feat(altair): implement chernoff-basic

plots/chernoff-basic/implementations/altair.py

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+""" pyplots.ai
+chernoff-basic: Chernoff Faces for Multivariate Data
+Library: altair 6.0.0 | Python 3.13.11
+Quality: 87/100 | Created: 2025-12-31
+"""
+
+import altair as alt
+import numpy as np
+import pandas as pd
+
+
+# Data - Iris dataset features for 12 representative flowers
+np.random.seed(42)
+# Diverse samples from iris-like measurements (normalized 0-1)
+data = pd.DataFrame(
+    {
+        "observation": [f"Sample {i + 1}" for i in range(12)],
+        "sepal_length": [0.22, 0.83, 0.45, 0.12, 0.91, 0.67, 0.33, 0.78, 0.55, 0.95, 0.28, 0.61],
+        "sepal_width": [0.63, 0.45, 0.78, 0.89, 0.32, 0.56, 0.71, 0.41, 0.65, 0.25, 0.82, 0.48],
+        "petal_length": [0.07, 0.69, 0.42, 0.05, 0.83, 0.55, 0.18, 0.76, 0.38, 0.95, 0.11, 0.62],
+        "petal_width": [0.04, 0.54, 0.33, 0.02, 0.79, 0.48, 0.12, 0.67, 0.29, 0.88, 0.08, 0.52],
+        "species": [
+            "setosa",
+            "virginica",
+            "versicolor",
+            "setosa",
+            "virginica",
+            "versicolor",
+            "setosa",
+            "virginica",
+            "versicolor",
+            "virginica",
+            "setosa",
+            "versicolor",
+        ],
+    }
+)
+
+# Map species to colors
+species_colors = {"setosa": "#306998", "versicolor": "#FFD43B", "virginica": "#4B8BBE"}
+data["color"] = data["species"].map(species_colors)
+
+# Create descriptive labels including species name
+data["label"] = [f"{obs} ({sp})" for obs, sp in zip(data["observation"], data["species"], strict=True)]
+
+# Grid positions for 12 faces (4 columns x 3 rows for better canvas utilization)
+data["col"] = [i % 4 for i in range(12)]
+data["row"] = [i // 4 for i in range(12)]
+data["x_center"] = data["col"] * 200 + 130
+data["y_center"] = (2 - data["row"]) * 240 + 160
+
+# Calculate face feature dimensions based on variables with more pronounced variation
+# face_width: sepal_length, face_height: sepal_width
+# eye_size: petal_length, mouth_width: petal_width
+# eyebrow_slant: derived from petal_length (maps to eyebrow angle)
+data["face_width"] = 40 + data["sepal_length"] * 70  # 40-110
+data["face_height"] = 50 + data["sepal_width"] * 80  # 50-130
+data["eye_size"] = 6 + data["petal_length"] * 22  # 6-28
+data["mouth_width"] = 15 + data["petal_width"] * 35  # 15-50
+data["eyebrow_slant"] = -15 + data["petal_length"] * 30  # -15 to 15
+
+# Build face components using layered shapes
+face_records = []
+
+for _, r in data.iterrows():
+    xc, yc = r["x_center"], r["y_center"]
+    fw, fh = r["face_width"], r["face_height"]
+    es = r["eye_size"]
+    mw = r["mouth_width"]
+    eb_slant = r["eyebrow_slant"]
+
+    # Face outline - single smooth ellipse using many small points on perimeter
+    # This creates a clean ellipse shape instead of blobby overlapping circles
+    for angle in np.linspace(0, 2 * np.pi, 48, endpoint=False):
+        px = xc + (fw * 0.9) * np.cos(angle)
+        py = yc + (fh * 0.75) * np.sin(angle)
+        face_records.append(
+            {
+                "x": px,
+                "y": py,
+                "size": 350,
+                "color": r["color"],
+                "part": "outline",
+                "observation": r["observation"],
+                "species": r["species"],
+                "opacity": 0.7,
+            }
+        )
+
+    # Face fill - concentric rings of points to fill the ellipse smoothly
+    for scale in [0.8, 0.6, 0.4, 0.2]:
+        for angle in np.linspace(0, 2 * np.pi, int(36 * scale) + 8, endpoint=False):
+            px = xc + (fw * 0.9 * scale) * np.cos(angle)
+            py = yc + (fh * 0.75 * scale) * np.sin(angle)
+            face_records.append(
+                {
+                    "x": px,
+                    "y": py,
+                    "size": 400,
+                    "color": r["color"],
+                    "part": "face_fill",
+                    "observation": r["observation"],
+                    "species": r["species"],
+                    "opacity": 0.5,
+                }
+            )
+
+    # Center fill point
+    face_records.append(
+        {
+            "x": xc,
+            "y": yc,
