feat(plotnine): implement sn-curve-basic (#7523)

## Implementation: `sn-curve-basic` - python/plotnine

Implements the **python/plotnine** version of `sn-curve-basic`.

**File:** `plots/sn-curve-basic/implementations/python/plotnine.py`

**Parent Issue:** #3826

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/anyplot/actions/runs/26168552769)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Markus Neusinger <2921697+MarkusNeusinger@users.noreply.github.com>

Author: github-actions[bot]

plots/sn-curve-basic/implementations/python/plotnine.py

@@ -1,7 +1,7 @@
 """ anyplot.ai
 sn-curve-basic: S-N Curve (Wöhler Curve)
 Library: plotnine 0.15.4 | Python 3.13.13
-Quality: 85/100 | Updated: 2026-05-20
+Quality: 91/100 | Updated: 2026-05-20
 """
 
 import os
@@ -11,12 +11,15 @@
 from plotnine import (
     aes,
     annotate,
+    element_blank,
     element_line,
     element_rect,
     element_text,
     geom_hline,
     geom_line,
     geom_point,
+    geom_ribbon,
+    geom_rug,
     ggplot,
     labs,
     scale_shape_manual,
@@ -43,10 +46,10 @@
 yield_strength = 350
 endurance_limit = 250
 
-# Generate realistic S-N curve data with scatter
 # Using Basquin equation: S = A * N^b
-A = 1200  # Fatigue strength coefficient
-b = -0.12  # Fatigue strength exponent
+A = 1200
+b = -0.12
+sigma_logN = 0.3  # Log-normal scatter in cycles
 
 stress_levels = np.array([500, 450, 400, 375, 350, 325, 300, 280, 270, 260, 255])
 
@@ -57,7 +60,7 @@
 for s in stress_levels:
     n_expected = (s / A) ** (1 / b)
     n_tests = np.random.randint(3, 6)
-    scatter = np.random.lognormal(0, 0.3, n_tests)
+    scatter = np.random.lognormal(0, sigma_logN, n_tests)
     n_values = n_expected * scatter
     cycles.extend(n_values)
     stress.extend([s] * n_tests)
@@ -72,57 +75,62 @@
 
 df = pd.DataFrame({"cycles": cycles, "stress": stress, "type": point_type})
 
-# Create Basquin fit line
+# Basquin fit line with ±2σ scatter band (converts log-N scatter to stress bounds)
 fit_cycles = np.logspace(2, 7, 100)
 fit_stress = A * fit_cycles**b
-df_fit = pd.DataFrame({"cycles": fit_cycles, "stress": fit_stress})
+fit_stress_upper = fit_stress * np.exp(2 * sigma_logN * abs(b))
+fit_stress_lower = fit_stress * np.exp(-2 * sigma_logN * abs(b))
+df_fit = pd.DataFrame(
+    {"cycles": fit_cycles, "stress": fit_stress, "stress_upper": fit_stress_upper, "stress_lower": fit_stress_lower}
+)
 
 anyplot_theme = theme(
+    figure_size=(8, 4.5),
     plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
     panel_background=element_rect(fill=PAGE_BG),
     panel_grid_major=element_line(color=INK, size=0.3, alpha=0.10),
     panel_grid_minor=element_line(color=INK, size=0.2, alpha=0.05),
-    panel_border=element_rect(color=INK_SOFT, fill=None),
+    panel_border=element_blank(),
+    axis_line_x=element_blank(),
+    axis_line_y=element_line(color=INK_SOFT),
     axis_title=element_text(color=INK, size=10),
     axis_text=element_text(color=INK_SOFT, size=8),
-    axis_line=element_line(color=INK_SOFT),
     plot_title=element_text(color=INK, size=12),
     legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
     legend_text=element_text(color=INK_SOFT, size=8),
     legend_title=element_text(color=INK, size=9),
+    plot_margin=0.05,
 )
 
 plot = (
     ggplot()
+    # ±2σ scatter band via geom_ribbon — idiomatic plotnine uncertainty visualization
+    + geom_ribbon(df_fit, aes(x="cycles", ymin="stress_lower", ymax="stress_upper"), fill=OKABE_ITO[0], alpha=0.12)
     # Basquin fit line
     + geom_line(df_fit, aes(x="cycles", y="stress"), color=OKABE_ITO[0], size=1.2, alpha=0.85)
     # Data points — shape mapped to type for runout vs failure distinction
-    + geom_point(df, aes(x="cycles", y="stress", shape="type"), color=OKABE_ITO[0], size=2.5, alpha=0.75)
-    # Reference lines with Okabe-Ito colors
+    + geom_point(df, aes(x="cycles", y="stress", shape="type"), color=OKABE_ITO[0], size=4.2, alpha=0.80)
+    # geom_rug exposes marginal data density along both axes — distinctive plotnine feature
+    + geom_rug(df, aes(x="cycles", y="stress"), color=OKABE_ITO[0], alpha=0.35, size=0.5)
+    # Reference lines with Okabe-Ito colors; endurance limit slightly thicker as focal point
     + geom_hline(yintercept=ultimate_strength, linetype="dashed", color=OKABE_ITO[1], size=0.9, alpha=0.85)
     + geom_hline(yintercept=yield_strength, linetype="dashed", color=OKABE_ITO[2], size=0.9, alpha=0.85)
-    + geom_hline(yintercept=endurance_limit, linetype="dashed", color=OKABE_ITO[3], size=0.9, alpha=0.85)
-    # Reference line labels with matching colors
+    + geom_hline(yintercept=endurance_limit, linetype="dashed", color=OKABE_ITO[3], size=1.2, alpha=0.90)
+    # Reference line labels with matching colors; shifted right to reduce left-edge crowding
     + annotate(
         "text",
-        x=1.2e2,
-        y=ultimate_strength + 18,
+        x=3e2,
+        y=ultimate_strength + 20,
         label="Ultimate Strength (550 MPa)",
-        size=8,
+        size=10,
         color=OKABE_ITO[1],
         ha="left",
     )
     + annotate(
-        "text", x=1.2e2, y=yield_strength + 18, label="Yield Strength (350 MPa)", size=8, color=OKABE_ITO[2], ha="left"
+        "text", x=3e2, y=yield_strength + 20, label="Yield Strength (350 MPa)", size=10, color=OKABE_ITO[2], ha="left"
     )
     + annotate(
-        "text",
-        x=1.2e2,
-        y=endurance_limit - 22,
-        label="Endurance Limit (250 MPa)",
-        size=8,
-        color=OKABE_ITO[3],
-        ha="left",
+        "text", x=3e2, y=endurance_limit - 25, label="Endurance Limit (250 MPa)", size=10, color=OKABE_ITO[3], ha="left"
     )
     # Logarithmic scales
     + scale_x_log10()
@@ -136,7 +144,6 @@
         title="sn-curve-basic · python · plotnine · anyplot.ai",
     )
     + theme_minimal()
-    + theme(figure_size=(8, 4.5))
     + anyplot_theme
 )