feat(plotnine): implement line-timeseries (#6120)

## Implementation: `line-timeseries` - python/plotnine

Implements the **python/plotnine** version of `line-timeseries`.

**File:** `plots/line-timeseries/implementations/python/plotnine.py`

**Parent Issue:** #2006

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/anyplot/actions/runs/25590428403)*

---------

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/line-timeseries/implementations/python/plotnine.py

@@ -1,16 +1,19 @@
-""" pyplots.ai
+""" anyplot.ai
 line-timeseries: Time Series Line Plot
-Library: plotnine 0.15.2 | Python 3.13.11
-Quality: 92/100 | Created: 2025-12-26
+Library: plotnine 0.15.4 | Python 3.13.13
+Quality: 92/100 | Updated: 2026-05-09
 """
 
+import os
+
 import numpy as np
 import pandas as pd
 from mizani.breaks import breaks_date
 from mizani.labels import label_date
 from plotnine import (
     aes,
     element_line,
+    element_rect,
     element_text,
     geom_line,
     geom_point,
@@ -22,36 +25,51 @@
 )
 
 
-# Data: Daily stock prices over one year
+THEME = os.getenv("ANYPLOT_THEME", "light")
+PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
+ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
+INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
+INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
+BRAND = "#009E73"
+
+# Data: Bitcoin prices over one year with explicit trend and volatility
 np.random.seed(42)
-dates = pd.date_range(start="2024-01-01", periods=252, freq="B")  # Business days
-price = 100.0
+dates = pd.date_range(start="2023-01-01", periods=365, freq="D")
+price = 16500.0
 prices = []
-for _ in range(252):
-    price = price * (1 + np.random.randn() * 0.015)
+for i in range(365):
+    # Trend component: upward over the year, with mid-year dip
+    trend = 20000 + 15000 * np.sin(i / 365 * np.pi * 2) + i * 5
+    # Volatility: crypto is more volatile than stocks
+    volatility = price * (1 + np.random.randn() * 0.035)
+    # Mix trend and volatility
+    price = 0.7 * trend + 0.3 * volatility
     prices.append(price)
 
 df = pd.DataFrame({"date": dates, "price": prices})
 
 # Plot
 plot = (
     ggplot(df, aes(x="date", y="price"))
-    + geom_line(color="#306998", size=1.5, alpha=0.9)
-    + geom_point(color="#306998", size=0.8, alpha=0.5)
-    + scale_x_datetime(breaks=breaks_date(7), labels=label_date("%b %Y"))
-    + labs(title="line-timeseries · plotnine · pyplots.ai", x="Date", y="Stock Price ($)")
+    + geom_line(color=BRAND, size=1.5, alpha=0.9)
+    + geom_point(color=BRAND, size=0.8, alpha=0.5)
+    + scale_x_datetime(breaks=breaks_date(30), labels=label_date("%b"))
+    + labs(title="line-timeseries · plotnine · anyplot.ai", x="Date", y="Bitcoin Price (USD)")
     + theme_minimal()
     + theme(
         figure_size=(16, 9),
-        text=element_text(size=14),
-        axis_title=element_text(size=20),
-        axis_text=element_text(size=16),
+        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
+        panel_background=element_rect(fill=PAGE_BG, color=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),
+        text=element_text(size=14, color=INK),
+        axis_title=element_text(size=20, color=INK),
+        axis_text=element_text(size=16, color=INK_SOFT),
         axis_text_x=element_text(angle=45, hjust=1),
-        plot_title=element_text(size=24),
-        panel_grid_major=element_line(color="#cccccc", size=0.5, alpha=0.3),
-        panel_grid_minor=element_line(color="#dddddd", size=0.3, alpha=0.2),
+        plot_title=element_text(size=24, color=INK),
     )
 )
 
 # Save
-plot.save("plot.png", dpi=300)
+plot.save(f"plot-{THEME}.png", dpi=300)