+            "size": 600,
+            "color": r["color"],
+            "part": "face_fill",
+            "observation": r["observation"],
+            "species": r["species"],
+            "opacity": 0.5,
+        }
+    )
+
+    # Left eyebrow (line represented by two points)
+    face_records.append(
+        {
+            "x": xc - fw * 0.38,
+            "y": yc + fh * 0.32 + eb_slant * 0.3,
+            "size": 120,
+            "color": "#2C3E50",
+            "part": "eyebrow",
+            "observation": r["observation"],
+            "species": r["species"],
+            "opacity": 0.9,
+        }
+    )
+    face_records.append(
+        {
+            "x": xc - fw * 0.22,
+            "y": yc + fh * 0.32 - eb_slant * 0.3,
+            "size": 120,
+            "color": "#2C3E50",
+            "part": "eyebrow",
+            "observation": r["observation"],
+            "species": r["species"],
+            "opacity": 0.9,
+        }
+    )
+    # Right eyebrow
+    face_records.append(
+        {
+            "x": xc + fw * 0.22,
+            "y": yc + fh * 0.32 - eb_slant * 0.3,
+            "size": 120,
+            "color": "#2C3E50",
+            "part": "eyebrow",
+            "observation": r["observation"],
+            "species": r["species"],
+            "opacity": 0.9,
+        }
+    )
+    face_records.append(
+        {
+            "x": xc + fw * 0.38,
+            "y": yc + fh * 0.32 + eb_slant * 0.3,
+            "size": 120,
+            "color": "#2C3E50",
+            "part": "eyebrow",
+            "observation": r["observation"],
+            "species": r["species"],
+            "opacity": 0.9,
+        }
+    )
+    # Left eye
+    face_records.append(
+        {
+            "x": xc - fw * 0.30,
+            "y": yc + fh * 0.15,
+            "size": es * 45,
+            "color": "#1A252F",
+            "part": "eye",
+            "observation": r["observation"],
+            "species": r["species"],
+            "opacity": 1.0,
+        }
+    )
+    # Right eye
+    face_records.append(
+        {
+            "x": xc + fw * 0.30,
+            "y": yc + fh * 0.15,
+            "size": es * 45,
+            "color": "#1A252F",
+            "part": "eye",
+            "observation": r["observation"],
+            "species": r["species"],
+            "opacity": 1.0,
+        }
+    )
+    # Left pupil (white highlight)
+    face_records.append(
+        {
+            "x": xc - fw * 0.30 + 3,
+            "y": yc + fh * 0.15 + 3,
+            "size": es * 12,
+            "color": "#FFFFFF",
+            "part": "pupil",
+            "observation": r["observation"],
+            "species": r["species"],
+            "opacity": 0.95,
+        }
+    )
+    # Right pupil (white highlight)
+    face_records.append(
+        {
+            "x": xc + fw * 0.30 + 3,
+            "y": yc + fh * 0.15 + 3,
+            "size": es * 12,
+            "color": "#FFFFFF",
+            "part": "pupil",
+            "observation": r["observation"],
+            "species": r["species"],
+            "opacity": 0.95,
+        }
+    )
+    # Nose
+    face_records.append(
+        {
+            "x": xc,
+            "y": yc - fh * 0.05,
+            "size": 90,
+            "color": "#5D6D7E",
+            "part": "nose",
+            "observation": r["observation"],
+            "species": r["species"],
+            "opacity": 0.7,
+        }
+    )
+    # Mouth - using horizontal ellipse shape for better representation
+    mouth_y = yc - fh * 0.30
+    for dx in np.linspace(-mw * 0.4, mw * 0.4, 7):
+        # Parabolic curve for mouth (smiling effect based on width)
+        dy = -(dx**2) / (mw * 1.2) + mw * 0.08
+        face_records.append(
+            {
+                "x": xc + dx,
+                "y": mouth_y + dy,
+                "size": 80 if abs(dx) < mw * 0.3 else 50,
+                "color": "#C0392B",
+                "part": "mouth",
+                "observation": r["observation"],
+                "species": r["species"],
+                "opacity": 0.9,
+            }
+        )
+
+face_df = pd.DataFrame(face_records)
+
+# Reorder facial features drawing order
+part_order = {"outline": 0, "face_fill": 1, "eyebrow": 2, "nose": 3, "mouth": 4, "eye": 5, "pupil": 6}
