Refactor code structure for improved readability and maintainability

Code/src/hysteretic_curves/hysteretic_curves.py

@@ -73,13 +73,14 @@ def load_tfdmap(case_folder_path):
             f"TFDMap file must contain at least two columns after ignoring headers: {tfdmap_file}"
         )
 
-    df_tfdmap = df_tfdmap.iloc[:, 1:].copy()
     n_cols = df_tfdmap.shape[1]
 
-    if n_cols == 1:
-        df_tfdmap.columns = ["TFDMapF"]
+    if n_cols == 2:
+        df_tfdmap.columns = ["Time"] + ["TFDMapF"]
     else:
-        df_tfdmap.columns = [f"TFDMapW{i + 1}" for i in range(n_cols - 1)] + ["TFDMapF"]
+        df_tfdmap.columns = (
+            ["Time"] + [f"TFDMapW{i}" for i in range(1, n_cols - 1)] + ["TFDMapF"]
+        )
 
     return df_tfdmap
 
@@ -166,6 +167,9 @@ df["CumDispl"] = df.groupby("Case")["Displ"].transform(
     lambda s: s.diff().abs().fillna(0).cumsum()
 )
 
+# DeltaDispl: actual Displ minus previous Displ, computed per case
+df["DeltaDispl"] = df.groupby("Case")["Displ"].transform(lambda s: s.diff().fillna(0))
+
 
 # ------------------------------------------------------------------
 # 2) CycleNum based on slope reversals (LoadDir changes)
@@ -190,7 +194,7 @@ df["MaxAmpl"] = df.groupby("Case")["Displ"].transform(lambda x: x.abs().max())
 ordered_cols_point = (
     ["Case"]
     + thickness_cols
-    + ["Displ", "CumDispl", "LoadDir", "CycleNum", "MaxAmpl", "Force"]
+    + ["Displ", "CumDispl", "DeltaDispl", "LoadDir", "CycleNum", "MaxAmpl", "Force"]
 )
 df = df[ordered_cols_point]
 

Code/src/hysteretic_curves/predict_hysteretic_curves.py

@@ -127,7 +127,9 @@ def make_windows_for_case_fast(
 CaseWindows = Dict[
     int, Tuple[np.ndarray, np.ndarray, np.ndarray]
 ]  # case -> (x, y, idx)
-WINDOW_CACHE: Dict[Tuple[str, int], CaseWindows] = {}  # (target_col, window_size) -> cache
+WINDOW_CACHE: Dict[
+    Tuple[str, int], CaseWindows
+] = {}  # (target_col, window_size) -> cache
 
 
 def precompute_windows_for_cases(
@@ -748,13 +750,19 @@ def main():
     feature_cols = [
         "Displ",
         "CumDispl",
+        "DeltaDispl",
+        # "LoadDir",
+        # "CycleNum",
+        # "MaxAmpl",
     ] + thickness_cols
     target_cols = sorted(
         [c for c in df.columns if c == "Force" or c.startswith("TFDMap")],
         key=target_sort_key,
     )
     if not target_cols:
-        raise RuntimeError("No target columns found. Expected Force and/or TFDMap* columns.")
+        raise RuntimeError(
+            "No target columns found. Expected Force and/or TFDMap* columns."
+        )
 
     all_cases = sorted(df["Case"].unique())
     train_pool = [int(c) for c in all_cases if c <= n_train_case]

Code/src/hysteretic_curves/predict_hysteretic_curves_1d_cnn.py

 import numpy as np
 import pandas as pd
-from sklearn.preprocessing import StandardScaler
-
 import torch
 import torch.nn as nn
-from torch.utils.data import Dataset, DataLoader
+from sklearn.preprocessing import StandardScaler
 
 # Si quieres weight_norm moderno (sin warning):
 from torch.nn.utils.parametrizations import weight_norm
+from torch.utils.data import DataLoader, Dataset
 
 # ============================================================
 # 0) Config
@@ -17,7 +16,7 @@ np.random.seed(SEED)
 torch.manual_seed(SEED)
 
