[GR-71966] Add matplotlib benchmarks

PullRequest: graalpython/4246

Author: Andrija Kolic

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

@@ -0,0 +1,127 @@
+#!/usr/bin/env python3
+# Copyright (c) 2026, Oracle and/or its affiliates. All rights reserved.
+# DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
+#
+# The Universal Permissive License (UPL), Version 1.0
+#
+# Subject to the condition set forth below, permission is hereby granted to any
+# person obtaining a copy of this software, associated documentation and/or
+# data (collectively the "Software"), free of charge and under any and all
+# copyright rights in the Software, and any and all patent rights owned or
+# freely licensable by each licensor hereunder covering either (i) the
+# unmodified Software as contributed to or provided by such licensor, or (ii)
+# the Larger Works (as defined below), to deal in both
+#
+# (a) the Software, and
+#
+# (b) any piece of software and/or hardware listed in the lrgrwrks.txt file if
+# one is included with the Software each a "Larger Work" to which the Software
+# is contributed by such licensors),
+#
+# without restriction, including without limitation the rights to copy, create
+# derivative works of, display, perform, and distribute the Software and make,
+# use, sell, offer for sale, import, export, have made, and have sold the
+# Software and the Larger Work(s), and to sublicense the foregoing rights on
+# either these or other terms.
+#
+# This license is subject to the following condition:
+#
+# The above copyright notice and either this complete permission notice or at a
+# minimum a reference to the UPL must be included in all copies or substantial
+# portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+"""
+3D plotting example: surface, wireframe, and contour projections.
+Saves to PDF.
+"""
+
+
+def run():
+    # Execute all imports inside the `run` method so they're measured
+    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
+
+    # Ensure we have version info in the logs
+    print(f"Using matplotlib version '{matplotlib.__version__}'")
+    print(f"Using numpy version '{np.__version__}'")
+
+    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)
+
+
+def warmupIterations():
+    return 0
+
+
+def iterations():
+    return 1
+
+
+def summary():
+    return {
+        "name": "OutlierRemovalAverageSummary",
+        "lower-threshold": 0.0,
+        "upper-threshold": 1.0,
+    }

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 +1,166 @@
+#!/usr/bin/env python3
+# Copyright (c) 2026, Oracle and/or its affiliates. All rights reserved.
+# DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
+#
+# The Universal Permissive License (UPL), Version 1.0
+#
+# Subject to the condition set forth below, permission is hereby granted to any
+# person obtaining a copy of this software, associated documentation and/or
+# data (collectively the "Software"), free of charge and under any and all
+# copyright rights in the Software, and any and all patent rights owned or
+# freely licensable by each licensor hereunder covering either (i) the
+# unmodified Software as contributed to or provided by such licensor, or (ii)
+# the Larger Works (as defined below), to deal in both
+#
+# (a) the Software, and
+#
+# (b) any piece of software and/or hardware listed in the lrgrwrks.txt file if
+# one is included with the Software each a "Larger Work" to which the Software
+# is contributed by such licensors),
+#
+# without restriction, including without limitation the rights to copy, create
+# derivative works of, display, perform, and distribute the Software and make,
+# use, sell, offer for sale, import, export, have made, and have sold the
+# Software and the Larger Work(s), and to sublicense the foregoing rights on
+# either these or other terms.
+#
+# This license is subject to the following condition:
+#
+# The above copyright notice and either this complete permission notice or at a
+# minimum a reference to the UPL must be included in all copies or substantial
+# portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+"""
+Categorical plots: grouped bar chart with error bars and a boxplot.
+Saves a multi-page PDF using 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 run():
+    # Execute all imports inside the `run` method so they're measured
+    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
+
+    # Ensure we have version info in the logs
+    print(f"Using matplotlib version '{matplotlib.__version__}'")
+    print(f"Using numpy version '{np.__version__}'")
+
+    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,
+            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)
+
+
+def warmupIterations():
+    return 0
+
+
+def iterations():
+    return 1
+
+
+def summary():
+    return {
+        "name": "OutlierRemovalAverageSummary",
+        "lower-threshold": 0.0,
+        "upper-threshold": 1.0,
+    }