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feat(bokeh): implement sn-curve-basic (#3836)
## Implementation: `sn-curve-basic` - bokeh Implements the **bokeh** version of `sn-curve-basic`. **File:** `plots/sn-curve-basic/implementations/bokeh.py` **Parent Issue:** #3826 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/21045723821)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/sn-curve-basic/implementations/bokeh.py‎

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""" pyplots.ai
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sn-curve-basic: S-N Curve (Wöhler Curve)
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Library: bokeh 3.8.2 | Python 3.13.11
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Quality: 93/100 | Created: 2026-01-15
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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, Label, Span
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from bokeh.plotting import figure
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# Data - Typical S-N fatigue test results for steel specimens
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np.random.seed(42)
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# Generate realistic S-N curve data points
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# Basquin equation: S = A * N^b (power law relationship)
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# Using typical steel fatigue parameters
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A = 1200 # MPa (coefficient)
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b = -0.12 # Basquin exponent
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# Create stress levels with multiple specimens at each level
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stress_levels = np.array([450, 400, 350, 320, 300, 280, 260, 250, 240, 230, 220, 210])
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# Generate cycles for each stress level with realistic scatter
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cycles_list = []
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stress_list = []
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for stress in stress_levels:
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# Calculate theoretical cycles from Basquin equation
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N_theoretical = (stress / A) ** (1 / b)
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# Add scatter (typical for fatigue data)
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n_specimens = np.random.randint(2, 5)
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scatter = np.random.lognormal(0, 0.3, n_specimens)
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cycles_actual = N_theoretical * scatter
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cycles_list.extend(cycles_actual)
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stress_list.extend([stress] * n_specimens)
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cycles = np.array(cycles_list)
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stress = np.array(stress_list)
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# Material properties reference values
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ultimate_strength = 500 # MPa
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yield_strength = 350 # MPa
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endurance_limit = 200 # MPa (below which infinite life expected)
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# Fit line data (using Basquin equation)
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cycles_fit = np.logspace(2, 7, 100)
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stress_fit = A * (cycles_fit**b)
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# Create ColumnDataSource for data points
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source = ColumnDataSource(data={"cycles": cycles, "stress": stress})
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# Create ColumnDataSource for fit line
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source_fit = ColumnDataSource(data={"cycles_fit": cycles_fit, "stress_fit": stress_fit})
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# Create figure with log scales
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p = figure(
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width=4800,
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height=2700,
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title="sn-curve-basic · bokeh · pyplots.ai",
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x_axis_label="Number of Cycles to Failure (N)",
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y_axis_label="Stress Amplitude (MPa)",
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x_axis_type="log",
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y_axis_type="log",
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y_range=(150, 600),
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x_range=(100, 2e7),
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)
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# Plot S-N curve fit line
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p.line(
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x="cycles_fit",
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y="stress_fit",
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source=source_fit,
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line_width=5,
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line_color="#306998",
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line_alpha=0.9,
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legend_label="Basquin Fit (S = A·N^b)",
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)
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# Plot data points
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p.scatter(
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x="cycles",
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y="stress",
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source=source,
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size=22,
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fill_color="#306998",
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fill_alpha=0.7,
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line_color="#1a3d5c",
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line_width=2,
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legend_label="Fatigue Test Data",
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)
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# Add horizontal reference lines for material properties
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ultimate_line = Span(
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location=ultimate_strength, dimension="width", line_color="#c0392b", line_width=4, line_dash="dashed"
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)
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p.add_layout(ultimate_line)
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yield_line = Span(location=yield_strength, dimension="width", line_color="#e67e22", line_width=4, line_dash="dashed")
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p.add_layout(yield_line)
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endurance_line = Span(
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location=endurance_limit, dimension="width", line_color="#27ae60", line_width=4, line_dash="dashed"
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)
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p.add_layout(endurance_line)
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# Add labels for reference lines
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ultimate_label = Label(
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x=150,
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y=520,
