feat(letsplot): implement chernoff-basic (#6835)

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

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

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

**Parent Issue:** #3003

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/anyplot/actions/runs/25925524639)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Markus Neusinger <2921697+MarkusNeusinger@users.noreply.github.com>

Author: github-actions[bot]

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

@@ -1,15 +1,18 @@
-""" pyplots.ai
+""" anyplot.ai
 chernoff-basic: Chernoff Faces for Multivariate Data
-Library: letsplot 4.8.2 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-31
+Library: letsplot 4.9.0 | Python 3.13.13
+Quality: 80/100 | Updated: 2026-05-15
 """
 
+import os
+
 import numpy as np
 import pandas as pd
 from lets_plot import (
     LetsPlot,
     aes,
     element_blank,
+    element_rect,
     element_text,
     geom_path,
     geom_polygon,
@@ -26,48 +29,65 @@
 
 LetsPlot.setup_html()
 
-# Data - Iris dataset (4 measurements per flower, 3 species)
+# Theme tokens
+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"
+
+# Data - Iris dataset
 np.random.seed(42)
 iris = load_iris()
 df = pd.DataFrame(iris.data, columns=["sepal_length", "sepal_width", "petal_length", "petal_width"])
 df["species"] = [iris.target_names[i] for i in iris.target]
 
-# Sample 12 flowers (4 per species) for clear visualization
+# Sample 12 flowers (4 per species)
 sample_idx = []
 for species in range(3):
     species_idx = np.where(iris.target == species)[0]
     sample_idx.extend(np.random.choice(species_idx, 4, replace=False))
 df_sample = df.iloc[sample_idx].reset_index(drop=True)
 
-# Normalize data to 0-1 range for facial feature mapping
+# Normalize data to 0-1 range
 features = ["sepal_length", "sepal_width", "petal_length", "petal_width"]
 for col in features:
     min_val = df_sample[col].min()
     max_val = df_sample[col].max()
     df_sample[col + "_norm"] = (df_sample[col] - min_val) / (max_val - min_val)
 
+# Generate faces in a grid
+grid_rows = 3
+grid_cols = 4
+all_face_data = []
+label_data = []
+species_colors = {"setosa": "#009E73", "versicolor": "#D55E00", "virginica": "#0072B2"}
+
+for idx, row in df_sample.iterrows():
+    col = idx % grid_cols
+    row_pos = idx // grid_cols
+    center_x = col + 0.5
+    center_y = (grid_rows - 1 - row_pos) + 0.5
 
-# Chernoff face generator - maps 4 variables to facial features
-def create_face(row_data, center_x, center_y, scale=0.4):
-    """Generate face components based on normalized data values."""
-    sepal_len = row_data["sepal_length_norm"]  # Face width
-    sepal_wid = row_data["sepal_width_norm"]  # Eye size
-    petal_len = row_data["petal_length_norm"]  # Mouth curvature
-    petal_wid = row_data["petal_width_norm"]  # Eyebrow slant
+    sepal_len = row["sepal_length_norm"]
+    sepal_wid = row["sepal_width_norm"]
+    petal_len = row["petal_length_norm"]
+    petal_wid = row["petal_width_norm"]
 
+    scale = 0.42
     face_data = []
 
-    # Face outline (ellipse) - face width controlled by sepal_length
-    face_width = 0.35 + 0.2 * sepal_len  # Range: 0.35 to 0.55
+    # Face outline (ellipse)
+    face_width = 0.35 + 0.2 * sepal_len
     face_height = 0.45
     theta = np.linspace(0, 2 * np.pi, 50)
     face_x = center_x + scale * face_width * np.cos(theta)
     face_y = center_y + scale * face_height * np.sin(theta)
     for i in range(len(theta)):
         face_data.append({"x": face_x[i], "y": face_y[i], "part": "face", "order": i})
 
-    # Eyes - eye size controlled by sepal_width
-    eye_size = 0.03 + 0.04 * sepal_wid  # Range: 0.03 to 0.07
+    # Eyes
+    eye_size = 0.03 + 0.04 * sepal_wid
     eye_y = center_y + scale * 0.12
     eye_spacing = 0.12
 
@@ -96,19 +116,19 @@ def create_face(row_data, center_x, center_y, scale=0.4):
     for i in range(len(theta_eye)):
         face_data.append({"x": right_pupil_x[i], "y": right_pupil_y[i], "part": "right_pupil", "order": i})
 
-    # Mouth - curvature controlled by petal_length
+    # Mouth
     mouth_y = center_y - scale * 0.15
     mouth_width = 0.12
-    curvature = -0.08 + 0.16 * petal_len  # Range: -0.08 (sad) to 0.08 (happy)
+    curvature = -0.08 + 0.16 * petal_len
     mouth_x = np.linspace(-mouth_width, mouth_width, 20)
     mouth_curve_y = mouth_y + scale * curvature * (1 - (mouth_x / mouth_width) ** 2)
     mouth_curve_x = center_x + scale * mouth_x
     for i in range(len(mouth_x)):
         face_data.append({"x": mouth_curve_x[i], "y": mouth_curve_y[i], "part": "mouth", "order": i})
 
-    # Eyebrows - slant controlled by petal_width
+    # Eyebrows
     brow_y = center_y + scale * 0.22
-    brow_slant = -0.03 + 0.06 * petal_wid  # Range: -0.03 (angry) to 0.03 (surprised)
+    brow_slant = -0.03 + 0.06 * petal_wid
     brow_length = 0.06
 
     # Left eyebrow
@@ -147,87 +167,66 @@ def create_face(row_data, center_x, center_y, scale=0.4):
         }
     )
 
-    # Nose - simple vertical line
+    # Nose
     nose_top = center_y + scale * 0.02
     nose_bottom = center_y - scale * 0.08
     face_data.append({"x": center_x, "y": nose_top, "part": "nose", "order": 0})
     face_data.append({"x": center_x, "y": nose_bottom, "part": "nose", "order": 1})
 
