Refactor manuscript title and enhance clarity in results discussion

.gitignore

@@ -22,6 +22,9 @@ Code/**/*.png
 Code/**/*.html
 Code/**/*.svg
 
+# Local browser used by Plotly/Kaleido static image export
+.plotly_chrome/
+
 # GiD results
 *.post.lst
 *.post.bin

Manuscript/Figures/OptimizedWindowThicknessEvolution/optimized_window_thickness_evolution.csv

+family,height_cm,width_cm,surrogate,iteration,source_model,window,window_index,thickness_mm,objective_surrogate
+H30_B29,30,29,RBF,1,opt1,tw1,1,12.34,0.0
+H30_B29,30,29,RBF,1,opt1,tw2,2,14.34,0.0
+H30_B29,30,29,RBF,2,opt2,tw1,1,12.68,0.0
+H30_B29,30,29,RBF,2,opt2,tw2,2,14.91,0.0
+H30_B29,30,29,RBF,3,opt3,tw1,1,12.56,0.0
+H30_B29,30,29,RBF,3,opt3,tw2,2,14.79,0.0
+H30_B29,30,29,Supervised ML,1,opt1,tw1,1,12.6,0.0
+H30_B29,30,29,Supervised ML,1,opt1,tw2,2,14.64,0.0
+H30_B29,30,29,Supervised ML,2,opt2,tw1,1,12.53,0.0
+H30_B29,30,29,Supervised ML,2,opt2,tw2,2,14.75,0.0
+H30_B34,30,34,RBF,1,opt1,tw1,1,15.5,507.37762
+H30_B34,30,34,RBF,1,opt1,tw2,2,20.0,507.37762
+H30_B34,30,34,RBF,2,opt2,tw1,1,14.8,700.7361
+H30_B34,30,34,RBF,2,opt2,tw2,2,18.93,700.7361
+H30_B34,30,34,RBF,3,opt3,tw1,1,14.77,688.53041
+H30_B34,30,34,RBF,3,opt3,tw2,2,18.95,688.53041
+H30_B34,30,34,Supervised ML,1,opt1,tw1,1,15.69,541.94744
+H30_B34,30,34,Supervised ML,1,opt1,tw2,2,20.0,541.94744
+H30_B34,30,34,Supervised ML,2,opt2,tw1,1,15.14,830.19116
+H30_B34,30,34,Supervised ML,2,opt2,tw2,2,20.0,830.19116
+H30_B34,30,34,Supervised ML,3,opt3,tw1,1,15.2,796.29365
+H30_B34,30,34,Supervised ML,3,opt3,tw2,2,20.0,796.29365
+H45_B29,45,29,RBF,1,opt1,tw1,1,5.94,0.0
+H45_B29,45,29,RBF,1,opt1,tw2,2,8.38,0.0
+H45_B29,45,29,RBF,1,opt1,tw3,3,9.28,0.0
+H45_B29,45,29,RBF,2,opt2,tw1,1,5.69,0.0
+H45_B29,45,29,RBF,2,opt2,tw2,2,7.97,0.0
+H45_B29,45,29,RBF,2,opt2,tw3,3,9.02,0.0
+H45_B29,45,29,RBF,3,opt3,tw1,1,5.81,0.0
+H45_B29,45,29,RBF,3,opt3,tw2,2,7.88,0.0
+H45_B29,45,29,RBF,3,opt3,tw3,3,8.98,0.0
+H45_B29,45,29,Supervised ML,1,opt1,tw1,1,5.96,0.0
+H45_B29,45,29,Supervised ML,1,opt1,tw2,2,8.23,0.0
+H45_B29,45,29,Supervised ML,1,opt1,tw3,3,9.47,0.0
+H45_B29,45,29,Supervised ML,2,opt2,tw1,1,5.7,0.0
+H45_B29,45,29,Supervised ML,2,opt2,tw2,2,7.9,0.0
+H45_B29,45,29,Supervised ML,2,opt2,tw3,3,9.02,0.0
+H45_B29,45,29,Supervised ML,3,opt3,tw1,1,5.72,0.0
+H45_B29,45,29,Supervised ML,3,opt3,tw2,2,7.87,0.0
