Refactor code for improved readability and consistency

Code/models/width_optimization/3W/ml_models/per_output_models_B29_H45/it0/cv_detailed_per_output_B29_H45.csv

+output,model,cv_rmse,std_rmse,cv_rmse_dispersion,cv_mae,cv_r2,BEST_PARAMS,fit_time_sec,gpr_kernel
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Code/models/width_optimization/3W/ml_models/per_output_models_B29_H45/it0/cv_summary_per_output_B29_H45.csv

+output,best_model,cv_rmse,std_rmse,cv_rmse_dispersion,cv_mae,cv_r2,BEST_PARAMS,model_path,train_time_sec,gpr_kernel,selected_by
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Code/models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/cv_detailed_per_output_B34_H45.csv

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Code/models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/cv_summary_per_output_B34_H45.csv

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Code/models/width_optimization/5W/ml_models/per_output_models_B34_H60/it0/cv_detailed_per_output_B34_H60.csv

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+tfmmax_tw5,GradientBoosting,8.288650919529477,2.5383297612272155,0.3062416050417176,6.480972296773851,0.8595808521301442,"{""learning_rate"": 0.016680603387731113, ""max_depth"": 2, ""max_features"": 0.6196325519809496, ""n_estimators"": 2496, ""subsample"": 0.6282288764493943}",78.57,
+tfmmax_tw5,XGBoost,8.817898824785466,2.224511432578745,0.2522722790066562,6.942297413636819,0.841110236661875,"{""colsample_bytree"": 0.7606729661729963, ""learning_rate"": 0.013344479664965542, ""max_depth"": 3, ""min_child_weight"": 7, ""n_estimators"": 1282, ""subsample"": 0.609690494793553}",27.91,
+tfmmax_tw5,RandomForest,10.882512731029662,3.019489833328643,0.2774625592414033,8.687589892726612,0.7586123331793203,"{""max_depth"": 10, ""max_features"": 0.9048675595752709, ""min_samples_leaf"": 1, ""min_samples_split"": 2, ""n_estimators"": 1183}",102.6,

Code/models/width_optimization/5W/ml_models/per_output_models_B34_H60/it0/cv_summary_per_output_B34_H60.csv

+output,best_model,cv_rmse,std_rmse,cv_rmse_dispersion,cv_mae,cv_r2,BEST_PARAMS,model_path,train_time_sec,gpr_kernel,selected_by
+exymax_tw5,GaussianProcess,0.0037397014620559475,0.0018524494485466028,0.4953468792474669,0.002564404130278725,0.9436289596508454,"{""gpr__amplitude"": 66.2941072502412, ""gpr__kernel_type"": ""Matern52"", ""gpr__length_scale"": 9.110383232654469, ""gpr__n_restarts_optimizer"": 2, ""gpr__noise"": 1.1864143515274533e-09, ""gpr__rq_alpha"": 0.09863897962847874}",../../models/width_optimization/5W/ml_models/per_output_models_B34_H60/it0/best_model_exymax_tw5.joblib,1245.86,"2.7**2 * Matern(length_scale=5.93, nu=2.5) + WhiteKernel(noise_level=1.19e-09)",lowest_cv_rmse_dispersion_within_5pct_rmse_band
+exymax_tw2,GaussianProcess,0.005176366137444404,0.0013486668112550314,0.2605431639580415,0.003913884402047848,0.9629242548153287,"{""gpr__amplitude"": 0.4369339947510315, ""gpr__kernel_type"": ""Matern52"", ""gpr__length_scale"": 53.88550972627239, ""gpr__n_restarts_optimizer"": 10, ""gpr__noise"": 3.3825346762279944e-05, ""gpr__rq_alpha"": 0.014523514317963207}",../../models/width_optimization/5W/ml_models/per_output_models_B34_H60/it0/best_model_exymax_tw2.joblib,1452.49,"3.2**2 * Matern(length_scale=6.89, nu=2.5) + WhiteKernel(noise_level=2.91e-12)",lowest_cv_rmse_dispersion_within_5pct_rmse_band
+exymax_tw1,SVR,0.0065461931973238765,0.0016275998090011273,0.2486330237956466,0.004822458246245301,0.941070262320398,"{""svr__C"": 1535.8841058246376, ""svr__epsilon"": 0.00014352665858615663, ""svr__gamma"": 0.0018620005892167522}",../../models/width_optimization/5W/ml_models/per_output_models_B34_H60/it0/best_model_exymax_tw1.joblib,1235.52,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+exymax_tw4,GaussianProcess,0.006603966975088371,0.001775601440057033,0.2688689157221706,0.004692998066122281,0.9507369331408887,"{""gpr__amplitude"": 50.805556206537666, ""gpr__kernel_type"": ""Matern32"", ""gpr__length_scale"": 21.652164558122173, ""gpr__n_restarts_optimizer"": 1, ""gpr__noise"": 2.9548225428033293e-06, ""gpr__rq_alpha"": 2.558711165466693}",../../models/width_optimization/5W/ml_models/per_output_models_B34_H60/it0/best_model_exymax_tw4.joblib,2001.22,"4.2**2 * Matern(length_scale=13.2, nu=1.5) + WhiteKernel(noise_level=2.95e-06)",lowest_cv_rmse_dispersion_within_5pct_rmse_band
+exymax_tw3,XGBoost,0.011296370910068609,0.0028544467636858533,0.25268706086320575,0.008438430960753036,0.8887122017660922,"{""colsample_bytree"": 0.922975236986968, ""learning_rate"": 0.09616759998521371, ""max_depth"": 3, ""min_child_weight"": 2, ""n_estimators"": 645, ""subsample"": 0.8928281167713635}",../../models/width_optimization/5W/ml_models/per_output_models_B34_H60/it0/best_model_exymax_tw3.joblib,1263.38,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_tw5,GaussianProcess,3.7384297568091562,1.3274899260227968,0.3550929166463309,2.7368501751524046,0.9723615024471302,"{""gpr__amplitude"": 0.4369339947510315, ""gpr__kernel_type"": ""Matern52"", ""gpr__length_scale"": 53.88550972627239, ""gpr__n_restarts_optimizer"": 10, ""gpr__noise"": 3.3825346762279944e-05, ""gpr__rq_alpha"": 0.014523514317963207}",../../models/width_optimization/5W/ml_models/per_output_models_B34_H60/it0/best_model_tfmmax_tw5.joblib,1810.68,"3.38**2 * Matern(length_scale=7.88, nu=2.5) + WhiteKernel(noise_level=2.91e-12)",lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_tw4,GaussianProcess,5.133123529769486,1.3469976699248127,0.26241286852204443,3.8771940588901117,0.978992912143702,"{""gpr__amplitude"": 0.4369339947510315, ""gpr__kernel_type"": ""Matern52"", ""gpr__length_scale"": 53.88550972627239, ""gpr__n_restarts_optimizer"": 10, ""gpr__noise"": 3.3825346762279944e-05, ""gpr__rq_alpha"": 0.014523514317963207}",../../models/width_optimization/5W/ml_models/per_output_models_B34_H60/it0/best_model_tfmmax_tw4.joblib,1117.45,"3.2**2 * Matern(length_scale=7.86, nu=2.5) + WhiteKernel(noise_level=2.91e-12)",lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_tw2,GaussianProcess,6.576362420451899,1.5033765740006209,0.22860306015453988,5.3484335100709925,0.9804048469267181,"{""gpr__amplitude"": 0.4369339947510315, ""gpr__kernel_type"": ""Matern52"", ""gpr__length_scale"": 53.88550972627239, ""gpr__n_restarts_optimizer"": 10, ""gpr__noise"": 3.3825346762279944e-05, ""gpr__rq_alpha"": 0.014523514317963207}",../../models/width_optimization/5W/ml_models/per_output_models_B34_H60/it0/best_model_tfmmax_tw2.joblib,2133.81,"4.15**2 * Matern(length_scale=8.2, nu=2.5) + WhiteKernel(noise_level=2.91e-12)",lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_frame,SVR,7.362379567538453,2.0424917941910397,0.2774227782545486,5.5591589929609775,0.6082838664236161,"{""svr__C"": 193.7932286428093, ""svr__epsilon"": 0.00012005001235516034, ""svr__gamma"": 0.04170927843815279}",../../models/width_optimization/5W/ml_models/per_output_models_B34_H60/it0/best_model_tfmmax_frame.joblib,2858.91,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_tw3,GaussianProcess,8.477766506568951,2.0065315944971487,0.23668162987768046,6.501673146641316,0.9810053418137713,"{""gpr__amplitude"": 1.102823280779196, ""gpr__kernel_type"": ""RBF"", ""gpr__length_scale"": 0.4345689693488521, ""gpr__n_restarts_optimizer"": 6, ""gpr__noise"": 7.227269699156097e-05, ""gpr__rq_alpha"": 0.05336277986968177}",../../models/width_optimization/5W/ml_models/per_output_models_B34_H60/it0/best_model_tfmmax_tw3.joblib,2040.23,4.53**2 * RBF(length_scale=4.85) + WhiteKernel(noise_level=0.000709),lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_tw1,SVR,9.729020339610338,3.3410734652444916,0.3434131442445218,6.791137893865481,0.9561686375984351,"{""svr__C"": 3513.591055460674, ""svr__epsilon"": 0.08923278628428069, ""svr__gamma"": 0.013747294826820433}",../../models/width_optimization/5W/ml_models/per_output_models_B34_H60/it0/best_model_tfmmax_tw1.joblib,785.03,,lowest_cv_rmse_dispersion_within_5pct_rmse_band

Code/reports/width_optimization/3W/ml_optimization/it0/optimization_results_3W_B29_H45_TFD_W90.csv

