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feat(bokeh): implement box-horizontal (#2581)
## Implementation: `box-horizontal` - bokeh Implements the **bokeh** version of `box-horizontal`. **File:** `plots/box-horizontal/implementations/bokeh.py` --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20593349206)* --------- 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/box-horizontal/implementations/bokeh.py‎

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""" pyplots.ai
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box-horizontal: Horizontal Box Plot
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Library: bokeh 3.8.1 | Python 3.13.11
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Quality: 91/100 | Created: 2025-12-30
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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 FactorRange
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from bokeh.plotting import figure
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# Data - Response times (ms) by service type
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np.random.seed(42)
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categories = ["Cache Layer", "API Gateway", "Authentication", "Database Query", "File Storage"]
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# Generate different distributions for each service (sorted by median)
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data = {
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"Cache Layer": np.random.normal(15, 5, 80),
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"API Gateway": np.random.normal(45, 12, 80),
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"Authentication": np.concatenate([np.random.normal(65, 15, 75), [130, 145, 150]]), # Outliers
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"Database Query": np.concatenate([np.random.normal(120, 30, 80), [220, 240]]), # Some slow queries
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"File Storage": np.random.normal(200, 50, 80),
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}
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# Calculate box plot statistics for each category
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stats = {}
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outliers_x = []
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outliers_y = []
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for cat in categories:
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values = data[cat]
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q1 = np.percentile(values, 25)
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q2 = np.percentile(values, 50)
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q3 = np.percentile(values, 75)
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iqr = q3 - q1
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upper_fence = q3 + 1.5 * iqr
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lower_fence = q1 - 1.5 * iqr
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# Whiskers extend to furthest data point within fence
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mask = (values >= lower_fence) & (values <= upper_fence)
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upper = values[mask].max() if mask.any() else q3
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lower = values[mask].min() if mask.any() else q1
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# Find outliers
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outlier_mask = (values < lower_fence) | (values > upper_fence)
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for o in values[outlier_mask]:
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outliers_x.append(o)
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outliers_y.append(cat)
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stats[cat] = {"q1": q1, "q2": q2, "q3": q3, "upper": upper, "lower": lower}
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# Create figure with categorical y-axis (horizontal orientation)
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p = figure(
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width=4800,
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height=2700,
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y_range=FactorRange(*categories),
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x_axis_label="Response Time (ms)",
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y_axis_label="Service Type",
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title="box-horizontal · bokeh · pyplots.ai",
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)
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# Colors
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box_color = "#306998"
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median_color = "#FFD43B"
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whisker_color = "#444444"
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# Box and whisker dimensions
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box_height = 0.6
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cap_height = 0.3
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for cat in categories:
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s = stats[cat]
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# Draw box (IQR)
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p.hbar(
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y=[cat],
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left=[s["q1"]],
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right=[s["q3"]],
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height=box_height,
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fill_color=box_color,
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fill_alpha=0.7,
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line_color="#1a3a5c",
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line_width=2,
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)
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# Draw median line
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p.hbar(
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y=[cat],
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left=[s["q2"] - 1],
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right=[s["q2"] + 1],
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height=box_height,
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fill_color=median_color,
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line_color=median_color,
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line_width=0,
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)
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# Draw whiskers (horizontal lines from box to whisker ends)
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p.hbar(y=[cat], left=[s["lower"]], right=[s["q1"]], height=0.02, fill_color=whisker_color, line_color=whisker_color)
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p.hbar(y=[cat], left=[s["q3"]], right=[s["upper"]], height=0.02, fill_color=whisker_color, line_color=whisker_color)
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# Draw whisker caps (vertical lines at whisker ends)
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p.hbar(
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y=[cat],
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left=[s["lower"] - 0.5],
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right=[s["lower"] + 0.5],
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height=cap_height,
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fill_color=whisker_color,
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line_color=whisker_color,
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)
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p.hbar(
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y=[cat],
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left=[s["upper"] - 0.5],
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right=[s["upper"] + 0.5],
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height=cap_height,
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fill_color=whisker_color,
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line_color=whisker_color,
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)
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# Draw outliers
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if outliers_x:
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p.scatter(
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x=outliers_x, y=outliers_y, size=15, fill_color="white", line_color=box_color, line_width=2, marker="circle"
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)
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# Style settings
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p.title.text_font_size = "28pt"
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p.xaxis.axis_label_text_font_size = "22pt"
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p.yaxis.axis_label_text_font_size = "22pt"
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p.xaxis.major_label_text_font_size = "18pt"
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p.yaxis.major_label_text_font_size = "18pt"
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# Grid styling
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p.xgrid.grid_line_alpha = 0.3
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p.xgrid.grid_line_dash = [6, 4]
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p.ygrid.grid_line_alpha = 0.3
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p.ygrid.grid_line_dash = [6, 4]
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# Background
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p.background_fill_color = "#fafafa"
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p.border_fill_color = "white"
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# Axis styling
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p.xaxis.axis_line_color = "#666666"
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p.yaxis.axis_line_color = "#666666"
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# Save
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export_png(p, filename="plot.png")

‎plots/box-horizontal/metadata/bokeh.yaml‎

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library: bokeh
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specification_id: box-horizontal
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created: '2025-12-30T09:32:28Z'
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updated: '2025-12-30T09:43:47Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 20593349206
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issue: 0
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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/box-horizontal/bokeh/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/box-horizontal/bokeh/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/box-horizontal/bokeh/plot.html
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quality_score: 91
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review:
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strengths:
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- Excellent horizontal box plot implementation with all statistical elements (median,
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quartiles, whiskers, outliers)
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- Categories sorted by median value for easy comparison as suggested in spec
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- Realistic service response time scenario with appropriate variation across categories
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- Clean color scheme with good contrast (blue boxes, yellow median lines)
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- Proper use of bokeh hbar for horizontal orientation
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weaknesses:
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- Code does not use ColumnDataSource which is the idiomatic bokeh way to handle
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data
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- Median line implementation uses a narrow hbar with fixed width which may not scale
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well with different data ranges

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