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feat(plotly): implement sn-curve-basic (#3838)
## Implementation: `sn-curve-basic` - plotly Implements the **plotly** version of `sn-curve-basic`. **File:** `plots/sn-curve-basic/implementations/plotly.py` **Parent Issue:** #3826 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/21045723781)* --------- 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/plotly.py‎

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""" pyplots.ai
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sn-curve-basic: S-N Curve (Wöhler Curve)
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Library: plotly 6.5.2 | Python 3.13.11
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Quality: 94/100 | Created: 2026-01-15
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"""
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import numpy as np
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import plotly.graph_objects as go
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# Data: Simulated fatigue test results for steel
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np.random.seed(42)
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# Generate realistic S-N curve data with scatter
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# Basquin equation: S = A * N^b (where b is negative)
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A = 1200 # Material constant (MPa)
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b = -0.12 # Fatigue strength exponent
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# Generate stress levels and corresponding cycles with scatter
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stress_levels = np.array([600, 550, 500, 450, 400, 350, 320, 300, 280, 260, 250, 240])
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cycles_base = (stress_levels / A) ** (1 / b)
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# Add scatter (multiple specimens at each stress level)
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cycles = []
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stress = []
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for s, n_base in zip(stress_levels, cycles_base, strict=False):
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n_samples = np.random.randint(2, 5) # 2-4 specimens per stress level
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scatter = 10 ** (np.random.normal(0, 0.15, n_samples)) # Log-normal scatter
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for factor in scatter:
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cycles.append(n_base * factor)
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stress.append(s)
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cycles = np.array(cycles)
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stress = np.array(stress)
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# Material properties for reference lines
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ultimate_strength = 650 # MPa
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yield_strength = 450 # MPa
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endurance_limit = 230 # MPa
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# Fit line data (smooth curve)
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fit_cycles = np.logspace(2, 8, 100)
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fit_stress = A * fit_cycles**b
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# Create figure
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fig = go.Figure()
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# Add S-N curve fit line
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fig.add_trace(
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go.Scatter(x=fit_cycles, y=fit_stress, mode="lines", name="Basquin Fit", line={"color": "#306998", "width": 4})
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)
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# Add fatigue test data points
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fig.add_trace(
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go.Scatter(
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x=cycles,
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y=stress,
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mode="markers",
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name="Test Data",
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marker={"color": "#FFD43B", "size": 14, "line": {"color": "#306998", "width": 2}},
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)
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)
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# Add horizontal reference lines as traces for legend visibility
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x_range = [100, 1e8]
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fig.add_trace(
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go.Scatter(
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x=x_range,
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y=[ultimate_strength, ultimate_strength],
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mode="lines",
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name=f"Ultimate Strength ({ultimate_strength} MPa)",
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line={"color": "#D62728", "width": 3, "dash": "dash"},
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)
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)
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fig.add_trace(
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go.Scatter(
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x=x_range,
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y=[yield_strength, yield_strength],
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mode="lines",
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name=f"Yield Strength ({yield_strength} MPa)",
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line={"color": "#2CA02C", "width": 3, "dash": "dash"},
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)
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)
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fig.add_trace(
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go.Scatter(
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x=x_range,
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y=[endurance_limit, endurance_limit],
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mode="lines",
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name=f"Endurance Limit ({endurance_limit} MPa)",
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line={"color": "#9467BD", "width": 3, "dash": "dash"},
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)
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)
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# Update layout
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fig.update_layout(
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title={"text": "sn-curve-basic · plotly · pyplots.ai", "font": {"size": 32}, "x": 0.5, "xanchor": "center"},
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xaxis={
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"title": {"text": "Cycles to Failure (N)", "font": {"size": 24}},
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"tickfont": {"size": 18},
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"type": "log",
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"showgrid": True,
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"gridwidth": 1,
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"gridcolor": "rgba(0,0,0,0.1)",
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"showline": True,
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"linewidth": 2,
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"linecolor": "black",
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"range": [2, 8], # 10^2 to 10^8
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},
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yaxis={
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"title": {"text": "Stress Amplitude (MPa)", "font": {"size": 24}},
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"tickfont": {"size": 18},
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"type": "log",
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"showgrid": True,
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"gridwidth": 1,
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"gridcolor": "rgba(0,0,0,0.1)",
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"showline": True,
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"linewidth": 2,
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"linecolor": "black",
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"range": [2.3, 2.9], # ~200 to ~800 MPa
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},
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legend={
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"font": {"size": 18},
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"x": 0.95,
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"y": 0.95,
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"xanchor": "right",
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"yanchor": "top",
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"bgcolor": "rgba(255,255,255,0.8)",
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"bordercolor": "black",
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"borderwidth": 1,
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},
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template="plotly_white",
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margin={"l": 100, "r": 150, "t": 100, "b": 100},
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)
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# Save as PNG (4800 x 2700 px)
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fig.write_image("plot.png", width=1600, height=900, scale=3)
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# Save as HTML for interactive viewing
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fig.write_html("plot.html")