+Parameter,Value
+Configuration_W,3.0
+Configuration_B,29.0
+Configuration_H,45.0
+Configuration_TFD_W,90.0
+Iteration,0.0
+tw1_optimal,5.525014015514342
+tw2_optimal,8.471896290298302
+tw3_optimal,9.333863643334162
+Objective_score,-1.1926307429844958e-06
+Exy_tw1,0.062156306095972946
+Exy_tw2,0.04245576123008932
+Exy_tw3,0.04710451673468341
+TFM_tw1,90.00000000228151
+TFM_tw2,90.00000000327702
+TFM_tw3,90.00000000384767
+TFM_frame,70.8310257119319

Code/reports/width_optimization/3W/ml_optimization/it0/optimization_results_3W_B34_H45_TFD_W90.csv

+Parameter,Value
+Configuration_W,3.0
+Configuration_B,34.0
+Configuration_H,45.0
+Configuration_TFD_W,90.0
+Iteration,0.0
+tw1_optimal,7.333816578195384
+tw2_optimal,9.279818580001832
+tw3_optimal,10.121143978945401
+Objective_score,-1.0932680460256248e-06
+Exy_tw1,0.030541286356165126
+Exy_tw2,0.038966386800767694
+Exy_tw3,0.04736176051237062
+TFM_tw1,90.00000000888014
+TFM_tw2,90.0000000363739
+TFM_tw3,90.00000004969976
+TFM_frame,71.65152648580627

Code/reports/width_optimization/5W/ml_optimization/it0/optimization_results_5W_B34_H60_TFD_W90.csv

+Parameter,Value
+Configuration_W,5.0
+Configuration_B,34.0
+Configuration_H,60.0
+Configuration_TFD_W,90.0
+Iteration,0.0
+tw1_optimal,5.694179976812822
+tw2_optimal,7.254866605325219
+tw3_optimal,8.140135383492023
+tw4_optimal,6.653343430447909
+tw5_optimal,5.0
+Objective_score,17.106344387688598
+Exy_tw1,0.06127584151539267
+Exy_tw2,0.05514946520902724
+Exy_tw3,0.06789993494749069
+Exy_tw4,0.06857663218038944
+Exy_tw5,0.053409088461247346
+TFM_tw1,90.00000005473717
+TFM_tw2,90.00000072572061
+TFM_tw3,90.0000006322008
+TFM_tw4,90.00031542767577
+TFM_tw5,72.89365290337983
+TFM_frame,73.03210418991345

Code/src/hysteretic_curves/hysteretic_curves.py

@@ -29,7 +29,7 @@ THICKNESS_FILE = os.path.join(BASE_DIR, f"widthsH{H}_B{B}.txt")
 
 # === Read window thickness data ===
 thickness_data = []
-with open(THICKNESS_FILE, "r", encoding="utf-8") as f:
+with open(THICKNESS_FILE, encoding="utf-8") as f:
     for line in f:
         parts = line.strip().split()
         if parts:
@@ -130,7 +130,7 @@ for folder_name in case_folders_sorted:
     df_curve["Case"] = i
 
     # thickness correcto por case
-    trow = thickness_by_case.get(i, None)
+    trow = thickness_by_case.get(i)
     if trow is None:
         print(f"⚠️ No thickness data for case {i}, skipping.")
         continue
@@ -290,10 +290,7 @@ for (case, cycle), g in df.groupby(["Case", "CycleNum"]):
     peak_displ = df.loc[idx_peak, "Displ"]
     peak_force = df.loc[idx_peak, "Force"]
 
-    if peak_displ != 0:
-        K_sec = peak_force / peak_displ
-    else:
-        K_sec = np.nan
+    K_sec = peak_force / peak_displ if peak_displ != 0 else np.nan
     row["SecantStiffness"] = K_sec
 
     cycle_rows.append(row)

Code/src/hysteretic_curves/plot_data_hysteretic_curves.py

 import os
-import pandas as pd
+
 import matplotlib.pyplot as plt
+import pandas as pd
 import seaborn as sns
 
 sns.set_theme(style="whitegrid", context="talk")
@@ -29,6 +30,7 @@ os.makedirs(PLOT_DIR, exist_ok=True)
 output_file_cycles = os.path.join(BASE_DIR, "merged_dataset_cycles.csv")
 df_cycles = pd.read_csv(output_file_cycles)
 
+
 def plot_envelope_per_case(df_cycles, case_number, outdir=PLOT_DIR):
     g = df_cycles[df_cycles["Case"] == case_number]
 
@@ -51,6 +53,7 @@ def plot_envelope_per_case(df_cycles, case_number, outdir=PLOT_DIR):
 
     print(f"Saved: {fname}")
 
+
 def plot_energy_degradation(df_cycles, case_number, outdir=PLOT_DIR):
     g = df_cycles[df_cycles["Case"] == case_number]
 
@@ -78,6 +81,7 @@ def plot_energy_degradation(df_cycles, case_number, outdir=PLOT_DIR):
 
     print(f"Saved: {fname}")
 
+
 def plot_stiffness_degradation(df_cycles, case_number, outdir=PLOT_DIR):
     g = df_cycles[df_cycles["Case"] == case_number]
 
@@ -118,7 +122,7 @@ def plot_all_envelopes(df_cycles, outdir=PLOT_DIR):
             g["PosEnvForce"],
             marker="o",
             linestyle="-",
-            label=f"Case {case} (pos)"
+            label=f"Case {case} (pos)",
         )
 
         # Plot negative envelope
@@ -127,7 +131,7 @@ def plot_all_envelopes(df_cycles, outdir=PLOT_DIR):
             g["NegEnvForce"],
             marker="o",
             linestyle="--",
-            label=f"Case {case} (neg)"
+            label=f"Case {case} (neg)",
         )
 
     plt.axhline(0, color="black", linewidth=0.8)
@@ -139,7 +143,7 @@ def plot_all_envelopes(df_cycles, outdir=PLOT_DIR):
     plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
     plt.tight_layout()
 
-    fname = os.path.join(outdir, f"envelopes_all_cases.png")
+    fname = os.path.join(outdir, "envelopes_all_cases.png")
     plt.savefig(fname)
     plt.close()
 

Code/src/hysteretic_curves/predict_hysteretic_curves.py

@@ -27,9 +27,10 @@ Stability fix:
 from __future__ import annotations
 
 import argparse
+import contextlib
 import gc
 import os
-from typing import Dict, List, Optional, Tuple, cast
+from typing import cast
 
 import joblib
 import numpy as np
@@ -86,8 +87,8 @@ def _make_grad_scaler(use_amp: bool):
 
 
 def make_windows_for_case_fast(
-    df_case: pd.DataFrame, feature_cols: List[str], target_col: str, window_size: int
-) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    df_case: pd.DataFrame, feature_cols: list[str], target_col: str, window_size: int
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
     """
     Vectorized sliding windows.
     xw: (n_windows, window_size, n_features) float32
@@ -124,18 +125,18 @@ def make_windows_for_case_fast(
 # -------------------------
 # Window cache per window_size
 # -------------------------
-CaseWindows = Dict[
-    int, Tuple[np.ndarray, np.ndarray, np.ndarray]
+CaseWindows = dict[
+    int, tuple[np.ndarray, np.ndarray, np.ndarray]
 ]  # case -> (x, y, idx)
-WINDOW_CACHE: Dict[
-    Tuple[str, int], CaseWindows
+WINDOW_CACHE: dict[
+    tuple[str, int], CaseWindows
 ] = {}  # (target_col, window_size) -> cache
 
 
 def precompute_windows_for_cases(
     df: pd.DataFrame,
-    cases: List[int],
-    feature_cols: List[str],
+    cases: list[int],
+    feature_cols: list[str],
     target_col: str,
     window_size: int,
 ) -> CaseWindows:
@@ -151,8 +152,8 @@ def precompute_windows_for_cases(
 
 def get_cache_for_window_size(
     df: pd.DataFrame,
-    cases_to_cache: List[int],
-    feature_cols: List[str],
+    cases_to_cache: list[int],
+    feature_cols: list[str],
     target_col: str,
     window_size: int,
 ) -> CaseWindows:
@@ -169,8 +170,8 @@ def get_cache_for_window_size(
 
 
 def build_from_cache(
-    cache_ws: CaseWindows, cases: List[int]
-) -> Tuple[Optional[np.ndarray], Optional[np.ndarray]]:
+    cache_ws: CaseWindows, cases: list[int]
+) -> tuple[np.ndarray | None, np.ndarray | None]:
     xs, ys = [], []
     for c in cases:
         x, y, _ = cache_ws[int(c)]
@@ -229,17 +230,15 @@ class LSTMRegressor(nn.Module):
 def maybe_compile(model: nn.Module, device: str, enable: bool) -> nn.Module:
     if not enable or device != "cuda":
         return model
-    try:
+    with contextlib.suppress(Exception):
         model = torch.compile(model)  # type: ignore
-    except Exception:
-        pass
     return model
 
 
 # -------------------------
 # CV folds by CASE
 # -------------------------
-def make_case_folds(train_pool: List[int], seed=123, n_splits=5):
+def make_case_folds(train_pool: list[int], seed=123, n_splits=5):
     tp = np.array(sorted(train_pool), dtype=int)
 
     if len(tp) < 2:
@@ -291,8 +290,8 @@ def make_loader(
 def train_one_fold_cached(  # type: ignore
     cache_ws: CaseWindows,
     feature_dim: int,
-    train_cases: List[int],
-    val_cases: List[int],
+    train_cases: list[int],
+    val_cases: list[int],
     hidden_dim,
     dense_dim,
     num_layers,
@@ -449,7 +448,7 @@ def cv_score_for_params_cached(
     grad_clip,
     device,
     compile_model: bool,
-    trial: Optional[object] = None,
+    trial: object | None = None,
 ):
     fold_rmses = []
 
