Add the benchmark files as-is from the issue description.

Author: Andrija Kolic

graalpython/com.oracle.graal.python.benchmarks/python/matplotlib/3d_surface_wireframe.py

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+#!/usr/bin/env python3
+"""
+3D plotting example: surface, wireframe, and contour projections.
+Saves to PDF.
+
+Run:
+  python tst/3d_surface_wireframe.py
+"""
+
+from pathlib import Path
+
+# Use a non-interactive backend to work in headless environments
+import matplotlib
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt
+import numpy as np
+from mpl_toolkits.mplot3d import Axes3D  # noqa: F401  # needed for 3D projection
+
+
+def main() -> None:
+    out_path = Path(__file__).parent / "surface_3d.pdf"
+
+    # Domain and function
+    x = np.linspace(-4, 4, 200)
+    y = np.linspace(-4, 4, 200)
+    X, Y = np.meshgrid(x, y)
+
+    R = np.sqrt(X**2 + Y**2) + 1e-12
+    Z = np.sin(R) / R + 0.15 * np.cos(3*X) * np.sin(3*Y) / (1 + 0.5 * (X**2 + Y**2))
+
+    fig = plt.figure(figsize=(7.5, 5.8), dpi=150)
+    ax = fig.add_subplot(111, projection="3d")
+
+    # Surface with colormap
+    surf = ax.plot_surface(X, Y, Z, cmap="viridis", linewidth=0, antialiased=True, alpha=0.95)
+
+    # Wireframe overlay (sparser grid to avoid clutter)
+    step = 10
+    ax.plot_wireframe(X[::step, ::step], Y[::step, ::step], Z[::step, ::step],
+                      rstride=1, cstride=1, color="k", linewidth=0.3, alpha=0.5)
+
+    # Contour projections on Z, X, and Y planes
+    z_offset = Z.min() - 0.4
+    ax.contour(X, Y, Z, zdir="z", offset=z_offset, cmap="viridis", levels=18, linewidths=0.8)
+
+    x_offset = x.min() - 0.6
+    ax.contour(X, Y, Z, zdir="x", offset=x_offset, cmap="magma", levels=14, linewidths=0.7)
+
+    y_offset = y.max() + 0.6
+    ax.contour(X, Y, Z, zdir="y", offset=y_offset, cmap="plasma", levels=14, linewidths=0.7)
+
+    # Axes labels and limits
+    ax.set_xlabel("X")
+    ax.set_ylabel("Y")
+    ax.set_zlabel("Z")
+
+    ax.set_xlim(x_offset, x.max())
+    ax.set_ylim(y.min(), y_offset)
+    ax.set_zlim(z_offset, Z.max())
+
+    # Colorbar
+    cb = fig.colorbar(surf, ax=ax, shrink=0.6, aspect=12, pad=0.08)
+    cb.set_label("Z value")
+
+    # View angle
+    ax.view_init(elev=25, azim=-55)
+
+    ax.set_title("3D Surface + Wireframe + Contour Projections")
+    fig.tight_layout()
+
+    fig.savefig(out_path, format="pdf")
+    plt.close(fig)
+
+    print(f"Wrote PDF: {out_path.resolve()}")
+
+
+if __name__ == "__main__":
+    main()

graalpython/com.oracle.graal.python.benchmarks/python/matplotlib/OWNERS.toml

@@ -0,0 +1,6 @@
+[[rule]]
+files = "*"
+any = [
+    "francois.farquet@oracle.com",
+    "andrija.kolic@oracle.com",
+]

graalpython/com.oracle.graal.python.benchmarks/python/matplotlib/categorical_bar_and_box.py

