feat(plotnine): implement line-stress-strain

plots/line-stress-strain/implementations/python/plotnine.py

@@ -1,12 +1,26 @@
-""" pyplots.ai
+""" anyplot.ai
 line-stress-strain: Engineering Stress-Strain Curve
-Library: plotnine 0.15.3 | Python 3.14.3
-Quality: 90/100 | Created: 2026-03-20
+Library: plotnine 0.15.7 | Python 3.13.14
+Quality: 83/100 | Updated: 2026-06-21
 """
 
+import os
+import sys
+
 import numpy as np
 import pandas as pd
-from plotnine import (
+
+
+# Work around naming conflict between plotnine.py script and plotnine package
+script_dir = os.path.dirname(os.path.abspath(__file__))
+if script_dir in sys.path:
+    sys.path.remove(script_dir)
+if "" in sys.path:
+    sys.path.remove("")
+if "." in sys.path:
+    sys.path.remove(".")
+
+from plotnine import (  # noqa: E402
     aes,
     annotate,
     coord_cartesian,
@@ -21,8 +35,6 @@
     ggplot,
     labs,
     scale_color_identity,
-    scale_fill_identity,
-    scale_linetype_identity,
     scale_size_identity,
     scale_x_continuous,
     scale_y_continuous,
@@ -31,7 +43,15 @@
 )
 
 
-# Data - Mild steel stress-strain curve
+THEME = os.getenv("ANYPLOT_THEME", "light")
+PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
+INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
+INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
+REGION_ALPHA = 0.18 if THEME == "light" else 0.13
+
+# Imprint palette — position 1 is always the first categorical series
+IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]
+
 np.random.seed(42)
 
 youngs_modulus = 210000  # MPa
@@ -40,120 +60,126 @@
 fracture_strain = 0.35
 necking_strain = 0.22
 
-# Elastic region (0 to yield)
+# Elastic region
 elastic_strain = np.linspace(0, yield_stress / youngs_modulus, 40)
 elastic_stress = youngs_modulus * elastic_strain
 
-# Yield plateau (short flat region for mild steel)
+# Yield plateau (part of the plastic region — not a separate band per spec)
 plateau_strain = np.linspace(elastic_strain[-1], 0.025, 15)
 plateau_stress = np.full_like(plateau_strain, yield_stress)
 
 # Strain hardening (power law)
 hardening_strain = np.linspace(0.025, necking_strain, 80)
 hardening_stress = yield_stress + (uts - yield_stress) * ((hardening_strain - 0.025) / (necking_strain - 0.025)) ** 0.45
 
-# Necking to fracture (stress decreases)
+# Necking to fracture
 necking_strain_vals = np.linspace(necking_strain, fracture_strain, 40)
 necking_stress = (
     uts - (uts - 320) * ((necking_strain_vals - necking_strain) / (fracture_strain - necking_strain)) ** 1.3
 )
 
-# Combine all regions
 strain = np.concatenate([elastic_strain, plateau_strain[1:], hardening_strain[1:], necking_strain_vals[1:]])
 stress = np.concatenate([elastic_stress, plateau_stress[1:], hardening_stress[1:], necking_stress[1:]])
-
 df = pd.DataFrame({"strain": strain, "stress": stress})
 
-# 0.2% offset line data - extended for better visibility
+# 0.2% offset line
 offset = 0.002
-offset_strain_start = offset
+elastic_end = yield_stress / youngs_modulus  # ~0.00119
 offset_strain_end = (yield_stress + 50) / youngs_modulus + offset
 
