feat(bokeh): implement chernoff-basic

plots/chernoff-basic/implementations/bokeh.py

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+""" pyplots.ai
+chernoff-basic: Chernoff Faces for Multivariate Data
+Library: bokeh 3.8.1 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+from bokeh.io import export_png
+from bokeh.models import ColumnDataSource, HoverTool, Label
+from bokeh.plotting import figure
+
+
+# Generate synthetic company performance data (4 metrics for 12 companies)
+# Metrics: Revenue Growth, Profit Margin, Customer Satisfaction, Market Share
+np.random.seed(42)
+
+# Three company sectors with different profiles
+sectors = ["Tech", "Retail", "Energy"]
+sector_idx = np.array([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2])
+
+# Tech companies: high growth, high margin
+tech_data = np.column_stack(
+    [
+        np.random.uniform(0.6, 1.0, 4),  # Revenue Growth
+        np.random.uniform(0.5, 0.9, 4),  # Profit Margin
+        np.random.uniform(0.6, 0.95, 4),  # Customer Satisfaction
+        np.random.uniform(0.3, 0.7, 4),  # Market Share
+    ]
+)
+
+# Retail companies: moderate growth, moderate margin
+retail_data = np.column_stack(
+    [
+        np.random.uniform(0.2, 0.5, 4),  # Revenue Growth
+        np.random.uniform(0.15, 0.4, 4),  # Profit Margin
+        np.random.uniform(0.5, 0.8, 4),  # Customer Satisfaction
+        np.random.uniform(0.4, 0.8, 4),  # Market Share
+    ]
+)
+
+# Energy companies: low growth, variable margin
+energy_data = np.column_stack(
+    [
+        np.random.uniform(0.05, 0.3, 4),  # Revenue Growth
+        np.random.uniform(0.3, 0.6, 4),  # Profit Margin
+        np.random.uniform(0.3, 0.6, 4),  # Customer Satisfaction
+        np.random.uniform(0.5, 0.9, 4),  # Market Share
+    ]
+)
+
+data = np.vstack([tech_data, retail_data, energy_data])
+
+# Normalize each feature to 0-1
+data_norm = (data - data.min(axis=0)) / (data.max(axis=0) - data.min(axis=0) + 1e-10)
+
+# Colors for sectors
+colors = ["#306998", "#FFD43B", "#8B4513"]
+
+# Store face center data for hover tooltips using ColumnDataSource
+face_centers_x = []
+face_centers_y = []
+face_labels = []
+face_revenue = []
+face_margin = []
+face_satisfaction = []
+face_market_share = []
+
+# Create figure with 4x3 grid for 12 faces
+p = figure(
+    width=4800,
+    height=2700,
+    title="chernoff-basic · bokeh · pyplots.ai",
+    x_range=(-0.1, 4.1),
+    y_range=(-0.2, 3.2),
+    tools="",
+)
+
+# Style
+p.title.text_font_size = "32pt"
+p.title.align = "center"
+p.xaxis.visible = False
+p.yaxis.visible = False
+p.xgrid.visible = False
+p.ygrid.visible = False
+p.outline_line_color = None
+p.background_fill_color = "#FAFAFA"
+
+# Draw faces in a 4x3 grid (inline, no helper function)
+face_size = 0.4
+for i, (features, sec_idx) in enumerate(zip(data_norm, sector_idx, strict=True)):
+    col = i % 4
+    row = 2 - i // 4  # Start from top row
+    cx = col + 0.5
+    cy = row + 0.5
+    color = colors[sec_idx]
+    label_text = f"{sectors[sec_idx]} #{i % 4 + 1}"
+
+    # Store data for hover tooltip
+    face_centers_x.append(cx)
+    face_centers_y.append(cy)
+    face_labels.append(label_text)
+    face_revenue.append(f"{data[i, 0] * 100:.1f}%")
+    face_margin.append(f"{data[i, 1] * 100:.1f}%")
+    face_satisfaction.append(f"{data[i, 2] * 100:.1f}%")
+    face_market_share.append(f"{data[i, 3] * 100:.1f}%")
+
+    # Features mapping:
+    # - revenue_growth (features[0]) -> face width
+    # - profit_margin (features[1]) -> face height
+    # - customer_satisfaction (features[2]) -> eye size
+    # - market_share (features[3]) -> mouth curvature
+    face_width = (0.3 + features[0] * 0.3) * face_size
+    face_height = (0.35 + features[1] * 0.25) * face_size
+    eye_size = (0.03 + features[2] * 0.05) * face_size
+    mouth_curve = features[3]
+
+    # Draw face outline (ellipse approximation using patches)
+    theta = np.linspace(0, 2 * np.pi, 50)
+    face_x = cx + face_width * np.cos(theta)
+    face_y = cy + face_height * np.sin(theta)
+    p.patch(face_x, face_y, fill_color=color, fill_alpha=0.3, line_color=color, line_width=3)
+
+    # Draw eyes
+    eye_spacing = face_width * 0.5
+    eye_y = cy + face_height * 0.25
+    eye_theta = np.linspace(0, 2 * np.pi, 30)
+
+    # Left eye
+    left_eye_x = cx - eye_spacing
+    left_ex = left_eye_x + eye_size * np.cos(eye_theta)
+    left_ey = eye_y + eye_size * np.sin(eye_theta)
+    p.patch(left_ex, left_ey, fill_color="white", line_color="#333333", line_width=2)
+
+    # Left pupil
+    pupil_size = eye_size * 0.5
+    left_px = left_eye_x + pupil_size * np.cos(eye_theta) * 0.6
+    left_py = eye_y + pupil_size * np.sin(eye_theta) * 0.6
+    p.patch(left_px, left_py, fill_color="#333333", line_color="#333333")
+
+    # Right eye
+    right_eye_x = cx + eye_spacing
+    right_ex = right_eye_x + eye_size * np.cos(eye_theta)
+    right_ey = eye_y + eye_size * np.sin(eye_theta)
+    p.patch(right_ex, right_ey, fill_color="white", line_color="#333333", line_width=2)