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text=f"Ultimate Strength ({ultimate_strength} MPa)",
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text_font_size="22pt",
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text_color="#c0392b",
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text_font_style="bold",
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)
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p.add_layout(ultimate_label)
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yield_label = Label(
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x=150,
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y=365,
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text=f"Yield Strength ({yield_strength} MPa)",
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text_font_size="22pt",
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text_color="#e67e22",
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text_font_style="bold",
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)
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p.add_layout(yield_label)
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endurance_label = Label(
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x=150,
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y=208,
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text=f"Endurance Limit ({endurance_limit} MPa)",
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text_font_size="22pt",
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text_color="#27ae60",
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text_font_style="bold",
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)
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p.add_layout(endurance_label)
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# Style the plot
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p.title.text_font_size = "36pt"
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p.title.text_color = "#2c3e50"
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p.xaxis.axis_label_text_font_size = "26pt"
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p.yaxis.axis_label_text_font_size = "26pt"
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p.xaxis.major_label_text_font_size = "22pt"
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p.yaxis.major_label_text_font_size = "22pt"
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# Legend styling
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p.legend.location = "bottom_left"
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p.legend.label_text_font_size = "22pt"
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p.legend.background_fill_alpha = 0.9
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p.legend.border_line_width = 2
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p.legend.border_line_color = "#cccccc"
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p.legend.glyph_width = 50
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p.legend.glyph_height = 35
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p.legend.spacing = 12
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p.legend.padding = 20
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# Grid styling
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p.grid.grid_line_alpha = 0.3
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p.grid.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 = "#ffffff"
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# Axis styling
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p.xaxis.axis_line_width = 2
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p.yaxis.axis_line_width = 2
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p.xaxis.major_tick_line_width = 2
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p.yaxis.major_tick_line_width = 2
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# Save as PNG
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export_png(p, filename="plot.png")

‎plots/sn-curve-basic/metadata/bokeh.yaml‎

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library: bokeh
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specification_id: sn-curve-basic
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created: '2026-01-15T20:47:36Z'
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updated: '2026-01-15T20:50:12Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 21045723821
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issue: 3826
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python_version: 3.13.11
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library_version: 3.8.2
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preview_url: https://storage.googleapis.com/pyplots-images/plots/sn-curve-basic/bokeh/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/sn-curve-basic/bokeh/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/sn-curve-basic/bokeh/plot.html
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quality_score: 93
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review:
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strengths:
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- Excellent implementation of the Basquin equation for realistic S-N curve generation
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- All three material property reference lines (Ultimate Strength, Yield Strength,
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Endurance Limit) are clearly displayed with appropriate colors and labels
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- Clean code structure with proper use of ColumnDataSource for both data points
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and fit line
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- Appropriate use of log-log scales as required by the specification
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- Professional styling with good font sizes and color choices
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weaknesses:
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- Legend text appears relatively small compared to other text elements in the plot
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- Could benefit from HoverTool to show exact values when hovering over data points
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(better use of Bokeh interactive capabilities)
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image_description: 'The plot displays an S-N curve (Wöhler curve) on a log-log scale.
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The x-axis shows "Number of Cycles to Failure (N)" ranging from 10² to 10⁷, and
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the y-axis shows "Stress Amplitude (MPa)" ranging from approximately 200 to 600
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MPa. Blue circular markers represent fatigue test data points (approximately 40
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points) with realistic scatter at each stress level. A solid blue line shows the
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Basquin fit curve (S = A·N^b) decreasing from upper left to lower right. Three
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horizontal dashed reference lines are displayed: red for Ultimate Strength (500
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MPa), orange for Yield Strength (350 MPa), and green for Endurance Limit (200
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MPa). Each reference line has a corresponding colored label on the left side.
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The legend is positioned in the bottom-left corner showing "Basquin Fit (S = A·N^b)"
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and "Fatigue Test Data". The title "sn-curve-basic · bokeh · pyplots.ai" appears
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at the top. The background is a light gray (#fafafa) with a subtle dashed grid.'
