feat(letsplot): implement line-timeseries

plots/line-timeseries/implementations/python/letsplot.py

@@ -1,47 +1,74 @@
-""" pyplots.ai
+""" anyplot.ai
 line-timeseries: Time Series Line Plot
-Library: letsplot 4.8.2 | Python 3.13.11
-Quality: 92/100 | Created: 2025-12-26
+Library: letsplot 4.9.0 | Python 3.13.13
+Quality: 89/100 | Updated: 2026-05-09
 """
 
+import os
+
 import numpy as np
 import pandas as pd
 from lets_plot import *
 
 
 LetsPlot.setup_html()
 
+# Theme tokens
+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"
+ACCENT_SOFT = "#D4A574" if THEME == "light" else "#8B7355"
+BRAND = "#009E73"
+
 # Data - Daily temperature readings over one year
 np.random.seed(42)
 dates = pd.date_range(start="2024-01-01", periods=365, freq="D")
 
 # Simulate realistic temperature data with seasonal pattern
 day_of_year = np.arange(365)
-seasonal_pattern = 15 * np.sin(2 * np.pi * (day_of_year - 80) / 365)  # Peak in summer
-baseline = 12  # Average temperature
-noise = np.random.randn(365) * 3  # Daily variation
+seasonal_pattern = 15 * np.sin(2 * np.pi * (day_of_year - 80) / 365)
+baseline = 12
+noise = np.random.randn(365) * 3
 temperature = baseline + seasonal_pattern + noise
 
 df = pd.DataFrame({"date": dates, "temperature": temperature})
 
-# Create plot
+# Find peak and trough for visual emphasis
+max_idx = df["temperature"].idxmax()
+min_idx = df["temperature"].idxmin()
+extremes = pd.DataFrame([df.iloc[max_idx], df.iloc[min_idx]])
+
+# Theme-adaptive styling with enhanced refinement
+anyplot_theme = theme(
+    plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
+    panel_background=element_rect(fill=PAGE_BG),
+    panel_grid_major=element_line(color=INK_SOFT, size=0.25, linetype="solid"),
+    panel_grid_minor=element_blank(),
+    axis_title=element_text(size=20, color=INK, face="bold"),
+    axis_text=element_text(size=16, color=INK_SOFT),
+    axis_text_x=element_text(angle=45, hjust=1),
+    plot_title=element_text(size=24, color=INK, face="bold"),
+    legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
+    legend_text=element_text(size=16, color=INK_SOFT),
+    legend_title=element_text(size=16, color=INK),
+    axis_line=element_line(color=INK_SOFT, size=0.4),
+)
+
+# Create plot with layered visual hierarchy
 plot = (
     ggplot(df, aes(x="date", y="temperature"))
-    + geom_line(color="#306998", size=1.5, alpha=0.9)
-    + labs(x="Date", y="Temperature (°C)", title="line-timeseries · letsplot · pyplots.ai")
-    + theme_minimal()
-    + theme(
-        plot_title=element_text(size=24, face="bold"),
-        axis_title=element_text(size=20),
-        axis_text=element_text(size=16),
-        axis_text_x=element_text(angle=45),
-        panel_grid_major=element_line(color="#CCCCCC", size=0.5),
-        panel_grid_minor=element_line(color="#EEEEEE", size=0.3),
-    )
-    + ggsize(1600, 900)
+    + geom_smooth(method="loess", span=0.15, color=ACCENT_SOFT, size=1.2, alpha=0.5, se=False)
+    + geom_line(color=BRAND, size=1.0, alpha=0.8)
+    + geom_point(color=BRAND, size=1.5, alpha=0.6)
+    + geom_point(data=extremes, color=BRAND, size=3.5, alpha=0.95)
+    + labs(x="Date", y="Temperature (°C)", title="line-timeseries · letsplot · anyplot.ai")
     + scale_x_datetime(format="%b %Y")
+    + ggsize(1600, 900)
+    + anyplot_theme
 )
 
 # Save
-ggsave(plot, "plot.png", path=".", scale=3)
-ggsave(plot, "plot.html", path=".")
+ggsave(plot, f"plot-{THEME}.png", path=".", w=4800, h=2700, unit="px", dpi=100)
+ggsave(plot, f"plot-{THEME}.html", path=".")