@@ -491,12 +490,12 @@ def cv_score_for_params_cached(
 
 def strip_compile_prefix(state_dict: dict) -> dict:
     # torch.compile suele guardar keys como "_orig_mod.xxx"
-    if not any(k.startswith("_orig_mod.") for k in state_dict.keys()):
+    if not any(k.startswith("_orig_mod.") for k in state_dict):
         return state_dict
     return {k.replace("_orig_mod.", "", 1): v for k, v in state_dict.items()}
 
 
-def target_sort_key(col_name: str) -> Tuple[int, int]:
+def target_sort_key(col_name: str) -> tuple[int, int]:
     if col_name == "Force":
         return (0, 0)
     if col_name.startswith("TFDMapW"):
@@ -520,7 +519,7 @@ def target_prediction_col(target_col: str) -> str:
 def train_final_full_trainpool_cached(  # type: ignore
     cache_ws: CaseWindows,
     feature_dim: int,
-    train_cases: List[int],
+    train_cases: list[int],
     hidden_dim,
     dense_dim,
     num_layers,
@@ -713,9 +712,7 @@ def main():
     predict_case = 0
     if w_val == 2:
         n_train_case = 8
-        if b_val == 29:
-            predict_case = 12
-        elif b_val == 34:
+        if b_val == 29 or b_val == 34:
             predict_case = 12
         else:
             print(f"Warning: No predict_case set for W={w_val}, B={b_val}")
@@ -818,7 +815,7 @@ def main():
             direction="minimize", sampler=sampler, pruner=pruner
         )
 
-        def objective(trial) -> float:
+        def objective(trial, target_col=target_col) -> float:
             params = suggest_params(trial, device=device)
 
             ws = int(params["window_size"])
@@ -1041,7 +1038,7 @@ def main():
 
         pred_col = target_prediction_col(target_col)
         df_test_pred[pred_col] = np.nan
-        for idx, pred in zip(test_indices, y_pred):
+        for idx, pred in zip(test_indices, y_pred, strict=False):
             df_test_pred.loc[idx, pred_col] = pred
 
         del final_model, test_loader, test_ds

Code/src/hysteretic_curves/predict_hysteretic_curves_1d_cnn.py

@@ -418,7 +418,7 @@ print(f"Test RMSE on Case {PREDICT_CASE}: {test_rmse:.4f}")
 df_test_pred = df_test.copy()
 df_test_pred["Force_TCN"] = np.nan
 
-for idx, pred in zip(test_indices, y_pred):
+for idx, pred in zip(test_indices, y_pred, strict=False):
     df_test_pred.loc[idx, "Force_TCN"] = pred
 
 OUT_FILE = f"../../data/hysteretic_curves/{W}W/H{H}_B{B}/case_{PREDICT_CASE}_with_tcn_preds.csv"

Code/src/lhs_sample_generator/lhs_sample_generator_resilink.py

-# -*- coding: utf-8 -*-
 """
 Created on Tue Jan 10 08:23:00 2023.
 
 @author: jig
 """
-from pyDOE import lhs
+
 import pandas as pd
+from pyDOE import lhs
 
 
-class GenerateLHSCases(object):
+class GenerateLHSCases:
     """
     Class for generating a number of cases by varying a set of geometric features.
 
@@ -22,14 +22,20 @@ class GenerateLHSCases(object):
         """Define the desired parameters for this case."""
         # Info available in: https://github.com/tisimst/pyDOE/blob/master/pyDOE/doe_lhs.py
         self.n_calculations = 64  # Number of desired calculations
-        self.criterion = 'maximin'  # 'center', 'maximin', 'centermaximin', 'correlation'
-        self.iterations = 5  # Number of iterations for maximin and correlation (Default: 5)
-        self.ranges = [[4.0, 14.0],
+        self.criterion = (
+            "maximin"  # 'center', 'maximin', 'centermaximin', 'correlation'
+        )
+        self.iterations = (
+            5  # Number of iterations for maximin and correlation (Default: 5)
+        )
+        self.ranges = [
+            [4.0, 14.0],
             [4.0, 14.0],
             [4.0, 14.0],
             [4.0, 14.0],
             #    [4.0, 14.0],
-                       [4.0, 14.0]]
+            [4.0, 14.0],
+        ]
         self.lhs_parameters = None
 
     def run(self):
@@ -39,14 +45,15 @@ class GenerateLHSCases(object):
 
     def create_lhs_design(self):
         """Create the Latin Hypercube Sampling design."""
-        self.lhs_parameters = lhs(len(self.ranges),
-                                  samples=self.n_calculations,
-                                  criterion=self.criterion)
+        self.lhs_parameters = lhs(
+            len(self.ranges), samples=self.n_calculations, criterion=self.criterion
+        )
 
     def generate_features_dataframe(self):
         """Generate the DataFrame from the Latin Hypercube Sampling design."""
         features = [
-            [f'ml{ii + 1}'] + [
+            [f"ml{ii + 1}"]
+            + [
                 round(r[0] + (r[1] - r[0]) * self.lhs_parameters[ii][i], 2)
                 for i, r in enumerate(self.ranges)
             ]
@@ -54,14 +61,21 @@ class GenerateLHSCases(object):
         ]
 
         # Define the column names for the DataFrame
-        column_names = ['Model', 'tw1', 'tw2', 'tw3', 'tw4', 'tw5',]  # 'tf']
+        column_names = [
+            "Model",
+            "tw1",
+            "tw2",
+            "tw3",
+            "tw4",
+            "tw5",
+        ]  # 'tf']
 
         # Create the DataFrame
         features_df = pd.DataFrame(features, columns=column_names)
 
         # Save as CSV and Excel
-        features_df.to_csv('ml_features.csv', index=False)
-        features_df.to_excel('ml_features.xlsx', index=False)
+        features_df.to_csv("ml_features.csv", index=False)
+        features_df.to_excel("ml_features.xlsx", index=False)
 
         print("DataFrame saved as 'ml_features.csv' and 'ml_features.xlsx'.")
 

Code/src/width_optimization/flexible_gpr.py

@@ -10,7 +10,11 @@ of kernels during Bayesian optimization.
 from sklearn.base import BaseEstimator, RegressorMixin
 from sklearn.gaussian_process import GaussianProcessRegressor
 from sklearn.gaussian_process.kernels import (
-    RBF, Matern, RationalQuadratic, WhiteKernel, ConstantKernel
+    RBF,
+    ConstantKernel,
+    Matern,
+    RationalQuadratic,
+    WhiteKernel,
 )
 
 
@@ -69,30 +73,27 @@ class FlexibleGPR(BaseEstimator, RegressorMixin):
         if self.kernel_type == "RBF":
             base = RBF(length_scale=ls, length_scale_bounds=(1e-6, 1e3))
         elif self.kernel_type == "Matern32":
-            base = Matern(
-                length_scale=ls, nu=1.5, length_scale_bounds=(1e-6, 1e3)
-            )
+            base = Matern(length_scale=ls, nu=1.5, length_scale_bounds=(1e-6, 1e3))
         elif self.kernel_type == "Matern52":
-            base = Matern(
-                length_scale=ls, nu=2.5, length_scale_bounds=(1e-6, 1e3)
-            )
+            base = Matern(length_scale=ls, nu=2.5, length_scale_bounds=(1e-6, 1e3))
         elif self.kernel_type == "RQ":
             base = RationalQuadratic(
                 length_scale=ls,
                 alpha=float(self.rq_alpha),
                 length_scale_bounds=(1e-6, 1e3),
-                alpha_bounds=(1e-6, 1e3)
+                alpha_bounds=(1e-6, 1e3),
             )
         else:
             raise ValueError(f"Unknown kernel_type: {self.kernel_type}")
 
-        k = ConstantKernel(
-            constant_value=float(self.amplitude),
-            constant_value_bounds=(1e-6, 1e3)
-        ) * base
+        k = (
+            ConstantKernel(
+                constant_value=float(self.amplitude), constant_value_bounds=(1e-6, 1e3)
+            )
+            * base
+        )
         k += WhiteKernel(
-            noise_level=float(self.noise),
-            noise_level_bounds=(1e-12, 1e-2)
+            noise_level=float(self.noise), noise_level_bounds=(1e-12, 1e-2)
         )
         return k
 

Code/src/width_optimization/flexible_mlp.py

@@ -6,8 +6,8 @@ This module provides a scikit-learn compatible wrapper for MLPRegressor
 that allows dynamic configuration of hidden layer architecture.
 """
 
-from sklearn.neural_network import MLPRegressor
 from sklearn.base import BaseEstimator, RegressorMixin
+from sklearn.neural_network import MLPRegressor
 
 
 class FlexibleMLP(BaseEstimator, RegressorMixin):
@@ -17,9 +17,16 @@ class FlexibleMLP(BaseEstimator, RegressorMixin):
     optimization.
     """
 
-    def __init__(self, n_layers=2, n_neurons=128, activation='relu',
-                 alpha=1e-4, learning_rate_init=1e-3,
-                 random_state=42, max_iter=5000):
+    def __init__(
+        self,
+        n_layers=2,
+        n_neurons=128,
+        activation="relu",
+        alpha=1e-4,
+        learning_rate_init=1e-3,
+        random_state=42,
+        max_iter=5000,
+    ):
         """
         Parameters
         ----------
@@ -76,7 +83,7 @@ class FlexibleMLP(BaseEstimator, RegressorMixin):
             n_iter_no_change=100,
             validation_fraction=0.2,
             max_iter=self.max_iter,
-            random_state=self.random_state
+            random_state=self.random_state,
         )
 
         self.model_.fit(x_data, y)

Code/src/width_optimization/ml_optimization_de.py

@@ -10,13 +10,15 @@ Author: Joaquín Irazábal González
 Date: 5th September 2025
 """
 
-import os
 import argparse
+import contextlib
+import os
+
 import joblib
 import numpy as np
 import pandas as pd
-from scipy.optimize import differential_evolution
 import plotly.graph_objects as go
+from scipy.optimize import differential_evolution
 
 BASE_DIR = os.path.dirname(os.path.abspath(__file__))
 