-# Key points
-yield_point_strain = yield_stress / youngs_modulus + offset
+# Critical points
+yield_point_strain = elastic_end + offset
 yield_point_stress = yield_stress
-uts_strain = necking_strain
-uts_stress = uts
-fracture_strain_pt = fracture_strain
-fracture_stress_pt = necking_stress[-1]
+fracture_stress_pt = float(necking_stress[-1])
 
 df_points = pd.DataFrame(
     {
-        "strain": [yield_point_strain, uts_strain, fracture_strain_pt],
-        "stress": [yield_point_stress, uts_stress, fracture_stress_pt],
-        "label": ["Yield Point\n(0.2% offset)", "UTS", "Fracture"],
-        "color": ["#C0392B", "#C0392B", "#C0392B"],
-        "size": [6.0, 6.0, 6.0],
+        "strain": [yield_point_strain, necking_strain, fracture_strain],
+        "stress": [yield_point_stress, uts, fracture_stress_pt],
+        "color": [IMPRINT[4]] * 3,
+        "size": [3.0, 3.0, 3.0],
     }
 )
 
-# Region labels - repositioned for clarity
+# Region labels: 3 regions per spec (elastic, strain hardening, necking)
+# Yield plateau is a critical point, not a separate shaded band
 df_regions = pd.DataFrame(
     {
-        "strain": [0.005, 0.015, 0.13, 0.29],
-        "stress": [410, 215, 310, 370],
-        "label": ["Elastic", "Yield\nPlateau", "Strain\nHardening", "Necking"],
-        "color": ["#5D6D7E", "#5D6D7E", "#5D6D7E", "#5D6D7E"],
+        "strain": [elastic_end / 2, 0.13, 0.29],
+        "stress": [350, 335, 385],
+        "label": ["Elastic", "Strain\nHardening", "Necking"],
     }
 )
 
