add tqdm reqiuirement, add Pareto plot subtitle

femmt/optimization/io.py

@@ -16,6 +16,7 @@
 import pandas as pd
 from matplotlib import pyplot as plt
 import matplotlib.patches as mpatches
+import tqdm
 
 # onw libraries
 from femmt.optimization.io_dtos import (InductorOptimizationDTO, InductorOptimizationTargetAndFixedParameters, FemInput,
@@ -358,7 +359,7 @@ def show_study_results(config: InductorOptimizationDTO) -> None:
                                       storage=f"sqlite:///{config.inductor_optimization_directory}/{config.inductor_study_name}.sqlite3")
 
             fig = optuna.visualization.plot_pareto_front(study, targets=lambda t: (t.values[0], t.values[1]), target_names=["volume in m³", "loss in W"])
-            fig.update_layout(title=f"{config.inductor_study_name}")
+            fig.update_layout(title=f"{config.inductor_study_name} <br><sup>{config.inductor_optimization_directory}</sup>")
             fig.write_html(f"{config.inductor_optimization_directory}/{config.inductor_study_name}"
                            f"_{datetime.datetime.now().isoformat(timespec='minutes')}.html")
             fig.show()
@@ -574,7 +575,7 @@ def fem_simulations_from_reluctance_df(reluctance_df: pd.DataFrame, config: Indu
             fem_working_directory = os.path.join(
                 target_and_fix_parameters.working_directories.fem_working_directory, f"process_{process_number}")
 
-            for index, _ in reluctance_df.iterrows():
+            for index, _ in tqdm.tqdm(reluctance_df.iterrows(), total=reluctance_df.shape[0]):
 
                 destination_json_file = os.path.join(
                     target_and_fix_parameters.working_directories.fem_simulation_results_directory,
@@ -647,8 +648,6 @@ def fem_logs_to_df(reluctance_df: pd.DataFrame, fem_results_folder_path: str) ->
             :rtype: pd.DataFrame
             """
             files_in_folder = [f for f in os.listdir(fem_results_folder_path) if os.path.isfile(os.path.join(fem_results_folder_path, f))]
-            print(files_in_folder)
-            print(len(files_in_folder))
 
             # add new columns to the dataframe, init values with None
             reluctance_df['fem_inductance'] = None
@@ -804,7 +803,7 @@ def single_fem_simulation(fem_input: FemInput, show_visual_outputs: bool = False
             return fem_output
 
         @staticmethod
-        def full_simulation(df_geometry: pd.DataFrame, current_waveform: List, inductor_config_filepath: str, process_number: int = 1):
+        def full_simulation(df_geometry: pd.DataFrame, current_waveform: List, inductor_config_filepath: str, process_number: int = 1) -> tuple:
             """
             Reluctance model (hysteresis losses) and FEM simulation (winding losses and eddy current losses) for geometries from df_geometry.
 
@@ -816,6 +815,8 @@ def full_simulation(df_geometry: pd.DataFrame, current_waveform: List, inductor_
             :type inductor_config_filepath: str
             :param process_number: process number to run the simulation on
             :type process_number: int
+            :return: volume, loss
+            :rtype: tuple
             """
             for index, _ in df_geometry.iterrows():
 

femmt/optimization/sto.py

@@ -25,6 +25,7 @@
 import matplotlib.patches as mpatches
 import magnethub as mh
 import pandas as pd
+import tqdm
 
 class StackedTransformerOptimization:
     """Perform optimizations for the stacked transformer."""
@@ -483,7 +484,7 @@ def show_study_results(config: StoSingleInputConfig) -> None:
                                       storage=f"sqlite:///{config.stacked_transformer_optimization_directory}/{config.stacked_transformer_study_name}.sqlite3")
 
             fig = optuna.visualization.plot_pareto_front(study, targets=lambda t: (t.values[0], t.values[1]), target_names=["volume in m³", "loss in W"])
-            fig.update_layout(title=f"{config.stacked_transformer_study_name}")
+            fig.update_layout(title=f"{config.stacked_transformer_study_name} <br><sup>{config.stacked_transformer_optimization_directory}</sup>")
             fig.write_html(f"{config.stacked_transformer_optimization_directory}/{config.stacked_transformer_study_name}"
                            f"_{datetime.datetime.now().isoformat(timespec='minutes')}.html")
             fig.show()
@@ -700,8 +701,7 @@ def fem_simulations_from_reluctance_df(reluctance_df: pd.DataFrame, config: StoS
                 target_and_fix_parameters.working_directories.fem_working_directory, f"process_{process_number}")
 
             # pd.read_csv(current_waveforms_csv_file, header=0, index_col=0, delimiter=';')
-
-            for index, _ in reluctance_df.iterrows():
+            for index, _ in tqdm.tqdm(reluctance_df.iterrows(), total=reluctance_df.shape[0]):
 
                 destination_json_file = os.path.join(
                     target_and_fix_parameters.working_directories.fem_simulation_results_directory,
@@ -1044,8 +1044,10 @@ def full_simulation(df_geometry: pd.DataFrame, current_waveform: List, stacked_t
 
                 print(f"P_winding_both reluctance: {reluctance_output.winding_losses}")
                 print(f"P_winding_both FEM: {fem_output.p_loss_winding_1 + fem_output.p_loss_winding_2}")
-                print(f"P_winding_both derivation: {(fem_output.p_loss_winding_1 + fem_output.p_loss_winding_2 - reluctance_output.winding_losses) / \
-                                                    (fem_output.p_loss_winding_1 + fem_output.p_loss_winding_2) * 100}")
+                winding_derivation = ((fem_output.p_loss_winding_1 + fem_output.p_loss_winding_2 - reluctance_output.winding_losses) / \
+                                      (fem_output.p_loss_winding_1 + fem_output.p_loss_winding_2) * 100)
+                print(f"P_winding_both derivation: "
+                      f"{winding_derivation}")
                 print(f"P_hyst reluctance: {reluctance_output.p_hyst}")
                 print(f"P_hyst FEM: {fem_output.core}")
                 print(f"P_hyst derivation: {(reluctance_output.p_hyst - fem_output.core) / reluctance_output.p_hyst * 100}")

setup.py

@@ -50,7 +50,8 @@
                       'materialdatabase==0.3.0',
                       'optuna',
                       'plotly',
-                      'mag-net-hub'],
+                      'mag-net-hub',
+                      'tqdm'],
     license="GNU General Public License v3",
     long_description=readme + '\n\n' + history,
     long_description_content_type="text/markdown",