+H45_B29,45,29,Supervised ML,3,opt3,tw3,3,8.96,0.0
+H45_B34,45,34,RBF,1,opt1,tw1,1,7.21,0.0
+H45_B34,45,34,RBF,1,opt1,tw2,2,9.27,0.0
+H45_B34,45,34,RBF,1,opt1,tw3,3,9.82,0.0
+H45_B34,45,34,RBF,2,opt2,tw1,1,6.81,0.0
+H45_B34,45,34,RBF,2,opt2,tw2,2,9.02,0.0
+H45_B34,45,34,RBF,2,opt2,tw3,3,9.65,0.0
+H45_B34,45,34,Supervised ML,1,opt1,tw1,1,7.34,0.0
+H45_B34,45,34,Supervised ML,1,opt1,tw2,2,9.28,0.0
+H45_B34,45,34,Supervised ML,1,opt1,tw3,3,10.13,0.0
+H45_B34,45,34,Supervised ML,2,opt2,tw1,1,6.87,0.0
+H45_B34,45,34,Supervised ML,2,opt2,tw2,2,9.08,0.0
+H45_B34,45,34,Supervised ML,2,opt2,tw3,3,9.86,0.0
+H45_B34,45,34,Supervised ML,3,opt3,tw1,1,6.84,0.0
+H45_B34,45,34,Supervised ML,3,opt3,tw2,2,9.05,0.0
+H45_B34,45,34,Supervised ML,3,opt3,tw3,3,9.73,0.0
+H60_B34,60,34,RBF,1,opt1,tw1,1,5.98,15.02082
+H60_B34,60,34,RBF,1,opt1,tw2,2,7.29,15.02082
+H60_B34,60,34,RBF,1,opt1,tw3,3,8.53,15.02082
+H60_B34,60,34,RBF,1,opt1,tw4,4,6.73,15.02082
+H60_B34,60,34,RBF,1,opt1,tw5,5,5.0,15.02082
+H60_B34,60,34,RBF,2,opt2,tw1,1,5.71,16.67562
+H60_B34,60,34,RBF,2,opt2,tw2,2,7.44,16.67562
+H60_B34,60,34,RBF,2,opt2,tw3,3,8.51,16.67562
+H60_B34,60,34,RBF,2,opt2,tw4,4,6.15,16.67562
+H60_B34,60,34,RBF,2,opt2,tw5,5,5.0,16.67562
+H60_B34,60,34,RBF,3,opt3,tw1,1,5.77,17.74124
+H60_B34,60,34,RBF,3,opt3,tw2,2,7.38,17.74124
+H60_B34,60,34,RBF,3,opt3,tw3,3,8.37,17.74124
+H60_B34,60,34,RBF,3,opt3,tw4,4,6.18,17.74124
+H60_B34,60,34,RBF,3,opt3,tw5,5,5.0,17.74124
+H60_B34,60,34,Supervised ML,1,opt1,tw1,1,5.97,10.58275
+H60_B34,60,34,Supervised ML,1,opt1,tw2,2,7.38,10.58275
+H60_B34,60,34,Supervised ML,1,opt1,tw3,3,8.56,10.58275
+H60_B34,60,34,Supervised ML,1,opt1,tw4,4,6.7,10.58275
+H60_B34,60,34,Supervised ML,1,opt1,tw5,5,5.0,10.58275
+H60_B34,60,34,Supervised ML,2,opt2,tw1,1,5.76,16.96082
+H60_B34,60,34,Supervised ML,2,opt2,tw2,2,7.37,16.96082
+H60_B34,60,34,Supervised ML,2,opt2,tw3,3,8.46,16.96082
+H60_B34,60,34,Supervised ML,2,opt2,tw4,4,6.67,16.96082
+H60_B34,60,34,Supervised ML,2,opt2,tw5,5,5.0,16.96082
+H60_B34,60,34,Supervised ML,3,opt3,tw1,1,5.74,16.90444
+H60_B34,60,34,Supervised ML,3,opt3,tw2,2,7.46,16.90444
+H60_B34,60,34,Supervised ML,3,opt3,tw3,3,8.5,16.90444
+H60_B34,60,34,Supervised ML,3,opt3,tw4,4,6.37,16.90444
+H60_B34,60,34,Supervised ML,3,opt3,tw5,5,5.0,16.90444

Manuscript/Figures/OptimizedWindowThicknessEvolution/plot_optimal_thickness_evolution.py