@@ -31,7 +33,7 @@ def save_optimization_results(
     b_val,
     h_val,
     tfd_w_val,
-    it_val
+    it_val,
 ):
     """
     Save optimization results to CSV file.
@@ -69,40 +71,42 @@ def save_optimization_results(
     frame_pred = model_frame.predict(inp_best)[0]
 
     # Create results dictionary
-    results_dict = {
-        'Parameter': [],
-        'Value': []
-    }
+    results_dict = {"Parameter": [], "Value": []}
 
     # Add configuration
-    results_dict['Parameter'].extend([
-        'Configuration_W', 'Configuration_B', 'Configuration_H',
-        'Configuration_TFD_W', 'Iteration'
-    ])
-    results_dict['Value'].extend([w_val, b_val, h_val, tfd_w_val, it_val])
+    results_dict["Parameter"].extend(
+        [
+            "Configuration_W",
+            "Configuration_B",
+            "Configuration_H",
+            "Configuration_TFD_W",
+            "Iteration",
+        ]
+    )
+    results_dict["Value"].extend([w_val, b_val, h_val, tfd_w_val, it_val])
 
     # Add optimal widths
     for window_num in range(w_val):
-        results_dict['Parameter'].append(f'tw{window_num+1}_optimal')
-        results_dict['Value'].append(result.x[window_num])
+        results_dict["Parameter"].append(f"tw{window_num + 1}_optimal")
+        results_dict["Value"].append(result.x[window_num])
 
     # Add objective score
-    results_dict['Parameter'].append('Objective_score')
-    results_dict['Value'].append(result.fun)
+    results_dict["Parameter"].append("Objective_score")
+    results_dict["Value"].append(result.fun)
 
     # Add Exy predictions
     for window_num, exy_val in enumerate(exy_preds):
-        results_dict['Parameter'].append(f'Exy_tw{window_num+1}')
-        results_dict['Value'].append(exy_val)
+        results_dict["Parameter"].append(f"Exy_tw{window_num + 1}")
+        results_dict["Value"].append(exy_val)
 
     # Add TFM predictions
     for window_num, tfm_val in enumerate(tfm_preds):
-        results_dict['Parameter'].append(f'TFM_tw{window_num+1}')
-        results_dict['Value'].append(tfm_val)
+        results_dict["Parameter"].append(f"TFM_tw{window_num + 1}")
+        results_dict["Value"].append(tfm_val)
 
     # Add frame prediction
-    results_dict['Parameter'].append('TFM_frame')
-    results_dict['Value'].append(frame_pred)
+    results_dict["Parameter"].append("TFM_frame")
+    results_dict["Value"].append(frame_pred)
 
     # Create DataFrame
     df_results = pd.DataFrame(results_dict)
@@ -114,7 +118,7 @@ def save_optimization_results(
     # Save to CSV
     csv_path = os.path.join(
         it_dir,
-        f'optimization_results_{w_val}W_B{b_val}_H{h_val}_TFD_W{int(tfd_w_val)}.csv'
+        f"optimization_results_{w_val}W_B{b_val}_H{h_val}_TFD_W{int(tfd_w_val)}.csv",
     )
     df_results.to_csv(csv_path, index=False)
 
@@ -137,7 +141,7 @@ def make_input_df(x, w_val):
         pd.DataFrame: DataFrame with the appropriate number of tw columns
     """
     # Create column names based on number of windows
-    columns = [f"tw{i+1}" for i in range(w_val)]
+    columns = [f"tw{i + 1}" for i in range(w_val)]
 
     # Verify input length matches number of windows
     if len(x) != w_val:
@@ -220,16 +224,13 @@ def objective(x, models_exy, models_tfm, model_frame, w_val, b_val, tfd_w_val):
     raw_vals = []
 
     # --- 1. Equivalent strain maximization (negative sign) ---
-    for model_i, tw_i, i in zip(models_exy, x, range(w_val)):
+    for model_i, tw_i, i in zip(models_exy, x, range(w_val), strict=False):
         # Calculate predicted equivalent strain squared multiplied by volume
         pred_i = model_i.predict(inp)[0]
         v_factor = get_window_v_factor(w_val, b_val, i)
 
         if v_factor == 1.0 and w_val not in [2, 3, 5]:
-            print(
-                "Warning: Not considering window area for W not equal to "
-                "2, 3, or 5"
-            )
+            print("Warning: Not considering window area for W not equal to 2, 3, or 5")
             raw_vals.append(pred_i * pred_i * tw_i)
         else:
             raw_vals.append(pred_i * pred_i * tw_i * 1e-3 * v_factor)
@@ -245,10 +246,7 @@ def objective(x, models_exy, models_tfm, model_frame, w_val, b_val, tfd_w_val):
 
     # --- 3. Penalize frame failure exceeding the limit (TFD_MAX) ---
     frame_pred = model_frame.predict(inp)[0]
-    penalty_frame = (
-        (frame_pred - tfd_max) ** 3 if frame_pred > tfd_max
-        else 0.0
-    )
+    penalty_frame = (frame_pred - tfd_max) ** 3 if frame_pred > tfd_max else 0.0
 
     # --- Combine all objective terms ---
     total_score = score_exy + penalty_windows + penalty_frame
@@ -264,7 +262,7 @@ def plot_3d_tw_pairs_at_optimum(
     h_val,
     tfd_w_val,
     it_val,
-    output_dir="figures_3D"
+    output_dir="figures_3D",
 ):
     """
     Generate a 3D surface plot for every pair (tw_i, tw_j), fixing the
@@ -296,7 +294,6 @@ def plot_3d_tw_pairs_at_optimum(
 
     for i in range(w_val):
         for j in range(i + 1, w_val):
-
             # Define variable ranges
             tw_i_vals = np.linspace(bounds[i][0], bounds[i][1], 50)
             tw_j_vals = np.linspace(bounds[j][0], bounds[j][1], 50)
@@ -326,7 +323,7 @@ def plot_3d_tw_pairs_at_optimum(
                         z=z_vals,
                         colorscale="Viridis",
                         showscale=True,
-                        name="Objective surface"
+                        name="Objective surface",
                     )
                 ]
             )
@@ -339,35 +336,36 @@ def plot_3d_tw_pairs_at_optimum(
                     z=[opt_z],
                     mode="markers",
                     marker=dict(size=6, color="red"),
-                    name="Optimum"
+                    name="Optimum",
                 )
             )
 
             fig.update_layout(
                 title=(
-                    f"RBF 3D Objective Surface – (tw{i+1}, tw{j+1}) "
+                    f"RBF 3D Objective Surface – (tw{i + 1}, tw{j + 1}) "
                     f" at optimum for other windows width"
                 ),
                 scene=dict(
-                    xaxis_title=f"tw{i+1} (mm)",
-                    yaxis_title=f"tw{j+1} (mm)",
-                    zaxis_title="Objective score"
+                    xaxis_title=f"tw{i + 1} (mm)",
+                    yaxis_title=f"tw{j + 1} (mm)",
+                    zaxis_title="Objective score",
                 ),
                 width=950,
-                height=850
+                height=850,
             )
 
             # Save HTML
-            fname_base = f"surface_tw{i+1}_tw{j+1}_B{b_val}_H{h_val}_TFD_W{int(tfd_w_val)}"
+            fname_base = (
+                f"surface_tw{i + 1}_tw{j + 1}_B{b_val}_H{h_val}_TFD_W{int(tfd_w_val)}"
+            )
             html_path = f"{it_dir}/{fname_base}.html"
             fig.write_html(html_path)
 
             # Save PNG (optional if Kaleido installed)
-            try:
-                fig.write_image(html_path.replace(".html", ".png"),
-                                width=1200, height=1000)
-            except Exception:  # pylint: disable=broad-except
-                pass
+            with contextlib.suppress(Exception):
+                fig.write_image(
+                    html_path.replace(".html", ".png"), width=1200, height=1000
+                )
 
             print(f"Saved 3D RBF surface: {html_path}")
 
@@ -405,7 +403,11 @@ def main():
         bounds_vals = [(5, 14), (5, 14), (5, 14)]  # tw1, tw2, tw3 between 5 and 14 mm
     elif w_val == 5:
         bounds_vals = [
-            (5, 12), (5, 12), (5, 12), (5, 12), (5, 12)
+            (5, 12),
+            (5, 12),
+            (5, 12),
+            (5, 12),
+            (5, 12),
         ]  # tw1 to tw5 between 5 and 12 mm
     else:
         # General case for any W
@@ -415,17 +417,24 @@ def main():
     # Path to trained models
     input_models_dir = os.path.join(
         BASE_DIR,
-        "..", "..", "models", "width_optimization",
-        f"{w_val}W", "ml_models",
-        f"per_output_models_B{b_val}_H{h_val}"
+        "..",
+        "..",
+        "models",
+        "width_optimization",
+        f"{w_val}W",
+        "ml_models",
+        f"per_output_models_B{b_val}_H{h_val}",
     )
 
     # Output directory for optimization cruves
     output_graphs_dir = os.path.join(
         BASE_DIR,
-        "..", "..", "reports", "width_optimization",
+        "..",
+        "..",
+        "reports",
+        "width_optimization",
         f"{w_val}W",
-        "ml_optimization"
+        "ml_optimization",
     )
     os.makedirs(output_graphs_dir, exist_ok=True)
 
@@ -440,10 +449,11 @@ def main():
 
     # Check Exy and TFM models for each window
     for window_idx in range(w_val):
-        exy_name = f"best_model_exymax_tw{window_idx+1}.joblib"
-        tfm_name = f"best_model_tfmmax_tw{window_idx+1}.joblib"
-        if not os.path.exists(os.path.join(input_models_dir_it, exy_name)) or \
-           not os.path.exists(os.path.join(input_models_dir_it, tfm_name)):
+        exy_name = f"best_model_exymax_tw{window_idx + 1}.joblib"
+        tfm_name = f"best_model_tfmmax_tw{window_idx + 1}.joblib"
+        if not os.path.exists(
+            os.path.join(input_models_dir_it, exy_name)
+        ) or not os.path.exists(os.path.join(input_models_dir_it, tfm_name)):
             all_models_exist = False
             break
 
