feat(matplotlib): implement chernoff-basic (#3041)

## Implementation: `chernoff-basic` - matplotlib

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

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

**Parent Issue:** #3003

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20617517947)*

---------

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

Author: github-actions[bot]

plots/chernoff-basic/implementations/matplotlib.py

@@ -0,0 +1,198 @@
+""" pyplots.ai
+chernoff-basic: Chernoff Faces for Multivariate Data
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-31
+"""
+
+import matplotlib.patches as patches
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# Data - Car performance metrics (9 vehicles with 4 attributes)
+# Attributes: fuel efficiency, power, reliability, comfort (all 0-1 normalized)
+np.random.seed(42)
+
+# Create 3 categories of cars with distinct characteristics
+# Economy cars: high efficiency, low power, medium reliability, medium comfort
+# Sports cars: low efficiency, high power, medium reliability, low comfort
+# Luxury cars: medium efficiency, medium power, high reliability, high comfort
+categories = ["Economy", "Sports", "Luxury"]
+n_per_category = 3
+
+# Generate synthetic data with category-specific distributions
+data = []
+labels = []
+category_ids = []
+
+# Economy cars - high efficiency, low power
+for i in range(n_per_category):
+    data.append(
+        [
+            0.7 + np.random.rand() * 0.25,  # fuel_efficiency: 0.7-0.95
+            0.2 + np.random.rand() * 0.2,  # power: 0.2-0.4
+            0.4 + np.random.rand() * 0.3,  # reliability: 0.4-0.7
+            0.3 + np.random.rand() * 0.3,  # comfort: 0.3-0.6
+        ]
+    )
+    labels.append(f"Economy {i + 1}")
+    category_ids.append(0)
+
+# Sports cars - low efficiency, high power
+for i in range(n_per_category):
+    data.append(
+        [
+            0.15 + np.random.rand() * 0.2,  # fuel_efficiency: 0.15-0.35
+            0.75 + np.random.rand() * 0.2,  # power: 0.75-0.95
+            0.4 + np.random.rand() * 0.25,  # reliability: 0.4-0.65
+            0.25 + np.random.rand() * 0.25,  # comfort: 0.25-0.5
+        ]
+    )
+    labels.append(f"Sports {i + 1}")
+    category_ids.append(1)
+
+# Luxury cars - high reliability and comfort
+for i in range(n_per_category):
+    data.append(
+        [
+            0.35 + np.random.rand() * 0.25,  # fuel_efficiency: 0.35-0.6
+            0.5 + np.random.rand() * 0.25,  # power: 0.5-0.75
+            0.7 + np.random.rand() * 0.25,  # reliability: 0.7-0.95
+            0.7 + np.random.rand() * 0.25,  # comfort: 0.7-0.95
+        ]
+    )
+    labels.append(f"Luxury {i + 1}")
+    category_ids.append(2)
+
+X_norm = np.array(data)
+colors = ["#306998", "#FFD43B", "#4CAF50"]  # Python Blue, Yellow, Green
+
+# Create figure - 3x3 grid of faces (square format for symmetric grid)
+fig, ax = plt.subplots(figsize=(12, 12))
+
+# Calculate grid positions with better spacing
+n_cols = 3
+n_rows = 3
+x_positions = np.linspace(0.22, 0.78, n_cols)
+y_positions = np.linspace(0.76, 0.28, n_rows)  # More space between rows
+
+# Draw each Chernoff face
+for idx in range(len(X_norm)):
+    row = idx // n_cols
+    col = idx % n_cols
+    x_center = x_positions[col]
+    y_center = y_positions[row]
+    features = X_norm[idx]
+    color = colors[category_ids[idx]]
+    label = labels[idx]
+
+    # Feature mappings (all features in 0-1 range):
+    # - features[0]: face width (fuel efficiency)
+    # - features[1]: face height (power)
+    # - features[2]: eye size (reliability)
+    # - features[3]: mouth curvature (comfort) - happy = high comfort
+
+    # Scale down faces to prevent overlap
+    face_width = 0.12 + features[0] * 0.06  # 0.12-0.18
+    face_height = 0.14 + features[1] * 0.06  # 0.14-0.20
+    eye_size = 0.015 + features[2] * 0.015  # 0.015-0.03
+    mouth_curve = -0.05 + features[3] * 0.10  # -0.05 to 0.05 (sad to happy)
+
+    # Face ellipse
+    face = patches.Ellipse(
+        (x_center, y_center), face_width, face_height, facecolor=color, edgecolor="black", linewidth=2.5, alpha=0.75
+    )
+    ax.add_patch(face)
+
+    # Eyes - position relative to face
+    eye_y = y_center + face_height * 0.18
+    eye_x_offset = face_width * 0.22
+
+    # Left eye (white with pupil)
+    left_eye = patches.Ellipse(
+        (x_center - eye_x_offset, eye_y), eye_size * 1.6, eye_size, facecolor="white", edgecolor="black", linewidth=1.5
+    )
+    ax.add_patch(left_eye)
+    left_pupil = patches.Circle((x_center - eye_x_offset, eye_y), eye_size * 0.35, facecolor="black")
+    ax.add_patch(left_pupil)
+