@@ -0,0 +1,117 @@
+#!/usr/bin/env python3
+"""
+Categorical plots: grouped bar chart with error bars and a boxplot.
+Saves a multi-page PDF using PdfPages.
+
+Run:
+  python tst/categorical_bar_and_box.py
+"""
+
+from pathlib import Path
+
+# Use a non-interactive backend to work in headless environments
+import matplotlib
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.backends.backend_pdf import PdfPages
+
+
+def _colorize_boxplot(bp, facecolor="#1f77b4", edgecolor="black", alpha=0.6):
+    for box in bp["boxes"]:
+        box.set(facecolor=facecolor, edgecolor=edgecolor, alpha=alpha)
+    for median in bp["medians"]:
+        median.set(color="black", linewidth=1.2)
+    for whisker in bp["whiskers"]:
+        whisker.set(color=edgecolor, linewidth=1.0)
+    for cap in bp["caps"]:
+        cap.set(color=edgecolor, linewidth=1.0)
+    for flier in bp["fliers"]:
+        flier.set(marker="o", markersize=3, markerfacecolor="white", markeredgecolor=edgecolor, alpha=0.7)
+
+
+def main() -> None:
+    out_path = Path(__file__).parent / "categorical_plots.pdf"
+
+    rng = np.random.default_rng(2024)
+    categories = ["A", "B", "C", "D"]
+    n = len(categories)
+
+    with PdfPages(out_path) as pdf:
+        # Page 1: Grouped bar chart with error bars
+        x = np.arange(n)
+        bar_w = 0.35
+
+        means1 = rng.normal(3.0, 0.4, n)
+        errs1 = rng.uniform(0.1, 0.4, n)
+
+        means2 = rng.normal(2.2, 0.5, n)
+        errs2 = rng.uniform(0.1, 0.4, n)
+
+        fig1, ax1 = plt.subplots(figsize=(7, 4), dpi=150)
+        b1 = ax1.bar(x - bar_w / 2, means1, yerr=errs1, width=bar_w, capsize=3,
+                     label="Series 1", color="#1f77b4", edgecolor="black", alpha=0.85)
+        b2 = ax1.bar(x + bar_w / 2, means2, yerr=errs2, width=bar_w, capsize=3,
+                     label="Series 2", color="#ff7f0e", edgecolor="black", alpha=0.85)
+
+        ax1.set_xticks(x, categories)
+        ax1.set_ylabel("Value")
+        ax1.set_title("Grouped Bar Chart with Error Bars")
+        ax1.grid(axis="y", linestyle="--", alpha=0.35)
+        ax1.legend(loc="best")
+
+        # Annotate bars with heights
+        for bars in (b1, b2):
+            for rect in bars:
+                h = rect.get_height()
+                ax1.text(rect.get_x() + rect.get_width() / 2.0, h + 0.05,
+                         f"{h:.2f}", ha="center", va="bottom", fontsize=8, rotation=0)
+
+        fig1.tight_layout()
+        pdf.savefig(fig1)
+        plt.close(fig1)
+
+        # Page 2: Boxplot across categories
+        # Generate some synthetic distributions with varying mean/variance
+        mus = [2.8, 3.2, 2.5, 3.5]
+        sigmas = [0.50, 0.60, 0.45, 0.55]
+        data = [rng.normal(loc=m, scale=s, size=400) for m, s in zip(mus, sigmas)]
+
+        fig2, ax2 = plt.subplots(figsize=(7, 4), dpi=150)
+        bp = ax2.boxplot(
+            data,
+            tick_labels=categories,
+            widths=0.6,
+            patch_artist=True,
+            showfliers=True,
+            whis=(5, 95),
+        )
+        # Colorize boxes with a palette
+        palette = ["#1f77b4", "#ff7f0e", "#2ca02c", "#9467bd"]
+        for box, color in zip(bp["boxes"], palette):
+            box.set(facecolor=color, edgecolor="black", alpha=0.6)
+
+        # Style the rest
+        for median in bp["medians"]:
+            median.set(color="black", linewidth=1.4)
+        for whisker in bp["whiskers"]:
+            whisker.set(color="black", linewidth=1.0)
+        for cap in bp["caps"]:
+            cap.set(color="black", linewidth=1.0)
+        for flier in bp["fliers"]:
+            flier.set(marker="o", markersize=3, markerfacecolor="white", markeredgecolor="black", alpha=0.7)
+
+        ax2.set_title("Boxplot by Category")
+        ax2.set_ylabel("Distribution")
+        ax2.grid(axis="y", linestyle="--", alpha=0.35)
+        fig2.tight_layout()
+
+        pdf.savefig(fig2)
+        plt.close(fig2)
+
+    print(f"Wrote PDF: {out_path.resolve()}")
+
+
+if __name__ == "__main__":
+    main()

graalpython/com.oracle.graal.python.benchmarks/python/matplotlib/distributions_hist_2d.py