 # --- Geometry identifiers (as in your workflow) ---
-W = 2   # Number of windows
+W = 2  # Number of windows
 B = 29  # Width identifier
 H = 30
 if W == 2:
@@ -42,7 +41,7 @@ elif W == 5:
 else:
     print("Warning: PREDICT_CASE not set for W != 2, 3, or 5")
 
-WINDOW_SIZE = 60   # típicamente 40-80
+WINDOW_SIZE = 60  # típicamente 40-80
 
 BATCH_SIZE = 128
 LR = 1e-3
@@ -85,7 +84,14 @@ def choose_val_cases(train_pool):
 df = pd.read_csv(DATA_FILE)
 
 thickness_cols = [c for c in df.columns if c.startswith("tw")]
-feature_cols = ["Displ", "CumDispl", "LoadDir", "CycleNum", "MaxAmpl"] + thickness_cols
+feature_cols = [
+    "Displ",
+    "CumDispl",
+    "DeltaDispl",
+    "LoadDir",
+    "CycleNum",
+    "MaxAmpl",
+] + thickness_cols
 TARGET_COL = "Force"
 
 df_train_all = df[df["Case"] != PREDICT_CASE].copy()
@@ -111,12 +117,16 @@ def make_windows_for_case(df_case, feature_cols, target_col, window_size):
     idx = df_case.index.to_numpy()
 
     if len(df_case) < window_size:
-        return np.empty((0, window_size, len(feature_cols))), np.empty((0,)), np.array([])
+        return (
+            np.empty((0, window_size, len(feature_cols))),
+            np.empty((0,)),
+            np.array([]),
+        )
 
     Xw, yw, iw = [], [], []
     for end in range(window_size - 1, len(df_case)):
         start = end - window_size + 1
-        Xw.append(X[start:end+1])
+        Xw.append(X[start : end + 1])
         yw.append(y[end])
         iw.append(idx[end])
 
@@ -127,7 +137,9 @@ def build_windows_for_cases(df_all, cases, feature_cols, target_col, window_size
     X_list, y_list = [], []
     for case in sorted(cases):
         df_case = df_all[df_all["Case"] == case]
-        Xc, yc, _ = make_windows_for_case(df_case, feature_cols, target_col, window_size)
+        Xc, yc, _ = make_windows_for_case(
+            df_case, feature_cols, target_col, window_size
+        )
         if len(Xc) > 0:
             X_list.append(Xc)
             y_list.append(yc)
@@ -141,15 +153,18 @@ def build_windows_for_cases(df_all, cases, feature_cols, target_col, window_size
 
 print("\n--- Building TRAIN windows ---")
 X_train, y_train = build_windows_for_cases(
-    df_train_all, train_cases, feature_cols, TARGET_COL, WINDOW_SIZE)
+    df_train_all, train_cases, feature_cols, TARGET_COL, WINDOW_SIZE
+)
 
 print("\n--- Building VAL windows ---")
 X_val, y_val = build_windows_for_cases(
-    df_train_all, val_cases, feature_cols, TARGET_COL, WINDOW_SIZE)
+    df_train_all, val_cases, feature_cols, TARGET_COL, WINDOW_SIZE
+)
 
 print("\n--- Building TEST windows ---")
 X_test, y_test, test_indices = make_windows_for_case(
-    df_test, feature_cols, TARGET_COL, WINDOW_SIZE)
+    df_test, feature_cols, TARGET_COL, WINDOW_SIZE
+)
 print("Test windows:", X_test.shape)
 
 if X_train is None or X_val is None:
@@ -165,7 +180,9 @@ n_features = X_train.shape[-1]
 scaler_X = StandardScaler()
 scaler_X.fit(X_train.reshape(-1, n_features))
 
-X_train_scaled = scaler_X.transform(X_train.reshape(-1, n_features)).reshape(X_train.shape)
+X_train_scaled = scaler_X.transform(X_train.reshape(-1, n_features)).reshape(
+    X_train.shape
+)
 X_val_scaled = scaler_X.transform(X_val.reshape(-1, n_features)).reshape(X_val.shape)
 X_test_scaled = scaler_X.transform(X_test.reshape(-1, n_features)).reshape(X_test.shape)
 