+    # Right eye
+    right_eye = patches.Ellipse(
+        (x_center + eye_x_offset, eye_y), eye_size * 1.6, eye_size, facecolor="white", edgecolor="black", linewidth=1.5
+    )
+    ax.add_patch(right_eye)
+    right_pupil = patches.Circle((x_center + eye_x_offset, eye_y), eye_size * 0.35, facecolor="black")
+    ax.add_patch(right_pupil)
+
+    # Eyebrows - angle based on power (higher power = more intense)
+    brow_y = eye_y + eye_size * 1.4
+    brow_length = eye_size * 1.3
+    brow_angle = (features[1] - 0.5) * 0.015  # Angle variation
+
+    ax.plot(
+        [x_center - eye_x_offset - brow_length / 2, x_center - eye_x_offset + brow_length / 2],
+        [brow_y + brow_angle, brow_y - brow_angle],
+        color="black",
+        linewidth=2.5,
+        solid_capstyle="round",
+    )
+    ax.plot(
+        [x_center + eye_x_offset - brow_length / 2, x_center + eye_x_offset + brow_length / 2],
+        [brow_y - brow_angle, brow_y + brow_angle],
+        color="black",
+        linewidth=2.5,
+        solid_capstyle="round",
+    )
+
+    # Nose - simple vertical line with base
+    nose_height = 0.015 + features[1] * 0.01
+    nose_y_top = y_center + nose_height * 0.3
+    nose_y_bottom = y_center - nose_height * 0.7
+    ax.plot([x_center, x_center], [nose_y_top, nose_y_bottom], color="black", linewidth=2)
+    # Nose base
+    ax.plot([x_center - 0.005, x_center + 0.005], [nose_y_bottom, nose_y_bottom], color="black", linewidth=2)
+
+    # Mouth - curved based on comfort
+    mouth_y = y_center - face_height * 0.28
+    mouth_width_val = 0.02 + features[0] * 0.015
+
+    mouth_x = np.linspace(-mouth_width_val / 2, mouth_width_val / 2, 30)
+    mouth_y_curve = mouth_y + mouth_curve * (1 - (2 * mouth_x / mouth_width_val) ** 2)
+    ax.plot(x_center + mouth_x, mouth_y_curve, color="black", linewidth=3, solid_capstyle="round")
+
+    # Label below face (positioned further down to avoid overlap)
+    ax.text(
+        x_center, y_center - face_height * 0.65 - 0.02, label, ha="center", va="top", fontsize=13, fontweight="bold"
+    )
+
+# Styling
+ax.set_xlim(0, 1)
+ax.set_ylim(0, 1)
+ax.set_aspect("equal")
+ax.axis("off")
+
+# Title
+ax.set_title("Car Ratings · chernoff-basic · matplotlib · pyplots.ai", fontsize=24, fontweight="bold", pad=20)
+
+# Feature mapping legend (bottom left, moved down to avoid overlap)
+legend_text = (
+    "Feature Mapping:\nFace Width = Fuel Efficiency\nFace Height = Power\nEye Size = Reliability\nMouth Curve = Comfort"
+)
+ax.text(
+    0.02,
+    0.01,
+    legend_text,
+    transform=ax.transAxes,
+    fontsize=11,
+    verticalalignment="bottom",
+    fontfamily="monospace",
+    bbox={"boxstyle": "round", "facecolor": "white", "alpha": 0.95, "edgecolor": "gray"},
+)
+
+# Category legend (upper right)
+for i, category in enumerate(categories):
+    ax.scatter([], [], c=colors[i], s=250, label=category, alpha=0.75, edgecolors="black")
+ax.legend(loc="upper right", fontsize=14, title="Category", title_fontsize=16, framealpha=0.95, edgecolor="gray")
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight", facecolor="white")

plots/chernoff-basic/metadata/matplotlib.yaml

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+library: matplotlib
+specification_id: chernoff-basic
+created: '2025-12-31T11:00:11Z'
+updated: '2025-12-31T11:10:43Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20617517947
+issue: 3003
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/chernoff-basic/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/chernoff-basic/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent implementation of Chernoff faces with clear visual differentiation between
+    car categories
+  - 'Well-designed feature mapping: face width for efficiency, height for power, eye
+    size for reliability, mouth curve for comfort creates intuitive visual patterns'
+  - Clean, informative legends that explain both the category colors and feature mappings
+  - Good use of square figure format (12x12) for the 3x3 grid layout
+  - Category-specific data generation creates meaningful visual clusters (Economy=wide
+    faces, Sports=tall faces, Luxury=happy faces)
+  - Professional appearance with consistent styling (line widths, alpha values, edge
+    colors)
+  weaknesses:
+  - Some wasted vertical space at the top of the figure above the first row of faces
+    could be reduced
+  - The within-category variation could be more pronounced to better demonstrate the
+    multivariate nature of the visualization
+  - Could potentially add more facial features (e.g., ear size, hair) to map additional
+    variables as mentioned in the spec