+
+    # Right pupil
+    right_px = right_eye_x + pupil_size * np.cos(eye_theta) * 0.6
+    right_py = eye_y + pupil_size * np.sin(eye_theta) * 0.6
+    p.patch(right_px, right_py, fill_color="#333333", line_color="#333333")
+
+    # Draw eyebrows
+    brow_y = eye_y + eye_size * 1.8
+    brow_width = eye_size * 1.2
+    eyebrow_slant = (features[0] - 0.5) * 0.02 * face_size
+
+    p.line(
+        [left_eye_x - brow_width, left_eye_x + brow_width],
+        [brow_y + eyebrow_slant, brow_y - eyebrow_slant],
+        line_color="#333333",
+        line_width=3,
+    )
+    p.line(
+        [right_eye_x - brow_width, right_eye_x + brow_width],
+        [brow_y - eyebrow_slant, brow_y + eyebrow_slant],
+        line_color="#333333",
+        line_width=3,
+    )
+
+    # Draw nose
+    nose_length = (0.02 + features[1] * 0.03) * face_size
+    nose_y_top = cy + face_height * 0.1
+    nose_y_bottom = cy - face_height * 0.1
+    p.line([cx, cx], [nose_y_top, nose_y_bottom], line_color="#333333", line_width=2)
+    p.line(
+        [cx - nose_length * 0.5, cx, cx + nose_length * 0.5],
+        [nose_y_bottom, nose_y_bottom - nose_length * 0.3, nose_y_bottom],
+        line_color="#333333",
+        line_width=2,
+    )
+
+    # Draw mouth (curved based on market_share)
+    mouth_y = cy - face_height * 0.4
+    mouth_width = face_width * 0.5
+    mouth_x = np.linspace(cx - mouth_width, cx + mouth_width, 20)
+    curve_amount = (mouth_curve - 0.5) * 0.08 * face_size
+    mouth_y_curve = mouth_y + curve_amount * (1 - ((mouth_x - cx) / mouth_width) ** 2) * 4
+    p.line(mouth_x, mouth_y_curve, line_color="#333333", line_width=3)
+
+    # Add label below face
+    label_obj = Label(
+        x=cx,
+        y=cy - face_height - 0.1,
+        text=label_text,
+        text_align="center",
+        text_font_size="20pt",
+        text_color="#333333",
+    )
+    p.add_layout(label_obj)
+
+# Create ColumnDataSource for hover tooltips (Bokeh-specific feature)
+hover_source = ColumnDataSource(
+    data={
+        "x": face_centers_x,
+        "y": face_centers_y,
+        "label": face_labels,
+        "revenue": face_revenue,
+        "margin": face_margin,
+        "satisfaction": face_satisfaction,
+        "market_share": face_market_share,
+    }
+)
+
+# Add invisible scatter for hover interaction
+hover_renderer = p.scatter("x", "y", source=hover_source, size=80, fill_alpha=0, line_alpha=0)
+
+# Add HoverTool for interactivity (distinctive Bokeh feature)
+hover_tool = HoverTool(
+    renderers=[hover_renderer],
+    tooltips=[
+        ("Company", "@label"),
+        ("Revenue Growth", "@revenue"),
+        ("Profit Margin", "@margin"),
+        ("Satisfaction", "@satisfaction"),
+        ("Market Share", "@market_share"),
+    ],
+)
+p.add_tools(hover_tool)
+
+# Add legend manually using patches and labels (positioned below grid)
+legend_y_base = 2.95
+legend_x_positions = [0.5, 1.5, 2.5]
+for i, (name, color) in enumerate(zip(sectors, colors, strict=True)):
+    lx_center = legend_x_positions[i]
+    theta = np.linspace(0, 2 * np.pi, 30)
+    lx = lx_center + 0.06 * np.cos(theta)
+    ly = legend_y_base + 0.06 * np.sin(theta)
+    p.patch(lx, ly, fill_color=color, fill_alpha=0.3, line_color=color, line_width=2)
+    legend_label = Label(
+        x=lx_center + 0.12, y=legend_y_base - 0.02, text=name, text_font_size="20pt", text_color="#333333"
+    )
+    p.add_layout(legend_label)
+
+# Add subtitle with feature mapping explanation (increased font size)
+subtitle = Label(
+    x=2.0,
+    y=-0.02,
+    text="Face width=Revenue Growth, Face height=Profit Margin, Eye size=Satisfaction, Mouth=Market Share",
+    text_align="center",
+    text_font_size="22pt",
+    text_color="#666666",
+)
+p.add_layout(subtitle)
+
+# Save
+export_png(p, filename="plot.png")

plots/chernoff-basic/metadata/bokeh.yaml

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+library: bokeh
+specification_id: chernoff-basic
+created: '2025-12-31T11:00:13Z'
+updated: '2025-12-31T14:50:05Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20617520899
+issue: 3003
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/chernoff-basic/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/chernoff-basic/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/chernoff-basic/bokeh/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Clear visual differentiation between sectors through color and facial expression
+    patterns
+  - Excellent business context with realistic company performance metrics
+  - Proper normalization of data as spec requires
+  - 'Good use of Bokeh-specific features: ColumnDataSource, HoverTool for interactivity'
+  - Feature mapping subtitle explains what each facial feature represents
+  - Well-organized 4x3 grid layout makes comparison easy
+  weaknesses:
+  - Legend positioned too far from the faces (at very top of plot)
+  - Faces could be slightly larger to better utilize the canvas space