@@ -190,10 +207,12 @@ class WindowDataset(Dataset):
         return self.X[idx], self.y[idx]
 
 
-train_loader = DataLoader(WindowDataset(X_train_scaled, y_train_scaled),
-                          batch_size=BATCH_SIZE, shuffle=True)
-val_loader = DataLoader(WindowDataset(X_val_scaled, y_val_scaled),
-                        batch_size=BATCH_SIZE, shuffle=False)
+train_loader = DataLoader(
+    WindowDataset(X_train_scaled, y_train_scaled), batch_size=BATCH_SIZE, shuffle=True
+)
+val_loader = DataLoader(
+    WindowDataset(X_val_scaled, y_val_scaled), batch_size=BATCH_SIZE, shuffle=False
+)
 
 
 # ============================================================
@@ -201,6 +220,7 @@ val_loader = DataLoader(WindowDataset(X_val_scaled, y_val_scaled),
 # ============================================================
 class Chomp1d(nn.Module):
     """Remove padding on the right to keep causality and correct length."""
+
     def __init__(self, chomp_size: int):
         super().__init__()
         self.chomp_size = chomp_size
@@ -208,16 +228,22 @@ class Chomp1d(nn.Module):
     def forward(self, x):
         if self.chomp_size == 0:
             return x
-        return x[:, :, :-self.chomp_size]
+        return x[:, :, : -self.chomp_size]
 
 
 class TemporalBlock(nn.Module):
-    def __init__(self, in_ch, out_ch, kernel_size, dilation, dropout, use_weight_norm=True):
+    def __init__(
+        self, in_ch, out_ch, kernel_size, dilation, dropout, use_weight_norm=True
+    ):
         super().__init__()
         padding = (kernel_size - 1) * dilation
 
-        conv1 = nn.Conv1d(in_ch, out_ch, kernel_size, padding=padding, dilation=dilation)
-        conv2 = nn.Conv1d(out_ch, out_ch, kernel_size, padding=padding, dilation=dilation)
+        conv1 = nn.Conv1d(
+            in_ch, out_ch, kernel_size, padding=padding, dilation=dilation
+        )
+        conv2 = nn.Conv1d(
+            out_ch, out_ch, kernel_size, padding=padding, dilation=dilation
+        )
 
         if use_weight_norm:
             conv1 = weight_norm(conv1)
@@ -250,16 +276,29 @@ class TemporalBlock(nn.Module):
 
 
 class DeepTCNRegressor(nn.Module):
-    def __init__(self, n_features, channels=(64, 64, 128, 128, 256),
-                 kernel_size=5, dropout=0.15, head_dim=128, use_weight_norm=True):
+    def __init__(
+        self,
+        n_features,
+        channels=(64, 64, 128, 128, 256),
+        kernel_size=5,
+        dropout=0.15,
+        head_dim=128,
+        use_weight_norm=True,
+    ):
         super().__init__()
         layers = []
         in_ch = n_features
         dilation = 1
         for out_ch in channels:
             layers.append(
-                TemporalBlock(in_ch, out_ch, kernel_size, dilation=dilation,
-                              dropout=dropout, use_weight_norm=use_weight_norm)
+                TemporalBlock(
+                    in_ch,
+                    out_ch,
+                    kernel_size,
+                    dilation=dilation,
+                    dropout=dropout,
+                    use_weight_norm=use_weight_norm,
+                )
             )
             in_ch = out_ch
             dilation *= 2
@@ -270,14 +309,14 @@ class DeepTCNRegressor(nn.Module):
             nn.Linear(in_ch, head_dim),
             nn.ReLU(),
             nn.Dropout(dropout),
-            nn.Linear(head_dim, 1)
+            nn.Linear(head_dim, 1),
         )
 
     def forward(self, x):
-        x = x.transpose(1, 2)          # (N, L, F) -> (N, F, L)
-        z = self.tcn(x)                # (N, C, L)
-        z = self.pool(z).squeeze(-1)   # (N, C)
-        y = self.head(z).squeeze(-1)   # (N,)
+        x = x.transpose(1, 2)  # (N, L, F) -> (N, F, L)
+        z = self.tcn(x)  # (N, C, L)
+        z = self.pool(z).squeeze(-1)  # (N, C)
+        y = self.head(z).squeeze(-1)  # (N,)
         return y
 