-# Region boundary strains for shading
-elastic_end = yield_stress / youngs_modulus
-plateau_end = 0.025
-
-# Plot using plotnine grammar of graphics with layered composition
 plot = (
     ggplot()
-    # Region shading using annotate("rect") - plotnine-distinctive feature
-    + annotate("rect", xmin=0, xmax=elastic_end, ymin=0, ymax=440, alpha=0.15, fill="#3498DB")
-    + annotate("rect", xmin=elastic_end, xmax=plateau_end, ymin=0, ymax=440, alpha=0.15, fill="#2ECC71")
-    + annotate("rect", xmin=plateau_end, xmax=necking_strain, ymin=0, ymax=440, alpha=0.12, fill="#F39C12")
-    + annotate("rect", xmin=necking_strain, xmax=fracture_strain, ymin=0, ymax=440, alpha=0.12, fill="#E74C3C")
-    # Main stress-strain curve
-    + geom_line(df, aes(x="strain", y="stress"), color="#306998", size=2.8)
-    # 0.2% offset line using geom_segment - plotnine-distinctive
+    # Three region shadings: elastic, plastic (strain hardening), necking
+    + annotate("rect", xmin=0, xmax=elastic_end, ymin=0, ymax=460, alpha=REGION_ALPHA, fill=IMPRINT[2])
+    + annotate("rect", xmin=elastic_end, xmax=necking_strain, ymin=0, ymax=460, alpha=REGION_ALPHA, fill=IMPRINT[3])
+    + annotate("rect", xmin=necking_strain, xmax=fracture_strain, ymin=0, ymax=460, alpha=REGION_ALPHA, fill=IMPRINT[4])
+    # Main stress-strain curve (Imprint position 1 — first categorical series)
+    + geom_line(df, aes(x="strain", y="stress"), color=IMPRINT[0], size=1.0)
+    # 0.2% offset construction line
     + geom_segment(
-        aes(x=offset_strain_start, xend=offset_strain_end, y=0, yend=yield_stress + 50),
-        color="#C0392B",
-        size=1.2,
+        aes(x=offset, xend=offset_strain_end, y=0, yend=yield_stress + 50),
+        color=IMPRINT[4],
+        size=0.6,
         linetype="dashed",
     )
-    # Offset label near the line
-    + annotate("text", x=0.012, y=60, label="0.2% offset", size=11, color="#C0392B", fontstyle="italic")
-    # Key points with identity scales for direct aesthetic mapping
-    + geom_point(df_points, aes(x="strain", y="stress", color="color", size="size"), fill="#C0392B")
+    + annotate("text", x=0.011, y=52, label="0.2% offset", size=3.5, color=INK_SOFT, fontstyle="italic")
+    # Critical point markers
+    + geom_point(df_points, aes(x="strain", y="stress", color="color", size="size"))
     + scale_color_identity()
     + scale_size_identity()
-    # Point labels - larger text
-    + geom_text(
-        df_points, aes(x="strain", y="stress", label="label"), nudge_y=32, size=15, color="#2C3E50", fontweight="bold"
+    # Critical point labels — individual positions for clarity near y-axis
+    + annotate(
+        "text",
+        x=yield_point_strain + 0.018,
+        y=yield_point_stress + 22,
+        label="Yield Point\n(0.2% offset)",
+        size=3.0,
+        color=INK,
+        fontweight="bold",
     )
-    # Region labels with identity color scale - larger text
-    + geom_text(df_regions, aes(x="strain", y="stress", label="label", color="color"), size=14, fontstyle="italic")
-    + scale_fill_identity()
-    + scale_linetype_identity()
-    # Modulus annotation - larger and repositioned
+    + annotate("text", x=necking_strain, y=uts + 28, label="UTS", size=3.0, color=INK, fontweight="bold")
     + annotate(
-        "text", x=0.03, y=140, label=f"E = {youngs_modulus // 1000} GPa", size=16, color="#306998", fontweight="bold"
+        "text",
+        x=fracture_strain - 0.012,
+        y=fracture_stress_pt + 28,
+        label="Fracture",
+        size=3.0,
+        color=INK,
+        fontweight="bold",
+    )
+    # Region labels
+    + geom_text(df_regions, aes(x="strain", y="stress", label="label"), size=3.0, color=INK_SOFT, fontstyle="italic")
+    # Elastic modulus annotation
+    + annotate(
+        "text", x=0.028, y=145, label=f"E = {youngs_modulus // 1000} GPa", size=3.5, color=IMPRINT[2], fontweight="bold"
+    )
+    + labs(
+        x="Engineering Strain",
+        y="Engineering Stress (MPa)",
+        title="line-stress-strain · python · plotnine · anyplot.ai",
     )
-    + labs(x="Engineering Strain", y="Engineering Stress (MPa)", title="line-stress-strain · plotnine · pyplots.ai")
     + scale_x_continuous(breaks=np.arange(0, 0.40, 0.05))
     + scale_y_continuous(breaks=np.arange(0, 500, 50))
-    # Coordinate control - plotnine-distinctive
     + coord_cartesian(xlim=(0, 0.38), ylim=(0, 460))
     + theme_minimal()
     + theme(
-        figure_size=(16, 9),
-        plot_title=element_text(size=26, weight="bold", color="#1A2530"),
-        axis_title=element_text(size=22, color="#2C3E50", weight="bold"),
-        axis_text=element_text(size=16, color="#555555"),
+        figure_size=(8, 4.5),
+        text=element_text(size=7),
+        plot_title=element_text(size=12, weight="bold", color=INK),
+        axis_title=element_text(size=10, color=INK, weight="bold"),
+        axis_text=element_text(size=8, color=INK_SOFT),
+        panel_grid_major=element_line(color=INK, size=0.3, alpha=0.15),
         panel_grid_major_x=element_blank(),
         panel_grid_minor=element_blank(),
-        panel_grid_major_y=element_line(color="#E8E8E8", size=0.4, alpha=0.5),
-        plot_background=element_rect(fill="white", color="white"),
-        panel_background=element_rect(fill="white", color="white"),
+        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
+        panel_background=element_rect(fill=PAGE_BG),
+        panel_border=element_rect(color=INK_SOFT, fill=None),
+        axis_line=element_line(color=INK_SOFT),
     )
 )
 
-# Save
-plot.save("plot.png", dpi=300, verbose=False)
+plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)