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feat(letsplot): implement sn-curve-basic (#3837)
## Implementation: `sn-curve-basic` - letsplot Implements the **letsplot** version of `sn-curve-basic`. **File:** `plots/sn-curve-basic/implementations/letsplot.py` **Parent Issue:** #3826 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/21045722559)* --------- 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/letsplot.py‎

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""" pyplots.ai
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sn-curve-basic: S-N Curve (Wöhler Curve)
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Library: letsplot 4.8.2 | Python 3.13.11
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Quality: 91/100 | Created: 2026-01-15
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"""
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import numpy as np
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import pandas as pd
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from lets_plot import * # noqa: F403
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from lets_plot.export import ggsave as export_ggsave
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LetsPlot.setup_html() # noqa: F405
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# Generate realistic S-N curve data for steel specimen
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np.random.seed(42)
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# Material properties (typical structural steel in MPa)
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ultimate_strength = 500
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yield_strength = 350
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endurance_limit = 200
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# Generate data points at different stress levels
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stress_levels = np.array([450, 400, 350, 320, 300, 280, 260, 240, 220, 210])
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# Base cycles following Basquin equation: S = A * N^b (b typically -0.05 to -0.15)
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# Rearranged: N = (S/A)^(1/b)
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A = 800
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b = -0.10
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base_cycles = (stress_levels / A) ** (1 / b)
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# Generate multiple test specimens per stress level with scatter
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all_stress = []
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all_cycles = []
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for stress, base_N in zip(stress_levels, base_cycles, strict=True):
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n_specimens = np.random.randint(3, 6)
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scatter = np.random.lognormal(0, 0.15, n_specimens)
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cycles = base_N * scatter
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all_stress.extend([stress] * n_specimens)
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all_cycles.extend(cycles)
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df = pd.DataFrame({"stress": all_stress, "cycles": all_cycles})
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# Create fit line data
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fit_cycles = np.logspace(2, 7, 100)
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fit_stress = A * fit_cycles**b
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df_fit = pd.DataFrame({"cycles": fit_cycles, "stress": fit_stress})
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# Create the S-N curve plot
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plot = (
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ggplot() # noqa: F405
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# Basquin fit line
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+ geom_line( # noqa: F405
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data=df_fit,
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mapping=aes(x="cycles", y="stress"), # noqa: F405
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color="#306998",
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size=2,
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alpha=0.8,
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)
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# Data points with tooltips
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+ geom_point( # noqa: F405
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data=df,
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mapping=aes(x="cycles", y="stress"), # noqa: F405
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color="#306998",
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size=5,
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alpha=0.7,
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tooltips=layer_tooltips() # noqa: F405
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.line("Cycles|@cycles")
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.line("Stress|@stress MPa"),
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)
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# Reference lines for material properties
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+ geom_hline(yintercept=ultimate_strength, color="#DC2626", size=1.5, linetype="dashed") # noqa: F405
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+ geom_hline(yintercept=yield_strength, color="#B8860B", size=1.5, linetype="dashed") # noqa: F405
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+ geom_hline(yintercept=endurance_limit, color="#22C55E", size=1.5, linetype="dashed") # noqa: F405
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# Labels for reference lines (positioned on left side)
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+ geom_text( # noqa: F405
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data=pd.DataFrame({"cycles": [200], "stress": [ultimate_strength * 1.04], "label": ["Ultimate Strength"]}),
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mapping=aes(x="cycles", y="stress", label="label"), # noqa: F405
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color="#DC2626",
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size=14,
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hjust=0,
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)
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+ geom_text( # noqa: F405
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data=pd.DataFrame({"cycles": [200], "stress": [yield_strength * 1.04], "label": ["Yield Strength"]}),
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mapping=aes(x="cycles", y="stress", label="label"), # noqa: F405
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color="#B8860B",
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size=14,
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hjust=0,
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)
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+ geom_text( # noqa: F405
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data=pd.DataFrame({"cycles": [200], "stress": [endurance_limit * 1.04], "label": ["Endurance Limit"]}),
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mapping=aes(x="cycles", y="stress", label="label"), # noqa: F405
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color="#22C55E",
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size=14,
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hjust=0,
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)
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# Scales - log on both axes
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+ scale_x_log10() # noqa: F405
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+ scale_y_log10() # noqa: F405
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# Labels
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+ labs( # noqa: F405
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title="sn-curve-basic · letsplot · pyplots.ai", x="Number of Cycles to Failure (N)", y="Stress Amplitude (MPa)"
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)
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# Theme
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+ theme_minimal() # noqa: F405
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+ theme( # noqa: F405
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plot_title=element_text(size=24), # noqa: F405
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axis_title=element_text(size=20), # noqa: F405
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axis_text=element_text(size=16), # noqa: F405
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panel_grid=element_line(color="#CCCCCC", size=0.5, linetype="dashed"), # noqa: F405
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)
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+ ggsize(1600, 900) # noqa: F405
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)
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# Save PNG (scale 3x to get 4800 x 2700 px)
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export_ggsave(plot, filename="plot.png", path=".", scale=3)
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# Save HTML for interactive version
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export_ggsave(plot, filename="plot.html", path=".")

