feat(seaborn): implement chernoff-basic (#3150)

## Implementation: `chernoff-basic` - seaborn

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

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

**Parent Issue:** #3003

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20627533498)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
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/chernoff-basic/implementations/seaborn.py

@@ -0,0 +1,210 @@
+""" pyplots.ai
+chernoff-basic: Chernoff Faces for Multivariate Data
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 82/100 | Created: 2025-12-31
+"""
+
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+
+
+# Load and prepare data (Iris dataset - 4 measurements per flower)
+iris_df = sns.load_dataset("iris")
+feature_cols = ["sepal_length", "sepal_width", "petal_length", "petal_width"]
+data = iris_df[feature_cols].values
+species_map = {"setosa": 0, "versicolor": 1, "virginica": 2}
+target = iris_df["species"].map(species_map).values
+
+# Select subset of samples with maximum variation within each species (5 per species = 15 total)
+np.random.seed(42)
+indices = []
+for species in range(3):
+    species_indices = np.where(target == species)[0]
+    species_data = data[species_indices]
+    # For each feature, find samples with min and max values to maximize visible variation
+    # Then add centroid sample
+    selected = set()
+    for feat_idx in range(4):
+        feat_values = species_data[:, feat_idx]
+        min_idx = species_indices[np.argmin(feat_values)]
+        max_idx = species_indices[np.argmax(feat_values)]
+        selected.add(min_idx)
+        selected.add(max_idx)
+    # Add sample closest to mean
+    mean_vals = species_data.mean(axis=0)
+    distances = np.sum((species_data - mean_vals) ** 2, axis=1)
+    selected.add(species_indices[np.argmin(distances)])
+    # Convert to list and take first 5
+    selected = list(selected)[:5]
+    while len(selected) < 5:
+        for idx in species_indices:
+            if idx not in selected:
+                selected.append(idx)
+                break
+    indices.extend(selected[:5])
+indices = np.array(indices)
+
+subset_data = data[indices]
+subset_target = target[indices]
+species_names = ["Setosa", "Versicolor", "Virginica"]
+
+# Global normalization for heatmap (shows species differences)
+data_min = data.min(axis=0)
+data_max = data.max(axis=0)
+heatmap_normalized = (subset_data - data_min) / (data_max - data_min + 1e-8)
+
+# WITHIN-species normalization for faces (maximizes visible variation within each species)
+# This ensures each species shows its full range of variation in facial features
+normalized_data = np.zeros_like(subset_data)
+for species in range(3):
+    species_mask = subset_target == species
+    species_subset = subset_data[species_mask]
+    for feat_idx in range(4):
+        feat_min = species_subset[:, feat_idx].min()
+        feat_max = species_subset[:, feat_idx].max()
+        feat_range = feat_max - feat_min if feat_max > feat_min else 1.0
+        normalized_data[species_mask, feat_idx] = (species_subset[:, feat_idx] - feat_min) / feat_range
+
+# Set seaborn style
+sns.set_style("white")
+sns.set_context("poster", font_scale=1.0)
+
+# Color palette for species (using seaborn palette)
+palette = sns.color_palette("Set2", 3)
+face_colors = [palette[t] for t in subset_target]
+
+# Create figure - 16:9 aspect ratio for 4800x2700 at 300dpi
+fig = plt.figure(figsize=(16, 9))
+
+# Create grid layout: heatmap on left, faces on right
+gs = fig.add_gridspec(3, 7, width_ratios=[1.5, 1, 1, 1, 1, 1, 0.1], hspace=0.25, wspace=0.15)
+
+# Add heatmap showing feature values on left side using seaborn (global normalization)
+ax_heatmap = fig.add_subplot(gs[:, 0])
+sns.heatmap(
+    heatmap_normalized,
+    ax=ax_heatmap,
+    cmap="YlOrRd",
+    cbar=False,
+    xticklabels=["Sepal\nLength", "Sepal\nWidth", "Petal\nLength", "Petal\nWidth"],
+    yticklabels=[f"{species_names[subset_target[i]][0]}{(i % 5) + 1}" for i in range(15)],
+    linewidths=1,
+    linecolor="white",
+    annot=True,
+    fmt=".2f",
+    annot_kws={"size": 9},
+)
+ax_heatmap.set_title("Feature Values\n(Normalized)", fontsize=16, fontweight="bold", pad=10)
+ax_heatmap.tick_params(axis="x", labelsize=12, rotation=0)
+ax_heatmap.tick_params(axis="y", labelsize=11)
+
+# Draw Chernoff faces in grid (3 rows x 5 cols on the right side)
+for idx in range(15):
+    row = idx // 5  # Rows 0, 1, 2
+    col = idx % 5 + 1  # Columns 1-5 (column 0 is heatmap)
+    ax = fig.add_subplot(gs[row, col])
+
+    features = normalized_data[idx]
+    color = face_colors[idx]
+
+    # Map features to facial characteristics with WIDER ranges for visible variation
+    # Feature 0: sepal_length -> face width (0.45 to 1.0)
