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feat(bokeh): implement chernoff-basic (#3042)
## Implementation: `chernoff-basic` - bokeh Implements the **bokeh** version of `chernoff-basic`. **File:** `plots/chernoff-basic/implementations/bokeh.py` **Parent Issue:** #3003 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20617520899)* --------- 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>
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‎plots/chernoff-basic/implementations/bokeh.py‎

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

‎plots/chernoff-basic/metadata/bokeh.yaml‎

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

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