@@ -462,12 +472,12 @@ def main():
 
     for window_idx in range(w_val):
         # Load Exy models
-        exy_model_name = f"best_model_exymax_tw{window_idx+1}.joblib"
+        exy_model_name = f"best_model_exymax_tw{window_idx + 1}.joblib"
         exy_model_path = os.path.join(input_models_dir_it, exy_model_name)
         models_exy.append(joblib.load(exy_model_path))
 
         # Load TFM models
-        tfm_model_name = f"best_model_tfmmax_tw{window_idx+1}.joblib"
+        tfm_model_name = f"best_model_tfmmax_tw{window_idx + 1}.joblib"
         tfm_model_path = os.path.join(input_models_dir_it, tfm_model_name)
         models_tfm.append(joblib.load(tfm_model_path))
 
@@ -490,7 +500,7 @@ def main():
         maxiter=500,  # Maximum number of iterations
         popsize=25,  # Population size
         tol=1e-6,  # Tolerance for convergence
-        seed=42       # Random seed for reproducibility
+        seed=42,  # Random seed for reproducibility
     )
 
     # Save results to CSV
@@ -504,7 +514,7 @@ def main():
         b_val,
         h_val,
         tfd_w_val,
-        it
+        it,
     )
 
     # Extract optimum for plotting
@@ -514,15 +524,13 @@ def main():
     print(f"\nResults for {w_val} windows optimization (Iteration {it}):")
     print("Best solution found:")
     for window_num, tw in enumerate(optimum):
-        print(f"tw{window_num+1} = {tw:.2f}")
+        print(f"tw{window_num + 1} = {tw:.2f}")
     print(f"Objective score = {result.fun:.4f}")
 
     # Calculate predictions for optimal solution
     inp_best = make_input_df(optimum, w_val)
     print("\nPredictions:")
-    print(
-        "Exy:", [m.predict(inp_best)[0] for m in models_exy]
-    )
+    print("Exy:", [m.predict(inp_best)[0] for m in models_exy])
     print("TFM tw:", [m.predict(inp_best)[0] for m in models_tfm])
     print("TFM frame:", model_frame.predict(inp_best)[0])
 
@@ -536,7 +544,7 @@ def main():
         h_val,
         tfd_w_val,
         it,
-        output_dir=output_graphs_dir
+        output_dir=output_graphs_dir,
     )
 
 

Code/src/width_optimization/ml_surrogate_train.py

@@ -33,51 +33,40 @@ Author: Joaquín Irazábal González
 Date: 5th September 2025
 """
 
+import argparse
+import itertools
+import json
 import os
 import time
-import json
-import itertools
 import warnings
-import argparse
+
 import joblib
 import numpy as np
 import pandas as pd
+from flexible_gpr import FlexibleGPR
+from flexible_mlp import FlexibleMLP
+from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
+from sklearn.exceptions import ConvergenceWarning
+from sklearn.metrics import make_scorer, mean_absolute_error, root_mean_squared_error
 from sklearn.model_selection import KFold, LeaveOneOut, RepeatedKFold
-from sklearn.metrics import (
-    make_scorer, mean_absolute_error, root_mean_squared_error
-)
-from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
-from sklearn.svm import SVR
-from sklearn.preprocessing import StandardScaler
 from sklearn.pipeline import Pipeline
-from sklearn.exceptions import ConvergenceWarning
+from sklearn.preprocessing import StandardScaler
+from sklearn.svm import SVR
 from skopt import BayesSearchCV
-from skopt.space import Real, Integer, Categorical
+from skopt.space import Categorical, Integer, Real
 from xgboost import XGBRegressor
-from flexible_mlp import FlexibleMLP
-from flexible_gpr import FlexibleGPR
-
 
 # Silence convergence warnings
 warnings.filterwarnings("ignore", category=ConvergenceWarning)
 
 # --- Custom scorers ---
-rmse_scorer = make_scorer(
-    root_mean_squared_error,
-    greater_is_better=False
-)
+rmse_scorer = make_scorer(root_mean_squared_error, greater_is_better=False)
 mae_scorer = make_scorer(mean_absolute_error, greater_is_better=False)
 
-SCORING = {
-    "rmse": rmse_scorer,
-    "mae": mae_scorer,
-    "r2": "r2"
-}
+SCORING = {"rmse": rmse_scorer, "mae": mae_scorer, "r2": "r2"}
 
 DEFAULT_RMSE_BAND_DELTA = 0.05
-DISPERSION_SELECTION_CRITERION = (
-    "lowest_cv_rmse_dispersion_within_5pct_rmse_band"
-)
+DISPERSION_SELECTION_CRITERION = "lowest_cv_rmse_dispersion_within_5pct_rmse_band"
 
 
 def param_product(grid_dict):
@@ -96,7 +85,7 @@ def param_product(grid_dict):
     """
     keys = list(grid_dict.keys())
     for values in itertools.product(*[grid_dict[k] for k in keys]):
-        yield dict(zip(keys, values))
+        yield dict(zip(keys, values, strict=False))
 
 
 def get_scoring_for_cv(cv, base_scoring):
@@ -165,11 +154,8 @@ def extract_best_fold_rmse(cv_results, best_index):
     """
     fold_rmse = []
     split_keys = sorted(
-        [
-            k for k in cv_results
-            if k.startswith("split") and k.endswith("_test_rmse")
-        ],
-        key=lambda k: int(k.split("_", 1)[0].replace("split", ""))
+        [k for k in cv_results if k.startswith("split") and k.endswith("_test_rmse")],
+        key=lambda k: int(k.split("_", 1)[0].replace("split", "")),
     )
 
     for key in split_keys:
@@ -204,8 +190,7 @@ def compute_rmse_dispersion(fold_rmse):
     return mean_rmse, std_rmse, cv_rmse_dispersion
 
 
-def select_best_model_by_rmse_band(candidate_models,
-                                   delta=DEFAULT_RMSE_BAND_DELTA):
+def select_best_model_by_rmse_band(candidate_models, delta=DEFAULT_RMSE_BAND_DELTA):
     """
     Select best model using RMSE competitiveness plus dispersion stability.
 
@@ -219,20 +204,15 @@ def select_best_model_by_rmse_band(candidate_models,
 
     best_mean_rmse = min(m["cv_rmse"] for m in candidate_models)
     threshold = (1.0 + delta) * best_mean_rmse
-    competitive = [
-        m for m in candidate_models
-        if m["cv_rmse"] <= threshold
-    ]
+    competitive = [m for m in candidate_models if m["cv_rmse"] <= threshold]
 
     stable_candidates = [
-        m for m in competitive
-        if np.isfinite(m.get("cv_rmse_dispersion", float("nan")))
+        m for m in competitive if np.isfinite(m.get("cv_rmse_dispersion", float("nan")))
     ]
 
     if stable_candidates and len(stable_candidates) == len(competitive):
         selected = min(
-            stable_candidates,
-            key=lambda m: (m["cv_rmse_dispersion"], m["cv_rmse"])
+            stable_candidates, key=lambda m: (m["cv_rmse_dispersion"], m["cv_rmse"])
         )
         return selected, DISPERSION_SELECTION_CRITERION
 
@@ -258,8 +238,9 @@ def extract_gpr_kernel(estimator):
     try:
         if hasattr(estimator, "named_steps") and "gpr" in estimator.named_steps:
             gpr = estimator.named_steps["gpr"]
-            if (getattr(gpr, "model_", None) is not None and
-                    hasattr(gpr.model_, "kernel_")):
+            if getattr(gpr, "model_", None) is not None and hasattr(
+                gpr.model_, "kernel_"
+            ):
                 return str(gpr.model_.kernel_)
     except (AttributeError, KeyError):
         pass
@@ -292,59 +273,71 @@ def adjust_tree_search_space(model_name, grid, n_samples):
 
     if model_name == "RandomForest":
         if very_small:
-            g.update({
+            g.update(
+                {
                     "n_estimators": Integer(200, 800),
                     "max_depth": Integer(1, 4),
                     "min_samples_split": Integer(2, 8),
                     "min_samples_leaf": Integer(2, 4),
                     "max_features": Real(0.5, 1.0),
-            })
+                }
+            )
         elif small:
-            g.update({
+            g.update(
+                {
                     "n_estimators": Integer(300, 1200),
                     "max_depth": Integer(2, 10),
                     "min_samples_split": Integer(2, 10),
                     "min_samples_leaf": Integer(1, 6),
                     "max_features": Real(0.5, 1.0),
-            })
+                }
+            )
 
     elif model_name == "GradientBoosting":
         if very_small:
-            g.update({
+            g.update(
+                {
                     "n_estimators": Integer(200, 1500),
                     "learning_rate": Real(0.01, 0.08, prior="log-uniform"),
                     "max_depth": Integer(1, 3),
                     "subsample": Real(0.7, 0.9),
                     "max_features": Real(0.6, 1.0),
-            })
+                }
+            )
         elif small:
-            g.update({
+            g.update(
+                {
                     "n_estimators": Integer(300, 2500),
                     "learning_rate": Real(1e-3, 0.1, prior="log-uniform"),
                     "max_depth": Integer(1, 4),
                     "subsample": Real(0.6, 1.0),
                     "max_features": Real(0.5, 1.0),
-            })
+                }
+            )
 