+    # Feature 1: sepal_width -> face height (0.55 to 1.1)
+    # Feature 2: petal_length -> eye size (0.04 to 0.16)
+    # Feature 3: petal_width -> mouth curvature (-0.35 to 0.35)
+
+    face_width = 0.45 + features[0] * 0.55
+    face_height = 0.55 + features[1] * 0.55
+    eye_size = 0.04 + features[2] * 0.12
+    mouth_curve = -0.35 + features[3] * 0.7
+
+    # Draw face outline (ellipse)
+    face = mpatches.Ellipse((0.5, 0.5), face_width, face_height, facecolor=color, edgecolor="black", linewidth=2.5)
+    ax.add_patch(face)
+
+    # Draw eyes
+    eye_y = 0.58
+    eye_spacing = 0.11 + features[1] * 0.05
+
+    # Left eye
+    left_eye = mpatches.Ellipse(
+        (0.5 - eye_spacing, eye_y), eye_size * 1.6, eye_size, facecolor="white", edgecolor="black", linewidth=2
+    )
+    ax.add_patch(left_eye)
+    left_pupil = mpatches.Circle((0.5 - eye_spacing, eye_y), eye_size * 0.35, facecolor="black")
+    ax.add_patch(left_pupil)
+
+    # Right eye
+    right_eye = mpatches.Ellipse(
+        (0.5 + eye_spacing, eye_y), eye_size * 1.6, eye_size, facecolor="white", edgecolor="black", linewidth=2
+    )
+    ax.add_patch(right_eye)
+    right_pupil = mpatches.Circle((0.5 + eye_spacing, eye_y), eye_size * 0.35, facecolor="black")
+    ax.add_patch(right_pupil)
+
+    # Draw eyebrows
+    eyebrow_angle = -0.12 + features[2] * 0.24
+    eyebrow_y = eye_y + eye_size + 0.05
+
+    ax.plot(
+        [0.5 - eye_spacing - 0.05, 0.5 - eye_spacing + 0.05],
+        [eyebrow_y + eyebrow_angle, eyebrow_y - eyebrow_angle],
+        color="black",
+        linewidth=3.5,
+        solid_capstyle="round",
+    )
+    ax.plot(
+        [0.5 + eye_spacing - 0.05, 0.5 + eye_spacing + 0.05],
+        [eyebrow_y - eyebrow_angle, eyebrow_y + eyebrow_angle],
+        color="black",
+        linewidth=3.5,
+        solid_capstyle="round",
+    )
+
+    # Draw nose
+    nose_size = 0.03 + features[0] * 0.025
+    nose = mpatches.Polygon(
+        [[0.5, 0.50], [0.5 - nose_size, 0.40], [0.5 + nose_size, 0.40]],
+        facecolor=tuple(c * 0.85 for c in color[:3]),
+        edgecolor="black",
+        linewidth=1.5,
+    )
+    ax.add_patch(nose)
+
+    # Draw mouth
+    mouth_width = 0.08 + features[0] * 0.05
+    mouth_x = np.linspace(0.5 - mouth_width, 0.5 + mouth_width, 25)
+    mouth_y = 0.30 + mouth_curve * ((mouth_x - 0.5) ** 2) * 18
+    ax.plot(mouth_x, mouth_y, color="#8B0000", linewidth=3.5, solid_capstyle="round")
+
+    # Set axis properties
+    ax.set_xlim(0, 1)
+    ax.set_ylim(0, 1)
+    ax.set_aspect("equal")
+    ax.axis("off")
+
+    # Add label below face
+    species_idx = subset_target[idx]
+    sample_num = (idx % 5) + 1
+    ax.text(
+        0.5, -0.02, f"{species_names[species_idx]} #{sample_num}", ha="center", va="top", fontsize=11, fontweight="bold"
+    )
+
+# Add overall title
+fig.suptitle("chernoff-basic · seaborn · pyplots.ai", fontsize=24, fontweight="bold", y=0.98)
+
+# Add legend for species (positioned to the right)
+legend_handles = [mpatches.Patch(color=palette[i], label=species_names[i], ec="black", lw=1.5) for i in range(3)]
+fig.legend(
+    handles=legend_handles, loc="center right", fontsize=14, frameon=True, bbox_to_anchor=(0.99, 0.5), title="Species"
+)
+
+# Add feature mapping explanation at bottom
+feature_text = (
+    "Face Width ← Sepal Length  |  Face Height ← Sepal Width  |  Eye Size ← Petal Length  |  Mouth Curve ← Petal Width"
+)
+fig.text(0.55, 0.02, feature_text, ha="center", fontsize=12, style="italic")
+
+plt.savefig("plot.png", dpi=300, bbox_inches="tight", facecolor="white")

plots/chernoff-basic/metadata/seaborn.yaml

@@ -0,0 +1,26 @@
+library: seaborn
+specification_id: chernoff-basic
+created: '2025-12-31T21:36:50Z'
+updated: '2025-12-31T21:57:25Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20627533498
+issue: 3003
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/chernoff-basic/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/chernoff-basic/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 82
+review:
+  strengths:
+  - Excellent use of the Iris dataset with intelligent sample selection to maximize
+    visible variation within each species
+  - Good colorblind-safe palette (Set2) with clear species differentiation
+  - Comprehensive facial feature mappings with wide parameter ranges for visible variation
+  - Clean feature mapping explanation at the bottom helps interpretation
+  - Species labels on each face aid identification
+  weaknesses:
+  - The heatmap (coded at lines 86-101) does not appear in the rendered output image,
+    reducing the complementary value
+  - Within-species normalization makes cross-species comparisons less intuitive (Setosa
+    faces look similar to Virginica despite different actual values)