+face_df["order"] = face_df["part"].map(part_order)
+face_df = face_df.sort_values("order")
+
+# Create labels for each face - positioned below faces with descriptive text
+label_df = data[["x_center", "y_center", "label", "face_height"]].copy()
+label_df["y_label"] = label_df["y_center"] - label_df["face_height"] * 0.7 - 35
+
+# Face features chart (includes outline, fill, and features)
+features = (
+    alt.Chart(face_df)
+    .mark_point(filled=True)
+    .encode(
+        x=alt.X("x:Q", axis=None, scale=alt.Scale(domain=[0, 900])),
+        y=alt.Y("y:Q", axis=None, scale=alt.Scale(domain=[0, 800])),
+        size=alt.Size("size:Q", legend=None, scale=alt.Scale(range=[40, 1600])),
+        color=alt.Color("color:N", legend=None, scale=None),
+        opacity=alt.Opacity("opacity:Q", legend=None),
+        order="order:O",
+    )
+)
+
+# Labels with species info
+labels = (
+    alt.Chart(label_df)
+    .mark_text(fontSize=13, fontWeight="bold", color="#2C3E50")
+    .encode(x=alt.X("x_center:Q", axis=None), y=alt.Y("y_label:Q", axis=None), text="label:N")
+)
+
+# Legend for species (positioned on right side, higher up to avoid overlap)
+legend_data = pd.DataFrame(
+    {
+        "species": ["setosa", "versicolor", "virginica"],
+        "x": [850, 850, 850],
+        "y": [780, 730, 680],
+        "color": ["#306998", "#FFD43B", "#4B8BBE"],
+    }
+)
+
+legend_points = (
+    alt.Chart(legend_data)
+    .mark_point(filled=True, size=600, opacity=0.5)
+    .encode(x=alt.X("x:Q", axis=None), y=alt.Y("y:Q", axis=None), color=alt.Color("color:N", scale=None, legend=None))
+)
+
+legend_text = (
+    alt.Chart(legend_data)
+    .mark_text(align="right", fontSize=14, dx=-25, fontWeight="bold")
+    .encode(x="x:Q", y="y:Q", text="species:N")
+)
+
+# Feature mapping explanation - positioned at top left to avoid overlap with faces
+mapping_data = pd.DataFrame(
+    {
+        "text": [
+            "Feature Mapping:",
+            "Face width ← sepal length",
+            "Face height ← sepal width",
+            "Eye size ← petal length",
+            "Mouth width ← petal width",
+            "Eyebrow slant ← petal length",
+        ],
+        "x": [30, 30, 30, 30, 30, 30],
+        "y": [785, 760, 735, 710, 685, 660],
+    }
+)
+
+mapping_text = (
+    alt.Chart(mapping_data)
+    .mark_text(align="left", fontSize=12, color="#34495E")
+    .encode(x="x:Q", y="y:Q", text="text:N")
+)
+
+# Combine all layers
+chart = (
+    (features + labels + legend_points + legend_text + mapping_text)
+    .properties(
+        width=1600,
+        height=900,
+        title=alt.Title(
+            "chernoff-basic · altair · pyplots.ai",
+            fontSize=28,
+            anchor="middle",
+            subtitle="Iris Dataset: Each face represents a flower sample with features encoding measurements",
+            subtitleFontSize=16,
+        ),
+    )
+    .configure_view(strokeWidth=0)
+)
+
+# Save as PNG and HTML
+chart.save("plot.png", scale_factor=3.0)
+chart.save("plot.html")

plots/chernoff-basic/metadata/altair.yaml

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+library: altair
+specification_id: chernoff-basic
+created: '2025-12-31T11:00:16Z'
+updated: '2025-12-31T11:38:44Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20617521051
+issue: 3003
+python_version: 3.13.11
+library_version: 6.0.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/chernoff-basic/altair/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/chernoff-basic/altair/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/chernoff-basic/altair/plot.html
+quality_score: 87
+review:
+  strengths:
+  - Creative implementation of Chernoff faces using Altair mark_point with layered
+    scatter points
+  - Excellent color coding by species with colorblind-friendly palette
+  - Well-organized grid layout with good canvas utilization
+  - Feature mapping legend clearly explains variable-to-feature correspondence
+  - Good use of Iris dataset as a classic multivariate example
+  weaknesses:
+  - Feature mapping text overlaps with Sample 9 label causing text collision
+  - Does not leverage Altair distinctive features like interactivity or tooltips
+  - Face feature variations could be more visually pronounced between samples