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feat(pygal): implement sn-curve-basic (#3863)
## Implementation: `sn-curve-basic` - pygal Implements the **pygal** version of `sn-curve-basic`. **File:** `plots/sn-curve-basic/implementations/pygal.py` **Parent Issue:** #3826 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/21047244255)* --------- 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/sn-curve-basic/implementations/pygal.py‎

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""" pyplots.ai
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sn-curve-basic: S-N Curve (Wöhler Curve)
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Library: pygal 3.1.0 | Python 3.13.11
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Quality: 78/100 | Created: 2026-01-15
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"""
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import numpy as np
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import pygal
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from pygal.style import Style
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# Data: Fatigue test results for steel specimens
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np.random.seed(42)
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# Stress levels (MPa) - from high to low
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stress_levels = np.array([450, 400, 350, 300, 275, 250, 225, 210, 200, 195])
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# Generate cycles to failure with realistic scatter (Basquin relationship: S = A * N^b)
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base_cycles = np.array([1e2, 5e2, 2e3, 1e4, 3e4, 1e5, 4e5, 1e6, 5e6, 1e7])
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# Add multiple test specimens per stress level (3-5 samples each) with scatter
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cycles_data = []
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stress_data = []
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for stress, base_n in zip(stress_levels, base_cycles, strict=True):
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n_samples = np.random.randint(3, 6)
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scatter = np.exp(np.random.normal(0, 0.3, n_samples))
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cycles = base_n * scatter
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cycles_data.extend(cycles)
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stress_data.extend([stress] * n_samples)
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cycles_data = np.array(cycles_data)
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stress_data = np.array(stress_data)
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# Fit Basquin equation: S = A * N^b (linear in log-log space)
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log_cycles = np.log10(cycles_data)
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log_stress = np.log10(stress_data)
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coeffs = np.polyfit(log_cycles, log_stress, 1)
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b = coeffs[0] # slope (negative for S-N curve)
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log_A = coeffs[1] # intercept
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A = 10**log_A
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# Generate fitted curve points with more points for smooth line
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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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# Material reference values (MPa)
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ultimate_strength = 520
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yield_strength = 350
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endurance_limit = 190
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# Create XY data points for pygal
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xy_points = [(float(c), float(s)) for c, s in zip(cycles_data, stress_data, strict=True)]
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# Fitted curve points as list of tuples
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fit_points = [(float(c), float(s)) for c, s in zip(fit_cycles, fit_stress, strict=True)]
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# Custom style for 4800x2700 canvas with larger fonts
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custom_style = Style(
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background="white",
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plot_background="white",
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foreground="#333333",
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foreground_strong="#333333",
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foreground_subtle="#666666",
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colors=("#306998", "#FF6B35", "#E74C3C", "#27AE60", "#8E44AD"),
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title_font_size=72,
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label_font_size=48,
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major_label_font_size=42,
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legend_font_size=42,
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value_font_size=36,
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stroke_width=6,
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opacity=0.9,
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opacity_hover=1.0,
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)
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# Create XY chart with logarithmic x-axis
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chart = pygal.XY(
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width=4800,
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height=2700,
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style=custom_style,
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title="sn-curve-basic · pygal · pyplots.ai",
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x_title="Cycles to Failure (N)",
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y_title="Stress Amplitude (MPa)",
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logarithmic=True,
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show_dots=True,
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dots_size=16,
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stroke=True,
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show_x_guides=True,
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show_y_guides=True,
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x_label_rotation=0,
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legend_at_bottom=True,
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legend_box_size=32,
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margin=80,
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truncate_legend=-1,
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range=(150, 550),
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)
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# Add series in logical order for legend: data → fit → references (high to low)
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# 1. Test data points (primary data - most important)
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chart.add("Test Data", xy_points, dots_size=20, stroke=False, show_dots=True)
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# 2. Fitted Basquin curve (derived from data)
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chart.add(
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"Basquin Fit (S-N Curve)",
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fit_points,
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stroke=True,
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show_dots=False,
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stroke_style={"width": 8, "dasharray": "20, 10"},
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)
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# 3. Reference lines in descending order of stress value
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# Ultimate Strength (highest)
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ultimate_line = [(100, ultimate_strength), (1e7, ultimate_strength)]
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chart.add(
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f"Ultimate Strength ({ultimate_strength} MPa)",
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ultimate_line,
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stroke=True,
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show_dots=False,
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stroke_style={"width": 4},
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)
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# Yield Strength (middle)
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yield_line = [(100, yield_strength), (1e7, yield_strength)]
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chart.add(f"Yield Strength ({yield_strength} MPa)", yield_line, stroke=True, show_dots=False, stroke_style={"width": 4})
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# Endurance Limit (lowest)
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endurance_line = [(100, endurance_limit), (1e7, endurance_limit)]
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chart.add(
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f"Endurance Limit ({endurance_limit} MPa)", endurance_line, stroke=True, show_dots=False, stroke_style={"width": 4}
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)
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# Save outputs
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chart.render_to_file("plot.html")
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chart.render_to_png("plot.png")

