feat(pygal): implement line-loss-training (#6654)

## Implementation: `line-loss-training` - python/pygal

Implements the **python/pygal** version of `line-loss-training`.

**File:** `plots/line-loss-training/implementations/python/pygal.py`

**Parent Issue:** #2860

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/anyplot/actions/runs/25843905278)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/line-loss-training/implementations/python/pygal.py

@@ -1,62 +1,79 @@
-""" pyplots.ai
+""" anyplot.ai
 line-loss-training: Training Loss Curve
-Library: pygal 3.1.0 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-31
+Library: pygal 3.1.0 | Python 3.13.13
+Quality: 92/100 | Updated: 2026-05-14
 """
 
+import os
+import sys
+
 import numpy as np
-import pygal
-from pygal.style import Style
 
 
+# Remove local directory from sys.path to avoid shadowing the pygal package
+_local_dir = os.path.abspath(os.path.dirname(__file__))
+sys.path = [p for p in sys.path if os.path.abspath(p) != _local_dir]
+
+import pygal  # noqa: E402
+from pygal.style import Style  # noqa: E402
+
+
+THEME = os.getenv("ANYPLOT_THEME", "light")
+
+# Theme tokens (from default-style-guide.md)
+PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
+INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
+INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
+
+# Okabe-Ito palette (first series = brand green #009E73)
+OKABE_ITO = ("#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442")
+
 # Data: Simulated training loss curves showing typical overfitting behavior
 np.random.seed(42)
 epochs = np.arange(1, 51)
 
 # Training loss: Steadily decreasing with some noise
 train_loss = 2.5 * np.exp(-0.08 * epochs) + 0.1 + np.random.normal(0, 0.02, len(epochs))
 
-# Validation loss: Decreases then increases (overfitting after epoch ~30)
+# Validation loss: Decreases then increases (overfitting after epoch ~25)
 val_loss = 2.3 * np.exp(-0.07 * epochs) + 0.15 + 0.003 * np.maximum(0, epochs - 25) ** 1.5
 val_loss += np.random.normal(0, 0.03, len(epochs))
 
 # Find minimum validation loss epoch for annotation
 min_val_epoch = int(epochs[np.argmin(val_loss)])
 min_val_loss = float(np.min(val_loss))
 
-# Custom style for large canvas
+# Custom theme-adaptive style
 custom_style = Style(
-    background="white",
-    plot_background="white",
-    foreground="#333333",
-    foreground_strong="#333333",
-    foreground_subtle="#666666",
-    colors=("#306998", "#FFD43B"),  # Python Blue, Python Yellow
-    title_font_size=48,
-    label_font_size=36,
-    major_label_font_size=32,
-    legend_font_size=32,
-    value_font_size=28,
-    stroke_width=4,
-    opacity=0.9,
-    opacity_hover=1.0,
+    background=PAGE_BG,
+    plot_background=PAGE_BG,
+    foreground=INK,
+    foreground_strong=INK,
+    foreground_subtle=INK_MUTED,
+    colors=OKABE_ITO,
+    title_font_size=28,
+    label_font_size=22,
+    major_label_font_size=18,
+    legend_font_size=16,
+    value_font_size=14,
+    stroke_width=3,
 )
 
 # Create line chart
 chart = pygal.Line(
     width=4800,
     height=2700,
     style=custom_style,
-    title="line-loss-training · pygal · pyplots.ai",
+    title="line-loss-training · pygal · anyplot.ai",
     x_title="Epoch",
     y_title="Cross-Entropy Loss",
-    show_x_guides=False,
+    show_x_guides=True,
     show_y_guides=True,
     dots_size=6,
-    stroke_style={"width": 4},
+    stroke_style={"width": 3},
     legend_at_bottom=False,
     legend_box_size=24,
-    margin=50,
+    margin=80,
     x_label_rotation=0,
     truncate_label=-1,
     show_dots=True,
@@ -69,6 +86,7 @@
 chart.add("Training Loss", list(train_loss))
 chart.add("Validation Loss", list(val_loss))
 
-# Save as PNG and HTML
-chart.render_to_png("plot.png")
-chart.render_to_file("plot.html")
+# Save as PNG and HTML with theme suffix
+chart.render_to_png(f"plot-{THEME}.png")
+with open(f"plot-{THEME}.html", "wb") as f:
+    f.write(chart.render())