feat(letsplot): implement timeseries-decomposition

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

@@ -1,7 +1,7 @@
-""" pyplots.ai
+""" anyplot.ai
 timeseries-decomposition: Time Series Decomposition Plot
-Library: letsplot 4.8.2 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-31
+Library: letsplot 4.9.0 | Python 3.13.13
+Quality: 88/100 | Updated: 2026-05-14
 """
 
 import os
@@ -13,6 +13,8 @@
     LetsPlot,
     aes,
     element_blank,
+    element_line,
+    element_rect,
     element_text,
     geom_line,
     gggrid,
@@ -22,13 +24,21 @@
     ggtitle,
     labs,
     theme,
-    theme_minimal,
 )
 from statsmodels.tsa.seasonal import seasonal_decompose
 
 
 LetsPlot.setup_html()
 
+# Theme tokens
+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"
+
+# Okabe-Ito palette for components
+OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7"]
+
 # Data: Monthly temperature readings over 5 years (60 months)
 np.random.seed(42)
 n_months = 60
@@ -49,79 +59,60 @@
 
 # Extract components and create plotting DataFrames
 df_original = pd.DataFrame({"date": dates, "value": values, "component": "Original"})
-
 df_trend = pd.DataFrame({"date": dates, "value": decomposition.trend, "component": "Trend"})
-
 df_seasonal = pd.DataFrame({"date": dates, "value": decomposition.seasonal, "component": "Seasonal"})
-
 df_residual = pd.DataFrame({"date": dates, "value": decomposition.resid, "component": "Residual"})
 
-# Combine all components
-df_all = pd.concat([df_original, df_trend, df_seasonal, df_residual])
-
-# Convert date to string for plotting
-df_all["date_str"] = df_all["date"].dt.strftime("%Y-%m")
-
 # Create individual plots for each component
-colors = {"Original": "#306998", "Trend": "#DC2626", "Seasonal": "#059669", "Residual": "#7C3AED"}
+component_colors = {"Original": OKABE_ITO[0], "Trend": OKABE_ITO[1], "Seasonal": OKABE_ITO[2], "Residual": OKABE_ITO[3]}
+
+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=0.5),
+    axis_title=element_text(color=INK, size=16),
+    axis_text=element_text(color=INK_SOFT, size=14),
+    axis_line=element_line(color=INK_SOFT),
+    plot_title=element_text(color=INK, size=20, face="bold"),
+)
 
 # Plot 1: Original Series
 p1 = (
     ggplot(df_original, aes(x="date", y="value"))
-    + geom_line(color="#306998", size=1.2)
+    + geom_line(color=component_colors["Original"], size=1.2)
     + labs(x="", y="Temperature (°C)", title="Original Series")
-    + theme_minimal()
-    + theme(
-        plot_title=element_text(size=20, face="bold"),
-        axis_title=element_text(size=16),
-        axis_text=element_text(size=14),
-        axis_text_x=element_blank(),
-    )
+    + anyplot_theme
+    + theme(axis_text_x=element_blank())
     + ggsize(1600, 200)
 )
 
 # Plot 2: Trend Component
 p2 = (
     ggplot(df_trend.dropna(), aes(x="date", y="value"))
-    + geom_line(color="#DC2626", size=1.2)
+    + geom_line(color=component_colors["Trend"], size=1.2)
     + labs(x="", y="Temperature (°C)", title="Trend")
-    + theme_minimal()
-    + theme(
-        plot_title=element_text(size=20, face="bold"),
-        axis_title=element_text(size=16),
-        axis_text=element_text(size=14),
-        axis_text_x=element_blank(),
-    )
+    + anyplot_theme
+    + theme(axis_text_x=element_blank())
     + ggsize(1600, 200)
 )
 
 # Plot 3: Seasonal Component
 p3 = (
     ggplot(df_seasonal, aes(x="date", y="value"))
-    + geom_line(color="#059669", size=1.2)
+    + geom_line(color=component_colors["Seasonal"], size=1.2)
     + labs(x="", y="Temperature (°C)", title="Seasonal")
-    + theme_minimal()
-    + theme(
-        plot_title=element_text(size=20, face="bold"),
-        axis_title=element_text(size=16),
-        axis_text=element_text(size=14),
-        axis_text_x=element_blank(),
-    )
+    + anyplot_theme
+    + theme(axis_text_x=element_blank())
     + ggsize(1600, 200)
 )
 
 # Plot 4: Residual Component
 p4 = (
     ggplot(df_residual.dropna(), aes(x="date", y="value"))
-    + geom_line(color="#7C3AED", size=1.2)
+    + geom_line(color=component_colors["Residual"], size=1.2)
     + labs(x="Date", y="Temperature (°C)", title="Residual")
-    + theme_minimal()
-    + theme(
-        plot_title=element_text(size=20, face="bold"),
-        axis_title=element_text(size=16),
-        axis_text=element_text(size=14),
-        axis_text_x=element_text(angle=45),
-    )
+    + anyplot_theme
+    + theme(axis_text_x=element_text(angle=45))
     + ggsize(1600, 200)
 )
 
@@ -132,21 +123,22 @@
 final_plot = (
     combined
     + ggsize(1600, 900)
-    + ggtitle("timeseries-decomposition · letsplot · pyplots.ai")
-    + theme(plot_title=element_text(size=24, face="bold"))
+    + ggtitle("timeseries-decomposition · letsplot · anyplot.ai")
+    + theme(plot_title=element_text(color=INK, size=24, face="bold"))
 )
 
 # Save as PNG with scale for 4800x2700 resolution
-ggsave(final_plot, "plot.png", scale=3)
+ggsave(final_plot, f"plot-{THEME}.png", scale=3)
 
 # Save HTML for interactive version
-ggsave(final_plot, "plot.html")
+ggsave(final_plot, f"plot-{THEME}.html")
 
-# Move files from lets-plot subdirectory to current directory
+# Move files from lets-plot subdirectory to current directory if needed
 lp_dir = "lets-plot-images"
 if os.path.exists(lp_dir):
-    for f in ["plot.png", "plot.html"]:
-        src = os.path.join(lp_dir, f)
+    for fname in [f"plot-{THEME}.png", f"plot-{THEME}.html"]:
+        src = os.path.join(lp_dir, fname)
         if os.path.exists(src):
-            shutil.move(src, f)
-    os.rmdir(lp_dir)
+            shutil.move(src, fname)
+    if not os.listdir(lp_dir):
+        os.rmdir(lp_dir)