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

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library: letsplot
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specification_id: sn-curve-basic
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created: '2026-01-15T20:48:18Z'
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updated: '2026-01-15T20:51:05Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 21045722559
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issue: 3826
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python_version: 3.13.11
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library_version: 4.8.2
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preview_url: https://storage.googleapis.com/pyplots-images/plots/sn-curve-basic/letsplot/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/sn-curve-basic/letsplot/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/sn-curve-basic/letsplot/plot.html
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quality_score: 91
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review:
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strengths:
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- Excellent implementation of S-N curve with proper log-log scaling
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- Clear horizontal reference lines with color-coded labels for Ultimate Strength,
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Yield Strength, and Endurance Limit
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- Realistic steel fatigue data using Basquin equation
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- Interactive tooltips for data points showing cycles and stress values
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- Clean code structure following KISS principles
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- Well-balanced canvas utilization with appropriate 16:9 aspect ratio
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weaknesses:
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- Missing legend identifying the fit line and data points
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image_description: |-
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The plot displays an S-N curve (Wöhler Curve) for steel fatigue data on a log-log scale. The X-axis shows "Number of Cycles to Failure (N)" ranging from 100 to 10^7, and the Y-axis shows "Stress Amplitude (MPa)" ranging from approximately 158 to 501 MPa. Blue data points represent individual fatigue test specimens with visible scatter at each stress level. A solid blue Basquin fit line passes through the data showing the characteristic inverse relationship between stress and cycles.
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Three horizontal dashed reference lines are prominently displayed:
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- Red dashed line at ~501 MPa labeled "Ultimate Strength"
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- Dark yellow/olive dashed line at ~355 MPa labeled "Yield Strength"
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- Green dashed line at 200 MPa labeled "Endurance Limit"
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The title "sn-curve-basic · letsplot · pyplots.ai" appears at the top. The plot uses a minimal theme with light gray dashed grid lines. Text labels for the reference lines are positioned on the left side of the plot in matching colors. Overall the layout is clean and uses the canvas space effectively.
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criteria_checklist:
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visual_quality:
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score: 36
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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, axis labels, and tick labels are all clearly readable at appropriate
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sizes
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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; reference line labels are well positioned
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- id: VQ-03
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name: Element Visibility
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score: 6
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max: 8
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passed: true
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comment: Data points are visible with appropriate alpha; size could be slightly
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larger
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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: Red, yellow, green reference lines are distinguishable; blue data/line
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has good contrast
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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; good use of 16:9 aspect
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ratio
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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: Both axes have descriptive labels with units
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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 is subtle and appropriate, but no legend for the data points/fit
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line
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spec_compliance:
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score: 24
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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-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: 4
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max: 5
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passed: true
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comment: Has reference lines and fit line; missing explicit legend
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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 data range appropriately
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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: Reference line labels are accurate and color-matched
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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 · letsplot · pyplots.ai'
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data_quality:
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score: 19
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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 from multiple specimens, Basquin fit, reference lines
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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: Realistic steel fatigue test scenario with appropriate material properties
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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 200-500 MPa appropriate for structural steel
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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 imports → data → plot → save structure, 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 necessary imports used
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- id: CQ-04
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name: No Deprecated API
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score: 0
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max: 1
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passed: false
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comment: Uses non-standard ggsave import pattern
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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 layer_tooltips for interactivity, ggplot grammar, but could
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leverage more lets-plot specific 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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- annotations
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- layer-composition
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- hover-tooltips
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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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- alpha-blending
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- grid-styling

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