-    return pd.DataFrame(face_data)
-
-
-# Generate faces in a grid (3 rows x 4 columns = 12 faces)
-grid_rows = 3
-grid_cols = 4
-all_face_data = []
-label_data = []
-species_colors = {"setosa": "#306998", "versicolor": "#FFD43B", "virginica": "#DC2626"}
-
-for idx, row in df_sample.iterrows():
-    col = idx % grid_cols
-    row_pos = idx // grid_cols
-    center_x = col + 0.5
-    center_y = (grid_rows - 1 - row_pos) + 0.5  # Flip y so row 0 is at top
-
-    face_df = create_face(row, center_x, center_y, scale=0.42)
+    # Convert to DataFrame and add metadata
+    face_df = pd.DataFrame(face_data)
     face_df["face_id"] = idx
     face_df["species"] = row["species"]
     all_face_data.append(face_df)
 
-    # Add label
+    # Add species label
     label_data.append({"x": center_x, "y": center_y - 0.45, "label": row["species"].title(), "species": row["species"]})
 
+# Combine all face data
 faces_df = pd.concat(all_face_data, ignore_index=True)
 labels_df = pd.DataFrame(label_data)
 
-# Separate dataframes for different face parts
+# Separate face parts for layering
 face_outline = faces_df[faces_df["part"] == "face"]
 eyes = faces_df[faces_df["part"].isin(["left_eye", "right_eye"])]
 pupils = faces_df[faces_df["part"].isin(["left_pupil", "right_pupil"])]
 mouth = faces_df[faces_df["part"] == "mouth"]
 brows = faces_df[faces_df["part"].isin(["left_brow", "right_brow"])]
 nose = faces_df[faces_df["part"] == "nose"]
 
-# Create the plot
+# Create plot with theme-adaptive styling
+anyplot_theme = theme(
+    plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
+    panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
+    panel_grid=element_blank(),
+    axis_title=element_blank(),
+    axis_text=element_blank(),
+    axis_ticks=element_blank(),
+    plot_title=element_text(size=24, face="bold", color=INK),
+    legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
+    legend_title=element_text(size=18, color=INK),
+    legend_text=element_text(size=16, color=INK_SOFT),
+    legend_position="right",
+    plot_margin=[40, 20, 20, 20],
+)
+
 plot = (
     ggplot()
-    # Face outlines (colored by species)
     + geom_polygon(
-        aes(x="x", y="y", group="face_id", fill="species"), data=face_outline, color="#333333", size=1.5, alpha=0.3
+        aes(x="x", y="y", group="face_id", fill="species"), data=face_outline, color=INK_SOFT, size=1.5, alpha=0.3
     )
-    # Eyes (white fill)
-    + geom_polygon(aes(x="x", y="y", group=["face_id", "part"]), data=eyes, fill="white", color="#333333", size=1.0)
-    # Pupils (black fill)
-    + geom_polygon(aes(x="x", y="y", group=["face_id", "part"]), data=pupils, fill="#333333", color="#333333", size=0.5)
-    # Mouth (line)
-    + geom_path(aes(x="x", y="y", group="face_id"), data=mouth, color="#333333", size=2.0)
-    # Eyebrows (lines)
-    + geom_path(aes(x="x", y="y", group=["face_id", "part"]), data=brows, color="#333333", size=2.5)
-    # Nose (line)
-    + geom_path(aes(x="x", y="y", group="face_id"), data=nose, color="#333333", size=1.5)
-    # Species labels (no legend for text color)
-    + geom_text(aes(x="x", y="y", label="label"), data=labels_df, color="#333333", size=12, fontface="bold")
-    # Color scale
+    + geom_polygon(aes(x="x", y="y", group=["face_id", "part"]), data=eyes, fill="white", color=INK_SOFT, size=1.0)
+    + geom_polygon(aes(x="x", y="y", group=["face_id", "part"]), data=pupils, fill=INK, color=INK, size=0.5)
+    + geom_path(aes(x="x", y="y", group="face_id"), data=mouth, color=INK, size=2.0)
+    + geom_path(aes(x="x", y="y", group=["face_id", "part"]), data=brows, color=INK, size=2.5)
+    + geom_path(aes(x="x", y="y", group="face_id"), data=nose, color=INK, size=1.5)
+    + geom_text(aes(x="x", y="y", label="label"), data=labels_df, color=INK_SOFT, size=12, fontface="bold")
     + scale_fill_manual(values=species_colors)
-    # Labels
-    + labs(title="Iris Species Comparison · chernoff-basic · lets-plot · pyplots.ai", fill="Species")
-    # Theme
-    + theme(
-        plot_title=element_text(size=24, face="bold"),
-        axis_title=element_blank(),
-        axis_text=element_blank(),
-        axis_ticks=element_blank(),
-        panel_grid=element_blank(),
-        legend_title=element_text(size=16),
-        legend_text=element_text(size=14),
-        legend_position="right",
-        plot_margin=[40, 20, 20, 20],  # top, right, bottom, left
-    )
+    + labs(title="chernoff-basic · letsplot · anyplot.ai", fill="Species")
+    + anyplot_theme
     + ggsize(1600, 900)
 )
 
-# Save as PNG (use path parameter to avoid subdirectory creation)
-ggsave(plot, "plot.png", scale=3, path=".")
-
-# Save as HTML for interactivity
-ggsave(plot, "plot.html", path=".")
+# Save outputs
+ggsave(plot, f"plot-{THEME}.png", scale=3, path=".")
+ggsave(plot, f"plot-{THEME}.html", path=".")