     elif model_name == "XGBoost":
         if very_small:
-            g.update({
+            g.update(
+                {
                     "n_estimators": Integer(200, 1200),
                     "max_depth": Integer(1, 4),
                     "learning_rate": Real(0.01, 0.2, prior="log-uniform"),
                     "subsample": Real(0.7, 0.95),
                     "colsample_bytree": Real(0.7, 1.0),
                     "min_child_weight": Integer(3, 20),
-            })
+                }
+            )
         elif small:
-            g.update({
+            g.update(
+                {
                     "n_estimators": Integer(300, 1600),
                     "max_depth": Integer(2, 6),
                     "learning_rate": Real(1e-3, 0.3, prior="log-uniform"),
                     "subsample": Real(0.6, 1.0),
                     "colsample_bytree": Real(0.6, 1.0),
                     "min_child_weight": Integer(1, 15),
-            })
+                }
+            )
 
     return g
 
@@ -361,9 +354,8 @@ model_grids = {
             "min_samples_leaf": Integer(1, 6),
             "max_features": Real(0.5, 1.0),
         },
-        "bayes"
+        "bayes",
     ),
-
     "GradientBoosting": (
         GradientBoostingRegressor(random_state=42),
         {
@@ -373,14 +365,10 @@ model_grids = {
             "subsample": Real(0.6, 1.0),
             "max_features": Real(0.5, 1.0),
         },
-        "bayes"
+        "bayes",
     ),
-
     "FlexibleMLP": (
-        Pipeline([
-            ("scaler", StandardScaler()),
-            ("mlp", FlexibleMLP())
-        ]),
+        Pipeline([("scaler", StandardScaler()), ("mlp", FlexibleMLP())]),
         {
             "mlp__n_layers": Integer(1, 5),
             "mlp__n_neurons": Integer(64, 512),
@@ -388,29 +376,24 @@ model_grids = {
             "mlp__alpha": Real(1e-5, 1e-2, prior="log-uniform"),
             "mlp__learning_rate_init": Real(1e-4, 1e-2, prior="log-uniform"),
         },
-        "bayes"
+        "bayes",
     ),
-
     "SVR": (
-        Pipeline([
-            ("scaler", StandardScaler()),
-            ("svr", SVR(kernel="rbf", tol=1e-4))
-        ]),
+        Pipeline([("scaler", StandardScaler()), ("svr", SVR(kernel="rbf", tol=1e-4))]),
         {
             "svr__C": Real(1e0, 1e4, prior="log-uniform"),
             "svr__gamma": Real(1e-4, 1e-1, prior="log-uniform"),
             "svr__epsilon": Real(1e-4, 1e-1, prior="log-uniform"),
         },
-        "bayes"
+        "bayes",
     ),
-
     "XGBoost": (
         XGBRegressor(
             objective="reg:squarederror",
             random_state=42,
             n_jobs=-1,
             tree_method="approx",
-            verbosity=0
+            verbosity=0,
         ),
         {
             "n_estimators": Integer(200, 2000),
@@ -420,25 +403,19 @@ model_grids = {
             "colsample_bytree": Real(0.6, 1.0),
             "min_child_weight": Integer(1, 10),
         },
-        "bayes"
+        "bayes",
     ),
-
     "GaussianProcess": (
-        Pipeline([
-            ("scaler", StandardScaler()),
-            ("gpr", FlexibleGPR())
-        ]),
+        Pipeline([("scaler", StandardScaler()), ("gpr", FlexibleGPR())]),
         {
-            "gpr__kernel_type": Categorical(
-                ["RBF", "Matern32", "Matern52", "RQ"]
-            ),
+            "gpr__kernel_type": Categorical(["RBF", "Matern32", "Matern52", "RQ"]),
             "gpr__amplitude": Real(1e-2, 1e2, prior="log-uniform"),
             "gpr__length_scale": Real(1e-2, 1e2, prior="log-uniform"),
             "gpr__rq_alpha": Real(1e-2, 1e2, prior="log-uniform"),
             "gpr__noise": Real(1e-12, 1e-4, prior="log-uniform"),
             "gpr__n_restarts_optimizer": Integer(1, 10),
         },
-        "bayes"
+        "bayes",
     ),
 }
 
@@ -471,7 +448,9 @@ def main():
         start_rows = 10
 
     # Path configuration
-    data_path = f"../../data/width_optimization/{w_val}W/ml_FEMdata_B{b_val}_H{h_val}.csv"
+    data_path = (
+        f"../../data/width_optimization/{w_val}W/ml_FEMdata_B{b_val}_H{h_val}.csv"
+    )
     output_models_dir = (
         f"../../models/width_optimization/{w_val}W/ml_models/"
         f"per_output_models_B{b_val}_H{h_val}"
@@ -496,9 +475,9 @@ def main():
         )
         return
 
-    print(f"\n\n{'='*60}")
+    print(f"\n\n{'=' * 60}")
     print(f"ITERATION {iteration_idx}: Training with {n_current_rows} rows")
-    print(f"{'='*60}")
+    print(f"{'=' * 60}")
 
     # Create iteration specific directory
     it_dir = os.path.join(output_models_dir, f"it{iteration_idx}")
@@ -508,7 +487,7 @@ def main():
     df = df_full.iloc[:n_current_rows].copy()
 
     # Define features and targets dynamically
-    input_features = [f"tw{i+1}" for i in range(w_val)]
+    input_features = [f"tw{i + 1}" for i in range(w_val)]
     target_features = [
         c for c in df.columns if c not in input_features and c != "eyymax_tf"
     ]
@@ -549,7 +528,7 @@ def main():
             start_model = time.time()
 
             # Initialize variables
-            best_rmse, mean_mae, mean_r2 = float('inf'), None, None
+            best_rmse, mean_mae, mean_r2 = float("inf"), None, None
 
             scoring_used, refit_metric = get_scoring_for_cv(cv, SCORING)
 
@@ -557,9 +536,7 @@ def main():
             # Bayesian Search CV
             # ---------------------------------------------------------
             # Shrink tree search spaces automatically for small N
-            grid_used = adjust_tree_search_space(
-                model_name, grid, n_samples
-            )
+            grid_used = adjust_tree_search_space(model_name, grid, n_samples)
 
             rs = BayesSearchCV(
                 estimator=est,
@@ -572,7 +549,7 @@ def main():
                 random_state=42,
                 optimizer_kwargs={"base_estimator": "RF"},
                 return_train_score=False,
-                verbose=0
+                verbose=0,
             )
 
             rs.fit(features_df, y)
@@ -588,8 +565,8 @@ def main():
 
             # RMSE scorer is negative, so invert sign
             fold_rmse = extract_best_fold_rmse(rs.cv_results_, i)
-            fold_mean_rmse, std_rmse, cv_rmse_dispersion = (
-                compute_rmse_dispersion(fold_rmse)
+            fold_mean_rmse, std_rmse, cv_rmse_dispersion = compute_rmse_dispersion(
+                fold_rmse
             )
             best_rmse = float(-rs.cv_results_["mean_test_rmse"][i])
             if np.isfinite(fold_mean_rmse):
@@ -616,7 +593,7 @@ def main():
                 "cv_r2": float(mean_r2),
                 "BEST_PARAMS": json.dumps(best_params),
                 "fit_time_sec": round(elapsed_model, 2),
-                "gpr_kernel": gpr_kernel
+                "gpr_kernel": gpr_kernel,
             }
             detailed_rows.append(detailed_row)
 
@@ -628,7 +605,8 @@ def main():
                 f"time={elapsed_model:6.1f}s"
             )
 
-            candidate_models.append({
+            candidate_models.append(
+                {
                     "output": tgt,
                     "model": model_name,
                     "cv_rmse": float(best_rmse),
@@ -639,31 +617,26 @@ def main():
                     "BEST_PARAMS": best_params,
                     "BEST_ESTIMATOR": best_estimator,
                     "CV_RESULTS": rs.cv_results_,
-                "gpr_kernel": gpr_kernel
-            })
-
-        best_for_tgt, selected_by = select_best_model_by_rmse_band(
-            candidate_models
+                    "gpr_kernel": gpr_kernel,
+                }
             )
+
+        best_for_tgt, selected_by = select_best_model_by_rmse_band(candidate_models)
         best_cv_results = best_for_tgt["CV_RESULTS"]
 
         # =============================================================
         # SAVE BEST MODEL AND RESULTS FOR THIS OUTPUT
         # =============================================================
         # Modified filename to save in itX folder without suffix
-        out_model_path = os.path.join(
-            it_dir, f"best_model_{tgt}.joblib"
-        )
+        out_model_path = os.path.join(it_dir, f"best_model_{tgt}.joblib")
         joblib.dump(best_for_tgt["BEST_ESTIMATOR"], out_model_path)
-        joblib.dump(
-            best_cv_results,
-            os.path.join(it_dir, f"cv_results_{tgt}.pkl")
-        )
+        joblib.dump(best_cv_results, os.path.join(it_dir, f"cv_results_{tgt}.pkl"))
 
         elapsed_target = time.time() - start_target
 
         # --- Summary for this output ---
-        summary_rows.append({
+        summary_rows.append(
+            {
                 "output": tgt,
                 "best_model": best_for_tgt["model"],
                 "cv_rmse": best_for_tgt["cv_rmse"],
@@ -675,33 +648,34 @@ def main():
                 "model_path": out_model_path,
                 "train_time_sec": round(elapsed_target, 2),
                 "gpr_kernel": best_for_tgt.get("gpr_kernel", ""),
-            "selected_by": selected_by
-        })
+                "selected_by": selected_by,
+            }
+        )
 
-        print(f"  🏁 Best model for {tgt}: {best_for_tgt['model']} "
+        print(
+            f"  🏁 Best model for {tgt}: {best_for_tgt['model']} "
             f"(RMSE={best_for_tgt['cv_rmse']:.5f}, "
             f"CV={best_for_tgt['cv_rmse_dispersion']:.5f}, "
             f"R²={best_for_tgt['cv_r2']:.5f}) "
             f"selected by {selected_by} "
-              f"→ saved to {out_model_path}")
+            f"→ saved to {out_model_path}"
+        )
 
