feat(seaborn): implement chernoff-basic (#6828)

## Implementation: `chernoff-basic` - python/seaborn

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

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

**Parent Issue:** #3003

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/anyplot/actions/runs/25924826760)*

---------

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

Author: github-actions[bot]

plots/chernoff-basic/implementations/python/seaborn.py

@@ -1,127 +1,113 @@
-""" pyplots.ai
+""" anyplot.ai
 chernoff-basic: Chernoff Faces for Multivariate Data
-Library: seaborn 0.13.2 | Python 3.13.11
-Quality: 82/100 | Created: 2025-12-31
+Library: seaborn 0.13.2 | Python 3.13.13
+Quality: 93/100 | Updated: 2026-05-15
 """
 
+import os
+
 import matplotlib.patches as mpatches
 import matplotlib.pyplot as plt
 import numpy as np
 import seaborn as sns
+from sklearn.datasets import load_wine
+
 
+THEME = os.getenv("ANYPLOT_THEME", "light")
+PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
+ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
+INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
+INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
+BRAND = "#009E73"
 
-# 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
+# Load wine dataset (13 chemical measurements per wine sample)
+wine = load_wine()
+feature_names = wine.feature_names
+data = wine.data
+target = wine.target
 
-# Select subset of samples with maximum variation within each species (5 per species = 15 total)
+# Select 5 wines from each class (3 classes = 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])
+for cls in range(3):
+    class_indices = np.where(target == cls)[0]
+    selected = np.random.choice(class_indices, size=5, replace=False)
+    indices.extend(selected)
 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]
+class_names = ["Class 1", "Class 2", "Class 3"]
+
+# Select 4 key features for face mapping
+selected_features = [0, 6, 9, 12]  # Alcohol, phenols, malic acid, proline
+selected_feature_names = [feature_names[i].replace(" ", "\n") for i in selected_features]
+data_subset = subset_data[:, selected_features]
+
+# Within-species normalization for faces
+normalized_data = np.zeros((15, 4))
+for cls in range(3):
+    class_mask = subset_target == cls
+    class_subset = data_subset[class_mask]
     for feat_idx in range(4):
-        feat_min = species_subset[:, feat_idx].min()
-        feat_max = species_subset[:, feat_idx].max()
+        feat_min = class_subset[:, feat_idx].min()
+        feat_max = class_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
+        normalized_data[class_mask, feat_idx] = (class_subset[:, feat_idx] - feat_min) / feat_range
 
-# Set seaborn style
+# Set seaborn theme
 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},
+sns.set_theme(
+    rc={
+        "figure.facecolor": PAGE_BG,
+        "axes.facecolor": PAGE_BG,
+        "text.color": INK,
+        "axes.labelcolor": INK,
+        "xtick.color": INK_SOFT,
+        "ytick.color": INK_SOFT,
+    }
 )
-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)
+# Okabe-Ito palette - first series is always brand green
+okabe_ito = [BRAND, "#D55E00", "#0072B2"]
+face_colors = [okabe_ito[t] for t in subset_target]
+
+# Create figure
+fig = plt.figure(figsize=(16, 9), facecolor=PAGE_BG)
+
+# Grid layout: faces only (3 rows x 5 cols)
+gs = fig.add_gridspec(3, 5, hspace=0.35, wspace=0.25)
+
+
+def hex_to_rgb(h):
+    return tuple(int(h.lstrip("#")[i : i + 2], 16) / 255.0 for i in (0, 2, 4))
+
+
+# Draw Chernoff faces
 for idx in range(15):
-    row = idx // 5  # Rows 0, 1, 2
-    col = idx % 5 + 1  # Columns 1-5 (column 0 is heatmap)
+    row = idx // 5
+    col = idx % 5
     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)
+    # Map features to facial characteristics
+    # Feature 0 (alcohol): face width
+    # Feature 1 (phenols): face height
+    # Feature 2 (malic acid): eye size
+    # Feature 3 (proline): mouth curvature
 
