input_args):  # noqa: D103
     # Try running the main_function and catch any exceptions
     try:
         main_function(command_list)
-    except Exception as e:  # noqa: BLE001
+    except Exception:  # noqa: BLE001
         # Write the exception message to the .err file
         with err_file.open('a') as f:
-            f.write(f'ERROR: {e!s}\n')
+            f.write('ERROR: An exception occurred:\n')
+            f.write(f'{traceback.format_exc()}\n')
 
 
 # ======================================================================================================================

modules/performUQ/UCSD_UQ/mainscript_hierarchical_bayesian.py

@@ -68,7 +68,7 @@ def loglikelihood_function(residual, error_variance_sample):  # noqa: D103
     return ll
 
 
-def main(input_args):  # noqa: D103
+def main(input_args):  # noqa: C901, D103
     # Initialize analysis
     working_directory = Path(input_args[0]).resolve()
     template_directory = Path(input_args[1]).resolve()  # noqa: F841
@@ -78,24 +78,38 @@ def main(input_args):  # noqa: D103
 
     # input_file_full_path = template_directory / input_file
 
-    with open(input_file, encoding='utf-8') as f:  # noqa: PTH123
-        inputs = json.load(f)
-
-    uq_inputs = inputs['UQ']
-    rv_inputs = inputs['randomVariables']
-    edp_inputs = inputs['EDP']
-
-    (
-        parallel_pool,
-        function_to_evaluate,
-        joint_distribution,
-        num_rv,
-        num_edp,
-        list_of_model_evaluation_functions,
-        list_of_datasets,
-        list_of_dataset_lengths,
-        restart_file,
-    ) = preprocess_hierarchical_bayesian.preprocess_arguments(input_args)
+    try:
+        with open(input_file, encoding='utf-8') as f:  # noqa: PTH123
+            inputs = json.load(f)
+
+        uq_inputs = inputs['UQ']
+        rv_inputs = inputs['randomVariables']
+        edp_inputs = inputs['EDP']
+    except FileNotFoundError as fnf_error:
+        msg = f"Input file '{input_file}' not found. Please check the file path."
+        raise FileNotFoundError(msg) from fnf_error
+    except json.JSONDecodeError as json_error:
+        msg = f"Error decoding JSON from file '{input_file}'. Ensure the file contains valid JSON."
+        raise ValueError(msg) from json_error
+    except KeyError as key_error:
+        msg = f'Missing required key in JSON data: {key_error}. Please check the input file format.'
+        raise KeyError(msg) from key_error
+
+    try:
+        (
+            parallel_pool,
+            function_to_evaluate,
+            joint_distribution,
+            num_rv,
+            num_edp,
+            list_of_model_evaluation_functions,
+            list_of_datasets,
+            list_of_dataset_lengths,
+            restart_file,
+        ) = preprocess_hierarchical_bayesian.preprocess_arguments(input_args)
+    except Exception as e:
+        msg = "Error during the preprocessing of arguments in 'preprocess_hierarchical_bayesian'."
+        raise RuntimeError(msg) from e
     transformation_function = joint_distribution.u_to_x
 
     prior_inverse_gamma_parameters = uq_utilities.InverseGammaParameters(

modules/performUQ/UCSD_UQ/runTMCMC.py

@@ -183,7 +183,7 @@ def write_data_to_csvfile(  # noqa: D103
     # Finished writing data
 
 
-def run_TMCMC(  # noqa: N802, PLR0913
+def run_TMCMC(  # noqa: C901, N802, PLR0913
     number_of_samples,
     number_of_chains,
     all_distributions_list,
@@ -264,25 +264,25 @@ def run_TMCMC(  # noqa: N802, PLR0913
 
     # Evaluate log-likelihood at current samples Sm
     if run_type == 'runningLocal':
-
         processor_count = mp.cpu_count()
-        if processor_count > 32:
+        max_num_processes = 32  # max number of processes to use for multiprocessing when running locally
+        if processor_count > max_num_processes:
             processor_count = 8
-        
+
         pool = Pool(processes=processor_count)
         write_eval_data_to_logfile(
             logfile,
             parallelize_MCMC,
             run_type,
             proc_count=processor_count,
             stage_num=stage_number,
-            )
+        )
         outputs = pool.starmap(runFEM, iterables)
-        
+
         # pool does not start
-        #mp.set_start_method('forkserver', force=True)
-        #processor_count = mp.cpu_count()
-        #with mp.Pool(processes=processor_count) as pool:
+        # mp.set_start_method('forkserver', force=True)
+        # processor_count = mp.cpu_count()
+        # with mp.Pool(processes=processor_count) as pool:
         #    write_eval_data_to_logfile(
         #        logfile,
         #        parallelize_MCMC,
@@ -292,8 +292,8 @@ def run_TMCMC(  # noqa: N802, PLR0913
         #    )
         #    outputs = pool.starmap(runFEM, iterables)
 
-        #mp.set_start_method('spawn')
-        #with mp.Pool(processes=processor_count) as pool:
+        # mp.set_start_method('spawn')
+        # with mp.Pool(processes=processor_count) as pool:
         #    write_eval_data_to_logfile(
         #        logfile,
         #        parallelize_MCMC,
@@ -302,7 +302,7 @@ def run_TMCMC(  # noqa: N802, PLR0913
         #        stage_num=stage_number,
         #    )
         #    outputs = pool.starmap(runFEM, iterables)
-            
+
         log_likelihoods_list = []
         predictions_list = []
         for output in outputs:

modules/performUQ/common/CMakeLists.txt

@@ -14,4 +14,13 @@ target_include_directories(commonUQ PUBLIC ${CONAN_INCLUDE_DIRS_JANSSON})
 simcenter_add_python_script(SCRIPT uq_utilities.py)
 simcenter_add_python_script(SCRIPT quoFEM_RV_models.py)
 simcenter_add_python_script(SCRIPT parallel_runner_mpi4py.py)
-add_subdirectory(ERAClasses)
+add_subdirectory(ERAClasses)
+
+simcenter_add_python_script(SCRIPT adaptive_doe.py)
+simcenter_add_python_script(SCRIPT common_datamodels.py)
+simcenter_add_python_script(SCRIPT convergence_metrics.py)
+simcenter_add_python_script(SCRIPT gp_ab_algorithm.py)
+simcenter_add_python_script(SCRIPT gp_model.py)
+simcenter_add_python_script(SCRIPT principal_component_analysis.py)
+simcenter_add_python_script(SCRIPT space_filling_doe.py)
+simcenter_add_python_script(SCRIPT tmcmc.py)