     # =============================================================
     # SAVE SUMMARY TABLES
     # =============================================================
-    summary_df = (pd.DataFrame(summary_rows)
-                  .sort_values(["cv_rmse"])
-                  .reset_index(drop=True))
-    detailed_df = (pd.DataFrame(detailed_rows)
-                   .sort_values(["output", "cv_rmse"])
-                   .reset_index(drop=True))
-
-    summary_csv = os.path.join(
-        it_dir, f"cv_summary_per_output_B{b_val}_H{h_val}.csv"
+    summary_df = (
+        pd.DataFrame(summary_rows).sort_values(["cv_rmse"]).reset_index(drop=True)
     )
-    detailed_csv = os.path.join(
-        it_dir, f"cv_detailed_per_output_B{b_val}_H{h_val}.csv"
+    detailed_df = (
+        pd.DataFrame(detailed_rows)
+        .sort_values(["output", "cv_rmse"])
+        .reset_index(drop=True)
     )
 
+    summary_csv = os.path.join(it_dir, f"cv_summary_per_output_B{b_val}_H{h_val}.csv")
+    detailed_csv = os.path.join(it_dir, f"cv_detailed_per_output_B{b_val}_H{h_val}.csv")
+
     summary_df.to_csv(summary_csv, index=False)
     detailed_df.to_csv(detailed_csv, index=False)
 

Code/src/width_optimization/rbf_model.py

@@ -19,7 +19,7 @@ class RBFModel:
     """
 
     def __init__(
-        self, x_data, y_data, function='multiquadric', smooth=0.0, epsilon=None
+        self, x_data, y_data, function="multiquadric", smooth=0.0, epsilon=None
     ):
         """
         Initialize and fit the RBF model.
@@ -49,10 +49,11 @@ class RBFModel:
     def fit(self):
         """Fit the RBF model using the stored data."""
         self.model = Rbf(
-            *self.x_data.T, self.y_data,
+            *self.x_data.T,
+            self.y_data,
             function=self.function,
             smooth=self.smooth,
-            epsilon=self.epsilon
+            epsilon=self.epsilon,
         )
 
     def predict(self, x_new):

Code/src/width_optimization/rbf_optimization_de.py

@@ -6,8 +6,11 @@ This module performs structural optimization using Radial Basis Function
 surrogate models and Differential Evolution. It finds the optimal window
 widths to maximize strain while maintaining failure within limits.
 """
-import os
+
 import argparse
+import contextlib
+import os
+
 import joblib
 import numpy as np
 import pandas as pd
@@ -27,7 +30,7 @@ def save_optimization_results(
     b_val,
     h_val,
     tfd_w_val,
-    it_val
+    it_val,
 ):
     """
     Save optimization results to CSV file.
@@ -65,40 +68,42 @@ def save_optimization_results(
     frame_pred = model_frame.predict(inp_best)[0]
 
     # Create results dictionary
-    results_dict = {
-        'Parameter': [],
-        'Value': []
-    }
+    results_dict = {"Parameter": [], "Value": []}
 
     # Add configuration
-    results_dict['Parameter'].extend([
-        'Configuration_W', 'Configuration_B', 'Configuration_H',
-        'Configuration_TFD_W', 'Iteration'
-    ])
-    results_dict['Value'].extend([w_val, b_val, h_val, tfd_w_val, it_val])
+    results_dict["Parameter"].extend(
+        [
+            "Configuration_W",
+            "Configuration_B",
+            "Configuration_H",
+            "Configuration_TFD_W",
+            "Iteration",
+        ]
+    )
+    results_dict["Value"].extend([w_val, b_val, h_val, tfd_w_val, it_val])
 
     # Add optimal widths
     for window_num in range(w_val):
-        results_dict['Parameter'].append(f'tw{window_num+1}_optimal')
-        results_dict['Value'].append(result.x[window_num])
+        results_dict["Parameter"].append(f"tw{window_num + 1}_optimal")
+        results_dict["Value"].append(result.x[window_num])
 
     # Add objective score
-    results_dict['Parameter'].append('Objective_score')
-    results_dict['Value'].append(result.fun)
+    results_dict["Parameter"].append("Objective_score")
+    results_dict["Value"].append(result.fun)
 
     # Add Exy predictions
     for window_num, exy_val in enumerate(exy_preds):
-        results_dict['Parameter'].append(f'Exy_tw{window_num+1}')
-        results_dict['Value'].append(exy_val)
+        results_dict["Parameter"].append(f"Exy_tw{window_num + 1}")
+        results_dict["Value"].append(exy_val)
 
     # Add TFM predictions
     for window_num, tfm_val in enumerate(tfm_preds):
-        results_dict['Parameter'].append(f'TFM_tw{window_num+1}')
-        results_dict['Value'].append(tfm_val)
+        results_dict["Parameter"].append(f"TFM_tw{window_num + 1}")
+        results_dict["Value"].append(tfm_val)
 
     # Add frame prediction
-    results_dict['Parameter'].append('TFM_frame')
-    results_dict['Value'].append(frame_pred)
+    results_dict["Parameter"].append("TFM_frame")
+    results_dict["Value"].append(frame_pred)
 
     # Create DataFrame
     df_results = pd.DataFrame(results_dict)
@@ -110,7 +115,7 @@ def save_optimization_results(
     # Save to CSV
     csv_path = os.path.join(
         it_dir,
-        f'optimization_results_{w_val}W_B{b_val}_H{h_val}_TFD_W{int(tfd_w_val)}.csv'
+        f"optimization_results_{w_val}W_B{b_val}_H{h_val}_TFD_W{int(tfd_w_val)}.csv",
     )
     df_results.to_csv(csv_path, index=False)
 
@@ -133,7 +138,7 @@ def make_input_df(x, w_val):
         pd.DataFrame: DataFrame with the appropriate number of tw columns
     """
     # Create column names based on number of windows
-    columns = [f"tw{i+1}" for i in range(w_val)]
+    columns = [f"tw{i + 1}" for i in range(w_val)]
 
     # Verify input length matches number of windows
     if len(x) != w_val:
@@ -216,16 +221,13 @@ def objective(x, models_exy, models_tfm, model_frame, w_val, b_val, tfd_w_val):
     raw_vals = []
 
     # --- 1. Equivalent strain maximization (negative sign) ---
-    for model_i, tw_i, i in zip(models_exy, x, range(w_val)):
+    for model_i, tw_i, i in zip(models_exy, x, range(w_val), strict=False):
         # Calculate predicted equivalent strain squared multiplied by volume
         pred_i = model_i.predict(inp)[0]
         v_factor = get_window_v_factor(w_val, b_val, i)
 
         if v_factor == 1.0 and w_val not in [2, 3, 5]:
-            print(
-                "Warning: Not considering window area for W not equal to "
-                "2, 3, or 5"
-            )
+            print("Warning: Not considering window area for W not equal to 2, 3, or 5")
             raw_vals.append(pred_i * pred_i * tw_i)
         else:
             raw_vals.append(pred_i * pred_i * tw_i * 1e-3 * v_factor)
@@ -241,10 +243,7 @@ def objective(x, models_exy, models_tfm, model_frame, w_val, b_val, tfd_w_val):
 
     # --- 3. Penalize frame failure exceeding the limit (TFD_MAX) ---
     frame_pred = model_frame.predict(inp)[0]
-    penalty_frame = (
-        (frame_pred - tfd_max) ** 3 if frame_pred > tfd_max
-        else abs(0.0)
-    )
+    penalty_frame = (frame_pred - tfd_max) ** 3 if frame_pred > tfd_max else abs(0.0)
 
     # --- Combine all objective terms ---
     total_score = score_exy + penalty_windows + penalty_frame
@@ -260,7 +259,7 @@ def plot_3d_tw_pairs_at_optimum(
     h_val,
     tfd_w_val,
     it_val,
-    output_dir="figures_3D"
+    output_dir="figures_3D",
 ):
     """
     Generate a 3D surface plot for every pair (tw_i, tw_j), fixing the
@@ -292,7 +291,6 @@ def plot_3d_tw_pairs_at_optimum(
 
     for i in range(w_val):
         for j in range(i + 1, w_val):
-
             # Define variable ranges
             tw_i_vals = np.linspace(bounds[i][0], bounds[i][1], 50)
             tw_j_vals = np.linspace(bounds[j][0], bounds[j][1], 50)
@@ -322,7 +320,7 @@ def plot_3d_tw_pairs_at_optimum(
                         z=z_vals,
                         colorscale="Viridis",
                         showscale=True,
-                        name="Objective surface"
+                        name="Objective surface",
                     )
                 ]
             )
@@ -335,35 +333,36 @@ def plot_3d_tw_pairs_at_optimum(
                     z=[opt_z],
                     mode="markers",
                     marker={"size": 6, "color": "red"},
-                    name="Optimum"
+                    name="Optimum",
                 )
             )
 
             fig.update_layout(
                 title=(
-                    f"RBF 3D Objective Surface – (tw{i+1}, tw{j+1}) "
+                    f"RBF 3D Objective Surface – (tw{i + 1}, tw{j + 1}) "
                     f" at optimum for other windows width"
                 ),
                 scene={
                     "xaxis_title": f"tw{i + 1} (mm)",
                     "yaxis_title": f"tw{j + 1} (mm)",
-                    "zaxis_title": "Objective score"
+                    "zaxis_title": "Objective score",
                 },
                 width=950,
-                height=850
+                height=850,
             )
 
             # Save HTML
-            fname_base = f"surface_tw{i+1}_tw{j+1}_B{b_val}_H{h_val}_TFD_W{int(tfd_w_val)}"
+            fname_base = (
+                f"surface_tw{i + 1}_tw{j + 1}_B{b_val}_H{h_val}_TFD_W{int(tfd_w_val)}"
+            )
             html_path = f"{it_dir}/{fname_base}.html"
             fig.write_html(html_path)
 
