Refactor code for improved readability and consistency

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
+exymax_tw1,SVR,0.006585985009931142,0.0064982791163432545,0.9866829497097801,0.006585985009931142,,"{""svr__C"": 1245.9545122809943, ""svr__epsilon"": 0.0012027645559314329, ""svr__gamma"": 0.005025785483194675}",../../models/width_optimization/3W/ml_models/per_output_models_B29_H45/it0/best_model_exymax_tw1.joblib,395.13,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+exymax_tw3,SVR,0.006837992343800181,0.004553580884860971,0.6659236594480216,0.006837992343800181,,"{""svr__C"": 448.7880306471758, ""svr__epsilon"": 0.00013403355081127117, ""svr__gamma"": 0.005847007418075073}",../../models/width_optimization/3W/ml_models/per_output_models_B29_H45/it0/best_model_exymax_tw3.joblib,271.87,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+exymax_tw2,SVR,0.0074343683500841745,0.004806222418653051,0.6464869901958292,0.0074343683500841745,,"{""svr__C"": 1578.3879853890564, ""svr__epsilon"": 0.002061045404501547, ""svr__gamma"": 0.003800674800490907}",../../models/width_optimization/3W/ml_models/per_output_models_B29_H45/it0/best_model_exymax_tw2.joblib,357.86,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_frame,SVR,2.619870006205032,2.179555686783801,0.8319327606414179,2.619870006205032,,"{""svr__C"": 5387.889822738673, ""svr__epsilon"": 0.00023269836732785153, ""svr__gamma"": 0.07097468738318204}",../../models/width_optimization/3W/ml_models/per_output_models_B29_H45/it0/best_model_tfmmax_frame.joblib,379.78,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_tw3,SVR,5.238414599827629,6.046768431344908,1.1543126868086917,5.238414599827629,,"{""svr__C"": 5330.328717919815, ""svr__epsilon"": 0.05582902448723831, ""svr__gamma"": 0.04143240634749279}",../../models/width_optimization/3W/ml_models/per_output_models_B29_H45/it0/best_model_tfmmax_tw3.joblib,503.18,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_tw2,GaussianProcess,7.603551373657773,7.080381390450335,0.9311939963975333,7.603551373657773,,"{""gpr__amplitude"": 17.76576664980768, ""gpr__kernel_type"": ""RBF"", ""gpr__length_scale"": 2.467108843522573, ""gpr__n_restarts_optimizer"": 4, ""gpr__noise"": 2.3000512910597964e-12, ""gpr__rq_alpha"": 0.024337729656929853}",../../models/width_optimization/3W/ml_models/per_output_models_B29_H45/it0/best_model_tfmmax_tw2.joblib,593.72,1.91**2 * RBF(length_scale=2.5) + WhiteKernel(noise_level=2.3e-12),lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_tw1,FlexibleMLP,9.347145297566009,11.923942423962007,1.275677444221071,9.347145297566009,,"{""mlp__activation"": ""relu"", ""mlp__alpha"": 1.9379088771032204e-05, ""mlp__learning_rate_init"": 0.0004224115725246676, ""mlp__n_layers"": 4, ""mlp__n_neurons"": 489}",../../models/width_optimization/3W/ml_models/per_output_models_B29_H45/it0/best_model_tfmmax_tw1.joblib,1210.15,,lowest_cv_rmse_dispersion_within_5pct_rmse_band

Code/models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/cv_summary_per_output_B34_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
+exymax_tw3,SVR,0.007202212127447048,0.004481301562810276,0.6222118265209654,0.007202212127447048,,"{""svr__C"": 1117.125712746634, ""svr__epsilon"": 0.0006465958496706061, ""svr__gamma"": 0.004670356278081838}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_exymax_tw3.joblib,889.41,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+exymax_tw1,GradientBoosting,0.007300943573175188,0.007955575607590385,1.089664031484974,0.007300943573175188,,"{""learning_rate"": 0.016754195403105226, ""max_depth"": 3, ""max_features"": 0.6676595772974903, ""n_estimators"": 1169, ""subsample"": 0.7322018725625201}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_exymax_tw1.joblib,425.58,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+exymax_tw2,SVR,0.008460412231077893,0.011444731445050746,1.3527392203196034,0.008460412231077893,,"{""svr__C"": 9808.35589449746, ""svr__epsilon"": 0.00016322664292135178, ""svr__gamma"": 0.00038258041488189597}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_exymax_tw2.joblib,382.41,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_frame,SVR,1.7183858166563564,1.8940457119290446,1.1022237809285973,1.7183858166563564,,"{""svr__C"": 2338.7077018101695, ""svr__epsilon"": 0.0015121721150378067, ""svr__gamma"": 0.08059242367780602}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_tfmmax_frame.joblib,334.62,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_tw2,SVR,6.570230475951083,8.231729016702195,1.252882839777489,6.570230475951083,,"{""svr__C"": 9598.629415551462, ""svr__epsilon"": 0.008177764525277858, ""svr__gamma"": 0.06450806413377351}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_tfmmax_tw2.joblib,497.18,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_tw3,SVR,6.867884256625402,7.773194090294852,1.131817864110931,6.867884256625402,,"{""svr__C"": 4770.813296275575, ""svr__epsilon"": 0.0156951512570451, ""svr__gamma"": 0.04171961411779374}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_tfmmax_tw3.joblib,505.18,,lowest_cv_rmse_dispersion_within_5pct_rmse_band
+tfmmax_tw1,GaussianProcess,12.729753975937573,16.8283131798545,1.3219668826015203,12.729753975937573,,"{""gpr__amplitude"": 42.64486576023699, ""gpr__kernel_type"": ""Matern32"", ""gpr__length_scale"": 1.5803508571081364, ""gpr__n_restarts_optimizer"": 10, ""gpr__noise"": 2.537051332183834e-05, ""gpr__rq_alpha"": 0.01469218297101831}",../../models/width_optimization/3W/ml_models/per_output_models_B34_H45/it0/best_model_tfmmax_tw1.joblib,872.27,"1.98**2 * Matern(length_scale=4.65, nu=1.5) + WhiteKernel(noise_level=2.91e-12)",lowest_cv_rmse_dispersion_within_5pct_rmse_band

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/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_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"]
+