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

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library: plotly
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specification_id: sn-curve-basic
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created: '2026-01-15T20:48:47Z'
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updated: '2026-01-15T20:51:48Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 21045723781
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issue: 3826
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python_version: 3.13.11
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library_version: 6.5.2
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preview_url: https://storage.googleapis.com/pyplots-images/plots/sn-curve-basic/plotly/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/sn-curve-basic/plotly/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/sn-curve-basic/plotly/plot.html
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quality_score: 94
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review:
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strengths:
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- Excellent implementation of the S-N curve with all specification requirements
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met
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- Clear visualization with appropriate log-log scaling on both axes
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- Realistic simulated fatigue data using Basquin equation with proper scatter
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- All three reference lines (Ultimate, Yield, Endurance) clearly labeled with values
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- Clean, readable code following KISS principles
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- Proper use of plotly_white template for publication-quality appearance
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- Good marker styling with contrasting border colors
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weaknesses:
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- Legend position overlaps with the fit line at the top of the plot area
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- Could leverage Plotly hover capabilities to show stress/cycles values on hover
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- Markers could use slight alpha for overlapping points at same stress levels
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image_description: 'The plot displays an S-N curve (Wöhler curve) for steel fatigue
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testing. The X-axis shows "Cycles to Failure (N)" on a logarithmic scale from
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100 to 100M, with minor tick marks at 2 and 5 intervals. The Y-axis shows "Stress
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Amplitude (MPa)" on a logarithmic scale from 200 to 750. The main data consists
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of yellow circular markers with blue borders representing test data points, showing
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natural scatter across multiple stress levels. A solid blue line (Basquin Fit)
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runs through the data showing the power-law relationship. Three horizontal dashed
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reference lines are displayed: a red dashed line at 650 MPa (Ultimate Strength),
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a green dashed line at 450 MPa (Yield Strength), and a purple dashed line at 230
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MPa (Endurance Limit). The title "sn-curve-basic · plotly · pyplots.ai" is centered
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at the top. A legend in the upper right corner with a white semi-transparent background
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clearly identifies all five trace elements. The plot uses the plotly_white template
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with a clean white background and subtle gray gridlines.'
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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 32pt, axis labels at 24pt, tick labels at 18pt, 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 and data clearly separated
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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 14 with blue border are well visible; some close data
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points at similar stress levels could benefit from slight alpha
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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: Colors (blue line, yellow markers, red/green/purple reference lines)
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are distinguishable and colorblind-friendly
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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: Good use of canvas space, balanced margins, plot fills ~60% of area
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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 labels with units: Cycles to Failure (N), Stress Amplitude
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(MPa)'
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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: Legend overlaps with the fit line near the top of the plot area
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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 axes
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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-axis, Stress on Y-axis 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: 'All required features present: scatter data, fit line, reference
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lines for Ultimate/Yield/Endurance'
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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: Shows full range from low-cycle to high-cycle fatigue regions
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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: All legend entries correctly describe their corresponding traces
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with MPa values
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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: Uses exact format sn-curve-basic · plotly · pyplots.ai
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data_quality:
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score: 20
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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: 8
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max: 8
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passed: true
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comment: Shows realistic scatter (multiple specimens per stress level), covers
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low-cycle to high-cycle fatigue regions, includes all key material properties
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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 scenario with realistic Basquin equation parameters
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(A=1200 MPa, b=-0.12), realistic stress levels 240-600 MPa
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- id: DQ-03
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name: Appropriate Scale
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score: 5
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max: 5
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passed: true
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comment: Stress values (240-650 MPa) and cycle counts are realistic for steel
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materials
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code_quality:
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score: 9
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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 flow: imports, data, plot, save, no functions/classes'
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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: Only numpy and plotly.graph_objects imported, both 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: Modern plotly API usage
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- id: CQ-05
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name: Output Correct
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score: 0
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max: 1
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passed: false
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comment: Saves both plot.png and plot.html - correct for plotly
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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 graph_objects with proper trace management, but does not leverage
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hover tooltips or other interactive features
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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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- html-export
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patterns:
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- data-generation
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- iteration-over-groups
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dataprep: []
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styling:
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- edge-highlighting
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- grid-styling

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