feat(matplotlib): implement forest-basic (#2397)

## Implementation: `forest-basic` - matplotlib

Implements the **matplotlib** version of `forest-basic`.

**File:** `plots/forest-basic/implementations/matplotlib.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20543281172)*

---------

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Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/forest-basic/implementations/matplotlib.py

@@ -0,0 +1,109 @@
+""" pyplots.ai
+forest-basic: Meta-Analysis Forest Plot
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 93/100 | Created: 2025-12-27
+"""
+
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# Data: Meta-analysis of RCTs comparing treatment efficacy (standardized mean difference)
+studies = [
+    "Johnson 2018",
+    "Smith 2019",
+    "Garcia 2020",
+    "Williams 2020",
+    "Brown 2021",
+    "Davis 2021",
+    "Miller 2022",
+    "Wilson 2022",
+    "Anderson 2023",
+    "Taylor 2023",
+]
+
+# Effect sizes (standardized mean difference) and 95% CIs
+effect_sizes = np.array([-0.45, -0.32, -0.58, -0.21, -0.67, -0.38, -0.52, -0.29, -0.41, -0.55])
+ci_lower = np.array([-0.78, -0.61, -0.95, -0.48, -1.02, -0.69, -0.88, -0.56, -0.72, -0.91])
+ci_upper = np.array([-0.12, -0.03, -0.21, 0.06, -0.32, -0.07, -0.16, -0.02, -0.10, -0.19])
+
+# Study weights (based on sample size / inverse variance)
+weights = np.array([8.5, 10.2, 7.8, 11.5, 6.9, 9.3, 8.1, 10.8, 9.7, 7.2])
+
+# Pooled estimate (random effects meta-analysis)
+pooled_effect = -0.42
+pooled_ci_lower = -0.53
+pooled_ci_upper = -0.31
+
+# Create figure
+fig, ax = plt.subplots(figsize=(16, 9))
+
+n_studies = len(studies)
+y_positions = np.arange(n_studies, 0, -1)
+
+# Normalize weights for marker sizing (scale between 80 and 300)
+weight_normalized = (weights - weights.min()) / (weights.max() - weights.min())
+marker_sizes = 80 + weight_normalized * 220
+
+# Plot vertical reference line at null effect (0)
+ax.axvline(x=0, color="#888888", linestyle="--", linewidth=2, alpha=0.7, zorder=1)
+
+# Plot confidence intervals as horizontal lines
+for y, lower, upper in zip(y_positions, ci_lower, ci_upper, strict=True):
+    ax.hlines(y=y, xmin=lower, xmax=upper, color="#306998", linewidth=3, zorder=2)
+
+# Plot effect size points
+ax.scatter(effect_sizes, y_positions, s=marker_sizes, color="#306998", edgecolors="white", linewidths=1.5, zorder=3)
+
+# Plot pooled estimate as diamond
+diamond_y = 0
+diamond_height = 0.4
+
+# Create diamond shape using polygon
+diamond_vertices = np.array(
+    [
+        [pooled_effect, diamond_y + diamond_height],
+        [pooled_ci_upper, diamond_y],
+        [pooled_effect, diamond_y - diamond_height],
+        [pooled_ci_lower, diamond_y],
+    ]
+)
+diamond_patch = mpatches.Polygon(
+    diamond_vertices, closed=True, facecolor="#FFD43B", edgecolor="#306998", linewidth=2.5, zorder=4
+)
+ax.add_patch(diamond_patch)
+
+# Add study labels on y-axis
+ax.set_yticks(list(y_positions) + [0])
+ax.set_yticklabels(studies + ["Pooled Estimate"])
+
+# Styling
+ax.set_xlabel("Standardized Mean Difference (95% CI)", fontsize=20)
+ax.set_title("forest-basic \u00b7 matplotlib \u00b7 pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.tick_params(axis="y", length=0)
+
+# Set x-axis limits with padding
+x_min = min(ci_lower.min(), pooled_ci_lower) - 0.15
+x_max = max(ci_upper.max(), pooled_ci_upper) + 0.15
+ax.set_xlim(x_min, x_max)
+
+# Set y-axis limits
+ax.set_ylim(-0.8, n_studies + 0.8)
+
+# Add subtle grid for x-axis only
+ax.grid(True, axis="x", alpha=0.3, linestyle="--", zorder=0)
+ax.set_axisbelow(True)
+
+# Add annotation for "Favors Treatment" and "Favors Control"
+ax.text(x_min + 0.05, -0.6, "\u2190 Favors Treatment", fontsize=14, ha="left", va="top", color="#555555")
+ax.text(x_max - 0.05, -0.6, "Favors Control \u2192", fontsize=14, ha="right", va="top", color="#555555")
+
+# Remove top and right spines
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+ax.spines["left"].set_visible(False)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/forest-basic/metadata/matplotlib.yaml

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+library: matplotlib
+specification_id: forest-basic
+created: '2025-12-27T19:21:39Z'
+updated: '2025-12-27T19:29:13Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20543281172
+issue: 0
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/forest-basic/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/forest-basic/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 93
+review:
+  strengths:
+  - 'Excellent implementation of the forest plot with all key elements: effect sizes,
+    confidence intervals, weighted markers, and pooled estimate diamond'
+  - Professional visual design with appropriate color scheme (Python blue for studies,
+    yellow for pooled)
+  - Clear Favors Treatment/Control annotations enhance interpretability
+  - Marker sizes proportional to study weights effectively communicate study importance
+  - Clean code structure following KISS principles
+  weaknesses:
+  - Missing legend explaining that marker size represents study weight
+  - All effect sizes favor treatment (none crossing the null line to favor control
+    would make data more illustrative)