Merge pull request #72 from upb-lea/pecst

pre-summary update for the PE-CST integration

Author: SevenOfNinePE

dct/capacitor_selection.py

@@ -155,7 +155,7 @@ def _start_optimization(circuit_filtered_point_file: str, act_cst_config: pecst.
 
             loss_total_array = np.full_like(circuit_dto.calc_modulation.phi, np.nan)
 
-            new_circuit_dto_directory = os.path.join(act_cst_config.results_directory, "01_circuit_dtos_incl_capacitor_1_loss")
+            new_circuit_dto_directory = os.path.join(act_cst_config.results_directory, "01_circuit_dtos_incl_capacitor_loss")
             if not os.path.exists(new_circuit_dto_directory):
                 os.makedirs(new_circuit_dto_directory)
 
@@ -182,11 +182,13 @@ def _start_optimization(circuit_filtered_point_file: str, act_cst_config: pecst.
                     loss_total_array[vec_vvp] = loss_per_capacitor * n_series * n_parallel
 
                 capacitor_losses = CapacitorResults(
-                    p_combined_losses=loss_total_array,
-                    volume=volume_total,
-                    pcb_area=area_total,
+                    loss_total_array=loss_total_array,
+                    volume_total=volume_total,
+                    area_total=area_total,
                     circuit_trial_file=circuit_filtered_point_file,
-                    capacitor_order_number=df_geometry_re_simulation_number,
+                    capacitor_order_number=df_geometry_re_simulation_number['ordering code'].values[0],
+                    n_series=n_series,
+                    n_parallel=n_parallel
                 )
 
                 pickle_file = os.path.join(new_circuit_dto_directory, f"{ordering_code}.pkl")

dct/circuit_optimization.py

@@ -77,6 +77,8 @@ def load_filepaths(project_directory: str) -> circuit_dtos.ParetoFilePaths:
 
         file_path_dto = circuit_dtos.ParetoFilePaths(
             circuit=loaded_file["circuit"],
+            capacitor_1=loaded_file["capacitor_1"],
+            capacitor_2=loaded_file["capacitor_2"],
             transformer=loaded_file["transformer"],
             inductor=loaded_file["inductor"],
             heat_sink=loaded_file["heat_sink"]

dct/circuit_optimization_dtos.py

@@ -12,6 +12,8 @@ class ParetoFilePaths:
     """File paths for the sub simulation optimization parts."""
 
     circuit: str
+    capacitor_1: str
+    capacitor_2: str
     inductor: str
     transformer: str
     heat_sink: str

dct/datasets_dtos.py

@@ -180,7 +180,9 @@ class CapacitorResults:
     volume_total: float
     area_total: float
     circuit_trial_file: str
-    capacitor_order_number: int
+    capacitor_order_number: str
+    n_parallel: int
+    n_series: int
 
     def __init__(self, **kwargs):
         names = set([f.name for f in dataclasses.fields(self)])

dct/dctmainctl.py

@@ -1564,10 +1564,15 @@ def run_optimization_from_toml_configurations(self, workspace_path: str) -> None
         # Create list of inductor and transformer study (ASA: Currently not implemented in configuration files)
         inductor_study_names = [self._inductor_study_data.study_name]
         stacked_transformer_study_names = [self._transformer_study_data.study_name]
+        capacitor_1_study_names = [self._capacitor_1_selection_data.study_name]
+        capacitor_2_study_names = [self._capacitor_2_selection_data.study_name]
         # Start summary processing by generating the DataFrame from calculated simulation results
         s_df = self._summary_pre_processing.generate_result_database(
             self._inductor_study_data, self._transformer_study_data, pre_summary_data,
-            inductor_study_names, stacked_transformer_study_names, filter_data)
+            inductor_study_names, stacked_transformer_study_names, filter_data,
+            self._capacitor_1_selection_data, self._capacitor_2_selection_data,
+            capacitor_1_study_names, capacitor_2_study_names
+        )
         #  Select the needed heat sink configuration
         self._summary_pre_processing.select_heat_sink_configuration(self._heat_sink_study_data, pre_summary_data, s_df)