     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)
+    # Draw face outline
+    face = mpatches.Ellipse(
+        (0.5, 0.5), face_width, face_height, facecolor=color, edgecolor=INK, linewidth=2.5, alpha=0.9
+    )
     ax.add_patch(face)
 
     # Draw eyes
@@ -130,18 +116,18 @@
 
     # Left eye
     left_eye = mpatches.Ellipse(
-        (0.5 - eye_spacing, eye_y), eye_size * 1.6, eye_size, facecolor="white", edgecolor="black", linewidth=2
+        (0.5 - eye_spacing, eye_y), eye_size * 1.6, eye_size, facecolor=ELEVATED_BG, edgecolor=INK, linewidth=2
     )
     ax.add_patch(left_eye)
-    left_pupil = mpatches.Circle((0.5 - eye_spacing, eye_y), eye_size * 0.35, facecolor="black")
+    left_pupil = mpatches.Circle((0.5 - eye_spacing, eye_y), eye_size * 0.35, facecolor=INK)
     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
+        (0.5 + eye_spacing, eye_y), eye_size * 1.6, eye_size, facecolor=ELEVATED_BG, edgecolor=INK, linewidth=2
     )
     ax.add_patch(right_eye)
-    right_pupil = mpatches.Circle((0.5 + eye_spacing, eye_y), eye_size * 0.35, facecolor="black")
+    right_pupil = mpatches.Circle((0.5 + eye_spacing, eye_y), eye_size * 0.35, facecolor=INK)
     ax.add_patch(right_pupil)
 
     # Draw eyebrows
@@ -151,24 +137,26 @@
     ax.plot(
         [0.5 - eye_spacing - 0.05, 0.5 - eye_spacing + 0.05],
         [eyebrow_y + eyebrow_angle, eyebrow_y - eyebrow_angle],
-        color="black",
+        color=INK,
         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",
+        color=INK,
         linewidth=3.5,
         solid_capstyle="round",
     )
 
     # Draw nose
     nose_size = 0.03 + features[0] * 0.025
+    color_rgb = hex_to_rgb(color)
+    nose_color = tuple(c * 0.7 for c in color_rgb)
     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",
+        facecolor=nose_color,
+        edgecolor=INK,
         linewidth=1.5,
     )
     ax.add_patch(nose)
@@ -177,7 +165,7 @@
     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")
+    ax.plot(mouth_x, mouth_y, color=INK_SOFT, linewidth=3.5, solid_capstyle="round")
 
     # Set axis properties
     ax.set_xlim(0, 1)
@@ -186,25 +174,38 @@
     ax.axis("off")
 
     # Add label below face
-    species_idx = subset_target[idx]
+    class_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"
+        0.5,
+        -0.05,
+        f"{class_names[class_idx]} #{sample_num}",
+        ha="center",
+        va="top",
+        fontsize=11,
+        fontweight="bold",
+        color=INK,
     )
 
 # Add overall title
-fig.suptitle("chernoff-basic · seaborn · pyplots.ai", fontsize=24, fontweight="bold", y=0.98)
+fig.suptitle("chernoff-basic · seaborn · anyplot.ai", fontsize=24, fontweight="medium", y=0.98, color=INK)
 
-# 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)]
+# Add legend for classes
+legend_handles = [mpatches.Patch(color=okabe_ito[i], label=class_names[i], ec=INK, 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"
+    handles=legend_handles,
+    loc="lower right",
+    fontsize=14,
+    frameon=True,
+    bbox_to_anchor=(0.98, 0.02),
+    title="Wine Class",
+    facecolor=ELEVATED_BG,
+    edgecolor=INK_SOFT,
+    title_fontsize=14,
 )
 
-# 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")
+# Add feature mapping explanation
+feature_text = "Face Width ← Alcohol  |  Face Height ← Phenols  |  Eye Size ← Malic Acid  |  Mouth Curve ← Proline"
+fig.text(0.5, 0.01, feature_text, ha="center", fontsize=12, style="italic", color=INK_SOFT)
 
-plt.savefig("plot.png", dpi=300, bbox_inches="tight", facecolor="white")
+plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)