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criteria_checklist:
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visual_quality:
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score: 37
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max: 40
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items:
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- id: VQ-01
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name: Text Legibility
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score: 10
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max: 10
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passed: true
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comment: Title at 36pt, axis labels at 26pt, tick labels at 22pt - all perfectly
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readable
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- id: VQ-02
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name: No Overlap
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score: 8
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max: 8
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passed: true
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comment: No overlapping text elements, all labels clearly positioned
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- id: VQ-03
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name: Element Visibility
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score: 7
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max: 8
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passed: true
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comment: Markers size 22 with good alpha, visible but some data points overlap
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slightly at similar stress levels
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- id: VQ-04
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name: Color Accessibility
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score: 5
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max: 5
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passed: true
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comment: Blue for data, red/orange/green reference lines are distinguishable
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and colorblind-safe
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- id: VQ-05
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name: Layout Balance
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score: 5
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max: 5
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passed: true
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comment: Plot fills canvas well with balanced margins
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- id: VQ-06
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name: Axis Labels
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score: 2
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max: 2
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passed: true
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comment: 'Descriptive with units: Stress Amplitude (MPa), Number of Cycles
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to Failure (N)'
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- id: VQ-07
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name: Grid & Legend
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score: 0
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max: 2
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passed: false
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comment: Grid alpha 0.3 is good, but legend text appears small relative to
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other elements
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spec_compliance:
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score: 25
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max: 25
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items:
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- id: SC-01
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name: Plot Type
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score: 8
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max: 8
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passed: true
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comment: Correct S-N curve with log-log scales
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- id: SC-02
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name: Data Mapping
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score: 5
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max: 5
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passed: true
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comment: Cycles on X, Stress on Y as specified
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- id: SC-03
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name: Required Features
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score: 5
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max: 5
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passed: true
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comment: Includes data points, fit line, and all three reference lines
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- id: SC-04
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name: Data Range
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score: 3
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max: 3
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passed: true
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comment: Axes show full range from 10² to 10⁷ cycles
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- id: SC-05
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name: Legend Accuracy
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score: 2
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max: 2
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passed: true
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comment: Legend correctly identifies Basquin Fit and Fatigue Test Data
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- id: SC-06
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name: Title Format
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score: 2
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max: 2
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passed: true
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comment: 'Correct format: sn-curve-basic · bokeh · pyplots.ai'
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data_quality:
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score: 18
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max: 20
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items:
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- id: DQ-01
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name: Feature Coverage
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score: 7
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max: 8
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passed: true
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comment: Shows scatter at multiple stress levels, fit curve, but could show
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more distinct fatigue regions
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- id: DQ-02
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name: Realistic Context
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score: 7
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max: 7
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passed: true
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comment: Steel fatigue testing is a perfect engineering scenario with realistic
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Basquin parameters
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- id: DQ-03
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name: Appropriate Scale
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score: 4
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max: 5
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passed: true
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comment: Values are realistic (200-500 MPa for steel), though some generated
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points slightly exceed expected scatter
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code_quality:
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score: 10
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max: 10
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items:
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- id: CQ-01
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name: KISS Structure
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score: 3
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max: 3
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passed: true
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comment: 'Clean linear structure: imports → data → plot → save'
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- id: CQ-02
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name: Reproducibility
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score: 3
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max: 3
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passed: true
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comment: Uses np.random.seed(42)
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- id: CQ-03
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name: Clean Imports
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score: 2
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max: 2
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passed: true
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comment: All imports are used
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- id: CQ-04
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name: No Deprecated API
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score: 1
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max: 1
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passed: true
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comment: Uses current Bokeh API
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- id: CQ-05
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name: Output Correct
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score: 1
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max: 1
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passed: true
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comment: Saves as plot.png
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library_features:
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score: 3
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max: 5
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items:
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- id: LF-01
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name: Distinctive Features
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score: 3
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max: 5
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passed: true
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comment: Uses ColumnDataSource, Span for reference lines, Label for annotations
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- good but could leverage HoverTool
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verdict: APPROVED
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impl_tags:
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dependencies: []
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techniques:
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- annotations
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patterns:
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- data-generation
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- columndatasource
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dataprep: []
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styling:
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- alpha-blending
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- grid-styling

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