@@ -0,0 +1,73 @@
+#!/usr/bin/env python3
+"""
+Distribution plots: 1D histogram, 2D histogram, and hexbin with colorbars.
+
+Run:
+  python tst/distributions_hist_2d.py
+"""
+
+from pathlib import Path
+
+# Use a non-interactive backend to work in headless environments
+import matplotlib
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def main() -> None:
+    out_path = Path(__file__).parent / "distributions_2d.pdf"
+
+    rng = np.random.default_rng(7)
+    n = 6000
+
+    # Create two correlated variables
+    x = rng.normal(0.0, 1.0, n)
+    y = 0.65 * x + rng.normal(0.0, 0.8, n)
+
+    fig, axs = plt.subplots(1, 3, figsize=(12, 4), dpi=150)
+
+    # 1) 1D histograms overlayed
+    ax = axs[0]
+    ax.hist(x, bins=40, alpha=0.8, label="x", color="#1f77b4", edgecolor="white")
+    ax.hist(y, bins=40, alpha=0.6, label="y", color="#ff7f0e", edgecolor="white")
+    ax.set_title("1D Histograms")
+    ax.set_xlabel("Value")
+    ax.set_ylabel("Frequency")
+    ax.grid(True, linestyle="--", alpha=0.3)
+    ax.legend(loc="best")
+
+    # 2) 2D histogram via pcolormesh
+    ax = axs[1]
+    H, xedges, yedges = np.histogram2d(x, y, bins=60)
+    X, Y = np.meshgrid(xedges, yedges)
+    pcm = ax.pcolormesh(X, Y, H.T, cmap="viridis", shading="auto")
+    ax.set_title("2D Histogram")
+    ax.set_xlabel("x")
+    ax.set_ylabel("y")
+    cb = fig.colorbar(pcm, ax=ax)
+    cb.set_label("Count")
+    ax.grid(False)
+
+    # 3) Hexbin with log color scale
+    ax = axs[2]
+    hb = ax.hexbin(x, y, gridsize=45, cmap="plasma", mincnt=1, bins="log")
+    ax.set_title("Hexbin (log density)")
+    ax.set_xlabel("x")
+    ax.set_ylabel("y")
+    cb = fig.colorbar(hb, ax=ax)
+    cb.set_label("log10(count)")
+    ax.grid(False)
+
+    fig.suptitle("Distributions: 1D and 2D Density", fontsize=14)
+    fig.tight_layout(rect=[0, 0.03, 1, 0.95])
+
+    fig.savefig(out_path, format="pdf")
+    plt.close(fig)
+
+    print(f"Wrote PDF: {out_path.resolve()}")
+
+
+if __name__ == "__main__":
+    main()

graalpython/com.oracle.graal.python.benchmarks/python/matplotlib/polar_quiver_stream.py

