feat(matplotlib): implement scatter-matrix (#2248)

## Implementation: `scatter-matrix` - matplotlib

Implements the **matplotlib** version of `scatter-matrix`.

**File:** `plots/scatter-matrix/implementations/matplotlib.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20525444128)*

---------

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

Author: github-actions[bot]

plots/scatter-matrix/implementations/matplotlib.py

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+""" pyplots.ai
+scatter-matrix: Scatter Plot Matrix
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-26
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.gridspec import GridSpec
+
+
+# Data: Iris-like flower measurements (4 variables, 3 species)
+np.random.seed(42)
+
+# Species parameters (mean, std for each measurement)
+species_params = {
+    "Setosa": {"sl": (5.0, 0.35), "sw": (3.4, 0.38), "pl": (1.5, 0.17), "pw": (0.2, 0.1)},
+    "Versicolor": {"sl": (5.9, 0.52), "sw": (2.8, 0.31), "pl": (4.3, 0.47), "pw": (1.3, 0.2)},
+    "Virginica": {"sl": (6.6, 0.64), "sw": (3.0, 0.32), "pl": (5.5, 0.55), "pw": (2.0, 0.27)},
+}
+
+n_per_species = 50
+data = {var: [] for var in ["Sepal Length (cm)", "Sepal Width (cm)", "Petal Length (cm)", "Petal Width (cm)"]}
+species_labels = []
+var_keys = ["sl", "sw", "pl", "pw"]
+var_names = ["Sepal Length (cm)", "Sepal Width (cm)", "Petal Length (cm)", "Petal Width (cm)"]
+
+for species, params in species_params.items():
+    for key, name in zip(var_keys, var_names, strict=True):
+        mean, std = params[key]
+        data[name].extend(np.random.normal(mean, std, n_per_species))
+    species_labels.extend([species] * n_per_species)
+
+# Convert to arrays
+data_arrays = [np.array(data[name]) for name in var_names]
+n_vars = len(var_names)
+
+# Colors for species
+colors_map = {"Setosa": "#306998", "Versicolor": "#FFD43B", "Virginica": "#4CAF50"}
+colors = [colors_map[s] for s in species_labels]
+
+# Create scatter plot matrix using GridSpec for tighter control (square format)
+fig = plt.figure(figsize=(12, 12))
+gs = GridSpec(n_vars, n_vars, figure=fig, wspace=0.08, hspace=0.08)
+axes = [[fig.add_subplot(gs[i, j]) for j in range(n_vars)] for i in range(n_vars)]
+
+# Plot each cell
+for i in range(n_vars):
+    for j in range(n_vars):
+        ax = axes[i][j]
+
+        if i == j:
+            # Diagonal: histograms for each species
+            for species, color in colors_map.items():
+                mask = [s == species for s in species_labels]
+                species_data = data_arrays[i][mask]
+                ax.hist(species_data, bins=12, alpha=0.7, color=color, edgecolor="white", linewidth=0.5)
+        else:
+            # Off-diagonal: scatter plots with increased marker size
+            ax.scatter(data_arrays[j], data_arrays[i], c=colors, s=70, alpha=0.6, edgecolors="white", linewidth=0.5)
+
+        # Grid styling
+        ax.grid(True, alpha=0.3, linestyle="--")
+        ax.tick_params(axis="both", labelsize=16)
+
+        # Axis labels only on edges with proper font sizes
+        if i == n_vars - 1:
+            ax.set_xlabel(var_names[j], fontsize=20)
+        else:
+            ax.set_xticklabels([])
+
+        if j == 0:
+            ax.set_ylabel(var_names[i], fontsize=20)
+        else:
+            ax.set_yticklabels([])
+
+# Legend (add to top-right subplot with larger marker size)
+legend_elements = [
+    plt.Line2D([0], [0], marker="o", color="w", markerfacecolor=color, markersize=14, label=species)
+    for species, color in colors_map.items()
+]
+axes[0][n_vars - 1].legend(handles=legend_elements, loc="upper right", fontsize=16, framealpha=0.9)
+
+# Title with proper font size
+fig.suptitle("scatter-matrix · matplotlib · pyplots.ai", fontsize=24, y=0.995)
+
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/scatter-matrix/metadata/matplotlib.yaml

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+library: matplotlib
+specification_id: scatter-matrix
+created: '2025-12-26T16:13:20Z'
+updated: '2025-12-26T16:23:50Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20525444128
+issue: 0
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-matrix/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-matrix/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent implementation of the scatter plot matrix with clear visual separation
+    of species clusters
+  - Proper use of GridSpec for tight subplot spacing creating a cohesive matrix appearance
+  - Appropriate use of transparency (alpha=0.6 for scatter, 0.7 for histograms) to
+    handle overlapping points
+  - Clean axis label placement with labels only on edges (left and bottom)
+  - Well-chosen colorblind-safe palette with good contrast
+  - Realistic Iris-like data with biologically plausible parameters
+  weaknesses:
+  - Legend placement in top-right subplot overlaps with scatter data in that cell;
+    consider placing legend outside the matrix or in a dedicated area
+  - Scatter marker size (s=70) is adequate but could be slightly larger for better
+    visibility given the data density
+  - Could leverage more distinctive matplotlib features like shared axes linking or
+    colorbar for additional encoding