             # Save PNG (optional if Kaleido installed)
-            try:
-                fig.write_image(html_path.replace(".html", ".png"),
-                                width=1200, height=1000)
-            except Exception:  # pylint: disable=broad-except
-                pass
+            with contextlib.suppress(Exception):
+                fig.write_image(
+                    html_path.replace(".html", ".png"), width=1200, height=1000
+                )
 
             print(f"Saved 3D RBF surface: {html_path}")
 
@@ -401,7 +400,11 @@ def main():
         bounds_vals = [(5, 14), (5, 14), (5, 14)]  # tw1, tw2, tw3 between 5 and 14 mm
     elif w_val == 5:
         bounds_vals = [
-            (5, 12), (5, 12), (5, 12), (5, 12), (5, 12)
+            (5, 12),
+            (5, 12),
+            (5, 12),
+            (5, 12),
+            (5, 12),
         ]  # tw1 to tw5 between 5 and 12 mm
     else:
         # General case for any W
@@ -411,17 +414,24 @@ def main():
     # Path to trained models
     input_models_dir = os.path.join(
         BASE_DIR,
-        "..", "..", "models", "width_optimization",
-        f"{w_val}W", "rbf_models",
-        f"per_output_models_B{b_val}_H{h_val}"
+        "..",
+        "..",
+        "models",
+        "width_optimization",
+        f"{w_val}W",
+        "rbf_models",
+        f"per_output_models_B{b_val}_H{h_val}",
     )
 
     # Output directory for optimization cruves
     output_graphs_dir = os.path.join(
         BASE_DIR,
-        "..", "..", "reports", "width_optimization",
+        "..",
+        "..",
+        "reports",
+        "width_optimization",
         f"{w_val}W",
-        "rbf_optimization"
+        "rbf_optimization",
     )
     os.makedirs(output_graphs_dir, exist_ok=True)
 
@@ -436,10 +446,11 @@ def main():
 
     # Check Exy and TFM models for each window
     for window_idx in range(w_val):
-        exy_name = f"rbf_exymax_tw{window_idx+1}.joblib"
-        tfm_name = f"rbf_tfmmax_tw{window_idx+1}.joblib"
-        if not os.path.exists(os.path.join(input_models_dir_it, exy_name)) or \
-           not os.path.exists(os.path.join(input_models_dir_it, tfm_name)):
+        exy_name = f"rbf_exymax_tw{window_idx + 1}.joblib"
+        tfm_name = f"rbf_tfmmax_tw{window_idx + 1}.joblib"
+        if not os.path.exists(
+            os.path.join(input_models_dir_it, exy_name)
+        ) or not os.path.exists(os.path.join(input_models_dir_it, tfm_name)):
             all_models_exist = False
             break
 
@@ -458,12 +469,12 @@ def main():
 
     for window_idx in range(w_val):
         # Load Exy models
-        exy_model_name = f"rbf_exymax_tw{window_idx+1}.joblib"
+        exy_model_name = f"rbf_exymax_tw{window_idx + 1}.joblib"
         exy_model_path = os.path.join(input_models_dir_it, exy_model_name)
         models_exy.append(joblib.load(exy_model_path))
 
         # Load TFM models
-        tfm_model_name = f"rbf_tfmmax_tw{window_idx+1}.joblib"
+        tfm_model_name = f"rbf_tfmmax_tw{window_idx + 1}.joblib"
         tfm_model_path = os.path.join(input_models_dir_it, tfm_model_name)
         models_tfm.append(joblib.load(tfm_model_path))
 
@@ -486,7 +497,7 @@ def main():
         maxiter=500,  # Maximum number of iterations
         popsize=25,  # Population size
         tol=1e-6,  # Tolerance for convergence
-        seed=42       # Random seed for reproducibility
+        seed=42,  # Random seed for reproducibility
     )
 
     # Save results to CSV
@@ -500,7 +511,7 @@ def main():
         b_val,
         h_val,
         tfd_w_val,
-        it
+        it,
     )
 
     # Extract optimum for plotting
@@ -510,7 +521,7 @@ def main():
     print(f"\nResults for {w_val} windows optimization (Iteration {it}):")
     print("Best solution found:")
     for window_num, tw in enumerate(optimum):
-        print(f"tw{window_num+1} = {tw:.2f}")
+        print(f"tw{window_num + 1} = {tw:.2f}")
     print(f"Objective score = {result.fun:.4f}")
 
     # Calculate predictions for optimal solution
@@ -530,7 +541,7 @@ def main():
         h_val,
         tfd_w_val,
         it,
-        output_dir=output_graphs_dir
+        output_dir=output_graphs_dir,
     )
 
 

Code/src/width_optimization/rbf_surrogate_train.py

@@ -6,17 +6,25 @@ This module handles the training of RBF surrogate models and performs
 Leave-One-Out (LOO) validation to assess their predictive performance.
 It automates the process for multiple output variables.
 """
-import os
+
 import argparse
+import os
+
 import joblib
 import numpy as np
 import pandas as pd
 from rbf_model import RBFModel
-from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
 
 
-def loo_validation_rbf(df_val, input_features, target_features,
-                       function="multiquadric", smooth=0.0, epsilon=None):
+def loo_validation_rbf(
+    df_val,
+    input_features,
+    target_features,
+    function="multiquadric",
+    smooth=0.0,
+    epsilon=None,
+):
     """
     Leave-One-Out (LOO) validation for RBF models.
 
@@ -68,11 +76,7 @@ def loo_validation_rbf(df_val, input_features, target_features,
 
             # Train RBF with N-1 samples
             model = RBFModel(
-                x_train,
-                y_train,
-                function=function,
-                smooth=smooth,
-                epsilon=epsilon
+                x_train, y_train, function=function, smooth=smooth, epsilon=epsilon
             )
 
             pred_i = model.predict(x_test)[0]
@@ -86,11 +90,10 @@ def loo_validation_rbf(df_val, input_features, target_features,
         rmse = np.sqrt(mean_squared_error(y_true, y_pred))
         mae = mean_absolute_error(y_true, y_pred)
         r2 = r2_score(y_true, y_pred)
-        rmse_std, rmse_cv, mae_std, mse_std = compute_error_dispersion(
-            y_true, y_pred
-        )
+        rmse_std, rmse_cv, mae_std, mse_std = compute_error_dispersion(y_true, y_pred)
 
-        results.append({
+        results.append(
+            {
                 "output": tgt,
                 "LOO_RMSE": rmse,
                 "LOO_MAE": mae,
@@ -98,8 +101,9 @@ def loo_validation_rbf(df_val, input_features, target_features,
                 "LOO_RMSE_STD": rmse_std,
                 "LOO_RMSE_CV": rmse_cv,
                 "LOO_MAE_STD": mae_std,
-            "LOO_SQERR_STD": mse_std
-        })
+                "LOO_SQERR_STD": mse_std,
+            }
+        )
 
         print(
             f"   LOO_RMSE = {rmse:.5f} | LOO_MAE = {mae:.5f} | "
@@ -164,7 +168,9 @@ def main():
         print("Warning: H not set for W != 2, 3, or 5")
 
     # Path configuration
-    data_path = f"../../data/width_optimization/{w_val}W/rbf_FEMdata_B{b_val}_H{h_val}.csv"
+    data_path = (
+        f"../../data/width_optimization/{w_val}W/rbf_FEMdata_B{b_val}_H{h_val}.csv"
+    )
     output_models_dir = (
         f"../../models/width_optimization/{w_val}W/rbf_models/"
         f"per_output_models_B{b_val}_H{h_val}"
@@ -187,9 +193,9 @@ def main():
         )
         return
 
-    print(f"\n\n{'='*60}")
+    print(f"\n\n{'=' * 60}")
     print(f"ITERATION {i_iter}: Training RBF with {n_curr} rows")
-    print(f"{'='*60}")
+    print(f"{'=' * 60}")
 
     # Create iteration specific directory
     it_dir = os.path.join(output_models_dir, f"it{i_iter}")
@@ -197,7 +203,7 @@ def main():
 
     df = df_tot.iloc[:n_curr].copy()
 
-    input_features = [f"tw{i+1}" for i in range(w_val)]
+    input_features = [f"tw{i + 1}" for i in range(w_val)]
     target_features = [
         c for c in df.columns if c not in input_features and c != "eyymax_tf"
     ]
@@ -210,12 +216,7 @@ def main():
         print(f"→ Training RBF for {tgt} ...")
         y = df[tgt].values
 
-        rbf = RBFModel(
-            x_values, y,
-            function="multiquadric",
-            smooth=0.0,
-            epsilon=None
-        )
+        rbf = RBFModel(x_values, y, function="multiquadric", smooth=0.0, epsilon=None)
 
         joblib.dump(rbf, os.path.join(it_dir, f"rbf_{tgt}.joblib"))
         print(f"   saved to it{i_iter}/rbf_{tgt}.joblib")
@@ -224,8 +225,12 @@ def main():
     # Execute LOO validation
     # =============================================================
     loo_results = loo_validation_rbf(
-        df, input_features, target_features,
-        function="multiquadric", smooth=0.0, epsilon=None
+        df,
+        input_features,
+        target_features,
+        function="multiquadric",
+        smooth=0.0,
+        epsilon=None,
     )
 
     loo_csv = os.path.join(it_dir, "rbf_LOO_results.csv")

pyproject.toml

+[tool.ruff]
+target-version = "py310"
+line-length = 88
+src = ["Code/src"]
+
+[tool.ruff.lint]
+select = [
+    "E",
+    "F",
+    "I",
+    "UP",
+    "B",
+    "SIM",
+]
+ignore = [
+    "E501",
+]
+
+[tool.ruff.lint.per-file-ignores]
+"Code/src/hysteretic_curves/predict_hysteretic_curves.py" = ["F821"]
+"Code/src/hysteretic_curves/predict_hysteretic_curves_1d_cnn.py" = ["F821"]
+