@@ -0,0 +1,111 @@
+#!/usr/bin/env python3
+"""
+Vector and polar plots: polar plot, quiver, and streamplot on separate pages.
+Saves a multi-page PDF using PdfPages.
+
+Run:
+  python tst/polar_quiver_stream.py
+"""
+
+from pathlib import Path
+
+# Use a non-interactive backend to work in headless environments
+import matplotlib
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.backends.backend_pdf import PdfPages
+
+
+def page_polar(ax):
+    # Polar demo with multiple radii and an area fill
+    theta = np.linspace(0, 2*np.pi, 512)
+    r1 = 1.0 + 0.3*np.sin(5*theta)
+    r2 = 0.7 + 0.2*np.cos(3*theta + 0.5)
+
+    ax.plot(theta, r1, color="#1f77b4", linewidth=2.0, label="r1(θ) = 1 + 0.3 sin(5θ)")
+    ax.plot(theta, r2, color="#ff7f0e", linewidth=2.0, linestyle="--", label="r2(θ) = 0.7 + 0.2 cos(3θ+0.5)")
+    ax.fill_between(theta, r2, color="#ff7f0e", alpha=0.25, step="mid")
+    ax.set_theta_zero_location("N")
+    ax.set_theta_direction(-1)
+    ax.set_title("Polar Plot", va="bottom")
+    ax.grid(True, alpha=0.4)
+    ax.legend(loc="upper right", bbox_to_anchor=(1.25, 1.15), frameon=False)
+
+
+def page_quiver(ax):
+    # Quiver plot for a simple rotational vector field
+    n = 25
+    x = np.linspace(-2.0, 2.0, n)
+    y = np.linspace(-2.0, 2.0, n)
+    X, Y = np.meshgrid(x, y)
+
+    # Vector field: rotation around origin
+    U = -Y
+    V = X
+    speed = np.hypot(U, V)
+
+    q = ax.quiver(X, Y, U, V, speed, cmap="viridis", pivot="mid", angles="xy", scale=35, width=0.006)
+    ax.set_aspect("equal", adjustable="box")
+    ax.set_title("Quiver: Rotational Field")
+    ax.set_xlabel("x")
+    ax.set_ylabel("y")
+    cb = ax.figure.colorbar(q, ax=ax, pad=0.01)
+    cb.set_label("|v|")
+    ax.grid(True, linestyle="--", alpha=0.3)
+
+
+def page_streamplot(ax):
+    # Streamplot with linewidth and color mapped to speed
+    x = np.linspace(-3.0, 3.0, 200)
+    y = np.linspace(-3.0, 3.0, 200)
+    X, Y = np.meshgrid(x, y)
+
+    # Double-vortex-like field
+    U = 1 - (X**2) + (Y**2)
+    V = -2*X*Y
+    speed = np.sqrt(U**2 + V**2)
+
+    lw = 1.5 * speed / (speed.max() + 1e-12)
+    strm = ax.streamplot(X, Y, U, V, color=speed, linewidth=lw, cmap="plasma", density=1.4, arrowsize=1.2)
+    ax.set_aspect("equal", adjustable="box")
+    ax.set_title("Streamplot: Speed-coded")
+    ax.set_xlabel("x")
+    ax.set_ylabel("y")
+    cb = ax.figure.colorbar(strm.lines, ax=ax, pad=0.01)
+    cb.set_label("|v|")
+    ax.grid(True, linestyle="--", alpha=0.25)
+
+
+def main() -> None:
+    out_path = Path(__file__).parent / "vector_and_polar.pdf"
+
+    with PdfPages(out_path) as pdf:
+        # Page 1: Polar
+        fig1 = plt.figure(figsize=(6.2, 5.5), dpi=150)
+        ax1 = fig1.add_subplot(111, projection="polar")
+        page_polar(ax1)
+        fig1.tight_layout()
+        pdf.savefig(fig1)
+        plt.close(fig1)
+
+        # Page 2: Quiver
+        fig2, ax2 = plt.subplots(figsize=(6.2, 5.0), dpi=150)
+        page_quiver(ax2)
+        fig2.tight_layout()
+        pdf.savefig(fig2)
+        plt.close(fig2)
+
+        # Page 3: Streamplot
+        fig3, ax3 = plt.subplots(figsize=(6.6, 5.2), dpi=150)
+        page_streamplot(ax3)
+        fig3.tight_layout()
+        pdf.savefig(fig3)
+        plt.close(fig3)
+
+    print(f"Wrote PDF: {out_path.resolve()}")
+
+
+if __name__ == "__main__":
+    main()