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

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library: pygal
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specification_id: sn-curve-basic
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created: '2026-01-15T21:43:12Z'
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updated: '2026-01-15T22:04:17Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 21047244255
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issue: 3826
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python_version: 3.13.11
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library_version: 3.1.0
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preview_url: https://storage.googleapis.com/pyplots-images/plots/sn-curve-basic/pygal/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/sn-curve-basic/pygal/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/sn-curve-basic/pygal/plot.html
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quality_score: 78
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review:
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strengths:
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- Correctly implements S-N curve with logarithmic X-axis and fitted Basquin equation
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- Includes all three key material reference lines (Ultimate Strength, Yield Strength,
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Endurance Limit)
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- Good use of pygal custom Style with appropriately sized fonts for 4800x2700 canvas
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- Data shows realistic scatter with multiple test specimens per stress level
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- Color palette is distinct and accessible
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weaknesses:
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- X-axis tick labels overlap/truncate at high cycle values due to logarithmic scale
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rendering in pygal
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- Y-axis is linear but spec recommends both axes use logarithmic scales
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- Legend order (Basquin Fit first) does not match visual prominence order (Test
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Data is primary)
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image_description: 'The plot displays an S-N curve (Wöhler curve) on a semi-logarithmic
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scale. The X-axis shows "Cycles to Failure (N)" with a logarithmic scale ranging
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from approximately 100 to 10^7, and the Y-axis shows "Stress Amplitude (MPa)"
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ranging from about 150 to 550 MPa (linear scale). Orange circular markers represent
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the test data points showing the characteristic downward trend of fatigue data
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- high stress at low cycles transitioning to low stress at high cycles. A dashed
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blue line represents the Basquin fit (S-N curve) that follows the data trend.
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Three horizontal reference lines are visible: a red line at 520 MPa for Ultimate
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Strength, a green line at 350 MPa for Yield Strength, and a purple line at 190
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MPa for Endurance Limit. The title "sn-curve-basic · pygal · pyplots.ai" appears
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at the top. The legend is positioned at the bottom, showing all five series. The
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background is white with subtle grid lines. X-axis tick labels show some crowding/overlap
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at higher values.'
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criteria_checklist:
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visual_quality:
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score: 30
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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: 7
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max: 10
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passed: true
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comment: Title and axis labels readable; tick labels somewhat crowded at high
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cycle counts
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- id: VQ-02
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name: No Overlap
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score: 4
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max: 8
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passed: false
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comment: X-axis tick labels overlap at higher cycle values due to logarithmic
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scale rendering
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- id: VQ-03
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name: Element Visibility
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score: 8
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max: 8
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passed: true
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comment: Data markers clearly visible at appropriate size, lines distinguishable
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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: Orange, blue, red, green, purple are colorblind-friendly and distinct
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- id: VQ-05
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name: Layout Balance
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score: 4
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max: 5
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passed: true
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comment: Good canvas utilization, slight imbalance with legend at bottom
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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 (MPa, 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: Legend order differs from visual prominence order
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spec_compliance:
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score: 23
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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 XY scatter plot with fitted curve for S-N data
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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 (log scale), Stress on Y correctly assigned
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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 data points, fit curve, and reference lines; Y-axis is linear
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not logarithmic
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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: Full range of data visible (10^2 to 10^7 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 labels correctly identify all series with 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 correct format: sn-curve-basic · pygal · 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: 8
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max: 8
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passed: true
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comment: Shows scatter from multiple specimens, low/high cycle regions, and
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endurance limit region
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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 real engineering application with appropriate
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terminology
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- id: DQ-03
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name: Appropriate Scale
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score: 3
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max: 5
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passed: true
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comment: Stress values realistic for steel (190-520 MPa); could extend cycles
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further
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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: 'Simple script: 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: np.random.seed(42) ensures reproducibility
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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 (numpy, pygal, Style)
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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 pygal 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 both plot.png and plot.html
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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 pygal XY chart, custom Style, logarithmic scale; could leverage
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tooltips more
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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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- regression
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styling:
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- grid-styling

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