+#!/usr/bin/env python3
+"""Plot optimized window thickness evolution in a single combined figure.
+
+Input CSV expected columns:
+family, surrogate, iteration, window, window_index, thickness_mm
+
+The plot groups the x-axis by geometry family and shows adaptive iterations
+inside each family block. Window color identifies each thickness variable, and
+line style identifies the surrogate strategy:
+solid = RBF, dashed = Supervised ML.
+"""
+
+from __future__ import annotations
+
+import csv
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
+
+plt.rcParams.update(
+    {
+        "font.family": "serif",
+        "font.serif": ["Times New Roman", "DejaVu Serif"],
+        "mathtext.fontset": "stix",
+        "font.size": 12,
+        "axes.labelsize": 12,
+        "axes.titlesize": 11,
+        "xtick.labelsize": 12,
+        "ytick.labelsize": 12,
+        "axes.linewidth": 0.7,
+        "axes.spines.top": False,
+        "axes.spines.right": False,
+        "figure.dpi": 300,
+        "savefig.dpi": 300,
+    }
+)
+
+BASE_DIR = Path(__file__).resolve().parent
+INPUT_CSV = BASE_DIR / "optimized_window_thickness_evolution.csv"
+FIG_PATH = BASE_DIR / "optimized_window_thickness_evolution"
+
+SURROGATE_LINESTYLES = {
+    "RBF": "-",
+    "Supervised ML": "--",
+}
+SURROGATE_ORDER = ["RBF", "Supervised ML"]
+WINDOW_COLORS = {
+    "tw1": "#1a6faf",
+    "tw2": "#E07060",
+    "tw3": "#4D9A57",
+    "tw4": "#8B6BBE",
+    "tw5": "#D49A2A",
+}
+COLOR_GRID = "#d8d8d8"
+ITERATION_STEP = 0.78
+FAMILY_GAP = 1.05
+
+
+def load_rows(path: Path):
+    with path.open(newline="", encoding="utf-8") as f:
+        reader = csv.DictReader(f)
+        rows = []
+        for row in reader:
+            row["iteration"] = int(row["iteration"])
+            row["window_index"] = int(row["window_index"])
+            row["thickness_mm"] = float(row["thickness_mm"])
+            rows.append(row)
+    return rows
+
+
+def style_axis(ax, *, include_x_grid: bool = False) -> None:
+    ax.grid(
+        True,
+        axis="both" if include_x_grid else "y",
+        color=COLOR_GRID,
+        linewidth=0.55,
+        alpha=0.75,
+    )
+    ax.tick_params(axis="both", which="major", direction="in", length=4, width=0.7)
+    ax.tick_params(axis="both", which="minor", direction="in", length=2.5, width=0.6)
+    ax.tick_params(axis="x", which="both", length=0)
+    ax.minorticks_on()
+
+
+def save_figure_all_formats(fig, out_base: Path) -> None:
+    fig.savefig(f"{out_base}.png", format="png", bbox_inches="tight", dpi=300)
+    fig.savefig(f"{out_base}.svg", format="svg", bbox_inches="tight")
+    fig.savefig(f"{out_base}.pdf", format="pdf", bbox_inches="tight")
+
+
+def family_sort_key(family: str) -> tuple[int, int, str]:
+    height_text, width_text = family.split("_")
+    return int(height_text.replace("H", "")), int(width_text.replace("B", "")), family
+
+
+def window_sort_key(window: str) -> int:
+    return int(window.replace("tw", ""))
+
+
+def plot_all_families(rows: list[dict], out_base: Path):
+    rows = sorted(
+        rows,
+        key=lambda r: (
+            family_sort_key(r["family"]),
+            r["window_index"],
+            SURROGATE_ORDER.index(r["surrogate"])
+            if r["surrogate"] in SURROGATE_ORDER
+            else len(SURROGATE_ORDER),
+            r["iteration"],
+        ),
+    )
+    families = sorted({r["family"] for r in rows}, key=family_sort_key)
+    windows = sorted({r["window"] for r in rows}, key=window_sort_key)
+    surrogates = [s for s in SURROGATE_ORDER if s in {r["surrogate"] for r in rows}]
+
+    fig, ax = plt.subplots(figsize=(10.2, 4.85))
+    fig.subplots_adjust(left=0.075, right=0.985, bottom=0.3, top=0.95)
+
+    family_centers = {}
+    tick_positions = []
+    tick_labels = []
+    family_start_positions = []
+    x_by_family_iteration = {}
+    current_x = 0.0
+
+    for family in families:
+        iterations = sorted({r["iteration"] for r in rows if r["family"] == family})
+        family_start_positions.append(current_x)
+        family_positions = []
+        for offset, iteration in enumerate(iterations):
+            x = current_x + offset * ITERATION_STEP
+            x_by_family_iteration[(family, iteration)] = x
+            family_positions.append(x)
+            tick_positions.append(x)
+            tick_labels.append(f"it{iteration}")
+
+        family_centers[family] = (family_positions[0] + family_positions[-1]) / 2
+        current_x = family_positions[-1] + FAMILY_GAP
+
+    for family_start in family_start_positions[1:]:
+        ax.axvline(
+            family_start - FAMILY_GAP / 2,
+            color=COLOR_GRID,
+            linewidth=0.7,
+            zorder=0,
+        )
+
+    for family in families:
+        family_rows = [r for r in rows if r["family"] == family]
+        for window in sorted({r["window"] for r in family_rows}, key=window_sort_key):
+            for surrogate in surrogates:
+                subset = [
+                    r
+                    for r in family_rows
+                    if r["window"] == window and r["surrogate"] == surrogate
+                ]
+                if not subset:
+                    continue
+                subset = sorted(subset, key=lambda r: r["iteration"])
+                ax.plot(
+                    [x_by_family_iteration[(family, r["iteration"])] for r in subset],
+                    [r["thickness_mm"] for r in subset],
+                    marker="o",
+                    color=WINDOW_COLORS.get(window, "#555555"),
+                    markerfacecolor="white",
+                    markeredgewidth=0.8,
+                    linewidth=1.5,
+                    markersize=4.2,
+                    linestyle=SURROGATE_LINESTYLES.get(surrogate, "-"),
+                    zorder=3,
+                )
+
+    ax.set_ylabel("Window thickness [mm]")
+    ax.set_xticks(tick_positions)
+    ax.set_xticklabels(tick_labels)
+    ax.tick_params(axis="x", labelsize=10, pad=3)
+    ax.set_xlim(min(tick_positions) - 0.35, max(tick_positions) + 0.35)
+    style_axis(ax)
+
+    y_values = [r["thickness_mm"] for r in rows]
+    y_margin = (max(y_values) - min(y_values)) * 0.06
+    ax.set_ylim(max(0, min(y_values) - y_margin), max(y_values) + y_margin)
+
+    for family, center in family_centers.items():
+        ax.text(
+            center,
+            -0.115,
+            family,
+            transform=ax.get_xaxis_transform(),
+            ha="center",
+            va="top",
+            fontsize=12,
+            clip_on=False,
+        )
+
+    window_handles = [
+        Line2D(
+            [0],
+            [0],
+            color=WINDOW_COLORS.get(window, "#555555"),
+            marker="o",
+            markerfacecolor="white",
+            markeredgewidth=0.8,
+            linewidth=1.5,
+            markersize=4.2,
+            label=window,
+        )
+        for window in windows
+    ]
+    surrogate_handles = [
+        Line2D(
+            [0],
+            [0],
+            color="#444444",
+            linestyle=SURROGATE_LINESTYLES.get(surrogate, "-"),
+            linewidth=1.5,
+            label=surrogate,
+        )
+        for surrogate in surrogates
+    ]
+    fig.legend(
+        window_handles + surrogate_handles,
+        [h.get_label() for h in window_handles + surrogate_handles],
+        loc="lower center",
+        ncol=len(window_handles) + len(surrogate_handles),
+        frameon=False,
+        bbox_to_anchor=(0.5, 0.035),
+        columnspacing=1.4,
+        handlelength=2.0,
+    )
+
+    save_figure_all_formats(fig, out_base)
+    plt.close(fig)
+    return out_base
+
+
+def main():
+    rows = load_rows(INPUT_CSV)
+    created = plot_all_families(rows, FIG_PATH)
+    print(f"Saved: {created}.png/.svg/.pdf")
+
+
+if __name__ == "__main__":
+    main()