 
@@ -287,10 +326,10 @@ model = DeepTCNRegressor(
     kernel_size=5,
     dropout=0.15,
     head_dim=128,
-    use_weight_norm=USE_WEIGHT_NORM
+    use_weight_norm=USE_WEIGHT_NORM,
 ).to(DEVICE)
 
-criterion = nn.SmoothL1Loss(beta=HUBER_BETA)   # Huber
+criterion = nn.SmoothL1Loss(beta=HUBER_BETA)  # Huber
 optimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=WEIGHT_DECAY)
 
 # ============================================================
@@ -338,12 +377,16 @@ for epoch in range(MAX_EPOCHS):
     val_true = scaler_y.inverse_transform(val_true_scaled.reshape(-1, 1)).ravel()
     val_rmse = rmse(val_true, val_pred)
 
-    print(f"Epoch {epoch+1:03d} | Train loss={avg_train_loss:.6f} | Val RMSE={val_rmse:.4f}")
+    print(
+        f"Epoch {epoch + 1:03d} | Train loss={avg_train_loss:.6f} | Val RMSE={val_rmse:.4f}"
+    )
 
     # early stopping on val RMSE
     if val_rmse < best_val_rmse:
         best_val_rmse = val_rmse
-        BEST_STATE = {k: v.detach().cpu().clone() for k, v in model.state_dict().items()}
+        BEST_STATE = {
+            k: v.detach().cpu().clone() for k, v in model.state_dict().items()
+        }
         WAIT = 0
     else:
         WAIT += 1

Manuscript/Manuscript.blg

@@ -3,44 +3,44 @@ Capacity: max_strings=200000, hash_size=200000, hash_prime=170003
 The top-level auxiliary file: Manuscript.aux
 The style file: WileyNJD-AMA.bst
 Database file #1: wileyNJD-AMA.bib
-You've used 34 entries,
+You've used 24 entries,
             2086 wiz_defined-function locations,
-            783 strings with 11196 characters,
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-> -- 298
-< -- 3
-+ -- 129
-- -- 95
-* -- 718
-:= -- 1173
-add.period$ -- 54
-call.type$ -- 34
-change.case$ -- 2
+            718 strings with 9842 characters,
+and the built_in function-call counts, 4996 in all, are:
+= -- 367
+> -- 260
+< -- 0
++ -- 118
+- -- 94
+* -- 645
+:= -- 882
+add.period$ -- 24
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 chr.to.int$ -- 0
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+if$ -- 1038
 int.to.chr$ -- 0
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-missing$ -- 31
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-num.names$ -- 38
-pop$ -- 75
+int.to.str$ -- 24
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+pop$ -- 41
 preamble$ -- 1
 purify$ -- 0
 quote$ -- 0
-skip$ -- 40
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 stack$ -- 0
 substring$ -- 0
-swap$ -- 48
-text.length$ -- 3
+swap$ -- 96
+text.length$ -- 0
 text.prefix$ -- 0
 top$ -- 0
 type$ -- 0
 warning$ -- 0
-while$ -- 36
-width$ -- 36
-write$ -- 388
+while$ -- 24
+width$ -- 26
+write$ -- 298

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+["bibtex Manuscript"] 1776846350.22825 "Manuscript.aux" "Manuscript.bbl" "Manuscript" 1776846351.50549 0
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   (generated)
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   "Manuscript.blg"
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-  "/home/cimne/Articles_in_process/2026_Article_RESILINK_ML/Manuscript/Manuscript.tex" 1776327265.87996 87744 49a228ccac70c171aff96da1aaba9077 ""
+  "/home/cimne/Articles_in_process/2026_Article_RESILINK_ML/Manuscript/Manuscript.tex" 1776846348.81427 90576 bccc967433e5d59c517ed302559efaeb ""
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@@ -31,7 +31,6 @@
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Manuscript/Manuscript.fls

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 INPUT /usr/share/texlive/texmf-dist/fonts/tfm/adobe/times/ptmri8r.tfm
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Manuscript/Manuscript.pag

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